From 344992b3d59c5f0446257dc1bef69a6fdfb3fdba Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Fri, 5 Jul 2024 12:23:10 +0200
Subject: [PATCH 001/250] doc: transfer learning

---
 .../finetuning_imgs/finetuning_11_0.png       | Bin 0 -> 34328 bytes
 .../finetuning_imgs/finetuning_17_3.png       | Bin 0 -> 113262 bytes
 .../finetuning_imgs/finetuning_18_3.png       | Bin 0 -> 113262 bytes
 .../finetuning_imgs/finetuning_19_3.png       | Bin 0 -> 65797 bytes
 .../finetuning_imgs/finetuning_20_0.png       | Bin 0 -> 143570 bytes
 .../finetuning_imgs/finetuning_23_0.png       | Bin 0 -> 127786 bytes
 .../finetuning_imgs/finetuning_27_4.png       | Bin 0 -> 143291 bytes
 .../finetuning_imgs/finetuning_29_4.png       | Bin 0 -> 108211 bytes
 .../finetuning_imgs/finetuning_30_0.png       | Bin 0 -> 178106 bytes
 .../finetuning_imgs/finetuning_7_1.png        | Bin 0 -> 38911 bytes
 docs/methods/transfer-learning.md             | 417 ++++++++++++++++++
 11 files changed, 417 insertions(+)
 create mode 100644 docs/methods/finetuning_imgs/finetuning_11_0.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_17_3.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_18_3.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_19_3.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_20_0.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_23_0.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_27_4.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_29_4.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_30_0.png
 create mode 100644 docs/methods/finetuning_imgs/finetuning_7_1.png
 create mode 100644 docs/methods/transfer-learning.md

diff --git a/docs/methods/finetuning_imgs/finetuning_11_0.png b/docs/methods/finetuning_imgs/finetuning_11_0.png
new file mode 100644
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diff --git a/docs/methods/transfer-learning.md b/docs/methods/transfer-learning.md
new file mode 100644
index 00000000..c402905a
--- /dev/null
+++ b/docs/methods/transfer-learning.md
@@ -0,0 +1,417 @@
+
+## Transfer Learning
+
+Generally, transfer learning refers to the process of leveraging the knowledge learned in one model for better performance on a different task. A task is a vague term, but it essentially includes learning a different objective, for example, transitioning from regression to classification. It can also involve learning the same objective with a different loss function or optimizer, or using the same loss and objective but with different data. In cases where the dataset is too small to train a model from scratch without overfitting, we start from a pretrained model that has good performance on a larger dataset. This last type is the transfer learning we are using in alphaDIA.
+
+## Transfer Learning for alphaDIA
+
+In alphaDIA, we use the output obtained after the first search run and introduce a signal from the identified peptides to fine-tune the predictions from PeptDeep models on the custom dataset we are interested in. We then explore how this affects a second search run with fine-tuned models used to predict the spectral library.
+
+Training neural networks, and specifically transformers (such as those used in MS2 prediction), usually requires a lot of hyperparameter fine-tuning. Users try out different parameters like learning rate and the number of epochs to see what works better. For users with limited experience, this may seem like a trial and error process that is very time-consuming. The goal of the transfer learning module in alphaDIA is to provide robustness with minimal intervention from users, thereby increasing the accessibility of such processes for users from all backgrounds with minimal experience in deep learning.
+
+
+In this notebook, we will be going over the transfer learning done in alphaDIA, starting with two components that help achieve the robustness we are targeting: **Learning Rate Schedulers** and **Early Stopping**. If you understand these concepts and want to jump straight into how to use our APIs to fine-tune the model, skip to the [finetuning section](#transfer-learning-in-alphadia)
+
+
+
+```python
+
+from alphadia.transferlearning.train import CustomScheduler
+
+```
+
+## Learning Rate Scheduler
+
+Learning rates are crucial parameters that define the magnitude of updates made to the model weights, essentially controlling "how fast we learn". While a higher learning rate might seem beneficial, it can cause the weights to converge quickly to sub-optimal values and oscillate around them. If the learning rate is too high, it can even cause the model to diverge by overshooting the weights. This is where learning rate schedulers come into play. A learning rate scheduler adjusts the learning rate of a neural network (or part of it) dynamically based on time/epochs or the loss of the model (more on that later).
+
+For alphaDIA, we use a custom learning rate scheduler with two phases:
+
+### 1) Warmup Phase
+In this phase, the learning rate starts small and gradually increases over a certain number of "warmup epochs". Our default is **5**. This technique significantly helps in training transformers when using optimizers like Adam or SGD ([https://arxiv.org/abs/2002.04745](https://arxiv.org/abs/2002.04745)). Since we are not training from scratch, we set the default number of warmup epochs to 5. The user only needs to define the maximum learning rate and the number of epochs for warm-up. During this phase, the learning rate lr(t) is calculated as:
+
+$$
+\text{lr}(t) = \text{max\_lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)
+$$
+
+### 2) Reduce on Plateau LR Schedule
+After the warmup phase, the learning rate reaches the maximum value set by the user and remains there until the training loss reaches a plateau. A plateau is defined as the training loss not significantly improving for a certain number of epochs, referred to as "patience". For this phase, we use the PyTorch implementation `torch.optim.lr_scheduler.ReduceLROnPlateau` with a default patience value of 3 epochs. 
+
+This approach makes the fine-tuning process less sensitive to the user-defined learning rate. If the model is not learning for 3 epochs, it is likely that the learning rate is too high, and the scheduler will then reduce the learning rate to encourage further learning.
+
+
+
+
+```python
+"""
+To show how our LR scheduler works, we will use a dummy optimizer with a dummy model parameters.
+"""
+
+import torch
+
+NUM_WARMUP_STEPS = 5
+MAX_LR = 0.01
+
+class DummyModel(torch.nn.Module):
+    def __init__(self):
+        super(DummyModel, self).__init__()
+        self.fc = torch.nn.Linear(1, 1)
+    
+    def forward(self, x):
+        return self.fc(x)
+    
+model = DummyModel()
+optimizer = torch.optim.Adam(model.parameters(), lr=MAX_LR)
+
+```
+
+
+```python
+"""
+Now since our lr scheduler uses reduce_lr_on_plateau, we need to pass the training loss per each epoch to the scheduler.
+"""
+
+# Dummy training loss
+losses = [0.12,0.1, 0.09, 0.08, 0.07, 0.06, 0.06,0.06,0.06,0.06,0.06, 0.06] 
+scheduler = CustomScheduler(optimizer, max_lr=MAX_LR, num_warmup_steps=NUM_WARMUP_STEPS)
+
+learning_rates = []
+for epoch, loss in enumerate(losses):
+    scheduler.step(epoch, loss)
+    learning_rates.append(optimizer.param_groups[0]['lr'])
+
+fig, ax1 = plt.subplots()
+
+color = 'tab:red'
+ax1.set_xlabel('Epoch')
+ax1.set_ylabel('Learning Rate', color=color)
+ax1.plot(learning_rates, color=color)
+ax1.tick_params(axis='y', labelcolor=color)
+
+ax2 = ax1.twinx()  
+
+color = 'tab:blue'
+ax2.set_ylabel('Loss', color=color)  
+ax2.plot(losses, color=color)
+ax2.tick_params(axis='y', labelcolor=color)
+
+fig.tight_layout() 
+plt.show()
+```
+
+  
+    
+
+
+    
+![png](finetuning_files/finetuning_7_1.png)
+    
+
+
+Notice how in the first 5 epochs the learning rate started from 
+$\frac{\text{max\_lr}}{\text{number of epochs}} = \frac{0.01}{5} = 0.002$ 
+and increased till it reached $0.01$ in the 5th epoch.
+
+When the loss plateaus for more than 3 epochs (the value set for patience), the learning rate is halved. We will see how much this learning rate halving actually helps retention time (RT) and MS2 fine-tuning to consistently achieve much better performance without requiring extensive experimentation with hyperparameter changes.
+
+
+
+
+```python
+from alphadia.transferlearning.train import EarlyStopping
+```
+
+## Early Stopping
+
+With the learning scheduler we are using, we could theoretically keep training indefinitely, as the learning rate is reduced whenever the loss becomes steady until it reaches an infinitesimally small value. However, there are two issues with this approach:
+
+1. The performance gains when the learning rate is very small are often not significant enough to justify continued training.
+2. Longer training times increase the risk of overfitting on the small dataset we are fine-tuning on.
+
+To address these issues, we implement two measures:
+
+### 1) Maximum Number of Epochs
+We set the maximum number of epochs to 50. From our experiments, we find that 50 epochs are usually sufficient to achieve significant performance gains without spending unnecessary time/epochs on insignificant improvements.
+
+### 2) Early Stopping
+We use an Early Stopping implementation that monitors the validation loss and terminates the training if one of the following criteria is met for more than the patience epochs (this is different from the learning rate scheduler's patience value, but they are related, more on this later):
+
+a) The validation loss is increasing, which may indicate overfitting.
+
+b) The validation loss is not significantly improving, indicating no significant performance gains on the validation dataset.
+
+The early stopping patience value represents the number of epochs we allow the model to meet the criteria without taking any action. This is because training neural networks with smaller batches can be a bit unstable, so we allow for some volatility before intervening. We set the early stopping patience to be a multiple of the learning rate scheduler patience. The idea is to give the learning rate scheduler a chance to address the problem before terminating the training.
+
+It's important to note that there are many implementations of Early Stopping algorithms, some offering better performance against overfitting by monitoring the generalization gap (val_loss - train_loss). However, we found that the simple implementation we use is sufficient for our fine-tuning tasks.
+
+
+
+```python
+"""
+To illustrate how our early stopping works we will try it on simulated validation losses in differnet cases and see how and when the early stopping is triggered.
+"""
+simulated_losses = {
+    "diverging": [0.5, 0.3 ,0.2, 0.1, 0.125, 0.15, 0.2, 0.3, 0.5, 0.7, 1.0],
+    "converging": [0.5, 0.3 ,0.2, 0.1, 0.05, 0.03, 0.02, 0.01, 0.005, 0.002, 0.0005],
+    "not significantly improving" : [0.5, 0.3 ,0.2, 0.1, 0.07, 0.0695, 0.0689, 0.06883, 0.06878,0.06874, 0.06869],
+}
+
+stopped_at = {case: len(losses)-1 for case, losses in simulated_losses.items()}
+for case, losses in simulated_losses.items():
+    early_stopping = EarlyStopping(patience=3)
+    for epoch, loss in enumerate(losses):
+        continue_training = early_stopping.step(loss)
+        if not continue_training:
+            stopped_at[case] = epoch
+            break
+
+fig, ax = plt.subplots()
+for case, losses in simulated_losses.items():
+    ax.plot(losses, label="Loss "+case)
+    ax.scatter(stopped_at[case], losses[stopped_at[case]], color='red')
+ax.legend()
+ax.set_xlabel("Epoch")
+ax.set_ylabel("Validation Loss")
+# remove axis
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+plt.show()
+
+```
+
+
+    
+![png](finetuning_files/finetuning_11_0.png)
+    
+
+
+## Transfer Learning in alphaDIA
+
+Our fine-tuning interface in the `FinetuneManager` has a method implemented for each model (RT, Charge, MS2). Each function fine-tunes the respective model and runs tests on the validation dataset every epoch. By the end of the fine-tuning process, the method returns a pandas DataFrame that includes all metrics accumulated throughout the epochs. This DataFrame contains the training loss, learning rate, and all test metrics for the model. Additionally, the method provides an evaluation of the model on the full dataset before fine-tuning, and another evaluation on a separate test dataset that is used only once after the fine-tuning. This is included to facilitate easier interpretation of how well the fine-tuning performed on the given dataset.
+
+The metrics accumulated are calculated as the average over all validation samples and are as follows:
+
+| Model   | Metrics |
+|---------|---------|
+| **RT**  | L1 loss, Linear regression analysis, 95th percentile of the absolute error |
+| **Charge** | Cross Entropy, Accuracy, Precision, Recall |
+| **MS2**    | L1 loss, Pearson Correlation Coefficient, Cosine Similarity, Spectral Angle, Spearman Correlation |
+
+
+```python
+
+tune_mgr = FinetuneManager(
+    device="gpu",
+    settings=settings)
+tune_mgr.nce = 25
+tune_mgr.instrument = 'Lumos'
+```
+
+
+
+## RT Fine-tuning
+
+
+
+```python
+transfer_lib.precursor_df = tune_mgr.predict_rt(transfer_lib.precursor_df)
+plt.scatter(transfer_lib.precursor_df['rt_norm'], transfer_lib.precursor_df['rt_norm_pred'], s=1, alpha=0.1)
+plt.xlabel('RT observed')
+plt.ylabel('RT predicted')
+```
+    
+![png](finetuning_files/finetuning_17_3.png)
+    
+
+
+
+
+```python
+rt_stats = tune_mgr.finetune_rt(transfer_lib.precursor_df)
+
+transfer_lib.precursor_df = tune_mgr.predict_rt(transfer_lib.precursor_df)
+
+plt.scatter(transfer_lib.precursor_df['rt_norm'], transfer_lib.precursor_df['rt_norm_pred'], s=0.1, alpha=0.1)
+plt.xlabel('RT observed')
+plt.ylabel('RT predicted')
+
+```
+
+    2024-07-05 11:11:20>  RT model tested on all dataset with the following metrics:
+    2024-07-05 11:11:20>  l1_loss                       : 0.2398
+    2024-07-05 11:11:20>  r_square                      : 0.4829
+    2024-07-05 11:11:20>  r                             : 0.6949
+    2024-07-05 11:11:20>  slope                         : 0.5390
+    2024-07-05 11:11:20>  intercept                     : 0.1113
+    2024-07-05 11:11:20>  abs_error_95th_percentile     : 0.4317
+    2024-07-05 11:11:20>  Fine-tuning RT model with the following settings:
+    2024-07-05 11:11:20>  Train fraction:      0.70     Train size:      20226     
+    2024-07-05 11:11:20>  Validation fraction: 0.20     Validation size: 5779      
+    2024-07-05 11:11:20>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:11:30>  Epoch 0   Lr: 0.00020   Training loss: 0.1619   validation loss: 0.1270
+    2024-07-05 11:11:38>  Epoch 1   Lr: 0.00030   Training loss: 0.0657   validation loss: 0.0482
+    ...
+    2024-07-05 11:17:52>  Epoch 49  Lr: 0.00030   Training loss: 0.0128   validation loss: 0.0206
+    2024-07-05 11:18:00>  Epoch 50  Lr: 0.00030   Training loss: 0.0128   validation loss: 0.0196
+    2024-07-05 11:18:01>  RT model tested on test dataset with the following metrics:
+    2024-07-05 11:18:01>  l1_loss                       : 0.0203
+    2024-07-05 11:18:01>  r_square                      : 0.9229
+    2024-07-05 11:18:01>  r                             : 0.9607
+    2024-07-05 11:18:01>  slope                         : 0.9672
+    2024-07-05 11:18:01>  intercept                     : 0.0147
+    2024-07-05 11:18:01>  abs_error_95th_percentile     : 0.0846
+
+![png](finetuning_files/finetuning_19_3.png)
+    
+
+
+
+```python
+g = sns.relplot(data=rt_stats, x='epoch', y='value', hue='data_split', marker= 'o',dashes=False, col='metric_name', kind='line', col_wrap=2, facet_kws={'sharex': False, 'sharey': False, 'legend_out': False})
+g.set_titles("{col_name}")
+g.legend.set_title('Data split')
+
+```
+
+
+    
+![png](finetuning_files/finetuning_20_0.png)
+    
+
+
+## Charge Fine-tuning
+
+
+```python
+
+charge_stats = tune_mgr.finetune_charge(psm_df=transfer_lib.precursor_df)
+```
+
+    2024-07-05 11:18:13>  Charge model tested on all dataset with the following metrics: 
+    2024-07-05 11:18:13>  ce_loss                       : 0.5444
+    2024-07-05 11:18:13>  accuracy                      : 0.6414
+    2024-07-05 11:18:13>  precision                     : 0.3108
+    2024-07-05 11:18:13>  recall                        : 0.2975
+    2024-07-05 11:18:13>  Fine-tuning Charge model with following settings:
+    2024-07-05 11:18:13>  Train fraction:      0.70     Train size:      20226     
+    2024-07-05 11:18:13>  Validation fraction: 0.20     Validation size: 5779      
+    2024-07-05 11:18:13>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:18:24>  Epoch 0   Lr: 0.00020   Training loss: 0.6341   validation loss: 0.6309
+    2024-07-05 11:18:35>  Epoch 1   Lr: 0.00030   Training loss: 0.4453   
+    ...
+    2024-07-05 11:27:54>  Epoch 50  Lr: 0.00015   Training loss: 0.0618   validation loss: 0.1364
+    2024-07-05 11:27:55>  Charge model tested on test dataset with the following metrics: 
+    2024-07-05 11:27:55>  ce_loss                       : 0.1476
+    2024-07-05 11:27:55>  accuracy                      : 0.9515
+    2024-07-05 11:27:55>  precision                     : 0.9107
+    2024-07-05 11:27:55>  recall                        : 0.8994
+    
+
+
+```python
+g = sns.relplot(data=charge_stats, x='epoch', y='value', hue='data_split', marker= 'o',dashes=False, col='metric_name', kind='line', col_wrap=2, facet_kws={'sharex': False, 'sharey': False, 'legend_out': False})
+g.set_titles("{col_name}")
+g.legend.set_title('Data split')
+
+```
+
+
+    
+![png](finetuning_files/finetuning_23_0.png)
+    
+
+
+## MS2 Fine-tuning
+
+
+
+
+```python
+res = tune_mgr.predict_all(transfer_lib.precursor_df.copy(), predict_items=['ms2'])
+
+precursor_after_df = res['precursor_df']
+fragment_mz_after_df = res['fragment_mz_df']
+fragment_intensity_after_df = res['fragment_intensity_df']
+similarity_after_df = calculate_similarity(precursor_after_df, transfer_lib.precursor_df, fragment_intensity_after_df, transfer_lib.fragment_intensity_df)
+print(similarity_after_df['similarity'].median())
+plt.scatter(similarity_after_df['index'], similarity_after_df['similarity'], s=0.1)
+plt.xlabel('Index')
+plt.ylabel('Similarity')
+plt.title('Similarity between observed and predicted MS2 spectra before fine-tuning')
+```
+
+    
+![png](finetuning_files/finetuning_27_4.png)
+    
+
+
+
+```python
+
+# Testing the ms2 finetuning on the transfer library
+ms2_stats = tune_mgr.finetune_ms2(psm_df=transfer_lib.precursor_df.copy(), matched_intensity_df=transfer_lib.fragment_intensity_df.copy())
+```
+
+    100%|██████████| 5779/5779 [00:01<00:00, 3818.99it/s]
+    100%|██████████| 2889/2889 [00:00<00:00, 4138.71it/s]
+    
+
+    2024-07-05 11:38:06>  Ms2 model tested on validation dataset with the following metrics:
+    2024-07-05 11:38:06>  l1_loss                       : 0.0323
+    2024-07-05 11:38:06>  PCC-mean                      : 0.7480
+    2024-07-05 11:38:06>  COS-mean                      : 0.7649
+    2024-07-05 11:38:06>  SA-mean                       : 0.5544
+    2024-07-05 11:38:06>  SPC-mean                      : 0.6447
+    2024-07-05 11:38:06>  Fine-tuning MS2 model with the following settings:
+    2024-07-05 11:38:06>  Train fraction:      0.70     Train size:      20226     
+    2024-07-05 11:38:06>  Validation fraction: 0.20     Validation size: 5779      
+    2024-07-05 11:38:06>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:38:39>  Epoch 0   Lr: 0.00020   Training loss: 0.0212   validation loss: 0.0249
+    ...
+    2024-07-05 12:05:54>  Epoch 50  Lr: 0.00030   Training loss: 0.0110   validation loss: 0.0176
+    2024-07-05 12:05:56>  Ms2 model tested on test dataset with the following metrics:
+    2024-07-05 12:05:56>  l1_loss                       : 0.0176
+    2024-07-05 12:05:56>  PCC-mean                      : 0.9302
+    2024-07-05 12:05:56>  COS-mean                      : 0.9348
+    2024-07-05 12:05:56>  SA-mean                       : 0.7688
+    2024-07-05 12:05:56>  SPC-mean                      : 0.7802
+    
+
+
+```python
+res = tune_mgr.predict_all(transfer_lib.precursor_df.copy(), predict_items=["ms2"])
+
+precursor_after_df = res["precursor_df"]
+fragment_mz_after_df = res["fragment_mz_df"]
+fragment_intensity_after_df = res["fragment_intensity_df"]
+similarity_after_df = calculate_similarity(
+    precursor_after_df,
+    transfer_lib.precursor_df,
+    fragment_intensity_after_df,
+    transfer_lib.fragment_intensity_df,
+)
+print(similarity_after_df["similarity"].median())
+plt.scatter(similarity_after_df["index"], similarity_after_df["similarity"], s=0.1)
+plt.xlabel("Index")
+plt.ylabel("Similarity")
+plt.title("Similarity between observed and predicted MS2 spectra after fine-tuning")
+```
+    0.9401921591393136 
+![png](finetuning_files/finetuning_29_4.png)
+    
+
+
+
+```python
+g = sns.relplot(data=ms2_stats, x='epoch', y='value', hue='data_split', marker= 'o',dashes=False, col='metric_name', kind='line', col_wrap=2, facet_kws={'sharex': False, 'sharey': False, 'legend_out': False})
+g.set_titles("{col_name}")
+g.legend.set_title('Data split')
+
+```
+
+
+    
+![png](finetuning_files/finetuning_30_0.png)
+    
+

From c465beab113ab16eac7bb466871ce872923e90c3 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Fri, 5 Jul 2024 12:24:36 +0200
Subject: [PATCH 002/250] fix images path

---
 docs/methods/transfer-learning.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/methods/transfer-learning.md b/docs/methods/transfer-learning.md
index c402905a..a653de0a 100644
--- a/docs/methods/transfer-learning.md
+++ b/docs/methods/transfer-learning.md
@@ -103,7 +103,7 @@ plt.show()
 
 
     
-![png](finetuning_files/finetuning_7_1.png)
+![png](finetuning_imgs/finetuning_7_1.png)
     
 
 
@@ -182,7 +182,7 @@ plt.show()
 
 
     
-![png](finetuning_files/finetuning_11_0.png)
+![png](finetuning_imgs/finetuning_11_0.png)
     
 
 
@@ -221,7 +221,7 @@ plt.xlabel('RT observed')
 plt.ylabel('RT predicted')
 ```
     
-![png](finetuning_files/finetuning_17_3.png)
+![png](finetuning_imgs/finetuning_17_3.png)
     
 
 
@@ -262,7 +262,7 @@ plt.ylabel('RT predicted')
     2024-07-05 11:18:01>  intercept                     : 0.0147
     2024-07-05 11:18:01>  abs_error_95th_percentile     : 0.0846
 
-![png](finetuning_files/finetuning_19_3.png)
+![png](finetuning_imgs/finetuning_19_3.png)
     
 
 
@@ -276,7 +276,7 @@ g.legend.set_title('Data split')
 
 
     
-![png](finetuning_files/finetuning_20_0.png)
+![png](finetuning_imgs/finetuning_20_0.png)
     
 
 
@@ -318,7 +318,7 @@ g.legend.set_title('Data split')
 
 
     
-![png](finetuning_files/finetuning_23_0.png)
+![png](finetuning_imgs/finetuning_23_0.png)
     
 
 
@@ -342,7 +342,7 @@ plt.title('Similarity between observed and predicted MS2 spectra before fine-tun
 ```
 
     
-![png](finetuning_files/finetuning_27_4.png)
+![png](finetuning_imgs/finetuning_27_4.png)
     
 
 
@@ -398,7 +398,7 @@ plt.ylabel("Similarity")
 plt.title("Similarity between observed and predicted MS2 spectra after fine-tuning")
 ```
     0.9401921591393136 
-![png](finetuning_files/finetuning_29_4.png)
+![png](finetuning_imgs/finetuning_29_4.png)
     
 
 
@@ -412,6 +412,6 @@ g.legend.set_title('Data split')
 
 
     
-![png](finetuning_files/finetuning_30_0.png)
+![png](finetuning_imgs/finetuning_30_0.png)
     
 

From e0e8870c430236cc391a64cfc5f592ce1610c62f Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <65254084+mo-sameh@users.noreply.github.com>
Date: Fri, 5 Jul 2024 12:30:28 +0200
Subject: [PATCH 003/250] Update transfer-learning.md

---
 docs/methods/transfer-learning.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/methods/transfer-learning.md b/docs/methods/transfer-learning.md
index a653de0a..b6ad3765 100644
--- a/docs/methods/transfer-learning.md
+++ b/docs/methods/transfer-learning.md
@@ -29,9 +29,7 @@ For alphaDIA, we use a custom learning rate scheduler with two phases:
 ### 1) Warmup Phase
 In this phase, the learning rate starts small and gradually increases over a certain number of "warmup epochs". Our default is **5**. This technique significantly helps in training transformers when using optimizers like Adam or SGD ([https://arxiv.org/abs/2002.04745](https://arxiv.org/abs/2002.04745)). Since we are not training from scratch, we set the default number of warmup epochs to 5. The user only needs to define the maximum learning rate and the number of epochs for warm-up. During this phase, the learning rate lr(t) is calculated as:
 
-$$
-\text{lr}(t) = \text{max\_lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)
-$$
+$$\text{lr}(t) = \text{max lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)$$
 
 ### 2) Reduce on Plateau LR Schedule
 After the warmup phase, the learning rate reaches the maximum value set by the user and remains there until the training loss reaches a plateau. A plateau is defined as the training loss not significantly improving for a certain number of epochs, referred to as "patience". For this phase, we use the PyTorch implementation `torch.optim.lr_scheduler.ReduceLROnPlateau` with a default patience value of 3 epochs. 
@@ -108,7 +106,9 @@ plt.show()
 
 
 Notice how in the first 5 epochs the learning rate started from 
-$\frac{\text{max\_lr}}{\text{number of epochs}} = \frac{0.01}{5} = 0.002$ 
+$$
+\text{lr}(t) = \text{max\_lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)
+$$
 and increased till it reached $0.01$ in the 5th epoch.
 
 When the loss plateaus for more than 3 epochs (the value set for patience), the learning rate is halved. We will see how much this learning rate halving actually helps retention time (RT) and MS2 fine-tuning to consistently achieve much better performance without requiring extensive experimentation with hyperparameter changes.

From 461da8a18787585bdef0869726768bd1fc9e2f8f Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Fri, 5 Jul 2024 12:33:02 +0200
Subject: [PATCH 004/250] Update methods.md

---
 docs/methods.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/methods.md b/docs/methods.md
index 762dd2e8..83e3ad8b 100644
--- a/docs/methods.md
+++ b/docs/methods.md
@@ -5,5 +5,6 @@
 methods/command-line
 methods/configuration
 methods/calibration
+methods/transfer-learning
 
 ```

From 35c2302f9e000e0f968606ac6bd056c5690c291b Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Sat, 6 Jul 2024 07:24:03 +0200
Subject: [PATCH 005/250] format

---
 docs/methods/transfer-learning.md | 84 +++++++++++++++----------------
 1 file changed, 41 insertions(+), 43 deletions(-)

diff --git a/docs/methods/transfer-learning.md b/docs/methods/transfer-learning.md
index b6ad3765..75eb1808 100644
--- a/docs/methods/transfer-learning.md
+++ b/docs/methods/transfer-learning.md
@@ -32,7 +32,7 @@ In this phase, the learning rate starts small and gradually increases over a cer
 $$\text{lr}(t) = \text{max lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)$$
 
 ### 2) Reduce on Plateau LR Schedule
-After the warmup phase, the learning rate reaches the maximum value set by the user and remains there until the training loss reaches a plateau. A plateau is defined as the training loss not significantly improving for a certain number of epochs, referred to as "patience". For this phase, we use the PyTorch implementation `torch.optim.lr_scheduler.ReduceLROnPlateau` with a default patience value of 3 epochs. 
+After the warmup phase, the learning rate reaches the maximum value set by the user and remains there until the training loss reaches a plateau. A plateau is defined as the training loss not significantly improving for a certain number of epochs, referred to as "patience". For this phase, we use the PyTorch implementation `torch.optim.lr_scheduler.ReduceLROnPlateau` with a default patience value of 3 epochs.
 
 This approach makes the fine-tuning process less sensitive to the user-defined learning rate. If the model is not learning for 3 epochs, it is likely that the learning rate is too high, and the scheduler will then reduce the learning rate to encourage further learning.
 
@@ -53,10 +53,10 @@ class DummyModel(torch.nn.Module):
     def __init__(self):
         super(DummyModel, self).__init__()
         self.fc = torch.nn.Linear(1, 1)
-    
+
     def forward(self, x):
         return self.fc(x)
-    
+
 model = DummyModel()
 optimizer = torch.optim.Adam(model.parameters(), lr=MAX_LR)
 
@@ -69,7 +69,7 @@ Now since our lr scheduler uses reduce_lr_on_plateau, we need to pass the traini
 """
 
 # Dummy training loss
-losses = [0.12,0.1, 0.09, 0.08, 0.07, 0.06, 0.06,0.06,0.06,0.06,0.06, 0.06] 
+losses = [0.12,0.1, 0.09, 0.08, 0.07, 0.06, 0.06,0.06,0.06,0.06,0.06, 0.06]
 scheduler = CustomScheduler(optimizer, max_lr=MAX_LR, num_warmup_steps=NUM_WARMUP_STEPS)
 
 learning_rates = []
@@ -85,27 +85,27 @@ ax1.set_ylabel('Learning Rate', color=color)
 ax1.plot(learning_rates, color=color)
 ax1.tick_params(axis='y', labelcolor=color)
 
-ax2 = ax1.twinx()  
+ax2 = ax1.twinx()
 
 color = 'tab:blue'
-ax2.set_ylabel('Loss', color=color)  
+ax2.set_ylabel('Loss', color=color)
 ax2.plot(losses, color=color)
 ax2.tick_params(axis='y', labelcolor=color)
 
-fig.tight_layout() 
+fig.tight_layout()
 plt.show()
 ```
 
-  
-    
 
 
-    
+
+
+
 ![png](finetuning_imgs/finetuning_7_1.png)
-    
 
 
-Notice how in the first 5 epochs the learning rate started from 
+
+Notice how in the first 5 epochs the learning rate started from
 $$
 \text{lr}(t) = \text{max\_lr} \times \left( \frac{t}{\text{number of warmup epochs}} \right)
 $$
@@ -181,9 +181,9 @@ plt.show()
 ```
 
 
-    
+
 ![png](finetuning_imgs/finetuning_11_0.png)
-    
+
 
 
 ## Transfer Learning in alphaDIA
@@ -220,9 +220,9 @@ plt.scatter(transfer_lib.precursor_df['rt_norm'], transfer_lib.precursor_df['rt_
 plt.xlabel('RT observed')
 plt.ylabel('RT predicted')
 ```
-    
+
 ![png](finetuning_imgs/finetuning_17_3.png)
-    
+
 
 
 
@@ -246,9 +246,9 @@ plt.ylabel('RT predicted')
     2024-07-05 11:11:20>  intercept                     : 0.1113
     2024-07-05 11:11:20>  abs_error_95th_percentile     : 0.4317
     2024-07-05 11:11:20>  Fine-tuning RT model with the following settings:
-    2024-07-05 11:11:20>  Train fraction:      0.70     Train size:      20226     
-    2024-07-05 11:11:20>  Validation fraction: 0.20     Validation size: 5779      
-    2024-07-05 11:11:20>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:11:20>  Train fraction:      0.70     Train size:      20226
+    2024-07-05 11:11:20>  Validation fraction: 0.20     Validation size: 5779
+    2024-07-05 11:11:20>  Test fraction:       0.10     Test size:       2889
     2024-07-05 11:11:30>  Epoch 0   Lr: 0.00020   Training loss: 0.1619   validation loss: 0.1270
     2024-07-05 11:11:38>  Epoch 1   Lr: 0.00030   Training loss: 0.0657   validation loss: 0.0482
     ...
@@ -263,7 +263,7 @@ plt.ylabel('RT predicted')
     2024-07-05 11:18:01>  abs_error_95th_percentile     : 0.0846
 
 ![png](finetuning_imgs/finetuning_19_3.png)
-    
+
 
 
 
@@ -275,9 +275,9 @@ g.legend.set_title('Data split')
 ```
 
 
-    
+
 ![png](finetuning_imgs/finetuning_20_0.png)
-    
+
 
 
 ## Charge Fine-tuning
@@ -288,25 +288,25 @@ g.legend.set_title('Data split')
 charge_stats = tune_mgr.finetune_charge(psm_df=transfer_lib.precursor_df)
 ```
 
-    2024-07-05 11:18:13>  Charge model tested on all dataset with the following metrics: 
+    2024-07-05 11:18:13>  Charge model tested on all dataset with the following metrics:
     2024-07-05 11:18:13>  ce_loss                       : 0.5444
     2024-07-05 11:18:13>  accuracy                      : 0.6414
     2024-07-05 11:18:13>  precision                     : 0.3108
     2024-07-05 11:18:13>  recall                        : 0.2975
     2024-07-05 11:18:13>  Fine-tuning Charge model with following settings:
-    2024-07-05 11:18:13>  Train fraction:      0.70     Train size:      20226     
-    2024-07-05 11:18:13>  Validation fraction: 0.20     Validation size: 5779      
-    2024-07-05 11:18:13>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:18:13>  Train fraction:      0.70     Train size:      20226
+    2024-07-05 11:18:13>  Validation fraction: 0.20     Validation size: 5779
+    2024-07-05 11:18:13>  Test fraction:       0.10     Test size:       2889
     2024-07-05 11:18:24>  Epoch 0   Lr: 0.00020   Training loss: 0.6341   validation loss: 0.6309
-    2024-07-05 11:18:35>  Epoch 1   Lr: 0.00030   Training loss: 0.4453   
+    2024-07-05 11:18:35>  Epoch 1   Lr: 0.00030   Training loss: 0.4453
     ...
     2024-07-05 11:27:54>  Epoch 50  Lr: 0.00015   Training loss: 0.0618   validation loss: 0.1364
-    2024-07-05 11:27:55>  Charge model tested on test dataset with the following metrics: 
+    2024-07-05 11:27:55>  Charge model tested on test dataset with the following metrics:
     2024-07-05 11:27:55>  ce_loss                       : 0.1476
     2024-07-05 11:27:55>  accuracy                      : 0.9515
     2024-07-05 11:27:55>  precision                     : 0.9107
     2024-07-05 11:27:55>  recall                        : 0.8994
-    
+
 
 
 ```python
@@ -317,9 +317,9 @@ g.legend.set_title('Data split')
 ```
 
 
-    
+
 ![png](finetuning_imgs/finetuning_23_0.png)
-    
+
 
 
 ## MS2 Fine-tuning
@@ -341,9 +341,9 @@ plt.ylabel('Similarity')
 plt.title('Similarity between observed and predicted MS2 spectra before fine-tuning')
 ```
 
-    
+
 ![png](finetuning_imgs/finetuning_27_4.png)
-    
+
 
 
 
@@ -355,7 +355,7 @@ ms2_stats = tune_mgr.finetune_ms2(psm_df=transfer_lib.precursor_df.copy(), match
 
     100%|██████████| 5779/5779 [00:01<00:00, 3818.99it/s]
     100%|██████████| 2889/2889 [00:00<00:00, 4138.71it/s]
-    
+
 
     2024-07-05 11:38:06>  Ms2 model tested on validation dataset with the following metrics:
     2024-07-05 11:38:06>  l1_loss                       : 0.0323
@@ -364,9 +364,9 @@ ms2_stats = tune_mgr.finetune_ms2(psm_df=transfer_lib.precursor_df.copy(), match
     2024-07-05 11:38:06>  SA-mean                       : 0.5544
     2024-07-05 11:38:06>  SPC-mean                      : 0.6447
     2024-07-05 11:38:06>  Fine-tuning MS2 model with the following settings:
-    2024-07-05 11:38:06>  Train fraction:      0.70     Train size:      20226     
-    2024-07-05 11:38:06>  Validation fraction: 0.20     Validation size: 5779      
-    2024-07-05 11:38:06>  Test fraction:       0.10     Test size:       2889      
+    2024-07-05 11:38:06>  Train fraction:      0.70     Train size:      20226
+    2024-07-05 11:38:06>  Validation fraction: 0.20     Validation size: 5779
+    2024-07-05 11:38:06>  Test fraction:       0.10     Test size:       2889
     2024-07-05 11:38:39>  Epoch 0   Lr: 0.00020   Training loss: 0.0212   validation loss: 0.0249
     ...
     2024-07-05 12:05:54>  Epoch 50  Lr: 0.00030   Training loss: 0.0110   validation loss: 0.0176
@@ -376,7 +376,7 @@ ms2_stats = tune_mgr.finetune_ms2(psm_df=transfer_lib.precursor_df.copy(), match
     2024-07-05 12:05:56>  COS-mean                      : 0.9348
     2024-07-05 12:05:56>  SA-mean                       : 0.7688
     2024-07-05 12:05:56>  SPC-mean                      : 0.7802
-    
+
 
 
 ```python
@@ -397,9 +397,9 @@ plt.xlabel("Index")
 plt.ylabel("Similarity")
 plt.title("Similarity between observed and predicted MS2 spectra after fine-tuning")
 ```
-    0.9401921591393136 
+    0.9401921591393136
 ![png](finetuning_imgs/finetuning_29_4.png)
-    
+
 
 
 
@@ -411,7 +411,5 @@ g.legend.set_title('Data split')
 ```
 
 
-    
-![png](finetuning_imgs/finetuning_30_0.png)
-    
 
+![png](finetuning_imgs/finetuning_30_0.png)

From 99ce208e41afcc52a3fbf51caf14235bff3a861e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 10:13:54 +0200
Subject: [PATCH 006/250] introduce custom exceptions

---
 alphadia/cli.py                     | 20 +++++++---
 alphadia/exceptions.py              | 53 +++++++++++++++++++++++++++
 alphadia/outputtransform.py         | 10 +----
 alphadia/planning.py                | 57 ++++++++++++++++-------------
 alphadia/workflow/peptidecentric.py | 15 +-------
 alphadia/workflow/reporting.py      | 29 ++++++++++-----
 6 files changed, 123 insertions(+), 61 deletions(-)
 create mode 100644 alphadia/exceptions.py

diff --git a/alphadia/cli.py b/alphadia/cli.py
index 4b78126c..6bff7b65 100644
--- a/alphadia/cli.py
+++ b/alphadia/cli.py
@@ -15,6 +15,7 @@
 # alphadia imports
 import alphadia
 from alphadia import utils
+from alphadia.exceptions import CustomError
 from alphadia.workflow import reporting
 
 logger = logging.getLogger()
@@ -325,7 +326,7 @@ def run(*args, **kwargs):
     try:
         import matplotlib
 
-        # important to supress matplotlib output
+        # important to suppress matplotlib output
         matplotlib.use("Agg")
 
         from alphadia.planning import Plan
@@ -341,8 +342,15 @@ def run(*args, **kwargs):
         plan.run()
 
     except Exception as e:
-        import traceback
-
-        logger.info(traceback.format_exc())
-        logger.error(e)
-        sys.exit(1)
+        if isinstance(e, CustomError):
+            message = f"An error occurred: {e.error_code}: {e.msg} {e.detail_msg}"
+            exit_code = 1
+        else:
+            import traceback
+
+            logger.info(traceback.format_exc())
+            message = f"An unknown error occurred: {e}"
+            exit_code = 127
+
+        logger.error(message)
+        sys.exit(exit_code)
diff --git a/alphadia/exceptions.py b/alphadia/exceptions.py
new file mode 100644
index 00000000..c2245a81
--- /dev/null
+++ b/alphadia/exceptions.py
@@ -0,0 +1,53 @@
+"""Module containing custom exceptions."""
+
+
+class CustomError(Exception):
+    """Custom alphaDIA error class."""
+
+    _error_code = None
+    _msg = None
+    _detail_msg = ""
+
+    @property
+    def error_code(self):
+        return self._error_code
+
+    @property
+    def msg(self):
+        return self._msg
+
+    @property
+    def detail_msg(self):
+        return self._detail_msg
+
+
+class BusinessError(CustomError):
+    """Custom error class for 'business' errors.
+
+    A 'business' error is an error that is caused by the input (data, configuration, ...) and not by a
+    malfunction in alphaDIA.
+    """
+
+
+class NoPsmFoundError(BusinessError):
+    """Raise when no PSMs are found in the search results."""
+
+    _error_code = "NO_PSM_FOUND"
+
+    _msg = "No psm files accumulated, can't continue"
+
+
+class NoRecalibrationTargetError(BusinessError):
+    """Raise when no recalibration target is found."""
+
+    _error_code = "NO_RECALIBRATION_TARGET"
+
+    _msg = "Searched all data without finding recalibration target"
+
+    _detail_msg = """Search for raw file failed as not enough precursors were found for calibration.
+                 This can have the following reasons:
+                   1. The sample was empty and therefore no precursors were found.
+                   2. The sample contains only very few precursors.
+                      For small libraries, try to set recalibration_target to a lower value.
+                      For large libraries, try to reduce the library size and reduce the calibration MS1 and MS2 tolerance.
+                   3. There was a fundamental issue with search parameters."""
diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 33714410..c9d1c1d3 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -18,6 +18,7 @@
 
 from alphadia import fdr, grouping, libtransform, utils
 from alphadia.consensus.utils import read_df, write_df
+from alphadia.exceptions import NoPsmFoundError
 from alphadia.outputaccumulator import (
     AccumulationBroadcaster,
     TransferLearningAccumulator,
@@ -27,12 +28,6 @@
 logger = logging.getLogger()
 
 
-class OutputGenerationError(Exception):
-    """Raised when an error occurs during output generation"""
-
-    pass
-
-
 def get_frag_df_generator(folder_list: list[str]):
     """Return a generator that yields a tuple of (raw_name, frag_df)
 
@@ -524,8 +519,7 @@ def build_precursor_table(
                     logger.warning(e)
 
         if len(psm_df_list) == 0:
-            logger.error("No psm files accumulated, can't continue")
-            raise OutputGenerationError("No psm files accumulated, can't continue")
+            raise NoPsmFoundError()
 
         logger.info("Building combined output")
         psm_df = pd.concat(psm_df_list)
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 50f81b46..25b387af 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -23,7 +23,9 @@
 
 # alphadia imports
 from alphadia import libtransform, outputtransform
+from alphadia.exceptions import CustomError
 from alphadia.workflow import peptidecentric, reporting
+from alphadia.workflow.base import WorkflowBase
 from alphadia.workflow.config import Config
 
 logger = logging.getLogger()
@@ -324,6 +326,7 @@ def run(
                     logger.info(f"No existing quantification found for {raw_name}")
 
                 workflow.load(dia_path, speclib)
+                raise ValueError("123")
                 workflow.calibration()
 
                 psm_df, frag_df = workflow.extraction()
@@ -340,36 +343,21 @@ def run(
                 psm_df.to_parquet(psm_location, index=False)
                 frag_df.to_parquet(frag_location, index=False)
 
-                workflow.reporter.log_string(f"Finished workflow for {raw_name}")
-                workflow.reporter.context.__exit__(None, None, None)
-                del workflow
+            except CustomError as e:
+                log_exception_event(e, raw_name, workflow)
 
-            except peptidecentric.CalibrationError:
-                # get full traceback
-                logger.error(
-                    f"Search for {raw_name} failed as not enough precursors were found for calibration"
-                )
-                logger.error("This can have the following reasons:")
-                logger.error(
-                    "   1. The sample was empty and therefore nor precursors were found"
-                )
-                logger.error("   2. The sample contains only very few precursors.")
-                logger.error(
-                    "      For small libraries, try to set recalibration_target to a lower value"
-                )
-                logger.error(
-                    "      For large libraries, try to reduce the library size and reduce the calibration MS1 and MS2 tolerance"
-                )
-                logger.error(
-                    "   3. There was a fundamental issue with search parameters"
-                )
                 continue
+
             except Exception as e:
-                logger.error(
-                    f"Search for {raw_name} failed with error {e}", exc_info=True
-                )
+                log_exception_event(e, raw_name, workflow)
+
                 raise e
 
+            finally:
+                workflow.reporter.log_string(f"Finished workflow for {raw_name}")
+                workflow.reporter.context.__exit__(None, None, None)
+                del workflow
+
         base_spec_lib = SpecLibBase()
         base_spec_lib.load_hdf(
             os.path.join(self.output_folder, "speclib.hdf"), load_mod_seq=True
@@ -383,3 +371,22 @@ def run(
     def clean(self):
         if not self.config["library_loading"]["save_hdf"]:
             os.remove(os.path.join(self.output_folder, "speclib.hdf"))
+
+
+def log_exception_event(e: Exception, raw_name: str, workflow: WorkflowBase) -> None:
+    """Log exception and emit event to reporter if available."""
+
+    if isinstance(e, CustomError):
+        logger.error(f"Search for {raw_name} failed with error: {e.error_code} {e.msg}")
+        logger.error(e.detail_msg)
+    else:
+        logger.error(
+            f"Search for {raw_name} failed with unknown error {e}", exc_info=True
+        )
+
+    if workflow.reporter:
+        workflow.reporter.log_string(
+            value=str(e),
+            verbosity="error",
+        )
+        workflow.reporter.log_event(name="exception", value=str(e), exception=e)
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index c07c6cdc..e2be1a81 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -15,6 +15,7 @@
 
 # alphadia imports
 from alphadia import fragcomp, plexscoring, utils
+from alphadia.exceptions import NoRecalibrationTargetError
 from alphadia.peakgroup import search
 from alphadia.workflow import base, manager
 
@@ -98,12 +99,6 @@
 )
 
 
-class CalibrationError(Exception):
-    """Raised when calibration fails"""
-
-    pass
-
-
 class PeptideCentricWorkflow(base.WorkflowBase):
     def __init__(
         self,
@@ -488,13 +483,7 @@ def calibration(self):
                 else:
                     # check if last step has been reached
                     if current_step == len(self.batch_plan) - 1:
-                        self.reporter.log_string(
-                            "Searched all data without finding recalibration target",
-                            verbosity="error",
-                        )
-                        raise CalibrationError(
-                            "Searched all data without finding recalibration target"
-                        )
+                        raise NoRecalibrationTargetError()
 
             self.end_of_epoch()
 
diff --git a/alphadia/workflow/reporting.py b/alphadia/workflow/reporting.py
index d08ec45d..12eb393a 100644
--- a/alphadia/workflow/reporting.py
+++ b/alphadia/workflow/reporting.py
@@ -21,6 +21,8 @@
 # As soon as its instantiated the default logger will be configured with a path to save the log file
 __is_initiated__ = False
 
+from alphadia.exceptions import CustomError
+
 # Add a new logging level to the default logger, level 21 is just above INFO (20)
 # This has to happen at load time to make the .progress() method available even if no logger is instantiated
 PROGRESS_LEVELV_NUM = 21
@@ -361,7 +363,14 @@ def __exit__(
         self.entered_context = False
         self.start_time = 0
 
-    def log_event(self, name: str, value: typing.Any):
+    def log_event(
+        self,
+        name: str,
+        value: typing.Any,
+        exception: Exception | None = None,
+        *args,
+        **kwargs,
+    ):
         """Log an event to the `events.jsonl` file.
 
         Important: This method will only log events if the backend is in a context.
@@ -379,16 +388,18 @@ def log_event(self, name: str, value: typing.Any):
 
         if not self.entered_context:
             return
+        message = {
+            "absolute_time": self.absolute_time(),
+            "relative_time": self.relative_time(),
+            "type": "event",
+            "name": name,
+            "value": value,
+            "verbosity": 0,
+        }
+        if exception is not None and isinstance(exception, CustomError):
+            message["error_code"] = exception.error_code
 
         with open(self.events_path, "a") as f:
-            message = {
-                "absolute_time": self.absolute_time(),
-                "relative_time": self.relative_time(),
-                "type": "event",
-                "name": name,
-                "value": value,
-                "verbosity": 0,
-            }
             f.write(json.dumps(message) + "\n")
 
     def log_metric(self, name: str, value: float):

From e2004f967ad431c56f0f71241e824be6dae05586 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 10:14:26 +0200
Subject: [PATCH 007/250] unify reporter interfaces

---
 alphadia/workflow/reporting.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/reporting.py b/alphadia/workflow/reporting.py
index 12eb393a..c45211db 100644
--- a/alphadia/workflow/reporting.py
+++ b/alphadia/workflow/reporting.py
@@ -223,6 +223,8 @@ def log_figure(
         name: str,
         figure: Figure | np.ndarray,
         extension: str = "png",
+        *args,
+        **kwargs,
     ):
         """Log a figure to the figures folder.
 
@@ -402,7 +404,7 @@ def log_event(
         with open(self.events_path, "a") as f:
             f.write(json.dumps(message) + "\n")
 
-    def log_metric(self, name: str, value: float):
+    def log_metric(self, name: str, value: float, *args, **kwargs):
         """Log a metric to the `events.jsonl` file.
 
         Important: This method will only log metrics if the backend is in a context.
@@ -432,7 +434,7 @@ def log_metric(self, name: str, value: float):
             }
             f.write(json.dumps(message) + "\n")
 
-    def log_string(self, value: str, verbosity: int = "info"):
+    def log_string(self, value: str, verbosity: int = "info", *args, **kwargs):
         """Log a string to the `events.jsonl` file.
 
         Important: This method will only log strings if the backend is in a context.
@@ -460,9 +462,10 @@ def log_string(self, value: str, verbosity: int = "info"):
                 "value": value,
                 "verbosity": verbosity,
             }
+
             f.write(json.dumps(message) + "\n")
 
-    def log_figure(self, name: str, figure: typing.Any):
+    def log_figure(self, name: str, figure: typing.Any, *args, **kwargs):
         """Log a base64 image of a figure to the `events.jsonl` file.
 
         Important: This method will only log figures if the backend is in a context.
@@ -517,7 +520,7 @@ def __init__(self, path: str = None) -> None:
         self.logger = logging.getLogger()
         super().__init__()
 
-    def log_string(self, value: str, verbosity: str = "info"):
+    def log_string(self, value: str, verbosity: str = "info", *args, **kwargs):
         if verbosity == "progress":
             self.logger.progress(value)
         elif verbosity == "info":
@@ -558,7 +561,7 @@ def __exit__(self, exc_type, exc_value, exc_traceback):
 class Pipeline:
     def __init__(
         self,
-        backends: list[type[Backend]] = None,
+        backends: list[Backend] = None,
     ):
         """Metric logger which allows to log metrics, plots and strings to multiple backends.
 
@@ -576,7 +579,7 @@ def __init__(
         self.context = Context(self)
 
         # instantiate backends
-        self.backends = backends
+        self.backends: list[Backend] = backends
 
     def __enter__(self):
         for backend in self.backends:

From 40b1178d6379ebbbb824e6b462d0336c4d7d6234 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 10:18:16 +0200
Subject: [PATCH 008/250] adapt readme

---
 docs/installation.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index 5648c49d..329f9104 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -214,3 +214,10 @@ pip install "alphadia[stable]"
 ```
 Afterwards, verify the alphaDIA installation by running:
 `alphadia --version` which should output the current version.
+
+### Notes on running alphaDIA as part of automated workflows
+AlphaDIA is designed to be run in a headless mode. In case of an error, a nonzero exit code is returned.
+A exit code of 127 indicates that there was an unknown error. All other nonzero exit codes pertain to
+'business errors', i.e. those caused most likely by user input (data and/or configuration).
+
+Further details on such errors can be found in the `events.jsonl` file in the `.progress` folder(s) of the output directory.

From 59d0ef1695c6652f7cb1392610b4503767e8f398 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 10:21:29 +0200
Subject: [PATCH 009/250] make method private

---
 alphadia/planning.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/alphadia/planning.py b/alphadia/planning.py
index 25b387af..a8417af4 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -344,13 +344,11 @@ def run(
                 frag_df.to_parquet(frag_location, index=False)
 
             except CustomError as e:
-                log_exception_event(e, raw_name, workflow)
-
+                _log_exception_event(e, raw_name, workflow)
                 continue
 
             except Exception as e:
-                log_exception_event(e, raw_name, workflow)
-
+                _log_exception_event(e, raw_name, workflow)
                 raise e
 
             finally:
@@ -373,7 +371,7 @@ def clean(self):
             os.remove(os.path.join(self.output_folder, "speclib.hdf"))
 
 
-def log_exception_event(e: Exception, raw_name: str, workflow: WorkflowBase) -> None:
+def _log_exception_event(e: Exception, raw_name: str, workflow: WorkflowBase) -> None:
     """Log exception and emit event to reporter if available."""
 
     if isinstance(e, CustomError):

From 18c54d70960a1a3a3f74af5d2f810d81689c4fbf Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 10:22:51 +0200
Subject: [PATCH 010/250] remove accidental commit

---
 alphadia/planning.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/alphadia/planning.py b/alphadia/planning.py
index a8417af4..439e07a8 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -326,7 +326,6 @@ def run(
                     logger.info(f"No existing quantification found for {raw_name}")
 
                 workflow.load(dia_path, speclib)
-                raise ValueError("123")
                 workflow.calibration()
 
                 psm_df, frag_df = workflow.extraction()

From 0108fe2b258624f54c570bc9f2f4605921a59c48 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 16 Jul 2024 16:30:32 +0200
Subject: [PATCH 011/250] add NotDiaDataError

---
 alphadia/data/bruker.py | 6 ++----
 alphadia/exceptions.py  | 8 ++++++++
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/alphadia/data/bruker.py b/alphadia/data/bruker.py
index 73f0f629..e34126cc 100644
--- a/alphadia/data/bruker.py
+++ b/alphadia/data/bruker.py
@@ -12,6 +12,7 @@
 
 from alphadia import utils
 from alphadia.data.stats import log_stats
+from alphadia.exceptions import NotDiaDataError
 
 logger = logging.getLogger()
 
@@ -62,10 +63,7 @@ def __init__(
                 try:
                     cycle_shape = self._cycle.shape[0]
                 except AttributeError as e:
-                    logger.error(
-                        "Could not find cycle shape. Please check if this is a valid DIA data set."
-                    )
-                    raise e
+                    raise NotDiaDataError() from e
                 else:
                     if cycle_shape != 1:
                         msg = f"Unexpected cycle shape: {cycle_shape} (expected: 1). "
diff --git a/alphadia/exceptions.py b/alphadia/exceptions.py
index c2245a81..8cd35be6 100644
--- a/alphadia/exceptions.py
+++ b/alphadia/exceptions.py
@@ -51,3 +51,11 @@ class NoRecalibrationTargetError(BusinessError):
                       For small libraries, try to set recalibration_target to a lower value.
                       For large libraries, try to reduce the library size and reduce the calibration MS1 and MS2 tolerance.
                    3. There was a fundamental issue with search parameters."""
+
+
+class NotDiaDataError(BusinessError):
+    """Raise when data is not from DIA."""
+
+    _error_code = "NOT_DIA_DATA"
+
+    _msg = "Could not find cycle shape. Please check if this is a valid DIA data set."

From d54a80d3cb51b0aeb63159ac60a801d90363f87d Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 22 Jul 2024 15:45:52 +0200
Subject: [PATCH 012/250] raise on missing checkpoint path

---
 alphadia/libtransform.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index 03e78584..0daade51 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -314,6 +314,11 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
 
         model_mgr = ModelManager(device=device)
         if self.checkpoint_folder_path is not None:
+            if not os.path.exists(self.checkpoint_folder_path):
+                raise ValueError(
+                    f"PeptDeep model checkpoint folder {self.checkpoint_folder_path} does not exist"
+                )
+
             logging.info(f"Loading PeptDeep models from {self.checkpoint_folder_path}")
             model_mgr.load_external_models(
                 ms2_model_file=os.path.join(self.checkpoint_folder_path, "ms2.pth"),

From a43d48023a499080ddbf84eb6689005b0627abd7 Mon Sep 17 00:00:00 2001
From: despardo <odespard@gmail.com>
Date: Mon, 22 Jul 2024 23:40:50 +0200
Subject: [PATCH 013/250] added ability to select classifier version

---
 alphadia/workflow/manager.py      | 22 +++++++++----
 tests/unit_tests/test_workflow.py | 52 +++++++++++++++++++++++++++++++
 2 files changed, 68 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 109280f9..6a0f0dc0 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -499,7 +499,7 @@ def __init__(
             self.feature_columns = feature_columns
             self.classifier_store = {}
             self.classifier_base = classifier_base
-
+        self.current_version = -1
         self.load_classifier_store()
 
     def fit_predict(
@@ -512,6 +512,7 @@ def fit_predict(
         df_fragments: None | pd.DataFrame = None,
         dia_cycle: None | np.ndarray = None,
         decoy_channel: int = -1,
+        version: None | int = -1,
     ):
         """Update the parameters dict with the values in update_dict."""
         available_columns = list(
@@ -559,7 +560,7 @@ def fit_predict(
         self.reporter.log_string(f"Decoy channel: {decoy_channel}")
         self.reporter.log_string(f"Competetive: {competetive}")
 
-        classifier = self.get_classifier(available_columns)
+        classifier = self.get_classifier(available_columns, version)
         if decoy_strategy == "precursor":
             psm_df = fdr.perform_fdr(
                 classifier,
@@ -619,7 +620,13 @@ def fit_predict(
             raise ValueError(f"Invalid decoy_strategy: {decoy_strategy}")
 
         self.is_fitted = True
-        self.classifier_store[column_hash(available_columns)] = classifier
+
+        self.current_version += 1
+        classifier_hash = column_hash(
+            np.append(available_columns, str(self.current_version))
+        )
+        self.classifier_store[classifier_hash] = classifier
+
         self.save()
 
         return psm_df
@@ -657,14 +664,17 @@ def load_classifier_store(self, path=None):
                         torch.load(os.path.join(path, file))
                     )
 
-    def get_classifier(self, available_columns):
-        classifier_hash = column_hash(available_columns)
+    def get_classifier(self, available_columns, version):
+        classifier_hash = column_hash(np.append(available_columns, str(version)))
         if classifier_hash in self.classifier_store:
-            classifier = self.classifier_store[classifier_hash]
+            classifier = deepcopy(self.classifier_store[classifier_hash])
         else:
             classifier = deepcopy(self.classifier_base)
         return classifier
 
+    def get_current_version(self):
+        return self.current_version
+
     def predict(self):
         """Return the parameters dict."""
         raise NotImplementedError(
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 0a3362d4..d2df38ae 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -11,6 +11,7 @@
 
 from alphadia.calibration.models import LOESSRegression
 from alphadia.calibration.property import Calibration
+from alphadia.fdrexperimental import BinaryClassifierLegacyNewBatching
 from alphadia.workflow import base, manager
 
 
@@ -296,3 +297,54 @@ def test_workflow_base():
             # os.rmdir(os.path.join(my_workflow.path, my_workflow.FIGURE_PATH))
             # os.rmdir(os.path.join(my_workflow.path))
             shutil.rmtree(os.path.join(my_workflow.parent_path))
+
+
+FDR_TEST_BASE_CLASSIFIER = BinaryClassifierLegacyNewBatching(
+    test_size=0.001, batch_size=50, learning_rate=0.001, epochs=1
+)
+FDR_TEST_FEATURES = ["feature_a", "feature_b"]
+
+
+def fdr_testdata(features):
+    test_dict = {}
+
+    for feature in features:
+        test_dict[feature] = np.random.normal(50, 2, 100)
+    test_dict["decoy"] = np.random.randint(0, 2, 100)
+    test_dict["precursor_idx"] = np.random.randint(1000, 10000, 100)
+
+    return pd.DataFrame(test_dict)
+
+
+def test_fdr_manager():
+    fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
+
+    assert fdr_manager.is_loaded_from_file is False
+    assert fdr_manager.is_fitted is False
+
+    assert fdr_manager.feature_columns == FDR_TEST_FEATURES
+    assert fdr_manager.classifier_base == FDR_TEST_BASE_CLASSIFIER
+
+
+@pytest.mark.slow
+def test_fdr_manager_fit_predict():
+    fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
+    test_features_df = fdr_testdata(FDR_TEST_FEATURES)
+
+    assert len(fdr_manager.classifier_store) == 1
+
+    fdr_manager.fit_predict(
+        test_features_df,
+        decoy_strategy="precursor",
+        competetive=False,
+        df_fragments=None,
+        dia_cycle=None,
+        # neptune_run=self.neptune
+    )
+
+    assert len(fdr_manager.classifier_store) == 2
+    assert fdr_manager.get_current_version() == 0
+    assert (
+        manager.column_hash(np.append(FDR_TEST_FEATURES, str(0)))
+        in fdr_manager.classifier_store
+    )

From 178eb7d0aebdaa5cf181e250d67b3ad01ba4ed8e Mon Sep 17 00:00:00 2001
From: odespard <despard@PCMANN464.w2k.biochem.mpg.de>
Date: Tue, 23 Jul 2024 10:52:56 +0200
Subject: [PATCH 014/250] improved version default and lists in classifier
 store

---
 alphadia/workflow/manager.py      | 1408 +++++++++++++++--------------
 tests/unit_tests/test_workflow.py |    4 +-
 2 files changed, 717 insertions(+), 695 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 6a0f0dc0..005d9335 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -1,693 +1,715 @@
-# native imports
-import logging
-import os
-import pickle
-import typing
-from copy import deepcopy
-
-import numpy as np
-
-# alpha family imports
-# third party imports
-import pandas as pd
-import torch
-import xxhash
-
-# alphadia imports
-import alphadia
-from alphadia import fdr
-from alphadia.calibration.property import Calibration, calibration_model_provider
-from alphadia.workflow import reporting
-
-logger = logging.getLogger()
-
-
-class BaseManager:
-    def __init__(
-        self,
-        path: None | str = None,
-        load_from_file: bool = True,
-        figure_path: None | str = None,
-        reporter: None | reporting.Pipeline | reporting.Backend = None,
-    ):
-        """Base class for all managers which handle parts of the workflow.
-
-        Parameters
-        ----------
-
-        path : str, optional
-            Path to the manager pickle on disk.
-
-        load_from_file : bool, optional
-            If True, the manager will be loaded from file if it exists.
-        """
-
-        self._path = path
-        self.is_loaded_from_file = False
-        self.is_fitted = False
-        self.figure_path = figure_path
-        self._version = alphadia.__version__
-        self.reporter = reporting.LogBackend() if reporter is None else reporter
-
-        if load_from_file:
-            self.load()
-
-    @property
-    def path(self):
-        """Path to the manager pickle on disk."""
-        return self._path
-
-    @property
-    def is_loaded_from_file(self):
-        """Check if the calibration manager was loaded from file."""
-        return self._is_loaded_from_file
-
-    @is_loaded_from_file.setter
-    def is_loaded_from_file(self, value):
-        self._is_loaded_from_file = value
-
-    @property
-    def is_fitted(self):
-        """Check if all estimators in all calibration groups are fitted."""
-        return self._is_fitted
-
-    @is_fitted.setter
-    def is_fitted(self, value):
-        self._is_fitted = value
-
-    def save(self):
-        """Save the state to pickle file."""
-        if self.path is not None:
-            try:
-                with open(self.path, "wb") as f:
-                    pickle.dump(self, f)
-            except Exception:
-                self.reporter.log_string(
-                    f"Failed to save {self.__class__.__name__} to {self.path}",
-                    verbosity="error",
-                )
-
-    def load(self):
-        """Load the state from pickle file."""
-        if self.path is not None:
-            if os.path.exists(self.path):
-                try:
-                    with open(self.path, "rb") as f:
-                        loaded_state = pickle.load(f)
-
-                        if loaded_state._version == self._version:
-                            self.__dict__.update(loaded_state.__dict__)
-                            self.is_loaded_from_file = True
-                        else:
-                            self.reporter.log_string(
-                                f"Version mismatch while loading {self.__class__}: {loaded_state._version} != {self._version}. Will not load.",
-                                verbosity="warning",
-                            )
-                except Exception:
-                    self.reporter.log_string(
-                        f"Failed to load {self.__class__.__name__} from {self.path}",
-                        verbosity="error",
-                    )
-                else:
-                    self.reporter.log_string(
-                        f"Loaded {self.__class__.__name__} from {self.path}"
-                    )
-            else:
-                self.reporter.log_string(
-                    f"{self.__class__.__name__} not found at: {self.path}",
-                    verbosity="warning",
-                )
-
-    def fit(self):
-        """Fit the workflow property of the manager."""
-        raise NotImplementedError(
-            f"fit() not implemented for {self.__class__.__name__}"
-        )
-
-    def predict(self):
-        """Return the predictions of the workflow property of the manager."""
-        raise NotImplementedError(
-            f"predict() not implemented for {self.__class__.__name__}"
-        )
-
-    def fit_predict(self):
-        """Fit and return the predictions of the workflow property of the manager."""
-        raise NotImplementedError(
-            f"fit_predict() not implemented for {self.__class__.__name__}"
-        )
-
-
-class CalibrationManager(BaseManager):
-    def __init__(
-        self,
-        config: None | dict = None,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        """Contains, updates and applies all calibrations for a single run.
-
-        Calibrations are grouped into calibration groups. Each calibration group is applied to a single data structure (precursor dataframe, fragment fataframe, etc.). Each calibration group contains multiple estimators which each calibrate a single property (mz, rt, etc.). Each estimator is a `Calibration` object which contains the estimator function.
-
-        Parameters
-        ----------
-
-        config : typing.Union[None, dict], default=None
-            Calibration config dict. If None, the default config is used.
-
-        path : str, default=None
-            Path where the current parameter set is saved to and loaded from.
-
-        load_from_file : bool, default=True
-            If True, the manager will be loaded from file if it exists.
-
-        """
-
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.estimator_groups = []
-            self.load_config(config)
-
-    @property
-    def estimator_groups(self):
-        """List of calibration groups."""
-        return self._estimator_groups
-
-    @estimator_groups.setter
-    def estimator_groups(self, value):
-        self._estimator_groups = value
-
-    def disable_mobility_calibration(self):
-        """Iterate all estimators and remove the mobility estimator from each group."""
-        for group in self.estimator_groups:
-            for estimator in group["estimators"]:
-                if estimator.name == "mobility":
-                    group["estimators"].remove(estimator)
-                    self.reporter.log_string(
-                        f'removed mobility estimator from group {group["name"]}'
-                    )
-
-    def load_config(self, config: dict):
-        """Load calibration config from config Dict.
-
-        each calibration config is a list of calibration groups which consist of multiple estimators.
-        For each estimator the `model` and `model_args` are used to request a model from the calibration_model_provider and to initialize it.
-        The estimator is then initialized with the `Calibration` class and added to the group.
-
-        Parameters
-        ----------
-
-        config : dict
-            Calibration config dict
-
-        Example
-        -------
-
-        Create a calibration manager with a single group and a single estimator:
-
-        .. code-block:: python
-
-            calibration_manager = CalibrationManager()
-            calibration_manager.load_config([{
-                'name': 'mz_calibration',
-                'estimators': [
-                    {
-                        'name': 'mz',
-                        'model': 'LOESSRegression',
-                        'model_args': {
-                            'n_kernels': 2
-                        },
-                        'input_columns': ['mz_library'],
-                        'target_columns': ['mz_observed'],
-                        'output_columns': ['mz_calibrated'],
-                        'transform_deviation': 1e6
-                    },
-
-                ]
-            }])
-
-        """
-        self.reporter.log_string("Loading calibration config")
-        self.reporter.log_string(f"Calibration config: {config}")
-        for group in config:
-            self.reporter.log_string(
-                f'Calibration group :{group["name"]}, found {len(group["estimators"])} estimator(s)'
-            )
-            for estimator in group["estimators"]:
-                try:
-                    template = calibration_model_provider.get_model(estimator["model"])
-                    model_args = estimator.get("model_args", {})
-                    estimator["function"] = template(**model_args)
-                except Exception as e:
-                    self.reporter.log_string(
-                        f'Could not load estimator {estimator["name"]}: {e}',
-                        verbosity="error",
-                    )
-
-            group_copy = {"name": group["name"]}
-            group_copy["estimators"] = [Calibration(**x) for x in group["estimators"]]
-            self.estimator_groups.append(group_copy)
-
-    def get_group_names(self):
-        """Get the names of all calibration groups.
-
-        Returns
-        -------
-        list of str
-            List of calibration group names
-        """
-
-        return [x["name"] for x in self.estimator_groups]
-
-    def get_group(self, group_name: str):
-        """Get the calibration group by name.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        Returns
-        -------
-        dict
-            Calibration group dict with `name` and `estimators` keys\
-
-        """
-        for group in self.estimator_groups:
-            if group["name"] == group_name:
-                return group
-
-        self.reporter.log_string(
-            f"could not get_group: {group_name}", verbosity="error"
-        )
-        return None
-
-    def get_estimator_names(self, group_name: str):
-        """Get the names of all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        Returns
-        -------
-        list of str
-            List of estimator names
-        """
-
-        group = self.get_group(group_name)
-        if group is not None:
-            return [x.name for x in group["estimators"]]
-        self.reporter.log_string(
-            f"could not get_estimator_names: {group_name}", verbosity="error"
-        )
-        return None
-
-    def get_estimator(self, group_name: str, estimator_name: str):
-        """Get an estimator from a calibration group.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        estimator_name : str
-            Name of the estimator
-
-        Returns
-        -------
-        Calibration
-            The estimator object
-
-        """
-        group = self.get_group(group_name)
-        if group is not None:
-            for estimator in group["estimators"]:
-                if estimator.name == estimator_name:
-                    return estimator
-
-        self.reporter.log_string(
-            f"could not get_estimator: {group_name}, {estimator_name}",
-            verbosity="error",
-        )
-        return None
-
-    def fit(
-        self,
-        df: pd.DataFrame,
-        group_name: str,
-        skip: list | None = None,
-        *args,
-        **kwargs,
-    ):
-        """Fit all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        skip: TODO
-
-        """
-
-        if skip is None:
-            skip = []
-        if len(self.estimator_groups) == 0:
-            raise ValueError("No estimators defined")
-
-        group_idx = [
-            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
-        ]
-        if len(group_idx) == 0:
-            raise ValueError(f"No group named {group_name} found")
-
-        # only iterate over the first group with the given name
-        for group in group_idx:
-            for estimator in self.estimator_groups[group]["estimators"]:
-                if estimator.name in skip:
-                    continue
-                self.reporter.log_string(
-                    f"calibration group: {group_name}, fitting {estimator.name} estimator "
-                )
-                estimator.fit(
-                    df, *args, neptune_key=f"{group_name}_{estimator.name}", **kwargs
-                )
-
-        is_fitted = True
-        # check if all estimators are fitted
-        for group in self.estimator_groups:
-            for estimator in group["estimators"]:
-                is_fitted = is_fitted and estimator.is_fitted
-
-        self.is_fitted = is_fitted and len(self.estimator_groups) > 0
-
-    def predict(self, df: pd.DataFrame, group_name: str, *args, **kwargs):
-        """Predict all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        """
-
-        if len(self.estimator_groups) == 0:
-            raise ValueError("No estimators defined")
-
-        group_idx = [
-            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
-        ]
-        if len(group_idx) == 0:
-            raise ValueError(f"No group named {group_name} found")
-        for group in group_idx:
-            for estimator in self.estimator_groups[group]["estimators"]:
-                self.reporter.log_string(
-                    f"calibration group: {group_name}, predicting {estimator.name}"
-                )
-                estimator.predict(df, inplace=True, *args, **kwargs)  # noqa: B026 Star-arg unpacking after a keyword argument is strongly discouraged
-
-    def fit_predict(
-        self,
-        df: pd.DataFrame,
-        group_name: str,
-        plot: bool = True,
-    ):
-        """Fit and predict all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        plot : bool, default=True
-            If True, a plot of the calibration is generated.
-
-        """
-        self.fit(df, group_name, plot=plot)
-        self.predict(df, group_name)
-
-
-class OptimizationManager(BaseManager):
-    def __init__(
-        self,
-        initial_parameters: dict,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.__dict__.update(initial_parameters)
-
-            for key, value in initial_parameters.items():
-                self.reporter.log_string(f"initial parameter: {key} = {value}")
-
-    def fit(self, update_dict):
-        """Update the parameters dict with the values in update_dict."""
-        self.__dict__.update(update_dict)
-        self.is_fitted = True
-        self.save()
-
-    def predict(self):
-        """Return the parameters dict."""
-        return self.parameters
-
-    def fit_predict(self, update_dict):
-        """Update the parameters dict with the values in update_dict and return the parameters dict."""
-        self.fit(update_dict)
-        return self.predict()
-
-
-class FDRManager(BaseManager):
-    def __init__(
-        self,
-        feature_columns: list,
-        classifier_base,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.feature_columns = feature_columns
-            self.classifier_store = {}
-            self.classifier_base = classifier_base
-        self.current_version = -1
-        self.load_classifier_store()
-
-    def fit_predict(
-        self,
-        features_df: pd.DataFrame,
-        decoy_strategy: typing.Literal[
-            "precursor", "precursor_channel_wise", "channel"
-        ] = "precursor",
-        competetive: bool = True,
-        df_fragments: None | pd.DataFrame = None,
-        dia_cycle: None | np.ndarray = None,
-        decoy_channel: int = -1,
-        version: None | int = -1,
-    ):
-        """Update the parameters dict with the values in update_dict."""
-        available_columns = list(
-            set(features_df.columns).intersection(set(self.feature_columns))
-        )
-
-        # perform sanity checks
-        if len(available_columns) == 0:
-            raise ValueError("No feature columns found in features_df")
-
-        strategy_requires_decoy_column = (
-            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
-        )
-        if strategy_requires_decoy_column and "decoy" not in features_df.columns:
-            raise ValueError("decoy column not found in features_df")
-
-        strategy_requires_channel_column = (
-            decoy_strategy == "precursor_channel_wise" or decoy_strategy == "channel"
-        )
-        if strategy_requires_channel_column and "channel" not in features_df.columns:
-            raise ValueError("channel column not found in features_df")
-
-        if decoy_strategy == "channel" and decoy_channel == -1:
-            raise ValueError("decoy_channel must be set if decoy_type is channel")
-
-        if (
-            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
-        ) and decoy_channel > -1:
-            self.reporter.log_string(
-                "decoy_channel is ignored if decoy_type is precursor",
-                verbosity="warning",
-            )
-            decoy_channel = -1
-
-        if (
-            decoy_strategy == "channel"
-            and decoy_channel > -1
-            and decoy_channel not in features_df["channel"].unique()
-        ):
-            raise ValueError(f"decoy_channel {decoy_channel} not found in features_df")
-
-        self.reporter.log_string(
-            f"performing {decoy_strategy} FDR with {len(available_columns)} features"
-        )
-        self.reporter.log_string(f"Decoy channel: {decoy_channel}")
-        self.reporter.log_string(f"Competetive: {competetive}")
-
-        classifier = self.get_classifier(available_columns, version)
-        if decoy_strategy == "precursor":
-            psm_df = fdr.perform_fdr(
-                classifier,
-                available_columns,
-                features_df[features_df["decoy"] == 0].copy(),
-                features_df[features_df["decoy"] == 1].copy(),
-                competetive=competetive,
-                group_channels=True,
-                df_fragments=df_fragments,
-                dia_cycle=dia_cycle,
-                figure_path=self.figure_path,
-            )
-        elif decoy_strategy == "precursor_channel_wise":
-            channels = features_df["channel"].unique()
-            psm_df_list = []
-            for channel in channels:
-                channel_df = features_df[
-                    features_df["channel"].isin([channel, decoy_channel])
-                ].copy()
-                psm_df_list.append(
-                    fdr.perform_fdr(
-                        classifier,
-                        available_columns,
-                        channel_df[channel_df["decoy"] == 0].copy(),
-                        channel_df[channel_df["decoy"] == 1].copy(),
-                        competetive=competetive,
-                        group_channels=True,
-                        df_fragments=df_fragments,
-                        dia_cycle=dia_cycle,
-                        figure_path=self.figure_path,
-                    )
-                )
-            psm_df = pd.concat(psm_df_list)
-        elif decoy_strategy == "channel":
-            channels = list(set(features_df["channel"].unique()) - set([decoy_channel]))
-            psm_df_list = []
-            for channel in channels:
-                channel_df = features_df[
-                    features_df["channel"].isin([channel, decoy_channel])
-                ].copy()
-                psm_df_list.append(
-                    fdr.perform_fdr(
-                        classifier,
-                        available_columns,
-                        channel_df[channel_df["channel"] != decoy_channel].copy(),
-                        channel_df[channel_df["channel"] == decoy_channel].copy(),
-                        competetive=competetive,
-                        group_channels=False,
-                        reuse_fragments=True,
-                        figure_path=self.figure_path,
-                    )
-                )
-
-            psm_df = pd.concat(psm_df_list)
-            psm_df.loc[psm_df["channel"] == decoy_channel, "decoy"] = 1
-        else:
-            raise ValueError(f"Invalid decoy_strategy: {decoy_strategy}")
-
-        self.is_fitted = True
-
-        self.current_version += 1
-        classifier_hash = column_hash(
-            np.append(available_columns, str(self.current_version))
-        )
-        self.classifier_store[classifier_hash] = classifier
-
-        self.save()
-
-        return psm_df
-
-    def save_classifier_store(self, path=None):
-        if path is None:
-            path = os.path.join(
-                os.path.dirname(alphadia.__file__), "constants", "classifier"
-            )
-
-        logger.info(f"Saving classifier store to {path}")
-
-        for classifier_hash, classifier in self.classifier_store.items():
-            torch.save(
-                classifier.to_state_dict(), os.path.join(path, f"{classifier_hash}.pth")
-            )
-
-    def load_classifier_store(self, path=None):
-        if path is None:
-            path = os.path.join(
-                os.path.dirname(alphadia.__file__), "constants", "classifier"
-            )
-
-        logger.info(f"Loading classifier store from {path}")
-
-        for file in os.listdir(path):
-            if file.endswith(".pth"):
-                classifier_hash = file.split(".")[0]
-
-                if classifier_hash not in self.classifier_store:
-                    self.classifier_store[classifier_hash] = deepcopy(
-                        self.classifier_base
-                    )
-                    self.classifier_store[classifier_hash].from_state_dict(
-                        torch.load(os.path.join(path, file))
-                    )
-
-    def get_classifier(self, available_columns, version):
-        classifier_hash = column_hash(np.append(available_columns, str(version)))
-        if classifier_hash in self.classifier_store:
-            classifier = deepcopy(self.classifier_store[classifier_hash])
-        else:
-            classifier = deepcopy(self.classifier_base)
-        return classifier
-
-    def get_current_version(self):
-        return self.current_version
-
-    def predict(self):
-        """Return the parameters dict."""
-        raise NotImplementedError(
-            f"predict() not implemented for {self.__class__.__name__}"
-        )
-
-    def fit(self, update_dict):
-        """Update the parameters dict with the values in update_dict and return the parameters dict."""
-        raise NotImplementedError(
-            f"fit() not implemented for {self.__class__.__name__}"
-        )
-
-
-def column_hash(columns):
-    columns.sort()
-    return xxhash.xxh64_hexdigest("".join(columns))
+# native imports
+import logging
+import os
+import pickle
+import typing
+from copy import deepcopy
+
+import numpy as np
+
+# alpha family imports
+# third party imports
+import pandas as pd
+import torch
+import xxhash
+
+# alphadia imports
+import alphadia
+from alphadia import fdr
+from alphadia.calibration.property import Calibration, calibration_model_provider
+from alphadia.workflow import reporting
+
+logger = logging.getLogger()
+
+
+class BaseManager:
+    def __init__(
+        self,
+        path: None | str = None,
+        load_from_file: bool = True,
+        figure_path: None | str = None,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
+    ):
+        """Base class for all managers which handle parts of the workflow.
+
+        Parameters
+        ----------
+
+        path : str, optional
+            Path to the manager pickle on disk.
+
+        load_from_file : bool, optional
+            If True, the manager will be loaded from file if it exists.
+        """
+
+        self._path = path
+        self.is_loaded_from_file = False
+        self.is_fitted = False
+        self.figure_path = figure_path
+        self._version = alphadia.__version__
+        self.reporter = reporting.LogBackend() if reporter is None else reporter
+
+        if load_from_file:
+            self.load()
+
+    @property
+    def path(self):
+        """Path to the manager pickle on disk."""
+        return self._path
+
+    @property
+    def is_loaded_from_file(self):
+        """Check if the calibration manager was loaded from file."""
+        return self._is_loaded_from_file
+
+    @is_loaded_from_file.setter
+    def is_loaded_from_file(self, value):
+        self._is_loaded_from_file = value
+
+    @property
+    def is_fitted(self):
+        """Check if all estimators in all calibration groups are fitted."""
+        return self._is_fitted
+
+    @is_fitted.setter
+    def is_fitted(self, value):
+        self._is_fitted = value
+
+    def save(self):
+        """Save the state to pickle file."""
+        if self.path is not None:
+            try:
+                with open(self.path, "wb") as f:
+                    pickle.dump(self, f)
+            except Exception:
+                self.reporter.log_string(
+                    f"Failed to save {self.__class__.__name__} to {self.path}",
+                    verbosity="error",
+                )
+
+    def load(self):
+        """Load the state from pickle file."""
+        if self.path is not None:
+            if os.path.exists(self.path):
+                try:
+                    with open(self.path, "rb") as f:
+                        loaded_state = pickle.load(f)
+
+                        if loaded_state._version == self._version:
+                            self.__dict__.update(loaded_state.__dict__)
+                            self.is_loaded_from_file = True
+                        else:
+                            self.reporter.log_string(
+                                f"Version mismatch while loading {self.__class__}: {loaded_state._version} != {self._version}. Will not load.",
+                                verbosity="warning",
+                            )
+                except Exception:
+                    self.reporter.log_string(
+                        f"Failed to load {self.__class__.__name__} from {self.path}",
+                        verbosity="error",
+                    )
+                else:
+                    self.reporter.log_string(
+                        f"Loaded {self.__class__.__name__} from {self.path}"
+                    )
+            else:
+                self.reporter.log_string(
+                    f"{self.__class__.__name__} not found at: {self.path}",
+                    verbosity="warning",
+                )
+
+    def fit(self):
+        """Fit the workflow property of the manager."""
+        raise NotImplementedError(
+            f"fit() not implemented for {self.__class__.__name__}"
+        )
+
+    def predict(self):
+        """Return the predictions of the workflow property of the manager."""
+        raise NotImplementedError(
+            f"predict() not implemented for {self.__class__.__name__}"
+        )
+
+    def fit_predict(self):
+        """Fit and return the predictions of the workflow property of the manager."""
+        raise NotImplementedError(
+            f"fit_predict() not implemented for {self.__class__.__name__}"
+        )
+
+
+class CalibrationManager(BaseManager):
+    def __init__(
+        self,
+        config: None | dict = None,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        """Contains, updates and applies all calibrations for a single run.
+
+        Calibrations are grouped into calibration groups. Each calibration group is applied to a single data structure (precursor dataframe, fragment fataframe, etc.). Each calibration group contains multiple estimators which each calibrate a single property (mz, rt, etc.). Each estimator is a `Calibration` object which contains the estimator function.
+
+        Parameters
+        ----------
+
+        config : typing.Union[None, dict], default=None
+            Calibration config dict. If None, the default config is used.
+
+        path : str, default=None
+            Path where the current parameter set is saved to and loaded from.
+
+        load_from_file : bool, default=True
+            If True, the manager will be loaded from file if it exists.
+
+        """
+
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.estimator_groups = []
+            self.load_config(config)
+
+    @property
+    def estimator_groups(self):
+        """List of calibration groups."""
+        return self._estimator_groups
+
+    @estimator_groups.setter
+    def estimator_groups(self, value):
+        self._estimator_groups = value
+
+    def disable_mobility_calibration(self):
+        """Iterate all estimators and remove the mobility estimator from each group."""
+        for group in self.estimator_groups:
+            for estimator in group["estimators"]:
+                if estimator.name == "mobility":
+                    group["estimators"].remove(estimator)
+                    self.reporter.log_string(
+                        f'removed mobility estimator from group {group["name"]}'
+                    )
+
+    def load_config(self, config: dict):
+        """Load calibration config from config Dict.
+
+        each calibration config is a list of calibration groups which consist of multiple estimators.
+        For each estimator the `model` and `model_args` are used to request a model from the calibration_model_provider and to initialize it.
+        The estimator is then initialized with the `Calibration` class and added to the group.
+
+        Parameters
+        ----------
+
+        config : dict
+            Calibration config dict
+
+        Example
+        -------
+
+        Create a calibration manager with a single group and a single estimator:
+
+        .. code-block:: python
+
+            calibration_manager = CalibrationManager()
+            calibration_manager.load_config([{
+                'name': 'mz_calibration',
+                'estimators': [
+                    {
+                        'name': 'mz',
+                        'model': 'LOESSRegression',
+                        'model_args': {
+                            'n_kernels': 2
+                        },
+                        'input_columns': ['mz_library'],
+                        'target_columns': ['mz_observed'],
+                        'output_columns': ['mz_calibrated'],
+                        'transform_deviation': 1e6
+                    },
+
+                ]
+            }])
+
+        """
+        self.reporter.log_string("Loading calibration config")
+        self.reporter.log_string(f"Calibration config: {config}")
+        for group in config:
+            self.reporter.log_string(
+                f'Calibration group :{group["name"]}, found {len(group["estimators"])} estimator(s)'
+            )
+            for estimator in group["estimators"]:
+                try:
+                    template = calibration_model_provider.get_model(estimator["model"])
+                    model_args = estimator.get("model_args", {})
+                    estimator["function"] = template(**model_args)
+                except Exception as e:
+                    self.reporter.log_string(
+                        f'Could not load estimator {estimator["name"]}: {e}',
+                        verbosity="error",
+                    )
+
+            group_copy = {"name": group["name"]}
+            group_copy["estimators"] = [Calibration(**x) for x in group["estimators"]]
+            self.estimator_groups.append(group_copy)
+
+    def get_group_names(self):
+        """Get the names of all calibration groups.
+
+        Returns
+        -------
+        list of str
+            List of calibration group names
+        """
+
+        return [x["name"] for x in self.estimator_groups]
+
+    def get_group(self, group_name: str):
+        """Get the calibration group by name.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        Returns
+        -------
+        dict
+            Calibration group dict with `name` and `estimators` keys\
+
+        """
+        for group in self.estimator_groups:
+            if group["name"] == group_name:
+                return group
+
+        self.reporter.log_string(
+            f"could not get_group: {group_name}", verbosity="error"
+        )
+        return None
+
+    def get_estimator_names(self, group_name: str):
+        """Get the names of all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        Returns
+        -------
+        list of str
+            List of estimator names
+        """
+
+        group = self.get_group(group_name)
+        if group is not None:
+            return [x.name for x in group["estimators"]]
+        self.reporter.log_string(
+            f"could not get_estimator_names: {group_name}", verbosity="error"
+        )
+        return None
+
+    def get_estimator(self, group_name: str, estimator_name: str):
+        """Get an estimator from a calibration group.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        estimator_name : str
+            Name of the estimator
+
+        Returns
+        -------
+        Calibration
+            The estimator object
+
+        """
+        group = self.get_group(group_name)
+        if group is not None:
+            for estimator in group["estimators"]:
+                if estimator.name == estimator_name:
+                    return estimator
+
+        self.reporter.log_string(
+            f"could not get_estimator: {group_name}, {estimator_name}",
+            verbosity="error",
+        )
+        return None
+
+    def fit(
+        self,
+        df: pd.DataFrame,
+        group_name: str,
+        skip: list | None = None,
+        *args,
+        **kwargs,
+    ):
+        """Fit all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        skip: TODO
+
+        """
+
+        if skip is None:
+            skip = []
+        if len(self.estimator_groups) == 0:
+            raise ValueError("No estimators defined")
+
+        group_idx = [
+            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
+        ]
+        if len(group_idx) == 0:
+            raise ValueError(f"No group named {group_name} found")
+
+        # only iterate over the first group with the given name
+        for group in group_idx:
+            for estimator in self.estimator_groups[group]["estimators"]:
+                if estimator.name in skip:
+                    continue
+                self.reporter.log_string(
+                    f"calibration group: {group_name}, fitting {estimator.name} estimator "
+                )
+                estimator.fit(
+                    df, *args, neptune_key=f"{group_name}_{estimator.name}", **kwargs
+                )
+
+        is_fitted = True
+        # check if all estimators are fitted
+        for group in self.estimator_groups:
+            for estimator in group["estimators"]:
+                is_fitted = is_fitted and estimator.is_fitted
+
+        self.is_fitted = is_fitted and len(self.estimator_groups) > 0
+
+    def predict(self, df: pd.DataFrame, group_name: str, *args, **kwargs):
+        """Predict all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        """
+
+        if len(self.estimator_groups) == 0:
+            raise ValueError("No estimators defined")
+
+        group_idx = [
+            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
+        ]
+        if len(group_idx) == 0:
+            raise ValueError(f"No group named {group_name} found")
+        for group in group_idx:
+            for estimator in self.estimator_groups[group]["estimators"]:
+                self.reporter.log_string(
+                    f"calibration group: {group_name}, predicting {estimator.name}"
+                )
+                estimator.predict(df, inplace=True, *args, **kwargs)  # noqa: B026 Star-arg unpacking after a keyword argument is strongly discouraged
+
+    def fit_predict(
+        self,
+        df: pd.DataFrame,
+        group_name: str,
+        plot: bool = True,
+    ):
+        """Fit and predict all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        plot : bool, default=True
+            If True, a plot of the calibration is generated.
+
+        """
+        self.fit(df, group_name, plot=plot)
+        self.predict(df, group_name)
+
+
+class OptimizationManager(BaseManager):
+    def __init__(
+        self,
+        initial_parameters: dict,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.__dict__.update(initial_parameters)
+
+            for key, value in initial_parameters.items():
+                self.reporter.log_string(f"initial parameter: {key} = {value}")
+
+    def fit(self, update_dict):
+        """Update the parameters dict with the values in update_dict."""
+        self.__dict__.update(update_dict)
+        self.is_fitted = True
+        self.save()
+
+    def predict(self):
+        """Return the parameters dict."""
+        return self.parameters
+
+    def fit_predict(self, update_dict):
+        """Update the parameters dict with the values in update_dict and return the parameters dict."""
+        self.fit(update_dict)
+        return self.predict()
+
+
+class FDRManager(BaseManager):
+    """
+    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation. 
+    --------------------------------
+    Parameters:
+    feature_columns: list
+        List of feature columns to use for the classifier
+    classifier_base: object
+        Base classifier object to use for the FDR estimation
+
+    """
+    def __init__(
+        self,
+        feature_columns: list,
+        classifier_base,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.feature_columns = feature_columns
+            self.classifier_store = {}
+            self.classifier_base = classifier_base
+        self._current_version = -1
+        self.load_classifier_store()
+
+    def fit_predict(
+        self,
+        features_df: pd.DataFrame,
+        decoy_strategy: typing.Literal[
+            "precursor", "precursor_channel_wise", "channel"
+        ] = "precursor",
+        competetive: bool = True,
+        df_fragments: None | pd.DataFrame = None,
+        dia_cycle: None | np.ndarray = None,
+        decoy_channel: int = -1,
+        version: None | int = -1,
+    ):
+        """Update the parameters dict with the values in update_dict."""
+        available_columns = list(
+            set(features_df.columns).intersection(set(self.feature_columns))
+        )
+
+        # perform sanity checks
+        if len(available_columns) == 0:
+            raise ValueError("No feature columns found in features_df")
+
+        strategy_requires_decoy_column = (
+            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
+        )
+        if strategy_requires_decoy_column and "decoy" not in features_df.columns:
+            raise ValueError("decoy column not found in features_df")
+
+        strategy_requires_channel_column = (
+            decoy_strategy == "precursor_channel_wise" or decoy_strategy == "channel"
+        )
+        if strategy_requires_channel_column and "channel" not in features_df.columns:
+            raise ValueError("channel column not found in features_df")
+
+        if decoy_strategy == "channel" and decoy_channel == -1:
+            raise ValueError("decoy_channel must be set if decoy_type is channel")
+
+        if (
+            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
+        ) and decoy_channel > -1:
+            self.reporter.log_string(
+                "decoy_channel is ignored if decoy_type is precursor",
+                verbosity="warning",
+            )
+            decoy_channel = -1
+
+        if (
+            decoy_strategy == "channel"
+            and decoy_channel > -1
+            and decoy_channel not in features_df["channel"].unique()
+        ):
+            raise ValueError(f"decoy_channel {decoy_channel} not found in features_df")
+
+        self.reporter.log_string(
+            f"performing {decoy_strategy} FDR with {len(available_columns)} features"
+        )
+        self.reporter.log_string(f"Decoy channel: {decoy_channel}")
+        self.reporter.log_string(f"Competetive: {competetive}")
+
+        classifier = self.get_classifier(available_columns, version)
+        if decoy_strategy == "precursor":
+            psm_df = fdr.perform_fdr(
+                classifier,
+                available_columns,
+                features_df[features_df["decoy"] == 0].copy(),
+                features_df[features_df["decoy"] == 1].copy(),
+                competetive=competetive,
+                group_channels=True,
+                df_fragments=df_fragments,
+                dia_cycle=dia_cycle,
+                figure_path=self.figure_path,
+            )
+        elif decoy_strategy == "precursor_channel_wise":
+            channels = features_df["channel"].unique()
+            psm_df_list = []
+            for channel in channels:
+                channel_df = features_df[
+                    features_df["channel"].isin([channel, decoy_channel])
+                ].copy()
+                psm_df_list.append(
+                    fdr.perform_fdr(
+                        classifier,
+                        available_columns,
+                        channel_df[channel_df["decoy"] == 0].copy(),
+                        channel_df[channel_df["decoy"] == 1].copy(),
+                        competetive=competetive,
+                        group_channels=True,
+                        df_fragments=df_fragments,
+                        dia_cycle=dia_cycle,
+                        figure_path=self.figure_path,
+                    )
+                )
+            psm_df = pd.concat(psm_df_list)
+        elif decoy_strategy == "channel":
+            channels = list(set(features_df["channel"].unique()) - set([decoy_channel]))
+            psm_df_list = []
+            for channel in channels:
+                channel_df = features_df[
+                    features_df["channel"].isin([channel, decoy_channel])
+                ].copy()
+                psm_df_list.append(
+                    fdr.perform_fdr(
+                        classifier,
+                        available_columns,
+                        channel_df[channel_df["channel"] != decoy_channel].copy(),
+                        channel_df[channel_df["channel"] == decoy_channel].copy(),
+                        competetive=competetive,
+                        group_channels=False,
+                        reuse_fragments=True,
+                        figure_path=self.figure_path,
+                    )
+                )
+
+            psm_df = pd.concat(psm_df_list)
+            psm_df.loc[psm_df["channel"] == decoy_channel, "decoy"] = 1
+        else:
+            raise ValueError(f"Invalid decoy_strategy: {decoy_strategy}")
+
+        self.is_fitted = True
+
+        classifier_hash = column_hash(available_columns)
+        if classifier_hash not in self.classifier_store.keys():
+            self.classifier_store[classifier_hash] = []  
+
+        self._current_version += 1  
+        # The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index of the classifier_store
+        self.classifier_store[column_hash(available_columns)].append(classifier) 
+
+        self.save()
+
+        return psm_df
+
+    def save_classifier_store(self, path=None):
+        if path is None:
+            path = os.path.join(
+                os.path.dirname(alphadia.__file__), "constants", "classifier"
+            )
+
+        logger.info(f"Saving classifier store to {path}")
+
+        for classifier_hash, classifier in self.classifier_store.items():
+            torch.save(
+                classifier.to_state_dict(), os.path.join(path, f"{classifier_hash}.pth")
+            )
+
+    def load_classifier_store(self, path=None):
+        if path is None:
+            path = os.path.join(
+                os.path.dirname(alphadia.__file__), "constants", "classifier"
+            )
+
+        logger.info(f"Loading classifier store from {path}")
+
+        for file in os.listdir(path):
+            if file.endswith(".pth"):
+                classifier_hash = file.split(".")[0]
+
+                if classifier_hash not in self.classifier_store:
+                    self.classifier_store[classifier_hash] = deepcopy(
+                        self.classifier_base
+                    )
+                    self.classifier_store[classifier_hash].from_state_dict(
+                        torch.load(os.path.join(path, file))
+                    )
+
+    def get_classifier(self, available_columns, version):
+        """
+        Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
+        --------------------------------
+        Parameters:
+        available_columns: list
+            List of feature columns
+        version: int
+            Version of the classifier to get
+        """
+        classifier_hash = column_hash(available_columns)
+        if classifier_hash in self.classifier_store:
+            classifier = deepcopy(self.classifier_store[classifier_hash][version])
+        else:
+            classifier = deepcopy(self.classifier_base)
+        return classifier
+        
+    @property
+    def current_version(self):
+        return self._current_version
+
+    def predict(self):
+        """Return the parameters dict."""
+        raise NotImplementedError(
+            f"predict() not implemented for {self.__class__.__name__}"
+        )
+
+    def fit(self, update_dict):
+        """Update the parameters dict with the values in update_dict and return the parameters dict."""
+        raise NotImplementedError(
+            f"fit() not implemented for {self.__class__.__name__}"
+        )
+
+
+def column_hash(columns):
+    columns.sort()
+    return xxhash.xxh64_hexdigest("".join(columns))
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index d2df38ae..9ed33953 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -343,8 +343,8 @@ def test_fdr_manager_fit_predict():
     )
 
     assert len(fdr_manager.classifier_store) == 2
-    assert fdr_manager.get_current_version() == 0
+    assert fdr_manager.current_version == 0
     assert (
-        manager.column_hash(np.append(FDR_TEST_FEATURES, str(0)))
+        manager.column_hash(FDR_TEST_FEATURES)
         in fdr_manager.classifier_store
     )

From 68b22728f269d2eabac246493acff41228a2607b Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 11:08:56 +0200
Subject: [PATCH 015/250] formatting

---
 alphadia/workflow/manager.py      | 13 +++++++------
 tests/unit_tests/test_workflow.py |  5 +----
 2 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 005d9335..8af5a71c 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -484,7 +484,7 @@ def fit_predict(self, update_dict):
 
 class FDRManager(BaseManager):
     """
-    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation. 
+    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
     --------------------------------
     Parameters:
     feature_columns: list
@@ -493,6 +493,7 @@ class FDRManager(BaseManager):
         Base classifier object to use for the FDR estimation
 
     """
+
     def __init__(
         self,
         feature_columns: list,
@@ -632,12 +633,12 @@ def fit_predict(
         self.is_fitted = True
 
         classifier_hash = column_hash(available_columns)
-        if classifier_hash not in self.classifier_store.keys():
-            self.classifier_store[classifier_hash] = []  
+        if classifier_hash not in self.classifier_store:
+            self.classifier_store[classifier_hash] = []
 
-        self._current_version += 1  
+        self._current_version += 1
         # The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index of the classifier_store
-        self.classifier_store[column_hash(available_columns)].append(classifier) 
+        self.classifier_store[column_hash(available_columns)].append(classifier)
 
         self.save()
 
@@ -692,7 +693,7 @@ def get_classifier(self, available_columns, version):
         else:
             classifier = deepcopy(self.classifier_base)
         return classifier
-        
+
     @property
     def current_version(self):
         return self._current_version
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 9ed33953..b7952eca 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -344,7 +344,4 @@ def test_fdr_manager_fit_predict():
 
     assert len(fdr_manager.classifier_store) == 2
     assert fdr_manager.current_version == 0
-    assert (
-        manager.column_hash(FDR_TEST_FEATURES)
-        in fdr_manager.classifier_store
-    )
+    assert manager.column_hash(FDR_TEST_FEATURES) in fdr_manager.classifier_store

From e06d6c7b9569d2d5f9fa726e93465c081b533fd8 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 11:29:20 +0200
Subject: [PATCH 016/250] format whitespace

---
 alphadia/workflow/manager.py | 1432 +++++++++++++++++-----------------
 1 file changed, 716 insertions(+), 716 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 8af5a71c..f4bbbe18 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -1,716 +1,716 @@
-# native imports
-import logging
-import os
-import pickle
-import typing
-from copy import deepcopy
-
-import numpy as np
-
-# alpha family imports
-# third party imports
-import pandas as pd
-import torch
-import xxhash
-
-# alphadia imports
-import alphadia
-from alphadia import fdr
-from alphadia.calibration.property import Calibration, calibration_model_provider
-from alphadia.workflow import reporting
-
-logger = logging.getLogger()
-
-
-class BaseManager:
-    def __init__(
-        self,
-        path: None | str = None,
-        load_from_file: bool = True,
-        figure_path: None | str = None,
-        reporter: None | reporting.Pipeline | reporting.Backend = None,
-    ):
-        """Base class for all managers which handle parts of the workflow.
-
-        Parameters
-        ----------
-
-        path : str, optional
-            Path to the manager pickle on disk.
-
-        load_from_file : bool, optional
-            If True, the manager will be loaded from file if it exists.
-        """
-
-        self._path = path
-        self.is_loaded_from_file = False
-        self.is_fitted = False
-        self.figure_path = figure_path
-        self._version = alphadia.__version__
-        self.reporter = reporting.LogBackend() if reporter is None else reporter
-
-        if load_from_file:
-            self.load()
-
-    @property
-    def path(self):
-        """Path to the manager pickle on disk."""
-        return self._path
-
-    @property
-    def is_loaded_from_file(self):
-        """Check if the calibration manager was loaded from file."""
-        return self._is_loaded_from_file
-
-    @is_loaded_from_file.setter
-    def is_loaded_from_file(self, value):
-        self._is_loaded_from_file = value
-
-    @property
-    def is_fitted(self):
-        """Check if all estimators in all calibration groups are fitted."""
-        return self._is_fitted
-
-    @is_fitted.setter
-    def is_fitted(self, value):
-        self._is_fitted = value
-
-    def save(self):
-        """Save the state to pickle file."""
-        if self.path is not None:
-            try:
-                with open(self.path, "wb") as f:
-                    pickle.dump(self, f)
-            except Exception:
-                self.reporter.log_string(
-                    f"Failed to save {self.__class__.__name__} to {self.path}",
-                    verbosity="error",
-                )
-
-    def load(self):
-        """Load the state from pickle file."""
-        if self.path is not None:
-            if os.path.exists(self.path):
-                try:
-                    with open(self.path, "rb") as f:
-                        loaded_state = pickle.load(f)
-
-                        if loaded_state._version == self._version:
-                            self.__dict__.update(loaded_state.__dict__)
-                            self.is_loaded_from_file = True
-                        else:
-                            self.reporter.log_string(
-                                f"Version mismatch while loading {self.__class__}: {loaded_state._version} != {self._version}. Will not load.",
-                                verbosity="warning",
-                            )
-                except Exception:
-                    self.reporter.log_string(
-                        f"Failed to load {self.__class__.__name__} from {self.path}",
-                        verbosity="error",
-                    )
-                else:
-                    self.reporter.log_string(
-                        f"Loaded {self.__class__.__name__} from {self.path}"
-                    )
-            else:
-                self.reporter.log_string(
-                    f"{self.__class__.__name__} not found at: {self.path}",
-                    verbosity="warning",
-                )
-
-    def fit(self):
-        """Fit the workflow property of the manager."""
-        raise NotImplementedError(
-            f"fit() not implemented for {self.__class__.__name__}"
-        )
-
-    def predict(self):
-        """Return the predictions of the workflow property of the manager."""
-        raise NotImplementedError(
-            f"predict() not implemented for {self.__class__.__name__}"
-        )
-
-    def fit_predict(self):
-        """Fit and return the predictions of the workflow property of the manager."""
-        raise NotImplementedError(
-            f"fit_predict() not implemented for {self.__class__.__name__}"
-        )
-
-
-class CalibrationManager(BaseManager):
-    def __init__(
-        self,
-        config: None | dict = None,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        """Contains, updates and applies all calibrations for a single run.
-
-        Calibrations are grouped into calibration groups. Each calibration group is applied to a single data structure (precursor dataframe, fragment fataframe, etc.). Each calibration group contains multiple estimators which each calibrate a single property (mz, rt, etc.). Each estimator is a `Calibration` object which contains the estimator function.
-
-        Parameters
-        ----------
-
-        config : typing.Union[None, dict], default=None
-            Calibration config dict. If None, the default config is used.
-
-        path : str, default=None
-            Path where the current parameter set is saved to and loaded from.
-
-        load_from_file : bool, default=True
-            If True, the manager will be loaded from file if it exists.
-
-        """
-
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.estimator_groups = []
-            self.load_config(config)
-
-    @property
-    def estimator_groups(self):
-        """List of calibration groups."""
-        return self._estimator_groups
-
-    @estimator_groups.setter
-    def estimator_groups(self, value):
-        self._estimator_groups = value
-
-    def disable_mobility_calibration(self):
-        """Iterate all estimators and remove the mobility estimator from each group."""
-        for group in self.estimator_groups:
-            for estimator in group["estimators"]:
-                if estimator.name == "mobility":
-                    group["estimators"].remove(estimator)
-                    self.reporter.log_string(
-                        f'removed mobility estimator from group {group["name"]}'
-                    )
-
-    def load_config(self, config: dict):
-        """Load calibration config from config Dict.
-
-        each calibration config is a list of calibration groups which consist of multiple estimators.
-        For each estimator the `model` and `model_args` are used to request a model from the calibration_model_provider and to initialize it.
-        The estimator is then initialized with the `Calibration` class and added to the group.
-
-        Parameters
-        ----------
-
-        config : dict
-            Calibration config dict
-
-        Example
-        -------
-
-        Create a calibration manager with a single group and a single estimator:
-
-        .. code-block:: python
-
-            calibration_manager = CalibrationManager()
-            calibration_manager.load_config([{
-                'name': 'mz_calibration',
-                'estimators': [
-                    {
-                        'name': 'mz',
-                        'model': 'LOESSRegression',
-                        'model_args': {
-                            'n_kernels': 2
-                        },
-                        'input_columns': ['mz_library'],
-                        'target_columns': ['mz_observed'],
-                        'output_columns': ['mz_calibrated'],
-                        'transform_deviation': 1e6
-                    },
-
-                ]
-            }])
-
-        """
-        self.reporter.log_string("Loading calibration config")
-        self.reporter.log_string(f"Calibration config: {config}")
-        for group in config:
-            self.reporter.log_string(
-                f'Calibration group :{group["name"]}, found {len(group["estimators"])} estimator(s)'
-            )
-            for estimator in group["estimators"]:
-                try:
-                    template = calibration_model_provider.get_model(estimator["model"])
-                    model_args = estimator.get("model_args", {})
-                    estimator["function"] = template(**model_args)
-                except Exception as e:
-                    self.reporter.log_string(
-                        f'Could not load estimator {estimator["name"]}: {e}',
-                        verbosity="error",
-                    )
-
-            group_copy = {"name": group["name"]}
-            group_copy["estimators"] = [Calibration(**x) for x in group["estimators"]]
-            self.estimator_groups.append(group_copy)
-
-    def get_group_names(self):
-        """Get the names of all calibration groups.
-
-        Returns
-        -------
-        list of str
-            List of calibration group names
-        """
-
-        return [x["name"] for x in self.estimator_groups]
-
-    def get_group(self, group_name: str):
-        """Get the calibration group by name.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        Returns
-        -------
-        dict
-            Calibration group dict with `name` and `estimators` keys\
-
-        """
-        for group in self.estimator_groups:
-            if group["name"] == group_name:
-                return group
-
-        self.reporter.log_string(
-            f"could not get_group: {group_name}", verbosity="error"
-        )
-        return None
-
-    def get_estimator_names(self, group_name: str):
-        """Get the names of all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        Returns
-        -------
-        list of str
-            List of estimator names
-        """
-
-        group = self.get_group(group_name)
-        if group is not None:
-            return [x.name for x in group["estimators"]]
-        self.reporter.log_string(
-            f"could not get_estimator_names: {group_name}", verbosity="error"
-        )
-        return None
-
-    def get_estimator(self, group_name: str, estimator_name: str):
-        """Get an estimator from a calibration group.
-
-        Parameters
-        ----------
-
-        group_name : str
-            Name of the calibration group
-
-        estimator_name : str
-            Name of the estimator
-
-        Returns
-        -------
-        Calibration
-            The estimator object
-
-        """
-        group = self.get_group(group_name)
-        if group is not None:
-            for estimator in group["estimators"]:
-                if estimator.name == estimator_name:
-                    return estimator
-
-        self.reporter.log_string(
-            f"could not get_estimator: {group_name}, {estimator_name}",
-            verbosity="error",
-        )
-        return None
-
-    def fit(
-        self,
-        df: pd.DataFrame,
-        group_name: str,
-        skip: list | None = None,
-        *args,
-        **kwargs,
-    ):
-        """Fit all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        skip: TODO
-
-        """
-
-        if skip is None:
-            skip = []
-        if len(self.estimator_groups) == 0:
-            raise ValueError("No estimators defined")
-
-        group_idx = [
-            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
-        ]
-        if len(group_idx) == 0:
-            raise ValueError(f"No group named {group_name} found")
-
-        # only iterate over the first group with the given name
-        for group in group_idx:
-            for estimator in self.estimator_groups[group]["estimators"]:
-                if estimator.name in skip:
-                    continue
-                self.reporter.log_string(
-                    f"calibration group: {group_name}, fitting {estimator.name} estimator "
-                )
-                estimator.fit(
-                    df, *args, neptune_key=f"{group_name}_{estimator.name}", **kwargs
-                )
-
-        is_fitted = True
-        # check if all estimators are fitted
-        for group in self.estimator_groups:
-            for estimator in group["estimators"]:
-                is_fitted = is_fitted and estimator.is_fitted
-
-        self.is_fitted = is_fitted and len(self.estimator_groups) > 0
-
-    def predict(self, df: pd.DataFrame, group_name: str, *args, **kwargs):
-        """Predict all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        """
-
-        if len(self.estimator_groups) == 0:
-            raise ValueError("No estimators defined")
-
-        group_idx = [
-            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
-        ]
-        if len(group_idx) == 0:
-            raise ValueError(f"No group named {group_name} found")
-        for group in group_idx:
-            for estimator in self.estimator_groups[group]["estimators"]:
-                self.reporter.log_string(
-                    f"calibration group: {group_name}, predicting {estimator.name}"
-                )
-                estimator.predict(df, inplace=True, *args, **kwargs)  # noqa: B026 Star-arg unpacking after a keyword argument is strongly discouraged
-
-    def fit_predict(
-        self,
-        df: pd.DataFrame,
-        group_name: str,
-        plot: bool = True,
-    ):
-        """Fit and predict all estimators in a calibration group.
-
-        Parameters
-        ----------
-
-        df : pandas.DataFrame
-            Dataframe containing the input and target columns
-
-        group_name : str
-            Name of the calibration group
-
-        plot : bool, default=True
-            If True, a plot of the calibration is generated.
-
-        """
-        self.fit(df, group_name, plot=plot)
-        self.predict(df, group_name)
-
-
-class OptimizationManager(BaseManager):
-    def __init__(
-        self,
-        initial_parameters: dict,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.__dict__.update(initial_parameters)
-
-            for key, value in initial_parameters.items():
-                self.reporter.log_string(f"initial parameter: {key} = {value}")
-
-    def fit(self, update_dict):
-        """Update the parameters dict with the values in update_dict."""
-        self.__dict__.update(update_dict)
-        self.is_fitted = True
-        self.save()
-
-    def predict(self):
-        """Return the parameters dict."""
-        return self.parameters
-
-    def fit_predict(self, update_dict):
-        """Update the parameters dict with the values in update_dict and return the parameters dict."""
-        self.fit(update_dict)
-        return self.predict()
-
-
-class FDRManager(BaseManager):
-    """
-    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
-    --------------------------------
-    Parameters:
-    feature_columns: list
-        List of feature columns to use for the classifier
-    classifier_base: object
-        Base classifier object to use for the FDR estimation
-
-    """
-
-    def __init__(
-        self,
-        feature_columns: list,
-        classifier_base,
-        path: None | str = None,
-        load_from_file: bool = True,
-        **kwargs,
-    ):
-        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
-        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
-        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
-        if not self.is_loaded_from_file:
-            self.feature_columns = feature_columns
-            self.classifier_store = {}
-            self.classifier_base = classifier_base
-        self._current_version = -1
-        self.load_classifier_store()
-
-    def fit_predict(
-        self,
-        features_df: pd.DataFrame,
-        decoy_strategy: typing.Literal[
-            "precursor", "precursor_channel_wise", "channel"
-        ] = "precursor",
-        competetive: bool = True,
-        df_fragments: None | pd.DataFrame = None,
-        dia_cycle: None | np.ndarray = None,
-        decoy_channel: int = -1,
-        version: None | int = -1,
-    ):
-        """Update the parameters dict with the values in update_dict."""
-        available_columns = list(
-            set(features_df.columns).intersection(set(self.feature_columns))
-        )
-
-        # perform sanity checks
-        if len(available_columns) == 0:
-            raise ValueError("No feature columns found in features_df")
-
-        strategy_requires_decoy_column = (
-            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
-        )
-        if strategy_requires_decoy_column and "decoy" not in features_df.columns:
-            raise ValueError("decoy column not found in features_df")
-
-        strategy_requires_channel_column = (
-            decoy_strategy == "precursor_channel_wise" or decoy_strategy == "channel"
-        )
-        if strategy_requires_channel_column and "channel" not in features_df.columns:
-            raise ValueError("channel column not found in features_df")
-
-        if decoy_strategy == "channel" and decoy_channel == -1:
-            raise ValueError("decoy_channel must be set if decoy_type is channel")
-
-        if (
-            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
-        ) and decoy_channel > -1:
-            self.reporter.log_string(
-                "decoy_channel is ignored if decoy_type is precursor",
-                verbosity="warning",
-            )
-            decoy_channel = -1
-
-        if (
-            decoy_strategy == "channel"
-            and decoy_channel > -1
-            and decoy_channel not in features_df["channel"].unique()
-        ):
-            raise ValueError(f"decoy_channel {decoy_channel} not found in features_df")
-
-        self.reporter.log_string(
-            f"performing {decoy_strategy} FDR with {len(available_columns)} features"
-        )
-        self.reporter.log_string(f"Decoy channel: {decoy_channel}")
-        self.reporter.log_string(f"Competetive: {competetive}")
-
-        classifier = self.get_classifier(available_columns, version)
-        if decoy_strategy == "precursor":
-            psm_df = fdr.perform_fdr(
-                classifier,
-                available_columns,
-                features_df[features_df["decoy"] == 0].copy(),
-                features_df[features_df["decoy"] == 1].copy(),
-                competetive=competetive,
-                group_channels=True,
-                df_fragments=df_fragments,
-                dia_cycle=dia_cycle,
-                figure_path=self.figure_path,
-            )
-        elif decoy_strategy == "precursor_channel_wise":
-            channels = features_df["channel"].unique()
-            psm_df_list = []
-            for channel in channels:
-                channel_df = features_df[
-                    features_df["channel"].isin([channel, decoy_channel])
-                ].copy()
-                psm_df_list.append(
-                    fdr.perform_fdr(
-                        classifier,
-                        available_columns,
-                        channel_df[channel_df["decoy"] == 0].copy(),
-                        channel_df[channel_df["decoy"] == 1].copy(),
-                        competetive=competetive,
-                        group_channels=True,
-                        df_fragments=df_fragments,
-                        dia_cycle=dia_cycle,
-                        figure_path=self.figure_path,
-                    )
-                )
-            psm_df = pd.concat(psm_df_list)
-        elif decoy_strategy == "channel":
-            channels = list(set(features_df["channel"].unique()) - set([decoy_channel]))
-            psm_df_list = []
-            for channel in channels:
-                channel_df = features_df[
-                    features_df["channel"].isin([channel, decoy_channel])
-                ].copy()
-                psm_df_list.append(
-                    fdr.perform_fdr(
-                        classifier,
-                        available_columns,
-                        channel_df[channel_df["channel"] != decoy_channel].copy(),
-                        channel_df[channel_df["channel"] == decoy_channel].copy(),
-                        competetive=competetive,
-                        group_channels=False,
-                        reuse_fragments=True,
-                        figure_path=self.figure_path,
-                    )
-                )
-
-            psm_df = pd.concat(psm_df_list)
-            psm_df.loc[psm_df["channel"] == decoy_channel, "decoy"] = 1
-        else:
-            raise ValueError(f"Invalid decoy_strategy: {decoy_strategy}")
-
-        self.is_fitted = True
-
-        classifier_hash = column_hash(available_columns)
-        if classifier_hash not in self.classifier_store:
-            self.classifier_store[classifier_hash] = []
-
-        self._current_version += 1
-        # The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index of the classifier_store
-        self.classifier_store[column_hash(available_columns)].append(classifier)
-
-        self.save()
-
-        return psm_df
-
-    def save_classifier_store(self, path=None):
-        if path is None:
-            path = os.path.join(
-                os.path.dirname(alphadia.__file__), "constants", "classifier"
-            )
-
-        logger.info(f"Saving classifier store to {path}")
-
-        for classifier_hash, classifier in self.classifier_store.items():
-            torch.save(
-                classifier.to_state_dict(), os.path.join(path, f"{classifier_hash}.pth")
-            )
-
-    def load_classifier_store(self, path=None):
-        if path is None:
-            path = os.path.join(
-                os.path.dirname(alphadia.__file__), "constants", "classifier"
-            )
-
-        logger.info(f"Loading classifier store from {path}")
-
-        for file in os.listdir(path):
-            if file.endswith(".pth"):
-                classifier_hash = file.split(".")[0]
-
-                if classifier_hash not in self.classifier_store:
-                    self.classifier_store[classifier_hash] = deepcopy(
-                        self.classifier_base
-                    )
-                    self.classifier_store[classifier_hash].from_state_dict(
-                        torch.load(os.path.join(path, file))
-                    )
-
-    def get_classifier(self, available_columns, version):
-        """
-        Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
-        --------------------------------
-        Parameters:
-        available_columns: list
-            List of feature columns
-        version: int
-            Version of the classifier to get
-        """
-        classifier_hash = column_hash(available_columns)
-        if classifier_hash in self.classifier_store:
-            classifier = deepcopy(self.classifier_store[classifier_hash][version])
-        else:
-            classifier = deepcopy(self.classifier_base)
-        return classifier
-
-    @property
-    def current_version(self):
-        return self._current_version
-
-    def predict(self):
-        """Return the parameters dict."""
-        raise NotImplementedError(
-            f"predict() not implemented for {self.__class__.__name__}"
-        )
-
-    def fit(self, update_dict):
-        """Update the parameters dict with the values in update_dict and return the parameters dict."""
-        raise NotImplementedError(
-            f"fit() not implemented for {self.__class__.__name__}"
-        )
-
-
-def column_hash(columns):
-    columns.sort()
-    return xxhash.xxh64_hexdigest("".join(columns))
+# native imports
+import logging
+import os
+import pickle
+import typing
+from copy import deepcopy
+
+import numpy as np
+
+# alpha family imports
+# third party imports
+import pandas as pd
+import torch
+import xxhash
+
+# alphadia imports
+import alphadia
+from alphadia import fdr
+from alphadia.calibration.property import Calibration, calibration_model_provider
+from alphadia.workflow import reporting
+
+logger = logging.getLogger()
+
+
+class BaseManager:
+    def __init__(
+        self,
+        path: None | str = None,
+        load_from_file: bool = True,
+        figure_path: None | str = None,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
+    ):
+        """Base class for all managers which handle parts of the workflow.
+
+        Parameters
+        ----------
+
+        path : str, optional
+            Path to the manager pickle on disk.
+
+        load_from_file : bool, optional
+            If True, the manager will be loaded from file if it exists.
+        """
+
+        self._path = path
+        self.is_loaded_from_file = False
+        self.is_fitted = False
+        self.figure_path = figure_path
+        self._version = alphadia.__version__
+        self.reporter = reporting.LogBackend() if reporter is None else reporter
+
+        if load_from_file:
+            self.load()
+
+    @property
+    def path(self):
+        """Path to the manager pickle on disk."""
+        return self._path
+
+    @property
+    def is_loaded_from_file(self):
+        """Check if the calibration manager was loaded from file."""
+        return self._is_loaded_from_file
+
+    @is_loaded_from_file.setter
+    def is_loaded_from_file(self, value):
+        self._is_loaded_from_file = value
+
+    @property
+    def is_fitted(self):
+        """Check if all estimators in all calibration groups are fitted."""
+        return self._is_fitted
+
+    @is_fitted.setter
+    def is_fitted(self, value):
+        self._is_fitted = value
+
+    def save(self):
+        """Save the state to pickle file."""
+        if self.path is not None:
+            try:
+                with open(self.path, "wb") as f:
+                    pickle.dump(self, f)
+            except Exception:
+                self.reporter.log_string(
+                    f"Failed to save {self.__class__.__name__} to {self.path}",
+                    verbosity="error",
+                )
+
+    def load(self):
+        """Load the state from pickle file."""
+        if self.path is not None:
+            if os.path.exists(self.path):
+                try:
+                    with open(self.path, "rb") as f:
+                        loaded_state = pickle.load(f)
+
+                        if loaded_state._version == self._version:
+                            self.__dict__.update(loaded_state.__dict__)
+                            self.is_loaded_from_file = True
+                        else:
+                            self.reporter.log_string(
+                                f"Version mismatch while loading {self.__class__}: {loaded_state._version} != {self._version}. Will not load.",
+                                verbosity="warning",
+                            )
+                except Exception:
+                    self.reporter.log_string(
+                        f"Failed to load {self.__class__.__name__} from {self.path}",
+                        verbosity="error",
+                    )
+                else:
+                    self.reporter.log_string(
+                        f"Loaded {self.__class__.__name__} from {self.path}"
+                    )
+            else:
+                self.reporter.log_string(
+                    f"{self.__class__.__name__} not found at: {self.path}",
+                    verbosity="warning",
+                )
+
+    def fit(self):
+        """Fit the workflow property of the manager."""
+        raise NotImplementedError(
+            f"fit() not implemented for {self.__class__.__name__}"
+        )
+
+    def predict(self):
+        """Return the predictions of the workflow property of the manager."""
+        raise NotImplementedError(
+            f"predict() not implemented for {self.__class__.__name__}"
+        )
+
+    def fit_predict(self):
+        """Fit and return the predictions of the workflow property of the manager."""
+        raise NotImplementedError(
+            f"fit_predict() not implemented for {self.__class__.__name__}"
+        )
+
+
+class CalibrationManager(BaseManager):
+    def __init__(
+        self,
+        config: None | dict = None,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        """Contains, updates and applies all calibrations for a single run.
+
+        Calibrations are grouped into calibration groups. Each calibration group is applied to a single data structure (precursor dataframe, fragment fataframe, etc.). Each calibration group contains multiple estimators which each calibrate a single property (mz, rt, etc.). Each estimator is a `Calibration` object which contains the estimator function.
+
+        Parameters
+        ----------
+
+        config : typing.Union[None, dict], default=None
+            Calibration config dict. If None, the default config is used.
+
+        path : str, default=None
+            Path where the current parameter set is saved to and loaded from.
+
+        load_from_file : bool, default=True
+            If True, the manager will be loaded from file if it exists.
+
+        """
+
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.estimator_groups = []
+            self.load_config(config)
+
+    @property
+    def estimator_groups(self):
+        """List of calibration groups."""
+        return self._estimator_groups
+
+    @estimator_groups.setter
+    def estimator_groups(self, value):
+        self._estimator_groups = value
+
+    def disable_mobility_calibration(self):
+        """Iterate all estimators and remove the mobility estimator from each group."""
+        for group in self.estimator_groups:
+            for estimator in group["estimators"]:
+                if estimator.name == "mobility":
+                    group["estimators"].remove(estimator)
+                    self.reporter.log_string(
+                        f'removed mobility estimator from group {group["name"]}'
+                    )
+
+    def load_config(self, config: dict):
+        """Load calibration config from config Dict.
+
+        each calibration config is a list of calibration groups which consist of multiple estimators.
+        For each estimator the `model` and `model_args` are used to request a model from the calibration_model_provider and to initialize it.
+        The estimator is then initialized with the `Calibration` class and added to the group.
+
+        Parameters
+        ----------
+
+        config : dict
+            Calibration config dict
+
+        Example
+        -------
+
+        Create a calibration manager with a single group and a single estimator:
+
+        .. code-block:: python
+
+            calibration_manager = CalibrationManager()
+            calibration_manager.load_config([{
+                'name': 'mz_calibration',
+                'estimators': [
+                    {
+                        'name': 'mz',
+                        'model': 'LOESSRegression',
+                        'model_args': {
+                            'n_kernels': 2
+                        },
+                        'input_columns': ['mz_library'],
+                        'target_columns': ['mz_observed'],
+                        'output_columns': ['mz_calibrated'],
+                        'transform_deviation': 1e6
+                    },
+
+                ]
+            }])
+
+        """
+        self.reporter.log_string("Loading calibration config")
+        self.reporter.log_string(f"Calibration config: {config}")
+        for group in config:
+            self.reporter.log_string(
+                f'Calibration group :{group["name"]}, found {len(group["estimators"])} estimator(s)'
+            )
+            for estimator in group["estimators"]:
+                try:
+                    template = calibration_model_provider.get_model(estimator["model"])
+                    model_args = estimator.get("model_args", {})
+                    estimator["function"] = template(**model_args)
+                except Exception as e:
+                    self.reporter.log_string(
+                        f'Could not load estimator {estimator["name"]}: {e}',
+                        verbosity="error",
+                    )
+
+            group_copy = {"name": group["name"]}
+            group_copy["estimators"] = [Calibration(**x) for x in group["estimators"]]
+            self.estimator_groups.append(group_copy)
+
+    def get_group_names(self):
+        """Get the names of all calibration groups.
+
+        Returns
+        -------
+        list of str
+            List of calibration group names
+        """
+
+        return [x["name"] for x in self.estimator_groups]
+
+    def get_group(self, group_name: str):
+        """Get the calibration group by name.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        Returns
+        -------
+        dict
+            Calibration group dict with `name` and `estimators` keys\
+
+        """
+        for group in self.estimator_groups:
+            if group["name"] == group_name:
+                return group
+
+        self.reporter.log_string(
+            f"could not get_group: {group_name}", verbosity="error"
+        )
+        return None
+
+    def get_estimator_names(self, group_name: str):
+        """Get the names of all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        Returns
+        -------
+        list of str
+            List of estimator names
+        """
+
+        group = self.get_group(group_name)
+        if group is not None:
+            return [x.name for x in group["estimators"]]
+        self.reporter.log_string(
+            f"could not get_estimator_names: {group_name}", verbosity="error"
+        )
+        return None
+
+    def get_estimator(self, group_name: str, estimator_name: str):
+        """Get an estimator from a calibration group.
+
+        Parameters
+        ----------
+
+        group_name : str
+            Name of the calibration group
+
+        estimator_name : str
+            Name of the estimator
+
+        Returns
+        -------
+        Calibration
+            The estimator object
+
+        """
+        group = self.get_group(group_name)
+        if group is not None:
+            for estimator in group["estimators"]:
+                if estimator.name == estimator_name:
+                    return estimator
+
+        self.reporter.log_string(
+            f"could not get_estimator: {group_name}, {estimator_name}",
+            verbosity="error",
+        )
+        return None
+
+    def fit(
+        self,
+        df: pd.DataFrame,
+        group_name: str,
+        skip: list | None = None,
+        *args,
+        **kwargs,
+    ):
+        """Fit all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        skip: TODO
+
+        """
+
+        if skip is None:
+            skip = []
+        if len(self.estimator_groups) == 0:
+            raise ValueError("No estimators defined")
+
+        group_idx = [
+            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
+        ]
+        if len(group_idx) == 0:
+            raise ValueError(f"No group named {group_name} found")
+
+        # only iterate over the first group with the given name
+        for group in group_idx:
+            for estimator in self.estimator_groups[group]["estimators"]:
+                if estimator.name in skip:
+                    continue
+                self.reporter.log_string(
+                    f"calibration group: {group_name}, fitting {estimator.name} estimator "
+                )
+                estimator.fit(
+                    df, *args, neptune_key=f"{group_name}_{estimator.name}", **kwargs
+                )
+
+        is_fitted = True
+        # check if all estimators are fitted
+        for group in self.estimator_groups:
+            for estimator in group["estimators"]:
+                is_fitted = is_fitted and estimator.is_fitted
+
+        self.is_fitted = is_fitted and len(self.estimator_groups) > 0
+
+    def predict(self, df: pd.DataFrame, group_name: str, *args, **kwargs):
+        """Predict all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        """
+
+        if len(self.estimator_groups) == 0:
+            raise ValueError("No estimators defined")
+
+        group_idx = [
+            i for i, x in enumerate(self.estimator_groups) if x["name"] == group_name
+        ]
+        if len(group_idx) == 0:
+            raise ValueError(f"No group named {group_name} found")
+        for group in group_idx:
+            for estimator in self.estimator_groups[group]["estimators"]:
+                self.reporter.log_string(
+                    f"calibration group: {group_name}, predicting {estimator.name}"
+                )
+                estimator.predict(df, inplace=True, *args, **kwargs)  # noqa: B026 Star-arg unpacking after a keyword argument is strongly discouraged
+
+    def fit_predict(
+        self,
+        df: pd.DataFrame,
+        group_name: str,
+        plot: bool = True,
+    ):
+        """Fit and predict all estimators in a calibration group.
+
+        Parameters
+        ----------
+
+        df : pandas.DataFrame
+            Dataframe containing the input and target columns
+
+        group_name : str
+            Name of the calibration group
+
+        plot : bool, default=True
+            If True, a plot of the calibration is generated.
+
+        """
+        self.fit(df, group_name, plot=plot)
+        self.predict(df, group_name)
+
+
+class OptimizationManager(BaseManager):
+    def __init__(
+        self,
+        initial_parameters: dict,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.__dict__.update(initial_parameters)
+
+            for key, value in initial_parameters.items():
+                self.reporter.log_string(f"initial parameter: {key} = {value}")
+
+    def fit(self, update_dict):
+        """Update the parameters dict with the values in update_dict."""
+        self.__dict__.update(update_dict)
+        self.is_fitted = True
+        self.save()
+
+    def predict(self):
+        """Return the parameters dict."""
+        return self.parameters
+
+    def fit_predict(self, update_dict):
+        """Update the parameters dict with the values in update_dict and return the parameters dict."""
+        self.fit(update_dict)
+        return self.predict()
+
+
+class FDRManager(BaseManager):
+    """
+    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
+    --------------------------------
+    Parameters:
+    feature_columns: list
+        List of feature columns to use for the classifier
+    classifier_base: object
+        Base classifier object to use for the FDR estimation
+
+    """
+
+    def __init__(
+        self,
+        feature_columns: list,
+        classifier_base,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.feature_columns = feature_columns
+            self.classifier_store = {}
+            self.classifier_base = classifier_base
+        self._current_version = -1
+        self.load_classifier_store()
+
+    def fit_predict(
+        self,
+        features_df: pd.DataFrame,
+        decoy_strategy: typing.Literal[
+            "precursor", "precursor_channel_wise", "channel"
+        ] = "precursor",
+        competetive: bool = True,
+        df_fragments: None | pd.DataFrame = None,
+        dia_cycle: None | np.ndarray = None,
+        decoy_channel: int = -1,
+        version: None | int = -1,
+    ):
+        """Update the parameters dict with the values in update_dict."""
+        available_columns = list(
+            set(features_df.columns).intersection(set(self.feature_columns))
+        )
+
+        # perform sanity checks
+        if len(available_columns) == 0:
+            raise ValueError("No feature columns found in features_df")
+
+        strategy_requires_decoy_column = (
+            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
+        )
+        if strategy_requires_decoy_column and "decoy" not in features_df.columns:
+            raise ValueError("decoy column not found in features_df")
+
+        strategy_requires_channel_column = (
+            decoy_strategy == "precursor_channel_wise" or decoy_strategy == "channel"
+        )
+        if strategy_requires_channel_column and "channel" not in features_df.columns:
+            raise ValueError("channel column not found in features_df")
+
+        if decoy_strategy == "channel" and decoy_channel == -1:
+            raise ValueError("decoy_channel must be set if decoy_type is channel")
+
+        if (
+            decoy_strategy == "precursor" or decoy_strategy == "precursor_channel_wise"
+        ) and decoy_channel > -1:
+            self.reporter.log_string(
+                "decoy_channel is ignored if decoy_type is precursor",
+                verbosity="warning",
+            )
+            decoy_channel = -1
+
+        if (
+            decoy_strategy == "channel"
+            and decoy_channel > -1
+            and decoy_channel not in features_df["channel"].unique()
+        ):
+            raise ValueError(f"decoy_channel {decoy_channel} not found in features_df")
+
+        self.reporter.log_string(
+            f"performing {decoy_strategy} FDR with {len(available_columns)} features"
+        )
+        self.reporter.log_string(f"Decoy channel: {decoy_channel}")
+        self.reporter.log_string(f"Competetive: {competetive}")
+
+        classifier = self.get_classifier(available_columns, version)
+        if decoy_strategy == "precursor":
+            psm_df = fdr.perform_fdr(
+                classifier,
+                available_columns,
+                features_df[features_df["decoy"] == 0].copy(),
+                features_df[features_df["decoy"] == 1].copy(),
+                competetive=competetive,
+                group_channels=True,
+                df_fragments=df_fragments,
+                dia_cycle=dia_cycle,
+                figure_path=self.figure_path,
+            )
+        elif decoy_strategy == "precursor_channel_wise":
+            channels = features_df["channel"].unique()
+            psm_df_list = []
+            for channel in channels:
+                channel_df = features_df[
+                    features_df["channel"].isin([channel, decoy_channel])
+                ].copy()
+                psm_df_list.append(
+                    fdr.perform_fdr(
+                        classifier,
+                        available_columns,
+                        channel_df[channel_df["decoy"] == 0].copy(),
+                        channel_df[channel_df["decoy"] == 1].copy(),
+                        competetive=competetive,
+                        group_channels=True,
+                        df_fragments=df_fragments,
+                        dia_cycle=dia_cycle,
+                        figure_path=self.figure_path,
+                    )
+                )
+            psm_df = pd.concat(psm_df_list)
+        elif decoy_strategy == "channel":
+            channels = list(set(features_df["channel"].unique()) - set([decoy_channel]))
+            psm_df_list = []
+            for channel in channels:
+                channel_df = features_df[
+                    features_df["channel"].isin([channel, decoy_channel])
+                ].copy()
+                psm_df_list.append(
+                    fdr.perform_fdr(
+                        classifier,
+                        available_columns,
+                        channel_df[channel_df["channel"] != decoy_channel].copy(),
+                        channel_df[channel_df["channel"] == decoy_channel].copy(),
+                        competetive=competetive,
+                        group_channels=False,
+                        reuse_fragments=True,
+                        figure_path=self.figure_path,
+                    )
+                )
+
+            psm_df = pd.concat(psm_df_list)
+            psm_df.loc[psm_df["channel"] == decoy_channel, "decoy"] = 1
+        else:
+            raise ValueError(f"Invalid decoy_strategy: {decoy_strategy}")
+
+        self.is_fitted = True
+
+        classifier_hash = column_hash(available_columns)
+        if classifier_hash not in self.classifier_store:
+            self.classifier_store[classifier_hash] = []
+
+        self._current_version += 1
+        # The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index of the classifier_store
+        self.classifier_store[column_hash(available_columns)].append(classifier)
+
+        self.save()
+
+        return psm_df
+
+    def save_classifier_store(self, path=None):
+        if path is None:
+            path = os.path.join(
+                os.path.dirname(alphadia.__file__), "constants", "classifier"
+            )
+
+        logger.info(f"Saving classifier store to {path}")
+
+        for classifier_hash, classifier in self.classifier_store.items():
+            torch.save(
+                classifier.to_state_dict(), os.path.join(path, f"{classifier_hash}.pth")
+            )
+
+    def load_classifier_store(self, path=None):
+        if path is None:
+            path = os.path.join(
+                os.path.dirname(alphadia.__file__), "constants", "classifier"
+            )
+
+        logger.info(f"Loading classifier store from {path}")
+
+        for file in os.listdir(path):
+            if file.endswith(".pth"):
+                classifier_hash = file.split(".")[0]
+
+                if classifier_hash not in self.classifier_store:
+                    self.classifier_store[classifier_hash] = deepcopy(
+                        self.classifier_base
+                    )
+                    self.classifier_store[classifier_hash].from_state_dict(
+                        torch.load(os.path.join(path, file))
+                    )
+
+    def get_classifier(self, available_columns, version):
+        """
+        Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
+        --------------------------------
+        Parameters:
+        available_columns: list
+            List of feature columns
+        version: int
+            Version of the classifier to get
+        """
+        classifier_hash = column_hash(available_columns)
+        if classifier_hash in self.classifier_store:
+            classifier = deepcopy(self.classifier_store[classifier_hash][version])
+        else:
+            classifier = deepcopy(self.classifier_base)
+        return classifier
+
+    @property
+    def current_version(self):
+        return self._current_version
+
+    def predict(self):
+        """Return the parameters dict."""
+        raise NotImplementedError(
+            f"predict() not implemented for {self.__class__.__name__}"
+        )
+
+    def fit(self, update_dict):
+        """Update the parameters dict with the values in update_dict and return the parameters dict."""
+        raise NotImplementedError(
+            f"fit() not implemented for {self.__class__.__name__}"
+        )
+
+
+def column_hash(columns):
+    columns.sort()
+    return xxhash.xxh64_hexdigest("".join(columns))

From e1e0b70945d3292103fe3225ed07765169030a19 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 11:46:18 +0200
Subject: [PATCH 017/250] changed docstring formatting and extended test

---
 alphadia/workflow/manager.py      | 32 +++++++++++++++++++++----------
 tests/unit_tests/test_workflow.py | 12 +++++++++++-
 2 files changed, 33 insertions(+), 11 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index f4bbbe18..53a4fdb6 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -259,6 +259,7 @@ def get_group_names(self):
         -------
         list of str
             List of calibration group names
+
         """
 
         return [x["name"] for x in self.estimator_groups]
@@ -300,6 +301,7 @@ def get_estimator_names(self, group_name: str):
         -------
         list of str
             List of estimator names
+
         """
 
         group = self.get_group(group_name)
@@ -483,10 +485,10 @@ def fit_predict(self, update_dict):
 
 
 class FDRManager(BaseManager):
-    """
-    Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
-    --------------------------------
-    Parameters:
+    """Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
+
+    Parameters
+    ----------
     feature_columns: list
         List of feature columns to use for the classifier
     classifier_base: object
@@ -525,7 +527,12 @@ def fit_predict(
         decoy_channel: int = -1,
         version: None | int = -1,
     ):
-        """Update the parameters dict with the values in update_dict."""
+        """Fit the classifier and perform FDR estimation.
+
+        Notes
+        -----
+            The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index in self.classifier_store.
+        """
         available_columns = list(
             set(features_df.columns).intersection(set(self.feature_columns))
         )
@@ -637,7 +644,6 @@ def fit_predict(
             self.classifier_store[classifier_hash] = []
 
         self._current_version += 1
-        # The classifier_hash must be identical for every call of fit_predict for self._current_version to give the right index of the classifier_store
         self.classifier_store[column_hash(available_columns)].append(classifier)
 
         self.save()
@@ -678,14 +684,20 @@ def load_classifier_store(self, path=None):
                     )
 
     def get_classifier(self, available_columns, version):
-        """
-        Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
-        --------------------------------
-        Parameters:
+        """Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
+
+        Parameters
+        ----------
         available_columns: list
             List of feature columns
         version: int
             Version of the classifier to get
+
+        Returns
+        ----------
+        object
+            Classifier object
+
         """
         classifier_hash = column_hash(available_columns)
         if classifier_hash in self.classifier_store:
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index b7952eca..b5f5cd11 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -339,9 +339,19 @@ def test_fdr_manager_fit_predict():
         competetive=False,
         df_fragments=None,
         dia_cycle=None,
-        # neptune_run=self.neptune
     )
 
     assert len(fdr_manager.classifier_store) == 2
     assert fdr_manager.current_version == 0
     assert manager.column_hash(FDR_TEST_FEATURES) in fdr_manager.classifier_store
+
+    fdr_manager.fit_predict(
+        test_features_df,
+        decoy_strategy="precursor",
+        competetive=False,
+        df_fragments=None,
+        dia_cycle=None,
+        version=0,
+    )
+
+    assert fdr_manager.current_version == 1

From 9e2444775c506d91872fab24b5e0a5c025eca8bd Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 14:36:14 +0200
Subject: [PATCH 018/250] responses to comments 1

---
 alphadia/workflow/manager.py | 38 ++++++++++++++++--------------------
 1 file changed, 17 insertions(+), 21 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 53a4fdb6..27394cd9 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -3,6 +3,7 @@
 import os
 import pickle
 import typing
+from collections import defaultdict
 from copy import deepcopy
 
 import numpy as np
@@ -485,17 +486,6 @@ def fit_predict(self, update_dict):
 
 
 class FDRManager(BaseManager):
-    """Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
-
-    Parameters
-    ----------
-    feature_columns: list
-        List of feature columns to use for the classifier
-    classifier_base: object
-        Base classifier object to use for the FDR estimation
-
-    """
-
     def __init__(
         self,
         feature_columns: list,
@@ -504,13 +494,23 @@ def __init__(
         load_from_file: bool = True,
         **kwargs,
     ):
+        """Contains, updates and applies classifiers for target-decoy competitio-based false discovery rate (FDR) estimation.
+
+        Parameters
+        ----------
+        feature_columns: list
+            List of feature columns to use for the classifier
+        classifier_base: object
+            Base classifier object to use for the FDR estimation
+
+        """
         super().__init__(path=path, load_from_file=load_from_file, **kwargs)
         self.reporter.log_string(f"Initializing {self.__class__.__name__}")
         self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
 
         if not self.is_loaded_from_file:
             self.feature_columns = feature_columns
-            self.classifier_store = {}
+            self.classifier_store = defaultdict(list)
             self.classifier_base = classifier_base
         self._current_version = -1
         self.load_classifier_store()
@@ -525,7 +525,7 @@ def fit_predict(
         df_fragments: None | pd.DataFrame = None,
         dia_cycle: None | np.ndarray = None,
         decoy_channel: int = -1,
-        version: None | int = -1,
+        version: int = -1,
     ):
         """Fit the classifier and perform FDR estimation.
 
@@ -639,10 +639,6 @@ def fit_predict(
 
         self.is_fitted = True
 
-        classifier_hash = column_hash(available_columns)
-        if classifier_hash not in self.classifier_store:
-            self.classifier_store[classifier_hash] = []
-
         self._current_version += 1
         self.classifier_store[column_hash(available_columns)].append(classifier)
 
@@ -683,7 +679,7 @@ def load_classifier_store(self, path=None):
                         torch.load(os.path.join(path, file))
                     )
 
-    def get_classifier(self, available_columns, version):
+    def get_classifier(self, available_columns: list, version: int = -1):
         """Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
 
         Parameters
@@ -701,10 +697,10 @@ def get_classifier(self, available_columns, version):
         """
         classifier_hash = column_hash(available_columns)
         if classifier_hash in self.classifier_store:
-            classifier = deepcopy(self.classifier_store[classifier_hash][version])
+            classifier = self.classifier_store[classifier_hash][version]
         else:
-            classifier = deepcopy(self.classifier_base)
-        return classifier
+            classifier = self.classifier_base
+        return deepcopy(classifier)
 
     @property
     def current_version(self):

From 559dac02119e6076ec103aaff3610c80ccc112e2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 15:25:48 +0200
Subject: [PATCH 019/250] updates with issues over load and save

---
 alphadia/workflow/manager.py      | 37 ++++++++++++++++++++-----------
 tests/unit_tests/test_workflow.py | 18 +++++++++++----
 2 files changed, 38 insertions(+), 17 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 27394cd9..451860da 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -654,10 +654,13 @@ def save_classifier_store(self, path=None):
 
         logger.info(f"Saving classifier store to {path}")
 
-        for classifier_hash, classifier in self.classifier_store.items():
-            torch.save(
-                classifier.to_state_dict(), os.path.join(path, f"{classifier_hash}.pth")
-            )
+        for classifier_hash, classifier_list in self.classifier_store.items():
+            for version, classifier in enumerate(classifier_list):
+                os.makedirs(os.path.join(path, classifier_hash), exist_ok=True)
+                torch.save(
+                    classifier.to_state_dict(),
+                    os.path.join(path, f"{classifier_hash}/{version}.pth"),
+                )
 
     def load_classifier_store(self, path=None):
         if path is None:
@@ -667,17 +670,25 @@ def load_classifier_store(self, path=None):
 
         logger.info(f"Loading classifier store from {path}")
 
-        for file in os.listdir(path):
-            if file.endswith(".pth"):
-                classifier_hash = file.split(".")[0]
+        for subpath in os.listdir(path):
+            if subpath.endswith(".pth"):
+                continue
+            else:
+                classifier_hash = subpath
+                num_classifiers = len(os.listdir(os.path.join(path, classifier_hash)))
 
                 if classifier_hash not in self.classifier_store:
-                    self.classifier_store[classifier_hash] = deepcopy(
-                        self.classifier_base
-                    )
-                    self.classifier_store[classifier_hash].from_state_dict(
-                        torch.load(os.path.join(path, file))
-                    )
+                    self.classifier_store[classifier_hash] = [None] * num_classifiers
+
+                for file in os.listdir(os.path.join(path, classifier_hash)):
+                    if file.endswith(".pth"):
+                        version = int(file.split(".")[0])
+
+                        classifier = deepcopy(self.classifier_base)
+                        classifier.from_state_dict(
+                            torch.load(os.path.join(path, classifier_hash, file))
+                        )
+                        self.classifier_store[classifier_hash][version] = classifier
 
     def get_classifier(self, available_columns: list, version: int = -1):
         """Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index b5f5cd11..95997b3d 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -300,7 +300,7 @@ def test_workflow_base():
 
 
 FDR_TEST_BASE_CLASSIFIER = BinaryClassifierLegacyNewBatching(
-    test_size=0.001, batch_size=50, learning_rate=0.001, epochs=1
+    test_size=0.001, batch_size=2, learning_rate=0.001, epochs=1
 )
 FDR_TEST_FEATURES = ["feature_a", "feature_b"]
 
@@ -326,12 +326,11 @@ def test_fdr_manager():
     assert fdr_manager.classifier_base == FDR_TEST_BASE_CLASSIFIER
 
 
-@pytest.mark.slow
 def test_fdr_manager_fit_predict():
     fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
     test_features_df = fdr_testdata(FDR_TEST_FEATURES)
 
-    assert len(fdr_manager.classifier_store) == 1
+    #   assert len(fdr_manager.classifier_store) == 1
 
     fdr_manager.fit_predict(
         test_features_df,
@@ -341,7 +340,7 @@ def test_fdr_manager_fit_predict():
         dia_cycle=None,
     )
 
-    assert len(fdr_manager.classifier_store) == 2
+    #   assert len(fdr_manager.classifier_store) == 2
     assert fdr_manager.current_version == 0
     assert manager.column_hash(FDR_TEST_FEATURES) in fdr_manager.classifier_store
 
@@ -355,3 +354,14 @@ def test_fdr_manager_fit_predict():
     )
 
     assert fdr_manager.current_version == 1
+    assert fdr_manager.get_classifier(FDR_TEST_FEATURES, 0).fitted is True
+    temp_path = os.path.join(tempfile.tempdir, "classifier")
+    fdr_manager.save_classifier_store(temp_path)
+    fdr_manager.load_classifier_store(temp_path)
+
+    assert os.path.exists(
+        os.path.join(temp_path, f"{manager.column_hash(FDR_TEST_FEATURES)}/0.pth")
+    )
+    assert fdr_manager.get_classifier(FDR_TEST_FEATURES, 0).fitted is True
+
+    os.remove(temp_path)

From 4baaa2aa8b7aad473da3d46093dae38ec0194c32 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 16:22:43 +0200
Subject: [PATCH 020/250] improved load and save

---
 alphadia/workflow/manager.py      | 53 +++++++++++++++++--------------
 tests/unit_tests/test_workflow.py | 20 +++++++-----
 2 files changed, 41 insertions(+), 32 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 451860da..149c010d 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -646,7 +646,17 @@ def fit_predict(
 
         return psm_df
 
-    def save_classifier_store(self, path=None):
+    def save_classifier_store(self, path: None | str = None, version: int = -1):
+        """Saves the classifier store to disk.
+
+        Parameters
+        ----------
+        path: None | str
+            Where to save the classifier. Saves to alphadia/constants/classifier if None.
+        version: int
+            Version of the classifier to save. Takes the last classifier if -1 (default)
+
+        """
         if path is None:
             path = os.path.join(
                 os.path.dirname(alphadia.__file__), "constants", "classifier"
@@ -655,14 +665,20 @@ def save_classifier_store(self, path=None):
         logger.info(f"Saving classifier store to {path}")
 
         for classifier_hash, classifier_list in self.classifier_store.items():
-            for version, classifier in enumerate(classifier_list):
-                os.makedirs(os.path.join(path, classifier_hash), exist_ok=True)
-                torch.save(
-                    classifier.to_state_dict(),
-                    os.path.join(path, f"{classifier_hash}/{version}.pth"),
-                )
+            torch.save(
+                classifier_list[version].to_state_dict(),
+                os.path.join(path, f"{classifier_hash}.pth"),
+            )
 
     def load_classifier_store(self, path=None):
+        """Loads the classifier store from disk.
+
+        Parameters
+        ----------
+        path: str
+            Whence to load the classifier. Loads from alphadia/constants/classifier if None.
+
+        """
         if path is None:
             path = os.path.join(
                 os.path.dirname(alphadia.__file__), "constants", "classifier"
@@ -670,25 +686,14 @@ def load_classifier_store(self, path=None):
 
         logger.info(f"Loading classifier store from {path}")
 
-        for subpath in os.listdir(path):
-            if subpath.endswith(".pth"):
-                continue
-            else:
-                classifier_hash = subpath
-                num_classifiers = len(os.listdir(os.path.join(path, classifier_hash)))
+        for file in os.listdir(path):
+            if file.endswith(".pth"):
+                classifier_hash = file.split(".")[0]
 
                 if classifier_hash not in self.classifier_store:
-                    self.classifier_store[classifier_hash] = [None] * num_classifiers
-
-                for file in os.listdir(os.path.join(path, classifier_hash)):
-                    if file.endswith(".pth"):
-                        version = int(file.split(".")[0])
-
-                        classifier = deepcopy(self.classifier_base)
-                        classifier.from_state_dict(
-                            torch.load(os.path.join(path, classifier_hash, file))
-                        )
-                        self.classifier_store[classifier_hash][version] = classifier
+                    classifier = deepcopy(self.classifier_base)
+                    classifier.from_state_dict(torch.load(os.path.join(path, file)))
+                    self.classifier_store[classifier_hash].append(classifier)
 
     def get_classifier(self, available_columns: list, version: int = -1):
         """Gets the classifier for a given set of feature columns and version. If the classifier is not found in the store, gets the base classifier instead.
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 95997b3d..f17e5bc2 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -330,7 +330,7 @@ def test_fdr_manager_fit_predict():
     fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
     test_features_df = fdr_testdata(FDR_TEST_FEATURES)
 
-    #   assert len(fdr_manager.classifier_store) == 1
+    assert len(fdr_manager.classifier_store) == 1
 
     fdr_manager.fit_predict(
         test_features_df,
@@ -340,7 +340,7 @@ def test_fdr_manager_fit_predict():
         dia_cycle=None,
     )
 
-    #   assert len(fdr_manager.classifier_store) == 2
+    assert len(fdr_manager.classifier_store) == 2
     assert fdr_manager.current_version == 0
     assert manager.column_hash(FDR_TEST_FEATURES) in fdr_manager.classifier_store
 
@@ -355,13 +355,17 @@ def test_fdr_manager_fit_predict():
 
     assert fdr_manager.current_version == 1
     assert fdr_manager.get_classifier(FDR_TEST_FEATURES, 0).fitted is True
-    temp_path = os.path.join(tempfile.tempdir, "classifier")
-    fdr_manager.save_classifier_store(temp_path)
-    fdr_manager.load_classifier_store(temp_path)
 
-    assert os.path.exists(
-        os.path.join(temp_path, f"{manager.column_hash(FDR_TEST_FEATURES)}/0.pth")
+    fdr_manager.save_classifier_store(tempfile.tempdir)
+
+    fdr_manager_new = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
+    fdr_manager_new.load_classifier_store(tempfile.tempdir)
+
+    temp_path = os.path.join(
+        tempfile.tempdir, f"{manager.column_hash(FDR_TEST_FEATURES)}.pth"
     )
-    assert fdr_manager.get_classifier(FDR_TEST_FEATURES, 0).fitted is True
+
+    assert os.path.exists(temp_path)
+    assert fdr_manager_new.get_classifier(FDR_TEST_FEATURES).fitted is True
 
     os.remove(temp_path)

From 9acfd4e2f3f8b25782d1664e1ef24e6388db4112 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 16:39:05 +0200
Subject: [PATCH 021/250] formatting

---
 alphadia/workflow/manager.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 149c010d..863eaf71 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -675,8 +675,8 @@ def load_classifier_store(self, path=None):
 
         Parameters
         ----------
-        path: str
-            Whence to load the classifier. Loads from alphadia/constants/classifier if None.
+        path: None | str
+            From where to load the classifier. Loads from alphadia/constants/classifier if None.
 
         """
         if path is None:

From 599eb3a80e9d5a688de646fcee471a05af612c9d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 23 Jul 2024 16:59:01 +0200
Subject: [PATCH 022/250] formatting

---
 alphadia/workflow/manager.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 863eaf71..af73b947 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -670,13 +670,13 @@ def save_classifier_store(self, path: None | str = None, version: int = -1):
                 os.path.join(path, f"{classifier_hash}.pth"),
             )
 
-    def load_classifier_store(self, path=None):
+    def load_classifier_store(self, path: None | str = None):
         """Loads the classifier store from disk.
 
         Parameters
         ----------
         path: None | str
-            From where to load the classifier. Loads from alphadia/constants/classifier if None.
+            Location of the classifier to load. Loads from alphadia/constants/classifier if None.
 
         """
         if path is None:

From af236cbf6e8e1167ee96bc4b53517da23fcb897e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 24 Jul 2024 10:16:36 +0200
Subject: [PATCH 023/250] move error logic from CLI

---
 alphadia/cli.py      | 12 +++++-------
 alphadia/planning.py | 32 ++++++++++++++++++++------------
 2 files changed, 25 insertions(+), 19 deletions(-)

diff --git a/alphadia/cli.py b/alphadia/cli.py
index 6bff7b65..581db62b 100644
--- a/alphadia/cli.py
+++ b/alphadia/cli.py
@@ -1,8 +1,9 @@
 #!python
+"""CLI for alphaDIA.
+
+Ideally the CLI module should have as little logic as possible so that the search behaves the same from the CLI or a jupyter notebook.
+"""
 
-# native imports
-# alpha family imports
-# third party imports
 import argparse
 import json
 import logging
@@ -12,7 +13,6 @@
 
 import yaml
 
-# alphadia imports
 import alphadia
 from alphadia import utils
 from alphadia.exceptions import CustomError
@@ -343,14 +343,12 @@ def run(*args, **kwargs):
 
     except Exception as e:
         if isinstance(e, CustomError):
-            message = f"An error occurred: {e.error_code}: {e.msg} {e.detail_msg}"
             exit_code = 1
         else:
             import traceback
 
             logger.info(traceback.format_exc())
-            message = f"An unknown error occurred: {e}"
             exit_code = 127
 
-        logger.error(message)
+        logger.error(e)
         sys.exit(exit_code)
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 439e07a8..116ec2c6 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -355,13 +355,17 @@ def run(
                 workflow.reporter.context.__exit__(None, None, None)
                 del workflow
 
-        base_spec_lib = SpecLibBase()
-        base_spec_lib.load_hdf(
-            os.path.join(self.output_folder, "speclib.hdf"), load_mod_seq=True
-        )
+        try:
+            base_spec_lib = SpecLibBase()
+            base_spec_lib.load_hdf(
+                os.path.join(self.output_folder, "speclib.hdf"), load_mod_seq=True
+            )
 
-        output = outputtransform.SearchPlanOutput(self.config, self.output_folder)
-        output.build(workflow_folder_list, base_spec_lib)
+            output = outputtransform.SearchPlanOutput(self.config, self.output_folder)
+            output.build(workflow_folder_list, base_spec_lib)
+        except Exception as e:
+            _log_exception_event(e)
+            raise e
 
         logger.progress("=================== Search Finished ===================")
 
@@ -370,18 +374,22 @@ def clean(self):
             os.remove(os.path.join(self.output_folder, "speclib.hdf"))
 
 
-def _log_exception_event(e: Exception, raw_name: str, workflow: WorkflowBase) -> None:
+def _log_exception_event(
+    e: Exception, raw_name: str | None = None, workflow: WorkflowBase | None = None
+) -> None:
     """Log exception and emit event to reporter if available."""
 
+    prefix = (
+        "Error:" if raw_name is None else f"Search for {raw_name} failed with error:"
+    )
+
     if isinstance(e, CustomError):
-        logger.error(f"Search for {raw_name} failed with error: {e.error_code} {e.msg}")
+        logger.error(f"{prefix} {e.error_code} {e.msg}")
         logger.error(e.detail_msg)
     else:
-        logger.error(
-            f"Search for {raw_name} failed with unknown error {e}", exc_info=True
-        )
+        logger.error(f"{prefix} {e}", exc_info=True)
 
-    if workflow.reporter:
+    if workflow is not None and workflow.reporter:
         workflow.reporter.log_string(
             value=str(e),
             verbosity="error",

From 4f39e8a1d5d30eda2fc3ae188ace7ef8c36681b9 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 24 Jul 2024 19:21:39 +0200
Subject: [PATCH 024/250] temporary save

---
 alphadia/workflow/optimizer.py    | 128 ++++++++++++++++++++++++++++++
 tests/unit_tests/test_workflow.py |  31 +++++++-
 2 files changed, 158 insertions(+), 1 deletion(-)
 create mode 100644 alphadia/workflow/optimizer.py

diff --git a/alphadia/workflow/optimizer.py b/alphadia/workflow/optimizer.py
new file mode 100644
index 00000000..12938532
--- /dev/null
+++ b/alphadia/workflow/optimizer.py
@@ -0,0 +1,128 @@
+# native imports
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+# alpha family imports
+# third party imports
+import pandas as pd
+
+# alphadia imports
+from alphadia.workflow import reporting
+
+
+class BaseOptimizer(ABC):
+    def __init__(
+        self,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
+    ):
+        self.reporter = reporting.LogBackend() if reporter is None else reporter
+
+    @abstractmethod
+    def check(self):
+        pass
+
+    @abstractmethod
+    def update(self):
+        pass
+
+
+class RTOptimizer(BaseOptimizer):
+    pass
+
+
+class MS2Optimizer(BaseOptimizer):
+    def __init__(self, initial_tolerance: float):
+        """This class automatically finds an appropriate MS2 tolerance for the search.
+
+        Parameters
+        ----------
+        initial_tolerance: float
+            The initial tolerance to start the optimization
+        """
+        super().__init__()
+        self.estimator = "mz"
+        self.df = "fragment"
+        self.proposed_new_tolerance = initial_tolerance
+        self.tolerances = []
+        self.ids = []
+        self.round = -1
+        self.optimal_tolerance = None
+
+    def check(self):
+        """Optimization should stop if continued narrowing of the MS2 tolerance is not improving the number of precursor identifications.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
+        If so, it continues optimization and appends the proposed new tolerance to the list of tolerances. If not, it stops optimization and sets the optimal tolerance attribute.
+
+        Notes
+        -----
+            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if the round variable is less than 2.
+
+            Once the optimal tolerance is set, the optimizer will no longer check for improvements.
+
+        Returns
+        -------
+        stop_optimizaton: bool
+            True if there has been no recent improvement in the number of identifications and optimization should stop. False otherwise.
+
+        """
+
+        if self.optimal_tolerance is not None:
+            self.end_of_optimization_message()
+            stop_optimizaton = True
+
+        elif (
+            self.round < 2
+            or self.ids[-1] > 1.1 * self.ids[-2]
+            or self.ids[-1] > 1.1 * self.ids[-3]
+        ):
+            self.reporter.log_string(
+                f"❌ MS2: optimization incomplete in round {self.round}. Will search with tolerance {self.proposed_new_tolerance}.",
+                verbosity="progress",
+            )
+            stop_optimizaton = False
+
+        else:
+            self.optimal_tolerance = self.tolerances[np.argmax(self.ids)]
+            self.end_of_optimization_message()
+            stop_optimizaton = True
+
+        return stop_optimizaton
+
+    def initiate(self):
+        """If the optimization continues (which will use the proposed new tolerance), the round variable should be incremented and the proposed new tolerance should be added to the list of tolerances."""
+        if self.optimal_tolerance is None:
+            self.tolerances.append(self.proposed_new_tolerance)
+            self.round += 1
+
+    def update(self, precursors_df: pd.DataFrame, proposed_new_tolerance: float):
+        """This function appends the number of precursors identified to the list of identifications.
+        It also saves the proposed new tolerance, which will be added to the list of tolerances if the optimization continues after check is called.
+
+        Parameters
+        ----------
+
+        precursors_df: pd.DataFrame
+            The filtered dataframe of precursors obtained after FDR correction.
+
+        proposed_new_tolerance: float
+            The tolerance which will be used for the next round of optimization (if optimization continues).
+
+        """
+        if self.optimal_tolerance is None:
+            self.ids.append(len(precursors_df))
+            self.proposed_new_tolerance = proposed_new_tolerance
+
+    def end_of_optimization_message(self):
+        self.reporter.log_string(
+            f"✅ MS2: optimization complete. Optimal tolerance {self.optimal_tolerance} found in round {self.round}.",
+            verbosity="progress",
+        )
+
+
+class MS1Optimizer(BaseOptimizer):
+    pass
+
+
+class MobilityOptimizer(BaseOptimizer):
+    pass
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index f17e5bc2..fe179bc9 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -1,6 +1,7 @@
 import os
 import shutil
 import tempfile
+from collections import defaultdict
 from pathlib import Path
 
 import numpy as np
@@ -12,7 +13,7 @@
 from alphadia.calibration.models import LOESSRegression
 from alphadia.calibration.property import Calibration
 from alphadia.fdrexperimental import BinaryClassifierLegacyNewBatching
-from alphadia.workflow import base, manager
+from alphadia.workflow import base, manager, optimizer
 
 
 def test_base_manager():
@@ -369,3 +370,31 @@ def test_fdr_manager_fit_predict():
     assert fdr_manager_new.get_classifier(FDR_TEST_FEATURES).fitted is True
 
     os.remove(temp_path)
+
+
+def ms2_optimizer_test():
+    test_dict = defaultdict(list)
+    test_dict["var"] = list(range(100))
+
+    ms2_optimizer = optimizer.MS2Optimizer(100)
+
+    assert ms2_optimizer.optimal_tolerance is None
+    assert ms2_optimizer.round == -1
+    assert ms2_optimizer.check() is False
+
+    ms2_optimizer.initiate()
+    test_dict["var"].append(1)
+    ms2_optimizer.update(pd.DataFrame(test_dict), 20)
+
+    assert ms2_optimizer.round == 0
+
+    ms2_optimizer.initiate()
+    test_dict["var"].append(1)
+    ms2_optimizer.update(pd.DataFrame(test_dict), 10)
+
+    ms2_optimizer.initiate()
+    test_dict["var"].append(1)
+    ms2_optimizer.update(pd.DataFrame(test_dict), 2)
+
+    assert ms2_optimizer.check() is True
+    assert ms2_optimizer.optimal_tolerance == 10

From e8d8296379f7029d7d7d20285f677d992136d11e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 25 Jul 2024 09:23:53 +0200
Subject: [PATCH 025/250] updated base optimizer and added decorator to check
 if optimum already found

---
 alphadia/workflow/optimizer.py    | 81 +++++++++++++++++++------------
 tests/unit_tests/test_workflow.py |  8 ++-
 2 files changed, 56 insertions(+), 33 deletions(-)

diff --git a/alphadia/workflow/optimizer.py b/alphadia/workflow/optimizer.py
index 12938532..81f46415 100644
--- a/alphadia/workflow/optimizer.py
+++ b/alphadia/workflow/optimizer.py
@@ -1,5 +1,5 @@
 # native imports
-from abc import ABC, abstractmethod
+from functools import wraps
 
 import numpy as np
 
@@ -11,20 +11,53 @@
 from alphadia.workflow import reporting
 
 
-class BaseOptimizer(ABC):
+def check_if_optimum_found(method):
+    """If an optimum has been found, the optimizer should cease to execute its methods to check, initiate and update.
+    This function can be used as a decorator and placed before the methods in an optimizer to prevent execution of the methods if an optimum has been found.
+    If the optimum has been found, the decorator will print the end of optimization message and, for consistency with stop_or_continue methods, return True.
+    """
+
+    @wraps(method)
+    def wrapper(self, *args, **kwargs):
+        if self.optimal_tolerance is not None:
+            self.end_of_optimization_message()
+            return True
+        return method(self, *args, **kwargs)
+
+    return wrapper
+
+
+class BaseOptimizer:
     def __init__(
         self,
+        initial_tolerance: float,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
+        """This class serves as a base class for organizing the search parameter optimization process, which defines the tolerances used for search.
+
+        Parameters
+        ----------
+        initial_tolerance: float
+            The initial tolerance to start the optimization
+        """
+        self.proposed_new_tolerance = initial_tolerance
+        self.tolerances = []
+        self.round = -1
+        self.optimal_tolerance = None
         self.reporter = reporting.LogBackend() if reporter is None else reporter
 
-    @abstractmethod
-    def check(self):
-        pass
+    def check_stopping_conditions(self):
+        raise NotImplementedError(
+            f"check_stopping_conditions() not implemented for {self.__class__.__name__}"
+        )
 
-    @abstractmethod
     def update(self):
-        pass
+        raise NotImplementedError(
+            f"update() not implemented for {self.__class__.__name__}"
+        )
+
+    def initiate(self):
+        raise NotImplementedError(f"initiate() not implemented for {self.__class__}")
 
 
 class RTOptimizer(BaseOptimizer):
@@ -33,23 +66,13 @@ class RTOptimizer(BaseOptimizer):
 
 class MS2Optimizer(BaseOptimizer):
     def __init__(self, initial_tolerance: float):
-        """This class automatically finds an appropriate MS2 tolerance for the search.
-
-        Parameters
-        ----------
-        initial_tolerance: float
-            The initial tolerance to start the optimization
-        """
-        super().__init__()
+        super().__init__(initial_tolerance)
         self.estimator = "mz"
         self.df = "fragment"
-        self.proposed_new_tolerance = initial_tolerance
-        self.tolerances = []
         self.ids = []
-        self.round = -1
-        self.optimal_tolerance = None
 
-    def check(self):
+    @check_if_optimum_found
+    def check_stopping_conditions(self):
         """Optimization should stop if continued narrowing of the MS2 tolerance is not improving the number of precursor identifications.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
         If so, it continues optimization and appends the proposed new tolerance to the list of tolerances. If not, it stops optimization and sets the optimal tolerance attribute.
@@ -67,11 +90,7 @@ def check(self):
 
         """
 
-        if self.optimal_tolerance is not None:
-            self.end_of_optimization_message()
-            stop_optimizaton = True
-
-        elif (
+        if (
             self.round < 2
             or self.ids[-1] > 1.1 * self.ids[-2]
             or self.ids[-1] > 1.1 * self.ids[-3]
@@ -89,12 +108,13 @@ def check(self):
 
         return stop_optimizaton
 
+    @check_if_optimum_found
     def initiate(self):
         """If the optimization continues (which will use the proposed new tolerance), the round variable should be incremented and the proposed new tolerance should be added to the list of tolerances."""
-        if self.optimal_tolerance is None:
-            self.tolerances.append(self.proposed_new_tolerance)
-            self.round += 1
+        self.tolerances.append(self.proposed_new_tolerance)
+        self.round += 1
 
+    @check_if_optimum_found
     def update(self, precursors_df: pd.DataFrame, proposed_new_tolerance: float):
         """This function appends the number of precursors identified to the list of identifications.
         It also saves the proposed new tolerance, which will be added to the list of tolerances if the optimization continues after check is called.
@@ -109,9 +129,8 @@ def update(self, precursors_df: pd.DataFrame, proposed_new_tolerance: float):
             The tolerance which will be used for the next round of optimization (if optimization continues).
 
         """
-        if self.optimal_tolerance is None:
-            self.ids.append(len(precursors_df))
-            self.proposed_new_tolerance = proposed_new_tolerance
+        self.ids.append(len(precursors_df))
+        self.proposed_new_tolerance = proposed_new_tolerance
 
     def end_of_optimization_message(self):
         self.reporter.log_string(
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index fe179bc9..beb2fe56 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -380,7 +380,7 @@ def ms2_optimizer_test():
 
     assert ms2_optimizer.optimal_tolerance is None
     assert ms2_optimizer.round == -1
-    assert ms2_optimizer.check() is False
+    assert ms2_optimizer.check_stopping_conditions() is False
 
     ms2_optimizer.initiate()
     test_dict["var"].append(1)
@@ -396,5 +396,9 @@ def ms2_optimizer_test():
     test_dict["var"].append(1)
     ms2_optimizer.update(pd.DataFrame(test_dict), 2)
 
-    assert ms2_optimizer.check() is True
+    assert ms2_optimizer.round == 2
+    assert ms2_optimizer.check_stopping_conditions() is True
+    assert ms2_optimizer.initiate() is True
+    assert ms2_optimizer.update(pd.DataFrame(test_dict), 2) is True
+    assert ms2_optimizer.round == 2
     assert ms2_optimizer.optimal_tolerance == 10

From cdaa80649ea28660d682c6f745abac4459fb5797 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 25 Jul 2024 15:24:48 +0200
Subject: [PATCH 026/250] refactored optimizer

---
 alphadia/workflow/optimizer.py    | 147 ------------------------------
 tests/unit_tests/test_workflow.py |  26 ++----
 2 files changed, 8 insertions(+), 165 deletions(-)
 delete mode 100644 alphadia/workflow/optimizer.py

diff --git a/alphadia/workflow/optimizer.py b/alphadia/workflow/optimizer.py
deleted file mode 100644
index 81f46415..00000000
--- a/alphadia/workflow/optimizer.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# native imports
-from functools import wraps
-
-import numpy as np
-
-# alpha family imports
-# third party imports
-import pandas as pd
-
-# alphadia imports
-from alphadia.workflow import reporting
-
-
-def check_if_optimum_found(method):
-    """If an optimum has been found, the optimizer should cease to execute its methods to check, initiate and update.
-    This function can be used as a decorator and placed before the methods in an optimizer to prevent execution of the methods if an optimum has been found.
-    If the optimum has been found, the decorator will print the end of optimization message and, for consistency with stop_or_continue methods, return True.
-    """
-
-    @wraps(method)
-    def wrapper(self, *args, **kwargs):
-        if self.optimal_tolerance is not None:
-            self.end_of_optimization_message()
-            return True
-        return method(self, *args, **kwargs)
-
-    return wrapper
-
-
-class BaseOptimizer:
-    def __init__(
-        self,
-        initial_tolerance: float,
-        reporter: None | reporting.Pipeline | reporting.Backend = None,
-    ):
-        """This class serves as a base class for organizing the search parameter optimization process, which defines the tolerances used for search.
-
-        Parameters
-        ----------
-        initial_tolerance: float
-            The initial tolerance to start the optimization
-        """
-        self.proposed_new_tolerance = initial_tolerance
-        self.tolerances = []
-        self.round = -1
-        self.optimal_tolerance = None
-        self.reporter = reporting.LogBackend() if reporter is None else reporter
-
-    def check_stopping_conditions(self):
-        raise NotImplementedError(
-            f"check_stopping_conditions() not implemented for {self.__class__.__name__}"
-        )
-
-    def update(self):
-        raise NotImplementedError(
-            f"update() not implemented for {self.__class__.__name__}"
-        )
-
-    def initiate(self):
-        raise NotImplementedError(f"initiate() not implemented for {self.__class__}")
-
-
-class RTOptimizer(BaseOptimizer):
-    pass
-
-
-class MS2Optimizer(BaseOptimizer):
-    def __init__(self, initial_tolerance: float):
-        super().__init__(initial_tolerance)
-        self.estimator = "mz"
-        self.df = "fragment"
-        self.ids = []
-
-    @check_if_optimum_found
-    def check_stopping_conditions(self):
-        """Optimization should stop if continued narrowing of the MS2 tolerance is not improving the number of precursor identifications.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
-        If so, it continues optimization and appends the proposed new tolerance to the list of tolerances. If not, it stops optimization and sets the optimal tolerance attribute.
-
-        Notes
-        -----
-            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if the round variable is less than 2.
-
-            Once the optimal tolerance is set, the optimizer will no longer check for improvements.
-
-        Returns
-        -------
-        stop_optimizaton: bool
-            True if there has been no recent improvement in the number of identifications and optimization should stop. False otherwise.
-
-        """
-
-        if (
-            self.round < 2
-            or self.ids[-1] > 1.1 * self.ids[-2]
-            or self.ids[-1] > 1.1 * self.ids[-3]
-        ):
-            self.reporter.log_string(
-                f"❌ MS2: optimization incomplete in round {self.round}. Will search with tolerance {self.proposed_new_tolerance}.",
-                verbosity="progress",
-            )
-            stop_optimizaton = False
-
-        else:
-            self.optimal_tolerance = self.tolerances[np.argmax(self.ids)]
-            self.end_of_optimization_message()
-            stop_optimizaton = True
-
-        return stop_optimizaton
-
-    @check_if_optimum_found
-    def initiate(self):
-        """If the optimization continues (which will use the proposed new tolerance), the round variable should be incremented and the proposed new tolerance should be added to the list of tolerances."""
-        self.tolerances.append(self.proposed_new_tolerance)
-        self.round += 1
-
-    @check_if_optimum_found
-    def update(self, precursors_df: pd.DataFrame, proposed_new_tolerance: float):
-        """This function appends the number of precursors identified to the list of identifications.
-        It also saves the proposed new tolerance, which will be added to the list of tolerances if the optimization continues after check is called.
-
-        Parameters
-        ----------
-
-        precursors_df: pd.DataFrame
-            The filtered dataframe of precursors obtained after FDR correction.
-
-        proposed_new_tolerance: float
-            The tolerance which will be used for the next round of optimization (if optimization continues).
-
-        """
-        self.ids.append(len(precursors_df))
-        self.proposed_new_tolerance = proposed_new_tolerance
-
-    def end_of_optimization_message(self):
-        self.reporter.log_string(
-            f"✅ MS2: optimization complete. Optimal tolerance {self.optimal_tolerance} found in round {self.round}.",
-            verbosity="progress",
-        )
-
-
-class MS1Optimizer(BaseOptimizer):
-    pass
-
-
-class MobilityOptimizer(BaseOptimizer):
-    pass
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index beb2fe56..05ec3610 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -13,7 +13,7 @@
 from alphadia.calibration.models import LOESSRegression
 from alphadia.calibration.property import Calibration
 from alphadia.fdrexperimental import BinaryClassifierLegacyNewBatching
-from alphadia.workflow import base, manager, optimizer
+from alphadia.workflow import base, manager, searchoptimization
 
 
 def test_base_manager():
@@ -376,29 +376,19 @@ def ms2_optimizer_test():
     test_dict = defaultdict(list)
     test_dict["var"] = list(range(100))
 
-    ms2_optimizer = optimizer.MS2Optimizer(100)
+    ms2_optimizer = searchoptimization.MS2Optimizer(100)
 
     assert ms2_optimizer.optimal_tolerance is None
-    assert ms2_optimizer.round == -1
-    assert ms2_optimizer.check_stopping_conditions() is False
 
-    ms2_optimizer.initiate()
-    test_dict["var"].append(1)
-    ms2_optimizer.update(pd.DataFrame(test_dict), 20)
-
-    assert ms2_optimizer.round == 0
+    ms2_optimizer.step(pd.DataFrame(test_dict), 20)
 
-    ms2_optimizer.initiate()
     test_dict["var"].append(1)
-    ms2_optimizer.update(pd.DataFrame(test_dict), 10)
+    ms2_optimizer.step(pd.DataFrame(test_dict), 10)
 
-    ms2_optimizer.initiate()
     test_dict["var"].append(1)
-    ms2_optimizer.update(pd.DataFrame(test_dict), 2)
+    ms2_optimizer.step(pd.DataFrame(test_dict), 2)
 
-    assert ms2_optimizer.round == 2
-    assert ms2_optimizer.check_stopping_conditions() is True
-    assert ms2_optimizer.initiate() is True
-    assert ms2_optimizer.update(pd.DataFrame(test_dict), 2) is True
-    assert ms2_optimizer.round == 2
     assert ms2_optimizer.optimal_tolerance == 10
+
+
+ms2_optimizer_test()

From e6c18e8539b461356602909e3cda0e61800e5c86 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 25 Jul 2024 17:22:42 +0200
Subject: [PATCH 027/250] added managers to optimizer to calculate updates
 internally

---
 alphadia/workflow/searchoptimization.py | 175 ++++++++++++++++++++++++
 tests/unit_tests/test_workflow.py       |  35 +++--
 2 files changed, 201 insertions(+), 9 deletions(-)
 create mode 100644 alphadia/workflow/searchoptimization.py

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
new file mode 100644
index 00000000..08fce959
--- /dev/null
+++ b/alphadia/workflow/searchoptimization.py
@@ -0,0 +1,175 @@
+# native imports
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+# alpha family imports
+# third party imports
+import pandas as pd
+
+# alphadia imports
+from alphadia.workflow import manager, reporting
+
+
+class BaseOptimizer(ABC):
+    def __init__(
+        self,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
+    ):
+        """This class serves as a base class for organizing the search parameter optimization process, which defines the parameters used for search.
+
+        Parameters
+        ----------
+
+        calibration_manager: manager.CalibrationManager
+            The calibration manager for the workflow, which is needed to update the search parameter between rounds of optimization
+
+        optimization_manager: manager.OptimizationManager
+            The optimization manager for the workflow, which is needed so the optimal parameter can be saved to the manager
+
+        """
+        self.optimal_parameter = None
+        self.calibration_manager = calibration_manager
+        self.optimization_manager = optimization_manager
+        self.reporter = reporting.LogBackend() if reporter is None else reporter
+
+    @abstractmethod
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """This method evaluates the progress of the optimization, and either concludes the optimization if it has converged or continues the optimization if it has not.
+        This method includes the update rule for the optimization.
+
+        Parameters
+        ----------
+
+        precursors_df: pd.DataFrame
+            The precursor dataframe for the search
+
+        fragments_df: pd.DataFrame
+            The fragment dataframe for the search
+
+
+        """
+        pass
+
+    @abstractmethod
+    def _update_parameter(self, df):
+        """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The rule is specific to the parameter being optimized.
+
+        Parameters
+        ----------
+
+        df: pd.DataFrame
+            The dataframe used to update the parameter. This could be the precursor or fragment dataframe, depending on the search parameter being optimized.
+
+
+        """
+        pass
+
+    @abstractmethod
+    def _check_convergence(self):
+        """This method checks if the optimization has converged according to parameter-specific conditions and, if it has, sets the optimal parameter attribute and updates the optimization manager."""
+        pass
+
+    def has_converged(self):
+        """If the optimal parameter has been set, the optimization must have converged and the method returns True. Otherwise, it returns False."""
+        return self.optimal_parameter is not None
+
+
+class RTOptimizer(BaseOptimizer):
+    """TODO: Implement this class. It will be used to optimize the RT parameter for the search."""
+
+    pass
+
+
+class MS2Optimizer(BaseOptimizer):
+    def __init__(
+        self,
+        initial_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+
+        """
+        super().__init__(calibration_manager, optimization_manager, **kwargs)
+        self.parameters = [initial_parameter]
+        self.precursor_ids = []
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+
+        """
+
+        if (
+            len(self.precursor_ids) > 2
+            and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-2]
+            and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-3]
+        ):
+            self.optimal_parameter = self.parameters[np.argmax(self.precursor_ids)]
+
+            self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
+            "fragment", "mz"
+        ).ci(df, 0.99)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .precursor_ids) and determine whether it has converged."""
+        if not self.has_converged():
+            self.precursor_ids.append(len(precursors_df))
+            self._check_convergence()
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ MS2: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                verbosity="progress",
+            )
+
+        else:
+            proposed_parameter = self._update_parameter(fragments_df)
+
+            self.reporter.log_string(
+                f"❌ MS2: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+
+
+class MS1Optimizer(BaseOptimizer):
+    """TODO: Implement this class. It will be used to optimize the MS1 parameter for the search."""
+
+    pass
+
+
+class MobilityOptimizer(BaseOptimizer):
+    """TODO: Implement this class. It will be used to optimize the mobility parameter for the search."""
+
+    pass
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 05ec3610..9ee96e2f 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -373,22 +373,39 @@ def test_fdr_manager_fit_predict():
 
 
 def ms2_optimizer_test():
+    temp_path = os.path.join(tempfile.tempdir, "calibration_manager.pkl")
+    calibration_manager = manager.CalibrationManager(
+        TEST_CONFIG, path=temp_path, load_from_file=False
+    )
+
+    temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
+
+    optimization_manager = manager.OptimizationManager(
+        OPTIMIZATION_TEST_DATA, path=temp_path, load_from_file=False
+    )
+
+    test_fragment_df = calibration_testdata()
+    calibration_manager.fit(test_fragment_df, "fragment", plot=False)
+
     test_dict = defaultdict(list)
     test_dict["var"] = list(range(100))
 
-    ms2_optimizer = searchoptimization.MS2Optimizer(100)
+    ms2_optimizer = searchoptimization.MS2Optimizer(
+        100, calibration_manager, optimization_manager
+    )
 
-    assert ms2_optimizer.optimal_tolerance is None
+    assert ms2_optimizer.optimal_parameter is None
 
-    ms2_optimizer.step(pd.DataFrame(test_dict), 20)
+    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
 
-    test_dict["var"].append(1)
-    ms2_optimizer.step(pd.DataFrame(test_dict), 10)
+    assert len(ms2_optimizer.parameters) == 2
 
     test_dict["var"].append(1)
-    ms2_optimizer.step(pd.DataFrame(test_dict), 2)
-
-    assert ms2_optimizer.optimal_tolerance == 10
+    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
 
+    test_dict["var"].append(1)
+    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
 
-ms2_optimizer_test()
+    assert ms2_optimizer.optimal_parameter is not None
+    assert ms2_optimizer.precursor_ids == [100, 101, 102]
+    assert optimization_manager.ms2_error == ms2_optimizer.optimal_parameter

From 96e0bb47157bf7bb284278d260f5b020c56d1287 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 12:12:20 +0200
Subject: [PATCH 028/250] Altered the optimizer so it updates the optimization
 manager with the current classifier version. Added a new instance attribute
 to the FDR manager to achieve this.

---
 alphadia/workflow/manager.py            |   8 +
 alphadia/workflow/searchoptimization.py | 190 +++++++++++++++++++++++-
 2 files changed, 191 insertions(+), 7 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index af73b947..c2407035 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -641,6 +641,9 @@ def fit_predict(
 
         self._current_version += 1
         self.classifier_store[column_hash(available_columns)].append(classifier)
+        self._num_classifiers = len(
+            self.classifier_store[column_hash(available_columns)]
+        )
 
         self.save()
 
@@ -722,6 +725,11 @@ def get_classifier(self, available_columns: list, version: int = -1):
     def current_version(self):
         return self._current_version
 
+    @property
+    def num_classifiers(self):
+        """This is the number of classifiers in the last classifier_hash that was saved to."""
+        return self._num_classifiers
+
     def predict(self):
         """Return the parameters dict."""
         raise NotImplementedError(
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 08fce959..ca1d054f 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -16,6 +16,7 @@ def __init__(
         self,
         calibration_manager: manager.CalibrationManager,
         optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class serves as a base class for organizing the search parameter optimization process, which defines the parameters used for search.
@@ -27,12 +28,16 @@ def __init__(
             The calibration manager for the workflow, which is needed to update the search parameter between rounds of optimization
 
         optimization_manager: manager.OptimizationManager
-            The optimization manager for the workflow, which is needed so the optimal parameter can be saved to the manager
+            The optimization manager for the workflow, which is needed so the optimal parameter and classifier version can be saved to the manager
+
+        fdr_manager: manager.FDRManager
+            The FDR manager for the workflow, which is needed to update the optimal classifier version in the optimization manager
 
         """
         self.optimal_parameter = None
         self.calibration_manager = calibration_manager
         self.optimization_manager = optimization_manager
+        self.fdr_manager = fdr_manager
         self.reporter = reporting.LogBackend() if reporter is None else reporter
 
     @abstractmethod
@@ -78,9 +83,89 @@ def has_converged(self):
 
 
 class RTOptimizer(BaseOptimizer):
-    """TODO: Implement this class. It will be used to optimize the RT parameter for the search."""
+    """TODO Finish this optimizer"""
 
-    pass
+    def __init__(
+        self,
+        initial_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.parameters = [initial_parameter]
+        self.feature = []
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+
+        """
+
+        if (
+            len(self.feature) > 2
+            and self.feature[-1] < 1.1 * self.feature[-2]
+            and self.feature[-1] < 1.1 * self.feature[-3]
+        ):
+            self.optimal_parameter = self.parameters[np.argmax(self.feature)]
+
+            self.optimization_manager.fit({"rt_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
+            "precursor", "rt"
+        ).ci(df, 0.99)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        if not self.has_converged():
+            self.feature.append(len(precursors_df))
+            self._check_convergence()
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ RT: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                verbosity="progress",
+            )
+
+        else:
+            proposed_parameter = self._update_parameter(precursors_df)
+
+            self.reporter.log_string(
+                f"❌ RT: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"rt_error": proposed_parameter})
 
 
 class MS2Optimizer(BaseOptimizer):
@@ -89,6 +174,7 @@ def __init__(
         initial_parameter: float,
         calibration_manager: manager.CalibrationManager,
         optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
         **kwargs,
     ):
         """See base class.
@@ -100,7 +186,9 @@ def __init__(
 
 
         """
-        super().__init__(calibration_manager, optimization_manager, **kwargs)
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
         self.parameters = [initial_parameter]
         self.precursor_ids = []
 
@@ -121,9 +209,16 @@ def _check_convergence(self):
             and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-2]
             and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-3]
         ):
-            self.optimal_parameter = self.parameters[np.argmax(self.precursor_ids)]
+            backtrack_by = (
+                len(self.precursor_ids) - np.argmax(self.precursor_ids)
+            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
+            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
 
+            self.optimal_parameter = self.parameters[-backtrack_by]
             self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
+            self.optimization_manager.fit(
+                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+            )
 
     def _update_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
@@ -161,12 +256,93 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             )
 
             self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"ms2_error": proposed_parameter})
 
 
 class MS1Optimizer(BaseOptimizer):
-    """TODO: Implement this class. It will be used to optimize the MS1 parameter for the search."""
+    """TODO Finish this optimizer"""
 
-    pass
+    def __init__(
+        self,
+        initial_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.parameters = [initial_parameter]
+        self.feature = []
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+
+        """
+
+        if (
+            len(self.feature) > 2
+            and self.feature[-1] < 1.1 * self.feature[-2]
+            and self.feature[-1] < 1.1 * self.feature[-3]
+        ):
+            self.optimal_parameter = self.parameters[np.argmax(self.feature)]
+
+            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
+            "precursor", "mz"
+        ).ci(df, 0.99)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        if not self.has_converged():
+            self.feature.append(len(precursors_df))
+            self._check_convergence()
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ MS1: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                verbosity="progress",
+            )
+
+        else:
+            proposed_parameter = self._update_parameter(precursors_df)
+
+            self.reporter.log_string(
+                f"❌ MS1: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"ms1_error": proposed_parameter})
 
 
 class MobilityOptimizer(BaseOptimizer):

From f6694b333ac794fc21923769d8295a96cb329fef Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 13:01:04 +0200
Subject: [PATCH 029/250] updated unit test

---
 tests/unit_tests/test_workflow.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 9ee96e2f..1729a165 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -387,11 +387,14 @@ def ms2_optimizer_test():
     test_fragment_df = calibration_testdata()
     calibration_manager.fit(test_fragment_df, "fragment", plot=False)
 
+    fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
+    fdr_manager._num_classifiers = 1
+
     test_dict = defaultdict(list)
     test_dict["var"] = list(range(100))
 
     ms2_optimizer = searchoptimization.MS2Optimizer(
-        100, calibration_manager, optimization_manager
+        100, calibration_manager, optimization_manager, fdr_manager
     )
 
     assert ms2_optimizer.optimal_parameter is None
@@ -408,4 +411,9 @@ def ms2_optimizer_test():
 
     assert ms2_optimizer.optimal_parameter is not None
     assert ms2_optimizer.precursor_ids == [100, 101, 102]
+    assert (
+        ms2_optimizer.optimal_parameter
+        == ms2_optimizer.parameters[np.argmax(ms2_optimizer.precursor_ids)]
+    )
     assert optimization_manager.ms2_error == ms2_optimizer.optimal_parameter
+    assert optimization_manager.classifier_version == 0

From 281e5e5827a5d78c0e20468bd789034fac252f85 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 13:35:47 +0200
Subject: [PATCH 030/250] added RT, MS1 and mobility optimizers for automatic
 calibration

---
 alphadia/workflow/searchoptimization.py | 111 ++++++++++++++++++++++--
 1 file changed, 105 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index ca1d054f..756a24e1 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -125,9 +125,16 @@ def _check_convergence(self):
             and self.feature[-1] < 1.1 * self.feature[-2]
             and self.feature[-1] < 1.1 * self.feature[-3]
         ):
-            self.optimal_parameter = self.parameters[np.argmax(self.feature)]
+            backtrack_by = (
+                len(self.feature) - np.argmax(self.feature)
+            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
+            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
 
+            self.optimal_parameter = self.parameters[-backtrack_by]
             self.optimization_manager.fit({"rt_error": self.optimal_parameter})
+            self.optimization_manager.fit(
+                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+            )
 
     def _update_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
@@ -145,7 +152,7 @@ def _update_parameter(self, df: pd.DataFrame):
         return proposed_parameter
 
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
             self.feature.append(len(precursors_df))
             self._check_convergence()
@@ -302,9 +309,16 @@ def _check_convergence(self):
             and self.feature[-1] < 1.1 * self.feature[-2]
             and self.feature[-1] < 1.1 * self.feature[-3]
         ):
-            self.optimal_parameter = self.parameters[np.argmax(self.feature)]
+            backtrack_by = (
+                len(self.feature) - np.argmax(self.feature)
+            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
+            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
 
+            self.optimal_parameter = self.parameters[-backtrack_by]
             self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
+            self.optimization_manager.fit(
+                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+            )
 
     def _update_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
@@ -322,7 +336,7 @@ def _update_parameter(self, df: pd.DataFrame):
         return proposed_parameter
 
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
             self.feature.append(len(precursors_df))
             self._check_convergence()
@@ -346,6 +360,91 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
 
 class MobilityOptimizer(BaseOptimizer):
-    """TODO: Implement this class. It will be used to optimize the mobility parameter for the search."""
+    def __init__(
+        self,
+        initial_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.parameters = [initial_parameter]
+        self.feature = []
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+
+        """
 
-    pass
+        if (
+            len(self.feature) > 2
+            and self.feature[-1] < 1.1 * self.feature[-2]
+            and self.feature[-1] < 1.1 * self.feature[-3]
+        ):
+            backtrack_by = (
+                len(self.feature) - np.argmax(self.feature)
+            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
+            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
+
+            self.optimal_parameter = self.parameters[-backtrack_by]
+            self.optimization_manager.fit({"mobility_error": self.optimal_parameter})
+            self.optimization_manager.fit(
+                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+            )
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
+            "precursor", "mz"
+        ).ci(df, 0.99)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        if not self.has_converged():
+            self.feature.append(len(precursors_df))
+            self._check_convergence()
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ Mobility: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                verbosity="progress",
+            )
+
+        else:
+            proposed_parameter = self._update_parameter(precursors_df)
+
+            self.reporter.log_string(
+                f"❌ Mobility: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"mobility_error": proposed_parameter})

From 918bfda43b4042a46322750528a7b11bfcb768fb Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 15:11:50 +0200
Subject: [PATCH 031/250] added optimizers for targeted optimization of MS1,
 MS2, RT and mobility

---
 alphadia/workflow/searchoptimization.py | 342 +++++++++++++++++++++++-
 1 file changed, 329 insertions(+), 13 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 756a24e1..4a5cd0b3 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -82,7 +82,7 @@ def has_converged(self):
         return self.optimal_parameter is not None
 
 
-class RTOptimizer(BaseOptimizer):
+class AutomaticRTOptimizer(BaseOptimizer):
     """TODO Finish this optimizer"""
 
     def __init__(
@@ -159,7 +159,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ RT: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'rt_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
                 verbosity="progress",
             )
 
@@ -167,7 +167,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             proposed_parameter = self._update_parameter(precursors_df)
 
             self.reporter.log_string(
-                f"❌ RT: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'rt_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
                 verbosity="progress",
             )
 
@@ -175,7 +175,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.optimization_manager.fit({"rt_error": proposed_parameter})
 
 
-class MS2Optimizer(BaseOptimizer):
+class AutomaticMS2Optimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
@@ -184,7 +184,7 @@ def __init__(
         fdr_manager: manager.FDRManager,
         **kwargs,
     ):
-        """See base class.
+        """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
 
         Parameters
         ----------
@@ -250,7 +250,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ MS2: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'ms2_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
                 verbosity="progress",
             )
 
@@ -258,7 +258,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             proposed_parameter = self._update_parameter(fragments_df)
 
             self.reporter.log_string(
-                f"❌ MS2: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'ms2_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
                 verbosity="progress",
             )
 
@@ -266,7 +266,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.optimization_manager.fit({"ms2_error": proposed_parameter})
 
 
-class MS1Optimizer(BaseOptimizer):
+class AutomaticMS1Optimizer(BaseOptimizer):
     """TODO Finish this optimizer"""
 
     def __init__(
@@ -343,7 +343,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ MS1: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'ms1_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
                 verbosity="progress",
             )
 
@@ -351,7 +351,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             proposed_parameter = self._update_parameter(precursors_df)
 
             self.reporter.log_string(
-                f"❌ MS1: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'ms1_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
                 verbosity="progress",
             )
 
@@ -359,7 +359,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.optimization_manager.fit({"ms1_error": proposed_parameter})
 
 
-class MobilityOptimizer(BaseOptimizer):
+class AutomaticMobilityOptimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
@@ -434,15 +434,331 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ Mobility: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'mobility_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                verbosity="progress",
+            )
+
+        else:
+            proposed_parameter = self._update_parameter(precursors_df)
+
+            self.reporter.log_string(
+                f"❌ {'mobility_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"mobility_error": proposed_parameter})
+
+
+class TargetedRTOptimizer(BaseOptimizer):
+    """This class optimizes the RT search parameter until it reaches a user-specified target value."""
+
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        target_parameter: float
+            Optimization will stop when this parameter is reached.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.target_parameter = target_parameter
+        self.parameters = [initial_parameter]
+
+    def _check_convergence(self, proposed_parameter):
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
+
+        Parameters
+        ----------
+        proposed_parameter: float
+            The proposed parameter for the next round of optimization.
+        """
+
+        if proposed_parameter <= self.target_parameter:
+            self.optimal_parameter = self.target_parameter
+            self.optimization_manager.fit({"rt_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values, and
+            2) take the mean of the endpoints of the central 95% of these deviations
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = self.calibration_manager.get_estimator(
+            "precursor", "rt"
+        ).ci(df, 0.95)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class."""
+        if not self.has_converged():
+            proposed_parameter = self._update_parameter(precursors_df)
+            self._check_convergence(proposed_parameter)
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ {'rt_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+        else:
+            self.reporter.log_string(
+                f"❌ {'rt_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"rt_error": proposed_parameter})
+
+
+class TargetedMS2Optimizer(BaseOptimizer):
+    """This class optimizes the MS2 search parameter until it reaches a user-specified target value."""
+
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        target_parameter: float
+            Optimization will stop when this parameter is reached.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.target_parameter = target_parameter
+        self.parameters = [initial_parameter]
+
+    def _check_convergence(self, proposed_parameter):
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
+
+        Parameters
+        ----------
+        proposed_parameter: float
+            The proposed parameter for the next round of optimization.
+        """
+
+        if proposed_parameter <= self.target_parameter:
+            self.optimal_parameter = self.target_parameter
+            self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values, and
+            2) take the mean of the endpoints of the central 95% of these deviations
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = self.calibration_manager.get_estimator(
+            "fragment", "mz"
+        ).ci(df, 0.95)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class."""
+        if not self.has_converged():
+            proposed_parameter = self._update_parameter(fragments_df)
+            self._check_convergence(proposed_parameter)
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ {'ms2_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+        else:
+            self.reporter.log_string(
+                f"❌ {'ms2_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"ms2_error": proposed_parameter})
+
+
+class TargetedMS1Optimizer(BaseOptimizer):
+    """This class optimizes the MS1 search parameter until it reaches a user-specified target value."""
+
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        target_parameter: float
+            Optimization will stop when this parameter is reached.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.target_parameter = target_parameter
+        self.parameters = [initial_parameter]
+
+    def _check_convergence(self, proposed_parameter):
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
+
+        Parameters
+        ----------
+        proposed_parameter: float
+            The proposed parameter for the next round of optimization.
+        """
+
+        if proposed_parameter <= self.target_parameter:
+            self.optimal_parameter = self.target_parameter
+            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values, and
+            2) take the mean of the endpoints of the central 95% of these deviations
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = self.calibration_manager.get_estimator(
+            "precursor", "mz"
+        ).ci(df, 0.95)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class."""
+        if not self.has_converged():
+            proposed_parameter = self._update_parameter(precursors_df)
+            self._check_convergence(proposed_parameter)
+
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ {'ms1_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
                 verbosity="progress",
             )
 
         else:
+            self.reporter.log_string(
+                f"❌ {'ms1_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+            self.parameters.append(proposed_parameter)
+            self.optimization_manager.fit({"ms1_error": proposed_parameter})
+
+
+class TargetedMobilityOptimizer(BaseOptimizer):
+    """This class optimizes the mobility search parameter until it reaches a user-specified target value."""
+
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        calibration_manager: manager.CalibrationManager,
+        optimization_manager: manager.OptimizationManager,
+        fdr_manager: manager.FDRManager,
+        **kwargs,
+    ):
+        """See base class.
+
+        Parameters
+        ----------
+
+        initial_parameter: float
+            The parameter used for search in the first round of optimization.
+
+        target_parameter: float
+            Optimization will stop when this parameter is reached.
+
+        """
+        super().__init__(
+            calibration_manager, optimization_manager, fdr_manager, **kwargs
+        )
+        self.target_parameter = target_parameter
+        self.parameters = [initial_parameter]
+
+    def _check_convergence(self, proposed_parameter):
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
+
+        Parameters
+        ----------
+        proposed_parameter: float
+            The proposed parameter for the next round of optimization.
+        """
+
+        if proposed_parameter <= self.target_parameter:
+            self.optimal_parameter = self.target_parameter
+            self.optimization_manager.fit({"mobility_error": self.optimal_parameter})
+
+    def _update_parameter(self, df: pd.DataFrame):
+        """See base class. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values, and
+            2) take the mean of the endpoints of the central 95% of these deviations
+        This is implemented by the ci method for the estimator.
+
+
+        """
+        proposed_parameter = self.calibration_manager.get_estimator(
+            "precursor", "mobility"
+        ).ci(df, 0.95)
+
+        return proposed_parameter
+
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class."""
+        if not self.has_converged():
             proposed_parameter = self._update_parameter(precursors_df)
+            self._check_convergence(proposed_parameter)
 
+        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+            self.reporter.log_string(
+                f"✅ {'mobility_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
+                verbosity="progress",
+            )
+
+        else:
             self.reporter.log_string(
-                f"❌ Mobility: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'mobility_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
                 verbosity="progress",
             )
 

From 25ef9d23c15f0ac5ae104a603b8ac540ade26c19 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 16:11:09 +0200
Subject: [PATCH 032/250] added plotting for automatic optimizers

---
 alphadia/workflow/searchoptimization.py | 81 +++++++++++++++++++++++++
 1 file changed, 81 insertions(+)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 4a5cd0b3..37e6aad0 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -1,6 +1,7 @@
 # native imports
 from abc import ABC, abstractmethod
 
+import matplotlib.pyplot as plt
 import numpy as np
 
 # alpha family imports
@@ -174,6 +175,26 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.parameters.append(proposed_parameter)
             self.optimization_manager.fit({"rt_error": proposed_parameter})
 
+    def plot(self):
+        """Plot the optimization of the RT error parameter."""
+        fig, ax = plt.subplots()
+        ax.vlines(
+            self.optimal_parameter,
+            0,
+            max(self.feature),
+            color="red",
+            zorder=0,
+            label="Optimal RT error",
+        )
+        ax.plot(self.parameters, self.feature)
+        ax.scatter(self.parameters, self.feature)
+        ax.set_ylabel("Number of precursor identifications")
+        ax.set_xlabel("RT error")
+        ax.xaxis.set_inverted(True)
+        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
+        ax.legend(loc="upper left")
+        plt.show()
+
 
 class AutomaticMS2Optimizer(BaseOptimizer):
     def __init__(
@@ -265,6 +286,26 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.parameters.append(proposed_parameter)
             self.optimization_manager.fit({"ms2_error": proposed_parameter})
 
+    def plot(self):
+        """Plot the optimization of the MS2 error parameter."""
+        fig, ax = plt.subplots()
+        ax.vlines(
+            self.optimal_parameter,
+            0,
+            max(self.precursor_ids),
+            color="red",
+            zorder=0,
+            label="Optimal MS2 error",
+        )
+        ax.plot(self.parameters, self.precursor_ids)
+        ax.scatter(self.parameters, self.precursor_ids)
+        ax.set_ylabel("Number of precursor identifications")
+        ax.set_xlabel("MS2 error")
+        ax.xaxis.set_inverted(True)
+        ax.set_ylim(bottom=0, top=max(self.precursor_ids) * 1.1)
+        ax.legend(loc="upper left")
+        plt.show()
+
 
 class AutomaticMS1Optimizer(BaseOptimizer):
     """TODO Finish this optimizer"""
@@ -358,6 +399,26 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.parameters.append(proposed_parameter)
             self.optimization_manager.fit({"ms1_error": proposed_parameter})
 
+    def plot(self):
+        """Plot the optimization of the MS1 error parameter."""
+        fig, ax = plt.subplots()
+        ax.vlines(
+            self.optimal_parameter,
+            0,
+            max(self.feature),
+            color="red",
+            zorder=0,
+            label="Optimal MS1 error",
+        )
+        ax.plot(self.parameters, self.feature)
+        ax.scatter(self.parameters, self.feature)
+        ax.set_ylabel("Number of precursor identifications")
+        ax.set_xlabel("MS1 error")
+        ax.xaxis.set_inverted(True)
+        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
+        ax.legend(loc="upper left")
+        plt.show()
+
 
 class AutomaticMobilityOptimizer(BaseOptimizer):
     def __init__(
@@ -449,6 +510,26 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.parameters.append(proposed_parameter)
             self.optimization_manager.fit({"mobility_error": proposed_parameter})
 
+    def plot(self):
+        """Plot the optimization of the mobility error parameter."""
+        fig, ax = plt.subplots()
+        ax.vlines(
+            self.optimal_parameter,
+            0,
+            max(self.feature),
+            color="red",
+            zorder=0,
+            label="Optimal mobility error",
+        )
+        ax.plot(self.parameters, self.feature)
+        ax.scatter(self.parameters, self.feature)
+        ax.set_ylabel("Number of precursor identifications")
+        ax.set_xlabel("Mobility error")
+        ax.xaxis.set_inverted(True)
+        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
+        ax.legend(loc="upper left")
+        plt.show()
+
 
 class TargetedRTOptimizer(BaseOptimizer):
     """This class optimizes the RT search parameter until it reaches a user-specified target value."""

From a9eae04a057ccbc5c7acb40b374c383e8fbe1f0c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 16:43:34 +0200
Subject: [PATCH 033/250] changed calibration and removed now-unused methods

---
 alphadia/workflow/peptidecentric.py | 367 +++++++++++-----------------
 1 file changed, 143 insertions(+), 224 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index c07c6cdc..e22deddf 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -1,5 +1,6 @@
 # native imports
 import logging
+from numbers import Number
 
 # third party imports
 import numpy as np
@@ -16,7 +17,7 @@
 # alphadia imports
 from alphadia import fragcomp, plexscoring, utils
 from alphadia.peakgroup import search
-from alphadia.workflow import base, manager
+from alphadia.workflow import base, manager, searchoptimization
 
 logger = logging.getLogger()
 
@@ -324,119 +325,67 @@ def get_batch_plan(self):
 
         return plan
 
-    def start_of_calibration(self):
-        self.batch_plan = self.get_batch_plan()
+    def extract_optimization_data(self, target):
+        """Search parameter optimization (i.e. refinement of tolerances for RT, MS2, etc.) is performed on a subset of the elution groups in the spectral library.
+        The number of elution groups which must be searched to get a sufficiently large number for robust calibration varies depending the library used and the data.
+        This function searches an increasing number of elution groups until a sufficient number (determined by target) of precursors are identified at 1% FDR.
+        It then returns the elution group indexes which will be used to find the data in the spectral library for search parameter optimization.
 
-    def start_of_epoch(self, current_epoch):
-        self.com.current_epoch = current_epoch
+        Parameters
+        ----------
 
-        # if self.neptune is not None:
-        #    self.neptune["eval/epoch"].log(current_epoch)
+        target : int
+            The number of precursors which must be identified at 1% FDR to stop the extraction.
+
+        """
 
         self.elution_group_order = self.spectral_library.precursor_df[
             "elution_group_idx"
         ].unique()
         np.random.shuffle(self.elution_group_order)
 
-        self.calibration_manager.predict(
-            self.spectral_library._precursor_df, "precursor"
-        )
-        self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
+        batch_plan = self.get_batch_plan()
 
-        # make updates to the progress dict depending on the epoch
-        if self.com.current_epoch > 0:
-            self.com.recalibration_target = self.config["calibration"][
-                "recalibration_target"
-            ] * (1 + current_epoch)
-
-    def start_of_step(self, current_step, start_index, stop_index):
-        self.com.current_step = current_step
-        # if self.neptune is not None:
-        #    self.neptune["eval/step"].log(current_step)
-
-        #    for key, value in self.com.__dict__.items():
-        #        self.neptune[f"eval/{key}"].log(value)
-
-        self.reporter.log_string(
-            f"=== Epoch {self.com.current_epoch}, step {current_step}, extracting elution groups {start_index} to {stop_index} ===",
-            verbosity="progress",
-        )
+        features = []
+        fragments = []
+        for current_step, (start_index, stop_index) in enumerate(batch_plan):
+            self.reporter.log_string(
+                f"=== Step {current_step}, extracting elution groups {start_index} to {stop_index} ===",
+                verbosity="progress",
+            )
 
-    def check_epoch_conditions(self):
-        continue_calibration = False
+            eg_idxes = self.elution_group_order[start_index:stop_index]
+            batch_df = self.spectral_library._precursor_df[
+                self.spectral_library._precursor_df["elution_group_idx"].isin(eg_idxes)
+            ]
 
-        self.reporter.log_string(
-            "=== checking if epoch conditions were reached ===", verbosity="info"
-        )
-        if self.dia_data.has_ms1:
-            if self.com.ms1_error > self.config["search"]["target_ms1_tolerance"]:
-                self.reporter.log_string(
-                    f"❌ {'ms1_error':<15}: {self.com.ms1_error:.4f} > {self.config['search']['target_ms1_tolerance']}",
-                    verbosity="info",
-                )
-                continue_calibration = True
-            else:
-                self.reporter.log_string(
-                    f"✅ {'ms1_error':<15}: {self.com.ms1_error:.4f} <= {self.config['search']['target_ms1_tolerance']}",
-                    verbosity="info",
-                )
+            feature_df, fragment_df = self.extract_batch(batch_df)
+            features += [feature_df]
+            fragments += [fragment_df]
+            features_df = pd.concat(features)
+            fragments_df = pd.concat(fragments)
 
-        if self.com.ms2_error > self.config["search"]["target_ms2_tolerance"]:
-            self.reporter.log_string(
-                f"❌ {'ms2_error':<15}: {self.com.ms2_error:.4f} > {self.config['search']['target_ms2_tolerance']}",
-                verbosity="info",
-            )
-            continue_calibration = True
-        else:
             self.reporter.log_string(
-                f"✅ {'ms2_error':<15}: {self.com.ms2_error:.4f} <= {self.config['search']['target_ms2_tolerance']}",
-                verbosity="info",
+                f"=== Step {current_step}, extracted {len(feature_df)} precursors and {len(fragment_df)} fragments ===",
+                verbosity="progress",
             )
+            precursor_df = self.fdr_correction(features_df, fragments_df)
+
+            precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
-        if self.com.rt_error > self.config["search"]["target_rt_tolerance"]:
-            self.reporter.log_string(
-                f"❌ {'rt_error':<15}: {self.com.rt_error:.4f} > {self.config['search']['target_rt_tolerance']}",
-                verbosity="info",
-            )
-            continue_calibration = True
-        else:
             self.reporter.log_string(
-                f"✅ {'rt_error':<15}: {self.com.rt_error:.4f} <= {self.config['search']['target_rt_tolerance']}",
-                verbosity="info",
+                f"=== checking if recalibration conditions were reached, target {self.com.recalibration_target} precursors ===",
+                verbosity="progress",
             )
 
-        if self.dia_data.has_mobility:
-            if (
-                self.com.mobility_error
-                > self.config["search"]["target_mobility_tolerance"]
-            ):
-                self.reporter.log_string(
-                    f"❌ {'mobility_error':<15}: {self.com.mobility_error:.4f} > {self.config['search']['target_mobility_tolerance']}",
-                    verbosity="info",
-                )
-                continue_calibration = True
-            else:
-                self.reporter.log_string(
-                    f"✅ {'mobility_error':<15}: {self.com.mobility_error:.4f} <= {self.config['search']['target_mobility_tolerance']}",
-                    verbosity="info",
-                )
+            self.log_precursor_df(precursor_df)
 
-        if self.com.current_epoch < self.config["calibration"]["min_epochs"] - 1:
-            self.reporter.log_string(
-                f"❌ {'current_epoch':<15}: {self.com.current_epoch} < {self.config['calibration']['min_epochs']}",
-                verbosity="info",
-            )
-            continue_calibration = True
-        else:
-            self.reporter.log_string(
-                f"✅ {'current_epoch':<15}: {self.com.current_epoch} >= {self.config['calibration']['min_epochs']}",
-                verbosity="info",
-            )
+            if precursors_01FDR > target:
+                final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
+                break
 
-        self.reporter.log_string(
-            "==============================================", verbosity="info"
-        )
-        return continue_calibration
+        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
+        self.com.fit({"classifier_version": self.fdr_manager.current_version})
 
     def calibration(self):
         if (
@@ -449,95 +398,104 @@ def calibration(self):
             )
             return
 
-        self.start_of_calibration()
-        for current_epoch in range(self.config["calibration"]["max_epochs"]):
-            if self.check_epoch_conditions():
-                pass
-            else:
-                break
+        if (
+            isinstance(self.config["search"]["target_ms2_tolerance"], Number)
+            and isinstance(self.config["search"]["target_rt_tolerance"], Number)
+            and isinstance(self.config["search"]["target_ms1_tolerance"], Number)
+            and isinstance(self.config["search"]["target_mobility_tolerance"], Number)
+        ):
+            self.reporter.log_string(
+                "A complete list of target tolerances has been specified. Will perform targeted search parameter optimization.",
+                verbosity="info",
+            )
+            self.ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
+                self.config["search"]["initial_ms2_tolerance"],
+                self.config["search"]["target_ms2_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
+            self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
+                self.config["search"]["initial_rt_tolerance"],
+                self.config["search"]["target_rt_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
+            self.ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
+                self.config["search"]["target_ms1_tolerance"],
+                self.config["search"]["target_ms1_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
+            self.mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
+                self.config["search"]["target_mobility_tolerance"],
+                self.config["search"]["target_mobility_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
 
-            self.start_of_epoch(current_epoch)
+            order_of_optimization = [
+                [
+                    self.ms2_optimizer,
+                    self.rt_optimizer,
+                    self.ms1_optimizer,
+                    self.mobility_optimizer,
+                ]
+            ]
 
-            features = []
-            fragments = []
-            for current_step, (start_index, stop_index) in enumerate(self.batch_plan):
-                self.start_of_step(current_step, start_index, stop_index)
+        else:
+            raise NotImplementedError(
+                "Automatic search parameter optimization is not yet implemented"
+            )
 
-                eg_idxes = self.elution_group_order[start_index:stop_index]
+        for optimizers in order_of_optimization:
+            for current_step in range(self.config["calibration"]["max_epochs"]):
+                if np.all([optimizer.has_converged() for optimizer in optimizers]):
+                    break
                 batch_df = self.spectral_library._precursor_df[
                     self.spectral_library._precursor_df["elution_group_idx"].isin(
-                        eg_idxes
+                        self.eg_idxes_for_calibration
                     )
                 ]
 
-                feature_df, fragment_df = self.extract_batch(batch_df)
-                features += [feature_df]
-                fragments += [fragment_df]
-                features_df = pd.concat(features)
-                fragments_df = pd.concat(fragments)
+                features_df, fragments_df = self.extract_batch(batch_df)
 
                 self.reporter.log_string(
-                    f"=== Epoch {self.com.current_epoch}, step {current_step}, extracted {len(feature_df)} precursors and {len(fragment_df)} fragments ===",
+                    f"=== Step {current_step}, extracted {len(features_df)} precursors and {len(fragments_df)} fragments ===",
                     verbosity="progress",
                 )
-                precursor_df = self.fdr_correction(features_df, fragments_df)
 
-                if self.check_recalibration(precursor_df):
-                    self.recalibration(precursor_df, fragments_df)
-                    break
-                else:
-                    # check if last step has been reached
-                    if current_step == len(self.batch_plan) - 1:
-                        self.reporter.log_string(
-                            "Searched all data without finding recalibration target",
-                            verbosity="error",
-                        )
-                        raise CalibrationError(
-                            "Searched all data without finding recalibration target"
-                        )
-
-            self.end_of_epoch()
-
-        if self.config["calibration"].get("final_full_calibration", False):
-            self.reporter.log_string(
-                "Performing final calibration with all precursors",
-                verbosity="progress",
-            )
-            features_df, fragments_df = self.extract_batch(
-                self.spectral_library._precursor_df
-            )
-            precursor_df = self.fdr_correction(features_df, fragments_df)
-            self.recalibration(precursor_df, fragments_df)
+                precursors_df = self.fdr_correction(
+                    features_df, fragments_df, self.com.classifier_version
+                )
+
+                precursors_df_filtered, fragments_df_filtered = self.filter_dfs(
+                    precursors_df, fragments_df
+                )
 
-        self.end_of_calibration()
+                self.recalibration(precursors_df_filtered, fragments_df_filtered)
 
-    def end_of_epoch(self):
-        pass
+                self.reporter.log_string(
+                    "=== checking if optimization conditions were reached ===",
+                    verbosity="info",
+                )
 
-    def end_of_calibration(self):
-        # self.calibration_manager.predict(self.spectral_library._precursor_df, 'precursor')
-        # self.calibration_manager.predict(self.spectral_library._fragment_df, 'fragment')
-        self.calibration_manager.save()
-        pass
+                for optimizer in optimizers:
+                    optimizer.step(precursors_df_filtered, fragments_df_filtered)
 
-    def recalibration(self, precursor_df, fragments_df):
+                self.reporter.log_string(
+                    "==============================================", verbosity="info"
+                )
+
+    def filter_dfs(self, precursor_df, fragments_df):
         precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.01]
         precursor_df_filtered = precursor_df_filtered[
             precursor_df_filtered["decoy"] == 0
         ]
 
-        self.calibration_manager.fit(
-            precursor_df_filtered,
-            "precursor",
-            plot=True,
-            skip=["mz"] if not self.dia_data.has_ms1 else [],
-            # neptune_run = self.neptune
-        )
-
-        rt_99 = self.calibration_manager.get_estimator("precursor", "rt").ci(
-            precursor_df_filtered, 0.95
-        )
-
         fragments_df_filtered = fragments_df[
             fragments_df["precursor_idx"].isin(precursor_df_filtered["precursor_idx"])
         ]
@@ -557,9 +515,24 @@ def recalibration(self, precursor_df, fragments_df):
             ),
             max_fragments,
         )
-        fragments_df_filtered = fragments_df_filtered.iloc[:stop_rank]
+        fragments_df_filtered = fragments_df_filtered.iloc[
+            :stop_rank
+        ]  # QUESTION: Should this raise an exception if the length of fragments_df_full is less than min_fragments?
+
+        self.reporter.log_string(
+            f"fragments_df_filtered: {len(fragments_df_filtered)}", verbosity="info"
+        )
 
-        print(f"fragments_df_filtered: {len(fragments_df_filtered)}")
+        return precursor_df_filtered, fragments_df_filtered
+
+    def recalibration(self, precursor_df_filtered, fragments_df_filtered):
+        self.calibration_manager.fit(
+            precursor_df_filtered,
+            "precursor",
+            plot=True,
+            skip=["mz"] if not self.dia_data.has_ms1 else [],
+            # neptune_run = self.neptune
+        )
 
         self.calibration_manager.fit(
             fragments_df_filtered,
@@ -568,49 +541,21 @@ def recalibration(self, precursor_df, fragments_df):
             # neptune_run = self.neptune
         )
 
-        m2_99 = self.calibration_manager.get_estimator("fragment", "mz").ci(
-            fragments_df_filtered, 0.95
+        self.calibration_manager.predict(
+            self.spectral_library._precursor_df,
+            "precursor",
         )
 
+        self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
+
         self.com.fit(
             {
-                "ms2_error": max(m2_99, self.config["search"]["target_ms2_tolerance"]),
-                "rt_error": max(rt_99, self.config["search"]["target_rt_tolerance"]),
                 "column_type": "calibrated",
                 "num_candidates": self.config["search"]["target_num_candidates"],
             }
         )
 
-        if self.dia_data.has_ms1:
-            m1_99 = self.calibration_manager.get_estimator("precursor", "mz").ci(
-                precursor_df_filtered, 0.95
-            )
-            self.com.fit(
-                {
-                    "ms1_error": max(
-                        m1_99, self.config["search"]["target_ms1_tolerance"]
-                    ),
-                }
-            )
-
-        if self.dia_data.has_mobility:
-            mobility_99 = self.calibration_manager.get_estimator(
-                "precursor", "mobility"
-            ).ci(precursor_df_filtered, 0.95)
-            self.com.fit(
-                {
-                    "mobility_error": max(
-                        mobility_99, self.config["search"]["target_mobility_tolerance"]
-                    ),
-                }
-            )
-
-            # if self.neptune is not None:
-            #    self.neptune['eval/99_mobility_error'].log(mobility_99)
-
         percentile_001 = np.percentile(precursor_df_filtered["score"], 0.1)
-        print("score cutoff", percentile_001)
-
         self.optimization_manager.fit(
             {
                 "fwhm_rt": precursor_df_filtered["cycle_fwhm"].median(),
@@ -619,34 +564,7 @@ def recalibration(self, precursor_df, fragments_df):
             }
         )
 
-        # if self.neptune is not None:
-        # precursor_df_fdr = precursor_df_filtered[precursor_df_filtered['qval'] < 0.01]
-        # self.neptune["eval/precursors"].log(len(precursor_df_fdr))
-        # self.neptune['eval/99_ms1_error'].log(m1_99)
-        # self.neptune['eval/99_ms2_error'].log(m2_99)
-        # self.neptune['eval/99_rt_error'].log(rt_99)
-
-    def check_recalibration(self, precursor_df):
-        self.com.accumulated_precursors = len(precursor_df)
-        self.com.accumulated_precursors_01FDR = len(
-            precursor_df[precursor_df["qval"] < 0.01]
-        )
-
-        self.reporter.log_string(
-            f"=== checking if recalibration conditions were reached, target {self.com.recalibration_target} precursors ===",
-            verbosity="progress",
-        )
-
-        self.log_precursor_df(precursor_df)
-
-        perform_recalibration = False
-
-        if self.com.accumulated_precursors_01FDR > self.com.recalibration_target:
-            perform_recalibration = True
-
-        return perform_recalibration
-
-    def fdr_correction(self, features_df, df_fragments):
+    def fdr_correction(self, features_df, df_fragments, version=-1):
         return self.fdr_manager.fit_predict(
             features_df,
             decoy_strategy="precursor_channel_wise"
@@ -657,6 +575,7 @@ def fdr_correction(self, features_df, df_fragments):
             if self.config["search"]["compete_for_fragments"]
             else None,
             dia_cycle=self.dia_data.cycle,
+            version=version,
             # neptune_run=self.neptune
         )
 

From 20c91c6fa6cd9dcca466cd7d89dbab45d0fc6c74 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 26 Jul 2024 17:10:20 +0200
Subject: [PATCH 034/250] fixed errors

---
 alphadia/workflow/peptidecentric.py | 66 +++++++++++++++++------------
 1 file changed, 40 insertions(+), 26 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index e22deddf..caf142c1 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -149,8 +149,6 @@ def com(self):
     def init_calibration_optimization_manager(self):
         self._calibration_optimization_manager = manager.OptimizationManager(
             {
-                "current_epoch": 0,
-                "current_step": 0,
                 "ms1_error": self.config["search_initial"]["initial_ms1_tolerance"],
                 "ms2_error": self.config["search_initial"]["initial_ms2_tolerance"],
                 "rt_error": self.config["search_initial"]["initial_rt_tolerance"],
@@ -164,9 +162,7 @@ def init_calibration_optimization_manager(self):
                 "recalibration_target": self.config["calibration"][
                     "recalibration_target"
                 ],
-                "accumulated_precursors": 0,
-                "accumulated_precursors_01FDR": 0,
-                "accumulated_precursors_001FDR": 0,
+                "classifier_version": -1,
             }
         )
 
@@ -405,44 +401,58 @@ def calibration(self):
             and isinstance(self.config["search"]["target_mobility_tolerance"], Number)
         ):
             self.reporter.log_string(
-                "A complete list of target tolerances has been specified. Will perform targeted search parameter optimization.",
+                "A complete list of target tolerances has been specified. Targeted search parameter optimization will be performed.",
                 verbosity="info",
             )
+
             self.ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
-                self.config["search"]["initial_ms2_tolerance"],
+                self.config["search_initial"]["initial_ms2_tolerance"],
                 self.config["search"]["target_ms2_tolerance"],
                 self.calibration_manager,
                 self.com,
                 self.fdr_manager,
             )
+
             self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
-                self.config["search"]["initial_rt_tolerance"],
+                self.config["search_initial"]["initial_rt_tolerance"],
                 self.config["search"]["target_rt_tolerance"],
                 self.calibration_manager,
                 self.com,
                 self.fdr_manager,
             )
-            self.ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
-                self.config["search"]["target_ms1_tolerance"],
-                self.config["search"]["target_ms1_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
-            )
-            self.mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
-                self.config["search"]["target_mobility_tolerance"],
-                self.config["search"]["target_mobility_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
-            )
+
+            if self.dia_data.has_ms1:
+                self.ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
+                    self.config["search_initial"]["initial_ms1_tolerance"],
+                    self.config["search"]["target_ms1_tolerance"],
+                    self.calibration_manager,
+                    self.com,
+                    self.fdr_manager,
+                )
+            else:
+                self.ms1_optimizer = None
+
+            if self.dia_data.has_mobility:
+                self.mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
+                    self.config["search_initial"]["initial_mobility_tolerance"],
+                    self.config["search"]["target_mobility_tolerance"],
+                    self.calibration_manager,
+                    self.com,
+                    self.fdr_manager,
+                )
+            else:
+                self.mobility_optimizer = None
 
             order_of_optimization = [
                 [
-                    self.ms2_optimizer,
-                    self.rt_optimizer,
-                    self.ms1_optimizer,
-                    self.mobility_optimizer,
+                    optimizer
+                    for optimizer in [
+                        self.ms2_optimizer,
+                        self.rt_optimizer,
+                        self.ms1_optimizer,
+                        self.mobility_optimizer,
+                    ]
+                    if optimizer is not None
                 ]
             ]
 
@@ -451,6 +461,10 @@ def calibration(self):
                 "Automatic search parameter optimization is not yet implemented"
             )
 
+        self.extract_optimization_data(
+            self.config["calibration"]["recalibration_target"]
+        )
+
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_epochs"]):
                 if np.all([optimizer.has_converged() for optimizer in optimizers]):

From a0c3211b6607c11b667538947420039c405d198f Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Sat, 27 Jul 2024 18:14:15 +0200
Subject: [PATCH 035/250] fix plot style

---
 .../finetuning_imgs/finetuning_11_0.png       | Bin 34328 -> 30397 bytes
 .../finetuning_imgs/finetuning_17_3.png       | Bin 113262 -> 111270 bytes
 .../finetuning_imgs/finetuning_18_3.png       | Bin 113262 -> 0 bytes
 .../finetuning_imgs/finetuning_19_3.png       | Bin 65797 -> 61442 bytes
 .../finetuning_imgs/finetuning_20_0.png       | Bin 143570 -> 124721 bytes
 .../finetuning_imgs/finetuning_23_0.png       | Bin 127786 -> 116195 bytes
 .../finetuning_imgs/finetuning_27_4.png       | Bin 143291 -> 132519 bytes
 .../finetuning_imgs/finetuning_29_4.png       | Bin 108211 -> 108554 bytes
 .../finetuning_imgs/finetuning_30_0.png       | Bin 178106 -> 164988 bytes
 .../finetuning_imgs/finetuning_7_1.png        | Bin 38911 -> 35482 bytes
 docs/methods/transfer-learning.md             |   2 +-
 11 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 docs/methods/finetuning_imgs/finetuning_18_3.png

diff --git a/docs/methods/finetuning_imgs/finetuning_11_0.png b/docs/methods/finetuning_imgs/finetuning_11_0.png
index 9d491d137835ab1b6bc48c45867a6616ae399f6d..e4e151d1e4a0c5dfae5c7dd1e9e5ca517c9f7342 100644
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diff --git a/docs/methods/transfer-learning.md b/docs/methods/transfer-learning.md
index 75eb1808..5edaf684 100644
--- a/docs/methods/transfer-learning.md
+++ b/docs/methods/transfer-learning.md
@@ -3,7 +3,7 @@
 
 Generally, transfer learning refers to the process of leveraging the knowledge learned in one model for better performance on a different task. A task is a vague term, but it essentially includes learning a different objective, for example, transitioning from regression to classification. It can also involve learning the same objective with a different loss function or optimizer, or using the same loss and objective but with different data. In cases where the dataset is too small to train a model from scratch without overfitting, we start from a pretrained model that has good performance on a larger dataset. This last type is the transfer learning we are using in alphaDIA.
 
-## Transfer Learning for alphaDIA
+## Transfer Learning in alphaDIA
 
 In alphaDIA, we use the output obtained after the first search run and introduce a signal from the identified peptides to fine-tune the predictions from PeptDeep models on the custom dataset we are interested in. We then explore how this affects a second search run with fine-tuned models used to predict the spectral library.
 

From e06d172b4c8e2841828842db9a3deb2fdb8419e3 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 29 Jul 2024 14:01:45 +0200
Subject: [PATCH 036/250] refactor to use dataframe

---
 alphadia/workflow/manager.py            |  8 -----
 alphadia/workflow/searchoptimization.py | 44 +++++++++++++++----------
 2 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index c2407035..af73b947 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -641,9 +641,6 @@ def fit_predict(
 
         self._current_version += 1
         self.classifier_store[column_hash(available_columns)].append(classifier)
-        self._num_classifiers = len(
-            self.classifier_store[column_hash(available_columns)]
-        )
 
         self.save()
 
@@ -725,11 +722,6 @@ def get_classifier(self, available_columns: list, version: int = -1):
     def current_version(self):
         return self._current_version
 
-    @property
-    def num_classifiers(self):
-        """This is the number of classifiers in the last classifier_hash that was saved to."""
-        return self._num_classifiers
-
     def predict(self):
         """Return the parameters dict."""
         raise NotImplementedError(
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index ca1d054f..baa21662 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -189,8 +189,10 @@ def __init__(
         super().__init__(
             calibration_manager, optimization_manager, fdr_manager, **kwargs
         )
-        self.parameters = [initial_parameter]
-        self.precursor_ids = []
+        self.history_df = pd.DataFrame(
+            columns=["parameter", "precursor_ids", "classifier_version"]
+        )
+        self.proposed_parameter = initial_parameter
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
@@ -205,19 +207,22 @@ def _check_convergence(self):
         """
 
         if (
-            len(self.precursor_ids) > 2
-            and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-2]
-            and self.precursor_ids[-1] < 1.1 * self.precursor_ids[-3]
+            len(self.history_df) > 2
+            and self.history_df["precursor_ids"].iloc[-1]
+            < 1.1 * self.history_df["precursor_ids"].iloc[-2]
+            and self.history_df["precursor_ids"].iloc[-1]
+            < 1.1 * self.history_df["precursor_ids"].iloc[-3]
         ):
-            backtrack_by = (
-                len(self.precursor_ids) - np.argmax(self.precursor_ids)
-            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
-            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
-
-            self.optimal_parameter = self.parameters[-backtrack_by]
+            self.optimal_parameter = self.history_df.loc[
+                self.history_df["precursor_ids"].idxmax(), "parameter"
+            ]
             self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
             self.optimization_manager.fit(
-                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+                {
+                    "classifier_version": self.history_df.loc[
+                        self.history_df["precursor_ids"].idxmax(), "classifier_version"
+                    ]
+                }
             )
 
     def _update_parameter(self, df: pd.DataFrame):
@@ -238,25 +243,28 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .precursor_ids) and determine whether it has converged."""
         if not self.has_converged():
-            self.precursor_ids.append(len(precursors_df))
+            self.history_df.loc[len(self.history_df)] = [
+                self.proposed_parameter,
+                len(precursors_df),
+                self.fdr_manager.current_version,
+            ]
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ MS2: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ MS2: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df['parameter'])} searches.",
                 verbosity="progress",
             )
 
         else:
-            proposed_parameter = self._update_parameter(fragments_df)
+            self.proposed_parameter = self._update_parameter(fragments_df)
 
             self.reporter.log_string(
-                f"❌ MS2: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ MS2: optimization incomplete after {len(self.history_df['parameter'])} search(es). Will search with parameter {self.proposed_parameter}.",
                 verbosity="progress",
             )
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"ms2_error": proposed_parameter})
+            self.optimization_manager.fit({"ms2_error": self.proposed_parameter})
 
 
 class MS1Optimizer(BaseOptimizer):

From 6faa3eb8f8ec2928c66b8b59d244ddff6efd1191 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 29 Jul 2024 14:28:39 +0200
Subject: [PATCH 037/250] update to use df

---
 alphadia/workflow/searchoptimization.py | 136 ++++++++++++++----------
 1 file changed, 80 insertions(+), 56 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 5f7e5bef..2842ab8a 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -2,7 +2,6 @@
 from abc import ABC, abstractmethod
 
 import matplotlib.pyplot as plt
-import numpy as np
 
 # alpha family imports
 # third party imports
@@ -106,8 +105,10 @@ def __init__(
         super().__init__(
             calibration_manager, optimization_manager, fdr_manager, **kwargs
         )
-        self.parameters = [initial_parameter]
-        self.feature = []
+        self.history_df = pd.DataFrame(
+            columns=["parameter", "feature", "classifier_version"]
+        )
+        self.proposed_parameter = initial_parameter
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
@@ -122,19 +123,22 @@ def _check_convergence(self):
         """
 
         if (
-            len(self.feature) > 2
-            and self.feature[-1] < 1.1 * self.feature[-2]
-            and self.feature[-1] < 1.1 * self.feature[-3]
+            len(self.history_df) > 2
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-2]
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-3]
         ):
-            backtrack_by = (
-                len(self.feature) - np.argmax(self.feature)
-            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
-            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
-
-            self.optimal_parameter = self.parameters[-backtrack_by]
+            self.optimal_parameter = self.history_df.loc[
+                self.history_df["feature"].idxmax(), "parameter"
+            ]
             self.optimization_manager.fit({"rt_error": self.optimal_parameter})
             self.optimization_manager.fit(
-                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+                {
+                    "classifier_version": self.history_df.loc[
+                        self.history_df["feature"].idxmax(), "classifier_version"
+                    ]
+                }
             )
 
     def _update_parameter(self, df: pd.DataFrame):
@@ -155,25 +159,28 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.feature.append(len(precursors_df))
+            self.history_df.loc[len(self.history_df)] = [
+                self.proposed_parameter,
+                len(precursors_df),
+                self.fdr_manager.current_version,
+            ]
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ {'rt_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'rt_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
         else:
-            proposed_parameter = self._update_parameter(precursors_df)
+            self.proposed_parameter = self._update_parameter(precursors_df)
 
             self.reporter.log_string(
-                f"❌ {'rt_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'rt_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
                 verbosity="progress",
             )
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"rt_error": proposed_parameter})
+            self.optimization_manager.fit({"rt_error": self.proposed_parameter})
 
     def plot(self):
         """Plot the optimization of the RT error parameter."""
@@ -338,8 +345,10 @@ def __init__(
         super().__init__(
             calibration_manager, optimization_manager, fdr_manager, **kwargs
         )
-        self.parameters = [initial_parameter]
-        self.feature = []
+        self.history_df = pd.DataFrame(
+            columns=["parameter", "precursor_ids", "classifier_version"]
+        )
+        self.proposed_parameter = initial_parameter
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
@@ -354,19 +363,22 @@ def _check_convergence(self):
         """
 
         if (
-            len(self.feature) > 2
-            and self.feature[-1] < 1.1 * self.feature[-2]
-            and self.feature[-1] < 1.1 * self.feature[-3]
+            len(self.history_df) > 2
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-2]
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-3]
         ):
-            backtrack_by = (
-                len(self.feature) - np.argmax(self.feature)
-            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
-            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
-
-            self.optimal_parameter = self.parameters[-backtrack_by]
+            self.optimal_parameter = self.history_df.loc[
+                self.history_df["feature"].idxmax(), "parameter"
+            ]
             self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
             self.optimization_manager.fit(
-                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+                {
+                    "classifier_version": self.history_df.loc[
+                        self.history_df["feature"].idxmax(), "classifier_version"
+                    ]
+                }
             )
 
     def _update_parameter(self, df: pd.DataFrame):
@@ -387,25 +399,28 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.feature.append(len(precursors_df))
+            self.history_df.loc[len(self.history_df)] = [
+                self.proposed_parameter,
+                len(precursors_df),
+                self.fdr_manager.current_version,
+            ]
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ {'ms1_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'ms1_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
         else:
-            proposed_parameter = self._update_parameter(precursors_df)
+            self.proposed_parameter = self._update_parameter(precursors_df)
 
             self.reporter.log_string(
-                f"❌ {'ms1_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'ms1_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
                 verbosity="progress",
             )
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"ms1_error": proposed_parameter})
+            self.optimization_manager.fit({"ms1_error": self.proposed_parameter})
 
     def plot(self):
         """Plot the optimization of the MS1 error parameter."""
@@ -449,8 +464,10 @@ def __init__(
         super().__init__(
             calibration_manager, optimization_manager, fdr_manager, **kwargs
         )
-        self.parameters = [initial_parameter]
-        self.feature = []
+        self.history_df = pd.DataFrame(
+            columns=["parameter", "precursor_ids", "classifier_version"]
+        )
+        self.proposed_parameter = initial_parameter
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
@@ -465,19 +482,22 @@ def _check_convergence(self):
         """
 
         if (
-            len(self.feature) > 2
-            and self.feature[-1] < 1.1 * self.feature[-2]
-            and self.feature[-1] < 1.1 * self.feature[-3]
+            len(self.history_df) > 2
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-2]
+            and self.history_df["feature"].iloc[-1]
+            < 1.1 * self.history_df["feature"].iloc[-3]
         ):
-            backtrack_by = (
-                len(self.feature) - np.argmax(self.feature)
-            )  # Corresponds to the 1 + the number of searches since the last increase in identifications, with 1 indicating an increase in the most recent search.
-            # This is done instead of directly indexing the parameter of interest because the self.fdr_manager.classifier_store and self.parameters will be of different lengths, but the relevant entries will be the same index from the end.
-
-            self.optimal_parameter = self.parameters[-backtrack_by]
-            self.optimization_manager.fit({"mobility_error": self.optimal_parameter})
+            self.optimal_parameter = self.history_df.loc[
+                self.history_df["feature"].idxmax(), "parameter"
+            ]
+            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
             self.optimization_manager.fit(
-                {"classifier_version": self.fdr_manager.num_classifiers - backtrack_by}
+                {
+                    "classifier_version": self.history_df.loc[
+                        self.history_df["feature"].idxmax(), "classifier_version"
+                    ]
+                }
             )
 
     def _update_parameter(self, df: pd.DataFrame):
@@ -498,25 +518,29 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.feature.append(len(precursors_df))
+            self.history_df.loc[len(self.history_df)] = [
+                self.proposed_parameter,
+                len(precursors_df),
+                self.fdr_manager.current_version,
+            ]
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ {'mobility_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.parameters)} searches.",
+                f"✅ {'mobility_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
         else:
-            proposed_parameter = self._update_parameter(precursors_df)
+            self.proposed_parameter = self._update_parameter(precursors_df)
 
             self.reporter.log_string(
-                f"❌ {'mobility_error':<15}: optimization incomplete after {len(self.parameters)} search(es). Will search with parameter {proposed_parameter}.",
+                f"❌ {'mobility_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
                 verbosity="progress",
             )
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"mobility_error": proposed_parameter})
+            self.parameters.append(self.proposed_parameter)
+            self.optimization_manager.fit({"mobility_error": self.proposed_parameter})
 
     def plot(self):
         """Plot the optimization of the mobility error parameter."""

From afc0c9f86b3050d6bc0f2cc1ca5a10567422dbc8 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 29 Jul 2024 14:57:36 +0200
Subject: [PATCH 038/250] fix df dtype

---
 alphadia/workflow/searchoptimization.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index baa21662..f4ba0489 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -243,11 +243,20 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .precursor_ids) and determine whether it has converged."""
         if not self.has_converged():
-            self.history_df.loc[len(self.history_df)] = [
-                self.proposed_parameter,
-                len(precursors_df),
-                self.fdr_manager.current_version,
-            ]
+            new_row = pd.DataFrame(
+                [
+                    {
+                        "parameter": float(
+                            self.proposed_parameter
+                        ),  # Ensure float dtype
+                        "precursor_ids": int(len(precursors_df)),  # Ensure int dtype
+                        "classifier_version": int(
+                            self.fdr_manager.current_version
+                        ),  # Ensure int dtype
+                    }
+                ]
+            )
+            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence

From 27789a2e8dd74800e191b46249302b20018df702 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 29 Jul 2024 15:10:23 +0200
Subject: [PATCH 039/250] add unknown alphabase mods

---
 alphadia/constants/default.yaml   | 33 +++++++++++-
 alphadia/planning.py              | 27 ++++------
 alphadia/workflow/config.py       | 83 +++++++++++++++----------------
 tests/unit_tests/test_planning.py | 16 ++++++
 4 files changed, 99 insertions(+), 60 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 97168d82..108d6d31 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -41,7 +41,38 @@ library_prediction:
   # maximum charge state for predicted fragments
   max_fragment_charge: 2
   instrument: Lumos
-  checkpoint_folder_path: None
+  checkpoint_folder_path: null
+
+# define custom alphabase modifications not part of unimod or alphabase
+# also used for decoy channels
+custom_modififcations:
+  # Dimethyl @K channel decoy
+  Dimethyl:d12@K:
+    composition: H(-2)2H(8)13C(2)
+
+  # Dimethyl @Any_N-term channel decoy
+  Dimethyl:d12@Any_N-term:
+    composition: H(-2)2H(8)13C(2)
+
+  # Dimethyl @Protein_N-term channel decoy
+  Dimethyl:d12@Protein_N-term:
+    composition: H(-2)2H(8)13C(2)
+
+  # mTRAQ @K channel decoy
+  mTRAQ:d12@K:
+    composition: H(12)C(1)13C(10)15N(2)O(1)
+
+  # mTRAQ @Any_N-term channel decoy
+  mTRAQ:d12@Any_N-term:
+    composition: H(12)C(1)13C(14)15N(2)O(1)
+
+  # mTRAQ @Protein_N-term channel decoy
+  mTRAQ:d12@Protein_N-term:
+    composition: H(12)C(1)13C(14)15N(2)O(1)
+
+  # SILAC heavy @K channel decoy
+  Label:13C(12)@K:
+    composition: C(12)
 
 search:
   channel_filter: '0'
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 50f81b46..5373df8b 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -28,22 +28,6 @@
 
 logger = logging.getLogger()
 
-modification.add_new_modifications(
-    {
-        "Dimethyl:d12@Protein N-term": {"composition": "H(-2)2H(8)13C(2)"},
-        "Dimethyl:d12@Any N-term": {
-            "composition": "H(-2)2H(8)13C(2)",
-        },
-        "Dimethyl:d12@R": {
-            "composition": "H(-2)2H(8)13C(2)",
-        },
-        "Dimethyl:d12@K": {
-            "composition": "H(-2)2H(8)13C(2)",
-        },
-        "Label:13C(12)@K": {"composition": "C(12)"},
-    }
-)
-
 
 class Plan:
     def __init__(
@@ -134,6 +118,7 @@ def __init__(
         level_code = logging.getLevelName(level_to_set)
         logger.setLevel(level_code)
 
+        self.init_alphabase()
         self.load_library()
 
         torch.set_num_threads(self.config["general"]["thread_count"])
@@ -174,6 +159,16 @@ def log_environment(self):
         logger.progress(f"{'directlfq':<15} : {directlfq.__version__}")
         logger.progress("===================================================")
 
+    def init_alphabase(self):
+        """Init alphabase by registering custom modifications."""
+
+        # register custom modifications
+        if "custom_modififcations" in self.config:
+            n_modifications = len(self.config["custom_modififcations"])
+            logging.info(f"Registering {n_modifications} custom modifications")
+
+            modification.add_new_modifications(self.config["custom_modififcations"])
+
     def load_library(self):
         """
         Load or build spectral library as configured.
diff --git a/alphadia/workflow/config.py b/alphadia/workflow/config.py
index 4af78454..8b30904b 100644
--- a/alphadia/workflow/config.py
+++ b/alphadia/workflow/config.py
@@ -77,15 +77,7 @@ def print_w_style(
         # Check what the config name in string inside the brackets ( )
         # If the source is default, remove the brackets and set style to default
         # Else set style to new
-        if string.find("(") != -1:
-            config_name = string[string.find("(") + 1 : string.find(")")]
-            if config_name == "default":
-                string = string[: string.find("(")] + string[string.find(")") + 1 :]
-                style = "default"
-            else:
-                style = "new"
-        else:
-            style = "default"
+        style = "new" if "user defined" in string else "default"
 
     if style == "update":
         # Green color
@@ -362,32 +354,47 @@ def update_recursive(
             )
         return default_config
 
-    for default_key, default_value in default_config.items():
-        is_last_item = default_key == list(default_config.keys())[-1]
+    all_keys = list(default_config.keys())
+    for experiment_config in experiment_configs:
+        all_keys += [key for key in experiment_config if key not in all_keys]
+
+    for key in all_keys:
+        style = "auto"
+        if key not in default_config:
+            style = "new"
+            for experiment_config in experiment_configs:
+                if key in experiment_config:
+                    default_config[key] = experiment_config[key]
+                    break
+
+        default_value = default_config[key]
+
+        is_last_item = key == all_keys[-1]
 
         if not isinstance(
             default_value, tuple
         ):  # If the default value is not a leaf node, print it's key on separate line
             print_w_style(
-                f"{default_key}",
-                style="auto",
+                f"{key}",
+                style=style,
                 last_item_arr=last_item_arr + [is_last_item],
             )
 
         # Collect potential updates for this item
         potential_config_updates = []
         for experiment_config in experiment_configs:
-            if default_key in experiment_config:
-                potential_config_updates.append(experiment_config[default_key])
+            if key in experiment_config:
+                potential_config_updates.append(experiment_config[key])
 
-        default_config[default_key] = update_recursive(
-            {"key": default_key, "value": default_value},
+        default_config[key] = update_recursive(
+            {"key": key, "value": default_value},
             potential_config_updates,
             level + 1,
             print_output,
             is_last_item,
             last_item_arr=last_item_arr + [is_last_item],
         )
+
     return default_config
 
 
@@ -537,25 +544,6 @@ def __repr__(self) -> str:
             print_recursively(self.config, 0, "auto")
         return ""
 
-    def translate(self):
-        """
-        Translate the config dict so that every leaf node is a tuple (value, experiment_name), instead of just value
-
-        and sets the translate_config attribute, uses the general translate_config function
-        """
-        temp = copy.deepcopy(self.config)
-        self.translated_config = translate_config(temp, self.experiment_name)
-        # Let's make sure that the main config was not translated
-        return self.translated_config
-
-    def align_config_w_translation(self) -> None:
-        """
-        Translate the config dict back so that every leaf node is a value, instead of a tuple (value, experiment_name)
-        uses the general translate_config_back function
-        """
-        temp = copy.deepcopy(self.translated_config)
-        self.config = translate_config_back(temp)
-
     def update(self, experiments: list["Config"], print_modifications: bool = True):
         """
         Updates the config with the experiment configs,
@@ -572,17 +560,26 @@ def update(self, experiments: list["Config"], print_modifications: bool = True):
             contains updated and unmodifed values.
         """
 
-        # Translate the config dict first.
-        self.translate()
+        # The translated config contains the source of the modifications on leaf nodes
+        translated_config = translate_config(
+            copy.deepcopy(self.config), self.experiment_name
+        )
 
         if len(experiments) > 0:
             translated_experiments = []
             for config in experiments:
-                translated_experiments.append(config.translate())
-            self.translated_config = update_recursive(
-                {"key": "", "value": self.translated_config},
+                translated_experiments.append(
+                    translate_config(
+                        copy.deepcopy(config.config), config.experiment_name
+                    )
+                )
+
+            # Update the config with the experiments iteratively
+            translated_config = update_recursive(
+                {"key": "", "value": translated_config},
                 translated_experiments,
                 print_output=print_modifications,
             )
-        # Translate the config dict back
-        self.align_config_w_translation()
+
+        # Remove the source of modifications
+        self.config = translate_config_back(translated_config)
diff --git a/tests/unit_tests/test_planning.py b/tests/unit_tests/test_planning.py
index b8bb38c8..d9a23ce8 100644
--- a/tests/unit_tests/test_planning.py
+++ b/tests/unit_tests/test_planning.py
@@ -3,6 +3,7 @@
 
 import pytest
 from alphabase.constants import _const
+from alphabase.constants.modification import MOD_DF
 
 from alphadia import planning
 from alphadia.test_data_downloader import DataShareDownloader
@@ -72,3 +73,18 @@ def test_library_loading():
         plan = planning.Plan(temp_directory, library_path=test_data_location)
         assert len(plan.spectral_library.precursor_df) > 0
         assert len(plan.spectral_library.fragment_df) > 0
+
+
+def test_custom_modifications():
+    temp_directory = tempfile.gettempdir()
+
+    config = {
+        "custom_modififcations": {
+            "ThisModDoesNotExists@K": {
+                "composition": "H(10)",
+            },
+        }
+    }
+
+    plan = planning.Plan(temp_directory, [], config=config)  # noqa F841
+    assert "ThisModDoesNotExists@K" in MOD_DF["mod_name"].values

From 7c3382b295bd10c0a5f6583222e97bad26bb8c50 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 29 Jul 2024 15:12:05 +0200
Subject: [PATCH 040/250] update default

---
 alphadia/constants/default.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 97168d82..1e5119fe 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -41,7 +41,7 @@ library_prediction:
   # maximum charge state for predicted fragments
   max_fragment_charge: 2
   instrument: Lumos
-  checkpoint_folder_path: None
+  checkpoint_folder_path: null
 
 search:
   channel_filter: '0'

From 19dde17dd81be6c5370d7d0e0e3344a0be23180f Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 29 Jul 2024 15:19:16 +0200
Subject: [PATCH 041/250] use df for optimization history in automatic
 optimization

---
 alphadia/workflow/searchoptimization.py | 57 ++++++++++++++++++-------
 1 file changed, 42 insertions(+), 15 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 207c242f..e6068028 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -159,11 +159,20 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.history_df.loc[len(self.history_df)] = [
-                self.proposed_parameter,
-                len(precursors_df),
-                self.fdr_manager.current_version,
-            ]
+            new_row = pd.DataFrame(
+                [
+                    {
+                        "parameter": float(
+                            self.proposed_parameter
+                        ),  # Ensure float dtype
+                        "feature": int(len(precursors_df)),  # Ensure int dtype
+                        "classifier_version": int(
+                            self.fdr_manager.current_version
+                        ),  # Ensure int dtype
+                    }
+                ]
+            )
+            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
@@ -408,11 +417,20 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.history_df.loc[len(self.history_df)] = [
-                self.proposed_parameter,
-                len(precursors_df),
-                self.fdr_manager.current_version,
-            ]
+            new_row = pd.DataFrame(
+                [
+                    {
+                        "parameter": float(
+                            self.proposed_parameter
+                        ),  # Ensure float dtype
+                        "feature": int(len(precursors_df)),  # Ensure int dtype
+                        "classifier_version": int(
+                            self.fdr_manager.current_version
+                        ),  # Ensure int dtype
+                    }
+                ]
+            )
+            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
@@ -527,11 +545,20 @@ def _update_parameter(self, df: pd.DataFrame):
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if not self.has_converged():
-            self.history_df.loc[len(self.history_df)] = [
-                self.proposed_parameter,
-                len(precursors_df),
-                self.fdr_manager.current_version,
-            ]
+            new_row = pd.DataFrame(
+                [
+                    {
+                        "parameter": float(
+                            self.proposed_parameter
+                        ),  # Ensure float dtype
+                        "feature": int(len(precursors_df)),  # Ensure int dtype
+                        "classifier_version": int(
+                            self.fdr_manager.current_version
+                        ),  # Ensure int dtype
+                    }
+                ]
+            )
+            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
             self._check_convergence()
 
         if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence

From ecbe63a8b7433fe2b61a1dadd2ed2c2b167a29d3 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 29 Jul 2024 15:23:07 +0200
Subject: [PATCH 042/250] change check for targeted calibration

---
 alphadia/workflow/peptidecentric.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index caf142c1..65e0d3e7 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -1,6 +1,5 @@
 # native imports
 import logging
-from numbers import Number
 
 # third party imports
 import numpy as np
@@ -395,10 +394,10 @@ def calibration(self):
             return
 
         if (
-            isinstance(self.config["search"]["target_ms2_tolerance"], Number)
-            and isinstance(self.config["search"]["target_rt_tolerance"], Number)
-            and isinstance(self.config["search"]["target_ms1_tolerance"], Number)
-            and isinstance(self.config["search"]["target_mobility_tolerance"], Number)
+            self.config["search"]["target_ms2_tolerance"] > 0
+            and self.config["search"]["target_rt_tolerance"] > 0
+            and self.config["search"]["target_ms1_tolerance"] > 0
+            and self.config["search"]["target_mobility_tolerance"] > 0
         ):
             self.reporter.log_string(
                 "A complete list of target tolerances has been specified. Targeted search parameter optimization will be performed.",

From de6da9b8e86918edf90e13e0a9d908df8cc01aa1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 09:41:12 +0200
Subject: [PATCH 043/250] temporary_save

---
 alphadia/workflow/peptidecentric.py     | 105 ++-
 alphadia/workflow/searchoptimization.py | 943 ++++++++----------------
 tests/e2e_tests/e2e_test_cases.yaml     |  35 +
 3 files changed, 413 insertions(+), 670 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 65e0d3e7..cabde883 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -162,6 +162,9 @@ def init_calibration_optimization_manager(self):
                     "recalibration_target"
                 ],
                 "classifier_version": -1,
+                "fwhm_rt": self.config["optimization_manager"]["fwhm_rt"],
+                "fwhm_mobility": self.config["optimization_manager"]["fwhm_mobility"],
+                "score_cutoff": self.config["optimization_manager"]["score_cutoff"],
             }
         )
 
@@ -393,17 +396,7 @@ def calibration(self):
             )
             return
 
-        if (
-            self.config["search"]["target_ms2_tolerance"] > 0
-            and self.config["search"]["target_rt_tolerance"] > 0
-            and self.config["search"]["target_ms1_tolerance"] > 0
-            and self.config["search"]["target_mobility_tolerance"] > 0
-        ):
-            self.reporter.log_string(
-                "A complete list of target tolerances has been specified. Targeted search parameter optimization will be performed.",
-                verbosity="info",
-            )
-
+        if self.config["search"]["target_ms2_tolerance"] > 0:
             self.ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
                 self.config["search"]["target_ms2_tolerance"],
@@ -411,7 +404,15 @@ def calibration(self):
                 self.com,
                 self.fdr_manager,
             )
+        else:
+            self.ms2_optimizer = searchoptimization.AutomaticMS2Optimizer(
+                self.config["search_initial"]["initial_ms2_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
 
+        if self.config["search"]["target_rt_tolerance"] > 0:
             self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
                 self.config["search_initial"]["initial_rt_tolerance"],
                 self.config["search"]["target_rt_tolerance"],
@@ -419,8 +420,15 @@ def calibration(self):
                 self.com,
                 self.fdr_manager,
             )
-
-            if self.dia_data.has_ms1:
+        else:
+            self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
+                self.config["search_initial"]["initial_rt_tolerance"],
+                self.calibration_manager,
+                self.com,
+                self.fdr_manager,
+            )
+        if self.dia_data.has_ms1:
+            if self.config["search"]["target_ms1_tolerance"] > 0:
                 self.ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
                     self.config["search_initial"]["initial_ms1_tolerance"],
                     self.config["search"]["target_ms1_tolerance"],
@@ -429,9 +437,16 @@ def calibration(self):
                     self.fdr_manager,
                 )
             else:
-                self.ms1_optimizer = None
-
-            if self.dia_data.has_mobility:
+                self.ms1_optimizer = searchoptimization.AutomaticMS1Optimizer(
+                    self.config["search_initial"]["initial_ms1_tolerance"],
+                    self.calibration_manager,
+                    self.com,
+                    self.fdr_manager,
+                )
+        else:
+            self.ms1_optimizer = None
+        if self.dia_data.has_mobility:
+            if self.config["search"]["target_mobility_tolerance"] > 0:
                 self.mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.config["search"]["target_mobility_tolerance"],
@@ -440,25 +455,39 @@ def calibration(self):
                     self.fdr_manager,
                 )
             else:
-                self.mobility_optimizer = None
-
-            order_of_optimization = [
-                [
-                    optimizer
-                    for optimizer in [
-                        self.ms2_optimizer,
-                        self.rt_optimizer,
-                        self.ms1_optimizer,
-                        self.mobility_optimizer,
-                    ]
-                    if optimizer is not None
-                ]
+                self.mobility_optimizer = searchoptimization.AutomaticMobilityOptimizer(
+                    self.config["search_initial"]["initial_mobility_tolerance"],
+                    self.calibration_manager,
+                    self.com,
+                    self.fdr_manager,
+                )
+        else:
+            self.mobility_optimizer = None
+
+        self.reporter.log_string(
+            "A complete list of target tolerances has been specified. Targeted search parameter optimization will be performed.",
+            verbosity="info",
+        )
+        optimizers = [
+            self.ms2_optimizer,
+            self.rt_optimizer,
+            self.ms1_optimizer,
+            self.mobility_optimizer,
+        ]
+        targeted_optimizers = [
+            [
+                optimizer
+                for optimizer in optimizers
+                if isinstance(optimizer, searchoptimization.TargetedOptimizer)
             ]
+        ]
+        automatic_optimizers = [
+            [optimizer]
+            for optimizer in optimizers
+            if isinstance(optimizer, searchoptimization.AutomaticOptimizer)
+        ]
 
-        else:
-            raise NotImplementedError(
-                "Automatic search parameter optimization is not yet implemented"
-            )
+        order_of_optimization = targeted_optimizers + automatic_optimizers
 
         self.extract_optimization_data(
             self.config["calibration"]["recalibration_target"]
@@ -569,7 +598,7 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         percentile_001 = np.percentile(precursor_df_filtered["score"], 0.1)
-        self.optimization_manager.fit(
+        self.com.fit(
             {
                 "fwhm_rt": precursor_df_filtered["cycle_fwhm"].median(),
                 "fwhm_mobility": precursor_df_filtered["mobility_fwhm"].median(),
@@ -625,8 +654,8 @@ def extract_batch(self, batch_df, apply_cutoff=False):
             if self.dia_data.has_ms1
             else "mz_library",
             fragment_mz_column=f"mz_{self.com.column_type}",
-            fwhm_rt=self.optimization_manager.fwhm_rt,
-            fwhm_mobility=self.optimization_manager.fwhm_mobility,
+            fwhm_rt=self.com.fwhm_rt,
+            fwhm_mobility=self.com.fwhm_mobility,
         )
         candidates_df = extraction(thread_count=self.config["general"]["thread_count"])
 
@@ -635,11 +664,11 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         if apply_cutoff:
             num_before = len(candidates_df)
             self.reporter.log_string(
-                f"Applying score cutoff of {self.optimization_manager.score_cutoff}",
+                f"Applying score cutoff of {self.com.score_cutoff}",
                 verbosity="info",
             )
             candidates_df = candidates_df[
-                candidates_df["score"] > self.optimization_manager.score_cutoff
+                candidates_df["score"] > self.com.score_cutoff
             ]
             num_after = len(candidates_df)
             num_removed = num_before - num_after
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index e6068028..ed9964b2 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -6,17 +6,16 @@
 # alpha family imports
 # third party imports
 import pandas as pd
+import seaborn as sns
 
 # alphadia imports
-from alphadia.workflow import manager, reporting
+from alphadia.workflow import peptidecentric, reporting
 
 
 class BaseOptimizer(ABC):
     def __init__(
         self,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class serves as a base class for organizing the search parameter optimization process, which defines the parameters used for search.
@@ -24,20 +23,12 @@ def __init__(
         Parameters
         ----------
 
-        calibration_manager: manager.CalibrationManager
-            The calibration manager for the workflow, which is needed to update the search parameter between rounds of optimization
-
-        optimization_manager: manager.OptimizationManager
-            The optimization manager for the workflow, which is needed so the optimal parameter and classifier version can be saved to the manager
-
-        fdr_manager: manager.FDRManager
-            The FDR manager for the workflow, which is needed to update the optimal classifier version in the optimization manager
+        workflow: peptidecentric.PeptideCentricWorkflow
+            The workflow object that the optimization is being performed on.
 
         """
         self.optimal_parameter = None
-        self.calibration_manager = calibration_manager
-        self.optimization_manager = optimization_manager
-        self.fdr_manager = fdr_manager
+        self.workflow = workflow
         self.reporter = reporting.LogBackend() if reporter is None else reporter
 
     @abstractmethod
@@ -58,297 +49,242 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """
         pass
 
-    @abstractmethod
-    def _update_parameter(self, df):
-        """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The rule is specific to the parameter being optimized.
-
-        Parameters
-        ----------
-
-        df: pd.DataFrame
-            The dataframe used to update the parameter. This could be the precursor or fragment dataframe, depending on the search parameter being optimized.
-
-
-        """
-        pass
-
-    @abstractmethod
-    def _check_convergence(self):
-        """This method checks if the optimization has converged according to parameter-specific conditions and, if it has, sets the optimal parameter attribute and updates the optimization manager."""
-        pass
-
-    def has_converged(self):
-        """If the optimal parameter has been set, the optimization must have converged and the method returns True. Otherwise, it returns False."""
-        return self.optimal_parameter is not None
-
-
-class AutomaticRTOptimizer(BaseOptimizer):
-    """TODO Finish this optimizer"""
 
+class AutomaticOptimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
-        """See base class.
+        """This class automatically optimizes the search parameter and stores the progres of optimization in a dataframe, history_df.
 
         Parameters
         ----------
-
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
-        """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.history_df = pd.DataFrame(
-            columns=["parameter", "feature", "classifier_version"]
-        )
-        self.proposed_parameter = initial_parameter
-
-    def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
 
         """
+        super().__init__(workflow, **kwargs)
+        self.history_df = pd.DataFrame()
+        self.workflow.com.fit({self.parameter_name: initial_parameter})
+        self.has_converged = False
 
-        if (
-            len(self.history_df) > 2
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-2]
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-3]
-        ):
-            self.optimal_parameter = self.history_df.loc[
-                self.history_df["feature"].idxmax(), "parameter"
-            ]
-            self.optimization_manager.fit({"rt_error": self.optimal_parameter})
-            self.optimization_manager.fit(
+    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        if self.has_converged:
+            return
+
+        new_row = pd.DataFrame(
+            [
                 {
-                    "classifier_version": self.history_df.loc[
-                        self.history_df["feature"].idxmax(), "classifier_version"
-                    ]
+                    "parameter": float(
+                        self.workflow.com.__dict__[self.parameter_name]
+                    ),  # Ensure float dtype
+                    self.feature_name: self._get_feature_value(
+                        precursors_df, fragments_df
+                    ),  # Ensure int dtype
+                    "classifier_version": int(
+                        self.workflow.fdr_manager.current_version
+                    ),  # Ensure int dtype
                 }
-            )
+            ]
+        )
+        self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
+        just_converged = self._check_convergence()
 
-    def _update_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
-        This is implemented by the ci method for the estimator.
+        if just_converged:
+            self.has_converged = True
 
+            index_of_optimum = self.history_df[self.feature_name].idxmax()
 
-        """
-        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
-            "precursor", "rt"
-        ).ci(df, 0.99)
+            optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
+            classifier_version_at_optimum = self.history_df["classifier_version"].loc[
+                index_of_optimum
+            ]
 
-        return proposed_parameter
+            self.workflow.com.fit({self.parameter_name: optimal_parameter})
+            self.workflow.com.fit({"classifier_version": classifier_version_at_optimum})
 
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
-        if not self.has_converged():
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "parameter": float(
-                            self.proposed_parameter
-                        ),  # Ensure float dtype
-                        "feature": int(len(precursors_df)),  # Ensure int dtype
-                        "classifier_version": int(
-                            self.fdr_manager.current_version
-                        ),  # Ensure int dtype
-                    }
-                ]
-            )
-            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-            self._check_convergence()
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
             self.reporter.log_string(
-                f"✅ {'rt_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
         else:
-            self.proposed_parameter = self._update_parameter(precursors_df)
+            new_parameter = self._propose_new_parameter(
+                precursors_df
+                if self.estimator_group_name == "precursor"
+                else fragments_df
+            )
+
+            self.workflow.com.fit({self.parameter_name: new_parameter})
 
             self.reporter.log_string(
-                f"❌ {'rt_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
+                f"❌ {self.parameter_name:<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.workflow.com.__dict__[self.parameter_name]}.",
                 verbosity="progress",
             )
 
-            self.optimization_manager.fit({"rt_error": self.proposed_parameter})
-
     def plot(self):
         """Plot the optimization of the RT error parameter."""
         fig, ax = plt.subplots()
-        ax.vlines(
-            self.optimal_parameter,
-            0,
-            max(self.feature),
+
+        # Plot the vertical line
+        ax.axvline(
+            x=self.workflow.com.__dict__[self.parameter_name],
+            ymin=0,
+            ymax=self.history_df[self.feature_name].max(),
             color="red",
             zorder=0,
-            label="Optimal RT error",
+            label=f"Optimal {self.parameter_name}",
+        )
+
+        # Plot the line and scatter plot using Seaborn
+        sns.lineplot(
+            x=self.history_df["parameter"],
+            y=self.history_df[self.feature_name],
+            ax=ax,
+        )
+        sns.scatterplot(
+            x=self.history_df["parameter"],
+            y=self.history_df[self.feature_name],
+            ax=ax,
         )
-        ax.plot(self.parameters, self.feature)
-        ax.scatter(self.parameters, self.feature)
-        ax.set_ylabel("Number of precursor identifications")
-        ax.set_xlabel("RT error")
+
+        # Set labels and other properties
+        ax.set_xlabel(self.parameter_name)
         ax.xaxis.set_inverted(True)
-        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
+        ax.set_ylim(bottom=0, top=self.history_df[self.feature_name].max() * 1.1)
         ax.legend(loc="upper left")
+
         plt.show()
 
+    @abstractmethod
+    def _propose_new_parameter(self, df):
+        """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The rule is specific to the parameter being optimized.
+
+        Parameters
+        ----------
 
-class AutomaticMS2Optimizer(BaseOptimizer):
+        df: pd.DataFrame
+            The dataframe used to update the parameter. This could be the precursor or fragment dataframe, depending on the search parameter being optimized.
+
+
+        """
+        pass
+
+    @abstractmethod
+    def _check_convergence(self):
+        """This method checks if the optimization has converged according to parameter-specific conditions and, if it has, sets the optimal parameter attribute and updates the optimization manager."""
+        pass
+
+    @abstractmethod
+    def _get_feature_value(
+        self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
+    ):
+        """Each parameter is optimized according to a particular feature. This method gets the value of that feature for a given round of optimization.
+
+        Parameters
+        ----------
+
+        precursors_df: pd.DataFrame
+            The precursor dataframe for the search
+
+        fragments_df: pd.DataFrame
+            The fragment dataframe for the search
+
+
+        """
+        pass
+
+
+class TargetedOptimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        target_parameter: float,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
-        """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
+        """This class optimizes the search parameter until it reaches a user-specified target value.
 
         Parameters
         ----------
+
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
+        target_parameter: float
+            Optimization will stop when this parameter is reached.
 
         """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.history_df = pd.DataFrame(
-            columns=["parameter", "precursor_ids", "classifier_version"]
-        )
-        self.proposed_parameter = initial_parameter
-
-    def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+        super().__init__(workflow, **kwargs)
+        self.workflow.com.fit({self.parameter_name: initial_parameter})
+        self.target_parameter = target_parameter
+        self.has_converged = False
 
+    def _check_convergence(self, proposed_parameter):
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
 
+        Parameters
+        ----------
+        proposed_parameter: float
+            The proposed parameter for the next round of optimization.
         """
 
-        if (
-            len(self.history_df) > 2
-            and self.history_df["precursor_ids"].iloc[-1]
-            < 1.1 * self.history_df["precursor_ids"].iloc[-2]
-            and self.history_df["precursor_ids"].iloc[-1]
-            < 1.1 * self.history_df["precursor_ids"].iloc[-3]
-        ):
-            self.optimal_parameter = self.history_df.loc[
-                self.history_df["precursor_ids"].idxmax(), "parameter"
-            ]
-            self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
-            self.optimization_manager.fit(
-                {
-                    "classifier_version": self.history_df.loc[
-                        self.history_df["precursor_ids"].idxmax(), "classifier_version"
-                    ]
-                }
-            )
+        return proposed_parameter <= self.target_parameter
 
-    def _update_parameter(self, df: pd.DataFrame):
+    def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
+            2) take the mean of the endpoints of the central 95% of these deviations, and
+            3) take the maximum of this value and the target parameter.
         This is implemented by the ci method for the estimator.
-
-
         """
-        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
-            "fragment", "mz"
-        ).ci(df, 0.99)
-
-        return proposed_parameter
+        return max(
+            self.workflow.calibration_manager.get_estimator(
+                self.estimator_group_name, self.estimator_name
+            ).ci(df, 0.95),
+            self.target_parameter,
+        )
 
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The number of precursor identifications is used to track the progres of the optimization (stored in .precursor_ids) and determine whether it has converged."""
-        if not self.has_converged():
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "parameter": float(
-                            self.proposed_parameter
-                        ),  # Ensure float dtype
-                        "precursor_ids": int(len(precursors_df)),  # Ensure int dtype
-                        "classifier_version": int(
-                            self.fdr_manager.current_version
-                        ),  # Ensure int dtype
-                    }
-                ]
+        """See base class."""
+        if self.has_converged:
+            self.reporter.log_string(
+                f"✅ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
+                verbosity="progress",
             )
-            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-            self._check_convergence()
+            return
 
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
+        new_parameter = self._propose_new_parameter(
+            precursors_df if self.estimator_group_name == "precursor" else fragments_df
+        )
+        just_converged = self._check_convergence(new_parameter)
+        self.workflow.com.fit({self.parameter_name: new_parameter})
+
+        if just_converged:
+            self.has_converged = True
             self.reporter.log_string(
-                f"✅ {'ms2_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
+                f"✅ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
                 verbosity="progress",
             )
 
         else:
-            self.proposed_parameter = self._update_parameter(fragments_df)
-
             self.reporter.log_string(
-                f"❌ {'ms2_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
+                f"❌ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} > {self.target_parameter:.4f}",
                 verbosity="progress",
             )
 
-            self.optimization_manager.fit({"ms2_error": self.proposed_parameter})
 
-    def plot(self):
-        """Plot the optimization of the MS2 error parameter."""
-        fig, ax = plt.subplots()
-        ax.vlines(
-            self.optimal_parameter,
-            0,
-            max(self.precursor_ids),
-            color="red",
-            zorder=0,
-            label="Optimal MS2 error",
-        )
-        ax.plot(self.parameters, self.precursor_ids)
-        ax.scatter(self.parameters, self.precursor_ids)
-        ax.set_ylabel("Number of precursor identifications")
-        ax.set_xlabel("MS2 error")
-        ax.xaxis.set_inverted(True)
-        ax.set_ylim(bottom=0, top=max(self.precursor_ids) * 1.1)
-        ax.legend(loc="upper left")
-        plt.show()
-
-
-class AutomaticMS1Optimizer(BaseOptimizer):
+class AutomaticRTOptimizer(AutomaticOptimizer):
     """TODO Finish this optimizer"""
 
     def __init__(
         self,
         initial_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
         """See base class.
@@ -360,13 +296,11 @@ def __init__(
             The parameter used for search in the first round of optimization.
 
         """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.history_df = pd.DataFrame(
-            columns=["parameter", "precursor_ids", "classifier_version"]
-        )
-        self.proposed_parameter = initial_parameter
+        self.parameter_name = "rt_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "rt"
+        self.feature_name = "precursor_count"
+        super().__init__(initial_parameter, workflow, **kwargs)
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
@@ -380,26 +314,15 @@ def _check_convergence(self):
 
         """
 
-        if (
+        return (
             len(self.history_df) > 2
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-2]
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-3]
-        ):
-            self.optimal_parameter = self.history_df.loc[
-                self.history_df["feature"].idxmax(), "parameter"
-            ]
-            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
-            self.optimization_manager.fit(
-                {
-                    "classifier_version": self.history_df.loc[
-                        self.history_df["feature"].idxmax(), "classifier_version"
-                    ]
-                }
-            )
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-3]
+        )
 
-    def _update_parameter(self, df: pd.DataFrame):
+    def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
             2) take the mean of the endpoints of the central 99% of these deviations, and
@@ -408,126 +331,59 @@ def _update_parameter(self, df: pd.DataFrame):
 
 
         """
-        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
-            "precursor", "mz"
+        return 1.1 * self.workflow.calibration_manager.get_estimator(
+            self.estimator_group_name, self.estimator_name
         ).ci(df, 0.99)
 
-        return proposed_parameter
-
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
-        if not self.has_converged():
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "parameter": float(
-                            self.proposed_parameter
-                        ),  # Ensure float dtype
-                        "feature": int(len(precursors_df)),  # Ensure int dtype
-                        "classifier_version": int(
-                            self.fdr_manager.current_version
-                        ),  # Ensure int dtype
-                    }
-                ]
-            )
-            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-            self._check_convergence()
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'ms1_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
-                verbosity="progress",
-            )
-
-        else:
-            self.proposed_parameter = self._update_parameter(precursors_df)
-
-            self.reporter.log_string(
-                f"❌ {'ms1_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
-                verbosity="progress",
-            )
-
-            self.optimization_manager.fit({"ms1_error": self.proposed_parameter})
-
-    def plot(self):
-        """Plot the optimization of the MS1 error parameter."""
-        fig, ax = plt.subplots()
-        ax.vlines(
-            self.optimal_parameter,
-            0,
-            max(self.feature),
-            color="red",
-            zorder=0,
-            label="Optimal MS1 error",
-        )
-        ax.plot(self.parameters, self.feature)
-        ax.scatter(self.parameters, self.feature)
-        ax.set_ylabel("Number of precursor identifications")
-        ax.set_xlabel("MS1 error")
-        ax.xaxis.set_inverted(True)
-        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
-        ax.legend(loc="upper left")
-        plt.show()
+    def _get_feature_value(
+        self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
+    ):
+        return len(precursors_df)
 
 
-class AutomaticMobilityOptimizer(BaseOptimizer):
+class AutomaticMS2Optimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
-        """See base class.
+        """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
 
         Parameters
         ----------
-
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
+
         """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.history_df = pd.DataFrame(
-            columns=["parameter", "precursor_ids", "classifier_version"]
-        )
-        self.proposed_parameter = initial_parameter
+        self.parameter_name = "ms2_error"
+        self.estimator_group_name = "fragment"
+        self.estimator_name = "mz"
+        self.feature_name = "precursor_count"
+        super().__init__(initial_parameter, workflow, **kwargs)
 
     def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
 
         Notes
         -----
-            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
 
         """
 
-        if (
+        return (
             len(self.history_df) > 2
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-2]
-            and self.history_df["feature"].iloc[-1]
-            < 1.1 * self.history_df["feature"].iloc[-3]
-        ):
-            self.optimal_parameter = self.history_df.loc[
-                self.history_df["feature"].idxmax(), "parameter"
-            ]
-            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
-            self.optimization_manager.fit(
-                {
-                    "classifier_version": self.history_df.loc[
-                        self.history_df["feature"].idxmax(), "classifier_version"
-                    ]
-                }
-            )
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-3]
+        )
 
-    def _update_parameter(self, df: pd.DataFrame):
+    def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
             2) take the mean of the endpoints of the central 99% of these deviations, and
@@ -536,79 +392,23 @@ def _update_parameter(self, df: pd.DataFrame):
 
 
         """
-        proposed_parameter = 1.1 * self.calibration_manager.get_estimator(
-            "precursor", "mz"
+        return 1.1 * self.workflow.calibration_manager.get_estimator(
+            self.estimator_group_name, self.estimator_name
         ).ci(df, 0.99)
 
-        return proposed_parameter
-
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
-        if not self.has_converged():
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "parameter": float(
-                            self.proposed_parameter
-                        ),  # Ensure float dtype
-                        "feature": int(len(precursors_df)),  # Ensure int dtype
-                        "classifier_version": int(
-                            self.fdr_manager.current_version
-                        ),  # Ensure int dtype
-                    }
-                ]
-            )
-            self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-            self._check_convergence()
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'mobility_error':<15}: optimization complete. Optimal parameter {self.optimal_parameter} found after {len(self.history_df)} searches.",
-                verbosity="progress",
-            )
-
-        else:
-            self.proposed_parameter = self._update_parameter(precursors_df)
-
-            self.reporter.log_string(
-                f"❌ {'mobility_error':<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.proposed_parameter}.",
-                verbosity="progress",
-            )
-
-            self.parameters.append(self.proposed_parameter)
-            self.optimization_manager.fit({"mobility_error": self.proposed_parameter})
-
-    def plot(self):
-        """Plot the optimization of the mobility error parameter."""
-        fig, ax = plt.subplots()
-        ax.vlines(
-            self.optimal_parameter,
-            0,
-            max(self.feature),
-            color="red",
-            zorder=0,
-            label="Optimal mobility error",
-        )
-        ax.plot(self.parameters, self.feature)
-        ax.scatter(self.parameters, self.feature)
-        ax.set_ylabel("Number of precursor identifications")
-        ax.set_xlabel("Mobility error")
-        ax.xaxis.set_inverted(True)
-        ax.set_ylim(bottom=0, top=max(self.feature) * 1.1)
-        ax.legend(loc="upper left")
-        plt.show()
+    def _get_feature_value(
+        self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
+    ):
+        return len(precursors_df)
 
 
-class TargetedRTOptimizer(BaseOptimizer):
-    """This class optimizes the RT search parameter until it reaches a user-specified target value."""
+class AutomaticMS1Optimizer(AutomaticOptimizer):
+    """TODO Finish this optimizer"""
 
     def __init__(
         self,
         initial_parameter: float,
-        target_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
         """See base class.
@@ -619,75 +419,57 @@ def __init__(
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
-        target_parameter: float
-            Optimization will stop when this parameter is reached.
-
         """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.target_parameter = target_parameter
-        self.parameters = [initial_parameter]
+        self.parameter_name = "ms1_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "mz"
+        self.feature_name = "precursor_count"
+        super().__init__(initial_parameter, workflow, **kwargs)
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
-    def _check_convergence(self, proposed_parameter):
-        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
 
-        Parameters
-        ----------
-        proposed_parameter: float
-            The proposed parameter for the next round of optimization.
         """
 
-        if proposed_parameter <= self.target_parameter:
-            self.optimal_parameter = self.target_parameter
-            self.optimization_manager.fit({"rt_error": self.optimal_parameter})
+        return (
+            len(self.history_df) > 2
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-3]
+        )
 
-    def _update_parameter(self, df: pd.DataFrame):
+    def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values, and
-            2) take the mean of the endpoints of the central 95% of these deviations
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
         This is implemented by the ci method for the estimator.
 
 
         """
-        proposed_parameter = self.calibration_manager.get_estimator(
-            "precursor", "rt"
-        ).ci(df, 0.95)
-
-        return proposed_parameter
-
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class."""
-        if not self.has_converged():
-            proposed_parameter = self._update_parameter(precursors_df)
-            self._check_convergence(proposed_parameter)
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'rt_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
-
-        else:
-            self.reporter.log_string(
-                f"❌ {'rt_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
-
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"rt_error": proposed_parameter})
+        return 1.1 * self.workflow.calibration_manager.get_estimator(
+            self.estimator_group_name, self.estimator_name
+        ).ci(df, 0.99)
 
+    def _get_feature_value(
+        self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
+    ):
+        return len(precursors_df)
 
-class TargetedMS2Optimizer(BaseOptimizer):
-    """This class optimizes the MS2 search parameter until it reaches a user-specified target value."""
 
+class AutomaticMobilityOptimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        target_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
         """See base class.
@@ -698,218 +480,115 @@ def __init__(
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
-        target_parameter: float
-            Optimization will stop when this parameter is reached.
-
         """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.target_parameter = target_parameter
-        self.parameters = [initial_parameter]
+        self.parameter_name = "mobility_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "mobility"
+        self.feature_name = "precursor_count"
+        super().__init__(initial_parameter, workflow, **kwargs)
+
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
-    def _check_convergence(self, proposed_parameter):
-        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
 
-        Parameters
-        ----------
-        proposed_parameter: float
-            The proposed parameter for the next round of optimization.
         """
 
-        if proposed_parameter <= self.target_parameter:
-            self.optimal_parameter = self.target_parameter
-            self.optimization_manager.fit({"ms2_error": self.optimal_parameter})
+        return (
+            len(self.history_df) > 2
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-3]
+        )
 
-    def _update_parameter(self, df: pd.DataFrame):
+    def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values, and
-            2) take the mean of the endpoints of the central 95% of these deviations
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central 99% of these deviations, and
+            3) multiply this value by 1.1.
         This is implemented by the ci method for the estimator.
 
 
         """
-        proposed_parameter = self.calibration_manager.get_estimator(
-            "fragment", "mz"
-        ).ci(df, 0.95)
-
-        return proposed_parameter
-
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class."""
-        if not self.has_converged():
-            proposed_parameter = self._update_parameter(fragments_df)
-            self._check_convergence(proposed_parameter)
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'ms2_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
-
-        else:
-            self.reporter.log_string(
-                f"❌ {'ms2_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
+        return 1.1 * self.workflow.calibration_manager.get_estimator(
+            self.estimator_group_name, self.estimator_name
+        ).ci(df, 0.99)
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"ms2_error": proposed_parameter})
+    def _get_feature_value(
+        self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
+    ):
+        return len(precursors_df)
 
 
-class TargetedMS1Optimizer(BaseOptimizer):
-    """This class optimizes the MS1 search parameter until it reaches a user-specified target value."""
+class TargetedRTOptimizer(TargetedOptimizer):
+    """This class optimizes the RT search parameter until it reaches a user-specified target value."""
 
     def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
-        """See base class.
-
-        Parameters
-        ----------
-
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-        target_parameter: float
-            Optimization will stop when this parameter is reached.
-
-        """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.target_parameter = target_parameter
-        self.parameters = [initial_parameter]
-
-    def _check_convergence(self, proposed_parameter):
-        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
-
-        Parameters
-        ----------
-        proposed_parameter: float
-            The proposed parameter for the next round of optimization.
-        """
-
-        if proposed_parameter <= self.target_parameter:
-            self.optimal_parameter = self.target_parameter
-            self.optimization_manager.fit({"ms1_error": self.optimal_parameter})
-
-    def _update_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values, and
-            2) take the mean of the endpoints of the central 95% of these deviations
-        This is implemented by the ci method for the estimator.
-
+        """See base class."""
+        self.parameter_name = "rt_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "rt"
+        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
 
-        """
-        proposed_parameter = self.calibration_manager.get_estimator(
-            "precursor", "mz"
-        ).ci(df, 0.95)
 
-        return proposed_parameter
+class TargetedMS2Optimizer(TargetedOptimizer):
+    """This class optimizes the MS2 search parameter until it reaches a user-specified target value."""
 
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        workflow: peptidecentric.PeptideCentricWorkflow,
+        **kwargs,
+    ):
         """See base class."""
-        if not self.has_converged():
-            proposed_parameter = self._update_parameter(precursors_df)
-            self._check_convergence(proposed_parameter)
+        self.parameter_name = "ms2_error"
+        self.estimator_group_name = "fragment"
+        self.estimator_name = "mz"
+        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
 
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'ms1_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
 
-        else:
-            self.reporter.log_string(
-                f"❌ {'ms1_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
+class TargetedMS1Optimizer(TargetedOptimizer):
+    """This class optimizes the MS1 search parameter until it reaches a user-specified target value."""
 
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"ms1_error": proposed_parameter})
+    def __init__(
+        self,
+        initial_parameter: float,
+        target_parameter: float,
+        workflow: peptidecentric.PeptideCentricWorkflow,
+        **kwargs,
+    ):
+        """See base class."""
+        self.parameter_name = "ms1_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "mz"
+        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
 
 
-class TargetedMobilityOptimizer(BaseOptimizer):
+class TargetedMobilityOptimizer(TargetedOptimizer):
     """This class optimizes the mobility search parameter until it reaches a user-specified target value."""
 
     def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        calibration_manager: manager.CalibrationManager,
-        optimization_manager: manager.OptimizationManager,
-        fdr_manager: manager.FDRManager,
+        workflow: peptidecentric.PeptideCentricWorkflow,
         **kwargs,
     ):
-        """See base class.
-
-        Parameters
-        ----------
-
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-        target_parameter: float
-            Optimization will stop when this parameter is reached.
-
-        """
-        super().__init__(
-            calibration_manager, optimization_manager, fdr_manager, **kwargs
-        )
-        self.target_parameter = target_parameter
-        self.parameters = [initial_parameter]
-
-    def _check_convergence(self, proposed_parameter):
-        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
-
-        Parameters
-        ----------
-        proposed_parameter: float
-            The proposed parameter for the next round of optimization.
-        """
-
-        if proposed_parameter <= self.target_parameter:
-            self.optimal_parameter = self.target_parameter
-            self.optimization_manager.fit({"mobility_error": self.optimal_parameter})
-
-    def _update_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values, and
-            2) take the mean of the endpoints of the central 95% of these deviations
-        This is implemented by the ci method for the estimator.
-
-
-        """
-        proposed_parameter = self.calibration_manager.get_estimator(
-            "precursor", "mobility"
-        ).ci(df, 0.95)
-
-        return proposed_parameter
-
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class."""
-        if not self.has_converged():
-            proposed_parameter = self._update_parameter(precursors_df)
-            self._check_convergence(proposed_parameter)
-
-        if self.has_converged():  # Note this may change from the above statement since .optimal_parameter may be set in ._check_convergence
-            self.reporter.log_string(
-                f"✅ {'mobility_error':<15}: {self.target_parameter:.4f} <= {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
-
-        else:
-            self.reporter.log_string(
-                f"❌ {'mobility_error':<15}: {proposed_parameter:.4f} > {self.target_parameter:.4f}",
-                verbosity="progress",
-            )
-
-            self.parameters.append(proposed_parameter)
-            self.optimization_manager.fit({"mobility_error": proposed_parameter})
+        self.parameter_name = "mobility_error"
+        self.estimator_group_name = "precursor"
+        self.estimator_name = "mobility"
+        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 6d6ddfc8..8cfe2fa6 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -99,6 +99,41 @@ test_cases:
       - BasicStats
 
 
+  - name: astral_automatic_calibration
+    config:
+      library_prediction:
+        predict: true
+        fixed_modifications: 'Carbamidomethyl@C'
+        variable_modifications: 'Oxidation@M;Acetyl@Protein N-term'
+        max_var_mod_num: 2
+        missed_cleavages: 1
+        precursor_mz:
+          - 380
+          - 980
+        nce: 25
+        instrument: Lumos
+      search:
+        target_num_candidates: 3
+        target_ms1_tolerance: 4
+        target_ms2_tolerance: -1
+        target_rt_tolerance: -1
+      search_initial:
+        initial_num_candidates: 1
+        initial_ms1_tolerance: 10
+        initial_ms2_tolerance: 15
+        initial_rt_tolerance: 300
+      search_output:
+        peptide_level_lfq: true
+        precursor_level_lfq: true
+    fasta:
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=2024_01_12_human.fasta
+    raw_data:
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_02.raw
+    metrics:
+      - BasicStats
+
 #  - name: astral_mixed_species
 #    config:
 #      library_prediction:

From d74927c33dbe031e9979571371871c199e4ad614 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Tue, 30 Jul 2024 10:14:09 +0200
Subject: [PATCH 044/250] add multiplexing step

---
 alphadia/constants/default.yaml       | 23 ++++++++++++
 alphadia/libtransform.py              | 54 +++++++++++++++++++++++++++
 tests/unit_tests/test_libtransform.py | 52 ++++++++++++++++++++++++++
 3 files changed, 129 insertions(+)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 108d6d31..475d0034 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -158,6 +158,29 @@ scoring_config:
   precursor_mz_tolerance: 10
   fragment_mz_tolerance: 15
 
+# perform non-isobaric multiplexing of any input library
+library_multiplexing:
+  # if true, the library is multiplexed
+  enabled: False
+
+  # define channels by their name and how modifications should be translated from the input library to the multiplexed library
+  multiplex_mapping:
+    0:
+      mTRAQ@K: mTRAQ@K
+      mTRAQ@Any_N-term: mTRAQ@Any_N-term
+
+    4:
+      mTRAQ@K: mTRAQ:13C(3)15N(1)@K
+      mTRAQ@Any_N-term: mTRAQ:13C(3)15N(1)@Any_N-term
+
+    8:
+      mTRAQ@K: mTRAQ:13C(6)15N(2)@K
+      mTRAQ@Any_N-term: mTRAQ:13C(6)15N(2)@Any_N-term
+
+    12:
+      mTRAQ@K: mTRAQ:d12@K
+      mTRAQ@Any_N-term: mTRAQ:d12@Any_N-term
+
 multiplexing:
   enabled: False
   target_channels: '4,8'
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index 0daade51..442059f9 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -2,11 +2,13 @@
 import logging
 import os
 import typing
+from functools import reduce
 from pathlib import Path
 
 # third party imports
 import numpy as np
 import pandas as pd
+from alphabase.constants.modification import MOD_DF
 
 # alpha family imports
 from alphabase.peptide import fragment
@@ -602,6 +604,58 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
         return input
 
 
+class MultiplexLibrary(ProcessingStep):
+    def __init__(self, multiplex_mapping: dict) -> None:
+        """Initialize the MultiplexLibrary step."""
+
+        self._multiplex_mapping = multiplex_mapping
+
+    def validate(self, input: str) -> bool:
+        """Validate the input object. It is expected that the input is a path to a file which exists."""
+        valid = True
+        valid &= isinstance(input, SpecLibBase)
+
+        # check if all modifications are valid
+        for _, channel_multiplex_mapping in self._multiplex_mapping.items():
+            for key, value in channel_multiplex_mapping.items():
+                for mod in [key, value]:
+                    print(mod)
+                    if mod not in MOD_DF.index:
+                        logger.error(f"Modification {mod} not found in input library")
+                        valid = False
+
+        return valid
+
+    def forward(self, input: SpecLibBase) -> SpecLibBase:
+        """Apply the MultiplexLibrary step to the input object."""
+
+        if "channel" in input.precursor_df.columns:
+            channel_unique = input.precursor_df["channel"].unique()
+            logger.info(f"Library already multiplexed for channel {channel_unique}")
+            input.precursor_df = input.precursor_df[input.precursor_df["channel"] == 0]
+
+        channel_lib_list = []
+        for channel, channel_mod_translations in self._multiplex_mapping.items():
+            logger.info(f"Multiplexing library for channel {channel}")
+            channel_lib = input.copy()
+            for original_mod, channel_mod in channel_mod_translations.items():
+                channel_lib._precursor_df["mods"] = channel_lib._precursor_df[
+                    "mods"
+                ].str.replace(original_mod, channel_mod)
+                channel_lib._precursor_df["channel"] = channel
+
+            channel_lib.calc_fragment_mz_df()
+            channel_lib_list.append(channel_lib)
+
+        def apply_func(x, y):
+            x.append(y)
+            return x
+
+        speclib = reduce(lambda x, y: apply_func(x, y), channel_lib_list)
+        speclib.remove_unused_fragments()
+        return speclib
+
+
 class FlattenLibrary(ProcessingStep):
     def __init__(
         self, top_k_fragments: int = 12, min_fragment_intensity: float = 0.01
diff --git a/tests/unit_tests/test_libtransform.py b/tests/unit_tests/test_libtransform.py
index 7326171c..4eb8b23f 100644
--- a/tests/unit_tests/test_libtransform.py
+++ b/tests/unit_tests/test_libtransform.py
@@ -1,6 +1,8 @@
 import tempfile
 
 import numpy as np
+import pandas as pd
+from alphabase.spectral_library.base import SpecLibBase
 
 from alphadia import libtransform
 
@@ -79,3 +81,53 @@ def test_library_transform():
 
     assert speclib.precursor_df["decoy"].sum() == 2
     assert np.all(speclib.precursor_df["cardinality"] == [2, 2, 1, 1])
+
+
+def test_multiplex_library():
+    # given
+    repeat = 2
+    peptides = ["AGHCEWQMK"] * repeat
+    mods = ["mTRAQ@K"] * repeat
+    sites = ["0;9"] * repeat
+
+    precursor_df = pd.DataFrame(
+        {"sequence": peptides, "mods": mods, "mod_sites": sites}
+    )
+    precursor_df["nAA"] = precursor_df["sequence"].str.len()
+    precursor_df["charge"] = [2, 3]
+
+    test_lib = SpecLibBase()
+    test_lib.precursor_df = precursor_df
+    test_lib.calc_precursor_mz()
+    test_lib.calc_fragment_mz_df()
+
+    test_multiplex_mapping = {
+        0: {"mTRAQ@K": "mTRAQ@K"},
+        "magic_chanel": {"mTRAQ@K": "mTRAQ:13C(3)15N(1)@K"},
+        1337: {"mTRAQ@K": "mTRAQ:13C(6)15N(2)@K"},
+    }
+
+    # when
+    multiplexer = libtransform.MultiplexLibrary(test_multiplex_mapping)
+    result_lib = multiplexer.forward(test_lib)
+
+    # then
+    assert result_lib.precursor_df["sequence"].shape == (6,)
+    assert result_lib.precursor_df["charge"].nunique() == 2
+    assert result_lib.precursor_df["frag_stop_idx"].nunique() == 6
+
+    for channel in [0, 1337, "magic_chanel"]:
+        assert (
+            result_lib.precursor_df[
+                result_lib.precursor_df["channel"] == channel
+            ].shape[0]
+            == repeat
+        )
+
+    for modification in ["mTRAQ@K", "mTRAQ:13C(3)15N(1)@K", "mTRAQ:13C(6)15N(2)@K"]:
+        assert (
+            result_lib.precursor_df[
+                result_lib.precursor_df["mods"].str.contains(modification, regex=False)
+            ].shape[0]
+            == repeat
+        )

From 00d533d73da84b927d213afc18ea7e2ecbb0b300 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 10:48:04 +0200
Subject: [PATCH 045/250] automatic calibration in peptidecentric.py with
 required alterations to config and tests, also refactor optimizers

---
 alphadia/constants/default.yaml         |   8 +-
 alphadia/workflow/peptidecentric.py     | 106 ++++++++++++++----------
 alphadia/workflow/searchoptimization.py |  28 ++++---
 tests/e2e_tests/e2e_test_cases.yaml     |   4 +-
 4 files changed, 82 insertions(+), 64 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 97168d82..c9e3be40 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -68,11 +68,11 @@ calibration:
   # Number of precursors searched and scored per batch
   batch_size: 8000
 
-  # recalibration target for the first epoch. For subsequent epochs, the target will increase by this amount.
-  recalibration_target: 200
+  # minimum number of precursors to be found before search parameter optimization begins
+  min_precursors_for_optimization: 200
 
-  # TODO: remove as not relevant anymore
-  max_epochs: 20
+  # the maximum number of steps that a given optimizer is permitted to take
+  max_steps: 20
 
   # TODO: remove this parameter
   final_full_calibration: False
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index cabde883..00dbedd6 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -158,9 +158,6 @@ def init_calibration_optimization_manager(self):
                 "num_candidates": self.config["search_initial"][
                     "initial_num_candidates"
                 ],
-                "recalibration_target": self.config["calibration"][
-                    "recalibration_target"
-                ],
                 "classifier_version": -1,
                 "fwhm_rt": self.config["optimization_manager"]["fwhm_rt"],
                 "fwhm_mobility": self.config["optimization_manager"]["fwhm_mobility"],
@@ -372,7 +369,7 @@ def extract_optimization_data(self, target):
             precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
             self.reporter.log_string(
-                f"=== checking if recalibration conditions were reached, target {self.com.recalibration_target} precursors ===",
+                f"=== checking if minimum number of precursors for optimization found yet; minimum number is {target} ===",
                 verbosity="progress",
             )
 
@@ -397,82 +394,64 @@ def calibration(self):
             return
 
         if self.config["search"]["target_ms2_tolerance"] > 0:
-            self.ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
+            ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
                 self.config["search"]["target_ms2_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
+                self,
             )
         else:
-            self.ms2_optimizer = searchoptimization.AutomaticMS2Optimizer(
+            ms2_optimizer = searchoptimization.AutomaticMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
+                self,
             )
 
         if self.config["search"]["target_rt_tolerance"] > 0:
-            self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
+            rt_optimizer = searchoptimization.TargetedRTOptimizer(
                 self.config["search_initial"]["initial_rt_tolerance"],
                 self.config["search"]["target_rt_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
+                self,
             )
         else:
-            self.rt_optimizer = searchoptimization.TargetedRTOptimizer(
+            rt_optimizer = searchoptimization.AutomaticRTOptimizer(
                 self.config["search_initial"]["initial_rt_tolerance"],
-                self.calibration_manager,
-                self.com,
-                self.fdr_manager,
+                self,
             )
         if self.dia_data.has_ms1:
             if self.config["search"]["target_ms1_tolerance"] > 0:
-                self.ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
+                ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
                     self.config["search_initial"]["initial_ms1_tolerance"],
                     self.config["search"]["target_ms1_tolerance"],
-                    self.calibration_manager,
-                    self.com,
-                    self.fdr_manager,
+                    self,
                 )
             else:
-                self.ms1_optimizer = searchoptimization.AutomaticMS1Optimizer(
+                ms1_optimizer = searchoptimization.AutomaticMS1Optimizer(
                     self.config["search_initial"]["initial_ms1_tolerance"],
-                    self.calibration_manager,
-                    self.com,
-                    self.fdr_manager,
+                    self,
                 )
         else:
-            self.ms1_optimizer = None
+            ms1_optimizer = None
         if self.dia_data.has_mobility:
             if self.config["search"]["target_mobility_tolerance"] > 0:
-                self.mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
+                mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.config["search"]["target_mobility_tolerance"],
-                    self.calibration_manager,
-                    self.com,
-                    self.fdr_manager,
+                    self,
                 )
             else:
-                self.mobility_optimizer = searchoptimization.AutomaticMobilityOptimizer(
+                mobility_optimizer = searchoptimization.AutomaticMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.calibration_manager,
                     self.com,
                     self.fdr_manager,
                 )
         else:
-            self.mobility_optimizer = None
+            mobility_optimizer = None
 
-        self.reporter.log_string(
-            "A complete list of target tolerances has been specified. Targeted search parameter optimization will be performed.",
-            verbosity="info",
-        )
         optimizers = [
-            self.ms2_optimizer,
-            self.rt_optimizer,
-            self.ms1_optimizer,
-            self.mobility_optimizer,
+            ms2_optimizer,
+            rt_optimizer,
+            ms1_optimizer,
+            mobility_optimizer,
         ]
         targeted_optimizers = [
             [
@@ -489,13 +468,32 @@ def calibration(self):
 
         order_of_optimization = targeted_optimizers + automatic_optimizers
 
+        self.reporter.log_string(
+            "Starting initial classifier training and precursor identification",
+            verbosity="progress",
+        )
+
         self.extract_optimization_data(
-            self.config["calibration"]["recalibration_target"]
+            self.config["calibration"]["min_precursors_for_optimization"]
+        )
+
+        self.reporter.log_string(
+            "Target number of precursors found. Starting search parameter optimization.",
+            verbosity="progress",
         )
 
         for optimizers in order_of_optimization:
-            for current_step in range(self.config["calibration"]["max_epochs"]):
-                if np.all([optimizer.has_converged() for optimizer in optimizers]):
+            for current_step in range(self.config["calibration"]["max_steps"]):
+                if np.all([optimizer.has_converged for optimizer in optimizers]):
+                    self.reporter.log_string(
+                        f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
+                        verbosity="progress",
+                    )
+
+                    for optimizer in optimizers:
+                        if isinstance(optimizer, searchoptimization.AutomaticOptimizer):
+                            optimizer.plot()
+
                     break
                 batch_df = self.spectral_library._precursor_df[
                     self.spectral_library._precursor_df["elution_group_idx"].isin(
@@ -531,6 +529,22 @@ def calibration(self):
                 self.reporter.log_string(
                     "==============================================", verbosity="info"
                 )
+        self.reporter.log_string(
+            "Search parameter optimization finished. Values taken forward for search are:",
+            verbosity="progress",
+        )
+        self.reporter.log_string(
+            "==============================================", verbosity="progress"
+        )
+        for optimizers in order_of_optimization:
+            for optimizer in optimizers:
+                self.reporter.log_string(
+                    f"{optimizer.parameter_name:<15}: {self.com.__dict__[optimizer.parameter_name]:.4f}",
+                    verbosity="progress",
+                )
+        self.reporter.log_string(
+            "==============================================", verbosity="progress"
+        )
 
     def filter_dfs(self, precursor_df, fragments_df):
         precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.01]
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index ed9964b2..49752676 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -9,13 +9,13 @@
 import seaborn as sns
 
 # alphadia imports
-from alphadia.workflow import peptidecentric, reporting
+from alphadia.workflow import reporting
 
 
 class BaseOptimizer(ABC):
     def __init__(
         self,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class serves as a base class for organizing the search parameter optimization process, which defines the parameters used for search.
@@ -54,7 +54,7 @@ class AutomaticOptimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """This class automatically optimizes the search parameter and stores the progres of optimization in a dataframe, history_df.
@@ -74,6 +74,10 @@ def __init__(
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if self.has_converged:
+            self.reporter.log_string(
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
+                verbosity="progress",
+            )
             return
 
         new_row = pd.DataFrame(
@@ -204,7 +208,7 @@ def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """This class optimizes the search parameter until it reaches a user-specified target value.
@@ -284,7 +288,7 @@ class AutomaticRTOptimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class.
@@ -345,7 +349,7 @@ class AutomaticMS2Optimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
@@ -408,7 +412,7 @@ class AutomaticMS1Optimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class.
@@ -469,7 +473,7 @@ class AutomaticMobilityOptimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class.
@@ -533,7 +537,7 @@ def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class."""
@@ -550,7 +554,7 @@ def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class."""
@@ -567,7 +571,7 @@ def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class."""
@@ -584,7 +588,7 @@ def __init__(
         self,
         initial_parameter: float,
         target_parameter: float,
-        workflow: peptidecentric.PeptideCentricWorkflow,
+        workflow,
         **kwargs,
     ):
         """See base class."""
diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 8cfe2fa6..543870d9 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -114,9 +114,9 @@ test_cases:
         instrument: Lumos
       search:
         target_num_candidates: 3
-        target_ms1_tolerance: 4
+        target_ms1_tolerance: -1
         target_ms2_tolerance: -1
-        target_rt_tolerance: -1
+        target_rt_tolerance: 200
       search_initial:
         initial_num_candidates: 1
         initial_ms1_tolerance: 10

From 3b243244282fa2d8628447a061c2ad007eca330b Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 10:51:36 +0200
Subject: [PATCH 046/250] formatting

---
 alphadia/workflow/peptidecentric.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 00dbedd6..fb47991a 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -469,7 +469,7 @@ def calibration(self):
         order_of_optimization = targeted_optimizers + automatic_optimizers
 
         self.reporter.log_string(
-            "Starting initial classifier training and precursor identification",
+            "Starting initial classifier training and precursor identification.",
             verbosity="progress",
         )
 

From 09c0d707527c52064cfc23d1ebfeb6f7ff69a576 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 11:13:01 +0200
Subject: [PATCH 047/250] changed some TODOs and comments

---
 alphadia/workflow/searchoptimization.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 49752676..03aec284 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -72,7 +72,7 @@ def __init__(
         self.has_converged = False
 
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
-        """See base class. The TODO is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        """See base class. The feature is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if self.has_converged:
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
@@ -88,7 +88,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
                     ),  # Ensure float dtype
                     self.feature_name: self._get_feature_value(
                         precursors_df, fragments_df
-                    ),  # Ensure int dtype
+                    ),
                     "classifier_version": int(
                         self.workflow.fdr_manager.current_version
                     ),  # Ensure int dtype
@@ -283,8 +283,6 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
 
 class AutomaticRTOptimizer(AutomaticOptimizer):
-    """TODO Finish this optimizer"""
-
     def __init__(
         self,
         initial_parameter: float,
@@ -307,7 +305,7 @@ def __init__(
         super().__init__(initial_parameter, workflow, **kwargs)
 
     def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        """Optimization should stop if continued optimization of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
 
@@ -407,8 +405,6 @@ def _get_feature_value(
 
 
 class AutomaticMS1Optimizer(AutomaticOptimizer):
-    """TODO Finish this optimizer"""
-
     def __init__(
         self,
         initial_parameter: float,
@@ -431,7 +427,7 @@ def __init__(
         super().__init__(initial_parameter, workflow, **kwargs)
 
     def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
 
@@ -492,7 +488,7 @@ def __init__(
         super().__init__(initial_parameter, workflow, **kwargs)
 
     def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the TODO parameter is not improving the TODO feature value.
+        """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
 

From 60b884e7333ff6e89c9f53d484f986714aee0276 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 11:52:45 +0200
Subject: [PATCH 048/250] e2e now runs automatic calibration test

---
 .github/workflows/e2e_testing.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/e2e_testing.yml b/.github/workflows/e2e_testing.yml
index 0a6c083a..7198e790 100644
--- a/.github/workflows/e2e_testing.yml
+++ b/.github/workflows/e2e_testing.yml
@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         # test case name as defined in e2e_test_cases.yaml
-        test_case: [ "basic", "synchropasef", "astral", ]
+        test_case: [ "basic", "synchropasef", "astral", "astral_automatic_calibration", ]
     env:
       RUN_NAME: alphadia-${{github.sha}}-${{github.run_id}}-${{github.run_attempt}}
       BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

From 8351156ba1528a97402b4f60334d299bdf2e6743 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Tue, 30 Jul 2024 12:58:47 +0200
Subject: [PATCH 049/250] rename checkpoint to peptdeep_model_path

---
 alphadia/constants/default.yaml |  4 +++-
 alphadia/libtransform.py        | 20 ++++++++++----------
 alphadia/planning.py            |  4 ++--
 3 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 1e5119fe..515c06bd 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -41,7 +41,9 @@ library_prediction:
   # maximum charge state for predicted fragments
   max_fragment_charge: 2
   instrument: Lumos
-  checkpoint_folder_path: null
+
+  # set path for custom peptdeep model. If set to null, the default model will be used
+  peptdeep_model_path: null
 
 search:
   channel_filter: '0'
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index 0daade51..ad65e6ec 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -252,7 +252,7 @@ def __init__(
         fragment_mz: list[int] | None = None,
         nce: int = 25,
         instrument: str = "Lumos",
-        checkpoint_folder_path: str | None = None,
+        peptdeep_model_path: str | None = None,
         fragment_types: list[str] | None = None,
         max_fragment_charge: int = 2,
     ) -> None:
@@ -276,7 +276,7 @@ def __init__(
         instrument : str, optional
             Instrument type for prediction. Default is "Lumos". Must be a valid PeptDeep instrument.
 
-        checkpoint_folder_path : str, optional
+        peptdeep_model_path : str, optional
             Path to a folder containing PeptDeep models. If not provided, the default models will be used.
 
         fragment_types : List[str], optional
@@ -295,7 +295,7 @@ def __init__(
         self.nce = nce
         self.instrument = instrument
         self.mp_process_num = mp_process_num
-        self.checkpoint_folder_path = checkpoint_folder_path
+        self.peptdeep_model_path = peptdeep_model_path
 
         self.fragment_types = fragment_types
         self.max_fragment_charge = max_fragment_charge
@@ -313,17 +313,17 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
         device = utils.get_torch_device(self.use_gpu)
 
         model_mgr = ModelManager(device=device)
-        if self.checkpoint_folder_path is not None:
-            if not os.path.exists(self.checkpoint_folder_path):
+        if self.peptdeep_model_path is not None:
+            if not os.path.exists(self.peptdeep_model_path):
                 raise ValueError(
-                    f"PeptDeep model checkpoint folder {self.checkpoint_folder_path} does not exist"
+                    f"PeptDeep model checkpoint folder {self.peptdeep_model_path} does not exist"
                 )
 
-            logging.info(f"Loading PeptDeep models from {self.checkpoint_folder_path}")
+            logging.info(f"Loading PeptDeep models from {self.peptdeep_model_path}")
             model_mgr.load_external_models(
-                ms2_model_file=os.path.join(self.checkpoint_folder_path, "ms2.pth"),
-                rt_model_file=os.path.join(self.checkpoint_folder_path, "rt.pth"),
-                ccs_model_file=os.path.join(self.checkpoint_folder_path, "ccs.pth"),
+                ms2_model_file=os.path.join(self.peptdeep_model_path, "ms2.pth"),
+                rt_model_file=os.path.join(self.peptdeep_model_path, "rt.pth"),
+                ccs_model_file=os.path.join(self.peptdeep_model_path, "ccs.pth"),
             )
 
         model_mgr.nce = self.nce
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 50f81b46..bb96e503 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -226,8 +226,8 @@ def _parse_modifications(mod_str: str) -> list[str]:
                 nce=self.config["library_prediction"]["nce"],
                 instrument=self.config["library_prediction"]["instrument"],
                 mp_process_num=self.config["general"]["thread_count"],
-                checkpoint_folder_path=self.config["library_prediction"][
-                    "checkpoint_folder_path"
+                peptdeep_model_path=self.config["library_prediction"][
+                    "peptdeep_model_path"
                 ],
                 fragment_types=self.config["library_prediction"][
                     "fragment_types"

From 1170dcd596df4efbac38893f1ea214ae2e6640e1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 15:01:22 +0200
Subject: [PATCH 050/250] fixed bugs with classifier version and
 workflow.extraction()

---
 alphadia/workflow/peptidecentric.py     |  6 ++----
 alphadia/workflow/searchoptimization.py | 21 ++++++++++++++-------
 2 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index fb47991a..54bde250 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -512,6 +512,8 @@ def calibration(self):
                     features_df, fragments_df, self.com.classifier_version
                 )
 
+                self.log_precursor_df(precursors_df)
+
                 precursors_df_filtered, fragments_df_filtered = self.filter_dfs(
                     precursors_df, fragments_df
                 )
@@ -731,10 +733,6 @@ def extraction(self):
         self.com.fit(
             {
                 "num_candidates": self.config["search"]["target_num_candidates"],
-                "ms1_error": self.config["search"]["target_ms1_tolerance"],
-                "ms2_error": self.config["search"]["target_ms2_tolerance"],
-                "rt_error": self.config["search"]["target_rt_tolerance"],
-                "mobility_error": self.config["search"]["target_mobility_tolerance"],
                 "column_type": "calibrated",
             }
         )
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 03aec284..6475d91a 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -92,6 +92,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
                     "classifier_version": int(
                         self.workflow.fdr_manager.current_version
                     ),  # Ensure int dtype
+                    "score_cutoff": float(self.workflow.com.score_cutoff),
                 }
             ]
         )
@@ -107,9 +108,13 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             classifier_version_at_optimum = self.history_df["classifier_version"].loc[
                 index_of_optimum
             ]
+            score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[
+                index_of_optimum
+            ]
 
             self.workflow.com.fit({self.parameter_name: optimal_parameter})
             self.workflow.com.fit({"classifier_version": classifier_version_at_optimum})
+            self.workflow.com.fit({"score_cutoff": score_cutoff_at_optimum})
 
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
@@ -134,7 +139,6 @@ def plot(self):
         """Plot the optimization of the RT error parameter."""
         fig, ax = plt.subplots()
 
-        # Plot the vertical line
         ax.axvline(
             x=self.workflow.com.__dict__[self.parameter_name],
             ymin=0,
@@ -144,19 +148,19 @@ def plot(self):
             label=f"Optimal {self.parameter_name}",
         )
 
-        # Plot the line and scatter plot using Seaborn
         sns.lineplot(
-            x=self.history_df["parameter"],
-            y=self.history_df[self.feature_name],
+            data=self.history_df,
+            x="parameter",
+            y=self.feature_name,
             ax=ax,
         )
         sns.scatterplot(
-            x=self.history_df["parameter"],
-            y=self.history_df[self.feature_name],
+            data=self.history_df,
+            x="parameter",
+            y=self.feature_name,
             ax=ax,
         )
 
-        # Set labels and other properties
         ax.set_xlabel(self.parameter_name)
         ax.xaxis.set_inverted(True)
         ax.set_ylim(bottom=0, top=self.history_df[self.feature_name].max() * 1.1)
@@ -267,6 +271,9 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         )
         just_converged = self._check_convergence(new_parameter)
         self.workflow.com.fit({self.parameter_name: new_parameter})
+        self.workflow.com.fit(
+            {"classifier_version": self.workflow.fdr_manager.current_version}
+        )
 
         if just_converged:
             self.has_converged = True

From 599e3a206427fca2afb0f5f22f6ae33489e88bed Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 15:16:55 +0200
Subject: [PATCH 051/250] make precursor_df consistent

---
 alphadia/workflow/peptidecentric.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 54bde250..ddda447b 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -508,17 +508,17 @@ def calibration(self):
                     verbosity="progress",
                 )
 
-                precursors_df = self.fdr_correction(
+                precursor_df = self.fdr_correction(
                     features_df, fragments_df, self.com.classifier_version
                 )
 
-                self.log_precursor_df(precursors_df)
+                self.log_precursor_df(precursor_df)
 
-                precursors_df_filtered, fragments_df_filtered = self.filter_dfs(
-                    precursors_df, fragments_df
+                precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+                    precursor_df, fragments_df
                 )
 
-                self.recalibration(precursors_df_filtered, fragments_df_filtered)
+                self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
                 self.reporter.log_string(
                     "=== checking if optimization conditions were reached ===",
@@ -526,7 +526,7 @@ def calibration(self):
                 )
 
                 for optimizer in optimizers:
-                    optimizer.step(precursors_df_filtered, fragments_df_filtered)
+                    optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
                 self.reporter.log_string(
                     "==============================================", verbosity="info"

From 0878a7710f022738fda06a6aab709e2c63b44996 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 15:21:43 +0200
Subject: [PATCH 052/250] formatting

---
 alphadia/workflow/peptidecentric.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index e38fa06e..34959fe9 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -15,7 +15,6 @@
 
 # alphadia imports
 from alphadia import fragcomp, plexscoring, utils
-from alphadia.exceptions import NoRecalibrationTargetError
 from alphadia.peakgroup import search
 from alphadia.workflow import base, manager, searchoptimization
 

From 1becbc9515b2509c33681f98250495b41e39b5ba Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 17:49:16 +0200
Subject: [PATCH 053/250] improved logging

---
 alphadia/workflow/peptidecentric.py     | 10 +++++++++-
 alphadia/workflow/searchoptimization.py |  4 ++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 34959fe9..f18f2fe0 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -478,7 +478,10 @@ def calibration(self):
 
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_steps"]):
-                if np.all([optimizer.has_converged for optimizer in optimizers]):
+                if (
+                    np.all([optimizer.has_converged for optimizer in optimizers])
+                    and len(optimizers) > 0
+                ):
                     self.reporter.log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
                         verbosity="progress",
@@ -506,6 +509,11 @@ def calibration(self):
                     features_df, fragments_df, self.com.classifier_version
                 )
 
+                self.reporter.log_string(
+                    f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+                    verbosity="progress",
+                )
+
                 self.log_precursor_df(precursor_df)
 
                 precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 6475d91a..c5709953 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -117,7 +117,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.workflow.com.fit({"score_cutoff": score_cutoff_at_optimum})
 
             self.reporter.log_string(
-                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
@@ -131,7 +131,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             self.workflow.com.fit({self.parameter_name: new_parameter})
 
             self.reporter.log_string(
-                f"❌ {self.parameter_name:<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.workflow.com.__dict__[self.parameter_name]}.",
+                f"❌ {self.parameter_name:<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.workflow.com.__dict__[self.parameter_name]:.4f}.",
                 verbosity="progress",
             )
 

From b37e70c6b5a677143e581851e88ce50444c8d194 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 30 Jul 2024 22:18:50 +0200
Subject: [PATCH 054/250] remove empty list in  completely automatic
 calibration

---
 alphadia/workflow/peptidecentric.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index f18f2fe0..ecef0a27 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -358,7 +358,15 @@ def extract_optimization_data(self, target):
                 f"=== Step {current_step}, extracted {len(feature_df)} precursors and {len(fragment_df)} fragments ===",
                 verbosity="progress",
             )
-            precursor_df = self.fdr_correction(features_df, fragments_df)
+
+            precursor_df = self.fdr_correction(
+                features_df, fragments_df, self.com.classifier_version
+            )
+
+            self.reporter.log_string(
+                f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+                verbosity="info",
+            )
 
             precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
@@ -460,7 +468,11 @@ def calibration(self):
             if isinstance(optimizer, searchoptimization.AutomaticOptimizer)
         ]
 
-        order_of_optimization = targeted_optimizers + automatic_optimizers
+        order_of_optimization = (
+            targeted_optimizers + automatic_optimizers
+            if any(targeted_optimizers)
+            else automatic_optimizers
+        )
 
         self.reporter.log_string(
             "Starting initial classifier training and precursor identification.",
@@ -478,10 +490,7 @@ def calibration(self):
 
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_steps"]):
-                if (
-                    np.all([optimizer.has_converged for optimizer in optimizers])
-                    and len(optimizers) > 0
-                ):
+                if np.all([optimizer.has_converged for optimizer in optimizers]):
                     self.reporter.log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
                         verbosity="progress",
@@ -511,7 +520,7 @@ def calibration(self):
 
                 self.reporter.log_string(
                     f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
-                    verbosity="progress",
+                    verbosity="info",
                 )
 
                 self.log_precursor_df(precursor_df)

From 0cb800f113f83659bef6cbc31fd1931653e91ed3 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 31 Jul 2024 09:56:44 +0200
Subject: [PATCH 055/250] enforce minimum training iterations and backtrack for
 kernel

---
 alphadia/constants/default.yaml         |  4 ++--
 alphadia/workflow/peptidecentric.py     |  2 +-
 alphadia/workflow/searchoptimization.py | 14 +++++++++++++-
 3 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index d59ec2f0..b0597ad5 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -95,8 +95,8 @@ search_advanced:
 
 calibration:
 
-  # minimum number of times (epochs) the updated calibration target has to been passed
-  min_epochs: 3
+  # minimum number of times the classifier is trained.
+  min_training_iterations: 3
 
   # Number of precursors searched and scored per batch
   batch_size: 8000
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index ecef0a27..3be6fe5d 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -484,7 +484,7 @@ def calibration(self):
         )
 
         self.reporter.log_string(
-            "Target number of precursors found. Starting search parameter optimization.",
+            "Required number of precursors found. Starting search parameter optimization.",
             verbosity="progress",
         )
 
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index c5709953..6a874bd1 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -93,6 +93,8 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
                         self.workflow.fdr_manager.current_version
                     ),  # Ensure int dtype
                     "score_cutoff": float(self.workflow.com.score_cutoff),
+                    "fwhm_rt": float(self.workflow.com.fwhm_rt),
+                    "fwhm_mobility": float(self.workflow.com.fwhm_mobility),
                 }
             ]
         )
@@ -111,10 +113,16 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[
                 index_of_optimum
             ]
+            fwhm_rt_at_optimum = self.history_df["fwhm_rt"].loc[index_of_optimum]
+            fwhm_mobility_at_optimum = self.history_df["fwhm_mobility"].loc[
+                index_of_optimum
+            ]
 
             self.workflow.com.fit({self.parameter_name: optimal_parameter})
             self.workflow.com.fit({"classifier_version": classifier_version_at_optimum})
             self.workflow.com.fit({"score_cutoff": score_cutoff_at_optimum})
+            self.workflow.com.fit({"fwhm_rt": fwhm_rt_at_optimum})
+            self.workflow.com.fit({"fwhm_mobility": fwhm_mobility_at_optimum})
 
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
@@ -241,7 +249,11 @@ def _check_convergence(self, proposed_parameter):
             The proposed parameter for the next round of optimization.
         """
 
-        return proposed_parameter <= self.target_parameter
+        return (
+            proposed_parameter <= self.target_parameter
+            and self.workflow.current_version
+            > self.workflow.config["min_training_iterations"]
+        )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is

From 7d3f930d6081d2687d3daa419600ede9de53a8a3 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 31 Jul 2024 10:54:27 +0200
Subject: [PATCH 056/250] impose minimum iterations during extraction of
 optimization data

---
 alphadia/workflow/peptidecentric.py     | 19 ++++++++++++++-----
 alphadia/workflow/searchoptimization.py |  6 +-----
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 3be6fe5d..44151f7d 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -314,7 +314,7 @@ def get_batch_plan(self):
 
         return plan
 
-    def extract_optimization_data(self, target):
+    def extract_optimization_data(self, target, min_iterations):
         """Search parameter optimization (i.e. refinement of tolerances for RT, MS2, etc.) is performed on a subset of the elution groups in the spectral library.
         The number of elution groups which must be searched to get a sufficiently large number for robust calibration varies depending the library used and the data.
         This function searches an increasing number of elution groups until a sufficient number (determined by target) of precursors are identified at 1% FDR.
@@ -371,13 +371,21 @@ def extract_optimization_data(self, target):
             precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
             self.reporter.log_string(
-                f"=== checking if minimum number of precursors for optimization found yet; minimum number is {target} ===",
+                f"=== Checking if minimum number of precursors for optimization found yet; minimum number is {target} ===",
                 verbosity="progress",
             )
 
             self.log_precursor_df(precursor_df)
 
-            if precursors_01FDR > target:
+            self.reporter.log_string(
+                f"=== Classifier has been trained for {self.fdr_manager.current_version + 1} iteration(s); minimum number is {min_iterations} ===",
+                verbosity="progress",
+            )
+
+            if (
+                precursors_01FDR > target
+                and self.fdr_manager.current_version >= min_iterations - 1
+            ):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
                 break
 
@@ -480,11 +488,12 @@ def calibration(self):
         )
 
         self.extract_optimization_data(
-            self.config["calibration"]["min_precursors_for_optimization"]
+            self.config["calibration"]["min_precursors_for_optimization"],
+            self.config["calibration"]["min_training_iterations"],
         )
 
         self.reporter.log_string(
-            "Required number of precursors found. Starting search parameter optimization.",
+            "Required number of precursors found and required number of training iterations performed. Starting search parameter optimization.",
             verbosity="progress",
         )
 
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 6a874bd1..9c8d392a 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -249,11 +249,7 @@ def _check_convergence(self, proposed_parameter):
             The proposed parameter for the next round of optimization.
         """
 
-        return (
-            proposed_parameter <= self.target_parameter
-            and self.workflow.current_version
-            > self.workflow.config["min_training_iterations"]
-        )
+        return proposed_parameter <= self.target_parameter
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is

From 4542a7eafcfb8a5aaf8825517b72bca294d7fb13 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 31 Jul 2024 14:58:11 +0200
Subject: [PATCH 057/250] introduce extra optimization before loop and enforce
 minimum steps for optimization

---
 alphadia/constants/default.yaml     |  3 +++
 alphadia/workflow/peptidecentric.py | 39 ++++++++++++++++++++++++-----
 2 files changed, 36 insertions(+), 6 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index b0597ad5..9a4202f2 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -107,6 +107,9 @@ calibration:
   # the maximum number of steps that a given optimizer is permitted to take
   max_steps: 20
 
+  # the maximum number of steps that a given optimizer is permitted to take
+  min_steps: 3
+
   # TODO: remove this parameter
   final_full_calibration: False
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 44151f7d..8768b6c5 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -389,8 +389,10 @@ def extract_optimization_data(self, target, min_iterations):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
                 break
 
-        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
-        self.com.fit({"classifier_version": self.fdr_manager.current_version})
+        return self.elution_group_order[:final_stop_index], precursor_df, fragments_df
+
+    #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
+    #        self.com.fit({"classifier_version": self.fdr_manager.current_version})
 
     def calibration(self):
         if (
@@ -487,19 +489,38 @@ def calibration(self):
             verbosity="progress",
         )
 
-        self.extract_optimization_data(
-            self.config["calibration"]["min_precursors_for_optimization"],
-            self.config["calibration"]["min_training_iterations"],
+        self.eg_idxes_for_calibration, precursor_df, fragments_df = (
+            self.extract_optimization_data(
+                self.config["calibration"]["min_precursors_for_optimization"],
+                self.config["calibration"]["min_training_iterations"],
+            )
+        )
+
+        self.com.fit({"classifier_version": self.fdr_manager.current_version})
+
+        precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+            precursor_df, fragments_df
         )
 
+        self.recalibration(precursor_df_filtered, fragments_df_filtered)
+
         self.reporter.log_string(
             "Required number of precursors found and required number of training iterations performed. Starting search parameter optimization.",
             verbosity="progress",
         )
 
+        # Perform an initial optimization step based on the extracted data to update the initial search parameters.
+        # This ensures that the classifier is trained at least once prior to the end of optimization, even if the min_steps parameter is 0.
+        for optimizers in order_of_optimization:
+            for optimizer in optimizers:
+                optimizer.step(precursor_df_filtered, fragments_df_filtered)
+
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_steps"]):
-                if np.all([optimizer.has_converged for optimizer in optimizers]):
+                if (
+                    np.all([optimizer.has_converged for optimizer in optimizers])
+                    and current_step > self.config["calibration"]["min_steps"] - 1
+                ):
                     self.reporter.log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
                         verbosity="progress",
@@ -551,6 +572,12 @@ def calibration(self):
                 self.reporter.log_string(
                     "==============================================", verbosity="info"
                 )
+
+                self.reporter.log_string(
+                    f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config["calibration"]["min_steps"]} ===",
+                    verbosity="progress",
+                )
+
         self.reporter.log_string(
             "Search parameter optimization finished. Values taken forward for search are:",
             verbosity="progress",

From eba03f71ff3f3aea3d7016c2f064c706d0b1f964 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 31 Jul 2024 16:04:57 +0200
Subject: [PATCH 058/250] CHORE: update slurm install instructions

---
 docs/installation.md | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 329f9104..d19eb1b8 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -165,33 +165,34 @@ docker run -v $DATA_FOLDER:/app/data/ --rm alphadia-docker
 
 ### 1. Set up environment
 Check if conda is available on your cluster. If not, install it, or, if provided, load the corresponding module, e.g.
-`module load anaconda/3/2023.03`.
+`module load anaconda/3/2023.03`. You might be asked to run `conda init bash` to initialize conda.
 
 Create and activate a new conda environment
 ```bash
-conda create -n alphadia python=3.11
+conda create -n alphadia -y
 conda activate alphadia
 ```
 ### 2. Installing mono
 Install mono to support reading proprietary vendor formats like Thermo `.raw` files.
 
-Please make sure you include the conda-forge channel
+
+Install python into your new environment
 ```bash
-conda install python=3.11 -c conda-forge
+conda install python=3.11 -y
 ```
 Then install mono by
 ```bash
-conda install mono -c conda-forge
+conda install mono=6.12.0.182 -c anaconda -y
 ```
 
-Make sure mono is installed by running
+Test that mono is correctly installed by running
 ```bash
 mono --version
 ```
 
-Make sure the output looks something like this:
+The output should look something like this:
 ```
-Mono JIT compiler version 6.12.0.90 (tarball Fri Mar  5 04:37:13 UTC 2021)
+Mono JIT compiler version 6.12.0.182 (tarball Mon Jun 26 17:39:19 UTC 2023)
 Copyright (C) 2002-2014 Novell, Inc, Xamarin Inc and Contributors. www.mono-project.com
 	TLS:           __thread
 	SIGSEGV:       altstack
@@ -212,7 +213,7 @@ Install alphaDIA using pip:
 ```bash
 pip install "alphadia[stable]"
 ```
-Afterwards, verify the alphaDIA installation by running:
+Afterward, verify the alphaDIA installation by running:
 `alphadia --version` which should output the current version.
 
 ### Notes on running alphaDIA as part of automated workflows

From 087a85642ee0f8726d4486558d6979392395dc97 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 31 Jul 2024 16:34:28 +0200
Subject: [PATCH 059/250] check min_steps as part of _check_convergence method

---
 alphadia/workflow/peptidecentric.py     | 24 +++++++++++++++++-------
 alphadia/workflow/searchoptimization.py | 23 ++++++++++++++++++-----
 2 files changed, 35 insertions(+), 12 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 8768b6c5..b195c6b4 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -509,6 +509,10 @@ def calibration(self):
             verbosity="progress",
         )
 
+        self.reporter.log_string(
+            "Perform initial optimization on extracted data.",
+            verbosity="info",
+        )
         # Perform an initial optimization step based on the extracted data to update the initial search parameters.
         # This ensures that the classifier is trained at least once prior to the end of optimization, even if the min_steps parameter is 0.
         for optimizers in order_of_optimization:
@@ -517,10 +521,7 @@ def calibration(self):
 
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_steps"]):
-                if (
-                    np.all([optimizer.has_converged for optimizer in optimizers])
-                    and current_step > self.config["calibration"]["min_steps"] - 1
-                ):
+                if np.all([optimizer.has_converged for optimizer in optimizers]):
                     self.reporter.log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
                         verbosity="progress",
@@ -567,14 +568,16 @@ def calibration(self):
                 )
 
                 for optimizer in optimizers:
-                    optimizer.step(precursor_df_filtered, fragments_df_filtered)
+                    optimizer.step(
+                        precursor_df_filtered, fragments_df_filtered, current_step
+                    )
 
                 self.reporter.log_string(
                     "==============================================", verbosity="info"
                 )
 
                 self.reporter.log_string(
-                    f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config["calibration"]["min_steps"]} ===",
+                    f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
                     verbosity="progress",
                 )
 
@@ -794,7 +797,14 @@ def extraction(self):
             apply_cutoff=True,
         )
 
-        precursor_df = self.fdr_correction(features_df, fragments_df)
+        self.reporter.log_string(
+            f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+            verbosity="info",
+        )
+
+        precursor_df = self.fdr_correction(
+            features_df, fragments_df, self.com.classifier_version
+        )
 
         precursor_df = precursor_df[precursor_df["qval"] <= self.config["fdr"]["fdr"]]
 
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 9c8d392a..264a2ca5 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -240,16 +240,24 @@ def __init__(
         self.target_parameter = target_parameter
         self.has_converged = False
 
-    def _check_convergence(self, proposed_parameter):
+    def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
         """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
 
         Parameters
         ----------
         proposed_parameter: float
             The proposed parameter for the next round of optimization.
+
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
+
+
         """
 
-        return proposed_parameter <= self.target_parameter
+        return (
+            proposed_parameter <= self.target_parameter
+            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
@@ -265,7 +273,12 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             self.target_parameter,
         )
 
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+    def step(
+        self,
+        precursors_df: pd.DataFrame,
+        fragments_df: pd.DataFrame,
+        current_step: int = -1,
+    ):
         """See base class."""
         if self.has_converged:
             self.reporter.log_string(
@@ -277,7 +290,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         new_parameter = self._propose_new_parameter(
             precursors_df if self.estimator_group_name == "precursor" else fragments_df
         )
-        just_converged = self._check_convergence(new_parameter)
+        just_converged = self._check_convergence(new_parameter, current_step)
         self.workflow.com.fit({self.parameter_name: new_parameter})
         self.workflow.com.fit(
             {"classifier_version": self.workflow.fdr_manager.current_version}
@@ -292,7 +305,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         else:
             self.reporter.log_string(
-                f"❌ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} > {self.target_parameter:.4f}",
+                f"❌ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} > {self.target_parameter:.4f} or insufficient steps taken.",
                 verbosity="progress",
             )
 

From 70dba731bb1da8c470ff0665503b0a44e397cb5c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 31 Jul 2024 20:06:28 +0200
Subject: [PATCH 060/250] add changes to automatic optimizers

---
 alphadia/workflow/peptidecentric.py     |  2 +-
 alphadia/workflow/searchoptimization.py | 37 +++++++++++++++++++++----
 2 files changed, 32 insertions(+), 7 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index b195c6b4..8a71513b 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -510,7 +510,7 @@ def calibration(self):
         )
 
         self.reporter.log_string(
-            "Perform initial optimization on extracted data.",
+            "=== Performing initial optimization on extracted data. ===",
             verbosity="info",
         )
         # Perform an initial optimization step based on the extracted data to update the initial search parameters.
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/searchoptimization.py
index 264a2ca5..05837a44 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/searchoptimization.py
@@ -71,7 +71,12 @@ def __init__(
         self.workflow.com.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
 
-    def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+    def step(
+        self,
+        precursors_df: pd.DataFrame,
+        fragments_df: pd.DataFrame,
+        current_step: int = -1,
+    ):
         """See base class. The feature is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if self.has_converged:
             self.reporter.log_string(
@@ -99,7 +104,7 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
             ]
         )
         self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-        just_converged = self._check_convergence()
+        just_converged = self._check_convergence(current_step)
 
         if just_converged:
             self.has_converged = True
@@ -332,7 +337,7 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, **kwargs)
 
-    def _check_convergence(self):
+    def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued optimization of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
@@ -341,6 +346,10 @@ def _check_convergence(self):
         -----
             Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
+        Parameters
+        ----------
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
 
@@ -350,6 +359,7 @@ def _check_convergence(self):
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
+            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -393,7 +403,7 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, **kwargs)
 
-    def _check_convergence(self):
+    def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
@@ -402,6 +412,10 @@ def _check_convergence(self):
         -----
             Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
+        Parameters
+        ----------
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
 
@@ -411,6 +425,7 @@ def _check_convergence(self):
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
+            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -454,7 +469,7 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, **kwargs)
 
-    def _check_convergence(self):
+    def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
@@ -463,6 +478,10 @@ def _check_convergence(self):
         -----
             Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
+        Parameters
+        ----------
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
 
@@ -472,6 +491,7 @@ def _check_convergence(self):
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
+            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -515,7 +535,7 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, **kwargs)
 
-    def _check_convergence(self):
+    def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
@@ -524,6 +544,10 @@ def _check_convergence(self):
         -----
             Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
+        Parameters
+        ----------
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
 
@@ -533,6 +557,7 @@ def _check_convergence(self):
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
+            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):

From e6987c1250bdefaea94219b93b73f6d6dfab2f97 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 1 Aug 2024 14:12:38 +0200
Subject: [PATCH 061/250] improved names and formatting

---
 alphadia/constants/default.yaml               |  6 +-
 ...{searchoptimization.py => optimization.py} | 70 ++++++++------
 alphadia/workflow/peptidecentric.py           | 91 +++++++++----------
 tests/unit_tests/test_workflow.py             |  4 +-
 4 files changed, 91 insertions(+), 80 deletions(-)
 rename alphadia/workflow/{searchoptimization.py => optimization.py} (92%)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 9a4202f2..390c2862 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -95,14 +95,14 @@ search_advanced:
 
 calibration:
 
-  # minimum number of times the classifier is trained.
-  min_training_iterations: 3
+  # minimum number of steps taken during the optimization lock (during which the elution groups used for optimization are extracted)
+  optimization_lock_min_steps: 3
 
   # Number of precursors searched and scored per batch
   batch_size: 8000
 
   # minimum number of precursors to be found before search parameter optimization begins
-  min_precursors_for_optimization: 200
+  optimization_lock_target: 200
 
   # the maximum number of steps that a given optimizer is permitted to take
   max_steps: 20
diff --git a/alphadia/workflow/searchoptimization.py b/alphadia/workflow/optimization.py
similarity index 92%
rename from alphadia/workflow/searchoptimization.py
rename to alphadia/workflow/optimization.py
index 05837a44..7fa6283e 100644
--- a/alphadia/workflow/searchoptimization.py
+++ b/alphadia/workflow/optimization.py
@@ -49,13 +49,25 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         """
         pass
 
+    @abstractmethod
+    def plot(self):
+        """
+        This method plots relevant information about optimization of the search parameter.
+
+        Notes
+        -----
+            This can be left blank if there is nothing of interest to plot.
+
+        """
+        pass
+
 
 class AutomaticOptimizer(BaseOptimizer):
     def __init__(
         self,
         initial_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class automatically optimizes the search parameter and stores the progres of optimization in a dataframe, history_df.
 
@@ -66,7 +78,7 @@ def __init__(
 
 
         """
-        super().__init__(workflow, **kwargs)
+        super().__init__(workflow, reporter)
         self.history_df = pd.DataFrame()
         self.workflow.com.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
@@ -88,18 +100,14 @@ def step(
         new_row = pd.DataFrame(
             [
                 {
-                    "parameter": float(
-                        self.workflow.com.__dict__[self.parameter_name]
-                    ),  # Ensure float dtype
+                    "parameter": self.workflow.com.__dict__[self.parameter_name],
                     self.feature_name: self._get_feature_value(
                         precursors_df, fragments_df
                     ),
-                    "classifier_version": int(
-                        self.workflow.fdr_manager.current_version
-                    ),  # Ensure int dtype
-                    "score_cutoff": float(self.workflow.com.score_cutoff),
-                    "fwhm_rt": float(self.workflow.com.fwhm_rt),
-                    "fwhm_mobility": float(self.workflow.com.fwhm_mobility),
+                    "classifier_version": self.workflow.fdr_manager.current_version,
+                    "score_cutoff": self.workflow.com.score_cutoff,
+                    "fwhm_rt": self.workflow.com.fwhm_rt,
+                    "fwhm_mobility": self.workflow.com.fwhm_mobility,
                 }
             ]
         )
@@ -226,7 +234,7 @@ def __init__(
         initial_parameter: float,
         target_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class optimizes the search parameter until it reaches a user-specified target value.
 
@@ -240,7 +248,7 @@ def __init__(
             Optimization will stop when this parameter is reached.
 
         """
-        super().__init__(workflow, **kwargs)
+        super().__init__(workflow, reporter)
         self.workflow.com.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.has_converged = False
@@ -314,13 +322,17 @@ def step(
                 verbosity="progress",
             )
 
+    def plot(self):
+        """Empty method for consistency with AutomaticOptimizer."""
+        pass
+
 
 class AutomaticRTOptimizer(AutomaticOptimizer):
     def __init__(
         self,
         initial_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class.
 
@@ -335,7 +347,7 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_count"
-        super().__init__(initial_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, workflow, reporter)
 
     def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued optimization of the parameter is not improving the TODO feature value.
@@ -386,7 +398,7 @@ def __init__(
         self,
         initial_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
 
@@ -401,7 +413,7 @@ def __init__(
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
         self.feature_name = "precursor_count"
-        super().__init__(initial_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, workflow, reporter)
 
     def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
@@ -452,7 +464,7 @@ def __init__(
         self,
         initial_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class.
 
@@ -467,7 +479,7 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
         self.feature_name = "precursor_count"
-        super().__init__(initial_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, workflow, reporter)
 
     def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
@@ -518,7 +530,7 @@ def __init__(
         self,
         initial_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class.
 
@@ -533,7 +545,7 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
         self.feature_name = "precursor_count"
-        super().__init__(initial_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, workflow, reporter)
 
     def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
@@ -587,13 +599,13 @@ def __init__(
         initial_parameter: float,
         target_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class."""
         self.parameter_name = "rt_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
-        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, target_parameter, workflow, reporter)
 
 
 class TargetedMS2Optimizer(TargetedOptimizer):
@@ -604,13 +616,13 @@ def __init__(
         initial_parameter: float,
         target_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class."""
         self.parameter_name = "ms2_error"
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
-        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, target_parameter, workflow, reporter)
 
 
 class TargetedMS1Optimizer(TargetedOptimizer):
@@ -621,13 +633,13 @@ def __init__(
         initial_parameter: float,
         target_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class."""
         self.parameter_name = "ms1_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
-        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, target_parameter, workflow, reporter)
 
 
 class TargetedMobilityOptimizer(TargetedOptimizer):
@@ -638,10 +650,10 @@ def __init__(
         initial_parameter: float,
         target_parameter: float,
         workflow,
-        **kwargs,
+        reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
         """See base class."""
         self.parameter_name = "mobility_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
-        super().__init__(initial_parameter, target_parameter, workflow, **kwargs)
+        super().__init__(initial_parameter, target_parameter, workflow, reporter)
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 8a71513b..98ed982c 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -16,7 +16,7 @@
 # alphadia imports
 from alphadia import fragcomp, plexscoring, utils
 from alphadia.peakgroup import search
-from alphadia.workflow import base, manager, searchoptimization
+from alphadia.workflow import base, manager, optimization
 
 logger = logging.getLogger()
 
@@ -314,17 +314,13 @@ def get_batch_plan(self):
 
         return plan
 
-    def extract_optimization_data(self, target, min_iterations):
+    def get_optimization_lock(self):
         """Search parameter optimization (i.e. refinement of tolerances for RT, MS2, etc.) is performed on a subset of the elution groups in the spectral library.
         The number of elution groups which must be searched to get a sufficiently large number for robust calibration varies depending the library used and the data.
-        This function searches an increasing number of elution groups until a sufficient number (determined by target) of precursors are identified at 1% FDR.
+        This function searches an increasing number of elution groups until a sufficient number of precursors are identified at 1% FDR and a sufficient number of steps have been taken.
+        The values deemed sufficient are specified in by "optimization_lock_target" and "optmization_lock_min_steps" in the config.
         It then returns the elution group indexes which will be used to find the data in the spectral library for search parameter optimization.
 
-        Parameters
-        ----------
-
-        target : int
-            The number of precursors which must be identified at 1% FDR to stop the extraction.
 
         """
 
@@ -371,20 +367,22 @@ def extract_optimization_data(self, target, min_iterations):
             precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
             self.reporter.log_string(
-                f"=== Checking if minimum number of precursors for optimization found yet; minimum number is {target} ===",
+                f"=== Checking if minimum number of precursors for optimization found yet; minimum number is {self.config['calibration']['optimization_lock_target']} ===",
                 verbosity="progress",
             )
 
             self.log_precursor_df(precursor_df)
 
             self.reporter.log_string(
-                f"=== Classifier has been trained for {self.fdr_manager.current_version + 1} iteration(s); minimum number is {min_iterations} ===",
+                f"=== Classifier has been trained for {self.fdr_manager.current_version + 1} iteration(s); minimum number is {self.config['calibration']['optimization_lock_min_steps']} ===",
                 verbosity="progress",
             )
 
             if (
-                precursors_01FDR > target
-                and self.fdr_manager.current_version >= min_iterations - 1
+                precursors_01FDR
+                > self.config["calibration"]["optimization_lock_target"]
+                and self.fdr_manager.current_version
+                >= self.config["calibration"]["optimization_lock_min_steps"] - 1
             ):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
                 break
@@ -394,49 +392,39 @@ def extract_optimization_data(self, target, min_iterations):
     #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
     #        self.com.fit({"classifier_version": self.fdr_manager.current_version})
 
-    def calibration(self):
-        if (
-            self.calibration_manager.is_fitted
-            and self.calibration_manager.is_loaded_from_file
-        ):
-            self.reporter.log_string(
-                "Skipping calibration as existing calibration was found",
-                verbosity="progress",
-            )
-            return
-
+    def get_optimizers(self):
         if self.config["search"]["target_ms2_tolerance"] > 0:
-            ms2_optimizer = searchoptimization.TargetedMS2Optimizer(
+            ms2_optimizer = optimization.TargetedMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
                 self.config["search"]["target_ms2_tolerance"],
                 self,
             )
         else:
-            ms2_optimizer = searchoptimization.AutomaticMS2Optimizer(
+            ms2_optimizer = optimization.AutomaticMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
                 self,
             )
 
         if self.config["search"]["target_rt_tolerance"] > 0:
-            rt_optimizer = searchoptimization.TargetedRTOptimizer(
+            rt_optimizer = optimization.TargetedRTOptimizer(
                 self.config["search_initial"]["initial_rt_tolerance"],
                 self.config["search"]["target_rt_tolerance"],
                 self,
             )
         else:
-            rt_optimizer = searchoptimization.AutomaticRTOptimizer(
+            rt_optimizer = optimization.AutomaticRTOptimizer(
                 self.config["search_initial"]["initial_rt_tolerance"],
                 self,
             )
         if self.dia_data.has_ms1:
             if self.config["search"]["target_ms1_tolerance"] > 0:
-                ms1_optimizer = searchoptimization.TargetedMS1Optimizer(
+                ms1_optimizer = optimization.TargetedMS1Optimizer(
                     self.config["search_initial"]["initial_ms1_tolerance"],
                     self.config["search"]["target_ms1_tolerance"],
                     self,
                 )
             else:
-                ms1_optimizer = searchoptimization.AutomaticMS1Optimizer(
+                ms1_optimizer = optimization.AutomaticMS1Optimizer(
                     self.config["search_initial"]["initial_ms1_tolerance"],
                     self,
                 )
@@ -444,13 +432,13 @@ def calibration(self):
             ms1_optimizer = None
         if self.dia_data.has_mobility:
             if self.config["search"]["target_mobility_tolerance"] > 0:
-                mobility_optimizer = searchoptimization.TargetedMobilityOptimizer(
+                mobility_optimizer = optimization.TargetedMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.config["search"]["target_mobility_tolerance"],
                     self,
                 )
             else:
-                mobility_optimizer = searchoptimization.AutomaticMobilityOptimizer(
+                mobility_optimizer = optimization.AutomaticMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.calibration_manager,
                     self.com,
@@ -469,13 +457,13 @@ def calibration(self):
             [
                 optimizer
                 for optimizer in optimizers
-                if isinstance(optimizer, searchoptimization.TargetedOptimizer)
+                if isinstance(optimizer, optimization.TargetedOptimizer)
             ]
         ]
         automatic_optimizers = [
             [optimizer]
             for optimizer in optimizers
-            if isinstance(optimizer, searchoptimization.AutomaticOptimizer)
+            if isinstance(optimizer, optimization.AutomaticOptimizer)
         ]
 
         order_of_optimization = (
@@ -484,20 +472,33 @@ def calibration(self):
             else automatic_optimizers
         )
 
+        return order_of_optimization
+
+    def calibration(self):
+        if (
+            self.calibration_manager.is_fitted
+            and self.calibration_manager.is_loaded_from_file
+        ):
+            self.reporter.log_string(
+                "Skipping calibration as existing calibration was found",
+                verbosity="progress",
+            )
+            return
+
+        order_of_optimization = self.get_optimizers()
+
         self.reporter.log_string(
             "Starting initial classifier training and precursor identification.",
             verbosity="progress",
         )
 
-        self.eg_idxes_for_calibration, precursor_df, fragments_df = (
-            self.extract_optimization_data(
-                self.config["calibration"]["min_precursors_for_optimization"],
-                self.config["calibration"]["min_training_iterations"],
-            )
+        eg_idxes_for_calibration, precursor_df, fragments_df = (
+            self.get_optimization_lock()
         )
 
         self.com.fit({"classifier_version": self.fdr_manager.current_version})
 
+        # Perform a first recalibration on the optimization lock.
         precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
             precursor_df, fragments_df
         )
@@ -513,12 +514,13 @@ def calibration(self):
             "=== Performing initial optimization on extracted data. ===",
             verbosity="info",
         )
-        # Perform an initial optimization step based on the extracted data to update the initial search parameters.
-        # This ensures that the classifier is trained at least once prior to the end of optimization, even if the min_steps parameter is 0.
+
         for optimizers in order_of_optimization:
             for optimizer in optimizers:
                 optimizer.step(precursor_df_filtered, fragments_df_filtered)
+        # End of first recalibration
 
+        # Start of optimization/recalibration loop
         for optimizers in order_of_optimization:
             for current_step in range(self.config["calibration"]["max_steps"]):
                 if np.all([optimizer.has_converged for optimizer in optimizers]):
@@ -528,13 +530,12 @@ def calibration(self):
                     )
 
                     for optimizer in optimizers:
-                        if isinstance(optimizer, searchoptimization.AutomaticOptimizer):
-                            optimizer.plot()
+                        optimizer.plot()
 
                     break
                 batch_df = self.spectral_library._precursor_df[
                     self.spectral_library._precursor_df["elution_group_idx"].isin(
-                        self.eg_idxes_for_calibration
+                        eg_idxes_for_calibration
                     )
                 ]
 
@@ -623,9 +624,7 @@ def filter_dfs(self, precursor_df, fragments_df):
             ),
             max_fragments,
         )
-        fragments_df_filtered = fragments_df_filtered.iloc[
-            :stop_rank
-        ]  # QUESTION: Should this raise an exception if the length of fragments_df_full is less than min_fragments?
+        fragments_df_filtered = fragments_df_filtered.iloc[:stop_rank]
 
         self.reporter.log_string(
             f"fragments_df_filtered: {len(fragments_df_filtered)}", verbosity="info"
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 1729a165..3337859e 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -13,7 +13,7 @@
 from alphadia.calibration.models import LOESSRegression
 from alphadia.calibration.property import Calibration
 from alphadia.fdrexperimental import BinaryClassifierLegacyNewBatching
-from alphadia.workflow import base, manager, searchoptimization
+from alphadia.workflow import base, manager, optimization
 
 
 def test_base_manager():
@@ -393,7 +393,7 @@ def ms2_optimizer_test():
     test_dict = defaultdict(list)
     test_dict["var"] = list(range(100))
 
-    ms2_optimizer = searchoptimization.MS2Optimizer(
+    ms2_optimizer = optimization.MS2Optimizer(
         100, calibration_manager, optimization_manager, fdr_manager
     )
 

From d91689abede6b11e4418115fedaa2cb619cb8cd0 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 1 Aug 2024 15:42:50 +0200
Subject: [PATCH 062/250] improve docstrings

---
 alphadia/workflow/optimization.py   | 115 ++++++++++++++++------------
 alphadia/workflow/peptidecentric.py |  69 ++++++++++++++++-
 2 files changed, 132 insertions(+), 52 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 7fa6283e..f5b14acd 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -24,7 +24,10 @@ def __init__(
         ----------
 
         workflow: peptidecentric.PeptideCentricWorkflow
-            The workflow object that the optimization is being performed on.
+            The workflow object, which includes the calibration, calibration_optimization and FDR managers which are used as part of optimization.
+
+        reporter: None | reporting.Pipeline | reporting.Backend
+            The reporter object used to log information about the optimization process. If None, a new LogBackend object is created.
 
         """
         self.optimal_parameter = None
@@ -56,7 +59,7 @@ def plot(self):
 
         Notes
         -----
-            This can be left blank if there is nothing of interest to plot.
+            This can be overwritten with pass if there is nothing of interest to plot.
 
         """
         pass
@@ -76,6 +79,7 @@ def __init__(
         initial_parameter: float
             The parameter used for search in the first round of optimization.
 
+        See base class for other parameters.
 
         """
         super().__init__(workflow, reporter)
@@ -157,7 +161,7 @@ def step(
             )
 
     def plot(self):
-        """Plot the optimization of the RT error parameter."""
+        """Plot the value of the feature used to assess optimization progress against the parameter value, for each value tested."""
         fig, ax = plt.subplots()
 
         ax.axvline(
@@ -199,13 +203,24 @@ def _propose_new_parameter(self, df):
         df: pd.DataFrame
             The dataframe used to update the parameter. This could be the precursor or fragment dataframe, depending on the search parameter being optimized.
 
+        Returns
+        -------
+        float
+            The proposed new value for the search parameter.
+
 
         """
         pass
 
     @abstractmethod
     def _check_convergence(self):
-        """This method checks if the optimization has converged according to parameter-specific conditions and, if it has, sets the optimal parameter attribute and updates the optimization manager."""
+        """This method checks if the optimization has converged according to parameter-specific conditions.
+
+        Returns
+        -------
+        bool
+
+        """
         pass
 
     @abstractmethod
@@ -247,6 +262,8 @@ def __init__(
         target_parameter: float
             Optimization will stop when this parameter is reached.
 
+        See base class for other parameters.
+
         """
         super().__init__(workflow, reporter)
         self.workflow.com.fit({self.parameter_name: initial_parameter})
@@ -254,7 +271,7 @@ def __init__(
         self.has_converged = False
 
     def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
-        """The optimization has converged if the proposed parameter is equal to or less than the target parameter. At this point, the target parameter is saved as the optimal parameter.
+        """The optimization has converged if the proposed parameter is equal to or less than the target parameter and the a sufficient number of steps has been taken.
 
         Parameters
         ----------
@@ -264,6 +281,11 @@ def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
         current_step: int
             The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
+        Returns
+        -------
+        bool
+            True if proposed parameter less than target and the current step is greater than the minimum required, False otherwise.
+
 
         """
 
@@ -334,15 +356,7 @@ def __init__(
         workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
-        """See base class.
-
-        Parameters
-        ----------
-
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-        """
+        """See base class. Optimizes retention time error."""
         self.parameter_name = "rt_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
@@ -352,7 +366,7 @@ def __init__(
     def _check_convergence(self, current_step: int = -1):
         """Optimization should stop if continued optimization of the parameter is not improving the TODO feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+        It also checks if the current step is greater than the minimum number of steps required for optimization.
 
         Notes
         -----
@@ -363,6 +377,11 @@ def _check_convergence(self, current_step: int = -1):
         current_step: int
             The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
+        Returns
+        -------
+        bool
+            True if the convergence conditions are met, False otherwise.
+
         """
 
         return (
@@ -381,6 +400,10 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             3) multiply this value by 1.1.
         This is implemented by the ci method for the estimator.
 
+        Returns
+        -------
+        float
+            The proposed new value for the search parameter.
 
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
@@ -400,15 +423,7 @@ def __init__(
         workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
-        """This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number.
-
-        Parameters
-        ----------
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-
-        """
+        """See base class. This class automatically optimizes the MS2 tolerance parameter by tracking the number of precursor identifications and stopping when further changes do not increase this number."""
         self.parameter_name = "ms2_error"
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
@@ -429,6 +444,11 @@ def _check_convergence(self, current_step: int = -1):
         current_step: int
             The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
+        Returns
+        -------
+        bool
+            True if the convergence conditions are met, False otherwise.
+
         """
 
         return (
@@ -447,6 +467,10 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             3) multiply this value by 1.1.
         This is implemented by the ci method for the estimator.
 
+        Returns
+        -------
+        float
+            The proposed new value for the search parameter.
 
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
@@ -466,15 +490,7 @@ def __init__(
         workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
-        """See base class.
-
-        Parameters
-        ----------
-
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-        """
+        """See base class. Optimizes MS1 error."""
         self.parameter_name = "ms1_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
@@ -514,6 +530,11 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         This is implemented by the ci method for the estimator.
 
 
+        Returns
+        -------
+        float
+            The proposed new value for the search parameter.
+
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
@@ -532,15 +553,7 @@ def __init__(
         workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
-        """See base class.
-
-        Parameters
-        ----------
-
-        initial_parameter: float
-            The parameter used for search in the first round of optimization.
-
-        """
+        """See base class. Optimizes mobility error."""
         self.parameter_name = "mobility_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
@@ -561,6 +574,11 @@ def _check_convergence(self, current_step: int = -1):
         current_step: int
             The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
+        Returns
+        -------
+        bool
+            True if the convergence conditions are met, False otherwise.
+
         """
 
         return (
@@ -579,8 +597,13 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             3) multiply this value by 1.1.
         This is implemented by the ci method for the estimator.
 
+        Returns
+        -------
+        float
+            The proposed new value for the search parameter.
 
         """
+
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
         ).ci(df, 0.99)
@@ -592,8 +615,6 @@ def _get_feature_value(
 
 
 class TargetedRTOptimizer(TargetedOptimizer):
-    """This class optimizes the RT search parameter until it reaches a user-specified target value."""
-
     def __init__(
         self,
         initial_parameter: float,
@@ -609,8 +630,6 @@ def __init__(
 
 
 class TargetedMS2Optimizer(TargetedOptimizer):
-    """This class optimizes the MS2 search parameter until it reaches a user-specified target value."""
-
     def __init__(
         self,
         initial_parameter: float,
@@ -626,8 +645,6 @@ def __init__(
 
 
 class TargetedMS1Optimizer(TargetedOptimizer):
-    """This class optimizes the MS1 search parameter until it reaches a user-specified target value."""
-
     def __init__(
         self,
         initial_parameter: float,
@@ -643,8 +660,6 @@ def __init__(
 
 
 class TargetedMobilityOptimizer(TargetedOptimizer):
-    """This class optimizes the mobility search parameter until it reaches a user-specified target value."""
-
     def __init__(
         self,
         initial_parameter: float,
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 98ed982c..88b76fa7 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -297,6 +297,7 @@ def get_exponential_batches(self, step):
         return int(2**step)
 
     def get_batch_plan(self):
+        """Gets an exponential batch plan based on the batch_size value in the config."""
         n_eg = self.spectral_library._precursor_df["elution_group_idx"].nunique()
 
         plan = []
@@ -316,11 +317,21 @@ def get_batch_plan(self):
 
     def get_optimization_lock(self):
         """Search parameter optimization (i.e. refinement of tolerances for RT, MS2, etc.) is performed on a subset of the elution groups in the spectral library.
+        This subset is termed the optimization lock.
         The number of elution groups which must be searched to get a sufficiently large number for robust calibration varies depending the library used and the data.
         This function searches an increasing number of elution groups until a sufficient number of precursors are identified at 1% FDR and a sufficient number of steps have been taken.
         The values deemed sufficient are specified in by "optimization_lock_target" and "optmization_lock_min_steps" in the config.
-        It then returns the elution group indexes which will be used to find the data in the spectral library for search parameter optimization.
 
+        Returns
+        -------
+        eg_idxes_for_calibration : np.ndarray
+            The indices (in .spectral_library._precursor_df) of the precursors which will be used for calibration.
+
+        precursor_df : pd.DataFrame
+            Dataframe of all precursors accumulated during the optimization lock, including q-values from FDR correction.
+
+        fragments_df : pd.DataFrame
+            Dataframe of all fragments accumulated during the optimization lock, including q-values from FDR correction.
 
         """
 
@@ -387,12 +398,25 @@ def get_optimization_lock(self):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
                 break
 
-        return self.elution_group_order[:final_stop_index], precursor_df, fragments_df
+        eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
+        return eg_idxes_for_calibration, precursor_df, fragments_df
 
     #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
     #        self.com.fit({"classifier_version": self.fdr_manager.current_version})
 
     def get_optimizers(self):
+        """Select appropriate optimizers. Targeted optimization is used if a valid target value (i.e. a number greater than 0) is specified in the config;
+        if a value less than or equal to 0 is supplied, automatic optimization is used.
+        Targeted optimizers are run simultaneously; automatic optimizers are run separately in the order MS2, RT, MS1, mobility.
+        This order is built into the structure of the returned list of lists, order_of_optimization.
+        For MS1 and mobility, the relevant optimizer will be excluded from the returned list of lists if it is not present in the data.
+
+        Returns
+        -------
+        order_of_optimization : list
+            List of lists of optimizers
+
+        """
         if self.config["search"]["target_ms2_tolerance"] > 0:
             ms2_optimizer = optimization.TargetedMS2Optimizer(
                 self.config["search_initial"]["initial_ms2_tolerance"],
@@ -475,6 +499,14 @@ def get_optimizers(self):
         return order_of_optimization
 
     def calibration(self):
+        """Performs optimization of the search parameters. This occurs in two stages:
+        1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
+        2) Optimization loop: the search parameters are optimized iteratively using the locked set of precursors.
+            In each iteration, the data are searched with the locked library from stage 1, and the properties -- m/z for both precursors and fragments (i.e. MS1 and MS2), RT and mobility -- are recalibrated.
+            The optimization loop is repeated for each list of optimizers in order_of_optimization.
+
+        """
+        # First check to see if the calibration has already been performed. Return if so.
         if (
             self.calibration_manager.is_fitted
             and self.calibration_manager.is_loaded_from_file
@@ -485,6 +517,7 @@ def calibration(self):
             )
             return
 
+        # Get the order of optimization
         order_of_optimization = self.get_optimizers()
 
         self.reporter.log_string(
@@ -492,6 +525,7 @@ def calibration(self):
             verbosity="progress",
         )
 
+        # Get the optimization lock
         eg_idxes_for_calibration, precursor_df, fragments_df = (
             self.get_optimization_lock()
         )
@@ -600,6 +634,25 @@ def calibration(self):
         )
 
     def filter_dfs(self, precursor_df, fragments_df):
+        """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
+
+        Parameters
+        ----------
+        precursor_df : pd.DataFrame
+            Precursor dataframe after FDR correction.
+
+        fragments_df : pd.DataFrame
+            Fragment dataframe.
+
+        Returns
+        -------
+        precursor_df_filtered : pd.DataFrame
+            Filtered precursor dataframe. Decoy precursors and those found at worse than 1% FDR are removed from the precursor dataframe.
+
+        fragments_df_filtered : pd.DataFrame
+            Filtered fragment dataframe. Retained fragments must have a correlation greater than 0.7 and belong to the top 5000 fragments sorted by correlation.
+
+        """
         precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.01]
         precursor_df_filtered = precursor_df_filtered[
             precursor_df_filtered["decoy"] == 0
@@ -633,6 +686,18 @@ def filter_dfs(self, precursor_df, fragments_df):
         return precursor_df_filtered, fragments_df_filtered
 
     def recalibration(self, precursor_df_filtered, fragments_df_filtered):
+        """Performs recalibration of the the MS1, MS2, RT and mobility properties. Also fits the convolution kernel and the score cutoff.
+        The calibration manager is used to fit the data and predict the calibrated values.
+
+        Parameters
+        ----------
+        precursor_df_filtered : pd.DataFrame
+            Filtered precursor dataframe (see filter_dfs)
+
+        fragments_df_filtered : pd.DataFrame
+            Filtered fragment dataframe (see filter_dfs)
+
+        """
         self.calibration_manager.fit(
             precursor_df_filtered,
             "precursor",

From 8d5c989b5c2a8c2982c7e08eb52ce4413a7509e6 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 1 Aug 2024 17:58:29 +0200
Subject: [PATCH 063/250] add unit tests for optimizers

---
 tests/unit_tests/test_workflow.py | 425 +++++++++++++++++++++++++++---
 1 file changed, 395 insertions(+), 30 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 3337859e..638a12c7 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -1,7 +1,6 @@
 import os
 import shutil
 import tempfile
-from collections import defaultdict
 from pathlib import Path
 
 import numpy as np
@@ -13,7 +12,8 @@
 from alphadia.calibration.models import LOESSRegression
 from alphadia.calibration.property import Calibration
 from alphadia.fdrexperimental import BinaryClassifierLegacyNewBatching
-from alphadia.workflow import base, manager, optimization
+from alphadia.workflow import base, manager, optimization, peptidecentric, reporting
+from alphadia.workflow.config import Config
 
 
 def test_base_manager():
@@ -131,12 +131,21 @@ def calibration_testdata():
         rt_library + np.random.normal(0, 0.5, 1000) + np.sin(rt_library * 0.05)
     )
 
+    mobility_library = np.linspace(0, 100, 1000)
+    mobility_observed = (
+        mobility_library
+        + np.random.normal(0, 0.5, 1000)
+        + np.sin(mobility_library * 0.05)
+    )
+
     return pd.DataFrame(
         {
             "mz_library": mz_library,
             "mz_observed": mz_observed,
             "rt_library": rt_library,
             "rt_observed": rt_observed,
+            "mobility_library": mobility_library,
+            "mobility_observed": mobility_observed,
         }
     ).copy()
 
@@ -372,48 +381,404 @@ def test_fdr_manager_fit_predict():
     os.remove(temp_path)
 
 
-def ms2_optimizer_test():
-    temp_path = os.path.join(tempfile.tempdir, "calibration_manager.pkl")
-    calibration_manager = manager.CalibrationManager(
-        TEST_CONFIG, path=temp_path, load_from_file=False
+def create_workflow_instance():
+    config_base_path = os.path.join(
+        Path(__file__).parents[2], "alphadia", "constants", "default.yaml"
     )
 
-    temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
+    config = Config()
+    config.from_yaml(config_base_path)
+    config["output"] = tempfile.mkdtemp()
+    workflow = peptidecentric.PeptideCentricWorkflow(
+        "test",
+        config,
+    )
+    workflow.reporter = reporting.Pipeline(
+        backends=[
+            reporting.LogBackend(),
+            reporting.JSONLBackend(path=workflow.path),
+            reporting.FigureBackend(path=workflow.path),
+        ]
+    )
+    workflow._calibration_manager = manager.CalibrationManager(
+        workflow.config["calibration_manager"],
+        path=os.path.join(workflow.path, workflow.CALIBRATION_MANAGER_PATH),
+        load_from_file=workflow.config["general"]["reuse_calibration"],
+        reporter=workflow.reporter,
+    )
 
-    optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_TEST_DATA, path=temp_path, load_from_file=False
+    workflow.init_calibration_optimization_manager()
+    workflow.init_fdr_manager()
+
+    return workflow
+
+
+def automatic_ms2_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df2, "fragment", plot=False)
+
+    ms2_optimizer = optimization.AutomaticMS2Optimizer(
+        100,
+        workflow,
     )
 
-    test_fragment_df = calibration_testdata()
-    calibration_manager.fit(test_fragment_df, "fragment", plot=False)
+    assert ms2_optimizer.has_converged is False
+    assert ms2_optimizer.parameter_name == "ms2_error"
 
-    fdr_manager = manager.FDRManager(FDR_TEST_FEATURES, FDR_TEST_BASE_CLASSIFIER)
-    fdr_manager._num_classifiers = 1
+    workflow.fdr_manager._current_version += 1
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert len(ms2_optimizer.history_df) == 1
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert ms2_optimizer.has_converged is True
+    assert (
+        ms2_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+    ).all()
+    assert (
+        workflow.com.ms2_error
+        == ms2_optimizer.history_df.parameter[
+            ms2_optimizer.history_df.precursor_count.idxmax()
+        ]
+    )
+    assert workflow.com.classifier_version == 2
+
+
+def automatic_rt_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    rt_optimizer = optimization.AutomaticRTOptimizer(
+        100,
+        workflow,
+    )
+
+    assert rt_optimizer.has_converged is False
+    assert rt_optimizer.parameter_name == "rt_error"
+
+    workflow.fdr_manager._current_version += 1
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert len(rt_optimizer.history_df) == 1
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert rt_optimizer.has_converged is True
+    assert (
+        rt_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+    ).all()
+    assert (
+        workflow.com.rt_error
+        == rt_optimizer.history_df.parameter[
+            rt_optimizer.history_df.precursor_count.idxmax()
+        ]
+    )
+    assert workflow.com.classifier_version == 2
+
+
+def automatic_ms1_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    ms1_optimizer = optimization.AutomaticMS1Optimizer(
+        100,
+        workflow,
+    )
+
+    assert ms1_optimizer.has_converged is False
+    assert ms1_optimizer.parameter_name == "ms1_error"
+
+    workflow.fdr_manager._current_version += 1
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert len(ms1_optimizer.history_df) == 1
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
-    test_dict = defaultdict(list)
-    test_dict["var"] = list(range(100))
+    assert ms1_optimizer.has_converged is True
+    assert (
+        ms1_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+    ).all()
+    assert (
+        workflow.com.ms1_error
+        == ms1_optimizer.history_df.parameter[
+            ms1_optimizer.history_df.precursor_count.idxmax()
+        ]
+    )
+    assert workflow.com.classifier_version == 2
+
+
+def automatic_mobility_optimizer_test():
+    workflow = create_workflow_instance()
 
-    ms2_optimizer = optimization.MS2Optimizer(
-        100, calibration_manager, optimization_manager, fdr_manager
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    mobility_optimizer = optimization.AutomaticMobilityOptimizer(
+        100,
+        workflow,
     )
 
-    assert ms2_optimizer.optimal_parameter is None
+    assert mobility_optimizer.has_converged is False
+    assert mobility_optimizer.parameter_name == "mobility_error"
+
+    workflow.fdr_manager._current_version += 1
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert len(mobility_optimizer.history_df) == 1
 
-    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
+
+    assert workflow.com.classifier_version == -1
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
-    assert len(ms2_optimizer.parameters) == 2
+    calibration_test_df1 = pd.concat(
+        [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
+        ignore_index=True,
+    )
+    workflow.fdr_manager._current_version += 1
 
-    test_dict["var"].append(1)
-    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
+    assert workflow.com.classifier_version == -1
 
-    test_dict["var"].append(1)
-    ms2_optimizer.step(pd.DataFrame(test_dict), test_fragment_df)
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
-    assert ms2_optimizer.optimal_parameter is not None
-    assert ms2_optimizer.precursor_ids == [100, 101, 102]
+    assert mobility_optimizer.has_converged is True
     assert (
-        ms2_optimizer.optimal_parameter
-        == ms2_optimizer.parameters[np.argmax(ms2_optimizer.precursor_ids)]
+        mobility_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+    ).all()
+    assert (
+        workflow.com.mobility_error
+        == mobility_optimizer.history_df.parameter[
+            mobility_optimizer.history_df.precursor_count.idxmax()
+        ]
     )
-    assert optimization_manager.ms2_error == ms2_optimizer.optimal_parameter
-    assert optimization_manager.classifier_version == 0
+    assert workflow.com.classifier_version == 2
+
+
+def targeted_ms2_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    optimizer = optimization.TargetedMS2Optimizer(
+        100,
+        7,
+        workflow,
+    )
+
+    assert optimizer.has_converged is False
+    assert optimizer.parameter_name == "ms2_error"
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 0
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 1
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert optimizer.has_converged is True
+    assert workflow.com.classifier_version == 2
+
+    assert workflow.com.ms2_error == optimizer.target_parameter
+    assert workflow.com.classifier_version == 2
+
+
+def targeted_rt_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    optimizer = optimization.TargetedRTOptimizer(
+        100,
+        7,
+        workflow,
+    )
+
+    assert optimizer.has_converged is False
+    assert optimizer.parameter_name == "rt_error"
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 0
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 1
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert optimizer.has_converged is True
+    assert workflow.com.classifier_version == 2
+
+    assert workflow.com.rt_error == optimizer.target_parameter
+    assert workflow.com.classifier_version == 2
+
+
+def targeted_ms1_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    optimizer = optimization.TargetedMS1Optimizer(
+        100,
+        7,
+        workflow,
+    )
+
+    assert optimizer.has_converged is False
+    assert optimizer.parameter_name == "ms1_error"
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 0
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 1
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert optimizer.has_converged is True
+    assert workflow.com.classifier_version == 2
+
+    assert workflow.com.ms1_error == optimizer.target_parameter
+    assert workflow.com.classifier_version == 2
+
+
+def targeted_mobility_optimizer_test():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    optimizer = optimization.TargetedMobilityOptimizer(
+        100,
+        7,
+        workflow,
+    )
+
+    assert optimizer.has_converged is False
+    assert optimizer.parameter_name == "mobility_error"
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 0
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+
+    assert optimizer.has_converged is False
+    assert workflow.com.classifier_version == 1
+
+    workflow.fdr_manager._current_version += 1
+    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+
+    assert optimizer.has_converged is True
+    assert workflow.com.classifier_version == 2
+
+    assert workflow.com.mobility_error == optimizer.target_parameter
+    assert workflow.com.classifier_version == 2

From 0d9e2ebcaebeae183681b122b5de10bc1353ea4f Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 1 Aug 2024 18:39:02 +0200
Subject: [PATCH 064/250] merge optimization and calibration_optimization
 managers

---
 alphadia/workflow/base.py           |  16 ++++-
 alphadia/workflow/optimization.py   |  52 ++++++++------
 alphadia/workflow/peptidecentric.py | 101 ++++++++++------------------
 3 files changed, 82 insertions(+), 87 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index b06fc5d7..3268fd5d 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -95,8 +95,22 @@ def load(
             self._calibration_manager.disable_mobility_calibration()
 
         # initialize the optimization manager
+        optimization_manager_config = {
+            "ms1_error": self.config["search_initial"]["initial_ms1_tolerance"],
+            "ms2_error": self.config["search_initial"]["initial_ms2_tolerance"],
+            "rt_error": self.config["search_initial"]["initial_rt_tolerance"],
+            "mobility_error": self.config["search_initial"][
+                "initial_mobility_tolerance"
+            ],
+            "column_type": "library",
+            "num_candidates": self.config["search_initial"]["initial_num_candidates"],
+            "classifier_version": -1,
+            "fwhm_rt": self.config["optimization_manager"]["fwhm_rt"],
+            "fwhm_mobility": self.config["optimization_manager"]["fwhm_mobility"],
+            "score_cutoff": self.config["optimization_manager"]["score_cutoff"],
+        }
         self._optimization_manager = manager.OptimizationManager(
-            self.config["optimization_manager"],
+            optimization_manager_config,
             path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH),
             load_from_file=self.config["general"]["reuse_calibration"],
             figure_path=os.path.join(self.path, self.FIGURE_PATH),
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index f5b14acd..79d7f9f8 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -84,7 +84,7 @@ def __init__(
         """
         super().__init__(workflow, reporter)
         self.history_df = pd.DataFrame()
-        self.workflow.com.fit({self.parameter_name: initial_parameter})
+        self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
 
     def step(
@@ -96,7 +96,7 @@ def step(
         """See base class. The feature is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if self.has_converged:
             self.reporter.log_string(
-                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
             return
@@ -104,14 +104,16 @@ def step(
         new_row = pd.DataFrame(
             [
                 {
-                    "parameter": self.workflow.com.__dict__[self.parameter_name],
+                    "parameter": self.workflow.optimization_manager.__dict__[
+                        self.parameter_name
+                    ],
                     self.feature_name: self._get_feature_value(
                         precursors_df, fragments_df
                     ),
                     "classifier_version": self.workflow.fdr_manager.current_version,
-                    "score_cutoff": self.workflow.com.score_cutoff,
-                    "fwhm_rt": self.workflow.com.fwhm_rt,
-                    "fwhm_mobility": self.workflow.com.fwhm_mobility,
+                    "score_cutoff": self.workflow.optimization_manager.score_cutoff,
+                    "fwhm_rt": self.workflow.optimization_manager.fwhm_rt,
+                    "fwhm_mobility": self.workflow.optimization_manager.fwhm_mobility,
                 }
             ]
         )
@@ -135,14 +137,22 @@ def step(
                 index_of_optimum
             ]
 
-            self.workflow.com.fit({self.parameter_name: optimal_parameter})
-            self.workflow.com.fit({"classifier_version": classifier_version_at_optimum})
-            self.workflow.com.fit({"score_cutoff": score_cutoff_at_optimum})
-            self.workflow.com.fit({"fwhm_rt": fwhm_rt_at_optimum})
-            self.workflow.com.fit({"fwhm_mobility": fwhm_mobility_at_optimum})
+            self.workflow.optimization_manager.fit(
+                {self.parameter_name: optimal_parameter}
+            )
+            self.workflow.optimization_manager.fit(
+                {"classifier_version": classifier_version_at_optimum}
+            )
+            self.workflow.optimization_manager.fit(
+                {"score_cutoff": score_cutoff_at_optimum}
+            )
+            self.workflow.optimization_manager.fit({"fwhm_rt": fwhm_rt_at_optimum})
+            self.workflow.optimization_manager.fit(
+                {"fwhm_mobility": fwhm_mobility_at_optimum}
+            )
 
             self.reporter.log_string(
-                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.com.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
                 verbosity="progress",
             )
 
@@ -153,10 +163,10 @@ def step(
                 else fragments_df
             )
 
-            self.workflow.com.fit({self.parameter_name: new_parameter})
+            self.workflow.optimization_manager.fit({self.parameter_name: new_parameter})
 
             self.reporter.log_string(
-                f"❌ {self.parameter_name:<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.workflow.com.__dict__[self.parameter_name]:.4f}.",
+                f"❌ {self.parameter_name:<15}: optimization incomplete after {len(self.history_df)} search(es). Will search with parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f}.",
                 verbosity="progress",
             )
 
@@ -165,7 +175,7 @@ def plot(self):
         fig, ax = plt.subplots()
 
         ax.axvline(
-            x=self.workflow.com.__dict__[self.parameter_name],
+            x=self.workflow.optimization_manager.__dict__[self.parameter_name],
             ymin=0,
             ymax=self.history_df[self.feature_name].max(),
             color="red",
@@ -266,7 +276,7 @@ def __init__(
 
         """
         super().__init__(workflow, reporter)
-        self.workflow.com.fit({self.parameter_name: initial_parameter})
+        self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.has_converged = False
 
@@ -317,7 +327,7 @@ def step(
         """See base class."""
         if self.has_converged:
             self.reporter.log_string(
-                f"✅ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
+                f"✅ {self.parameter_name:<15}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
                 verbosity="progress",
             )
             return
@@ -326,21 +336,21 @@ def step(
             precursors_df if self.estimator_group_name == "precursor" else fragments_df
         )
         just_converged = self._check_convergence(new_parameter, current_step)
-        self.workflow.com.fit({self.parameter_name: new_parameter})
-        self.workflow.com.fit(
+        self.workflow.optimization_manager.fit({self.parameter_name: new_parameter})
+        self.workflow.optimization_manager.fit(
             {"classifier_version": self.workflow.fdr_manager.current_version}
         )
 
         if just_converged:
             self.has_converged = True
             self.reporter.log_string(
-                f"✅ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
+                f"✅ {self.parameter_name:<15}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} <= {self.target_parameter:.4f}",
                 verbosity="progress",
             )
 
         else:
             self.reporter.log_string(
-                f"❌ {self.parameter_name:<15}: {self.workflow.com.__dict__[self.parameter_name]:.4f} > {self.target_parameter:.4f} or insufficient steps taken.",
+                f"❌ {self.parameter_name:<15}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} > {self.target_parameter:.4f} or insufficient steps taken.",
                 verbosity="progress",
             )
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 88b76fa7..649ada78 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -123,42 +123,9 @@ def load(
             f"Initializing workflow {self.instance_name}", verbosity="progress"
         )
 
-        self.init_calibration_optimization_manager()
         self.init_fdr_manager()
         self.init_spectral_library()
 
-    @property
-    def calibration_optimization_manager(self):
-        """Is used during the iterative optimization of the calibration parameters.
-        Should not be stored on disk.
-        """
-        return self._calibration_optimization_manager
-
-    @property
-    def com(self):
-        """alias for calibration_optimization_manager"""
-        return self.calibration_optimization_manager
-
-    def init_calibration_optimization_manager(self):
-        self._calibration_optimization_manager = manager.OptimizationManager(
-            {
-                "ms1_error": self.config["search_initial"]["initial_ms1_tolerance"],
-                "ms2_error": self.config["search_initial"]["initial_ms2_tolerance"],
-                "rt_error": self.config["search_initial"]["initial_rt_tolerance"],
-                "mobility_error": self.config["search_initial"][
-                    "initial_mobility_tolerance"
-                ],
-                "column_type": "library",
-                "num_candidates": self.config["search_initial"][
-                    "initial_num_candidates"
-                ],
-                "classifier_version": -1,
-                "fwhm_rt": self.config["optimization_manager"]["fwhm_rt"],
-                "fwhm_mobility": self.config["optimization_manager"]["fwhm_mobility"],
-                "score_cutoff": self.config["optimization_manager"]["score_cutoff"],
-            }
-        )
-
     def init_fdr_manager(self):
         self.fdr_manager = manager.FDRManager(
             feature_columns=feature_columns,
@@ -367,11 +334,11 @@ def get_optimization_lock(self):
             )
 
             precursor_df = self.fdr_correction(
-                features_df, fragments_df, self.com.classifier_version
+                features_df, fragments_df, self.optimization_manager.classifier_version
             )
 
             self.reporter.log_string(
-                f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+                f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
                 verbosity="info",
             )
 
@@ -402,7 +369,7 @@ def get_optimization_lock(self):
         return eg_idxes_for_calibration, precursor_df, fragments_df
 
     #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
-    #        self.com.fit({"classifier_version": self.fdr_manager.current_version})
+    #        self.optimization_manager.fit({"classifier_version": self.fdr_manager.current_version})
 
     def get_optimizers(self):
         """Select appropriate optimizers. Targeted optimization is used if a valid target value (i.e. a number greater than 0) is specified in the config;
@@ -465,7 +432,7 @@ def get_optimizers(self):
                 mobility_optimizer = optimization.AutomaticMobilityOptimizer(
                     self.config["search_initial"]["initial_mobility_tolerance"],
                     self.calibration_manager,
-                    self.com,
+                    self.optimization_manager,
                     self.fdr_manager,
                 )
         else:
@@ -530,7 +497,9 @@ def calibration(self):
             self.get_optimization_lock()
         )
 
-        self.com.fit({"classifier_version": self.fdr_manager.current_version})
+        self.optimization_manager.fit(
+            {"classifier_version": self.fdr_manager.current_version}
+        )
 
         # Perform a first recalibration on the optimization lock.
         precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
@@ -581,11 +550,13 @@ def calibration(self):
                 )
 
                 precursor_df = self.fdr_correction(
-                    features_df, fragments_df, self.com.classifier_version
+                    features_df,
+                    fragments_df,
+                    self.optimization_manager.classifier_version,
                 )
 
                 self.reporter.log_string(
-                    f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+                    f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
                     verbosity="info",
                 )
 
@@ -626,7 +597,7 @@ def calibration(self):
         for optimizers in order_of_optimization:
             for optimizer in optimizers:
                 self.reporter.log_string(
-                    f"{optimizer.parameter_name:<15}: {self.com.__dict__[optimizer.parameter_name]:.4f}",
+                    f"{optimizer.parameter_name:<15}: {self.optimization_manager.__dict__[optimizer.parameter_name]:.4f}",
                     verbosity="progress",
                 )
         self.reporter.log_string(
@@ -720,7 +691,7 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
 
         self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
 
-        self.com.fit(
+        self.optimization_manager.fit(
             {
                 "column_type": "calibrated",
                 "num_candidates": self.config["search"]["target_num_candidates"],
@@ -728,7 +699,7 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         percentile_001 = np.percentile(precursor_df_filtered["score"], 0.1)
-        self.com.fit(
+        self.optimization_manager.fit(
             {
                 "fwhm_rt": precursor_df_filtered["cycle_fwhm"].median(),
                 "fwhm_mobility": precursor_df_filtered["mobility_fwhm"].median(),
@@ -761,11 +732,11 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         config.update(
             {
                 "top_k_fragments": self.config["search_advanced"]["top_k_fragments"],
-                "rt_tolerance": self.com.rt_error,
-                "mobility_tolerance": self.com.mobility_error,
-                "candidate_count": self.com.num_candidates,
-                "precursor_mz_tolerance": self.com.ms1_error,
-                "fragment_mz_tolerance": self.com.ms2_error,
+                "rt_tolerance": self.optimization_manager.rt_error,
+                "mobility_tolerance": self.optimization_manager.mobility_error,
+                "candidate_count": self.optimization_manager.num_candidates,
+                "precursor_mz_tolerance": self.optimization_manager.ms1_error,
+                "fragment_mz_tolerance": self.optimization_manager.ms2_error,
                 "exclude_shared_ions": self.config["search"]["exclude_shared_ions"],
                 "min_size_rt": self.config["search"]["quant_window"],
             }
@@ -776,16 +747,16 @@ def extract_batch(self, batch_df, apply_cutoff=False):
             batch_df,
             self.spectral_library.fragment_df,
             config.jitclass(),
-            rt_column=f"rt_{self.com.column_type}",
-            mobility_column=f"mobility_{self.com.column_type}"
+            rt_column=f"rt_{self.optimization_manager.column_type}",
+            mobility_column=f"mobility_{self.optimization_manager.column_type}"
             if self.dia_data.has_mobility
             else "mobility_library",
-            precursor_mz_column=f"mz_{self.com.column_type}"
+            precursor_mz_column=f"mz_{self.optimization_manager.column_type}"
             if self.dia_data.has_ms1
             else "mz_library",
-            fragment_mz_column=f"mz_{self.com.column_type}",
-            fwhm_rt=self.com.fwhm_rt,
-            fwhm_mobility=self.com.fwhm_mobility,
+            fragment_mz_column=f"mz_{self.optimization_manager.column_type}",
+            fwhm_rt=self.optimization_manager.fwhm_rt,
+            fwhm_mobility=self.optimization_manager.fwhm_mobility,
         )
         candidates_df = extraction(thread_count=self.config["general"]["thread_count"])
 
@@ -794,11 +765,11 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         if apply_cutoff:
             num_before = len(candidates_df)
             self.reporter.log_string(
-                f"Applying score cutoff of {self.com.score_cutoff}",
+                f"Applying score cutoff of {self.optimization_manager.score_cutoff}",
                 verbosity="info",
             )
             candidates_df = candidates_df[
-                candidates_df["score"] > self.com.score_cutoff
+                candidates_df["score"] > self.optimization_manager.score_cutoff
             ]
             num_after = len(candidates_df)
             num_removed = num_before - num_after
@@ -812,8 +783,8 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         config.update(
             {
                 "top_k_fragments": self.config["search_advanced"]["top_k_fragments"],
-                "precursor_mz_tolerance": self.com.ms1_error,
-                "fragment_mz_tolerance": self.com.ms2_error,
+                "precursor_mz_tolerance": self.optimization_manager.ms1_error,
+                "fragment_mz_tolerance": self.optimization_manager.ms2_error,
                 "exclude_shared_ions": self.config["search"]["exclude_shared_ions"],
                 "quant_window": self.config["search"]["quant_window"],
                 "quant_all": self.config["search"]["quant_all"],
@@ -825,14 +796,14 @@ def extract_batch(self, batch_df, apply_cutoff=False):
             self.spectral_library._precursor_df,
             self.spectral_library._fragment_df,
             config=config,
-            rt_column=f"rt_{self.com.column_type}",
-            mobility_column=f"mobility_{self.com.column_type}"
+            rt_column=f"rt_{self.optimization_manager.column_type}",
+            mobility_column=f"mobility_{self.optimization_manager.column_type}"
             if self.dia_data.has_mobility
             else "mobility_library",
-            precursor_mz_column=f"mz_{self.com.column_type}"
+            precursor_mz_column=f"mz_{self.optimization_manager.column_type}"
             if self.dia_data.has_ms1
             else "mz_library",
-            fragment_mz_column=f"mz_{self.com.column_type}",
+            fragment_mz_column=f"mz_{self.optimization_manager.column_type}",
         )
 
         features_df, fragments_df = candidate_scoring(
@@ -844,7 +815,7 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         return features_df, fragments_df
 
     def extraction(self):
-        self.com.fit(
+        self.optimization_manager.fit(
             {
                 "num_candidates": self.config["search"]["target_num_candidates"],
                 "column_type": "calibrated",
@@ -862,12 +833,12 @@ def extraction(self):
         )
 
         self.reporter.log_string(
-            f"=== FDR correction performed with classifier version {self.com.classifier_version} ===",
+            f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
             verbosity="info",
         )
 
         precursor_df = self.fdr_correction(
-            features_df, fragments_df, self.com.classifier_version
+            features_df, fragments_df, self.optimization_manager.classifier_version
         )
 
         precursor_df = precursor_df[precursor_df["qval"] <= self.config["fdr"]["fdr"]]

From f6d56f17f56485892ab317fc8f2c2818deef50dc Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 2 Aug 2024 11:36:39 +0200
Subject: [PATCH 065/250] review comments

---
 alphadia/workflow/optimization.py   |  40 +++++----
 alphadia/workflow/peptidecentric.py | 125 ++++++++++++++++++----------
 tests/unit_tests/test_workflow.py   |  16 ++--
 3 files changed, 111 insertions(+), 70 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 79d7f9f8..b7ed45d5 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -50,18 +50,13 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
 
         """
+
         pass
 
     @abstractmethod
     def plot(self):
-        """
-        This method plots relevant information about optimization of the search parameter.
-
-        Notes
-        -----
-            This can be overwritten with pass if there is nothing of interest to plot.
+        """This method plots relevant information about optimization of the search parameter. This can be overwritten with an empty function if there is nothing to plot."""
 
-        """
         pass
 
 
@@ -355,7 +350,7 @@ def step(
             )
 
     def plot(self):
-        """Empty method for consistency with AutomaticOptimizer."""
+        """See base class. There is nothing of interest to plot here."""
         pass
 
 
@@ -394,13 +389,16 @@ def _check_convergence(self, current_step: int = -1):
 
         """
 
+        min_steps_reached = (
+            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
         return (
-            len(self.history_df) > 2
+            min_steps_reached
+            and len(self.history_df) > 2
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
-            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -460,14 +458,16 @@ def _check_convergence(self, current_step: int = -1):
             True if the convergence conditions are met, False otherwise.
 
         """
-
+        min_steps_reached = (
+            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
         return (
-            len(self.history_df) > 2
+            min_steps_reached
+            and len(self.history_df) > 2
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
-            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -523,13 +523,16 @@ def _check_convergence(self, current_step: int = -1):
 
         """
 
+        min_steps_reached = (
+            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
         return (
-            len(self.history_df) > 2
+            min_steps_reached
+            and len(self.history_df) > 2
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
-            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -591,13 +594,16 @@ def _check_convergence(self, current_step: int = -1):
 
         """
 
+        min_steps_reached = (
+            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
         return (
-            len(self.history_df) > 2
+            min_steps_reached
+            and len(self.history_df) > 2
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
-            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 649ada78..cb85d4e1 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -342,7 +342,7 @@ def get_optimization_lock(self):
                 verbosity="info",
             )
 
-            precursors_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
+            num_precursors_at_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
 
             self.reporter.log_string(
                 f"=== Checking if minimum number of precursors for optimization found yet; minimum number is {self.config['calibration']['optimization_lock_target']} ===",
@@ -357,9 +357,9 @@ def get_optimization_lock(self):
             )
 
             if (
-                precursors_01FDR
+                num_precursors_at_01FDR
                 > self.config["calibration"]["optimization_lock_target"]
-                and self.fdr_manager.current_version
+                and current_step
                 >= self.config["calibration"]["optimization_lock_min_steps"] - 1
             ):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
@@ -371,66 +371,69 @@ def get_optimization_lock(self):
     #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
     #        self.optimization_manager.fit({"classifier_version": self.fdr_manager.current_version})
 
-    def get_optimizers(self):
+    def get_ordered_optimizers(self):
         """Select appropriate optimizers. Targeted optimization is used if a valid target value (i.e. a number greater than 0) is specified in the config;
         if a value less than or equal to 0 is supplied, automatic optimization is used.
         Targeted optimizers are run simultaneously; automatic optimizers are run separately in the order MS2, RT, MS1, mobility.
-        This order is built into the structure of the returned list of lists, order_of_optimization.
+        This order is built into the structure of the returned list of lists, ordered_optimizers.
         For MS1 and mobility, the relevant optimizer will be excluded from the returned list of lists if it is not present in the data.
 
         Returns
         -------
-        order_of_optimization : list
+        ordered_optimizers : list
             List of lists of optimizers
 
         """
-        if self.config["search"]["target_ms2_tolerance"] > 0:
+        config_search_initial = self.config["search_initial"]
+        config_search = self.config["search"]
+
+        if config_search["target_ms2_tolerance"] > 0:
             ms2_optimizer = optimization.TargetedMS2Optimizer(
-                self.config["search_initial"]["initial_ms2_tolerance"],
-                self.config["search"]["target_ms2_tolerance"],
+                config_search_initial["initial_ms2_tolerance"],
+                config_search["target_ms2_tolerance"],
                 self,
             )
         else:
             ms2_optimizer = optimization.AutomaticMS2Optimizer(
-                self.config["search_initial"]["initial_ms2_tolerance"],
+                config_search_initial["initial_ms2_tolerance"],
                 self,
             )
 
-        if self.config["search"]["target_rt_tolerance"] > 0:
+        if config_search["target_rt_tolerance"] > 0:
             rt_optimizer = optimization.TargetedRTOptimizer(
-                self.config["search_initial"]["initial_rt_tolerance"],
-                self.config["search"]["target_rt_tolerance"],
+                config_search_initial["initial_rt_tolerance"],
+                config_search["target_rt_tolerance"],
                 self,
             )
         else:
             rt_optimizer = optimization.AutomaticRTOptimizer(
-                self.config["search_initial"]["initial_rt_tolerance"],
+                config_search_initial["initial_rt_tolerance"],
                 self,
             )
         if self.dia_data.has_ms1:
-            if self.config["search"]["target_ms1_tolerance"] > 0:
+            if config_search["target_ms1_tolerance"] > 0:
                 ms1_optimizer = optimization.TargetedMS1Optimizer(
-                    self.config["search_initial"]["initial_ms1_tolerance"],
-                    self.config["search"]["target_ms1_tolerance"],
+                    config_search_initial["initial_ms1_tolerance"],
+                    config_search["target_ms1_tolerance"],
                     self,
                 )
             else:
                 ms1_optimizer = optimization.AutomaticMS1Optimizer(
-                    self.config["search_initial"]["initial_ms1_tolerance"],
+                    config_search_initial["initial_ms1_tolerance"],
                     self,
                 )
         else:
             ms1_optimizer = None
         if self.dia_data.has_mobility:
-            if self.config["search"]["target_mobility_tolerance"] > 0:
+            if config_search["target_mobility_tolerance"] > 0:
                 mobility_optimizer = optimization.TargetedMobilityOptimizer(
-                    self.config["search_initial"]["initial_mobility_tolerance"],
-                    self.config["search"]["target_mobility_tolerance"],
+                    config_search_initial["initial_mobility_tolerance"],
+                    config_search["target_mobility_tolerance"],
                     self,
                 )
             else:
                 mobility_optimizer = optimization.AutomaticMobilityOptimizer(
-                    self.config["search_initial"]["initial_mobility_tolerance"],
+                    config_search_initial["initial_mobility_tolerance"],
                     self.calibration_manager,
                     self.optimization_manager,
                     self.fdr_manager,
@@ -457,20 +460,56 @@ def get_optimizers(self):
             if isinstance(optimizer, optimization.AutomaticOptimizer)
         ]
 
-        order_of_optimization = (
+        ordered_optimizers = (
             targeted_optimizers + automatic_optimizers
-            if any(targeted_optimizers)
+            if any(
+                targeted_optimizers
+            )  # This line is required so no empty list is added to the ordered_optimizers list
             else automatic_optimizers
         )
 
-        return order_of_optimization
+        return ordered_optimizers
+
+    def first_recalibration_and_optimization(
+        self,
+        precursor_df: pd.DataFrame,
+        fragments_df: pd.DataFrame,
+        ordered_optimizers: list,
+    ):
+        """Performs the first recalibration and optimization step.
+
+        Parameters
+        ----------
+        precursor_df : pd.DataFrame
+            Precursor dataframe from optimization lock
+
+        fragments_df : pd.DataFrame
+            Fragment dataframe from optimization lock
+
+        ordered_optimizers : list
+            List of lists of optimizers in correct order
+        """
+        precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+            precursor_df, fragments_df
+        )
+
+        self.recalibration(precursor_df_filtered, fragments_df_filtered)
+
+        self.reporter.log_string(
+            "=== Performing initial optimization on extracted data. ===",
+            verbosity="info",
+        )
+
+        for optimizers in ordered_optimizers:
+            for optimizer in optimizers:
+                optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
     def calibration(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
         2) Optimization loop: the search parameters are optimized iteratively using the locked set of precursors.
             In each iteration, the data are searched with the locked library from stage 1, and the properties -- m/z for both precursors and fragments (i.e. MS1 and MS2), RT and mobility -- are recalibrated.
-            The optimization loop is repeated for each list of optimizers in order_of_optimization.
+            The optimization loop is repeated for each list of optimizers in ordered_optimizers.
 
         """
         # First check to see if the calibration has already been performed. Return if so.
@@ -485,7 +524,7 @@ def calibration(self):
             return
 
         # Get the order of optimization
-        order_of_optimization = self.get_optimizers()
+        ordered_optimizers = self.get_ordered_optimizers()
 
         self.reporter.log_string(
             "Starting initial classifier training and precursor identification.",
@@ -501,30 +540,18 @@ def calibration(self):
             {"classifier_version": self.fdr_manager.current_version}
         )
 
-        # Perform a first recalibration on the optimization lock.
-        precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-            precursor_df, fragments_df
-        )
-
-        self.recalibration(precursor_df_filtered, fragments_df_filtered)
-
         self.reporter.log_string(
             "Required number of precursors found and required number of training iterations performed. Starting search parameter optimization.",
             verbosity="progress",
         )
 
-        self.reporter.log_string(
-            "=== Performing initial optimization on extracted data. ===",
-            verbosity="info",
+        # Perform a first recalibration on the optimization lock.
+        self.first_recalibration_and_optimization(
+            precursor_df, fragments_df, ordered_optimizers
         )
 
-        for optimizers in order_of_optimization:
-            for optimizer in optimizers:
-                optimizer.step(precursor_df_filtered, fragments_df_filtered)
-        # End of first recalibration
-
         # Start of optimization/recalibration loop
-        for optimizers in order_of_optimization:
+        for optimizers in ordered_optimizers:
             for current_step in range(self.config["calibration"]["max_steps"]):
                 if np.all([optimizer.has_converged for optimizer in optimizers]):
                     self.reporter.log_string(
@@ -587,6 +614,12 @@ def calibration(self):
                     verbosity="progress",
                 )
 
+            else:
+                self.reporter.log_string(
+                    "Optimization did not converge within the maximum number of steps, which is {self.config['calibration']['max_steps']}.",
+                    verbosity="warning",
+                )
+
         self.reporter.log_string(
             "Search parameter optimization finished. Values taken forward for search are:",
             verbosity="progress",
@@ -594,7 +627,7 @@ def calibration(self):
         self.reporter.log_string(
             "==============================================", verbosity="progress"
         )
-        for optimizers in order_of_optimization:
+        for optimizers in ordered_optimizers:
             for optimizer in optimizers:
                 self.reporter.log_string(
                     f"{optimizer.parameter_name:<15}: {self.optimization_manager.__dict__[optimizer.parameter_name]:.4f}",
@@ -621,7 +654,9 @@ def filter_dfs(self, precursor_df, fragments_df):
             Filtered precursor dataframe. Decoy precursors and those found at worse than 1% FDR are removed from the precursor dataframe.
 
         fragments_df_filtered : pd.DataFrame
-            Filtered fragment dataframe. Retained fragments must have a correlation greater than 0.7 and belong to the top 5000 fragments sorted by correlation.
+            Filtered fragment dataframe. Retained fragments must either:
+                1) have a correlation greater than 0.7 and belong to the top 5000 fragments sorted by correlation, if there are more than 500 with a correlation greater than 0.7, or
+                2) belong to the top 500 fragments sorted by correlation otherwise.
 
         """
         precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.01]
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 638a12c7..a5b5f27b 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -413,7 +413,7 @@ def create_workflow_instance():
     return workflow
 
 
-def automatic_ms2_optimizer_test():
+def test_automatic_ms2_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -467,7 +467,7 @@ def automatic_ms2_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def automatic_rt_optimizer_test():
+def test_automatic_rt_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -521,7 +521,7 @@ def automatic_rt_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def automatic_ms1_optimizer_test():
+def test_automatic_ms1_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -575,7 +575,7 @@ def automatic_ms1_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def automatic_mobility_optimizer_test():
+def test_automatic_mobility_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -628,7 +628,7 @@ def automatic_mobility_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def targeted_ms2_optimizer_test():
+def test_targeted_ms2_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -667,7 +667,7 @@ def targeted_ms2_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def targeted_rt_optimizer_test():
+def test_targeted_rt_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -706,7 +706,7 @@ def targeted_rt_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def targeted_ms1_optimizer_test():
+def test_targeted_ms1_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()
@@ -745,7 +745,7 @@ def targeted_ms1_optimizer_test():
     assert workflow.com.classifier_version == 2
 
 
-def targeted_mobility_optimizer_test():
+def test_targeted_mobility_optimizer():
     workflow = create_workflow_instance()
 
     calibration_test_df1 = calibration_testdata()

From 157c14a0155357169f8869985815f1d4f3bce01c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 2 Aug 2024 13:48:31 +0200
Subject: [PATCH 066/250] update unit tests

---
 tests/unit_tests/test_workflow.py | 96 +++++++++++++++++++------------
 1 file changed, 59 insertions(+), 37 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index a5b5f27b..02e4434a 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -407,7 +407,29 @@ def create_workflow_instance():
         reporter=workflow.reporter,
     )
 
-    workflow.init_calibration_optimization_manager()
+    optimization_manager_config = {
+        "ms1_error": workflow.config["search_initial"]["initial_ms1_tolerance"],
+        "ms2_error": workflow.config["search_initial"]["initial_ms2_tolerance"],
+        "rt_error": workflow.config["search_initial"]["initial_rt_tolerance"],
+        "mobility_error": workflow.config["search_initial"][
+            "initial_mobility_tolerance"
+        ],
+        "column_type": "library",
+        "num_candidates": workflow.config["search_initial"]["initial_num_candidates"],
+        "classifier_version": -1,
+        "fwhm_rt": workflow.config["optimization_manager"]["fwhm_rt"],
+        "fwhm_mobility": workflow.config["optimization_manager"]["fwhm_mobility"],
+        "score_cutoff": workflow.config["optimization_manager"]["score_cutoff"],
+    }
+
+    workflow._optimization_manager = manager.OptimizationManager(
+        optimization_manager_config,
+        path=os.path.join(workflow.path, workflow.OPTIMIZATION_MANAGER_PATH),
+        load_from_file=workflow.config["general"]["reuse_calibration"],
+        figure_path=os.path.join(workflow.path, workflow.FIGURE_PATH),
+        reporter=workflow.reporter,
+    )
+
     workflow.init_fdr_manager()
 
     return workflow
@@ -440,7 +462,7 @@ def test_automatic_ms2_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
@@ -450,7 +472,7 @@ def test_automatic_ms2_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
@@ -459,12 +481,12 @@ def test_automatic_ms2_optimizer():
         ms2_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
     ).all()
     assert (
-        workflow.com.ms2_error
+        workflow.optimization_manager.ms2_error
         == ms2_optimizer.history_df.parameter[
             ms2_optimizer.history_df.precursor_count.idxmax()
         ]
     )
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_automatic_rt_optimizer():
@@ -494,7 +516,7 @@ def test_automatic_rt_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
@@ -504,7 +526,7 @@ def test_automatic_rt_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
@@ -513,12 +535,12 @@ def test_automatic_rt_optimizer():
         rt_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
     ).all()
     assert (
-        workflow.com.rt_error
+        workflow.optimization_manager.rt_error
         == rt_optimizer.history_df.parameter[
             rt_optimizer.history_df.precursor_count.idxmax()
         ]
     )
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_automatic_ms1_optimizer():
@@ -548,7 +570,7 @@ def test_automatic_ms1_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
@@ -558,7 +580,7 @@ def test_automatic_ms1_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
@@ -567,12 +589,12 @@ def test_automatic_ms1_optimizer():
         ms1_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
     ).all()
     assert (
-        workflow.com.ms1_error
+        workflow.optimization_manager.ms1_error
         == ms1_optimizer.history_df.parameter[
             ms1_optimizer.history_df.precursor_count.idxmax()
         ]
     )
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_automatic_mobility_optimizer():
@@ -602,7 +624,7 @@ def test_automatic_mobility_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
     mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
     calibration_test_df1 = pd.concat(
@@ -611,7 +633,7 @@ def test_automatic_mobility_optimizer():
     )
     workflow.fdr_manager._current_version += 1
 
-    assert workflow.com.classifier_version == -1
+    assert workflow.optimization_manager.classifier_version == -1
 
     mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
@@ -620,12 +642,12 @@ def test_automatic_mobility_optimizer():
         mobility_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
     ).all()
     assert (
-        workflow.com.mobility_error
+        workflow.optimization_manager.mobility_error
         == mobility_optimizer.history_df.parameter[
             mobility_optimizer.history_df.precursor_count.idxmax()
         ]
     )
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_ms2_optimizer():
@@ -649,22 +671,22 @@ def test_targeted_ms2_optimizer():
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 0
+    assert workflow.optimization_manager.classifier_version == 0
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 1
+    assert workflow.optimization_manager.classifier_version == 1
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
     assert optimizer.has_converged is True
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
-    assert workflow.com.ms2_error == optimizer.target_parameter
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.ms2_error == optimizer.target_parameter
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_rt_optimizer():
@@ -688,22 +710,22 @@ def test_targeted_rt_optimizer():
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 0
+    assert workflow.optimization_manager.classifier_version == 0
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 1
+    assert workflow.optimization_manager.classifier_version == 1
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
     assert optimizer.has_converged is True
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
-    assert workflow.com.rt_error == optimizer.target_parameter
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.rt_error == optimizer.target_parameter
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_ms1_optimizer():
@@ -727,22 +749,22 @@ def test_targeted_ms1_optimizer():
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 0
+    assert workflow.optimization_manager.classifier_version == 0
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 1
+    assert workflow.optimization_manager.classifier_version == 1
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
     assert optimizer.has_converged is True
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
-    assert workflow.com.ms1_error == optimizer.target_parameter
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.ms1_error == optimizer.target_parameter
+    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_mobility_optimizer():
@@ -766,19 +788,19 @@ def test_targeted_mobility_optimizer():
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 0
+    assert workflow.optimization_manager.classifier_version == 0
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
 
     assert optimizer.has_converged is False
-    assert workflow.com.classifier_version == 1
+    assert workflow.optimization_manager.classifier_version == 1
 
     workflow.fdr_manager._current_version += 1
     optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
     assert optimizer.has_converged is True
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 2
 
-    assert workflow.com.mobility_error == optimizer.target_parameter
-    assert workflow.com.classifier_version == 2
+    assert workflow.optimization_manager.mobility_error == optimizer.target_parameter
+    assert workflow.optimization_manager.classifier_version == 2

From 8c61993063fd907d7d974605cc192fdaf369e114 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 2 Aug 2024 17:32:01 +0200
Subject: [PATCH 067/250] change feature for AutomaticMS1Optimizer

---
 alphadia/workflow/optimization.py | 163 +++++-------------------------
 1 file changed, 27 insertions(+), 136 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index b7ed45d5..2370af1c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -169,7 +169,7 @@ def plot(self):
         """Plot the value of the feature used to assess optimization progress against the parameter value, for each value tested."""
         fig, ax = plt.subplots()
 
-        ax.axvline(
+        ax.vlines(
             x=self.workflow.optimization_manager.__dict__[self.parameter_name],
             ymin=0,
             ymax=self.history_df[self.feature_name].max(),
@@ -217,16 +217,33 @@ def _propose_new_parameter(self, df):
         """
         pass
 
-    @abstractmethod
-    def _check_convergence(self):
-        """This method checks if the optimization has converged according to parameter-specific conditions.
+    def _check_convergence(self, current_step: int = -1):
+        """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
 
-        Returns
-        -------
-        bool
+        Notes
+        -----
+            Because the check for an increase in feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+        Parameters
+        ----------
+        current_step: int
+            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
-        pass
+
+        min_steps_reached = (
+            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+        )
+        return (
+            min_steps_reached
+            and len(self.history_df) > 2
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 1.1 * self.history_df[self.feature_name].iloc[-3]
+        )
 
     @abstractmethod
     def _get_feature_value(
@@ -368,39 +385,6 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _check_convergence(self, current_step: int = -1):
-        """Optimization should stop if continued optimization of the parameter is not improving the TODO feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
-        It also checks if the current step is greater than the minimum number of steps required for optimization.
-
-        Notes
-        -----
-            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
-        Parameters
-        ----------
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
-
-        Returns
-        -------
-        bool
-            True if the convergence conditions are met, False otherwise.
-
-        """
-
-        min_steps_reached = (
-            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
-        )
-        return (
-            min_steps_reached
-            and len(self.history_df) > 2
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-3]
-        )
-
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
@@ -438,38 +422,6 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _check_convergence(self, current_step: int = -1):
-        """Optimization should stop if continued narrowing of the MS2 parameter is not improving the number of precursor identifications.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the number of identifications.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in identifications requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
-        Parameters
-        ----------
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
-
-        Returns
-        -------
-        bool
-            True if the convergence conditions are met, False otherwise.
-
-        """
-        min_steps_reached = (
-            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
-        )
-        return (
-            min_steps_reached
-            and len(self.history_df) > 2
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-3]
-        )
-
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
@@ -504,37 +456,9 @@ def __init__(
         self.parameter_name = "ms1_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
-        self.feature_name = "precursor_count"
+        self.feature_name = "mean_isotope_intensity_correlation"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _check_convergence(self, current_step: int = -1):
-        """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
-        Parameters
-        ----------
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
-
-        """
-
-        min_steps_reached = (
-            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
-        )
-        return (
-            min_steps_reached
-            and len(self.history_df) > 2
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-3]
-        )
-
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
@@ -556,7 +480,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df)
+        return precursors_df.isotope_intensity_correlation.mean()
 
 
 class AutomaticMobilityOptimizer(AutomaticOptimizer):
@@ -573,39 +497,6 @@ def __init__(
         self.feature_name = "precursor_count"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _check_convergence(self, current_step: int = -1):
-        """Optimization should stop if continued narrowing of the parameter is not improving the TODO feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the TODO feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in TODO feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
-        Parameters
-        ----------
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
-
-        Returns
-        -------
-        bool
-            True if the convergence conditions are met, False otherwise.
-
-        """
-
-        min_steps_reached = (
-            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
-        )
-        return (
-            min_steps_reached
-            and len(self.history_df) > 2
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-3]
-        )
-
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,

From 724085752767f3fcc673f33bd4cb893b7896346b Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 2 Aug 2024 22:23:41 +0200
Subject: [PATCH 068/250] fixed unit test

---
 tests/unit_tests/test_workflow.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 02e4434a..9de6eee7 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -138,6 +138,8 @@ def calibration_testdata():
         + np.sin(mobility_library * 0.05)
     )
 
+    isotope_intensity_correlation = np.linspace(0, 100, 1000)
+
     return pd.DataFrame(
         {
             "mz_library": mz_library,
@@ -146,6 +148,7 @@ def calibration_testdata():
             "rt_observed": rt_observed,
             "mobility_library": mobility_library,
             "mobility_observed": mobility_observed,
+            "isotope_intensity_correlation": isotope_intensity_correlation,
         }
     ).copy()
 
@@ -585,16 +588,13 @@ def test_automatic_ms1_optimizer():
     ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
 
     assert ms1_optimizer.has_converged is True
-    assert (
-        ms1_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
-    ).all()
     assert (
         workflow.optimization_manager.ms1_error
         == ms1_optimizer.history_df.parameter[
-            ms1_optimizer.history_df.precursor_count.idxmax()
+            ms1_optimizer.history_df.mean_isotope_intensity_correlation.idxmax()
         ]
     )
-    assert workflow.optimization_manager.classifier_version == 2
+    assert workflow.optimization_manager.classifier_version == 0
 
 
 def test_automatic_mobility_optimizer():

From 84899f8ec662ae94abdb0de94994d94299ff2426 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 5 Aug 2024 09:21:02 +0200
Subject: [PATCH 069/250] change e2e test for automatic calibration

---
 tests/e2e_tests/e2e_test_cases.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 543870d9..74e842ba 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -116,7 +116,7 @@ test_cases:
         target_num_candidates: 3
         target_ms1_tolerance: -1
         target_ms2_tolerance: -1
-        target_rt_tolerance: 200
+        target_rt_tolerance: -1
       search_initial:
         initial_num_candidates: 1
         initial_ms1_tolerance: 10

From a5d01f2f25e3ac535a031ba7c89a9782360968b0 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 5 Aug 2024 10:20:20 +0200
Subject: [PATCH 070/250] adjust min_steps and optimization_lock_min_steps in
 default.yaml

---
 alphadia/constants/default.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 390c2862..365c1b96 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -96,7 +96,7 @@ search_advanced:
 calibration:
 
   # minimum number of steps taken during the optimization lock (during which the elution groups used for optimization are extracted)
-  optimization_lock_min_steps: 3
+  optimization_lock_min_steps: 0
 
   # Number of precursors searched and scored per batch
   batch_size: 8000
@@ -108,7 +108,7 @@ calibration:
   max_steps: 20
 
   # the maximum number of steps that a given optimizer is permitted to take
-  min_steps: 3
+  min_steps: 2
 
   # TODO: remove this parameter
   final_full_calibration: False

From 4f9b0949d4fa6c1da28f2d2fa7163a05a59a467d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 5 Aug 2024 10:22:29 +0200
Subject: [PATCH 071/250] formatting

---
 alphadia/workflow/optimization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 2370af1c..16e2e2b6 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -18,7 +18,7 @@ def __init__(
         workflow,
         reporter: None | reporting.Pipeline | reporting.Backend = None,
     ):
-        """This class serves as a base class for organizing the search parameter optimization process, which defines the parameters used for search.
+        """This class serves as a base class for the search parameter optimization process, which defines the parameters used for search.
 
         Parameters
         ----------

From bd55b6cb3736346404682dfa8d9bcba47a818384 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 5 Aug 2024 10:59:30 +0200
Subject: [PATCH 072/250] update unit test

---
 tests/unit_tests/test_workflow.py | 91 ++++++++++---------------------
 1 file changed, 28 insertions(+), 63 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 9de6eee7..b62737a9 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -664,29 +664,20 @@ def test_targeted_ms2_optimizer():
         workflow,
     )
 
-    assert optimizer.has_converged is False
     assert optimizer.parameter_name == "ms2_error"
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    for current_step in range(workflow.config["calibration"]["min_steps"]):
+        assert optimizer.has_converged is False
 
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 0
+        workflow.fdr_manager._current_version += 1
+        optimizer.step(
+            calibration_test_df1, calibration_test_df2, current_step=current_step
+        )
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
-
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 1
-
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+        assert workflow.optimization_manager.classifier_version == current_step
 
     assert optimizer.has_converged is True
-    assert workflow.optimization_manager.classifier_version == 2
-
     assert workflow.optimization_manager.ms2_error == optimizer.target_parameter
-    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_rt_optimizer():
@@ -703,29 +694,20 @@ def test_targeted_rt_optimizer():
         workflow,
     )
 
-    assert optimizer.has_converged is False
     assert optimizer.parameter_name == "rt_error"
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    for current_step in range(workflow.config["calibration"]["min_steps"]):
+        assert optimizer.has_converged is False
 
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 0
+        workflow.fdr_manager._current_version += 1
+        optimizer.step(
+            calibration_test_df1, calibration_test_df2, current_step=current_step
+        )
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
-
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 1
-
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+        assert workflow.optimization_manager.classifier_version == current_step
 
     assert optimizer.has_converged is True
-    assert workflow.optimization_manager.classifier_version == 2
-
     assert workflow.optimization_manager.rt_error == optimizer.target_parameter
-    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_ms1_optimizer():
@@ -742,29 +724,20 @@ def test_targeted_ms1_optimizer():
         workflow,
     )
 
-    assert optimizer.has_converged is False
     assert optimizer.parameter_name == "ms1_error"
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    for current_step in range(workflow.config["calibration"]["min_steps"]):
+        assert optimizer.has_converged is False
 
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 0
+        workflow.fdr_manager._current_version += 1
+        optimizer.step(
+            calibration_test_df1, calibration_test_df2, current_step=current_step
+        )
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
-
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 1
-
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+        assert workflow.optimization_manager.classifier_version == current_step
 
     assert optimizer.has_converged is True
-    assert workflow.optimization_manager.classifier_version == 2
-
     assert workflow.optimization_manager.ms1_error == optimizer.target_parameter
-    assert workflow.optimization_manager.classifier_version == 2
 
 
 def test_targeted_mobility_optimizer():
@@ -781,26 +754,18 @@ def test_targeted_mobility_optimizer():
         workflow,
     )
 
-    assert optimizer.has_converged is False
     assert optimizer.parameter_name == "mobility_error"
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
-
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 0
-
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+    for current_step in range(workflow.config["calibration"]["min_steps"]):
+        assert optimizer.has_converged is False
 
-    assert optimizer.has_converged is False
-    assert workflow.optimization_manager.classifier_version == 1
+        workflow.fdr_manager._current_version += 1
+        optimizer.step(
+            calibration_test_df1, calibration_test_df2, current_step=current_step
+        )
 
-    workflow.fdr_manager._current_version += 1
-    optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+        assert workflow.optimization_manager.classifier_version == current_step
 
     assert optimizer.has_converged is True
-    assert workflow.optimization_manager.classifier_version == 2
 
     assert workflow.optimization_manager.mobility_error == optimizer.target_parameter
-    assert workflow.optimization_manager.classifier_version == 2

From fe5e0ea49a800656244669b0479ec87c480f8dfa Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 6 Aug 2024 22:01:17 +0200
Subject: [PATCH 073/250] fits lock only

---
 alphadia/workflow/peptidecentric.py | 47 +++++++++++++++++++++--------
 1 file changed, 35 insertions(+), 12 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index cb85d4e1..f2eacb51 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -504,6 +504,13 @@ def first_recalibration_and_optimization(
             for optimizer in optimizers:
                 optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
+    def adjust_batch_frag_idx(self, batch_df):
+        num_frags = batch_df["flat_frag_stop_idx"] - batch_df["flat_frag_start_idx"]
+        batch_df["flat_frag_stop_idx"] = num_frags.cumsum()
+        batch_df["flat_frag_start_idx"] = batch_df["flat_frag_stop_idx"].shift(
+            fill_value=0
+        )
+
     def calibration(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
@@ -536,6 +543,23 @@ def calibration(self):
             self.get_optimization_lock()
         )
 
+        self.batch_df = self.spectral_library._precursor_df[
+            self.spectral_library._precursor_df["elution_group_idx"].isin(
+                eg_idxes_for_calibration
+            )
+        ]
+
+        batch_fragment_idx = np.concatenate(
+            [
+                np.arange(row["flat_frag_start_idx"], row["flat_frag_stop_idx"])
+                for i, row in self.batch_df.iterrows()
+            ]
+        )
+        self.batch_fragment_df = self.spectral_library._fragment_df.iloc[
+            batch_fragment_idx
+        ].reset_index(drop=True)
+        self.adjust_batch_frag_idx(self.batch_df)
+
         self.optimization_manager.fit(
             {"classifier_version": self.fdr_manager.current_version}
         )
@@ -563,13 +587,10 @@ def calibration(self):
                         optimizer.plot()
 
                     break
-                batch_df = self.spectral_library._precursor_df[
-                    self.spectral_library._precursor_df["elution_group_idx"].isin(
-                        eg_idxes_for_calibration
-                    )
-                ]
 
-                features_df, fragments_df = self.extract_batch(batch_df)
+                features_df, fragments_df = self.extract_batch(
+                    self.batch_df, self.batch_fragment_df
+                )
 
                 self.reporter.log_string(
                     f"=== Step {current_step}, extracted {len(features_df)} precursors and {len(fragments_df)} fragments ===",
@@ -720,11 +741,11 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         self.calibration_manager.predict(
-            self.spectral_library._precursor_df,
+            self.batch_df,
             "precursor",
         )
 
-        self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
+        self.calibration_manager.predict(self.batch_fragment_df, "fragment")
 
         self.optimization_manager.fit(
             {
@@ -757,7 +778,9 @@ def fdr_correction(self, features_df, df_fragments, version=-1):
             # neptune_run=self.neptune
         )
 
-    def extract_batch(self, batch_df, apply_cutoff=False):
+    def extract_batch(self, batch_df, batch_fragment_df=None, apply_cutoff=False):
+        if batch_fragment_df is None:
+            batch_fragment_df = self.spectral_library._fragment_df
         self.reporter.log_string(
             f"Extracting batch of {len(batch_df)} precursors", verbosity="progress"
         )
@@ -780,7 +803,7 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         extraction = search.HybridCandidateSelection(
             self.dia_data.jitclass(),
             batch_df,
-            self.spectral_library.fragment_df,
+            batch_fragment_df,
             config.jitclass(),
             rt_column=f"rt_{self.optimization_manager.column_type}",
             mobility_column=f"mobility_{self.optimization_manager.column_type}"
@@ -828,8 +851,8 @@ def extract_batch(self, batch_df, apply_cutoff=False):
 
         candidate_scoring = plexscoring.CandidateScoring(
             self.dia_data.jitclass(),
-            self.spectral_library._precursor_df,
-            self.spectral_library._fragment_df,
+            batch_df,
+            batch_fragment_df,
             config=config,
             rt_column=f"rt_{self.optimization_manager.column_type}",
             mobility_column=f"mobility_{self.optimization_manager.column_type}"

From b5ea3bb5211baedc854e24e69464baaed4f6a69c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 7 Aug 2024 09:07:58 +0200
Subject: [PATCH 074/250] modify_e2e_test_for_automatic_calibration

---
 tests/e2e_tests/e2e_test_cases.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 74e842ba..4b286aa9 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -119,9 +119,9 @@ test_cases:
         target_rt_tolerance: -1
       search_initial:
         initial_num_candidates: 1
-        initial_ms1_tolerance: 10
-        initial_ms2_tolerance: 15
-        initial_rt_tolerance: 300
+        initial_ms1_tolerance: 100
+        initial_ms2_tolerance: 100
+        initial_rt_tolerance: 600
       search_output:
         peptide_level_lfq: true
         precursor_level_lfq: true

From a8e196afcf84f9f5687eef65aca96082cbc0a02c Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 12:49:34 +0200
Subject: [PATCH 075/250] #296: make saving of libraries optional

---
 alphadia/constants/default.yaml |  3 +++
 alphadia/outputtransform.py     | 10 +++++-----
 alphadia/planning.py            |  4 +++-
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index d936fd5f..948524d3 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -185,6 +185,9 @@ search_output:
   normalize_lfq: True
   # can be either "parquet" or "tsv"
   file_format: "tsv"
+  # whether to save the (MBR) library to the output directory
+  save_library: True
+  save_mbr_library: True
 
 # configuration for the optimization manager
 # initial parameters, will nbe optimized
diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index c9d1c1d3..d37f49dd 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -365,7 +365,11 @@ def build(
         )
         _ = self.build_stat_df(folder_list, psm_df=psm_df, save=True)
         _ = self.build_lfq_tables(folder_list, psm_df=psm_df, save=True)
-        _ = self.build_library(base_spec_lib, psm_df=psm_df, save=True)
+        _ = self.build_library(
+            base_spec_lib,
+            psm_df=psm_df,
+            save=self.config["general"]["save_mbr_library"],
+        )
 
         if self.config["transfer_library"]["enabled"]:
             _ = self.build_transfer_library(folder_list, save=True)
@@ -804,14 +808,10 @@ def build_library(
         precursor_number = len(mbr_spec_lib.precursor_df)
         protein_number = mbr_spec_lib.precursor_df.proteins.nunique()
 
-        # use comma to separate thousands
         logger.info(
             f"MBR spectral library contains {precursor_number:,} precursors, {protein_number:,} proteins"
         )
 
-        logger.info("Writing MBR spectral library to disk")
-        mbr_spec_lib.save_hdf(os.path.join(self.output_folder, "speclib.mbr.hdf"))
-
         if save:
             logger.info("Writing MBR spectral library to disk")
             mbr_spec_lib.save_hdf(os.path.join(self.output_folder, "speclib.mbr.hdf"))
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 0ce9fad9..0a02d02e 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -361,11 +361,13 @@ def run(
         except Exception as e:
             _log_exception_event(e)
             raise e
+        finally:
+            self.clean()
 
         logger.progress("=================== Search Finished ===================")
 
     def clean(self):
-        if not self.config["library_loading"]["save_hdf"]:
+        if not self.config["general"]["save_library"]:
             os.remove(os.path.join(self.output_folder, "speclib.hdf"))
 
 

From d84f7578631fff7f10f170aca85960b8fd3cd62a Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 12:58:51 +0200
Subject: [PATCH 076/250] #296: set tqdm log minimum to 5 seconds

---
 alphadia/fdrexperimental.py        | 4 ++--
 alphadia/outputaccumulator.py      | 3 ++-
 alphadia/transferlearning/train.py | 2 +-
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/alphadia/fdrexperimental.py b/alphadia/fdrexperimental.py
index c8d4cc20..901258eb 100644
--- a/alphadia/fdrexperimental.py
+++ b/alphadia/fdrexperimental.py
@@ -368,7 +368,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
         y_test = torch.from_numpy(y_test).float().to(self.device)
 
         batch_count = 0
-        for epoch in tqdm(range(self.epochs)):
+        for epoch in tqdm(range(self.epochs), mininterval=5):
             train_loss_sum = 0.0
             test_loss_sum = 0.0
 
@@ -1122,7 +1122,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
 
         batch_count = 0
 
-        for epoch in tqdm(range(self.epochs)):
+        for epoch in tqdm(range(self.epochs), mininterval=5):
             # shuffle batches
             order = np.random.permutation(num_batches)
             batch_start_list = batch_start_list[order]
diff --git a/alphadia/outputaccumulator.py b/alphadia/outputaccumulator.py
index 4d0ef135..63010c63 100644
--- a/alphadia/outputaccumulator.py
+++ b/alphadia/outputaccumulator.py
@@ -553,7 +553,8 @@ def ms2_quality_control(
                 precursor_df["frag_stop_idx"],
                 strict=True,
             )
-        )
+        ),
+        mininterval=5,
     ):
         # get XIC correlations and intensities for the precursor
         fragment_correlation_view = fragment_correlation_df.iloc[start_idx:stop_idx]
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 69eaeed7..343be3a2 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -287,7 +287,7 @@ def _order_intensities(
             The reordered fragment intensity dataframe.
         """
         reordered = unordered_frag_df.copy()
-        for i in tqdm(range(len(reordered_precursor_df))):
+        for i in tqdm(range(len(reordered_precursor_df)), mininterval=5):
             new_start_idx = reordered_precursor_df.iloc[i]["frag_start_idx"]
             new_end_idx = reordered_precursor_df.iloc[i]["frag_stop_idx"]
 

From bd6485eb87ceacac31c536f433912c8a855525d9 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 12:59:32 +0200
Subject: [PATCH 077/250] #296: add debugging instructions

---
 alphadia/cli.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/alphadia/cli.py b/alphadia/cli.py
index 581db62b..b67165fd 100644
--- a/alphadia/cli.py
+++ b/alphadia/cli.py
@@ -352,3 +352,13 @@ def run(*args, **kwargs):
 
         logger.error(e)
         sys.exit(exit_code)
+
+
+# To debug e2e tests with PyCharm:
+# create a configuration with
+# - module: alphadia.cli
+# - script parameters: "--config /abs/path/to/tests/e2e_tests/basic/config.yaml"
+# - working directory: "/abs/path/to/tests/e2e_tests"
+# - uncomment the following:
+# if __name__ == "__main__":
+#     run()

From 5f0c3c82b66d70f6f9f3eeb56cc78e90d72dd519 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:00:00 +0200
Subject: [PATCH 078/250] #296: fix bug when error occurs before reporter is
 instantiated

---
 alphadia/planning.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/alphadia/planning.py b/alphadia/planning.py
index 0a02d02e..57481f4a 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -346,8 +346,9 @@ def run(
                 raise e
 
             finally:
-                workflow.reporter.log_string(f"Finished workflow for {raw_name}")
-                workflow.reporter.context.__exit__(None, None, None)
+                if workflow.reporter:
+                    workflow.reporter.log_string(f"Finished workflow for {raw_name}")
+                    workflow.reporter.context.__exit__(None, None, None)
                 del workflow
 
         try:

From 98a4b48486302766c15af2b5b7bae4103e3d0987 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:05:57 +0200
Subject: [PATCH 079/250] #296: fix bug when two levels of parent path do not
 exist

---
 alphadia/workflow/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index b06fc5d7..dadfcc95 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -51,7 +51,7 @@ def __init__(
 
         if not os.path.exists(self.parent_path):
             logger.info(f"Creating parent folder for workflows at {self.parent_path}")
-            os.mkdir(self.parent_path)
+            os.makedirs(self.parent_path)
 
         if not os.path.exists(self.path):
             logger.info(

From 430b7727d1f8e4bf1a112f975fe5b8029e322d33 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:06:25 +0200
Subject: [PATCH 080/250] #296: declare variables in __init__

---
 alphadia/workflow/base.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index dadfcc95..65fb0043 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -45,9 +45,14 @@ def __init__(
             Configuration for the workflow. This will be used to initialize the calibration manager and fdr manager
 
         """
-        self._instance_name = instance_name
-        self._parent_path = os.path.join(config["output"], TEMP_FOLDER)
-        self._config = config
+        self._instance_name: str = instance_name
+        self._parent_path: str = os.path.join(config["output"], TEMP_FOLDER)
+        self._config: dict = config
+        self.reporter: reporting.Pipeline | None = None
+        self._dia_data: bruker.TimsTOFTranspose | alpharaw.AlphaRaw | None = None
+        self._spectral_library: SpecLibBase | None = None
+        self._calibration_manager: manager.CalibrationManager | None = None
+        self._optimization_manager: manager.OptimizationManager | None = None
 
         if not os.path.exists(self.parent_path):
             logger.info(f"Creating parent folder for workflows at {self.parent_path}")

From 17acc734d0cd2a2945b3fff0c961dbe6502c3fa0 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:32:27 +0200
Subject: [PATCH 081/250] #296: remove tqdm settings from code, set in e2e test

---
 .github/workflows/e2e_testing.yml  | 1 +
 alphadia/fdrexperimental.py        | 4 ++--
 alphadia/outputaccumulator.py      | 3 +--
 alphadia/transferlearning/train.py | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/e2e_testing.yml b/.github/workflows/e2e_testing.yml
index 0a6c083a..87952292 100644
--- a/.github/workflows/e2e_testing.yml
+++ b/.github/workflows/e2e_testing.yml
@@ -24,6 +24,7 @@ jobs:
       NUMBA_BOUNDSCHECK: 1
       NUMBA_DEVELOPER_MODE: 1
       NUMBA_FULL_TRACEBACKS: 1
+      TQDM_MININTERVAL: 10  # avoid lots of tqdm outputs
     steps:
       - uses: actions/checkout@v4
       - name: Conda info
diff --git a/alphadia/fdrexperimental.py b/alphadia/fdrexperimental.py
index 901258eb..c8d4cc20 100644
--- a/alphadia/fdrexperimental.py
+++ b/alphadia/fdrexperimental.py
@@ -368,7 +368,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
         y_test = torch.from_numpy(y_test).float().to(self.device)
 
         batch_count = 0
-        for epoch in tqdm(range(self.epochs), mininterval=5):
+        for epoch in tqdm(range(self.epochs)):
             train_loss_sum = 0.0
             test_loss_sum = 0.0
 
@@ -1122,7 +1122,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
 
         batch_count = 0
 
-        for epoch in tqdm(range(self.epochs), mininterval=5):
+        for epoch in tqdm(range(self.epochs)):
             # shuffle batches
             order = np.random.permutation(num_batches)
             batch_start_list = batch_start_list[order]
diff --git a/alphadia/outputaccumulator.py b/alphadia/outputaccumulator.py
index 63010c63..4d0ef135 100644
--- a/alphadia/outputaccumulator.py
+++ b/alphadia/outputaccumulator.py
@@ -553,8 +553,7 @@ def ms2_quality_control(
                 precursor_df["frag_stop_idx"],
                 strict=True,
             )
-        ),
-        mininterval=5,
+        )
     ):
         # get XIC correlations and intensities for the precursor
         fragment_correlation_view = fragment_correlation_df.iloc[start_idx:stop_idx]
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 343be3a2..69eaeed7 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -287,7 +287,7 @@ def _order_intensities(
             The reordered fragment intensity dataframe.
         """
         reordered = unordered_frag_df.copy()
-        for i in tqdm(range(len(reordered_precursor_df)), mininterval=5):
+        for i in tqdm(range(len(reordered_precursor_df))):
             new_start_idx = reordered_precursor_df.iloc[i]["frag_start_idx"]
             new_end_idx = reordered_precursor_df.iloc[i]["frag_stop_idx"]
 

From 7fffdb79118e04a7c6213ae2dae64b1742d69876 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:32:40 +0200
Subject: [PATCH 082/250] #296: mov config keys to correct place

---
 alphadia/constants/default.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 948524d3..034a30f3 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -11,6 +11,9 @@ general:
   wsl: false
   mmap_detector_events: false
   use_gpu: true
+  # whether to save the (MBR) library to the output directory
+  save_library: True
+  save_mbr_library: True
 
 library_loading:
   rt_heuristic: 180
@@ -185,9 +188,6 @@ search_output:
   normalize_lfq: True
   # can be either "parquet" or "tsv"
   file_format: "tsv"
-  # whether to save the (MBR) library to the output directory
-  save_library: True
-  save_mbr_library: True
 
 # configuration for the optimization manager
 # initial parameters, will nbe optimized

From 03fc914449c7fabecd894707e2718118a9217d4c Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 13:54:57 +0200
Subject: [PATCH 083/250] #296: allow to skip neptune upload

---
 .github/workflows/e2e_testing.yml |  2 +-
 tests/e2e_tests/calc_metrics.py   | 11 +++++++++--
 tests/run_e2e_tests.sh            |  7 ++++---
 3 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/e2e_testing.yml b/.github/workflows/e2e_testing.yml
index 87952292..04cfaa24 100644
--- a/.github/workflows/e2e_testing.yml
+++ b/.github/workflows/e2e_testing.yml
@@ -40,7 +40,7 @@ jobs:
         shell: bash -el {0}
         run: |
           cd tests
-           . ./run_e2e_tests.sh ${{ matrix.test_case }} $RUN_NAME ${GITHUB_SHA::7} $BRANCH_NAME
+           . ./run_e2e_tests.sh ${{ matrix.test_case }} $RUN_NAME True ${GITHUB_SHA::7} $BRANCH_NAME
       - name: Cleanup
         if: always()
         shell: bash -el {0}
diff --git a/tests/e2e_tests/calc_metrics.py b/tests/e2e_tests/calc_metrics.py
index 5c86f9d7..fdb2cc32 100644
--- a/tests/e2e_tests/calc_metrics.py
+++ b/tests/e2e_tests/calc_metrics.py
@@ -184,8 +184,9 @@ def _get_history_plots(test_results: dict, metrics_classes: list):
 if __name__ == "__main__":
     test_case_name = sys.argv[1]
     run_time_minutes = int(sys.argv[2]) / 60
-    short_sha = sys.argv[3]
-    branch_name = sys.argv[4]
+    neptune_upload = sys.argv[3] == "True"
+    short_sha = sys.argv[4]
+    branch_name = sys.argv[5]
 
     test_case = get_test_case(test_case_name)
     selected_metrics = test_case["metrics"]  # ['BasicStats', ]
@@ -213,6 +214,12 @@ def _get_history_plots(test_results: dict, metrics_classes: list):
     except Exception as e:
         print(e)
     finally:
+        print(test_results)
+
+        if not neptune_upload:
+            print("skipping neptune upload")
+            exit(0)
+
         neptune_run = neptune.init_run(
             project=NEPTUNE_PROJECT_NAME,
             tags=[test_case_name, short_sha, branch_name],
diff --git a/tests/run_e2e_tests.sh b/tests/run_e2e_tests.sh
index 8de69669..dfd46c7d 100755
--- a/tests/run_e2e_tests.sh
+++ b/tests/run_e2e_tests.sh
@@ -2,8 +2,9 @@
 set -e -u
 TEST_CASE_NAME=$1
 ENV_NAME=$2
-SHORT_SHA=${3:-sha_na}
-BRANCH_NAME=${4:-branch_na}
+NEPTUNE_UPLOAD=${3:-False}
+SHORT_SHA=${4:-sha_na}
+BRANCH_NAME=${5:-branch_na}
 
 cd e2e_tests
 
@@ -18,6 +19,6 @@ ls */*
 
 RUN_TIME=$(($(date +%s) - $TIMESTAMP_START))
 
-conda run -n $ENV_NAME --no-capture-output python calc_metrics.py $TEST_CASE_NAME $RUN_TIME $SHORT_SHA $BRANCH_NAME
+conda run -n $ENV_NAME --no-capture-output python calc_metrics.py $TEST_CASE_NAME $RUN_TIME $NEPTUNE_UPLOAD $SHORT_SHA $BRANCH_NAME
 
 cd -

From e4d9a1464d3b3bd939811fd32776ffe8add07d1a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 7 Aug 2024 14:05:54 +0200
Subject: [PATCH 084/250] refactor optimization lock

---
 alphadia/workflow/peptidecentric.py | 65 ++++++++++++++++-------------
 tests/e2e_tests/e2e_test_cases.yaml |  6 +--
 2 files changed, 40 insertions(+), 31 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index f2eacb51..cbeb15bb 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -291,7 +291,7 @@ def get_optimization_lock(self):
 
         Returns
         -------
-        eg_idxes_for_calibration : np.ndarray
+        optimization_lock_eg_idxes : np.ndarray
             The indices (in .spectral_library._precursor_df) of the precursors which will be used for calibration.
 
         precursor_df : pd.DataFrame
@@ -365,11 +365,31 @@ def get_optimization_lock(self):
                 final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
                 break
 
-        eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
-        return eg_idxes_for_calibration, precursor_df, fragments_df
+        optimization_lock_eg_idxes = self.elution_group_order[:final_stop_index]
 
-    #        self.eg_idxes_for_calibration = self.elution_group_order[:final_stop_index]
-    #        self.optimization_manager.fit({"classifier_version": self.fdr_manager.current_version})
+        optimization_lock_library_precursor_df = self.spectral_library._precursor_df[
+            self.spectral_library._precursor_df["elution_group_idx"].isin(
+                optimization_lock_eg_idxes
+            )
+        ]
+
+        optimization_lock_fragment_idxes = np.concatenate(
+            [
+                np.arange(row["flat_frag_start_idx"], row["flat_frag_stop_idx"])
+                for i, row in optimization_lock_library_precursor_df.iterrows()
+            ]
+        )
+        optimization_lock_library_fragment_df = self.spectral_library._fragment_df.iloc[
+            optimization_lock_fragment_idxes
+        ].reset_index(drop=True)
+        self.adjust_batch_frag_idx(optimization_lock_library_precursor_df)
+
+        return (
+            optimization_lock_library_precursor_df,
+            optimization_lock_library_fragment_df,
+            precursor_df,
+            fragments_df,
+        )
 
     def get_ordered_optimizers(self):
         """Select appropriate optimizers. Targeted optimization is used if a valid target value (i.e. a number greater than 0) is specified in the config;
@@ -539,26 +559,12 @@ def calibration(self):
         )
 
         # Get the optimization lock
-        eg_idxes_for_calibration, precursor_df, fragments_df = (
-            self.get_optimization_lock()
-        )
-
-        self.batch_df = self.spectral_library._precursor_df[
-            self.spectral_library._precursor_df["elution_group_idx"].isin(
-                eg_idxes_for_calibration
-            )
-        ]
-
-        batch_fragment_idx = np.concatenate(
-            [
-                np.arange(row["flat_frag_start_idx"], row["flat_frag_stop_idx"])
-                for i, row in self.batch_df.iterrows()
-            ]
-        )
-        self.batch_fragment_df = self.spectral_library._fragment_df.iloc[
-            batch_fragment_idx
-        ].reset_index(drop=True)
-        self.adjust_batch_frag_idx(self.batch_df)
+        (
+            self.optimization_lock_library_precursor_df,
+            self.optimization_lock_library_fragment_df,
+            precursor_df,
+            fragments_df,
+        ) = self.get_optimization_lock()
 
         self.optimization_manager.fit(
             {"classifier_version": self.fdr_manager.current_version}
@@ -589,7 +595,8 @@ def calibration(self):
                     break
 
                 features_df, fragments_df = self.extract_batch(
-                    self.batch_df, self.batch_fragment_df
+                    self.optimization_lock_library_precursor_df,
+                    self.optimization_lock_library_fragment_df,
                 )
 
                 self.reporter.log_string(
@@ -741,11 +748,13 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         self.calibration_manager.predict(
-            self.batch_df,
+            self.optimization_lock_library_precursor_df,
             "precursor",
         )
 
-        self.calibration_manager.predict(self.batch_fragment_df, "fragment")
+        self.calibration_manager.predict(
+            self.optimization_lock_library_fragment_df, "fragment"
+        )
 
         self.optimization_manager.fit(
             {
diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 74e842ba..4b286aa9 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -119,9 +119,9 @@ test_cases:
         target_rt_tolerance: -1
       search_initial:
         initial_num_candidates: 1
-        initial_ms1_tolerance: 10
-        initial_ms2_tolerance: 15
-        initial_rt_tolerance: 300
+        initial_ms1_tolerance: 100
+        initial_ms2_tolerance: 100
+        initial_rt_tolerance: 600
       search_output:
         peptide_level_lfq: true
         precursor_level_lfq: true

From 3e6e811b7e51169a906bb2067ecbcd9bf30a7c0e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 14:36:25 +0200
Subject: [PATCH 085/250] #296: tweak install instructions

---
 docs/installation.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 329f9104..11211001 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -39,7 +39,7 @@ A detailed guide to installing alphaRaw with mono can be found [here](https://gi
 It is highly recommended to use a conda environment for the installation of alphaDIA.
 This ensures that all dependencies are installed correctly and do not interfere with other packages.
 ```bash
-conda create -n alphadia python=3.11
+conda create -n alphadia python=3.11 -y
 conda activate alphadia
 ```
 
@@ -169,7 +169,7 @@ Check if conda is available on your cluster. If not, install it, or, if provided
 
 Create and activate a new conda environment
 ```bash
-conda create -n alphadia python=3.11
+conda create -n alphadia python=3.11 -y
 conda activate alphadia
 ```
 ### 2. Installing mono

From 26cb82e8e163dc08db917c12573c3eba3512bc71 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Thu, 8 Aug 2024 10:59:11 +0200
Subject: [PATCH 086/250] fix test case data

---
 tests/e2e_tests/e2e_test_cases.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/e2e_tests/e2e_test_cases.yaml b/tests/e2e_tests/e2e_test_cases.yaml
index 4b286aa9..7c5cf79d 100644
--- a/tests/e2e_tests/e2e_test_cases.yaml
+++ b/tests/e2e_tests/e2e_test_cases.yaml
@@ -92,9 +92,9 @@ test_cases:
     fasta:
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=2024_01_12_human.fasta
     raw_data:
-      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_02.raw
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_03.raw
     metrics:
       - BasicStats
 
@@ -128,9 +128,9 @@ test_cases:
     fasta:
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=2024_01_12_human.fasta
     raw_data:
-      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_01.raw
       - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_02.raw
+      - source_url: https://datashare.biochem.mpg.de/s/WTu3rFZHNeb3uG2/download?files=20231024_OA3_TiHe_ADIAMA_HeLa_200ng_Evo01_21min_F-40_iO_before_03.raw
     metrics:
       - BasicStats
 

From 62312118a8dd9e462e2ecfb3da8f0728ee4ce8cd Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Thu, 8 Aug 2024 14:04:44 +0200
Subject: [PATCH 087/250] #299: add slurm e2e script

---
 tests/e2e_tests/e2e_slurm.sh | 54 ++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 tests/e2e_tests/e2e_slurm.sh

diff --git a/tests/e2e_tests/e2e_slurm.sh b/tests/e2e_tests/e2e_slurm.sh
new file mode 100644
index 00000000..ee4db7e8
--- /dev/null
+++ b/tests/e2e_tests/e2e_slurm.sh
@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+# A simple SLURM script to run AlphaDIA end2end tests on a SLURM cluster.
+# Prerequisites:
+# - conda environment with working AlphaDIA installation
+# - input parameters (see below) set to desired values
+# - (optional) SBATCH directives adapted to current use case
+#SBATCH --job-name=alphadia
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=24
+#SBATCH --mem=128G
+#SBATCH --time=01:00:00
+
+# input parameters:
+CONDA_ENV=alphadia
+BRANCH=development  # branch to take test cases from
+TEST_CASE=basic
+
+#
+set -u -e
+OUTPUT_FOLDER=output_${CONDA_ENV}_${TEST_CASE}_${SLURM_JOB_ID}
+URL=https://raw.githubusercontent.com/MannLabs/alphadia/${BRANCH}/tests
+export TQDM_MININTERVAL=10  # avoid lots of tqdm outputs
+
+echo CONDA_ENV=$CONDA_ENV
+echo BRANCH=$BRANCH
+echo OUTPUT_FOLDER=$OUTPUT_FOLDER
+
+echo CONDA_ENV ">>>>>>"
+conda info
+conda run -n $CONDA_ENV pip freeze
+echo "<<<<<<"
+
+echo MONO_VERSION ">>>>>>"
+conda run -n $CONDA_ENV mono --version
+echo "<<<<<<"
+
+echo "Preparing test data.."
+
+mkdir -p $OUTPUT_FOLDER && cd $OUTPUT_FOLDER
+
+# note: if the locations of these files change, this script will need to be updated
+wget $URL/run_e2e_tests.sh
+
+mkdir -p e2e_tests && cd e2e_tests
+wget $URL/e2e_tests/e2e_test_cases.yaml
+wget $URL/e2e_tests/prepare_test_data.py
+wget $URL/e2e_tests/calc_metrics.py
+cd ..
+
+chmod +x ./run_e2e_tests.sh
+
+echo "Running alphadia.."
+./run_e2e_tests.sh $TEST_CASE $CONDA_ENV

From de0fd84e92cc43c73565ad24ba42adbd6c980edc Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 12 Aug 2024 11:51:37 +0200
Subject: [PATCH 088/250] refactor fragment index adjustment

---
 alphadia/workflow/peptidecentric.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index cbeb15bb..7bc98ca0 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -379,10 +379,25 @@ def get_optimization_lock(self):
                 for i, row in optimization_lock_library_precursor_df.iterrows()
             ]
         )
+
+        # Extract the fragments for the optimization lock and reset the indices to a consecutive range of positive integers. This simplifies future access based on position
         optimization_lock_library_fragment_df = self.spectral_library._fragment_df.iloc[
             optimization_lock_fragment_idxes
         ].reset_index(drop=True)
-        self.adjust_batch_frag_idx(optimization_lock_library_precursor_df)
+
+        # Change the fragment indices in the precursor_df to match the fragment indices in the optimization lock fragment_df instead of the full spectral library.
+        num_frags = (
+            optimization_lock_library_precursor_df["flat_frag_stop_idx"]
+            - optimization_lock_library_precursor_df["flat_frag_start_idx"]
+        )
+        optimization_lock_library_precursor_df["flat_frag_stop_idx"] = (
+            num_frags.cumsum()
+        )
+        optimization_lock_library_precursor_df["flat_frag_start_idx"] = (
+            optimization_lock_library_precursor_df[
+                "flat_frag_stop_idx"
+            ].shift(fill_value=0)
+        )
 
         return (
             optimization_lock_library_precursor_df,
@@ -524,13 +539,6 @@ def first_recalibration_and_optimization(
             for optimizer in optimizers:
                 optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
-    def adjust_batch_frag_idx(self, batch_df):
-        num_frags = batch_df["flat_frag_stop_idx"] - batch_df["flat_frag_start_idx"]
-        batch_df["flat_frag_stop_idx"] = num_frags.cumsum()
-        batch_df["flat_frag_start_idx"] = batch_df["flat_frag_stop_idx"].shift(
-            fill_value=0
-        )
-
     def calibration(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.

From cec81639e77a669ebd0b3d62f249d53892d1dc19 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 13 Aug 2024 10:17:39 +0200
Subject: [PATCH 089/250] add parameters to stat.tsv and refactor optimization
 manager

---
 alphadia/outputtransform.py              | 20 ++++++++++++++++++--
 alphadia/workflow/base.py                | 17 ++---------------
 alphadia/workflow/manager.py             | 14 +++++++++++++-
 tests/unit_tests/test_outputtransform.py | 18 ++++++++++++++++++
 4 files changed, 51 insertions(+), 18 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index c9d1c1d3..c0f21317 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -1,6 +1,7 @@
 # native imports
 import logging
 import os
+import pickle
 from collections.abc import Iterator
 
 import directlfq.config as lfqconfig
@@ -653,9 +654,13 @@ def build_stat_df(
         stat_df_list = []
         for folder in folder_list:
             raw_name = os.path.basename(folder)
+            optimization_manager_path = os.path.join(folder, "optimization_manager.pkl")
             stat_df_list.append(
                 _build_run_stat_df(
-                    raw_name, psm_df[psm_df["run"] == raw_name], all_channels
+                    raw_name,
+                    psm_df[psm_df["run"] == raw_name],
+                    optimization_manager_path,
+                    all_channels,
                 )
             )
 
@@ -820,7 +825,10 @@ def build_library(
 
 
 def _build_run_stat_df(
-    raw_name: str, run_df: pd.DataFrame, channels: list[int] | None = None
+    raw_name: str,
+    run_df: pd.DataFrame,
+    optimization_manager_path: str,
+    channels: list[int] | None = None,
 ):
     """Build stat dataframe for a single run.
 
@@ -866,6 +874,14 @@ def _build_run_stat_df(
         if "mobility_fwhm" in channel_df.columns:
             base_dict["fwhm_mobility"] = np.mean(channel_df["mobility_fwhm"])
 
+        with open(optimization_manager_path, "rb") as f:
+            optimization_manager = pickle.load(f)
+
+        base_dict["ms2_error"] = optimization_manager.ms2_error
+        base_dict["ms1_error"] = optimization_manager.ms1_error
+        base_dict["rt_error"] = optimization_manager.rt_error
+        base_dict["mobility_error"] = optimization_manager.mobility_error
+
         out_df.append(base_dict)
 
     return pd.DataFrame(out_df)
diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index 3268fd5d..dfd6528b 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -95,22 +95,9 @@ def load(
             self._calibration_manager.disable_mobility_calibration()
 
         # initialize the optimization manager
-        optimization_manager_config = {
-            "ms1_error": self.config["search_initial"]["initial_ms1_tolerance"],
-            "ms2_error": self.config["search_initial"]["initial_ms2_tolerance"],
-            "rt_error": self.config["search_initial"]["initial_rt_tolerance"],
-            "mobility_error": self.config["search_initial"][
-                "initial_mobility_tolerance"
-            ],
-            "column_type": "library",
-            "num_candidates": self.config["search_initial"]["initial_num_candidates"],
-            "classifier_version": -1,
-            "fwhm_rt": self.config["optimization_manager"]["fwhm_rt"],
-            "fwhm_mobility": self.config["optimization_manager"]["fwhm_mobility"],
-            "score_cutoff": self.config["optimization_manager"]["score_cutoff"],
-        }
+
         self._optimization_manager = manager.OptimizationManager(
-            optimization_manager_config,
+            self.config,
             path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH),
             load_from_file=self.config["general"]["reuse_calibration"],
             figure_path=os.path.join(self.path, self.FIGURE_PATH),
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index af73b947..0347550d 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -454,7 +454,7 @@ def fit_predict(
 class OptimizationManager(BaseManager):
     def __init__(
         self,
-        initial_parameters: dict,
+        config: dict,
         path: None | str = None,
         load_from_file: bool = True,
         **kwargs,
@@ -463,6 +463,18 @@ def __init__(
         self.reporter.log_string(f"Initializing {self.__class__.__name__}")
         self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
 
+        initial_parameters = {
+            "ms1_error": config["search_initial"]["initial_ms1_tolerance"],
+            "ms2_error": config["search_initial"]["initial_ms2_tolerance"],
+            "rt_error": config["search_initial"]["initial_rt_tolerance"],
+            "mobility_error": config["search_initial"]["initial_mobility_tolerance"],
+            "column_type": "library",
+            "num_candidates": config["search_initial"]["initial_num_candidates"],
+            "classifier_version": -1,
+            "fwhm_rt": config["optimization_manager"]["fwhm_rt"],
+            "fwhm_mobility": config["optimization_manager"]["fwhm_mobility"],
+            "score_cutoff": config["optimization_manager"]["score_cutoff"],
+        }
         if not self.is_loaded_from_file:
             self.__dict__.update(initial_parameters)
 
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 350432e9..3af2faba 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -7,6 +7,7 @@
 from conftest import mock_fragment_df, mock_precursor_df
 
 from alphadia import outputtransform
+from alphadia.workflow import manager
 
 
 def test_output_transform():
@@ -36,6 +37,18 @@ def test_output_transform():
         "multiplexing": {
             "enabled": False,
         },
+        "search_initial": {
+            "initial_ms1_tolerance": 4,
+            "initial_ms2_tolerance": 7,
+            "initial_rt_tolerance": 200,
+            "initial_mobility_tolerance": 0.04,
+            "initial_num_candidates": 1,
+        },
+        "optimization_manager": {
+            "fwhm_rt": 2.75,
+            "fwhm_mobility": 2,
+            "score_cutoff": 50,
+        },
     }
 
     temp_folder = os.path.join(tempfile.gettempdir(), "alphadia")
@@ -62,6 +75,11 @@ def test_output_transform():
         frag_df.to_parquet(os.path.join(raw_folder, "frag.parquet"), index=False)
         psm_df.to_parquet(os.path.join(raw_folder, "psm.parquet"), index=False)
 
+        optimization_manager = manager.OptimizationManager(
+            config, os.path.join(raw_folder, "optimization_manager.pkl")
+        )
+        optimization_manager.fit({"ms2_error": 7})
+
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)
     _ = output.build_stat_df(raw_folders, save=True)

From 5449f1fb28ca916986dbfce3316a2639ce8f286d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 13 Aug 2024 10:33:15 +0200
Subject: [PATCH 090/250] modify workflow unit tests

---
 tests/unit_tests/test_workflow.py | 41 ++++++++++++++-----------------
 1 file changed, 18 insertions(+), 23 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index b62737a9..48b524d4 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -206,14 +206,24 @@ def test_calibration_manager_save_load():
     os.remove(temp_path)
 
 
-OPTIMIZATION_TEST_DATA = {
-    "fwhm_cycles": 5,
-    "fwhm_mobility": 0.01,
+OPTIMIZATION_CONFIG = {
+    "search_initial": {
+        "initial_ms1_tolerance": 4,
+        "initial_ms2_tolerance": 7,
+        "initial_rt_tolerance": 200,
+        "initial_mobility_tolerance": 0.04,
+        "initial_num_candidates": 1,
+    },
+    "optimization_manager": {
+        "fwhm_rt": 2.75,
+        "fwhm_mobility": 2,
+        "score_cutoff": 50,
+    },
 }
 
 
 def test_optimization_manager():
-    optimization_manager = manager.OptimizationManager(OPTIMIZATION_TEST_DATA)
+    optimization_manager = manager.OptimizationManager(OPTIMIZATION_CONFIG)
 
     assert optimization_manager.fwhm_cycles == 5
     assert optimization_manager.fwhm_mobility == 0.01
@@ -226,7 +236,7 @@ def test_optimization_manager_save_load():
     temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
 
     optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_TEST_DATA, path=temp_path, load_from_file=False
+        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
     )
 
     assert optimization_manager.is_loaded_from_file is False
@@ -235,7 +245,7 @@ def test_optimization_manager_save_load():
     optimization_manager.save()
 
     optimization_manager_loaded = manager.OptimizationManager(
-        OPTIMIZATION_TEST_DATA, path=temp_path, load_from_file=True
+        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=True
     )
 
     assert optimization_manager_loaded.is_loaded_from_file is True
@@ -247,7 +257,7 @@ def test_optimization_manager_save_load():
 def test_optimization_manager_fit():
     temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
     optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_TEST_DATA, path=temp_path, load_from_file=False
+        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
     )
 
     assert optimization_manager.is_loaded_from_file is False
@@ -410,23 +420,8 @@ def create_workflow_instance():
         reporter=workflow.reporter,
     )
 
-    optimization_manager_config = {
-        "ms1_error": workflow.config["search_initial"]["initial_ms1_tolerance"],
-        "ms2_error": workflow.config["search_initial"]["initial_ms2_tolerance"],
-        "rt_error": workflow.config["search_initial"]["initial_rt_tolerance"],
-        "mobility_error": workflow.config["search_initial"][
-            "initial_mobility_tolerance"
-        ],
-        "column_type": "library",
-        "num_candidates": workflow.config["search_initial"]["initial_num_candidates"],
-        "classifier_version": -1,
-        "fwhm_rt": workflow.config["optimization_manager"]["fwhm_rt"],
-        "fwhm_mobility": workflow.config["optimization_manager"]["fwhm_mobility"],
-        "score_cutoff": workflow.config["optimization_manager"]["score_cutoff"],
-    }
-
     workflow._optimization_manager = manager.OptimizationManager(
-        optimization_manager_config,
+        OPTIMIZATION_CONFIG,
         path=os.path.join(workflow.path, workflow.OPTIMIZATION_MANAGER_PATH),
         load_from_file=workflow.config["general"]["reuse_calibration"],
         figure_path=os.path.join(workflow.path, workflow.FIGURE_PATH),

From e78252e72fcf2018975647e1b836753a06dfe2e9 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 13 Aug 2024 10:45:13 +0200
Subject: [PATCH 091/250] update workflow unit test

---
 tests/unit_tests/test_workflow.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 48b524d4..5b504477 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -215,8 +215,8 @@ def test_calibration_manager_save_load():
         "initial_num_candidates": 1,
     },
     "optimization_manager": {
-        "fwhm_rt": 2.75,
-        "fwhm_mobility": 2,
+        "fwhm_rt": 5,
+        "fwhm_mobility": 0.01,
         "score_cutoff": 50,
     },
 }
@@ -225,7 +225,7 @@ def test_calibration_manager_save_load():
 def test_optimization_manager():
     optimization_manager = manager.OptimizationManager(OPTIMIZATION_CONFIG)
 
-    assert optimization_manager.fwhm_cycles == 5
+    assert optimization_manager.fwhm_rt == 5
     assert optimization_manager.fwhm_mobility == 0.01
 
     assert optimization_manager.is_loaded_from_file is False

From 4d2a9747cceb2737441548fb22d592e9a7aa4190 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Tue, 13 Aug 2024 11:06:22 +0200
Subject: [PATCH 092/250] extend transfer-learning to include mobility

---
 alphadia/transferlearning/train.py | 164 +++++++++++++++++++++++++++++
 nbs/tutorial_nbs/finetuning.ipynb  |  72 ++++++++++---
 2 files changed, 224 insertions(+), 12 deletions(-)

diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 69eaeed7..fb4484f9 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -4,6 +4,7 @@
 import pandas as pd
 import torch
 from alphabase.peptide.fragment import remove_unused_fragments
+from alphabase.peptide.mobility import ccs_to_mobility_for_df, mobility_to_ccs_for_df
 from peptdeep.model.charge import ChargeModelForModAASeq
 from peptdeep.model.model_interface import CallbackHandler, LR_SchedulerInterface
 from peptdeep.pretrained_models import ModelManager
@@ -896,3 +897,166 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         metrics = test_metric_manager.get_stats()
 
         return metrics
+
+    def _test_ccs(
+        self,
+        epoch: int,
+        epoch_loss: float,
+        test_df: pd.DataFrame,
+        metric_accumulator: MetricManager,
+        data_split: str,
+    ) -> bool:
+        """
+        Test the CCS model using the PSM dataframe and accumulate both the training loss and test metrics.
+
+        Parameters
+        ----------
+        epoch : int
+            The current epoch number.
+        epoch_loss : float
+            The train loss value of the current epoch.
+        test_df : pd.DataFrame
+            The PSM dataframe.
+        metric_accumulator : MetricManager
+            The metric manager object.
+        data_split : str
+            The dataset label to test on. e.g. "validation", "train"
+        Returns
+        -------
+        bool
+            Whether to continue training or not based on the early stopping criteria.
+        """
+        continue_training = True
+        if epoch % self.settings["test_interval"] == 0:
+            self.ccs_model.model.eval()
+
+            pred = self.ccs_model.predict(test_df)
+
+            test_input = {
+                "predicted": pred["ccs_pred"].values,
+                "target": test_df["ccs"].values,
+            }
+            val_metrics = metric_accumulator.calculate_test_metric(
+                test_input, epoch, data_split=data_split, property_name="ccs"
+            )
+            if epoch != -1 and data_split == "validation":
+                metric_accumulator.accumulate_metrics(
+                    epoch,
+                    metric=epoch_loss,
+                    metric_name="l1_loss",
+                    data_split="train",
+                    property_name="ccs",
+                )
+                current_lr = self.ccs_model.optimizer.param_groups[0]["lr"]
+                metric_accumulator.accumulate_metrics(
+                    epoch,
+                    metric=current_lr,
+                    metric_name="lr",
+                    data_split="train",
+                    property_name="ccs",
+                )
+                val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
+                    "value"
+                ].values[0]
+                continue_training = self.early_stopping.step(val_loss)
+                logger.progress(
+                    f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+                )
+            else:
+                logger.progress(
+                    f" CCS model tested on {data_split} dataset with the following metrics:"
+                )
+                for i in range(len(val_metrics)):
+                    logger.progress(
+                        f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
+                    )
+
+            self.ccs_model.model.train()
+
+        return continue_training
+
+    def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Fine tune the CCS model using the PSM dataframe.
+
+        Parameters
+        ----------
+        psm_df : pd.DataFrame
+            The PSM dataframe.
+
+        Returns
+        -------
+        pd.DataFrame
+            Accumulated metrics during the fine tuning process.
+        """
+        if "mobility" not in psm_df.columns and "ccs" not in psm_df.columns:
+            logger.error(
+                "Failed to finetune CCS model. PSM dataframe does not contain mobility or ccs columns."
+            )
+            return
+        if "ccs" not in psm_df.columns:
+            psm_df["ccs"] = mobility_to_ccs_for_df(psm_df, "mobility")
+        elif "mobility" not in psm_df.columns:
+            psm_df["mobility"] = ccs_to_mobility_for_df(psm_df, "ccs")
+
+        # Shuffle the psm_df and split it into train and test
+        train_df = psm_df.sample(frac=self.settings["train_fraction"])
+        val_df = psm_df.drop(train_df.index).sample(
+            frac=self.settings["validation_fraction"]
+            / (1 - self.settings["train_fraction"])
+        )
+        test_df = psm_df.drop(train_df.index).drop(val_df.index)
+
+        # Create a test metric manager
+        test_metric_manager = MetricManager(
+            test_metrics=[
+                L1LossTestMetric(),
+                LinearRegressionTestMetric(),
+                AbsErrorPercentileTestMetric(95),
+            ],
+        )
+        # Create a callback handler
+        callback_handler = CustomCallbackHandler(
+            self._test_ccs,
+            test_df=val_df,
+            metric_accumulator=test_metric_manager,
+            data_split="validation",
+        )
+        # Set the callback handler
+        self.ccs_model.set_callback_handler(callback_handler)
+
+        # Change the learning rate scheduler
+        self.ccs_model.set_lr_scheduler_class(CustomScheduler)
+
+        # Reset the early stopping
+        self.early_stopping.reset()
+
+        # Test the model before training
+        self._test_ccs(-1, 0, psm_df, test_metric_manager, data_split="all")
+        # Train the model
+        logger.progress(" Fine-tuning CCS model with the following settings:")
+        logger.info(
+            f" Train fraction:      {self.settings['train_fraction']:3.2f}     Train size:      {len(train_df):<10}"
+        )
+        logger.info(
+            f" Validation fraction: {self.settings['validation_fraction']:3.2f}     Validation size: {len(val_df):<10}"
+        )
+        logger.info(
+            f" Test fraction:       {self.settings['test_fraction']:3.2f}     Test size:       {len(test_df):<10}"
+        )
+        self.ccs_model.model.train()
+        self.ccs_model.train(
+            train_df,
+            batch_size=self.settings["batch_size"],
+            epoch=self.settings["epochs"],
+            warmup_epoch=self.settings["warmup_epochs"],
+            lr=settings["max_lr"],
+        )
+
+        self._test_ccs(
+            self.settings["epochs"], 0, test_df, test_metric_manager, data_split="test"
+        )
+
+        metrics = test_metric_manager.get_stats()
+
+        return metrics
diff --git a/nbs/tutorial_nbs/finetuning.ipynb b/nbs/tutorial_nbs/finetuning.ipynb
index c4c7e9d9..c61e3d9d 100644
--- a/nbs/tutorial_nbs/finetuning.ipynb
+++ b/nbs/tutorial_nbs/finetuning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,6 +13,7 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "from alphabase.spectral_library.base import SpecLibBase\n",
+    "from alphadia.workflow.reporting import *\n",
     "from alphadia.transferlearning.train import *\n",
     "\n",
     "import seaborn as sns\n",
@@ -21,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,18 +42,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "transfer_lib.precursor_df = transfer_lib.precursor_df[~transfer_lib.precursor_df['mods'].str.contains('Dimethyl@C')]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "tune_mgr = FinetuneManager(\n",
+    "    device=\"gpu\",\n",
+    "    settings=settings)\n",
+    "tune_mgr.nce = 25\n",
+    "tune_mgr.instrument = 'Lumos'"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Util function to plot the metrics"
+    "## CCS Fine-tuning\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfer_lib.precursor_df = transfer_lib.precursor_df.dropna(subset=['mobility'])  # drop rows with na in the mobility column\n",
+    "transfer_lib.precursor_df = tune_mgr.predict_mobility(transfer_lib.precursor_df)\n",
+    "plt.scatter(transfer_lib.precursor_df['mobility'], transfer_lib.precursor_df['mobility_pred'], s=1, alpha=0.1)\n",
+    "plt.xlabel('mobility observed')\n",
+    "plt.ylabel('mobility predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ccs_stats = tune_mgr.finetune_ccs(transfer_lib.precursor_df)\n",
+    "\n",
+    "transfer_lib.precursor_df = tune_mgr.ccs_model.predict(transfer_lib.precursor_df)\n",
+    "plt.scatter(transfer_lib.precursor_df['ccs'], transfer_lib.precursor_df['ccs_pred'], s=1, alpha=0.1)\n",
+    "plt.xlabel('ccs observed')\n",
+    "plt.ylabel('ccs predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = sns.relplot(data=ccs_stats, x='epoch', y='value', hue='data_split', marker= 'o',dashes=False, col='metric_name', kind='line', col_wrap=2, facet_kws={'sharex': False, 'sharey': False, 'legend_out': False})\n",
+    "g.set_titles(\"{col_name}\")\n",
+    "g.legend.set_title('Data split')"
    ]
   },
   {
@@ -69,11 +122,6 @@
    "outputs": [],
    "source": [
     "\n",
-    "tune_mgr = FinetuneManager(\n",
-    "    device=\"gpu\",\n",
-    "    settings=settings)\n",
-    "tune_mgr.nce = 25\n",
-    "tune_mgr.instrument = 'Lumos'\n",
     "transfer_lib.precursor_df = tune_mgr.predict_rt(transfer_lib.precursor_df)\n",
     "plt.scatter(transfer_lib.precursor_df['rt_norm'], transfer_lib.precursor_df['rt_norm_pred'], s=1, alpha=0.1)\n",
     "plt.xlabel('RT observed')\n",
@@ -144,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

From 70b27d071b96bdc71ee7b6f640cdec1eee4a9d13 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 13 Aug 2024 16:00:32 +0200
Subject: [PATCH 093/250] renamed_config_in_test

---
 tests/unit_tests/test_workflow.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 5b504477..6c675344 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -206,7 +206,7 @@ def test_calibration_manager_save_load():
     os.remove(temp_path)
 
 
-OPTIMIZATION_CONFIG = {
+TEST_OPTIMIZATION_CONFIG = {
     "search_initial": {
         "initial_ms1_tolerance": 4,
         "initial_ms2_tolerance": 7,
@@ -223,7 +223,7 @@ def test_calibration_manager_save_load():
 
 
 def test_optimization_manager():
-    optimization_manager = manager.OptimizationManager(OPTIMIZATION_CONFIG)
+    optimization_manager = manager.OptimizationManager(TEST_OPTIMIZATION_CONFIG)
 
     assert optimization_manager.fwhm_rt == 5
     assert optimization_manager.fwhm_mobility == 0.01
@@ -236,7 +236,7 @@ def test_optimization_manager_save_load():
     temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
 
     optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
+        TEST_OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
     )
 
     assert optimization_manager.is_loaded_from_file is False
@@ -245,7 +245,7 @@ def test_optimization_manager_save_load():
     optimization_manager.save()
 
     optimization_manager_loaded = manager.OptimizationManager(
-        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=True
+        TEST_OPTIMIZATION_CONFIG, path=temp_path, load_from_file=True
     )
 
     assert optimization_manager_loaded.is_loaded_from_file is True
@@ -257,7 +257,7 @@ def test_optimization_manager_save_load():
 def test_optimization_manager_fit():
     temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
     optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
+        TEST_OPTIMIZATION_CONFIG, path=temp_path, load_from_file=False
     )
 
     assert optimization_manager.is_loaded_from_file is False
@@ -421,7 +421,7 @@ def create_workflow_instance():
     )
 
     workflow._optimization_manager = manager.OptimizationManager(
-        OPTIMIZATION_CONFIG,
+        TEST_OPTIMIZATION_CONFIG,
         path=os.path.join(workflow.path, workflow.OPTIMIZATION_MANAGER_PATH),
         load_from_file=workflow.config["general"]["reuse_calibration"],
         figure_path=os.path.join(workflow.path, workflow.FIGURE_PATH),

From a1a4160630cda16dda489ef51c8239bb40244d56 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 13 Aug 2024 16:05:15 +0200
Subject: [PATCH 094/250] update outputtransform test

---
 tests/unit_tests/test_outputtransform.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 3af2faba..a2814763 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -78,7 +78,7 @@ def test_output_transform():
         optimization_manager = manager.OptimizationManager(
             config, os.path.join(raw_folder, "optimization_manager.pkl")
         )
-        optimization_manager.fit({"ms2_error": 7})
+        optimization_manager.fit({"ms2_error": 6})
 
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)
@@ -113,6 +113,8 @@ def test_output_transform():
         os.path.join(temp_folder, f"{output.STAT_OUTPUT}.tsv"), sep="\t"
     )
     assert len(stat_df) == 3
+    assert stat_df["ms2_error"][0] == 6
+    assert stat_df["rt_error"][0] == 200
     assert all([col in stat_df.columns for col in ["run", "precursors", "proteins"]])
 
     # validate protein_df output

From 0bfb56fa366f55a274c0055b3bef3e6953861ec1 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:20:19 +0200
Subject: [PATCH 095/250] #299: review comments

---
 alphadia/constants/default.yaml | 6 +++---
 alphadia/outputtransform.py     | 7 +------
 2 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 034a30f3..54e6c6af 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -11,9 +11,9 @@ general:
   wsl: false
   mmap_detector_events: false
   use_gpu: true
-  # whether to save the (MBR) library to the output directory
-  save_library: True
-  save_mbr_library: True
+  # whether to save the libraries to the output directory
+  save_library: True       # input library
+  save_mbr_library: True   # output library
 
 library_loading:
   rt_heuristic: 180
diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index d37f49dd..2ee131f2 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -368,7 +368,6 @@ def build(
         _ = self.build_library(
             base_spec_lib,
             psm_df=psm_df,
-            save=self.config["general"]["save_mbr_library"],
         )
 
         if self.config["transfer_library"]["enabled"]:
@@ -775,7 +774,6 @@ def build_library(
         self,
         base_spec_lib: base.SpecLibBase,
         psm_df: pd.DataFrame | None = None,
-        save: bool = True,
     ):
         """Build spectral library
 
@@ -788,9 +786,6 @@ def build_library(
         psm_df: Union[pd.DataFrame, None]
             Combined precursor table. If None, the precursor table is loaded from disk.
 
-        save: bool
-            Save the generated spectral library to disk
-
         """
         logger.progress("Building spectral library")
 
@@ -812,7 +807,7 @@ def build_library(
             f"MBR spectral library contains {precursor_number:,} precursors, {protein_number:,} proteins"
         )
 
-        if save:
+        if self.config["general"]["save_mbr_library"]:
             logger.info("Writing MBR spectral library to disk")
             mbr_spec_lib.save_hdf(os.path.join(self.output_folder, "speclib.mbr.hdf"))
 

From c03c68a90c5e1ecf19b39fa7034903f211f1899e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:21:53 +0200
Subject: [PATCH 096/250] #299: add code review action

---
 .github/workflows/branch-checks.yaml | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/branch-checks.yaml b/.github/workflows/branch-checks.yaml
index 7245169e..a7467d7b 100644
--- a/.github/workflows/branch-checks.yaml
+++ b/.github/workflows/branch-checks.yaml
@@ -9,9 +9,10 @@ jobs:
     name: Run all pre-commit hooks
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
-    - uses: actions/setup-python@v3
+    - uses: actions/checkout@v5
+    - uses: actions/setup-python@v5
     - uses: pre-commit/action@v3.0.1
+
   # For feature branches, we don't test the full matrix (os x [stable, loose]) in order to save time & resources.
   run-tests-stable:
     name: Test stable pip installation on ubuntu-latest
@@ -25,3 +26,11 @@ jobs:
       os: ${{ matrix.os }}
       install-script: ./pip_install.sh stable,test
       test-script: ./run_unit_tests.sh
+
+  get-code-review-input:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: MannLabs/alphashared/actions/get-code-review-input@v1
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          PR_NUMBER: ${{ github.event.number }}

From 6fe674a814d3d1992a1d6018f1ada43222d8b4ab Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:23:14 +0200
Subject: [PATCH 097/250] #299: fix action versions

---
 .github/workflows/branch-checks.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/branch-checks.yaml b/.github/workflows/branch-checks.yaml
index a7467d7b..62aa3936 100644
--- a/.github/workflows/branch-checks.yaml
+++ b/.github/workflows/branch-checks.yaml
@@ -9,7 +9,7 @@ jobs:
     name: Run all pre-commit hooks
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v5
+    - uses: actions/checkout@v4
     - uses: actions/setup-python@v5
     - uses: pre-commit/action@v3.0.1
 

From 4c0f5765d4de93ecb56885d3b3acf92547c9d774 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:25:41 +0200
Subject: [PATCH 098/250] #299: add link to script to Readme.

---
 docs/installation.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index 329f9104..405f923a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -221,3 +221,6 @@ A exit code of 127 indicates that there was an unknown error. All other nonzero
 'business errors', i.e. those caused most likely by user input (data and/or configuration).
 
 Further details on such errors can be found in the `events.jsonl` file in the `.progress` folder(s) of the output directory.
+
+### Slurm script example
+You can find an example of a Slurm script here: [./tests/e2e_tests/e2e_slurm.sh](./tests/e2e_tests/e2e_slurm.sh).

From 1f1e768e010c2555bf927c0c4b1b75f99f523cd4 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:42:16 +0200
Subject: [PATCH 099/250] #296: limit code review action

---
 .github/workflows/branch-checks.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/branch-checks.yaml b/.github/workflows/branch-checks.yaml
index 62aa3936..5dc68ab6 100644
--- a/.github/workflows/branch-checks.yaml
+++ b/.github/workflows/branch-checks.yaml
@@ -34,3 +34,4 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           PR_NUMBER: ${{ github.event.number }}
+          EXCLUDED_EXTENSIONS: ipynb;js

From 720d3da53b8cc962fab9c28edba8366c052c88de Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:51:29 +0200
Subject: [PATCH 100/250] #296: move debug instructions to docs

---
 alphadia/cli.py                              |  7 +------
 docs/{contributing.md => developer_guide.md} | 14 ++++++++++++--
 docs/installation.md                         |  2 +-
 3 files changed, 14 insertions(+), 9 deletions(-)
 rename docs/{contributing.md => developer_guide.md} (75%)

diff --git a/alphadia/cli.py b/alphadia/cli.py
index b67165fd..829e3390 100644
--- a/alphadia/cli.py
+++ b/alphadia/cli.py
@@ -354,11 +354,6 @@ def run(*args, **kwargs):
         sys.exit(exit_code)
 
 
-# To debug e2e tests with PyCharm:
-# create a configuration with
-# - module: alphadia.cli
-# - script parameters: "--config /abs/path/to/tests/e2e_tests/basic/config.yaml"
-# - working directory: "/abs/path/to/tests/e2e_tests"
-# - uncomment the following:
+# uncomment for debugging:
 # if __name__ == "__main__":
 #     run()
diff --git a/docs/contributing.md b/docs/developer_guide.md
similarity index 75%
rename from docs/contributing.md
rename to docs/developer_guide.md
index 43838d7e..56038125 100644
--- a/docs/contributing.md
+++ b/docs/developer_guide.md
@@ -1,5 +1,5 @@
-# Contributing
-This document gathers information on how to contribute to the alphaDIA project.
+# Developer Guide
+This document gathers information on how to develop and contribute to the alphaDIA project.
 
 ## Release process
 ### Tagging of changes
@@ -23,6 +23,16 @@ specifying the release tag (e.g. `vX.Y.Z`).
 
 
 ## Notes for developers
+### Debugging
+To debug e2e tests with PyCharm:
+1. Create a "Run/Debug configuration" with
+ - "module": `alphadia.cli`
+ - "script parameters": `--config /abs/path/to/tests/e2e_tests/basic/config.yaml`
+ - "working directory": `/abs/path/to/tests/e2e_tests`
+2. Uncomment the lines following the `uncomment for debugging` comment in `alphadia/cli.py`.
+3. Run the configuration.
+
+
 ### pre-commit hooks
 It is highly recommended to use the provided pre-commit hooks, as the CI pipeline enforces all checks therein to
 pass in order to merge a branch.
diff --git a/docs/installation.md b/docs/installation.md
index 776f970b..a5b605bc 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -65,7 +65,7 @@ AlphaDIA can be installed in editable (i.e. developer) mode. This allows to full
 
 Make sure you have a conda environment and Mono installed for reading `.raw` files as described [here](https://github.com/MannLabs/alpharaw#installation).
 
-See also the [developer guide](contributing.md) for more information on how to contribute to alphaDIA.
+See also the [developer guide](developer_guide.md) for more information on how to contribute to alphaDIA.
 
 ### 1. Setting up the repository
 

From eeb3d60f651aad0d96956c9b2c35d6d197385a24 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 13:53:49 +0200
Subject: [PATCH 101/250] #296: simplify calc_metrics script

---
 tests/e2e_tests/calc_metrics.py | 66 ++++++++++++++++-----------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/tests/e2e_tests/calc_metrics.py b/tests/e2e_tests/calc_metrics.py
index fdb2cc32..fdfc2f38 100644
--- a/tests/e2e_tests/calc_metrics.py
+++ b/tests/e2e_tests/calc_metrics.py
@@ -213,36 +213,36 @@ def _get_history_plots(test_results: dict, metrics_classes: list):
             test_results |= metrics
     except Exception as e:
         print(e)
-    finally:
-        print(test_results)
-
-        if not neptune_upload:
-            print("skipping neptune upload")
-            exit(0)
-
-        neptune_run = neptune.init_run(
-            project=NEPTUNE_PROJECT_NAME,
-            tags=[test_case_name, short_sha, branch_name],
-        )
-
-        # metrics
-        for k, v in test_results.items():
-            print(f"adding {k}={v}")
-            neptune_run[k] = v
-
-        # files
-        for file_name in OutputFiles.all_values():
-            print("adding", file_name)
-            file_path = os.path.join(output_path, file_name)
-            if os.path.exists(file_path):
-                neptune_run["output/" + file_name].track_files(file_path)
-
-        try:
-            history_plots = _get_history_plots(test_results, metrics_classes)
-
-            for name, plot in history_plots:
-                neptune_run[f"plots/{name}"].upload(plot)
-        except Exception as e:
-            print(f"no plots today: {e}")
-
-        neptune_run.stop()
+
+    print(test_results)
+
+    if not neptune_upload:
+        print("skipping neptune upload")
+        exit(0)
+
+    neptune_run = neptune.init_run(
+        project=NEPTUNE_PROJECT_NAME,
+        tags=[test_case_name, short_sha, branch_name],
+    )
+
+    # metrics
+    for k, v in test_results.items():
+        print(f"adding {k}={v}")
+        neptune_run[k] = v
+
+    # files
+    for file_name in OutputFiles.all_values():
+        print("adding", file_name)
+        file_path = os.path.join(output_path, file_name)
+        if os.path.exists(file_path):
+            neptune_run["output/" + file_name].track_files(file_path)
+
+    try:
+        history_plots = _get_history_plots(test_results, metrics_classes)
+
+        for name, plot in history_plots:
+            neptune_run[f"plots/{name}"].upload(plot)
+    except Exception as e:
+        print(f"no plots today: {e}")
+
+    neptune_run.stop()

From 6f52f559a40f6177a5ff351ab319f6e23a6c42af Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 14 Aug 2024 14:16:39 +0200
Subject: [PATCH 102/250] #296: allow code review action to fail

---
 .github/workflows/branch-checks.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/branch-checks.yaml b/.github/workflows/branch-checks.yaml
index 5dc68ab6..5fa58954 100644
--- a/.github/workflows/branch-checks.yaml
+++ b/.github/workflows/branch-checks.yaml
@@ -31,6 +31,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: MannLabs/alphashared/actions/get-code-review-input@v1
+        continue-on-error: true
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           PR_NUMBER: ${{ github.event.number }}

From 231ebd99dab2bbc511585bb3266fd02e329bf63c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 14 Aug 2024 21:01:13 +0200
Subject: [PATCH 103/250] add timing manager and unit test

---
 alphadia/outputtransform.py              | 21 +++++---
 alphadia/workflow/base.py                | 12 +++++
 alphadia/workflow/manager.py             | 68 +++++++++++++++++++-----
 alphadia/workflow/peptidecentric.py      |  7 ++-
 tests/unit_tests/test_outputtransform.py | 24 ++++++++-
 5 files changed, 111 insertions(+), 21 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 51fcb6e1..10c88e2b 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -1,7 +1,6 @@
 # native imports
 import logging
 import os
-import pickle
 from collections.abc import Iterator
 
 import directlfq.config as lfqconfig
@@ -25,6 +24,7 @@
     TransferLearningAccumulator,
 )
 from alphadia.transferlearning.train import FinetuneManager
+from alphadia.workflow import manager
 
 logger = logging.getLogger()
 
@@ -657,12 +657,11 @@ def build_stat_df(
         stat_df_list = []
         for folder in folder_list:
             raw_name = os.path.basename(folder)
-            optimization_manager_path = os.path.join(folder, "optimization_manager.pkl")
             stat_df_list.append(
                 _build_run_stat_df(
+                    folder,
                     raw_name,
                     psm_df[psm_df["run"] == raw_name],
-                    optimization_manager_path,
                     all_channels,
                 )
             )
@@ -820,9 +819,9 @@ def build_library(
 
 
 def _build_run_stat_df(
+    folder: str,
     raw_name: str,
     run_df: pd.DataFrame,
-    optimization_manager_path: str,
     channels: list[int] | None = None,
 ):
     """Build stat dataframe for a single run.
@@ -830,6 +829,9 @@ def _build_run_stat_df(
     Parameters
     ----------
 
+    folder: str
+        Directory containing the raw file and the managers
+
     raw_name: str
         Name of the raw file
 
@@ -845,6 +847,8 @@ def _build_run_stat_df(
         Dataframe containing the statistics
 
     """
+    optimization_manager_path = os.path.join(folder, "optimization_manager.pkl")
+    timing_manager_path = os.path.join(folder, "timing_manager.pkl")
 
     if channels is None:
         channels = [0]
@@ -869,14 +873,19 @@ def _build_run_stat_df(
         if "mobility_fwhm" in channel_df.columns:
             base_dict["fwhm_mobility"] = np.mean(channel_df["mobility_fwhm"])
 
-        with open(optimization_manager_path, "rb") as f:
-            optimization_manager = pickle.load(f)
+        optimization_manager = manager.OptimizationManager(
+            path=optimization_manager_path
+        )
+        timing_manager = manager.TimingManager(path=timing_manager_path)
 
         base_dict["ms2_error"] = optimization_manager.ms2_error
         base_dict["ms1_error"] = optimization_manager.ms1_error
         base_dict["rt_error"] = optimization_manager.rt_error
         base_dict["mobility_error"] = optimization_manager.mobility_error
 
+        base_dict["optimization_duration"] = timing_manager.optimization["duration"]
+        base_dict["extraction_duration"] = timing_manager.extraction["duration"]
+
         out_df.append(base_dict)
 
     return pd.DataFrame(out_df)
diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index 4cf8a073..ba81995c 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -23,6 +23,7 @@ class WorkflowBase:
 
     CALIBRATION_MANAGER_PATH = "calibration_manager.pkl"
     OPTIMIZATION_MANAGER_PATH = "optimization_manager.pkl"
+    TIMING_MANAGER_PATH = "timing_manager.pkl"
     FDR_MANAGER_PATH = "fdr_manager.pkl"
     FIGURE_PATH = "figures"
 
@@ -53,6 +54,7 @@ def __init__(
         self._spectral_library: SpecLibBase | None = None
         self._calibration_manager: manager.CalibrationManager | None = None
         self._optimization_manager: manager.OptimizationManager | None = None
+        self._timing_manager: manager.TimingManager | None = None
 
         if not os.path.exists(self.parent_path):
             logger.info(f"Creating parent folder for workflows at {self.parent_path}")
@@ -109,6 +111,11 @@ def load(
             reporter=self.reporter,
         )
 
+        self._timing_manager = manager.TimingManager(
+            path=os.path.join(self.path, self.TIMING_MANAGER_PATH),
+            load_from_file=self.config["general"]["reuse_calibration"],
+        )
+
         self.reporter.log_event("section_stop", {})
 
     @property
@@ -141,6 +148,11 @@ def optimization_manager(self) -> str:
         """Optimization manager for the workflow. Owns the optimization data"""
         return self._optimization_manager
 
+    @property
+    def timing_manager(self) -> str:
+        """Optimization manager for the workflow. Owns the timing data"""
+        return self._timing_manager
+
     @property
     def spectral_library(self) -> SpecLibBase:
         """Spectral library for the workflow. Owns the spectral library data"""
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 0347550d..fdc1e196 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -454,7 +454,7 @@ def fit_predict(
 class OptimizationManager(BaseManager):
     def __init__(
         self,
-        config: dict,
+        config: None | dict = None,
         path: None | str = None,
         load_from_file: bool = True,
         **kwargs,
@@ -463,19 +463,21 @@ def __init__(
         self.reporter.log_string(f"Initializing {self.__class__.__name__}")
         self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
 
-        initial_parameters = {
-            "ms1_error": config["search_initial"]["initial_ms1_tolerance"],
-            "ms2_error": config["search_initial"]["initial_ms2_tolerance"],
-            "rt_error": config["search_initial"]["initial_rt_tolerance"],
-            "mobility_error": config["search_initial"]["initial_mobility_tolerance"],
-            "column_type": "library",
-            "num_candidates": config["search_initial"]["initial_num_candidates"],
-            "classifier_version": -1,
-            "fwhm_rt": config["optimization_manager"]["fwhm_rt"],
-            "fwhm_mobility": config["optimization_manager"]["fwhm_mobility"],
-            "score_cutoff": config["optimization_manager"]["score_cutoff"],
-        }
         if not self.is_loaded_from_file:
+            initial_parameters = {
+                "ms1_error": config["search_initial"]["initial_ms1_tolerance"],
+                "ms2_error": config["search_initial"]["initial_ms2_tolerance"],
+                "rt_error": config["search_initial"]["initial_rt_tolerance"],
+                "mobility_error": config["search_initial"][
+                    "initial_mobility_tolerance"
+                ],
+                "column_type": "library",
+                "num_candidates": config["search_initial"]["initial_num_candidates"],
+                "classifier_version": -1,
+                "fwhm_rt": config["optimization_manager"]["fwhm_rt"],
+                "fwhm_mobility": config["optimization_manager"]["fwhm_mobility"],
+                "score_cutoff": config["optimization_manager"]["score_cutoff"],
+            }
             self.__dict__.update(initial_parameters)
 
             for key, value in initial_parameters.items():
@@ -750,3 +752,43 @@ def fit(self, update_dict):
 def column_hash(columns):
     columns.sort()
     return xxhash.xxh64_hexdigest("".join(columns))
+
+
+class TimingManager(BaseManager):
+    def __init__(
+        self,
+        path: None | str = None,
+        load_from_file: bool = True,
+        **kwargs,
+    ):
+        super().__init__(path=path, load_from_file=load_from_file, **kwargs)
+        self.reporter.log_string(f"Initializing {self.__class__.__name__}")
+        self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
+
+        if not self.is_loaded_from_file:
+            self.__dict__.update(
+                {
+                    "optimization": {
+                        "start": None,
+                        "end": None,
+                        "duration": None,
+                    },
+                    "extraction": {
+                        "start": None,
+                        "end": None,
+                        "duration": None,
+                    },
+                }
+            )
+
+    def start(self, workflow_stage: str):
+        self.__dict__.update({workflow_stage: {"start": pd.Timestamp.now()}})
+        self.save()
+
+    def end(self, workflow_stage: str):
+        self.__dict__[workflow_stage]["end"] = pd.Timestamp.now()
+        self.__dict__[workflow_stage]["duration"] = (
+            self.__dict__[workflow_stage]["end"]
+            - self.__dict__[workflow_stage]["start"]
+        ).total_seconds() / 60
+        self.save()
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index cb85d4e1..c92960a5 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -522,7 +522,7 @@ def calibration(self):
                 verbosity="progress",
             )
             return
-
+        self.timing_manager.start("optimization")
         # Get the order of optimization
         ordered_optimizers = self.get_ordered_optimizers()
 
@@ -637,6 +637,8 @@ def calibration(self):
             "==============================================", verbosity="progress"
         )
 
+        self.timing_manager.end("optimization")
+
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
 
@@ -850,6 +852,7 @@ def extract_batch(self, batch_df, apply_cutoff=False):
         return features_df, fragments_df
 
     def extraction(self):
+        self.timing_manager.start("extraction")
         self.optimization_manager.fit(
             {
                 "num_candidates": self.config["search"]["target_num_candidates"],
@@ -894,6 +897,8 @@ def extraction(self):
 
         self.log_precursor_df(precursor_df)
 
+        self.timing_manager.end("extraction")
+
         return precursor_df, fragments_df
 
     def log_precursor_df(self, precursor_df):
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index a2814763..d2396e46 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -80,6 +80,13 @@ def test_output_transform():
         )
         optimization_manager.fit({"ms2_error": 6})
 
+        timing_manager = manager.TimingManager(
+            os.path.join(raw_folder, "timing_manager.pkl")
+        )
+
+        timing_manager.start("extraction")
+        timing_manager.end("extraction")
+
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)
     _ = output.build_stat_df(raw_folders, save=True)
@@ -115,7 +122,19 @@ def test_output_transform():
     assert len(stat_df) == 3
     assert stat_df["ms2_error"][0] == 6
     assert stat_df["rt_error"][0] == 200
-    assert all([col in stat_df.columns for col in ["run", "precursors", "proteins"]])
+    assert isinstance(stat_df["extraction_duration"][0], float)
+    assert all(
+        [
+            col in stat_df.columns
+            for col in [
+                "run",
+                "precursors",
+                "proteins",
+                "optimization_duration",
+                "extraction_duration",
+            ]
+        ]
+    )
 
     # validate protein_df output
     protein_df = pd.read_parquet(os.path.join(temp_folder, "pg.matrix.parquet"))
@@ -128,3 +147,6 @@ def test_output_transform():
             assert np.corrcoef(protein_df[i], protein_df[j])[0, 0] > 0.5
 
     shutil.rmtree(temp_folder)
+
+
+test_output_transform()

From fc1182c900f690dfd4d9234a6556ba20c2c3e44a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 14 Aug 2024 21:50:17 +0200
Subject: [PATCH 104/250] remove erroneous call to test

---
 tests/unit_tests/test_outputtransform.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index d2396e46..7725c4fe 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -147,6 +147,3 @@ def test_output_transform():
             assert np.corrcoef(protein_df[i], protein_df[j])[0, 0] > 0.5
 
     shutil.rmtree(temp_folder)
-
-
-test_output_transform()

From 29c1c9993f9ac13fa264e5c81634d9388c54ff73 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 14 Aug 2024 22:12:59 +0200
Subject: [PATCH 105/250] handling for absent managers

---
 alphadia/outputtransform.py | 35 +++++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 10c88e2b..bc1ed95a 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -873,18 +873,33 @@ def _build_run_stat_df(
         if "mobility_fwhm" in channel_df.columns:
             base_dict["fwhm_mobility"] = np.mean(channel_df["mobility_fwhm"])
 
-        optimization_manager = manager.OptimizationManager(
-            path=optimization_manager_path
-        )
-        timing_manager = manager.TimingManager(path=timing_manager_path)
+        if os.path.exists(optimization_manager_path):
+            optimization_manager = manager.OptimizationManager(
+                path=optimization_manager_path
+            )
 
-        base_dict["ms2_error"] = optimization_manager.ms2_error
-        base_dict["ms1_error"] = optimization_manager.ms1_error
-        base_dict["rt_error"] = optimization_manager.rt_error
-        base_dict["mobility_error"] = optimization_manager.mobility_error
+            base_dict["ms2_error"] = optimization_manager.ms2_error
+            base_dict["ms1_error"] = optimization_manager.ms1_error
+            base_dict["rt_error"] = optimization_manager.rt_error
+            base_dict["mobility_error"] = optimization_manager.mobility_error
+
+        else:
+            logger.warning(f"Error reading optimization manager for {raw_name}")
+            base_dict["ms2_error"] = np.nan
+            base_dict["ms1_error"] = np.nan
+            base_dict["rt_error"] = np.nan
+            base_dict["mobility_error"] = np.nan
 
-        base_dict["optimization_duration"] = timing_manager.optimization["duration"]
-        base_dict["extraction_duration"] = timing_manager.extraction["duration"]
+        if os.path.exists(timing_manager_path):
+            timing_manager = manager.TimingManager(path=timing_manager_path)
+
+            base_dict["optimization_duration"] = timing_manager.optimization["duration"]
+            base_dict["extraction_duration"] = timing_manager.extraction["duration"]
+
+        else:
+            logger.warning(f"Error reading timing manager for {raw_name}")
+            base_dict["optimization_duration"] = np.nan
+            base_dict["extraction_duration"] = np.nan
 
         out_df.append(base_dict)
 

From b26c9c8d59359498befcda724b35eff494e6bd33 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Thu, 15 Aug 2024 13:59:44 +0200
Subject: [PATCH 106/250] fix #305 segfault

---
 alphadia/data/bruker.py       | 23 +++++++++++++----------
 tests/unit_tests/test_data.py |  5 +++--
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/alphadia/data/bruker.py b/alphadia/data/bruker.py
index 73f0f629..f714ddaf 100644
--- a/alphadia/data/bruker.py
+++ b/alphadia/data/bruker.py
@@ -107,7 +107,10 @@ def transpose(self):
 
         logger.info("Transposing detector events")
         push_indices, tof_indptr, intensity_values = transpose(
-            self._tof_indices, self._push_indptr, self._intensity_values
+            self._tof_indices,
+            self._push_indptr,
+            len(self._mz_values),
+            self._intensity_values,
         )
         logger.info("Finished transposing data")
 
@@ -861,7 +864,7 @@ def build_chunks(number_of_elements, num_chunks):
 
 
 @nb.njit
-def transpose(tof_indices, push_indptr, values):
+def transpose(tof_indices, push_indptr, n_tof_indices, values):
     """
     The default alphatims data format consists of a sparse matrix where pushes are the rows, tof indices (discrete mz values) the columns and intensities the values.
     A lookup starts with a given push index p which points to the row. The start and stop indices of the row are accessed from dia_data.push_indptr[p] and dia_data.push_indptr[p+1].
@@ -879,6 +882,9 @@ def transpose(tof_indices, push_indptr, values):
     push_indptr : np.ndarray
         start stop values for each row (n_rows +1)
 
+    n_tof_indices : int
+        number of tof indices which is usually equal to len(dia_data.mz_values)
+
     values : np.ndarray
         values (n_values)
 
@@ -898,28 +904,25 @@ def transpose(tof_indices, push_indptr, values):
         values (n_values)
 
     """
-    # this is one less than the old col count or the new row count
-    max_tof_index = tof_indices.max()
-
-    tof_indcount = np.zeros((max_tof_index + 1), dtype=np.uint32)
+    tof_indcount = np.zeros((n_tof_indices), dtype=np.uint32)
 
     # get new row counts
     for v in tof_indices:
         tof_indcount[v] += 1
 
     # get new indptr
-    tof_indptr = np.zeros((max_tof_index + 1 + 1), dtype=np.int64)
+    tof_indptr = np.zeros((n_tof_indices + 1), dtype=np.int64)
 
-    for i in range(max_tof_index + 1):
+    for i in range(n_tof_indices):
         tof_indptr[i + 1] = tof_indptr[i] + tof_indcount[i]
 
-    tof_indcount = np.zeros((max_tof_index + 1), dtype=np.uint32)
+    tof_indcount = np.zeros((n_tof_indices), dtype=np.uint32)
 
     # get new values
     push_indices = np.zeros((len(tof_indices)), dtype=np.uint32)
     new_values = np.zeros_like(values)
 
-    chunks = build_chunks(max_tof_index + 1, 20)
+    chunks = build_chunks(n_tof_indices, 20)
 
     with nb.objmode:
         alphatims.utils.set_threads(20)
diff --git a/tests/unit_tests/test_data.py b/tests/unit_tests/test_data.py
index d2fa02d5..e1b643a0 100644
--- a/tests/unit_tests/test_data.py
+++ b/tests/unit_tests/test_data.py
@@ -8,13 +8,14 @@ def test_transpose():
     values = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
     tof_indices = np.array([0, 3, 2, 4, 1, 2, 4])
     push_ptr = np.array([0, 2, 4, 5, 7])
+    n_tof_indices = 7
 
     push_indices, tof_indptr, intensity_values = bruker.transpose(
-        tof_indices, push_ptr, values
+        tof_indices, push_ptr, n_tof_indices, values
     )
 
     _push_indices = np.array([0, 2, 1, 3, 0, 1, 3])
-    _tof_indptr = np.array([0, 1, 2, 4, 5, 7])
+    _tof_indptr = np.array([0, 1, 2, 4, 5, 7, 7, 7])
     _intensity_values = np.array([1.0, 5.0, 3.0, 6.0, 2.0, 4.0, 7.0])
 
     assert np.allclose(push_indices, _push_indices)

From a5f67bfb1baca45bce92a03d0715cb1ea558414d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 15 Aug 2024 15:08:20 +0200
Subject: [PATCH 107/250] debugging information and path changed in
 outputtransform

---
 alphadia/outputtransform.py              | 10 +++++++---
 alphadia/workflow/peptidecentric.py      | 24 ++++++++++++++++++++++--
 tests/unit_tests/test_outputtransform.py | 12 +++++++++---
 3 files changed, 38 insertions(+), 8 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index bc1ed95a..024a2786 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -24,7 +24,7 @@
     TransferLearningAccumulator,
 )
 from alphadia.transferlearning.train import FinetuneManager
-from alphadia.workflow import manager
+from alphadia.workflow import manager, peptidecentric
 
 logger = logging.getLogger()
 
@@ -847,8 +847,12 @@ def _build_run_stat_df(
         Dataframe containing the statistics
 
     """
-    optimization_manager_path = os.path.join(folder, "optimization_manager.pkl")
-    timing_manager_path = os.path.join(folder, "timing_manager.pkl")
+    optimization_manager_path = os.path.join(
+        folder, peptidecentric.PeptideCentricWorkflow.OPTIMIZATION_MANAGER_PATH
+    )
+    timing_manager_path = os.path.join(
+        folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
+    )
 
     if channels is None:
         channels = [0]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 7e48daeb..d2373d27 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -292,7 +292,7 @@ def get_optimization_lock(self):
         Returns
         -------
         optimization_lock_eg_idxes : np.ndarray
-            The indices (in .spectral_library._precursor_df) of the precursors which will be used for calibration.
+            The elution group (eg) indices (in .spectral_library._precursor_df) of the precursors which will be used for calibration.
 
         precursor_df : pd.DataFrame
             Dataframe of all precursors accumulated during the optimization lock, including q-values from FDR correction.
@@ -376,7 +376,7 @@ def get_optimization_lock(self):
         optimization_lock_fragment_idxes = np.concatenate(
             [
                 np.arange(row["flat_frag_start_idx"], row["flat_frag_stop_idx"])
-                for i, row in optimization_lock_library_precursor_df.iterrows()
+                for _, row in optimization_lock_library_precursor_df.iterrows()
             ]
         )
 
@@ -819,6 +819,26 @@ def extract_batch(self, batch_df, batch_fragment_df=None, apply_cutoff=False):
             }
         )
 
+        self.reporter.log_string("=== Search parameters used === ", verbosity="debug")
+        self.reporter.log_string(
+            f"{'rt_tolerance':<15}: {config.rt_tolerance}", verbosity="debug"
+        )
+        self.reporter.log_string(
+            f"{'mobility_tolerance':<15}: {config.mobility_tolerance}",
+            verbosity="debug",
+        )
+        self.reporter.log_string(
+            f"{'precursor_mz_tolerance':<15}: {config.precursor_mz_tolerance}",
+            verbosity="debug",
+        )
+        self.reporter.log_string(
+            f"{'fragment_mz_tolerance':<15}: {config.fragment_mz_tolerance}",
+            verbosity="debug",
+        )
+        self.reporter.log_string(
+            "==============================================", verbosity="debug"
+        )
+
         extraction = search.HybridCandidateSelection(
             self.dia_data.jitclass(),
             batch_df,
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 7725c4fe..6129a6c4 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -7,7 +7,7 @@
 from conftest import mock_fragment_df, mock_precursor_df
 
 from alphadia import outputtransform
-from alphadia.workflow import manager
+from alphadia.workflow import manager, peptidecentric
 
 
 def test_output_transform():
@@ -76,12 +76,18 @@ def test_output_transform():
         psm_df.to_parquet(os.path.join(raw_folder, "psm.parquet"), index=False)
 
         optimization_manager = manager.OptimizationManager(
-            config, os.path.join(raw_folder, "optimization_manager.pkl")
+            config,
+            os.path.join(
+                raw_folder,
+                peptidecentric.PeptideCentricWorkflow.OPTIMIZATION_MANAGER_PATH,
+            ),
         )
         optimization_manager.fit({"ms2_error": 6})
 
         timing_manager = manager.TimingManager(
-            os.path.join(raw_folder, "timing_manager.pkl")
+            os.path.join(
+                raw_folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
+            )
         )
 
         timing_manager.start("extraction")

From 38f747ddd83dc08c7b07daf24953dc52185cd99a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 15 Aug 2024 15:10:36 +0200
Subject: [PATCH 108/250] batch_df to batch_precursor_df

---
 alphadia/workflow/peptidecentric.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index d2373d27..9f3dcfcb 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -318,11 +318,11 @@ def get_optimization_lock(self):
             )
 
             eg_idxes = self.elution_group_order[start_index:stop_index]
-            batch_df = self.spectral_library._precursor_df[
+            batch_precursor_df = self.spectral_library._precursor_df[
                 self.spectral_library._precursor_df["elution_group_idx"].isin(eg_idxes)
             ]
 
-            feature_df, fragment_df = self.extract_batch(batch_df)
+            feature_df, fragment_df = self.extract_batch(batch_precursor_df)
             features += [feature_df]
             fragments += [fragment_df]
             features_df = pd.concat(features)
@@ -797,11 +797,14 @@ def fdr_correction(self, features_df, df_fragments, version=-1):
             # neptune_run=self.neptune
         )
 
-    def extract_batch(self, batch_df, batch_fragment_df=None, apply_cutoff=False):
+    def extract_batch(
+        self, batch_precursor_df, batch_fragment_df=None, apply_cutoff=False
+    ):
         if batch_fragment_df is None:
             batch_fragment_df = self.spectral_library._fragment_df
         self.reporter.log_string(
-            f"Extracting batch of {len(batch_df)} precursors", verbosity="progress"
+            f"Extracting batch of {len(batch_precursor_df)} precursors",
+            verbosity="progress",
         )
 
         config = search.HybridCandidateConfig()
@@ -841,7 +844,7 @@ def extract_batch(self, batch_df, batch_fragment_df=None, apply_cutoff=False):
 
         extraction = search.HybridCandidateSelection(
             self.dia_data.jitclass(),
-            batch_df,
+            batch_precursor_df,
             batch_fragment_df,
             config.jitclass(),
             rt_column=f"rt_{self.optimization_manager.column_type}",
@@ -890,7 +893,7 @@ def extract_batch(self, batch_df, batch_fragment_df=None, apply_cutoff=False):
 
         candidate_scoring = plexscoring.CandidateScoring(
             self.dia_data.jitclass(),
-            batch_df,
+            batch_precursor_df,
             batch_fragment_df,
             config=config,
             rt_column=f"rt_{self.optimization_manager.column_type}",

From ca2aafcc1a91d7f1189b37d2508adf1d675c0cb0 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 16 Aug 2024 13:19:02 +0200
Subject: [PATCH 109/250] introduced OptimizationLock class

---
 alphadia/workflow/optimization.py   | 142 +++++++++++++++
 alphadia/workflow/peptidecentric.py | 262 +++++-----------------------
 2 files changed, 187 insertions(+), 217 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 16e2e2b6..51c0491b 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -2,6 +2,7 @@
 from abc import ABC, abstractmethod
 
 import matplotlib.pyplot as plt
+import numpy as np
 
 # alpha family imports
 # third party imports
@@ -579,3 +580,144 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
         super().__init__(initial_parameter, target_parameter, workflow, reporter)
+
+
+class OptimizationLock:
+    def __init__(self, workflow):
+        """Sets and updates the optimization lock, which is the data used for calibration and optimization of the search parameters.
+
+        Parameters
+        ----------
+        workflow: peptidecentric.PeptideCentricWorkflow
+            The workflow object, which includes the spectral library and the calibration and optimization managers.
+
+        """
+        self.workflow = workflow
+
+        self.reached_target_before = False
+        self.reached_target = False
+
+        self.elution_group_order = self.workflow.spectral_library.precursor_df[
+            "elution_group_idx"
+        ].unique()
+        np.random.shuffle(self.elution_group_order)
+
+        self.target_count = self.workflow.config["calibration"][
+            "optimization_lock_target"
+        ]
+
+        self.batch_idx = 0
+        self.set_batch_plan()
+
+        eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
+        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
+                eg_idxes
+            )
+        ]
+
+        self.features = []
+        self.fragments = []
+
+    def _get_exponential_batches(self, step):
+        """Get the number of batches for a given step
+        This plan has the shape:
+        1, 2, 4, 8, 16, 32, 64, ...
+        """
+        return int(2**step)
+
+    def set_batch_plan(self):
+        """Gets an exponential batch plan based on the batch_size value in the config."""
+        n_eg = self.workflow.spectral_library._precursor_df[
+            "elution_group_idx"
+        ].nunique()
+
+        plan = []
+
+        batch_size = self.workflow.config["calibration"]["batch_size"]
+        step = 0
+        start_index = 0
+
+        while start_index < n_eg:
+            n_batches = self._get_exponential_batches(step)
+            stop_index = min(start_index + n_batches * batch_size, n_eg)
+            plan.append((start_index, stop_index))
+            step += 1
+            start_index = stop_index
+
+        self.batch_plan = plan
+
+    def extract(self):
+        """Extract features and fragments from the current batch of the optimization lock."""
+
+        feature_df, fragment_df = self.workflow.extract_batch(self.library_precursor_df)
+        self.features += [feature_df]
+        self.fragments += [fragment_df]
+
+    def fdr(self):
+        """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization."""
+        self.precursor_df = self.workflow.fdr_correction(
+            self.features_df,
+            self.fragments_df,
+            self.workflow.optimization_manager.classifier_version,
+        )
+
+        self.count = len(self.precursor_df[self.precursor_df["qval"] < 0.01])
+
+        if self.count < self.target_count:
+            self.reached_target = False
+        else:
+            self.reached_target = True
+
+    def adjust_upwards(self):
+        """If the optimization lock does not contain enough precursors at 1% FDR, the optimization lock proceeds to include the next step in the batch plan in the library attribute."""
+        self.batch_idx += 1
+
+        eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
+
+        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
+                eg_idxes
+            )
+        ]
+
+    def adjust_downwards(self):
+        """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly. If not, the same library is used as before."""
+        self.batch_idx += int(np.ceil(np.log2(self.target_count / self.count)))
+
+        eg_idxes = self.elution_group_order[: self.stop_index]
+
+        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
+                eg_idxes
+            )
+        ]
+
+    def update(self):
+        """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached."""
+        if not self.reached_target:
+            self.adjust_upwards()
+
+        else:
+            self.adjust_downwards()
+            self.features = []
+            self.fragments = []
+
+    @property
+    def features_df(self):
+        return pd.concat(self.features)
+
+    @property
+    def fragments_df(self):
+        return pd.concat(self.fragments)
+
+    @property
+    def start_index(self):
+        if self.reached_target:
+            return 0
+        else:
+            return self.batch_plan[self.batch_idx][0]
+
+    @property
+    def stop_index(self):
+        return self.batch_plan[self.batch_idx][1]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 9f3dcfcb..a93ed932 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -256,156 +256,6 @@ def norm_to_rt(
         else:
             raise ValueError(f"Unknown norm_rt_mode {mode}")
 
-    def get_exponential_batches(self, step):
-        """Get the number of batches for a given step
-        This plan has the shape:
-        1, 2, 4, 8, 16, 32, 64, ...
-        """
-        return int(2**step)
-
-    def get_batch_plan(self):
-        """Gets an exponential batch plan based on the batch_size value in the config."""
-        n_eg = self.spectral_library._precursor_df["elution_group_idx"].nunique()
-
-        plan = []
-
-        batch_size = self.config["calibration"]["batch_size"]
-        step = 0
-        start_index = 0
-
-        while start_index < n_eg:
-            n_batches = self.get_exponential_batches(step)
-            stop_index = min(start_index + n_batches * batch_size, n_eg)
-            plan.append((start_index, stop_index))
-            step += 1
-            start_index = stop_index
-
-        return plan
-
-    def get_optimization_lock(self):
-        """Search parameter optimization (i.e. refinement of tolerances for RT, MS2, etc.) is performed on a subset of the elution groups in the spectral library.
-        This subset is termed the optimization lock.
-        The number of elution groups which must be searched to get a sufficiently large number for robust calibration varies depending the library used and the data.
-        This function searches an increasing number of elution groups until a sufficient number of precursors are identified at 1% FDR and a sufficient number of steps have been taken.
-        The values deemed sufficient are specified in by "optimization_lock_target" and "optmization_lock_min_steps" in the config.
-
-        Returns
-        -------
-        optimization_lock_eg_idxes : np.ndarray
-            The elution group (eg) indices (in .spectral_library._precursor_df) of the precursors which will be used for calibration.
-
-        precursor_df : pd.DataFrame
-            Dataframe of all precursors accumulated during the optimization lock, including q-values from FDR correction.
-
-        fragments_df : pd.DataFrame
-            Dataframe of all fragments accumulated during the optimization lock, including q-values from FDR correction.
-
-        """
-
-        self.elution_group_order = self.spectral_library.precursor_df[
-            "elution_group_idx"
-        ].unique()
-        np.random.shuffle(self.elution_group_order)
-
-        batch_plan = self.get_batch_plan()
-
-        features = []
-        fragments = []
-        for current_step, (start_index, stop_index) in enumerate(batch_plan):
-            self.reporter.log_string(
-                f"=== Step {current_step}, extracting elution groups {start_index} to {stop_index} ===",
-                verbosity="progress",
-            )
-
-            eg_idxes = self.elution_group_order[start_index:stop_index]
-            batch_precursor_df = self.spectral_library._precursor_df[
-                self.spectral_library._precursor_df["elution_group_idx"].isin(eg_idxes)
-            ]
-
-            feature_df, fragment_df = self.extract_batch(batch_precursor_df)
-            features += [feature_df]
-            fragments += [fragment_df]
-            features_df = pd.concat(features)
-            fragments_df = pd.concat(fragments)
-
-            self.reporter.log_string(
-                f"=== Step {current_step}, extracted {len(feature_df)} precursors and {len(fragment_df)} fragments ===",
-                verbosity="progress",
-            )
-
-            precursor_df = self.fdr_correction(
-                features_df, fragments_df, self.optimization_manager.classifier_version
-            )
-
-            self.reporter.log_string(
-                f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
-                verbosity="info",
-            )
-
-            num_precursors_at_01FDR = len(precursor_df[precursor_df["qval"] < 0.01])
-
-            self.reporter.log_string(
-                f"=== Checking if minimum number of precursors for optimization found yet; minimum number is {self.config['calibration']['optimization_lock_target']} ===",
-                verbosity="progress",
-            )
-
-            self.log_precursor_df(precursor_df)
-
-            self.reporter.log_string(
-                f"=== Classifier has been trained for {self.fdr_manager.current_version + 1} iteration(s); minimum number is {self.config['calibration']['optimization_lock_min_steps']} ===",
-                verbosity="progress",
-            )
-
-            if (
-                num_precursors_at_01FDR
-                > self.config["calibration"]["optimization_lock_target"]
-                and current_step
-                >= self.config["calibration"]["optimization_lock_min_steps"] - 1
-            ):
-                final_stop_index = stop_index  # final_stop_index is the number of elution groups that will be included in the calibration data
-                break
-
-        optimization_lock_eg_idxes = self.elution_group_order[:final_stop_index]
-
-        optimization_lock_library_precursor_df = self.spectral_library._precursor_df[
-            self.spectral_library._precursor_df["elution_group_idx"].isin(
-                optimization_lock_eg_idxes
-            )
-        ]
-
-        optimization_lock_fragment_idxes = np.concatenate(
-            [
-                np.arange(row["flat_frag_start_idx"], row["flat_frag_stop_idx"])
-                for _, row in optimization_lock_library_precursor_df.iterrows()
-            ]
-        )
-
-        # Extract the fragments for the optimization lock and reset the indices to a consecutive range of positive integers. This simplifies future access based on position
-        optimization_lock_library_fragment_df = self.spectral_library._fragment_df.iloc[
-            optimization_lock_fragment_idxes
-        ].reset_index(drop=True)
-
-        # Change the fragment indices in the precursor_df to match the fragment indices in the optimization lock fragment_df instead of the full spectral library.
-        num_frags = (
-            optimization_lock_library_precursor_df["flat_frag_stop_idx"]
-            - optimization_lock_library_precursor_df["flat_frag_start_idx"]
-        )
-        optimization_lock_library_precursor_df["flat_frag_stop_idx"] = (
-            num_frags.cumsum()
-        )
-        optimization_lock_library_precursor_df["flat_frag_start_idx"] = (
-            optimization_lock_library_precursor_df[
-                "flat_frag_stop_idx"
-            ].shift(fill_value=0)
-        )
-
-        return (
-            optimization_lock_library_precursor_df,
-            optimization_lock_library_fragment_df,
-            precursor_df,
-            fragments_df,
-        )
-
     def get_ordered_optimizers(self):
         """Select appropriate optimizers. Targeted optimization is used if a valid target value (i.e. a number greater than 0) is specified in the config;
         if a value less than or equal to 0 is supplied, automatic optimization is used.
@@ -505,11 +355,10 @@ def get_ordered_optimizers(self):
 
         return ordered_optimizers
 
-    def first_recalibration_and_optimization(
+    def first_recalibration(
         self,
         precursor_df: pd.DataFrame,
         fragments_df: pd.DataFrame,
-        ordered_optimizers: list,
     ):
         """Performs the first recalibration and optimization step.
 
@@ -521,24 +370,17 @@ def first_recalibration_and_optimization(
         fragments_df : pd.DataFrame
             Fragment dataframe from optimization lock
 
-        ordered_optimizers : list
-            List of lists of optimizers in correct order
         """
+
+        self.optimization_manager.fit(
+            {"classifier_version": self.fdr_manager.current_version}
+        )
         precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
             precursor_df, fragments_df
         )
 
         self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
-        self.reporter.log_string(
-            "=== Performing initial optimization on extracted data. ===",
-            verbosity="info",
-        )
-
-        for optimizers in ordered_optimizers:
-            for optimizer in optimizers:
-                optimizer.step(precursor_df_filtered, fragments_df_filtered)
-
     def calibration(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
@@ -566,27 +408,7 @@ def calibration(self):
             verbosity="progress",
         )
 
-        # Get the optimization lock
-        (
-            self.optimization_lock_library_precursor_df,
-            self.optimization_lock_library_fragment_df,
-            precursor_df,
-            fragments_df,
-        ) = self.get_optimization_lock()
-
-        self.optimization_manager.fit(
-            {"classifier_version": self.fdr_manager.current_version}
-        )
-
-        self.reporter.log_string(
-            "Required number of precursors found and required number of training iterations performed. Starting search parameter optimization.",
-            verbosity="progress",
-        )
-
-        # Perform a first recalibration on the optimization lock.
-        self.first_recalibration_and_optimization(
-            precursor_df, fragments_df, ordered_optimizers
-        )
+        optlock = optimization.OptimizationLock(self)
 
         # Start of optimization/recalibration loop
         for optimizers in ordered_optimizers:
@@ -601,54 +423,62 @@ def calibration(self):
                         optimizer.plot()
 
                     break
-
-                features_df, fragments_df = self.extract_batch(
-                    self.optimization_lock_library_precursor_df,
-                    self.optimization_lock_library_fragment_df,
+                self.reporter.log_string(
+                    f"=== Step {current_step}, extracting elution groups {optlock.start_index} to {optlock.stop_index} ===",
+                    verbosity="progress",
                 )
+                optlock.extract()
 
                 self.reporter.log_string(
-                    f"=== Step {current_step}, extracted {len(features_df)} precursors and {len(fragments_df)} fragments ===",
+                    f"=== Step {current_step}, extracted {len(optlock.features_df)} precursors and {len(optlock.fragments_df)} fragments ===",
                     verbosity="progress",
                 )
 
-                precursor_df = self.fdr_correction(
-                    features_df,
-                    fragments_df,
-                    self.optimization_manager.classifier_version,
-                )
+                optlock.fdr()
 
                 self.reporter.log_string(
                     f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
                     verbosity="info",
                 )
 
-                self.log_precursor_df(precursor_df)
+                self.log_precursor_df(optlock.precursor_df)
 
-                precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-                    precursor_df, fragments_df
-                )
+                if optlock.reached_target:
+                    if not optlock.reached_target_before:
+                        self.first_recalibration(
+                            optlock.precursor_df, optlock.fragments_df
+                        )
+                        optlock.reached_target_before = True
+                        optlock.update()
+                        continue
 
-                self.recalibration(precursor_df_filtered, fragments_df_filtered)
+                    precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+                        optlock.precursor_df, optlock.fragments_df
+                    )
 
-                self.reporter.log_string(
-                    "=== checking if optimization conditions were reached ===",
-                    verbosity="info",
-                )
+                    self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
-                for optimizer in optimizers:
-                    optimizer.step(
-                        precursor_df_filtered, fragments_df_filtered, current_step
+                    self.reporter.log_string(
+                        "=== checking if optimization conditions were reached ===",
+                        verbosity="info",
                     )
 
-                self.reporter.log_string(
-                    "==============================================", verbosity="info"
-                )
+                    for optimizer in optimizers:
+                        optimizer.step(
+                            precursor_df_filtered, fragments_df_filtered, current_step
+                        )
 
-                self.reporter.log_string(
-                    f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
-                    verbosity="progress",
-                )
+                    self.reporter.log_string(
+                        "==============================================",
+                        verbosity="info",
+                    )
+
+                    self.reporter.log_string(
+                        f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
+                        verbosity="progress",
+                    )
+
+                optlock.update()
 
             else:
                 self.reporter.log_string(
@@ -758,13 +588,11 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         self.calibration_manager.predict(
-            self.optimization_lock_library_precursor_df,
+            self.spectral_library._precursor_df,
             "precursor",
         )
 
-        self.calibration_manager.predict(
-            self.optimization_lock_library_fragment_df, "fragment"
-        )
+        self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
 
         self.optimization_manager.fit(
             {

From 2f840a641ae832d56fecc9fc53144f5afd76c06e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 16 Aug 2024 14:58:32 +0200
Subject: [PATCH 110/250] adjusted update rules

---
 alphadia/workflow/optimization.py   | 24 +++++++++++++-----------
 alphadia/workflow/peptidecentric.py | 26 +++++++++++++-------------
 2 files changed, 26 insertions(+), 24 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 51c0491b..ad27bf1c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -383,7 +383,7 @@ def __init__(
         self.parameter_name = "rt_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
-        self.feature_name = "precursor_count"
+        self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -401,12 +401,12 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, 0.975)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df)
+        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
 
 
 class AutomaticMS2Optimizer(AutomaticOptimizer):
@@ -420,7 +420,7 @@ def __init__(
         self.parameter_name = "ms2_error"
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
-        self.feature_name = "precursor_count"
+        self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -438,12 +438,12 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, 0.975)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df)
+        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
 
 
 class AutomaticMS1Optimizer(AutomaticOptimizer):
@@ -476,7 +476,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, 0.975)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -495,7 +495,7 @@ def __init__(
         self.parameter_name = "mobility_error"
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
-        self.feature_name = "precursor_count"
+        self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -514,12 +514,12 @@ def _propose_new_parameter(self, df: pd.DataFrame):
 
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, 0.975)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df)
+        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
 
 
 class TargetedRTOptimizer(TargetedOptimizer):
@@ -683,7 +683,9 @@ def adjust_upwards(self):
 
     def adjust_downwards(self):
         """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly. If not, the same library is used as before."""
-        self.batch_idx += int(np.ceil(np.log2(self.target_count / self.count)))
+        self.batch_idx += max(
+            int(np.ceil(np.log2(self.target_count / self.count))), -self.batch_idx
+        )
 
         eg_idxes = self.elution_group_order[: self.stop_index]
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index a93ed932..41d358c1 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -408,7 +408,7 @@ def calibration(self):
             verbosity="progress",
         )
 
-        optlock = optimization.OptimizationLock(self)
+        self.optlock = optimization.OptimizationLock(self)
 
         # Start of optimization/recalibration loop
         for optimizers in ordered_optimizers:
@@ -424,36 +424,36 @@ def calibration(self):
 
                     break
                 self.reporter.log_string(
-                    f"=== Step {current_step}, extracting elution groups {optlock.start_index} to {optlock.stop_index} ===",
+                    f"=== Step {current_step}, extracting elution groups {self.optlock.start_index} to {self.optlock.stop_index} ===",
                     verbosity="progress",
                 )
-                optlock.extract()
+                self.optlock.extract()
 
                 self.reporter.log_string(
-                    f"=== Step {current_step}, extracted {len(optlock.features_df)} precursors and {len(optlock.fragments_df)} fragments ===",
+                    f"=== Step {current_step}, extracted {len(self.optlock.features_df)} precursors and {len(self.optlock.fragments_df)} fragments ===",
                     verbosity="progress",
                 )
 
-                optlock.fdr()
+                self.optlock.fdr()
 
                 self.reporter.log_string(
                     f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
                     verbosity="info",
                 )
 
-                self.log_precursor_df(optlock.precursor_df)
+                self.log_precursor_df(self.optlock.precursor_df)
 
-                if optlock.reached_target:
-                    if not optlock.reached_target_before:
+                if self.optlock.reached_target:
+                    if not self.optlock.reached_target_before:
                         self.first_recalibration(
-                            optlock.precursor_df, optlock.fragments_df
+                            self.optlock.precursor_df, self.optlock.fragments_df
                         )
-                        optlock.reached_target_before = True
-                        optlock.update()
+                        self.optlock.reached_target_before = True
+                        self.optlock.update()
                         continue
 
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-                        optlock.precursor_df, optlock.fragments_df
+                        self.optlock.precursor_df, self.optlock.fragments_df
                     )
 
                     self.recalibration(precursor_df_filtered, fragments_df_filtered)
@@ -478,7 +478,7 @@ def calibration(self):
                         verbosity="progress",
                     )
 
-                optlock.update()
+                self.optlock.update()
 
             else:
                 self.reporter.log_string(

From aa40d6b8c0efbf7a200e25b60f7314f8b05627a3 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 16 Aug 2024 15:42:40 +0200
Subject: [PATCH 111/250] update unit test

---
 tests/unit_tests/test_workflow.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 6c675344..bddc2533 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -430,6 +430,10 @@ def create_workflow_instance():
 
     workflow.init_fdr_manager()
 
+    workflow.optlock = pd.DataFrame(
+        {"library_precursor_df": np.zeros(2000)}
+    )  # Note this allows the test to run but it is not of the correct type for the genuine optlock attribute
+
     return workflow
 
 
@@ -476,12 +480,13 @@ def test_automatic_ms2_optimizer():
 
     assert ms2_optimizer.has_converged is True
     assert (
-        ms2_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+        ms2_optimizer.history_df.precursor_proportion_detected
+        == pd.Series([1000 / 2000, 1001 / 2000, 1002])
     ).all()
     assert (
         workflow.optimization_manager.ms2_error
         == ms2_optimizer.history_df.parameter[
-            ms2_optimizer.history_df.precursor_count.idxmax()
+            ms2_optimizer.history_df.precursor_proportion_detected.idxmax()
         ]
     )
     assert workflow.optimization_manager.classifier_version == 2
@@ -530,12 +535,13 @@ def test_automatic_rt_optimizer():
 
     assert rt_optimizer.has_converged is True
     assert (
-        rt_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+        rt_optimizer.history_df.precursor_proportion_detected
+        == pd.Series([1000 / 2000, 1001 / 2000, 1002 / 2000])
     ).all()
     assert (
         workflow.optimization_manager.rt_error
         == rt_optimizer.history_df.parameter[
-            rt_optimizer.history_df.precursor_count.idxmax()
+            rt_optimizer.history_df.precursor_proportion_detected.idxmax()
         ]
     )
     assert workflow.optimization_manager.classifier_version == 2
@@ -634,12 +640,13 @@ def test_automatic_mobility_optimizer():
 
     assert mobility_optimizer.has_converged is True
     assert (
-        mobility_optimizer.history_df.precursor_count == pd.Series([1000, 1001, 1002])
+        mobility_optimizer.history_df.precursor_proportion_detected
+        == pd.Series([1000 / 2000, 1001 / 2000, 1002 / 2000])
     ).all()
     assert (
         workflow.optimization_manager.mobility_error
         == mobility_optimizer.history_df.parameter[
-            mobility_optimizer.history_df.precursor_count.idxmax()
+            mobility_optimizer.history_df.precursor_proportion_detected.idxmax()
         ]
     )
     assert workflow.optimization_manager.classifier_version == 2

From d6b7e2bf7185caefc98719d3e8eb03fde78c81e2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 16 Aug 2024 15:54:42 +0200
Subject: [PATCH 112/250] improved logging and removed
 optimization_lock_min_steps

---
 alphadia/constants/default.yaml     | 5 +----
 alphadia/workflow/peptidecentric.py | 6 +++++-
 tests/unit_tests/test_workflow.py   | 2 +-
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index cd19ccc8..df2cd3ab 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -98,9 +98,6 @@ search_advanced:
 
 calibration:
 
-  # minimum number of steps taken during the optimization lock (during which the elution groups used for optimization are extracted)
-  optimization_lock_min_steps: 0
-
   # Number of precursors searched and scored per batch
   batch_size: 8000
 
@@ -110,7 +107,7 @@ calibration:
   # the maximum number of steps that a given optimizer is permitted to take
   max_steps: 20
 
-  # the maximum number of steps that a given optimizer is permitted to take
+  # the minimum number of steps that a given optimizer must take before it can be said to have converged
   min_steps: 2
 
   # TODO: remove this parameter
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 41d358c1..560ced7a 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -404,7 +404,7 @@ def calibration(self):
         ordered_optimizers = self.get_ordered_optimizers()
 
         self.reporter.log_string(
-            "Starting initial classifier training and precursor identification.",
+            "Starting initial search for precursors.",
             verbosity="progress",
         )
 
@@ -450,6 +450,10 @@ def calibration(self):
                         )
                         self.optlock.reached_target_before = True
                         self.optlock.update()
+                        self.reporter.log_string(
+                            "Required number of precursors found. Starting search parameter optimization.",
+                            verbosity="progress",
+                        )
                         continue
 
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index bddc2533..eb18c549 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -481,7 +481,7 @@ def test_automatic_ms2_optimizer():
     assert ms2_optimizer.has_converged is True
     assert (
         ms2_optimizer.history_df.precursor_proportion_detected
-        == pd.Series([1000 / 2000, 1001 / 2000, 1002])
+        == pd.Series([1000 / 2000, 1001 / 2000, 1002 / 2000])
     ).all()
     assert (
         workflow.optimization_manager.ms2_error

From 7fefb259e23a0b6c70b9f6e3c009abc22fcf8227 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Fri, 16 Aug 2024 16:37:00 +0200
Subject: [PATCH 113/250] fix test functions

---
 alphadia/transferlearning/train.py | 360 ++++++++++++++---------------
 1 file changed, 174 insertions(+), 186 deletions(-)

diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index fb4484f9..0addaa32 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -340,56 +340,58 @@ def _test_ms2(
 
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] == 0:
-            self.ms2_model.model.eval()
-            if "instrument" not in precursor_df.columns:
-                precursor_df["instrument"] = default_instrument
-            if "nce" not in precursor_df.columns:
-                precursor_df["nce"] = default_nce
-
-            precursor_copy = precursor_df.copy()
-            pred_intensities = self.ms2_model.predict(precursor_copy)
-
-            test_input = {
-                "psm_df": precursor_df,
-                "predicted": pred_intensities,
-                "target": target_fragment_intensity_df,
-            }
-            val_metrics = metric_accumulator.calculate_test_metric(
-                test_input, epoch, data_split=data_split, property_name="ms2"
+        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+            return continue_training
+
+        self.ms2_model.model.eval()
+        if "instrument" not in precursor_df.columns:
+            precursor_df["instrument"] = default_instrument
+        if "nce" not in precursor_df.columns:
+            precursor_df["nce"] = default_nce
+
+        precursor_copy = precursor_df.copy()
+        pred_intensities = self.ms2_model.predict(precursor_copy)
+
+        test_input = {
+            "psm_df": precursor_df,
+            "predicted": pred_intensities,
+            "target": target_fragment_intensity_df,
+        }
+        val_metrics = metric_accumulator.calculate_test_metric(
+            test_input, epoch, data_split=data_split, property_name="ms2"
+        )
+        if epoch != -1 and data_split == "validation":  # A training epoch
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=epoch_loss,
+                metric_name="l1_loss",
+                data_split="train",
+                property_name="ms2",
             )
-            if epoch != -1 and data_split == "validation":  # A training epoch
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=epoch_loss,
-                    metric_name="l1_loss",
-                    data_split="train",
-                    property_name="ms2",
-                )
-                current_lr = self.ms2_model.optimizer.param_groups[0]["lr"]
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=current_lr,
-                    metric_name="lr",
-                    data_split="train",
-                    property_name="ms2",
-                )
-                val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                    "value"
-                ].values[0]
-                continue_training = self.early_stopping.step(val_loss)
-                logger.progress(
-                    f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-                )
-            else:
+            current_lr = self.ms2_model.optimizer.param_groups[0]["lr"]
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=current_lr,
+                metric_name="lr",
+                data_split="train",
+                property_name="ms2",
+            )
+            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
+                "value"
+            ].values[0]
+            continue_training = self.early_stopping.step(val_loss)
+            logger.progress(
+                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+            )
+        else:
+            logger.progress(
+                f" Ms2 model tested on {data_split} dataset with the following metrics:"
+            )
+            for i in range(len(val_metrics)):
                 logger.progress(
-                    f" Ms2 model tested on {data_split} dataset with the following metrics:"
+                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
                 )
-                for i in range(len(val_metrics)):
-                    logger.progress(
-                        f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                    )
-            self.ms2_model.model.train()
+        self.ms2_model.model.train()
         return continue_training
 
     def _normalize_intensity(
@@ -497,12 +499,10 @@ def finetune_ms2(
             unordered_frag_df=test_intensity_df,
         )
 
-        # Create a metric manager
         test_metric_manager = MetricManager(
             test_metrics=[L1LossTestMetric(), Ms2SimilarityTestMetric()],
         )
 
-        # create a callback handler
         callback_handler = CustomCallbackHandler(
             self._test_ms2,
             precursor_df=reordered_val_psm_df,
@@ -511,13 +511,10 @@ def finetune_ms2(
             data_split="validation",
         )
 
-        # set the callback handler
         self.ms2_model.set_callback_handler(callback_handler)
 
-        # Change the learning rate scheduler
         self.ms2_model.set_lr_scheduler_class(CustomScheduler)
 
-        # Reset the early stopping
         self.early_stopping.reset()
 
         # Test the model before training
@@ -592,50 +589,52 @@ def _test_rt(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] == 0:
-            self.rt_model.model.eval()
-
-            pred = self.rt_model.predict(test_df)
-            test_input = {
-                "predicted": pred["rt_pred"].values,
-                "target": test_df["rt_norm"].values,
-            }
-            val_metrics = metric_accumulator.calculate_test_metric(
-                test_input, epoch, data_split=data_split, property_name="rt"
+        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+            return continue_training
+
+        self.rt_model.model.eval()
+
+        pred = self.rt_model.predict(test_df)
+        test_input = {
+            "predicted": pred["rt_pred"].values,
+            "target": test_df["rt_norm"].values,
+        }
+        val_metrics = metric_accumulator.calculate_test_metric(
+            test_input, epoch, data_split=data_split, property_name="rt"
+        )
+        if epoch != -1 and data_split == "validation":
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=epoch_loss,
+                metric_name="l1_loss",
+                data_split="train",
+                property_name="rt",
             )
-            if epoch != -1 and data_split == "validation":  # A training epoch
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=epoch_loss,
-                    metric_name="l1_loss",
-                    data_split="train",
-                    property_name="rt",
-                )
-                current_lr = self.rt_model.optimizer.param_groups[0]["lr"]
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=current_lr,
-                    metric_name="lr",
-                    data_split="train",
-                    property_name="rt",
-                )
-                val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                    "value"
-                ].values[0]
-                continue_training = self.early_stopping.step(val_loss)
-                logger.progress(
-                    f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-                )
-            else:
+            current_lr = self.rt_model.optimizer.param_groups[0]["lr"]
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=current_lr,
+                metric_name="lr",
+                data_split="train",
+                property_name="rt",
+            )
+            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
+                "value"
+            ].values[0]
+            continue_training = self.early_stopping.step(val_loss)
+            logger.progress(
+                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+            )
+        else:
+            logger.progress(
+                f" RT model tested on {data_split} dataset with the following metrics:"
+            )
+            for i in range(len(val_metrics)):
                 logger.progress(
-                    f" RT model tested on {data_split} dataset with the following metrics:"
+                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
                 )
-                for i in range(len(val_metrics)):
-                    logger.progress(
-                        f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                    )
 
-            self.rt_model.model.train()
+        self.rt_model.model.train()
 
         return continue_training
 
@@ -662,7 +661,6 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
-        # Create a test metric manager
         test_metric_manager = MetricManager(
             test_metrics=[
                 L1LossTestMetric(),
@@ -671,20 +669,16 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             ],
         )
 
-        # Create a callback handler
         callback_handler = CustomCallbackHandler(
             self._test_rt,
             test_df=val_df,
             metric_accumulator=test_metric_manager,
             data_split="validation",
         )
-        # Set the callback handler
         self.rt_model.set_callback_handler(callback_handler)
 
-        # Change the learning rate scheduler
         self.rt_model.set_lr_scheduler_class(CustomScheduler)
 
-        # Reset the early stopping
         self.early_stopping.reset()
 
         # Test the model before training
@@ -747,49 +741,51 @@ def _test_charge(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] == 0:
-            self.charge_model.model.eval()
-
-            pred = self.charge_model.predict(test_df)
-            test_input = {
-                "target": np.array(test_df["charge_indicators"].values.tolist()),
-                "predicted": np.array(pred["charge_probs"].values.tolist()),
-            }
-            val_metrics = metric_accumulator.calculate_test_metric(
-                test_input, epoch, data_split=data_split, property_name="charge"
+        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+            return continue_training
+
+        self.charge_model.model.eval()
+
+        pred = self.charge_model.predict(test_df)
+        test_input = {
+            "target": np.array(test_df["charge_indicators"].values.tolist()),
+            "predicted": np.array(pred["charge_probs"].values.tolist()),
+        }
+        val_metrics = metric_accumulator.calculate_test_metric(
+            test_input, epoch, data_split=data_split, property_name="charge"
+        )
+        if epoch != -1 and data_split == "validation":
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=epoch_loss,
+                metric_name="ce_loss",
+                data_split="train",
+                property_name="charge",
             )
-            if epoch != -1 and data_split == "validation":  # A training epoch
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=epoch_loss,
-                    metric_name="ce_loss",
-                    data_split="train",
-                    property_name="charge",
-                )
-                current_lr = self.charge_model.optimizer.param_groups[0]["lr"]
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=current_lr,
-                    metric_name="lr",
-                    data_split="train",
-                    property_name="charge",
-                )
-                val_loss = val_metrics[val_metrics["metric_name"] == "ce_loss"][
-                    "value"
-                ].values[0]
-                continue_training = self.early_stopping.step(val_loss)
-                logger.progress(
-                    f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-                )
-            else:
+            current_lr = self.charge_model.optimizer.param_groups[0]["lr"]
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=current_lr,
+                metric_name="lr",
+                data_split="train",
+                property_name="charge",
+            )
+            val_loss = val_metrics[val_metrics["metric_name"] == "ce_loss"][
+                "value"
+            ].values[0]
+            continue_training = self.early_stopping.step(val_loss)
+            logger.progress(
+                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+            )
+        else:
+            logger.progress(
+                f" Charge model tested on {data_split} dataset with the following metrics: "
+            )
+            for i in range(len(val_metrics)):
                 logger.progress(
-                    f" Charge model tested on {data_split} dataset with the following metrics: "
+                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
                 )
-                for i in range(len(val_metrics)):
-                    logger.progress(
-                        f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                    )
-            self.charge_model.model.train()
+        self.charge_model.model.train()
         return continue_training
 
     def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
@@ -842,7 +838,6 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
-        # Create a test metric manager
         test_metric_manager = MetricManager(
             test_metrics=[
                 CELossTestMetric(),
@@ -851,7 +846,6 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             ],
         )
 
-        # Create a callback handler
         callback_handler = CustomCallbackHandler(
             self._test_charge,
             test_df=val_df,
@@ -859,13 +853,10 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             data_split="validation",
         )
 
-        # Set the callback handler
         self.charge_model.set_callback_handler(callback_handler)
 
-        # Change the learning rate scheduler
         self.charge_model.set_lr_scheduler_class(CustomScheduler)
 
-        # Reset the early stopping
         self.early_stopping.reset()
 
         # Test the model before training
@@ -927,51 +918,53 @@ def _test_ccs(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] == 0:
-            self.ccs_model.model.eval()
+        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+            return continue_training
 
-            pred = self.ccs_model.predict(test_df)
+        self.ccs_model.model.eval()
 
-            test_input = {
-                "predicted": pred["ccs_pred"].values,
-                "target": test_df["ccs"].values,
-            }
-            val_metrics = metric_accumulator.calculate_test_metric(
-                test_input, epoch, data_split=data_split, property_name="ccs"
+        pred = self.ccs_model.predict(test_df)
+
+        test_input = {
+            "predicted": pred["ccs_pred"].values,
+            "target": test_df["ccs"].values,
+        }
+        val_metrics = metric_accumulator.calculate_test_metric(
+            test_input, epoch, data_split=data_split, property_name="ccs"
+        )
+        if epoch != -1 and data_split == "validation":
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=epoch_loss,
+                metric_name="l1_loss",
+                data_split="train",
+                property_name="ccs",
             )
-            if epoch != -1 and data_split == "validation":
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=epoch_loss,
-                    metric_name="l1_loss",
-                    data_split="train",
-                    property_name="ccs",
-                )
-                current_lr = self.ccs_model.optimizer.param_groups[0]["lr"]
-                metric_accumulator.accumulate_metrics(
-                    epoch,
-                    metric=current_lr,
-                    metric_name="lr",
-                    data_split="train",
-                    property_name="ccs",
-                )
-                val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                    "value"
-                ].values[0]
-                continue_training = self.early_stopping.step(val_loss)
-                logger.progress(
-                    f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-                )
-            else:
+            current_lr = self.ccs_model.optimizer.param_groups[0]["lr"]
+            metric_accumulator.accumulate_metrics(
+                epoch,
+                metric=current_lr,
+                metric_name="lr",
+                data_split="train",
+                property_name="ccs",
+            )
+            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
+                "value"
+            ].values[0]
+            continue_training = self.early_stopping.step(val_loss)
+            logger.progress(
+                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+            )
+        else:
+            logger.progress(
+                f" CCS model tested on {data_split} dataset with the following metrics:"
+            )
+            for i in range(len(val_metrics)):
                 logger.progress(
-                    f" CCS model tested on {data_split} dataset with the following metrics:"
+                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
                 )
-                for i in range(len(val_metrics)):
-                    logger.progress(
-                        f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                    )
 
-            self.ccs_model.model.train()
+        self.ccs_model.model.train()
 
         return continue_training
 
@@ -1007,7 +1000,6 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
-        # Create a test metric manager
         test_metric_manager = MetricManager(
             test_metrics=[
                 L1LossTestMetric(),
@@ -1015,20 +1007,16 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
                 AbsErrorPercentileTestMetric(95),
             ],
         )
-        # Create a callback handler
         callback_handler = CustomCallbackHandler(
             self._test_ccs,
             test_df=val_df,
             metric_accumulator=test_metric_manager,
             data_split="validation",
         )
-        # Set the callback handler
         self.ccs_model.set_callback_handler(callback_handler)
 
-        # Change the learning rate scheduler
         self.ccs_model.set_lr_scheduler_class(CustomScheduler)
 
-        # Reset the early stopping
         self.early_stopping.reset()
 
         # Test the model before training

From 07577cf824f3d1510de5886e7f8dc552e509aa50 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 16 Aug 2024 20:03:17 +0200
Subject: [PATCH 114/250] fixed feature tracking in optimizer

---
 alphadia/workflow/optimization.py   | 51 +++++++++++++++--------------
 alphadia/workflow/peptidecentric.py | 10 +++---
 tests/unit_tests/test_workflow.py   | 47 +++++++++++---------------
 3 files changed, 51 insertions(+), 57 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index ad27bf1c..efdd00b9 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -82,12 +82,12 @@ def __init__(
         self.history_df = pd.DataFrame()
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
+        self.current_step = -1
 
     def step(
         self,
         precursors_df: pd.DataFrame,
         fragments_df: pd.DataFrame,
-        current_step: int = -1,
     ):
         """See base class. The feature is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
         if self.has_converged:
@@ -97,6 +97,7 @@ def step(
             )
             return
 
+        self.current_step += 1
         new_row = pd.DataFrame(
             [
                 {
@@ -114,7 +115,7 @@ def step(
             ]
         )
         self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-        just_converged = self._check_convergence(current_step)
+        just_converged = self._check_convergence()
 
         if just_converged:
             self.has_converged = True
@@ -218,7 +219,7 @@ def _propose_new_parameter(self, df):
         """
         pass
 
-    def _check_convergence(self, current_step: int = -1):
+    def _check_convergence(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
         This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
         If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
@@ -227,15 +228,11 @@ def _check_convergence(self, current_step: int = -1):
         -----
             Because the check for an increase in feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
 
-        Parameters
-        ----------
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
 
         """
 
         min_steps_reached = (
-            current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+            self.current_step >= self.workflow.config["calibration"]["min_steps"] - 1
         )
         return (
             min_steps_reached
@@ -292,8 +289,9 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.has_converged = False
+        self.current_step = -1
 
-    def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
+    def _check_convergence(self, proposed_parameter: float):
         """The optimization has converged if the proposed parameter is equal to or less than the target parameter and the a sufficient number of steps has been taken.
 
         Parameters
@@ -301,9 +299,6 @@ def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
         proposed_parameter: float
             The proposed parameter for the next round of optimization.
 
-        current_step: int
-            The current step in the optimization process. By default it is set to -1, which prevents the optimizer from converging unless min_steps has been set to 0.
-
         Returns
         -------
         bool
@@ -314,7 +309,8 @@ def _check_convergence(self, proposed_parameter: float, current_step: int = -1):
 
         return (
             proposed_parameter <= self.target_parameter
-            and current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+            and self.current_step
+            >= self.workflow.config["calibration"]["min_steps"] - 1
         )
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -335,7 +331,6 @@ def step(
         self,
         precursors_df: pd.DataFrame,
         fragments_df: pd.DataFrame,
-        current_step: int = -1,
     ):
         """See base class."""
         if self.has_converged:
@@ -344,11 +339,11 @@ def step(
                 verbosity="progress",
             )
             return
-
+        self.current_step += 1
         new_parameter = self._propose_new_parameter(
             precursors_df if self.estimator_group_name == "precursor" else fragments_df
         )
-        just_converged = self._check_convergence(new_parameter, current_step)
+        just_converged = self._check_convergence(new_parameter)
         self.workflow.optimization_manager.fit({self.parameter_name: new_parameter})
         self.workflow.optimization_manager.fit(
             {"classifier_version": self.workflow.fdr_manager.current_version}
@@ -406,7 +401,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
+        return len(precursors_df) / self.workflow.optlock.total_precursors
 
 
 class AutomaticMS2Optimizer(AutomaticOptimizer):
@@ -443,7 +438,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
+        return len(precursors_df) / self.workflow.optlock.total_precursors
 
 
 class AutomaticMS1Optimizer(AutomaticOptimizer):
@@ -519,7 +514,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / len(self.workflow.optlock.library_precursor_df)
+        return len(precursors_df) / self.workflow.optlock.total_precursors
 
 
 class TargetedRTOptimizer(TargetedOptimizer):
@@ -610,12 +605,14 @@ def __init__(self, workflow):
         self.set_batch_plan()
 
         eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
-        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
             self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
                 eg_idxes
             )
         ]
 
+        self.total_precursors = len(self.batch_precursor_df)
+
         self.features = []
         self.fragments = []
 
@@ -650,7 +647,7 @@ def set_batch_plan(self):
     def extract(self):
         """Extract features and fragments from the current batch of the optimization lock."""
 
-        feature_df, fragment_df = self.workflow.extract_batch(self.library_precursor_df)
+        feature_df, fragment_df = self.workflow.extract_batch(self.batch_precursor_df)
         self.features += [feature_df]
         self.fragments += [fragment_df]
 
@@ -675,13 +672,15 @@ def adjust_upwards(self):
 
         eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
 
-        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
             self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
                 eg_idxes
             )
         ]
 
-    def adjust_downwards(self):
+        self.total_precursors += len(self.batch_precursor_df)
+
+    def reset_downwards(self):
         """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly. If not, the same library is used as before."""
         self.batch_idx += max(
             int(np.ceil(np.log2(self.target_count / self.count))), -self.batch_idx
@@ -689,19 +688,21 @@ def adjust_downwards(self):
 
         eg_idxes = self.elution_group_order[: self.stop_index]
 
-        self.library_precursor_df = self.workflow.spectral_library._precursor_df[
+        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
             self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
                 eg_idxes
             )
         ]
 
+        self.total_precursors = len(self.batch_precursor_df)
+
     def update(self):
         """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached."""
         if not self.reached_target:
             self.adjust_upwards()
 
         else:
-            self.adjust_downwards()
+            self.reset_downwards()
             self.features = []
             self.fragments = []
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 560ced7a..5a36cb30 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -412,7 +412,9 @@ def calibration(self):
 
         # Start of optimization/recalibration loop
         for optimizers in ordered_optimizers:
-            for current_step in range(self.config["calibration"]["max_steps"]):
+            for current_step in range(
+                self.config["calibration"]["max_steps"]
+            ):  # Note current_step here refers to a different step than the attribute of the same name in the optimizer -- this should be rectified
                 if np.all([optimizer.has_converged for optimizer in optimizers]):
                     self.reporter.log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
@@ -468,9 +470,7 @@ def calibration(self):
                     )
 
                     for optimizer in optimizers:
-                        optimizer.step(
-                            precursor_df_filtered, fragments_df_filtered, current_step
-                        )
+                        optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
                     self.reporter.log_string(
                         "==============================================",
@@ -478,7 +478,7 @@ def calibration(self):
                     )
 
                     self.reporter.log_string(
-                        f"=== Optimization has been performed for {current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
+                        f"=== Optimization has been performed for {optimizer.current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
                         verbosity="progress",
                     )
 
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index eb18c549..107d1e70 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -430,9 +430,10 @@ def create_workflow_instance():
 
     workflow.init_fdr_manager()
 
-    workflow.optlock = pd.DataFrame(
-        {"library_precursor_df": np.zeros(2000)}
-    )  # Note this allows the test to run but it is not of the correct type for the genuine optlock attribute
+    class MockOptlock:
+        total_precursors = 2000
+
+    workflow.optlock = MockOptlock()
 
     return workflow
 
@@ -454,7 +455,7 @@ def test_automatic_ms2_optimizer():
     assert ms2_optimizer.parameter_name == "ms2_error"
 
     workflow.fdr_manager._current_version += 1
-    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert len(ms2_optimizer.history_df) == 1
 
@@ -466,7 +467,7 @@ def test_automatic_ms2_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     calibration_test_df1 = pd.concat(
         [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
@@ -476,7 +477,7 @@ def test_automatic_ms2_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    ms2_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+    ms2_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert ms2_optimizer.has_converged is True
     assert (
@@ -509,7 +510,7 @@ def test_automatic_rt_optimizer():
     assert rt_optimizer.parameter_name == "rt_error"
 
     workflow.fdr_manager._current_version += 1
-    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert len(rt_optimizer.history_df) == 1
 
@@ -521,7 +522,7 @@ def test_automatic_rt_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     calibration_test_df1 = pd.concat(
         [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
@@ -531,7 +532,7 @@ def test_automatic_rt_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    rt_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert rt_optimizer.has_converged is True
     assert (
@@ -564,7 +565,7 @@ def test_automatic_ms1_optimizer():
     assert ms1_optimizer.parameter_name == "ms1_error"
 
     workflow.fdr_manager._current_version += 1
-    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert len(ms1_optimizer.history_df) == 1
 
@@ -576,7 +577,7 @@ def test_automatic_ms1_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     calibration_test_df1 = pd.concat(
         [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
@@ -586,7 +587,7 @@ def test_automatic_ms1_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    ms1_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+    ms1_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert ms1_optimizer.has_converged is True
     assert (
@@ -615,7 +616,7 @@ def test_automatic_mobility_optimizer():
     assert mobility_optimizer.parameter_name == "mobility_error"
 
     workflow.fdr_manager._current_version += 1
-    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=0)
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert len(mobility_optimizer.history_df) == 1
 
@@ -626,7 +627,7 @@ def test_automatic_mobility_optimizer():
     workflow.fdr_manager._current_version += 1
 
     assert workflow.optimization_manager.classifier_version == -1
-    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=1)
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     calibration_test_df1 = pd.concat(
         [calibration_test_df1, pd.DataFrame(calibration_test_df1.loc[0]).T],
@@ -636,7 +637,7 @@ def test_automatic_mobility_optimizer():
 
     assert workflow.optimization_manager.classifier_version == -1
 
-    mobility_optimizer.step(calibration_test_df1, calibration_test_df2, current_step=2)
+    mobility_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert mobility_optimizer.has_converged is True
     assert (
@@ -672,9 +673,7 @@ def test_targeted_ms2_optimizer():
         assert optimizer.has_converged is False
 
         workflow.fdr_manager._current_version += 1
-        optimizer.step(
-            calibration_test_df1, calibration_test_df2, current_step=current_step
-        )
+        optimizer.step(calibration_test_df1, calibration_test_df2)
 
         assert workflow.optimization_manager.classifier_version == current_step
 
@@ -702,9 +701,7 @@ def test_targeted_rt_optimizer():
         assert optimizer.has_converged is False
 
         workflow.fdr_manager._current_version += 1
-        optimizer.step(
-            calibration_test_df1, calibration_test_df2, current_step=current_step
-        )
+        optimizer.step(calibration_test_df1, calibration_test_df2)
 
         assert workflow.optimization_manager.classifier_version == current_step
 
@@ -732,9 +729,7 @@ def test_targeted_ms1_optimizer():
         assert optimizer.has_converged is False
 
         workflow.fdr_manager._current_version += 1
-        optimizer.step(
-            calibration_test_df1, calibration_test_df2, current_step=current_step
-        )
+        optimizer.step(calibration_test_df1, calibration_test_df2)
 
         assert workflow.optimization_manager.classifier_version == current_step
 
@@ -762,9 +757,7 @@ def test_targeted_mobility_optimizer():
         assert optimizer.has_converged is False
 
         workflow.fdr_manager._current_version += 1
-        optimizer.step(
-            calibration_test_df1, calibration_test_df2, current_step=current_step
-        )
+        optimizer.step(calibration_test_df1, calibration_test_df2)
 
         assert workflow.optimization_manager.classifier_version == current_step
 

From 1a799ca0aea3899c7661fef1ee39911f57beb7e5 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 19 Aug 2024 12:59:35 +0200
Subject: [PATCH 115/250] max lr bugfix

---
 alphadia/transferlearning/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index fb4484f9..1f1d367a 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -547,7 +547,7 @@ def finetune_ms2(
             epoch=self.settings["epochs"],
             batch_size=self.settings["batch_size"],
             warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            lr=self.settings["max_lr"],
         )
 
         self._test_ms2(
@@ -706,7 +706,7 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             batch_size=self.settings["batch_size"],
             epoch=self.settings["epochs"],
             warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            lr=self.settings["max_lr"],
         )
 
         self._test_rt(
@@ -888,7 +888,7 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             batch_size=self.settings["batch_size"],
             epoch=self.settings["epochs"],
             warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            lr=self.settings["max_lr"],
         )
 
         self._test_charge(
@@ -1050,7 +1050,7 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             batch_size=self.settings["batch_size"],
             epoch=self.settings["epochs"],
             warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            lr=self.settings["max_lr"],
         )
 
         self._test_ccs(

From fd8e675a19d8b369b650f8dbe767c3036130e6b0 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 19 Aug 2024 15:53:20 +0200
Subject: [PATCH 116/250] remove local settings use

---
 alphadia/transferlearning/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 1f1d367a..f3262b45 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -68,7 +68,7 @@ def __init__(self, optimizer: torch.optim.Optimizer, **kwargs):
         self.reduce_lr_on_plateau = torch.optim.lr_scheduler.ReduceLROnPlateau(
             optimizer,
             mode="min",
-            patience=settings["lr_patience"],
+            patience=3,
             factor=0.5,
         )
         self.warmup_lr = LambdaLR(optimizer, self._warmup)

From de8441c3bf6d6755ac9bbc589165c836cb7fff3e Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 19 Aug 2024 16:17:13 +0200
Subject: [PATCH 117/250] enable selection of alternative peptdeep models

---
 alphadia/constants/default.yaml      |  3 +++
 alphadia/libtransform.py             | 11 +++++++++++
 alphadia/planning.py                 |  3 +++
 gui/workflows/PeptideCentric.v1.json | 16 ++++++++++++++--
 4 files changed, 31 insertions(+), 2 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index cd19ccc8..b25cf506 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -48,6 +48,9 @@ library_prediction:
   # set path for custom peptdeep model. If set to null, the default model will be used
   peptdeep_model_path: null
 
+  # set peptdeep model type. Possible values are 'generic', 'phospho', 'digly'. If set to null, the generic model will be used
+  peptdeep_model_type: null
+
 # define custom alphabase modifications not part of unimod or alphabase
 # also used for decoy channels
 custom_modififcations:
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index ad65e6ec..f55eaefc 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -253,6 +253,7 @@ def __init__(
         nce: int = 25,
         instrument: str = "Lumos",
         peptdeep_model_path: str | None = None,
+        peptdeep_model_type: str | None = None,
         fragment_types: list[str] | None = None,
         max_fragment_charge: int = 2,
     ) -> None:
@@ -279,6 +280,10 @@ def __init__(
         peptdeep_model_path : str, optional
             Path to a folder containing PeptDeep models. If not provided, the default models will be used.
 
+        peptdeep_model_type : str, optional
+            Use other peptdeep models provided by the peptdeep model manager. Default is None.
+            Possible values are ['generic','phospho','digly']
+
         fragment_types : List[str], optional
             Fragment types to predict. Default is ["b", "y"].
 
@@ -296,6 +301,7 @@ def __init__(
         self.instrument = instrument
         self.mp_process_num = mp_process_num
         self.peptdeep_model_path = peptdeep_model_path
+        self.peptdeep_model_type = peptdeep_model_type
 
         self.fragment_types = fragment_types
         self.max_fragment_charge = max_fragment_charge
@@ -313,6 +319,11 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
         device = utils.get_torch_device(self.use_gpu)
 
         model_mgr = ModelManager(device=device)
+
+        if self.peptdeep_model_type is not None:
+            logging.info(f"Loading PeptDeep models of type {self.peptdeep_model_type}")
+            model_mgr.load_installed_models(self.peptdeep_model_type)
+
         if self.peptdeep_model_path is not None:
             if not os.path.exists(self.peptdeep_model_path):
                 raise ValueError(
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 57481f4a..32ae8bd0 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -226,6 +226,9 @@ def _parse_modifications(mod_str: str) -> list[str]:
                 peptdeep_model_path=self.config["library_prediction"][
                     "peptdeep_model_path"
                 ],
+                peptdeep_model_type=self.config["library_prediction"][
+                    "peptdeep_model_type"
+                ],
                 fragment_types=self.config["library_prediction"][
                     "fragment_types"
                 ].split(";"),
diff --git a/gui/workflows/PeptideCentric.v1.json b/gui/workflows/PeptideCentric.v1.json
index f0c8f1bb..b302bb73 100644
--- a/gui/workflows/PeptideCentric.v1.json
+++ b/gui/workflows/PeptideCentric.v1.json
@@ -239,11 +239,23 @@
                     ]
                 },
                 {
-                    "id": "checkpoint_folder_path",
-                    "name": "PeptDeep Model",
+                    "id": "peptdeep_model_path",
+                    "name": "PeptDeep Model Path",
                     "value": "",
                     "description": "Select a custom PeptDeep model for library prediction. This can be a DDA or DIA trained model. Please make sure that you use the same instrument type and NCE for prediction as the model was trained on.",
                     "type": "singleFolderSelection"
+                },
+                {
+                    "id": "peptdeep_model_type",
+                    "name": "PeptDeep Model Type",
+                    "value": "generic",
+                    "description": "Select a custom pretrained PeptDeep model for library prediction. Possible values are 'generic', 'phospho' and 'digly'.",
+                    "type": "dropdown",
+                    "options": [
+                        "generic",
+                        "phospho",
+                        "digly"
+                    ]
                 }
 
             ]

From 5dd2c4c40a91c59b9f9a90bea44dec9aa2eb5828 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 19 Aug 2024 18:46:54 +0200
Subject: [PATCH 118/250] code review and unit test

---
 alphadia/workflow/optimization.py   | 195 +++++++++++++++-------------
 alphadia/workflow/peptidecentric.py |  57 ++++----
 tests/unit_tests/test_workflow.py   |  89 +++++++++++++
 3 files changed, 222 insertions(+), 119 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index efdd00b9..9cb1deef 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -31,9 +31,9 @@ def __init__(
             The reporter object used to log information about the optimization process. If None, a new LogBackend object is created.
 
         """
-        self.optimal_parameter = None
         self.workflow = workflow
         self.reporter = reporting.LogBackend() if reporter is None else reporter
+        self.num_prev_optimizations = 0
 
     @abstractmethod
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
@@ -82,7 +82,7 @@ def __init__(
         self.history_df = pd.DataFrame()
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
-        self.current_step = -1
+        self.num_prev_optimizations = 0
 
     def step(
         self,
@@ -97,7 +97,7 @@ def step(
             )
             return
 
-        self.current_step += 1
+        self.num_prev_optimizations += 1
         new_row = pd.DataFrame(
             [
                 {
@@ -232,7 +232,8 @@ def _check_convergence(self):
         """
 
         min_steps_reached = (
-            self.current_step >= self.workflow.config["calibration"]["min_steps"] - 1
+            self.num_prev_optimizations
+            >= self.workflow.config["calibration"]["min_steps"]
         )
         return (
             min_steps_reached
@@ -289,7 +290,6 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.has_converged = False
-        self.current_step = -1
 
     def _check_convergence(self, proposed_parameter: float):
         """The optimization has converged if the proposed parameter is equal to or less than the target parameter and the a sufficient number of steps has been taken.
@@ -306,12 +306,11 @@ def _check_convergence(self, proposed_parameter: float):
 
 
         """
-
-        return (
-            proposed_parameter <= self.target_parameter
-            and self.current_step
-            >= self.workflow.config["calibration"]["min_steps"] - 1
+        min_steps_reached = (
+            self.num_prev_optimizations
+            >= self.workflow.config["calibration"]["min_steps"]
         )
+        return proposed_parameter <= self.target_parameter and min_steps_reached
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
@@ -339,7 +338,7 @@ def step(
                 verbosity="progress",
             )
             return
-        self.current_step += 1
+        self.num_prev_optimizations += 1
         new_parameter = self._propose_new_parameter(
             precursors_df if self.estimator_group_name == "precursor" else fragments_df
         )
@@ -578,43 +577,39 @@ def __init__(
 
 
 class OptimizationLock:
-    def __init__(self, workflow):
+    def __init__(self, library, config: dict):
         """Sets and updates the optimization lock, which is the data used for calibration and optimization of the search parameters.
 
         Parameters
         ----------
-        workflow: peptidecentric.PeptideCentricWorkflow
-            The workflow object, which includes the spectral library and the calibration and optimization managers.
+        library: alphabase.spectral_library.flat.SpecLibFlat
+            The library object from the PeptideCentricWorkflow object, which includes the precursor and fragment library dataframes.
 
+        config: dict
+            The configuration dictionary from the PeptideCentricWorkflow object.
         """
-        self.workflow = workflow
+        self.library = library
+        self.config = config
 
-        self.reached_target_before = False
-        self.reached_target = False
+        self.first_time_to_reach_target = True
+        self.has_target_num_precursors = False
 
-        self.elution_group_order = self.workflow.spectral_library.precursor_df[
-            "elution_group_idx"
-        ].unique()
-        np.random.shuffle(self.elution_group_order)
+        self.elution_group_order = library._precursor_df["elution_group_idx"].unique()
+        rng = np.random.default_rng(seed=772)
+        rng.shuffle(self.elution_group_order)
 
-        self.target_count = self.workflow.config["calibration"][
-            "optimization_lock_target"
-        ]
+        self.target_count = self.config["calibration"]["optimization_lock_target"]
 
         self.batch_idx = 0
         self.set_batch_plan()
 
-        eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
-        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
-            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
-                eg_idxes
-            )
+        eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
+        self.batch_precursor_df = self.library._precursor_df[
+            self.library._precursor_df["elution_group_idx"].isin(eg_idxes)
         ]
 
-        self.total_precursors = len(self.batch_precursor_df)
-
-        self.features = []
-        self.fragments = []
+        self.feature_dfs = []
+        self.fragment_dfs = []
 
     def _get_exponential_batches(self, step):
         """Get the number of batches for a given step
@@ -625,102 +620,116 @@ def _get_exponential_batches(self, step):
 
     def set_batch_plan(self):
         """Gets an exponential batch plan based on the batch_size value in the config."""
-        n_eg = self.workflow.spectral_library._precursor_df[
-            "elution_group_idx"
-        ].nunique()
+        n_eg = self.library._precursor_df["elution_group_idx"].nunique()
 
         plan = []
 
-        batch_size = self.workflow.config["calibration"]["batch_size"]
+        batch_size = self.config["calibration"]["batch_size"]
         step = 0
-        start_index = 0
+        start_idx = 0
 
-        while start_index < n_eg:
+        while start_idx < n_eg:
             n_batches = self._get_exponential_batches(step)
-            stop_index = min(start_index + n_batches * batch_size, n_eg)
-            plan.append((start_index, stop_index))
+            stop_idx = min(start_idx + n_batches * batch_size, n_eg)
+            plan.append((start_idx, stop_idx))
             step += 1
-            start_index = stop_index
+            start_idx = stop_idx
 
         self.batch_plan = plan
 
-    def extract(self):
-        """Extract features and fragments from the current batch of the optimization lock."""
+    def update_with_extraction(self, feature_df, fragment_df):
+        """Extract features and fragments from the current batch of the optimization lock.
 
-        feature_df, fragment_df = self.workflow.extract_batch(self.batch_precursor_df)
-        self.features += [feature_df]
-        self.fragments += [fragment_df]
-
-    def fdr(self):
-        """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization."""
-        self.precursor_df = self.workflow.fdr_correction(
-            self.features_df,
-            self.fragments_df,
-            self.workflow.optimization_manager.classifier_version,
-        )
+        Parameters
+        ----------
+        feature_df: pd.DataFrame
+            The feature dataframe for the current batch of the optimization lock (from workflow.extract_batch).
 
-        self.count = len(self.precursor_df[self.precursor_df["qval"] < 0.01])
+        fragment_df: pd.DataFrame
+            The fragment dataframe for the current batch of the optimization lock (from workflow.extract_batch).
+        """
 
-        if self.count < self.target_count:
-            self.reached_target = False
-        else:
-            self.reached_target = True
+        self.feature_dfs += [feature_df]
+        self.fragment_dfs += [fragment_df]
 
-    def adjust_upwards(self):
-        """If the optimization lock does not contain enough precursors at 1% FDR, the optimization lock proceeds to include the next step in the batch plan in the library attribute."""
-        self.batch_idx += 1
+        self.total_precursors = len(self.features_df)
 
-        eg_idxes = self.elution_group_order[self.start_index : self.stop_index]
+    def update_with_fdr(self, precursor_df):
+        """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization.
 
-        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
-            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
-                eg_idxes
-            )
-        ]
+        Parameters
+        ----------
+        precursor_df: pd.DataFrame
+            The precursor dataframe for the current batch of the optimization lock (from workflow.perform_fdr).
+        """
 
-        self.total_precursors += len(self.batch_precursor_df)
+        self.count = len(precursor_df[precursor_df["qval"] < 0.01])
 
-    def reset_downwards(self):
-        """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly. If not, the same library is used as before."""
-        self.batch_idx += max(
-            int(np.ceil(np.log2(self.target_count / self.count))), -self.batch_idx
-        )
+        self.has_target_num_precursors = self.count >= self.target_count
 
-        eg_idxes = self.elution_group_order[: self.stop_index]
+    def increase_batch_idx(self):
+        """If the optimization lock does not contain enough precursors at 1% FDR, the optimization lock proceeds to include the next step in the batch plan in the library attribute.
+        This is done by incrementing self.batch_idx.
+        """
+        self.batch_idx += 1
 
-        self.batch_precursor_df = self.workflow.spectral_library._precursor_df[
-            self.workflow.spectral_library._precursor_df["elution_group_idx"].isin(
-                eg_idxes
-            )
-        ]
+    def decrease_batch_idx(self):
+        """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly.
+        If not, the same library is used as before.
+        This is done by taking the smallest step in the batch plan which gives more precursors than the target number of precursors.
+        """
 
-        self.total_precursors = len(self.batch_precursor_df)
+        batch_plan_diff = np.array(
+            [
+                stop_at_given_idx - self.stop_idx * self.target_count / self.count
+                for _, stop_at_given_idx in self.batch_plan
+            ]
+        )  # Calculate the difference between the number of precursors expected at the given idx and the target number of precursors for each idx in the batch plan.
+        smallest_value = np.min(
+            batch_plan_diff[batch_plan_diff > 0]
+        )  # Take the smallest positive difference (i.e. the smallest idx that is expected to yield more than the target number of precursors).
+        self.batch_idx = np.where(batch_plan_diff == smallest_value)[0][
+            0
+        ]  # Set the batch idx to the index of the smallest positive difference.
 
     def update(self):
-        """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached."""
-        if not self.reached_target:
-            self.adjust_upwards()
+        """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
+        If the target has been reached, the feature and fragment dataframes are reset (since decrease will )"""
+        if not self.has_target_num_precursors:
+            self.increase_batch_idx()
+            eg_idxes = self.elution_group_order[
+                self.start_idx : self.stop_idx
+            ]  # Take only additional elution groups in the new step of the batch plan. The target has not been reached and hence recalibration and optimization have not been performed, so previous extractions can be re-used.
 
         else:
-            self.reset_downwards()
-            self.features = []
-            self.fragments = []
+            self.first_time_to_reach_target = False
+            self.decrease_batch_idx()
+            eg_idxes = self.elution_group_order[
+                : self.stop_idx
+            ]  # Take all elution groups up the the stop index. The target has been reached and hence the recalibration and optimization have been performed, so everything must be extracted afresh.
+            self.feature_dfs = []
+            self.fragment_dfs = []
+
+        # Sets the batch to be used in the next round of optimization. Note that this batch will only include additional precursors if the target was not reached in this step (as the precursors from this step are stored in the feature_dfs attribute).
+        self.batch_precursor_df = self.library._precursor_df[
+            self.library._precursor_df["elution_group_idx"].isin(eg_idxes)
+        ]
 
     @property
     def features_df(self):
-        return pd.concat(self.features)
+        return pd.concat(self.feature_dfs)
 
     @property
     def fragments_df(self):
-        return pd.concat(self.fragments)
+        return pd.concat(self.fragment_dfs)
 
     @property
-    def start_index(self):
-        if self.reached_target:
+    def start_idx(self):
+        if self.has_target_num_precursors:
             return 0
         else:
             return self.batch_plan[self.batch_idx][0]
 
     @property
-    def stop_index(self):
+    def stop_idx(self):
         return self.batch_plan[self.batch_idx][1]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 5a36cb30..f0ac1e7b 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -408,7 +408,7 @@ def calibration(self):
             verbosity="progress",
         )
 
-        self.optlock = optimization.OptimizationLock(self)
+        self.optlock = optimization.OptimizationLock(self.spectral_library, self.config)
 
         # Start of optimization/recalibration loop
         for optimizers in ordered_optimizers:
@@ -426,31 +426,40 @@ def calibration(self):
 
                     break
                 self.reporter.log_string(
-                    f"=== Step {current_step}, extracting elution groups {self.optlock.start_index} to {self.optlock.stop_index} ===",
+                    f"=== Step {current_step}, extracting elution groups {self.optlock.start_idx} to {self.optlock.stop_idx} ===",
                     verbosity="progress",
                 )
-                self.optlock.extract()
+
+                feature_df, fragment_df = self.extract_batch(
+                    self.optlock.batch_precursor_df
+                )
+                self.optlock.update_with_extraction(feature_df, fragment_df)
 
                 self.reporter.log_string(
                     f"=== Step {current_step}, extracted {len(self.optlock.features_df)} precursors and {len(self.optlock.fragments_df)} fragments ===",
                     verbosity="progress",
                 )
 
-                self.optlock.fdr()
+                precursor_df = self.fdr_correction(
+                    self.optlock.features_df,
+                    self.optlock.fragments_df,
+                    self.optimization_manager.classifier_version,
+                )
+
+                self.optlock.update_with_fdr(precursor_df)
 
                 self.reporter.log_string(
                     f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
                     verbosity="info",
                 )
 
-                self.log_precursor_df(self.optlock.precursor_df)
+                self.log_precursor_df(precursor_df)
 
-                if self.optlock.reached_target:
-                    if not self.optlock.reached_target_before:
+                if self.optlock.has_target_num_precursors:
+                    if self.optlock.first_time_to_reach_target:  # Recalibrates and updates classifier, but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
                         self.first_recalibration(
-                            self.optlock.precursor_df, self.optlock.fragments_df
+                            precursor_df, self.optlock.fragments_df
                         )
-                        self.optlock.reached_target_before = True
                         self.optlock.update()
                         self.reporter.log_string(
                             "Required number of precursors found. Starting search parameter optimization.",
@@ -459,7 +468,7 @@ def calibration(self):
                         continue
 
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-                        self.optlock.precursor_df, self.optlock.fragments_df
+                        precursor_df, self.optlock.fragments_df
                     )
 
                     self.recalibration(precursor_df_filtered, fragments_df_filtered)
@@ -478,7 +487,7 @@ def calibration(self):
                     )
 
                     self.reporter.log_string(
-                        f"=== Optimization has been performed for {optimizer.current_step + 1} step(s); minimum number is {self.config['calibration']['min_steps']} ===",
+                        f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
                         verbosity="progress",
                     )
 
@@ -540,22 +549,18 @@ def filter_dfs(self, precursor_df, fragments_df):
             fragments_df["precursor_idx"].isin(precursor_df_filtered["precursor_idx"])
         ]
 
-        min_fragments = 500
-        max_fragments = 5000
-        min_correlation = 0.7
         fragments_df_filtered = fragments_df_filtered.sort_values(
-            by=["correlation"], ascending=False
-        )
-        stop_rank = min(
-            max(
-                np.searchsorted(
-                    fragments_df_filtered["correlation"].values, min_correlation
-                ),
-                min_fragments,
-            ),
-            max_fragments,
-        )
-        fragments_df_filtered = fragments_df_filtered.iloc[:stop_rank]
+            by="correlation", ascending=False
+        )
+        # Determine the number of fragments to keep
+        min_fragments, max_fragments = 500, 5000
+        min_correlation = 0.7
+
+        high_corr_count = (fragments_df_filtered["correlation"] > min_correlation).sum()
+        stop_rank = min(max(high_corr_count, min_fragments), max_fragments)
+
+        # Select top fragments
+        fragments_df_filtered = fragments_df_filtered.head(stop_rank)
 
         self.reporter.log_string(
             f"fragments_df_filtered: {len(fragments_df_filtered)}", verbosity="info"
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 107d1e70..ba0da2bd 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -764,3 +764,92 @@ def test_targeted_mobility_optimizer():
     assert optimizer.has_converged is True
 
     assert workflow.optimization_manager.mobility_error == optimizer.target_parameter
+
+
+class TEST_LIBRARY:
+    def __init__(self):
+        idxes = np.arange(0, 100000)
+        self._precursor_df = pd.DataFrame({"elution_group_idx": idxes})
+        self._fragments_df = pd.DataFrame(
+            {
+                "precursor_idx": np.concatenate(
+                    [np.full(10, precursor_idx) for precursor_idx in idxes]
+                )
+            }
+        )
+
+
+TEST_OPTLOCK_CONFIG = {
+    "calibration": {
+        "batch_size": 8000,
+        "optimization_lock_target": 200,
+    }
+}
+
+
+def test_optlock():
+    library = TEST_LIBRARY()
+    optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
+
+    assert optlock.start_idx == optlock.batch_plan[0][0]
+
+    feature_df = pd.DataFrame({"col": np.arange(0, 1000)})
+    fragment_df = pd.DataFrame({"col": np.arange(0, 10000)})
+
+    optlock.update_with_extraction(feature_df, fragment_df)
+
+    assert optlock.total_precursors == 1000
+    precursor_df = pd.DataFrame(
+        {"qval": np.concatenate([np.full(100, 0.005), np.full(1000, 0.05)])}
+    )
+    optlock.update_with_fdr(precursor_df)
+
+    assert optlock.has_target_num_precursors is False
+    assert optlock.first_time_to_reach_target is True
+    optlock.update()
+
+    assert optlock.start_idx == optlock.batch_plan[1][0]
+
+    feature_df = pd.DataFrame({"col": np.arange(0, 1000)})
+    fragment_df = pd.DataFrame({"col": np.arange(0, 10000)})
+
+    optlock.update_with_extraction(feature_df, fragment_df)
+
+    assert optlock.total_precursors == 2000
+
+    precursor_df = pd.DataFrame(
+        {"qval": np.concatenate([np.full(200, 0.005), np.full(1000, 0.05)])}
+    )
+
+    optlock.update_with_fdr(precursor_df)
+
+    assert optlock.has_target_num_precursors is True
+    assert optlock.first_time_to_reach_target is True
+    optlock.update()
+
+    assert optlock.first_time_to_reach_target is False
+    assert optlock.start_idx == 0
+
+
+def test_optlock_batch_idx():
+    library = TEST_LIBRARY()
+    optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
+
+    optlock.batch_plan = [[0, 100], [100, 2000], [2000, 8000]]
+
+    assert optlock.start_idx == 0
+
+    optlock.increase_batch_idx()
+    assert optlock.start_idx == 100
+
+    optlock.increase_batch_idx()
+    assert optlock.start_idx == 2000
+
+    precursor_df = pd.DataFrame({"qval": np.full(4500, 0.005)})
+
+    optlock.update_with_fdr(precursor_df)
+
+    optlock.decrease_batch_idx()
+
+    assert optlock.start_idx == 0
+    assert optlock.stop_idx == 2000

From b6cf73655b26b5904166057e6c7e4c851460a28d Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 19 Aug 2024 23:15:11 +0200
Subject: [PATCH 119/250] implement fixes

---
 alphadia/constants/default.yaml | 41 ++++++++++++++++++++-------------
 alphadia/libtransform.py        | 24 +++++++++++++++----
 alphadia/planning.py            |  9 ++++++++
 3 files changed, 53 insertions(+), 21 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 475d0034..7ea66ee0 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -163,23 +163,32 @@ library_multiplexing:
   # if true, the library is multiplexed
   enabled: False
 
+  # if the input library already contains multiplexed channels, the input channel has to be specified.
+  input_channel: 0
+
   # define channels by their name and how modifications should be translated from the input library to the multiplexed library
-  multiplex_mapping:
-    0:
-      mTRAQ@K: mTRAQ@K
-      mTRAQ@Any_N-term: mTRAQ@Any_N-term
-
-    4:
-      mTRAQ@K: mTRAQ:13C(3)15N(1)@K
-      mTRAQ@Any_N-term: mTRAQ:13C(3)15N(1)@Any_N-term
-
-    8:
-      mTRAQ@K: mTRAQ:13C(6)15N(2)@K
-      mTRAQ@Any_N-term: mTRAQ:13C(6)15N(2)@Any_N-term
-
-    12:
-      mTRAQ@K: mTRAQ:d12@K
-      mTRAQ@Any_N-term: mTRAQ:d12@Any_N-term
+  # channels can be either a number or a string
+  # for every channel, the library gets copied and the modifications are translated according to the mapping
+  # the following example shows how to multiplex mTRAQ to three sample channels and a decoy channel
+  multiplex_mapping: {}
+
+    #0:
+    #  mTRAQ@K: mTRAQ@K
+    #  mTRAQ@Any_N-term: mTRAQ@Any_N-term
+
+    #4:
+    #  mTRAQ@K: mTRAQ:13C(3)15N(1)@K
+    #  mTRAQ@Any_N-term: mTRAQ:13C(3)15N(1)@Any_N-term
+
+    #8:
+    #  mTRAQ@K: mTRAQ:13C(6)15N(2)@K
+    #  mTRAQ@Any_N-term: mTRAQ:13C(6)15N(2)@Any_N-term
+
+    #12:
+    #  mTRAQ@K: mTRAQ:d12@K
+    #  mTRAQ@Any_N-term: mTRAQ:d12@Any_N-term
+
+
 
 multiplexing:
   enabled: False
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index 442059f9..f294e424 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -605,10 +605,11 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
 
 
 class MultiplexLibrary(ProcessingStep):
-    def __init__(self, multiplex_mapping: dict) -> None:
+    def __init__(self, multiplex_mapping: dict, input_channel: str | int | None = None):
         """Initialize the MultiplexLibrary step."""
 
         self._multiplex_mapping = multiplex_mapping
+        self._input_channel = input_channel
 
     def validate(self, input: str) -> bool:
         """Validate the input object. It is expected that the input is a path to a file which exists."""
@@ -619,20 +620,33 @@ def validate(self, input: str) -> bool:
         for _, channel_multiplex_mapping in self._multiplex_mapping.items():
             for key, value in channel_multiplex_mapping.items():
                 for mod in [key, value]:
-                    print(mod)
                     if mod not in MOD_DF.index:
                         logger.error(f"Modification {mod} not found in input library")
                         valid = False
 
+        if "channel" in input.precursor_df.columns:
+            channel_unique = input.precursor_df["channel"].unique()
+            if self._input_channel not in channel_unique:
+                logger.error(
+                    f"Input library does not contain channel {self._input_channel}"
+                )
+                valid = False
+
+            if (len(channel_unique) > 1) and (self._input_channel is None):
+                logger.error(
+                    f"Input library contains multiple channels {channel_unique}. Please specify a channel."
+                )
+                valid = False
+
         return valid
 
     def forward(self, input: SpecLibBase) -> SpecLibBase:
         """Apply the MultiplexLibrary step to the input object."""
 
         if "channel" in input.precursor_df.columns:
-            channel_unique = input.precursor_df["channel"].unique()
-            logger.info(f"Library already multiplexed for channel {channel_unique}")
-            input.precursor_df = input.precursor_df[input.precursor_df["channel"] == 0]
+            input.precursor_df = input.precursor_df[
+                input.precursor_df["channel"] == self._input_channel
+            ]
 
         channel_lib_list = []
         for channel, channel_mod_translations in self._multiplex_mapping.items():
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 5373df8b..fe092e02 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -247,6 +247,15 @@ def _parse_modifications(mod_str: str) -> list[str]:
         )
         spectral_library = harmonize_pipeline(spectral_library)
 
+        if self.config["library_multiplexing"]["enabled"]:
+            multiplexing = libtransform.MultiplexLibrary(
+                multiplex_mapping=self.config["library_multiplexing"][
+                    "multiplex_mapping"
+                ],
+                input_channel=self.config["library_multiplexing"]["input_channel"],
+            )
+            spectral_library = multiplexing(spectral_library)
+
         library_path = os.path.join(self.output_folder, "speclib.hdf")
         logger.info(f"Saving library to {library_path}")
         spectral_library.save_hdf(library_path)

From 56f4ee3222f1fa705cd4925edefca47b067b5d72 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Mon, 19 Aug 2024 23:53:50 +0200
Subject: [PATCH 120/250] fix missing column multiplexing

---
 alphadia/plexscoring.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/alphadia/plexscoring.py b/alphadia/plexscoring.py
index af2e16f4..0a33c8fa 100644
--- a/alphadia/plexscoring.py
+++ b/alphadia/plexscoring.py
@@ -1722,8 +1722,10 @@ def collect_candidates(
             "frame_center",
             "frame_start",
             "frame_stop",
-            "score",
         ]
+
+        candidate_df_columns += ["score"] if "score" in candidates_df.columns else []
+
         df = utils.merge_missing_columns(
             df,
             candidates_df,

From b7fbf48cc061bc96b3c0c3d29fee297ef9cce1b1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 11:32:14 +0200
Subject: [PATCH 121/250] update managers

---
 alphadia/exceptions.py                   | 12 +--
 alphadia/outputtransform.py              | 94 ++++++++++++++++++++----
 alphadia/workflow/manager.py             | 33 +++------
 alphadia/workflow/optimization.py        |  4 +
 alphadia/workflow/peptidecentric.py      | 17 ++++-
 tests/unit_tests/test_outputtransform.py | 15 ++--
 6 files changed, 123 insertions(+), 52 deletions(-)

diff --git a/alphadia/exceptions.py b/alphadia/exceptions.py
index 8cd35be6..94efbc7e 100644
--- a/alphadia/exceptions.py
+++ b/alphadia/exceptions.py
@@ -37,19 +37,19 @@ class NoPsmFoundError(BusinessError):
     _msg = "No psm files accumulated, can't continue"
 
 
-class NoRecalibrationTargetError(BusinessError):
-    """Raise when no recalibration target is found."""
+class NoOptimizationLockTargetError(BusinessError):
+    """Raise when the optimization lock target is not found."""
 
-    _error_code = "NO_RECALIBRATION_TARGET"
+    _error_code = "NO_OPTIMIZATION_LOCK_TARGET"
 
-    _msg = "Searched all data without finding recalibration target"
+    _msg = "Searched all data without finding optimization lock target"
 
-    _detail_msg = """Search for raw file failed as not enough precursors were found for calibration.
+    _detail_msg = """Search for raw file failed as not enough precursors were found for calibration and optimization.
                  This can have the following reasons:
                    1. The sample was empty and therefore no precursors were found.
                    2. The sample contains only very few precursors.
                       For small libraries, try to set recalibration_target to a lower value.
-                      For large libraries, try to reduce the library size and reduce the calibration MS1 and MS2 tolerance.
+                      For large libraries, try to reduce the library size and reduce the initial MS1 and MS2 tolerance.
                    3. There was a fundamental issue with search parameters."""
 
 
diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 024a2786..c505878b 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -297,6 +297,7 @@ class SearchPlanOutput:
     PSM_INPUT = "psm"
     PRECURSOR_OUTPUT = "precursors"
     STAT_OUTPUT = "stat"
+    INTERNAL_OUTPUT = "internal"
     PG_OUTPUT = "protein_groups"
     LIBRARY_OUTPUT = "speclib.mbr"
     TRANSFER_OUTPUT = "speclib.transfer"
@@ -365,6 +366,7 @@ def build(
             folder_list, save=False, base_spec_lib=base_spec_lib
         )
         _ = self.build_stat_df(folder_list, psm_df=psm_df, save=True)
+        _ = self.build_internal_df(folder_list, save=True)
         _ = self.build_lfq_tables(folder_list, psm_df=psm_df, save=True)
         _ = self.build_library(
             base_spec_lib,
@@ -678,6 +680,50 @@ def build_stat_df(
 
         return stat_df
 
+    def build_internal_df(
+        self,
+        folder_list: list[str],
+        save: bool = True,
+    ):
+        """Build internal data table from a list of seach outputs
+
+        Parameters
+        ----------
+
+        folder_list: List[str]
+            List of folders containing the search outputs
+
+        save: bool
+            Save the precursor table to disk
+
+        Returns
+        -------
+
+        stat_df: pd.DataFrame
+            Precursor table
+        """
+        logger.progress("Building internal statistics")
+
+        internal_df_list = []
+        for folder in folder_list:
+            internal_df_list.append(
+                _build_run_internal_df(
+                    folder,
+                )
+            )
+
+        internal_df = pd.concat(internal_df_list)
+
+        if save:
+            logger.info("Writing internal output to disk")
+            write_df(
+                internal_df,
+                os.path.join(self.output_folder, self.INTERNAL_OUTPUT),
+                file_format="tsv",
+            )
+
+        return internal_df
+
     def build_lfq_tables(
         self,
         folder_list: list[str],
@@ -850,9 +896,6 @@ def _build_run_stat_df(
     optimization_manager_path = os.path.join(
         folder, peptidecentric.PeptideCentricWorkflow.OPTIMIZATION_MANAGER_PATH
     )
-    timing_manager_path = os.path.join(
-        folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
-    )
 
     if channels is None:
         channels = [0]
@@ -894,20 +937,45 @@ def _build_run_stat_df(
             base_dict["rt_error"] = np.nan
             base_dict["mobility_error"] = np.nan
 
-        if os.path.exists(timing_manager_path):
-            timing_manager = manager.TimingManager(path=timing_manager_path)
+        out_df.append(base_dict)
 
-            base_dict["optimization_duration"] = timing_manager.optimization["duration"]
-            base_dict["extraction_duration"] = timing_manager.extraction["duration"]
+    return pd.DataFrame(out_df)
 
-        else:
-            logger.warning(f"Error reading timing manager for {raw_name}")
-            base_dict["optimization_duration"] = np.nan
-            base_dict["extraction_duration"] = np.nan
 
-        out_df.append(base_dict)
+def _build_run_internal_df(
+    folder_path: str,
+):
+    """Build stat dataframe for a single run.
 
-    return pd.DataFrame(out_df)
+    Parameters
+    ----------
+
+    folder_path: str
+        Path (from the base directory of the output_folder attribute of the Plan class) to the directory containing the raw file and the managers
+
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe containing the statistics
+
+    """
+    timing_manager_path = os.path.join(
+        folder_path, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
+    )
+    raw_name = os.path.basename(folder_path)
+
+    internal_dict = {}
+
+    if os.path.exists(timing_manager_path):
+        timing_manager = manager.TimingManager(path=timing_manager_path)
+        for key in timing_manager.timings:
+            internal_dict[f"{key}_duration"] = [timing_manager.timings[key]["duration"]]
+
+    else:
+        logger.warning(f"Error reading timing manager for {raw_name}")
+
+    return pd.DataFrame(internal_dict)
 
 
 def perform_protein_fdr(psm_df):
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index fdc1e196..d55936f2 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -487,7 +487,6 @@ def fit(self, update_dict):
         """Update the parameters dict with the values in update_dict."""
         self.__dict__.update(update_dict)
         self.is_fitted = True
-        self.save()
 
     def predict(self):
         """Return the parameters dict."""
@@ -761,34 +760,22 @@ def __init__(
         load_from_file: bool = True,
         **kwargs,
     ):
+        """Contains and updates timing information for the portions of the workflow."""
         super().__init__(path=path, load_from_file=load_from_file, **kwargs)
         self.reporter.log_string(f"Initializing {self.__class__.__name__}")
         self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
-
         if not self.is_loaded_from_file:
-            self.__dict__.update(
-                {
-                    "optimization": {
-                        "start": None,
-                        "end": None,
-                        "duration": None,
-                    },
-                    "extraction": {
-                        "start": None,
-                        "end": None,
-                        "duration": None,
-                    },
-                }
-            )
+            self.timings = {}
 
-    def start(self, workflow_stage: str):
-        self.__dict__.update({workflow_stage: {"start": pd.Timestamp.now()}})
+    def set_start_time(self, workflow_stage: str):
+        """Starts the timer for a portion of the workflow and stores it in the timings attribute under the name given by the parameter workflow_stage"""
+        self.timings.update({workflow_stage: {"start": pd.Timestamp.now()}})
         self.save()
 
-    def end(self, workflow_stage: str):
-        self.__dict__[workflow_stage]["end"] = pd.Timestamp.now()
-        self.__dict__[workflow_stage]["duration"] = (
-            self.__dict__[workflow_stage]["end"]
-            - self.__dict__[workflow_stage]["start"]
+    def set_end_time(self, workflow_stage: str):
+        """"""
+        self.timings[workflow_stage]["end"] = pd.Timestamp.now()
+        self.timings[workflow_stage]["duration"] = (
+            self.timings[workflow_stage]["end"] - self.timings[workflow_stage]["start"]
         ).total_seconds() / 60
         self.save()
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 9cb1deef..ac769556 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -9,6 +9,8 @@
 import pandas as pd
 import seaborn as sns
 
+from alphadia.exceptions import NoOptimizationLockTargetError
+
 # alphadia imports
 from alphadia.workflow import reporting
 
@@ -727,6 +729,8 @@ def fragments_df(self):
     def start_idx(self):
         if self.has_target_num_precursors:
             return 0
+        elif self.batch_idx >= len(self.batch_plan):
+            raise NoOptimizationLockTargetError()
         else:
             return self.batch_plan[self.batch_idx][0]
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index f0ac1e7b..a12a0062 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -399,7 +399,7 @@ def calibration(self):
                 verbosity="progress",
             )
             return
-        self.timing_manager.start("optimization")
+        self.timing_manager.set_start_time("optimization")
         # Get the order of optimization
         ordered_optimizers = self.get_ordered_optimizers()
 
@@ -516,7 +516,8 @@ def calibration(self):
             "==============================================", verbosity="progress"
         )
 
-        self.timing_manager.end("optimization")
+        self.save_managers()
+        self.timing_manager.set_end_time("optimization")
 
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
@@ -751,8 +752,16 @@ def extract_batch(
 
         return features_df, fragments_df
 
+    def save_managers(self):
+        """Saves the calibration, optimization and FDR managers to disk so that they can be reused if needed.
+        Note the timing manager is not saved at this point as it is saved with every call to it.
+        The FDR manager is not saved because it is not used in subsequent parts of the workflow.
+        """
+        self.calibration_manager.save()
+        self.optimization_manager.save()
+
     def extraction(self):
-        self.timing_manager.start("extraction")
+        self.timing_manager.set_start_time("extraction")
         self.optimization_manager.fit(
             {
                 "num_candidates": self.config["search"]["target_num_candidates"],
@@ -797,7 +806,7 @@ def extraction(self):
 
         self.log_precursor_df(precursor_df)
 
-        self.timing_manager.end("extraction")
+        self.timing_manager.set_end_time("extraction")
 
         return precursor_df, fragments_df
 
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 6129a6c4..2c27ef6d 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -83,19 +83,20 @@ def test_output_transform():
             ),
         )
         optimization_manager.fit({"ms2_error": 6})
-
+        optimization_manager.save()
         timing_manager = manager.TimingManager(
             os.path.join(
                 raw_folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
             )
         )
 
-        timing_manager.start("extraction")
-        timing_manager.end("extraction")
+        timing_manager.set_start_time("extraction")
+        timing_manager.set_end_time("extraction")
 
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)
     _ = output.build_stat_df(raw_folders, save=True)
+    _ = output.build_internal_df(raw_folders, save=True)
     _ = output.build_lfq_tables(raw_folders, save=True)
 
     # validate psm_df output
@@ -128,7 +129,7 @@ def test_output_transform():
     assert len(stat_df) == 3
     assert stat_df["ms2_error"][0] == 6
     assert stat_df["rt_error"][0] == 200
-    assert isinstance(stat_df["extraction_duration"][0], float)
+
     assert all(
         [
             col in stat_df.columns
@@ -136,12 +137,14 @@ def test_output_transform():
                 "run",
                 "precursors",
                 "proteins",
-                "optimization_duration",
-                "extraction_duration",
             ]
         ]
     )
 
+    internal_df = pd.read_csv(
+        os.path.join(temp_folder, f"{output.INTERNAL_OUTPUT}.tsv"), sep="\t"
+    )
+    assert isinstance(internal_df["extraction_duration"][0], float)
     # validate protein_df output
     protein_df = pd.read_parquet(os.path.join(temp_folder, "pg.matrix.parquet"))
     assert all([col in protein_df.columns for col in ["run_0", "run_1", "run_2"]])

From 16aa10f311fb2806561d27455fea83bb1cdaa1a6 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 12:41:52 +0200
Subject: [PATCH 122/250] fix unit test

---
 alphadia/workflow/peptidecentric.py | 2 +-
 tests/unit_tests/test_workflow.py   | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index a12a0062..c079db78 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -758,7 +758,7 @@ def save_managers(self):
         The FDR manager is not saved because it is not used in subsequent parts of the workflow.
         """
         self.calibration_manager.save()
-        self.optimization_manager.save()
+        self.optimization_manager.save()  # this replaces the .save() call when the optimization manager is fitted, since there seems little point in saving an intermediate optimization manager.
 
     def extraction(self):
         self.timing_manager.set_start_time("extraction")
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index ba0da2bd..49249cf5 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -271,6 +271,7 @@ def test_optimization_manager_fit():
     assert optimization_manager.fwhm_cycles == 10
     assert optimization_manager.fwhm_mobility == 0.02
 
+    optimization_manager.save()
     os.remove(temp_path)
 
 

From bf0329651e212c60ebfbb7a37957d996fb58f354 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 13:34:40 +0200
Subject: [PATCH 123/250] permit rt proportion

---
 alphadia/workflow/base.py         |  4 +++-
 alphadia/workflow/manager.py      |  5 ++++-
 tests/unit_tests/test_workflow.py | 15 +++++++++++++++
 3 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index ba81995c..3fbb2afb 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -102,7 +102,9 @@ def load(
             self._calibration_manager.disable_mobility_calibration()
 
         # initialize the optimization manager
-
+        self.config["optimization_manager"]["gradient_length"] = (
+            self.dia_data.rt_values.max()
+        )
         self._optimization_manager = manager.OptimizationManager(
             self.config,
             path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH),
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index d55936f2..bb470e80 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -467,7 +467,10 @@ def __init__(
             initial_parameters = {
                 "ms1_error": config["search_initial"]["initial_ms1_tolerance"],
                 "ms2_error": config["search_initial"]["initial_ms2_tolerance"],
-                "rt_error": config["search_initial"]["initial_rt_tolerance"],
+                "rt_error": config["search_initial"]["initial_rt_tolerance"]
+                if config["search_initial"]["initial_rt_tolerance"] > 1
+                else config["search_initial"]["initial_rt_tolerance"]
+                * config["optimization_manager"]["gradient_length"],
                 "mobility_error": config["search_initial"][
                     "initial_mobility_tolerance"
                 ],
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 49249cf5..3236587b 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -218,6 +218,7 @@ def test_calibration_manager_save_load():
         "fwhm_rt": 5,
         "fwhm_mobility": 0.01,
         "score_cutoff": 50,
+        "gradient_length": 1000,
     },
 }
 
@@ -275,6 +276,17 @@ def test_optimization_manager_fit():
     os.remove(temp_path)
 
 
+def test_optimization_manager_rt_proportion():
+    temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
+    TEST_OPTIMIZATION_CONFIG_PROPORTION = TEST_OPTIMIZATION_CONFIG.copy()
+    TEST_OPTIMIZATION_CONFIG_PROPORTION["search_initial"]["initial_rt_tolerance"] = 0.5
+    optimization_manager = manager.OptimizationManager(
+        TEST_OPTIMIZATION_CONFIG_PROPORTION, path=temp_path, load_from_file=False
+    )
+
+    assert optimization_manager.rt_error == 500
+
+
 @pytest.mark.slow
 def test_workflow_base():
     if pytest.test_data is None:
@@ -854,3 +866,6 @@ def test_optlock_batch_idx():
 
     assert optlock.start_idx == 0
     assert optlock.stop_idx == 2000
+
+
+test_optimization_manager_rt_proportion()

From e488edae14603a8060c4f775ecfc6bd1d7c1b0c1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 15:06:59 +0200
Subject: [PATCH 124/250] take proportion of gradient length if initial value <
 1

---
 alphadia/workflow/manager.py        |  4 ++--
 alphadia/workflow/optimization.py   | 18 +++++++++---------
 alphadia/workflow/peptidecentric.py | 21 +++++++++------------
 3 files changed, 20 insertions(+), 23 deletions(-)

diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index bb470e80..1b88e97a 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -771,12 +771,12 @@ def __init__(
             self.timings = {}
 
     def set_start_time(self, workflow_stage: str):
-        """Starts the timer for a portion of the workflow and stores it in the timings attribute under the name given by the parameter workflow_stage"""
+        """Stores the time at which a given stage of the workflow started in the timings attribute."""
         self.timings.update({workflow_stage: {"start": pd.Timestamp.now()}})
         self.save()
 
     def set_end_time(self, workflow_stage: str):
-        """"""
+        """Stores the time at which a given stage of the workflow ended in the timings attribute."""
         self.timings[workflow_stage]["end"] = pd.Timestamp.now()
         self.timings[workflow_stage]["duration"] = (
             self.timings[workflow_stage]["end"] - self.timings[workflow_stage]["start"]
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index ac769556..768c7b0b 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -590,8 +590,8 @@ def __init__(self, library, config: dict):
         config: dict
             The configuration dictionary from the PeptideCentricWorkflow object.
         """
-        self.library = library
-        self.config = config
+        self._library = library
+        self._config = config
 
         self.first_time_to_reach_target = True
         self.has_target_num_precursors = False
@@ -600,14 +600,14 @@ def __init__(self, library, config: dict):
         rng = np.random.default_rng(seed=772)
         rng.shuffle(self.elution_group_order)
 
-        self.target_count = self.config["calibration"]["optimization_lock_target"]
+        self.target_count = self._config["calibration"]["optimization_lock_target"]
 
         self.batch_idx = 0
         self.set_batch_plan()
 
         eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
-        self.batch_precursor_df = self.library._precursor_df[
-            self.library._precursor_df["elution_group_idx"].isin(eg_idxes)
+        self.batch_precursor_df = self._library._precursor_df[
+            self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
         ]
 
         self.feature_dfs = []
@@ -622,11 +622,11 @@ def _get_exponential_batches(self, step):
 
     def set_batch_plan(self):
         """Gets an exponential batch plan based on the batch_size value in the config."""
-        n_eg = self.library._precursor_df["elution_group_idx"].nunique()
+        n_eg = self._library._precursor_df["elution_group_idx"].nunique()
 
         plan = []
 
-        batch_size = self.config["calibration"]["batch_size"]
+        batch_size = self._config["calibration"]["batch_size"]
         step = 0
         start_idx = 0
 
@@ -713,8 +713,8 @@ def update(self):
             self.fragment_dfs = []
 
         # Sets the batch to be used in the next round of optimization. Note that this batch will only include additional precursors if the target was not reached in this step (as the precursors from this step are stored in the feature_dfs attribute).
-        self.batch_precursor_df = self.library._precursor_df[
-            self.library._precursor_df["elution_group_idx"].isin(eg_idxes)
+        self.batch_precursor_df = self._library._precursor_df[
+            self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
         ]
 
     @property
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index c079db78..8d5e669d 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -269,42 +269,41 @@ def get_ordered_optimizers(self):
             List of lists of optimizers
 
         """
-        config_search_initial = self.config["search_initial"]
         config_search = self.config["search"]
 
         if config_search["target_ms2_tolerance"] > 0:
             ms2_optimizer = optimization.TargetedMS2Optimizer(
-                config_search_initial["initial_ms2_tolerance"],
+                self.optimization_manager.ms2_error,
                 config_search["target_ms2_tolerance"],
                 self,
             )
         else:
             ms2_optimizer = optimization.AutomaticMS2Optimizer(
-                config_search_initial["initial_ms2_tolerance"],
+                self.optimization_manager.ms2_error,
                 self,
             )
 
         if config_search["target_rt_tolerance"] > 0:
             rt_optimizer = optimization.TargetedRTOptimizer(
-                config_search_initial["initial_rt_tolerance"],
+                self.optimization_manager.rt_error,
                 config_search["target_rt_tolerance"],
                 self,
             )
         else:
             rt_optimizer = optimization.AutomaticRTOptimizer(
-                config_search_initial["initial_rt_tolerance"],
+                self.optimization_manager.rt_error,
                 self,
             )
         if self.dia_data.has_ms1:
             if config_search["target_ms1_tolerance"] > 0:
                 ms1_optimizer = optimization.TargetedMS1Optimizer(
-                    config_search_initial["initial_ms1_tolerance"],
+                    self.optimization_manager.ms1_error,
                     config_search["target_ms1_tolerance"],
                     self,
                 )
             else:
                 ms1_optimizer = optimization.AutomaticMS1Optimizer(
-                    config_search_initial["initial_ms1_tolerance"],
+                    self.optimization_manager.ms1_error,
                     self,
                 )
         else:
@@ -312,16 +311,14 @@ def get_ordered_optimizers(self):
         if self.dia_data.has_mobility:
             if config_search["target_mobility_tolerance"] > 0:
                 mobility_optimizer = optimization.TargetedMobilityOptimizer(
-                    config_search_initial["initial_mobility_tolerance"],
+                    self.optimization_manager.mobility_error,
                     config_search["target_mobility_tolerance"],
                     self,
                 )
             else:
                 mobility_optimizer = optimization.AutomaticMobilityOptimizer(
-                    config_search_initial["initial_mobility_tolerance"],
-                    self.calibration_manager,
-                    self.optimization_manager,
-                    self.fdr_manager,
+                    self.optimization_manager.mobility_error,
+                    self,
                 )
         else:
             mobility_optimizer = None

From 6899d03c0994f23125d4cff1507b9016866a1a73 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Tue, 20 Aug 2024 18:34:57 +0300
Subject: [PATCH 125/250] refactor train.py

---
 alphadia/transferlearning/metrics.py |   6 +-
 alphadia/transferlearning/train.py   | 466 +++++++++++++++------------
 nbs/tutorial_nbs/finetuning.ipynb    |  11 +-
 3 files changed, 259 insertions(+), 224 deletions(-)

diff --git a/alphadia/transferlearning/metrics.py b/alphadia/transferlearning/metrics.py
index c10e7454..81f2e5cf 100644
--- a/alphadia/transferlearning/metrics.py
+++ b/alphadia/transferlearning/metrics.py
@@ -392,8 +392,10 @@ def calculate_test_metric(
             for j in range(n_classes):
                 confusion_matrix[i, j] = np.sum((predictions == i) & (targets == j))
 
-        precision = np.diag(confusion_matrix) / np.sum(confusion_matrix, axis=0)
-        recall = np.diag(confusion_matrix) / np.sum(confusion_matrix, axis=1)
+        precision = np.diag(confusion_matrix) / (
+            np.sum(confusion_matrix, axis=0) + 1e-6
+        )
+        recall = np.diag(confusion_matrix) / (np.sum(confusion_matrix, axis=1) + 1e-6)
 
         new_stats = pd.DataFrame(
             np.array([np.mean(precision), np.mean(recall)]).reshape(1, 2),
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 0addaa32..a498c17d 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -25,23 +25,6 @@
 
 logger = logging.getLogger()
 
-settings = {
-    # --------- USer settings ------------
-    "batch_size": 1000,
-    "max_lr": 0.0005,
-    "train_fraction": 0.7,
-    "validation_fraction": 0.2,
-    "test_fraction": 0.1,
-    "test_interval": 1,
-    "lr_patience": 3,
-    # --------- Our settings ------------
-    "epochs": 51,
-    "warmup_epochs": 5,
-    # --------------------------
-    "nce": 25,
-    "instrument": "Lumos",
-}
-
 
 class CustomScheduler(LR_SchedulerInterface):
     """
@@ -65,10 +48,11 @@ def __init__(self, optimizer: torch.optim.Optimizer, **kwargs):
         self.optimizer = optimizer
         self.num_warmup_steps = kwargs.get("num_warmup_steps", 5)
         self.num_training_steps = kwargs.get("num_training_steps", 50)
+        self.lr_patience = kwargs.get("lr_patience", 3)
         self.reduce_lr_on_plateau = torch.optim.lr_scheduler.ReduceLROnPlateau(
             optimizer,
             mode="min",
-            patience=settings["lr_patience"],
+            patience=self.lr_patience,
             factor=0.5,
         )
         self.warmup_lr = LambdaLR(optimizer, self._warmup)
@@ -220,22 +204,36 @@ def __init__(
         self,
         mask_modloss: bool = False,
         device: str = "gpu",
-        settings: dict | None = None,
+        lr_patience: int = 3,
+        test_interval: int = 1,
+        train_fraction: float = 0.7,
+        validation_fraction: float = 0.2,
+        test_fraction: float = 0.1,
+        epochs: int = 51,
+        warmup_epochs: int = 5,
+        batch_size: int = 1000,
+        max_lr: float = 0.0005,
+        nce: float = 25,
+        instrument: str = "Lumos",
     ):
-        if settings is None:
-            settings = {}
         super().__init__(mask_modloss, device)
+        self.lr_patience = lr_patience
+        self.test_interval = test_interval
+        self.train_fraction = train_fraction
+        self.validation_fraction = validation_fraction
+        self.test_fraction = test_fraction
+        self.epochs = epochs
+        self.warmup_epochs = warmup_epochs
+        self.batch_size = batch_size
+        self.max_lr = max_lr
+        self.nce = nce
+        self.instrument = instrument
+
         self.device = device
-        self.settings = settings
-        self.early_stopping = EarlyStopping(
-            patience=(settings["lr_patience"] // settings["test_interval"]) * 4
-        )
+        self.early_stopping = EarlyStopping(patience=(lr_patience // test_interval) * 4)
 
         assert (
-            settings["train_fraction"]
-            + settings["validation_fraction"]
-            + settings["test_fraction"]
-            <= 1.0
+            self.train_fraction + self.validation_fraction + self.test_fraction <= 1.0
         ), "The sum of the train, validation and test fractions should be less than or equal to 1.0"
 
     def _reset_frag_idx(self, df):
@@ -300,6 +298,141 @@ def _order_intensities(
             ]
         return reordered
 
+    def _log_training_progress(
+        self, epoch: int, current_lr: float, epoch_loss: float, val_loss: float
+    ):
+        """
+        Log the training progress.
+
+        Parameters
+        ----------
+        epoch : int
+            The current epoch number.
+        current_lr : float
+            The current learning rate.
+        epoch_loss : float
+            The training loss value of the current epoch.
+        val_loss : float
+            The validation loss value of the current epoch.
+        """
+        logger.progress(
+            f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+        )
+
+    def _log_test_metrics(self, val_metrics: pd.DataFrame, data_split: str):
+        """
+        Log the test metrics.
+
+        Parameters
+        ----------
+        val_metrics : pd.DataFrame
+            The dataframe containing the test metrics.
+        data_split : str
+            The dataset label used for testing e.g. "validation", "train", "test"
+        """
+
+        logger.progress(
+            f" Model tested on {data_split} dataset with the following metrics:"
+        )
+        for i in range(len(val_metrics)):
+            logger.progress(
+                f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
+            )
+
+    def _accumulate_training_metrics(
+        self,
+        metric_accumulator: MetricManager,
+        epoch: int,
+        epoch_loss: float,
+        current_lr: float,
+        property_name: str,
+    ):
+        """
+        Accumulate the training metrics (training loss and learning rate) for the given property.
+
+        Parameters
+        ----------
+        metric_accumulator : MetricManager
+            The metric manager object.
+        epoch : int
+            The current epoch number.
+        epoch_loss : float
+            The training loss value of the current epoch.
+        current_lr : float
+            The current learning rate.
+        property_name : str
+            The property name to accumulate the metrics for.
+        """
+        loss_name = "ce_loss" if property_name == "charge" else "l1_loss"
+        metric_accumulator.accumulate_metrics(
+            epoch,
+            metric=epoch_loss,
+            metric_name=loss_name,
+            data_split="train",
+            property_name=property_name,
+        )
+        metric_accumulator.accumulate_metrics(
+            epoch,
+            metric=current_lr,
+            metric_name="lr",
+            data_split="train",
+            property_name=property_name,
+        )
+
+    def _evaluate_metrics(
+        self,
+        test_input: dict,
+        metric_accumulator: MetricManager,
+        epoch: int,
+        data_split: str,
+        property_name: str,
+        epoch_loss: float,
+        current_lr: float,
+    ) -> bool:
+        """
+        Evaluate the model using the test_input, accumulate the metrics and return the continue_training flag based on the early stopping criteria.
+
+        Parameters
+        ----------
+        test_input : dict
+            The input data for calculating the metrics.
+        metric_accumulator : MetricManager
+            The metric manager object.
+        epoch : int
+            The current epoch number.
+        data_split : str
+            The dataset label to test on e.g. "validation", "train"
+        property_name : str
+            The property name to accumulate the metrics for.
+        epoch_loss : float
+            The training loss value of the current epoch.
+        current_lr : float
+            The current learning rate.
+
+        Returns
+        -------
+        bool
+            Whether to continue training or not based on the early stopping criteria applied on the metrics.
+        """
+        continue_training = True
+        val_metrics = metric_accumulator.calculate_test_metric(
+            test_input, epoch, data_split=data_split, property_name=property_name
+        )
+        if epoch != -1 and data_split == "validation":
+            loss_name = "ce_loss" if property_name == "charge" else "l1_loss"
+            self._accumulate_training_metrics(
+                metric_accumulator, epoch, epoch_loss, current_lr, property_name
+            )
+            val_loss = val_metrics[val_metrics["metric_name"] == loss_name][
+                "value"
+            ].values[0]
+            continue_training = self.early_stopping.step(val_loss)
+            self._log_training_progress(epoch, current_lr, epoch_loss, val_loss)
+        else:
+            self._log_test_metrics(val_metrics, data_split)
+
+        return continue_training
+
     def _test_ms2(
         self,
         epoch: int,
@@ -340,7 +473,7 @@ def _test_ms2(
 
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+        if epoch % self.test_interval and epoch != -1:
             return continue_training
 
         self.ms2_model.model.eval()
@@ -357,40 +490,20 @@ def _test_ms2(
             "predicted": pred_intensities,
             "target": target_fragment_intensity_df,
         }
-        val_metrics = metric_accumulator.calculate_test_metric(
-            test_input, epoch, data_split=data_split, property_name="ms2"
+
+        current_lr = (
+            self.ms2_model.optimizer.param_groups[0]["lr"] if epoch != -1 else 0
         )
-        if epoch != -1 and data_split == "validation":  # A training epoch
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=epoch_loss,
-                metric_name="l1_loss",
-                data_split="train",
-                property_name="ms2",
-            )
-            current_lr = self.ms2_model.optimizer.param_groups[0]["lr"]
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=current_lr,
-                metric_name="lr",
-                data_split="train",
-                property_name="ms2",
-            )
-            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                "value"
-            ].values[0]
-            continue_training = self.early_stopping.step(val_loss)
-            logger.progress(
-                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-            )
-        else:
-            logger.progress(
-                f" Ms2 model tested on {data_split} dataset with the following metrics:"
-            )
-            for i in range(len(val_metrics)):
-                logger.progress(
-                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                )
+        continue_training = self._evaluate_metrics(
+            test_input,
+            metric_accumulator,
+            epoch,
+            data_split,
+            "ms2",
+            epoch_loss,
+            current_lr,
+        )
+
         self.ms2_model.model.train()
         return continue_training
 
@@ -444,13 +557,10 @@ def finetune_ms2(
         self._normalize_intensity(psm_df, matched_intensity_df)
 
         # Shuffle the psm_df and split it into train and test
-        train_psm_df = psm_df.sample(frac=self.settings["train_fraction"]).copy()
+        train_psm_df = psm_df.sample(frac=self.train_fraction).copy()
         val_psm_df = (
             psm_df.drop(train_psm_df.index)
-            .sample(
-                frac=self.settings["validation_fraction"]
-                / (1 - self.settings["train_fraction"])
-            )
+            .sample(frac=self.validation_fraction / (1 - self.train_fraction))
             .copy()
         )
         test_psm_df = psm_df.drop(train_psm_df.index).drop(val_psm_df.index).copy()
@@ -529,26 +639,26 @@ def finetune_ms2(
         # Train the model
         logger.progress(" Fine-tuning MS2 model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.settings['train_fraction']:3.2f}     Train size:      {len(train_psm_df):<10}"
+            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_psm_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.settings['validation_fraction']:3.2f}     Validation size: {len(val_psm_df):<10}"
+            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_psm_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.settings['test_fraction']:3.2f}     Test size:       {len(test_psm_df):<10}"
+            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_psm_df):<10}"
         )
         self.ms2_model.model.train()
         self.ms2_model.train(
             precursor_df=train_psm_df,
             fragment_intensity_df=train_intensity_df,
-            epoch=self.settings["epochs"],
-            batch_size=self.settings["batch_size"],
-            warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            epoch=self.epochs,
+            batch_size=self.batch_size,
+            warmup_epoch=self.warmup_epochs,
+            lr=self.max_lr,
         )
 
         self._test_ms2(
-            self.settings["epochs"],
+            self.epochs,
             0,
             reordered_test_psm_df,
             reordered_test_intensity_df,
@@ -589,7 +699,7 @@ def _test_rt(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+        if epoch % self.test_interval != 0 and epoch != -1:
             return continue_training
 
         self.rt_model.model.eval()
@@ -599,40 +709,16 @@ def _test_rt(
             "predicted": pred["rt_pred"].values,
             "target": test_df["rt_norm"].values,
         }
-        val_metrics = metric_accumulator.calculate_test_metric(
-            test_input, epoch, data_split=data_split, property_name="rt"
+        current_lr = self.rt_model.optimizer.param_groups[0]["lr"] if epoch != -1 else 0
+        continue_training = self._evaluate_metrics(
+            test_input,
+            metric_accumulator,
+            epoch,
+            data_split,
+            "rt",
+            epoch_loss,
+            current_lr,
         )
-        if epoch != -1 and data_split == "validation":
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=epoch_loss,
-                metric_name="l1_loss",
-                data_split="train",
-                property_name="rt",
-            )
-            current_lr = self.rt_model.optimizer.param_groups[0]["lr"]
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=current_lr,
-                metric_name="lr",
-                data_split="train",
-                property_name="rt",
-            )
-            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                "value"
-            ].values[0]
-            continue_training = self.early_stopping.step(val_loss)
-            logger.progress(
-                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-            )
-        else:
-            logger.progress(
-                f" RT model tested on {data_split} dataset with the following metrics:"
-            )
-            for i in range(len(val_metrics)):
-                logger.progress(
-                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                )
 
         self.rt_model.model.train()
 
@@ -654,10 +740,9 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         """
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.settings["train_fraction"])
+        train_df = psm_df.sample(frac=self.train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.settings["validation_fraction"]
-            / (1 - self.settings["train_fraction"])
+            frac=self.validation_fraction / (1 - self.train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -686,26 +771,24 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning RT model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.settings['train_fraction']:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.settings['validation_fraction']:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.settings['test_fraction']:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.rt_model.model.train()
         self.rt_model.train(
             train_df,
-            batch_size=self.settings["batch_size"],
-            epoch=self.settings["epochs"],
-            warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            batch_size=self.batch_size,
+            epoch=self.epochs,
+            warmup_epoch=self.warmup_epochs,
+            lr=self.max_lr,
         )
 
-        self._test_rt(
-            self.settings["epochs"], 0, test_df, test_metric_manager, data_split="test"
-        )
+        self._test_rt(self.epochs, 0, test_df, test_metric_manager, data_split="test")
 
         metrics = test_metric_manager.get_stats()
 
@@ -741,7 +824,7 @@ def _test_charge(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+        if epoch % self.test_interval != 0 and epoch != -1:
             return continue_training
 
         self.charge_model.model.eval()
@@ -751,40 +834,18 @@ def _test_charge(
             "target": np.array(test_df["charge_indicators"].values.tolist()),
             "predicted": np.array(pred["charge_probs"].values.tolist()),
         }
-        val_metrics = metric_accumulator.calculate_test_metric(
-            test_input, epoch, data_split=data_split, property_name="charge"
+        current_lr = (
+            self.charge_model.optimizer.param_groups[0]["lr"] if epoch != -1 else 0
+        )
+        continue_training = self._evaluate_metrics(
+            test_input,
+            metric_accumulator,
+            epoch,
+            data_split,
+            "charge",
+            epoch_loss,
+            current_lr,
         )
-        if epoch != -1 and data_split == "validation":
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=epoch_loss,
-                metric_name="ce_loss",
-                data_split="train",
-                property_name="charge",
-            )
-            current_lr = self.charge_model.optimizer.param_groups[0]["lr"]
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=current_lr,
-                metric_name="lr",
-                data_split="train",
-                property_name="charge",
-            )
-            val_loss = val_metrics[val_metrics["metric_name"] == "ce_loss"][
-                "value"
-            ].values[0]
-            continue_training = self.early_stopping.step(val_loss)
-            logger.progress(
-                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-            )
-        else:
-            logger.progress(
-                f" Charge model tested on {data_split} dataset with the following metrics: "
-            )
-            for i in range(len(val_metrics)):
-                logger.progress(
-                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                )
         self.charge_model.model.train()
         return continue_training
 
@@ -831,10 +892,9 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         )
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.settings["train_fraction"])
+        train_df = psm_df.sample(frac=self.train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.settings["validation_fraction"]
-            / (1 - self.settings["train_fraction"])
+            frac=self.validation_fraction / (1 - self.train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -865,25 +925,25 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning Charge model with following settings:")
         logger.info(
-            f" Train fraction:      {self.settings['train_fraction']:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.settings['validation_fraction']:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.settings['test_fraction']:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.charge_model.model.train()
         self.charge_model.train(
             train_df,
-            batch_size=self.settings["batch_size"],
-            epoch=self.settings["epochs"],
-            warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            batch_size=self.batch_size,
+            epoch=self.epochs,
+            warmup_epoch=self.warmup_epochs,
+            lr=self.max_lr,
         )
 
         self._test_charge(
-            self.settings["epochs"], 0, test_df, test_metric_manager, data_split="test"
+            self.epochs, 0, test_df, test_metric_manager, data_split="test"
         )
         metrics = test_metric_manager.get_stats()
 
@@ -918,7 +978,7 @@ def _test_ccs(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.settings["test_interval"] != 0 and epoch != -1:
+        if epoch % self.test_interval != 0 and epoch != -1:
             return continue_training
 
         self.ccs_model.model.eval()
@@ -929,40 +989,19 @@ def _test_ccs(
             "predicted": pred["ccs_pred"].values,
             "target": test_df["ccs"].values,
         }
-        val_metrics = metric_accumulator.calculate_test_metric(
-            test_input, epoch, data_split=data_split, property_name="ccs"
+
+        current_lr = (
+            self.ccs_model.optimizer.param_groups[0]["lr"] if epoch != -1 else 0
+        )
+        continue_training = self._evaluate_metrics(
+            test_input,
+            metric_accumulator,
+            epoch,
+            data_split,
+            "ccs",
+            epoch_loss,
+            current_lr,
         )
-        if epoch != -1 and data_split == "validation":
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=epoch_loss,
-                metric_name="l1_loss",
-                data_split="train",
-                property_name="ccs",
-            )
-            current_lr = self.ccs_model.optimizer.param_groups[0]["lr"]
-            metric_accumulator.accumulate_metrics(
-                epoch,
-                metric=current_lr,
-                metric_name="lr",
-                data_split="train",
-                property_name="ccs",
-            )
-            val_loss = val_metrics[val_metrics["metric_name"] == "l1_loss"][
-                "value"
-            ].values[0]
-            continue_training = self.early_stopping.step(val_loss)
-            logger.progress(
-                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-            )
-        else:
-            logger.progress(
-                f" CCS model tested on {data_split} dataset with the following metrics:"
-            )
-            for i in range(len(val_metrics)):
-                logger.progress(
-                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-                )
 
         self.ccs_model.model.train()
 
@@ -993,10 +1032,9 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             psm_df["mobility"] = ccs_to_mobility_for_df(psm_df, "ccs")
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.settings["train_fraction"])
+        train_df = psm_df.sample(frac=self.train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.settings["validation_fraction"]
-            / (1 - self.settings["train_fraction"])
+            frac=self.validation_fraction / (1 - self.train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -1024,26 +1062,24 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning CCS model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.settings['train_fraction']:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.settings['validation_fraction']:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.settings['test_fraction']:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.ccs_model.model.train()
         self.ccs_model.train(
             train_df,
-            batch_size=self.settings["batch_size"],
-            epoch=self.settings["epochs"],
-            warmup_epoch=self.settings["warmup_epochs"],
-            lr=settings["max_lr"],
+            batch_size=self.batch_size,
+            epoch=self.epochs,
+            warmup_epoch=self.warmup_epochs,
+            lr=self.max_lr,
         )
 
-        self._test_ccs(
-            self.settings["epochs"], 0, test_df, test_metric_manager, data_split="test"
-        )
+        self._test_ccs(self.epochs, 0, test_df, test_metric_manager, data_split="test")
 
         metrics = test_metric_manager.get_stats()
 
diff --git a/nbs/tutorial_nbs/finetuning.ipynb b/nbs/tutorial_nbs/finetuning.ipynb
index c61e3d9d..f303e5c6 100644
--- a/nbs/tutorial_nbs/finetuning.ipynb
+++ b/nbs/tutorial_nbs/finetuning.ipynb
@@ -37,7 +37,7 @@
    "outputs": [],
    "source": [
     "transfer_lib = SpecLibBase()\n",
-    "transfer_lib.load_hdf('/Users/georgwallmann/Documents/data/alphadia_manuscript/2024_04_25_Dimethyl_GPF/transfer_learning_asms/speclib.transfer.hdf', load_mod_seq=True)"
+    "transfer_lib.load_hdf('/Users/georgwallmann/Documents/data/alphadia_manuscript/2024_04_25_Dimethyl_GPF/transfer_learning_asms/speclib.transfer.hdf', load_mod_seq=True)\n"
    ]
   },
   {
@@ -55,12 +55,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "tune_mgr = FinetuneManager(\n",
     "    device=\"gpu\",\n",
-    "    settings=settings)\n",
-    "tune_mgr.nce = 25\n",
-    "tune_mgr.instrument = 'Lumos'"
+    "    test_interval=3)\n"
    ]
   },
   {
@@ -76,7 +73,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transfer_lib.precursor_df = transfer_lib.precursor_df.dropna(subset=['mobility'])  # drop rows with na in the mobility column\n",
+    "transfer_lib.precursor_df[\"mobility\"] = transfer_lib.precursor_df[\"mobility_observed\"]\n",
     "transfer_lib.precursor_df = tune_mgr.predict_mobility(transfer_lib.precursor_df)\n",
     "plt.scatter(transfer_lib.precursor_df['mobility'], transfer_lib.precursor_df['mobility_pred'], s=1, alpha=0.1)\n",
     "plt.xlabel('mobility observed')\n",
@@ -151,7 +148,7 @@
    "source": [
     "g = sns.relplot(data=rt_stats, x='epoch', y='value', hue='data_split', marker= 'o',dashes=False, col='metric_name', kind='line', col_wrap=2, facet_kws={'sharex': False, 'sharey': False, 'legend_out': False})\n",
     "g.set_titles(\"{col_name}\")\n",
-    "g.legend.set_title('Data split')\n"
+    "g.legend.set_title('Data split')"
    ]
   },
   {

From 109c000ac3e50dfdd38ffe31ad57a371f1eb118b Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 17:37:40 +0200
Subject: [PATCH 126/250] only calibrate relevant subset

---
 alphadia/workflow/optimization.py   | 44 ++++++++++++++++++---
 alphadia/workflow/peptidecentric.py | 59 +++++++++--------------------
 tests/unit_tests/test_workflow.py   | 49 ++++++++++++++++++++----
 3 files changed, 97 insertions(+), 55 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 768c7b0b..813f61c2 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -606,9 +606,7 @@ def __init__(self, library, config: dict):
         self.set_batch_plan()
 
         eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
-        self.batch_precursor_df = self._library._precursor_df[
-            self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
-        ]
+        self.set_batch_dfs(eg_idxes)
 
         self.feature_dfs = []
         self.fragment_dfs = []
@@ -654,7 +652,7 @@ def update_with_extraction(self, feature_df, fragment_df):
         self.feature_dfs += [feature_df]
         self.fragment_dfs += [fragment_df]
 
-        self.total_precursors = len(self.features_df)
+        self.total_precursors = self.features_df.precursor_idx.nunique()
 
     def update_with_fdr(self, precursor_df):
         """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization.
@@ -704,7 +702,6 @@ def update(self):
             ]  # Take only additional elution groups in the new step of the batch plan. The target has not been reached and hence recalibration and optimization have not been performed, so previous extractions can be re-used.
 
         else:
-            self.first_time_to_reach_target = False
             self.decrease_batch_idx()
             eg_idxes = self.elution_group_order[
                 : self.stop_idx
@@ -713,9 +710,44 @@ def update(self):
             self.fragment_dfs = []
 
         # Sets the batch to be used in the next round of optimization. Note that this batch will only include additional precursors if the target was not reached in this step (as the precursors from this step are stored in the feature_dfs attribute).
+        self.set_batch_dfs(eg_idxes)
+
+    def set_batch_dfs(self, eg_idxes):
         self.batch_precursor_df = self._library._precursor_df[
             self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
-        ]
+        ].copy()
+
+        start_indices = self.batch_precursor_df["flat_frag_start_idx"].values
+        stop_indices = self.batch_precursor_df["flat_frag_stop_idx"].values
+
+        # Create ranges for each row and concatenate them
+        fragment_idxes = np.concatenate(
+            [
+                np.arange(start, stop)
+                for start, stop in zip(start_indices, stop_indices, strict=True)
+            ]
+        )
+
+        # Extract the fragments for the optimization lock and reset the indices to a consecutive range of positive integers. This simplifies future access based on position
+        self.batch_fragment_df = self._library._fragment_df.iloc[fragment_idxes].copy()
+
+        self.reindex_fragments()
+
+    def reindex_fragments(self):
+        """
+        Reindex the fragment dataframe to have a consecutive range of positive integers as indices. Change the precursor dataframe such that the fragment indices match the reindexed fragment dataframe.
+        """
+        self.batch_fragment_df.reset_index(drop=True, inplace=True)
+
+        # Change the fragment indices in batch_precursor_df to match the fragment indices in batch_fragment_df instead of the full spectral library.
+        num_frags = (
+            self.batch_precursor_df["flat_frag_stop_idx"]
+            - self.batch_precursor_df["flat_frag_start_idx"]
+        )
+        self.batch_precursor_df["flat_frag_stop_idx"] = num_frags.cumsum()
+        self.batch_precursor_df["flat_frag_start_idx"] = self.batch_precursor_df[
+            "flat_frag_stop_idx"
+        ].shift(fill_value=0)
 
     @property
     def features_df(self):
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 8d5e669d..7ba0a67c 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -352,32 +352,6 @@ def get_ordered_optimizers(self):
 
         return ordered_optimizers
 
-    def first_recalibration(
-        self,
-        precursor_df: pd.DataFrame,
-        fragments_df: pd.DataFrame,
-    ):
-        """Performs the first recalibration and optimization step.
-
-        Parameters
-        ----------
-        precursor_df : pd.DataFrame
-            Precursor dataframe from optimization lock
-
-        fragments_df : pd.DataFrame
-            Fragment dataframe from optimization lock
-
-        """
-
-        self.optimization_manager.fit(
-            {"classifier_version": self.fdr_manager.current_version}
-        )
-        precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-            precursor_df, fragments_df
-        )
-
-        self.recalibration(precursor_df_filtered, fragments_df_filtered)
-
     def calibration(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
@@ -428,7 +402,7 @@ def calibration(self):
                 )
 
                 feature_df, fragment_df = self.extract_batch(
-                    self.optlock.batch_precursor_df
+                    self.optlock.batch_precursor_df, self.optlock.batch_fragment_df
                 )
                 self.optlock.update_with_extraction(feature_df, fragment_df)
 
@@ -453,23 +427,25 @@ def calibration(self):
                 self.log_precursor_df(precursor_df)
 
                 if self.optlock.has_target_num_precursors:
-                    if self.optlock.first_time_to_reach_target:  # Recalibrates and updates classifier, but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
-                        self.first_recalibration(
-                            precursor_df, self.optlock.fragments_df
+                    precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+                        precursor_df, self.optlock.fragments_df
+                    )
+
+                    self.optlock.update()
+
+                    self.recalibration(precursor_df_filtered, fragments_df_filtered)
+
+                    if self.optlock.first_time_to_reach_target:  # Updates classifier but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
+                        self.optlock.first_time_to_reach_target = False
+                        self.optimization_manager.fit(
+                            {"classifier_version": self.fdr_manager.current_version}
                         )
-                        self.optlock.update()
                         self.reporter.log_string(
                             "Required number of precursors found. Starting search parameter optimization.",
                             verbosity="progress",
                         )
                         continue
 
-                    precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
-                        precursor_df, self.optlock.fragments_df
-                    )
-
-                    self.recalibration(precursor_df_filtered, fragments_df_filtered)
-
                     self.reporter.log_string(
                         "=== checking if optimization conditions were reached ===",
                         verbosity="info",
@@ -487,9 +463,8 @@ def calibration(self):
                         f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
                         verbosity="progress",
                     )
-
-                self.optlock.update()
-
+                else:
+                    self.optlock.update()
             else:
                 self.reporter.log_string(
                     "Optimization did not converge within the maximum number of steps, which is {self.config['calibration']['max_steps']}.",
@@ -595,11 +570,11 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
         )
 
         self.calibration_manager.predict(
-            self.spectral_library._precursor_df,
+            self.optlock.batch_precursor_df,
             "precursor",
         )
 
-        self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment")
+        self.calibration_manager.predict(self.optlock.batch_fragment_df, "fragment")
 
         self.optimization_manager.fit(
             {
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 3236587b..fa42404a 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -781,13 +781,24 @@ def test_targeted_mobility_optimizer():
 
 class TEST_LIBRARY:
     def __init__(self):
-        idxes = np.arange(0, 100000)
-        self._precursor_df = pd.DataFrame({"elution_group_idx": idxes})
-        self._fragments_df = pd.DataFrame(
+        precursor_idx = np.arange(1000)
+        elution_group_idx = np.concatenate(
+            [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
+        )
+        flat_frag_start_idx = precursor_idx**2
+        flat_frag_stop_idx = (precursor_idx + 1) ** 2
+        self._precursor_df = pd.DataFrame(
             {
-                "precursor_idx": np.concatenate(
-                    [np.full(10, precursor_idx) for precursor_idx in idxes]
-                )
+                "elution_group_idx": elution_group_idx,
+                "precursor_idx": precursor_idx,
+                "flat_frag_start_idx": flat_frag_start_idx,
+                "flat_frag_stop_idx": flat_frag_stop_idx,
+            }
+        )
+
+        self._fragment_df = pd.DataFrame(
+            {
+                "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
             }
         )
 
@@ -868,4 +879,28 @@ def test_optlock_batch_idx():
     assert optlock.stop_idx == 2000
 
 
-test_optimization_manager_rt_proportion()
+def test_optlock_reindex():
+    library = TEST_LIBRARY()
+    optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
+    optlock.batch_plan = [[0, 100], [100, 200]]
+    optlock.set_batch_dfs(
+        optlock.elution_group_order[optlock.start_idx : optlock.stop_idx]
+    )
+    optlock.reindex_fragments()
+
+    assert (
+        (
+            optlock.batch_precursor_df["flat_frag_stop_idx"].iloc[100]
+            - optlock.batch_precursor_df["flat_frag_start_idx"].iloc[100]
+        )
+        == (
+            (optlock.batch_precursor_df["precursor_idx"].iloc[100] + 1) ** 2
+            - optlock.batch_precursor_df["precursor_idx"].iloc[100] ** 2
+        )
+    )  # Since each precursor was set (based on its original ID) to have a number of fragments equal to its original ID squared, the difference between the start and stop index should be equal to the original ID squared (even if the start and stop index have been changed to different values)
+    assert (
+        optlock.batch_fragment_df.iloc[
+            optlock.batch_precursor_df.iloc[50]["flat_frag_start_idx"]
+        ]["precursor_idx"]
+        == optlock.batch_precursor_df.iloc[50]["precursor_idx"] ** 2
+    )  # The original start index of any precursor should be equal to the square of the its original ID

From e76f9a33b634324d5410cc64a8fd561d5e34cc84 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Tue, 20 Aug 2024 18:42:11 +0300
Subject: [PATCH 127/250] fix finetune manager initialization

---
 alphadia/outputtransform.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 024a2786..7400d564 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -396,7 +396,7 @@ def build_transfer_model(self):
         device = utils.get_torch_device(self.config["general"]["use_gpu"])
 
         tune_mgr = FinetuneManager(
-            device=device, settings=self.config["transfer_learning"]
+            device=device, **self.config["transfer_learning"]
         )
         tune_mgr.finetune_rt(transfer_lib.precursor_df)
         tune_mgr.finetune_charge(transfer_lib.precursor_df)

From 842e2b840ee21725eb33083b77194c6c9cfc14d6 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Tue, 20 Aug 2024 18:44:02 +0300
Subject: [PATCH 128/250] format

---
 alphadia/outputtransform.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 7400d564..8b67a0bd 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -395,9 +395,7 @@ def build_transfer_model(self):
 
         device = utils.get_torch_device(self.config["general"]["use_gpu"])
 
-        tune_mgr = FinetuneManager(
-            device=device, **self.config["transfer_learning"]
-        )
+        tune_mgr = FinetuneManager(device=device, **self.config["transfer_learning"])
         tune_mgr.finetune_rt(transfer_lib.precursor_df)
         tune_mgr.finetune_charge(transfer_lib.precursor_df)
         tune_mgr.finetune_ms2(

From 302d2ddf7e179e8568210bf6287e15ee4b3a98bc Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 17:49:33 +0200
Subject: [PATCH 129/250] remove unit test call

---
 tests/unit_tests/test_workflow.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 3236587b..b8022a84 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -866,6 +866,3 @@ def test_optlock_batch_idx():
 
     assert optlock.start_idx == 0
     assert optlock.stop_idx == 2000
-
-
-test_optimization_manager_rt_proportion()

From 2c4d85da59363dc0017774e5823a4224c0f96df4 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 17:58:59 +0200
Subject: [PATCH 130/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 36 +++++++++++++++++++++++++------
 1 file changed, 30 insertions(+), 6 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index fa42404a..8cfe4e4a 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -780,6 +780,30 @@ def test_targeted_mobility_optimizer():
 
 
 class TEST_LIBRARY:
+    def __init__(self):
+        precursor_idx = np.arange(100000)
+        elution_group_idx = np.concatenate(
+            [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
+        )
+        flat_frag_start_idx = precursor_idx * 10
+        flat_frag_stop_idx = (precursor_idx + 1) * 10
+        self._precursor_df = pd.DataFrame(
+            {
+                "elution_group_idx": elution_group_idx,
+                "precursor_idx": precursor_idx,
+                "flat_frag_start_idx": flat_frag_start_idx,
+                "flat_frag_stop_idx": flat_frag_stop_idx,
+            }
+        )
+
+        self._fragment_df = pd.DataFrame(
+            {
+                "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
+            }
+        )
+
+
+class TEST_LIBRARY_INDEX:
     def __init__(self):
         precursor_idx = np.arange(1000)
         elution_group_idx = np.concatenate(
@@ -817,8 +841,8 @@ def test_optlock():
 
     assert optlock.start_idx == optlock.batch_plan[0][0]
 
-    feature_df = pd.DataFrame({"col": np.arange(0, 1000)})
-    fragment_df = pd.DataFrame({"col": np.arange(0, 10000)})
+    feature_df = pd.DataFrame({"precursor_idx": np.arange(0, 1000)})
+    fragment_df = pd.DataFrame({"precursor_idx": np.arange(0, 10000)})
 
     optlock.update_with_extraction(feature_df, fragment_df)
 
@@ -834,8 +858,8 @@ def test_optlock():
 
     assert optlock.start_idx == optlock.batch_plan[1][0]
 
-    feature_df = pd.DataFrame({"col": np.arange(0, 1000)})
-    fragment_df = pd.DataFrame({"col": np.arange(0, 10000)})
+    feature_df = pd.DataFrame({"precursor_idx": np.arange(1000, 2000)})
+    fragment_df = pd.DataFrame({"precursor_idx": np.arange(10000, 20000)})
 
     optlock.update_with_extraction(feature_df, fragment_df)
 
@@ -849,9 +873,9 @@ def test_optlock():
 
     assert optlock.has_target_num_precursors is True
     assert optlock.first_time_to_reach_target is True
+
     optlock.update()
 
-    assert optlock.first_time_to_reach_target is False
     assert optlock.start_idx == 0
 
 
@@ -880,7 +904,7 @@ def test_optlock_batch_idx():
 
 
 def test_optlock_reindex():
-    library = TEST_LIBRARY()
+    library = TEST_LIBRARY_INDEX()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
     optlock.batch_plan = [[0, 100], [100, 200]]
     optlock.set_batch_dfs(

From 49d9a011671851054eee97b7c98e9cc15d2b6ca8 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 18:16:00 +0200
Subject: [PATCH 131/250] formatting

---
 tests/unit_tests/test_workflow.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 1c5144e6..8cfe4e4a 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -902,7 +902,7 @@ def test_optlock_batch_idx():
     assert optlock.start_idx == 0
     assert optlock.stop_idx == 2000
 
-    
+
 def test_optlock_reindex():
     library = TEST_LIBRARY_INDEX()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)

From c6b6a0a1a9ec46aad38c0258aa1fc76f3567141f Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 22:02:06 +0200
Subject: [PATCH 132/250] bug fix

---
 alphadia/workflow/optimization.py   |  2 +-
 alphadia/workflow/peptidecentric.py | 22 ++++++++++++++--------
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 813f61c2..058d3d25 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -593,7 +593,7 @@ def __init__(self, library, config: dict):
         self._library = library
         self._config = config
 
-        self.first_time_to_reach_target = True
+        self.previously_calibrated = False
         self.has_target_num_precursors = False
 
         self.elution_group_order = library._precursor_df["elution_group_idx"].unique()
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 7ba0a67c..6ead1552 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -435,8 +435,8 @@ def calibration(self):
 
                     self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
-                    if self.optlock.first_time_to_reach_target:  # Updates classifier but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
-                        self.optlock.first_time_to_reach_target = False
+                    if not self.optlock.previously_calibrated:  # Updates classifier but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
+                        self.optlock.previously_calibrated = True
                         self.optimization_manager.fit(
                             {"classifier_version": self.fdr_manager.current_version}
                         )
@@ -465,6 +465,8 @@ def calibration(self):
                     )
                 else:
                     self.optlock.update()
+                    if self.optlock.previously_calibrated:
+                        self.apply_calibration()
             else:
                 self.reporter.log_string(
                     "Optimization did not converge within the maximum number of steps, which is {self.config['calibration']['max_steps']}.",
@@ -569,12 +571,7 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
             # neptune_run = self.neptune
         )
 
-        self.calibration_manager.predict(
-            self.optlock.batch_precursor_df,
-            "precursor",
-        )
-
-        self.calibration_manager.predict(self.optlock.batch_fragment_df, "fragment")
+        self.apply_calibration()
 
         self.optimization_manager.fit(
             {
@@ -592,6 +589,15 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
             }
         )
 
+    def apply_calibration(self):
+        """Predicts values in the optimization lock batch."""
+        self.calibration_manager.predict(
+            self.optlock.batch_precursor_df,
+            "precursor",
+        )
+
+        self.calibration_manager.predict(self.optlock.batch_fragment_df, "fragment")
+
     def fdr_correction(self, features_df, df_fragments, version=-1):
         return self.fdr_manager.fit_predict(
             features_df,

From cfd91ccaefa05edab0390f57e983e8dc62c6f952 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 22:26:57 +0200
Subject: [PATCH 133/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 8cfe4e4a..a74f5fb4 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -853,7 +853,7 @@ def test_optlock():
     optlock.update_with_fdr(precursor_df)
 
     assert optlock.has_target_num_precursors is False
-    assert optlock.first_time_to_reach_target is True
+    assert optlock.is_calibrated is False
     optlock.update()
 
     assert optlock.start_idx == optlock.batch_plan[1][0]
@@ -872,7 +872,7 @@ def test_optlock():
     optlock.update_with_fdr(precursor_df)
 
     assert optlock.has_target_num_precursors is True
-    assert optlock.first_time_to_reach_target is True
+    assert optlock.is_calibrated is False
 
     optlock.update()
 

From 5a090c6c5fe1aef989aa34c6bccbdc5856880179 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 20 Aug 2024 23:07:10 +0200
Subject: [PATCH 134/250] fix typographical error

---
 tests/unit_tests/test_workflow.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index a74f5fb4..90fcf3f9 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -853,7 +853,7 @@ def test_optlock():
     optlock.update_with_fdr(precursor_df)
 
     assert optlock.has_target_num_precursors is False
-    assert optlock.is_calibrated is False
+    assert optlock.previously_calibrated is False
     optlock.update()
 
     assert optlock.start_idx == optlock.batch_plan[1][0]
@@ -872,7 +872,7 @@ def test_optlock():
     optlock.update_with_fdr(precursor_df)
 
     assert optlock.has_target_num_precursors is True
-    assert optlock.is_calibrated is False
+    assert optlock.previously_calibrated is False
 
     optlock.update()
 

From 8cf8e25c13ce36872325d046cb7a3af7c52d7098 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 09:31:33 +0200
Subject: [PATCH 135/250] reverse change to update rule

---
 alphadia/outputtransform.py       | 6 ++++--
 alphadia/workflow/optimization.py | 8 ++++----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index c505878b..f075a545 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -965,12 +965,14 @@ def _build_run_internal_df(
     )
     raw_name = os.path.basename(folder_path)
 
-    internal_dict = {}
+    internal_dict = {
+        "run": raw_name,
+    }
 
     if os.path.exists(timing_manager_path):
         timing_manager = manager.TimingManager(path=timing_manager_path)
         for key in timing_manager.timings:
-            internal_dict[f"{key}_duration"] = [timing_manager.timings[key]["duration"]]
+            internal_dict[f"duration_{key}"] = [timing_manager.timings[key]["duration"]]
 
     else:
         logger.warning(f"Error reading timing manager for {raw_name}")
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 058d3d25..ed6f3db0 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -397,7 +397,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -434,7 +434,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -472,7 +472,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -510,7 +510,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
 
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame

From ebde2113184213906a01098b49399f7252ffe879 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 09:43:17 +0200
Subject: [PATCH 136/250] fix unit test

---
 tests/unit_tests/test_outputtransform.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 2c27ef6d..489b1921 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -144,7 +144,7 @@ def test_output_transform():
     internal_df = pd.read_csv(
         os.path.join(temp_folder, f"{output.INTERNAL_OUTPUT}.tsv"), sep="\t"
     )
-    assert isinstance(internal_df["extraction_duration"][0], float)
+    assert isinstance(internal_df["duration_extraction"][0], float)
     # validate protein_df output
     protein_df = pd.read_parquet(os.path.join(temp_folder, "pg.matrix.parquet"))
     assert all([col in protein_df.columns for col in ["run_0", "run_1", "run_2"]])

From 7a580db97f5f71fff62bdf531a85120ac17ca423 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 10:01:22 +0200
Subject: [PATCH 137/250] review comments

---
 alphadia/outputtransform.py              |  6 ++++--
 alphadia/planning.py                     |  6 ++++++
 alphadia/workflow/manager.py             | 16 ++++++++++++++--
 alphadia/workflow/peptidecentric.py      |  6 +-----
 tests/unit_tests/test_outputtransform.py |  2 +-
 5 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index c505878b..f075a545 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -965,12 +965,14 @@ def _build_run_internal_df(
     )
     raw_name = os.path.basename(folder_path)
 
-    internal_dict = {}
+    internal_dict = {
+        "run": raw_name,
+    }
 
     if os.path.exists(timing_manager_path):
         timing_manager = manager.TimingManager(path=timing_manager_path)
         for key in timing_manager.timings:
-            internal_dict[f"{key}_duration"] = [timing_manager.timings[key]["duration"]]
+            internal_dict[f"duration_{key}"] = [timing_manager.timings[key]["duration"]]
 
     else:
         logger.warning(f"Error reading timing manager for {raw_name}")
diff --git a/alphadia/planning.py b/alphadia/planning.py
index 57481f4a..5f4f85b5 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -321,9 +321,15 @@ def run(
                     logger.info(f"No existing quantification found for {raw_name}")
 
                 workflow.load(dia_path, speclib)
+
+                workflow.timing_manager.set_start_time("optimization")
                 workflow.calibration()
+                workflow.timing_manager.set_end_time("optimization")
 
+                workflow.timing_manager.set_start_time("extraction")
                 psm_df, frag_df = workflow.extraction()
+                workflow.timing_manager.set_end_time("extraction")
+
                 psm_df = psm_df[psm_df["qval"] <= self.config["fdr"]["fdr"]]
 
                 if self.config["multiplexing"]["enabled"]:
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index d55936f2..54ef81e8 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -768,12 +768,24 @@ def __init__(
             self.timings = {}
 
     def set_start_time(self, workflow_stage: str):
-        """Starts the timer for a portion of the workflow and stores it in the timings attribute under the name given by the parameter workflow_stage"""
+        """Stores the start time of the given stage of the workflow in the timings attribute. Also saves the timing manager to disk.
+
+        Parameters
+        ----------
+        workflow_stage : str
+            The name under which the timing will be stored in the timings dict
+        """
         self.timings.update({workflow_stage: {"start": pd.Timestamp.now()}})
         self.save()
 
     def set_end_time(self, workflow_stage: str):
-        """"""
+        """Stores the end time of the given stage of the workflow in the timings attribute and calculates the duration. Also saves the timing manager to disk.
+        Parameters
+        ----------
+        workflow_stage : str
+            The name under which the timing will be stored in the timings dict
+
+        """
         self.timings[workflow_stage]["end"] = pd.Timestamp.now()
         self.timings[workflow_stage]["duration"] = (
             self.timings[workflow_stage]["end"] - self.timings[workflow_stage]["start"]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index c079db78..047cdd8e 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -399,7 +399,7 @@ def calibration(self):
                 verbosity="progress",
             )
             return
-        self.timing_manager.set_start_time("optimization")
+
         # Get the order of optimization
         ordered_optimizers = self.get_ordered_optimizers()
 
@@ -517,7 +517,6 @@ def calibration(self):
         )
 
         self.save_managers()
-        self.timing_manager.set_end_time("optimization")
 
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
@@ -761,7 +760,6 @@ def save_managers(self):
         self.optimization_manager.save()  # this replaces the .save() call when the optimization manager is fitted, since there seems little point in saving an intermediate optimization manager.
 
     def extraction(self):
-        self.timing_manager.set_start_time("extraction")
         self.optimization_manager.fit(
             {
                 "num_candidates": self.config["search"]["target_num_candidates"],
@@ -806,8 +804,6 @@ def extraction(self):
 
         self.log_precursor_df(precursor_df)
 
-        self.timing_manager.set_end_time("extraction")
-
         return precursor_df, fragments_df
 
     def log_precursor_df(self, precursor_df):
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 2c27ef6d..489b1921 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -144,7 +144,7 @@ def test_output_transform():
     internal_df = pd.read_csv(
         os.path.join(temp_folder, f"{output.INTERNAL_OUTPUT}.tsv"), sep="\t"
     )
-    assert isinstance(internal_df["extraction_duration"][0], float)
+    assert isinstance(internal_df["duration_extraction"][0], float)
     # validate protein_df output
     protein_df = pd.read_parquet(os.path.join(temp_folder, "pg.matrix.parquet"))
     assert all([col in protein_df.columns for col in ["run_0", "run_1", "run_2"]])

From 1483a792bc513556adccc86ded9abf8444562668 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 10:12:36 +0200
Subject: [PATCH 138/250] remove additional changes to config

---
 alphadia/workflow/base.py         |  4 +---
 alphadia/workflow/manager.py      | 11 +++++++----
 alphadia/workflow/optimization.py |  8 ++++----
 3 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index 3fbb2afb..811fb292 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -102,10 +102,8 @@ def load(
             self._calibration_manager.disable_mobility_calibration()
 
         # initialize the optimization manager
-        self.config["optimization_manager"]["gradient_length"] = (
-            self.dia_data.rt_values.max()
-        )
         self._optimization_manager = manager.OptimizationManager(
+            self.dia_data.rt_values.max(),
             self.config,
             path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH),
             load_from_file=self.config["general"]["reuse_calibration"],
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 1b88e97a..025ac420 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -454,6 +454,7 @@ def fit_predict(
 class OptimizationManager(BaseManager):
     def __init__(
         self,
+        gradient_length: float,
         config: None | dict = None,
         path: None | str = None,
         load_from_file: bool = True,
@@ -464,13 +465,15 @@ def __init__(
         self.reporter.log_event("initializing", {"name": f"{self.__class__.__name__}"})
 
         if not self.is_loaded_from_file:
+            rt_error = (
+                config["search_initial"]["initial_rt_tolerance"]
+                if config["search_initial"]["initial_rt_tolerance"] > 1
+                else config["search_initial"]["initial_rt_tolerance"] * gradient_length
+            )
             initial_parameters = {
                 "ms1_error": config["search_initial"]["initial_ms1_tolerance"],
                 "ms2_error": config["search_initial"]["initial_ms2_tolerance"],
-                "rt_error": config["search_initial"]["initial_rt_tolerance"]
-                if config["search_initial"]["initial_rt_tolerance"] > 1
-                else config["search_initial"]["initial_rt_tolerance"]
-                * config["optimization_manager"]["gradient_length"],
+                "rt_error": rt_error,
                 "mobility_error": config["search_initial"][
                     "initial_mobility_tolerance"
                 ],
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 768c7b0b..a629a89d 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -397,7 +397,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -434,7 +434,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -472,7 +472,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -510,7 +510,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
 
         return 1.1 * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.975)
+        ).ci(df, 0.99)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame

From 0a70756317cfb98ad5139f793a8d1321de30fe07 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 10:35:48 +0200
Subject: [PATCH 139/250] add unit test and improve optimization manager init

---
 alphadia/workflow/base.py         |  2 +-
 alphadia/workflow/manager.py      |  2 +-
 tests/unit_tests/test_workflow.py | 27 +++++++++++++++------------
 3 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index 811fb292..c3e5f067 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -103,8 +103,8 @@ def load(
 
         # initialize the optimization manager
         self._optimization_manager = manager.OptimizationManager(
-            self.dia_data.rt_values.max(),
             self.config,
+            gradient_length=self.dia_data.rt_values.max(),
             path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH),
             load_from_file=self.config["general"]["reuse_calibration"],
             figure_path=os.path.join(self.path, self.FIGURE_PATH),
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index 025ac420..4cb8153a 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -454,8 +454,8 @@ def fit_predict(
 class OptimizationManager(BaseManager):
     def __init__(
         self,
-        gradient_length: float,
         config: None | dict = None,
+        gradient_length: None | float = None,
         path: None | str = None,
         load_from_file: bool = True,
         **kwargs,
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index b8022a84..71aff9e2 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -218,7 +218,6 @@ def test_calibration_manager_save_load():
         "fwhm_rt": 5,
         "fwhm_mobility": 0.01,
         "score_cutoff": 50,
-        "gradient_length": 1000,
     },
 }
 
@@ -233,6 +232,21 @@ def test_optimization_manager():
     assert optimization_manager.is_fitted is False
 
 
+def test_optimization_manager_rt_proportion():
+    TEST_OPTIMIZATION_CONFIG_PROPORTION = TEST_OPTIMIZATION_CONFIG.copy()
+    TEST_OPTIMIZATION_CONFIG_PROPORTION["search_initial"]["initial_rt_tolerance"] = 0.5
+    optimization_manager = manager.OptimizationManager(
+        TEST_OPTIMIZATION_CONFIG_PROPORTION, 1200
+    )
+
+    assert optimization_manager.fwhm_rt == 5
+    assert optimization_manager.fwhm_mobility == 0.01
+    optimization_manager.rt_error = 600
+
+    assert optimization_manager.is_loaded_from_file is False
+    assert optimization_manager.is_fitted is False
+
+
 def test_optimization_manager_save_load():
     temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
 
@@ -276,17 +290,6 @@ def test_optimization_manager_fit():
     os.remove(temp_path)
 
 
-def test_optimization_manager_rt_proportion():
-    temp_path = os.path.join(tempfile.tempdir, "optimization_manager.pkl")
-    TEST_OPTIMIZATION_CONFIG_PROPORTION = TEST_OPTIMIZATION_CONFIG.copy()
-    TEST_OPTIMIZATION_CONFIG_PROPORTION["search_initial"]["initial_rt_tolerance"] = 0.5
-    optimization_manager = manager.OptimizationManager(
-        TEST_OPTIMIZATION_CONFIG_PROPORTION, path=temp_path, load_from_file=False
-    )
-
-    assert optimization_manager.rt_error == 500
-
-
 @pytest.mark.slow
 def test_workflow_base():
     if pytest.test_data is None:

From 4f4379d89e3dbba2ce3ce3fe2ce25b8ab5efc03e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 10:55:30 +0200
Subject: [PATCH 140/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 71aff9e2..1c8ea430 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -1,6 +1,7 @@
 import os
 import shutil
 import tempfile
+from copy import deepcopy
 from pathlib import Path
 
 import numpy as np
@@ -233,7 +234,7 @@ def test_optimization_manager():
 
 
 def test_optimization_manager_rt_proportion():
-    TEST_OPTIMIZATION_CONFIG_PROPORTION = TEST_OPTIMIZATION_CONFIG.copy()
+    TEST_OPTIMIZATION_CONFIG_PROPORTION = deepcopy(TEST_OPTIMIZATION_CONFIG)
     TEST_OPTIMIZATION_CONFIG_PROPORTION["search_initial"]["initial_rt_tolerance"] = 0.5
     optimization_manager = manager.OptimizationManager(
         TEST_OPTIMIZATION_CONFIG_PROPORTION, 1200

From 801de9db2219523c09f1837202d38d889fb512d2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 11:06:12 +0200
Subject: [PATCH 141/250] fix unit test

---
 tests/unit_tests/test_outputtransform.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 2c27ef6d..eacc8181 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -77,7 +77,7 @@ def test_output_transform():
 
         optimization_manager = manager.OptimizationManager(
             config,
-            os.path.join(
+            path=os.path.join(
                 raw_folder,
                 peptidecentric.PeptideCentricWorkflow.OPTIMIZATION_MANAGER_PATH,
             ),
@@ -85,7 +85,7 @@ def test_output_transform():
         optimization_manager.fit({"ms2_error": 6})
         optimization_manager.save()
         timing_manager = manager.TimingManager(
-            os.path.join(
+            path=os.path.join(
                 raw_folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
             )
         )

From 953333d755e957e350b8a1ee73f7fdcdd4f3e8b4 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 13:18:49 +0200
Subject: [PATCH 142/250] review comments

---
 alphadia/planning.py                | 2 +-
 alphadia/workflow/peptidecentric.py | 2 +-
 nbs/debug/debug_lvl1.ipynb          | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/alphadia/planning.py b/alphadia/planning.py
index 5f4f85b5..4160840d 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -323,7 +323,7 @@ def run(
                 workflow.load(dia_path, speclib)
 
                 workflow.timing_manager.set_start_time("optimization")
-                workflow.calibration()
+                workflow.search_parameter_optimization()
                 workflow.timing_manager.set_end_time("optimization")
 
                 workflow.timing_manager.set_start_time("extraction")
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index ca9974d1..efe144ee 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -378,7 +378,7 @@ def first_recalibration(
 
         self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
-    def calibration(self):
+    def search_parameter_optimization(self):
         """Performs optimization of the search parameters. This occurs in two stages:
         1) Optimization lock: the data are searched to acquire a locked set of precursors which is used for search parameter optimization. The classifier is also trained during this stage.
         2) Optimization loop: the search parameters are optimized iteratively using the locked set of precursors.
diff --git a/nbs/debug/debug_lvl1.ipynb b/nbs/debug/debug_lvl1.ipynb
index 73e18f1b..db0282cc 100644
--- a/nbs/debug/debug_lvl1.ipynb
+++ b/nbs/debug/debug_lvl1.ipynb
@@ -112,7 +112,7 @@
     "    plan.config,\n",
     ")\n",
     "workflow.load(dia_path, speclib)\n",
-    "workflow.calibration()"
+    "workflow.search_parameter_optimization()"
    ]
   },
   {
@@ -221,7 +221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   },
   "orig_nbformat": 4
  },

From a931b1c6a8137da5f1acc540c86955d6a7949e7e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 15:28:29 +0200
Subject: [PATCH 143/250] use alphabase functions

---
 alphadia/workflow/optimization.py   | 92 ++++++-----------------------
 alphadia/workflow/peptidecentric.py |  9 ++-
 2 files changed, 25 insertions(+), 76 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index ed6f3db0..46f6061c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -4,11 +4,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-# alpha family imports
 # third party imports
 import pandas as pd
 import seaborn as sns
 
+# alpha family imports
+from alphabase.peptide.fragment import remove_unused_fragments
+
 from alphadia.exceptions import NoOptimizationLockTargetError
 
 # alphadia imports
@@ -667,87 +669,33 @@ def update_with_fdr(self, precursor_df):
 
         self.has_target_num_precursors = self.count >= self.target_count
 
-    def increase_batch_idx(self):
-        """If the optimization lock does not contain enough precursors at 1% FDR, the optimization lock proceeds to include the next step in the batch plan in the library attribute.
-        This is done by incrementing self.batch_idx.
-        """
-        self.batch_idx += 1
-
-    def decrease_batch_idx(self):
-        """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly.
-        If not, the same library is used as before.
-        This is done by taking the smallest step in the batch plan which gives more precursors than the target number of precursors.
-        """
-
-        batch_plan_diff = np.array(
-            [
-                stop_at_given_idx - self.stop_idx * self.target_count / self.count
-                for _, stop_at_given_idx in self.batch_plan
-            ]
-        )  # Calculate the difference between the number of precursors expected at the given idx and the target number of precursors for each idx in the batch plan.
-        smallest_value = np.min(
-            batch_plan_diff[batch_plan_diff > 0]
-        )  # Take the smallest positive difference (i.e. the smallest idx that is expected to yield more than the target number of precursors).
-        self.batch_idx = np.where(batch_plan_diff == smallest_value)[0][
-            0
-        ]  # Set the batch idx to the index of the smallest positive difference.
-
     def update(self):
         """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
-        If the target has been reached, the feature and fragment dataframes are reset (since decrease will )"""
+        If the target has been reached, the feature and fragment dataframes are reset
+        """
         if not self.has_target_num_precursors:
-            self.increase_batch_idx()
-            eg_idxes = self.elution_group_order[
-                self.start_idx : self.stop_idx
-            ]  # Take only additional elution groups in the new step of the batch plan. The target has not been reached and hence recalibration and optimization have not been performed, so previous extractions can be re-used.
+            self.batch_idx += 1
 
         else:
-            self.decrease_batch_idx()
-            eg_idxes = self.elution_group_order[
-                : self.stop_idx
-            ]  # Take all elution groups up the the stop index. The target has been reached and hence the recalibration and optimization have been performed, so everything must be extracted afresh.
+            self.batch_idx = 0
             self.feature_dfs = []
             self.fragment_dfs = []
 
-        # Sets the batch to be used in the next round of optimization. Note that this batch will only include additional precursors if the target was not reached in this step (as the precursors from this step are stored in the feature_dfs attribute).
+        eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
         self.set_batch_dfs(eg_idxes)
 
     def set_batch_dfs(self, eg_idxes):
-        self.batch_precursor_df = self._library._precursor_df[
-            self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
-        ].copy()
-
-        start_indices = self.batch_precursor_df["flat_frag_start_idx"].values
-        stop_indices = self.batch_precursor_df["flat_frag_stop_idx"].values
-
-        # Create ranges for each row and concatenate them
-        fragment_idxes = np.concatenate(
-            [
-                np.arange(start, stop)
-                for start, stop in zip(start_indices, stop_indices, strict=True)
-            ]
-        )
-
-        # Extract the fragments for the optimization lock and reset the indices to a consecutive range of positive integers. This simplifies future access based on position
-        self.batch_fragment_df = self._library._fragment_df.iloc[fragment_idxes].copy()
-
-        self.reindex_fragments()
-
-    def reindex_fragments(self):
-        """
-        Reindex the fragment dataframe to have a consecutive range of positive integers as indices. Change the precursor dataframe such that the fragment indices match the reindexed fragment dataframe.
-        """
-        self.batch_fragment_df.reset_index(drop=True, inplace=True)
-
-        # Change the fragment indices in batch_precursor_df to match the fragment indices in batch_fragment_df instead of the full spectral library.
-        num_frags = (
-            self.batch_precursor_df["flat_frag_stop_idx"]
-            - self.batch_precursor_df["flat_frag_start_idx"]
+        self.batch_library = self._library.copy()
+        self.batch_library._precursor_df, (self.batch_library._fragment_df,) = (
+            remove_unused_fragments(
+                self.batch_library._precursor_df[
+                    self.batch_library._precursor_df["elution_group_idx"].isin(eg_idxes)
+                ],
+                (self.batch_library._fragment_df,),
+                frag_start_col="flat_frag_start_idx",
+                frag_stop_col="flat_frag_stop_idx",
+            )
         )
-        self.batch_precursor_df["flat_frag_stop_idx"] = num_frags.cumsum()
-        self.batch_precursor_df["flat_frag_start_idx"] = self.batch_precursor_df[
-            "flat_frag_stop_idx"
-        ].shift(fill_value=0)
 
     @property
     def features_df(self):
@@ -759,9 +707,7 @@ def fragments_df(self):
 
     @property
     def start_idx(self):
-        if self.has_target_num_precursors:
-            return 0
-        elif self.batch_idx >= len(self.batch_plan):
+        if self.batch_idx >= len(self.batch_plan):
             raise NoOptimizationLockTargetError()
         else:
             return self.batch_plan[self.batch_idx][0]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 6ead1552..794aec02 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -402,7 +402,8 @@ def calibration(self):
                 )
 
                 feature_df, fragment_df = self.extract_batch(
-                    self.optlock.batch_precursor_df, self.optlock.batch_fragment_df
+                    self.optlock.batch_library.precursor_df,
+                    self.optlock.batch_library.fragment_df,
                 )
                 self.optlock.update_with_extraction(feature_df, fragment_df)
 
@@ -592,11 +593,13 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
     def apply_calibration(self):
         """Predicts values in the optimization lock batch."""
         self.calibration_manager.predict(
-            self.optlock.batch_precursor_df,
+            self.optlock.batch_library._precursor_df,
             "precursor",
         )
 
-        self.calibration_manager.predict(self.optlock.batch_fragment_df, "fragment")
+        self.calibration_manager.predict(
+            self.optlock.batch_library._fragment_df, "fragment"
+        )
 
     def fdr_correction(self, features_df, df_fragments, version=-1):
         return self.fdr_manager.fit_predict(

From 696e5bf4f9d402de65c748b46315d8831309a3e5 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 15:33:28 +0200
Subject: [PATCH 144/250] only save timing manager after all timings are
 complete

---
 alphadia/planning.py                     | 1 +
 alphadia/workflow/manager.py             | 2 --
 tests/unit_tests/test_outputtransform.py | 1 +
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/planning.py b/alphadia/planning.py
index 4160840d..f093db22 100644
--- a/alphadia/planning.py
+++ b/alphadia/planning.py
@@ -329,6 +329,7 @@ def run(
                 workflow.timing_manager.set_start_time("extraction")
                 psm_df, frag_df = workflow.extraction()
                 workflow.timing_manager.set_end_time("extraction")
+                workflow.timing_manager.save()
 
                 psm_df = psm_df[psm_df["qval"] <= self.config["fdr"]["fdr"]]
 
diff --git a/alphadia/workflow/manager.py b/alphadia/workflow/manager.py
index ace9901b..6dc2e3e8 100644
--- a/alphadia/workflow/manager.py
+++ b/alphadia/workflow/manager.py
@@ -782,7 +782,6 @@ def set_start_time(self, workflow_stage: str):
             The name under which the timing will be stored in the timings dict
         """
         self.timings.update({workflow_stage: {"start": pd.Timestamp.now()}})
-        self.save()
 
     def set_end_time(self, workflow_stage: str):
         """Stores the end time of the given stage of the workflow in the timings attribute and calculates the duration. Also saves the timing manager to disk.
@@ -796,4 +795,3 @@ def set_end_time(self, workflow_stage: str):
         self.timings[workflow_stage]["duration"] = (
             self.timings[workflow_stage]["end"] - self.timings[workflow_stage]["start"]
         ).total_seconds() / 60
-        self.save()
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index aabd53a4..0fd5187e 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -92,6 +92,7 @@ def test_output_transform():
 
         timing_manager.set_start_time("extraction")
         timing_manager.set_end_time("extraction")
+        timing_manager.save()
 
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)

From c8db225735abf0f92a0c4f3006eb0e6e82c00cd4 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 17:17:14 +0200
Subject: [PATCH 145/250] review comments

---
 alphadia/workflow/optimization.py   |  75 +++++++++++++++--
 alphadia/workflow/peptidecentric.py |  75 +++++++----------
 tests/unit_tests/test_workflow.py   | 123 +++++++++++++++-------------
 3 files changed, 162 insertions(+), 111 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 46f6061c..afe801bc 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -10,6 +10,7 @@
 
 # alpha family imports
 from alphabase.peptide.fragment import remove_unused_fragments
+from alphabase.spectral_library.flat import SpecLibFlat
 
 from alphadia.exceptions import NoOptimizationLockTargetError
 
@@ -669,15 +670,65 @@ def update_with_fdr(self, precursor_df):
 
         self.has_target_num_precursors = self.count >= self.target_count
 
+    def update_with_calibration(self, calibration_manager):
+        """Updates the batch library with the current calibrated values using the calibration manager.
+
+        Parameters
+        ----------
+        calibration_manager: manager.CalibrationManager
+            The calibration manager object from the PeptideCentricWorkflow object.
+
+        """
+        calibration_manager.predict(
+            self.batch_library._precursor_df,
+            "precursor",
+        )
+
+        calibration_manager.predict(self.batch_library._fragment_df, "fragment")
+
+    def increase_batch_idx(self):
+        """If the optimization lock does not contain enough precursors at 1% FDR, the optimization lock proceeds to include the next step in the batch plan in the library attribute.
+        This is done by incrementing self.batch_idx.
+        """
+        self.batch_idx += 1
+
+    def decrease_batch_idx(self):
+        """If the optimization lock contains enough precursors at 1% FDR, checks if enough precursors can be obtained using a smaller library and updates the library attribute accordingly.
+        If not, the same library is used as before.
+        This is done by taking the smallest step in the batch plan which gives more precursors than the target number of precursors.
+        """
+
+        batch_plan_diff = np.array(
+            [
+                stop_at_given_idx - self.stop_idx * self.target_count / self.count
+                for _, stop_at_given_idx in self.batch_plan
+            ]
+        )  # Calculate the difference between the number of precursors expected at the given idx and the target number of precursors for each idx in the batch plan.
+        smallest_value = np.min(
+            batch_plan_diff[batch_plan_diff > 0]
+        )  # Take the smallest positive difference (i.e. the smallest idx that is expected to yield more than the target number of precursors).
+        self.batch_idx = np.where(batch_plan_diff == smallest_value)[0][
+            0
+        ]  # Set the batch idx to the index of the smallest positive difference.
+
     def update(self):
         """Updates the library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
         If the target has been reached, the feature and fragment dataframes are reset
         """
         if not self.has_target_num_precursors:
-            self.batch_idx += 1
+            self.increase_batch_idx()
+            eg_idxes = self.elution_group_order[
+                self.start_idx : self.stop_idx
+            ]  # Take only additional elution groups in the new step of the batch plan.
+            # The target has not been reached and hence recalibration and optimization have not been performed, so previous extractions can be re-used.
 
         else:
-            self.batch_idx = 0
+            self.decrease_batch_idx()
+            eg_idxes = self.elution_group_order[
+                : self.stop_idx
+            ]  # Take all elution groups up the the stop index.
+            # The target has been reached and hence the recalibration and optimization have been performed, so everything must be extracted afresh.
+
             self.feature_dfs = []
             self.fragment_dfs = []
 
@@ -685,13 +736,21 @@ def update(self):
         self.set_batch_dfs(eg_idxes)
 
     def set_batch_dfs(self, eg_idxes):
-        self.batch_library = self._library.copy()
+        """
+        Sets the batch library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
+
+        Parameters
+        ----------
+        eg_idxes: np.ndarray
+            The elution group indexes to use for the next round of optimization.
+        """
+        self.batch_library = SpecLibFlat()
         self.batch_library._precursor_df, (self.batch_library._fragment_df,) = (
             remove_unused_fragments(
-                self.batch_library._precursor_df[
-                    self.batch_library._precursor_df["elution_group_idx"].isin(eg_idxes)
+                self._library._precursor_df[
+                    self._library._precursor_df["elution_group_idx"].isin(eg_idxes)
                 ],
-                (self.batch_library._fragment_df,),
+                (self._library._fragment_df,),
                 frag_start_col="flat_frag_start_idx",
                 frag_stop_col="flat_frag_stop_idx",
             )
@@ -707,7 +766,9 @@ def fragments_df(self):
 
     @property
     def start_idx(self):
-        if self.batch_idx >= len(self.batch_plan):
+        if self.has_target_num_precursors:
+            return 0
+        elif self.batch_idx >= len(self.batch_plan):
             raise NoOptimizationLockTargetError()
         else:
             return self.batch_plan[self.batch_idx][0]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 794aec02..3afe3688 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -360,12 +360,13 @@ def calibration(self):
             The optimization loop is repeated for each list of optimizers in ordered_optimizers.
 
         """
+        log_string = self.reporter.log_string
         # First check to see if the calibration has already been performed. Return if so.
         if (
             self.calibration_manager.is_fitted
             and self.calibration_manager.is_loaded_from_file
         ):
-            self.reporter.log_string(
+            log_string(
                 "Skipping calibration as existing calibration was found",
                 verbosity="progress",
             )
@@ -374,7 +375,7 @@ def calibration(self):
         # Get the order of optimization
         ordered_optimizers = self.get_ordered_optimizers()
 
-        self.reporter.log_string(
+        log_string(
             "Starting initial search for precursors.",
             verbosity="progress",
         )
@@ -387,7 +388,7 @@ def calibration(self):
                 self.config["calibration"]["max_steps"]
             ):  # Note current_step here refers to a different step than the attribute of the same name in the optimizer -- this should be rectified
                 if np.all([optimizer.has_converged for optimizer in optimizers]):
-                    self.reporter.log_string(
+                    log_string(
                         f"Optimization finished for {', '.join([optimizer.parameter_name for optimizer in optimizers])}.",
                         verbosity="progress",
                     )
@@ -396,7 +397,7 @@ def calibration(self):
                         optimizer.plot()
 
                     break
-                self.reporter.log_string(
+                log_string(
                     f"=== Step {current_step}, extracting elution groups {self.optlock.start_idx} to {self.optlock.stop_idx} ===",
                     verbosity="progress",
                 )
@@ -407,7 +408,7 @@ def calibration(self):
                 )
                 self.optlock.update_with_extraction(feature_df, fragment_df)
 
-                self.reporter.log_string(
+                log_string(
                     f"=== Step {current_step}, extracted {len(self.optlock.features_df)} precursors and {len(self.optlock.fragments_df)} fragments ===",
                     verbosity="progress",
                 )
@@ -420,14 +421,17 @@ def calibration(self):
 
                 self.optlock.update_with_fdr(precursor_df)
 
-                self.reporter.log_string(
+                log_string(
                     f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
-                    verbosity="info",
                 )
 
                 self.log_precursor_df(precursor_df)
 
-                if self.optlock.has_target_num_precursors:
+                if not self.optlock.has_target_num_precursors:
+                    self.optlock.update()
+                    if self.optlock.previously_calibrated:
+                        self.optlock.update_with_calibration(self.calibration_manager)
+                else:
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
                         precursor_df, self.optlock.fragments_df
                     )
@@ -436,58 +440,56 @@ def calibration(self):
 
                     self.recalibration(precursor_df_filtered, fragments_df_filtered)
 
-                    if not self.optlock.previously_calibrated:  # Updates classifier but does not optimize the first time the target is reached. Optimization is more stable when done with calibrated values.
+                    self.optlock.update_with_calibration(self.calibration_manager)
+
+                    if not self.optlock.previously_calibrated:  # Updates classifier but does not optimize the first time the target is reached.
+                        # Optimization is more stable when done with calibrated values.
                         self.optlock.previously_calibrated = True
                         self.optimization_manager.fit(
                             {"classifier_version": self.fdr_manager.current_version}
                         )
-                        self.reporter.log_string(
+                        log_string(
                             "Required number of precursors found. Starting search parameter optimization.",
                             verbosity="progress",
                         )
                         continue
 
-                    self.reporter.log_string(
+                    log_string(
                         "=== checking if optimization conditions were reached ===",
-                        verbosity="info",
                     )
 
                     for optimizer in optimizers:
                         optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
-                    self.reporter.log_string(
+                    log_string(
                         "==============================================",
-                        verbosity="info",
                     )
 
-                    self.reporter.log_string(
+                    log_string(
                         f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
                         verbosity="progress",
                     )
-                else:
-                    self.optlock.update()
-                    if self.optlock.previously_calibrated:
-                        self.apply_calibration()
+
             else:
-                self.reporter.log_string(
-                    "Optimization did not converge within the maximum number of steps, which is {self.config['calibration']['max_steps']}.",
+                log_string(
+                    f"Optimization did not converge within the maximum number of steps, which is {self.config['calibration']['max_steps']}.",
                     verbosity="warning",
                 )
 
-        self.reporter.log_string(
+        log_string(
             "Search parameter optimization finished. Values taken forward for search are:",
             verbosity="progress",
         )
-        self.reporter.log_string(
+        log_string(
             "==============================================", verbosity="progress"
         )
         for optimizers in ordered_optimizers:
             for optimizer in optimizers:
-                self.reporter.log_string(
+                log_string(
                     f"{optimizer.parameter_name:<15}: {self.optimization_manager.__dict__[optimizer.parameter_name]:.4f}",
                     verbosity="progress",
                 )
-        self.reporter.log_string(
+        log_string(
             "==============================================", verbosity="progress"
         )
 
@@ -538,9 +540,7 @@ def filter_dfs(self, precursor_df, fragments_df):
         # Select top fragments
         fragments_df_filtered = fragments_df_filtered.head(stop_rank)
 
-        self.reporter.log_string(
-            f"fragments_df_filtered: {len(fragments_df_filtered)}", verbosity="info"
-        )
+        self.reporter.log_string(f"fragments_df_filtered: {len(fragments_df_filtered)}")
 
         return precursor_df_filtered, fragments_df_filtered
 
@@ -572,8 +572,6 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
             # neptune_run = self.neptune
         )
 
-        self.apply_calibration()
-
         self.optimization_manager.fit(
             {
                 "column_type": "calibrated",
@@ -590,17 +588,6 @@ def recalibration(self, precursor_df_filtered, fragments_df_filtered):
             }
         )
 
-    def apply_calibration(self):
-        """Predicts values in the optimization lock batch."""
-        self.calibration_manager.predict(
-            self.optlock.batch_library._precursor_df,
-            "precursor",
-        )
-
-        self.calibration_manager.predict(
-            self.optlock.batch_library._fragment_df, "fragment"
-        )
-
     def fdr_correction(self, features_df, df_fragments, version=-1):
         return self.fdr_manager.fit_predict(
             features_df,
@@ -685,7 +672,6 @@ def extract_batch(
             num_before = len(candidates_df)
             self.reporter.log_string(
                 f"Applying score cutoff of {self.optimization_manager.score_cutoff}",
-                verbosity="info",
             )
             candidates_df = candidates_df[
                 candidates_df["score"] > self.optimization_manager.score_cutoff
@@ -694,7 +680,6 @@ def extract_batch(
             num_removed = num_before - num_after
             self.reporter.log_string(
                 f"Removed {num_removed} precursors with score below cutoff",
-                verbosity="info",
             )
 
         config = plexscoring.CandidateConfig()
@@ -762,7 +747,6 @@ def extraction(self):
 
         self.reporter.log_string(
             f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
-            verbosity="info",
         )
 
         precursor_df = self.fdr_correction(
@@ -1031,7 +1015,6 @@ def requantify_fragments(
 
         self.reporter.log_string(
             f"creating library for charged fragment types: {fragment_types}",
-            verbosity="info",
         )
 
         candidate_speclib_flat, scored_candidates = _build_candidate_speclib_flat(
@@ -1040,7 +1023,6 @@ def requantify_fragments(
 
         self.reporter.log_string(
             "Calibrating library",
-            verbosity="info",
         )
 
         # calibrate
@@ -1051,7 +1033,6 @@ def requantify_fragments(
 
         self.reporter.log_string(
             f"quantifying {len(scored_candidates):,} precursors with {len(candidate_speclib_flat.fragment_df):,} fragments",
-            verbosity="info",
         )
 
         config = plexscoring.CandidateConfig()
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 90fcf3f9..5fc0090f 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -7,6 +7,7 @@
 import pandas as pd
 import pytest
 import yaml
+from alphabase.spectral_library.flat import SpecLibFlat
 from sklearn.linear_model import LinearRegression
 
 from alphadia.calibration.models import LOESSRegression
@@ -779,52 +780,56 @@ def test_targeted_mobility_optimizer():
     assert workflow.optimization_manager.mobility_error == optimizer.target_parameter
 
 
-class TEST_LIBRARY:
-    def __init__(self):
-        precursor_idx = np.arange(100000)
-        elution_group_idx = np.concatenate(
-            [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
-        )
-        flat_frag_start_idx = precursor_idx * 10
-        flat_frag_stop_idx = (precursor_idx + 1) * 10
-        self._precursor_df = pd.DataFrame(
-            {
-                "elution_group_idx": elution_group_idx,
-                "precursor_idx": precursor_idx,
-                "flat_frag_start_idx": flat_frag_start_idx,
-                "flat_frag_stop_idx": flat_frag_stop_idx,
-            }
-        )
+def create_test_library():
+    lib = SpecLibFlat()
+    precursor_idx = np.arange(100000)
+    elution_group_idx = np.concatenate(
+        [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
+    )
+    flat_frag_start_idx = precursor_idx * 10
+    flat_frag_stop_idx = (precursor_idx + 1) * 10
+    lib._precursor_df = pd.DataFrame(
+        {
+            "elution_group_idx": elution_group_idx,
+            "precursor_idx": precursor_idx,
+            "flat_frag_start_idx": flat_frag_start_idx,
+            "flat_frag_stop_idx": flat_frag_stop_idx,
+        }
+    )
 
-        self._fragment_df = pd.DataFrame(
-            {
-                "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
-            }
-        )
+    lib._fragment_df = pd.DataFrame(
+        {
+            "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
+        }
+    )
 
+    return lib
 
-class TEST_LIBRARY_INDEX:
-    def __init__(self):
-        precursor_idx = np.arange(1000)
-        elution_group_idx = np.concatenate(
-            [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
-        )
-        flat_frag_start_idx = precursor_idx**2
-        flat_frag_stop_idx = (precursor_idx + 1) ** 2
-        self._precursor_df = pd.DataFrame(
-            {
-                "elution_group_idx": elution_group_idx,
-                "precursor_idx": precursor_idx,
-                "flat_frag_start_idx": flat_frag_start_idx,
-                "flat_frag_stop_idx": flat_frag_stop_idx,
-            }
-        )
 
-        self._fragment_df = pd.DataFrame(
-            {
-                "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
-            }
-        )
+def create_test_library_for_indexing():
+    lib = SpecLibFlat()
+    precursor_idx = np.arange(1000)
+    elution_group_idx = np.concatenate(
+        [np.full(2, i, dtype=int) for i in np.arange(len(precursor_idx) / 2)]
+    )
+    flat_frag_start_idx = precursor_idx**2
+    flat_frag_stop_idx = (precursor_idx + 1) ** 2
+    lib._precursor_df = pd.DataFrame(
+        {
+            "elution_group_idx": elution_group_idx,
+            "precursor_idx": precursor_idx,
+            "flat_frag_start_idx": flat_frag_start_idx,
+            "flat_frag_stop_idx": flat_frag_stop_idx,
+        }
+    )
+
+    lib._fragment_df = pd.DataFrame(
+        {
+            "precursor_idx": np.arange(0, flat_frag_stop_idx[-1]),
+        }
+    )
+
+    return lib
 
 
 TEST_OPTLOCK_CONFIG = {
@@ -836,7 +841,7 @@ def __init__(self):
 
 
 def test_optlock():
-    library = TEST_LIBRARY()
+    library = create_test_library()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
 
     assert optlock.start_idx == optlock.batch_plan[0][0]
@@ -880,51 +885,55 @@ def test_optlock():
 
 
 def test_optlock_batch_idx():
-    library = TEST_LIBRARY()
+    library = create_test_library()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
 
     optlock.batch_plan = [[0, 100], [100, 2000], [2000, 8000]]
 
     assert optlock.start_idx == 0
 
-    optlock.increase_batch_idx()
+    optlock.update()
     assert optlock.start_idx == 100
 
-    optlock.increase_batch_idx()
+    optlock.update()
     assert optlock.start_idx == 2000
 
     precursor_df = pd.DataFrame({"qval": np.full(4500, 0.005)})
 
     optlock.update_with_fdr(precursor_df)
 
-    optlock.decrease_batch_idx()
+    optlock.has_target_num_precursors = True
+    optlock.update()
 
     assert optlock.start_idx == 0
-    assert optlock.stop_idx == 2000
 
 
 def test_optlock_reindex():
-    library = TEST_LIBRARY_INDEX()
+    library = create_test_library_for_indexing()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)
     optlock.batch_plan = [[0, 100], [100, 200]]
     optlock.set_batch_dfs(
         optlock.elution_group_order[optlock.start_idx : optlock.stop_idx]
     )
-    optlock.reindex_fragments()
 
     assert (
         (
-            optlock.batch_precursor_df["flat_frag_stop_idx"].iloc[100]
-            - optlock.batch_precursor_df["flat_frag_start_idx"].iloc[100]
+            optlock.batch_library._precursor_df["flat_frag_stop_idx"].iloc[100]
+            - optlock.batch_library._precursor_df["flat_frag_start_idx"].iloc[100]
         )
         == (
-            (optlock.batch_precursor_df["precursor_idx"].iloc[100] + 1) ** 2
-            - optlock.batch_precursor_df["precursor_idx"].iloc[100] ** 2
+            (optlock.batch_library._precursor_df["precursor_idx"].iloc[100] + 1) ** 2
+            - optlock.batch_library._precursor_df["precursor_idx"].iloc[100] ** 2
         )
     )  # Since each precursor was set (based on its original ID) to have a number of fragments equal to its original ID squared, the difference between the start and stop index should be equal to the original ID squared (even if the start and stop index have been changed to different values)
     assert (
-        optlock.batch_fragment_df.iloc[
-            optlock.batch_precursor_df.iloc[50]["flat_frag_start_idx"]
+        optlock.batch_library._fragment_df.iloc[
+            optlock.batch_library._precursor_df.iloc[50]["flat_frag_start_idx"]
         ]["precursor_idx"]
-        == optlock.batch_precursor_df.iloc[50]["precursor_idx"] ** 2
+        == optlock.batch_library._precursor_df.iloc[50]["precursor_idx"] ** 2
     )  # The original start index of any precursor should be equal to the square of the its original ID
+
+
+test_optlock()
+test_optlock_batch_idx()
+test_optlock_reindex()

From c853ac27138178ed0860d5db152f9f38ee8a3f70 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 18:44:13 +0200
Subject: [PATCH 146/250] make update rule and order of optimization
 configurable

---
 alphadia/constants/default.yaml     | 13 +++++-
 alphadia/workflow/optimization.py   | 26 ++++++++----
 alphadia/workflow/peptidecentric.py | 61 ++++++++++++++++++-----------
 3 files changed, 68 insertions(+), 32 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index df2cd3ab..b6cf16d5 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -189,8 +189,19 @@ search_output:
   # can be either "parquet" or "tsv"
   file_format: "tsv"
 
+optimization:
+  order_of_optimization: null
+  ms2_update_interval: 0.99
+  ms1_update_interval: 0.99
+  mobility_update_interval: 0.99
+  rt_update_interval: 0.99
+  ms2_update_factor: 1.1
+  ms1_update_factor: 1.1
+  mobility_update_factor: 1.1
+  rt_update_factor: 1.1
+
 # configuration for the optimization manager
-# initial parameters, will nbe optimized
+# initial parameters, will be optimized
 optimization_manager:
   fwhm_rt: 5
   fwhm_mobility: 0.01
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index a629a89d..a70c1710 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -380,6 +380,8 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
+        self.update_factor = workflow.config["optimization"]["rt_update_factor"]
+        self.update_interval = workflow.config["optimization"]["rt_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -395,9 +397,9 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             The proposed new value for the search parameter.
 
         """
-        return 1.1 * self.workflow.calibration_manager.get_estimator(
+        return self.update_factor * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, self.update_interval)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -417,6 +419,8 @@ def __init__(
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
         self.feature_name = "precursor_proportion_detected"
+        self.update_factor = workflow.config["optimization"]["ms2_update_factor"]
+        self.update_interval = workflow.config["optimization"]["ms2_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -432,9 +436,9 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             The proposed new value for the search parameter.
 
         """
-        return 1.1 * self.workflow.calibration_manager.get_estimator(
+        return self.update_factor * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, self.update_interval)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -454,6 +458,8 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
         self.feature_name = "mean_isotope_intensity_correlation"
+        self.update_factor = workflow.config["optimization"]["ms1_update_factor"]
+        self.update_interval = workflow.config["optimization"]["ms1_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -470,9 +476,9 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             The proposed new value for the search parameter.
 
         """
-        return 1.1 * self.workflow.calibration_manager.get_estimator(
+        return self.update_factor * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, self.update_interval)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -492,6 +498,10 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
         self.feature_name = "precursor_proportion_detected"
+        self.update_factor = workflow.config["optimization"]["mobility_update_factor"]
+        self.update_interval = workflow.config["optimization"][
+            "mobility_update_interval"
+        ]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
@@ -508,9 +518,9 @@ def _propose_new_parameter(self, df: pd.DataFrame):
 
         """
 
-        return 1.1 * self.workflow.calibration_manager.get_estimator(
+        return self.update_factor * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, 0.99)
+        ).ci(df, self.update_interval)
 
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index efe144ee..9da051b3 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -323,32 +323,47 @@ def get_ordered_optimizers(self):
         else:
             mobility_optimizer = None
 
-        optimizers = [
-            ms2_optimizer,
-            rt_optimizer,
-            ms1_optimizer,
-            mobility_optimizer,
-        ]
-        targeted_optimizers = [
-            [
-                optimizer
+        if self.config["optimization"]["order_of_optimization"] is None:
+            optimizers = [
+                ms2_optimizer,
+                rt_optimizer,
+                ms1_optimizer,
+                mobility_optimizer,
+            ]
+            targeted_optimizers = [
+                [
+                    optimizer
+                    for optimizer in optimizers
+                    if isinstance(optimizer, optimization.TargetedOptimizer)
+                ]
+            ]
+            automatic_optimizers = [
+                [optimizer]
                 for optimizer in optimizers
-                if isinstance(optimizer, optimization.TargetedOptimizer)
+                if isinstance(optimizer, optimization.AutomaticOptimizer)
             ]
-        ]
-        automatic_optimizers = [
-            [optimizer]
-            for optimizer in optimizers
-            if isinstance(optimizer, optimization.AutomaticOptimizer)
-        ]
 
-        ordered_optimizers = (
-            targeted_optimizers + automatic_optimizers
-            if any(
-                targeted_optimizers
-            )  # This line is required so no empty list is added to the ordered_optimizers list
-            else automatic_optimizers
-        )
+            ordered_optimizers = (
+                targeted_optimizers + automatic_optimizers
+                if any(
+                    targeted_optimizers
+                )  # This line is required so no empty list is added to the ordered_optimizers list
+                else automatic_optimizers
+            )
+        else:
+            opt_mapping = {
+                "ms2": ms2_optimizer,
+                "rt": rt_optimizer,
+                "ms1": ms1_optimizer,
+                "mobility": mobility_optimizer,
+            }
+            ordered_optimizers = []
+            for optimizers_in_ordering in self.config["optimization"][
+                "order_of_optimization"
+            ]:
+                ordered_optimizers += [
+                    [opt_mapping[opt] for opt in optimizers_in_ordering]
+                ]
 
         return ordered_optimizers
 

From d086de31756e13900e466b8e3dbf790397bc5e49 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 19:26:34 +0200
Subject: [PATCH 147/250] rewrite and unit test

---
 alphadia/constants/default.yaml     | 20 +++++++-----
 alphadia/workflow/optimization.py   | 47 ++++++++++++++++-------------
 alphadia/workflow/peptidecentric.py | 14 ++++++---
 tests/unit_tests/test_workflow.py   | 46 ++++++++++++++++++++++++++++
 4 files changed, 93 insertions(+), 34 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index b6cf16d5..183ff25b 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -191,14 +191,18 @@ search_output:
 
 optimization:
   order_of_optimization: null
-  ms2_update_interval: 0.99
-  ms1_update_interval: 0.99
-  mobility_update_interval: 0.99
-  rt_update_interval: 0.99
-  ms2_update_factor: 1.1
-  ms1_update_factor: 1.1
-  mobility_update_factor: 1.1
-  rt_update_factor: 1.1
+  ms2_error:
+      update_interval: 0.99
+      update_factor: 1.1
+  ms1_error:
+      update_interval: 0.99
+      update_factor: 1.1
+  mobility_error:
+      update_interval: 0.99
+      update_factor: 1.1
+  rt_error:
+      update_interval: 0.99
+      update_factor: 1.1
 
 # configuration for the optimization manager
 # initial parameters, will be optimized
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index a70c1710..316aa5ad 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -85,6 +85,12 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
         self.num_prev_optimizations = 0
+        self.update_factor = workflow.config["optimization"][self.parameter_name][
+            "update_factor"
+        ]
+        self.update_interval = workflow.config["optimization"][self.parameter_name][
+            "update_interval"
+        ]
 
     def step(
         self,
@@ -291,6 +297,12 @@ def __init__(
         super().__init__(workflow, reporter)
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
+        self.update_factor = workflow.config["optimization"][self.parameter_name][
+            "update_factor"
+        ]
+        self.update_interval = workflow.config["optimization"][self.parameter_name][
+            "update_interval"
+        ]
         self.has_converged = False
 
     def _check_convergence(self, proposed_parameter: float):
@@ -321,10 +333,10 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             3) take the maximum of this value and the target parameter.
         This is implemented by the ci method for the estimator.
         """
-        return max(
+        return self.update_factor * max(
             self.workflow.calibration_manager.get_estimator(
                 self.estimator_group_name, self.estimator_name
-            ).ci(df, 0.95),
+            ).ci(df, self.update_interval),
             self.target_parameter,
         )
 
@@ -380,15 +392,14 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
-        self.update_factor = workflow.config["optimization"]["rt_update_factor"]
-        self.update_interval = workflow.config["optimization"]["rt_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
+            2) take the mean of the endpoints of the central interval
+                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+            3) multiply this value by self.update_factor.
         This is implemented by the ci method for the estimator.
 
         Returns
@@ -419,15 +430,14 @@ def __init__(
         self.estimator_group_name = "fragment"
         self.estimator_name = "mz"
         self.feature_name = "precursor_proportion_detected"
-        self.update_factor = workflow.config["optimization"]["ms2_update_factor"]
-        self.update_interval = workflow.config["optimization"]["ms2_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
+            2) take the mean of the endpoints of the central interval
+                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+            3) multiply this value by self.update_factor.
         This is implemented by the ci method for the estimator.
 
         Returns
@@ -458,18 +468,16 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mz"
         self.feature_name = "mean_isotope_intensity_correlation"
-        self.update_factor = workflow.config["optimization"]["ms1_update_factor"]
-        self.update_interval = workflow.config["optimization"]["ms1_update_interval"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
+            2) take the mean of the endpoints of the central interval
+                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+            3) multiply this value by self.update_factor.
         This is implemented by the ci method for the estimator.
 
-
         Returns
         -------
         float
@@ -498,17 +506,14 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "mobility"
         self.feature_name = "precursor_proportion_detected"
-        self.update_factor = workflow.config["optimization"]["mobility_update_factor"]
-        self.update_interval = workflow.config["optimization"][
-            "mobility_update_interval"
-        ]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _propose_new_parameter(self, df: pd.DataFrame):
         """See base class. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central 99% of these deviations, and
-            3) multiply this value by 1.1.
+            2) take the mean of the endpoints of the central interval
+                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+            3) multiply this value by self.update_factor.
         This is implemented by the ci method for the estimator.
 
         Returns
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 9da051b3..185c4255 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -352,17 +352,21 @@ def get_ordered_optimizers(self):
             )
         else:
             opt_mapping = {
-                "ms2": ms2_optimizer,
-                "rt": rt_optimizer,
-                "ms1": ms1_optimizer,
-                "mobility": mobility_optimizer,
+                "ms2_error": ms2_optimizer,
+                "rt_error": rt_optimizer,
+                "ms1_error": ms1_optimizer,
+                "mobility_error": mobility_optimizer,
             }
             ordered_optimizers = []
             for optimizers_in_ordering in self.config["optimization"][
                 "order_of_optimization"
             ]:
                 ordered_optimizers += [
-                    [opt_mapping[opt] for opt in optimizers_in_ordering]
+                    [
+                        opt_mapping[opt]
+                        for opt in optimizers_in_ordering
+                        if opt is not None
+                    ]
                 ]
 
         return ordered_optimizers
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 1c8ea430..0daddeda 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -452,6 +452,12 @@ class MockOptlock:
 
     workflow.optlock = MockOptlock()
 
+    class MockDIAData:
+        has_mobility = True
+        has_ms1 = True
+
+    workflow._dia_data = MockDIAData()
+
     return workflow
 
 
@@ -870,3 +876,43 @@ def test_optlock_batch_idx():
 
     assert optlock.start_idx == 0
     assert optlock.stop_idx == 2000
+
+
+def test_configurability():
+    workflow = create_workflow_instance()
+    workflow.config["optimization"].update(
+        {
+            "order_of_optimization": [
+                ["rt_error"],
+                ["ms1_error", "ms2_error"],
+                ["mobility_error"],
+            ],
+            "rt_error": {"update_interval": 0.95, "update_factor": 1.3},
+            "ms2_error": {"update_interval": 0.995, "update_factor": 1.2},
+        }
+    )
+    workflow.config["search"].update(
+        {
+            "target_rt_tolerance": -1,
+        }
+    )
+
+    ordered_optimizers = workflow.get_ordered_optimizers()
+
+    assert len(ordered_optimizers) == 3
+
+    assert ordered_optimizers[0][0].parameter_name == "rt_error"
+    assert isinstance(ordered_optimizers[0][0], optimization.AutomaticRTOptimizer)
+    assert ordered_optimizers[0][0].update_interval == 0.95
+    assert ordered_optimizers[0][0].update_factor == 1.3
+
+    assert ordered_optimizers[1][0].parameter_name == "ms1_error"
+    assert ordered_optimizers[1][0].update_interval == 0.99
+    assert isinstance(ordered_optimizers[1][0], optimization.TargetedMS1Optimizer)
+
+    assert ordered_optimizers[1][1].parameter_name == "ms2_error"
+    assert isinstance(ordered_optimizers[1][1], optimization.TargetedMS2Optimizer)
+    assert ordered_optimizers[1][1].update_interval == 0.995
+    assert ordered_optimizers[1][1].update_factor == 1.2
+
+    assert ordered_optimizers[2][0].parameter_name == "mobility_error"

From 1f93052e8970522bc71d7d336be6e652cdbf99bd Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 20:10:10 +0200
Subject: [PATCH 148/250] change defaults to normal targeted values

---
 alphadia/constants/default.yaml | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 183ff25b..b192a031 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -192,17 +192,17 @@ search_output:
 optimization:
   order_of_optimization: null
   ms2_error:
-      update_interval: 0.99
-      update_factor: 1.1
+      update_interval: 0.95
+      update_factor: 1.0
   ms1_error:
-      update_interval: 0.99
-      update_factor: 1.1
+      update_interval: 0.95
+      update_factor: 1.0
   mobility_error:
-      update_interval: 0.99
-      update_factor: 1.1
+      update_interval: 0.95
+      update_factor: 1.0
   rt_error:
-      update_interval: 0.99
-      update_factor: 1.1
+      update_interval: 0.95
+      update_factor: 1.0
 
 # configuration for the optimization manager
 # initial parameters, will be optimized

From 175655f90eba053e57e867a16a9934656255ea83 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 21 Aug 2024 23:05:29 +0200
Subject: [PATCH 149/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 0daddeda..c77392d9 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -887,7 +887,7 @@ def test_configurability():
                 ["ms1_error", "ms2_error"],
                 ["mobility_error"],
             ],
-            "rt_error": {"update_interval": 0.95, "update_factor": 1.3},
+            "rt_error": {"update_interval": 0.99, "update_factor": 1.3},
             "ms2_error": {"update_interval": 0.995, "update_factor": 1.2},
         }
     )
@@ -903,11 +903,11 @@ def test_configurability():
 
     assert ordered_optimizers[0][0].parameter_name == "rt_error"
     assert isinstance(ordered_optimizers[0][0], optimization.AutomaticRTOptimizer)
-    assert ordered_optimizers[0][0].update_interval == 0.95
+    assert ordered_optimizers[0][0].update_interval == 0.99
     assert ordered_optimizers[0][0].update_factor == 1.3
 
     assert ordered_optimizers[1][0].parameter_name == "ms1_error"
-    assert ordered_optimizers[1][0].update_interval == 0.99
+    assert ordered_optimizers[1][0].update_interval == 0.95
     assert isinstance(ordered_optimizers[1][0], optimization.TargetedMS1Optimizer)
 
     assert ordered_optimizers[1][1].parameter_name == "ms2_error"

From a44711fc1c9e218b88227dadfa5831d78a11e423 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 10:49:39 +0200
Subject: [PATCH 150/250] bug fix

---
 alphadia/workflow/peptidecentric.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 185c4255..3556a9c2 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -365,7 +365,7 @@ def get_ordered_optimizers(self):
                     [
                         opt_mapping[opt]
                         for opt in optimizers_in_ordering
-                        if opt is not None
+                        if opt_mapping[opt] is not None
                     ]
                 ]
 

From df410eb4d3828276d0d8707ab20c93a270729cfb Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 11:08:02 +0200
Subject: [PATCH 151/250] update YAML with comments

---
 alphadia/constants/default.yaml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index b192a031..d3c81d33 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -190,7 +190,15 @@ search_output:
   file_format: "tsv"
 
 optimization:
+  # The order in which to perform optimization. Should be a list of lists of parameter names
+  # Example: [['ms1_error', 'ms2_error', 'rt_error', 'mobility_error']] means that all parameters are optimized simultaneously.
+  # Example: [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]] means that the parameters are optimized sequentially in the order given.
+  # Example: [["rt_error"], ["ms1_error", "ms2_error"]] means that first rt_error is optimized, then ms1_error and ms2_error are optimized simultaneously, and mobility_error is not optimized at all.
   order_of_optimization: null
+
+  # Parameters for the update rule for each parameter:
+  #   - update_interval: the percentile interval to use (as a decimal)
+  #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
   ms2_error:
       update_interval: 0.95
       update_factor: 1.0

From 74ef6e419cd24f5c2d12b85d76d1bce324093d10 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Thu, 22 Aug 2024 12:07:39 +0200
Subject: [PATCH 152/250] fixes

---
 alphadia/libtransform.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index f55eaefc..32740505 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -281,7 +281,8 @@ def __init__(
             Path to a folder containing PeptDeep models. If not provided, the default models will be used.
 
         peptdeep_model_type : str, optional
-            Use other peptdeep models provided by the peptdeep model manager. Default is None.
+            Use other peptdeep models provided by the peptdeep model manager.
+            Default is None, which means the peptdeep default model ("generic") is being used.
             Possible values are ['generic','phospho','digly']
 
         fragment_types : List[str], optional
@@ -320,6 +321,7 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
 
         model_mgr = ModelManager(device=device)
 
+        # will load other model than default generic
         if self.peptdeep_model_type is not None:
             logging.info(f"Loading PeptDeep models of type {self.peptdeep_model_type}")
             model_mgr.load_installed_models(self.peptdeep_model_type)

From 7a63adcf9f9e4e451ea3bcf2dc91e76dcf525cb8 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 12:52:10 +0200
Subject: [PATCH 153/250] use elution groups

---
 alphadia/workflow/optimization.py |  9 +++++----
 tests/unit_tests/test_workflow.py | 20 +++++++++-----------
 2 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index afe801bc..61c0fc8d 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -405,7 +405,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / self.workflow.optlock.total_precursors
+        return len(precursors_df) / self.workflow.optlock.total_elution_groups
 
 
 class AutomaticMS2Optimizer(AutomaticOptimizer):
@@ -442,7 +442,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / self.workflow.optlock.total_precursors
+        return len(precursors_df) / self.workflow.optlock.total_elution_groups
 
 
 class AutomaticMS1Optimizer(AutomaticOptimizer):
@@ -518,7 +518,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
-        return len(precursors_df) / self.workflow.optlock.total_precursors
+        return len(precursors_df) / self.workflow.optlock.total_elution_groups
 
 
 class TargetedRTOptimizer(TargetedOptimizer):
@@ -613,6 +613,7 @@ def __init__(self, library, config: dict):
 
         self.feature_dfs = []
         self.fragment_dfs = []
+        self.total_elution_groups = 0
 
     def _get_exponential_batches(self, step):
         """Get the number of batches for a given step
@@ -655,7 +656,7 @@ def update_with_extraction(self, feature_df, fragment_df):
         self.feature_dfs += [feature_df]
         self.fragment_dfs += [fragment_df]
 
-        self.total_precursors = self.features_df.precursor_idx.nunique()
+        self.total_elution_groups = self.features_df.elution_group_idx.nunique()
 
     def update_with_fdr(self, precursor_df):
         """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization.
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 4a74f67f..7d713b95 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -850,12 +850,12 @@ def test_optlock():
 
     assert optlock.start_idx == optlock.batch_plan[0][0]
 
-    feature_df = pd.DataFrame({"precursor_idx": np.arange(0, 1000)})
-    fragment_df = pd.DataFrame({"precursor_idx": np.arange(0, 10000)})
+    feature_df = pd.DataFrame({"elution_group_idx": np.arange(0, 1000)})
+    fragment_df = pd.DataFrame({"elution_group_idx": np.arange(0, 10000)})
 
     optlock.update_with_extraction(feature_df, fragment_df)
 
-    assert optlock.total_precursors == 1000
+    assert optlock.total_elution_groups == 1000
     precursor_df = pd.DataFrame(
         {"qval": np.concatenate([np.full(100, 0.005), np.full(1000, 0.05)])}
     )
@@ -867,12 +867,12 @@ def test_optlock():
 
     assert optlock.start_idx == optlock.batch_plan[1][0]
 
-    feature_df = pd.DataFrame({"precursor_idx": np.arange(1000, 2000)})
-    fragment_df = pd.DataFrame({"precursor_idx": np.arange(10000, 20000)})
+    feature_df = pd.DataFrame({"elution_group_idx": np.arange(1000, 2000)})
+    fragment_df = pd.DataFrame({"elution_group_idx": np.arange(10000, 20000)})
 
     optlock.update_with_extraction(feature_df, fragment_df)
 
-    assert optlock.total_precursors == 2000
+    assert optlock.total_elution_groups == 2000
 
     precursor_df = pd.DataFrame(
         {"qval": np.concatenate([np.full(200, 0.005), np.full(1000, 0.05)])}
@@ -887,6 +887,8 @@ def test_optlock():
 
     assert optlock.start_idx == 0
 
+    assert optlock.total_elution_groups == 2000
+
 
 def test_optlock_batch_idx():
     library = create_test_library()
@@ -910,6 +912,7 @@ def test_optlock_batch_idx():
     optlock.update()
 
     assert optlock.start_idx == 0
+    assert optlock.stop_idx == 2000
 
 
 def test_optlock_reindex():
@@ -936,8 +939,3 @@ def test_optlock_reindex():
         ]["precursor_idx"]
         == optlock.batch_library._precursor_df.iloc[50]["precursor_idx"] ** 2
     )  # The original start index of any precursor should be equal to the square of the its original ID
-
-
-test_optlock()
-test_optlock_batch_idx()
-test_optlock_reindex()

From f5c105dee6afb857fe918c572302fab36559622e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 13:06:21 +0200
Subject: [PATCH 154/250] fix unit test

---
 alphadia/workflow/optimization.py | 1 -
 tests/unit_tests/test_workflow.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 61c0fc8d..bd3681ca 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -613,7 +613,6 @@ def __init__(self, library, config: dict):
 
         self.feature_dfs = []
         self.fragment_dfs = []
-        self.total_elution_groups = 0
 
     def _get_exponential_batches(self, step):
         """Get the number of batches for a given step
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 7d713b95..ecba1fe4 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -449,7 +449,7 @@ def create_workflow_instance():
     workflow.init_fdr_manager()
 
     class MockOptlock:
-        total_precursors = 2000
+        total_elution_groups = 2000
 
     workflow.optlock = MockOptlock()
 

From 978731afa16c397bb8233a2f21cd385651b7ad2c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 15:45:35 +0200
Subject: [PATCH 155/250] separate target and automatic update configuration

---
 alphadia/constants/default.yaml   | 24 ++++++++++++++++--------
 tests/unit_tests/test_workflow.py | 11 +++++++++--
 2 files changed, 25 insertions(+), 10 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index d3c81d33..9df3989e 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -200,17 +200,25 @@ optimization:
   #   - update_interval: the percentile interval to use (as a decimal)
   #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
   ms2_error:
-      update_interval: 0.95
-      update_factor: 1.0
+      targeted_update_interval: 0.95
+      targeted_update_factor: 1.0
+      automatic_update_interval: 0.99
+      automatic_update_factor: 1.1
   ms1_error:
-      update_interval: 0.95
-      update_factor: 1.0
+      targeted_update_interval: 0.95
+      targeted_update_factor: 1.0
+      automatic_update_interval: 0.99
+      automatic_update_factor: 1.1
   mobility_error:
-      update_interval: 0.95
-      update_factor: 1.0
+      targeted_update_interval: 0.95
+      targeted_update_factor: 1.0
+      automatic_update_interval: 0.99
+      automatic_update_factor: 1.1
   rt_error:
-      update_interval: 0.95
-      update_factor: 1.0
+      targeted_update_interval: 0.95
+      targeted_update_factor: 1.0
+      automatic_update_interval: 0.99
+      automatic_update_factor: 1.1
 
 # configuration for the optimization manager
 # initial parameters, will be optimized
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index c77392d9..b2300876 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -887,8 +887,15 @@ def test_configurability():
                 ["ms1_error", "ms2_error"],
                 ["mobility_error"],
             ],
-            "rt_error": {"update_interval": 0.99, "update_factor": 1.3},
-            "ms2_error": {"update_interval": 0.995, "update_factor": 1.2},
+            "rt_error": {
+                "automatic_update_interval": 0.99,
+                "automatic_update_factor": 1.3,
+            },
+            "ms2_error": {
+                "automatic_update_interval": 0.80,
+                "targeted_update_interval": 0.995,
+                "targeted_update_factor": 1.2,
+            },
         }
     )
     workflow.config["search"].update(

From cbda5f8f2135aceb4b163b4f28b680cd8c464695 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 15:47:40 +0200
Subject: [PATCH 156/250] move parameter proposal to base class

---
 alphadia/workflow/optimization.py | 96 +++++--------------------------
 1 file changed, 14 insertions(+), 82 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 316aa5ad..d43d2d09 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -86,10 +86,10 @@ def __init__(
         self.has_converged = False
         self.num_prev_optimizations = 0
         self.update_factor = workflow.config["optimization"][self.parameter_name][
-            "update_factor"
+            "automatic_update_factor"
         ]
         self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "update_interval"
+            "automatic_update_interval"
         ]
 
     def step(
@@ -208,9 +208,13 @@ def plot(self):
 
         plt.show()
 
-    @abstractmethod
-    def _propose_new_parameter(self, df):
-        """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The rule is specific to the parameter being optimized.
+    def _propose_new_parameter(self, df: pd.DataFrame):
+        """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The update rule is
+            1) calculate the deviation of the predicted mz values from the observed mz values,
+            2) take the mean of the endpoints of the central interval
+                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+            3) multiply this value by self.update_factor.
+        This is implemented by the ci method for the estimator.
 
         Parameters
         ----------
@@ -223,9 +227,10 @@ def _propose_new_parameter(self, df):
         float
             The proposed new value for the search parameter.
 
-
         """
-        pass
+        return self.update_factor * self.workflow.calibration_manager.get_estimator(
+            self.estimator_group_name, self.estimator_name
+        ).ci(df, self.update_interval)
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
@@ -298,10 +303,10 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.update_factor = workflow.config["optimization"][self.parameter_name][
-            "update_factor"
+            "targeted_update_factor"
         ]
         self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "update_interval"
+            "targeted_update_interval"
         ]
         self.has_converged = False
 
@@ -394,24 +399,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _propose_new_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central interval
-                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
-            3) multiply this value by self.update_factor.
-        This is implemented by the ci method for the estimator.
-
-        Returns
-        -------
-        float
-            The proposed new value for the search parameter.
-
-        """
-        return self.update_factor * self.workflow.calibration_manager.get_estimator(
-            self.estimator_group_name, self.estimator_name
-        ).ci(df, self.update_interval)
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -432,24 +419,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _propose_new_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central interval
-                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
-            3) multiply this value by self.update_factor.
-        This is implemented by the ci method for the estimator.
-
-        Returns
-        -------
-        float
-            The proposed new value for the search parameter.
-
-        """
-        return self.update_factor * self.workflow.calibration_manager.get_estimator(
-            self.estimator_group_name, self.estimator_name
-        ).ci(df, self.update_interval)
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -470,24 +439,6 @@ def __init__(
         self.feature_name = "mean_isotope_intensity_correlation"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _propose_new_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central interval
-                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
-            3) multiply this value by self.update_factor.
-        This is implemented by the ci method for the estimator.
-
-        Returns
-        -------
-        float
-            The proposed new value for the search parameter.
-
-        """
-        return self.update_factor * self.workflow.calibration_manager.get_estimator(
-            self.estimator_group_name, self.estimator_name
-        ).ci(df, self.update_interval)
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -508,25 +459,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-    def _propose_new_parameter(self, df: pd.DataFrame):
-        """See base class. The update rule is
-            1) calculate the deviation of the predicted mz values from the observed mz values,
-            2) take the mean of the endpoints of the central interval
-                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
-            3) multiply this value by self.update_factor.
-        This is implemented by the ci method for the estimator.
-
-        Returns
-        -------
-        float
-            The proposed new value for the search parameter.
-
-        """
-
-        return self.update_factor * self.workflow.calibration_manager.get_estimator(
-            self.estimator_group_name, self.estimator_name
-        ).ci(df, self.update_interval)
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):

From 56e459dbdca5a12c26146cf82bcfcc94779ae6a2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 15:51:43 +0200
Subject: [PATCH 157/250] formatting

---
 tests/unit_tests/test_workflow.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 3ffe20a1..6861d6fc 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -992,4 +992,3 @@ def test_optlock_reindex():
         ]["precursor_idx"]
         == optlock.batch_library._precursor_df.iloc[50]["precursor_idx"] ** 2
     )  # The original start index of any precursor should be equal to the square of the its original ID
-

From c1df81a45fceb4578f55eed8676888b5b4cdd962 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 16:44:31 +0200
Subject: [PATCH 158/250] change convergence so RT can decrease beyond max

---
 alphadia/workflow/optimization.py | 122 +++++++++++++++++++++++-------
 1 file changed, 94 insertions(+), 28 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index d43d2d09..fcc1e104 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -128,33 +128,7 @@ def step(
         if just_converged:
             self.has_converged = True
 
-            index_of_optimum = self.history_df[self.feature_name].idxmax()
-
-            optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
-            classifier_version_at_optimum = self.history_df["classifier_version"].loc[
-                index_of_optimum
-            ]
-            score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[
-                index_of_optimum
-            ]
-            fwhm_rt_at_optimum = self.history_df["fwhm_rt"].loc[index_of_optimum]
-            fwhm_mobility_at_optimum = self.history_df["fwhm_mobility"].loc[
-                index_of_optimum
-            ]
-
-            self.workflow.optimization_manager.fit(
-                {self.parameter_name: optimal_parameter}
-            )
-            self.workflow.optimization_manager.fit(
-                {"classifier_version": classifier_version_at_optimum}
-            )
-            self.workflow.optimization_manager.fit(
-                {"score_cutoff": score_cutoff_at_optimum}
-            )
-            self.workflow.optimization_manager.fit({"fwhm_rt": fwhm_rt_at_optimum})
-            self.workflow.optimization_manager.fit(
-                {"fwhm_mobility": fwhm_mobility_at_optimum}
-            )
+            self._update_optimization_manager()
 
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
@@ -240,7 +214,7 @@ def _check_convergence(self):
         Notes
         -----
             Because the check for an increase in feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
+            This function may be overwritten in child classes.
 
         """
 
@@ -257,6 +231,54 @@ def _check_convergence(self):
             < 1.1 * self.history_df[self.feature_name].iloc[-3]
         )
 
+    def _find_index_of_optimum(self):
+        """Finds the index of the row in the history dataframe with the optimal value of the feature used for optimization.
+
+        Returns
+        -------
+        int
+            The index of the row with the optimal value of the feature used for optimization.
+
+        Notes
+        -----
+            This method may be overwritten in child classes if the "optimal" value differs from the value that maximizes the feature used for optimization.
+
+        """
+        return self.history_df[self.feature_name].idxmax()
+
+    def _update_optimization_manager(self):
+        """Updates the optimization manager with the results of the optimization, including:
+            the optimal parameter
+            the classifier version,
+            score cutoff, and
+            FWHM values
+        at the optimal parameter.
+
+        """
+        index_of_optimum = self._find_index_of_optimum()
+
+        optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
+        classifier_version_at_optimum = self.history_df["classifier_version"].loc[
+            index_of_optimum
+        ]
+        score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[index_of_optimum]
+        fwhm_rt_at_optimum = self.history_df["fwhm_rt"].loc[index_of_optimum]
+        fwhm_mobility_at_optimum = self.history_df["fwhm_mobility"].loc[
+            index_of_optimum
+        ]
+
+        self.workflow.optimization_manager.fit({self.parameter_name: optimal_parameter})
+        self.workflow.optimization_manager.fit(
+            {"classifier_version": classifier_version_at_optimum}
+        )
+        self.workflow.optimization_manager.fit(
+            {"score_cutoff": score_cutoff_at_optimum}
+        )
+        self.workflow.optimization_manager.fit({"fwhm_rt": fwhm_rt_at_optimum})
+        self.workflow.optimization_manager.fit(
+            {"fwhm_mobility": fwhm_mobility_at_optimum}
+        )
+
     @abstractmethod
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -404,6 +426,50 @@ def _get_feature_value(
     ):
         return len(precursors_df) / self.workflow.optlock.total_precursors
 
+    def _check_convergence(self):
+        """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
+        This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
+        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+
+        Notes
+        -----
+            Because the check for an increase in feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
+
+
+        """
+        if len(self.history_df) <= 2:
+            return False
+
+        min_steps_reached = (
+            self.num_prev_optimizations
+            >= self.workflow.config["calibration"]["min_steps"]
+        )
+
+        feature_substantially_decreased = (
+            self.history_df[self.feature_name].iloc[-1]
+            < 0.8 * self.history_df[self.feature_name].iloc[-2]
+            and self.history_df[self.feature_name].iloc[-1]
+            < 0.8 * self.history_df[self.feature_name].iloc[-3]
+        )
+
+        parameter_not_substantially_changed = (
+            self.history_df["parameter"].iloc[-1]
+            / self.history_df["parameter"].iloc[-2]
+            > 0.95
+        )
+
+        return min_steps_reached and (
+            feature_substantially_decreased or parameter_not_substantially_changed
+        )
+
+    def _find_index_of_optimum(self):
+        rows_within_thresh_of_max = self.history_df[self.feature_name].loc[
+            self.history_df[self.feature_name]
+            > self.history_df[self.feature_name].max() * 0.9
+        ]
+        index_of_optimum = rows_within_thresh_of_max.index[-1]
+        return index_of_optimum
+
 
 class AutomaticMS2Optimizer(AutomaticOptimizer):
     def __init__(

From 90b2f1f5b53e6e677b06842ee4b5312036ccb9c5 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 16:50:29 +0200
Subject: [PATCH 159/250] bug fix

---
 alphadia/workflow/optimization.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 62906e27..5a1a1cd1 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -89,10 +89,10 @@ def __init__(
         self.has_converged = False
         self.num_prev_optimizations = 0
         self.update_factor = workflow.config["optimization"][self.parameter_name][
-            "update_factor"
+            "automatic_update_factor"
         ]
         self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "update_interval"
+            "automatic_update_interval"
         ]
 
     def step(
@@ -301,10 +301,10 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.target_parameter = target_parameter
         self.update_factor = workflow.config["optimization"][self.parameter_name][
-            "update_factor"
+            "targeted_update_factor"
         ]
         self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "update_interval"
+            "targeted_update_interval"
         ]
         self.has_converged = False
 

From 6f68c9fc2e23560c2c357110e1a3c95a664353fb Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 16:55:40 +0200
Subject: [PATCH 160/250] take minimum not most recent

---
 alphadia/workflow/optimization.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index fcc1e104..81d2622c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -463,11 +463,11 @@ def _check_convergence(self):
         )
 
     def _find_index_of_optimum(self):
-        rows_within_thresh_of_max = self.history_df[self.feature_name].loc[
+        rows_within_thresh_of_max = self.history_df.loc[
             self.history_df[self.feature_name]
             > self.history_df[self.feature_name].max() * 0.9
         ]
-        index_of_optimum = rows_within_thresh_of_max.index[-1]
+        index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()
         return index_of_optimum
 
 

From 843436a5eb90b98abfe1af8412c2fd46c8f245ad Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 17:23:18 +0200
Subject: [PATCH 161/250] make rt convergence criteria configurable

---
 alphadia/constants/default.yaml   |  2 ++
 alphadia/workflow/optimization.py | 12 +++++++++---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index f46219f9..629b0e3b 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -254,6 +254,8 @@ optimization:
       targeted_update_factor: 1.0
       automatic_update_interval: 0.99
       automatic_update_factor: 1.1
+      maximal_decrease: 0.8
+      minimum_proportion_of_maximum: 0.95
 
 # configuration for the optimization manager
 # initial parameters, will be optimized
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index b69fe598..29490cf3 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -422,6 +422,12 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
+        self.maximal_decrease = workflow.config["optimization"]["rt_error"][
+            "maximal_decrease"
+        ]
+        self.minimum_proportion_of_maximum = workflow.config["optimization"][
+            "rt_error"
+        ]["minimum_proportion_of_maximum"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _get_feature_value(
@@ -450,15 +456,15 @@ def _check_convergence(self):
 
         feature_substantially_decreased = (
             self.history_df[self.feature_name].iloc[-1]
-            < 0.8 * self.history_df[self.feature_name].iloc[-2]
+            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-2]
             and self.history_df[self.feature_name].iloc[-1]
-            < 0.8 * self.history_df[self.feature_name].iloc[-3]
+            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-3]
         )
 
         parameter_not_substantially_changed = (
             self.history_df["parameter"].iloc[-1]
             / self.history_df["parameter"].iloc[-2]
-            > 0.95
+            > self.minimum_proportion_of_maximum
         )
 
         return min_steps_reached and (

From dad3490365ca2a779217eee263ff0f456be573e2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 17:30:38 +0200
Subject: [PATCH 162/250] reformat and remove duplicated test from merge

---
 alphadia/workflow/optimization.py |  4 ---
 tests/unit_tests/test_workflow.py | 47 -------------------------------
 2 files changed, 51 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 1070bafe..5238099d 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -402,7 +402,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -423,7 +422,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -444,7 +442,6 @@ def __init__(
         self.feature_name = "mean_isotope_intensity_correlation"
         super().__init__(initial_parameter, workflow, reporter)
 
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
@@ -465,7 +462,6 @@ def __init__(
         self.feature_name = "precursor_proportion_detected"
         super().__init__(initial_parameter, workflow, reporter)
 
-
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
     ):
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index a55cb943..48ef5f40 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -921,53 +921,6 @@ def test_optlock_batch_idx():
     assert optlock.stop_idx == 2000
 
 
-def test_configurability():
-    workflow = create_workflow_instance()
-    workflow.config["optimization"].update(
-        {
-            "order_of_optimization": [
-                ["rt_error"],
-                ["ms1_error", "ms2_error"],
-                ["mobility_error"],
-            ],
-            "rt_error": {
-                "automatic_update_interval": 0.99,
-                "automatic_update_factor": 1.3,
-            },
-            "ms2_error": {
-                "automatic_update_interval": 0.80,
-                "targeted_update_interval": 0.995,
-                "targeted_update_factor": 1.2,
-            },
-        }
-    )
-    workflow.config["search"].update(
-        {
-            "target_rt_tolerance": -1,
-        }
-    )
-
-    ordered_optimizers = workflow.get_ordered_optimizers()
-
-    assert len(ordered_optimizers) == 3
-
-    assert ordered_optimizers[0][0].parameter_name == "rt_error"
-    assert isinstance(ordered_optimizers[0][0], optimization.AutomaticRTOptimizer)
-    assert ordered_optimizers[0][0].update_interval == 0.99
-    assert ordered_optimizers[0][0].update_factor == 1.3
-
-    assert ordered_optimizers[1][0].parameter_name == "ms1_error"
-    assert ordered_optimizers[1][0].update_interval == 0.95
-    assert isinstance(ordered_optimizers[1][0], optimization.TargetedMS1Optimizer)
-
-    assert ordered_optimizers[1][1].parameter_name == "ms2_error"
-    assert isinstance(ordered_optimizers[1][1], optimization.TargetedMS2Optimizer)
-    assert ordered_optimizers[1][1].update_interval == 0.995
-    assert ordered_optimizers[1][1].update_factor == 1.2
-
-    assert ordered_optimizers[2][0].parameter_name == "mobility_error"
-
-
 def test_optlock_reindex():
     library = create_test_library_for_indexing()
     optlock = optimization.OptimizationLock(library, TEST_OPTLOCK_CONFIG)

From 70635fc6567f6b243478ad25448528c65b18ba6a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 22 Aug 2024 17:42:40 +0200
Subject: [PATCH 163/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 48ef5f40..8e87c9fb 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -959,6 +959,8 @@ def test_configurability():
             "rt_error": {
                 "automatic_update_interval": 0.99,
                 "automatic_update_factor": 1.3,
+                "maximal_decrease": 0.8,
+                "minimum_proportion_of_maximum": 0.95,
             },
             "ms2_error": {
                 "automatic_update_interval": 0.80,

From 09149db41ac135b6d9a7c7de50914399cd080b82 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 09:33:59 +0200
Subject: [PATCH 164/250] update YAML comments

---
 alphadia/constants/default.yaml | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index f46219f9..7da7deae 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -224,11 +224,20 @@ search_output:
   # can be either "parquet" or "tsv"
   file_format: "tsv"
 
+# Configuration for the optimization of search parameters. These parameters should not normally be adjusted and are for the use of experienced users only.
 optimization:
   # The order in which to perform optimization. Should be a list of lists of parameter names
+  # Example:
+    # order_of_optimization:
+    #   - - "rt_error"
+    #   - - "ms2_error"
+    #   - - "ms1_error"
+    #   - - "mobility_error"
+  # The above means that first rt_error is optimized, then ms2_error, then ms1_error, and finally mobility_error. (Other examples are shown in Python list format rather than YAML format to save space.)
   # Example: [['ms1_error', 'ms2_error', 'rt_error', 'mobility_error']] means that all parameters are optimized simultaneously.
   # Example: [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]] means that the parameters are optimized sequentially in the order given.
   # Example: [["rt_error"], ["ms1_error", "ms2_error"]] means that first rt_error is optimized, then ms1_error and ms2_error are optimized simultaneously, and mobility_error is not optimized at all.
+  # If order_of_optimizatoin is null, first all targeted optimizers run simultaneously, then any remaining automatic optimizers run sequentially in the order [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]]
   order_of_optimization: null
 
   # Parameters for the update rule for each parameter:

From f63de4f0683a33f4fff661f8f22d66fe6378f51c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 09:55:32 +0200
Subject: [PATCH 165/250] rename update_interval to update_percentile_range

---
 alphadia/constants/default.yaml   | 18 +++++++++---------
 alphadia/workflow/optimization.py | 18 +++++++++---------
 tests/unit_tests/test_workflow.py | 12 ++++++------
 3 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index f46219f9..0f210c64 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -232,27 +232,27 @@ optimization:
   order_of_optimization: null
 
   # Parameters for the update rule for each parameter:
-  #   - update_interval: the percentile interval to use (as a decimal)
+  #   - update_percentile_range: the percentile interval to use (as a decimal)
   #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
   ms2_error:
-      targeted_update_interval: 0.95
+      targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
-      automatic_update_interval: 0.99
+      automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
   ms1_error:
-      targeted_update_interval: 0.95
+      targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
-      automatic_update_interval: 0.99
+      automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
   mobility_error:
-      targeted_update_interval: 0.95
+      targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
-      automatic_update_interval: 0.99
+      automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
   rt_error:
-      targeted_update_interval: 0.95
+      targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
-      automatic_update_interval: 0.99
+      automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
 
 # configuration for the optimization manager
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 5238099d..01157e06 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -91,9 +91,9 @@ def __init__(
         self.update_factor = workflow.config["optimization"][self.parameter_name][
             "automatic_update_factor"
         ]
-        self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "automatic_update_interval"
-        ]
+        self.update_percentile_range = workflow.config["optimization"][
+            self.parameter_name
+        ]["automatic_update_percentile_range"]
 
     def step(
         self,
@@ -215,7 +215,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """This method specifies the rule according to which the search parameter is updated between rounds of optimization. The update rule is
             1) calculate the deviation of the predicted mz values from the observed mz values,
             2) take the mean of the endpoints of the central interval
-                (determined by the self.update_interval attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
+                (determined by the self.update_percentile_range attribute, which determines the percentile taken expressed as a decimal) of these deviations, and
             3) multiply this value by self.update_factor.
         This is implemented by the ci method for the estimator.
 
@@ -233,7 +233,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         """
         return self.update_factor * self.workflow.calibration_manager.get_estimator(
             self.estimator_group_name, self.estimator_name
-        ).ci(df, self.update_interval)
+        ).ci(df, self.update_percentile_range)
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
@@ -308,9 +308,9 @@ def __init__(
         self.update_factor = workflow.config["optimization"][self.parameter_name][
             "targeted_update_factor"
         ]
-        self.update_interval = workflow.config["optimization"][self.parameter_name][
-            "targeted_update_interval"
-        ]
+        self.update_percentile_range = workflow.config["optimization"][
+            self.parameter_name
+        ]["targeted_update_percentile_range"]
         self.has_converged = False
 
     def _check_convergence(self, proposed_parameter: float):
@@ -344,7 +344,7 @@ def _propose_new_parameter(self, df: pd.DataFrame):
         return self.update_factor * max(
             self.workflow.calibration_manager.get_estimator(
                 self.estimator_group_name, self.estimator_name
-            ).ci(df, self.update_interval),
+            ).ci(df, self.update_percentile_range),
             self.target_parameter,
         )
 
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 48ef5f40..ed4e7f74 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -957,12 +957,12 @@ def test_configurability():
                 ["mobility_error"],
             ],
             "rt_error": {
-                "automatic_update_interval": 0.99,
+                "automatic_update_percentile_range": 0.99,
                 "automatic_update_factor": 1.3,
             },
             "ms2_error": {
-                "automatic_update_interval": 0.80,
-                "targeted_update_interval": 0.995,
+                "automatic_update_percentile_range": 0.80,
+                "targeted_update_percentile_range": 0.995,
                 "targeted_update_factor": 1.2,
             },
         }
@@ -979,16 +979,16 @@ def test_configurability():
 
     assert ordered_optimizers[0][0].parameter_name == "rt_error"
     assert isinstance(ordered_optimizers[0][0], optimization.AutomaticRTOptimizer)
-    assert ordered_optimizers[0][0].update_interval == 0.99
+    assert ordered_optimizers[0][0].update_percentile_range == 0.99
     assert ordered_optimizers[0][0].update_factor == 1.3
 
     assert ordered_optimizers[1][0].parameter_name == "ms1_error"
-    assert ordered_optimizers[1][0].update_interval == 0.95
+    assert ordered_optimizers[1][0].update_percentile_range == 0.95
     assert isinstance(ordered_optimizers[1][0], optimization.TargetedMS1Optimizer)
 
     assert ordered_optimizers[1][1].parameter_name == "ms2_error"
     assert isinstance(ordered_optimizers[1][1], optimization.TargetedMS2Optimizer)
-    assert ordered_optimizers[1][1].update_interval == 0.995
+    assert ordered_optimizers[1][1].update_percentile_range == 0.995
     assert ordered_optimizers[1][1].update_factor == 1.2
 
     assert ordered_optimizers[2][0].parameter_name == "mobility_error"

From 0d6eb01f9a8c4a64dc5e87157b962b84de9696a9 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 12:00:35 +0200
Subject: [PATCH 166/250] improve timing manager to cope with failed searches
 and update unit test to test for good behaviour in this case

---
 alphadia/outputtransform.py              |  2 +-
 tests/unit_tests/test_outputtransform.py | 21 +++++++++++++++------
 2 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index f075a545..2620169b 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -966,7 +966,7 @@ def _build_run_internal_df(
     raw_name = os.path.basename(folder_path)
 
     internal_dict = {
-        "run": raw_name,
+        "run": [raw_name],
     }
 
     if os.path.exists(timing_manager_path):
diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index 0fd5187e..e98e1afc 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -63,7 +63,7 @@ def test_output_transform():
     psm_base_df = mock_precursor_df(n_precursor=100)
     fragment_base_df = mock_fragment_df(n_precursor=200)
 
-    for raw_folder in raw_folders:
+    for i, raw_folder in enumerate(raw_folders):
         os.makedirs(raw_folder, exist_ok=True)
 
         psm_df = psm_base_df.sample(50)
@@ -82,17 +82,23 @@ def test_output_transform():
                 peptidecentric.PeptideCentricWorkflow.OPTIMIZATION_MANAGER_PATH,
             ),
         )
-        optimization_manager.fit({"ms2_error": 6})
-        optimization_manager.save()
+
         timing_manager = manager.TimingManager(
             path=os.path.join(
                 raw_folder, peptidecentric.PeptideCentricWorkflow.TIMING_MANAGER_PATH
             )
         )
 
-        timing_manager.set_start_time("extraction")
-        timing_manager.set_end_time("extraction")
-        timing_manager.save()
+        if (
+            i == 2
+        ):  # simulate the case that the search fails such that the optimization and timing managers are not saved
+            pass
+        else:
+            optimization_manager.fit({"ms2_error": 6})
+            optimization_manager.save()
+            timing_manager.set_start_time("extraction")
+            timing_manager.set_end_time("extraction")
+            timing_manager.save()
 
     output = outputtransform.SearchPlanOutput(config, temp_folder)
     _ = output.build_precursor_table(raw_folders, save=True)
@@ -157,3 +163,6 @@ def test_output_transform():
             assert np.corrcoef(protein_df[i], protein_df[j])[0, 0] > 0.5
 
     shutil.rmtree(temp_folder)
+
+
+test_output_transform()

From 3443eefdb11e90b216abbf186ad7eeac25cdb571 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 12:36:45 +0200
Subject: [PATCH 167/250] review comments

---
 alphadia/workflow/optimization.py | 42 +++++++++++++++++--------------
 1 file changed, 23 insertions(+), 19 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 29490cf3..e7f5a414 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -250,34 +250,38 @@ def _find_index_of_optimum(self):
         return self.history_df[self.feature_name].idxmax()
 
     def _update_optimization_manager(self):
-        """Updates the optimization manager with the results of the optimization, including:
-            the optimal parameter
+        """Updates the optimization manager with the results of the optimization, namely:
             the classifier version,
-            score cutoff, and
-            FWHM values
+            the optimal parameter,
+            score cutoff,
+            FWHM_RT,
+            and FWHM_mobility
         at the optimal parameter.
 
         """
         index_of_optimum = self._find_index_of_optimum()
 
-        optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
         classifier_version_at_optimum = self.history_df["classifier_version"].loc[
             index_of_optimum
         ]
-        score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[index_of_optimum]
-        fwhm_rt_at_optimum = self.history_df["fwhm_rt"].loc[index_of_optimum]
-        fwhm_mobility_at_optimum = self.history_df["fwhm_mobility"].loc[
-            index_of_optimum
-        ]
-
-        self.workflow.optimization_manager.fit({self.parameter_name: optimal_parameter})
         self.workflow.optimization_manager.fit(
             {"classifier_version": classifier_version_at_optimum}
         )
+
+        optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
+        self.workflow.optimization_manager.fit({self.parameter_name: optimal_parameter})
+
+        score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[index_of_optimum]
         self.workflow.optimization_manager.fit(
             {"score_cutoff": score_cutoff_at_optimum}
         )
+
+        fwhm_rt_at_optimum = self.history_df["fwhm_rt"].loc[index_of_optimum]
         self.workflow.optimization_manager.fit({"fwhm_rt": fwhm_rt_at_optimum})
+
+        fwhm_mobility_at_optimum = self.history_df["fwhm_mobility"].loc[
+            index_of_optimum
+        ]
         self.workflow.optimization_manager.fit(
             {"fwhm_mobility": fwhm_mobility_at_optimum}
         )
@@ -446,7 +450,7 @@ def _check_convergence(self):
 
 
         """
-        if len(self.history_df) <= 2:
+        if len(self.history_df) < 3:
             return False
 
         min_steps_reached = (
@@ -454,16 +458,16 @@ def _check_convergence(self):
             >= self.workflow.config["calibration"]["min_steps"]
         )
 
+        feature_history = self.history_df[self.feature_name]
         feature_substantially_decreased = (
-            self.history_df[self.feature_name].iloc[-1]
-            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-3]
+            feature_history.iloc[-1] < self.maximal_decrease * feature_history.iloc[-2]
+            and feature_history.iloc[-1]
+            < self.maximal_decrease * feature_history.iloc[-3]
         )
 
+        parameter_history = self.history_df["parameter"]
         parameter_not_substantially_changed = (
-            self.history_df["parameter"].iloc[-1]
-            / self.history_df["parameter"].iloc[-2]
+            parameter_history.iloc[-1] / parameter_history.iloc[-2]
             > self.minimum_proportion_of_maximum
         )
 

From a9958d0c21f91d667fb64ddef8236af02760c70d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 13:49:06 +0200
Subject: [PATCH 168/250] make convergence configurable for all parameters

---
 alphadia/constants/default.yaml   |  14 ++++
 alphadia/workflow/optimization.py | 129 ++++++++++++++++--------------
 2 files changed, 83 insertions(+), 60 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 629b0e3b..48dd339a 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -234,26 +234,40 @@ optimization:
   # Parameters for the update rule for each parameter:
   #   - update_interval: the percentile interval to use (as a decimal)
   #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
+  #   - favour_narrower_parameter: if True, the optimization will not take the value that maximizes the feature used for optimization, but instead the smallest value compatible with the minimum_proportion_of_maximum value.
+  #       This setting can be useful for optimizing parameters for which many parameter values have similar feature values and therefore favouring narrower parameters helps to overcome noise.
+  #   - maximal_decrease: the maximal decrease of the parameter value before stopping optimization (only relevant if favour_narrower_parameter is True)
+  #   - minimum_proportion_of_maximum: the minimum proportion of the maximum value of the parameter that the designated optimum should have (only relevant if favour_narrower_parameter is True)
   ms2_error:
       targeted_update_interval: 0.95
       targeted_update_factor: 1.0
       automatic_update_interval: 0.99
       automatic_update_factor: 1.1
+      favour_narrower_parameter: False
+      maximal_decrease: 0.8
+      minimum_proportion_of_maximum: 0.95
   ms1_error:
       targeted_update_interval: 0.95
       targeted_update_factor: 1.0
       automatic_update_interval: 0.99
       automatic_update_factor: 1.1
+      favour_narrower_parameter: False
+      maximal_decrease: 0.8
+      minimum_proportion_of_maximum: 0.95
   mobility_error:
       targeted_update_interval: 0.95
       targeted_update_factor: 1.0
       automatic_update_interval: 0.99
       automatic_update_factor: 1.1
+      favour_narrower_parameter: False
+      maximal_decrease: 0.8
+      minimum_proportion_of_maximum: 0.95
   rt_error:
       targeted_update_interval: 0.95
       targeted_update_factor: 1.0
       automatic_update_interval: 0.99
       automatic_update_factor: 1.1
+      favour_narrower_parameter: True
       maximal_decrease: 0.8
       minimum_proportion_of_maximum: 0.95
 
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 29490cf3..2798cb02 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -94,6 +94,15 @@ def __init__(
         self.update_interval = workflow.config["optimization"][self.parameter_name][
             "automatic_update_interval"
         ]
+        self.favour_narrower_parameter = workflow.config["optimization"][
+            self.parameter_name
+        ]["favour_narrower_parameter"]
+        self.maximal_decrease = workflow.config["optimization"][self.parameter_name][
+            "maximal_decrease"
+        ]
+        self.minimum_proportion_of_maximum = workflow.config["optimization"][
+            self.parameter_name
+        ]["minimum_proportion_of_maximum"]
 
     def step(
         self,
@@ -211,8 +220,12 @@ def _propose_new_parameter(self, df: pd.DataFrame):
 
     def _check_convergence(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+        If self.favour_narrower_parameter is False:
+            1) This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
+            2) If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
+        If self.favour_narrower_parameter is True:
+            1) This function checks if the previous rounds of optimization have led to a meaningful disimprovement in the feature value or if the parameter has not changed substantially.
+            2) If not, it continues optimization and appends the proposed new parameter to the list of parameters. If so, it stops optimization and sets the optimal parameter attribute.
 
         Notes
         -----
@@ -220,22 +233,52 @@ def _check_convergence(self):
             This function may be overwritten in child classes.
 
         """
+        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+            if len(self.history_df) <= 2:
+                return False
 
-        min_steps_reached = (
-            self.num_prev_optimizations
-            >= self.workflow.config["calibration"]["min_steps"]
-        )
-        return (
-            min_steps_reached
-            and len(self.history_df) > 2
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < 1.1 * self.history_df[self.feature_name].iloc[-3]
-        )
+            min_steps_reached = (
+                self.num_prev_optimizations
+                >= self.workflow.config["calibration"]["min_steps"]
+            )
+
+            feature_substantially_decreased = (
+                self.history_df[self.feature_name].iloc[-1]
+                < self.maximal_decrease * self.history_df[self.feature_name].iloc[-2]
+                and self.history_df[self.feature_name].iloc[-1]
+                < self.maximal_decrease * self.history_df[self.feature_name].iloc[-3]
+            )
+
+            parameter_not_substantially_changed = (
+                self.history_df["parameter"].iloc[-1]
+                / self.history_df["parameter"].iloc[-2]
+                > self.minimum_proportion_of_maximum
+            )
+
+            return min_steps_reached and (
+                feature_substantially_decreased or parameter_not_substantially_changed
+            )
+
+        else:
+            min_steps_reached = (
+                self.num_prev_optimizations
+                >= self.workflow.config["calibration"]["min_steps"]
+            )
+            return (
+                min_steps_reached
+                and len(self.history_df) > 2
+                and self.history_df[self.feature_name].iloc[-1]
+                < 1.1 * self.history_df[self.feature_name].iloc[-2]
+                and self.history_df[self.feature_name].iloc[-1]
+                < 1.1 * self.history_df[self.feature_name].iloc[-3]
+            )
 
     def _find_index_of_optimum(self):
         """Finds the index of the row in the history dataframe with the optimal value of the feature used for optimization.
+        if self.favour_narrower_parameter is False:
+            The index at optimum is the index of the parameter value that maximizes the feature.
+        if self.favour_narrower_parameter is True:
+            The index at optimum is the index of the minimal parameter value whose feature value is at least self.minimum_proportion_of_maximum of the maximum value of the feature.
 
         Returns
         -------
@@ -244,10 +287,20 @@ def _find_index_of_optimum(self):
 
         Notes
         -----
-            This method may be overwritten in child classes if the "optimal" value differs from the value that maximizes the feature used for optimization.
+            This method may be overwritten in child classes.
 
         """
-        return self.history_df[self.feature_name].idxmax()
+
+        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+            rows_within_thresh_of_max = self.history_df.loc[
+                self.history_df[self.feature_name]
+                > self.history_df[self.feature_name].max() * 0.9
+            ]
+            index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()
+            return index_of_optimum
+
+        else:
+            return self.history_df[self.feature_name].idxmax()
 
     def _update_optimization_manager(self):
         """Updates the optimization manager with the results of the optimization, including:
@@ -435,50 +488,6 @@ def _get_feature_value(
     ):
         return len(precursors_df) / self.workflow.optlock.total_elution_groups
 
-    def _check_convergence(self):
-        """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
-        This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
-        If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-
-        Notes
-        -----
-            Because the check for an increase in feature value requires two previous rounds, the function will also initialize for another round of optimization if there have been fewer than 3 rounds.
-
-
-        """
-        if len(self.history_df) <= 2:
-            return False
-
-        min_steps_reached = (
-            self.num_prev_optimizations
-            >= self.workflow.config["calibration"]["min_steps"]
-        )
-
-        feature_substantially_decreased = (
-            self.history_df[self.feature_name].iloc[-1]
-            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-2]
-            and self.history_df[self.feature_name].iloc[-1]
-            < self.maximal_decrease * self.history_df[self.feature_name].iloc[-3]
-        )
-
-        parameter_not_substantially_changed = (
-            self.history_df["parameter"].iloc[-1]
-            / self.history_df["parameter"].iloc[-2]
-            > self.minimum_proportion_of_maximum
-        )
-
-        return min_steps_reached and (
-            feature_substantially_decreased or parameter_not_substantially_changed
-        )
-
-    def _find_index_of_optimum(self):
-        rows_within_thresh_of_max = self.history_df.loc[
-            self.history_df[self.feature_name]
-            > self.history_df[self.feature_name].max() * 0.9
-        ]
-        index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()
-        return index_of_optimum
-
 
 class AutomaticMS2Optimizer(AutomaticOptimizer):
     def __init__(

From 9496cf8eb78603214918f52af7b9ff2fe112903e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 13:54:36 +0200
Subject: [PATCH 169/250] fix typographical error

---
 alphadia/workflow/optimization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index bfd326ca..89282841 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -234,7 +234,7 @@ def _check_convergence(self):
 
         """
         if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
-            if len(self.history_df) <= 2:
+            if len(self.history_df) < 3:
                 return False
 
             min_steps_reached = (

From 01aec2f121d4a1727711137ea3a16ecfa4358a73 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 15:46:25 +0200
Subject: [PATCH 170/250] fix unit test and only assign relevant attributes

---
 alphadia/workflow/optimization.py | 14 ++++++++------
 tests/unit_tests/test_workflow.py |  2 ++
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 89282841..b1707634 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -97,12 +97,14 @@ def __init__(
         self.favour_narrower_parameter = workflow.config["optimization"][
             self.parameter_name
         ]["favour_narrower_parameter"]
-        self.maximal_decrease = workflow.config["optimization"][self.parameter_name][
-            "maximal_decrease"
-        ]
-        self.minimum_proportion_of_maximum = workflow.config["optimization"][
-            self.parameter_name
-        ]["minimum_proportion_of_maximum"]
+
+        if self.favour_narrower_parameter:
+            self.maximal_decrease = workflow.config["optimization"][
+                self.parameter_name
+            ]["maximal_decrease"]
+            self.minimum_proportion_of_maximum = workflow.config["optimization"][
+                self.parameter_name
+            ]["minimum_proportion_of_maximum"]
 
     def step(
         self,
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 8e87c9fb..3a3e32ea 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -959,6 +959,7 @@ def test_configurability():
             "rt_error": {
                 "automatic_update_interval": 0.99,
                 "automatic_update_factor": 1.3,
+                "favour_narrower_parameter": True,
                 "maximal_decrease": 0.8,
                 "minimum_proportion_of_maximum": 0.95,
             },
@@ -966,6 +967,7 @@ def test_configurability():
                 "automatic_update_interval": 0.80,
                 "targeted_update_interval": 0.995,
                 "targeted_update_factor": 1.2,
+                "favour_narrower_parameter": False,
             },
         }
     )

From f5ae652e09869083f57298e853218e0f9fce00b1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 16:14:50 +0200
Subject: [PATCH 171/250] formatting

---
 alphadia/workflow/optimization.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index d7452df3..352c4349 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -107,7 +107,6 @@ def __init__(
                 self.parameter_name
             ]["minimum_proportion_of_maximum"]
 
-
     def step(
         self,
         precursors_df: pd.DataFrame,

From 949b276a50a853fa4a029fe10e2fe73c148ce301 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 23 Aug 2024 16:38:33 +0200
Subject: [PATCH 172/250] make fragment calibration configurable

---
 alphadia/constants/default.yaml     | 6 ++++++
 alphadia/workflow/peptidecentric.py | 4 ++--
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 2d97e087..ca72e4bb 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -119,6 +119,12 @@ calibration:
   # TODO: remove this parameter
   norm_rt_mode: 'linear'
 
+  # the maximum number of fragments with correlation scores exceeding correlation_threshold to use for calibrating fragment mz (i.e. ms2)
+  max_fragments: 5000
+
+  # the correlation threshold for fragments used to calibrate fragment mz (i.e. ms2)
+  min_correlation: 0.7
+
 search_initial:
   # Number of peak groups identified in the convolution score to classify with target decoy comeptition
   initial_num_candidates: 1
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 238244d8..b203ea43 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -549,8 +549,8 @@ def filter_dfs(self, precursor_df, fragments_df):
             by="correlation", ascending=False
         )
         # Determine the number of fragments to keep
-        min_fragments, max_fragments = 500, 5000
-        min_correlation = 0.7
+        min_fragments, max_fragments = 500, self.config["calibration"]["max_fragments"]
+        min_correlation = self.config["calibration"]["min_correlation"]
 
         high_corr_count = (fragments_df_filtered["correlation"] > min_correlation).sum()
         stop_rank = min(max(high_corr_count, min_fragments), max_fragments)

From 96a1aeb1bb1ea0fbb96237563e7bf66d2fb1fcb2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 26 Aug 2024 10:04:09 +0200
Subject: [PATCH 173/250] refactor search parameter optimization

---
 alphadia/workflow/peptidecentric.py | 124 ++++++++++++++++------------
 1 file changed, 72 insertions(+), 52 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index b203ea43..95f38117 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -416,77 +416,35 @@ def search_parameter_optimization(self):
                         optimizer.plot()
 
                     break
-                log_string(
-                    f"=== Step {current_step}, extracting elution groups {self.optlock.start_idx} to {self.optlock.stop_idx} ===",
-                    verbosity="progress",
-                )
 
-                feature_df, fragment_df = self.extract_batch(
-                    self.optlock.batch_library.precursor_df,
-                    self.optlock.batch_library.fragment_df,
-                )
-                self.optlock.update_with_extraction(feature_df, fragment_df)
+                log_string(f"Starting optimization step {current_step}.")
 
-                log_string(
-                    f"=== Step {current_step}, extracted {len(self.optlock.features_df)} precursors and {len(self.optlock.fragments_df)} fragments ===",
-                    verbosity="progress",
-                )
-
-                precursor_df = self.fdr_correction(
-                    self.optlock.features_df,
-                    self.optlock.fragments_df,
-                    self.optimization_manager.classifier_version,
-                )
-
-                self.optlock.update_with_fdr(precursor_df)
-
-                log_string(
-                    f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
-                )
-
-                self.log_precursor_df(precursor_df)
+                precursor_df = self.process_batch()
 
                 if not self.optlock.has_target_num_precursors:
                     self.optlock.update()
+
                     if self.optlock.previously_calibrated:
-                        self.optlock.update_with_calibration(self.calibration_manager)
+                        self.optlock.update_with_calibration(
+                            self.calibration_manager
+                        )  # This is needed so that the addition to the batch libary has the most recent calibration
+
                 else:
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
                         precursor_df, self.optlock.fragments_df
                     )
 
                     self.optlock.update()
-
                     self.recalibration(precursor_df_filtered, fragments_df_filtered)
-
                     self.optlock.update_with_calibration(self.calibration_manager)
 
                     if not self.optlock.previously_calibrated:  # Updates classifier but does not optimize the first time the target is reached.
                         # Optimization is more stable when done with calibrated values.
-                        self.optlock.previously_calibrated = True
-                        self.optimization_manager.fit(
-                            {"classifier_version": self.fdr_manager.current_version}
-                        )
-                        log_string(
-                            "Required number of precursors found. Starting search parameter optimization.",
-                            verbosity="progress",
-                        )
+                        self.initiate_search_parameter_optimization()
                         continue
 
-                    log_string(
-                        "=== checking if optimization conditions were reached ===",
-                    )
-
-                    for optimizer in optimizers:
-                        optimizer.step(precursor_df_filtered, fragments_df_filtered)
-
-                    log_string(
-                        "==============================================",
-                    )
-
-                    log_string(
-                        f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
-                        verbosity="progress",
+                    self.step_all_optimizers(
+                        optimizers, precursor_df_filtered, fragments_df_filtered
                     )
 
             else:
@@ -514,6 +472,68 @@ def search_parameter_optimization(self):
 
         self.save_managers()
 
+    def process_batch(self):
+        self.reporter.log_string(
+            f"=== Extracting elution groups {self.optlock.start_idx} to {self.optlock.stop_idx} ===",
+            verbosity="progress",
+        )
+
+        feature_df, fragment_df = self.extract_batch(
+            self.optlock.batch_library.precursor_df,
+            self.optlock.batch_library.fragment_df,
+        )
+        self.optlock.update_with_extraction(feature_df, fragment_df)
+
+        self.reporter.log_string(
+            f"=== Extracted {len(self.optlock.features_df)} precursors and {len(self.optlock.fragments_df)} fragments ===",
+            verbosity="progress",
+        )
+
+        precursor_df = self.fdr_correction(
+            self.optlock.features_df,
+            self.optlock.fragments_df,
+            self.optimization_manager.classifier_version,
+        )
+
+        self.optlock.update_with_fdr(precursor_df)
+
+        self.reporter.log_string(
+            f"=== FDR correction performed with classifier version {self.optimization_manager.classifier_version} ===",
+        )
+
+        self.log_precursor_df(precursor_df)
+
+        return precursor_df
+
+    def initiate_search_parameter_optimization(self):
+        self.optlock.previously_calibrated = True
+        self.optimization_manager.fit(
+            {"classifier_version": self.fdr_manager.current_version}
+        )
+        self.reporter.log_string(
+            "Required number of precursors found. Starting search parameter optimization.",
+            verbosity="progress",
+        )
+
+    def step_all_optimizers(
+        self, optimizers, precursor_df_filtered, fragments_df_filtered
+    ):
+        self.reporter.log_string(
+            "=== checking if optimization conditions were reached ===",
+        )
+
+        for optimizer in optimizers:
+            optimizer.step(precursor_df_filtered, fragments_df_filtered)
+
+        self.reporter.log_string(
+            "==============================================",
+        )
+
+        self.reporter.log_string(
+            f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
+            verbosity="progress",
+        )
+
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
 

From 604409b1102a22e1f8304c0fb0cf0e402c91184d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 26 Aug 2024 15:16:30 +0200
Subject: [PATCH 174/250] review comments

---
 alphadia/workflow/optimization.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 352c4349..68acdc6a 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -236,10 +236,10 @@ def _check_convergence(self):
             This function may be overwritten in child classes.
 
         """
-        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
-            if len(self.history_df) < 3:
-                return False
+        if len(self.history_df) < 3:
+            return False
 
+        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             min_steps_reached = (
                 self.num_prev_optimizations
                 >= self.workflow.config["calibration"]["min_steps"]
@@ -273,7 +273,6 @@ def _check_convergence(self):
 
             return (
                 min_steps_reached
-                and len(self.history_df) > 2
                 and feature_history.iloc[-1] < 1.1 * feature_history.iloc[-2]
                 and feature_history.iloc[-1] < 1.1 * feature_history.iloc[-3]
             )

From 57db61f4e7cef62bb7c34bd4254c03a99076b02a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 26 Aug 2024 15:17:59 +0200
Subject: [PATCH 175/250] review comments

---
 alphadia/workflow/optimization.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 4a5d91a4..e787ff69 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -261,6 +261,9 @@ def _update_optimization_manager(self):
         """
         index_of_optimum = self._find_index_of_optimum()
 
+        optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
+        self.workflow.optimization_manager.fit({self.parameter_name: optimal_parameter})
+
         classifier_version_at_optimum = self.history_df["classifier_version"].loc[
             index_of_optimum
         ]
@@ -268,9 +271,6 @@ def _update_optimization_manager(self):
             {"classifier_version": classifier_version_at_optimum}
         )
 
-        optimal_parameter = self.history_df["parameter"].loc[index_of_optimum]
-        self.workflow.optimization_manager.fit({self.parameter_name: optimal_parameter})
-
         score_cutoff_at_optimum = self.history_df["score_cutoff"].loc[index_of_optimum]
         self.workflow.optimization_manager.fit(
             {"score_cutoff": score_cutoff_at_optimum}

From 13c205829b20994f87da4dfe13428becc3ea70f8 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Mon, 26 Aug 2024 15:19:57 +0200
Subject: [PATCH 176/250] add TODO

---
 alphadia/workflow/peptidecentric.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index b203ea43..deb2b86e 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -549,7 +549,10 @@ def filter_dfs(self, precursor_df, fragments_df):
             by="correlation", ascending=False
         )
         # Determine the number of fragments to keep
-        min_fragments, max_fragments = 500, self.config["calibration"]["max_fragments"]
+        min_fragments, max_fragments = (
+            500,
+            self.config["calibration"]["max_fragments"],
+        )  # TODO remove min_fragments as it seems to have no effect
         min_correlation = self.config["calibration"]["min_correlation"]
 
         high_corr_count = (fragments_df_filtered["correlation"] > min_correlation).sum()

From bb1534a908aaefa553962a2affe2def587d8dde4 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 27 Aug 2024 12:08:34 +0200
Subject: [PATCH 177/250] updated documentation

---
 docs/_static/images/methods_optimization.png  | Bin 0 -> 857727 bytes
 .../methods_optimization_calibration.png      | Bin 0 -> 254070 bytes
 docs/methods/calibration.md                   | 102 --------------
 docs/methods/search_parameter_optimization.md | 131 ++++++++++++++++++
 4 files changed, 131 insertions(+), 102 deletions(-)
 create mode 100644 docs/_static/images/methods_optimization.png
 create mode 100644 docs/_static/images/methods_optimization_calibration.png
 delete mode 100644 docs/methods/calibration.md
 create mode 100644 docs/methods/search_parameter_optimization.md

diff --git a/docs/_static/images/methods_optimization.png b/docs/_static/images/methods_optimization.png
new file mode 100644
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HcmV?d00001

diff --git a/docs/methods/calibration.md b/docs/methods/calibration.md
deleted file mode 100644
index 3363735f..00000000
--- a/docs/methods/calibration.md
+++ /dev/null
@@ -1,102 +0,0 @@
-# Calibration
-
-## Calibration and optimization
-The first step of every alphaDIA run entails the calibration of the empirical or fully predicted spectral library to the observed values. This step has two main purposes: 1. Removing the systematic deviation of observed and library values and 2. Optimizing the size of the search space to reflect the expected deviation from the library values. For DIA search this means calibration and optimization of retention time, ion mobility, precursor m/z and fragment m/z.
-
-AlphaDIA uses a iterative calibration schedule consisting of epochs. Every epoch, a defined number of precursors need to be identified at 1% FDR before calibration and optimization is performed and the next epoch is started (dashed green line). The number of precursors for the `n`'th epoch is derived from the parameter `recalibration_target` (default: 200) as `n*recalibration_target`. To reach this target library entries are searched, scored and aggregated, following an exponential batch plan (blue).
-
-<img src="../_static/images/methods_calibration.png" width="100%" height="auto">
-
-If one epoch has accumulated enough confident target precursors, they are calibrated to the observed values using locally estimated scatterplot smoothing (LOESS) regression. For calibration of fragment m/z values, a fixed number of up to 5000 (but at least 500) of the best fragments according to their  XIC correlation are used. For precursor calibration all precursors passing 1% FDR are used.
-For optimizing the search space, tolerances like retention time, ion mobility and m/z ratios need to be reduced. The goal is to cover the expected spectrum space but reduce it as much as possible to accelerate search and gain statsitical power. Starting with initial tolerances defined in `search_initial`.  At the end of every epoch the 95% deviation after calibration is adopted as new tolerance until the target tolerances defined in the `search` section are reached.
-
-The search is finished as soon as the minimum numberof epochs `min_epochs` has passed and all tolerances have been to the target tolerances defined in `search`.
-## Configuring the calibration schedule
-
-```yaml
-calibration:
-  # minimum number of times (epochs) the updated calibration target has to been passed
-  min_epochs: 3
-
-  # Number of precursors searched and scored per batch
-  batch_size: 8000
-
-  # recalibration target for the first epoch. For subsequent epochs, the target will increase by this amount.
-  recalibration_target: 200
-
-search_initial:
-  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
-  initial_num_candidates: 1
-
-  # initial ms1 tolerance in ppm
-  initial_ms1_tolerance: 30
-
-  # initial ms2 tolerance in ppm
-  initial_ms2_tolerance: 30
-
-  # initial ion mobility tolerance in 1/K_0
-  initial_mobility_tolerance: 0.08
-
-  # initial retention time tolerance in seconds
-  initial_rt_tolerance: 240
-```
-
-## Calibration using LOESS
-Individual properties like the retention time deviate from their library values and need to be calibrated (a). As a nonlinear but stable method, locally estimated scatterplot smoothing (LOESS) using both density and uniformly distributed kernels is used. (b) A collection of polynomial kernels is fitted to uniformly distributed subregions of the data. These consist of first and second degree polynomials basis functions of the calibratable property. (c) The individual functions are combined and smoothed using tricubic weights. (d) Combining the kernels with their weighting functions allows to approximate the systematic deviation of the data locally. (e), The sum of the weighted kernels can then be used for continuous approximation and calibration of retention times. The architecture is built on the scikit-learn package and can be configured to use different hyperparameters and arbitrary predictors for calibration.
-
-<img src="../_static/images/methods_loess.png" width="100%" height="auto">
-
-## Configuring the LOESS model
-
-The type of model, the hyperparameters and the columns used as input and target for calibration can be set in the `calibration_manager` section of the configuration file.
-
-```yaml
-calibration_manager:
-  - name: fragment
-    estimators:
-      - name: mz
-        model: LOESSRegression
-        model_args:
-          n_kernels: 2
-        input_columns:
-          - mz_library
-        target_columns:
-          - mz_observed
-        output_columns:
-          - mz_calibrated
-        transform_deviation: 1e6
-  - name: precursor
-    estimators:
-        - name: mz
-          model: LOESSRegression
-          model_args:
-            n_kernels: 2
-          input_columns:
-            - mz_library
-          target_columns:
-            - mz_observed
-          output_columns:
-            - mz_calibrated
-          transform_deviation: 1e6
-        - name: rt
-          model: LOESSRegression
-          model_args:
-            n_kernels: 6
-          input_columns:
-            - rt_library
-          target_columns:
-            - rt_observed
-          output_columns:
-            - rt_calibrated
-        - name: mobility
-          model: LOESSRegression
-          model_args:
-            n_kernels: 2
-          input_columns:
-            - mobility_library
-          target_columns:
-            - mobility_observed
-          output_columns:
-            - mobility_calibrated
-
-```
diff --git a/docs/methods/search_parameter_optimization.md b/docs/methods/search_parameter_optimization.md
new file mode 100644
index 00000000..28b2a329
--- /dev/null
+++ b/docs/methods/search_parameter_optimization.md
@@ -0,0 +1,131 @@
+# Search Parameter Optimization
+
+## Calibration and optimization
+### Overall process
+The first step of every alphaDIA run is the optimization of search parameters and the calibration of the empirical or fully predicted spectral library to the observed values. This step has two main purposes: 1) removing the systematic deviation of observed and library values, and 2) optimizing the size of the search space to reflect the expected deviation from the library values. For DIA search this means calibration and optimization of certain parameters: retention time, ion mobility, precursor m/z and fragment m/z. The process of iterative calibration and optimization is illustrated below.
+
+<img src="../_static/images/methods_optimization.png" width="100%" height="auto">
+
+Optimization can be performed either in either a targeted or automatic manner. In targeted optimization, the search space is progressively narrowed until a target tolerance is reached for a given parameter. In automatic optimization, the search space is progressively narrowed until an internal algorithm detects that further narrowing will reduce performance, at which point the optimal value is selected for search. Automatic optimization can be triggered by setting the target tolerance to 0.0 in the GUI, or by setting it to any nonpositive value in the command line. It is possible to use targeted optimization for some parameters and automatic optimization for others; currently, it is recommended to use targeted optimization for precursor m/z, fragment m/z and ion mobility, and automatic optimization for retention time.
+
+AlphaDIA iteratively performs calibration and optimization based on a subset of the spectral library used for search. The size of this subset is adjusted according to an exponential batch plan to balance accuracy and efficiency. A defined number of precursors, set by the ``optimization_lock_target`` (default: 200), need to be identified at 1% FDR before calibration and optimization are performed. If fewer precursors than the target number are identified using a given step of the batch plan, AlphaDIA will search for precursors from the next step of the batch plan in addition to those already searched. If more precursors than the target number are identified, AlphaDIA will check if any previous step of the batch plan is also likely to yield at least the target number, in which case it will use the smallest such step of the batch plan for the next iteration of calibration and optimization. In this way, AlphaDIA ensures that calibration is always performed on sufficient precursors to be reliable, while calibrating on the smallest-possible subset of the library to maximize efficiency.
+
+The process of choosing the best batch step for calibration and optimization is illustrated in the figure below, which shows optimization and calibration over seven iterations; the batch size is increased in the first iteration until sufficient precursors are detected, and subsequently reduced when the proportion is sufficiently high that a previous step should reach the target as well; if, however, fluctuations in the number of identifications mean that not enough precursors are actually identified, the next step in the batch plan will be searched as well to ensure that calibration is always performed on at least the target number of precursors.
+
+<img src="../_static/images/methods_optimization_calibration.png" width="100%" height="auto">
+
+### Calibration
+If enough confident target precursors have been detected, they are calibrated to the observed values using locally estimated scatterplot smoothing (LOESS) regression. For calibration of fragment m/z values, a fixed number of up to 5000 of the best fragments according to their  XIC correlation are used. For precursor calibration all precursors passing 1% FDR are used. Calibration is performed prior to every optimization.
+
+### Optimization
+For optimizing the search space, tolerances like retention time, ion mobility and m/z ratios need to be reduced. The goal is to cover the expected spectrum space but reduce it as much as possible to accelerate search and gain statsitical power. Search starts with initial tolerances as defined in `search_initial`.  For targeted optimization, the 95% deviation after calibration is adopted as the new tolerance until the target tolerances defined in the `search` section are reached. For automatic optimization, the 99% deviation plus 10% of the absolute value of the tolerance is adopted as the new tolerance, and search continues until parameter-specific convergence rules are met.
+
+The optimization is finished as soon as the minimum number of steps `min_steps` has passed and all tolerances have either 1) reached the target tolerances defined in `search` if using targeted optimization, or 2) have converged if using automatic optimization.
+
+## Configuring calibration and optimization
+The configuration below will perform targeted optimization of precursor m/z, fragment m/z and ion mobility, and automatic optimization of retention time.
+
+```yaml
+calibration:
+  # Number of precursors searched and scored per batch
+  batch_size: 8000
+
+  # minimum number of precursors to be found before search parameter optimization begins
+  optimization_lock_target: 200
+
+  # the maximum number of steps that a given optimizer is permitted to take
+  max_steps: 20
+
+  # the minimum number of steps that a given optimizer must take before it can be said to have converged
+  min_steps: 2
+
+search_initial:
+  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
+  initial_num_candidates: 1
+
+  # initial ms1 tolerance in ppm
+  initial_ms1_tolerance: 30
+
+  # initial ms2 tolerance in ppm
+  initial_ms2_tolerance: 30
+
+  # initial ion mobility tolerance in 1/K_0
+  initial_mobility_tolerance: 0.08
+
+  # initial retention time tolerance in seconds
+  initial_rt_tolerance: 240
+
+search:
+  channel_filter: '0'
+  exclude_shared_ions: True
+  compete_for_fragments: True
+
+  target_num_candidates: 2
+  target_ms1_tolerance: 15
+  target_ms2_tolerance: 15
+  target_mobility_tolerance: 0.04
+  target_rt_tolerance: 0
+
+  quant_window: 3
+  quant_all: True
+```
+
+## Calibration using LOESS
+Individual properties like the retention time deviate from their library values and need to be calibrated (a). As a nonlinear but stable method, locally estimated scatterplot smoothing (LOESS) using both density and uniformly distributed kernels is used. (b) A collection of polynomial kernels is fitted to uniformly distributed subregions of the data. These consist of first and second degree polynomials basis functions of the calibratable property. (c) The individual functions are combined and smoothed using tricubic weights. (d) Combining the kernels with their weighting functions allows to approximate the systematic deviation of the data locally. (e), The sum of the weighted kernels can then be used for continuous approximation and calibration of retention times. The architecture is built on the scikit-learn package and can be configured to use different hyperparameters and arbitrary predictors for calibration.
+
+<img src="../_static/images/methods_loess.png" width="100%" height="auto">
+
+## Configuring the LOESS model
+
+The type of model, the hyperparameters and the columns used as input and target for calibration can be set in the `calibration_manager` section of the configuration file.
+
+```yaml
+calibration_manager:
+  - name: fragment
+    estimators:
+      - name: mz
+        model: LOESSRegression
+        model_args:
+          n_kernels: 2
+        input_columns:
+          - mz_library
+        target_columns:
+          - mz_observed
+        output_columns:
+          - mz_calibrated
+        transform_deviation: 1e6
+  - name: precursor
+    estimators:
+        - name: mz
+          model: LOESSRegression
+          model_args:
+            n_kernels: 2
+          input_columns:
+            - mz_library
+          target_columns:
+            - mz_observed
+          output_columns:
+            - mz_calibrated
+          transform_deviation: 1e6
+        - name: rt
+          model: LOESSRegression
+          model_args:
+            n_kernels: 6
+          input_columns:
+            - rt_library
+          target_columns:
+            - rt_observed
+          output_columns:
+            - rt_calibrated
+        - name: mobility
+          model: LOESSRegression
+          model_args:
+            n_kernels: 2
+          input_columns:
+            - mobility_library
+          target_columns:
+            - mobility_observed
+          output_columns:
+            - mobility_calibrated
+
+```

From 43bf799c3c504fb70267ae5f35cebda7263dc1fe Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Tue, 27 Aug 2024 12:54:01 +0200
Subject: [PATCH 178/250] take index of optimum, not maximum

---
 alphadia/workflow/optimization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 5ec252ce..580432d4 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -171,7 +171,7 @@ def plot(self):
         ax.vlines(
             x=self.workflow.optimization_manager.__dict__[self.parameter_name],
             ymin=0,
-            ymax=self.history_df[self.feature_name].max(),
+            ymax=self.history_df.loc[self._find_index_of_optimum(), self.feature_name],
             color="red",
             zorder=0,
             label=f"Optimal {self.parameter_name}",

From 6ca1a0f51848109fd1ba97f873f9cd8e61d182dd Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 16:21:07 +0200
Subject: [PATCH 179/250] add new pypi workflow

---
 .github/workflows/legacy_publish_on_pypi.yml | 126 ++++++++++++++++++
 .github/workflows/publish_on_pypi.yml        | 132 ++-----------------
 2 files changed, 140 insertions(+), 118 deletions(-)
 create mode 100644 .github/workflows/legacy_publish_on_pypi.yml

diff --git a/.github/workflows/legacy_publish_on_pypi.yml b/.github/workflows/legacy_publish_on_pypi.yml
new file mode 100644
index 00000000..2efb2470
--- /dev/null
+++ b/.github/workflows/legacy_publish_on_pypi.yml
@@ -0,0 +1,126 @@
+# TODO delete once the new release workflow has run once
+on:
+  workflow_dispatch:
+    inputs:
+      tag_to_release:
+        description: 'Enter tag to release (example: v1.5.5)'
+        required: true
+
+name: LEGACY Publish on PyPi
+
+env:
+  PYTHON_VERSION: "3.11"
+
+jobs:
+  Create_PyPi_Release:
+    runs-on: ubuntu-latest
+    outputs:
+      new_version: ${{ steps.get_current_version.outputs.new_version }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag_to_release }}
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          miniconda-version: "latest"
+          auto-update-conda: true
+          python-version: ${{ env.PYTHON_VERSION }}
+      - name: Conda info
+        shell: bash -le {0}
+        run: conda info
+      - name: Get current version
+        id: get_current_version
+        shell: bash -l {0}
+        run: |
+         CURRENT_VERSION=$(./misc/get_current_version.sh)
+         echo "new_version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
+      - uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
+      - name: Prepare distribution
+        shell: bash -le {0}
+        run: |
+          conda create -n alphadia_build python=${{ env.PYTHON_VERSION }} -y
+          conda activate alphadia_build
+          python -m pip install --upgrade pip
+          pip install build twine
+          rm -rf dist
+          rm -rf build
+          python -m build
+          twine check dist/*
+          conda deactivate
+          conda env remove --name alphadia_build -y
+          conda clean --all -y
+      - name: Publish distribution to Test-PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/
+          user: __token__
+          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+      - name: Test Test-PyPI loose installation
+        shell: bash -le {0}
+        run: |
+          conda create -n pip_loose_test python=${{ env.PYTHON_VERSION }} -y
+          conda activate pip_loose_test
+          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple "alphadia==${{ steps.get_current_version.outputs.new_version }}"
+          alphadia -v
+          conda deactivate
+          conda env remove --name pip_stable_test -y
+          conda clean --all -y
+      - name: Test Test-PyPI stable installation
+        shell: bash -le {0}
+        run: |
+          conda create -n pip_stable_test python=${{ env.PYTHON_VERSION }} -y
+          conda activate pip_stable_test
+          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple "alphadia[stable]==${{ steps.get_current_version.outputs.new_version }}"
+          alphadia -v
+          conda deactivate
+          conda env remove --name pip_stable_test -y
+          conda clean --all -y
+      - name: Publish distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}
+  Test_PyPi_Release:
+    name: Test_PyPi_version_on_${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    needs: Create_PyPi_Release
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macOS-latest, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          miniconda-version: "latest"
+          auto-update-conda: true
+          python-version: ${{ env.PYTHON_VERSION }}
+      - name: Conda info
+        shell: bash -le {0}
+        run: conda info
+      - uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
+      - name: Test PyPI stable installation
+        shell: bash -le {0}
+        run: |
+          conda create -n pip_stable python=${{ env.PYTHON_VERSION }} -y
+          conda activate pip_stable
+          pip install "alphadia[stable]==${{ needs.Create_PyPi_Release.outputs.new_version }}"
+          alphadia -v
+          conda deactivate
+          conda env remove --name pip_stable -y
+          conda clean --all -y
+      - name: Test PyPI loose installation
+        shell: bash -le {0}
+        run: |
+          conda create -n pip_loose python=${{ env.PYTHON_VERSION }} -y
+          conda activate pip_loose
+          pip install alphadia==${{ needs.Create_PyPi_Release.outputs.new_version }}
+          alphadia -v
+          conda deactivate
+          conda env remove --name pip_loose -y
+          conda clean --all -y
diff --git a/.github/workflows/publish_on_pypi.yml b/.github/workflows/publish_on_pypi.yml
index 927408c0..e89d5b47 100644
--- a/.github/workflows/publish_on_pypi.yml
+++ b/.github/workflows/publish_on_pypi.yml
@@ -1,125 +1,21 @@
+# Publish and test releases on Test-PyPI and PyPI.
+name: Publish on PyPi
+
 on:
   workflow_dispatch:
     inputs:
       tag_to_release:
-        description: 'Enter tag to release (example: v1.5.5)'
+        description: 'Enter tag to release (example: v1.5.5). A tag with the same name must exist in the repository.'
+        type: string
         required: true
 
-name: Publish on PyPi
-
-env:
-  PYTHON_VERSION: "3.11"
-
 jobs:
-  Create_PyPi_Release:
-    runs-on: ubuntu-latest
-    outputs:
-      new_version: ${{ steps.get_current_version.outputs.new_version }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag_to_release }}
-      - uses: conda-incubator/setup-miniconda@v3
-        with:
-          miniconda-version: "latest"
-          auto-update-conda: true
-          python-version: ${{ env.PYTHON_VERSION }}
-      - name: Conda info
-        shell: bash -le {0}
-        run: conda info
-      - name: Get current version
-        id: get_current_version
-        shell: bash -l {0}
-        run: |
-         CURRENT_VERSION=$(./misc/get_current_version.sh)
-         echo "new_version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
-      - uses: actions/cache@v4
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
-      - name: Prepare distribution
-        shell: bash -le {0}
-        run: |
-          conda create -n alphadia_build python=${{ env.PYTHON_VERSION }} -y
-          conda activate alphadia_build
-          python -m pip install --upgrade pip
-          pip install build twine
-          rm -rf dist
-          rm -rf build
-          python -m build
-          twine check dist/*
-          conda deactivate
-          conda env remove --name alphadia_build -y
-          conda clean --all -y
-      - name: Publish distribution to Test-PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://test.pypi.org/legacy/
-          user: __token__
-          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
-      - name: Test Test-PyPI loose installation
-        shell: bash -le {0}
-        run: |
-          conda create -n pip_loose_test python=${{ env.PYTHON_VERSION }} -y
-          conda activate pip_loose_test
-          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple "alphadia==${{ steps.get_current_version.outputs.new_version }}"
-          alphadia -v
-          conda deactivate
-          conda env remove --name pip_stable_test -y
-          conda clean --all -y
-      - name: Test Test-PyPI stable installation
-        shell: bash -le {0}
-        run: |
-          conda create -n pip_stable_test python=${{ env.PYTHON_VERSION }} -y
-          conda activate pip_stable_test
-          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple "alphadia[stable]==${{ steps.get_current_version.outputs.new_version }}"
-          alphadia -v
-          conda deactivate
-          conda env remove --name pip_stable_test -y
-          conda clean --all -y
-      - name: Publish distribution to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_API_TOKEN }}
-  Test_PyPi_Release:
-    name: Test_PyPi_version_on_${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    needs: Create_PyPi_Release
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macOS-latest, windows-latest]
-    steps:
-      - uses: actions/checkout@v4
-      - uses: conda-incubator/setup-miniconda@v3
-        with:
-          miniconda-version: "latest"
-          auto-update-conda: true
-          python-version: ${{ env.PYTHON_VERSION }}
-      - name: Conda info
-        shell: bash -le {0}
-        run: conda info
-      - uses: actions/cache@v4
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
-      - name: Test PyPI stable installation
-        shell: bash -le {0}
-        run: |
-          conda create -n pip_stable python=${{ env.PYTHON_VERSION }} -y
-          conda activate pip_stable
-          pip install "alphadia[stable]==${{ needs.Create_PyPi_Release.outputs.new_version }}"
-          alphadia -v
-          conda deactivate
-          conda env remove --name pip_stable -y
-          conda clean --all -y
-      - name: Test PyPI loose installation
-        shell: bash -le {0}
-        run: |
-          conda create -n pip_loose python=${{ env.PYTHON_VERSION }} -y
-          conda activate pip_loose
-          pip install alphadia==${{ needs.Create_PyPi_Release.outputs.new_version }}
-          alphadia -v
-          conda deactivate
-          conda env remove --name pip_loose -y
-          conda clean --all -y
+  publish_on_pypi:
+    uses: MannLabs/alphashared/.github/workflows/publish_on_pypi.yml@v1
+    with:
+      # see the documentation of the workflow for more information on the parameters
+      package_name: alphdia
+      tag_to_release: ${{ inputs.tag_to_release }}
+    secrets:
+      test_pypi_api_token: ${{ secrets.TEST_PYPI_API_TOKEN }}
+      pypi_api_token: ${{ secrets.PYPI_API_TOKEN }}

From 9169189562c4081a4110df6c1401e431b50fa7a5 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 16:49:56 +0200
Subject: [PATCH 180/250] add new release workflow

---
 .github/workflows/create_release.yml          | 203 ++----------------
 .github/workflows/legacy_create_release.yml   | 201 +++++++++++++++++
 misc/.bumpversion.cfg                         |  13 +-
 release/macos/build_backend_macos.sh          |   2 +
 release/macos/build_gui_macos.sh              |   3 +
 release/macos/build_installer_macos.sh        |  12 ++
 release/macos/build_package_macos.sh          |  82 +++++++
 release/macos/build_pkg_macos.sh              |   1 +
 release/pyinstaller/alphadia.spec             |   2 +-
 release/windows/alphadia_innoinstaller.iss    |   3 +-
 release/windows/build_gui.ps1                 |   2 +
 release/windows/build_gui_windows.ps1         |  12 ++
 release/windows/build_installer.ps1           |   3 +
 ...ackend.ps1 => build_installer_windows.ps1} |   5 +-
 release/windows/build_package_windows.ps1     |   4 +
 15 files changed, 353 insertions(+), 195 deletions(-)
 create mode 100644 .github/workflows/legacy_create_release.yml
 create mode 100755 release/macos/build_installer_macos.sh
 create mode 100755 release/macos/build_package_macos.sh
 create mode 100644 release/windows/build_gui_windows.ps1
 rename release/windows/{build_backend.ps1 => build_installer_windows.ps1} (70%)
 create mode 100644 release/windows/build_package_windows.ps1

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index e9da72d8..c89824ce 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -1,200 +1,25 @@
+# Create a draft release and build and upload all installers to it.
+name: Create Draft Release
+
 on:
   workflow_dispatch:
     inputs:
       commit_to_release:
         description: 'Enter commit hash to release (example: ef4037cb571f99cb4919b520fde7174972aae473)'
+        type: string
         required: true
       tag_to_release:
         description: 'Enter tag to release (example: v1.5.5)'
+        type: string
         required: true
 
-
-name: Create Draft Release
-
 jobs:
-  Get_New_Version:
-    runs-on: ubuntu-latest
-    outputs:
-      new_version: ${{ steps.check_release_tag.outputs.new_version }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.commit_to_release }}
-
-      - name: Check release tag
-        id: check_release_tag
-        shell: bash -le {0}
-        run: |
-          CURRENT_VERSION=$(./misc/get_current_version.sh)
-          if [ "v${CURRENT_VERSION}" != "${{ inputs.tag_to_release }}" ]; then
-            echo Code version "v${CURRENT_VERSION}" does not match the tag to release ${{ inputs.tag_to_release }}
-            exit 1
-          fi
-          echo "new_version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
-
-      - uses: mukunku/tag-exists-action@v1.6.0
-        id: check-tag
-        with:
-          tag: ${{ inputs.tag_to_release }}
-
-      - name: Check if tag already exists
-        run: |
-          echo "Tag already exists!"
-          exit 1
-        if: steps.check-tag.outputs.exists == 'true'
-
-
-  Create_Draft_Release:
-    runs-on: ubuntu-latest
-    needs: Get_New_Version
-    outputs:
-      upload_url: ${{ steps.draft_release.outputs.upload_url }}
-    steps:
-      - name: Draft Release
-        id: draft_release
-        # TODO this action is deprecated, replace with https://github.com/ncipollo/release-action
-        # cf. https://github.com/actions/create-release/issues/119#issuecomment-783010321
-        uses: actions/create-release@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          tag_name: ${{ inputs.tag_to_release }}
-          release_name: ${{ inputs.tag_to_release }}
-          draft: true
-          prerelease: false
-
-  Create_MacOS_Installer:
-    needs: [Create_Draft_Release, Get_New_Version]
-    env:
-        ARCH: x64
-        EAGER_IMPORT: true
-    runs-on: macos-latest-xlarge
-    steps:
-    - name : Checkout code
-      uses: actions/checkout@v4
-      with:
-        ref: ${{ inputs.commit_to_release }}
-
-    # Build backend
-    - name: Install conda
-      uses: conda-incubator/setup-miniconda@v3
-      with:
-        miniconda-version: "latest"
-        auto-update-conda: true
-        activate-environment: alpha
-        python-version: "3.11"
-
-    - name: Check arm64
-      shell: bash -el {0}
-      run: |
-        python -c "import platform; print(platform.machine())"
-
-    - name: Build backend
-      shell: bash -el {0}
-      run: |
-        release/macos/build_backend_macos.sh
-
-    - name: Test backend
-      shell: bash -el {0}
-      run: |
-        dist/alphadia/alphadia --version
-
-    # Build GUI
-    - name: Setup Node.js
-      uses: actions/setup-node@v4
-
-    - name: Build GUI
-      run: |
-        release/macos/build_gui_macos.sh
-
-    # combine backend and GUI
-    - name: Build package
-      shell: bash -el {0}
-      run: |
-        release/macos/build_pkg_macos.sh
-
-    - name: List output files
-      run: |
-        ls dist
-
-    # Upload the package
-    - name: Upload a Release Asset
-      uses: actions/upload-release-asset@v1
-      env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      with:
-          upload_url: ${{ needs.Create_Draft_Release.outputs.upload_url }}
-          asset_path: dist/alphadia-${{ needs.Get_New_Version.outputs.new_version }}-darwin-${{ env.ARCH }}.pkg
-          asset_name: alphadia-${{ needs.Get_New_Version.outputs.new_version }}-darwin-${{ env.ARCH }}.pkg
-          asset_content_type: application/zip
-
-  Create_Windows_Installer:
-    needs: [Create_Draft_Release, Get_New_Version]
-    env:
-        ARCH: x64
-    runs-on: windows-latest
-    steps:
-    - name : Checkout code
-      uses: actions/checkout@v4
-      with:
-        ref: ${{ inputs.commit_to_release }}
-
-    # Build backend
-    - name: Install conda
-      uses: conda-incubator/setup-miniconda@v3
-      with:
-        miniconda-version: "latest"
-        auto-update-conda: true
-        activate-environment: alpha
-        python-version: "3.11"
-
-    - name: Build Backend
-      shell: powershell
-      run: |
-        release/windows/build_backend.ps1
-
-    - name: Test Backend
-      shell: powershell
-      run: |
-        dist\alphadia\alphadia.exe --version
-
-    # Build GUI
-    - name: Setup Node.js
-      uses: actions/setup-node@v4
-
-    - name: Build GUI
-      shell: powershell
-      run: |
-        release/windows/build_gui.ps1
-
-    # combine backend and GUI
-    - name: Check if Innosetup is installed
-      shell: powershell
-      run: |
-        if (-not (Test-Path "C:\Program Files (x86)\Inno Setup 6\ISCC.exe")) {
-          Write-Host "Inno Setup is not installed"
-          exit 1
-        }
-        else {
-          Write-Host "Inno Setup is installed"
-        }
-
-    - name: Build Installer
-      shell: powershell
-      run: |
-        release/windows/build_installer.ps1
-
-    - name: List output files
-      run: |
-        ls dist
-
-    - name: Upload a Release Asset
-      uses: actions/upload-release-asset@v1
-      env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      with:
-          upload_url: ${{ needs.Create_Draft_Release.outputs.upload_url }}
-          asset_path: dist/alphadia-${{ needs.Get_New_Version.outputs.new_version }}-win-${{ env.ARCH }}.exe
-          asset_name: alphadia-${{ needs.Get_New_Version.outputs.new_version }}-win-${{ env.ARCH }}.exe
-          asset_content_type: application/zip
+  create-release:
+    secrets: inherit
+    uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
+    with:
+      package_name: alphadia
+      commit_to_release: ${{ inputs.commit_to_release }}
+      tag_to_release: ${{ inputs.tag_to_release }}
+      build_nodejs_ui: true
+      test_app: true
diff --git a/.github/workflows/legacy_create_release.yml b/.github/workflows/legacy_create_release.yml
new file mode 100644
index 00000000..161d9898
--- /dev/null
+++ b/.github/workflows/legacy_create_release.yml
@@ -0,0 +1,201 @@
+# TODO delete once the new release workflow has run once
+on:
+  workflow_dispatch:
+    inputs:
+      commit_to_release:
+        description: 'Enter commit hash to release (example: ef4037cb571f99cb4919b520fde7174972aae473)'
+        required: true
+      tag_to_release:
+        description: 'Enter tag to release (example: v1.5.5)'
+        required: true
+
+
+name: LEGACY Create Draft Release
+
+jobs:
+  Get_New_Version:
+    runs-on: ubuntu-latest
+    outputs:
+      new_version: ${{ steps.check_release_tag.outputs.new_version }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.commit_to_release }}
+
+      - name: Check release tag
+        id: check_release_tag
+        shell: bash -le {0}
+        run: |
+          CURRENT_VERSION=$(./misc/get_current_version.sh)
+          if [ "v${CURRENT_VERSION}" != "${{ inputs.tag_to_release }}" ]; then
+            echo Code version "v${CURRENT_VERSION}" does not match the tag to release ${{ inputs.tag_to_release }}
+            exit 1
+          fi
+          echo "new_version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
+
+      - uses: mukunku/tag-exists-action@v1.6.0
+        id: check-tag
+        with:
+          tag: ${{ inputs.tag_to_release }}
+
+      - name: Check if tag already exists
+        run: |
+          echo "Tag already exists!"
+          exit 1
+        if: steps.check-tag.outputs.exists == 'true'
+
+
+  Create_Draft_Release:
+    runs-on: ubuntu-latest
+    needs: Get_New_Version
+    outputs:
+      upload_url: ${{ steps.draft_release.outputs.upload_url }}
+    steps:
+      - name: Draft Release
+        id: draft_release
+        # TODO this action is deprecated, replace with https://github.com/ncipollo/release-action
+        # cf. https://github.com/actions/create-release/issues/119#issuecomment-783010321
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ inputs.tag_to_release }}
+          release_name: ${{ inputs.tag_to_release }}
+          draft: true
+          prerelease: false
+
+  Create_MacOS_Installer:
+    needs: [Create_Draft_Release, Get_New_Version]
+    env:
+        ARCH: x64
+        EAGER_IMPORT: true
+    runs-on: macos-latest-xlarge
+    steps:
+    - name : Checkout code
+      uses: actions/checkout@v4
+      with:
+        ref: ${{ inputs.commit_to_release }}
+
+    # Build backend
+    - name: Install conda
+      uses: conda-incubator/setup-miniconda@v3
+      with:
+        miniconda-version: "latest"
+        auto-update-conda: true
+        activate-environment: alpha
+        python-version: "3.11"
+
+    - name: Check arm64
+      shell: bash -el {0}
+      run: |
+        python -c "import platform; print(platform.machine())"
+
+    - name: Build backend
+      shell: bash -el {0}
+      run: |
+        release/macos/build_backend_macos.sh
+
+    - name: Test backend
+      shell: bash -el {0}
+      run: |
+        dist/alphadia/alphadia --version
+
+    # Build GUI
+    - name: Setup Node.js
+      uses: actions/setup-node@v4
+
+    - name: Build GUI
+      run: |
+        release/macos/build_gui_macos.sh
+
+    # combine backend and GUI
+    - name: Build package
+      shell: bash -el {0}
+      run: |
+        release/macos/build_pkg_macos.sh
+
+    - name: List output files
+      run: |
+        ls dist
+
+    # Upload the package
+    - name: Upload a Release Asset
+      uses: actions/upload-release-asset@v1
+      env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      with:
+          upload_url: ${{ needs.Create_Draft_Release.outputs.upload_url }}
+          asset_path: dist/alphadia-${{ needs.Get_New_Version.outputs.new_version }}-darwin-${{ env.ARCH }}.pkg
+          asset_name: alphadia-${{ needs.Get_New_Version.outputs.new_version }}-darwin-${{ env.ARCH }}.pkg
+          asset_content_type: application/zip
+
+  Create_Windows_Installer:
+    needs: [Create_Draft_Release, Get_New_Version]
+    env:
+        ARCH: x64
+    runs-on: windows-latest
+    steps:
+    - name : Checkout code
+      uses: actions/checkout@v4
+      with:
+        ref: ${{ inputs.commit_to_release }}
+
+    # Build backend
+    - name: Install conda
+      uses: conda-incubator/setup-miniconda@v3
+      with:
+        miniconda-version: "latest"
+        auto-update-conda: true
+        activate-environment: alpha
+        python-version: "3.11"
+
+    - name: Build Backend
+      shell: powershell
+      run: |
+        release/windows/build_backend.ps1
+
+    - name: Test Backend
+      shell: powershell
+      run: |
+        dist\alphadia\alphadia.exe --version
+
+    # Build GUI
+    - name: Setup Node.js
+      uses: actions/setup-node@v4
+
+    - name: Build GUI
+      shell: powershell
+      run: |
+        release/windows/build_gui.ps1
+
+    # combine backend and GUI
+    - name: Check if Innosetup is installed
+      shell: powershell
+      run: |
+        if (-not (Test-Path "C:\Program Files (x86)\Inno Setup 6\ISCC.exe")) {
+          Write-Host "Inno Setup is not installed"
+          exit 1
+        }
+        else {
+          Write-Host "Inno Setup is installed"
+        }
+
+    - name: Build Installer
+      shell: powershell
+      run: |
+        release/windows/build_installer.ps1
+
+    - name: List output files
+      run: |
+        ls dist
+
+    - name: Upload a Release Asset
+      uses: actions/upload-release-asset@v1
+      env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      with:
+          upload_url: ${{ needs.Create_Draft_Release.outputs.upload_url }}
+          asset_path: dist/alphadia-${{ needs.Get_New_Version.outputs.new_version }}-win-${{ env.ARCH }}.exe
+          asset_name: alphadia-${{ needs.Get_New_Version.outputs.new_version }}-win-${{ env.ARCH }}.exe
+          asset_content_type: application/zip
diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 528f949c..28363e03 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -21,9 +21,13 @@ serialize =
 
 [bumpversion:file:../release/macos/info.plist]
 
-[bumpversion:file:../release/macos/build_backend_macos.sh]
+[bumpversion:file:../release/macos/build_installer_macos.sh]
 
-[bumpversion:file:../release/macos/build_pkg_macos.sh]
+[bumpversion:file:../release/macos/build_package_macos.sh]
+
+[bumpversion:file:../release/macos/build_backend_macos.sh] # TODO remove with old release workflow
+
+[bumpversion:file:../release/macos/build_pkg_macos.sh] # TODO remove with old release workflow
 
 [bumpversion:file:../release/macos/build_zip_macos.sh]
 
@@ -31,6 +35,9 @@ serialize =
 
 [bumpversion:file:../release/windows/alphadia_innoinstaller.iss]
 
-[bumpversion:file:../release/windows/build_backend.ps1]
+[bumpversion:file:../release/windows/build_installer_windows.ps1]
+
+[bumpversion:file:../release/windows/build_backend.ps1] # TODO remove with old release workflow
+
 search = {current_version}
 replace = {new_version}
diff --git a/release/macos/build_backend_macos.sh b/release/macos/build_backend_macos.sh
index 26c14894..5afe4671 100755
--- a/release/macos/build_backend_macos.sh
+++ b/release/macos/build_backend_macos.sh
@@ -1,3 +1,5 @@
+# TODO remove with old release workflow
+
 # navigate to the root directory
 
 pip install build
diff --git a/release/macos/build_gui_macos.sh b/release/macos/build_gui_macos.sh
index 6bf30054..cd4a64f9 100755
--- a/release/macos/build_gui_macos.sh
+++ b/release/macos/build_gui_macos.sh
@@ -1,5 +1,8 @@
 #!/bin/bash
 
+# Build the GUI for MacOS.
+# This script needs to be run from the root of the repository.
+
 # Cleanup the GUI build
 rm -rf gui/dist
 rm -rf gui/out
diff --git a/release/macos/build_installer_macos.sh b/release/macos/build_installer_macos.sh
new file mode 100755
index 00000000..4401eea8
--- /dev/null
+++ b/release/macos/build_installer_macos.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+# Build the installer for MacOS.
+# This script needs to be run from the root of the repository.
+
+pip install build
+python -m build
+pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+
+# Creating the stand-alone pyinstaller folder
+pip install pyinstaller
+pyinstaller release/pyinstaller/alphadia.spec -y
diff --git a/release/macos/build_package_macos.sh b/release/macos/build_package_macos.sh
new file mode 100755
index 00000000..5e3df255
--- /dev/null
+++ b/release/macos/build_package_macos.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# Build the install package for MacOS.
+# This script must be run from the root of the repository after running build_installer_macos.sh and build_gui_macos.sh
+
+
+# Set up package name and version
+PACKAGE_NAME="alphadia"
+APP_NAME="alphaDIA"
+PACKAGE_VERSION="1.7.2"
+PKG_FOLDER="dist/$APP_NAME.app"
+
+# BUILD_NAME is taken from environment variables, e.g. alphadia-1.7.2-macos-darwin-arm64 or alphadia-1.7.2-macos-darwin-x64
+rm -rf ${BUILD_NAME}.pkg
+
+# Cleanup the package folder
+echo "Cleaning up the package folder"
+
+rm -rf $PKG_FOLDER
+
+# === Prepare structure ===
+echo "Preparing package structure"
+
+mkdir -p $PKG_FOLDER
+mkdir -p $PKG_FOLDER/Contents/Resources
+mkdir -p $PKG_FOLDER/Contents/MacOS
+mkdir -p $PKG_FOLDER/Contents/Frameworks
+
+
+
+# === Backend ===
+echo "Copying backend"
+
+BACKEND_BUILD="dist/$PACKAGE_NAME"
+
+# Check if the backend build exists, otherwise exit with an error
+if [ ! -d "$BACKEND_BUILD" ]; then
+  echo "Backend build not found at $BACKEND_BUILD"
+  exit 1
+fi
+
+# Copy the backend
+cp -a ${BACKEND_BUILD}/. $PKG_FOLDER/Contents/Frameworks/
+
+
+# === GUI ===
+echo "Copying GUI"
+
+# TODO find out where this contract from ARCH & KERNEL is used in GUI building
+ARCH=$(uname -m)
+if [ "$ARCH" == "x86_64" ]; then
+  ARCH="x64"
+fi
+KERNEL=$(uname -s | tr '[:upper:]' '[:lower:]')
+echo ARCH=${ARCH} KERNEL=${KERNEL}
+
+ls ./gui/out
+
+# Set the path to the GUI build
+GUI_BUILD="./gui/out/${PACKAGE_NAME}-gui-${KERNEL}-${ARCH}"
+
+# Check if the GUI build exists, otherwise exit with an error
+if [ ! -d "$GUI_BUILD" ]; then
+  echo "GUI build not found at $GUI_BUILD"
+  exit 1
+fi
+
+# Copy the electron forge build
+cp -a ${GUI_BUILD}/. $PKG_FOLDER/Contents/Frameworks/
+
+# === Resources ===
+echo "Copying resources"
+cp release/logos/$PACKAGE_NAME.icns $PKG_FOLDER/Contents/Resources/
+cp release/logos/$PACKAGE_NAME.png $PKG_FOLDER/Contents/Resources/
+cp release/macos/$APP_NAME $PKG_FOLDER/Contents/MacOS/
+
+cp release/macos/Info.plist $PKG_FOLDER/Contents/
+
+# change permissions for entry script
+chmod +x $PKG_FOLDER/Contents/MacOS/$APP_NAME
+
+pkgbuild --root $PKG_FOLDER --identifier de.mpg.biochem.$PACKAGE_NAME.app --version $PACKAGE_VERSION --install-location /Applications/$APP_NAME.app --scripts release/macos/scripts dist/$BUILD_NAME.pkg --nopayload
diff --git a/release/macos/build_pkg_macos.sh b/release/macos/build_pkg_macos.sh
index af314e3a..99a39d59 100755
--- a/release/macos/build_pkg_macos.sh
+++ b/release/macos/build_pkg_macos.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+# TODO remove with old release workflow
 
 # Set up package name and version
 PACKAGE_NAME="alphadia"
diff --git a/release/pyinstaller/alphadia.spec b/release/pyinstaller/alphadia.spec
index 9bb248c6..7862707c 100644
--- a/release/pyinstaller/alphadia.spec
+++ b/release/pyinstaller/alphadia.spec
@@ -80,7 +80,7 @@ if sys.platform[:5] == "linux":
 		upx_exclude=[],
 		icon=icon
 	)
-else:
+else: # non-linux
 	exe = EXE(
 		pyz,
 		a.scripts,
diff --git a/release/windows/alphadia_innoinstaller.iss b/release/windows/alphadia_innoinstaller.iss
index 9c7c68bc..b3aff4e5 100644
--- a/release/windows/alphadia_innoinstaller.iss
+++ b/release/windows/alphadia_innoinstaller.iss
@@ -25,7 +25,8 @@ LicenseFile=..\..\LICENSE.txt
 PrivilegesRequired=lowest
 PrivilegesRequiredOverridesAllowed=dialog
 OutputDir=..\..\dist
-OutputBaseFilename=alphadia-1.7.2-win-x64
+; example for BUILD_NAME: alphadia-1.7.2-win-x64
+OutputBaseFilename={#GetEnv('BUILD_NAME')}
 SetupIconFile=..\logos\alphadia.ico
 Compression=lzma
 SolidCompression=yes
diff --git a/release/windows/build_gui.ps1 b/release/windows/build_gui.ps1
index f6b2f4eb..8503d72f 100644
--- a/release/windows/build_gui.ps1
+++ b/release/windows/build_gui.ps1
@@ -1,3 +1,5 @@
+# TODO remove with old release workflow
+
 # Powershell script to build the GUI for Windows
 
 cd gui
diff --git a/release/windows/build_gui_windows.ps1 b/release/windows/build_gui_windows.ps1
new file mode 100644
index 00000000..f6b2f4eb
--- /dev/null
+++ b/release/windows/build_gui_windows.ps1
@@ -0,0 +1,12 @@
+# Powershell script to build the GUI for Windows
+
+cd gui
+# delete old build using powershell
+Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./out
+Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
+
+
+npm install
+npm run make
+
+cd ..
diff --git a/release/windows/build_installer.ps1 b/release/windows/build_installer.ps1
index 282179a1..126891ed 100644
--- a/release/windows/build_installer.ps1
+++ b/release/windows/build_installer.ps1
@@ -1 +1,4 @@
+# TODO remove with old release workflow
+
+
 &  "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" .\release\windows\alphadia_innoinstaller.iss
diff --git a/release/windows/build_backend.ps1 b/release/windows/build_installer_windows.ps1
similarity index 70%
rename from release/windows/build_backend.ps1
rename to release/windows/build_installer_windows.ps1
index a3837c22..b227514e 100644
--- a/release/windows/build_backend.ps1
+++ b/release/windows/build_installer_windows.ps1
@@ -1,3 +1,6 @@
+# Build the installer for Windows.
+# This script must be run from the root of the repository.
+
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./build
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
 
@@ -6,5 +9,5 @@ python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install pyinstaller tbb
+pip install pyinstaller tbb==2021.13.1
 pyinstaller release/pyinstaller/alphadia.spec -y
diff --git a/release/windows/build_package_windows.ps1 b/release/windows/build_package_windows.ps1
new file mode 100644
index 00000000..1b66cdab
--- /dev/null
+++ b/release/windows/build_package_windows.ps1
@@ -0,0 +1,4 @@
+# Build the install package for Windows.
+# This script must be run from the root of the repository after running build_installer_windows.ps1
+
+&  "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" .\release\windows\alphadia_innoinstaller.iss

From c51761cbe0983ae2b66deb1901518c76b39303bd Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 16:50:47 +0200
Subject: [PATCH 181/250] remove unused build_zip_macos.sh

---
 misc/.bumpversion.cfg            |  2 --
 release/macos/build_zip_macos.sh | 54 --------------------------------
 2 files changed, 56 deletions(-)
 delete mode 100755 release/macos/build_zip_macos.sh

diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 28363e03..3a4123c0 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -29,8 +29,6 @@ serialize =
 
 [bumpversion:file:../release/macos/build_pkg_macos.sh] # TODO remove with old release workflow
 
-[bumpversion:file:../release/macos/build_zip_macos.sh]
-
 [bumpversion:file:../release/macos/distribution.xml]
 
 [bumpversion:file:../release/windows/alphadia_innoinstaller.iss]
diff --git a/release/macos/build_zip_macos.sh b/release/macos/build_zip_macos.sh
deleted file mode 100755
index f6abb2e6..00000000
--- a/release/macos/build_zip_macos.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-
-# Set up package name and version
-PACKAGE_NAME="alphadia"
-PACKAGE_VERSION="1.7.2"
-
-ARCH=$(uname -m)
-if [ "$ARCH" == "x86_64" ]; then
-  ARCH="x64"
-fi
-echo "ARCH=${ARCH}" >> $GITHUB_ENV
-
-KERNEL=$(uname -s | tr '[:upper:]' '[:lower:]')
-
-BUILD_NAME="${PACKAGE_NAME}-${PACKAGE_VERSION}-${KERNEL}-${ARCH}"
-
-# Cleanup the dist folder
-rm -rf ./dist/${BUILD_NAME}
-
-# Create the dist folder
-mkdir -p ./dist/${BUILD_NAME}
-
-# === GUI ===
-
-ls ./gui/out
-
-# Set the path to the GUI build
-GUI_BUILD="./gui/out/alphadia-gui-${KERNEL}-${ARCH}"
-
-# Check if the GUI build exists, otherwise exit with an error
-if [ ! -d "$GUI_BUILD" ]; then
-  echo "GUI build not found at $GUI_BUILD"
-  exit 1
-fi
-
-# Copy the electron forge build
-cp -a ${GUI_BUILD}/. ./dist/${BUILD_NAME}
-
-# === Backend ===
-
-BACKEND_BUILD="./dist/alphadia"
-
-# Check if the backend build exists, otherwise exit with an error
-if [ ! -d "$BACKEND_BUILD" ]; then
-  echo "Backend build not found at $BACKEND_BUILD"
-  exit 1
-fi
-
-# Copy the backend
-cp -a ${BACKEND_BUILD}/. ./dist/${BUILD_NAME}
-
-# create the zip file
-
-zip -q -r ./dist/${BUILD_NAME}.zip ./dist/${BUILD_NAME}

From 93013632c242a149a854b49bd8fabee00df01262 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 16:56:03 +0200
Subject: [PATCH 182/250] update Readme instructions

---
 docs/developer_guide.md | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/docs/developer_guide.md b/docs/developer_guide.md
index 56038125..74424fb0 100644
--- a/docs/developer_guide.md
+++ b/docs/developer_guide.md
@@ -2,24 +2,16 @@
 This document gathers information on how to develop and contribute to the alphaDIA project.
 
 ## Release process
+
 ### Tagging of changes
 In order to have release notes automatically generated, changes need to be tagged with labels.
 The following labels are used (should be safe-explanatory):
 `breaking-change`, `bug`, `enhancement`.
 
 ### Release a new version
-Note: Releases need to be done from the `main` branch.
-
-1. Bump the version locally to (e.g. to `X.Y.Z`) and merge the change to `main`.
-2. Create a new draft release on GitHub using the
-[Create Draft Release](https://github.com/MannLabs/alphadia/actions/workflows/create_release.yml) workflow.
-You need to specify the commit to release, and the release tag (e.g. `vX.Y.Z`).
-3. Test the release manually.
-4. Add release notes and publish the release on GitHub.
-5. Run the [Publish on PyPi](https://github.com/MannLabs/alphadia/actions/workflows/publish_on_pypi.yml) workflow,
-specifying the release tag (e.g. `vX.Y.Z`).
-6. Run the [Publish Docker Image](https://github.com/MannLabs/alphadia/actions/workflows/publish_docker_image.yml) workflow,
-specifying the release tag (e.g. `vX.Y.Z`).
+This package uses a shared release process defined in the
+[alphashared](https://github.com/MannLabs/alphashared) repository. Please see the instructions
+[there](https://github.com/MannLabs/alphashared/blob/reusable-release-workflow/.github/workflows/README.md#release-a-new-version)
 
 
 ## Notes for developers

From 53a616b15988ada2aba761267ed82328a74d39d9 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 17:13:55 +0200
Subject: [PATCH 183/250] add previously deleted file

---
 release/windows/build_backend.ps1 | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 release/windows/build_backend.ps1

diff --git a/release/windows/build_backend.ps1 b/release/windows/build_backend.ps1
new file mode 100644
index 00000000..b227514e
--- /dev/null
+++ b/release/windows/build_backend.ps1
@@ -0,0 +1,13 @@
+# Build the installer for Windows.
+# This script must be run from the root of the repository.
+
+Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./build
+Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
+
+pip install build
+python -m build
+pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+
+# Creating the stand-alone pyinstaller folder
+pip install pyinstaller tbb==2021.13.1
+pyinstaller release/pyinstaller/alphadia.spec -y

From d5e6b47e1e26e9c88c8f628265ea4adeef4181e2 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 27 Aug 2024 17:19:03 +0200
Subject: [PATCH 184/250] add bumpversion to ruff exclusions

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 61c80b2f..08974b35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,6 +59,8 @@ version = {attr = "alphadia.__version__"}
 [project.scripts]
 alphadia = "alphadia.cli:run"
 
+[tool.ruff]
+extend-exclude = ["misc/.bumpversion.cfg"]
 
 [tool.ruff.lint]
 select =  [

From b93d27e41ba902ffe91d2b1906212a14dd9235fb Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 10:12:31 +0200
Subject: [PATCH 185/250] fix error where config field was used in the wrong
 place

---
 alphadia/workflow/optimization.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 5ec252ce..ab30c0de 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -255,8 +255,7 @@ def _check_convergence(self):
 
             parameter_history = self.history_df["parameter"]
             parameter_not_substantially_changed = (
-                parameter_history.iloc[-1] / parameter_history.iloc[-2]
-                > self.minimum_proportion_of_maximum
+                parameter_history.iloc[-1] / parameter_history.iloc[-2] > 0.95
             )
 
             return min_steps_reached and (
@@ -298,7 +297,8 @@ def _find_index_of_optimum(self):
         if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             rows_within_thresh_of_max = self.history_df.loc[
                 self.history_df[self.feature_name]
-                > self.history_df[self.feature_name].max() * 0.9
+                > self.history_df[self.feature_name].max()
+                * self.minimum_proportion_of_maximum
             ]
             index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()
             return index_of_optimum
@@ -483,12 +483,6 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
-        self.maximal_decrease = workflow.config["optimization"]["rt_error"][
-            "maximal_decrease"
-        ]
-        self.minimum_proportion_of_maximum = workflow.config["optimization"][
-            "rt_error"
-        ]["minimum_proportion_of_maximum"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _get_feature_value(

From 763a7b02dc00a58f05a4566c737c89b9d4ea8e8c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 10:14:11 +0200
Subject: [PATCH 186/250] update default.yaml for consistency

---
 alphadia/constants/default.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index ca72e4bb..f77eae57 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -260,7 +260,7 @@ optimization:
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
       maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.95
+      minimum_proportion_of_maximum: 0.9
   ms1_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
@@ -268,7 +268,7 @@ optimization:
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
       maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.95
+      minimum_proportion_of_maximum: 0.9
   mobility_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
@@ -276,7 +276,7 @@ optimization:
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
       maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.95
+      minimum_proportion_of_maximum: 0.9
   rt_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
@@ -284,7 +284,7 @@ optimization:
       automatic_update_factor: 1.1
       favour_narrower_parameter: True
       maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.95
+      minimum_proportion_of_maximum: 0.9
 
 # configuration for the optimization manager
 # initial parameters, will be optimized

From 1567b3faceaea88113c53b2b1c56aaf354fdae4c Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 28 Aug 2024 13:05:19 +0200
Subject: [PATCH 187/250] less permissions for new release workflow

---
 .github/workflows/create_release.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index c89824ce..cb800352 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -16,6 +16,8 @@ on:
 jobs:
   create-release:
     secrets: inherit
+    permissions:
+      contents: write
     uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     with:
       package_name: alphadia

From e5665291f541eeec3a1f5589fec3d250bfa94e69 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 28 Aug 2024 13:14:02 +0200
Subject: [PATCH 188/250] adapt permissions for new release workflow

---
 .github/workflows/create_release.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index cb800352..73d9a2d8 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -18,6 +18,7 @@ jobs:
     secrets: inherit
     permissions:
       contents: write
+      actions: write
     uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     with:
       package_name: alphadia

From a5f74dce666571a74a582a645cb81d9ecb4ff713 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 28 Aug 2024 13:20:55 +0200
Subject: [PATCH 189/250] remove permissions for new release workflow

---
 .github/workflows/create_release.yml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index 73d9a2d8..c89824ce 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -16,9 +16,6 @@ on:
 jobs:
   create-release:
     secrets: inherit
-    permissions:
-      contents: write
-      actions: write
     uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     with:
       package_name: alphadia

From 65b7e7cfaaba24481beb874a1c2dc1d9d0c04764 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 28 Aug 2024 13:27:07 +0200
Subject: [PATCH 190/250] reduce permissions for new release workflow

---
 .github/workflows/create_release.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index c89824ce..cb800352 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -16,6 +16,8 @@ on:
 jobs:
   create-release:
     secrets: inherit
+    permissions:
+      contents: write
     uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     with:
       package_name: alphadia

From 584b76ce3e41f6c2be7f94fb4881325db96a072e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 15:37:38 +0200
Subject: [PATCH 191/250] review comments

---
 alphadia/workflow/base.py           | 10 +++----
 alphadia/workflow/optimization.py   |  6 -----
 alphadia/workflow/peptidecentric.py | 42 ++++++++++++++++++++---------
 3 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index c3e5f067..a0e6874b 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -3,7 +3,7 @@
 import os
 
 # alpha family imports
-from alphabase.spectral_library.base import SpecLibBase
+from alphabase.spectral_library.base import SpecLibBase, SpecLibFlat
 
 # alphadia imports
 from alphadia.data import alpharaw, bruker
@@ -139,22 +139,22 @@ def config(self) -> dict:
         return self._config
 
     @property
-    def calibration_manager(self) -> str:
+    def calibration_manager(self) -> manager.CalibrationManager:
         """Calibration manager for the workflow. Owns the RT, IM, MZ calibration and the calibration data"""
         return self._calibration_manager
 
     @property
-    def optimization_manager(self) -> str:
+    def optimization_manager(self) -> manager.OptimizationManager:
         """Optimization manager for the workflow. Owns the optimization data"""
         return self._optimization_manager
 
     @property
-    def timing_manager(self) -> str:
+    def timing_manager(self) -> manager.TimingManager:
         """Optimization manager for the workflow. Owns the timing data"""
         return self._timing_manager
 
     @property
-    def spectral_library(self) -> SpecLibBase:
+    def spectral_library(self) -> SpecLibFlat:
         """Spectral library for the workflow. Owns the spectral library data"""
         return self._spectral_library
 
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 352c4349..139f3878 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -484,12 +484,6 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
-        self.maximal_decrease = workflow.config["optimization"]["rt_error"][
-            "maximal_decrease"
-        ]
-        self.minimum_proportion_of_maximum = workflow.config["optimization"][
-            "rt_error"
-        ]["minimum_proportion_of_maximum"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _get_feature_value(
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 95f38117..51afd838 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -108,6 +108,7 @@ def __init__(
             instance_name,
             config,
         )
+        self.optlock = None
 
     def load(
         self,
@@ -419,7 +420,7 @@ def search_parameter_optimization(self):
 
                 log_string(f"Starting optimization step {current_step}.")
 
-                precursor_df = self.process_batch()
+                precursor_df = self._process_batch()
 
                 if not self.optlock.has_target_num_precursors:
                     self.optlock.update()
@@ -440,10 +441,10 @@ def search_parameter_optimization(self):
 
                     if not self.optlock.previously_calibrated:  # Updates classifier but does not optimize the first time the target is reached.
                         # Optimization is more stable when done with calibrated values.
-                        self.initiate_search_parameter_optimization()
+                        self._initiate_search_parameter_optimization()
                         continue
 
-                    self.step_all_optimizers(
+                    self._step_all_optimizers(
                         optimizers, precursor_df_filtered, fragments_df_filtered
                     )
 
@@ -472,7 +473,8 @@ def search_parameter_optimization(self):
 
         self.save_managers()
 
-    def process_batch(self):
+    def _process_batch(self):
+        """Extracts precursors and fragments from the spectral library, performs FDR correction and logs the precursor dataframe."""
         self.reporter.log_string(
             f"=== Extracting elution groups {self.optlock.start_idx} to {self.optlock.stop_idx} ===",
             verbosity="progress",
@@ -505,7 +507,8 @@ def process_batch(self):
 
         return precursor_df
 
-    def initiate_search_parameter_optimization(self):
+    def _initiate_search_parameter_optimization(self):
+        """Saves the classifier version just before search parameter optimization begins and updates the optimization lock to show that calibration has been performed."""
         self.optlock.previously_calibrated = True
         self.optimization_manager.fit(
             {"classifier_version": self.fdr_manager.current_version}
@@ -515,25 +518,40 @@ def initiate_search_parameter_optimization(self):
             verbosity="progress",
         )
 
-    def step_all_optimizers(
-        self, optimizers, precursor_df_filtered, fragments_df_filtered
+    def _step_all_optimizers(
+        self,
+        optimizers: list,
+        precursor_df_filtered: pd.DataFrame,
+        fragments_df_filtered: pd.DataFrame,
     ):
+        """All optimizers currently in use are stepped and their current state is logged.
+
+        Parameters
+        ----------
+        optimizers : list
+            List of optimizers to be stepped.
+
+        precursor_df_filtered : pd.DataFrame
+            Filtered precursor dataframe (see filter_dfs).
+
+        fragments_df_filtered : pd.DataFrame
+            Filtered fragment dataframe (see filter_dfs).
+        """
         self.reporter.log_string(
             "=== checking if optimization conditions were reached ===",
         )
 
         for optimizer in optimizers:
             optimizer.step(precursor_df_filtered, fragments_df_filtered)
+            self.reporter.log_string(
+                f"=== Optimization of {optimizer.parameter_name} has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
+                verbosity="progress",
+            )
 
         self.reporter.log_string(
             "==============================================",
         )
 
-        self.reporter.log_string(
-            f"=== Optimization has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
-            verbosity="progress",
-        )
-
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
 

From 9e8829b52d0869169d4633b5e28d2ddec83f2bc1 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 16:02:36 +0200
Subject: [PATCH 192/250] fix import

---
 alphadia/workflow/base.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py
index a0e6874b..08b16143 100644
--- a/alphadia/workflow/base.py
+++ b/alphadia/workflow/base.py
@@ -3,7 +3,8 @@
 import os
 
 # alpha family imports
-from alphabase.spectral_library.base import SpecLibBase, SpecLibFlat
+from alphabase.spectral_library.base import SpecLibBase
+from alphabase.spectral_library.flat import SpecLibFlat
 
 # alphadia imports
 from alphadia.data import alpharaw, bruker

From 9ffdc3d4eb90b13928666cb91cc597e917797d50 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 16:02:46 +0200
Subject: [PATCH 193/250] fix import

---
 tests/unit_tests/test_outputtransform.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py
index e98e1afc..23f5da92 100644
--- a/tests/unit_tests/test_outputtransform.py
+++ b/tests/unit_tests/test_outputtransform.py
@@ -163,6 +163,3 @@ def test_output_transform():
             assert np.corrcoef(protein_df[i], protein_df[j])[0, 0] > 0.5
 
     shutil.rmtree(temp_folder)
-
-
-test_output_transform()

From cb3e42c67eb3d579f4ae6b6d06519d979dc6069d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 16:34:22 +0200
Subject: [PATCH 194/250] allow definition of target RT as fraction

---
 alphadia/workflow/peptidecentric.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index deb2b86e..00a118bd 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -284,9 +284,15 @@ def get_ordered_optimizers(self):
             )
 
         if config_search["target_rt_tolerance"] > 0:
+            gradient_length = self.dia_data.rt_values.max()
+            target_rt_error = (
+                config_search["target_rt_tolerance"]
+                if config_search["target_rt_tolerance"] > 1
+                else config_search["target_rt_tolerance"] * gradient_length
+            )
             rt_optimizer = optimization.TargetedRTOptimizer(
                 self.optimization_manager.rt_error,
-                config_search["target_rt_tolerance"],
+                target_rt_error,
                 self,
             )
         else:

From 60d336c5f015036a6fefb55ccf89df0287b28e74 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 17:20:39 +0200
Subject: [PATCH 195/250] improve documentation of contract

---
 alphadia/constants/default.yaml      | 6 +++++-
 gui/workflows/PeptideCentric.v1.json | 4 ++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index ca72e4bb..b14ac364 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -88,9 +88,13 @@ search:
   compete_for_fragments: True
 
   target_num_candidates: 2
+  # target ms1 tolerance in ppm
   target_ms1_tolerance: 15
+  # target ms2 tolerance in ppm
   target_ms2_tolerance: 15
+  # target ion mobility tolerance in 1/K_0
   target_mobility_tolerance: 0.04
+  # target retention time tolerance in seconds if > 1, or a proportion of the total gradient length if < 1
   target_rt_tolerance: 60
 
   quant_window: 3
@@ -138,7 +142,7 @@ search_initial:
   # initial ion mobility tolerance in 1/K_0
   initial_mobility_tolerance: 0.08
 
-  # initial retention time tolerance in seconds
+  # initial retention time tolerance in seconds if > 1, or a proportion of the total gradient length if < 1
   initial_rt_tolerance: 240
 
 selection_config:
diff --git a/gui/workflows/PeptideCentric.v1.json b/gui/workflows/PeptideCentric.v1.json
index b302bb73..31bf8e47 100644
--- a/gui/workflows/PeptideCentric.v1.json
+++ b/gui/workflows/PeptideCentric.v1.json
@@ -318,7 +318,7 @@
                     "id": "target_rt_tolerance",
                     "name": "RT Tolerance",
                     "value": 100,
-                    "description": "Retention time tolerance in seconds. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
+                    "description": "Retention time tolerance in seconds if greater than 1 or as a proportion of the gradient length if less than 1. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
                     "type": "float"
                 },
                 {
@@ -434,7 +434,7 @@
                     "id": "initial_rt_tolerance",
                     "name": "RT Tolerance",
                     "value": 240,
-                    "description": "Initial retention time tolerance in seconds.",
+                    "description": "Initial retention time tolerance in seconds if greater than 1 or as a proportion of the gradient length if less than 1.",
                     "type": "float"
                 }
             ]

From 00d843d6effa7b0cc94900621505096ca8d3abec Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 18:09:15 +0200
Subject: [PATCH 196/250] take the least batch index from optimization to start
 the next round

---
 alphadia/workflow/optimization.py   | 48 +++++++++++++----------------
 alphadia/workflow/peptidecentric.py |  3 ++
 2 files changed, 25 insertions(+), 26 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 5ec252ce..cdd3e0a1 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -134,6 +134,7 @@ def step(
                     "score_cutoff": self.workflow.optimization_manager.score_cutoff,
                     "fwhm_rt": self.workflow.optimization_manager.fwhm_rt,
                     "fwhm_mobility": self.workflow.optimization_manager.fwhm_mobility,
+                    "batch_idx": self.workflow.optlock.batch_idx,
                 }
             ]
         )
@@ -143,7 +144,7 @@ def step(
         if just_converged:
             self.has_converged = True
 
-            self._update_optimization_manager()
+            self._update_workflow()
 
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
@@ -306,14 +307,14 @@ def _find_index_of_optimum(self):
         else:
             return self.history_df[self.feature_name].idxmax()
 
-    def _update_optimization_manager(self):
+    def _update_workflow(self):
         """Updates the optimization manager with the results of the optimization, namely:
             the classifier version,
             the optimal parameter,
             score cutoff,
             FWHM_RT,
             and FWHM_mobility
-        at the optimal parameter.
+        at the optimal parameter. Also updates the optlock with the smallest batch index.
 
         """
         index_of_optimum = self._find_index_of_optimum()
@@ -343,6 +344,12 @@ def _update_optimization_manager(self):
             {"fwhm_mobility": fwhm_mobility_at_optimum}
         )
 
+        self.workflow.optlock.batch_idx = self.history_df[
+            "batch_idx"
+        ].min()  # Typically (although not necessarily) the optimal parameter is compatible with the smallest batch index is used.
+        # If optimization has proceeded far beyond the optimal value, then it is possible that the most recent batch index will be bigger.
+        # If the optimal parameter needs a larger batch size to reach the target, the batch index will just increase in the next step so no harm is done.
+
     @abstractmethod
     def _get_feature_value(
         self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame
@@ -483,12 +490,6 @@ def __init__(
         self.estimator_group_name = "precursor"
         self.estimator_name = "rt"
         self.feature_name = "precursor_proportion_detected"
-        self.maximal_decrease = workflow.config["optimization"]["rt_error"][
-            "maximal_decrease"
-        ]
-        self.minimum_proportion_of_maximum = workflow.config["optimization"][
-            "rt_error"
-        ]["minimum_proportion_of_maximum"]
         super().__init__(initial_parameter, workflow, reporter)
 
     def _get_feature_value(
@@ -618,7 +619,7 @@ def __init__(
 
 
 class OptimizationLock:
-    def __init__(self, library, config: dict):
+    def __init__(self, library: SpecLibFlat, config: dict):
         """Sets and updates the optimization lock, which is the data used for calibration and optimization of the search parameters.
 
         Parameters
@@ -676,7 +677,9 @@ def set_batch_plan(self):
 
         self.batch_plan = plan
 
-    def update_with_extraction(self, feature_df, fragment_df):
+    def update_with_extraction(
+        self, feature_df: pd.DataFrame, fragment_df: pd.DataFrame
+    ):
         """Extract features and fragments from the current batch of the optimization lock.
 
         Parameters
@@ -693,7 +696,7 @@ def update_with_extraction(self, feature_df, fragment_df):
 
         self.total_elution_groups = self.features_df.elution_group_idx.nunique()
 
-    def update_with_fdr(self, precursor_df):
+    def update_with_fdr(self, precursor_df: pd.DataFrame):
         """Calculates the number of precursors at 1% FDR for the current optimization lock and determines if it is sufficient to perform calibration and optimization.
 
         Parameters
@@ -753,25 +756,16 @@ def update(self):
         """
         if not self.has_target_num_precursors:
             self.increase_batch_idx()
-            eg_idxes = self.elution_group_order[
-                self.start_idx : self.stop_idx
-            ]  # Take only additional elution groups in the new step of the batch plan.
-            # The target has not been reached and hence recalibration and optimization have not been performed, so previous extractions can be re-used.
 
         else:
             self.decrease_batch_idx()
-            eg_idxes = self.elution_group_order[
-                : self.stop_idx
-            ]  # Take all elution groups up the the stop index.
-            # The target has been reached and hence the recalibration and optimization have been performed, so everything must be extracted afresh.
-
             self.feature_dfs = []
             self.fragment_dfs = []
 
         eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
         self.set_batch_dfs(eg_idxes)
 
-    def set_batch_dfs(self, eg_idxes):
+    def set_batch_dfs(self, eg_idxes: None | np.ndarray = None):
         """
         Sets the batch library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
 
@@ -780,6 +774,8 @@ def set_batch_dfs(self, eg_idxes):
         eg_idxes: np.ndarray
             The elution group indexes to use for the next round of optimization.
         """
+        if eg_idxes is None:
+            eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
         self.batch_library = SpecLibFlat()
         self.batch_library._precursor_df, (self.batch_library._fragment_df,) = (
             remove_unused_fragments(
@@ -793,15 +789,15 @@ def set_batch_dfs(self, eg_idxes):
         )
 
     @property
-    def features_df(self):
+    def features_df(self) -> pd.DataFrame:
         return pd.concat(self.feature_dfs)
 
     @property
-    def fragments_df(self):
+    def fragments_df(self) -> pd.DataFrame:
         return pd.concat(self.fragment_dfs)
 
     @property
-    def start_idx(self):
+    def start_idx(self) -> int:
         if self.has_target_num_precursors:
             return 0
         elif self.batch_idx >= len(self.batch_plan):
@@ -810,5 +806,5 @@ def start_idx(self):
             return self.batch_plan[self.batch_idx][0]
 
     @property
-    def stop_idx(self):
+    def stop_idx(self) -> int:
         return self.batch_plan[self.batch_idx][1]
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 00a118bd..9bbf4a51 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -418,6 +418,9 @@ def search_parameter_optimization(self):
                         verbosity="progress",
                     )
 
+                    self.optlock.set_batch_dfs()
+                    self.optlock.update_with_calibration(self.calibration_manager)
+
                     for optimizer in optimizers:
                         optimizer.plot()
 

From c33be4283a9bb54f28e76cf8a5ced05c236ce6d2 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 18:20:48 +0200
Subject: [PATCH 197/250] use optimum not minimum

---
 alphadia/workflow/optimization.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index cdd3e0a1..96cbea73 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -344,9 +344,8 @@ def _update_workflow(self):
             {"fwhm_mobility": fwhm_mobility_at_optimum}
         )
 
-        self.workflow.optlock.batch_idx = self.history_df[
-            "batch_idx"
-        ].min()  # Typically (although not necessarily) the optimal parameter is compatible with the smallest batch index is used.
+        batch_index_at_optimum = self.history_df["batch_idx"].loc[index_of_optimum]
+        self.workflow.optlock.batch_idx = batch_index_at_optimum  # Typically (although not necessarily) the optimal parameter is compatible with the smallest batch index is used.
         # If optimization has proceeded far beyond the optimal value, then it is possible that the most recent batch index will be bigger.
         # If the optimal parameter needs a larger batch size to reach the target, the batch index will just increase in the next step so no harm is done.
 

From 5e9130c278be0e361d995ee2176a42b958310eb5 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Wed, 28 Aug 2024 18:45:25 +0200
Subject: [PATCH 198/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 8b8cc6e2..36db64bf 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -450,6 +450,7 @@ def create_workflow_instance():
 
     class MockOptlock:
         total_elution_groups = 2000
+        batch_idx = 1
 
     workflow.optlock = MockOptlock()
 

From 2f21b0cb781e6c809059542150c7f10b048cf4ad Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 08:58:42 +0200
Subject: [PATCH 199/250] update docstring

---
 alphadia/workflow/optimization.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 96cbea73..c34b1070 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -314,7 +314,7 @@ def _update_workflow(self):
             score cutoff,
             FWHM_RT,
             and FWHM_mobility
-        at the optimal parameter. Also updates the optlock with the smallest batch index.
+        at the optimal parameter. Also updates the optlock with the batch index at the optimum.
 
         """
         index_of_optimum = self._find_index_of_optimum()
@@ -345,9 +345,7 @@ def _update_workflow(self):
         )
 
         batch_index_at_optimum = self.history_df["batch_idx"].loc[index_of_optimum]
-        self.workflow.optlock.batch_idx = batch_index_at_optimum  # Typically (although not necessarily) the optimal parameter is compatible with the smallest batch index is used.
-        # If optimization has proceeded far beyond the optimal value, then it is possible that the most recent batch index will be bigger.
-        # If the optimal parameter needs a larger batch size to reach the target, the batch index will just increase in the next step so no harm is done.
+        self.workflow.optlock.batch_idx = batch_index_at_optimum
 
     @abstractmethod
     def _get_feature_value(

From d144c9b7efc4ccd0d04b6216f11ed31f79638737 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 09:29:34 +0200
Subject: [PATCH 200/250] update optimizers to allow skipping

---
 alphadia/constants/default.yaml   |  3 +
 alphadia/workflow/optimization.py | 99 +++++++++++++++++++++++--------
 2 files changed, 78 insertions(+), 24 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index ca72e4bb..11d23e4e 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -113,6 +113,9 @@ calibration:
   # the minimum number of steps that a given optimizer must take before it can be said to have converged
   min_steps: 2
 
+  # the maximum number of times an automatic optimizer can be skipped before it is considered to have converged
+  max_skips: 2
+
   # TODO: remove this parameter
   final_full_calibration: False
 
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index c34b1070..9d945ee1 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -49,10 +49,10 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
         ----------
 
         precursors_df: pd.DataFrame
-            The precursor dataframe for the search
+            The filtered precursor dataframe for the search (see peptidecentric.PeptideCentricWorkflow.filter_dfs).
 
         fragments_df: pd.DataFrame
-            The fragment dataframe for the search
+            The filtered fragment dataframe for the search (see peptidecentric.PeptideCentricWorkflow.filter_dfs).
 
 
         """
@@ -88,6 +88,7 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
         self.num_prev_optimizations = 0
+        self._num_consecutive_skips = 0
         self.update_factor = workflow.config["optimization"][self.parameter_name][
             "automatic_update_factor"
         ]
@@ -112,7 +113,9 @@ def step(
         precursors_df: pd.DataFrame,
         fragments_df: pd.DataFrame,
     ):
-        """See base class. The feature is used to track the progres of the optimization (stored in .feature) and determine whether it has converged."""
+        """See base class. The feature is used to track the progres of the optimization and determine whether it has converged.
+        It also resets the internal counter for the number of consecutive skips.
+        """
         if self.has_converged:
             self.reporter.log_string(
                 f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]} found after {len(self.history_df)} searches.",
@@ -120,28 +123,12 @@ def step(
             )
             return
 
+        self.num_consecutive_skips = 0
         self.num_prev_optimizations += 1
-        new_row = pd.DataFrame(
-            [
-                {
-                    "parameter": self.workflow.optimization_manager.__dict__[
-                        self.parameter_name
-                    ],
-                    self.feature_name: self._get_feature_value(
-                        precursors_df, fragments_df
-                    ),
-                    "classifier_version": self.workflow.fdr_manager.current_version,
-                    "score_cutoff": self.workflow.optimization_manager.score_cutoff,
-                    "fwhm_rt": self.workflow.optimization_manager.fwhm_rt,
-                    "fwhm_mobility": self.workflow.optimization_manager.fwhm_mobility,
-                    "batch_idx": self.workflow.optlock.batch_idx,
-                }
-            ]
-        )
-        self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
-        just_converged = self._check_convergence()
 
-        if just_converged:
+        self._update_history(precursors_df, fragments_df)
+
+        if self._just_converged:
             self.has_converged = True
 
             self._update_workflow()
@@ -165,6 +152,20 @@ def step(
                 verbosity="progress",
             )
 
+    def skip(self):
+        """Increments the internal counter for the number of consecutive skips and checks if the optimization should be stopped."""
+        self._num_consecutive_skips += 1
+        if (
+            self.num_consecutive_skips
+            > self.workflow.config["optimization"]["max_skips"]
+        ):
+            self.has_converged = True
+            self._update_workflow()
+            self.reporter.log_string(
+                f"✅ {self.parameter_name:<15}: optimization complete. Optimal parameter {self.workflow.optimization_manager.__dict__[self.parameter_name]:.4f} found after {len(self.history_df)} searches.",
+                verbosity="progress",
+            )
+
     def plot(self):
         """Plot the value of the feature used to assess optimization progress against the parameter value, for each value tested."""
         fig, ax = plt.subplots()
@@ -222,7 +223,57 @@ def _propose_new_parameter(self, df: pd.DataFrame):
             self.estimator_group_name, self.estimator_name
         ).ci(df, self.update_percentile_range)
 
-    def _check_convergence(self):
+    def _update_history(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
+        """This method updates the history dataframe with relevant values.
+
+        Parameters
+        ----------
+        precursors_df: pd.DataFrame
+            The filtered precursor dataframe for the search.
+
+        fragments_df: pd.DataFrame
+            The filtered fragment dataframe for the search.
+
+        """
+        new_row = pd.DataFrame(
+            [
+                {
+                    "parameter": self.workflow.optimization_manager.__dict__[
+                        self.parameter_name
+                    ],
+                    self.feature_name: self._get_feature_value(
+                        precursors_df, fragments_df
+                    ),
+                    "classifier_version": self.workflow.fdr_manager.current_version,
+                    "score_cutoff": self.workflow.optimization_manager.score_cutoff,
+                    "fwhm_rt": self.workflow.optimization_manager.fwhm_rt,
+                    "fwhm_mobility": self.workflow.optimization_manager.fwhm_mobility,
+                    "batch_idx": self.workflow.optlock.batch_idx,
+                }
+            ]
+        )
+        self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
+
+    @property
+    def _unnecessary_to_continue(self):
+        """This function checks if the optimization has already been optimized sufficiently many times and if it has been skipped too many times at the current parameter value.
+        (Being skipped at least twice indicates that the current parameter proposal significantly reduces the number of identified precursors and is unlikely to be optimal.)
+
+        Returns
+        -------
+        bool
+            True if the optimization has already been performed the minimum number of times and the maximum number of skips has been reached, False otherwise.
+
+        """
+        return (
+            self.num_prev_optimizations
+            > self.workflow.config["calibration"]["min_steps"]
+            and self.num_consecutive_skips
+            > self.workflow.config["calibration"]["max_skips"]
+        )
+
+    @property
+    def _just_converged(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
         If self.favour_narrower_parameter is False:
             1) This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.

From 4fb4b080a8f1307655e116a5399fd8f08956e20a Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 11:25:18 +0200
Subject: [PATCH 201/250] consider automatic optimizers to have converged if
 they are skipped sufficiently many times

---
 alphadia/workflow/optimization.py   | 15 +++++++--
 alphadia/workflow/peptidecentric.py | 30 ++++++++++++++++--
 tests/unit_tests/test_workflow.py   | 48 +++++++++++++++++++++++++++++
 3 files changed, 88 insertions(+), 5 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index df052a1d..56649f98 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -59,6 +59,11 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
         pass
 
+    @abstractmethod
+    def skip(self):
+        """This method is used to record skipping of optimization. It can be overwritten with an empty function if skipping of optimization does not need to be recorded"""
+        pass
+
     @abstractmethod
     def plot(self):
         """This method plots relevant information about optimization of the search parameter. This can be overwritten with an empty function if there is nothing to plot."""
@@ -88,7 +93,7 @@ def __init__(
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
         self.num_prev_optimizations = 0
-        self._num_consecutive_skips = 0
+        self.num_consecutive_skips = 0
         self.update_factor = workflow.config["optimization"][self.parameter_name][
             "automatic_update_factor"
         ]
@@ -154,10 +159,10 @@ def step(
 
     def skip(self):
         """Increments the internal counter for the number of consecutive skips and checks if the optimization should be stopped."""
-        self._num_consecutive_skips += 1
+        self.num_consecutive_skips += 1
         if (
             self.num_consecutive_skips
-            > self.workflow.config["optimization"]["max_skips"]
+            >= self.workflow.config["calibration"]["max_skips"]
         ):
             self.has_converged = True
             self._update_workflow()
@@ -525,6 +530,10 @@ def plot(self):
         """See base class. There is nothing of interest to plot here."""
         pass
 
+    def skip(self):
+        """See base class. There is nothing to record when skipping optimization here."""
+        pass
+
 
 class AutomaticRTOptimizer(AutomaticOptimizer):
     def __init__(
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index ab166fea..a3e7f21b 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -419,6 +419,7 @@ def search_parameter_optimization(self):
                         verbosity="progress",
                     )
 
+                    self.optlock.has_target_num_precursors = True
                     self.optlock.set_batch_dfs()
                     self.optlock.update_with_calibration(self.calibration_manager)
 
@@ -439,6 +440,8 @@ def search_parameter_optimization(self):
                             self.calibration_manager
                         )  # This is needed so that the addition to the batch libary has the most recent calibration
 
+                        self._skip_all_optimizers(optimizers)
+
                 else:
                     precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
                         precursor_df, self.optlock.fragments_df
@@ -529,7 +532,7 @@ def _initiate_search_parameter_optimization(self):
 
     def _step_all_optimizers(
         self,
-        optimizers: list,
+        optimizers: list[optimization.BaseOptimizer],
         precursor_df_filtered: pd.DataFrame,
         fragments_df_filtered: pd.DataFrame,
     ):
@@ -551,16 +554,39 @@ def _step_all_optimizers(
         )
 
         for optimizer in optimizers:
-            optimizer.step(precursor_df_filtered, fragments_df_filtered)
             self.reporter.log_string(
                 f"=== Optimization of {optimizer.parameter_name} has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
                 verbosity="progress",
             )
+            optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
         self.reporter.log_string(
             "==============================================",
         )
 
+    def _skip_all_optimizers(
+        self,
+        optimizers: list[optimization.BaseOptimizer],
+    ):
+        """All optimizers currently in use are stepped and their current state is logged.
+
+        Parameters
+        ----------
+        optimizers : list
+            List of optimizers to be stepped.
+
+        """
+        self.reporter.log_string(
+            "=== skipping optimization until target number of precursors are found ===",
+        )
+
+        for optimizer in optimizers:
+            self.reporter.log_string(
+                f"=== Optimization of {optimizer.parameter_name} has been skipped {optimizer.num_consecutive_skips} time(s); maximum number is {self.config['calibration']['max_skips']} ===",
+                verbosity="progress",
+            )
+            optimizer.skip()
+
     def filter_dfs(self, precursor_df, fragments_df):
         """Filters precursor and fragment dataframes to extract the most reliable examples for calibration.
 
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 36db64bf..34ea6070 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -997,3 +997,51 @@ def test_configurability():
     assert ordered_optimizers[1][1].update_factor == 1.2
 
     assert ordered_optimizers[2][0].parameter_name == "mobility_error"
+
+
+def test_optimizer_skipping():
+    workflow = create_workflow_instance()
+
+    calibration_test_df1 = calibration_testdata()
+    calibration_test_df2 = calibration_testdata()
+
+    workflow.calibration_manager.fit(calibration_test_df1, "precursor", plot=False)
+
+    rt_optimizer = optimization.AutomaticRTOptimizer(
+        100,
+        workflow,
+    )
+
+    assert rt_optimizer.has_converged is False
+    assert rt_optimizer.parameter_name == "rt_error"
+
+    workflow.fdr_manager._current_version += 1
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
+
+    assert len(rt_optimizer.history_df) == 1
+
+    rt_optimizer.skip()
+
+    rt_optimizer.skip()
+
+    assert len(rt_optimizer.history_df) == 1
+    assert rt_optimizer.has_converged is True
+
+    rt_optimizer = optimization.TargetedRTOptimizer(
+        100,
+        10,
+        workflow,
+    )
+
+    workflow.fdr_manager._current_version += 1
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
+
+    rt_optimizer.skip()
+
+    rt_optimizer.skip()
+
+    assert rt_optimizer.has_converged is False
+
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
+
+    assert rt_optimizer.has_converged is True

From 7cb6b64d3a02959bf90400832467f114369ec1cd Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 11:28:41 +0200
Subject: [PATCH 202/250] review comments

---
 alphadia/workflow/optimization.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 485aa992..f2a47638 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -345,6 +345,8 @@ def _update_workflow(self):
         )
 
         batch_index_at_optimum = self.history_df["batch_idx"].loc[index_of_optimum]
+        # Take the batch index of the optimum, at the cost of potentially getting the batch library twice if this is the same as the current batch index.
+        # The time impact of this is negligible and the benefits can be significant.
         self.workflow.optlock.batch_idx = batch_index_at_optimum
 
     @abstractmethod
@@ -768,8 +770,8 @@ def set_batch_dfs(self, eg_idxes: None | np.ndarray = None):
 
         Parameters
         ----------
-        eg_idxes: np.ndarray
-            The elution group indexes to use for the next round of optimization.
+        eg_idxes: None | np.ndarray
+            The elution group indexes to use for the next round of optimization. If None, the eg_idxes for the current self.start_idx and self.stop_idx are used.
         """
         if eg_idxes is None:
             eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]

From fd08419833e4d569dc551b578b63f4b786b64918 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 11:33:45 +0200
Subject: [PATCH 203/250] use property and use strict inequality

---
 alphadia/constants/default.yaml   | 2 +-
 alphadia/workflow/optimization.py | 5 +----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 11d23e4e..fea74bca 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -114,7 +114,7 @@ calibration:
   min_steps: 2
 
   # the maximum number of times an automatic optimizer can be skipped before it is considered to have converged
-  max_skips: 2
+  max_skips: 1
 
   # TODO: remove this parameter
   final_full_calibration: False
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 56649f98..03793270 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -160,10 +160,7 @@ def step(
     def skip(self):
         """Increments the internal counter for the number of consecutive skips and checks if the optimization should be stopped."""
         self.num_consecutive_skips += 1
-        if (
-            self.num_consecutive_skips
-            >= self.workflow.config["calibration"]["max_skips"]
-        ):
+        if self._unnecessary_to_continue:
             self.has_converged = True
             self._update_workflow()
             self.reporter.log_string(

From 1ebfdeacb10d56d118cd82b31d8df6c6d91f120e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 11:59:23 +0200
Subject: [PATCH 204/250] improve logging

---
 alphadia/workflow/peptidecentric.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index a3e7f21b..16d1f805 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -555,7 +555,7 @@ def _step_all_optimizers(
 
         for optimizer in optimizers:
             self.reporter.log_string(
-                f"=== Optimization of {optimizer.parameter_name} has been performed {optimizer.num_prev_optimizations} times; minimum number is {self.config['calibration']['min_steps']} ===",
+                f"=== Optimization of {optimizer.parameter_name} has been performed {optimizer.num_prev_optimizations + 1} time(s); minimum number is {self.config['calibration']['min_steps']} ===",
                 verbosity="progress",
             )
             optimizer.step(precursor_df_filtered, fragments_df_filtered)
@@ -582,7 +582,7 @@ def _skip_all_optimizers(
 
         for optimizer in optimizers:
             self.reporter.log_string(
-                f"=== Optimization of {optimizer.parameter_name} has been skipped {optimizer.num_consecutive_skips} time(s); maximum number is {self.config['calibration']['max_skips']} ===",
+                f"=== Optimization of {optimizer.parameter_name} has been skipped {optimizer.num_consecutive_skips + 1} time(s); maximum number is {self.config['calibration']['max_skips']} ===",
                 verbosity="progress",
             )
             optimizer.skip()

From 63421b5a1c1d95f0d049b61946c251eef6588c4d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 12:18:02 +0200
Subject: [PATCH 205/250] refactoring

---
 alphadia/constants/default.yaml   | 18 ++++++++--------
 alphadia/workflow/optimization.py | 36 ++++++++++++++++++++-----------
 2 files changed, 32 insertions(+), 22 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index d42a2874..026d8507 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -258,7 +258,7 @@ optimization:
   #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
   #   - favour_narrower_parameter: if True, the optimization will not take the value that maximizes the feature used for optimization, but instead the smallest value compatible with the minimum_proportion_of_maximum value.
   #       This setting can be useful for optimizing parameters for which many parameter values have similar feature values and therefore favouring narrower parameters helps to overcome noise.
-  #   - maximal_decrease: the maximal decrease of the parameter value before stopping optimization (only relevant if favour_narrower_parameter is True)
+  #   - maximal_decrease: the maximal decrease of the parameter value before stopping optimization (only relevant if favour_narrower_parameter is True). For example, a value of 0.2 indicates a 20% decrease.
   #   - minimum_proportion_of_maximum: the minimum proportion of the maximum value of the parameter that the designated optimum should have (only relevant if favour_narrower_parameter is True)
   ms2_error:
       targeted_update_percentile_range: 0.95
@@ -266,32 +266,32 @@ optimization:
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
-      maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.9
+      maximal_decrease: 0.2
+      maximum_decrease_from_maximum: 0.1
   ms1_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
-      maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.9
+      maximal_decrease: 0.2
+      maximum_decrease_from_maximum: 0.1
   mobility_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
       favour_narrower_parameter: False
-      maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.9
+      maximal_decrease: 0.2
+      maximum_decrease_from_maximum: 0.1
   rt_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
       favour_narrower_parameter: True
-      maximal_decrease: 0.8
-      minimum_proportion_of_maximum: 0.9
+      maximal_decrease: 0.2
+      maximum_decrease_from_maximum: 0.1
 
 # configuration for the optimization manager
 # initial parameters, will be optimized
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 6be63440..d726ea8a 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -109,9 +109,9 @@ def __init__(
             self.maximal_decrease = workflow.config["optimization"][
                 self.parameter_name
             ]["maximal_decrease"]
-            self.minimum_proportion_of_maximum = workflow.config["optimization"][
+            self.maximum_decrease_from_maximum = workflow.config["optimization"][
                 self.parameter_name
-            ]["minimum_proportion_of_maximum"]
+            ]["maximum_decrease_from_maximum"]
 
     def step(
         self,
@@ -301,15 +301,18 @@ def _just_converged(self):
 
             feature_history = self.history_df[self.feature_name]
             feature_substantially_decreased = (
-                feature_history.iloc[-1]
-                < self.maximal_decrease * feature_history.iloc[-2]
-                and feature_history.iloc[-1]
-                < self.maximal_decrease * feature_history.iloc[-3]
-            )
+                feature_history.iloc[-1] - feature_history.iloc[-2]
+            ) / np.abs(feature_history.iloc[-2]) < -self.maximal_decrease and (
+                feature_history.iloc[-1] - feature_history.iloc[-3]
+            ) / np.abs(feature_history.iloc[-3]) < -self.maximal_decrease
 
             parameter_history = self.history_df["parameter"]
             parameter_not_substantially_changed = (
-                parameter_history.iloc[-1] / parameter_history.iloc[-2] > 0.95
+                np.abs(
+                    (parameter_history.iloc[-1] - parameter_history.iloc[-2])
+                    / parameter_history.iloc[-2]
+                )
+                < 0.05
             )
 
             return min_steps_reached and (
@@ -326,8 +329,12 @@ def _just_converged(self):
 
             return (
                 min_steps_reached
-                and feature_history.iloc[-1] < 1.1 * feature_history.iloc[-2]
-                and feature_history.iloc[-1] < 1.1 * feature_history.iloc[-3]
+                and (feature_history.iloc[-1] - feature_history.iloc[-2])
+                / np.abs(feature_history.iloc[-2])
+                < 0.1
+                and (feature_history.iloc[-1] - feature_history.iloc[-3])
+                / np.abs(feature_history.iloc[-3])
+                < 0.1
             )
 
     def _find_index_of_optimum(self):
@@ -335,7 +342,7 @@ def _find_index_of_optimum(self):
         if self.favour_narrower_parameter is False:
             The index at optimum is the index of the parameter value that maximizes the feature.
         if self.favour_narrower_parameter is True:
-            The index at optimum is the index of the minimal parameter value whose feature value is at least self.minimum_proportion_of_maximum of the maximum value of the feature.
+            The index at optimum is the index of the minimal parameter value whose feature value is at least self.maximum_decrease_from_maximum of the maximum value of the feature.
 
         Returns
         -------
@@ -349,10 +356,13 @@ def _find_index_of_optimum(self):
         """
 
         if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+            maximum_feature_value = self.history_df[self.feature_name].max()
             rows_within_thresh_of_max = self.history_df.loc[
                 self.history_df[self.feature_name]
-                > self.history_df[self.feature_name].max()
-                * self.minimum_proportion_of_maximum
+                > (
+                    maximum_feature_value
+                    - self.maximum_decrease_from_maximum * np.abs(maximum_feature_value)
+                )
             ]
             index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()
             return index_of_optimum

From 8f3c6d39506836b1604946e2f4d0901225275384 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 12:23:55 +0200
Subject: [PATCH 206/250] update unit test

---
 alphadia/workflow/optimization.py | 9 ++++++---
 tests/unit_tests/test_workflow.py | 9 ++++++++-
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index bfb6531f..2cc2c45c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -267,12 +267,15 @@ def _unnecessary_to_continue(self):
             True if the optimization has already been performed the minimum number of times and the maximum number of skips has been reached, False otherwise.
 
         """
-        return (
+        min_steps_reached = (
             self.num_prev_optimizations
-            > self.workflow.config["calibration"]["min_steps"]
-            and self.num_consecutive_skips
+            >= self.workflow.config["calibration"]["min_steps"]
+        )
+        max_skips_reached = (
+            self.num_consecutive_skips
             > self.workflow.config["calibration"]["max_skips"]
         )
+        return min_steps_reached and max_skips_reached
 
     @property
     def _just_converged(self):
diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index 34ea6070..afd2d8e6 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -1020,11 +1020,15 @@ def test_optimizer_skipping():
 
     assert len(rt_optimizer.history_df) == 1
 
+    rt_optimizer.step(calibration_test_df1, calibration_test_df2)
+
+    assert rt_optimizer.has_converged is False
+
     rt_optimizer.skip()
 
     rt_optimizer.skip()
 
-    assert len(rt_optimizer.history_df) == 1
+    assert len(rt_optimizer.history_df) == 2
     assert rt_optimizer.has_converged is True
 
     rt_optimizer = optimization.TargetedRTOptimizer(
@@ -1045,3 +1049,6 @@ def test_optimizer_skipping():
     rt_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert rt_optimizer.has_converged is True
+
+
+test_optimizer_skipping()

From 8c4d3c83c1f7ee7948a9d3b752faec6b882d9bdd Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 14:13:26 +0200
Subject: [PATCH 207/250] split favour_narrower_parameter effects in two

---
 alphadia/constants/default.yaml   | 25 ++++++++++++++++---------
 alphadia/workflow/optimization.py | 22 ++++++++++++++--------
 2 files changed, 30 insertions(+), 17 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 026d8507..8fba4bab 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -250,47 +250,54 @@ optimization:
   # Example: [['ms1_error', 'ms2_error', 'rt_error', 'mobility_error']] means that all parameters are optimized simultaneously.
   # Example: [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]] means that the parameters are optimized sequentially in the order given.
   # Example: [["rt_error"], ["ms1_error", "ms2_error"]] means that first rt_error is optimized, then ms1_error and ms2_error are optimized simultaneously, and mobility_error is not optimized at all.
-  # If order_of_optimizatoin is null, first all targeted optimizers run simultaneously, then any remaining automatic optimizers run sequentially in the order [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]]
+  # If order_of_optimization is null, first all targeted optimizers run simultaneously, then any remaining automatic optimizers run sequentially in the order [["ms2_error"], ["rt_error"], ["ms1_error"], ["mobility_error"]]
   order_of_optimization: null
 
   # Parameters for the update rule for each parameter:
   #   - update_percentile_range: the percentile interval to use (as a decimal)
   #   - update_factor: the factor by which to multiply the result from the percentile interval to get the new parameter value for the next round of search
-  #   - favour_narrower_parameter: if True, the optimization will not take the value that maximizes the feature used for optimization, but instead the smallest value compatible with the minimum_proportion_of_maximum value.
-  #       This setting can be useful for optimizing parameters for which many parameter values have similar feature values and therefore favouring narrower parameters helps to overcome noise.
-  #   - maximal_decrease: the maximal decrease of the parameter value before stopping optimization (only relevant if favour_narrower_parameter is True). For example, a value of 0.2 indicates a 20% decrease.
-  #   - minimum_proportion_of_maximum: the minimum proportion of the maximum value of the parameter that the designated optimum should have (only relevant if favour_narrower_parameter is True)
+  #   - try_narrower_parameters: if True, the optimization will try narrower parameters until a substantial (as determined by maximal_decrease) decrease in the feature used for optimization is observed.
+  #   - maximal_decrease: the maximal decrease of the parameter value before stopping optimization (only relevant if favour_narrower_parameter is True).
+  #     For example, a value of 0.2 indicates up to 20% decrease from the previous parameter is permissible.
+  #   - favour_narrower_optimum: if True, the optimization will not take the value that maximizes the feature used for optimization, but instead the smallest value compatible with the maximum_decrease_from_maximum value.
+  #     This setting can be useful for optimizing parameters for which many parameter values have similar feature values and therefore favouring narrower parameters helps to overcome noise.
+  #   - maximum_decrease_from_maximum: the maximum proportional decrease from the maximum value of the parameter that the designated optimum should have (only relevant if favour_narrower_optimum is True).
+  #     For example, a value of 0.1 indicates that the optimum should no more than 10% less than the maximum value.
   ms2_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
-      favour_narrower_parameter: False
+      try_narrower_values: False
       maximal_decrease: 0.2
+      favour_narrower_optimum: False
       maximum_decrease_from_maximum: 0.1
   ms1_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
-      favour_narrower_parameter: False
+      try_narrower_values: False
       maximal_decrease: 0.2
+      favour_narrower_optimum: False
       maximum_decrease_from_maximum: 0.1
   mobility_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
-      favour_narrower_parameter: False
+      try_narrower_values: False
       maximal_decrease: 0.2
+      favour_narrower_optimum: False
       maximum_decrease_from_maximum: 0.1
   rt_error:
       targeted_update_percentile_range: 0.95
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
-      favour_narrower_parameter: True
+      try_narrower_values: True
       maximal_decrease: 0.2
+      favour_narrower_optimum: True
       maximum_decrease_from_maximum: 0.1
 
 # configuration for the optimization manager
diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index d726ea8a..9f608b1c 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -101,14 +101,20 @@ def __init__(
             self.parameter_name
         ]["automatic_update_percentile_range"]
 
-        self.favour_narrower_parameter = workflow.config["optimization"][
-            self.parameter_name
-        ]["favour_narrower_parameter"]
+        self.try_narrower_values = workflow.config["optimization"][self.parameter_name][
+            "try_narrower_values"
+        ]
 
-        if self.favour_narrower_parameter:
+        if self.try_narrower_values:
             self.maximal_decrease = workflow.config["optimization"][
                 self.parameter_name
             ]["maximal_decrease"]
+
+        self.favour_narrower_optimum = workflow.config["optimization"][
+            self.parameter_name
+        ]["favour_narrower_optimum"]
+
+        if self.favour_narrower_optimum:
             self.maximum_decrease_from_maximum = workflow.config["optimization"][
                 self.parameter_name
             ]["maximum_decrease_from_maximum"]
@@ -277,10 +283,10 @@ def _unnecessary_to_continue(self):
     @property
     def _just_converged(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
-        If self.favour_narrower_parameter is False:
+        If self.try_narrower_values is False:
             1) This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
             2) If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-        If self.favour_narrower_parameter is True:
+        If self.try_narrower_values is True:
             1) This function checks if the previous rounds of optimization have led to a meaningful disimprovement in the feature value or if the parameter has not changed substantially.
             2) If not, it continues optimization and appends the proposed new parameter to the list of parameters. If so, it stops optimization and sets the optimal parameter attribute.
 
@@ -293,7 +299,7 @@ def _just_converged(self):
         if len(self.history_df) < 3:
             return False
 
-        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+        if self.try_narrower_values:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             min_steps_reached = (
                 self.num_prev_optimizations
                 >= self.workflow.config["calibration"]["min_steps"]
@@ -355,7 +361,7 @@ def _find_index_of_optimum(self):
 
         """
 
-        if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+        if self.favour_narrower_optimum:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             maximum_feature_value = self.history_df[self.feature_name].max()
             rows_within_thresh_of_max = self.history_df.loc[
                 self.history_df[self.feature_name]

From 693617f80ad04b47b7986fb4fe9ddcc71ab761bf Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 14:31:42 +0200
Subject: [PATCH 208/250] fix unit test

---
 tests/unit_tests/test_workflow.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py
index afd2d8e6..35d7bf2f 100644
--- a/tests/unit_tests/test_workflow.py
+++ b/tests/unit_tests/test_workflow.py
@@ -960,15 +960,16 @@ def test_configurability():
             "rt_error": {
                 "automatic_update_percentile_range": 0.99,
                 "automatic_update_factor": 1.3,
-                "favour_narrower_parameter": True,
-                "maximal_decrease": 0.8,
-                "minimum_proportion_of_maximum": 0.95,
+                "try_narrower_values": True,
+                "maximal_decrease": 0.4,
+                "favour_narrower_optimum": True,
+                "maximum_decrease_from_maximum": 0.3,
             },
             "ms2_error": {
                 "automatic_update_percentile_range": 0.80,
                 "targeted_update_percentile_range": 0.995,
                 "targeted_update_factor": 1.2,
-                "favour_narrower_parameter": False,
+                "favour_narrower_optimum": False,
             },
         }
     )
@@ -1049,6 +1050,3 @@ def test_optimizer_skipping():
     rt_optimizer.step(calibration_test_df1, calibration_test_df2)
 
     assert rt_optimizer.has_converged is True
-
-
-test_optimizer_skipping()

From c9fda5abb96b888658034184f0ac1aabcb59ed29 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 17:51:37 +0200
Subject: [PATCH 209/250] review comments and reset after convergence

---
 alphadia/workflow/optimization.py   | 66 +++++++++++++++++++----------
 alphadia/workflow/peptidecentric.py | 12 +-----
 2 files changed, 45 insertions(+), 33 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index d7781344..4b868374 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -38,7 +38,7 @@ def __init__(
         """
         self.workflow = workflow
         self.reporter = reporting.LogBackend() if reporter is None else reporter
-        self.num_prev_optimizations = 0
+        self._num_prev_optimizations = 0
 
     @abstractmethod
     def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
@@ -61,13 +61,12 @@ def step(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFrame):
 
     @abstractmethod
     def skip(self):
-        """This method is used to record skipping of optimization. It can be overwritten with an empty function if skipping of optimization does not need to be recorded"""
+        """Record skipping of optimization. Can be overwritten with an empty method if there is no need to record skips."""
         pass
 
     @abstractmethod
     def plot(self):
-        """This method plots relevant information about optimization of the search parameter. This can be overwritten with an empty function if there is nothing to plot."""
-
+        """Plots the progress of the optimization. Can be overwritten with an empty method if there is no need to plot the progress."""
         pass
 
 
@@ -92,8 +91,8 @@ def __init__(
         self.history_df = pd.DataFrame()
         self.workflow.optimization_manager.fit({self.parameter_name: initial_parameter})
         self.has_converged = False
-        self.num_prev_optimizations = 0
-        self.num_consecutive_skips = 0
+        self._num_prev_optimizations = 0
+        self._num_consecutive_skips = 0
         self.update_factor = workflow.config["optimization"][self.parameter_name][
             "automatic_update_factor"
         ]
@@ -128,8 +127,12 @@ def step(
             )
             return
 
-        self.num_consecutive_skips = 0
-        self.num_prev_optimizations += 1
+        self._num_consecutive_skips = 0
+        self._num_prev_optimizations += 1
+        self.reporter.log_string(
+            f"=== Optimization of {self.parameter_name} has been performed {self._num_prev_optimizations} time(s); minimum number is {self.workflow.config['calibration']['min_steps']} ===",
+            verbosity="progress",
+        )
 
         self._update_history(precursors_df, fragments_df)
 
@@ -159,8 +162,12 @@ def step(
 
     def skip(self):
         """Increments the internal counter for the number of consecutive skips and checks if the optimization should be stopped."""
-        self.num_consecutive_skips += 1
-        if self._unnecessary_to_continue:
+        self._num_consecutive_skips += 1
+        self.reporter.log_string(
+            f"=== Optimization of {self.parameter_name} has been skipped {self._num_consecutive_skips} time(s); maximum number is {self.workflow.config['calibration']['max_skips']} ===",
+            verbosity="progress",
+        )
+        if self._batch_substantially_bigger:
             self.has_converged = True
             self._update_workflow()
             self.reporter.log_string(
@@ -257,9 +264,9 @@ def _update_history(self, precursors_df: pd.DataFrame, fragments_df: pd.DataFram
         self.history_df = pd.concat([self.history_df, new_row], ignore_index=True)
 
     @property
-    def _unnecessary_to_continue(self):
+    def _batch_substantially_bigger(self):
         """This function checks if the optimization has already been optimized sufficiently many times and if it has been skipped too many times at the current parameter value.
-        (Being skipped at least twice indicates that the current parameter proposal significantly reduces the number of identified precursors and is unlikely to be optimal.)
+        (Being skipped indicates that the current parameter proposal significantly reduces the number of identified precursors and is unlikely to be optimal.)
 
         Returns
         -------
@@ -268,11 +275,11 @@ def _unnecessary_to_continue(self):
 
         """
         min_steps_reached = (
-            self.num_prev_optimizations
+            self._num_prev_optimizations
             >= self.workflow.config["calibration"]["min_steps"]
         )
         max_skips_reached = (
-            self.num_consecutive_skips
+            self._num_consecutive_skips
             > self.workflow.config["calibration"]["max_skips"]
         )
         return min_steps_reached and max_skips_reached
@@ -298,7 +305,7 @@ def _just_converged(self):
 
         if self.favour_narrower_parameter:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             min_steps_reached = (
-                self.num_prev_optimizations
+                self._num_prev_optimizations
                 >= self.workflow.config["calibration"]["min_steps"]
             )
 
@@ -321,7 +328,7 @@ def _just_converged(self):
 
         else:
             min_steps_reached = (
-                self.num_prev_optimizations
+                self._num_prev_optimizations
                 >= self.workflow.config["calibration"]["min_steps"]
             )
 
@@ -474,7 +481,7 @@ def _check_convergence(self, proposed_parameter: float):
 
         """
         min_steps_reached = (
-            self.num_prev_optimizations
+            self._num_prev_optimizations
             >= self.workflow.config["calibration"]["min_steps"]
         )
         return proposed_parameter <= self.target_parameter and min_steps_reached
@@ -505,7 +512,7 @@ def step(
                 verbosity="progress",
             )
             return
-        self.num_prev_optimizations += 1
+        self._num_prev_optimizations += 1
         new_parameter = self._propose_new_parameter(
             precursors_df if self.estimator_group_name == "precursor" else fragments_df
         )
@@ -528,12 +535,12 @@ def step(
                 verbosity="progress",
             )
 
-    def plot(self):
-        """See base class. There is nothing of interest to plot here."""
+    def skip(self):
+        """See base class."""
         pass
 
-    def skip(self):
-        """See base class. There is nothing to record when skipping optimization here."""
+    def plot(self):
+        """See base class"""
         pass
 
 
@@ -824,6 +831,21 @@ def update(self):
         eg_idxes = self.elution_group_order[self.start_idx : self.stop_idx]
         self.set_batch_dfs(eg_idxes)
 
+    def reset_after_convergence(self, calibration_manager):
+        """Resets the optimization lock after all optimizers in a given round of optimization have converged.
+
+        Parameter
+        ---------
+        calibration_manager: manager.CalibrationManager
+            The calibration manager object from the PeptideCentricWorkflow object.
+
+        """
+        self.has_target_num_precursors = True
+        self.feature_dfs = []
+        self.fragment_dfs = []
+        self.set_batch_dfs()
+        self.update_with_calibration(calibration_manager)
+
     def set_batch_dfs(self, eg_idxes: None | np.ndarray = None):
         """
         Sets the batch library to use for the next round of optimization, either adjusting it upwards or downwards depending on whether the target has been reached.
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 16d1f805..4cfd0c41 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -419,9 +419,7 @@ def search_parameter_optimization(self):
                         verbosity="progress",
                     )
 
-                    self.optlock.has_target_num_precursors = True
-                    self.optlock.set_batch_dfs()
-                    self.optlock.update_with_calibration(self.calibration_manager)
+                    self.optlock.reset_after_convergence(self.calibration_manager)
 
                     for optimizer in optimizers:
                         optimizer.plot()
@@ -554,10 +552,6 @@ def _step_all_optimizers(
         )
 
         for optimizer in optimizers:
-            self.reporter.log_string(
-                f"=== Optimization of {optimizer.parameter_name} has been performed {optimizer.num_prev_optimizations + 1} time(s); minimum number is {self.config['calibration']['min_steps']} ===",
-                verbosity="progress",
-            )
             optimizer.step(precursor_df_filtered, fragments_df_filtered)
 
         self.reporter.log_string(
@@ -581,10 +575,6 @@ def _skip_all_optimizers(
         )
 
         for optimizer in optimizers:
-            self.reporter.log_string(
-                f"=== Optimization of {optimizer.parameter_name} has been skipped {optimizer.num_consecutive_skips + 1} time(s); maximum number is {self.config['calibration']['max_skips']} ===",
-                verbosity="progress",
-            )
             optimizer.skip()
 
     def filter_dfs(self, precursor_df, fragments_df):

From b344346af7c2d15da8d265113140fcfd8d9d75d9 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Thu, 29 Aug 2024 20:51:14 +0200
Subject: [PATCH 210/250] proceed if insufficient precursors for optimization

---
 alphadia/workflow/optimization.py   | 34 ++++++++++++++++++++++++++++-
 alphadia/workflow/peptidecentric.py | 16 +++++++-------
 2 files changed, 41 insertions(+), 9 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 7fd86ea0..3650fcf2 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -64,12 +64,41 @@ def skip(self):
         """This method is used to record skipping of optimization. It can be overwritten with an empty function if skipping of optimization does not need to be recorded"""
         pass
 
+    def proceed_with_insufficient_precursors(self):
+        self.workflow.reporter.log_string(
+            "No more batches to process. Will proceed to extraction using best parameters available in optimization manager.",
+            verbosity="warning",
+        )
+        try:
+            self._update_workflow()
+            self.workflow.reporter.log_string(
+                f"Information. Using current optimal value for {self.parameter_name}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.2f}.",
+                verbosity="warning",
+            )
+        except KeyError:
+            self.workflow.reporter.log_string(
+                f"No information available. Using initial value for {self.parameter_name}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.2f}.",
+                verbosity="warning",
+            )
+
     @abstractmethod
     def plot(self):
         """This method plots relevant information about optimization of the search parameter. This can be overwritten with an empty function if there is nothing to plot."""
 
         pass
 
+    @abstractmethod
+    def _update_workflow():
+        """This method updates the optimization manager with the results of the optimization, namely:
+        the classifier version,
+        the optimal parameter,
+        score cutoff,
+        FWHM_RT,
+        and FWHM_mobility
+
+        """
+        pass
+
 
 class AutomaticOptimizer(BaseOptimizer):
     def __init__(
@@ -552,6 +581,9 @@ def skip(self):
         """See base class. There is nothing to record when skipping optimization here."""
         pass
 
+    def _update_workflow(self, new_parameter: float):
+        pass
+
 
 class AutomaticRTOptimizer(AutomaticOptimizer):
     def __init__(
@@ -876,7 +908,7 @@ def start_idx(self) -> int:
         if self.has_target_num_precursors:
             return 0
         elif self.batch_idx >= len(self.batch_plan):
-            raise NoOptimizationLockTargetError()
+            raise NoOptimizationLockTargetError()  # This should never be triggered since introduction of the BaseOptimizer.proceed_with_insufficient_precursors method and associated code, and could be removed.
         else:
             return self.batch_plan[self.batch_idx][0]
 
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index 16d1f805..ad3574bf 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -407,9 +407,11 @@ def search_parameter_optimization(self):
         )
 
         self.optlock = optimization.OptimizationLock(self.spectral_library, self.config)
-
+        insufficient_precursors_to_optimize = False
         # Start of optimization/recalibration loop
         for optimizers in ordered_optimizers:
+            if insufficient_precursors_to_optimize:
+                break
             for current_step in range(
                 self.config["calibration"]["max_steps"]
             ):  # Note current_step here refers to a different step than the attribute of the same name in the optimizer -- this should be rectified
@@ -433,6 +435,11 @@ def search_parameter_optimization(self):
                 precursor_df = self._process_batch()
 
                 if not self.optlock.has_target_num_precursors:
+                    if self.optlock.batch_idx + 1 >= len(self.optlock.batch_plan):
+                        for optimizer in optimizers:
+                            optimizer.proceed_with_insufficient_precursors()
+                        insufficient_precursors_to_optimize = True
+                        break
                     self.optlock.update()
 
                     if self.optlock.previously_calibrated:
@@ -821,13 +828,6 @@ def save_managers(self):
         self.optimization_manager.save()  # this replaces the .save() call when the optimization manager is fitted, since there seems little point in saving an intermediate optimization manager.
 
     def extraction(self):
-        self.optimization_manager.fit(
-            {
-                "num_candidates": self.config["search"]["target_num_candidates"],
-                "column_type": "calibrated",
-            }
-        )
-
         self.calibration_manager.predict(
             self.spectral_library._precursor_df, "precursor"
         )

From 33b07cebc22373d1e4fec7986a760bfe73d39882 Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 09:41:48 +0200
Subject: [PATCH 211/250] perform calibration if more than 6 precursors

---
 alphadia/workflow/optimization.py   | 19 +++++++------------
 alphadia/workflow/peptidecentric.py | 14 ++++++++++++--
 2 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 3650fcf2..1afccf95 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -64,22 +64,17 @@ def skip(self):
         """This method is used to record skipping of optimization. It can be overwritten with an empty function if skipping of optimization does not need to be recorded"""
         pass
 
-    def proceed_with_insufficient_precursors(self):
+    def proceed_with_insufficient_precursors(self, precursors_df, fragments_df):
         self.workflow.reporter.log_string(
             "No more batches to process. Will proceed to extraction using best parameters available in optimization manager.",
             verbosity="warning",
         )
-        try:
-            self._update_workflow()
-            self.workflow.reporter.log_string(
-                f"Information. Using current optimal value for {self.parameter_name}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.2f}.",
-                verbosity="warning",
-            )
-        except KeyError:
-            self.workflow.reporter.log_string(
-                f"No information available. Using initial value for {self.parameter_name}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.2f}.",
-                verbosity="warning",
-            )
+        self._update_history(precursors_df, fragments_df)
+        self._update_workflow()
+        self.workflow.reporter.log_string(
+            f"Using current optimal value for {self.parameter_name}: {self.workflow.optimization_manager.__dict__[self.parameter_name]:.2f}.",
+            verbosity="warning",
+        )
 
     @abstractmethod
     def plot(self):
diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py
index ad3574bf..c945aa42 100644
--- a/alphadia/workflow/peptidecentric.py
+++ b/alphadia/workflow/peptidecentric.py
@@ -436,8 +436,6 @@ def search_parameter_optimization(self):
 
                 if not self.optlock.has_target_num_precursors:
                     if self.optlock.batch_idx + 1 >= len(self.optlock.batch_plan):
-                        for optimizer in optimizers:
-                            optimizer.proceed_with_insufficient_precursors()
                         insufficient_precursors_to_optimize = True
                         break
                     self.optlock.update()
@@ -480,6 +478,18 @@ def search_parameter_optimization(self):
         log_string(
             "==============================================", verbosity="progress"
         )
+        if insufficient_precursors_to_optimize:
+            precursor_df_filtered, fragments_df_filtered = self.filter_dfs(
+                precursor_df, self.optlock.fragments_df
+            )
+            if precursor_df_filtered.shape[0] >= 6:
+                self.recalibration(precursor_df_filtered, fragments_df_filtered)
+            for optimizers in ordered_optimizers:
+                for optimizer in optimizers:
+                    optimizer.proceed_with_insufficient_precursors(
+                        precursor_df_filtered, self.optlock.fragments_df
+                    )
+
         for optimizers in ordered_optimizers:
             for optimizer in optimizers:
                 log_string(

From 009144f24483f02955c34c11e27c034a4424d97e Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 11:11:07 +0200
Subject: [PATCH 212/250] add simulation notebook

---
 docs/_static/images/methods_optimization.png  | Bin 857727 -> 802232 bytes
 .../methods_optimization_calibration.png      | Bin 254070 -> 241751 bytes
 .../optimization_simulation.ipynb             | 792 ++++++++++++++++++
 3 files changed, 792 insertions(+)
 create mode 100644 nbs/tutorial_nbs/optimization_simulation.ipynb

diff --git a/docs/_static/images/methods_optimization.png b/docs/_static/images/methods_optimization.png
index 68b559a5cc66b013572b0df698e5db628760819b..af613d8d94a13341ad195d8ce9d3ca6a0c9ca0d9 100644
GIT binary patch
literal 802232
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diff --git a/nbs/tutorial_nbs/optimization_simulation.ipynb b/nbs/tutorial_nbs/optimization_simulation.ipynb
new file mode 100644
index 00000000..0256f13d
--- /dev/null
+++ b/nbs/tutorial_nbs/optimization_simulation.ipynb
@@ -0,0 +1,792 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Search Parameter Optimization Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import matplotlib as mpl\n",
+    "from alphadia.workflow import manager\n",
+    "\n",
+    "os.environ[\"NUMBA_BOUNDSCHECK\"] = \"1\"\n",
+    "os.environ[\"NUMBA_DEVELOPER_MODE\"] = \"1\"\n",
+    "os.environ[\"NUMBA_FULL_TRACEBACKS\"] = \"1\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpl.rcParams['figure.dpi']= 150\n",
+    "\n",
+    "#%config InlineBackend.figure_format = 'svg'\n",
+    "\n",
+    "# set all axis and text colors to #4e4f51\n",
+    "mpl.rcParams['text.color'] = '#4e4f51'\n",
+    "mpl.rcParams['axes.labelcolor'] = '#4e4f51'\n",
+    "mpl.rcParams['axes.edgecolor'] = '#4e4f51'\n",
+    "mpl.rcParams['xtick.color'] = '#4e4f51'\n",
+    "mpl.rcParams['ytick.color'] = '#4e4f51'\n",
+    "\n",
+    "# change all default line widths\n",
+    "mpl.rcParams['lines.linewidth'] = 0.6\n",
+    "mpl.rcParams['axes.linewidth'] = 0.6\n",
+    "mpl.rcParams['xtick.major.width'] = 0.6\n",
+    "mpl.rcParams['ytick.major.width'] = 0.6\n",
+    "mpl.rcParams['xtick.minor.width'] = 0.6\n",
+    "mpl.rcParams['ytick.minor.width'] = 0.6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mock Calibration "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0:01:42.320059 INFO: Initializing CalibrationManager\n",
+      "0:01:42.320571 INFO: Loading calibration config\n",
+      "0:01:42.320913 INFO: Calibration config: [{'name': 'precursor', 'estimators': [{'name': 'rt', 'model': 'LOESSRegression', 'model_args': {'n_kernels': 6}, 'input_columns': ['rt_library'], 'target_columns': ['rt_observed'], 'output_columns': ['rt_calibrated']}]}]\n",
+      "0:01:42.321170 INFO: Calibration group :precursor, found 1 estimator(s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "config =  [{'name': 'precursor',\n",
+    "  'estimators': [{'name': 'rt',\n",
+    "    'model': 'LOESSRegression',\n",
+    "    'model_args': {'n_kernels': 6},\n",
+    "    'input_columns': ['rt_library'],\n",
+    "    'target_columns': ['rt_observed'],\n",
+    "    'output_columns': ['rt_calibrated'],\n",
+    "    },\n",
+    "]}]\n",
+    "\n",
+    "config_calib_rt =  [{'name': 'precursor',\n",
+    "  'estimators': [{'name': 'rt',\n",
+    "    'model': 'CalibRT',\n",
+    "    'model_args': {},\n",
+    "    'input_columns': ['rt_library'],\n",
+    "    'target_columns': ['rt_observed'],\n",
+    "    'output_columns': ['rt_calibrated'],\n",
+    "    },\n",
+    "]}]\n",
+    "\n",
+    "\n",
+    "calibration_manager = manager.CalibrationManager(config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_precursors_at_01_FDR(rt_df, number_to_discover):\n",
+    "\n",
+    "    # Only allow values within the tolerance to be discovered\n",
+    "    rt_df['discoverable'] = False\n",
+    "    rt_df[\"discoverable\"] = rt_df.apply(lambda row: row['rt_observed'] < row[\"upper_bound\"] and row['rt_observed'] > row['lower_bound'], axis=1)\n",
+    "    rt_df.loc[rt_df['discoverable'] == False, \"discovered\"] = False\n",
+    "\n",
+    "    # Take a random sample at appropriate FDR of the remaining discoverable precursors\n",
+    "    target_indices = rt_df.loc[(rt_df['decoy'] == False) & (rt_df['discoverable'] == True) & (rt_df['discovered'] == False)].sample(n=int(0.99*number_to_discover)).index\n",
+    "    decoy_indices = rt_df.loc[(rt_df['decoy'] == True) & (rt_df['discoverable'] == True) & (rt_df['discovered'] == False)].sample(n=int(0.01*number_to_discover)).index\n",
+    "    rt_df.loc[target_indices, 'discovered'] = True\n",
+    "    rt_df.loc[decoy_indices, 'discovered'] = True\n",
+    "\n",
+    "    return rt_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_size = 3000\n",
+    "iterations = 15\n",
+    "update_rule = 0.99\n",
+    "\n",
+    "true_amplitude = 60\n",
+    "true_wavelength = 100\n",
+    "true_noise = 50\n",
+    "\n",
+    "rng = np.random.default_rng(42)\n",
+    "predicted_rt = rng.uniform(100, 1000, size=dataset_size)\n",
+    "observed_rt = predicted_rt + true_amplitude * np.sin(predicted_rt / true_wavelength) + rng.normal(0, true_noise, size=dataset_size)\n",
+    "true_rt = predicted_rt + true_amplitude * np.sin(predicted_rt / true_wavelength)\n",
+    "true_positive_95_bound = true_rt + 1.96 * true_noise\n",
+    "true_negative_95_bound = true_rt - 1.96 * true_noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0:01:42.411905 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.415518 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.442650 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.445316 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.470872 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.473066 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.499471 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.501854 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.527652 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.530212 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.566556 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.570127 INFO: calibration group: precursor, predicting rt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0:01:42.602170 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.604882 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.631466 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.635726 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.664146 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.666718 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.692421 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.696517 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.721915 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.724483 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.751780 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.754533 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.780657 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.783217 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.807731 INFO: calibration group: precursor, fitting rt estimator \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0:01:42.810789 INFO: calibration group: precursor, predicting rt\n",
+      "0:01:42.836908 INFO: calibration group: precursor, fitting rt estimator \n",
+      "0:01:42.839858 INFO: calibration group: precursor, predicting rt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14\n"
+     ]
+    }
+   ],
+   "source": [
+    "rt_df_target = pd.DataFrame({\"rt_library\": predicted_rt, \n",
+    "                             \"rt_observed\": observed_rt, \n",
+    "                             \"rt_true\": true_rt, \n",
+    "                             \"rt_true_positive_95_bound\": true_positive_95_bound,\n",
+    "                             \"rt_true_negative_95_bound\": true_negative_95_bound,\n",
+    "                             \"decoy\": False, \n",
+    "                             \"discovered\": False})\n",
+    "rt_df_decoy = pd.DataFrame({\"rt_library\": predicted_rt, \"rt_observed\": rng.uniform(100, 1000, size=dataset_size), \"decoy\": True, \"discovered\": False})\n",
+    "rt_df = pd.concat([rt_df_target, rt_df_decoy]).reset_index(drop=True)\n",
+    "rt_df[\"lower_bound\"] = rt_df[\"rt_library\"] - 300\n",
+    "rt_df[\"upper_bound\"] = rt_df[\"rt_library\"] + 300\n",
+    "\n",
+    "dfs = [rt_df.copy()]\n",
+    "tolerances = [300]\n",
+    "\n",
+    "for i in range(iterations):\n",
+    "    print(i)\n",
+    "    \n",
+    "    rt_df = find_precursors_at_01_FDR(rt_df, 100).copy()\n",
+    "    \n",
+    "    precursor_df = rt_df[rt_df[\"discovered\"]]\n",
+    "    calibration_manager.fit(precursor_df, \"precursor\")\n",
+    "\n",
+    "    # Get upper and lower bounds for next search\n",
+    "    tolerance = calibration_manager.get_estimator(\n",
+    "        \"precursor\", \"rt\"\n",
+    "    ).ci(precursor_df, update_rule)\n",
+    "\n",
+    "    calibration_manager.predict(rt_df, \"precursor\")\n",
+    "\n",
+    "    rt_df['upper_bound'] = rt_df['rt_calibrated'] + tolerance\n",
+    "    rt_df['lower_bound'] = rt_df['rt_calibrated'] - tolerance\n",
+    "\n",
+    "    tolerances.append(tolerance)\n",
+    "    dfs.append(rt_df.copy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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QUlIMwJPDh+Ho4EBMTCyhoSG1SvRx7Nhxdu7ajZOTE7M/nUWDBto370VFRXz66RyiY2JYs2Yt48ePM2i7ectW3prxJmFhoYA2gJq/4AuOHw9n2/Yd1a4Pa9SoEdOnv8LatetITEqid69e9OzZQ/8am7dw+vQZPDwa88msj3FwcAC0gch7739IeHgE27fvYMCA/gbn33/gIJ/M+pjmzYNq9FzI5XK2bt2GpaUlC7/+ErcK0ylVKhWxsXF6x/+9cROHDh+mefPmvPH6q7ogVKFQsGz5Cvbs2cu639YzdsxzujZhoSE83reP3rkB9uzdy+LF3/D9Dz/y6SezDPpW1X1a0/ukon379zN0yGDGjh2jC2g3bdrMDz/+xPrf/xBBmSAIgiDcB2JNmSCgfSOdkpLCosVLiIuLw9TUlJ49ehgc99xzowze6CYkJHL69BmaNvVlwoTxuoAMwMHBgZdenArA9h07dduPHTtGXl4+QUGBuoAMtKn5R40cYXCNqly6FM/58+ext7fn5TveaAO4ubnel1HFLVu3AtrpguUBGYC1tTWTJ09EIpGwY+cu5HK5QdsuXTrrAjIAExMTnhw+HIDoqOh77lvF/k0YP14XkIF2Hd64sWMAbXBoTK9ePWsckAEUFxejUChwd3c3CJpMTEy0I6W3qFQq/vrrbywsLHjzjdf0RgVNTU2ZNPEFHB0d2LVrN2q1WrfPz8/P4NwAvXr2JCDAn4sXoygqKjLaP2P36d3eJ25ubowePUpvhHHAgP7Y2Nhw6dIlFAqF0T4IgiAIglBzYqRMeKTdWa8MwNLSkunTX6F+fXe97RKJhJD2hmtoIiMjAQgNCTE6Nc7HxwcLCwvi4+N126KjYwDo0rmzwfEymYyOHTuwefOWGj2Gc+fOAdC1S2es7nijfb8olUri4i4B0K1bV4P9Xl5eeHl6kpiURGJiIn5+fnr7W7duZdCmwa1pcjk3b95z/zIzM8nMzMLe3s7oyE379sFYW1uTmprKzZs3DaZLGntdq+Lg4ICzszOJiYms/OVX+vbpjbu7u9Fjr1xJID8/n9atWukFi+XMzc1p0qQJp06dJiU1lUYNG+r2lZSUcPLkKRITEykoLESlVAFwM+cmGo2GtLQ0mjRpone+yu7Tu71Pyqc4VmRiYoKbqytXEhIoKCjAycmpxucTBEEQBMGQCMqER1p5nTKJ9Hbx6A4dwrCxsTE41t7e3uDNKUBGRgYAq1avYdXqNZVeq+KIQk5ODkClGQ7dXF1r/Bh0GQkrCQruh4KCApRKJXZ2dlhYWBg9xtXVlcSkJLKzcwz21XOuZ7CtPDBQ3oeRluqeT4lEgquLC4lFRWRn5xgEZS4uhv2rzv+mT2PBF1+wYcNfbNjwF64uLgQ1D6JL5860a9dWd1z5/RF57pzRDwEqKsjPh1tB2fnzF1jwxRfk5eVXenxJSanBtsru07u9TyobtS0faVMolLU6nyAIgiAIhkRQJjzS7qxTVhUzI290QZtYASAwIOCBBkb/epIqdkmq2PlPqaIPpqZmle6rTMuWLVj27VJOnjzFmbNnuXgxiv37D7B//wE6dAjj7bdmALfvj/r16xPg71/lOW1ttQk5SkpKmDd/AYWFhTzzzNN06dwZV1cXzMzMkEgkfPHFVxw6fBjNrXNXVNl9erek/4bXThAEQRD+40RQ9ohq1KgRiYmJD7sbd61Ro0YPuws69W6NJISGhjB06JAatSkfqcnMzDS6P6OS7VVdPy0trcZtasvW1haZTEZ+fj6lpaVGR8syMrR9dnb+56eylU+fq+z5rLjvfvbPysqKbt266qZ0xsXFMXfeAo4fD+fUqdMEB7fTvT6NGjas8YcA0dExFBQU0LFjB0YaScaSll771/qfuE8EQRAEQbg7Iih7RMlksgeWUv5R06p1K1avWUt4RESNg7KgwED27dvPkaPH6Nfvcb19KpWK48eP1/z6rVqxavUaDh0+wqhRIw0SONypvL6bqkJSierIZDL8/JoRFRXN4cNH6N27l97+5ORkkpKSsLCweCilFlxcXHBxqUdmZhbnzp3XpsOv4NSpUxQWFlK/fv1q0+/fCz8/P7o/1o0//tzA1atXCQ5uR9OmvlhbWXExKoqCggLdaFhVCgsLAeNTB1NTU0lIqP0HKrW9TwRBEARB+OeI7IuCcI/8mjWjdatWxMTEsmz5CoqLDdOKJyYmcubMGd3PHTt1xNbWlosXL7Jv337ddo1Gw5q168jMrL5OVrlmzZrSokVz8vLyWLp0GaWl+uuM0tMzSEpK1v1cPqp048aNGl8D0KWSX7vuN73RluKSElas+B6NRsPjffsYFJD+p5T378effiIvL0+3/ebNm/z08y8ADBo44L5cKzMzk71791FWVqa3XS6Xc+HiRQDq1dOuUzM1NeWJJ4ZSUlLC55/PMzpSlZ2dzf79B3Q/lydBCQ8P13sshYVFLF7yDUpl7ddx1fY+EQRBEAThnyNGygThPnj11el8POsTtm/fwaFDh/H29sbJyZHi4mKSkpLJyspi0MABujplVpaWvPLyS3w+dx4LFy1m+44duLu5k5iURGpqKn1692bX7t01v/7/pvP+Bx9x6PBhzkaeJSAgAFOZKWlpaSQmJTF+3Fi8vDwB8PNrhr29PceOHWfmzPdxc3dDIpHQq1fPKtc8derYkb59erNz125emfY/WrZsgbmZORejLpKXl4+fXzNGVlNv7EEaPGgQF85f5PSZM0yd+hItWrZAo9Fw/vwFSkpKCA0NMRiVvFsFBYUsWryE5Su+w9e3Cc7OzpSVlhEbF0teXj6+vk3o0CFMd/zw4cO4fuMGBw4c5KWXp+Hj442bmxtKhZIbKTe4du06Xp6edO/+GABNfX1p3aoVkefOMfXFl3Xp+i9ejMLOzo7QkBAiTpyodb9rc58IgiAIgvDPEUGZINwHDg4OzP38M3bt2s3hI0dITEggNlaOg4MD7m5uDBzY3yD9fWhoCJ9+Mou169YRH3+Z69dv4Ovry8svvciNGzdqFZQ5OzuzYME8Nm/azLHjx4mMPIdUKqVePWf6Pd6X9u2DdceamZnx/vszWfXrauLj44mKjkaj0RAYEFBtIooXX5xKQEAAO3bu5OLFKNRqNe7ubgwaOJDBgwdhbm5euyfuPjIxMWHmzHfYtn0H+/bt4+zZSAAaN25Ezx496Nu3j9GSBXfDvb47E8aP49z581y7dp34+MuYm5vj5ubKk8OH07dvH70MiFKplFf/N52OHTuwa9du4uMvk5CQiLW1NfXq1eOJoUPo3LmT3jXeffdtfv/jT44ePcqZM2ext7enS5fOjBo5kp9++vmu+l2b+0QQBEEQhH+ORGMsfZfwr5eensGkyVNYsXwZbm7G06er1Wri4uIA7VqX+/WGVBAEQaia+P9XEATh0VOT9+eVEX8lBEEQBEEQBEEQHiIRlAmCIAiCIAiCIDxEYk2ZIAiCIAiCIPxb5edDYSE0aGB8/y+/wK+/QlkZyOXaf1UqMDPTfpmaav+1twdnZ3By0n45O0PjxtC5M4gyKQ+dCMoEQRAEQRAE4WHLzITjx+H8ebh4EaKjITlZG5T16QM7dxpvl5gIe/bc/XXT0owHZaWlkJsLbm4gkdz9+YUaEUGZIAiCIAiCIPzTCgu1gdbOnXD4MMTGVn7s1auV77uXzMcWFuBaSUKKY8egZ0+oXx/CwiA0VPtvcDBYW9/9NQWjRFAmCIIgCIIgCP+EsjLYuFE75XDPHu3PNXH1Kmg0xkes7iUo8/CofBQsKkr7b2oq/PWX9gtAJoMOHaBXL23QFhKinSJ5HyhVauQqNfklSvJK5NhbmmFnKcPMRIrM5L+dCkMEZYIgCIIgCILwoH31FXz6KeTk1L5tcbF2KqGjo+G+bt3gyy+1wZm5uXb9mIkJKBTaL7lcOxUxLw+ys7XXz8nRTlts1qzya168aHy7Uqkd2Tt8GD78EGxstNMrhw6FgQON97EmD1GuZE9MOssPJhCVkq/bHtTAjsndfOgV4IaV2X83dPnvPjJBEARBEARB+LcwNa0+ILOy0k4PbNkSmjfXBk0eHtCoUeUjYm3bar/ut8uXa3ZcYSFs2KD9ksm0QeITT8DEidoAsQaK5UrmbIthVbjhNM2olHymrY1kdJgn7/b3/88GZv/NRyUIgiAIgiAI/yYjR8Lrr2tHrsrJZPDYY/D449ClC7Rpc9+mAt6z3bshPh4iIrRf4eFw7pw2s2NllErYuxcSEuDFF2t0GaVKzZ6YdKMBmYlUQjM3G2zMZZxOzmFfTAaPN3f/T05lFEGZIAiCIAiCINwPCQng7q4d8bqTk5N2BOm336B7dxg3DgYPBgeHf7qXNSOVgp+f9mvMGO22wkLttMU9e7Rf588bbzt6dI0zNspVapYdSNDbZm9pyugwT0aGelAqV5FTLMfJygxrcxkKlVoEZYIgCIIgCIIg3KGkRLtebP58+PhjeOcd48fNmgVz5oCPzz/bv/vFxgb69dN+AaSnw7Zt8PffsGuXdu0awHPPVX6Od98Ff394+mmwsCC/REl06u01ZF7OVqycEMKJxBwm/XLKYH3ZpK4+9A78760v+++FmYIgCIIgCILwT9m1S7v+a84cbWKNzz+HrCzjxzZrVncDMmPc3GD8eG1GycxM+PNPeP99aNrU+PFXr8LcuTB2rHad3NtvU5h0e9qivaUpv0wIZfnBBN7847xeQAba9WXT10UyZ1ssxXLlg3xk/zgRlAmCIAiCIAhCbeXlaacg9u2rnbZYLj9fO2r2CFGq1BSbWZDWsz9xL75BWl4pxXIlSpVa/8DvvgP1rW3Z2TB3Lk1CWvDZ9kV459xgdJgnEYnZrDlRRV02YFV4MntjMgzPX4f9t8b9BKGGhgwdZrDNxMQEOzs7/P38GDJ0MAH+/g+hZw/GhQsXee/9D+jRvTvTp7/yQK6xectWdu7cRWpqKkqlkuZBQcye/ckDudajbOHCxezbv59PP5lFixbNH3Z3BEEQHk2HDmnXWSUnG+6TybRFmf9lHlQNsBqnspfLtUHZHSRyOSPO7+KZ87spSxvMW80Ggqx+tddddvAKPQNc/zPry0RQJjzSenTvrvu+pKSExKQkjoeHEx4Rwav/m063bl0fYu/qjuPHw/n++x+wsbEhJKQ9FuYWNGzU8GF3SxAEQRDuL7lcOz1v/nxtMec7deoEy5dDUNA/37cqPKgaYLVKZW9iAsuWwdKl2syOd5CiwXLzRhaxkQFNw/iyy2jiXLwqvXZUSj75Jcr/zNqy/8ajEIS7dOeokVqt5tdVq9mw4S+++/4HOnXqiEwmfk2qEx4RAcBbM96kZcsWD7k3giAIgvAAXL0KTz0FJ04Y7rOz0wZqL7ygzVr4L/KgaoBVlcq+olXhyYR6O9GvuTuyoUO1RaYvXdIWvP75ZygrM2jTNz6c3vERbAnowtedRpLg3MjoufNLFbjb//tGJe/Gv+uuEYSHTCqVMnLEs5iYmFBQUMDVq9cedpfqhOzsbADc3Nweck8EQRAE4QHYtUtboNlYQNa1qzY1/KRJ/7qArDaBU23XaBlLZV+ZZQevIK947mbNtKNmSUnw9tvaoPYOUjQMjjnE7h9eZM6OxdQrumlwjJ3Fv6Sm230ghgAE4Q6mpqZYWVlRUFCASm1YIDEzM4vff/+dM2fPcvNmLtbWVgQEBPDk8GE0vSPbUHVruSpbHzRk6DBcXVxYtmwpf2/cyJ49e8nIyMTe3p5uXbswcuQITI0Ul7x69SqrVq3hYlQUKpUKH29vnnpqOKamZkYfq0KhYPfuPezdt4/09HTKyuQ42Nvj4elBt27d6Nqlc5XP1dq161j323rdz5MmT9F9X/Ex5ecX8OeGDUREnCArKwtzc3OaNvVlyODBtGnT2uC85Y9/6dIl/LnhLw4dOkx6ejrt2rbl3XffrrJPJSUlbN26jcNHjpCRkYlarcbB3h6fJj707t2Ltm3a6B1fVlbG5s1bOHL0GKmpqQB4eDSm3+OP06NHd4PzR0VFc+ToUaKiosnKykKhUODi4kJoaAjDhw3DxsZa7/iK98CYMaNZtXoNZ2/dO+PHjWXw4EGA9r766++/OXv2LFlZ2ZiZmeHm5kZI+2AGDx6ElZGaN1FRUaxd9xvx8ZeRSCQEBgYwbtxYPBo3rvI5EgRBEGpIrdZmVfzgA8PpimZmMHs2vPoqmJg8nP5Vo7aBU23WaN2Zyr4qlU41dHeHzz7TlhD49ls08+YhycnRO8REo2bkuZ1s8+vMEW9H3fagBnbYWf53Qpn/ziMRhPskPT2dgoICZDIZ9d31F5omJSXz/gcfkp+fT8OGDenQIYzMzEzCwyM4efIUr7/2Kp06dbxvffniy684ffoMLZo3p2GDhkRHR7Phr7/JzsnhtVf/p3ds/OXLvPfeB5SWluLp4YGHpwcpKanM+mQ2jz/e1+j5v/zqa44dO46lpSWBgQFYWVmRnZ1DTHQMpSWl1QZl3t7e9OjenTNnz5Kbm0uHDmFYWlgC4OjoAGhH0d599z3S0tNxcalHaGgI+Xn5nDt3nrNnI5kwYTxDbgUmFak1Gj77bC5R0dEEBQXi5emJrZ1tlf1RqVR88OFHXLoUj52dHc2bB2Fqakp2djanT5/BwtxCLyjLzc3lw48+JikpGUdHB4KCAkEDsbGxLFy0mMuXLzNp0kS9a/y8ciVJScl4enrSsmULFAoFV64ksGHDX5w6dYp5cz/H0tLSoG95+Xm8/sYM1GoVAQEBKOQKzM3NAW2gN3vOZxQVFeHq6kr74GDkcjnXb9xg7brfCAkJwcfHW+98J0+eZPOWrfj6NqFdu7YkJiZx+vQZLl2KZ/Gir3F0dDTogyAIglBLY8bA6tWG23194Y8/oFWrf75PtXBfAqdK5JXIa9eXqqYa2tnBW2+hmjSZyzM/pcEP32InL9bt3tOkPUe89T9UndKtCWb/kSQfIIIyoVxm5t23tbEBI29CAW2dDmMLYWvCygqsras/7j4pKSkhMTGJH378EYDHH++rN+qh0Wj48quvyc/PZ9gTQxkz5jkkt6rVHzt2nPkLvmDR4iUEBPjj5OR0z/3JyMzEzNycZd9+o3uDnZ6ezquvvcHBg4cY8eyz1K/vruvbwoWLKS0t5ZlnnmbkiGd159m2bTvLVxhmO0pPT+fYseO4urjwxRcLsKsQ8MjlchISEqvtY1hYKGFhocyc+T65ubmMHzcONzdXvWO+/XY5aenpdO3ahWmvvKwb4YuOjuGjj2fx888radG8uUHQkZWVhampKUu/WYyzs3ONnrOo6GguXYqnaVNf5sz+FDOz2yOExcXFpKSk6h2/aPESkpKSGTRwAGPHjtH1LTc3l08+nc3WbdsJDm5H27ZtdW2efeYZ/P39sK5wbyoUCr777nt27trNxk2befaZpw36dvr0GcLCQnn9tVf1+lVQUMDcefMoKipi3LgxDBk8GGmF6S+xsXE4ORkGWJu3bOWtGW8SFhYKaAPS+Qu+4PjxcLZt38GokSNq9JwJgiAIVXj2WVi79nYad9Cuifr5Z7C3f1i9qrH7Gjjdwd7S+CycytRkqqHM0QGPrz/nqw5DcVi6iPGnN2GqUjKn+/N6x40O8/xPZV4EsaZMKOfqevdft4IYowIC7v688+c/8Ic9ZOgw3dezI0bxzrszuXEjhUkTX+CF5yfoHXvh4kWSk5NxcanHqFEjdQEZQMeOHQgNCaG0tJQ9e/fdt/5NnPi83oiHm5sbj93KCBkdHa3Xt2vXruHu5sYzTz+ld47+/fvRrJlhEce8PO0nZ94+3noBGYCZmRn+/n733P+0tDROnjqFhYUFkya+oDflMjAwgMf79kGtVrNt+3aj7Z97blSNAzKA/FuPyd/fXy/wAbCyssLXt4nu54SERE6fPkPTpr5MmDBer28ODg689OJUALbv2Kl3nnbt2uoFZKCd8vr88xMwMTHhRISR9Qa3jpk08QWDfu3evYe8vHzatm3DE0OH6gVk2sfih4ODg8H5unTprAvIQFvS4cnhwwGIjoo2OF4QBEG4CwMHwhdfaL+XSrWFjzdsqBMBGTyYwEl3rKWMoAaGa8GMqc1UQyszGa8+E4bHsq+Z8t4aXh/wqi7RR1ADOxaPaMO7/f0xM5FSLFeS+8NKLiVnVl4brY4QI2XCI61iSnyFUkFGRibx8fH8tn497u7utGt3e4QkOjoGgE6dOhnNyPjYY904Hh6uFyzdC5lMRovmhnWoGjRoAMDNm7cXvJb3rWPHDpgYmdfetUsXLl2K19vWqFFDLCwsOH36DBv++ptu3brifB9G+CqKjtH2q23bNtjaGk49fOyxx9i4abOu/xVJJBJC2rev1fW8vb2RSqXs27sPj8aNCQsLMwg4y0VGRgIQGhJiEAgB+Pj4YGFhQXx8vMG+7OxsTpw8xY3r1ykuKUGj1o4Gy2QyUlJTDY4vP5+xAPPcufMA9O3Tp0aPsVzr1oZTZho01N4bOTcNF0MLgiAId2n6dEhLgz59oEePh92bWikPnCqmwa9MbddomZlImdzNh2lrI6s9trZTDa3MZPRr7k7PgMHkl/Tn5VIFdhamurpqcpWabRdTObdkJe9/P5Nsp4a82ucl8jt0vqcU/w9T3eqtINxnxpJvJCQk8O7M95k95zMWLfqaRg219bZybi08dXV1NWhTcXtOdo7R/bXl4OBgNMAqX6+kUCh028r75uLiUmXfKrKysuKlF6fyzdJvWbnyF1au/IUGDRrQokVzuj/WjYCAgHt+DDk5Nyu9fsXt5dkbK7K3tzeazKQqDRs2YOyY5/h11Wq+Wfot3y5bjodHY1q2bEnPHt3x8vLSHZuRkQHAqtVrWLV6TaXnrPg8A2zcuIlffl2FUqmsVd9cXOoZ3Z6VnQWAu7t7rc5Xz9nwfFa37g3lHX0WBEEQ7oFEAp9//rB7cVceZOAkM5HSK8CN0WEeVWZ3rDjV8HYBawW5xQrsrUyxNTelsEyBRAK2Fqa6PpQXui5VKHG2NsPK3ARTqTYgm7Mthg2H4tj929cANMm5wTcbP6dz/R+YlpJ/Vyn+H7a601NB+If4+PjQt28f/v57Izu27+CFF56vvhHa/7NrS62pfIhdejcnrKWuXbvQqlVLIk6cIPLsOS5GRbFz5y527tzFkMGDmDBh/AO9flUP0ayWAVm5oUOH0KlTJyIiIog8d47o6Bg2bdrM5s1beH7CeAYNGghoE4kABAYE1DggiouL48effsb6VkDbvHkQjo6OuuBx3Pjn9UYw9R9P7aaQVEfyD9wfgiAIjwK5Qo5m/lzyfRqR8lgwTpaOOFo6YCY1x0xWt1Ou303gVBtWZjLe7R9AqLczyw5eMShMPaVbE3oGuGJlJtMWsI5OZ/khwwLW4zp6EeLtxKI90bzR15/D8Zm640ykEpq52dCioT39m7vTqrEjR+Kz+N+RNTQsuJ0T4bPHxlFspv1wUq82Wh1ZdyaCMkHr1qjBXbGxqXxfTMy9Jfp4SNxujeBUnIpWnrwjM8N4UpSMW9udnG9PAZSZan/FSkpLjLbJyjIcIbobTrfWnWVWkrAlo4pELvb29vTp3Zs+vXuj0Wg4e/Ys8xd8ycZNm+nVqyceHh53369bCSoqf860911t1o3VhItLPQYOHMDAgQNQqVQcPnyERYuX8PPKX+je/TFsbGyod+uaoaEhDB06pEbnDQ/XFskeNXqUQbr8srIycnNza93Xes71uH79BmlpaXh5eda6vSAIgnD3CksLuDbpWQJ+3YaNDF4ZDYe9oLV7a97oMIMh/oOxMfvnko49CLUJnO72/Nqphq7klyjJv2OqoezW2q+qCli/+cd5RoV68E7/AL7aHc8PRxOxtzTlpe6+jAz1oFSuIqdYjpOVGUq1hj+ndODL7IOkND1MA98ski+783uj3nrnrW2K/4dNBGWCViXT3u5ZPeNTtv7t0tPTAbCwuJ2BKDBQO53v6LFjPPfcKIOphQcOHrx1XKBuW3mwdGfWP9Bm3UtIqFntkOqUX/PY8XCee260wRqpw4eP1Og8EomEtm3bEtyuHYcOH+bq1Wv3FJQF3poCeebsWQoLiwxqeB04eOhW/+99qmRlTExMeOyxbmzbvoO4uDhSUlJp1qwprVq3YvWatYRHRNQ4KCssLALQBXQVHT12DM1dfADRqlVLIs+dY9eu3XqJOwRBEIQHq7A4jwuDQuiw7xIAlkrYvBa6jYNIIhn910imtJvK/D7z/xOBWXWB072Q3TqHlZnMIHujUqVmT3T1BaxXR1ylvZcT5qZSvJytWDkhhBOJOUz65RRRKfmYS8roYHOBJ90j6WF7ktktb0BLbdt8N2u4qj+DpLYp/h+2uhE6CsI/KCEhgZ27dgMQ3K6dbnuL5s3x9PQkIyODNWvX6b0BPx4eTnh4BBYWFvTqeXsRsJubGy4u9UhOTiaiQla+0tJSvln6LcXFt2tw3IvmzYNo1KghaWlprP/9D719O3buJC4uzujjPH483GDNVEFBAZcuaf9A1bvHoNrd3Z3g4HaUlJTw/Q8/6K3Dio2NY8eOnUilUvr363dP1yl3/sIFIs+dQ63Wnxaanp7O9evXkUgkONfTBlR+zZrRulUrYmJiWbZ8hdHXIjExkTNnzuh+btBQW7du9549eo/l6rVr/PLLr3fV5969e2FnZ8fpM2fYtGmzQWAXFxd3VyNwgiAIQuXkxUXcHNhLF5CVsy+D0Bu3f152+ls2xW1Grqz7a3UrBk3N3Gxxt7fAykz2wEeS5Co1yw7W7EPo7w4nMLqDJ79MCGX5gSus2LyZMPlKVnp/wLmgEfzs/REDLf/GSnlDr12gfSIOJobJTPJL687rVjdCR0F4QBYuXKz7XqlUkpGZwaVL8ajVatq3D+axx7rp9kskEl579X+89/4H/PHHn4SHR+Dj7U1mViYxMbGYmJjwyssvGdQoe/aZZ1i85Bs+nzuPoKDAWxn9LmNlZUloSAgRJ4ynUK8NqVTKtGmv8MEHH7F27TqOHTuOp4cHqWmpXL58hX79Hmf79h16bTIyMvl87jysraxo4uuLo6MDRUVFREVFU1JSQvv2wfclLf6LU6fwzrvvsX//AaIuRuHn70deXj4XL15ErVYzYfw4gxpldyspMYkffvwJe3s7mjRpgq2tLXl5+URFRaFQKBgwoL9ehslXX53Ox7M+Yfv2HRw6dBhvb2+cnBwpLi4mKSmZrKwsBg0coKtT1rNHTzZu3MTJk6d48aVX8PVtQmFhIVFR0YSGhBAfH1/lVFFjbG1tmfHmG8ye8xk//PgTW7Zuw9e3ibZ49PUbpKam8tWXXxhNiy8IgiDchbIypE8/TeP9p/Q2KyUwfiisuiO57YJj8xjsNwgz6vb6svvpdsIOJXklcuwtTbGxMKWoVAESCbYWt0fgalPA+kb6dWxSk8i+vIVXCvZS369myzxMJGq62Z5mY67+0oLapPh/2ERQJjzS9u3fr/teKpVibW1FYGAg3R/rRo8e3Q2mAXp5efLVlwtYv/4Pzpw9y7Hjx7GysiI0NIQnhw83Wg+sV6+eSCQSNm7cRExMLDY2NrRvH8yY557jp59+vm+Pxa9ZM+Z+/hmrVq8mOiqa9PR0PD09eW/mO5ibWxgEZX5+zRg1aiQXzl/gxo0bREdHY2Njg5eXJ7169eSxbt0quVLtODs7s2D+PP7880/CI05w/Hg45ubmtGzZkqFDBtOmTev7ch2A9u2DKSgo4MLFiyQmJlFQUIC9vR0BAQH07/e4wfRABwcH5n7+Gbt27ebwkSMkJiQQGyvHwcEBdzc3Bg7sT5fOnXXH29nZsmD+PFb+8itRUVGcPHkKV1dXRo58lqFDhjBlyot31e8WLZqz8Osv2fDX35w5c5aIiBNYWlrg6urGyBHP4l6/dpkZBUEQhEqUlcGTTyLbuk1vc4kMnn4Kthj5LPJs2llyS27W+SmM90uxXMmemHSWH6w8Ycd7f11gUKsG9Apw42ZxzQpY97U7xreenyE9o8EOoAb5sTJUblj5DOXlAw04XthCb19QAztsLepOqCPR3M0iCOGhS0/PYNLkKaxYvgw3N+PpxtVqtW7amp+fn9FaTIIgCML9J/7/FYR/odJSGD4ctukHZIWmMGAUHPKqvOm5KRdo6WZYO/R+MBx1Mrtva73ut4oJO8qzItqYyygsU3IpvRCVWsOoUA8mdfVh8JKjvNDZm+HtGtHx830ASFEhAZDKDNq6m6RzNGBClddXaaScKfZnX3579ua351KZJ3tfe4yX154hJrVA79gFT7UkwN0OP3fbf+x5rMn788rUnfBREARBEARBEO5GaSkMGwbbt+ttLjCDfqPgaDXJb50sHB5It6oadfq3FUFWqtTsiUln87lUo1kRzU2lrD1xjVXhybTxcGBUqAeL9sYyLiCHmT7b8VKfJNQ6ip0OX9OxyyijbTNzvHDRJOld94bchUMFbThU2JZjha3IU9nq7b9ZLMfGXP85GhXqQXsvJ2b8cZ6fxrf/1wW3xvw7XmVBEARBEARBeBDkcu0I2R0BWaGFlMdHqjlWTZLhNu5tcLB0uO/dqi5N/LS1kf+qIshylZpNkSlsermTXlbEckEN7BjXoTHbx9hw+MiXvFI/nskBB7A9UMTECtWTWsjOMOkXP6NTH+3sB6BO/okDNwM5fCsQu1LWCKi8NqejlRmFZUrdecZ38qK9lxNjfzxBUnZxncnA+O/voSAIgiAIgiDcDaUSRo82mLKosbXl6Devcyzho2pP8UbHGZhJze9vt26NOq09cY2A+rYGUwDL/ZuKIBeUKvlgYBDLDl5hzQltIClBTYBFEmE2F+hgep6QmIvYy4p4xgTIAEsTw/OUXN9HVMoAvW3ltcouhYxg+qC5LP/1PBHZOdX2KaiBHa525nwypDmOt0bc1kRc5ZMtMeSVaDMv5pcqDNL0/xuJoEwQBEEQBEH471GrYeJE+P13/e12dkh27qRT2xZM3pXG8tPLKj3FlHZTtZkXZfc3i59cpeZmoZxDM7pXOgWwPKj41xRB1miISMxmzYmrPOGwj372xwixvoiDrLBWp2lhFY+VtIRitaXBvu9O5NKiSR7vDwhg4JKj1Z5rUlcfFCo1Hs5WbLuQxsI9l7hZrJ8Gv65kYBRBmSAIgiAIgvDfo1Zrpy5WZG0NO3ZAWBg2wII+C+jq2Y35x+YSmRapO6y1e2ve7PgWg/0GPZCsi2o1WJnLjE8B7OjFppc76abf3U0R5AeRPMTawpSfjiYBEGZzgT724TVuq7L2ZVdWANsz/YgobG40ICu3/OAV1k/uwHOhHqw5ec1oMhHQrhtr3diB7gsO0sjRknEdvfj7pdvPG2ifTzvLuhHu1I1eCoIgCIIgCEJtyGTwyy9gYwMrVoC5OWzaBB066A6xMbPmyYDhDPYbRG7JTXJKc3GycMDB0gEzqfl9HyED7Vqyn44mcig+E7VGg4lUogs0yqfxjQr1YOWEEAYvOUpeiaJWU/DuKXmIRg25FyHjAJSkQes5ul2FpQpdvbHwwhY847S70j4kltXHzbcvZU5d+euGL91at2Hqlwdr1P+olHwyCkp5va8fr/f142axnKzC2yOJe2PScbAyo3VjB8b+eIK8EgV5JQqjz9uUbk0we9gjjDUkgjJBEARBEAThv8nEBJYtA0dH6NQJevQwOMRMZooZptiYWdPIvtED7U6ZUkGZqpQ+Lc1o52uJs5UTVjI71p1IZVX4dd2UxdURV3UZDJceuFLjKXi1Th6iUUNeFKQf0AZiGQeh7FbBZqkpNJ8JMu1IYXnfAMKL9GuCJZXVJ7yoBeGFLQgvakGaoh7b+nYmPb+U7QlXaNGsZrXKdM+TQk3ktVzm74wzCCwndfWhY5N6TPj5pG5ErFzF5y2/VPnvmPZZQyIoEwRBEARBEP67JBL4/POH3QsK5UVsjtvEvGPzDKZKTg95g40v92Pcj+d0gcZPR5NYMSaYw/GZNZqCV548xFhAdpuGiLOHuGK3kSDZaaSZh24HYXdSKyhNPYKsQW9kJlLsLW9Xc05VuLAsYxhxpV6EF7UgVeFi0NzR2oxv9l+hsEyJk1UNKkFXYGlmwtwdsQa1x6JS8pm+LpJRoR4sGdlGNyJW0U9Hk1j9QihmMmmdyLpYru70VBAEQRAEQRAqo1Rqpyz+CxXKi3hz1xssM5JUJDItkvGbRjO53VR+nvApQ5acIq9EQVRKPqVyFW/08avRFDy5Ss2yAwl3bNXgY36DDtbn6WBzgVDrC7iY5kJazfr9987VWLVvTq8AN+wsZQQ1sNONXH2eVnmh56AGdthamJKUXciVzCIszEz02lYlqIEdJlIJcWkFlR5z50hiRVEp+ZQq1DjUMhB82OrGeJ4gCIIgCIIgVOavvyA4GFJTH3ZPDMiVCjbGbjIakFW0/PS3HLy2g1Ght6dQFsmVhPo41WgKXn6JUrfmq1wj03T2+U1hdqOlDHQ4rA3IauCq3I31Ob3YnubLtLWRzNkWiwSY3M2nRu0nd/WhoETBomfbsu/1blzOKGRcR68atZ3QyZs1EVepUBnAqJ+OJjGmgxcWplIC6tvS3suRgPq2mEgl5Jcqqm78L/Tv/DhBEARBEARBEGpi92549lltpsUuXWDPHvDyeti90pGr5cw/NrdGxy6MWMCmZ/ay7GAiag242ppjYVrN2/WiZJCakVtimCXyusKNFIUrDUwzqjyF0tKTYofOZFl3wMStOzI7L4qj05FeyiQgv5S1J67SxdeZXgFujA71YFVE5VMkR4V60KqxA48vPExeiYKgBnZM7OLD483dOXs1V1fjzJjRoR4Eezkya0t01Y8Z7YiYBg1n3u9NVqGcjPxSXa0yG/O6F+LUvR4LgiAIgiAIAsDRozB06O3U91euaAOzkyfB3f2hdq3czZKbnEs/V6Njz6adpUiRj5+7LVKJBDtLIwk+5HmQvh/SdkHqbii8jDroA2w83jI41MvZGjvPHpCyTm/7VQWcwxEXr6do3mIanx1QsXrntVt7EwlqkM2krj58/Wwb8kvkWJvLkCChqEzJW/38CfF2Yvkhw+yO4zt50d7LSZcVEbTB0/9+i2RaD1/e6e9PW08HfjqaZDQzZLdmrgxZcsRgnVhlcgrlfLX7EutPXdc/V1cfegVWkWXyX6ju9FQQBEEQBEEQyp09CwMGQLF+Bj769QM3tyqbPog6XpXJKblZ6+NtzGWM6eClXUumVkD2CW0AlrZL+71GpddGk74fmdc7euu27C1NWfl8K34/kE4fJewvhv0l2n8TlQA3IWkFUwpMmNt/Hofis7h2swTQT6jxdj9/9sVmsOJWEGZvacqoUA9+HNsehVpNUZkS61sjU2sirvLJlhijQdWifZdp4mpDPRtzVowJplSu4maxHEcrM5xszLicUUhOYZlBRsWqWJiacOFGnt62qJR8pq27I8tkHVA3eikIgiAIgiAI5WJjoW9fyNN/Q86zz8K332ozLlbinup43QUnS8faHW/hwGCfLPqa/oHs6D7tqJii6gQZ0pwI1h6PZVxHL9784zwAo8MasT95Oy9c2I+6irbLTn9LF4+ufDK0G+N+OqO3b3XEVYI9HYnPKNQ9V3klCpYeuMKyg1cIqG/H6hdCmfnXRbZfTK12HdiKQwmsGBNMt3n7aOpmi425jG5NXRjUugGTfznN5mmda5wQpEVDO2wsTLCzkBFQ31avsDTAqvBkQr2d6NfcvU6kxf/391AQHoAhQ4fpfQ19YjgjR47mnXdmsmv3bjSaav5XecD27t3HkKHDWLtWf7rBwoWLGTJ0GBcuXHxg105Pz2DI0GHMnPn+A7vGP23zlq28/Mp0hj/59H/usdXG2rXrGDJ0GHv37tPbXtl9NXHiZIYMHfZPdvGuDRk6jIkTJz/sbgiC8E9ITobevSEzU3/7wIHaYtEmJpU2La/jNW1tpMEb//I6XnO2xVIsV9637jpaOtLavXW1xwWbwxpPF3yPdee5rN6YRU6H6xurDcgA1FJrrl89TyffeowM8cBEKmFEaH2+jphfZUBWbsHxebTzskVmJDL4/kgio8I8kd4R56o1oNZoyC6Us/VC9QEZoMso2dRNOz1zTAcvxnX2ZtaWKPJKFayJuFptQhB7S1Ne6u7Lr8+HUqbQ8EZff5aMaMvBNx/jpe6+2FeY8rns4BXkqpo8Aw+fGCkTHmk9uncHQK1Wk5qWRkxsLNExMZw/f4E3Xn/tIffuwdi7dx+LFi/h2WeeZsSIZx92dx6448fD+f77H7CxsSEkpD0W5hY0bNSQ9PQMJk2eQvOgIGbP/uRhd1OowoULF3nv/Q/o0b0706e/8rC7IwjCw5SaCj17wvXr+tu7d4f168G08iLLNavjpR1hCfFypGszF8xk0hpPaZQrVShUGvJLFNwsVuBoZYqdpSmmUjNmdJzByA0jq2zfyRJGmGVCcWaVxwEgNQOXLlC/N0qXnpTatuTFfDlZhWW809+fPoGulKoKarWWLbc0lz5B7my7oJ8vvzyQ8nO3NagbZmMuI6e4doWh5So1q54PxcLUBIVKTUpuMe8NCOTjwVL+OnuDUB9nRoZ4GE0I4uVsxcoJIZxOvsmo7yMMRjrHdfRi08udGPvjCZKyi4lKySe/RFknpjD++3soCA/QnW/wIiMjmfXJbA4fPkK3rl1p3z74IfXMuOeeG8Xw4U/g4mJYpPF+cXZ24pslizA3N39g1/gnhUdEAPDWjDdp2bKFbnt6etWZqB4l/8R99aB9s2QRJibiT5og/KdlZkKfPtpkHhWFhMDGjWBpWWVz43W8jFt+KIF2Xk4890MEE7tWP6WxqEzJ7uh0vjtsOCXyxceaMMh/MJPbTuJc9AoiSsHYoJKd1wgoWlt5pxxagHsfcO8Nrl1AZlVhKmaE3lqyZaPbUqLMq/xcRuSU3MTZ2nhtr5vFcqMZDe+mMLSFqQm7otLo2syV/osO69aflQdVJhJ4sXsTg4Qg9pamrJkYxjf7L7PaSPbHqJR83vzjPKNCPVg5IURXWDq/VIG7vUWt+vgwiL9gglBB69ateeyxbuzdu4/wiIh/XVDm5OSEk5PTA72GTCajUaNG1R9YR2RnZwPgVs2i70fZP3FfPWj/pXtWEAQjMjKgRw+IitLf3qIFbN8OtrbVnsJYHa/KlI8OKdUapq2tOmlEcZmS2VtjjI7sxKdm8/vmfXjER7GUA0gbw6iiZqxJuaQ7prV7a/4X+ibdGj/Ota2HaSy7NQpo4a4NwOr3BvdeYFlf/7q3pmLeOfKXV6Lg1/Bk3hlYu797TpaOZBelG93naGVGYZnhlM5L6YW1LgxtaiJh5t8XmfdkS73izxWDqindfEjJLeGHse1RazSk5Jbg6WzFsSvZRgOyiu4sLG1nUfno6b9JnQ3KLl++QuS5c8THxxMfH092dg4AG//eUGW7vXv3sX3HDq5du45MJsOvWTOeevpJAvz9K20TExPD77//SdylSyiVSho3bkT//v10U9+MycrKYvWatZw9G0lhYSEuLvXo0qULTw4fhpnZw68wrlQruZ5/vfoD/6Ua2TVCJn0wt6+Ptzd7gaysbN22IUOH4eriwtKlS/hzw18cOnSY9PR02rVty7vvvg1AWVkZmzdv4cjRY6TeKl7p4dGYfo8/To8exu+VmJgY1qz9jUuXLmEildK0WTNGjqx8SuHChYvZt38/n34yixYtmuvtKy0tZeu2bRw9epyUlBTUajX16tWjZcsWDBo4kIYNGzBz5vtcvPUHbd1v61n323pd+2mvvEzPnj2qnda3f/8Bdu7aRVJSMiqVivr169O1S2cGDx5kcG9X7K9UKmHtut+Ij7+MRCIhMDCAcePG4tG4cRWvhr6EhEQOHTrM+fPnyczKori4GGdnZ9q2ac1TTz+Fc4XAYu3adXqPb9LkKbrvmwcF6Z6Hi1FReuum7pwiV1BQwF9/byQi4gQZGRnIZDKaNPFhyODBBkF7xedu5sx3WLvuN8LDI8jOzqZ/v8d54YXnq32MNXkdAeRyOQcPHuLEyZMkJ1/l5s2bmMpkeHp50a/f43Tt0rnGz2tV9xWARqNhy5at7Ni5i/T0dGxtbenQIYyRI0ZgY2Nd6bkUSgV//vkXiQkJFBUXs3rVr9jYWBMVFc2Ro0eJioomKysLhUKBi4sLoaEhDB82TO+c5ecD2Ld/v+57QG8Kbvnv6HffLTfo/6lTp9m0eTOXL19BLpfj4uJCWFiowbXg9n0z7ZWXadKkCatWryY6OgalUomvbxOee250lX8vBEF4ANLTtQFZ9B21q3x9YdcuqOGHSnkltZtmV3F0qLKkEXKlil3R6XoBmZW0hMdsT/O4/TG6257E1qQEym6fd2nrJ/hg6FRySm7iZOmIlcyOtRGpDNl4iiFWA7A1KWb8ky9Rr3FIpQlLqpuKuScmg7lP+tHavTWRaZHVPtY27m2wt3BgV5ThdMegBnaYm0qJSysw2KdSa3TrwMoTjFRlfCcvXWHon44msWJMMMsOXtFbj1YeVJUq1HSZt4/jb/fkyOVMvOt5sfxgzUY6y899OD4TO8u6Ee7UjV4asX7970ScOFGrNt9//wObt2zFzMyMNq1bI1fIiTx3jrORkbw1403CwkIN2hw7dpz5C75Ao9EQFBiIrZ0t589fYOHCxSQnJTN+/DiDNqmpqcx46x3y8/Px9PAgMDCAK5ev8Ntv6zl//jyfzPoY0yrmPP8Trudfx3uh90Ptw71InJ6Il4PXAzl3SYk2HazpHcUa1RoNn302l6joaIKCAvHy9MTWTvvJXG5uLh9+9DFJSck4OjoQFBQIGoiNjWXhosVcvnyZSZMm6p3v5MlTfPb5XFQqFU2bNsXd3Y3ExCTeffc9elYSxFUmJyeHDz/6mKtXr2FjY0Pz5kGYmpqSnpbOzp27aFC/Pg0bNqBN2zao1CpiYmLx9vLC2/v2PVC/fv0qrqC1dOm37Ny1GzMzM1q0aI65uTkXL0bx66rVnDh5ik9mfWR02uPJkyfZvGUrvr5NaNeuLYmJSZw+fYZLl+JZvOhrHB1rlpnqzw0bOH48HC9PTwICtG+MExOT2L5jJ+ERJ/jii/m6wMzb25se3btz5uxZcnNz6dAhDEsL7dSWho0aYmtny/Hj4Tg4ONC2TRvdNQICb7/hvnEjhQ8+/IisrCxcXV1p07o1JSUlxF26xKez5zBu3BieGDrUoJ9lcjnvznyfzMxMgoICaeLjg42NTbWPr6avI0BGRgZLvlmKk5MTDRs2oGlTX3Jv5hIbF0d0dDQ3rl+/b2sGV3z3Pbt27aZ5UBCenh5ERUWzdes2oqKi+GzObKysrAzaHDp0mN179uDr24S27dqSlpame2/x88qVJCUl4+npScuWLVAoFFy5ksCGDX9x6tQp5s39HMtb05ACAv25mXuTs2cjcXd3JzAgQHeNivdvZf74409+XbUaExMTgoKCsLOzJSYmlg0b/iI8PILP5nyKg4ODQbvLV66wfMV3uLu706ZNa25cv0FUVDQffPARC+bPxdPT8+6eTEEQaicry3hA5uWlLRJdi1pk9pa1+1Dc0cqMYrmKgPraLIG7otPoFeiqF5QpVBpWHErA3qSAPnbh9LU/RhebSMylldfZSjq3gbeP9cfGXEZhWT5xaQW6oOSXkkEENbDjJfd2VWaQrG4qplyp5kxyIW90mMHov6peywbwRocZnEkqQGkkJ0bFQMqYVeHJbJvWpUZFpdt7OfHJlhhAf63apfRCmrnZ3HpOlKw8lsSaiWFM7OqDmYmUESEeyFXqWo90vt67mbasQB1QZ4MyP/9meHp50tTXl6ZNfZk4aQoKReW/AJHnzrF5y1ZsbW2ZN/czGjTQvrGJjY1j5nvvs2jxEpo3b673qWlBQQGLl3yDWq3m7bdm0KFDGKB9A/72OzP5e+MmgoODDT5ZXrhoMfn5+QwcOICJtz4ZV6lUzJu/gPDwCP74489HIsFCXaTRaDh56jQAXl5eevuysrIwNTVl6TeLcXZ21tu3aPESkpKSGTRwAGPHjtEF3bm5uXzy6Wy2bttOcHA72rZtC0BxSQmLlyxBpVLxyisv0atnT931f/l1FRs2/FWrfn/19SKuXr1Gp04deeXll3RvaEE7elNSoq358eTwYTg6OBATE0toaEit7sNjx46zc9dunJycmP3pLN3vUFFREZ9+OofomBjWrFlr9IOKzVu26n3woVKpmL/gC44fD2fb9h2MGjmiRn3o27cPE194Xu9NtFqtZv3vf7B27TpWr17DtFdeBiAsLJSwsFBmznyf3Nxcxo8bh5ubq97zcvx4OI0aNjSaPEKlUjF33nyysrIYO3YMQ4cMRirV/seemprKhx/N4pdfVtG2TRuDN+jx8fH4+fmxfNm3BiMxVanp6whgZ2fPxx9/SKuWLZFU+MOdnp7Oe+9/yPrf/6BHjx56j/luHThwkLmff4avbxNA+8HFnDmfc/7CBdasWWt0BHDX7t288cZrdOlsOGL37DPP4O/vh7X17edGoVDw3Xffs3PXbjZu2syzzzwNQJ/evanvXp+zZyMJDAioVaKP+Ph4Vq9Zi4WFBbNmfYRfs2a6a3311UKOHjvGsuUrePutGQZtt23bzgsvPM+ggQN0277/4Uc2b97Chr/+5tX/Ta9xPwRBuAd2dtC4sX5Q5u0NBw6Ah0ftTmUpq9U0OwcrU209LYWKojIlLjbmqFSQXlwCEgl2shLkSX/zmvm3dA04i5m0Zhkb7cxUJFzJpkxjPEic0q1JlYFEiUJJTpG8ygDFRCrh56NXWTRyEFPaTWHZ6WWVHjul3VQGNhvIgIWGgx2j7wikjMkrUXDhRi6v9WlWq6LSAPmlCp4L86SbnyulchU5xXKcrMywMjfBRCLh0OVMlh64wqhQT1o2sq+0D8YUlSkJ9XGuE+nwoQ6nxB8+bBijRo4gJKR9jT5l37RxMwBPP/Wk7s0kgL+/H48/3peioiL27Nmj12b37j0UFxcTGhKiC8gAHBwcGDv2OQA2btyk1+bSpXhiYmKxt7dn3Ngxuu0mJiZMnTIZmUzGlq3bUKn0i/4JD5dKpSIlJYVFi5cQFxeHqakpPXv0MDjuuedGGQRkCQmJnD59hqZNfZkwYbzeKKiDgwMvvTgVgO07duq2Hzt2jLy8fIKCAnUBGYBEImHUyBEG16jKpUvxnD9/Hnt7e16+4408gJubq0GAeTe2bN0KaKeMVfwdsra2ZvLkiUgkEnbs3IVcbjg9pEuXznoj0SYmJjw5fDgA0VHRBsdXpmWLFgajGlKplGefeRpnZydOnDhZm4dUpZMnT5GcnEyHDmEMe2KoLiAD7aji+PFjUavV7Nq9x2j7iROfr1VAVtvX0c7OltatWukFZNrj3HjqqeGo1WpOnrw/z0f//v10ARmApaUlEye9gEQiYfeevUZf8+DgdkYDMoB27drqBWQApqamPP/8BExMTDgRUbtZEJXZum07arWagQP66wKy8mtNmvQCZmZmREScIDMzy6BtQIC/XkAG8PRTTwG1u2cFQbhHZmbwxx/aZB4APj53FZABmJlImdzNp0bHTu7qw/WbxUz65RQ9vzjI4CVH6fD5Pp5ZcZxD8VmYR3+E7O8GOEZOoJfdyWoDslNFAcxOmUBs8BlmFP9aaUA2OsyTngGulQYSxXIlq8KTSc0rNbq/PF38oRndeW9AIMlZSub1ns/a4WsN0vK3dm/N6mFrmdd7HnO3JegKR4M2kFrwVEve6ufPH6euGy0GXW5UqAcB9e14ff05ujRzYcWYYPa+1o0/pnRg72vdWP5cOxIyixi85KheYWgvZyt8XW0wk5lon+cvD/LUsuP0/PIgL6w8xY6oNJo3tKdUrqJngCvW5tpSByZSCQH1bWnv5UhAfVtM7szVf4uLrXlVg43/OnV2pKw2ysrKOH/hAgAdO3Yw2N+xQwe2bNnKyZOnGDp0iG77qdOnK20T3K4dZmZmnDt/HrlcrltLU96mfftggymKDg4OBAYEcP7CBaKjY4yu3RD+WcZqMFlaWjJ9+ivUr68/JUIikRDSvr3B8ZGRkQCEhoTovXEv5+Pjg4WFBfHx8bpt0dHaT5yMvWmVyWR07NiBzZu31OgxnDunnf/dtUtnrKrJPHW3lEolcXHaRcndunU12O/l5YWXpyeJSUkkJibi5+ent79161YGbRrcmoaXc/NmrfqSn1/AiZMnuJp8laKiYtRq9a0+qigoKKCgoADbGiz4rk7569ohLMzo/qDAQAC917Wco6MjTX19a3W9u30do6NjuHjxItk5OdrgSHP7OU25tbbxXhm7Tz0aN8bLy4vExEQSEhLx99d/zY39rlSUnZ3NiZOnuHH9OsUlJWhuzYuRyWT3rd/lv2fG7lkHBwfatG5NxIkTxMTG4uKi/xhbt25t0MbOzhZbW5ta37OCINwjGxvYuhXGjdMWhq7FWuSKZCZSegW4MTrMo8q0+KNDPQnzcab3V4cMgpHyZBTftFMzAOOBEYBCY8LxwpbszOvArvwwMpXaqfXPu/nzcvcC5u2MMxhNmtKtCT0DXCvN8li+jmzDmRv08DNM4lGeLv5EYg6Tfjmll7Xw1d7t2TfmEEWKfHKKb+Jo6YCDhQPx6aWsjUjjxR5Neb6LDwqVBmtzE2RSKb8cT2Lkd+EsGdkWr3rW/Hg0scoRsGs3SzCVSpjy6ymUao1uKmLF6ZnlyjMpzt0ea3TKY1RKPq+tP8eoUA9+fSGUwjIllmYmLHq2JWFNHChWFJBSkI6zlRNWMjvWnUhlVfh1vUyOUqmE3GIl7vZ1I9ypG728RzdupKBQKLC3t6NevXoG+5s00X5qkpScrLc9KTEJAJ8mhp+qmJqa4uHRmMuXr5CSkqL7BLu8TRMf45/E+DTx4fyFCyQlJ4ug7F+gPFmLRCrBytIST09POnQIM7r+x97e3uhawIwMbWr1VavXsGr1mkqvVXF6bU6ONjFNZSnI3VxrPuUs61Z2QfdazKuvrYKCApRKJXZ2dlhYGE8r6+rqSmJSki7pTkX1nA1/78oDD2UV047vdOjQYb5Z+i2lpZX/ISwpKbkvQVlGhrZOzJdffc2XX31d6XH5+YYLn11cDB9vdWr7OhYVFfH55/N0HzgZU74+8l65uhq/T11dXUhMTNTdzxVV9Rxs3LiJX35dhVJ5/4qzGlPeL9dKfp/KH1dOdrbBvnqVjFZbWlhSUFB4n3ooCEKN1asHW2r2YWVVrMxkvNs/gFBvZ5YdvGIQZLzQ2Ydufi4MW3oUe9VV8jC+3nrWuSD6B0qQVEhuX6Y25UBBO7bndWJfQXvyVfrvJYIa2GFrYUqItxOrXwjlZrGc7EI5jlZm2FiYcDr5Jmeu5tDWw8loYFa+jsxYxkN7S1N+mRDKsoNXDLJA5pUo+GhTLAv3XOGbkW1p7eHPzSIFsaklOFlZ0L9FA9ZGXCWjoJSPBwdx42YxthZmDG/biMGtGwAauvu78JifC7klCm4WaftsbiplTcRV5myNxc3enOFtG6LSaBjbwYs3qkn6MTrMk4jEnCrXoIE26ceglu7417dFJlPS0kvFldyLOFo60tDeiRVnVnDk6hHGt5rExpf7Me7HcyRlFzOpqw97YtJp7+VUJ9LhwyMSlGVmad9cVTYlzMLCAmtrawoLCykuKcHK0pLi4mKKirVDrJX9cXZ2duby5StkZGTqgjLdteoZb1N+rswMUSPp36A261PMKknOotZo/0MODAh4oIHRv14VUwTunGJ3NzIyMli4aDEALzw/geDgdjg5OekSi8x46x3i4uLQVLIQubbUGu0IXNu2bXCwd6j0ODs7wwDQzPTBZ1hd+cuvnL9wgeZBQYwY8SweHh5YW1thYmLC2bORfPTxLOOFcP4hppU8B3Fxcfz4089YW1nx0otTad48CEdHR90HHuPGP8/Nf2okqor78n7cs4Ig1EJpKbz1Frzxxl2PhFVHqVIjV6kpKFUS4uVID/8O5JcqyCmSYyEzwcJUirUkjz/XvcvXjpto3TCePnFLuFTmZXCudGU9Liha0cLsAmmWXZkf25rd+WEUqCuftj6pqw9SCczeFsOaiKu083TkiTYN6eHvRmGpEhdbC5yszCguUyEBLO8IzCqm9L8z46E2yMk2mpYf9EfRZm+LMVp0+fnO3sSk5vPZ9liKypRIJDAq1It6NmZMWaWdCebnbqsbAUvLK2VkaCM2TQumSJFPkSKPnNJ0+rZwxNayDTP+uGh02qOJVML4Tl6M+aH6qeqdmjgT1MiSLZf+Yt6xeXqZJFu7t2ZayDQmtJ7A46sfJ9z7KD9P+JRVx9Po4e/KhJ9P0tO/7pTDeSSCstIS7afqVRXDtbAwp6ioiJJbQVlJhU/iK2tXPmJQ8dPo8k/wK2tjbmF+q03ln/RXVFhYRFFRkcH2rCzDNRC10ciuEYnTE+/pHA9TI7t/T02i8kA7NDREb/prVcrXQWZmZhrdn1HJ9qqun5aWVuM2tWVra4tMJiM/P5/S0lKjo2XlI0vOzg+m3tXp02dQKpUMHTqEQYMGGuxPTzdeW+VulX+I07tXL6NTmO+32r6O4eERSKVSZs58xyD7Ydp9fi60HzwZZhssv39rU+MsPFxbzHvU6FEGpSLKysrIzc29+47ewcnJifT0dDIyM42WXigf5XaqxRpOQRAegPh4GDkSTp2CI0e0X/d5Ov7tIsu3k1BIJfBk20a8N8CfAwdW4ZT+K50sjvG85e1AYrjTPj5LnWD0nK8lTOKHSf2QWblhob5MQSUBEWjXXfUOcONAXAarwq/i5WzFgqdacTIxh0+3RpOeX0phmZJL6YX4u9sysYsPfYL0C1ZXTOm/KjyZTS93YmSIB7+dusbIUA8mrjxl9NpVjaLB7SmZo8M8eetxP97pH4C9hSnmplL+OnODLk2deba9B2tOXCUmVTs7xMvZir9eCubA1e0M+m2UQbA0o+MM9r85kLd+j2V3zO2BiKAGdrzWuxlyZfWZFBs7WrJsTHNm7H7TaKKSyLRIJmyawOR2k9kxagftv2tPV4+uvNh9KFKpBBOppM6kw4dHJCiryzZv3qxXa+l+kUllDyyl/KOmVetWrF6zlvCIiBoHZUGBgezbt58jR4/Rr9/jevtUKhXHjx+v+fVbtWLV6jUcOnyEUaNGGiSIuJNMpv21V6mN5L2too2fXzOioqI5fPgIvXv30tufnJxMUlISFhYWNUpTfjcKC7VTxoyNXEdFRdX6zbzMtPx5MJ50p3XrVuzZs5fwiIh/JCir7etYVFSElZWV0XT0R48eva99O3r0qEFQdv36dRITy19zrxqfq7BQ+yGTsdfx6LFjaIwMdVb3WlUmMDCA9PR0Dh86zKhR+imh8/LyOHs2EolEIuqOCcLDotHADz/A//4H5R9AnzkDEyfCr79WOZpdG6UKJT8eSeSrPfGoKixucja5Sb2r6yn6YxeDZWlgjsEMgycc9jMvdSwqTAzOe7nMg0yFHSt3xPBGHz/CfIxnHpzU1YdeAW4o1WoW77uCvaUpq54P5WJKLmG+NrTwVJNdnKO3PurDTVGcSr7JO/38KSxTUiJX6hVBzitRMPbHE6ycEEJ3fxfkSlWlQU51o2jlVoUn07qxPQmZRSw9cEU3giaRSHipexPaejrw09Ekrt8sYeXzrZhzbCYrKgmWRm4YydTgqXw1Yj6Fpc3JLVZgZ2GKnaUpplKIzzAccLjTp08Esiluc5WZIwGWn15OWKMwpgRPYcHxefRvOoArGaW8PyCwzqTDhzqcfbE2LCy1n+qXlZVVekxpqXZf+RshywojAZW1Kx8Vq/jmqXwEobI2Zbrr1Gx+66BBg1ixfJnB15zZn9aovfDg+TVrRutWrYiJiWXZ8hUUFxcbHJOYmMiZM2d0P3fs1BFbW1suXrzIvn23i+FqNBrWrF1nNBtcZZo1a0qLFs3Jy8tj6dJlBuut0tMzSEq6vV6yfFTjxo0bNb4GwIAB/QFYu+43vdGc4pISVqz4Ho1Gw+N9+zyw4ujliUEOHDyo9xizs7NZ+q1hweDq2N0a/UtLSzOaDbVjhw40btyYgwcP8dtv6w1Kbmg0GmJiYoiJqTxNcG3U9nVs0KA+hYWFHD5yRO+4jZs2c+HCxfvSp3Jbt24jIeF2PZyysjJWfKd9zXv27FHlLIQ7NWioXZ+xe88evTVlV69d45dffjXaxsnx7u7Z/v36IZVK2bJ1G/GXL+u2KxQKVnz3PXK5nLCw0LtaAygIwj1KS4Phw7UB2J0zgjZuhAq/s3dDqVJTLFeSllfC5Ywi+jWvz8E3H+Olx3zo63yepR5zOBYwjhn1f6G+rOoZCp7mlScfcrQy43JGIYOXHKVrMxd+n9KB4+/0YPv0Lhx/pwfrJ3egT6AbayKSySzQprF/uYcvluZK0hT7GfRbD/yXetHp57b4L/Vi8PoeuLieZePLwRyJz2RnVBorjyXR88tDqNEGeeWSsosZvOQomQVllCqMf9BqIpUwMtSDn44m1eh5++loEqPCPAlqYIuVmQk/Hk1k+cErqDSQklvCijHB7HujC0dv7DQakFX07alv2RS3mYiELN7/+yLPrzxJy4928v7fUThZV/1ewUwmpa2nDfOPza1RvxdFLGJK8BTOpZ+jUJHP3ph0vOtZ15l0+PCIjJS51NMu5s42spgbtMFVUVERNjY2uuQDVlZWWFtZUVRcTFZ2Nh5GPo0uP1/FRfAu9VxISEgkO8v4tcoX87vUMJGDjY11rdJqCw/Hq69O5+NZn7B9+w4OHTqMt7c3Tk6OFBcXk5SUTFZWFoMGDtDVKbOytOSVl1/i87nzWLhoMdt37MDdzZ3EpCRSU1Pp07s3u3bvrvn1/zed9z/4iEOHD3M28iwBAQGYykxJS0sjMSmJ8ePG6kY6/PyaYW9vz7Fjx5k5833c3N2QSCT06tWzyhGDTh070rdPb3bu2s0r0/5Hy5YtMDcz52LURfLy8vHza8bIGtYbuxsh7dvrkutMmfoi/v7+KBQKLly4iLe3F/7+fsTGxtX4fKamprRp05qTJ0/xv1dfw8fHB5lMRkCAP7169sTExIR333mLjz7+hDVr17F123a8vDyxt7cnP7+AxMRE8vLyeH7CeAIqFDS+F7V5HYcPH8ZXXy1kwYIv2bZtB/WcnUlMSuLGjRsMHjyITZs235c+AXR7rBtvznibFi2aY2VlRXR0NDdv5uLh0bjGNebK9ezRk40bN3Hy5ClefOkVfH2bUFhYSFRUNKEhIcTHxxtM39WWA/Dk8uUrvPHmDBo3boxUKiUkpD2h5WmyjWjWrCkjRzzLqtVreOutd2jePAg7OztiYmLJysqiQYP6TJk86a6eE0EQ7pJaDStWwNtvQ16e4f5mzWDDBmja9K4vYWyqooNJPtO9DjLVYTs2DaseMZJrzNiZF8qfN3tyuKANSGUE1L9d2PhSeiEqtYagBnaYm0qJSysgoL4dZiZSrMxkWJnJqG9/K5mVSs22i6nsik6ntYcjod6OPBvixpu732B5JaNM4zeNZnK7qfw84VPe+iOOL59prZ16eMc6MtCOmP0ankyot/Fp2M3cbCiVVz6KdqeolHykwLLRwaTll+J0K6FHTpF2UKHnFwc4+V4X5h2tWbC04Ng8Nj+7j1d/u6nLwPjn2RtM792syppxg1vVJ7c0l3Pp52p0nbNpZylWFNPctTk5xTfJKdKQX6rAyrzuhDp1p6f3oGHDBpiampKXl092drZBwo8rV7SfAHvdUQDWy9uLqKhoEq4kGKxHUCqVXL16DTMzM72aTV7eXkScOMGVBONV1hMquZZQtzk4ODD388/YtWs3h48cITEhgdhYOQ4ODri7uTFwYH+DtOKhoSF8+sks1q5bR3z8Za5fv4Gvry8vv/QiN27cqFVQ5uzszIIF89i8aTPHjh8nMvIcUqmUevWc6fd4X9q3D9Yda2Zmxvvvz2TVr6uJj48nKjoajUZDYEBAtdO4XnxxKgEBAezYuZOLF6NQq9W4u7sxaOBABg8eVKsRk9oyNTXlszmzWbVqNafPnOXUqdM4OzkxYEB/nn3maWbNqv3o8csvvchPP6/k3LlzHDp0GLVajVql1tWOa9CgAV99uYCt27YTfjycuLhLqNVqHBwc8PHxJqR9ezp37nTfHmNtXsfHunXDxtqG9b//TmJiIsnJyfj6NmHK5EloNJr7GpRNfOF53Fxd2b1nD+npGdjY2NC/fz9GjRxhUG+sOnZ2tiyYP4+Vv/xKVFQUJ0+ewtXVlZEjn2XokCFMmfKi0XZvvzWDn35eSXR0DFeuJKBWq6nn7FxlUAbw1FNP4u3txcZNW7gcf5kyuRwXFxeGPTGU4cOHGc20KgjCA3LhAkyaBOHhxvdPngxffAG1/H+lomK5kjnbYvRS3ve1O8ZCjwVYSOVQRcLf6BJvGodMZ19JT6ZvSMbe0pQp3T0ZGeqhV9jY3FTK2hPXaOZmw5qIq6g1MKGTt9HZlnKVmlXHrzKgRX0C3G35fmw7tlzaYDQgq2j56W/p2KgLjzcPQQI839mb9Seu8ceLHRgZ4qE3FdFYNkbQjpIFNbAjr7TmWY4B0vJLmbMthpNJ2qRL2tT33kzo5MPNojLyahksydUF+Lnb6tajqdQaowFmOS9nK97pF8D1wku16nd2cTZ25nY4WjqQXZRObokCd/sHUyroQZBojE3gr4OefOoZFAoFG//eYHT/rFmfcvrMGZ6fMJ7Bgwfp7fvu+x/YsmUr48eN1VsTtGHDX6z85VdCQ0J499239docDw/n88/n0T44mPfee1e3/dKleN6c8Rb29vb88P0KvRTqubm5PP/CJCwsLFj584+6tT13Iz09g0mTp7Bi+TLc3IyPuqnVauLitCMHfn5+RmtoCYIgCPef+P9XECooKoJZs7QBl5Hp4tSrp11bNnjwPV2mfFRq2tpIve31TTM57P88MonhFL8StTmbc7uwJrsfkSXN+OvFTrjbW7Am4ipPtmvEicQcfj6WZLBO7PnO3vQOdGPkdxG0bGTPpK4+WJia4Ganvzwls6CMYrmS6JR8WjZywNpCyWMru1QZ1DhaODK1/VReDH4RtUZDTslNnCwdsbdwIDalhMZONhy7nK1XN+yl7r5417Pmjd/PYW9pyugwbTCpVmuQSiV0+nxflc+diVRCMzftaOCXT7dmyqpTRKXol3wZHerJ2/39SMyNpeWyllWer6LzU87z6d8FRCTezqzrZGXGlmmd2Xo+hQ1nb+hGH+0tTdn8cmeOXM6ki78JHl/XPBNnzEsxfLj/Q74b/ANtPz7IoRk9aODwzwZlNXl/XplHYqQMYPCQQZw+c4b1v/9BcHA73ehWbGwcO3fuwtraml699JMX9O7di9//+JOIEyc4fjycDh20RWRzc3NZuVK79mHIEP3/QJo1a0pAgD8xMbGsXPkLL7zwPKBN3rBs2QqUSiUDB/S/p4BMEARBEAThX0+jgfXr4c034do148c89xwsWAC1qM9ZGblKzYqDCQTUt9WbapiqcGFHXicGOhzWHZuk9Mah5ct0+70heRXqiTlbmyGVSJjSrQlztsWwuprCxisnhFBUpuC5H06wYkywQVBmYSpl2YFkxnb04s8z1+nX2rzKgMzXyZcdo3ZwKPkQA9cONMhq+EaHGQQ2HEwnX2f6BrlTWKagVKHGzkKGuZkJ13KKGNa2ka6AdGxaAYdmdK90qqA2gGvEiND6FCnyuVl6E0xyWPZcC4OCzKsikhnSuj7uTg41fUkAcLBwwNlGXuF62oBRrlTTpakL/VrURyaV8mt4MhYyKRGJ2Xy0OZrTrbvQ2r213nNQmTbubbCUWTLUfxhnkgrwc7fDztJ4KaN/qzobGZw6dYrf1v+u+7l8wfibM97SbXvm6acIDtZO92ndqhWDBg5g85at/O/V12ndqhVKpZLIc+fQaDRMe+Vlg7Vbtra2vPLyS8xf8AVz582nefMgbG1tOXfuPEVFRQwZPMhoAehpr7zMjLfeYfOWrZy/cJHGjRtxOf4yaenp+Pv78eSTwx/EUyIIgiAIgvDv8eKLsKySaXq+vtp9t6aL35OSNIhbCLL6LB8z0ehUw/WRT9DH7jhb8zrza/YAzhT7s/fxx2jgeoa8W9PqQr2dcLYx50h8FvmlCqMBWUWrI64S7OnEtZtFJGUX62VHBO3I3f7YDCzMTIhIzOZwfCbBvpWP3DhaOLJz9E7mHp3LitMrDPZHpkUy+q+RTGk3lbm957E2IoWhbRphaSahoEyJRCJhUlfDYLKyqYJezlb8PKFVpWntp4e8oVeQGeDPM9d5Z4BXrYIlW3M7sguv6dVKm/TLKYPRx8ldfegZ4MZTy46jUmuQYMobHWYw+q+RVVxBa1roNM6mnmVgswH0//oE/+vVDJM6Vm6yzgZleXn5XLoUb7C94ra8PP1PBF544Xm8vb3Zum07kefOIZPJaNWqJU8//VSla2k6duzAnNmfsP73P4iLu4RSqaRx40YM6N/foMZOOe06lC9Ys3YtZ8+eJTz8Bi4uLjz99FM89eRwvSmNgiAIgiAI/0kjRxoGZaam8M472i8jNS9rpeAKxCyAhJ9AXYZU5spL+wKJTLmdAbs8rfvTz0/k6ZWeRGbeXvt8s1iOTYVEEB8MCmRfTDptvZwqrfl1p++PJLBiTDC7o9P1amIpVWrKlGpWHEpg+ZhgJq48hQYNzlbabLImEhOCXIOwM7cjvyyfqIwoprafysGkg0YDsoqWnf6Wbl5d6d9yAEpNCflleWQX56CW1sNeYo+DlQn2lqa3R7gq1DQrX4tmb2mqTWt/dCYrzlSfcGTIklPklSg4dz0PpVqlLdq8yXj9toqmhU6jTKFAraHaWmnfHrxCq8YORKfmE1DfluwCBYOaDWJyuylVrsGb3G4yg5oNwszEnLnbrtC1mQs9/F0xrUOZF6EOB2U9e/agZ88e/0i7gIAAPvzg/Vq1cXGpx/Rpr9SqjSAIgiAIwn9Gly7wzDPw22/an3v0gCVL4F4z1uachei5cO130NxeJ2ahzMC/dCOR3K7/WV4YeVSoBwvH9WXwkqO6YMXRyozCMu1MqzEdPPFytmbp/isENrCvVbZCuVLFrCHNdTWxiuVKTiRm4+lsjUqj0WU/NJFKsDGzZ1G/RQzxG0Kxopis4izqWdXDUmaJjZkNT/z2RI2uO+/oPPqM6UPvX3tzJvV2yZ3KRrh2Rafzbv8AXS21x/yc2Z+83WhAVtHy09/SoVFnRoW2ZumBRC6lF2IqNaOHdw8mtZtUZQA5ud1kunt1x9LMgpd7NK22VpqNuYyMgrLb3xfKefevS3z73Hy6enRj/vG5BqN5b3V8i4F+Awm/XEBTN3OGtGmEp5M1FqbSOpUOH+pwUCYIgiAIgiA8ZEqldr2Yt7fx/fPnw8WL8MknMHTovRWEzjgMUbMhdWelh0ys9zfrcvoC+tdZHXGVNh4OjAr10BVGtjY3wcxEwuIRbegV6EpesRJLM2mtsxWWKdQ4WZuRXSTH1lzGnO0xxKUVMONxf2zMZeQUa9dTNXa0xNREgqOFI0PWDTEIMN7s+CY/DfmJx1c/zuWcquu0nU07S0ZRBkq1Um/7nSNcr6yJYsnItpxIzOH5lSdp5+nIijHB2Fqo6Ppz9dMCARZGLGDTM3tZdjARlVpDqULK6ZTzzOg4gw6NOrAwYiEX0i/oRv4a2DSgf9P+dPbozOmU8xTkBjKkdWNGfhdb5XUKy5Q4WZnpfX/0SjYDFp7gk6HdODRuILmlubrEJ44WDoApmfkK7K3MMTMxIbC+PSYSsDCreyFO3euxIAiCIAiC8HDl5MCPP8LixdppiFFRYCyJWePG2lT49xSMHYELH0H63koPkatl/HmzBysyh3NnQFbup6NJrBgTzLKDV5jc1QdzUxPWTOyATCrBRCrB2tyE6T2bIZXWrq9W5jJeXx9JiLcz3vWsWRV+lYD6tjjdGolzsjLTTRd8/8DbRosuR6ZFMmrDKCa3m8yOUTto/117bdKNKpSngDdGm1K/Mz+Pf4Ivdl3WjVBFJOZwIC6DhaO9apXWvkiRj5+7LVKJBIUK2rp1YuGJ2ThZOrDnuT2YmpiSW5rLzZKbOFk6YS4z58SNkwS7d+b1QzF09nWrdvSxYmr/2LQCLMxMaNHQDqVawzf7EymRK/GqZ42vqw0jQuqx/UKm0cyYk7v60CvQDas6FpjVrd4KgiAIgiAI/zyNBhIS4OBB+PNP2L0bFBVGlH79FcaPN972bgOyGgRjahMbvkvrw4+Zg0lX1qvydFEp+ZTKVbzaqxmtGjvw2PwDhHg5Mu/JVhyOz2T5oYRqsxXeKaiBHbYWMhY81QpzUxMm/HQSuB1gyKQSLMxMeK13E+10wWrrky0nrFEYU4Kn8NmRz6o81tnKmfyyyvv4dcQCBjUbxLqT+lMGrc1lZBZmV/vYKsopuYmNuYxn2jdmdfhVtl1IZdUL72NhrmTLpS18HfG1kZG/GUgk4G5vQW6JvNprqNQadkelMamrDx9sjOJyRiG/Ph9KdqFcl7jF0swEc5mUpfuv8MPRxAqtNTQzT6aL4hQrNrbmRFJP3u3vX6cCs7rTU0EQBEEQBOG2khKIjtaORMXEQEqK9is1VVsbrKwM5HJQq7WjWZaW0KQJ7Nlj/Hzh4dqgy9RU2764GNLT4coViIvTfl+Zjz7SJvYwN6/8mJrKPgXn3oG0SvoJaMxdOGXxHCZ+L/HZ9zE1PnWpQsVTwY14doW2gPUHg4KYvyuONXdkKxzf0YsfjibqpdZXqfVL+5pIJfyvV1O2X0glu1DOuE5e3MgtAW4XSB7TwYt1J64yoUsDuq+cX6M+LopYxN/P/s3co3NRawxrq8HtFPAXMy5Wep6zaWfJLrmpV7gZtFMDyxOO1JSTpSNdm1rT3suJT7Zon281Gt7f9z7LKh35G8nkdlN5v98cpJKa3RcHL2WyaEQbwnycOXQpk1HfxxqMhL3QxYcxHT05HHuN+mUR9LA7QU+7kzQyywDgu8yhzA73JdTbiX7N3evM2jIRlAmCIAiCINQls2fD9u1w4oT+aFVV8vK0/5qZVX7M/v3w7rt316cGDbTBoJfX3bWvqCi58oDMsgEq/7fYXtqfbw6lsKS9K1DzoMzW0pRX1p4lKbuYmf39OXP1pl5AZm9pioVMyuPN69Pe24mMgjK91PqrwpMBGB3myfhOXsiValJyS3D0NkOl1rDo2dZMWxdJXolCl/UQoEiRV6vpgsWKYpq7Nud8+nmjx0wLncby08srDdrKZRbl6GWYBO0onrWpXa3S2rtYOzK4tQljfzxBXomCl7p7sy9pm9GArKLlp7+li0cXhvgN040+VixUfWfAW1imRAIs3hvPKiMlCTIzEonYvR4/jyh2eEQgVRUbHNPD7iSzU19g2cEr9AxwFUGZIAiCIAiC8ADs3g1Hj95dW8vK62RRWFi7c5mbwxNPwPTpEBZ2d/0xpvET4NAScisEJJb1IfAd8J1ImUrG0m+P661BqulUQxOJNjtiD38XRoR68vSy47r9FetoPbPiuMEIzbiOXmyb1hkTqYTjCTmM/fGEwTGTuvqw6eVOjP3xBNduljBrcxRfPt2apPy4Gj98E4kJJYoSOnt0BiAqIwqVRqXbP7ndZLp4dOG1na9Vey4nS0cK75jiqFJrWBuRyvSQNxi/aXS155jRcQbnrxUxfd0F8koUmEgljAitz8B1NUsU8sXx+fTzHcikLj7cLFEwoEV9ShUq0vJLDQLeUG9n9sVm6AIyU4mCdlYxdLU9Q1fbszS3vKI9aRWxaBPzG3ib3SAqBfJLlHVmCmPd6KUgCIIgCMKjRKOpfC1W9+7aaYZ3416DMldXbar7J56AwYPB1vbu+lEViRRafAiHh98Kxt4G30lgoq1rll9YqksaUVlhZGNe6OyDlbmMRc+2RaVRU1Ci0J3H3tK02jpan26NYfdrXVm0x/goTlRKPtPXRTIq1IPfp3REoVZTUqYis6AMJ0vHavvnaOHI1PZTmdxuMhIkjGk5hldCXsFSZsmKMys4cvUIU4On0qFRB3r92qvaRCBt3Ntga2aPnYWMgPq2eiNSq8Kvs/HlfkxqO6XKtPhT200lrEEfXZ0ygGZuNhQp8ms18pdflkuf5i7kleaRWpSIuYkl7vZ2rDtxlVNJeTwV3Jht07pgaSrlrZ//ZIzzIbranqGD9QWsTUprdJ2Ketid5IeshuSXKnC3v8d6eP8QEZQJgiAIgiD8W1y9Cp9/rl2/9eefxo/p3l27hgu0gVuTJtC8uTYtfYMGUL8+ODhoR7LMzEAq1a4/KympOogKCID+/bXr0KyttV8ODtrz+vhAixbg63tvmRQBFPkQPV87TbHjL8aPaTQUwn4Cj2dAph9IliqUmEglqNQao4WRjRkd6kk3Pxem/HqaiMQc2ns5MuNx/9v7wzyrraM1OsyTg3GZRgOyilZHXKW9lxO5JXI+2hSNi405+9/sUOV0QV8nX3aM2sGh5EOVpMufwVud3iE5s4wTNw5UmzIfYEanGZhKzXijr7/BiFReiYJxP57j5wmz6di4C19HzDesAdbpLQY0HUjElUIaOVrqgjIbc1m1AWG58kDT0dKOlIIbpBamUs+qHhYy+PvSKhrWd+fpVu35/vcf8LgcRbD5Sb5zSqrRuavS3fYkP2QNxc7C9J7P9U8RQZkgCIIgCMLDdv06fPwxrFypXScmkUBiovH6X6GhMGOGNjjr1On+jVZNmaL9elDUKrjyPZx/H8oytduavQj1jEx9lEjBZxwASpUauUpNfomSvBI5dhamHHzzMV2AMfbHE6ycEEJbTwd+OmokRXq3JoR5OzFs6VFdMeWKNbFMpBJGhnowceWpSrtek2Mq+u5wAt+NCeaXY0n8ND6EhKwy/hf6JuM2jjI41tHCkZ2jdzL36FyjxZgrJs14p+OnhFr1YHK7KSyvYj3X1OCpdG7Ul15fHtEFU+VTMMunVyZlFzNkySlGhbZm35hD5JXmklqQjZOlI1YyO84kFxKZXEJbTyd+GNeeolIlN4vluNlZgElOtc9BxUDzsZWP6YI+cwmMbuDLSx6B+CtvYHbxDJ84a0AFGC4RqxWVRsqpogD2F7TXZcasK+pOTwXhPhgydFitjnd1ceG775Y/oN78ew0ZOuwfeewXLlzkvfc/oEf37kyf/soDvda9mjnzfS5GRbFi+TLc3Fxr1Gbt2nWs+2090155mZ49ezzgHgqCUCcVFWkLLM+bpx3JKqfRwIoV8JmRtOjm5jB37j/Xx/shbR+c+R/kXtDbrDrzFpkhO8grVWBvaYadpQwzE6kuOUOxXMmemHSWH0wwusarPMAYvOQoo0I9WDEmmFK5ipvFchytzHCyNuNGbjG9vzqkC05AvyaWWqOhVK6qso5WMzebao+pKColH6kENr/cmS93x/PHmetseuVxprSbYpAcY2r7qRxMOmg0IKto+elv6dCoM2X57Xm7w2y6enRj/vG5BiNcr4fNoHOjvoz+LlLvMUel5PPmH+cZFerBygkhDF5ylLwSBXklKvKLpbz5+zVszGV0a6akVxD4N1STXXyd3LIS6lk7sS8mk93RmWQUlLJ8TIsqR/4qBpo/n1lBqDm87wTdLaGDBVhIL0NW9aN9NVGgtmVvXlv2FYRwsKAteSrthxQLnvJCoao6Ecq/iQjKhEdKj+7dDbZFx8SQlpaGt5cX3nd8Imln9wDmyj9CRFAiCIJQCY0GfvsN3ngDbtwwfszq1dpMi9K6kT3OqPx4iHwTrm80vj/zCGO//pG4Ui+gfGTLh14BbgDM2RbDqnDj67fuDDCWHrjCsoNX8HO3xcZcRrFcxeoXQpnxxwW94ARup6wf19GL9aeukVNcdR0tG3NZtcfcKTWvlEulSsZ09CQ2LR+ZxIJPu8+lQ6MufHVruqCJxITJ7SYzeO3gGp1zYcQCto3Yz/S1UfRv0Z5D4w6Tkp9NTslN3G2dsTe35/tD1xny9ymDx1xudcRV2ng48GrvpjhYmtHGw4EJP53Ey8WKWUOasidpO4N+M5zO+HrYm3w4+HHG/XiODWcyeKPDDEb/ZZjsw0wCc1sNIPfk6zyT9jdf+4Dlfb6Fy6ybku3YCZPGA5HbdSX+VCYHw6+Tp9I+5tGhHnTwqUdWYRkOVlVkHP0XEUGZ8EgxNhqzcOFi0tLSCA0NYcSIZx9Crx5dzZo15Zsli7CysnrYXXkgBgzoT5cunXF0rH6BtyAIj5Dr12HqVNiyxfh+CwuYNEk7RfFfEpDdOYXQ2KiWHnkuXPwULi0CtfHgYHd+CHNTx3G5zEO3LSoln2lrI1n+XDvKFCqjAVlF5QHGqFAPlh64gqTCerfRoR4UVzG6Vb4eLTWvVDeVsTIVpzvWlJ2FKe/+dYGe/q6sGBPMZ9ti2Hw+lVGhrdn8zD6KFPmUKkuQIKlV0gy5Jo+5T3lhIrGkRC7luwM3uXazhEXPujDm+5NcrEEmyp+OJrF+cgc2Rd5gU2QKv04MxcZcxVt73qy07thzf49icruprJk0ByuZFWpJAya3m8JPZ5YRYg6PWcFjltDRAixzVmkb3qc/7/kqyHcMRunWi5fPbmRrfAwQD/xMa/fWTA95g40v92P2liv0be5O70B33vv7AjP7B96fDvwDRFAmCMJDY25uTqNGjR52Nx4YOzs77OzsHnY3BEH4t9Bo4PvvtaNj+UbeOEul2mDsgw+0yTr+JaqaQlg+qiUB8kqU5BWX4p61Dru495HIs4yer9Q6kFfixrA7p7nR/SZSCc0b2td4/dZPR5P4bmwwUomEEaEeyJUqispU1LMxw8pMxkvdfXXJLSrKK1Ho1qPZW5pWmVr/btLvm5tKKVOoGN/Jm13R6boEIUsPJLLsYCJ+7rb0DXSjR8vcGj3OctfyrvH23rcplBfyZscZvNq7Hx9svERhqbJGARlog9/CMiVdm7ly7EoWu6NS0FiG16juWA+PjniWWHPx7Ga+8rrCkqZmyDS1G0WsjloDp8pgVzHsLILwUmjhruLvLlPZvn2e3rGRaZGM3zSaqcFT+eLZefx8JIWcwjI0GrCzrDuhTt3pqXB/qZVQfP1h9+LuWTUC6YO9fffu3ceixUt49pmn6dq1K2vWruXChYvk5+fz9lszCAsLZeLEyWRkZrLx7w0G7ataL6XRaDh8+Ai7du8mISERuVyOm5srnTt1YtiwJzA3N69RH9PTM5g0eQrNg4KYOfMd1q77jfDwCLKzs+nf73FeeOF5AAoKCvjr741ERJwgIyMDmUxGkyY+DBk8mPbtg2t0rfI+R5w4QUJCAtnZOUgkEho3bkSP7t15/PG+SCt8olv+3AAsWryERYuX6PZ9+sksWrRoXuVzpFKp2L59B/v27edGSgoAjRs3omePHvTp0xsTExO94yuu+UpMTOTPDRtITr6Kqakpbdq0ZtzYMdSrV0+vjUKhYPfuPezdt4/09HTKyuQ42Nvj4elBt27d6Nqls9HnIjw8okbnr2z6ZsW+xsTEsGnTZq5dv46lpQVt27bludGjcHZ2rtHrIghCHZGdDc8/DxsrmcbXqxd8+aU2w+Edaj1KdR8Vy5UGUwjNZFJ6BbjibG3GjotpnEnKYWJXX15d9jNv11uIvbXxmlxZSnvWlkykfrOXea9zPeJvJZu4U23XbxWVKbEzN6Wpqw2TfjlV5dqzO6+XlF3M4CVHWfRsayZ19WH6ukij16g43bEm6fcnd/XhrzPX+Xl8KBrgu0MJevvVGkjJLeXpEHdKVMaD18o4WzmTX5bP+fTzjNowkintpjJryBzS82tYSBxtCQArMxM+2xbLb6eucXBGBwaum2/0WFOgvYV2FKy7FXS6MAZLiYZQO6D6fB81dkOpDcB2FcOeYsi+YzlYdUW1vz31LaENO2NnGYqpTErfIHfM6kjhaBBB2aOr+DpsMpLRqa4YnAg2Xv/IpW6kpPDGmzOwtbWlRYvmFBYWIZOZVN+wEmq1mq++Wsihw4exsLDA19cXGxtrLl++wrrf1nP6zFlmfzqrxoEZQJlczrsz3yczM5OgoECa+PhgY2Oj7f+NFD748COysrJwdXWlTevWlJSUEHfpEp/OnsO4cWN4YujQaq+hUCj44suvsLW1pXHjxvj4+FBQUEBsbBzLV3xHfPxlvcCqY8cOnDt3nsSkJAIC/KnvfvtTX0dHhyqvpVKpmD3nM06fPoOVlRWtWrUEDZy/cIFly1cQee4cb814Uy8ILLd9+w42btpEYGAg7dq15dKleA4fPsKVK1f4+qsv9Z7XL7/6mmPHjmNpaUlgYABWVlZkZ+cQEx1DaUmp0aCsNuevzt8bN7J9+w4CAwMIDQ0hLu4S+/cf4Pz5C8yb+5lBkCcIQh116BCMGqWdtngnNzdYsgSGDzeaar4mo1QPqjiuUqVmT0y6LiBr7GjJp08E0tbThtzSXLJLcnC2dMPe3B7pmTdZ5/ktUiNVfeVqGT9mDeabjGcoUFtD4kVGhXqw+oUwBi05Qk6R/ihLbdZvldcX+2x7DKuNpKqPTSvgx6OJ3CyWs2ZiGAMXHTE4d16JgmnrItk2rQujQz0qTXm/KjyZ3a92rfIYgFGhHnT3d2XzuRRi0/Jp4mJjNMAcHdaI3Qlb6e7TucqkGRW1cW+DpcySixkXddu+O7OC/r4D6dy4h0E9ssqMDvNkX2wGa05cJaC+rV7dsYpB2GNW0MkCrPT+3FZ97poq1Jggdwrl00vH2FUMUTV4ybOLs7Ezr3wGysKIBex97iAnEvPpGeD6wD+0uJ9EUCYI1Th8+AgD+vfj+ecnGIzO3I2/N27i0OHDNG/enDdef1W33kihULBs+Qr27NnLut/WM3bMczU+Z3x8PH5+fixf9i02Nta67SqVirnz5pOVlcXYsWMYOmSwLpBJTU3lw49m8csvq2jbpg2enp5VXsPExIR33n6L4OB2yGS3/+vIy8tj1iefsm//fnr16kFQUBAA48ePY+3adSQmJdG7V69aJfrYvHkLp0+fwcOjMZ/M+hgHBwcAcnJyeO/9DwkPj2D79h0MGNDfoO227dv5bM5s/P39ACgrK+ODDz8iNjaOw4eP0KtXTwDS09M5duw4ri4ufPHFAr2kLnK5nISERKN9q+n5a2Lnzl28N/NdgoPbAaBUKlm0eAkHDx5ixYrveffdt2t8LkEQ/qVUKnjpJeMB2dix2tExJyejTY2NUpUrX3s1OsyTd/v7P5DATK5Ss+yAdoSnUxNnvn2uOZsvbeZ/P88zSALxm6czzYwEZDvzwpiTOoFkeQO97asjrhLq7cSe17ryw5EkvemFd6arb+Zmg425jMIypUHAUV5f7M6AzN7SlNFhjRgRWp8iRT7ZxTkoMGPX66H8dOQ6q8Kv601nzCtREJuax9v9AgjxdmL5IcMgeHwnbTa/KY81obWH8fT74zt50d7LiWs5xfQJdGfujlgc2xuuRTORShgRWp+B60Zy6WYU00KmMWHThGpfk2mh01h+ejlqjVqv2HSpspTrhfEsHu2FudSWdSdSDR5jxWs/F+bJhJ9PAuBgrkGetp93HLWBWCdLsH4AsUy+Cg6VwoFi2F8CaodA1vb9ga9OBNT4HOWjhJU5m3aWImU+3Zq5YWFat8KcutVbQXgI7O3tGDt2zH0JyFQqFX/99TcWFha8+cZrumADwNTUlEkTX+D06dPs2rWb50aPMjoSVJmJE5/XC8gATp48RXJyMh06hDHsiaF6++rXr8/48WP5/PN57Nq9h4m3pjpWxsTEhLCwUIPt9vb2PPfcaD788GMiIk7ogrJ7sWXrVgAmjB+v9xw5OTkxbuwYZs/5jM1bthoNygYPHqQLmEC7bm3I4MHExs4nKipaFzTl5Wn/U/f28TbIsmlmZqZ3jrs5f0106tRRF5AByGQyXnj+ecLDIzhx8iSZmVm4uIjRMkGo00xMtFkUQ0KgrEy7zcUFfvoJBgyotNmdo1SVWRWeTKi3E/2au9/3UYH8EiXRqfk0drRk2ZjmvLn7TaO1sSLTIgnNgKu+FthqSgFIKGvARzcmc6iwncHx5ZYfSiDU2xkvZyu96YWX0guxNDfh48GB9Ap0p1SuIrdEgZO1GZamUlaFX+XX8GQKy5RGa4d5OVvx84RWHLi6nUG/jTIIIP8X8iYbX9ZmESyfzjgq1AP/+vYs2XeJyd2aEOrjTLFcSW6x9rpO1mZ8e+AKn2yJ0R1/Z/p9c1MpayKu8smWGHa92hW5Ss3FlDymWDUxeOzN3Gx0o1NX865ycuJJJradyHdnvqv0+Wpg04Cvw7/mYsZFzEzMcLBw4KezP/Hl8S8pVZbqjrM1s8XP2R9fPx/Cr5RSWCJDggVSLJBoLPBysOPk+TN0Uh1ihncyodZJWF6Q0+I+/7lRSC3ZVVDC/mI4UAKRZdpSZOVMMqKxMrW6p1FCYzKLcrA3dUWjUWL5gEaRH4S601NBeEhatWxVqylpVblyJYH8/Hxat2qlF2yUMzc3p0mTJpw6dZqU1FQaNWxYo/M6OjrS1NfXYHtkZCQAHcKMFOYEgv7P3nmGR1Vubfienpn0XkgjlBBCbwm9Ix3BRhMBS+CIoAhYjx67oMfKUUClSbUgTQQB6SX0FkJLoaWSNklmJlO/H0OGDDNJJoifovu+rlziLu9+954wvM9eaz2rqdWV6OLFi67dAJCensGJEyfIy8+noqICLKC92VsnKzvb5XGqIz8/n/z8G3h7e9G6dSuH/e3bt8Pd3Z3s7GyKioocnA1bt2rpcE5YmPUNbWFRkW1beHg93NzcOHr0GGt+Wkv37t3wr+Zt9Z2M7wpduzimR3p5edKqVUuSkw+RmppKYGDXOo0pICDwF6RFC/jvf2HKFOjdG779tlYjj6pRqtqYtyuN3k3ufqpWiVZPXKgn7w5vyvrzG2psVlxshmeydXwdKuOA2yQeP90LvUVW4/iVZhML92Vw4mqxzdreVyVDJhbjpZQ7rRF7qlsM26Z3582NKQ61Z95KGUseb8m7+15hwTHnAnL8equL4OKJb/PCD+d5qF04Her7s+lMNo92qo/OYOZGmZ4QLwXBnm4Uaw2IgF0X8m2Rp9vt98sqjJzPKcVssc5RJILrxdpqDULkMi2bL/0CQJGuiPgvan+hmVWWRVaZtcbaZDKRUew8o6NUX8qR7MMcybZGwsQyaKOw1oP1VEIXJXhehSEhtV6yTpglnuS6tWdTXizbC+J49/FHWJ/8Oguq+b0xWUwcyz7GrE6zGL3G0Vr/dqpGCWtCJfVi8f5MHmkfgQX+sPTeu829MUsBgT+RgLsYqcjLywPgxMmTtTayLlWrwUVRVl00JS/ParTx0cef8NHHn1R7vlpdWus1DAYDn302l9179lR7jFarq3afqxQWWquGAwMDne4XiUQEBQaSUV5OQUGhgyhzZpChVCoBMBpupXGoVCqe/tdk/vfFlyxZspQlS5YSFhZG8+bN6NmjO3FxztMpXB3fFYKCnN9jUJC1OXXlsxAQEPgb8K9/QVCQtXbMhSyIyiiVK6RkqckvqyBIDMq7lLJlNJmp56ti7qg2BPuImLBpNoluECKBteXOz1laCrkejflqwBsYD+136TpqnQEvpcxmbf94l2iGtw7nk20XnNaIpWSpmbbqBGMTInlveAuuF9sbd4xNDGfH5V+cCrKqzD/6JT2iuzH/0fv5/sh1RFjwUcqqNQrp1SSIiZ3r8/z3t6zrzRZIzXb893NC52h+PpVNt0aBNoOQoW3cSM5dR4U4hQpxKpfzr7D+11tpmBWmCpeelyuIgBbyWyKsmxJ8fn+yjwOlZtijtUbBdmrgnFFCheEKEosWqeU6yV+d5fneCbzVI4rvU1famXO0CmnFC51foE9MHwCnTbWrktQ2ia6RXZm+ZXqNc2od0hqV1IuPt+0n1MeNuBAvYkM874naMkGUCQjUglxW85u+6rBYHAthzTe3hYaGEtekSY3ne3q63rhaLnPeO6XybVKbNq3x8fap9nxXmmSvW7+B3Xv2EBUVxfjHHiXmppmIVCrl+vUs/vX0FKvd8/8HTorhb+1y/Yu3W7eutGzZguRDhzhx/CRnUlLYsuVXtmz5lWFDhzBx4oTfNb6AgMA/jNRUqOaFDiIRPPSQy0OVaOtmMZ5fWsG1Ii0J9f1+9wK0qrlIlL+Kl/u7M9F8kinhUGKGA5ch1+R4ngXYnJ1CuUFNbIinU8FyO8Febnw2shVLD1xh0+kcPh/Viu2peU4FWVWWJV+hbZQv/eND8FbKKNEa7Oq0XGHOvjl0jejPffGhfLkzjRWHqm9S/USX+kzr04jRlyOdHlfJmIRI2kf7MWzuTlSeIsQ+y3jt4EF0ZMIf2MM4Xm4VYD1V0F0J/n+ACCu7TYQdvS0dEYpBXAxcAqDUAE9vviW0QtxDifSOoJF/Y+KDmiIWSfn62BJ+S9/Cx/0/JiE8gU+TP3VIN53V6QX6NehL4jeJFOlqzkiZljCDlcnZmC3WVgkLH2uP3mQWRJnAXxhVuNXB8F5F9dfobSW9+UZSq9XaoiWV3LjhaHEbcDPKEl6vntNG1nebyqhO3z596NSp4+8a6+DBZABmPP8ckZGRdvtyc3N+19hV8buZQph/007fGZX7/P1rTzesDW9vb/r17Uu/vn2xWCwcP36cDz78iHXrN9CnT2+He72b5OXlEx0d7bC98v78XEinFBAQ+Ivw1VcwaZLVuGPatN89nLeybit4X5Wcj7ddoHWkz+9agN5uLvJIvdME7XybqT43ryOBzwLhkRq+9ot1xXgpZcSFelZr0AHWKJTZYmHy0qPMvK8JT/dogMFsYd4u19I2v96bQc8mQUzv24jX15+1q9NyheM5x9GbS7mYI7MTWs7MRb7em0G7aF+m92tM+2g/vt7raAQyOjGYUsth2n85jRuWA4z86fdnj1RHhBT6qqDfzWhY0B+woi8zw16t1ZRjpwaOVYDxd4yXU55NTnk2h7IOOezbdGkTzYObMyJuBNM7TidAGUBj/8YEewSTmqVFUyGmd/0+XCq8VO34SW0n0z2iP8PWWWsMK9NjJWLRPZHC+NefocAfg1j6/2Yp/3fG19eXrKxssrKyaNDAvpD3xEnHfxQaNWqIu0rFmZQUSktL6xQNuxNatWrJtm3bOZic/LtFWXl5GeA8fW/vPudpKpUujSazk1eq1RAYGEhgYAD5+Tc4efKU1Q6/CkeOHKGsrIzQ0FCH1MXfi0gkok2bNrRr25bde/Zw5crVP1SU7du3nw4d2tttKy0t5cSJk4hEIuLiao6mCggI/AWwWODtt60NnwGee85aL/bww79rWC+ltM6NijedzubVQU3veAGq1Rv5NcVqLuIlKeM/YfMZUbHD4biHPeErtbWXlDPCvPz5bGQsZTojhRo9fjdNMFYeumrnsvhUtxjS8sr4cqy1X+bVIg2ebrI6pW0WafQMbx3OGxvO4qGQUqStW21vmb6ErWet/0ZZHRujGJ0QiU5vcpj7L6ez6dIokAZB7nyX1JH8sgrUWg17rv7K5oxFTNzyC3rzzYdSfULHHeEptjoj9lVZf5r8AVG3ShG28+bPUd3vE2F1wWQxcSLnhF2UTCaW0TqkDQMaDmVvig9fPPIK3aO6M2f/bEfzloSZdI+wmrdUdZws0uiRS//6UTIQRJmAwO8iPj6elJSz/PDjGmY8P93m0Lh79x727NnrcLxMJmP48PtZtnwF778/h2eeeZqQEPtK24KCAk6dOk3Pnj1+9/w6dexIREQEu3btpl5YGCNGDEdWJR3TYrFw7tw5gGprqCoJCwsjKyubzVu28MCIW/Vw+/bvZ8eOnU7PqYz0XL+eVad5Dxo0kMWLl7Jw0SLefOM/eHt7A1BUVMSixUsBGDK4etcyV0hPTyc3N4927draPZPS0lIuXLgA8If3Cdu7bx/de3SjTevWgLVw+5uFi9DpdLRv367aujoBAYG/CBYLvPwyvP++/bZHH4VmzeCmmdKdIJeISeoew9SVJ2o9dkLnaFYkX8FssdZohXi71fl6Gr0Rjd7Egt3p9PA8zOzwzwmWOda1as3wWgHsqEaQtQ5pjYfMm3HfHOb09eqbOA9tGUavJkFsPpPDnC3nSclS0z7alxcHuG6PDlBQpsdTIeO5Po3ZfTGfet51+972dfPhTFY60f4qlkzswKGMwmrryiZ3j+GrXWn8mprL031lvLb9c9LLN1NSUTch6AqSCmhnskbC+vpCoh/I7rK2KDfDvspImBaO/D+KMFcwmA0cykrmUJY1Uyfuy2do7N+EFkGtuL/xgzTwi6VtaFs8ZD6sTM5m2LojDi0AfFVylLI/IJfzD0AQZQICv4NBAwewZcsW9u8/wJTMaURHR5Gdlc3lK1cYPHgQ69dvcDjngQdGcO36dXbu3MXTU6YSE1Of4OBgjAYj17Ouc/XqNaKjou6KKJNIJLz80gv85423WLFyFT9v+oXo6Ci8vb1Rq0vJyMigpKSExydOqFWUjRh+P8eOHWfp0mXs23eAemFhZGVncelSGvffP4y1a9c5nNOqdSvkcjnr12/gypUr+Pn6gQiGD7+/RmfJoUOGcPrUGY4eO8bkyU/TvEVzLBYLp06dRqvVkpDQgQED+v+uZ5OXl8/7s+fgrlLRoGFDfH19KC8vJyXlLFqtlvbt21Vri3+36NevL2+++TbxTZvi6+fLhQsXyc3Nxc/Pj6SnnvxDry0gIPA7sVhg5kyrq+LtzJxZfW2Zi0glYvrEBbvUqLh9tJ/Nqt3Lre510EaTmUMZBUR6mhgneZuR9X91etx2DSTlQVoNnkbTE2fy1e6rdoIMbtVmjUmI5Kd/dUYmEfHupnN2aYNlFUb83OuetplfWsHjXerTPTYQD5mkThbrngpvckp0rHu6C/N21VxXdijzOt4BBzlcPo8h35+v0zyrpRzIAXIhuAwGBsLAGOgTBz7utZ1cN3QG2H8Bdpy1/hxKA4MIa61b5Y/stv+v/FEB7k5+/sA6udsxW8ycu3GWczfO2rbJRJ5IjU1QmOOtPzREhPXvQHyYF+4KKSqFIMoEBP72+Pj48O47b7N48VLOpKRQWFhIgwYxvPHG64gQORVlYrGY556dRqdOHfn1161cvHiJ9PQM3N3dCQgIYPj9w+jSpfNdm2NYWBgff/QhP2/6hYMHDnL+/AXMZjM+Pj7ExNSnQ/v2Ll0vPj6e9997h2XLVpCekUFWVhZRUZG8+MIsYmJinIoyfz8/Xn7pRVZ/9x1nz6ai01nz63t0716jKJNIJLzyykts+mUzv/32G8ePnwAgIiKc3r16cd99/erUw80ZsbGNGTNmNKdPneb69eucPXsWDw8PoqOj6NOnNz26d/9d47vC/cOG0bBBAzZs/JkLFy+iUCjo0aM7j44d84dH6QQEBH4HFos1TfHTTx33zZ1rbRZ9F1DJpbw0MI7Wkb4s3JdRbaPixxYeokRrID7MCy9l3Zd2epOZ8yfW0VHyFiP9HJtcayyeiNq9z/cXTpF2fX6140xuN5ku4fcxbO2Rao9ZnnyFUR0iuJhb5iCALuSWoZQ52sdXR2XaJsD6k9cZ1CKMH49eYVqHGUxYP7bW82d1nkWFXsLoBGsD6uoMPAyiHEqlG/jvma1YRNWECF0lH7gMXAXRVWjnA4Naw6Cu0C7m9w19OxVmKPFswmkCePP8XpJ1UCEFYiG2TSyL27zGmIVjoAQovvmTD7iWPWpFBngB3rf9eAE+N3/+QE1ksJRikBxGK7Ha/4sscuTmxqhMHZnQ+d8UlFUQ4PH/qBx/ByKLM4s4gb88ubl5PJU0iQXz5xEcHOT0GLPZzPnz1jc5sbGxv3sRKyAgcHd45ZV/cyYlpca/vwL3NsL3798YiwVmzYIPP7TfLhLBN9/ABEfX1t+D0WTmfI4aH3eF00bFy5Ov2FK2Ph/Vus5NpI0VpVQcmYn7Zediq8y/O+djXiI4qAU+Sk82nN/otKbnhc4v0CX8PsZ+dcLWkNkZErGIfS/0YuLiw05rx6b0bEijYA+mrTrhePJtfPhQC8oqjAxpEYbJbMFkttD/0z2sm9KO9/Y771NWSVLbyczpMwedXorZgsN8LFioEKeglq5FKz4Eopp7Y1WLDkjDakiYBl4G6NfcKsQGtIRg7zsb1hkGCxzSWdMRd2ggPu4J3uzzITGf1XdwLZSIJKRPS2fYqmGOUUUdVnGWB2QD127++U4egRirSPO3/sjD5AQ1DeKa+NofKtbifQZxcNJaxKL/3z5lrqzPq0OIlAkICAgICAgIuMp77zkKMrEYliyBsbVHZ+qKVCKmfqAH721K5cjlIodGxZWMTYyid1zdGkjrsvdh2T8W94pMh30GsRvvaQJ5/eAuOLgLgG5R3fio38fsmbCHIm0xRdoifJW++Cp9MBil9Phgt0NNz+00DvagvMKIBQvto30dnBm/PXiZbdO7MyYhskZb/Mq0zbNZJVwr0rA8+QoPtYugRGtg/MKTLJ74Dp0iuvJJ8gfVmkJcKzSRWVBKi3BvmyCzYEEnPkaJdDUVkrPVXL0WyoFzQAqQCWHecH87GN4fujeBu9RKDoAit0g2qLWsyM9nrxbKLdZ7fK7fTHpFD6BEV+7URt5kMTH/6HymdpjKxPUT7Xe6ARE3f27y1X1fEVYawePvr6I47Ri6a2fB5EIFmhkouvlzCfToufbTNasRSizQ8eZ17vJ7q9Gt+nEovYBuje+d2mxBlAkICAgICAgIuML//gevvGK/TSKBZctg5Mg/7LKVaYzbU/OYtyvNrv9XfJgXk7o3oHdcUJ0iAhq9kRX7LzBRd9nBKfCcJIz+l7K4bLxqt3335d20+6otk9r+i/90e4+Nx9RcK8qlXWQFnRoG1irIvJUyHk2MIMALPhkdRYGmEH+VHyqpF6sOZbPs4DVKtAYmLj7MiicTaBfly9d7q0/bXHP0OmMSI+n78W7CfNxI6mZ1Qc4s0DBs7hHGJLRiwyO/UW5QU6gtwk/pi0rqZTOFWPt0Zw6k3aB+gDsWLGjFyZTIVqMXX3T5OdrQYRVhN4VYoyAY3g5GPAYJDes+XHVkG6HcvyvnjIO4JErkqf4dGW7R001TRJm+BG83bzwVXugMBkxmEV4KL7pFdWP35d0OY315+EsOP3mYp9o+xYKjC6q95uR2kxnZZiRPLj6Dov2DBLd/ELNBR8W1VHSZx9FcSsZYeL1uN2LBKlzPAQFAJ6AFDsqkWWBz0govozXVJacSluxUss/3AokN/O+JHmUgiDIBAQEBAQEBgdpZtgymTHHcvnDhHyrIKlHJpQxoFkLvuCDUWiNqnQEvNxleSilyibhuKYsmM9tSc3n7cBD60If4V+B3ABhw42ToI3TYvYSaalvmHf2CxPDOSMWt2HTa2rBsQPOwGq8Z7a9i8cSW7Lq6mR5LHKNX0zrMYN2UAYxfeJLT10sY/dVBFk/oQM8mQRRp9BSU3Urb3Jaay+lrJTzWOZoRX+yjRGugrMKIm/xWPVqJ1sAXOzOYtyuD2JDKfmlqW4QxPswLpUzMsStFNI66TI5i+p2JsQzgOJAKrevdFGJJEH+X2qlqzLBLC/4xIzlg9CBVL+XFxHd4Ze4RSrRlNG1QSKdGnpzJO82acz9wNPsoZ/LOYLaYaRXSiucTZ7Lm4TUkfpPo0OOrSFdE/+X92TxmMx3DOzpt3PxC5xcY1Ggw01elciC9wLZPLHNDWb81yvqt6TLmWV7t5MWG9evYsmkDx45WX1PolBvAemAH1shZW0BhTTF9sePbDJ17GD+vQkZ11jNrwxp04vPoRZdA5Lzdjq+bH0pdBClZatRa4z3RowwEUSYgICDw/84777z1Z09BQECgLmzdCuPHO27/7DMYN+7/bRrSm+JLJZfeke19JXqTmXk7rQ2aP84ZxfCQs8hlCgwJ83hiwxM1CrJKPk3+kPWPbGfergwKyipwV1TfV81bKWPJ4y15d5/zOq8TOSeYsH4sSW0ns3ji2wybe4TT19X0+u8upvdtxMPtIvBUyCjVGZCIRAxpEYZYLCJp6VFb/ZrJbGFF8hXGd4pm5g+nbGObLdhFFitJ6hbD+9t+YUfBO2xaf6xO6XNKVGj36eCImeYeMLIjjJwAMXepRLjCPYZVhWqW5t9gnw7Gt0nipbYvEXT1MMPCetl6cXkrZTSt58bMX2cw76jz5/ro2jFMbjeZ3eN3E/9FvEMq46XCS7T/qj3fDF3I7vF7KNQWoa4owVfpi4/CmyOZpQz85BBXi7TVzjepWwwqfxUnA3pS0KctUQm5POKXzqcLPqQir8L1Gy8FfgXJXgnDxg9nSrMXGL/wJGqtkTeHduNCbim+BqsJlhkdevEFdOIUKsRnqRCfwyKyzrFVUAKPNo7ljQ1n77hFxJ/BvRHPExAQEBAQEBD4M8jMhAcfBNNtb+XffBOeeeZPmVKdMZRaDUpuotYaOZ9bSlyoJ62igni1+H0qeu5EowziZO5Jl4Y8nnMcjUFN/2YhRPipMJrNjO8U7fTYsYnh7Lj8S43GGwDzj37JrqubGZNgDTOVaA14KGTM/e0Sjy5MZtaPp3hnUypHLxeh1Zs4lGnfR23Zwct0qO/H6A6RNV5naGsFS88/z6dnHkArPubS/QJIjfUIP92Q8K81/NvDTMpLcOp9eHnY7xNkFWbYXA7P5EFMBhxpvZSvaUKhdysWDV/B7D5zkIt8uHq9KcPmHrEJ0bGJ4WzL+NmpIKvKl0e+ZNflXWx9dDutQlrZ7WsV0oqP75tL66BufLMri0BVKPW9Yzl4wUxBmYhfU27UKMjGJkSRGOPPuIWHbYI8rnFDJk59EsO/DKz/bT3+3f1B4frzMGlMrPniB1rFt+TUL8t4pFUw7aP97GoMxbjhZm6Bj3EUwfq3iNCtIkT3CXHKZxjfciIDm4ciFt1Zi4g/CyFSJiAgICAgICBQHZGRVov79967te255+DVV/+8OdWF/P2wfww0mQ6xVhEpwsLuWT3R6U0UavT4qeQoZDJulJfUaegKcymvDIwjq0SH3mhmQLNQjl8tZkWVxbNELGJUQiiDV412acyqEbhRHSJpF+3LmxvP2tWr5ap1vDrIsTF3idbAYwsPsWRiB9pE+bBoX6Zd5K5JqBv+IVv5+tLnaI2uW9u7mVrRsDCOoXnrGdH6Om1HuHxqtVwzwM8a+LkcftNYDToqifSOZPnw75CKlJRXwMaTRfx77Rk7Y5e6Ptc5++awe/xufnt0NwXaIvLLC201dttTCzmbpePJ7jFsPJmFSCRi5g+nbA21nT3L+DAvnuwaQ68mQQydu9fu8/FQSCnQFNIsuBmNmjeioGeBtWbsNHAYyHXtGZk0JRTtWMjS1F9Yk/gw+oY9EUmcSxcREhSWhmgKG9I5vDtGk5mBzUPvqEXEn8W9M1MBAQEBAQEBgf9vxGJ4911o2BCSkmDECKv7okhU+7l/JhYznJ0Dp14FiwmOz4TgHmhUcRzMKGTB7nS7RfbgFqHMHFg3pzql1Isnlh6xpQf2jQti9oMtSazvx/yb47eJ9MFgKatTBE5nKmX+o21pHOxp68FWlbIKIx4K50vYzAINQ+fuY0xCJAvGtbO1EbhYspdXd05iy7lLTs9zRoC5DaNUjRit2Edik5Uun1cdpytgTRmsLYcT1WT1tQ5pjY+bL6PmH+NsdilxoV4sGNfO4bjGwR6UG9R1eq45ZYWYjT6E+4ShkgRQVG5Ab5HQJy6UFclXOHmlhGf6NOLheQeA6p9l1do+rcHElUJ7gVtWYcRf5YeXwosbmhvWjQqgHdZ6satAMnAWXMmVzcnJhrWfIvX7Eb9eT6Bs4Pg8qlKk0eOhkDKqQyTye8TkAwRRJiAgICAgICBQOxMnQpMm0Lq1Vaj9ldHdgAOPQvbmW9vMFZj3jmKOYRGLk/McTvnlTA6vDm5Aq5BWjn2rnNA6pDUKsSfnc27Va21NzePQhzuZ/UBzVid1pKhcj5+7nAsFdbOW1xrV5Jd68sIPpwn2VjhY51/ILbMz9bgdq8lHGvN2pREVpCeLL7ig3ubStUXAzAaJPBPoS2jRLiRm19MbnXGsPBbv2Ic5o/Tl/k3Taz1+RsdZHM0s5UyW9bmmZKnR6U3Ehnja1cZVRqPqgtao5nSmhUe2HCTE282hvUJcqCePdoqy69dW9VneMky5dU7XhoEOc7uQW4a7zIswjzACVAH2kxABkTd/CoEDWI1SXHDXNxZeI++H/+BWvw2+PR9HHhgFWKOGjYM9bHPzVclRyiVEBajuGedFEESZgICAgICAgIBrdOr0Z8+gdvL2wr6RoHW0KC8yerDx6HnA12GfyWxh8+kbzOw0izFrak+Jm5Ywg5XJ2XYpdWBdxE9adowvx7SmW+MgFu3NoG+LunVIVkg80ZuMrJ/ajnKD2ql1vjNTj6pYsKAWb2d3yVeYReW1XrO+FJ7wDGCCr5lQ8UEoqPUUp5iRoPPrxrun4vhVnUCe0Z+IQiU/T+tAUu4F5tdQ/zWp7WQGNx7MoE8P2W2vjPxUpTIaVRfkYk++PXiZIo2BIo1j+wIPhZQ8tfMQXnWGKc7mZjJbWJmczcCGw1DJVNULfT9gENAdOASSwxJMWueOilXRZRwjO/ME/u0G8cysV3miT3NbKm6Ah5xATzckIlDezYZw/w/cO/JRQEBAQEBAQEDAORYzpLwP23s4EWQi9E1f47HL73PDaC/IJGIRcaGetI/25ZczuQxpPISktpNqvFRS28l0j+jP8uRr1R6TV1rBttRcPt5+EXeZl4PBRHW0DmlNiKcfImUyQ1b3oskX0XRe3IYmX0Qz9LteBAYdZ92Uduy+kE+H+n6MSXA09TCK8siTv06B/JMaBZkUGOEBv4a5kV4fXg64QaikbtEnAAsiDmlaslb2GprB19B1+QUaTSLP6A/A1SItk789w5w+H7B8+EqnZhvLh69kdp85TP72jIOxhq9KTlmFfSjpQm4ZfkqfOj1XldTLLrJ5O2UVRryVdTPGcDY3gGUHr9E9sj9Hs44xtcPUmgfxAHrBJ798QqfRnVCpVLVf2GKm4PAGPk4axKCp79Hrvzt5aN4Ben64i0fmH2Dr2Vw0ehfCb38h7i0JKSAgICAgICDwR3H5MowdC199ZU1VvFdwlq5YiVswdFpBobITZ1Ztt232VsoYmxjF6IRIdHoTWoMJX5UMLFLe6TWbjuFd+STZsZ/Yswkz6R7R32bL7gyJWESfpiE8ueSILWoyrcMMJqwfW+utzOo8i21p2xi/fozDvqrW+Z+PfpspK46x4slE2kT6snBfBmeySiiTbKJItthmj+6MKCk86Q2Pe4sIkViwdn6uO8fKGrOhpDvtuz1FVHhDevu7k6OuQCwy8MKAJjzcPpx3fj5HckYh+9IKGPTpIT5+pDd7JgyhSFtEkbYYX6UPPm4+HM0sZdCnh8gq0REXeitNUCoW4a6Q2ompygbaIuQ8nziTR9c6PqvbqS6yWZXa0kJvJz7My2FulZRoDYxfeJLlT/XATWaqtUF1UtskBjQfwEcdPuL0+6cZPnU4pzacgloCZyWFNzj57Vu4RbfGr99kZL5hpGSpmbrqBGMTo3h5YBOhT5mAgICAgICAwD2DVgvDh8Px49ChAyxdCvff/2fPqnZqSFckuDd0WgbKEEpybi2yK131jl8pZtvZHPo0DQGwuiiaFAR5qkgIHsjGkQMp05dQpC0ixNMfP6Uv83ZeYdi6I9UKMrCaUOj0Jltt0rKD19g4dSCT2k6q0b59crvJ9I3pS6PPG1V7jEQk4cC1faQWHuWJrk1Q6wyk55fx9gPhPPfr2+y68qvz84DB7pDkDfepQCwCl1wmbuP0VRHXPAaySDuKQzd8ADiUrOW7Vh4s2pvBV3szKNEaiA/z4vEu9fnmsfYUlleQV1qBv7ucQC8FUpEFb4UvImQUaovQ6E00DPbi7eFxNAxWUqYvsaVsBrn74SYVs2NGd/JL7U02jl8u5v64YSRdnVRjWuTkdtbI5rB1NTd1ruz19lS3GKatOlHrs3iqWww3ynTVCr3MAg1DPjvMbzM68mHfD+kR1YM5++c4Ng5PmEbXyK70X96f6R2nk1yczNXEq0wZOYXstdn8uOrHWueiyzxO1jdP493xYbwTHkQklbHs4GUS6vsxoFnIPVFb9tefoYCAgICAgIDAH820aVZBBlBaahVoK3+/494fhsUCqR9Vm65I8zeg5xZQWgWXt1J+878ylk5M4Icj12gd6YNKLuWppUfo/dGum+lfO3l4/gEu5JajkqpYdaCU/27SMGdTHvlqmL87kzAfN9pH+xIX6olE7OhC6aGQUqjR220Ti0T8u/trLBq2yGn63qJhi5jdZzYT1k1waHAM4Ovmy8tdXyZ9WjqrH1yNj5sHbWNEqBQmzhb/RoevWzsVZL5imOkL6dGwNgwGuFcKMtfJK4GPf4FW78KD6kYUdh3D11OG0bmBNT0xJUtNXqmOxiGerJ/SmWh/FSlZaqZ/d5L3fklFIhbz/dGreLjJsFDBj6k/0n1JFyI+CaHl/DiafBHNkNW9uKrbjtFSwuBVfWwpm72+7cZP53/E31NETIAHIpE1EvlQ2wjkUglPLj7Fix3fYfGw5U6f69L7lzOnzwe8szGtRiFdyZHMQvrEBdfa621sQiS9mgQRHeDB2MTqjy0s15OcpmZr2lbyNHn8PPpnzk85z94Je0l9OpW1j6zlQsEF2n/VnoyiDEY3H83KMys5/ORh2jRuQ1qPNHgSiKh16mAyULJ3OVmLnkF35TQA83aloTeZXTj5z0eIlAn8oxh2f92aiwQFBvLVV/P/oNncPa5evcZ333/P6dOnKS0tw8/Xl3bt2zFq5CN4eXk5HL99+2989vncasfr0qUzM2c8b7fNbDazctVqtm7dRnl5OY0bNeLJJx8nOjra4XyTycRzzz2Pm1LJ7PffRXSH1tE6nY4tW37l0OEjXL16lfLychQKBeHh9WjZsiX9+vYhMPCWhfPKlatYtfo7Rj7yMKNGjbyjawoICPwD+f57a8piVRo3hkGD/pz51IahDJKfgCurHffdTFckpJfdZi+llPgwL3rEBnHsShEPtYtg3q40Vhy64jCELf0rIZKXBsZRVmGkVGcg1EfO3hc7oa4ocWq+UbnoL6swEuAht403NjGcrRk/M/3XZ5jUbhLrRq5DY9BQoCnAX+WPUqpk7fm1ZJdls+HCBof5NPRryOYxm9l9eTfDVg2zi7QEqAJu2a5XIU4OU31gnCeo7iAEoTfChmOwZA/8chKMocBIQH6BsT+NZlLbyXz56BwGfXqIq0VaCsr0fLUnncbBniyZ2IGhc/dRojWwPPkKrSN9ePG+JkhlRmZunek0qnUi5wSP/vQoSW2T2DxmM+2/ak+RrogTOScYs8Z6vTl956AtMaLW6vFUyJiz5TwpWWqGzT3CmIRWbHjkNzQGNRUmHb5KH+RiJb+czqPCIKFffAhbUx1dN2/noXbhGExmZvWPrbY/2cTO9enSKIBT14qJD/PmuT6NaRXh/NgJnaOJr+eDt6ofs7bOJOLjCJoFNcNL4YW6Qs2ZvDOYLVbR9FaPt9EatHzS/xNm75t9K92xHjARSAG2ArW00zMWXiN35UuETvicFOqj1hrviRTGv/4MBQTuIr169nTYdjY1lZycHOpHR1O/fn27fV5env9fU7tjTp06zdvvvEtFRQXh4fVoEtuEy1eusGnTLyQnH2LO7PcICAhweq6zewZo3NgxdWTNmp/47rvvCQ+vR8OGDTh+/ASvvf4f5s37EpVSaXfsxp83cfXaNf774Zw7FmSp584xe/YcioqKUSgUxDZujI+PD+Waci5dusT58xf46ae1vPrqy7Rq2fKOriEgICBAZiY8+aT9Nnd3+OkncPJS6y9B6UW49pPDZnNwb3TtF6MRBaIpKEcll6JSSJBLxMglYiZ1j6FNlB/bU3I4l6PmYl4pcaGeNqv521mWfIWEGH8GNAvB3c3EuvM/Ok8/6zCDdVMGMH7hSTILNFzILcNXJSc+zItzOaW2JsdFuiLe2/ses/fNdliYd4roRNvQtg5z8HXzZcvYLfaL9CpUFWQioL8KnvWBfu539GQ5cRm+3gErD0Bh2c2N8cD9QBUPjHlHv6RrZDfeur874xcdsxleVIqwMQmRfLEzDYlYxM7z+QxqHsyac+trTDMEmH90PonhiUxqN4n39t5qWF55vUZevQnz8SD9RimPd6nP9O+stX0bTuaQEONHmyh/inXFFGjz8FP6MrxtCDKRmC6NAhjdIdKpCK9kTEIkHWMCkEnEaG72gvtqXDuMZgsavREvNxkeCikVBhN6s5kynZGyCiOjFhxkSMswu15mId5uqORSvt6TzpL9l/lqXFve6TWHjuFd+Tj5A/Ze2Wu7bquQVrzQ+QWGNh5Kka6YXZm7HD9rEdAMaAzsBvYDNQTAVLGdkQdZ1zdqnYEQb7can/tfAUGUCfyjmDbtGYdtn376OTk5OSQkdLjnoisVFRX896OPqaio4JFHHmb0zflbLBYWL1nK2rXr+HzuF7zxn9ecnu/qPRuNRtb8tJb60dF88MFsZDIZO3ft4uOPP2XLli0Mr1J3UVxczKpVq7mvX19iYmLu6L7S0zN47bX/oNfrGTFiOI88/BBubre+UM1mM8nJh1i8ZCkFN+7Qt1hAQEDAYIBRo6Dktlfv//sfNG3658zJFfxaQ/svrNGymxjiXuV80FT8kaHW5dgiWXqLN4VlZmICvOjbNJjyChMj2kWQX6pjVv8m+N2sUVp56CrLDl52SHFbdvAyPeO8mfnrDKf1YFXNNxZPfJthc48wuEUoJrOF8Z2iWbgvw6HJsdli5lSuvZW9ukLt2NMKmNx+svNFehUUInjME6b7Qqy82sOqpVwHqw7C/O1wOP22nd2AHjgt+PnwwBx2jR9My3AvFDKxzfBi0b5MvnqsHWKRiFE3jVRMGPhg/2yX5vNZ8mesHbmW2ftm26JIVa+XV2Ji2cErvDY4ntEdirhcUM6XjzZjw4UNPLvYUTTP6DiLYbFDmNq7YbXRrwmdo2kf7UdKdgmhXm48s/I4SyZ2YH/aDXaezye/VIe/h4KCsgoCPd3oERtIrybB7DiXS4i3GzvO5/HVnnQaBln7hQV7ufH+Ay14qG04k7o3YPbmc2w8lW2L6JUb1BRqi/BT+qKSepFyXUN5hRgvhSefJn9q9zwkIgnxQfE2EZ/SNwVTcxPuv7pTnuborimSK/Ht/ZTt/73c6uYo+WchiDIBgXuYAwcOUlxcTL169Rj5yMO27SKRiEfHjmH//gOcOHGCjIwMpxExV8nLy6O8vJwuI4Yjk1m/3Lp17cr//vclGRmZdscuXrIUiUTCmDG197lxhsVi4ZNPPkWv1zNq5COMHPmIwzFisZiOHRNp0aI5N244pq0ICAgIuMRrr8HBg/bbxo6FceP+nPnUhQaPQ8EhuPwdFR2WUOTXnSOXNvLpxg+dRrICPAfjpXAnOaOA/+1Ic1iUj+8UzfopnXls4SEyCzS2fe2ivVl/fn2NBh0A849+SafwLix9/D58lG6MX3SIuaPbUFSud6nJcUpeikNPK4lIQlLbJIauHOr0HC8xTPKG53wg5A5WtKeuwPzfYNleUN9u1igChgBtqj//eM5xinXFvDggjhXJV2yGF+UVRjwVUqID3Hlq6RHiQj2Z1i/ATpjWxPGc42gMGpoFNbMTr5XXs1i8ybhRTmF5BTP6NUYuMzJj64xq0yIr0y3n9J1DQVmFXUSr0jhkRfIVzmWX8myfxkxcfJjMAg1D5+5jzM0U1qrHu8nFXMxVozdp6dBQRGiA0iGV9XBmEc/1acyZ6yUYzBaWJ1sjdF/szGDerowqjajVtkbU34xrR2y4wfacfN18mdx+Mkltk9AYNNzQ3CBAFYBSqmTBsQV8EfEFsz3m8OqLb2HQ3HKA9Ok2Dqmntd4vPswLL+W9IXfujVkKCPwJVNZdjXzkYbp168aKlSs5ffoMarWaF1+YRWJiAk8+mURefj7r1q5xOP/06TO8+u/X6NWzp0OEzmKxsGfPXn7dupX09Az0ej3BwUF06dyZESOGo1AoXJrjpbQ0AOKbNkUstn+NJ5VKiYtrQl5eHsmHDv8uUVZWZn0T5eFxKx9ELBajUqkoKyuzbUs9d46dO3fxr8mT8PS8s9TPY8eOc/nKFfz9/XnooQdrPNbd3R139zvMUREQEPhns20bzL4tctGwIXzxBdxh2vX/B0aTGb3JjFprRB3+Nr6Rz6L0jeSNrbVHst7tNZurhRoHu/OULDUzfzjFmIRIu3ooiVhkSz10hU+SP+TXsYPp+999FGkMPLbwECueTERvqT18ZbKYmH90PlM7TGXi+okAxAfFozFoHMRMiASm+cBkb/CWuDQ1GzozrDoO89fDwUvVHCQBHgTiah+vRFdMbEgjJi07BtwyUnn/l3MsT75CtL+KlwbEca3sQp3mWaApwEvhmD5brCuiga8fL/RvhM5gIsBDxk/na0+LnHf0S7pGdWNwi0FM+vYIRrPFznb/sU7RdG4YQLneyKFMq4gu0Rr4Ymca83al2USUl1LKvwc3JFOznedWOn8BUJnKWqTR07lhAOMW2jfDrq4R9ZazOQT5W9cyNdURtgppxdQOUzmSdAStwcAebVN2LP0v5Sk7kIc0wrP1QNuxk7o3QH4POC+CIMoEBGrlelYWM2bOwtPTk+bNm1FWVo5UWsd/BapgNpv5+ONP2b1nD25ubjRs2BAPD3cuXUpj1ervOHrsOO+8/aZLwqxCVwHYi6WqVAqjzNuiWZWkpaWzaPEStBoNPr6+tGjenGbN4h2OCwy0ppRkXc+ybSsrK0OtVtuMNsxmMwsWfEWDBjH06dO71rlXx5GjRwHo3LkTEsmdP2cBAQGBaikqgvHjrQ6GlchksGoV3OELpapiqUSrx1spx0spRS4R37kdd9FJyN8LjZ8GQKM3si01l/m70m3C6ume9QkKXudyJGtUwgPM3nzeqY357fVQjYM9HFIPa+J4znEKNEWEeLtRpDGQWaBh8Gd7+fX5BLsIWHV8efhLjicdt1nneym8uKa+1aC6gczqpPiYJ7jV8ZFmGeGLGzB/Adw4X/1x7h7ulD9QDi6+x/RUeHP0cpFNxD7XtzFp+aUcu1JEtJ+KHyd3QioR46f0rX2wKvir/FFXOPYKk0lkTFg3gaGx9zMsbCjmOqRFfrh/DkMnDGHeo7ciZYGeCvzdFaxKvoxSKqGgvMKhzrBSRHkrZax/ph3v7nuFBcdqfgHw3eT38JCpKNYaba0RauP09RJ83GJqrSM8kXOCiesnktQ2idl95uDmVUjA4Odxb94HidITkdi6dhibGEXvuKB7wg4fBFEmUIku/87PlXqAVOl8n+4Gd9IHxDquCqR/fhRkz569DBo4gMcfn3hXRMLadevZvWcPzZo1Y8bzz+Hra/2iNhgMzJu/gG3btrNq9Xc8Nu7RWsfy8ra+RcvLd/755ebm1rj/8JEjHD5yq2/J6tXf0Sw+npkzn8fHx8e23dfXlwYxMWz/bQeJiYlERkbwzcJFmM1m2rW1FmZv3ryFjIxM5sx+3yFqVxcyMjIAaHCH9WgCAgICtTJtGly/zUb+/fehraPRhCs4E0tgTZ1K6h5Dn7hgl93fKsVdRfr3eJ94ApFZS4UiEnPoAN7dlMqyg7eMGu4kkjU8bihNQz05k+UYqQBrPdSCce2YtysND4XUqT19TRTrivFQ3LrXUB83ckqMzOo0i9Frap5nka6IE9mn+KDfB3SP6s6iE4uYtHESDWTwqh+M9QRpHYOYh3TwSTH8cAMM3wLXqj9WovLi5y0bePb0tFoFJEDrkNbIxZ40C1MwpWdDRiVEIgayS3TMH9sWPw8F28/m8vXedL58tJlLwrRyXKVUyZm8M063/5D6A9+d/Y63e7zNY60fq5NoLtYWMXtTLpfyy22f0+uDmzIqMQq1zoDBZGHuqDZO6wzHJoaz4/IvTgVZVeYf/ZJukd3wEXUh0FPl0tzA2rzaW+HDGz3fqLWO0Hqd+fSI7sHgFu05nFmEMspq+hUf5sWk7g3oHRd0T7guVnLvzFTgj2VN0J2f226u7S2eAz/HQcUd1vw0ex1a/OeOp3W38Pb24rHHxt0VQWYymfjpp7W4ubkxc8Z0O+Ejk8l46sknOHr0KL/+upVHx46pVdzExzflhx9+5MiRo6jVajv7+4KCAk6etOaia7X2yfK+fr6MGvkIHTp0ICQkGL1ez4ULF1mydClnUlJ46+13mDP7fbt7njBhPP95401eevkV27a2bdvQvn071OpSVqxcSe/eveycG/V6PVKptE4irbTUulCoFJwCAgICd5W1a+Hbb+239esHzz57R8Np9EYHsVRJSpaaqStPMDYxipcHNql1gajRG9mWkk3p0dcYo1ps2y46MJa99Tex7GCx3fF3Eskq1hXzcLsIUjemOnVcTMlSo9ObiA3xpKzCSIiHv0tjVxLm5c9HDzfiRlkFgZ4KfFVytqfm0qfhoFqbRye1nUzLoM7sOFfEkazzXL2+g1d9DIyNqpsYM1rghzL4tBgO6gAd8C3gpL92JcGhoUSOeQ/3sKbM8qxdQAJMS5jBt/uzGJtYnxbh3jy19AgpWWq8lTI2TOnCuz+n2twOVyZnM63DDCasH1vruFMTpjL/6Hw7kw9n2zenb2ZA4wG1jleVQl0xTUK92HI2j4md6/N0zwakZqsZ/dUBTl6rvs7wapG2Ti8APjwwh59H9Uejc11qmMwWDCYJY5qPodeSXrWfAMzZN4fdE/bQpWEwRRo99XyVeCtlvy9C/Sdxb81WQOBPoGWLli7XeNVGWlo6arWaJrGxdoKsEoVCQYMGDSgrKyMrO7vW8Vq3akWDmBh0Oh1vvPkWFy5cRKvVcu7ced54821MJhMA4ts6ZbZp3ZqRIx8hJqY+KpUKHx8fOnRoz4cfzCEsLIxLl9LYt2+/3TnNmzfj448+ZMSI4dx3Xz+eeeZpXnn5JQC+/XYZFgu26N7Jk6eY8sw0Hnp4JI+MHM0XX3yJXm/fSFRAQEDg/50bNyApyX6btzd88w3cQYTfaDKzLTXXqSCryrKDl9memoexhia2mgojczYeRXpgpJ0gA1CY1ZjPvO9wjodC6pKJRlWKdMX0iw9h18wePN2zId5KR2e6Io0eD4WUdlG++Kt8HZoSV0frkNZ4KbwRiyA22JOT14pZfjCTXk2CUclUvNLl3WqbHC8etpwXO77NQ1+v49+behKf+R9ORRgY7+W6IDOJ3Tjn35+YTBiVc1OQ6YHl1CjIJJ4BDH/1K75/8UFWH77GwEZDeKrNpBqvldR2Mt0j+rM8+Ro3Siv4as+tKOnYxCiSMwrs7OeXHbxGj8gBLoybRNfIrsw7Mq/W7eoKNR5yjxrHux0/Nx/6Nwvi6L+7MqaTO2nFZwn10/Dtk61YMrENEb7WzKfKOsMFu9NZMrEDbSJ96vwCoLSiBA83a388V4gP80JkETutI6zpOkXaIlYdvsLszeeoMJpRyaX3nCADIVImIFArAYHOe3zdCXl51saNJ06erLWRdalaDfXq1XiMSCTixRdn8dbb73DpUhozZ71g2+fj48PIkY+wfPkK3N1d+9JWKpUMHjyIBQu+4vjxE3Tr1tVuf2RkpENa5cVLl9i2fTtPPPE4Xl5eFBQU8PY77xIVFcmLL8zi6tWrrFy1GoWbG49PnFDrHCrr4NQlruWgCwgICLjMp59CXp7jtvDwOxpObzIzb2c6ErGIxsEeNuMEZ32/5u1Kq7a+Ras3suvEER4uGk1TnwyH/eqwcTyzZbjD9rIKI/4qvzrNWSH2ZMLiw4hFomodF/095DzdowENgz1ZlXzd5QjPswkz2HjyBgfSinh/RHPS88o5dqWIBkEeNA3z5rlVqdzfuiObRu2gtKKEgiqW6MsOXuWhn5OY7Pcj47wsdYqMFZjggLIdPe77gXBFAIMMN01PDMAq4Gr154bWC2P1+g0czHfjxNVimoZ5U6IR8VKnd+gU0ZVPkj9wMJl4NmEm3SP6M36htUeY+83PHawppaMTInlyyRG765RoDYxfeJLFE6sfd0anGXSJ6EKfb/vY0kZbhbRiWsI0ukZ2pf/y/nbppCl5KbhJ3eqUFumj9GHd5XV8uMbRpOP5js/z87RhTP42hX1p1nYzlXWGw1vXq/MLgAJtEUfSrb9nM384VevxEzpHk19egZ66pcwWaYsRicQczizC7XfU/P/ZCKJMQKAW5LI7629hsTimhZhvbgsNDSWuSZMaz3fVvTAoKIhPPv6IgweTST13Dr1eT2REBN27d+PATavnyMgIl+cdFhoKQGFR7V+KFouF+fO/Iioqiv739QNg06ZfMBgMzJwxg+DgIDp2TCQ7O4dNm35h7JjRtUYd69evT2rqOdLS0+nRo7vL8xYQEBColddftzaGfu01a4+yoUN/l/19qc5Izyb+LHisGeUGta03WFVr8Mp6nJQsNWqt0SGF0WgyY8zZTceLD+CjLLbbZ7KIWVIxleZRr2Kw3Gbdj7UGx13mVadFuUrqZbMgd+a4GB/mRaCHG+dzSrFYLCw9cJVvnxjgUupht4j+DJt7hP8MicdotvBw+wge6xRNfpkGlcLE2w9YF/bqCjHeCl+OpFv46Ph1Uq8v46ngD9gbVYKiDgGOqwZYZQojqs07DGz6EB5yax36+30+oF1wR6Y/+Qzq9Bpe8HlD9gNZTD38ONM6zKBt9ECkYn9MZgszV5+nbZTznlork7MZtu6I7XlV7VHWONgDnd7k1Nwis0DDsLlHHHp1hXsHIBV5cD5bg5vYg+3jtnO15Cr+Kn+UUiXzj85n+pbpDvV9lY6VMzvNZMyaMbU+r1mdZ7E1bStjf3IU2CdyTvDoT48yqe0kvnx0DoM+PczVImvpw6J9mSwc357iilovYYef0pePjl/jw4da1tq4emxCJP3jQzmcWUBEoLfL1xABwboMklpGkJEfdM/Y3zvj3p25wN1lRF7tx1SHtIYozKBUfpfRx18cqcz6V0ir1aJU2pudOOufFeBvzc0Pr1fPaSPrO0UikdC5cyc6d+5kt/3cOavFlDNHxeooK7da3Lu51Z6yuW3bdi5dusR7775tqz+7dv06Xl6eBAffqlNs1Kghv+3YQXZ2NtHR0TWO2a5tWzZt+oV9+/Yz/i7V8gkICAgAIJXCiy/CoEEwfTrMn/+77O/d5EYCg44zZPWYGq3BK6NQap2BEG83uzHMl77C49gziMT2TZtLjO48feVFCjy70d3d+fexyWypU63StIQZrEzOtnNevN1xcWLn+ny58xL/25nGhw+1YEjLMJ5ZcZaFE2bTPbo7s/fNdrjX5xJm0u1m5GhIi1C6xwYy/buTvD2sGRo07L3+s9PI0FPNH+VBnyUMcD+FVx2+6vWqaPKiJkL0GCa7B1FSbuFgmppODRSo5FI0OjH/e+F71CdrFmSMB3ztXQPn9JnDxlOFPNg2nJk/nKq2p1YlEzpH2/Uo81BIKdRUn65vtZm379X1nyGevLkxheSMIuLDPPnf2Ia8uP1F1BVqzuSdcagtq8qXh78k89nLtYrmye0m0yemD40/b1z9MwHmHZ1Hl6guvHV/T8YvOg5YXyiYzBbCvPzr/ALg6OUzPLbwkDUFsprG1ZO6N6BXkyAW7E5j7o40ds3qWON1PMXQVwWD3WGohxT//UM5LHmQOQ8uv2fs750hiDIBK26Bf9C4dy/176+Ir68vWVnZZGVl0aBBA7t9J0465kM3atQQd5WKMykplJaW3nEvL1coKipi//4DeHp60jEx0eXzDuy3vo2tzf2wrKycb5cto0eP7sTF2Tdzqaiw/wdJV2F9vSZyYfHTpk1rIiMjuHLlKt9//4PT5tGVaDQabty4QWRkZK3jCggICNho3hy2bv1dQ5Tpy3lx28xae4Mtnvg2w+ZaoypeblUyL8xGOPY88gufOZx/SRfOE5n/JlNfD4mmDDe5hPgwL4f+YmCtVVo3xVqrVJMrXmUN1LB1Rxz2VTouqrUG2kX78ubGs7bt34xvzyPtI9h3sRidtr1dhCfQ3Q8/pQ9ms4y3NqQyvW9jEmP8GfHFPh5oG45CYeTl7S86zEsKdNSeYPilE9amzy4KsoyKUObmjWTw0OeJDfFFKhGz9kgOI9qG89Xu0+y+UMDMfrG8OvNZju/YWP1AntgEWVXmH/2S7lHdkIo70qG+vy2646ynFlijO+2j/XhrY6ptW1mFET9V7X3ZqvbqcpNJUOus6Y/ncsqQipWU6cvsGkdXR7RPNBKRmDl9P6BrZHc+OOAommd2fIGhTQYz+sfRLrlp/nf/f9k1fhhSMRhv6kG11oCXm4LpiTMZt7b2qNyzVV4AVG1EXbVxdaUph1QsYvu5XD7dbm0c5+xFQ1M53KeCge7QTQly23LC+twidL+x92I+A5qH1jq3vyr3rpwUEPgLEB9vjUD98OMam6kGwO7de9izZ6/D8TKZjOHD70er1fL++3PIyclxOKagoIAdO3a6PIfLly87mGjcuHGDd959H61Wy8QJ4x1SBn/44UfUavt/3I1GI6tWrWbf/v3I5XJ6967Z+Wj5ihUYDEbGP2af+hMZEYFOpyM5+ZBt3P379yOTyQgJCan1fkQiEc89+yxyuZyVq1az9Ntl6HQ6u2MsFgvJhw7x/IyZXLxYXfdPAQEBAdcxmsxo9EZySnScz1GTU6JDozc6NefQGw2sO7fepd5gu65uZkxCOPFhXni6Sa3jGUph9zBwIsh2qNsy/NJ/ydRba4pNZgsrkq8wvlO002tU1iq91OmdWk00Hl98ijAfN9pH+xIX6onkpglUSpYai8XCv3o25LGFh2wpl9eKtLjLJczflc6zq0/wn/Xn6DZ7P8+uuMxHmzQ8syyTdm/t4ZOtl3h3RAsaBnrw9IpjBHu78WjHCDZe2OAgyEZ4QGoUfBGEVZC5QEZFENOvPEef8/NwazyBRiG+/HjsOmKxiF5Ng0m/Uc6HD7Xiya71mTlrJgu//qr6wVTAOBwEWSUf7J9D18bevPPzWWbeF8tnI1s5GFXEh3nx34da8sKAJnbPC6wppZUi2hXiw7xwV0ht6Y8ms4XtZwuZ0XGWS+fP6DiLjPwKLGYF3SMGsXv8Hq48e5UTSae48uxVdo/fQ7+YoZToSthwYYNLY1pdOovoF3/r32xvlYzDmSV0De9fq1nJ5HaT6d9gEMuTb/UfqGxE3XX2b0xZeYzvjlzFRyVDJZdiNFv4fHua7dhlB6/RKySBBc378E0QXI2GlCj4KBD6qKoKsluEyAr5dd9m9DWY6fzVESJlAgK/g0EDB7Blyxb27z/AlMxpREdHkZ2VzeUrVxg8eBDr1zt+AT7wwAiuXb/Ozp27eHrKVGJi6hMcHIzRYOR61nWuXr1GdFQUPXv2cGkOa9eu52ByMjExMfj5+lJcUkJqaioGg4GHH36IXr16Opzz7bLlrFr9HQ0bNiAgIACNRkNGRiaFhYXI5XKmP/cs/v7V2yBnZGSwefMWJox/zMFFcuDAAazfsJEPPvwvrVu3Ijs7h6tXr/LAAyNcdrGMianPm2+8zvuzP+DHH9ewcePPxMbG4uPjjUaj4dKlNIqLi5HL5QQE/L2jsQICAn88de0zpjfrXW7Y+2nyh6x/ZDsxgZ58f/QqYfIb9Ml9HHHJaYdj5+WNYE7OY5hvCx0tO3iZ9VM6V1uXU1mrNPuB7uweP5S8skLKDCW4SbxQSb1YcyyXDYUFLH08AZ3eRKFGj59KbteLqrBcz0dbL9gZfoxNjOK3c3l216wa4bHNL/kKCTH+dG0UwPsjWlCsNSARG/n44BzbMa0U8EkAdK9DZUK+yJ9DyueZeiqeJmG+fNCvPr2aBCEViwj1duOxhYfsPi/F6Z+4sOmb6gd0wyrIakgOOp5zHKOljI8facWm0zlkl2jtoju+N5/bqkNXKK3wx11hv5SuKqJdMbdI6haDt1LK1ue628Z3k4uRSQbyVNtJLKhB+E9qO5nBjQczbeVZXhvclCuFOlpGeCPDHx+pL3LEiCxSdAYDN+rYnqhQW4S/uzXiFx/mhZebjP/+ep6yCmONZiXPJsykd/QAzBYFpTqDw7iVvz//6tEQ2U3HU/XNBtNR8iwe9N1OV89jhP92iSdFFmuaqQtc1EVQWJTjtG7zXuHenLWAwF8EHx8f3n3nbRYvtvb3KiwspEGDGN5443VEiJyKMrFYzHPPTqNTp478+utWLl68RHp6Bu7u7gQEBDD8/mF06dLZ5TkkJHSgqLiIzMxMzp07h7u7O21at2bIkME0b97M6TmPPPIw58+d53rWddLS0gHw9/fnvvv6MXToEMJrcX1c8NU3RISHM2jQQId9vr6+/Oc/r7Fo0RKOHTuOu7s7998/jNGjRrp8TwBxcXHM+/J/bNnyK4cPH+Hy5UxSUspxc3OjXr0w+t/Xj759+wiiTEBAwDkWC0yZAuPHQ/v21R5Wlz5jcokYg9lMkbaoTpbdenMZnRoEMODTPXSU7aRftL0gMyHnaMj7vH/Keb1PidZgq8tJqO/Hgj1OxGO3GOJCfRj2+SGahHrxysA4nlh6hAqDiUUTOnAoo9DWR6vqeZUOjCIgV30rK0EuFfNoYhQTFx926T7n7UqjQ7QffT/eRWyIJ5+MjuJk7kmCJfCOP0zwArGL5XvZRhk75VMYPPQdwm7o+LWTjCAvBetPZlGqM/LlzjQHcao+so6i7TUIMhkwBqg9YYMsdQESix8L92WQml3KJ9suVqkrM9rqyiyImNC5PjO+t/9dqE1EVzI2IYoujQK4Uqjh2dUn7MaP9FNZxU+4c/Ezs9MLDGk8mElLz3AwoxC5TEJsiCdjv07GZLHYxsop0fH6kHjaxtTNpdNP6UtBudVvIKl7AwwmM2du/u44MyupaoIyaN1hfvpXJ2JDPJ2mfo5NjLJzIi3RWrN9IuS5PBO82qX5mUVyjmhasKmwDb+VtueK3pq26Kxu815BZHFmESfwlyc3N4+nkiaxYP48O0OFqpjNZs6ftxo9xMbG1qmBr4CAgIDAnSN8//4FmDcPJk8GicRq7vHaayC3r/UxmsxsOpPN1JUnah3u81GtiQlQseTAZe5vb6bz4jYuT+XUpNMs2K5nwylr/8mng1YzM8TawNoi9yc54mtCG/Zl/q70GhfxT3apz7N9G2FBRKnWQLHWgLdSZq1VE1mYuvIEv53LQyIWsXtWT6avPsEHD7Zk3i5HEVOVMQmRvDSgCV3n7MBssS6ax3WMwmS20On931y+z+3TuzNl5TE8FFKm9Naz+ZeevOxnNWZwhRKzmBWa/gS3+oi2DcJ5c2MKaq2REG83Xh0Ux2/n8pBJJA4iqOzUrxT84pgKWolCoaDikQqouVTaxsUpl/GUBdLxvd9w0l/bhrdSxvopnVmwO53lyfbPN9pfxZKJHTicWejU3GJi5/q0i/Zl8f5MnuoaQ0cnz9lbKWNMQjijE0PtxE+Ylz+lWhFisZhON+f4n6Hx9IkLosvsHQ7jyKVijv67K90Wd3XZpGPn+F20eWM3I9tH8vKgOK4Xaej78R6748QinIpVgC3PdmVF8hWWHLh882gLfcJLebrpNZq0eQilT7RtnJwSHYnvbUchquBk/CjcxM6NUm6Ygzlc0Y7WCaP5129+HMsyORxz8KXef6ooc2V9Xh1CpExAQEBAQEDg70V6OsyYYf2zyQTvvAMnTsBGe/OHyj5jrjBvVxoLxrXjzPUSHu8eVafpuEk8uZR/2dbP7JAiiYMU0cHjAuKeP9NCEc38XWkkdY+p1qHuiS4x9IsPZveFfL7YeQmj+VY0RCq2Rmw+fKglI77YR2aBhhXJV3h5YBOHJsbOWJ58hXZRfvyrRwP6xYdwKKOQ//56nofaud5OBaxNp5VyI+GGOdQ/sJn3XExk0Joh2b09LXuuYoQ4jG8PZPLG3H0APJoYxZPdYlBrDQxqHsbIBfatAcrP7aVg89zqBxeLWbpiKe/lv+eyIPFT+vLN7ss1CjK4FcFcN6ULbSJ9Wbgvw/a5ZRZomPXDKV4Z1ITVT3Ukr1RHYfmt9McVyVd4c+NZXhsch1wqdmrk4sypMdjLzKuDgtl3KZ/+zULo2SSQnJIKGgS6U3bTLOR29EYz57M1PN/xeR796VGnx1Tl+U7Pcz5by0cPt6L3zdRdTzdH8xJnqayVuMslvNxVxtORGUjyd+Fduh9pRRbkQEWuF/jcauLupZTa7j+5vBndPY8BoDErOFjWnD2lrdld1oa0inDiw7yZX68tJ7IdxWd8mJdgiS8gICAgICAg8JfAbIYJE6C83H77M45tSCprWVwhJUuNTm9CJKLOvcGCPf15oLWRAS3CbtV0uS1B427CTeWPSiImqXsDdl/Ip6zCaFfD5O8hty7kJWI+/PUC3+xzbC4NMP27k4xNiOS7pI70/Xg3K5OvMLl7A15ac8al+/t6bzqrn+rIe5tSWX7oCnGhniR1a1D7iTcxU8H+8+/ztPwTens71hI5w2SBRWpYRSzfDP6eMpEEBTAmMYogTzcGNg9lf9oNRn91ELPFwtxRbew+L23aEW5s+BCqsYwXiUQ0H/MKJQGtmdVoFqPXjK51TtMTZ+ImVbAsuWYhW0lmgYbCsgrUOoPtcyvW6gn1ViKTiFm0L4OVhw4T4u3mEFEakxBJu2g/ViRfcZoGWUlV8fPhQy1YeiCTL3amcfJqMZ8+0poSnYFfU3IJ91U6PR9gxvcpbJx2P0ltk5h/dH61x01qO4lhscPQ6KQ0C5Oi0ZtwV0jthJNzLETKc0h0P03/wHOE7kxCoruOs1iROXsnxN4SZXKJmKTuMUxdeYIfi3qTqq3PrtI2HNU0RW+x7xV7ewuCqiR1ixEs8QUEBAQEBAQE/hJ8+ins3m2/LSkJ7rvP4dDKWhZXKdLoUcmlLvUG8xRDawU8njADi1lKoJeb05quqkYifeKC0ZvMqLVGJBIR9QPcUckl6IxmtpzNrVaQVbIs+Qod6vvx7ePtWbA7g7xSnUuiUy4VE+WvwmA2oVJIkEvFdi6C1S/EQS+6jEn2M9OCtpGUr0dRvS6wY5sGnsuHM3pYNOxF5h+dz5BGD/PRpito9CaSusVgtlhs5iPto33t+n/prp4hf+271tYC1fDyux9z3qc9c3dksP6Z+1zq5TU0dgilOjPhvko7V8XqiA/zQiYV89bGs7y18awtouXlJuW1IfHEBLoT5qN0+Nyf7BpD+2g/xnx9kCKNgU1TuzImIdIhDbIqY26z4F+WfIU2Ub6k5ZUxb3c6/eJDqv28Mgs0/HyykA/6fkjXqK58uP9Dhzq1GZ1mMLTxMN7ZmEaH6AC0BiP3t7bWmMslYp7qFsO0VZXnWIiQ55LofopEj9Mkup+hnjz/1gXtTZPtkBXsstZ83myTI5WI6RMXzNjESJYd7M56urt0/7fv69Uk2Fandi8iiDIBAQEBAQGBvwepqfDSS/bboqPhgw+cHu6trL2fVFV8VXLKKoy23mDVLfLrSWFTGDRRSNAHhfHxtnSngup2IxGVXIpUInZwj7NgZMFu19Is5+9OZ+nEDrw+JJ4bZRU1Hhvhq+Tt4U1pE+VBsa6YK+oLjOviyzN9unL8chk/n8py6iJoRodGso8yyRb6ep3ls0CoL6vmIrdxQQ/P34CNNwOZSW2T6BrZlelbpvNYiyTKKtSkZpcyddUJxiREsmRiB4bO3WfX/6si+yJ5P7yBxVi9qPbt+TgTJj7OlJXHrGmG35xk8cR36BjelY+dGGe80OkFekUPYMxXJ+jWONBl98SJnaPZejaXtlG+lFUYuZBbhulmGOfIZWtvroWPtceCBbXOiJebDC+lFIlIZG3+/VQiheV6TGYzLw+Mc0iDBKuIm9A5mvbRfg4W/N/szWDBuHZ8uSuNFclXmNilPs9/5zzi9u6mc3Rp2Jk2AQPYPX4YxboiW52at5sPxzJLGfjJIbo1DqRNlA+p2Wpb5EkqEdOrSZDNvGRu5GwG+zi2/qmNPIMvnmFdkJo0IHW3bVfJpbw8MI6E+v7M25Xm1MimS6NAXvjhpN39Vz6bjjEB3MN6DBBEmYCAgICAgMDfAaMRxo2DiipCRCSCxYvB09PpKbWnZN0iPswLhUxsSz17fPEp1k75wGGRHy+HbREyQsQGwIRl78NsPj0bnCZyWVl28DIJ9f0Y0CzE6Zt+tdZQpzTLYo2Bj7aeZ8Z9sdUe17mBP18+2owNFzbw7OI5jlGTjrMYmTAEjU5iW4jrRZmUSTdTJtlBpKycbwJhmIdL06LYBG8Uwv+KwXDzGtMSptE1siv9l/cn2icaldTL1q8LrLVurSN9GJMQyfzd6bjJJUSKCtj33WtY9Npqr+XdeRQd738Mfw850puWj5VtA57rm8Bv43ZTblBTrCtCKfVCKfFi+cEs+q5JpkRr4Eqhplb3RG+ljDkPtKBzwwByS3U0q+ft0GagsjdXTKA7PWODaBzs+HuoUkjxVcn4eo9VtPeNC2b1Ux3JVevsLPhXJF/hrY2pDtG7lCw1YhH8NqMHSpkED4WU44lFTt1ES7QGxnx9iO+SOrIjtYCUrDI0ehEF5Xn8mnKK2BAvpvVpRPtoP9Ycu05SR1+730edwWyreyw53hCoXZQVmv3Yp47nQFkLDpY3R+kfx/edOoHUUYKo5FIGNAuhd1wQaq0RtdaASmFtD7Hm2DUyC8r5z7BmvDjA5PBsPBTWSPO9jCDKBAQEBAQEBO593nsPjhyx3/bss9DdeSoU2Ney1MbttSxyqZgbasjJuWUNbszZSpPU6UiMt4SFwpjPBxGfMDr93RrHn7crzWYTbjSZbWmMcomIG2V1T7O8UVaBXOo8/TDCV8m8cc2YuXUm851E+k7knGDsT6OZ1HYyr3d/naCQfajqfc3lwqNIgOd94A1/ULkQmTBZYI0xkLie35GkCuFBTQH+Kn+UUiXzj85n+pbpFOmKWDxsOSuTsx1qhRbty2TBuHbM25XG52v3cebrmZh1zs0lADzbDcO782ie7BqDQipm4YQOqDUGyiuMBHgq8FBI2Xgqi3Unsni4XQQ9mwQxedkRjlwutkW4qrYgcGa80jcuiNkPtmTvxXweWXCg2jYDjy08ROeGAXSMCcBYg2uIm0zKxC712X0hHz8PBWn5Zby45pRTV0NnlOqMdoLv5YFxtI9y3jphQudoKowmctU6xnWKxmKxUFiuZ+Z9sXgYr3M19Qekhw4yrSIZ8TYLDMu0pRm6KyRMWXGMtlG+TOg5EpIXO8ylwOjDgbLmZEo78OCAMXT64jpmy61eCJ93b1hj3ZdUIrZFi0O83dAajLz7cyrf3hSZztoTjE6IolvjwHs6dREEUSYgICAgICBwr3P8OLz5pv22Jk2sros1YF/L4notD4CHQsqNcr3NHe+JqMPM9HwXicg+kpGmC+eFa1NrvYWULDVqrRGRCLaetTayPpdTyv4Xe+GukNR6flV8VXLUOiMrkq/weJf6TL8tne3t4U1Zf36DU0FWlXlHv2TJycVojdaoVCsFfB0EbV10HD+o9UfVcT7b07cxcnFvmgU1w0vhhbpCzZm8M5hvGnQktZ1M94j+DFt3xGGMSoOVCGkpHz07EUNJXrXX82jRD99eTzA2MYrWkT7899cLTOvTiFk/n6WswshT3WJoHe6D3mjmo0daodObyCrW8sGDrRCLsYtwZRZoGDrXmn64YFw7LBYLaq0BP3c5nm4y3tuU6tQMJCVLzcwfTjEmIZKf/tUZiVhEiVaPh0KB0WSuVjhU1hQazWaCvBTVuho6w8vNPndUJZfSLz6Yzo0CKNEYqo24VZRk8FSTK4Tk7UKUtwuJ9rJjX+3yTPCoD1hfYoxJjGTqyhMs3K3nVLwcjcWdk/pWbCuIY39Zc9IqwgGR1ZAkpdxOkN3en8wVlDIpLw2Mo0OVtMbKZxMf5sWnI1vTOy7onm0YXZV7/w4EqkUkuvUXwWQyCX1yBAQEBP6fMJtvucFV/S4W+APQauHRR63pi5VIJLBkCShrd52orZZlUvcGdGkYwPAv9tmljlWtcZrgv5aXvb52GLvUswMPpDxLscnLpVsp0RrYcOo67/x8DoC4UE/KdEaXDDeqzjnY241R7SMI8FTQrVEQYxKKbAYScqmYNlEeTFs026U5aY1a3ETwuh/M8AWpC7/OatwoaPwae24MZff2Qj4f/Q49orsze99s9l65lfLWKqQVzybMpHtEf8YvPFmtscbpcxc4/L9naxRkqiZd6TrhJSZ2bWCrvcos0NCsnjePdYpm/KLD+Civ0qlBAO4KaY2NtCvPLaswsuN8HtF+Koa0CuVSbimX8sqoMJprdWesbDNwtaicj7ZedDB1cUZllMhbSZ0+b2c28Eq5FAuQcr2En09nc/p6CSU3MkhwP8UHEefo6nUGZekVqK03eO5Omyi7/SVGr3Nfcs0QBNj/Utz+EqPy79GdiieHtEadwVabJ7/5zP4OCKLsb4xIJLI2TKyoQK1W4+/v/2dPSUBAQOAfQflNO3a5XC6Isj+a55+HlBT7bS+9BB06uDxEbYs+vclMl0YBZBbcWohfyC3DTSbio0ZLGaH8zmHM/RVdCOm5huJ9h2zbKvuUVaZeVTWFAGt62Jpj123/76GQUqjRs/N4vsvGExM6R/PD0av4quS0j/bny10XeWlAE1pHWtPwovxVFOuKOZnr3AzidnooYUEQNHLBE8VsgXkl8EqBjujS75jWIZLP2w/gmRVnWTBuCD1GDaDMUEKZvgR/lS8SPFh2IIth645UK8gMRdlMHpWEtii32ut26tGHr79djae7m0Pt1Vd70lmd1JHEGF/evr85i/Zm8PH2i3bPHewjXN8+3oG1x7N4qF0ERrOZMp2RvFI9jYI9USmkJC096tKz+3pvOgvGteOTbRedmrpUR13Said1b1BtOqBKLqVzuInOhZ+DdCfSwLRbO13MiDXkH0DWYILdmFVfYlxz8hKjV5MgNHoT30/qeNfE0+1pjX9HBFH2N8fX15ecnBzy8vIwGo14enqiUCiERYKAgIDAH4DZbKa8vJzcXOsC0rMagwmBu8TatfDll/bbWrWCf/+7zkPVtOiTSsQO0TSJRY96+8OMUG5yGGvpjUG8mzeJnSpP4sO8uFakZWxiFKMTIm/1KbvNFCLcV4lULLYzuqiMxi07eNlmPLH6yNVqhV1lhGLoXGtUb0xCJC8OaILZbLH1P5NLRGSVX6z1eXiL4YMAeNLbted3WAf/yoMjN31WTuScYML6sSS1nczno9/GZBaR9O1JzBar2OzWyI2hrXxZnnytWkGmKMumdO1rFOZlV3vdgEatCR7+MtN/PONQe+WtlNEj1mqw8uFDrcgvq2BA81Dub1PPLlWxKvsu3WBGv1ii/FU8vuSwQzTtqW4xfPBQC1s0rSYqUy9jQzxtKXe1mbqA62m1rqQDShVecHkpmF3rHVdg9CK5rBkHyltwsKw5U5sOY8BtaZeuRK7cFVICPRUuXROwq6Ms0erxVsr/dpGw2hBE2d8cb29vdDodxcXFFBYWUlhY+GdPSUBAQOAfgZubm5Ch8EeiVsMTT9hvU6lg5UqQ183q3hWqLkRL1TfwPPQwquI9Dsd9emMi+sYz2T4uCokYnuvTiEbBnhzKKKwxZe5Sbinbz+USG+JpE1qVvcLCfZVMWXGMxRM6MKt/7E0jj1vCbntqLj4qOa0ifOws060pdL74uStwl0vpOvs3nuhSn3r1jtd4r31V8E0QRLhgc682wYsFML8EnLVwnn/0SzqFd2FI4xGkZt8STYcziwj1cWNMQiRf7ExzOM+v/DKZK1+jqLCg2mvHNm/DTxt+ZvJ3qWQW2NdgRfurWDKxA4cyCnlkfs1mHJkFGiRiEW0ifZg7qg3fHrzMp9VE06bdZtVfWy+zIo0eD4X9cruqqUt1uJJW27uhG6q8TZCzHULvg3oDHQeSuoN/B8jf5/Q6hUYvksubcaCsOQfLmnOxIhILt+Y1b3c6vZs69v+6m5Erjd7ItlRrHWVNffz+7vz97/AfjlgsJiQkBHd3d0pLSykvL8dkMv3Z0xIQEBD42yKXy/H09MTf3x+JpG4GDQJ1wMvLanc/YQLcuGHd9umnVoOPPwipRIy0IgtV8gAoOWO3z4yURcZXuX/MDJsAU2sN/DKtK+9uOufUWr1qytzLA+O4lF/G3FFt7CJoW1NybMLut3N5LN6f6TR606lBABMXH3aI3ny9N4OF49sTHaAiIS6LOcefp+TIeaf35y6yRscm+7j2PHYYvBh7VU1WLcuKT5I/ZGCjwQ7bqzorVtU/4usnObfmHbSa6iNRirAmaHq/yPJj+Sx9PIEhn++1CSRvpYylExOYtyut1ue+9PEE1p24zog24ZjMFvLLKhjUPJQRNUTTqlr1OxOUVansbXf79dVaY61CwyEipdHiX3ESz+LtyHJfRnT2AFhuPnxjmXNRBhDU85Yok/mg8+vC7GOhHChvwXldlJ0Ic/asXJnrnaLRG3l3U6rTiGBdUj7/Dvy9704AsNaWeXl54eVlLTS2WCxYLDV4qwoICAgI3BEikUhID///ZPBgOHkSHnsMfHzg8cf/2Otpc+DXjqC5Zr9d6oGl8w+MCuhtXWDeNIF4umdDNp/JqbbXVSWVi/z0/HK+2JlmF8lZvC+Dh9pF8M7PqdUKjMrozdzRrR2iNylZao5nH+Wp9c9zVZNc7Ry6KWFRMMS4EB0rNPuhbfU2vX78V+0HA8dzjqM1qnltSDwfb71gm19KlhqLxcLA5qFsPGVNUSw9vomS7QswmYzVjqeoF0fQQ28gVqhYlnyFtlF+dgJpbGIUyRkFLj33hPp+dGsU4JLxR1WqE5RVqdrb7nbUOoNLESapJgNpzlZU2b8SkvsbGEqcH5i7o/pBIh8AuQ8E9wSfllzOK2fRVscob3W4Ote6YjSZ2ZaaW2OKJriW8vl3QBBl/0CERYOAgICAwN+GsDDYssXqwvhH/9vmFmxNE0v7psq2EOixCYt3S7adybYJMolYxOiESJ5c4mjz7oxF+zL55rH2HL1ciFpn5MU1pxnZPoIZ/WLZlprrsrCrKk5MFFEkW8iAVdUv2N1E8K4/TPMBsQuPb3VhXzZLp/OkZ90a9WapC4jyC3EQOcUaA28MjefpbtG8Mms6P/26pMZxFPWaEvTQfxArVLZtlYYa83alIRLV7bnP3209N/W25txVo2nOUhWd1YvdTlK3GFZW6W1Xldtt7G0YyiBnG2RvhpytUJbu0n1QngFlmeAR7bjPt5X15ybeyrql91Y719+J3mRm3k7X7s+VlM97HUGUCQgICAgICNzbiMXg7v7HX0ckgvbzoKIQrv0EXrHQYzN4RKPXG+0WmI2DPdDpTZy9bbFfHSlZaswWC/8eHI9SJkEhE7Pq8FVEIpi/y7WFa2X05stdFygRb6RYthyLqPoUwA4KWBICTVxYo1+uCOGl61PYX9aKuFAVQR51q5f0cfPhP2vO0ybS107kKGUSHv54E8cWvU5xxukax1DWb0PnSe/i4+VpZ3KSkqVGb7QKJKDOz71SXF3ILXMwUakpVdFZvVglYxIi6dkkiG8PXnbYV52NPec+gRMvgLkOzcLFMgjoDCF9QOJaNMtLKf3dlvt3A7XWWKfP6Y9Mo/wr8Pe9MwEBAQEBAYG/DxbLHx8JcwWxFDqvgBMvQbNXQWEVJ7cvMCvt7OtCVrGW2ZvPcTiziPgwL6b1boS2jgLj6PVjFKiep9R8qdrjJMCrfvBvP5C48EiPKkYz9vQItBbrov9Cbhn+Sl9ahbTiRM6JWs9vHdIaldSL8zmlpGaX2kTOrgv57PltCzvffxyzppq0vJu06jmIH1d9g0Gko0BTiL/KD5XUi1WHsll28BrFGoNNINVmvnE7xVo9jyZG0z020Kk75vdHrvHRI60cUhVDvN0I9rIXQvFhXkzoHE37aD8yb5Q7vV61NvYe9V0TZN7xmIP7oA/sQ4lHR4oNMqtboUSKvIYG1ZXcLcv930uJtm5/P/6oNMq/CoIoExAQEBAQEPhrYzLB6NGQmAjPPltncXbX7bYlbtD2Y7tNty8wqzaXdpWqphApWWq+2pPOq4Oa1j4dsYiGQQquGpfzwI/fYrRUX48VLYVlIdC59r7aXDbAu4aG/HvgXCqO7LdtH9U+ArlEwaxOsxi9ZnSt40xLmMHK5GyboFm0L5NPH2zK9m/eZcIP39Z6/sSkSXR+shMP/NTfTgS2CmnFtA4zWDdlACq5ktkPtMBTIcVYx7r5UG8lBWX6auvKHm4XjslktktVjA/zQimT8NLAOJ7r05gijR7fm0KuslfamsmdbJ+nt6SUPl6HmBB9hpgGK5z/3gX3BrHcUZi5BUFI35s/fdBIg61uhevSSck6aDdfV9wK76bl/u/hr5JG+VdBEGUCAgICAgICf21mzYLvvrP+HDoEX3/tcrriHdltWyxw/lMIv995jY4Tbl9gVtrZ1yVNzJkphK+748K1sgl1sJcbPWKDCA3I5tlfn+LcbY6QtzPaE74IBG8XTEG/KoHnb0Cp+RLTDWpiQzwRi0QkdYuhV5MgctUVDGo8hEltJzHv6Lxqx0lqO5nuEf0Ztu5WjdeR/bvp+8kEMjNqdi6UyWR8+PGHpEal8vjGcQ77K3uhTWo7mTl955BTYuD0tRLa1/er03OXScRMWnbUofaral3ZrP5NCPFys4myCZ2j+WZvBvN2pREb4mlLeazslRYf5oXSlEt7/Speqb+fjh5nkIhMYAIKfgOPh53csAcE9YDc3yCoG4T2h9B+4NMcRFZhdLfcCl2y3I8L+kPTBf8qaZR/Ff7edycgICAgICBwb/O//8FHH936/1Wr4Pp12LWr1ojZHS1gLWY4Nt0qyi5+AX33WiMVteDrLmVa74ZsS83lXI611mlF8hXGd4pm5g+naj1/QudoVlQxhYj2V/HhQy3xcru1cPVWyhibGM6ohFC0RjUaQwnfn13IU6vfx1BDc2AvsVWMjfGqdRpcN8ITubC5SilahbmUbx9PQCIWse7EdTrPthqHTOkVw5y+H9Atqjtz9s92iGI9mzCT7hH9Gb/wJCVaA6byYop2fEN5Sg1OgTcRq3x44s3P8O4sYd666kUfwLyjX9I9uhtDG49g1/kbXCnUuPzcn+waw6J9GdU6KEIVl8bGgew4n29r0v3WxlTMFuyMPqLl17nP+wCTIk7iu+M4b9ZzMmDWzxDlRJQBtP8CFAEgd+zafbfdCl1pAv1H8ldJo/yrIIgyAQEBAQEBgb8ma9fC1Kn222QyeOONWgXZHS1gRSY4OBEyl1l3ll6EHf2hz06QOSqaW2mRBoo0Bh5pH8kTXWMwmMwsT77Cd4evsuyJBEZ3iKzRPbHqIh/s+2z5ussZ3yma/+24xOKJLdl55ReGrB7jUi0XQCc3WBYior6s9pS+b9UwNR+Kb+sC7af0Zee5PGb9eAqzxSoYK+cyeOUCOkd0Zt3IdWgMGop1xYR4hOCn9GdVcg7D5h6hWFNB2altFO9ciFlXVus85CGNCHngFV5+fDCDV/V06T7n7JtD98j+hHi7kRjjZ3VhrOW5j02Ion20H6+vT6l1/Pm701k4vj0BHgoS6vvx8PwDttq1+vLrDPbZzSDvvTRR3jT20NYwWNYvVvEvciIyPBtUe9of4VZ4N5tA15W/ShrlX4V/nCi7ePEiP/20jrOpqajVatwUCqKioujdpxe9e/VysIo3mUxs/HkT27dtJzsnBzc3N5o3a8aoUSOJiAiv9jqHDh1m7bp1pKdnANAgJobhw4fRrl27P/T+BAQEBAQE/hZs2QKPPALm2xTC119Dz9oX6nVewDbyQHpoFGRtsttnKTqB7spmKkLvRy4V2yIIGr2RbWdzmb/bMS1yQuf6jEmIYkTresz64RTvjmhO2yhfFu7LcHKs1RTisYWHbIv8qn22vJUyNj7TmW6xbXht54ssOFZz1KgSMfCKH7zuBxJRzYKsyARP5cEPTvRS65DWeMi8mPXjbswWq2Bc8nhL3t33im0uuy/vZva+2TQLaoaXwgt1hZpO4V14odNbfP/rfrLWfEzhpZMuzFqEV8IIfLqOpWm4H+UGNSdzXTnP2gtNXVHCwn2XOXG1mH/1aMCkHg1oE+XDon2ODbeTusfQKzaIwVWaTtdEZU+1nrGBWIBXuqu4cvJ7Ooh+pbmq5jRMGxI3COlnTYs1G0FSt5qqv6Nb4V8hjfKvwt//Dquwf/8BPvjwv5jNZhrExNC0aRxqtZqzZ1M5m5rKyZOneH76c7bjzWYzcz74kIMHk3F3d6dt2zaUqkvZf+AAR44e5e233qRx40YO11m/fgPfLFyERCKhZcsWyKQyjp84wVtvv8tTTz7BoEHVdFwXEBAQEBAQgN27Yfhw0N9mePD66zDOsbbIGXVZwF7NzUK26wUoOmC33WCR8PzV6aTdCGV8p1x6NQnCJDEjEot5b1Mqy5Odp0XO+P4kYxIimda7EXMebM7Pp3IYlRBJv/hg8tQVVhc5LzdMFovNFKJSGNze36xEa2DzmSw8fI+4LMiCJLA8WEIfd1Otx+7QwLhcuFaNN8iszrO4lKvD001GidbA2MRwdlz+xWEuZouZU7m30gVPpZ/i0tIL/Pbjb5hvF9ZOkHgGEDBoOm5RLQCre2WBprDW86pSqC3CQyG12dhnFWvRGcyseDIRjd5IYZkelUKCTCLmQFoB14u1Dk2hayK7RMe7m1KtVvnxX9DXfV3tJ8m8od5gCB8OYf1BeuetG/6uboV/dhrlX4V/jCgzmUzMm78As9nM9OeepXv3brZ9V69e46WXX2b37j307duHFs2bA7Bt+3YOHkwmLCyU9959Bx8fH8Aq7mbP+YCPPv6E/839DInkVsXstevXWbR4CTKZjLffepMmTWIBuH49ixdefIlvFi6iTZvWhIaG/v/dvICAgICAwL1CcjIMGmRtBl2VJ5+0ijIXcXUBGyAtYnmD15AVZdht15oVTMp8mV1lbaHYavgwNiGS5/o2Zs+5XKeCDG6ZcFzILeX09RIS6vsR4u3GS2tOM6t/E6asPIaXUsb/RrXmqcVHOH3dXjje3t9MIhYxoEUAA1e+59L99FDCj/UU+IkqajzOIpLyk7wtD11MpjrJNKntZLpH9udQWjnrp3Rm4qLDjEoIZfCqGhwXTcARYAds021zac7uzfvi23MiEqWnbVtZhRF/lZ9L51fip/SlrML63Cp7ti3em4FEBN8fucaei/k2M47YEE/+N7pNncb3cpNRVmGkRGvgw/OtWVy/GlHmFmyNhoUPh+CedY6IVcff2a3wz0yj/Kvwz5CewLVr1ygpKaFevXp2ggwgIiKc7t27A3Dp4q2+HuvWbQDgsXHjbIIMoFOnjnTo0J7s7GySkw/ZjbVxw0bMZjP97+tnE2QA9eqF8dBDD2IymdiwYePdvj0BAQEBAYF7nz17oG9fKLPPozOPHo3m08/JUVdwPkdNTokOjd6I0VR9BMaVBWyYLI/vGrxArMJekBUbPRiT/rZVkFVhWfIV9l26QbGT/mPeShlP96zPrlkd+WR0FM8NUBIVVAGiCsJ85EjE4O8uRywSkZxeyKL9lxnXMdphnNv7mzUO9iC96AKn82purCzG2ndsWz1qFWQGVUPWBn5P256bWDRsOa1CWtntbxXSihUjVvJe79kYDDLaRvtyLruUFU8lYLCUVZ9SmA7MA34BdDVOAYBGjWOZt2oDAQOn2QkysLpXusu8HOZWHVV7ocGtdMOnezVk85kcPtp6gcOZRaRmW90RL+SWoZBZ3TEdsdBedYa3wr5AIbJ+Fre7Y+4tbUWh8da5Omkg5kZToM8euP86dJgHYffdNUEGt9wKXeGf4Fb4d+MfI8pkMtfeFnh6Wr8UcnNzuXbtGnK5nHbt2joc16ljRwAOHz5it/3I0aPW/Z06uXyOgICAgIDAP56tW+G++6DU3hLeOOx+fp7+Lg99dYjE97Zz3yd7SHxvOw/NO8CmM9lo9M7z7mpbwEbLr/NdgxeIUWTZbc81+PFw2vsc08Q5PW/+7nT6xocgrlKCHu2vYt2UdgQGHWfI6l40+SKazovb0GhuFF0Xd+VC6TbeGRGLxWImqVsMYDUY6VDfj9EdIu3Gv72/WYX4PCO+H1ztfYA1XXFLPXjTv/Zm0KsK+zHyylyMPm0Ro6BVwH3sGLebS1Muc2rSaTKnXmHTqB1cvd6C7nMO0O2DnSR9exSzxYKnm4xibZHjoNnAcmApkF/z9QFEEhlPPPsi67fvo0/PHk6PMZktrEzOZlqHGbUPiGMvNIBijQGjycLCfZlOx690x6wkXJbL1KCV7Ix9iu8bvsijAZvo62XtA3a7O6YRKasL+7GyoB+j09/moZzV6Fp+DEFdQOxCz4E7oNKt0BX+CW6Ffzf+MZ9WcHAwISEhXL9+nV27dtvtu3r1Grt27cLDw4PExAQAMjIyAYiKikQqdXzT0KCB9S9F5uVM27aysnLy828AEBNT3+GcwMAAvLy8yMvPR6NxPYdZQEBAQEDgb82GDTB4sEPKoum+/rw15lWe+f6MQy+jSkv7dzedcyrMalrAxrpl8n2DFwiX2yuIyxUhPHBpDhcqogFr6mBcqCfto32JC/VEIhaRkqVGpzcRG2J9iVtpfPHe/leYsH6sgyviiZwTjFkzmllbZ4JYT7fG/jzRpT4lWgOPLTxEUvcYPnyohU1AVu1vphEfZFfhFAq0N6p9dD2UcCIS+qiqPQSAMrM7/7r8Ii9em8qlIhGZBRqUcimBnirS8414yoKI8GzMjlQTAz85xBc7M2x1bteKtMSHefP1ngxUsptW7RbgKrAamA9crPn6lbRI6Mpv+w8T3mss3h4q2706Y9nBa/SIHMBTbSbVOGZlL7TlydfstrsrJGgNpmprC5cdvEyHKE/eaXuepfX/zd64x5kespxoRbbtmAd8t9vcMW9PWZ2dM56Xrk9lf1krTmdpUGurb9p9N6jqVlgT/xS3wr8b/5i4pkQi4dlpz/DW2+/y0cefsG7dekLDQikpKeHs2VQiIsKZNvUZW6Qs/4b1C9Df39/peJXb8/NvfaHfuGH9s4eHB25uzvNh/f39UavV5OXlEx0dVeu8y8rKKS8vd9h+40b1X9ACAgICAgL3DOvWwYMPgtF+QWseNoxfXvmIJT+m1nh6paV910YB5Kp1eCvlNoMAZ3bbLZQXWFL/dXyl9hG5C7pIxqa/RZ7R/2Y/sChGJ0Si05so1OjxU8lRyMSsPHQVtc6Ah8K6hKrO+OJ2vjzyJYnhicQHNmNqn8b0ahLE2hPXmfH9SRLq+7FgXDv0RhOaChMquYTw8N1sKnwHq/pxRITVXfE/frVHxwoUzXmj8N8QGcMwsYhZ/ePYn3aDxxYecnC7G98pmrVPd+axhYdsJhiVbpCfbr9Ix9Ao6qXU4/q+65BV3RUdkXoH49vrCYobJbL1upinukXj6Sblx2PXqu0pVqI1MH7hSRZPfIdOEV35JPmDWnuhVb0XlVzKlULnL8HDZbmM9NqCx+ZRjJEUgqfTw+jhdZxGCSrGVnHHrI7/D2MNwa3w78s/6hOLi4vj3Xfe4r33ZpOWnk5autUqVyqV0qplS4KDg23H6m6+rVMoFE7HqhRdWu2tpGmtTnfznOrzh93cFDePramBxS02bNjAqtXfuXSsgICAgIDAPUfTpuDjA1VfNo4cie7rhXzxtWvp/vN2pdEmypcBn+7BbLlled4nLphXqixgxUVHWR7zCp4S+3+DDV6teSRlBkUmb6L9VSyZ2IFDGYU8tfSIU9HSMMgDL6UUiVhUu/FFFT45+AlrR67lpe0v8F7v2QR41MfTTYa7QkqF0YRULGHfpXxGrn6dM6XfWpWXE7zF8G0wDPGo/ZrbrjZl6dlWmCTb8PZ0R+XhzqObvuVkrg6RTIlI7oZY5oZIruR4nhsnUs4zol0U/+7qz6RFZyktzEfre47n1+0i58IJOrx31aV7rUSmUOKe8BBe7e9HJLWujyrdEXvGBuGjktMqwqfanmKZBRqGzT3CnAe6s2fCMAo0hWSpC/BT+qKSerEyOZth6444CKakbjFsOp1Nt0aBtm0STPT2OsRov8108zyGuJZWASbPWPYaB/HqN8e5Wq6s9V7/v4w1BLfCvyf/KFG2e/cePvt8LrGNG/P889OJjIygsLCQtWvXsXbdek6fPsPs2e+5XH/2/8GQIUPo1auXw/YbN27w8iuv/gkzEhAQEBAQuIs0agQ//2ztPabRwMSJsGAB6jJDnXoyVaYUpmaX2lIbxyZG8fLAJrYFbKk6FtnBb0F97NbJgZ0xdFpHWPpZzEVaW9NmZwIhJcvqwnjyagmvDY5HrT1Z515aGoOGvVf38tO59eTmtOKLnRm23mYtI9x5ftsT5Jl+q3aM5nJYEwINnb8ztlGigYkLYM3hs8BZl+ZXydwFMLfK/7/+Q51OtzHswQe5EDkcTWXKYxUW7cukf3wo7aL8WHXoCkndY6rtKfZEl/p0aRSMTm8iSBXKr6f1rDl2mfM5pXY1ZJWMTYiid1wwTyw5TL/4EDrWM9PW8B1j/TcRIqvZZt8s82FbeU9+0dzHjAGPMufbo1wtr/338P/bWENwK/z78Y8RZVlZWXz62ed4e3vx6qsvo1Ra33iEhYXxr39NprCwiMNHjrBt23YGDOiP2839FRXOHYx0N6NiSuWtvwjKm9GziorqbXh1uoqbx9b+xgXAw8MdD48772khICAgICDwl6dDB/jhB2vD6I8+ArGYEq1j6n5NFGn0tpTCSipTGwc0C0Ell6IKCIK+W2Fbdyg5Y23k220NcpGSpG4xnM8tszVtrollyZdpG+XD8Nb16txLq0BTgJfCi0+TP2T9I9uZt8vaUPr57w8jD/qEPNPuas8d5QlfB4CqltXbkXR45HNIz6vT1O4aDZs35MMP5vLxKTmaavqApWSpKa0w8Pr60/x7cDwnrhZTVmFkwbh26PQmijR6/D3k+KjkmM0WRi44gNkC0/s0YnSHKEK8lDWm78klYkYlRLIi+QpTm12lY/ayGud8uLwplgZPsLeiJ58dvQ5A/yw1Sd1imLrqRK33LBhrCPxe/jGibM+evRiNRlq3bm0TZFXp3LkTh48cIeXsWQYM6E9gQAAABQUFTser3B4YeCssHhBg/XNZWRk6nc5pXVnleUFBgQ77BAQEBAQE/rEMGGD9uUldezL5quSUVTgaLczblWZveqDwg15bIeU9aD0HJAqkQK8mwXRqGMC4bw45jOGMr/dmsPLJRPI0tRR03Ya/yh91hZpTuacoN6iJDfEkJTuPfPnb6EqdR9ykwAcB8Kxv7eN/tgVmroBqTCn/UGKbNuGFf7/OwAFDmLLyJJkFTpwaq6DWGhibGMWLP56iX3wIg1qEYjJZMJnNhHq7IZWI+Hp3OsuSr9jSEx9fepQnu8YwvW+jWtP3+sQFc/paCWFtRlOc/SY+5Npf3+TOj0W9WFlwH+1bdeOpljEsmbvPJu66NrKuBW+vS7wdwVhD4G7wjxFllWLIXeXcnkjlbt1edrM3Sv360QBcvnwFo9Ho4MCYlmatR4uOirZt8/BwJzAwgPz8G6SnZ9C0qb2dbn7+DdRqNUGBgaiqmYeAgICAgMDfkpwcWLUKpk0DUe1CptLS/nbXRWfc3kOqKilZatRao73xgTIE2n1qd1yxRo9YLHJImaxsBu2hkFJWYeRCbhkms4WULDXFWgMhnv60Cmnl4LrojNYhrVFKlZzJOwNAobYIhUxHnvw1KiTODU2CJfBdCHSrZdlQqoUJC+BH1zTlXcU/th1N+jyCR4N2rMmX0Ncs4ujlmgUZgFwqxkMh46vH2qPWGjCZLJzLUfPFzjRbk2dn6Ylf7UmnRbg3XRsFoDMY8XeXo5JokenLkHqE245TyaU817cxB9MKaNt4Clz4NwDHymNZUTiAjcVdaBAaxDMjGtAzNoj8Mh1bnu3mIO5+r7GG0WRGbzKj1hop0ertzGgEISdQyT9GlPn4Wl8vXUpLc7q/sml0UFAQYLXQDw8P59q1axw5ctRmlV/J/gMHAGjfvp3d9nZt2/LL5i3s37/fQZRVd46AgICAgMDfmpMnYcgQuHoVdDp48cVaT6m0tJ+68kStx97eQwpgqM8utpR0pMIid8kVTymXkHHjVsqk1YExnFEJoZQb1BRoCvFX+aGSerHqUDbLDl4jT63DaJIwq9MsRq+p3exjasJU5h+dj9libXotwsKOwmeokFxwenxHN/g+BOrVUuqeeh2Gfwzns2s+7m4i849EFdcV97huyPzqcQ0gt5z/PtTS4bNwRnyYF4EeCo5kFvH6+hTO5ZSye1ZPPthywaVawkpzlwc/Wc+j/huYGPgzRT7d8Om3FrlEbBNBpTo9baJ8kZmeokKXhrHBZEJVzXlSa2SG0j665uHm3Dnl9xhraPRGtqXmMn9XuoOgqzSjEZwSBeAfJMoSOrRn9ervSEk5yy+/bGbAgP62fefPn2f9ho0AdOrU0bZ92LAh/O9/X7Jk6VKaNInFx8cHgAMHDnLo0GFCQ0NJSOhgd53BQwaz5detbN7yK127diE2Nhaw1rR9//0PSCQShgypuQmkgICAgIDA34b162H0aKhs7/LSSxAaCo89VuNp0mos7W+nsofUWxsrI00Wng9exjPBq9mmbs+kzFdccsVTKST4e1hTJqP9VSye2JKdV35hyOoxDjbs0zrMYN2UAahkKjyVUgZ6DuGpNpNqtMVPaptE18iuTN8yHYDmQc2ZsH4sapNzQfa4F3wRCPJaAik/HIIJ86FMBxKpjKCQUAot7ogV7iCVIZLIABGY9LSPcOd0Zj5l5eWY9TosBh0WvRazEkgLmgABAABJREFUXgs3haIjImTegbRr3oQKj1CuyCJQhDdF6hXkcOTYxCh6NQniQPoNHk2MpKBcz7bUPPRG69hVo45P92jAvks3bFb4caGe6PTV9xS7nZL8i3icWs7euMW4iaz1+u4lm/h042bG39ePScuOkpxxq96veT0vnu/3PokB/oTKpIQ6eo/USE3GGkaTGYPJjMliTcks0Rrwc5fhqZDx7i+pTn9/bzejEYSZgMhisdTyLuPvw6LFS1i7dh0AkZERRIRHUFhUyPnzFzCbzdzXry//+tdk2/Fms5nZcz7g4MFkPDw8aNGiOWp1KSkpKchkMt5++01iGzd2uM669RtYuHAREomEVi1bIpVKOX7iBHq9niefeJzBgwf97nvJzc3jqaRJLJg/j+Bgxy9GAQEBAQGBPxWLBT78EF54wfrnqjRqBKdPQzVtZ6qi0RvZnprnNHVsQudo2kf7VempZeHl0IU8FfiT7bhdFb1oN2oTpRWWW6ljblJEIlDrjBRrrNu8lVIqjGaSvj3KnIdieXffKzWKrEltJ/NBvw/ILjLhrZSjMWjYd30LHx6Y4yjiEqbRNbIr/Zf351KhNTMnyjuayyWZDuNKgI8CYapPzc/FZIZP9geztfxhJEEN+M9jA/n3tiz+N7otvT/a5fScp3s2JNpf5dATzGKxgMmI2aDFotdhMRl4fURrssthYXI2zcJ9eaZXQzo3DGBLSo5Th8QpvRrSo7E/JgwUaYso0Bbir/TD282H01fLOZOlZlCLAMoNatT6YkI9/MHizspka9SxcbAHs/o34aF5B2q871i3TJICf2Sozy6kIkchuaLgPuSdviI9v5wvdjpmR91tEaTVG9EZzGw/l8eifRm25/J0z4bUD3Bnxve1O3N+Pqo1A5qFCKmMfwN+z/r8HyXLJ4x/jCZNYtm8+VfS0tK4fj0LpVJJfHxT+vXtS7duXe2OF4vFzJo5g40bf2bb9u0cOXIUhUJBx46JjBo1ksiICKfXGTZ0CKEhIfy0dh0pZ602tA0bNmTE8PuF1EUBAQEBgb8/ej1MmgSLFjnu69wZfvrJJUEG1aeOucnELNidzlsbU2+aQFh4PWwBEwI22J3fXfEbW/atJmmrv21bpQV9+2hfkr49SmaBhkldY5jWtxGvDI51qRn0vKNf0iWiK9k5LRmVUJ8XfjhPp4at2T1+D3nlueSU5eCv8kcpVTL/6Hymb5lOkc5aZxXgFuBUkPmK4btQ6FNL/Vi+HkZmQ1HLMNY/8gYLdl7D3S+Ii3kXcZNLqq3FW3bwMuundHboCSYSiUAqQyKVgdKLsYlRjOoRS3axlvE9miIRi1iRfIV3fk5lSMswO4fEQE8FgZ4KzFSw5tyPTkXprE6zeLBdPyauG8/6C+vt9lVGHd/aeAk/VfXmLk3d0pkWvIL7vA/W+Gy8JOV8uS+DBePaM29XmkMaZVVHzt8rgjR6I2UVRj7ZetHueUrEIkYnRPLkEtf77AlGIQL/KFEG0DExkY6JiS4fL5FIGDZsKMOGDa3TdTp0aE+HDu3rOj0BAQEBAYF7m+vX4aGH4ICTiMe4cbBggcuCrBJnqWMavVWglWgNiDDzVr0vGev/i915ZkQUxX/OrLUhwK3mwilZamZ8f5IxCZEsmdiBKSuOMalnAw6lF9A2xoNxGz5waV7/PfgB6x/ZzsrkKzzYNpyZP5xi3fFsvn2iDeqKdD5L/owfUn+w1ZA1D2pOQVEBWbosh7Hi5LA+FBrWYjp5WAcPZMNVI5BznHKDGoVMgo9KjslsYUXyFcZ3inaIhgGUaA08tvAQSyZ2qLYn2MTO9ekRG0jK9RKWJ1/hpYFx7E+7wa4L+Vwt0vLFzjTm7UpjYPNQHmxbj8bBnpgsFczcOoP5Rx2F7ImcE4xeM5qktkl8dN9H7LmyxyZOT+ScYML6sSS1ncy/h7yNXOIoKF0RYwaLhHVFPZiX/wCXKiKBUru+dbdzN0SQ0WQm40YZZ7NKHVooNA72qFMqplMzGoF/HMKnLyAgICAgIHB32L0bHn4Ycu2txxGJ4L33YNYsl5wXXUEll1pd8aJ9UBybRD83e0FmQUxh8wU8uDmGEq3zXlnLk6/QOtKHxRM6YLbAvrRCYkINdWoGbbCUceZ6CQ+1C7dFoAZ+msyYhHD+23ceb/aYQ6G2CEuFifsXDiTfzbF52EAVrAwBL0nN1/uqBJ7Jh4oq0Z9CbREpWTq8lDLiw7yqjYZVklmgYejcfcx5oAWrn+pIYbkejd6IQiYhwEPOlzvT+Py3i2x8piuebtkM+HQPYxIi7aJjvio5CpmYM9dLEIvMrDu/3qkgq8r8o/NJDE9kUrtJvLf3vdv2fUnH8C5EKXvZBGWcWzrTglfS37v6dEY9Sr7N78c3N4aRZbBPFXPWt66SuyGC9CYz/u5uLNp32mGfh0JKoab6nrXOcMWMRuDvjSDKBAQEBAQEBFzGqb23mwTFF/9DMnMGmEz2J6hUsHw53H//XZ9DqbaCPiUvonTbZLff8n/snXd4FNXbhu/t2ZAeUmhp1NCkJ9SAgHTsonQQKR8IFkDF3rArKEpRKUoVfyiggIJCgACh9yYhhZKE9E3ZZOv3x5CQze6maACRc19XLpKZMzNnNizMs+97nkemINr3M6atq+dUkIHUZrbjXBr9mtciM99AkclEhr5qYdDZhel8+Gg40384ydsPNKd9iA/f7L7IVzviWRAdT+NAd/yMV1h7bhyFde3nMsMb3vcFeTla1WSVxNiCHPt9PlpvsgoSOZSYxbiuYTy75miF1bCnuobRLsSbmAvpdGnoy0f/O4tGJWdI+6CSdVirDiQxvU/jknMsiI6jcaA7bholAR4udG/sR8cwX5Cb+GjPB5V6rT6P/ZyfH/+ZD2I+KKkeFjM39mM2Pt6f0JruLO58iHtzX3d6ngI8MdSfzODfW5OUb589C85z64r5pyKooMhMfpHJYTUsr8hUbiumIypjRiP4byNEmUAgEAgEgkrhyN5bayjkq50L6HFom/0BoaGwbh20alXtc/g6+jxPKd9gsNdOm/1WmZLs1t8zeqk7pVsWSyPZ3QczNCKIQoOZpMwCPLQqnr63IYWW9CrNR6vS8vWRz5k3fBprYlN4pF09Otb3Jb/IRGZ+Eb+sXsI7h1/A0txWhGhk8LU/jPAo//wZZngkGXbo7fe1DmyNq9KDcym5vPLTCTZN61pSIRs8L8ZhhctTq6KGWkFOkZH0/CLOpeax8Xgy4bXcbYTEvD8v0LdZAG4apcMq2brDl9Eo5WTp06tUWSwwFtDcvznHU23bKxOyE1ArzXi4KFG37411+9vIrLaiSmfxINF/Ip73PMvI786QlO9YcJfOrXOWM/dPRVCBweS0GnY+Na/ctX2O5uuhFY/kdzvib4BAIBAIBIIKKTCYmL3J1t67XnYKi9a9Q3hagv0BfftKFTIfn2qfw+p9F/k86EP6e+2x2V9kUbLD/yuCvfoDuxw+kNfz1rJsbAf2x2cy/ruDNg/NLep48N24VlUOg35tx2vU8wglIqwvSrmMh7/ei9Jq4uyPH3PJ+3foaHucnwLW14KOjos8JZwsgsFXId5JwWdaxHRWxSZjscKlLD0Tvz/EghFt6RDqw7e7L7L93DUOJWbi66bBw0VJn2aBtA/15a1fTrHm4GUaB7oz74k2gL2QyNEbGfGtVHHbG5fOjnNppOoKS0KdXx/UlCvZhVhVVassZhRk4KGxVaINfBqwZdgWtl78jY/2fMTRlKMs8IcJ123rixQ1oMnzyEKfZvOedFYsOH7d3MUxYzqHsO7wZSZ1b1AivDMLDPhcF5R/nEn9xyLIVa3Ex9WxgXlFa/vKMjGqPmph8nHXI0SZQCAQCASCcjGZLWw7k2qXt5Sv1uKtt68EWF5+Bfmbb4CigkVSf2MOP8TG8VXwB9xXxvihyKJiQuLLpGY05psOSib3sH0gr+mmpoZaiUopZ94fF/g2Jt7uGieu6Phm5yWei5zByJ+HVTin0mHQc2I/ZsOQfny45RxzBwfTtfcAcoLO2gmycDX8WhtCKyjUHFME0/VyIrlO4sMmtJ1EVL2+3L/+hsNfTFwGQxbuZcW4CHo3DUCnN5JVYMTbVYWHVkVuoRGdvohNJ1OxWO2FWFkhcSlLz/S1x3igdR1eGdCU/CJTSbUs0NOFudv+YkSXqoluX1dfdEU6asigoRoSZd78Nvw3Poj5gEWHFpWMm50J99eAL7Lhi5x8htdI48NwLQo55QqyYRFBRIb5YrVa2XfRXngXt2/+U1w1CkwWa5WdLssyPDJYOC8KABB/AwQCgUAgEJSLwWxhwY6LdtszXT15ZtB0zDLpcUKnduXNp96j8LXXq1WQFc/h2x1nWBD8rp0g01s0jE14nR257TCYLHi6qAmtWYPx3x2k56fRPLpgLz0+jmbM0gPsOJfGyE7BhPg69p1fvu8yXev2ZWLbieXOpzgMesFByeDiyHUnxK07dtK9c0dyap6FXrbH9NTCnroVC7K8BjOp1f8I8watoFVgK5t9rQJbsfT+FbzY8R1GLz5mI1BCfF1ZOqYDO/9K57GFe+n4/p/0/3wXHd//k8cW7mXXX+m4qFRM6VEfsK3oSPeeSIdQH57sHMrkHg3YObMH7z/UkkYB7uQXmXBzUXA+NY8jSVnk6I0s3ZuAl4uX3Ryd0TqwNVq5ii6FJ4gLkdwmn243juiEaBtBBpBkgnrxMDsLci0w/9B8NpzbyOQeYXzxRGua1battjWr7cEXT7TmmV4NkSNj/o6LzPjxuJ1gOnVVxzNrjjJ701kKDM7XnFWEWiEnI7+w5LUrS7HT5YSoMD5+tKXT+YrgaEEx4m+BQCAQCASCctHpHRsaAOwLasnnnR6n37kYJj44iwSfOkwo42zn0BxEq0R93eq+snPIzojnngbnbbbnm114MuE19uW3xFOrYsnoDszefIaVsfbViVNXdTy75miJFf7geTF2VZccvZHRi4+xesL7dAvuxod7yg+DLrZ3B/jxxx848OXLWEKNMND22k96wHx/UJVj6GFEwyvJz3IyvT9PqQro3/BBeof2J8+YQ6E5Fw+NFz5aLy6mFfHCj+euB2ZLRIT6sGB4Wz75/RzLndz7jB+PMywiiJf6hfPVjjiyCox2FZ1Z606wYERbtp255rDKNK5rGH2aBnAuNReDycLhxDymd5zJ8J+GOr+x67zfrAcuWyP50v9G298z3ip6xM51OL6sZPp4z4cMbjzIYW5d8d8no9nCbxdSy61OwT/PK1Mq5ITWdMPPzaVip8tHWrJ2Qkd0hfbzrdYKmdksZQQWFUnfq1SgVkt/VvOHJILqR2a1Wh03xAr+1fyTxHCBQCAQCKrCueQcBn/yJ0VKx45ycosZtdlIoUpys/v92W40CnAHHJuDgPSAPyEqjF7hAZWqFJxL0dFnzi7CXS6yMuxlvJW55Jq1jI5/k0MFTQGY3KMBIb6ulVrH8/GjLbmYls/CnRcdGkFEhPowf0Rj8ox5FBgLyCjIsAmDXnBwgY0gIxZkW2RYA6wwFrj+UsmA93zhhQq6/NLNNTkauoSG4T0YtXg/CRkFDIsIYny3MKavPUYNjZKoRn70aR6ISiZDo1KQX2Qip9CIp4sKjUrOjnNpPPdDxaYbnz/eiox8A29uPA1IFbZlYztw9FI27UN8mPfnhXJFzfO9G/Fw27p0ev9P6nlr+XVaB2Zum+HUFr+xCtY2CKKFyf6cZqUnvudyyHHSplmWS89coq5nXaf7CwwmHpm/t1IZYc1qe7B2Ysd/VKnSG0wUGi38efYai2Pi7f6OT4yqT89w/793jYwMSEyES5dAq4X77nM87q23pK+yzqelkcvB0xN8faV1nr6+0le9ejBjBnh7V31+Ajv+yfO5qJQJBAKBQCBwzrVrBI97knnn03jqoVcc5oxZ5AoK5Tc+iS92tnNkDlLMqas6pq46yvDI4Eq1cHlqJZVzpjCM4fFvsyD4PaYmzeBIQRNAsrcfGhHEU8sOlneaEpbEJPDdkx0YFhlsYwThopZz4ZqOBgEuqBQKHlz+ICaLCQ+NB7oiHSevnbS1c7cCfwC7wephhaGUCDKtDL4LgEfcy5/LKX193iv4gKKTPnQrulpSxSvOUesQ6sOfZ6+x6UQyaw9eQiaD5rU9ebFfOAt3XuCRtkF411DzzS77dXKOWLjzIiufimTd4cucuKIrqeh8O6odBxMyK6wyzfnjL0Z0DC5ZTzXp+5PMH/ER3YKi+GjvByWVRW85zKtXk8fVmcgdCDKAPM8WuMt3V1qUZRZmlyvKyqvqlqU68sq0aiUqhYX+LQLp2zyQHL0RXaERL60KDxcVamUlq2EpKbBrF+zZA8ePw8mTcK1Upl23bs5FmUxWviADsFggK0v6KssLLzg+Ji0Ntm2DNm2gYUNJ2AluGkKUCQQCgUAgcMzGjTBuHC7XrtEbGH1oI0vbDS73kGJ7b2fmIGWpbBuZh1ZZIgJO6Rtw77kFGK03Fmc1CnCj0GCu0gO53mDm/c1n+eV4MiBVjJY/1YrLhdt5ZvWH9G/YnyntpzB2w1jHJ7EAvwCHkYTYE8D1pUMBClhfGyIqiMIq8OuPX9tlvGlyKRGGHloVnz/eild+Pkl+kYlJ3RvwUOu6Ng6COXoj286kci3XgKdWVeV7zy8ysWZ8R1J0hWTmS+YdAZ4uvLb+VIXHmy1W1h+9woRuYUxdfZSYuAwGzN3P2w9EsXP0QLIL0pFfWEhgwnwURscRAwaP5phbf0yuWzMu76tXqXkD+Lh4lbs/R3/rQ5uVpdoQa2iU1KYCW02Q2gyjo2H9evjtN7hwofzxly4536fRVGG2ZfD0lL4csW8fDL3elurtDR06QGQkdOoEXbpIGYSCakOIMoFAIBAIBLbk5cFzz8HXX9tsnrV9MQfrNuVkYAOnhxbbezszB3HEgug4ewc6kx6UNx5u1Qo5E6LCmLrqKICNIANw0yid5kY5IyWnEBeVnPYhUuvWx4814e1ds1h0WGrDS8xO5MBTBxjfdrydEQUWYD1wDMk27RGglrSr6XWHxZAKDD0Wpw0motsSlv15iR8OXi7Z3qy2B+O7hbFpWld2/5XOkIV77Vs/u4XRp1kgEaE++LqpOZucW6V7T88r4pWfT5CcU4ibRomfu4bnezeutLD7dOtf7H6hR8l6qktZekYvOUyE2yk+Df2aOjLHIiPH6ssfmmn8kdOH7QsziH3Zq0oRBF5ar3LHFFdUK8stDW22WuHAAViyBFatghwHaeDOuHxZqoY5Whumrto92xAc7HzfyZM3vs/KksTjb7/duGanTtCzJ/TqBe3bi3Vr/xAhygQCgUAgENxgzx4YMQIu2gsqg5s7XnrnD/+l7b3T8wx/v42sMB2294a6D0GLVwGpGtErPIDhkUE21bfiLLJgX1dqulXu4bQ4PLpJoDuTezQgPc9AsK8LfySsLxFkAFmFWfRd0Zctw7bQsW5H5sbOlcSDGfgZOHF9YF+gkfRtdy38XAs8y3k+NVvlvHF1PN9nDOQPtZoTV2wfzk9d1TFt9dGSNWWXs/R2+6de3/9/3Rtw/FIW9XxqVOrei6mhVpKcU8iZ62KufYh3lURtjt5IQno+U+5tQJtgL9btOcJDss95xOcPh+Otcg1fJt/P/LRHyLe4AmkAVTIKmd5pJmp5+VWh0hXVirjloc2TJ8P8+VU7Ri6HOnUgKAhyc8HLy37M0KHQvbtUMdNoJMGkUIDRKH0ZDFBYKInAjAzIzJS+UlIgIMD5tU+VUzU1GGDHDunr1VehZk0YNAgefFASadpKVAsFNghRJhAIBAKBQHpoe+st+OADaf1JWQYPRv7lfIZkQFZ0XIWGBn+7jazwGvzRE3JOQtZRUKihqbTmxVWtZFb/cCJCfVm+L5F2IT4lWWQ5eiN+7i4VPpAXm1ocSMhiyKJ9nLqqQyGXET2zIx/EfGA3/kLmBdp/3Z6J7Say/vH16PQ6nh3/LNtObJMGtAc6SN8OcYNlAaApZ+lNnlnLlKSZ7MhtT7PaHmhUcs6lOBa6xWvKhkUE8dWOOIf7I0J9aFXPC09XdZXESNnr5hWZ8HGtWsXFVa1k2uojtA/2ZmnIK6hzTzgeGPQYhc3eZfP3yeRbbOf3yk+n+XXaICa0nejUKARgYttJDG48CLWy/MpW2Ypqedzy0OaePcsXZe7uUvUpMhJatoTmzSEsDJQVPK77+0tf1Y2vL9SqBcnJFY9NT5cqgEuWQI0a0K8fLF/+z1or7zKEKBMIBAKB4G5n714YOxbOnrXf5+YGc+fCmDG4ymT0q2Vxakdeuv3wb7WR6ZMlQaY7c2PH0RdB5QUNJwCSEOjXPJB7m/iz7UyqjW375B4NGNM5lOlrHTsQempVfDc2ggXRcTZmFo0C3Mg36jiW6vi4rMIs3tv9Hu/veh+v373I2nvdLCEYqUoGPOsFn/qVf49XDTV5MuE1zhRK4cVjOoewMjYJSzk+2EtiElg0sh0LouMcjlu486K0f0dcufdeGkfXLRskXRHFwu5QYhYHErKI9xzE/GBbUXZaH0rAvYvwDe2FymxhQpTWTixdytI7NQoBKYJgRqcXGNx4EG7qiquBziqqZbktoc2DBkkVpfRS6+w6dIDBgyUR07JlxQLsVjJ3LsyZI7VOxsZKa8z27IH9+8s3FsnPl44RgqxKCBsVgUAgEAjuVvLz4dlnoXNnx4Ksc2c4dkwSbNddF5UKOa5qJYGeLjQKcCfQ0wVXtdLu4ba4jawyNKvtgac1BbZF2QoyANcgqNW75EeT2YLBZOG9TWeYtvpoSaUrvJY7p67m0Km+L8MjgxxeZ3hkMLHxGXbugm4aJRkFmeVP0grW36w3BJkH8BjIFPBpzYoF2YmC+jxw4ZMSQTYsIoj2IT6scJApVppTV3UUGsw0DnRs4Vi8PzY+g/Yh3gzt4PjeixkeEcS9TQI4lGjrwtck0J3MvCLGdwsr/0auU1bYbc7pyHZdWwByzVreuDKeQX/NIcM1ArAVS2UpNgqprYli1+jdXHrmEscmnuDSM5fYNWYXj4Q/XClBVkxxRbW8kOmbEtpssUjVoStXHO9Xq2H4cAgJkarSiYmS2Hn5Zcnh8N8kyIqRySTb/EcegY8/lkRZZqZkAjRtGjRt6vi4ESOcn9NRJV4gKmUCgUAgENyVbN8O48Y5XDuGSgVvvgkzZ/7txftVaSN7tqMGl+iekF9mLjVCMfX4A4O6HrqcQrL1BjxcVGjVCjxd1dTz1jK4VR2phdFoJr/IhAx4qV84T3QI4q2Np4mNl8SWQi5jRGQwY5cesLt+XpEJX9cKgsSigX3Xv1cCj4PmervikAos77fmRDA1aQZ6q0uJScc99bwYtXi/XXi1I7IKDLhpnD+yZV1fCzZq8X6Wje1Am2AvlsQk2LWYjukcQodQX/536BKfDmlFocFMVoHkvFhDo8RFJcfTVe00DBlAJTPyWIf6tA/x4e1fSgtoGa9fnUimeSXvJ48hzSS9nqWNNEq3ny4o0wLroVWRW2hFLtNQ17Nuubb3laG4olqZqm61cPIkTJggiZYnn4RvvnE87p134JNP7mx7eQ8PGDhQ+gKIj5dcJH/6CXbvlv7NGDLE+fHdu0ttmf/3f5JBiIOYjbsRIcoEAoFAILjbiI6Ge+91vK9dO1i8GFq0+EeXqGwb2dMdFNx75TFkBYm2O9wbou/6O1vjVSyMtncfnNKjAROjwvjtZIpNC2Px/nFdQlk8uj25hUayCox4uCixYnVoPnI+NY8aKg/nLoD7gB2lfh4IXnUlQ4+oClzB84OeIqzhR3yvN+PtqsZTq8JVraDPnJ1cKmPg4QxvVzV5RaYK9xfnjQ2LCGLxqPZYsXI5S4/3dRv9lbFJvP3LGfKKTPx05ApNr1eRziTrOJOcy9Zno5i9+TSvDWxmJ+xqKrP4tP5iGvi5Yu28lmHf7LMTlEmGWjx/6fmSnx0ZadxKsVRsVV9c2b0pFBTA229LVSTT9d/RkiXwzDPSmrCy1KiaIcsdQWiodL/PPCNlmx0+LK1Hc8SBA1Ie265dsGyZJMpmzpQMQu5y90YhygQCgUAguNvo2lUKo92588Y2jUZqqXruuWproyqujESG+rLlVAqpukLyikycT82jSaA7z0XK6XHlceQFl20P9GiCvutvvLs9x6Ggu5ylp1ltT97bdNZhRefUVR3P/nCMYRFZzOjTGH83OasPJtGjsWMzBLPFyqrYZKZ1mM6YDcNtd54BtpT6OQLqtoPNtaF5BUtmDvk9im+T95j96zl0ehN5RSbOpeQyZ0grnuwayhsbTpd/AhybcpS3P0dv5KsdcSyIjmPfSz1ZfSCJU1d1nEvJxd1Fcp0sNkcpnXu2av8ldIVGdHpTibBbNLIdhUUm5InLCE58DbkxC3Rw6PQqEjLKce27jjMjjVsilm4FBw5I7Yjnz9tut1jgxRfhl19uz7xuJ35+0KeP8/1ljU4OHIBHH5XCqWfMkNoeXe7gvxP/gL/9r+74CZPo3Kkjo0aNrM75CAQCgUAguNnI5fDtt5KxgF4vrR379lto3LjaL6VWyLk33J92Id5kFRjx1Krw0KpQ5p5Fs/M+ZIVlnN08m2HqvpWtFyxOK2zO1oaVZUVsEm2CvOnXPBCd3oRXOe6Cy/ddZv2UfoxvO5FFxS6Al4H/lRoUAs0Hwea6ULccE0CjFcalwnd/rWVCUU1eHfgO9887WFJZWrjzIivGRfDWxtN25h3FFv9uGiV5RSbGVmAG4swsxGKFzAIDiRkFnEnOLXGd3B+f6bCyOLpTCA383fDQKkuE3a97YpgTNp/W6kM2526d+ipPRqzl21jbdWmluS1GGrcKkwnee0/6EMPkoIIZFia1Mgps0eth3TrH+/76C8aPh9deg+efl1ob77Jw6r8tyq5du0aOrnL5IwKBQCAQCG4DJpPzqleDBvDRR9Kn+pMn35Q1LgUGE9vOpLIw+qKNCOhXL4PP/KYjM6XZjDd6tMTYbQsylR/L99qv/QJJtAyNCOKpZQcrNYfFMfH0bR5II383PLSqct0Ft57O5pPeHxEVHMU7G97izKozUPzM7QlRY2B9UPkZZLkWeDgZthZIPy88NJ+OdbswPLIVf55NLxFbBQYzz/RqxKdbz6OQy2gT5MWDretwb5MA8otMZBYY8HfX4FNDzdKYBDy1Krt2wWKzENu1XTfw0qoI8HBx6jpZzKmrOmb8eJxjl3J4bWAzDiXsYqDrBl6pvQQXWaHdeLlFz4xOJlqFtbZbG+YoHuE/RXw8DBsmOZaWRaWSWvFeflnkdDlCq4Vz56T26IULJaOTsqSkSBWzTz6RXsennrprXBz/g+8WgUAgEAgE/Pmn9GnzJ5/AgAGOx0yefNMuX2AwMXvTGbtqV7jLRd5xfwUXk60wOl7QgBGnXqLuxQuM72bho0dbMmrxfhIyCmzGNQpwo9BgrlowdaGRLo38SNMVMq5rGM+uOWozpiS7LD6T/CIlWVebYF5ugfzrA1TwwGRYHVZ+BlmKCfpfhSNFN7Z5u3iTa8hkYvcgBrepQUZBJr6uPtRwMTGyUz16NvHD30OLi0rOn2eu8eSyAw4MOkLZMKVzyetRbNrRPsTHqVlIs9oeeLiouK9ZACG+NdifkFlhZXF5bCJRdQuI7fgRmvQ/HQ8KfhzazMFFG0C/gMrFI/xn+OUXqb0uO9t+X/v2sHSpczdCgURAALz0kiS8/vc/KRfxyBH7cSkp8PTT8OGHUvVs1ChJ9P6H+Q++YwQCgUAguIu5fBmGDpWCas+dgylTJDOCW4jJbGHbmVQbQaaQy2hVS8mqBm/gq7QVVFcVLRl+8R1yzO6cuqpj2uqjLNp5kWVjO+CptX0Qc9MoySyoWjB1Rp6B1JxCamgU3Nc0wMY2vriKtDD6It/GxKMrMDBjygTOnz1XMmbMFPixafmC7JwBOl6yFWQNfBpw4KkD1FDVoNvSrjT5KoTOS9vQ5KsQui/rxuYLP1HLW4HFauW9TWeZtuaoXRXv1FUd09ce4+ud8Wyc0oWYF3uwcERbLqblM3hejJ1oLWZiVH3USjm9wwOY1L0+i3fHV/AqWXnUeyudzt3rWJC51oWojdB5FWil9WSVjUe44zGb4ZVXpJyxsoJMoYDXX4eYGCHIqoJSKTk0HjoEv/8u/XvliEuXpGpZy5ZSPtp/mP/Yu0YgEAgEgrsUvV6y227cGFaturE9IQHeffeWTsVgtrBgh2Rv76lVMblHA3bO7MEnT3QmvclHWEs9fpwzt4B7fyfP6mZzjhWxSRxIyGRYhG2uVV6RCZ9y1oaVRSGX4eeuoZ6PFheVkgKDiZf6N2HOkFY0q+1hsz7NTaPk4w/fQ3/hxsPfjP+Dxe1AUY5r9x49dL4ECaWWF3m7ePPb8N/4cM+HjN0w1s7V8WjKUYauG8orf76AUmHg1xNl1taVYXlsItvOXsNVrWT+9ji+2hHn1E5/WEQQPcP9MZgtxMZncC23sNzKop8yk29C3uKjenOpIc+3H9BgAgw4BXUGljtHkAR5gcFESk4h51J0pOQUUmAwYTLfodlUaWnQt6/j91CDBpIYe+ON/3wV56Yhk0Hv3rBtm5TZ5swkJC4OvL1v7dxuMf+ofTE+Pp7Va374W8c+PuSxf3JpgUAgEAgEAFartHh++nRJgDli507p0/5bZDmt05s4naxzYi4RwP1ez/JZvU/JcO2AttN6THI3Gge6cybZ1mFwSUwCi0a2Y0F0XImRxfnUPFzUinLXhoEkBodH1mV4x9oYrOnEpafh7+ZLgJsPv51MJdDDlTUTOgLw2AJpfdDFw7v43zcflJzjw4kwo3P59/pzHgxNAX0Zo41J7ScRnRDNokOLyj1+/qH5RNbtwrCIVny1o/xq1je7LtK5gS/jo8JoXU4WWUSoD1YrvLf5DOdScpnZt4mTM1oZ7LWTt2rPx0uZZ7/btS5EfIvJvxcGswVdTiE5egOeWhXuLiqMZgtpuYW4u6jx1CqxgsM1hM1qezAhKoxe4QF33jqzESMkwVCWIUPg66/BvYKQOkHl6dABtmyR7PJfecXWHXbKFMmh8T/MP3pnJCQkkpDgYJFeOVitVmQymRBlAoFAIBD8U44fl7KBtm93vN/TU3KJGz/+lmYAFT+4OzOXWJ/dgxyzO7F5zXmIa8zs60Ogh4udKDt1VUehwWwj2MwWKytjkxjdKYQZPx53eP0QX1eWjr2HHUmb6b9qmE2VqlVgK6Z3nEldnz48t/owswY243SyDpeCa5xe+S5WqxWFHBb9H4ztWP59/lDkzRPJWZStASlkCia0ncDgVYMr9XrNif2YDUP+YEF0vFOXRZBeD5PZirerij7NAunbrBY5hUZy9ZKrpVopZ0VsIj8fuUqQryvL9yURXsvdaWVxmM9m3q37leOLhY6CtnMowI1tJ5MdCq3RnULoEOrDU98dYOGIdny5/QIrYh0biUxddZThkcHM6t/kzhJmX3wBkZGQKYWQo1RKmWRTp4rQ45tF166wY4ckhl98UTIEefXV2z2rm84/elcEBgbQpImzT18EAoFAIBDcFDIypMXvCxZI7ollkckkIfb221Ju0C3GU6uu0LZ+R247QGpTjAj1oVsjP7afS7Mbl1VgwMNFSXgtdzy0KnxrqDmbnMPQDkEM7RBkd35PrYplT97D7JiXWXR4gd35jqYcZfhPQ5nYdhKv3z+bVJ2Beu4y0jZ8TGF+Li4qWDUVHmhT/j1uVN1DfpOpWJKetNvXzL8ZBcYCjqUeK/8k1zmScoR8o85htbAsOr2R86m5/HjoMmm5hfi6acjIN+DnpiGqkR/3t6qDRiln7FLJnbK8yuL67O5M8PsfQZrUkm1ZFh9cu3yDJuRBp2YtcMOxcVhEEEvHdODPs9ccCrLSLN+XSESoD/2aB945684aNpQq0b17S++lH36QIiQEN5fitsaePSW7fGeti0ajZFj03HNwh2uSfyTKwsPDmfr0lOqai0AgEAgEgvLQ6+Hzz6XqV06O4zFdu0pjWrW6pVMrjYdWyZP1z/P51tNA8wrHL9x5kUUj2/H2L7a5XZ5aFfX9avDl0LYUGM3kF5nw1Kpw0yixWK28PCCcjvV9bWzZh0fWZUfSFoeCrDQLDs2na3A37gsdTK3Tq9l95jQeWlj/HHSvwK9hWhp8X5jEgZ5RjG873q5F0UPjQXpBeoX3XZpMfRZumoofy9xclITX8ODZXo3swp/f+fUM47qE8kjbuiVryMqrLOZZXHnm0nR+qP8CSpmFjdldUUV8Ra96TR2atThi9YFLzOjTmCUxCZW6zwXRcXdefllUlOQU2L49BAbe7tncXcjl5ecnfvqp1Ea6dKlUVXv55TvWQv8Oqh8LBAKBQHAXc/Gi9HB4+bLj/fXqSW1Vjz5629uqNKmb0B54lJfcLVx0e5WdeW3LHe+oTTHE15UV4yLZ9Vc6C3c6bp3r3KAmjQPc+GFCR3R6IzmFRmp7yem2dGil5vnxng8pPKJn7crv8feALS9A6xDn401WGJ0KK3IBsui7oi9bhm2hc73OfLbvs5I2SV2RDv8a/pWaQzE+Wm/yisq3+W9W24MaGiUjv43lxBX712PDlM58/Ns5MvJt3SmX70tkw5TODiuLhwvCeS95DKlGX7zCRzCrRROUCjkFBlOJWUt5NApwI6vAULWIAr3p39fCaDJJLb7O3juDBt3a+QgqJj4e3nxT+t5ohJ9+uqPbHO+gjykEAoFAILiLCQ4GX1/77S4ukiX32bPw2GO3XZBx6WcUMY8gsxrQyE0sCnmXTm5HKzwsq8BQUikqXo/21Y4LTF3t2CZ+xo/H+XL7BTQqBXOuBzC7qhSk5mdWvm3w9BGmTZlKiB/sfr18Qaa3wP1XiwWZxIXMC7T/uj0t/FsQPWoX5yYncHTCcTY8/gu13GrTKrBVpebROrA1rkoPzqWU37o4rksoX++8aCPI4MbrsWjnRV7qH04Nm4qblZ6aLUxf+isTosL4+NGWNKvtYXP8PvUo+vSfZrPeq9ispSLcNErS86oWUaArdOwaedsoKIAHHrijH+jvSt59V+oeKGbhwjvaBfNf9jGFQCAQCAQChygUUtBq3743tj3xBLz/PgTZ2sabzBbJLU9vum66ob41ob5JayFmKFhveMO7yA0M8tzJnrxW5R7q7aomr0g6bnhkMAcTMytco7QiNonWQV6olHJ2X0inV7g/J69lVG6uJuBHCPbM4/cXoXY5bttZZhibXZOGLYbhfew7sgqzSvY91uxx6ns3JL9Iwe/HDAzp0Ig3NpwitGYu0zpMZ8yG4RVOZWanmZy6UlCuycfwiCDaBHvzxsbTTsesiE2iTZAX3Rv706y2B0kpybxXdx4DvXaxJ68lD8xz54mIUBaNbEehwUxWgQFfNzX+7i5olLZ/N3L0lRNaVY0oAPBw+Rc9OGdnw8CBkrX9r79KH3w8++ztnpWgMsydK/2+PvkExo6FTp1u94z+EbdclCUkJLD2x/8xY/rzt/rSAoFAIBD8+0lNhYAAx/vuuw969ZLMPT78ENratwUWGEy3x5Y8YSXsHQFWW+ORtZm9ePnK5HIPbVbbA41KzrmUXFrU8eCprqEM/Tq2Upctts2f+P1B+jYPpJ6XHy0DWuKh8UBXpOPUtVOYrWb7A/+Adi6wZSb4luNqnmyCPlegyN2HwQH3cOCpA/Rd0Rc3tRvPRMwgql5fEtNNLNp5npf6h1NkMvPriWQ8tSrWT+nH+DYTy13fNqndJLoF9cVsVvHxoy0d2txP6BZG22Afhn2zz2k2WTGLYxK4NzyAGe3yCT03jWBNCgCd3I7zmNsPfLXjERZEx9E40B03jZIpPRpQz9vVTqx7aisntCobUVD6fjy0/5KaQHKylIt14sSNbc89BzVrSlb4gn83NWpIH1QNGwZ1697u2fxj/va74uef/lel8efOnWPt2v9x6PDhv3tJgUAgEAj+u6SnS+HP8+dLFveOPvWVySQnODc3h22KFbnl3TRb8ovLYN8YwLbUk+A9lJnHH7cJi3bEhG5hWCxWtj4bhZuLotKtcyDdl8FkxlWtJK/IgJeLF2sfXcu1/GvUdK2JVqll0eFFzD8w/0aF6wJ0zYJfZoGH1vm5LxjgvisQbwIyzjN2w1gmtZvEoacOkVtoYcW+q9y//iDrJnXiQloes346wfO9JVOCHL2R0YuPsXTsu3Sq15U5sR/ZWfM/EzGDB5oMZuGOS6yIvcSwiCAWjWyHwWSmoMiMdw01SrkMuQx6frqzQkEmvR45mM/OIyrpJWQa22rX8wHL+SW7G1eM/pxJzmV4ZDDtQ30cVk89tMpKCS2zxcq20ymM7xbGtNVHyx0LMDGqPup/g8nH5cvQowdcuGC73dtbCoW+Sdy2KvZ/mZYtb/cMqoV/9C9yYWEhGzf+wpGjR8nJycHT05M2bdowaOAANNedT/766y+++34FJ0+exGq1olar6dfXSVq3QCAQCAR3GwUFUhvO+++D7voD8IwZsHu34/VhTsJqK+uWV+225Be+hv0TKCvIaDgZ/5afMqzoHMv3JaGQy2gU4IabRklekYnzqXmYLVaGRQTRNtiHV34+QYquEA8XJdP7VM3a2mKBb0a34Jfz6/lwz4d24mdqh6klFa4Lly/Q5y9YNxNcyzFpO1oEfa9Aapki2/yD84ms04XUVCnsubjKl5JTyJD2QdR0u1FhSsgo4P55BxkW0YqNQ/4k36gjU5+Fj9YbV6UHq2KTyQ2WM6pTKI+2q4fBZMFgMhPo4cL2c9d4Y+Mp5DKY3qdJpQSZuzyf9+t+Ts2zMXb7sk1uvHB5KleMUmvjxKj69Az3dyrO1Qo5E6LCmLrqqMP9pfFyVdOpfk2HRiKlGR4Z/O9wXrx0SRJkcXG222vVgt9/h+YVO4b+HW5bFVtwR/C3f/OFhYW88OJLJCVdwmqV/iG+cuUqZ86c5cCBA8x+9x3W/vg/fvhhLRaLBZVKRd++fXj4oQfx8vKqrvkLBAKBQHBnYjbDd99J5gJXrtju27MH1q+XzAcqicFsqZRbHlSjLfn5L+Ggg2icxs9Am09xlcmY1T+cx9vXo6abC/lFphIb9xoaJddy9Xi7qhn2zT4SMgoAyg07doSnVoWfB8zYOoOFhxznko3dMJYJbSewZdgWZk1qwvf/Z6K8Z9+dehh8FXIcRMDBjbDn+TviGdM5hM0nkvl5cmcOxGcik8lsKkw5eiNf7YhnQXR8SctgXpGOcym5hNfyYGhECJ3f/4MFw9sRVtOVAHcNO8+nU2CwcCAhq9KvRzPtBb4Ker+kXbE0Bq8OFLX+nqeKavKikzVkZVEq5PQKD2B4ZFC5Qn9YRBCt6nkxdukB5g1tTfsQb77ZHW8nOioSgbeMxERJkMXH226vXx+2boXQ0Jty2dtWxRbcMfz99sX1G0hMTMLT05P7Bw8iKKgeBfpCDh8+THT0Tma/9z6HDx8B4L77evPE40Pwdhb8JhAIBALB3YLVClu2wMyZcPKk4zFqtWSBXwWq2vJX2pb8b7VUnfkUjjhYH970BbjnvZIqn9UKf6Xm8dK6E5gs1pJKmVIuY0znUNqF2JarqrpG6bne9fn1r40OBVlpFh5aSOekOFYOMlGeFv01Hx5JhsJyTDeKw56f69WIvs0C0RUamfdnHCv3JzG5RwOHmWAWK3bB0GM6h7AyNokh7YNoWtuDvXHpNDK406KeJ+4aJUeSglh94FIFr4eVEb6/8kqtb9DITXZ7F6Y9RM1GH6G7KGPTibPkFZlYMroDNTxdynu5AHBVK5nVP5yIUNssOJCE1pjOIbQP8WHU4v3U0Cg5k6yjb/NA+jQPRKc3oSs04uGi+ve05yUkSIIsIcF2e5Mm8OefUqXsJnDbqtiCO4q/LcpiY2NRq9W8/95satW6EaTXrWsXAvz9WfPDWmQyGdOff47One9sNxSBQCAQCKqFQ4ckMfbnn87HDBsmrS0LCanSqSvrlleMrtBIoKfL32upOvUeHJtlf9Lmr0GLN0oEWYHBxJyt51GrFCwYITn+ZRYYqOmmxlWtZPOJZJbvTWDZ2A4MnhdDjt5YbthxWRRyGYNb+9Nj2QcV3u9z7i6M8NpW7pg1uTAiBSpj2F5g0jG2SziHErPILDCUtO2VlwlWmmERQXQMq0kNjZIuDWqSmJFPpwY1cVEp2HD0CusOX2H+8Lb0burPoYRMxnQKYXqZ18NNXsD7dT9noNduu/Nnm9x4/tKzBDZ9lL6hASWvL9z43VcGV7WSfs0D6RnubyO03F2UGM0W0vOKWD2+o53wclUrK32NW0JCAnTvLlXKStO0qfR+dGauUw3cliq24I7jb4uy5OQUGjdubCPIiunVqxdrflhLWFioEGQCgUAgECQkwMsvw8qVzsf07Ck5KrZp87cuUVm3vBvjVVVvqbJa4eRbcOIN+xO2fAeav1zyo8lsYW9cOsM7BrM/PpPx3x20E33ju4XxSNu6XM7W82SXED7d+hcABxMyebJLKMMjglheji3+6wObkm/MqTCX7IPaPsyskVnumG9zYPw1cNKxaIeXixdZ+QYaBrgzdumBku05eiOjFu9n2dgOtAn2cuimOL5bGD2bBGC2WkjVFdLlg+0lgiki1IdZ/RvxUFs/UvPScHPLon6gL94u3ri7tGbm/06SozdSX3OJRcHvUt/FPkz8mD6cz/RvMbBvREklq/SatKpa0iuviy1HQsurinb4t4UrV+Dee+0FWbNm8McfN1WQwT+rYgvuHv7RmrKaNR2EWAJ+fjUBqFOnzt89vUAgEAgEdz6ZmTB7NnzxBRicVLJatpTE2H33/aPg58q65cF1W3IXJVur2lIll4GpwH5Q648gfLrNJoPZQngtT+b9ecFhxejUVR3TVh9lWEQQz9/XmCe7hNGjsbTmSKtWIAOm9mpIqyDnwqZ3eACn0k6Va4H/cYALz1cgyOZmwbPpklWJi8KFQnNhueNbB7ZGiRvZeiOuGqXdA3dCRgGD58WUuCkWZ4J5u6oJ8HDBYrWyIjaRD7actckmC/F15YNHGrMjaTOjN31sZ1gys9NMts8YyPwf1zFN9hxuCj1lSas7BbdGb/GORsPK2CTe/uWMjSD7V1nS3wrS0qB3b/s1ZC1awLZt4O9/06fwd6vYgruLf/SulFXwn4dSeRe96QUCgUAgKMvcuVKwqSPq1YO334bhw6Vg6H9IVdzyJkXVxwp/o6VKCa3eB0sRnJsr7Ww7FxpPtTvGYLIQcyG93BY+uBEA7a5R0irImykrDxMZ5kvLul6sjE2kWyM/lo3tgMFk4Wq2ZAyiUclJTM/nUlYejf3CWPPIGtIL0kss8L8+/DU7E3fwtOIijyqTy73+O5nw6vW86f4N+tOhTgfeiH6j3GOmRUxnVWwywyJCSNEVORwjGXzE2WSC5RWZ+ODhlmTkFfHe5rM24z21KpY9eQ+zY152mGt2NOUoQ9cNZVK7SXz46Nuo/vgcdDdihkxKLz7WvcSh013IO3KCcym5DsOo/zWW9LcCo1EKWz9zxnZ7ixZShczP75ZMo6pV7H9VuLbglvHPLPH1haSlpf2t/X636I0gEAgEAsFt47nn4MsvISPjxjYPD5g1C6ZOBW05IVlVpLJuecMjg+nZ1J+cgr/ZUiWTQZvPwGICr+bQcKLDYxRyGUtiEip1/uIA6Nj4DL4e2Q6VQs613EL+r0cDarqpqaFWYDBZ+GCLZFRhNFtYN7kdP53ZwJz19vlfL3SczmuaBNSJ9tbwpZmZDh9l3fh5cOPBDGn+OG/tfAuL1XEj44S2k4iq15cXYs8xqpMML235D9BlDT783DQsjLYXw8Mj67I9cXO5QdMgWfJ3C+rGgE6rUf/ZCZkhHbxbY4z8gbxoPQcq+t3fTeuVVCqYMAEmTpRabwEaN5YqZLfwObTKVey7qZIpKOEf/db37tvH3n37HO6TyWTl7v9p3Y//5NICgUAgEPz78fSE116DadOkB8TJk6W1ZTVr3pTLVeSWV2xLrlUpSdI7aEMsB5uWKpkM2s8rd3xeYdVEX6HBzO+nUunW0I/HF+3lxBXp2Mk9GhBWswaR9X0pMJi5ml3Ir9Pa88LWGSxw4Lh4KuUo8r3DUTuOcyth0jVYkGO7beGhhQxtMZSNj//Ky9tfshd7nV+gf8OBrNl/jaERQXy3N5GhEcF2D9zOMtma1fbAzUXJ5pO21TuFXMYTEbUYuHpopV6vD/d8SNQTf3IicB4d5JtRdpiPVqllVn9Thb/7u26t0vjxUtj6yJFQt65ke38LWhZLU5Uq9l1VyRTY8LffmX41a/6j3neBQCAQCP4TJCfDkSPQv7/j/RMnwoULkjCrX/+mT8eZW15Zd7yKWqrkmPFXZZFilASks5YqR3b6Xq5Kcgor42F4g6wCA6m6QjLyDJiu990p5DKGRgTx1LKDXEzPZ3SnEBIycvkzYbNDQaaRwY+1YGAN59cxW2F0KizPtd93JOUI2YXZ3OPXlc1P7EBXlE1GqbBnjULDOxv/IjLMl3ub+PPNrnhAsvafvvYYnloVwyODGRoRVOI06XO93XLV/ks0DnAjM99g11bYKMCNfKPOoWGJmwzyyow/knIEXVEObx+qRdvg6cyyqHCl8r/7u46hQ6UKdZMmUtvwLaZKVey7qZIpsOFvi7Kvv15YnfMQCAQCgeDOwmiEOXPgrbekn8+fd5xzpFbD55/f0qmV55ZXTHktVQrMfFLvUyLdTjAk7n3cajZx2FJV1k6/WJSM6RSCZwVtfWXxdlWTV2Qiq8CAm0a6VqMANwoNZk4n67iSrWfDlM50b+JB7+X2FSUXGfxcC/qUI8gMVng8GX7Kdz4muzAbX40v+YUKPF38OXhRxqdHLnMo8SQ/TOhIfHo+Pxy8zLCIIOYNbc3wb2LZMKULU+9twMNt6zp1mnyySyj3NgkgR2+/Bs1NoySjwNaMRAl87gcdtdDpEujLCLNMfRZuGqVdtlVlfvd3JQMH3tbLV7aKfddVMgUlCCkuEAgEAkFViYmRrOtnzoS8POnrpZdu96yqRHFLVVmUmJgb9BEPeEcTqMpkZdjLPNtRbddSVWynP3XVUU5d1RHi68qGKZ0J8XVlzNL9WK3Sw2ZlaFbbA41KzrmU3BJxBpJYySyQnOty9Ebe2niKQnOuXUXJVQa/1C5fkBVYYNDV8gUZgLfWm9mbztDz02jGLj2ISiHj40fvIcjH1WZuK2KTOJCQySsDmqJWypkQVZ9FOy8y48fjdkL31FUdz/1wjPc3n8XLVUNEqI/N/rwiE76uN7b5ymFrHZjkBa00sMBBt52P1rtkLgui4zCYK2vm/x/Faq1y4PqtpriSuXZiR/a91JPfn+3Gvpd6snZiR/o1DxSC7C5HiDKBQCAQCCpLRgY89RR06QInT9ruW7YM9u+/PfO6jslsocBgIiWnkHMpOlJyCikwmDA5eGAv3VJVjFpm5Kvg923CiOuo07g3ZTRK2Y1zmMwWtpWy0/fUqvhubAQLoyVRcuKKjhWxSYzpFFKpeY/pHMLK2CTCa90QZyCJFZ9SOVi6QhOXc2wNxNxksLkO9HR1fn6dGfpegd8rWEbXOrA1nhovtpxMASQxNePH4yzaeZEV4yJxUd+YG0gGJZ0b1mRVbCKbTiSzopxMNYDlsYn8efYarw0Mt9l+PjWPGioPWgW2orkaDgRB91L3M9ID/s/Tdp6uSo+SuRQbsdy1WK3SByT33APR0bd7NuVSuorZKMCdQE8XXNVK0bIoEKJMIBAIBIIKsVphxQppTco339jvl8lg7FgIDr71c7tOgcHEppPJPLpgL5Hv/UGfObuIfO8PHl2wl00nkykw2D+0qxVyXuoXzuePt6JVbQ0Lgt/lPk9bgy6r3AV52zkgv/EpvsFssbHTHx4ZTGx8ho39/fJ9ibQP9WFoRBDlMSwiiPYhPpKIuy7OitdcnU/Nw0WtKKm45RWZ8NHeqCh5yOG3OtCtHBPLDDP0vAK7yo8eA2Bm55kcS8rHVEbDFlfF/krNs1kPduqqjlRdIYNa1am00+SC6DhCa7oxopQYNlusrIpN5pPw7uytB6FlOj/1Fsi8Eb1WYslfei66Kq7h+0/x7rvw8cdSxbpvX9i06XbPSCCoMkKUCQQCgUBQHsnJ8MADUp5Yerr9/latYO9e+PZbCAi41bMD7FsJS3Pqqo6pq44ye9NZG2FWLOLGLj1AXHIqqxu/x70eB22OtSpcsXTbSEHNniXVt/TcInR6Y4mzYrEZR1lRkqM3MmrxfiZG1efzx1vZtDIq5DIGtqzF0jHtmNyjAeOWHmRcl1CiGvlzKCkLhVwyEjNbrKyMTWL09Ypb6YqSlxx+rwOdyhFkaSbocRkOOo4Ss2FSu0kMaDiQl9adcrj/610XaRTgjryMx5lOb8RktlQtXqDQxKwB4XzxROvrr4sV7bkPuffyHNzKPJldNkLXy7A6T/q52JJ/Rexlm3F3bbbV55/Dq6/e+LmwEB5+WHrfCgR3EKJ5VSAQCAQCR1itsHIlPP00ZGXZ73dzk8Kfp0wB5e3777RsK6EzShtCGMwWZm86w/J9SbjL85kufxOXGqdtxhtkrpi7/MrWtIYs/HFvidhrH+LNS/1utN+VNuMoS0JGAYO+2M2UHvVZM74jOYUGLBbwdlWRW2giM9+AWgk/Pd2O7MJskgvO8/GQergom7EqNplvdyWyfF8iG6Z0ZmiHIFbuT2Ld4Wu80mESoUcm0KYcH4sUk1QhO224sa22e218tD6cvHaj9bRVYCtmdnqBAY0GMvG7k1zK0js836mrOvQGM40D3W1yx7xc1WQVVK1KVRwv0K95ID0buiGLfRJtsn1U0D49PJgMKWZpns9EzCCqXl9GLz5Gjv7GNe/abKulSyVX07IsWODYdEcg+BdzF76DBQKBQCCogNRUKXR2/XrH+x9+WHJerFv3lk7LEWVbCctjQXQcPcP9S0Scl0LHd6Gv0dL1gs24XLMr+Z1+4Yv9vqyIPWqzL6/IZOOsWNqMwxE5eiPvbjrLL8eTWTy6PTEX0lm48yL5RSaWjr2HX/5ax9z9H9tlgs3sNJNfpvZlxDdHGbV4P8vGdqBNsBfyohQGX5mLshxBdsUE916G86W0kgwZDzZ5kLd6vEWBsYBMfTY+Ll54uHhxOCGXAXP2OxVkxZR2hgRJDGlVciwWRbnHlaW4qqUsvIJy5wOQddhujDF4KLWavMG6/PwSS/5Vscncv/6gjSCDuzTb6n//gyeftN/+xRcwatStn49A8A8RokwgEAgEgtL88QcMGyYJs7L4+0ufwj/44K2flxN0+qqFNOv0Rn47mYqfMovvQ1+hiTbRZkyuxYMddb+nKKsBK2KP252j9DqvU1d1dmYcjvDUqvjiiTZ88vt5Vu5PwlOrYsPT7Zgd8zKLDtvnjR1NOcrQdUOZ1G4SK56azSs/nWf62mNMjqhB18yRKHRnnV4ryQj3XoG40rrFAla5FYvVQpFRTm6+B6FeARQYTLR580+7NWTOKO2+CDChWxhbTqXQu2mg03iBYooDpVvU8cTTVQkZByB6MBSm2A6UyaHVh6iaPIe/yczvpxNZdziRcym5dvlmcJdmW/32GzzxBFjK/OLeeUeqXAsEdyBClAkEAoFAUBqTybEge/xx6VP4mjVv/ZzKIUfvvErliGy9EXNeImvqv0CY5qrNPpPan8TwdbSr0waZTMbkHg1Yvi/RpjIjmVIklQQmlxVpjihrBDI8si7bEzc7FGSlmX9wPt2CuvHW/QNQFKUSeGAA8rzzTscnGKU1ZAmlPE3UqOlWvxujW43l/iaDSM/XkWVIRaP3xb+GD68ObMKnW+Psqk9lKW3bD5JBSVRjf55ZfYS0XAOjO4Uw40d7EVs2UFpvNFMYtxaXY08iM5epzKk8oPNqqN0PAK1KybCIYAI9tCLbqpjdu6UPRYxlfl8zZ8KsWbdnTgJBNXAXvYsFAoFAIKgEffrA9OmSmxuAn59UHXvoods7Lyd4asuvUhVXaNw0SvKKTHibL/Gm6/8RoLCt0Fwz+/PY8bdJOJgN/Emz2h6M7hTChimdGbV4PwkZBSUCY1hkEO4aJUeSglgRm1RixuFIlBQbgTy17GDJz09E1GLgavsAaEd8uOdDfh3cjKItfZBrnJs3xBmgxxW4VNpk0gxfDP6C4S2HcyrtLANWDmBn4k4UMgXN/JvRtlZbHg5/lE3TOvLa+vPsOJeO2VE5Clvb/jGdQ2gf4kNOgYEUXSGHk7Jt1r0VE+LryrKxHWwCpQd77WBOvU+Qycpcx70hdNsAnk1sNhdnW/UM90enN6ErNOLhosJDq0R9PSj6rmHfPhgwAPRlxOzEifD++5ILqkBwh/K3RdnnX8z72xeVIePppyf/7eMFAoFAILipvPsubN8umQUsXiwJs38pHlqlwypV2QpNZoGBmm5qfM5NQVVGkCUU1WLYxXe5YryRUlyc0TUsIohlYzswZeUR5g1tzYH4TMYtO0h+kYllYzvQOsiLtQcv81i7unaiBOyNQBoFuJFv1NkFQDsjM/0Ivnv7oy5HkJ0zSGvIrpayjccCFEH/Rv35dO9nvLrjFbxdvJnVdRYT2k6gwFhAekE6NV1rolQW8fkTzcjRm1ix7wrL9122qZwNjwiid9NAMvOKGBoRxMrYJNYevMwnj91T0lZYet3bkpgELmfp+W5sBAui42xek5jcVlw2+BOkuVGNNfv1QBH1P1B7O7w/5XXxVZxvdVeyd6/0gUluru32oUPhyy+FIBPc8fxtUfbnn9sdbpddf1NYrVan22UyIcoEAoFAcJuxWp0/yKnV8Pvv4O39r3/YUyvkTIgKY+qqoyXbHFVoimldezgLgy7gXxADwPnCIIZdfIc0k0/ZUwNSRleHUB+WjG7Pp1vP2wiMwfNiGBYRxKdDWmE2W3ixXxMiw3xYuPNiyTXdNEobgeOmUZJRkFmpewtTwfY6oC687HTMqSLJZTG1tCCzApehdYfWuKpceT36NRr4NGDLsC3sTNzJ/avvtzMWeb7j8/Sp34ewehdY364Loxcfo4ZGyYRuYXSs78trP5/g/LW8EhH22WP32GSqJWQUlLwei0a2w0UpZ/eFdDuRmmH2YkzCG/zUYDoeinxWZ96HW9NF9FV4ivYlZ8TESPljeXm22wcPlhwY5XdRtVDwn+Vvv//fefstu20xe/awefMWGjVqSNeuXfH3lz5ZTLuWxs5duzl//jz9+/WlU6dOf3/GAoFAIBD8U7KypNyxUaPgscccj/FxLFL+bSgVcnqFBzA8Mojl+yQTDUcVmmKOXC2i57VnOdzZTOK1DB6Pe4NMs2e518guMBATZy8wcvRGvtoRx4LoOBoHuhPo4cKL/Zrw3dgOZBcYyS8yEejpgrnUB7V5RSZ8XSt+bdu7ebCtjgUPS57TMceLoNcVSDOX2XEQaCeFQS8/vhxPjSe/Df+ND2I+YNGhRXbnOZpylBE/jWBSu0k83/F55h2czcapb4BVQ16hiQKDmRl9m7Bq/yWuZhcy6J7a3NcsEK06zaZKmaM3En0+jaa1PLi3iT8Lox27YsYV1WNi4ku01F5gQdrDNNuZxL1N69xdrYiVJTnZsSDr3x/WrAHVXZrPJvjP8bdFWfPmzWx+PnzkCFu2/MaECePp17eP3fiBAwew5bffWLjwa9q1a/t3LysQCAQCwT/j4kXo1w/On4foaGjaFJo3v92z+ke4qpXM6h9ORKgvmflFNqYajqjr58fllmt56MsYdBa3cs+tkMvo1TSwZE2YIyxWOJOcy5nkXK7lFrFoZDs+23YOb1c1j7StS30/9xLxUjoAunS1qjR9/YLYGKBHWZTm9JqHC6H3Fcgs65y4HYiUwqD7N+xPyJwQJrWfRHRCtENBVpr5B+cTWTcStULNz2c3kpJyD19ujwckY40xnUPZMb07Lio5WrWSXuEBdGvk53CtV3qeoVxXzD15rdiT1woodsU03V2GHZWlVi145hnJWbGYAQMkS3yN5rZNSyCobqrtI5m1a/9H/fphDgVZMX379KF+/fr8sNY+IFEgEAgEgptObCxERkqCDCA/X3Jyy86+rdOqDooNIR5pW48lMQnXtzo2rXDTKEk3ulYoyKD8cGhHnLqqQw680DecB9vUJcjXFbkMxncLA4rdG5OZ1mG6w+M7uXvwi39euYJsf6HUsmgnyP6ABu0bsGLYKj7o/SHxWZfRFemY0HYCc2PnVmr+c/bNYWK7icyN/YQnImohl924r+lrj/HJ1vMlr2rpdV6NAtwJ9HTBVa1EqZBjTlzLF0EfIKdsGc8xusKqBVDfVbz1FowfL30/aJAQZIL/JNUmyuLj46lVifT0WrUCSUhIrHCcQCAQCATVys8/Q48ekFbmYV+ng/j42zKl6kapkJNbKOWW9fWIYU3Yi9SQF9iNqyhbTCGXEV7LnfYh3jSr7VFuOLQjUnSFfPjbWXxrqPnjTBpjlh6gVT0vhkYEAbB832W6B/VjfJuJNse10sDvtYwoDM7XnO3RSxWy7LKCbCdMjpjG4XeOMKDBA+QWyPHRetDMvxkFxoJKG4scSTlCgbEAo8VIvlFH40B3m/3L9yXyx5lrmMzlhJud+ZQ6J0cyyGsXs2otrtR1iwOlBQ6QyeCrryRH1B9/FIJM8J+k2urkCoWCpCTnrRLFJCUloVAoquuyAoFAIBBUzNKl8OST9mGzLVvCL79AvXq3ZVo3gxy9gUe9f+f9uvNQyCwsDnmLUfFvUGi94drnLFvMmWNjTTcNbwxuxo5z10jVFXI+Nc+pdTyAl1bFS/3CmffnhZI2ylGL9/PdkxFEhkpGIKMXH2Pp2HfpVK8rc2I/Qpl1lN/rQA2r3ul5d+mh/xXIK3vpfeCT2ALL6KEUGVQYzBae/eEonwwJp22ttqQXpFfpNcwoyMBD40GmPgs3jf2j0oLoOMeBzVYLHJ4O5z4r2TTObz0JRXVYntnf6fWa1fbAQytaF7FYnJt2KBTw/PO3dj4CwS2k2iplzZo2JSnpEqvX/GDnvFjMmjU/kJiYRLOmTavrsgKBQCAQlM9XX8GYMfaC7L77YNeu/5QgA6iV/BUf1fschUy63wi3kywMmY1KJrXHFeeWRZ9LY1rPhiiu9+eF+LqyYUpnQnxdGf/dQXp+Gs24ZQf58dAV8ovM9Ar354W+TfhyaBuiZ3Rnco8GeGrtqzvNanvg6aoipozzYEJGAYO+2E2qrpA1EzqyfFwEOr2cfmEPsHvQx8SGueNdzme2fxZAX0eC7BAod7qh7f0s9zatxeKYePbEpdO5QU3irhXxaNNHqelatcBvX1dfdEU6fLTe5BWZ7PYXrwGzwVwEMU/YCLJiGrsklHu9iVH1Ud/tJh8XLkCbNrBjx+2eiUBwW6i2j2WGjxjG8RMnWLPmB3bsiKZTp474Xc91SUtLY+/evaSkpOLi4sLw4ZULjBQIBAKB4B/x0Ucwc6b99iefhPnz/1vObVYrHH8FjzOz7XZdMgRQw8WFYZGhJVWwrAIDdby0xL7Uk9UHkni4bV2++ONGZau0rf6Tyw7YVNQcBUsXM7ZzCFYrpda13SBHb+TdTWf5akccM/o0YkCL2liv7cQl9kHkZucui7/nwwPJoC8ryI4Av0DklLfo1qMd7UN8ePuXM9T11rJmfEe2nUmlXWgkapWxXGOR0rQObI1WqUUlV+Gq9OBcSq7DcbpC443MMEM27HwArkXbjftN9QyvXu3p9HrDI4MdV93uJlauhP/7P8jJgUcfhYMHITj4ds9KILilVJsoC6pXj7fffpO5c7/g8uXLrFv3k11mWZ06dZg29WmCgoKq67ICgUAgEDjmzTfhjTfst7/0khQO/S/PH6sSFjMcnAwXFtrt+vLao6w1TmT9lAiHuWXNanswoVsYLkoFe+KkNr+KbPXLBksPnhdDjt7IsIggOtavSbbeWK4xSFaBkVk/neKXratYHPoWClmh07G/5sPDyVDkSJBtgGdefJ7+w5+gUYA7oxbvJ0dvJEdvJCO/iIU745izzcz6pzvwfMfnGfHTiPJfR2BqxFQWHlrI0x2eY1VsMs66NEvWgOVfgh39IOeU7QCZEiKX0LXO43zR6BoLouPsXveJUfXpGe5/97ou5uRIYmzlyhvb0tPhgQekbDJX19s2NYHgVlOt/wo0bNCAeV/M5cSJk5w+fZrMrCwAvL29ado0nBbNm5cINYFAIBAIbhqzZzsWZO+8Ay+/fMunc1MxG2DvSEhaY7frm7xJfJv7ID//X2S5Amvq6qM2Amt4ZHCFtvogBUu3CfLmvYeao1LIaRTgznubzjCyU0iF0+7qdpivQ97BRebcROTnPBiSAoaywugosAE8Wnjw+quv8XX0VaavPW4TUp2WW4SbRsmZ5FyeWHiINRMfYGLbiSw4tMDp9Sa0nUDXoK4cTz1JVL2+3L/ecQxAyRqw7BOwvR/or9gOULpD1/9Brd64Av2aB9Iz3N+hdf5dWyHbvBkmTgRHfgQWi5QlKESZ4C7ipnw006JFc1q0uLMzXwQCgUBwh/L5546F12efSXlHdygmswWD2YJObyJHb8BTq8ZDVYTLviHIU36zGWtFTkqTufQNHcsjGiV/nr1WKYHVOsiL4ZFBDI0IKjeXrDSLY+JZPT6SrHwjSoWMtsHe1Kzh3NkRoLv7ARYGz0Yjd24DvzYXhqaA3YquY8B6wBven/cxX++8zJc77N0zvV3VJevBTifnMvG7k8wf8RFdg6P4aM8HNq2MrQJbMS1iGj1CenDo6nGmtpvF6MXHbEReaSZG1UeTsRN2PwTGHNudLoHQYzN4tyrZpLwuvort8+9qkpOl9+EPPzjeP2KEtA7UreK4BoHgv8RNq5cbjUZyc3NRqVS4u7tXfIBAIBAIBP+UzZth2jT77QsWwIQJt34+1USBwcS2M6ksjL5Y0gLnochjdaN3aKo6aTPWaFXxdOJ0thwPRSHfQcwL9/LNrspZ/i+JSWDx6PbkXbfVrwynruq4pitiyqrDyGUyRncKwc/dhT5N/UnK0uOmUZJXZCpxbOztsY8vg95HLbc30ChmZS6MTME+4esY8DPgAo+/9wT9mj7M/fPsxWOz2h5oVHKb9WAxcRkMmLuftx+IYufogeQUZpNdmI2niyceGg/MVjMquRpXSxvun3fQqSAbFhFED+125NFjwVKmyufRBLpvBrcQp/fmUFzfDVUziwUWLoQXX5RiKMri5SW9T4cMueVTEwj+DVS7KPvtt9/ZvOU3EhOlLLIePboz9ekpAOzdu48d0dGMHjWyUplmAoFAIBBUiXvvhYcegnXrbmz74os7XpDN3nSG5ftuVLr8lFl8F/oq4aoE27EWF55KeIWYvFaAFPycX1Q1gWUyW5wKEmdkFRhw0yg5kJDFO7+ewcNFxadDWpOiKyQz34CPqxqNSs6hXd/QP/c9VDLngcrf6WBMKtilgB0HfgaZQsasr15l3KBpTqtZYzqHsDI2yW492KUsPaOXHEYph/uaBeLnpmZi95pYLQo+2nyWKfc2oEOoH68NbMrimHiHa++6WZbjdvBF+4n7dYZuG0Dj4/TeHInrknNHhdErPOC/ub7s6FGpVTE21vH+Hj1g2bL/nBOqQFAVqu2dbzab+eDDjzhw4CAKhYJ6deuSdOmSzZiQkBBiP/yI+vXr89ijj1TXpQUCgUAgkNBoYM0aGDsWvv9eWls2ZcrtntXfxmS2sO1Mqo0gq6tKYXnYq4Rokm3GGuSe/M9jITF5N9q+3DTKKgU/e2pVyGUyalWxxa64VbC0Y+NjC/faCI9JIQeY4fEO8nIE2Tc5MOGaA0F2AqlCZoUF33xLdkCE02rW8IigEhdGZ5gssOlECgDju9XnTHIOk7o34Mvtcfx6IplhEUEsGtmuxKXS+7qo/CvhPB4X3rM/Yd0HodMKUGqdXtORuC7m1FUdU1cdZXhkMLP6N/nvCLO0NHjlFfj6a8kdtCw+PlIg9OjR/y3jHYHgb1Bt7/pNmzazf/8B2rZtw9NTJuPl5cUDDz5sM6ZWrUBq1Qrk8OHDQpQJBAKB4OagVEph0Q8/DPfff7tn848wmC0s2HHRZtuHQfPtBJlRE0B25E8M8G7Np4d3klUgiZW8IhM+ruWv7yqmWFAdSMikQ6ivXbC0M4pbBVNyClk/uYuNoUhxJlp/t638n8ts5PZyq4T52TA5Dewe3YvXkFmg99jp9Bn4GIcTs6jrrbURZc1qezC+axg9wwN4/ocj1PZyoVGAm03rpKO5KxVyWtXzZvPJlJJ5f7UjjgXRcTQOdC9pvzyXkovFCksHfEPnS8NRUQSAsf5EVO3ngdx5yJojce2I5fsSiQj1oV/zwP9GK+P06fDdd473jRolRVZcj08SCO52qk2U/fHndry8PJkx/XlcXJx/wlavbj0uxMVV12UFAoFAILBHLr/jBRmATm/behji60q9/svI3dMDd2MaABcM0Ds+Fa/0/2NGp5lsnNqX4V8fJSGjgPOpebioFRUKrLIW+JN7NGB0pxBm/Hi8wjkWtwoOjbjh2OipVTE8si5PRNRClfg1/qfeRWYvt0qYmwXPpDvYcQj4BbCCZ5dhqO4ZzNFL2XRr5EfH+r7kF92oZAV4urDh6BWOXc7mg0fuISPPQGbBjdbJVfsvsXxfoo2QG9M5hIzcItxdlHbr7ixWOJNsn1H20ZEAWvRZgcfBIXyWMoyR/T8lsBxBBo7FtTMWRMf9d3LL3nxTMvQoLBV50KiRtHasR4/bNy+B4F9Itb3jr1y5QqOGjcoVZAAaFw06Rws8BQKBQCCoCmfOQHb27Z7FTSVHf6P10FOrYtmT9/DO4Xm0/iuNFBMcK4IulyHBBEdTjjJs3VDe3f0yS8feg6dWhdliZWVsEqMrsKgva4G/fF8iHUJ9GNqh/FzRYddbBVcfuMTQiCCWxCQQ4uvKhqfbUSvwKMvWtSTg1AvlCrKPnQmyfcBGwApP/t80Tm1YxJrxHfHSqnngyxg6vf8nU1Yd5oMtZ/lk6zky84ro3KAmV7P1DPsmlp6fRvPogr30/DSaCd8fkuY1pTMhvpLN+vCIILo39ifUv4ad+C2PU1d1ZHv3YXzmcr5KewxdkXPDkmKqen6dvuJz3hGEhMCMGdL3rq5SJMWxY0KQCQQOqLZKmVKpxGCsuG89PS0drdZ5z7VAIBAIBBVy7hx07w516sBvv/1nW6A8tTdaD4dH1mV74mYWHZZytnpchhQzZJfpCFxwaD6RdbswLKIVX+2IZ/m+RDZM6czQDkEObfEVcpmdBX6O3sioxftZNrYDbYK9WBKTYGdMMaZzCO1DfBi1eD8BHhoKDWZqaBQsHtOSN3e+iOrCAj73L//+3s2EVzIc7NgNbJO+De76INnNH2XcdwdRymWM7BjC8nERjFq8v6SS1aOxH16uat7ffLbCsOsV4yI5mJhJj8b+aJQyNColSfoCu2NcZIUUWl1KWjBLu0hmFRjIUwUBWTdCpMuhtLiuDDq98c6xzj97Fi5cgIEDHe9/4QXIzJRcF+vWvbVzEwjuIKpNlNWrV4+4uIsU6PW4OhFd2dnZXIyPp3HjRtV1WYFAIBDcbSQkQK9ecO2a9BUVBVu3SgLtv4TViofGSrPaHpxNyeWJiFoMXD20ZPfZcgwS58Z+zIYhf7AgOt5GYLUN9rZzFezXPBCr1WpXyUnIKGDwvBiHphf+Hhp+PHSZwfNiABjfLQw/dw1fDmvFtoT1uMYt4JMKBNnrGfBWpoMd24Fo6dvR4yfz1nsfsPrA5ZLWw2JxVRx0naM3EtXIj+3nKpfFFhHqg6dWhd5oxkMrCZ/S4hegjuoaK+q/RpzXSJp0f4VCg9mmFVKjlBqNSkKkK6Ds+SvCVaNAbzShVf1LDT+KiqQPQxYskGIovLwgPl76syw1asC8ebd6hgLBHUe1vdt7dI9i4aKvmT9/AVOfnoJKZfvJkdlsZuGirzEYDNwrytYCgUAg+DtcvSoJssuXb2w7cwZeesm5ocCdiMUMByfjUpjO+K6fsmBnAvlGHcdSj1Xq8CMpR8g36mgc6M6Z5NwSgfX9k+35YUJHruUWkpUvVWPcXZQkpNtXikCqmDkyvXj7/uZsOpGMt6vqujlIFsO+2cf8Ec1JP/Acn1RQuJyVDu9lOdjxO7BH+vbp6U9jvGckE5cfZnSnEDZM6cyoxftJyCgoCboeFhHEwp0X6d0ssNJh1wt3XmTxqPa4aW48AnlolSXr7hpqElnZ4HX8FOmEFMzmwzUKvoqPKBnbrLYH47uF8fGj9xB3LQ91JdZ+lT5/RTSr7QHA7F/P8tLfcWK0WCTXw/x8STwZDNI2FxfQaiWRVNXKssEgVcP27IHoaNi4EXJKhWZnZ8Mnn8Dbb1ftvAKBoIRqE2X33debmD172LVrN2fPnqN161YAJMQn8PU333LwwEFSr12jVat7iIrqVl2XFQgEAsHdQloa9O4NZc2i2rX7b30Sb9LDnqFw+WfkQL8wf1LveY6MAkdlJedk6rNshMfAlrWo6eZCiq6QKSsPl+wb0KIWfZoFlnuusqYXfu4aABtzkPBabricfYupLinlnmt6GnyS7WDHJmD/9e/vhXHPj+eXw3I2n0y1qY49+OUeAjw17I/P5Pn7GrPrrzQKDeYqrdkyWiwkZ+sJqVkDpUKOWiFnQlQYS9avZmnoG3gq8krGP+s2m+Nub7A7r3XJ8dNWH2VYRBCz+odXypCj+PxTVx2tcGyxccr319f1OXVizM+HvXulDLATJ6QPJ65ehZQUMDuPHSA4WKo2O+KHH2DxYlCppPPn50NqKly6JAm78vjsM3j6afCvoEQqEAgcUm2iTKFQ8Nqrr7B4yVK2bt3G779vBeBifDwX4+ORy+Xc17sX48Y9iUxkUQgEAoGgKmRnQ58+cPq07fYWLaQ2Kg+P2zKtYkxmCwazBZ3eRI7egKdWjYdWiVohr5qLXlEm7BwMaTElm9QX5/N4owAyPcZWaU4+Wm/yinQ0q+3BuC5hRDX24/X1JxnZKcRGYB1OyqZ3s8AqVXJ8a2h4dWA4+xMyr7cMWhnnsYSaF5eVe+zUa/BFTpmNFiTL++IiYB+gI3hrvRjXpSa/n0olIaOAX44nM6R9XbY+143sAiOZBQbMFitLx3TgSra+Cq8M5BWaWLU/iRf6NUF5/Xd0n/cx+jZ4BTWFNmMvFtXhr0L7UOMVsUlEhvlWyr5eqZDTKzyA4ZFB5driDyuTsebUiXHwYNiyBYxVC/kGpGqZM86fl95PVcXHRwpoV1W8vk4gEDimWpuV1Wo1EyeM54nHh3Dy5ClSr13DarXg6+tLixYt8PVxnnJ/K8nJyWHdup/Yf+Ag6enpqNVq/P39aNmyJWNGj7Ibv3//AX5ev56LFyW73PphYTz44P20a9fO6TWSkpJYtXoNJ0+eorCwkFqBgfTq1ZOBAwcgl/8HbG4FAoHgVqHTQf/+cOSI7fZGjaS1ZLf5/5YCg4ltZ1JZGH3RzgxjQlQYvcIDKteCln8JdvSFHFvhaZVr+OaYmpHhnrQKbMXRlKMVnqp1YGtqufsy7wlfNCo56w5fJr/Ii0YB7na5ZWaLlVX7k5jWsyFf77pYbq4XSJWcJXsuMjwihFnrThJey42JHou4X7Oy3DlNugYLygoyE7AWOAfIgPuBVtL8vVy8+G73JZaN7cCUlUeYN7Q1BxKyeGndSZvXeWDLWrzYr0mFr0lp1Eo5+xMy0elN0u8mcQ0ue0cAtiLnUH4Txia8To7Z3eF5qmJf76pWMqt/OB1CfFi40/7vSmnjlGLb/mInRru/P3L53xNkILkgOiMvz/k+R3TuDCNHwvDh5Z9XIBBUyE1ZQerp6Unnzp1uxqn/MRcuxPHGm2+Rm5tLUFA9OnRoj75Az6VLl9iwYaOdKNuwYSPfLl6CQqHgnntaolKqOHL0KG+/M5vxT41jwID+dtc4e/Ycr772OgaDgYYNG+Lv78fp06f5dvESzp49x4wZz4tqoUAgEFSG7Gzo2xdiY223BwfDtm0QEHBbplVMgcHE7E1nHFY/Tl3VMXXVUYZHBjOrorVBWcchegAUXLbdrvLE1HkdYamNWbL7MtM6TGfMhuEVzmt6p5lcuFbIyz+dLgk9Xnf4CivGRVJDcyO3zKeGmqn3htGvZU0MllxmDqyBh8YLV6UHq/cns3zfZZtcr2ERQXQMq8n6o5eQyy18M6oFmhPP4Z3kXJBZrPDUNVhctghXCKwCEgEV8BjQ8Mb8i4wK+raohadWxfInO/DBlnMOjTw2n0zhpf7hVar0qZVyzqXkois0Epi2HPaPp2xs9Q5dWyYlvoTe6twF0alocoKrWklEqA9tQ3xsjFM0KjkrY5N4+5cz5OiNyKwWauWmc9XDX5pjWSfGHj1g/fpKXRO5XPoyXbfZL69SVpEoq1MHunaFLl2kal09+wqiQCD4e1SbKFuyZCndu0cRGhpaXaesdnJycnjzrbcxGAzMmvUiER062Ow/f/4vm58vX7nCkqXLUKlUvPP2WzRp0hiAK1eu8sKLL/Ht4iW0adOaWrVqlRxjMpn49LM5GAwGxo4dw/2DBwGg1+t5/Y23iNmzh7Z/tqFnz3tv8t0KBALBHU5mptSyeLCMgUOtWpIgu80PhCazhW1nUsttRwMp8yuivLVByb/DrkfAVCaoWFsbemxB5dWC3r4mTlzOoXtQP8a3mVhii++ISW0nMbDhQAbM3c+lrBttfQkZBfT/fBd7XuzBlB71uZxdyJAO/vxyfiN9V35oU4FrFdiKaR2ms35KP0YvPkYNjZJxXcLo3TQAo8XCyM512Hj2f6gOT+MRtSNPewmzFcakwvdlM5jzgOVACuAKDAWuu6VPbDuJgY0GMmDOXi5l6XlzcFM8tGqnzoqls9iqEnZtsULg1flw5gW7Meuzoph++RmM1orb8RyKpnKwIqPrB3/YGKcUC2e5xczgs7uYsucHTAoF/Ud/7thy/97rzxBKJbRpA/fcA82bQ2go1K4tvUe8vECjAcX1YGuTCfT68tebPfywlC1mMEiGIDVqSOcJDYWwMPD2rvR9CgSCqlFtomz9ho1s2PgLdevWIapbN7p164r/v2yx56pVq9HpdEwY/5SdIANo1Kihzc+/bPwFi8XCgP79SgQZQJ06tXn00UdYvHgJGzf+wvjxT5Xs27cvltTUVEJDQkoEGYBWq2XC+HE89/wM1q/fIESZQCAQlEdGhuSyePSo7faAAEmQNWhwW6ZVGoPZwoIdFys1tnSbW+n1Z9a4bwk89wwya5mwYI8m0GML1AgGoIZGyXP3NWJvXDqvdp1Np3pdmRP7kZ2QmtHpBQY2HMik70/aCLJicvRG0vOK6NbInyKznplbZ7DwkL3AO5pylDEbhjOp3SQ2Tv0QGS5cyswnt8iIWmVixm/TaX95EU94Or9nkxVGpsKqsoIsC/geyAS8gBGA7/X5d3yBQY0Hkl+oQCGXoZDL6NW0YmfFirLYihkeEcS9TfxZc+Ag7zb4Hx5nltiNyQueyDPH+2OlcksNKpNTZjNeqyS8VpmqntVK3/N7mLHzO+pnXinZ/LDhkmPL/WbNJCv6zp3B3XFrpR1KZcVje/QQwc4CwW2i2kTZuCfHEr1zJ3/9dYHlK1ayYuUqmjRpQlRUV7p07oybm1t1XepvUVRUxI7onbi4uFRaEB08dAiATp3sWzE7dezI4sVLOHDgoI0ou3FMR7tj6tevT2BAAIlJSaSmXiMg4N8lWgUCgeBfweXL0K8fnDxpu71WLfjzT2hStfVDNwud3lQlx7/8IqlCIa0/i6Ov+SueDlhjP7hmJ4jaABpfm801NEq6NfLDZLYyuNFDPNhkMLqibLKLsvFy8cZT48mhhFy7CllpPLUqfN1c2H3hGsmG7Q4FWWnmH5xPt+Bu+Cm60dDfi2OXskg1bKPTlUWMKEeQGa0wNAV+LNsNl4pUIcuFe1rdw7drFqP0UOGj9cbTxYvDCbn0n7Ofbo38WDa2A9PXHquUs2LpLLbIMOdrtjqG1UQtl/F18zV4JdkLsh2q8TRs+glNDx2qdCtkZXLKSlPWibFZahyv/fE1EZdO2o195sKfqBUT7E8il0ttvQKB4D9DtYmygQMHMHDgAFJSUtgRvZPo6J2cOXOGs2fP8s03i2nTujVRUd3o0KG9XYbZreDChTj0ej1Nw8PRaDQcOnSYo8eOYTQYqF27Np27dLYxIsnLyyctLR2AsDD7lkw/v5p4eHhwLS2NgoICXK8vcE2IT7h+TJjDeYTVDyMlNZWExAQhygQCgcARr75qL8jq1pUEWcOGjo+5DeToDZUe66lV4aKSM3vTGX6IjeODunN5sOYOu3EnFPdRv+v/cNU4/iBTrVSgVoLr9f++5WhwU/kzf0cc3+4+iqkC1/LXB4Wz/UwqrUNceWH1R5Wa+4cxH/L78P7sOJNBZJgG2cap9CnH7NJghUeTYUN+mR2XgJWAHgYMuh9Z98l8e8CKbw3IyE/l91PHSuZfnEP2YOs6ZBZU7nUuzmLbNLULy8Z0ILfIRHaBAQ8XVcmarXd/OcmO7mvxumIvyE4HzGL01k5M9rhU6VbIiVH1K5VTVppiJ8YnW3hT/6O3efzYb8hxbKpSOzURhViCLhDcFVS70UdgYCCPD3mMx4c8xoULceyIjmb37hj2HzjAgYMH0bq4ENkxkqlPT6nuS5fLpcuXAMmEZPbs94ndv99m//fLV/D0lMl069YVgPT0NADc3NxwcXHcK+7r64tOp+PatTRCQqQWk7R0ScjVrOnr9BiAtGtp//COBAKB4D/K559LuUvXOw8ICoLt26U1Lf8iPLXqigddZ3hkMNvPXmPDgdN8F/oOkW72VZEF1x7mg5RRfN5ER7/mrpVy9JPLZaw/fJkG/m42gkwhl9EowK1kzVJcWj6t63nSp1ktZv54nEZ1fKsURK036biQkkmf7BfooyproXiDQgs8lAyby2ZRnwX+BxhhwIhJjJ32Em2vOw0mZDgOrl4Sk8Di0e3JKzQ53O+IHL0RuUyG3mgmv8iEv7sGpULG93uTWBGbRBP5MTwdCLKiVl8wfes9gK7yrZCRwZV2XiyL62+beXnmRORXrzq+jybNcXnrDTQPPyhVxQQCwX+em+K+WEyDBvVp0KA+Y8eM5viJE/yx7U927d7N9u07brkoy8uTPrLbf+AAcrmcCeOfonPnThQVFfHrps38/PN65n7+BXXr1iUsLBR9oZRTotE4/0/XxUUKz9QX3mgTKSw5TuPkGEng6fWVy1TJy8snP7/sx42Qfl38CQQCwX8Od3f49Vfo1EnKPfrtN8lt8V+Gh1ZZKcc/hVzGqI7BjF5yAA95PvU1tg6LZquc165MZEWm5OZbFZt1tUJO40B3gnxqMDwiiI3HkxkeGczQiKASdz9/Dxd8XNXIZFay9QbS84vIKsyq0r1m56fyYN5zuOt2Oh1TYIH7k2FbWY21H9gMKpWaD76agyksijd/OcPAlrVYNrYDg+fF2Dg8FnPqqo68QhM1NJV7nUFqJ5TJZER99CfFjv7NanswulMIG6Z0ZtRiFWmNP8Hv3POA9Np/mvsiT9Qay+nk7YBtK2SbYC+WxCTYRx10C6NX00pGHZQmKwsmT4ZVqxyuWDPXC8L0/vvUeOwxlEpF1c4tEAjuaG6qKCvm1KnTxOzew5GjRyoefJOwXk+iN5vNDB82lP79+5XsGzN6FGnX0ojZs4effv6Z55979nZN046NGzeyes0Pt3saAoFAcGsJCJAyyDw9wddx58HtpuzaIEd4alU827sRZqv1+rqoAJ5MeI3V9V/CVV5EvtmFyUkvsCO3fckxlbFZv2EWYsTLVY3JYuXF/k2Y2bcJv59OZfx3B+2ExJQe9Ylq5M+XT7RGb6n8B3taGTQ4+Rxai3NBlmeBgVchuuznjVuBGMAVjEMMfCdfxDR/jxJXx9ZBXgyLCOKrHXEOz5tVYCA2PqNKzorf7U2gdMTaqas6Zvx4nGERQawYF4lBFsGH0cd5xn8FTyfNJNNnAD11RTbnKW6FHBYRxKKR7ezs61UKedUFWUwMDB0KSQ6qbzVqwKxZKJ59FkV5lvUCgeA/y00TZRcvxhO9cye7du0mKysLq9WKVqulR/fuREV1vVmXdYpLqX/kHBl99Ox5LzF79nDq1CkAtNcrWkVFznvZCwuLro+9cW4XFxfy8vIoKipycoxUSdNW8h/dQYMGce+99vNNT09n1suvVOocAoFA8K+koAByciQDD0f8y9oVy1K8Nmh4ZJBDW/wQX1eWje1Aem4RqbqikpZCjSaCD3NfZ4Lrp4xLeIVTensnyRy9c5v1AoOJbadTbcwsPLUqNk/rypfbL7Ai1n4u+UUmmtb2ZMupFJbExDN/RPNKBVG7ymBHiBvadOeCTGeGfldhT2GpjWbgJ+AkEAA8DnjfcHWc0HYSS8e+wws/nuPTIa1YEB2Ho6xqb1c1Px25wseP3lNhO+GwiCDah/jw9i9nHO5fEZtE+xAfvFxVfHXtUTZldyLBUIfwGiY8tfZr3XP0Rr7aEceC6Dg7+/o9L/Z0Og87LBZ47z14/XXHdvR9+sDChf/KarBAILh1VKsoS01NJXrnLnbu3MWVK1ewWq0oFAratm1DVFQ3Ijp0QK2ufA9+deLv5wdIbYWenvaWUcX2/Tk50n9wNWtK4/Py8igsLHS4riwjI+P6sX4l2/xq1iQvL4/09AxCQkKcHuNX6pjycHOrgZtbjUqNFQgEgjuGS5fggQfAaoVdu6RKwR2Iq1rJrP7hRIT6siA6rkQk1fPW8tPkzpgtVlRyGVqNnOiZHck36sgoyMTXdShm2Si6H8rmcpmAZsCpo19BkYl3N52xE17DI4OJuZDuUJB5alV8NzaCBdFxJaJmVWxyhUHUbjLYVAfaK50HCmeboc8V2F/6c8hCYDWQALQEBgJl/utfeGg+Het2oW1wKwoNZhoHunMm2dY7v1ltDzQqOYcSs0raCSNCfVi0y95ZcWznUNqFeDNq8f5Sr6UVsHXJ+HrXRb4e2Q65DBIMdQA4n5qHi1rhtEXSYsVmblV2XJTJYP9+e0Hm5QVz5sDIkdIYgUBwV1NtomzmCy/y118XsFqlj7qaNGlMVLdudOnSGffKZmjcRIodFA0GA0aj0c4BMi9P+ge3WHy5udXAz68maWnpXLwYT9Om4Tbj09LS0el0+Pv5lTgvAoSEhhCfkMDFixdp166t3TwuxkmZNiHBIdV2bwKBQHBHsXs3PPIIpKZKP48ZA2vW3LEPpq5qJf2aB9Iz3B+d3oTeaMLf1cqFvR/xQ/ZAXhhQn1//Ws+He8oPaC42vGhW2wM3jfTfc3GbYm6hCXeNki2nUuyEl0IuY2hEkNMsr+GRwcTGZ9hUmZbvu8z6Kc6DqD3lsLk2dCynqSPDDPddgcOlBVkOkuV9BtAP6EBZXVTC3NiP2TDkD1KyDSX3W9qgZEK3+qzaL4U8J2QUsHxvIv/XowHtQ33QX28nrO2lxUurYs3BSzz45Z4Sp0Z/ZQZfh7zDO1ef5EBB85JrnrqqQ19GBBaHT4/vFsa01Ued3/B1quy4KJPB4sVSwPOV6xlkUVGwYgXUqVP58wgEgv801Wbpc/78X9SuXZuhQ59g4YL5vP/ebPr16/uvEGQAfn5+hIaEYLVaOXnylN3+4m1hoTfs79u1lUTVnj177Mbv2bsXgPbt29lsv3HMXrtjLl68SEpqKsFBQcIOXyAQ3H1YrfDxx9C9+w1BBrB2Lbz//m2bVnWgvL7GKNDThQB1NpkbOnNP6iu8FvYDM7fNYOi6oXatgsWtfO/vfYWlY+8paaEb0zkEs8WK3mhi08lkHl2wl+/3JWIwW/hmV7zdtRsFuDnN8ioWbEtiEmy25+iNjF58jJc6vcvS+1fQKrBVyT5vOewJ0ZYryNJMcO/lMoIsBfgG0AOjgQicCjKQXB3zjTpqXW/TnNwjlOiZHZkzNJiZg2rQuI6RJyICeHNwE+Y90YrhHYN58KsYBny+i11/pVLXR0GhJY3zWafp1EjO+qfbMblHKE3cM1lb/wXucf2LxaFv0kL7l811swpuiMBiDiZklrSilsffdlz09ZVEmEoFb70Ff/whBJlAILCh2ipln37ykdNsrn8LDz74AJ9+NoclS5cRHByEz/VcsosX41m/YQMAffv2KRk/cNBAfvt9K1t++52uXbvQuHFjAK5evcratT+iUCgYNGigzTUiIyMICAggPiGB9Rs2cv/gQYC0lmzBwq8BuP/+wTf9XgUCgeBfRXa2VBH7+Wf7fQ0bwkMP3eoZ3RRM6Ydh+yDqWSSrc82Z2WQll39McSvfsIhW5OjNdAyriVIhJ7/QzLmUPJKzCxkRGUxWgcFOeHlqVYyIDMFgdhxQVp5gS8go4P55BxkW0YqNQ/4k36hDp4vjnjNPo9addjrfFBP0vAKnSy+5/gv4EVq0aMGJqBNQyc9jswuzCfCtxUePNmFH0mYGrRlmV02c2WkmHUL68+RSySzsp8nt2JG0mX6rPrYb+3KrkfwS/inKwhQA3BV6loW+zpC49/irSFqz5e2qJq/I1mZ/eGQwGqXcYSsqSNXLiVH16RnuX3WDj2KioiA+XogxgUDgkGoTZa++9gbBwUHMfved6jpltRMV1Y2jR4/x5/btTHl6Gk2aNMZgMHD27DmMRiP39e5N586dSsbXrVOH0aNHsXjxEl6a9Qqt7rkHpVLJkaNHMRgMPDXuSWqVWaCuVCp59tlpvPbaGyxevITdu3fj5+fH6dNnyMrKolOnjtx7b49bfesCgUBw+zhyRGpXvHjRft9998Hq1eDtfevnVd1c+hnFnmG4mm094T/3g1/yocBxPjAgtfL9MTIaXYGcE1eyOX45h+jzaYzuFMIvU7tgtlhIz7M1nio2EjmXkouLynHlxk2jLDd8WTKziGdBdDyRtQy87/08aoV9Na6YK9crZOdLL4HbB/wOfr39WLFkBS0XtXR+o2UIdPdBrTTzwd6XWXDIvo3yaMpRhq4byoS2k5g37B3kMhnv7J7lsOUyL+MokWePoizj2ZFldifPIi0zKF6ndi7lxhqx0tUvpUJu04qqKzTi4aLCQ6tEfX2/UywW+PprGD0anMTiCEEmEAicUW2izGw2lwQj/5uZOnUK4eFN2PLb75w8eQqZTEZYWBh9+9znUCzdP3gQtQID+enn9Zw6LX1y2KBBAx568AG71sViwps04ZOPP2TlqtWcPHmKhIREAgMDePDBBxg0cACyO3TdhEAgEFQJiwU++QRefhmMZXKoZDJ47TV49VVQ3OF5TFYrnPkQjr6EDFvlFW+EQVfLF2Rwo5XvzFU54bU9eOF/J8jRG5nx43Fe7t+Efs1rUdvThfBa7pxPzcNNoywx71hz8BI7Z/ZwaFSRV2TCx7V8gy2FXEbHWno+8HqOOgrn7oZJRrj3CsQV/yotwCbgNDAUMhtm4unqWSlXR4DWga3xdfXmf6d/dijISrPw0Hy6BnWlkW9jh4KsiQr+qAu1yzzVnNUHMzz+HdJNkugf0zmElbHSOjVn1a9icVbcjlopDAapErxyJezYIbUqitBngUBQBapNlAUF1SMzI7O6TnfTkMlk3Hdfb+67r3elj+nQoT0dOrSveGApgoKCePGFmVWdnkAgEPw3uHRJcpXbscN+X/H6mj597PfdaZj0sH8CJHxvtyvPszUdjhwh3YELuiMyC7Lo3rgJr284RW0vFxRyGU90CGLQPXWwWq1k6418NawNKoWcuLR8DidlsXJ/Ep5aFReu5Tk0qijPWdBTq2J4ZDAjWljwje2PSu9ckF00ShWyxOKuv0JgLbgp3ciblAfuYLaaWX9uPc93fJ4RP42o8H5ndp6JxSrjk30fVOr1+WTvR2x8YiNymRyL9Ua7ZjM1/FEHAso80Rg8WjDs9ItkmCTH5eGRwfRtVosUnZ6RHUMqV/2qDAUF8OCD8Pvv0s+rV0sxD59++s/OKxAI7iqqTZQN6N+fOXM/5/TpM3ZOhQKBQCC4i1i9GiZNktaRlSUyEn74AerVu+XTqnbyk2DXQ5B5yG7XXutA6nacQ/pB+wwyZ3hrvThxJYf/69EAGeBTQ83vp1N5ctkBu/VN47uF0T7Eh871fZn9UAuOJGXTPsTHLsur2FmwbPhycevj6XMHUP7xGCpFmtN5nTdIa8guFwuyLJCtkjFxxAu89/ZL5BpzydZn4enihafGC7kMJradWG71a1K7SQxsNIicwmyOpR6r1OtzJOUIuYZcmvs353iqdC/3qGFbXahZpth6sBA8u3+Pf1wOgTKZTUWsgUs1GpDl58OgQbB9u+32L76AJ5+EZs2q71oCgeA/TbWJsqZNw+nduxdvvPkWvXv3on37dvjV9EOttg9kBMkNUSAQCAT/IaxWGDFCqoKVRSaDmTMl57nblFf5dyi2pNfpTeToDXhq1VKFJWMXyj1DoMhWzFiRsTh/Ih8kDuTQIwFVauXzdPFiyq9HWDSyPUUmC7M3nXUYlnzqqo5pq48yrksoC0e0LRlXLLTaBHuxJCahRMgt35fIpqldGRYRxIrYpJLcsp+3b+aJ/HHUVGU7ndcZA/S8DMnF1b5E8I+uRefxb7HJWIvf397F2/c3575mDUnTFbL7bAbHL2fzwYCP6BoUxUd7P7Az45jR8QUGNhqIxawivaBqHTYZBRl4aDwAaKOBrXXAp4wg26eHvldhp0LB8icj0KoV1VMRK0tuLgwYIOXslaZGDfjxRyHIBAJBlag2UfbU+InIZDKsViubNm1m06bN5Y7/ad2P1XVpgUAgEPwbkMnAkQtvUBB8953kPncHUWAwse1MKgujS4cVW3khbBsT3L4EbB38jLIa7PL7DF/f/jwVlIcMFdM7zmT4T0MrvNb0jjM5kpCLwWzFVaUg9mKGQ0FWGo1KwdYz11i5PwmFXIZWreD1DaeIauTHopHtKLye5eXtqsbNRcGMPo1pHeRFfpGJ82f+ZGTBaLxUzoOhTxRBrytwrViQHQPXC1Fs+W05s369CMm5mCzw0k8ncVEp6Ns8kNc3niY2PpPdFzJ4+4Eodo4eSHZhNpn6LHy03ni6eHE4IZdnVp/ho0da4KOtmsGLr6svuiIdHTTwWx3wKiPIduuh/1XItYCP1htfNyeGG/8UnQ769oW9ZeJvvL1hyxbo0OHmXFcgEPxnqTZR1qxp0zs2+FMgEAgE1cTLL0u5Y2fPSj8PHQpffgleXrd1WlWlwGBi9qYzLN93QxhpFUbm1l/IfS5b7Many4J5/OwLaNPDWfmUPxarlamrjjPn8UFMaDuRheW08k1sO4lBjQeSV6Rg3hNtQAYLoh04VZaiOH/s2dVHmdwjlCciapFv1JFRkImvqxyLtZBdf2USfT6DFF0hSrmMhSPbkZCez/81T4HtQ3BVFjg9/6FC6HMFMiyAFVz2aHm4/WyMfSKo7V8TuewiCrkMs0VyMPlmdzw9wwMY0r4esfGZXMrSM3rJYZRyuK9ZIL411GTkp/L7qWOYLPDxoy1ZsieBCVF1q1RNdFO74aE7wa91wKOMINteIJmq5FulsV4unhWe82+h18PgwfaCzNcXtm2DVq1uznUFAsF/mmoTZe+++3Z1nUogEAgEdyoaDXzzDTzwAMydK4mym4zTFsO/2bJmMlvYdia1RJAVG2IMbynHf/dBKGMkmVyjJ31in0Km8Wbj0Da8V6rtcNL3ZuaP+IhuwVF8tMe+le+FzlIr37EkPbM3ncVitTLviTYOc8VK0yjADbPZwoePNnaa7zWtw3ReH9yP0YuPkZBRgL7IjCb9D1x2zUAu1zs9d8z1apPOAhRC0/P30OH+D+gf0ZRe4QEUGEx8/ngbNCo5q/ZfYvm+RE5d1ZGWV0RkmG9JmySAyQKbTqTYnH9YRBDtQ3x4+5cztA/xqnQ1cUanmWSn7GNzbStuZX6tWwvg/qugv+5yOb3TTNTym1AlMxrhsccgOtp2u5+fFAjdokX1X1MgENwVVJsoEwgEAsFdgNV6oxL22muOx3TuDImJ4Op606fjuMVQMsOYEBVGr/CAKof9GswWFuyQKlXF67T2x2cybm0CbjnPsyLsZZQyyf2vsNFLjIvpg86Sx+TIYGLjbdsOY+IymPDdSRaMGETfBveRXpBOWn4avq6+aJVa1p9bz6/ntxBR617yi0z4uWvKzRUrJtDDBQ9XKy9se9mhRfzRlKOM2TCcCW0nsXTsO9w/7yDWyz8zkWeQW0wOziix7bq4KbAC6eCZ9DjL5s7jgy3nmPHjcYZHBDE+qj4Pzd9DXW8tozuFsGFKZ0Yt3k9WvoE5exJ4sV84kWGOA5jHdQkjqrEfD30VQ47eyCs/nebXaRVXEye1ncSgxoPAaubksUZEGs+X7NuUDw8lQ9F1QTax7SQGNx6Eumxg2T/FYpEyyH75xXa7n5/kMtq0afVeTyAQ3FXcNFF29epVdLpc3N3dqVOn9s26jEAgEAhuFbGxklnHzp1SBtMDD0BLJ0HBt0iQlW0xLObUVR1TVx1leGQws/o3qZIw0+lNnE7WlRhiLIiOKyW0WvD21aeYGbiMT/NeZmjQM5xdt5Nmtd0Z2TGYMUsO2JzLU6vi/UcaMXPbdL45sojm/s3x0HigK9Jx8trJEmv3YvE0Y+1ZfGpUbIQS1ciXTRc2OhRkpVl4aD4d63bhtfA4Qk+/jkLuPDBtYx48miKJG3mSG/6Kt9GEN8TfQ0tekSTklscm0SrIi2ERQXy1I44ZPx5nWEQQy8Z2wGKxciEtj/6f7yJ6RvcbAcx6Ix5aJW4uKgqKTKgVcro0rElCRhKXsvRM+v6kVE10ZgzS6QUGNx6Ei0KL0Wyh2aD9JP7Rj+CcvWy4PmeD1Xasm7pGha9hlbBa4emnpRyy0nh6Slb4QpAJBIJ/SLWKMqPRyOrVa/jt963k5+cD0KNHd6Y+PQWAHTuiWb9hA09PmUJYWGh1XlogEAgEN4uzZ6W1YuvW3dhmscDUqZIV+G1YT1y2xdAZy/clEhHqQ7/mgZVuZczRS5Wq4aUqXwq5jEYBbrhplOwveoI+FzrSMCQcpULGzpk9sFismCxWu7bD4ZF12Z64uUQ8FVu5l6VYPPVp2p4ADxeHuWLFKOQyejXzYeDqSuR7WeH0uqm81yij3CzjNbkwPEWyLmmU34nmbWYjV6gI8HDBt4aav1JzS8YuiUlg0ch2LIiOw2KFFbFJtAnypkcTP86l5GKxwpVsPfX9ahDo6WITwOymkR47ZvUPJyJUqqbFxGUwYO5+G2OQrMJsfFy88NJ6oZZrMFlkbD6RzG+nU8kvMhHV8FseqL2OpnWHEGvSU7OGD14unqjlmuqvkAF88AF89ZXtNldX+PVXsYZMIBBUC9UmyoqKinj1tdf5668LeHp60rZNGw4ess1uadGyBXM//4KYmBghygQCgeDfTkICvPsuLF4sibCyREdL62h69brlUyvdYlgRC6Lj6BnuX2lR5ms8y2u1v6ZPh5U8u+YYk3s0YGhEEIUGM5kFBnxc1dTQdEClkLP7QjqLdl7EVa1gZt8mNudRyGU8EVGLgasrt65ubuzHRI/eidli4ckuoTz3g+P8rkYBbuQbdRXne2XC0+nwwYCMcoct0cG4VLCYoJ6mHnOeeoWuQe24lJ2CVq3GQiGvDGjCZ9viyNEbOXVVR6HRTONAd84kS2JtcUw83RvfiLpxUSr4ZlcCY7uEOKxSuqqV9Ar3p2vDmmQVGMjIk1wicwrkXEhV4e9Rl5qubriolBQUmdj1VzptQnxoWtuz5HeQp3qW3WeuseOcnnbBesZ2CUCtvAkNQPHx8OqrtttUKulDis6dq/96AoHgrqTa/vX66aefOX/+L3r16sn4p8ahVqt54MGHbcb4+vhQr25djh0/zojqurBAIBAIqpe//oL33oPvvweTk/VHwcHwySfQs+etndt1ilsMK8Opqzp0elPlWhgvLsV3/yTG1iwk6+JcPnp0KvvjMxn/3cGSypWnVsXmaV35dOv5EkOL8Fru+Ljath1WWjxd50jKEbILs/krWUXbYG+7IOhi3DRKMsrL9zICe+BFD3jvsfKvOS8bpqYBBhljOozmje5vUGAs4HT6KWq51cKKlaXHluDvWYv1U+4tMQ0xmiw08HMrEWWnrurI0RtpHOiOXCZDo5Lz6dZzBPu6OqxSFreeroxNonGgO24aJXlFppJKG8A77S7ySO/7KVQGois02vwOQFqjNrpTCG8ObsaoxfudXusfExoKGzZIBh95eVJleMUK6NOneq8jEAjuaqpNlO3aHYNfzZpMmjgBhULhdFztOrU5W2yVLBAIBIJ/D6dOwezZsHq148oYgI8PzJoFkyeDi4vjMbeA4hbDyqIrNNq00dlh0sOhpyHuW4qbMb3Ov8oP8bV572BNm6HDI4OJuZBeIsgAzqfm4aJW2LQdViieHJBdmM3mE2YOJGQ6DIIGCPBwobZHTfuDrcBZ4Dd49z6YdX/51/ogE17MABdciP6/aE5dO8X9q++3W9M1tcNU2tVuzeJjc1k6dhr3zzuIVq3gpf7h7PwrnRy9ZEeZV2TCTaNkSPt6rIxNwmJ1XKUs23paLOxK84DXdp4o+gzZzq/41DCfRQfy7cacuqqzWdM288fjVaqIVol+/WD3bhg4EJ59Fh59tPqvIRAI7mqqTZRdu3aNtm3blCvIAFRKFXl59v+4CgQCgeA2YLXC1q0wZw5s3ux8nFYrPYzOnCmZG9xmPLUVm2GUxsOlnHVGuRdg1yOQbVvRklnNhKQtAF4p2VacD/bUsoM2Y80WKytjkxjdKYQZP0rrxvKKTPi6+lRpnl4uXpy8epGEjAIGz4thWESQXRC0RiXHRW2iba22GC1GPDQeXLt4jfMrzyOPhy/HwsR7y7/OqxnwTiZoFBqOTTrGJ3s/YdGhRXbjjqYcZeyGsUxoO4EZnWaw98qfPNu7NQq5jD1x6SWGHwB+bhqiGvqV2N2D4yplRa2nj3hv48O6c5HLrJB7locLR/Gj4j0yzY7/3q2ITaJ1kBdtg70rrIj+o/iEe+6BEyf+FX//BQLBf49qE2VqtZr8Soit1GupuLlVsyuSQCAQCP4eBgOMHAmpqY73a7Uwfrwkxmr/e5x0PbTKcs0wStOstgceWif/3SWsgv0TwGRfrdmgf4jpSbbN9o0C3Cg0mB22Ti7fl8iGKZ1L2g7Pp+ZRQ+VRpXBkD40n51KkueTojXy1I44F0XE2LX4pOYXMebwFv4/4nfOXz/PRux+xd/leVMB3z8Dj7cq/znNp8FkWIIPnOz1PTFKMQ0FWmoWHFhJZN5LcolyGt+rDNzuT2HE+rcTwI7yWB+4uKh5qW5ehX+8jR28syXeroVGQklNIjt6Abw0NJovFaevpEz5beK/uPJttjV2SeMRnG4vSHnZ4DNwwHymvIlot8Ql3WAi6QCC4c6i2Gn9oaAgX4uLIyclxOiY1NZWLF+Np0KBBdV1WIBAIBP8EjQb+7//st7u5wQsvSCYHc+b8qwQZgFohZ0JUWKXGToyqj7psFcSUD/vGwp6h9oJMWYPs1suY+tdYDFbbCpubRuk0RyxHb2TU4v1MiArj40db0iTQnVWxyUzrML1S85zZaSZFBkXJmqpiLFapxe9AQhZ6g5mfJrcjLmcr94y9h473dGTdsnVolGbWv1a+ILNYYXwqfJYNyEAhUzCh7QTmxs6t1Pw+j/2c+5vcT54xh/0JmZLhh0Ey/BjTOYT8IiNz/zhPQkYBIb6ubJjSmRBfVx5ftI/I9/6gz5xdTFpxiKvZhQ7PP8L3FztBBvB12gMsSnuo3LkVz6Xsur5iitewTV111E7IF8cnzN50lgKDCTKr1nIqEAgE1UG1ibL7evdGr9fz8SefodPZfwKWl5fP5198idlsps9991XXZQUCgUBQEfn59oG3pZkwAdTXH2a9vSWnuYQEeP99CAi4JVOsKkqFnF7hAQyPDCp33PDIYPt1RlnHYEtbuLjE/gCPcOhzgFTvBxyeL6/IhG85OWLFbYcX0/JZNLIdj7QJ4sHw+5nYdmK585zQdhIDGg3EipxmtT0cjvHUqlg6tiX/98UInhw4isurL4MevNzh9/ehXzmfd5qtMDIVrvgPLNnWzL8ZBcaCKhmRFBgLyNbf6IrJKjDwYOs6tA/xwWC2MK1nI+p5a/lubAQLoy8y48fjNiIor8iEp9a+lfTJmj/zdh37zLUvrz3Ku8lPAhXHLugKjbi52C+hqEp8wtFl67CGhEhGHgKBQHALqTZR1q1bV7p26cKJEycYP2ESb7z5FgBnzpzl3dnvMX78BE6dOkX3qCjat6+gt0IgEAgE/wyrFfbs4f/ZO+v4qgo2jn9vbveueyMWxGCMrg1GhzTYCkxKJRQFlbC7iNdESiWUMpFWUaQGjBw5emPEuu7i3t18/zjssrt7VzhCPd/3s88rp8/dGOd3nuf5/Zg4UahyDRki5I05IiBAMO9YtAiuXIG33wYfn9t7vTeBWinn5YERfD68jZ2QiazjzufD29gGR1ssGBM/xfJbFGjO2B+wwRjotx88IiqcWUvPL8Hfzdl6PplUQkSQGx1CvYgIckMmlVjbDrvO2sb/tp7FZFIyu+8cVt2/mtaBrW2O1zqwNcuGreTFTu+yZn8GaflaxnQOdXjuHj4aBg3szpa3NkOGsCwwFHbMhRj/ij8nnRnuvybh19wnWThwufUa3J3cySrOqnhHB2QXZ+Ph7GENkw70cGZwyzq8u/EUEokwZ/bRI625kFnAkcu5yKS2YqqsIUopE/1+5LU6X9mdKyfsJeakjaI6ggzAz80JuYMwturGJ9TRZBD53HgkBQUQGwtTp4LBUK1zi4iIiPxdajXQ4/nnpxLWIIxffvmFhAThzVtqaiqpqamo1WpiR47ggQcqb0EQEREREblJLBbBiGD1auHr0iXb9V9/DXPmON73jTdu/fXdAtRKOQOaB9I7wh+N1ohGa8BdJcfVWUGRzkCBThAPSmMe7B+L/Jp9xbDIIiWh/kRatZ2Nq0KYea5oZm14VDAXMgt4smsDzmXkMzwqiCKDhuziHHzU3qjl7qzZn8qKfVcoLDFyX5u6/HoilcRUDVP7DKNr/X4UGTTkaHPxVnmhlruzOj6VYesE45CNz8TQPdzPxg7fkJeGZtcKPju13eZawrrC1nHQsBLPkwIz3HfVlRP5r+Jqbs6a/Wk8FzWd0etGoinR4Kt24OJYCX4ufiilas6kFRBZxx0vtZIRX+7lsU6hrIpPYcfZTL6b0ImzaQXMG94WJ4WU1fsvs2LfJfK1BjtDlGf8V/NCoH1V6n/pj/HowNeIPHCw2nODXmqlQ7OO6sQnyE1GPl83G4+iMiMYn34qmHuMHVv1ByMiIiLyN6lVUSaRSLj/vnsZNnQIFy9eJCMjE7PFjK+PD40aNUKhqMT9SkRERESk5lgsQgXsp58EIXbqVMXbfvONEAatrJlz4d2O/LpznkQC5zML+GzbZTYfT7XOZvVq6s/isPcdCrKDOng0zcyF8/OZpLEw+545uCpdUMqkTO8XzuPLD2G6fqBS58VZW07z7n2NKZDuYMh3I+0s5Kd2nMbWFwYjl0gpNmlQu+TQvoE3CoWJTcdy2HBMMFUpLNHY5HIBPPb1flY+EcW0fk0I94C33n2Xa7t+BlOZvDgFRD4Kv/eBOpX8K55lgodTG3I29w2cEVwgV+y7wrZpg5nYbiJfHv4StUJdIyMSH5UvS3ZewWyBsTGh7DqXyZejOlBiNPHOxkTytQbS83W88stxElMLrFli6yfHMHrJfpKziwVDlKc7s6TNenqZ7AXZ+6ljWZz5AM2v5jOhWwOeXVP1tU3s3hAnuePmn+rEJ0yNW0W7a+UqyUOHwujRVe4rIiIiUhvUqigrRSaT0bhxYxo3bnwrDi8iIiLy38Zshn37YN06+OUXOHu26n2kUmjXDrKy7jrTjtqg1MjB0dzQ6M712ajpSQ/TL3iWGTmamwsvZwlZywALDi2gW2g3+ja4h2sFV2kU5M2+l2NYFneVFfuuUMfTGb3BxAv9Qpj+x3QWH7KfgUpIS2DM+lgmtZ/EzJiZtFnUhlxdLiAItmmdZjDnoX7WEObyJGcX0/9/f9LXfIgl8z+xN88KgKixsLkZeFeSQHPFAPNdH8DL7VnkOTeMTAa3DEKnl/NKl/fpHtKddWfW8WzHZxm3flzFB7vOc9HPYTErWBF/hZFRwXQI9WbovDjeGRZJukZnzSvLLdbj6iQ8XpTPEhs6L458rZ64n55khNsau3O8dfVJ9js9xufDG9K1sVDFi40OrnQezOHcYBmqik/odOkoT+39wXZhgwbCSwwH7ZAiIiIit4JbIsrKc/jwYS6lpODr60un6Gjk8ttyWhEREZF/H19+KRhxVGRhX56mTWHkSOGNf/36t/baapGa5ElVZuSglEtpG+JK16VLaFQEPwZBhhFGp8Ov9pqI2XGz6Vy/M60XtcZsMVuF1KYp/Zm1JQmp1MJfl7Y4FGRlWXBwAR3rdmRi+4l8sPsDQBBssWtHMLHdJJaNe5dh8w5ahQyAxWyi6ORf5MSv5uNsB9/f9tDnAfilHrhUohXO6qHvVdg9+RPeXHsNECpWY2NC6RDqzcQVh1kY2xZPSVe6NnHB1UnK/qv7WVjJPU1qP4lhTe7ltbXneG1wBB1CvRm9ZD/5WgOLdl5k8aj2fLDlNGYLeKmV1pmzUkqzxEZ2rI/32ZcY4bbO7hzpTf7HhEFPMb3c9/nlgRFEhfmwcMcFOyv7id0b0jvCv1Ir+8riE3yK8vh0w1yk3ChXWuRyJGvWiHlkIiIit5VaU0ebN2/hl3XreW7qs0RERFiXz549l7379ln/3KhRQ9579x2U/7L2GREREZHbgotL1YIsOBgefRSGDxdmYiTVM0q4W6hpnlRlRg59IvzJ0+VxNP0oR4HJGfBTIaSZHJ+71GGwuX9zjqUfswqpSe0nMev+2QB8El/BXF45Pov/jF8e/YVZcbMwW8zW5QsPLaBrcDee69uBN9efxmKxoLt4CG3cNxSkOrgPZ2AoPNgRVgaCspJv5xEd9LsG9fza4Kr04ONHAsguLEEhl7IqPoV3NiYyuWdDDl7KZcqaBKQS6NnUj7eHfUDn+l35aN8cu3bMmTEzGdR4MMV6Oe/e1wKDyczK+BRyiwVBWdYaXyqR4KSQWrPWyrI0LpkvR0SQffWkzXILEiQdFxPQ6AmH92Q3N6gz4O6sqHboc2l8wrOrE2yWSyxm5m7+GP+iXJvl5vfeR9ahQ6XHFBEREaltak2U7YuPp6SkhCZNmliXHT58mD179+Lj40OPHt05fvw4586d5/fftzJ48KDaOrWIiIjIPx+LBc6dg127YNs2+OILx0G1AweCXA5G20oEdevCsGGCEOvc+R/bdlVZG2JpnlRsdIiNq2KpkUM9RTrTAr/h1atPo8WF8ABX2gZ7kqO98dD9RcVRmlayi7Nxd7J1c1xwcAGDGg+hVWCLGlvIlwq8sszdO5sdY3biVXSND956lfi4nY4PUg94ECaHwKd+IK1EkO3UwpBroDHD3OipnM85RwPPRhxOyWf6j8cwW8BbrWR4xxAeXrQXELLL/kzM5GByHiOjWrPhkW0UGTRoSvIIcvPBRenBzjN5PLvqFGkaHWfSCogIsp8TK21ZfKRDfVbFp9hlrYHw/dOanXm7+GPekjxLM1USFiSYO36NrFHlZhqlc4NqpbzCcOjK9i2NTyj7czXu4Hp6Xjxks62p7z3Ipr1Qo+OLiIiI1Aa1JsquXr1GcHAw0jIPArt2xSGRSJg5Yzrh4Y3R6/U88eQEtu/YKYoyERGR/zYmk+CUuGsX7Nwp/H/ZCtgDD8D9DtxqPT2hZ0/YuhUiI+HeewUx1q7dP1aIlVKTPKmoMG8GNA9ELpOSX1zCg15/8EadRbjJtDQM9Ma790p0ehMWiwUn52oosTL4qH3QlNi3uv2c+CPBHvVqdCxHAg/gSOIRYkfGsv4n+zY+QHCB7wz0hvf84GXvys+zsQgeTgWtBSa0m0BM/Rg6fNmBRyIfZXbf2Xz2p4pLOVqe7dOYPK3ezo1QsPFPYuGOJNqFeDFveFu+O3iZT/44aiewHM2JeamVdGvsR4dQb97ZmFjhdeYW60HpTezFd9nU/G28O7yCU6PHKr+5WqA0PqG0DbLk2Alm7Fhus40lMBDZim//8X+PRERE/pnUmijTaDRERjazWXbi5El8fXwIDxcMP5RKJU2bNuF0RVk5IiIiIv9Wrl2DAwdufMXHQ3kTh7L89ptjUQYwa5ZQSfuXmSlVN08KYOGOC4K5Q8k1Qk48ydz6v1rXtShZy3tr/seXyW1QyqUceq1rjRwGVXIVJzJO2K07mHoQT2fP6t4O4EDgFQM7gf2w3lyBIHMB7gN5I1gcAGMdZ0lbWamBMenQPLA1U6Km0DW4K/1X9idXl8vCQwvoHtKNXybfy4ML9jGoRRCXcxwM013HbIGOYT7sPJfJR1srN5ApnRN7rm9jvF2VDG1dxzpnVhFeaiUB7s6MergHHk3ux8m5ZlWvv4O1DbKxN4ouU1GYyszzSSRIvv0W/CsJfBMRERG5hdSaKFOr1Wg0N/7hSU9PJzMzk549eths5+zsjE5XUlunFREREbl7+eILoaJ14IAgymrCb78JLY2O5sHatKmd67vLqE6eVCknr+VjOPMlnH4RZ4P9PoPl3/Alranj4YzZrGBapxnErh1R5XGfjXqWRYcW2cyAWc+ZcRJ3J/ebE3gGIB7YBVT2T2AYcD+o3eH7IBjkUvk5cuqPIDR4Isdd/FDJVSw6tIjnf3ve6vgIMHvPbPo2HMg346LQG014qCqOpym1/X9y+cEq7w+EObGVT0SRcDmPKWsSbASZHCNSiQW9RThfZB13Ajycmf1gy2rNgt0K5DIp8o/mwpHDNsslU6dCnz63/XpERERESqk1URYUFMjJk6fIzMzEz8+P337fikQioU1b24eH7KxsvBzNSYiIiIj8kzCZICUFdDooY25kw6ZNsGVLzY4bFARdu0KvXoL1vawS3/N/GdXJkwKop0jng3qf43E8weH6H3L68Na18XiolHwzLorP/rjAY537Mb7tRBYfrthhcEK7CXQN7srzvz3vcL3JYkJrMPB89HRG/TKyyut8NupZFh5YiDnBDH8ClelNKdAD6Ao+MthYB6JVlR//jWwJv2iO437+ZTQlGk5knHAoJo+kHSFHm0dKhhMRQe44K2UVuhGGB7ii05scimOZVEJ4gCuuTnIKS4ycTS8U5sQMJr7467yNIFNIDHwePBsZZp5KeRGDRcHE7g1xlt8ZMWbl6FF4+23bZU2bCvl9IiIiIneQWhNl/fv345NPPmPKlOcIDAwkKTkZd3d3OrRvZ92mpKSE8xcu0KJ589o6rYiIiMitwWKBvDy4ehUuX4YLF+D8eeHr3DlISgKDQZjv2rbN8TFatKhalDVsKIiwbt2E/2/Y8B/nllhbVJUnJcHMSJ8tvBi4DFeZ1m59jsmbmZefYqsmGoCno0PYn5TNnotZZBTqeG3A+3Su35VP4u0dBsu3/TmiTWAbTCY5Xev1Z2K7iZVayE9oNwHJBQlznpkDqVXdOPAAEAwhcvitLjSp5KOwICWp0Su8fe4d4HgVBxfIKsrht5MSohr4oDeaeLxLGM9/b29Y4uokJ6fYVhx7qBTERocwIioYnd5ETrEeb7USJ4WU1fsvU6A10DcigAPJwuemlBiYH/I+fdwPAPBp/TnsC/qi0iyx28auXbYmOVIpLFsGqioUsIiIiMgtptZEWY/u3UlKSmbz5i1cTErCx8eHKVOeQVXmF11c3B5KSkpo2bJFbZ1WRERE5O/z559Cu+DVq7ZfWvsHfzvOn694XfkXUG5u0L49dOggfHXu/K8Mcr5ZKsuTClFeY1a9z4h2tZ/1AtDWi6Xnb4PIN7kB4O2i5ImuYZQYzLQJ9iKnWI/BoGBI4/sY1HgwBfp88nR5eDp74u7kwv/2zrVr+yvPlKhprNx3jc3H0/ll8uwKLeQf9HqQ3z7+jUXbFlV9002BYYAKWirh17oQVMm/zCUWJbsD5tGwcX/Y8k7Vx7+Ot8qL41cvkZ6vY9GOC7w4MIJpfcPZcS7TWvUymS0UlhjxVt9QhKE+apaP68j+pBzGf3PQLqJgTOdQgjxVDGtTF29XJSvjzjBF+QY93G+4Gg703MM9AZ8iVy6t9vXeMiZPFtp/x4wR/u6++CJERd3pqxIRERGp3fDosWNGEztyBMXFxXg4CF1s0aIFH380l8DAwNo8rYiIiMgNiosFQXXlivBV+t/OzjB3ruN9du6EOdXLnrLj8mVBvDl60x4dDc88c0OEhYeLzm6V4ChPSiExMMHvJ57x/w4nqQMDCXU96LCIFFkX8jfvAgQhsfKJaHaezWTxTvusszGdQ+na2BcXVz9+OXyVe5q7kF2cS64uF5lERqR/JO5O7mhKNJzMOInJYmJCu0l0r9+fYesOUlhipEgnw1vajc3DB1JQkk+2NheTxsAXsz7jtZWvYbE48IQviwy4B7iuB3qo4Jcg8KikW9Ws8GJP4NdM/8uVHV29ajTbppa7cyatgDytntbBXuiNJh5oV48eTf1xcZKhkAlVr9XxKbg4CeL4Sq6Wb8ZFsXDHBVbtdxxRMP3HYxy9nM/47g3IyM1hdZP3cc62tZm3yF2QNxxT5XXeNmJiICEBPv0UXhDt70VERO4OalWUASgUCoeCDMDPzxc/P9/aPqWIiMh/jdxcOHXqRjth6dfFi5CT43ifoKCKRVndujd/Le7ukJoKDRrYr2vcGD777OaP/Q/DaDKjN5nRaI3ka/V4qJTVDvgF+zwpOUbWN3qOCFWy4x0aPglt5oDSA498HSC02n0zLoov/jpfqZAYGRXMUz0a0r9FEKeu5fN6tw94st14glz9KdAXkFWcha/aF1elK+mFmfg5h/L0ypPERofwWHQIFosFfzc1xTo5zhIPPp+ziB+++hSjrmJnQys+IH1YijlAmP8a6QZLAioPhTap6jE26W3e6XUP/TMv8vvJbGZ0nsGIn6s2L5kSNY3V8amYLRDkoSK7UM/jy+2rXuO7NmDHjB6YzBae7NqAcxmFxCdlO/wcy7Ii/hLt6skZnPMkzsYDtivlbkh6bgG/mCqv87bi4gIvv3ynr0JERETESq2LMoDs7GxOJSaSkyO0gXh7exHRtCm+vqIgExER+Rt8/LHwdflyzfdNSxNmwBQOnOfqVZE95ecHYWHQqJHtV+PG4OPzn50BK0ux3sgfieks2mFfmZrQvQF9IgKsYc+VUT5P6q+C9naizKwOQRr9FQTecMsrbX3s0cS/WkJiZXwK7UO8ySjQ4qlS4qFyZ2fKaZ7cMM6uHfGF6OkEhITy5aj27D6fxePLD3DymgaLxYL2wgGKdnxNcdbVan1O0tZSGCLFLBPmml71hnd8Kt/ntDYE5y6/kX05g7c3nOSTR9qQlFVIiO+Qasy23ajwRdZxRyGTMnHFIYfZY1O+S2BkVDDT7mlCnwh/ujT2ZdTX+6u8JzdpEU1OPUSwwra11KLwQNLzN/AV2wNFREREqqJWRVl2Tg6LF3/J/v0HHK7v0KE94598QhRnIiIijrFYBPEUFOR4vdl8c4Ks9NipqRAcbL8uPBweeUSomJX9qldPuBYnp5s753+EYr2R9zcnOgx9PnlNw7OrE4iNDuHlgU2rLcwGNA+kd4Q/msJI9NsPotQlY0GKJfwZpK3eAYWbzT5KmZSXBjalkZ8b45Y5/jeoPF/tvsh34zthkeiY/vs0h+ImIS2Bx34ZyaT2k3i92we8s/EU+VoDxvwMcv5YiPZ81aJFuECQPeqPqUEGYEYOLPKHcY4bS6zEF0by5KXXWCENYNnYUIr1Rq7ma/F3d0YuU/B+79l0De7OnL2z7MTk1KjpdK/fnzFLjpKvNfDW0EiWxiXZCbKylIpVmRTah3pXGVHgLivkm7DXaaawzTSzKLyQ9N4K3u0q2PM2YTSC/Ja8fxYRERGpVWrtN1V2Tg4zZ75IVlY2Tk5OtG7dCv/rIYyZGZkcSUhg//4DXLhwkdmzP8TH27u2Ti0iIvJPprBQcC/cvFn4MhqFOTBH1adWrap/XA+PG8KqXj3hvysSV40awZo1N3f9/3GMJjN/JKY7FGRlWbHvElFh3gxoHui4lbFcJpv8esuj2tsbohfAsdeQdFyExLut3flLWybreqowWyw1yDrTYMHE+jMbKq02ASw4uICoul14tF0ks//3Cflxq7EYqpe5GdK1GVe6ZmJSZgDgLoUfg6CvuvL9fs3vxJSUaZRYnPB2UfLWhpNsPHbDyjGyjjtP92zIvU3vp2+DgeRoc8nR5uKt8kItd2d1fCrD1h0kX2sgNiqEDqHevLH+ZJXX+9Xuiywb25FL2UWVbucp07CiwWs0V12wWW5R+gqCzKt1lee6pRQVCXOdY8fClCn/qXgJERGRfx61JspWfLuSrKxsunfrxhNPjMPNzfYtZmFhIV99vYTt23ewYsVKpjz7TG2dWkRE5J9GTg6sXQs//AB//QX6cvlUSUmOZ7TKijKFQrCPb9z4Rjthw4YQEiIIsHK/g0r5u3NPIrboTWYWbr9o/bOjLCvT9dLMin2X6BPhb/P5e8oK8b74LjKlB9I2Hzg+SZ3+EHQPSGy/P+VbJqPCvHhtcCQdQr3szu3o2pKzi5FIjczZM6ta9/reyjeRbpKTl5hYre3rhITRclJnthl+xGQSBFw9OWyuAy2qKL5+kzWIN6+Nx4yQKQaw+bitt/7JaxqeWnmEZ3s14sluDUjJLGbrKQnHr17iTFoBZosg3F4f3Ix7IgMY8vlumyyxijh5TYPOYMLHtWJffh9ZHisavGrXWmpxDkDS60/wjKzyPLecmTPhxAnBzGPtWsH6vmHDO31VIiIiIg6pNVF26PBhAvz9efbZycgcvI1ydXXlmclPk3gqkUOHDjk4goiIyL+a/HxYtw6++w5+/902K6g8u3Y5FmV+fsL+zZpBkyaO58MqobbmnkRuoNEaOZWqqTLLaufZTGY/2JLfT6WzeOdFEq/l8qj370wL/BaFXINZokAbHIvKp4KHeQeCrLRl0kOl4OmejXgsOgSzxcKM/k1tzr0+4SpDWwcyPCqIIoOG7OIcfNTeeDl7kafL5Wi6fV6XDXrgDzi3/1y1PhNXNzcmTJvKidAEfk1aaV3e2gk21YE6VfyIvXttHF9l3QcIlcOxMaGsik+psO3wi+0XaBPsia+rEy/c0wSz2UKaRoe7swInhZQdZzM5l1FIcnY1TEiuk1VYQh1PlcOIAj95DqsavEJjZ9tWYrNzHaR9toF7k2qf55bx55/wxRc3/rx7N0yaJPzuEREREbkLqbWnD51OR6tWLR0KslJkMhnh4eHE769mD76IiMg/m5IS2LgRVqwQQpRLqtfuxdFKHpIffvimLqW2555EBPK1+kqzrFrUdee5vuFM6tGQ9zclsmp/Cu3Up1jfeJFN25vUYuDy1gnUu+8v1E6Vi+2yLZNlz11qwFFKZB13JvdsxITudVmbuJ4h3420mbsa23oskztOrvwGk4F1QMXxZWWQ4NKiD3M+fZIZe6ZQkHSjsjVADd8HgWslxViz1Jm3MmeyPKuDddnIqGA6hHrzzkb76lxZIaw3migxmCnUGfFyUeLmJOfFn49x6FIuTQLdmDe8rd3+lRHo4YxCJmF81wZM+S6hzBoLX4R8aCfItIo6KHr9hdQ9vEbnuSXk58O4cbbLVCpbkSYiIiJyl1FrTx716tWzui1WRk5uLvX+jv20iIjI3U9xsdA6tGpVxRb1ZZHLoWtXGDhQ+IqIqNXLqbW5JxE7PNVKh1lWZQWDUibhz1Pp7DhykE/qL+derx0OjxVsPEDc0f10b9ep0s+/tGWy1P6+ohytK7lamtV1YsYf01nkYGbsUOoh1IoKBrv0wJ9AfKW3b0UZ2AiveyZSUv84k3cNx2QxWddN8IB5fiCvxKQz0whL3AcwdsSz/DLvIPW8VEzo1oBW9T0ZvWS/XdthVaHOE7o14IsRbXl40V7OphfirJRVGMxdnsg67pjNFiatOMTHj7RhRMfgMp+vhNevTmJ1g5fxkhcAkCupi3Of7cg9G1Xvw7rVPP88pJT7eZg9W2h1FhEREblLqbWnjqFDBnPq1CkOHzlS4TZHjiRw6tQpBg8ZVFunFRERuRtRqeDXXysXZE5OcN99gsFGVpZg9jFtmtCaWMsW8+Xnnipj4Y4L6E3mWj3/vxl3Zzn7k3NsRFGoj5r1k2MI9VEz8duDGHS56A9NZ1uTCRUKsk15MfQ+s5CP9pnRG82k5es4k6YhLV9Hsd6Iscz3pLRlMjY6xGp/L5NKiAhyo0OoFxFBbsikEmKj6/HXpS0OBRnAyYyTqBVqWge2tl1xFVhItQSZRKnGu+9E/B57jfyQleQpllsFmQz4xBcW+lcuyM7qodMVePHoWvZe+52tz3VhyZgOtKrvydB5cXZth6VidNGOi0z/8Zid0Dp5TcOzaxL47M/z/PJ0DA+0rcvq+BTGdA6t+oaA8d0asDI+hfikXEZ8uY8J3Rsw96GW1tm207owYpPeodDsSoEiFEW/nai87hJBtmEDLFliu6xnT3jqqTtzPSIiIiLV5KYrZZmZmTZ/joxsxoD+/XjvvQ/o0iWGrl1i8PPzs267O24Pu3fHMWBAf1o0b/73rlpEROTuRiKBMWPg1VdtlysU0K+fYD8/dKgQvHwbKH2Irw4nr2nQaI1iC2M1sQBLdidZ/1y2evXDgQs8H7aNgO0reUjtWKCf1QXz5rXx7ClsDcDVaxrSC3Q8tfIwialCJab8zF++Vo9MKmFEVDDPrUng6Z6NHM6yOSuN9Pm24nBlk8XEokOLeLbjs4xbPw7MwB5gG8J/V4E6ohtevZ7A4J5CmnIqJsmNbhF3KXwXCP1dKj/Gbi3cew2yr59vzp7Z9Ajpz/jlx/jkkbYOjTnKitHKWBF/iQ5hXrw8MAKZVIIESLicx8r4iveLjQ6hd9MAXl8nuDQmZxczdF4cI6OCWTyqPTq9idxiPV7q7hQVt+enUyYekNfBtfLbvD1kZ8OTT9ouc3ODpUtBKla+RURE7m5u+qnjyfETkTh4m22xWNi5cxc7d+5yuG7Lll/ZsuVX1v78482eWkRE5E5z9Sp8/TVcvCg4mjniscfgtdcEq/Pu3WHUKKEy5uV1Wy8VhLmnmqDRGQj0cL5FV/PvorzgjY0OIf5iFrlnVvF7+HLCnFLBgeGfxuTCx2kj+DZ7EMZy/xRlF+pxdbqx7HRaAQu2XyBdU0JsdDCeaiXhAa6YTGbmPNTSYQvf4JZBTBvgV6WJx4IDCzjw5AFiG8Sy4s0VUI2CqszNF5/+z+DcoBV58pVo5D+C5IYLRwMFbAiCZlU4LH5fAKPSoaSMgceRtCNoSvIxWyQOWw5LxeiTyw9WfaHA4p0XaR/qzcRvD/J0z8a8NCCCtsGeLIlLtmt5nNi9Ib0j/JEA9bxUVkGYrzUwf/sFFu64QJNAN6t75Zm0AiKC3BnT5y55gfHUU5Cebrvsk08ER1YRERGRu5yb/k0aeQtajERERO5izGb44w9YuBDWrwfT9ZmZV15xPKsRHCy0EXXr5thJ8TbioarY2tt2OwWx0fUI8pRwJf8K2dczn7xUniilTijlNXN7/C9QVvDKpBJGdKxH1rquPBTiuJXdYJGxInsgn6c/So7JcXKyl1pJYYnRoaNjdqEeL7WS5/uE46lW8uGW0w4rRukaHdc0WVVef64ul26vdaPkhxLIrHJzeg17FFmnUZzKSyVNORO99IzN+m4q+DkIfKqIxJqVAy9lC5XG8uRoc1ErZay63nI4/cdj1nXhAa7o9KYaVX51ehNGs4WJKw7xRJcwJvVoSKeGvmj1JjQ6A0EezrirFNZYCKPJzITuDZi/di1dXI/wVdb9AJgtWKuXpUzs3hDl3TB/uWYNfP+97bJBg4SMMhEREZF/ADctyt57753avA4REZG7ldxcoSq2cCFcuGC/fvFimDPH8b5jxtzSS6su7ip5lSYHoT5qlo1rxY7Lv9Jtma1LX+vA1kzrNINhTYfiqqyiH+1fRHUy3coK3vAAV3QGC0c1dWjlay/KtuR3ZlbqaJL1FZs9RdZxx0khpcRgYv3kmAqNLMZ3a4DJbGHPBcfCq7DEiI/au/IbtAC74dqf1yrfDlB5+lFvyFSeeW40uZYdTP3tefT6fJttxrnDAn9QVvK+0miBpzNgcSWaylvlRWGJhhX7LrF+coyN0Yark5yc4ppVfnOLb1Qev9qdRNMgN5Iyi9h2JoNXBzXD19XJxlhFLpPSN/AqfcJfRU0+ComJBZkP2R03NjqE3hH+d94UJyVFsLsvi7c3fPml+PJYRETkH8Nd0nMgIiJy13HuHHz6qdCeWFRU8XZLl8K77wrGHXcpSpmUCd0b8OzqBIfrPVQKlj/eivfjXmHxYXtTiIS0BGLXjmBiu0nMuWfOf0KYVTfTrazgLRUM32hGMtz3DxQIEQglHh3Y7/Uyk9ZXXWkcGxPKz4evsGxsxa6KJ69pmLImgZFRwSwf15Gh8+LsZq/OphfionCndWBrG4FtpQT4BahGDnTHvkNZuvgLfHyVTP1tKmtOrLZZLwVm+cK0Kjpzc0zwYCr8pa14mzaBbVDL3a3hz6OX7Gf5uI60DfFkaVwyhSVGvNXVq/yWUlp5LGVpXDKLR7VHozPSJtjTXlRl7Ue1sx8giM6ZQcvRW+R8nXUfYNvqqJRJKdYb71wYu8kEsbGQl2e7fP58CAq69ecXERERqSVqXZSlp6ej0Qj/gLu7uxMQEFDbpxAREblVWCywYwd8/LHgYmapIK0WwMUFRo6ECRPuakEGwpv/PhEBxEYHO7TFL3XpcyTIyrLw0AK6hnTjwYgH/tWtjNXJdJva0cz4/t1QOrlZBW9hiZEANye+njSUi/tH4561kXeujCJO34P1k7swouPFSs0pSjO54s5n2Tk6OmJlfAptgj0ZGRXM/O22VVyT2cLq+FSmdJzG2PWxtjtmA2uosl1RolQRPXI6vy6Yzhf7v+CtFW9QYrLN2nOTwqpAGFyFTs9XBhJ9No1zDubryjKt0wwOXyqkXYgXhSVGzqYXMuPHY7wyqCnfje9ETlEJ3q5ONbK3d1JIOZN2o+3w5DUNFouFlwc1RaUo9xiQuQf+6g9G2zbFmc1P80irzrirVVbRpTeZ2Xwi9c6Gsb/3nhA2X5ZHHxXMhERERET+QdTKb8uzZ8+xbv16EhKOUlxsa92rVqtp06Y1Q4cMITxczAgREbkr0evhu+/go48gIaHybVu0EFqFRo68be6JtYFaKeflgRFEhfmwcMcF60OkTCohtlMdBqyq2KWvLHP3zGZokyEo+XeKsqoy3Ro7XeIp/x8Yqt3J2T0zadzzXavg3Xw8DW8XJ97fnMi6g30wWAZgkigJD1Ax9/czvNg/wlrxKf8QPzYmlO7h/pgsFoa2qsPDi/ZV63pLqz4Ld1zAXO4dwop9V1g3eQDj2068IbgvA6uB4vJHsqV5qzao7nmOX9++l4ErBxB3ebf9Z6GAdXUgoorC1b7i1tTp9RM9neZwrhLhP6n9JIY2GUxhiYzwAA/cVQrcneUYTGZW7LvEa7+cZP7IdlzMLGRsTBjTfqjcxASEyuOq+BS7z6a4xEQ9r3KPAOk7YMcgMJarjAf0RNl9A+HyG8rzrghj12hg3jzbZaGhQqu1iIiIyD+Mv/2bcumy5axfvwHL9TfqcrkcV1fhF3dhYRFFRUXs3h1HXNwehg4dwtgxo//uKUVERGqbN96ADz+seL1MBg89BM88A506/WPnNNRKOQOaB9I7wh+N1ohGZ8BbrURnyarSpa+UI2lHyNPm/mtbGCvKdItUnWey//cM8NhjXVYvbR563QuoXXx4eWAEwzsGs/VUGqv2p+Ch8mBMOZMOvdFEr6b+9Gzqj1ZvIqdIj4tSToCHMwajmR1nM9lzPovJvRvX2MiiSaCbnQlFvtbAmCVHWTbuPXqEdufleS+RvDwZjI6PVYpfp/vZ/us3FJk19PqmF4dS7Z0OB6qFCplHFYYe3+UN5pWUJ6j37SWWjXuPzvW78kn8HLuZxZmdZ3JPw0E8tyaRrYkZ1nWRddwZ0zmUe9vU48G29Zn313k2HU+1mzVzRGnl8Z2N9j2a7qpyLxWu/Qq77gOTznZ54D3QbS3Ib4Rs3zVh7O7ucOiQ4PS6Y4fwe2rVKvBwbCAjIiIicjfzt0TZ10uWsmHDRhQKBQMHDqB7t26EhAQjkwn/SplMJlJSUtixYxebt2xh/foNmEwmnnh8XK1cvIiISC0xcSLMnQvGck+rHh5Ce+LkyVC//p25tlpGfn3WRa2UW23vj6ZVEnLtgBxdHvU86t2Ky7vjlLe4b6tOZLL/d/RytxcmbtICCk99BB3eQ62UE+brwvQfjhHqo2b5uI4VmnSM6RxKt3A/6nqpmLU5kZcHNWPFvkv8knCVlU9EkVV480YW5UnOLmbmj2doW3iVS19ecmx3eB2Jwokej7/GT3Ons2jvBuYdm8LVgqu22wAve8HbPiCt5N2EyQJH6ozhkvd0jClJJGcXM2zeQUZGtWbDI9soMmjI0ebio/Yi0M2HPefy6Tlnr91s3MlrGqb/eIyRUcG8cE8TNh1PJV9rsJs1K/8ZP9GlAW1DPBm9ZL/dMSPruOOuKvN5XV4LcY+AuVxvZZ1B0PVHkNnGQ9Q0jP2WmoHUrw9//im8VJLJhJdGIiIiIv9AblqUnTlzhg0bNuLr68Obb7xOvXr2DygymYywsDDCwsLo07c3b7zxFps2baZr1y40CQ//WxcuIiJSi4SEwIgR8M03wp8bNYIpUwT3RNe7Ihb2luKtqll2mrez5625kLsAweLeQozrUZ72/57Orscq3LbI5IzOJLMGB2u0Rq7madkwuUulJh2lIuPlgRFM6RPOlVwtg1sGMTYmjCVxSQxqUTODhvJGFmWxWCxIE37mxYVzKz1G3eBQFi9fTUxUawYvm0JcxmIs5RKkXSWwLBAeqOKvRJ4JHk6DrMKjrH8kiIU7kjBbSvO+kli4I8ma9zWopScuylwb23tHrIxPoW2wl3V+ruJQZyUuTnKcFFJ6zd1ObrH9EJuNjX3SStg3Giwm243q3Qcxa0Bm35t514Wxy2RCNIeIiIjIP5ib/i3566+/I5FIeOGF5x0KsvLUq1uXadOe56WXXuHXX38TRZmIyO0kOVmwrW/cGKZOdbzNiy8K2z3/PAweLDzo/EfwUnlV7NJXjjaBbfBUed7ya7ojmPQEZP/I5sbv0kyVVOFm+SYXNpU8xGn30TzfsvON5Vq9EB6dlF0tk462wV5cyCxk/vYLPN2zEWG+Lnz65zkeaFfvbxlZlGKxWAi9uI41P35V6TF8wtvRbvy7XFPK6Px1D05lx9tt01AB64IgsgpPm8tmZ/pe1nHGABQfQWcqsGutLM37kkklfPJom2oHQS+JS7KZn6ss1Hnrc90J9HC2E2WxUcH0bOqPwWRGnvQ17J+AXfkwdCRELwOp40cEMYxdREREpPa56X6Ck6dOERISQkTTptXeJ6JpU0JDQzlx4uTNnlZERKQmnD8vhKc2aiRYRH/wAWgr8OOOiBDmMoYN+08JMgClVMm0TjOqte20zjNQSu9ut8kao8+FU7NgfRieRx+vUJCZlL4k13sV05Akej44n+Fd21KsN1FYYkRnEMKeH4sOYWlccrVOuyQuiZHRIShkEkZEBbNkdxIms8UamlwdJnRrQE5RCRFBtqYzzQJdaXP1F3ZWIcgeHD6cZmPeJz43juk7+zkUZP3UcKB+1YKs0LcbwwsbCoLsOgX6/ApbK282CLpJoJvN8lKRdyA5l8RUwUpfozPYnDeyjjtzH2rJk90asHjHBSynP4H947ETZA2fhOjlFQoyqH4YeynuzrVkipOVJYTYi4iIiPwLuelKWW5u7k1Vu+rVq0t8/P6bPa2IiEh1uHBByA779lshx6eUjAwhV+ypp+7ctd2FKOUKhjUdyoR2E1l0qGLntontJgnOi/8AO/zqBD9bOTAZLq2q8FhpBm8WZz5AgvJBHmsYSUeLKyO/2kdytmBhGBXmzSuDImjs70pusaHGIqNvswAbceIoNNkRI6OCaR3sxe7zmXw3oRPp+Tpyi/V4qhS8OXMqK1cuqfwCeoD7Iy44m75j1/ElYOt2X+35MYCvSwJ4qNsvZJ6Ntlnu5+JNt8bOHEjOtdvn7wZBV0aguzMfPdya3CI9vm6Cmlyx7xLvbExkhOsq1FnL7XdqMhXaflSlkU91wthLsZtfu1lyc6FrVwgPF9qsRTMPERGRfxk3/ZtSqVRSUlJS9Ybl0JfoUSju/gcaEZF/JBcuCLk933xjK8bK8skngqX9P9RB8VbhqnRh7j1z6RbSnTl7Ztm5403vPJOhTYbYuS46FD/OciQS0OiM5BXfmkDdykSX/ro7XrXzo5o841CUXSypw9eZ9/Jjbh9KLErAwOHvboQ23zd/D22CPXhnWAv2XMhmxb5LPNS+ZoYwucV6fFyUNuKkOkYW47s1oEOoNyO+3MfkXo1Y8Nd5tp3JwEUp4+B3n3Jpxw+Vn3gI0A6WJDiupHlL4dtAGFiFyWahGcakw+DeH/Jn0nY2j9hMhy87kKvLpU1gG5xlbgxt7UmQp7PdfQS4OxNUw7a+yubnSoms447ZYmHiioOolXLGd2tAQkqeNcvtoqIuJosMmaTM74jIV6Hl29X6vVBVGHtZbObXbpaSErj/fjh9Wvjq0AF+/hmaN/97xxURERG5i7hpURYYGMDpM6cxmUxWt8WqMJlMnD5zmsDAwJs9rYiIiCMuXhTE2PLlFYsxlUpwUnzhhWoLshpVW/4FuCpdeDDiAYY2GUKeNpccXR7ezp54qjxRSp3sKmTFemOF4mdsTBgdQr2Y8O0hkrOLazVQt6LzRoV5s/Cxdvzv9zN2duVu0iIupOp4drXGPj/KNxp8O0OWYHcfXxjJV1n38YemIxYHXe4r41NoH+LNthe6I5NK+HDLaVbGpxAR5MaEbg1rdC9eaiXZRXq81bYtcZUZWTgppDjJpDy16jAxjXyttu/5WgN5u1aSv6cSQSYB7gNaVrxJByf4IQhCqnh/eEEPw1KhS4sJdA3uSocvO/BRv4+Y2H4iH+z+gGmdZrBk1xVWxl+p8D5cneQ83L4ex6/mcza9EFP5QLEyVDY/V5axMaGsjE/h5DVhu2L9ORaPas+C67Nov2k68/zlqXwS/BESLNDqA4h8sfKbLUNVYeylxEaH/H3nRYsFnnwStm+/sezcORg/HuLixJdLIiIi/xpu+smgfbt2fP/Dj/z881oeeujBau2zdu0vFBQUMnDAgJs9rYiISFmSkoQ2xcrEmFoNTz8N06aBv3+1D12Z4KgtcXE3opQrUKLAVelSqe19VeG50344aq0oDZ0XV2uBupWdt32oN3+css2PauKczCifjdzruZ0PUsexImeg4/yoZjMwJa1ibspgFif7VCoOAL7afZFeTf349UQaK+OF851NL8RZKauxScfWU+m8MqiZ3X4VGVnIpRK+HNWBRzrUp0Oot9X2PT/+Z/L3rK74hFLgEaBJxZtM8oBP/EBZxbP+r0Xwgbk50wa8QNfgrvRf2Z9cXS6fxX/GL4/+Qr4un27B/Ri09oDdfQS6O9M93Ic+kd7klmTzaGcJjytCUMvdWbM/lRX7rtjZ2ANM6N6QE1fz7YKgy+Iom8xRltu6vJ682DeYIFeJUCmtIRWFsYPwfZ3YvSG9I/z/3u8IiwVmzhTasMsSFCSE3YuCTERE5F/ETf+2HDJkMBs3bWbV6jVYLBYeeOD+CitmJpOJn35ey+rVa3BxcWHw4EE3fcEiIiJASgq8/bYgxspni5Vyk2IMqhYctSEu/i53sopX3fDclfEptAn2tNqYw98L1K3svDKpYJbx5PKDqKVaBnvs4hHv32nnctq6zWO+G1mRMwCQWPOjQMid0rj1I7dRTx5sImXkQCmr919mxb5LDsUBwOk0wT2wrKlHWZOOqizeQajorIpPwWCqfL9SI4tSPnu0NR4qOa5OcobOiyNfa6AgYQt52yuZIZMDo4EKuitdJLDIH0a6O15fliv1YgmNeJFvFK4sOrSI5397nlydMDN2JO0IZouZWX3nMPjTeJvPz2wBrd7EG0MbsT1lC0O+m2vXJjul4zTWTR7AmCVHrTN7ALFRIXRq4IPOYGTuQy0ryCYLo0+zAEZ8GW/3fXM0iyZp9CR4qKq+4QpwFMbu7qyovb+H774ruMaWxcUFNm361+QmioiIiJRy009Trq6uzJg+jXfefY/Va77j199+JyamM40aNsTDQ/hXLT9fw/kLF9izZy85OTnIZDJmTJ+G638g90hE5Jaydy98/bXjdSqVIMamT6+xGIPqC46/Iy7+Lne6ileT8Nylcck2NuZQvUBdR6LTzVlObqEeD5XC7qE7PMAFS8YeRslmMyRiJy4ynd0xmzinEO1ynH1FLSkqMWI2w+bEVIef45jOoayfHMPoJfttxMGN87mSr7PPq6qJSUfZik5N9msX4s2LPx9lZv9mvD64Ge98sohLv8+vcB+kwETA1/Hqpgr4KQiaVeGuqDHDW7pg9l9IRnNqBCcyTmC22LsBFpQUIjX52X1uHioFyx9vxftxr7D4sL2hTEJaAmPXxzKh3SSWjXuXYfMOUs9LxcTuDYlp5Mv98+PILTZU2Aq5Kj4FqVRC18a+nErVEBmgoJX6PAeLI+1m0SLruNeKK6KjMPZa4aOP4PXXbZdJpUKFrE2b2juPiIiIyF3C33pqadWqJR+8/y6ffPo5V69eZePGTXbbWCzCU0jdunWZOvVZGjdq9HdOKSIiAvDQQ/Dhh5CQcGOZSiW4Kk6fDgEBN33omgiO6oiL2uZuqOLVNDy3fOtYVYG6lYnO8mLJW5bPfV7bGOv9J/UOJvOod8XXYrRIiVRdINHcjm/GRfHBlkRr62H5ay4NeC5tvywsMRIe4Iqrk5xivYnIOm5oHFTRqmPSMTYm1KbtsKb7lTo/bj+TTRvzWU6s/kBodXOEK4Igq+BdYKwbLPAH1yp+hI+VwIOpcM6QAlT+wsLT2ROFg1m82Oh6/HVpi0NBVpZFhxbQPaQb26ffi1Imx2KxMG7ZQavIqyibzGwR5goXPNaGke1d8Dw4AlXuPtK6foW7R1teHBDOq2tPcTlXKxhwyO/SudAvvhBmX8uzdCkMEjttRERE/p387SeWxo0b88W8zzh06DAHDx0iOSkZTYHw4OHu5kZoWCjt2ralXbu2SMT+bxGR2kEqFYw9Bg2qNTFWSk0FR2Xiora5W6p4NQ3PddQ6VlGgblWic/qPxxjdMYAfBl7leNx8uqgPoJRW7saXafBkVU5/Vuf0J83gy9M9hYBnR4KsLCvjU+gY5s1nj7amUYAbOoOJohIjfq5OuDkrMJjNDqt2FZl01PFU4e6sYPf5TGvboaP9Zj/Qgu8ndKJAZyBfa0Apl6KQCZWgUkMPD5WCjsorfPPmC5gqmqdsAAwHHBSEXCXwhT+Mqka7oj54OBNOnuScoeqWzDaBbfB09kSjxWZGTiaVMDwqiMFrRlR9QmDOntkMaTKEAq2FIr2J/ck5NuvLt3QChPqomfVgE7ad+ZbwE1OpKxNEnNfBMTyz+x16dnyHTVOG8NOhzNv+MqXazJ0r/C4rz4IFMGrU7b8eERERkdtErT1JtWsnCC8REZFawGAQ2hMPHoSvKgi/HTAA/vc/GDECatHRtKaCoyJxURuUb+Gr66WutIonk0qs1ZzfTqbdsgfPmobnOrIxL9s6VnqfeqOZHWczKxWdLtJini/sgcfRInpV0gluQUq+V1+09UdjChqI/mAa2n2XkJmM1tmzqgj1UdM+xJuDl3IY/81Bh7b0m5/tapNZVkp5c4sWdT144Z5w7v1iF1+P6chrgyMcVsMmdG9Il8Z+FJQYKdab8HV1QiGTsnjnRVbGp5CvNRDqo2ZyMyMjH3gavd7Bz6sU6AF0k2AXjgy0cYI1gRBexbfRInVij+cbuAaPZ7L77+xbW7WgmtZ5BnqDDKVMwvhuDZiyJgEQ2j2LDBqOph+t8hggzKblanPZekxPi/qeVW5f2hr5xa6pjMr5luZlWjFVUnhTcYFGv4xgbNtJzLlnzt1n0mOxwDvvwBtv2K+bOxcmTrz91yQiIiJyG7nLfiuLiPzHMZth9WphluLidfExZgx06WK/rUQCzz9f65dQU8FRG3MpjijfwhcR5Ma84W0dVvE8VApio0MYERWMTm8ip1iwWDdbBMFT28KspuG55W3Mywbqlt7nlzsvsvCx9izaUXnraJFZzQltQ2LcHFdtChUh/JR3D/MvxZBu9L1+vmPWtse3N560CWquCA+Vgm/GRfHFX+cdznidvKZhypoEYsu0ODoyBSmt6DzeJYzley5xPlOohsVGB7NkTAeM10W3p1qBu0rBhcxCxi07QHzSjcpQZB13Hu8SxvrJMUxedZiJzaQ8MHQo+hL7uTncgQeAEHAkyJ71hNk+4FTFj0RKSQBzi9+la6N+dHJTMTRgSLXCxYeED+alH0+RXlDC3IdaWWfkXJ3kZBfnVLivI3K0uWiNznZxAY6Ija7HwXNLmZT3LWHlZuOyTHBvKpRYYOGhBXQN6caDEQ/cPSHoFgu8+CLMnm2/7t13HbcyioiIiPzLEEWZiMjdwu+/w4wZcLTcm/SXXoKdO2+b/XNNBUepuKhNHLXwuTrJbQKGSwn1UbN8XEf2Jzmu5twK44+ahOeWOgyWtTEvDdQte58RQW7XxVI+TZwvUU+Rzp8FUQ6P+UteDxtRpjMrMdW9j1VZ9/D+IT+7bLGyM2LvDGtBTlHl1VCZVMJzfcO5kFnAkcu5yKSSCi3yV8Sn0C7Ey8ZhsjyxZUw9ygroQp2RfJ2BIHdn3FUKdp/LYsZPx+zE3clrGp7//iixUcG81MmN/n16UVJcaH+ixggZZGr7VT5SWBoAQ6rhM7VV05EXLj+PxuTK+svC5/ZMr0bM7juHbsHdmbPXcbj4kPDBTPzmBHEXsgFsZuS2n8nER13JwJ8DvFVeJKZm8GiH4Er/TsqkEkZF5CDb+SK+5XTWZQPccxVOl/lI5+6ZzdAmQ1A66uu8Eyxa5FiQ/e9/t+TFk4iIiMjdiCjKRETuNEePCmLs998dr9+9G+LjITr6tlxOTQRHqbioTSqaGyssMdpVDEqrOQt3XKiwmnMrjD+qG57rKDOqNFAXKHOfFiJVSahOr+PPJt/R0OkqmQZPohLbY8Y+auTX/BjeqrsIk1cUK9K7oA+6jzpuAbz3W+UzT6UzYt3D/RyuLyuYpEBqvo55w9vipKjcIv+r3UmsfCKKXecyOX71hnBoUdedKb3D6dzQh1m/JtIj3JeZAyLYcz7LoYCuyvHxm9/i+eqX1yjIz7VdIQV6AQ4KygDdVbAyEOpW8e03WGTMSRvFl5n32Qjb0mgDLNC94WCGNr0RLu7l7ImXyhOLWcHAT+K4nKu17ld2tu6lgRG4q0y0DmxtI+gqonQ2rUcTC0q5lCe6NuC57xzvd2/dJHz3vYSs3GzhWT30vQop5UYOj6QdIU+bi6vSpcrruC3ExsKSJXDgwI1l8+fDpEl37ppEREREbjOiKBMRuVNcvgyvvQbffFOxc1x0NHzwwW0TZFB9wVEqLmq7NbAi90dHwcSx0YJhRWUW6nBrjD+qCs8dFxNG+1Avq8Ng+UBdnd7AqRPbmRG0ngHuuwhzSoUrwPXWMz9FHlEuJ9lb1NLu3AVmF6JPLefXGUO5Xyp8zx77an+1rnvxzov0iQigRV13GwFVVcWxMsF08poGjc7IV6M7UKgzYjKb8XNXopCbyNXmciEvkSd6eOHu5InBCJ0a+ZCUXcyVXK1V5J28puHFn48ztXdj1oyP5pnVRzickmet0Bk1GWR//yq63PRy3wjgQQRTj3IoJfC2N0z3AmkVheZLJYE8mzKdo1rHydJL45L5bkInFvx1nu8PpnBvm3oEuKtI12gY0dGTfJ3WRpCVUna27vsJUUzvNIOR1ZxNw6IgKauIwhIjHUO9HcYFdHc9xPue7yMzltgsP6KDftcgswIPlBxdXqXh6LcVV1che6xzZ6Fte8kSGD36Tl+ViIiIyG1FFGUiIreb/HzBzv6TT0DnYCYGIDIS3n8fhgy5bW2LZalKcJQVF7WNVm/CgsWuZa58MHHZsOTqcCvs+ysMz3WWI5GARmdk8aj21kBduUWPOe03ilPWo0jfxIuSq+C4aAXAAI84h6IMoH5gHdydFWw7nU7bEK8aOWZmF+l5pldjxn97CKhexbG8RX75ilmGRsec307j6izn40easencWmbvme0wHLlP2ECa13G3irzcYoPNTGBWoZ7/PdQaqRRW77/M0q2HObfmVYzlBVkd4BHAw/4+mythRSC0qiJ7DGB9XjdeufI0BeaKK0cnr2lI1+jYdiaDzEIDX+5Ksq4b1yUMldK+olkWswWe++4Ym6ZUfzZt8KdxqJ3k3N+mHo8vP2AXFzDYYycfB/8PhcRWee3UwpBrQq5aRXg7e1Z6vbcdPz/47Tc4cgQeeOBOX42IiIjIbUcUZSIitwu9XpidePttyMpyvE2dOoID2ejRIKv8Ie9WU6HgUMlRXg+MrS3KuizmFOkrbJkrGzB85HJutQwrSrlV9v2VheeqlHIClIVwbS3Gy+sg9TeU5qJqHfeq3o90Y8UzSONiwvjp8GWCvdUU6Cq3xC9PZoGOlvU8rJWX6lYcS9v4HM2PeV03Vnl9SCNm/jGdhQ5ER9lw5Bc7vcu3ey+x8okoTBaIv5jtsEL3QFMXTBvfxph7zfZgbYBB2P0rJgGe84T3q2HmYZaqmXnpCX7I7Xt9z8opKjHaRRtE1nHHZLbg7qyochbzcq6WSd+eYOGoOXQL6c6cPQ5m0zrNZEiTwRTpZEgkEussZfmYAeXFL/E9PQdJOTOTDYXwcBroKii+w/XWSJVnlfd722nQQPgSERER+Q8iijIRkduByQQdOsCxCmZ+XF0F97GpU8HlLpnzoHLBUVvUJCi5bMBwUlahw/mmysjT6tEbTSjlsgrFZXkbfg+V8uaFqNkEm5pCSXa1ftkanYMpDryXFdc6MvuYBxUJhdioYO6JDECjM7D1VAYN/arhXlEGT5WSV9Ye5/UhkbQL8SSmkR/jlh2oekeENr4lYzqw42wGp9MKMZktVofJqDBPtqf86lCQlWXRoQV0qtcFN1Vb1Eo5c34741AQ5mSk8t5nr3LxwrkbC2XAAKC9/XGD5bAsAHo6MPooT4lrBMZO3+B/WI3HvivV+lnyc3WiqFy0wbiYMFbFpzA2JpSxMWFM+6Fyy/u4C9nsOquhQ8AAdo4ZTJ4uj1xtHl4qTzycPTmcXMDAT/bTLdyP5eM6Mu2Ho9ZZytJWSMmpD5ge+I3dsZM8Yrj/XBxVSfRpnWeglFajhFjbbN8OH38Ma9YI+YoiIiIiIlZEUSYicjuQyYSg5/KiTCaDCROEbB5//ztzbXeQ6gQll2+ZK60YTO3TmP6R1Uj+LYNCKmXCikMkphY4dGasTCBW6uJoMYPEgWCTyjAH3IM0ZXWF13SpJJAt+TFszo9h1KAHyC02MKhnIP5hOQ5zvMbGhNIh1Jshn+/GxUnO2M6heLs41dii/68zmRy8JHyOFoulRhVHo9nCwtj2SCRCe2F4gCur96fUKBz50/i5/B67nT9PZzgUZCp9Lqnfv8GlpDLzhe7Aw4CDUahYN5jnBx7VKDDPy4Np5xOJSH2SqVHTWTe5P2OWHHVoLlJKZB133FQK6ng6c+p6aHNsVAhdGvsSn5SN3mSmQ6iXw7mvsoyMCiayjgdD58UxtGUQz/RuzMbDGq7kpvP7yaMYr7ccllYlO4Z5281SOrL6/6ngXvret5zHLTOrbI0c2mTI7bfD//prIWvMaBSyFX/88Y53A4iIiIjcTYiiTETkdjFzJnz55Y3WxfvuE0w8mjg2Fvi3U5HLYnkctczlaw28uymR+9rUvem8sPLOjEClAtHGxVEhhdwjcO1XSP0V5G7Qc7PD8xqCBuNURpSZLRKOFDfhD00UWzVRnC+pT2lFbOmeSywe1Z7Bn+1iZFQw30/ohEZrILOgBBcnOU4KKaviU3hnY6K1sjPtx2O4OStsgooro6xFf77WwObjqTSv62AoqxJS87TM+vU0xXoTj3cJo1dTf/5IzEBvLqxROLLWqOGv0xl264z56WT88gY5aVduLAwFHgLKFZIDZDDfH+6vRrEw3QiPZ8Cm6x2kCWkJjFk3kgntJrFs3LsMm3ewworZ2JhQ8ov1vHtvCwp0CYyMCqZX0wAuZhXybO/GOMtl/Hz8KhN7NLSZ+yqlrKB+d+MpXhscQYdQbx5etLdCMbg0LpklozuQVVTChG4NePb69/eLjIfxkml4wm8dAJ+mD+fj9BHErDgltEZWZNvfaSZDmw65va6LJpPgLvvRRzeW/fILPP00LFhwR2ZmRURERO5GRFEmIlKbFBcLDxmOWnM8PIRQ6FWrYM4cx4HQdzm12dpXkcuiI5bGJbN4VHsW7rhgzfsa3jEYJ/nfywuDG86MDXzVlQpEP3kOhaf/IkeRhEq3C0lJ5o2VUiUYi0Bu/7Cb59EblcmFvYUt+EMTxTZNB7JNng7PcfKaBp3BRKCHM3+ezmBMTBjHr+bz5a6LFJYYOZNWYHf9ADN+OsZf03oQGxXMiviqLfq/O3CZDqFeFJYYKdabqhVOXBYvtZLCEiOJqQU8//1RRkYF8969zbmkOV2j4+Tp8sgosDW70WdcJOOHNzEVlgla7gT0hXLxa8S6wad+4F2NgsvaQhifIQQpl6e0nXJkVGvmb0+yW1/6uemNZi5lF/H1mPb8eOgKMbO2WUVcabutBAu5RXoWj2qP3mimxGjCw1mBq7OcvGIDcpmEN4ZG2olrR5y8psGChSYBboT5upRxRJXwXurjuMuKSNSGsTR7GAChvi5oiiUEq3paWyNztLl4q7ysrZHFOhmuNft23zwFBUJVbONG+3WpqcKcrdMdaKMUERERuQsRRZmISG1gsQhzEjNnwuOPC+2IjnjqKZg8+R/5dvimW/sqQKM11qhlTqc30STQDalEYnV/VCnlN50XVpaFOy7w9egOSCVYRY+fPJdol2NEux4n2uUEDZ2vV23yHRzArIf0HVB3IHBDvGr1JrL1KoaeXImxmr9uDUYz3Rv74uGixEku5ZM/zlX5OeVrDcz88SifPNqGjg18WOTAMXNC94bENPTBYLbw4f0tySnW461W4qSQorJrj6uY8hVHEKqZvSP8aRzkVa17LMXT2ZPCkhviS3fpGBk/v4tFf71ypACGAi1s96sjg4X+1QuCLrJImZxhZlkVt/Zp/Fw2Dd/Gwh1J1p+B8tWtN4ZG8r+tZ1n4WHve3nDKRiCXbbcd360Bgz/bRR1PFcvGduTtjSe5mFlI22AvHukQzNB5ux2Ka0dodEYCPVTIZVI7R9QZV6YAEpvrHPmVMH8pl8I9kYH4uCjJLrrRGvn7c93wd78186E2JCfD0KFw/Lj9uhkzBHdZsX1RRERExIooykRE/i4HDsCUKbB3r/DnWbMEYVbPweDLP/QhpKrZr5sJaM7X6mt0DXqTmRWPR6FSymwqc1XZ95c+rJbmhTni5DUNOm0eb7U+iVfhHppKDtHI+YrDbSvCcHUzksD+6K+3ZS7acRGzxcK84W2rLcgAPNQKHu/aAL3RTKGu+sJ1a2IGGp2Bfs386ePAMVMC/Hk6gwXb7T+j9+9rflPtj2X5eOtZVk9oW6NwZA8nT6u4K0rcRdam/4Hpuk2FF/AoEGC73zh3+Mi3erNjJV4daH74AMnVMKc8knYEo6WQHdN7kq7R4XVdsJZWtF4bHMGq+BSOX73xgiAxtcDuOKXttsM7Cu22y/Yk062xHxuOpmK2wJjOYdUWZMHKVHx0CUBXwLEjqpuz3BoXUbbyZjTD5uNpdsd0d74Ns2S//w7Dh0NOju1yhQIWL4YxY279NYiIiIj8wxBFmYjIzXL1Krz8shD+XBatVnBSXLGiykPUqtPfLaK6s181DWj2UNW8Zc7H1cn6mWUV6m0+s36RATYPq65OcswW+4fVCu9Tm8VjpplQA1O4iyV12FHQjh0FbdFkdWaMMpXoBj589PtZkrOLkUklDkwaKiayjjsSJES9/ycdwrx5c0hk9S8GofqokErJLirBx8WJYG8VcqkUvcnMe5WI6lFLDrD1uW7Vbn90VHE8flWDwShjRucZjPi5arOPKVHTMJrlNA10Y+/apeTtWI7VwKIR8AA234v6cvjSH/pVYxxKb4GvTaF0br2I5P1tq97hOlfys/gpPotL2cU27aLl7zu3WG9njV+Wsu22ZWMcvjt4GWeljMEtg0jX6CgsMXI2vdAmj6+U5qrzfNvwLTwPSMBzN3hEAPaOqFqDkYcX7rUJAq+IyDruuKtu4T/7ZrOQwfjqq0L3QFl8fGDtWuja9dadX0REROQfjCjKRERqilYL//ufYNJRXIFbW1qaMC+hrFh41HY74K2iJrNfNQlodlfJayRW3FXyan1mgR7OBHo4k1VQQsf3/7BWJbxk+XR0Ocn5kvpcKKlvdw6JSyippiCCZKkVXodZ7s5pc1tWpTRjR2FbLusDb6wsMHB4TYKdW2TZwOuqGBsTyjd7kzFbQKM14OLgwV8mlRAe4Iqrk9zuoV6tlPHYknhrBSeyjjuzH2zBhYyiSkV1vtbAw4v2svapGKIa3HzFMatAT/9Gg6sMR57UbhI9gvuzdMc5Cn79hLwda4UVEoSiUE+saQBS4GkPeM8X3KrxniKhBMakgcXLnUFqn6p3KIOfizcnryXbfH6O7rt0pq4iyrfbJqZqeGlgUx7tWB+1QsaLA5qSmq+zto+Wz+Pr4nqEhSHv4yrVgh74qz/cswfUde3OpZBKebJb9eYqJ3ZviPJWvezJzxfyFdets18XESHMlYkZZCIiIiIVcuef+ERE/ilYLPD998I8REoFD7gNGwqCbejQSufGbkU74K2iprNf1Q1oVsqqb9IxsXtDJFBptaf8Z6aW5DM25Cj19PuIdjlOhCoZgM/SH+Gj9Mds9i+dk9qdH8lD3jdEmQ4XzL5dWJBYH79G/VD5t2f6TycrvdbybpFlKyVVWaWXrcacTS9ELhXmhU6nFdA22JP72tSjd4Q/hTqjzUzY6v2XOZicg0wqsZn1Op1WgKfaiYU7HMz1lCM5u5gPtiTy8sAI2oZ4oTeaKDGYUcikOCuk/H4qnTfXn0SllCGTShxWd5RyGdO+P8k7935Aj9DuzIqzdwB8Lmo6gxoPJvlSJu9MfJj8lOvmIE7AfUDTG8dr6wSL/KF9NUag9BZ4Jwc+zAEjIMs4iaezV43aKQNcvflylC+FOiNKuRSl3N7t0tFMncPrMZn5enR7zBbYcjyVAp2R5KwiXvr5eKV5fC1Nm5lb7xOU0jKirzgFEmZCZ/vqu1wmrdZcZWx0SLVfmNSYU6cEN9mzZ+3X3XcfLFsG7jWLrxARERH5ryGKMhGR6pCQAM88A7t3O17v7g6vvSZsU4Wb2K1qB7xV1HT2S6MzVCtouqKHyfKVoA6h3vSO8Gf72YxKPzN3WSFpp74nzTmVMPNBVHlHec3dXjj08j7FLpWXTZVpXEwYa/ankFTQDj95LnuLWrCvsAUntI2Y9VAbLE2L6B0VwpPLD1brMyjbvlY28Loiq/RxMWG0D/Xi7Y0nCQ9wtV7bz4ev8MH9LfB3c8JsgZwiPcV6I04KKdvPZLJi3yXqeakY0zmUx7uE8dOhyzbzSuEBruj0pmqL6qSsIlLzdQz6bBcRQe4sH9eRPeezaBfqTbfGfjSv61FhdSeyjjsyqYTDl/KQ4oSfrBtbYweiNWrI1eXh6eyJp5MnRpOM95dvZvFrT1GQc90O3xdhfsxX+KOrBN7xgWc8QVYNT5yDOhibDifK/Ki2DGiJwShhWqcZxK6tup1yRucZGIwytp5KY8fZTNI1OodulxXN1JXH1UmOXCrloYV7WPFENPP/Ou+wNbSsQcjPveLxPjPX/mABvaDD/ArPVdVcZakxzi15wXPoEHTvDkVFtsulUsHMY8aMf6SxkYiIiMjtRhRlIiKVkZ0tiK1Fi4R5ifJIJPDkk/DOO9UOf75V7YC3iprOftXESKDsw+SKfZdoH+rNiKhgdHoT+VoD/u5OeKmVSIGluy9Z95NJJbQOhPbqk4RzkCaSQzRzvohUYoGcis8HEKk8zYtdgvF088RJIeXkVQ2dG/mg0XmzirFM2dfLpj1vaVwyX4/uQFFJ1RXDUkGpVsowmy1EBLlx8lqBNfB6ZFQwi0e1R6c3oTeaUTvJ8HZRcvhSHgq5lJcHNBMqYC5KvNVKlHIpfyamV1ldmf7jMWKjghnfvSHz/rpgvX5XJzk5xdUX1YUlRtxVCtycFdzbpi4qhQyTBcZ/c7DS8ydnFzMuJoxV8SmMiAoh4XI+EUEe/JWYYxU4hSU5nE7V4Jq8kzNrP8OgLxEO1hShQnb9Xca9LvC5H9Srxo+RRaLg7Vw572RqKe90PyVqGkdTNAxrMpSJ7SaysLJ2yvaTGBQ+mM+2JvHlbntL/FKqcvEs+/nIZRJ2nsvk7WHNOZCcU+msnhQTjS6/grd2g/3K4Eeg03KQVf6yx5EJSKnJyy2dUW3ZUvgqNToCYX5szRro0+fWnFNERETkX8h/WpRpNAVMfuYZ8vM1BAYGsmhhxW8i//xzG1t+/ZXLl68gl8tpEh7OQw8/SETTphXuk5iYyA8//MSZs2cxGo3Ur1+PgQMH0Ktnz1txOyK1zdWrwsNGeQexUnr0gE8+gVatanTYW9UOeKsoO/tV2TwT3JyRQOnDpBBAnO5QAIzv1oA5D7Vk8qojdAv3Y4LXMtzOz0aCA6FcCTqLM/sLInhp2VauGvytx87XGnhr/UnuiQy0ERogfA9KjCYs5Y0LyuChUhAbHWIVlDnFeqRSCUvGdOSbvZesFaX52y+wcMcFmgS68dWo9vx2MpUH2gWTUaBj9m+nrfftoVKwZUpX5v5+hpVVVFeWj+vIg/P3klOsJ7tQx1tDI3lp7XFCfdSE+KjxrUEo1dn0QjxVcrZM6YpKIeO9TYkOWy7Ln3/F3ku0D/XivU2JbJzSBSwwb9t5m33NBh05v88n+cQ2YYEUYXbsuu9DmBw+8YOh1bC5B9irhbTmb1JSWIgp8wObdRPbTWJw48E4ydQUlxh5s/uHdKrXlY/j59i1U06Nmk6/hoMwGpVM6RNO0yB3lsQlOYwU6NrIl/vmx1VpGvNElzCUMik/HbrCl6M78PDCvRVuq5Lo+Cx4Dn094u1XNpkCbT8CSfUEVXkTkNuCQgGrV0Pr1pCXB+3awU8/QUjI7Tm/iIiIyL+E/7QoW7p0GRpN5XMBAF999TUbNm5CqVTSpnVr9AY9CUePciQhgZkzphMdHWW3z549e5kz939YLBYimzXDzd2NY8eO8+mnn3Mp+RJjx465BXckUqvUrQudOsGmTbbLw8KEubF7772ptpxb1Q54q1DKpEzu1ZATVwtsRIejNrabNRLQm8x8sMV2XsxJoqfEouTkNQ1TrhtorHoyij8TM1iy38JUt6oFmdbsBH6d2ZUfycLTIRzXNsJguVGCKXvsVwc3Y+i8OAa3DLIx6gDQGkx4VVAxDPVRs3xcR/Yn5VSromS2gFQiAQn0b16H2b+ethNesdEhxJ3PcijIyrLzbCZP9Qhj24zo60HBV2nXwIsDr3TBZJaTnK3Dz9Wp2oYq7UO8AAnHr+Sj0RkqnYEDYX6uY6g3U/uGM+izXQR4OKGQSth+JtNmX0POVTLXvo8h63q10w3BXTEUVBJ4yQume4FzNX50Cs3wWjZ8lgetjD/yy6O/MCtuFmaLmdaBrZnReSaDwgdzLddEdkEe1/K1vLspkZFRrdnwyDaKDBproLJa7s7q+FT6rYvnnXub0z7Ek55N/ejfPBCNzkBavg53ZwUuTnLcneWYLRDTyJfkbOHeHL2keLRDfdqGeLH7fBb3RAZSoDVU+BLGV57LV6Fv01p9zm7dxbqvEtzqTeTVFGR3lJAQWLpU+F35+efgfOd+X4mIiIj8U/nPirKjR4+x7a+/6HdPX377fWuF2yUcPcqGjZtwc3Nj9qwPqFOnDgCnT5/hlVdf47PP59G8eXNcXW/4NBcUFPD5vC8wm828OHMGnTpFA5CXl8eLL73CL+vW0759e1q0aH5rb1Lk7/PJJ7B1q+CkqFIJFvjTpv2th45b2Q54K5DLpHRr7I9Wb65UdPx8+OpNtVqWztj9GH+WLq6JdHY9SieX4zR2TqHdqZWUWITPa2V8Cu1DvDiXUcj6K42ZGmF/LANO7C9owr6iFuwtbEnnjgOo7+PJ9L8qdz4sb85R9r8BnORSkGAnbjxUCr4ZF8XCHReqVVEqFXpjY0I5n15IRkGJnfCSSSWMiAqucn4tpqEPCx5rzoazG5i7d7ZdBWhapxkMCR/CpuOpjI0JY9oPRys9HsArg5qy9WQaHRr4VHt+bvGui/RpFkBKTjEdQr1wkstYGpcMgMVioejUdnJ+n49FrxV2aIAgyFzgIVeY6wvB1fwRX1cIz2TC5eseGEfSjiBBwuEnT6JWqnFVuPPr8Sw+35pCbKcQwvyVvLc58XqVMomFO5JoEuh2XURprDNjHioFuUUluKuUpOfryNcWU9fLmSYBbmw5kYpcJiW3SM/gVnV4qkcjOoZ5k1esp0+zQOtLCl9XJZ5qJWazhTFL92O2wLKxHckqLHF4Lw2dLrMs7E3qK9NtlpslStIi5rPwfHveNFvunn+k8/IgKwsaNXK8/t57hS8RERERkZvirvl9fzspKSlh/oKF1K9fn3vvHVapKFu/Tujxf/ihB62CDKBp0yb079+PjRs38ccff3DvvcOs67Zu/YPi4mKiOna0CjIAT09PRo9+jA8/nM26detFUXa3YDCAXO646tWoEbzwApw/D3PnQnDw3z7dzVjB30mK9Ua7KlYppaIjNiqYFwdEcCApmw5hPtVrtzSVQNY+LNf+pMHRdRyLPGXrOAe0UZ9mX1FL65+/2p103UDjPFf0fvjJ8zhc3BRlnZ5ovbrx+EapVcTJpBI+jW50U+YcZf87IsgdX1cnDCYzsx5owcivblijx0aHEJ+UXa2KUqnQ02gN9GoaIBzv1wN221bHnKO+l4qFo5ozfet0h9bzCWkJxK4dwcR2k5jVdzYFxRJrTlZF7aePRQfTwNeNRTuSaFHPs0bzc8V6I/e1DqJpkAd6oxkLFigpJOu3+RQn7hR2kADdha/mTvCZH/RUV3oKK1cMMDkT1hXZr7umyWHjISkHLyUjl0oY0zmMXp2CeHvjSV4e0MzmPswW7AKfy1Y6H1m0175tsVsDejX156W1x+na2I+p3yWwZEwH9lzIqvAlxbwRbRm9ZD86gwlPtb3ijHY5xqKQ9/CQ295QntGVoqgfMPt14/iOg3e8ddnKtm2C3b2rKxw8CC7VCIsTEREREakRd8Fv+9vPmu++Jz09nffefQeZrOKPoKSkhGPHBSvpzp072a3v3KkTGzdu4sCBgzai7OChQxXu075dO5RKJUePHUOv16OsJMdK5Dbw228wZYpg1PHQQ463efddwUmslqipFfwtyxWqBtV2ioxPoXWwJxczi/jzdCYvDWyKSlHu75bZANkHIeMvSNuGJSsOiUmHAmhewV+DaNfjNqLsRv6TO6OT3uaKPgAXlSs7R/Xk4UV7KbHceECuqfNg2Wypsv89NiaUBdsvsONsJhO6NWDrc914eNFeLudqq1XRKmVpXDIrn4iiqMSEXCqhxGhxeG3VMed4975mrD+zodIsMICFhxbQNbgb4e69eeGecGb0b0JusZ6swhvtp38mpuOpVtIh1JuMAh0ZBbpKzy/Mz9VjeFQQRQYN+bp8pLJi3rovnDxdHteKzjIyNJWZc16g+OrV6zcF3A9ejeBNH3jKA+TV6Pw1WYQ2xdezobCCkb667j6M7uTBpB6NcFJIuZqrZei8OMIDXKv8HKtT6Xz2envrSwMiyCws4YsRbfl469lqVUZzi/U08nO1eQnTzPki34S9bvcC4rI+gHeL5vJWYA8sZjNn0grueOsyOp3QHfDxxzeWPfMMLFly565JRERE5F/Kf06UJScns27denr36kVkZDPS0zMq3Pbq1WsYDAY8PNzx9fW1W9+woRCEmXzpks3y5KRkABo0tA/KVCgUBAfX5/z5C1y7do3Q0NCbvxmRm+fiRXj++RtBpy+8AAMHOn4DXIuCDO6SXKFqUhOnyKVxyXzzeEd0BjPZhXqKSorxUJrxvroYedZ2pJm7wFho3b4603gdXewzwXKL9bg6yTlwPQD6pT6NydPq7QROTZ0Hyx679L/va1PX6raXrzXw7JoEYqOC+fmpGD7cnIhOb+JMegERQW4Vmp+UcvKaBo3OiJNMwpr9KbQJ8XJ4DYUlRrzVFb+sUcqltA1xZcrSWdW6p7l7Z7Nz7BC2HMtg2R57O/7x3RrQuaEvK+OT6RcZhJ+bU4XnD/VRs2xcK7anbGHIdyMp1BfyW+xvbD63iY/2fUTC5QT4C9hTZqcm4HQvTA6CV7zBS1aty+aADiZkwBHH3X+AkC3mqvRAIZFz6pqGE1fzGdq6LkNaBnEoJbfSzxFqVulsG+xF32b+/HYyvdqV0V5N/ZHLJEzo1oBn1yQAcEoXxsb8rtzv9Zd1+4TicJ5Ifo0X7+tORoGWX0+kY7bc4dblhASIjYWT5f4OLl0KvXvDyJF35LJERERE/q38p0SZ2Wxm3hfzcXFxYfToUVVun5mVCYCPj4/D9c7Ozri4uFBYWEixVotapaK4uJiiYsG1zbeC/Xx8fDh//gIZGZlVirLCwiKKyue/AFlZWVVev4gDiovhww9h9mwoKfO0d/kyzJoFb799Wy5DrZTzyqAIBjQPYvX+FLacSLM+yN/yXKEaUFOnSK3exIdbTrPxmBDALMFMQvP38ZDmV+sYRouUY8WN2VPYin1FLThUZD845uWipLBEqDLIpBIGtQjick6x3XZViRtHeKlvHLuOp4p6XioeXbzPxm1vRXwK7UK8eW1IM4pKjOyc0bNS8xOlXEqfCH98XJRYLGYu5ZRwNqOAvpEBDq/hbHohzkpZhS2ufSL8ydPlcTS96hkxEOaucrW5/HUmw+54V3K1nE0vJKaRL4+0DyZPa+CFvk0I8HB2OD+3/PFWvB/3CosPL8TL2YsjE47w4e4PBbv5JGADNyIJFCDpD490h/d9Iaya+sIg9+aQ/wPE7PiySm/NFzpN53KOgbqeTuQU67m/XX0e+2ofa5/uwp+n0nFxqrhVuLqze6UsiUuif/NAlu9Jrtb2S+OSGdA8CKVcRp9mZV/CSHjxyrMEKbLo5Hqc3/OjeTZlGg90DKdbYz9KjCZWxqfcudZlk0lo1X7tNaG1uzy9ekG3brf/ukRERET+5fynRNmmTZs5d+48zz4zGXd3tyq312l1ADhVEgbs7OxEUVER2uuiTKvTWddVtJ/zdZMIrVZb5TVs2LCBNd99X+V2IlVgsQg2zS+8ACkVvOXevl3IIqvlylh5jCYzepMZjdaIr6uSVwdF8OEDLSm6LgbcnG9xrlANqMop0klSQkeXk5RYlOwvak5avo50zY2/AxakbM9vzTCvHQ73N1skGD1asTGtCevTm3KgKJIic8WDRpF13Alwc6LEIKRShQe4ojOY8FDZP/FXJW4cHdtJIeV8RiGDWwbh5ixUvmQOZg03H79G38gA4pNyWLzzosO5ok3PduFqXhHN6qjI0+WRrc1BKs+jWR1PXhzQBGe5Y8FgMltYFZ/CmM6hTP/R3qDEx0VJtraKMLZy5Ghz8XGxFahlZ6lGL9lvcx1vDW3GE10b8Nx3CdZlsdH1+OvSFhYfFlomn49+nrjLcSzcvRC2AofLHLwOdB0DcxtCx2p23xktUn4pupfO935KpIcn4wtllWaLTWw3if4NB1FcImNZXBLjuzUks0CHk0JGTmEJ+ToDTgop47s1YMqaBLv9b6a9VaM1YKokGqH89gU6Iy5OcofhzhMuvUKs92Z2Kcby3oON6NnUH63eyGNfCzOL797b/Pa3Lp85A48/DnFx9uucnIQXWs8+e8t/R4qIiIj8F/nPiLLMzExWrFxF88hIevfudacvp9oMGTKEXr3srzcrK4uXX3n1DlzRP5DERGEO4s8/Ha/38xMeNsaMueUPG8V6I38kprNoh/2D/ITuDegTEXDHq2NlsXeKtNDYKYUebofo6naEKJcTOEkNbNe0Y39Rc5tKUym7CtvYiLJkYxh/5TVnb2FL4ouaE9utDaGt1Wx3IEDKMzYmlN3ns1g6VnAydHWSk6bRUcdTZSdwqhI3jo59IaOI7dN7YrFYSMoswkOt4JfJMSyLS+ar3Unkaw3U91Lxwf0t+XBzosNA4JPXNHzx13m6NnHnYuEfTFzm2BlxaJMhzOzflFFL9tsdY8W+S6yfHMOIjsF2rXLZRXp8VI6rbBXhrfIiu+iGy19Vs1QfbT1nc36ZVMLwqCAGrxkBgEwiY0K7CbSf0h6+B0q7UqXQZBB8OAjurfq9l5W9hS2Ym/00H49/jHk7LmCxZDN78By6h3ZnVtwsh9li3ev354H5h4hp5Mv4bg3YdT6TTg186BjqjUIu5Z2Np/jsz3NsfKYrsVHBdt+rsu2tVeXvlZJXbLC2t1YHTbHWOhNmDXdu6k++zkB+sQE31VAmOcsxmMys2JfM17uTydcabn/rstEoxHy88YZtB0EprVvDihUQGXl7rkdERETkP8jd8/R3i1m06EuMRiOTJk2o9j7OKuEf0xJH/0hdR6cT1qlUKuH/y1ill5SUoFbbv/XXXa+mle5TGa6uLjZ2+yI1oLBQMPD46CPhoaM8MhlMngxvvgmenrf8cor1Rt7fXLGL4bOrE4iNDuHlgU3vGmHmrpLTuo4T7po99HI/QG/3A3YW3gBRridoXccJJ4WUM2m27nY7C9qwMrs/lxRRjBwymh6fn7JZX5kAKcvIqGA6hHpz3/w9zHqgBTP7N2H3+Sy81coKxVdNjt0vMpA/EzMcOuo90SWM9ZNjePnn43w2vA07z2U5FGRwo83vje0vWqtKZSnrjDjnnjk83aMhX1y33i/dPzY6BCeFjJcGNiWqgbdNNe6PxAxmPdiE1oGtbcRKRbQJbIOHsye/n7zR7ljVLFW+1sDoJftZPq4jHcO8+SMxnSKDxtoy2UXZhcH9BpOy58b+oU3hjSfhsQCQVTO+T6uowwvnR7E5P4aneza2XlOHUC80xVKae93Dhkf6OcwWG7buIPlaA8nZwvxW6cuA5/qG88PBy5gtkFtsYORX+9j4TFfahnjx9e4bodCFJUb83ZQ83TPMalqSXZyDj9obtdydNftTWbHvik3rqodaYffSoSK6uR6iwf6p0HsruIYCN8KdJVJIz9ex5UQqfySmk5gqWPNH1nHn3Xub397W5WPHYNw4uG5QZYNUCjNnCr8jRVMqERERkVvK3fHkdxs4cPAgLi4uLFiwyGa53iC8Kc3JyeGVV14DYNq05/Hy8sLP1w+A7Oxsh8fU6XQUFRXh6uqK+rrAUqvVuKjVFBUXk5WdTbADUVZ6PH9/v9q5ORFbLBb4+Wd47jlhVswRPXvCZ59B89sTS1BtF8N9l4gK82ZA80Cbt+RlWx7ztXo8VErcVbewzbH4GlzbjPPVDfzotxW5b+WttippCc+3zmRVfArlCwwZRh9euTqZDqFe9DV42O1bVgC0DfFkaZy9GcXYmFA6hvnw28k0NjzTBb3RRD0vNVFhPgS4O1PPy5moBj524qu6x+4fGciiHRf5/K/zdtd38pqG574/SmxUCItGtafEYOLLnRWbn5Rv86uIUmfECT3uI8zPhaVxyRSVGK0thY8vO8CVXC0jo4JZPKo9eqOJ4hITPq5KZBIZ0zrNIHbtiErPATCt0wwOJxdgvD6gVd1ZquTsYobOi+O5vo2Z/WBLzuUkQhE47XJi1/5dmM3CAev4wqsT4IkIUFRTjOklar7Je5h77p+Flzwd2YHLNtdUrDfioVLy5DeHOJ2mcZgtVpbSCIPsQj1bjqcytE1d5v11gXytARcnOXqjiYZ+Liwe1R6d3kSR3oi/mxOuKjP+AQkM+W6kXTVuSsdprJs8gDFLjpKcXXx9xkuBXFrVTVoY7bOR1+t8iazIDDuGwD1xoHC3bqFSyGle14PwQDeGdwxBozPg7qy4tX+ny6PXw/vvw3vvOX5p1bAhLFsGXbrc+msREREREfnviDKAoqIiTpR3krqOXq+3rtPrhTejdevWQaFQkJ+vITs7287w48IF4cEsNCTEZnloWCgnT57i4oWLBNevb7POaDSSknIZpVJpk3smUktkZMCoUYLVvSPq1xfadB580HEu2S2iJi6GC3dcsGldum0tj0WX4MJSuLYRcoS35tLrX5Vhski5ImtBkwBXnllTseiszHijVACUChCd3kRusZ46niqkEthw9BoSLHiqFDaVLJlUwoDmgTzSvj5qhYyneja0E1/J2cXM+PEYrwxqynfjO5FZqKNAZ8TP1QlXZzl6o5md5zIdCrKyrIi/RIcwLzo38KlwDql8m19VzN07myFNBpOcWcTSMR1QK+V8sCXRJlB6/vYLLNxxwSpMujf25762QQxrOpQJ7SZWaos/sd0kBocPZtCnN1okazJLla818Ob6U3QPc2fFF9/AZzc6B3zd4MXh8FQXUFXTUdGMFEPo42SEvsiKlZfQHM3lxQFN6dHED73xxjXV8VRRWGKw/rl8tlh5SiMMdAYTO85lEujpbA3/HhsTyor4SwxrXZf5285z+HIujf1cef/BJkz/fZrDubWEtATGro9lQrtJLBv3LsPmHWRcTBj5xXqe6Op4Rg1AjpE36izmMd/NZT7EExA3ArqtA+mND6q0aqZWyu+M7f3Bg/DWW/bLpVKYOlXoMnDwUlFERERE5NbwnxFl63752eHy9PQMxk+YSGBgIIsWzrdZ5+TkRMsWLTh0+DBxcXsYOnSIzfo9e/cC0KFDe5vl7du14+TJU+zZs5cePbrbrDtw8CB6vZ4O7duLGWW3Ak9PSE62X65QwPTpQubOHQg+ramLoUZrQK2U396Wx6LLcMLBQ5oDrur92FHQjiRFDG063E9kaAiPLNlPvtbeBbGUqow38rUGGwHSoq4HL9wTzsBPd7H2qS4s2H5j/ql8VlZ28RU0Bh/8XbxpG+xBl0bt0RpM5BULOU8SJHyzN5nV+w8Q6OGMq5OcohIjMil8Nboj87ZdsLseRyzeeZGeTfyRSrCr1oAgeMq2+VXFkbQj5OnyuJhdxDd7L9E4wNVGkJWfdTqckseB5FyaBLnSsq4ns/vMoVtwd+bstZ+7mt5pJkObDuGDjRe4nHuj0lk6S1UdG3+LyUjh0d/o2GocWZlCfIivGzw3GJ7tB641cGwv9u/DgpxJ3NvoHjYfT+Xnp2IoKjEw4st9DO8YTD0vQQB4qBS8O6wF6QW6Ko5oS26xniAPZwpLjNbKmUZroEOoN0PnxfHz4assH9eRo5fzsGBmw5n1lRqJACw6tIBO9bow+4HudGkcwObjqQ4rsgDuskLmB39AFzcH33u5GiwGoJrq9XbQuTNMnAgLy3wGERFCBll09J27LhEREZH/KP8ZUXazDB02hEOHD/P9Dz/Svn07a3Xr9Okz/Pbb77i4uNCnTx+bffr27cMPP/5E/P797N27j06dhH/g8vLyWL78WwCGDRt6e2/kv4JSCV98AWW/J336wLx50KTJHbusqlwMy5NdpMfHRfm3Wh4dYjaBxQgyB86gvp3AOQB09jNjIMHiE40hcCAa73vIk4bTyUlOd5kUtZOcid8eIjm7YkEGgvHGH6fSbDKbHF6iRaiMPN4ljOV7LjG8Ywj7k3OsD8Hls7LKi5EZnWcwpMlQsMjQKy3IJBL0JjMSicR6bBAe/p/rG47Z7DjE2REnr2koLDHSJNDNYfXG1UlOdnHNnRH93JwYXqZ9r3SubERUsEO7/d3nsqjnpWb8woO8c293do4ZTJ4uzzp35eHsyeHkAlbtS6N1sBcr91+2HndQiyAiAt2YN7xthTb+FqOBwhN/kL/vR0z5ws9DkCdMGwQT+4K6BmLsoA6ym75Il/avsW3hQboX69l5LpN6Xmou5xZx/KoG475LRIUJnQix0SEcvZJHQz/XGn2OPq5K5DKJtb3RYrHwbO/GPLxoL/laA/laAzN+PMaC2LYoFUa6LKleztun8XPZMWYoFzML+f7gZb7467xdO2yY8ipfhb1NQ6er9gdo/ga0eB0kd6Fj4axZsGkTXLsGL70Er74quCyKiIiIiNx2RFFWBa1btWLI4EFs2LiJqc+9QOtWrTAajSQcPSr8o//MZDsjDjc3N56Z/DRz5v6PWbPn0Lx5JG5ubhw9eoyioiKGDR1Cixa3Z5bpP0nv3vDII7Brl2Dy8fDDt7VV0RH2LoaVo1bKMJotN93yaIPZAGnb4PKPcGUdtHwbGk+0304qg3r3wfnrb84V7hDUH+oOhqD+SJz9UAK+gKfJjMFsJr/YiN5oZkRUMPFJVYsRfzcnuob7V8t4o2OYD2n5OsbEhPHwQqEqXT4rqzwJaQmM+Fkw0ZjZ6V0eXiSYQZTa1K+fHMO4ZQcI9VXzzrAWnE0vIDW/ZhWZAp2RFnU9HIqywhIjPmrvGh3PW+WFUqaxthSWWtUfSMrhg82JpGt01mpW00A3xnQOpXNDX+QyCZdztTy+/AjNglzp0cQfZ4UziakZ/HriKEaz8HmVGp3suZBlnVd7ZPE+hzb+341rw+DJb5O8bTWmQmH2NcQXZg6BcT2gBsaDnNXDG9ngGT6ed1pPw2CUkphaYHXo/Gr3RRaPas8nf5zjQmYRrs5yHulQj1GdQnhy+QEWPNa+RnEGnmolX+28aK1gFpUYWbTvks3Lgofa12Pn2SzaNbDUqJqZWpCNp9qPwhKjTavtktEdUGRswf3wdOQm2+u0SJ2QRC+D0Eer/ZndEs6fh9BQkDv45rm7w8qV4OoKbdrc9ksTEREREbmBKMqqwRNPPE5YWBibNm8h4ehR5HI5rVq15OGHHyKiaVOH+3Tu3In333uH73/4kTNnzmI0Gqlfvx6DBg6kV6+et/kO/mWUlAjhpkOHQosWjreZN0944+tWA1/uW4i7quIQ2/JE1nFHpZCRV2yoYcuj8UYLo8UMWXsheRWkfA8lZcLGL//kWJQBhD0GMhXUGwJ+XUDquCRSOg+j8hDO1y8ygGn3hPPxH+cctsHBDaG162wmE7o3qNB4Y1xMGDGNfJFIoEmAG5kFOuvnUBMTjeh6XRgZ1Zr52wXHvek/HmNkVDDrnu6CBQsfbjnN4ZRc5g1vW+mxyuPmLKdfZCDfH7xit+5seiEuCvcaOyMev5rCgOZBeKgUrHg8ihPXNHRq5EubYC+7ata7mxIZ3DKIlwdG8MH9zekW7m9TTRvWui4hPi7WqtfoJftZ9WQ00/s3Yc6vZxyK4WPnLzP+p6/QH91Icb4grhsHwktD4bEuIK9B190lA7yVA0dVLXmm93P0a9gPhcSJ5XtSiAhytzp0mi2gN5h4fXAz+kYGUmPylGsAAOd6SURBVGI0Myo6FJPZwvzYdpzPKGRs51CmVSPO4IkuYZjNFhtHTA+VgrxiPTKpBJPZYnXvXBl/idCg6jkolpKrzcPTM8DqLCq02p5noHQpkVlzkGD7M59j9kbdcz3OQTE1Ok+totXCBx8I1bAPPoDnn3e8Xdeut/e6REREREQc8p8XZQEB/hXOm5Wld+9eNc43i4iI4I3XX7vZSxNxxM6dMGECnD4tmHns2OG4Cubre/uvrRKUsopDbMszNiaUP09n0La+V43OodEZCLScE4TYpdWCcYcDLOl/kZF5DTcPf3unN7/Owlc1KO8I+WC7esRGh7Au4SofbT1ntRIvdTjsEOqNwWTm823nuZqntTH1yNPq8XdzxttFic5gIquwBGeFjFbBnqRdr2TV1ETj0/i5rH/kTxbuSLJWT1bGp9Ah1JswXzUr44X8rZoGTMukEqLCHM8VmcwWVsenMqXjNMauj63yeKXOiPlaI/7uTkzu1Qj19TkvR9b8pdW+tzee5MjlPNqHeDHx24Mcv+p4u9FL9pOcXYxGq+dgcqHd9eozkyk4uJ6iU9uxGIUW287h8MJAuLddzWL7Uo3wk7QpYR3fY4ZfM1yVrqQVpuGqdMNgUPL17mReGxxhdegM9VHj7eqEq7PC4b0+2bUB/ZoHcjglr9KqamxUMPdEBvLSz0dtfuZcnRXM6B/BK4MiySjQ4qVW8tjX+2lWx73GOW+Bbj5sOpZq/TlSS7V833IRzbP+sNv2pLYB11qtoad/uxqdo9awWGDtWnjhhRvzta+/Dg89JBgdiYiIiIjclfznRZnIP4ScHJgxA77++sayXbvg228Ft8W7HLlMSu+mAdXO4pr2w1F6NfGv1rED5Fk84LWNsPgZUHiqyu0lFhOvf/kFV1z737R7Y6WOkN0aEDezF5mFOlQKGVw32fjuwGU+vL+ltepVaurRO8Kf9+9rScLlPD7786yNwBjcMogXBwjV6Jsx0SgyaOzmv77cdZGvR3dAKhFE1NaTaTUSzMv3JPNoh/o83bORw2rfweR8FnQdysR2Eys1kijrjNiziR9eaiUjOgbzQSWh1NN/PEZsVAifPNKGi5lFuDopWBjbjlVl5sHKVgWXj+vIin2XqOflwvPfH0MmldDYT0XB+YOc+eM7ss8KLpsyKdzXEV4YBNGNqvXxWskywccaJaEdZjOo8VAK9YU4yZwwmU1IkWE0ytGbzQxpGUSHUG/e2ZhoDa+e8+vpCu916ncJPNurES8NbFphVXVCtwb0igggLU9Hav6NPMmxMaHkFJbwwg9HKdabeKJLGO1DhVmpm8l581F5sixOcLDsU0/D/wLfxEN/1m7bjXldORQ0l+kt29y+4OeyHD8uOCdu22a7vKhIWP7TT7f/mkREREREqoUoykTubiwWWL1ayBzLyLBf/+KLwvzYHRxOL60YFeqMKOUgl5vI1eZaTRe8VJ4opU7IpJIKH+TLVpNGL9mPm7Mcd5WiwgqOUmKgr/s+HvL6g65uR5BJzFBY+XUmFIezKa8Lv2o6c1kfCJqbc2+s0hFyzY1jaktMxMzeRq+m/vSLDKDEaLa2k3moFDzRNYyxncPsbOBL2XIijZcGRhBZxx21UnZTJhqu5QahTl7TUFTGrGPH2Uw+ebRNtQXzOxsT6dXUny/+Ok9EkLu12qfRGQh0d0YhkyJDwpx75tK1EmfEweGDmbX5IlP6NKZTA8Hk4vdT6RWGUpeyIv4SrYM9uJhZxPztFxxWxuBGVXBqn8YU6Ay08tQTfnUXa+Z8Q3rqNQBcnWFcd5jaH8Kq9w7ASpoR5ubCwnwosuiR/vo8zQ8vwd3JHU2Jhi71u/BGt/cZOm8Pq8dH81TPRoz4ch/5WgNP92zE/uScKu/1s23naejviq+rk83n7KlWolJI+e1kmmCiI8Ea6lz6fTKZLRSWGElMLbDmzP0wsTP3z4/jTKqW6Z1nMPLnqquuU6KmkZyt5/sJnZEWXcQ3LgaJPs9mG7NFwvLi8fh2fo3pzWopoqImZGfDG2/AggVwPT/OBplMyB0zmYT/FhERERG56xBFmcjdy8WLMGkS/P674/Xt28OiRXdUkJVWjOIvZDNzYAM2nN3A3L2z7R7Cp3WawbCmQ7map+ViZpFNFpfX9XmhVfEpvLMxkXytgY8fbsXB5BzGdA5luoOZmnbqRL4Iqdo9rkDZiEUp0azP606KPsjhNtV1bzSazBhMZraeqp4jZJdGPnRt7MvBV/ui0RrIK9bjpVawY3oPthxPpX/zQHKK9Px6Ms2hIAOhkrUqPoUxnUNZEpd0UyYahSX2oja3WG8Va2kaHfnF+krn3MoK5nytAS+1kjSNjr/OZNpkiM0b0Qa9ycyDC/fx2fA2NPXsw64xQ8jV5ZKrzcNb5YmnsyclBhm5hSYm9WzEz4evYLaAtopQ6rKUWr4v3HHBrjI2dF6ctY1v4V+nyTm+nW+XL2X7tj+t+4f6wcTeML4XeNUwIeKyAWblwnHXjgxq9wCNT64mIS0Bs8XMsfRj1x0wXySmbj8eXniYy7lacor0fLT1LMnZxXirlYzrEspjX+2v+mQIMQSLR7Wn++xtNA64YeN/Jq2A2Q+2ZOupdPpEBCCXSpj7UEs6hHrz7sZTvDE00joDBoKYbR/qxS9Px3AmTcPQJlXnvE1oN4nu9fsz88czfDmqPQpVYySBfQTTnOuY5J7kt/2GR4IH3L7g51KMRuF34OuvC90EjujSBebPr3j+VkRERETkrkAUZSJ3HwaD4Jr41lvCsHp5XF3hvffg6afv6Fvf0orRjjOZbJ7akelbpzt8wEtISyB2reAIOOeeOew6B11nbbM+yJc+YJbOq8RGBdM2xIvYr+JZ8UQUI6KCWVVOtOwras5lfQD1lfb29RZVPSShw9HWfZhHVhdzKqPy4F2wd2/UG00YTBY0WgO5xQa81ArcVQosFgsrqxBkIMwLtajryZ+JGSzaad/iOL5bA5wUMoK9XXjp5xMOj1Ga0XXyWj4jo4I5fiWvxiYaarm7zYN5KaUOgDKpBKlEgkopZ86vp/Fzd6pSMEfWuWFWAdjY7KdrSqziw0Up59k1x1k8qj3fxhWgNVjIKUonJecizoob3/dJPRqRmKqhoZ9rjYxddHqTTWvmyvgU2gR7Mrx9XT5esY7iUzv44+weftMJJVSJBPq1hKf6wKDWNZsXAzivhw9yYY3Gk3f6zGJCyEPU91YxscNT5OnyyNPl4e3siZuTBwqpE8MXx1urdi5KOekaHaE+alY9GY22muHVZe+1cYBtG2ppRezktXycZFIWxLazfp/Kzq6V5ctdF+nSWDBQKSwx8WKn9+hUryufxM+xe5EyNWo63ev3Z8ySoyRnF5NZUMKWE2mMi/4Stea0EArt0RxZt1/wdmtYsw+zNvjjD6GD4ITjvz/Urw9z5twV7rMiIiIiIlUjijKRu4v9++HJJ+FYBY5rw4bB55/f8YF1o8nMH4npbDiayk+Tolh/ZkOlb9xBcATsGtKNp3veT3iAOwt3XOBAcq51fWQddyZ2b0jbYC9GfrUPT90xPlhxjrkTHqddsBdL4pKs4saClJ3GAYxULgOg2KxiQ15XUr0fZOKg0TgrleTn6ziV+qejS7GjrHtjUYmRP06lszUx3c6OfXy3Bsx5qKVNm1x5SueFvvjrvMN2wJPXNExZk8C0vuE80L6e3cO5o4wuncHE9P5NKdSV8FzUdEavG1nlPU2Jmsbq+FS7B3PBBEJO76a+fDm6BUUGDZc0p3mqjzcyXFkdn0R8Uh6AjWCWSSVEBLkxvV84W0+lI5FIhPbaMpSKj1LhBhZ0ehOLdzmugMmkEkZEBfPJ1rN4dahZbELZap/FYkGfdo7pLyxHe2YXGWlp1u28XGBsd5jUGxoF1ugUAJwoEcTY9xoF3pIHOTLxEzxU7uw8m8mLP58gMVVjfcEQ4K6ne7iF7uF+LHqsA/0+2Uk9LxVOCilp+To2PduVjceu0Sa4ZiY2Ze+1bOXy50NXGBkdwlOrDnPoUi5myw2Xz3c2Jtod5+Q1DfnFBq7kFKNSynh40UFGRrVmwyPbKDJorC3Hark7q+NTGbbuoLXqmFusZ8fZDEJ81Azo8jPyxA+h3aegqFme2t8mIQFmzqy4g8DZWZi/nTkT1OrbemkiIiIiIjePKMpE7g60WqEF56OPHM9E1Kkj2Nzfd9/tvzYH6E1m1idcY/3kGHxcYc7P1QuinbtnNkObDGFA80B6R/ij0RrR6Ay4OytwV8nR6wr5+OtX+dRrA63qnuOwvj2ZBSO5kFloV8FRGxpwKS6Rb9N74Nr4ER4a3BSd3kRStg5/Nwt5NQysztcacFfJ2X0ug3ZhaprW8yG7OAcftTdquTtr9qfy+rqTDG4ZZNcmV5bY6BDik7Irnc8C2HEukx5NbQeZSjO69iflOHTke7pnQ+5vNoy9Vyo30ShtOxu27qDduim9G+OsMODrf8Rh+PSUjtOY036AtUJSViQaTWaK9Sbqe7uwo1mATeBy2QranOttdW2DvdCXmaMri0wqoV9kAEaTmRPX8pmobmizLjzA1VpJPZteaLe/m1LKpRP7ydm3jeJz8Zg0tjOXncPh8e4wvDPUMCYPgN+L4H958HsxDGjwEAGZA5nZNwY/V0/m/GZrrV+2grXxWCojo4J54Z4mPN4llFAfF8xmC78/1409F7JZe+QqvZvWzP2wjqeKN4dE4qyQ4aSQ8kdiOgmX83iiWwOmfneEA8m51jiFnk390eqNeKkVDn8+c4v17DqXyfCOwdet7ZNYuCOpTOVaw5m0ArxlueQbb4jH0uqqUFXuhDz6a7tj33LOn4e2be1eBlh56CGhOhYScnuvS0RERETkbyOKMpE7j8kE0dGOq2MSidCm+N57QtDpXUKBzsjrgyPZfT6Trk1lNXIEzNPmUs/DBblMilopJ9DDGTRn4fh8nC4s462AfOv2bZUHOZaVyPztGTazS6UVnGDvDxyKmIggN+aPrFn+lodKTolRS6rhL178bq5DsbJpykBmbUniXHoBz/VtzKZjqTaiobTy8+RyezFUnsISIy5ON9pPSytsC3dcqLDC9tTKI0zp1ZjZfefQuX5XPtpXedtZ+YfyJ7qE0amRKzO2Tnco6hLSEhi7PpaJ7SaxbNy7PLPqJPNGtOVAcm6lFvWjl+xnbEwoPx++woKR7Yhp5Et6gY6W9TzxvD5HVyrgAKvIk2EhTyt8fs5KGVFh3nQI82RkdBBFBg1ZxTl4O3uhui6Kv9l5nvRT8SivHiJ68Sjycm3niPzcYVQXeLwHRNSt8ltgh94Cqwvgo1w4pocmnh3o5TyBz/s9wj2JO4mNDuHPxIwqBffK+BTaBnsxJiaMAq2R19cd5737WrJox0XrvdYkhqC0dTavWPh+9okI4I/EdCRImNI7nJcHNLO2mb698VSFLw5kUgkB7s6k5mtxVyloUded41c1Ni2oThI979RZTH+PPQw69xlpBl8i67jjXCZfzSYT8HbSqJHQLfDLL7bLW7WCTz+F7t1v/zWJiIiIiNQKoigTufPIZBAbK7TclKVlS1i8GKKi7sx1VYbFQnxSNompGpqFVPDWugJydHnU86gnvO1O/wtOfwTXNgHgaEIuOPtboJ/NgyNULmLOpheilFf/wTcqzBtXlZlpv8+ocC5u7PpYJrWfxIf3zQYJNC0x0TzEBXcnT1QyQTTsT8pDV815ofLXWN0K26fbzhEe6Ep9555seGSAte3MV+1FgJsPSZklzPzxjE17ZWnLW48mvvycuLbSKhsIrabdQ7vx3YT7eX/TmUot6kdGBfPjpM5IEaIPtp/J4JHFex0KuE3PdkEC7LmQzZvrTjDnoVa4WgRDkz8T01k8qjmbz29k4OoRgti0AOlQJ6MOnle8OJ9wDr3etgIqlcA9LeGJHjC0LShu4rd6rklwUfw8D1JNwrIGXg14vcdUBocPRYoTg1oEopBKWLI7qVrHXBKXRL/mgbyx/jjX8nUUlRitPxel5i2OTGzKMzYmlAV/nWfbmQybFxL/e6gVBrOZj7aeJV2js5nLLBWFM/o3YcW+S6RrShjeMZjHokOwWCw81D6Y/GID3z4exZe7kqzVznqKNOaHfEhL9XkAvgj+kEcvfsCEbg1QKWVM6tGIFfsuCU6bHs41/pxrhQ8+gA0bhJdZQUHC7O24caKrooiIiMg/HFGUidwdPP88/PijMFOmVMKbb8K0aaBQ3Okrc4iLs4KlccmE+qprHETrrVDBxWVw+mPIq/yh9JreD7VriENxVZmIKetaWJ0H31cHN2HDmfVVzsUtOLiAmPoxZGuzmfLrFOvy0kraJ1GDyC4UrMkdteABNstW709hfNcGPP/D0WpX2EDIOFs+riPR7/9RxpFPQ1r+GYZ3DOajR1pjNJnRGcy4q+S4OcnZfzEHicTAx/tmV+scs+Nm06/hoGpVhaLCvOkY5k1BiZG2IV70bOqPWilDKpXg46Iku7CEJXFJHL+Sz+Rejfjsz3OsfCIaswWc5TKe79uI+9r6ChW8HQvhInDh+lchXLv+v7I0rQOxMTCqK9T3qdYt2XG8BL7IhxUaKCr3buFi7kVGrh3JxHaTeKfnh4yJCSNfZ3QouJVyKX0i/IV7LdLzR2KGML+lNeDt4oSnWone9H/2zjs8qjptw/f0kmRSSQPSaAkdEkjoHQEp9gJRikpxESyAfT93rRTXhlJUFKXZUIqAivQWegu9JLQUUieTmcnU749hhkxmJplgWXf33Htxrcw5c86ZkvB7zvu+z2MjJSaIMwU6luzJZfXkbvWKIahe8cpMj6N/yyhGfrzHLdeuOot2XmT5Yxn0aBpBWICcn08U8MjifR5i+ZHuiaye3I35y97j2eA3CZHezJZIDTjF1+mriEm8jbfWnaJzYhirJ3f7Y6tkNhusWuWoiHlzY0lOdhh8hIQ4sscC6mmfKSAgICDwl0QQZQJ/DSQS+OwzhwX+vHnQsuW/+4pqRWc0cyJPy7nrOr+DaCMk8I+GMcRu7gpVXjLXbmBHxFZtR5aUDGGTNo1JMS0Y01XtJq78aRP0d+H7UEYciQ0UPLTGv7m4t3e/zQ8P/MBTPz2Fze6Y/6ve9jez/yyeGdicuzo2chl1RATICQt0DDZpDRbytUaiNUpUMgkBCgnPaVv4XWEDR5XKZLExLzOVdzeedTNM2XrmOikxGvq3jEQlk5JTpGP6N0dY+FAa1w159Wo1LdaXeoRPe2PBtgukJoRxx4c7ebJ/Mx7tnsio9DjK9GaCVTICFVKqLFZMVhuFFUaWPNKJ8EApVnsVR89no8o9xrAPl7J121Yo9n2e6BB4sAuM6gapiX69DA/Mdlipgw/LYLux7v3nH5hHl0bduaPF3VwtuxnQLBGL6Nksgin9mtA0SkGZsYxiQwnhqijeuqcFh3J1GKrMPDc4mXKDGaPZxtwHO6KQiVm+9zKTlx1k7siOtQZDd04M54GFu12CzPl4lyYRjPw4y6cgA8d3pFhXhUIm4Y11p3y2xM74+gCLOq7mzfBFHtvtEjWJzXpwsdzIhF5NUMjEZF/V0r1ZRN1v3K3wyy+OjoHDh+GLL+Chh7zvN3v2H3N+AQEBAYF/G4IoE/jz+OUXx2Jj+nTv21u2hK1b/9RLulWci0STxcbBXB3Tuswg83vfQbRvhcOUEFCJ86DKx07yUGjyKFUJj3L2uISrB68i0ulYffgqKx/vRmZ6nKuNrnlUIEZz7SKm3GBm9KK9LB7XudaFb9/kSMoMBfUSK3qzntaRrTla4F6Fc7b99Ww+kPFf7OdKqYHJfZrwYOd4ygwminUmYkLkJMfIKdIXc76smOigcB7MiKLCICJY5d2cwRt55QauV1R5tbAv15uR3agyqOVS3r2/A0uzcmkV75/oc3K9ssQjfNob2de0VJmt/DilB7vOFTHqkyyv7YupjYLYk7WXiivZfPvTt+zbvQ9zae2vN1AJd3VyCLF+reBWY7CuWWBBOSwsh/wbLYoqqQqDxUvsRA3ezZrDbU1uJ0QtdxmfjOkaj0JuYe2ZNYxb7z2bLzVhOJ/vuMScn8+4tjnfi/tGdmTyskP0aBbh8RkGKKQEq6ToTVY+Hd0JrdFMWICcsAA587ec5+VV2X59TyRiEbvO+26JbSTL5/242XS0nPbYViFvwrMFL7Puywhgl9u1myw2FFLb75dJ5s1R8aWXHMYdyn9Tm6SAgICAwJ+KIMoE/ni0Wkcr4scfO9pxevb8a86J1YNg1c22ype+P8GPU4fVGkQrAlS+1m9BzaDFk1jiH8IkUqE1WOjRzMTg1jHIJWIkYhEHL5UyqXdT2sc5xJVaLqGyylLndeYU6xk+dyej0uNYMT6DkkoTBpOVQKUUqVjMF7tzOH6tnNs7Wuv1+ov1xWgU3o1XZu2cxer7b8NgsrDmie4culTK/Qt3U1ll4fNx7Vh37nve2+tpJDKj6wxWTb7N5XpYFyEqOV/uyeWlH457GKCkxGj4ZmIXpBIxQUopG7ILWHnoKj1T6udK5yt82hsmq42NJwp4Y/0pAOxWC+aSK5jyzrLt57NsfPMs1qKLWC11f25qBQxuB/emw7AOjr/fClY7rNfDp+WwthKcZw6XpvH2kKcZs9r3jYTqHMo/RLmxjOjAWNZN6cH+nBKs9ipm/PJcndl8M/vP4qt9l7lc6hB/1Wfx5o7swPC5Oz1MbB7pnkhppYmBraK5UKTjlxMF6KosPDOgBR9uOe/XNUvEIsIC5D5n4IYE7+CtRu+jkXh+146Kb+PBg49QaXO3lK9+7S8MSfntoiw3F15+GZYs8XRUvHTJ4Tg7bdpvO4eAgICAwH8EgigT+GPZsgXGjHEsPsAxLzF2LBw6BIpbXGn+m7FYbShlNw0qLpcamPTlceY9NJuecb2YvXumh+Bo2mk89jNPIbJVK5NF9YHkpyF2CHqzI/dswdbDXmdeUuNDmbhkPz2aNWDhw2nYbHakEv8CYR223+d5uEs8gXIJPx3P5/vDV13GCEPaRNd7Li5cHY7Wh1g5lH8IvUXL0kcz+GDzOZZlXSJYJWP1E2m8sfNFFh70vogfuXKky/VwxNz9tVZCnPbz5wp1tIgOIkQto3VsMCaLlcSIADaeLERrMCMSQaXJysfbHK5/v1f4dHXsdjtWbSF7tvzCnCU/cf3CGcxFuZiLr4CtbgHmJEABt3eAezrDkHYQ8BsKJOdNsEgLn2vh2g29LRFJiZX1xqa9ne7xnWkWpqrXMUsMpcQExnL8aik9WgSz4byf2XxxPXn1jl6M+eyg2zZn2PWo9Dg+2nLe1SLqnCMbPncnczefZ1R6HFP7N8dostIgSOHTvKbmDGPTyEAqvMzAKUVG/h77MSPDf/I4hl0k43TM3xm+oT2OWynecc4RDmkTc2vCrLTUYdjx/vtQ5aN03rIltG5d/2MLCAgICPxHIogygT8GgwFeeAHefddz28mTsHgxjB//p1/WrWKx2jBZbWgNDhv3VYeuuplo7DxfzIvzP+aBAQPYNmY7ZcYyVxBtiDKEgnIrV65n0ah0GaL4ByH5KQhtD4DeZOGNdSdZssf7zMvTXx+5UVXo6KoqDGkTzVt3tauXrXigQsbVMj1vrD+FRCxyVSauluoJUYXUS6yopCqOFx73uU+FqZyrRUqW3Wi3zMxoxObc9V4FWXXmH5hHRqPujEpvz0dbfLv8je+ZxPlCHdtm9EEuFSOXiNEZLZQbzASrZcy6ux0Wm42rxXpEYrFrYb48K4+pnacxdnVmna9zRtcZ/HqyBJv9hvDSlWApy8NSeg1z6TUspXmYS/OwlOVhNxkYW/tL80qgEoZ2cFTEBre7tTwxJ0YbfKeDT7Sw1eAwbgQQ24OJEN3O5LSJPNW3C8WVVeiMFjSBtYvNmkQHhWOlinzzFgyWXsza6Wc23+5ZbB0zFKnYMS9ZXTgt3pXD/IfSmL/1PCkxGh7rkUS/lEgmfnnAJco/2nLeVUl7KCOecd0Teebrm6223sLGw9RyIjUKLpW4V8GaK3KYGz+L5krPn7WLVTHQbTmfHgkBrtT5uhZsu0D/llH1E2VVVfDhh/Daaw5h5o3YWPjnP2H0aJAK/0QLCAgI/K8g/MYX+P3Ztw8efhhOnfLcFhjoGFJ/9NE//7puEb3JcqOKdQGb3c7cBzvyr41nXSYap47/xDNRX9It6Cjv/fwAo5dmMrBVNOEBclrGWBnaTsHmk5cY1v7/MCpeQRWS4Dq2xeqokHkTZNWpWVUo0FZRXFlVL1vxUr2JBoEKJvdJ5IF0Rw5Wsb6E2KAoTBYTUzpPYdzqcXUea2r6VFadXkXXxl3RVmnJLszGandvf4wKDGfR5quAo4LxYHoMQ1f41yr3XtYcVt//K/O3XsTmJW1gSt+m9E2OZOe5Ymw22HSykM93ec7LPdYjiQEtoyg3mFzhzUv2XGHV5MGM7zjxpkC0ATpAC1Q4/r9DQAe+O/U9p89/wNXzuVgrirCbfQ0D1o+YEIcQG54K/VuB8jcIMYDtBlhaAV9VQFm13PVETWuCLMMoK+6ECDmfbtNyuegQs+9tz+USPQ00QfUS4hEBoUz/eQa7ruwkvXHH+mXzGct4+772pCWEuQknhUyMWi5h+4w+2HFY5R+/Ws4bd7VxyxhzxkHM3HCazdN6u8xragsbH9o2hucGJ7v+/kDYBl6JXYhS7BmqvrK0Dy9fncQvd3bm+LV9fr2u7Gta//PKbDZYsQJefBFycrzvExQEzz3ncFRUq73vIyAgICDwX4sgygR+P8xmePVVeOMNR4ZOTXr1cjgsJt6ibdy/gZpVrE4JoZToTZQbzLz55WLeSfkeVdONrv3HhK/h4+t3kVusp29yJJ0SwsgvM3J/5zi+2neJYe0aUr1pzGS1MX/LBb+u5bOdOSx82FFV0FVZADudE8P8thUHO1KZmQZRhxn21SjXYrx7XHf+NfBf9IzvyfjU8Sw8sNDrcUKVoXw6/FP6JfUjryKPjjEdiVBHoJKqWHhwIfP2zaPUWEqH6A5oFMGsP+44fvOoQCrN2not4q3oGNImhrVH81yPt4rVMKlXEn2So1i6J5cR7Rvy1f7LvPfrWaw11Fv2NS1PfnWY+9o3YFSbIN7qIuK7bcfZuv003X6eR+sQE4m5SVy5chmz1nyzrOS8hhv/+71oGwfDOjpyxDo3+e3HO2mCL7WwrAJyq3VIBsgCGdr0Hm5vMorbmnYjQOkwTrlWZnCZoCze5ahAtmwYx/SuMxi1sm6xPL3rDM4Wn2X+gXl0j+tOkb6oXtdbaiyjcWis1wDu8T2SyGgSzoMLd3OhyFHZSo4Jct2AqM7QtjEYTBYm9m5CelIYnRLCmLvpnNfv//rj+Tw/JMVVTQ6W6DwEWaVVyctXJ7GyrN+NarK0znbV6viVV7Zjh0NoHTjgfbtUCo8/7jD2aNDA73MLCAgICPx3IYgygd+H7GxHdezgQc9tCoVjfmLqVO+5O7+B6m2F5QYTwSo5GpUUuUT8m4fwvVWxdFUWoi0n+SThH/TX7POwLw+WVrL1jlMYmt3OsqxLvLr2JGue6M5ra08wdUBzj0Wc1uA998kb2de0GE1WWkQ7sp4C5DKeX3mUl4a29OmuOLZbAp0SwvjXz6d57a7mjhysGnNA2iotQYogBi0dxIZRG+jSqAvvZb3nVkEZ3nw4n474lJ/P/0yvz3t5zMxN6TyFfY/tY9DSQTyVMY1Snd1V5QpUSCnWl/j1Gp2U6Et57Y7WPDsomQKtkVC1HLVCgkRsx2QzMKCtnKuVZ+iWJKGZNIBlPx9kY9YxdEXXsFQUYa0oxlpRzGxjBd7Mw695eez3RCaBXikOETY8FeJ/Bwf1PAssr4AlFXCoRtFObmtBoOU2gow9KM+LZHWJlGU79vH52E7YgZkbTrlMUJyfy2c7c1gzZVCtBjUAk9ImMbT5UG5fNgRwfF8i1PV7QaHKEN7ceNGj1Tb7mpapXx1mVHocn43t7KqOVb8BYbO7f5dHfZJFqd7M+w+0Z19Oic8bEjWz+hZev4tugUfoGeQQ29mGJJ64NIMLVY0AGNctkSqz1Wt11hcapR85imfO+BZk997ruInVtKn/JxUQEBAQ+K9EEGUCvw2rFd55x3GX19vAelqaI28nJeV3P3X1tkIPq/deSfRPifpNIa81q1hNFJeZKv+Sxnt30di78SDlNODni7Bkz35O5etIjg5CLIJvD15hQq8mhKnde9XKDZ6tVLVRqjcRqJBitdnZe7GYoe1iXe6K3qzhncJw6WNprDq12kOQAWQXZqOWqQmUB9Lp405MTJvIqgdWoTfrKdYX0yCgATGBMcz4ZYbX5x/OP8y41eOYkDqBbWO2YbOpMJlumiToqiw0CAir1+uMCAjjSqmBAIWExmFq9p7NQ2O6wrJflrByy0qKLhRBEVBZr8P+ocRHwKB2cFsb6NcaNPXz0fBKoQVWVsK3FbDZ4Oi0dCKxRRBg7UmAtS9ye4LrcadhRqtYDWarHZFIhN5k9chaK9Wbyfz4MJ+Pe50ujXrwbtZsD7H9VPp0+iYMplivZful7cDN70t9Wh+DlSH8nO27UlqzPTf7mhaJCHY/3xd9lRWZ9OZ3udxgRiIW0TQqiAlf7CclJsgtoLx61bRmVt8zl59mXbMnWFvegzfzxmGyO0TVqPQ40hJCMdvt9ZrT1Kj8+P0yejT861+OG1dOuneHOXP+411oBQQEBAR+PwRRJnDrXLoEmZmwfbvnNqnUYfX8/PMg8+Nucj2pyxxjyvLDZGbE88KQ5FsWZs4qVgNpCU9GLeP+sJ+Rimxe960URWBOeY6SqNE0rRLz/g1RVFJZxcqDV7DZHa1ODUPdW52C6+nsEKqW32hdhMW7c1g0pjP7Lpa4mSFUt4a32R3h0PHhcjJXz/J6TKvdyoIDC1wzZW/ueJOZO2fSOrI1GoWGe1veS5A8yKsgq86CAwvoFd+byrJUBrdp5FrcFpRXER0UXq9FfMG1Cm5/4UMMl7ORFJ6m4tp5rN5aYv+NqOSOatigtnBbW0iO/X2OW2BxGHZ8q4NtBqj+qjXyUG5LGsG2I8kobC0R4VkNdroQTr+tOb+cKKC40uRz9jCnWM+IufsZld6eXzK3Umku55q2mDBVKGqphuVZebx86CyP9b35M1zz+1IX07rM4GBOBRbvPzoualbH8sqN6KostGkYzIxvj/DziZuB63d3bIhKJmbBjRsR1WfU1u7ey7x9jhbj6ll9GUlhDnOOM/MotwYBDmH1aPdEOsaHMnrRXm5vG+v3nObEXk2Q+1ONl0hg1iy4/XZo0QJmzoThw0Hkn3uqgICAgMD/BoIoE7h1ZDL3u79OWrZ0VMdSU/+Q0/prjrFkTy7piWEMbh19S62M2opinopaymMNVqIWezd5KLMGY0+ZwWbbPXy6rYDsa1muba1iNYzvmcQdHRqx8uBVGgQpkNZo39SopPW6M6+QiV0zL/tzyxCL4JmBLVz5Zd7aF3s0i6DMWFzrTNe8ffPY99g+/tn7n/x84WeXgQfA0ruWMnz58DqvD2D2rlmsvv9XlldrG3swPY4LhVW1ux5WAReBs3C18BqdJrb063x/NikNYUiqiAEd7PRKAuXv9Bs0r5oQ216jIhaqDGVA0hCOnU0mXt2Vz+7oyZvyk64gcSfBKhkPZcSTmRGP2WpDV2WhcVgAapmEAIWUM/kVfOwls6vcYGbBthzGdW/CrB+vU1hhRFeldYn6lJggwtXukQnO70ttM4gAE1NvtD6+t7fO96B6e+7JvAo0ShkvfH+MjnGh/Ou+DlRUmakwWhytrHIJG08WsHDbzSq5FAuvNf2O8erl3DFyNQ/8oCanWO/K6ls/tQf9UqIo0BopqXRUk0PUMtRyCVvPXCdAIfWorPkiMyOefimRjt8rdjusXetoUXzlFe9PGDwYVq6EYcMER0UBAQEBAa8I/zoI3DoxMTBvHtx/v+PvIhE884zD7EP5G0KW6qA+5hjzt56/uXiqD5e+ocneyUyNKvS6WWsLwtbiGezNn2D2r1dZtvesxz7Z17RMXeGYl1n6aAYhapnHdcglYib0TGLKisN1XtLYbgksy7rkNvNitNiY8OUB0hJCvbYvrjx4BREi8iuKfR43VBnKpE6TUEgVjGk/htub3U6gIhCFRMGq06swWoz1MumoNGvJuljMvWmNyEyPY2R6HE+tOMyse2u4HpYBJ4GzQC6uklAhBX6d6w9BIkMaFI4kMJyWTeMYlB5CbNgl2kfk0URyhRiZCQ9XkFskuwrWVMLqSsgyuguxOE0ciYG9ubflXYxo2ZePt+VAeAVao4UCrZG/9W1K56RwFmx1tPq1aRjMojGd2HW+iEcW7/PqRDm1f3NSE0L5YNM5j+3TBjbHbLHx47E8auIt363UWFrrDGL76PZM7/osw5sPY8IXx1zB0XXhbM+tfgPiZF4FHeJCGNgymphgldcqeQtlDnMavUMbtcMYRLH/Eb54aD/DFhx3VcwsNjvjPt+L1uiovt3eJoaYkDBUcin9U6Lo2bwBWoMFg9nCC0NS6NIknPk33t/q79XEXk3olxLpqMBfuACTJ8P69Y7ff0OHOlq2ayISwZ13+vUeCAgICAj8byKIMoHfxn33wfffQ1aWI3usR48//JT+mmNIxCJsdjsGk7X+LYwSFRKTpyAz2BR8en0EC67fzfiYDjQ8r6/1jjo45mU6J4QxuE20xzapREy/lCivd+arh+H2ataALkkRvLr2pGv74NbRVFZZ2JtTwt6cEq/ti5N6N+Xw5TLiI0O8XlvTsKZsGLWBbbnbGLZ8mJeF9XSiAqIY2nwoZcYynxb41SkxOPKXRi/ay7LHMrDZ7ezNKWHMoiPMHDwZ3fZKfvjuO/QX9D6P8UcgVWsQBYQhCQpHGhiO5Ib4kgSFkdAkmEdvj2T+r2vpG3aaXkFX6Cg5SUNpHT139cBid1TBVlc6xNj5atnYEpGEtOiODEgayLDmI2ge2gaFXEq53oTWaGFCr6YEKqRUWawopBJ+OpGPQiLm6wld0FWZCVTIeGPdSZZmeW/nffKGmcaT/Zvz8eg0DFU3xXuwSkaQUsLFIu+fh9VmZ/3RImZ0ncHIak6N50rOeZ1BbKhpSIQ6gjP5Rsr1Inae931DoCbO9txHuie63YD4bGcOQ1rHeFTJZSIzjzf4hr9Ffo1cfNOGMtSeR/n+KYxKf42PtpynVayGsAA5r97RBo1S5mEGJL3x39V/TwxuHU2/lEi0Bgtao9n9eVaLw6Dj1VfBaHQ8wW53mBnt2CG0JgoICAgI1BtBlAn8dubNc8xNBAX9KaeryxyjZphsobYKs9VeP1fG2NuxN+iF6PpWAKx2Md+U9ued/JEUWCKQiEXcldqIxxbv9+uaF26/wIBWUXibINObrEzoleRyULxSanBdv9lqo7LKQliAnCCljK3Te5NfZkQmFROkkpJfbnQdx5nl5EQiFjEyPY4JX+xn/sOtPWa6QpWh/JT5EzN3zvTagpZblkv29Wz6J/XnoyEfcan8EuHqcA8L/JqEqULRVWnJKdazYOt5bmuipuLQOvac3EbGjGx+r0qTGxIgFAiDe7rcz6nyMPIsaqRB4bx4X3caN4ylfWIUQz5cTXHVGUziy5hFl4iUZ9Et4Aq91SZ6X4DxzX/fyyq3wnq9Q4itr3TPEZPaYhiQNJD72w6hZ1wf1LJAdpwtIiE4gi1nili086JHlWZM1wS6NokgSCEjJSYIk8WKRilj/fF8r4KsOk4zjYvXK9l0utAl3vPLjXwzsQvBau+zn8EqGYNbN0QqjXSvdOKomFWfQXy47cNMTJuEwSimQaCaIKWs3u25qXGhdEoIc7sBkX1Ni9ZoQaOSuqrkrVTnmNPoXVJUOR7HMtslbM0LY9Q9cczfep6JvZqglIppHuX/76jqQs3N9n7rVpg0CU6e9HzSrl3w44+OipmAgICAgEA9EESZwG8nJORPPV1t5hi1hcnWy5VRJELUYRb8nM5JcU+mnLyfs1Xxrs3NowIxmqz1srPXGszIxCLMNrubhX+AQsKKfdew2+18OqYTAXIJm04Ver/+nkn0TYniuwNX+PbAZeZl+p7bc17j8Wtalmflecx0Teo0ia05W70KsuoVtNuW3FarBf65knOubR2iO6CWajh1tQT9uf38su8D3tj2KxaLxeMct4QU1A3VpHVMo3d6bz65/AnXZNdAA07Pi9cen8nUpTmUFlwgvUUZZ2RreHHdFsTyi2ikRQwKgt5q6K2CxN/fg4aDRvhJDxv0sNsAZgC7CJk9kUFJPQmTtmXrsQikhCEq1zCoSScsVhtTlh9i9j3t+eBX77lb2de0TP/2KKPS4xjfM4kluy/x5IBmmCw2PtnuOSvmjc925vDp6DTm3TDTSAhX88PfurE/p5RuTSO8CqjMjHh2Xyjmw83n+Hzc63Rt7OnU2DaqLU+mT2dos6GYLVJySnSs2HeJjnGhjO2WyLRv6m5/HdctkXK9mYm9mpD5aZYrONqJ0WIFA5zLL2Za1HImRn7r1XjnrLExT15+hmxDU7qbbTw9oMWttTDXpLzc0Z796afet8fE3DT0EBAQEBAQqCeCKBP4y1Mzi6xhqNrr4jFYJeOLcenM33re56LW5co4qCnqS4ugaA90/cL7iSM6U9L7EGpFMyY0K3Ez0ghUSD0WjXWRV27kbIGOpAaBjPpkDznFjnaxmxWQMBQSMTM3nPLtKrniMI91T+LpAc24o0NDJGKRz0pEoEJKid5RVVyy5wqrJt+c6ZKIJExIneDVwKOuClp1C/wNozbQ6eNOrorZHSH38ODYKVz6eTU2g5br9XqHvBAGxN340wgIB71Ezza2kdIqhW2PbXM7f8Oghjz102Q2a3diUpaSdRX6lMA7kdBHDUl/gAgrssJPlQ4h9rMeCm50dqbGpDG13WD2nAolN68hD6WnML5nEsPn7kTqkGpkX9OiM1qwY2dE+4ZkXSz2qx22Q1wIMqmIfReLaRIZVK+bAzY7pMaHcqZA5/bz8rc+TT2cB53V1scW73dzalxz/yYqzVpKDKVuTo2PZ2Xz0aiOhKrlZF/T8suJQv+MM9LjuK1VNLnFOpQyMXe0j+VMoY6NJwsx3bBtDFJI0V3dydpmU2mu9DyWxS5mfuE9vF/4oMvq3mix8miPBJSy3/hP3U8/waOPwpUrntvEYsdc2auvgsZHVoaAgICAgEAdCKJM4C+Ntyyyv/Vp6vXue2ZGvF+L2rPH1mKxfgFVN9qP4h+AhkO87ntdnMR9XnLAojRKpOL6zY2EqGW89MNx2jcOYfG4m0G52de0fLj5HANbdWPzqeu1ukomhKvJ7BLHTycK+Hj7BXq3iPS6kG4eFUhihJrYYCWdEkIdczqfH+XTMY5Kx7qzjhkgbwYetVXQqrPgwAIyGmXwUJOHeP+T9wk/E87/XXyxXu9JTYLDG9ClZ18SOjdgftFcqKXbbMGBBRTriwlVhbpEmV1/ldDCq8xtAH1U0LR+iQN+YbXDbiNsuCHEDla5m3SAI3D5xW5vYDJLMRSfYWr3BnRKCGP0or0eYr5UbyIxIoBBrWPI/CQLf3Dax7//6xnGBNcvEK1Ub+LODg25WmZ0+3nx5jxYsyJcbjDz0ZaLzN96sdr8otYtlFprtBColLgeG71oL98/3s2ncca4bol0bRrOibwSUmLVlBmL6NPGxN2qKN66pwWHcnWs2HUazannCDvzPiKlZ3XspCGBaVeeJNvgHsIcHqD4bYKsrupYWhrMn/+HOc0KCAgICPzvIIgygT8ck8WMyWai1FDqurMeqgpBLlYgl/ouXfjKIvO2eKx+R98X4ZIyXoj9lLtDNzss2J0cfAqi+4PEcwUfHqBAV2XxyAHTmywsfLhTveZlIgIVLHgoleV7L3P4cpkrKDcsQM7yxzIwW+0s3ObbVTJM7dhvzZFrfH/4KmcKdFwpNbjeix+P5ZGZEc+ojDhkYhEKqYRKk4XnB6cQrJKhkIn5/tBVbPaOfDBoGHmVni1vtVXQ3LADufDUY09ReaQSzFCM/4YO1ZFFJqFulsFdI4bx8pjbkcut3L68t/tn5IMdZ76ltwpmRDpEWPM/QITZ7HCoCrYYYL81kIDY2xjY/j4a6osxH1iArZqwbR/dnme7PcuQpkN555dcnhnYgueHJLN0z83g45qEB8oRiaDKXL92WKPJSoXRQoiqfuW/QIWUfilRWGx2t5+X6plezvlGtVziqrbWfE9qhlE70VdZ2H2+zCXSujdrgEIm5rZWUfRoFkGxzuTmELrzbBEBSitnK35lwuezPFpl323Vlw+Dv0Z8xrNKZbZLmFtwPx9dvxez3f198Dvc2RebNjmCn71Vx4KCHEYfkyY55mkFBAQEBAR+I4IoE/hD0ZkqWXVqNXN2ey62pnWZwYjk4QTKAzyeV1sWmbfFo81u9znjJcLGg2E/8Wz05wRLKz0vsuIMVZd/RNJ4BIBbq2SwSsbOZ/vy5Z5cluzJdVuILquWxVUXY7slMG/Lebaeuc6YrgmkxYeRkRSOWCTi0Z6JHM4tpXFYgNfrDwuQM6VvEsM7RFJhKqJ1gpZeLeNRSzWs2JvH5GUHmZ+ZyrTbWrD3Ygl2G2w5e53Pd3nmlo3pmkDnxDBeWJnNs0M93SBbRbbyWUEDwAAcAfYDRVBGWZ2v3Ruy8DjUKT0ISO5B+zYtmdAziYykcJ5feZQZt0f5PH+UBHqpHK2IvVWQ/AeJsNNWJRsqjGw2OBwTbxp06KD0O6TKCF7p/QrDWgxDZ9JRrC92maAcyjsEIujQOIQL13UEq2SsO5bnVZC1itUQopaz/lhevUwowFHxKtZVEaisZ9adVIzeZEEqFnt835yZXs7KsM1mR1zPinBogJzvD131sI/XmyxkfpKF1W53mYzoqyysndqZ6b9MZ4GXcPLj+YeJUhxG7OVzPqZvwvQrT3LKmOj1OvwOd/bGwoUwcaLDUbEm/fs7Kmdxcbd2bAEBAQEBAS8IokzgD0NnqmTaz9O8LrYO5x8m8/uRTEydxOyBsz2EWV1ZZNUXj4tGd0IqEVGo9SyttFRe4PWGH9Ih4LTX45wwJPKPa+PRmBsx6x4L289eZ0G1QFq4KWZWT+7G6EV7XbNg/gbNjkqPc7nJlRvMLrOG5wenkJ4YilQsZv3xfO7r5L7IC1bJmNw3ifs7R7H2zFr6fekpbKd2nsb89NtRSCV8s/8yg9vE8OHmuo0iXh7aCqnU7OHIqFFoKNIXuT/RDlzFIcSOA7fi2SECWVQUqQP7kvnAbagbBJNXUYTFrkNn+ZW5Bxfz9Xkrp6su0vWz466nNXCKMJVDhLVU3MK5/SDH1ghxdH8OGDpiiJEz6seJte7vbN08W3yWtWfXolFo0FZpOV54HJvdxtK7ljOw6Qj6vb2doW1j3NpVq/No90QUEjG7LxSTnhher2sOD5AzuHUMUpGI8T2TmOpH1t2Enkl8uTuX/i2jUMq8V3gcLYqOynBKTBCLxnSul+gLUkr54MGOHm6nWoOF4zWOsXhcKqtPr/H6OwIcX7WJhbCl0c3H7GIFGyXjmXSuHxYf/4S5hTvfCv36QWAgVFSrBgYGwttvw2OPCZb3AgICAgK/O4IoE/hDMFnMrDq12udiy8n8A/PoEd+Te1Ludmtl9CeLrPri8cBLAwgNuHk7PUCs5+mopYyJWIPEi0Ob1qpmTv5DLCkeQpBKyZqhrZj90+k6xUz1xXX1il2nxDA+2e4p5sZ1S6BLkwjeXHeS2BAluioLVpvdkV2WGEZyVBBmq42nB7bAWi0VOiFczRePtEettDH9l2k+DTfGrs5kUtok3uw3i7s7NmTnef+NIiICZTyZPp0xq0bdfF+qtESoIxx/sQIngN3ANR8HEwMhOAw5Qm/8dwCgvvH/ASALkmGWmDFTwB6Ws2fvcu/HugxhYoczYp8GDhHW+g8SYVUBLTFF9GJjcTLvHIklQBPJ3IEd6SQ3M3RFH7+O8X7W+/zwwA+8tfMtbHb379icXbPoEzeICqPZ9X4721WdZKbH0b9lFJ/vvMi0gS2QScR+i58BKZE0CFIQqJQx5vN9zLqnrV9mGl2aRPDyqmzuSWuEVFy7YLHZIftaBV/szvW7IjyxVxNUMglBSs+WyppRFnKpmI7xgUz9bGatx9xqgM+1MEYDlsjeSDsvpJsykXeaF9Yd7nyrNGniiPrIvOFW2q+fozoWH1/78wQEBAQEBG4RQZQJ/CGYbCZm76p9seVkzq5ZDG8xDDk3F3J1ZZFVx2aH67oqGoWqaBWrIbLiV15v+CGx8iKv+68q7cVreY9w3RIG+G8QsjTLYfFdfXEdoJBy+HIZvVs0YEBKFBVGM/laIyEqGcFqCTbMlBgKebCbiHD1zZbDJXuusPbINfo/0IGNJwr4ZMcF5j+URqtYDVdKDSx+pB0HCrZSZiyt03Bj3v55pDfszrDmd7Fgq+/qYnU+25nDV+O70FE0nD1XJjL/hnjOLszGXGkm5kgMeb/mQXV9IAaigYZA7I0/DXBZ0fvCjG+XSo0Yet6ohPVVQ1s51LNbzi/OGOPYrWtD3573ka/szN9/KuT0npvmFBKDjkCllApzie/WzRocyj+E3qyndWRrjhYc9dimNZXTIjqIk3kVLmMOR/VJw4ReTejZLILxX+xn94USIoOVNAhU+CV+glUyZt3TjjfWnXJ9Z2u289YUKo92T2Rgq2geXbyPRqEq5BIxErF/InDJnlzWTelBZnocS2rJQvNVnbJYbZhtNjQ1hFr/lEjKjGUcKThCpARCxXDax1dl2nWHs+WsIV/QSNMYNXWEO/9W+3uAUaNgyxZISYEnn3S4LAoICAgICPxBCKJM4A+h1FBar8VtmaHUrYWxtiwyb2iUMuSWEhY0eYdG5d973eeqpREzLk1kp6696zF/DEKqs2jnRb4a34W+yZGEBsiJClKw81wRE788wLzMVGw39nl2cCKrz6zivb1zvLYcrn5iMDKRijfXnXQtdJ0zajnFFWzJ3UCfpO51G27c4L2sOdyZMpxT+f4bRVzXGXn9xxO8NPR1Mhr1YOaa1zi59iSpb6ZiNt5YHUcBSUAikABe06/rgVoE3auJsFQFSP4AEXbW2Jg9ujbsqWxDVmVriiyhAPRs1Is4uZTT+eepVpjEarPz68lCkhuX1es8xfpiNArvNuglhlICFY5fsU7hs31GXyRi+Ck7ny2nr/PW3W0ZvWgvn+3M4ePRaSREBNRZ8Zp1d1u2nb3utk/NWTCnS2h0sBKVTMKnOy4iFhfQIS6UFtFBDlEmEfFI90Se/rr2n9Nyg5ljV8uY2r857eO8i74JPZPo0ayBxwyX0z314203bzo4nxseIKe4soCpIfCPMDhjhvTL3qPFi22wvAKeqyqnEY2BWsKd60NREYSG+jbrWLhQaFUUEBAQEPhTEESZwB9CiaG0fvsby2gUfHNwRKOqn3mBRiVFeuJfXgWZyS5H12Q6fVelUmV3VxW3EgJ9XWdkxb5L5Bbrua1VFA90iqdVw2BKK01YbDam3ZbAq9tfYOFB77N0Y1dnMil1Eq/2eYs1R/Nc25wzar2TNfxt/SukN+5YP2FrLHNVZvyhWGei3GBhwHNLUGSv5tSeUwCY48yQjONPqF+H8olCBF2UN0VYZyXI/4A17nljI/ZUtma3ri1ZlW24bvG88FaxGmQSMTqjxev79P2hK/wruX7mDeHqcLRV3r87YapQdNW2FWqNLM3KZeXBqy5B+FBGHMsey+CF749htdp57cdsXrq9FRlJYV5nG8d1S6Bbswjum7/b43zV23mdLqFRGiXPD0lxVegWj+uMyWLj8WUHOVeoY9Xk7oxKj2NpLRWwUelxpMRouOujnQxrF+sm+kLVcsIC5Ry5XMbEJQf4bGwnV5WqpntqTWOciModtNr/Lu82cJynkwTGamBRLT+KYcoQ3xvry/79cOedjorYW29530cQZAICAgICfxKCKBP4QwhT1W81X3OxJZeImdAriSnLD9f5XJfLWquXIHcFVOa6tm2vaM/XPM/D0f2psnsuZKsHLPtLsc5EldnGnHvbsfdiCfcv3E32NS0pMUEsHpfGunNrvAqy6sw7MI+MRt15sn9n1h0rRFdl4UyBjn+uyeaVO2PJ0+V5Gm7UQZmxzFWZqQu73c7J/bvY/M4rFJ854AhmHgK0xDEL9htoJYeBasefXipQ/QFdX+XyKESRvakI6cQV8UDuX1J76yk4HDCXZV2ib3Kk1/fp4KUyJLT0MD/xRYfoDqikKo4XHve6TS3VcDr/pvDTKGWu8OZglYzMjHhGpsdhs9t5un9zxGIRr93Rlq/3XaZ7swiWPZZOucGC3mRBo5QRqJBisdqoMJhrvYlQ067+qf7NaREdRPY1LSaLjZd/OMa+nFISwtUEyCVMG9iCjnGhLNp50WsFrGN8KJmfZHG51OAh+no2a8Dw9rFMXXGYcoMZrcGCWi716p7qvOnweJqENtdfZbBoF+jcr/3NcPhOB+WeY6B0iO5AiCqkzs/FL374AUaOBIMBZs505I3dc4/HbjWD64NV8t+3RVJAQEBAQOAGgigT+EMIVYXWa3Fbc7EllYjpnxJFZkZcrWHKbnMskkDovBA234ZdFoKp3WyaRY7iGasNhY8FlK7KQpi6fj15oQFyXhiSwgeb3F0Oz1+vRCW38W7WbL+O827WHLaM2UbrhmGE3chs2namiCvlRe6GG/5elzKEKE3tAtNut2E4m4Xp4EoeXngS2gK3AfUz/nMjQgID1DeFWOwf8FvFpIqjVNMWa2RPbBE9+OjEaubvns/RSW/TRhHJyM747YB5b1ojdFWeFpJWm52le67xZOdpjFmdWec1TUmfwoIDCzxMPgCeyZjO8qw8V0WsVawGhUzM6fwKEsLVLB7Xmb0XSxj/xX6vTp8JEQFYrfDDoavsPHed8EAFxboqGoepub9T/ap5pXqTS4TmlRvQGi2kJ4YxPzOVt38+zZqjeR5tj84MseyrWgLljoqb03XUZgexSMSo9Hg6xofy8KdZLldJrdFMdLDSq3uqvaqUnd8+yvTg7xEFew+h26AHmY/i1LSuM5CLfwfnl/feg6eecre7HzPGMTvWqpXrIW/B9XBDrPZKon9K1G8zExEQEBAQEKiG8C+KwB+CXCxnWpcZZH4/ss59fS221HIpLwxJIT0x3M1lTYKV5NhQ7y5rMQOh00eIGt2BQhWDM4lLb7J4bYc8U6BDKZfUq1UyMkjBz9kFHLpcSqeEUFeVq0mDAIr09Zuly9MW8/dVjvyzVrEapvZrRqPgCLILs1HL1PUStkqphl7N7ayt1hLpxG6zUnlyG+VZX2OJuAy9cMyK3QJyEXRV3hRhqbc4zlMbOWbYpIfNBthmFBESFoKmrAzt2SUcL3wOm91Gh+gOaBTBVJps/K1PU58mF2O7JdApIYzRi/bSKFRFiFruVsGqzpI9V1g1eTDjO06stdo5IXUCPeJ68PRPT3tsm5g6ieHJw+i5apfrsXHdElmx9xJBShlfjEtn/tbztTp9ZqbH80TfpozrlkjDEJWriqU13sJNBLXcJUJjglXMHdkBpUzCrycLXfOMNStguioLp/MdRijvPdCehQ+lUlQt9DlAIaVAa0BUYwLMaeZR3T1VJjIzKmw9U6JWECbVgpcq2CVxBA/mFrHL6P01TEyd5DADqiVsvk5sNnjmGXj3Xc9taWnQoIHrr76C68HxGU1ZfpjMjHheGJIsCDMBAQEBgd8F4V8TgT8EuVTGiOThTEidWKstfl2LLbVc6uayZs9ZToOLr2LuvQlZULT3FqJmkzyvx0c7pNVmr1cI9BN9myKT2OnaLJC2CQmU6EsIU4ehlASx/XQZRZX1nKWrYQYxaelB9rzQjTZRbVhwYAFTOk9h3OpxdR7nyfRpLNtzjTvaN3YzirBbzOiO/ULZ8a+xNSuChwDvvhS1EiWBIQEwNMBRFQv6nTu3LIoYDlW1J7HVULYYq7h3Q3WxY+dSgednM63LDCTI2XP+OojgwvVKr9WeZVmXXBlx7z3QHqVEzMjO3p0Eyw1mxiw6woqJb9EnsRczd870MGqZ0fVZBjTpzyOrHqHUWOq27cn06fRqPIhSnYjoYCWlejOZ6XEMbBWF1hhGp8Rwv5w+l2Tl0ikxlG5Nwjl/Xef2uqI0ivqFRd+o0LWK1aBRSjldoKNFVCCfbL/otm/NtkcnC7ddIC0hjCkrDqKWuwu26jERjUJVaFTSG++jCbAzOHgnM6IXk6jwvFEAUGEPhnavE9Y0k7+dWY9+t+f7Pb3rswxvMYwSHegkxltrH7Ra4dFH4fPPPbeNGwfz54PM8TuotuD66izZk0t6YhiDW/v4PSQgICAgIFAPBFEm8IcRKA9gzsA59IzvxexdvhdbNYOjayKViJFaSlEfexwufe147OB46POT39dSWzukvyHQU/s1o3tzDd+f+o5ZuzyDnJ/pMp3mEUNpGtaUcyXn/LqummYQVpudz3deZVqXGTyx/m/se2wf41PH12qLPyltEnckD+f29/by/cE8Fo/rTJsYFa/960NOZS/C1loLj1KndX11REAHhUOE3R7gMOj4PblugUuqJrRs/QTfXW7BqgtBxIcH8FRcc9LsRsZfP8PCWsT8hNRJdG04kHd/Ocfkvk2RSsRkXSyhx8xNXqs94BAQHeJCKTOYmdKvmU8nwbHdEpDYFQxMHEGPxoMwWLRUmstRSjSopRpWHixgWWERHw7+nFn9tZQYSglThaKWalielceIVfv5dHQabRoG82j3JNISQhn2wQ6ClFK+fCSdkR+f8us9WrjtAn2TI5m/1b2KdXubGL9vIjjn6Gx2eKxHEvtzS5m54RRzH+xYL3Mbo8mKzQ77ctxvOlTPYEuJ0bjcFyOMB/muyXRSA7y/VrNdwjflQ+lx14fM21XC9h8P8Oodvdg2ZihlxjLXexqsDOFqiZnyShj2gSMfsN7tg2azI2/s6689t736Krz4opuhR13B9dWZv/X8bwupFhAQEBAQuIEgygT+UALlAdyTcjfDWwyjzFBKibGMMGUIIaoQ5GKFf+1IV9dB1iNgzL/5WP4vcHYeNH/c72vx1Q5ZbjDz2toTzLqnHV2ahHsNpH2ib1O6N9cw/edprkyv6hzOP8xD349iUtokNj60kQ4LOrhVUbzhzQwCHC10P04dxP2t7mfQ0kFsGLWBLo268F7Wex5C8NluzzKk2VD2ntcxrF0sH248Rdcx0yktXoahTTnc7/fbg1oE/dU3hdjvORtWanWEADtbErNNYOc8S1pEMqR7b7q0t1CiNyEWiQiQBvBWv1n0iuvFbC+Vk6fSp9MvYQh/X3WGLaeLaNc4hEiNgsl9mtKreQPe//Wsm3io3sL4U3Y+ISoZr/140ucclbOy9vLQFC5er+RicSUv3d6SRxbvcxN5726s3u6nddsWG6Li70NbsurINVfYeEpMEMU6U73EUHWnSGcV60yBzq+bCNXn6Jz/LRKDRll/c5vqc2k1+WxnDovHdUYtlzjEid1G6NEJhAec8br/+vKuzMwbze3derHnis31GsZ8dhCpGAa2inbZ5f+cfQSLDebc29aVD1iv9kGjEe67D9ascX9cKoXPPrsZDl0Nf4LrnWRf07rMTQQEBAQEBH4Lwr8kAn84cqkMOTIC5QFutvd1Yq6Ag8/A+Y+9by89WO9rUcul9E+JpEezCEr1Jop1JsIC5EQGKcgt1lNhNLst1MMD5YSq5Shl8P2p77wKsurM2z+PbnHdmJg2kTd3vFnrvlPTp7mZQTgpN5jJ/Pgwq6fM5oeTqxm3ehzdGndj1QOr0Jv1lBnLiA6MJkwVzoqsfDafLKewrAJ99mouH/gn9g6VkObf+xEsdoiwuwNhkPr3c0o022G3EX6udIT+HqjyOkrE27tn0zdhMFNWHHW1xknFIib1bsKgJnfQJ34QWlM5pYZSooLCiVCHYqgSc6WsipeGtGT2PRIQmdGbtVwsv0TT6AiWju+A2SLherkZmVTsElpvrDvFmie689ji/V7t42tW1j7bmcOnozvRe85m/j60FWKRyO2z8tXu1ypWg1gEBdoqluzJdZlg3IrTZ7nRTJuGwW7nKTeYGb1oL0sfzfBpne8Uoa+tPcHLQ1PolBDGqE/28OnoToQHKn7TXFpNsq9psVhtqOU35kJFYuyt/w573AXPwcoWvJ73CAf0LX3mA1pssO5YPjWpHr7t/AzqbB80GGDECPjlF/fHFQr47ju4/Xavr6c+wfVw09xEQEBAQEDgtyCIMoE/Hb9spov3wc4HQXfe8wDyMOj0EcTXowx0A+cA/7KsS7SIDiJKo+TNO9vw5rpTLL1xx14swm2hfq5Qx67nuzNz50y/zvH2rrfZkLmBmTtnenXmA0f7Xa/GgxixyntodU6xnpzrFiRVXVh6x2AqzVqulpcQExhJYnBD1h0t5P1fd1NSoUd5bR1XDcupbKGD3nVfX4QERtwQYv3Uv19u2FVLQ3Lk3UlsN4Dkbx+lwvtLd+NQ/iEsdh3zM9PI1xpdLpTL917mYG4ZD3WJZ+7GEl4ckszRK+VM2XSIY1e1JISr+XxcO9ac3cC7WbO9zH3NoE/8EF78/gSbT13HZoeUmCCPTDpfwgq4YV9vZ+ezfbhUYmBCzySmrDjsdd/qjO2WwIbsfPqnRLlVQW/F6dNxEyGKr/dfcXs8p1jPSz8c45XhrRw3EcxWKqssNAhUEKiUUlppwmqz83/DW7nN1JXqTRRXmuptbuOcS/OF1mghOvjm3yWJD2I78SZibTa5VdHMzB/DuvJuOBpjby0f0GiyeuTL+WwfNJvh3ns9BZlaDatXQ79+Ps91K8H1AgICAgICvxVBlAn8qdRpM53cAPX5d+DIS2D3cmc+ZjCkfwLq2Hqfu+YA/8m8CvomR7HjXJFLkIHnQj0lJohSY/1cFU1WEz8++CPPb3reQzA4zSDGLDriqqJ4I0AuZcmey7z8w4lqIvEcp/MrsFrMlJ/9Dq30W+wtjSCp/ZpiJXBnoEOI9VSB5HcQYlprADt17Tgn6cKDwx6hx/sXSI0P5Sm1yi9B5uSatpg5P15ytRw6beE7J4ZxtqCC/xvakn/9csbV5haskrH4kXa8sfNFnwHdI1eOZELqJP4x4nVsNsjXGuvVtufMEQtUSCnVm1BKJfRNjiIz3btBiBNnm+Dhy2WuWS4nt+L0KRWLaBEd5LVVMV9rxGK102fOzTm6yioLIhEehhxOQtVytAZzvcxtqs+lOYmVFTKpwbcsLxnECWOSpzARiRF3nI2p9ATHTHeTu/0ylN98zW0aBmMwW+s8d3W8tVB6bR+0WuGhh+DHH90PoNHAunXQrVut57mV4HoBAQEBAYHfivCvicCfRl0206998yutWn9EE1uWx3abJABzuzlImj6GVFqHAvFBzQF+Xy1UNQlUSNFWldXrXCWGMmzGNqy5fxOVZocZROPgCCSiQL7cdY0Rq/Z7FWRyqZj+KZG0bhhMiFrKxaJKN5Fot1kpv/AdWunX2NsZazXvCBfDPUEwMsghxH4Pzhobs6miE5u0nThQmYLlxq+Qe9UJ7H4+gdJKMwFq/6ofTkKUIeiqyl1/d9rCj0qP4+kBzT3cCjMzGrE5d32dAd0LDsyjV3xPXhl+OxariACFBIlYRLBKVqsYrp4j9uDHe1yL84RwNUsfzSAtIYyPt/tuGfzuwBXuTm3E31dlux23vk6fY7slsGRPLuuOOcxbalr+nynQEaCQkhJT/4rXtTJjvefSAOLkeUxq8A13h25CLrYQI7/OO6a3vQuT2MHIYwczyGqjb6tGaA0WtEYzGqWMYLWUcr3vz8AbvlooPdoH16+Hr75y3yk4GDZudFjf18EtBdcLCAgICAj8RgRRJvCnYLHa2HuxmOV7L3vd3i8oi9mN3yPM5rm4PFCZzJOXp6EpaM6EqvxbDm2tOcDvbwuV3mQlKrB+6cqhyhCe23SeU/kVN13z2loIkFfw0ZaLHvs3DlXx2p0t6Rgf6HKf01uL2PtSDw7l6nhxZTbZx5ZRLlmOvY3B53kDRDAi0CHEBqp9B/H6i8kmZU9lGzZpO7GpohOXTDFe97taZiA5WoNMLEYmC6lXvlqAzNPsBBzOfumJYZRU3qxuScQiHkyPYeiKuvPvAGbvmsW6Bwfz+NIjiEUiHu2RxLopPRj1yR5XGHJ1glW+c8RyivUMeX+7ywbebLFxrdxAiMrRcrnxZAGHL5dxd2ojRi/a61X4+ev0WV0MlRvMDJ+702VMYrHaMJptBKukBKuk9WqrdFa8nHNp3sQeOATc+B5JtI8LYfSivTSwneeVxl8zPGQbEtHNMmh/zT4UiWUuYVJba3J0sNJNPNlV/C4tlB5VuqFD4Z13HAHR4GhZXLfOL0EGtxhcLyAgICAg8BsRRJnAH0b1BVqZwUR8eABbp/dm+d7L1QwQ7Pxf7ELGRqzxeL7VLmZu4X28X/AgViRQz9DW6uc3WazoTe7tUv4bL9gJVtZPaAQrQhCJ3FshfbnmDWwZydv3p7D2zBqe/NzTar+NpS3HLq1C27y85qkAxw/xoACHEBsRAOrfuEa0yCIwNBjEtF1x7NC1p9KmrvM5ISo53xy4QoBcQmWViafSpzN61ag6nzetywyUUiVxYWqvImnBtgssfDiNf649gc3uENKVZm39WkntFczPTEMkguV7L3P8SrkrW6umcMrMiK81R6y6QciiMZ1IjQ+loNyI2WrjntTGfHvgstfjVn9+XSYdE3om0b1ZA5799mZ7a7nBzNYz10mO0TAgJRK73Y5ILEImFtO/Zd0CombFCxwi0yn2Fo3uhB07WqMFjVKGRiVFBCzdsIoZAf9iUOxuxCK712N31X+IRDKs7tbkGjdT6lOR8tZC6Ty21yrdk09CYCBMmQKrVkHXrnWeozq+nFqd5/QaXC8gICAgIPAbEP5FEfhDqG2BNqZrAqsnd2P0or3kFOvRWT0X/ddMETx5eRp7K1u7PR6skhGskmGy2NAaDJQbzF6NQvQmC7+eLOCn7AIKtEaiNApeHNLS7Vj+Gi8kRQRSaRQxtfM0xq72tNCuydT0aZRV2j0WbNWrE6nxoSzaeRGNUsa/Hkhh+s/TPZ0dC+HwtcMcjj0MUZ7naSWHsRrIDIKo3/iTfNkMK3VwVNGUfp1eYXjKHdhyT1B5srDO57aK1RCilvHZjgt88Ug6X+zK5aHkQYxPnehX1tj8LRd9iqSaBg+BCinF+pJ6vbar5UX8a90l9Cara1btdH6Fy2Ldib/trOAQ27N/Os3XE7pw+EoZPZo1wGCyEKqS19oaCQ4xtD+3hGCVjIUPp2G32ympNBEgl6KQiVl58Ao5xZW8MqI1L9xuxWSxE6iUopSKWXP0Gl3f2kSp3syAlEhm3dOOg7klTOrVlPaNfeeudUoI81q9c4rM5OgghrSJITpYBXY7FGyCk2/zqG49hHh/HVaRElvSOGRtnq+zNdnbzRR/K1LeBKWTWtsHH33UUTWLjvZ57NqoGVzvbL28pfBqAQEBAQGBOhBEmcDvjt5k4a31J9mXU0qgQkpKTBBnCnRYbXa3eSHnQvy9ggfpFnSEjmpH0Oz68q48d+UJyq1BbsetPusz6pMsn3fjRcCOs0V0TgynY1wopXozwSoZQSqZW7uUP8YLwSoZLwxJwWK10TtusF9Co1fjQVhtIq/zL9WrE19P6IJIZOa7k9+6C7JiwAg09Dx+iBgeDHKIsU6/0YX7nAm+0zn+7K8CRxHiHIuuZjLpyiRm3juTvbNL6xQZ47olcrXMwBePpPPCymO8cVcbTuZpeb3PTJ9ZY9XNTnKK9STHBHmIJCfVDR50VRbC1WH1ep3OgO6TeRWu797jvZuSnhjG9rPXOXbV8dk3jwrEZKmfI2Cp3kSF0UKXN38lNT6U9x7o4FdrYvvGIQyfu5MKo5khbWL4v2GtGL0oi1NuOWhnaREdxGdjOvHPtSfYcDzftS1YJePloa2Y/dNplu29RLBK5jV3LVglQyYVMfT9HVwu9d72Oio9jj7JkUixwMWv4NS/oPSwz+u3SQKwNpmIKPlpZIGxDgOd43m1CivwbmFfV0WqNkHpV/vgLQoyJ9Ib4kstlwq29wICAgICfyiCKBO4JXzNjkjFIi4W6ZjQqymju1gp0ZvcbM6dbYtLsy7RIS7EtRB/3/AKn4RM5aUL97Ki5Dac1tlOapv1Afe78c8PTnbMIulNFOlunl+rN7uZLPhjvJCZEc/Oc0V0aRrB/E1XmD1kFj3jujNn9xwPoTGt6zSGNx/B5zvzuLNDQ58W4s7qxMMZjZErqnh799uODVqgAg8xJgIGqB1C7I4AUP6GG/QF0gjyQ7vz0P4fOFZL5+a8/fPoGd+TWXf3YMKSwz73G5UeR1pCKMPn7mRY21jefaADoxdl0Sc5ku7NGlBV0cnN7CRMFYpaqmF5Vp6b2Ym3HCon4YE3DR7OFOgIkGnq1UpaM6Db+d3rlxLF1xO6oKuyUKwzERogd5tf84frFVUMbRvLq2tPsC+nlJEf7/ErP6y6yFh7NI+n+jfHDl5z0K6UGrheUeW2rWabZW25a+/e354PR3Xg+ZXHvbZKpsaHwbmP4ezrYLjm+8XKgqHFFMQtpiJW3JyxrGmgUxveLOx9VaSClFJyiiqZ8e1Rt9ZWt/bB77+DFi2gfXu/zi8gICAgIPBXRRBlAvXGV2viwJaRvHN/B07m6fhs5zGPBeC4jGi3tsXqC/FKWWMOdzjANwePkBIT6FpUOitsdc36OFmyJ5e0+FDKDCZeWX3C9fjQtjE8PySZzolhPD2gOdvPXkdXZWF51iW+/1tXr9WN6u1seVojTw9K5P+2vIJKpnAFORfriwlXh6OSqlhwYAGH8g7x916vYDFL+PvQlmw9c518rdH1OqofO0gt4mpFEUdyj0ApEA1obp4/QgLjNDAhGJJ+QxTSBTMsq4CvdWLWTjrA6OXDaxVkTmbtnMXWMUN59/72tboNOgXGkqxc0hJC+fKRdGZ8e4RiXRX/WHOKV9dWz33Teti0g+8cKkdrpNwlqqw2O8uz8urVSuotoPuznTnc1ioai82OxWbHaLFhsdoIUdXvjdYoZdjsNoa0iWHt0TxXftg/R7T2qFo5Q6yd5h3V8Wb37qSm62BtbZbectcWbLvAF+M6e70em83Owm3neTGxzLcgU0RA8lPQ7G8gD/bYXNNApza8WtjjuyLVIjqIz8Z28t4+uHoVjBoFQUEOI48uXfy6BgEBAQEBgb8igigTqBe+ZkeCVTJeGd6aN9adZKmXHKfGuvV0P7mA5Zc/ZfG4AQyfu9NtIQ4QHxnOthl9MJrcK2wr9l1mZOc4HvVj1gfg4+03zCHWnHAtxnedL0Ytl2K22rg3rRF9kyMJUEiQScSsP5bHpN5Jrjkvp/io3s42qE0kv5z/kU05G9EoNKw/tx4RIgLkAWirtBwvPO4Kim4T2RG7Pp3QACXvPtCBcr0JsVjkVil8OCOO7Pxsxnw6GuRANVPDbkqYFAz3BILiFqtiBRZYUQHLdZBldDzWNqo1erO+XiYZBboS+ibHkJEUTmWVpU6B8fH2C3RvFsG793fgSpmjXa62gObqeBMm47olYrPZeaDTTdG8ZM8VVk0ezPiOE2u1xa8toDv7mhad0cLnu3JYsM1R5ZGIRex8tm+9HAHVCgkF2ir+b1grXhiS4ppTNNtsdHtrk0fVqqY4dBKqlqM3WUmJCXK7IZEcHeThOngrwctlejNPLD+IHYfBjd5kwW6Hjx9OY2jbGKwNHsN+6g1ElsqbTwxs6hBjSaNBGuDz+OWG+lUXPSzsa8Fn++BPP8H99zsyycrKYMAARyh03771uhYBAQEBAYG/CoIoE/CbmuHL1cnMiGfX+WIPQSYTmXk++jPGNVgNwO3lUzh44RdX22Kp3kTTyECmDWzBrvPFLPTS8jW1XzOAei1CTRYbg1pH81N2AY1DVSwe15nNpwr5dMdFr9bfoWo5t7eJZlDraMoMZkxmK6EBcvK1RsIC5GRmNMRgCaR9THuK9EVEqCNQSVUsPLiQefvmuQQZwHtZc1h9/6/0nLmLBzvHMb5nEs98fYR7UhuxenI3xn+5lb0XnueVfWtdP4FBYodhx8RgaKvw7/OoiU2i5otSPUsrYLMeakbzahQaivRF9TqmwaJFVxVB95mb/RIY2de0lOvNFOmqiAmpXzhazYqQszVyzGd7mTuyo5t1+5hFR/h83Ot0a9yDd7Jm31JAd7nBzOHLpa6/W212vtyT63eO2KPdE5FLxAQppIxelMUX49JpHuW4wVBhNNcrPyxELXNVsqrfkCiprGLlwStu77X/rqE30Zbn82DQV3xf0I7Oyb0YmR6HyWKlwmihYagarU2EPmoU4VcXQoMekPIMxA4Fcd2ZgMGqus1yquNhYV9ftm2DO+8Ec7XPtbISfv5ZEGUCAgICAv+xCKJMwG98zY74aqdqKCvkw/i3aK8+43qsmfIyl49NYdSd3zN/63nCA+Q8PziFuZvO+ZwV+3j7BV66vaXHNolYRHJ0ECkxjoXwyTwtp/IdbYIGk5UXh6Tw0u0tUUjFfLT5PJ/u9MwHy76mZepXh8lMj+PxPk1ZefAK96Q2BpHIIcjUclY/kcaG82uYvcvTrn5K5ynse2wfg5YO4lzJOcBRYao0a2kRHeSaX0qND2XatwdooP6Kw1XfYJVbQepoS5wSDOOCHcLsVsjSteKY4k4yeg5l7KcZPvfTVmmJUEfU69gaRTAg8rvaBY6K1/eHrvDS0Fa3lENVszWyujlK9RY8pUzF8OZ3MaTZUMqMZRgsWuTiIK8za95QK6Roje5mLEv25LLmie6MTI9jmZeKr5NR6XF0jA9l57kiujeLQCIWoVbcFDBWm91vcTehZxJXSvW88L3nzNf4nknc0aERKw9edc1V+esaCnY6qk8xMmwD7Q7soIOyiju6jWWDejDjv9jvca5JncbRtVsmqpguqOph9a5RSev1OXu1sPeXvXsdjoqGGqYljz0Gb75568cVEBAQEBD4NyOIMgG/8TU74q2dqlvgYT6Im0WY1HP/UoMZY1UVQ9rEEKVR8svJglpnxXRVFre2tmCVjEe7JzIyIw6T2UaZwYxGJUOjdLQnLs26hEom5tEv9rsCgx/uGs+vpwq8ZmEB7DhXxNMDWxAbouKRxfvIvqYlIUzN2ic78/efX2CBF8fFw/mHGbd6HBNSJ7Bh1AY6fdyJUqOj8lJiKHVd86IdF0iN28Vlyz+4ZK8COXRXwlOhDuMO8S0EPFuUMXxT3J/5l3uQa4olJSaIXsraneayC7NRy9T1MsmQi4MIkssQi/DZeleTULWcI1fKMZgsjO2WyLRv6m6XnNAzCYlYxC9P9fLaGunNyGLW3W15+6dzfHfwKikxGhaP7cwra7JZd+x4ndfaKlaDVCzyMGMJVcuQiGDawBakxrm3szqfN6FnEh3jQ8n8JIsAhZRuTSMY39Pdml0uFdO1SUSdToyZ6fFkJIUz4J1tXuMApq447OZUWm4w1+kaGiYp587QTTwQ9jPNlDfC2m8UchVXlvPq8dvQ2gI9zjV5lUNsThtoQyax+W35Xp+8sVot7Ovi6FEYNAgqatwcGDUK5s0D0W9MShcQEBAQEPg3IogyAb/xNTvi3k5lZ3yDlTwbvRiJyOa2n9Em5+9XJ/J16QC+NdoZ2TkORLBga+3ObdUXoZVVFpY8ms6B3FIe/nSvF/OJREalx6OQiqgyW7lQpOeprzwXttVxOjvOuWEv7uQfd6Sw+vRqr4KsOgsOLCCjUQYT0yby5g7H3XqnDbu+ahc/XZzL+jIt0iC4NwieDoG0W3DXtotk/FAJC0utvDd2Myd2mFDrS5EU6DhToEMlrd2V0Gq3suDAAqZ0nsK41ePqPN/U9Gl8uesaD2Ukuows6qJVrIYAhQSNUkpxhYmuTcL9sojv0iQCETB5+b5aZ6+qV+zkMonLsTD7mpbPduXQu3kDv65zbLcEluzJdTuP83vw4ebz/Hgsz6vFvEIm5vjVciRiEVEaBboqKxarnQEt3R0F5RIxp/LLmdArya3tsvr79GiPJHo1b8BdH/kOmwY8nEq9uYaKsdIz6BD3hf5Mf81e5GLPOAYAOUbuDv2Vz4pH+DxXx7hQhrSJ9luU+Zs35peFvS+OH4f+/aG01P3xO++Ezz8HSd1tlgICAgICAn9lBFEm4De+Zkec7VQqkZHZjd9jaMh2j30uVsXweO7znDQmAdAgSEGURkGZvm7nNuci9LEeDjOO+VvOs8RLa1n2NS3TvjnCqPQ4pvZrxuJxnRn6wU6vFvzV8ebsKJeK6RgfyNTPZtb5vgC8n/U+PzzwAzN3zqRdVDvyywr59cxozJE5BIkds2JTgqHRLYzT6GThSJs/zqjDm0hO6sWC1AmIEDEszUhmt3jUUg0r9uax8mBhna6E8/bNY99j+5iYOtEzrLoa1U0yBrWO5e6ODf0SOxN6JiERiZh2WzIatQyZRMQzA5uTlhDqdZ7Pacn+wsojvHN/B8QikV8VuVaxGjRKKSerfXeW7Mll9eRufolAb2HENb8Hvizmg5Qylj6Wxidj2lJmLKPAeBazKIwQezBysQK5VIZUIqZLkwje+eUMMonYQ9wpZWLUCikTvjjgs3pbnZqRAc7XOjkNZJeXcG/oRmLltc8Lmu1Sfizrxj59q1r3W7TzIoNa+666eovDCFbVnjfmsrCvR1ukiyNHoF8/KC52f/y222D5cpAK/4wJCAgICPznI/xrJuA3vmZHzhToCDDnsD5lOglSz7mtn8ozeOby0+hsasCxSIsIVKCUSSk31L0gBccidNMzvdh+tsirIKuOU4A1jwp0E2HesrB8zcP1T4mkzFhWL6dCvVlPSkQK0mI5vb9IJzzazpQQeCIEQut5I9+OiMqIntiajscY2YcJax9nzrDFbMvdxogVIzxm26Z2nkbf+CGIRENqDbguNZay+MgXzOw/i26Ne/L2Hs85uZomGQEKKRGBCj9a8eJIjQ+j/zvbHH/PiOehjHhMditdmoQzsGU0FUYz13VVBCikBMilyKUi+r29Fa3RgsVuZ3zPJJ7++gjNozxjEaozrlsiZqvNbc6t3GBm9KK9LB7XmfSkMK+mMRN6JtGucYhHGLGv70HNWbqEcDWfj2vH1ssbeGiNp8HIjK4zGNZiOEqJCrVcylMDmvPryUImfrkfi83uek1NIwN5dlAye3NKfL6f1XGY1zicSi/kFzNcvRbL+meYJj4OUbU/t1jUGFGTcQxc3ZQiS6hf53J+7jXxFYfRKlbD5L5N6J8S6ZE35rKwv5UK2cGDDmfFkhrvU48esHIlKG7RFUdAQEBAQOAvhiDKBPzG1+xIj4B9hGx7EGWN+TGbXcTbBZl8VHgvdm4uyCb2aoJCKsZgtngs/CRiEc2jAtGoZIQHyCnWVaE1Wjh/vRKz1c7Cbf6F1H62M4dFozsxKiPeJcK8ZWH5shcPD5BTYij1dmifvLvhXU5dOkV0kJV/RcP4YAio5zrUIgulKOYOrEnjEAUm8eG+D1m+5iU2PryRmTtnsvDAQo/nHM4/zNjVmUxIncRL3d7gha6v062Rb1fCfgmD2X9Rz6Amd9AvYTBaU7nPYOdWsRoUUjGZn+zhs7Gd6ZwY5jWz7NHuiXSMD2XUJ3sIVctYPK4zey+WuObzwDE7N6RNDHd3bEhEoIIjl8s4cqWMUr2ZYJUMm8VO7xYNyHqhH+V6s8/g8cz0OPomR/LlnhyPqprTFGTjUz1JTwyn0mShsspCg0AFQUrHbNzYz/d5VKf8sZkPVslY/Eg73tj5olcr/sP5hxm5ciQTUyfxet+ZKKUqn8HIIWopuX5UyKpjsthZ8kg6xRWVNN3xGGJzsc997WIlJ6QD+NeFHlRoujA9siVFlt1+n0trNBOLu3umrzgMcPxsTVpyiMyMeF4Ykkx0sNJv23uf7NsHAwc6LO+r0707/PgjqNW/7fgCAgICAgJ/IQRRJuA3NWdHRNh4PPIbnolagtjuvjrWWgOYcmkaWyo6uT3unCsxWW28tf4kE3o1pVWshiulBjIzGjEyIxaTrYIKUxmhylCCFMGYzBLsdhESsYirZTVc13yQfU2LrsqCXCp2E2E1s7B82YuLxRCurruqAIAeMMBG2QLmxcNoDcjr6TlwriqekI5Ps7C0mPVnN6E9/jdX9tkLPV5ga85Wr4KsOgsOzKNLo+6UF3dEo0pn08PbqDRrKTWUEqIMIVgRgtUmRSaWEqnR0e/t7VQYzbUGO4/tlsCyrEtcKNKzZPclnh7QjB7NIijTmynVm4jUKAlSSvl42wVeWeMI614zuTvzt573qKrZ7LD2aB5rjzrmtZ4fnMJrPzqeM7lvUxCL+CW7gM92ec5fjemawLopPTh2tYz0xHAAPt2R4/V9aBSqospqo/fszTSLcry2yioLTSIDef2ONoxKjyPronvlxR+b+cyMRmzOXV9rNhrA/APzyGjUnWhZb3o0c7Tsecvaqq+VfLBKRniggvBABSTcD2c/8tjngrUF6/SD+fhSBuVWh5lHisLmp1vjTWqGaNcWh1GdJXtySU8MY3Br/2fSvPLLL3DXXaDTuT/euzesWQOBgV6fJiAgICAg8J+KIMoE6oVafnN25OjuRUzXfOmxj1aRzNN5f2dLhcb1WPW5ErlEzLrjeXyx+xJBSjlP9GlKcqyCrZc2MHTFKK+tef0Th3DsShWrJ3dz2aTXhVOAVRdhNbOwvNmLB6tkPNK9CQqxuXanQgtwHZIaw98THTljknqKsUOWDNRtp3HbCgUJRQF8Pq4dDUNa8G7WbGx2GxKRhAmpExi+fLhfx3svaw5bxmxDqxfzydZL7M0pQSyC8MAqSiovERag4OGMeJpFBXF7mxiW7b3k0+reOXv1zf4rzLm3LT2bNaDcaMZstXGhSMfGkwU8PaAFD32SxfEbIupvfZp6zOd5Y2nWJVLjQxnWLpYV+y7zYKc43lh30mcswvRvj5KZHs/U/s145psjvHFnayqM3s0xnELSYnNvPTyRV8Fzg5Lpkxzp0YpZl828RCziwfQYhq4YWevrcuLIqhvMrycLfQoUb+3AcpGZ7oGHGBK8kwCJgUm5LwBerOQTMm+KMkUExD+AofHDLNqtZEm2+3tYl1tjTRzzeu6izFcchjfmbz1/64YeTrZv9xRk/fo5AqKFCpmAgICAwH8hgigTqDeulqzkZzHsOoQqb6Vrm63xPag7fcr7YrXPuRK9yeJa4G0+VcCkPo2Y/vM0r8YT1VvznuvyGl/uzvXpoliTULUcuVTsEmHOLCwR0CkhFF2Voy2y5oI1MyOerAvF5BRX8GTnaYypaZxhB65BXCS83A7GaEBaDzFmtMlZWdqXRUXDkYW1Ym5sR2ArOcV6Rszdz6j09qy5fxOVZi1GiwERonrNthXoSpi6JIcTXsTWqPQ4UmKDyfwki7kjO/h0BpzQM4leLSIp15t4+752Lov6l4emcK3MgNFs4+kBLbDZ7C5B5msuyxef7rjIwofTiNIo+PVU7bEIAEuycmkfF0xydBCVVe5tqE4y0+MY1Cqaz3blEKySeXxHKowWygxmD1fEuoRL86hAKs3aen0OlWYtP2UX+xQoznbg6Sv20iPoEEOCd9Bfk4VG4rjhYLWLCZeUUWwN8bSSj8iA5k9AzG0QMxDEMlTAC0MsHmYbVpudjSfyeaxHEk9+dbjOa5/Yqwlyqfv1+orD8Eb2NS1ag+XWTD2c/OMfcPUqLFrk+Pttt8H334OqfoHkAgICAgIC/ykIokzglpA6B/e7L4afz4D2BLR7C3HKNMQiEVLwaNdyUn2B9+zg5qw6tbpWJ0C42Zonl7ZnX06JVxfF6jis2aVYbDZO51cQrJIx8+42BMilfPBgR9e8UoBCitVm47nBLXj/13PoTRaXsJCIYWmv4ey5Ws2p8Do0FMOL7eGR4Pq1KdqkIXxwdRCLi4ZRYg0GQFJDDDjyuC4yf+tFWkQHkRYfyu2pvmeHvKEzldO6YbCbKHMGMndJikBrMHHsajnD5+7koYw4vprQhesVRop1N50Bfz5RwJMrDpGvNbq1NDrNUnrM3MSB3BKeH5LiOoc/c1nVcc743d4mlocX7fXrOc7zF2qNbhXQ6oHTT311mIGtor1WVZ3fCW9h1HKJiPE9k5i64rDHeQMVUor1/plyOCkxlFKgNXoXKKZypHk/MUT7PQPbrEFJpcfzJSIbtwXvRtx8oqewE4kg7X2P5/iaYXNW2W7Vtt5XHIYvtEbzb5spE4lgwQIoKICgIIftvWDqISAgICDwX4wgygR+G7JA6Pk96C5CzAC/nuJc4NXXdt7REvYrk748zscPd+JAbglao3d3vrHdEiisMLDheAFxYWq+ntCFPReKefa7Y64KQrBKxtMDmjGifUOaRgbx8tCWhKrliIGrZQZWP5HG7F2zmdZ1Gk0UTfjwm5d5qq2RCSGgqEdnlk0RRWXiE5gTx/Pzl6cosd4ULd4yp8Dd9S+zW7z/JwNClCE8M7ABE3o2ccvXWpZ1iTfWnWLTM70Y2jaGAq2RC0WVFJQbmbz8oJvtuy9b+upmKVqjhSDFzTY3f+ayamK22qg01a8KYzRZaRyq4r0HOnCtzOD2+pyB07+cLPTIpmsVq0EkArlUQqNQlYflPcCce9t5dZnUVVkIV4fV67U5s+pcAkWXA1fXwNXVULAF7Bak1P5L+KkWRwgYnFyvqpPzhom3myK3altf3/m3mu2Pt4RUCt984xBj4t/QCikgICAgIPAfgCDKBG4J96yiSILVjdCYLH5ZXzsXeLdiO19p1mKx2bHZ7bw8tBUqmcTDnW9Uehw9mzWgymLlVH4F3z/ejbd/Pu1mpZ8QrnY5BI76JMu1QO2UEMrzg1Ncpg6vbXmVj754i6mtzBzrCYH1WBtaVI1YUfEg55T38XjT1ny1/7KH+ILa87XOFOgIkNUeCl2dDtEdkBJIt7d+dZlcVM/XmnV3W2QSMc8NTiav3EhEoILwADl9k6Nc719dOGf1Dl4qc6vy1TWX5Y0QtYx8rdHndqcbZ3V7fK3RTKRGwejPdqOWOx4/V6ijaWQgzaMCXfvVzKZzzpq1axzChJ5JTFlx2MPy3mmpX7Ot80yBDo0iuF6fg1qq4Wx+GbG5b8CBdVB2tM7nOTGFdoG4ewiNuxvpb2kDrEFtlbTafnZ9xWF4w2P+rTZOnYKiIoejojeEdkUBAQEBgf8RBFEmUG9qZhU5F85tGgYzpE00nRPDa72z71zghQfIKTbUvyUsUCHlWpmBmRtOsS+n1M2d7/jVcjonhiGViDCarcy5ty2bTxeyJOuS6zqjNErevLMNH24+55F5pquyEKyS8WB6DL1fHcxEJbzS10xUPX5STlfB66Uw9d4f6K9pyex3t9OxSQn9kyPRqOQe4qt6vlZNMWC12fn1RAnTuswg8/u6TSZmdJ1BSaWNFtHuC+gBKZHMvKcd289e574Fu726G/prouI0S3FW+Zwtf7diKBGokBIZ5NnmFqySkZnRiAfTY6g0aynWlxCuDkMt1RCoULHnQhHZ1ypu7BfPJ6M7YTRZKdGbiNYoUcjErDuWz3cHrjDnvvZoDWa6JEUQqJDSo1kE4L2Vz2mp76yyWaw2iitNKKUSFBIJz2RM56EfRtX52qamT2N5Vh4tYkJRX18H5XUJMhE06A5x90Lju5CrG9Z5jlultkqaL3zFYXjDY/7NF+vXwwMPOCpi+/ZBUpJf1yIgICAgIPDfiCDKBOpF9ayiYJWMv/Vpysj0ONeCOEwtR19lRQSovAgzvcnCxSIdY7slsulUAeEq78m3EpGEVpGt0Cg0aKu0ZBdmY7VbXS1h1V0Uq7vzPT8kmaulet5cf4rCCiMLH+7Eir2X3a6z0mTBarczsXcTgtVytwrRmQIdJYWX+XLZbfzY+jIt6jHGcski46UiM8sqwAq8qIzg2NVyRqXHsWDreZY9mo7Jauf5IcmkJ4XxyfYLboHCz313lOeHJPPV+C4UVhgpqbwx4yUXI5MMYWLqxFpn7yamTqJn3CA2nyrnq/FduK4zUqY3ExWsRKOQ1eluWLPdzxtOs5TT+Y7q0v6cEh7v3cQlNL21YvpibLcE5m09z8j0eDch5wxo3nJpPcO+8nTjnNF1Brc3H8ZdHRoytX8z9l4s4emvDpOWEOb6jK9XVNE/JZK7OzbEarPzwpAURCLonxLlqgb5auVrFKoiJUaDWi5BLVcQrJLxyfaLLNp5ke//NojxHSe6bPFFQKoCwiXw0w0tOyF1Er0aD2LEqv28dkdrsA/3LsrEcojqCw2HQeM7QRVT53v276JmHIYvfM2kuWGzwcyZ8NJLjv8GGD4cdu0Cjcb38wQEBAQEBP6LEUSZgN9Uzyqq3v43/ov9HpWXx3okMbBVlFvFTG+y8M4vZ9CoZIztmsixK478rOotYaHKUCZ1msSE1AnozXqK9EVEqCNQSVWsObOGQFkwUrHITRg4WZKVS8f4EPqnRLHjXBFNIwOxWm3Mvretz+usXiG6WFBOk+KZmHfs4f3W/r8vOUYwt36Ox7K3sbViF3Czfe39X48z/6E05m89T1GliSeWHSQpIoBnBzdn+YSOlBpKXcHNocoQSivh+ZVHuFCkRy2XuFoP48LULHnsDXrF92LmrpkeQuXZrs8yuNkQzhdfIb1pIDaRka2nCzh/Xc9TA5qz/nieXzb11dv9vOFsAXTOnN2X1hiz1caEXkl0Sghjxb5L3JfWyGsrZnWcdvuvrj0JiFxCrj4BzbPvnM28TZf58Vherd/FCT2T6N8yyuMmgb+tfCq5lEd6JJIQEcDra8/z9wFTyAyCityv6CQqpYEUzprgvipHOHevxoMYs+gIw9rF0i8lEnHFCDjxmuOkinCIHeoQYjEDQRZU62fyV6J6HEZ9Z9JclJTAww87wp+rk50Nr74Ks2f/QVcvICAgICDw10Zkt9t9jPQL/JUpKChk/ISJLFwwn6ioyD/lnHqThXvm7eZqmcFnQHB1MjPieWGIw6TAYrVxILeE6GAVey+W8MuJfP5vWGukEhubclczcuVImoY1ZcOoDWzL3cb7e9/3EB7PdHmGgYnDOHbZwIWiSl5fd8rtfBKxiMGto5l1T1v0VRa2nr7OgFbRvLX+VJ0CoUXlL6iuzeDeRla/348rVfBaGSzSwtG/neT+b+/naIGjIvL5iKUU5Lfnoy0X+fXpXkxefpB37mvPxpMFPNw1hjVn1jBn9yyP1zi96wyGNR+GwSRl8a5ctypesErGL091x4oJbVU5BouWCHUoKpmapceW8MqWVyg1lrqONbXzNHrHD6ZBoIZ75+32y0yjVazG5a5Y0+xjVHoc43smuSppo9LjeGZgC5bsyeHTHTk80j2BR7onUaY3EaSUseV0IQu2XfBYvDtdEp2tksEqGasnd2PB1guEBkhoEHmIsTVjCLyw9M7lxKn6EBcWVK/vYr0xlcP1HdjyN0HBr4jLvM9AXut5gCXHlRzILXdVjNRyKdhtcOwfED0AIrqAWFL/a/gLUX2e1N+ZNAD274d77oHcXM9t99/vsL8XMsgEBAQEBP6D+S3rc6FSJuA3Tit7fwOCl+zJJT0xjB7NIiisMNIyNpj3N55FLpPwf8NbYzJbARHDWgznqYynmdz5b8zcOZOFBxZ6HOtw/mEe+v4hJqVOYuaAWSTHBDF383nKDWbXXJGzdS23WI9KLuG2NtFsOllY63VKyi+i2pnJve3LUDXy730oMsNrxTBfB1V2R1VMJVVxvPA44N6+BtVDrCWM6R7LtJ+nscBHJtuolSOZkDqJl7q/Tkp0IGue6M7Dn2aRU6yn3GDmUomR2T+dol9yFKO6NOFfu//FK1v/D5vd5nGssaszmXjj/bpaZvDrtWVf02KyuGeA1RRSjUJVvDw0hZ7NGmAwWfh0Rw6NQlXEhwdgslgJUcspN5joGB/KV+O7UGE0ozWakUslyCQiN5dEcJ+p06ht9FnsX0DznN2z2DZ2GOuPev8uVjcJOZBbwqaThQzyEeTshrkCCrdD4RYo2AylB8Fuo64pKUnBFh7u8jiT+9YQKCIxtP2HX6/pP4F6z6TZ7fDhh/DMM2Cq4c4pFjsqZM8/77DBFxAQEBAQ+B9FEGUCflNuMNU7IHj+1vN0jA9l9eE8WsVqyOwS79FmNuvuNvyj96usOr3SqyCrzrwD88ho1J12EbcxKj2OdT5a1yRiETuf7cv8rRe8Hsdm0tNX+zqvdjlCYz8N3gw2eKcQZlaCtpoGmpI+hQUHFtA2qq1b+5pTdISq5URplASrpaw6tdKrIKuOM5OtIL89ey6UsOTRdJ66EfobE6xkYq8mtGsczLfZK/n7lpdrPdb8A/PoGd+TUelt+WjLRb9eZ5XFxicPp5GvNdIgSElYgBxDlYXCiiqWPZaBRCyiqMKISi5BW25h5aSuBCgkSMRiNp4s5PNdN41KxCIY0iaG+zs1pkmDQDI/2cOFIk8jkZxiPR9sOsuTAyPq5cZZZixjy+nrbo/7MgkJUqgx2yxIJe4Okc7KT2XxWQIPPISy4jAiu/8VUwA0LYgKCYbfks3130hBAYwbB+vWeW6LjIRly6Bfvz//ugQEBAQEBP5iCKJMwG+CVfJbDgh+uGs8FquduZvOeVQ1Vuy9xJB24czZNcevY76bNYcfHxxCZkY8D3SK89q61jwqkMoqz/wru91OUv5X/LPdMnpEu1eXfGGzw+fX4e+VcNXivm1S2iTuaHEn1yu1TOo4neVZeYxYtd8lyJzGGP2ToxCJLMzeVb9Mtp4zd9E5KZx5ozpyqcSAWCwiLSEMkdjEO1mz/DrW7F2zWH3/r8zfetFn/lh1FFIxr687yfWKKnRVFuQSEU/1b06nxHC0BjNmq411x/IpqDDyVP/mPPnVYd6+tz3zt57x+Bxsdlh7NI+1R/MYlR7HZ2N9G4moZBKK6unGWWoopbDipqW+PyYhw1oMJ1AeALg7iZ7PK+RIqyOIxH4IMmkQRPeHmNscfwIT6nXd/xOsWQOPPALXr3tu694dVqyAhn+cy6SAgICAgMB/EoIoE/AbjUpKm4bB9Q4INpitRGoUbDid77FoD1bJWPBQGqWGonpVSHRmLWGKKHKKKvhq/2WPfbwFGStLspkS8hoTB1Yg9rNTaps+kMtN/8YHup+4Wn7Y9Xj7aIepQ7+Ewbz8w3nOFeq8hi6P7ZbA8avlDGwVRbExv96ZbC2ig1iw9TzdmoRzz/xd2OwwtG0M04c0qNexjNYKt5ZEX7SK1dAgSMkzA1pgtFgJD1AQpJRSWWXm0KVSXl930vU6JWIRTw1owdC2MX61s9ZlJFJcafLpxumLEGUIuiqHkPNlEqISQVcl9DYfJm7vSL451497b1+FGIXLSdSBksP6FqQHZnueSCyHiAyI7APRfW/Mhv0OAcn/jeh0MG0aLFjgffszz8Cbb4JMeP8EBAQEBAScCKJMwG/kEjFD2kTXOyA4VC1DBGw5fZ1OCaGucF+rzU5mRjyXS/VIFKX1OqbBUk4pZqLDZGyZ3oUVe/NYsueKa8bs9raxNApx9CVaDVr6l7/Omz2zifazu+yoUcymoKF8Ly2jm1XMqgdWoTfrKTOWER0YTYQqggM5Om5/b59P+/hR6XH0TY6isspMRZWFEkP9XqMzk21fTikmi43k6CBO5FVQoDVytbyoXsfSmcoJVNT94z62WwLzNp9j0+lC2jQM5h/DW/HmupOsOpLH6snd6NA41CXsrDY7y/de4tHuiYz8OMuv6/hsZw4LH3a4UdYUsBtPFjLznhb1CmgOUYa4XDidgd9fHJpPHxX0UUFvNaQrQV5NhJ8p+JXVp9fQu/FQD3v3PZVtSQ/MxmSTctjQnN26tnTqfCed025Hqgjw6zX+T2O3O9oR9+713BYd7TDzGDz4z78uAQEBAQGBvziCKBPwG6lETOfEcPQma70CggMUUhCJeHpAc1eWmUImZsXey4zMiOPzHTk81D20Xtcil8i4+5u7OFpw1OU0uGryYF5ceYY37mrDvosl2O12WhWv5Klmn9E/3T+T0SILfGtLJS9+EJsubUVbpWXWzlnM3DmT1pGtXblp3Rv3ZGb/mcwd2YGv9l1m/fF8rDdUhtOGvW9yJCv2XuKnEwXMvrcdYar6vUZnJhs4zDBaxmo4kVeBrspCuDqsXseKCAijZzMl+3J8C8PqNvXlBjMn8yp4sn9zTuZX+Ay4zrpQzKjOcfVuZ/VWtTNZbBzM1fkdlD2tywzsdhltYxSoK/YzIXQrZ459RFkSKGpx5eijgrt2zWLYmKFIxWCp1sW6srQPeytbcaAyGaPdoeBbHdDwTSeF8MvSH0QieOopePBB98dHjICPP4YGDf491yUgICAgIPAXR1hnCNQLtVyKCHisRxJP3jCfAHenO12VhfPXK2nSIIDnBydTWGHkma+PcOxqTWv0RIIUUjafLuSJ/o3rVSGp7nbodBqckDqJeQ/N4r1fcvnsh/X8Lex1VvYqrXWB7sRihy+0GloN/Jb7YlMprCxkQLNBroy0hQcXMm/fPABXjlqRoZCgQC3P3h7Jm3c3p6wSJGIxKpmUdcfz+GzXBe7sGEmPFCnl5gskBSbU6zWqpRpXFShIdbPV60yBjgCZpl7H0iiCGdc9ivhwdZ029dUrf6WVJj54sAMPLNxDTrGe4XN3Mio9joUPp2E0WbHa7T4rhb5wulF6Y9/FMib2Gc6E1Im1GqK81v4e7rYdw/DzB3zX4ACSiCo4B2l+/EZLkEHJFYdJyMBW0aw7lu/almuKJdcU67Z/9jUtWoPl1uz0/xe5/35YssSRRaZWw3vvOWbLBHdFAQEBAQEBnwirDIF6o5JLGdgqisyMONYcyXNzuisxlBAdFEG4OhSTWUKJzoxELGZeZirL91525W5lX9My7ZsjHL4Uxyej0zh6Wcf0rjMYtbLuConT7bCmDfyCA/NIC03j6oaP+TljD0397Db7RSciN+EZ7r37Zdad/ZGJX/TzMIiY0nkKhyccxoaNzRc3M2LFCI99pnWZwfAWw5j+9VGeG5LElks7GPbVHNd+L/R4gSfTn2TMqjF1XtPU9Gksz8rDZneIJo1Syskb1Sirzc7yrDymdp7mV57X1PRpzN98iSKdmecGJ5OaEIbRZKVUbyL0RtWypk29E6VMwjf7r7B43E2Djo+2nGf+1vO0iA7iib5NadsopM5rqE50sJIojXsfaXVh+PfvzzL7jtn0jO/FbC9B2dO7PMvd5gMoTryBws9zWuyw3whbDI4/BVZHe2h4gH+tuFqj2T/79/8lLBaQevknRCSCjz6Cxx+Hd9+Fpk3/9EsTEBAQEBD4T+N/RpRVVVVx6NBh9u3bz4mTJ7l+/TpisZiYmGi6dOnCiOHDUKm8e6P/+usm1m/YwOXLV5BKpbRo3px777uHlORkn+c7efIk33zzHafPnMFisdC4cSOGDBlM3z59/qiX+Keilkt5cUgKTw+MZ82ZtV6d7qZ2nkbvuMGMWXSEAIWUMV0TWD25mys0GGBJ1iXax4VQrKviwYxhTEydyPxaKiQTUifQI64HT//0tPsGOwSfBZH0EeZ38+815Jrhvco2ZA77nuTAMJ7d+KzP/LBnfn6G7Mez+efWf3q9vsP5h8n8fiQTUycx676Z/N/mV3g3619u+8zbN499j+1jfOr4Wq3/a+acjeuWiNlqc2v3W7LnCqsmD2Z8x4lupha1HUtXZWFq/+ZM/HI/FpvdVdX0ZlACN50j39l4hpgQpZtBh83uqNi1axyKRCSqVzurSibh+SEpPDOwOfoqK6EBckQi+HJ3Lt/sv8JD6bHIig9ynzSf4WN3UGoopcRQSpgqlGBlCAdzKnhu0yXeqSWT0WqHA1WwWe8QYTuNUFHDbDNMFUpxZUGd1wygUQqmFC6KiuDll+HCBdiwwXsFLC4O1q79869NQEBAQEDgP5T/GVG2dds2PvzQ0X7WqFEjOnfqhN6g59Sp0yxfvoLt27fz+muvEhIS4va8Tz75lDVrf0Qul9OhfXtMZhOHjxzh0OHDPDtjOhkZ6R7n2rVrN7PnvO2YaWrZkiBNEEePHuO99z4gNyeXsWPH/Amv+I/HShUvbnrWp0hxthR+Pu41Rszdz/RvjzIqPc6t6gI3zR/+9uURPnpoNr0SejFzp2eFZGr6VHrE9WDQ0kGUGqvNRl2H4TaYNxBi/Vg7m+0wvzyQJr2W8HLiYNYcvoxNta7WdrlJnSax4dyGWgUjOHLBusd1QyXzrOGUGksZtHQQG0ZtoEujLryX9Z7Ha6yZczYqPY4+yZEs2ZPjJpzKDWbGLDrC5+Nep3dCL2Z5qSh5y0xblnWJh7skMP3bo3W+T2O7JbAs6xI2u3eDDmc8wspDVxnT1b9jTuiZRJXFht1uJ1qjRCwCndGELn8fkyJ3oJZtR3xmB6ITlQAExt2NRBXNoYsiFmTn83P2ESw2UImaMDNCilzsyCiwIcYe2o4lBVf4+vp1tnsRYdVxmoT8nF23g2WrWA0a1f/Mr0rf6PXwwQfw1ltQVuZ47Icf4M47/51XJSAgICAg8F/B/8xKQyqRctvAAQwbNozGjRu5Hi8pKeHV117nwoWLfPrpZzzzzFOubYePHGHN2h8JCgpi1sw3iY11zJqcOnWaF196mfc/mEvr1q0JDLzZJ1dRUcEHcz/EZrPx3LMz6NIlA4CysjKee/5Ffli1mrS0NNq0af0nvfI/BpPFzKpTq+sUKc4g5FHp7floy0WvtujZ17RUma0UVZr4YOMlHuk+jP6ZQzBYtJQZywhWBmO321hwYAFP//T0TUFWBQ0uwfsZ8ICfvhfb9SI+qLyfcvtjvB7Qnm/3X+aB9Ch6fDbb53MkIgkTUicwfPlwv87x9u63+eGBH5i5c6ZHi+W5knN0+rgTE9MmsvbBtdixU1RZSqA8GIU4yJVz1ihUxZx729KzWQMMJguf7si5eT3V5vc+2nyZ5waPoEfj27CLKrmmLSZEGYJaqvHITANYsieX1ZO7MbJzXK0W9tVNP8C7QYczdsDfY2amx9EvJYrFO89Tln+Yae2uIC/ahqJwK+Hmcu9PKtyKLuJe2jUOwWS1kVusJ/uaFoNdycqyvshVIUQ1vY3GLW4jTy9D2vBX1vljEtJ1BhUGd5MPX0zs1QS5xI/BxP9WLBb47DN45RW4ds1929NPw6BB4KPLQEBAQEBAQMA//mdEWd++fejb17N1MCwsjAnjx/Psc8+ze88ezGYzshv5OatXrQHgvnvvcQkygOTkFgwadBtr1/7Ixo0bueOOEa5tv/yyEb1eT3rnzi5BBhASEsLo0Q/x1luzWLVq9X++KLOZ6h2E7AwvdlZdPt5+gaaRDnEhEkFMsJKd54sZ2y2RLq9tITk6iH4pUdzbWUrS+41uChw7cA1GxcF7t0G4pO5ruG6B1663oHX6UmanJDN60V5C1XIOXS5jQBt5rZlfrSJboTfr65ULpjfraR3ZmqMFntWjUmMpb+54k5k7Z3LtqWvEBDRBJpFSWmmib3I0d3eMI0AhpbDCQJXFykOf7nVZ/WdmxDMyPQ6jyUqJ3kRMsBKJWMr201p6tWjAnB8voasq99mSWN1FMSMpzGX64RR6bRoGM6BlFM2jgjxNP2oYdOiqLISp5ZQbzExedojlj6XTKSGUT3ZcrGEkEsSUNCtpqo1cXTOdCfb9SCgGf97Ogs0ENHyAycsOkhof6jIYcczD9XLNwy1dkI1GKWXdk8PqNAmZmDqJ4S2GIUZBZkachy1+dTIz4umXEon0f1GUWSzw1Vfw6qtw+rT3fQoLYf9+6NHjz702AQEBAQGB/zL+Z0RZbSQmJgBgNpupqKggLCyMqqoqjh47BkDXrl08ntO1SxfWrv2Rffv2u4my/QcO+HxOWmoqcrmcI0ePYjKZkMvrl/f1V6LUUHpLQcgn8yrIvqbFarOz49m+6IwWSvQmlFIJHzzYge1nrqNWSGgVq+HYVS2nC3Tc37kLbaPaOtrzdBAthoVpMCzQv2tdVKxidsFzGCyprFqbT851OUsfzQCRnWJdFfm64lqfr1FoKNLXLxesWF+MRqGpdR+b3cbF0jwu5Bnp3SIStUKCVKJAJZMQoJBy+LKRhz515KAlhKtZPK4zey+WMP6L/R7uieN7JhGklHG9oso1r+cLp4viT0/2ICMpHJEIFFIJuioL5QYzAXIpUomI29vGuoxZAELVcnRVFtdxzhToUModn1XP5g3YeLKQs4U6N+EUprAQv6s9kpw8AMLr8yaGtIEgR5VqVEYcU5YfdhmMeJuHKzeYOX7ZyJyBc3ybhHR9luEthhEod1S3XxiSQnpiOPO3nvd4Tyf2akK/lEivrosWqw2T1YbWYKHcYCI8QEGgUoJULP7PF3AmE3z5pSPg+bxnyLeLBx6AWbOgceM/79oEBAQEBAT+SxFEGZCf77DElkqlBAUFAXD16jXMZjPBwRoiIiI8ntOkSRIAObm5bo/nXMwBIOnG9urIZDLi4hpz7tx5rl27RkJCwu/4Kv5cbjUI2UmFwczc3TlsPFHIlP5JxIfLKDaUERVeRIXZzhePtueTbZdZsucK3x7IIyMmg8MXDnNPNMyP9K86dr5KxLQrd3GwcjQibi6Ul2ZdolNCGCFqGVqjhVBl7flh2iotEWrP70BthKvD0VbVbXzRICCMLcWO+anjV8r55WQBx66WU2W28tnYzvx9aEu+3n+Z2fe0Y/7W817bA7OvaZm64jCZ6fF88Ug6wz7YUadNfbnBjFgswmaDHWeL+GxnzeqWxs2YJUAhJUAhdVn0AyRHB1GmNzGxZxIdE8J4bPF+TuRpPYTTrEA1bfzobjtvbMQuXVt2V7alUfJgnuzfFbVcihTonxLlqmrVzDdzkpkRT7u4ENRyKfek3M3wFsMoM5RSYiwjTBlCiCoEuViBXHpz8FAtlzK4dTT9UiLRGixojWY0ShkalRS5xLvA0pssbDxZwNI9l0hLCObB9BiKjfmcKyslJiiccFUocon7ef4j0Ovh889h5ky45Lt6yIABDsGWmvqnXZqAgICAgMB/O4IoA9as/RGADh3au1oXrxddByA83Pu9faVSSUBAADqdDr3BgFqlQq/XU6l3VCkifDwvPDycc+fOU1h4/T9alP2WIGQAtVxCelIILw9rQrGhiPNlxwlVhRKmDmbe/o/YcWkH49o/xmO3mfjHr49gsV1hSSKMqr34BDic9z7Kb8bcolepsgfiLR3p4+0X+PjhNM4V1p35lV2YjVqmvuUctdr2iwoKY2z3BpgsNlJiNRy+Us61sgLKDWaGz93J+w90YNGYNDaeLKx1XgtgSVYuaQmhbvN6vkhPDCNALuWNdSdZmuVd6FU3ZjmZpyVYJWXXc/0cwkUhJdh8CnnhxyQX/4zO0IYTeX0BhzNjdeG0PboDbVSe15NnjWVreWt269qwW9eWQku1n5msCtokFTK4dTRSiRi1XFqvqpZcKkOOjEB5AI2CG9U8tRvSG+JLLZfWaXuvN1l4Y91Jdpwt4vNx7dhyab1X59EZXWcwrMVwV0XuL01VlWNebOFCKCnxvV9qqsPko3//P+3SBAQEBAQE/lf4nxdl+/cfYOPGX5FKpYwa+aDrcaPBCIBC4TsJSalUUFlZieGGKDMYja5tvp6nVDoWfQaDwa/r0+kqqays9Hi8qKh+7XS/N6Gq0FsOQh7YMpIIDZhku+j5+SyPBe3kTpNpGNiQx1aPw2w3M1ANi6KgoR/f1pN6FTOuvsAxQ4da98u+psVgstI0MpDlWXm1ZqRZ7VYWHFjAlM5TGLd6XJ3X8EzXZ7zmqNVkavo0LhSasNtFhKjlvLYmm4Gtol3VqculBppGBaIzWlmw9UKd5wWH2KzpkuiNF29P5ufsfK+CrDpLsy6RFh/KoNbRqEQGVNqNRF9dC3kbwHDT9EGpvgT09XqM7RUdeTzyW/JM4eyqbMtuXTseHjGGV7dUsu+K74rr/K3n3ea5bqWq9XtisdrYeLKANUfyWP1EGm/sfNFrHMHh/MOMXDmSSWmTmDVg9l9fmMnlsG6db0GWluawwB86FMT/4a2ZAgICAgICf1H+p0XZlStXeOfd97Db7YwZ/TCJiYn/7kvyYM2aNaz46ut/92V4IBfL/Q57npo+jRV780iND+WBTg0Z3KYB03+Zzkf7P/LY93D+YR5d/SiIQC2CdxvA4yF1X4/JBmfDx3LHseGY7f61jTmNK/bnlDOh97BaM7/m7ZtH9uPZdeaoTUqdxIgWIzhw7WCt53bmhz377Wn+dX975m0+x0tDWzJ87k6Gto1h8bjOTPvmCDabHZ3Fyom8ulshwSE2bTa7m0tiTR7KiCMpIojnvqu9kgfQWJ5Pwf5fkJeegOtbwWbyup9Cf5oY2XXyzA08tu3Xp9D71AJyTLFwo245KawJuqra36Psa1q0BovbTFd9qlq/NyarjflbLpCZ0YjNuetrzYcDmLd/Ht3jenJPyt1/7VZGkQimToVHHnF/vFs3hxgbONB7FpmAgICAgIDA78b/rCgrLi7mH/94FZ1Ox4jhwxg2bKjbdqXKseCrqqryeQyj0bHNGTqtUt5cJFZVVaFWq708x+j2nLoYNmwYfft6ViCKiop44cWX/DrGH4FcKmN4i+F1ipRnujzD0GbDMSVZeCBDQ4mhmEK9nRndZhCiCmHevnnumWMAIuisgKXR0NQPL5QjFTHMN7/OtIH3Yt661e/X4DSueKR7Il9lFfB819fp2rgH72bN9jSI6PIsQQoNswfOoUdcL2bv9mIi0eVZhicPw2CU8Eqvf5AW08nnfkObD2XSl8fJuliC0WTlwKVS2ueUuFoPO8SFcGeHhuiqLG7mGv6gN1l5cUgKb64/5WkI0iOJjCbhFFYYvQo9CVZSA07SN2gv/TT7aKa87NjgR8by/Y1O8e5FT1FmtsvIMTV0uw6FTOw2n+YLrdH8p4svX2gNFk4XVLBwdGuGrqj7ZgTAnF2zGN5iGHL+jaLMbocdOxyZYrNne692jRwJzz7rCIYePBhmzIBevQQxJiAgICAg8CfxPynKKioq+L9X/knh9ev069fXa5hzgwjH4rK42Lszn9FopLKyksDAQNQ3BJZarSZAraZSr6eouJg4L6LMebzISM/FqzcCAwPcctD+KuhNFmw2O//s80+GtRjG4sOL+e7kd1jtVsAhPl7q8RIDmwxk7Zk1zNrl2aY4KXUST2U8xT+3/ROLzSE8xMCMUPhnOMjqWA9abCLmFt7P3MIHsItlvHzDCbC6EPGFUxikxoXSKSGM4XN3svFkEC8PHcT2sSMoM5RSaiwjVBlCsDKEAzkVDH4niyCllPmZtzN87DBKq+0Tqgwhr9zKrHUXmHZbMu/9cgGFrC1r7t9EpVlLiaGUMFWoKz/s/UuXeOOuNgyfu9NVsase0PzZzhwWjemEyWKrd0aWXCrmbKGORaM7YbbZsFjtBCmlqBUSJCIRNrudkkrPile3wMN8GPcWIVKd/ydTx0HMbVijBtC8pBVcvFjnU6qHUteFRvnXqTCVG0w0jwqk0qytl/NomaH039PCeOECLF/ucFJ0WtoPGwa9e3vuq1Q6ssiaNYMWLf7UyxQQEBAQEBD4HxRlBoOBf/zzNS5fvkyXjAz+9vgkRF7uBjdsGItMJqO8XEtxcbGH4cf5844Zn4T4eLfHExITyM4+wYXzF4irYRVtsVi4dOkycrncLffsPw2L1UaVxYRdZKLEUEIDdQPmDJzDx8M/pqiyCIvdgkqqQi6RM+2XaSw8sNDjGIfzDzPhxwluj8VK4Mto6OupZT04p4/hmavTOGJwLCATQlUopGIe7ZHEU18drvP547olUq438ezgZHKKKlk5qSsBCgm/nirk1bWnqDBamJeZSrhKgd0OydESPhzVkQC5FIVMQkmFnS/3aInSqCjQalm86wgmK7wyrCWbTxfy6U6HOJm/9WI1J0Ktm4V7ckwQo9LjXBW7k3kVroDm7GtadEYLAQopMonYL7EpEYsY3DqaQIWEDcfzKKk0MSo9HrvdxnVdFSFWuWv+KlTtWYI8Y4xDI/GcX3RDJIXIXtBwKMQOhqDmIBIhAXrHWsi8ZK0z96tLUoQrlNp53c4wbF2VhTMFOqw2O61iNWhUf51fUcEqOYEKKcX6WswwvFBiLKvTbOR3Iy8Pvv4ali2DvXs9t3/yiXdRBo6ZMQEBAQEBAYF/C3+dFc//t3ff4VFVWwOHf1PTK2kESKGF0GtCEZCOCEFUkCbFBtgLYLmWq6j3U7GiCBZsgAUsFFERlA6h1wQIoYQakpCeSSZTvj+GDAkzk0wCJAHW+zx5hHP2OWdP3GRmZe+9VjUoLi7mzTf/j6SkJNq1a8szzzyFSmU/t7qLiwutW7Vix86dbNy4ibi4IWXOb9q8GYBOnTqWOd6xQwcOHEhg06bN3HprzzLntm3fjl6vp1PHjtd1jbJCo44/k5fanf16MvZJbo24lcUJi/F19bUbkNkz1AO+DHYu1f28tDjeOjeeIrMlmYqPm4Zv74tl9r/JjOsazuiYsHIzFY6NDWNAi2C+2niMdUmWhCmX17wCcFErUSggW2fAYDTTONCTbccvsGL/We7u0IDP15edFVIpFfRrEcKD32y3Hrs8E2FpX208bp3NKlnKV7pAc2aBnvhjGdb09NMW2xaiLnn9JUWlzWYz57ILef+edrhpVSzdfZr3/k6yFJ9W5TI+bC/t2vYlpm0Pm0AvzeDPjoJoOnkklLl/lskXz8g41A3ioG4/0NhPgelshsTNyekOi2H7u2tx0Sj5futJWtXzrvQs4bXk7aYm2NuVOu7+lbrO39X32nSoxKlTsHQp/PwzrFkDpnISzCxeDLNmgV/lsqcKIYQQ4tq6aYIyo9HIzHffY+++fTRv3pznn3vWmv7ekbihQ9ixcyc/LVpMx44drLNbBw8e4q+/VuLh4UHfy9JD9+vXl0WLfyZ+61Y2b95Cly6dAcjKyuKbb74DYOjQuGvwCqtHnj6faSun2t1HtvvcbiYsmcCkDpN4u9/bDF5Y8W/eXRXwXgBM8a342ZmE8HDyY2zOb1Pm+NjO4cQfy+DLjcdYfTCVb+6LoX24L19tPG4TGDzYvSHdmwQQ9/EGjqY7LrLcItQbL1cN98zdzJ5TOaiUCtqH+TKxWwQvD25Ojs52n1fTYE8K9ZVLylFsMvF3Qqo1GCxdoNnPXcuvu07TvUkAPZsG2g02Kyoq/UCsP6uHHeXo9i9p57ILjcLI51sS0Xs2Z1KPhjz+w+4y9/szuwudPBI4oGvI6pxO/JMTw/1DR3Bbq1BwIkByJkNil0YBPNGnMXe2r++w3/ffEkn3JoG1qhCzVqVkQItgPCpZHsHXzffqdsRshoQEyx6xJUtg2zbnrouKggceAAe/iBJCCCFEzblpgrIVK/5gy5Z4ALy9vZgzx/4MzsSJ4/H2tswEtG3ThiGDb2fZ8t958qlnaNumDQaDgd179mA2m3n8sUdt9nt5eXnx2KOP8M7Md3nr7Xdo2bIFXl5e7Nmzl/z8fIbGDaFVq5bX9sVeI3pDMUsOLi03sQfA3B1z6Rnek5ZBLVmfst5hu5Za+CEEWjiuOmB1yu9uTO0+5K4oIzmlgi2VUsG9ncO572vLB9PjGQXEfbyRMbFhfDauI4V6I5kFevwuzsCsSkylyGjieIbjgAzg/lsiKSgyoFWreKRX4zKzOeeyiwjxcS0z0+TjpuHezhHojeWnwb9cXqGBtYctNfFKJ8Ao+fOOE5nc++VWfp7SlWkDougQ7se8i4WeS2YILy8q7aIoopfXdoZq1tI7aRsuymICSuXKGOizmcGL97Bmai9rQeYSizP7siL7FmsWxbGdw+nT3FIrzGA0oTeayNEZyNbp8XHT2k1HX1GGRHetmkk9GvFGOTXSnv5pD2M7Z/HCoGZlsi/WJLVKSZ/oYNYnneeJmKlMXDq2wmumdp2OVunEAK+Ml16CN95wrm1AAIwYYUnk0bWrJO4QQgghaqna8WmnGuTlXUpeUBKc2TNy5D3WoAzggQfuJzIykt9X/MHuPXtQq9W0adOaESOGE92smd17dO3ahTffmMFPixZz6NBhDAYDDRrU5/ZBg+jdu9fVe1HVTG/S886mt5xq+8iKR8gpcjxjdL83fBwIrhVMhBQr3Slo+xE+EePILSomok4hP03qQmaBnrxCA34eGkwmc5nZqWxdMbPXJDNnbXKp/VyXlif2igoqN2X82Nhw+jUP5nSmjg9HtmPjkXSb2ZxX45pb96+VzFYdOpeLq6bySTlScywZOUsnwCj5c3Rdbyb1aEihwcjqhFSGtK3HgBYhZBcWo1JA/LELLNyaggojXT33MNR3LQN8NuGlclwHr4E2lfocZPpifz4Y2a7McsNsoxfZRi+bgswFegOrElOZu/aozazWpJ4N6Rsd7HTwZDCaWHUwtcIaafO3nCA20t9aQLo2cNeq6d4kiEJDHJM6TGZuOb+gmNxhiiXzYlXS4ZvNjgOoHj3KD8o8PeHOO2HUKOjTBypYESCEEEKImnfTBGWjRo1k1KiRVbq2T5/e9OljvzCuI9HR0bzy8ktVel5tlanLdDrrnE2a+4vcFfBpEIyzvy2pjGK/jhTGfIVO3Yjz6XnsP5ODm0ZNdF1vXNUqVhw+y+HzudzTKczu9Y72c+mKjTw7sBnv/HXIbsr4bk0C2Ho0g66NA/hu8wl+3X2aw6llMxK+93cSSx/txv3dIhnfNYI5a5P5cftJ1k3vVakMkFq1ZWZsTGwYnSL8mbE8kbGx4QxoHkK+3pKuf0H8CV5acoBsXTH/XZZAdF1L0PTS7S1YseEfngv5jTv9/iVI47gQc4lCk5a1ue3BrODvxPPkFRkqXG5YoDfw5opEuwk8DpzJ4fHvdzO2c7jTs1ol9b6ccXkB6drAXatGq/JgZv+Z3Brek7c22Sl70PVZ4qKGOJ910WyGgwdh3TpYvx7+/Rc2b4YwO2P71lvB2xtySo0xX1+4/Xa44w7Lf50suSGEEEKI2uGmCcrElbugq/hDf3mitbDIieWKZhTom03lu9yJzPr4JPX9MpnYLZK41vWI+3g97hoVhQYjb/5xkOi6XvjbySRYHo1KybH0fJvljT5uGlRKWLT9FMM7NiAjX0+7cD/6RAdbk0/M33KCbF0x2bpixs+zLCtcn5RuXT64MD6l3KQcpU3sFsGqxFTevrs1nSL8eX15Aq/FtaBfi2DWH07jw9VJHLws+YjJDAfO5HJvVBYu/z7IXP/tjh9wkd6kZk1uR1p3m8yUf4PZddZoPZetKybY29XhckOD0cSqxNRyMypC5Wa1cnSGSu27u7yAdG2gVinxVHlwZ/RdDI4aQpYuiwuFmfi7+uLr5otW6VL+DJnBAHv2XArC1q+31AgrbeVKyx6wy2m1lsBr/XpLEHbHHZbZM5kRE0IIIa5bteuTjqjV/N2qnrFttBd8FgQeFUx4mNzqsdpvJtOX+5NZYJlNydYVM3XRHnanZPLd/Z1RKOGBry3ByOHUPFwrWZ/MQ6tmxvIEZixPICrEi1b1fBjQIoTout6YDGb8PLSM/TLeZhZtQtcIlj7ajfHztpJZUMzgNqG4aFR8tu7SrM/8LSdY+mg3pzJADmxRFzNmMvP1GE1mXolrwapES5Xm7k0D0RvNDrMY9os0oFm+0/H30axgS34rlmT15I/sbuQYPVl8WxfULgeBS8F1RXXArsWsVrbOtkZaea52AWln98Y5Q6vWoEWDp9aD+j717DcymeDIEUtCjpKvXbtA53iJKQB//WU/KAOYMwe8vGSPmBBCCHGDkKBMOM3PzY+WQS3Zf36/09e4KODDQJjkU3FbXfBgRu5+gD1paqDY5vz8+BTah/vRvXGAdabFaDJXanbqgVsa4qJRsvqZnmQVFOPrrkGrUvLzzlM0Dfbi0zXJdoOpA2dymLZ4L2Niw5j/QCxms5nEs7mk5Rba7GcbP29ruRkgH+phyQD55I87OZtdiLu27J63gS3qEuLjWuGywsLA/rim/Vmmn3sLGrMk61aWZ91CqiGgzLnSmR1L+lJRHbBrMavl41a5mc2qFpC+PPjy93DBXatidWIqc9dd+d44p9x/vyVVfXZ25a9dtcoyo6a20x9vJ9b/CiGEEOK6IUGZcJpWqWVsq3E8t3q6U+0baSzLFdtVMMlhVmiI932eSRu6km0n1XxpX244Rr/mwWhUCoqNlnV9lZmdah/uS++ZawjxcS2TAGTKrY3ZcjSj3OsBFsSnEBvpj1at5MsNx5g+0DbZS3kZIAO9XNhwJI1Cg4l/DqaVWZpYIkenJ6RoK+qkT1H7tsK9xQt2Z4oUjSZA2p+kFfvyc2ZvFmf25UiR/f11pTM7lpjcs1GFdcCuxayWt5u6UjObVSkgfXliEh83DcsevYX3/z7sMOh2em+c2Wwp0nzkiOXL09OS4dCenJzKBWT161uWInbvDr17S/p6IYQQ4iYhQZlwmlat4ZGYh5m9/RNSsk+U23aoB3wTBD4VjLBsZSi5HRYy8ss8oPyADCwfnvMKDWx4thffbk6x7vGqaHbqvm6RDGwZwpM/7CKzoJjMgkszcSqlgtGxYWWKPpdn7rqjfDauI/lFBof72RxlgPx4VHtm/XOEj0e1t8kA6aXMZ5jfP0RunQ55Fws4n18H0dNAaTtbpKofR3zY14xZ7ouhgn/KpTM7giXDpDNLDa/FrJZWpWRSz4Y8/v3uCts6Ezhezl5ikpJadhUF3fO3nKBzA28GBoD63Fk4fdrydfIkJCdfCsQKSpVU6NzZcVDWqpWlYLMjTZteCsJ69IDwcFmSKIQQQtyEJCgTlXIhD+bePpfbFg60e14J/Bd4KbTie51068OJJp/gogkENjvdh9NZOn7afpJOEf7WPV6OZqfqeGoJ9HJlY1IaxQYTg1qH8nfi+TL3q0rR50K9EYWCCvezlc4AWXq2KrNAj6eL5Z9fI5eT3BewhDt81+ChKoTSiR51Z+D0Mmhwp8291Vo3WsWMYWS6/cyIJUpndmwR6s3EbhEMaBHi1DK9azGrpVYp6RsdbFMj7XJjOzsXOJZmLzGJM0H3qN1/MnLPX4TkZRDwdhYqcyXqzR054vhcq1aX/hwSAp06Xfrq2NFSR0wIIYQQNz0JykSlaFUqXv9ZTYs6HTiQsePSCRP4HoQFTWFQdPn3MKEiu+lr6MMeo623G0UGIz5uGrJ1tvvI7PFz17LvdDY/bT/FmNgwvrkvhriPN9qdnXqkV2Nc1Eq6NQ6ksNjALY0DbJY5erqouVBQuWV6mQV63LXqSmdbLJmt8nPT0Ni0hYcjFtLLe0f5Fx75wm5QBpb07C8MiiYmwt/+PqkeDenVLIjj6fn8MqUrLholC+NTKNAb8arBWa2SfpeukVa636VrpDktN5fiw0ls/2wVo06cJCQ3nbq56TTSZxP4yzO8mqdk+Ni37V7qp8uhzbkk559VWno6ZGVZ0tJf7pZb4JdfLEFYvXoyCyaEEEIIuyQoE5Xi7abGx13Lvp2NIXwH6IGd0OI4/PYgNA4p//psgtDFLGDAAhPZunXWwGHF490Z88UWjmcUlHv95XujFsSn0C7MlzGxYcxekwxcmp0aExtGkyAvdqZcoGeTIGYsT2DPqWy+uS+GDuF+zNt4zLIcspxliI6UJM1wdj9byWzVW7/v4YnIjTTY+iz/8z1Q7jMMHo0xNXoIZcOJ5f5Dddeq6dc8mNjIOuQVGaz711w0Sr6PT+G7LZalpiX756LrevNo78ZOvc5rOavlrlVXmMzEhskEx45Z0sknJFxaTnjkCKSm4ga85uB5jV29HPYl1bOO0/22cnWFRo2gSRPIz7cflAUGwrBhlb+3EEIIIW4qEpSJSinMz0O/ZjY5v/0IMcBeGN4avpoGHhUk9Eh1744+9lvunX+MbF0RcDHBwg+7GXvZjJcjl++NAvhq43E+G9eROWuTMZlLMuk1olOEH6M+swR6Y2PDmD4wmkEfrbcuc/zxoS6k5RWSVVBMsI9rpZbplQSGCoWC15Yd4I1hrejVLJCPViex77TtfraYUCN/L3qUFeG/EajJAtua1gAYzSpW5sQyP2MQm/Ja0/yEL5OMRfSNLj+roUalJP54Bp+uSS6TwMReIpHK7tO6JrNaF6kvBl/2aqTZ9dRT8NFHlX4OgF9hLq7FhRRqbJ9z1stOUKbRQGioJflGvXoQGQmNG1/6Cg0FZe0pai2EEEKI65cEZaJS/vxjBat+/QEA1TZ4YwQ8O6Ti63IaP8+3GWOY/9lhu0HX/PgUOoT7lZnxulzpvVGlHTiTgwJY9ugtuGpUuGiUHDidg8Fosib0mB+fQttSM2qz1yTTu1kQP2xL4WSmjq8ndGJit0imLtpT4Wu5r1skv+w8xZRbIxkVW5f84hyOZR+kcUgACx5qR6FeyenMInzdNQR6uvLpmiNE7B7Ofd6OSwnoVX58m9afz8/dViaVvbNZAUtmtLYeu3DVZ7SgirNaVZGcDAcOQFyc/fPNm1/R7UNyMzjub1tP7FBgBC8MeIRznnXwaBjGW4/fhntoiARdQgghhKgWEpSJShk1ahQ//PADm/5dxvePQr9W5bc3q72ZW/xf3v61KSbz8XLbfrHhGAseiGV9UprNbNPEbhF0ivBn/LytdoO61JxCftl5iu0nMq0zRDOHty4T5F0+o+bnrrXO+mTpiukU4edUWv2+0UFkFebx74k/GPLjGHaf22093zakLU/GTqNvxCCOp+tYvP0Un6xJRh8xgg5utkGZ2aspez0ncs/KJhSaHc8Uzd9ygthIf25rGeIwALqWM1pQhVktZxQVwbp1sGKF5evwYUuK+cxM+/W52rQp93ZmhYJMLz9OutfhnFfJVwAtY1rg0TCM1MP2X3uGhy8L294GwKxR7dDWlYBMCCGEENVHgjJRKQqFgq/ee5z8P34nrE75GeryXZtSEPsT/zfrlFP3PnAmh5xCA1+M70ReoYHCYiNebhqUClgYn8KM5YkOlzZ6u2rYfiKzTIr5y4OwA2dy0BuMRNe1FN4tWYLYIdyPrIJipszfUW5a/YndIujSsA4qdTH/2/Qf5u6YY9OP3ed2M2HJGCZ3mMI7/d/B203DiE4NCPDoh/nvhShyD1oaBveGZk+hC+jP83PiKTRXvGxyztrkCme5qm1G60oUFcHKlfDjj7BkCeTllT2flwd790L79rbXtmplSZZRpw60bg1RUZY9XReXFBrDI9iQdMEmMYmPm4alw7sxbO3R8oPuKs4kCiGEEEJcCQnKROWcWkqdHfdQp4KAzFhvGKpOX5KerQKcC8oAzucU8s5fB8kpNNCqng/P9I+i2/+txlDO4+wVRoZLqetL1wMrKjbx1cROnMvW8cvOU5jMEOztip+7xm5a/WKTCRe1Eo3KkrWwro+W4yf/sQnI2rrAq/7wbhas08GcHZ/SPbwH/SLi8HTV4qZRQ8v/wNm/IHoq+FlmfHKyCyuVij9HV/7eMrhGM1pXqrgYVq+2BGK//lpxQeUNG+wHZR4ecOYMBAfbzWSoBruJSZypZXelM4lCCCGEEFUlnz5E5XhHg9IFjIUOGijQt5zBn6bxrPztGE/1bVqp2/u5a8kpNJB4NpfEs7lM6tGIJsFliyxfzl7yjxKl64GBJSHGa8sS6Nc8mDva1eeXnacZ2CIEbzeNNdFH6bT693aOIMBTy+T5O1CrlEzpVZ9HvnrLer+WWni1Dtzpafm7VmEJygBmbnqbuKghloAMIHKs5auUbF3lUvHnFBbXnkCrImYz7NgBX38NP/wAGRnOXRccXP7SwZDyU3w6WsZ5PKOA6Yv38vLg5iya1IWcwlo6kyiEEEKIm44EZaJyvJtA1wWwdghwWRSk8aUwdj6v7wxj/pY9qJQKnh8UXaWshiXyiwxlgqrLOUr+UaIkdX3p+6/Yd5ble88yJjaMnyZ1wdNVbVOPqySt/lt/HmTpo90Y2SmMLJ2erMIs9qTuoZkG/lsH7rksy/pAD2jvAjuLYNe5XWTpMvHUejjsv49b5VLxeztRW6yEwWhCbzSRozOQrdPj46atvuAjPx86d4b9jpObWCkUEBMDgwbB7bdDu3ZXvJ/LmWWc7i61aCZRCCGEEDc1CcpE5dW7HVq9CvtevnTMtxWGbj+z8rg787fsBsBoMle5uHKJQG8XJvVoRIH+sN09XuUl/7g8yLv8/gviU+jcsI41eUZ5y94WTe7KmSwdeRkb+DYYRnuBykEd4Md9YUKq5c8XCrOo71Pf4Wv2dlNXKmj1dnPun2yB3sCqxFTmrrVTULpnQ/pGB1/bZXoeHvbrdpXWrRvccw/cfTfUrXvVu1Arl3EKIYQQQtghQZmompb/gQvb4fRSCB8JsV+gN7kwZ81maxMfNw2uGiW3tazL7pNZLIivuLhy6RmvFqHeuKpVdIjw48eHupBdWEyxwYi7ixqz2cy3m0+Um/yjdBDmaEatdPIMR8veTmbqQHeWgMRXCM1YQLS3/ddwwQjvZMLHWZeO+bv6lvttvHyGrjzO1hcr0Bt4c0Wi3dT4zqbYd1peniVboj0TJlj2hpUWE2MJxIYPhwYNruzZQgghhBA3CAnKRNUolNDlW0j5ERo9CApFmaQVEXXc+ea+GLYeu8BTP+7ixcHNaRfmOKuhvRmvh3o0ZMX+s3wff4LZY9rh76UkS5dFakEmHhofRsYEAybmbzllE5iVBGGLtp9i5vDWDmfULk+ecfmyt9y8TOqd/QTXf0egNObb/VZkGeG9LPgwC3JKJSRpF9IOXzffcr+NjmboLudsVkCD0cSqxNRy7wXOpdh3SK+3JOuYMwfS0mDfPrtJNxg+HB57zLJHbMIEuPdeaNiwcs8SQgghhLgJSFAmqk7rA40fsv61JGmFj5uGb++LZc7aZGv68a3HMxkTG8bn4zoCkJ5bhIeLGheN0m66+zGxYfRpFsTXG4/zw+T2LDm4jJmb37ZbE2zJowOZMG8PxzMKLMvzejSkZ1QQ2QV63h3RpsJ0+pcnz1CrlKgVRtzPf0XIvlehMNXudbkm+CDTEpBl2ckOObXrdLRKlwq/jVezvpjeaGLOmqMVtgPnUuyXcfQofP45fPmlJRgrsWEDdO9u297bG3buhKZNpeaXEEIIIUQ5JCgTV01J0oqxncOJP5ZRph5Utq7YmtWwV1Qgbwxrxb7T2Xy0OsluoeguDQNQKGFC91Cmrpxabk2wKR2nsOSxt8ktUKBWKdGqlTz5wy7O5RRaC0mXx27yjH2vwoE37LY3K11ZqW7GmMTdZDhI1T+5wxTiooagVTuXmONq1RfL0Rmubop9s9lSU+zDD+HPPy1/v9ycOfaDMoBmzZzqixBCCCHEzUyCMnHVeLupaVXPm9GxYTz4zXa7bUxmWH0wje0frGdMbBhfjOuEyWzmdJYOP3etdebMTaOiR5M6LE1aajcgK+3T7Z9yS1gPhkXdSdysTXx9fyznc4tIPJuLSqkgOsQTbzcNdTy0ZOQVkVNo4HBqHkaT2XHyjKaPwMH3wVhQqu8KFmX2ZYPro7wxtjcfNV3OO5vfspm9m9blWeKaDSk366I9VyMxxVVLsa/TwYIF8MEHcOBA+TdZt85SENql4llBIYQQQghhS4IycdVoVUqe6d+UQr2xwtma0jNna6f14odtKRw4k8Ohc7mMjgmjc8M6KFQG3tn0Vrn3KVFSE+yLCZ04dDaHR3s1Yv+ZXMbEhmEwmckvMuDjpsHTRU2RwYjeaGJh/Ela1fO2nzzDra6lyPP+1wAw1b2Nte5P8+yvRQDs/nArM+7oyboJg8kqzOKCLhN/Nz98XH3J0ylwVblV7pt3lVxxiv3UVJg92/KVnl7+xT17wuTJMGyYBGRCCCGEEFdAgjJx1ahVSro0rMPJCzqi63pZZ6PKYzJDak4hJzIKUCoUvH13a8vSRYWCTF0me1L3OPXsXed2kanLJKfQk66N6mA0g67YxIPfbrfZozWhawRdGwXQJtSNW4JSUascpGOPngoZ26D5NJTBvYjVGxh71pLV8GSmjglf7USthP4tQiyzcPmpBHhk8dygZjVWhLjKKfYTEmDmTMvsmL6c2TZfXxg/HiZNgujoq9NpIYQQQoibnARl4qooXai42GTm41HtcdEo+X7rSeZvOeEwyQZAXV833hvRFoUCa1KOv57sTlZhZqX6kFmYRUqGkkYBnvzvD8cp4act3surHU8wWvMBRfsvUBB3CHePOrY31HhBrxXWv9pLyGEwwYp95yqdkONaqXKK/d9/h6++ctw4OhqefBLGjgV396vSVyGEEEIIYSFBmbgiBqOJIoMlDftn62wLFU/oGsHSR7sxft5WjmcU2FzfItQbtVLBfd9uI/HspaQcumIjXlrfSvXFz9WXDK2Kv8tJCV9Pc57/hs6lnz4e9KABkjf8h/C+s52a3bpaCTmulSqn2J88Gd58E7Kyyjbs1w+efhr695cMikIIIYQQ14h8yhJVVqA3kFtk4I3fE3nih902S+ZKZqU+W3eUb+6LwcfNNhPhxG4RfLPpOAfOlM2SqFEpcVF50TakrVN9aRfSDj9XX6JCvJm71jYlvEZRzOTAxayKmkI/n/gy58LTv6A4K9HmGkdKJ+NoGuxFiI8r7lp1jQdkJUpm9GaNakeL0EuVrhVmEx3qqJk1qp1t4WgvL0tNMbDsD7v/fkv9sZUrYeBACciEEEIIIa4hmSkTVVKgN7A+KZ0cXXGZ1Pf2LIhPoV2YL2Niw5i9Jtl6vKTA84zliaiUCpoGe+LpoibY2xW1Usn8zWd4ImYqE5eOrbA/U7tMJyPPjMFom2QkxmM/r9ebTVNX+/1MNjShToEONzsrGK9XZWb0cgsx//A9AR+/Dz16oHi6n/0A8vHHLcHXww9DUFD1d1oIIYQQ4iYlQZmoNJ3ewN8HUukY6e8w9f3lvtp4nM/GdWTO2mSi61pqkXWK8OfRhTsZ2zmc0bFhFOqNXCjQU9fHFY1KgYtGQ6/w23io/WQ+2+k4Lf6UjlOIazaEV5ckMSom3HrcX5XNC3Xncbf/arvXpRX7khc9g5d2tuV1bRQBlfs21Hpqswn1wvm4v/46JF8Mho8dhf++AnXtJDcJCID//rda+yiEEEIIISQoE5VUoDdQoDeyMiGVlvV8KlWoWAFseLY3JrOZhfEpfLPpBB+Pbs/WYxd4yEGWRLXClRdveZNuDbrzfvw7NjXBnoydxh3N4vh6wxn2n8khyNsFMDPC729eqDsPX3WeTV9MZgULMm7jndRx/Hrb7eRu3mm/gPT1ymCA+fOhdDBWoqgI3n8f3n67ZvomhBBCCCFsSFAmnGYwmth6LIMGfh6czy3kQkHlChWn5RZRz9eNNYfPs+NEJp+Mbs+ctcl2lz+W7EcbExvG87dFc1ujOxjaLI6coiyyCrPw1PqgUXiycMtZMuvD7/vO0jHcDz/TKX5r9gpttTvt9mFfQSNePP0we3RRtAj1xkWjRK1U2C8gfb0xGCwp7WfMsA3GStuwAcxmUCiqr29CCCGEEMKhG+CTqKgueqOJ3/eeY0SnBuQVGfB3r1yhYk8XNVMW7KBThD9z7u3AusNpTu1Hax/mR3JaHnPWJhMV4kWrej70jdYQFaIlKTUfV42a+2+J5FbNClxWPklbbaHNfXKNbsw8N47vMgZhQgVYkowsjE/hoR6N7BeQvl4YDLBwoSUYO3LEcbuYGPjPf2DwYAnIhBBCCCFqkev4k6iobjk6A/vPZOPvruVwah6uWlWZ7H7lKZmV2nEik7nrjlKgN9rNkmjPvI3HGNPZslcs8WwuP20/xUPf7eCzdUd5++42uKgVHE3L56W/0lGbbQOyZVnd6XNoDt9kDLEGZCVJRoqN5rKp4a8nBgN89x00b24p6OwoIIuJgT/+gC1bIC5OMikKIYQQQtQyMlMmnJat01uDsWYhXiyMT2FC1wimLd5b4bUls1ImM0SHeFKot82S6MiBMzkU6o1EhXiReDbXenxBfAqxkf6cyMi/mNWxLT+79eYu/38AKFCH8n8ZT/JtSnPrNS1CLUlGujQM4NC5HJ7q16RGiz1X2Zo1MGkSHD7suE1MjCVxx8CBMjMmhBBCCFGLXYefRkVN8XHTYjSZrcHY678nsvTRboyOCSt3GeKY2DC6NAzgzRUHAcsyxsruR8ss0OPpYjtc5647yufjOvL+qiRMZphx9gG6e+3in/xbOBf5CtPiWvOQrpj8IgPebho8XdQYTWa0aiU9mgZenzNkAO7ujgOyTp0swdhtt0kwJoQQQghxHbhOP5GKmuDtpqZFqDfzt5wgJtKf21vVZfy8rTzauzEfjmxrs5SxRag3H41sy/SBzTCaTCx9tBuP9GoMQJCXS6We7eeuJa/IgAojA703ApZK0wfO5GAwmYkK8QIgy+hN30Of8lzKJD5Ye47zOUW8uSKRl5fsZ82hNDQqBb7u2lpV7LlKYmLgjjvKHuvYEZYvh/h4GDRIAjIhhBBCiOuEzJQJpymAh3o05IkfdjN+3la+uS+G9uG+fLDqMNP6R9Exwp+iYiPFRhMeLmrUSiXfbj7OS0sOkK0rvrh0MJL7b4lEq1bSItS7TBp8R0r2o+kvHGRRo/do73GIx1OmsjTrVsCSpt/b9dJQzjF5lrnuz/3nMJkh/lgmni6WosrXTUBWUGCZFbPn9ddhyRJo394yM3b77RKICSGEEEJch66TT6aiNigoMtK7WRCjY8I4nlFA3McbOZqWzxN9m+KmVfHMT7tx06pYdziNB7/dTtf/W83sNclk64oBy6zW1EV7eHflIYoMRiZ0jXDquRO7hpG49n8sb/I47T0OATAj9FOC1ekAeLtqqONpO/NWeh9biTlrk9EbTVf2jagOhw7BiBEQGwtGo/02LVrA1q2wbZtkVBRCCCGEuI5JUCaclq83cCw9n0k9GzJzeGvq+7kxe00y3d/6h8U7TvHCoGg2HsngjRUHSTybWyYYKm1BfAobktK5NcoS4JXn4U5abs+YSL+C13FTFlmP+6jz+V/9j2kR6o2ni5qMvKIy15VkV1wQX3av24EzOeToDFX7BlSHkyfh/vstGRUXLYL9+y21xxzp2FGCMSGEEEKI65wsXxROc9eqKTaaift4I2Niw/hsXEcK9UYyC/TU8dAS5O3Kcz/vc+pen607SmzDOkzq2ZDODf2Zu+5omaWMLUK9ebnVLjqlvYwyI9vm+qNFocw6P5KJt0dQVGwkp9BgvW5itwg6Rfgzft5W6yxdaTmFxYT4uFbxu3CNZGfD//4HH3wARWUDTF55BUaOBG3l6sIJIYQQQojrgwRlwmnuLioMJrN1hqykmLOni5pALxem9o+qVJr7/EIDr/+ewCej29O1cQDZBcVkFujxdymkwZFpaE4utHvt1+mDeevsBO6MaUqXhgG4uaiZMbQlfu5aXDRKFsanMGN5ot2ADCzLHWsNvR4+/dRS+Dkjw36brCzYtw86dKjWrgkhhBBCiOohQZlwmlalJCO/0FqbzGTGWjesU4QfGfmVT3M/omN9/k5M5akfdxMV4kVbt8M87vYKGtUZm/Zn9AFMO/UkWd49mHG3ZTbs0Lkcujauw0/bT7LvdDaHzjleNgmWmTRvt1ow7M1mWLwYnn8ekpPtt3F3hyeegGnTwM+vevsnhBBCCCGqTS34dCquF2qVksgATwI9XW1qkxXojQR4Vm55nZ+7lqbBXoz8bAtms4lbDfN42mM+GoVtYovckJGkR/yPmf7BmMxmFsSncOhcHk/1a4JaqeSWJgH8tP1Uhc+c3LMR2prOvLhxI0ydClu22D+vVsNDD8FLL0FISPX2TQghhBBCVDsJykSluGvVmMxmpg6IokO4Hz9tP0nHCH9Gx4bh46quVJp7X3cNeUUGMtKOMj/yPbp57bVpl2N05z+nH+VI2u14Hj7DeyOCePuvg/RvHkKf6CDctZYh3Dc6mLGdw5i/xXER67Gdw+kTHVRz6fAPHbLMjP36q+M2w4bB//0fNG1aff0SQgghhBA1SoIyUWn5RUZ+3JbC+K4RDGwZwqrEVB76dju3RgVZlzZW5MHuDVEpFZzNKmBO+P9odzHVfWnb86N5MmUqp4qDIcuyTDJfb+Cdu1ujVirLBFfuWjUvDIomNrIOc9Ym2yQNmdyzUZkgrtoZjTBgAJw4Yf98587wzjtwyy3V2y8hhBBCCFHjJCgTleblqmbjkQzuaFufOWuTrcsYT2XqWPpoN5uljZcbGxtOt8YBPPXjbl4c3JynzzzE4sbTrcsWjWYlH5+/h49SR2JEVeZaXzctrhr7w9ZdaykM3Sc6iBydgZzCYrxdNXi7qdGqlDVbMFqlgpdftqS7L61RI8vM2F13SWp7IYQQQoiblNQpE5WmVSl5eUg08ccyygRf2bpixs/baq1j1iLUu8x1LUK9+WhUO54f1AyA5wdFE+LtisGvE++dGwvAaX0go46+yfupY2wCshah3ni7lv97BLVKibtWTYiPK02DvQjxccVdq67ZgKzE+PHQsqXlz3XqwEcfQUIC3H23BGRCCCGEEDcxmSkTlaZWKYmo48m0RbY1yY5nFNjUMcspLKaujyverhq0asuMlcfFNPo6vYGJ3SKZtugu1AoD32QMIcfoafe593WLrP2xy+7dEBgI9erZnlOpYOZMWLMGnnsOfHyqu3dCCCGEEKIWqgXTB+J6lFtocFiTLFtXzOw1yXR/6x8e/X4nb65IxGgoJmX7J+gNhjJtcwoNdIrwY1RMBLPOj3IYkI2JDaNjhJ+1SHStk5oKDz4I7dvDs886bjdggKVItARkQgghhBDiIgnKRJVk6yquSVZSx+zEqSN4bx5IsxNPsm7p8xToLwVWWQX6Cpc8zhzemod6NGT8vK0OC0LXmKIiePttaNIEvvjCUn9swQLH6e6FEEIIIYS4jCxfFFXi4+ZcTbJunrv5MOwdvHKyAeir/4D47d2JjY1DrVLi46a1u+Qxs0CPn7sWF42ShfEpzFieSLbOkrijVjCbYckSS70xe8Wfn3wSNm0CpfzeQwghhBBClE+CMlEl3m7l1yRTYOLRoB95KnghSoXZelytMNEoeQr69gNQq9zK3Gf2mmTmrE0mKsQLTxc1eUUGDp3LxXTx8hah3ni71YIhu3evJej691/7593dYfBgSxp8CcqEEEIIIUQF5BOjqBKtSsmkHg3tnvNS5vNZ+Bs8E7KgTEAGcEofxKSj08gpUly6T89L9ylZ8rjteCaJZy8FZACTezZCW5NZFNPSYPJkaNfOcUA2bhwkJcGLL4KmlszqCSGEEEKIWk2CMlEleqOJ7k0CGR0TVuZ4Y5cUfmvyNP184m2uWZXTiduTPmSPLoqcQsveMLVKSd/oYMZ2DrNpX9rYzuH0iQ6qmdT2BoMlfX2TJjB3LphMtm26doWtW+GbbyA0tPr7KIQQQgghrlu1YC2YuN4U6A28uSKRDUnpfHNfDO3Dfflq43Hq5/3Juw3ex1OlK9PeaFby9rlxfJZ2J+aLvwcovTfMXavmhUHRxEbWYc7a5DJLIluEejO5ZyP6RAfhrq2B4bpmDTz2GOzfb/98gwbw1lswcqTUGhNCCCGEEFUiQZmoFIPRxKrEVOZvsRSNjvt4I2Nj6vFD7FK8kmfatNcp/LgveRqb81tbj9nbG+auVXNbyxD6RAeRozOQU2hJ6uHtpkarUtbMDNnp09Cvn2Wm7HLu7pbU91OnWv4shBBCCCFEFcnyRVEpeqOJOWuOljpwgZiUcXYDsqTiKLJv3UJ8Qesyxx3tDVOrlLhr1YT4uNI02IsQH1fcteqaCcjAUgD68cdtj48ZA4cOwcsvS0AmhBBCCCGumARlolJydJeKRjd1Oc7Sxk/R02unTbvFF/ow+OCb5KtCiQrxsh6v0b1hVfHKKxAcbPlz27awfj3Mnw/169dot4QQQgghxI1Dli+KSildNNqICn91dpnzxWYVr515kO8ybgcUZBbo8XRR1/zesPIkJVn2hrm62p7z9rYk+bhwAR58EFSq6u+fEEIIIYS4oV0n0xWitihdNDq5qAHPnHza+ve0Yl9GJ7/BdxmDAUvSi1BfNz4d04FFk7twW8uQ2hWQ5eXBc89Bixbw7ruO240YYUmFLwGZEEIIIYS4BiQoE5VSUuy5xMqcLnyUeg+78qMYnPQB2wpaWs+1CPXG111DgJdLze4Nu5zZDAsXQlSUJXNicTG8+SacPFnTPRNCCCGEEDehWvIpWVwvLi/2DPBB6mjuOfp/pBoCyhyv8WLP9uzZAz17WpJ1nDlz6XhBgSWTohBCCCGEENWsln1iFrWdvWLPJlTozZoy7WpdQo8LF+CRR6B9e0uyjstpNBARYb8wtBBCCCGEENdQLdrgI64XtbrY8+VMJpg3z7J3LCPDfpuBA+GDDyzLGYUQQgghhKhmteBTs7ge1cpiz5fbuRMefhji4+2fj4y0BGNDhoBCUa1dE0IIIYQQooQEZaLK1BeDr5KCz7VGVha8+CJ8+qn95YhubvDCC5Y9ZPbS4AshhBBCCFGNJCgTN5ZTp6BDBzh/3v75u++2pL8PC7N/XgghhBBCiGpWC9aYCXEV1atnSeZxuaZNYeVKWLRIAjIhhBBCCFGryEyZuGJ6QzF6k55MXSYXdJn4u/nh5+aLVumCVq2p+AZXk0IBs2ZBy5ZQVGRZqvjii/DMM+DiUr19EUIIIYQQwgkSlIkrkqfPZ8nBpczc/Da7z+22Hm8b0papXaYztFkcnlqPq/9gs9nyX3sJOho3hmeftdQk++ADS6p7IYQQQgghailZviiqLE+fz9SVUxn76+gyARnA7nO7GfvraKatnEaePv/qPvjwYejXD5YscdzmlVfgt98kIBNCCCGEELWeBGWiSvSGYpYcXMrcHXPKbTdnx6csPbQMvaH4yh9aVASvvQatWsHq1fDEE5DvIOBTytAWQgghhBDXB/nkKqpEb9Lzzqa3nGo7c9Pb6E36K3vg+vXQtq1lBkx/8V4pKfDGG1d2XyGEEEIIIWqYBGWiSjJ1mexJ3eNU213ndpGly6zigzLhwQehRw84eND2/E8/WWbQhBBCCCGEuE5JUCaq5EIlg6wLhVmVe4DZDN9/D82awRdf2J5XqSzFn/fskayKQgghhBDiuibZF0WV+Lv5Va69q6/zjY8dgylT4K+/7J/v2BE+/9yynFEIIYQQQojrnARl10hRURGLf/6FDRs2kJaWjqenJ+3bt2PM6FHUqVOnprt3xfzc/Ggb0tYm66I97ULa4evmW/FNjUZ4/314+WXQ6WzPe3pa9pA98ohlpkwIIYQQQogbgCxfvAb0ej0vvfwKP/20CJ2ukJiYTgQEBLB69T889fQznDt3rqa7eMW0Si1Tu0x3qu3UrtPRKitYYnjgAHTtCtOm2Q/I4uIgIQEef1wCMiGEEEIIcUORoOwa+GnRYg4dOkxUVBSfzv6Y6dOmMvOdt7hv4gSys3OYNeuTmu7iFdOqNQxtFsekDpPLbTe5wxTiooagVWscNzIaLUHX1q2250JD4eefLTXHGjS4sk4LIYQQQghRC0lQdpUVFxez4vcVAEye9CBubm7Wc0OHxhEREc7+Awc4ciS5prp41XhqPZjZfyYL7vyetiFty5xrG9KWBXd+zzv938FT61H+jVQqmDWr7DGFwrJMMSEB7rzT8nchhBBCCCFuQLKn7CpLPHiQ/IICQkJCaNiwoc35rl26cPz4CbZt20bjxo1qoIdXl6fWg7uj7yIuaghZukwuFGbh7+qLr5svWqVL+TNkpQ0aBOPGwbffQtOmMG8edOt2bTsvhBBCCCFELSBB2VV2/NhxABrZCcgAGjWyHD9+4kR1dema06o1aNHgqfWgvk/98hubTKB0MEH7/vsQGQnPPgulZhiFEEIIIYS4kcnyxassLT0dgDoB9jMslmReTDufVm19qhWKiuA//4Fhwyw1yOzx94f//lcCMiGEEEIIcVORmbKrrFBXCICLg4LGrq6uAOgK7WQYtCMvL5/8/Hyb46mpqQCkXwwCa7X9++DppyHpiOXvn86Bu+6q2T4JIYQQQghxFZV8LjcajZW+VoKyWm7ZsmX88ONPDs+/8J8Xq7E3VyCykeULYOXfli8hhBBCCCFuMJmZmYSG1q3UNRKUXWWubpaZsKKiIrvnCwstM2lurs4t0RsyZAi9e/e2Oa7X60k9f556oXVRKqVu1/UgPT2dF/7zIm++8ToBAQE13R1xg5BxJa4FGVfiWpGxJa6F2jKuTCYjFy5k0qRJ40pfK0HZVRZ4cSBkpGfYPZ+RYTkeGBTo1P08PT3w9LSfUr5BgwqSaohaKSAggODgoJruhrjByLgS14KMK3GtyNgS10JtGFd161ZuhqyEJPq4yiIiIwBIPnrU7vnkZMvxiPDw6uqSEEIIIYQQohaToOwqi27WDA93d86dO8fRo8dszm/avBmATp06VXfXhBBCCCGEELWQBGVXmUajYdDtgwCY+9ln1j1kAEuWLOX48RO0bNHihigcLYQQQgghhLhysqfsGhgx/G727NnDwYOHmDzlEZo3jyYtLY3Dh5Pw8fHmscceqekuCiGEEEIIIWoJmSm7BrRaLa/PeI0RI4bj4uJCfPxW0tLS6N27F++9O5OQkJCa7qKoAR4eHoy8ZwQeHvYTtwhRFTKuxLUg40pcKzK2xLVwI4wrhdlsNtd0J4QQQgghhBDiZiUzZUIIIYQQQghRgyQoE0IIIYQQQogaJEGZEEIIIYQQQtQgCcqEEEIIIYQQogZJUCaEEEIIIYQQNUiCMiGEEEIIIYSoQVI8WogKFBUVsWvXbrZt205CYiJpaWkolUrq1g2hS5cuDI0bgpubm91rV6/+hz/+/JOTJ0+hVquJatqU4SPuJrpZM4fPS0xMZNGinzl0+DAGg4EGDeozaNBt9O7V61q9RFFL5OTk8uhjj5GdnUNISAhz58x22FbGlnBGdnY2v/zyK1u3bSc9PR2tVktQUCCtW7dm4oTxNu23bt3Gb0uWcPToMQAaNWzIsGFD6dixo8NnpKSk8P0PP7J//wEKCwupGxJC3759GDz4dpRK+d3vjSYpKYlff11CQmIiOTk5uLq4EB4eTp++venTuzcKhaJMe6PRyPLfV7B61WrOnjuHq6srrVq2ZNSokTRoUN/hc6oyFkXtduRIMrv37CEpKYmkpCQyMi4AsOS3X8q9rrre79LT01mw8Ht27dpNXl4egYEBdO/enbvvuhOtVlu1F10JUqdMiAqs/PtvPvnkUwDq169PeFgYBboCDh48hE6no379erzx+gx8fX3LXPfFF1+ybPnvaLVa2rVti75Yz969+zCbzTw7fRqdO8faPGvTps28M/NdzGYzLZo3x8vbi71795Gfn88dQ+OYOHFCNbxiUVM+/HAW/65Zg9lsLjcok7ElnHHkSDL/ffU1cnNzCQtrQFhYGLoCHSdPniQ9I4Nff1lcpv3Spcv4ct5XqFQq2rRpjUatYdfu3ej1eh568AFuv32QzTMOHjzESy+/gl6vp0mTJgQFBZKQkEBmZhbdunZl2rRnbD6ki+tXyc8Rk8lEo4YNCakbQk5ODgkJiRiNRnr06M4zTz9lbW8ymXjr7XfYsiUeDw8PWrduRW5OLgcSEtBqtbw+4zWaNm1i85yqjEVR+7355v8Rv3WrzfHygrLqer87e/Ys0599npycHMLDwqjfoD7JR5I5l5pKdHQzZrz2KhqN5opef4XMQohyrV79j/mTT2abU1JOljmekZFhfvKpp81xQ4eZZ858r8y5Xbt3m+OGDjOPGTvOfPr0aevxxMSD5jvvGm4eNXqsOTc3r8w1OTk55pGjxpjjhg4zb9q02Xo8MzPTPGnyw+a4ocPMe/fuuwavUNQGu3fvMccNHWb+5JPZ5rihw8wPTZpit52MLeGMrKws89h7x5uHjxhp3hIfb3P+0KHDZf5+8tQp8x3D7jLfdfcIc2LiQevxU6dOm8eMHWcedufd5jNnzpS5pri42PzgQ5PNcUOHmX9bstR6vKCgwDxt+nPmuKHDzKtWrb7Kr0zUFIPBYL533ARz3NBh5jVr1pY5l5Jy0jxm7L3muKHDzHv27rUe/2vlSnPc0GHmyVMeNmdmZlqPb9y4yRw3dJh50uSHzQaDocy9qjIWxfVh8c8/m+cvWGiOj99qvnDhgvmuu0eY44YOc9i+Ot/vnn3ueXPc0GHmzz7/wnrMYDCY3/zf/5njhg4zL1z4/ZW8dKfIugIhKtC7dy8efniKzTILf39/Jj30EACbt2yhuLjYem7pkmUAjBh+N6GhodbjzZpFMXDgAPLz81m1alWZ+/399yoKCgqIjYmhS5fO1uO+vr6MH38vAEuWLL26L07UCkVFRcz+dA4NGjTgjjuGlttWxpZwxvff/0BOTg4Txo8jNibG5vzlsxPLly3HZDIxcEB/mjWLsh6vVy+U4cPvxmg0smzZ8jLXbNkST2pqKpEREQyNG2I97ubmxqSHHgBkXN1ITp06RXZ2NvXq1aNnzx5lzjVoUJ+ePXsCcCTpiPX4kos/r8aPG1dmNUnXrl2IienE2bNniY8vO3NSlbEorg933XknY0aPIiamE35+fhW2r673u8OHk0hMPIiPjw8Txo+zHlepVEyZPAm1Ws3y31dgNBor/6IrQYIyIa5AZGQEAMXFxeTm5gKWD9h79+0DLG88l+vaxXJs27btZY5v37HD4TUdO3RAq9WyZ+9e9Hr9Veu/qB1++PEnUlNTmTJ5EiqV462+MraEM4qKilizdh2urq706dPbqWsujZGuNueqMq4aNWpESHAwJ1JSSE09X6n+i9rJ2aVbXl5eAKSmpnLq1Cm0Wi0dO3awaVfxuHJ+LIobT3W+35Vc06lTR5tx7uvrS/PoaPLy8khISLyCV1QxCcqEuALnzp0DQK1WW9+ITp8+Q3FxMT4+3gQEBNhc06hRQwCOnzhR5vjxY8cBaHjxfGkajYawsAbo9XrOnDlzNV+CqGHHjx9nyZKl9OndmxYtmpfbVsaWcMaRI8nodDoaRkbi4uLCjh07+XLeV8yZM5elS5eRceFCmfZ5efmkpaUD0LBhpM39AgMD8Pb25nxaGgUFBdbj1nHV0HZcwaXxdvzE8avwqkRNCw4OJiQkhNOnT7N27boy506ePMXatWvx9PS07vE5dnF8hIeHoVbb/rKpkZ3xUdWxKG481fl+V3JNowp/lp2we/5qkeyLQlyBZct/B6Bdu7bW366kpacBUKdOHbvXuLq64uHhQV5eHgU6He5ubhQUFJB/8Q0mwMF1derU4ciRZM6fTyMiIuIqvxJRE0wmEx9/MhsPDw/Gl1oy4YiMLeGMk6dOAuDj42N3Y/138xfw2KOP0KNHdwDSL44rT09PXF1d7d6zTp065OTkXBwj4QCkpVs+PAcEOB5XAGnn067wFYnaQKVS8eQTjzHj9Td57/0PWLJkKXVD65KdnU1CQiINGtTniccfs/6CsmR8OPp5ZR0faZfGR1XHorjxVOf7nfVZDn6WBVh/ll3bWX+ZKROiirZv38GqVatRq9WMGT3KerxQVwiAi4uLw2tdXS3ndDqd5b+FhdZzjq4reYMquUZc/37/fQVJSUeYMH4c3t5eFbaXsSWckZeXD8DWbdvYuWsXkx56kG+/+YrPP5vDHXcMRa/X8+FHs6ypxkvGiIuL45TP1nFVeGmMFBaWPx5lXN14oqOjefONGYQEB5N89CgbNmxk3779KBQK2rZpQ3BwsLVt4cX/7xWPj0s/o6o6FsWNpzrf7yr6WeZifU6h3fNXiwRlQlTBqVOneP+DDzGbzUwYP47ISNtlFkKUJy0tjfkLFtKyRQun9/0I4QyzyQRY6kONHjWSQYNuw8fHh6CgICZOGE+3rl0xGAz8+ttvNdtRcd1Zt249U6c9S0BAAO+8/RY//rCQT2d/TJ/evfhtyVJeeumVMkmvhBDOk6BMiErKyMjg1VdnkJeXx9C4IQwZMrjMeVc3y29hioqKHN6jsNByrqTotFupZRqOriv5TY6jQtXi+jJ37ucYDAamTJnk9DUytoQzXEv9f7QX8JccO3DgAHBpjBQVOU70Yh1Xrpfu7epa/niUcXVjOXPmDB9+NAtvby9efPEFmjZtgqurK6GhoTz88BQ6dexI8tGjrFq1Grg0DiseH5d+RlV1LIobT3W+31X0s6zI+hz7S2qvFtlTJkQl5Obm8sp/X+N8Whp9+vS2W4AwMCAQsARv9hQWFpKfn4+npyfuF38ouLu74+HuTn5BAekZGYS5u9tcV3K/oKDAq/RqRE3atn07Hh4efPrp3DLH9cWWDyMXLlzgP/95CYCpU5/Gz89PxpZwSlCg5f+ji4sLPj4+tueDggDIzs4BIODiuMrLy6OwsNDuXh57YyQwIIC8vDzS0zPs7kUsuSZQxtUNYf36DRgMBtq1a2c30O7WrSvbtm/nQEICt902kMCLyRkc/byyjo/AS+OjqmNR3Hiq8/0uMCCQo0ePkZFu/1np1p9lQVV/QU6QmTIhnKTT6Xj1tdc5efIkXTp35pGHp6BQKGza1asXikajITs7x+4Pk+TkowBEhJfdoBxxMb3+0YvnSzMYDKSknESr1Zap1SGub/n5+ew/cKDM1+HDSQDo9XrrMb3eshxIxpZwRknWOr1eb3cpWV6epXxHyQdeT08PAgMtH6BL9pmVlpaWTk5ODkGBgbiX+pBjHVdHbccVXBpvEeERVXsholYp+ZnjYeeDLoC7h+V4Xl4ecKlkzIkTKRgMBpv2yXbGR1XHorjxVOf7Xck1yRX+LLu2iWUkKBPCCcXFxbz55v+RlJREu3ZteeaZp1CpVHbburi40LpVKwA2btxkc37T5s2ApR5GaR07WOq4bNq02eaabdu3o9fradO6NVqt4w3Q4vqx5Ldf7H59NncOACEhIdZjwcGW387J2BLOCAwMJDIiArPZzP79B2zOlxxrWGov7KUxcnXG1dGjRzmXmkp4WJh1/Irrm+/FYr9HkpPtni8pGl0yExscHEz9+vXR6/Vs377Dpn3F48r5sShuPNX5fldyzbZt221+kZWVlUVCYiKenp5ERze7gldUMQnKhKiA0Whk5rvvsXffPpo3b87zzz1bYRHNuKFDAPhp0eIytTAOHjzEX3+txMPDg759+5a5pl+/vri7uxO/dSubN2+xHs/KyuKbb74DYOjQuKv1ssR1SsaWcMawYXcA8NXX33ChVF2yo0ePsWTpUgAGDhxgPT54yGCUSiV//rWSQ4cOWY+fOXOGRYsWo1KpbPbPdu4cS3BwMMeOH2fJ0mXW44WFhcyZ+zkg4+pGEhvTCYADBxL4448/y5w7dOgQS5ctB8oW7R168efVN99+S1ZWlvX45s1b2Lp1G3Xr1iU2NqbMvaoyFsWNqbre75o2bUJ0dDOys7P55ptvrceNRiNz5nyGwWBg8O2D7Nbbu5oUZrPZfE2fIMR1btmy5Xzx5TzA8iHE3c3+komJE8fj7e1t/fsXX3zJsuW/4+LiQts2bTAYDOzeswez2cyz06dZC2yWtmnTZt6Z+S5ms5mWLVvg5eXFnj17yc/PZ2jcEO67b+K1eZGi1khNPc9DkyYTEhLC3Dmz7baRsSWc8eGHs/jn33/x8PCgWbMo9Ho9Bw8eori4mP79+vHII1PKtF+ydBnz5n2FSqWibZs2qNVqdu3ejV6v58EH7mfw4NttnpF48CAvv/xf9Ho9TZs2ITAwkISERDIzM+natQvTp021u8xbXJ+++vobfvttCQBhYQ1oUL8BFzIvcOjQYUwmEwP69+Phhy+NK5PJxFtvv8OWLfF4enrSunUrcnJyOXDgABqNhtdff42opk1tnlOVsShqv+3bt/PjT4usf09KOoLZbKZp0ybWY/eMGE7Hjpdmv6rr/e7MmTNMf/Z5cnNzCQ8Pp0GD+hxJOsK51FSaNYvi9RmvVfgL+SslQZkQFfj++x/44cefKmz32dw5Nst0Vq/+h99X/MGpU6dQq9VERTVlxIjhRDdzPAWemJjIT4sWc+jQYQwGAw0a1Of2QYPo3bvXFb8WUfs5E5SBjC1RMbPZzN9/r+LPv1Zy6tQpFAoF4eHhDBzQ3+H/861bt/Hrb0us+8QaNmzIncPuKHe5WEpKCgu//4H9+w9QVFRESEgwffv2Zcjg21EqZUHOjWbzli38+edKkpOTKSgowM3NjcjICPr362ctSF6a0Whk+fLfWbV6NefOpeLi4kKrVi0ZNWokYQ0aOHxOVcaiqN1Wr/6Hj2Z9XG6bxx971CZrbHW936WlpbPw++/ZtWsXubl5BAYG0r37LQy/+65qWd4vQZkQQgghhBBC1CD5FZYQQgghhBBC1CAJyoQQQgghhBCiBklQJoQQQgghhBA1SIIyIYQQQgghhKhBEpQJIYQQQgghRA2SoEwIIYQQQgghapAEZUIIIYQQQghRgyQoE0IIIYQQQogaJEGZEEIIIYQQQtQgCcqEEEIIIYQQogZJUCaEEEIIIYQQNUhd0x0QQgghqmLoHXcCsOS3Xypsu3r1P3w062NG3jOCUaNGWo9/+OEs/vn3X16f8RqtWrW8Zn2tbfbt28+LL71c5phSqcTLy5OGDRty28CBxMbGAJe+d5Vx+fdZCCFE+SQoE0IIIW5Svr6+tG/XDgB9sZ6TKSfZtWs3u3bttgZWdevWpXevXjbX/vPvvwB06dIZN1e3MuciIyOvfeeFEOIGIkGZEEKIG17nzrFERTXF29u7prtSq9SvV48nnniszLFly5bzxZfz+GnRYnr37k3z5tE0bx5tc21JUDZxwgSCg4Oqpb9CCHGjkj1lQgghbngeHh7Ur19fgjInDBkymPr162Eymdi7d29Nd0cIIW4KMlMmhBDihudoT1lpO3bsZNHixRw7dhyVSkWrVi25d+wY6tev7/BePXr0YOH337Nv335ycnJ47tnpdO4cy9mzZ1mzZi27du8mNfU8eXl5+Ph406pVK0YMH069eqE2zx96x50EBQYye/bH/PzLr6xbt57U1FQ6tG9PdPNmfP31t9x9153ce+9Yu/1/5ZVX2b1nDzNmvErrVq2u6PvVoEEDTp06TXZ29hXdRwghhHNkpkwIIcRNb+OmTcx4/Q0MBgOdOnXE39+fLVvimT79OY4dO2b3mtNnzjB12nSSko7QqlVL2rRpg1qtAmDl36v48adFFBYW0aRxYzp16oibmztr1qxl6rTpHD9+3O49TWYz//vfW/z662+EhAQTGxODn78ffXr3RqPRsPqffzAajTbXpaamsmfvXkJD615xQAag0xUC4OPjc8X3EkIIUTGZKRNCCHHT++OPP3nk4Sn0798PALPZzLffzeeXX37lw48+5oP337W5Zv36Ddw+6Dbuv/8+VCpVmXOdY2MYOKA/wcHBZY6vWr2aWbM+4Ysv5/H6jNds7pmeno5Go2H2J7OoU6dOmXNdunRm3br1bNu2nc6dY8uc+3vVasxmM/369q3S6y8tNzeXpKQklEolbdq0vuL7CSGEqJjMlAkhhLjpNWsWZQ3IABQKBWNGj6JOnTocO3aMhIREm2t8fLwZP36cTUAGEBUVZROQAfTt04fo6Gbs33+A/Px8u325994xNgEZwMABAwBY+fffZY4bjUb++ecf1Go1vXvbZkl0ll6vJ+nIEf73f2+Rn5/PvfeOJShIEngIIUR1kJkyIYQQN73ut9xic0ytVtO1axeWLVtOQkKCTQbCNq3b4OLi4vCeOp2Obdu2c+zYMXLz8jAaLMsOMy9kYjabOXfuHI0aNSpzjUKhIKZTJ7v3a9GiOWFhDdi1azdpaekEBgYAlr1wGRkX6Nq1C76+vpV52ew/cMBa7610H5584nF69bq1UvcSQghRdRKUCSGEuOkFBgXaPR58cabowoULNucCLgZF9uzdu4+Z775LdnaOwzYl+7ZK8/HxQaPROLxmQP/+fP7Fl6xevZqRI+8BLs2c9e/Xz+F1jpSuU5ZfkM+hQ4fJysris8+/IDIygoiIiErfUwghROVJUCaEEEJUgdZB8KTT6Xj7nZnk5eVxzz0j6H7LLQQFBaLValEoFLz77vusW78es9ns9D1L9Op1K99+N59Vq1czYsRwMrOy2LFjJ0FBQbRt26bSr+HyOmXFxcXMfPc9tmyJ5/0PPuT9995FqZSdDkIIca3JT1ohhBA3vbTzaXaPn0+zHPf393f6XgkJieTm5tKlS2dGjxpJgwb1cXFxQaFQAHAu9VyV++nh4UH3W24hLS2dXbt2s3r1P5hMJvr162u9/5XQaDQ8/tij+Pj4cPz4CdasXXvF9xRCCFExCcqEEELc9DZu3GRzzGg0snnzZgCio6NtzjuSl5cHYDdZx9mzZzl61H6KfWcNHNgfgL/+WsmqVatRKpX06dP7iu5ZmoeHB3ffZdln9vPPv2Ayma7avYUQQtgnQZkQQoibXkJiIqtWrS5zbOH3P5CWlk5ERDgtWjR3+l6hFwtDb9mypUzx5by8fGZ9/AkGg+GK+tqkSRMaNWxI/NatpKam0rFjB+pUYibPGQMG9MfPz49Tp06zefOWq3pvIYQQtmRPmRBCiOvatOnPOjzXr19fpxJg3DZwAB9/Mps//1pJ3ZAQjp84TkrKSdzd3Xni8ccqvL60Jo0b07ZNG3bv2cOUhx+lZcsWAOzffwBvb29iY2KI37q1Uve83MCBA/hk9qcADOhf+QQfFXFxceGuu+7kiy++ZPHPP9OtW9er/gwhhBCXSFAmhBDiunb4cJLDcyWZBSvSrVs3OnTowKLFPxO/dStqlYrYmBjG3juGsAYNKt2nF154jkWLf2bjxo3s3LkLHx8fune/hTGjR/PVV19X+n6Xa9WqFWBZItnOyddYWQP69+PXX3/l6NFjbN++g44dO1yT5wghhACF2V76JyGEEELUWosX/8x38xcw8p4RjBo1sqa7I4QQ4grJnjIhhBDiOlJQUMDy31egVqvpP6B/TXdHCCHEVSDLF4UQQojrwKrVqzmwP4EDCQlkZmYyZMjgq57gQwghRM2QmTIhhBDiOnBgfwL//PsvhYU6Bg26jfHj7q3pLgkhhLhKZE+ZEEIIIYQQQtQgmSkTQgghhBBCiBokQZkQQgghhBBC1CAJyoQQQgghhBCiBklQJoQQQgghhBA1SIIyIYQQQgghhKhBEpQJIYQQQgghRA2SoEwIIYQQQgghapAEZUIIIYQQQghRgyQoE0IIIYQQQogaJEGZEEIIIYQQQtQgCcqEEEIIIYQQogZJUCaEEEIIIYQQNUiCMiGEEEIIIYSoQRKUCSGEEEIIIUQN+n/Q/WAqMx0R/QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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LAHh6yGAc7O2Ji7tIeHhYrRJ9HD16jB07d+Ho6Micjz/E01Pz8F5cXMzHH39CbFwcq1evYezYMXplN23ewqyZM4iICAc0AdTn87/g2LFItm7bftf5YV5eXkyfPpU1a9aSlJxMj+7d6datq+45Nm3m1KnTeHvX56MPP8De3h7QBCJvv/MekZFRbNu2nb59++jVv2//AT768AOaNg2u0bWQyWRs2bIVCwsLFnz9JW6VhlMqlUouXozXOf7vDRs5eOgQTZs25fX/vaoNQuVyOUuXLWf37j2s/W0do0eN1JaJCA+j15M9deoG2L1nD4sWfcP3P/zIxx99qNe26u7Tmt4nle3dt49BAwcwevQobUC7ceMmfvjxJ9b9/ocIygRBEAThPhBzygQBzYN0SkoKCxctJj4+HhMTE7p17ap33MiRw/UedBMTkzh16jSNGjVk3Lix2oAMwN7enpdfmgzAtu07tNuPHj1Kfn4BwcFB2oAMNKn5hw8bqneO6ly6lMC5c+ews7Njyh0P2gBubq73pVdx85YtgGa4YEVABmBlZcXEieORSCRs37ETmUymV7Zjxw7agAzA2NiYp4cMASA2JvYft61y+8aNHasNyEAzD2/M6FGAJjg0pHv3bjUOyABKSkqQy+W4u7vrBU3GxsaantJblEolf/31N+bm5sx4/TWdXkETExMmjH8RBwd7du7chUql0u4LCAjQqxuge7duBAY24cKFGIqLiw22z9B9eq/3iZubGyNGDNfpYezbtw/W1tZcunQJuVxusA2CIAiCINSc6CkTHmt3rlcGYGFhwfTpU/HwcNfZLpFICGujP4cmOjoagPCwMIND4/z9/TE3NychIUG7LTY2DoCOHTroHS+VSmnXri2bNm2u0Ws4e/YsAJ06dsDyjgft+0WhUBAffwmAzp076e339fXF18eHpORkkpKSCAgI0NkfEtJCr4znrWFyObm5/7h9mZmZZGZmYWdna7Dnpk2bUKysrEhNTSU3N1dvuKSh97U69vb2ODk5kZSUxIpffuXJnj1wd3c3eOyVK4kUFBQQ0qKFTrBYwczMjAYNGnDy5ClSUlPxqldPu6+0tJQTJ06SlJREYVERSoUSgNycXNRqNWlpaTRo0ECnvqru03u9TyqGOFZmbGyMm6srVxITKSwsxNHRscb1CYIgCIKgTwRlwmOtYp0yidHtxaPbto3A2tpa71g7Ozu9h1OAjIwMAFauWs3KVaurPFflHoWcnByAKjMcurm61vg1aDMSVhEU3A+FhYUoFApsbW0xNzc3eIyrqytJyclkZ+fo7XN2ctbbVhEYKO5DT8vdrqdEIsHVxYWk4mKys3P0gjIXF/323c0r06cx/4svWL/+L9av/wtXFxeCmwbTsUMHWrdupT2u4v6IPnvW4IcAlRUWFMCtoOzcufPM/+IL8vMLqjy+tLRMb1tV9+m93idV9dpW9LTJ5Ypa1ScIgiAIgj4RlAmPtTvXKauOqYEHXdAkVgAICgx8oIHRQ09SzS5JNTv/LdW0wcTEtMp9VWnevBlLv13CiRMnOX3mDBcuxLBv33727dtP27YRvDFrJnD7/vDw8CCwSZNq67Sx0STkKC0tZd7n8ykqKuK5556lY4cOuLq6YGpqikQi4YsvvuLgoUOob9VdWVX36b0yehjeO0EQBEH4jxNB2WPKy8uLpKSkum7GPfPy8qrrJmg53+pJCA8PY9CggTUqU9FTk5mZaXB/RhXbqzt/WlpajcvUlo2NDVKplIKCAsrKygz2lmVkaNrs5PTvD2WrGD5X1fWsvO9+ts/S0pLOnTtph3TGx8czd958jh2L5OTJU4SGtta+P1716tX4Q4DY2DgKCwtp164twwwkY0lLr/17/W/cJ4IgCIIg3BsRlD2mpFLpA0sp/7hpEdKCVavXEBkVVeOgLDgoiL1793H4yFF69+6ls0+pVHLs2LGan79FC1auWs3BQ4cZPnyYXgKHO1Ws76aslFTibqRSKQEBjYmJieXQocP06NFdZ//Vq1dJTk7G3Ny8TpZacHFxwcXFmczMLM6ePadJh1/JyZMnKSoqwsPD467p9/+JgIAAujzRmT/+XM+1a9cIDW1No0YNsbK05EJMDIWFhdresOoUFRUBhocOpqamkphY+w9UanufCIIgCILw7xHZFwXhHwpo3JiQFi2Ii7vI0mXLKSnRTyuelJTE6dOntT+3a98OGxsbLly4wN69+7Tb1Wo1q9esJTPz7utkVWjcuBHNmjUlPz+fJUuWUlamO88oPT2D5OSr2p8repVu3rxZ43MA2lTya9b+ptPbUlJayvLl36NWq+n1ZE+9BaT/LRXt+/Gnn8jPz9duz83N5aeffwGgf7++9+VcmZmZ7Nmzl/Lycp3tMpmM8xcuAODsrJmnZmJiwlNPDaK0tJTPPptnsKcqOzubffv2a3+uSIISGRmp81qKiopZtPgbFIraz+Oq7X0iCIIgCMK/R/SUCcJ98Oqr0/ngw4/Ytm07Bw8ews/PD0dHB0pKSkhOvkpWVhb9+/XVrlNmaWHB1Ckv89nceSxYuIht27fj7uZOUnIyqamp9OzRg527dtX8/K9M55133+fgoUOciT5DYGAgJlIT0tLSSEpOZuyY0fj6+gAQENAYOzs7jh49xuzZ7+Dm7oZEIqF7927Vznlq364dT/bswY6du5g67RWaN2+GmakZF2IukJ9fQEBAY4bdZb2xB2lA//6cP3eBU6dPM3nyyzRr3gy1Ws25c+cpLS0lPDxMr1fyXhUWFrFw0WKWLf+Ohg0b4OTkRHlZORfjL5KfX0DDhg1o2zZCe/yQIYO5cfMm+/cf4OUp0/D398PNzQ2FXMHNlJtcv34DXx8funR5AoBGDRsS0qIF0WfPMvmlKdp0/RcuxGBra0t4WBhRx4/Xut21uU8EQRAEQfj3iKBMEO4De3t75n72KTt37uLQ4cMkJSZy8aIMe3t73N3c6Nevj176+/DwMD7+6EPWrF1LQsJlbty4ScOGDZny8kvcvHmzVkGZk5MT8+fPY9PGTRw9dozo6LMYGRnh7OxE715P0qZNqPZYU1NT3nlnNit/XUVCQgIxsbGo1WqCAgPvmojipZcmExgYyPYdO7hwIQaVSoW7uxv9+/VjwID+mJmZ1e7C3UfGxsbMnv0mW7dtZ+/evZw5Ew1A/fpedOvalSef7GlwyYJ74e7hzrixYzh77hzXr98gIeEyZmZmuLm58vSQITz5ZE+dDIhGRka8+sp02rVry86du0hIuExiYhJWVlY4Ozvz1KCBdOjQXuccb731Br//8SdHjhzh9Okz2NnZ0bFjB4YPG8ZPP/18T+2uzX0iCIIgCMK/R6I2lL5LeOilp2cwYeIkli9bipub4fTpKpWK+Ph4QDPX5X49kAqCIAjVE///CoIgPH5q8nxeFfFXQhAEQRAEQRAEoQ6JoEwQBEEQBEEQBKEOiaBMEARBEARBEAShDomgTBAEQRAEQRAEoQ6JoEwQBEEQBEEQBKEOiZT4giAIgiAIgiDUOYVShUypoqBUQX6pDDsLU2wtpJgaGyE1/m/3JYmgTBAEQRAEQRCEOlUiU7A7Lp1lBxKJSSnQbg/2tGViZ3+6B7phafrfDV3+2yGnIAiCIAiCIAgPtRKZgk+2xjFtTbROQAYQk1LAtDXRfLL1IiUyRR218METQZkgCIIgCIIgCHVCoVSxOy6dlZHX9PYZG0kI9LChja8Dp67msDcuA4VSVQetfPD+u32AgiAIgiAIgiA81GRKFUv3J+pss7MwYUSED8PCvSmTKckpkeFoaYqVmRS5UvWfnF8mgjJBEARBEARBEOpEQamC2NTbQxZ9nSxZMS6M40k5TPjlpN78sgmd/OkR9N+bX/bfCzMFQRAEQRAEQXgk5JfKtN/bWZjwy7hwlh1IZMYf5wzOL5u+9r85v0wEZYIgCIIgCIIg3DOFUkWJTEFafhnxaQWk5ZdRIlPUaP6XnYWp9vsRET5EJWWz+rj+/LLKVkZeZc9/bH7Zf6vfTxBqaOCgwXrbjI2NsbW1pUlAAAMHDSCwSZM6aNmDcf78Bd5+5126dunC9OlTH8g5Nm3ewo4dO0lNTUWhUNA0OJg5cz56IOd6nC1YsIi9+/bx8Ucf0qxZ07pujiAIgvCIeFBrgP3TVPa2FlKCPW25mFbIsHBvxq84WaPzLj1whW6Brv+Z+WUiKBMea127dNF+X1paSlJyMsciI4mMiuLVV6bTuXOnOmzdo+PYsUi+//4HrK2tCQtrg7mZOfW86tV1swRBEARB4MGtAVaRyt5Q5sSKVPYjInx4q0+TKus3NTZiYmd/vt1/hTKZUmd+WXViUgooKFX8Z+aW/TdehSDcozt7jVQqFb+uXMX69X/x3fc/0L59O6RS8WtyN5FRUQDMmjmD5s2b1XFrBEEQBEGocD8CJ0OqS2Vf2crIq4T7OdK7qbvBXi2psRHdA91ILygjp0RmoIaqFZTJcbczr1WZh9V/o79PEO4TIyMjhg19HmNjYwoLC7l27XpdN+mRkJ2dDYCbm1sdt0QQBEEQhAq1CZxqO0fLUCr7qiw9cAVZNXVbmkoZEeGDRy0DLFtzk1od/zATXQCCcAcTExMsLS0pLCxEqVLq7c/MzOL333/n9Jkz5ObmYWVlSWBgIE8PGUyjRo10jr3bXK6q5gcNHDQYVxcXli5dwt8bNrB79x4yMjKxs7Ojc6eODBs2FBMT/f+Irl27xsqVq7kQE4NSqcTfz49nnhmCiYmp3rEAcrmcXbt2s2fvXtLT0ykvl2FvZ4e3jzedO3emU8cO1V6rNWvWsva3ddqfJ0ycpP2+8msqKCjkz/XriYo6TlZWFmZmZjRq1JCBAwbQsmWIXr0Vr3/JksX8uf4vDh48RHp6Oq1bteKtt96otk2lpaVs2bKVQ4cPk5GRiUqlwt7ODv8G/vTo0Z1WLVvqHF9eXs6mTZs5fOQoqampAHh716d3r1507dpFr/6YmFgOHzlCTEwsWVlZyOVyXFxcCA8PY8jgwVhbW+kcX/keGDVqBCtXrebMrXtn7JjRDBjQH9DcV3/9/TdnzpwhKysbU1NT3NzcCGsTyoAB/bG0tDTQlhjWrP2NhITLSCQSgoICGTNmNN7161d7jQRBEITHQ20Dp9rM0bozlX11ajLU0MJEiqOVZkjlnVkXDQn2tMXW4r8Tyvx3Xokg3Cfp6ekUFhYilUrxcPfQ2ZecfJV33n2PgoIC6tWrR9u2EWRmZhIZGcWJEyf532uv0r59u/vWli++/IpTp07TrGlT6nnWIzY2lvV//U12Tg6vvfqKzrEJly/z9tvvUlZWho+3N94+3qSkpPLhR3Po1etJg/V/+dXXHD16DAsLC4KCArG0tCQ7O4e42DjKSsvuGpT5+fnRtUsXTp85Q15eHm3bRmBhbgGAg4M9oOlFe+utt0lLT8fFxZnw8DAK8gs4e/YcZ85EM27cWAbeCkwqU6nVfPrpXGJiYwkODsLXxwcbW5tq26NUKnn3vfe5dCkBW1tbmjYNxsTEhOzsbE6dOo25mblOUJaXl8d7739AcvJVHBzsCQ4OAjVcvHiRBQsXcfnyZSZMGK9zjp9XrCA5+So+Pj40b94MuVzOlSuJrF//FydPnmTe3M+wsLDQa1t+QT7/e30mKpWSwMBA5DI5ZmZmgCbQm/PJpxQXF+Pq6kqb0FBkMhk3bt5kzdrfCAsLw9/fT6e+EydOsGnzFho2bEDr1q1ISkrm1KnTXLqUwKKFX+Pg4FDttRIEQRD+++534FRZ5VT2NWpLDYYaVswvm7Ym+q71TercANP/SJIPEEGZUCEz897LWluDgYdQALKyQK2+t3otLcHK6u7H3SelpaUkJSXzw48/AtCr15M6vR5qtZovv/qagoICBj81iFGjRiKRSAA4evQYn8//goWLFhMY2ARHR8d/3J6MzExMzcxY+u032gfs9PR0Xn3tdQ4cOMjQ55/Hw8Nd27YFCxZRVlbGc889y7Chz2vr2bp1G8uWf6dXf3p6OkePHsPVxYUvvpiPbaWARyaTkZiYdNc2RkSEExERzuzZ75CXl8fYMWNwc3PVOebbb5eRlp5Op04dmTZ1iraHLzY2jvc/+JCff15Bs6ZN9YKOrKwsTExMWPLNIpycnGp0zWJiY7l0KYFGjRryyZyPMTW93UNYUlJCSkqqzvELFy0mOfkq/fv1ZfToUdq25eXl8dHHc9iydRuhoa1p1aqVtszzzz1HkyYBWFW6N+VyOd999z07du5iw8ZNPP/cs3ptO3XqNBER4fzvtVd12lVYWMjcefMoLi5mzJhRDBwwACOj239kLl6Mx9FRP8DatHkLs2bOICIiHNAEpJ/P/4JjxyLZum07w4cNrdE1EwRBEP67HkTgVKFyKvuaqMlQw4r5ZSMivKsdcjkiwuc/lXkRxJwyoYKr671/3QpiDAoMvPd6P//8gb/sgYMGa7+eHzqcN9+azc2bKUwY/yIvvjBO59jzFy5w9epVXFycGT58mDYgA2jXri3hYWGUlZWxe8/e+9a+8eNf0OnxcHNz44lbGSFjY2N12nb9+nXc3dx47tlndOro06c3jRvrDqsEyM/XfHLm5++nE5ABmJqa0qRJwD9uf1paGidOnsTc3JwJ41/UGXIZFBRIryd7olKp2Lptm8HyI0cOr3FABlBw6zU1adJEJ/ABsLS0pGHDBtqfExOTOHXqNI0aNWTcuLE6bbO3t+fllyYDsG37Dp16WrdupROQgWbI6wsvjMPY2JjjUccNts3ExIQJ41/Ua9euXbvJzy+gVauWPDVokE5ApnktAdjb2+vV17FjB21ABpolHZ4eMgSA2JhYveMFQRCEx8+DCJy0x95KZV8TtRlqaGkq5a0+gSwa2lKv/mBPWxYNbclbfZpgamx0z2ujPYxET5nwWKucEl+ukJORkUlCQgK/rVuHu7s7rVvf7iGJjY0DoH379gYzMj7xRGeORUbqBEv/hFQqpVlT/XWoPD09AcjNzdVrW7t2bTE2NtYr06ljRy5dStDZ5uVVD3Nzc06dOs36v/6mc+dOON2HHr7KYuM07WrVqiU2NvpDD5944gk2bNykbX9lEomEsDZtanU+Pz8/jIyM2LtnL9716xMREaEXcFaIjo4GIDwsTC8QAvD398fc3JyEhAS9fdnZ2Rw/cZKbN25QUlqKWqXpDZZKpaSkpuodX1GfoQDz7NlzADzZs2eNXmOFkJAWets862nujZxK94YgCILw+KoInB7EHK0HOdTQ0lRK76budAt0paBUQUGZHFtzE+26ajKliq0XUu97iv+69Gi1VhDuM0PJNxITE3lr9jvM+eRTFi78Gq96mvW2cnJyAHB1ddUrU3l7TnbOfWmbvb29wQCrYr6SXC7Xbqtom4uLS7Vtq8zS0pKXX5rMN0u+ZcWKX1ix4hc8PT1p1qwpXZ7oTGBg4D9+DTk5uVWev/L2iuyNldnZ2RlMZlKdevU8GT1qJL+uXMU3S77l26XL8PauT/PmzenWtQu+vr7aYzMyMgBYuWo1K1etrrLOytcZYMOGjfzy60oUCkWt2ubi4mxwe1Z2FgDu7u61qs/ZSb8+y1v3huKONguCIAiPpwcZON3LUMPbC1jLySuRY2dpgo2ZCUXlciQSsDE30bahYqHrMrkCJytTLM2MMTHSBGQPIsV/XXt0WioI/xJ/f3+efLInf/+9ge3btvPiiy/UqFyl0Yw1plJX3cVudC8V1lKnTh1p0aI5UcePE33mLBdiYtixYyc7duxk4ID+jBs39oGev7qXaFrLgKzCoEEDad++PVFRUUSfPUtsbBwbN25i06bNvDBuLP379wM0iUQAggIDaxwQxcfH8+NPP2N1K6Bt2jQYBwcHbfA4ZuwLOj2Yuq+ndkNI7kbyL9wfgiAIj4PbgYKC/FIZdham2h6ZR33O0oOeo1Ux1DDcz4mlB67o9VpN6tyAboGuWJpKNQtYx6az7KB+79aYdr6E+TmycHcsrz/ZhEMJmdrjjI0kNHazplk9O/o0dadFfQcOJ2Td0RI1cPvv4t3WRnsYiaBM0LjVa3BPrK2r3hcX988SfdQRt1s9OJWHolUk78jMMJwUJePWdken20MApSaaX7HSslKDZbKy9HuI7oXjrXlnmVUkbMmoJpGLnZ0dPXv0oGePHqjVas6cOcPn879kw8ZNdO/eDW9v73tv160EFVVfM819V5t5YzXh4uJMv3596devL0qlkkOHDrNw0WJ+XvELXbo8gbW1Nc63zhkeHsagQQNrVG9kpGaR7OEjhuulyy8vLycvL6/WbXV2cubGjZukpaXh6+tT6/KCIAjCvSuRKdgdl/6fGgZ3p9oETlUqKYG0NKh4LmrfXqf+iqGG8u9+xGT1SqQKGVK5DMlyGZLyclRyOWVlCpqXq1gkkaCWSFAjQWlkRJnUjFITM8pc7Hnz4C6+2nuZH44kYWdhwstdGjIs3BtZeialMXFY56lRWqr5c2IEr36/mmD1IbranmBnfgTfZQ3WaXJtU/zXtUf7LhPunyqGvf1jzoaHbD3s0tPTATA3v52BKChIM5zvyNGjjBw5XG9o4f4DB24dF6TdVhEs3Zn1DzRZ9xITa7Z2yN1UnPPosUhGjhyhN0fq0KHDNapHIpHQqlUrQlu35uChQ1y7dv0fBWVBt4ZAnj5zhqKiYr01vPYfOHir/f98qGRVjI2NeeKJzmzdtp34+HhSUlJp3LgRLUJasGr1GiKjomoclBUVFQNoA7rKjhw9ivoePoBo0aI50WfPsnPnLp3EHYIgCMKDVSJT/CeHwRlS5RwtiQLTmzeQHorTBFyVvyqCsNRUKKg0Jy00FE6c0KlfWtGrmJkCRw/pnd8IcLz1VRXZTSlbL2VjZmKEr5MlK8aFcTwphwm/nMTzwE6+2/QxBAEtQd0Sfqk0yMUrL52duRFcdfDUbqttiv+69miEjoLwL0pMTGTHzl0AhLZurd3erGlTfHx8yMjIYPWatToP4MciI4mMjMLc3Jzu3bpqt7u5ueHi4szVq1eJqpSVr6ysjG+WfEtJScl9aXPTpsF4edUjLS2Ndb//obNv+44dxMfHG3ydx45F6s2ZKiws5NKlSwA4/8Og2t3dndDQ1pSWlvL9Dz/ozMO6eDGe7dt3YGRkRJ/evf/ReSqcO3+e6LNnUal0h4Wmp6dz48YNJBIJTs6agCqgcWNCWrQgLu4iS5ctN/heJCUlcfr0ae3PnvU069bt2r1b57Vcu36dX3759Z7a3KNHd2xtbTl1+jQbN27SC+zi4+PvqQdOEARBqJpCqWJ3XHq1Q/pAMwxuT1zGI5vRrzKpsRGWplLc7cxp/MVHuAf4YmlngzQoELp3h5EjYeZM+OorWLsW9u+H+HjdgAygur9Jt9bfvBelJmZ8dyiREW19+GVcOMv2X2H5pk1EyFbwesQvsByYCfQAyR2PJx4e2fjIU/TqLCh7dOZYPxqhoyA8IAsWLNJ+r1AoyMjM4NKlBFQqFW3ahPLEE521+yUSCa+9+gpvv/Muf/zxJ5GRUfj7+ZGZlUlc3EWMjY2ZOuVlvTXKnn/uORYt/obP5s4jODjoVka/y1haWhAeFkbUccMp1GvDyMiIadOm8u6777NmzVqOHj2Gj7c3qWmpXL58hd69e7Ft23adMhkZmXw2dx5WlpY0aNgQBwd7iouLiYmJpbS0lDZtQu9LWvyXJk/izbfeZt++/cRciCGgSQD5+QVcuHABlUrFuLFj9NYou1fJScn88ONP2NnZ0qBBA2xsbMjPLyAmJga5XE7fvn10Mky++up0PvjwI7Zt287Bg4fw8/PD0dGBkpISkpOvkpWVRf9+fbXrlHXr2o0NGzZy4sRJXnp5Kg0bNqCoqIiYmFjCw8JISEiodqioITY2Nsyc8TpzPvmUH378ic1bttKwYQPN4tE3bpKamspXX35hMC2+IAiCcG9kShVL99dstMojMwxOLoeYGE0v1qlT4OkJ775r+Njycrg1KqjW8vIMzMMzwdrcBLXKCMM5j++uxMScm+k3sE5NJvvyZqYW7sEjoIbTPIzAr34KB8t1N9cmxX9dE0GZ8Fjbu2+f9nsjIyOsrCwJCgqiyxOd6dq1i94wQF9fH776cj7r1v3B6TNnOHrsGJaWloSHh/H0kCEG1wPr3r0bEomEDRs2Ehd3EWtra9q0CWXUyJH89NPP9+21BDRuzNzPPmXlqlXExsSSnp6Oj48Pb89+EzMzc72gLCCgMcOHD+P8ufPcvHmT2NhYrK2t8fX1oXv3bjzRuXMVZ6odJycn5n8+jz///JPIqOMcOxaJmZkZzZs3Z9DAAbRsGXJfzgPQpk0ohYWFnL9wgaSkZAoLC7GzsyUwMJA+vXvpDQ+0t7dn7mefsnPnLg4dPkxSYiIXL8qwt7fH3c2Nfv360LFDB+3xtrY2zP98Hit++ZWYmBhOnDiJq6srw4Y9z6CBA5k06aV7anezZk1Z8PWXrP/rb06fPkNU1HEsLMxxdXVj2NDncfeoXWZGQRAEoXoFpQpiU++eJh4e4mFwhYVw6BDs3QvHjsGZM1BaaQ57ixZVB2U+9ziH2dISlYMjW8+nsOxgkt78tOm+LQj9bD6bL+bQ1N+VYD8XMmTw7rZLSNRqjNRqjNSqW9+rkKqUmCtkWMjLaOx3jdNBwzE6rcYWoCb5sbKA06CMlrDuyR6Vc30Q7GmLjflD9p5VQ6K+l0kQQp1LT89gwsRJLF+2FDc3w+nGVSqVdthaQECAwbWYBEEQhPtP/P8rCA+3+LQCnvxaf+5TVXa+2onGbvfaB1S9Gmd/VCohMhK2b9cEYsePQ3XLs5ibQ1ERGFhehz//hKefvv2znR3UqwceHoa/3N3Bw4MSMws+2XaRlZHXtFkRrc2kFJUruJRehFKlZni4NxM6+TNg8RFe7ODHkNZetPtsLwBGKDVxk5FUr6y7cTpHAsdVe62UaiMuFPhzLq0xyUkecBOmNrbg6NmrvNRpos6x859pTqC7LQHuNv9aL2dNns+r8uiEj4IgCIIgCIJwH9hZ1G6Zkgc1DO6u2R99bLA8sA82boRNm6A2Q+TLyuDqVfD319/XoYOmPh8fzZet7V2rUyhV7L6QyqazqdqsiGUyJTklMhwtTTEzMWLN8eusjLxKS297hod7s3DPRcYE5jDbfxu+qhOEW8Www/5r2nUcbrBsZo4vLupknfPelLlwsLAlB4tacbSoBfnKW8Gxp+ar96S2/LT9IiTfXpJmeLg3bXwdmfnHOX4a2+bhH3qKCMoEQRAEQRCEx4ythZRgT1udQKgqwZ622Frc/0fmmmR/XH5zFz1XLqhdxf7+EBYGISFgZWX4GDc36NevVtXKlCo2RqewcUp7bVZEvfXG2tZn2yhrDh3+kqkeCUwM3I/N/mLGV1o9qZn0NBN+CTC4VpmtXV9UV39if24Qh24FYlfKvdAZl3gHB0tTisoV2nrGtvelja8jo388TnJ2ycM59NSAh7+FgiAIgiAIgnAfmRobMbGzP9PWRN/12EmdG2B6n3taKrI/rjl+nUAPG70hgBXmmQXQs7qKTEwgIgK6dIG2bTXp6h/QckSFZQre7RfM0gNXWH1cE0hKUBFonkyE9XnampwjLO4CdtJinjMGMsDCwMhJo8wDqNTPY2wk0b7WmJQCZvxxjkthQ5nefy7Lfj1HVHbOXdsU7GmLq60ZHw1sisOtHrfVUdf4aHMc+aWazIsFZXLc7czvUlPdE0GZIAiCIAiC8FiRGhvRPdCNERHe1abFHxHh80AyL8qUKnKLZBwZ1gjTX34mecSL2Lm76AwBzC+Vc9mpPjed61Ev6+btwsHB0KcPdOumGYZYVW/Y/aZWE5WUzerj13jKfi+97Y4SZnUBe2lRrappJI1nwbPOWJg3Y+3xVFZG3tAGUN8dz6NZg3ze6RtIv8VH7lrXhE7+yJUqvJ0s2Xo+jQW7L5FbopsG/1HJwPjwD7AUBEEQBEEQhPvM0lTKW30CWTS0JcGeunOqgj1tWTS05YNZOFqhQLJxEz3eeBGX5k1w/PwT1r/+Od2+PMDEX0/h62TJxint8XWyBImEjQ3aImvfEb78Ei5fhgsXYN48ePLJKgMyhVJFiUxBWn4Z8WkFpOWXUSJT/KP11qzMTfjpSDIAEdbn6WkXWeOArMDUnT9lTgxLA69EJQHLgxmwrisurmfYMCVU81pvWXbgCn4u1owM98bYSEKghw1tfB0I9LDB2Oj2MMbh4d6E1Leny/wDjP3pBFamxvz9cnuduh7U0NMH4dFopSAIgiAIgiDcZ5amUno3dadboCsFpQoKyuTYmpsYzn74T6Wlwbffovr+eyxSUrCotGt49DZWtuyjHcY3PNybFePCGLD4CHM7j6bba51rnP3xrslDAt2qDjTVKsi7ABn7oTQNQj7R7ioqk2uXEYgsasZzjruqbENSuQduDZ9E4t6BD+P289mZlXrHRKdFM3bjCCa2nszP4z5m4OKT5JfKiUkpIKOwjP89GcD/ngwgt0RGVtHthCB74tKxtzQlpL49o388Tn6pnPxSud51yy+VP5Chpw+KCMoEQRAEQRCEx5b0VvBlaSp9MHOPzpyBr7+GNWtALjc4TC0wM5kOOYkcdmoAwKqoa9oMhkv2X6nxELyaJA8ZEeFzuwdQrYL8GEjfrwnEMg5A+a0Fm41MoOlskGp64yqGGAJEFjfTqTu53IPI4mZEFjUjsrgZaXJnjj31BPuubTQYkFW27NS3tPXqwPDwEJbsT9L0hqkhKauYHw4nse1CmnbuWbCnLRM6+dOugTPjfj5BcnaJTl2Vr1tBmeLRWPT7FhGUCYIgCIIgCML9pFRqUs5//TUcOFDtoUXmRtzo14XPJrZnxL4CbaDx05Fklo8K5VBCZo2G4FUkD6kckOmvJVZI1JmDXLHdQLD0FEaZB28HYXdSySlLPYzUswdSYyOdZQRS5S4szRhMfJkvkcXNSJW76BQ1NpJgbaFm3tG5d203wIKo+Wx6fi9GEng+3AO1JI/ysiym9nBiVm8/7dyzmJQCpq+NZni4N4uHtdT2iFX205FkVr0YjqnU6JHIuljh0WmpIAiCIAiCIDzMSkrghx80wVhiYrWHRtWDZa1hXbCKYrM9TEz7QW8YX5lMyes9A2o0BE+mVLF0v+acdhYmjIjwYmiYO7L8aNRpO3ApPotVveMYyzIhrWYv5+8dq7Bs05TugW56ywh8llb1Qs+9m7qTX57P2fSzNTrPmbQzqCiikU8S/X8bTnRatHZfiHsI08NeZ8OU3oz58SzJ2SV6PYmVxaQUUCZXYW9Zu7Xo6tqj0Z8nCIIgCIIgCA+zS5fA1xemTasyICszhu9aQchEiBgPP7WCYjPNvmWnvuXA9e0MD/fSHl8sUxDu71ijIXgFpQpiUwvwdbJkw5RQXFzPMPH3Dvgf7UaDxE+wTd+iCchq4JrMjXU53dmW1pBpa6L5ZOtFJMDEzgYWojbg+Tb1ySm9e0r7ytKKUpl/7DOdgAxuzz377Njb/DyuBXYWmqGcPx1JZlRbX8xNjPSSgRSUyQ2c4eEmesoEQRAEQRAE4Z9q2BCcnCBTP/DJtJWyoLWCZaGQVU0G+wVR89n43B6WHkhCpQZXGzPMTe7yuF58FYxMySu1ws7ChBUvtOCTI7NZfnopANf9jKgvrT7rosLChxL7DmRZtcXYrQtSW19KYtMxupRJYEEZa45fo2NDJ80yAuHerIyqehmB4eHe+LtYU6SwrfIYQ2zMbCgor3ox7zvnnsWkFKBGzel3epBVJCOjoEy7Vpm12aMX4jx6LRYEQRAEQRCEh42REbz5JowefXtby5ZkTxxNvRuvIK/BU/eZtDMUywsIcLfBSCLB1sJAgg9ZPqTvg7SdkLoLii6jCn4Xa+9ZjIjwYt/VbdqArKFjQ+y8m0PKep0qrsnhLA64+D5D02bT+HS/klU7rt/am0SwZzYTOvnz9fMtKSiVYWUmRYKE4nIFs3o3IczPkWUH9bM7jm3vSxtfR0Z+H8WKF1sQ4h6i1/NlSEv3llhILbiQcaHa4+4MWnOKZHy16xLrTt7QacfETv50D6omy+RD6NFpqSAIgiAIgiDcBwqlCplSRUGpgvxSGXYWpjVLg5+aCtu2wbgq5lMNHQrvvQdNmsAbb0CnTtxIv4B8Wc3bllOai7WZlFFtfTVzyVRyyD6uCcDSdmq+Vyt1yqjT9yH1fZMRbT3pvXoYAA7mDuwYsYM/971ATwXsK4F9pZp/kxQAuZC8nEmFxsztM4+DCVlczy0F0Emo8UbvJuy9mMHyW0GYnYUJw8O9+XF0G+QqFcXlCqxu9UytjrrGR5vjyC+VsyYqlelhrzN244i7vuZp4dNYdmoZKnX1PXqVg9a41ELMTYw5fzNf55iYlAKmrb0jy+Qj4NFopSAIgiAIgiDcB/e0jldGhmbB5m++gbIyaN0aWrTQr9zERJMC395eu8nRwqFW7XM0t2eAfxZPmvyB9MheTa+YvOphfQBGOVHsOp9Ex2BrbXKNyW0mcyD5AC+e3091oc7SU9/S0bsTHw3qzJifTuvsWxV1jVAfBxIyirTXKr9UzpL9V1h64AqBHrasejGc2X9dYNuFVG5lrgdgZeQNNkzpzYRWk7Q9d4ZMDp1MR++OvLbjtWpfY4WKoLVZPVuszY2xNZcS6GHDpfQibep8zfmvEu7nSO+m7o9EWvyHv4WC8AAMHDRY52vQU0MYNmwEb745m527dqFWq+9eyQO0Z89eBg4azJo1a3W2L1iwiIGDBnP+fPXd+/9EenoGAwcNZvbsdx7YOf5tmzZvYcrU6Qx5+tn/3GurjTVr1jJw0GD27Nmrs72q+2r8+IkMHDT432ziPRs4aDDjx0+s62YIgvCQq1jHa9qaaJ2ADG6v4/XJ1ouUyBSajTk5miGJ/v7wxReagAw0vWFVqRSQAThYOBDiHnLXtoWawWofFxoe7cLIrB6YRk+HGxvuGpABqIysyM+IpUCWB4CxxJiJrSeyIGpBtQFZhfnH5tHa1wapgcjg+8NJDI/wwUhyxznVoFKryS6SseW8bkAGmuBtzI9nebPdHH4euErvGoS4h7B68Brmdp/LKzteqXY+WWVOlg70be7Jry+EUy5X8/qTTVg8tBUHZjzBy10aahOBACw9cAWZsiZXoO6JnjLhsda1SxcAVCoVqWlpxF28SGxcHOfOnef1/9XsE5tHzZ49e1m4aDHPP/csQ4c+X9fNeeCOHYvk++9/wNramrCwNpibmVPPqx7p6RlMmDiJpsHBzJnzUV03U6jG+fMXePudd+napQvTp0+t6+YIgvCIMrSOlyErI6/S1tWUrltXYr5oAZICA8HChg1w8iSEhmo3yRRK5Eo1BaVyckvkOFiaYGthgomRKTPbzWTY+mHVnre9BQw1zYSSGmRINDIFl47g0QOFSzfKbJozsESB2liT8TDYNZgSeUmtUtLnleXRM9idred18+VXpOavGDJYmbWZlJwSWZX1JmeXMHDxSYaHh7Dpub0UywsoVxXiaOGAvbkdMqWM6/nX+aLnFyzuvZjlp5fz7YlvyS3LNVhfS/eWuNs4YWuex/Dvo/R6Ose082XjlPaM/vE4ydklxKQUUFCqeCSGMD78LRSEB+jOB7zo6Gg+/GgOhw4dpnOnTrRpE1pFyboxcuRwhgx5ChcXl7sffI+cnBz5ZvFCzMzMHtg5/k2RUVEAzJo5g+bNm2m3p6dn1FWTHjr/xn31oH2zeCHGxuJPmiAIVau8jldVjFRKnj6/h7bfjsKioIqU7lIpjB0L7u7aTcXlCnbFpvPdIf0hkS890YD+TQYwsdUEzsYuJ6oMDI3HsfUdCsVrqm6cfTNw7wnuPcC1I0gtKw3FjOJiWiEHZrYlxD0Ea1Nrskqyqn2td8opzcXJyvDaXrklMoMZDYvKFTjeZT0wzXDHJJYeSKJLgAsLhzZl39UDvLf/bU6n3h4uGeIewrSwaZwYf4Jeq3pxOeeyXl0z283kaEI+r63TBJt3Lo79xvrzPN+mPivGhWkXli4ok+NuZ16bS1EnxF8wQagkJCSEJ57ozJ49e4mMinrogjJHR0ccHR0f6DmkUileXl53P/ARkZ2dDYCbm1sdt+Th9W/cVw/af+meFQThwahYx6sqHZLOMHvfDwRmJhvcrzYyQjJyJLz7rmY44y0l5QrmbIlj9XH9HriE1Gx+37QX74QYlrAfo/owvLgxq1MuaY8JcQ/hlfAZdK7fi+tbDlFfeiuToLm7JgDz6AHu3cHCQ6fuiqGYlXv+KpJrfBU1D2dL55pcFi1HCweyi9MN7nOwNKWoXKG3/VJ6EeamxjqLSlfFxtyEDwY15vVd/2PpKf05ZtFp0YzbOI6JrSeyffh22nzXRqfHbHLoZHo26EuXz4/dXhw73INieQHZJTk4WTpiKbVl7fFUoq/naReWtjU3kMHyIfTIBmWXL18h+uxZEhISSEhIIDtb82nGhr/XV1tuz569bNu+nevXbyCVSglo3Jhnnn2awCZNqiwTFxfH77//SfylSygUCurX96JPn97aoW+GZGVlsWr1Gs6ciaaoqAgXF2c6duzI00MGY2pa9yuMK1QKbhTcuPuBDykvWy+kRg/m9vX382MPkJWVrd02cNBgXF1cWLJkMX+u/4uDBw+Rnp5O61ateOutNwAoLy9n06bNHD5ylNTUVAC8vevTu1cvunY1fK/ExcWxes1vXLp0CWMjIxo1bsywYVUPKVywYBF79+3j448+pFmzpjr7ysrK2LJ1K0eOHCMlJQWVSoWzszPNmzejf79+1KvnyezZ73AhJgaAtb+tY+1v67Tlp02dQrduXe86rG/fvv3s2LmT5OSrKJVKPDw86NSxAwMG9Ne7tyu318hIwpq1v5GQcBmJREJQUCBjxozGu379at4NXYmJSRw8eIhz586RmZVFSUkJTk5OtGoZwjPPPoNTpcBizZq1Oq9vwsRJ2u+bBgdrr8OFmBideVN3DpErLCzkr783EBV1nIyMDKRSKQ0a+DNwwAC9oL3ytZs9+03WrP2NyMgosrOz6dO7Fy+++MJdX2NN3kcAmUzGgQMHOX7iBFevXiM3NxcTqRQfX1969+5Fp44danxdq7uvANRqNZs3b2H7jp2kp6djY2ND27YRDBs6FGtrqyrrkivk/PnnXyQlJlJcUsKqlb9ibW1FTEwsh48cISYmlqysLORyOS4uLoSHhzFk8GCdOivqA9i7b5/2e0BnCG7F7+h33+mnODt58hQbN23i8uUryGQyXFxciIgI1zsX3L5vpk2dQoMGDVi5ahWxsXEoFAoaNmzAyJEjqv17IQjCwyu/1PAwu4ZZ13hr3490TTxpcL8KCZsCO2Hz6Yd06tdBJ2mETKFkZ2y6TkBmaVTKEzan6GV3lC42J7AxLoXy2/UtCXmKdwdNJqc0F0cLByyltqyJSmXghpMMtOyLjXEJY59+Gef6YSC5YyLXLVUNxaxIrhF18yiWJpa1SklvZ27Pzhj94Y7BnraYmRgRn1aot0+pUrM66hpj2vky449z1Z5jRIQXB65tMxiQVbbs1DIivCKYFDqJTw9/Soh7CK+3ncmAgP68ujYOB0sTfh7Xgv3XttH/t+E6ry/EPYTpYa/T2rcP4f5OHErIxNbi0Qh3Ho1WGrBu3e9EHT9eqzLff/8DmzZvwdTUlJYhIcjkMqLPnuVMdDSzZs4gIiJcr8zRo8f4fP4XqNVqgoOCsLG14dy58yxYsIiryVcZO3aMXpnU1FRmznqTgoICfLy9CQoK5MrlK/z22zrOnTvHRx9+gIlJ3UbtNwpu4LfAr07b8E8kTU/C1973gdRdWqpJB2tyx2KNKrWaTz+dS0xsLMHBQfj6+GBjawNAXl4e773/AcnJV3FwsCc4OAjUcPHiRRYsXMTly5eZMGG8Tn0nTpzk08/molQqadSoEe7ubiQlJfPWW2/TrYogrio5OTm89/4HXLt2HWtra5o2DcbExIT0tHR27NiJp4cH9ep50rJVS5QqJXFxF/Hz9cXP7/Y94OHhUc0ZNJYs+ZYdO3dhampKs2ZNMTMz48KFGH5duYrjJ07y0YfvGxz2eOLECTZt3kLDhg1o3boVSUnJnDp1mkuXEli08GscHGqWmerP9es5diwSXx8fAgM1D8ZJScls276DyKjjfPHF59rAzM/Pj65dunD6zBny8vJo2zYCC3MLAOp51cPG1oZjxyKxt7enVcuW2nMEBt1+4L55M4V333ufrKwsXF1daRkSQmlpKfGXLvHxnE8YM2YUTw0apNfOcpmMt2a/Q2ZmJsHBQTTw98fa2vqur6+m7yNARkYGi79ZgqOjI/XqedKoUUPycvO4GB9PbGwsN2/cuG9zBpd/9z07d+6iaXAwPj7exMTEsmXLVmJiYvj0kzlYWlrqlTl48BC7du+mYcMGtGrdirS0NO2zxc8rVpCcfBUfHx+aN2+GXC7nypVE1q//i5MnTzJv7mdYWGjeq8CgJuTm5XLmTDTu7u4EBQZqz1H5/q3KH3/8ya8rV2FsbExwcDC2tjbExV1k/fq/iIyM4tNPPsb+jkn5AJevXGHZ8u9wd3enZcsQbt64SUxMLO+++z7zP5+Lj4/PvV1MQRDqjJ3FHR+Kq9W8v3sZI85sRVpFKvaTgeH80H8iWX4BuJeY01al0gnK5Eo1yw8mYmdcSE/bSJ60O0pH62jMjORVtiP57HreONrn1pC7AuLTCrVJMn4p7U+wpy0vu7euMiCDqodiViTX+HncR2SVXOWV8FcYs2FM1RflltfbzuR0ciEKA5dhbHtfVkdd00vkUWFl5FW2TutY7aLSxkYSxrSvR4+V1c+rq7AwaiH7R+/npTYvY29uh0IppbhczYnkXDZODdVZHLuy6LRoxm4cwcTWk5nXfR6zngzQLCvwCHhkg7KAJo3x8fWhUcOGNGrUkPETJiGXV/0LEH32LJs2b8HGxoZ5cz/F01PzYHPxYjyz336HhYsW07RpU51PTQsLC1m0+BtUKhVvzJpJ27YRgOYB/I03Z/P3ho2EhobqfbK8YOEiCgoK6NevL+NvfTKuVCqZ9/l8IiOj+OOPPx+LBAuPIrVazYmTpwDw9fXV2ZeVlYWJiQlLvlmEk5OTzr6FixaTnHyV/v36Mnr0KG3QnZeXx0cfz2HL1m2EhramVatWAJSUlrJo8WKUSiVTp75M927dtOf/5deVrF//V63a/dXXC7l27Trt27dj6pSXtQ+0oOm9KS0tAeDpIYNxsLcnLu4i4eFhtboPjx49xo6du3B0dGTOxx9qf4eKi4v5+ONPiI2LY/XqNQY/qNi0eYvOBx9KpZLP53/BsWORbN22neHDhtaoDU8+2ZPxL76g8xCtUqlY9/sfrFmzllWrVjNt6hQAIiLCiYgIZ/bsd8jLy2PsmDG4ubnqXJdjxyLxqlfPYPIIpVLJ3Hmfk5WVxejRoxg0cABGRpr/2FNTU3nv/Q/55ZeVtGrZUu8BPSEhgYCAAJYt/VavJ6Y6NX0fAWxt7fjgg/do0bw5kkp/uNPT03n7nfdY9/sfdO3aVec136v9+w8w97NPadiwAaD54OKTTz7j3PnzrF69xmAP4M5du3j99dfo2EG/x+75556jSZMArKxuXxu5XM53333Pjp272LBxE88/9ywAPXv0wMPdgzNnogkKDKxVoo+EhARWrV6Dubk5H374PgGNG2vP9dVXCzhy9ChLly3njVkz9cpu3bqNF198gf79+mq3ff/Dj2zatJn1f/3Nq69Mr3E7BEF4ONhaSHWH2UkkmClkBgOyZA8/HL9diEfnbrwuV1JcrsDF2gylEtJLSkEiwVZaiiz5b14z+5ZOgWcwNdIf3mewHaZKEq9kU642PHJqUucG1QYSpXIFOcWyKodiJmeXMHjJKd7s3ZghzYcQeSOy2t6pSa0n069xP/ou0O/sGBHuTRtfRz7aHFdl+fxSOedv5vFaz8ZVLir9SvdGlCkLa5V4pEBWSHqOBZM3nWFomA/Nvez0FseuyrJT39LZpxNPNRn8SKTDh0c4Jf6QwYMZPmwoYWFtavQp+8YNmwB49pmntQ+TAE2aBNCr15MUFxeze/dunTK7du2mpKSE8LAwbUAGYG9vz+jRIwHYsGGjTplLlxKIi7uInZ0dY0aP0m43NjZm8qSJSKVSNm/ZilKpu+ifULeUSiUpKSksXLSY+Ph4TExM6Na1q95xI0cO1wvIEhOTOHXqNI0aNWTcuLE6vaD29va8/NJkALZt36HdfvToUfLzCwgODtIGZAASiYThw4bqnaM6ly4lcO7cOezs7Jhyx4M8gJubq16AeS82b9kCaIaMVf4dsrKyYuLE8UgkErbv2IlMpj88pGPHDjo90cbGxjw9ZAgAsTGxNW5D82bN9Ho1jIyMeP65Z3FycuT48RO1eUnVOnHiJFevXqVt2wgGPzVIG5CBpldx7NjRqFQqdu7abbD8+PEv1Cogq+37aGtrQ0iLFjoBmeY4N555ZggqlYoTJ+7P9ejTp7c2IAOwsLBg/IQXkUgk7Nq9x+B7Hhra2mBABtC6dSudgAzAxMSEF14Yh7GxMcejajcKoipbtm5DpVLRr28fbUBWca4JE17E1NSUqKjjZGbqT4YPDGyiE5ABPPvMM0Dt7llBEB4epsZGTOzsr7Pty44jKTa5nQQi08qeN56cwrlN+0lq1Z4Jv5yk2xcHGLD4CG0/28tzy49xMCELs9j3kf7tiUP0OLrbnrhrQHayOJA5KeO4GHqamSW/VhmQjYjwoVuga5WBRIlMwcrIq6Tmlxncb2dhwstdGrJpagdaeTuRnKlgXo/PWTNkjcGU9KsGr2Fej3nM3ZqoXTgaNIHU/GeaM6t3E/44eYP80qo7PoaHexPoYcv/1p2lY2MXlo8KZc9rnfljUlv2vNaZZSNbk1lYzo38GmSVrCSzOId6DhaUlCvpFuiKlZkxQ8M9WHT8S5q7NaeDdweauzXHWGJssPznR+ehUFfd7ofNI9tTVhvl5eWcO38egHbt2urtb9e2LZs3b+HEiZMMGjRQu/3kqVNVlglt3RpTU1POnjuHTCbTzqWpKNOmTajeEEV7e3uCAgM5d/48sbFxBuduCP8uQ2swWVhYMH36VDw83HW2SyQSwtq00Ts+OjoagPCwMJ0H9wr+/v6Ym5uTkJCg3RYbq/nEydBDq1QqpV27tmzatLlGr+HsWc2nTp06dsDyjgf5+0WhUBAfr5mU3LlzJ739vr6++Pr4kJScTFJSEgEBATr7Q0L0F9j0vDUMLyfXcNrbqhQUFHL8xHGuXb1GcXEJKpXqVhuVFBYWUlhYiI2NTa3qNKTifW0bEWFwf3BQEIDO+1rBwcGBRg0b1up89/o+xsbGceHCBbJzcjTBkfr2NU25NbfxnzJ0n3rXr4+vry9JSUkkJibRpInue27od6Wy7Oxsjp84yc0bNygpLUV9a1yMVCq9b+2u+D0zdM/a29vTMiSEqOPHibt4ERcX3dcYEhKiV8bW1gYbG+ta37OCIDwE1GqkxkZ0D3RjRIS3di5WprUD30Y8zcvHfue7NoNYFj6EpzoFEtHIlR5fHdQLRmJSCpjxxzm+aa2iL4YDIwC52phjRc3Zkd+WnQURZCo0Q+tfcGvClC6FzNsRr9ebNKlzA7oFulaZvr1iHtn60zfpGqCfvMrXyZIV48I4npTDhF9Oauu3szDh1R5t2DvqIMXyAnJKcnGwsMfe3J6E9DLWRKXxUtdGvNDRH7lSjZWZMVIjI345lsyw7yJZPKwVvs5W/HgkSa/NY9v70sbXkdE/Hud6bikmRhIm/XoShUqtzYgYn1ZIgLsNXw+r3bBvS6ktv5+8wa8vhlNUrsDGTIpUqmDr8K2UyEu4WXgTZ0tnLKQWBlPpn0k7Q15pLtamNf+AtC49FkHZzZspyOVy7OxscXbWz0TToIHmU5Pkq1d1ticnJQPg38D/ziKYmJjg7V2fy5evkJKSov0Eu6JMA3/9MhV1nTt/nuSrV0VQ9hCoSNYiMZJgaWGBj48PbdtGGJz/Y2dnZ3AuYEaGJrX6ylWrWblqdZXnqjy8NidHk5imqhTkbq41H3KWdSu7oLu7+12OvHeFhYUoFApsbW0xNzecVtbV1ZWk5GRt0p3KnJ30f+8qAg9FNcOO73Tw4CG+WfItZWVV/yEsLS29L0FZRobmE70vv/qaL7/6usrjCgr0Jz67uNQu4xXU/n0sLi7ms8/maT9wMqRifuQ/5epq+D51dXUhKSlJez9XVt012LBhI7/8uhKFomZDfe5VRbtcq/h9qnhdOdnZevucq+ittjC3oLCw6D61UBCEBy4xEWbNgg4dYPp0LE2lvNUnkHA/J5YeuEJMSgHftxnEn0274dikAR938KdzgAuDlxzBTnmNfAzPt/7wbDB9giRIKiW3L1eZsL+wNdvy27O3sA0FSt1niWBPW2zMTQjzc2TVi+HklsjILpLhYGmKtbkxp67mcvpaDq28HQ0GZhXzyAxlPLSzMOGXceEsPXBFLwtkfqmc9zdeZMHuK3wzrBUh3k3ILZZzMbUUR0tz+jTzZE3UNTIKy/hgQDA3c0uwMTdlSCsvBoR4Amq6NHHhiQAX8krl5BZr2mxmYsTqqGt8suUibnZmPN26Hmrgfz0aM3dHPKev5aG89YHbpfQirExsa5V4xFJqy1e7j9LG154mHjZIpQqySvJILUzFwcKB+rb1WX56OYevHWZMizEGU+nnlOXhZfdoZOd9LIKyzCzNw1VVQ8LMzc2xsrKiqKiIktJSLC0sKCkpobhEM3+jqj/OTk5OXL58hYyMTG1Qpj2Xs+EyFXVlZog1kh4GtZmfYlpFchaVWvMfTlBg4AMNjB56Vc9H1htidy8yMjJYsHARAC++MI7Q0NY4OjpqE4vMnPUm8fHxqKuYiFxbqltzDFq1aom9nX2Vx9na6geApiYPPsPqil9+5dz58zQNDmbo0Ofx9vbGysoSY2NjzpyJ5v0PPjS8EM6/xKSKaxAfH8+PP/2MlaUlL780maZNg3FwcNB+4DFm7Avk/ls9UdXcl/fjnhUEoQ7l5sKcObBoEchksGcPimHDkdnZU1imIMzXga5N2lJQJienWIa51BhzEyOsJPn8ufYtvnbYSEi9BHrGL+ZSua9e9ekKZ87LW9DM9DxpFp34/GIIuwoiKFRV3SszoZM/RhKYszWO1VHXaO3jwFMt69G1iRtFZQpcbMxxtDSlpFyJBLC4IzCrnNL/zoyHIyJ8iErKNpiWH3R70eZsjTO46PILHfyISy3g020XKS5XIJHA8HBfnK1NmbRSMxIswN1G2wOWll/GsHAvNk1vg0xVSJEsj7zyNFr62vLLi8HI5MasjkphZaRm+GNFuv6xG0fc9e2bHv46a6JSaevvRLCXBZsv/cW8o/P0Mi1OC5vGuJBx9FrVi6ibUXqp9B3N7e96rofFYxGUlZVqPlWvbjFcc3MziouLKb0VlJVW+iS+qnIVPQaVP42u+AS/qjJm5ma3ylT9SX9lRUXFFBcX623PyqrdgoB38rL1Iml60j+qoy552T48n3pUBNrh4WE6w1+rUzEPMjPT8PjqjCq2V3f+tLS0GpepLRsbG6RSKQUFBZSVlRnsLavoWXJyejDrXZ06dRqFQsGgQQPp37+f3v70dMNrq9yrig9xenTvbnAI8/1W2/cxMjIKIyMjZs9+Uy/7Ydp9vhaaD570h51U3L+1WeMsMlKzmPfwEcP1loooLy8nLy/v3ht6B0dHR9LT08nIzDS49EJFL7djLeZwCoLwkJPLYelSeP99qNyLn5vL1dfeYlqbkdqAxEgCT7fy4u2+Tdi/fyWO6b/S3vwoL1jcHsExxHEvn6aOM3iq1xIn8MOE3kgt3TBXXaawioAINPOuegS6sT8+g5WR1/B1smT+My04kZTDx1tiSS8oo6hcwaX0Ipq42zC+oz89g910eswqp/RfGXmVjVPaMyzMm99OXmdYuDfjVxhO6V9dLxrcHpI5IsKHWb0CeLNPIHbmJpiZGPHX6Zt0bOTE8228WX38GnGpmtEhvk6W/PVyKAeubaffWv209NPCptHVrysN6l9hQ2h7xvx4lpWRN9gyvQ+TWk+qMvGIscSY9zp/QE+/fnx8IYGlo5oyc9eMGq9rVjmVfkv3lthb2Ff5njxsHoug7FG2adMmnbWW7hepkfSBpZR/3LQIacGq1WuIjIqqcVAWHBTE3r37OHzkKL1799LZp1QqOXbsWM3P36IFK1et5uChwwwfPkwvQcSdpFLNr71SZTj9b1VlAgIaExMTy6FDh+nRo7vO/qtXr5KcnIy5uXmN0pTfi6IizZAxQz3XMTExtX6Yl5pUXAfDSXdCQlqwe/ceIqOi/pWgrLbvY3FxMZaWlgbT0R85cuS+tu3IkSN6QdmNGzdISqp4z31rXFdRkeZDJkPv45GjR1Eb6Oq823tVlaCgQNLT0zl08BDDh+umYc7Pz+fMmWgkEolYd0wQ/gvUati4EWbOhEuXDB5i89cfXHF9Em4l9nAyzsX52jqK/9jJAGkamKE3wuAp+33MSx2NEv1kEpfLvcmU27Jiexyv9wwgwt9w5sEJnfzpHuiGQqVi0d4r2FmYsPKFcC6k5BHR0JpmPiq9xY/f2xjDyau5vNm7CUXlCkplCp1FkPNL5Yz+8TgrxoXRpYkLMoWyymyMd+tFq7Ay8ioh9e1IzCxmyf4r2h40iUTCy10a0MrHnp+OJHMjt5QVL7Tgk6OzWX6XYGlGuxksPvkJG6a+T0m5MbYWJnzecz6dfDoz7+hcbTDnYO7AB10+YHiz4RTLSrian8j7T/my+dKmWq1rtjBqIX8//zdzj8xlRruZmBpV3SHzsHlksy/WhrmF5pevvLy8ymPKyjT7Kh6ELCr1BFRVrqJXrPLDU0UPQlVlyrXnMTwv5079+/dn+bKlel+fzPm4RuWFBy+gcWNCWrQgLu4iS5ctp6SkRO+YpKQkTp8+rf25Xft22NjYcOHCBfbuvb0YrlqtZvWatQazwVWlceNGNGvWlPz8fJYsWao33yo9PYPk5NvzJSt6NW7evFnjcwD07dsHgDVrf9PpzSkpLWX58u9Rq9X0erLnA1scvSIxyP4DB3ReY3Z2Nku+1V8w+G5sb/X+paWlGcyG2q5tW+rXr8+BAwf57bd1ektuqNVq4uLiiIurOk1wbdT2ffT09KCoqIhDhw/rHLdh4ybOn79wX9pUYcuWrSQm3l4Pp7y8nOXfad7zbt26VjsK4U6e9TTzM3bt3q0zp+za9ev88suvBss4OtzbPdund2+MjIzYvGUrCZdvzzGQy+Us/+57ZDIZERHh9zQHUBCEh8jp09C1KwwaZDAgU5mYkDfxZeTnzvFCjyCedDrHEu9POBo4hpkev+AhrX6Ego9Z1cmHHCxNuZxRxIDFR+jU2IXfJ7Xl2Jtd2Ta9I8fe7Mq6iW3pGeTG6qirZBZq0thP6doQCzMFafJ99P+tK02W+NL+51Y0WeLLgHVdcXE9w4YpoRxOyGRHTBorjibT7cuDqNAEeRWSs0sYsPgImYXllMkNf9BqbCRhWLg3Px1JrsmV5KcjyQyP8CHY0wZLU2N+PJLEsgNXUKohJa+U5aNC2ft6R47c3GEwIKts2allHLp2CFNjUzbGb2T7hZs0e28Hn29NoleDQWx6bi8XX0rm9PgYkqYn42zhTLdfuuH9dX06/BxKqaKYuUfm1qjdC6MWMil0EmfTz1IiL+GDzh/SP6A/ptK6XRe4Nh6LnjIXZ81k7mwDk7lBE1wVFxdjbW2tTT5gaWmJlaUlxSUlZGVn423g0+iK+ipPgndxdiExMYnsLMPnqpjM71LDRA7W1la1Sqst1I1XX53OBx9+xLZt2zl48BB+fn44OjpQUlJCcvJVsrKy6N+vr3adMksLC6ZOeZnP5s5jwcJFbNu+HXc3d5KSk0lNTaVnjx7s3LWr5ud/ZTrvvPs+Bw8d4kz0GQIDAzGRmpCWlkZScjJjx4zW9nQEBDTGzs6Oo0ePMXv2O7i5uyGRSOjevVu1PQbt27XjyZ492LFzF1OnvULz5s0wMzXjQswF8vMLCAhozLAarjd2L8LatNEm15k0+SWaNGmCXC7n/PkL+Pn50qRJABcvxte4PhMTE1q2DOHEiZO88upr+Pv7I5VKCQxsQvdu3TA2NuatN2fx/gcfsXrNWrZs3Yavrw92dnYUFBSSlJREfn4+L4wbS2ClBY3/idq8j0OGDOarrxYwf/6XbN26HWcnJ5KSk7l58yYDBvRn48ZN96VNAJ2f6MyMmW/QrFlTLC0tiY2NJTc3D2/v+jVeY65Ct67d2LBhIydOnOSll6fSsGEDioqKiImJJTwsjISEBL3hu5rlAHy4fPkKr8+YSf369TEyMiIsrA3hYWFVnqtx40YMG/o8K1etZtasN2naNBhbW1vi4i6SlZWFp6cHkyZOuKdrIgjCQ+DaNXj7bVi5kqomFG9t3I65T4wh38ma6Xs+ZrL9NqzrVd9jJFObsiM/nD9zu3GosCUYSQn0sNbOpbqUXoRSpSbY0xYzEyPi0woJ9LDF1NgIS1MplqZSPOxuJbNSqth6IZWdsemEeDsQ7ufA82FuzNj1Osuq6GWqWPz453EfM+uPeL58LkQz9PCOeWSg6TH7NfIq4X6Gh2E3drOmTFZ1L9qdYlIKMAKWjgglraAMx1sJPXKKNZ0K3b7Yz4m3OzKvFsHS38//zeDfBvP3s7v4aDOsPH6NCU80YPKvFzA3MebroUHM2j1L53oEuwZTIi+p1bpmJfISmro2pVReysRW0ygtN8b6wU/xvm8ei6CsXj1PTExMyM8vIDs7Wy/hx5Urmk+Afe9YANbXz5eYmFgSryTqzUdQKBRcu3YdU1NTnTWbfP18iTp+nCuJ+qusAyRWcS7h0WZvb8/czz5l585dHDp8mKTERC5elGFvb4+7mxv9+vXRSyseHh7Gxx99yJq1a0lIuMyNGzdp2LAhU15+iZs3b9YqKHNycmL+/Hls2riJo8eOER19FiMjI5ydnejd60natAnVHmtqaso778xm5a+rSEhIICY2FrVaTVBg4F2Hcb300mQCAwPZvmMHFy7EoFKpcHd3o3+/fgwY0L9WPSa1ZWJiwqefzGHlylWcOn2GkydP4eToSN++fXj+uWf58MPa9x5Pefklfvp5BWfPnuXgwUOoVCpUSpV27ThPT0+++nI+W7ZuI/JYJPHxl1CpVNjb2+Pv70dYmzZ06ND+vr3G2ryPT3TujLWVNet+/52kpCSuXr1Kw4YNmDRxAmq1+r4GZeNffAE3V1d27d5NenoG1tbW9OnTm+HDhuqtN3Y3trY2zP98Hit++ZWYmBhOnDiJq6srw4Y9z6CBA5k06SWD5d6YNZOffl5BbGwcV64kolKpcHZyqjYoA3jmmafx8/Nlw8bNXE64TLlMhouLC4OfGsSQIYMNZloVBOEhl58Pn34KX38NVYxMivZoxMddX+SkVzBP2h5lgfd8zI1kUE3C39hSP+qHTWdvaTemr7+KnYUJk7r4MCzcmzKZkpwSmTZIWXP8Oo3drFkddQ2VGsa19zOYO0imVLHy2DX6NvMg0N2G70e3ZvOl9QYDssqWnfqWdl4d6dU0DAnwQgc/1h2/zh8vtWVYmLfOUERD2RhB00sW7GlLflnt1upKKyjjk61xnEjWJMvQpL73Y1x7f3KLy8kvy6t1sCRXySmWFxDgbkNcaiGro64xqq0vydmF7Erconc9bM1sySqpXf6E7JJsbM1scbF0Ye72K7zY0R8Xm5qNTHsYSNSGBvA/gp5+5jnkcjkb/l5vcP+HH37MqdOneWHcWAYM6K+z77vvf2Dz5i2MHTNaZ07Q+vV/seKXXwkPC+Ott97QKXMsMpLPPptHm9BQ3n77Le32S5cSmDFzFnZ2dvzw/XKdFOp5eXm88OIEzM3NWfHzj9q5PfciPT2DCRMnsXzZUtzcDPe6qVQq4uM1PQcBAQEG19ASBEEQ7j/x/68gPCDXrkGrVlDF6KcSj3q8ETqUTYGdUEs0v3ceJpkcavICUon+EL9SlRmb8jqyOrs30aWN+eul9rjbmbM66hpPt/bieFIOPx9N1psn9kIHP3oEuTHsuyiae9kxoZM/5ibGuNnqBgGZheWUyBTEphTQ3MseK3MFT6zoWG1Q42DuwOQ2k3kp9CVUajU5pbk4WjhgZ27PxZRS6jtac/Ryts66YS93aYifsxWv/34WOwsTRkRogkmVSo2RkYT2n+2t9rIaG0lo7KbpDfzy2RAmrTxJTIruki8jwn14o08ASXkXab60ebX1VXZ47GHe2PMG87ot4sutJZxIzsXR0pTN0zpgbiqn6y+d9K5Hc7fm/Pb0bwR+U/ORKHEvx/Hevvf4bsAPtPrgAAdndsXT/sGs31qVmjyfV+Wx6CkDGDCwP6dOn2bd738QGtpa27t18WI8O3bsxMrKiu7ddZMX9OjRnd//+JOo48c5diyStm01i8jm5eWxYoVm7sPAgQN0yjRu3IjAwCbExV1kxYpfePHFFwBN8oalS5ejUCjo17fPPwrIBEEQBEEQHkv160PTpnDggO52GxtkM2cxwjycUhNTQisNNUyVu7A9vz397A9pD09W+GHffAqdf69HfqX1xJysTDGSSJjUuQGfbI1jVZThbIWvrTvL8HBvVowLo7hczsgfjrN8VKheUGZuYsTS/VcZ3c6XP0/foHeIWbUBWUPHhmwfvp2DVw/Sb00/vayGr7edSVC9AbRv6MSTwe4Ulcspk6uwNZdiZmrM9ZxiBrfy0i4gfTGtkIMzu+j1olXQBHBeDA33oFheoEklb5zD0pHNWHs8VZvOHmBl1FUGhnjg7mhf3Tukx8nSiYLyAhwtHIASXu7SkGHh3hhLoERRYPB6xGTEYGliWat1zSykFgxqMpjTyYUEuNtia/HozCeDRzgoO3nyJL+t+137c8WE8RkzZ2m3PffsM4SGaob7hLRoQf9+fdm0eQuvvPo/Qlq0QKFQEH32LGq1mmlTp+jN3bKxsWHqlJf5fP4XzJ33OU2bBmNjY8PZs+coLi5m4ID+BheAnjZ1CjNnvcmmzVs4d/4C9et7cTnhMmnp6TRpEsDTTw95EJdEEARBEAThv00igfnzoU0bzc9SKUwZAc/YoLC1YZFXe4NDDddFP0VP22Nsye/Ar9l9OV3ShD29nsDT9TT5t9K8h/s54mRtxuGELArK5AYDsspWRV0j1MeR67nFJGeX6GRHBM18sn0XMzA3NSYqKZtDCZmENqy658bB3IEdI3Yw98hclp9arrc/Oi2aEX8NY1LrycztMY81USkMaumFhamEwnIFEomECZ30g0lDc9FAk9b+53Et2H9tG/1/009rPz3sdTZM6c2YH8+SnK1JYvbn6Ru82de31sGSiZEJNqZ2zH/GmeNJObz2WzQLhgaTUphisJxSrWTZqWWadcg2Gl6SoLJp4dM4k3qGfo370ufr47zSvTHGj9hyk49sUJafX8ClSwl62ytvy8/X/UTgxRdfwM/Pjy1btxF99ixSqZQWLZrz7LPPVDmXpl27tnwy5yPW/f4H8fGXUCgU1K/vRd8+ffTW2KmgmYfyBavXrOHMmTNERt7ExcWFZ599hmeeHqIzpFEQBEEQBEG4w9GjEBEBhob+hobC0KEgzYaRjpC9BpLLMZK68vLeIKJTbs8zq0jr/uwL43l2hQ/RmbfnPueWyLA2u/0o/G7/IPbGpdPK17HKNb/u9P3hRJaPCmVXbDq2FrfrUihVlCtULD+YyLJRoYxfcRI1apwsNdlkjSXGBLsGY2tmS0F5ATEZMUxuM5kDyQcMBmSVLT31LZ19O9GneV8U6lIKyvPJLslBZeSMncQOe0tj7CxMbvdwVVrTrGIump2FiSat/ZHZLD9ddcKRSa0ns3L8HAYsOklOsYyzN/JRqJS1CpaWnVrG9PDXMDM2Y/Gey6w+fo2Xu/gRlXKIAOcGVZb99sS3nBh/ggmtJ1R7TSa2nkj/xv0xNTZj7tYrdGrsQtcmrpgYP1rDxh/ZoKxbt65069b1XykXGBjIe+++U6syLi7OTJ82tVZlBEEQBEEQHmtHjsA778C+fbBuHTzzjP4xOWdgsgJu7IbM2/PEzBUZNCnbQDS31/+sWBh5eLg3C8Y8yYDFR7TBioOlKUXlmpFWo9r64OtkxZJ9VwjytKtVtkKZQsmHA5tieisIKJEpOJ6UjY+TFUq1Wpv90NhIgrWpHQt7L2RgwEBK5CVklWThbOmMhdQCa1NrnvrtqRqdd96RefQc1ZMev/bgdOrtJXeq6uHaGZvOW30CtWupPRHgxL6r2wwGZJUtPfUtnXw6seu1vvx4+Dprjt/ExMiUrn5daxQsdfTuyIWMC/Rr3J8DF3NYffwaxkYShoZ7MPC3kfz1/F9V9rrlluXSa1Uvtg/fTluvtiyIWqDXmzer3Sz6BfQj8nIhjdzMGNjSCx9HK8xNjJCKoEwQBEEQBEEQauH4cXj3Xdix4/a2996DwYPB+NbCzRmHIGYOpO4wXAcw3vlv1uY8CeiOXVsVdY2W3vYMD/fWLoxsZWaMqbGERUNb0j3IlfwSBRamRrXOVlguV+FoZUp2sQwbMymfbIsjPq2Qmb2aYG0mJadEBkB9BwtMjCU4mDswcO1AvQBjRrsZ/DTwJ3qt6sXlnMtVnE3jTNoZMoozUKgUOtvvTKk/dXUMi4e14nhSDi+sOEFrHweWjwrFxlxJp5+H1ej1fX50Hp18OuLqGs36l3ohVxhzKuUcM9vNpJ1XO76O+prz6ee1PX+e1p70adSHJ3yf4Gz6Wd5o/y7GmLH8oCYDeWM3a4rlBZxOO33XIYqXcy7T5rs2TAqdxJ5Re8gryyO1MBUXKxc8rD0wwoyMAhl2lmaYGhsT5GGHsQTMTR+9EOfRa7EgCIIgCILw3xAVBR99BFu26O+Li4M1a6CnL5x/H9L3VFmNTCXlz9yuLM8cwp0BWYWfjiSzfFQoSw9cYWInf8xMjFk9vi1SIwnGRhKszIyZ3q0xRka1m4xkaSblf+uiCfNzws/ZipWR1wj0sMHxVk+co6WpdrjgO/vfMLjocnRaNMPXD2di64lsH76dNt+10STdqEZFCnhDNCn1O/Dz2Kf4Yudl7bDFqKQc9sdnsGCEb63S2hfLi9l3dSfXC5KYHjGdUPcOfHX8Yxwt7Nk9cjcmxibkleWRV5aHo4UjdmZ2qNQq2tfrwuvr4ni7b5C299HaTEp2SQ5QsyGKuWW55JTmkFuay5i/x4AEPK09+bbvd4z+IYrzN3UzY07s5E/3IDcsH7HA7NFqrSAIgiAIgvDoO3xYE4zt3Fn1MR1cwHge7D5f5SEqY2u+S+vJj5kDSFc4V3vKmJQCymRKXu3emBb17Xni8/2E+Tow7+kWHE7IZEdsOplF5Sx8viXN6tnqPOxXJdjTFhtzKfOfaYGZiTHjfjoB3F47TGokwdzUmNd6NNAMF7zr+mTLiPCKYFLoJD49/Gm1x1ZkNazK11Hz6d+4P2tP6CYssTKTkllkeEmBqpQpynit7WuYGpsiV8pIzZMzI+JdTE3lbL60ma+jvjbQ8zeTjl69sDaXklcq0+4rKldo59ZVHqLY0bsjXxz7Qj/hSPh0Onp31OlB/HXQar4/eKPSe6SmsdlVOspPsnxDCMeTu/FWnyaPVGD26LRUEARBEARBeHSp1bB/P3z4oebfqrR2hCnOYHoJ1JmGqzJz4aT5SIwDXubT7+Nq3IQyuZJnQr14fnkkAO8PCCYqKYuWvpY0rudEdkkOJcpMVrzQgo1nMjlwKZu0gjIupRehVOku7WsqNeLtvoGcTM4hq7Ccfi08uZlXCoBSpdYukLz2+DXGdfSky4rPa9TGhVEL+fv5v5l7ZC4qtf7aanA7q2ETpyZ08O5ARnEGGy9uRKa6HfycSTtDdmmudsHmCpWDopoyNTbluT+e41z6OULcQ3i749sEWjzJrF1vsLTKnr9hTGw9mXd6f4KR5HaClUvpRViZ2GrnklUMUXw14lUOjDlAZnEmaUVpOFk6YSG1YNmpZby24zVtz+Hk0MkMCOjPmIORdLY+RVfb43SzPYGXaQYA32UOYk5kQ8L9HOnd1P2RmVsmgjJBEARBEAThwVGrYft2mDNHk8ijKs7OMGsWDPSEE8MNH2PhibLJLLaV9eGbgyksbuMK1Dwos7EwYeqaMyRnlzB3cDPMzRSkyffx5m/z9XpoZrabyVfP96OgxAgjIwlrjl9nZeRVbM2lfPxUEK18rMkryyO7NIcm9RxBUs6fk8MZ+9MprueWarMeAhTL82s1XLBEXkJT16acSz+nt9/P3o+/n/sbBwsH5vecT3ZpNk4WTvww4AeOXT/G5C2TScpLAiCzOEcnwyToB0V3UxEAXsi4AGgCrtNppymRlxgMyCpbdupbOnp3ZGDAYJ210nbH5PBq+AxGb9C8z7lluby7/11Wnl/J7pG7yS/PZ2HUQv6I+0MbmIa4h/BmqxfpaiInY/Mg/nA9hpFTid45u9qeYE7qiyw9cIVuga6PTFD2aLRSEARBEARBeDSNGAF9+lQdkLm5adYeS06G11+Hhs+DfXPdYyw8oPVCGHCF8gYvseRgqnaIYLCn4XlVdwr2tNUsWCxT0L+5O31DnJi9dxZjN47QC06i06IZtn4Yb+yZhYpy/rfuLL5Olux4pSNbXwnjRtk+Ov3cEe+v69NyWQu8v65Pp587EpW2lS3Tw2jfwImicgUfboqhZ5Ab2SXVzw+rzFhiTKm8lA7eHWju1hxjibF2Xze/bkRPiubgtYN0+rkT3l9703JZS7y/9qbzz53JLMkkelI03fy6AeBo4aDNMFlBqVKzJiqV6WGv16g9FWntK4IjY4kxE1tP5ItjX9So/BfHPkemlDGhoz/vDwgm8s1u9AjyYEBAfya1nqRz7KXsS7RY2oLD1w7zec/PufTSBS4/9wPZPScTVU/FswlTcI59lYayPRgp9QMygAZmN/EzvUlMSgEFpQqDxzyMRE+ZIAiCIAiC8OD06werV+tv9/TU9IyNHw8WlRZVlhhBs/fg0BBNMBb0BjScAMbmABQUlWmTRlS1MLIhL3bwx9JMysLnW2FtLmFT/EaW1aCnp61XB1r7hLBwTwK9mjkyY9cMg+UqL+68dNQ8isqNKSlTkllYjqOFw13b52DuwOQ2k5nYeiISJIxqPoqpYVOxkFqw/PRyruRc4bv+3906/zKD5x/510gmtZ7E+ufWM/zP4diY2mFrLiXQw0ZnCObKyBtsmNKbCa0mVZsWvyKt/Ws7XtNuC3YNpkReUquev4LyPHo2dSG/LJ/U4iTMjC0wktnzwROf0a5+R76M/FwbGDsrc7G9+guS4u34lV7CSFlco/NU1tX2BD9k1aOgTI67nXmty9cFEZQJgiAIgiAI906p1KS09/fX9Hrd6emnYcYMuHlT83MjL3ijGTR2gA7TDNfpNQgifgLv50BqobOrTK7A2EiCUqU2uDCyISPCfegc4MKkX09x8mouR95oz9wjc2v08hZEzWfjc3sI93dkYw0CuaWnvqWjdyca2XbjmaXHcbE2Y9+MttUOF2zo2JDtw7dz8OrBKtLlz+Ttjm/zZ9yfBgMy3fMvpYNPB5b0XYKJkSmvP9kER0tTzEyMtEMw80vljPnxLD+Pm0O7+h35OurzahNsVM4EaWtmS1ZJ1l2vG9wONB0sbEkpvElqUSrOls6YS+GP+F/wsKpPH+9O9DNTYpy5A/PM/ZiW3QRuQtHNGp3DkC42J/ghaxC25ib3XMe/TQRlgiAIgiAIQs2Vl8Pp03D0KBw7BgcOQFYWLFgA0wwEWSYmMGUK/LoCZoSCzQ4o3wbXgKyp4ByhX0ZiBP5jAFAoVciUKgpKFeSXyrA1N+HAjCe0AcboH4+zYlwYrXzs+elIsnbeEtxKkd65ARF+jgxecoTk7BICPWworPUcrwJa+Tgx/aeaBXLzj83jwJh+NHSx5IcxYSRmlfNK+AzGbNCfK+dg7sCOETuYe2SuwbTw0WnRjP17DP1nZjH/6Pwanf+Lo18wMGAgneceJrtYs+5asKctY9r5snFKe0b/eJzk7BIGLj7J8PAQDow+RE5ZFulF6XjYeKBWq9kQv4Gp26ZiaWKJscQYpVoJQEF5Ac6W1We6BN1A84kVT2iDPjMJjPBsyMveQTTJuonp2dMYo662rppSqo04WRzIvsI22syYj4pHp6WCcB8MHDS4Vse7urjw3XfVfyL1XzRw0OB/5bWfP3+Bt995l65dujB9+tQHeq5/avbsd7gQE8PyZUtxc3OtUZk1a9ay9rd1TJs6hW7duj7gFgqCIDwgqama4OvoUc3XqVMgk+kft2WL4aAMYERLaLYa8ldC+e3NytOzyAzbTn6ZHDsLU2wtpJgaG2mTM5TIFOyOS2fZgUS9YKtygDFg8RGGh3uzfFQoZTIluSUyHCxNcbQy5WZeCT2+Okh+qSY4sb6HlPDlyjLyyvJqFcjll+WxaUoH5u9M4I/TN9g4tReTWk/SS44xuc1kDiQfqHKdLoABTQbU+vy5pbmE+zux9XwaoFkSYMYf5xge7s2KcWEMWHyE/FI5+aVK8kskmBo7cCX3GP4O/pQqSunZoCetPFrhbOmsHUL57YlvicmIwdLEstqev8qB5s+nlxNuBu84QhcLaGsO5kaXIav6BbJrqlBlw578VuwtDONAYSvylTYAzH/GF7nScPbKh5EIyoTHStcuXfS2xcbFkZaWhp+vL35+fjr7bG1t/q2m/SeJoEQQBOERo1TChQua4OvIEc2/SUk1K7t/PxQVgbX17W0FCRA9A25sMFwm8zCjv/6R+DJfoKJny5/ugZphkJ9sjWNlpP6wREMBxpL9V1h64AoB7jZYm0kpkSlZ9WI4M/84rw3I4N5SwjtY2JNdmlGrMjmluWQVWDOqnQ8X0wqQSsz5uMtc2np15KtbwwUrkmYMWDOg2rpcrVzJLq1dIJlTmouTlane9lVR12jpbc+rPRphb2FKS297xvx4gjB/B97s248tlzYz7+g8veGM08KmcWL8CXqt6sWyU8uYFjaNcRvH6dVvKoG5LfqSd+J/PJf2N1/7g8V9Ti1YbtWIbIf2GNfvh8y2EwknMzkQeYN8peZ9HhHuTVt/Z7KKyrG31L8GDyMRlAmPFUO9MQsWLCItLY3w8DCGDn2+Dlr1+GrcuBHfLF6IpaVlXTflgejbtw8dO3bAweHuE7wFQRAeCk8/DX//fW9lGzWC69chMBBkeXDhY7i0EFRyg4fvKghjbuoYLpd7a7fFpBQwbU00y0a2plyuNBiQVVYRYAwP92bJ/itIJBLtvhHh3pTIlNqkIBXuJSW8qZFFjZJ1VOZg4cCne67g72zF8lGhfLo1jk3nUhkeHsKm5/ZSLC+gTFGKBMlde8AyijNwsnCq1fkdLRzILjYcSP50JJl1E9uyMfomG6NT+HV8ONZmSmbtmlHlumPjNo5jYuuJbB++nR6/9mDXyF1Maj2JH08vJcwMnrCEJyygnTlY5KzUFLxPf94LlFDgEIrCrTtTzmxgS0IckAD8rJn/FvY6G6b0Zs7mKzzZ1J0eQe68/fd5ZvcJuj8N+BeIoEwQhDpjZmaGl5dXXTfjgbG1tcXWtmapmgVBEB4KnTvXKChTIeGahy+0a4dH766oO3Yiz82L/JIy3M8vxTb+HSQyw8kgyqyCmBo/il05TQ3uNzaS0LSeHeNXnKxRk386ksx3o0MxkkgYGu6NTKGkuFyJs7UplqZSXu7SUJvcAnRTwo/dOOKu9U8Pf51fj91gQmfvWgVydub2XEpL4KOBTdkZm87KKE2AuWR/EksPJBHgbsOTQW50bZ531/o2XtzIjwN+rPX5d8YYzkoZk1JAUbmCTo1dOXoli10xKagtImuw7tgy2tULZVbj9iQffpFFFrCokSlStYHhrP+ASg0ny2FnCewohsgysDa/TLsCV7am6q5LF50WzdiNIwhwCiCsXjhfnSzH3mYGarUEW4tHJ9R5dFoq3F8qBZTcqOtW3DtLLzB6sLfvnj17WbhoMc8/9yydOnVi9Zo1nD9/gYKCAt6YNZOIiHDGj59IRmYmG/5er1e+uvlSarWaQ4cOs3PXLhITk5DJZLi5udKhfXsGD34KMzOzGrUxPT2DCRMn0TQ4mNmz32TN2t+IjIwiOzubPr178eKLLwBQWFjIX39vICrqOBkZGUilUho08GfggAG0aRNao3NVtDnq+HESExPJzs5BIpFQv74XXbt0oVevJzEyuj0+oeLaACxctJiFixZr93380Yc0a9a02mukVCrZtm07e/fu42ZKCgD163vRrWtXevbsgbGxsc7xled8JSUl8ef69Vy9eg0TExNatgxhzOhRODvrTkyWy+Xs2rWbPXv3kp6eTnm5DHs7O7x9vOncuTOdOnYweC0iI6NqVH9VwzcrtzUuLo6NGzdx/cYNLCzMadWqFSNHDMfJqXafiAqC8N91Z6ILQ3OvaiQvD379VZMp8ddfDR/Tuze8+qre5gJTS6I9AzhdrwlnvYNx6NIBKxdHsotluFqbMt6lHq8u+Yk3nBdgZxVvsOoshR1rSsfj0XgKb3dwJuFWsok7NXazpsxAD1dVissV2JqZ0MjVmgm/nKx27lnF+WqeEn4ynev3YuCGk4T6OvJ625mM+GvYXdv0eruZnLtWzI9jwlED3x1M1NmvUkNKXhnPhrlTqrx7JkOZSsbR60f5X9v/MfKvkXc9/n/t/sfp5EIUVUypsrMwwdLUmE+3XuS3k9c5MLMt/dZ+bvBYE6CNuaYXrIsltI+dgIXkVmKOors2pcZuKjQB2M4S2F0C2Xe0Pa8sj60JW6ssH58dT3y25t7LKRvNk8EtMH1EFo4GEZQ9vkpuwEa/ux/3sBqQBNa+/8qpbqak8PqMmdjY2NCsWVOKioqRSo3vXrAKKpWKr75awMFDhzA3N6dhw4ZYW1tx+fIV1v62jlOnzzDn4w9rHJgBlMtkvDX7HTIzMwkODqKBvz/Wt8b037yZwrvvvU9WVhaurq60DAmhtLSU+EuX+HjOJ4wZM4qnBg266znkcjlffPkVNjY21K9fH39/fwoLC7l4MZ5ly78jIeGyTmDVrl1bzp49R1JyMoGBTfBw99Duc3Cwr/ZcSqWSOZ98yqlTp7G0tKRFi+aghnPnz7N02XKiz55l1swZOkFghW3btrNh40aCgoJo3boVly4lcOjQYa5cucLXX32pc12//Oprjh49hoWFBUFBgVhaWpKdnUNcbBxlpWUGg7La1H83f2/YwLZt2wkKCiQ8PIz4+Evs27efc+fOM2/up3pBniAIj5/qEl1UzL2yNL3L49yFC7BoEaxcCSW3gqBZs6CpgZ6qxo3B1xckElTt2nHetymzUqy55OxNPSdrPn4qiBd8rMkryyO7NAcnCzfszOwwOj2DtT7fYoR+FCBTSfkxawDfZDxHocoKki4wPNybVS9G0H/xYXKKdXtZrM2k5JTUrOfFzsKEX8aF8+m2OFZF6Q91vJhWyI9HksgtkbF6fAT9Fh4mp0RWo5Twr4TPoHP9Xoz58Sz5pXLe/iuWra/0Z2LrSdWmxZ/UejIDAvqz/VwOJbICGrhYGwwwR0R4sStxC138O+j0gBlLjAl2DcbWzJaC8gJiMmJQqpVM3jKZ6EnRTGw9sdq0+JNaT6J/owH0XXCiymNGRPiw92IGq49fI9DDhmJ5gXYIpTYIu/XV3hIsdR577k+mxHwlHCyFfaWaQCzmPna2Xc8pZ3I719p9aFHHRFAmCHdx6NBh+vbpzQsvjNPrnbkXf2/YyMFDh2jatCmv/+9V7XwjuVzO0mXL2b17D2t/W8foUXf/JKxCQkICAQEBLFv6LdbWVtrtSqWSufM+Jysri9GjRzFo4ABtIJOamsp773/IL7+spFXLlvj4+FR7DmNjY958Yxahoa2RSm//15Gfn8+HH33M3n376N69K8HBwQCMHTuGNWvWkpScTI/u3WuV6GPTps2cOnUab+/6fPThB9jb2wOQk5PD2++8R2RkFNu2badv3z56Zbdu28ann8yhSZMAAMrLy3n3vfe5eDGeQ4cO0717NwDS09M5evQYri4ufPHFfJ2kLjKZjMREwxPba1p/TezYsZO3Z79FaGhrABQKBQsXLebAgYMsX/49b731Ro3rEgThv6dEpqg20cW0NdGMiPDhrT5NDAdmp07Bxx8bHo64Zg3MmaO/XSKB8+fB2poymYI3vz3GRUUB7Rs48e3Ipmy6tIlXftZPAvGbjxONDQRkO/Ij+CR1HFdlnjrbV0VdI9zPkd2vdeKHw8k6wwuLyhU43krOYGwkobGbNdZmUorKFToLIIMmuIhKytYLyOwsTBgR4cXQcA+K5QVkl+Qgx5Sd/wvnp8M3WBl5Qycl/Kbn9qKkmMLyPGzM7DA1suHXoykM3HCS/FI5wZ62jG3vS3GZMfN6fE4n7858fmyu/npibWfRr3E/0nJVPBHgytztF3Foo59owthIwtBwD/qtHcal3BimhU3jfzv/p108ukReQlZJll7mw2PXj/F5j8/p6NOR+Ufn653/9XavMzBgIFvOZlNQpjB4Da9kFvN0C1dm/7oPz/I4VDcSWfDNGd5UwRMu0N4erP75446eAiUcLIP9JZpALLocA3fM/WFbnn33DyseMo9WawWhDtjZ2TJ69Kj7EpAplUr++utvzM3NmfH6a9pgA8DExIQJ41/k1KlT7Ny5i5EjhhvsCarK+PEv6ARkACdOnOTq1au0bRvB4KcG6ezz8PBg7NjRfPbZPHbu2s34W0Mdq2JsbExERLjedjs7O0aOHMF7731AVNRxbVD2T2zesgWAcWPH6lwjR0dHxowexZxPPmXT5i0Gg7IBA/prAybQzFsbOGAAFy9+TkxMrDZoys/XfGrp5++nl2XT1NRUp457qb8m2rdvpw3IAKRSKS++8AKRkVEcP3GCzMwsXFxEb5kgPI4UShW749LvmuhiZeRVwv0c6d3U/XavQHQ0vPUWbNtWdcE1azQBW6XEGFq3RloUlCqITS2gvoMFS0c1ZcauGQZ7iKLTognPgGsNzbFRlwGQWO7J+zcncrCotd7xFZYdTCTczwlfJ0ud4YWX0ouwMDPmgwFBdA9yp0ymJK9UjqOVKRYmRqyMvMavkVcpKlcwLNxbb+6Zr5MlP49rwf5r2+j/23D9HrCwGWyYoukBS84uYcn+JApKlUzt1ohDiQoGhLhRKlfxTKg3PYI8cLTSpNb/dv8VPtoch625lI8GdebgmH7kleWRU5qLo4UDdub2nE4upO+C4/wxuR0ypYoLKflMsmyg99obu1lre6eu5V8jemI0F166wI7LOwwuHj0tbBoxL8WgUqvo9FMnvu71NYfGHiK3NJec0hwcLRyxM7fj6LWjPP/H8zwdOJTfJ3Rl9c4zuKvTOR27iTPXT5NalkKBURbDPinjifowOgg6WIDVA+hQKlDCoTLYVwL7bwVhyvt/GoNOnzxDaRcFFo9QYPbotFQQ6kiL5i1qNSStOleuJFJQUEBIixY6wUYFMzMzGjRowMmTp0hJTcWrXr0a1evg4ECjhg31tkdHRwPQNsLAwpxAcJAmK1FCQkLNXgCQmJhEdHQ0GZmZlJeXgxpKS0sBSElNrXE9VcnMzCQzMws7O1tatgzR29+mTShWVlakpqaSm5url9mwZUgLvTKenppPaHNyc7XbvLzqYW5uzqlTp1n/19907twJJ8e7p0iuaf010bGD/vBIW1sbQkJaEBV1nLi4OFxcOtaqTkEQ/htkShVL9yfe/UBg6YErdGviijQjHd5+G376CdRVDDEzNoannoIXXtAcYygouyW/VEaghw2fPBXExvhN1Q7Zy1PB1NQyvvcw4Zj5pP+zd95hTZ3tH/9kQgKEPWWJW9ygiHvXbbW1zraOVqVLa9XOX7dtHW9bu9QOR7WOLrd1b1ScOBA3ojJlBkhCEpLfH5FITFi2fd+O87muXC3nPOc5T04wnO+57/t7M/FcD/RmWZXrLjebWBKfQuKtAqu1vadShkwsRqWQO6wRm9Qlgl3Tu/Lu5iS72jN3hYzlE1vyQfzrDmvFEjMTGbdxDJOj4lg24X1e/vkSw6ODaVfXm41n0unfPJCS0jJyivUEqJzwd3OmQGtABOy/fOduXy8D45aeQiqGPpEBeLvIyS3JYkfSGYwmyxpFIkgr0HI5qxhnuYTIIJXN+3B1kpKrybP+bMbMe/vfq9L5MC46jtc7v05haSEh7iHM3DGTHG0Ofi5+pOems2nfJspul0E+bGELuAMBIPaDNr7QPdjSJ6yTAtz+RBG2764IO/1fFGH3k6Z1pkhnxAx/m4jZ32OVAgL/Q3z+wEhFdrbFmjbxzJlqG1kXqdVQQ1FWWTQlO9titPHxJ5/y8SefVnq8Wl1U7TkMBgOfffYFBw4erHSMVqurdp7qyMuz/JHy9fV1uF8kEuHn60tKSQm5uXl2osyRQYZCoQDAaLhny6xUKnn2mTi+/Gohy5d/z/Ll3xMUFETz5s3o3q0rTZo0cXj+ms5fE/z8HL9HPz9Lc+ryayEgIPDvozxKVRMu38ql9L33UXw6H1FJieNBnp7w7LMwZUqN/rYYy0zU8VTyxag2+HuIGL91Du2dIUAC6ys5xfdFkOXakG/6vYPx2OEarV2tM6BSyKzW9hM7hTO0dTCf7rrssEYsKV3N1DWJjI0J5cOhLUgrsDUKGds+mL2pv1Vp3gGw+ORCuoV3YfHjD/PTiTREmPFQyCo1CunR2I8JHevy0k/3rOuNJqyNmSsyvmM4W85m0KWBL2UmM6sSbjKuQzgzf77nhFixV1pc2zj2pOyp1vlw4YmFtA9uz1cDvmLb+W0sWrUI0gA94AIEAq1ApIQWcospR3cFdFGAx5+QjlhkgoNaiwDbp4FTtRFhJkCHpZG4ATBW8SrPcTTf999yxPavoy4yDly5Q5MAFY0C3P4WtWWCKBMQqAa5rOonfZVhdvCU0nR3W2BgIE0aN67yeDe3mjeulsscN0Y0mS3fZG3atMbD3aPS42vSJHvDxk0cOHiQsLAwxj35OBF3zUSkUilpaek88+xzlT+Z/aOp4smuSFTzL94uXTrTsmULEo4dI/H0Gc4nJbF9+w62b9/BkMGDmDBh/O+aX0BAQOBBKdTWzPWgTVoyH277HM+cStIcfXzgpZfgmWeghi06KpqLhHkrea2vCxNMZ3guGApNcCQVshzcfZuBbRlJlBjUNApwIzmj+gd+/ipnPhvZiu+P3GTruUw+H9WK3cnZDgVZRVYm3CQqzJO+kQG4K2QUag02dVo1YW78XDqH9OWhyEAW7rvGqmOVN6l+qlNdpvZqwOjUUIfjyhkTE0rbcC+GfnmYPpEBRAapWHk0lY3PdWR0u3vHlvdKiwqMshhzrB5U9WJNQAZMf3c6brhxo+wGhAB3s+kj5RYB1l0JXRXg/SeIsOL7RNjJchFmwuLCWFLFS4tFgOnuvmr3DLPWyKc0YGn8DZY82RZ9mUkQZQJ/YZTBFgfDvyvKv0ZvK6nM8k9Iq9VaoyXl5OTYW9z63I2yBNep47CR9R9NeVSnd69edOgQ+7vmOno0AYAZL71IaGiozb6sLPsnhQ+K190Uwjt37fQdUb7P27v6dMPqcHd3p0/v3vTp3Ruz2czp06eZN/9jNmzcRK9ePe3e6x9JdvYdwsPD7baXvz+vGqRTCggI/DNxVzh+2FaRyMyr/LxyFmJHbniurvDKKzBtGri42O+vhPvNRUbUOYffvvd5wcOy31MCn/nCiCq+9gt0BagUMpoEulVq0AGWKJTJbCbu+5PMfKgxz3arh8FkZtH+mqVtfnsohe6N/ZjeuwFvbbxgU6dVE05nnkZvKuJKpsxGaDkyF/n2UArR4Z5M79OQtuFefHvI3g1zfMdw2oZ78eSSY+Rp9DYRsieXHGP5hHa0CfNgafwNktLVrE7I4KOe8wEcr9kI3AQKsVgihkJunVxyySVECr2V0OduNMzvT7ijLzbBIS3sVcO+TDiXI0VZ5o672AtvqTeNS1zJylHwdKe2bEorQuKsZErfUOK2T7SkTkoBieW/IomIht4NcZG6gAlm95jNd6e+4+fzP4MBXEWuPBT6EF2CulBQVEBuQS6Ugr5Iz/Xb2WgKc0m+dZ7cnFyLyKsyJCdC4uplTY+ViEV/ixTGv/4KBf4cxNL/mqX8PxlPT0/S0zNIT0+nXj3bQt7EM/ZfsA0a1MdFqeR8UhJFRUW1ioY9CK1atWTXrt0cTUj43aKspMTSjMRR+t6heMdpKuUujWWmmmeV+/r64uvrw507OZw5c9Zih1+BEydOUFxcTGBgoF3q4u9FJBLRpk0boqOiOHDwIDdv3vpTRVl8/GHatWtrs62oqIjExDOIRCKaNKk6miogIPDPRaWQ2tUh3U+Sfz32RUTR4/o9owuzSIRo4kR47z0ICKjVObV6IzuSLOYiKkkxbwctZljpXrtxj7nBN2pLLylHBKm8+WxkI4p1RvI0eryUcpxkYlYfu2XjsjipSwTXsotZONbSL/NWvgY3Z1mN0zaT0tXka/QMbR3MO5su4OokJV9bu9reYn0hOy9Y/kZZHBvDGB0Tik5fhtZQhqdShlQiYsXRm/x2LoNODXyp5+fCj5NjuVNcik5fhkwixkkmZlXCTd7bnGx9f/dHyAZ/Ec+YmFC+fiIanb6MEr2R+v7hnM8+f29BJiALi/BQARGWzW5iS5+w3krLq3H1mr3WFJfBoULYlwv78uFkMRhlgAu0imhF6ts7KNDqeOGHFMQiEU91jqBDPW9KSo2s+s9+2oZ70qKTAq7az23GzCXzJTDAa51fI0ORwc+5P1tSLoFiivnF/Avr0tfRzK8ZKg9LO4BXOrzGexGDmbr6DGsea8yBm9uYE/8RZ1LOgBpQQzDBtFZGIS9WsPvEZbRaHSKJJcspX6NHLv3rR8nAknkpICDwgJQ7Df78y6+Uld0THgcOHOTgwUN242UyGUOHPoxWq+Wjj+aSmWn/qDE3N5e9e/f9IevrEBtLSEgI+/cfYO3aHzHcV/NkNptJTk4mOTm52rnKzSy2bd9usz3+8OFK11se6UlLS6/VustdFZcsXUphYaF1e35+PkuXfQ/AoIEDajXn/Vy/fp0jR47aXZOioiIuX74M8Kf3CTsUH8+p06etP5eVlfHdkqXodDqio6MqrasTEBD45yOXiJncNaLqQSIRbzz0DEaFEoDz/vW4uW0ffPNNrQWZRm+kRF/G1weu083tODsbPsMwT3tBpjXBzDsWRz1HtA5ojavMnaeWH6fnx/sZvugIPT/ez+QVJ60ui+HeSl7oUZ8ejf3ILirl6e9P0OGjPbyx/jwZhbWrTc4t1qM3mnixV0MA6rjX7nvb09mD8+mF1rU18HNl14V0nOQGZPICUtWXKDLcYUKnQD58pBnfx6fw8i9nySzU8cWeK5iB51afosvcvXy175pVkAEUag08ueQYk7tGMH94C4I9FXy17xqd5+zhudWnuJ1fwr4b+zGWGSEfyMQSHQsESX2ICYX/84IDwZAbARuD4HmPP06QlSJlRzG8mgOxt8DzOvTLhTlAgicYQ4AAwA0S7ySSq81FV6bB1UlKUrqaF9cmsjExHf3dDtUV6+QqQyKSMDlqMgsSFjjcbzKbOJt1lkM3D3E26yzzjszBjIFPR7ZGKVHQt97DbB65l4szbnD41dNc/PQGxxcmMfKpTxF1fR6v4e8RMHaudT5PpRyF7E/I5fwTECJlAgK/gwH9+7F9+3YOHz7CczemEh4eRkZ6Bqk3bzJw4AA2btxkd8wjjwzjdloa+/bt59nnXiAioi7+/v4YDUbS0tO4des24WFhdO/e7XevTyKR8NqrL/P2O++xavUatmz9jfDwMNzd3VGri0hJSaGwsJCJE8ZXamxRzrChD3Pq1Gm+/34l8fFHqBMURHpGOlevXuPhh4ewfv0Gu2NatW6FXC5n48ZN3Lx5Ey9PLxDB0KEPV+ksOXjQIM6dPc/JU6eIi3uW5i2aYzabOXv2HFqtlpiYdvTr1/d3XZvs7Dt8NGcuLkol9erXx9PTg5KSEpKSLqDVamnbNrpSW/w/ij59evPuu+8T2bQpnl6eXL58haysLLy8vJg86ek/9dwCAgJ/baQSMb2a+DM2JpSVVdRXde8dTWHEByzfcZ4vWw0iPqZdrc9lLDNxLCWXULcynpC8z8i6OxyO262BydlwrYp6oOntZ/LNgVucS7ONdpXXZo2JCWXdMx2RSUR8sPWiTdpgcakRL5faKQ5PpZw7RaVM7FSXro18cZVJbBoxV0XrgNa4ObmTWahjw7Od+OnELR6J9mXfzX0MWmvfA2x6+5mM7TCYm/ka8jR6ktLVyCXiKmvnbuRqbCJkIizmJvmlN/nt0mI+2fsJxU7F4An+EujvAv2V0Ev5x5tz6ExwWGfpEZbr1pwnun/BQ8u61vj4XE0u9b0aoHK+97D019O36dfc8gCgvE6uqusf6ReJxqCpVYppnjafjDwFzYJUyEUS4lacx1kmYWjrQMKbuFNYmk94QD7vP1IHpbQJa45lsPLobYI9Fbg4SVE6CaJMQOAfj4eHBx/Mfp9ly77nfFISeXl51KsXwTvvvIUIkUNRJhaLeXHaVDp0iGXHjp1cuXKV69dTcHFxwcfHh6EPD6FTp45/2BqDgoL45OP5bNn6G0ePHOXSpcuYTCY8PDyIiKhLu7Zta3S+yMhIPvpwNitXruJ6Sgrp6emEhYXyysuziIiIcCjKvL28eO3VV1j7449cuJCMTmd5Atqta9cqRZlEIuH1119l62/b2LNnD6dPJwIQEhJMzx49eOihPrXq4eaIRo0aMmbMaM6dPUdaWhoXLlzA1dWV8PAwevXqSbeuNf9D9aA8PGQI9evVY9PmLVy+cgUnJye6devK42PH/OlROgEBgb8+SrmUV/s3oWf+NdZcK2Z72b2U7Yo1TI9czeFGm/pEBqlQKWp/a6cvM3EpcQOxkvcY6XXbbr/G7IYo+iN+unyWa2mLK50nLjqOTsEPMWT9iUrH/JBwk1HtQriSVWxnmHE5qxiFzN4+vjIig1Q4ySx/CzaeSWNAiyB+OXmTqe1mMH7j2GqPn9VxFqV6CaNjwjh1M59H2/pWaaX/xPoxxEXH8W7fjyjSiCu1u7+fQq2Br/Zd4+TtSzRrcJ4VJ5ZzpfgKIiDaHQa4WF7RztUuuVaUmiDftRFJIl+aNX+ewTtncyzT4gDZQibiBRe/Ws3n6+KLVCzh/wbV50RqAYVaA5ezipGKxdZrsDohw+H1l4gkRPpF0im0U61TTPO0+Ww9V0ADfzeWHb7OM93q0yTIiX03f6P/anvxPLXdDDY814+LGaXkFpfi4/on5Hr+CYjMjiziBP7yZGVlM2nyFL5evAh/f8f/qEwmE5cuXQKgUaNGv/smVkBA4I/h9df/j/NJSVX++xX4eyN8/wr8IZjNlH32OeIZL2GsV59bv+0lTyTH826N1qqEm/yQcNOaMvf5qNa2TaRrgLG0iNITM3FJdSy2ir27crX+64QGRiOXyth8eTNz4ufY3Qi/3PFlOgU/xNhvErmRW0luIxYTjfiXezBh2XGHtWPPda9PA39Xpq5JtD/4PuYPb0FxqZFBLYIoM5kpM5npu+AgG56L5sPDjsVVOZOj4pjbay46vRSTGbadTwPl0RqJudXDVtOn3sM8/u1xujXyI9xbaWN3XxGj6A4aySFEinjyjBdRiS3mHANcoJ8S/P/A8IjBDMfuRsL2aqBpkwlM6/gabb9pS1zbOOp71mfCxgmARSRdn3rdrlF1ZbQOaM3eJ/fyecLnhLhHkJXZiq/2WQzjnu1en4Z3PzN3hczm+ns6exLXNo7JUZPRGDRoDVp8lD6Eflrzeu2Lz9xg2qpUvhjVhve3XGDB6Ka8vHNmlS0EpkTFMa/PPMQ4/VdNPmpyf14Zwl8JAQEBAQEBAYG/Gno9TJyIZNpUREYjsksXuTVsNHN+S3ZYwzS2fRg9m/jVSpDpMuIxbG7hUJAZxM68qwuh9eXbuHlGsOHSegasGsDZrLNsGLmB5GeTOTLxCClTUzg4/iB96z3MgAXHqxRkgMUhsdSIGTNtwz1pEuiGRHyvzcmKo6l0qOfDmJiqb9rLref93Zy4na9h/o5L3C7QWho7LznDqx1ms2zID7QKaGVzXKuAViwb8gOvxL7P7bwyTqTmYzSZ6BXpxacJ82p03eYengsYGNchnJVHU2lX14vR7e6t14SWYskuMuWvkuY8HoXzd4x0ucjOOpATAT8FwjjVHyPIChXhfK/3pW8aeF6DTrfhF1krnu63mte6vU/fH/qSr8tn4fGFdAnrwqSoSQCUmctYfHIxL7R7oUbnmdZ+GtfyrzG6xWh+TV7L6PaBlH9sK4+m0r2RH2NjQm2u/y/DN3J68mnqe9bn8XWPs+LsCryV3rg7u9t9LpXROqA1SqmKS5lFFGj1vNi7Hlsub6q2p9uikwvZeGkTUvHfJ/YkpC8KCAgICAgICPyVKCiARx6BPXtsNndN3MvhY7tY7B9t3RYZpGJK13r0bOJXq4iARm9k1eHLTNClwn2tHy9Kguh7NR21tJgTk04wJ34OX5/8GoADqQeYEz/H4pDnZHHI6xTSiQ96zqn2nO4KGY+3D8FHBZ+ODiNXk4e30gulVGWtAyrUGpiw7Dirno4hOsyTbw+lVGo9/+vJNMa0D6X3JwcI9nTmeVUD2oZ7UlxqZNiXJxnZrhWbRuyhxKAmT5uPl8ITpVTF6oQMhmw4wfpnO3LkWg6NA9wwUDsr/XxtPh3r+zKgeSBPLjnG0vHRyFyS+O7UMtJL91FfXspEVxjmCjF/YFpihhF2aGCnBoLrP8kbfb9gqBm6aPIp1hfi7uyOm5OKUoMBhdyJn4b/xNRtUzmQeoC+P/Rl25htdAzpyCdHP2Hh8YUcf/o4k6ImWT9fR0yOmkzHkI60/aYtj0U+xscPfYzWWGztRVeoNVCg0TPjoUb0aOLHtvOZvL3hKnMf68Jb+15hV8outo3ZxoHUAwxZM4T+DfrzQrsXrFG7qpgaM4PVCRmYzBDkrkDhZOSJzdX/rgHMPzyXwY0GIefB+s3+txFEmYCAgICAgIDAX4WUFBgwABy44ppeeZWpb77CeL0Ztc6AylmGSiFFLhHXLmWxzMSu5CzeP+6HPnA4z/j+CIABZ84EjqDdgeWYgddi49h/Y7/dDXu5Q145Z7PO0jm0M2NiWlhT2u4n3FvJsgkt2X9rG92Wz6u0DmjckjOcSytk9DdHWTa+Hd0b+5Gv0ZNbrLembe5KzuLc7UKe7BjOk0sSGNs+mFExgZhFebw0QImns6dV6A367DgB7s53e46puZRZhMlsEXcKmZhTN/OJ61aPzMK8Gl8/gBxNPisP5vBYezlXdBtotnAkjWTZTHaFYQEQ6VSr6SpFaxaxX2Nmh8YixpIq9hQ/vZwWdfvycOMhnM8+x68Xf+ZkxknOZ5/HZDbRKqAVM2NnsXnUZrJLsskszqTMXEb/Bv3pV78fedo8CnQFzOs1j+7h3R2mpU6NmUrn0M7WiNvik4tpH9yerqG9iAxS4eokpa6PC54uUhDpaRhkwNdTjKciCKmkjDpuddj5+E4+PPSh9fcotSC1hmIwjq4hfRmy4QSRQSqcZRKyirNrJZ4LtPm4ymvep+9/iSDKBAQEBP7LzJ793v96CQICAn9FEhJg8GDIzrbdrlTCypWIhw5FCSgVEOD+4OEXfZmJRfssDZo/yRzF0IALyGVOGGIW8dSmpzBzz7p88OrBNZpz7uG5bBm1h0X7U7ivRzTuChnLJ7as0kRj/MaxTI6KY9mE9xnyxQnOpanp8Z/9TO/dgMeiQ3BzklGkMyARiRjUIgixWMRbG5L4fHQk+27+xqC1YxwKvXXPWoTe/Q6Jk7tEsOJIKhczi5FLJPi52vfgrIqTGcdYk/IN+1OPM9IN5oRCxB8UkDG41GebVsynNy8TrzNTWkUG3vzDc+kV0YMhawdhMpts9iVmJjJm3WjiouN4q+tbzD44m7SiNGtftPJoZwOvBnze73M6h3amSF9EriYXb6U3CqmCxScXM337dPJ198w5Pkv4jEebPsrkHiaKdHLq+wSz+fKvzD0814Fj5XRc5a7sSbkX9c3X5VujdrHBsSxIWGB33LSYmXQN6cu4JWco1Bp4Z3Akv53PIDygduI5T1dAsHtwrY75XyGIMgEBAQEBAQGB/zXbt8PQoaDV2m4PCIBNmyA62vFxNcFQBFJXEFnyFNVaI5eyimgS6Iark5Q3Cj7ivce6ojPnWaMQD2Jdri8rsqa0VWRs+2D2pv5WpfEGwOKTC4kN7sTj7Vux+2IOrk5S6ngo+XLvVXZfzMbVSYq/ypnBLYNoHuzOSw+F1UroWevvYsLoWN+H/9uQRJnJTHqhlhBvzxpb6Td1kpKZMIn9QdD0D4iIlZos5hxbSiyv9U/9wtytz3JIe7naYy2W8Xk082tmE72syMITlnqy97rP5qlNE6zi7WzWWVoFtGJoo+Hk6/IJ+zTMJi21POLm6JzpRelM3DiR5Q8vZ+bOmQ4jXhbHyieYHDWZbWO20fabtlZxdzXvKm2/acuU6ClsGLkBjUFDga6AYLdgzCYXa4ppodbA2Jgw2oZ7MW3tad5/pHLnZkd4OXvUavz/EkGUCQgICAgICAj8L/nlFxg1Cu5rZk+zZrBlC4TW3KnOjjuH4fAYaDwdGj0PgAgzB2Z1R6cvI0+jx0spx0kmI6fkXv8plZOKHE1OrU5VpC9kaOs6JGdctG6TiEWMiglk4JrRNZpjQcJ89o87wKA2Lqj1BQS4Gmgc5I8ZEyuP3ub4jXyy1Do+HdGiVkJvTEwr9l/OZXzHcLo08KX0bsPjMTGheCplOElkTG8/kyfWj3E4T4gURrjCSDeIcjbW8IpUzm09bLkrxPZooKRCNMzD2QN1afUtAcrJ1eSiclJVOWZu/FwOjDvAnscPkKvN505JnrXG7txtDbmaPLu01Kq4U3KHsS3GOkxvvZ/ylMcp0VP48NCH1u35unw+PPShTY3iT4/+zNQ1ySSk5BMZpOLNgU3pE+nPoM8PcStfi4usSa360HkoPGr0fv4KCKJMQEBAQEBAQOB/xbJlMHEimO6LSPTpAz/+CO7uDzav2QQX5sLZN8BcBqdngn83NMomHE3J4+sD120MNAa2CGRmf1/rz+pSNT7KmvVLLO9B5ensy6h2XgSqnFl0d/42oR4YzMW1irhlFKfz2C/DrQLh/pozjd6Im4IauyWWC70nYuvy/ZFU3tuczJsDm7JiYlvcFXLe3pTE8DYhDG08mClRU6zOfi4ieMQVnlRBD2WNTlUl5/LhVwOs10FiqeMxrQNa4+HsYU0xrAneSu9qRdzpzNNkFudhMnoQ7BGEUuJDfokBvVlCVJgXmjL7iFh15xzYYCADVg2o0fjPEj5j/cj1zImfYxd9qygGM4rT+WJ0K7QGcHOWsvVcBtdzSqyunpX1QXPEjA6zkIv/oOK+/wKCKBMQEBAQEBAQ+F/w2Wcwdar99jFjYOlSkD1gkZIuB448Dhnb7m0zlWI6NIq5hqUsS8i2O+S385m8MbCeNQqRlJ2EUqasMipRsQeVzqgjV5dNiUFHp0Ze9GwaQ36JEQ+lnMu5F2q1/Dsld2wiP/enIr78yyUKSwtqJfQKSwuZszWTTWcyAVgSn8Kqp9tTojfyWr+m5Gv0aPRSZrR/Cb/iJOoVHGKYqxnX39k86uhNJSW+Aylq1o6h22ZUO35Wx1kYTUZa+LeocTRIIVXUSMRpjWrO3TAzYvvRCuYnRq5mF7N3ZmytIlAquQq1vnaOlRqDpso0SwAXuQtKJxHPrz7NnEdakleix8dFbnXVXJ2Qxq/P9mNSmylVRknjouMszovSv4fzIgiiTEBAQEBAQEDgv8+33zoWZFOmwJdfwoM2HM8+BPEjQZtmtyvf6Mrmk5cAT7t9ZSYz287lMLPDLMb8Otqmj5Uj6/L6XvVtbM7vN2qYETuLIY0H8+uJ27RrULtoX2WRn8UnF9I5tDOPRnUgqzi3VnPma/PxUMitPyelqynSGfjot4tsPptBgPwED3svY6THDd6RAVVnA1aKsQz2XoDNNyPY7/okBZ5RhIgUbGnZjsl3rrK4iv5acVFx9G/Qn88SPquxZfwLMS+w+ORih7Vf9yMXu7HiaCr5GgP5GttU2dUJGbwYM5MnNzhO37z/nJuvbKapb9Nqx1akujTL1gGtcZI4kafNpYGfG5lqDeM7BVGgs3XV3HYuh9c7fkDHkM58kmDv5Plyx5cZ2HDQ38Z1sRyhebSAgICAgICAwH+bXr0gPNx228svw1dfPZggM5sg6SPY3c2BIBOhb/omT6Z+RI7RVpBJxCKaBLrRNtyT385nMajhICZHTQGwazhcjqezJ9vHbmfu4blM2DjBLrqSmJnI2HWjmbVzJp0bqXCV165ZcFWRn/8cmUf/5r54ONsLy6rwcPYgt0Rvs+1OYTG+RV/zbb3RHG38Nq/43iD8AQIrJhPsSYLJS0Q8u3c4ro8k4vboego8owC4la8lbsV55vaaxw9DVztsaP3D0NXM6T2X67m3+fjIxw6v+/3ERcfRPbw7i05UXVcHtk2YHbHy6G16hPdjyt3PvjImR02mc2hnVpxZUeP01nKqS7MsF5glBg0v94/gatEuOi/tRMgnIcQuaU3jr8IZ/GMPZG7HEIthYINh7Hn8AFeeS+Xw+NNcfOYGGx/bg07dloOXCtHof3/t338TIVImICAgICAgIPDfJjwc9u2D7t0tvclmz4bXXnuwuRylK5bj7A8dVpGn6MD5Nbutm90VMsa2D2N0TCg6fRlaQxmeShmYpczuMYfY4M58mjDPoXV5XFvH/cvuZ+GJhcTU6USYonuN64Cqi/xYaqMy8Hf1r1W6naezBwnXLWmUdWSZPOazgpD4I7zlpq/m6Mo5cg3WxMP603I69nuC176chbNnIGIRvNzMicfaBjN7y0USUvKIv5bLgAXH+GRETw6OH0S+Np98bQGeCg88nD04eaOIF1Zd4JWB/oS4h/DG3jf4qOdHlVrGT42ZSt/6fTmedtzGrr4yKjZhdkSh1sCT351h/XPziA12HIGq2LMsJT+l2vTWilQntsvF3rv73+XVTq8yc8cMFp1chEQkoYV/C6sj5Lmsc4zfOJa46Dje7Pwh/T5NcNiHDmBs+zBe69+4Vk3V/5f8PVYpICAgICAgIPBPIywM9u+HrVth8uQHm6OKdEX8e0KHlaAIoDDzXoQi3FvJ8gntOH2zgF0XMunVNACA9EId+jIn/NyUxPj3Z/PI/hTrC8nTqBnYYBCPNn2UOyX5eClUdF3WtUbLW5Awn00j+9OQATYmGo4ovzGfvn16pWMkIgnqUjUSkZQZsbMYu656V8cZHWZRoi3jjVaX8Mz8jC7Kq4hFNVq+HWczYdU+WHsUUvPluLbuT7vnn+SbmQNZeiiFb9YcplBrIDJIxcROdfnuybbklZSSXVSKt4scX5UTUpEZdydPRMjI0+aj0ZdR31/FuI4hBKnc+Gn4T2SXZFNaVsrAhgMZ0GAAedo8cjQ5Nv3DfrnwC6sfXc3kqClVp0VG32vCXBXX7pSwLD6D7o36smlEP0oMavK0+XgrPAlQebEscamNrX1V6a33M6vjLPK0eXa1cveLvemx09l8eTNrk9byWufXmBw1GY1BQ44mBx+lDwqpgq9Pfc3C4xbBP6pdq0oblq88mkpMXS/6NQuoVXP1/xWCKBMQEBAQEBAQ+F8REvJggsxshoufQOIsi7uiDSJo/jZEvg5iCQDud+up3BUyvp8Qw08nbvFodDDHUvKY9P0JGyfGyCAVk7tE0LmBL9/uv8359CJ83W7wUu9GfLLrDtP7mmtl8GCiGGeJMx/2+pAuYV0cNhmueGPuKPJT0VREhIis4iwGNape6L3UZjxDTZcw7niGocZ8eIAyo2wd/HAYlu+CM6kglUmZMHEi2REDOJ0r5moxZBfpaBjgxsbnOvLkkmMkpauZ/uMZxsTk80y3+vx08hYz+jTGTCm/JG9i/hH7a/BS7EuEazszYNUAruZdvXdt2k2lZ0RPvjj+BRfuXLD2D/th2Go2Jd7hldjZ1simffPmmQxtPIQX11yw9mmrikNXcpjQsS4fbEnm1K18awSqZ+MSfPy8bT6bhccXcvzp40yKmlRl1DQuOo7eEb356cJPbBm9BX2ZnqLSIlzlrohFYmuDanWpmtHNRzNu/TiOP3280nrFF9q9wPGnjzN9+3S+7NfPYcPychbtv0bPJn6CKBMQ+Ksx5OFhtRrv5+vLN98s/pNW88dx69ZtfvzpJ86dO0dRUTFenp5Et41m1MgRqFT2RbW7d+/hs8+/qHS+Tp06MnPGSzbbTCYTq9esZefOXZSUlNCwQQOefnoi4ffXRABlZWW8+OJLOCsUzPnoA0SiB3skqdPp2L59B8eOn+DWrVuUlJTg5OREcHAdWrZsSZ/evfD1vWfhvHr1Gtas/ZGRIx5j1KiRD3ROAQEBgT+cGzfs68d+D4ZiSHgKbq6133c3XZGAHjabVQopkUEqujXy49TNfIZHh7Bo/zVWHbtpN0VSupoX1iQyNiaUV/s3objUSJHOQKCHnLeHNCCtyHFkojLSim4zc+dMkrKTmBI9hc2jNmMwGVCXqnGWOhPgGsCyxGU2UZiKVGYqUt+rPrse30WnsE7MPzzf5uZ9WFBjPggOoF7BD0iTap+iqDfBphRYvgV+O2Ex8EAMtAFjFyPinmKW9+rOgAXHuJWvJbdYzzcHr9PQ343lE9ox+It4CrUGfki4SetQD155qDFSmZGZO2c6jGolZiby+LrH7RotW5wnxzM5ajLvd3+ftt+0xWQ2ERcdR5eQhxiw4DgAY2JasWnEHjQGNaVlOjwVHsjFCn47l02pQUKfyAB2Jtu7bt7P8OhgDGUmZvZtxK7kLJbG3yA5o4j0Ah0bnutnE5XL1+U7TG8tp1VAK2bGvszARgMRi0QEuARgNBnRl+kp1hfjJHXCSeJkvUd4u+s7aA1aPu37KXPi51TakHrCxglMjprMxw99jNZY7LBheTlJ6WrUWuPfIoXxr79CAYE/kB7du9ttu5CcTGZmJnXDw6lbt67NPpXK7b+1tAfm7NlzvD/7A0pLSwkOrkPjRo1JvXmTrVt/IyHhGHPnfIiPj+NiXEfvGaBhwwZ22379dR0//vgTwcF1qF+/HqdPJ/LmW2+zaNFClAqFzdjNW7Zy6/Zt/jN/7gMLsuSLF5kzZy75+QU4OTnRqGFDPDw8KNGUcPXqVS5dusy6det5443XaNWy5QOdQ0BAQOBP5+hR6NkTnnkG5s6FB/xOtKHoCtxeZ7fZ5N8TXdtlaES+aHJLUMqlKJ0kyCVi5BIxU7pG0CbMi91JmVzMVHMlu4gmgW5cziqmzEGoYWXCTWIivOnXLAAX5zI2XPqF9RfX8073d2q13PJmyPc3C368xeOMiBzB5wmfE6wKrjRCtn3sdoc36VfzrtJ6cWumRE9h+9jt6I06jLc343d7FcrceMi/aDdfdSQWw7fxsHo95JUHD0VAS6Ar4GXZtOjkQjqHduG9h7sybukpPJVyikuNVhE2JiaUr/ZdQy4VcyWrmIEt/PkleWOVaYZQeaPl8u1vd3sbP6Uf/RoM5Oi1Ymv0a9OZTGIivGgT5k2BroBcbTZeCk+GRgUgE4np1MCH0e1CHYrwcsbEhBIb4YNMIkZTasTVSco3T0RjNJnR6I0oZHLm955Pt/CuzImfQ2JmIlfzrtL2m7a83e1tdj+xG41BQ57GtlburXVXeXdofbRGrcPI17SYaVx5/gpKqZI8XX6tGlJ3De2Fq1PVckatMxDg7lzlmL8CgigT+FcxderzdtsWLPiczMxMYmLa/e2iK6Wlpfzn408oLS1lxIjHGH13/WazmWXLv2f9+g18/sVXvPP2mw6Pr+l7NhqN/LpuPXXDw5k3bw4ymYx9+/fzyScL2L59O0Mfftg6tqCggDVr1vJQn95EREQ80Pu6fj2FN998G71ez7BhQxnx2HCcne99oZpMJhISjrFs+ffk5tTOFllAQEDgv8aFCzBgAGg0MH8+5ObC11+D9Hfefnm1hrZfWaJldzE0eYNLfi/gjQy1LpNcTR7eSi/0Znfyik1E+Kjo3dSfktIyhkWHcKdIx6y+jfFSynGSiVl97BYrj6bapbitPJpK9ybuNsYL8/rM+10GD+XNgmfunImP0geT2US38G4OUxGrMxXJ1+XzcfyH5J/7iE/Dw3DS3Kj5dbxLiQnWZMDizXD8wH076wN9AD/74+Yfmcv+cQNpGazCSSa2Ohsujb/Bt+OiiYnwoE2YKwW6AkrLtMw7PKdG66ms0fJnCZ9xYPwBNDoxH2+/zsyHGjO6XSipuSUsfLwZmy5vYtoy+7TIGbGzGNJoEC/0rE+bMA+Wxt+wS1cd3zGctuFeJGUUEqhy5vnVp1k+oR2Hr+Ww79Id7hTp8HZ1QmcoY0irjux5/AAlRjV3SvJwk7ujlKpYdjCd9EI9CpkEpbyI53sGUNcb3humYsaOGZVGCMdtGEdcdBxzes1F5eTGgoQFNmPKm5OXm30kZSdRZi7js4TPGNbkkWpdFlXOf49eZYIoExD4G3PkyFEKCgqoU6cOI0c8Zt0uEol4fOwYDh8+QmJiIikpKQ4jYjUlOzubkpISOg0biuxuM9MunTvz5ZcLSUm5YTN22fLvkUgkjBlTffG1I8xmM59+ugC9Xs+okSMYOXKE3RixWExsbHtatGhOTk7OA51HQEBA4E8lLQ0eegjy8u5tW7oUWrZ03J+sttSbCLnHIPVHStstJ9+rKyeubmbB5vn29VrtZuDjNhCVkwsJKbl8ufea3U35uA7h1nqoG7ka677ocHc2XtpoFUvV9S+7n+rcFMsFSN+VfTn61FFiQ2L55OgnJGYmIhFJmBw1mcGrBzs8ViWGKe7wogcESM1QS0F2thQWX4GVP4P6yn07/bCIsfqVH3868zQFugJe6deEVQk3rXVNHgoZbs5l3NbtZdqyuZjMJtY+uvZ3N1o+nXmafG0BpTp3LmSoySspZUafhshlRmbsrFz0jF03milRccztPZfc4lK+fiIanb6MfI0ez7uifFXCTS5mFDGtV0MmLDvOjVwNg7+IZ8zdFNaK453lYs7cUtOsjoogNxkZ6jzKTGaGtw1kzbEMVh69TaHWwGPRIWgNeg5d3F5thHDhiYV0Cu1C3/p9rNepYh1hZWYfhbpCzJXUk4Hld1ul+HvInb/HKgUE/geU112NHPEYXbp0YdXq1Zw7dx61Ws0rL8+iffsYnn56Mtl37rBh/a92x587d543/u9NenTvbhehM5vNHDx4iB07d3L9egp6vR5/fz86dezIsGFDcXJyqtEar167BkBk06aI7+trI5VKadKkMdnZ2SQcO/67RFlxcQkArq73KqTFYjFKpZLi4mLrtuSLF9m3bz/PxE3Bze3BUj9PnTpN6s2beHt7M3z4o1WOdXFxwcXl79UcUkBA4F9AcTEMGgS3b9tu79sX4uJ+19TGMhP6MhNqrRF18Pt4hk5D4RnKOztnODS8sNQkjWVyVBwf9JjDrTyNjSADS93NzJ/PMiYm1KYeSiIWMSomkIFrbB+y1dTgoSZuiuUCRCaR8VnCZzxU7yE2jNyAxqBBa9AiQmQnZgIkMNUD4tzBXVKDi1YBnQnWFMPi03B0I5Bpu9/b15vc9rnQmhp18y3UFdAooAFTVp4CIMRTwaInmjFjx73asU6hncjR1O4BYmWNlgt0+dTz9OLlvg3QGcrwcZWx7lL1aZGLTi6kc1gXBrYYwJQVJzCazHdNPIxIxSKe7BBOx/o+lOiNHLtheZBQqDXw1b5rLNp/jUYBbrg6SVEppPzfwPrc0OzmxR8cPwDY8Fw/xi05Q75GT6NAOWM31SxCOP/wXAY26I9YJCbCM6LK5uTlZh+FumJcqkhfnNK1HvK/gckHCKJMQKBa0tLTmTFzFm5ubjRv3ozi4hKk0lr+FaiAyWTik08WcODgQZydnalfvz6uri5cvXqNNWt/5OSp08x+/90aCbNSXSlgK5YqUi6MbtwXzSrn2rXrLF22HK1Gg4enJy2aN6dZs0i7cb6+lpq09LR067bi4mLUarXVaMNkMvH1199Qr14EvXr1rHbtlXHi5EkAOnbsgETy4NdZQEBA4L+JVSwVl+I66jFcT5+2HRAbCz//DHJ5zSfNPwN3DkHDZwHQ6I3sSs5i8f7rVmH1bPe6+PlvqNKBEGDxyYV0CO7EqJhHmLPtkkO3uvvroRr6u1JiUNuJopoYPFTnpliRcgHy8ZGPebzF43x08CPib8fTKbQTY5vf621WTwYzPeFJN3Cu5X12uhG+KoDFJyBnB5Blu99ZoaDZQ6P5/vMZNP2uSY3ndXNy52RqvlXEfjKiBZsubbIRSepS9R/WaFkmkTF+w3gGN3qYIUGDMWGocVrk/MNzGTx+EIsevxcp83VzwtvFiTUJqSikEnJLSu3qDE1mSM4owl0hY+Pz0XwQ/zpfn6r6AcCPcR/iJleSo82qVYSwsLSQDsEdWD50eY3MPub0mgvk2U+GpU/Z38V5EQRRJlCO7s6DHyt1BanC8T5dDlBFXLnKeZUg/d9HQQ4ePMSA/v2YOHHCHyIS1m/YyIGDB2nWrBkzXnoRT09PAAwGA4sWf82uXbtZs/ZHnnzi8WrnUrlbnqJl33H8+WVlZVW5//iJExw/ca9vydq1P9IsMpKZM1/Cw8PDut3T05N6ERHs3rOX9u3bExoawndLlmIymYiOigJg27btpKTcYO6cj+yidrUhJcXi6lXvAevRBAQEBP7bVBRLj3w/nwknf7PZb6rfAPGmTVCDyH65uCu9/hPuiU8hMmkpdQrFFNiPD7Yms/LoPaOGyiJZlfFpwnyGNhlM00A3zqc7dqtbGn+Dr5+IZtH+a7g6SSsVVOUGD1Oip1gjW7maXELdQ3F3duejQx8xffv0GjU2Lhcg+bp8EjPOMrfPXDZf2sy6i+vwVHhSTwZveMFYN5DW0iflmA4+LYCfT4NhD5DuYFBL2LliL3P26Qn2Da5VvZxc7EazICee616fUTGhqBRlPLXNViQlZSf9IY2Wy7f/nPwzP174kfe7vc+TrZ+slegp0OYzZ2sWV++UWA0y3hrYlDGxYZToyxCLRHwxqo3DOsOx7YPZm/qbQ0FWkcUnF9IltAt1XXqgcHEsmCqjUFfItPbTamz20S28GwNbtOX4jXu/Z5FBKqZ0rUfPJn5/C9fFcv4+KxX4c/nVQQVrTYn+wvoUz44tTaD0AWt+mr0FLd5+4GX9Ubi7q3jyySf+EEFWVlbGunXrcXZ2ZuaM6TbCRyaTMenppzh58iQ7duzk8bFjqhU3kZFN+fnnXzhx4iRqtdrG/j43N5czZyy56Fqt1uY4Ty9PRo0cQbt27QgI8Eev13P58hWWf/8955OSeO/92cyd85HNex4/fhxvv/Mur772unVbVFQb2raNRq0uYtXq1fTs2cPGuVGv1yOVSmsl0oqKLDcK5YJTQEBA4K+MRm+0iqUnTm5iwsmNNvvznd1Y9uInTHZzR1mDuXYlZVB08k3GKJdZt4uOjOVQ3a2sPFpgM76ySFZllNdAPRYdQvLmZIeOi0npanT6MhoFuFFcaiTA1bvS+e53U1Q5qfjp0Z8RIea3q7/VSJBVFCCTo+Jo6deRA5fUtPR5iIE9miC/+B4Xw2onxoxm+LkYFhTA0QvAXuCW/biYjjEca3YMc6AZdz8Xwr1lXMrQMavDLEb/Wr3QnRozgxWH0xnbvi4tgt35cGsyz/f2tvs8/qg6vPu3b7u+jX4N+1U7X0XydAU0DlSx/UI2z3Srx6Qu4RhMpaQVpZNdnIu30gulTMXu5Dwa+Lla6wxv5Wtr9QBg/pG5bB/TH12ZV63W56HwoFdErxo3J58bP5cD4w/Sqb4/+Ro9dTwVuCtkyCXiv02ErJy/12oFBP4HtGzRssY1XtVx7dp11Go1jRs1shFk5Tg5OVGvXj2Ki4tJz8iodr7WrVpRLyICnU7HO+++x+XLV9BqtVy8eIl33n2fsjJLQ1Gx2PavWZvWrRk5cgQREXVRKpV4eHjQrl1b5s+bS1BQEFevXiM+/rDNMc2bN+OTj+czbNhQHnqoD88//yyvv/YqACtWrMRsxhrdO3PmLM89P5Xhj41kxMjRfPXVQvT62veJERAQEPgrYywzsSs5i5VHb9L92nHe2v2Nzf5SiZRJw15nwU3YnZyNscyx2QWAptTI3M0nkR4ZaSPIAJxMakznP7I7xtVJSq6mdpGIfF0BfSID2D+zG892r4+7wt6ZLl+jx9VJSnSYJ95KT1oFtKpyznI3xRJ9CQqZEoNRwqwOs2q0nhdiXmDDpQ0sGbyCV2LfZ9ySM/yy/wChl19AuT0KeeqqGguyEhN8mg8RN2DUKTi6GPgee0HmBVM/mcq4T8bRvFVzJCIJLjJ3Xu3fhLXHb9O/wSAmtZlS5bkmR8XRNaQvPyTcJqeolG8OXidLrav081h4fCFdwrowKWpSNfNa6vAWnVhU7XZ1qRpXuWuV892Pl7MHfZv5cerNLkzoHMD6Sz/TbXkXGnwRRsdlbWj8VTiDf+yBWXGUVqEKfjl5m+UT2tEm1KPWDwDytfl4OHtU+/tTTuuA1rg7eVCkL6r1edYcv8mcbRcpNZpQyqV/O0EGQqRMQKBafHxrlwdeFdnZlsaNiWfOVNvIukithjp1qhwjEol45ZVZvPf+bK5evcbMWS9b93l4eDBy5Ah++GEVLi41+9JWKBQMHDiAr7/+htOnE+nSpbPN/tDQULu0yitXr7Jr926eemoiKpWK3Nxc3p/9AWFhobzy8ixu3brF6jVrcXJ2ZuKE8dWuobwOTl1on0svICAg8FdCX2Zi0b7rNMi/zZeb5iG5L7Ixq99Ujoc0A2DR/muV1rdo9Ub2J57gsfzRNPWwb8ysDnqC57cPtdteXGrEW1m7SIST2I3xy44jFokqdVz0dpXzbLd61Pd3Y01CGlPbzWD8xrFVzGphWswMNp/J4cwtNW8OHuzQ4r4icdFxDG08jGKdiR+OpjNlyxaecf+eYZ57kKZVLmDvJ7cMPi+ALwogNw/YDZzHvnpCAXSFSZMn8U7fd0jOSWbto2txlbuikHhw6EoeTYPcKdSIeLXDbDqEdObThHkO+mrNpGtIX8YtOUOh1oDLXcMMAJ9KPo+a1OHN6DCDTiGd6LWilzXKWFV9XlJ2Es5S51qlRXooPPjt9m/EyGOYfXB2pTVb5bVhr8S+T+KtAoa2rlPrBwC52nxKtK7MiJ3F2HXVR9hmxM6ipFREvrb6CGtFCnQFKGQyjt/Ix/l31Pz/rxFEmYBANchlD9bfwuzAo9V0d1tgYCBNGjeu8viauhf6+fnx6Scfc/RoAskXL6LX6wkNCaFr1y4cOXoUgNDQkBqvOygwEIC8/Oq/FM1mM4sXf0NYWBh9H+oDwNatv2EwGJg5Ywb+/n7ExrYnIyOTrVt/Y+yY0dVGHevWrUty8kWuXb9Ot241S18QEBAQ+F9QpDPyUIicZ76Yj6xUY7Mvfuyz7KvfB+7W4ySlq1FrjXY1LsYyE8bMA8ReeQQPRYHNvjKzmOWlL9A87A0M5qN257+cVYyLTFWrm3KlVMWlzCJMZhw6LkYGqfB1deZSZhFms5nvj9xixVP9qhVYk6Pi6BLSlyFfnODtQZGYymTM7T2PrhUaDZfTKqAVL3d8mV51+/PGuqscTrrIM74/siFsC07iqntOVeSWAf5TAN8WQokWOAgkAPdPIQZioNmjzXil1yt0Cu1E9DfRXM27al3PS+1n0i28H1KxN2UmMzPXXiIqrBWbRuyhxKAmT5uPl8ITpVTF6oQMhmw4Yb1e5T3KRCIRbk7ulX4e99fh6Yw6So2leDh7IhW5cilDg7PYld1P7OZW4S28ld4opAoWn1zssD6vPC1yZoeZjPl1TLXXa1bHWey8tpPErESK9cU1qNlaSGxwJ8yaGPo1q0NBabWnsMFL4clHm6+xYNQgJkdNqdIhckpUHIMbD+L0jRICvd1rfA4R4K29wpQ2YeQWh/xt7O8d8fdducAfy7DsBz9WWkUUZkAyv8vo4y+OVGb5J6TValEobM1OHPXP8vG25OYH16njsJH1gyKRSOjYsQMdO3aw2X7x4iUAh46KlVFcYrG4d3auPmVz167dXL16lQ8/eN9af3Y7LQ2Vyg1//3t1ig0a1GfP3r1kZGQQHh5e5ZzRUVFs3fob8fGHGfcH1fIJCAgI/Bk4S/UM+zQO2dWrNtu3tvUk+8X2bAiLZtySM9YolFpnIMDd2Was6eo3uJ56HpHYtmlzodGFZ2++Qq5bF7q6OP4+LjOZWZ2QUeNI1tSYGaxOyLBxXrzfcXFCx7os3HeVL/ddY/7wFgxqGcTzqy6wZPycSgXWizEz6XI3cjSoRSBdG/kybe0ZZj/cjL71BtGnXh9yNDncKbljFRobLm1gz+UNvFsvGXnZAlzEGvsFV8IVPXyQDz+owVAGHAf2A1r7sX0H92X8jPHEtohFLpGzIGEBz//2vI3AScxM5PH1Y5gcFcfcXnPZfDaPR6OCmfnzWRbtT7HawReXqq2CtpzxHcPv9Sgzm9mbnM/MDrMYU0lNWsU6vHWPbaK4sCktg914d3MSCSn5RAa58eXY+ryy+xXUpWrOZ5+vtMcbWNIib0xLrVFUsldEL5p80YSTk09W2vvtfhYkzGfjiN2UmcwEqbxr/QBg/+XzxK04z8LH59EltCvzjtj//szs8DKDGw3ip+NZvL/lIvtnxVZ5Hjcx9FbCQBcY7CrF+/AwDsqa8urghL+N/b0jBFEmYMHZ90+a949L/fsr4unpSXp6Bunp6dSrV89mX+IZ+3zoBg3q46JUcj4piaKiogfu5VUT8vPzOXz4CG5ubsS2b1/j444ctjyNrc79sLi4hBUrV9KtW1eaNLG1Dy4tta0f05VaHq+JRNUXBrRp05rQ0BBu3rzFTz/97LB5dDkajYacnBxCQ0OrnVdAQEDgj6RYX8I3nz/B9IO21vcnA+GRPvnoNj3O5Kg4lk14nyFfWKIqKucKmRcmI5x6Cfnlz+zmvqoL5qkb/8cNfR0kmmKc5RIig1R2/cUAVh69zYbn+jGpzZQqXfHKa6CGbDhht6/ccVGtNRAd7sm7my9Yt383ri0j2oYQf6UAnbatTeTI18ULL4UHJpOM9zYlM713Q9pHeDPsq3geiQpGLjfy+bEF7ErZiZ+LH3dK7pCvy+di9jkmqsw85gWeUmrscHBFD+/lwaoiKANIAnYBDhI73MOa0GvcSyx5ZSTpxWmczjjN0B+HVilwFp9cSNewLkjFsbSr683odqGsOnaT5AzHTpVjY0JpG+7Fe5uTrdvWnc7gu3GDqv08nm4zmRa+7Rmy9gS/xnVArbOE9y5mFiMVKyjWF9s0jq6McI9wJCIxc3vPo3Nloif2ZQY3HsjoX0YTpApCY9DUqmarxKBGjgyVs4zp7WfyxPrqo3LTKjwAiL+Wy4AFx3jv4a4cGDeQAl0Bedp8vBWeeCg8kImdOHglh3c2Wa6jowcNTeXwkBL6u0AXBcittxOW6xahvcDGS5t4tOmj/F0tM/6eqxYQ+IsQGWmJQP38y69WUw2AAwcOcvDgIbvxMpmMoUMfRqvV8tFHc8nMzLQbk5uby969+2q8htTUVDsTjZycHGZ/8BFarZYJ48fZpQz+/PMvqNW2f9yNRiNr1qwl/vBh5HI5PXv2qPK8P6xahcFgZNyTT9hsDw0JQafTkZBwzDrv4cOHkclkBAQEVPt+RCIRL06bhlwuZ/WatXy/YiU6nc5mjNlsJuHYMV6aMZMrV65WMpOAgIBAzTGWmdDojWQW6riUqSazUIdGb3RozqE3GthwcSMzdRvoNxby7wa/spUwdATo7mqvxScXsv/WNsbEBBMZpMLNWWqZz1AEB4aAA0G2Vx3F0Kv/4YbeUlNcZjKzKuEm4zqEO1x3odbAuCVneLXDbJYN+cHOVKFVQCuWDfmBV2LfZ+KyswR5ONM23JMmgW5I7ppAJaWrMZvNPNO9Pk8uOWa1QL+dr8VFLmHx/utMW5vI2xsv0mXOYaatSuXjrRqeX3mD6PcO8unOq3wwrAX1fV15dtUpIvxcGd8xBJHIwPDIR5ndYzbvdX+P5UOW8XJofZLDRHzlBwE1DA1c0cMTmdAkFVYUQdlN4FvgJ+wEWZ3gEOZ+8S2Lf/yNEs8G5BaZ8VEE8Oa+N6sUZOXMOzyXzg3dmb3lAjMfasRnI1sRGWTrBhwZpOI/w1vycr/GNtcL4NTNAjR6Kf/X+YNqP49xS84Q7KnAxUnKpUyL8Cszmdl9IY8ZsTUzSpkRO4uUO6WYTU50DRnAgXEHuTntFomTz3Jz2i0OjDtIn4jBFOoK2XR5EyonVa0bWedp83FTyDh+o5DOwX2tJigSkYQW/i3oFNqJFv4tkIgsmS1x0XH0rTeAHxLuNU+/la9l3NJTtH5nPx9uziL+ogIfZQCuclfKTCI+3nHvb/nKo7fpERDD18178Z0f3AqHpDD42Bd6KSsKsnvUkcKmhHfQm/6+pmJCpExA4HcwoH8/tm/fzuHDR3juxlTCw8PISM8g9eZNBg4cwMaNm+yOeeSRYdxOS2Pfvv08+9wLRETUxd/fH6PBSFp6Grdu3SY8LIzu3bvVaA3r12/kaEICEREReHl6UlBYSHJyMgaDgcceG06PHt3tjlmx8gfWrP2R+vXr4ePjg0ajISXlBnl5ecjlcqa/OA1v78ptkFNSUti2bTvjxz1p5yLZv38/Nm7azLz5/6F161ZkZGRy69YtHnlkWI1dLCMi6vLuO2/x0Zx5/PLLr2zevIVGjRrh4eGORqPh6tVrFBQUIJfL8fH5Z0djBQQE/nwcNWUGy8335K4R9Grib1MLpjfprQ17d9SHtk/DTz/BtL5wy8N27vL0rwhfN346eYsgeQ69siYiLjxnt45F2cOYm/kkJmzTtlceTWXjcx2tkZv7uZGrYcgXJ5jzSFcOjBtMdnEexYZCnCUqlFIVv57KYlNeLt9PjEGnLyNPo8dLKbfpRZVXoufjnZdtDD/Gtg9jz8Vsm3OWNxK2WV/CTWIivOncwIf/DG+JSmli0+V1zD081xq1aeUE3wa5MFpaAjUs1b56NzL2Q3lkLBdLZCzZfqxIrqRhn7G8+/pMOjWug9ls5lyaGqWTlAJddq0iQ0ZzMZ+MaMXWc5lkFGr5+ol7zZY97163NcduUlTqjYuT7a10mcnMiiOpNPBzJSur+pq0z0a2wl0hZeeLXa3zO8vFyCT9mRQ1ha+rqcMa2HAgU1df4M2BTbmZp6NliDsyvPGQeiJHjMgsRWcwkHO3PdGDNLL2dfHCRS7hPzsuUVxq5PuJH/B01CQCXf0o0heRo8nBR+mDq9yVzOI7BLrUxWR2okhnsJvLaIKt5zLp1ywQ6d12OWqtkQsZasLk6TzquZvObqcI3nOVp0VmqGF52YVSuJN/iQJtPq7y/32P2wdBEGUCAr8DDw8PPpj9PsuWWfp75eXlUa9eBO+88xYiRA5FmVgs5sVpU+nQIZYdO3Zy5cpVrl9PwcXFBR8fH4Y+PIROnTrWeA0xMe3IL8jnxo0bXLx4ERcXF9q0bs2gQQNp3ryZw2NGjHiMSxcvkZaexrVr1wHw9vbmoYf6MHjwIIKrcX38+pvvCAkOZsCA/nb7PD09efvtN1m6dDmnTp3GxcWFhx8ewuhRI2v8ngCaNGnCooVfsn37Do4fP0Fq6g2SkkpwdnamTp0g+j7Uh969ewmiTEBA4HdRsc/Y/SSlq3lhdSJj24fxWv/GyCViDCYT+dp8m5v8a94QNQnMDvKPTmeeRm8qpkM9H/otOEisbB99wm0FWRlyTgZ8xEdnGzpcY6HWwJNLjrF8Qjti6nrx9UEH4rFLBE0CPRjy+TEaB6p4vX8Tnvr+BKWGMpaOb8exlDwmfX/C7rhyB0YRkKW+l5Ugl4p5vH0YE5Ydr9F1XLT/GjF1vTCLSnll9+tWUwd/Ccz2hvEqEItKajRXmhHezoWl6rtirARLzdgJ4P5glxjGPzWJ3c5d0SncmbX+EmNitMR1q8fkrhFoSg2oDQU1Om856epcJGYvlsSnkJxRxKe7rlSoKzNa68rMiBjfsS4zfrIVfOUi+lhKHl3mHK60Jm1sTBidGvhwM0/DtLWJNvOHeilZNmE2HYIdO0DO7PAygxoOZMr35zmakodcJqFRgBtjv02gzGy2zpVZqOOtQZFERVhcIR+kkbW3wpNSo4nz6WrCvZWYMXPxTjJPb5pgt66X2s/E1zmMMiy97hylfo5tH2bjRFqotUS3QuRZPO+/tkafkREJB7Qi1hcZ2aKB63f1X56ugGD34BrN8VdDZHZkESfwlycrK5tJk6fw9eJFNoYKFTGZTFy6ZDF6aNSoUa0a+AoICAgIPDjC9+/fA2OZia3nM3hhdWK1Yz8f1ZoIHyXLj6TycFsTHZe1qfF5zk45x9e79Ww6a+k/+azfWmYGrADALPcmIeRbAuv3ZvH+6w4jYeU83aku03o3wIyIIq2BAq0Bd4Wl1geRmRdWJ7LnYjYSsYgDs7ozfW0i8x5tyaL916qcd0xMKK/2a0znuXsxmS03zU/EhlFmMtPhoz01fp/HXu/Ob9fWM27DGJxE8KIHvOZlMWaoCfll8FG+xd5eawYMwFHgEODI+a8J0Asu/t8Npq1KtREA84e3IL1AS+f6vvh6agj5pOY36leeS8VN5kvsh3tw0F/birtCxsbnOvL1gev8kGB7fcO9lSyf0I7jN/JYGn/DTgxP6FiX6HBPlh2+waTOEcQ6uM7uChljYoIZ3T7QJtoWpPKmSCtCLBbT4e4a3x4cSa8mfnSas9duHrlUzMn/60yXZZ1JzEzktc6vUd+zfo0aWa94eBWPNH2E23k6Hl10lI3PR/NB/OvV1i/O6TWXhXtusejA9btbzfQKLuLZprdp3GY4Co9w6/jMQh3tP9yNk6iUM5GjcBY7TkHMMjtxyODCqtx8dpSYKXbw2dyadut/Kspqcn9eGUKkTEBAQEBAQOBfSXmfsZqwaP81vnm4IefTCpnYNaxW53GWuHH1TioSsYiG/q4cc5rMUfJp53oZcfcttHAKZ/H+a0zuGkGbMA+HN/FPdYqgT6Q/By7f4at9VzGa7kVDpGJLxGb+8JYM+yqeG7kaViXc5LX+jUlIya1SkIHFgTE6zItnutWjT2QAx1Ly+M+OSwyPrnk7FYlYhJPMxCdH5/CIK8zzgbo1TFPUmixC7KN8yDdhMW0+A+wBHLSsjGoXReTYSDZpNpGvyydPm4/rfWmE5eYlQz4/xN5q3Pwq0jqgNV4KT747kFqlIIN7EcwNz3WiTagnS+JTrJ/bjVwNs34+y+sDGrN2UizZRTrySu6lP65KuMm7my/w5sAmyKVih0YuhVoDX+1LsXGA9FeZeGOAP/FX79C3WQDdG/uSWVhKPV8XinWO2wnojSYuZWh4KfYlHl/3OAuPL+T408eZFDWpSlv8uOg4Hm4yGIVMjpuzibHtg9mb+luVggwstZTdQjvzYmwrJgSmILmzH/eiw0hL0yETSrNU4DHZOl6lkFrff0JJM7q6nQJAY3JC5N+Fz29dYElmGpcMpThW5xbK+7D9XRFEmYCAgICAgMC/kvJalpogPnkS/zcH03vAFFyenFWrm3x/N28eaW2kX4ugezVdzsvRuJThrPRGKREzuWs9Dly+Q3Gp0aaGydtVbrmRl4iZv+My38XbN5cGmP7jGcbGhPLj5Fh6f3KA1Qk3ietaj1d/PV+j9/ftoeusnRTLh1uT+eHYTZoEujG5S73qD7xLQ39XNLmnmCs5S5/Amh1TZrakKL6dZ0lZBOA6sAOw98ECX6A7GLsb6RbTjTfD3qTvD33xUnhSXGr7OSalq9Hpy/BVObEmIYNZHWYxuhKb+opMbz8TZ6kTKxOqFrLl3MjVkFdcilpnsH5uBVo9ge4KZBIxS+NTWH3sOAHuznbpj2NiQokO92JVwk2HaZDlVKzjmz+8Bd8fucFX+65x5lYBC0a0plBnYEdSFsGeCofHA8z4KYnNUx9mctRkFp9cXG0j65c7vszAhoMwGGQgtwinsbFB9FtV+TWMkEE3xd3X2SdxOm/AUazIlLEPGt0TZXKJmMldI3hhdSK/5PckWVuX/UVtOKlpytO+DfFrfZpLNWj5MKPDLOTimtWu/xURRJmAgICAgIDAv5LyWpbqUOmK+WrDR0gKC3hx1Uecy7nE9Lee54nMiZUe4yaG1k4wMWYGZpMUX5Wzw5quikYivZr4oy8zodYakUhE1PVxQSmXoDOa2H4hq1JBVs7KhJu0q+vFiolt+fpACtlFuhqJTrlUTJi3EoOpDKWTBLlUzOWsqq34K+Is0vGS/wb8DywjsIYeC7s08OIdOF/+EWQBOwFHhrpeQDegGSCGM1lnmLBxAlOiprDniT2IzW5W98KK5Gv0uDpJ+WJvChuff6hGvbwGNxpEkc5EsKfCxlWxMiKDVMikYt7bfIH3Nl+wRrRUzlLeHBRJhK8LQR4Ku8/96c4RtA33Ysy3R8nXGNj6QmfGxITapUFWZMx9FvwrE27SJsyTa9nFLDpwnT6RAZV+XjdyNWw5k8e83vPpHNaZ+Yfn2zWyLtGX4Ovii1zsRlZBGdvO5tG/hUVhyyVi9KYim1rKulLoprwnxEJtIqOVXztZ7n4wm+FumxypREyvJv6MbR/KyqNd2UhX69iatnyYEmX57OTSGoZn/4IIokxAQEBAQEDgX4m7Ql79ILOZ+Vs/JaQwy7qp+Y511OnQqtKb/DpS2BoEjZ0k6P2C+GTXdZYduUFDf1fahntSXGrkclaxnZGIUi5FKhHbOD0CmDHy9YGapVkuPnCd7ye0461BkeQUV57qBRDiqeD9oU1pE+ZKga6Am+rLPNHJk+d7deZ0ajFbzqYzrkM4M3+uvF9WT7cE3q7zNSGmrErHVOSyHl7Kgc3lnh9FWNIUE7GkLVbEA+gKtID7DCkBWHRyEV3Du1JX2cPGiONyVjFlJjOeSjnFpUZLmuF3Z1g2YTaxwZ35xIFxxssdXqZHeD/GfJNIl4a+1b7vciZ2DOfEjXzGxISSW6JnV3I2eqPFjeREajxjYkJZ8mRbzJhR64yonGWoFFIkIpGl+fek9uSV6CkzmXitfxO7NEiwiLjxHcNpG+5lZ8H/3aEUvn4imoX7r7Eq4SYTOtXlpR8dR9w+2HqRTvU70sanHwfGDaHgbuqnCBFBbnW4kqnljZ+vE+yp4OkuEbg6l1qbMUslYtSlBda51gbAYw/QajXb4IlbUCekZRqQ3lPwSrmU1/o3IaauN4v2X7O+/0KtgdmbrzFn+Fy613XcvHxm7MsMajQQKX/fKBkIokxAQEBAQEDgX0rFWpbKmHh8PX2uHLXZdrxOU15XdOCX3u3tbvIj5bArREaA2ACUYT70GH6uq9g/K5YSg5pcTR7eSi+UUhVrjmWw8uhtVh5NJaauF/2aBVgd6Sqi1hpqnGaZlK6mQGPg452XmPFQo0rHdaznzcLHm7Hp8iamLZtrd6M7I3YWI2MGodFJHFrxB8uyeCvoa3q7J9RoXQVl8E4efFlwN4ZSChy++7ovqBJYJwhJVzG3I25Xeafq6exJjiaHgQ2VLBgTRk7JvWu7JzkPH1c50ru92MrbBrzYO4Y9TxygxKCmQJePQqpCIVHxw9F0ev+aQKHWwM08TZUtCMAiaL95sg3BXjLytfmEB5rxVvjz0aONOJ1azBvrLnArX8tX+64R4etC90Z+NPS3VzFKJymeShnfHrREQXs38WftpFiy1DobC/5VCTd5b3OyXfQuKV2NWAR7ZnRDIZPg6iTldPt8h26ihVoDY749xo+TY9mbnEtSejEavYjckmx2JJ2lUcA98ffrqTQmx3ra/D56Onta//9CDduBFZq9OVDYlCPFLTha0hyFdxN+6tABpPYfrFIupV+zAHo28UOtNaLWGlA6WdT4T8fTiQzqbtN82kvhibuzB6duFHHsejFdGyprtqi/KIIoExAQEBAQEPhXUrGWxRFtbifzyv5lNttyFSqeHzwLTyc5OWrIzLzXi8qYuZPGydORGO+l0jkZ7zBYP5X+a+V2wmdquxlseK4f45acYdH+a1abcGOZyZrGKJeIyCmuXUPcfI2enOJS5FLH6YchngoWPdGMmTtnWq3rK5KYmcjYdaOZEhXH3N5zmdarPt0b+/LZ7itcSMtnou96XvRfhUJcdSQOLHVjiwrhrVzINWHxuD8BHMBidV8BlbcfQV1GsHbBs7Rc0rjKeet71WfbmG0cSD1A56WdHYpKiTSA5RNiyC/RU1JqxMfNCVcnKZvPprMhMZ3HokPo3tiPuJUnOJFaQNldZ4+KLQgcGa+MaRfCKwMi2HhpE6M2OBa0W6YOIm7FecJ9XIiN8MFYhWuIs0zKhE51OXD5Dl6uTly7U8wrv561q0GrjCKd0Ubwvda/CW3DHLdOGN8xnFJjGVlqHU90CMdsNpNXomfmQ41wNaZxK/lnpMeOMrU0AfEuMwy5YU0z9FB4Wmsp92kdryXLCPu0cFUWwtRB64j6JAOj6V6358+71rdG3xwhlYit0eIAd2e0BiMfbElmxV2RKRVDn8gAvF3k5JZksSPpDCPbWSLNjh5o/J0QRJmAgICAgIDAvxLbWhbbyIKnppAvNs5BZiqzbjMhYtqgGWSqfAhxkpJTore64z0VdpyZbh8gEdlGMjLFHnRMukDKfcZ4iZmJjN84lslRcSyb8D5DvjiBWmtEJIKdFyyNrC9mFnH4lR64ODnI3asCT6Uctc7IqoSbTOxUl+n3pbO9P7QpGy9tcijIKrLo5EK6hnWhc/BAGvmrWDvcGdnJt5AVJtZoHQe18Gw2nCvXlOexpCrm2Y5z9XFl1qw3+bGwCY2DvSg2aagKT2dPto/dzpz4OQ7dA8tFZVxUHHP7zOOTnZfIKiplUpcIWgd7oDea+HhEK3T6MtILtMx7tBViMdZG2oVaAzdyNQz+wpJ++PUT0ZjNZtRaA96uclycTczYMaNaQbv4ibmYTE4UavW4OjlhLDNVKhzKawqNJhN+KieH/b0qQ+VsW0ellEvpE+lPxwY+FGoMlUbcSgtTmNT4JgHZ+xFl70eiTcX3/slLboBrXQDkYjkzYmcxdt1ojupAZ4JCk0WE7dPCPg1cvPvr/8PQuZzIC8JouufYcn9/spqgkEl5tX8T2lVIa9x6zjJnZJCKT0a0pmcTP7uU378jf/93IFApItG9JxNlZWVCnxwBAQGB/xIm070OtxW/iwX+ejiqZRGZTXy6+T8EFeXYjP28wwgO1rX0JysuNeKltNSkjfdez2uqb+3mvqNsSOS5y+Td3/C4AotPLiQ2uBNjYlpRqDWw6Wwas7dcBKBJoBvFOmONDTfAcqPq7+7MqLYh+Lg50aWBH2Ni8q0GEnKpmDZhrkxdOqdG12fu4bl0erQLW1Y8w9O+G5GKqm9vm22EmTnwfbmuSMFi4pFuO07uI2d03OO8Gvc+k1YmU2TUUFxqxNfVu8r549rGsf/G/irt3AEWnlxIp9AuTO3dj94fH8JDcYsO9XxwcZJW2Uj7ySXHuJFrWcveS9mEeykZ1CqQq1lFmChj143tNRK0nUO7EKroztjvTtiZujiiPErkrqBWn7dKYT+fQi7FDCSlFbLlXAbn0gopzEkhxuUs80Iu0ll1HkXRTaiuN3jWvnuiTCpjSOPBTI6awuKTi2icCqkOXPinRMUxsOFABiw4Zl3jlK71Hlg82aU16gzW2jz53Wv2T0AQZf9gRCIRTk5OlJaWolar8fau+ktOQEBAQOCPoaTEkpcll8sFUfY34P6bPulHH+CTcspmzPVmbVnQcZT158tZxTjLRHzc4HuGKX60m1PnP4CBV25WKcjKWZAwn40jdoNZwq+n0qzbXZ2k5Gn07Dt9p8bGE+M7hvPzyVt4KuW0Dfdm4f4rvNqvMa1DLWl4Yd5KCnQFNi56lWIG97TTmDfXI85PV+1w091UxddzocCExVFxF3DFdpx/qD9vvfEWgx55jFXHshj+9VlrrdTlrGLcZO6VthyQiCRMjprM4NWDq18/MP/IXA6MH0RsPU/ef7g5Sw+l8MnuK9ZUxXKS0tXM/PksY2JCWTGxHetPpzM8OgSjyUSxzkh2kZ4G/m64K82M3VgzQTv/yFz2jxuIVIxDU5fKqC6ttiJTutarNB1QKZfSMbiMjnmfg3QfUt9r93bWMCPWcOcIsnrjrT+7yl2Y32c+XcK6Mu/wHFLvN93o8DKDGw1Cr5fx3bi2f5h4uj+t8Z+IIMr+4Xh6epKZmUl2djZGoxE3NzecnJyEmwQBAQGBPwGTyURJSQlZWRYnOje3B7AnE/ifYL3pO3wI5s623RkQQMCWX1mQL7ZG0yRmPerdjzFMsdVurk3ah2nU/D8cO1SzPl+nM0+jKyvCQ660sXYvj8atPJpqNZ5Ye+IWDf1d7ZwG4Z5l+uAv4i2mDjGhvNKvMSaT2dr/TC4RkV5yxfFCyoAM4Ba4S2FeV3i6DkD1guy4Dp7JhhOlQCGwF0sD6IraJxhoD1lNsjgXeI4xysfZdCbTxryizGRm27kcZnaYxRgHfcUi/SLRGDQ1E5VYrm2BroBPR7TmTlEp/ZoH8nCbOjapihWJv5rDjD6NCPNWMnH5cZto1cAWgczs71vrc/eJDLCm3FVn6gJVp9VWpCbpgFInFaR+D6bq7f0Bco0qEoqbcaSkBUeLm/NC0yH0uy/t0lXuwqNNHmFwo0EUaPPJ0xXg5eyBh8IDudjJYksvBy/XGrib3qViHWWhVo+7Qv6Pi4RVhyDK/uG4u7uj0+koKCggLy+PvLy86g8SEBAQEPjdODs7CxkKfzcyM2HkSKiQfmopNlqNMjSYfnVM9GziR5E6B7djj6EsOGg3xYKcCfi2exOduWZuieUU6wu5niWnUYCbVWiV9woL9lTw3KpTLBvfjll9G9018tDjdbdOaHdyFh5KOa1CPGws039IuEl0mCdeLk64yKV0nrOHpztHMLbjPRc9CrH0BrsKZAPNoXc3+C4cQmrQ8kldBq/kwuJCMBViMfBIxCLwwGJl3xhoD4TcO648tfC9h7sybqltVPKzPdfZP2sQk6Im2aUoqpxU5Ghs00qrI60wh/lbUjl+Ix9wnKooEYtoE+rBF6PasOJoKgscRNOy1DrSCmt37jxtPt4utuKkoqlLZVRmEV++/ild69GzvjPK7K2QuRsCH4I6/e0nkrqAdzu4E+94fUYVCSXNOFLcnKPFzblSGoqZe+tadOA6PZv6261VLpUhR4ar3IVg9+CaXIpK0eiN7Eq21FFW1cfvn84//x3+yxGLxQQEBODi4kJRURElJSWUlZVVf6CAgICAwAMhl8txc3PD29sbiaR2Bg0C/0PKymD0aMi6r9/We+9Bt27A3WhaaTrKhH5QeN5mmAkpS41v8PCYGVzKLEIpda/V6X1cvFCIlXwxqg1OMrE1krMzKZMXezWggb8bey5ms+zwDbsb10ldIuhQz4cJy45zI9fWJOPbQyksGdeWcB8lI9uGcvjaHYZHyglKCiI9Pt1S5xUOtAOXJjDPD+I8arbmDcWW6Fh6DhAPnOaeGAvF0l8sElA4Pr5iep+xgg4uM5kpK5PyQY8PiA2OZUHCAmsqo7pUjZ+LX80WeBcPZw+KSwutP1dMVfx+YgwbEtMY1iaYMpOZO8WlDGgeyDAH0bTiUiPeSq9andtL4Uluie3vVFK6GrXWWK3QsKul0mjxLj2DW8FuZFmvIbpwBMx3L7ix2LEoA/Drfk+UyTzQeXVizqlAjpS04JIuzEaE3U9N1/qgaPRGPtia7DAiWJuUz38C/+x3JwBYastUKhUqlQoAs9mM2Vx9oa6AgICAQO0QiURCevjflXfegb17bbf17QuvvHLvZ20m7IgFzW3bcVJXzB1/ZpRPTz7Ymszq47fYPyu20rqo+2kd0Bql1I3Vp9P5ct81m0jOsvgUhkeHMHtLssOeWUnpaqauSWRMTChfjG5tTV2suL/MZGbO1vM00SWzffUSGk49hFlqtoimhwE/6KKApf4QUYPoWKYRnsuGX05jMYq4CojBtZErU8dOZXbWbPCsZhLupfdN7BTB4grNsaf3bkBGQSkBHnLcndzZMHIDGoOGXE0u3kpvAl0Da3ltVTZpoeX8kHCTmLpedGngUyPjj8tZxbjIVLU6t7uzBzuS7NMd1TpDjWqjpJoUpJk7UWbsICBrDxgKHQ/M2ut4O0DoIyD3AP/u4NGS1OwSlu60j/JWRk3XWluMZSZ2JWdVmaIJNUv5/CcgiLJ/IcJNg4CAgICAwH2EhYGzM+ju1k8FB8OKFZb0xXKc/S1pYte+q7AtALptxezekl3nM1h51+VwzbFMZnWYxWgHdVH3MzVmBhsT7/B4bDgnUvNQ64y88us5RrYNYUafRuxKzqq0iXE5PyTcpHWoB2NiQvlq3z1DhzJNIe+/+w6rv19CQe4dcAd6AW0ABTiL4ANvmOoB4hrcGnyXCzO3gTHJDZyKLBGxjkAQtKrfir49+zJ76ezqprGSry3gmW4NeSw6hHyNHn+VEz6uMoxmPbkaNd3Du7Pt6nbWXfyV9OJ01KVqBjUcxLSYaYzbMK7a+afGzGB1Qkalfb4WH7jO109Ek3xfc+6K0bTlE9pZxe7qhAymtpvB+I1jqz33jA6zOHWjyCYKWM79NvZWDMWQuQsytkHmTii+7njc/ZSkQPENcA233+fZyvK6i7ui5rVeVa71d6IvM7FoX83eX01SPv/uCKJMQEBAQEBAQGDiRIiOhuHDISUF1q4FHx/bMSIRtF0EpXlwex2oGkG3beAajl5vtLnBTLiez3ddBjGpzRS+PlW5ffrkqDi6hvRlyBcn6NM0iP8bGIlCJsFJJmbN8VuIRLB4f81uXJfG3+DrJ6JZtP8a+qI81Md+pTjxNxYaSsEXeARLOuHd+9p2TrA8ABrX4B79mhaeO1KXY5kDada6Pd8t70jDL8NtxqhL1fgofRxPUAmeCg9eW3eO6zkl1Pdz5bUBEay/tJG5hy1NmT2dPZkSPYWPH/oYg8lAUWkRrnJPfJQeHL19lEVVWNNbr+2GE5WOSUpXo9OXWWv57jdRuV/srjx6mw3P9av2c50SFcfgRoN4/ocku32V2dhz8VNIfBlMtWgWLpaBT0cI6AWSmkWzVArp77bcr5SCAsjNhdJS25fBYHnAUf4SidBqDDidOE0TmRyNzBmt1IlsVy9rs+qK/NlplH8F/rnvTEBAQEBAQECgNrRsCSdOwIED0KGD4zFiKXRcBYmvQrM3wMli5qLWGrlwX7TlRo6RVzvMpkNIZz5NmGeT8tYqoBXTYmbSNaQv45acoVBrIL1Ay5xtFzl+I5/IIBVTezZAqy/jQoYaiVhUqetiOUnpajIyszEdXkZa/AYoM0AA0AVoem+cBHjDC/7PCyQ1iI7tNfXkmatxaH2ccfOBNJMIH6WXXRpfUnYSSpmy1ul925PO4OIkY9HjzXjnwKs2PcDydfl8eOhD5sTPoZlfM55o8QRPt3ma3JIi5vSeR6fQLsw/Mrfaa1sVBVo9j7cPp2sjX3T6MvI090xUVh+7xU8nbvPxiFYs2n+NQq2BcUvOsGzCbLqHd2XO4Tl2554Z+zIDGw4kLa+M4lL7Rl6V2ti71q2ZIHOPxOTfC71vLwpdYykwyCxuhRIp8ioaVJdTE8t9hV6Hb0k+rzU24LTuF0utZWYmZGSAvz989JHjA+fNgw8+qP49AN7Auvu21Z21ETP2v5RNsq+jiFsLIYGWhyUBAZZodvlLUUnh4t8IQZQJCAgICAgI/KOpld22SgUDB1Y9ocQZoj6x2VSotb2ZLi414iKXMuSLw4yJacWmEXsoMajJ0+bjpfBEKVWxOiGDIRtOWEWDp1JuvYlPSlfzzcHrvDUokme712VUTCAlBjW5mjy8lV4opSrWHMtg5dHbFJcaqe/tRObRDfRsPwZ1YaGlpqsH0Nx26eFSWBkAHWtwD5tqgA8M9fm/R3+iNOmwdfuotiHIJU526Zll5jIWn1zMC+1eYMLGCdXOPyP2Xnrf2PbB7E39jW9PfUML/xaonFSoS9UkZSdRZi7DZDZxNussM3bOwFvpzeXcy8Tfimd+7084MO4ghboCqzW7u7MHC/fetLm2VRHoriC3WF9pXdlj0cGUlZloFOBGckYRN3I1vPzzJb55cigDGg6kQFdg/VzdnT04daOIAQuOsWx8O+vn6S4popfqGOPDzxNRb5Vj4eTfE8Rye2Hm7AcBve++eqGR+lvcCjdcJyn9qM16a+JWKJWI6S0v5j1JCpePJ1GnMJugohz8ivPwLcnHrzgPN73WMthRf+4WLSoXZc4PXnumlTphFjkWlPVyb+O+cUXlB3t7w6+/QpcuD3z+/zWCKBMQEBAQEBD4x/JAdttmM1xaAMEPO67RccD9dToV7ey/2pfCov0pNApwuxvpUnMps8imzikySIWTTGxjSBHg7kyojwRfv9MMWjvGGpGRiCQ80uQRxrWcyN7OsWzfto9XZ0wmNeWaxenwIaAdlpBYBUa7wVe+4F4DU9BvCuGlHCgyXWW6QU2jADfEIhGTu0TQo7EfWepSBjQcxJSoKTYphAuPL+T408cd2tlXJC46jkGNBtL/02NIxCLGxNbhl+TdXJ96HY1BQ44mBx+lDwqpgq9Pfc3C4wvJ11ks7T9L+Iz1I9czJ34O7b6NIi7qGeb1mYeu1J0rGRoaBUjYfzm3RoIsMkiFTCJmysqTdnVnFevKZvVtTIDKmeQMy+czPDqYRftS+PrANfpEBuDtIie3JIsdSWcwmizzKsqyaKtfw+t1DxPreh6JqMziTpm7B1wfs1+MzBX8ukHWHvDrAoF9IbAPeDSHu2Klxm6Ffeqj1JZYxIoDFLPf4/Effqj2+jikoKDyfU5ODzYnoJFVfmxTeTURxNxc8PB44HP/FRBEmYCAgICAgMA/kspuYMPz0gi+dJgX0jvY222bTXBqukWUXfkKeh+yRCqqwctFytxHmnPgyh12XMhGbzSxKuEm4zqEM/Pns5jMWG/oHTG+YzirEm5ahUHzOipmD2vIyztnWkWPp7MncW3jmBw1GY1Bw5VbVxj/5Fg2/rwRREAUFhOP+6JgKrFFjI1RVX/N0ozwVBZsq+CsX2oqYsXEGCRiERsS0+g4x+L091yPCOb2nkeXsK7MvZvGl6/Lp+8Pfdn1+C66hHVh/uH5DlMLH248mE92pHIrX8uEjnVxdRLhKndlyJohduNfaPcCx58+Tt8f+nI17yqnM0+jMWho5teMs1lnWXjyK7qEd2Zww2H8di6T82lq63Wvjqc7R7A0PqVSIxCo4NLY0Je9l+5Ym3S/tzkZowlrY2iAcHkaD7kfYUrIGTz3nubdOg4mTN8CYQ5EGUDbr8DJB+T2LRUqcysUmU1E5KbRMvMyLTMu0/L7Kzjl3MDUozvibdscnycsrPI3XB01FWUymeVnJyfL/5vNlh6Ad19mk4kyvQGRTofEbEIjrzx829OnBk8Sgn9fv7T/NYIoExAQEBAQEPjHUdkNrFKvZfG62TTKuclX7R9lvunxe3bbojI4OgFurLQMLroCe/tCr30gs1c0xjITBpMRo0lPga6AxqGFdGjkz4ePNOLsrRJmb7nEwrFRjG4XWqV7YsWbfAB3hYxl46PZcHGdVZDV96rPtjHbOJB6gMGrB3Nm5xn4DdAC/sBAbJozl9PB2ZKuWLcGBnor1PDCHSi4zy3QS+HJvovZzPrFIi7DvZUsm9CSfTd/Y+Dqr+kY0tFqW1+gKyDANQBPhRf1XHqxeeQAivWFdmmbecFw5HousRFeTH8ojJk7Zjg07UjMTGTCxglMjprMtjHbaPtNW/J1+eRqclE53ftM5sbPpWtoXwLcnWkf4YVIJKr2uo+NCaNtuBdvbbQ347ifxQeus2RcW3xcnYip68Vji49YI3F15WkM9DjAAPdDNFakWg7QVjFZ+m8W8e8oVc+tXqWHWd0KzWbq5d6mw80zdEw9Q/ub5/DQFduNN128WPkaairKXFwgMNBSwxUYeO//zWaHhhzExcHkySCX2zqXOkAE6PVGPthygZ/ir+FkdBwNG9s+jDBJd5Ab4M4dyys9HW7fBvXd6LdCAZ416MPwF+ZfJ8quXLnCunUbuJCcjFqtxtnJibCwMHr26kHPHj3srOLLysrYvGUru3ftJiMzE2dnZ5o3a8aoUSMJCalckR87dpz1GzZw/XoKAPUiIhg6dAjR0dF/6vsTEBAQEBAQqMRu22xm7m+f0SjHcqP+zNGfaZZ5jS+93qdng05Ij42C9K22h+Qnoru1ibI6jyIWSax1aFq9Ea1Rw6bLmx2aeMzqMIu1Uwbx1vorTO3VgKgwT5bEp9ilUI7vGE7bcC+eXHLMepP/eGwYYrGR/xyZC1giZNvHbmdO/By+PvQ1bAaSABnQG4iF+/v/ioHXveCtGph55JfBpGz42f6+ntYBrXGVqZj1ywFMZotgXD6xJR/Ev251HzyQesBqxFFeC9YhuBMvd3iP6WsuEOKlBCA54ybJGWqaBKoYHRNOZqGOHyfHsOHir1W6KAIsPrmY9sHtmRI9hQ8PfYi30ht16b1reTrzNOrSQpbEp5J4q4BnutVjSrd6tAnzYGm8fcPtyV0j6NHIj4GfH6pRmmNSuhqz2Uz3Rr6Ygde7Krl55ifaiXbQXHmt2uMBSy1iQB9LWqzJCJJaWNOXlFC6fgvjv/2Grikn8SvJr/YQcWoqlJRYhNX9hIdbDDvCwiyv0FCoU+ee8Cp/ubnVfI1gEWO1QCmX8tqApsRE+LBo/zW7z2lK13r0bOKHs7wZDBpgP4FaDWlpkJPjWCT+jfhXibLDh48wb/5/MJlM1IuIoGnTJqjVai5cSOZCcjJnzpzlpekvWsebTCbmzpvP0aMJuLi4EBXVhiJ1EYePHOHEyZO8/967NGzYwO48Gzdu4rslS5FIJLRs2QKZVMbpxETee/8DJj39FAMGVNJxXUBAQEBAQOAPwZEb4qRjvzLwom3TXN+SfG7k3kF2oB/kHbbZZzDDE5lmLu6cz4xYMYMbDUJnkINRTIm+hDf3veLQFj0xM5HRv44mLjqOOQ/P5dcT2QyLCqZPpD/Z6lJLM16VM2VmM6sSbvLe5mSrMJCIRTzePowcbTZnsixNh+PaxrH/xn6+/vlri12dGktUbCjgZf/e/STwQwD0UlZ/nfZq4IksuG1vEgjArI6zuJqlw81ZRqHWYDXkuP99lxtxlHM26yxdwzrz+ajBpObpbNwMG/q7sirhJmNjwxCJjMw7PKf6hXKvlmzHtR0opArOZ5+32Z+nzcfVSWq1sU8v0KIzmFj1dHs0eiN5xXqUThJkEjFHruWSVqDlRq6mkrPZk1Go44OtyRar/Miv6O2yofqDZO5QZyAED4WgviB1IJCqYs8e+PRT2LkTT52O4bU7Gi5fhtat7bf36WNxU/wLoJRL6dcsgJ5N/FBrjah1BlTOssrNeCqiUlle/wD+NaKsrKyMRYu/xmQyMf3FaXTtes+d5dat27z62mscOHCQ3r170aK5xapo1+7dHD2aQFBQIB9+MBuPuwWEhw8fYc7ceXz8yad8+cVnSCT38lxvp6WxdNlyZDIZ77/3Lo0bNwIgLS2dl195le+WLKVNm9YEBgb+9968gICAgIDAv4z73RA7pZzm5f3Lbcc4ufDKY8/zQ+S7yPJso2oaEwzLgO0aoDiRsetGMyUqjrm953Ir18CxrK0OBZlEJCHSLxKVk4r4W/FsvbKFqPDevPrrOWb1bcxzq0+hUsj4clRrJi07wbk0W+HY0N8VY5mJO8W51vmeavUUsY/HwlYs5h09gE7YRccAuilgVQAEVnOHZxZJWSdvw/Arx3DQ2xiw9NrqGtqXY9dK2PhcRyYsPc6omEAGrqm+ITbA3MNz6RTyECMWH8FktkQ+JnaqS68m/uy7mM0bA5uSr7tjFZ/VUV5L9n9d/o/FJxdjMtuu3EvhSfHd6Fl5z7Zlh1KQiOCnE7c5eOUOxaVGLmUW0SjAjS9Ht6nRectROcsoLjVSqDUw/1JrltWtRJQ5+1uiYcFDwb977SJi95OZCZs21Whohqs3Z4IacjagAYmBDfnkw3H413VU1PbXQ3pXfCnlUgLcH9zB8e/Mv0aU3b59m8LCQurUqWMjyABCQoLp2rUrmzdv4eqVq1ZRtmGD5R/Bk088YRVkAB06xNKuXVuOHTtOQsIxOnSIte7bvGkzJpOJAf37WQUZQJ06QQwf/ihLlixl06bNTJr09J/4bgUEBAQEBP451MrS/i4V3RDD89L4fONcJBVu4k2IeOexp/kk6j9EyNNtjs0rgwHpcFRnO+eikwvpHNaFQQ0HMXrjPJt995twlLsHuspdcXdyRiIBbxc5YpGIhOt5LD2cyhOxtmYUErGIyCAVpUYTvq4W17xGLo14cviTZO3JAh8sDaAdPNetTbriZT28oglg3shVfFfvIAsSFjhIv3yZfvUHoNaIiQr3JClNzapJMWjKcmolorRlRQxrU4ekdDUXM4uY/uMZxsTk88Gw5pQaTeQW59VornKKSouIDopm/IbxNttbB7RGKVVZ3SvL0w2f7VGfbecz+XjnZdtrkFWMk0xSSRNlM22VSQz2OMD7GU9RapbbuWMeKmpFnlGFl9RyrE7qi7zuCMRhI8AnFsQ1MKYox2SqvP6qf3+QSsFoH8pMd/PhcFhLDoe14EhoCzJUvtZ9kUEq3Or413wNAv9z/jWiTCarQYUr4HY3dzYrK4vbt28jl8uJjo6yG9chNpZjx45z/PgJG1F24uRJy34HTSc7xMayZMlSjh8/IYgyAQEBAQGBGvBAlvaASiElMkjF7WtpfPfLu3jqbJ0Pvxs4mOk9fiBYfsdme7oR+qRBUiUO3PMPz2VQwwGcyz5n3VbRhMORe+CsDrOYPXQwZpOJyV0ieGFNIiuPprLxuY6MbhfKlnMZjG0fzKiYQLRlRRQZUqirCqehviHXPrrGhZwL0AwYDDgIutQmXfHbQph2B0rMt9n3TVumRE9hw8gNiBBRoCtEJXfHWapi14U8nl5+jlM3C2gc4MZz3evj5iwjPaf6WqaKaI1qJnaKQC4RW9MXVx5NpV+zAOp6u+CtcJB/WQUBrgFM2TLFao9fztSYGaxOyLBxUSzQGHCWSlgSf8NunjKT2cYdEyBYlsUwzz0M89xDuFMGAAklzdhc2MXOHdOIlLV5ffCQqNlU2IUiVQfWPty5yv5gdty+DUuWwHffwebN0Ly5/RgPD+jaFXbvxiyRkNMmhq9UkeyLiCbFM6jSOqpKG1QL/GX514gyf39/AgICSEtLY//+A3bpi/v378fV1ZX27WMASEm5AUBYWChSqf1lqlcvAoAbqTes24qLS7hzJweAiIi6dsf4+vqgUqnIvnMHjUaDUlmDb08BAQEBAYF/KTXuyVTR0v4ucomYKR2C8fnPs9TLS7PZF9+xBQ8/uh9fWYHN9mt66JUGNyqprwJL9CdfV2C1Y7cx4XDQl6tifdnc3nPp0tCbpzrV5dtDKTy55Bg/PBXDSw+FsfnKZpteZENNQ0n5TwoGowEGAG0dr6em6YqFZfDUfWYe+bp8Pjz0IXPi53Dx2UvU82xEaVkpd0pyqRtQwPuP1EEpbcK609k0r+POtwdTeKiFvVV7VTiJ3Zi2JpHkjCJrM+aNz3Vk2eEbTOocgcrZg1YBrWyEbGW0DmiNGTNbr9iasUyOiqNrSF+GbDhhs93FSYLWUGZXW1jOyqOpbHymHbOjLhGSt5IubqftxjziuRv3xmNt3DHLmZM57t4PxRrUWmP1oqysDH77Db7+GrZssUTJAL75Bj77zPExM2bAuHGI+vfHxVWFYWsyKQ7+TZQztn0YPZv4VV2LJfCX418jyiQSCdOmPs9773/Ax598yoYNGwkMCqSwsJALF5IJCQlm6gvPWyNld3Is4sq7kqZ75dvv3Ln3hC0nx/L/rq6uOFfS0dzb2xu1Wk129h3Cw6u3Iy0uLqGkpMRue87d9QkICAgICPwTqczS/n5WHk0lpq4XnRv4kKXW3UttFIvo9+W7SG+esxl/LSqIyEnX8ZDaWg0mlULvNMgoq35thboCqx271YSjikbJAAtPLKR9cHsifZvxQq+GDGwRyMnUfKRSI2/ue5WvTnxlGWgG4mHdrnXgAQwHHJQFibCkK75dg3TFEzoYkQnXKzEZNJlNOEud2XV9J2/tf90u0vdS+5konALZkJjGsKiWtRJR96cUljdjntQlAjdnKRczipkRO4ux66qvU5vWfhqLTiyy1pKV9zzrGtKXcUvO2LgoRgapUMql3MxzbOQRLMtipGo7rttGMUaSB5WYDHZTnaZBjJKxFdwxK0OtM1ReD5WfD99+C59/Drdu2e9fsQI++ggcPbDv29f6v0rgtf5NiKnrXaVbYa0idgJ/Cf5Vn1iTJk34YPZ7fPjhHK5dv86165aiXqlUSquWLfH3v5d7q9NaGkw4VdKZvFx0abX3Es61Ot3dYyov6HR2dro7tqoGFvfYtGkTa9b+WKOxAgICAgIC/xQcWtpXwqL912gT5km/BQethhLzrm+j6bJlNuNyIlUEvJCHi9S2WEyvak7X0+fIrczx4j48FZ408GrAkVtHmBw1mcGrB9fouE+Pfsr2sdvRlWnw8dDT1qkEbZmYWR1n4aHw4KujX1HwSwGcAsKAEVjuwu/DXQwr/GGQa/Xn/KwAZuaAvormyK0DWiOXyBj64yA784zEzEQeXz+GKVFxfDfufdadymJquxmM3zi22nM7SikErO6I3Rv5kV1kYFDDQUyOmsLiKmzx46LjGNZkGPnaQgY1eMym59mQDSfsBNPkLhFsPZdBlwb36qwklNFTdYzRXtvo4nYKsaiKiwKUuTXikHEAb3x3mlsllTc2Lkfl7KBU5vJlWLAAli0DTRVOjwUFsH07DB1a7Xl+l1uhwF+Wf5UoO3DgIJ99/gWNGjbkpZemExoaQl5eHuvXb2D9ho2cO3eeOXM+rHH92X+DQYMG0aNHD7vtOTk5vPb6G/+DFQkICAgICPz5OLK0r4ykdDU6fRmNAtxIzigieN82Gq/70GaMWalE9PnPSEpeAfWpezt8O6Lv8CMhaQPIrWn0R6bk+XbPczLjJBqDplbGFyWGEl7e+TI/Xrj3wLVVQCueavIUkTsiiT8VD62AQVicFu+juRx+DYT61Rj6lUncOFxnIlOvfFrtul6KfYkFCQvsBFlFFp1cSPvgTpjMbegW2o9JbaY4dJ8sZ0olKYXlLI2/Qd/IQFoGe/LT8Qzm9ppHl9CuzDsyxy5SNzP2ZQY3HoTBIMNH4c+2szp+PZXKpcwiO8EHlqbQPZv489Ty4/SJDCC2jokow4+M9d5KgKxqYxGTzINdJd35TfMQM/o9ztwVJ7lVUv3vYWSQCpXi7m212Wyxsv/kE0uKYlW4usKYMfD00xBl72FQGYJb4T+Pf40oS09PZ8Fnn+PuruKNN15DobA88QgKCuKZZ+LIy8vn+IkT7Nq1m379+uJ8d39paanD+XR3o2IKxb1/CIq70bPS0kqqgwGdrvTu2OqfuAC4urrg6lrLnhYCAgICAgJ/c+63tK+OfI0eVycp0beTWLBpPmLu3a2bRSJEK1fi3b03lEbBrq5QeN7SyLfLr8iRM6vDLEb/Wn0K3QsxL/DD2R8Y33o8T7R4ghxN7coJ0tRppBfbuj0mXk3kubeeg2wszaA7Oj52lBt86wfKagIhZR5tOBb0JaGhDZmUr6tSPMVFx9EptBPP//Z8tWtfkDCfjSN288S3iSwZP5sOIZ0raZz9Ml1C+jLmm9OVpvwlpaspKjXw1sZz/N/ASA5dKaSBqicHxg2kQFdAnjYfL4UnHs4emM0ynl52CrXOyPReDRjdLowAlaLK9D25RMyomFBWJdzkhWa3iM1YWeV7O17SFHO9pzhU2p3PTlpqEPumq63GLNUxpWs95JhhzRpLGuKZaoR6VBRMngyjRlmEmcC/nn+NKDt48BBGo5HWrVtbBVlFOnbswPETJ0i6cIF+/fri6+MDQG5ursP5yrf7+t4Li/v4WP6/uLgYnU7nsK6s/Dg/P1+7fQICAgICAgIWKlra1wRPpZziUiP+pVrM2BZZGd7/AHl5WpiTF/TYCUkfQuu5IHFCDgxoOJBJUZOqrA2bHDWZzqGdaftNW/rXH8IzbZ8hqySrVuv0VnqjLq0QeSkAvgeKgZFAI/tjpMA8H5jmWf38xvrPskUynalrkgn3PsOyCZWLpxdjZjKk8UCiv4myczN0hCXSp8ZJJmHIFycYE9OKTSP2UGJQW0WUUqrCSeLEs6sSq23MrNYaGNs+jFd+OUufyACiw70o1popM3oQ5OKDWASL995gRcJNq7ib+P1Jnu4cwfTeDapN3+vVxJ9ztwsJajOagox38cD2s1KXufBLfg9W5z5E21ZdmNQyguVfxFvFXecGlnvBse1Dq6xttBprpKbA6NGWSJkjJBIYPhymTYOYmGqvt8C/i3+NKCsXQy6VOB4qXSzbi4sthb9164YDkJp6E6PRaOfAeO2aJc89PCzcus3V1QVfXx/u3Mnh+vUUmjZtYnPMnTs5qNVq/Hx9BedFAQEBAQGBKii3tLfvIWVPxR5SyfWiroR0kQABAABJREFUGT1yNkt+fgdPXRGrW/Sh+zNTCah4gCIAohfYzJGuTmdWh1nEBsc67Ns1NWYqnUM70/eHvuTr8skuycNN7oeP0rdWxhcKqYLz2ectG3KwCDITMB6H/cf8JfBjIHSpJsFGhxJillDs/zCLvzsGwI1cjVU8/TZqH3pzEWmFOVbxdOByAZfu3OBq3tVq115OnjYfVycphVoDX+1LYdH+FBoFuOHqJKW4VI1ULGLh2ChOplYv8uRSMa5OMr55si1qrYGyMjMXM9V8te+atcmzo/TEbw5ep0WwO50b+KAzGPF2kaOUaJHpi5G6BlvHKeVSXuzdkKPXcolq+Bxc/j8ATpU0YlVePzYXdKJeoB/PD6tH90Z+3CnWsX1aFztxV2NjjQYNLKLrR1svALOnJ8aJT5E/7mnyvf0tZjR6o1D/JWDDv0aUeXhaHi9dvXbN4f6rVyxfSH5+foDFQj84OJjbt29z4sRJq1V+OYePHAGgbdtom+3RUVH8tm07hw8fthNllR0jICAgICAgYItcImZy1wheWJ1Y7dj7e0idrtOYL6cPp972W7zR41l+KzXaijIHuMpdaLGohbVvl8agIVeTi7fSG4VUweKTi5m+fbo1ouSt9OSOuhSFnFqlPi4+udhSu5UJrACcgLGAg3Zdsc7wUyDUqeZuTa9siKnjzxzN9SNMY7CpxSsXT/sv57BobDQfb71JcamaS5lFNApw49PR1TtBV8RL4UlxhUifyQzJGfd6wP1neEubz6IyIoNU+Lo6ceJGPm9tTOJiZhEHZnVn3vbLNaolLDd3efTTjTzuvYkJvlvI9+iCR5/1yCVia7PxIp2eNmGeyMomUaq7hrFeHIHK5jytNTJDYRtdc3V2nEZoZ6yh1aOqrIH5q6/eE2UNG6J/9jl2tuvLV8cySVpxGbhsff9V9dkT+Pfxr5HnMe0sDT6Ski7w22/bbPZdunSJjZs2A9g0gh4yZBAAy7//noKCAuv2I0eOcuzYcQIDA4mJaWcz18BBAxGLxWzbvoNLly5Zt6enp/PTTz8jkUgYNGjgH/reBAQEBAQE/mlIJWJ6NfFnbPvQKseNiQmlbbgXPySUp5eZecl/Bf/P3nmGR1WtbfiengnpIQUIKfRQpAQIvfdqQVE6qAQOCEqzYTkWELCAohBEEKUKIk0EESRAgFBD7xACpJA+SWYmkynfjyFDJjMpKPodZd3Xda5j9lp77RIC8+R91/PMbLAMvxeykUgtzl3xSuCl9ibEK4TZB2YTtiCMwRsG89ru1xi8YTA1Pq/B7AOzbYKsaWBTAt198HNXUcWrEn1q92dss3Flrl/U+rj42GKrIFsBeABjcCrInveAvUHlC7Jdue2IPDELs0c4v5xJIVPrfC/exZQ8pFIJWoOJC8nWCtTl1DwqKTxoEtikvNcDOFrcl2RYqxC61PPnbq6e4a2C6dMoEKX8/kdNmVRCeBV3WoR6M71HHWKvpjN9w2nOJWmoE+CG3lB6plhJctKu4Hb6FQ6Ej2FywFrcpbkE5mwnetsOcvVGRi8/SqvZu+n+2X6avLeLwd9e4pDfR8gqN6OKp5q6ge4EerrgqpRXqFoll0lxNRoIXPoldQb1IbCS3Hau0WRGZzCSV2AkKbQuGeNeIvu71Wjjz/BecCcmbLrsUPEtytmbtf0iWkMZwXiCR4ZHRprXrFmTxx8fyKZNm1kcvYTtv/xC9aDqZGZlcunSZcxmMz17dKdJ48a2c7p17crx4yc4fDiOCRMn8dhjjdBocjl37hxKpZJXXpmMTGZvjRRUrRqjRo1k2bLlvP7GTJo0boxcLudkfDwGg4EXX3ieKlWc9CcIBAKBQCCww1UpL7N1bHSbEFqE+TLSliFl4Y0qyxjr9xMA3TyOsqvV17i5dOZWTrpt35O3ixdyqRKNzkyW1oCnWomXq8JW8TJbzJxOPV3qfU2KnMTu67vpXrM7tzOt57/e5kM6hHTg40NznRhfzKBltZbW1sebWdaWxUCse8hKJO/IgE/9YJJX2e/GZIGTAc9y0nsmrd30aHSFnE3KYZxrTefzzRZWxyUyqk0o0zecth1bE5dcYYv7GW1nkJlvJryKh8P3YmKXWnSq44uJQib3qEyGLhNfdQAfDarLmVv5nE3S0PexyuQXatAYsqniVgiWSkzoHMbKw7dxU8lLFZTFqeuSQJTfjwzwikF+20zx7YNSiYWAOwvZfbExESHexN2477R45o6GUcuPlRo2XiZ6vTXsefZsSEmxHlu5EkaNQmcwoi80s/viXZbH3rC+F8+eTPCvRdj5uxXO2evdMFC0Mj7iPDKiDGD0qJHUq1eXHTt+5dq1a9y5k4RaraZBg/r06N6dDh3a282XSqXMmD6Nbdt+5rfduzl27DgqlYrWrVvx3HPPEly9utPrDBzQnyqBgfy0aTPnzp8HoFatWjz5xOOidVEgEAgEggegtEwmF6mF5P6DWOFem4QG3QEL71RdwujKW+3OD9P+zLjvmxF95/72hSaBTZjWegb96/TnvXXnib2WwctdavFCpwGMixjH4jLysoqbfXzR60uSUxrzXGQYr264RJtaTdk3aj9381NJyUuxtT66Kd148ocnuXrxqrVCFgQ8g8OnMG+pdf9Yt3K2nacZ4dkUyMq7xJbBVVmy9zbZWgOXU/NwUcpK3Yu38vBNtkxsy5CWwaw+knjv2G02Tyzf4n58xHjaVOvJphNpLBnRHL3BRJbWgJ+7Cj93FWYK2Hjxx1JF6aDmPRizeRRbLm+xG5vcchqbJ/bm/W1X8XEt3dylvst1Jgespqfn4TLfjYcsn0WxN1gyogWLY645tFE+kAgyGGDZMvjwQ7h9237svffQPj2YPLOE+buu2N4nWCuCQyKDeXGF8ziAkiyOuWY1ChGi7JHmkRJlAK1btaJ1q1YVni+TyRg4cAADB1YsGLKIli1b0PJey6RAIBAIBII/jkMmk9mM8fnnCY/ZztuALE9DyJMpDPP9xe48MzA2Fb7R2O8nj0+JZ9hPQxgXMZ5Fw+cS9d0ZRrUL4/wtDfN6zKN19dZ8dvizcs0+Pjk8jy2Dd7MmLpFBEUF88PMFBkUE4q7y4FLGJT6P+5wNFzbwWrvX6OnZk30r9llDoQfhkEEWroQtFcgfO6qHp5LhlhHQ3XdD9HJVOq2GFSdHV8jIZUdYMaYlzUK8WB6bwLkkDaOWWV0a21Zvz2dOXBqnt36VDsE9Gfa11VFx/m9X6NOoCoMiqlEnwB2TpYDpu6Y5DX+OT4lnyMYhREVE8WnPT9mfuN/WBhqfEs/oLcOIihjPW/0/QClzFJQVEWOFFhmbszqxOO0prhYEA7l2uXUlKVcEWSywfj289hrcuOF8zo0bpG3cypHw1naCDHjgVsxzSRo0OqPYW/aII777AoFAIBAI/jlYLDB1KvJvv7V+LYE3ay8HX/tpZiSMSLGwyvn2J8AaiNw+uAPRI57AaILfLqZRzc+Di+kXK2T2cTLlJIWWPM7eyeHp5kHMeaohu278zJRfX2Jc83HM6zGP/3b+L5euXWLCiAlQA3gChx39fVxhTSB4OAmLLs7XOfBSGhQUq/5k6rI4l6THQ62gQVUPp9Ww4iRkaBmwMJa5Tz3GurGtycw3oDUYkSBjUP1B9KndjyxdFgWmXFwVniilblxN1SOVuPDNyBZkaQ14uypRKaScvZODVGJm86UtTgVZcaKPR9MqqBXjmo9j9oHZJcYW0TqoHSHqLjZBGe5ynckBa+jleajUNQ2o+T6tB9+kDySp0N9urCi3zhlliqBDh2DqVOv/l0aPHujfehtVowiWf3vUYbiirZjF0egLRQj0I44QZQKBQCAQCCqM0WS2Odvl6Kz7qZy60P1V/Pe/MH++9b8lQBRgv/sAi0TGa/nVWJVb9n4egI8PzWVAvf7kayUUGI2kaTOYfWA2c2Ln0NC/IR4qDzQFGs7ePWt1TSxBtj6duU+HM2P9Wb4Y2oD2y+eRpc+yrVFPXY8bn95AV0UHA6BEhBrTveEjX5BKHJa2YbRYxdjiHMcxH7U3WdqbHL+ZxQvta/DKunin1bAiGlT14MX2NWge6k3s1XTa1fZl3o8XUSmkDG4RzODoQ9QNdOc/nWrRoY4fu86nsDw2gQvJGpv1fYCHC53q+tG6hi9Ijcw7OKfc9wzwedznbHp2E3Ni5zi8ywVxH7P12T6EVXZnWdvjdMl9p9R1tHhiqDmBAb82JTHfeVZAUW5daTiIoBs3rM6J69aV/gCdOsH770O7duTlFpCvK3RaDcsrMJbZiumMipjRCP7dCFEmEAgEAoGgQmgNRn67kEp0zHWHD/p/i733Z59ZRRlYq03jgTb2UywSORlNv2LeurEVWvJkykmydFko5ZV5qUtt9OZ0gHLNPopQK9R8ffJzFg57hfT8DE6lnrKNmfVmzkefh2pAf/vzVBL42h+Ge5S9foYJBiXDXp3jWHE3xJk/nWH75Pa2CtmAhbEMjQy22//l7arEU62gklJGTkEh6fkFXErNY+vpZMKruOPjqrRZ3M/cdJZtL7XFTSV3WEOlkLLxxG1UcilZunS7Zy6Lkykn0RZqaejf0OHdJmQnoJSb8HCRo2zRHcvv7yOx2IsqjdmDm/7j8Gz8CiO+u0BivvNw6uK5dTKphDoBbvdy1IxcTs3DZC7myJmdDbNmwYIF1j1kzmjdGj74ALp0sR3SGoylVsPK29vn7H491OIj+aOO+BMgEAgEAoGgXLQGI7O2X3DqJldk7/2HnO0qytKlMGWK9b9lwETAPpWGArOcU6Ffo/KKeKClb+ek8+n2RPSFJr57ockDh0G/vfdtIqq0pIp7wP1BI7AW8MdBkPnJYHMVaF1OIPTZAhiQBDdKKfhMjpzGmrhkzBa4laVj3PfHWTw8gpZhPnxz4Dq/X7rL8ZuZ+Lqp8HCR07NBIC3CfHlv2znWHbtN3UB3Fj7XDHAUEjm6QoZ/Y624HbqWzt5LaaRq9LZQ53f61+dOth6LItP5zZVChjYDD5W9Eq3lU4sdQ3ew6/pO5h207mlb7A9RntbxAlklqDcVSdhL/HIwnVWLT99z23TO6LahbDxxm/GdajEkMhi9wUSm1oDPPUG5+0KqVQRZLNCxI5wuRXzXrAlz5sCTT4LEvpTpqpTj4+o8jK28vX0lGdexJkph8vHII0SZQCAQCASCMjGazPx2IfX/z9573TosY8daO//kwEtACTPjArOCqJtv4ippxOuNvB9o+aJA5AvJuSzdd4spraYzYtPQcs8rHga9PH4pH/f4xDpgBjYCbsBA+3PClfBzVQgrp1vtlCyE9rdvkuvYMQlAVMR4OlbvxcDN9x3+Yq9lMDj6EKteiKR7/QA0ukKytIV4uyrwUCvI1Rei0RWw/WyqLaesuBArKSRuZemYtv4Ujzetxsy+9ckvMNqqZYGeLiz47QrD2zkJWSsDX1dfNAUaKkmgthJuSrzZOWwnc2LnsOT4Etu8WZkwsBJ8kQ1f5OQzrFIac8PVyKSUKciGRgbTqoYvFouFw9czGfvdMaftm4BVaE2aBC+8YL+Ilxe8/TZMmABK522IrioZRrPlgZwunTGsVYhwXhQAj1B4tEAgEAgEgj+GwWRm8d7rFZq7OOYaBlMpSuKPsHUrDBuGxGIBBfAyDoJMZ1YxJuEd9uY250pqHt5q7z8ciLzy8G3aB/ViXMQDhEEDP174EU8XT+t1d9+b9AR2e8i6quFgUPmCLK/WDKr0OcnC/qscnqNJYBO+HbiK11p/wKhlp+wESqivK9+Obsm+K+k8E32I1h/toc/n+2n90R6eiT7E/ivpuCgUTOxszTIrXtGxPvtNWob58HzbMCZ0rsW+GZ356MnHqBPgTn6BETcXGZdT8ziZmEWOrpBvDyXg5eL1QO9aLVXQTn+Ga6FWt8mXmr9ATEKMnSADSDRC9RswKwtyzbDo+CK2XNrKhM41+OK5pjSoal9ta1DVgy+ea8rL3WojRcKivddtwdTFOZek4eV1xUKbR42Cxx6zDsrlMHkyXL0Kr7xSqiADUMqkZOTrbe+uJEVOl1Eda/Dx04+Ver9/WWVZ8I9D/CkQCAQCgUBQJhqd8U/Ze/9hc5Cff4anngLjvf49H6BENnK+yYXnE97mcP5jeKoVfDOyBQcuZ1c4ELl4CyBYP0yPWnaKtVEf0SGkA3MPOuZulbTGBzBZTGTk59D0TnPi0+OtOWTFHu15D1jkD4oyDD0KUTEz+RXOpvfhRYWWPrWfoHtYH/IKc9CbcvFQeeGj9uJ6WgGvbrhEQsb9PVWRYT4sHhbBJ79eYmWc8xbT6RtOMzQymNd7h/PV3mtkaQsdKjpvbDzD4uER/HbhrtMq0wvta9CzfgCXUnMxGM2cuJnHtNYzGPbTkHLf9UcNOuOyqxVf+t9v+3vZW0HnuAVO55fs2vz44FwG1O3vNLeu6M9TocnMzqupdtUp/9wMJFhIda9sO2ZX1f34Y/jqK2urYp065T4HWGMawiq74efmUr7T5aDHWB/VGo3e8X5FhUxQhMRisThviBX8T5OaepexUeNYEr2YgAD/8k8QCAQCgeAPcilFQ8/5+ys8/9dXOlAnwB34E+Ygv/wCjz/uYL6wqWcHOg45ibc8l1yTmlE3/stxbX0AJnSuRaivKx/8fIHNE5sz++Cb5QYiz2jzAdN/uAhgZwQRGebDouF1ySvMc2qNv/jYYpsgK2J5w1U8//pwzIPNtl97S4DZvvBqOV1+6abKxIctp3Z4Z0YuO0JChpahkcGM7VCDaetPUUklp2MdP3o2DEQhkaBSyMgvMJKjL8TTRYFKIWXvpTSm/FC+6cbnzzYhI9/Af7eeB6wVthVjWhJ/K5sWoT4s3HO1zJa7qd3r8FREEG0+2kN1bzU/T27JjN+ml2qLX1cB62sF08jouKZJ7onvpRxyKlhcvfXyLYI8g0od1xqMDFp0iPPJGtQGPWOPbCTqyI/srtmSlwa+aje3QVUP1o9r/acqVTqDEX2hmT0X77Is9obDn/FxHWvSNdxfVMMeEf7M53PxJ0QgEAgEAkGZeKr/mL33HzYH2bkTnnjCQZCtfawHrzeeSP0b11kcMptJidM5qa0HgEwqYUhkMC+uOGardn075kPaVG/PfCeByK9ETueJ8AHkF8j56MnHbEYQLkopV+9qqBXggkIm44mVT2A0G8u1xq9nrseEN17E/Mx9QaaWwHcBMMi97Pd1TleT2do5FJz1oUNBEivGtGTAwlhWxSXSNNiLlmE+7Ll4l+1nkll/7BYSCTSs6slrvcOJ3neVQRHBeFdSsnR/KUHHJYjed53VL7Zi44nbnLmjsVV0vhnZnGMJmWUKMoD5u68wvHWIbT/V+O/Psmj4PDoEd2TeoTm2d+0thYXVK/OsMhOpE0EGkOfZCHfpgQqLskx9dpmiTKMzcvFOFk+f3cPU/d8TmGc1Iul/cT/Lmg/kZLV6trkPI7RZrZSjkJnp0yiQXg0DydEVotEX4qVW4OGiQCkX1TBBxRCiTCAQCAQCQZl4qOUPbO/9p8xBkpMdBJlxxAhWNh2DJSWPc7padLm0mELL/c1ZdQLc0BtMtjbLhAwtAxceY2hkE7YO3kN+oYZMXRY+am9c5R6siUtGEypl9vbzbDudDFgrRitfbMJt/e+8vHYufWr3YWKLiYzZMqbsh86D5O0paJ/QWve9AQEy2FwVIsvJA9b69cEvYgX/NbrYhKGHWsHnzzZh5qaz5BcYGd+pFk82DbJzEMzRFfLbhVTu5hrwVCvsnr08ziVpyC8wsm5sa1I0ejLzreYdAZ4uvL35XLnnm8wWNsffIapDDSatjSf2WgZ9Fxzh/cc7sm9UP7K16UivRhOYsAhZYbrTNQweDTE1/ZhctwbcPly9QvcN4OPiVeZ44a5dbF3xCg3uOu6BnLlnKU8Nm2fnpPgwQpvlxdoQK6nkVKUcW02BwAlClAkEAoFAICgTpUxKVMcaTFoTX+7cInvvBzUHsXOgGzUKJIUwOspqWz58OHy9lLEX7truobggA3BTyR1yo3J0hXy19waLY27Ygo/zCjRcSsnFbIEu9QJxUUhpEWp1a/z4mXq8v/8NW8vjzeybHH3xKGMjxjoYUdgwQuXf/UjvlUbRZ/H69xwWQ8sx9FiWNoDIDstZsecWPxy7bTveoKoHYzvUYPvk9hy4ks7g6EOOrZ8datCzQSCRYT74uim5mJxb9sVKkJ5XwMxNZ0jO0eOmkuPnrmJq97oVFnaf7rrCgVc72/ZT3crSMWr5CSLdzvFp2NdUk1x1el6OxZfdqsnszunJ79EZxL3p9UARBF5qL+eDFy/C9OlU37bN6bBJIuWSXwgqo4EChcp2XIQ2C/5XEKJMIBAIBAJBmchlUrqFBzCsVXCZla/i9t7peYY/bg6iTwf/r+C7J2FXJVi2DLlM5vQeisKBQ3xdqezmvM2yKBC5CE+1gmGtQqgX6M6EzrVIzzMQ4uvC7oTNdnvQsvRZ9F3dl13DdzGw7kBWxK/gxws/YrKYAGsbpOseNw62PgD3zPU6qWFTFfCUlf68JouUd5PG8n1GP3YrlZy5k+PwPiavjbftKbudpXMYn3Rv/D+danH6VhbVfSqV+56LU0kpJzlHb3svLUK9Sw1DdkaOrpCE9HwmdqlFsxAvNh48yZOSzxnks9vpfItUxZfJA1mUNoh8syuQBvBARiHT2sxAKVXZH0xLswaKL14MJpPT8/aFNuXDLs9zyS/U7rgIbRb8LyH+JAoEAoFAICgXV6WcN/qEExnmy+KYa+UaGuToKv4BH4q1kenvwu6ukHMWpPHw6kcgkzncw8rDN2ke6mMLB87RFeLn7lJum2WRqcXRhCwGLznMuSQNMqmEmBmtmRM7xzbP28Wb8S3GExURhbZQi5+rHx/3+JivB3xNpjYThUzB0qXf8a7nG3DP1G+wG6wIAFUZW4jyTGomJs5gb24LGlT1QKWQ2uz4S1K0p2xoZDBf7b3mdDwyzIcm1b3wdFU+UItpyevmFRjxcX2wvYOuSjmT156kRYg334bORJl7xvnE4GfQN/iQX75PJt9sf38zfzrPz5P7ExUxrlSjEIBxEeMZULc/Svm9ypZOB/Pnw0cfgcb5M1+qHMyszs8TU8N5mLgIbRb8LyFEmUAgEAgEggrhqpSXaUde3NCgouYgnrpcctTu1jYyXbJVkGku3J8Q/xoovKB2lN09dKnnz28XUu1s2yd0rsXotmFMW+/cgdBTreC7MZEsjrlmZ2ZRJ8CN/EINp1Kt59XyqcWOoTvYd3MfA9cOdDAJeTnyZfwy/Xk3/k2bRf8rXvCpX9nPmmSozPMJb3NBbw0vHt02lNVxiTY7fmcsj01gyYjmLI655nRe9L7r1vG918p89uI4u27JIOnyKBJ2x29mcTQhixue/VkUYi/KzuvCCOiyBN+wbihMZqI6qh1aYG9l6Uo1CgHr+57e5lUG1O2Pm7KStRr2/ffw1ltw+zbOsPj7s+XJKKa4N8ckdV6yFKHNgv81xJ9EgUAgEAgEFUYuk+KqlBPo6UKdAHcCPV1wVcodPtwWmYOURfcrhzmweAzDNBfxtKTAbx3tBRmAazBU6W770mgyYzCamb39ApPXxtsqXeFV3DmXlEObmr4MaxXs9HrDWoUQdyPDwV3QTSUnQ2t16fN28WbnsJ3MPTiXMVvGOOx1ik+JZ9T3oxiwpD/UtCABPq1cviA7o63J41c/sQmyoZHBtAj1YZWTTLHinEvSoDeYqBvo3MKxaDzuRgYtQr0Z0tL5sxcxLDKYLvUCOH7T3s6/XqA7mXkFjO1Qo+wHuUdJYfdLTmt+11grUrkmNe/eGUv/K/PJcI0E7FtgS1JkFFJV1ZH9ow5w6+VbnBp3hlsv32L/6P0MCn/KKsjAagIzbpxzQaZSweuvI7lyhe4L3mb+0OYitFnwj0H8aRQIBAKBQPDQKc8cpO+F/czf9jEKs4n/bnkTacdPwJRsP6lSGMbOuzEoq6PJ0ZOtM+DhokCtlOHpqqS6t5oBTapZWxgLTeQXGJEAr/cO57mWwby39TxxN6xiSyaVMLxVCGO+PepwL3kFRnxdrUFiU1pNITYxtkxjD66AKcKESmJtVxxcjuX9Lk0kk25OR2dxsZl0NK7uxchlR8jRFZZ9MpClNeCmKv0jW9a9vWAjlx1hxZiWNAvxYnlsgkOL6ei2obQM8+XH47f4dHAT9AYTWVqr82IllRwXhRRPV2WpYcgACkkhz7SsSYtQH97fVlxAS3gnaRyZptV8lDyaNKP1fRY30iirBdZDrSBXb0EqURHkGVS67X1QEEyeDHPn2h8fOhRmzYJgq+hzhQpXdQWC/wWEKBMIBAKBQPDQKcsc5Mmzu5m3fQEyixn8QDa9wFGQuddG1/5Xdt1QEB3j6D44sXMtxnWswc6zKXYtjEXjL7QLY9moFuTqC8nSFuLhIseCxan5yOXUPCopPHi87uNMajWJDss7lP5gZ4AI8JJaDT06upb9HvKChxMZ8QU7tVJMZgnuLgpclTJ6zt/HrRIGHqXh7aokr8BY7nhR3tjQyGCWjWyBBQu3s3R437PRXx2XyPvbLpBXYOSnk3eof6+KdCFZw4XkXHa90pFZv5zn7X4NHIRdZXkWn9ZcRi0/Vyxt1zN06WEHQZloqMLUW1NtXzsz0niQFthSef11+OYbyMiALl1g3jxo1sxhWpFVfVFlVyD4X0aIMoFAIBAIBH8JRZWRVmG+7DiXQqpGT/vfNzLx5/lIsUAA8CbgW+JEj3ro2u/kw99znLo93s7S0aCqJ7O3X3Ra0TmXpOGVH04xNDKL6T3r4u8mZe2xRDrX9Xd6nyazhZ9OpLJs4HLS8tNse8scuAY8BkFy+KUqNFQ5n1bEG+kw+8r3NDl3hlcip9MltDfPRB/l5W51eL59GO9uOV/2Ajg35Shr3BoDcI3FMdc4/HpX1h5N5FySNQbA3cXqOllkjlI892zNkVto9IVodEabsFsyojn6AiPSmysIufk20sIs0MDx82tIyAgo995LM9IoVyxdvmw18Jg7FypXdhz38oIvvgBPT+jd2y53TCD4p/KHRdnYqPG0bdOakSNHPMz7EQgEAoFA8C9CKZPSJdyf5qHe8MVCAn/4xDpQBasg8y5xgmcDjJ12seuquVT7/dL2hpVkVVwizYK96d0wEI3OiFcZ7oIWJBxLOoZaUUpFJROoBg3VVkEWVEa8VaEFXkiF7+7pqPiUeEZuHkpUxHiWjf6AVzdcInp4BO9tPe9g3lFk8W/NVDMyphwzkNLMQswWyNQauJmh5UJyrs118siNTKeVxVFtQqnl74aHWm4Tdj8fjGV+jUU0VR63W7tp6ls8H7meb+Ls96UV5w8Zady4Ae+9B999B2Yz+PnBnDnO5z73XMXXFQj+AfxhUXb37l1ySrEgFQgEAoFAINAajPx2IZXovdfoumEJUw6ssg5UA94AvOznF3o8RmGHHUgUfqw85Lj3C6yiZUhkMC+uOFahe1gWe4NeDQOp4++Gh1rh1F1QJpXwVEQAr/z6Fu91fs9xEQMgh47esLmcDLJcMzyVDLu0jmPRxxfRtno7ejVsidZg4uVudfh012VkUgnNgr14omk1utQLIL/ASKbWgL+7Cp9KSr6NTcBTrXBoFywyC7Hf23UfL7WCAA+XUl0niziXpGH6htOcupXD2/0acDxhP/1ctzCz6nJcJHqH+VKzjultjDSp0bRC8QjlkpgIH34Iy5aBsVib5sKFMGUKBJRflRMI/umI9kWBQCAQCAQPHa3ByKztF1h1KIG3d3/N6ONbrQPBwOvYwpaLOK2txfBzrxN0/SpjO5iZ9/RjjFx2hIQMe3VTJ8ANvcH0YMHU+kLa1fEjTaPnhfY1eGVdvN2cTnUqYyaPjRc28nGPj2kS2OS+66IFyIPHq8PawLIzyFKM0CcJThaUPuezwx+zd1QMFouREW2q07WeH/4ealwUUvZcuMvzK446MegIY8vEtrb3UWTa0SLUp1SzkAZVPfBwUdCjQQChvpU4kpBZbmVxZdxNOgZpiWs9D1X6HueTQp6FZvNxUQfQO8D85/aGXb8Os2fDihVQ6MTwRKuFjz+27hkTCP7lCFEmEAgEAoHgoWI0mfntQiprY6/z2fbPePx8jHVABbyKgyBLkj3GsOtvoDG7kZOkYfLaeIZGBrNiTEsGLIy1Ex1uKjmZ2gcLps7IM2A2Wwj0VNGjfoCdu6CnWsGsJx4jMfcSJouJ6OPRTGo5iTFbxlhPzoTRYfC1P8jK2Lp0yQC97kBC6X4cAJxMOUlS7h3e+f0dHq/3OD1r9qPQaClzf9y09acYFhnC1ont0BQUYrFgM+0ozb1xXMeaKOVSuocH0LVeAE8vPlTOW7LwtPdvtLm0FJU033HYNQhaLIJq/WyH/rCRxuXLVqfElSutuWPOcHW1uixOm1bxdQWCfzDCD1QgEAgEAsFDxWAys/zX83y98f37ggygACzfg8VyX91cMjWCLr+SZ3GzW2NVXCJHEzIZGmmfa5VXYMSnjL1hJZFJJfi5q6juo8ZFIUdrMPJ6n3rMH9yEBlU9GNYqhFO3s/FQeQGw6OgiOoR0YGzEWNDA9FqwLKBsQaZxb0jbW+ULsiIytBkk5SUxZOMQZu55FbnMwM9nkss8Z2XcTX67eBdXpZxFv1/jq73XShVkQyOD6Rruj8FkJu5GBndz9WVWFv3kmSwNfY951RdQyZkgqxUFfc/ZCbLSMJrMaA1GUnL0XErRkJKjR2swYjSZ4fx5q3V9eLi1OuZMkLm4WFsWb9ywCjcfn3KvKRD8G/hTlbIbN26wdt0Pf+jcZwc/82cuLRAIBAKB4H+U3OQ03lwwmeZ37Pc6FUplTPWbguSWmc+qf0qGa0vUbTZjlLpRN9CdC8n2DoPLYxNYMqI5i2Ou2YwsLqfm4aKUOd0bVhxPtYJhrYIY1roqBks619LT8HfzJcDNh51nUwn0cGVdVGsAno0+xOIRDW1ti71W9WJaxFTmhsH0cjTBnkIPqrX8jowTjpbspeHr6oumwHrvi44volVQO4ZGNuGrvTfKPG/p/uu0reXL2I41aFpGFllkmA8WC8z+5QKXUnKZ0ateKStaGOC1j/eqLsJLnuc47BoEkd9g9O+GwWRGk6MnV2/Az12JXG4iW59FhjYLH7U33movZBIley+ls3CP/T6zpwoSmXpmK1X27EBiKcWxxMUFXnwRXnsNqlYt8z0IBP9G/pQoS0i4SULCzQc6x2KxIJFIhCgTCAQCgeBfimX9egdBppOrGPfEG8TUiIBsyDG5E5fXkCe5y4xePgR6uDiIsnNJGvQGk51gM5ktrI5LZFSbUKZvOO30+qG+rnw7pjF7E3+hz5qh9/eHAU0CmzCt9QyCfHoyZe0J3ujXgLNJGjaeuMvUVtMZvmkoNzKuojg5vlxBtsHgy5DELK66+NrvQyuDpoFNUcvVnL171nZsftzHbBm8m8UxN0p1WSx6H0aTBW9XBT0bBNKrQRVy9IXk6grxVCtQyqWsirvJppNJBPu6svJwIuFV3EutLA71+YUPg75yfrGwkRAxHy1u/HY2meiY6+QXGPl2TGM2XdrKgiMfO7zXKa2m0z6oF/klMtUid2+k6pnfnF/H1RXGjbO2KVapUvrDCwT/cv6UKAsMDKBevdJ++yIQCAQCgeCR5IUXOXroJC02fgtAtosbYwa9w4lq4bYpe3ObA9Y2xcgwHzrU8eP3S2kOS2VpDXi4yAmv4o6HWoFvJSUXk3MY0jLYbm9YEZ5qBSueb8ys2DdZcmKxw3rxKfEM+2kI4yLG887AWaRqDIT6uvJEkyBcVVUY13gkPZNX8Lhn2Y+4370judVGUHjzecd9aGUwKXIS0cejMVvMtmMnU06SX6hxWi0siUZXyOXUXDYcv01arh5fNxUZ+Qb83FR0rOPHwCbVUMmljPnW6k5ZVmVxc3Ynovx+JFiVajuWZfbBtd1SVKFP2MxaVh5OxFOtYMtLzct8ryM2WW3/vx3zAQMXHrO1Vy5vPoBnSooyNzeYMMHaqujvPD9OIHiU+FOiLDw8nEkvTXxY9yIQCAQCgeBfgIergpqzHydeforA7TcZ/sz7XPELKXV+9L7rLBnRnPe32ed2eaoV1PSrxJdDItAWmsgvMOKpVuCmkmO2WHizbzg9GwTw85lkztzJ4XJqHsNaBbE3cYdT4VCcxccX0T6kAz3CBvDdmEgW7b1G/LUbfOi/iablCLL383z57NZpjrbtyNiIsSw6uoijLx5lbMRYlhxfUup5URFRtA9uz5SdUxzGMnVZuKnK/1jm5iInvJIHr3Sr4xD+/MHPF3ihXRiDIoJse8jKqizmmV15+dY0fqj5KnKJma3Z7VFEfkW36vVtZi1FWXHDWgXx+81fHN6rtxY6JUDfK7C0GUSziNYl2jEv+NcgNuQx2t48Tb5LJZSvTEYxdQr4lkwNFwgeXYT7okAgEAgEgoeKKnU76uPP4NbfzLRak7liKl2QgfM2xVBfV1a90Ir9V9KJ3nfdrsoTGebDG33qUMNfRY1AA8O9pHi51MBT5YVMKqX3qnEVus+PD85lQN3+7L2Uxa8nTrEs7CUeU+SUOt9ohgl3XVmSmwFAr1W92DF0B22rt2XKzil82vNTWge1ZkHcAofWvsmRk2kf3J5eq3qRpXcMXfZRe5NXULbNf4OqHlRSyRnxTRxn7jiGP2+Z2JaPd14iI9/enXLl4ZtsmdjWaWXxhDac2cmjSS30xSt8OG80qodcJkVrMLJ473XAapbyXGQV+q9+jvC70Po2tLll/V94+v21MtVwuDoscNKOubD1YPbUaMEPjXuw69UBD+bWKBA8AghRJhAIBAKB4I9z+zYEBd3/+tYmZLHPgKUQpRTmhn9BeoI3B/OalLlMltZgqxQVhR1/tfcqq+LsRUSorytzBtVlb+IvjNruuK9pepsZLB+4nF6renE182qZ1zyZcpJsfTanL57kh5qTqaHKLnWuzgSDLsB21f3ctKuZV2nxdQt2j9jN43Wf5G5+Ft3CuvNU+FPkFORwW3MbLxcv1HI10cejmbJzilNB1jSwKa5yDy6llN26+EK7ML7ed91OkMH98OehkcG83iecAqO52KiFrqodTPs2m49H9aWZE4OQw8qRjOtuH/is0RlJuJlK6+TL9NJcx2vQPPbEnsbbMUvaRt/LMKOH83bMQyGNORTS2Lq2vlCIMoGgBEKUCQQCgUDwL8NoMlvd8nRGcnQGPNXKBwv1rQhmM0ydCsuXQ2wsNGgAieshdghY7hs9uEgN9PfcV64o83ZVknfPIGJYqxCO3cx0EGQV2S82dOMQoiKi2DF0By2+buFUBBVHm3aEyZYx+JYhyLKM8GPuSJp0q8aho4vs1nymwbPU9K5NfoGMHfF6BrcM5vUfz9Ei1BO/yhqitkVx9u5Zuz1kJZnRZgbn7mjLNPkYFhlMsxBv3t16vtQ5q+ISaRbsRae6/jSo6kFiSjKzgxbSz2s/B/Me4/GF7jwXGcaSEc3RG0xkaQ34uinxd3dBJb/3Z2P7dvjlF7xi9nP67BnkZdx3SeqnQ2gWJHiX3Y7p4aKo8JoCwaPC3y7KEhISWL/hR6ZPm/p3X1ogEAgEgn89WoOR3y6kEh1z3cEuPapjDbqFB9iqIX8YvR6GD4cNG6xf9+4Nm1+Fi5OgxIf49ZndePPOhDKXa1DVA5VCyqWUXBpV8+DF9mEM+TrOYV5p+5pKEn08mjbV2/BOp3eYunMqJovzgOLmKqhxYjRyaekti8mF0PMnkPaKZ7J3R46+eJReq3rhpnTj5cjpdKzei5vpRpbsu3yvSmVi2+lk9l9JZ/PE5rSq1obTqc5dIgHGNx9Ph+BemEwKPn76Mac291EdahAR4sPQpYdLzSYrYllsAl3CA5jePJ+wS5MJUaUA0MbtNM+4/cBXewexOOYadQPdcVPJmdi5FtW9Xe+L9Y0b4ZtveJA6lk4OB4Lh59qQe8/osbR2zAZVPfBQi5qAQFCSP/xTsemnHx9o/qVLl1i//keOnzjxRy8pEAgEAoGgDIq75ZXkXJKGSWviGdYqhDf61Pvjwiw9HZ54Ag4cuH8s9BacnwglApYTvIcw4/SzWCi7OhfVoQZms4Vdr3TEzUWGRmd0CDsu2tfUb+2QCt3m/MPz2TtqL4/XfZwlJ5awqESFq70LbA+SIi8sXZBdNUCPNXCjOZB6ijFbxjC++XiOv3icXL2ZVYeTGLj5GBvHt+FqWh5v/HSGqd3rApCjK2TUslN8O+ZD2lRvz/y4eQ6tli9HTufxegOI3nuLVXG3GBoZzJIRzTEYTWgLTHhXUiKXSpBKoOun+8oVZADnknIwXVxIx8TXkajs95ZNDVjJtuwOGLLkXLBYK5Itwnzsq6e9e8M335R5jUQPOFgdDlW3/v+pACgs9seprHbMcR1ronxY1dp/IH9LFVvwj+RP/apCr9ezdes2TsbHk5OTg6enJ82aNaN/v76oVCoArly5wnffr+Ls2bNYLBaUSiW9e/V8KDcvEAgEAoHASkm3vNJYefgmkWE+9G4Y+OAfAi9cgH794Pr1+8c6A2NwEGTUnoD/Y58ytOASKw8nIpNKqBPghptKTl6BkcupeZjMFoZGBhMR4sPMTWdI0ejxcJEzradj3E6dADfyCzWcSj1VoVs9mXKSpNwk3vn9HXrV6mWrcF3NvEpPV9hYBVwlpbfmxRdAr02QWgsoFvO16NgiWlVrR2qq1V2wqMqXkqNncItgKrvdn5yQoWXgwmMMjWzC1sF7yC/UkKmzhi27yj1YE5dMboiUkW3CeLp5dQxGMwajiUAPF36/dJd3t55DKoFpPetVSJC5S/P5KOhzKl+MdRjLMVZi2/52LN38HkH5Gezbe4rOjas7ivOuXUEmA5O1ulgolXEuoAbGyEjyWnkxPHUht8txp5wcOY01cckO7ZjDWoXQNdz/kRUff0sVW/CP5Q9/5/V6Pa++9jqJibew3Etnv3MniQsXLnL06FFmffgB6zf8yA8/rMdsNqNQKOjVqydPPfkEXl5eD+v+BQKBQCAQAAaT2eaWVx6LY649+IfjX3+FZ56BnGKVpe7AKCdz674MzT7FVSLhjT7hPNuiOpXdXMgvMNps3Cup5NzN1eHtqmTo0sMkZFgNNEoLO3ZTycnQZlb8foEMbQZJeUmM2TLGts/svZWN+do3H2VJEVmMfToYsB9yZICTCK2isOdFe28wum0ov5xJZtOEthy9kYlEIrHLBMvRFfLV3hssjrlhaxnMK9BwKSWX8CoeDIkMpe1Hu1k8rDk1KrsS4K5i3+V0tAYzRxOyygx/Lk4D9VW+Cv7I1q5YHJMmEPfZeQxN3Gk71uvOKWQtwhwX8vKCV14Bb2/0LVsxJ8ON5SfT8FQr2PxCc/ocNJbZPjq++Xi6hvam7+aj9++tqgfjOtobiTxq/C1VbME/mj/evrh5CzdvJuLp6cnAAf0JDq6OVqfnxIkTxMTsY9bsjzhx4iQAPXp057lnB+Pt7f3QblwgEAgEAsF9nLX8lca5JA0andH24a/clqovv4TJk23VEwB6A8OcLF7/VWg8GyRW1WOxwJXUPF7feAaj2WKrlMmlEka3DaN5qMru9NLCjvMKjPi6+jzQO/F19UVzb19T9PFohrqZWF5ZW2Yz5c/5MOgs6C/ee0YnFLkLTulWh14NAtHoC1m45xqrjyQyoXMtp5lgZgsOwdCj24ayOi6RwS2CqV/Vg0PX0qljcKdRdU/cVXJOJgaz9uitUsOfrVgY7vszM6ssRSU1Oowat0uQr02BEtvqZGvXwuOPO3/AefMAcAGmG4w0q3eXxTHXym3HnN7mVQbU7Y8MFTtf7oBGX4iHi+KRb8/7W6rYgn88f1iUxcXFoVQq+Wj2LKpUCbQd79C+HQH+/qz7YT0SiYRpU6fQtm2bh3KzAoFAIBAInJOjM5Q/qRhFtuRltVSNaxtMr2/molj0lf3JA4DBThZt+DY0etcmyLQGI/N3XUapkLF4uNXxL1NroLKbElelnF/OJLPyUAIrxrRkwMJYcnSFpYYdX07No5LCgyaBTezEQGk0DWyKWq7m7N2zAEytrKZ90tIyz1mXC8MTofBX4Kmy19caNYxpF87xm1lkag22/K+yMsGKMzQymNY1KlNJJaddrcrczMinTa3KuChkbIm/w8YTd1g0LILu9f05npDJ6DahTCsh9NykWj4K+px+Xgcc1rfkgWQxyE+WYukYH28V2TJZmc/pqpTTu2EgXcP90eiM5BUUMrDOkzxVfyDZ+mxrO6aLF15qL5RSFUq51VlRrZQL2/t7/OVVbMG/gj8sypKTU6hbt66dICuiW7durPthPTVqhAlBJhAIBALB34CnuvwWN/v5ijJbqhJvJOMx/2UUN0oYdD2Jc8Hy2AfQ8E3bl0aTmUPX0hnWOoQjNzIZ+90xB9E3tkMNBkUEcTtbx/PtQvl01xUAjiVk8ny7MIZFBrPyni2+yWxhTVwyk1tOY/QWZyU6eyZFTiL6eDRmi5m5VX2ZXimjzPnf5MDYu2DeAnQDynFt93LxIivfQO0Ad8Z8e79VL0dXyMhlR1gxpqXTTLCi5+5aLwCTxUyqRk+7Ob/b9owVBWM/GeFHal4abm5Z1Az0xdvFG3eXpsz48Sw5ukJqqm6xJORDarrcdry5KyD5AijxyGYk7K7Vgog5M/F5vC9IK/bBX36vymWtrN4XWm7KSgR5VKvQGo8yf6aKLXh0+FN7yipX9nU65udXGYBq1cQPqkAgEAgEfwceankZLW72NKjqgYeLnF2ltFRVz05h2Yb/UjvjluPJndoAB+2PNZ0H4dPsDhlMZsKreLJwz1WnFaNzSRomr41naGQwU3vU5fl2Nehc17rnSK2UIQEmdatNk+D7wmbl4dtsntibqIhxRB+37muSSWQ08G+Ah8oDTYGGc3fP8UKzF2gf3J4pO1/h4wAXppYjyBZkwSvpYNkLlRpVIr9yfpnzmwY2RY4b2bpCXFVyhw/cCRlaBiyMtbkpFmWCebsqCfBwwWyxsCruJnN2XLQzwygvGHtGmxn8Pr0fizZsZLJkCm4ynePNbQN+wK5dsUAmZ32j7iyOfArP+nVY3691hQWZ4M/zR6vYgkeLPyXDJZIydskCcrlQ+QKBQCAQ/B0oZVKiOtZg0pr4cueO71gTCzhtqfLLy2Lzd1Pw0dkLDYtKheSbb2DIEDjxClxaYB2IWAB1JzmsYzCaib2aXmYLH1gDj5sGe+GuktMk2JuJq0/QqoYvjwV5sTruJh3q+LFiTEsMRjNJ2TqkqJjXfR49anbnTu4dBtYdiLZQS7o2Hf9K/viqfTFZTLz8yySiq6h5Wp5d5vU/yIS3MoCLMKbuGEKfC+XtvW+Xec7L99wFh0aGkqIpcDrHavBxzS4TLK/AyJynHiMjr4DZv1y0m1+RYOwhG4cwvvl45j79Pordn4OmWBUzD1gMnLx/qECmYHWTXkS3fIoUD+svzF99xC3p/z940Cq2CNd+NPlzlvg6PWlpaX9o3M/P789cWiAQCAQCQTHkMindwgMY1iq4TEOBYa1C6Frfnxyt85aqNDdvtoR3YNSJbfePuXoh3fQTvt07WQ80+wzMRvBqCLXHOb2OTCpheWxChe59eWwCS0Y0J+5GBl+PaI5CJuVurp7/dK5FZTcllZQyDEYzc3ZcJK/AyNDIIPo16cIvV35h4NqBDhWlV1tP49sqcpQ3k8u87ox0mJcFpAEHodPGTgxsOJB3Y97FbHFul291F+zDlLgLjGwjwUtd9gfokgYffm4qomMcxXBFg7EXHVtEh+AO9G2zFuVvkUiMWXADmA+k35+3JbwDczqO4o7nffvIR92S/v+LB65ii3DtR5I/9V0/dPgwhw4fdjomkUjKHP9p44Y/c2mBQCAQCAQlcFXKeaNPOJFhviyOueZo3HHPllytkJOo05a6zgddXiA8LYHIW2e5WDmEFwa9zbLHIrBtWpBIoMXCMu8lT/9g+2j0BhO/nkulQ20/nl1yiDN3rOdO6FyLGpUr0aqmL1qDiVRNAQOb+jNt11SWHF/iuFZKPNJDw1C6l33N8XdhcQ6gBzYBT8Nnxz7jiUZPsPXZrbz5+5sOYm9y5GSeDH+SXec0DIkM5rtDNxkSGeLwgbu0TLYGVT1wc5Hzy1l7sfigwdhzD86l43N7OFN1EZHn5yH773G4F2OW1aAJH3Qfy4+qYNt8YUn//8uDVLEf9XDtR5k//JPpV7myzV1JIBAIBALB/wYl3fJKsyUvq6XKKJMzceAM3ji5jLda/Ic8lWupLVXO7PS9XOXk6MsPOy5OltZAqkZPRp4B472NVjKphCGRwby44hjX0/MZ1SaU/AIDWy9vdSrIVBLYUAX6VSr9OiYLjEqFlbmABfgR6Ap4Wa3u07XpBHkGsfnZzWgLtWRoM/B19UUtV7Pl0hb0BmtOdpd6/izdfwOwWvtPW38KT7WCYa1CGBIZbHOa9HFVolJIWXPkFnUD3MjMNziEKpcVjO0mgbwS80+mnERTkMP7x6vQImgp7zYYjfTKFZg1C/dx43kfCdPL+N4L/l4eqIotKpmPLH9YlH39dfTDvA+BQCAQCAQPieJueaUZBnio5TT3kXMs0zHbSoaJN+t9Q6vmZ/C9lk1I5UCnLVUl7fSLRMnoNqF4ltPWVxJvVyV5BUaytAbcVNZr1QlwQ28wcT5Zw51sHVsmtsXNxUTX7+c4nO8igU1VoGcZgsxggWeT4aciH4/dQChQ4/6clLwUanrXpOqnVanvV99mIHL27llWPrGKXeczePOn8wyNDGbhkKYMWxrHlontmNSlFk9FBJXqNPl8uzC61AsgR+e4B81ZMLYc+NwPWquhzS3QlRBmmbos3FRyvjt6h85vz6d9kxDkYaHI751b1vde8PdT0Sq2qGQ+uojvvEAgEAgEjxoWC6qvo1n10dv0fmoW132DbENyjMwP/tiWfbW6xptcaLDVoaWqpJ1+qK8rK8a05MiNTEZ/e4To4c0faB+NSiHlUkquTZyBVaxkaq3OdTm6Qt7beo53n6jqUFFylcCWqtDVtfRraM3wRDL8WtS1eRbIwlolK4aXixd5hjzq+9XndOr9XLCoiPH0rd2P57+1HisyKJnZtz5KuZSojjWZtf0Cq+KcO01O+eEUwyKzeb1PPSLDfIi7cV+ElQzG9pVaK36d7j1PvAE+jYfoFvfX9FF7k3cvGPvjW1Ii+waJD3X/41S0ii14NBHffYFAIBAI/iUYTWa0BiMpOXoupWhIydGjNRgxmoqZVhQUwNixyCZORJWVydpf5uBWYFUqSkkhX4V8ZBdGXE2ZRpeUUcglZrvr/FbMTt9TreC7MZFEx1xn+obTnLmjYVVcIqPbhFbovke3DWV1XCLhVe6LM7CKFR/X+22WGr2R2zn2BmJuEvilWtmCTGOCXneKCbIUrK7+A7H2It6jKHA6KTcJD5UHYN1L9v3jq5nXfS5ag4zjN7Ns85fHJtC2dmXWxN1k+5lkp4KsOCvjbrLn4l3e7hdud7x4MHZDJRwNvi/IAOrUh4WF0PL2/ft0lXvY3lNRtpXgf5/iFew6Ae4EerrgqpQLQSYQokwgEAgEgn8DWoOR7WeTeXrxIVrN3k3P+ftpNXs3Ty8+xPazyWgNRkhIgHbtYOlS23n+d26w6+RSmgYqWBzyIT087Q26LFIXpBHzQXq/DmMwme3s9Ie1CiHuRoad/f3KwzdpEebDkMhgymJoZDAtQn2sIu6eOCvac3U5NQ8XpYwGVa0CKa/ASOViFSUPKeysBh3Upa+fYYKud2C/vuhFAT8Bg4AS2+qKAqeDPYNZ2OtrLv4ngS3P7CEpuTH7Lmm4mJxrtx/sXJKGVI2e/k2qVdhpcnHMNcIquzG81f33UhSM/Ul4Jw5Vh7CSnZ8GkOfChh+gcj5MvmfJX/xeNA+4h08gEPxvISrdAoFAIBD8wynZSlicc0kaJq2J57+KREZ8NRNJZqbDnMRq1VgT/hEuGcfsjltkrpjbb6agcic0OXpydAZ8K6kwms02Z8XiZhzFydEVMnLZEb57PpJWYT5E77tua2WUSSX0bhjIoIhq1AnwYOQ3R3ihXRgd6/iz/vgJZFIJJrMFk9nC6rhERrUJZfqG06TmFBDo7kuTwCYk3I1nRzWILGPbVJrRKsjOFGX3mrEae/QBfOznRkVE0T64Pd/Gf8vE5jOYteUWGr2RSylWIdagqgdfj2iOVIK9GNIV4iKXPpDTpEZv5I2+4bS07S/KQX1pLl0qr3D8dXkm8ClwA3wUMNO9Hx2r92LgZvv3LbKtBIJ/NkKUCQQCgUDwD6ZkK2FJpGYTk2PXMPzgOiTYu0VoFSpmDhzPc51+xSXjvN2YQeKKqd3P7EqrTfSGQzZB1SLUm9d732+/K27GUZKEDC39vzjAxM41WTe2NTl6A2YzeLsqyNUbycw3oJTDTy81J1ufTbL2Mh8Pro6LvAFr4pL5Zv9NVh6+yZaJbRnSMhjvSjKuZl7jzZbjqHFyHM3KEGQp9wTZeUOxgzshuEMwiSH331WR1X374Pb0WtWLme3/y8pDScTdyLJb71ySBp3BRN1Ad7vcMS9XJVnaB6tSafSFBHq6WPcX1XZDEvc86mQnUUFXgc+AbEj0U3Lyy/8ysMtYRi07RY7u/jVFtpVA8M9H/AQLBAKBQPAPpmQrYXG8tTks2PoxHRJOOoxd967KjKcn8XaLpTzmetVuLNfkSn6bbXxxxJdVcfF2Y3kFRjtnxeJmHM7I0RXy4faLbDudzLJRLYi9mk70vuvkFxj5dkxjtl3ZyIIjHztkgs1oM4Ntk3oxfGk8I5cdYcWYlniozWw9Hc2Q9OXIyxBkd4zQ5TZcLq6VTkLrWq3Z9uU2qwDMTbZZ3Ucfj2bKzik80+BZp1WoIoo7Q4JVDKkVUsxmWek344SiqpZcfwf5vsch64TjpH3AMqAQtG1bIf1+NZcu6Xh74TE7QQYi20og+DcgRJlAIBAIBP9gNDrnIc1N71zky80fUTU33WHsZIsuvNzxWaLrfUg99U27sVyzB3uDvqcgqxar4k47nFt8n9e5JI2DGYczPNUKvniuGZ/8epnVRxLxVCvY8lJzZsW+yZITix3mx6fEM2TjEMY3H8+G/3zEu5uu8MWeK0zuYOLZu0uQ510p9VqJhdDlDlwrrltuAdeh8YeNMVssfLDvA65kXrFZ3T8W8Bif9VxIx+q9HKpQxSnuDAkQ1aEGO86l0L1+YLlOk0WB0o2qeeLpKoeMoxAzAPQp9hPNwBpgu/XLy72eZGq3/3BuyXmHfDMQ2VYCwb8FIcoEAoFAIPgHk6MrUaWyWBhxYhsz93yD0mzvyGeUSMl4+32iA+uxXPUSNVRJ9uNKf26Gb6R5tWZIJBImdK7FysM37USK1ZQi0RaYXFKkOaOkEciwVkH8fvMXp4KsOIuOLaJtcFs+GvQEhZoUKsV2Q6m9Uer8hELofBsSij+2BryOePHp0vk83eQp9IWFfNbzMzQFGnIKcnBTeCKXuLH6cDIDNztWoYoobtsPVoOSjnX9eXntSdJyDbZ9byUpGSitKzShv7Yel1PPIzHp7CdrgYVAkeP/e+8RNOM1xl5ME9lWAsG/HPFTLBAIBALBPxhP9f0qlVuBltk7vqD/xf0O8zI9fZk14h2mv9iF/+7pRoDMvkJz1+TPM6ffJ+FYNrCHBlU9GNUmlC0T2zJy2RESMrQ2gTG0VTDuKjknE4NZFZdoZ8ZRkpJGIDKphOciq9Bv7ZAKPd8nBz+hZ2A4XrGDkJchyK4ZoPMduFVckBVCwL4ADm8/TJokk76r+7Lv5j4UUgUD6w2kWWAz2lZvT02vhlxKzbOrgpWkuG3/6LahtAj1IUdrIEWj50Ritm3fW3EHyuLZbUWB0gO89jK/+idIJCXKXsnAJ/f+X6GA5cth6FBcQWRbCQSPAH9YlH3+xcI/fFEJEl56acIfPl8gEAgEAoEVD7XcVqXqeP24U0Gma9Oem/OjmRBWHZ9LE1GUEGQJBVUYev1D7hT6246dS9IwfcNphkYGs2JMSyauPsnCIU05eiOTF1YcI7/AyIoxLWka7MX6Y7d5pnmQgygBRyOQOgFu5BdqHAKgSyMz/SQe+wcg198pdc4lg3UPWZKpxMBO2PjNRlZeW8Vbe2fi7eLNG+3fICoiCm2hlnRtOpVdKyOXF/D5cw3I0RlZdfgOKw/ftquYDYsMpnv9QDLzChgSGczquETWH7vNJ880trkzFu17axbixfLYBG5n6fhuTCSLY67ZvZPY3CbcNvgTrEq9f59ngQVYK2VqNWzcCL162Ybl98RXUb6VQCD49/GHRdmePb87PS6RWFMYLRZLqcclEiHKBAKBQCB4GChlUqI61mDSmnh+Dm9Pl+tHeersHtv4+q5DeK3ZYEw/Xgeu07TqMKKDr+KvjQXgsj6Yodc/IM3o43T9VXGJtAzzYfmoFny667KdwBiwMJahkcF8OrgJJpOZ13rXo1UNe/t7N5XcTuC4qeRkaB1t+Z1RQwG/VwNlGYLsXIHVZTG1pCA7BHV71qVe7Xq0n9eeWj612DF0B/tu7mPg2oEOxiJTW0+lZ82e1Kh+lc3N2zFq2SkqqeREdahB65q+vL3pDJfv5tlE2GfPNLbLVEvI0Nrex5IRzXGRSzlwNd1BpGaYvBid8C4/h07GRWWA34HlgAnw8IBt26B9+wq9H4FA8O/hD4uyD95/z+FY7MGD/PLLDurUqU379u3x9/cDIO1uGvv2H+Dy5cv06d2LNm3a/PE7FggEAoFAYEMuk9ItPIBhrYJZeTiRef0nMUB/m8KUFCb1mMxvtSPt5p9MKqDr3Vc40dbEzbsZPHvtXTJNnmVeI1trIPaao8DI0RXy1d5rLI65Rt1AdwI9XHitdz2+G9OSbG0h+QVGAj1dMBX7RW1egRFfV+cCsDgt3Dz4rZoZD3NeqXNOF0C3O5BWUpBdAQLh3WffZeXplXiqPNk5bCdzYuew5PgSh3XiU+IZ/tNwxjcfz9TWU1l4bBZbJ70LFhV5eiNag4npveqx5sgtkrL19G9clR4NAlEr0+z20uXoCom5nEb9Kh50qedPdIxzV8xrBdV5/vZbTLv9PU2XXgbA4uuLZOdOiIgo990IBIJ/H39YlDVs2MDu6xMnT7Jjx06iosbSu1dPh/n9+vVlx86dREd/TfPm4i8cgUAgEAgeFq5KOW/0CScyzJfM/AJ2N/mK9/YmkuTh73R+kJ8ftx9bz5NfxqIxu5W5tkwqoVv9QIdw6OKYLXAhOZcLybnczS1gyYjmfPbbJbxdlQyKCKKmn7tNvFxOzaOSwoMmgU3sqlXF6eUXzNYAHfKCtFKveUIP3e9AprnEQCpghPHPjadP7T6Ezg9lfIvxxCTEOBVkxVl0bBGtglqhlCnZdHErKSmN+fJ36z62BlU9GN02jL3TOuGikKJWyukWHkCHOn5O93ql5xnKDJSOzW/KQa/GvNd0Mb0uH4Sfd+EX0bTM+xMIBP9eHtru0PXrf6RmzRpOBVkRvXr2pGbNmvyw3klAokAgEAgEgvIxmeDDD2HLFrvDrko5vRsGMiiiOp8nK+8JMice6lhbCNMLXcsVZFB2OLQzziVpkAKv9grniWZBBPu6IpXA2A41rLdvtrAmLpmXI6c7Pb+Nuwfb/PPKFGRH9NaWRQdBlguBuYGsenMNc7rN5UbWbTQFGqIiolgQt6BC9z//8HwmtJhATMJuRrQOQiGT2J5r2vpTfLLrsu2tFt/nVSfAnUBPF1yVcuQyKaab6/kieA5SSpbx7mORSHmr+3j6jZxPVmitCt2fQCD4d/LQRNmNGzeoUqVKufOqVAkkIeFmufMEAoFAIBCUICXFagAxcyaMGgWJ9u2EcpmUXL01t6yXRyzrarxGJanWYZnyssVkUgnhVdxpEepNg6oeZYZDOzs3R1fIjyduEejhwp4LaYz+9ihNqnsxJDIYgJWHb9MxuBfjIsbZndtEBb9WKURmKH3P2UGdtUKWXVKQGeA5l+FcXnqFvrUeJ1crxUftQQP/BmgLtRU2FjmZchKzxcyElhMwWHKImdGGCZ3DbIHZKw/fZPeFuxhNJW+gGBc+pdrZEfT32s8bVZaVfUGJhFT3yrZAaYFA8Gjy0ESZTCYjscQ/Ds5ITExEJpM9rMsKBAKBQPBosH07NGkCv/1m/TorC4YMAaO9jXuOzsDT3r/yZcgcIt3OsSz0PVwkers5xbPFiuOpVjChcy32zejMwueaMaNXPSZ0rkW9QHfeHdCATnX9CK/ijkwqcbg967lhxMxoTSVXDV0aGdCb02hZoxLNQz2ZuPoE4zrW5PNnmxDkrWbUN6f4b6ePWPnESpoENqG5CvZUg0oWncPaRezXQc87oCmphyxQ9VpHvMMnU2BQkF9g4ZUf4pFYKtGiaguydFkVfs0AiTmJTNoxieD5wfRd2xk//5NsnticUF9XABbHXMPgTJRZzHB8Cpycajv0gt9mluS8j29+dqnXa1DVAw+1SCkSCB5lHpooa1C/PomJt1i77gcH58Ui1q37gZs3E2lQv/7DuqxAIBAIBP9u8vNh/Hjo2xdSU+3HDh2C/fYW+FWSv2Je9c+RSayiIdLtLNGhs1BIrA6IMqmEOgFuxFxKY3LX2jaBFerrypaJbQn1dWXsd8fo+mkML6w4xobjd8gvMNEt3J9Xe9XjyyHNiJneiQmda9mqR6G+rmye2Bw//5P0X9eF4PnVabz4MWp+EcKAH7rg53+SL4Y0YMKqE6Rq9KyLas3KFyJJy4HH6z3B7j4fcLiGG95l/M52jxZ63YE8Jx8xvC41RB4yjc71/FkWe4OD19LpUs8fvUHCJz0+Icgj6IFeua+rL5oCa7tmfEo8o7cM46NDM/l2TGM81QrOJWnQ6EpkmpkKIPY5uPSZw3o99HH8sPpVquXcdXq9cR1rohR5YwLBI81D+7XMsOFDOX3mDOvW/cDevTG0adMaP7977otpaRw6dIiUlFRcXFwYNqxigZECgUAgEDzSHDkCw4bBlSuOY9WqwZo19+3TLRY4PROPC7Mcpt4yBFDJxYWhrcIYEhmM3mAiS2ugmpeauNe7svZoIk9FBPHF7qs2h8XiwcfPrzhqcxgE7IKlJ64+wcKhDZgV+yZLTix2uHaRqImKGM8XQz5g4MJjfLX3GtN71qFvo6qYk/fjHfcsEmPpLou/5sPjyaBzIsiC00Jp0X4Rdat40iLUh/e3XaBlqDefPVefzRe38NmmT9g4eGOZxiLFaRrYFLVczdm7Z+2ORx9fROugdgyNbMJXe2+g0RfezwwzZMO+x+FujOOCa4BtUJM7bFg5nRHPvMcVvxDb8LBWIXQN9xch0ALBI85DE2XB1avz/vv/ZcGCL7h9+zYbN/7kkFlWrVo1Jk96ieDg4Id1WYFAIBAI/n0YjVYzj/fftxp7lKR/f1i2DCpXtn5tNsGxCXA12mHql3efZn3hODZPjOTIjUzGfnfMQWBFdaiBi1zGwWvpgLUV0VnwcRHFg6WXj27Oz1c2OhVkxSkpat746RzH4jbyccBbSEyO+96K+DkfnkqGAmdNOAmwfdavJGW5UyfAnZHLjgAws39Npv86jcXHF9+7djSTWk5izJYxZd4jwKTISUQfj8ZscWxPXBD3MVsG72ZxzI37e8Dyb8He3pBzrsRsGXxlgtj7R/zzswjJTuGKXwgNqnowrmNNuob746oUrYsCwaPOQ/1boHatWiz8YgFnzpzl/PnzZGZZe7i9vb2pXz+cRg0b2oSaQCAQCAQCJ1y5Yq2OHTniOObqCp99Bi++CEX/npoMcGgEJK5zmL40bzzf5D7Bpv+0KlNgTVobz9DIYFaMacmAhbEMaxVC3I0Mp/OLs/boLab1DOWzuHkVerTioqaHz2k+9n8PqUlf6vxNeTA4BQzOBNltaNSsEdV9qrD91G2mrT9Njq6QCZ3D+P3mLzZBBrDo6CKOvniUsRFjy7TFj4qIon1we6bsnOJ0/GTKSfILNfRpVMW6Byz7DPzeG3Qlwq0lrjCvEE7aC+q8LxYxY9BzfFDMOl9UyAQCATxkUVZEo0YNadSo4V+xtEAgEAgE/04sFliyBKZMAa1j5cjSMhL98m/JqRZKTmounmolHooCXA4PRpqy034uUlLqLaBX2BgGqeTsuXi3XIG1Ki6RpsFeDGsVzJDI4DJzyYqoE+BGhi7rgZwN8ws1DAs+x9ue7yI1l+7quD4XhqSA0dlgKuAH09q/RvTeRL7ca80Sk0klPBdZhX5r7bdJZOmz6LWqFzuG7qB1UGsWxC2wa2VsEtiEyZGTaR/cnl6repGlL90YJFOXxXMtg1Fl7IMDT0Jhjv0EhR/MMkJ8ie/hnDl4ThhL2THdAoHgUeUvq5cXFhaSm5uLQqHA3d39r7qMQCAQCAT/fFJT4YUXYNs2xzGZDMObM9k5YDSLf0vkXNI1ADxkeayt8wH1FfZ7nwotCl66OY0dp8OQSfcS+2oXlu6/UaHbWB6bwLJRLci7Z6tfHm4qOXfzMpyOySQyGvg3wEPlgaZAw7m75zBZTFhub+RtjzeRO5dbAKzOhREpOE/4ygQ8IKrNeDpU78XAzffFY50AN/ILNU5F4tXMq7T4ugXjmo9j87Ob0RZqydBmEOwZjEQi4aujXzFl55QyBRlAgJsvgRk7kcaMgZKislJtmG2C+Ov2x8eNg+nWXDajyYzBZEajM5KjM1jFtaiaCQSPPA9dlO3c+Su/7NjJzZvWLLLOnTsx6aWJABw6dJi9MTGMGjmiQplmAoFAIBA8EiQnw86djsdr10a//Fs+SHNn5fr74stPnsV3YW8Rrkiwm641u/Biwkxi85oA90RKQcUEFlhbGY0mMzm6wgrNzysw4uvqY3fM28Wb8S3GExURhbZQS7o2ncqulVHL1cQenELdizOQSEoXZN9pYHQqOE0By4W6QXV5vfvbdKzei1HLTtndq5tKToa29IyzLH0Wsw/MZk7sHBr6N8RD5cHygcuZvX82y+LLyRPDagJSNWkVLqdfdxz0bQPzZRBn74ZJz57wxRcgkaA1GPntQirRMdcd9/V1rEG38ACxv0wgeER5aL+SMZlMzJr9EYujl3D79m2qBwU5WOOHhoYSF3eE/QdiS1lFIBAIBIJHkCZN4L337I+NH4/x2HF+dQth5eH7rYdBihTW15xBuDrBbrpB6smPXt/aBBlYRcqDBD97qhVIJRKqFLkKlsPl1DwqKTxoEmi9Zi2fWhx98Si1vGsxcO1Awr8Mp/3y9oR/Gc6iHzvybPYmJJbSBdnSnNIFmUwrY8uwrewbH0dqShMGLjxGQoZ9i2BegZFAd99y79tsMXM69TQHEg/gInfhqfqDKvS8M1u8iPL8R44DQU/Axjqwo4Qga9QIfvgB5HK0BiOztl9g0pp4O0EG9/b1rYln1vaLaA2lvx+BQPDv5aGJsu3bf+HIkaM0a9aUpV9H8/nn8x3mVKkSSJUqgZw4ceJhXVYgEAgEgn8H06db7e0DA61B0V99hcFFzeK99q1wc4MXEapKtjtWqAogu/0v9O0yEG9Xhe14XoERH1dlhS5flFN2NCETiUTiECztDJPZwu7zmcxoMwNvF292DtvJ3INzGbNljN2ereHu8KHLTaTO618ALMqGsXdLqZDlw4ZnNnEruy66Ajm1/T0I8lbbTWlQ1YPxHWri7+ZrE4nl0TSwKZ4qLzqEdGBcxLgy50ZFjKdZyGBiqy2jEJXteGHNcXC2Cyz51v6EwEBrO6qHB0aTmd8upNqJa2esPHyT3RfuYnQWTC0QCP7VPDRRtnvP73h5eTJ92lS8vLxKnVc9qDp376Y9rMsKBAKBQPDPwWCw2t07QyaD1avhzBno3RsAjc6+9TDU15XqfVaQq/CzHbtqgDoXU+m57T/suPYTWye1INTXFbBWslyUsnIFVpEFfnTMdSatjef7wzcZ1Sa0Qo/k5epC19A+LBuwjJiEGAd3wzEe8G0AyMowX16QBf9JA2cmi2itFbiIGm355UwK8bey6VDHj29GNmf3lI5sGNea3VM6si6qNXkGI3dzTMxoM6NC9z6t9QxydRJkqHiz3Sy+HbjKQdA1CWzCtwNX8VrrDxi17BQfHfdDE7GKQouMuckj0KQ/CZNfsV9YrYatW+FeBJDBZHYQ16WxOOYaBiHKBIJHjocmyu7cuUOd2nVwcSm75UHlokKjqVhvu0AgEAgE/xouXIDWreGDD0qfExR0P3sMyNHdbz30VCtY8XxjPjixkKZX0kgxwqkCaHcbEozWkOahG4fw4YE3+XZMYzzVCkxmC6vjEssVWCUt8FcevknLMB+GtCw7V3RoZDBNqnsR9f1ZutXsxoK4BXbjUZ7wTQBIyxBkH2fBy+mlDOoAM7zX933cVGrWvNgaL7WSx7+Mpc1He5i45gRzdlzkk12XyMwroG2typy+nUv76r0Y26zsyte4iPH0r9sP70oqcnRG+i44SmpKE7YO3sPF/yRwcPRJLv4ngS3P7LFrlzyXpCHbuydjM1fy07XOeI4a6ii0v/sOmje3fVlSXJfFuSQNGp1oYRQIHjUemiiTy+UYCsvvW09PS0etVpc7TyAQCASCfwVms9XooVkzOHHCKsri4ip0qqf6fuvhsFZB/H7zF5acWMy1Quh8GzrdhtQSFoWLjy8i5tYOhkYGAeULLJlUwpDIYJbHJtiO5egKGbnsCFEda/Dx0485VNoaVPXg46cfY2yHGoxcdoQqni5k6bLtXA9f8oTF/mU/34eZML0UQSbRS8AE4zpaXRbHfneC574+RFqunnVRrenbyGoYdiIxG53BhJerkuiY67y8Lp5hX8fzepsPS618rXpyDfO6z0MldUGtkJOjM5CjK+SrvTfoMOcgL6++ycLtmby8+ibdPz1MQoaOAY2r0KdRIEq5lCytgTxFMHKzCYufn/2Nv/EGDLLfo1ZcXFcETQWNVgQCwb+Hh2bxU716da5du45Wp8O1FNGVnZ3N9Rs3qFu3zsO6rEAgEAgE/7vcuQNjxsCvv94/ZjJZw6FPngQ3t9LPtVjwUFloUNWDiym5DvlbF8v43F48pLlIYK0Y05KIEG+Wxd6wM5ro3TAQi8XiUMlJyNAyYGEsQyODWTKiOXqDiSytAW9XJf4eKjYcv82AhVbjrieaViNbf39rwhQv+KSEVinJOxnwXmlGiQWwvO9yVJ4u9KnTl3VH7pKSo2dAk0Ba1XJDb07jxS4KXOQhqGUepOYYOXA1zVbpS8jQMnDhMYZGWitf+YUaMnVZ+Ki98a/kQ3qeGW2BDDcP68eg4uLXbAFN+jWia72NoUEUAS1fJlufTYYuE191AB8NqovFLMdNJcejfh0KPzyAYuwLsHGjte20pGFLifUrgqtKhq7QiFohnBgFgkeFh/bT3rlTR6KXfM2iRYuZ9NJEFAqF3bjJZCJ6ydcYDAa6dO78sC4rEAgEAsH/JuvXQ1QUZDnJvVIorNlkpYkyswmOTcBFn87Y9p+yeF9CqflbzigKaa4b6M6F5FybwPr++Rb8ENWau7l6svILCfR0wd1FTkK6Y1g1cK96dI3FMdeoG+iOm0pOXoGR9wc2ZPuZZLxdFawY05LLqXmo5dZY5Ne8YXZlp8vZeCMdZpcWB1YAFELXx7py7JqZF789w6g2ofw8uSU/XdhC/3VDHYKfZ7SZQfvqvQj1dbU5MhZVvhbH3Ch27xrk0kSWjmhBJdX9j0AeajkNqnpwLklDbdVN1tV+Fx9pGtx6i9cvL+Wj2zftrjet9QwWPNefc7f1KL08rd/rL7+E4cOtewNLUHz98iiqSs76+SKv96knLPIFgkeEh/aT3qNHd2IPHmT//gNcvHiJpk2bAJBwI4Gvl37DsaPHSL17lyZNGtOxY4eHdVmBQCAQCP63yMmBiRNh5Urn45MmwUcfWc0gnGHUwcEhcHsTUqB3DX9SG08pM3/LGZm6LNyKCY9+j1WhspsLKRo9E1efsI31bVSFng0Cy1zLbIELybm2r/3cre6D342JZHHMNdYdu8WBV1vzVXAg41UpZa41LQ0+yS5lUI81MdoNMrXZnE+WcjEll/rVVMz4bTrRxxc7nBKfEs+QjUOIihjPt2M+YODCY3bZZSXvHaDQbCY5W0do5UrIZVKUMilRHWuwfPNaVoT9Fw/p/fnvudzkmCv8pr1/vWE/DWFcxHjm9Zh3P/D5pZdKfeai9SetiS/z3QCMbhvK6rhEvr/Xdtq7YaAIlRYIHgEe2k+5TCbj7bdm0qtXTzIzM/n1110AXL9xg59/3k5aejo9unfjjddfQyIpY8evQCAQCAT/MIwmM1qDkYyfd1HYoJFzQVa1qrWNccGC0gVZQSb83h1ub7IdUl5fxLNuq6jqUU75qQQ+am/yCow0qOrBZ880YWqPuszefoHMfAMXknM5mpDF0YQs3v/5AmaokAU+9+b5VlLxVr9wjiRksvpIIiazmYzYt8oVZJPuliPILEAl65feai9eaFeDiZ3D+P3mL04FWXGiS+ylK4s8vZHvD9+0uRzKZVJ6eJ9iXa2ZeMjsBdwlA5wvcFxj8fFFbLm0FYOx/P1fcpmUbuEBDGtVvnFKi1AfVsVZ2zCFE6NA8OjwUGviSqWScVFjee7ZwZw9e47Uu3exWMz4+vrSqFEjfH18Hubl/jA5OTls3PgTR44eIz09HaVSib+/H4899hijR410mH/kyFE2bd7M9es3AKhZowZPPDGQ5sWclUqSmJjImrXrOHv2HHq9niqBgXTr1pV+/foilYrfeAkEAsG/Ba3ByJ5Tiehfm8mTe9YidWbs/swzsGgRlPXvYP4t2NsLcs7bHbZIVSw9pWREuCdNApvYte6VRtPAplRx92Xhc76oFFI2nrhNfoEXdQLcHXLLTGYLa44kMrlrbb7ef528AiOXU/MwmZ0a1DO6bSjLD15nWGQob2w8S3gVN8Z5LKFh9uoy72n8XVicU8qgDpAA6vv37+Xixfextxjetgrtl88r95nBfi9dKbcPgFIu5UhCJhqd0doeeHMdLoeGA/YC66AO+iVBlhkkZhh5Cr5/DEz3OhQ/PjiXAXX7o0TheJESuCrlvNEnnJahPkTvu27Xytigqgej24bSItSHkcuO2Cp9RU6MooVRIPj385f8lHt6etK2bZu/Yuk/zdWr13j3v++Rm5tLcHB1WrZsgU6r49atW2zZstVBlG3ZspVvli1HJpPRuPFjKOQKTsbH8/4Hsxj74gv07dvH4RoXL17irbffwWAwULt2bfz9/Th//jzfLFvOxYuXmD59qqgWCgQCwb8ArcHIN4s20+3DaYSnJTiMa1SV2Dl+Jn0/moqrqowP7lmnIaYvaG/bH1d4Ymy7kRqpdVl+4DaTW05j9JZh5d7XtDYzuHpXz5s/nedSSi5mC2w8cYdVL7Sikkpm29/kU0nJpC416P1YZQzmXGb0q4SHygtXuQdrjySz8vBtu1bAoZHBtK5Rmc3xt5BKzSwd2QjVmSl4J5YuyMwWePEuLCttO5UW66eRYlpxWpsZFBTK6N+kGjkFmX94L50zGlT1QCmXciklF42+kMC0lXBkLCVT0n7Jh0HJoL13eEYsfLQbRsXDc09Bsof1etm6LNyUlSp0f65KOZFhPkSE+tgZp6gUUlbHJfL+tgt27xuw3qNn2XFDAoHgn89DE2XLl39Lp04dCQsLe1hLPnRycnL473vvYzAYeOON14hs2dJu/PLlK3Zf375zh+XfrkChUPDB++9Rr15dAO7cSeLV117nm2XLadasKVWqVLGdYzQa+fSz+RgMBsaMGc3AAf0B0Ol0vPPue8QePEjEnmZ07drlL35agUAgEPyVGE1mzn65grHTx6EyOeZKHQpuxNS+r5Ck8sflYlrpe4OSf4X9g8BYQkSoq0LnHSi8GtHd18iZ2zl0Cu7N2GbjWHKi9Fa+8RHj6Ve7H30XHOFWls52PCFDS5/P93Pwtc5M7FyT29l6Brf0Z9vlrfRaPdfBPGNyy2lsntibUctOUUkl54V2NeheP4BCs5kRbaux9eKPKE5MZpAyo9R7MVlgdCp871wfORVk45uPp1+dfvSdf4hATxdeH1AxwVNEyb10JSnas2W2QGDSIrjwqsOc1bkwKuV+3axDAny4x/rfHW/CyWh4dhDsDYNMfTZBnuW3TBZhQUL7ObvtjFOKhLMzPFzKr8IJBIJ/Pg+tj27zlq1MmTqdlyZNZsOGH7l79+7DWvqhsWbNWjQaDaNGjnAQZAB16tS2+3rb1m2YzWZ69exhE2QA1apV5emnB2Eymdi6dZvdOYcPx5GamkpYaKhNkAGo1Wqixr4AwObNWx7mYwkEAoHg/wGDycyCXB+0Cvv9YQUyOR90HsOQZz8kycMa1FV8b1DR/rOUHD3JJ77EsrevoyDzqAc9DoJXIwAqqeRM6VGHq6kG3mo/q8z8rY+6zWX892ftBFkRObpC0vMK6FDHn6ci/JixazpDNw5xaImMT4ln9JZhzD08k62TWrAuqjX1qriRW1CImQKm75yCLnZYmYLMaIHhZQgyiVYCCmyCrElgE1Y8voL3O7+PVCIh2McVrcGEXyXfUq/hjKK9dM4YFhlMl3r+HE3I4MNaP+LhRJDlhoxiWDFB5p8HazaArJhoqqwFyb2vfVy8Huj+PNRywqt42Pb1XUguXZA1qOqBh1q0LgoEjwIP7Sf9hefHELNvH1euXGXlqtWsWr2GevXq0bFje9q1bYtbWVksfwMFBQXsjdmHi4tLhatUx44fB6BNG8dWzDatW7Ns2XKOHj3G2LEvOjmntcM5NWvWJDAggJuJiaSm3iUgoJxUTYFAIBD8z6LRGYnVKnm910QWb5oNwAW/UF7pN5WL/vZdI+eSNOQXWFOef7uQSnTMNXqZvuKlgHWOC1duAx23gMpejFRSyelQxw+jycKAOk/yRL0BaAqyyS7IxsvFG0+VJ8cTch0qZMXxVCvwdXPhVlYuJ9N2lGuesejYIjqEdMBP1oHa/l6cupVFquE32txZwnDP0s8rtMCQFNiQ53xcrVezc9xO/Nz9yNBm4Ovqi1quJvp4NC/veJnBDZ5l0fC5vLz2PJ4qrwfaS+dfyQe5NNHueNGerdY1KqOUSvi64Tq8Epc7nB+jGEuziI9pfDae+JR4pGZY9SNULfEc73SC32vc2/um9ir3vorzIE6M4zrWRCmcFwWCR4KHJsr69etLv359SUlJYW/MPmJi9nHhwgUuXrzI0qXLaNa0KR07dqBlyxYOGWZ/B1evXkOn01E/PByVSsXx4yeIP3WKQoOBqlWr0rZdWzsjkry8fNLS0gGoUcOxJdPPrzIeHh7cTUtDq9Xi6uoKWCMArOfUcHofNWrWICU1lYSbCUKUCQQCwT+YHJ0BgB112/JDo25kqT34tP0wCuSOQcGeagUuCimztl/gh7hrzAlawBOV9zrMOyPrQc32P+Kqcv6LTKVchlIOrvf++Zaiwk3hz6K91/jmQDzGcoz65g5qhMGkxd/TwpxNcyr0nHNj5/LrsD7svZBBqxoqJFsn0bMMo0aDBZ5Ohi35zsc9CjzQKDV0+r4TDf0b4qHyQFOg4ezds5gt1gdYfHwR7YM7MLFLN7LyLRXeS/dy5DQsFjnLR7Ukt8BIttaAh4vCtmfrw21n2dtpPV53HAXZ+YA3GLmrDSsa5zOt9QyG/TSEt2Kg2w37eTtqwqz21v+e1mYGSqmq3PsqTnEnxpWHE0udN6xVCF3D/YUdvkDwiPDQa+KBgYE8O/gZnh38DFevXmNvTAwHDsRy5OhRjh47htrFhVatWzHppYkP+9Jlcuv2LcBqQjJr1kfEHTliN/79ylW8NHECHTpY/6ZNT08DwM3NDRcX5xtsfX190Wg03L2bRmhoCABp6VYhV7my83YLX1/r8bS7aX/yiQQCgUDwt/H779C0KXh52Q55qu+Lrxm9J0MZBk7DWoXw+8W7bDl6nu/CPqCV21mHOYvvPsWclJF8Xk9D74auFfowLpVK2HziNrX83ewEmUwqoU6Am23P0rW0fNrV8qVtbQ8+O/QZTzcYVGHzjNOppzGadegLdHideJGeitIsFEFvhieT4RfnWdSEEIJbNTfOpZ/DbDFzOvV0qWt9fGguMaP6ka6hQnvpoiLG06F6LwxGKDSZyC8w4u+uQi6T8P2hRFbFJVJPegpPJ4KsoMkXTNvVGNAw86fz/Dy5P/OlfXkp5me7ebfdYfiTYJHCuIjxVudF+YP/ornIiTEyzJfFMdccnBjHdaxJ13B/4booEDxC/KU/7bVq1aRWrZqMGT2K02fOsPu3Pew/cIDff9/7t4uyvDzrr+yOHD2KVColauyLtG3bhoKCAn7e/gubNm1mwedfEBQURI0aYej0egBUKsffeBbh4mL97ZhOf79NRG87z/lvzooEnk7nvLXE2X3n5zv+ujH9nvgTCAQCwV+ITgevv27NFhs61C5/zEMtt7kYliXIZFIJI1uHMGr5UTyk+dRU2TssmixS3r4zjlWZVjffxTHXKlwhUcqk1A10J9inEsMig9l6OplhrUIYEhlsc/fz93DBx1WJTGpm06Uf+fX6TjqHdSp3bW8Xb8a3GE9URBSGQg0D86Jwy9hb6nytGQYm3w9ZLkldWT1iJu/l86Ofk3Q0iSx9VpnXP5lykmx9Nu4ufry+8QJv9vuQNtXbMz9unoMpycuR0+lYvRcfbrvG+483ouO8PbZ9Wg2qejCqTShbJrZl5DIFaXU/we/SVMD67j/NfY3nqozhfPLvANzK0vHG53v4/Is4u433RgkMfhqCajZhQZtXGVC3f4VdF53hqpTTu2EgXcP90eiMaPSFeLgo8FDLUcqkokImEDxi/C2/gjl37jyxBw5yMv7k33E5p1jM1l8hmkwmhg0dQp8+vW1jo0eNJO1uGrEHD/LTpk1MnfLK/9dtOrB161bWrvvh//s2BAKB4NEjPt4qxM7fyw1btQr694fBg4GK7Q3yVCt4pXsdTBYL55M1QADPJ7zN2pqv4yotIN/kwoTEV9mb28J2TkWyqYwmMwaTGY2uEC9XJUazhdf61GNGr3r8ej6Vsd8ds6u+NKrmwXcvNGFO7BwsWKjsWnYQdS2fWuwYuoN9N/cxeG1/3pWepmcZ+iPPbM3ziint943ZcMnrIr1W92JSy0kcffEovVb14mrm1TLvI1OXxd1MOT0aBDJw4TGGRjZh6+A95BdqyNRl4aP2xlXuwZq4ZAZuPsZb/cL57lCCnXHGuSQN0zecZmhkMKteaIVBEsncmNO87L+KlxJnkOnTl66a++nQMrOJEV+8jjTN/pefd9+awtopr+Dp4oXZLMdN+edt6uX3xJerUi5s7wWCR5y/TJRdv36DmH372L//AFlZWVgsFtRqNZ07daJjx/Z/1WVLxUV93x3LmdFH165diD14kHPnzgGgvlfRKigwlLqmXl9wb+79tV1cXMjLy6OgoKCUc6yVNLVa7XS8JP3796dLF8f7TU9P5403Z1ZoDYFAIBA8ACYTfPwxvPUWFNpnRjFuHHTsCIGB5e4NCvV1ZcWYlqTnFpCqKbC1FKpUkczNfYco1095IWEm53S1HM7N0ZWeTaU1GPntfKpdALGnWsEvk9vz5e9XWRXneC/+7iryDDmcSj2FTCLDVeFaqnmGt4s3O4ftZE7sHFaeWMLWqtDFtfTXpTFB7yQ4qC9lQhbgbf3P+JR4xmwZQ1REFDuG7qDF1y3KrJj5qL35/lwKL3WpTd9GVfhq7w0Wx9woZievsdnJD40MpkWoD+9vu+B0rVVxibQI9cHLVcFXd59me3YbEgzVCK9kxFN9vwVx2r7vaXnbPsD7dNP2rG8ykoxtqfx67hQHXu1qC7oWCASCh8FDFWWpqanE7NvPvn37uXPnDhaLBZlMRkREMzp27EBky5YolaW3A/6V+Pv5Ada2Qk9PR8sof3+r6UZOjvUfuMqVrfPz8vLQ6/VO95VlZGTcO9fPdsyvcmXy8vJIT88gNDS01HP8ip1TFm5ulXBz++PtEQKBQCB4ABISYMQI2L/fcUylgnffBf/7Jk2l7Q2q7q3mpwltMZktKKQS1CopMTNak1+oIUObia/rEEySkXQ6ns3tEgHNQKk26NoCIx9uv+AgvIa1CiH2arpTQeapVjDricdIzL0EgMliIvp4NJNaTmLMljEO88e3GE9MQgyrTyxhRzVoX4b4yDZBzztwxNnvIc2ABpsgK0708WhaBbViXPNxzD4w2+naTQOb4unixaaTpziWkMWKMS2JDPNhyf7rDnuwxrQNo3moNyOXHSn2Li2AfVvp1/uv8/WI5kglkGCoBsDl1DxclNZA7YB9vzE+boPdObc8AxjWYQKauFu26wmbeoFA8LB5aH+rzHj1Na5cuYrFYu0ZqFevLh07dKBdu7a4u7s/rMv8YYocFA0GA4WFhQ4OkHl51iCVIvHl5lYJP7/KpKWlc/36DerXD7ebn5aWjkajwd/Pz+a8CBAaFsqNhASuX79O8+YRDvdx/dp167yQ0If2bAKBQCD4k1gs8N138NJLkOskWKtJE2v7Yv36DkMl9wbpCo34u1q4emgeP2T349W+Nfn5ymbmHiw7oDkhw7oZq0FVD1v4cVGbYq7eiLtKzo5zKQ7CSyaVMCQymBdXHHP6aMNahXDqdjYh/l62Y4uOLuLoi0cZGzGWJceX3F9LIiMqIoqha/ryazVoXYYgyzBBjztwwpkgKwQKAC8nY/f4PO5zNj27iTmxc2yui8WZ1mYGJxJyMZqtwdcrD93kP51r0SLMB929/XJVvdR4qRWsO3aLJ748SKbW2t3iL8/g69AP+CDpeY5qG9rWPJekQWcwUTfQnQvJ1u+zyWxhdVwik2spafnqp3b3YJDKmTDwVTQu990whU29QCD4K3hof6tcvnyFqlWrMmTIc0QvXsRHs2fRu3ev/wlBBuDn50dYaCgWi4WzZ885jBcdqxF23/6+eYRVVB08eNBh/sFDhwBo0aK53fH75xxyOOf69eukpKYSEhws7PAFAoHgf4X0dHj6aRg1ylGQSSRWo4+4OKeCrIji+4IClNlkbmlL49SZvF3jB2b8Np0hZQQ0f3RoJt+OaWxroRvdNhST2YKu0Mj2s8k8vfgQ3x++icFkZun+Gw7XrhPght5gurdnzZ4iwfb57itUUnjYAqez9Fn0WtWLGW1msHzgctvxBv4N0GuT+Ex5tkxBlmaELrdLEWQ6wASUE096MuUk2kItDf0bOoyNbz6efnX6UDtQzX8H1GPhc00Y1jqEJ76Kpe/n+9l/JZUgHxl6cxqXs87Tpo6UzS81Z0LnMOq5Z7K+5qs0dr3CsrD/0kh9xW7tLK3BJnqLOJaQSZevPsBLbx9I9kGX5zldpY7ta2FTLxAI/ioe2t8qn34yjy8Xfs4zTw/6nxUcTzzxOADLv11BZmam7fj16zfYvGULAL169bQd79e/H1KplB07f+XSpUu240lJSaxfvwGZTEb//v3srtGqVSQBAQHcSEhg85attuN6vZ7F0V8DMHDggIf+bAKBQCD4A+zYAY0awY8/Oo6FhsK+fTBrFlSw9d6YfgJ2RlLdfAYA1YVZZF0qO6A5+vgiYm7tYGhkEEMjg2ldozJymZR8vYlLKXkkZ+sZ3iqELK3BQXh5qhUMbxWKweQ8oKxIsJ1N0rAmLpnJLafZxq5mXqXF1y24nHGZzc9u5sKEC6zp+znVj46keRmeEylG6HQHTjvZcq3Wq609OBX0rMjWZ+Ohuh961iSwCcsHLmdq66lELImg39rOKN2P0LKGGxNXW83CfprQHFwP03tNJ2p+EULT6MbU+yqUAT90IcxrJ9vC3yBElQKAu0zHirB3qK26abuGt6uSvAKj3X0MaxUC0dGYOna0HdtWtx3fNbP+G9+gqgdfPNeUN/rUEzb1AoHgL+Gh/c3y1tvvEhISzKwPP3hYSz50OnbsQHz8Kfb8/jsTX5pMvXp1MRgMXLx4icLCQnp0707btm1s84OqVWPUqJEsW7ac19+YSZPGjZHL5ZyMj8dgMPDiC89TpUoVu2vI5XJeeWUyb7/9LsuWLefAgQP4+flx/vwFsrKyaNOmNV26dP67H10gEAgExdFqYfp0+Oor5+OjR8P8+eBRRkpySW5tQnZwKK4me0/4z/1gWz5oLaWcByyI+5jdI2LQaKWcuZPN6ds5xFxOY1SbULZNaofJbCY9z14FFRmJXErJxUXh/Hesbiq5raVv5eHbbJ5on/eVpc9i9oHZzImdQ8eAunznkYzakl3qfd65VyG7XOg4Js+Rs2vyLtp92670By1BkEcQG57eQGJOIu4qd9RyNdHHo5myc4rNAGTIxiFERYxn4dAPkEokfHDgDad5ZXkZ8bS6GE/J2LAskzt5Zus2gwZVPVAppFxKuV8RtVW/lHL47TfM77wL69fTYteP/KpUC5t6gUDwt/DQRJnJZLIFI/8vM2nSRMLD67Fj56+cPXsOiURCjRo16NWzh1OxNHBAf6oEBvLTps2cu2eLXKtWLZ584nGH1sUiwuvV45OP57J6zVrOnj1HQsJNAgMDeOKJx+nfry+SMvJsBAKBQPA38PHHzgWZry8sWQJPPlnxtSwWuDAX4l9Hgr3yulEI/ZPKFmRgbeXLL9RwIUlKeFUPXv3xDDm6QqZvOM2bferRu2EVqnq6EF7Fncupebip5Hw3JpLFMddYd+wW+2Z0vp+ZVoy8AiM+rtYqX46ukFHLTvHtGMe8r0CpmW9crxNkce4cDJBYCF3uwDUngoy7YPY3U92reqmujiVpGtgULxcvfr++j7mHZqMp0HD27lmn+8uijy+ifXB76vjWdSrI6ilgdxBULfGp5qIuhGE3PiDdaHUbGd02lNVxiZgtpYQ0y+VIP/wA3nyDAFdXAsp9CoFAIHg4PDRRFhxcncyMzPIn/j8jkUjo0aM7PXp0r/A5LVu2oGXLFuVPLEZwcDCvvTrjQW9PIBAIBH8H06bBmjVw8eL9Y717w7JlEBhY8XWMOjgSBQnfOwzleTal5cmTpJsqtlSmNotOdevxzpZzVPVyQSaV8FzLYPo3robFYiFbV8hXQ5uhkEm5lpbPicQsVh9JxFOt4OrdPMZ2qMHktfF2axZ3FjyXpCEhQ2vL+9r27O8YzLkU5Jyj1sko5DpH58YirhdaK2Q3jSUGzMBdIBDMmNl8aTNTW09l+E/Dy33eGW1nYLZIeGvvG5xKPVXu/E8OzWPrc1uRSqR2wq2BEnZXg4ASn2gMHo0Yev41MoxWx+VhrULo1aAKKRodI1qHll39ci0jA0AgEAj+Ah6aKOvbpw/zF3zO+fMXHJwKBQKBQCD4n8LVFb7/Hlq3BoUCPv0UoqKsxh4VJT8R9j8JmccdhjIDHye/yTzSj9Wu8HLeai/O3MnhP51rIQF8Kin59Xwqz6846mABP7ZDDVqE+tC2pi+znmzEycRsWoT6MKRlMKuP3BdXRc6Co9qEMn3DacBaMdt+JpXBLUK4k3CThpdGIuduqfd12QBd78DtEoJMUiBhXtt5DG7/LNm6LDxdvPBUeSGVwLiIcSw+XvpeOquRR39y9NkVEmRgrSbmGnJp6N+Q06nWZ2mshN+CoLLMfu4xPXh2+h7/azkESiR2FbFaLvcMyCyWB/t+CwQCwV/IQxNl9euH0717N97973t0796NFi2a41fZD6VS4XS+n1/FcroEAoFAIPhLaN4cvvkGWrWCOnWcTimypNfojOToDHiqldYKS8Z+5AcHQ0Ga3XyzBV5Nh8+ubuF6xIIHauXzdPFi4s8nWTKiBQVGM7O2X7QTWEWcS9IweW08L7QLI3p4hG1e0R6zZiFeLI9NsAm5lYdvsn1Se4ZGBrMqzlpZ+25MJJt+/4Xn8l/AS5Fd6n1dMEDX25Bcotonz5Xz/ZM/svSQO1UrG+nRoDZpGj0HLmZw+nY2c/rOo31wR+YdmuMQAzC99av0q9MPs0lBuvbBOmwytBk2Y5BmKthVDXxKCLLDOuiVBPtkMlY+H4laKXOsiC1dCps2wcqV4OX1QPcgEAgEfwUPTZS9OHYcEokEi8XC9u2/sH37L2XO/2njhjLHBQKBQCD4U6SmwuTJ8MEHUKuW8zkjRpR6utZg5LcLqUTHFA8rtvBqjd+IcvsSsC8d5ZphaApszQcwlxnQXJJprWdwMiEXg8mCq0JG3PUMVh9JRCaVUCfADTeVnLwCI5dT8zCZrRvUVAoZuy7ctc1TK2W8s+UcHev4sWREc/T3sry8XZW4uciY3rMuTYO9yC8wcvnCHkZoR+GlyCv1ns4UQLc7cLeEIKucV5nXuv3E7F1acnRaXv/pLC4KGb0aBvLO1vPE3cjkwNUM3n+8I/tG9SNbn02mLgsftTeeLl6cSMjl5bUXmDeoET5qJ8nSZeDr6oumQENLFeysBl4lBNkBHfRJsn4vfNTe+LqpHBeJi4MJE8BggBYt4KefoKGjLb9AIBD8nTw0Udagfn3RBiAQCASC/w22bIEXXoC0NEhMtFrbyyv+T57WYGTW9gusPHy/UqWWFbKgZjQ9XHY4zL9sgMeTrZWlIkoLaC7JuIjx9K/bj7wCGQufawYSWHk4kQmdw3gusgr5hRoytJn4uvrgKvdg7ZFk1hy5w5DIYF5ZG+9knhSzRc/+K5nEXM4gRaNHLpUQPaI5Cen5/KdhCvw+GFe5ttR7Oq6Hnncgo4TnRmBaQ+L+e4Dxq07Z2covPXCDruEBDG5RnbgbmdzK0jFq+QnkUujRIBDfSkoy8lP59dwpjGb4+OnHWH4wgaiOQQ9UTXRTuuGhOcPP1cCjhCD7XWs1Vcm3FJmIeDoukpoKTz1lFWQAV69aK6UXL0JQULn3IBAIBH8VD02Uffjh+w9rKYFAIBAIKkzxFkNNeiZB783E9bvl9yccOgQffQQzZ1Z4vd8upNoEmadawbBWIQx7TIr/gWNQwn1wX6E7A27lklNCwBQFNO8YuoPWQa1ZELfAoZXv1bbWVr5TiTpmbb+I2WIhelgEc5+uy97EX+i/bqjDOZNbTmPLS32QWCzlzntnQG9GLTtFQoYWXYEJVfpuXPZPRyrVlfr8sfeqTZriz2OAHsrhrJ2zFK3ByOfPNkOlkLLmyC1WHr7JuSQNaXkFtKrha2uTBDCaYfuZFLv1h0YG0yLUh/e3XaBFqBfTWs9g2E9Dyv2+TG8zg+yUw/xS1YJbCW+OXVoYmAS6ey6X09rMQCktUSUrLIRnnoE7d+yPjx0rBJlAIPh/RwRuCAQCgeAfi9ZgZPvZZJ5efIj/TFyIsnlze0FWxOLF1myyCmAwmVm89zpgzQLbMrEtob6uvLA+hSEXp2K03P+nU1N7Ep0SHAVZEVczrzJ4w2D61O7D7hG7uTTxEgdGH+DChAtsGryJu/l3+fnyDqp5y8gvMBLo4YKHq4XZB99k9JZhDhWk+JR4Rm8ZxmdxH+JRyczsQ2XP++jQTL4d0xhPtQLL7U2Ml7yC1Fy6IPtNCz3u2AsyabaU5saZXNIN5pNfL2GywJOLDhL1/XG795OVb2D29gv8p1MtvniuKQ2q2me8NajqwWfPNGFqj7qMXHaEHF0hM386T/86/YmKGFfm92R8xHj61+1PaEgPrldqbDe2Pd9aISsSZOMixjOgbn+UJQPLpk+3VkyL06kTzJ1b5rUFAoHg7+Avi6VPSkpCo8nF3d2datWq/lWXEQgEAsEjSlGL4drY60yKXcOEw+uROcm4Mvbrj/ybpRW2OdfojJxP1tgMMRbHXCtmuNGI95NeZEbgCtYrP6Bp9R5Y+LzUtbxdvPnh6R946/e3WHpiKQ39G+Kh8nDI5IqKGM+3Yz7g4NUctl/d6jSLqzguChWbLm5iSRkOh2DN92od1I4Pmtyh5qXXkVC6P//WPHg6BQqKZapJElzZ/p/fmX9QT1pyLivjEmkS7MXQyGC+2nuN6RtOMzQymBVjWmI2W7ialkefz/cTM70TXcP9rdVLXSEeajluLgq0BUaUMintalcmISORW1k6xn9/lkXD59GhNGOQNq9aRZZUicFswKfLZjKPRuFzdyda/24cljTDNfNrwr1CbHPdlJXsH27JEliwwP5Y9eqwbt0DtbUKBALBX8VD/ZuosLCQtWvXsfPXXeTn5wPQuXMnJr00EYC9e2PYvGULL02cSI0aYQ/z0gKBQCB4hChqMTz480F+3PYJjVOuOMzJV7jwXtcXaff+q/T2rVzhf/BydNb9RsNahRB3w9Fw40jBc/S82pouTZvRtRyjivEtxhOTEGPbU1Zk5V6SIvE0oNEAOn83p8w1ZRIZURFRDFgzoELPk3DidUa43HIIti7OulwYlmJvXeJxuQbPdfuWDo2bsfzkEVtw9fLYBJaMaM7imGuYLbAqLpFmwd50rufHpZRczBa4k62jpl8lAj1dCPR0sa3pprJ+F97oE05kmC+LY64Rey2DvguO2BmDZOmz8XHxwkvthVKqwmA2sOH8j2y+9BNJeUloC3LoVCBhX+5dXmnTmISXbyKTSFFIlY4Vst9+g//8x/6YSgUbN4K/f4XeoUAgEPzVPDRRVlBQwFtvv8OVK1fx9PQkolkzjh23z25p9FgjFnz+BbGxsUKUCQQCgeAPYzCauPXBJ/y86SvUxgKH8eNV6/FKv6kkelfh7L7rdK0f4Dwk2Am+hRd5u+rX9Gy5mlfWnWJC51oMiQxGbzCRqTXg46qkkqolCpkUqcxQqlHFg4qnBXEf82R4f87cPVPmvAb+DdAWaiuU7/WSJ7zjUnooNMByDbyQas2BBkAPw91H8NFXc3F3cSVDn8KMfpVwU3qhllmNRgxGE3UD3bmQnAvAstgbdKp7P+rGRS5j6f4ExrQLxVXp+FHDVSmnW7g/7WtXJktrICPP6hKZo5VyNVWBv0cQlV3dcFHIyTPk89u1XbQLaUOzqk1I16ZT2bUyarkalxNLmLh9IgduHmBuj3mOguzCBRg0CEwlKoTR0dZIBIFAIPgf4aGJsp9+2sTly1fo1q0rY198AaVSyeNPPGU3x9fHh+pBQZw6fZrhD+vCAoFAIHi0SE5GNmIUE3771WGoUCpjftshLG41CJPUas93LkmDRmd0Kg4cuP4tvkfGM6aynqzrC5j39CSO3Mhk7HfHbLb4nmoFv0xuz6e7LuPlKmNyy2mM3jLMYakHEU9gDUfO0v8fe+cdFsW59uF7K+wCS5UmUiwoghURxELsXdOLvSSiJ0ZT1PR2Uozl5MTExJYYTWzpsURNTKKoqFixYIsoYAGkL7C9fH+sIMsuRT+TnCRzX9e5Tph5531nhnWZ3zzP83tK7ZojO8Pb1ZsSbUmD8z3nDXP96h+zuBRmFFAdQxNfFzO3+1ym3DuFbb9tY/6++U4NRHzcQ+nW3LdalGVcU1OmNdI60AOxSISLTMy7O84R5qtkcEyggyCuSj1dl5ZD60CPasv/qkgbwJtdLvLAgOHopO6U6EoYuWGkw7nM6DqDQ48dYtDaQWw6t5n7o+67KcwKCmDoUCgrs7/o55+H8eMbvH8CAgICfyR3TJTt2ZtKEz8/pk1NRiKR1DkuuGkwZ8+evVPLCggICAj8k/j+e3j0UVyKihx2ZfqE8OSwZzgZ1Mphn1pntEujc8CkhSNPQOYnVDV38Tr/Ml9eCmbuYXtlMyYhjNQLhdWNmDdOH8yUzlMd6sBULioKNYW3dHllutLq5si18Xb1ZlrcNKbHTcdkMTkdU8VbvvCCT/1rzSuG52rcRrfzbrRp34b7B9/Psz8/69TGv8pA5OC1acwbMJ8dp/O5XGIzDqnQm3B3kfJQXDPWpeVgscLSlEz6RvnbibLa7pZVwq4md3vt5BH9fxGlLOYda1f+c2yN03OZtGkSybHJbB+9ncmbJttqz5CBXg/33AOXLtkfdP/9tr51AgICAv9j3DH3xevXr9OiZYt6BRmATCqjoqLyTi0rICAgIPBPIjsbnAiyVZ2HMXTCe04FGYDKVeZ0OwDlF+CnbpD5id1mkdVMeIG90JKIRYyKD+XT1CwAyrRGJqw8zvOJb7HmnnV0DOxYPVatV+Pvdms1S94Kb4LdHc2xWvq05NBjh2jp3ZLh64djxUpsUCztA9rTI7QH7QPaIxFJEANL/BsWZC8X1RBkBvA/6U9leCUD4wba1cDVxZLDS9h8bgtv3B1Vva2JuwtJrZoQF+5TbYlfFaWsSU13S2fc7/0z7zZ7F4nIgrj8PBOK1uBXz6PFsiPL2JOzh8RmiZRqS8BigUmTIDXVfmBcHKaVn6IxWcgr03EuT01emQ6NwYTJXId9poCAgMAfxB2LlMnlciobIbbyr+fj7u7W4DgBAQEBAQEHnngCtmyxmTcA+e4+zB48k93NY+s8JDpYhUpRx5+7rPVwMBlMjtGaTdp7mZVjn2wfGeCOzmDmdK765hRFGkYuPsykHp3YPWEPBZUl5FcU4aPwJsjd95aaI6tcvBjSciRfnv6yeru3qzc/jvmReanzWH5kOd6u3pwpOMNPY3/ieuX1mzVWYgllu0bSXnem3nWeLoD/lt74IR/6y/uzI2YHEvGt1cAt3D+flAnDkIqhdaAKD1cZ98aGMGrFAcq0xur+bm4uEvLKdJRpDfi6uWCyWOzuX00e8dnO3JDFdttiXGCCBywsdXoIAO+nvc/3D39Psa6UkI+/gHXr7Ac0a4bm62/5+WIZy1KOVaeigu3zkZzUnH5RAY1LcRUQEBD4Hbhj3z4REeFcyMykrKwMT09Pp2Py8/O5ePESHTt2cLpfQEBAQECgXsRi+PRTrO3akdslkSFtx1CqcJ7uV8XUpBbIa5t8mCrh8BNw0UlPM6kbpe0+YsZqX4dd7i5SijUGh+1lWiP/3ZHJxmO5rHk0Ab1Wzgd7rxLuW1lnzVlt5iTOQW8QkxQ6yC4dsqaDY0uflmwfvZ3d2bvp+1nfarGnFMFP4R50lzqKyyosVph6HVZU6ZETsHLMShblL4L826uBK9WVMiA6kD5t/KnUG1n0y3myijSE+ypZPakrBy8V8/DyA9UiKC7cm+cHRzmdb6zvFt5o6mjx/5+S+gVZ1blojBoClE1g8mTYtOlmTzJ3d7Tffc/bR0uqUyZrknFNzYz16YxJCOOFIW0EYSYgIPCncMfSFwf0749Wq2Xhf/6LWu34BqyiopL3P/gQs9nMwAED7tSyAgICAgJ/RwrrqcUKCUGUno7Xlu8Z1jum3mnGJIQ51DRRchy2xzoXZKooGHiIfO+7nc5XoTfh6yavc72LhRoGL9rDwUslPD8kivs6h3JP1EimNtAcOTl2GkMjh2GxinlrSybPJ77FqpFriQ2KJTk2mUVpi6ojZvP3zWfSpknVgsxLDD81pV5BZrbCuPwbgswIpECbLm3o1rNbtQi7nRq4Ym0Jg6IDiQv3wWC2MLNvJM28FXw2KZ5lKReZ/fUJu6hUhd6Ep8IxlXSy3/dOBdnbxTCrkadUqivFw9UDvLzgxx9tNWVSKeYNX7BDGuBUkNVkzYFsfjlzXUhlFBAQ+FO4Y6KsV6+e9OzRg5MnTzIleRqvvf5vAM6cOctbb89lypRkMjIyuCspibg4wYZWQEBAQMAJFoutyW9Y2M1IhzPCwlC6yHhhSBQfPNKJ6GD7aFl0sIoPHulkH/mwWjGdWYT1x3hQn3Ocs/kEGHgQPKPwVDgXXvllevw9XKvXk4hFRAV5EBfuTVSQBxKxiDKtkY92ZdJz3q/8Z8d5zGY58/svYN296+1qzsDmILhq5Fqe6/YmGw5eJ69My4DoQEYuPkx+Xkd+ePhXRIg4nn/coecZQKAEUkKgu6LuW6WzwL25sLYcKACvdC/oDn6BfnYiTK1X46dswK6xFj4KbxKa+/LmltOIRCL2ZRby7kMdySwo59jlEiRikd348/kVuMoldr+vqU2+5uXgjx3mLmv1FC86lg/WSaB7IDLRDcHn6gpffQW7d6PvP6DeGraaLE3JxCCIMgEBgT+BOxqjf/rpJ4loHsH3339PerrtzVtubi65ubkolUrGjB7FfffdeyeXFBAQEBD4u3DlCkycWF0vxrhxcPw41JESD7Z+V4NjAukb5Y9aa0KtNaJSSHF3lVGpM1Kus5lMyE2lcHAi0mtbHOaotIpJbzaVDp3n4y6z1TyrFFKig1V2UR6AR+JDySwo57GezfntehmPxAdRaVRTpCnGV+mDUmrr47XmwBUq9Cbu6dSU7adyOZOr5sl+I+nZbCCVRjXF2hJ8FN4opSrWp+UycuNhALY80Z2kyCYcyynlo12XOJJdylNDlE57nkVIYUdTaFF34I5yC4y4Bru0wEkxD0S+wbA3wxm/cbSDCMu4noFSprylGjhvhRdjVxxidEI469JySDlfwBfJ3TifV87iRzrjIhOz/uBl1hzIpkxrxGyxsi4thwmJ4cz++gRP+K/nmcC1DnO/e30cj933bzoe3dnoc/FT+iGr2adMIoFu3VCX6eqsYavNLbVPEBAQELiD3NFvHZFIxL333M3IEcO5ePEi168XYLFa8PP1pWXLlshk9bhfCQgICAj8c/niC5g6FUpLb27LzoaZM2HVqnoPlUrESCViRCK4UFDO+79eZuvJ3Op+V33a+LM84m2nguywDh7Os5B54SOmqa3MH7AAd7kbcomY2QMjmbz6COYbE1U5L87bdpY372lFuTiF4V+Mduid9WTXWex4ZhhSkRiNWY3SrZguzX2Qycz8cKKYzSfyAajQqzmXV45IJCIywB13Fymvbcrg3yNjmDWwNbFh3uw8dx1vV2+Heq9ouS1lMbiev+KFZhh0FY6UgyTFB/82b3LOpQVLWiUwNXYqK46usBNhZquZZUeWMaPrDCZtmtTgr2xWtzlcLjKybGwcepOZN7acoUxrJL9Mx4vfn+RMbjnRwSomJIazaXp3xq88SFaRhjUHstn0eCIrO22ij9lRkL2dO5HlBfcxpMTErG5zGPPdqDrPoUURXFXBnO5zkItdnI4p0zrWANZHg+0TBAQEBH4HfpdXQRKJhFatWtGqlXNrYgEBAQEBAcBmb//44zZR5gwfH1tKo7j+bPuqZsTO6obGJzZji7o3d5m/x6uGtfrCEnih0FZiBbDkyBJ6hfeif/MBXCu/SssgHw680J1VqVdZc+AKwV6uGIxmnhkYxuyfZ7P8iGMNVHpeOhM2jWFal2k82/1ZOi3rRInO1ui5Y2BHZnd7lvceHsw7WzPJU+v4V+8IHu5qH22zivTojRYGRAcwMDoQq0hHbFBsdaphvCtsDQafemzirxih/1U4mwOux+Jo0n0OYrmCYe2D0BmkvNjjbZLCkth4bqOdCFtyaAmHHjvElNgp9driT42dxrDIYQxddJDZA1uTr9ZRprXdyRKNAXcX2+NFxjU1s78+wej4UFZP6sqIxamUaQ2kfvMYozw2OMz7+tXHOOgylg8eaUGojzshPiNIjp3KMif3ukUR7PkUysODCX4y6WbT6FrUlYpaF/W2TxAQEBD4nfhD4vNHjx4lOycHPz8/uiUkIJUKaQECAgIC/3i2bIHHHoO8PIdd1pAQ9Cs+obRbL8quV+CpkKNSSJHfiIrVpHYz4prIpWI6h7nT89OVtKyEr4PgugnG58N2jeMpzU+dT2KzRDou64jFaqFjYEdmdZvDDzMHsWZ/Hgq5mLTc/XxydEW9l7bk8BK6Nu3K1C5Tmbt3Lt6u3gxpNYQeYYnozIU81leGt6s/SpmSdafW8OrOV+3E25zEOSQ2Hcibmy9wd+dg7m1zP35KP/op4fsgcKtHo5432ATZ1X0S4n1nkntXb2KaejKxezhx4T5MXXOUpWM64yXqSc/Wbri7iDl49SBLjyylRFfCoLWD2D56O91CurEobZFDJHB2t2cZFjmMaZ+f4nKJlmW7L7J8XBfmbjuLxQreSjkVevveZGvTcugU6sXors3wOf88ozw2Opx3fuv/kDz0X8y2+z1LWThgIb3Ckliwb171uYSUwe61MoIqjASdugZDRsK2beDr6JhZVyqqM+ptnyAgICDwO3LHvnm2bt3G9xs38dSTM4iKuml3O3/+QvYfOFD9c8uWLXjrzTeQy2/tzZWAgICAwN+EsjJ4+mlYudLpbtNDD7Pj8ZdYfKyYjF2/VG+vq59Ufc2I+0X5U6or5Xj+cY4D06/DNxWQZ3Z+alXW6jH+MZzIP0F2aTanC0/Rv0VfxnT3oECbSVSTVmTOyGT50eUsObSkWkzVpqp31jdnvmHrqK3szt7NyA0jHUTOjK4zOPTYIQatHcSF4guk56Uz6ttRJMdO48VhbzL243RWT+7KkYOP80MwyEVOl7Odvw4GXoDyX13ZuegXOrSPo6hCj0wqZl1aDm9sOcP03i04nF3CzA3piEXQu00T/j1yLonNevLugQWk56UTtyKOqV2msuWRLRgtRioMFXi5eOHl6oXVKmP9wSucuiFyMq6p0RnMtA70QCwS4SITcy7P0Qny09QsVoyKouhqht12KyJEXZcT0PJRp9fkLnfj/qj7GNF6OKXaEsouZxJ572RkxZk3Bx06BM8+Cx87GobIJWKSk5ozY326w77aOG2fICAgIPAHcMdE2YG0NPR6Pa1bt67edvToUfbt34+vry933ZXEyZMn+e23C/z00w6GDRt6p5YWEBAQEPir8MsvMGkS5DixJ/f2Rv/+B7zh3p41P2Q57K6rn5Raa+J0rpoQWT6zAj/jpauPo8WNyAB3Ood6Uay9KZo+LGv4FIs0RahcVHY9wQauGdigmKrNsbxjaI1afhn3C2/sfsNpOmB6XjqTNk0iOTaZ7aO3E7cirlrkLTuyhG4hPUiKasrSzzvwjv81xPUIst1aGL4P/K415+CmXyg2lWG0aDmaU8bsr09gsYKPUs4jXcN4cNl+wNa77JczBRzOKmV0fEc2P/QrlUY1an0pQR6+KGUq0rMr2X9Rw5WSfH7KOE7rQMc6saqUxYfimrEuLae6nq/2709rceXfmv/yumgGbRWXsCLC0vUTJC0n1vs7kUtlyJHhrjESMvZJ+C3TfkDnzvCf/zg9VioR0y8qgDEJofXa4jttnyAgICDwB3HHRNnVq9cIDQ1FXCPvf8+eVEQiEc/OmU1kZCsMBgOPPpbMrpTdgigTEBAQ+CdRUQHPPw+LFzvfP3QopqXL+LEI1jQQ0VhzIJv4CB8GxwQilYgp0+i53/tnXg1ehodES4tAH3z6rkVnMGO1WnFxbYQSq4Gv0pYC9+OYH5mXOu+WxVRNyg3lZBZn1lufBbDsyDISQhKq0x2reG3XC/xLeo3ZAcZ6joYtFfDgF6BVgTrhIvvz93Oh+ALXynOZ338+7/+iILtYy4x+rSjVGhzcCG02/pdYmnKJ2DBvFj/SmS8OX+a9n487CCxndWLeSjm9WjUhLtyHN7acqfM8SzQGkPsw5uKb/BDzb3ziXsSl5dh6r62aykoYOhTS0+23t2kD27c36NL5wpAo4iN8WZqSaZfKGB2sYmpSC/pG+QuuiwICAn8ad+zbR61WEx3d1m7bqYwM/Hx9iYy0GX7I5XLatGnN2bNn79SyAgICAgJ/Be6556bVfU08POC992DiRAxGM0u/2d+o6ZamZNqiGvprhJ16jIXNtlfva6f/jrc2/IcVWZ2QS8UcebnnLdm8K6QKksKSHHqCOaMuMVVFgFsA81PnN+qaqtId56XOw2K1IDXBK67ZTFTVf9zaIpiwEkw9gBsO9wv2LeD7h78nYlEESWG9+H763dy/5ABD2wVxudhJMd0NLFboGuHL7t8KeHfH+frXvVEn9lT/Vvi4yxnRMZjxKw9WG344w1spJ0DlyrgH78Kz9b24uDbS5VCvt32G9u2z3x4RYftcNWnS4BQO7RN0RlSusjprFQUEBAT+SO7YN5BSqUStvvnmKT8/n4KCAmJiYuzGubq6otPp79SyAgICAgJ/BV54wXFb795w8qQtnVEkqk5DbAwZ18ownlsBP0TjWrDdYf8w6WeAlWBPVywWGbO6zWnUvDPiZ7Di6AqmxE5hUdqiRh3zftr7TO0yFbHI/k9qp8BOeLp68s2ZbxqcQyKSYLbaIntJYUkoTfB9KA0KssWZMHY9mIZSLcjAvjZu/r75WDDw2aR4DCYznoq63QWrbP8/Tc1q8JzBVid2d8emHL9cyojFqWQV3RR8UkzIRTcFWnSwigBPV+bf357BMYEoGyvITCZ45BHYscN+e1CQTZA1bdq4ebClMirlUgI9XYkM8CDQ0xWlXCoIMgEBgT+dO/YtFBQUSEbGaQoKCgD48acdiEQiOnXuZDeuqLAIby+vO7WsgICAgMBfgd69YfJk238rFPD++7YH6rCw6iGN7ScVIsvn84iX8Tz5LzA6irivivsx+uJbeCrkfDYpnvd/zqR704FM6Ty13nmTY5PpGdqTlKwUu55gDVFTANXkyYQnKawsxGK11Hmst6s3L/R8gYszL7L23rW8uetNTmbv5JdwGOpW/7qv7IcnToF1IOBEZ1XVxh3LO0axtpSzeWpEIhGucgnRwc7VXmSAOzqD2ak4lohFRAV5EBfuTVSQBxKxyFYnZjTz4c4LdhEymcjI4rB5fBj6DrIbwmxqUgtcpeJbE0EmE4wdC999Z7/d19cm0po3b9w8AgICAv/j3LH0xUGDBvLee+8zc+ZTBAYGcikrC5VKRVyX2Ooxer2eC5mZtKsVPRMQEBAQ+JtQX0+xBQtArYa33gInfSwb6iclwsJo3208F7gKd4nWYX+x2YdnL/+LHeoEAB5PCOPgpSL2XSzkeoWOlwe/TWKznryXtsDR5j1xNkNbDaXCUMHYDmMp0hQ1/pq5KYCqSI5NJiksCb257syQmkYig9YM4sy1M4QpYW8otK7nVpitMHUbfOwHtK17nK/SF7XeJq4KK4v5MUNEfHNfDCYzk3tE8PSXjqLT3UVKscZeHHsqZIxJCGNUfCg6g5lijQEfpRwXmZj1By9TrjXSPyqAQ1m2mjq5yMhHYW/TT3UIgEXNFnAg6MNbN9Ewm2H8eNhQq5+Zhwf8+CNERzd+LgEBAYH/ce6YKLsrKYlLl7LYunUbFy9dwtfXl5kzn0ChUFSPSU3dh16vp337dndqWQEBAQGB/wV0Oli4EH76CXbuBImTzsbe3vDll3VOUV8/qTD5NeaFvE+C+ymnx2pDxtD7x6GUmT0A8HGT82jPCPRGC51CvSnWGDAaZQxvdQ/DIoehNakpqCzATe6Gi8SFZUeWMX3rdMK8wng16VVCPUNv6fKrBFDHwI48nfA0ic0SGbhmID+N/clpPZu3qzc/jvmRd/a+w4rDK8AK7d1he1MIqucvs9YED+2CzS2oN9elqjbu1HXb/fJReHPyajb5ZTqWpWTy3JAoZvWPJOW3Air0Js7nV2C2WKnQm/BR3lSE4b5KVk/qysFLxUz57LCDQcaExHCCvBSM7NQUH3c5a1PPMVP+KnepjlSPG+K1jwEBi5DKP238DTWbYeJEWLfOfrtCYetvFxvr/DgBAQGBvyh31GZo4oTxjBk9Co1Gg6cTF6R27drx33cXEhgYeCeXFRAQEBD4M9m6FWbMgMwbNuUrVsDU+lMFneGsn5RMZCS5yTc84f8FLmInBhLKEIhbRo6kB2Vb9wA2IbH20QR2ny9g+e6LdkIiPsKHj8bGsOrYKn68+CNqvZpT109Vpxiq89X8O+Xf/DzuZx5s+yDfnPkGs7WOpmY36BTYiUD3IL68bxMeck88FXK2nN+KQqZg2ZFlzOg6g0mbJtkdMy1uGlvObWHFgRUgh7sUtqbQnk60bBWlOjHfKx9lc1j95iNgq41bdmQZFquFToGdUEpVnMsrp1RroGOoNwaTmftiQ7irjT9uLhJkElvUa31aDm4uNnF8pUTLZ5PiWZqSybqDjlbyVS6Mxy+XMSWpOddLilnf+m1ci47YjbNK3ZC2mNDgOdvx8svw+ef221xdYfNm6NXr1uYSEBAQ+Atwx71fZTKZU0EG0KSJH02a+DndJyAgICDwFyMrC558EjZutNtsef4FLvYciHtI0C0529XuJyXFxKaWTxGlyHJ+QIvHoNMCkHviWaYDbKl2n02K58OdF5wKiS7hnmw+t4mXd71st93b1ZtpcdNIjk1GY9SQWZLJvP7zmN9/foNNoud0n8PF6ybmbr3KkexTfDMtEXdrArsn7KFMX4a7zJ2DVw+y9MhSwGbq0ca3DRO+ngByGO0BKwPqbwpdpHfjSUsbXhswhynu1OsKWVUb9/SPTwMwM34W69NysVghyFNBUYWByasdo15TejYnZc5dmC1WHuvZnN+uV5B2qcjpfazJmrRsYkOkDCt+DFfTIfudUg9EvbdBk+71zuHA1KnwxRdw8UZTcBcX2+esb99bm0dAQEDgL8LvYjdUVFTEnr172bhpMxs3bWbP3r0UFhb+HksJCAgICPzRVFTAq69CVJSDIAOwlpXx/vNLSJj7Cw8s3c/WU7loDKZGTV3VT+qDRzrROtiHneVdHMZYlGHQZwfELwe57SVgVerjmISwOoWERCzikfgg3ktbYLe9pU9LDj12iJbeLRm5YSRRH0bRdUVXIhZFcPcXd9PS+8Z+n5YOcybHTqNHyEByinQsfKADoT5KVK4ydp4tJLvQjNXkzcvfZ/Jij7dZNXIt7QPa4ynxZNz347BILbzkA2sC6xdkJ8uhpP8WTpiMDFo7iGe7P8vn93xOx8COduM6Bnbk83s+Z073OQxaO4gSXQnJsdNIajaItWlXiA5WIZOImbrmiEOKaMY1NTO/SOedbWexWqFflD8Tu4c3yoXRQ1xJ69MPEFpLkFllnrbf060KMoDQUEhJgZYtQS6H77+HAQNufR4BAQGBvwh3NFJWVFzM8uUrOHjwkNP9cXFdmPLYo/j5CdEyAQEBgb8cZjOsWmVLLcvNdTrkSHAbXhkwjYyAFoDtYX/G+nTGJITxwpA2jWrOa9dPqiIaw67DyHVZWBFjjXwCcYc3QOZhd4xcIub5IW1o2cSDSauc/w2KDHCn0qi2c1Wsqu261SbRHQM78mT8bJKaDWLMinSyijSMjg9l7aMJeCpktGvqhUIuQSwWcbGgkqGLDtE7xpXi0hKKTcVIgWX+MKnufscA7K6Akddhi0WGykWFxqhBhIjWvq3Z8sgWyg3lFGmK8FX64i53J7c8FxEi2vq15aWer5PUbBATVh6nTGvk9RHRfJp6yaEZdE3WpuXQJcwHiRi6hPs02KJAJangs4hXaCuz72lmlXkj6rsDfP4ftV8hIbBrF5w+Df373/48AgICAn8B7pgoKyou5tlnn6OwsAgXFxc6duyAv78/AAXXCziWns7Bg4fIzLzI/Pnv4Ovjc6eWFhAQEBD4vfnpJ5g1y9ZXzAmFSk/euWsi38T0wSpyTMJYcyCb+AgfBscEOk9ltFpBdDNcJL2R8qj08YGEJXDiZURdlyHy6Wx3mMlswWC2oNaaaOqlwGK11ikk3F2kFGmK7bZNi5vW6CbRSWF3ceSxExjMZjxdvPn1TDGzvzrL5RKbE+TatBziwn1o6q3ATS4hwMOVb45eZly3UJK/eZlFx9eAxIpKDF8HQX9lvUvybTmMzged1WYk0sK7BV/c/wWfHP2E11Nex4qVGP8YVC4qu9q4aV2m8fm9a1m19wojNx6mTGtkTHwYceE+vLopo/5FgY/3XmTVxK5kF1XWO85LomZN85eJUWTabbfK/WyCzLtjg2s1SNOmt9SHTEBAQOCvyh0TZWs+X0thYRFJvXrx6KOT8PCwf4tZUVHBx5+sZNeuFNasWcvMGU/cqaUFBAQEGk3Nh/gyrQFPhfyW6p7+cZw8CXPmwHbHBs0AVrGYzd3v5qXOD6J2dUciFhEZ4I67i9TO1Q9swqxflL/d/feSVOBz8U0kck/EneY6P4fgQRA0AGqJPY3BxM9n8lmWYjPziI/w5uVh0cSFezusXTU+2COAHqE9UOvVnC04S3JsMiPWj2jUrVh2ZCmDWw5HazRSpi8hPLCEN+9rilIaxYaDuaw5cIUVey7y6cQ4Xtt0CpVChkhSyFt7kylTHAMgRApbg6GdS/1rfVgKMwrAgs1IJNgjmFeSXiG3PJcHoh9gXIdxdda6LTm8hPimPdAZOxLireCVYW0ZEB3A8A/22vUSq4uMa2p0RjO+7nX78vtKSlnT/CWHej+rawCiPr+AVyPt6k0m+OQTePRR546dAgICAv8Q7pgoO3L0KAH+/syYMR2Jky9Wd3d3npj+OGdOn+HIkSNOZhAQEBD4fan9EF9FdLCK5KTm9IsKaFR63T+G11+3/c9aR77boEEUvfImMzbm4amQ8Xg9vax2ny9g/v3t+el0Pst3X+TMtRIe9vmJWYGfI5OqsYhkaEPHoPCt42HeiSB7e+sZ1hzIsa3duyVjE8KwWK3MGdTGbu1N6VcZ0TGQR+KDsIoqmd9vPt4Kb1RyFTqzrlFNolv6tOSTEZ+w9cImFuyb79DnbGbXWWycPpgJK49jNFtZOqYLG899wewf/4VOZDMh6egCPwRDcAMfsWcK4N3SGj93e4ZV6auYuX2m3Zozus7g0GOHGLR2EBeKL9jNsShtIbsm7GZUfDgp5wv47XoFWUWaBq+zisIKPcFeCqctCppIi1nX/EVauV62225xDUbc71dQtW7cIkYjjB4NX30FaWnw8cd197gTEBAQ+Jtzx54+dDodHTq0dyrIqpBIJERGRpJ28OCdWlZAQECgUdR8iK/N7dQ9/SPo0sW5IGvXztaTbMAAivLUhPuq6+xl1a6piqf6RzLtrha8/cMZ1h3MIVZ5mk2tltmlvYmtRi7vSCbknp0oXWT1npbJbOHnM/msOZBj10dr8upDDmJ7eu+WJCc15bszmxj+xWg7MTWx40Qej3u8wdvQmLqziZvGkBw7jVWT3iSvrJAH1ozlkikFbmRkDlbCl0HgXo/m0FpgbD58U3Fz29TYqSSEJBC3Is5hTWe1blUcyzvG9YpiZqzJwgosfsQ+7bMhAj1dkUlETOnZnJlfpNfYY+XDsHccBJlWFoysz07EqsjGLaDXw4MPwqZNtp8//dRmef/hh3ZprAICAgL/FO7YK6mQkBCKi53bBdekuKSEECE/XEBA4A+k5kN8faw5kM0vZ65jMlv+oDP7H2fIEEhIuPlzUJAt1ezYsWonPC+lnM8mxbMs5SKzvz5RLYqqoldLx3ahXVNPfjmdT8qxw7zXbAHftJzjUIcEEGo6xP7jBxu8/wazhaW7Llbb39deu4orJVraNnVhzs+zmbBptEMD5yO5R3CTuzV4Gxpfd7aE5cf+S//POtoE2Q2SPWFTcP2CrMQioc/Vm4KsY2BH1ty7hlmJs6qdFJ2vuYw9OXuY2sWxL5zWpKZFE3fO51fgKpcQHaxq8FrBJmYtFivT1hwhLsKHUV1rNtIW8crVaZSYbpYolIiaQr8UpF6NFGQaDYwceVOQVbFyJZw507g5BAQEBP5m3DFRNmL4ME6fPs3RY8fqHHPsWDqnT59m2PChd2pZAQEBgQapeohvDEtTMjH8k0TZ5cuQnu58n0gEb74JHh7w2mtw/jxMmmRX+6NylXIwq9jOgj7cV8mm6d0J91Uy9fPDGHUlGI7M5tfWydztneJkIfihtDt9zy3l3QMWDCYLeWU6zuWpySvToTGY7ISaWmvidK7azv5eIhYRFeRBXLg3UUEeSMQixiSEsDN7G8tu9AerTcb1DJQypYO1fE0kIgnJscksSltU55hqDDB375uUWGxmIhLgPT9Y6g/SeoI/hRJvtH1+5dPJZzg65SiXn7zMnol7KNIUEbciziE1sTbvp73P1C5TEddK8XSTefLWPe24r3NT1qflMCExvOFrAKb0as7atBzSLpUwasUBkpOas/CB9tWi7qwugjGX3qDC4k65LBzZwN0ovB3bBTilogKGDoUff7TfrlTCDz9A27aNm0dAQEDgb8Zt5+gUFBTY/Rwd3ZbBgwby1ltz6dGjOz17dKdJkybVY/em7mPv3lQGDx5Eu5iY/99ZCwgICNwCVQ/xjSHjmhq11vT3T2E8dw7mzYPPP4dOnWw1Pc7Sxvr0sQk3T+fe7VZg5d5L1T9XRa+WpmTy1aFMno74lYBda3lAWez0+PO6UF67NoV9FR0BuHpNTX65jn+tPcqZ3HLAseavTGtAIhYxKj6Upzak83jvlk5r2VzlJvp9PsrpulXNot1kbsxOnM3ob0c7HRftH43GqGm47kwLKG7+qBLDF4EwqIFA3F4t3H2thJJzvYnxj6FLUBcW9F9EXnmxXQ1ZfRzLO4bGqCHGP4YT+ScAmzmITOTOznPXeWFIFBKxCBGQfrmUtWl1R4zHJITRt00Ar2y0uTRmFWkYsTiV0fGhLB/XBZ3BTInGgLcyiUpNF745beY+aTDujTnR0lKbINu3z367hwds3Qo9ejTqegUEBAT+jtz2U8djU6YicvIH3Gq1snv3Hnbv3uN037Zt29m2bTvfffv17S4tICAgcEuUaQ23NF6tMxLo6fo7nc2fzNGjMHcufPPNzXqxQ4fgl1+gXz/H8SJRnYIMHAXvmIQw0i4WUnJuHT9FribCJRecGP6pzW78N28UnxcNxVTrT1FRhQF3l5vbzuaVs2RXJvlqPWMSQvFSyokMcMdstrDggfZOa9mGtQ9i1uAmTsVUS5+WbB+9nd3Zu7n3y3tZOWIlU2KnOE1PVLmoKNQU1nn9GLHVjdUQZM1lsDkI2jbgsPhlOYzLB70VwMKJ/BOcyD/Bq0lvUahxLmLrokhThMrlZnrizPhZrE/LJeV8EV3CfZj6+WEe792K5wdH0TnUi5WpWQ71d1OTWtA3yh8REOKtqHZqLNMa+WhXJktTMmkd6FHtrHkur5yoIBUT+jXiUSI3FwYNghMn7Ld7edmiZl273tL1CggICPzduG1RFt22rVCMKyAg8JfAU1G3tbczVK5SCsv1FFXq/x6W+VYr/PqrzZyjDmt73n7buShrgJqCVyIWMaprCIUbe/JAmPNUdqNVwpqiIXyQ/zDFZudiz1spp0JvwlMhY0wtR8eiCgPeSjlP94vESynnnW1n7VInq8hX67imdhRTzkw7Bq0dxPbR2+kW0o1FaYvsas+C3YMJUYU4v/ha0TGAXgr4Ngh8G3B3n1cMzxfZIo21KdGW4qe8tV6evkpf1HqbyEqOnUZSs0HVPcp0BjMmi5Wpa47waI8Ipt3Vgm4t/NAazKh1RoI8XVEpZNWfcZPZQnJScz767jt6uB/j48J7AbBYqY5eVjE1qQXyhv5dZGbaahAv1koh9vWFHTtskVoBAQGBfzi3LcreeuuNO3keAgICAr8bKoXUqbW3M2x1MyK6vv0zVS2u/rKW+TodrFsH771XZ9NnwOZ6FxNjsyiX3XQ+bExPt5qCNzLAHZ3RynF1MB38HEXZtrJE5uWOJ8tQt9lTdLAKF5kYvdHMpundnUbBooNVTOnVHLPFyr5M51GsCr0JXyfCxplpx4XiC8StiGNql6lsfHgjGqOGIk0RTVVNcZd54yqV0DGwIyfzTxLtH43BaOBc/jmsCntJNUkFS/xBXs/7SpMVHr8Oy+v5KLpKFbjJVHQM7OhgTuKMToGdUEgVSMVSVo1cS1KzQUxYebw60lWiuRl5/HjvJdoEeXCpoJJfz13npaFt8XN3sXvhIJWI6R94lX6RL6GkDJnIzJKCBxzWHZMQRt8o//pfVhw/DgMHQn6+/fagIJsgi25kPzMBAQGBvzl/oacLAQEBgdtDLhGTnNScGevTGxw7sXs4n+3PokbP4b+eZX5+PixZYvvf9et1j1Op4PHH4cknwd/fbldje7rVFLzuLlKKNQY+U4/mEb+fkaEHQO8Zx0HvF5i2qX6re7Dd/2+PXmHVRFtdmrMoWMY1NTM3pDM6PpTVk7oyYnGqQ1Pk8/kVDsKmyrTDWbPoEl0Jc/fOZV7qPGL8Y1C5qAh2D+Y//ZeSma9n5YiVKGQKnt74NNvyt0GNSxED8/xglnf911ZshvtzYae27jGdAjuhlKpYu/8aM7vOYuKmMfVPCsxKnI2LxI3vH/iZ9Wm51RGyKqoij1V8mprF8nFdUOtMdAr1chRVhQdR7B4IlAHwbNBqDFYpnxTeA9inOsolYjQGk3Phvi8Vhg+HsjL7+Vu0sAmyiIgGr01AQEDgn8Idf7LIz89Hrbb9AVepVAQEBNzpJQQEBARuCalETL+oAMYkhNZriz86PpS4cB/e2OLclnvNgWziI3wYHBP4v5nKePy4LUXxq69sfaDqokkTeOop+Ne/nNaLNaan25NdLUwZ1Au5i0e14K3QmwjwcOGTaSO4eHA8qsItvHFlHKmGu9g0vQejul50KrKqqLr/qRcKHRwdnbE2LYdOoV6Mjg/lo132Fvtmi5X1abl2wqYxph0Wq6XaLANgXt93advUk0UHfuD1H1/H5GKyG+8hhnWBMKwBQ48yeSAJ5/P4zUl9XU1mdZvD0ewKMq5V8Eb8EKZ0nsryo87dIwGmxk4jqdlgnlh7miPZJXYvE+Bm5PFc3s20w4xraqxWKy8MbYNCVusxoGAf7BwEJvs0xWdjzvJQh0RUSkW16DKYLWw9letUuL8sySJ+1hREOp39/B062GrIhGcDAQEBATvuiCg7f/43Nm7aRHr6cTQajd0+pVJJp04dGTF8OJGRre7EcgICAgK3jFIu5YUhUcRH+LI0JdPhIXJS9wi6hHszfuVBh6hLTZamZDacsvVncfYsrFlT9/7ISJg5EyZMsFmQO6Ghnm6tXLL5l/9XjNDu5vy+Z2nV+81qwbv1ZB4+bi68vfUMGw/3w2gdjFkkJzJAwcKfzvHcoCg6h3nxqROTiYndw0mK9MdstTKiQzAPLjvQqEuuivosTcl0ECRrDlxh4/TB1cKmQdOOWni7eqNyVdBraU/Si9OhlnFHKxlsDIaoBkoWD2g6EtznG3q7LOC3egTWtC7TGNF6GBV6CZEBnkjFcv4zcCG9I5KYlzrPLpWxY2BHnoyfTVKzQYxecYysIo3TOSd2D2ddWo7DvdHozYR413oEyE+BlKFgqrTfHtAbedJmIqU3lWdDwv2162V8J5ahoIYo69XL1pusHuMYAQEBgX8q/29R9umq1WzatBnrDRcvqVSKu7vti7uiopLKykr27k0lNXUfI0YMZ+KE8f/fJQUEBARuC6VcyuCYQPpG+aPWmlDrjKhcZbi7Svlo5wX+veV0vYIM/sct8++5x5aGWDtlsW9fW2Rs8GAQ1y8m6+rpFq24wHT/LxnsedPOPCRvMQbdMyjdfHlhSBSPdA1lx+k81h3MwVPhyYRaJh0Gk5k+bfzp3cYfrcFMcaUBN7mUAE9XjCYLKecL2HehkOl9W91SCwOdwUzrQA8HE4oyrZEJK4+zatJb3BWexPdnv8NP6deoeQEeafcI7f7TjmuWaw5dPYcobREyzwYMPb4oHcaLOY8S8nk2qya9RWKznryXtsBBYD2b+CwDWgzlqQ1n2HHm5u8vPsKHF4b0Z/eEYZTqSsmvKEbl4kmghy/7fitj5OLDdX5m64v8qhS1UkmvbYc994C5VmQrcAD0+g6kN0V8Y5qxn/WPYOw9L/HFN68i0elsaYxffAEKRZ3HCAgICPyT+X89VXyy8lM2b96CTCZjyJDBJPXqRVhYKJIbjUXNZjM5OTmkpOxh67ZtbNq0GbPZzKOTJ92RkxcQEBC4VaQ3TCqUcmm17f25PLVD+lt9/GmW+YWFsH49REU5d0qUy2HyZJvlvVwOo0fb6sXat2/0ErUt7jsrzzDd/wv6qA47jPUQl1Nx+l2IewulXEqEnxuzvzpBuK+S1ZO61mnSMSExnF6RTWjqrWDe1jO8MLQtaw5k8336VdY+Gk9hxa21MKhpZFGbrCINz359jmXj7mZIq2GIRNZGGWiIjCKWH1iOSWKfrigCXvCGf/uCuB5DD7MVjgVPINtnNqacS2QVaRi5+DCj4zuy+aFfqTSqKdaW4Kv0rhZYvRfsdxBYaZeKGfnhAcbEhzJ7UBte/uka+epS9MaLfDqxKy8Pi3IaeXy0R3M6h3k5jfxGB6tQKWrcr8vfQepDYKkl7oKHQs+vQWL/WW9sM/bDIdH8e9zrvGw8i3T5cpD+D77IEBAQEPgf4ba/Ic+dO8fmzVvw8/PltVdfISTE0TJYIpEQERFBREQE/fr35dVXX+eHH7bSs2cPWkdG/r9OXEBAQOBOceuW+Q0bVtwxjEabjf2qVbB5s+3nwYPrtq9PTgZvb1uKYpMmt7yczeLeSnf34zzu/yWJ7ifqHFtpdkVnllQ3DlZrTVwt1bJ5eo96TTpmf32C0fGhvDAkipn9IrlSomVY+yAmdo9gZeolhrYLuqVzrm1kUZsHuoTw/dFc+kYFcPJqMXMS5zDqW+dNpQEoB6uHFRP2c7qLYFUg3NdAp+RSMzyYB4UVx9n0UBBLUy5hsVb1+7rE0pRL1f2+hrb3wk1ewuyv677PAGvScugU6k3bIBW7zhUA1NHUWY6bixQXmZg+C3dRonGMotnZ2F9aCwfGg9VsPyjkHui+ASSO/zZupRn7au9opj0/g0BBkAkICAjUy20XRWzf/hMikYhnnnnaqSCrTUjTpsya9TRWq5Xt23+83WUFBAQE7jhVDoKNwSHK8HtgtdoaOj/9NISEwIgR8O23NkEGNqOEa9ecHxsWBrNn35Ygw2wgoOhrtraawdrmL9UpyMrMbqzTTGCeyzak7V+6uV1rsDWPvlTUKJOO7afyWJuWw7AP9vL10atsz8hj0S+/4SqX3NLvo7aRRU0e6xHBXa396B/jDZJiwvw1DI0cypTYKY6DTUAF4OG4q4UMDjRrWJBdtriScBl2aOBY3jF05nJaB9pPWNXv62hOKf3bBvBpalajrnVl6iVGJ4RVR+iqmjr3nPcr09cfZd72s0xff5TEd36huMLgNJo7Jj6U3m38MZotcGEF7B/rKMjCR0OPL50KMtu6NyOZvpWlTDy88WYjcieodQ24mwgICAgI3L4oyzh9mrCwMKLatGn0MVFt2hAeHs6pUxm3u6yAgIDAHafKMr8xNKpZ7u1gtdrcE194AVq2hK5d4b//dW5pb7HUb+hxqxhK4PQ82BSB1/HJtFVccjrMIvelqOWrWIZfovf9H/FIz85oDGYq9CZ0Rluz57EJYbcsMmQSEaPiQ1m59xJmi5V1aTlMSAxv1BzJvZpTXKknKshexEUHq1g6pjMzB4SyPfM7hm3oTeh7zZi6dTLni84zJ3EOn478lI6BHW0HVAV+nIiugUo41AyiXRz31aTCrxePVLTgXA0NUm4oqzO1MjLAHZ3BfFv1czWpEnmHsko4k1uOxWoTQjXXjQ5WsfCB9jzWqznLUzKxnn0PDk7BoX11i8cgYTWI637xUBVZDi++yrdrZvHqLyuYfOj7Osf/oZFlAQEBgb8ot/26t6Sk5LZSEENCmpKWdvB2lxUQEBC44zTWMr9RzXJvlTNnbAYIX3xhc09sCBcXm6FHYmK9wxrT+LmaQ9Mhe12dc101wYISSHMNZnpka0YoFMz67DipmUWAzYzixaFRtPJ3p0RjvGWR0b9tgJ04WXMgm03TuzOqa2iDFvodQ73Ze6GAL5K7kV+mq07hc5WLkUlNzPppNsuO3HQ8VOvVuMvdq5tFrx65mgeWPcB51XmH+RtbPwbwiT6AB3p9T8H5BLvtTdx86NXKlUNZJQ7HVPV1uxXqq5+rSaDKlXcf7EhJpQE/D5uaXHMgmze2nGGU+zqUhasdD2r9JHR+F0T1X6xKIeU+fQ4vrnkOH63td/byzk/IVTVha5sedmP/kMiygICAwN+A2/6mlMvl6Ovrg1MHBr0BmUx4ayYgIPC/RUOW+VXNcmu7LtYWP15KOSpXMWaMlGhLKNaW4KPwxlvhhVzsglx64/uvvBzi4uDcucadYEKCrU7soYfAy8u2bh1New033PEaavxcTesnnIqy8wZ4txRWqUFvBTjJge9GMTV2GkvGzueexYfpEenLE70jOXa5lHVp2dwX26xx13ODEo0BXze5nTgp0xoZv/Igqyd1rdNCf0qv5sSF+zBqxQGm92nJkp0X+PXcddxdpFToTfRt44ef/zE7QQaQcT0DpUxJmFcYc7fPZe72uU6jYz5i+DwQhjTQf6zCAhPyYVjfd/jl0i62jtpK3Io4SnQldArshKvEgxEdvQjycnW4jgCVK0G3aBjTUP0c2O6PxWpl6prDKOVSpvRqTnpOabWZzUVZU8xWCRJRjbTF6Jeg/b8bFGQALt99w/wlTyGp9QzwzJ41/NQqAZPk5mfrd4ssCwgICPzNuG1RFhgYwNlzZzGbzdVuiw1hNps5e+4sgYGBt7usgIDAH8gtRVv+BtRlmV/XNWsMJjvxE+6rZNWkDmy/uN2p7fmsbnMY2WYE7nI38PCwRb3qo0ULmwgbOxZqpIrXXreK+Agflo6N5T8/nXOI+HmIK8nM1TFjvZoxCWG8MKTNTWHmlwB+iVBos7vfrYX/lMDmSofkNgCWHllCr7BebJ4xkmJdEZfUZwlt4sWMpmFIxXI8FbIGWwtU4a2UU1RpwEdpX7+UVaSp08jCRSbGRSLmX+uO0r2lX7Xte9WaErGIFePbMWyDo5mH2Wpm6eGlyAvkoMRpEn+cC3wVBGENvD/MNMDIXOjRLpmeoT2JWxHHuwPfZWqXqczdO5dZ3eawcs8V1qZdqfM63F2kPNglhJNXyzifX4G5dkOxGjRUP1fFxO7hrE3LIeOabZzG8BvLx3VhyY1ebj+qE3n68pO8F/ouIqzQYS5EP1f/xYItxfbNN5G88orDrpMBLZh0/2t2gux3iSwLCAgI/E25bVHWJTaWL7/6mm+//Y4HHri/Ucd89933lJdXMGTw4NtdVkBA4A+irgf/OqMtfxOcWeY7o3bzXE+FjNWTO/B26ossP7KUNoUw8wLopLAsDtLz0hlzI8q0YMACmzAbMQJO1DLTCA21CbGHHoLOnR0iF/U17e0S7sPPp+37R7V2zWKc7xbu9trF3NxJrCkewpoD2cRH+DA4JvDmA3PbORgvfs6EsydYl/tbg/dpwb759ArrSdh7YVisFsAmPOckzuGHmYMYsyK9zobGVVSJjB2n83lxaFuig1V2n7UqI4ulKZnVboUVehNSsYgV4+J4KK4ZceE+drbvngoZT/ePxCqq5Hj+ccdFtfDOt+9g9bc6FWTTPOG9JiBvIGC0vRLmWmKYNfgZeob2ZNDaQZToSng/7X2+f/h7ynRl9AodyNDvDjlcR6DKlaRIX/pF+1CiL+LhRBGTZWEopSo2HMxlzYErTkVtclILTl0tc2gEXRNnvcmc9XLbWNqb5/qHEuQuskVKG0Kns7VbWOcYUT3UNoHxA59BI7f1IKsvsiwgICAg4Jzb/rYcPnwYW37Yyrr1G7Bardx33711RszMZjPffPsd69dvwM3NjWHDht72CQsICPz+1Pfgn3FNzYz16Y7Rlj+YPzOK56x57qPR7lxa8SpxXyzlxUwIvaEtzvvYRFkVS48soWdYL+6Pug/5yJHw5psQFAQPPAAPPwzx8XU2eK6vaa9EbDPLeGz1YZRiLcM89/CQz0/Eut2sUxvrt4U1xYMBEUtTMukb5Q/Y+k5pPAeh7RDLup2NSz88lneMckM5Mf4xnMi3Ccv0vHRGfTuKaV2msWrSm/U2NgZbRGddWg5G801zD2fW8FVGFlW8/3BHPBVS3F2kjFicWr1Gu6aerJwQx+XiStS6ItoHtCfjegbmKnfBXECFTZDVwk0Ey/xhdCNMH6+EjCE86jk+k7mz7Mgynv7xaUp0JdX3xWK1MK//AoYtSrO7fosVtAYzr45oya6cbQz/YqFDNHVm11lsnD6YCSuP24naMfFhdGvui85oYuED7evoTRZBv7YBjFqR5nDfndWiiVo+Bp6NaOacnw933w0HDjjssiQnE/3ue/xqpMHIsoCAgIBA3dz205S7uztzZs/ijTffYv2GL9j+4090755IyxYt8PS0/VUrK1NzITOTffv2U1xcjEQiYc7sWbi7N+ApLCAg8KdR34N/TZxGW/4g/uwonsFsYc3W4/T7LY2ul08RfyWD9gsuILJYuKvW2MhiCC+BLO+b2xbum8+I1sORx8bCwYMQG+sgxJyJTg9XKSUVBqfpgZEBbliv72OcZD7Do3bjJtE5nHdr1xwS3E5yoLI9lXoTFgtsPZPLspSLKOUSnhrciAf0GhRpilC5OKqYJYeXkBjSk9HxHfhol3Mnx9oRnVsx94gN8+G5b4/z7KC2vDKsLStTLxHk6coHj3SgwqDFw70CjVHLF/d/gUKq4P3U91n09SLM4Wanc7aRwTdB0LaBbFK1BV7XhXIwMwv16VGcun6qOkpYk3J9BWJzE4dIoV009ehSh+PS89KZuGkMybE3RW2It4KpSS3o3tKPez9KpURjrDMVcl1aDmKxiJ6t/DidqyY6QEYH5QUOa6IdatGig1WNc0U8eRKGDYOcWr8TsRj++1/ETzyBUiRCCX9OQ3UBAQGBvwn/r6eWDh3aM/ftN3lv0QdcvXqVLVt+cBhjvdG7pGnTpjz55AxatWz5/1lSQEDgd8ZgtrB018VGja2KtvyRouxPi+Ll58Pu3bB7N7KdKWzIOIXYacWVIwMyYXmXmz8fyztGqbbElsIYF+cwvj7ROSExnE3TuzN+5UGyijT4SMq4x/tXJvr8QsjhLB72qfs8TFYx0YpMzlhi+WxSPHO3nWFtmu0+RgV54Kus52An+Cp9UeudOy2+e2ABv4xNYc9vRZy8an8NE7uHO6QdNsbco+q40R8fIKtIw65zRYxNCGXN5HhkMiPfnv2Ghfvn20Wfwo3hXC29WqcgG+MBS/zBvYGP8Ak93J8LvxlzgPpfWHi5eiFzkhs5JiGEndnbnAqymiw7soSksF7smn03cokUq9XKpFWHq0Wes5TOc3k2K/z4CB+WjO3E6C5ueB0ehaLkAHk9P0bl2ZnnBkfy0nenuVyitRlwSBu46B9+sEVvKyrst3t42NxChVIEAQEBgTvG//uJpVWrVny4+H2OHDnK4SNHyLqUhbrclmai8vAgPCKc2M6diY3tjKgRrk4CAgJ/Lmqt6ZYszdVa0x+WwvinRfFSU6HHTavvxvrHXlbBjy3glL/jvmJdKSGeIQ7bGxKds78+wfiuAXw15ConUz+ih/IQcnH9bnwFRi/WFQ9iffEg8ox+PN7b1uC5SpABnM+vwE2momNgRztRUxedAjuhkCo4df2U0/3H8o5Rqi/l4/FxVOhMlGgMBHspULnK2HuhwC7tsIoqc4/597Xjy+RulOuMlGmNyKViZBJbJKjK0MNTIWNMQhiju4UikxmZ/dMsltZ0WrQA5yErPAuc9NF2F8GH/jCuEemKhtBHSM7I4Dej82bate+Ll6sXai12NXISsYhH4oOcmo84Y8G++QxvPZxyrZVKg5mDWcXV+yRiEZEB7tWC7Hx+BRYrhPsqmXd/a3499zmRp56kqcQm4rwPT+CJvW/Qu+sb/DBzON8cKaj/ZYrVauuRN3u2rSdeTcLCYMsWiIlp1HUICAgICDSOO/YkFRtrE14CAgJ/bcq0t9Y3Sa0z/m5pS7VT+Jp6K+uN4tV8WP0xI6/xUTyrFS5cgCZNwMvLcX+nTiCTgbF+R0GNFHaFw48t4acWcNYPW7MrJ/i43lyn6joNJgsp5wvqFZ1uYg1PV9yF5/FK+tSTCW5FTJl3f7TNxmMOGoLhcB7aA9lIzKbq2rOamC1W1qfl8mT8bCZsHF3vdQLMiJ/BsiPLnKbvVZFXXsR72y6j1plo19STZwZEcveHe/hkQldeHhblNBqWnNSCHq2aUK43oTGY8XN3QSYRs3z3Rdam5VCmNRLuq2T1pK4cvFTMtZIKzpX9bC/ISoDrQBuHUwKgkwtsCIRIufP9VVjFLuzzehX30ClMV/3Ege8aFlSzEudgMEqQS0RM6dWcmRvSAVuj6Eqj2rn5iBOO5R2jRFvCjhMG2jXzArghREN4JD6ISqOaIk0xvkoflFIV3x/LZ2SnABbvnsm44s+JqZGKqRDDa7JMWn4/iomdbUYz9b5Iyc+31TrWFmSJifDdd+Dv5C2DgICAgMD/C8EWSUBAwA5PRQNPqrVoVF3KbVA7hS8qyIPFj3R2GsWripqMig9FZzBTrLFZrFusNsHjIMwsFjh9ujodkd27ITcXVq2C8eMdT0aptKUZ7ttnt1krdeFo09YcataO0XMeZFj2yxwsamQ0ReFld50rdl9k6dguLEupP3W00qLklLYF3T2cr1MhC+Ob0gF8lN2dfJMfANHBJ6rTHv+9JcOuUXNN1hy4wtaZQ5gaO9Ve5NQiOdZmAf/0j0/Xe64+Cm/UOjVncsuZ3COC1fuyuVBgi4aNSQhl5YQ4TDdEt5dShkohI7OggkmrDpF26WZkKDpYxeQeEWya3p3p647y4ahYlqZk8vXRKxx5uSeTt82zDbQC5wBfoLXzc5rhBfN9waUBrZ6jD2Ch5k16thxINw8FIwKGkxw71aHvWU2mxk5jeOQwnv/6NPnlehY+0KG6Rs7dRUqRprjOY51RrC1Ba3LFRymvbrdgMwgZ7WAQsmLYJ6RnrmJa6edE1KqNKzTD3bm2XnN2RjPSOv7tBgbC+vUwZMhNYTZ6NHz8MbgKdWMCAgICvweCKBMQELBDpZA6WJPXRXSwCpXizn+NOEvhc3eR2jUYrqJm1GTKZ4edG39E+qHMOHlTgO3ZA8VOHpB373YuygB69bKZHvTogblHT/Y1bcukU1aMEtuDrVEewbTuczi4aUyD1zcrcQ5ysYvddUYFedwQS2W0ds0mRJbPL+XxTo//vvQuO1Gms8gxN72HdYUDePtIE6y16pmq0h5Hx4fyxsh2FFc6j4aWaY28svE8/31oHvEh8SxKW+Tw8P9UwlN0b9a92gK+LjoFdkIpVXEur5wxNUw9agroCp2JMp2RIJUrKoWMvb8VMuebEw5pjRnX1Dz95XHGxIfy2eR4fj6dz7qDOQxpF0iprtQWfaoEzgAdcJpf6iuGTwNgeCN8pnaou/LM5adRm93ZdNl2357o05L5/RfQKzSJBfvnOdyX2YnPMjxyGFM/O0VqZhGAXY3crnMFt1yz56Pw5kzudR6JC2XNY514Y8/zTuvRTuafpKmlgPCzc/Crde2XjTDgKpytcUurjWbqS8QdOBDeeQfmzIE33oAXX2xUY2kBAQEBgdtDEGUCAgJ2yCVikpOaM2N9eoNjpya1QH6HTT7qqhur0JscGgx7KmR8NimepSmZDo59zUrz6JC+DemHx+DaKahoRJ3c7t1173vhBdvDqVSKBIg1mHiohnBcc+AKG6cPZkrnqfUaOUyNncaI1sMRiyT8fCb3xvFWohWXUJzdyC+tv6CFy1UKjF7En+mCBcdWI9vLuvN602WYveNZk98DQ9A9BHsE8NaP9Ufp1qbl0DXCh6RIJ0VW2O5nVJAnIpEYtV7Nxoc3ojFqKNIU4av0xUfhQ3ZpNnEr4uoVZAAz42fx65lilo/tQmILX+ZtP8NdkX48OziKfRcKnQro2iYmtVl/6DKzBrbm09QsAHzd5BRrSyATMANdHA4BIEkBawOhaQN/8YxWCQvyxrGi4B47Ybs2LYdOoV5ghaQWwxjRZjil2hKKdaV4u3rhrfDCapEx5L1ULpdoq4+r2QD7+SFRqBTmW6rZ83L14q7WVlzlIrZdrNsgZHRQOP77H0BSq7bwvAH6X4WcWiWHdkYz9TFrlu1lRLzzlwMCAgICAncOQZQJCAjYIZWI6RcVwJiE0Hprm8YkhP0uzot1uT+ez6/AVS6xi+KNSbAZVtQUZP/a/yUPH/+R0LL8W1+8shLKysDT03Gfh4fdj0q5lBeGRBEf4cvSlEwyrqmZsPI4qya9RfdmPflv2gKn0ZQRrYfjLndDZzBy+tQu5gRtYrBqDxEuuXAFuJF61kRWSrxbBvsr2zucSrnFjYTTq9k+ZwT3im2/s7EfH2zUJS7ffZF+UQG0a6qyc0WsGXH87kg+TTyaELEoghj/GFQuKtR6NVfVV0l7NI0Hoh9g+ZHlda4xrcs07osaASIRFfoKciuzmdbHH4XMDaMJurX05VKRhisl2uqoWMY1NS9+f4rXh7flm2mJzN12hk3HczGYLNW1gtHBKir1Js7l28yk8orVPPfmm+AHOPmVyUXwbx+Y7Q3iBoI82fpAZuTM5rjWed7jp6lZfJHcjSU7L/Dl4Rzu7hRCgEpBvlrNqK5elOm0doKsipqNo79Mjmd2tzmMbmRtGlYZWUWVmK0GFuyb53TcQCUsU15CYrKv/zqmg4HXoMC56aTNaMa1CXz9tS010RkikSDIBAQEBP4gBFEmICDggDPBUUV0sIqpSS3oG+X/u7guGk0Wwv2UXCiowFDjQdNssW8wXLNZck28terGC7LwcFskoFcvSEqCFi1uKUVLKZcyOCaQvlH+qLUm1DojCpmMB9rez71tR1ZHU3xcvfBSeCGzgjVvF5qcTcjyf+A50VWnzoBVDPZMdSrKAJoFBqNylfHr2Xw6h3nfkmNmUaWBJ/q0YsrnRwDHiKOnQsaOZ4YwpfMUh9qyQWsHsX30drqFdHOa3vh0wmzuiRpBdmk2Xq5elBvKKdQU4qf0w2Q1kld+nSD35sQEq6qjYmaLlTfvaUvnMHdKdaVcqTjPzAF+vDKiJdfLTChdRVToSynTl2IRF7Pn2UTmfradpV+PwhSnxon7PDFyWBMIHRroPQawqbQXL155nHJL3ZGjjGtq8tU6fj13nYIKIyv23Oy/NqlHBAq5Y0SzJhYrPPXFCX6Y2fjatGGLUolu6nkzRbMWD7rbrlEmshdku7Uw/Jqtr1pd+JXo4O67bA2hKyogObne8xcQEBAQ+H0RRJmAgIBTnAkOlasMlUKKXCK+oxEyg8mIwWKgRFtCsbaE54cF8M79rTmWXVHdVwng5+2HWBdaymdnDjO3zySnhhV7wzvx2KHvna5jahWJtPddNhHWsyeEhv6/z116414o5dJaLpRy3OVuhLi4wrWtmC5vhNwfkVoqGzXvVUMT8k32NUg13SWTe7Xg26OXaeajpFxXvyV+bQrKdbQP8aw2oagdcSzTGpn86UnWPjafxGaJvHvg3WrxdaH4ApM2TWJh/4WkTEihUFNIhaESb1dvpCJ3XKRSKgwVHLx6kPcPvl99nEQk4b6o+xjfYTxhXs1oF6JgVWo2G6YkoHQ1s/ncZp5cNd9B5D3T7Rl6hvZk6IZ+XCi+AGZocroJBZ4F4CSIIwKe8oK3G2HmYREreTb7Ub4q6U+dNpk1qNSbcHex/7MZHazCbLGicpU1WIt5uUTLtM9PsXTcAnqFJbFgn5PatG7PMrz1MCp1EkQiEb5ucoq0jvWPU1S2/mq1I4CbK+DBPNDV00JvQkUrAvsMh9w824YnnrBZ3Hfv3uA9EBAQEBD4fRBEmYCAQJ3ULTjuHBWGSjae3eTQ9LdjYEdeiJrGj5E+7FryLS2PphJZZBMNvQDJs7McDCEA0ppFo5dIcTGbKFB6kRregb3hndgb1pFVr9yDUiZBLpXYxKUTZ8baNvyeCvntC1GLGX5oA/qiRn3ZmlxD0QTezZprXZl/wpMqoVDbCl1tKCXIw0jXFk04n6vDw6X+KE1tvBRyXvzuJK8MjyY2zIvuLZswadUhuzEnr5YxekU6n068m6GRQynUFFJQWYCv0heFVMHGcxvJLM6mZ7P+fH1Uz/juvnx9OJuHEprw6q5Xq9MbvV29mRY3jeTYZDRGDYWaQtR6Nf5u/gyM8cPd1cKsHbOdRo7S89IZ+91YkmOT2T56O53f6Yx6n5qCHgWgcLyuUCmsCoDeyobvgd49ClO3z/A/qsTzwBWnn6XaNHF3oVJvL4AndY9gXVoOE7uHM7F7BLO+coxo1RTTxRoDe8+riQsYzO4JwyjVlVKiLcVb4YWnqxdHs8oZ8t5BekU2YfWkrnzw62/4Kvzs5nvOG+b6OSxDpiqBe387QJ0S3QpPHoD//HIRsalGXqPRCOPGwblzIBUeCwQEBAT+DIRvXwEBgdvHZLI9yB0/Dpcu2Wzl8/KgoMD2oGcywX33wbPPOj1ct3IF2/Z9yt68/SSZYIgR/DTQvATaFKbTusiWUjXE2dLbdxD09FTHOWWuzBryFBf8mnG2SThW0U0hJROLSV5zhDO55TedGaMCqtMwa9vwV+FsrB1WC4icCDaxBEvAAMQ56+u8hdn6QLaVdWdrWXfGDb2PEo2Rob0D8Y8o5tPULCr1pnqt0Gd1m0Ob4BH0j/Jnx5nrda5T81pcZGJ2nivgcHYqT/ZrhdVqdZr+ePKqmr7/2c+Y+BDGJYbgqlJRoi3FTSZhQsdJmEwS1qZdoXWQis/2ZzO5Zwibz39TLcha+rRk++jt7M7ezcgNIx3OfeWIlWw+f7beVD6AZYeXUX6wnPIL5dDf+ZgxHrC4CXg2Qp8uLoVZF84QlfsYT8bPZuP0QUxYedypuUgV0cEqPBQygr1cOZ1rq2kbEx9Gj1Z+pF0qwmC2EBfuXR19BOd9xQI9fPFzU7Eq9SoVGjOP9mrOlqNqrpTk81PGcaoydqvMRQI9XfFy9bIzCHH2amB5uQt9B37GJBY6rffz0MEnm+CB02BzRalBRISt/5ggyAQEBAT+NIRvYAEBgcZjMEBaGuzYAT//DEePgl5f/zG9ezufymSk8t13eCDjIg/cxqmUfLsJxXOPO00Z29w2yWF8lRg5l2d7oM64pmbG+nTGJITxwhBbl+HaNvxV1B6rlImh5Bhc2w6520HqAb23Oj1PY9AwXGqIMotVxDFNa35Wx7NDHc8FfTOqImKf7stm+bguDHt/D6PjQ/kyuRsWdMzZMdtp37D0vHTGfDeKaV2mMe+BeRxcUNJgxGdi93DWpeVgsdrSFLeezCWmqROXjBuUaY18uOsSS1Iu0TrQA3cXKa8ND2LWhhNU6M1M7hFBnzb+/Hr2OlKJlf8e+C9gi5D9OOZH5qXOcyoSTuafxFfpy7xU5wYW1VQAR2Bdp3U2u/taBEjgI3+4txFW9/kmmHwdfriRQZqel86EjaNJjp3GqklvMnLx4Trv38Tu4ZRpDLx5dzvKdemMjg+lT5sALhZWMKNvK1ylEr49eZWpd7Wgc5gXP57K48VhLeoV0xMihzPt8/RqC/3afJqaxcrxcVRozczqNocxNwxC3i4BXwk87W0b93oRvFasp+W6IRyYfIC7wnozv0ZqZHQ+bPnWhfB8J/9W+/WDDRvA17fhGyggICAg8LshiDIBAYH6MRhg+3bYsAHr5s2IKipu7Xg35+YJBouB0qKr3OqjYImrB3siOqEdMhwXaePt+2uKkZqsOZBNfIQPzf2U9bpNNpEWU3F2J8WySyh0exDpC27uFMvBVAlSx2st9eyLwuzG/op2/KyO51d1HEVmL6drZFxTozOaCfR05Zez15mSFMHm85vrbeQMsOTwEnqF9WL+fT1JXpNe57jRN/qFfXHoMnHh3lToTWgMZodWA86wWOHMjQiRq0yCWmfiTG45T395nNHxobx1dwzFutxqQ4ppcdNIyUqp06Ux2j8ajVHj1MCimt8AA+CosQFbdGxRE/BpRHTsuwqYct3WSLk2y44soVtID0bHd+SjXZcc9lfdN4PJQnZRJZ9M6MLXR67Qfd6v1SKuytJfhBWN3sS7D0c1KKaTY6fx1r11i8GMa2qsWGnioWBkmxF2BiGzCsFLDMcN8H6pbXy/iP7IJC5EuPWpTo20rFlDs/n/RqxxdIbkpZfgtddAcmvprwICAgICdx5BlAkICNRPcTHccw9YLI2wQnCCu/MQRom2BJmmgSgboJHCoabQ8ZGnWebanj2eYUzpHcnIKH8Ucmmj7PtH12he7IylKZl8Mj4OsYhq0dZEWkKC2wkS3E+S4HaKFq5XbDvKnExgMUB+CjS1JVpW1aVpDWaKDApGZKzF1MivW6PJQlIrP7zc5IjFpjqt0GszP3U+uycO5/1HOrHMiWNmclILurfwxWix8s697SnWGPBRynGRiVHUajVQH7UjjmBLtbu3c1NkrqWAzdQjOTaZEetH1DmPykVFoabQ+U49cAKIApx8fIIlsNS/cY2gK61idB3mcf+3c7BY63a/WJS2kB8e+ZWlKZeqPwPRwSomdg8nLtyHN7ec5tUR0fxnx3mWju3CvzefthP4NRt0P9W/Jd+e/rZBMd2QGARQ60wEeipwl7ixcMBCO4OQyTeyVasMQoZFDmPajebVSouRpUfX0uuXrx0n9faGzz+HoUPrPT8BAQEBgT8OQZQJCAjUT2Ag5l5JSHbtrHNIrrsv5a3a0LxDK6RNm0JAALi62mpUEhOdHlOsLeF4OARVgMIIOiloZKB2gUvecNEbTvlDeiCYJHB86kSSPSKZLhXbmW40ZN9f9VA9fuXBOlPTMq6p0WlLeb1jBt4V+2gjOkLLKhHWSIxXtyIKHIThRvPrZSkXsVitLH6kc6MFGYCnUsbkns2xWK2UaIvqjyTVoKoh8KC2gfRz4pgpAn45e50luxzv0dv3xDClV3NmbkhvcJ26Io6r9mXx7FB/25yNiIKp9Wr8lE7cKq4ClUCc8+MmqeBdv8bVju3Rwrg8C9sGDiPG/3NO5NfdXPtY3jFM1gpSZvcmX63D+4ZgXZeWwxtbzvDysCjWpeVw8qoancFM60CP6shhTTYcusysgeH8N21BwyeITQxueugXOzFYRag8F19dOtATAHe5G/dH3ceI1sPt2i2obhiEDF10kMslWpqWXefDjXPpmPubw3rGjp2RffeNrR2EgICAgMD/DIIoExD4E7mjTn//HyorbSYdLVs6PceMHgPpUEOUZfg3Z094R/aFdeBEUCtKFSoAPnikE4NjAht17j4Kb8bf2/hT9HH1wlMpr75nhRUGu3s2MDrAzr7f3UWKxWqtfqhuqNbKpC1krPlZp65+dXFRH0xKeSwp5Z1RFyYyQZ5LQnNf3v3pPFlFGiRikUPD6/qIDlYhQkT8278wtlsY93atx9fcCcW6Utxl/uSrdfi6uRDqo0AqFmMwW3irnnq5cSsPseOpXoyJD2VN2u1FHLeezGPuvZF0DOyIu9y97ihY1brXM1DKlDcNLMzAWSACaOo4vpkUVvjDwLpbiVVjsMLLRbCwBCxAkaYIlYuqweOulBXyTVoh2UUaKvQmzuWVY7E6XneJxuBgjV9FZIA7RdqSWxLTlUa1g8iLUVzg8xav43VIBF57wTMKALlUhhyZrd2CZwg6o4mHlu3n+BXb56tb9nGWfD8XL51jmvHWxBHctW0tMlUjQowCAgICAn8ogigTEPiTuG2nvztJaSm8/77tfy1a2BrJ1mqebDBbeFsRzVPNYvihTQ+2R3anwN3b6XRLUzLpG+XfKFHmrfC2c5Srj06BnfBSeDXqngV6uhLo6UphuZ6ub/9cHX3wlpTR1S2DC/pmZOqbOawhcgsn1xxEkCS3zvOwSFWctXRmXU5bUio6c9kQeHNnuZGjG2wGEKsndWXE4lTKtEa7htcNMbF7OJ/tz8JihYJyPT6KgAaPqYm3qxcvfHeSrSfzqu/L/PvbkXm9st70zjKtkQeX7ee7f3UnvvntRRzNFiuVehFzEufwTuo7zqNgNcdbzSw7sowZXWcwadUkUAPRjuPEwOOe8JYfeDTiPUW6Hibk2WqtqvBV+qLWNyyKm7j5kHEtq1oc1XXd3ko5FXrnxvPuLlKuVzg37qiLCmMZASrX6nV7uB9jadjbuIu1tpq6nYNgwD5QOqpVqVjM5J436yqvqvyRWOwL57RSF14c+C96vzULuVsj+gUICAgICPzhCKJMQOBPQGMwNd7p7/cQZpWVNiE2f75NmAEUFcHOndCnj91QtdZEmlrEw6PeaXDajGtq1FpTo85ZLpbbOcrVx6zEOUhFct78ofH3TCkqY2LYcUIMB0hwO0mUIguA9/Mf4t38sXbHV9VJ7S2L5gGfm6JMhxsWvx4sOdOMJi0HovDvwuxvMuo91yor89HxoXy0K5M1B7LZNL27nVW6M2pHY34+c51597e+NeHq6sVPGTcjNGfzyvFSurA05WSDx2cVaZi77QwvDImic5g3BpMZvdGCTCLGVSbmp9P5vLYpA4VcgkQswlw71w4wmCApdDB7c/baR8Hq4MMDH9LL0ssWnfRx3N/ZBZb5Q5dGtMgzWOGNYninGLs+XZ0CO6GQKjh1/VS9x3cK7ESAuw8rxvlRoTMhl4qRS8UOkVZnNXU1qdCb8FU6uZh68FN48/Y9foxacYD25q0sDHkPubjGVWhyIP1ZSFzjcKxUIrarq8zxDuL5QU+weNN8AC55BzHt7hfoMuKuRr8wERAQEBD44xFEmYDAH4zpRs1RfZELuOkK2Nh0wEah18Py5fDmm3DdSU+ruXMdRFmZ1uA4rh7UOmOjGk3LpTIHRzlnTI2dxojWw9l7oajee6aSVJB3+kvyXHOJsBxGUXqcl1WOwqGPz2n2KGzOg+fzKzBbrEzqHsGGgzlcKo+libSE/ZXtOFDRjlPalsx7oBPWNpX0jQ/jsdWHG3UPPk3NYvm4LixNyaRMa2T8yoOsntSVzmFefJqa5RCFmtQ9gi7h3vx7SwaRAe7V53biciWzE+cw+tuGheszic9gsYoI8VZW99uKDHBHZzA77UHmjEuFleSW6Rj6/h6iglSsntSVU1dKiW7qRb+oAGKaelabg6w/eJk1B7LtxIpIJGLa56dY89g8LhRf4Mn4J5mwcYLzxUqhrKKMzSGbHVJG3UXwhi884QWSRrjLHNbBxHw45eSj+kziMyw7sgyL1VLvHHMS52A0SdhxOo+U8wXkq3XV6Ys1qaumrorz+RW4yVS3JKbFuPPRzgt82ycNn3MLHQcF9IG4j+qco3Zd5RZ6kZh9Ag99JZ9NeJ7HB3Wgb5T/7x95FxAQEBC4bYRvaAGBPxiD2cLSXRcbNfZW0gHrxWSCzz6D11+HnHrEoKsr6HS2/7+Bp6Jhu/SaqFxljR7rLnd0lKuiY2BHnk18lmGthyPDheUpWdX75FIxw6KUdHI9SZDuAIG6NNq6ZiIWWaG4/jWj5Wd5rkcoXh5euMjEZFxVk9jSF7XOh3VMZOaBPnbpeZ+mZvHJ+Dgq9aYGxY1ELCIywB2lXILFYiUqyIOMa+VkFWkYsTiV0fGhLB/XBZ3BjMFkQekiwcdNztHsUmRSMS8MbmtzRXST46OUI5eKiTIPa1C4Jscmk9A0gTd2v8rqyS8w4gObxbq7i5RiTeNFdYXehEoho6mXgleGt8FVbiIi0EBm6Rm8Xb0J9FSx4WAOh7PKuD82hE3TuzN+5UGyijRM6h7BurQcerRqwsGLlXRs1ppWPi05cOWAvQuhFcgBAgEvx3O42w0+aAIhjfgYGRHzcqGFhSUO7ZABmNZlGiNbj+TotaP1zjOtyzSGRg7j/R2XWLHXuQsiNOziCbY0zl9OFzMncQ6jGiGmZ8bPYsOBK7S4/CI+2s2OA0Ifgm6rQeJiv12jAbG4+t+qUi5lcEzgzbrK6d1QuSvoq5T98TWqAgICAgK3zD9alKnV5Ux/4gnKytQEBgaybGndbyJ/+eVXtm3fzuXLV5BKpbSOjOSBB+8nqk2bOo85c+YMX331DefOn8dkMtGsWQhDhgymTx3NdAX+Gai1DT/cV3Er6YBOsVph40Z47jk4d67ucQ88YOtZ1L69wy6VQlptVFElOtxdpHaRpiqig1WoFLd2rjUd5Yo0xeSVF+Pl6oVSqmJ9Wi6T951gVHwoCx5oz3PfnGTqXeEkVLyH/OwCRHqLre9yI805dFZXDpZH8fyqHVw1+hMdrGJKr+aUaY28vimDAdGBdkIDbL8DvcmMtR47dU+FjDEJYYyKD0VnMFOsMSAWi1g5oSuf7c+ujih9tCuTpSmZtA704ONxXfgxI5f7YkO5Xq5j/o9nqyNongoZ22b2ZOFP50i9UMiX096hZ2gPFu5f6CBcZ8bPpGdoT5788UlKdaX0Dk9jfPdwdmQUEOarxM+98aL6fH4F3kopPzzZlR/Ob2bqp/Md1+s6i3ldBjNh5XHSL/uxelJX1uzPpku4N2/9cIYtM3uAFf7z0wUOXCzks0ffJrFZT949sID039JtzophjmtHSOG9JjCikR4Ux4wuhA/6lZj8i7Tb/x9HQd/9WYZFDkdvkDIn8RU6BMTy37QFDuOejJ/NwBZDMZnkzOwXSZsgFStTLzltKdCzpR/3fJTaoGmMr5srSZHDGyGmp3FXUE8uHnyQ3n4HHAe0ngmd3wVRLUGVkQEPPghJSfDRzb9b0hviSymXNipaLSAgICDwv8M/WpR9+ukq1GrndQE1+fjjT9i85QfkcjmdOnbEYDSQfvw4x9LTeXbObBIS4h2O2bdvPwsW/ger1Up027Z4qDw4ceIkixZ9QHZWNhMnTvgdrkjgr8DvlQ7owNGj8PTTkJJS95ghQ2ypjJ061TlELhEzvU8LTl0ttxMdztLYpia1QH4bb+RNFhHztl7kcHbJDcFXxqX8InQWm6BIu1TMjD4tWTEhho1nNzP32DpeU9SfjgagtbhAk0T2lEWz9GwYJ7UtMVpvhmAyrqmZecOc46VhbRmxOJVh7YPsjDoAtEYz3nVEDMN9laye1JWDl4qZ8tlhh4f5CYnhdkLPYgWxSAQiGBQTzPztZ1lby/FwTEIYqRcKWZuWg0Qswmi2UqQtYuPDG9EYNRRpivBV+uImcyOjIAO5RM6C/gso1BTi7+ZPUngwk7tHkFWko4m7S6PdH5Mim2DBwLP1ND2euGkMybHTWDXJ1vS4a7gPT/aPZOj7ewjwdEEmFrHrXEF1/dzQRYd4OK4dLTStSXdNBw/7ORUieN4bZnuDayM+OhUWm7Pi+6V6em57gW8e/IZeYb3QGDWU6koJdA/ET+lHuVZExmUdOSUa3vzhDKPjO7L5oV+pNKop1pbgo/CuFv4DN6bxxt0xdAnzonebJgyKCUStM5JXpkPlKsPNRYrKVYrFCt1b+pFVZLs2Zy8pHo5rRrsQL9Iyy3mz9zzuCktinpMo8JPxs+nt35nKbf3p7e74wuRi05cI7fAa0pqCzGqFTz6BGTNAq4XTp6F3b9tLFQEBAQGBvzT/WFF2/PgJft25k4ED+vPjTzvqHJd+/Dibt/yAh4cH8+fNJTg4GICzZ8/x4ksv8/4Hi4mJicHd/aZPc3l5OR8s/hCLxcJzz86hW7cEAEpLS3nu+Rf5fuMmunTpQrt2Mb/vRQr8T/J7pgMCkJ8Pzz5rS1esK7rTsye8/Tb06NHgdFKJmF6t/NEaLPWKjm+PXr2tVMuqGruv0s7Txe0MidbjdHM7SauoHGJPr0VvleOpkHFflybM/skmFsKl8FqE41wGJJh9Eln5WwQ7S6JJ7DqYZr5ezN5Zv/NhbXOOmv8N4CIVgwgHceOpkPHZpHiWpmQ6NfGo2VC4ptCb2D2cC/kVXC/XOwgyiVjEqPjQ6vq1yAB3KgxlzNw+k6d+fIoY/xhULiq8XL14b+B75Jbn8vwvzzs89M/qNofhkcP54WQuE7tHMOurhi3a3xgZxcazm26p6fHyPRfp1zaAnGINceHeuEglfJqaVT02r/AwL/0wF4t/BdT6KD/gDgv9ILSRH/GNFfBEAVy+4YGRkp2CxqihRKPBS+GJn3dT9p4v4fTVHEZ3C6OZn5LXt5y+EaW8xNKUS7QO9LghotTVNWOeChkllXpUCjn5ZTrKtBqaervSOsCDbadykUrElFQaGNYhmH/d1ZKuET6Uagz0axtY/ZLCz12Ol1KOxWJlwqcHsVhh9cSuJIUOp0ezgQ5i8Md9e5Ecv4soeb7dNVpEcvKiPmLphS68ZrHe/CNdWAhTpsB339nflEcfhdhYaN68cTdRQEBAQOB/kn+kKNPr9Xy0ZCnNmjXj7rtH1ivKNm205fg/+MD91YIMoE2b1gwaNJAtW37g559/5u67R1bv27HjZzQaDfFdu1YLMgAvLy/Gjx/LO+/MZ+PGTYIo+4dSMx2wIW4nHRCrFb7+2rkg69zZJsYGDHCwvq8LjcHE3G11ux7O/voEY+JDeW5wFIcuFREX4du4dEuzHgoPYL32C82Pb+RE9Gl7xzmgk/IsByrbMyYhhJ3Z26rFQpYJso0QIIH9OtilhZ0aOKg3s2zEVCrpyLHdWSxKaHlb5hw1/zsqSIWfuwtGs4V597Vj9Mc3rdHHJISRdqmoXldFsBd9aq2RPm0CbPNtP+QwtrY5h7uLlCKNrVDOYrVwIv8E3q7eHJ5ymHmp81h+ZLnDHOl56Yz5bhRTY6cxr/98yjUiRnUN5YvDl+tMPx3fLRRPNxEL9s1r1P1alLaQHx75lSPZpWgMJu7pGESbIE8MJgtWrIgwkp/3DtqQNAcxFiOH95tA70a6s18xwvQC2FjpuO+aupgtR8Qczv4NqVjEhMQIRncL499bMnhhcFu7VGGLFYeGzzUjnQ8t2++YttirOX3a+PP8dyfp2aoJT36RzsoJcezLLKzzJcXiUZ0Zv/IgWqPZ9lJj3j47MehVnsrS0LfwlNlfUKnJncr4r7A06cXJlMM3U5e3bYNJkyAvz/EGtGhR98sXAQEBAYG/DP9IUbbhiy/Jz8/nrTffQCKp+xbo9XpOnLRZSScmdnPYn9itG1u2/MChQ4ftRNnhI0fqPKZLbCxyuZzjJ05gMBiQy28taiLw10cuEZOcdLOvUH3cVjpgYKCthuzll29uCw2FefPgoYcaLcbgFpwi03LoGOrFxYJKfjlbwPND2qCQ1fq3ZTFC0WG4vhPyfsVamIrIrEOG7SHdGQnuJzmk7cAj8UEM22BvmjDwqk2c6Ws9jy5KW8imh34h5XzhLTkPZlxTozOYaR3oYfffE7uHs2RXJinnC0ju1ZwdT/XiwWX7uVyitYtoNcSnqVmsfTSeSr0ZqViE3mR1em61zTmcWaxPi5tGSlaKU0FWk6VHltAztBeRqr48MyCSOYNaU6IxUFhxM/30lzP5eCnldGvuS4m28JaaHpus5bw00g+xRMPr90RSqivlWuV5hrc5x+6sJ9FGaOyO8RbDa77wL0+QNuJjaLbC+6XwShFU1KE7mqp8Gd/Nk2l3tcRFJuZqiZYRi1OJDHBv0OSkMZHOGTfSW58fHEVBhZ4PR3XmvzvONyoyWqIx0LKJO1FBN1/CtHW9yOqWrzi8gLhsCODNyoW8HngXVouFc3nllBeXEfjCM3Z1Y3Y88QQsWAAuLs73CwgICAj8ZfjHibKsrCw2btxE3z59iI5uS36+E1vwG1y9eg2j0Yinpwo/P8dGqC1a2NJFsrKz7de4lAVA8xaO6SQymYzQ0GZcuJDJtWvXCA8Pv/2LEfhLUruvUF2MSQi7fefFp5+GZcugrAxeeAFmzgRFI90wanArTpGfpmbx2eSu6IwWiioMVOo1eMot+FxdjrRwF+KCPWCqqB7fGGnY1S2DSLE7lUa1g1g4V4fXwrG8Y1Qa1UQFqW7JeRCgRGPA3UVa/d/3dGpa7bZXpjUyY0M6Y+JD+fZf3Xln6xl0BjPn8suJCvKo0/ykioxratQ6Ey4SERsO5tApzHkD7gq9CR/lTZVa22JdIpKQHJvMiPUjGnVNC/fPZ/fE4Ww7cZ1V+xzt+Kf0ak5iCz92nsundUjZrdwurqivEOYVxtbffuDdA++SfjkdLmMz8vC6Oc5FBNM94UUf8JY0bu5DOki+Dsf0dY/pFNgJd7knMpGU09fUnLpaxoiOTRnePogjOSV299EZtxLp7BzqTf+2/vyYkd/oyGifNv5IJSKSezVnxoZ0AE7rIthS1pN7vXdWj0/XRPJo1ss8d08S18u1bD+VT8y180QMeAou/Oa4gJ8ffPwxjBzpuE9AQEBA4C/JP0qUWSwWFn/4EW5ubowfP67B8QWFBQD4+vo63e/q6oqbmxsVFRVotFqUCgUajYZKje3tsF8dx/n6+nLhQibXrxc0KMoqKiqprHTM2SksLGzw/AX+d1HKpbw4NIrBMUGsP5jDtlN51Q/y0cEqpia1qL+v0PXrtjTEf/8bVConCyjhyy9tdSYBAbd9nrfqFKk1mHln21m2nLA1YBZhIT3mbTzFjXvYN1nFnNC0Yl9FBw5UtuNIZRQxYTfT9xpLsbYEoMGH8tp4K+VU6G0RjGAvBSHeCh5efsDObW9NWg6xYT68PLwtlXoTu+f0rtf8RC4V0y/KH183OVarhexiPeevl9M/2vnv5Xx+Ba5ySXWKq9liZX1aLjO7zmLipjFE+0ejMWpuKaJVoi1h57nrDimzV0q0nM+voHtLP3pH+qOz3toLgBBVCCuPruTlXS/bbO6VQI13USLgIXd42w8iGlk3VilScCzgIZL2rKIhK5dZiXMoKrfg5yGlRGPg3thmjP34AN893oNfTufj5lJ3qnDt2r2GWJl6iUExgazel9Wo8Z+mZjE4Jgi5VEK/tjVfwoh47soMgmSFdHM/yU9lCczImcV9XSPp1aoJep0e93lv893udUgsToz+hw61CbLAwEadh4CAgIDAX4N/lCj74Yet/PbbBWY8MR2VyqPB8TqtDgCXelJDXF1dqKysRHtDlGl1uup9dR3neqOvjFarbfAcNm/ezIYvvmxwnMBfB5PZgsFsQa014ecu56WhUbxzX3sqb4gBD1dp3X2FjEZbKtOrr9qiYFIpLHTSbBagm2P67K3SkFOki0hPV7cM9FY5BytjyCvTka+++W/AiphdZR0Z6e3cAdJiFWHy7MCWvNZsym/DocpoKi32hUbO0vcawkfhzZncHDtx0xDRwSpcZGIuXK9gWPsgPFxtkS+Jk3TPrSev0T86gLRLxSzffdFpXdEPM3pwtbSStsEKSnWlFGmLEUtLaRvsxXODW+MqdS4YzBYr69JymJAYzuyvbQYlaw5cYeP0wUzpPJXThaco1NzaS5libQm+bvYCtaqW6lxeOfO3nyXjWhnLxrW7pabHbnI3Xv7uZcgHIoEaH9merrCwCXRtpHGoyQoflsJ7lXJ+HvgSj2rk9aZnTusyjWGRQ7hceoVinQcDor3RGqz4urtQXKGnTGfERSZmSq/mzNzgeD232ljb1p7CiLmR9VsZ19SU60y4uUgdmjtnXFOTnP0iY3y2skc2kbfub0nvNv4Yzpyl/L6H+VeOkz5oSiW8+67N7OMWUpAFBAQEBP4a/GNEWUFBAWvWriMmOpq+ffv82afTaIYPH06fPo7nW1hYyAsvvvQnnJHA/weNwcTPZ/JZluL4IJ+c1Jx+UQF1R8d+/dVmhZ2RcXPbokU297V6+uX9f3B0irTSyiWHuzyO0NPjGPFup3ARG9mljuVgZYxdpKmKPRWd7ERZlimCnaUx7K9oT1plDGN6dSK8o5JdXzt3SKydvtcQnQI7oZSqOJOrdhA39TGxeziZ1yvZNbs3VquVSwWVeCplfD+9O6tSs/h47yXKtEaaeSuYe2973tl6hjVpjmlsGdfUfLjzAj1bq7hY8TNTVzn2+prVbQ4jWg/n2UFtGLfyoMMcaw5ks2l6d0Z1DWXdwRzKtEYmrDzOqklvcbLgAP5u/g1eT018FN4UVd50+WvmreCbaQlYMBDmr+dudwuTlWEoZDKeSZjN2O9HNzjnk7FPMmD2AJuJR42PX2sZvOMHdzey3xjYTFpmFMApA0AZg9YOYvvo7XQL6caitEVO7t8seoT2IHZ5LBeKL1Rvn5M4h8Wjh2C1SHhjy2ne/+U3tjzRkzHxoQ6/q5q1ew3136uiVGOsTm9tDGqNtrqdRXVz5zb+lOmMlGmMeChGMM1VitFs4dcVXzN0ziQCjU7yNbt2hc8/h8jIRq8tICAgIPDX4h8jypYtW4HJZGLatORGH+OqsP0x1evrLmrQ6Wz7FDfqdRSuN18L6/V6lEpHezHdjWiaohE1Pu7ubnZ2+wJ/XTQGE29vrdvFcMb6dMYkhPHCkDb2wiw7G2bNsjkq1sZkguefd7TJvkOoFFI6BrugUu+jj+oQfVWHaFbLwhsg3v0UHYNdcJGJOZdn7263u7wTa4sGkS2LZ/Tw8dz1wWm7/bUFSG2q0veeTpjNuEaIhZnxs1iflovF2vDcVYyOD2VgdCC/nLnu1FHv0R4RbJrenRe+Pcn7j3Ri92+FTgUZ2MwjVk/uwKu7nmP5Uee9vqqcERcMWMDjd7XgwxvW+1XHj0kIw0Um4fkhbYhv7lMdjRu5+DDjuoXTu3nwLYlUT1cvfsqwpTuG+yr5clpntl/8noX77QVjr7BefPPgN0yNnVq3Lb4VknRJ/Ov9f1HZ4mZqdbgUXvWFsR4gaWQgp1Lmy4ScIr6usN9+ofgCcSvimNplKhsf3ojFaqFYo6aJmw9yiYxFaYt4YtsTlOhKqo9Jz0tn1LejmNZlGq8nvYOHq4wSjZHRHx9gyxM96RzmzSd7bzaFrtCb8PeQ83jvCB6JD6LSqKZIU4yv0gelVMWGg7msOXDFLnXVUylzeOlQF73cj9D84JPQdwe4hwM3mzuLxJBfpmPbqVx+PpPPmdxyXPQudPNqQtOCKzcnkUhshj0vvmiLigsICAgI/G35x3zLHzp8GDc3N5YsWWa33WC0vSktLi7mxRdtbnWzZj2Nt7c3TfyaAFBUVOR0Tp1OR2VlJe7u7ihvCCylUombUkmlRkNhURGhTkRZ1Xz+/k3uzMUJ/M/TaBfDA9nER/gwOCYQqUFvc1Z75x1bo1gnWEaPRjx//p0/Yc01uLYV16ub+brJDqR+9afaKsR6nu5YwLq0HGoHGK6bfHnx6nTiwr3pb/R0OLZMa2T8yoOsntSVzmFefJrqaEYRGeBJ7zYj6hcLwNTYaSQ1G8TIjYcbPffE7uEMig5kWcpFPth5wWHOjGtqnvryOGPiw1g2rgt6o5kVu+s2P6my73cmyGpS5YyYfNc9RDRx49PULCr1pmp79smrDnGlRMvo+FCWj+uCwWRGozfj6y5HjITZ3eYw+rtR9a4BMKvbHI5mlWOy2AtGZ/dxd/Zuun3Sjd0TdtMrLIn5tZoet9S0RJ2hJqVtCrSwbQuWwEs+8KgnyBopxgwiJV9UPMzIR97Gd9dr4ORcSnQlzN07lxJtGS/1eJtLuSIuux3n3i9HYLHWXW225PASeoX2YlKPjvx3RyZuLlIMJjMtmrixfFwXdAYzlQYT/h4uuCss+AekM/yL0Q7RuJldZ7Fx+mAmrDxOVpHmRnsKGVJxQxdpZbzvFl4JXoGk0gIpw2FAKshu1n4qZFJimnoSGejBI13DUOuMqFxleN0XhDWpFyKzGVq1gjVrbFEyAQEBAYG/Pf8YUQZQWVnJqZqpXzUwGAzV+wwG25vRpk2DkclklJWpKSoqcjD8yMy0PZiFh4XZbQ+PCCcj4zQXMy8S2qyZ3T6TyUROzmXkcrld3zOBvze34mK4dNcFBpzfh3TObMjKcjrmVEALVj38NL0mjqSfnz+NbPdUP5XZkPkpXNsCxba2DmLsyoScYraKuSJpR+sAd57YULforO0qWJOsIg0jFqdWCxCdwUyJxkCwlwKxCDYfv0al1ps3e8+jW0hP/pu2oNqJMNo/mtigWB6MfpDEZj3Y91s5Id6K6ghHVpGGOV+f4MWhbfhiSjcKKnSU60w0cXfB3VWKwWRh928FTgVZTdakZRMX4U1ic98665AkYpFT+/66WLh/PsNbDyOroJJPJ8ShlEuZu+2MXUPpj3ZlsjQls7rPVVIrf+7pHMSINiNIjp3KsgZE6rDIYQxdZEuRHJMQwq6c7fUK2wvFF4j+KJqfxvzMjjEpFGlKuHjpNO+89w67XXZDnG2cnwSe87bZ2ysa6Q9iRYIhfBLXw5/jk7XZGNMrWThgIb3CklhQSwB2DOzIk/GzSWo2iHGfHGP15A4M2/BivYKsivn75vPTmF0s+jmTid3DWZOWzciOTfno1wscvVxCqybuvH1/a2b/NMvpvUjPS2fipjEkx05j1aQ3Gbn4MJO6R1CmMfBoT+c1agBSTLwavJyxfltvbiw7BamjoNdGEN+0nqyKminl0uoUR7onwiuvQG6urVbUTciSEBAQEPin8I8RZRu//9bp9vz860xJnkpgYCDLltr3gnFxcaF9u3YcOXqU1NR9jBgx3G7/vv37AYiL62K3vUtsLBkZp9m3bz933ZVkt+/Q4cMYDAbiunQRepT9g2isi2GLwss8+8VyXLKOOd1frFCxsNdYNrQfgEUs4eu6Uh5vh8rLcOr1Rg29amhCSnksl2Td6RR3L9HhYTy08iBlWk2dx9R2FaxNmdZoJ0DaNfXkmQGRDFm0h+/+1YMPfs3kh5O5jI7vyJaHdyIRm1DIZKj1akp0pXi6eCEGIgPc+GR8F7RGM6UaI4GerogQ8dn+LNYfPESgpyvuLlIq9SYkYvh4fFcW/5rpeMJOWL77Ir1b+yMW4RARBJt5hDP7/ro4lneMUl0pF4sq+Wx/Nq0C3O0EWU3nxqJKAz+fuc6hrBJaB7nTvqkX8/stoFdoEgv2Owqa2d2eZUSb4czdksnlEu0tCcYSXQlTtjzK6gFfM3zKbC7LvscSZwGxTYw95QUzvMD9FswajUGDeb/gUe5uOYCtJ3P59l/dqdQbeWzVCWYP6kfK+GHklhdRoi0hxNMPDxdPlu26zMiNhwn2crW7r1ViXOWiQq1Xk3E9A7P1plPhsbxjaE1qnu4XSVy4DyMWp/Lt0ausntSV45dLsWJh87lN9YpTgGVHltAtpAfz70uiR6sAtp7MJb65r9N0WJWkgo9C59LDw8nvXqoEqxGQwL59EBQEERHOF335ZcHIQ0BAQOAfyD9GlN0uI0YO58jRo3z51dd06RJbHd06e/YcP/74E25ubvTr18/umP79+/HV19+QdvAg+/cfoFu3BABKS0tZvfpzAEaObFyPIYG/Bw25GHroK5mRup4JRzYjc2KDbRaJWdNpMO/2GEOZwt451C7lsaGeZhYzWE0gceIM6tcNXANA51gzBiKsvgkYA4eg9hlAqTiSbi5SkiRilC5Spn5+hKyiugUZ2GrDfj6dZ9ezyekpWuFMbjmTe0Swel82j3QN42BWcfVD8NaT+TwYF8jPl3/ivRsRsyqqzB6Gtx4BVgkGuRWJSITBbEEkElXPDbZUvqf6R2KxOG/i7IyMa2oq9CZaB3pUz1MTd5fbs+9v4uHCIzXs2Zt5K3jznrZ0DnOvdm70VQTwzv2tOZZdwf4LJYR4K5my9DBv3J3E7gnDKNWVUqwtwUfhjaerF0ezyll3II+Ood6sPXiZyAB3NKZGCkYdHNtwjLhPotH30oEHBElgljdM9QTlLYixwzooavMcPbq8zK9LD5OkMbD7twJCvJVcLqlk/8ViXvzuNIsf6cyT67MZmxCOxeBKgErER7suATaxW6QpxtvVm2lx00iOTUZj1FCoKcRP6YdCqmD50eUsObSkus6sVFfK6IRW3PPRPsq0Rsq0RuZ8fYIlYzojl5nosXJeo85/UdpCUiaM4GJBBV8evsyHOy84pMNGyK/yccS/aeFy1XGCmFeh3StgMsOrL8HcuTZn1JQUW81YbQRBJiAgIPCPRBBlDdCxQweGDxvK5i0/8ORTz9CxQwdMJhPpx49jtVqZ8cR0ByMODw8Pnpj+OAsW/od58xcQExONh4cHx4+foLKykpEjhtOuXcyfdEUCfwaOLob2RBZk89ih753uS2sWw2v9pnDG37EZeRVLUzLrbjRtMULer1ZHigwAAOuZSURBVHD5a7iyEdr/G1pNdRwnlkDIPXDhRvRApoKgQdB0GAQNQuTaBDngB3iZLRgtFso0JgwmC6PiQ0m71LAY8fdwoWekf6OMN7pG+JJXpmNC9wgeXGqLSlfVRL2d+mKdJhqjvrWZaDzb7U0eXHaYMq2x2qZ+0/TuTFp1iHA/JW+MbMf5/HJyy3QO89RHuc5Eu6aeTkXZ7dr3yyXqanv27i18WTI2hi3nt/DRpu+4VnGtOhrULqAds7rN4fG+w9EbZVwu0TJ59THaBrlzV2t/XGWunMm9zvZTx6tryKqMTsp1RnSmBsSnATgIXAKSQB+qI0wKz3rDJBW43IIYO2+AV4vAK3IKb3SchdEk5kxuebVD58d7L7J8XBfe+/k3MgsqcXeV0j7Ek75R/jy2+hBLxnapjqpW6E0Eevhz6LFD7M7ezcgNIx3E+IyuMzj02CEGrR3EheILeLl6sS4tx+5lwQNdQth9vpDY5tZbimbmlhfhpWxChd5kl2q7cnwcsuvbUB2djdRsf2+tYhdECasg/GE4cwbGjoUjtrRgUlNh/nybSY+AgICAgACCKGsUjz46mYiICH7Yuo3048eRSqV06NCeBx98gKg6rMgTE7vx9ltv8OVXX3Pu3HlMJhPNmoUwdMgQ+vTp/QdfgcCfjUpRdxNbgCMhbfmu7V3cc3pX9TZzcDBPxo5mc1SvBt+e23oomW6mMFotULgfstZBzpegr9HX6vI3zkUZQMRYkCggZDg06QFi5x1/q+phFJ629QbFBDD33hhe3XQag8l5zU+V0NpzvoDkpOZ1Gm9M6h5B95Z+iETQOsCDgnJddSTrVkw0EkJ6MDq+Ix/tsjnuzf76BKPjQ9n4eA+sWHln21mO5pSw+JHO9c5VGw9XKQOjA/ny8BWHfbdj3+/p6sXJqzkMjgmimbeCpeNi+OXiz/QIS6RzcEeHaNAT2x5nb84eFgxYwH8eaE9CCz+75tUjOzYlzNetunn1+JUHWfdYAu4uUgq1uc5PxAgcAU4AicBYaCWD531sborSWwjeZBvh9WI4oezA9L5PMrDFQGQiF1bvyyEqSFXt0GmxgsFo5pVhbekfHYjeZGFcQjhmi5WPxsRy4XoFExPDmfX1CfLVepq4eTJrx7+d9i5Lz0tn0qZJJMcms330diZvmoyXqxfn8vORiEWYLVZGx4cSF+7D2rRswoMa56BYRYm2FC+vgGpnUVuq7QWGiD8lunABIuxzWYstPih7b8I1MBEWL4bZs0FXS/y/8gqMGAHR0bd0LgICAgICf0/+8aIsIMC/znqzmvTt2+eW+5tFRUXx6isv3+6pCfyNkEvqbmJbxdy7JtL/QhoKq5kzox5D+tKLbP74qMO4unoqqXVGAq2/2YRY9nqbcYcTrPk7uV5wDQ9Pf8cm1U0Sbf9rBAaTEYPFQIm2hGJtCT1ae3Pk5Z6cy9Xy1BcnuVxic2yscjiMC/fBaLbwwa8XuFqqtTP1KNUa8PdwxcdNjs5oprBCj6tMQodQL/JuRLJu1URjUdpCNj30C0tTLlXXf61NyyEu3IcIPyVr03KQiEW33GBaIhYRH+G8rqjKvn9m11lM3DSmwfmqnBHLtCb8VS58MKojerOOEl1Jg9GgTec207fVcB5dfZiTV9XV92hwTCD3dW7K6PhQxn6cRmZhJWqtgSNZxXQK97AXjAZsYuwI0AmYBInu8Iw33O0GDRoN1rx2F38KIqZw1acPc9yDcJe7k1eRh7vcA6NRzid7s3h5WFS1Q2e4rxIfdxfcXWVO2xA81rM5A2MCOZpTirebhI1nN9XbTBpg2ZFlJIQksLD/u0hEcmYPjOKFIdFcL9firZQz9pODtA1W4asIaPyFAYEevvxwIrf6c6QUa/my/bL/Y++9w5q+2/b/V/YAAgFkiYBbxI0Ibq1at3a3im0dddBarVbtvHt3t47erV2OtlZbZ4dWbdU66qiouDduEQegQiBAEjJ/f8REQsKy7fN97t/zeR2Hh20+MwN5n7mu6zxpcXur176njA240XolPa2R0L8//P679wmlUnjrrX8sX1BAQEBA4L+P//OiTEDgfwKpREyvhsHMNhzlJWVL7GLvWZKbASGsfeF9ejxyH28dKuHT0CCP7c78qmivTKUAawkXMr6mwf4ZUHza67wVETlsvPHVF1zz71d9YHUllJhLWXtmnVfOlSsYeeOUIehKQCYRwx2TjVUHrvLhQ63cVS+XqUev+DDef7AVR68W8um2c26BATCoVSQv93cuXO/FRKPUovea//rqz0t883QSYpFTRG05lVutYHYxqnMcS/Zk8URSPZ7r2chnte9gVhHzutbMvt/ljNizaR20ajlBfnZe3PxijatBg5sMYv6IRFYfuY4DOw+1C7vz2biByV7Gmufbc/ByMdFaP6b+cJxet0J5q/t7fLD1HS5vvkzerjxoCZLR8GAYvBgEKdXHJ3qQbxOREdiTll2+xCqSE2AuQSFRYLPbECPBapVittsZ3CqSpLhg3vk1k0CVjO9GJzN705lKA7hfWHWUSfc14pUBzbDaTfT8rmZi/NOMT9k1chcFJRbmbjuPWCSib0IEWrVzjnJr5k1mPtK0VtXMEFUQi9OdDpa9o/V8FPEmgeZzXvv+WtiVQ5FzePnCJaR9+0GBj5beZs2cVveJiTV6PgICAgIC/zcQRJmAwF/EarNjttkpMVmRS0EqtbmrR8EqLVpVEIqde1C/MJVHT5+iwWsf8kZYJ595WUlxPUhdtJ8ApRSNSuau4MSFqFk8ujU7sjcyeFUqp/OOMtQPRmngfjVE1aCicdTQhN8Ku7BJ34mr5gjQVxFYXQUl5lKmbZ7m04q9YjCyuUxMp1l/cF+zMPomhFNmtbvbyQJVMp7pWp9Rnep72cC72Hgyl1cGxJMQpUEtl9yTiYa/wvN5nbqhp7ScWcfOc7f45Im2NZpzc4mK+5qF8cX2C8RHatzVPr3JQoRGiUwiRoKI2ffPoWsVzoiDmgxi5oZLTO7dmI4NQpCI7aw5s77G1aBO9TpxQ5/Pyr3FTOwVw+ZLvzF4lbdIntFpBg7RYP49pBnBoiL+8/F6ji05hjHBiP8EGB0JLwRBfd+dqpVy2y5F0nwGgw+sR2I1U/LjE4gQ4Sf3Q1+mp0u9Lvy72/sM+XwPK8al8GzPRgz/ah9FRgvP9WzE/qyCSgO4XXz6xwXaxgTRIMJSO0fLskKC/IMZf18wt0ryCVGbUMiVrJ3YiWELMzibY2R6pxmkrq5e6E1OnkZWvpkfxndCXHqJ0PTOiMyFHvvYHSKWGMYR0XIyry18H+nSpb5P9vzzztxBH/mVAgICAgL/txFEmYDAX8BgtrI1M4+Mi/m8NKAB68+t96gexRTC17sC6XO4yH1M/Gcz+erYSYxqDTqDGa1ajkImZnlGNu/8mkmR0cLHj7XmYFYBIzvF8e5vmV7mFj1U8ENk9fdXLG/EguwU1hV2J9vs+4CaujdabXasditrz6yrMhsL7gQjx3bjwaYPcfD1PuiNFgoNZrRqGTun92DjiRz6tYigoNTMplO5PgUZOCtZyzOyGdkpjkXpl+/JRKOkzLstUWcwu8Vart5EkcFc5Zybq/3y6UX7KTJa0Krl5OpNbD97yyND7PPhbTHb7Dwyfx+fDmtLs6De/DlyMDqTDp2xkGBVEEHKIMosEnQlNtJ6NmL14WvYHWC1W5iZXjNHwE8zPuWXJ37hRlEBaT0b8tr2GZWK5Dd2vEHdsrq8NfMt/vj1D2gNcc/BhEgYFwhaHwaAVXHVAp+VqFA1m8i0hJf5Lm4UuSW57rm3defWEeEXRVJETx6bf5irOiMFpWb+s+UcWfkGgtVyRneJ48mv99foehtP5jJCWwuHEeBW6S3e2P4G3x791v2Yq4q7asJgzueaGdK0+py38XfCyF/66SxfPdUemaoxoojeTtOcO9ikQRS1+45hV/xRPNkf0RUfbcORkfDtt9C3b62eh4CAgIDA/x0EUSYgcI8YzFbe35DJzrO32PBCB6Zvme5e4KnMMCMdXkoHlbXI4zi1XscPj0xg1ZPT3HNhLuMDgBHJMbSL1TLi6wyWPpPMzIdbeJlb7DTCZYvv6oZDFY0obhjGuo/x+AoDp296uwRWpKJ7o9lqw2JzoDda0BksaNUyNCoZDpGF2XtqJhzm7JlF/0YDeerrox4tiQlRGsZ1a4BCJiEm2I9XVp/0ebxrdu7UjSJSk2M4ca2w1iYaaqnGbc5QHpcDoEQsQiwSoZJLmb3pDHU0Co/wal+COSHqrlkF4GGzn6cvc4sPP7mUSStPsPCp9nyfXozR4qCgNI/sgksoZXff97QejbhaYACJqVbVIIPFQP3gSA7m7PYtLBzgf9WfqD+j6P5yd0RJ0O81eDYMBtZyXgzgghk+0MFeaUPWp/7Oriu76La4m8/KXP/GfZm2KtPtfOgnl5KnNxEXomb52BSMd5wma8KJ60VolZU7j/pCJVNxKOeQx2OuKm5a+zRm9ZmNqUzKyx3fo2N0V5/RCq7g6pGLjpGVb+BWcRkbT+YyOuUr1PozzlDowBZIUlYRPGsJzJ4NDh/hdY8+CvPmQUhIrZ6DgICAgMD/LQRRJiBwD1htdrZm5rH+WA4/pyWz7ux658LYAY+chjmbIbbI97H2Nm1oNHEMYr2IA1k69+MJURomdG9IuxgtqV/vI8h0nJnLLjBrQipdFs32OIcDWKKHN++s84rtsKoY9ska8cnDh/FXBFBUZOJ0zrYaPZ/y7o2lZVa2ns5jS2YeeXqT20ykf4sIpg+oUyvhcLOkAGuFlOVTN/RMXnmUaX2a8HD7aK/FuXN2LpbhyTFuV0GTxcb0fs0oMZUxJXk6T69Nrfb6k5OnsSIjxyvkOSFKg79SSq9moXz1dEtKLXqu6M/wbO9gJPizIuMyGZcLATwEs0QsIj4ygOl9m7DldB4ikchrEe4SHy7hBg5MZhsL/7zk8x4lYhHDk2NYe+Q6av+qc94qUmgqJEYTw+t/vOa5wYLTRXEflFhKONF9F1PfgbRgaHQPefUny5xibFUxaJRaDo7czMz0mZXOvQ1f7RQ+sx+bxalPitGoZChkYnKLTPw2qSu/Hr9B2xhtja9/Lq8EjSKoVmJcJVVx8qZvsT/v4Dy6xnSjZ8xghn5+kNTkNqx//A9KLXp3y7FaqmFFRg5D1zojFcBZXd157iaxIWr6d1mNNPNDSJwLS3902ttXRKNxOi+OGCFkjwkICAgIVIsgygQE7gGzzc66ozdYN7EzIf4we/VMWubC3E3QM8v3MbdVsODBekz+eicpcn9+tNnRG63oTRY0ShkalRSzqYSPv3mdudr1tK57nkxHMjrjAJ9CaLEeuqmcf/9cAgYHwAX+bSrCalNQWE1gdUWKjBY0Kim7z98ksb6aZtEhbjMRtVTDn+cKySvWVX+icvia6XKx8/wtejQL83gsLkTNktEd2H+5wKcj33M9G/JQ86HsvVa1iYar7Wzo2oNe2yb3aoxSZiE07AiDV6V6VUgmd5jG7Pb93RWS8iLRarNjMNuoF+zHzubhrNh/1W09X76CNvuRVmw5nUe7GC3mcnN05ZGIRfRNCMdqs3PyRhGD29XSEdA/glJLKUdyjzgfyAcOAkeBUOj0AIxpC8MCQFW77j8ANpfCR4WwuZxWTEtKY2fWzmrn3uYdnEe32G588FBPCkqt2O0ONk/pxp6L+aw5cp1ezWr+XG12B3a7tMYzYJOSJ7Hg0ALsDt/RDACz98xiUJNBFJssfLnjMvN3Xna3oJaU6TmbW0ywREeR9a54dFVXnVXljkhTvnFuePpp+P572L797gW6d4clSyA2tsbPU0BAQEDg/zaCKBMQuAeKTVbeGJTA7gu36B6mZ+yiY0w4CBIf3Us2EXyZBP/uATr1VZ4264lWaZBKxKjlUiIClaA/Bye+RHFxMW+Fl5s/E2VwOv+Iz3u4YoVe170fv1Z0m58zbvNcz0a1ek6BKillViM5lu28vGqOz3auZnUG0Si4ERcKLtTonJXNdIGzCuWnuDvQ5HLkm7/zok/DjVM39Dy77AiT72vMrD6z6VSvK//ZV3XbmavK4eKZLvXp2MifGVum+xR1R3OPMmrdCCYkprF49Ls8v/wUnw9vx4EsnU+R6AqkfnrRfkZ1jmP14WvMS02kc6NQ8opNtIoOIujOHJ1LwAFukSfBQaHRek+OgMGqYI7fOA6ngQPANajTAZ56FcZEQ/w9VMXsIik3grsz8PA2jlfQ9BKRhPGJ4xmyYkiNzjUrfRa7Rg2mqFTMG2tP8N6DrViw8xLn8kpqHUMgk0gZ3GRwtY6W4xPH0zWmK1N/n1rlOY/kHqGorIiBrSJZfyzHowVVITLzTtRC+gXuYeD5T8m1hJIQpUFZLl/NIxNQLIbFi6FVKzAY4L33YOpUkNRyWE9AQEBA4P80gigTELgXHA72n8tBs+hrwn+ex3OVFJD+iIPJ/eFkucJAgamQ6MBoZ+tb3nY48x+48RsAvpZx0Xm/1urWglVaMnOykUtrvvBNrh+Mv8rOtM2VG0aMXJtKxvU0tj65lbYL2qIzVV01q2qmC5xtaeXvcURKLBmX86t0QASY+8d5mkT4U0/Zk/WP93e3nYWqtYQHhHD5Vhkv/XTWPc8Ed806ejQNZXXmmioX9uA0Kuke141V4x/i/d/OVmrb7gqk/imtE2Kc0Qc7zt7k8YV7fQq43yZ1QQTsuZjPm2tPMvvR1vg7wGy1cy7XxIxOMxheg2rQqPqjePL5J9m7bi9iG9w/GJ5JgiEakN1Dp5zOBvOLoHe/n4gKa0+K5W2OV6iGJYQlYLAYaueCaCrk6z8LuFFkorTM6m5VdZm3TP/pePXPtXMc87Zf4EyenreHfkDH6K587GMGbHqnGSTX7UC/Zf2q/WwC6Iw63n+wJTHBd4O2o2W5fBn7Ia3Uzi8dvoj5kCcufcD4bg1QySWk9WjE0n1XnE6bgcq7J4uJcdrc16sHrVvX6PUREBAQEBAojyDKBATuAT+ljJPf/sSbX3/oc/uVQJjaF1bHAxUWycEyFVxaDGc+hsKqF6U3LHUI1jSqtblFZo6+Vgvf1wc1Zf3Z6l0V5x2cR+eYzkxoP4EPdn9Q5b6Tk6ex9XQ+ibFa91wa4BF8vWJ/NuO6NmDqj8cYnhzD2CXe7Ya++HLHRZaM7kDK+1tpHH637Sy36CzDOsTwn8fbYLXZMVnsaFRSAhRS9l8qQCSy8PE+H/M/PpiVPou+DQdWKxKXZWSTXD+YDvWDKS6z0i5WS89mYajlEsRiESF+cvJLyliUfpkT14qYeF8jPt12nmXPpGB3gFIqYWqfRjSJUBIV3INxieN8tweagUyoe6kukz6YRLMuMPl5eCoa6tXSzt7FiTL4ogiW6qHUAQ8GNWXCbxP4pO8ndIzuyNyMue7PnUahQWf0LXYkIgkJYQloFBr0ZXpO3TyFzeGMhgjTqEiIsmG22YmPDOBcXglL911h3cTOtYohKDJaOJhVSGpyGzan7qTMXsyNotsEKoMIkAciFltp/mWTGgkyALk4gFdWn+C+ZmGsm9iZ+cvn8lLgBwRJS9z7JPqd4YfktURpO5DV7yHajxzDYxP7+o6PGDSoRtcVEBAQEBDwhSDKBATugRKThTVRbRib0I66pw67HzdI4cMuMLszmCoslEMl8FbdSKK2d4Kym5We24GInfp2LC0YwB/69nzbsQPTOkYzYk31FZQXkqez9XQBibFa9l/O57H20dUufJ9MiaF+HQVPrq+Zq+JHez5i44iNzEyfWencTlpiGg/FD+G2HlrUDSbUT06wv7OfTm+0kqs3EaFRopJJ8FNIeFnfFFMtHPlO3dBjttqZNyKRT7ae9zBM2XnuFvGRGno3D0Mlk5J1u4TpPx5j4ZPtuWXMqVWlJ9+g8wqf9sWCXZdIjAvmgS/SeaF3Y57pUp/U5BgKDRYCVTL8FVLKrE5hcrPYxNIxSYT4S7FjpsBYxJiu0azOXM3bu95mU+qmu4Io5yhkA0dBlCkivLGDx/pfJ/V5SKxlyLMLiwNWl8AXhfCn6e7jLoOMDec3kJ6dzoT2E1j7xFoMFgNGi5GogCjMNs+eRq1SS1pSGuMTx2OwGLhtuO22xl94eCHBKi2pyXUoKDVjstj5fFg7FDIxK/ZfZeLyw3w+vF2lMQTjuzWgQ/0Qnli4192GGq1V0SwiEBwKZv52kdwiIyVlRVy4WcL26R2JDYpFl1u9KHN9ebHhxEk2Hb/Konbr+CBkkdd+DomaRrYQFB2TCM+5gfVaJjtatKJjm7h7eu0FBAQEBAQqQxBlAgL3gMFsY+3zSRyNG0zdUU5RtioBpveBq0He+38YApOCQCXOgbJKTirXQsNnKIt7hvMnJVw/fB1RSQkzN55j1YTBVWYqaZVavhmyiN4NepNTnE/TaBUh6mBkUgtT729EcoNgFu665HPhe1+zMAqNebUSK2armdWPrebNnW96tZFN6zSNQY2HMHVlJvuzdEzs2ZBhHWIpNJrJLzETGSSnWaSc24Z8LhbmExEQwrCUcIqNIgJVMq85sMrIKTJyq7jMp4V9kcGCTOx0t1DLpXzyeFuWZVwhIbZmos/FrdKCSo1KynPqhp4yi43fJnVlz4XbpH6d4bN9sVPDUIqMFuJCpaw79zOz7hhOTEmZwkd7P+JCwQWSvkrikTqP0P1Sd/J/zEfnuMpD/SB1rINeGpDco5HfDSssKIKFRZBr895e3iBDZ9Lxwe4PmJk+kxZhLXij2xvkleTRPKy5u2rbKLgRm1I3sevKLoauHOpjtu8FApWBLNl9hdmbz3m9Fo8Nb8fE5Ufo2jjU6z30U0gJVEkxmG1883QSepOFYD85wX5y5u+4yL/WnvL6nKzIyGFyh2mMWjei2tfC5cwZJc3l05jZtLOe9dqnRNaAfSsa03vNR+7HpFmXafj+65iXf4dCaq8y109AQEBAQKA2CKJMQKAqTp2C776DDz/0sLWuo4Fpm19j4ZX5zOylJHXqYv6w/8HVSlzpRFThgBfQGJq+gDX2ScwiFXqjla6NzfRvEYlcIkYiFnH8qp7ZfWbTLaY7s/fO9FgAD2kyhG+GfsPmi1votrir9+K4wzT6NxpEr/gU8kvMGM02/JVSpGIx3+3N4uSNIga287FKr4Kr+qvklea5Kyn5hnxC1CHO8OCz6ygps3HxVgnrn+/CkWwdjy/cS2mZlcWjW7Phwhrm7vc2EpnRaQZrJ/Z1ux5WR5BKzvf7rvD6LyfLOec5LezjIzX8OKEjUomYAKWUTafyWH3kOt3ia+eGV5VRSUXMNjtbT+fx/sYzXttc82fPdKnPpD4xTL9jNCIRSdgwfAO3Dbc5dvoYnALdSR3LFF/Rvw/MngKDtaC+x7W/zQG5mrass4Qy6cQWrJXsV5lBht1h59TNUyTVTWLIiiEMbDKQSR0m8eLmF/l9xO9VWuOPXDuSjOsZfNhrFisPXOWqzujxWqQmx/D58LYM+TzdI4C7pMzKmC710ZWauT8hgku3S9hyOo+SMisv9mnKFzsu+nwOS/ddY+3E/oxrN8Ej08/7uTqdOT/f9h82NPkYjcT7s3bO2BFmXKX39d+9tgX//ivzVu7m+RHdBFEmICAgIPC3IYgyAQFfnDoFb78NP/7oNOTo1AmGDgXAbLXw67n17oXfS11NfHXxdc+2swqCo1HSOBznpiCylyuThfeEZlMhagAGizP3bMHOo14VljFd6pMYq2XckuOM796dXSMHUWgqpNBUiFISSESAtko3wZHrRjA+MY3ZfWYRIJfy+8lc1hy97naSG9AyghBV7ezYg1XBfHHgC9J+S6NFWAv3LNHJmyexO+z0bTiEZc+k8Nn2CyzPyCZQJWPd8+15P/01nwtmV76Vy/XwoS8OER6ocC/Sz+WVeFjKu+znL98upV+LCMI0CuoGqrhRaKB+qB9bM2+iN1oQiaDUbOOrXU7Xv78rfNoX/gopa476sMMsh0IG68+ud79XjcWN+ezjz/j5p5/xK4WB98EjQ2FAMPj9hfV+Hn4YYp5A0/RF/rysIrV1KIGNfvUS9G0i2jA5eTJdY7pWapBR3uAjuyibA2MP8M2Qb2psjd+lXjfeeaA7I7897LFtWUY2bWOCSE2O4csdF90toq45siGfp/P59oukJscwuXcTTGYbdQIUlZrXlJRZeefXC7w99AM61/NtBvJC8nR6RHXj+OqnmBP5m9c5HCIZt670JO61bcjt3l9U7I5tzbQBU8g9b6R5Zh4DWkYKwkxAQEBA4G9BEGUCAi4cDti2DT7+GDZs8Nz05psY+w1Ab7Lhr7IxM91z/upCwQUmf9eGlIQxHtWjOn51iAqIIrfQxrVbGUTrliOKHQbNpoC2DQAGs5X3N2SydJ9vh7+pPxwjNTmGDx9uxZDP0yktszCmS30m92rGlswc0h1rq3UTXHBoHj3iutGj3mDe33gGiVjkrkxc1xkIUt1bOK/dYed4nreRSLG5iOu3lSy/41o4IiWa7Vc2VlnBAFh1aiVj2o1l84vJ5Bt0HjlpK/fnsHTfNYqMFp7r2RCTxcKB17tSZCpEZ9IRpFQQqAjHZpcy6+HWWO12rucbEInF7lm12rS4zeg0g22ZBV7h075IiNIgFYurFHASsYjHk8LpPevfsAe4CNdCz3C52wd8MAH6a+4tT8yFye7Mq9smjiMh4VmeSBhH708z0BksfLFdxQcP9eTPUYPRGXXojIVoVYHYHXYWHFrA1N+nVmqQoVFouG24DYDOpGPg8oHsH7ufbt92q9F9zdk7i50jByEVO+clyxu9LNmTxfwn2zN/50XiIzWM7dqAXvFhTPj+kLs98csdF92VtCdTYhndpT4v/nC31bZi2Hie3kK/Bg/wUPOh6Iw6rhXddgdCb0nfhfVIZwb7ef+sXTWGEbo2krD1m722lUlkfNhjJIsTB+MQOd+kBbsu0bt5uCDKBAQEBAT+FgRRJiBQWgqrVsEnn8CJEz53ER09ykcTZ5PToy+vDAr3mL/qqIR3QqCXuoS3z8yl/t7PPKpHvw3fwLFsO53b/BuT4k1UQXHuY602Z4XMlyArT8WqwuHsQm6VlNGtSRA9v5tdo6c5K30Wg0YO5vme9Xk8OZJSi558QwFRAeGYrWYmdZjE6HWjqz3P5OTJrD27lk71Onk47ZUn3D+ERdudVSOJWMSw5EgGrazaqMQ1o7Q7ezdj14/2Geq8dmJ/Dl4uoVtTDWvPrGfOmlk+9+tdfwCZN8ruzHCZ3eHNNW1xm5CYxn31+7P3Qkml+7ieW5Nwf17p34yNJ3MQiUROcV8Oh9WCKfs48usHafvLUyii8hnbGYakQm8/UP7FNf2fRlhWDKuKIS6sDVM6TOeBZkPYc6GYqCAVOoOFqzojI745SN+EMOY82gad3h+/AAl9lvXgcM7hKs+vL9MT5nc35FstU5NXUrsZxEJTIR891ob2ccGYzDYKDGaC78z/qeUS/pzREwdOq/yT14t4/6GWDPk83S3MXDliMzedZfu0Hm7zmqrCxge3juT1gfH8Z0M2JWV62pT9yL+jFqJUeIeqH7zcjCazrqDSexvwnAprwAuDXuR8Hc/W11M39J55ZQICAgICAn8B4beJwP9NHA44dAi+/hqWL4fiyiscJrU/89sO5ke/hgz1k5NvLAAgSQFvh0A/v7v7Ph8Ecwo9q0cFRh39WjZjecYVBreuS3nTPLPNzvwdl2p0y9+mZ7HwKWdVoaTMikwiosRSVLvFcZmOxrFZDF6V6hYzXWK68EX/L3i4+cPsv76/0qqb00zkG3o16EVOcQ7tItt5OO3NOzAPnUlH24i2aBSBbDzpPH+TcH9KLfoq71Or1FY7ozRq3Qhe7Pgib/Z4k+mbqw5/Hp+Yxssd3+XjLeeY0qcJf77Ug2X7nOHNIxcdY/Ho9+hUryuf+Ghxm9HpJbrW68uYb0/w+fC2pCbHsKxCTpmzOhPNsORIzPZiSi259AgNok9CR5bvu8E3v+7lVuZ+DHkZlMlP0aq1jcEPwpBg6KDkL3PJ5sdpZR8SO79BhCKEkaUFvBQQQqhai7FMTNZtM4kxWn4Y35Eio4UbhUa3Ccq36ZcBGNExhhc7TiO1mly0UzdPEaoOpVtsN7rEdGFSh0nkluTW6n51pkLqaaN8BnCP69qAlIYhDFu4l0u3nfNdzSID3F9AlGdQq0iMZisTejQkuUEwSXHBfP7HBZ/uohtO5PJy/3gMZhuZOcV0q1OCUuwpyAw2BefWx9D+R+85QDsi5qc8zMddUrFIfGcOeOWVCQgICAgI3COCKBP4r8Zqs2O22dEbrRQZzQSq5GhUUuQSse+2olOnnFWxH36As96Oa+VxBAdz7pGneFSVgl7pD0B+qZkI8y3WRcJgf+9jtBKYEAizy3WCaZVBLNx5kcc6xHgt4vRGa61s4E1mG00jnFlParmUcwW3a3Ssi5ziHGbv/cCrTTEuKI7Ze2YzrdM0kqOTvebiXGYiWy5uofvi7l5CZlKHSRwYe4B+y/oxJWUauhKHu+3PXyEl31BQ5X2lJaVVO6OkVWrp06CPx0xWZSw4NI+O0V2QS9uw5XQedbUq6oeo2Ti5Kx9syGT6j2dIrt+G9Y//4Q6fDlZp0SgCUUgU9ProT3QGC08v2s+6iV1IbhDMgp1O98q4EDWLR7dm59VNd8WtAbgEgbpArGU2zNoSuifDkGgY4gex95gjVp58h4w/RfVom/wpDy6VUmS0Ij58g6YRxe6ctvN5x8vltllZPCoJBzBz0xm3CYrrffk2PYv1k/pV6eoJMC5xHCqJip8f+5n1Z9czaeMk3ur5Vq3uXasM4oOtl71mwU7d0DN51VFSk2P4dlQHd3Ws/BcQdsfd8O+kuGBSv3a2ZH76RBsOZBVUGvdgszs8svoW3nqIzv7H6BZwBICsokj8PzLQ5uJ5r2OvBoYzdeAUDtRrUeXz0ij/hjdWQEBAQEAAQZQJ/BdjMFvvmGP4sHrv3oDe8eGerUXXrkGLqhdZADRpAlOmYHxiOFOWHEN/RzQ1VFxlaPH7ROze41OQAVy3Qm45i7u2EW0JVAbxxY5jDGlTl2C13GP/IqN3K1VV6Axm/BVSbHYHJ64VEhMWUqvjAxQB6Cu4CXaP7c5v53/j3V3v8sX+LzzyqVxzcRH+EczYMoMFhxZ4nfNo7lFGrxvN+MTx7Bq5C395ACaz2G1vX1JmpY5fcKX3JBFJGJ84niErhlS6j6u1UavSct+S+2r0XOdmzGHd49tI+/4kK8d1JCpIhcVm59mejZBLxSikYlZmXCXjcj4AJWV6xnSpz6VbuegMzra5rHwDxWUWorUqVo3riM5QRoDKweSfnuO79d9BAeAAgiC2MfSLKKKvGnqpQCOp0W1WyU0rrC6Fn4qhafPRzLx/Dh9tukKRMQu429ZXnnN5JTQJ96dl3UD3Y65qUXl0BgsjvjrK4tHv0THad9VwSvJ0BjYehNVh5V9//MvtGDn7/tm1mkEMVAax+VTlldKK7bmnbuiRiGDvK/dhKLMhk4pZnpHtDpGWiEU0Cg9g/HcHiY8MqNQQpmJI9YtXp7Kh8fP8WtSVjD9bMP+idwD6lg79mdJpFCUKdZXPKyFKg0Yl/AoVEBAQEPh7EH6jCPxXUpk5htJi4tbZfCbd0DMiJZZXBzS7K8yioyE5GTIyvE8oEkG/fpCWBgMHgliMvsjE6Rw9daQFvBC+nMeDNyMV+Q5LzrXC+wWwUA9l5UaKpneawfHsUqx2Z6tTXa1nq1OgSk5t0KrllJQ5Vd+XOy7w1cjW92TQ4UIikjAucZxbEFXMp9IoNDza/FEC5AE+BVl5FhxaQPfY7lhsFmQSGb9N7seIr46SV1RGREBIpfdZ3t3P53O+09q47PgyHk14tFbtmqUWPVa7A53BzIcbz/Dr8Zy7172Tl/Vo+2ieXrSfzo1CSYoL5p1fMz32CVTK+HrjPs5e2MzhS9u5ajqJwc+Aqil010A/NfT1g2a1eysrJc/qNOz4qQR2GcE1rbft0AI6x/ZgbNdBfLsny+u48i2VRlsxpeZCjLbb+MsDef/B5jy16JBXtldWvoGhnx8kNbkNW0bspNRSxA19vtsYY0VGDrsppFSy212dtDlsLDi0oMYziNM6zuBwVjFW3z86bipWx3KKTJSUWWlZN5AZPx1j8+m7814Pt6uLSiZmwZ18s/Izar/u3c+8A2aKjBaKjM5q55LRHUhpEOw05zg3jyJbADSETR0H0W/vrwAUqTVsn/oulqEPUPKTt3lNRSZ0b4hcMPkQEBAQEPibEESZwH8dLnOMVekXSbidTfO8SzS/eYk2N87RIu8CWxol8+yDr7J03xWS6wfTv0XE3VbGxx/3FGWxsTB6NIwcCTExHtfRF+czJXwZY+usRi32nfh8ywozdfBlERgruPRNSEyjb8OBGMokxIWoqROgQCr2XMRpVNJKLb4r4rKBdzn87c8qRCqSMaPTDIZXMxcEnuHA7nOWE0QSkYSEsAS3Qcmpm6cAWPbQMq8qlq99bQ4bs/fM5pcnfqH+3PqMTxzP4tHvsv5YPpdulnm4HpY/vqG2odvdzxeu1sbNlzbTs37Pap9neQqMOvwVUnKLTOTpTR7byudlrX62M6VlFp78Zj86QynG0kMYi/dyrCSL4BevYQk2gxTim8B4NfRVQ3fVXzfpcJFrE/FTsYOfSpzGHZXplzl7ZjFk1GCeTI7h+3JzbnEhapaMac2f5Vsq7+DKgNs8dQCPzz/slQFXZLSwYFcWo7s0ZNZvt7hZbKKkTM/Z3GJEIhG7X+5E/+WzPI6Zd2AeB8YeYFziuCpbTickpjGoySAGzt1f7WtQvj03M6cYjVLGq2tO0C5Gy38ea0txmYVikxWtWo5aLmFrZp5HILoUK+82+plx6hU8MHwdT/yiJivfQFa+gSGfp7Nxcld6xYeTpzdRUOoMqQ6anIy9czL5deOYcv9ETtj9WFc/2F1Zq4wRKbH0ig8TnBcFBAQEBP42BFEm8L8fnQ7On7/75+xZEnYd5FRuFnK7dxxu0vXTTiMPkYj5Oy96Lp4efRT+8x945BGnQEtO9giFdpP9Iw33T2RyuLcbG0CJIwBpi6nsJI5t++ZiLDzq3ubKQ+perx8PfXGQzo1CWfZMCkFqmdciTi4RM75bAyatPEp1jOocx/KMbA+LdpMFutXrz4TECVXOWaW1T/MZDqxRaCg0FfJq11cZnzgeg8XAbcNtt4HH2rNrMVlN7uqUVqklLSnN574usw+DxUCLsBbMOziPTtFdSU15kAnfHWHWo/15IXkqarnS4/i6AXWRin3/U1S+tdGBg1B1aLWvU3lc4c9av7sVxvI4MPPN/i2cv7WIg5d3kWU4hyOwBNRAHYiQwVMq6KmCHmqo+zf+i3mVAHbbtKSkvE+j5SMqFWIVBbDOqGNS78YkNQhhwc6LXNMZWTa2Le/ufqVS85Phq4eT1j6N1c/NJO37k2RcvjvjlxClYdr9TbBY7fx2Isfj2PgIf4rLvM1kdCYd/Zb1qzKbb3qnlxjSZDDjvzvhDo6uDld7bvkvIDJzimkbE8T9zSOIDFT5rJI3VWYxJ/pjWqqdxiCKg2P47smDDF5wEvmtPG45tFjtDkYv3o/e5Ky+DWwZSWT9YMTpu9HWCWOh3YHeaMVosfLqgHg6Ngxh/s6LXq3RE7o3pFd8mOC6KCAgICDwtyL8VhH4a+TkwNSpEBLi+UetBqUSVCrn33I52O1gs939Y7FAjx4g9vFt87p1MH063LgBJZ625FKgYRW3VKe0kNjCHK6F1MXucGA02zxbGLOzfQux8khUSMzegsxoV/DNraEsuPUwz0cnktJQy/rHh3qYRbjavoauPUiR0UJWfjYd4oLp3zLC63xSiZhe8eE+v5l32a37K6R0b1yHjg1CPVrr+reIoKTMyoivj7B49HukVDIXNL3TSwxsPID2XyV6ZVEFKYOID40n81YmQ1cO9RG2+wJ11HUY1GQQ/nJ/ZvWexbZL23zu6zL7KDQVolVqaRXeiu1ZW3kwfihHrup4bfU55j35JuvOrfU4XiKScOWFKz7bG12VPAcOtEotAfKAWoc/S8UiwgMUGMxGykRnKROdw2Q+ill0HptaB2IHP+YASqjv7xRfPVXQQwX1/kYfB6tDxAFjLHGtRvLJqRb0TEphaBMt5wsu+BRklQlgjSKAm3oTMjH8ML4jFruVjRfWVGt+Mu/gPLrFdmP+kw9QUGJFZ3BWiwJVMgKUEi7fNngd46+QcrsSk5YLBRdI+irJawaxXmA9glXBnMs1UWQQkX4xv8avkas9d0yX+h5fQHybnsWAFpFeERIykYVn6/zIc2E/IBffFd1aRw5FBycxU9eargvfZ97j0wj268c7D7REo5R5mgFFRiLF+e9KeaHVv0UEveLD0But6E0W7+MEBAQEBAT+RgRRJvDXuH4dVq689+MLCyEw0Pe2c+fu6ZQ2kZgXIsx0mNITk9nGTX0ZFpujdguqqIE46nRHdGun85wOMT/qevNx7nDyrKFIxCIGto5i7JKDnMnVu4OYXW1fFQOHF/55iT4J4fgaOzKYbYzv3oB2sUF8m57FNZ3RHYZrsdkpLbMS7CcnQClj5/Qe5BaakEnFBKicbXnl54LKuwmGqLREakL4+VAeJWV2/OWe7iRapZa5/eby8taXfS7orxRe4VzBOSx2C18O+BKdSYfZZqZn/Z6c153nSuEVt8grb/Yxu89slj+0HL1Zz23DbQqM+ex6qRMyiYS3dr3N1kub0Sg0tApv5W57/PLgl14zSlqllmeTniXCP4JVj6zituE2ErGkxu2aQ5s+zOR1czH4naXZJ6PIMV3GrrwjfxTOv2KlTvHVU+38++9wSSxPmVjJEWsSK68l80dxe+qFR7GgcSI/rtnOqrNHWTyyPQ0jvU1QXMYmu67s8imAp3eawaDGg7Ha7Eil3mHmlTErfRa96w9g0sqjqOVOc4zcIhM/TuhIoNr7yZeUWQmtwqTF1wziT4/+hMUso46/mAClrNbtuYkxWq/ZvlM39OhNVjQqqTtCIkF1gTnRnxCvyvI6l9UuQbzuAP2++hmAyb/MRfTWaJo0qF/tfbiQ3vm3Qi2XCrb3AgICAgL/OIIoE/hr5Nf8W3CfFBX5FmVRUTU+xZWgCE6FNSAzrD434tsw+dURWG9bfGYi+XRl9IVIhKjtLNicTKa4G5MyH+d82d3w2GYR/tjtDvwUErdFva2iEivHqRt6ik0WpGIHZrsZnVHnrqxplUGcyyslv6SMb0Ym4SeX8MeZm77vv1sD7osP5+dD1/jp0FXmjUgEnHNBX+64zPydl90CsU6AjRf7hPDmujPcKq7vMdMFd2e1fAmy8qKg79K+VVrgXyi44N624NACusZ2Jd+Qz+RNk92Pd4vtxsd9P+bf3f/F+MSx3Cy96dH2uOLECrY8ucU9o1Q+SLrnkp7u62uVWo6MP1J1u6YDxCIxb+58vcJ7CvWkd1sRe6ig/j/gaH7YBL8b4KKyEZ+MyOC9r05yotA5B1ixBfWjLedYPq6tR/WvJpltqauHk5aYxqz7Z1NkKqyV+YnJqsfhgANZOuJC1PzyXGcOZuno3CjUS0CdyyshWKWttjppdziz+dpGtEUt8+NsTikrD2TTLkbLqM71mfZj9fc3unN9igwWJnRvyIhvMrxMSUxWGxjhQm4+08JXMCHsJ5/GOzf0oajmmog5c7fyLCsphtGjYNs2kPwNtpgCAgICAgJ/M4IoE/hr/B2izBcVRJlDIsEWE4sprgHy+KZ8kydluyKSzLD6FCuc6c2BKhnrJ3Zh/s6LPof0T93QM2nFUacrY79GqLMXwe190Ok73/cQ2oGCHkdQKxozvnFBuSpWNE92rIvZUcCU/ipC1MGopRpW7s9h6b5rXotJcBoxKGRWfjr9C7P3zvISOdM6zmB4ymDsNjEzN53xcpV03//Ko4zt0oCpfRrzQNu6SMQij4V0eYv0pDgtBQan5f7SfddYO7E/49pNYOHh+VXa0NdEFLiqYptSN5H0VZJHW+RHez7ilyd+YcrvU7A77DQKbsSiIYvYdWUXY9aN8Snwtjy5hfG/jmfBoAX0jOtJp3qdeO/P97yurzPp6PVdLz4f8DlltjJ+OfOLV0smIrDfaQisK73bithTDQ3+ARF22wa/lzqF2GYD5LnsErnAv8sMNI3QcOJ6ManJMV4VoBPX9RjNYqZ3muEOca5JZhvAvEPz6BrbjW4xXWt1vzpTIYmxWq4XmvhudLL75+W5no3cmV7lEYvkvJA8nZFrU6s995SUaTgcMrRq5+d1y+mbHpb0lTEiOYa+CRFcyS9BKRPzQJsozt0sYWvmTcx3bBsDFFJKrqfza+PJNFF6n8vqEHNmXxwt5l8C7/FBZ9SFxSKIMgEBAQGB/5UIokzgr9GsGbz8slOc3b7t/LugAEwm5x+j0fm32excDInFzr8lEpBKocy3qyHh4bByJabQMNINMj49b+bYTadZwHM9G1E/1I/9Fb59H5ESS8bl/CoXfwDnT/yK1fYdlN1ZHMc+AXUH+Nz3lrgBj32eTmpyDN+MTEItt/Lr+fX0X+HtcDe5wzTWTuzPyEXHPBzuAlUyloxpzSvbZlRqxDBijdOI4Z2eM1l/LMdrHxdxIWpGdIxh7+V8Np3MpXlUIKM6xTGt3ELaNYtWP1RNVKCSpDitc05n8XG+Gfkenep1ZcP5tZXa0NdUFCw4tICU6BQmtJ/AB7vv5j0dyT3iNvu4WnS1xgJvwaAF9Pm+D18O/JL07PRKr39Rd5H+y/r73BYluVsF66mCRn+TTX15bA7Ya4JNd4TY4bLK3RJ1pkLqBqmY82grkuKCeXrRfi/Rfl1XxuAmg5mQOIGvDn9VbWZbeWbvmcWgJgMRi8QerppVEaQMomNDMRqV3OPnpWKmF0CTcH+KSq10r9fPLegrY3xiGl2j+6ErteKvlLjbeJ9etJ81z3au1DhjdOf6dGoUwumcAuKj1BSabtOzpZmHVeF8+EhTjlwpYeWes2jOvEzwuU8RKb2f52V9JPIvLbQ4ccn7xurUga+/hiE1e00FBAQEBAT+XyCIMoG/Rrt2zj9VYLZavFv2VEHIxQrk0kpKFxIJhgcf9plF5mvxKBGLGJ4cw9glByu9jxBJIa9GfcPD2u1QXgsengIRvUHivYIP8VNQUmZlWUY2j3cI481dr7GgEmE1at0IxiemsXj0uwz9/KB78T0iJZod2ZtqZMSQUrcLqclt+HLHZa/tccFqfnmuI2ClQZiNB5LshPqJiAjQMl/Zlvd+O8OQNnVJTYlBJhahkEooNVt5pX88gSoZCpmYNUeuY3e047N+g8kp9b5GTYKcy/Npxqf88sQvzEyf6SEK8g35aBSaWgm8SP9IOtfrTHZRNm/ueLNG14+QOAWYy5yjyT8gwuwOOFIGp0Sh9Ep5l4SfJlBUM/1DsErL8ORgluy54g4+rkiovxyRCN69710GNB7g4XZZHUdyj1BUVsQj8Y/ww+kfqt3fFeScGCuldT2tx89L+UyvxNggtp+9hVYto9RsY/LKu4Lel5mMy2105KJjfDG8HekXCt0tml0a10EhE9M3IZyujUPJLzG7TUYUMjHp52/jp7Rxvngb4xd7V5E/SbiPLwJ/QHzumtfzsTgkHDoQT/svTiG1ercPWwcOQrroGwgLq9HrKSAgICAg8P8KQZQJ/KOUmEtZe2Ydcypp2RvabAj+cj+v4yq6rJWn/OLRZY5hdzgwmW2czvE2FBBhZ1jw77wUsZhAaan3TRafo+zqb0jqDQXAbLOjN1opMpoJVMlIf+k+zuYVsuPKRp+CrDwLDs2jY/RdYSURixiWHMmgldUbUwB8kjGHdY9vY/7Oy+5FbbCfnNcGNKFvixDyjXncKL6BVqWlbmAwCw8vZHf2biYkTmDDC4M5csWIww47zt9i8Z4sr6rEyE5xdKgfzKurT/HSIG83yOqCnCtSvip2PO9utS7zViZZBVm0rtOaafungQOoxvDyzZ1vArD0xNJK9wmXOPPBXMYcf1dgc3kciLgqDubn/Hy2G525YYV2kIh0XHqoP/XDau7+GCDXUFhqZ8OJHJ+CLLl+MFo/mL55GqtOreLD3h8SGxTr42yVU2gqZGSbZ2okyl5InobdJsVgtiIVi71+XnQGC2uPXWN4ciQdGonQGXUEqopZPLo1vxzJw+5o52Em48ttVOsnZ82R61728QazlRFfZ2BzOO6Y4lgxlFn5dXIHpm+Z7vNn62TuUcIVRxH7eJ/PFcdgXwgph096bSuVKbnw8tskvDEVpEK7ooCAgIDA/34EUSbwj1FiLmXa5mmVVpZGrBnOhMQ0Zt8/20uYmW12t8uaL1yBsKnJMSx6OgmpRMRNvXcrZHPlJd6r+wVt/c76PM9pY33eujEOjSWaWY9Y+fP8LRaUC6QFaFlXw3dj2jD5u9k1et5zywmrJuH+mO3FtRI5pRanm+ONQhMT72vA4x3C+e3cr3T1UUWY1GESo9uMpt+yfvx55U8+7DOLuZsv8026dxXMFZb8ZEoMbw9tiURi9jJw0Cg0VQY5e2ACCmDiFxM5f+M8KIFAQAJjfx0LwBeHvnDuW40gq4w6LhF2Zy6sueLezlMd12mMJPI+9pYkYIyUk/rbBK99bA4bCw4t8HKJrIwZnWewOP06N/UWlozuwJDP072E2RuDm7L+3Dp3FfWLA1/QLbZbre49SKmlXkD9GrUXDmoymO/3ZtOlcR2UMk+xEheiZvHo1uzI3kifpXN8tuf2iOnPU18fRSGT+HQbTYjSEKCU8tmwdl5up3qjlZMVXBiXjE5k3dn1lX7ZYQUm3IQd0Xcfc4gVnLnYkbh39qAqM3sdcziqKTte/w8TxtyPVBBkAgICAgL/JQiiTOAfwWy1sPbMumorS/PvmBU8Ev+wRyuj3mj1WfUqj9Nx8CLzd17k0Ot90Prd/TrdT2xgavgyRoauR+LDoU1vUzMn90mW5g8gQKVk/aAEZv9+1uc8mtXu4GZpQa2F1YCWkTySWJcSi+8A6sooMOpoFObPgidbolbamb5lWo0NN7rEdkMha+XzvIEqGSNSohmWHInZcRuxzdNgAkBfpq88oNkG3ABKATkQCUTBNrZBzc0yqyRY7JkT1uIfEmFlfs0xh3Zna34zPj4WhZ8mjM97tyNJbmHQyp6VHjfvwDwOjD3gdomsjPGJ4xnYeBDdVu9BZ7DQNiaI1OQYvtxx0b3PC70aERsqJ3XdXTv7UzdPoZapa5XFJsGPEpOIt7p/WGV7Yd+GA8Gu4OvdWQxqHYW0XD6ga+7x/fTXfAq78u25i0Z5tueWZ0L3hqhkEgKU3m3JRUZPASWXimkX68/kb6u2899phMV6GKkBa1gPpFtiiJ/lbc5jFYlZ0X8U2rffYELLKCHcWUBAQEDgvwrht5bAP4LZbmb2npplJ83ZM4shTQcj5+5CruICrirsDrhVUka0VkVClIaw4m28V/cLouS+Kz5rdd15N2cMt6zO/KXqDEL8FVLyKwnQrQyrw8B7DzbHjpUSc1Ctjg1Vh/BSf38O5Oyk0KSrleHGnD2zvNofwbMKMniV06REq9R6CQwvUWAHrgLFQBAQzT1XvXyhEUO3O5Ww+9TQSg7iv/H8Liz+TVlxpRE9uz5GrrIDb/x+k7P77lZ4JMYS/JVSii1Vi2+dSUe/Zf3YlLqJjtEdmZsx17uilDyZrjFduVVaRESgEp3BwrfpWSx8qj3zd14kPlLDxPsa0b1JEDklNzyuV9tq3OTkaXy/5wYbTuTx/ZgOtAnty4ZhA9CXFbnbCzWKQG7r7QTI/XhmyQGitSrkEjESsdjt3DkiJZrtVzZWWWkD7/bc8oxIiaVXfJhXDqDVZsdit6OpINR6x4dReMfOP0wCWjGc9dZ5AEy75XS2nDXgO6JD8+HjFU4nxTuYGzVGv3ARD3dOEcKdBQQEBAT+KxF+cwn8I+iMulpVlgqNnrbmgaraDQtplDLk1gIWNPyYb+u/5VOQXbdGk3rpXSZfne4WZC6DkG/Tsyo9d0mZlRB15QG6FWkU3IhoTV1+ObuaPt/3wIGDNhFtanRs24i2BKu0bLu8kcSodszNmFuj4z7N+JQJ7SdwLO+Yu/3RhasK8sGe1xi1boRbRLgExoxOM/h26Le0iWjjFgWPxz4O54AcIBZowd8iyNQiuF8NH4RARj0oaADro2CqFtoo/j5BdroMviyEx3Ig/BJc6vg7K+wzsEY/RExUnFfAt83uYFvmTfRlhdWe+0LBBZK+SuJc/jl2PL2DcxPPsXvUbjKfy+SXx3/hXP45kr5KIq/kFv4K5/dernbYP2fcxzdPt6eOv5Td2enklHg7bc47MI9usd0YlziuyvsYn5hG93r9WJZxjax8A4M+S+f3k7ew2xWoJXXQyhoQpAhDLlax8eRNtmbm0TZGy7huDZBLxMilIsZ0qe+ee/wko+btucNTIt3vVUKUhk+faMOLfZogryCGDGYrG07m8Nj8vdjv7OsixE9Ofmkek4PgXCx8H1H5xyvfDiuKoaCsCNq0gXfeubvxueeQHztKaM8uqOVSQZAJCAgICPxXIlTKBP4RCiqIrGr3NxUSHXh3cESjknoF2VZGQpQGjUqK9PR/iC5a47Xd7JBT0nA6961NpMzhKfaahPtXahDi4lxeCX4yTY1ayrRKLdue2sbLW19yzwnVpvLxUqeXkIgd/HbhF5Lrtbsnw40Co84tBoAqqyAugTGh/QTWPLaGJb8vYeGar7ihuQ5NanTpKlGIoKPybiWsgxLk/0Al7IwZdhihZ4e3mHH8N9Zl7/fYXmDU0bJuIDKJmBKTlaYRAe48NxdrjlzjP81ianQ9nUnHB7s/4KnWT/Gv7f8ityQXfZmekzdPul0og1VaSsrufq5u6k0sy7jC2qM32D69I7O2LOTtnm/7PHd11bgpydPpdsft0NVGWL6d1xUgHq5R8sqAeHeFbsnoDpitdp5dfpgLN0tYO7ELL/RqTKlFX6vPmkNUyvqJXVDKJAT7yzl2tZAJSw/x7agktygymK0e7qnLM7I9ctBCS3eTcPATPqnjPG+SBEZpYFEVP/LByiDnf0ybBocPw6hR0K9fje5bQEBAQEDgfzPCV4oC/wjBKm3t9ncttu4gl4gZ371BjY6d0L2h8xv6hNfBz9O57s/iNrxY8j0XI6Z6CTJwtia6ApYrw2Z3sCIjh8kdplV7L2lJaey6ssvD/r5WlY/Y/hSbi8gpyam54cYdXDb0TjHgTM+tSRVEZ9DxwQ8fUP/f9Xnz7JvcCL3unBm7RxLkMCUINoaBrj5sj4Y3QqCL6u8TZEXycPTRjzOlOJyoSxB/BdJugiP2MbLKTF77B6u09GkezvKMbHQGs4dodXE4uxAJ/rWqavrL/VmduZrd2bs5nnfcLcjaRrRFLdVwNveu8NMoZZy6oadRmD8Gq57VmavdraIVKV+NW/vEWjKfy2TvmL1cnXKVXSP/JDe3DUM/P+iRh+fCFSB+IEvHr8dzMJltNI0I4NQNPWarnX/9coIDWToCVTL85BJGdoqj1FJJiPsdJCIJrcJb0SWmC63CW3FDn8/KA9lsOJGD3mhh8sqjZFwuQG90fu58uacu3XeFDvWDeba9hHmx7zNZ9CyyknMe1/kgBALFILPCcxkgtd3d1jaiLUGqoDs3JIFVq/5xQWa12TGYreQWmTibqye3yITBbMVqq2EmgoCAgICAQA0RKmUC/whalbZWZgXuxdYdpBIxvePDGZES49MW34XHHIvEHzoshO19cciCMLeeTeOwVF602VFU0tJUUmYlWF29Alm67xprJ/av0uFOIpIwOXky939/v8fjNal8zOj0EgObDGLnGT2h2ttVG25UQog6hCj/KA8x0CTcv/IqiB24BWj4S0YdoQ7o44D7A+D+YIhS3vu5KsOsikEf1A7CemAN7cznp37hy71fojPdrci2jWiLSqri5E1Pi/S2EW3RqoJQhkuZ9uNxHm0f7Rat5bHZHSzbd4MXOkxj5LoRgPM9TQhLQKPQoC/Tc+rmKWwOp1KYkjIFuUROoCLQ4z4AXkyZzoqMHA9XQoVMzNncYga2jMRk1Vc7P+aqxs1Mn0mLsBbM6v0RZqMMlczsM8euMsqL0JwiI3qTleT6wcwfkchHm89yNq+YWY/V83msVqklLSmN8YnjMVgM3DbcJlQdSlRAFMEd7ajkckZ8neGu1ulNFiIClT7dUx1lOtJ/eobpgWsQBfoOjd9kgITb8NkP0C4X6hjgzTu+K9M6zUAu/oecX3xgMFvZmpnHgp2XvKIlxndvQO/4cMFMREBAQEDgb0P4jSLwjyAXy5nWcQYj1lSfz1XZYkstl/LqgHiS64cwf+dF98JIgo1mUVqPDCQ3kfdD0peIoh9AoYrElcRlMFt9tkOeyytBKZdU2ypZZLQwctExlo59n571uzNnzxwsdot7sS4Ty3ij29uYrWafAqh8m+DaJ9ZisBjIN+QTExiDhACW7r3B2D0nGNOlPtGBoffkwqeSqnig6UMeYsCnSYkDKAICgPBqT+2FXAS9REp6KW30CLKQ6B0z95e5YZexqdhCSNzDxDUZSWidtm5RoJX4M7bdWBw4mHdgnlsQTUqexIJDCzxCrAGmdZyBGDlPL9pHtFZFkFruUcEqz9J919jy4iCmpkxFKVN6iRGVVMXCwwsxWox0qteJrZe2MqH9BD7Y/YH7HBMS0xjSbDDd1u5xPza6c31W7s8mQCnj1QHxGO23gJq5OdoddjrX60xCaCJTVmby4UO+3TUrQ6uWu0VoZKCKz4e3RSmTsC3zJkszspGIRcjE8V6ftUbBjdiUuoldV3YxdOVQry8SpneawaAmg6kbpHJX7FxmHuXdU2UiC6nBG5kUvpJgqd75ZUAFssWhDM+6TbudsHUrqO5o5td2wcZG0PaBNKcZUGVh838zFVsvy3Pqhp5JK44yIiWWVwc0E4SZgICAgMDfgvDbROAfQS6VMbTZEMYnTqjSFn9CYtWLLbVcSv8WEfSKD0NvtOLIWkGdy+9g6fEHsoAI30P9jdO87+dOO+SkFUc9HrfZHV6zLpWRlW/gbE4ZDzR9kIGNB1Fo0pFvLCBEFUygMoiLeWXkFHsbN7ioWPnQKDR83u8r3l93lYzLTmFx8IqOfa92pmV4y1q68E3mQsFFhjQdzDJdDhKxCJvd4WlS4sBpZ++H00mxFoRL4GGZiqfCg0lU6pDavdvm/gpWRSTHLW2JbzOQP00SrjvkfLD7fX5PmcW+a/uYvWKQz4y2A2MP0G9ZP3rV70XXmK5M/X2qx3nT2qfRu35/HvxiL1d1RuY+0QalRMzwDjEszfBecJeUWREh5t89/s26s+t8ipEXO77IkKZDeGjVQxQYC/jliV+YmT6TVuGteCF5Ot3r9UNXInI7L45IjuH+hHD0pmCS6oeQfuE2KY0C3CKo2vmxlCk8FP8QBcUiXurXjHCNolbzlq4KXUKUBo1Sytm8EpqG+/P1n85qW/n23FF3KoRapZbfR/zOzPSZlcYxpK525gzOe3IWA+fuR6OSoVE5f6U43VMd9A9MZ0bEEuorfP9cFDsCofV7hCh78uPcJ4jcd8Jju9QBm3bURfr5TApKoERi8so/+7upKri+PEv3XSG5fjD9W1Ty75CAgICAgEAtEESZwD+Gv9yPOffPoVtsd2bvmenjm/aXGNJ0sFdwdEWkEjFSqw71iWch+wfnY4fHQc/fa3wvVbVDLt13hXUTOzO8Q0yltvgAk3s1JrlhAGvOrGbWHu8g5xc7Tmdwk0E0Cm7EhYILlZ7H7rBzPM8pAJVSFXrT3VY6m93B4vTrTOs4g+c3PldtFUWr1LJo6CJ61e9FoamIkzfP0b9NKI8kdWH90Vt8suUih24cQmVVYZQawb8mr5bTBa+tAgbJRTwUoKa1XylgBK77rHTUlltWyFY1pHmL5/n5alPWXgogNsSPaZFNSVFasdjM9GvUjzd3vlltRtveMXsxWozc99197qpZeTOMx+Yf5qrOSGpyDG1jtBQaLUzq1Zg2MUF8m57lIW4eblcXhQymb5le6XWfXPMk4xPHs2DQApK+SgLg4sSrSEQqVmTkMHTtQb55uj0t6wbyTJcGtI/TMviz3QQopXw/JpnhX53hcn6IWwRVVkUNUYegkqq4VZrPt7uvseHETfwVUga2jKzRlwgAozrHsTwjG7sDxnZtwMErOmZuOsPnw9p5mNtUbM9NS0pjZ9bOauMY5h+aR9eYbrzzQHeKTQ63+2Ko6TA/N5xOot8Zn8dZHBJ+LBpE1wc/Z98HS+j3VQciTaVe+9nim1HwyXwURjGDP3OGb//T7YPVBdeXZ/7Oiz5jAAQEBAQEBGqLIMoE/lH85X48Ev8wQ5oOptCoo8BUSLAyiCBVEHKxombtSNc3QMYYMOXefSx3C5yfB02erfG9VNYOWWS08O6vp5n1SGs6NvTcBs5qw/P3NaJLEw3TN0/zMPFw4Vysp5LWPo2tT26l7YK2XnNGFfFlBgHOBfJvk/vxeMLjVVZRhjQZwqKhi9h8cTPdFnfz2NY6vDUDGg/AHvwbqb8cr9FPuloEvdUwyA8GKkVEKRzcLa/9NXQ2ZwjwHwbYboRTZnBwkaVNwxjQpQcd21gpMJiRiETYbQrUMhmbL26uUUZbj9geDG46lN2j0tEZi5CKFSjEAW6BFK1VMefRViTFBfP7qVyCVDLe/S2T1OQYFj7VHpPZhs5gRquWExog5ddzq2uVDZdbrOOHvQ5WH77ubhuNClLxxqDmrD12gyGfO8VEfGQA+SVmTufouV5o9BBBvqqo+jI9Xep14ZXO7/L1nwfcc1vn8kpq9CVCanIMSXHBvPNrpvu/RWLQKL3NbVztuYtHv0fnet3o1aAbA5cPrME7C3P2zmLXyMGIRQqnOHHY0R4fT4jfOZ/7byzqxMycp3m0eQLi4c/w6A7fX65s6zeMSS0eofSPEuaEFLjDt//p9sGaBNe7OHVDj95oFVoYBQQEBAT+MsJvEoF/HLlUhhwZ/nI/D9v7arEUw+EX4eJXvrfrDtf6XtRyKb3jw+jaOBSdwUx+iZlgPzlhAQqu5BsoNlk8Fuoh/nK0ajlKGaw587NPQVaeeQfn0Tmms9eckS8mJ0/zmP9yUWS0MOKro6ybNJtfMtcxet1oOtfr7K6iFJoKnUYLqmCmb57u856O3ThWI4vzQLFThD3sD/3UoHJ/4e+o6rBqsThgrwk2lzpDfw+V+S6wfbR3Nr3iBrD7wm36tgil0HKTgqICmoc1ZGZ6zcLHZ+2ZxcAmA8nS3SDcP4QQtRazVcKQVtE83C4GhUzM8oxs3t9whvXPd2HskoM+7eMNZitfj2zFrPRZNbrupxmf8ssTv2A0Ozh144qHqYdYBHn6Mpbuu+IWU+WdPsuLoE71uvJJxmyO5h51V1HbRLRhaspLPNBsCAt2XHWfw3Xs04v2s+yZFFIaBLNgl7cRxajOcSTFBfPur6f516B4kuKCSf16H988nUSIv8KnuU1WvoGhnx/khd4dsDsctcsZNOnu/myLxDhavAH7Rnjsd7i0Ke/ljOGQoTkDz6Uz4cvXkOR7O4zm+IcwbeAU0uPauB8rH77tep3/qfbB2gTXw11zEwEBAQEBgb+CIMoE/sex2uyYbXb0RitFRjOBKrn3nEj+AUgfBiUXvU8gD4akLyH28Vpf2zXAvzwjm6YRAYRrlHzwYEs+2HCGZXeqDmIR7oV6SZmVCzdL2PNKlxqLhI/2fMSmEZuYmT7Ty3TChSv4d+jagz63Z+UbyLplRVLWkWUP9KfUoud6UQGR/mHUD6xLdr6R7ZfWegsyO86gC0nl9xcqgaF3hFgv9d9nU3/dWpcseRfqt+5Ds5+eobgGbY7F5mLUChHygP30X+FsCW0V3opVj6yqlSi4Xnydcb897RY0MzrNoGfsAF5bc5rtZ25hd0B8ZIBXJp3LPh6c24vLimp1XZPVRKAigrO5dx0fR3WOY9OpXHrHh3tUQSs6fbpE0IiUNmwatpMyuwGdsRClVIVaqmFFRg7FsWLa1POOl8jKN/D6Lyd4c0iC80sEi43SMit1/BX4K6XoSs3Y7A7+PSSB5RnZvPNrJkVGi/OLiFJzpeY2RUYLG07k0TRaVaPXwEXFnEFJ/WHYT3+AWH+KK2URzMwdyYaizgQaS/hk62weOL3T53l+TujJW73Ho1d69tmeuqF3W/uXz5f7J9oH7yW4XkBAQEBA4K8iiDKB/1GqtZluVgf1xY/h2Ovg8LYtJ7I/JH8N6tp7uFcc4M/MKea+ZuHsvnDbLcjAc6EOzsW6zqSr1WLdbDPz27DfeOWPV7xmz1xmEOWDf33hJ5eydN9V/vXL6XIi8QIXbpawfXpHxm4qlz3mwDkIVsnaNEoCD/o7hVg3FUj+BiGmt/mRXtKaC5KODBs8hq6fXiIxVssUtapGgsxlJvHSlpc8xKVGobnnjDZwtpIOXz2c8YlpvDX0Pex2yNWbfLbtlcdfIeV2RafKaig1l3LwQoG7euNqEzx6tdA9y+WiotNnoErGiJRohiVHUlimQ2fSEaoORi3VsHzfDQ5dKWJEioimEQE+WxVz9SasNgc95/zh/nyUllkRiZwV4ZIyK2dziz3uQauWozdaqjS38TCHqYR6UnhZCwuL4JjZO2cQkRhxu9mYdac5YX6YK39epc/Bzby7+UvCS7xf49vqQF7r+xy/N+lU6TV95cv9E+2D9xJcLyAgICAg8FcRfpsI/I9Rnc30uz9uI6HFlzS0Z3htt0v8sLSeg6TRWKTSKspAVVBxgF8iFjE8OYaxS3xXq1z4K6Toywprda0CYyF2U0vWP/4HpRY9BUYd9QJDkYj8+X7PDYauPehTkMmlYnrHh9GibiBBaimXb5f6FIle2WM+RFaIGB4JgOEBTiH2d3DeVI8/ipP4Q5/EUWNzGoQH4a+QMsy/PntfiUNXasFPXbN5HJeZRMVq371mtOnLPK+74NA8usd2480hA7HaRPgpJEjEIgJVMp+vfU3ESEVC1XVYcyTbo2Xw50PXeDgxmjfWnvLYt7zT5xfbL7B4dGt2ZG9k8KpUL+E+ucM0vuwymNWHbrAsI5slozvQLtbTmORcXgl+CinxkbV3YrxRaKp0Lu1cXgl+Mo3POIYGMqcYe1rjrLDWk8K/Hd45gwBE9Uce1Z9+Njv3JUQjSv8SlQ9BtrFJJ17r+xwF6sAq77+8tX95/u72wcqcWn3hDq4XEBAQEBD4iwiiTOB/BKvNzv7L+azYf9Xn9l4BGcyuN5dgu/fi8lBpM164Og1NXhPGl+Xes+taxQH+JuH+Xu1sFZGIRYRrlET4h9TqWlplEC//cZEzucXuKsbAVlb85MU+g3/raVW8+2Bz2sX6U2gqpMCow2C7zf7Xu3LkSgmvrznNVZ0RALG0gGlbPr5bHSuHnwiG+juF2P1qkP3FipjZLmVfaUv+0CfxR3ES2eZId4Xno+RISi168g0FGOy3CVYGoZYrQBRUbb6aRCRhfOJ4hqwY4rXtXjPaKoZGA8zeM4sNw/rz7LJjiEUinunagA2TupL69T53tpaLc3kl+MsDa3VdjSKQDx9qhUImZmtmHkevFvJwYjRPL9rvU/gt3XeFX5/vTLem7Xhjx8s+g8iP5h5l1LoRpLVP46WO7/L59osM+TzdbUxitdkxWewEqqQEqqSM79aASSurv9/yToyuuTRfYs9md7DtdIFHzmC8HF7VwrAAzyrrYH+QN3zQnTNYZWvy55/Cti2Qnw+AIyiI2YOf58vIDiCq+oNaXlBW5O9uH7yn4HoBAQEBAYG/iCDKBP4xyi/QCo1mYkP82Dm9Byv2Xy1ngODg31ELGRW63ut4m0PM5zcf49O8YdiQQC1d18pf32y1YTDbPLaXN16oSPnWMqOtmABFQK0W64GKIEQiz1bIylzz7m8exkePx/PrufW8sNjban9axxn8NnkwY5bsYfPVr9h0ex3222a3IJMC/fycQmyoH6j/4hrRKgvFWKcf0/bEsLukDaV2tXtbXIi6ygrPtI4z6NtwIG90fZuHfvQWXC4SwhIwWAw+W0JtDlutMtoqC42GO62kjmLmj2iPSAQr9l/l5LUilozu4HZFdF/X7uBCnqlWoecysQKHowyLzc4jifX46dBVr/OWp8hoYdPJG/hrD/oUZOWZd3AeyXW7kJrchi93XGbnuVs0i9TQJz4Mh8OBSCxCJhbTu3n1AqK8E6OLrHyDW+wtejoJBw70JisapTNvzE4E77Z5hKa5P/GQv3PW0he9Sv5EKv1X9a3J8eGoP/kEnnwS+vXDtmAhzXRiqEFFqrygLM8/1T5YmVOr65o+g+sFBAQEBAT+AsJvFIF/hKoWaCM7xbFuYmeeXrSfrHwDJTa11/E3zKG8cHUa+0tbeDweqJIRqJJhttrRG40UGS0+jUIMZivbMvP4/VQeeXoT4RoFrw1o7nGuisYLLnwJj1e7vlqLIOdpFJY6vBZs5asTibFaFqVfRqOU8Z8n4it1UTyae5QRa4bTsW5HMgvPoJfdtdlPkMMoDYwIgPC/+JN81QKrS+C4ohG9kt5kSPwD2K+cpjTzpnufQJWMJWNa8376a5VWeEasGU5aYhof9p7JCylT+WTff3xez2X5XhnzDsyrNqMNYHzieJ+h0eW5XnSb/2zIxmC2MbJTHB3qB3M2t9htse5CIhbRODyQho5+bqv6yq+bRrd6fXE4xBy9VkjXxnUwmq1oVfIq5wQlYhH9W4UyaGXNTGPmZsxhx9O7SE2pj1IqZv3xG3T68A90Bgt94sOY9UhrDl8pIK17I9rU885dK99W6at653KhbBYRwICWkUQEqsDhgLw/IPMjXivdCAG+780qkmFvMBp5y9fdrck/7L5I/M1LENXUvZ+Hhf2jj6MOC4M+fZCKRPSOsN6ToHTxT7YPVgyu15ssbsH6T4ZXCwgICAj830QQZQJ/OwazlQ83ZnIgS4e/Qkp8ZADn8kqw2R2cuqFn+k/HSU2OcVcr5uYNo3PAMdqpnUGzG4s68fK15ymyea4G40LULBndgf2XC0j9OqPSb+NFwO7zt+lQP4R2MVp0BguBKhkBKpnHAH9F4wWoXHjUXCQ4XRVtdpHP+Zfy1YkfxndEJLLwc+ZP1Vrt772+F4AgsbOFbJQGkv7iGM0FM/xc4vxzsMxlgn+BJTee5q2it/jo8SmMWWzhcHYhNruDESnRbL+ysfoKz6F5dIntxlvd3yYpMonZe30Eh3d8hUj/ys1adCZdlRltbSLaMDl5Ml1jutJvWb8qM+GCVVpKyvRk5hS7P3vP9mhEcv1g/jx/ixPXne99k3B/yiw2Rn171Muqvvx1XUYtI746ytJnkik2Wen4wTYSY7XMfaJtlflhTcL9MduLa2Uaoy8rZEm6nm92X3ZXigJVMv41KIHZv59l+f5sAlUyn7lrgSoZMqmIQZ/udre/ViQ1OYaezcKQYoXLq+DMf0B31Oe+AHaJGmvDsdD0ReQB9ZwGOidzyF6+hk1bF1CnVMd9Yxdwy99zPs9tYd+rN9I77YrVVaSqEpT/E+2D0jviSy2XCrb3AgICAgL/KCKHw/HXAokE/p+Ql3eTceMnsHDBfMLDw/7Hr1/Z7IhULOL8zWKC1ApMZhsFBjPBajkKmbhC2yLMebQVl26V8uWOi/SoW8LXkZN5/dKjrCzoS8VhqUCVjPUTuzB/58UqA3NHpMTySv9mWG12Cgxmbpc4rx+gkmK1Oki/eNvDce65no2IC1G7H3uuZ33qhB1h1LoRXufuVb8Xqx9fzfqz65mzd453616naQxpMpTF6Tk82LYu3WZt92q3Ks++l3sgV5Rx35L7qlyki4A+aqcQe8APlH9hDZonDSVX24UnD/7CiQqdm1qllrSkNMYnjsdgMWCwGAhR1UHs8GfVgVyGJUcycEWPGgmKthFt2TVqF5k3DERp5ZSYiygw6ghWaVFLNazcn8PwlCgGrexZZUuoVqllQvsJPJf0HHaHg2tFt4kODEUkgi8PfMn8g/OrFGRtI9qy9rFtdJ+1x+O9mPNoK3rFh6OUiikps5JfYkbrJ6eg1Ez/uX/eETnRDE+JdBu1uO59RUYOyzKuUWS0sObZTkRr1SS/vxW7w/nFwbJnUjh0pcBnftj0vk0IDrxJ6wWtqn0NXRwdf5yf9sH35apJFT+3LirGOZzNLeaTx9sQF6rmldUnvb/I6NaAxNhgNNe/JeD8e2C8UfmNyAKh6SRoOhkUd2csjRcucfjhUXQ+vsv92JrmPZgyeJrXKRKiNPw4oaNXFbn8vyeuilSAUkrW7VLe/vU0GZcLPM4htA8KCAgICPxv5K+sz4XfaAK1prLWxPubh/Hx423JzCnh2/QTXgvA0SkRHm2L5QNhS2X1ONr2ED8ePkZ8pL97UemqsI1IiSXjcn6Vggyc38a3j9VSaDTz5rrT7scHtYrklQHN6FA/mKl9mvDn+VuUlFlZkZHNmuc6MbxDDKsOXmVYciSDVnrPE2mVWhYOXsi/d/wblVTlDnLON+QTog5BJVWx4NACjuQc4Y3ub2K1SHhjUHN2nrtFrt7kfh4uJGIRAWoR14tv+xY5DgiVwmgNjA90ut7dK1dtUpYUWfmhRMyvaYd4esUQL0HWKLgRm1I3sevKLoauHOolOKemTMdfUZdSS2mNrnkk9wg3im9ww3SeOtaOzPotD73JSkmZ3sOmfXKHaT4FsAudSccHuz+gWUhLbKUd2HDCwMBWZYhUGdWGc0PlAd3fpmfRNyECq92B1e7AZLVjtdkJUjlfaGdb32Xm77xcTuTovSzmNUoZdoedAS0j+fV4jjs/7O2hLbyqVgqZmC2n84gK0dToNXQRrNKSX5rn/v+qXEMrOnUCLNh1ie9Gd/B5P3a7g4W7LvJa/cLKBZkiFJpNgcbPgbycQ2JZGXz0Ecp336Wz0bMK9+DpHaxo04/99TzbjyuzsK+sItU0IoBvRyUJ7YMCAgICAv+/RxBlArWiMlv7QJWMN4e04P0NmSzL8BZO9Uo20iVzASuufsOS0X0Y8nm6RyAsQGxYCLtm9PSqsK08cJXhHWJ4phrrehdf/XmJhU+15+31p90L6D0X81HLpVhsdh5tH819zcLwU0iQScRsPJFDWo8G9GoWhh0TAYoAWoW34tTNU9gcTnOQtKQ0dmXt4o/Lf6BRaNh4YSMiRPjJ/dCX6Tl586TbaKJlWDschmS0fko+eaItRQYzYrHIo1L4VEoMpZZSbpbe9Lr/zkpIC4RH/EFxj+tOmzyUDEkcb2adZYveuUhvFd7Cp7mGKy9sZvpMn62ZR3OP8tQvqaS1T2NT6iaSvkqqsjrl4lbpLebs/ZCEOi351+B3Gfq5ZwzA0n3XWDuxf7XzW2nt07i/wQD6fpxBkdHCubwS1k6s2dxXZQHdp27oKTFZWbwniwW7nDEJErGI9Jfu82hn9SVyXCREaVArJOTpy/j34AReHRDvnlO02O10/vAPr6qVSCRiZ0LH2pnGKIPYfOrue1YT19CKz7XQYOH5FYdx4DS4MZitOBzw1VPtGdQqEludsTjOvI/IWk50+zdyirEGT4PU7+7jDgf8+CO8/DJcvuwrjYEjkU0pVnjPikLtLOyF9kEBAQEBgf8rCKJMoMZUDF8uz4iUWPZczPcSZDKRhVcivmV0nXUADCyaxOFLW9wmCzqDmUZh/ky7vyl7Luaz0EfL1+RejQFqtQg1W+30axHB76fyqKdVsWR0B7afuck3uy97nf+5Hg0J8ZeR3MifAmM+H/T6gFB1KCqpioWHF/LDyR+YmjIVk9VESr0Ubhtue2yfd2Ceh/Pf3Iw5rHt8G91m7mFYhxjGdWvAiz8c45HEaNZN7MzE5UeYen9TrhZdIkTlbAMLEDsNOyYEQitFDd+QCtglan43+/OLyY9vL2RhcXgGMFcWyuzKC6tqVg6cboAd6nZgQvsJNapSubLDFhyaR8fouy6CLoqMFkYuOlbl/NZLnV6iS72+2Gwy5jzaii2n8zhxvYgxi4/zzcj36FyvKx9XMfdVVUB3kdHC0at3xaXN7uD7fVcqDVWuyDNd6iOXiAlQSHl6UQbfjU6mSbjzC4Zik8V3fpjDwYqMnGorhC6md5rB8exSrOWMJatyDa0MfVEuwwJWsSavNR2adWd4cgxmq41ik5W6WjV6uwhDeCoh1xdCna4Q/yJEDQJxhUzAvXth6lTYt8/ndfJVGmZ2H8mPrXrjEPn+RuHvtrAXEBAQEBD4/wOCKBOoMRXDl11U1k5VV3aTL2I/pI36nPuxxsqrXD0xidQH1zB/50VC/OS80j+ez/+44LM18dQNPV/9eYnXBzb32iYRi2geGUCPpnVQyiRk5ujZfPomZqsdo9nGawPieX1gcxRSMV9uv8g36d75YKVlVprXVfJz5k/8Z5/34v71rq/zUueX+PXcr8ze4719UodJHBh7gH7L+nGh4ALgbN0rtehpGhHAsoxs2sYEkRirdZtMfD+mA8eu6jCJ8vg2fQafhMLoQKcwuxcyShI4oXiQlG6DGPBNSqX7+QplriovzBefZnzKL0/8wsz0mT4t6F1UzA5zCdX5Oy97tP9l5RsY+vlBpvRJZtfIP7mhz6fQVEhEQDBalZY1h/JYcziPhxIjaBRhITxEjFbZAI0iEIddxpCmDzGg8SAKTYUYrXrk4gD33FdlAd0u1AopepOnGcvSfVdY/3wXhifHsNxHxddFanIM7WK1pF+4TZfGoUjEItSKuwLGZndUKu5qWiGckJhG34YDMZRJiAtRu3PVKnMN9cZBO/UZhgdvovWh3bRVlvFA51FsUvdn3HcHvb6cSEsaTafOI1BFdkRVcVbr0iVnZezHH31eyS4SsaxNf+Z0fZIiVSV2jfxzFvYCAgICAgL/7Qi/HQVqTMXwZRe+2qk6+x/ls5hZBEu999cZLZjKyhjQMpJwjZItmXlVzoqVlFnxV9z9qAaqZDzXoyHDkqNBZKHQVIjOpGNoYjgfPNyU41dL8ZOLeea7g+7A4Kc6xbLtTJ5HYHB1Fu9XCq/QNrItM7bMYMGhBV7bj+YeZfS60YxPHO/V1ldg1Lnvufzs3LKMbNrFqDl0+mU6mFbzvdqOWFvpU68UqzKSnwp6My+7K1fMUcRHBtBdGVHlMb5CmavKC/PFkdwjGCwGWoS14Hhe5dWkitlh5YVqxXbAIqOFYLWSEpMEtaQO2qBwfjuewy9HjjJ3WHN2ZG9k0EpvY5XpnWZwX+wAhi88jEouZcmoDry5/hQbTpys0mAFnOJAKhZ5hRFr1TIkIph2f1MSY5yxBb7MMdrFahnxdQZ+CimdG4UyrpunNbtcKqZTw1CfTozlK4RdY7rx0T7vbDpXpe+hLw7SuVGoR66aL9fQ8gRLinhQ+wdPBG+msfJOWPsd/ay4toJ3TvZFb/f3OObUDT0T1zrF5rT77cgkdufM1s2b8OGH8PnnYPEtcB0dO/Hn86/zr2OVi3QX/6SFvYCAgICAwH8zgigTqDFFRt8tU57tVA7G1VnNSxFLkIg8F2kmu5w3rk/gB10ffjI5GN4hBkSwYKd39a085RehpWVWVo5LQSazsPrMzz5b3mZ0mkHrmCGE+sn580I+U1Yd9bDgd1VPqrN4d7X1+RJk5VlwaAEp0SkebX0uG3bAPTvXPEJFPdMX1D++nocVFvCr6qzeWO1iikJ6khM1gJD6j9NbosF07Cafbr3EubwSVFJNlbNKvkKZK2tprIrismI0isrNKirLDisvVMuTmhxDx4ahiIDRizM4m1tMgFLGuufbV5mJlrp6OBMS01g0yjmv9u2eLHo0qcOvx3OqfQ6jOsexdN8VD/EWqJLx3ehkvth+kd9O5Pi0mFfIxJy8XoRELCJco6CkzIbV5qBPc09rdrlEzJncIsZ3b0C7WO/8MD+FlJPXTQxt+hC94vqjr+BOWb7Sl5XvrLa6Wn5tdgfLM7I9KnFibHQLOMJj2s301uxHLvaOYwCQY+Jh7Ta+zR/qc7vzSwMtA1pGOJ/Pv/8N8yup5jVsCDNnInroIZIsNkaovGdNy/M/YWEvICAgICDw34ogygRqTKDKd8uUq51KJTIxu95cBgX96bXP5bJInr3yCpmmBgDUCVAQrlFQaPBdfSuPaxE6tmsDkuK0iMRmXt32cqWL9eGrh5PWPo0vnpzFwE/2c1VndLcRuha2ErGoUqdF+Gttfa3DW6OWatxVGH+xAfOpf/GV9ksipbWbBQK4XiTFUn8Un5UZUCpiGR/3CEXmYnTGbHo0j+ShxBRWZeSy+vDNameVXHlrExInMP/QfJ8tjdUR4R/JjI6vUGJ+rVbZYXX8gt1CFTwt2V9dfYyPH2+LWCTC7qheMLuYf2geKXfm1Zbuu8K6iZ2rzAmDysOIKzp8frnjIvN3XvQy6whQylg2tj1fj2xFoamQPNN5LKJgghyByMUK5FIZUomYjg1D+XjLOWQSsZe4U8rEqBVSxiw+xMErBVU6PIJntdXuwP1cJ7YH2dWlPKrdSpS8anFtcUj5rbAzBwwJVe63KP0y/Vrcqbq+/DJ8841Hlcyh1WJ59TUKnh5LkQ0C9WUEqqrOGxMs7AUEBAQEBKpG+A0pUGM0KqnPlqlzeSX4WbLYGD+dOKn33NbvRSm8eHUqJXanG1tClIZQfwVKmZQio8Frf18s3XeFP17szo2iUg7drEGA8cF5dI3pxjsPdGfkt4cBz4Vtk3B/Si36Stv2/kpbn8uGPVBcxMjQtTwd+gtBOeZa/bTZ7bDtjBxr42dIfupNJq4fx5z751RqWT+90wz6RfcDBjAucQILKwmj1pl0LDn2HTP7zKJ7XHfm7Jnj1dJYFW0j2iJy+HHuShy7Rv5JTskNbpXe8ogFmPr7VC9B1jaiLRH+wSx6ug63SsrwU0jxk0uRS0X0+mgnepMVq8PBuG4NmP7TcUZ0jKL/ct+CuSLl59WeXrSfJaM7kNwg2KdpzPhuDWhdL8grjLiyuciK7otxIWoWj27NzqubeHK97yrt4KZDUEpUqOVSpvRpwrbMm0z4/iBWu8Mt7hqF+fNSv2bsz3Lmb1Xm8OjCaV7jdCq9lJvPEPWvWDe+yDTxSQiv+vXJF9VD1HA0969rxG1r9b2yp27oKTJa8FNIITYWxo2DL74AmQzLs8+x9eFn+PxIAac+uvvlS0KUhon3NaR3fBi94sMEC3sBAQEBAYFaIogygRojl4gZ370Bk1Yc9Xi8q98BgnYNQ1lhfszuEPFR3gi+vPkoDu4uyCZ0b4hCKsZosToXfuWQiEU0CfdHo5IR4icnv6QMvcnKxVulWGwOQgPEfLJ+do3ud/aeWewcOQipGKx2PCz4/RVS8g0FlR57L219+YZ8nmz1FPfVacmO9NdJj/8Vtbhyowlf3CqBRTtEFEcPY+y097lpuUn7r5PZ+tTWKi3rU1cPJy0xjVc7v8+rnd6jc3TlroS94vqTW2inaUhTVj++GrlEzvRO00ldnVrt/U3rOIOtpwv4bPtlHEDj2MvM2fuhVyxARSYnT2Pejmyu5Jt4uF1dQv0VHLtayLFrhegMFgJVMuxWBz2a1iHj1V6UWG7WShCXn1cb8nk6W6d0I7l+CKVmK6VlVur4KwhQyhCLYNTiAx6zhVAzm/nqZhBdVdoJiWm8d99MlFKnMOvfIsJLqASppVzJr9kXEi7MVgdLxySTX1xKo91jEVvyK93XIVZyWtqH/1zqSrGmI9PDmnPbutdrP42phKcO/4pRpuSbpAfcj+tNFqJQOf/n1VfBaMQ442XeyzSx9Lcsr/OcuqEnbekRRqTE8uqAZkQEKgULewEBAQEBgVogiDKBGiOViOkdH86IlBiW7stGhJ1nw37kxfCliB2e/VZ6mx+TsqexozjJ43HXXInZZufDjZmM796IhCgN13RGRqREMzwlCrO9mGJzIVqllgBFIGaLBIdDhFwq5pahqFaL9UJTIfcnRLDhRC4AOoPZXa0IUQdXeuy9tPU1kNlp7ziJeMfLPKn1PdNTGSdvw8drIMPSCOWgQOzaTMYpRIxdPZZhLYfVzLL+0DySo7tQlN8OjSqZP57aRalFj86oI0gZRKAiCJtdikwsRaM00W3xKE7cPEGnep1Y9tAyxiWOq/IaaYlpDGk2GLNZStdG4ehNFhqGx7Dpwq/Mr6QyB555YUVGC78ed85rvdI/nnd/cwZ8T7yvEYhFbDmVx/6sAh5Iqt40ojzl59WitSrKbHZ6zN5O43CnAC8ts9IwzJ/3HmhJanIMGZc9BXlNbOZr21IZIetB18bOlj1fWVuVtQNXRqBKRoi/ghB/BcQ9Due/9Nrnkq0pGwz9+So7hSKb08wjXmH3cmusU6JjzMFfSD2ygQCzkUKlPytb3U/pnWwxV4g2AFFRWBd+xZaTOSzdd46qWLrvCsn1g+nfIkKojAkICAgICNQCQZQJ1Aq1/O7syPG9i5iu+d5rH72iGVNz3mBH8V0ziPJzJXKJmA0nc/hubzYBSjnP92xEsygFO7M3MWhlqld1Z0rydPo0GMCFPDsKVc2yylzoTIWE+N1dkGrVckrKrM6WS1nlxhi+nAoro4EMPqkbTMT2Hoiw4zNNtxI23oD/rIQdFyNo9sDz/DpnAjuvbmLD+bUYLAZO3jzJumHrajzbNjdjDjtG7kJvEPP1zmz2ZxUgFkGIfxkFpdkE+ymY0K0BwZpSt7jdnb2bXt/1YlPqJjpGd2RuxlyfeWEDmwzCWCbBZLFy6XYJWzPzmN63Ga91eZ+UaN85Y5XlhS3LyCYxVsvg1lGsPHCVYUkxvL8hk+X7s4mPDGBM99iav4h4GquM6hzH8oxsrHbPtsDTOcW83K8ZPZuFec2dVWczX90MYkWcLZX92ZZ5s1KB4qsdWC6y0MX/CAMC0/GTGEm78irgw0o+bsRdUaYIhdgnMNZ7ikV7lSw95TlPV94opyjzPOMyVvP48c0obHffjyBTCcOObeLrDg85r1UhS6yyOAxfzN95UTD0EBAQEBAQqCWCKBOoNe6WrGYvYdxzBFXOavc2e71HUCd9w6didaVzJQaz1b3A234mj7Se0UzfPM1nteVo7lGeXptKWvs0ZvaeRZm1Llql1mtmqTK0yiDyS/MA58JWIRMjAtrFBLH1VEGlxhi+nAorEiOFfwXDSA1IRZW3QlbEaIfv8mDucjh3UskL017iXFkbiqUyhn5+kNTkNnzWbzA5pZfvabYtr6SAyUuzOO1jTik1OYYGYQGcunnF4/ELBRdI+iqJCe0nsPYJpyDMN+S7Z8VESHn5p7P0bBbGjUIjJoudqX2aYrHaGTj3AKnJbVj/+B84RKXc0OcTpAyqNi/sm92XWfhUe8I1CraduRuLUJ1grkjbiLZuY5URyTH0S4jg2z1ZBKpkXtctNlkpNFq8XBGrs5mvbgaxIq6Wyt9P5VcqUFztwNNX7qdrwBEGBO6mtyYDjcTZ1mhziAmRFJJvC/K2kg9NgSbPQ2RfiLwfxDJUwKsDrF5mGza7g6M/bmLBhiVEbF6PtJIW07EHfmFx4hDntaSe91tZHIYvTt3QozdaBVMPAQEBAQGBWiD81hS4J6Suwf0uS2DzOdCfhtYfIo6fhlgkQgpe7Vouyi/wXurfhLVn1lXZ/gZO446U6BRahrXkyPgj9P6+tzusuTLaRrQlUBnE5lPHCFTJmPlwS/zkUj4b1o4Cg5lQPzmhmqFkXJ/g8/q/X/idl55+if3X93tsryuF17QwJhDktaiK6Wzw2W34bB3c3gXqpt2pO3Y0U6Y/SuadMN8io4Uvd1xmx9nbzB0Rd0+zbSXmIlrUDfQQZQlRGkZ1jqNjg1BKTBaClN6GDzqTjg92f8DM9Jm0CGuBRqFxz4qdTrvEpdulZN0xS+k68w8OXSnglQHx7nveee4280e0Z85v2ZSUFfl0ESyPa8ZvYMsonlq03/24ze5gRUZOtU6SLiYnT2NbZgGzHmlFUlwwU1Yd5f6ECNZN7MzTi/Z7zI/5KaRY7XaGfJ7uZXkvl4gY160Bk1ce9bpGdTOIvigw6sjTm3wLFHMR0pzfGaBfw/0t16Ok1Ot4ichO38C9iJtM8BZ2IhG0/9TrGI8ZtmIT9tWrCf36S+QzMyq9T5tIzIamnZmX8ihPdG7oU0RWFodRGXqTRZgpExAQEBAQqAWCKBP4a8j8odsaKLkMkX1qdIhrgSeXimkX68/kb2fW6LhP9n3itJ3fPdMrrNkX0zvN4HBWMdFaNT+M78i+S/m89PMJdwUhUCXj34Pjmd1nNt3jujMzfaa7MqNValn9+Grm7JnDtE7TSI5OZun+OQyxnmK8BhS16MzKtcJ/CmF+OhRvAJkqjvAnJqCs1wLAK3MK4GxeCSpJAFH+UbWebQtSBvHi/XUY362hR77W8oxs3t9whj9e7I5YHFRpJcrusHsEQ5evRNkduM1S9CYrAYq7bW5quZRcvYkDWTWrYgJYbHZKzd5VmKX7rrF2Yn/GtZtQ5QxXWvs0Hmw2hGKTmGX7snnn10yKjBa2ZN70yqZLiNIgEoFcKiFaq/KyvAeY82hrn5b61c0g+sLVUukWKCVZcH09XF8HeTvAYUVK1f8IT2l6DL/+zWpVdZLqi5AuWoT6s8/gypVK9zOLpfzc4j4WJD+MX4t40qqwra/t/FvF9kcBAQEBAQGBqhFEmcA9YbXZMdvs6I1WioxhBKqj0ZitNbK+DlTJkYhFPN0xlkJTYa1t53df3e0V1lyRtPZpDGwyiNd+PseaZzvz0eazLM24u9COC1GzZHQH9l8uYMziEyTGtmL9439gshVjsBQR5hfJ9qwtvLPrHRYf+IxvmyawOfAyYlvNX6MsC8zSwbeXwbQe1LcDGfPsdLY4WiISS9z7+crXclWLBjQaWmvLein+dP5wm9vkony+1qyHWyGTiLE75EzvNIPU1dXPSLks/l1VL5dZyuHsQo+Wv+rmsnwRpJaRqzd5PV5ktDBy0TEWj36PgY0H88vZnziUc4hTN09hc9jcs4a94gYwZdVpdp27TaMwf5qE+7tnBitm07lmzVrXC2J8twZMWnnUy/LeZalfMfD5XF4JGkVgrVsqz+cWEnXlfTi0AQqPV3ucC7O2I8Q8gjbmYaS1aQP8/nsYPx6Mxkp3cajVWMc8Q+GE50kMCWdlDWzrK4vD8IXX/JuAgICAgIBAtQi/OQVqjcFsZWtmHgt2OnOgXDb2LesGMqBlBB3qh1T5zX6gWszulzthc5SRb8yt1bXzDfloFBo+3vcxW5/ayu8Xf+dwzmH39jYRbXip80sMajIYfamIt4YmsP3sTZZmZCOXiukdH0a0VsXYLg347I/zfLfPKYIyLhfw1Z9ZjOwUx7hujUFUxkfpHzIhEN4MLiS8NL3G93jWDO8VwIoCsO4C9oro/+CT/OuNN4mpG0HY9gselZgio8WnGHBWi/px6MauKmfbyjOj0wwKSu00jfBcQPeJD2PmI6358/wtHluwl2s6I2sn9q22ElXeOdGFyyzFFertavmrbi6rIglRGvwVUsICvNvcAlUyBrYKRyYV0zKsJXHaemiVQQQoNNjsNiQiGQt3XuWJ3w8zpE1d3h7aEpPZRoHBTIRGiUImZsOJXH4+dI05j7VBb7TQsUEo/gopXRs7K48uF9HyZOUb3K2NS0Z3wGqzk19qRimVoJBIeDFlOk/+Un10gEvINo3Uor61AYqqE2QiqNMFYh6Feg8hV9et9ho+adWqckFWty48/zyisWORBQcTBoTV8LSVxWH4wmv+TUBAQEBAQKBaBFEmUCsMZivvb8hk6b5sAlUynuvZiOHJMe4FcbBajqHMhghQ+RBmJeZS1p1dx+w9s7A77Kx6ZJXP60hEEhLCEtxzTa4KSYg6BH2ZnuN5x9GbStnwxHYKy3QUGHWE+4cQ5q+l1Cji5R/PcDm/lIVPJbH+WA5LRifSLtafQlMhBUYdJsdtpvWvT6/mdZi58Rw9m4UyLDkSg0VPkfkaQbe3s0x5nKYan7fnk4tmeKsAlheD7RywAVo2SuG+uS9x3hpC8wZ1MdscvDKgGckNgvn6z0segcIv/3ycVwY0Y9W4jtwsNlFQakaMgl4NemO2lVVrWT8hMY1uMf3YfqaIVeM6cqvERKHBQnigEo1C5nY3dOGqRHWqV3PnRJdZytlcZ3XpYFYBz/Zo6K7y+WrFrIxRneOYt/Miw5NjPYScK6B5R/ZGn26cMzrNYGCTweQWmVn6TDL7LxcwddVR2scFuz+Lt4rL6B0fxsPt6mKzO3h1QDwiEfSOD3dXg1wuouVNMcBpqR8fqUEtl6CWKwhUyfj6z8ssSr/Mmuf6eQhZEZCogBAJ/H5ndK28kH33gRbgGOJblInlEH4f1B0M9R4EVWS1r5kbqxWkPv75bt0aOnaEveUyyTp0gClT4OGHQXZvbYUV4zAqwxV5ITgvCggICAgI1A5BlAnUGKvNztbMPJbuy/Zo/xt3x6TCRUKUhrFdG3B/QrhHxazEXOrhsigRSbxa87RKLWlJaYxPHI/BYuC24Tah6lBUUhXrz63HT+bHyZsnAcgpzueTjVfRm6x3hI2esV1F9I4P5/fTeTQK80fkcDB3WDzrz63nhcWzvBb40zrO4Ie0wWy+sJ3Bq1LxKzzKT40bEGq6RGgNO/GyLPBOAXynB2sRsBGkeVI+mTOfjaWNmT48ia4z/+B2qZnnlx+mQagfL/Vvworx7dAZnYIyWKVFqwxCVwqvrD7GpdsG1HIJJWVWcotMPNujAXP6fETPuJ4es2+u5/FSp5fo33gAF/OvkdzIH7vIxM6zeVy8ZWBKnyZsPJnjNSeVlW9wuz2uf/wPSi16DFY9QcogpPj7dE50tQC6Whkfa18Pi83O+O4NSIoLZuWBbB5rH+1zLqs8qckxJMUF886vmYDILeRqE9A8+8HZzPvjKr+dyKnyszi+WwN6Nw/3+pKgsmDniq18KrmUMV3rExfqx3u/XuSNPpMYEQDFV1aRJNJRRwrnzfBYmaeQHdw6il7xYYiLh8Lpd50XVYRA1CCnEIu8H2QBNfiE3eHmTVi2DBYvhkcfhddf971fWhocOAAPPQSTJztFmqgWjjSVUD4Oo6KQLR95IbguCggICAgI1B6Rw+Gowh9N4H8reXk3GTd+AgsXzCc8vKZNSH8Ng9nKI/P2cr3QyPqJXZi/82KVC+8RKbG8OsBpUmC2Wvgp8yf3DJOrEvZs0rMoJUpGrh1Jo+BGbErdxK4ru/h0/6dewuPFji/Su0Fvun7blQsFFzjzbBYvLL/ingmSiEX0bxHBrEdaYSizsudCPvclBDFt8zQWVOHuOCFxAi+3HcaRjb15QO1t3V4Z1yzwrg4WFYHFBuwFdgMd4NtZ3xIXdD+TV55kdVonJq44zMePtWFrZh5PdYpk/bn1zNnrLRKnd5rB4CaDMZqlLNlzhaX7rrhFkVYtY+uLXbE5zBSVFVFqLiJUrUUlU7PsxFLe3PGm2/ikTUQbJneYRo/Y/tTx1/DovL1VWpqLRdA0IoCWdQN58f6mdP5wG9YKzumpyTGM69bAbZyRmhzDi/c3Zem+LL7ZncWYLnGM6dKAQoOZAKWMHWdvsmDXJa/F+6jOcSTFBbudEQNVMtZN7MyCnZfQ+kmoE3akRq6Lyx5cQYyqJzHBAbX6LNYacxHc2o099w/I24a40PcM5I1uh1h6UsmhK0XuipFaLgWHHU68BRF9ILQjlJsnrP7aZvjtN6cQ27DBWSEDaNgQzp/3LbZMJigshIiIWj/VmlB+nrQyISsgICAgIPB/kb+yPhe+0hSoMS4r++d6NiLjcn6Vi2BwGlgk1w+ma+NQxGIzs9JnelXCCk2FxIfGMzVvKs91eI6Z6TN9tugdzT3Kk2ueZHzieDalbmLMujH4yZyOgIEqGSNSYt2ta1fyDajkEnon1GHNmZ+rFGQqEURcnk/d4m+IraEgu22DdwtgfhGUOYBzwO84B3TGw/je4+nZtCfp2emsndiLYqOzkuevkDCyS1SlIvFo7lFSVw9nfGIar3d5j/gIf9Y/34WnvskgK9+AzmDh8i0js38/Q69m4aR2bMh/9v6HN3f+G3uF7KmjuUcZtW4EExLTmNlnFtcLKzd+ANyGF5k5xaT1aEjj8AC32K0opKK1Kv41KJ5ujetgNFv5ZncW0VoVsSF+mK02gtRyioxm2sVqWTWuI8UmC3qTBblUgkwiYnnGXZdE8Jyp06jt9FxSs4DmOXtnsWvUYDYe9/1ZdM06+iukHLpSwB+ZN+lXSZCzB5ZiuPkn3NwBedtBdxgcdqqTG5K8HTzV8Vkm3ldBoIjE0OqtGj0nAGw22LkTVq2Cn3+G/HzvfS5ehPR06NLFe5tS+Y8JMrgbh1FZ5IWAgICAgIBA7RFEmUCNKTKakYhFDE+OYeySg9UfAMzfeZEO9YMx2HSUWko5MPYAu67sYujKoe4qUaPgRuwds5dNFzZVOTMFsODQAlKiU/jP/R9zIc9ETLDvNkq5VMyhf3Vl9p7K7fYf94fZoVBPBjiqF2RGO3xcCDN1oLcDt4BN0MivEQ98+ABbbVuZnDyZrjFd3Tlq4xPTmNV7FvVD/QhUS1l7ZnWVItH5HOfRMboLt/LakHXbwA8TOjJr0xmuFhiIDFQyoXtDWtcL5KdTq3ljx7+qPNf8Q/PoFtuN1ORWfLnjcrXPEaDMaufrp9qTqzdRJ0BJsJ8cY5mVm8VlLB+bgkQs4naxCZVcgr7Iyuq0TvgpJEjEYrZm3mTxnruuhWIRDGgZyeNJ9WhYx58RX+/j0m2D1zWz8g189sd5Xrg/tFZunIWmQnacveXxuFOkRzMsOZJSi558QwEh6mACFGosditSiWdfqqvyU5p/Hv9DT6IsPorIUQubTQBNU8KDAuFeRYrdDnv2OIXYTz9Bbg0McNas8S3KBAQEBAQEBP7rEESZQI0JVMlpEu6PyWyrshWuPKdu6LHa7Bgsxfw+4neflbDLussYLUbm7JlTo3N+mvEpO0fuosRPwnejk322rvWOD6vUbr+NAj6tA11VNbocdgcs1sMbBXDdChiB7cBF6DO2Dz/++0cKTYVMZCILDi1g6u9T3W2ECw7No0dsN55MuR+RyFqlSCzP3Iw57By5ixv6fK6XnOOF+0MJUARit4tpHxeMSGzm44xZNTrX7D2zWPf4NubvvFxlmLMLhVTMexsyuVVcRkmZFblExJTeTUiqH4LeaMFis7PhRC55xSam9G7CC6uO8tGjbZi/85zX+2B3wK/Hc/j1eA6pyTF8O+publhFVDIJt421C2jWGXXcLL5rqV/eJGTwKt8mIYObDsFf7gd4OolezLnJsYRjiGqSeyANgIjeENnX+cc/rlb3DTgrYnv3OsXVDz/AtWvVH6NSOQ07Ro6Enj1rf00BAQEBAQGB/5UIokygxmhUUlrWDaTAYK7VcUazjUhNBGvPrPVZCUsIS6DUUlqrCsnNkgKCFOFcvlXMqoNXvfYJ8ZOTX2GBHyiGd0Pg2UBnBacm/FYKL9+Gk2agDNgHHIG6fevy5udv0qdxH4xWIw+seoDjece92ggBZu2Zxe7RQ8k35NfqOeaU3OCxnx91Bzm75sT6NhyIHWOtzmWyFdM0IsAjk8sXCVEa6gQoebFPU0xWGyF+CgKUUkrLLBzJ1vHehkx3iLRELGJKn6YMahVZo3bWirlhFckvNROiCq/Rc3IRpAyipMz5PldmEqISQScl9LAcJWb/cH680ItHB65FjMLtJOpEyVFDU5L9T3lfSCyH0BQI6wkR992ZDfuLAckmE/Tp4/y7Orp2dQqxRx4BTS0sQQUEBAQEBAT+KxCmsgVqjFwiZkDLiFoHBKvkEuQSOXMz5vrcrlFouG24XatzllqL0JVdIyLYwI7pHXmuZ30CVc5FcqBKRvu4YEJUwe79hwXAmRiYGFQzQXa0DO67BoNuwEkDsAc032mYkDyBg0cOkj4/nUv6S6RfTefTDKcpiS9BBk5RVFxWTIFRV6vneKv0FhrF3QW4a07s7V2v4idToVVqa3yuEnMR/orqv4MZ1TmOedsvMHHFYZbsySJQJWXmxkz6fPwn9YLVtK2ndVfbbHYHK/ZnM7RNXb5Nz6rRfXybnkVqSqzP92Br5k2ClEG0iWhTo3O1jWhLkDLIbc8/IiWa7Vc28t2R+fRUwdvBsCsaChvC1mh4PRg6q4C8baw7u55Cg8nL3n1faSsAzHYp+0ubMzfvCfbErsL6YAH03gmt3oSwbrUTZOZKvsTw84PevSs/rk0b+OAD5/zYrl0werQgyAQEBAQEBP5/iiDKBGqMVCKmQ/0QAtUyEqJqtjhMiNIQoJRV2koIoC/TE6oOrdW9yCUyHv7xIRp+FsuQH+6jTtgR1k5sT+eGIayb2Bmr1Y7YJqajJYQtdWB5BETUYB192wbj8yAxG7brgf3AQhDZRdSbVo+TTU4yetNoGn/WGJVURffY7vx55U8koqod9YrK9ISoai6iAHcm2//X3n2HR1VtDRz+TU+d9EIghVBDqFISunRECHptIEjxKsVeANstn2K5drFRVLCgWLBQRFRQOoTeQwsltIT0NpNM/f4YMmSYSaOL632ePMope84ZNpOzZu+91vlmbJnOL4eWMqHDhFq3FeobTI8mYdUeU5Gmfm5qBmmni/l28wnyDWbSMoudyTjG94znjTtaO//+Uw/nYqzjdNYyk5Vmke6p4E0WG1uPlTCp85RatTWp8xTsdg2t6+no4r+L8UGf0nLXeAri4Y8G8O8QxxRV7XkBYC9veGPda/h7g/q8T8Af8nsx4vCLtN7zNXemv8bbWSN5aUs9TApdra4JcBRv/vVXePJJaNkS7rqr6mOHDnX9c0ICPP887NsH27bB009DfHztX1sIIYQQf0kyfVHUiY9WjQK4v3s8j32z3bm9cqa7knIL6dmlNArz5ZmbmlNoNHGqtOqRsD1n9rjVK6tOu8h2eKu9nfXKKkaQxjS/l9sih3Db42+yz/QHU5Ly+LM56Grx1YPFDu8XOIo/F5QCGyHsaBg3ptzIvv772FWwiz2FewgqD+L5Xs8zotUISk2lHCs8xkcpH+Gt9mbW1llM3zTduZ6sMi+1jgCvgAu+x/O9vu41Fg1fxKtrX61yhK5yW3pdAPd2iyA2xKfGNPWV13vll5p4b3g7hs3awNFcAynvr2VEUgyzRnWgzGTFard7XB9WnXyDqcpRu01HCpjQK4Xx7SdUmxDlxba3c5ttF8bf3uP7sC2oQsvhEHSoxSdanAbyTjiShPRPjGTJrnNJNY6ZojhminI5fs+pIoqMlqrT6dtssHs3/P67IxhbtQrKy8/tz8gAs9lz4eYhQ6B1a0hJgTvvdARxl6CmmBBCCCH+WiQoE3XmrVXTPzGCkckxLNpx2iXTXZ4xj0j/UEJ8gjCZVeSVmAEFUfqqR8Ksdiszt8zkkU6PcO/Ce2t8/UeSHuGdP9/Blm6DTBw/+fBpxGw+bTeb3i1hezg0ruUsy99K4bEcSDuDY83YSaAjmPuaaZzUmPeT38dkNVFiKqGeXz2WHFxCn8/7uCWReKTTI2y6fxMDvxzIobxDzn3tIttht9t4de2rPJb0GGMWjKnVPc7cMrPaKZFmm5mW4S2da86q8mjSJGb8mUFOiZmnb2pO+7hgykxW8g0mgny06DRKtzT1Fbw0Kr7bfILP7j2XoOPDFenMWJlOs0h/Hu7dmNYNAmu8n8oiA7yI0LtmKawcGP7nx4O8fsvr9Ijtyevr3AtlT+78FLeZt6Db+zK1Hb+y2GFzGawwOn6yrJBnzCfEt3adpKjMfC79u8XiGMVatcqRun7NGsivZmpqcTGkpnrOlBgRATtqtzZQCCGEENevv01QVl5ezrZt29m0aTN709LIzs5GqVRSr14knTt3ZmjKELy9PafjW778D35ZupTjx0+gVqtp1rQpd9x5OwnNm1f5emlpaXz33ffsP3AAi8VCdHQDBg26id7XQca08vJycrKyeKxnDI/3i2HxwZ89Zrp7tNMkboy5iTFztjL3/rbVjhJN3zSdTfdvYlz7cY5kIDbAABQDBUAekAtR5iienvk0WaezHL23GdAKaAIBGngjFO4LqN19HDPDI9mwcCeot6kJ9POjIKEABgAKiAuJo2lIU4rLi/nXn//i3YHv8vSyp5lRRY2xexfe66yj1vGjjs4Rs4oAa8bmGa73WIXx7cfTPaY7T/z6RLXXX1RezMhWo5iSNamatibSM3ogQxdspqTcwqN9mzLhi81YbHbnqGZF4o7zJUbp0WmUvL3sAPUCvVwSdNjscCCrhDbRQagUChKj9C6jb1VJjNLjrVHxzKAEnuzfFEO5lSBfLQoFfLH+GN9tPsE9SVFocrdypzqTlLFryDfmk2fMJ9g7iACvQLYeLebpPzJ4u5qajFY7bCmHPw2OIGxtGRSfF98GeweRW5pV4zUDBOZnwyfvOYKwdeugpKRW5zktXy7p64UQQghRJYXdbq9Fkuy/vt9+/50PPpgOQIMGDYiNicFgNLBv336MRiMNGtTnpRenEhgY6HLexx9/wqLFP6PVamnXti0ms4mdO3dht9t5aspkkpOT3F5r3br1vP7Gm9jtdhJbtMBf78/OnbsoLS3llqEpjB075qLv52Iqhl+sLVu20KFDBwBUGhVWLyv4AN6c+68WUEPH2E70bXwzB7LL0XplMG/XXEfAZQesODIalgNl4G3zpol3E06cOkFedp7juPMpgIZAayABKoZKUnxhejhE1eJrBrMd3syBqcsgrjiRrsnDGDaqO43rN8RgNpBryCXEJwRvtbczmJrYcSKNgxrXaiRvztA5HMg9wCtrXmF8+/FM7jLZGaQ1Dm7M0hFLWZ2xmmmp09wD2bN1zs4fbfPkwINH8VEHsur4L7zmYUTpsaTJ9IweyJjZOzia66gN9mCvxsSF+DB5fvWjawBv3NGaw9mlfLgincQoPbNGdaD7q384A7iEev68P/wGfth2stZtvjusLR3igrHZ7YT4alEqoKTMREnmJoKL1+CTvxpl7hoUllLHCUMzMGoi+X3vGZbuyeS3PZlYbOCtKGNH4jC0SgsANpTYg9owN+sE32Zns9pDEFZZu8h2rByzinbPr8RS/exPEqP0zO8ZgHfbNjXen5NKBUlJMGAA9O8PHTs6tgkhhBDiunUxz+d/m5EytUrNgP79GDJkCNHRDZzb8/LymPriSxw+fIRPPpnDk08+7ty3fccOFi3+GX9/f1579RWiohxrTfbt289z//o37773Pi1btsTPz9d5TnFxMe+9/wE2m42nn5pC587JABQUFPD0M8/x04KFdOjQgVatWl6hO7/0cnNznf9vNVvBjGNEy4NNqzeyiY21ateIkZ14eLBXArE4grAEoFKOiDCVo+bYMPe8ER6tKoQpq0KwKW7jqykTOGPXMywpgu5zurHrzC5ahrdEr9NTVF7E7jO7sdltqBQqxrcfT8q8lFq9xrup77Jo+CJah7cmOTqZfl/0c46aHco7RMePOvJ48uOsGrOKM6VnyCzJJMQnhCj/KD7d/qnLKFtV2kW2w08bwDu/HeWpm4bSPXoAdkUpp4pyCfQKxEetZ17qaYYu2OwyJXHuhmMsfKgrd3eKqTaFfUXSj6mL0wDXBB0VafX9dGryDKZatzkyKYY+CRF8tjadgsztTGpzAm3OKnRnVhJiLvR80pmVlITeQZvoQExWG8dyDew5VYTR7sUPBb3RegcS0XgA0c0GcNqgQV1/OUt+vLva9w5gUpcpFBvBYgP/8lI6nNhL0vHd/NTiRvaFN3Q5dkLPRmgSIyA0FHKqyRIaH+/IpjhgAPTuDed9wSOEEEIIUZW/TVDWu3cvevd2nzoYHBzM+HHjeOrpZ1i/YQNmsxnN2QX5CxcsAuDOO253BmQAzZs3Y+DAASxe/DPLli3jllvOZVD7/fdlGAwGkjp1cgZkAIGBgYwefQ//+99rLFiw8C8dlOVU92B6qahwjIi1wDFF0df9kBH+MC0MQmoxAJFdruCNg11o2PVNvnm/JaNnb6Rlsya8+ft++rXSOjNDelqflRieiMFsqFNdMJvdRphvGHa7nVxDrsv+/LJ8yqxlfL/3e95OfdsZBA5pOoRGQY1qDMgAJneZgsms4uE+TVAp1azeX0TPZmG88XMGJeWFVU5JrMii+Nm9nUiOD3Ym/ahI1NKqfgD9WkTQNMLfPenHeQk6SsotBPtoKTSaeeirbcy7P4mOcUF8vObIeYlE/Hmkg5UO3ss4uWgy4+2bUZELtXk7s/7Et/4wHvpqK+1jg5wJRhzr4Xo618N9OXMPei81Sx4bUmOSkEmNx3Brmg31rGdZt+R3Ig/vR4njzSrw8ncJykYmx9InIRy1WgU9esAPP5xrKCHBsa1nT0cdsQYNzn8pIYQQQoha+dsEZdVp2DAOALPZTHFxMcHBwZSXl7Nz1y4AunTp7HZOl86dWbz4ZzZt2uwSlG3esqXKczq0b49Wq2XHzp2YTCa02rrV+7pWVB4pu6S8gEY4grCmZ//sQaQKZoXDEL/aNTsvtzevZt5HgU0Pq3M4bjrMl/clg8JObkk5mSXV38+F1FHLKMzg/1b+H/9s908mdJjAK2tece6rPPJWOQg8Xni8VmvOJrSfSOeo/qS8v55Co5nEKD3jesTj76Uhu7jcOVWxKhVZFH99rDvJ8SEoFKBTqygpt1BoNOOrVaNWKbi5dRRzNxxzBmZBPlpKyi3Odg5kleClVZEYpadH0zCWpZ3h4JkSl8ApWGchdl1bVEdPAxBSlzcxsBX4N0KrUjIiOYZH5m13JhjxtB6u0Ghm9/Ey3uj/hkuSkCAD9DkCd2WG0O+kFwGHPwU+BSDqvJfsdHw3M5JvJzFKz4SejeiTEH4u6+IddzgCrx49sHTpiikklCKjhUKjiQBvLXqTBa1KiVollUaEEEIIUTcSlAGZmY6U2Gq1Gn9/xzy4kydPYTabCQjQExrqnjmwUSNH7aCjx465bD965CgA8Y3cawtpNBpiYqI5dCidU6dOERcXdwnv4sq5pEFZONDk7E8MNVbOu90PZoTXbnTM5B1DQat3OZHeBHveCTgbXHyZmkHHuGACfTQUlVlqLMJ8IXXUKmqMvZv6Lj8N+8kldX1VI2/5ZfkM/HIgS0cspXODzh7XnFVeJ1YRLO05VcSjX29nZFIsn/8ziSHvrakxTX2h0YxSqcBmgzUHc5iz9vzRLT1jusSx8KGujJ69EV+dGl+d2lmoGaB5pD8FBhMTesRzQ1ww93+2mb2ni9wCp9f8fGjlOYeOi/SyBqwrac360tY0aH4Tj/Xtgo9WjRrom+DI9jl3Q4Zz+uT5RibH0iYmEB+7lTsyg7n9cF/43YZm+y4UdjtQc7/tmX2ADVNuRO+ncw+whg2DYcMwmCwsS8ti5nfr3d6zcT3i6ZsQgU4twZkQQgghak+CMmDR4p8BaNeurXPqYnZONgAhIZ6/2/fy8sLX15eSkhIMRiM+3t4YDAZKDY5RitAqzgsJCeHQoXTOnMn+ywZlFzV90QuIwzEi1gQIrN1pgUp4PwxG1KJmtdUO7xTAvw9l0OzM//Fop0kseOgml4QXH60+zEejOnDoTAm+Gn21mSEvpI6an9bPuSbNYDa4pK6vbuStYs3ZhA4TWDBsgTPxSHRADBqFP1+sO+W2TqzC3NRjdIgLcsmSWJWkhsH4atW8vCSNL1Pd14HtOVXE5Pk7GZEUw2f3diLtdBEB3mrWPd2HojIzep2aAPM+tGc+onnub5QYW7H3dG/AkZmxcuC0OrIdrbzdr+e0NYqVhS1ZX9KK9SWtOWOp9G8mtZhW8We4qWUkapUSH62aZwclkNQwhBkr092CIZdRraG3oVm4sNr7d6PTQVISqp49idTaoYqaZAaThZeXpDF3g+f37NGvtzMiKYZJ/Zuh0yirrm0mhBBCCFHJ3/6JYfPmLSxbthy1Ws2Iu4c7t5cZywDQ6aquhOTlpaO0tBTj2aDMWFbm3FfVeV5ejjl5RqOxVtdXUlJKaWmp2/Yrsq6rCi+//DL/nHAfd31+JwdPHHSkrjcAxrP/LcOR/OPsTDdtpBa/GH/yQ4qxh5kcGRTroL8PzI6A+rXorQcsWu45ZWLj2dq9FYWlx7efyKf3vsjQ9x0BzZ5TRRhNVhqH+zEv9TSTu0xhxA+eE0TUtY7apC6T+PnAz86RsVxDLkFeQbSOaI1epyfSL7Lakbf8snxeWfMKr6591Zl45Mtbv+W+z3ay51QVGVXO+mj1YWaN6sCMleke15RVeO7m5vy2J9NjQFbZl6kZdIgNYmDLSLwVRryLlhF5cjGcXgrGU87jvHwygN4e21hdfAMPhM/ntCmEdaWtWV/ShlFDxzB1RSmbTlS9fm7GynTHeq6zI04+WjU3tYykT0I4RXnFFCnU6L006L3VrqNaPXtCTUGZry907epYE9ajhyM7olcV82XPslhtLEvL8hiQVfZlagbtYgLx99LQvUmoBGZCCCGEqNHf+mnhxIkTvP3ONOx2O2NGj6Jhw4Y1n3SFLVq0iK+/+fZqX4YLvV5PYvNE/u+e56sMZCozYSKvFlPHzuejgNdD4YHAmo+1oOTNQi3/PlOGp4l7M7dMp3ODboxIasuHK44A5xJXbD5ayPgbhzDuhgnM2uo5QcT0TdPZ88AeJrSf4LFOWYXx7cdzc5ObGTxvMABBXkE0Cm7El//4kmJTMTmGHEJ9QqnvX593b3qX//753yoTe9jsNnZm7aRdZDsUdq8qp+1VtudUETab3SVL4vnuSY4hPtSfp7/fXWN70dpMsjb/jjZ/L2SvBJvJ43E6w37qabI5bQ5z27fZkMCN+2Zy1BRFRUQ+MbgRJeVba7yXIqPFEdRYrbBxI+qlS1EvXYrPiRNEHj8OSg9TBPv1c9/m7e0I1vr0cfy3XTtQ1+3jz2S1MWPF4VodO2ftUWaN6sDytHOjfUIIIYQQVfnbBmW5ubk8//xUSkpKGJoyhCFDBrvs9/J2fGteXl5eZRtlZY59FUWnvSt9015eXo6Pj4+Hc8pczqnJkCFD6N3bfQQiJyeHZ5/7V63auBy0ag1Dmg5heOJw5u2Zd8nb76SDLyOhcS1yoaRZvbnzhJHdprJqj5uW+gYL71rOjJVHsNnPJa74Z7eGfJOaxTNdXqJLdHfeSX3dbS3X48mPY7aaearbUyQ1SKqyxlivuF6UmEpYd3wdjYMbs+yeZfx55E9eW/ea2/FPdn6STfdvqrEm2eQuU5iXerraka/KDCYrzw1K4JVf9rmveeoeT3KjEM4Ul7H3tHuxZxVW2vum0dt/I330m2jiddyxoxY1lu9qsI93DoeiLy8l2FCI2mZFbbNS5OXLUX19l+vQaZQu69Nc2O0EG4tolHsc1ZvbYNcWR9Hm/POC1507oW1b9/NbtoSoKMdPv36Ony5dHFMUL0KR0eLxPfOkooTAr3syXUb7hBBCCCE8+VsGZcXFxfz3/17gTHY2ffr09ljMOSzU8Y1/VUktysrKKC0txc/PD5+zAZaPjw++Pj6UGgzk5OYS4yEoq2gvPNx9RMETPz9flzpoV5vdbuftDW+z4ugqNpxYR7Yh+5K2rwSmBMELIaCpYZqjHRV58Q/R+tdpWKo/FHCkqi81F9Es0h+lQoFOo6R9TBAd44JJeX8ty9L8+ffggaweO5QCYz75ZQUEeQUS4BXIgdwDtJ3ZFsBtvVflQtNalZbdZ3YToAvg15G/8r81//M4srY9czv3/HgPE9pPYOmIpVXWJpvYYSI3NxlM9x/W1eIOHbRqJQfPlDB7dEfMNhsWqx1/LzU+OhUqhQKb3U5eqfuIV1e/7XwQ8z8C1SW1fi3sIZBdD9sOO/evmsuEjLfxsri2Pbt9Ci/0Hef889iucXyVmoHNDh/++DJNco9TptaitZrxMZcTZCzCz1SL6b1Ll3oOyhQKSE+vcTpiXRUaPY8SViXfYCKrqOzcaJ8QQgghRBX+dk8KRqOR5194kePHj9M5OZkHH5iIQuH+9F+/fhQajYbCwiJyc3PdEn6kpzumMcXFxrpsj2sYx549ezmcfpiY6GiXfRaLhYyM42i1Wpe6Z38lCoWCOds/ZfeZXZesTZVChUKh4IVO4+mVOZtkTc0P5EesOoJ7L0IV3JG4TT9XO9JUWZ4xHz+dmmEdYyg0mHjqpuYczSnlh4ld8NWpWL7vDFMX76O4zML0ke0J8dZht0PTkKbclXgXM7bMcFvvVVFo+v4b7iepfhIPLXmIiR0nsjZjbbVTHQFmbJlBt9hu/N+N/8ejSx91bm8b2ZYpXZ5icNPBlJk0NAjyrjGjokqp4KaWkfjpVCzdfZq8UhMjkmKx221kl5QTaNU6118F+bgPQR4oi0Gvcl+/6MKugtwwWGuA1UVwOhfIRYnHUnIAGLTngqORybF0jg91FqWOKzhNk9zj1b9mVf74A55+2vO+SxyQAQR4162ERcVIbFGZmciAS389QgghhLh+/K2CMrPZzMsv/4+DBw/Srl1bnnzycVQqz7nVdTodrVu1YsvWraxdu46UlCEu+9etXw9Ax44dXLZ3aN+ePXv2sm7dem68safLvk2bN2MymejYocNftkYZQFL9Tpc0KJs2cBoxJVsZlDsXVS0Csnfy4Znccrbpovl+0wfVjjSdL9g7iJ5NfBmQGMGctUdYddCRMOX8mlcAOrUShQIKjRawq3i9/xt0r1T/qiKbYtvItnyS8gndY7oz8MuBxAfF82TnJ+n9mefEF+d7c92brByzigHxQymzFuGrCUCn9MduU/PrrnyaRPgxpksck+e7F7YGCPDWMDI5lruTYrDb7WQWlvH2Xe3w1qpYuP0kb/1+kEKjmQBVMaNjdtKubV86te1BYpTeZXpjtiWYLYYEOvrudWm/0KrH/6ge5S+nYbsVjJm1uq8KBo2XS4bE9ek5juvx1lBPbatTW7RsCQMHOn66davbuRdJ7612e8+qUnmKpt5LcwWuTgghhBB/ZX+boMxqtfLGm2+xc9cuWrRowTNPP+VMf1+VlKFD2LJ1K99+N58OHdo7R7f27dvPr7/+hq+vL3379nU5p1+/vnw3/3tSN25k/foNdO6cDEBBQQGfffYFAEOHplyGO7wySkylHMk/csna81JA+N7/Y4i25mySx8wwNgv+PBu35RpyWZq+lOiAaLcCzZ60i2xHmG8ww5O0pLy/hsM5VRdZTozS4++l4a6Z69lxogiVUkGPJqFMHjiA1WOHUGDM50RhDhH+IYT4BGIwlXOiKJM/Rv2JRqGn0FjoVoesKtsyt3GqKJdpv+Xw3KAEXlqSxtLdu/j98Z58s/k4b93Zhp5Nw7i7UwxfbXTN/BcX4sNn93Zi45E8xn2+2W0N2X1JwSy/9TCHN39CO902NAorH21Iw+TXgvE94nnk6+0u7S0t7ExH373sMcazvKgjfxR14r5Bt3Dzox0g31qr+6nMrlQyYUg7HprQ2ZkhsXOjUB7t05h/3NAAr/erDsLtGg2KhARHlsTOnaFXL0fx5qtEq1Iyvmc8j8zbXuOxFVM0E+rp0Xv/bT5mhRBCCHGB/jZPC0uW/MKGDakA6PX+zJgxy+NxY8eORq93FMNq26YNQwbfzKLFP/PY40/Stk0bLBYL23fswG6388jDD7mt9/L39+fhhx7k9Tfe5NXXXqdly0T8/f3ZsWMnpaWlDE0ZQqtWLS/vzV4mJouZBfsW8sfRP2o8NlAXSIRfBPtz91d5TEstfB0JibUIyD4rgkeyoajSwEpFgeZ3NrzjVqDZkyldppB+poz6QWpnvbKq/LNbQwzlFrRqFQ/2aszdSTGUmazkGUxkFdiJCozk3d9z2XXyCAezimkXE8S4Ho0I1voy6YedPDqwdolcKlRMq7QDS3dnklDPMdKy5Vg+93yyke8ndmHygGa0jw1i9tlCzwHeGj6/N4kZK9NdgjWdopxe/psZqllJ74Ob0CnNhFaaPTcwYD2D5+9gxaRezoLMFebn92VJYTdnFsWRybH0bhOD4rbb4OOP3a7b1qABtG+Psm1bxyhW/fpQrx5ERIBOh0Kp5PzScj5aNeN7NOKlJWkc7/Ug+vJSvCwmylUajBovSnTeHA+IpHffG3hmSOI1sx5LrVK6FLGuyoikGDrGBTN1cRov3tISrST5EEIIIUQNro2nnSugpORc8oKK4MyTYcPucgZlAPfd908aNmzIz0t+YfuOHajVatq0ac2dd95BQvPmHtvo0qUzL780lW+/m8/+/QewWCxERzfg5kGD6N2716W7qSvMZDPx+rpXa3VsXGAcC4YvoOG0hh4DpX/qHcWgvWp4Xi20woQz8PV5uSfaRbbDW+3tLNBstppdCjSfb0L7iSRH9Wfo+5tZ+FDXalPGj0yKpV+LCE7mG5k2rB1rD+W4jUI9n9KCzo1C+XbzCeJCfHjjjjbszyxGoYCiMgshPsHV39h5gr2D6NdC50yAUXmkZXyPeMosVpbvzWJI2/oMSIyksMyMSgGpR/L4amMGKqx08dvB0MCVDAhYh7+q6hGoaG0WDdjHlPnBvDOsHcnRAaRN+4jcMwV83XYghVZ/t4LMZbfdgdfHH2NSa1kV24aV8e1ZE9cOn8TmjL+xEX0TImodPFmsNpbty3LUSItvX+VxX2w6QafGYddUSvmailiP7RpHx7hgRs/eyJA2UZJ5UQghhBC1orDb7bVMtC2uJVlZZxg3fgKzZs4gIiL8irzm8cITxLwTXfOBZ6U9mMZd8+9yCZR8FDA9HEadP3ziwXoj3J0JRz2kVpwzdA4Hcg84pyzumLCTk0WZPPvHFLfU848lTaZn9EDGzN7B0VwDSx/rTmZhGa//ut9jyviuTULZkZFPl8ahfLH+GD9uP8mBrBKslRacBXhrWPhQVz5fd4zRXeKYsTKdbzYfZ9WUXkz4YjPT72nJkG96u1xLVdpFtmPpiD8xlqtJeX8tQ1pH8dTAZpSaLGhUSr5MPcYna45SaDSjVEBCPUfQ9O+bE5n0yde0s/zEP4L+JFxT85q6MpuWlcU38G7WcPYZ4tgacwr9W6+hOHIEW2AQ6Vv24B8S5FKQ2WCy8Mqi3RR/OpfljTtRrHNP6TEyOZZnBzWvVWBmMFm4ffr6WqWXT4zS892EztfMaFkFi9WGyWIjz2Aiu7gcvZcGnUbJV6kZbDmWz8jkWGdAK4QQQoi/h4t5PpcnBlFrecaaH/orKygrQK87F30laOG7SEisoVyUDXg5D57PxWOq+/Htx9M9pjtP/PqEc5uXSk95SSC/3r0Kg7mY0yVnCPYOwketZ17qaYYu2OzMXqhRKTmSU8qsUR0oM1nJN5gI8tES4K1BpYTvNp/gjg7R5JaaaBcbRJ+ECHQaJfM2HmfuhmMUGs0UGs2Mnu2YVrj6YI5jtEqp4Pe9WTzRrynL9uTxaKdJjF04ssb3aVLnKSjR8uLi3byQkki/xAhWH8hm2vKD7Dsv+YjNDntOFXNPswJ0f97PzODNNbZvsqlZUdyB1l0nMPHPCLafMnPzvjW8u+Y1AvJOOo9TFuTTZMHX8Pjjzm0Wq41laVl8sekkJFY9yjt3wzGSGgbXalSrrvW+rsWU8uqzAatWrSTUT0tJmWNq66jOcTzUu7EzoBVCCCGEqI1r60lHXNOCvYPqdHykXyRF5Y6H77v9YVY4+NbwnGrzrk/RDbNoXFxEy7WveizQXJHlsCLbYrvIdtTzD8beII9DeYcI8wshLjCOX3flMm35JvIN51LJJ0bp8dWqmbp4L1MX76VZpD+t6gcwIDGShHp6bBY7Qb5aRn6S6jaKNqZLHAsf6sro2RvJN5gZ3CYKnUbFV6kZPNirIcOT6mGwFFFmOUPDiCCCfYaSenJCtWnxJ3aYyOCmg8kvsfLflESWpTmqNHdvGobJavc4RW5Cz0b0a2hBs3hr1e+jXcGG0lYsKOjJL4VdKbL6MX9gMp0OzOGl+R/S4kwVyVrefBMeeMBZaNlktTFjxeGq/8IqmbEyvVbT9epa7+tSp5S3WG2YrDaKjBYKjSYCvLUuI4N1URGceWnUhPpfXHFqIYQQQvx9SVAmai3IO4i2kW1rPSUvxDuEnvWTeMC+k/EBNbdvjBjMsO33cXSnmmnDerB8VH/OlJ5xK9D8xK9PuKS/f7LLk8zZPsetztejnSbx44M3OactAtzXLR6dRsnyJ3tSYDAT6KNBq1Ly/dYTNI3wZ/qKdLcMh+AYsZk8fycjkmKYe18SdrudtNPF5JeW89odzViR8QtDvhnh8t6kNE3hk6Gf0CO2J6+tcw8wn0ieTK/Ym3juxwMcOlPiTMk/MLEekQFe3NQykj4J4RQZHbWu9F4al+ChLKw/XtlLXa5zp6ExCwpuZHFBN7Isoc7tyRk7SbxrKh02Vb2ekk6d4JlnoFJW0ssxqlXXel8XmlL+/OAr2FeHj1bF8rQsZq467Bbsju8ZX6e1cUIIIYQQl4o8fYha0yq1TOo8hZE/3l3jsVO6TCE9fS9vsAFtDQGZXaEhNfAZxq/p4qgJhplHv97Ngoc68N2e+fx2+Fdngebzk4ZMaD+B5PrJdPyoo8v27ZnbGbtwJOPbT+TTe19k6PubGdK6HjfEBtL7jRVEBnjhp1M765NNvLExGw7negzIKvsyNYOkhsFo1Uq+2XSct4e14KllzzFrq/to2MIDC2n6XlNmp8xm9djV5BsLKCwrwF8XQLB3EDNWZHDzT5vcikIXGU1Elm9EfXA66sBW+CQ+63GkSNFoDGQvJdscyPf5vZmf35dD5TEuxzTKOc6zK2bTJ31T1TfVoQO89BL06wfnFVK/HKNada33dSEp5Q0mC8vSspi58rAzU+Wih7rx9u8Hqgy6H5m3vU5r44QQQgghLhV58hC1plVrGNo8hfHtJzCzmil5E9pPpLfKjPeW3mhV1aeeL1RGUdz+K4Z9UkLlFWSFRjMPf7WXL8c9SlxgLG9teMslIGsb2ZZJnSeR3CDZZSrj+WZumU6XBt344p8DaRSm57Gvt5FvMLtMaVQpFdydFMP9n9W8Pgtg5qrDzBrVgcxCA0sOLfIYkFXIL8vn1m9v5YtbvsLP1pWOcfG89HMaT/YL4o99OS4Bmb+ylFuD/qDhxilQcraA85lVkDAZlO6jRaoGKaTGfMqIxYFYzvunHGQo5LG1XzFi2y+oqygTYEtogfKlF+GWW9yCsQqXY1SrLvW+JvRsVOeU8gaThZeXpLmkrR+ZHEvqkZqD7rqsjRNCCCGEuFTkqUPUSV4JvNb3db68dR5tI9u67Gsb2ZbPUr7glVAfwrePwr+GgOy4dx92tfyD05pWHvf3aBrM4v1LSctJY8GwBaQ9mMaasWtIezCNlWNWkmvMpeNHHTmUd8h5jkqhonVEa7rFdKN1RGtUChXvpL5BTIgGs8XGoNZRbq/TNMKPMpO1TtP0ykxWBrQK4bW1tSsR8NaG10lqpHfWIcs3mPDTOQKpRrrjvFT/fTYkjOaF+jPRVARkAMZTcHKRxzbVWm9adRrBsOR41/vJPsrKWeMYvfVnjwFZSf1ojJ/MRrlrJ9x6a5UBGZwb1aqN2o5qVa73VZ2KDIZ1CY4qEpNUDsgqgu45a4/Wqo0ZK9MxWauudyeEEEIIcanJSJmoE61Kxc3T1jH1lp6sGjOYgrIC8oz5hHgH4Wu3kff7SALNq6ttw4aK4uZTUTZ8hBt8dBjNVgK8NS6jRiqlguFJ9Rj89d3syNrBq2tfpWV4S/Q6PZF+kUztNdVlDVmQVxATO05kfPvxGMwGcgw5hPqE4q32ZtbWWZSai/FRaenWOJS7O8W4jJj46dTkGeo2Ta/MbMWuKmJH1o5aHb8tcxsmWzF/7LVgs0OQt4bGtg08EPcVvfRbqj/50McQ/Q+PuyrqZnWKC3aukzoUEs1JfRj67FKXYy1h4RRPeYZPmvXmnp5N8VaparzuyzWqVVO9r8o10urCU2KSCwm6r8WMj0IIIYS4fslTh6gTvbcavbeGMXO2olZC/8RIQny1BJg28KjmKYLM1WfqK9NEYuvyOVm6puQUHcBoCyHYO5A/J/Vg9OxN7DrpeHBuGuFHqflc0GOz25z1zrrFdCPHkONss3FwY5aOWMqqY6sY+vVQt4Qaj3R6BD+tLz9tOcmcdcf47N5OtI8NYvbaI+w5VURJuYVgn7pN0wv00XC8JK9O5xSVF7LuQD6PNlxD9ManeCVwT7XHW3wbY2s0DmX82Gr/ofpo1fRrEUFSwxBKyi3kG0wEdHwX7kwBwKTRsqjPcL68cRjbc60kHCnkgQG1+6dfeVSr8ujT+S5kVMtHq64xmUldeUpMciFB96XO+CiEEEIIUR0JykSdVB45sdhgya5Mbg5YzTPR76CxlFd7rimsF0tC72LqYvcCz08kT+bL+1MY8dF2dp0sxE+nJtfgOegpKi8i1MeRWTDIK4hfR/7Kq2tfZdaWWW7Hbs/czr0L72XTqU282vc1Pl5zlJT31zIiKYZvxnUmu6SMAoOZiACvOiWfUKsUBHkFoVKoSAxPRK/TU1RexJ4ze7DarW7nhCgh6uhsXtN8SpAuH4o9t221q/itKIm5uYNYV9KaFscCGW8tp2+Ch5EboxG8vQFH7bXUo7lMX5F+NoFJAJMadaRI58vrPUdxSh8O+Y7rqus6rcs1qgXnUsr7aNWXJAjylJjkQoLuC834KIQQQghxISQoE3VSeeRk3oYjTI78nAnh39d4XnnC0zx+Io/pCye47dueuZ1RP41gYoeJfDLmFfq9lUpJuYUQn2CPbe05swcfjQ9tI9syqMkgVh5d6TEgq2z65ul0i+7B1Ft6MmbOVj5ckU7v5uF8vSmD4/lGPh3TkbFdGzLpu5qnI97btSHfbj7OiOQojj12jGJTsdt0yembprskH/kt1peQ9LerXMVpUgXxeXZ/Psq8ySWVvcesgBYLzJoF//0vLF0K7ds7/142HslzjmiNv/U5LCrXf+IXMqIFl2dU63LwlJjkQFYJXlrVZc/4KIQQQghxoeTJQ9SZj1bNs33CeFTxIGGl1a8fs6v1bKz3Fml4MX3ryGqPnb55Oj1ie/BEv468sHg/vhq9x7poVruVmVtm8minR+kd35uUeSm1uu431r/GyjGDUSvBYoMgH63zIb3AaKZjXJDberPzjUyKoW9COAVlJSw5uJA3N7zucbrkpvs3MfDLgc4kJEVx98GJae7vj39TdvqN5a7fmlBmr3qkyJkVMO8A6scfgx1ng8eHH4a1a0GhuKwjWnDpR7UuB0/p9q02O1+lZjCmSxyT5++ssY0LyfgohBBCCHExJCgTdZe3DZ/Vt+JTeqzaw0q9mmJI+pb6vjGM/7pXrZp+be1rrByzim6NI1ChYEqXKdz9g2tdtCCvILzV3tyeeDtZJVl1SrZRUFbAwJb1OJJTik6jZH9mMe1jgygwmJk4dwuf3duJG2IDmbP2qFtQM7ZrHJ3jQ1Cpzbyy7jmPZQEqpkuObz+epSOW0vGjjgxrOYwOSVOxF/6Konif48CI3tD8cYyh/XlmRipl9upHcCKLcvC/dzTqrctcd6xfD3Pnwj33AH+dEa3LparEJHM3HGPhQ11rDrovcCRRCCGEEOJiSFAm6ubEQlh7F1jLqj3MWv9WVB0/obhUg9mcW6fAKaskj7d+zybMT8ekgUMYd8MEZy2wykk9Xlr1EsNaDqvT5eeXFTD1lkSO5xn4YesJbHaI0HsR5KPhaK7Bud5s1qgOlJmsmG02dGolGpWSr1IzqBeg5ejxP9wCsrY6eD4Y3iyAVUaYuWUmXaK7sHzUcpqENMVP6wstn4PTv0LCJAhqA0BRYVm1WQG1FjP3bfqRB9d/i6+5ivd80yZnUAZ/jRGty6WqxCSFRjOjZ2+sNui+2JFEIYQQQogLJU8fom70CaDUVROUKTC1nMpS22h+++kIz9zUnMyiumUpzDXkk11czpJdmfyzW0P+3f1lukR3Z/b2mcwZOseZ1KN1RGvGtB1Tp7aDvAKZseIwLaL03NKuAT9sPcnAxEj03hrntLcPV6QzY2U6zSL9uSc5jlA/LRPmbkGtUjKxVwMenHOuNllLLTwfAv/wc/xZq3AEZQDvbHiHVWNXOwIygIYjHT+VeEpMUaH3oY38Z/lHxBWc9nxAy5bw3ntw4411eg+ud1VN4zyaa2DK/J38Z3ALvhvfmaKyv99IohBCCCGuTRKUibrRN4EuX8LKIYDddZ8mkLKkuby4NYa5G3agUir49+AWhPuF1OklAr0CKSkvBKDAYObtZQdJqNeWH+/8maXpi5xJPfac2YO3xtvjujNP2kW2I8ArkE/W7MBigxFJMXw7vjN+Xmq3aW82O6SdLubVpftY+FBXhnWMocBooqCsgB1ZO2iugf8Lgbv8XV9joC/coIOt5WenSxrzzwVlHnhKTBGXd5L/LP+I3oc3V/EGBcLUqTBhAqir/idssdowWW0UGS0UGk0EeGv/NsFHbaZx+uj+fiOJQgghhLg2Xd9PZuLyqH8ztHredVtgKyz9N/JbflvntDGrzc4XG44R4h1E28i2tWq6XWQ7fNR69mc6csaH6XWMSIpl9cFcDCYzr609N0pVkfDjkU6P1KrtSZ2nsPVoMRab489fpmaQeiTP+ZBeMe2tsoppb4/3a8r4Ho0oyd3O5xGwO9Y9IKvwSOC5/88rK6j2mioSUwD4mIw8teJTfvvkQY8BmU2hwPzP++DAAXjooWoDMoPJwpLdp7ljxnqSX1nOgHdWk/zKcu6YsZ4lu09jMFmqva7rQeUpnE0j/IkM8MJHq77uA1IhhBBC/PXI04m4MC2fg/pnsx7GDoP+6zF5N2TGinPFowO8NejUSnRqHU8kT65Vs48lTWJe6mlsdsc6Hy+1ivZxQXwzrjN2RSl27HSL6UbriNaoFCqmb3JkbBzXfly17U5oP5HBTQfz75/SXLbPWJmOyeqI0iqmvb03vJ0zUAI4nm8E42lC0x6leeot3KMHlcL9NfKs8EwOPHTm3LZgr8Bqr6tihK7tqf388dF4JqbOR2tzD5i2RDVnzZdLUMycCWFh1bZpMFl4eUkaj8zb7pYCviLF/stL9v0tAjMhhBBCiL8Cmb4oLoxCCZ0/h4xvoNH9oFC4JK2IC/Hhs3s7sfFIHs98v5upt6Ywof0EZnjIWFhhfPuJ9IgeyNAFjlGicT3iWbL7NPNSj/Hx6Pbodf58c/s3bjXB7pp/F9/c/g2dG3RmWuo0txT1kzs/xeCmg5n4xW5HgFXJnlNFFBnPFWY+f9pbcUk+9U9/gNefd6K0lnq87gIrvFUA0wqgyHZue7vIdgR6B1b7NlaM0O3q2xHdd2a3/Wd8g3jlxrH43TuaZwa3qHGUx2K1sSwtyyXJhSfOFPstI2XkSAghhBDiKpOgTFw4bQA0PjdCVZG0IsBbw+f3JjFjZboz/fjhHAOfjPkfPWN78uq6V90Cp8eTJtMjeiBjZu+g0GhmRFIMfZqH8+nao3w94QYW7FvIG+tf81gT7N7b7+Wu+XfRv1F/FgxbgMFsINeQS0xADIFeQWw5WszN0za6BWQVisrMLmuL1ColaoUVnzNziNz1PJRleTyv2Abv5DsCsgKb+/5JXaagVepqfBt9tGoeH9aZQ/ufIejlZwEwKdXM7pDCstvuZ/SA1rXOCmiy2lxGK6szY2W6pH8XQgghhLgGSFAmLpmKpBUjk2NJPZLrUg9q18lC+r2Vysjk1iy9ewVmWzE5hnz8tAHolP7MSz3N0AWbaRDkzRt3tKZzfCgKJYzpHsWk3ybVWBPsm9u/oeNHHXl17au0DG9Jh3odeKX327R/YSUma/XXrffSuG/c9Tzsecnj8XalF7+pmzMibTu5HoIxcEyXTGk2BK3aQ9se+GjVtPjvJGw/f4M5NJxTz/+PW1q2YFQdE3MUGS3Vptiv7PxRQiGEEEIIcXXIV+TiktF7q2lVX8/dSTHMWXvUbX+h0cwHfx6h88treHxeBnsz9ARpI1ArvOjdPJIfJnZh5j3tOZxdyvbj+disNhbsW+gxIKts5paZrM5YzYQOE7DZbezM2knP2D58uvYEVruChHr+JMUHM6hVJEkNg0io549K6VgUlhilR+/tIShp+iCofFw22ewKvsnrxyMl39B58GreHTrPLYFJ28i2fHnrPF7v/7p71sVVq6BHD8jM9Hgfaq0G5Z9/ovv9Vxp2veGCElNUl2Lfk6Iy9ymTQgghhBDiypKvyMUlo1UpebJ/U8pM1mpHa2x22HQ0n01H8/nXT7tZObkXX2/KYM+pIvZnFnN3pxiS40NQqCy8vu7VKtup7N3Ud/lp2E+8uvZVxrcfz+CmQ/gyL5PVU3phsdkpLbcQ4K3BT6em3GLFZLXxVepxWtXXo/UU9HjXcxR53v2C45rr3cRKnyd46sdyALZP28jUW3qyasxgCsoKyDPmE+wdRIBXICVGBV4q73NtnTgBU6bAvHmOPz/9NHz6qecbCQqq1f1WxVOK/ep4HCUUQgghhBBXlARl4pJRq5R0jg/heJ6RhHr+HMgqwWqzV3uOzQ5ZRWUcyzWgVCh47fazUxcVCvKN+ezI2lGr196WuY0ySxmLhv9Mj9juKNFRP9Cb+z/f7JKBMDFKz5gucXRpFEqbKG+6hWehVtXz3GjCJMjdBC0mo4zoRZLJwsjTaczdkMHxfCNj5mxFrYT+iZGE+GrJLc0i1LeApwc1d4xulZXBW2/Byy9DaaUkIZ995qgxlpxcq3uri4oU++dnXfSkylFCIYQQQghxRcn0RXFJWKw2DCYL+QYLZpud94ffwMrJN/Jgr8YEeFc/GlMv0Ju37mzrnLo46N3VmCxWCsry63QNJouJON9klHYdLy9J44lvd3hMCT95/k6WLZ9Dr8N9sS3vi6E013ODGn/otQQiegGeU+ZbbLBkVyZbMwq4qWU9nh7UHB+NChYuhMREeO4514Cswgcf1OneaqsixX5tTOjZyPMooRBCCCGEuKLka3JxUSxWG+UWRxr2WasOexyVWvhQV0bP3sjRXIPb+YlRetRKBfd+vom008VUDKwZzVb8tYF1upYAr0AOnS7n4JnSKlPC19ec4f+iZtLPlAom0ADpa54jtu+HtVq7dX7K/KIyM3ovDfqKhBwH9sNjj8Fvv3luIDAQpk51jJRdBpWLYFeXFn9kcqxkXhRCCCGEuEbIE5m4YAaTheJyCy/9nMajX3suVDx5/k5mrTrMZ/d28jhiNrZrHJ+tO8qeU+cCMgCNSolO5e+WSKMq7SLbEeQVSLNIPTNXuqeE1yjMTAibz7JmE+kXkOqyLzbnY8wFaW7nVEWtUuKjVRMZ4EXTCH9HQg5jKerJk6B1a88BmUIB998PBw7AQw+B+vJ9H1JVEWxwBMHvDW/Hs4OaS9ZFIYQQQohrhDyViQtiMFlYfTCHIqPZJfW9J1+mZtAuJpARSTF8uCLduX1EUgwd44KZujgNlVJB0wg//HRqIvReqJVK5q4/xaOdJjF24cgar2dS5ynkltixWN2TjHTy3c2L9T+kqZfn60y3NCHEYMQ7pBY3fj6bDebMgWeegexsz8d06QLvvQc33HABL3BhahzRkxEyIYQQQohrhgRlos6MJgu/78miQ8Ng7v9sc63OmbP2KLNGdWDGynQS6ukZ2zWOjnHBPPTVVkYmx3J3UgxlJit5BhP1ArzQqBToNBp6xd7EuBsmMGtr1WnxJ3aYSErzITy/4CDDO8U6twerCnm23mxuD17u8bxscyAlCVP599a2vKhtRmjd3gbYvh3GjnX815OoKHj9dRg+3DFSdoWpzwZfFaN6QgghhBDi2iRBmagTg8mCwWTlt71ZtKwfUKdCxQpgzVO9sdntfJWawWfrjvH+3Tew8Uge46rIkqhWePGvbi/TNbo7b6e+zvbM7c5j2ka25bGkydzSPIVP15xi96kiwvU6wM6dQb/zbL3ZBKpL3K7FZlfwZe5NvJ41ih9vupni9VsvLDW8Xg979rhv12rhySfh2WfBz6/u7QohhBBCiL8VCcpErVmsNjYeySU6yJczxWXkGepWqDi7uJz6gd6sOHCGLcfy+eDuG5ixMt3j9MeK9WgjkmJ45qYEbmp0C0Obp1BUXkBBWQF+2gA0Cj++2nCa/Abw867TdIgNIsh2gp+a/5e22q0er2GXoRH/OvkAO4zNSIzSo9MoUSsVF5YaPj7esT7s7bfPbUtJcaTBb9So7u0JIYQQQoi/JQnKRK2ZrDZ+3pnJnR2jKSm3EOxTt0LFfjo1E7/cQse4YGbc055VB7JrtR7thpgg0rNLmLEynWaR/rSqH0DfBA3NIrUczCrFS6Pmn90acqNmCbrfHqOttsytnWKrN29kjuKL3EHYUAGOJCNfpWYwrkcNqeHLy0Gn87zvX/9yrCmLinIEYwMG1Pr9EEIIIYQQAiT7oqiDIqOF3acKCfbRciCrBC+tyi27X1UqRqW2HMtn5qrDGExWj1kSPZm99ggjkh1rxdJOF/Pt5hOM+2ILs1Yd5rXb26BTKzicXcq/f81BbXcPyBYVdKfP/hl8ljvEGZBVJBkxW+1Vp4bPynJMQ4yLg/wqaqYFB8OqVbBjhwRkQgghhBDigkhQJmqt0GhyBmPNI/35KjWDMV3ianVuxaiUzQ5NI/woM7lnSazKnlNFlJmsNIv0d9n+ZWoGqw9mM3fDMT5ckc4vuW35Pq+3c79BHcV/Cl/j4YynOGNxpFZMjNLzxh2teeDGxqSfKeHxfk3cU8NnZMATT0DDho7Rr8xMeOedqi+wVavLmuJeCCGEEEJc3+RJUtRagLcWq83uDMZe/DmNhQ915e5OMdVOQxyRFEPn+FBeXrIPcExjrOt6tHyDCT+de3edueowH43qwNvLDmKzw9TT99Hdfxt/lHYjs+F/mZzSmnFGM6XlFvTeGvx0aqw2O1q1kh5Nw1xHyDZudARh8+eD1er6Qu+84ygKHRRUp+sWQgghhBCiJjJSJmpN760mMUrP3A3H6NQwmJtb1WP07I081Lsx04a19Vio+N1hbZkysDlWm42FD3XlwV6NAQj3r2KNVhWCfLSUlFtQYWWgfi3gqDS951QRFpvdOYpWYNXTd/90ns4YzzsrMzlTVM7LS9L4z4LdrNifjUalINBHi49W7QjISkvh008dtcSSkuCbb9wDMgCDAVaurPN7JoQQQgghRE1kpEzUmgIY1yOeR7/ezujZG/ns3k7cEBvIO8sOMLl/MzrEBVNutmK22vDVqVErlXy+/ij/XrCHQqOZxCg9Y7s25J/dGqJVK0mM0rukwa9KxXo0U94+vmv0Fjf47ueRjEksLLgRcKTp13ud68pFNj+X85buzsRmh9Qj+fjp1NzUIhx16gaYOxe++gqKi6t+cZUKxoxxpLePj7+Id08IIYQQQgjPZKRM1Jqh3Erv5uHc3SmGo7kGUt5fy+HsUh7t2xRvrYonv92Ot1bFqgPZ3P/5Zrr8bzkfrkin0GgGHKNak77bwZu/7afcYq39erQuMaStfIXFTR7hBt/9AEyNmk6EOgcAvZeGED/3kbfK69gAAo1FWB99DGXDOOjeHWbOrDog8/V1pLs/cAA+/lgCMiGEEEIIcdlIUCZqrdRk4UhOKeN7xvPGHa1pEOTNhyvS6f7qH8zfcoJnByWw9lAuLy3ZR9rpYmcwdL4vUzNYczCHG5s5ArzqPNBRy825Y+lneBFvZblze4C6lFcavE9ilB4/nZrcknKX8yqyK36Zem6tm1Gto9+6RShPnqz6BevXh1dfhePH4b33JBgTQgghhBCXnUxfFLXmo1VjttpJeX8tI5JimDWqA2UmK/kGEyG+WsL1Xjz9/a5atTVr1WGS4kMY3zOe5PhgZq467DKVMTFKz39abaNj9n9Q5ha6nX+4PIr3zgxj7M1xlJutFBtMNM0+SkrxETq0a0i9HjcyevZG5ygdQLlGx7LGSQxNO29tmEIBAwfCfffBkCGg0VzYGySEEEIIIcQFkKBM1JqPToXFZneOkFUUc/bTqQnz1zGpf7M6pbkvLbPw4s97+eDuG+jSOJRCg5l8g4lgXRnRhyajOf6Vx3PnnrmJn3b2ZJRXMf2mv4j3nl0s2rkTlaEUgBN7buBmUxOXgKzC4oTu54KyxES46y7HmrHo6At6T4QQQgghhLhYEpSJWtOqlOSWljGmSxyT5+/EZof9JwvQWcy0bBJJbmnd09zf2aEBv6dl8fg322kW6U9b7wM84v1fNKpTbseXF2gomePFyM2/MJJfqmw3bP9OyopLQa1125fdpSemRCXau+50BGVCCCGEEEJcZRKUiVpTq5Q0DPUjzM/LWZusYf4pln88EZNaizI0hF+sOgq8/TGqdZSrtZSptZSrtZhVapR2G0qbDZXd8dPsQAhN58xh2Kdbsdtt3GiZzRO+c9EoPKSkXwO6z8zoDO6jX+fTWS20zDzElgYt3Pb9s28LlC17g0qWUwohhBBCiGuDBGWiTny0amx2O5MGNKN9bBA7vj4BgNZigszTJNSlsd2Q+dob5GYfZm7Dt+jqv9P9GAPwCbChdk2e0IezqUELyjTu2RhHJsfSJyHctWC0EEIIIYQQV5kEZaLOSsutfLMpg9Fd4rg5Keyi2so5kcWM2FdodzbVvYsDwAdAjvsuu0KBonFjaNcO2raFtm0xtGjJtgIVH69Md0saMqFnI/okhOOjlS4vhBBCCCGuLfKEKurM30vN2kO53NK2AZv+3M1tF9FWgLWch06NY37jKc5pi1a7ko9O3co9/1tMlk8IZ6KDORZYj6PBUYy7dwBBbRMdAZmPj0tbPsBNDWz0SQinyGihqMyM3kuD3luNVqWUETIhhBBCCHFNkqBM1JlWpeQ/QxJIPZLLv7xaMm3cRwQZiwgyFtFEVc7E1kGcPJ3PjkNZFOcXo7OY0NgsBPp5kdggiOgwP8woKbMrCImuhyWoI29ljuSpep9x0hTG48efZGNJIv97bKzL6yZG6Xn4rs6gq7rbqs8GXz5aNZEBXpf7rRBCCCGEEOKiSVAm6kytUhIX4sfk73ZRrtGREVSPjKB6AKwAvjVrGHFzDCOSYykzWSkqM1MvwAu9lwat2hE0aXCMbBlNFsZ21TH5u9tQKyx8ljuEIqsfKNxf996uDVF42C6EEEIIIcRfmcznEhekuMxSZU2yQqOZD1ek0/3VP3ho3lZeXpKG1WImY/MHmCwWl2OLyix0jAtieKc43jsz3BGQeTAiKYYOcUEUlVk87hdCCCGEEOKvSoIycUEKjTXXJLPZIe10McdOHEK/fiDNjz3GqoXPYDCdC6wKDCZGz97I+J7xvHFHaxKj9C5tJEbpeeOO1ozrEc/o2Rs9FoQWQgghhBDir0ymL4oLEuDtXpjZk65+25kW8zr+RYUA9DW9Q+rm7iQlpaBWKQnw1nI010DK+2sZkRTDrFEdKDNZyTeYCPLRotMo+So1g6mL0yg0OhJ3CCGEEEIIcT2RoExcEL23msQovUvq+coU2Hgo/Bsej/gKpcLu3K5W2GiUPhHTDQNQq7xd2vlwRTozVqbTLNIfP52aknIL+zOLsZ09PTFKj95buqwQQgghhLi+yPRFcUG0KiXje8R73OevLGVW7Es8GfmlS0AGcMIUzvjDkykqV5xrp+e5diqmPG46mk/a6XMBGcCEno3QSlp7IYQQQghxnZEnXHFBTFYb3ZuEcXenGJftjXUZ/NTkCfoFpLqds6yoIzcfnMYOYzOKyhxrw9QqJX0TIhiZHON2fGUjk2PpkxAutcaEEEIIIcR1R+aCiTozmCy8vCSNNQdz+OzeTtwQG8ictUdpULKUN6Pfxk9ldDnealfyWuYoZmX/A/vZ7wEqrw3z0ap5dlACSQ1DmLEy3WVKZGKUngk9G9EnIRwfrXRXIYQQQghx/ZGnXFEnFquNZWlZzN2QAUDK+2sZ2ak+XyctxD/9DbfjjYog7k2fzPrS1s5tntaG+WjV3NQykj4J4RQZLRSVOZJ66L3VaM8WhBZCCCGEEOJ6JEGZqBOT1caMFYcrbcijU8Yz+OdvdTv2oLkZ/v0XkrrzoMv2qtaGqc8GXz5aNZEBXpf82oUQQgghhLgWyfCDqJMi47mi0U11R1nY+HF6+rsHZPPz+jB438uUqqJoFunv3C5rw4QQQgghhHAlI2WiTioXjbaiIlhd6LLfbFfxwqn7+SL3ZkBBvsGEn04ta8OEEEIIIYSogjwdizqpXDQ6vTyaJ48/way4lwDINgfywLGn2WRo6TwmKtCb6SPa46NTydowIYQQQgghPJAnZFEnFcWeK/xW1Jl3s+5iW2kzBh98xyUgS4zSE+ijIdRfh49WLQGZEEIIIYQQHshTsqiT84s9A7yTdTd3Hf4fWZZQl+1S7FkIIYQQQoiayROzqBNPxZ5tqDDZNS7HSUIPIYQQQgghakfWlIk6k2LPQgghhBBCXDry1CwuiBR7FkIIIYQQ4tKQoExcMCn2LIQQQgghxMWT4QwhhBBCCCGEuIokKBNCCCGEEEKIq0imL4qLZrHaMFltFBktFBpNBHhrZW2ZEEIIIYQQtSRBmbgoBpOFZWlZzFx52C0L4/ie8fRNiJAsjEIIIYQQQlRDhjHEBTOYLLy8JI1H5m13CcgA9pwq4pF523l5yT4MJstVukIhhBBCCCGufRKUiQtisdpYlpbF3A0Z1R43d8MxlqedwWK1XaErE0IIIYQQ4q9FgjJxQUxWGzNWHK7VsTNWpmOSoEwIIYQQQgiPJCgTF6TIaGHv6aKaD8QxlbHIKFMYhRBCCCGE8ESCMnFBCo2mOh1fVGa+TFcihBBCCCHEX5sEZeKCBHhr63S83ktzma5ECCGEEEKIvzbJVX6ZlJeXM//7H1izZg3Z2Tn4+flxww3tGHH3cEJCQq725V00vbeaxCi9W9ZFTxKj9Oi9pasJIYQQQgjhiYyUXQYmk4l//+e/fPvtdxiNZXTq1JHQ0FCWL/+Dx594kszMzKt9iRdNq1Iyvmd8rY6d0LMRWikiLYQQQgghhEfypHwZfPvdfPbvP0CzZs2Y/uH7TJk8iTdef5V7x46hsLCI99774Gpf4kVTq5T0TYhgZHJMtceNTI6lT0I4agnKhBBCCCGE8EielC8xs9nMkp+XADBh/P14e3s79w0dmkJcXCy79+zh0KH0q3WJl4yPVs2zgxJ4b3g7EqP0LvsSo/S8N7wdzw5qjo9Wpi4KIYQQQghRFXlavsTS9u2j1GAgMjKS+Hj36X1dOnfm6NFjbNq0icaNG12FK7y0fLRqbmoZSZ+EcIqMForKzOi9NOi91WhVShkhE0IIIYQQogYSlF1iR48cBaCRh4AMoFEjx/ajx45dqUu67NRngy8frZrIAK+rfTlCCCGEEEL8pcgwxiWWnZMDQEio5wyLFZkXs89kX7FrEkIIIYQQQly7ZKTsEiszlgGg0+k87vfycowkGcuMtWqvpKSU0tJSt+1ZWVkA5JwNAoUQQgghhBBXT8VzudVqrfO5EpRd4xYtWsTX33xb5f5nn/vXFbwaIYQQQgghRHXy8/OJiqpXp3MkKLvEvLwdI2Hl5eUe95eVOUbSvL28Pe4/35AhQ+jdu7fbdpPJRNaZM9SPqodSqbrAqxVXUk5ODs8+9y9efulFQkNDr/bliOuE9CtxOUi/EpeL9C1xOVwr/cpms5KXl0+TJo3rfK4EZZdY2NmOkJuT63F/bq5je1h4WK3a8/Pzxc/P1+O+6OgGF3CF4moLDQ0lIiL8al+GuM5IvxKXg/QrcblI3xKXw7XQr+rVq9sIWQVJ9HGJxTWMAyD98GGP+9PTHdvjYmOv1CUJIYQQQgghrmESlF1iCc2b4+vjQ2ZmJocPH3Hbv279egA6dux4pS9NCCGEEEIIcQ2SoOwS02g0DLp5EAAzZ81yriEDWLBgIUePHqNlYuJ1UThaCCGEEEIIcfFkTdllcOcdt7Njxw727dvPhIkP0qJFAtnZ2Rw4cJCAAD0PP/zg1b5EIYQQQgghxDVCRsouA61Wy4tTX+DOO+9Ap9ORmrqR7OxsevfuxVtvvkFkZOTVvkRxFfj6+jLsrjvx9fWcuEWICyH9SlwO0q/E5SJ9S1wO10O/UtjtdvvVvgghhBBCCCGE+LuSkTIhhBBCCCGEuIokKBNCCCGEEEKIq0iCMiGEEEIIIYS4iiQoE0IIIYQQQoirSIIyIYQQQgghhLiKJCgTQgghhBBCiKtIikcLUYPy8nK2bdvOpk2b2ZuWRnZ2Nkqlknr1IuncuTNDU4bg7e3t8dzly//gl6VLOX78BGq1mmZNm3LHnbeT0Lx5la+XlpbGd999z/4DB7BYLERHN2DQoJvo3avX5bpFcY0oKirmoYcfprCwiMjISGbO+LDKY6VvidooLCzkhx9+ZOOmzeTk5KDVagkPD6N169aMHTPa7fiNGzfx04IFHD58BIBG8fHceutQOnToUOVrZGRkMO/rb9i9ew9lZWXUi4ykb98+DB58M0qlfPd7vTl48CA//riAvWlpFBUV4aXTERsbS5++venTuzcKhcLleKvVyuKfl7B82XJOZ2bi5eVFq5YtGT58GNHRDap8nQvpi+LaduhQOtt37ODgwYMcPHiQ3Nw8ABb89EO1512p33c5OTl8+dU8tm3bTklJCWFhoXTv3p3bb/sHWq32wm66DqROmRA1+O333/ngg+kANGjQgNiYGAxGA/v27cdoNNKgQX1eenEqgYGBLud9/PEnLFr8M1qtlnZt22Iym9i5cxd2u52npkwmOTnJ7bXWrVvP62+8id1uJ7FFC/z1/uzcuYvS0lJuGZrC2LFjrsAdi6tl2rT3+HPFCux2e7VBmfQtURuHDqXzf8+/QHFxMTEx0cTExGA0GDl+/Dg5ubn8+MN8l+MXLlzEJ7PnoFKpaNOmNRq1hm3bt2MymRh3/33cfPMgt9fYt28///7PfzGZTDRp0oTw8DD27t1Lfn4BXbt0YfLkJ90e0sVfV8XniM1mo1F8PJH1IikqKmLv3jSsVis9enTnyScedx5vs9l49bXX2bAhFV9fX1q3bkVxUTF79u5Fq9Xy4tQXaNq0idvrXEhfFNe+l1/+H6kbN7ptry4ou1K/706fPs2Up56hqKiI2JgYGkQ3IP1QOplZWSQkNGfqC8+j0Wgu6v5rZBdCVGv58j/sH3zwoT0j47jL9tzcXPtjjz9hTxl6q/2NN95y2bdt+3Z7ytBb7SNGjrKfPHnSuT0tbZ/9H7fdYR9+90h7cXGJyzlFRUX2YcNH2FOG3mpft269c3t+fr59/IQH7ClDb7Xv3LnrMtyhuBZs377DnjL0VvsHH3xoTxl6q33c+Ikej5O+JWqjoKDAPvKe0fY77hxm35Ca6rZ///4DLn8+fuKE/ZZbb7Pfdvud9rS0fc7tJ06ctI8YOcp+6z9ut586dcrlHLPZbL9/3AR7ytBb7T8tWOjcbjAY7JOnPG1PGXqrfdmy5Zf4zsTVYrFY7PeMGmNPGXqrfcWKlS77MjKO20eMvMeeMvRW+46dO53bf/3tN3vK0FvtEyY+YM/Pz3duX7t2nT1l6K328RMesFssFpe2LqQvir+G+d9/b5/75Vf21NSN9ry8PPttt99pTxl6a5XHX8nfd089/Yw9Zeit9lkffezcZrFY7C+/8j97ytBb7V99Ne9ibr1WZF6BEDXo3bsXDzww0W2aRXBwMOPHjQNg/YYNmM1m576FCxYBcOcdtxMVFeXc3rx5MwYOHEBpaSnLli1zae/335dhMBhI6tSJzp2TndsDAwMZPfoeABYsWHhpb05cE8rLy/lw+gyio6O55Zah1R4rfUvUxrx5X1NUVMSY0aNI6tTJbf/5oxOLFy3GZrMxcEB/mjdv5txev34Ud9xxO1arlUWLFrucs2FDKllZWTSMi2NoyhDndm9vb8aPuw+QfnU9OXHiBIWFhdSvX5+ePXu47IuObkDPnj0BOHTwkHP7grOfV6NHjXKZTdKlS2c6derI6dOnSU11HTm5kL4o/hpu+8c/GHH3cDp16khQUFCNx1+p33cHDhwkLW0fAQEBjBk9yrldpVIxccJ41Go1i39egtVqrftN14EEZUJchIYN4wAwm80UFxcDjgfsnbt2AY5fPOfr0tmxbdOmzS7bN2/ZUuU5Hdq3R6vVsmPnTkwm0yW7fnFt+Pqbb8nKymLihPGoVFUv9ZW+JWqjvLycFStX4eXlRZ8+vWt1zrk+0sVt34X0q0aNGhEZEcGxjAyyss7U6frFtam2U7f8/f0ByMrK4sSJE2i1Wjp0aO92XM39qvZ9UVx/ruTvu4pzOnbs4NbPAwMDaZGQQElJCXv3pl3EHdVMgjIhLkJmZiYAarXa+Yvo5MlTmM1mAgL0hIaGup3TqFE8AEePHXPZfvTIUQDiz+6vTKPREBMTjclk4tSpU5fyFsRVdvToURYsWEif3r1JTGxR7bHSt0RtHDqUjtFoJL5hQ3Q6HVu2bOWT2XOYMWMmCxcuIjcvz+X4kpJSsrNzAIiPb+jWXlhYKHq9njPZ2RgMBud2Z7+Kd+9XcK6/HT129BLclbjaIiIiiIyM5OTJk6xcucpl3/HjJ1i5ciV+fn7ONT5HzvaP2NgY1Gr3L5saeegfF9oXxfXnSv6+qzinUY2fZcc87r9UJPuiEBdh0eKfAWjXrq3z25XsnGwAQkJCPJ7j5eWFr68vJSUlGIxGfLy9MRgMlJ79BRNaxXkhISEcOpTOmTPZxMXFXeI7EVeDzWbj/Q8+xNfXl9GVpkxURfqWqI3jJ44DEBAQ4HFh/Rdzv+Thhx6kR4/uAOSc7Vd+fn54eXl5bDMkJISioqKzfSQWgOwcx8NzaGjV/Qog+0z2Rd6RuBaoVCoee/Rhpr74Mm+9/Q4LFiykXlQ9CgsL2bs3jejoBjz6yMPOLygr+kdVn1fO/pF9rn9caF8U158r+fvO+VpVfJaFOj/LLu+ov4yUCXGBNm/ewrJly1Gr1Yy4e7hze5mxDACdTlfluV5ejn1Go9Hx37Iy576qzqv4BVVxjvjr+/nnJRw8eIgxo0eh1/vXeLz0LVEbJSWlAGzctImt27Yxftz9fP7ZHD6aNYNbbhmKyWRi2rvvOVONV/QRna7qlM/OflV2ro+UlVXfH6VfXX8SEhJ4+aWpREZEkH74MGvWrGXXrt0oFAratmlDRESE89iys3/vNfePc59RF9oXxfXnSv6+q+mzTOd8nTKP+y8VCcqEuAAnTpzg7XemYbfbGTN6FA0buk+zEKI62dnZzP3yK1omJtZ63Y8QtWG32QBHfai7hw9j0KCbCAgIIDw8nLFjRtO1SxcsFgs//vTT1b1Q8ZezatVqJk1+itDQUF5/7VW++forpn/4Pn169+KnBQv597//65L0SghRexKUCVFHubm5PP/8VEpKShiaMoQhQwa77PfydnwLU15eXmUbZWWOfRVFp70rTdOo6ryKb3KqKlQt/lpmzvwIi8XCxInja32O9C1RG16V/h49BfwV2/bs2QOc6yPl5VUnenH2K69zbXt5Vd8fpV9dX06dOsW0d99Dr/fnX/96lqZNm+Dl5UVUVBQPPDCRjh06kH74MMuWLQfO9cOa+8e5z6gL7Yvi+nMlf9/V9FlW7nwdz1NqLxVZUyZEHRQXF/Pf/3uBM9nZ9OnT22MBwrDQMMARvHlSVlZGaWkpfn5++Jz9UPDx8cHXx4dSg4Gc3FxifHzczqtoLzw87BLdjbiaNm3ejK+vL9Onz3TZbjI7Hkby8vJ47rl/AzBp0hMEBQVJ3xK1Eh7m+HvU6XQEBAS47w8PB6CwsAiA0LP9qqSkhLKyMo9reTz1kbDQUEpKSsjJyfW4FrHinDDpV9eF1avXYLFYaNeuncdAu2vXLmzavJk9e/dy000DCTubnKGqzytn/wg71z8utC+K68+V/H0XFhrG4cNHyM3x/Fo5zs+y8Au/oVqQkTIhasloNPL8Cy9y/PhxOicn8+ADE1EoFG7H1a8fhUajobCwyOOHSXr6YQDiYl0XKMedTa9/+Oz+yiwWCxkZx9FqtS61OsRfW2lpKbv37HH5OXDgIAAmk8m5zWRyTAeSviVqoyJrnclk8jiVrKTEUb6j4oHXz8+XsDDHA3TFOrPKsrNzKCoqIjwsDJ9KDznOfnXYvV/Buf4WFxt3YTcirikVnzm+Hh50AXx8HdtLSkqAcyVjjh3LwGKxuB2f7qF/XGhfFNefK/n7ruKc9Bo/yy5vYhkJyoSoBbPZzMsv/4+DBw/Srl1bnnzycVQqlcdjdTodrVu1AmDt2nVu+9etXw846mFU1qG9o47LunXr3c7ZtHkzJpOJNq1bo9VWvQBa/HUs+OkHjz+zZs4AIDIy0rktIsLx7Zz0LVEbYWFhNIyLw263s3v3Hrf9FdviK62FPddHLk2/Onz4MJlZWcTGxDj7r/hrCzxb7PdQerrH/RVFoytGYiMiImjQoAEmk4nNm7e4HV9zv6p9XxTXnyv5+67inE2bNrt9kVVQUMDetDT8/PxISGh+EXdUMwnKhKiB1WrljTffYueuXbRo0YJnnn6qxiKaKUOHAPDtd/NdamHs27efX3/9DV9fX/r27etyTr9+ffHx8SF140bWr9/g3F5QUMBnn30BwNChKZfqtsRflPQtURu33noLAHM+/Yy8SnXJDh8+woKFCwEYOHCAc/vgIYNRKpUs/fU39u/f79x+6tQpvvtuPiqVym39bHJyEhERERw5epQFCxc5t5eVlTFj5keA9KvrSVKnjgDs2bOXX35Z6rJv//79LFy0GHAt2jv07OfVZ59/TkFBgXP7+vUb2LhxE/Xq1SMpqZNLWxfSF8X16Ur9vmvatAkJCc0pLCzks88+d263Wq3MmDELi8XC4JsHeay3dykp7Ha7/bK+ghB/cYsWLebjT2YDjocQH2/PUybGjh2NXq93/vnjjz9h0eKf0el0tG3TBovFwvYdO7Db7Tw1ZbKzwGZl69at5/U33sRut9OyZSL+/v7s2LGT0tJShqYM4d57x16emxTXjKysM4wbP4HIyEhmzvjQ4zHSt0RtTJv2Hn/8+Se+vr40b94Mk8nEvn37MZvN9O/XjwcfnOhy/IKFi5g9ew4qlYq2bdqgVqvZtn07JpOJ++/7J4MH3+z2Gmn79vGf//wfJpOJpk2bEBYWxt69aeTn59OlS2emTJ7kcZq3+Gua8+ln/PTTAgBiYqKJbhBNXn4e+/cfwGazMaB/Px544Fy/stlsvPra62zYkIqfnx+tW7eiqKiYPXv2oNFoePHFF2jWtKnb61xIXxTXvs2bN/PNt985/3zw4CHsdjtNmzZxbrvrzjvo0OHc6NeV+n136tQppjz1DMXFxcTGxhId3YBDBw+RmZVF8+bNeHHqCzV+IX+xJCgTogbz5n3N1998W+Nxs2bOcJums3z5H/y85BdOnDiBWq2mWbOm3HnnHSQ0r3oIPC0tjW+/m8/+/QewWCxERzfg5kGD6N2710Xfi7j21SYoA+lbomZ2u53ff1/G0l9/48SJEygUCmJjYxk4oH+Vf+cbN27ix58WONeJxcfH849bb6l2ulhGRgZfzfua3bv3UF5eTmRkBH379mXI4JtRKmVCzvVm/YYNLF36G+np6RgMBry9vWnYMI7+/fo5C5JXZrVaWbz4Z5YtX05mZhY6nY5WrVoyfPgwYqKjq3ydC+mL4tq2fPkfvPve+9Ue88jDD7lljb1Sv++ys3P4at48tm3bRnFxCWFhYXTv3o07br/tikzvl6BMCCGEEEIIIa4i+QpLCCGEEEIIIa4iCcqEEEIIIYQQ4iqSoEwIIYQQQgghriIJyoQQQgghhBDiKpKgTAghhBBCCCGuIgnKhBBCCCGEEOIqkqBMCCGEEEIIIa4iCcqEEEIIIYQQ4iqSoEwIIYQQQgghriIJyoQQQgghhBDiKpKgTAghhBBCCCGuIvXVvgAhhBDiQgy95R8ALPjphxqPXb78D959732G3XUnw4cPc26fNu09/vjzT16c+gKtWrW8bNd6rdm1azf/+vd/XLYplUr8/f2Ij4/npoEDSUrqBJx77+ri/PdZCCFE9SQoE0IIIf6mAgMDuaFdOwBMZhPHM46zbdt2tm3b7gys6tWrR+9evdzO/ePPPwHo3DkZby9vl30NGza8/BcvhBDXEQnKhBBCXPeSk5No1qwper3+al/KNaVB/fo8+ujDLtsWLVrMx5/M5tvv5tO7d29atEigRYsEt3MrgrKxY8YQERF+Ra5XCCGuV7KmTAghxHXP19eXBg0aSFBWC0OGDKZBg/rYbDZ27tx5tS9HCCH+FmSkTAghxHWvqjVllW3ZspXv5s/nyJGjqFQqWrVqyT0jR9CgQYMq2+rRowdfzZvHrl27KSoq4umnppCcnMTp06dZsWIl27ZvJyvrDCUlJQQE6GnVqhV33nEH9etHub3+0Fv+QXhYGB9++D7f//Ajq1atJisri/Y33EBCi+Z8+unn3H7bP7jnnpEer/+//32e7Tt2MHXq87Ru1eqi3q/o6GhOnDhJYWHhRbUjhBCidmSkTAghxN/e2nXrmPriS1gsFjp27EBwcDAbNqQyZcrTHDlyxOM5J0+dYtLkKRw8eIhWrVrSpk0b1GoVAL/9voxvvv2OsrJymjRuTMeOHfD29mHFipVMmjyFo0ePemzTZrfzyiuv8uOPPxEZGUFSp04EBQfRp3dvNBoNy//4A6vV6nZeVlYWO3buJCqq3kUHZABGYxkAAQEBF92WEEKImslImRBCiL+9X35ZyoMPTKR//34A2O12Pv9iLj/88CPT3n2fd95+0+2c1avXcPOgm/jnP+9FpVK57EtO6sTAAf2JiIhw2b5s+XLee+8DPv5kNi9OfcGtzZycHDQaDR9+8B4hISEu+zp3TmbVqtVs2rSZ5OQkl32/L1uO3W6nX9++F3T/lRUXF3Pw4EGUSiVt2rS+6PaEEELUTEbKhBBC/O01b97MGZABKBQKRtw9nJCQEI4cOcLevWlu5wQE6Bk9epRbQAbQrFkzt4AMoG+fPiQkNGf37j2UlpZ6vJZ77hnhFpABDBwwAIDffv/dZbvVauWPP/5ArVbTu7d7lsTaMplMHDx0iFf+9yqlpaXcc89IwsMlgYcQQlwJMlImhBDib697t25u29RqNV26dGbRosXs3bvXLQNhm9Zt0Ol0VbZpNBrZtGkzR44cobikBKvFMe0wPy8fu91OZmYmjRo1cjlHoVDQqWNHj+0lJrYgJiaabdu2k52dQ1hYKOBYC5ebm0eXLp0JDAysy22ze88eZ723ytfw2KOP0KvXjXVqSwghxIWToEwIIcTfXlh4mMftEWdHivLy8tz2hZ4NijzZuXMXb7z5JoWFRVUeU7Fuq7KAgAA0Gk2V5wzo35+PPv6E5cuXM2zYXcC5kbP+/fpVeV5VKtcpKzWUsn//AQoKCpj10cc0bBhHXFxcndsUQghRdxKUCSGEEBdAW0XwZDQaee31NygpKeGuu+6ke7duhIeHodVqUSgUvPnm26xavRq73V7rNiv06nUjn38xl2XLl3PnnXeQX1DAli1bCQ8Pp23bNnW+h/PrlJnNZt548y02bEjl7Xem8fZbb6JUykoHIYS43OSTVgghxN9e9plsj9vPZDu2BwcH17qtvXvTKC4upnPnZO4ePozo6AbodDoUCgUAmVmZF3ydvr6+dO/WjezsHLZt287y5X9gs9no16+vs/2LodFoeOThhwgICODo0WOsWLnyotsUQghRMwnKhBBC/O2tXbvObZvVamX9+vUAJCQkuO2vSklJCYDHZB2nT5/m8GHPKfZra+DA/gD8+utvLFu2HKVSSZ8+vS+qzcp8fX25/TbHOrPvv/8Bm812ydoWQgjhmQRlQggh/vb2pqWxbNlyl21fzfua7Owc4uJiSUxsUeu2os4Wht6wYYNL8eWSklLee/8DLBbLRV1rkyZNaBQfT+rGjWRlZdGhQ3tC6jCSVxsDBvQnKCiIEydOsn79hkvathBCCHeypkwIIcRf2uQpT1W5r1+/vrVKgHHTwAG8/8GHLP31N+pFRnL02FEyMo7j4+PDo488XOP5lTVp3Ji2bdqwfccOJj7wEC1bJgKwe/ce9Ho9SZ06kbpxY53aPN/AgQP44MPpAAzoX/cEHzXR6XTcdts/+PjjT5j//fd07drlkr+GEEKIcyQoE0II8Zd24MDBKvdVZBasSdeuXWnfvj3fzf+e1I0bUatUJHXqxMh7RhATHV3na3r22af5bv73rF27lq1btxEQEED37t0YcffdzJnzaZ3bO1+rVq0AxxTJdrW8x7oa0L8fP/74I4cPH2Hz5i106ND+sryOEEIIUNg9pX8SQgghxDVr/vzv+WLulwy7606GDx92tS9HCCHERZI1ZUIIIcRfiMFgYPHPS1Cr1fQf0P9qX44QQohLQKYvCiGEEH8By5YvZ8/uvezZu5f8/HyGDBl8yRN8CCGEuDpkpEwIIYT4C9izey9//PknZWVGBg26idGj7rnalySEEOISkTVlQgghhBBCCHEVyUiZEEIIIYQQQlxFEpQJIYQQQgghxFUkQZkQQgghhBBCXEUSlAkhhBBCCCHEVSRBmRBCCCGEEEJcRRKUCSGEEEIIIcRVJEGZEEIIIYQQQlxFEpQJIYQQQgghxFUkQZkQQgghhBBCXEUSlAkhhBBCCCHEVSRBmRBCCCGEEEJcRRKUCSGEEEIIIcRVJEGZEEIIIYQQQlxFEpQJIYQQQgghxFX0/xxVQxr5bcxRAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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G4IXnB+Jgb09sbByhoSE1SvRx7Nhxdu7ajaOjI198/ikeHpoP70VFRXz++ZfExMayatVqRo0aqVd285atvDN9GmFhoYAmgJoz9xuOHw9n2/Ydd50fVqdOHaZOfZ3Vq9eQlJxM927d6Nq1i+45Nm/h1KnTeHrW5bNPP8He3h7QBCLvf/AR4eERbN++g969e+nVv//AQT779BMaNw6s1rWQyWRs3boNCwsL5n3/LW4VhlMqlUri4uJ1jt+wcROHDh+mcePGvP3WG9ogVC6Xs3jJUvbs2cuatesYMXyYtkxYaAg9nn1Gp26APXv3smDBD/z8y698/tmnem2r6j6t7n1S0b79+xnQvx8jRgzXBrSbNm3ml19/Y92ff4mgTBAEQRDuAzGnTBDQfJC+efMm8xcsJD4+HhMTE7p26aJ33LBhQ/Q+6CYmJnHq1GkaNPBl9OhR2oAMwN7enkmvTQRg+46d2u3Hjh0jLy+fwMAAbUAGmtT8QwYP0jtHVS5eTODcuXPY2dkx+Y4P2gBubq73pVdxy9atgGa4YHlABmBlZcX48WORSCTs2LkLmUymV7Z9+3bagAzA2NiYF55/HoCY6Jh/3baK7Rs9apQ2IAPNPLyRI4YDmuDQkG7dulY7IAMoLi5GLpfj7u6uFzQZGxtrekpvUSqV/PPPBszNzZn29ps6vYImJiaMG/sqDg727Nq1G5VKpd3n5+enVzdAt65d8fdvxIUL0RQVFRlsn6H79F7vEzc3N4YOHaLTw9i7dy+sra25ePEicrncYBsEQRAEQag+0VMmPNXuXK8MwMLCgqlTX6dWLXed7RKJhJBW+nNooqKiAAgNCTE4NK5evXqYm5uTkJCg3RYTEwtA+3bt9I6XSqW0adOazZu3VOs1nD17FoAO7dtheccH7ftFoVAQH38RgI4dO+jt9/b2xtvLi6TkZJKSkvDz89PZHxTUTK+Mx61hctk5Of+6fRkZGWRkZGJnZ2uw56ZVq2CsrKxISUkhJydHb7ikofe1Kvb29jg5OZGUlMSy5X/w7DPdcXd3N3js5cuJ5OfnE9SsmU6wWM7MzIz69etz8uQpbqakUKd2be2+kpISIiNPkpSUREFhIUqFEoCc7BzUajWpqanUr19fp77K7tN7vU/KhzhWZGxsjJurK5cTEykoKMDR0bHa9QmCIAiCoE8EZcJTrXydMonR7cWjW7cOw9raWu9YOzs7vQ+nAOnp6QCsWLmKFStXVXquij0K2dnZAJVmOHRzda32a9BmJKwkKLgfCgoKUCgU2NraYm5ubvAYV1dXkpKTycrK1tvn7OSst608MFDch56Wu11PiUSCq4sLSUVFZGVl6wVlLi767bub/02dwtxvvmH9+n9Yv/4fXF1cCGwcSPt27WjZsoX2uPL7I+rsWYMPASoqyM+HW0HZuXPnmfvNN+Tl5Vd6fElJqd62yu7Te71PKuu1Le9pk8sVNapPEARBEAR9IigTnmp3rlNWFVMDH3RBk1gBIMDf/4EGRo88SRW7JFXs/K9U0QYTE9NK91WmadMmLP5xEZGRJzl95gwXLkSzf/8B9u8/QOvWYbz7znTg9v1Rq1Yt/Bs1qrJOGxtNQo6SkhJmz5lLYWEhL7/8Eu3btcPV1QVTU1MkEgnffPMdhw4fRn2r7ooqu0/vldGj8N4JgiAIwhNOBGVPqTp16pCUlPSwm3HP6tSp87CboOV8qychNDSEAQP6V6tMeU9NRkaGwf3plWyv6vypqanVLlNTNjY2SKVS8vPzKS0tNdhblp6uabOT038/lK18+Fxl17PivvvZPktLSzp27KAd0hkfH8+s2XM5fjyckydPERzcUvv+1Kldu9oPAWJiYikoKKBNm9YMNpCMJTWt5u/1f3GfCIIgCIJwb0RQ9pSSSqUPLKX806ZZUDNWrlpNeEREtYOywIAA9u3bz5Gjx+jZs4fOPqVSyfHjx6t//mbNWLFyFYcOH2HIkMF6CRzuVL6+m7JCUom7kUql+Pk1JDo6hsOHj9C9ezed/VeuXCE5ORlzc/OHstSCi4sLLi7OZGRkcvbsOU06/ApOnjxJYWEhtWrVumv6/X/Dz8+Pzp068tff67l69SrBwS1p0MAXK0tLLkRHU1BQoO0Nq0phYSFgeOhgSkoKiYk1f6BS0/tEEARBEIT/jsi+KAj/kl/DhgQ1a0ZsbByLlyyluFg/rXhSUhKnT5/W/tymbRtsbGy4cOEC+/bt125Xq9WsWr2GjIy7r5NVrmHDBjRp0pi8vDwWLVpMaanuPKO0tHSSk69ofy7vVbpx40a1zwFoU8mvXrNWp7eluKSEpUt/Rq1W0+PZZ/QWkP6vlLfv199+Iy8vT7s9JyeH335fDkDfPr3vy7kyMjLYu3cfZWVlOttlMhnnL1wAwNlZM0/NxMSE554bQElJCV9/PdtgT1VWVhb79x/Q/lyeBCU8PFzntRQWFrFg4Q8oFDWfx1XT+0QQBEEQhP+O6CkThPvgjTem8smnn7F9+w4OHTqMj48Pjo4OFBcXk5x8hczMTPr26a1dp8zSwoLXJ0/i61mzmTd/Adt37MDdzZ2k5GRSUlJ4pnt3du3eXf3z/28qH3z4MYcOH+ZM1Bn8/f0xkZqQmppKUnIyo0aOwNvbCwA/v4bY2dlx7NhxZs78ADd3NyQSCd26da1yzlPbNm149pnu7Ny1m9en/I+mTZtgZmrGhegL5OXl4+fXkMF3WW/sQerXty/nz13g1OnTTJw4iSZNm6BWqzl37jwlJSWEhobo9Ureq4KCQuYvWMiSpT/h61sfJycnykrLiIuPIy8vH1/f+rRuHaY9/vnnB3L9xg0OHDjIpMlTqFfPBzc3NxRyBTdu3uDatet4e3nRuXMnABr4+hLUrBlRZ88y8bXJ2nT9Fy5EY2trS2hICBEnTtS43TW5TwRBEARB+O+IoEwQ7gN7e3tmff0Vu3bt5vCRIyQlJhIXJ8Pe3h53Nzf69Omll/4+NDSEzz/7lNVr1pCQcInr12/g6+vL5EmvcePGjRoFZU5OTsydO5vNmzZz7PhxoqLOYmRkhLOzEz17PEurVsHaY01NTfngg5ms+GMlCQkJRMfEoFarCfD3v2siitdem4i/vz87du7kwoVoVCoV7u5u9O3Th379+mJmZlazC3cfGRsbM3Pme2zbvoN9+/Zx5kwUAHXr1qFrly48++wzBpcsuBfutdwZPWokZ8+d49q16yQkXMLMzAw3N1deeP55nn32GZ0MiEZGRrzxv6m0adOaXbt2k5BwicTEJKysrHB2dua5Af1p166tzjlmzHiXP//6m6NHj3L69Bns7Oxo374dQwYP5rfffr+ndtfkPhEEQRAE4b8jURtK3yU88tLS0hk3fgJLlyzGzc1w+nSVSkV8fDygmetyvz6QCoIgCFUT//4KgiA8farz+bwy4q+EIAiCIAiCIAjCQySCMkEQBEEQBEEQhIdIBGWCIAiCIAiCIAgPkQjKBEEQBEEQBEEQHiIRlAmCIAiCIAiCIDxEIiW+IAiCIAiCIAgPnUKpQqZUkV+iIK9Ehp2FKbYWUkyNjZAaP9l9SSIoEwRBEARBEAThoSqWKdgTm8aSg4lE38zXbg/0sGV8x3p083fD0vTJDV2e7JBTEARBEARBEIRHWrFMwZfbYpmyOkonIAOIvpnPlNVRfLktjmKZ4iG18METQZkgCIIgCIIgCA+FQqliT2waK8Kv6u0zNpLgX8uGVt4OnLqSzb7YdBRK1UNo5YP35PYBCoIgCIIgCILwSJMpVSw+kKizzc7ChKFhXgwO9aRUpiS7WIajpSlWZlLkStUTOb9MBGWCIAiCIAiCIDwU+SUKYlJuD1n0drJk2egQTiRlM275Sb35ZeM61KN7wJM3v+zJCzMFQRAEQRAEQXgs5JXItN/bWZiwfHQoSw4mMu2vcwbnl01d82TOLxNBmSAIgiAIgiAI90yhVFEsU5CaV0p8aj6peaUUyxTVmv9lZ2Gq/X5omBcRSVmsOqE/v6yiFeFX2PuEzS97svr9BKGa+g8YqLfN2NgYW1tbGvn50X9AP/wbNXoILXswzp+/wPsffEiXzp2ZOvX1B3KOzVu2snPnLlJSUlAoFDQODOSLLz57IOd6ms2bt4B9+/fz+Wef0qRJ44fdHEEQBOEx8aDWAPu3qextLaQEetgSl1rA4FBPxi47Wa3zLj54ma7+rk/M/DIRlAlPtS6dO2u/LykpISk5mePh4YRHRPDG/6bSsWOHh9i6x8fx4+H8/PMvWFtbExLSCnMzc2rXqf2wmyUIgiAIAg9uDbDyVPaGMieWp7IfGubFjF6NKq3f1NiI8R3r8eOBy5TKlDrzy6oSfTOf/BLFEzO37Ml4FYJwj+7sNVKpVPyxYiXr1//DTz//Qtu2bZBKxa/J3YRHRADwzvRpNG3a5CG3RhAEQRCEcvcjcDKkqlT2Fa0Iv0KojyM9G7sb7NWSGhvRzd+NtPxSsotlBmqoXH6pHHc78xqVeVQ9Gf19gnCfGBkZMXjQKxgbG1NQUMDVq9cedpMeC1lZWQC4ubk95JYIgiAIglCuJoFTTedoGUplX5nFBy8jq6JuS1MpQ8O8qFXDAMvW3KRGxz/KRBeAINzBxMQES0tLCgoKUKqUevszMjL5888/OX3mDDk5uVhZWeLv788Lzw+kQYMGOsfebS5XZfOD+g8YiKuLC4sXL2LDxo3s2bOX9PQM7Ozs6NihPYMHD8LERP8foqtXr7JixSouREejVCqp5+PDiy8+j4mJqd6xAHK5nN2797B33z7S0tIoK5Nhb2eHp5cnHTt2pEP7dlVeq9Wr17Bm7Trtz+PGT9B+X/E15ecX8Pf69UREnCAzMxMzMzMaNPClf79+NG8epFdv+etftGghf6//h0OHDpOWlkbLFi2YMePdKttUUlLC1q3bOHzkCOnpGahUKuzt7KhXvx7du3ejRfPmOseXlZWxefMWjhw9RkpKCgCennXp2aMHXbp01qs/OjqGI0ePEh0dQ2ZmJnK5HBcXF0JDQ3h+4ECsra10jq94DwwfPpQVK1dx5ta9M2rkCPr16wto7qt/NmzgzJkzZGZmYWpqipubGyGtgunXry+WlpYG2hLN6jVrSUi4hEQiISDAn5EjR+BZt26V10gQBEF4OtQ0cKrJHK07U9lXpTpDDS1MpDhaaYZU3pl10ZBAD1tsLZ6cUObJeSWCcJ+kpaVRUFCAVCqllnstnX3JyVf44MOPyM/Pp3bt2rRuHUZGRgbh4RFERp7krTffoG3bNvetLd98+x2nTp2mSePG1PaoTUxMDOv/2UBWdjZvvvE/nWMTLl3i/fc/pLS0FC9PTzy9PLl5M4VPP/uCHj2eNVj/t999z7Fjx7GwsCAgwB9LS0uysrKJjYmltKT0rkGZj48PXTp35vSZM+Tm5tK6dRgW5hYAODjYA5petBkz3ic1LQ0XF2dCQ0PIz8vn7NlznDkTxejRo+h/KzCpSKVW89VXs4iOiSEwMABvLy9sbG2qbI9SqeTDjz7m4sUEbG1tadw4EBMTE7Kysjh16jTmZuY6QVlubi4fffwJyclXcHCwJzAwANQQFxfHvPkLuHTpEuPGjdU5x+/LlpGcfAUvLy+aNm2CXC7n8uVE1q//h5MnTzJ71tdYWFjotS0vP4+33p6OSqXE398fuUyOmZkZoAn0vvjyK4qKinB1daVVcDAymYzrN26wes1aQkJCqFfPR6e+yMhINm/Ziq9vfVq2bEFSUjKnTp3m4sUEFsz/HgcHhyqvlSAIgvDku9+BU0UVU9lXqy3VGGpYPr9syuqou9Y3oWN9TJ+QJB8ggjKhXEbGvZe1tgYDH0IByMwEtfre6rW0BCurux93n5SUlJCUlMwvv/4KQI8ez+r0eqjVar797nvy8/MZ+NwAhg8fhkQiAeDYsePMmfsN8xcsxN+/EY6Ojv+6PekZGZiambH4xx+0H7DT0tJ44823OXjwEINeeYVatdy1bZs3bwGlpaW8/PJLDB70iraebdu2s2TpT3r1p6WlcezYcVxdXPjmm7nYVgh4ZDIZiYlJd21jWFgoYWGhzJz5Abm5uYwaORI3N1edY378cQmpaWl06NCeKa9P1vbwxcTE8vEnn/L778to0rixXtCRmZmJiYkJi35YgJOTU7WuWXRMDBcvJtCggS9ffvE5pqa3ewiLi4u5eTNF5/j5CxaSnHyFvn16M2LEcG3bcnNz+ezzL9i6bTvBwS1p0aKFtswrL79Mo0Z+WFW4N+VyOT/99DM7d+1m46bNvPLyS3ptO3XqNGFhobz15hs67SooKGDW7NkUFRUxcuRw+vfrh5HR7T8ycXHxODrqB1ibt2zlnenTCAsLBTQB6Zy533D8eDjbtu9gyOBB1bpmgiAIwpPrQQRO5Sqmsq+O6gw1LJ9fNjTMs8ohl0PDvJ6ozIsg5pQJ5Vxd7/3rVhBjkL//vdc7Z84Df9n9BwzUfr0yaAjvzZjJjRs3GTf2VV4dM1rn2PMXLnDlyhVcXJwZMmSwNiADaNOmNaEhIZSWlrJn77771r6xY8fo9Hi4ubnR6VZGyJiYGJ22Xbt2DXc3N15+6UWdOnr16knDhrrDKgHy8jRPznzq+egEZACmpqY0auT3r9ufmppK5MmTmJubM27sqzpDLgMC/Onx7DOoVCq2bd9usPywYUOqHZAB5N96TY0aNdIJfAAsLS3x9a2v/TkxMYlTp07ToIEvo0eP0mmbvb09k16bCMD2HTt16mnZsoVOQAaaIa9jxozG2NiYExEnDLbNxMSEcWNf1WvX7t17yMvLp0WL5jw3YIBOQKZ5LX7Y29vr1de+fTttQAaaJR1eeP55AGKiY/SOFwRBEJ4+DyJw0h57K5V9ddRkqKGlqZQZvfxZMKi5Xv2BHrYsGNScGb0aYWpsdM9roz2KRE+Z8FSrmBJfrpCTnp5BQkICa9etw93dnZYtb/eQxMTEAtC2bVuDGRk7derI8fBwnWDp35BKpTRprL8OlYeHBwA5OTl6bWvTpjXGxsZ6ZTq0b8/Fiwk62+rUqY25uTmnTp1m/T8b6NixA073oYevophYTbtatGiOjY3+0MNOnTqxcdNmbfsrkkgkhLRqVaPz+fj4YGRkxL69+/CsW5ewsDC9gLNcVFQUAKEhIXqBEEC9evUwNzcnISFBb19WVhYnIk9y4/p1iktKUKs0vcFSqZSbKSl6x5fXZyjAPHv2HADPPvNMtV5juaCgZnrbPGpr7o3sCveGIAiC8PQqD5wexBytBznU0NJUSs/G7nT1dyW/REF+qRxbcxPtumoypYptF1Lue4r/h+nxaq0g3GeGkm8kJiYyY+YHfPHlV8yf/z11amvW28rOzgbA1dVVr0zF7dlZ2felbfb29gYDrPL5SnK5XLutvG0uLi5Vtq0iS0tLJr02kR8W/ciyZctZtmw5Hh4eNGnSmM6dOuLv7/+vX0N2dk6l56+4vTx7Y0V2dnYGk5lUpXZtD0YMH8YfK1byw6If+XHxEjw969K0aVO6dumMt7e39tj09HQAVqxcxYqVqyqts+J1Bti4cRPL/1iBQqGoUdtcXJwNbs/MygTA3d29RvU5O+nXZ3nr3lDc0WZBEATh6fQgA6d7GWp4ewFrObnFcuwsTbAxM6GwTI5EAjbmJto2lC90XSpX4GRliqWZMSZGmoDsQaT4f9gen5YKwn+kXr16PPvsM2zYsJEd23fw6qtjqlWuwmjGalOpK+9iN7qXCmuoQ4f2NGvWlIgTJ4g6c5YL0dHs3LmLnTt30b9fX0aPHvVAz1/VSzStYUBWbsCA/rRt25aIiAiizp4lJiaWTZs2s3nzFsaMHkXfvn0ATSIRgAB//2oHRPHx8fz62+9Y3QpoGzcOxMHBQRs8jhw1RqcHU/f11GwIyd1I/oP7QxAE4WlwO1BQkFciw87CVNsj87jPWXrQc7TKhxqG+jix+OBlvV6rCR3r09XfFUtTqWYB65g0lhzS790a2cabEB9H5u+J4e1nG3E4IUN7nLGRhIZu1jSpbUevxu40q+vAkYTMO1qiBm7/Xbzb2miPIhGUCRq3eg3uibV15ftiY/9doo+HxO1WD07FoWjlyTsy0g0nRUm/td3R6fYQQKmJ5lespLTEYJnMTP0eonvheGveWUYlCVvSq0jkYmdnxzPdu/NM9+6o1WrOnDnDnLnfsnHTZrp164qnp+e9t+tWgorKr5nmvqvJvLHqcHFxpk+f3vTp0xulUsnhw0eYv2Ahvy9bTufOnbC2tsb51jlDQ0MYMKB/teoND9cskj1k6BC9dPllZWXk5ubWuK3OTs5cv36D1NRUvL29alxeEARBuHfFMgV7YtOeqGFwd6pJ4HSv9Vc11FB6a+5XVb1b0/46x5BQT97r5c93uxP45WgSdhYmTOrsy+BQT0plSrKLZThamqJQqfl7Qmve+GklgerDdLGNZFdeGD9lDtSpt6Yp/h+2x/suE+6fSoa9/WvOhodsPerS0tIAMDe/nYEoIEAznO/osWMMGzZEb2jhgYMHbx0XoN1WHizdmfUPNFn3EhOrt3bI3ZSf89jxcIYNG6o3R+rw4SPVqkcikdCiRQuCW7bk0OHDXL167V8FZQG3hkCePnOGwsIivTW8Dhw8dKv9/36oZGWMjY3p1Kkj27bvID4+nps3U2jYsAHNgpqxctVqwiMiqh2UFRYWAWgDuoqOHjuG+h4eQDRr1pSos2fZtWu3TuIOQRAE4cG6W6DwuA6DM6Q6gdO/Ib1Vh6WpVC97o0KpYk/M3RewXhlxlVbejpiZGOHtZMmy0SGcSMpm3PKTRN/Mx0xSRmvr87zgHkUXm0iWu9/QllWDXlBW0xT/D9vjEToKwn8oMTGRnbt2AxDcsqV2e5PGjfHy8iI9PZ1Vq9fofAA/Hh5OeHgE5ubmdOvaRbvdzc0NFxdnrly5QkSFrHylpaX8sOhHiouL70ubGzcOpE6d2qSmprLuz7909u3YuZP4+HiDr/P48XC9OVMFBQVcvHgRAOd/GVS7u7sTHNySkpISfv7lF515WHFx8ezYsRMjIyN69ez5r85T7tz580SdPYtKpTssNC0tjevXryORSHBy1gRUfg0bEtSsGbGxcSxestTge5GUlMTp06e1P3vU1qxbt3vPHp3XcvXaNZYv/+Oe2ty9ezdsbW05dfo0mzZt1gvs4uPj76kHThAEQaicQqliT+zdA4UV4VfYG5v+2Gb0q6hi0NTQzQZ3O3MsTaUPvCdJplSx+GDlD6ElahXm8lLMFDJ+OpzI0NZeLB8dypIDl1m6eTNhsmX87fE25wNe5nefj+ljsQFLxQ2dOlpaxuKm1h99lF/6+MyxfjxCR0F4QObNW6D9XqFQkJ6RzsWLCahUKlq1CqZTp47a/RKJhDff+B/vf/Ahf/31N+HhEdTz8SEjM4PY2DiMjY15ffIkvTXKXnn5ZRYs/IGvZ80mMDDgVka/S1haWhAaEkLECcMp1GvCyMiIKVNe58MPP2b16jUcO3YcL09PUlJTuHTpMj179mD79h06ZdLTM/h61mysLC2p7+uLg4M9RUVFREfHUFJSQqtWwfclLf5rEyfw3oz32b//ANEXovFr5EdeXj4XLlxApVIxetRIvTXK7lVyUjK//Pobdna21K9fHxsbG/Ly8omOjkYul9O7dy+dDJNvvDGVTz79jO3bd3Do0GF8fHxwdHSguLiY5OQrZGZm0rdPb+06ZV27dGXjxk1ERp7ktUmv4+tbn8LCQqKjYwgNCSEhIaHKoaKG2NjYMH3a23zx5Vf88utvbNm6DV/f+prFo6/fICUlhe++/cZgWnxBEATh3siUKhYfqN5olcdtGNx/QX8engnWZlJK4i9hlJWBdW420qwMjDMzUV+7yajDcdjIirApK8KmrBibsiJsS4uwlhVjptQ85JzTfhgrzXpinZJM1qUtvF6wl1p+1ZvmYSxR08vsCL/JdEe+1CTF/8MmgjLhqbZv/37t90ZGRlhZWRIQEEDnTh3p0qWz3jBAb28vvvt2LuvW/cXpM2c4dvw4lpaWhIaG8MLzzxtcD6xbt65IJBI2btxEbGwc1tbWtGoVzPBhw/jtt9/v22vxa9iQWV9/xYqVK4mJjiEtLQ0vLy/en/keZmbmekGZn19DhgwZzPlz57lx4wYxMTFYW1vj7e1Ft25d6dSxYyVnqhknJyfmzpnN33//TXjECY4fD8fMzIymTZsyoH8/mjcPui/nAWjVKpiCggLOX7hAUlIyBQUF2NnZ4u/vT6+ePfSGB9rb2zPr66/YtWs3h48cISkxkbg4Gfb29ri7udGnTy/at2unPd7W1oa5c2azbPkfREdHExl5EldXVwYPfoUB/fszYcJr99TuJk0aM+/7b1n/zwZOnz5DRMQJLCzMcXV1Y/CgV3CvVbPMjIIgCELV8ksUxKTcPU08PH7D4O47tRry8uDqVbh2jZJGAewuNNWbh9fY3ZpNb3fDSKmbndgKeJG7C/BM5HTAEIxOq7EFqE5+rEzgNHAG9rRvBRXSHAR62GJj/vi8ZxL1vUyCEB66tLR0xo2fwNIli3FzM5xuXKVSaYet+fn5GVyLSRAEQbj/xL+/gvBoi0/N59nvD1f7+F1vdKChm+F1L/+tRyL7o1oNmZmQkKD7demS5iv/dvC1ftInvGndUpsV0dpMSmGZgotphRxbMAy3wiqWBpLc+jIwGnRVl2cZPGZn1e1UAQnAmVtf12/vajl5BVlW9tqf577YFH93W/zcbf6z61idz+eVeXzCR0EQBEEQBEG4D+wsarZMyYMaBvdQsj8qFLB9O1y4ANHREBenCbzy8qpVPDX6EpPefVkvK6KZiRGsc4OKQZkE8AL8b301AhYA5/XrbWTlRIbEGxd1ss72GzIXpFFKrE4WI483QVUkQX1rWRiJhRo7UyNkZTIURrcTsA0J9aSVtyPT/zrHb6NaPRZDT0VQJgiCIAiCIDxVbC2kBHrY6gRClQn0sMXW4v5/ZH7g2R/VasMLgkok8NJLUFp6D62G4bUlbHey1GZFLBfoYcsvrs6ojUwobmqGRRNjlHUKMbFQ6pRPGduZX6K6El0sodDUkhITMzxrO9KvYwCBlktQXfmNAzkBHC5ozqHCFlwuqwPmEmiH5usOe9/syOTVp8lLKSDQw5ZRbb1p5e3IiF9PkJxV/NgMPX30WygIgiAIgiAI95GpsRHjO9Zjyuqoux47oWN9TO9zT0t59sfVJ67hX8tGZwigUnV7ZlG1F0HOyIBTpzRfJ09q/v/RRzBmjP6xxsbg7w9nztS43UpXV85nyZj21zkAJKjwN08mzPo8rU3OYTHhAhKpHCs0WQ8NtbjALo2jTdvrvNZLZbBv11WiQwYxte8slvxxjoisKoZB3hLoYYurrRmf9W+Mw63eulURV/lsSyx5JZo25JfK9dL0P4pEUCYIgiAIgiA8VaTGRnTzd2NomGeVafGHhnk9kMyLMqWKnEIZh6Z31hsCuPrENVaEX9EGFXrZH1UqiI2FI0dufyUn65/k5EnDQRlAYKDhoMzEBOrVgwYNdL98fEizceLQ1QKm/XWO5+z30dPuGCFWF7CXFtbotTeQxjPvJWcszJuw5kQKK8Kva1/rTydyaVI/jw96+9Nn4dG71jWuQz3kShWeTpZsO5/KvD0XySnWTYP/uGRgFEGZIAiCIAiC8NSxNJUyo5c/oT5OLD54WW8o3oSO9enq7/pAhr6pVGBpJjU4BHBkG282TW6rHX4XfSOP4tPnsAw/BHv3wtGjkJNz13MoT54kI6/UcPKQsDC4ckUTnAUEgJ+fJvjy9NT0pBlgVabgt6OayWBh1ud5xi682q8339Sd3YVy/s7O4mCxkpsJgQS5BzE15G02Tu7JyF/PkpylWS90ycHLrBvfmmGhnqyKvKaXTKS8d21IqCdBde3pPPcgdRwsGNnGmw2Tbl+38uv5IIaePgiPRysFQRAEQRAE4T6zNJXSs7E7Xf1dyS9RkF8qx9bc5IFmPyyWKfjtaBKHEjJQqdUYG0m0gUb0zXym/XWOIaGeLBsdwp4BY+h7ZhfOs+8ehN1JGXWO9p/vQG6s6SnSSR4yaRJMmqRfSK2CnHOQfgBKUiHoS+2uwlK5dhmB8MImvOy4u9JzJ5XVws33WSTu7fg09gBfn1mhd0xUahSjNg1lfMuJ/D76c/ovPEleiZzom/mkF5Ty1rN+vPWsHznFMjILb/ck7o1Nw97SlKC69oz49QR5JXLySuQ6163fwqPklcgfyNDTB0UEZYIgCIIgCMJTS3or+LI0lT7wuUdlCjllylKeaWpKS18LnCwdsZTa6g3jWxlxleae9rSwBdeiagZkFhYog4I44ejDX2o3zrvX18lIaDB5iFoFedGQdkATiKUfhLJbCzYbmUDjmSC1AtC2DSC8qInOqZPLahFe1ITwwiaEFzUhVe7M8ec6sf/qJoMBWUVLTv1I6zrtGBIaxKIDSRgbSUANSZlF/HIkie0XUrVBa6CHLeM61KNNfWdG/x6p7RErV37dhoR6kl+qeKwW/RZBmSAIgiAIgiA8YIWyIjbHb2L2sdlEpUZhLocuSdBRXhvX92bpDeP77Wgyy1/pD9vWGq7Q0xPatdN8tW2Lwq8R2+IydJKX6K8lVkDEmUNctt1IoPQURhmHbgdhd1LJKU05gtSjO1JjI51lBFLkLixOH0h8qTfhRU1IkbvoFDU2kmBtoWb2sVnVujbzIuay+ZV9GEngldBaqCW5lJVm8np3J97p6aMNWqNv5jN1TRRDQj1ZOLi5tkesot+OJrPy1VBMpUaPRdbFco9PSwVBEARBEAThMVQoK2Larrf5PXwxvRLgvQvQOwGs5FBmfANn36EMaaM/jC+/RzscjYyQqFRQty507w5du0L79pqfK5DJFCw+kAiAnYUJQ8PqMCjEHVleFOrUnbgUncWq9gmMZRmQWr12b9i5EstWjenm76a3jMDXqaMrLdezsTt5ZXmcTTtbrfOcST2DikIaeCXRd+0QolKjtPsMzT2r2CO26MBlnbqib+ZTKldhb1mztegetsejP08QBEEQBEEQHkOyokJOLv6IdjMWkz4H/l4HL8VoAjIAMyU8c1kzjO/gtR0MCa2jLVtgaYN8+R8QH69JzPHLLzB4sF5ABpBfoiAmJR9vJ0s2Tg7GxfUM4/9sR71jXamf+CW2aVs1AVk1XJW5sS67G9tTfZmyOoovt8UhAcZ3rFet8q+0qkt2yd1T2leUWpjC3ONf6wRkcHvu2dfH3+f30c2ws9DMkfvtaDLDW3tjbmKEfy0bWnk74F/LBmMjCfmlcgNneLSJnjJBEARBEARBuJ9UKjhwAP74A5N//qFTXl6Vh/eNh/UBmmF8m17ey+KDSajU4GpjhumQwVWfq+gKGJmSW2KFnYUJy8Y048ujM1l6ejEA13yMqCtVVVmFwsKLYvt2ZFq1xtitM1Jbb4pj0jC6mIF/fimrT1ylva+TZhmBUE9WRFS+jMCQUE/quVhTqLCtut13sDGzIb+s8sW875x7Fn0zHzVqTn/QncxCGen5pdq1yqzNHr8Q5/FrsSAIgiAIgiA8ii5dgmXLYPlyuKoJXCRVHK6UwNG6cPxWx9eZ1DMUyfPxc7fBSCLB1sLAGluyPEjbD6m7IGU3FF5CFfgh1p7vMDSsDvuvbNcGZL6Ovth5NoWb63WquCqHszjg4v0ijZtM4asDSlbuvHZrbxKBHlmM61CP719pTn6JDCszKRIkFJUpeKdnI0J8HFlyKFEvnf+ott608nZk2M8RLHu1GUHuQXo9X4Y0d2+OhdSCC+kXqjzuzqA1u1DGd7svsu7kdZ12jO9Qj24BbmJOmSAIgiAIgiA8qhRKFTKlivwSheF1vGpKrYZevWDHjrseqpTAAW9YFwjr/SHTSnd/dkkO1mZShrf21qRzV8kh64QmAEvdpflerdQ9fdp+pN7vMbS1Bz1XaXrWHMwd2Dl0J3/vH8MzCthfDPtLNP9PUgDkQPJSJhQYM6vXbA4lZHItpwRAJ6HGuz0bsS8unaW3gjA7CxOGhHry64hWyFUqisoUWN3qmVoVcZXPtsSSVyJndUQKU0PeZtSmoXe9JlNCp7Dk1BJU6qp79CoGrbEpBZibGHP+hm4vZPTNfKasuSPL5GPg8WilIAiCIAiCINwHxTIFe2LTWHJQv6dHu45XTT/ISyTg7l7pbhVwxBPWNoa//SHNpvKqHM3t6Vcvk2dN/kJ6dJ+mV0xe+bA+AKPsCHafT6J9oLU2ucbEVhM5mHyQV88foKpQZ/GpH2nv2YHPBnRk5G+ndfatjLhKsJcDCemF2muVVyJn0YHLLD54Gf9atqx8NZSZ/1xg+4UUbmWuB2BF+HU2Tu7JuBYTtD13hkwMnkh7z/a8ufPNKl9jufKgtUltW6zNjbE1l+Jfy0ZnYWnN+a8Q6uNIz8buj0VafBGUCU+l/gMG6vwskUiwtLDAy8uLzl060b1bNySSqgYcPFh79+5j/oKFvPLySwwa9Ip2+7x5C9i3fz+ff/YpTZo0fiDnTktLZ9z4CTQODOSLLz57IOf4r23espWdO3eRkpKCQqF4ol5bTaxevYY1a9cx5fXJdO3aRbu9svtq7NjxpGdksHHDekPVPVL6DxiIq4sLP/205GE3RRCER1ixTMGX22JZEa4/J8rgOl53UirB2Fh/O8DIkfD777rbmjShbMgg+qpXsLsspsq2BZvBm+4u+B7rjF9xMmRW6yUBoDKyIi89hvwGDQEwlhgzvuV4+q3uV2VAVm7u8dkcHNkHqREo7ijw85Eklg4PZvHByzpBl0oNKrWarEIZW8+n6NWZVyJn5K9n+X30F7Sp257vI+boZVWc3uYd+jTszeD1g6ucT1aRk6UDvZvaMyDIg/wSBW8/20i7sPTqE9dYEX5FmyZ/8cHLj81aZSIoE55qXTp3BkClUpGSmkpsXBwxsbGcO3eet9+q3hObx01lAd+T6vjxcH7++Resra0JCWmFuZk5tevUfiKDzyfV+fMXeP+DD+nSuTNTp77+sJsjCMJjSqFUsSc2zWBAVtGK8CuEeDvQoaELplIjzZDGC+dh8WLYtg1iY8HSUq+crHUbpF7eqAsLyR3wIqrhw7AMCUZqDKPivNm9vuqEHW0tYJBpBhRXI0OikSm4tIda3VG4dKXUpin9ixWojTUZDwNdAymWF9coJX1uaS7PBLqz7bxuvvzom/mUypTaIYMVWZtJyS6WVVpvclYx/ReeZEhoEJtf3keRPJ8yVQGOFg7Ym9shU8q4lneNb575hoU9F7L09FJ+jPyRnFLDC2Y3d2+Ou40Ttua5DPk5Qq+nc2QbbzZNbsuIX0+QnFWsWVagRPFYDGF89FsoCA/QnR/woqKi+PSzLzh8+AgdO3SgVavgh9Qyw4YNG8Lzzz+Hi4vL3Q++R05OjvywcD5mZmYP7Bz/pfCICADemT6Npk2baLenpaU/rCY9cv6L++pB+2HhfIyNxZ80QRAqJ1OqtOt43c2SQ4m0rGvHD+/OZ/yZTTifiri9c906Ta9YBUVlCnbHpLN16BccKLNEbmwC27IJjArntU716duoH+NbjONszFIiSkGNPlvvQVC0uvJG2TcB92fAvTu4tgepZYWhmBHEpRZwcHprgtyDsDa1JrO4Bl1taIYFOlkZXtsrp1hmMKNhYZkCx7usB6YZ7pjE4oNJdPZzYf6gxuy/cpCPDrzP6ZTbwyWD3IOYEjKFyLGR9FjZg0vZl/Tqmt5mOscS8nhznSbYvHNx7HfXn+eVVnVZNjpEu7B0fqkcdzvzmlyKh0L8BROECoKCgujUqSN79+4jPCLikQvKHB0dcXR0fKDnkEql1KlT5+4HPiaysrIAcHNze8gteXT9F/fVg/Yk3bOCIDwY5et43Y1VWTGtNm3C9dsRzLySpH/A4sU6QVlxmYIvtsay6sRVwA4qjG5MSMniz8378EyIZhEHMKoLQ4oasurmRe0xQe5B/C90Gh3r9uDa1sPUld7KJGjurgnAanUH925gUUunGYaGYpYn1/guYjbOls7Vui7lHC0cyCpKM7jPwdKUwjKF3vaLaYWYmxrrLCpdGRtzEz4Z0JC3d7/F4lP6c8yiUqMYvWk041uOZ8eQHbT6qZVOj9nE4Ik8U783neccv704dmgtiuT5ZBVn42TpiKXUljUnUoi6lqtdWNrW3EAGy0fQYxuUXbp0maizZ0lISCAhIYGsLE137d3mPuzdu4/tO3Zw7dp1pFIpfg0b8uJLL+DfqFGlZWJjY/nzz7+Jv3gRhUJB3bp16NWrp3bomyGZmZmsXLWaM2eiKCwsxMXFmfbt2/PC8wMxNX34K4wrVAqu51+/+4GPqDq2dZAaPZjbt56PD3uBzMws7bby+SqLFi3k7/X/cOjQYdLS0mjZogUzZrwLQFlZGZs3b+HI0WOkpGjGVnt61qVnjx506WL4XomNjWXV6rVcvHgRYyMjGjRsyODBlQ8prGpOWWlpKVu3bePo0ePcvHkTlUqFs7MzTZs2oW+fPtSu7cHMmR9wIToagDVr17Fm7Tpt+fJ5Rncb1rd//wF27tpFcvIVlEoltWrVokP7dvTr11fv3q7YXiMjCavXrCUh4RISiYSAAH9GjhyBp4EFMCuTmJjEoUOHOXfuHBmZmRQXF+Pk5ESL5kG8+NKLOFUILMrnT5UbN36C9vvGgYHa63AhOlpnjuGdQ+QKCgr4Z8NGIiJOkJ6ejlQqpX79evTv108vaK947WbOfI/Va9YSHh5BVlYWvXr24NVXx9z1NVbnfQSQyWQcPHiIE5GRXLlylZycHEykUry8venZswcd2rer9nW921xFtVrNli1b2bFzF2lpadjY2NC6dRiDBw3C2tqq0rrkCjl///0PSYmJFBUXs3LFH1hbWxEdHcORo0eJjo4hMzMTuVyOi4sLoaEhPD9woE6d5fUB7Nu/X/s9oDMEt6o5ZSdPnmLT5s1cunQZmUyGi4sLYWGheucC3Xl39evXZ8XKlcTExKJQKPD1rc+wYUOr/HshCMKjK6+k8mF2ALXz0hl5ahMvn92Fray48gMjIyExEerVQ6ZQsism7VZApmFpVEInm1P0sDtGZ5tIbIxLoOx28UVBz/HhgIlkl+TgaOGApdSW1REp9N94kv6WvbExLmbUC5NwrhuiSSBiQGVDMcuTa0TcOIaliWWNUtLbmduzK1p/uGOghy1mJkbEpxbo7VOq1KyKuMrINt5M++tclecYGlaHg1e3GwzIKlpyaglhdcKYEDyBr458RZB7EG+3nk4/v768sSYWB0sTfh/djANXt9N37RC9eWpTQ96mpXcvQus5cTghA1uLxyPceTxaacC6dX8SceJEjcr8/PMvbN6yFVNTU5oHBSGTy4g6e5YzUVG8M30aYWGhemWOHTvOnLnfoFarCQwIwMbWhnPnzjNv3gKuJF9h1KiRemVSUlKY/s575Ofn4+XpSUCAP5cvXWbt2nWcO3eOzz79BBOThxu1X8+/js88n4fahn8jaWoS3vbeD6TukhJNOlgTE91fD5VazVdfzSI6JobAwAC8vbywsdWkT8rNzeWjjz8hOfkKDg72BAYGgBri4uKYN38Bly5dYty4sTr1RUae5KuvZ6FUKmnQoAHu7m4kJSUzY8b7dK0kiKtMdnY2H338CVevXsPa2prGjQMxMTEhLTWNnTt34VGrFrVre9C8RXOUKiWxsXH4eHvj43P7HqhVq1YVZ9BYtOhHdu7ajampKU2aNMbMzIwLF6L5Y8VKTkSe5LNPPzY47DEyMpLNW7bi61ufli1bkJSUzKlTp7l4MYEF87/HwcGhWq/z7/XrOX48HG8vL/z9NR+Mk5KS2b5jJ+ERJ/jmmznawMzHx4cunTtz+swZcnNzad06DAtzCwBq16mNja0Nx4+HY29vT4vmzbXn8A+4/YH7xo2bfPjRx2RmZuLq6krzoCBKSkqIv3iRz7/4kpEjh/PcgAF67SyTyZgx8wMyMjIIDAygfr16WFtb3/X1Vfd9BEhPT2fhD4twdHSkdm0PGjTwJTcnl7j4eGJiYrhx/fp9mzO49Kef2bVrN40DA/Hy8iQ6OoatW7cRHR3NV19+gaWBuRWHDh1m9549+PrWp0XLFqSmpmo/W/y+bBnJyVfw8vKiadMmyOVyLl9OZP36fzh58iSzZ32NhYXmvfIPaERObg5nzkTh7u5OgL+/9hwV79/K/PXX3/yxYiXGxsYEBgZia2tDbGwc69f/Q3h4BF99+Tn29vZ65S5dvsySpT/h7u5O8+ZB3Lh+g+joGD788GPmzpmFl5fXvV1MQRAeGjsLww/FG2YkMzH8L/rGHkJaRUr2PHsnLCdNxGT8OLj1QFGuVLP0UCJ2xgU8YxvOs3bHaG8dhZmRvNJ6ks+u591jvW4NucsnPrVAm0BjeUlfAj1smeTestKADCofink7ucZnZBZf4X+h/2PkxpGV1lPu7dbTOZ1coJfkA2BUW29WRVzVSfJR0YrwK2yb0r7KRaWNjSSMbFub7ivushD2LfMj5nNgxAFeazUJe3M7FEopRWVqIpNz2PR6sM7i2BVFpUYxatNQxrecyOxus3nnWT/NsgKPgcc2KPNr1BAvby8a+PrSoIEvY8dNQC6v/Bcg6uxZNm/Zio2NDbNnfYWHh+aDTVxcPDPf/4D5CxbSuHFjnaemBQUFLFj4AyqVinffmU7r1mGA5gP4u+/NZMPGTQQHB+s9WZ43fwH5+fn06dObsbeejCuVSmbPmUt4eAR//fX3U5Fg4XGkVquJPHkKAG9vb519mZmZmJiYsOiHBTg5Oensm79gIcnJV+jbpzcjRgzXBt25ubl89vkXbN22neDglrRo0QKA4pISFixciFKp5PXXJ9Gta1ft+Zf/sYL16/+pUbu/+34+V69eo23bNrw+eZL2Ay1oem9KSjRP/F54fiAO9vbExsYRGhpSo/vw2LHj7Ny1G0dHR774/FPt71BRURGff/4lMbGxrFq12uCDis1btuo8+FAqlcyZ+w3Hj4ezbfsOhgweVK02PPvsM4x9dYzOh2iVSsW6P/9i9eo1rFy5iimvTwYgLCyUsLBQZs78gNzcXEaNHImbm6vOdTl+PJw6tWsbTB6hVCqZNXsOmZmZjBgxnAH9+2FkpPmHPSUlhY8+/pTly1fQonlzvQ/oCQkJ+Pn5sWTxj3o9MVWp7vsIYGtrxyeffESzpk11MoWmpaXx/gcfse7Pv+jSpYvOa75XBw4cZNbXX+HrWx/QPLj48suvOXf+PKtWrTbYA7hr927efvtN2rfT77F75eWXadTIDyur29dGLpfz008/s3PXbjZu2swrL78EwDPdu1PLvRZnzkQR4O9fo0QfCQkJrFy1GnNzcz799GP8GjbUnuu77+Zx9NgxFi9ZyrvvTNcru23bdl59dQx9+/TWbvv5l1/ZvHkL6//ZwBv/m1rtdgiC8GiwtZDqDLNrmnKRScfX8WxCeJXlypo0I23MBEwGDcLY1orsMjnkl2IrLUGWvIE3zX6kg/8ZTI30h/cZbIepksTLWZSpDQeJEzrWrzKQKJEryC6SVToUMzmrmIGLTvFez4Y83/R5wq+HV9k7NaHlRPo07EPvefqdHUNDPWnl7chnW2IrLZ9XIuf8jVzefKZhpYtK/69bA0qVBTVKPJIvKyAt24KJm88wKMSLpnXs9BbHrsySUz/S0asDzzUa+FhkXgR4PFppwPMDBzJk8CBCQlpV6yn7po2bAXjpxRe0HyYBGjXyo0ePZykqKmLPnj06ZXbv3kNxcTGhISHagAzA3t6eESOGAbBx4yadMhcvJhAbG4ednR0jRwzXbjc2NmbihPFIpVK2bN2GUqm76J/wcCmVSm7evMn8BQuJj4/HxMSErl266B03bNgQvYAsMTGJU6dO06CBL6NHj9LpBbW3t2fSaxMB2L5jp3b7sWPHyMvLJzAwQBuQgSY1/5DBg/TOUZWLFxM4d+4cdnZ2TL7jgzyAm5urXoB5L7Zs3QpohoxV/B2ysrJi/PixSCQSduzchUymPzykfft2Oj3RxsbGvPD88wDERFedIriipk2a6PVqGBkZ8crLL+Hk5MiJE5E1eUlViow8yZUrV2jdOoyBzw3QBmSg6VUcNWoEKpWKXbv3GCw/duyYGgVkNX0fbW1tCGrWTG/pBjc3N1588XlUKhWRkffnevTq1VMbkAFYWFgwdtyrSCQSdu/Za/A9Dw5uaTAgA2jZsoVOQAZgYmLCmDGjMTY25kREzUZBVGbrtu2oVCr69O6lDcjKzzVu3KuYmpoSEXGCjAz9yfD+/o10AjKAl158EajZPSsIwqPD1NiI8R3rAdD6ylk2LX+z0oBMBexvZsuOn94nZd9ehsv8aP3tEV5eepxDCZmYxXyMdIMHDlGj6WYbedeA7GSRP1/cHE1c8GmmF/9RaUA2NMyryhTuxTIFK8KvkJJXanC/nYUJkzr7svn1drTwdCI5Q8Hs7nNY/fxqgtyDdI4Ncg9i5cDVzO4+m1nbErULR4MmkJr7YlPe6dmIv05e16aYN2RIqCf+tWx5a91Z2jd0YenwYPa+2ZG/JrRm75sdWTKsJRkFZVzPq0ZWyQoyirKp7WBBcZmSrv6uWJkZMyi0FgtOfEtTt6a082xHU7emGEsML1Ew59hsFOrK2/2oeWx7ymqirKyMc+fPA9CmTWu9/W1at2bLlq1ERp5kwID+2u0nT52qtExwy5aYmppy9tw5ZDKZdi5NeZlWrYL1hija29sT4O/PufPniYmJfWDrTAnVd+d6ZaD5wDl16uvUqqW7CKREIiGkVSu946OiogAIDQnR+eBerl69epibm5OQkKDdFhOjeeJk6EOrVCqlTZvWbN68pVqv4exZzVOnDu3bYXnHB/n7RaFQEB+vmZTcsWMHvf3e3t54e3mRlJxMUlISfn5+OvuDgprplfG4NQwvO8dw2tvK5OcXcCLyBFevXKWoqBiVSnWrjUoKCgooKCjAxqaKVTmrqfx9bR0WZnB/YEAAgM77Ws7BwYEGvr41Ot+9vo8xMbFcuHCBrOxsTXCkvn1Nb6borxtzLwzdp5516+Lt7U1SUhKJiUk0aqT7nhv6XakoKyuLE5EnuXH9OsUlJahvjYuRSqX3rd3lv2eG7ll7e3uaBwURceIEsXFxuLjovsagoCC9Mra2NtjYWNf4nhUE4dEgNTaim78bQ8M8WaVScsWxNl7ZN3SOKTKBX5rD/FC47JQPNz5nfHgWv4/+nP4LTxJ9M59pf53jh5YqemM4MAKQq405XtiUnXmt2ZUfRoZCM7R+jFsjJncuYPbOeL3epAkd69PV37XS9O3l88jWn75BFz/95FXeTpYsGx3CiaRsxi0/qa3fzsKEN7q3Yt/wQxTJ88kuzsHBwh57c3sS0kpZHZHKa10aMKZ9PeRKNVZmxkiNjFh+PJnBP4WzcHALvJ2t+PVokl6bR7X1ppW3IyN+PcG1nBJMjCRM+OMkCpVamxExPrUAP3cbvh9cs2HfllJb/jx5nT9eDaWwTIGNmRSpVMG2Idsolhdzo+AGzpbOWEgtDKbSP5N6htySHKxNq/+A9GF6KoKyGzduIpfLsbOzxdlZPxNN/fqapybJV67obE9OSgag3q39FZmYmODpWZdLly5z8+ZN7RPs8jL16+mXKa/r3PnzJF+5IoKyR0B5shaJ0e3Fo1u3DjM4/8fOzs7gXMD0dE1q9RUrV7Fi5apKz1VxeG12tiYxTWUpyN1cqz/kLPNWdkF3d/e7HHnvCgoKUCgU2NraYm5uOK2sq6srScnJ2qQ7FTk76f/elQceiiqGHd/p0KHD/LDoR0pLK/9DWFJScl+CsvR0zRO9b7/7nm+/+77S4/Lz9Sc+u7jULOMV1Px9LCoq4uuvZ2sfOBlSPj/y33J1NXyfurq6kJSUpL2fK6rqGmzcuInlf6xAoajeUJ97Vd4u10p+n8pfV3ZWlt4+50p6qy3MLSgoKLxPLRQE4b9maSplRi9/Qn2cuC6djNdX7wGQbQ4LQjXBWPatabL1TCBRrhkK17pOO4aEBrHogCYb46dnA+kVIEFSIbl9mcqEAwUt2Z7Xln0FrchX6n6WCPSwxcbchBAfR1a+GkpOsYysQhkOlqZYmxtz6koOp69m08LT0WBgVj6PzFDGQzsLE5aPDmXxwcs6SUdAM7zw401xzNtzmR8GtyDIsxE5RXLiUkpwtDSnVxMPVkdcJb2glE/6BXIjpxgbc1Oeb1GHfkEegJrOjVzo5OdCbomcnCJNm81MjFgVcZUvt8bhZmfGCy1rowbe6t6QWTvjOX01F+WtB24X0wqxMrGtUeIRS6kt3+05RitvexrVskEqVZBZnEtKQQoOFg7Uta3L0tNLOXL1CCObjTSYSj+7NJc6do9Hdt6nIijLyNR8uKpsSJi5uTlWVlYUFhZSXFKCpYUFxcXFFBVr5m9U9sfZycmJS5cuk56eoQ3KtOdyNlymvK6MdLFG0qOgJvNTTCtJzqJSa/7BCfD3f6CB0SOv8vnIekPs7kV6ejrz5i8A4NUxowkObomjo6M2scj0d94jPj4edSUTkWtKdWuyd4sWzbG3s6/0OFtb/QDQ1OTBZ1hdtvwPzp0/T+PAQAYNegVPT0+srCwxNjbmzJkoPv7kU8ML4fxHTCq5BvHx8fz62+9YWVoy6bWJNG4ciIODg/aBx8hRY8j5r3qiqrgv78c9KwjCQ3b0KBw+DO9qsiQrlCpkShUFpQpCvB2wmjGe86s/Y1mDYpYEQ6EZOBrBazYwwhZCzKHxFYiWwbyIuWx6eS+LDyahUkOawpnz8mY0MT1PqkUH5sQFsTs/jAJV5b0y4zrUw0gCX2yLZVXEVVp6OfBc89p0aeRGYakCFxtzHC1NKS5TIgEs7gjMKqb0vzPj4dAwLyKSsvQCsnIVe9G+2BZrcNHlMe18iE3J56vtcRSVKZBIYEioN87WpkxYoRkJ5uduo+0BS80rZXBoHTZPbYVMVUChLJfcslSae9uy/NVAZHJjVkXcZEW4Zvhjebr+UZuG3vWtmxr6NqsjUmhdz4nAOhZsufgPs4/N1su0OCVkCqODRtNjZQ8ibkTopdJ3NLe/67keFU9FUFZaonmqXtViuObmZhQVFVFyKygrqfAkvrJy5T0GFZ9Glz/Br6yMmbnZrTKVP+mvqLCwiKKiIr3tmZk1WxDwTnVs65A01cDaG4+JOraPzlOP8kA7NDREZ/hrVcrnQWZkGB5fnV7J9qrOn5qaWu0yNWVjY4NUKiU/P5/S0lKDvWXlPUtOTg9mvatTp06jUCgYMKA/ffv20duflmZ4bZV7Vf4Qp3u3bgaHMN9vNX0fw8MjMDIyYubM9/SyH6be52uhefCkP+yk/P6tyRpn4eGaBViHDB2it1REWVkZubm5997QOzg6OpKWlkZ6RobBpRfKe7kdazCHUxCEx0BkJHz4IezYoXn40rs3xX7+txZZ1iSh8K+lGU7XdEQxEgk8Ywmv2kI/azCt8ExmhC1Mz9QMhSuS5+PnbkNsimaExJuJ4/hlXE+klm6Yqy5RUElABJp5V9393TgQn86K8Kt4O1ky98VmRCZl8/nWGNLySyksU3AxrZBG7jaMbV+PZwLddHrMKqb0XxF+hU2T2zI4xJO1J68xONSTsctOGjx3Vb1ogHZI5tAwL97p4cd7vfyxMzfBzMSIf07foH0DJ15p5cmqE1e1r93byZJ/JgVz8OoO+qzRT0s/JWQKXXy6UL/uZTYGt2Xkr2dZEX6drVN7MaHlhEoTjxhLjPmo4yc849OHzy8ksHh4Y6bvnlbtdc0qptJv7t4cewv7St+TR81TEZQ9zjZv3qyz1tL9IjWSPrCU8k+bZkHNWLlqNeEREdUOygIDAti3bz9Hjh6jZ88eOvuUSiXHjx+v/vmbNWPFylUcOnyEIUMG6yWIuJNUqvm1V6oqT/trqIyfX0Oio2M4fPgI3bt309l/5coVkpOTMTc3r1aa8ntRWKgZMmao5zo6OrrGH+alJuXXwXDSnaCgZuzZs5fwiIj/JCir6ftYVFSEpaWlwXT0R48eva9tO3r0qF5Qdv36dZKSyt9z72rXVVioechk6H08euwYagNdnXd7ryoTEOBPWloahw8dZsgQ3TTMeXl5nDkThUQiEeuOCcKT4tIleO89+Ouv29vUapQffMivU2bz3Z4E7XA6azMp+bkXec8RxtqBTyUrFQ21gfcyQQlkl+RgbXb7o/OlMk8y5LYs2xHL28/4EVbPcObBcR3q0c3fDYVKxYJ9l7GzMGHFmFAu3MwlzNeaJl4qvcWPP9oUzckrObzXsxGFZQpKZAqdRZDzSuSM+PUEy0aH0LmRCzKFstJsjHfrRSu3IvwKQXXtSMwoYtGBy9oeNIlEwqTO9WnhZc9vR5O5nlPCsjHN+PLYTJbeJVia1mYaC09+ycbXP6a4zBhbCxPmPDOXDl4dmX1sljaYczB34JPOnzCkyRCKZMVcyUvk4+e82XJxc43WNZsfMZ8Nr2xg1tFZTGszHVOjyjtkHjWPbfbFmjC30DzVLysrq/SY0lLNvvIPQhYVegIqK1feK1bxw1N5D0JlZcq05zE8L+dOffv2ZemSxXpfX37xebXKCw+eX8OGBDVrRmxsHIuXLKW4WH/ByaSkJE6fPq39uU3bNtjY2HDhwgX27bu9GK5arWbV6jUGs8FVpmHDBjRp0pi8vDwWLVqsN98qLS2d5OTb8yXLezVu3NCd3Hw3vXv3AmD1mrU6vTnFJSUsXfozarWaHs8+88AWRy9PDHLg4EGd15iVlcWiH/UXDL4b21u9f6mpqQazobZp3Zq6dety8OAh1q5dp7fkhlqtJjY2ltjYytME10RN30cPj1oUFhZy+MgRneM2btrM+fMX7kubym3duo3ExNvr4ZSVlbH0J8173rVrlypHIdzJo7ZmPbzde/bozCm7eu0ay5f/YbCMo8O93bO9evbEyMiILVu3kXDp9hwDuVzO0p9+RiaTERYWek9zAAVBeIRkZcH//gcBAboB2S3GGzfQX5nKwWmdmNSpHs86nWOq2QyCT/biS+fKA7Jy9W/td7RwoLBMdy6sg6Upl9IL6bfwKB0auvDnhNYcf68L26e25/h7XVg3vjXPBLixKuIKGQWaNPaTu/hiYaYgVb6fvmu70GiRN21/b0GjRd70W9cFF9czbJwczJGEDHZGp7LsWDJdvz2ECk2QVy45q5h+C4+SUVBGqdzwg1ZjIwmDQz357WhytS7lb0eTGRLmRaCHDZamxvx6NIklBy+jVMPN3BKWDg9m39vtOXpjp8GArKIlp5Zw+OphTI1N2RS/iR0XbtDko53M2ZZEj/oD2PzyPuJeS+b02GiSpibjbOFM1+Vd8fy+Lu1+D6ZEUcSso7Oq1e75EfOZEDyBs2lnKZYX80nHT+nr1xdT6cNdF7gmnoqeMhdnzWTuLAOTuUETXBUVFWFtba1NPmBpaYmVpSVFxcVkZmXhaeBpdHl9FSfBuzi7kJiYRFam4XOVT+Z3qWYiB2trqxql1RYejjfemMonn37G9u07OHToMD4+Pjg6OlBcXExy8hUyMzPp26e3dp0ySwsLXp88ia9nzWbe/AVs37EDdzd3kpKTSUlJ4Znu3dm1e3f1z/+/qXzw4cccOnyYM1Fn8Pf3x0RqQmpqKknJyYwaOULb0+Hn1xA7OzuOHTvOzJkf4ObuhkQioVu3rlX2GLRt04Znn+nOzl27eX3K/2jatAlmpmZciL5AXl4+fn4NGVzN9cbuRUirVtrkOhMmvkajRo2Qy+WcP38BHx9vGjXyIy4uvtr1mZiY0Lx5EJGRJ/nfG29Sr149pFIp/v6N6Na1K8bGxsx47x0+/uQzVq1ew9Zt2/H29sLOzo78/AKSkpLIy8tjzOhR+FdY0PjfqMn7+PzzA/nuu3nMnfst27btwNnJiaTkZG7cuEG/fn3ZtGnzfWkTQMdOHZk2/V2aNGmMpaUlMTEx5OTk4ulZt9przJXr2qUrGzduIjLyJK9Neh1f3/oUFhYSHR1DaEgICQkJesN3NcsBeHHp0mXenjadunXrYmRkREhIK0JDQio9V8OGDRg86BVWrFzFO++8R+PGgdja2hIbG0dmZiYeHrWYMH7cPV0TQRAeAaWlMH8+fPkl5OUZPCTDyp5FYS+ybU8SE5JXMtF+O9a1b/UYVTLvtlQFG4pgWT7sLtb0kjV3b46tmR1GEk2go1SpCfSwxczEiPjUAvxr2WJqbISlqRRLUym17G4ls1Kq2HYhhV0xaQR5OhDq48ArIW5M2/02SyrpZSpf/Pj30Z/zzl/xfPtykGbo4R3zyEDTY/ZH+BVCfQwPw27oZk2prPJetDtF38zHCFg8NJjU/FIcbyX0yC7SdCp0/eYAke+3Z3YNgqUNr2xg4NqBbHhpN59tgRUnrjKuU30m/nEBcxNjvh8UwDt73tG5HoGugRTLi2u0rlmxvJjGro0pkZcwvsUUSsqMsX7wU7zvm6ciKKtd2wMTExPy8vLJysrSS/hx+bLmCbD3HQvAevt4Ex0dQ+LlRL35CAqFgqtXr2FqaqqzZpO3jzcRJ05wOVF/lXWAxErOJTze7O3tmfX1V+zatZvDR46QlJhIXJwMe3t73N3c6NOnl15a8dDQED7/7FNWr1lDQsIlrl+/ga+vL5MnvcaNGzdqFJQ5OTkxd+5sNm/azLHjx4mKOouRkRHOzk707PEsrVoFa481NTXlgw9msuKPlSQkJBAdE4NarSbA3/+uw7hee20i/v7+7Ni5kwsXolGpVLi7u9G3Tx/69etbox6TmjIxMeGrL79gxYqVnDp9hpMnT+Hk6Ejv3r145eWX+PTTmvceT570Gr/9voyzZ89y6NBhVCoVKqVKu3ach4cH3307l63bthN+PJz4+IuoVCrs7e2pV8+HkFataNeu7X17jTV5Hzt17Ii1lTXr/vyTpKQkrly5gq9vfSaMH4darb6vQdnYV8fg5urK7j17SEtLx9raml69ejJk8CC99cbuxtbWhrlzZrNs+R9ER0cTGXkSV1dXBg9+hQH9+zNhwmsGy737znR++30ZMTGxXL6ciEqlwtnJqcqgDODFF1/Ax8ebjZu2cCnhEmUyGS4uLgx8bgDPPz/QYKZVQRAecSoVrF4NM2bAVcND8nLNrfkx7AWWN+9DB+fTHPQci7mRDKpI+BtVBkvzYHUB5N7R8TStzXRkcmPmv9ICMxMjVp+4RkM3a1ZFXEWlhtFtfQzmDpIpVaw4fpXeTWrh727DzyNasuXieoMBWUVLTv1Imzrt6dE4BAkwpp0P605c46/XWjM4xFNnKKKhbIygCR4DPWzJK63ZWl2p+aV8uS2WyGRNsgxN6nsfRretR05RGXmluTUOluQquc6cvFURVxne2pvkrAJ2J27Vux62ZrZkFtcsf0JWcRa2Zra4WLowa8dlXm1fDxeb6o1MexRI1IYG8D+GXnjxZeRyORs3rDe4/9NPP+fU6dOMGT2Kfv366uz76edf2LJlK6NGjtCZE7R+/T8sW/4HoSEhzJjxrk6Z4+HhfP31bFoFB/P++zO02y9eTGDa9Hews7Pjl5+X6qRQz83NZcyr4zA3N2fZ779q5/bci7S0dMaNn8DSJYtxczPc66ZSqYiP1/Qc+Pn5GVxDSxAEQbj/xL+/gvCA7N8Pb78NFaYEVKQ0MeXn5n34ofVL5JtrHrrUMsngcKMxSCX6Q/xURhYcN/LkzcR4TlQyy2VCy4m801qzTlleiZxAD1vGtPOhe4Abg3+KoGkdO8Z1qIe5iTFutrpBQEZBGcUyBTE382laxx4rcwWdlrWvMqhxMHdgYquJvBb8Giq1muySHBwtHLAztyfuZgl1Ha05dilLZ92wSZ198XG24u0/z2JnYcLQMC8Gh3qiUqkxMpLQ9ut9VV5WYyMJDd2ssTaT8u1LQUxYcZLom7pLvgwN9eLdXn4k5cbRdHHTKuur6MioI7y7911md13At9uKiUzOwdHSlC1T2mFuKqfL8g5616OpW1PWvrAW/x+qPxIldlIsH+3/iJ/6/UKLTw5yaHoXPOwfzPqtlanO5/PKPBU9ZQD9+vfl1OnTrPvzL4KDW2p7t+Li4tm5cxdWVlZ066abvKB79278+dffRJw4wfHj4bRurVlENjc3l2XLNHMf+vfvp1OmYcMG+Ps3IjY2jmXLlvPqq2MATfKGxYuXolAo6NO7178KyARBEARBEJ4qSiU8/zxs3FjpIYqXX2G83wBuOtbC71ba9otphaTIXdiR15Y+9oe1xyYrfLBvOplhexuwYGRbXmu4A1nEHL0sgv8LnUbHuj0Y+etZ8ko0PU7RN/N5c91ZhoR6smx0CEVlcob9coKlw4P1gjJzEyMWH7jCiDbe/H36Oj2DzKoMyHwdfdkxZAeHrhyiz+o+eu15u/V0Amr3o62vE88GulNYJqdUrsLWXIqZqTHXsosY2KKOdgHpuNQCDk3vrNeLVk4TwNVhUGgtiuT5mlTyxtksHtaENSdStOnsAVZEXKF/UC3cHe2reqf0OFk6kV+Wj6OFA1DMpM6+DA71xFgCxYp8g9cjOj0aSxPLGq1rZiG1YECjgZxOLsDP3RZbi8dnPhk8xkHZyZMnWbvuT+3P5RPGp01/R7vt5ZdeJDhYM9wnqFkz+vbpzeYtW/nfG28R1KwZCoWCqLNnUavVTHl9st7cLRsbG16fPIk5c79h1uw5NG4ciI2NDWfPnqOoqIj+/foaXAB6yuuTmf7Oe2zespVz5y9Qt24dLiVcIjUtjUaN/HjhhecfxCURBEEQBEF4MhkbQ2Xz8Xu0hikNkdVtwad1+lEqU5JdLNPOh1p94hrrop7jGdvjbM1rxx9ZvTld3Ii9PTqhkJ6m/8KTDAkNYvugAygoJLMoGwdze4ywZnVECv03ntQGJhWtjLhKsJcj13KKSM4q1smOCJr5ZPvj0jE3NSYiKYvDCRkE+1bec+Ng7sDOoTuZdXQWS08t1dsflRrF0H8GM6HlRGZ1n83qiJsMaF4HC1MJBWUKJBIJ4zrU58ttsayMuD280dBcNNCktf99dDMOXN1O37X6ae2nhrzNxsk9GfnrWa7llNDQzZpTV3IY4uFGy1otOZVyqtLXUq48WDIxMsHG1I65LzpzIimbN9dGMW9QIDcLbhosp1QrWXJqiWYdsk2j73qeKaFTOJNyhj4Ne9Pr+xP8r1tDjB+z5SYf26AsLy+fixcT9LZX3JaXp/tE4NVXx+Dj48PWbduJOnsWqVRKs2ZNeemlFyudS9OmTWu+/OIz1v35F/HxF1EoFNStW4fevXrprbFTTjMP5RtWrV7NmTNnCA+/gYuLCy+99CIvvvC8zpBGQRAEQRAEoRo++QRWrYLy9Vtb14P/NQTVfsg5jlHBdibtCyDq5u1xiOVp3V8aM5aXlnkRlXF77nNOsQxrMyl5JXIWHUiid9PaZOZLCajdkI83RbP9wjlUd5nk8/ORRJYOD2Z3TBq2Frc/ViuUKsoUKpYeSmTJ8GDGLjuJGjVOlppsssYSYwJdA7E1syW/LJ/o9GgmtprIweSDBgOyihaf+pGO3h3o1bQ3CnUJ+WV5ZBVnozJyxk5ih72lMXYWJrd7uCqsaVY+F83OwkST1v7oTJaerjzhyJthb7F+0seUyBUUyvLIKc0hr8ycnUN38m34t/wY+aN2oWZDpoROYcmpJUwNfRMzYzMW7r3EqhNXmdTZh4ibh/Fzrl9p2R8jfyRybCTjWo6r8pqMbzmevg37Ympsxqxtl+nQ0IUujVwxMX68ho0/tkFZ165d6Nq1y39Szt/fn48+/KBGZVxcnJk65fUalREEQRAEQXiqKRSgVoOhB9i1amnmk22aD1PrgekZUNxOrGauSKdR6UaiuL3+Z/nCyENCPZk38ln6LTyqDVYcLE21Ke6Ht/bC28mKRfsv86ajFVvPp1SruXGpBaBW881LQZjeCgKKZQpOJGXh5WSFUq3WZj80NpJgbWrH/J7z6e/Xn2J5MZnFmThbOmMhtcDa1Jrn1j5XrfPOPjqbZ4Y/Q/c/unM65fb8ujt7uJKzNMv07IpJY0Yvf+1aap38nNh/ZbvBgKycr6Mvr7WayLZLG/jm+Dd6PWlvhr1J5NhIeqzswaXsS3rlx7ccT3vP9lxIv0Cfhn05GJfNqhNXMTaSMCi0Fv3XDuOfV/6pdIhiTmkOPVb2YMeQHbSu05p5EfP02vBOm3fo49eH8EsFNHAzo3/zOng5WmFuYoRUBGWCIAiCIAiCUANqNWzfDtOnw5gx8MYb+sekH4Z2R6FhDnDKYEr7sc4bWJP9LKA7dm1lxFWae9ozJNRTuzCylZkxpsYSFgxqTrcAV/KKFViYGlUrW2HFuVgqSTal6hxSCh1wMLfn6KV81py4zmudfbE2k5JdLAOgroMFJsYSHMwd6L+mv16AMa3NNH7r/1ulQU5FZ1LPkF6UjkKlu27anSn1X18VzcLBLTiRlM2YZZG09HJg6fBgbMyVdPh9cKX1V2co5fANw5kYPJE9w/YQvDQYD1sPbM1s8bD2oFeDXnTy7sTZtLO82/ZDjDFj6SFNAN3I3RqFqgRLU0s2xG2ocojipexLtPqpFROCJ7B3+F5yS3NJKUjBxcqFWta1MMKM9HwZdpZmmBobE1DLDmMJmJs+fiHO49diQRAEQRAE4clx+jRMmwb7bmUI/OwzGDkSHBw0P6cfgfMfQ9reSquQqaT8ndOFpRnPc2dAVu63o8ksHR7M4oOXGd+hHmYmxqwa2xqpkQRjIwlWZsZM7doQI6OqJyNVZy7WZ8/1QqXU9MQ5Wppqhwt+cOBdg4suR6VGMWT9EMa3HM+OITto9VOrKocFwu0U8IZoUuq34/dRz/HNrkvaYYsRSdkciE9n3lDvKhOOVHco5ZoLaxjXchyJUxPJKc0htzQXRwtH7MzsUKlVtK3dmbfXxfJ+7wBu5JYwqbMPw1rXRqbO46uuX+Fq5Yq7tTsRNyJYcmqJwXPklOaQXZJNTkkOIzeMBAl4WHvwY++fGPFLBOdv3J6uFOhhy/gO9egW4IblYxaYPV6tFQRBEARBEJ4MV6/CzJmwYoXu9pwczYLQ0/rfNRhTGVvzU+oz/JrRjzSFc5Wni76ZT6lMyRvdGtKsrj2d5hwgxNuB2S8040hCBjtj0sgoLGP+K81pUttW58N+uerOxRrfciJzus/G3sIEc1Nj3uxeXzNc8K7rky0hrE4YE4In8NWRr6o8tjyrYWW+j5hL34Z9WROpu5ablZmUjMKsSssZS4wZ33I8/Vb309l25xw4HwcfdgzZwZGrRxi1cZSBnr/ptK/TA2tzKSVyBRsnB3Pg6nZ6rtYNZPs17Mcv/X+hk3cnZh2dpR/khk6lvWd7nR7EPwas4udD1yu8R2oaml2hvfwkSzcGcSK5KzN6NXqsArPHp6WCIAiCIAjC4y83F776CubNgzIDi4P5AE5LYc/cSqtQm7lw0nwYxn6T+Orn2GqfulSu5MXgOryyNByAj/sFEpGUSXNvSxrWdiKrOJtiZQbLxjRj05kMDl7MIjW/lItphShVaoaG1dHOxTIUqCjVSkDTU9Xesz3v9nyGNSeuMrq9B52XzalWG+dHzGfDKxuYdXQWKrX+2mpwO6thI6dGtPNsR3pROpviNiFTybTHnEk9Q1ZJjnbB5nKFZQptwhFDAl0DKZYXczbtrHbNtPEtx+vMgbM0scRCasGXR77k+/Dv9erQ9PwNZnzLiXzY6ytszI15e9c0g4HspoubaLigIb/0+4XDow6TXpROSkEKTpZOWEgtWHJqCW/ufFPbczgxeCL9/Poy8lA4Ha1P0cX2BF1tI6ljmg7ATxkD+CLcl1AfR3o2dn9s5paJoEwQBEEQBEF48MrK4Mcf4fPPIauSnpo6deCTAWC80PB+Cw+Ujd5he2kvfjh0k4WtXIHqB2U2Fia8vvoMyVnFzBrYBHMzBany/by3dq5eD830NtP57pU+5BcbYWQkYU3kNQaFujFo/QRmtJ+hF6hYSC1YenqpNiPhN8fncHBkX7zSLCmS51U5XLCiM6lnKJYX09i1MefSzunt97H3YcPLG3CwcGDuM3PJKsnCycKJX/r9wvFrx5m4dSJJuUkAZBRlY22m+3H/YlohVia2lSbYsDWzJbM4U2fNNENz4N5q/RaTW01my8Utlc6BW3LqR8a1HMvBq3FVJhXJKc1h4LqBLOu/jD5+fYjLjGN+xHz+iv1LG5gGuQfxXotX6WIiJ33LAP5yPY6RU7FeXV1sI/ki5VUWH7xMV3/XxyYoezxaKQiCIAiCIDyeVCpNKnt/f00CD0MBmY2NZsjixYswdB7YN9Xdb1ELWs6Hfpcpq/8aiw6lcDGtEHNTYwI9DM+rulOgh61mwWKZgr5N3ekd5MTMfe8watNQveAkKjWKwesH8+7ed1BRxlvrztK0th1GRqX81v83fB186b+mP/4/+NP+t/b4/+DPgLUD8HXwJXJsJL6OvppkHIXZPNvYnaziqueHVWQsMaZEXkI7z3Y0dWuKscRYu6+rT1eiJkRx6OohOvzeAc/vPWm+pDme33vS8feOZBRnEDUhiq4+XQFwtHDQZpgsp1SpWR2RwtSQtw2eP78sH1crV3YO3cnsY7MZvWm0wesz7J9hzDk2hx1DduBg7lDpa3GzcmXW0VnVeu3fR3xPkayIo1ePMueZOVx87QKXXv6FrGcmElFbxUsJk3GOeQNf2V6MlPoBGUB9sxv4mN4g+mY++SUKg8c8ikRQJgiCIAiCIDwYe/ZAq1YwZAgkJenvl0ph8mS4fBneew8sLEBiBE0+0uy3qAUt50G/RPB7HYzNyS9REJOSj1Kl1i6MXB2vtquHpZmU+a+04P0+jdgcv4kld53j9SMHr+2gpZcdG6NuYGliUWWgMnrTaJ1ApUiRT36xHEcLw0FLRQ7mDsxoP4PEqYk4WzozvOlw1r6wlstTLjOj/QxeDnyZf17+h+m7pzPsn2GVBkrv7H6H9S+vp0+DPtiY2mFrLsW/lg3GFRKYrAi/TifPnoxrMUGvHdHp0ThZOHH4yuG7JvpYcmoJh68eZkKwfj2gGQpZICvgQvoFmro1NRhoVnQm9Qyy3AvUz9iO5OAAfPa2ov7pMTgm/Yhpvn6vYWW62EYCkF+NTJqPCjF8URAEQRAEQbj/jh+H7t31t1sAvYEWdeH5PdCwof4xdQZA2G/g+TJILXR2lcoVGBtJUKrUBhdGNmRoqBcd/VyY8McpTl7J4ei7bavdezMvYi6bXt5Laq6SrQnbqhWolCfrMDOy4e2/z/LT8KaVDhcE7jpUcFqb6bzf/n3+jv270iyF5RafWkw7r3Ys6r0IEyNT3n62EY6WppiZGLH6xDVWhF8hr0TOyF/P8vvoL2hTtz3fR8zRa9t34d9V5/JUOQeutk1tTI1MSZyaWOlQT6Ush64W8IwVPGsJPkd7UR+gsFqnN6izTSS/ZA7A1tzAenePKBGUCYIgCIIgCPdfWBh06ACHDml+lgCdgUEmYCkHroFjtuGyEiOoNxIAhVKFTKkiv0RBXokMW3MTDk7rpA0wRvx6gmWjQ2jhZc9vR5OJvnlHivSO9QnzcWTgoqMkZxXjX8uGghrP8crHr5YTr+6oXiA3P2I+mwdtRqK24uuBziRmlvG/0GmM3DhE79jqrAk2asNI+k7PZO6xypOfVPTNsW/o79efjrOOkFWk6S0K9LBlZBtvNk1uy4hfT5CcVUz/hScZEhrEwRGHyS7NJK0wDU87T22ij+qobA6cr6MvP/b+kaPXjjLn2O2gz0wCQz18meQZwPtN6mGScwpp1asQVJtSbcTJIn/2F7Qi0MMWG/PHJ9R5fFoqCPdB/wEDa3S8q4sLP/1U9ROpJ1H/AQP/k9d+/vwF3v/gQ7p07szUqa8/0HP9WzNnfsCF6GiWLlmMm5trtcqsXr2GNWvXMeX1yXTt2uUBt1AQBOERI5HA7Nma4CwAeNUM3MqA20PKlKffISNkB3mlcuwsTLG1kGJqbKRNzlAsU7AnNo0lBxP1gq2KAUa/hUcZEurJ0uHBlMqU5BTLcLA0xdHKlBu5xXT/7hB5JZrzWt8lJbwhZcpScktzaxSoyFVy3K2smLMjgb9OX2fT6z2Y0HICi+8YMlmdNcH6NepX4/PnlOQQWs+JbedTAc2SANP+OseQUE+WjQ6h38Kj5JXIyStRklcswdTYgcs5x/Fz8uNqfuW9jobcuWZaeaD55ZEv+f30UkLN4ANH6GwBrc3B3OgSZN5KDvIvA7IClQ1781qwryCEgwUtyFPaADD3RW/kSsPZKx9FIigTnipdOnfW2xYTG0tqaio+3t74+Pjo7LO1tfmvmvZEEkGJIAjCEy49HX77TbP4s5GBVAX+jrDABxyTAAPp7zOOMOL7X4kv9QbKe7bq0c3fDYAvt8WyIlw/QDAUYCw6cJnFBy/j526DtZmUYpmSla+GMv2v89qADO6eEt4QBwt7skrSa1SmsKyQhMIihrfxIi41H6nEnM87z6J1nfZ8d2u4oKE1wQxxtXIlq6RmgWR2SQ5OVqZ621dGXKW5pz1vdG+AvYUpzT3tGflrJCH1HHivdx+OXj1Cfcf6NTpXxTXTTCUwq1lvciPf4uXUDXxfDyzucxaLMqsGZDm0xbhuH2S2HUg4mcHB8OvkKTXv89BQT1rXcyazsAx7S/1r8CgSQZnwVDHUGzNv3gJSU1MJDQ1h0KBXHkKrnl4NGzbgh4XzsbS0fNhNeSB69+5F+/btcHC4+wRvQRCER9mdQwgdb1zBftnPSH/5GUlxMdSvDy+8cLuALBcufA4X54Oj4WQLu/NDmJUykktlntpt0TfzmbI6iiXDWlImVxoMyCoqDzCGhHqy6MBlJJLb3S5DQz0plimJSdFdYPluKeHv1Ny9OaZGFtVK1lGRnbk9X+25TD1nK5YOD+arbbFsPpfCkNAgNr+8jyJ5PqWKEiRI7toDll6UjpOFU43O72jhQFaR4UDyt6PJrBvfmk1RN9gUdZM/xoZibabknd3T+On0TyROTaz29Qlxb4Z9/nn6y8/zbW1oYw4W2bcWBL9Pf97zlZBn3wLn+kMYdWINaxMigQTgd80C0yFvs3FyT77YcplnG7vTPcCd9zecZ2avgPvTgP+ACMoEQXhozMzMqFOnzsNuxgNja2uLrW31UjULgiA8qsqHEP607yK1Du9l6Jlt+CWf0TlG9fHHpHftRV6pDPfMNdjGf4BElmmwvlKrAF6PH87u7MYG9xsbSWhc246xy05Wq32/HU3mpxHBGEkkDAr1RKZQUlSmxNnaFEtTKZM6+2qTW4BuSvhRm4betf6poW/zx/HrjOvoWaNAzs7cnoupCXzWvzG7YtJYEaEJMBcdSGLxwST83G14NsCNLk1z71rfprhN/Nrv1xqff1e04YyF0TfzKSxT0KGhK8cuZ7I7+iZqi3Dt0Molp5YwJWQKozeN1itrAoSYQycL6GQJnaxikYa/xKc1ixmrpFLDyVTYlQg7EyH8BlibJNLB6yDxmflQ4daKMopilNFQ/Fz9CKkbynd/lWH/3jTUagm2Fo9PqPP4tFS4v1QKKL7+sFtx7yzrgNGDvX337t3H/AULeeXll+jQoQOrVq/m/PkL5Ofn8+470wkLC2Xs2PGkZ2SwccN6vfJVzZdSq9UcPnyEXbt3k5iYhEwmw83NlXZt2zJw4HOYmZlVq41paemMGz+BxoGBzJz5HqvXrCU8PIKsrCx69ezBq6+OAaCgoIB/NmwkIuIE6enpSKVS6tevR/9+/WjVKrha5ypvc8SJEyQmJpKVlY1EIqFu3Tp06dyZHj2exajC0JXyawMwf8FC5i+4vRDo5599SpMmjau8Rkqlku3bd7Bv335u3LwJQN26dejapQvPPNMdY2PddLoV53wlJSXx9/r1XLlyFRMTE5o3D2LkiOE4OzvrlJHL5ezevYe9+/aRlpZGWZkMezs7PL086dixIx3atzN4LcLDI6pVf2XDNyu2NTY2lk2bNnPt+nUsLMxp0aIFw4YOwcnpPv51EwThsXZnL5WhuVcPSrFMwfyVh5H+9itLonbgUWA40DKKjmbl9Kl0DjmMnVW8wWMyFXasLhlLrYaTeb+dMwm3kk3cqaGbNaUGergqU1SmwNbMhAau1oxbfrLKuWfl51sRfp2NkzUp4ata1Hh8y4l0rNuD/htPEuztyNutpzP0n8F3bdPbbaZz7moRv44MRQ38dChRZ79KDTdzS3kpxJ0SpeFrWpFMJePYtWO81fothv0z7K7Hv9XmLU4nF6CoZEqVnYUJlqbGfLUtjrUnr3Fwemv6rJmj3f9j5I9Ejo1kXMtx/HZqKa1uBWGdLTU9YZYVbzu17K7tqY4b2bDzHOw6D3suQNYd2RdzyWXT6U2Vlo+/9R9A9tgRPBvYDNPHZOFoEEHZ06v4Omzyuftxj6p+SWDt/Z+c6sbNm7w9bTo2NjY0adKYwsIipFLD62tUh0ql4rvv5nHo8GHMzc3x9fXF2tqKS5cus2btOk6dPsMXn39a7cAMoEwmY8bMD8jIyCAwMID69ephbW2taf+Nm3z40cdkZmbi6upK86AgSkpKiL94kc+/+JKRI4fz3IABdz2HXC7nm2+/w8bGhrp161KvXj0KCgqIi4tnydKfSEi4pBNYtWnTmrNnz5GUnIy/fyNqudfS7nNwsK/yXEqlki++/IpTp05jaWlJs2ZNQQ3nzp9n8ZKlRJ09yzvTp+kEgeW2b9/Bxk2bCAgIoGXLFly8mMDhw0e4fPky33/3rc51/fa77zl27DgWFhYEBPhjaWlJVlY2sTGxlJaUGgzKalL/3WzYuJHt23cQEOBPaGgI8fEX2b//AOfOnWf2rK/0gjxBEJ4+VSW6KJ97ZWn6AD7OqdUoDh4ie9Z3vLlrK6aqyhfhVRsbofi4IW/U+xUj9KMAmUrKr5n9+CH9ZQpUVpB0gSGhnqx8NYy+C4+QXaT7od7aTEp2cfU+6NtZmLB8dChfbY9lZYT+UMe41AJ+PZpETrGMVWPD6DP/CNnFsrumhA9yD+J/odPoWLcHI389S16JnPf/iWHb//oyvuWEKtc3m9ByIv38+rLjXDbFsnzqu1gbDDCHhtVhd+JWOtdrp9MDZiwxJtA1EFszW/LL8olOj0apVjJx60SiJkQxvuX4KtPiT2g5gb4N+tF7XmSlxwwN82JfXDqrTlzFv5YNRfJ87RBKE8BfncM/q4L5xLUu8+sbY2akrLSue5VXAocSYH8s7IqC6JrlFqnStdwyJvZwfeAPLe4nEZQJwl0cPnyE3r16MmbMaL3emXuxYeMmDh0+TOPGjXn7rTe0843kcjmLlyxlz569rFm7jhHD7/4krFxCQgJ+fn4sWfwj1tZW2u1KpZJZs+eQmZnJiBHDGdC/nzaQSUlJ4aOPP2X58hW0aN4cLy+vKs9hbGzMe+++Q3BwS6TS2/905OXl8elnn7Nv/366detCYGAgAKNGjWT16jUkJSfTvVu3GiX62Lx5C6dOncbTsy6fffoJ9vb2AGRnZ/P+Bx8RHh7B9u076N27l17Zbdu389WXX9CokR8AZWVlfPjRx8TFxXP48BG6desKQFpaGseOHcfVxYVvvpmrk9RFJpORmGhgkdMa1F8dO3fu4v2ZMwgObgmAQqFg/oKFHDx4iKVLf2bGjHerXZcgCE+eYpmiykQXU1ZHMTTMixm9Gt2/wKygAFasgEWLkF64QFUDzDMt4JcWcKinH9+19KBhdpzeMTvzwvgyZTRXZB4621dGXCXUx5E9b3bglyPJOsMLC8sUON5KzmBsJKGhmzXWZlIKyxRcTCtEqVJr6xka5kVEUpZeQGZnYcLQsDoMCq1FkTyfrOJs5Jiy661QfjtynRXh13VSwm9+eR9Kiigoy8XGzA5TIxv+OHaT/htPklciJ9DDllFtvSkqNWZ29zl08OzInOOzDKwn9g59G/Yhu0BNJz9XZu2Iw6GVfqIJYyMJg0Jr0WfNYC7mRDMlZApv7XqLia0mMr7l+ErX9Dp+7Thzus+hvVd75h6bq3f+t9u8TX+//mw9m0V+qcLgNbycUcSwMC8+2xJDkJsUb/VVDq/9g/dyoJMLtK0NmvwguaDIhfsU1+Qr4VApHCiG/SUQVQYqC6DFrS8VmlwwJbe+iu/4f8Gtr/xbXyWVn6ulj9ODeVjxAD1erRWEh8DOzpYRI4bfl4BMqVTyzz8bMDc3Z9rbb2qDDQATExPGjX2VU6dOsWvXboYNHWKwJ6gyY8eO0QnIACIjT3LlyhVatw5j4HMDdPbVqlWLUaNG8PXXs9m1ew9jbw11rIyxsTFhYaF62+3s7Bg2bCgfffQJEREntEHZv7Fl61YARo8apXONHB0dGTliOF98+RWbt2w1GJT169dXGzCBZt5a/379iIubQ3R0jDZoysvTPLX0qeejl2XT1NRUp457qb862rZtow3IAKRSKa+OGUN4eAQnIiPJyMjExUX0lgnC00ihVLEnNu2uiS5WhF8h1MeRno3d/32vQHIyNGkChVWv2hteGxa1gnWBUGYCKGI5FhnLVV9zbNSlACSWefDxjfEcKmxZaT1LDiUS6uOEt5OlzvDCi2mFWJgZ80m/ALoFuFMqU5JbIsfRyhQLEyNWhF/lj/ArFJYpGBzqqTf3zNvJkt9HN+PA1e30XTtEvwcsZBobJ2t6wJKzill0IIn8EiWvd23A4UQF/YLcKJGreDHYk+4BtXC00qTW//HAZT7bEoutuZTPBnTk0Mg+5JbmklOSg6OFA/YW9pTIy4jLSMTdxhEzU3sCPaxxNpABsaGbtbZ36mreVaLGR3HhtQvsvLTT4OLRU0KmEP1aNCq1ig6/deD7Ht9zeNRhckpyyC7JxtHCETtzO45dPcYrf73CC/6D2PR6T3ZFZ9OziRMpmdc4dfYUGVfS8My5zIypb+Jlk0S/2mW08wArKRBytxukZvKVcLgU9hfDgVtBWJX9bUZoFha3qOqgCuRogrScCl+5mv8XKkopkSmweIwCs8enpYLwkDRr2qxGQ9KqcvlyIvn5+QQ1a6YTbJQzMzOjfv36nDx5ipspKdSpXbta9To4ONDA11dve1RUFACtw8IMlgsM0GQlSkhIqN4LABITk4iKiiI9I4OysjJQQ0mJ5nHVzZSUatdTmYyMDDIyMrGzs6V58yC9/a1aBWNlZUVKSgo5OTl6mQ2bBzXTK+PhoXlCm52To91Wp05tzM3NOXXqNOv/2UDHjh1wcrx7iuTq1l8d7dvpD4+0tbUhKKgZEREniI2NxcWlfY3qFAThySBTqlh8IPHuBwKLD16ma6P7MFTLy0uTRfGsfiZAhbkpv/vL+LEVnPbQL5qrgtdTSvm5lgnHzScw5nwXZGqTKk9Xnmzi16NJRF3L1aa2d7A0wcTICFsLU4NzxMZ1qMeeNzvy6ZZovblndhYmLBvTjC+PzjQ4VywqNYqRm4YwvuVEfh/9Oe/8Fc+LwXUI8XFi09mb9GpSi6IyJZmFMtxtzXCzMSe3RI4EOHgx49a6XnJG/nYaqREMCfNkcmdvjt84zCeHPuB0ymntucqzAjrb9qe7vyu7Y29nQrQ2k5JVfHvhbDVqPjv4md4aZuVtHr1pNBODJzKz/UzyyvKoa1eXabumkVmSiauVK+lF6WyI24BCqYA82LpxK83VzbHOt2Zy6jGa+ijp7Aed68D/wsDm3z9n1lMehB24FYSduVsQ9m+ZAI63vu7w+anFhLRsjxoemx6zx6OVgvAQOd/Hnor0dM0/yFFnz951IeuC/HyoZlBWWW9Kerom0ca3333Pt999X2n5/PyCu55DLpczf/5CDh0+XOkxJSWld63nbrKzNX+kXFxcDO6XSCS4uriQVFREVla2XlBmKEGGhYXmsZtCfjsts6WlJZNem8gPi35k2bLlLFu2HA8PD5o0aUznTh3x9/c3eP7q1l8drq6GX6Orq2Zx6vJrIQjC0ye/RFHtRBfRN/PJKCzD1QgsTKrx0U6t1izsfCeJBF57DcaP126S+dRHPXHs/9k7z6iozq4NX1Nh6E2aNLGgYkdFsWDvPcWeqImFxKixJqZ325vENDUmahJrij323rFiwa6IhaYwMMDMMPX7MTIyztDyJt8bk3OtxVrynOc8pwzCuc/e+950Ev+EwZBMmAROFzpe9sd8yHSrxeLu72E4fqRC567S6vFQyKzW9i+0jqB/4xA+33XVYY1YcpqKiauTGBYbxif9G3Av19YoZFiLEPambi3TvANg0akFtItoy6Lh/fjl5D1EmPFSyEo1CulQ259Rraox5ZdHgtXVScao1lV5c9/0UgXgyI3DOH4vgVlPz+L4PKVNimZxr7SEZgnsSdnjUJCVZMHJBbQIacE3Pb9h/639lvkFwB0g7eGXFkRVoEE9iI86Q3t/aKsAr79AhOWb4KDGIsD2qeH0Xy3CKsGVdDcOXLtPnUAPogLdn4jaMkGUCQiUg1xW9pu+0jCbzXZjpodjQUFB1Kldu8z93d0r3rhaLnPcGNFkthRcN2nSGC9Pr1L3r0iT7A0bN3Hg4EHCw8MZ8fxwIh+aiUilUu7dS+Oll8db/tD/f+DoYcK6qeK/eNu2bUPDhg1IPH6cpDNnuZCczPbtO9i+fQd9+/Rm1KiR/9X6AgICAn+UPE3lHO3u5xdxV6khtppP6Q+gKSkwdy5cuQK7dzueM3Qo5tdfJ6NBM76q04Xclm14vYc7z6x5jfGekGeCo6mQ6eDp2wxsS0+mUK8iKtCdS+nlv/AL8HDmi0GN+PHobbacz+DLwY3YfSnLoSAryfLE28SEe9MtOhBPhYw8jd6mTqsizDk8hzah3egaHcSCfTdYebz0JtUvtq7GxE41GZIaZp1XUQG44NQC2oa3ZfZTbRi3PAl41CstJijGYsyxqnf5J2yGWRtmMT5gPG/9+BZcsYxRDaIbQvte0N4L4hXg+xeIsILHRNipv5EIexy5Uy2WHr7FkueboTOaBFEm8DfGJcTiYPik4vL36G0lffhGUqPRWKMlxTx4YG9x6/cwyhJStarDRtZ/NsVRnc6dOhEX1/K/WuvYsUQApk55lbCwMJttmZkZ/9XaJfF5mEJ4/6GdviOKt/n6lp9uWB6enp506dyZLp07YzabOXPmDHPnfcqGjZvo1Kmj3bX+mWRl3SciIsJuvPj6fCqQTikgIPDPxFPh+GVbaXi7yPls11Uah3nZP4BevQoff2wx8DA+fIxOTIRY+zphtcyJeV/9zpKz2QC873IC/30fMsHr4XEk8EUVGFjGr/1cbS4eChl1gtxLNegASxTKZDaT8OMppnWtzcvtqqM3mVm4v2Jpm98dSqF9bX8md67JOxsv2tRpPY4jR8MzGWfQmfK5liGzEWSOzEW+O5RC0whvJnepRbMIH5Ycvlk5AXhkDvtH9GLeMw1YevgWyWkqViWmM6vjPIDSm0fnAynADeAmXDJd4uXIlwmNhn49oIsftFeA/1/wRF9ggkMaiylHcSSsdA/OvxdSc4A1PVYiFj0RKYx//zMU+GsQS//fLOX/yXh7e5OWlk5aWhrVq1e32ZbkICe/Zs0auLq4cCE5mfz8/EpFw/4IjRo1ZNeu3RxLTPyvRVlhoaXw21H63qHDjtNUil0ajaaKv0urUqUKVar4cf/+A86ePWexwy/ByZMnKSgoICgoyC518b9FJBLRpEkTmsbEcODgQW7fvvOXirLDh4/QvHkzm7H8/HySks4iEomoU6fsaKqAgMA/Fw+FlOhgD5tUutKIDvbASSZmy/l03uxZ99EDaGoqvPkmrFwJpses6r/4AlassBnS6AzsSM5kydlsPCQFvBu8iAFFe+2O96w7LFbBLvsWYwAEe/jyxaAoCrQGctQ6fFzkOMnErDp+x8ZlcUzbSG5kFbBgmKVf5h2lGndnWaXSNpVqHf0bh/Depou4OUlRamxre72dvct0NNToC9h50RKVtDg2hjMkNgytzohGb8TbRYZUIuKnY7fZej6d1jWrUN3flZ/HtuSBJqN0MfUYZzLOkJ6fTb7WxLfPNUWrM1KoM1AjIIILWRceTTQAt4DrwE3gPhAM7nWhXQ/oHAKdXaB25TR7hSgWYfsefp1SPxRhIvBSeDG8UX+cJW7sSTYiF7vTPboGseFhSFCQsPw8Dav6Ma6DJ/1+6VbmcabGTSXUPZSJ2yfabRMhItg9GKlYSqG+kLp+dXGVu3L1fhqFxvtkFqRjppysHLMYqdlSHqBU65BL//5RMhBEmYDAf0V0dDTJyRf59be1TJ0y2erQeODAQQ4ePGQ3XyaT0b9/P5avWMmsWXN45ZWXCQwMtJmTnZ3NuXPnad++3X99fnEtWxIaGsr+/QeoGhzMgAH9kZVIxzSbzVy+bLEwLq2Gqpjg4GDS0tLZtn07Tw14VA93+MgR9u7d53Cf4kjPvXtplTrvnj17sGzZjyxZupT333sXT09PAJRKJUuX/QhA7149K7Xm49y8eZPMzCyaNo2xuSf5+flcvXoV4C/vE3bo8GHi27WlSePGgMWd8/slS9FqtTRr1rTUujoBAYF/PnKJmLHxkUxYlVTu3JGtIliZeBuT2VKjFWjSWCJjX3wBulLSINetA5UKPDwAi/2+Wmfk2wM3aed+gtkhXxIgs69r1Zjg7WyLo54jGgc2xk3myXPfn+D8vdKbOPdpGEyH2v5su5DBnO1XSE5T0SzCm9e6l/236HGyC3S4O8l4tVMtDly7T1XPR7+3a/jUYNvQbRxIPVCqo2GIZ1Vy1JeJ8HXhh1HNOXM7l10X0+hY1weDSEWqKgd/N19GtQ7CWerMskMpbDqfznfPNSWjMLtS56rU5LLlfCEfbL5IVKA7r3Sozl3NUbxNXpCEJR3xBiACSQ1o2hO6RENnL2jhDLLSM/f/EIUmOKyGvYWwrwhOPh4JK6FlcrW5TG81Hb1RxP272Zy4pWTNAajRowZtavohN6vA6E7tKuW01xFJeKX5K/RZ1cfhdjNm7uXfs36fr8vn12c2gdkJqQgM6LmVm0pK7jWu51xHVZTHfXUWp+9d40bOTQoMaYjNPogeShxvFzkK2V+Qy/kXIIgyAYH/gp49urN9+3aOHDnK+FsTiYgIJz0tndTbt+nVqycbN26y2+eppwZw99499u3bz8vjJxAZWY2AgAAMegP30u5x585dIsLD/xRRJpFImPn6DN597wNWrlrN71u2EhERjqenJypVPikpKeTl5fHCqJHlirIB/ftx+vQZfvxxOYcPH6VqcDBp6Wlcv36Dfv36sn79Brt9GjVuhFwuZ+PGTdy+fRsfbx8QQf/+/cp0luzTuzfnz13g1OnTJCS8TP0G9TGbzZw7dx6NRkNsbHO6dy/7TVx5ZGXdZ9bsObi6uFC9Rg28vb0oLCwkOfkiGo2GZs2almqL/2fRpUtn3n//Q6Lr1sXbx5urV6+RmZmJj48PY8eM/kuPLSAg8PdGKhHTqU4Aw2LDWF5GfdXQ2DCaRfjwweZLiE1GAn/6Hj5+H0pzg3VxgXHj4NVXrYLMYDRxPCWbMHcjz0k+ZFC1HQ533a2GsVlwowxPo8ktprH4wB0bQQaParOGxoax7qVWyCQiPt5y2SZtsKDIgI8D+/iy8HaRcz+/iBdaVyM+qgpuMgmNAhuRmpvK9mHbmX14Nt+e+tZuv2JHwxNpJ3i794dIcGbNiTs81bQK+27vo/ca+x5gk1tMY1hcH24r1SjVerwUlcvWCPbw5eshUeSodSjTb7N3y498/sNnZF/JBncIaAI9+kGPMOjk+uebc2hNcERrSUe8p6jJyA4L6PpTpwrvn63OpoZPTTyc86xja8/cpXt9y8vl4jq5ko2wHyfaPxq1Xl2pCGOORkl6joJ6wR44SZ14f10OzjJ/+jduSPtG3qiK8shW5+Dr4oOzxI2lR66w7lQBId4KXJ2kuDgJokxA4B+Pl5cXH3/0IcuW/ciF5GRycnKoXj2S9957BxEih6JMLBbz6qSJxMW1ZMeOnVy7dp2bN1NwdXXFz8+P/v360rp1qz/tHIODg/ns03n8vmUrx44e48qVq5hMJry8vIiMrEbzZs0qdLzo6GhmffIRy5ev5GZKCmlpaYSHh/HajOlERkY6FGW+Pj7MfP011vz8MxcvXkKrtbgztouPL1OUSSQS3njjdbZs3caePXs4cyYJgNDQEDp26EDXrl0q1cPNEVFRtRg6dAjnz53n3r17XLx4ETc3NyIiwunUqSPt4uP/q/UrQr++falRvTqbNv/O1WvXcHJyol27eIYPG/qXR+kEBAT+/rjIpbzeow6Nw7xZcjjFzhVwZKsImkX48PyS44SkXOKzPQvxuGPfwBkANzcYPx4mT4bHovA6o4krSRtoKfmAQT537XZVm90RNZ3FL1fPcePeolLPN6FpAq1DutJ3/clS56xIvM3g5qFcyyywM9a4mlmAQiapdNomwMaz9+jZIJjfTt1mYvOpXFNeZP+t/Q4FWUkWnFxAq9A2NPDtytPNqpRppf/c+qEkNE3g/W6zyFeLkUncyxQgJWkc2JiM2zls3/Qjv/36C0lnkhD5QdO28PIw6BkITZ3LXaZSFJkgVRbIVUlVmjaeRt9dH3M84xwADQIUTPeomMNzMVVcqyAVS3irdw1OpuaSp9FzNbMAqVhs/cxWJaYzsflURm4cZrNvcU1f67DWdimm5ZGjUbLlfC41A9xZduQmL7WrQZ1gJ/bd3kqPVfbieWLzqWxo2Z3L6UVkFxTh5/YX5Hr+BYjMjiziBP72ZGZmMWbsOL5dtJCAAH+Hc0wmE1euXAEgKirqv36IFRAQ+HN44423uJCcXOb/X4EnG+H3r8CfhcFo4kqGCi9XJ7Q6I0q1Du+HNVorE2+z7sBlXty5jBGnNiExm+wXkEotNvdvvmknxgAMRfkUnZyGa6pjsVXgG8/1Gm8QFtQUuVTG5qubmX14tt2D8IxWM2gd0pVhi5O4lV1KbiMWE43DMzowatkJh7Vj49vXoGaAGxNXJ9nv/BjznmlAQZGB3g2CMZrMGE1mus0/yIbxTVE4Gei6vEuFIjKNAxuza/g+Nl3ZzIiNQ8udv2rAKrpU78empHtIXBMZvr6MffKAZKiZXotr56/iEQ5dOkLPetDdFwL+xPCI3gzHH0bC9qqhbp1RTGo1k2aLm5HQLIEa3jUYtXEUYBFJNyfetEvrLI3GgY3Z+/xevkz8klDPSDIzGvHNPoth3Mvta1Dr4Wfm4yJn04TmrEz+juXnfyRNlcaYpmOsNX0avQY/Fz/CPq94vfbll24xaWUqXw1uwoe/X2T+kLrM2DmtzBYC42ISmNtlLmKc/l9NPiryfF4aQqRMQEBAQEBAQOBvilQiploVNz7ZcomTqUqrI+CVjHx885Vs+PFVgvPt3X4BeOYZS21ZjRoON2vTD2M+MgzXolt22/RiZz5RV+Gnq3fZ1iySDVfWs+zsMlqFtmLDoA2o9WpytbkEugXi5+KHwSglfs4Bq4lHadQKcKOwyIAZM80ivO2cGX86lsquyfEMjQ0r0xa/OG3zYloed5VqViTe5pmmoeRp9Hyw6Trv9g+uVIpcoV7F79fXV2j+nCNz6FK9J54KZxqHdmNczDhbgVAEXATOArcguBZ07nKVbxIg3uPPrQ1Ll1bhtiKKdy4f4pAGCs0PRXL3GcRHxNN2WVuUWiULTizgxOgTjIkZw7envsVoNrLo1CImNJ9gFWplManFJG4obzCkwRBe3fYqX3fvzsL9KZjMsPxYKgent2fRsMZEhyhQG3LpWqMLz0Y/jbfCmysPrvDS7y/RMLAhY2PG4unsWakIo4vUgysZ+eRqdLzauTq/X91Ubk+3hacW0Ca8LU/Xeaqit/J/jiDKBAQEBAQEBAT+xhSnMe6+lMXC/Tes/b/uu3pxs3o9gpP22e5QuzYsXAhlpGGrdQZWHrnKKG0qPCYSLkuC6XY9DZW0gJNjTtrUZR1IPcDsw7Op51/Pai/fOrQ1H3ecXe51eCpkDG8Rip8HfD4k3FoH5CL1YPXxdJYfu0ueRs+oZSdYOTqWpuHefHeo9LTNtafuMbRFGJ0/O0CItzOveNSkWYQ33q5y7uaV3lbFEUqtkvSC9ArNPZNxBqVGSfNqVViVeJuZrT6mTWgb3l72Njf23oCLUDMS+veBAXUh9k80Wk43wA417FRDcI3hvNz2A6JdfFnYWkmBLg9PZ0/cnTwo0utRyJ345ZlfmLhtIgdSD9BtRTe2Dd1Gq9BWfHbsMzuhVhpjY8bSKrQVzRY349noZ/m066doDAXWXnTeLjIQFaE0H2DKznWkFaRZ2w7UD6jPxNiJrHxqJTtv7KTv6r70qNmjwmJwYuxUViWmYzJDsKcChZOB5zaX/7MGMO/IHPpE9UbOH+s3+/+NIMoEBAQEBAQEBP7muMildK8XSMc6/qg0BlRaPR7OMjwmNcPcpBGijAxwcrKkKU6bZvl3KRiMJnZdyuTDE/7ogp7hpSo/A6DHmbNBA2l+4AfMwMyWCQ7rskxmE+cyz1m/P5d5jjZhbRga28Ca0vY4Eb4uLBvVkP13ttHuh7mO64DGd2fEkrOcv5fHkMXHWDayOe1r+6NU68gueJS2uetSJufv5vF8qwieX5LIsBYhDI4NwizKYUpPF3xdfAhw9WJmm5ksOLEApbb8GiYvZ09URRWz4gd4oFay/OADno2SM23Ge+ze9Auhvmm83xkGvAzRrhVeqkzUJtivsYiwHWpILmmmeeYn6oZ1oE9UH1LzbvHD2aWcSj/FhawLmMwmGgU2YlrL6WwevJmswiwyCjIwmo30qNmD7jW6k6PJIVeby9xOc2kf0d5hWurE2Im0CWtDtxXdUGqVLDq1iBYhLYgP60R0sAeBHs7Meqo2e1J2ERcWR6OgRnZtByZvn0zi3USmxk0lNTe1EmIwgfjQbvTdcJLoYA+cZRIyC7IqFQHN1Shxk/9JH8ZfjCDKBAQEBP6f+eijD/7XpyAgIPAEIpWIkUrEuMilBHo+dIXwdIYlS+DTT2HBglJTFUuiM5pYuM/SoPmzjMH0D7yIXOaEPnYhL256ETOWmqOxMWNLtS5/nDlH5vD74D3WlLaSeCpk/PBCwzJNNEZuHMbYmASWjfqQvl+d5Pw9FR3+s5/JnWvybNNQ3J1k5Gv1SEQiejcIRiwW8c6GZL4cEs2+21vpvWaonaCYFDuJE6NP0G1FN67nXC/13BsHNsbL2cu2X1hZGOHU3uOs/mQx+/UnGNQDPv4AIl0qtnt5ZEp8OKB3Z2FaKoe1UFSG+8MXiV/Qv3Z/3t73JgdSD9hsS8pIYui6ISQ0TeCd+Hf46OBH3Mu/Z73O4mhnTZ+afNn9S9qEtSFfl0+2OhtfF18UUgWLTi1i8vbJNsL2i8QveLru04ztYMJZIkMi1aHUKkttO1D8GcSGxDKu6Tg+OfSJNWrXMqQl8xPnO/jsphEf2o0RS86Sp9HzXp9otl5IJyLQvk1DWeRocwnxDKnUPv8rBFEmICAgICAgIPB3Jj8f7t2zpCU6ont36NYNRKUUK+nzQepm3a7SGLh+v4Ae9QPxdZWzSDefMe2bozXnWKMQf8S6XGfMt6a0lWRYixD2pm51KMhKsujUAlqGtGZ4i0bsvvwANycpVb1c+HrvdXZfzsLNSUqAhzN9GgZTP8STKV3DyxR6IzaMYGzMWLYN3Uazxc1KjZhNi5uOCCkNAhqUXeekAk5DnTQJGa3HsH8i1PUo786UT5HJYs7xe6Hla8OL+/hiy0sc0qSWu++ZjDOoilT4u5ZuKrHg5ALahrflg/Yf8eKmUZgeGsKcyzxHo8BG9I96BqVWSfjn4TZpqcURN0fHTMtPY8ymMWwavImpO6eW2Xag+DN4YeML/Nj/R2Yfns31nOs0W9yMcU3H2dQohriHYDa5sioxnb4bTpKn0TMsNpxmET5MWnOGD5+qnGOkj7NXpeb/LxFEmYCAgICAgIDA35Xz5+Hpp0GrhdOnwdfX8bzSBNn9I3BkKNSeDFGvAOAsg1NvtSFXm0u2JgdfRS3cncFcJMXb2RulVomHkwcP1KUYiJRCvi6P/o2rcin9kS2/RCxicGwQvVYPqdAa8xPnsX/EAXo3cUWlyyXQTU/t4ADMmFh+7C4nbinJVGn5fGCDCgo9S7pdcYTmccbFJNCzVi8kIimTW0zjucedFM3ATQi9AgPDYVB/iPEzVuhayuKuHn5XW0TYHrXFoKMYL2evSqVS5mpzySrMKnPOnMNzODDiAHuGHyBbo+R+YQ4+Cm9cpB6cv6smW51jl5ZaFvcL7/Nys5fZdHVTuW0Hij+DuNA41Ho19fzrcS7zHEqtkk8OfWJTo/jL078ycfUlElOURAd78HavunSJDqD3l4e4o9TgKqtTKZMQL4VXha7n74AgygQEBAQEBAQE/o6sXQvDh4P6ocX8c8/Bpk1QkRYLZhNcnAPn3gSzEc5Mg4B2FLhE8vv1jcw7OscuZWx63HRrqpmqSIWfS8X6JRb3oPJ2rsLg5j4EeTiz8MBNktNUNAnzQm8uqFTELb0gjWd/e8YqEB6vOVPrDLgr4PPEuRVa84vEL9g0eBOzD8+2Rn4s1zuDnjV78cnvN4iv6U//2n0eOSnqwfUSPGWA51tCh24VOlSZnC+CtQWwvhCSihzPaRzYGE9nz4qnUgKucldytbllzjmTcYaMghxMBi9CvIJxkfihLNSjM0uICfdBbXTQTqEMfF18qeFTg67Lu1Zo/heJX7B+0Hru5N3Bw8k2vFhSDKYXpPHVkEZo9ODuLGXL+XRuPii0tlkorQ+aI6bGTUcuLr228u+GIMoEBAQEBAQEBP5OmM3wySfwxhu241u2wH/+YzHyKAvtAzg6HNK3PRozFWE8NIjXzXF8dfo7u12SMpIYsnaINdWsxXctcJG5lBmV8Hb2JqFZAmNjxqI1aMnWZlGo1xJf24/O0S3I0xhwc5JxNftipS7/fuF9mwf3x2vOZvx2hbyi3EoJPTNmUibc5Z4qEx+FNx5Onng4uaAqMvJi6+oo1TrUOilT603B/2AykaZDPNXHjNt/adx3VAPnnWogDXuGF/baR+oeZ3qr6RhNxvJTKR/SOLAxUrG0QiJOY1Bx/paZgduPEejpbG2vcD2rgL3TWlYqAuUh9yCvKK9Sn4FarybYPbjMKKCr3BUXJxGvrDrD7KcaklOow89Vbm2fsCrxHmtf7s6YJuPKjJImNE2wOC9KnwznRRBEmYCAgICAgIDA3wetFl58EVassN8WE2NJZSyLrENweBBo7tltyjaY+PmCvSArSXGq2eiY0WX2sarhU4NtQ7dxIPUAfVf35W7eXd6Kf4un6z5NljrdWuekNXgQ5B5sTYusCL4uvg4f3BedWkCbsDY8HRNHZkF2hdYqRqnJ5cBFJ/ZdUVNQpOJKRj4Hprdn1tbLbD6XTmD+SfqKljA4+jbvNazU0jYYzJb6sLUFsKEA0o3g7ZzNiR4vMEaVXWaqX0JMAj1q9uCLxC+YGDuRkRtGlnu8ibETWXhyocPar8eRi9356VgqSrUepdq2n9yqxHRejZ3G8xvKb549IXYCm69tpm6VuuXOLUmuNpcIz4hSBWTjwMY4SZzI0WRT09+dDJWaka2DydVaXDW9nS3pltvOP+CNVh/TKrQNnyXaO3nOaDWDXrV6PzGui8VUIP4tICAgICAgICDwl5OZCR06OBZkI0fCoUNQrZrjfc0mSJ4Fu9s5EGQiiurOpFuGnKwKlEN9kfgF45qOY9HJRbQNb8uYmDE2272dvdk+bDtzjsxhyo4pDG8wnBsTb+Dn4kfPlT2p+WVNYr6NIfSzUDotj2fvrZ2cGXuGGj7lO0M2DmyMQqoo9cH9P0fn0qN+Fbycvcu/kBJ4OXuRnKbixC0ll9LzMZnhfl4BVW59y3eeAznW6l1ej7tNhGellgXAZIYDGhlfmusTeBO63IOFeRZBBpYeaN1WdGN63HSW9l1Ko8BGNvs3CmzEiv6rmN15Djez7/Lp0U9pE9bG7r4/TkLTBNpFtGPhybLr6sC2CbMjlh+7S4eI7oyLGVfmOmNjxtImrA0/nf2pwumtxQS6BfLzxZ9LFZATYiew6NQiCvVqZvSI5Hr+LtosbU3oZ6G0XNKY2t9E0OfnDsjcjyMWQ6+aA9gz/ADXxqdyZOQZLr90i43P7kGrasbBK3modYZKnd//GiFSJiAgICAgICDwv+bKFejaFVIfc9wTiWDuXJg8uXQzD0fpisU4B0DcSrIUtTiz4WOHuxfXhBW77p3LPIfJbKJjtY4OrcsTmln6l+1J2cOpMadwd3Jn2s5ppTrwDVlrsWUvzwURHj2Yl/bgbqmNSifALaBS6Xbezl4k3rSkUVaVZvCMbBlV9xzlnYg/btpxVANr86rhGzWRXg2HMdLfmYs7plpq0h6j2G1wSZ8lHBx5EKUmF6UmF2+FF17OXpy6lc+ElRd5rVcAoZ6hvLn3TWZ1nFWqZfzE2Il0q9GNE/dOVCgCWbIJsyPyNHqe//4s68fPpWWI4whUyZ5lKcqUctNbS9I4sDE+Ch/e3/++w+3FYu/9/e/zeuvXmfbwPkpEEhoENLD+bJ7PPM/IjcNIaJrA220+ofvniSVSMS0R0OJrHNYinJk9auMifzLkzpNxlgICAgICAgIC/1QSE6FnT8h+LCXPzQ1WrYJevUrft4x0RQI6QtxyUASSk3HebnPJmjC1Xm3T9FcqlvF19+/I1uSQo1bRq2Zvnq77NPcLlfgoPOi9qjfbh20nKSOJXG1uuQ58C04uoGVIy1JdEOHRg/nk7ZNLXUcikqAqUiERSZnacjrD1pXv6jg1bjqFGiNvNryC183/EF8lFXEp+rY8zhXBTkMgG3O6cFPZHQnuREs9eKWTJ2tP3uW1uI9oEdKGzx2ImulxM+hUvTNSkQxPJ29EyMjRKFHrjNQI8GBEq1CCPdz55ZlfyCrMoshYRK9avehZsyc5mhweqB/Y9A/77eJvrHp6FWNjxrHIgRAsJqHpoybMZXHjfiHLDqfTPqobmwZ2p1CvIkejxFfhTaCHD8uSltqI6rLSWx9neqvp3Mm7Q6hnKNmaRz/nj4u9yS0ns/nqZtYkr2Fmm5kOfza/Pf0tC04sILZqawY3b1Rqw/Llx1KJreZD93qBSCV//+RAQZQJCAgICAgICPyv2LrVUidW7LBYTHi4xWmxfn3H+5nNcPkzSJpucVe0QQT134XoN0AsAcBHYZvu93hNmCMnxt61+rAjsYgD1/Kp4n6LKZ2j+GzXfSZ3M9MmvA0Hbh2gQ2SHCjeY/uzYZ+x6bhfbb2zndPppm+OVfDB3FPkpKSBFiMgsyKR3VM9HbomlMKXJSPqZrlC0eQz9xflQejuvUskywAoV/JAPWvco3mr7Dptq9SbhpwscvpFNcpqKrHwtAZ7OiHGiSNWMTQP3UKhXka/LI8DNByeJO84SZ8xmPb9d/s2h++WUllOI0LSh58qe1mbXFufJiXSM7MhXJ77i4v2L1v5hKwasYlPSfV5r+REtSxGCk1tMo3/tvry6+iJ5Gj3lcejaA0a1qsbHv1/i9B2lNQLVsXYhfv6+Np/NghMLODH6BGNixpRdK9c0gS7Vu4AZtg7ditagJb8oHze5G2KR2NqgWlWkYkj9IYxYP4ITo0+U+rNZ3JB68vbJfN29u8OG5cUs3H+DjnX8BVEmIPB3o2+/AZWa71+lCosXL/qLzubP486du/z8yy+cP3+e/PwCfLy9adqsKYMHDcTDw76z5e7de/jiy69KXa9161ZMmzrFZsxkMrFq9Rp27txFYWEhtWrWZPToF4iIiLDb32g08uqrU3BWKJg962NEpaXclINWq2X79h0cP3GSO3fuUFhYiJOTEyEhVWnYsCFdOneiSpUq1vmrVq1m9ZqfGTTwWQYPHvSHjikgICDw/8ZPP8GoUWB4rPalWTOLIAsIcLyfvgASX4Tba+y3PUxXJLCDzbC3wtuaalZcEzb78OwyUw7HxSQwt8tcBjYLQ6XVE+Ql592+NbmXn8LYmLFM3TGVFqEtKuXAl6vNZcuQLWgMGlRFKpylzgS6BbIsaVmpqY2lCcgaPjXYNXwXrcNbM+/IPJuH9wHBtfkguArVc3/EKdmISyWfyXVm2FQIP+TCVg1YP6HsKwxbZ7k3C4bPoef849xRasgu0LH44E1qBbgzpm0kvb84bE2rG9PWn6ZhzojEBqbtnOYwqpWUkcTwdcPtml1bnCdHMjZmLB+2/5Bmi5thMptIaJpA29Cu9Jx/AoChsY3YNHAPar2KIqMWb4UXcrGCreezKNJL6BIdyM5LZfcyA3imaQh6o4lp3aLYdSmTpYdvcSk9n7RcLRvGd7eJyhXXyj2e3lpMo8BGzIibQa+oXkiQsOfWHuoH1Edn1FGgK8BJ6oSTxMn6jPBu/Hto9Bo+7/Z5mT+bxQ2pP+36KRpDgcOG5cUkp6lQaQxPRArj3/8MBQT+RDq0b283dvHSJTIyMqgWEUG1xwqoPTzc/79O7Q9z7tx5PvzoY4qKiggJqUrtqNqk3r7Nli1bSUw8zpzZn+Dn57gY19E1A9SqVdNubO3adfz88y+EhFSlRo3qnDmTxNvvvMvChQtwUShs5m7+fQt37t7lP/Pm/GFBdunyZWbPnoNSmYuTkxNRtWrh5eVFobqQ69evc+XKVdatW8+bb86kUcP/wipLQEBA4H/BqlWWvmOP07Ur/PqrJXWxNPKvwd11dsOmgI5omy1DLaqCOrsQF7kUFycJcokYuVhuTfcrrgkrL+Vw4akFtAlvy9N1nsLF2ciGK7+x/vJ6ZnWahVqvJr0gvdINptPz05m5Zya52lyGNxjOwOiBfJn4JSEeIaVGyEoTkNdzrtN4UWPGNR3H9mHb0Rm0GO5uxj1lCb6qU5B3GSr5JyipCH7Ik/JjnoGcMuYtPLWANmFt+aBfPCOWnsbbRU5BkYEVibdpHObF4OZhfLPvBnKpmJO3lLSp6cNvlzaWmWYIpTe7Lh5/t927+Lv4071mL47dKLBGvzadzSA20ocm4b4Pm4Jn4aPwpn9MIDKRmNY1/RjSPIyVx2+XeuyhsWG0jPRDJhGjLjLg5iRl8XNNMZjMqHUGFDI58zrPo11EPLMPzyYpI8laK/duu3fZ/dxu1Ho1OepcfB7Wyl1K1/D2uuu8168GqiKVw8jXpNhJXHvlGi5SF3K0ygr9bBbfj/iwTrg5lS1nVFo9gZ7OZc75OyCIMoF/FRMnvmI3Nn/+l2RkZBAb2/yJi64UFRXxn08/o6ioiIEDn2XIw/M3m80s++FH1q/fwJdffcN7777tcP+KXrPBYGDtuvVUi4hg7tzZyGQy9u3fz2efzWf79u3079fPOjc3N5fVq9fQtUtnIiMj/9B13byZwttvv4tOp2PAgP4MfPYZnJ0f/UI1mUwkJh5n2Q8/kv2gcrbIAgICAn8LOnSA2rXh8uVHY8OGwZIlICunt5JPY2j2jSVa9hB9nTe54j8BX2SotBlkq3PwdfFBZ/Ykp8BEpJ8HfWv34aWmLzE2ZmyFUw7nHZlDn6heTN8xnQWnFiARSfii+xdkFGRUqsF0Mb4uFtFwLvMc03ZOw8/FD5PZRLuIdg5TEcsTkEqtkk8Pf0LOuU+YFRKAlz6zUucDUGiC1fmwTVuN7vFvsjvxc3Kwr8F7nHlH57B/RC8ahnjgJBNbnQ2XHr7FdyOaEhvpRZNwN3K1uRQZNcw9MrtC51PcaLlks+vi8QMjD6DWivl0+02mda3NkOZhpGYXsmB4PTZd3cSkZfZpkVNbTqdvVG8mdKxBk3Avlh6+RXLao5YD0cEejGwVQbMIH5LT8wjycGbS6iR+ejGWq5kqjt7I4dxdJd6uTmj1Rvo2asWe4QcoNKi4X5iDu9wTF6kHyw6mkZanQyGT4CLP55WOgfi7wfv9azJ1x9RSI4QjNowgoWkCszvNwcPJnfmJ823mPG5Ek5yVjNFs5IvELxhQ56lyXRY9nJ+MXmWCKBMQeII5evQYubm5VK1alUEDn7WOi0Qihg8bypEjR0lKSiIlJcVhRKyiZGVlUVhYSOsB/ZE9fFho26YNX3+9gJSUWzZzl/3wIxKJhKFDyy++doTZbObzz+ej0+kYPGgggwYNtJsjFotp2bIFDRrU58GDyr2lFRAQEPhbEBAAu3dD27Zw4wZMmQJz5oC4gnl21V+A7OOQ+jNFzX9A6RPPyeubmb95nr1rXvOp+Ln3wtPJlY87zEFZdL9SKYcP1A84fPcwAEazkZ8v/kzvWr1JzkqutAPf43b3xQKk2/JuHHvxGC1DW/LZsc9IykhCIpKUKSA9xDDOE171gkApUElBdq4Ivs2BHI+eKE1DqeZfi87VA3hh0wsV2r84HfO17nVYmXjbWtfkpZDh7mzkrnYvk5bNwWQ2sebpNZVutFzPvx7nMs/ZjCs1uRRpPbmYriKnsIipXWohlxmYurN00VOcbjmn8xyyC4r49rmmaHVGlGod3i5ynGRiVibe5nJ6PpM61WLj2Tv88GJDCgxZuLnlMqyVL55OoeQWmjGaRMhlYs7eUVGvqgfB7jLSVTkYTWaeaRbE6uPpLD92lzyNnmebhqLR6zh0eXu5EcIFJxfQOqwt3Wp0sd6nsoxois0+8rR5mEupJwOL4PRQPBly58k4SwGB/wHFdVeDBj5L27ZtWblqFefPX0ClUvHajOm0aBHL6NFjybp/nw3r19rtf/78Bd586206tG9vF6Ezm80cPHiIHTt3cvNmCjqdjoAAf1q3asWAAf1xcnKq0Dlev3EDgOi6dRE/9odcKpVSp05tsrKySDx+4r8SZQUFhQC4uT1qxCgWi3FxcaGgoMA6dunyZfbt289LCeNwd/9jqZ+nT58h9fZtfH19eeaZspukurq64ur6ZDWHFBAQELASHGwRZhs2wIQJFdrFYDShM5pQaQyoQj7EO2wSCu8w3tvp2IrdUpM0jLExCXzcYTbHbqjw98mr1GlmFGTg4fSoPvn9fe/zfMPnqR9Qv1IOfI7s7osFiEwi44vEL+havSsbBm1ArVej0WsQIbITM4ESmOgFCZ7gKanUpaA1weoCWHQPGtQezWHFEd5sMYw7aQ1QaYxka8pKWrQnT5tLVGBNxi23GJeEeitY+Fw9pu54VDvWOqx1pdM8s9XZNve8mFytkurePszoVhOt3oifm4x1V8pPiyxORe3VoCfjfjqJwWR+aOJhQCoW8XxcBG1rVcEs0qKTH6H3Gntx/2rsNDpV64HJBLfUu3l1heMXABvGd2fEkrMo1TqiguQM21SxCOG8I3PoVbMHYpGYSO/IMo1ois0+8rQFuJaRvjguvjryJ8DkAwRRJiBQLvfS0pg6bTru7u7Ur1+PgoJCpNJK/hUogclk4rPP5nPg4EGcnZ2pUaMGbm6uXL9+g9VrfubU6TN89OH7FRJmRdoiwFYslaRYGN16LJpVzI0bN1m67Ac0ajVe3t40qF+fevWi7eZVqWJJT0m7l2YdKygoQKVSWY02TCYT3367mOrVI+nUqWO5514aJ0+dAqBVqzgkkj9+nwUEBAT+PykplvI0OjwVcjwUUuQScdnOb+HhpQsy5Vm4fwhqvQyAWmdg16VMFu2/aU0/e7l9NfwDNpTpQAiw6NQC4kJaM6DOU9xTVa6prpezF6qiR+luDzQPWHxqMdPipjF+y/gKOfCNixlXqt19sQD59OinDG8wnFkHZ3H47mFah7VmWP1h1nnVZTDNG553B+dKPmenGeAbJSy6Ag9kgA/Ma/M8F3dfYs6ROfw+eA8fbb6Br6IUc5VScHfy5FSqkjyNHolYxGcDG7DpyiYbkfRH0zxL3vNiZBIZIzeMpE9UP/oG98GEvsJpkfOOzKHPyN4sHP4oUlbF3QlfVyfWnbqDi9zItJ3TShX3z28YSkLTBN5s+xaTd7xiVwNY8gXAzwmf4C534YEms1IRwryiPOJC4vih/w8VMvuY3WkOlFL9N6xF+BPjvAiCKBMoRnv/j+8rdQOpwvE27QOgjLhymeu6gPR/HwU5ePAQPXt054UXRv0pImH9ho0cOHiQevXqMXXKq3h7W2yK9Xo9Cxd9y65du1m95meef254uWt5eFreomXdd/z5ZWZmlrn9xMmTnDj5qG/JmjU/Uy86mmnTpuDl5WUd9/b2pnpkJLv37KVFixaEhYXy/ZKlmEwmmsbEALBt23ZSUm4xZ/Ysu6hdZUhJsfQbqf4H69EEBAQE/r9xJJbAkjo1tm01ut48iVO/PhVKTSwWd0U3f8Ez6UVEJg1FTmGYgrrz8ZZLLD/2yKhBIhYxODaIXqsrli7+eeI8+tfpg4eT53+Vcgjw0cGPODnmJM9GP1uuA9/0uOnEhsTSdXlXh2YexQJEqVWSlH6OOV3msPnKZtZdXoe3wpvqMnjTB4a5g7SSxh3HtfB5Fvx6BvQ+QKD9cc9lnkOtV5GarcbL2atS90YudqdesBPj29dgcGwYHgojL26zFUl/RppnyfFfL/3Kzxd/5sN2H/J84+cr536pUTJ7SybX7xdaDTLe6VWXZ5qHcOj2fo7cPYJEJMFo12bBwoKTC2hetXmZ/eYWnVpA27C2VHPtgMK1spHHPCa1mFRhs492Ee3o1aAZJ249+rmKDvZgXHx1OtbxfyJcF4t5cs5U4K9l7R9o3FFM06+sb/Hs+L0OFP3Bmp9670CDd//waf1ZeHp68Pzzz/0pgsxoNLJu3XqcnZ2ZNnWyjfCRyWSMGf0ip06dYseOnQwfNrRccRMdXZdff/2NkydPoVKpbOzvs7OzOXvWkouu0Whs9vP28WbwoIE0b96cwMAAdDodV69e44cff+RCcjIffPgRc2bPsrnmkSNH8O577/P6zDesYzExTWjWrCkqVT4rV62iY8cONs6NOp0OqVRaKZGWn28plC4WnAICAgJ/Z9Q6g51YKiY5TcX1V16jz6EVGEaNQrp4cZnCTK0zsCs5nfxTbzPUZZl1XHR0GIeqbWH5sVyb+bUC3CjUqyptSZ+ldGZi86mM3Dis3H2mxU23SzkEi8FG1+Vd2TZ0Gy1CWjBq4yhahbayph7manMJdAvER+HD4lOLafptU4eCrFhoXLp/iffbfUjz4HjOpKpp6NeVOo3kPNjdi8vhlRNjBjP8WgDzb8CxJCAMqOv4uMXCR21QEeHrypV0LdPjpjNkbflCd2LsVH46ksawFtVoEOLJJ1su8UpnX7vPw2g2/tdpno7Gt93cRvda3ctdryQ52lxqB3mw/WIWL7Wrzpi2EehNRdxXZ+Iqd2XN02tsarYcfWalGZGUZN7ROWwf2gOt0adS5+el8KJTZCfil8VXaP6cw3M4MPIgrWsEoFTrqOqtwFMhKz9C/TfkyTpbAYH/AQ0bNKxwjVd53LhxE5VKRe2oKBtBVoyTkxPVq1enoKCAtPT0ctdr3KgR1SMj0Wq1vPf+B1y9eg2NRsPly1d47/0PMRotb7rEYtu/Zk0aN2bQoIFERlbDxcUFLy8vmjdvxry5cwgODub69RscPnzEZp/69evx2afzGDCgP127duGVV17mjZmvA/DTT8sxm7FG986ePcf4VybyzLODGDhoCN98swCdTvdHbpmAgIDA3xaD0cSuS5kOBRnAuGO/MvnQCgCkS5Zgev55+55kD1EXGZiz+RTSo4NsBBmAk0mF6cIsu33cnKRkqysXiVBqcwnxcWVA3b6MixlX5txxMQn0jupFRn4GrcNa0yCgARLRo5d1xXboV7Ov8lP/nxjRaAQqrQpvZ2/qVqnLxisbiZwfydSdUx0+3ANMjZvKHdUdMqZmMKLR89zKvUFG9g+k7o4m+vhTtDbeqLAgKzTB5zkQuQMGL4JjmUArINR+7qQWk9h2fRv1/OshEUlwlXnyeo86rDlxlx41ezOmSdn3ZmxMAvGh3ViReJcH+UUsPniTTJW21M9jwYkFtA1vy5iYMeWsO5Y2YW1YeHJhueOqIhVu8jJaJzjAx9mLbvX8Of12W0a1CWT9lV9p90Nbwj4PJW5JHHW+rkO/Nf2o4V2DE6NPUMOnht0aJY1ISsNiSqK0Rh4rQuPAxng6eZGvy6/UiwalRsnqE7eZve0yRQYTLnLpEyfIQIiUCQiUi1+VyuWBl0VWlqVxY9LZs+U2ss5XqaBq1TLniEQiXnttOh98+BHXr99g2vQZ1m1eXl4MGjSQFStW4upasV/aCoWCXr168u23izlzJom2bdvYbA8LC7NLq7x2/Tq7du/mxRdfwMPDg+zsbD786GPCw8N4bcZ07ty5w6rVa3ByduaFUSPLPYfiOjhVnn0uvYCAgMDfCZ3RxMJ9Nx1uG3ViA6/tX2YzJl6+HEaOtNjhl0CjM7A/6STPKodQ1yvFbi1V8HO8sr2/3XhBkQFfl8pFIpzE7oxYeoIQLwWznp5NfHg8s4/Mtks5nBY3g961eqE3wtvt3uau6q6d851Sq0SpVfLJoU+YfXg265/dhJOpETvP5TI63o1L9y+RrSm9bcnbbd+mT1QfNl3ZxMtbXiY3+xzv+MBzHiCtxDN1thG+vA9f7QT5rSDq9KzDnd57Sg09JDRNoG9UXy49uMSap9fgJndDIfHi0LUc6gZ7kqcW8XrcR8SFtuHzxLkO+mpNIz60GyOWnCVPo8f1oWEGgF8pn0dFGi1PjZtK69DWdPqpk1XENgpsxMTYibQJa0O3Fd1sxG1yVjLOUudKpUV6KbzYencrsfJYPjr4Ubk1WyUbWdvcc3U2jQMb29nU28zRKCnUuFn745XH1JbTKSwSodQ4FvClkavNRSGTceKWEuf/oub/f40gygQEykFeXr+YUjA78Gg1PRwLCgqiTu3aZe5fUfdCf39/Pv/sU44dS+TS5cvodDrCQkOJj2/L0WPHAAgLc/CasBSCg4IAyFGW/0vRbDazaNFiwsPD6da1CwBbtmxFr9czbepUAgL8admyBenpGWzZspVhQ4eUG3WsVq0aly5d5sbNm7RrV7H0BQEBAYH/BSqNgYvp9i+Qhp3Zwtt7FtuN5336BZ6PCTKD0YQh4wAtrz2FlyLXZpvRLOaHognUD38TvfmY3XpXMwtwlXlU6qHcRerBlYx8LqXnc3yektlPteHAiF6k52eTrVZSxdWHQHcfzGYxO25s5cODH5bqfNdtRTeu51wHYHSTsdSv0oK+X53k3d7RmIwy5nSeS3yJRsMl13ir7Vt0juxCwuaX2Jr8E2/4wEvh4FQJMXZHD/9Jhe82giTFgzdmvMGECRMowsDmq5uYd9RRz66ptA5rTdPFTa3n3iiwEVNaTKNdRHekYl+MJjPT1lwhJrwRmwbuoVCvIkejxEfhjYvUg1WJ6fTdcJI8jZ7o4Ec9ykQiEe5l1OsVRxbHNR3HhkEb0Bq0FBmK8HL2Ripy40q6GmexG7uf282dvDv4uviikCpYdGoRk7dPthNGxWmR0+KmMXTt0HLv1/RW09l5YydJmUkU6Aoq3KC5ZP1YsU19/YD6zGwzk6zCLIdiHcBH4c2szTeYP7g3Y2PGlekQOS4mgT61e3PmViFBvp7lXksxIsBXc41xTcLJLgh9YuzvHfHknrnAn8uArD++r7SMKEzPS/xXRh9/c6Qyy38hjUaDQmFrduKof5afry8AIVWrOmxk/UeRSCS0ahVHq1ZxNuOXL18BcOioWBoFhRaLe2fn8lM2d+3azfXr1/nk4w+t9Wd3793Dw8OdgIBHdYo1a9Zgz969pKenExERUeaaTWNi2LJlK4cPH2HEn1TLJyAgIPBXkKuxT8vuc3EfH+74xm78vY6jGTxkBI8/bpquL8bt9CuIxHqb8TyDKy/ffo1s97bEuzr+fWw0mVmVmF7h+rCJsVNZlZhu7aeVp9EzbnkS855pQL7WxJbzal5uF4SHk4m39k7j29OOXfhKRlFGbRzFC43G0vZh5Kh3gyDio6owac1ZPupXj27Ve9OlehceqB9wv/C+VWgcuXOEZ1d1p2n+IW5GgEclftVf08HHZ2HFWiBdxnOjXqTHly/TLSYKZ7mUPJWWB/cbc2DEQbIKM8kszCTUIxS5RM78xPm8stXWOTApI4nh64cyNiaBOZ3msPlcDk/HhDDt13Ms3J9CVKD7Q/t4FVcy8q33D2Bkq4hHPcrMZvZeUjItbjpDS6lJKxlZXPfsJgry6tIwxJ33NyeTmKIkOtidr4fV4LXdr6EqUnEh60KpdVtgSYu8NSnVYePtkiQ0TaBTZCfqfFWHU2NPVbh5eMn6sWKb+kO3DxG/LL5Mse4ud8dF6sH+qxdI+OkCC4bPpW1YPHOPOo7K9onqzS8nMvnw98vsn96yzBcN7mLo7AK9XKGPmxTfIwM4KKvL630Snxj7e0cIokzAgnOVv2jdPy/17++It7c3aWnppKWlUb16dZttSWft86Fr1qyBq4sLF5KTyc/P/8O9vCqCUqnkyJGjuLu707JFiwrvd/SI5W1see6HBQWF/LR8Oe3axVOnTh2bbUVFtg8q2iKLdb9IVH5hQJMmjQkLC+X27Tv88suvDptHF6NWq3nw4AFhYWHlrisgICDwZ6LRG1DIbJVE/M1T/Of3z+zmftJuBEub9mWsc4nMC5MBTk9BfvULu/nXtSG8eOstbumqIlEX4CyXEB3sYePsWMzyY3fZML47Y5qMcyiiiimugeq74aTdtqWHb/Htc025kVVAvRAPfr+2tsy1wBJFiQ9vx+bBWyjSi/lg0yUmd65Fi0hfBnxzmKdiQpDLDXx5fD67Unbi7+rP/cL7pOWncSvnGqM9RSz1MRPoW+ZhbLimhQ+OwMqfwZgP/Z8aQOfhr/J8txaMXHacpKzLTO9aGzcnKTuS73PjvpoP+0WhNxk5k36G/j/3L1PgLDq1gPjwtkjFLWlezZchzcNYefw2l9LzHc4fFhtGswgfPth8yTq27kw634/oXe7nMbrJWBpUaUHfNSdZmxCHSmtJf7ycUYBUrKBAV2DTOLo0IrwikIjEzOk8lzaliZ6WM+hTuxdDfhtCsEcwar260o2s40Lj+KFfxWzqtw3dxol7Z6wvAA7fyKbn/ON80C+eAyN6kavNJUejxFfhjZfCC5nYiYPXHvDeJst9dPSioa4curpAD1doqwC59XHCct8iNRfZeGUTT9d9mifVMuPJPGsBgb8J0dGWCNSvv621mmoAHDhwkIMHD9nNl8lk9O/fD41Gw6xZc8jIyLCbk52dzd69+yp8DqmpqXYmGg8ePOCjj2eh0WgYNXKEXcrgr7/+hkpl+8fdYDCwevUaDh85glwup2NH2xSbx1mxciV6vYERzz9nMx4WGopWqyUx8bh13SNHjiCTyQgMDHS0lA0ikYhXJ01CLpezavUafvxpOVqt1maO2Wwm8fhxpkydxrVr18tdU0BAQKA8DEYTap2BjDwtVzJUZORpUesMGIz2D/EGo4ldFzORSsREB1ucYhulXWHB+o+RmWzraj5rNYRFsU8THeyBu7PUsp4+Hw70BQeCbK8qhv7X/8MtnaWm2GgyszLxNiPiIko9950Xc5jbZS4rB6yyM1VoFNiIZX1X8FrLD3lh2TmCvZxpFuFNnSB3JA9NoJLTVJjNZl5qXwOdsYjPEudW6J7NPTKHPI2O+Tuv8/GABtSo4sbLK08T6e/GyFahiER6nol+mo86fMSg6EFIxRLq665xMRy+9jcTWMHQwDU1PLcJ6oyFn74HoxfwAgSODGBYt0akZquZ90wjxrSNJE+jI/VBISPiIriWWUB2vhk/RSBv73u7TEFW8pra1PLko98vMq1rFF8MamT9jIuJDvbgP880ZEb32jy/5Dh5mkdRztO3c1HrpLzV5mOW9V1R5ucxYslZQrwVuDpJuZJhEX5Gk5ndF3OY2nJ6he7N1JbTSblfhNnkRHxoTw6MOMjtSXdIGnuO25PucGDEQbpE9iFPm8emq5vwcPL4Q42shzUYVmGb+iN3jtC9RndWJN61jt9Rahix9DSN39vPJ5szOXxZgZ9LIG5yN4wmEZ/uePS3fPmxu3QIjOXb+p343h/uREByOHxaBTq5lBRkj6gqhU2J76EzPbmmYkKkTEDgv6Bnj+5s376dI0eOMv7WRCIiwklPSyf19m169erJxo2b7PZ56qkB3L13j3379vPy+AlERlYjICAAg97AvbR73Llzl4jwcNq3b1ehc1i/fiPHEhOJjIzEx9ub3Lw8Ll26hF6v59lnn6FDh/Z2+/y0fAWr1/xMjRrV8fPzQ61Wk5Jyi5ycHORyOZNfnYSvb+mvL1NSUti2bTsjRzxv5yLZo0d3Nm7azNx5/6Fx40akp2dw584dnnpqQIVdLCMjq/H+e+8wa/ZcfvttLZs3/05UVBReXp6o1WquX79Bbm4ucrkcP79/djRWQEDgr6fMPmPxkXSqE2DT70hvMnHg6gOyC3SMiItg4cLfWfrLu7joi2zWXRLTh/mtBgOWNLdfTt0hWP6ATpkvIM47b3ceC7MGMCfjeUzYRuCWH0tl4/hW1shNMRG+Liwb1ZB9t7fSe9W3pVjS+7Lu1H02nc3mxxdi0eqM5Kh1+LjIcZKJWXX8DsuPpZJTqOOXk3cY1sqjUlGUQr2Kk7eV7LmcRZuafvznmYZ4uJjYdHUdc448qulq5ASf+0F8UIWWBuB6AXywBVZsAqMJnAOcMcZroQ4gsvTMiq3amsyMRnyzL4XoYA/GtI2kWYQPcdV9OXsnFxcnKbnarEpdk8FcwGcDG7HlfAbpeRq+fe5Rs2Xvh/dt9fHb5Bf54upk+yhtNJn56WgqNf3dyMwsvybti0GN8FRI2flqvHV9Z7kYmaQHY2LG8W05dVi9avVi4qqLvN2rLrdztDQM9USGL15Sb+SIEZmlaPV6HjxsT/RHG1n3jepLt+XdKjT/s2Of0a92fwqL9HbbDCbYcj6D7vWCkD5sD1FcmxkuT+Np7920cT9NyJ7rjBaZscv3LYWLRXBfeYVcjRI3+f++x+0fQRBlAgL/BV5eXnz80YcsW2bp75WTk0P16pG89947iBA5FGVisZhXJ00kLq4lO3bs5Nq169y8mYKrqyt+fn7079eX1q1bVfgcYmObo8xVcuvWLS5fvoyrqytNGjemd+9e1K/v2K524MBnuXL5CvfS7nHjhsU5zNfXl65du9CnT29CynF9/Hbx94SGhNCzZw+7bd7e3rz77tssXfoDp0+fwdXVlX79+jJk8KAKXxNAnTp1WLjga7Zv38GJEydJTb1FcnIhzs7OVK0aTLeuXejcuZMgygQEBP4ryuszNmFVEsNahDOzR23kEjE6o4k8tZ5nm4Xi5yrHPzeLzuvfxUtrm+K2vm48H3R8EUQihj5Mc+vz1WFayvbRJcJWkBmRcypwFrPO1XJ4jnkaPc8vOc4Po5oTW82Hbw/e5K5Sww8vNOTjw29Y0+QOpB5g9uHZ1POvZ3XFiwtpzexOs9lzKY8xP560E50j4iLYOL4VIkCjN1baYj9Ho8TNScrC/TeIreaDWVTEa7vfsJo6BEjgI18Y6QHiClrb31PDu+tg6TaLGMMF3nzrTTa4b+D8A9t7Nz9xHhsH7mbh/hSS01RMXJ3E0NgwEtpVZ2x8JOoiPSp9bqWuKU2VjcTsw5LDKVxKz+fzXddK1JUZrHVlZkSMbFWNqb/YCr5iEX08JYe2s4+UWpM2LDac1jX9uJ2jZtKaJJv1w3xcWDbqI+JCHDtAFrtjjvvxAsdScpDLJEQFujPsu0SMZrN1rYw8Le/0jiYm0uIK+UcaWYd4hJCrza10P7wu0YFsOW+fETSsRTgd6/hbbevzHtZmhsozeSVgTYWOYUDCAY2I9fkGflfDzYf6L0ebS4hnSIXW+LshMjuyiBP425OZmcWYseP4dtFCG0OFkphMJq5csRg9REVFVaqBr4CAgIDAH0f4/ftkYDCa2HIhnQmrksqd++XgxkT6uTD9t/NWYeOlUbH1l5kEpd+ymbuvWgyjn3qTWqG+jGwVQbMIH55fcpxb2WoAXvZfw7TAnwAwy31JDP2OoBqdWbT/pk0k7HFGt67GpM41MSNCbzSw9ca6Ug0lSvJTv5WkpTfkm332dvsAQ2PDeL17bV744QQfPFWVqK8jyl2zmMsv3WLSylSLm+Mb7VmVvJRJ28fjJIJXvWCmj8WYoSIotTBrE3y5BTQ6LKGDFtDo6Ub8PvJ3Bq8dTK42185+veQ5FDPvmQak5WpoU6MKVbzVhH5W8Qf1a+NTcZdVoeUne2xMPR7HUyFj4/hWfHvgJisSbT+3CF8XfhjVnBO3clh6+JadGB7VqhpNI7xZduQWY9pE0nLWHofrD40NYUiLIJtoW7CHL/kaEWKxmLiH5/hun2g61fGn9ey9duvIpWJOvdWGtsvakJSRxMw2M6nhXaNCjayX91/J03Wf5vKDyzRa1KDc+cWcH3eepJtuvLkh+eGImU4h+bxc9y61mzyDwivCOjcjT0uLT3bjJCribPRgnMWOUxAzzU4c0ruyMlvJjkIzBQ4+mzuT7vxPRVlFns9LQ4iUCQgICAgICPwrKavP2OMs3H+Db59ryqWHFvgis4lFaz+yE2S6ps0IWLWB/X5eeDjLOJWqZNh3idxRapCIRdQKcOO401iOoaS521XE7X+ngVMEi/bfYGx8JE3CvRw+xL/YOpIu0QEcuHqfhfuvs/C5+sw5PLtC5/7psbn8PngPiw/eQm+0f5JdkXibpuE+dK4TQJC77x+y2EdcwCeH32R+4mc85QZz/aBaBTvKaPTw5Q6YtQGUhQ8HG4Jnd0/Gdx7PlJZTyNPmMavjLLwV3nb268XRupIUm5f0/fIQe8tx83v8mnwU3nx/ILVMQQaPIpgbxremSZg3Sw6nWD+3W9lqpv96jjd61mbNmJZk5WvJKXyU/rgy8Tbvb77I273qIJeKHRq55Gn0fLMvxcYBMsDDxJs9Azh8/T7d6gXSvnYVMvKKqF7FlQKt48bkOoOJK+lqprScwvB1w1lwYgEnRp9gTMyYMmvEEpom0Ld2H5ykMnwU3uXeu5K4yV15praWLs4pSO7vxzP/CNKiNMiAokwP8BprneuhkFqvP7GwHvHupwEwiRUU+bbgyztXWZJxjyv6IqColCM+6sP2pCKIMgEBAQEBAYF/JaX1GXNEcpoKrc5IVKA7l9LzMYvEfNe8Pw0zr+P8sJbsdkA4U/u/hXlXCgVFBqRiEc+1jGDl6Fi2XcigR4PgRzVdzj+gdjXi7OKLi0TM2PjqHLh6n4Iig00Nk6+b3PIgLxEzb8dVvj+cQp0gd1RFeZWsk8rn4IzW/HT0LsuP3bUxpwD47tBN1oxpyeFrWZWy2F925DpKyc8UyNby++kCtgVDlwqW9BhNsPQAvPsb3CvOmqwGdIGYJjFsHrKZvSl76fBjhzLt130U3hQU2X6OxZ9XFQ8nViemMz1uOkMqEFWc3GIazlInlpeIfBWL6eKUwKuZBRgfKrZb2WpyCopQafXWzy1XoyPIU4FMImbp4RRWHT9BoKezXfrj0Ngwmkb4sDLxtsM0yGJMZqxRwHnPNODHo7f4Zt8Nzt7JZf7AxuRp9exIziTEW+Fwf4CpvySzeWI/xsaMZdGpReU2sp7Raga9avVGr5eBHLwV3uUK20gZtFPAAB9vQva2QqpNx1GsyJS+D6IeiTK5RMzY+EgmrEriN2VHLmmqkekaT5dOzdmcspEajYdzpQJRvWlx05CLK1a7/ndEEGUCAgICAgIC/0ryHPQZKwulWmcTkTneoA2547rg+mx/8o0iBg54l3SVGFRK3MRqohU3+HqvgfhaLfH3cHZY01XSSKRTnQB0RhMqjQGJREQ1P1dc5BK0BhPbL2by/WFL+qGbk7TStV+3827z/ZnvaRvWkQ3juzNiyVlrOqVcKibc1wW9yciFNBVPNS3fYv+FxqO5mZPKxydfRibP5W1vmO5d8ebPuy7Aq8vhwh3L987Bzmjba6EGxATHsOf5PczYOcNh761i+/WEpgnseW4PYrO71b2wJMWf11d7U9j4StcK9fLqE9WbfK3JKnCGtQhhcKwlfTBbnYOviw8uUg9WH09n+bG7hHgrkEnFfLD5Ih9svmiNaHk4S3m7dzSRVVwJ9lLYfe6j21gMSYZ+dwylWs+WCW0YGhtmlwZZkqGPWfAvT7xNk3BvbmQVsPDATbpEB5baOuFWtprfz+Ywt/M82oS3Yd6ReXaNrAt1hVRxrYJc7E5mrpFt53Lo0cDizCIXy+2EbTUptHOxCLF2CgizRkaVoLU7BSuy7P1gNsPDNjlSiZhOdQIY1iKM5cfi2Ug8dYLcGeQWWOGo3tiYsfSs1Qu5tILh2b8hgigTEBAQEBAQ+FfiqZBXar63i5yCokcpYsNahHPQ24X5g+ag0GlJ97D0/AyUPWBpxLtUc0ojt+VOPt91jTUn71ArwI1mEd7WaMvjRiIucilSidjG6RHAjIFvDzxKsywoMuDr4lOpc/d18eVU+imWJi1lbEwCy0Z9yEvLLzCjey2ahLuRq83ltuoqz8Z64+UsZ07neXSK7MTHhz60iY7U969PlG9d1l9eT7bmPr1c4YsqFU9VvJoOU1bA5jOW79193Xnv/ffoP6g/EomEzII86lSJZO2l38oUUGBxX2wb3pZqLh3oVi8QX1c52YU6dl3KQmcwWT+vPI2e578/y7JRH9EypA2fOTDOmBE3gw4R3Rm6OIm2taowvn0N6gQ7WZwt1wy1mz+x+VQ2jO/O1fQiTt5SMjQ2zObYACdTDzM0NowlzzfDjBmV1oCHswwPhRSJSGRp/j2mBTmFOowmEzN71LFLgwSLiCtZm1gyyvn9oRS+fa4pC/bfYGXibUa1rsaUnx1H3D7ecpnWNVrRxK87B0b0Jfdh6qcIEcHuVbmWoeHNX28S4q1gdNtI3JyLrM2Y5VIZPWr2sIqjNYHw7B9otZql98Y9uDVSoxqkj0KqLnIpM3vUIbaaLwv33+ByRj6uMg/CvcLLjepNjJ1I+4j2SERPdu2uIMoEBAQEBAQE/pWUrGUpj+hgD5xkYmtERiIWMSQ2jNE/nOSuZ4B1Xi2nWyyr9i7BcosFudfJpwl3X8n+6S1LjbYsP5ZKbDUfutcLtDrSlUSl0dukWV7NLMBV5lGpOimFVMGFrAuApUly18jurBnXno1XNjFp2Ry7B92pLafTJ6o33Wt2I6sgh8sPrrHp6jpWnv+R81nnCZfC90HQ163cwwOQWwjvrYWvd4LeCGInMdOmTWPomKEMWD+AFb+uYPOgPVy7ZyDSR8t/jv6n3DWreVUj1COU6EAXXu/lT7YmB1+FP7OersXVdA1V3ORIH1o+3spW0/erk7zaOZY9zx2gUK8iV6tEIfVAIfFgxbE0Oq9NJE+jJ1etY2y7qkzfOc3qIlmSpIwkJu94hSV9l9CpVieUmlwigsz4KgKY9XQUZ1ILeHPdRe4oNXyz7waRVVxpH+VPrQB7FePiJMXbRcZ3By1R0M51AlgzpiWZKq2NBf/KxNt8sPmSXdppcpoKsQj2TG2HQibBzUnKmRZKh26ieRo9Q787zs9jW7L3UjbJaQWodSKyC7PYkXyOqMBH4m/t6XuMbelt8/Oo0WuYHjedliEtyTnzGpBZ7meUZ/blQF5djhY04FhhfRS+dfglLg6k9hLERS6le71AOtbxR6UxIMJkjc6VjOqp9Wqy1dn4uviikCpYdGoRbnIP+tTqXe75/J0RRJmAgICAgIDAv5KStSzlMap5MCsTb1vNH2oFuKHVGW3EUqzreRZHfIiHpNA65my8Tx/dRHqskZcabRmx5CwL99+w2oQbjCZrGqNcIuJBgW2apdFkZlVieoVrvybETmDRqUXW5snezt7EVG3I9J3TSk0PHLZuCONiEpgQO4H5x75i2dklFBk1SICpXvCeL7hUIDBhNMHC3fDOr5BdAIiB5vDN7G8IrxpO/Mp4lFoly/qu4Kejd3muZQgP1Bnl1st1rNaRtQPXsvHKRsZvHW93b6e0nELt4H789EIs2QU6CosM+Lk74eYkZfO5NDYkpfFs01Da1/YnYflJTqbmWuvE+jQKZN3FjQ4FmUQkoXvN7nzd42v2puylzdI2DgXt7xN7k/DTBSL8XGkZ6YehDNcQZ5mUUa2rceDqfXzcnLhxv4DX1p6zq0ErjXytwUbwzexRh2bhltYJjiJuRQYjmSotz8VFYDabySnUMa1rFG6Ge9y59CvS48eYWJSIeJcZ+t6yphm6Obkz8LeBtAptxcT4+XDMvtVNllHEXrWZ67JQJvZeR8xn6RhMj3ohfBlfwxp9c4RUIraJFveO6sPYmHEsOrWQTw59Ytfy4ULWBcY0GUu3Gl2f6NRFEESZgICAgICAwL8U21qW0mt5xtVU0GdIF95q/AzUbANY6rpy1I/EUk/Pg3wa+h+cxLYOeBliL1olXyTlMWO8pIwkRm4cZk0l7PvVSUt0QAQ7L1oaWV/OyOfIax1wdbJtJg2w/NhdNowvv/ZrbMxY2ke05+fkn2kQ0IDkrGQSmiWw++buctMDF55awOLT31rt5xs5wXf+EONc5m5WDl6Gl5fB+Yd1Y54xnuS3ymd059F0atCJZoubodQqGRuTQHxoN6Ydu8zAWB+yCrPKXLeaVzXWDlzL9J3TWXRqkd32pIwkhq8bzriYccztMpdF+6+Tkq1mTNtIGod4oTOY+HRgI7Q6I2m5GuY+3QixGFYdv8OqxNsMjg2i12pbUxBvZ28SmiWQ0DQBD7kHM3aVXu9WLGgXPTcHk8mJPI0ONycnDEaTw0goYK0pNJhM+Hs42dj7l4eHs60YcZFL6RIdQKuafuSp9aVG3IryUhhT+zaBWfsRZe1HokmlyuOLF94Ct2qApa5sTJNxDFs3hM+PiFBGiihEygmDCxuVKvaozVzWW9Tjiv5zOJkTjMH0qE/Z4/3JKoKb3JV5XebRNjyeuUdmk5SRxLnMc4BFAP/UfwV9ono/sQ2jSyKIsn8wItGjNxNGo1HokyMgICDw/4TJZLL+u+TvYoG/H4/XsjweWXipZQhdXxmCNOUms1Jm49fmHl+1HEhBkQEfF0tN2gt+63kr+Du7te+71CL6/FVyTHabrCw6tYCWIa0ZGtuIPI2eTefu8dHvlwGoE+ROgdaAs1xil2aZp9EzYomlTiou1HGD4Sktp9Anqg/3C+8zs81MqrpXRSFVIJPI6PhjxwrdH6PZiLMI3vGBqd4grcCPc1YeTFsFPx60fN+8VXNenPEi8XHx+Cp8MZqN9FjRg3CvcD6L/Yr40G6MWHKWKu5O5Khz8XPxK3P9BT0XsPHKRoeCrCQLTy2kdXhrpnTtR+zH+/BS3CGuuh+uTtJSG2lvntCaQkOWTaSuhk8Ntg3dxoHUA6y7vA43mVuFBG2bsLaEKdoz7PuTdqYujiiOEnkqqFRarYfCfj2FXIoZSL6Xx+/n0zl/L4+8BynEup5jbuhl2nhcQJF/G06Uc4DMfY9EmVRG39qPIldRtyDVoAfybHYZF5NAr1q96Dn/uPUcx8VXp2Md/1KvvSzc5K48Xecp+kT1JlejJEebi4+zF14KL+Ripyc+QlaMIMr+wYhEIpycnCgqKkKlUuHr6/u/PiUBAQGBfwWFhZb0NblcLoiyJ4DHa1lUWr3FkMFZgvP4lxAnHrPOnXpwOUUSOUtaPoWzTMSnNX9kgOJnuzW1AT3pde12mYKsmPmJ89g4cDeYJaw9fc86XhyN23fmPiPiIpj26zmb/YrrpIbGNmLL4L0YzPnczrtNFdcqBLoFcuXBFYb8NoSGgQ0ZGzMWtV6NqkiFj8Knwnb67RTwrT/UrIAniulhquIbP0OuGkJqhCDvKue433GOnz4Opy1icVrcNPY8vweNDn44fJe+G06Sp9GjkEtQyBQoZKJS6+XkYjlxoXG0WdqmQuf/nyP/oW9UX+JqePNhv/osPZTCZ7uvWVMVi0lOUzHt13PM7FGbFrXU1nFvZ2+2D9vO7MOz+f7099yceJM+q/pU6Njzjs5h/4heSMU4NHUpjcqk1Y6Lr15qOqCLXEqrECOtcr4E6T6kVW482lhB41H9/aPIqo+0fv945Cr1sRcB0+Jm0CeqNzqdjO9HNLMam8gfCs4/ilwqQ44MN7krIR5VLe6N/zAEUfYPx9vbm4yMDLKysjAYDLi7u+Pk5CQ8JAgICAj8BZhMJgoLC8nMtBTAu7v/AXsygf8JJWtZAj0f5uctXAjf2UbAMnwC+bV+RyRmHardzzJAscVurU2afkTV/w/HD1Wv0LHPZJxBa8zHS+5iY+1eHI1bfiyVjeNbMaR5mNXFsWTPrG/2pbD44C0S32jD6gtzmNF6BtN3Tmd3ym5rhKfv6r4kZSTROqw1n3T8pNxz8hRbGkCP9qzQJXDiBry0DE7ehKCqQTQcXYv9nvstNWQlSMpIYujaoSTEJDCr0xw2nc2wmlcUG5isu7KcCc0nMMpBb6o+tfuQq82tVI82pUbJl4OakJ5XRPf6QfRrUpVVx++w/FiqnXFG4s1sejQMtn6f0CyB/bf28+2pb2kQ0AC1Xl2pY+dqc+kSHciW85Y0vvJMXaDiabUVSQeUOnlA6o9g0pc6pyTZBg8SC+pxtLABxwrqM6FuX7o/lnZZbuRKIgVtPj75WXAjC7KyIDcX8vJsv3JzQaUCrRazRoPuhRdRDn6ePI0OT4XcVsw1aQJnzjg+aZHIYhxy7RqEh1foOv+OCKLsH46npydarZbc3FxycnLIyalcXxMBAQEBgT+Gs7OzkKHwJHPwILzyis2QWaHAc/tmtkVUxf34s7jkHrTbbf6DUVRp/jZac8WaUhdToMvjZqacqEB3a3Piq5kFOMslhHgrGL/yNMtGNmd6tyiUah0PCnT4PKwT2n0pEy8XOSaTlOcbjmLnjZ38nPwzJ8ecZPbh2Tb9nVRFqnLTAzu7wPf+EFqBrDCVBl5bDYt2g8kduk3qRv9B/Rm7bWyZ+y04tYDWYW35oF88I5aeBh4ZmFQNCiQ2pInD3lT+rv5ka7LLP7ES5GiUfHssl58eCpziVMWN41vx/JLj3MpWIxGLaBLmxcf9GyCV6mgU2IjzmecZGzPWGhnzcPLggfpBpY/t62obZixp6lIa5aXVjouvTscazrhkbYGM3RDUFar2sF9I6gq+zeH+YcfnZ/AgsbAeRwvqc6ygPteKwjCXUNILD9ykY92AR+daUAASCXKF4lHkyjPEss1kgshIyMiAoqIK3iELIuA333rMvBVic53WlM+yImNmM+j1ILGvvXySEETZPxyxWExgYCCurq7k5+dTWFiI0Wj8X5+WgICAwD8WuVyOu7s7vr6+SJ7wh4R/LXfuwNNPg8HWnUO0dCmKugEo9naGvAs220xIWWp4k35Dp3IlIx8XaQVDTA/xc/VBIXbhq8FNcJKJrZGcnckZvNqpJjUD3NlzOYtlR27ZPaCPaRtJXHU/Jq46w8Ln6vHippE2EZ6SXMi8wLjN4xyeg6vIEh1L8KrYOW84BS8thTQz0APETcQsmrLoD6X3PWztxfJjd9n4Ske+P/u51X69ZG+qrMIsfBWVe9nho/Amu4R5SHGq4tDYMH58IZYNSfcY0CQEo8nM/YIi/D1kTI+bzqzDs2wiYxURtI6PbWsdn5ymQqUxlFtfZZdWq9bgW3QW99zdyDJnIrp4FB6asGAocCzKAPzbPxJlMi+0Pq2ZfTqIo4UNuKINtxFhYpORqvmZROSkEaFMI2J3GuKtBki7A7dvW6JbK1bAkCH2xxGLobCw0oKsGJXS9kVGyZTP97ELutoje7JrywRR9i9AJBLh4eGBh4cHAGazGfM/MBdXQEBA4H+NSCQS0sOfdDQa6N/fknJVktdegz7xsL0ZqO/abpO6YW71K4P9OvLxlkusOnGH/dNbVqqPmIvUnVVn0vh63w2bSM6ywyk80zSUj36/xMrj9qlsyWkqJq5OYmhsGHOfaUhmwX0uZF1g4+CNDsWRCRP7U/fbjbdVwNIAiKzAc21GLrzyA/yqBLoDNQAx1Auo94fS+15oHcmih82x8zR6Tt0qZErL6exO2YGqSGXTm6qKaxW8Fd6Vureezl7sSD5nt21F4m1iq/nQtqafjfGHp0LG7xO7kdYgzSYylpyVjIvM5Q8c2/5+qLT6R2myZSBVpyDN2IlL+g4CM/eAPs/xxMy9pS8S9hTIvSCgPXg1JDWrkF83b6Pm/dsMfLCD6jl3iVCmE6FMIyw3HSejofS1wCLOSsPfHx5ULppYjJPRcYrl8mOpTC0y4lXeAg56nz1JPNlnL/CHEB4aBAQEBAQEHGA2w9ixcOqU7Xj37vDhh5ZIQFBXuPH9o23OgdBuC2bPhuy6kM7yRMsD6+rjGdbGt+UxMXYqG5PuM7xlBCdTc1BpDby29jyDmoUytUsUuy5lOhRkJVmReJvOdf1xc1MS7R9dYXHkLIKPfWGiF4gr8Giw5KCMhXf7Mm3ec/y6x1b0/ZH0PqUml5fa1eLZpqEo1ToCPJzwc5NhMOtoG9YeZ5mMB+oH6Iw6QjxCkIll3M27y5SWUxi+bni560+Jm8LFe2prJO5xFh24ybfPNeVSiX5zeRo9wxYnsWL0i+hRWseNZiOLTi0qtd7tcabGTef0rXyHx37cxt6KvgAydkH6NsjYCQU3yz0OAIUpUHAL3CLst3k3snw9xFNs5sz8wUjNFXChccSdO6Vv8/eHixcffa9QWMZ8fMDLCzw9LV9eXujd3Fl2IYdUDWilcq75hpa67NSnXufLAXVQyGWWGrLi51izGYxGS1Tbs3LR6b8bgigTEBAQEBAQEACYPx9++sl2rGZNWLnyUb1Ks4VQlAN314FHFLTbBm4R6HQGFu579ACdeFPJ9217V6CPmKVHV9+vTtKlbjBv9YpGIZPgJBOz+sQdRCJYtL9iD+a/nrrHjJ7+FRZHzZ3gh0CoXQFnxZvZMqYk9eW09xDqdvKlcd0I2GM754+k93krvJi57jw3HxRSw9+NmT0jWX9lI3OOzCEpIwlfhS9vx7/N4HqD0Rl1ZGuy8XDyoV/tfoyNGVumLf64mHH0qdWHPLURT4XMztQDLJFGrc5oreUraaLS76sTbH21uU1kbMGJBZwYfcJhvZvtsRPoE9WbV1Yk220rzcaey59D0gwwVdAaEUAsA79WENgJtEZIOgQnT1peLAwbBl272u3i4eVKRpWqhGSVIa7KoqxI2bx5FoHk72/5ci29f1h2npaPPtldoUPu0nuQF1kbRQWii08qgigTEBAQEBAQENi9G6ZOtR1zc4P16y1v+IsRS6HVSkh6Heq9CU6W+iaVxsDFdNuamFsPDLweV3ofsUmx06w9uvI0etJyNczedpkTt5REB3swsWNNNDojF9NVSMQiO9fFx23dt17I4OMBtajiUoVFJ0sXKxLgTR94ywckFYiOHdB2Y2zai2iqOCPC4pLo6yCF8I+m921PPourk4yFw+vx3oHXWVSiB1i2JpuJ2yby6vZXqedfj+caPMfoJqPRGYzM7TyXNuFtmHdknt29nRo3ld61etN/TX+GNxjF0NhGfLMvxeF55Gp0DG8RQXxUFbQ6IznqRyYqyfdUvBo7jec3DAVAqVXSbUU3tg3dZlfvVnzsaS1n0KtWL+7lGCkosk8FLNXG3q1axQSZZzQm/47odbXRni6CdSdwPbscydW3EJUsTwkKcijK5BIxknrRsKd0UZYvV3DLOxifRtEExdRHXD0SwsIgNNTyVRoxMeWf/0PyNJUQn1Q85fNJRRBlAgICAgICAv9oDEYTOqMJlcbg2G47JQWefdaSBlWS5cuhbl37BSXOEPOZzdDjD5gFRQZc5VL6fnWEobGN2DRwD4V6FTkaJT4Kb1ykHqxKTLf26ALwdpFbH+KT01QsPniTd3pH83L7agyODaJQryJbnYOviw8uUg9WH09n+bG7FBQZrIJty5W9HEw9yAON40hZhBSWB0IrRfn3LVUHHxtq8NbTKymafcQ6PrhZKHKJk116ZqXT+1o+Su8b1iKEvalb+e70YhoENMDDyQNVkYrkrGSMZiMms4lzmeeYunMqfi5+9KjZg+G/juCV5q9wYMQBcrW55Ghy8FH44OnsyZHbR2i0sBEpuSnkaHLYOHA3C/enYHJQUh/kqSC7QOewofToNpH0q9OHcXfHWRtGX8+5TrPFzRjXdJy13i2/KJ8g9yA8nbw4dSufnvOPs2xkc+vn6SnJp5PHcUZGXCCy+krHzosBHUEstxdmzv4Q0Al0UXBGj2HVWYyHfsIpV4lTWTf48TTch0glYvxaxGDct5Ob3sFc8w3lpm8It7yDSfEOJtU7iAcuXgxrGcHMHrUR/4GGzxXBU1GBEG0JSk35/IcgiDIBAQEBAQGBfyxqnYFdlzJZtP+m3QP32PhIOlXzxKVfP3i8ZcznXaFjwwof5/EHzJJ29t/sS2Hh/hSiAt0fRrpUXMnItxEI0cEeOMnENn3KAj2dCfOTUMX/DL3XDLVGZCQiCU/VeYoRDV9gb5uW6AwycgoKeO/gTAavX1zqOQ5xh2+qgGcFTEEX58GUB5Bvus5kvYqoQHfEIhFj20bSobY/maoietbqzbiYR2IFKp7el9A0gd5Rvejx+XEkYhFDW1blt0u7uTnxJmq9mgfqB/i5+KGQKvj29LcsOLEApdZS37Xl2hY6VOvAlutb2HJ9C1KxlH61++Hv6k9WYRbrL6/HYHoUoTqTcYbCh9dwKT3f5jyigz2QScSMW37KTrAlp6mYtCaJCR1qMLfzPOJC2/DpMUvEU6lV8smhT9h+Yzvvtv2AFlXb8NXeW6w4dhaDybKuwphJM91q3qh2hJZuF5CIjGAEsveA27P2N0XmBv7tIHMP+LeFoG5w1gQ/n4bdO+DBSsDy8F6hB/hTpyw1Vw58BGSvzUD9+kwu38h1aLn/Tnx1OtbxL9ch8r/BQyElOtjD5tilUWrK5z+If/bVCQgICAgICPxrUesMfLzlksMGvFa77dgw3n5+BPLp0yyRMhHwdiRU2Q57u0DnQ5ZIRTn4uEqZ81R9Dly7z46LWegMJlYm3mZEXATTfj2HyYydICjJyFYRrEy8bRUG9at68NGAWszYOc0qerydvUlolsDYmLFW4aLUZpGjUdJv5TDS1Fccru0htoixoR7l37N7BngxE7apH40VmfL56YVYJGIRG5Lu0Wq2xelvfIdI5nSeS9vweOYcmW0VK91WdGPX8F20DW/rMLVwUuw0+tXuw2c7Urmj1DCqVTXcnES4yd2sTa5Lzp/QfAInRp+g24puXM+5TlpBGjmaRyLaYDLw68Vfy7yuHI0SNyf7x97RbSJZethxBK2YL/Zcp06QO7U9O7NpYHd0pgIK9Xk4SdxRSCwRzzfXHCVPoydCfo+unkcZF3oW771neL+qgwXTfodwB6IMoNk34OQH8oemFe8PgjU/l3ltJTGIxFzzC8M1LpaqHVsj0etB7iAi5e6OC9C9nvMjy32tHg9nmW0U+S9ELhEzNj6SCauSyp1basrnPwhBlAkICAgICAj84zAYTey6lOlQkJVkeeJtYgcPpMe2BkiGPAsvGKHWQ2ON/Guwtxt02gcye0VjMJrQmwwYTDpytbnUDssjLiqAT56K4tydQj76/QoLhsUwpHlYme6JQ2PDaBbhwwebLwEWS/ZlI5uy4fI6qyCr4VODbUO3cSD1gJ1wEZlFmEWOVUWcsyVdsVoFMr9+UsGE+5D7mCmfj8KbfZezmP6bRVxG+LqwbFRD9t3eSq9V39IqtJU1jS9Xm0ugWyDeCh+qu3Zi86CeFOjy7NI2c0Lg6M1sWkb6MLlrONN2TLWJuBWTlJHEqI2jGBszlm1Dt9FscTNURSq8Fd7lX9Bj11BQZBuRGRYbTrMIH97ZaG/G8Thf7b3BkhHNOJ6SQ6vq/ny27T7X76dyJSOfcNk9nvM6QM+QQ9RWpFp20JSxWNpWMJtAJAalEi5dgrg4yzb36rZzO3eGNWtKXSrDzYcTIdGcDKnLucCaXPSvRpHMiehgD34Z27LcSJf0ofhykUv/3+u1pBIxneoEMKxFWJn/T4e1CC+32fY/gX+dKLt27Rrr1m3g4qVLqFQqnJ2cCA8Pp2OnDnTs0MHOKt5oNLL59y3s3rWb9IwMnJ2dqV+vHoMHDyI0NKSUo8Dx4ydYv2EDN29aikqrR0bSv39fmjZt+pden4CAgICAgADojCYbN8SyWLj/Bh1fbIbLsoaQa9vvyaxMQntnE8aqTyMWSawRBI3OgMagZtPVzQ5NPKbHTWfNuN68s/4aEzvVpGm4N3uuZJGp0lqNOmoHujOyVQTNInx4fslxa23Z8JbhiMUG/nN0DmCJkG0ftp3Zh2fbpgQaAQkOBZkYeMMH3qmAmYfSCGOy4NcC+22NAxvjJvNg+m8HMJktgvGHFxry8eE3rK6SB1IPMPvwbOr517PWgsWFtGZG3AdMXn2RUB8XAC6l3+ZSuoo6QR4MiY0gI0/Lz2Nj2XB5rUNBVpJFpxbRIqQF45qOY87hOXg5e1WyD5yHNTW0OHW1Q5Q/vb485NCV8XGS01SYzWbaR1XBDPSpoed24WqaV99BfZcb5e4PWGoRA7uAR3v45kv4bQMcOADu7pa+eI6aH3fqZPPtLa8gjoQ3JDE0mlMhdbnr4e8wPbGiDar/17jIpczsUYfYar4O0yjH/T+kUf5d+OdfYQmOHDnK3Hn/wWQyUT0ykrp166BSqbh48RIXL13i7NlzTJn8qnW+yWRiztx5HDuWiKurKzExTchX5XPk6FFOnjrFhx+8T61aNe2Os3HjJr5fshSJRELDhg2QSWWcSUrigw8/ZszoF+nZs5SO6wICAgICAgJ/Co7cEEvjTmYasgOvQe4Rm3G9GZ7LMHN55zymthTTJ6o3Wr0cDGIKdYW8ve81h3b3SRlJDFk7hISmCczuN4cr6Wq61PemeU3IVivxVnjj7eyF3iBh+bE79PnqsFUYSMQihrcI54Emy9pnLKFZAvtv7bcVZDqgFJ8EfwmsCIROLuVf+141PJcJd0vpFzy91XSuZ2pxd7ZYyhcbcjx+3cVGHMWcyzxHfHgbvhzch9QcrdXNcNXxO9QKcGNl4m2GtQxHJDIw98js8k8U+CLxC9YPWs+OGzvQGXUVNhSZ3mo6bk4KNo1vjYuTBJlEzNEb2dzL1XArW13u/sWk52n5eMslrmYWsCL6Gzq7bih/J5knVO0Fnh3hcAF8ux72TQFTiXBkbi4cPgzt2tnvHx4O48dDgwbcbBhLh7UVt7F/UtwKXeRSutcL/J+lUf5d+NeIMqPRyMJF32IymZj86iTi49tat925c5fXZ87kwIGDdO7ciQb16wOwa/dujh1LJDg4iE8+/givh5a4R44cZfacuXz62ed8/dUXSCSPKmbv3rvH0mU/IJPJ+PCD96ldOwqAe/fSmPHa63y/ZClNmjQmKCjo/+/iBQQEBAQE/mVU1G7bT6pkdY13kOXYRtXUJhiQDtvVQEESw9YNYVxMAnM6z+FOtp7jmVscCjKJSEK0fzQeTh7cUd1BZyziav4uRi+d47C2qk+jbqw9fc8qymoFuGEwmrhfkG1db2zMWPqsetio2QiYKVWQtVPAykAIKucJzyySsk7ehGeuHae0FsLjYhKID+vG8RuFbBzfilFLTzA4Noheq8tviA0w58gcWod2ZeCio5jMlsjHC62r0alOAPsuZ/Fmr7ootfcr1OQaLIYdar2at9q+xYITCxhSf0j5hiIxCfSq1ZsVR++x61ImBUUGrmTkExXoztdDmlTouMV4OMsoKDKQp9Ez70pjllUrRZQ5B0BIP/DpAgez4b1fYfdoe3fPkmza5FiUAXz5JQAueVqg4qLsSXIr/F+mUf5d+HdIT+Du3bvk5eVRtWpVG0EGEBoaQnx8PADXr123jm/YsAmA5597zirIAOLiWtK8eTPS09NJTDxus9bmTZsxmUx069rFKsgAqlYN5plnnsZoNLJp0+Y/+/IEBAQEBAT+sRiMJtQ6Axl5Wq5kqMjI06LWGTAYS5MTju22RWYTc7Z8TovblohOsCyLn6vPoIbcVpDlGKHjvYeCrAQLTy1g09XNhPrK+Txxrs02b2dvZraZyc2JN1nz9BpmdZzFNz2+wWAykHz/Aqm5qTbzkzKSGLFhKLOOvsmyUQ3xVMiQiEVEB3tQZDBRxc3S/yzaPxq1Xm0RLgVYntwcCC4xlr5ju6qWL8iu6uApZSAN26/k+75LaRTYyGZ7o8BGrBywik86zkavlxET4c3l9HxWjolFby6olIjSGPMZ0KQqdYLcuZyRz+Sfz/Lxlkt8PKA+ErEIpUZZobWKyS/Kp2lwUz479hndVnRjetx0lpZyDasGrGJO5zkcuqLik62W/m+X0i2ul1czC3CSSYgOduR+YqaZywU+CP4GJ5FF3D/ujnkovxE5hkf7aqVVMNUcDx32gfsP8GUhNBgGo8bAjh1lCzJnZ9Bqy732YrfCivBvcCv8p/Gv+bRkjvJ0HeDu7g5AZmYmd+/eRS6X07SpfSO8uJYtOX78BCdOnCQurqV1/OTDnhBxxQWbj+2zZMlSTpw4yZgxo//IZQgICAgICPyrKNfSvk6Aw3oTR3bbkw6t4tnzuxhwYQ9f93+Wp3rvJkR+32a/NAN0uQfJpQTa5h2ZQ+9aPTmfdd46VpYJhyP3wJIsOrWAuJDWzB/UjhoBCjTGfPL1KVTziKBRYCPc5G5kFGRADuDj+Jwqk674XR5Mug+F5rvsK9FrS4SIXG0eHnJPnKUe7LqYw+gfznP6di61A90Z374G7s4y0h5UTkRpDCpeaB2JXCK2pi8uP5ZK93qBVPN1xcelcoYdQe5BJPyegFKrRKlVWvuF7X5uN3naQjLys62GIhKRnCKdlC/2XLdbx2gy27hjAoTIMhngvYcB3nuIcEoHILGwHpvz2tq5YxqQsianC14SFZvy2pLv3pK15/Yi/nE43KlANMvVFfr0gWeegS5dLN+Xg+BW+M/mXyPKAgICCAwM5N69e+zff8AufXH//v24ubnRokUsACkptwAIDw9DKrW/TdWrRwJwK/WWdaygoJD79y3NGiMjq9ntU6WKHx4eHmTdv49arcbFpQK/PQUEBAQEBP6lVMjSvkU4M3vUthNmjz/Adr16hIlHVgEgDTExseNquxTAGzrodA9ulVJfBZboj1KrpJ5/Pc5lnivdhOMhjtwDi/ttFfN54jx2PdeNzj914nT6aQBmtpnJpNhJzN4/m0ELB5UqyCqarphnhBcfM/Mo7rU1+/BsLr98hereURQZi7hfmE21wFw+fKoqLtI6rDuTRf2qnnx3MIWuDTzLPtBjOIndmbQ6iUvp+UQHezAiLoKN41ux7MgtXoqvjrOTZ6UMO4rTQkteQ44mF5Uapv9iGS/uA7d7SjxGk7HU2sLlx1LZ+FJzPoq5QmjOctq6n7Gb85T3bjxrD7NxxyxmdsaIR98UaDCdPlG2IFMooFcvGDgQuneHSj4HCm6F/2z+NaJMIpEwaeIrfPDhx3z62eds2LCRoOAg8vLyuHjxEqGhIUyc8Io1Unb/gUVc+fr6OlyvePz+/Udv2B48sPzbzc0NZ2fH+bC+vr6oVCqysu4TERFe7nkXFBRSWFhoN/7g4fkJCAgICAj8E6mwpf2xVGKr+dCmph+ZKi2eCrnVIKD4ATZx00H+8/tnlh0igemAu+06yUXQ+R6kl5FlVkyuNhcPJ0samUMTDgc87h5YXHemKlJxLvMcWYVZNg2PF5xYwDetv+Fq6lWMnvYnJcLirvhuBdwVT2phYAbcLMVk0GQ24Sx1ZtfNnbyz/w27SN+UFtNQOAWxIekeA2Ia/mHXw+Q0FdN+PcfQ2DDGtI3E1UlKtia7woYdE2In8KDwAW5yN+u5TYqdRnxoN0YsOWtj2hEd7IGLXMrtHMdGHiGyTAZ5bMdt22CGSnLsfh6KaedxhpqxLgwr4Y5ZGtlPDSJ453bbQanUIsCGDLEIMje3cq+zLAS3wn8u/6pPrE6dOnz80Qd88slsbty8yY2blhxyqVRKo4YNCQgIsM7VaiwNJpycnByuVSy6NJpHOcCah/nATk6lVN8Czs5OD+eW1cDiEZs2bWJ1JZoGCggICAgI/BOorKV9k3Bvus8/aDWUKE5tfKNlIMYJc3HTaaAaMBNQPHYsj/rEnzlPduklajb4KHyo6VOTo3eO2ppwlMMXiV+wfdh2xjUdZ23+7Ofih0KqwGgyUtW9qtXBUHlTyeDsweAgMOUphp8CoHcFnu+/yIVpD0BXRnPkxoGNkUtk9P+5Nyaz7U1Iykhi+PqhjItJ4PsRH7LudCYTm09l5MZh5R57YuxUViWm2zVmXpF4m8ZhXnSqEwAiaBvetlzDjrExY2kT1gYnqRNfdVuMs1Rh7XnWd8NJO8E0tm0kW86n07ZmFeuYBCMdPY4zxGcbbd1PIy6lt1sxRvcoDhl68ub3Z8jMk9L76hE63jjOq72mYBbZR6HE/frBjEmQlweNGsHzz1vEmH/5zccrg+BW+M/kXyXKDhw4yBdffkVUrVpMmTKZsLBQcnJyWL9+A+s3bOT8+QvMnv1JhevP/j/o3bs3HTp0sBt/8OABM994839wRgICAgICAn89lbG0T05TodUZiQp051J6vjW1cXizEN5dNB3JnVuWiRlAGlCyP2+VVujifib0Xk+yKxz9cSGhaQKn0k89MuGoAGcyzlCoL2TGzhn8fPHRC9dGgY2YFjeNBT0X0OmnTlw/dh3CAAdJN/XlsDYIapT+/hcAo8SdI1VfYOK1z8s9ryktpzA/cb6dICvJwlMLaBHSGpO5Ce3CujOmyTiH7pPFjItJID60G303nHS4fenhW3SLDsLL2ZNha1/i066f0jKkJfMT59tF6ibGTqRNWBsmb5/MiqdWsPWslt9OWxo3Py74wNIUumOdAF784QRdogNpWdVEjP5nhvluIVCWU+a9MMm82FXYnq3qrkztPpzFX+/g6W1rGJq0FT91HgC/1evIwWq2zo3RwR54eLvB4sVQqxY0bFjmcf5bBLfCfx7/GlGWlpbG/C++xNPTgzffnIlCYXlNFhwczEsvJZCTo+TEyZPs2rWb7t274fxwe1FRkcP1tA+jYgrFo/8IiofRs6Ki0m14tdqih3MVpc4piZubK25u5Rd/CggICAgI/JOoqKV9MUq1Djcn28ea4LkfIknc8WhAA+xoDDN0oEq2NPJtuxY5cqbHTWfI2vKt3ifETmDF+RU83+h5hjcYzgN15coJ7qnukVaQZjOWlJHE0LVDGd1oNNVuVeN6tevg4P3wYHf4zh9cygmEGL2acDz4a8LCajFGqS1TPCU0TaB1WGte2fpKuec+P3EeGwfu5rnvklgy8iPiQtuU0jh7Bm1DuzF08ZlSU/6S01TkF+k5ciObQfUGWQ07NgzagFqvJludja+LLwqpgkWnFjF5+2Q+7/oVaUoDg5uHE+ChKDN9Ty4RMzg2jJWJt5lQ7w4t05eXeW0nCutirv4ih4ra88Wpe9TNvAk7RvLDlvVI9LY/i0OTttqJMquxxjPPlHsfBQQc8a8RZQcPHsJgMNC4cWOrICtJq1ZxnDh5kuSLF+nevRtV/PwAyM7Odrhe8XiVKo/C4n5+ln8XFBSg1Wod1pUV7+fvX8Vum4CAgICAgIAFR5b2ZeHtIqeg6FFNVp+L+0hI/NV2UkAArNoIvlJI/gQazwGJE3KgZ61eFU6ha7a4GV1rdGVY/WFojeVbmZfE18UXVZGDCGARLF6wGGKxa1gkBeb6waQKGBUaarzM75LJTFx9iQjfsywbVbp4ejV2Gn1r96Lp4hg78xFHWCJ9KpxkEvp+dZKhsY3YNHAPhXoVORql1fXQSeLEyyuTym3MrNLoCXBXUM2/OwOjB1pNR+r517PW213IuoDJbGJsTAJtQ7vR96vjPNs0lMmda5abvtepTgDn7+YR3GQIuenv40Wm7fGNrvym7MCq7K40a9SWMfUjuDXhUzae3ECD6/amH8V0upZIQP4DMt0tz4qCsYbAn8G/RpQViyHXUpxuXFwt4wUFFluiatUiAEhNvY3BYLBzYLxxw5LnHhEeYR1zc3OlShU/7t9/wM2bKdStW8dmn/v3H6BSqfCvUkVwXhQQEBAQECgDR5b2pfF4D6n66deYs/UL20kyGfz2G4SEWL5vOt9mc5oqjelx08tNoeu2ohtKrZL7hfeZvH2ytUdWRY0vFFIFF7Iu2G7IB84Brez3CZDAz0HQtpwEGy0uELuEgoB+LPre0kP1VrbaKp62Dt6HzpzPvbwHVvF04GouV+7fsrPpL4scjRI3Jyl5Gj3f7Eth4f4UogLdcXOSUlCkQioWsWBYDKdSyxd5cqkYZ5kUL4UbczrPJT48ntlHZjtssl1s5pGn0bP44E0ahHjSpqYfWr0BX1c5LhINMl0BUrcQ674ucimvdq7FsRvZxNQaD1ffAuB0YRQrc7qzObc11YP8mdgrhI4ndyBp0Z8vbt4o85yPhdZjRaPuKBWeFTbWMBhN6IwmVBoDeRqdjRmNIOQEivnXiDIvb8vrpes3HP9nK24a7f+wGDMgIICQkBDu3r3LyZOnrFb5xRw5ehSAZs2a2ow3jYlh67btHDlyxE6UlbaPgICAgICAgC2V6clUsodUlYIcvl37Ic7NdHASKM6e+/JLaOVA9TzETe5Kg4UNyk2hK44o+bn4cUd1h88TP6+Ue+CiU4tsa7eygYtAG/v5LZ3hlyCoWs7Tms6lFqZWv3Is259wtd6mFq9YPO2/+oCFw5ry6ZbbVsv4qEB3Ph9SvhN0SXwU3hSUiPSZzHApPd/6/X+eaWjTz6s0ooM9qOLmxMlbSt7ZmEx6npZdU/oRH94NVVGeTeTNkZlHsbnL059vZLjvJkZV+R2lV1u8uqxHLhFbRVC+IeKQ6gABAABJREFUVkeTcG9kxjEUaW9gqJ5AkEt9RmsMTDPp8F65DFn/wYju3Sv9ZJ2cMA0dStH4V4iIrM0rWj1vVNBY44/22RP49/Gv+SmIbd6MNWt+Jjn5Ilu3bqN7927WbVeuXGHjps0ANo2g+/btzddfL+CHH3+kdu0ovLy8ADh69BjHj58gKCiI2NjmNsfp1bsX23fsZNv2HbRp05qoqCjAUtP2yy+/IpFI6N271198tQICAgICAk82Fe3JNDQ2zNpDSm7Qs3DdRwR1z4Z+wGngc1CPGIPL2LFlHs9L4U24V3iZKXTFNA5sTJB7EFqDlgUnFnBi9IkKpz5O3j750WAacB1oaz//BQ/4xh/k5djd78xvzfTLkzjYvQ5b9yXzbLNQh/MuZxQgFotQ64xWEXU1swBXmccftrh/nGEtwulQ25+jNx8wvEUY2YU6dl3KQmew3DuJWEStADfcnKS83K46h68/sDZurhPkjrLAQOfPDttE3koz88i7fw23cys4VGcZziJLvb5r3hbmb97GiK5dGLf8FIkpj0w96lf1YEqXWbTw8yVIJiXo0jGLRX0pZSqAJd315Zdh7FjE/v4osBh3Pm6sYTCa0BtNGM2WlMw8jR4fVxnuTjI+3vrH+uwJ/Pv41/wEVK9enX79+rJ+/QYWLvqWLVu3EhoSSo4yhytXrmIymejapTONSrjldOrYkVOnTnPsWCIvj59Agwb1UanySU5ORi6X8+qrE5FIJDbHCalalREjnmfJkqW8PvNNGjVsiFQq5UxSEjqdjtEvvkBQUND/9+ULCAgICAg8cZTXk2lkqwiaRfjw/JLj5Kl1zN3+FTHtrkDPh5OagGamD6bpH3Mn7641+uLt7IVULEelMaFUW9LJvFxkVrMPk9lktaZ3xITYCey+uZstQ7bQbHEzuq3oxtEXjhIf3p65DtLvpsdNp3nV5tbURwBuAnexE2QS4NMqMMGr7HtjNMOZgEGc8X6Tlm5aVBo9F9LyGOdS3fF8k5mVibcZERdhFUJGk5lViekVtrif3mo6OYUm6gR52H0W4zvUoF0tX4zomdjFj2xNDr6KAGY9HcX5O4VcSFPRs4EfhXoVKl0uQW56MLvycvtqLD92FzcnKTlqnV3k7XGinG8xtspv9PHaj/SuydKw7SFikZmAe1+x+3JDYsK9bUTZ+XsqRiw9+UgERUeDqRS3yYYN4dVXYdAgKKU1UjEanQGt3sTuy1ksPZxivS8vt69BNT/XCvfZ614vUEhl/JfzrxFlACNHPE/t2lFs27aDGzducO9eGgqFgujounTp3Jm2bW1zB8RiMdOnTWXz5t/ZtXs3J0/+H3vnHVd1/f3x593cC1ymgKgMNzlCUXDvXDnamqKpmWiZljna0zSzaUOxcpQzzRxlWpp74ERzT0RlyZAL3AuXO35/XLlyuZdh0e9b+X4+Ht/HN977cxG4r3vO+3UOo1KpaNu2DY8/PpiQOq4/jRo4oD81g4L4ce06Tp46BUD9+vV56MEHROqiQCAQCAR3QHk1mdwUUubvvMQ7P52+ldZmpf79V6G543x142zGrogh/vrt6wuRQZFMbjuV/g378/bKU+y5mMVz3eozussAxkaNZd7h8t0KS5t9fNTrI8a2GsvNwpsUFcvJy27JzhG7yChIJy0/zZ766KH04KHvH7p9d+sskI6TIPOR2u6P9ajk2vkNEwxOg5z8s6wfFMz87de4qTdyLj0fN6Ws3Lt4S/ZfYf349gyJDmHZgeRbbddYN75yi/txUeNoV6sXa4/cYP7wVhQazeTojdTwVFHDU4WFItac+YEP9r3vUpQ+0qono9aNYP259Q59E6Mns258H9756QK+mvLNXe5xu8TEwGX08tpf4WujlRUwd89l5g9vzbwdF52ibA4iaMIEeOut252dO8PLL8N994GkkhAlttTE/CITn/x23v56gi0iOCQmhKcWuy4HUJZ5Oy4KoxDB3SXKANq2aUPbNm2qPF4mkzFw4AAGDqxaYcgSoqNbEx3d+k6PJxAIBAKBoAyuajLpjTaBlmsoRoKFd2rPpYXfOYd5Vis8lQHf6BzvkyemJRL74xDGRo1j7rD3ifv2D0Z0COfUVR2ze86mbZ22fLz/40rNPuYkzGHL8C2YzDIOX9bz5fYk+t0bgKdKy9mss8xJmMPq06t5scOLjLh3BDuv7LTdH8vCSZBFKGF9FeqPHSyEh1Phqgkw3HZD9NYoXUbDSpNrKOaJBQdYPCqalqHeLNyTxMkUHSMW2Fwa29fpyMcuXBqntJ1Gp5BexH5lc1T8ZMt5+jarySNRtWgY6InZWsSU3yYT70LMJqYlMmTNEOKi4vio10fsSt5ljxYmpiUycn0scVHjeK3/dJQyZ0FZFTFWbJWxLqcL8248zIWiECCPQqOZFl4SVIlH2RsW6TDeLoKefRY+/BC6dIGXXoJ27Sp+8UthMlu4nJnPqZQ8B0EG0DDQg0Kj+Y7q7OkMJpHCeJcjvvsCgUAgEAj+ddhTG8O8UR0ZS0+3Xxz6rVYJw9KtLC0/E455h+fSMaQT8cMfxGSGLWduUKuGljOZZ6pk9nE07Sj5RXpm/ZzO1N6NmfVwU367/DOTfn2Wsa3GMrvnbN7q+ha5hbk09m/M0qVL2Zq2Fbo5nqOvBpYHgVbm4pCl+CoXnr0BRaWiP9mGHE6mFKJVK2gSrHUZDStNUpaeAZ/v4f2Hm7NyTFuyC4zojSYkyHjknkfo26AfOYYcisx5aBReKKUeXEgvRCpx45snWpOjN+KjUaJSSDlxPRepxMK6s+tdCrLSxB+Op03tNoxtNZaZu2eW6ZtL29odCFV3swvKCLdLTAxcTm+vfeWuaUTNdzd68k3mQFKKA+ztGqMB1Yfvs/jzT5AWG+kY9w1Z7t72frsI8vODixchIMDF6hVjNFvwc3dj4Z4/nPpKUjHvBF1hsSgCfZcjRJlAIBAIBIIq80+w9y45Q56hiB65L6J22+jQb5XImKavxdK8iu/zAHyw730GNO5PgV5CkcnEDX1Wlcw+Ssgy3OCNAQ2ZuuoEnw1tQseFs8kpzHFeY7+O4yeOQ0/H+VN84D0/kFaQLWey2sTYvFznPl+1Dzn6Kxy+ksPojnV5fmWiy2hYCU2CtTzVsS6twnzYcyGTDg38mP3DGVQKKYNahzAofh+Ngjx5ukt9OjWswW+n0li4J4nTqTq7AUeg1o0ujWrQtq4fSE3M3jur0tcZYE7CHNYOXsusPbOcXstPEz5gw+C+hPt7sqD9YbrlvVHuOnq8MNZ7hgG/tiC54HatALnZxOPHNjFxz3L89bdfrLEJq3m322iHNewi6E8IMgB9kZmCIpPLaFh+kanCVExXaN1cVAsX3FUIUSYQCAQCgaBK/BPsvUvO8NWOczwlf5MB3jsd+q0SOVktvmT2yjFVWu9o2lFyDDko5f48260BhZZMgErNPkpQK9R8dXQOn8c+T2ZBFsfSj9n77GscwnaH7P7b81QS+CoAhmkrXj/LDI+kwnaDc19pN8RXf/yDjRM72iNkAz7fw9CYEIf7Xz4aJV5qBe5KGblFxWQWFHE2PZ8Nx1OJqOmJr0ZpN9p4de0Jfnq2PR4qudMaKoWUNUeuoZJLyTFkOjxzRRxNO4q+WE/TgKZOr23SzSSUcjNaNznK1vdh3fYOEqvJYYzOouVKwFi87n2e4d+eJrngVnFqq5Ve5/cxdcdi6mU7W9sPT9zIoYdGkKz24Vx6PmaL9S+LIL3RVG40rLK7fWVpEqxFqxZvye92xL8AgUAgEAgElaI3mpix8X9r711yhpX7LrDVbRwhjdId+ossco6FfYXKO+qO1r2Wm8lHG5MpLDbz7ejIOy4G/fr214mqGU1Nz0DnQUexWd+XuppeQwbrakLbSgpCnyiCASlw2eS6f2LMZJYnpGKxwtUcA2O/O8y8YVFEh/vyze5LbDubweEr2fh5qNC6yenVJIjW4X68/dNJVh66RqMgTz5/vCXgLCRyDcUM+8YWcdt3MZPtZ2+Qriskv8jE2bQ83uh/D9dvFmJVZLs+XDlk6bPQqhyVaH3f+mwauonfLm1m9l7bnbZ5ARDnZesvkrlD4xeQhD/LL3szWTrvuL1mWWTKWV7e9g3R106Vu2fxfb14ukMoHg3ro1JI2Xo6/S+LII1Sjq/GdTG2yu72lWVs53oohcnHXY8QZQKBQCAQCCrEZLaw5XT6/9Teu+QM3ydc5FePZwipV1aQKYi78goaSTNeauZzR2uXFEQ+nZrH1zuvMqnNFIavHVrpvNLFoBcmfs0HPT90HHAcm/X9Q7ebIpTwczCEVxKoOSYLpeO1K+SV49oeFzWOznV6M3DdbYe/PRezGBS/j6WjY7jvnkB0hmJy9MX4aBRo1QryCovRGYrYeCIdi9VZiJUVEldzDExedYwHWtTi1fvvoaDIZI+WBXm58emW8wzr4Fvp61QaP40fuiId7hJooIQrEh82x25m1p5ZDnXeZmTDQHf47CZ8lltArPsN3o9QI5PaDEvq3Exj2o7F9Duzq9y9Utt3Y2bbx1kvqwk/XQeu29M3/yoalQyTxXpHTpeuiG0TKpwXBQCIfwECgUAgEAgqxGi2MG/7pSqNnbfjIkZzOUriL57hm+2n2eAzkfB6qQ59xSYZo5LeYHteK86n5+Oj9iEyKLJK65YtiLxk/zU61u7N2KixFc4rscafd8hmcPHD6R/wcvO6ve9J4ATwIPZaWt3VsLd25YIsv/5UavY9yuf9lzo9R2RQJIsGLuXFttMZseCYPWIEEOanYdHIaHaez+Sx+H20fe93+s7ZRdv3fuex+H3sOp+Jm0LB+K62WmalIzq2Z79CdLgvT7YP55mu9dk5tSvvPdSchoGeFBSZ8HCTcS49n6PJOeQailm0LwlvN+87eq3VUgUdCv/gYpjNbfLZVqPZkbTDqfB2sgnqXIYZOZBngbmH57L+7Aaeae7F79fW8vvXY8sVZMaWUcx/ewFtO0yyCbJSnEzR8dzKRGZsPIPeWE4IsgooZVKyCgrtr11ZSpwu4zrX5YNHm9Mk2DE62CRYy2ePtxCFowV2xL8CgUAgEAgEFaIzuDY0cIUre+/qMAfRGUy0OLCKhh0dow7WQnjv8BPs8YzES63gmydas/vczSoXRC6dAgi2N9MjFhxjRdx7dArtxPt7netulbXGBzBbzWQV5PJ8zBSemDUUDgJDsX/8/aQW5gaAogJDj2JUvJr6PCcy+/KUQk/fBg9yX3hf8otzKTTnoVV546v25tKNIqatPktSlt4+Nybcl3mxUXz461mWJLhOMZ2y+jhDY0J4qU8EX26/SI6+2Cmi8/KaP5g3LIotpzMY8+0hp7uDozvWpdc9gZxNz8NosnDkSj6T204l9schlb7W7zXpittvbfgi4Hba33M+CromfOpyfGnJJLVA+psvoPpdT92bN12Ot4aFUfzOdDZGdGLGqorTBv9qVFcukxLu70END7fKnS4fac6quLboCm/X2fv/NscR/PMRokwgEAgEAkGF5Br+vL13dZmDFG/axMsrFiDZDbwMeAIG+HLno3xT05YfGNsmlITLWUz/+XSVCyJ3DunNlP1naB3mQ36RiXPp+SRl6Zm4/BRzh/WifUj7Sq3xSzBZrLhdcUOyU4J1uBUUtiDZTD+YVkmWX6bZn8TwhTzdqytPLDjAcysTGRoTwphOdXl59XXcVXI6N3SnV1MZwVp3vhreioIiE7mFxXi5KVAppGw7m+FSkJVmaUIyMeG+TOjegLc2nHKoXRZT15fWYb7M3HjGpcg4maLj+ZWJvHBfQx6Oqg3Aqz+e4ueJ/YmLGluuLX4jBayqH0Kz5I+c+rQXviDphgtbyTJYJND8aAqSmy46fXzg1VeRPPMMJomM+XPLt9EvzV8t2qxRypEAU3o1IirUhwV7Ljv9Gx/buR4dG/ijUcrRqOTC9l5QLkKUCQQCgUAgqBAv9Z+z9642c5CDB6n1ZCxSiwmSgfeAibBi133MbvAEADKphCExITy1+JA92rVo1Lu0q9ORT1wURH4+ZgoPRgygoEjOew81J1tvxFejxE0p5UKGjvqBbqjkCqb8NgWdUceNghvkFOaUa43fIqgFJ4+cYtjYIViHWsEN1BL4NhAe8az49TppqMdM/SyKTvjSqSiFxaOiGfD5HpYmJNMixJvocF9+P5PBxj9SWXXoKhIJNA324sU+EcTvvMAjUSH4uCv5etflyr41AMTvvMSyp9qw5sg1/rius0d0vnmiFYeSsiu8AwXwydbzDGsbar9PNe67E8wdNptOIZ2ZvW+W/bX2kcLndfwZrMxGanK9Zr5XMzylu8mtLONVAlPugyOlsxyVShg/Hl55BXxtqleXW/j/WrRZrZSjkFno2yyI3k2DyDUUoyssxlutQOumQCkX0TBB1RCiTCAQCAQCQYVo1fI7tveuNnOQM2egTx+kBQW325Lgu6V9eK3b0/amhoEeFBrN9jfkSVl6Bn5+iKExkWwY9DsFxTqyDTn4qn3QyLUsT0hFFyZl5sZT/HTcdkctzE/D0jEt8PRMBUkA6QVFPN36adyV7qjlauYfmc/V3KtOETKAR/wHM2T4oxgHFYEnBMpgXTDEVBIY0dfoS42oxbxlcrMLQ61awZzBkby69gQFRSbGdanPQy1q2/tVCim5hmK2nE4nI8+Il1rh8OyVcTJFR0GRiZVj2pKmKyS7wGbeEejlxuvrTlY632yxsi7xOnGd6jJhRSJ7LmZx/6cHeOeBzuwc0Y+b+kykF+IJSpqLrDjT5RpGbVPMLT4gz6MJ1/bXqdK5jwZDwWMP4f79Ghg8GGbMgPBwhzF/Jar7Z5GXSkN0V8kJphJbTYHABUKUCQQCgUAgqBClTEpc57pMWJ5Y6dgSe+87NQdxSiMzGSA1E3r2hKwsh/EbGnfk9W7jQHL7gpaHSu5UNyrXUMyX2y8zb8dle+Hj/CIdZ9PysFihW+Mg3BRSWofZ3Bo/GXwPEqmBY2mJzDkwxym6NiF6AgefOkjvpb25kH3B3jekTiyfvvARhgfzwBfuueWwGFaJoceCGwOI6bSQxb9f5ftD1+ztTYK1jOlUl40TO7L7fCaD4vc5p352qkuvJkHEhPvi56HkTGpepa+zTCqhYaDHrdehmNfX/cH1m4V4qOTU8FTxwn2NqizsPvrtPLundbXfp7qaY2DEwiPEeJzko/CvqCW54HJertWPraqJbM3txbb4LBJe8baXIAjMgze3w88N4adGznNbBLWA2HfhhWkQHe1y/T8b1RUI/tcIUSYQCAQCgaBC5DIpPSICiW0TUmHkq7S9d2a+8c+nkRVmwq9dYF0KXHWMSp2PbMekHpOwSmwCrkRohPpp8Pdw/Ya8pCByCV5qBbFtQmkc5MkzXeuTmW8kzM8NqczAy7+/6eQECJCYlsio9aOIi4pj09BNtP6qNaHeoYxqEMeH494no1s6BEEXNaytCV6y8p/XbJXyZsoYvsvqx1alkj+uO96pOpmiY+KK23fKruUYnPon3Op/ukt9jl/NoY6ve7n72Z63No/H1KSgWEeWPhu1m463H2jI0v0pLNlvE4TlFUN2Ra6hmKTMAsZ3q0/LUG/W7D3KQ5I5POK71eV4q1TFF6kDmXvjEQosGuAGAEeu5DPt3gmcWjGKqXvAoxg6X4Ff6oO5zGs4ud1UFKH1oF75QurPRHUFgn8C4l+iQCAQCASCStEo5bzcN4KYcD/m7bjo0tCge0SAXVj96TSywgzY3BkKzkB3IAP46dag6Ghq/fIzH13Vs2T/FVqF+TIkJoRCo5lcQzE1PN0qfUMe5qdh8ahoDiblMGj+fk6m6JBJJRx8tRMbzv3kUpCVJv5wPJ1DO3NhwgXSb+Rz3319uN7iMoTCIA9YHAiqCq4Q5ZvVjE+eyva81jQJ1qJSSO12/GUpuVM2NCaEL7dfdNkfE+5LZB1vvDRKl88e5qdh0ah72Z78C/1XDnV2koyezLrxfXjnpwv4au4syqRRypm44iitQ31YFPYqyrw/XA8MeYzCJu/yy3epFFhun09qMbP/pdlM2bsMadrt4RGZMPoIxLe+3TY2ahwDGvVHKa84svVnoroCwT8BIcoEAoFAIBBUCY1STp+mQXSPCEBnqNje+0+lkRlSbRGygnO3Ox4H9EDaPfDzz2h8venjpaVb4wC2nE53sG1/pmt9RrYPZ/KqYy738FIr+HZUDPN2XHQws2gc5IFcZuXj/R9X6ayz986mV3gv7uv7KNfrnoIIeN4bPqpR8bwUoz9PJr3O6UJb8eKR7cNYlpBst+N3xcI9Scwf3op5Oy66HBe/85Ktf/tFp2f3UitY/OS9zNjziksXysS0REaujyUuahyv9Z+OUia7oyiTSiHl8JUcDiblcNmrP3NDHUXZKUM4gd3m4xfeA4XZQlxntU0sWa10uXSIl7YvpFGm68jr1D3wVRQ0D45kSrtpDGjUHw9l+dHAEv5MVFcg+Ccg/iUKBAKBQCCoMnKZFI3SZu3dMNCTIC83NEq505vbkjSyqtAkWIuXNQ22dHYUZACZQEE4bN0K/v62mmcmCzM3nmbiikR7pCuipicnU3JpV8+P2DYhLvcpscwv6y4YUVPLzcIcjqW7FnNlOZp6lCeffJLrngeQtIGP/CsXZH/o6/HAhQ/tgmxoTAitw3xZWomF/ckUHYVGM42CXFs4lvQnXM6idZgPQ6JvP3tsm9psu/JLhWUBAOIPz2Vn8iasFjNjOtWt+EFuUVZQ/pLblm26KADyzGrevD6G/uc/IUsTA9wWS1Nq5LNk5assWv2WS0FmlUgoiB1E4bYtXJl0lV0jd/FIxMNVEmQllER1P3u8hSjaLPjXIP41CgQCgUAgqHbuJI3s+bYq3HZ0h4IyxiAZYF0SSuHq7eSqvbmZpkPrpkCtlOGlUVLHR82AyFq2FMZiMwVFJiTAS30ieDw6hLc3nCLhcjZgu3s2rE0ooxYddHmGHMPNqj/cdlibsRbVIFgcBIMqsbz/TRfDhCtTMFjd7CYd99bx5okFB8g1FFe6XY7eiIeq/LdsObfugpXUG2sZ6s3ivUk8HlOTfisqL+oM8EnCBzx0z0BahPiUWwwZQCEp5rHoerQO8+Wdn06X6pHwRspYss3LeC91JDdMNot6u5HG5ctoXnmFZ5YvL/cMGe264DnnQ9yjWnJPlU5dPncS1RUI/gkIUSYQCAQCgaDaqWoa2bPRMrpdfwyJ/opjR7EfloUatry/ik/XX+Fkyu3UuCbBWsZ3rc/YznXZfCLNIYWxpH90h3AWjGhNXmExOfpitG5yrFhdmo+cTs3DVx3u1O6SROASeI+AtbWgs6bi4fkhw4iJ+ozNeilmiwRPNwUapYxen+zkahkDj/Lw0SjJLzJV2l9Sb2xoTAiLRkSjt9yoevQv7Sg5hhy+2Z1DXOe6tAz1ZuGeJPvr6i/P4aN6C6hfQ4O1/SqGfr3fSVAmG2vywtUX7F83CdaiLbgJb7wHX34Jxa4FaHHT5phnzcK3V89qFUslVvUlkV2B4J+MEGUCgUAgEAj+FkrSyNqE+7HpZBrpukLyi0ycS8+ncZAnk9pI6Xp9MFL9NceJ2sYYOm5mtjKFBdsynNa9lmOgSbAXMzeecRnROZmi4/nvjzE0JocpvRoR4CFlxaFkujYKcHnOM2l5aORauzV7uVwCdkPtOPglHJqqKn7+lzNh5vnviDz5B8/HTKFbWB8eiz/Icz0a8mTHcN5cf6riBaBSM5Cy/bYyABc5fCWbVx6oJIRXhhxDDhYLdmE3f3grCotMSK8sJvTK60iLc0AHh08tJykrsNL1ptUoQB3RCHTl3FGrVQvefRdFbCwKWQV2lQLBXcCfFmVj4sbRvl1bnnhieHWeRyAQCAQCwX8IpUxKt4gAWoX5kKMvxkutQKtWIM87g2pnTySFqY4TvJpg6vIbv12wsOBYlss1y7sbVpalCcm0DPGhT9MgdAYT3uW4C5otVk6n6pncbjJP/PgETQKaoFVp0RXpOJlxErPVbHOB3ABNx8AvDaB2BSaAxVYYnQ7f3tJRiWmJPLFuKHFR41gwcjrTVp8lflgUb2845WTe4VhLzMSoSsxAyjML0RWa8HHzqfD1KYuvxhepNNsu7H7eu4dP6s6lhfKww7gW6a/xZMwqvklwLqJdQmybUFr1qIvkVX9nUabVwksvwYQJoKkk1CgQ3CX8aVGWkZFBbnmffAgEAoFAILjr0RtNbDmdTvyOSw7phX3qZPGJ3yQkFkfRVaxtTnGnTUgUNViyz/XdL5lUwpCYEJ5afKhKZ1iw5zK9mwbRMMADrVrh0l1QJpVQL0DDPYo+pLyQQrYhm0x9Jv4af9RyNR9t+YjPPv+MTk9aWdek4hpkeRZ4OBV+0zv3xR+eS/s6HejdNBq90cxzPRry0W/nkEkltAzx5sEWtejWOJCCIhPZeiMBnip83ZUs2pOEl1rhlC5YYhbieLfLxrn0fLzdvCuP/t2iRVALlFIPRrX3Zt3R6/TTrOfV4IW4SQqdxkotBqa0MxFZt0Xl5RHefRcef9zWqVTCM8/AK6+An1+lZxII7iZE+qJAIBAIBIJqR280MWPjaaf7ZBFul5jh8Qoqi2M63nF9fYadfInaly4wppOF2Y8254kFB0jKclQ3DQM9KDSa76wwdWExHRrW4IaukNEd6/L8ykSHMY9F1cJdBWtOr+fThE8dRExz3+bo4nUMHGplxb0V1yBLM0HfFDhaVP6Yj/d/wPYRO7BaTQxvV4fujWsQoFXjppDy++kMnlx80EnkjGwfzvrx7e2vh60tjNZhvuWahTQO8kQuUTK57VRif6zc7GNizGQW7b5OC798EtrORpX5u+uBoYOh5Se4qQPpE2ihe+MaGHbtJatZS9dGGo89Bh99BA0bwvTpEBZW6VkEgrsRIcoEAoFAIBBUKyazhS2n0x0EmUwqoVmgjMW+r+MlcxRk5ssyXrs6llxvT3JTdExckcjQmBAWj4pmwOd7HESHh0pOtv7OClNn5RuxWKwEeanoeU+gg7tgHR81L/erz9TfJjPvcBnreCsc/+Y4IzvCV61BJil/j7NG6H0dksr34wBshhqpeSm8vu11Hmj8AL3q9aPYZK3wftzkVceIjQllw/gO6IqKsVphWUIy7/x0ulz3xrGd66GQyRnYeABjo8Y6P1sp4qLG0bl2LxZsm0G7GvGopAXOgzS1ofVcqNXP9rXJhHzDBuQzZqA5dAi/AwegdWvneVIp7NwJbsJoQyCoCOEHKhAIBAKBoFoxmi3M226zt/dSK3ima312Tu3KJ13CUa9SgaXU4HNgXRjETYm3wxpLE5I5mJTN0BjHmmP5RSZ8y7kb5gqZVEINTxV1fNW4KeTojSZe6tuYTwZF0iRYy/QH72H92fWuRct+mBINCzpWLMhyPe+h/dXKBVkJecY8UvJTGLJmCK/+Pg25zMjPf6RWOGdJwhW2nMlAo5Qzd9tFvtx+sVxBNjQmhO4RARjNFk5eMzCrx2wWDVxKZFCkw7jIoEgWDVzKKy3Hk7KuH28HfoS7K0FWPw7uP2kTZOnptpTEunXhoYfg0K000rfeAmyCXG80kZZbyNk0HWm5heilckxmi/O6AoHAzl+KlF2+fJkVK7//U3MHD3rsr2wtEAgEAoHgH4rOYOJUqo4wPw2LR0Vz4HI2r368gTfmPIfyZjpkAeOAs1C8MoSMVb+g2ZkJqY4RtIV7kpg/vBXzdly0G1mcS8/HTSlzeTesNF5qBbFtahPbNhijNZOLmTcI8PAj0MOXzSfSCdJqWBnXFiRFTFwwy3mBS/B+GEzpVPGzbi32pFH7H8k60qjKr0+geyD5xnwA5h6eS5vaHRgaE8mX2y9XOO/rXZdoX9+PMZ3r0qKMZT1gT2uMCffFaoWZv5zmbFoeL/WNID0tkg2DfqegWEe2IQdftQ8amSeJu75B+0cc7dT5zhtqakPMN5hqdMe0cxfWL7/Ebf1aJK6s7X/+mcK9+/jVPcTpDmGTYC1xnevSIyJQFGwWCMrhL/1kJCVdISnpSuUDS2G1WpFIJEKUCQQCgUDwHyXXYMRLreDbUTHM23GRAxt3s2TlqwTl2wo5sxcogKS0QL5/52viGjcgKDGf02VE2ckUHYVGM42CPO19ZouVZQnJjGgXxpTVx13uH+anYdGoe9me/At9lw91uCMWGRTJ5LZTqe3bi482n2ZkZx+nWl6yHJjvA6OiK37O+Fz4SlqP392D7shQw8vNy6Htk4QPWD9oK/N2XC7XZRFsr4fJbMVHo6BXkyB6N6lJbmExeQabq6VSLmVpwhXWHk0hxE/Dkv3JRNT0xMtNwZfbLzNvx2UaBXnecnbUEWVcyfRaX4Ir45LwJ6DROxiXradwzkS0589U+FymevVZuuUk7+izXZ57wvJEYtuE8nLfxkKYCQQu+Es/FUFBgTRu3Li6ziIQCAQCgeA/gJdaabetP7n2N1atehOfQkfBdfFqbR4f/C4ZF400PptBp4Y12Hb2htNaOXojWjc5ETU90aoV+LkrOZOay5DoEIe7Ybf3VrD4yXuZsecV5h9xTklMTEsk9schjI0ax9tdZ5FSkOTQ71YMy93hgSYVP+M7WfB6NnQI8SCzIJMJ0RMYtX5Upa/NhJgJZBZk4qH0sLcdTTtKQbHOQXyWh85QzLn0PFYfvsaNvEL8PFRkFRip4aGic8MaDIyshUouZdQiW1ph2chi6fWvSrswxv8HQlTp9rYciy+a0NdRLT2P9dsmKPPyqChZNLlBM3ymPs/mpl14Z23FddeW7L9CTLgvfZoGVWuRaIHgv8BfEmURERFMeHZ8dZ1FIBAIBALBfwCtWs6T9c6xYeE6lq1Yinuxo636Gf9Qhg2azg0PWx2t+J2XmD+8Fe/85Fi3y0utoF4Nd74YEoW+2ExBkQkvtQIPlRyL1cor90fQq0kgP/+Ryh/XczmXnk9sm9psT97kUpCVZt7hufRvNIDmAU1vn1sC69yhS2jFz/darpbp2bb0PF2RDpPVRKfQToyJGsP8w/PLnRcXFUfHkI6YrCZ0RY6pl9mGHDxUlb8t83CTE+Gu5fkeDcnWG/HVKFEppCw/cJXpP59mdIdwHomqbXenrCiymG/R8NzVyXxfbxpyiYXfbkTTcpUF1Z7nACjvGl2hXMm6iM581/J+Tgc34PCQHiz4KqHSswPM23GR7hEBQpQJBGUQ8WOBQCAQCATViip9I+qEhxne0ISkMfDH7b7DwY0Z+eib6NxuR4pcpSmG+WlYOroNu85nEr/T8Y5STLgvL/dtSN0AFXWDjAzzluLtVhcvlTcyqZQ+S8dW6ZxvbHuNbSO2ERkUScqNRDZ5QYsKymeZrBCvaMO7GbcFyMmMk7jJ3Xh247N81Osj2tZu62SrHxkUycSYiXQM6cikzZOY02cOJzJOOKztq/Yhv6him/8mwVrcVXKGf5PAH9cd72yNaBfG+vHt+WDzWbIKHN0pl+y/wvrx7V1GFo/oI5iZOpL0Yj98Gwym+6X7y93/sk9NlrS4n9VNu5Or9gQgItCDHL3xzkoUGEwihVEgKIP4iRAIBAKBQFB9XF2LbOcjIDGDApgEfACchJ1hLYh78BUMSmd79By90R4pKrmP9uX2CyxNcBQRYX4aZj3SiO3JvzBi4wdO4mdKu6ksHLiQ3kt7cyH7QoVHPZR6CKPZyOstn6BpYiIN3Msfa7DA8wW18arbCSv77e1mq5n4w/E80PgBWn/VmrGtxrJu8Dr0xXqy9Fn4afxQy9XEH45n0uZJfNTrI+IPx2Ox3nYjbBHUAo1cy9m0ilMXR3cI56udlxwEGdiEzpTVxxkaE8JLfSMoMpV2OrTSXbWJyYtu8sGI+2npwiBkv/IJxt5nK/gsPTHG7qQIYJZI2Vo/mu9a9GV3WCRWiWOEy0MlJzP/zkoU6AqLCfISFvkCQWmEKBMIBAKB4D+GyWzBaLagM5humW4onYv6/h0kr4Ldj9sEWQlKoC38ZO7I8/0mUSxTuJzqo1GSX2TzlI9tE8qhK9lOgqwq98WGrhlCXFQcm4ZuovVXrckpzHG9n5sP41qPQ6k7x/2X30VZgSDLMcPgDA3zn9zN/CPO6YlzD87l4FMHebTJo8zcPZNZe2bRNKApWpUWXZGOExknsFgt9vTFSZsnOcyf2m4qJ6/rKzT5iI0JoWWoD29uKP/e1tKEZFqGeNOlUQBNgrUkp6Uys/bn9PPexd785jz8qYbXjWdZbr7BjSmvkqM34uehJMDTDZX81r+NMWNg+nRMPr7MbdCN5ZG9SNEGlLvnnZYoANC6uf43IBDczfy/i7KkpCRWrf6BKZNf+P/eWiAQCASC/zx6o4ktp9P//23Jk5bBvmE4FiEDdsDyQ714pf/TWKSubP5sZ1MppJxNy6NZLS1PdQxniIs7SrFtarPtyi+V3heLPxxPuzrteKPLG7yw+QXMVrNDf33f+mwauolTZxZj+q0DHlJzOStBqgkGpCqZ9dgGDqUcYlr7abQMasn0XdPtUbqcwhwmbZ7EgoEL6BrWlVl7ZpWbvth7aW8HoTiu1Tg6hfTGbFbwwaPNXdrcx3WqS1SoL0O/3l9ubbISFuxJoltEIFNaFRB+diKhqjQA2nkc59czQ/BdlYdZIuVZTUv0NWszvmt96vhobov14GDYto3MRs358KPdFe4FVS9RUPp5tGoRExAIyvKnfyrW/vjDHY0/e/Ysq1b9wOEjR/7slgKBQCAQCCpAbzQxY+NpluxPdur7W23JLy2G/SOBMqGe3+HzU4/yQc/hICm/+nJcp7pYLFZ+e74zHm4ye52z0sikEh6PqUm/FUOqdKRP9n/C9hHbeaDRA8w/Mp/5h+YTrA2mlmct5vWbx/fbxjMmbwNa1zoRgAtG6H1dQuvGD/HI94+QU5jDuFbjmNVjFj3r9SJTn0N6Xha1vPyR4cGus3l0Cu9Ph0G9KCjWcbPwJsFaPzQKDUv/WOIQuYsMiuS5mCk80HgA8duvsjThKkNjQpg/vBVGkxl9kRkfdyVyqQSpBLp/tLNSQQZwMiUX85nP6Zz8EhKVY1qhb7882AayTAttN63k2ktv0Trc1zl62rEjWqOpSkLLbLGy5VQaYzrVZeKKxErPN7ZzPZR3scnH/yyKLfjH85d+IxcWFrJhw08cTUwkNzcXLy8vWrZsSf9+96NSqQA4f/483363lBMnTmC1WlEqlfTp3ataDi8QCAQCgcCGyWxhy+l0l4KsNNVuS37hKzgQh5MgC32Koi4dSesXCfuTkUklNAz0uFUjy8S59HzMFitDY0KICvXl1bV/kKYrROsmZ3Iv53I7DQM9KCjWOdUUK4+jaUdJyUvhjW1v0K9hPy5OvMiNghtIkHDx5Bc8nb8BTQWCLLEIel+HFmG9WHFihb197qG5tKnVgYz0SLaeyaRZLS9e6OlH30938Wz3hkiQ0mnWXns9MCggOsybx9sMo1fdgbcLN8u1LE9IJS9UyhPtwnm0VR2MJgtGk5kgrRvbzmbw5oaTSCUwuVfjKgkyT2kB79Weg/+ZPc6d+cBXQKbty1Gnt2LqvKBcca6USYnrXJcJyxMr3ddbo6RdPX+XRiKliW0Telc7L/7PotiCfwV/+jtfWFjItBdfIjn5Klar7Rfx9espnD59hoMHDzLj3emsWv0D33+/CovFgkKhoHfvXjz80IN4e3tX1/kFAoFAIBAARrOFedsvVWlstdmSn/oUEp9zbm/0HLT8CFV7CS8bTQxuXQd/DzcKikx2G3d3lZyMPAM+GiVDv95PUpYegIiani7vKHmo5GS5KExcEVn6LFLyUxi+djhxUXFMaTcFbcZvdLg8G0UFj77TAANSQKHyJ75/POGfhjsYc5QUe/5y+2We7BDO+sQU1jzdnoOXs5FIJETUdIwwHUzKYW6Zws1n0/KIqKllSEwY7d/byrzYVtT11xDoqWLnuUz0RgsHk3LKfT3K0kR9gS9D3rOnKzpwHvgMyLJ9aW7ZEtmUKSg9y79IJ5dJ6RERSGybkAqF/tCYECLreDNq0UE+H9KC1mE+fL37spPoGNvZZiRyt4qO/1kUW/Cv4c+nL65bz5UryXh5eTFwQH9CQuqgNxRy5MgRduzYyYyZ73HkyFEAeva8j8cHD8LHx6faDi4QCAQCgeA2rlL+yqOsLfmfSqlaPQqMC53b75kG9860pytarXA+PZ+X1vyByWK1R8rkUgkj24fTKkzlML28O0r5RSb83X2r9Hwl+Gn87PXA4g/HM8bHjbpXPkMiKd9R4+cCeCQVCk2AJRN9sZ6mAU05nn67xtfRtKMUmvN494Gm9G4ShK6wmM9/v8iyA8k807W+y5pgFitOhaFHtg9jWUIyg1qHcE+wln0XM2lo9KRZHS88VXKOJoew4uDVSu5sWRnm9zOv1fwapdTk3P0T8D1ghrN1m7KkxzCemfUsQd7qSl8/jVLOy30jiAn3Y96Oi05Ca2T7MFqH+fLEggO4q+ScTtXRu2kQvZoGoTOY0BUWo3VT3PXpef+zKLbgX8WfFmUJCQkolUremzmDmjWD7O2dOnYgMCCAld+vQiKRMPmFSbRv365aDisQCAQCgcA1uYY/Z0t+xylVVivM7QPem50Xbfo6NHvTLsj0RhOf/HYOpULGvGGtKDSaydYb8fdQolHK+eWPVJbsS2LxqGgGfL6HXENxucWOz6Xn46v2ITIo0sFEozxaBLVALVfb64G94K+m5ZVPK5yzMg+GpUGxCfs7pCx9FlqV1mms3nST7k1rYrIWkq0v5uc/UoGKa4KVZmhMCG3r+uOuktOhvj9XsgpoV98fN4WM9YnXWXPkOnNjo7jvngAOJ2Uzsl0Yk8sIPQ+pnvdqz6GftwtDjnxgHnAUjM2aM+HewWwKbgYSCcOKTAQ5z3CJRimnT9MgukcEOAgtTzc5xWYLmflFrBjT1kl4aZRyYXt/i/9JFFvwr+NPi7LU1DQaNWrkIMhK6NGjByu/X0XduuFCkAkEAoFA8P+Al/rObMm91Io7T6kyGOD9NtDguPOCzd6GZq/ZvzSZLey7mEls21AOXM5mzLeHnETfmE51eSSqNtduGniyQxgf/XYegENJ2TzZIZzYmBCWlLLFl0qkPBfzHCPWjaj0+SbETLDXA3s/2I8p7lkVjv8mF8ZkgKWUIAPHaFtplDIl933X49aZprBufG9GLDhGUpaeJxYcYPGoaJc1wUqeu3vjQMxWC+m6QjrM2ma/M1ZSGPuhqBqk59/AwyOHekF++Lj54OnWgqk/nCDXUEw91VW+DnmHcHWK88PcSlcsUNTC8OVbPKoL43JOob37Ti3p5bfEliuh5X2Hdvh3I38lii24e/hLd8r8/V2Xva9Rwx+AWrVq/dnlBQKBQCAQ3AFatfzObMnd5Px2JylV/iB/+EEIPw4Nygw6GwUPT3VoMpotRNT04vPfL7iMGJ1M0TFxRSJDY0J4oWcjnuxQl66NbHeO1EoZEmBCjwZEhtiEjcVqJUufQ4eQDoyJGsP8w7Z6YTKJjCYBTew1wU5mnGR0y9G36oE9zweBbrxQiSD7NAeezwSrBYd3RmWjba7aLVYLI9YNJS5qHItGTWfg54dIytIz4PM9djfFQqOZHL0RH42SQK0bFquVpQlXmLXpjENtssoKY09tN5VtU/oxd/UaJkom4SEzOD/MT2BZpyRv4jS+bvMw3x5NJ9dwW5AJS/r/f/5sFFtwd/GXfiolFdjbAsjl4odeIBAIBIL/D+7ELW9c53pYocopVb8u/YX7l78J165BArZ3D31udd4cAG+udbK8N5os7LmQWWEKH9gKHrcI8cZTJScyxIfxy47Qpq4fzWt7syzhCp0a1mDxqGiwwrWCszy66mE2Dd1E59DOZBmyGNhoIPpiPZn6TALcA/BT+2G2mnnulwnE11TzqPxmhftPz4bXytFspaNtlbXHH55L29odGBoTyZfbL5NrKObL7ReZt+NiKYMPE7Mebk5WfhEzfznjsGZVCmMPWTOEca3G8f6j76DYOgd0pcoM3UpXPG5oxafPP882sxeWvdec1rnbLen/F9xpFFsU1747+WuW+IZCbty48af6a9So8Ve2FggEAoFAUIqquuXFtgml+z0B5OqrllLV7/ROZv3yKdLiotuNSwA3BfQeDU9/6XKeTCph4Z6kKp194Z4k5g9vRcLlLL4a3gqFTEpGXiFPd62Pv4cSd6WMvEITHgpvLmRfYNDqQfw05Ce2Xd7GwBUDnSJK09pOZlFNOcorqRXuOzUTZpfUcS6TthgXFXcr2jbJYU557QCf3nJlnLfjsj0CVtbgo4aHivgdzmK4qoWx5x6aS6eQTtzfbgXK39shMWaCsRbWjwpZdf8UpsqbgMn1h+Z3uyX9/4o7jmKLSOZdyV/6ru/bv599+/e77JNIJBX2/7hm9V/ZWiAQCAQCQRkqc8srsSVXK+QkG/QVriWzmJm2fRFjDv7o3BkWBhPWQ7Nm5c7PL7yzezSFRjO/nkynU4MaDJ6/jz+u2+Y+07U+df3daVPPD3eFlk6hnVg4cCFvbH/DnsLosFZaItJ9sSg9K95zXAbMy731hRG4FcyIDIpkSrsptK/Tnh7f9XAo9jwxZiIdQzrSe2lve3tpjqYdpaBYR0RNLRar1akmW5NgLR5ucn454SgWKy2MbQVNMehvnfH9ve/T+fHf+SPoc6KlvyCP+hzJACP9PLSoT2dU+L0Xd5X+/7mTKLaIZN69/OmfzBr+/k6pCgKBQCAQCP63lOeWV9Ydr6KUKr+Cm3y+4T3a5p1w7uzYEX74AW5lvLiy0/fWyMktrLzYcWly9EbSdYVk5Rsx3QozyaQShsSE8NTiQ1zKLKDnPf582PMTdiTtcCnIVBJYXRP6lV9+C7MVRqTDklvBqzpudfjxqR8pNBXip/Ej2DMYk8WEQqrg9+G/k1OYg4+bD15uXuy9upf7vruPyzcvl7t+fnEui0ZGozMU22uyqRRSlh+4SqNAD7ILjA73yKDiwtiBxfDROqiZD/cNA7PMJv50Rbm8c7gmUaGTeVnihsbXAw1U6Xsv+P/ljqLYIpJ51/KnRdlXX8VX5zkEAoFAIBBUExW55ZVQXkpVZMpZvlw/g+ChWRABTAfSb3WOHg1ffAFKm6Ara6fvpVYQ2yaUke3C8FLf2b0YH42S/CITOXojHirb25OGgR4UGs2cStVx/aaBwa1r4+3egNHrRzrNd5PA2prQqwJBZrTC4FT4saCkAa5xjVHrR9mNQuL7xdOkRhO6fdsNo9lob5dL5YxvPZ7fhv1G76W9uZB9AXA2GvFX+/DWhpP8dPx2NKxJsJYnO4TTrXEguYYip3O5KowtBxZp4eFicDtnO+vb2+CVHrb+bEMOHiq5U22rqnzvBf//VDWKLSKZdy/iOy8QCAQCwV2Iq5SqhjeSWLlyKqo4M7S51fgymN9TwBufIYuLs48ta6cf5qdh8ahoDlzOZuSiA8QPa3VH92hUCiln0/Ls4gxsYiVbb3OuyzUU8+b6k7z5YLBTREkjgfXB0F1T/h56CzyYCr+WZG2aAQVYsToUhvZ28yZTn4nRbHRoBxi1fhRxUXFsGrqJ+767j8ebPU5cVJyD0YiPxpswvzy81Aq7zf3JFB2Tvj9GbMxNXurbmJhwXxIu3xZh+UUm/DS3C2P7SWGLD0SWNI0C5sHLu2FPCGxsCL5qH/JvWfWL2lb/DqoaxRbcnYjvvkAgEAgE/xFMZgt6o4m03ELOpulIyy1EbzRhMlucxpZOqSohqUYw2S963xZkAP4g+SQY2VNPOuyzpZSdvpdawbejYojfcYkpq4/zx3UdSxOSGdkurErnHtk+jGUJyUTUvC3OwCZWfEvVwdIVmriW62gg5iGBX2pVLMh0Zuh9vZQgA7ACZW5hlFjdp+SluCwYDRB/OJ7EtET2PrmX+j71GbhiIBFfRNBxYUcafd6Izos64R9wlHXjWxHm53ioJQlX+P1MBq/3i3BoP5eej7tCS2RQJE2VcCawlCAD6AjcBzdVILHazqmRa+2vU0ltK8E/n9JRzIaBngR5uaFRyoUgEwhRJhAIBALBfwG90cTGE6k8Om8fbWZupdcnu2gzcyuPztvHxhOp6I3Ob9qVMikv9YlgzuBIIoNVzAubQc1Gjv7wVqkKaYd4kN5OrjGaLczbfgmZVEJETU+m9W7MmTQdKw9dtY9Zsv8KrcN9GRITQkUMjQmhdZivTcTdEmcld67OpefjppTRJNgmkPKLTPiXiihppbC5FnRSl79+lhm6X4ddhaUaDbjMFSqxui+vYDSAj5sPrWu15q3tbzFq/SgH50ewWdePXB/Le/teZdGoe53SOOftuEi4vwfDSolhs8XK8oRUPmzUmcM1wd+jzKZGuCKDlnHwcyOYGDOZ5QmpDnfTdHd4h08gEPyzEKJMIBAIBIJ/OSWphBOWJzqlC55M0TFheSIzNp5xEGYlIm7UooNcTE1nRaOZdNMecphrlWmwdPoJvX93e/QtM6+IvMJiujb2Y8fUtnw6JJSmYbmEBxnZPqUtz3QNt6fuPbHgAGM712PO4Ei7sAKbgUe/5jVZNLIVz3Stz+hFhxjdIZzODQM4nJyDTGoLYZktVpYlJDPiVsQtPbeIIE8/IoMi8ZbCr7WgXQWC7IYJul6DQ6WvceUDLuaUWN3vvbrXZcHoEsa1HsfWS1uZd7hi6/r4w3PZcXUTQ2NqO7SfTNGhKzTx8v0RfPZ4i1uvixXtqRl0S/m05LrebbJhx2Jo0gIu+0Jc1Dg61+nN0gTHGmSitpVA8O9G3CkTCAQCgeBfTNlUwvIobQhhLCzi62828VGKEk9pAZOlb+HmfsphvFGiwdzhZ3670YD41fvsYq//vTV554EG1Ag4Sv+VQ51qhE2Mnsy68X0YseAYSVl6+n+2m/Fd67FyTFtyC41YLOCjUZBXaCK7wIhSDj8+24qbhTdJ1Z/jg0F1cJM3YXlCKt/susKS/VdYP749Q6JD8HGXcSH7Iq9Ej6Xu0bG0rMDHIs1ki5CdMt5u8zX7UrtebYe7YmWt7l/p+IrLgtFgM/WIi4pjwPIBFb7WJbiqWwa2qFaQl5vtflEDD6S7YnHL2uC8wAX4bDdM7AT3BkfyXMwUOtfpzYgFx+z31UDUthII/guIn2CBQCAQCP7FlKQSVoV5Oy7Sw9OI4eHHGHbmDBufepf3m8+hueaCw7g8s4aCdj/x2QE/liYk2tu91Aqm9Qnnpa1TXUaKSlL34qLGsWjUdAZ+fohcQzHvbjzDT8dTWTCiNXsuZBK/8xIFRSYWjbqXn86v4dMDHziJu6ntpvLThN4M+zqRJxYcYPGoaLRqCxuOxzMkcyHyCgTZdRN0uwbnSmf0GWBIpyG81fUtmwDMS8VP44darib+cDyTNk/isSaPlVsYGqBJQBP0xXqX1vWuKKlb1ijI06GAdElUS154HfmWvlDgHJWz7paQ1Wwmrb7uxWm1Dxq5luUJqQxcd8hBkIGobSUQ/BcQokwgEAgEgn8xOkPVizT7796G6u3PUGdlghf8GDoZtcboMCbPomV77e8oyqnP0gRH98HYNrXZcnljlVL32tbuwNCYSL7cfhkvtYLPHm/Jh7+eY9mBZLzUCtY/24oZe15h/hHX4m7ImiGMazWO1U+/x5trz/PZ7+eZ2MnM4Iz5yPPPl7t3cjF0uw4XS+uWYkABZqsZi9XK9J3TOZ99Hl2RjhMZJ2ge2Jwv+35J2zptHQpGl0Wr0pKpz6zw2ctSYl0vk0poGOhBs1peeGnkkHUQtvYFU5n1LMA6NZIJm3Fv05YT+6+w5sgVzqblOdU3A1HbSiD4ryBEmUAgEAgE/2JyDcZKx8gsZp7bvYxn9620NfgCL4O6puNckzKAKxFraFWrJRKJhGe61mfJ/ivkGoqRSSU8HlOTfiuGVOlcpVP3YtuEknA5i2UHbCmWsW1qs+3KLy4FWWnmHppL+5D2vPfIgxTr0nDf0wOlvvzCzUnFtjtkSWU8TVoFtOK5LpMY2Lg/hcXFfNzrY3RFOnKLcvFQeCGXeHAh3cCe5D32+mOu0BXp8Nf4V+n5S/DT+HB/c28+GdyCQqMZQ7GZwourcDs6Eom1TM0yPbCyBny2Exo3Rg0MjQklSKsWta0Egv844qdYIBAIBIJ/MV7qss4QjgTpc5i/8UOaX0y0NQQALwM1HMdlmAN47Pg7JB26CfxOk2AtI9qFsX58e55YcAC1UobepLvj1L2ImlqGxITw1GKbicidirsP935Ir6AIvPc8grwCQXbRCF2vw9Uygiw2NJb4IfGcvHGG+5fdz84rO1FIFQxsPJCWQS1pX6cj9byb8sORNF7o1Z0xLceWKxZPZpzEV+1LZFCkk+uiK1oEtSDI0w93ZQ5jvj3EyRQdA7y380mdD5FIyoS9UoH1DWDZNqhVy94salsJBHcHf1qUzfns8z+9qQQJzz77zJ+eLxAIBAKBwIZWLXdZpNlLreBlVQqPLJiG7EbG7Y6BOAmypKKaDL30LteLA+xtJ1N0TFl9nKExISweFc2X2y9SaKpammQJ2YYcImp6Umg021MsGwZ6UFBcdXGXnXkU7a4ByAuvlzvmrNF2hyzFXKYjE2Y8N4OP9n3Ma9tfxcfNh5c7vuxQ8Nlf449cXsR7Dzciv9DMO13fo32djnycMNvFPbdpuCs8eKHNFIatHVrp2ae2m8re87lMWX07DXRPXiTXjAGEqNJvDzwBHGgDazeCj4/TOvJb4qukvpVAIPjv8adF2e+/b3PZLpHYbGytVmu57RKJEGUCgUAgEFQHSpmUuM51mbA80d5Wz1vFD5m/4fXWbCRl/h6bvpViaBmKp9YWdTpXGMLQS9O5YfLFFUsTkokO9+XF3o3JKUq7o7P5qn2AHAdjCg+VnCx9dpXm11XAtlqgrECQnSyyuSymlxVkGRDZLBJ3pTtv7Hid+r712TR0Ezuv7GTgioFOguuFti/Qq14v9l89RN/6/bkvvC8FJh35xlz8NT4opJ5sPH6Dvp/sZ/GTvRkbNbbCu3XjWo2jZ7376Tp7n0N7ltmbkUlv8mP9yWhlBWy7EkXjgzWosXY1cg/3Kr0uAoHgv8efFmXT33nbqW3P3r388ssmGjZsQMeOHQkIsH0UdyPjBjt37ebcuXP07dObdu3a/fkTCwQCgUAgsCOXSekREUhsmxCW7E+mcVE26378EtWB/U5jr2lr8OyAaVy4FsaR9h9wJSOLwRffJNvsVeEeN/VGdp2/QcswzztM3fNlaq9AzKWEYX6RCT+NawFYmtYeWrbUsqC15Jc75ngR9LgON1xEyPCHaR2msenCJrxUXmyO3cysPbOYf3i+0zqJaYkM+3EY41qN44W2L/De3rd4o/ObaKmJwRiI0Wyh2GQlM9/MTYOJH49kMrvnB3QM7czsvbOcBN6UdtMY0Kg/zy0/5eSUCHCxqA5jr7xEc/UF5uU+TJP+XqxSqsSdEoHgLuZP//w3bdrE4esjR4+yadNm4uLG0Kd3L6fx/frdz6bNm4mP/4pWraL+7LYCgUAgEAjKoFHKeblvBA9c2Eezt6egynNOM9xSrzUv3D+JXLUnETU8udZ8FQ99sQedxaPCtWVSCT3uCeKpxYfomu7HxOjJjFwfW+mZnm8zhf0XdQRpNdSr4WlPsTyXno+7QluhuOtdI4QNgQbkRTfKXf9IIdx3HbLLlhPLBTwgrnUcfer34fEfHmdc63HsSNrhUpCVZu6hubSp3QalTMnaMxtIS7uXL7bZIopNgrWMbB/O9sldcFNIUSvlPBLxMAMa9eemIYfswpv4unnjrfZGKVWRozez5bQtbbRGfg43PBzTEvfmR7I3PxK4VVDaYBKGHQLBXUy13Q5dteoH6tWr61KQldC7Vy/q1avH96tWV9e2AoFAIBAIAM0Lz9NqSpyTICuWypjZZQRPPfwauWpPwJZCmFmsqVSQge0OWMmdsCX7r9ElpA9jWo6tcM64VuMY2Lg/QV7uhPhpkEpgTKe6AJgtVpYnpPJczBSXc9t5avkpIL9CQXag0Jay6CTIDFDPvx4LBy1kSrspFBQXkJafRlxUHJ8mfFrpswJ8sv8Tnmn9DDuStjK8bW0UMtv1i5MpOiavOsaHv52jJO6nlCvwULpT26s2zQObUturNh5KD5RyBaakVXwWMosulw6yM340D/+xtcJ9dYXOETWBQHD3UG2i7PLly9SsWbPScTVrBpGUdKW6thUIBAKBQAAQGur4dWsofFPBsOFvEx/zCFbJ7T/5+UUmfDXluzbKpBIianrSOsyHJsFasvU26/xcQzEjFhzjpXbvsmjgUiKDIh3mRQZFsmjgUmZ2f581hzKo4aHi99M3GLnoIJF1vBkSEwLAkv3X6Bxiu5flMF8Fv9YsRmYs/87ZXoMtQnazrCAzwdvRb7P16a2cyzrHk+ufxEPpgRXrHRd8tlgtPBP9DEZrLjumtuOZruF4qRW3zn6FraczMJnLHqAUpz+i1onh9Pfexddu76A2FTF74yc8nrip3CklBaUFAsHdSbXFyWUyGcnJyZWOS05ORiaTVde2AoFAIBAIACZNgt9+g19/hc5gHQ1u0mKer7mcJy43otB627XvXHo+bkqZk2ujl1pBbJtQhsSEUGg0k6034u+hxN9DxZsDmrD9bAbpukIe+vIwg1tHsmHQ7xQU68g25OCn8cFH7Y1MIiXfmEvL+gUUWm4QXdeTS5lejF92hC+GRtEm3Jf4nZcY8c0xvh/3Hh1COvDBvg+Q5yTyay1wtxrKfcRdBuh7HfLLFlG2QC1DLVZnrObNOW9isVr49oGlmMwSompGkWNwXQy6PJJzk3lx64vsTt5NZFAkE6Mns258H0YsOEZSlp55Oy66LthstcCRyXD2Y3uTvLcFUkC61crMzZ9zxTuIvWGRDtOaBGvRqkXqokBwN1NtvwGa3HMPBw8dYsXK7xn02KN2t8XSrFz5PVeuJBPdunV1bSsQCAQCgQBAKoXFizFPboKsbzYlf4VjPE4QHzaD0UmvUWxVIJNKaBjowY6zN5jYvQHjlh7BbLES5qdh8ahoDlzOttfUKhFpw9qE0iMigJhwX1RyKUq5lOUHrtJ/zkGCvNyoX8ODF/t6s/Hcz3xywNlKfmL0ZD5r1Ydnlh5hwL01WRnXlpt6Izdyi3mg8YP08fHGa99gZKbyTT1+10P/FNCXFWSAd6o312td53q6zaVxXKtx9KnXj5sFZj7s+SG6ojuz8vfT+NnnJKYlMnJ9LHFR41g0ajoDPz/k+g6YuQj2DYfk750XrGP7vx+adGV/SDOn7rGd66EU9cYEgruaahNlscOGcvyPP1i58nu2b99Bu3ZtqVHjlvvijRvs27ePtLR03NzciI2tWsFIgUAgEAgEZbh+3aG4sB2rFdI/Q9bXOfXvqjEQdzc3hrYJt0fBcvRGanmrSXipOysOJvNwVG0+23qBZQdsWS+lRdqTiw86RNRKCkuvfaY945cdYXLvMN7e9aLLosulRc1nQ2yi5svtF5nSqyH3NwvGkroLn4TBSCoQZL8WwAOpYHAhyJrlNMOrrZc9qvV8zBR61r0fN6WMzZd+5uMfP2TNoDV35Bqplqs5kXHCoT3+8Fza1u7A0JhIvtx+GV1h8e2aYcabsPMByNjhvOBy4Cf4qXFHpvZ9DovUMVsotk2o66ibQCC4q6g2URZSpw7vvPMWn376GdeuXWPNmh+dapbVqlWLiROeJSQkpLq2FQgEAoHg7sBkgunTYeZM+P13aN/+dp/FDIeegQvxTtO+yHiUVcVjWTc+xiEKVkKTYC1xneriJpex92ImYEtj/HZUDPN2XLSLtNKULiy9cGQrfj6/xqUgK01ZUfPyjyc5lLCGDwJfQ2LWlzvv5wJ4OBWKXAgyzsKGLzZgMBlxV6qR4cHqQ2kYQy1M+XWyvY5Y/OF4JkRPYNT6URWeEWBCzATiD8djsTrfGfs04QPWD9rKvB2Xb98BK7gK2/tA7knHwSZgPrAHUrv2Zn7/qZjTbz9nk2AtYzvXo3tEgHBdFAgE1VsSo0H9+nz+2af88ccJTp06RXaOLYfbx8eHe+6JoFnTpi7TGgUCgUAgEFTA5cswbBjs2WP7esgQSEwEHx8wG2+lza10mvZ1/ji+yXuQtU+3qVBgTViRyNCYEBaPimbA53uIbRNKwuUsl+NLs+LgVSb3CuPjhNlVeozSoqan73E+CHgbqbmw3PFr82FQGhhdCbIL0Lxdc3w1fvyUmMWao8lcyMjnk8HN2HNtk0Nh57kH53LwqYOMiRpToS1+XFQcHUM6MmnzJJf9R9OOUlCso2+zmrY7YDf/gG19wFCmuLUB+AQ4AfTtS41Vq1khV6AzmNAVFqN1U6BVy1HKpCJCJhAIgGoWZSU0a9aUZs2a/h1LCwQCgUBw92C1wrffwrPPQl7e7fbkZApHjMKybCFuCYORpm12nIaUtMaf0jt8FI+o5Px+JqNSgbU0IZkWId7EtglhSEwITy0+VOnxGgZ6kGXIuSNnw4JiHbEhJ3nd602kFmO5Y1flwZA0W8DJictACLzQYRrztifzxbbLhPlp+PGZVmg1MHHxLIfhOYU59F7am01DN9G2dls+TfjU+d5bzEQ6hnSk99Le5BSWbwySbcjh8egQVFk7YfdDUJzrOCAHmA1cATp2hNWrkavVyLHVk7OnPAoEAkEp/rZ4eXFxMXl5eSgUCjw9Pf+ubQQCgUAg+G+SnQ1xcbDaubanSSIlXq/ivlUduUfpePep2Krg2SuT2XQ8HJl0O3umdePrXZertOXCPUksGNGa/EITp1IrN8fwUMnJyM9y2SeTyGgS0AStSouuSMfJjJOYrWas19bwuvYV5K7lFgDL8mB4GphddV4CgiCu7Tg61enNwHWH8FIrWPzkvXz7x5c82uQRlyLxQvYFWn/VmrGtxrJu8Dr0xXqy9FmEeIUgkUj48uCXTNo8qUJBBhDo4UdQ1makO0ZBWVF5HXgfyASaN4f160GtdhhiMlswmi3oDCZyDUa81EoRNRMIBNUvyjZv/pVfNm3myhVbLbKuXbsw4dnxAOzbt5/tO3Yw4onhVappJhAIBALBXcmWLfDEE5CS4tSV7BXImw+PYUq774hQJjn06S1uPJX0KnvyIwFbJKugqGoCC2ypjCazhVxD1QoZ5xeZ8NP4OrT5uPkwrvU44qLi0BfrydRn4q/xRy1Xs2fvJBqdmYpEUr4g+1YHI9PBZRWwS9C4cWNe7PManev0ZsSCY+QainmmazjbrvzCr5c20zW8S7lr5xTmMHP3TGbtmUXTgKZoVVoWDlzIzF0zWZC4oNLnbRHUguCUpbgdf8m58yzwIVAAhIfDpk3g7e0wRG80seV0OvE7Ljnf6+tclx4RgeJ+mUBwl1JtP/lms5lZ78/m4MFDyGQy6tSuTfLVqw5jwsLCSHh/NvXq1eOxRx+prq0FAoFAIPhvUFgIL78MH3/ssvv7Zj34ps8DxDd+lzBVqkOfUerFD9p49uR72Ns8VHJ74eeq4KVWIJVIqFnFFLtz6fm4K7R2Z8P6vvXZNHQTO6/sZOCKgQ4pgtNqhzJDfRWJa7kFwNe5EJfhWpApryrZOOEXmjWKYtHu6wxcd4hcQzEyqYTHY2rSb8UQrFjx1/hXem6L1cLx9OMAuMndePieR6okyl5t/RTKUy4E2UHgC6AYCAiw1Yor8+Gz3mhixsbTLNlfzr2+5YnEtgnl5b6NhTATCO5Cqi1OvnHjLxw4cJCWLVvw9VfxzJnzidOYmjWDqFkziCNHjlTXtgKBQCAQ/Df44w+IjnYpyHLcPBk38EWm9n2ON+ovdBJkxapAbnb8hfu7DcRHo7C35xeZ8NUoq7R9mJ+G9ePbczApG4lEQpNgbaVzzBYrW09lM7XdVHzcfNgcu5n3977PqPWjHATZME941+0K0goE2dybMKYcQSZJlvDTmE2czauDoUhOgwAttX1saYENAz0oKNZxLP0YJzNOolFoiAyKrNIztwhqgZfKm06hnRgbNbbCsXFR42gZOog9tRZQjOp2x2/Ap9gEmaenLUJWv77DXJPZwpbT6S4FWWmW7L/C1tMZmMzlv04CgeC/SbWJsq2/b8Pb24spk1/Au0y4vjR1atchI+NGdW0rEAgEAsG/G4sFPvkEWre2CbMy7AqNpPeoz/ilcQfC/DTU6buYPEUNe/8FIzQ8k06vn55m08Uf2TChNWF+GsAWyXJTyioVWCUW+PE7LjFhRSLf7b/CiHZhVTq+t8aN7mF9WTBgATuSdji5G47SwqJAkFVgvvxpDjx9A1yZLHIF+kf2p1njKDafSCPx6k06NazBN0+0Yuukznz8WCSFZlsqoNlqttvfV4XJbaeSZ5AgQ8UrHWawaOBSJ0EXGRTJooFLebHtdEYsOMZ7h2ugi1pKsVXG+6nDyW32CUikoFTa7pC1aOG0j9FsYd72S1U607wdFzEKUSYQ3HVUW3z8+vXrtIiMxM2t4pQHlZsKna5que0CgUAgEPznGT4cli51blepSH/lLYbn34NVIrWbWUzf8wpbz99gd21IN0Ov67b/Jy2RoWuGMDZqHItG2Yo05xqKWZaQzIh2YUxZfbzcI5S1wF+y/wrrx7dnSHRIha6NQ2NCiKzjzbgliSx+sgcdFnRw6I/zgnkBFT/+BzkwJdN1n/yynGVPLKNe43qY0LH0qRak5ZoZ9k0CZ9LyaBTkSZNgLeO63b7XVlX7+3FR4+jfqB9yqYpcvYn7Pz3I0JhINgz6nYJiHdmGHHzVPmjkWpYnpNrTJQFu+vRicvYStt3w5IHnO+HVuAHo9dCli8u9dIY7u9enM5hECqNAcJdRbZEyuVyOsbjyvPXMG5moyzgRCQQCgUBw1zJkiHNbs2Zw8CDWCROxSmx/qmPb1GbblV+Yf2QeF4uh6zXocu2WICvFvMNz2XF1E0NjagM2gRUd7suQ6BCX28ukEobEhLBwT5K9LddQzBMLDhDXuS4fPNrcKdLWJFjLB482Z2znuvx6MoV5w5qRU8Ya/9kqCLJ3sysQZOflhNcO57GdjxE1P4o6H9eh46KOJKRtZOXYlvZI3vpjqXgovewRrhL7+6ntprJw4EKXka/lDy/n/ftmo5K6oVbIyTUYyTUU8+X2y3SatZfnll3h843ZPLfsCt0+2Me2s5k0DPQgoqYnMqmEHL2RfIXt9dS6KaBvX3ik/LvyuYaq3+sD0FXRaEUgEPx3qLaPYerUqcPFi5fQGwxoyhFdN2/e5NLlyzRq1LC6thUIBAKB4N9N377w9NPw5Ze2r194AaZPB5UKbVEhTYK1nEnLs5tZlHCmgvftpYs0lwisxaOiiQr1YcGeyw7Of32aBmG1Wp0iOUlZegZ8voehMSHMH96KQqOZHL0RH42SAK2KX0+mYaEIpecBpm3dyqgWo+xzJ3nDhzWokDey4O3scjpPwOqnV/PQroccchoT0xKJ/XEI41qNY+Z9MxlSoEUl9aDICC93eJXHVtuEUXn2934aPwLcAyguVqEvkuGhtb0N8lLfvndnsYIu8yJz673OxaDhNH7iVQqNZrL1Rnw1SlQKKSq5TSg3CdbaikhXQun1q4JGJcNQbEKtENEygeBuodoiZV27dCYvL4+5c+dRXOz8l8JsNhM//yuMRiPdunatrm0FAoFAIPj3M3s29Ohhs8L/4ANQKuDgONwODGNMx1AHM4uqUFKkuVGQrU5oicBqEOjO93Ft2Ta5M2vGtWPvi92Y+VAzcgpcKzxb9OgiHWf9zvjlR5i16Qzjlx/hQkY+bep7MmPvK4xYH8vh1MN218MXfSoXZC9nViDIjgBe0KhZI5oGNHU5ZO6hufx45kcWJM5lwPfd2JOykV71e/Fqx9fsY0rs78M/DWfQ6kG8uPVFVp38AX2RBAky3FW3BY9WLbdHAxuorrC24VTClNforp/B0pUz6T17C1f7PcLYl74l7rvD7L2YxQeP3ssL9zVEWYXaYqXXr4yScTN+PoPeWH7pAIFA8N+i2j6C6dnzPvbs3cuuXbs5c+YsLVpEApB0OYmvvv6GQwcPkZ6RQWTkvXTu3Km6thUIBAKB4J9PcbGtCPTgwSBx4Xih0cBvv9n+22SAvUPg2lqkQJ+6AaTfO4ksfXkqxjXZhhw8SgmPfs1r4u/hRpqukPHLjtj77m9Wk15Ngipcy2KF06l59q/r+Lqx8cKPzD88D4CTGSfxVHjwZUgQ41RpFa41+QZ8eLOczgNALdv/svRZaFXOQqakKPWuK7uY3m06H+z9gCFrhhAXNY73e8zinhrNeX/vu3b3R4vVglQiZXSLcXSu05vYrxJZMjoG3U0DYf7uyGVSlDIpcZ3rsnDdChaFv4mXLN++3/MeM2h1rgXdTh6ix4UDxOlfZmKKjqExIbzcN6JKBZ9L1p+wPLHSsSPbh7EsIZnvbqWd9mkaJIpKCwR3AdUmymQyGa+/9ioLFi7it9+28Ouvtj8uly5f5tLly0ilUnre14PRo59E4uoPkkAgEAgE/1JMZgtGswWdwUSuwYiXWolWLUcpkyK/dBFiY+HAAVsdspEjy1+oKBt2DoAbe+xNyktzGdwwkGztqPLnucBX7UN+kY4mwVpGd6hL50Y1eGPdCYa3C3MQWEeSb3JfkyCaBGsd0hrLo1ktLW5KCx/tf9/eZraaSd83plJBNiEDPsstp3Mb0Bi4Vd7LT+OHruj2eVwVpTZZTFyaeIm5h+Yy9+Bc2tXuQIi6S6VmHfmFJpYfSGZan8bIZVLkMik9fY7Ru/6rKCl0OFb2TS+67ToEgLaogG+/f53n+73AUjrRpq5flUSTXCalR0QgsW1CKrTFHxoTQuswX9756TRgc2LsHhEgRJlAcBdQrcnKSqWSsXFjeHzwIE6cOEl6RgZWqwU/Pz+aNWuGn69v5Yv8P5Cbm8uaNT9y4OAhMjMzUSqVBATUoHnz5owc8YTT+AMHDrJ23TouXboMQL26dXnwwYG0atWq3D2Sk5NZvmIlJ06cpLCwkJpBQfTo0Z1+/e5HKhW/XAUCgeC/gt5oYsvpdOJ3XHIQNU1qevJu1n7u/fAtJHq9rXHCBOjUCerVc16o4Cps7w25pxyarVIVXx9TMjzCy16kuTJaBLWgpqcfnz/uh0ohZc2RaxQUedMw0NOpbpnZYmX5gWQmdm/AV7sukV9k4lx6PmaLS4N6JnRvwI2CbI6lH7sVtbqHaZpsWmb/UuGZxmXAPFeCzAL8AkQBQbfPr5arOZFxAqDCotSRQZFMiJ7AwacOMmnzJL7o04cu7++lQaAnHio5+UU6zqblUfpxlHIpB5Kyb7scXlmJ275h2IqN3eZsTgjhL12H2xoWk1TOZd9awJ2JJo1Szst9I4gO8yV+Z5l/K8FaRrYPo3WYL08sOGB3eRROjALB3cPf8lPu5eVF+/bt/o6l/zIXLlzkzbfeJi8vj5CQOkRHt8agN3D16lXWr9/gJMrWr9/ANwsWIpPJuPfe5ijkCo4mJvLO9BmMeWo099/f12mPM2fO8trrb2A0GmnQoAEBATU4deoU3yxYyJkzZ5ky5QURLRQIBIL/AHqjiRkbTztFP3z1uUz8YjqR5/c7TsjPt0XKduxwTGPMOQ477gf9NcfxCi9M7ddQN70RC3dfY2L0ZEauj630XJPbTeVCRiGv/HjKLkjWHLnO0tFtcFfJ7FExX3clE7rVpU9zf4yWPKb2c0er8kYj17LiQCpL9l+zCwQoscD34XreeV7u+DJxLcfgceI1fJO/K/csFis8lQELXAXhjMB6oBNQyqlxQswE4g/HY7Fa7EWpZ+2Z5dLiPjEtkVHrRxEXFcdHvT7CYMqnQaCnQzSwNE2CtSjlUs6m5aErLCboxhI4MIayVdL2ZTej/uvJKPMc7S2n9pnAyUCbqL5T0aRRyokJ9yUqzNfBOEWlkLIsIZl3fjrt8HoDtjN6VVxuSCAQ/PupNlG2cOEiunTpTHh4eHUtWe3k5uby1tvvYDQaefnlF4mJjnboP3fuvMPX165fZ+GixSgUCqa/8zaNGzcC4Pr1FKa9+BLfLFhIy5YtqFmzpn2OyWTio48/wWg0MmrUSAYO6A+AwWDgjTffZs/evUT93pLu3bv9zU8rEAgEgr8Tk9nCltPpToKsy8VDzP7lE2oU3HSe1LAhfPSRoyBL/RV2PQKmMiJCHQxdN6HwbsZ9fib+uJZLl5A+jGk5lvlH5pV7rnFR4+jXoB/3f3qAqzkGe3tSlp6+c3ax98WujO9aj2s3CxkUHcBP5zbQe9n7TtGnidGTWTe+DyMWHMNdJWd0h7rcd08gFizU9QuhvnddDqyN4hFlVrlnMVthZDp850of6YE1QC+glDHIuFbj6BTaiUmbJ9m+bj3OZVHqssQfjqdN7TZ0DunhcJeuLCV3tixWCEqZC6enOY3ZkN2R0PdSqZHjGNqbF/MwG+7p7NB2p6LJioSOs7bSKKgkkmdyiuSVRuumqPLaAoHg30u15dGtW7+BSS9M4dkJE1m9+gcyMjKqa+lqY/nyFeh0OkY8MdxJkAE0bNjA4eufNvyExWKhd6+edkEGUKtWMI8++ghms5kNG35ymLN/fwLp6emEh4XZBRmAWq0mbsxoANatW1+djyUQCASC/wFGs4V52y/Zv1YVF/Hmb/NYtPpNl4Ks+KkxcOQIphYt0RtNpOUWknrkC6zb73cWZNrG0HMveDcDwF0lZ1LPhlxIN/JaxxksGrjUZf2tpQ8t570e7zPuuxMOgqyEXEMxmflFdGoYwMNRNZj62xSGrhnilBKZmJbIyPWxvL//VTZMaM3KuLY0rulBXlExxZYiXvx1CtaE0RUKMpMVhpUnyG6CfJUc+mAXZJFBkSx+YDFvd30buUSOn8YPmURGXFQcnyZ8Wu4+pZmTMAc/d69yXQtjY0Lo1jiAg0lZvFv/B7QuBFl+SBxF8QqaX7/g0L4jvCXvdxruNP5ORZNWLSeippbTqXkcTMrhdGr5gqyqlvsCgeDfT7X9pI9+chQ7du7k/PkLLFm6jKXLltO4cWM6d+5Ih/bt8fDwqK6t/hRFRUVs37ETNze3KkepDh0+DEC7ds6pmO3atmXBgoUcPHiIMWOecjGnrdOcevXqERQYyJXkZNLTMwgMrKSqpkAgEAj+segMJnttrybpF/lkwwc0yLrqNC5T48XUPhOZ9eHLuCtkbDmRSvyOi/Q2f8mzgSudF/ZvB53Xg8rPodldJadTwxqYzFYGNHyIBxsPQFd0k5tFN/F288FL5cXhpDynCFlpvNQK/DzcuJqTx9Ebm4g/XH7EDWzW851CO1FD1okGAd4cu5pDunELba/PZ5hX+fOKrTAkDVbnu+hMgcBdgfy4/kd8gnzs9cPUcjXxh+N5btNzDGoyiE1DNzFi7Qj0xfo7KgWgK8qlbg0PTqbcVoMld7ba1vVHKZXwVdOVeCcvdJq/XTGGyN31eOREvEP7ZZ+aPDtgKhapzKH9z4imO3FiHNu5XpUs9wUCwb+fahNl/frdT79+95OWlsb2HTvZsWMnp0+f5syZM3z99QJatmhB586diI5ujULx/x+Kv3DhIgaDgXsiIlCpVBw+fITEY8coNhoJDg6mfYf2DkYk+fkF3LiRCUDdus4pmTVq+KPVasm4cQO9Xo9GowFsJQBsc+q6PEfdenVJS08n6UqSEGUCgUDwLybXYERqMfPUwR95YecSlBbn6MzWeq2Z1mcCxf4BuCmkzNh4mu8TLjKr9qc86L/dafwfsp7U6/gDGpXrDzKVchlKOWhu/fmWosJDEcDc7Rf5ZnciJkvFZ37/kWYYzXoCvKzMWjurSs/5/p73+TW2L9tPZ9GmrgrJhgn0qqDkltEKj6bC+gIXnach5EQIyfcn02FNB5oGNEWr0qIr0nEi4wQWq+0B5h2eR4fQDkxsM5FMfWaVzllCjuEm7wxsyqT7GnFTb0TrprDf2Xr3pxNs77IK7+vOguxU4MvEf+3O0u9fcmjPV6oZ8+Cr6Nycvyd/RjRV1Ykxtk2ocF4UCO4iqj0mHhQUxOBBjzF40GNcuHCR7Tt2sHv3Hg4cPMjBQ4dQu7nRpm0bJjw7vrq3rpCr12yfXnp5eTFjxnskHDjg0P/dkqU8O/4ZOnXqCEBm5g0APDw8cHNznSvu5+eHTqcjI+MGYWGhANzItP3x8Pf3K3cOwI2MG3/xiQQCgUDwv8QnM42lK1+lbfIfTn0GuYp3uz3Jksg+IJHwTJtQtp3JYP3BU3wbPp02Hiec5szLeJhZaU8wp7GOPk01VXozLpVKWHfkGvUDPBwEmUwqoWGgh/3O0sUbBXSo70f7Blo+3vcxjzZ5pMrRp+PpxzFZDBQWGfA+8hS9FOV52kOhBR5KhV/0Ljr3QDtDO3RP6kBnqx92PP14uWt9uPdDdo/aTZa+/BRJV/i4eVNYbKagyESApwq5TMJ3+5JZmpBMY+kxvFwIsqLIz5i9ogZfrBuD1OJo7DHp/kmcrxHqNOeviKYSJ8aYcD/m7bjo5MQ4tnM9ukcECNdFgeAu4m/9aa9fvx7169dj1MgRHP/jD7Zu+Z1du3ezbdv2/3dRlp9v+8juwMGDSKVS4sY8Rfv27SgqKuLnjb+wdu06Pp3zGbVr16Zu3XAMhbY6JSqVstw13dxUABgKb6eJFNrnqcqZYxN4BoPr1BJX5y4ocP64MTPzzj45FAgEAkH14vfMUwS4EGR/BNbjuf6TuehXB7AJpCfahjJi4UG00gLqqRwdFs1WKa9fH8vSbJub753YrCtlUhoFeRLi605sTAgbjqcS2yaUITEhdne/AK0bvholMqmFtWd/4NdLm+ka3qXStUvXBTMW6xiYH4dH1vZyx+stMDAVtpQVZBbgZ+jq153l65cy5+gcrh+8Tk5hToX7H007SpY+Gx833zsqBeDl5k2Lt363i9QmwVpGtAtj/fj2PLFAwY1GH1Lj7AuA7bX/KO9FhmgHMSU+Bl+Do0Xkqaee43rTXlBGNMV1qkuPewL/kmjSKOX0aRpE94gAdAYTusJitG6K2/XtRIRMILir+H/5CObkyVPs2b2Xo4lH/z+2c4nVYvvtbDabiR06hL59+9j7Ro54ghsZN9izdy8/rl3LC5Oe/18d04kNGzawYuX3/+tjCAQCgaAM1jmfYY6ORmYsAsCChLltHuGTDkMoltnS9L3UCp6/ryFmq/XW/bNAnkx6nRX1XkIjLaLA7MYzydPYntfavm5VbNZvF6suxlujxGSx8mLfxkzt3ZhfT6Uz5ttDDtGXZrW0fDs6kll7ZmHFir/Gv8JnK10XbNCK/rwpPU4v9/LH51ugXwrsKPt5YyHwPeAB22K20vv73vaaYr2X9uZC9gUXq90m25CDvtCDyW2nEvvjkArHAkxuO5XDSXkOUcOTKTqmrD7O0JgQlo5ug1ESw/s7jvNcwFKeTZ5Ktk9fnhwXR62Myw5r/d6oLYceeIr57cKd7OsVMmm1RLFKCldrlHJhey8Q3OX8baLs0qXL7Ni5k127dpOTk4PVakWtVtO1Sxc6d+74d21bLm5qtf2/XRl9dO/ejT1793Ly5EkA1LciWkVFxnLXLCwsujX29tpubm7k5+dTVFRUzhxbJE1d6jwV0b9/f7p1cz5vZmYmL7/yapXWEAgEAkH1I7+3OcYZM5BNfoFr2gCe7zeJg3Wa2vvD/DQsHhVNZl4R6boie0qhShXD+3lvEKf5iNFJr3LSUN9p7VxD+TbreqOJLafSHQoQe6kV/DKxI19su8DSBOd7SgGeKvKNufaCzxqFptzoU+m6YEuOzGdDMHTTlP866MzQJwX2FpbpyAGWAQ2B7oDUsabYpqGbaP1V6wojZr5qH+Zvu8qU3v2JixpboTHJ2Khx9GtoKwXgiqUJybQO88Vbo+DLjEfZeLMdScZaNFcZUOJ4Ge+8Xx0m9Hme/J2XmbfrspN9/d4Xu5f/gggEAsGfoFpFWXp6Ojt27mLnzl1cv34dq9WKTCYjKqolnTt3IiY6GqWy/HTAv5OAGjbPXZVKhZeXs2VUQIDNdCM31/YHzt/fNj4/P5/CwkKX98qysrJuzb1dYKWGvz/5+flkZmYRFhZW7pwapeZUhIeHOx4eFXw8KRAIBIL/Gcrnn8NoLOaPjgPRH7lhT3Or46Pmx2faY7ZYUUglqFVSdkxtS0Gxjix9Nn6aIZglT9Dl8E2ulSnQDJTr6KcvMvHuxtNOwiu2TSh7LmS6FGReagUzHmxOct5ZAMxWM/GH45kQPYFR60c5jS+pC7bsyHw21YKOFXyGeNMMva7DgbKfQ14FVgCdAecKNPaaYmNbjWXm7pku1y5JRVx+4BiXbhQwd9hsOoV0Zva+WU411aa0nUa/hv3KlAKwAhKHNb/adYmvhrdCKoEkYy0ATmYZyf12GWuGTST252/IV2kY89Cr5KtsStRixaEQtbCpFwgEfwfV9ltl6rQXOX/+AlarrdhG48aN6NypEx06tMfT07O6tvnTlDgoGo1GiouLnRwg8/Ntv3BLxJeHhzs1avhz40Ymly5d5p57IhzG37iRiU6nI6BGDbvzIkBYeBiXk5K4dOkSrVpFOZ3j0kVbTZuw0LBqezaBQCAQ/E2cOQOzZkF8PLj6UFEqRfnSNO4zW+gcXR+dwYSh2ESAxsqFfbP5/mY/pt1fj5/Pr+P9vRUXaE7Ksl3GahKstRc/LklTzCs04amSs+lkmpPwkkklDIkJ4anFh1w+QmybUI5du0logLe9be7BuRx86iBjosY4FGUuqQs2dPn9/FoL2lYgyLLM0PM6HCkryI4Am4EHgcblz5+TMIe1g9cya88su+tiaSa3m8qRW6mIey5m8dWOa4zp/CD9G/XjZuFNsg05+Kp98FF7czrFwIA5B0nKtr2GAfIsvgqbzvSUJzmovx29PJmiw2A00yjI0y60zBYryw5cpeH0NxntFoxZKuOyb61yzy1s6gUCwd9Btf1WOXfuPMHBwQwZ8jjx8+by3swZ9OnT+x8hyABq1KhBeFgYVquVEydOOvWXtNUNv21/3yrKJqr27t3rNH7vvn0AtG7dyqH99px9TnMuXbpEWno6oSEhwg5fIBAI/slYrTYh1rIlLFoEb75Z4fDS94IClTfJXt+ee9Nf5fW63zN1yxSGVFCg+b19r7Jo1L14qW0fFo5sH4bZYsVQbGLjiVQenbeP7/ZfwWi28PWuy057Nwz0oNBottdMK02JYJuz9TzuCq294HROYQ69l/ZmarupLBy40N7eJKAJhfoUPlaeqFCQ3TBBt2tlBJkF2AhsAYZRoSADm5GHvlhP04CmTn3jWo2jX8O+NAhS89aAxnz+eCSPtKpD/8/20uX9vRy5ZKW2ZzgSZFzIvoTWXc93T0XyTNdwGntms6reNO7VnGdB+Fs0U593WDtHb7SL3hIOJWXTIyKQ4NhH2FHX+QPVEoRNvUAg+Luott8qH304my8+n8Njjz7yjxUcDz74AAALFy0mOzvb3n7p0mXWrV8PQO/evezt/fr3QyqVsmnzr5w9e9benpKSwqpVq5HJZPTv389hjzZtYggMDORyUhLr1m+wtxcWFjIv/isABg4cUO3PJhAIBIJq4uZNeOwxGDsWSpxy33sPdu6sdKop8whsjqGOxebKqDo9g5yzFRdojj88lx1XNzE0pjZDY0JoW9cfuUxKQaGZs2n5pN4sZFibUHL0Rifh5aVWMKxNGEaz6wJlJYLtRIqO5QmpTIyebO+7kH2B1l+15lzWOdYNXsfpZ06z/P451Dn4BK0q8JxIM0GX63C89JVrPfAd1L5RG0YDtSt8ZDs3C2+iVd0uehYZFMnCgQt5oe0LRM2Pot+Krig9DxBd14Pxy2xmYT883YocdtJjSUfqfBJMi/h7afxlGAO+70a492Z+iniZUFUaAJ4yA4vD36CB6op9Dx+Nkvwix5pysW1CUcmlvNw3gs8eb0GTYMdCbE2CtXz2eAte7ttY2NQLBIK/BYm1JN/wLzI0djihoSHMeHd6dSz3t/Hpp5/x+7ZtuLu707hxI4xGI2fOnKW4uJie993HM8+Mcxi/bv0GFixYiEwmI/Lee5HL5RxNTMRoNPLU6Cfp1+9+pz1OnznD66+/idFopGHDBtSoUYNTp06Tk5NDu3ZtmTplMhKJxGnenZCensGYuLHMj5/3jxXBAoFA8K9j/34YPBiuXHHu690bfvml/LlX12LdOxSJ2dETPt0EdZNAX8Ff2xZBLdg6fAc6vZTTqTqOX8tlx7kbjGgXRvv6/oCV6zcLeXTe7SyMEiORs2l5NAj0oOsHO5zWbR3mw9TejXl03j681ArWjW/FzL2vMP+Io1CUSqR0DmzEt9pUaltvlnvO67ciZOdKX4FLB5aDu787v/30G+1WtCv/QcuQ/FwySpmS5NxkPFWeqOVq4g/HM+/QPAcDkLiocbzUbjpSiYTpu192Oj9AfQVsqwW1HW8ncLGoFrGXppNaXIMmwVq+k52izxkN6e6+gE2QlRZbt50thU29QCC4M/7K+/Nq+7jHbDbbCyP/k5kwYTwREY3ZtPlXTpw4iUQioW7duvTu1ZNu3bo6jR84oD81g4L4ce06Tp46BUD9+vV56MEHnFIXS4ho3JgPP3ifZctXcOLESZKSrhAUFMiDDz5A/373/2VBJhAIBIJqxmKB2bPhlVfAbHbuf+YZeP9913OtVjj9PiS+hARH5XW5GPqnVCzIwJbKV1Cs43SKlIhgLdN++INcQzFTVh/nlb6N6dO0JsFebkTU9ORcej4eKjnfjoph3o6LrDx0lZ1Tu9IkWOtggw+QX2TCV2O7C5drKGbEgmMsGvUu7ep05JOE2faUyiCphW80l6htde0cDJBcDN2uw8XSguwUsBZoAoZ+BmoF1bqjmmIquYr9V/cze99sdEU6TmSccHm/LP7wXDqGdKShXyOXgqyxArbWhuAy72rOGEKJvTydTJMPAK8Un8V34ljWefgya8wMug3v71SkWdjUCwSC/wXVJspCQuqQnZVd+cD/MRKJhJ4976Nnz/uqPCc6ujXR0a0rH1iKkJAQXpw29U6PJxAIBIL/b9LSYPhw+O035z4fH1i4EAYOdD3XZIADcZD0nVNXvlcLoo8eJdOFxnNFtj6HLo0a88b6kwR7uyGTSng8OoT+99bCarVy01DMl0NbopBJuXijgCPJOSw7kIyXWsGFjHzGdKrLxBWJDmueS8/HTSmzC7akLD0DPz/E0JhIfhq8DaMlj6Lck9Q/Gofc4OzcWMKlYluE7ErprL9twE6gFxADFomFdWfX8ULbFxj247BKn3dCzARkEhmvb3+dY+nHKh3/4b7ZbHh8A1KJ1EG4NVHC1loQWOYdjVHbjKGnXiTLZHNcfr6WibYv2+qQBuVn89HnE7A0VSK7d2SlewsEAsHfTbWJsvv79uWTT+dw6tRpJ6dCgUAgEAj+kfz6KwwbBhkZzn0dOsCyZVCnjuu5Bcmw6yHIPuzUlR30AAWRs8k81KDKR/FRe/PH9Vye7lofCeDrruTXU+k8ufigQwSsSbCWMZ3q0jrMl/b1/JjxUDOOJt+kdZgvQ6JDWHbgtrgyW6wsS0hmRLswpqw+DtgiZhv/SGdQ61CuJ12h6dknkOPi+W9xzgjdr8O1EkFWBPwImmsalqxbQuvO0dw05ODl5o2XyhupFMZGjWVeBTXF4qLi6BrWlSxDVpUEGdiiiXnGPJoGNOV4uu1Z7lXCltrgL3Mce6gQvLp8R8DFXIIkEp5t4UfPkQOR5Ofbx0iMRmS5uVXaWyAQCP5uqi05+p57Irjvvh68+dbbfPX1NyQeO8b16yncuHHD5f8EAoFAIPifUVwML74IvXo5CzKJBF5/HbZtwxRcC73RRFpuIWfTdKTlFqI3mjClboNNrZwEmcUKU25AwO71WGVKu6thZZTU5Jr+8yncFDKQSJix8QyTvj/mlJJ4MkXHxBWJLNxzmfhhUcTvuMRzKxMZ+vV+4jrX5YNHmzsYVSzZf4V29fwZGhMC2MxBvh0Vw7ptm2h0oj/eFQiy00bocq2UIMsGvoHAgkDW/bqFL855s+u0iZruDSgo8OTnY1l8s/M6s+6b5eDqWEKJkceUdlN4ceuLZBSUv7crsvRZdmOQlir43YUg22+AHtfBIJOx5MkYVj0VTc93JyG9eMFx4LBhMHHiHe0vEAgEfxfVFil7asxYJBIJVquVjRt/YePGCi5DAz+uWV1dWwsEAoFAUHUuX4bHH4eEBOe+4GBYuhS6dEFvNLHldCrxOy6VEkZWptXdQpzHF4Cjg1+eBYamwYYCAEuFBZrLMrntVI4m5WE0W9EoZCRcymLZgWRkUgkNAz3wUMnJLzJxLj0fs8V2QU2lkPHb6Qz7OLVSxhvrT9K5YQ3mD29FodFMjt6Ij0aJh5uMKb0a0SLEm4IiE+dO/85w/Qi8FfnlnumPIpu4yShJvzwL/AgdYzrx6LQPeeHXbHINxbz04wncFDJ6Nw3ijQ2nOHQlh0ejg9AV6Vg3eB36Yj1Z+iz8NH52I49JmydRx6sO/hr/Sl+b0vhp/NAV6YhWweZa4F1GkO02QN8U2/fCV+2Dn4cKXnoJNm92HNiqla3kgbjjLRAI/iFUmyhrcs894pebQCAQCP75TJ7sWpDdf7+tJpm/P3qjiRkbT7Nk/+1UQLWsmE/rxdPTbZPT1HNGeCDVFlkqobwCzWUZGzWO/o36kV8k4/PHW4IEluxP5pmu4TweU5OCYh1Z+mz8NL5o5FpWHEhl+YHrDIkJ4fkViS7GSbFYC9l1Ppsd57JI0xUil0qIH96KpMwCnm6aBtsGoZHryz3T4ULodR2yLIAV2/2xXRDVN5Zli78kbulRB1v5r3dfpntEIINa1yHhcjZL912nRoAf4Z+G0zSgKVqV1snIQ1ekQ6PQ3JExiIfSA63uD36uBdoygmyb3maqUmC1jfV284Lvv7eVMyhNQACsWQPqCgqxCQQCwf8z1SbK3n33nepaSiAQCASCKlPawjzXYMRLrazYwvyLL2D37ttpiwqFzVlx4kSQSDCZLWw5nW4XZF5qBbFtQoltLiVg9yEodlxuZ7EnA67mkVvGNLCkQPOmoZtoW7stnyZ86iA+IoMimdZ+Gv0a9uNYsoEZG89gsVqJj43i/UcbsT35F/qvHOo0Z2L0ZNY/2xeJ1VrpuDcG9GHEgmMkZekxFJlRZW7FbdcUpFJDua/nnlvRJp0FMAA/gPyqG0+/M5s3nxuD3mhizuCWqBRSlh+4ypL9VziZouNGfhFt6voxNCaEJfuvsW58H0a3GOPSLRHAbDVzJOUIU9tNZciaIeWep4Qp7aZyM20/vwRb8Sjzbf1NDwNTwHDL5XJyu6mojp2CESMcB8rlsHp1+fcEBQKB4H+EKLghEAgEgn8teqOJjSdSeXTePtrM3EqvT3bRZuZWHp23j40nUtEbTc6TgoLg229t/12/PuzbB889Z8/2MJotzNt+CbDVAls/vj1hfhpGr0pjyJkXMFlv/+nUNZhAlyRnQVbChewLDFo9iL4N+rJ1+FbOjj/L7pG7Of3MadYOWktGQQY/n9tELR8ZBUUmgrRuaDVWZu59hdEbnsBitdAhpAPNA5sjk8hITEtk5PpYPk54F627hZn7XmHk+linSFPJuPf2vcqiUffipVZgvbaWcZLnkVrKF2Rb9NDz+i1BlgbMh9rF4XSdMp91eeF8+OtZzFZ4aO5e4r477PD65BQYmbnxNE93qc/0B5ry7k8XeanduywauNTl3bJFA5cSFdSZfg37Exc1toLvMoyLGkf/Rv0JC+3JJfd7Hfo2FtgiZCWCbGzUOAb6xKB4+JHbxb9L+Owz6Nixwr0EAoHgf8HfVpY+JSUFnS4PT09PatUK/ru2EQgEAsFdiqsUwxJOpuiYsDzRqTCwnV69YOVK6NMHPD0dunQGE6dSdXZDjHk7LpZyNGzGOylPMTVoMauU02lRpydW5pR7Rh83H75/9Hte2/YaXx/5utxUvriocSwaNZ29F3LZcnkz/u6+XJp4CX2xnkx9Jv4af9RyNfOPzGfuwbm4KVSsPbOW+RU4HIKtvlfb2h2YHnmdemdfQkL5/vwb8uHRNCiyAseAn0Ad0pqNv61l6voLkJrHkoRkIkO8GRoTwpfbLzJl9XGGxoSweFQ0FouVCzfy6TtnFzumdKF7RAB5hSZ6hz/AQxEDyC3KJceQg4/aB6XUg0W7r9Nv3UGaBmuZO2w2nUI6M3vfLKeI35R20xjQqD9KqRKjxYhvt3VkH4zDN2Mz+oAe7Je0RJP9FRHeobax4b1w7zMArl51fMCnnoK4uApfL4FAIPhfUa2irLi4mBUrVrL5198oKCgAoGvXLkx4djwA27fvYN369Tw7fjx164ZX59YCgUAguIsom2JYFm1hPpN2LeGjwlhiwn3p0zTIOZXxscdczs012C6GxbYJJeGys+HGgaLH6XWhLd1atKS72qfCc45rPY4dSTvsd8pKrNzLUiKeHmg+kJtFrcgr0jFwxUAngTIhegIHnzqIWqGm79K+Fe5dQtKRlxjudtWpsHVpVuZBbBqYzMBm4ADcc/+TdHt8LPVrBeClTrIXrl64J4n5w1sxb8dFLFZYmpBMyxAfujauwdm0PCxWuH7TQL0a7gRqbxdf9lR5UFtbC7AJ6oia3tT2yWLPxSzu//QA7zzQmZ0j+nGz8CY5hTfxdfPGW+2NUqrCaDGy+tQPrDv7Iyn5KeiLculSJGFnXgbPt7uXpOeuIJNIUUgUKJ8Zb0tPLU2HDvD55+Luu0Ag+MdSbaKsqKiI115/g/PnL+Dl5UVUy5YcOuxoFdyseTM+nfMZe/bsEaJMIBAIBH+a0imGZWl5/TRz1s+mti4D/4KbzA2vSfeIANf3y1zgV3yG14O/olf0Mp5feYxnutZnSEwIhUYz2Xojvhol7qpoFDIpUpmxXKMKmURGXFQcA5YPqNK+CxPn8/A9A5ixe4ZLY5DEtERGrR9FXFQcs3rMIjm3/GLPJTzrBW+4VTxuoQ5Gp4MlD/ge3G+6s3DNIjp074SHSk1WYRpT+7njofRGLbMZjRhNZhoFeXI6NQ+ABXsu06VRDfuabnIZX+9KYlSHMOcoJaBRyukREUDHBv7k6I1k5dtcInP1Ui6kKwjQ1sZf44GbQk6+sYAtF3+jQ2g7WgZHOkQO3Y7MZ/zG8ey+spv3e85GOf8r+Oorx83q1IEffgClstLXSyAQCP5XVJso+/HHtZw7d54ePboz5qnRKJVKHnjwYYcxfr6+1Kldm2PHjzOsujYWCAQCwV1HSYphaSRWC2MTfuCFnd8hv5UW2O/sbnb/sgrdiGiX4sCJS4vwOzCOUf6F5Fz6lNmPTuDA5WzGfHvIbovvpVbwy8SOfPTbObw1MiZGT2bk+linpZoENEFfrK9yceT2Ie3ZcHZDhU6NAPGH4+kU2omxrcYyc/fMcse96AMzK3Gc//wmTLgB1ivAKrgn/B7mrJlDVNMofjn/C+/vfd+lgYivRwht6/rZRdnJFB25hmIaBXkilUhQKaR89NtZQv00LqOUJamnyxKSaRTkabf8L4m0AUxvdYlHe/anUO5BTmFOhZHD3kt7s/7MegZv2OB4WV6thnXrbI6LAoFA8A+m2ow+du3eQw1/f8aNjUNZwadRwbWCyczMrK5tBQKBQHAXUpJiWEKN/Gy+W/ka03YstguyEibsXUHezbyKFzQZIGE07B+JxFIIgPe519i0bTVTVh93KOAc2yaUPRcyWZqQzJL91+gS0ocxLZ2NKrQqLZn6qv29K4mqzdozq0rjP9j7AWNbjUUqcf1n/F2/ygXZrGx49gZY9wOLoUuvLvg+60t4vXCmbZnGkDVDyjUQeWnrVJ7vGUodn9u28vlFJjxUcka2D2NZQjIWK8zbcRGj2fH7UTr11GKF06l5HEzK4XTqbUH2gPc2Hi96DsWOXry35XlGrR/l8iyj1o9i9t7ZbBq6iflH4tGvWelYEHrhQmjRouIXQiAQCP4BVJsoy8jIoF79eshksgrHKeQK8vMLqmtbgUAgENyFeKlvf/jX5eJBNi0YT4crzhGphNpNeCj2Azy9PZ367ORdgF/bwsVvHJolVjNhNxyNNGRSCUNiQli4JwmAXEMxIxYc46V277LkwWUOLoO6Ih0B7lWL0NxpVO1o2lEKTYU8FPGQgzujFJgbAC/7Vjz/tSx4MQ1YA5pdGrpN6sb2ZtvpVK+Twx248ph7aC4bzv7EOw9E2NtqeKjo3KAGrcN8WZpgS5k8maJDZ3B0wKwo9RTgEZ8tfFTnI2QSC9K8c4zIWoJ/BW8t4g/Hsyt5F+3qtONmcR588gksWABvvgmDBjmMNZkt6I0m0nILOZumIy23EL3RhMlcjn2mQCAQ/D9RbemLSqWSgiqIrfSMdDw83KtrW4FAIBDchWjVcu4NcGPA8s948tA6p36zRMpn7QbxWbvBNK7tg1Zdzp+7pOVwIA5MzpG09YaHmJzsmGzfMNCDQqPZIXUyKUvPwM8PMapDC3aO2MWNghzS87PwVftQ08OvSsWRtSotOYacyh+8FEWmIj647wOu6q7a7lhJZVz/tTvtLFcrnDfpBnx8GVgBNTQ1eO7b53jlj1fu+A7cB/veZ8eIfsil0ChIi6ebgoeiajPkq/3kGort9d3cVTLScgvJNRjxc1dhslicUk9LeNx3EzNrf+7Q1lQFIzzhg5vln2VOwhzWDl5LduFNanvVhpEjncbojSa2nE4nfsclh8hnk2AtcZ3r0iMisGoprgKBQPA3UG2/fcLDw7hw8SK5ubl4eXm5HJOens6lS5eJjLzXZb9AIBAIBFVBeeE8386fiNfZk059qR5+PNd/MgkhzQAY27keyrImH6YCOPQsXFrovLjcnZvNvmTCYj+nLg+VnGy90ak911DMx79dZN3RVJaMbkORQclnu68T5ldQ7p2z0uiKdNT0rFnhmLKo5CoGrBjA8fTjaCSwobaSbm7OZyvBYoWxGfDVQeBHkERI2PXzLgats0WT/ky07mbhTXo2CeL/2DvvuKrq/48/74R7gcuUJTIcKOJGBHGQe2u7nKmV6DdTK7W9y3LUN8tylZo5smXu0XAUCiiKeyQKLkD2Be4evz+uIJd7GZZ9q1/n+Xj0eMT5fM75nHO4Xs7rvN/v17t3K38q9EYW/nSerEIN4b5KPp/YhbRLRTy8LKVKBMWGe/P8oCinxxvru5U3Gzta/L9XXLcgqzwXjVFDgLKR0/E/1D5BQEBA4H/AHUtf7N+vH1qtlgXv/Re12vENWHl5BR9+9DFms5kB/fvfqWUFBAQEBP5NWK2wciXS2M5OBdnuFvEMmvhRlSAbEx/m6LxYfAx2xjgXZKooGHCIPO+7nS5frjfh61Z73fTFAg2DFv5C2qVinh8cxX2dQrknagST62mO3CO0B35KP4cmy7XRMbAjLhIXTt44iZcYdjemTkFmtsK4XFj+LfA1MBDaTmqLVWatEmG3UwNXSZG2mIHRgcSG+2AwW5jeJ5Im3gpWT4xj6b6LDvV45XoTngqZw3Ee9fveqSCbUwQzazml0cegUfmtn0t0JXi4Oqap1tc+oZI1Kdn8dOaGkMooICDwl3DHRFnPnj3o0b07J06cYFLSFF57/Q0Azpw5y9tz3mHSpCROnTrFXYmJxMZ2vlPLCggICAj8WygogJEjYeJE0GjshgxSOS/1m8Kke16kRKEiOljFRyM72kc+rFZMZxZi3RUH6nOOx286HgakgWeUXc1adfJK9fh7uBIdrAJsNWZRQR7EhnsTFeSBRCyiVGvkk72Z9Jj7M+/9cB6zWc68fvNZd+96B9HVIbADK0esZFbCLLKKs5mdMLtBt2Ja3DSWpi/FX2xhXwh0U9Q+V2eBey/B2kXQoiCSli+0hA6OIkytV+OnrMcdpAY+Cm/im/ry1tbTiEQiDmQW8P5DHcjML+PolWIkYvu+YOfzynGVS6ruH8DkRt/wcvCnDscubfEULxY6X/eBk7BmI6R8Cq3ybdsC3QORiRwFX301bNVxZkwiICAg8L/gjsbon356BhFNI/j+++/JyLC9ecvJySEnJwelUsmY0aO477577+SSAgICAgL/FlauhA0bHDZbW7fG/MUapjaLYpzWiEohxd1VRoXOSJnOZjIhN5VA2gSk17c67F9hFZPRZDLtO83DXWareVYppEQHq+yiPAAj40LJzC/j8R5N+e1GKSPjgqgwqinUFOGr9EEptfXxWpNylXK9iXs6NmbnyRyyCiqY0W84vSN6U6wrolBTiK/SF4VUwdL0pTy962kATv3nFJNjJrMk3TFqVElSTBLdm3Rn3q7J/BoCzepov1VmgeFHYO9ycAvrxbi35hIa/huPbBrtIMJO3TiFUqZsUA0c2KJ13govxi4/xOh4m+PivvP5bEjqyvncMhaN7ISLTMz6tCusScmmVGvEbLGyLvUy4xPCmfXNcZ70X88zgWsdjv3+jXE8ft8bdDiyx+Fcul6G1Rtt/9+0BA58Bs9OaY6f0g+Z1FGUOWufUBuVxiRCCqOAgMD/mjv6rSMSibj3nrsZMXwYFy9e5MaNfCxWC36+vjRv3hyZzPHLUkBAQEBAoEHMmAHr1kFGxq1tSUmI3n8fhVIJRhMX8sv48OcrbD+RU2Wv3ruVP8si5jgVZId18HCuhcwLnzBFbWVe//m4y92QS8TMGhDJo5+nY755oErnxbk7zvLWPS0oE+9j2IbRDr2znoqbxU8zhyIRiakwlaJ0K6JP6yZsOreJMRvH0Ma/DSoXFWq9mpM3TmKpZuHfc1VPUh5NITE8kbnJcx17hMVNp2NgR0atbMePQXqC6/grXmCGgdsh/TsXfPtOwa1NH74+VszP/YYyOWYyy48stxNhZquZpelLmdZlGhM3T6z31zGz62yuFBpZOjYWvcnMm1vPUKo1kleq48XvT3Amp4zoYBXjE8LZPLUbj6xII6tQw5qUbDY/kcCKjpvpbXYUZHNyJrAs/z4GF5uY2XU2YzaOqhprfQO2rgNX86353jqYIolDLnZxep412yfUh1pnJNDT9bb2ERAQEPij3LH0xepIJBJatGhBt24J9OjenaioKEGQCQgICAj8MWQyW7RMKgUvL/jmG1iyBJRKNAYTb287w5hP09h6/JYgA3gkoQlb3XpRYrY/3IJiSLgCmUbbz4vTF7P1/BYKNUWcKThF8yATKS9044leEXgqZEQGuGMwmnlmQBizfpzFuO9HO+2d9cim0byy9znUhkLaLW5Lj1Wx6C0a5ibPRYR9Ol/Nny8UXeC+r+5jeORwfpnwK1eeusqJKSe48OQF9jyyh+TLyUz5vCM7A7R1CrKrRuixDDKTIwketxD3tn0RiUQMbReEziDlxe5zWHPPGjad28S0LtOq9lt8aDE9w3oyKWZSnb+KyTFTGBo5lEmrj5JysZCdJ3Mp1dpuZLHGgLuL7eROXVcz65vjLNt/kc8ndsFTIaNUayD528fpbXa03X/92uMkyybw0ciOhPq4M6LVcJJu1uOFFcPuL8BHZ7/P/kGtafHWEuROomRAramotaFyFZ5XBAQE/vf8T+LzR44cIfvyZfz8/OgaH49UKqQFCAgICAjUgkaDycUVg9mCWmuiVGvAUyFHpZAib9sO6RdfQM+eEBwM1G3kIJeK6RTmTo+VK2heAd8EwQ0TPJIHOzUO05mXPI+EJgl0WNoBi9VCh8AOzOw6m23TB7LmYC4KuZjUnIN8dmR5nZew+PBiujTuwuTOk9n22zZ0Jh1DIoeweeRmNEYNBZoCm429VMGyI8tYfGgxxTqbJf6+7H3c0NxALvLms31F9IsOoFByjm/PfEvWmU/5oTG41fFK9bwG+r0D/QY8imncePZmlhIdrGJCt3Biw32YvOYIS8Z0wkvUgx4t3XB3EZN2LY0l6Uso1hUzcO1Ado7eSdeQrixMXegQrZvV9VmGRg5lyhcnuVKsZen+iywb15l3dpzFYgVvpZxyvX1vsrWpl+kY6sXoLk3wOf88ozwc2xjktXyPpCH/YZZCilwivmnOImVB/wX0c+9A54em07hMb7fPtR4d6PTNXtxd3Gu9H7WlojojOlhVe/sEAQEBgT+RO/bNs337Dr7ftJmnZkwjKuqW3e28eQs4mJJS9XPz5s14+603kctv782VgICAgMD/c246K1qffZZfF61hfp7SeT+pe++3q/mpy8ihb5Q/JboSjuUd4xgw9QZ8Ww65ZqfTq6zV2/i34XjecbJLsjldcJJ+zfowppsH+dpMohq1IHNapoOYqkll76yskiwaKRvR3Ls5I74c4SBypnWZxqHHDzFw7UAuFF0A4Jr6Ggevfssj3SYwenka4c03YMhexbYQkIucLmc7/2J48H0VQfdFMf/1/yLBlcJyPTKpmHWpl3lz6xmm9mrG4exipn+ZgVgEvVo14o0R75DQpAfvp8wnIzeD2OWxTO48ma0jt2K0GCk3lOPl4oWXqxdWq4z1aVc5efN3c+q6Gp3BTMtAD8QiES4yMedyHfu+rUzOYvmoKAqv2btmWhEh6rKMgOaPOb0md52Ze55eiviGvSAzd42j0dZdyJXO2/BUIpeISUpsyrT1GXXOg1raJwgICAj8D7hj3zwpqano9XpatmxZte3IkSMcOHgQHx8f7r33Hlq0aM6FC5ns3v3DnVpWQEBAQOD/A9nZMGAAPPooooIC/KdN4dyVIrsplf2k5mw/i8ZwKxJTaeQQIsvjgybzcRdrqlwRO4V6UVStKfPHpbULskoKNYWoXFQ092nOoccP0dy7OQPWDKDJf5vQbkk7Wi5qyd0b7qa5981xn+ZOj3M09yhao5b3+r/HCz+9wMTNE52mO07cPJH5B+azc/ROvF29AfBV+vLF8dV8ffZzMpmGz9VVbAisW5DtvwJ3LYL4V4az9bWtXCq+iNGi5cjlYnrO28MnezORiESM7BLG0n02EWuxwk9n8hm8MI2cnA5seehnzv4ni12j9/Cfzv9BKVNxKdeVzYfFzNmaR4fX9/HwsjS8lXI2T+1GuK8SuJWyOKGbzfCjevpo9d+f1uLKG5r/clobAdgEmaXLZ1CLIEOvh3vuQXz0qP32Nm2QbNuBXFW3IAOQSsT0jQpgTHxonfOctk8QEBAQ+B9xxyJl165dJzQ0FLH41pfZL78kIxKJeHb2LCIjW2AwGHjs8ST27tvP0KFD7tTSAgICAgL/VCwWWLoUZs+G8ltNp1rfuMSUlK/5qNtIh13WpGQTF+HDoDaBSCViSjV67vf+kVeDl+Ih0dIs0AefPmvRGcxYrVZcXEtv65R8lbam0bvG7GJu8lyWpTvWPlWKqaSYJHaO3kns8linEbMyQxmZRZl1uikCLE1fSnxIPJM7T2bnhZ0opApO5J3g2ZyneasRvOBT9zlvPQ0PHgLtg7Amaw2DMwdzoegC18tymNdvHh/+pCC7SMu0vi0o0Roc3AhtNv6XWLLvEjFh3iwa2YkNh6/wwY/HHARWZZ3Y6LhQPp/YheGLkvFWyunZohGx4T68ufVMredZrDGA3IcxF99iW5s38Il9EZfmY51PNhjg/vvh55/tt4eHw65d4O1d902phlIu5YXBUcRF+LJkX6ZDBHZyYjP6RPkLrosCAgJ/GXfs20etVhMd3dpu28lTp/Dz9SUysgUAcrmcVq1acvbs2Tu1rICAgIDAP5XMTHjsMdi71+lwsDrfltIocgwPLdmXaYtq6K8TdvJxFjTZWTXWVr+Rt798j+VZHZFLxaS/3OO2bN4VUgWJYYnsy9rnVJBVp7qYeufXdxzGA9wCmJc8r9514Va6YzPvZty7/l4kWFkWBBNUde+39jCMLwNTwq1t8w/M5/uHvydiYQSJYT35furd3L84hSFtg7hS5KSY7iYWK3SJ8GX/b/m8/8P5ute9WSf2VL8W+LjLGd4hmEdWpFUZfjjDWyknQOXKuAfvwrPlvbi41uJyaDTCww/D1hqOmf7+sHt3VT3h7aCUSxnUJpA+Uf6otSbUOiMqV5mtVrGqhk1AQEDgr+GOfQMplUrU6ltvnvLy8sjPz6dNmzZ281xdXdHp9DV3FxAQEBD4t2A2wwcfQNu2TgVZjrsvE+5/lecHTXMqyABOXS/FeG45bIvGNX+nw/hQ6WrASrCnKxaLjJldG96UefmR5UyKmcTC1IUN2ufD1A+Z3HkyYpH9n9SOgR3xdPXk2zPf1nsMiUiC2WomtyyX+Xvnczb/CN8H1y/IFh2GsXIwhdhvr14bN+/APCwYWD0xDoPJjKeidnfBStv/lclZ9Z4z2OrE7u7QmGNXShi+KJmswluCT4oJueiWQIsOVhHg6cq8+9sxqE0gytoEmckEY8bAxo322z08YMcOaNGiQefmDKlEjFIuJdDTlcgADwI9XVHKpYIgExAQ+Mu5Y99CQUGBnDp1mvz8fAB27f4BkUhEx04d7eYVFhTi7eV1p5YVEBAQEPgncfYs9OgBTz0FWq3D8Lr2A+j/2CfsaRZb6yFCZHl8EfEynif+A0ZHR72vi/oy+uLbeCrkrJ4Yx4c/ZtKt8QAmdZpc56klxSTRI7QH+7L2oTFqOJZ3rEGXVF0AVWdG/AwKKgrs+pDVxNvVmxd6vMDF6RdJ6phEz097UlBxjp8awxC3utd95QQ8qQKrwvl4ZW3c0dyjFGlLOJurRiQS4SqXEB3sXO1FBrijM5idNluurNOLDfcmKsgDiVhkqxMzmvl4zwW7CJlMZGRR2Fw+Dn0X2U1hNjmxGa5Scf0i6IMP4Kuv7Le5ucHOndCpU533REBAQOCfyh1LXxw4cAAffPAh06c/RWBgIJeyslCpVMR2jqmao9fruZCZSdsa0TMBAQEBgf/nmEzw3nvw6qs284aahIVRtPATXjjoxCHiJiIsjPbdwXOBq3CXOAq6IrMPz175Dz+o4wF4Ij6MtEuFHLhYwI1yHS8PmkNCkx58kDrf0eY9YRZDWgyh3FDO2PZjKdQU3tblVQqgSpJikkgMS0Rvrj0zpLlPc3aO3smeS3vo9EEnCikkTAa7GkPLOgyKzRaYfAk+rae/sa/SF7XeJq4KKorYdUpEXFNfDCYzj3aP4OmvHEWnu4uUIo19s2VPhYwx8WGMigtFZzBTpDHgo5TjIhOzPu0KZVoj/aICOJRlq6mTi4x8EjaHvqpDACxsMp+UoI8bbqIxdaqtjmzHDtvPCgVs3w4JCXXvJyAgIPAP5o6JsrsSE7l0KYvt23dw8dIlfH19mT79SRSKW6/wkpMPoNfradeu7Z1aVkBAQEDg786JEzBxIhw+7Hz8iSfg3XdxlbsSnX3QaT+pMPl15oZ8SLz7SaeH0IaModeuIZSaPQDwcZPzWI8I9EYLHUO9KdIYMBplDGtxD0Mjh6I1qcmvyMdN7oaLxIWl6UuZun0qYV5hvJr4KqGedTv11aRSAHUI7MDT8U+T0CSBAWsGsHvsbqf1bN6u3uwas4uXf36ZdQfXgQe0k8POxhBUx19mrQkeyoUttQffgFu1cSdv2O6Xj8KbE9eyySvVsXRfJs8NjmJmv0j2/ZZPud7E+bxyzBYr5XoTPspbijDcV8nnE7uQdqmISasPOxhkjE8IJ8hLwYiOjfFxl7M2+RzT5a9ylyq9at5grwP0D1iIVL6yYTfT1dWWuvjgg7b6sS1bbH3pBAQEBP4fc0dthiaMf4Qxo0eh0Wjw9HS0qW3bti3/fX8BgYGBd3JZAQEBAYG/K3o99O8PubmOY82bw2efVT1wy80Wh35SMpGRpEbf8qT/BlzETgwklCEQu5TLku6Ubv8FsAmJtY/Fs/98Psv2X7QTEnERPnwytg2rjq5i18VdqPVqTt44WZViqM5T88a+N/hx3I882PpBvj3zLWZr3R76HQM7EuQexIb7N+Aud0cukfPjxR9RyBQsTV/KtC7TmLh5ot0+U2KnsODnBaw7bBNkdyng+yDwlNS+jlHkwS+Rz7Dl0mt1ng/YauOWpi/FYrXQMbAjSqmKc7lllGgNdAj1xmAyc19MCHe18sfNRYJMYot6rU+9jJuLrdny1WItqyfGsWRfJuvSHBtzV7owHrtSyqTEptwoLmJ9yzm4FqbbzbNK3ZA2G1/vOdvh4gJff20T9DEx9c8XEBAQ+Idzx71fZTKZU0EG0KiRH40a+d3pJQUEBAQE/gaYzBYMZgtqrYlSrQFPhRyVQorL3LlIHnnk1kSRyFZT9uaboFRWba7eT2pNymWkmNjc/CmiFFnOF2z2OHScD3JPPEt1gC3VbvXEOD7ec8GpkOgc7smWc5t5ee/Ldtu9Xb2ZEjuFpJgkNEYNmcWZzO03l3n95tXbJHp2t9mIRTJ+K7jIa/tf5lLxJSZ3nsymhzdhMBvwU/qRdi2tyhZfjJjT507zfd734AqjPWBFQN09yEwuwTxW3phXIscwqeB6na6QlbVxT+96GoDpcTNZn5qDxQpBngoKyw08+rlj1GtSj6bsm30XZouVx3s05bcb5aReKnR6H6uzJjWbmBApQ4sex9V0yH5Q6oGo1w5o1K3OYzhFLhcEmYCAwL+GP6UhR2FhIafPnKGoyPYHzMfHm6hWrfDzEwSZgICAwP9HNAYTP57JY+m+iw4P+0k9ezNk0GAkO7ZDq1awciXExzs9Ts1+UnvKOjuIMosyDHH8pxDYt2qbSmGL7tzV0r9WISERixgZF8TQL0fZba+s7dqfvZ8RX45wqDeb1mUahx4/xMC1A7lQdMFu36SYKXQPGcD+cyV0bdqLrQ/9hIly8sqLECEm0C2YPWeKeKn7O8SH9GDuvre4cOkC3/M9iOAlH3jTt+57e0IPip5rydg6nYFrB7JrzC56hPbgvYPvOZzrM12fqUqdLNYVkxQzhcQmAxmx6TDRwSpkEjGT16Q77T02fUMGo+NCmdm/JX2j/Onewo9xn6XVfXKAh7iClqcfIFRmn1pqlXki6rUL/OKc72g2w5w5tvRVn3oasQkICAj8P+eOirLCoiKWLVtOWtohp+OxsZ2Z9PhjgjgTEBAQ+H+ExmBizvYzrD2YhbWGLfyp62qmfXmMM/c9zdOdOiF76UVbzVAd2PWTKo/GsPcwcl0WVsRYI59E3P5NkHnY7SOXiHl+cCuaN/Jg4irnf4MiA9ypMKrtXBUra7tut0l0h8AOzIibRWKTgYxZnkFWoYbRcaH8567m+CrdOXlZQoDCk1KtiCX7L3F1k5YWnpmczz2P2duMFFjqDxOdJ5ZUsV8LI67DVosMlYsKjVGDCBEtfVuydeRWygxlFGoK8VX64i53J6csBxEiWvu15qUer5PYZCDjVxyjVGvk9eHRrEy+5CDIqrM29TKdw3yQiKFzuI9TF8bqqCTlrI54hdYy+55mVpk3oj4/gE8tkS6TCcaPh7VrYfNm+OEHEJyZBQQE/sXcMVFWWFTEs88+R0FBIS4uLnTo0B5/f38A8m/kczQjg7S0Q2RmXmTevHfxFd6KCQgICPzjMZkt7D10gZZvPMdCXTnThjvvB7b4Nx2tR05hkEzu/A9PjSbR0pvNfJU+PhC/GI6/jKjLUkQ+9pbo1VMmG3spsFittQoJdxcphZoiu21TYqc0uEl0YthdpD9+HIPZjKeLNz+fKWLW12e5UmxzglybepnYcB8aeyuo0JvwVsr59sgVxsSFMPGTBzgeng4KUInhmyDop6xzSb4rh9G5oLPajESaeTdjw/0b+OzIZ7y+73WsWGnj3waVi8quNm5K5yl8ce9aVv16lRGbDlOqNTImLozYcB9e3Xyq7kWBT3+9yKoJXcgurKhznpdEzZqmL9NGkWm33Sr3swky7w7OdzQaYexY2LDB9vPhwzBwoM3UQ1VPYzYBAQGB/6fcMVG25ou1FBQUktizJ489NhEPD/u3mOXl5Xz62Qr27t3HmjVrmT7tyTu1tICAgECDqa3uSX5TBAjcHubvN9Hl0ST8Sm09Kre27YV0+DB83eQUVhj48cwNDCabicaalGz6Rvnb3X8vSTk+F99CIvdE3PEd54sED4Sg/lAjClczZTIuwpuXh0YTG+5t5yhYfX6wRwDdQ7uj1qs5m3+WpJgkhq8f3qBrXZq+hEHNh6E1GinVFxMeWMxb9zVGKY3iy7Qc1qRcZfkvF1k5IZbXNp9EpZBRXnKBl3Y8hK6pLZ0/RArbg6GtS91rfVwC0/LBgs1IJNgjmFcSXyGnLIcHoh9gXPtxtda6LT68mLjG3dEZOxDireCVoa3pHx3AsI9+teslVhunrqvRGc34utfuy+8rKWFN05ccUkutrgGIev8EXtHOdzQYYORI+O47++1Hj0JGhuCyKCAg8K/ljomy9CNHCPD3Z9q0qUgkjvZR7u7uPDn1Cc6cPkN6erqTIwgICAj8udRZ95TYlL5RASjlf0qp7f8/cnNh2jRcvv6a6vpiSfKnXH/vYQqkBnwVAbx7f0uOZpezZG8W797Xlt2n81i2/yJnrhfzsM9uZgZ+gUyqxiKSoQ0dg8K3lod5J4JszvYzrEm5jKdCxhO9mjM2PgyL1crsga3s+mhtzrjG8A6BjIwLwiqqYF7feXgrvFHJVejMugY1iW7u05zPhn/G9gubmX9gnkMt1/QuM9k0dRDjVxzDaLayZExnFm54h/cz38bqZxOGHVxgWzAE1/MReyYf3i+p9nPXZ1iVsYrpO6fbrVlXrdvC1AXsHb+fUXHh7Dufz283yskq1NR7nZUUlOsJ9lIQHaxyaFHQSFrEuqYv0sL1it12i2sw4r4/g6ql84PqdDab+y1b7Le7usL33wuCTEBA4F/NHXv60Ol0tG/fzqkgq0QikRAZGUlqWv2FwwICAgJ3kuoP8TU5dV3NtPUZjIkP44XBrQRhVhdWq82o45lnoKTEYVh/4zqPvBjFz01tP3cI7MDMrrNZPn44S/dc5aM9F4hRnmZzi6V2aW9iq5ErPyQRcs8elC6yOk/BZLbw45k81qRctuuj9ejnhxzE9tRezUlKbMzGM5sZtmG0nZia0GECT8Q+Ue8lN6TubMLmMSTFTGHVxLfIyctn8OsDyG9+Fm4mjQxSwldB4F5HMFZrgbF58G35rW2TYyYTHxJP7PJYhzWd1bpVcjT3KDfKi5i2JgsrsGikfdpnfQR6uiKTiJjUoynTN2RUG7Hycdi7DoJMKwtG1nsPYlWk8wOWlcGIEbBnj/12hcIm0vr0ua3zExAQEPj/xh3L1QkJCalyW6yLouJiQho3vlPLCggICNRL9Yf4uliTks1PZ25gMtfTmfffyvHjtmjGo486FWS7m0L0FGuVIAObeBizcRSzf5jFg9E6Pg5/j2+bz3aoQwIINR3i4LG0eu+/wWxhyd6LVfb3S/ddZNY3xx0iOleLtbRu7MLsH2cxfvNohwbO6TnpuMnd6r3shtedLWbhxnfoObcV+S3Pws13lEmesDm4bkFWbJHQ+9otQdYhsANr7l3DzISZDFw7sFY7/qXpS/nl8i9M7jzZYUxrUtOskTvn88pxlUuIDm5YvVZ0sAqLxcqUNenERvgwqkv1RtoiXrk2hWLTrRKFYlFj6LsPqVctgqygAHr3dhRkbm6wY4cgyAQEBAS4g6Js+LChnD59miNHj9Y65+jRDE6fPs3QYUPu1LICAgIC9VL5EN8QluzLxCCIMntKS2HGDOjUCX791WFY7+nOuLthwFi45MTDyVMMYZcWE/xLLENUexwnANtKunF39qdcNQWiM2u5UnqVY7knuFJ6lXJDOQbTrVootdbE6Rw1Y+LDquzvJWIRUUEexIZ7ExXkgUQsYkx8CHuyd7D0Zn+wmpy6cQqlTEmHwA61XrpEJCEpJomFqQvrukNgAtLgg5ML0IbZjD8kwAd+sMQfpHX0ICuQeKPt/TMrHz3DkUlHyJyWyb7x+yjUFBK7PNYhNbEmH6Z+yOTOkxHXSPF0k3ny9j1tua9TY9anXmZ8Qnjd13CTST2bsjb1MqmXihm1PIWkxKYseKBdlag7q4tgzKU3Kbe4UyYLRzZgPwrv5s4PduUK9OhhM/OojkoFu3ZBYmKDzklAQEDg/zu/O0cnPz/f7ufo6NYMGjiAt99+h+7du9GjezcaNWpUNffX5AP8+msygwYNpG2bNn/srAUEBARug8qH+IZw6roatdYkpDCCLVVx7VqYORPy8pxOMT78IAM7nmSv9rTDmAyY7Amv+IKfBLA6mkyc14Xy2vVJXHdJYNXk9uy9vIMeKxc41GzN7DqbEa2G4y53o1RrQCIWMSoulKe+zOCJXs0ZFReKzmCmSGOoqidzlZvo+8UohzXhVrNoN5kbsxJmMfq70U7nRftHozFq6q47ywfOAl2p+quqEsOGQBhYTyDuVy3cfb2Y4nO9aOPfhs5BnXm377tcL7tuV0NWF0dzj6Ixamjj34bjeccBmzmITOTOnnM3eGFwFBKxCBGQcaWEtam1R4zHxIfRp1UAr2yyuTRmFWoYviiZ0XGhLBvXGZ3BTLHGgLcykQpNZ749beY+aTDuzg52/jz06weXa6zXqJFNkHXs2KDrExAQEPg38LufOh6fNBmRyPHVn9VqZf/+X9i//xenYzt27GTHjp1s/O6b37u0gICAwG1RqjXc1ny1zkigZ929tP7fk54OTz0Fvzh+lwMQEQGLFpHbrR17P2jiMHyfO7zjCy1qMfBTm934b+4ovigcgptCweZH2zMn+UWWHXGMalWmQE7pPIV5/ebjrZQTGeCO2Wxh/gPtSLtUxKTVh+3SF4e2C2LmoEZOxVT1ZtH3fnUvK4avYFLMJKfpiSoXFQWaAucXYQWOAC5Aj1ubm8pgSxC0rsdh8asyGJcHeiuAheN5xzmed5xXEl+hWFt/OUB1CjWFqFxupSdOj5vJ+tQc9p0vpHO4D5O/OMwTvVrw/KAoOoV6sSI5y6H+bnJiM/pE+SMCQrwVVU6NpVojn+zNZMm+TFoGeuDuIqVcb+JcbhlRQSrG93XyKHHkiM3mvsYLXEJDbT3JImtJdRQQEBD4l/K7RVl069Z2PWUEBAQE/q54Kmq39naGylVKQZmewgr9v9cyPyXFuSBzcYHnn4fZs0GhoCj3hN2wCPipMfSqpQeXRSRlp+5uXrpwD0VmW+fkyjRDZ4KsOosPL6ZnaE9GtLyXp/tG4qWU8+6Os6xLc4z85Kl1XFc7iilnph0D1w5k5+iddA3pysLUhXZRumD3YEJUIY4nUw78DMQD/rc291TAd0HgW7vnFQBzi+D5Qpuuq0mRtgg/pV/dB6iBr9IXtd4mspJippDYZGBVjzKdwYzJYmXymnQe6x7BlLua0bWZH1qDGbXOSJCnKyqFrOozbjJbSEpsyicbN9Ld/SifFtwLgMUKZ3LK7NadnNgMec1/F6mptjqxihp9zlq1svUia+Io4gUEBAT+7fxuUfb222/eyfMQEBAQ+NNQKaROrb2dYaubEdFlzo9Utrj6V1rmT5qE9cMPEZ0/X7VJ138g1g8+QBbZokqg+ii87XazAqcNzkXZt+XQsf8PfLRJTJHZ9nAvEYsYGRfE0C+dpxnWZN6BeSSGDURjNGO2WDmQ6TyKVa434at0LHBzZtpxoegCsctjmdx5Mpse3oTGqKFQU0hjVWPcZd64SiV0COzAibwTRPtHYzpj4uwvZ7EMtFC9H8BEFSz2B3kd7ytNVnjiBiyr46OokCmqat1qmpM4o2NgRxRSBVKxlFUj1pLYZCDjVxyrinQVawy4u9g+t5/+eolWQR5cyq/g53M3eGlIa/zcXexeOEglYvoFXqNv5EsoKUUmMrM4/wGHdcfEh9Enyt/xZUV0NLRsaYuWVdK5s83Uw+/2xKaAgIDAv4V/0WtfAQGBfytyiZikxKb1TwQmdAtn9cEsqvUcrrLMn7P9LBqD6U86y78XGquIQ1OeBeCKZwCP3fsyrTo8wf0/3GD7yZyq++Ct8HYwyni7yGbvXkmKFrpdgbetHZF5tudc7q1oS2SAOxVGdYN6hYGtfkqtL2XJvkwW7D7H5xO74KlwtNA/n1eOm0xld251mXYU64p559d3iFgYwUPfPMRzPz3Hsz88i85o4dQ1DSuGr+DkYydpmdqS02dPYxlxS5CJgfl+8FlA3YKsyAz9r9UtyCoF1uLDi5kRN6NB92RmwixcJG58/8CP5OV2YMSiw3Y9ybyVcsr1tz63K5OzGBUfRucwHzqGejmKqoI0FPsHoKQUgGeDPudRv41Vw9HBKj4a2ZEXBrdCLhGjMZjILdVxLldNbqkOjdwV0+YtEBZm26FXL/jpJ0GQCQgICNTBHX/lm5eXh1pt+4ujUqkICAi400sICAgI3BZSiZi+UQGMiQ+t0xZ/dFwoseE+vLn1jNPxNSnZxEX4MKhN4D8/lVGvh88+s9ncL7FPG6zq6Zbjzz1DnmZ7q+7opbYU0EqBOqOLhUkDeyKXK5nZdTZjNt6KdOWY4eNSuMcNniuEb27avH8RP4v1qTl2gtfdRUqhpui2Tr1IW4y7i5S1qZfpGOrF6LhQPtlrb7FvtlhZn5rD9C4zmbB5DNAw0w6L1VJllgEwt8/7tG7syYcbt/PGC29guMsACbfme4hhXSAMrcfQo1QeSPz5XH5z9DqxY3bCbLBKmdhhMo3cvEi5msKSWtwjASbHTCGxySCeXHua9Oxiu3sLNgHlIhPbCeFT19VYrVZeGNIKhazGY0D+AdgzEEz2aYrPtjnLQ+0TUCkVVem8BrOF7Sdzam3G3m/TFhRLPoEPPrClvQoICAgI1ModEWXnz//Gps2bycg4hkajsRtTKpV07NiB4cOGERnZ4k4sJyAgIHDbKOVSXhgcRVyEL0v2ZTo8RE7sFkHncG8eWZFWlfbljCX7Mp2nbP1TMBhg1Sp46y2bXTnAY4/Z0suo0dNNJGJjm952u7dwyeY//l8zXLuf8weepUWvtxjRajhJMZPtrOdfL4QXC8FwUyRMjpnCiFbD+NlaapdKWq434e/ue1uX4KPwpvxm/dTK5CyWjevMkn2ZDoJkTcpVNk0dxKROk1l2ZEndph1O8Hb1RuWioO/jfUj7JQ3uo6oZNEALGWwKhqh6ShaPmzrj1XsDvVzm81sddXNTOk9haOQw9EYppVojFXop8/svIDE8kbnJcx0cKWfEzSKxyUBGLz9qFxmrzoRu4axLvexwbzR6MyHeNR4B8vbBviFgqlELFtALeeIWIqW3lGeDm7Ev/Ojfk/IrICAg8Af4w9+UK1d9zubNW7Babd/4UqkUd3fbF3d5eQUVFRX8+msyyckHGD58GBPGP/JHlxQQEBD4XSjlUga1CaRPlD9qrQm1zojKVYa7q5RP9lzgja2n6xRk8A+2zC8vt0XG3n/f0aL81Vdh2zag9p5u0YoLTPX/ikGeB6q2heQuwqB7Bnc3Xxb0X0BiWCLzDtjEQ/lNEdAhsAOzE55lUPMhlFSI6NHCj8SWjTCYLJRoDEhEYgI8RLdVP6WUqqoiP6euq9EZzLQM9HAwoSjVGhm/4hirJr7NXeGJfH92420ZaIxsMpKuPbpy3vU8jMMu4X+w0hYh86zH0CM38AHmnJ9K7hfZrJr4NglNevBB6nwHgfVswrP0bzaEp748zQ9nblSNxUX48MLgfuwfP5QSXQl55UWoXDwJ9PDlwG+ljFh0uNbPbF2RX1XNlM/rO+GXe8Css98e2B96bgTprSLBSuG+b2caH+5fzXMDn0QjVzis8f8qsiwgICDwJ/OHnio+W7GSLVu2IpPJGDx4EIk9exIWFopEYvsrZTabuXz5Mvv2/cL2HTvYvHkLZrOZxx6deEdOXkBAQOB2kd50mFPKpVW29+dy1Q7pb3Xxj7LMv3EDFi2y/Vdci8369u1w8iS0aePQ062T8gxT/TfQW3XYYTcPcRnlp9+H2Ldxl7txd6t7uStsIOWGUnTmMlRyT+QSD744cJ1h2w8xvEMgY7oGozeUoTaU4OPqjVzsgRhXnomfxdjvnfcKq06l1Xv1yE91I4uaZBVqePabcywddzeDWwxFJLI2SACKTolYvWg15b3LoXW17cAL3vCGL4jrqB8zW2F6Pjwz6D1KT1wiq1DDiEWHGR3XgS0P/UyFUU2RthhfpXeVwOo1/6CDwEq9VMSIj1MYExfKrIGteHn3dfLUJeiNF1k5oQsvD41ipRN7+8e6N6VTmJfTyG90sAqVotr9urIRkh8CSw1xFzwEenwDEvvPusFs4ddPv2PT6pfw0aqRm41Muft5rCJH4fWPjywLCAgI/I/43aLs3LlzbNmyFT8/X1579RVCQhwtgyUSCREREURERNC3Xx9effV1tm3bTo8e3Wkp9CgREBD4m3D7lvmOxhJ/O377Df77X1i5EnS62uf17AlvvAFt2gCVPd2sdHM/xhP+X5HgfrzWXSvMrujMkqrGwaUaC4MXprF9Wg/2nDKxMeMS53LLCPVR8sVjHdh7eQeD1492iBK92OMl7o4azuQrk+usn6pu9V6dmkYWNXmgcwjfH8mhT1QAJ64VMTthNqO+q8Xt0QDsBOtVK+UPl0O1wJq7CFYF2nqw1UWJGR7MhQJVR+YovfgtzxbBs/X7usSSfZeq+n0NaeeFm7yYWd/Ufp8B1qRepmOoN62DVOw9Z+v95bypsxw3FykuMjG9F+ylWOMYRbOzsb+0FlIeAavZflLIPdDtS5A4/tswLlnG2588hcxi22fg+YM8/cta3us51mHuPzayLCAgIPA/5ne/utq5czcikYhnnnnaqSCrSUjjxsyc+TRWq5WdO3f93mUFBAQE7jiVlvkNwSHK8HfCbIbNm2HAAFtz3sWLaxdkCQnw44+wdy8kJt7c30BA4TdsbzGNtU1fqlWQlZrdWKcZz1yXHUjbvXRru9bAqLgwfr1QwJwdZzmTU4aHq4zPH23POwdeZMLmMQ4RqozcDB74+n7mJ89nXr/5fD5irYObY4fADqwasZbnur5lZ/UOzo0sqvN49wjuaulHvzbeICkizF/DkMghTIqZ5Dg5D1gOGIHHsBNkzWSQ0qR+QXbF4kr8FfhBA9PipqEzGmgR4GE3p7Lf15HLJfRrHcDK5Ky6D3qTFcmXGB0fVhWhq2zq3GPuz0xdf4S5O88ydf0REt79iaJyg9No7pi4UHq18sdotsCF5XBwrKMgCx8N3b9yFGQmE8yYgef0J6oEWSX9fkvB1ej8s6bW1eNuIiAgICDw+yNlp06fJiwsjKhWrRq8T1SrVoSHh3Py5Knfu6yAgIDAHafSMn/a+ox65zptlvt34PRpGDwYsrPrnjdsmK3xc/fut7YZiuHCMjj3IV7a63g5lgcBYJH7Uhw6FXHkE/TCnY5aIxqDGanEhFQMngoZY+PDmLjqUNU+DW0M/cb+N4jya0uo4i5+GDOUCkMp18sK8VF4o5SqWJ+aU9UMuTpJPZtSVKEnKkjlkML3ZO/mdI/04PszG3kvxVbH1T20O/8d8F9mJ8y+1Sw6JwPSgd1AHyDO/twGKGF9IHjXUz9W7teTkRcLOWc8RVJMEj1Ce5BbnldramVkgDs6g9kuXbQuaqufc9bUWa0z2q0bHaxiQrdwYsN9WLYvk8mBW+DkLMdFmj0OsYtBXONiS0rg4Ydhl+NL1R+bxTJj2Cx0Mucpvf+IyLKAgIDAX8zvFmXFxcW/KwUxJKQxqalpv3dZAQEBgTtOQy3za22W+3egaVOoqHA6ZJXJ0D7wMKJZzyBr29bx/A9Nhex1tR76mgnmF0OqazBTI1syXKFg5upjJGcWAjYziheHRNHC351ijbFKZNx+Y+g5bH7oJwa8n8KWJ3tw4pKBjRnZnMstc3APBJuRRYdQb369kM+GpK7kleqqUvhc5WJkUhMzd8+yc4VU69W4y92rmkWvHriamVNnsnv/bhgLNLl1/IbWjwF8pg/ggZ7f06hoIivjZ9IjtAcD1w5kzyN76dlCxqEsx3o+dxcpRRpDg+5NJXXVz1UnUOXK+w92oLjCgJ+HzY5+TUo2b249wyj3dSgLPnfcqeUM6PQ+iGpc7PnzMHw4nDvnsMuSuPuY13Mclpoi7iZ/68iygICAwN+I3/1NKZfL0ev1t72fQW9AJhPemgkICPy9qM8yf3JiM/pE+TvUxpjMFgxmC2qtiVKtAU+FHJWrFJEI1DoTJZqb2272dvrDgk6vBxcXp+v6TpiIbP68qqllSg/WtenHypjh5Kr8iN6vJkmUQ9+oAPvraPmkU1F23gDvl8AqNeitACdI2TiKyTFTWDx2HvcsOkz3SF+e7BXJ0SslrEvN5r6YW6rm9zSGrjCq8Ve5Mmp5Cp9P7IKPu9ypkcWknk2JDfdh1PIUpvZuzuI9F/j53A3cXaSU6030aeWHn/9RO0EGcOrGKZQyJWFeYbyz/h3e+fYd8AImAdVSE33E8EUgDK6n/1i5BcbnwdA+7/LzpX18fs/nLExZSOzyWMK9wnGVeDC8gxdBXq4O1xGgciXoNg1j6qufq7w/FquVyWsOo5RLmdSzKRmXS6rMbC7KGmO2SpCIqqUgRr8E7d5wFGSbNsG4caC2j+aZZXJm93uCb9v2qfNc/raRZQEBAYG/Gb9blAUGBnD23FnMZnOV22J9mM1mzp47S2Bg4O9dVkBA4H+IU8Fxp8TF35DaLPNru2aNwcSPZ/KcNs+d0C2C2HBvkr5IJ6tQU9VQ10EQNYT8fPjuO/jiC3BzQ7Nlm9N1h3jFsUgs5npEKz6I7MfmqJ7oZbYoiYe4gswcHdPWq239owa3unUefvHglwAFNrv7/Vp4rxi2VICTABVL0hfTM6wnW6aNoEhXyCX1WUIbeTGtcRhSsRxPhYxSrfEPNYY+k1NWq5GFi0yMi0TMf9YdoVtzvyrb98rURolYxPJH2jqN0JmtZpYcWkKzU83IWJEBXYG+2FVYx7rA10EQVs/7w0wDjMiB7m1tqYqxy2N5f8D7aE1ainXFLOr6CSt+ucra1Ku1Xoe7i5QHO4dw4lop5/PKMTsLCd6kvvq5SiZ0C2dt6mVOXbfN0xh+Y9m4ziy+2cttlzqBp6/M4IPQ9xFhhfbvQPRzNW6UGV57zdbPrib+/hi/+hpFsTf8UyPLAgICAn8zfrco6xwTw1dff8N3323kgQfub9A+Gzd+T1lZOYMHDfq9ywoICPyPqEtw/G5x8Q/AmWW+M+prnjvz62OMjgvl84ldGL4o2b6hbnVBVBtFRbBxI2zYAD//bHtIBqwiER+v3sPHFxzT3sJjotn51c9MSSurini0dM1inO9W7vbayzs5E1lTNNh5/6jWszFe/ILxZ4+zLue3eu/T/APz6BnWg7APwrBYLUBlT7LZbJs+kDHLMyjXm/BV+tR7rOpUbwxdaWSxZF9mlVthud6EVCxi+bhYHoptQmy4j53tu6dCxtP9IrGKKpxH6Mpg/pT5mC6b4AHs7O4BpnjCB41AXk+64s4KeMfShpmDnqlKVSzWFfNh6od8//D3lOpK6Rk6gCEbDzlcR6DKlcRIX/pG+1CsL+ThBBGPysJQSlV8mZbDmpSrTnuPJSU24+S1UqepnJU4603mrBZtU0kvnusXSpC7yBYprU5REYwa5bR+jPbtYdMmXMPCeMFguu3IsoCAgICAc373t+WwYUPZum0769Z/idVq5b777q01YmY2m/n2u42sX/8lbm5uDB065HefsICAwJ9PfYLjtsTFn8RfGcWrbJ5bV/0ZwNrUy3QM9WJ0XGhV6lidDXULC2HrVvjqK/jhBzA6PpiLrFZY/QUkPGS3XSIWMSoulMc/v4FSomOo5y885LObGLezVXPG+m1lTdEgQFTVPwpsfac0ngPRto9h3Z4mNISjuUcpM5TRxr8Nx/NsLo0ZuRmM+m4UUzpPYdXEt7j3k3TcZKrf3Ri6kppGFh8+3AFPhRR3FynDFyVXCZi2jT1ZMT6WK0UVqHWFtAtox6kbpzBXugteADaCSWGCx4FGt9ZwE8FSfxjdABPOqyFjCI96jtUyd5amL+XpXU9TrCuuui8Wq4W5/eYzdGGqnbiyWEFrMPPq8ObsvbyDYRsWOLQHmN5lJpumDmL8imNkFWqqxsbEhdG1qS86o4kFD7SrpTdZBH1bBzBqeaqDqHNWiyZq/jh41nB1ycyEfv3g0iXHCx81CpYvB6WtkfTtRpYFBAQEBGrndz9Nubu7M3vWTN58623Wf7mBnbt2061bAs2bNcPT0/ZXrbRUzYXMTA4cOEhRURESiYTZs2bi7l6Pp7CAgMBfRkMFR53i4k/mr47iGcwWluy92KC5K5OzWDauM0tupo5BjYa6587Bli02K/vkZLBY6j1m/0uH+biGKIsMcMN64wDjJPMYFrUfN4mjPXlL18vEu50gpaIdFXoTFgtsP5PD0n0XUcolPDWoFtvFWijUFKJycVQxiw8vJiGkBw/Htmd9ag7Tu8xkwuYx9R5vdsJsTl3T1BsJignz4bnvjvHswNa8MrQ1K5IvEeTpykcj21Nu0OLhXo7GqGXD/RtQSBUsTl3MB3M+QL9fD9HACKCa23srGXwbBK1d6j4/tQVe14WSlpmF+vQoTt44WRUlrE6ZvhyxuZGdqAJbFO/zR9szJ/lFp26UGbkZTNg8hqQYm6gdsegwId4KJic2o1tzP+79JJlijbHWVMh1qZcRi0X0aOHH6Rw10QEy2isvcFgT7VCLFh2scu6KGBwMnp722yQSeP99ePJJh5qzhkaWBQQEBATq5g89tbRv34535rzFBws/4tq1a2zdus1hjtVq++vauHFjZsyYRovmzf/IkgICAn8ytyM47MTF/4i/QxRPrTX9IRvzU9dKscx+FnZstTnbNQCt1IVDbROQjBxJyJj7CV+TQVahBh9JKfd4/8wEn58IOZzFw3VkC5qsYqIVmZyxxLB6Yhzv7DjD2lTbfYwK8rjtVENfpS9qvfP78H7KfH4au48pa07ybudBTOo0uU5b/KSYKXQLGQBWea2RoEpL99GfppBVqGHvuULGxoey5tE4ZDIj3539lgUH59lFn6IkUeg26NCf1kN/IMF+3TEesNgf3Ov5CB/Xw/058JvxMlD3CwsvVy9kTtqANrQ9wNL0xSSG9WTvrLuRS6RYrVYmrjpcJfKcpXRWOlTGRfiweGxHRnd2w+vwKBTFKeT2+BSVZyeeGxTJSxtPc6VYazPgkDq5aIXCVr/YubMtjTEgwBa57dmz7hskICAgIPCH+MNPLC1atODjRR+Snn6Ew+npZF3KQl1me/BQeXgQHhFOTKdOxMR0QlTT1UlAQOBvx+0KDrXW9D9LYfy7RPFKtX/QxlwkQnLwQL2CTC+RsadZZ7a26sFPzbqglbtCITxyMJOvB1/jRPIndFceQi6u240v3+jFuqKBrC8aSK7Rjyd6hZF6qbBKkAGczyu/7VRDhVTByRsnnY4fzT1Kib6EhQ93RG80M7fvPHqFJzL3wFyHlL0ZcbNIbDKQsZ9mUKwxMu++tnyV1JUynZFSrRG5VIxMYosEVRp6eCpkjIkPY3TXUGQyI7N2z2RJDadFTsKZLWdABowDwm8NuYvgY38Y14B0RUPoSJJOneI3o/Nm2jXvi5erF2qtTUhWCsvbbQ8w/8A8hrUcRpnWSoXBTFrWLcMUiVhEZIB7lSA7n1eOxQrhvkrm3t+Sn899QeTJGTSW2ESc9+HxPPnrm/Tq8ibbpg/j2/T8ul+mRETA+vXwxhu2msbGjRt0zgICAgICv5879iQVE2MTXgICAv9sbldwqHXGPy1tqWbdWGNvZZ1RvOoPq7tO5f5pUTxPhbz2QauVsJIculw5hdxsZG3HwU5tzE2Dh0BaisPuJldX9oS0Z1ur7vzYPI5yF6XduJtYw9Pld+F5rILedWSCWxFT6t0PbZNHMAcNxnA4F21KNhKz6Wbt2WG7+WaLlfWpOcyIm8X4TaPrvQfT4qaxNH2p0/S9SnLLCvlgxxXUOhNdwn14qv/dJIYOxEQ5BZpiPOSeuIg9qhpDh3greLp/S7q3aESZ3oTGYMbP3QWZRMyy/RdZm3qZUq2RcF8ln0/sQtqlIq4Xl3Ou9Ed7QWYAdgJHgFBshh4et4Y7usCXgRBZx68RwCp24YDXq7iHTmKqajcpG+sXVDMTZmMwSpBLREzq2ZTpX2YAv689QLG2mB+OG2jbxAvgphANYWRcEBVGNYWaInyVPiilKr4/mseIjgEs2j+dcUVf0KZaKqZCDK/JMmn+/SgmdJrC/P7zbS9SdDpwreXfbv/+ttoy4WWqgICAwP8EwRZJQEDAjjoFhxOc1qXcAWrWjUUFebBoZCenUbzKqMmouFB0BjNFGgM+SjkWq03Y3WlhplJIq6IgEouZqBuXiL16itgrp4i9dppGFSUA5Lt5kTH4IQcb8+hgFdLEEfDaywDoGgWwt0UXYp98hEeveJJRWHsPyAqLkpPaZnTzcB61KZeF8W1Jfz7J7kaeye/mescZnxDO5qndeGPrKXQGs9P7uCblKtunD2ZyzGTHqFM1kmJsFvBP73q6zvvko/BGrVNzJqeMR7tHsGxfFkv2ZRIT5s09HRszIDoAndHMsHYhjI2PQKWQkZlfzsRVh0i9dCsyFB2s4tHuEWye2o2p647w8agYluzL5JsjV0l/uQeP7ph7a9E84BugAOgB9MLO7n6aF8zzBZd6PhKX9QEs0LxFj+YD6OqhYHjAMJJiJjv0PavO5JgpDIscyvPfnCavTM+CB9ozqkso69Iu/+72AFqTKz5KOeG+SlZNbH/TIGS0Q7Rx+dDPyMhcxZSSL4ioURtXYIa7c2y95pakL6ZncAIPrs9AsvsHOHiwyrjDAUGQCQgICPzPEESZgICAHdUFR31EB6tQKe7814izujF3FylFGic28NWiJpNWH/5zjT+sVsjMxOXwYT44up+8PQfokHMed4PW6fRGFSXMCNCxLvWynXnF5MRmyKIDMMx5l8+kocwrcKdVsCeLenci4/29tHTNJkSWx09lcU6P+33JXXaiTGeRY258D+sK+jMnvRHWGvVMp66rmfXNcUbHhfLmiLYUVTiPhpZqjbyy6Tz/fWgucSFxLExd6PDw/1T8U3Rr0q3KAr42qjspjqlm0+7hKqNLhC+JLf0p0Rgp1RkJUrmiUsj49bcCZn973ME58NR1NU9/dYwxcaGsfjSOH0/nsS7tMoPbBlKiK7FFn6zAYWAXNhOP0UC1EmZfMawMgGEN8Jn6Qd2FZ648jdrszuYrtvv2ZO/mzOs3n56hicw/6JiCOSvhWYZFDmXy6pMkZxYC8MiKND6f2IVOYV7sPZf/u9oDnMm5wcjYUNY83pE3f3neaT3aibwTNLbkE352Nn413pFcMUL/a3D25i0NVkOrhyYjuVBh2/Dkk/DZZ7d1XgICAgICdx5BlAkICNghl4hJSmzKtPUZ9c6dnNgM+R2OQtVWN1auN+GjtI/ieSpkrJ4Yx5J9maxL+5OMPy5fho8+gvR0OHIESkuRAC1u/lcfMb8dYWa1DL/KhrqIROwcPIa56zNAZCVacQnF2U381HIDzVyukW/0Iu5MZyw4thrZWdqN1xsvxewdx5q87hiC7iHYI4C3d9Vd87Q29TJdInxIjGzkdNxTISMqyBORSIxar2bTw5vQGDUUagrxVfrio/AhuySb2OWxdQoygOlxM/n5TBHLxnYmoZkvc3ee4a5IP54dFMWBCwVOBXRlNO+RFWkOzoUA6w9dYeaAlqxMzgLA101OkbYYNMAW4AwQBtwHVKsVS1TA2kBoXM+v32iVMD93HMvz77ETtpWtDbBCYrOhDG81jBJtMUW6ErxdvfBWeGG1yBj8QTJXim8J9KxCTVUD7OcHR6FSmG+rZs/L1Yu7WlpxlYvYcbF2g5DRQeH4H3wASY3awvMG6HcNLt/c3P8CrPkOGmkqbk1asQISE2HcuHrPSUBAQEDgz0MQZQICAnZIJWL6RgUwJj60TkONSnFxp1MDa3N/PJ9XjqtcYhfFGxNvM6xwJsiqU6/xx83GzDjrtajXw4IFt30dOt9GmLt1Y94lK6X+RoeGunqjkfzsFJ4L/ooBHr8Q4ZIDV4GbqWeNZCXEuZ3iYEU7h2OXWdyIP/05O2cP516x7Xc29tO0Bp3Xsv0X6RsVQNvGKk5cuyWKqkccN6bn0cijERELI2jj3waViwq1Xs019TVSH0vlgegHWJa+rNY1pnSewn1Rw0EkolxfTk5FNlN6+6OQuWE0Qdfmvlwq1HC1WFsVFTt1Xc2L35/k9WGt+XZKAu/sOMPmYzkYTJaqWsHoYBUVehPn8mypoIUVBn47mgVLgDIc0hXlInjDB2Z5g7ieTLxsfSDTLs/imLal0/GVyVlsSOrK4j0X+OrwZe7uGEKASkGeWs2oLl6U6rR2gqyS6o2jv0qKY1bX2YxuYG0aVhlZhRWYrQbmH5jrdN4AJSxVXkJisq/tO6qDAdch3wwSM7y2F176xckBZDLQOo/0CggICAj87xBEmYCAgANKuZQXBkcRF+HLkn2ZDhGN6uLiTqM1mLFiRSIWYa6W82e2WFmXepnxCeHM+uZ4tWbJh+s42i2q7PutFjh79lbkKz0dMjJsfcJ693bcsVkz8PCAsjLHsWqYmregIjYeuvdAflcPaNaMcr2ZiVojM2421JVYdVhyt1J++Tvkebt5VJ8HfrUfc5BnslNRBtAkMBiVq4yfz+bRKcz7thwzCysMPNm7BZO+SAccI46eChk/PDOYSZ0mOdSWDVw7kJ2jd9I1pKvT9Man42dxT9Rwskuy8XL1osxQRoGmAD+lHyarkdyyGwS5N6VNsKoqKma2WHnrntZ0CnOnRFfC1fLzTO/vxyvDm3Oj1ITSVUS5voRSfQkWcRG/PJvAFweu8OH8BSzdtwbcgDFAs1vn2UYOawKhfT29xwA2l/TkxatPUGZxq/O+5al1/HzuBvnlRpb/cqu58sTuESjkTgR9NSxWeGrDcbZNb3ht2tCFyUQ39ryVolmDB91t1ygT2Quy/VoYdt3WVy2wDNZ/A3dlO1koPBy+/tpmfy8gICAg8JciiDIBAQGnKOVSBrUJpE+UP2qtCbXOiOqmuJDfbBh7p6juslhUYWDRyE64yMSsT7vCmpTsqmjKmpRsNk/txqguoRy9UlyrYUUlYouZpkXXaJf7G21/vIB0XT4cP+Y8MnDkiHNRJhZDx46wf/+tbY0aQUwMdOpke6BNSEAaEECNlrsoXGQEyMvh+kZMV77DmrMLF4tjU2dnXDM0Is9kX4NU3V0yqWczvjtyhSY+Ssp0dVvi1yS/TEe7EM8qE4qaEcdSrZFHV55g7ePzSGiSwPsp71eJrwtFF5i4eSIL+i1g3/h9FGgKKDdU4O3qjVTkjotUSrmhnLRraXyY9mHVfhKRhPui7uOR9o8Q5tWEtiEKViVn8+WkeJSuZrac28KMVfMcRN4zXZ+hR2gPhnzZlwtFFwBoLW+N5RsL14+chdbAMOBm32sR8JQXzGmAmYdFrOTZ7Mf4urjfzT3rpkJvsm9tgO0lhdliReUqq7cW80qxlilfnGTJuPn0DEtkvpP2ALO6PsuwlkOp0EkQiUT4uskp1DoahExS2fqr1YwAbimHB3NBZ4W+mbZ0xYAKh90xDRuC9PMvwNu73usWEBAQEPjzEUSZgIBArUhvii+lXPqn2d7XdFmsxFmNUanWWGWecKmg3MEQIlh9g85Xz9Au5zxtcy/QJi8TN2PDRBDptqhRTRt+T4Uc71GjkfXoiTi2s02MNW7cMGc6ixm2tQJ9YYO+bC3KUCoC7uaLa12Yd9yTSqFQ0wpdbSghyMNIl2aNOJ+jw8Ol7ihNTbwUcl7ceIJXhkUTE+ZFt+aNmLjqkN2cE9dKGb08g5UT7mZI5BAKNAXkV+Tjq/RFIVWw6dwmMouy6dGkH98c0fNIN1++OZzNQ/GNeHXvq1Xpjd6u3kyJnUJSTBIao4YCTQFqvRp/N38GtPHD3dXCzB9mOY0cZeRmMHbjWJJiktg5eqetlu1oMac3nbYZe4wAOt6aHyqFVQHQqxYzwero3aMwdV2N/xElnilXHT5Lzmjk7kJFjdYGE7tFsC71MhO6hTOhWwQzv3aMaFUX00UaA7+eVxMbMIj944dSoiuhWFuCt8ILT1cvjmSVMfiDNHpGNuLziV346Off8FXYh1Of84Z3nERYM1Xx3PtbCiITvLsHnk12nGMUw/FnxtL27U9BdntOqwICAgICfx6CKBMQEPjLcOayWEl1x8DPJ3Zh+KJkSrXGKvOEGX1bMDC6mpuD1cqWVTPw1TYsjc+B9PQ6BGIUSQ8Mqd3F0WoBkZOwjFiCOaAfkstf1rpspgG+Kbf999Sgd2nm0YchkW74RxSxMjmLCr2pTiv0mV1n0yp4OP2i/PnhzI16LzM6WIWLTMyec/kczrbdR6vV6jTieOKamj7vHWRMXAjjEkJwVako1pbgJpMwvsNETCYJa1Ov0jJIxeqD2TzaI4Qt57+tEmTNfZqzc/RO9mfvZ8SXIxzOfcXwFWw5f7bOVD6ApelLaefZjtB9oRTvKIYmwL1AtSDPGA9Y1Ag8G6BPF5XAzAtniMp5nBlxs9g0dSDjVxxzai5S/b55KGQEe7lyOseWyjomLozuLfxIvVSIwWwhNty7KvoIzvuKBXr44uemYlXyNco1Zh7r2ZStR9RcLc5j96ljVJaGVZqLBHq64uXqZWcQ4iwAuKzMhT4DVjM75xVGvPYlXa47zrmsgi9fvof/TFuMXBBkAgICAn8rBFEmICDwl1Cby2JNKh9Ox7f3I23DLgwSKekhrXlr2xnu6dj4VsqYSER6SGv6/+bYkNkZV1WNOBnYHGnnGLo/NABLx451CkQ7F0eZGIqPwvWdkLMTpB7Qa7vTdYxBA+1EmcUKKTrYXGH770w1d/oFKfPZN34Yd81LZmSXUL5K6ooFHbN/mOW0b1hGbgZjNo5iSucpzH1gLmnzi+uN+EzoFl5l0V+qNbL9RA5tGtdMvLxFqdbIx3svsXjfJVoGeuDuIuW1YUHM/PI45Xozj3aPoHcrf34+ewOpxMp/U/4L2CJku8bsYm7yXKemICfyTuCr9GVusnMDCzvOw4wPZmAsN0I/oCtVyiRAAp/4w70NsLrPM8GjN2DbzXS+jNwMxm8aTVLMFFZNfIsRiw7Xev8mdAunVGPgrbvbUqbLYHRcKL1bBXCxoJxpfVrgKpXw3YlrTL6rGZ3CvNh1MpcXhzarU0yPjxzGlC8yqiz0a7IyOYsVj8RSrjUzs+tsxtw0CJlTDL4SePqmKH29EF4r0vPU0z1YsLECsZPyx/1tVdxYtID/xI/CXV577ZyAgICAwF+DIMoEBAQajLPUvt9bY1abyyKAq1FHs8KrNC+8Qvuc32i37jz3Xf8NkdnMT81iefT+VxnZJRQXqb19/6HGzkXZdQ8/rDExXAxtyadaX04ENqdIeUuIfBTWkaZiJWtSMmo930bSIsrP7qFIdgmF7hdE+vxbg2I5mCpA6viwW+AZi7sZ9mhtImxbhc0RzxlHc49SoishrqkvP529waTECLac31JnI2eAxYcX0zOsJ/Pu60HSmtqvYfTNfmEbDl0hNtybcr0JjcHs0GrAGRYrnLkZIXKVSVDrTJzJKePpr44xOi6Ut+9uQ5Eup8qQYkrsFPZl7avVpTHaPxqNUePUwKIKDbATOA7GUCOMBXxvDY/xgIWNwKcB0bGN5TDphq2Rck2Wpi+ma0h3Rsd14JO9lxzGK++bwWQhu7CCz8Z35pv0q3Sb+3OViKtMtxVhRaM38f7DUfWK6aSYKbx9b+1i8NR1NVasNPJQMKLVcDuDkJkF4CWGYwb4sMQ2/8ErnojL8uyOYZHLKH39RTrMeJoj2eVodBLchSCZgICAwN8OQZQJCAg0iLpqv5w2aLZaa6+7Sk/HuuoLxvxyFpVeg7teg7tBg4e+ApWugoDyIsRYne4ad/0MHz3Unj7RgSjkUjv7/vSQKEpc3TkS3IpjQZEcD2rByYDm9O/Tnkk9mzL1ZgpkTZbsy+SzR2IRi6hq8txIWky823Hi3U8Q73aSZq5XbQOlTk7KYoC8fdB4MHBLvBpNFgpNZiIuQkOtOIp1JSQ09aVdiCdisalWK/SazEuex/4Jw/hwZEeWOnHMTEpsRrdmvhgtVt69tx1FGgM+SjkuMjGKGq0G6qIy/fFc7q1wzNrUy9zbqTEy1xLAZuqRFJPE8PXDaz2OykVFgaag9oVOA9sAIzAY6HJrKFgCS/wb1gi6wipG134u9383G4vV+WcKYGHqAraN/Jkl+y5VfQaig1VM6BZObLgPb209zavDo3nvh/MsGduZN7actmsIXj3d9ql+zfnu9Hf1iun6xCCAWmci0FOBu8SNBf0X2BmEPHozW7XSIKT1fxLJbdOFwBu2z2peYCjfzpzHycDm7H5jPyYL7H6qJ/6qP6c+VEBAQEDg9yOIMgEBgXqpq/brzNViPvxoM0ZlEcOtecguXYQLF8DFBY7VEgU5dw63RQupv1uTI+7acgZJipHKQwB7+/6le9zpGLwO6836ruhgFc/efKh+ZEVaralpp66r0WlLeL3DKbzLD9BKlE7zShHWQIzXtiMKHIjhZlrm0n0XCfNV8vzQgAYLMgAfVy8Gt2uE0WyhWFtYdySpGkdzj1KiLWZg60D6OnHMFAE/nb3B4r2Ogm3OPW2Y1LMp07/MqHed6umP1Vl1IItnh/jbjtmAKJhar8ZP6cStohzYAZwCmgLDAa9bwxNV8L5fw2rHftHCuFwLOwYMpY3/FxzPq7259tHco5is5eyb1Ys8tQ7vm4J1Xepl3tx6hpeHRrEu9TInrqnRGcy0DPSoihxW58tDV5g5IJz/ps6v/wSxicHND/1kJwYrCZXn4KvLwNaADdzlbtwfdR/DW95qXu3j6oXqpkHIkBVn8Bw0k+++mMnm1om80m8ymnwF5OdWHVPlKmvQeQkICAgI/G8RRJmAwF/InUwH/LOoWfslsZhpm3uBblkZdMs+Rrvc33A3OLGYl8lsTZmdNWRWqRy3NQSJBGuHDpiKiyko1dndswHRAXb2/e4uUixWa9VDdX21ViZtAWPNz1ZZqzeEi/pg9pXFsK+sE+qCBMbLc4hv6sv7u8+TVajhtxvlzL2/pZ1JQ110DOyIp6sXHV//kdHxYdzbpfbIjjOKdCW4y/zJU+vwdXMh1EeBVCzGYLbwdh31cuNWHOKHp3oyJi6UNam11/hVpvG9ufWMw9j2E7m8c28kHQI74C53rzsKBpy6cQqlTHnr3liBk8B2bP8/HOh0a34TKSz3hwENKIcyWOHlQlhQDBagUFOIyqX+z9zV0gK+TS0gu1BDud7EudwyLFbH6y7WGBys8SuJDHCnUFt8W2K6wqh2EHltFBf4otnreB0Sgdev4BkFgFwqQ26R4O7pRohnCDqjiYeWHuTYVZvQvhLYnIETF3HRN8RhrehgFSqF8GdfQEBA4O+I8O0sIPAXcdvpgH8RBrOFT388S6/MQww8d4B+F1LxaYjDodEIV69CWJjjWANEmQURVz39+c0vlGst2/HgtIewxMby45UK2z3b8VPV3Or3LNDTlUBPVwrK9HSZ82NV9MFbUkoXt1Nc0DchU9/EYT2RWzg55iCCJDm1n5NUxVlLJ9Zdbs2+8k5cMQTeGiwzcuTLDAe3yCPZ5XYmDXUxs+ts0rPKMFkgv0yPjyKg3n2q4+3qxQsbT7D9RG7VfZl3f1syb1TUaahSqjXy4NKDbPxPN+KaOm8YPqGeiKPZYqVCL2J2wmzeTX7XeRSs+nyrmaXpS5nWZRoT10y0pSqeA6KBQcDN1EQx8IQnvO0HHg14T5Ghh/G5tlqrSnyVvqj19X9mG7n5cOp6VpU4qu26vZVyyvXO45/uLlJulDs37qiNcmMpASrXqnW7ux9lSdgc3MVaMAB7BkL/A6BsDD/9BNOnw44d0KQJUrGYR3vcqqsEnAoygMmJzZD/TV72CAgICAjY89c/8QkI/AupzwrezunvrxRmhw4hWvA+a77fjMpQu114rWRmOhdlTZpgGTuWLKOEbdlayl0UlLm4US5XUuaiJM/dl4s+wehkttqXj0Z2xBrlf1v3TCkqZULYMUIMKcS7nSBKkQXAh3kP8X7eWLv9K+ukfi2N5gGfW6JMhxsWv+4sPtOERs0HoPDvzKxvT9V5yZVukaPjQvlkbyYvbTzNtunD7EwanDE5ZgpDI4cyZGEaAD+euXHbUTYvVy92n7oVoTmbW4aX0oUl+07Uu39WoYZ3dpzhhcFRdArzxmAyozdakEnEuMrE7D6dx2ubT6GQS5CIRZhr5toBBhMkhg7i18u/2kfBauGTtE943v155EvlGGQGGA20uDXeyQWW+kPnBpRAGazwZhG8W2Rfv9cxsCMKqYKTN07WuX/HwI4EuPuwfJwf5ToTcqkYuVTsEGl1VlNXnXK9CV+lj9Ox2vBTeDPnHj9GLU+hnXk7C0I+QC6udhWay3BkFmwNhXnzbPWaY8bAzz8jlUjs6iprY0x8GH2i/P82EXgBAQEBAXsEUSYg8D+moVbwa1KyiYvwYVCbwL/uQeriRRRffdmgjL7rHn6cCGxO56E98e0QDc2aQfv2zidHRCBevZpAg4m8GkKrstFuWxcp5XoTseE+9InyZ+/5G3XeM5WknNzTX5HrmkOE5TCKkmO8rHIUDr19TvOLwuY8eD6vHLPFysRuEXyZdplLZTE0khZzsKItKeVtOaltztwHOmJtVUGfuDAe//xwA+6Ezcp82bjOLNmXyZViLVO+OMnisfPpGZrI/INzHezRZ3V9lqGRQ3lneyaBnq64u0o5n1fO8SsVzEqYzejv6o+yPZPwDBariBBvZVW/rcgAd3QGs9MeZM64VFBBTqmOIR/+QlSQis8nduHk1RKiG3vRNyqANo09q8xB1qddYU1Ktp1YEYlETPniJGsen8uFogvMiJvB+E3jnS9WAiVbS3j24rMQD/QCbpY7uYvgTV940gskDejRfVgHE/LgpMFx7JmEZ1iavhSL1VLnMWYnzMZokvDD6Vz2nc8nT62rSl+sTm01dZWczyvHTaa6LTEtxp1P9lzgu96p+Jxb4DjJPQ6eOQlp629t278f3n4bXnnFrq7SWZRzcmIz+kT5/y0i7wICAgICzhG+oQUE/sfUZQVfkyX7Mv/at9sjRmDx8kZcUuwwdM4vlOSwDhwMa0dGUEvy3W1Nk1Ke7wOeDXN3q/4wuSYlm87hPoyKC0VnMFOqNeKvcsFbKUcMrPw1u2o/uVTM0CglHV1PEKRLIVCXSmvXTMQiKxTVvWa0/CzPdQ/Fy8MLF5mYU9fUJDT3Ra3zYR0TmJ7S2y49b2VyFp89EkuF3lSvuKkUlEq5BIvFSlSQB6eul5GcWciQhWm8eXci+8cPpURXQrGuBG9XL7xdvcgtNVNhkPBY92Y2V0Q3OT5KOXKpmCjz0HqjbEkxScQ3jufN/a/y+aMvMPwjm8W6u4uUIo0TpVIL5XoTKoWMxl4KXhnWCle5iYhAA5klZ/B29SbQU8WXaZc5nFXK/TEhbJ7ajUdWpJFVqGFitwjWpV6me4tGpF2soEOTlrTwaU7K1RR7F0ILcATYDTQCJgHVskDvdoOPGkFIA/wojIh5ucDCgmJw1mVgSucpjGg5giPXj9R5nCmdpzAkcigf/nCJ5b86d0GEumvqKjFbrPx0uojZCbMZ1QAxPT1uJl+mXKXZlRfx0W5xnGDoBKMzoFzvOJaWBhYLiMUo5VIGtQm0q6usNHn5O9WoCggICAg4518tytTqMqY++SSlpWoCAwNZuuSTWuf+9NPP7Ni5kytXriKVSmkZGckDD95PVKtWte5z5swZvv76W86dP4/JZKJJkxAGDx5E7169/ozLEfiHoNbW/3BfyanratRa05/3hru0FFauhMRE6NjRcdzVFfPIkYgX2/5tpDeOIr1zLzJie3PJvVFVpKmS32MkUPkw2buVPz+eyWPS6sMOb/on9WzK/Afa8dy3J5h8Vzjx5R8gPzsfkd4CIhpszqGzupJWFsXzq37gmtG/6tilWiOvbz5F/+hAO6EBtt+B3mTGWoeduqdCxpj4sCpBWaQxIBaLWDG+C6sPZrMmJZsrxVrGrzyCVAz9owN5fXg0yRfy6dXKhaOXc1l1IKvquj0VMnZM78GC3edIvlDAV1PepUdodxYcXOAQZZseN50eoT2YsWsGJboSeoWn8ki3cH44lU+YrxK/22hKdT6vHG+llG0zurDt/BYmr5znuF6XmcztPIjxK46RccWPzyd2Yc3BbDqHe/P2tjNsnd4drPDe7gukXCxg9WNzSGjSg/dT5pNxOgM2AzlAH2w29zcjYRFS+KARDG+AzT3AUaML4QN/pk3eRdoefM/hPJ/t9ixDI4ehN0iZnfAK7QNi+G/qfId5M+JmMaDZEEwmOdP7RtIqSMWK5EtOWwr0aO7HPZ84b6tQHV83VxIj609ZTYqZwl1BPbiY9iC9/Jw0PT8TBm8fwaE7hEwGc+bA00+D+JbYkt4UX0q5lMAGvhgREBAQEPh78K8WZStXrkKtdl4XUJ1PP/2MLVu3IZfL6dihAwajgYxjxziakcGzs2cRHx/nsM+BAweZv+A9rFYr0a1b46Hy4PjxEyxc+BHZWdlMmDD+T7gigX8CpdqGRy4A1DrjnX/AunIF3n8fli+HigoYPRrWrHE6VTR5Mqc9A9nbNpERg2PpYzDTsVqPq+ppbL/XSMBgtvDODvs0RheRAb1VzqnraqZ/mcG03s1ZPr4Nm85u4Z2j63hNUXc6GoDGAsk6OCcJJCJyEtGRU3hl1RmuGW8Jruk3zTleGtqa4YuSGdouyM6oA0BrNOOtcC5uwn2VfD6xC2mXipwKyvEJ4XZCz2SB7EINBrOFLhF+zNt5lrU1HA/HxIeRfKGAtamXkYhFGM1WCrWFbHp4ExqjhkJNIb5KX9xkbpzKP4VcImd+v/kUaArwd/MnMTyYR7tFkFWoo5G7S4N7kCVGNsKCgWfraHo8YfMYkmKmsGqirelxl3AfZvSLZMiHvxDg6YJMLGLvuXzWpdmuacjCQ4yMbcc9JQ9x9tOz6MJ08ARw0+tFIYLnvWGWN7g24KNTbrE5K35YoqfHjhf49sFv6RnWE41RQ4muhED3QPyUfpRpRZy6ouNysYa3tp1hdFwHtjz0MxVGNUXaYnwU3iilKtan5jBgUypv3t2GzmFe9GrViIFtAlHrjOSW6lC5ynBzkaJylWKxQrfmfmQV3nQivRkZdb+Zans+r5yHY5vQNsSL1Mwy3uo1l7vCEpl7wDFldUbcLHr5d6JiRz96uZ9zuE7T90qkX2c7bKd1a1i7Fjp0qP9mCQgICAj8Y/jXirJjx47z8549DOjfj127f6h1XsaxY2zZug0PDw/mzX2H4OBgAM6ePceLL73Mhx8tok2bNri73/JpLisr46NFH2OxWHju2dl07RoPQElJCc89/yLfb9pM586dadu2zZ97kQJ/SzxrebivjTvaV+jMGZtRwJo1YKpmJPDVVzB/PgQFOewibdeWsJZRBJ7KrVN0fHfk2u9Ktayssfsm9Tzd3c+Q4H6Mrm4naOF6mZjTa9Fb5XgqZNzXuRGzdtvEQrgUXotwPJYBCb9ozOzVwh4NHNLbDCAgFy6/QVJxfpWYqB7tqGnO0THUizHxofx89gbuLlI8XKQgwkHceCpkrJ4Yx5J9mVUipDrVGwpXF3oTuoVzIa+cG2V6B0EmEYsYFRdaVb8WGeBOuaGU6Tun89Sup2jj3waViwovVy8+GPABOWU5PP/T8w4P/TO7zmZY5DC2nchhQrcIZn5dv0X7myOi2HR28201PV72y0X6tg7gcpGG2HBvXKQSViZnVc0tuHqJ15Y/hb7stM3mvlpywQPusMAPQhv4Ed9UDk/mw5WbH9192fvQGDUUazR4KTzx827Mr+eLOX3tMqO7htHET8nrW09TqjXyyd5LLNl3iZaBHjdFlLqqZsxTIaO4Qo9KISevVEepVkNjb1daBniw42QOUomY4goDQ9sH85+7mtMlwocSjYG+rQOrIqN+7nK8lHIsFivjV6ZhscLnE7qQGDqM7k0GOIjBXQd+RXLsLqLkeXbXaDWLES2xID3gxFjnySdh7lxQ3EbfBgEBAQGBfwT/SlGm1+v5ZPESmjRpwt13j6hTlG3eZMvxf/CB+6sEGUCrVi0ZOHAAW7du48cff+Tuu0dUjf3ww49oNBriunSpEmQAXl5ePPLIWN59dx6bNm0WRNm/FJVC2uDIxR3rK5SWBu++C99/b3Nuq4nRCEuXwmuvOQxpDCaHKFYllaJjTFwozw2K4tClQmIjfBuWbmnWQ0EK1us/0fTYJo5Hn7Z3nAM6Ks+SUtGOMfEh7MneUSUWskyQbYQACRzUgcW/B3rfBO79aS76Olp7VRcTn+y1rx2qNOdYn3aZCr2ByXeFMLyTgmJdMVZJEUoXL5aP68hDS9O4UmzryzYmPozUS4VOBVl1qos+tdZI71YBGM0W5u485DC3pjmHu4uUQo2tUM5itXA87zjert4cnnSYuclzWZa+zOEYGbkZjNk4iskxU5jbbx5lGhGjuoSy4fAVh8hOZfrpI11D8XQTMf/A3DqvpZKFqQvYNvJn0rNL0BhM3NMhiFZBnhhMFqxYEWOhOHUjJclrIMYIvYGb7yPayOHDRtBL2aCluGqEqfmwqcJx7Lq6iK3pYg5n/4ZULGJ8QgSju4bxxtZTvDCotV2qsMWKQ8Pn6pHOh5YedExb7NmU3q38eX7jCXq0aMSMDRmsGB/LgcyCWl9SLBrViUdWpKE1mpFKxPSce8BODHqVJbMk9G08ZfYXZK4QIXnfAmdrXGRgoC3NeODAht0wAQEBAYF/HP9KUfblhq/Iy8vj7bfeRCKp/Rbo9XqOn7BZSSckdHUYT+jala1bt3Ho0GE7UXY4Pb3WfTrHxCCXyzl2/DgGgwG5/PaiJgL/fOQSMUmJ9n2FauMP9xVKTrYJrR9/rH2ORAL33AMDBjgMNdgpMvUyHUK9uJhfwU9n83l+cCsUshr/tixGKDwMN/ZA7s9YC5IRmXXIsD2kOyPe/QSHtO0ZGRfE0C/tTRMGXLOJMxMSLj6whuHrh9cpyCpZmLqAzQ/9xJJ9l+wc9E5dV2M2W9gyLZYfL22n5yrH+q2ZXWezfcYwJq8+ScqlIruIVn2sTM5i7WNxVOjNSMUi9Car09rCmuYczizWp8ROYV/WPqeCrDpL0hfTI7Qnkao+PNM/ktkDW1KsMVBQfiv99KczeXgp5XRt6kuxtuC2mh6brGW8NMIPsUTD6/dEUqIr4XrFeabG6Hlx+mtcyjkCE4Cb77O8xfCaL/zHE6QNcFU0W+HDEnilEMpr+d02VvnySFdPptzVHBeZmGvFWoYvSiYywL1ek5OGRDqn3UxvfX5QFPnlej4e1Yn//nC+QZHRYo2B5o3ciQq69RKmtetFPm/+isMLCOsNkMyz2urtqnP33bY0Y7+6+74JCAgICPyz+deJsqysLDZt2kyf3r2Jjm5NXt6NWudeu3Ydo9GIp6cKPyd/EJs1a2o7ZrZ93n/WpSwAmt4cr45MJiM0tAkXLmRy/fp1wsPDf//FCPwjkUrEf35foZQUePVV2L279jmurvDYY/DMM1DL5/B2nCJXJmex+tEu6IwWCssNVOg1eMot+FxbhrRgL+L8X8BUXjW/Ac/kdHE7RaTYnQqj2kEsnLuZfdguIBqNUXNbYqLCqKZloIdd1MRTIUOltPLcTy84NWeoHn1aMm4eC3ZkoTOYuZBfzuC2gfi6ySmsMPDjmRsYTI71bqeuq1HrTLhIRHyZdpmOYd5Oz69cb8JHeUul1rRYl4gkJMUkMXz98AZd74KD89g/YRg7jt+wMxOBWyYqCc382HMuj5YhpQ06ZiVX1VcJ8wpj+2/beD/lfTKuZcCvwAHgLmAoIAYXEUz1hBd9wFvSsGMf0kHSDTjqxHSwko6BHXGXeyITSTl9Xc3Ja6UM79CYYe2CSL9cbHcfnXE7kc5Ood70a+3PrlN5DY6M9m7lj1QiIqlnU6Z9mQHAaV0EW0t7cK/3nqr512/4EfxqAVTT6BUyV0zv/RfPqUkgasi/FgEBAQGBfzL/KlFmsVhY9PEnuLm58cgj4+qdn1+QD4Cvr6/TcVdXV9zc3CgvL0ej1aJUKNBoNFRobLUAfrXs5+vry4ULmdy4kV+vKCsvr6CiwjFnp6CgoN7zF/j7opRLeXFIFIPaBLE+7TI7TuZWpZH9ob5Chw7ZxNiOHbXP8fKCqVNt9Sn+/nUe7nadIrUGM+/uOMvW47bX/SIsZLSZg6e4YQ/7JquY45oWHChvT0pFW9IromgTdit9zxkqFxUFmtv791CkLcbdxf7ejokPYfuFLXW65cGt6NOzg+/FYLKQ/nIPSnQlFGqL8FUEMPf+lpzL0THzqxNkFWmQS8X0jfLH102O1Wohu0jP+Rtl9IsOcHr883nluMolVSmuZouV9ak5TO8ykwmbxxDtf/sitFhbzJ5zNxxSZq8WazmfV0635n70ivRHZ729FwAhqhBWHFnBy3tfhqvYnBV9gCmAl014P+QOc/wgooF1YxUiBUcDHiLxl1XUZ+UyM2E2hWUW/DykFGsM3BvThLGfprDxie78dDoPN5faU4Vr1u7Vx4rkSwxsE8jnB7IaNH9lchaD2gQhl0ro27r6SxgRz12dRpCsgK7uJ9hdGs+MnKfZ2mQeTU/ZzuV441Z8OOEVPkx6QBBkAgICAv8S/lWibNu27fz22wWmPTkVlcqj3vk6rQ4AFxeXWue4urpQUVGB9qYo0+p0VWO17efqanPS02q19Z7Dli1b+HLDV/XOE/jnYDJbMJgtqLUm/NzlvDQkinfva0eF3pbO5OH6B/oK/fe/tQuyoCCbhfakSaBSNehw9TlFuoj0dHE7hd4qJ62iDbmlOvLUt/4NWBGzt7QDI7z3Od3fYhVh8mzP1tyWbM5rxaGKaCos9oVGztL3qqPWq/FT3l5ql4/Cm3L9rQd1iVjEmK7BDFo3ColIQrR/NCoXFWq9mlM3TmG22nfB2nBqPcNbDWXbhS0sOOhoGz8rYTZbZwyjpMKKp1JUJdrE0hJaB3vx3KCWuEqdCwazxcq61MuMTwhn1jfHAViTcpVNUwcxqdNkThec/F0i1NfNPmpUWUt1LreMeTvPcup6KUvHtb2tpsducjde2f0K/AycBwYCkbbxHq6woBF0aaBxqMkKH5fABxVyfhzwEo9p5HWmZz4R+wTDWw4jvzyfcqOW/tHeaA1WfN1dKCrXU6oz4iITM6lnU6Z/6Xg9t9tY29aewoi5jtYINeeX6Uy4uUidNndOyn6RMT7b+UU2gTcfbI7PhPWYu8XzRachvNnxPj64v/MfS10WEBAQEPhH8a8RZfn5+axZu4420dH06dP7rz6dBjNs2DB693Y834KCAl548aW/4IwE/ggag4kfz+SxdN9FJ32QmtI3KuCP9SR75RXYsMHWULaSwEB47jmbGLtN1zZHp0grLVwuc5dHOj08jhLndhIXsZG96hjSKtrgrZRTrrevlfmlvKOdKMsyRbCnpA0Hy9uRWtGGMT07Et5Byd6bAqQmNdP3anLqximUMuVtiQmlVMW53Fupi5EB7hgtFQyJHMLmkZvRGDUUaArwU/qhkCpYdmQZiw8tplhXjLerN/8d+N8qJ8iaZORmMPq7UUzpPIU3er3B0PX3sT97f9V4ZW3a8JbDeHZgK8atSHM4xpqUbDZP7caoLqGsS7tMqdbI+BXHWDXxbU7kp+DvVneEsyY+Cm8KK265/DXxVvDtlHgsGAjz13O3u4VHlWEoZDKeiZ/F2O9H13vM2d1m8+LSF7EusUI74D+AFFrK4F0/uLuB/cbA5pQ5LR9OGgBKGbh2IDtH76RrSFcWpi60+732DOvJe/3fo6VvS/IrbnCt/Bp+Sj+smMivyOfj0W2wWCS8ufU0H/70G1uf7MGYuFDW1HC5rF6758zavnr/vUpKNEaHCGtdqDXaqnYWSrmUQe46+iR1pVRnpFRjxEMxnCmuUoxmC6tTstkwaTnXrPI/lrosICAgIPCP5F8jypYuXY7JZGLKlKQG7+OqsP0x1etrL2rQ6WxjipsPuwrXW6+F9Xo9SqWjvZjuZjRN0YAHZHd3Nzu7fYF/LhqDiTnba3cxnLY+gzHxYbwwuNXvF2atWsGoUTbLe39/mxhLSgInn8OGoFJI6RDsgkp9gN6qQ/RRHaJJDQtvgDj3k3QIdsFFJrYTOwD7yzqytnAg2bI4Rg97hLs+Om03XlOA1KQyfe/p+FmMcyIWzFYzS9OXMq3LNCZunljvNU2Pm8n61Bw7k49Huobj6epOc+/mjPhyhEPka1qXaRx6/BAD1w7kwegH2Ze1r17b+MWHF9OlcRe6NelmJ8qq16bN7z+fJ+5qxsd7M6vGKxtRu8gkPD+4FXFNfVi23ybiRyw6zLiu4fRqGnxbItTT1Yvdp2zpjuG+Sr6a0omdF793iPL1DOvJtw9+y+SYyXVe3yPNH+H7Od+z4fAGGAP4QLgUXvWFsR4gaWDGXYXMl/GXC/mm3H77haILxC6PZXLnyWx6eBMWq4UCTQHert40cmvEprObeHzL405/T4HNA3GReuDhKqNYY2T0pylsfbIHncK8+ezXW02hy/Um/D3kPNErgpFxQVQY1RRqivBV+qCUqvgyLYc1KVftWid4KmUOLx1qo6d7Ok3TZkCfH0AaCM8/j3TRIqT79yPqHEteqY4dJ3P48UweZ3Js1vzRwX4893tTlwUEBAQE/tH8a771Dx0+jJubG4sXL7XbbjDa3pQWFRXx4osvAzBz5tN4e3vTyK8RAIWFhU6PqdPpqKiowN3dHeVNgaVUKnFTKqnQaCgoLCTUycNw5fH8/RvdmYsT+NvTYBfDlGziInwY1CbQ7i15ZcpjWV4RkvcXIOnQHpeRDzlPc3zlFWjfHqZMAbffKeg11+H6dlyvbeGbRj8g9as71VYh1vN0h3zWpV6mZoDhhsmXF69NJTbcm35GT4d9S7VGHlmRxucTu9ApzIuVyY5mFJEBnvRqNbxWsbD40GIOPX6IpJgklqYvdRivZHLMFBKbDGTEpsNVx57Usyl9Wnsxa/fMWiNfEzdPJCkmiZ2jd+IidWHouqF13o9KPkz9kO8f/p65yXOxWO0rpCpr05LuuoeIRm6sTM6iQm+qsmd/dNUhrhZrGR0XyrJxnTGYzGj0Znzd5YiRMKvrbEZvHFXLyreY2XU2R7LKMFlsgu/zR9vz6t7nnF7r/uz9dP2sK/vH76dnWCLzajQ9bh/Qni4FXfj6la8p6VICoyFYAi/5wGOeIGugGDOIlGwof5gRI+fgu/c1cHIuxbpi3vn1HYq1xbzQ4wXmJc9jQf8FzPphVq1tACp/T2/3fpspd4Xz7o7fcHORYjCZadbIjWXjOqMzmKkwmPD3cMFdYcE/IINhG0Y7CLzpXWayaeogxq84Rlah5mZ7ChlScX0XaeUR3628ErwcSYUFdvWDOTI4csY2PGYMiowM2jT2JDLQg5FdwlDrjKhcZagUfyB1WUBAQEDgH82/RpQBVFRUcPLUKadjBoOhasxgsL0Zbdw4GJlMRmmpmsLCQgfDj8xMmytdeFiY3fbwiHBOnTrNxcyLhDZpYjdmMpm4fPkKcrncru+ZwP9vbsfFcMm+TLvUJY3BxM/HLnPtnQ+4f+fn+GrVXPEMYHxhII/2aeWY8tiiBcycefsnWZENmSvh+lYosrV1EN/8ry7MVjFXJW1pGeDOk1/WLjprugpWJ6tQw/BFyVUCRGcwU6wxEOylQCyCLceuU6H15q1ec+ka0oP/ps6vciKM9o8mJiiGa2XXWNB/AT3D7mJ+DTHRIbADsxOeZVjLYRSXwxePdqGRuwvurlLAwqaz3zWgYfJSEpok0K9pv9sy2dAYNbTxb8PxPMf0zAUH5zGs5VCy8itYOT4WpVzKOzvO2DWU/mRvJkv2ZVb1uUps4c89nYIY3mo4STGT6zQmmRwzhaGRQxmy0JYiOSY+hL2Xd9Z5rReKLhD9STS7x/zID2P2UagpxmApI+9iLs8+9yzLXZfDaPCTw3PeNnt7RQM1hBUJhvCJ3Ah/js/WZmPMqLj5O0t0+jubHjedHqE96L26Nw9GP8hPF3+qtw1A5e9pVNx9zNv5GxO6hbMmNZsRHRrzyc8XOHKlmBaN3Jlzf8s6hfiEzWNIiplS1Wx8YrcISjUGHuvhvEYNQIqJV4OXMdZv+62N+gu2Hm1HAStw8SJMn450xQqkEjFKubQqxVFAQEBA4N/Lv0aUbfr+O6fb8/JuMClpMoGBgSxd8ondmIuLC+3atiX9yBGSkw8wfPgwu/EDBw8CEBvb2W5755gYTp06zYEDB7nrrkS7sUOHD2MwGIjt3FnoUfYv4nZdDNVaI0q5FI3eyKaXPqT78vkMLa1WE1SaR8ftXzHtxvA/nvJYScUVOPl6g6ZeMzRiX1kMl2Td6Bh7L9HhYTy0Io1SrabWfWq6CtakVGu0EyBtG3vyTP9IBi/8hY3/6c5HP2ey7UQOo+M6sPXhPUjEJhQyGWq9mmJdCZ4uXmCFfuHDGBY5lBJdCcXaYnwU3ni6epGeVcaQD1JRukhxd5FSoTchk8Caxzs1uGHyBykfMKLlCMQisUPkqzYKNYWoXJwbqxzNPUqJroSLhRWsPphNiwB3O0FW3bmx0m7/UFYxLYPcadfYi3l959MzNJH5Bx0FzayuzzK81TDe2ZrJlWItErHIab83ZxTripm09TE2P/QTU1ecJjR7GyvTP8GaaMVPBU95wTQvcL+NgI4xaBAf5j/G3c37s/1EDt/9pxsVeiOPrzrOrIF92Td+KKW6Yi6XXsZX6YtCqmBp+lKe3vU0ar36ttoAfJDyAfe0uodn+kYSG+7D8EXJfHfkGp9P7MKxKyVYsbDl3OYGCHFbs/F59yXSvUUA20/kENfU12mqrUpSzieh79Ddw4lg12P7a2sEXFygY0dbE3fBWVFAQEBA4Cb/GlH2exk+YhjpR47w1dff0LlzTFV06+zZc+zatRs3Nzf69u1rt0+/fn35+ptvSU1L4+DBFLp2jQegpKSEzz//AoARIxr2cCHw/4P6XAxrUlhhwPfcSbRTnmTkkVSnc/6T8jVrOw6qNeXRKRYzWE0gceIM6tcVXANA51gzBiKsvvEYAwej9ulPiTiSri5SEiVilC5SJn+RTlZh7YIMbLVhP57OtevZ5PQUrXAmp4xHu0fw+YFsRnYJIy2rqOohePuJPB6MDeTHK7v54GbErBJbRGw2w1oORyHywyrzQSmRUKoxcyhLTYnWRHaRLRXTUyHjjRHRlOhKbivypdara418OcNX6YtaX7sgL9IW08jDhZHV7NmbeCt4657WdApzt7Pbf/f+lhzNLufghWJCvJVMWnKYN+9OZP94mwgtqiZCj2SVsS4llw6h3qxNu0JkgDsak2O/t1qvNecoX365ml/Wv4K+m5qgYTDTGyZ7gvI2xNhhHRS2eo7unV/m5yWHSdQY2P9bPiHeSq4UV3DwYhEvbjzNopGdcHf15LOjn5Gek87JGyerhG+7gHa33QagRFfC2IQwhi9KplRrpFRrZPY3x1k8phNymYnuKxomxBemLmDf+OFczC/nq8NX+HjPBYdU2wj5NT6NeINmLtccD/AtsBFblKx9e1i7FqKjG3bzBAQEBAT+NQiirB46tG/PsKFD2LJ1GzOeeoYO7dtjMpnIOHYMq9XKtCenOhhxeHh48OTUJ5i/4D3mzptPmzbReHh4cOzYcSoqKhgxfBht27b5i65I4K/A0cWwdhqVFxM660mk69fgW4v99q4W8czr+QhGia35U82URzssRsj9Ga58A1c3Qbs3oMVkx3liCYTcAxduRg9kKggaCI2HQtBARK6NkAN+gJfZgtFioVRjwmCyMCoulNRLtfcSq8Tfw4Uekf61mnpUMjoulC4RvuSW6hjfLYIHl9ii0pU1UXOSX2TZEedpZ6O+s5loPNvVlnZWqjUSHaxifEI4m6d2Y+KqQ4T7KXlzRFvy1DqKtFfrPe/qlOpLiQmKaZAo6xjYEYVUwckbJ2ud46PwRi5RV9mzd2vmy+Kxbdh6fiufbN7I9fLrVdb8bQPaMrPrbJ7oMwy9UcaVYi2Pfn6U1kHu3NXSH1eZK2dybrDz5LGqGrJKE5UynRGdqWHRWo8bHriecmVWxAzCHoBnvWGiClxuQ4ydN8CrheAVOYk3O8zEaBJzJqesyqHz018vsmxcZz748Tcy8ytwd5VyJLuQfk37sTJjpd2xVC4qirT1f76qU6Ir4dAF7F4WPNA5hP3nC4hpar0tgZdTVoiXshHlepNdqu2KR2KR3diB6sgspOYa99YALAVSsEXEZs2EN9+0RcoEBAQEBARqIIiyBvDYY48SERHBtu07yDh2DKlUSvv27XjwwQeIatXK6T4JCV2Z8/abfPX1N5w7dx6TyUSTJiEMGTyY3r17/Y+vQOCvRqWovYltJS4mAxMPb+LJlK9Q6p0baxxq3Jp37prAkZAou+22lEfTrRRGqwUKDkLWOrj8Feir9bW68q1zUQYQMRYkCggZBo26g9h5x1/pTTMChadtvYFtAnjn3ja8uvk0BpPztL5KofXL+XySEpvWauoxsVsE3Zr7IRJBywAP8st0VamfY+JD2JO9w6kgq86S9MXEh3RndFwHPtlrc9yb9c1xRseFsumJ7lix8u6OsxRrDDw/1HkT59rwcvXivqgHHISDM6bFTWNp+tJaUx0rnRFPXLvMoDZBNPFWsGRcG366+CPdwxLoFNzBwZr/yR1P8OvlX5jffz7vPdCO+GZ+6AxmijQGfJRyRnRoTJivG2tSsqtMVNY9Ho+7i5QCbU7dJ1wMimMKyoLLCOxfxlwfm5ui9Day7LKN8HoRHFe2Z2qfGQxoNgCZyIXPD1wmKkhV5dBpsYLBaOaVoa3pFx2I3mQhwteDxr7DHA1drNBE1aT2RZ3grfDm/A01ErEIs8XK6LhQYsN9WJuaTXhQwxwUKynWluDlFVDlLGpLtb3AYPFKogvmI6LGy5Ni4L9AJhASAqtXQy/he19AQEBAoHb+9aIsIMC/1nqz6vTp0/u2+5tFRUXx6isv/95TE/h/hFxSexNbrFYGnD/Ii3s+I7TUWeogXPEMYM5dE9jRshsSiZgoJz2V1DojgdbfbEIse73NuMMJ1rw93Mi/joenv6PTW6ME238NwGAyYrAYKNYWU6QtpntLb9Jf7sG5HC1PbTjBlWKbsIwOVjGhWzix4T4YzRY++vkC10q0dqYeJVoD/h6u+LjJ0RnNFJTrcZVJaB/qRW6prYXE7dREgS3tbPNDP7Fk36UqR8i1qZeJDfchwk/J2tTLyKVi5t7f8vbs5V28SAzvWa9tfFJMEj1Ce/D0rqdrnVPpjFiqNeGvcuGjUR3Qm3UU64rrtebffG4LfVoM47HPD3PimrrqHg1qE8h9nRozOi6UsZ+mkllQgVprID2riI7hHs6vVQscAbyg40Atz3jD3W5Qr9FgNcwu/uRHTOKaT29muwfhLncntzwXd7kHRqOcz37N4uWhUVUOneG+SnzcXXB3lTFp9eEqcR7uq2TN43PoUa1W7q6Iu3CTu93W78nL1YvRcT6MT2jKjTIt3ko5Yz9Lo3WwCl/F7QnxQA9fth2/1UZBKdbyVbultCn40XFyFvAeUAQ89BAsXgze3re1noCAgIDAv49/vSgTEPhfIJWI6dMqwCFtr1F5EQu2f0DipSNO96uQufJx1wf5LPZuXD3ceCI+xKGnkoepnPA7Iw4AAOAKSURBVAupn9I0bTaUnXZ6nOqIrGZeWf4xV90H/u6G1eWGCjad3ezQ56qyMfKOp4ZTXA4yiRgQsfpgFhsOXeHde9tVRb0qTT36RPkz5552ZFwp4cOfzlcJDICh7YJ4bpAtGh0Z4E6F8TZqonKPUmFU0zLQgzM5t3qnLf/lIp89EotYBAaThXM5WmZ2nc2YBtrLp2eVEenvybx+80kMT2RusjOnx9kkhieSuCqRYl2x02NVd0bs1bIR3ko5Xm4Wntn9TL2W7ztH7+TRzY8yLHIoS8bE8N3Ra1ixcG8n/5ufjevoLHo2PtmZw5fKCPF24+mvjtMn34/XE99m/sF3UOvVnLxyEsspCxI3uKc/POMN8bfXX5xCs4hUz1607f4JJpEcD0M5LhIXzBYzYiSYTFIMFgvD2gURG+7Dm1vP4KmQsXpiHPN3nnVo6pxVqGHIwkOMjmvHD2P2oTOX4SF3Z/Wx1Q3uRTe722wMRgkLfzqHWCRiQHQg3kpb2uCPZ27cthD3VXixKtnmYNk3RM17ga/haTjvODkFWAZWFxWiLz6G0aMFMw8BAQEBgQYhiDIBgT9IVQ8xnQmsVtxcZZTrbMYCngp5Ve8hiRie6NXcLm2vXK6kWeEVh2NaRCLMY8eR1HwEv1bICfdVsmpie/Ze3sGwDaM5nZfBCDeYoIL+SghuwHNfhiaSbSXd2alO4IohENS/r2F1uaGCmbtnOrVir9kY2aAXkzDvZ3q38mdAdAB6k6UqncxTIeOxHhFMSIhwsIGvZMfJXJ4fHEV0sAqlXEKh5vbqioq0xbi72F/XqetqKvSmKrG2Kvky795/e/by/32oA6u2X2Js1978MmEoBZoCcstz8XL1QiFV8M3pbzBbzLx212ssOLDAqTPi0MihzN1+kel9W9C1qS8SsYWNZ7c0yPI9PiSehCYJXFcX8uXBMqb2CWX3xW0M2+AokmcnzMYqGsarw1vROlhBqd6Dt3u8zZK1S7hy4zRje1mY4QURzjNVa6XAIkXSejbDDm1BYjJQ/vXDiBDhJndDrVfTvUl3Xu05h+GLDrB+Ujz/6dWcUctTKNUaeaJXc9KyihwEWSW29MBLLNl3iS8nxRPoU8are1/l0OOHmBQzqc57lBSTxOAWgxGLrCT19iG/vBBfpQ4XuSubpiYwclkq53K0zEqY/X/svXdc0/fa///MHkAggAxluUXciIADt3XbbRXbqq1VuuwQu3va02EdPa1djlZrW0endbWutu6Be9S9EBegEAiQhMzfHzGRkIRh2/t3n+/9eT4ePs5p8tlEeb9yXdfrReby2oX45NQp5BaZ+X5iV8QV5wnf0Q2RucRzIzvwHbAGbN27I/nmG0hIqPOzFBAQEBAQEESZgMBfwGC28tuJAlYfusqrQ1uz50Ixi3Z6z0m5KlJnr+s5f73CI4tL0m42jLu1ONwT05qK92Yi7tyZEWWVHP3lhJe5RS8VfB9d+/WVyZsxLy+NVSU9yTP73qGu7o1Wmx2r3crKk6tqFC9wMxg5PoO7Wt7Nvlf7ozdaKDGY0aplbMnuxdqj1xjYJoriCjPrj+VzIE9HSoLWox0TnI6NS3PyGNs1gYU7LhCmDq39pqsQqtJS7sP5UGcwu8XauRsV6A2iWu3lh7YYStY3f3JJ52yFO3ejgpHz9xIWIOO1oa3o0aINVnsFhYbrDG9xL8GKEDIaDWPE2DvRmXTojCWEqkIIUYZQaZGgK7eR1bsZyw9cxu4Aq93C9B11cwR0hVJfLS0mq3dTXtk01a9Ifn3z6/RK6MX58s1MmDeNo7uPkhAKk1rBp01AK6nXI+WSBT4uV6Fq9SRTkl7k64Rx5Jfnu+feVp1eRVRAQ1KienP/3ANc0hkprjDzn42nyS0yEKqWM757Ag9+safWc9kd8OP+y4zpIUZn0jFwyUDWZa4jPSad2Tmz/eaaXS69zKxdszzm/lxV3O8mDeNMvpnhLWsX4hNvho2/8OMpPn+oMzJVc0RR/ZymOTdxWOSI3jfjOCHF/vabSF58AST1fKgCAgICAv/nEUSZgMBtYjBbeffXE6w+fI3VT3Vn7uZzPh0Fj129VZF6aVArtp8posf0P9xhwOWmhmQ3TaF1wTne7vMoIQ9nMqFnU8Z8kcPiR1OZfk8bL3OLLUa4YPFd3XCoYhAljMLY6H5GLjNwvLDMe6NqVHdvNFttWGwO9EYLOoMFrVqGRiXDIbLUOdNr1s4ZDGo2hIe+OOTRkpjUUMNjGU1QyiQ0baAmIlhEm7h4dzumWqrh2z3XWLbnCpEaBceulpKZGsfRyyUEyDT1ajtTSzVuc4aquBwAJWIRYpEImVTC++vO0zvRv738kNl7uKQzktTwllkFQFGFhWe+O4pYBOue6cHq/WK2nbnIJ6PCePrbQ8x/qDPf7CjDaHFQXFFAXvF5lDLnPOCp/DKyejXjUrEBJKZ6h1I3Do1m37XtfoWFVqll/Zj1PLfiOb5b/y13NIV3esGQes6LAZw1wzQd7JI2ZXXmerZe3ErGogyflblBze9gyncn3M6HAXIpBXoTCWFqlk5Iw3jTabIuHL1SilbZxHkNxWdJ+TyFSZ0nsfKBlRgsBooMRV65Zjsf2cn+a/s9juOq4mZ1zmJG/5mYKqW8mP4O6TE9fEYrPJOaTc/YgYxdeJjcIgPXyypZ+2c+49M+R60/CaV/QnAbRJ2XwA8vI1r4BpLOnpmVAgICAgICdUUQZQICt4HVZndWyA5f46NRHTmYp/Nr8S6zWXhk70p+1XUjtXEoz/VvQbuYEOZuOcfeXOe80QsDnyY+rgEP39GWTnFaMr/YTYjpCNOXnGXGpEy6L5zpcUwH8JUe3ghz/neZHb4rg92yZnx4zwECFUGUlpo4fu33Ot1PVffGikorvx0vYOOJAgr0Jnf1alCbKLIHN6iXcCgsL8Zq93SmO3ZVzwcbT9O1WSdWn/mVGTu9W+6eT8tmy9Th6MpFFJRVYrLYyB7YinJTJc+mZvPwysxazz85dQrLcm6ZM7hIaqghUCmlb6twPn+4LRUWPRf1JxnXMxQJSuZvzuOa3oxKJqGoooANx5z28hKxiMToILLvaMHG4wWIRCJnAPBN7A6QiMRsO3MDsUiEQiYGHJjMNuZvO+/zGiViEaNT41h58ArqwJpz3qpTYiohThPHq3+84nebwZGDGfFWDwY0usrpftDsNvLq/6x0irHvykCj1LJv7Aam75jud+5t9HKn8Jl5/wyOfViGRiVDIROTX2ril6d7sObIVTrG1d344nRBORpFiFuM60w6pm2fxvQd02kT0QaNQuOcj7uZa1ZbDMGcfXPoEZdB77hhjPhkH5mpHVg98g8qLHq3EFdLNSzLucaIlfuo1Jdx78nt6AzpbDldSHyYmkHdlyM98R4kzwZZIKxZU/8HKyAgICAgUAVBlAkI3AZmm51Vh66y6sluBKtkjP7cd8Bz+sUjvLXhM5oVXyYt7ygzW7egb2IEg9pE0TcxAr3Rit5kQaOUoVFJMZvK+WDBq8zWrqZ9ozOccKSiMw72KYQW6SFD5fzfn8rB4AA4y79MpVhtCkrqGVhdarSgUUnZfqaQ5MZqWsWEeVSvtp0uoaDMt2mFP3zNdLmyxl7b/KLflrsHV2S6s8Ye/epW1tgTvZtyd+sR7Lpcm/Ohs+1sxMp9Xu9N7tscpcxCeMRBhn2X6d0C12UK93Qe5K6QBKtkjEmLZ3RqHFabHYPZRmxoAFtaR7JszyW39XzVCtrMe9ux8XgBneK0mKvM0VVFIhZxR1IkVpudP6+WMqxTPR0BA6OosFRwMP+g5xsO4AZ0DYA+xiV83glU9cgXc7GhAt4vgQ1VtGJWShZbcrfUOvc2Z98cMuIzmHZ3b4orrNjtDjY8m8HOc0X8fPAKfVvV/V5tdgd2u9RrBszusPvMiqsthgBg5s4ZDG0xlDLTrdk1d+W6Us+p/DJCJToaXdExe/VMmhddomBtW8rNzW5WldORpi2o8z0ICAgICAjUhiDKBARugzKTldeHJrHi0BWGtI32asVqUK7j5U0LuOv4ZvdrvS7sZ9nm9ejHdiEqWIlUIkYtlxIVrAT9aTj6GYpzi3gzstS9T6Ioh+NF1RbdN7lohb5XvF+/XHqDn3Ju8ETvZvW6p2CVlEqrkWuWTbz4nbc5xTOp2bRqMJRmoc04W3y2Tsf0NdPlyhqry1xa9ayxx5ccZHKf5szoP5OusT34z+6a285KjRaPYz7avTHpzQKZujHbp6g7lH+IcavGMCk5i0Xj3+appcf4ZHQn9ubqPGzbAY9A6ocX7mFctwSWH7jMnMxkujULR2cwY2gaTujNOTqXgHM+A6fIk+CgxGi9LUfAUFUox69Xcds0QwM9PBQPjyRA4m1UxWxIuBrWk6EH/uBINU0vEUmYmDyR4cuG1+lYM3bMYOu4YZRWiHl95VHeuasd87ac53RBOUq5pNbcPhdJDTXIJFKGtfCRX1aNusQQgLOKW1pZypB20aw+7Kymuhw6FSIzb0XP587AzUjnWVEUOTPNGrz4HGWZH3BZE+GZCSggICAgIPA3IPxWERC4HRwOci4Use3MddKbhrlfFtttZB5aS/bWb9BUVnjt9tofX6Ave94pxBwOKNgEJ/8DV38BwJc9QExB/VqjQlVaTlzLQy6t+8I3tXEogSo7Uzb4N4wYuzKTnCtZ/Pbgb3Sc19Gv1bsLXzNdf0fW2Ow/ztAiKpBYZW9WjxzkbjsLV2uJDArjwvVKXvjxlHueCW5lpfVqGc7yEz/XuLAHpyDsmZDBdxPv5t1fTvl0CXQFUj+YFsfPT3RDAojFIi4Wl1FmLsdgK6WoshgkoQQpghnQOpzRqbHggJ3ninhj5Z/MvK89gQ6nNf/pfBNTu05ldB0cAbO7ZvPziZ9pH9Uesd7pwPloPAwPANltOLDrrDBXD/0G/kTDiM6kWf7NkWrVsKSIJAwWQ73aV0tMJXyxrZirpSYqKq3uLy9c5i3ZP3pXuqozrlsCczad5WSBnn+PmEZ6TA8+8DEDlt11KqmNujBwycBaP5sAOqOOd+9qS1zoraDtGFk+82LfISnwgnOjJ4C3ARuI9aV8vfdL7hr6qjMTMFhZp+cgICAgICBQFwRRJiBwGwQoZXy5IxcHDkLVchKjg2iXf4ZHvp1Ji0unfO5zOKo5rw54nM/VEji/CE5+ACU1L0qvWhoQqmlWb3OLE9f09Vr4vjq0JatP1e6qOGffHLrFdWNS50lM2z6txm0np07ht+NFJMffclVsFRWE0Vb2l7PGPtt8jq/GdyHt3d9oHnmr7Sy/9BSjusTxn5EdsNrsmCx2NCopQQope84XIxJZ+GD3jDqde8aOGdzRdIjfWUFnW6MzN05vLsBoNdAwKJojReuZsdp7Tu6Z1GwGhgxBKlLy0e9nWPJoGnYHKKUSnuvfjBZRShqG9qqT5XuFoYLZyycxqpGN3HYQW087exdHjfBpGSzWQ4UD7gppyaRfJvHhHR96ORxqFBp0Rt9iRyKSkBSR5J7vOlZ4DJvDhs6oI0KjIqmhDbPNTmJ0EKcLylm8+yKrnuzmldtXnczUOHe2WanRwr7cEjJTO7AhcwuV9jKult4gWBlCkDwYsdhK689a1EmQAcjFQby0/Ch9WkWw6sluzF06m5eD3iZIbry1UQtgFLAYrjRuxZkX3mRV7y5ClUxAQEBA4G9H+M0iIHAblJssXCkx8mB6LBGOEpYd+YLgLxcjcji8ti1VBDCz58Os65TG5CZbiNzUEioL/B7bgYgt+k4sLh7MH/rOfJnehSnpMXUKN34mNZvfjheTHK9lz4Ui7u8cU+vC98G0OBo3UPDg6rq5Kr6/833WjlnL9B3T/c7tZCVncXficG7ooU2jUMID5IQGypGIRZzXnazTeVz4yxozW+3MGZPMh7+dcRumAGw5fZ3EaA39WkegkknJvVFO9g+Hmf9gZ64br9VLEBYZdF6CEPDKjbtYcpF9j+0je2O2XwOMsSszyeqcxbS+M1j6aAqhgVLsmCk2lvJIjxiWn1jOv7f+26/lewttC5pYVQSfXkDHwnkcbV+Ph1gFix2W651ibJvp1usug4xfz/zKjrwdHg6HRouRhkENMds8exq1Si1ZKVlMTJ6IwWLghuGG2xp//oH5hKq0ZKY2oLjCjMli55NRnVDIxCzbc4knlx7gk9GdPHL7XCQ11DAxowldGofxwPxd7jbUGK2KVlHB4FAw/Zdz5JcaKa8s5WxhOZuy04kPiUeXX7soc3158evRP1l35BILO6xgWtgi7w1NwAUofPRxTP96i6QAJceu6OnePPx2Hr2AgICAgIBfBFEmIHAbGMw2Vj6RTN7cVzHf+wVhZVaf263v1J9Xuj7Eo01XsD38UZRiM1T6OahcC00fpTLhUc78KeHKgSuIysuZvvY0300aVmOmklapZcHwhfRr0o9rZUW0jFERpg5FJrXw3IBmpDYJZf7W8z4Xvn1aRVBiLKiXWDFbzSy/fzlvbHnDqyI0pesUhjYfznPfnmBPro4nezdlVJd4SoxmKiqthKrq7rwH/rPGrpUauV5W6ZH5plXLUcjElBosyMROdwu1XMqHIzuyJOciSfF1s2F3cb2i2K9RSdXcuJd7vFxnA4zucRnc2XI4K0/9xIybhhPPpj3L+7ve97B8XzFyBYfyD7F06zxC9Vu4S3WavmqQ3EZ7IsDVSpinh/llkG/zfr+qQUZ1h8PXM16noLyA1hGt3VXbZqHNWJe5jq0XtzLi2xE+KoPPEKwM5qvtF5m54bT7Pdcs3v2jO/Hk0oP0aB7u9TMMUEgJVkkxmG0seDgFvclCaICc0AA5czef47WVx7zmBZflXGNylymMWzWm1mfhcuZsKM1nTqNptLWf8/HAoGx+IE+3f55NYSnwyS73tZutdhRSe425fgICAgICAvVBEGUCArdB5Kl9FDz6ABmnfFe8jofD2udGcM9jC7F8sg9wOAWZL4KaQ8tnsMY/iFmkQm+00qO5mUFtopFLxEjEIo5c0jOzv+9w4+EthrNgxAI2nNtIxqIe3ovjLlMY1GwofRPTKCo3YzTbCFRKkYrFfL0rlz+vljKkk49Veg1c0l+ioKLAZ1bUqlOrKK+0ce56Oauf6s7BPB0j5++iotLKovHtUcvVf0vWWIhKzje7L/Lqij+rOOc5s78SozX8MCkdqURMkFLKumMFLD94hYzE+HrdZ01GJS5BVl8DjFk7Z9CvSR/G/DwGESJ+Hf0rNww33KJYZ9Qxe8M0Tu+exn3h8GUAqAPrddlubA44Rwt+U8Qx+cxv+P7qwL9Bht1h51jhMVIapTB82XCGtBjC012e5vkNz7N+zPoarfHHrhxLzpUc3us7g2/3XuKSztkW6JrFy0yN45PRHRn+yQ7mbjnn8TN8pHtjdBVmBiRFcf5GORuPF1BeaeX5/i35dLMPAQUs3n2ZlU8O4rFOkzwy/bzv1enM+cnv/2FDs1moZT6+JdkNu9cm8fSAqRQG3ZoZrXrtLw9OFESZgICAgMDfhiDKBATqiblEh2zwEJqUeedKGaTwZi/4IA0s5pWEX1rHs/1TiZa9guPcL4jsVRaAkb2h1XPQcDAGizP3bN6WQ17VrEe6NyY5XstjXx1hYs9b4cYlphKUkmCigrQ1ugmOXTWGiclZzOw/gyC5lPV/5vPzoSucyi/D7oDBbaMIU9XPjj1UFcqnez8l65csn1lRdzQdzpJH0/h401mW5uQRrJKx6qnOvLvjFcIDQnm6y9OMXzXe7/FdM0rv9JnOb8eLvTLBXPbzF25UMLBNFBEaBY2CVVwtMdA4PIDfThSiN1oQiaDCbOPzrU7Xv78aPu3LqOR2DDCKjcW0iWgDgMFiYP2Z9QSUw5BQuDcUBgdAwF9Y7182yaloOJLw5Jc4cEHFmPbhhLRY4yXoO0R1YHLqZHrE9fBrkFH1/vJK89g7YS8Lhi+oe2UwNoO37uzJ2C8PeLy3JCePjnEhZKbG8dnmc+4WUdcc2fBPdvDJpnNkpsYxuV8LTGYbDYIUfs1rjBYbX+64wtu9ZzC0+TDe3Pq6R4C0a66vV8MM/vxxDLOi13pfsBUcy0S8XzGaz+66H7vYl/WO89pTG4cyuG20IMwEBAQEBP4WBFEmIFAHrDY7ZpsdvdFKoFrOvAHBPP+TpyjbPgCebAeHq1Q1ZufMYuu4bey/EEReyEjiSpYhih8FrZ4FbQcADGYr7/56gsW7fTv8Pff9YTJT43jvnnYM/2QHFZUWHunemMl9W7HxxDV2OFbW6iY4b/8ceiVk0Ct2GO+uPYlELHJXJq7oDISoQuolVlzhvP6yosrMpVy5oWTpTdfCqtUlrVLL3gl7fRpaVJ1RstgsVJgraKqVsrl1Ot/uucbi3ZcpNVp4ondTTBYLe1/tQampBJ1JR4hSQbAiEptdyox72mO127lSZEAkFrtd/+rT4ja161R+P1HsET7dIjKQCoveQ4BpFBpuGG7UeryqXNBdwGA0YLhRwBsLWnJXsI3r7W8vT8yFyQY/XYPfgxJIavc4DyQ9Rr+PctAZLHy6ScW0u3uzbdwwdEYdOmMJWlUwdoedefvn8dz65/waZFS9P51Jx5ClQ9gzYQ8ZX2bU6bpm7ZrBlrFDkYqd85ItIgPdFbGvduYy98HOzN1yjsRoDRN6NKFvYgSTvtnvbk/8bPM5dyXtwbR4xndvzPPf33r+sVoVb9/Vmk7xgZSYSrhakUvH6HZsHruZSquZ88VX0Cg0qKUaNu7YivVgN4YE+ZixzAfLtxHMHzadTyoa1Hpf87aep1/rSEGUCQgICAj8LQiiTECgFgxm680q1nniw9S8NDSSlxKvMSwUWhTDpWQofgi6h8PdRXC4+Na+LlvwG2V2WnZ8A5PiTVQhCe73rTZnhcyXIKtK9arCgbwSrpdXktEihN5fz6zTfczYMYOhY4fxVO/GjEyNpsKip8hQTMOgSMxWc63VKxeTUyez8tRKusZ29XDaq0pkYBgLNzlD1KpXl3QmHQOXDPQytHDNKG3P2+5zRmlylymsfHIQ+y6Uk9FSw8qTq5n1s7fL4eQuU+jXeDAnrlbStWk4pUazO7y5ri1uk5Kz6NN4ELvOlnu8HqiQUmS49QOWiCREBUYRq4mlXWQ7n8/CjQOogGgJ/LxmMB8EQr8GoPyLa/pt12GJHr4DEiI68GxqNne2Gs7Os2U0DFGhM1i4pDMyZsE+7kiKYNZ9HdDpAwkIktB/SS8OXDtQ4/H1lXoiAiLc/62WqSkor98MYomphPfv70DnhFBMZhvFBjOhN+f/1HIJ26b2xoHTKv/PK6W8e3dbhn+ywy3MXDli09edYtOUXm7zmm5Nw5jzYBtWn17NM4u8PwtTu04lI3YQr688TUTBLP7VcD5KhY824u3w29YUklb/zJpV56DCu122Oseu6oW8MgEBAQGBvw3ht4mAgC+sVvjqK4zdevDuyUq3aOoUF0KRsRiLFD59ALISoFUjiL2521MhMKsEyqqYEhYbdQxs24qlORcZ1r4RqiqnMdvszN18vk6X9OWOXOY/5KwqlFdakUlElFtK67c4rtTRPD6XYd9luhew3eO68+mgT7mn9T3subLHb9XNaSaygL5N+nKt7Bqdojt5OO3N2TsHnUlHx6iOaBTBrP3TeXxf1aWqhhYrH1iJyWoiIiCCl357qcZQ5+fTn+eNXm+QvaHm8OeJyVm8mP42H2w8zbP9W7DthV4s2e0Mbx678DCLxr9D19gefOgj72pq1xfoEXsHj3x5lE9GdyQzNY4lNyt+5ZVWwtShXq6DpZWl/HDfDygkCo9ngREwQrsQGBYGw+Ogy98Qb3WmTM5xe3+6DHuLKFUYYyuKeSEojHC1FmOlmNwbZpLjtHw/MZ1So4WrJUa3CcqXO5wZXGPS43g+fQqZteSiHSs8Rrg6nIz4DLrHdefpLk+TX55fr+vVmUqI1Tb0GcD9WI8mpDUNY9T8XZy/4aw+t4oOcn8BUZWh7aIxmq1M6tWU7s3D6NFCQ/bGbL/ZeqOXj2ZSchavDX2bn5cYvec6TWD5SsKbisc4OOEBlkVFcKoO1WIXQl6ZgICAgMDfhSDKBP6rqdpWWGo0E6ySo1FJkUvEt9dWVFkJX38NM2fCmTPcGHovi5PGut8uqjATZb7OqmgY1tx7d60EJgXDzCqdYFplCPO3nOP+LnFeizi98Vagbm0cu6rHZLbRMsqZ9aSWSzldXL+2uWtl15i5a5pXm2JCSAIzd85kStcppMaketmxu8xENp7bSM9FPb2EzNNdnmbvhL0MXDKQZ9OmoCt3uNv+qleXXFR1+Pvgjg8IVgTX2IapVWrp36Q/q0+trlO7ZnpMd+TSDmw8XkAjrYrGYWrWTu7BtF9PkP3DSVIbd2D1yD/c4dOhKi0aRTAKiYK+729DZ7Dw8MI9rHqyO6lNQpm35Twn88sIVoRwcOJB/rjwh1dFL1YaS4g9BEmljX7BMLyBM9A5/jZzxKpSaJKwtawRKf0/494fpJQarYg/vUrLqDJ3TtuZgiNVctusLBqXggOYvu6k2wTF9XP5ckcuq58eWKOrJ8BjyY+hkqj46f6fWH1qNU+vfZo3e79Zr2vXKkOY9tsFr1mwY1f1TP7uEJmpcXw5rou7Olb1Cwi741b4d0pCKJlfOFsyf8pKZdWp1bVm683dP4e0mO5Ymj/D1rwDZAQddJ7b2JhTy+L5vOndnIhowvvdm1BpsXm0q9aGRvk3/GAFBAQEBAQQRJnAfzFV2wq9rN57NqFfYmTdW4tKS2HuXPjwQ8i/VQVo+MtymkX24Wx4HE0VlxhR9i5R23cyzI8b3hUr5FexuOsY1ZFgZQifbj7M8A6NCFXLPU9r9OPI6AedwUygQorN7uDo5RLiIsJq36kKQYog9NXcBHvG9+SXM7/w9ta3+XTPpx75VEWGIhoENCAqMIqpG6cyb/88r2Meyj/E+FXjmZg8ka1jtxIoD8JkFhOsklFqtFBeaaVBQKjfaxIh4s5Wd9boXuhqbdSqtPT5qk+d7nV2ziw2jNnMk0uOseDhFBqGqLDY7DzeuxlyqRiFVMy3OZfIuVAEQHmlnke6N+b89Xx0BmfbXG6RgbJKCzFaFd89lo7OUEmAwsGbG99j7p65oAcMgBziI2FgwCXuUF+irwo0vj0i6kWhEZZfhR8d0LLTo0wfM4v3112k1JgL3Grrq8rpgnJaRAbStlGw+zWD2ea1nc5gYcznh1g0/h3SY3xXDZ9NzWZI86FYHVZe++M15u6fi0QkYeaAmfWaQQxWhrDhmP+KbvX23GNX9UhEsOulPhgqbcikYpbm5LlDpOVSMZHBEmYuryFbzwH3HIefE52fhVUjf+func/xS7OnWFPag2nXxmPu7BRVmalxdE7QYnE4/BqJVCepoQaNSvgVKiAgICDw9yD8RhH4r6Q2c4ynlx1iTFo8Lw9uVbMwO38e5syB+fNB770QkzjsvHRwIfljwxkZugGpyHdYcr4V3i2G+XqorPJNe3bXqRzJq8Bqd7Y6NdJ6tjoFq+TUB61aTnmlU/V9tvksn49tf1sGHe77E0l4LPkxtyCqnk+lUWi4r/V9BMmDfAqyqszbP4+e8T2x2CzIJDJ+mTyQMZ8foqC0kqigML/XWZt7oVapZf2Y9Sw5soT7ku6rV7tmuaWY9+9vTZnJwrRfT7DmyLVb572ZOXVf5xgeXriHbs3CSUkI5a01Jzy2CVbK+GlPLqu3/sG2k6s4o99Knu0iqnDo2QgGquGOAGhVvx+lXwqM8NMV+NEOW0XgmlD7ff88usX3YkKPoXy5M9drv2CVjDFpMYxKjcZoK6PCXILRdoNAeTDv3tWahxbu98r2yi0yMOKTfWSmdmDjmC1UWEq5qi8iVKVFLdWwLOca2ymhQrLdXZ20OWzM2z+vzjOIU9KnciC3DKvvvzpuqlfHrpWaKK+00rZRMFN/PMyG44XubbN6NqHUVOLzsxAjhQo9LFwJd56CV3vDOz0PYqOcLomt6XdsDqW2IMD58320e2M6xWt5eOEehrRryNiuCWT/6G1eU51JPZsiF0w+BAQEBAT+JgRRJvBfR13NMRbvvkhq41AGtYnybGW022H9evj0U/j1Vw+rdQ+UkD8ylO59DqOQWnxuct0K03XwWSkYqx1mUnIWdzQdgqFSQkKYmgZBCqRiz0WcRiWt1zfzCpnYbdG+J7cEqUjG1K5TGV3LXBB4hgO7j1lFELls6F329scKjwGw5O4lXlUsX9vaHDZm7pzJigdW0Hh2YyYmT2TR+LdZfbiI84WVHq6HVfdvqm1ao3thVkoWW3K3sOH8Bno37l3rfVbliv4Kuy7/xKMdH6Oi0jOlq2rm1PLHu1FRaeHBBXsoKTdgvn4Ro+4QV4rOEjfxJCVBRTgaOEgMgHsawB1q6Kn66yYdLvKNIn4scPCjA7ZZwQ7gIyR61s4ZDB83jAdT4/gm59bnPyFMzVePtGfbpXUe84Jwy/Biw3ODGTn3ALlFnq6hpUYL87bmMr57U2b8cp3CMhPllXpO5ZchEonY/mJXBi2d4bHPnL1z/DpoVmVSchZDWwxlyOw9tT6Dqu25J66VoVHKePnno3SK0/Kf+ztSVmmhzGRFq5YTqJBw8sZxj/2lwL/C4MUQKP4QIk45X39jM2xOAH1lKTPvbce1/i0ornCGVIeoZajlEracvk6AQsri3RdZ9WQ3t5GIP8akxdM3MUJwXhQQEBAQ+NsQRJnAfx31MceYu+XcrcWTTgdffumsjJ09W/OO49tg73uZKLxnoQDKHUFI2zzHFhL4ffdsjCWH3O+58pB6xg7k7k/30a1ZOEseTSNELfNaxMklYiZmNOHpbw9RG+O6JbA0J89j5sVkgYzYQUxKnlTjnFVW5yyf4cAahYYSUwkv93jZbVpxw3DDbeCx8pTThMNVkahucFF1W5fBhcFioE1EG+bsm0PXmB5kpt3FpK8PMuO+QTyT+hxqudJj/0ZBjZCKff9TVDWY2YGDcHV4rc+pKmHqML458jWNghqR0aIzm05d93jfYTWzcMVv3Nj3C3tObuFo8X5s2hsQ54DGUC6Du1XQWwW91NDob/wXM9cQwC5zGGn93qXZsjH4KyRVF8A6o46n+zUnpUkY87ac47LOyJIJHXl7u3+TlNHLR5PVOYvlT0wn65s/yblw63Od1FDDlAEtsFjt/HL0mse+iVGBlFV6m8n4c9B00SGqA9ldX2B4i2FM/PqoOzi6NlztuVW/gDhxrYyOcSEMaB1FdLAKg9nKgu0X6N/2VntmGzksioTkm4XoiIeA44ARpA5YshzE05T8a+WfHL2qp22jYIa0jSY6JBSVXEq/xEgyWjRAb7RitFh5eXAi6U3DmLvlnFdr9KSeTembGCG4LgoICAgI/K0Iv1UE/uuoqzmGRCzC7nBgNNtQv/OW07zDWMPiUCSCe+6BqVOhYQHiLcO8NjHaFSy4PoJ51+/hqZhk0ppqWT1yhIdZhKvta8TKfZQaLeQW5dElIZRBbaO8jieViOmbGOnzm3mJ+FamU8/mDUhvEu7RWjeoTRTllVbGfHGQRePfIc3PXFB21xcY0nwwnT9P9sqiClGGkBieyInrJ3za0D+T+gwN1A0Y2mIogfJAZvSbwe/nf/e5rcvso8RUglappV1kOzbl/sZdiSM4eEnHK8tPM+fBN1h1eqXH/hKRhIvPXPTZ3uiq5DlwoFVqCZIH3Va75sydM/jpzl959dopTNfOYio4TaXtBDbNNYhzMN8CtIPGUqf46q2CXiqI/Rt9HKwOEYdL44jrNJ4lV5NJbNeBES20nCk+61OQ+RPAGkUQhXoTMjF8PzEdi93K2rM/12p+MmffHDLiM5j74J0Ul1vRGZzVomCVjCClhAs3vMPQAxVSbvgwaQFvB03XDGJscCyhqlBO55soNYjYca6ozs/I1Z77SPfGHl9AfLkjl8Ftot1V8g9+O8OdndLpHNWOweYjvKIFedXvO8KBh4B5UKiGmQ824T1tFI9mONAoZV5mQNKb/7+q0BrUJoq+iRHojVb0JovP/QQEBAQEBP4uBFEm8F9HbeYYztmaeEanxmEy2yjUVyIXywjyJ8iCg2HcOHj8cWh+01LR4cDRoCei61sAsDnE/KDrxwf5oymwhiMRixjSviETvtrHyXy9O4jZ1fZV3cFt/rbz9E+KxNfYkcFsY2LPJnSKD+HLHblc1hnd12+x2amotBIaICdIKWNLdi/yS0zIpGKCVFLyS00ec0FV3QTDVFqiNWEs319ApRl6xPVALVO7Ww21Si2zB87mxd9e9Lmgv1hykdPFp7HYLXw2+DN0Jh1mm5nejXtzRneGiyUX3SKvqtnHzP4zWXr3UvRmPTcMNyg2FrH1ha7IJBLe3Ppvfju/AY1C45Hr9dm+z7xmlLRKLY+nPE5UYBTf3fsdNww3kIgldWvXtMLwoOFMenMS9q12DhYcpPmMGGwxNogDOgMKiJc6xVdvtfN//w6XxKpU2CSsLrVxRBXPS/dvI9js4Jq1lEFNNailaq6WWAhTeZuguIxNtl7c6lMAZ3edytDmw7Da7EilNqbvqMHwogozdsygX+PBPP3tIdRyp0NjfqmJHyalE6z2vvnySivhNZi0+JpB/PG+H7GYZTQIFBOklNW7PTc5Tus123fsqh69yYpGJWXu5vPY7A5+3/E768NLCK30cTArcA1+bwwP3gWzxr6DXKygRWTdf8BVhZpgey8gICAg8E8jiDKB/zr8mWOI7TbiGgTx1fgu7LlQ7JGJ1KA8gd1iCRJ7lWDftm3hySchMxMCAjwPJhIh6jgDNqRyQpzB0ydGcqYy3v12q6hA7HYHAQqJ26LeVoOX9rGrespMFqRiB2a7GZ1R566saZUhnC6ooKi8kgVjUwiQS/jjZKHPTKeJGU3okxjJT/sv8+P+S8wZkww454I+23yBuVsuuAVifBhMHdiAvq1DqbCV8mTKk6jlaneroUqiYkvuFp+CrKoouGPxHTVa4J8tvtUKOm//PHrE96DIUMTkdZPdr2fEZ/DBHR/wr56vMTF5AoUVhR5tj8uOLmPjgxvdM0pVg6R7f9XbfX6tUsvBiQe92zXLgLybfy6BqFTEmw3fhHigA9AQbFIbsdJbrYi9VND4H3A0P2CA9SY4p2zG6/esJfPjltgdFxlrNnLfD/dxpOCI+zk+mzqVu1sP86j+uYxNpu+Y7nNe61D+ITKXjyYrOYsZA2b6NbzwxcH8g5isehwO2JurIyFMzYonurEvV0e3ZuFeAup0QTmhKm2t1Um7w86RgiN0jOqIWhbAqWsVfLs3j05xWsZ1a8yUH2q/vvHdGlNqsDCpZ1PGLMjxMiUxWW1ghLP5RUyJXEZmyY++jXeugHUO/KsxvPcgTEzJYnjLYcilgn29gICAgMD/XgRRJvC/nupZZI206luLR4eD9tdOc+fxzQw7uR3zho18suW8Vyvg9UAtvzdNofe5vZzo2p8Wb0xBGXsSbmyHgMd8nzi8C8W9DqJWNGdi8+IqVawYHkxvhNlRzLODVISpQ1FLNXy75xqLd1/2WkyC04hBIbPy4/EVzNw1w0vkTEmfyui0YdhtYqavO+nfVfLbQ0zo3oTn+jfnzo6NkIhFHgtpl0V6QpiaDx9I5Lfclczc6X2+yV0mc3fru8n4MsPrPHURBa6q2LrMdaR8nuLRFvn+zvdZ8cAKnl3/LHaHnWahzVg4fCFbL27lkVWP+BR4Gx/cyMQ1E5k3dB69E3rTNbYr72x7x+v8OpOOvl/15ZOUT6i0VrJi4wp0p3VgwSnA4oCO4Ih0gMg5A+ZqReythib/wLr8hhXWG5x/NhigwK37zzLFZqVNRBuOFBzhesV1NAqNx3N8eOVoNMqfPap/LmOTmgw0AObsn0OP+Awy4nrU63p1phKS47VcKTHx9fhU5m45x9I9eTzRu5lP50GxSM4zqdmMXZlZ67GfTZuCwyFDq3Z+XjceL6ybcUZqHHckRXGxqBylTMydHRpyurCc304UYr5p2xikkFJ+ZQdrmk+mhdLHsWzAasj9A0bdCaaUDizu+gLDWg4jUB7gvb2AgICAgMD/IgRRJvC/Gl9ZZE/0bsZTsXDy+yUMP76FJrqr7u1PfTqPpbEjfB7r3V7jeGXAEzSJvMznJU+hLLjZHhX/ADQa7HOf6+Im3P/JDjJT41gwNgW13MqaM6sZtMzb4W5ylymsfHIQYxce9nC4C1bJ+OqR9rz0+1S/RgxjfnYaMbzVezqrD1/z2sZFQpiaMelx7LpQxLo/82ndMJhxXROYUmUhHRogZ/GEDry1/WWfwbqH8g/xQc4H9Ijv4bPCUldRMG//PNJi0pjUeRLTtk9zv34w/6Db7ONS6aU6C7x5Q+fR/5v+fDbkM3bk7bi1vQW4irsKdi7vHIOCBkEsThGWCtzssGsouVUF662CZn+TTX1VbA7YZYJ1FU4hdqASvyYdRYYitxALU4d5ZcQBjF85njNPnWFS8iQ+P/C529ikLszcOYOhLYYgFok9XDVrIkQZQnpTMRqVnJwLRW6x5Mt5sEVkIKUVVnrGDuSxTpOYf8D/3NrE5Cx6xAxEV2ElUClxt/E+vHAPPz/eza9xxvhujenaLIzj14pJbKimxHSD3m3N3KOK5L17W3LwYjnf7jyF5uSLhJ7+CJHSx31eBOaDvv0QCje9wKKoONRSDUEKFYFyofVQQEBAQOB/P4IoE/jHqV7pClbJ6zQwXz2LLFp/nUGndjLomy20uXqagT72iVr5PbKswVgk3iWRsgYBvNxwAfdoN0HVOZQDz0JUP5B4r+DDAhSUV1pZkpPHyC4RvLH1Fb9CZ9yqMUxMzmLR+LcZ8ck+d8VsTFoMm/PW1cmIIa1RdzJTO/DZ5gte7yeEqlnxRDpgpUmEjTtT7IQHiIgK0jJX2ZF3fjnJ8A6NeLRHPOvO/ezzOl1oFBqulXuLv6puh3Xho5yPWPHACqbvmO4hClxipFaBZwUqYd7GeUSXR9PN3o283Xm88dMbcAEovrlNONAA5yzYnYDauXuUxCnAXOYcLf4BEWZ3wMFKOCYKp2/a2yT9OInSuukftxDzlRHnQmfSMX7leBbfvZjBzQd7uF3WxsH8g5RWlnJv4r18f/z7Wrd3BTknx0tpH6tlwlf73O+VGi08vHAPX43vQnJ8CJtOXUerllFhtjH52yMsGPsOXWN9m8m43EbHLjzMp6M7seNsiXuusnvzBihkYu5IiqRH83CKys1ukxGFTMyOMzcIUNo4U/Y7Exd5V3U/TOrDp8HfIz592fuGrMBKMPwq59+9J3G4190E7pBQXnkRqVjEdxPT6/QcBQQEBAQE/v9GEGUC/yi+Kl1wcz6qZxP6JUb6tJZ2uaxtW7ubSad2MvD0TjpcO13r+YIrSkm/eIStTZLdr4mwMyp0PS9ELSJYWuG9U9lpKi/9guRmhc1TQMrY8UIfThWUsPni2hqFDsC8/XNIj7klrCRiEaNSoxn6be05YgAf5sxi1cjfmbvlgntRGxog55XBLbijTRhFxgKull1Fq9LSKDiU+Qfmsz1vO5OSJ/HrM8M4eNGIVGKt1fhBX6n3aS9fW5BzdapWxY4UHHG2kJXCyZyT5G7MJTk2mezd2VAEGAATTkEswvmvjxIIAALhjaNvQCAs1iyGaKAFbvHlIlLizAdzGXP8XYHNVXEg4pI4lJ+KithkhG1GKLGDRKTj/N2DaBxRf/fHBcMXeGXEVWXV6VVcK7+GyWbCZDXV63pLTCWM7fBonUTZM6lTsNukGMxWpGKxl4upzmBh5eHLjE6NpkszETqjjmBVGYvGt2fFwQIgmV9HbaLCoqfIoEOrDEFVzW1UGyDn54NXvOzjDWYrY77IweZw3DTFsWKotLJmcheyN2b7/Lv1Z/4hIhWHEPv6OV8A5sHxysY8lTmVc+GxkF/ufvvjUR2FcGcBAQEBgf8aBFEm8I9RvdJVlWNX9Ty97BBj0uJ5eXArT2GWl4d9wULafLGYLVfP1elctsaNKR5xHw+UN+ZcWKz79dbK87zT6FM6Bpzyud9xY2PevPoYGksMM+61su3MdeZt9RSQbRtp+PqRDkz+emadrmV2FWHVIjIQs72sXiKnwuJ0c7xaYuLJPk0Y2SWSX06voYePKsLTXZ5mfIfxDFwykG0XtzFjwCwKy4tqPJ9EJEEikqBRaLwMHDQKTY1Bzh7YgQJY+MVCCtYWwCnguvP1Rz99FMJgduRsZ2thMyDk5h8NIKnbKRq4RNjNubDWirrtV1+u0BxJdB92lSdhjJaT+cskr21sDhvz9s/zcon0hyuse0KnCT4z4qpzveI6v5z+hbbd29br2kOUWmKDGtepvXBoi2F8syuP7s0boJR5/hASwtQsGt+ezXlr6b94ltdn7fm0bIa3Gs6NilLKKw1EB0UiF6tZvOsyS3Kcs5RJDTUEKaV8PKqTVzVcb7TyZzUXxq/GJ7Pq1Gq/X3ZYgUmFsDnm1msOZIi+s8Av8GWHYbzXaxyVUk/VJoQ7CwgICAj8tyGIMoF/BFely5cgq8ri3RdJbRzKoDZRtxZQR48i//ebNKnlHDfUwaxp1YNVSb1Y8NmT2GwOzk37HYAAsYHnIpcwNnw1Eh8ObXqbmln5D7K4aDBBKiWrhyYxc/0pn2YEVruDworiegurwW2juTe5EeWWwjrt56LYqKNZRCDzHmyLWmkne+OUOhtu3JV4D1ql74Dl6rlXJouJ7K7ZZC6/ZeDgr4Lmpgyn+DqHs1JhgpmimRCJ02ijK87/34A6C6+qhIo9c8La/EMirDKgNebwnvxW1IoPDjckQBPBJ/06kSK3MPTb3n73m7N3Dnsn7HW7RPpjYvJEeif0Ri6Rk9oolYFLBnplxFUnTB3GkYIjqGXqemWxSQig3CTizZ7v1dheeEfTIWBX8MX2XIa2b4hUfEuwuOYe393xik9hdyj/EA+uyGRi8kSyu2bT55uu6Ew6r1nKST2bopJJCFJ6tw9Xj7KQS8V0ig9k8pc1V3W3GGGRHsZqwBrRC2mX+Vh2zGb/R+n8aI2jUgh3FhAQEBD4fwDht5bAP4LZZmfu5vN+348pyadYHYxBrmLulnOe32r37o1dqURs8m7jKlEG8luzVFYnZrA9oQM2sXPlf73CTIxWRVJDDRFlv/NOo09pKPdd8Vmp68nb1x7hutXpDjEmLd7D8KA6gQopRX4CdP1hdRh4567W2LFSbg6p177h6jBeGBTI3mtbKDHp6mW48e2fS3mz95te2/jKvdIqtV4C41jhMW9RUAKcAI4Dl3C2HkYBKTiNNmJxtiHeBhoxZNyshPVRQzs5iEW3d6yasAS2ZNnFZvTucT/5yi68vr6QU7tv5clJjOUEKqWUWWoW3zqTjoFLBrIucx3pMenMzpntI0MsmyHNhyAVS7lhuOHlTukLV6vj4YLD9arGTU6dwjc7r/Lr0QK+eaQLHcLv4NdRg9FXlrojFzSKYG7o7QTJA3j0q73EaFXIJWIkYrHbuXNMWgybLq6tsdIG3uYuVWcpv500DY0iwKs6ZbXZsdjtaKoJtX6JEZTctPOPkIBWDKcskFgIszbAuDuhMNC57ZTrTmfLGYO/JkYTi+zTT+hss/PDzVZjIdxZQEBAQOC/HUGUCfwj6I1Wj3mVsIoSul48THreEbpdPEx8ST5PD5vCqta9nMGwRuutb7bVaswZvVBuWAfAdXUIG5unsbZlV3bFtcMq8f7YapQy5NZi5jX9gJjSn31e0xVrDFPzJrGjvIP7NYlYxOjUOA/Dg+qUV1oJU/sP0K1Os9BmxGgaseLkcj7e8x+Wj1xer8pHqErLmtOr6N2ke70MN1aNWkWltdJLVPmzuPcnMObtn8eEpAk88esTcBCnEFMATYEROFsRg+r8ODxQi6B7FRGWrADJPyDCjlfCZqPzzxYjbJ24nmUXLtIjphNxcimn8s95BHzb7A5+P1FIq9iSWo99tvgsKZ+nMKnzJDY/vJnCikIKKwoJU4cRKA/kl9O/MHTZUL4esYwQRXitggxutTraHfY6V+OyOmdxV+Jwviq+jM5gYejHTpfQzLR41JIGOGRaQhQypGIxa/+8QMsoIx3jtLSMCnKKMomIR7o3JvvHI/Wae/Rl7jJv/xx6JWRwd6t7PLZ1zZR+vvU8cx/s7BHfEBYgp6iigMkh8GYonDHDiqXw2hZQ2OCTX+H++53HKbLDsjJ4sbKUGJztyUK4s4CAgIDA/0sIokzgH6Hsaj59z+bQLdcpxBKv53pt0/vcPla17gWA3mTxWFiJxmSy0hTAkpgU9jVKxC723wuX1FCDRiVFevw/PgWZ2SGnvGk2fVYmU+nwnD1pERmIyWzzMjyoyumCcgJk3vNXvtAqtfz+0O+8+NsLbrfF+lQ+Xuj6AhKxg1/OriA1tlO9WiYdDgdLjy7FZDN5nK8mB8SqAmPFyBXs2bWHJfOXMnvNbKdpRysgA2jCbbUjKkSQrrwlwrooQf4PiLCTZqcA693lTaYe+YVVeXs83i826mjbKBiZREy5yUrLqCBOXCvz2Obng5f5T6u4Op1PZ9Ixbfs0Hmr/EK9teo388nz0lXr+LPzTLVSsdgd9EgbVYdZrosfMWV2qcZNTJ9MjrgcPLs/krlb3u9sHP9t8jrlbzrkDxCM1Sl4anMjcLedIjNbw1fgumK12Hl96gLOF5ax8sjvP9G1OhUV/++YuN5mxYwZDWwxDjrMiVn2mdGlOnkcOWnjFdpL2fciHDZz7d1ZBZxvOzx1w33G45xj8lHTr3KHKkDpdo4CAgICAwH8bgigT+Os4HHD6NOzYATt3wo4dND95kgW17NbzwgHEdht2scSrtUkyejSidr3Zs+xQraef1LOp02Ut6VW4+C1UXHS/t62sA9/zEg9F9aPSsctr30CFlGKD2ev1qtjsDpblXGNylymMWzWmxm2zUrLYenGrh/193eeQsugZP4gycynXyq/V3XDjJjcMN9hwfgPHCo+5z7fgwIJaLe51ZTqmfTSNaXumOV0SE3FWxJpyW0IsSQ4D1M4/PVWg+gc6yUrlkYgievGvE5v57noB124u5E/E30/u3p+8tg9VaenfWsHSnDz6tIogUOH9T9+BvBIktK5XVTNQHsjyE8u9XBU7RnVEKQnkoS8OsXCcfyv5F7q9QHpMOv2+6edRUXOJ5WfTnmXL2C1cr7hOfnk+YeowVFIV8/bP47n1z6Ez6Vh1epVXFENVwflsvxa0jAri2FU9Zqud11YcZW+ujoQwNQFyCWO7JnBGd6LGe5WIJCRFJKFRaNBX6j3y11wczD9IiVFHoDzA50ypKwft8c4S2l5/i0GinVBe7UQjgb04nTqBp3Pgp9aAyPlMQ1QhtfxU/l5uN85DQEBAQECgvgiiTOCv8fbb8OGHUFRU710dQGxpAYFJrdCoPD+KUomYfomRjEmLq9EsxMNlTRIIXebDpjtwyEIwt59J84hMnrfZUfhZQJVXWglV1+6rvnj3ZVY+WXPVQyKSMDl1MgO+GeDxel0qH1O7vsCQFkPZclJPuPZG7YYbPgiQB6Cv1Hucb3CzGnKvSoF9N/9E4Axhbo2zVbEehEugv/qWEGv4D/yrYlbFoQ/pBBG9sIZ345NjK/hs12ceQsZfFljHqI5oVSEoI6VM+eEI93WOobzS6nUOm93Bkt1XeabLFMbeFN/VxcixwmPYHE4F+Gzas8glcoIVwV4tis+nZbMs5xrnbxgY8ck+nu2fytax27iqL6KsspTIIC12h50TN06wPW87Z4vPel2PzqTDZDOx/PhyPsj5wH0NVatxLqpHMXgcx2B2i9BrpUb0JiupjUOZOyaZ9zec4lRBGTPuj8UX1c1hbhhuEK4Op2FQQ+5rfR/HCo953HuxqYSY4BifM6WOSh07fnyU7OCfEQVXVj+VkyOABGwimNUV3uiFc4YRmNJ1KnLxP+T84oPbjfMQEBAQEBC4HYTfKAJ/DYmkzoKsXK4iJ7YNO+PbszO+HScbJOAQifnYVemqhlou5eXBiaQ2DmPulnPuhZEEG60aan27rEUPgJTPEMXciUIVTdTNlw1mq8c8i4vTBeUo5RKf71Wl1Ghh7MLDLJ7wLr0b92TWzllY7Bb3QlkmlvF6xr8xW80+BVDVNsGVD6zEYDFQZCgiLjgOCUEs3nWVCTuP8kj3xsQEh/s23KiBjlEdUUgUbkHiOt97/d6jsbax58bXgR1ALtAOmIDTtr6OyEXQVXlLhCX/A+M8V+0y1pVZCEu4h4QWYwlv0NEtCrSSQCZ0moADB3P2znGLgqpzWVWZkj4VMXIeXribGK2KELWcU/llvk7L4t2X2fj8UJ5Lew6lTOklRlRSFfMPzMdoMdI1tiu/nf/NbXrhYlJyFsNbDSNj5U7A+dkJUiiYv/kyuy8UMyczGZO9mCYfxRCsCPZbRa0a5F21TdAfs31k3AFo1XK3CI0OVvHJ6I4oZRJ+P1HI4pw8JGIRMnGi12fNlzmMC1ccw94Jexm4ZKBbVLraC6vOlMpEFjJD1/J05LeESvXOKIXqXAC+Bk7Dnw1g3AOwr9GttyclZzG85TDkUm9Xx3+C247zEBAQEBAQuE2E3yYCf41u3fy+5VCpuNC0Dcu1LdkZ354jUc29TDpqyxNSy6UMahNF38QI9EYrjtxlNLjwFpZefyALivK9X/Msr5fkEjETezbh6WrtkDa7w2vWxR+5RQZOXavkzpZ3MaT5UEpMOoqMxYSpQglWhnCuoJJrZdf87u+aQ5q+YzptItqgUWj4ZODnvLvqEjkXnMJi30Udu1/uRtvItvWaRcvums3KUyvpGtvVXdHRmXR8uvdTMuIznBtdBrYDFqALMByoYwdWpAQGB8DQAGdVLOhv7tyyKqI5YulIYochbDNJuOKQM237u6xPm8Huy7uZuWxojaKgb+O+PrPAsjpn0a/xIO76dBeXdEZmP9ABpUTM6C5xLM7xXnCXV1oRIeZfvf7FqlOrfIqR59OfZ3jL4dz93d0UG4tZ8cAKZu2cxV2t7mJsh0dp16ALunIRUcFKdAYLY1LjGJAUid4USufGYWw+VUhas0DaRbbjUP4hv1XUpIgk/1VOH1TNuHO1LyY11KCQiTmVX+acvVRKOVVQTsvIQL7Y5qyo+WrP9WcO48JXHENCSIK7vdBpf+9gUPAOpkZ9RWOFn78XZcB3wGawSyR8MTicpzoVYJbeet7ZXV9geMthFJdDucT0j7cP/qU4DwEBAQEBgdtEEGUCf43OnUEqBasVGjZ0irSuXaFbN0QdOhDlENHyRCGbtpzDept5QlKJGKlVh/ro45D3vfO1A49B7/V1vsya2iFdsy6ju8T5tcUHmNy3OalNg/j55HJm7PQOcn4+PZthLYbSLLSZz3Y0F3aH3V35UEpV6E23WulsdgeLdlxhSvpUnlr7RK2zaFqlloUjFtKvST+u6K/QKbqTR0Vn3t557N++n4AfA6gIqoD+QFjtz0sEdFQ4RdiQAKdBx9/JdSvkqZrSus1T/HSpJSvPBxEfFsCU6JakKa1YbGYGNhvIG1veqFUU7HpkF0aLkT5f93FXzTpEdeDZ1GwyYgdy/9wDXNIZyUyNo2OclhKjhaf7NqdDXAhf7sj1qJDe06kRChlkb8z2e94Hf36QickTmTd0Hv2/6U+ALICiqUWUmEopKC+iwlpCZGAoT/VpitUG7WNDGPbxdoKUUr55JJXRn5/kQlGYWwT5q6LGaGIorSyt13MtNuo85uXGdUtgaU4edgdM6NGEfRd1TF93kk9GdfIwt6nenluTOUxVqlrkt4lo524vDDcd4Kem2SQHnPS9oxX4HfgRMMDphs3Qf/oZowalM8hU4rbzD1aGcKXYQmkFDPt4hzug+p9sH6wtzqMqXnEeAgICAgICt4kgygT+Gmo1rFoFiYkQHw8iT2s9NXhUum4rT+jKr5DzCJjyb72WvxHOzIEWj9f9Uv20Q5YaLby95jgz7m1PelPP98ApIJ/q04zuLTRkb5jiYeLhwrlYzySrcxa/PfgbHed1rFM2lVqq8WqlW7z7Mr9MHsjIpJE1zqINbzGchSMWsuHcBnp82cPjvfaR7elHPxpsbMCYnWOcxh21dH6pRdBPfUuI/Z2zYTaphjUlen43wCYjHDODg3MsbhnB4O69SO9gpdhgRiISYbcpUMtkbDi3oU6ioFd8L4a1HMH2cTvQGUuRihUoxEEsy7nGiJX7iNGqmHVfO1ISQll/LJ8QlYy3fzlBZmoc8x/qjMlsQ2cwo1XLCQ+Ssub08jqdNz0mnd2P7mb9ufXM3Olt4jG161T6Nx/CuIUHyS0ykBgdRFG5mePX9FwpMXqIIF9V1KjAKN7r+169nnOoSkt5pfOzm5kaR0pCKG+tOeH+/yIxaJTe5jau9txF49+hW2wGfZtkMGTpkDqd86Ocj1g3Zh1qWYCzvdBhR3tkImEBp33vsAf4FigAm0zOf3qMZF7qPVh3m5Hu2cKApCi3Xf6GY4ex2mHWfe3ITI3js83n/vH2wepxHjXhFechICAgICBwmwi/SQT+OoMG1fj2becJWcrgwPNw7nPf7+sO1OMinajlUvolRtCjeTg6g5micjOhAXIighRcLDJQZrJ4LNTDAuVo1XKUMvj55E8+BVlV5uybQ7e4bl5zRr6YnDqFZTnXPOZ/wLlAHvP5IVY9PZMVJ1YxftV4usV2c1dRSkwlNAxqSKgqlOwN2d7XdAEOrzvM4WaHoUfNzyNY7BRh9wTCQPXf55RoccAuE2yogLSU19hVYebdE9O9tnt/10z6Jgxm+9kb3NEmnBJLIcWlxbSOaMr0Hd7b+2LGzhkMaTGEXN1VIgPDCFNrMVslDG8Xwz2d4lDIxCzNyePdX0+y+qnuTPjK6VBY3T7eYLbyxdh2zNgxo07nnZ0zm4HNB/Lgzw96zbEdyj/E6OWjmZScxcejna6IVZ0+q4qgqs6Mripqh6gODGt+N5GBkfWaK1RLNUjFIrcIfXvNcV4bmkhKQiiZX+xmwcMphAUqfJrb5BY5TUme6dcFu8NRr7ZJs81MRECE8wWRGEeb12F3NafSM8BS4KZWMyd35ul+T7GuSunWaodfj+ZTnS935DL/oc7M3XIrX+6fah90tl7WnepxHgICAgICAreDIMoE/sepk8100V7YMQrKz3kfQB4KKZ9B/Mh6n9s1wL80J4+WUUFEapRMu6st0349yZKbrYtiEe6FenmllbOF5ex8qXudRcL7O99n3Zh1zNo5i8QGiT6d+yYmZ9EzdiAjVvoOrc4tMpB73YqkMp0ldw6iwqLnSmkx0YERNA5uRF6RkU3nV3oKslwgD2gODPR/feESGHFTiPVV/325YWfMsN4AGwzOzLAyuzODK7Hlwzz0eYrPfcrMZagVIuRBexi0zNkS2i6yHd/d+129RMGVsis89svDbkEztetUescP5pWfj7Pp5HXsDkiMDvLKpLM7cM9fJUYHUVZZWq/z6ow6r7yuqszdP4e0m66Im07d8BBDLhE0Jq0D60ZtodJuQGcsQSlVoZZqWJZzDXNzCVPSpzLm59qDnbO7TiVQoWLphDR0FWZsdgf/Gp7E0pw83lpzglKjxflFRIXZr7lNqdHCr0cLaBmjqtMzcFFsLCFGE+P+b0njUdiPT0OsP8bFyij+c2UMT378Lc2LLmOSylk8+BF6z3mXdR/tqNPxj13VYzLbvPLl/on2wWBV7W6sVake5yEgICAgIHA7CKJM4H+UWm2mWzVAfe4DOPwqOLxty4keBKlfgLphvc9dfYD/xLUy+rSKZPvZG25BBp4LdXAu1nUmXZ0X67kluUjFUgqnFFJoKKSwotA957Xq9CrCVdGkN+zD2IWHKTVa/B4nQC5l8e5LvLbieBWReJazheVsyk5nwrqZzg0vA9eAFkCC72M1lMBdgU4hlqECyd8gxMrtAYiienNEEsE7Z3bxy7VbWVdVA44HLhnos5XTZSbxwsYXPMSlRqGpd0Zb1dwsV5VqYnIWb454B7sd8vUmn217VQlUSLlhKL7t8/rD5Yq47UwRGpWMoe2iWftnPoEKKWPSYhiVGk1JpQ6dSUe4OhS1VMPS3VfZf7GUB9NhWIthTEyexLwaqrSTkrPIiB3IxK8PUGF2ttOVV1o5lV/m5cSoN1pqNLcpr7QSpq7ZjjNWCi9qYX4pHDb7CHUWiRF3molZd5yj5ns4W3aJqYOieGHLIl4a+BQpA1JJc9TvQ1jV2t/FP9E+qFFJa3VjdeEKrhcQEBAQEPirCL9NBP7HqM1m+u0ffiepzWc0ted4vW+XBGBpPwtJswlIpbeRaIz3AL9ELGJ0ahwTvvJdrXIRqJCiryyp0zlcNuLrzq7zO2fUN6E/jyw6Sm6RwWt/uVRMv8QI2jQKJkQt5cKNCp8iscKi5/Cfh+EKzspYjNehCBPDvUEwOsgpxP4Ozphi+aMshT/0KRwytqZdeTh3dozm24cbUGIqodhUglYZjN1h9wg49oXLTKJ6++XtZLSFqcPQV3ououftn0PP+AzeGD4Eq01EgEKCRCwiWCXzKYbrIkbqct6qaJVaBjUfhEYl5T+j47moP8mUQQ14eUgT5BIJG86vY9h3mV6fk8ldpvBZ92Es33+VzaeuM+fBmWTE9WTmrule27oMTTI/P+jzM+WiqhPj1RKTX3Ob0wXlBMg0Ptsmm8icYuxhjbPCGiuFfzk6EqIMhu+/h6tX4ZlnnBs3HIS84SAG2uz0SYpB/3AKeuMElqnkBKullBr8fyHh81lWsfavyt/dPujPqdUXk/zEeQgICAgICNQXQZQJ/I9gtdnZc6GIZXsu+Xy/b1AOM2NnE2r3XuDur2jFM5emoClowcTK/Nt2Xas+wN8iMtCrna06ErGISI2SqMDaLQvrYiPuquC45oxc4iBWq+Ltu1rTKT7QKW6MOgy2G+x5tQcHL5bz6s/HuaQzAmCvuMKdb2RBINDB8xwBIhgR6BRiA9Qg+4sVMbNdyu6KtvyhT+GPshTyzNGEBsh5uk8TZrcNp8xSyvXyIopNMsJVoYQqI3CIrGQs6sGBa/5n/qpmcFXndjLafIVGA8zcOYNfRw3i8SWHEYtEPNqjCb8+3YPML3Z7CZjTBeUEyoP/lvOCZ85Xz0UZXmLqubTn6BrblXJzucd+h/IPMW7VGLI6Z/FC+tt8sukcQ2bv4a07e7J17FBKTCXoTCWEKkMIUYWQe93s8VnyR1UnxlKjhYcX7uGr8V3oFO/pQmmzO/j9eLFH22SiHF7WwqggzyrrsEDQhPRBfe8oWPMLDpkMU+8+lCa08GpNjgpWeognh4p6VaRcgrI6f3f74G0F1wsICAgICPxFBFEm8I9RdXasxGgmPiyALdm9WLbnEot3X7y5iHTwr4bzGRe+2mt/m0PMJ4X381HBKGxIoJ6ua1XPb7baMJhtHu9XNV6oTrBK5m4tM9rKCFIE1bpYr7uN+BzSb84Zfbb5AgNaR/D+yETWnF7NM4u8rfanpE/ll8nDeHjO76zd9woXY45BxK3jSYGBAU4hNiIA1H9xjWiVhWNsMJApO+PYXt6BCrsacD6TV4Y0YWSXSNacXuOeAat+rXc0HcKr3d/g7h+8BZeLpIgkDBaDz5ZQm8NWr4w2f6HR4Jz7sjjKmDumMyIRLNtziT8vl/LV+C4M/2SHh5Cx2R2cLTDVeYarpvPWRaA/tOIhj5yv6hXFOfvmkNro1udk5vqzTOzZlAGto2igigIRSMVimjRQMqx9dI0CoqoTo4vcIgPDP9lBZmocCx9OwYEDvcnqdke1E8XbHe6lZf6P3B3onLX0Rcb2DxGtcf7dElks5N45msH3T8Mhcn4Q/VnY16ciVVVQVuWfah/059TqOmdd4zwEBAQEBATqivAbReAfoabZsbFdE1j1ZDceXriH3CID5Ta11/5XzeE8c2kKeyraeLwerJIRrJJhttrRG42UGi0+jUIMZiu/nyhg/bECCvQmIjUKXhnc2uNY5ZVWny50CWFqFo1vz+a8te7Wspd7vFyjSKip8uOL2Tmz+GXUHxy+pOc/DyT6dlHEuXgf8/1oGusbk6vMxdHi1qo0SQ7jNDAmCCL/4t/kSxZYXg5HFM3om/IGwxPvxH7xOBUnCgHnM/n6kQ6olXayN07xKzTG/DyarOQs3us3nWfSnuPD3f/xeT6X+Yk/5uydU2tGGziNRHyFRldFZ9IRqQ5m3dFCmkcE0jEuhFP5ZW6LdRcSsYjmkcE0dQx0W9X7I6tzFr0Tevs97+3kfPly65ydM4vND28lM60xSqmY1Ueukj7tD3QGC/0TI5hxb3sOXCwmq2czOsR6564lNdQwrlsCKQmhPLxwj1c1zeVC2SoqiMFto4kKVoHDAQV/wIn3eaViLQT5vnaHRYToD4dbkLlIzD1G37N7+a15KoBfC/u6VqR8CUoX/2T7YPXg+tuK8xAQEBAQEKgjgigT+NsxmK28t/YEe3OdQbaJ0UGcLijHZndw7Kqe7B+PkJka565WzC4YRbegw3RSO4Nm15Z25cXLT1Fq81wNJoSp+Wp8F/ZcKCbzixzfRiGJkYiA7Wdu0KVxGJ3itOgMFoJVMoJUMo92qdMF5V4udMEqGV890p53d7zisSivTSTUVPnxxcH8g9io4IuxHfjx+A++rfYdOE08guBC2AUAQsTOFrJxGkj5i2M0Z83wU7nzz75K5+ngLF9dfZg3S9/k/ZHP8sgiC+cKK/jqkfbsL9hCiUlXq9CYs38O3eMzeLPnv0mJTvE5B5Wd/hLRgf7NWnQmXY0ZbXUxEnEhl8h4et2jDGo2nA5xg/hp/2VGpsSR2jiUbWeuc/SK82ffIjKQSouNcV8e8rKqr3reZ1KzGdRsCNvytvg8b30F+kc5H7HigRVM3zHdq+p2MP8g+soSvtqhZ8H2C+5KUbBKxmtDk5i5/hRL9+QRrJL5zF0LVsmQSUUM/Wi7u/21OpmpcfRuFYEUK1z4Dk7+B3SHfG4L4LDJYL0N0Ro7VMu2vq4O4V/9J/Fbsy5e+/mysK+tIlWToPyfaB+87TgPAQEBAQGBeiKIMoHbwp+tvVQs4sKNcib2bMbD6TaKDWZC1XIUMrFH2+KSnDw6xoW4qxUfGd/gi5DJvHr+Pr4tvgPw7JUKVsn4enwqc7ec8zImAM9v418a1IrUxqEUG8zcKL91fr3B4uE4Z7M7vFzoxqTFsOniWq8qic6kY+KaiSwfuZyMuAxm7ZrlsVhPjk7GYPFvsuALnVFHoDyA93e97/2my3ww1vkk+qudQuzOAFD+hTVogTScfG13Hty3gqPVOje1Si1ZKVlMTJ6IwWLgrO4ksx6IRS3TcLHkPCmNOjN06dA6nWfWzhkMbzmU5po+rHlgIOXmUoqNOkJVWtRSDd/uuUZydGCNLaFni8+S8nkKkzpPYs2oNdgdDi6X3iAmOByRCD7b+1mNRiJwa+7rxxM/8v3x75mYnMWL6W+z72IxGS0a8P3EdMorrRSVm9EGyCmuMLut6jNTO7B65B9UWPQe174s5xr3rN/PN492Z1LyJC9BfTsC3WAx+LXWLzLqMFnwaN0bkxZPzoUi998FX7lrLvfFD0d24NPMjry0/E/vLzIympAcHwpnP4cz74Dxag1XqobfpYi+10O597vfte3Pu73HU6ryU1rDt4W9v4pUkFJK7o0Kpv54xGP+T2gfFBAQEBD4fxHhN5pAvfHXmjigdQQfjOzIiWvlfLnjqNcCcHxalEfbYtVA2ApZLIc67ueHA4dJjA50LypdFbbqi1B/LN59kc7xWkqMZt5Yddz9+tB20bw0uBVdGofyXP8WbDtznfJKK8ty8vj5ia6M7hLHd/suMSo1mqHfes8TaZVa5g+bz782/wuVVOUOci4yFBGmDkMj12Cymer1HCMCGnDDcMNz8V4OlAHRzjyx8RqYGOx0vbtdLtmkfFVq5ftyMWuy9vPwsuFegqyqKcWIb0d4O/ylPUvzsBFUWCrqdM6D+Qe5WnaVq6YzNLCmM+OXAvQmK+WVeg+b9sldpjBu1Ri/x9GZdEzbPo1WYW2xVXTh16MGhrSrRKTKqTWcG7znvlzzfA5DKlKxCKvdgdXuwGS1Y7XZCVE5H7RT5Fxg7pYLVUSO57WPXXiYVU/NpGdCT6bvuFUN1Cg06Iz+haIvarLWD1VpKaoocP93Ta6h1Z06AeZtPc/X47t4VdEUMjF2u4P5W8/xSuMS/4JMqoW9DeDT0+Cj2GZt3ISHuoxnZ0KHWu/Tn4W9v4pUy6ggvhyXIrQPCggICAj8P48gygTqhT9b+2CVjDeGt+HdX0+wJMdbOMWWr6X7iXksu7SAr8b3Z/gnOzwCYQHiI8LYOrU3JrNnhe3bvZcY3SWOR2uxrnfx+bbzzH+oM/9efdy9gN55rgi1XIrFZue+zjH0aRVBgEKCTCJm7dFrZPVqQt9WEdgxEaQIol1kO4+w56yULLbmbuWPC3+gUWhYe3YtIkQEyAPQV+o5cf0EZ58+Wy/nvkB5EGeKzzhfsODMGmsI3cIhKxjuDQTFba47bfJwciQJvJF7io165yK9XWQbnxWcuphSPLziYbI6Z/k1pfDF9YrrzNr1HkkN2vLasLe9HAIX777MyicH1Wl+a0CTwdzxQQ6lRgunC8pZ+WTtc1/+5s1cuWHlJiuLduYyb6szJkEiFrHjhT4e7ay+RI6LAIUUg1lCj0ZDGTJ2GCUmHTpTCZHqBlgc9bN792et3zGqI8HKEDYcu/Uzq4traFWOXdVTYrDw1LIDOHAa3BjMVhwO+PyhzgxtF42twQQcJ99FZK0iugOawNnm8MYfUO7j5y2VwnPPcX7is+ycv7/O91ofC3uhfVBAQEBA4P8KgigTqDPVw5erMiYtnp3nirwEmUxk4aWoLxnfYBUAQ0qf5sD5je62RZ3BTLOIQKYMaMnOc0XM3+ptDDK5b3OAei1CzVY7A9tEsf5YAbFaFV+N78Kmk4Us2H7B6/hP9GpKWKCM1GaBFBuLmNZ3mjvsef6B+Xz/5/c8l/YcJquJrnFd3dUxhUTB/APzmbN3Dha7pV6OgVO7TUVvKiNUGQqXISgUxrSFScHQTlGn2/TCLlGz3hzIClMAX57NxeLwDGD2F8pcV1OKOfvm0KVRF7+mFNVxCY3qbpMuSo0Wxi48XOP81gtdX6B77B3YbDJm3deOjccLOHqllEcWHWHB2HfoFtuDD3zsV9O82cH8g1RY9MiRcejSrfdsdgff7L7oN1S5Oo92b4xMLMZYaWPUvP1880gasZHOwLhyc8XfYq2f3XUqR/IqsFYZNavJNdQf+tJ8RgV9x88F7enSqiejU+MwW22Umaw00qrR20UYIjMJuzIfGvSAxOdhzm549z3fB+zVCz79FFq3RlNavwrx321hLyAgICAg8P8CgigTqDPVw5dd+GunaiQr5NP49+igPu1+rbnyEpeOPk3mXT8zd8s5wgLkvDQokU/+OOt3Vuzzbed5dUhrr/ckYhGto4Po1bIBSpmEE9f0bDheiNlqx2i28crgRF4d0hqFVMxnm86xYMcFr2NUVFpp3UjJTyd+5D+7vRf3r/Z4lRe6vcAvp39hxk5vC/jn0p5j32P7uGPxHXV2DMzqnMUdTYewYvdKZm/I4sMOMD4Ygm6zKpZTnsRRxV2kZQxl8II0v9v5CmX+O00pqlJdaLiqU3O3XPCYjXLNbz3bP5WtY7dxVV9EiamEqKBQtCotP+8v4OcDBdydHEWzKAuRYWK0yiZoFME47DKGt7yboS2GUW7Rcan0EmHqMFRSVa3B1cVGHU1CItGbPMOIF+++yOqnujM6NY6lPiq+LjJT4+gUr2XH2Rt0bx6OSCRCIavyA3RI/7K1/qRk5+fEUCkhIUztnqvy5xrqjYNO6pOMDl1H+/3b6ais5M5u41inHsRjX+/z+nIiK2U8XbuNQRWdjkouhed7wGdzoaTk1iGjo+H99+GBB0DknPvUqKT1yhv7JyzsBQQEBAQE/tsRfjsK1Jnq4csufLVTdQs8xMdxMwiVem+vM1owVVYyuG00kRolG08U1DgrVl5pJVBx66MarJLxRK+mjEqNAZHlZpiujhHJkUy7pyVHLlUQIBfz6Nf73IHBD3WN5/eTBR6GAf6cFl1cLLlIx+iOTN04lXn753m978qayuqcxcYHN9JpXie3Y2DP+F7M9CHinknNplfDnjz7XleGR//JgTb+859qwqZsyA/FfZmT14OL5oYkRgfRUxlV4z6+Qpn/blMKF9WFhqs61TIqyKsdsNRoIVStpNwkQS1pgDYkkl+OXGPFwUPMHtWazXlrGfrtLG8Hx65T6RM/mFd/PsWrw2J48fcX0Vfq+bPwzxoFI0CDgFBE4BVGrFXLkIhgyoCWJMdpWbjDu7I6MaMJneK1jPkihwCFlG7Nwnksw9OaXSySMLzlMCYmT2KeL2fNm/iy1nd9TnrGDuTuT/fRrVm4R66aL9fQqoRKSrlL+wcPhG6gufJmWPvNx6G4vIy3/rwDvT3QY59jV/U8udIpNqcMsCOT2JGGhsJrr8Hzz4NEApMnw7/+BRrP2bf65I39kxb2AgICAgIC/80IokygzpQafbdMebZTOXiswXJeiPoKichzYWyyy3n9yiS+1/XnR5OD0V3iQATztnhX36pSdRFaUWnl28fSkMksLD/5k8+Wt6ldp9I+bjjhAXK2nS3i2e8OeVjwu+aa/DktunC19fkSZFWZs28OPeJ68Gzas7y++XVSPk/h+OOn6R0/CH2l03UwJjgciV3Bsi8ncD1wDItaOmo8pi8cIhnlkf24GNaPsMYj6SfRYDpcyEe/ned0QTkqqabGljlfocz+WhproqyyzK8pBcCk5Ek+Z7mKjToPce0iMzWO9KbhiIDxi3I4lV9GkFLGqqc6+xXMh/IPkbl8NJOSs3h16NvIJGLKzeU1CkUXHaM6EqoKYcHWXI+qncvh89NN5/jl6DWfFvMKmZg/r5QiEYuI1Cgor7RhtTno39rTUVAuEbPngpEZ/WaSEdfTdyxA1xcY3nIohko7u8Yf9HJ4HLHSOYOXW+TpVOrLNVSMjYygg9yv3UA/zR7kYs8KoPu6MHGP9ne+LBoBQMvrubTNP8uPbfsBsCQnj05xWga3vWlb/8QTcPw4PPssJCX5PGZd88b+JyzsBQQEBAQE/lsRRJlAnQlW+W6ZcrVTqUQmZsbOZmjINq9tLlRG8/jFlzhhagJAgyAFkRoFJQbf1bequBahE3o0ISVBi0hs5uXfX/S7WB+9fDRZnbP49MEZDPlwD5d0Ri8LfolY5NdpEerf1jdz50zWj1nPG1veICEkAYtVQu+ZO2geGUQDRSUvJ3xJ0JVPeT7KVvvBqlEuC0PW4glezj2HkngmJtxLqbkMnTGPXq2juTs5je9y8ll+oLBWN0NXi6XLyt1XS2NtRAVGMSV9CuXmcp+CODUmlTsW3+HVOtggIJTySu+qU3J8KC8vP8wHIzsiFomwO2oXzC7m7p9DWkx34lW9eabLFMbWcO8unkvLxm6Xsbhae2J1h09/FvNBShlLJnTmi7HtKDGVUGA6g0UUSogjGLlYgVwqQyoR0yFOy5zNZ0mO78nWsUMpMZW4hVeIMgSQ8dSSI2w5XejX4dFFVadSu8PZZrnqyW482RlklxZzn/Y3GsprFtcWh5RfSrqx15BEsxt5PLNjGUNPbqNSImN7fAfyNc7PwcIdFxjY5mbVVaGAL77wOI6vOIxgVc15Y4KFvYCAgICAQM0IvyEF6oy/2ZHTBeUEWHJZm5hNgtR7bmt9aRrPX3qOcrsacC7SwgMVKGVSSo11y/ZavPsifzzfk6ulFewvrH2x7qxeZfDWnT0Z++UBwHNh2yIykAqL3m/b3u209ZmsJtpFtmNy6hSW5VwjSFTKQOvXPBS8HG2xFVR1OhQADkRUhGfgaDYRa1R/Hlw1gVkDZvm1rM/uOpWBMQOBwTyWPIn5flrmdCYdXx3+mun9Z9AzoSezds7yammsiY5RHZGIJey5sscdC1BiKiEqMIpQVSif7/+czvM7ewmyjlEdiQoMZeHDDbheXkmAQkqAXIpcKqLv+1vQm6xYHQ4ey2hC9o9HGJPekEFLa5/HAue82uoHBpNgHVTjvYOzXXB4y2Fkfu7pBOlvLrK6+2JCmJpF49uz5dI6Hlztu0o7rOVwlBIVarmUrF7N+P1EIWM+P0gjrZqwADlFFQVIJYVMvaMVm04VAv4dHl04zWucTqXn84sYrl6Dde3zTBH/CZE1P58iUSyipuMZsKoZkVeKmZjzE0NPbEN8My5cYbPw+O4feH1AlvtcpUYLAT6qmv7iMJIaaniyT1P6JUZ45Y0JFvYCAgICAgK1I4gygTrjb3akR8BeQraOQlltfszuEPF+wRg+K7wPB7cWZJN6NkUhFWO0WL0WfhKxiBaRgWhUMucCtrwSvcnKuesVWGwOwoPEfLh6Zp2ud+bOGWwZOxSpGKx2PCz4AxVSigzFfve9nbY+nUnHM6nP0qdBWzbveJXtLdcQIK2fNbpRFEBp7P3YmoxHpW3NueJzjPy8C7899FuNlvWZy0eTlZzFy93e5eWu79Atxrcr4TOp2fRNGER+iZ2WYS1ZPnI5comc7K7ZZC7PrPX6srtORSXV8FC7x7heUUJUYBghihC+P/49/97yb4qMRT73m5w6hTmb87hYZOKeTo0ID1Rw+FIJhy+XoDNYCFbJsFsd9GrZgJyX+1JuKayXIC43lzLj1wJeG+r/3l/o9gJDWwzjlZ9Oc/SK52e1Ljbztc0guqq0k5KzeKfPdJRSld9g5BC1lItF9QsbN1sdLH4klaKyCpptn4DY4vtZAzjESo5L+/Of8z0oC0rjrdPX+c+St8nIPehz+wcOr2du2j1c1UQATtv6htW+RfAXhwHOv1tZiw8yJi2elwe3IipYKVjYCwgICAgI1ANBlAnUmeqzIyLsPB7xA89HLkbs8Oy30tsCeDpvCpvLUjxed82VmG123lt7gok9m5HUUMNlnZExaTGMTmuI2V5GmbkErVJLkCIYs0WCwyFCLhVz3VBar8V6iamEAUlR/Ho0HwCdwexuRQtTh/rd93ba+hrYShlt2ASbH+FBre+ZHn+cKA8gLOVtvijVsfbMH1Qce5oVD6xgwuoJjGo7qm6W9fvnkBrTndKiTmhUqfzx0FYqLHp0Rh0hyhCCFSHY7FJkYikapYmMReM4WniUrrFdWXL3ktpdI5OzGNZyGGazFLWkASJ7KMFKCVM3TGHO/jl+95uYnEXP2IHuGak1R5zzWi8NSuTtX5wB30/2aQZiERuPFbAnt5g7U2o26qhOsVGH3mj1cHK8pi9CZyohTO2c09p7oQy7TU7fxAhWHvYMSq6LzXx9WyqjZL3o0dzZsucra8tfO7A/glUywgIVhAUqIGEknPnMa5vztpb8ahjE53lplFtUDDq1gxcPPErzy6dp6ee4ekUAn6fciV5xy/zDFaLtoqY4jKos3n2R1MahDGoTJVTGBAQEBAQE6oEgygTqhVp+a3bkyK6FZGu+8dpGr2jFc9deZ3PZLTOIqnMlcomYX/+8xte78ghSynmqdzNaNVSwJW8dQ7/N9KpwPJuaTf8mgzlbYEehqltWmQudqYSwgFuLX61aTnml1dlyKfNvjOHLqdAfTWTwYaNQojb3QoQd6uGmuKFYjixxOuOONCOhNIBF49vTKKQlv55xtgb+Wfgnq0atqvNs2+ycWWweuxW9QcwXW/LYk1uMWARhgZUUV+QRGqBgUkYTQjUVbnG7PW87fb/uy7rMdaTHpDM7Z7bPvLAhLYZirJRgslg5mV/GbycKyL6jFS93f4fUmO4+TVdcLoJjFx72aBdckpNHcryWYe0b8u3eS4xKiePdX0+wdE8eidFBPNIzvu4PEQhVaSmvdLbdBSrkzN10mT9OFd4U4KXuOa0dL4TRu1UEo7vEeTh+1mYzX9sMYnWcEQCD+P1EoV+B4qsdWC6y0D3wIIODdxAgMZJ18WXAh5V8wphbokwRDvEPYIx9iIW7lKzdc4TMI7+SeXAtjcqu+73GMrmKhZ1HsCDlTvTKW4IsqaHGK0vMXxyGL+ZuOScYeggICAgICNQTQZQJ1Bt3S1arFzDuPIjq2nL3e/bYe1GnLOAjsdrvXInBbHUv8DadLCCrdwzZG6Yw18cs0KH8Qzy8MpOszllM7zeDSmsjtEqt3/yp6miVIRRVFADOxaZCJkYEdIoL4bdjxTyTms3Yld5te76cCqsTJ4XXQmGsBqQi/62Q1THa4WudiC8vDEUbP4VP2naCjVvcmV2ZqR34eOAwrlVcuK3ZtoLyYiYvzuW4jzmlzNQ4mkQEcazwosfrZ4vPkvJ5CpM6T3LPirlCslVSFSKkvPjjKXq3iuBqiRGTxc5z/VtisdoZMnsvmakdWD3yDxyiCq7qiwhRhni5CFZnwfYLzH+oM5EaBb+fvBWLUJtgrk7HqI6opRpO5ZcxJjWOgUlRfLkzl6slJq/zlpmslBgtTOzZhE7xIXy5I5djV/W12szXNoNYHVcEwPpjRX4FiqsdOPvbPfQIOsjg4O300+SgkTjbGm0OMWGSEopsId5W8uFp0OIpiL4DogeAWIYqL483vn+RN3/8CYnFf9XvhjqYL5OHs7jjYEpVQV7vT+rZFLnU83r9xWH44thVPXqjVTD1EBAQEBAQqAfCb02B20LqGtzv/hVsOA3649D+PcSJUxCLREjBq13LRdUF3guDWrDy5Cqfgqwqc/bNIS0mjbYRbTk48SD9vunH2eKzNe7TMaojwcoQNhw7TLBKxvR72hIgl/LxqE4UG8xEBMoJ0wxn1+WJPm3v159dzwsPv8CeK3s8rq+RFF7RwiPBIK9HVUxng49LYN6ZxoiUbyGVh3C9mhgoNVr4bPMFNp+6wewxCbc121ZuLqVNo2APUZbUUMO4bgmkNwmn3GQhRKn1vj6TjmnbpzF9x3TaRLRBo9C4c7+OZ53n/I0Kcm+apfSY/gf7Lxbz0uBE9zVvOX2DuWM6M+uXPI/qlD9cM35D2jbkoYV73K/b7A6W5Vyr1UnSxeTUKfx+opgZ97YjJSGUZ787xICkKFY92Y2HF+7xyKYLUEix2u0M/2SHl+W9XCLisYwmTP72kNc5aptB9EWxUUeB3uRboJhLkV5bz2D9zwxouxolFV77S0R27gjehbjFJG9hJxJB5488d1CrkS5fDn4Emb1JU1b3H8XUgI5UyhQ+t/FnW+8vDsMfepNFmCkTEBAQEBCoB4IoE/hryAIh42covwDR/eu0i2uBJ5eK6RQfyOQvp9dpvw93f8iKB1Ywfft01mWuI+XzlBorZtldp3Igt4wYrZrvJ6az+3wRL/x01F0JeW5AM+Ibnia7azZpMWkebXtapZblI5cza+cspnSdQmpMKov3zGK49RgTNaCoR2dWvhX+UwJfX1cRJXsemTrN/Z6vzCmAUwXlqCRBNAxsWO/ZthBlCM8PaMDEjKYe+VpLc/J499eT/PF8T8TiEL+VKLvD7pH3VbUSZXfgNkvRm6wEKW61uanlUvL1Jvbm1q2KCWCx2akwe1dhFu++zMonB/FYp0k1znBldc7irlbDKTOJWbI7j7fWnKDUaGHjiUKvbLqkhhpEIpBLJcRoVV6W9wCz7mvv1doI1DqD6AtXS6VboJTnwpXVcGUVFGwGhxUpNf8j/GzLwwQMalW3qlN4OIwcCV9/7fl6SgpkZyO++2762xzMOlFYb9v6+s6/VW9/FBAQEBAQEKgZQZQJ3BaeWUURBKtj0JitdbK+DlbJkYhFPJweT4mppF4tYQaLge2XtpMWk8akzpOYtn2az22zOmcxpMVQXvnpND8/3o33N5zyyKVqGh7AuG6N6PHlSPJK87za9pqGNmXThU28tfUtFu39mC9bJrEh+ALiesSM5Vpghg6+1EOv8GE83+NNEkLDPcQX3MqcqioGXNWiwc1G1NuyXkog3d77neaR3vlaM+5ph0wixu6Qk911KpnLa5+Rcln8u6peLrOUA3klHlW+2uayfBGilpGvN3m9Xmq0MHbhYRaNf4chzYex4tSP7L+2n2OFx7A5bO5Zw74Jg3n2u+NsPX2DZhGBtIgMdM8MVs+mG9ctgaU5ebSPDWFiRhOe/vaQl+X9wwv38NX4Lh6tjeBsqdQoguvdUnkmv4SGF9+F/b9CSe3B1i7M2nSIuxdt3D1IXSLp5En45huwWGDGDN87PvGEU5QpFPDAA87/TrlltqOW4NMNsjbben9xGL7wmn8TEBAQEBAQqBXhN6dAvameVeSysW/bKJjBbaPo0jisxm/2g9Vitr/YFZujkiJjfr3OXWQoQqPQ8MHuD/jtod9Yf249B64dcL9f1fpcXyHizRFJbDpVyOKcPORSMf0SI4jRqpiU0ZTrFQVuQTht+zRm7ZzFiFYjiAqM4rWM13h/x3tMCoY3QkuIrNhR52s8ZYZ3imFZGQQTRiNbNlndHqR5gwA0KrlXJabUaPEpBpzVooHsv7q1xtm2qkztOpXiCjstozwX0P0TI5h+b3u2nbnO/fN2cVlnZOWTd9RaiarqnOjCZZbiqvK5Wv5qm8uqTlJDDYEKKRFB3m1uwSoZQ9pFIpOKaRvRlgRtLFplCEEKDTa7DYlIxvwtl3hg/QGGd2jEv0e0xWS2UWwwE6VRopCJ+fVoPj/tv8ys+zugN1pIbxJOoEJKj+bOyqPLRbQquUUGd2vjV+O7YLXZKaowo5RKUEgkPJ+WzYMrao8OcAnZltFa1Nd/hdLaBJkIGnSHuPsg9m7k6kbOl8+ehY/ege+/hyM3j6FSwauvgkbjfZguXWDBAhg+3Fk584Gr9dhfe7Ev/MVh+MJr/k1AQEBAQECgVgRRJlAvqmYVBatkPNG7GaNT49wL4lC1HEOlDRGg8iHMys0VrDq1ipk7Z2B32Pnu3u98nkcikpAUkeSea3JVSMLUYegr9RwpOILeVMGvD2yipFJHsVFHZGAYEYFaKowiXvzhJBeKKpj/UAqrD1/jq/HJdIoPpMRUQrFRh8F2nTB1MC/3eJllR5cxqu0oJiZPxGAxYLZWIr68kiXKI7T0se71xzkzvFkMS8vAZofMlpm8O/g9/jhmYd6Wcyx9NBWzzcFLg1uR2iSUL7adx2p3uKtZL/50hJcGt+K7x9IpLDNRXGFGjIK+TfphtlXWalk/KTmLjLiBbDpZynePpXO93ESJwUJksBKNQuZ2N3ThqkR1je1RZ+dEl1nKqXxndWlfbjGP92rqFpq+WjH9Ma5bAnO2nGN0aryHkHMFNG/OW+vTjXNq16kMaTGM/FIzix9NZc+FYp777hCdE0Ldn8XrZZX0S4zgnk6NsNkdvDw4EZEI+iVGuqtBLhfR6q18MVoVidEa1HIJarmCYJWML7ZdYOGOC/z8xEAPISsCkhUQJoH1N0fXqgrZt+9sA47hvkWZWA6RfaDRMIi9C1TRztdzc+H7GfDdd3DggPd+RiP88AM88ojvBzu+dvFeX6rHYfjD30yagICAgICAQM0IokygzlTNKkoIU/PV+C7suVDMY1/v85pPmdCjCQOSIj0qZuXmCg+XRYlI4tWap1VqyUrJcgukG4YbhKvDUUlVrD69mgBZAH8W/gnAtbIiPlx7Cb3JelPY6JnQQ0S/xEjWHy+gWUQgIoeD2aMSWX16Nc8smuG1wJ+cOplDkw6x8dxGRnw7goCSQ/zYvAnhpvOE17ETL9cCbxXD13qwArHyWD6880OahTYDkZ17OjfgRrmRYoOZJ5YcoEl4AC8MasGyiZ3QGZ2CMlSlRasMQVcBLy0/zPkbBtRyCeWVVvJLTTzeqwmz+r9P74TeTN8x3adl/aDmgzlXdJnUZoHYRSa2nCrg3HUDz/Zvwdo/r3nNSVV1e1w98g8qLHoMVj0hyhCkBPp0TnS1ALpaGe/vHIvFZmdizyakJITy7d487u8c43MuqyqZqXGkJITy1poTgMgt5Ooa0JzVOYsZd81k7qZLrDlyrcbP4sSMJvRrHen1JYG/YOfqrXwquZRHejQmITyAd9ac4/X+TzMmCMoufkeKSEcDKZwxw/2VnkJ2WPuG9E2MQFw2Ao6/7TypIgwaDnUKsegBIAsCux3274dVn8GqVbcqYjWxaJF/UfYPUTUOo74zaQICAgICAgI1I3I4HDX4own8b6WgoJDHJk5i/ry5REZG/I+c02C2cu+cXVwpMbL6ye7M3XKuxoX3mLR4Xh7sNCkwWy38eOJH9wyTqxL2eMrjKCVKxq4cS7PQZqzLXMfWi1v5aM9HXsLj+fTn6dekHz2+7MHZ4rOcfDyXZ5ZedM8EScQiBrWJYsa97TBUWtl5tog+SSFM2TCFeTW4O05KnsSLHUdxcG0/7lR7W7f747IF3tbBwlKwAGKHmKHNh3Kx7KLHnJyr6nRnq+GsOniDER3DWX16NbN2eYvE7K5TGdZiGEazlK92XmTx7otuUaRVy/jt+R7YHGZKK0upMJcSrtaikqlZcnQxb2x+w2180iGqA5O7TKFX/CAaBGq4b86uGi3NxSJoGRVE20bBPD+gJd3e+x1rtfzmzNQ4Hsto4jbOyEyN4/kBLVm8O5cF23N5pHsCj3RvQonBTJBSxuZThczbet5r8T6uWwIpCaFuZ8RglYxVT3Zj3pbzaAMkNIg4WCfXxWV3L2Nw82FYrGI+23SBBTsu+N226mex3phL4fp27Pl/QMHviEt8z0BezdjP4j+V7L9Y6q4YqeVScNjh6JsQ1R/C00EsAYcDfvnFKcLWrIFr1+p2LVFRTjOPceOgffv638vfQNV50rrOpAkICAgICPxf4K+szwVR9l/K/x+iLL/URNq033midzMSwtR1alH7eFRHejQPRyw2k7GoO3mleR6VsBJTCYnhifx7y795ossTTN8xvcYWvYnJE8nums0jqx5h8Z0/kzF9J0FKGWPS4t2ta2abHZVcQkSQjJ9P/sSYn/2bWahE8IIWXgmXIXXUTZDdsMHbxTC3FCpv/u1pH9aelpEt+f749373ez79ed7o9QbZG7JrjACYmJzFq93f4dBFA+1itTy0IMdt6/7jpHRmrj9J31aRZKZH859d/+GNLf/C7rD7PNak5Cym959Bj/d2+swK88WmKT15fMkBt9itLqQCFFLGdUsgo3kDLDY7gz/aToxWxWMZTchoHo5cKqHUaMbugBCVnDKTBb3JglwqQSYRsTQnjyU5eR7X46q8atR2en/Vo07mLx2jOrJ61Gq25G6ja6MBjF142MP+3jXr6GoPfaJXMwb6CXL2wFIGhdugcDMUbALdAaewqoWClu/jaPZ43QVKYqLTvKM2GjSAe+91irHu3UEiqX0fAQEBAQEBgf9x/sr6XOgzEagzpUYzErGI0alxTPhqX+07AHO3nKNL41AMNh0Vlgr2TtjL1otbGfHtCHeVqFloM3Y9sot1Z9fVKMgA5u2fR1pMGv8Z8AFnC0zEhfpuo5RLxex/rQczd/q32x8ZCDPDIVYG1EGQGe3wQQlM14HetUa3w/hO45mYPJHUBak17q+QKlhxckWtmWzz9s8hPaY71ws6kHvDwPeT0pmx7iSXig1EByuZ1LMp7WOD+fHYcl7f/FqNx5q7fw4Z8Rlkprbjs83+K0lVqbTa+eKhzuTrTTQIUhIaIMdYaaWwrJKlE9KQiEXcKDOhkkvQl1pZntWVAIUEiVjMbycKWbTzlmuhWASD20YzMiWWpg0CGfPFbs7fMHidM7fIwMd/nOGZAeH1cuMsM5cxfec00mO2smj824z4xPm5HJMWw6jUaCoseooMxYSpQwlSqLHYrUglnn2prspPRdEZAvc/iLLsECJHPWw2ATQtiQwJBpdxhsMB589DQQGkp/veZ9gw/6IsNBTuvtspxHr1AqnwT7WAgICAgMD/ywi/6QXqTLBKTovIQExmW42tcFU5dlWP1WbHYClj/Zj1PithF3QXMFqMzNo5q07H/CjnI7aM3Up5gISvx6f6bKPslxjh126/gwI+agA9VHU6HXYHLNLD68VwxVrlDQeMaT+G2YNmM3jJ4BqPIRFJmJg8keHLhtfpnLNzZrFl7Fau6ou4Un6aZwaEE6QIxm4X0zkhFJHYzAc5fmzRqzFz5wxWjfyduVsu1Bjm7EIhFfPOrye4XlZJeaUVuUTEs/1akNI4DL3RgsVm59ej+RSUmXi2Xwue+e4Q79/XgblbTnv9HOwOWHPkGmuOXCMzNY4vx93KDauOSibhhrF+Ac0uN06XkH2ydxf6J4WyOW8tw77zbRIyrOVwAuUBgKeT6LlrhRxOOoyoLrkH0iCI6gfRdzj/qGLh6FH4+GPYvt355+pVaNnSv/AaPhxmzrz13y1bOoXa8OFOIScIMQEBAQEBgf8zCL/1BeqMRiWlbaNgig3meu1nNNuI1kSx8uRKn5WwpIgkKiwV9aqQFJYXE6KI5ML1Mr7bd8lrm7AAOUXVFvjBYnhbC49rnRWcuvBLBbx4A/6sessOSNAm8HrG6wxoOoCyyjK25W2r8ThJEUkYLIZ63eO18qvc/9N97iBn15zYHU2HYMdYr2OZbGW0jAryyOTyeZ0NNTQIUvJ8/5aYrDbCAhQEKaVUVFo4mKfjnV9PuEOkJWIRz/ZvydB20eRcKKpxvhDwyg2rTlGFmTBVZJ3uyYXLjRPgy0PzWT1qOFM2ZHuYhKhE0FUJvSyHiNszmh/O9uW+ISsRo3A7iTpRcsjQktTAY94nEsshPA0iekNUH7A1gYNH4Nv9sGMS7NwJZT6e7alTcOkSxMZ6v5eeDkOGOCthw4Y5RZmAgICAgIDA/0mEqWyBOiOXiBncNqreAcEquQS5RM7snNk+39coNNww3KjXMSuspegqLxMVamBzdjpP9G5MsEoGODOuOieEEqYKdW8/Sgwno+HJ0LoJskOV0OcyDL16S5CJRWLubHknORNy2PzwZs4Un+H49eNc1l+u9Xi3c4/XK66jUdzy5D+Uf4hxq8bw760vEyBToVVq63yscnMpgYrav4MZ1y2BOZvO8uSyA3y1M5dglZTpa0/Q/4NtxIaq6RirdVfbbHYHy/bkMaJDI77ckVun6/hyRy6ZafE+fwa/nSgkRBlCh6gOdTpWx6iOqKQqtxtnt7hurD69mq8PzqW3Cv4dCltjoKQp/BYDr4ZCNxVQ8DurTq2mxGDysnffXdEOALNdyp6K1swueICciEXYlIthRx949QB0HAUNY52C6vXXYf1634LMxcaNvl+XSJwmH1OmCIJMQEBAQEDg/ziCKBOoM1KJmC6NwwhWy0hqWLcAr6SGGoKUMr+thAD6Sj3hat9Bt/6QS2Tc88PdNP04nuHf96FBxEFWPtmZbk3DWPVkN6xWOyqRil5FDdkohqVNIUpd+3Fv2GBiASTnwSaj53tNtU25YbzBhNUTaP5xc1RSFe0i2xGiDKn1uLdzj1WrQFWZu38Oa8+uY1LnSXU+VnhAKBnNG9S4jcumfnFOHieulfH9vsvoDBZO5Je5A64n9mzCrPvauX/+OeeLMNazndVkttEyKsjrPbPVzoGL5UxJn1qnYz2d+jTz9s9Dhp0+ajFTgypocXACJU3gjxh4LczZoiqvJgB7q2DWzhkEqUBa7V/A5breZJ5/m3bHvuX+czP4oGAMS9ZbkNx1P7zxBqxe7WxLrCsNGkBFRd23FxAQEBAQEPg/iSDKBOqFWi4lQC5hQo8mHq9LxCISo4NISdCSGB2EXComMTqIlwa1otRo5kqp/yrRscJj7ryyulC9QuKqIL2361XmPNiGGUs2kPXkJBY8HsO65Kv0a1r7Ma0O+FAHzXNhvh5cPh5apZb44HgAzhSf4VjhMR7t9Cj5U/IZ22Es53TniAyMrPXa/+o9Vmfmzhk8kfIEYlHtf4U7RnVEowhmfPfGfPRABy9BndRQw6z72vFYRhMeXrjHY95LV2Hm41EdSQhTk1tkYPgnOzh/vYL5D3Xm9+d68s5dbevs6ug+psHst2q390IJI1oNZ2JyzYLz7Q73MNx8kEEXZ1LSBH5vZCf4zGw6SytQ1PJIEmRQfP0gZaeOMtVwnClbv2bR9/+idcF5LpobsqO8AyaH0r39WsJxyGR1u7mmTWHsWPjiC2frYkEBPPVU3fYVEBAQEBAQ+D+LMFMmUG9UcikDkiIZkxbH6sPXPJzuio3FRAWFE6bWYrZIKC63ACIaavxXiWwOG/P2z+PpLk8zftX4Ws/vqpB42MAbYN7cOWx8cQMJnGPneGgWVbf72VABz9yAE9VG5ZQSJeM7jmdqt6mYbWbKzeVEB0bz65lf6ft1X7eJxMs9XuaZ1GcYu3Jsrff4fPrzPPjzg7d3j1U4mH8Qi91Cm4g27pkzf0xOncLcTXncKLfw4qBWJCeEYjLb0BnMaNVyFDIxS3PyeGvNCS+BpZRJ+GHfZb4af8ug47PN55i75Rwto4J4qk8z2sWE1Ho/VYkKVhKpUXq8VtV2//WfzzDzzplkxPdk5k7voOzJqZMZYdqD9uxH9FBSJ6wOOK+DG7mgOAr7d0PYa115rMo2G5uncjyyide+FomMysQklEcOeb4REAAdOkDnztCtm9OuPjq6bhckICAgICAgIFCF/zOirLKykoMHD7F37z6OnzjB9evXEYvFREdHkZ6ezojhw1CpfNvx/f77H6xdt45Lly4jlUpp2aIF991/L4mtWvk934kTJ/jhh584dfo0VquV2NgYBg8eRJ/evf+pW/wfRS2X8srgRJ4bEM/q02t8Ot1N7jKFXnGDGPvlARZP6ECHqA4e21Rlzt457J2wl8eSH6s1p6xHXA+eW/+cM7H5FHAUOON0I39p9DkereMjvmiBp6/DKj/dZa0atKJNRBvKKst4ddOrfDTwI1787UUvS/u6XrvRYmR4y+H1u8ca0FeWMabtQ0wtmFLDsbLoGTuQESv3UV5pZXK/Fkz6Zh9Wu8Od3+Uy7qhOUkMNCpmYD347TXSI0sOgw+6A0wXltI/VIhGJSGqo8QiJ9kdSQw0qmYSXBify/IAWGCptaAPkiETwza6L/LDvMg+mNkRWdID7pfkMH7edG4brXNFfIUwdhkqqYt7+eTxy9BuW1xD/YXNA/nWoPA1h+yH4KLQwQouarq3A23zEfbzuPSBEA8nJzj+dOkGLFkJmmICAgICAgMDfwv8ZUbZl61Y+/XQOADExMXRJScFgNHDy5CmWLfuWbdu28c7bbxESEuKx3xdfLGD1ml+Qy+V07NABs8XMocOHOXjoEC9MzSYtzTubaufOXcyc9T4Oh4Ok1q0J0gRx5MhRZs/+mIu5Fxk3buz/wB3/89io5JU/XvCZu+VqKZyYnMWCsW/z4758JneZwrhVY3weS2fSMXDJQNZlriMjPoNZO2f5rJB0bdSVjNcy0O3QwQngZnVreDLMGQcN6+B9YXHA+zp4qxgMVcRIU21TXur+Eq3CW3kIgOfWP0dWShZrTq/xea9Vrz09Jp3ZObN9XnuPuB7c/d3dzBs6r9btBi4ZiM6kq/E+VBINo5MeJUbTiBk+KkrPpGbTM3YgYxcedlfAlubk8VB6Qp2Cv8d1S2BpTh52h9OgY/5DnZm75ZxbwLniEZYfvMLYrnU75sSMJlRa7TgcDqI0SsQiKDeZKc/fS1bEdtSybYhPb0d03KmUA+PuQRwQyU/Hf+LrI1/zZ+Gf2B121CIwN7g1L2ZHhCOkA/oVR9DutSE5BY2MNVyID9rmn/X5elJDDaJJ/wH5/5l/LgUEBAQEBAT+h/k/s8qQSqTcMaA/w4YNIzY2xv168f/X3n2HR1WsDxz/bnazm90km14IkIRO6DWFKlVAiliQJiAqxYIN0KvXn1exYcdGUVFURBSVJgICUgQSCBBKCC0QQktI78lmy++PkCVhN40WxPfzPDxX5sycnRPmJufNzLyTns6s19/g5MlTfPXV1zz33DPWazH797Nq9e+4urryzuy3CAgIAODIkaO89N+X+fiTT2nVqhUuLs7WNjk5OXzy6WeYzWZeeH4mERHhAGRmZvLCf15i+YqVdOrUidatW92kJ78xDMZiVhxZWe2DkM2WDtwROJBJHaaUS1de1on0E3y+ey6v9XqNAQ2HkG/MJrMwE2eVM1HbI3nvf+8xcdNELDmXIykfPXw8DkZWcD7vlbYWwNSLcLjMUkW9Ro+LowsnM07y8a6P0Wv0ZBdlWwOA6pwxdiL9BJ2/6MyUTlNYPWo1ZouZ1PxUvHXeKBQKPt/9Oc+ue5aMwgw6f9GZZ8KfYeuErVzMu0hSbhJeOi8CXAP4JuYbOn/RucqArL1/e1zUbny0PoHnBw6je/07sSjyOJ+dhruTOzqVniVRFxi2IrrcksTvI0+z8omujA4NrDSFfWnSj1mr44DyCTpK0+q7aFSk5xuqfc+xYYH0CfFj0fZ4MpNimN72LOrUrWgubsGrOMt+o4tbKPQZwaMdJlPHpS4fb3mLU4kHuOgC32aDRutDYOPxtG/zFCcztfi/0gNiDlf6tSsrw8mVQ36NOOTfmJgA+/NoU3o2Qq2U7bdCCCGEuHH+NUFZ79696N3bdl2bp6cnkydN4vkX/sPOyEiKi4txvLSpf+WKVQCMuP8+a0AG0Lx5MwYMuJPVq39nw4YN3H33MOu1P//cQH5+PmGhodaADMDd3Z3x4x/k7bffYcWKlf/8oMxs4N0ds6tVd07Ue6x8YCPjvoxh4UNv0LV+dz6MetdmZufZ8Bn0ChpIXqEDYa9vwjvrGA6JuzmxexPpabaHCo/pCnMeBC/bRH42UowwIxUWlclcrkBBiP5uvh/5Mh0WdACwuz+rumeMZRRm8NbfbzF7+2zOPXMOs8WMg8KBtvPaklaQVq5eoamQXw7/wodRH1qDwCFNh9DIo1GVARnAjC4zMRQrebJPE5QOKrYdzaZnMx/e+z2R3KKsCpcklmZRXDQxlPCGnszfepLY89koHRQ09XOhdV03+rXwo6mfq23SjysSdOQWGfHUqckqKOaJH/ax5NEwOgd78OXfp8otZWwZ4Mq0TiY6aTdwbtUMJluiUZIG1Tlm7eRqnKOd+Our5dyVdIRRh+PIv/tuTnz8Gp5aD9yc3NmbkMNdc0uCxw19B0O0/aAs3Qn2BICpQ3t63vssdAjn/dh8vo+yPeeu1NjwIPqE+KKSoEwIIYQQN9C/JiirTIMGwQAUFxeTk5ODp6cnRUVFHDh4EIAuXWynYbpERLB69e/s3h1dLiiL3rOnwjadOnZErVaz/8ABDAYDanXNzvu6lWQUZNTo8OK84mw0jkqGfRrNmLB2bJ2wjazCTNILM3F3csfDyZ3crHw++2opx6O3cnb5ShIK7W/28neHBQ/DkA7V6+uXWfB8KqSXyZkRXrc7r/aczZ8x2iozGNb0jDGzxUx8RjwvbHyBh9s/zKSOk3jr77es18vOvJUNAs9knanW3rQpHacSEdCfoZ/uJKugmJYBeib1aIirkyMpOUUkpOVX2r/SLIrrnu5OeEMvFArQqJTkFhnJKijGWa1CpVRwV5sAvo88bQ3MPHRqcouM1vscS87FSa2kZYCeHk192BB3keMXc1kwrpM1kYinxkjQjnYoEy4A4FXtryJw1gGWLkWzcikDyhQXbopkWSSk5SWzPnY/xjL/rvHNOtICMLhoiamrZLNXLtEBEB0AbiFtmdH1BYY2G4JWXTK7/WKwkbCG3szbEn9FIKlnSs9G9AnxRWdn2aLRZMZgMpNdYCSrwICbVo1eq0KtdJAATgghhBA1JkEZkJSUBIBKpcLVtWTa5dy58xQXF+Pmpsfb2zZzYKNGJVnaEk6fLleecCoBgIaNbLO4OTo6EhhYnxMn4jl//jzBwcHX8SlurvSCqmdzrqzvolFdyt53irta12VdbC7bdh1En3qQ49Fb2RUZiclkqvQ+94XCvInVmx2LN8CjF8ufN6Yy++FR/DAzO0xFbVZj4TweWvdK73MtZ4x9HPUxy0cuZ/b22dZMihXNvFVnb5q9fWKx57N56scYxoYF8e3DYQz55O8q09RnFRTj4KDAbIa/j6fy9fYrZ7f0TOgSzMonujJ+4S6cNSqcNSqOJl2eamzu70pmvoEpPRrSIciDlz5cjebQfr595wJtC1NRKxW8fN9zvOOio7X9HDrlnQdigcOU7BfMsZ950istiQ3r93BBX/7ctbHhQQTfEQgd90OzprRQmAgoyGBAYSaeTu64a91RO2hQqy6nt9epVQxs5U+fEF+yC4xkFxajd3KsNMDKNxjZEJfM/C0nbb5mk3o0pG+IHxqVBGdCCCGEqD4JyoBVq38HoH37dtaliympKQB4edn/3b6TkxPOzs7k5uaSX1CATqslPz+fvPySWQrvCtp5eXlx4kQ8Fy+m/KODMk9tNTJqXFE/tygbU2EuRYkHeOu/y1m/7g9SLpyrVnt3HXw6oWTJYlVMFvgoE15Og4JLS/icHZ3xZRSm7EGoHDRsPHKR/w1pyS97zuDu5FFpZsiyZ4xVVKeskv1eLtY9afnF+eVS11c281Z2b9qKkSvIL84nLT+N+m6BOCpc+W7HeZt9YqW+jzpNp2CPclkSKxLWwBNntYo318SxOMp2H1js+WxmLDvAmLBAFk0MJe5CNm5aFTte6FMSuDhY8Ni3DMefZtHCuBdLbB7ffFNY7h55jk7EdXmUbXXa01prpz/JlARgpX8yK+2ylUnhQPOUBGtQZjOr5d4GNaAGXNTO1HOrV+n9VJeCL51ahb9b5Tn28w1G3lwTx/eR9r9mT/0Yw5iwQKb3b4bG0cHuLJsQQgghxJX+9W8M0dF72LBhIyqVijGjR1nLCwtKXjA1Gk2FbZ2cNOTl5VFwKSgrKLz8UlpROyenkpe+goLqpYbLzc0jL892GV9qavWX090IHtrKAxkrAzTKacRHr3/App/WYEiKBywsrsFn9W8NCydBXc+q6x4qgoeTYVdR+fJ7Qu7j9Z7vsDzmIvd08CWvOJtzecd46k4fFCj4YsgX9P+uv939XDU9R216l+n8fux368xYWn4aHk4etPFrg16jx9/Fv9KZt7J701r5tkKv0bN4+E88sugAsedzKmwH8MW2kzZZEu156a7mrI9NshuQlbU4KpFOQR4MaOWPNus82rXv4X96BXicAQ8LXEpLr7DzWc7FhXjnZ7ItpwOP+S7jgsGLHXlt2Jnblqn1W9Do2Um2jexxdYWwMOjSBVN4BEWdQ3nLUVetWa3ryWgysyEu2W5AVtbiqETaB7rj6uRI9ybeEpgJIYQQokr/6reFs2fP8uFHc7BYLEwYP44GDRrUdpdsrFq1ih+X/lTb3bChdlAzo8tMxvw6uvyFAuAskAicLvnveHM88Xxc48/QaeDdUfBYv6rrGizwZnrJH3sL93ae3Y7OyYSn1x6GLLVNMjKzy0wOPXaInt/05ES6bWr0ubvnEvtYLFM6Tqk04+TkjpO5q8ldDF4yGAAPJw8aeTZi8T2LyTHkWDMy1nWty8cDP+aVv16pMLGH2WLmQPIB2vu3R2FxsmY9rEzs+WzMZku5LIlXejA8kIberrzwy6Eq7xdiicfj5w9x2hgDvmngCNiuzIW6gCdwRT6WwIwkol1CuOPIfBIMAUBJDvunQj1J9vAnXeVEjkZHrlpHjsaZXI2WHLWOZFcvTnjV5/3/G4lvi8agKGmnBHSX/lQ1q3W9GUxm5m0+Wa26pUcIbIy7yMBW/rKUUQghhBCV+tcGZWlpabz66ixyc3MZNnQIQ4YMLnfdSVvywldUVGSvOQCFhSXXSg+d1jpdfkksKipCp9PZaVNYrk1VhgwZQu/evW3KU1NTefGl/1brHjeCWuXIkKZDGFVnFEv+WFIShJ0BUq7P/UMbweLHoLF/1XX3FMKEZDhksH/dw8mDdWPX8dKml+wm0IhJimH0r6OZ2mkqOx/eyb0/3cvW01ut19v5t+OZ8GcoNhXzfLfnCasXVuEZY72Ce5FryGXHmR009mzMhgc38Nepv3hnxzs29Z+LeI7dj+5mwOIBdgPBUjO6zGRJ1IVKZ77KyjeYeGlQCG/9ccR2z1P3hoQ38uJiTiGHL9ge9qzEREfnOHq77qKPfjdNnCrOTGijNWREuXLMJ4gE9zqc9qjDRVdPii2OJBjqluuHol1bIiZ/WeUzZXr743spIKtt2QVGu18ze0qPEFgXmyTZG4UQQghRpX9lUJaTk8Mr/3uNiykp9OnT2+5hzj7eJftV0tLSbK5BSXCVl5eHi4sLuksBlk6nw1mnIy8/n9S0NALtBGWl9/P19bG5Zo+Li3O5c9Bqm8Vi4cMPP2Tzlq1ERu4g5eJ1isIucVDAzPvgtSHgqKy8rtECb6TD6+lgrKTe1M5T2ZKwpdKMhgBzo+fSPbA7vz3wGxfzLpKWn4aXzgtvnTenM0/Tbn47AJv9XmUPmlYr1Ry6eAg3jRvrxq7j7b/frvBw7Qd/e5ApHaewdszaCs8mm9ppKnc1GUz3X3dU/sUoQ61y4PjFXBaO70yx2YzRZMHVSYVOo0SpUGC2WEjPs41gu7rE8Fng27ircqv9WWQ5gqEZJp++bJs+gAnbiq2zWhUpeyh1VfROjlVXukmyCiqI+iuQkW8gObuQ7AKjLGEUQgghRKX+dW8KBQUFvPra65w5c4aI8HAef2wqCjsvkXXrBuDo6EhWVjZpaWk2CT/i40uWMQUHBZUrD24QTGzsYU7GnySwfv1y14xGI4mJZ1Cr1eXOPfsnUSgUfP3NNxy6dFzA9aBUKlEEKnht4mR6+S0k3Lnq/XZHDDAuCXZfMZGpVChp6dvSevbXkZQjVR78XNa7O95lcNMhODno8XNR46p2xknlRDOvZjzQ8gHm7Zlns9+r9KDpRzs8SljdMJ5Y8wRTO09le+L2Kg/XnrdnHt2CuvG/O/7HU2ufspaXLKl8nsFNB1NocKSeh7bKjIpKBwUDW/njolGy9tAF0vMMjAkLwmIxk5JbhLvpctp2D93l4xhaJseTonPnmCoQvdL+MQRWRiDVG3z6Q7fHITgCFAqUQKjByFij/SQYpcaGBxHR0Nt6KHVpv5v6ueCiUZFbZORYci4ms4WWAXr02lvnW5SbtmZHWJQeIZBdWHzTl1oKIYQQ4p/l1nnjuQmKi4t58823OX78OO3bt+O5555BqbQ/HaPRaGjTujV79u5l+/YdDB06pNz1HTt3AtC5c6dy5Z06diQ29jA7duzkjjt6lru2Ozoag8FA506d/tFnlIWFh157UKYHGgCNYc60OQQa9zIg9TscjVUHZB9lwH/SoNACgW6BfDzgY55Z9wyjWo9icsfJ5BfnW/du6dV6Ck2FNTpT7Xx2GnPWpzJrWCtcNEoy8o1gsfBu//foHtSTd3fMJiYpxppNsZ1/O74a+hXdA7szYPEAGno05LmI5+i9yHbZqT3v73ifLRO2cmfDYRSasnF2dEPj4IrFrGLdwQya+LkwoUswM5bZHmwN4KZ1ZGx4EKPDArFYLCRlFfLhA+3RqpWsjDnHB38eJ6ugGDdlDuMDD9C+XV9C23Tj4dT93LluMaFnDzMv7F7evuMh9uSH0Nm5/OHLuQZndOe8cGh0Lwx+Djzr2u2HTq3ixUEhhDXwqvTcr53xqSX9KdPvQoOJ9HwDnjo1GkcHluw6Q+u6etS30LI/vVZFywB9ueeqSMsAPRpHB44m5dxSs31CCCGEuDX9a4Iyk8nEe+9/wIGDB2nRogX/eeF5a/r7igwdNoQ9e/fy08/L6NSpo3V268iRo6xbtx5nZ2f69u1brk2/fn35edkvRO3axc6dkUREhAOQmZnJokXfATBsWPVmbW5FuYY8TrmcqnlDLSVBWMNL/+sJKMBJAb4nX2GI2v4y0bJOF8NDyZfPHXv1jldZH78es8XMjod38MfxPxj247Bye7ceavcQj3d+vEZdLT1TzclRyYj5O9l/Nhulg4IeTbyZMeBOtj00hMyCDM5mpeLn6oWXzp18QxFns5PYNO4vHBV6sgqyrioQfGlQCG+siWPtoYP8+UxPlkaf4YMRbenZ1IfRoYH8sKv8LFSwl45FE0PZdSqdSd9G2wRCj4R5snH4SU5Gf0V7zT4cFSa2rF6HZVQ2L5+6vI9t5P51fNR1FGuzIujsfJjYgoZszO7MpuxQHh42goEPBuBQjQCpOud+RTTy5qk+jbmnQ70K+/1wtwZ0b+JzS+3FUisdmNyzIdOWxFRZt3SJZkidW2u2TwghhBC3pn/N28KaNX8QGRkFgF7vyrx59vcXPfTQePR6PQDt2rZlyOC7WLX6d55+5jnatW2L0WgkZv9+LBYL0558wma/l6urK08+8Tjvvvc+s995l1atWuLq6sr+/QfIy8tj2NAhtG7d6sY+7A1iMBaz4shKNpk2VVnX3dsdvxA/jmqPlgRhfsAV79et1PCjP7SsRkC2KBumpUB2mfOER7QcwZ8n/6RjQEde3fyq3aWCey7swVldsz15nloP7mzpRH6REbVKyeO9GpebzUnOtBDg7s/Hf6Zx8Nwpjifn0D7Qg0k9GuGpdmb6rwd4akD1ErmUKg0ELcDaQ0mE1CmZadlzOoMHv9rFL1O7MOPOZnQM8mDhpYOe3bSOfDsxjHlb4ssFaxpFEb1coxnmuIXex3ejcSjGu8zquZ5ee+GKuNq9MJe7YzezzKkva7K6caG4ZM/j2PAg+rQoyR5oNJkxmMxkFxjJKjDgplXbTUdf1blfOrWKyT0a8UYlZ6Q9+9N+xoZn8uKg5rfMfiyV0oG+IX6MDQ+sdInmmLBAOgd7Mmt1HK/f3eqWmu0TQgghxK3p1njbuQlycy8nLygNzuwZOfIBa1AG8MgjD9OgQQN+X/MHMfv3o1KpaNu2DSNG3E9I8+Z279GlSwRvvjGLn35extGjxzAajdSvX4+7Bg2id+9e1++hbjKD2cC7O2aDB+AClM0H4QfUv/QnEIKbB7Ni1AoazGlgPa+rrIf18KkPOFXxvpplgikX4ccrck+092+PVqWlZ1BPNp7cWOHeras5+NlD605AAx3nMgqYM7I920+k2szmvDq0BRGNvPkp+izBXjreu78tR5NyUCggu9CIl64ah6qV4an1oF8LjTUBRtmZlsk9GlJoNLHxcDJD2tXlzpb+ZBUWo1RA1Kl0ftiViBITXVz2M8x9C3e67cBVWckyUF8gGEi4XJSm98Lk4ECWyZUsk6vNgcz5BiMb4pKZv+WkzazW5J4N6RviV+3gyWgys+FIcpVnpH0feZqwBp63VEr5qpZoPtQ1mM7BnoxfuIshbQMk86IQQgghqkVhsViqmWhb3EqSky8yafIUFsyfh5+f7035zDNZZwn86FLyki2AmZIgrB5gJ49B3ONxPLDsAeveKwCdAub6wji9bf0r7SyA0UmQYCe14tfDvuZE+gkmdZzE0CVDK10q+GL3F2ns0bhaBz8vHr6EwU2Hs/tkBl0ae/PdztP8FnPOmnyilJvWkZVPdOXbHacZ3yWYeVviWRp9hq0zezHlu2jmPtiKIUt7VzsQXDvmLwqKVAz9dDtD2gTw/IBm5BmMOCodWBx1mq/+TiCroBgHBYTUKQmaXr6rJdO/+pH2xuXc4/EXvo72zzsrxwDsB34DTkNxq9Y4zJxB0fB7yTY52F1umG8w8uaaqhN4VHdWK99g5L65O6uVXr5lgJ6fp0TcMrNlpYwmMwajmfR8Ayk5ReidHNE4OvBDVCJ7TmeUzDBeCmiFEEII8e9wLe/n8sYgqi29oMxLf8+K65XKLMxEr7kcfYWo4Wd/aKmpvJ3ZAm9mwBdFdWjfKJTMhC1kFmVar0/uOJnugd35au9XjG0ztsq9W3N3z2X3o7uZ1HFSpWnxp3aaytBmg/kl+jxD29UlLc9A+yAP+oT4WZNPfB95mqyCYrIKihm/sGRZ4bbjqSWzVQ4K/jyczLP9mrIhNp2nQqfz0MqxVX6dpkfMxAE1r68+xGtDW9KvpR/bjqUwZ+NxjiTllEsdb7ZA7PkcHmyWieavR5nvGV3l/TECMcBOYB9QBFH1WjL3vvt48ZNnaeqvr/BAZqPJzIa45EoDMqjZrFZNz/u6FVPKly7RVKsc8HZRk1tYsrR1XEQwT/RubLOkUwghhBCiMrfWm464pXlqPWpU39/Fn+yikpfv0a6wwBecq3hPPVMMY5NhawG08fNhWLNhvN//fQYsHoCL2oWnwp6yZjn0d/EnNT+1yn5kFGYwYPEA1o5ZS8+gO3jXzkHOM7o8z5Cmg8ktVKLTqBj7VZTN0rQJXYJZ+URXxi/cRUZ+MYPbBqBxVPJDVCKP92rAqLA65BuzKTRepIGfB566YUSdm1JpWvypnaYyuOlgMnJNvDK0JRvikgHo3tQHg8lSYRbDfg2MOK7eW/FDm4E4YAewC8gvKY5u3ZW32gxjT70WALxdRZp3g8nMvM0nK61Tat6W+Got16vpeV/XO6V8dffGVUdpcObkqMLbtYrfNgghhBBCVECCMlFtHlqPGu3NSi9Ip7Awk3m+MNmt6vv/mguPJEPGpS1oB5IPMHHlRKZ2mkr0o9FkF2UzN3ouz657lozCDHSOOrx13tXq+4n0E3T+ojPHnjzO0GZDySjIIKMgAw+tB+5O7uxPzCM734HP/jphk+EQSmZsZiw7wJiwQL5/JAyLxULchRwy8op45/5mbE78gyFLx5T72gxtOpSvhn1Fj6CevHMpjX6pdv7teDZ8Br2CBvLSb8c4cTGXo5dmxQa0rIO/m1OVWQwLffrjlLK2XD9PZgbQcNV5iALKTGyubRpBy3nv8/KBYuIu5ABU6xywGzGrVdPzvq42pfyVwZenswadWsnGuGTmb732vXFCCCGEENeLvH2IalM7qJkeMZOxv42usm4dlzqM+iaUH/1MtK9iksNggWdT4LMs+9fnRs8lvF44R1OP8tbfb1nLYy/G4qn1rHagGOwejFqpodc72+ncwAsvZzVpecmsj93P5J6NuZBlsBuQlfXj7jP0C/FFq1by9/EUQhu68PyGl1iw13Y2bOWxlTT9pCkLhy5k20PbyCjIJKswE1eNG55aD+ZtTuSu5bttDoXOLjDgX7QL1fG5qNxbo2v5ot2ZIkWjCZCylpRid37J6M2yjL6cKKzPT4eeJzSj5Kyx9U3C+ajraBTt2zG/XVuOrv/L2n5Kz0ZVZga8EbNaNT3v62pSyl+ZmMRN68iqJ7rx4Z/HKgy6py2JqdHeOCGEEEKI60U2PYhqU6scGdZ8KJM7Tqm0npPKCccLa9hVr+qALKEYup6pOCAr9VHkR0zuNBkHRcmQbeffjkV3LwLgqbCnqtX/mV1n8uehNFJyi1lzMInvIhNZczAJCwpGhwXy9faECtu6aR15vFcDtsyMoJ5PEVrnC0zs6Y4ZA97Onng42V/amVGYwfCfhrM8bhV7TprxUDdk9u8XuZgFm46klgvIXB3yGOe1iga7ImBDTzj9Ixz7DMzlgzY2bIC9e1HWG0pU4DdExH3D20kTOVEUCAoFH3Ybw4ZGnRk8/iMm3fNfDvs1tGZzLN2fNjYsqFpLDW/ErFbpeV/VUZ3A8UqliUmmLYmxBn5jw4OIOpVWZdD9feRpNsZdxGiyzRgqhBBCCHGjSFAmaiQ9F97p+y6Lhy+hnX+7ctecVE44AC/qC1keAG7Kyu91yLEh7RMhuqjqz92XtA8FCqIfjSbu8Ti2TNhCWkEa4V+G0z2wO5M6TgJAqVDSxq8N3QK70cavDUpFSSemdprK4CaD0dg5MLypnwuFBlOFy/SCvXSseKITPr77GLK0N00+DaLtvDY0+DiQPt/2obFHY3Y/upvGno0r7P8Hke8S1khvPYcsI9+Ai6ZkNqaR5gxv1P2UyJDxvFZ3Po65hy83LDgP51aV/PfevdC/P/TrB08/jcrRidahYxgZXj7A2RnUlkfue4VD/iX9KT03a3FUIi0D9Lx3fxueH9isWrNBpbNa1VHdWa2y531VpjSDYU32edlLTKJ0qDroLmvelngMEpQJIYQQ4iaSNTqiRtRKJXfN2cGsu3uydcJg9l3Yxyub/4/NpzfjZC7klwAYVMVZzSaLguRGM3Fq/AiBafeSczEWk8VU5WefyznHdwe+42LeRWb1msVTa0tmyAYsHsD6set5tMOj1HGpQ44hh9T8VLx13rioXUjKTaKpVzNMJjVhDbwYHRpYbsbERaMiPd/+Mj03rSOLHm7Lm9vtL1GMSYph4sqJTO44mbVj1tL5i85kFNqmpt+XtA+DOYdNh42YLeChdaSxOZLHgn+gl35P5Q9+6BN4fhksWXK5bNs2+P13dIMH8+KgEEKDPe3vk+rRkF7NfUlIzePXqV2sadvzDSZcazCrNW1JTJV1azKrVdV5X2XPSKsJe4lJqgq6r3SrZnwUQgghxO1L3jpEjei1KvRaR8Z9vY1sx6XkqFZixkhLNSyvA42rWO2W4+CFsvsS8p2akVecydL7lqJVaVmwdwFzd8+1G9CUcndy5+/Ev9Fr9DZZFy1YiEuJ49FVj9pJqPEsTTybsHzfWb7ecZpFE0PpGOTBwu2niD2fTW6REU+d/Y6PDa/HX6f/sBuQlTV/z3zC64UzpdOUcvveysouymLHsQyeavA39Xc9z1vusZXe06htiOJkAxym/Y0izc504gsvwMCB6NQq+rXwI6yBF7lFRjLyDXjo1CVp/KMS+S7yNAC5RUaOJuUQUkfPE70rntUrq+ysVlXnlNV0VkunVlWZzKSm7CUmqSzorvA+1znjoxBCCCFEZSQoEzWiVFgIDtrB2vS3MCtKNoLd7wJf+1Wd7t7g04s/vR9g1uqZNoHTtNBp7H50NwMWD+BE+gmbtu3926NVaTl08RCtfFtZsy56OHmwbuw6Zm+fbfcMspikGMYtH8fUTlOZ3fcdvvw7gaGfbmdMWCBLJ0WQkltIZn4xfm5ONsknlA4KRoXVYfCPFSc2USqUtPRtiV6j54/jf/Bu/3eZvX02Zkv55W9eDhCQsJB3HL/BQ5MBOfbvZ7IoWZ8VSsKeOgz/+S/8cypIRx8QAE8/bf2ro9KBqIQ05m6Ox0WjsgZgZjtHw9d0n9aNmtWCyynldWrVdQmC7CUmqSzorsjVZnwUQgghhLgaEpSJGllzYjWfx7wAClACb3jB855VtysKeYFnzqYzd6VtkpDqLAGcFjaN+XvmY7aYib0Yi85RRzv/dgxqMogtCVsqPRQaSjI4dqvfg1l392TC13v5fHM8vZv78uPuRM5kFPDNhM481LUB03++fBB1Uz8X8oqz7R5O7eHkwdTOU5nccTL5xfnW5ZLuTu58eOeH/G/z/8o9w/ogZ7ziP6xwF6dB6cG3Kf2J3NeEJ/5YysALO+1XdHMrmSGbNg10Omtx6YzWrlPp131GC27MrNaNYC8xybHkXJzUyhue8VEIIYQQ4mrdGm9S4h9jWPNhhNYNxcsB/qhbdUBmUemJqv8lS2jF3L2VB07z98xnW+I2pnQqH7hN7jiZ7oHdmRddsoTQZDExf898ngp9iskdJzMnak61+v7eznfoGOyK6tKo99CpL+0fKiazoJjOwR6MDr2cfMJFoyItP93mPo09LyX28GjMsB+HEfJZCN2/7k7IZyH0/KYnbho3m8Qf2cGP2P/6uDZlf523uHPbm/h+foEvF75OuwvHbeoZFQ6cGvkQxmPHS4KyMgFZqdIZrU9GtbdJztEyQM8no9pfU7r3sjNaTf1c8XdzQqdW3TIBGdhPTGIyW/ghKpEJXYKrdY+ryfgohBBCCHEt5NfBokYcFA580e0x9FG7CK5ihVeeU1Pyw36irnMgk3/sVa37fxz1MctHLmf29tm08WvD9C7TCa8bzoDFA6wzTx5OHmhVWu5reR/Jucl2Z7Ls2Ze0j8zCTAa0qsOp1Dw0jg4cTcqhY5AHmfnFTP1+D4smhtIhyJ2vtyeQW2TES1c+6qzOcskJKyaUm/Ub2WokncJmYclahyLnSElFv97Q/BkK9Hewb9RzrFm3CK3RfhrKrcHtmdX7EdRtW/Ozu0el/6f9p8xo3SgVJSb5PvI0K5/oapPk5UpXO5MohBBCCHEtJCgTNXN2JW32T4EqAjJT3eEoO39FTp4jxcVpNQqcAE5OO4tOpcNZ7chLm1627jNr7NmYtWPWsvX0Vt7Y+gYjW42sUfczCjOZdXdLzqTn8+ves5gt4Kd3wkPnSEJavnW/2YJxnSg0mPBxpdzh1FM7T7W7XLKdBl71hPczYWtByaxfl/pd2DhuI028muKidoZWL8GFdRAyHTzaApAbe5wR6+0HZPGedXm99yP81bATKBRQzayA13uf1j9JRYlJsgqKGb9wV7mg+3rujRNCCCGEuBby9iFqRh8CDhowFVZQQYGh1SzWmsezfvkp/jOwOUnZtksAK3MxN5Mfd5j5de85Nk+/g+fC/0s7/44sjJnP18O+ts5StfFrw4R2E2p0bw8nd+ZtPkmLAD13t6/Hr3vPMaClP3qto3XP0eeb45m3JZ5m/q68Obwl0yNmMva30SgVSiZ3nMzQJUOt92ulhle94B6Xkr+rFSVBGZQceL31oW0lARlAg7Elf8rI8PLjq66j+M/mb6xlWRpnPuo2mu/a34VRWf7/opIVsGoVJSZJSMtn5rID/N/gFvw8OYLswn/fTKIQQgghbk0SlIma0TeBLothyxDgitR+ju4Uhn3P63sD+T5yP0oHBS8PboGvi1eNPsJV7Ubs+dOYLZCZX8yHG44TUqcdv434nbXxq6yzVLEXY9E6asvNZFWmvX973Jzc+erv/RjNJYcq/zQ5Ahcnlc2yN7MF4i7kMG3Jfn5/agiTO05h59kd5Bfnsz95P80d4X9e8IBr+c8Y4AwdNLC36NJyyYKMy0GZHW5aNQs7DePeg5tonHaGxe0H8kG3MWTo3OzWr0lWQKPJjMFkJrvASFaBATet+l8TfFRnGadO8++bSRRCCCHErUmCMlFzde+C1q/Cwf+7XObeGmPXX1ifoOP7yBigJMHCd5GnebhbQI0CJ51Kz9GkkpzxPnoNY8KCmLPxGKPD6/DO9tnWuqUJP6aFTmPiyolV3nt6xEz2JuRgvJStfnFUIuENvRjYyr/CZW9nMgqY+t0h5o17l8izURSkH+BbPxjtCkqF/c+Z5g4Tkkv+O70wk3pu9WDnTmjRoiR7Yhl6rYqm9b2YMegpzAoHDtZpUmH/a5IVMN9gZENcMvO32DlQumdD+ob43fbL9P7NyziFEEII8c9ye/+6XNw4rV6CupeW8QWNhP47MWgbMG/z5XO13LSOaFQOaFQang2fUa3bPh02nSVRFzBbSgIIJ5WSjsEeLJ0UgUWRhwUL3QK70cavDUqFkrm759IjqAeTOk6q9L5TOk5lcNPBvLw8rlz5vC3xGEwlUVpF2QsjT6VTlJVK6NkfaLdnNA/q7Qdk6Sb4Tyo8cfFymXeeGR5+GLp0gVdesWlTOkO3P6BZpQEZVD8rYL7ByJtr4pi2JMYmBXzs+WymLYnhzTVHyDcYq7yXEEIIIYS48W7vX5WLG0fhABHfQuJSaPQoKBRkZxVy+EJJEBDspWPRxFB2nUrnP78cYtbwoUzpOIV5e+ZVeMvJHafSo/4Ahq2IBmBSj4asOXSBJVGn+XJ8R/QaV5bet9R6JphWpWXB3gU8sOwBlt63lIh6EcyJmmNzMPWMiOcZ3HQwU787xJmMgnKfGXtF8owrl73l5GZQ98JnOP01AgdTnt1+Z5rgg0yYkwnZl2bhFGZ4Nb4+dTrfAemXziv75BOYMAHatbO2rWiG7krVzQpoNJnZEJdc6b2gJBthWANP6yyhEEIIIYSoPRKUiaundoPGl2eosgoMQMkM2bcTw5i3Jd6afvxkaj5fTXibnkE9mb1jtk3g9EzYDHrUH8CEhfvJKihmTFggfZr78s32BH6c0oEVR1by3s53bNpNC53GxPsm8sCyB+jfqD8rRq4gvziftPw0At0CcXfyYE9CDnfN2WUTkJW6MnmGSumASmFCd/Fr/A++CoXJdtvlmOGjjJKALNN8ubztBZj7O0ScPVO+gdkMjz0Gf/8NDpcDoYoSU0DNswIaTOZys5WVmbclXtK/CyGEEELcAiQoE9eNm1YNlMzqRJ1KK3ce1MFzWfT7IIqx4W1YO3ozxeYcUvMzcFG7oXFwZUnUBYatiKaeh5b37m9DRENvFA4woXsA09dPZ76dGbaYpBgmrpzI5I6TWXrfUjp/0ZnZ22fTyrcVnep04q3eH9LxtS0YTJX3227yjIOvQuwbdutbHJxYr2rOmLgY0soEY66F8Npf8OQuUFrsNHR1hREjwGJ78XqdL5ZdYLTOVlblyllCIYQQQghRO+RX5OK60WtVtK6rZ3RYIF9vT7C5nlVQzGd/nSLizb95ZkkihxP1eKj9UCmc6N3cn1+ndmH+gx05mZJHzJkMzCYzK46stBuQlTV/z3y2JW5jSqcpmC1mDiQfoGdQH77ZfhaTRUFIHVfCGnoyqLU/YQ08CKnjitKhZFNYhckzmj4OSl25IrNFwdL0fkzLXUrE4G18PGwJ7fzbgQXuPwRxn8HTURUEZA88AEeOwNNPg1Jp9znKJqVo6ueKv5sTOrWqRjNZpbOV1ZVdWFyj+kIIIYQQ4vqTX5GL60atdOC5/k0pNJgqna0xW2B3Qga7EzL47/JDbJnRix93JxJ7PpujSTmMDg0kvKEXCqWRd3fMrvA+ZX0c9THLRy5n9vbZTO44mcFNh7A4PYltM3thNFvIKzLipnXERaOiyGjCYDLzQ9QZWtfV20+eoa1TcsjzoddK+lxnIFt0z/L8byWHPMfM2cWsu3uyrVtzlNOeQrtpq/2ONWkCn30G/fpV6zmuVelsZXXVJMW+EEIIIYS4MSQoE9eNSulAREMvzqQXEFLHlWPJuZjM9qaNLjNbIDm7kNNp+TgoFLxz36WliwoFGQUZ7E/eX63P3pe0j0JjIatG/U6PoO44oKGuu5ZHv4222aM1oUswXRp50zZASzffZFTKOvZvGjId0nZDixk4+PUizGBk7IU4vo9MJCMpjSPjn6RL9ArUJjtZDDUaeOklmDEDnG5eOna9VmU9BLsqNUmxL4QQQgghbhx5IxPXRdmDiovNFj4d1QGNowNLdp3h+8jTZBVUvEyujruWD0a0Q6GAH6ISmbU6jnVPdyezMKNGfTAYDQQ7h+Ng0fDmH3F2MxDGns9mxrIDvNrpNKMdP6LoUDr5Q4+ic7ZzwLWjK/RaY/1raUKOLn5OhA7sgndWqv2ODBgAn34KjRrVqP/Xw5WHYFemuin2hRBCCCHEjSVBmbgmRpOZImNJGvYFW20PKp7QJZiVT3Rl/MJdJKTl27RvGaBH5aBg4re7ibuQQ+nEWkGxCVe1e4364ubkzokLRRy/mFdhSvi6jhf5X8B8+hmiwACOQPzfLxHU9/Nq7d3SqVX0D22EZcQ98MWCctcsdeuimDMH7rkHFBWcLH2DXe8U+0IIIYQQ4saTNzJx1fINRnKKjLzxexxP/Wj/oOIZyw6wYOtJFk0MxU1ru3/poa7BLNqRQOz5ywEZgKPSAY3StSSRRjW092+Ph5M7zfz1zN9imxLeUVHMFJ9lbGg2lX5uUeWuBaV+SXFmnE2biqiUDji+8Tq4uV0qUMFzz6GIi4N77621gKxURYdgQ0kQ/Mmo9rw4qLlkXRRCCCGEuEXIW5m4KvkGI9uOp5JdUFwu9b09i6MSaR/ozpiwQD7fHG8tHxMWSOdgT2atjkPpoKCpnwsuGhV+eidUDg58v/M8T4VO56GVY6vsz/SImaTlWjCabJOMhDof4vW6n9PUyX4/441N8MovQHvlCsajR6FOHdDrbRv5+MArr8Bff8F770HTplX28Wa6Xin2hRBCCCHEjSdvZqLGCgxG/oxNplVdN7up7+35ensCY8KDcFCUzNa8d38bJvVoyBM/7GVseBBbZ/bi01EdmDmgOS8MbI6jUoHG0ZFeQQOZ1GFKpfee2mkqQ5sP4dNN8eX2rnkqs3iv3of81OgFuwFZSrE7pxp/wssFX5Cubnb5wpEj8NBD0KIFfPBBxR/89NOwcuUtF5CVuh4p9oUQQgghxI0nM2WiRvINRvINJtYfLgnKanJQsQL4+/nemC0WfohKZNGO03w6ugO7TqUzqYIsiSqFE//t9iZd63fnw6h3iUmKsdZp59+Op8NmcHfzoXzz93kOnc/GV68BLIzw+JMX6yzEXZVr0xezRcHitIG8mzyO3wbeRc7OvSWp4SMjYfZsWLHi8gHP778PTzwB3t62D1XLyxSFEEIIIcTtQYIyUW1Gk5ldp9Ko7+HMxZxC0vNrdlBxSk4Rdd21bD52kT2nM/hsdAfmbYm3u/yxdD/amLBA/jMwhIGN7mZY86FkF2WSWZiJi9oNR4ULP0ReIKMe/H7wAp2CPPAwn2V581dop95rtw8H8xvx33OPsb+gGS0D9GgcLEQc3onPkP/Ctm22DXJzSwK1d9+t0bMKIYQQQghRXRKUiWozmMz8fiCJEZ3rk1tkxFNXs4OKXTQqpi7eQ+dgT+Y92JGtx1KqtR+tQ6AH8Sm5zNsSTzN/V1rXdaNviCPN/NUcT87DyVHFw90acIfjGjTrn6adutDmPjkmLe8ljeO7tEGYUeJekM1b8TtwaT2a/0s6W3EHnJ3t7ykTQgghhBDiOpGgTFRbdoGRQ+ezmKJrxLHkXJzUyhodVKxxdGDP6Qz2JmYyJjzIbpZEexZuP8WCcZ2YtyWeuAs5xF3I4afos4wJC+Sd+9oCFk6m5LHxYCpz69oGZKsyuzPr/CNcNHrRKukE4/auZvjRbTgaiir+UHd3eOwxeOop8PWtVj+FEEIIIYS4GhKUiWrLKjBYg7Hm/q78EJXIhC7BzFh2oNJ2SgcFT/dtwtZjKTTzd8VBAYUG2yyJFYk9n02hwUQzf1fiLuRYyxdHJRLWwJPTaXmXsjq24xdtb+713ARAviqAt9Oe5tvEFgw68jeP7P6NDuePVv5hAQHw7LMwaRK4ularf0IIIYQQQlwLCcpEtblp1ZjMFmsw9vrvcax8oiujQwP5YVdiubT2uUVGkrOLGBValwld61JoysElK52PRgeh17hhKLbgpnUsly2xMhn5Blw0tsN1/taTfDGuEx9uOI7ZArMuPEJ3131syutGUoNXmDG0DZMKitE9vwbPygKydu1g2jQYPRo0mqv8CgkhhBBCCFFzEpSJatNrVbQM0PN95GlWPtGVu1rXYfzCXSyZFM7o8Lp4uTiQXZRFWn46/q6+eDvrWX1sNf0Xj7HJmjizy0xWPHEnExbuJyEtv8rP9tCpyS0yosREP30ka7O7AApiz2djNFuss2iZJj19j84l2+wCiUkM6diM9/88is4/gvf4vPxNHR3h/vvh8cchIkKyKQohhBBCiFohBxaJalMAk3o0JKugmPELdzG5Z0NeGNgchcLAwbR1DFrSi+afBzP4xz6gKGLGn9MZ8+vocgEZQExSDKN/Hc3snf/lm4ltcdM6Vvq5pfvRDOlH+LnRTOYFv8VQ9y3W6wU5efSJ286ny9/GOy+jJCAr027toSSWGTyIqVNynpilbl2YNQvOnIHFi6FLFwnIhBBCCCFErZGZMlFt+UUmejf3tS5XHPtlFL883olXNj/PvD3zrPWmdp7KloQtLNizoNL7zdszl/B63RgT1o7PN5+qsN5DXQKJ2/IWq5u8i9ahJDnHrIC5FB9T0mPPPprM28X0rEwADtRpwoKwe0vadQ3mh6hEzJeOHPug2xiauTrwzCfT0emcruErIYQQQgghxPUjQZmotjyDkXNZxUzu2ZAOQe7kFRlYc3xVuYBMqVAyueNkhi4ZWq17zol6j5UPbGTellPW4KmsxzqruSvtIbT5f5Wb13VT5THXazZckWPkgQN/siD0HsaEB9E52JNZq+Os17Y27MhW4OFi0NXkwYUQQgghhLiBZPmiqDadWoWzWsXQT7dzKjWPezr68VFU+UOVW/q2JL84n/3J+6t1z31J+zCRy6DWdcrfJ0DP0jvjmW66H23aX7YNLwDLbYsbpZ/l66ZFTOrRkPELd9lNJJJdWL3kIkIIIYQQQtwMMlMmqk2nUWI0W6jnoWXTkYsM7aC1Cb70Gj2p+ak1um+OIYvX727FM32bkpFvwFOVR2DURFQXNthvsA5YClxxzFhBRFcK7x/JAbf6PP3p9gozO+qdKt/DJoQQQgghxM0kQZmoNrXSgbS8QiZ0Cean6DOk5afb1MkuysZb512j+7qq3Rj9RSQWoJ32GDMdZqDSZdpWTAPmA7GXi3KatcBp3Fje9WjPNoOOo0k5mJNTKvyslgF69FoZ9kIIIYQQ4tYhb6ei2lRKBxp4u+Dj4sSFrEK8dGqbOrEXY9E56mjn384m62I5FlBYoF1AexwVrhxNymKS9y886/w9jgqTbf2/gUVAPhgDg4gO78+BHoMZ++ggFEoHWh26wIIllXzeJVN6NkKtlFW7QgghhBDi1iFBmagRnVqF2WJhbHgQCkWRTfBlspiYv2c+00KnMXHlxHJt/XOg9ynocxL6nIJJQ+Ch+2ayfPs+vg3+L11dr8jaAZAPfAVnTgayu8cddJk5iTcuONG/ZR3GhviiU5cM4b4hfowND+T7yMQK+z42PIg+Ib6oJCgTQgghhBC3EAnKRI3lFZlYujuRh7sFMbPLTEb/Orrc9bm757L70d083Ww8CcsXWQOxFldsNXshqzXtGw0icVcYbVyP2HxO/ikNi7cP4KcW/TneIxAUCtaGtObdbjpUDg7lgiudWsWLg0IIa+DFvC3xxJ7Ptl5rGaBnSs9G9CkTxAkhhBBCCHGrkDdUUWOuTiq2n0jjnvb16BU0iEkdprBgzzwaZkCXM9A1MYPij1vwwYViFHbS3JfqfjCH8wUOvHz2UZY1nmldtmiyOPBZ8v3MyRmNqY2yXBt3rRonR/vDVqdWMbCVP31CfMkuMJJdWIzeyRG9VoVa6SAzZEIIIYQQ4pYkQZmoMbXSgf8bEsLOk2kcmvMlH2Sc4aNIN7RpWWVqGaq8jyohAe/zpzF6dOaDpLE8X2cR5ww+PHPmOXbltbKp3zJAj96p8iGruhR86dQq/N3kgGghhBBCCHHrk6BM1JhK6UCwlwszfj7I4BOHcY78vdptLW5uKO64A3r3hj59UDRuykOGZGb8fC8qhZFFaUPINrnYbTuxawMUiuvzDEIIIYQQQtwqJCgTVyWn0MjhC9nUqRtSab0iRzVHG7UhaMQgMlrk4Dv4dXTOOuv17OxCOgd7MCo0mE92jarwPmPCAukU7EF2oRGt7AsTQgghhBC3EXm7FVclq6BkeeLegOblylN1buypG0J03RbsqRvCIf/GuDtls7HzPIKzd7J2jYIew2ZbE25k5huY/N0eFk0MpUOQO19vT7BJ0vFQ12A6B3syfuEuFozrhJ9eliUKIYQQQojbhwRl4qq4aUvOKMvQufFJxAMkeAQQXS+E0+51KLvGsKtLDHMC38U1u2S/WV/DR0RFdycsbCgqpQNuWjUJafkM/XQ7Y8ICWTCuE4UGExn5Bjx0ajSODvwQlcis1XFkFZQk7hBCCCGEEOJ2IkGZuCp6rYqWAXpiz2fzfo8Hba4rMPOE71Ke8fsBhzIpGFUKM43ip2LocCcqpbbcfT7fHM+8LfE083fFRaMit8jI0aQczJeatwzQo9fKkBVCCCGEELcXyREuropa6cDkHg3tXnN1yGNB0Bs857+4XEAGcNbgy+STM8guUly+T8/L9zFbIO5CDrsTMoi7cDkgA5jSsxFqSWsvhBBCCCFuM/KGK66KwWSmexMfRocGlitvrElkeZNn6ecWZdNmQ3Zn7jo+h/0FzcguLAZKMjn2DfFjbHigTf2yxoYH0SfEV84aE0IIIYQQtx1ZCyZqLN9g5M01cfx9PLVcgo56uWt5v/6HuCgLytU3WRx4J2kcC1LuwXLp9wBl94bp1CpeHBRCWAMv5m2Jt0n0MaVnI/qE+FqTgwghhBBCCHE7kbdcUSNGk5kNccl8H5kIwNBPtzM2tC4/hq3ENf49m/oFCg8mxs9gZ14ba5m9vWE6tYqBrfzpE+JLdoGR7MKSpB56rQr1pQOhhRBCCCGEuB1JUCZqxGAyM2/zyTIF6YQm/gfXjL02dY8XN8O1/0qiDhwvV17R3jDVpeBLp1bh7yZp74UQQgghxL+DTD+IGskuKDk0GqCpJoGVjZ+hp6ttQLYsvQ+Dj7xJnjKAZv6u1nLZGyaEEEIIIUR5MlMmaqT00GgAE0o8VVnlrhdblLx2/lG+S7sLUJCRb8BFo5K9YUIIIYQQQlRA3o5FjZQeGg0QX1Sf5848y4LgNwBIKXbnsdMvsDu/lbVOgLuWuWM6otMoZW+YEEIIIYQQdsgbsqiR0sOeS63PjuDj5AfYl9eMwcc/KheQtQzQ465zxNtVg06tkoBMCCGEEEIIO+QtWdTIlYc9A3yUPJoHTr5NstG7XLkc9iyEEEIIIUTV5I1Z1Ii9w57NKDFYHMvVk4QeQgghhBBCVI/sKRM1Joc9CyGEEEIIcf3IW7O4KnLYsxBCCCGEENeHBGXiqslhz0IIIYQQQlw7mc4QQgghhBBCiFokQZkQQgghhBBC1CJZviiumdFkxmAyk11gJKvAgJtWLXvLhBBCCCGEqCYJysQ1yTcY2RCXzPwtJ22yME7u2ZC+IX6ShVEIIYQQQohKyDSGuGr5BiNvrolj2pKYcgEZQOz5bKYtieHNNUfINxhrqYdCCCGEEELc+iQoE1fFaDKzIS6Z7yMTK633feRpNsZdxGgy36SeCSGEEEII8c8iQZm4KgaTmXmbT1ar7rwt8RgkKBNCCCGEEMIuCcrEVckuMHL4QnbVFSlZyphdIEsYhRBCCCGEsEeCMnFVsgoMNaqfXVh8g3oihBBCCCHEP5sEZeKquGnVNaqvd3K8QT0RQgghhBDin01yld8gRUVFLPvlV/7++29SUlJxcXGhQ4f2jBk9Ci8vr9ru3jXTa1W0DNDbZF20p2WAHr1WhpoQQgghhBD2yEzZDWAwGHj5/17hp59+pqCgkNDQznh7e7Nx4yaeefY5kpKSaruL10ytdGByz4bVqjulZyPUcoi0EEIIIYQQdsmb8g3w08/LOHr0GM2aNWPu558yc8Z03nt3NhMfmkBWVjaffPJZbXfxmqmUDvQN8WNseGCl9caGB9EnxBeVBGVCCCGEEELYJW/K11lxcTFrfl8DwJTJj6LVaq3Xhg0bSnBwEIdiYzlxIr62unjd6NQqXhwUwiej2tMyQF/uWssAPZ+Mas+Lg5qjU8vSRSGEEEIIISoib8vXWdyRI+Tl5+Pv70/DhrbL+7pERJCQcJrdu3fTuHGjWujh9aVTqxjYyp8+Ib5kFxjJLixG7+SIXqtCrXSQGTIhhBBCCCGqIEHZdZZwKgGARnYCMoBGjUrKE06fvllduuFUl4IvnVqFv5tTbXdHCCGEEEKIfxSZxrjOUlJTAfDytp9hsTTzYsrFlJvWJyGEEEIIIcStS2bKrrPCgkIANBqN3etOTiUzSQWFBdW6X25uHnl5eTblycnJAKReCgKFEEIIIYQQtaf0vdxkMtW4rQRlt7hVq1bx49KfKrz+4kv/vYm9EUIIIYQQQlQmIyODgIA6NWojQdl15qQtmQkrKiqye72wsGQmTeuktXv9SkOGDKF379425QaDgeSLF6kbUAcHB+VV9lbcTKmpqbz40n95843X8fb2ru3uiNuEjCtxI8i4EjeKjC1xI9wq48psNpGenkGTJo1r3FaCsuvM59JASEtNs3s9La2k3MfXp1r3c3FxxsXF2e61+vXrXUUPRW3z9vbGz8+3trshbjMyrsSNIONK3CgytsSNcCuMqzp1ajZDVkoSfVxnwQ2CAYg/edLu9fj4kvLgoKCb1SUhhBBCCCHELUyCsusspHlznHU6kpKSOHnylM31HTt3AtC5c+eb3TUhhBBCCCHELUiCsuvM0dGRQXcNAmD+ggXWPWQAK1asJCHhNK1atrwtDo4WQgghhBBCXDvZU3YDjLj/Pvbv38+RI0eZMvVxWrQIISUlhWPHjuPmpufJJx+v7S4KIYQQQgghbhEyU3YDqNVqXp/1GiNG3I9GoyEqahcpKSn07t2LD95/D39//9ruoqgFzs7OjHxgBM7O9hO3CHE1ZFyJG0HGlbhRZGyJG+F2GFcKi8Viqe1OCCGEEEIIIcS/lcyUCSGEEEIIIUQtkqBMCCGEEEIIIWqRBGVCCCGEEEIIUYskKBNCCCGEEEKIWiRBmRBCCCGEEELUIgnKhBBCCCGEEKIWyeHRQlShqKiIffti2L07msNxcaSkpODg4ECdOv5EREQwbOgQtFqt3bYbN27ij7VrOXPmLCqVimZNm3L/iPsIad68ws+Li4vj559/4eixYxiNRurXr8egQQPp3avXjXpEcYvIzs7hiSefJCsrG39/f+bP+7zCujK2RHVkZWXx66+/sWt3NKmpqajVanx9fWjTpg0PTRhvU3/Xrt0sX7GCkydPAdCoYUOGDx9Gp06dKvyMxMRElvy4lEOHYiksLKSOvz99+/Zh8OC7cHCQ3/3ebo4fP85vv63gcFwc2dnZOGk0BAUF0advb/r07o1CoShX32Qysfr3NWzcsJELSUk4OTnRulUrRo0aSf369Sr8nKsZi+LWduJEPDH793P8+HGOHz9OWlo6ACuW/1ppu5v18y41NZXFPyxh374YcnNz8fHxpnv37tx37z2o1eqre+gakHPKhKjC+j//5LPP5gJQr149ggIDyS/I58iRoxQUFFCvXl3eeH0W7u7u5dp9+eVXrFr9O2q1mvbt2mEoNnDgwEEsFgvPz5xBeHiYzWft2LGTd997H4vFQssWLXDVu3LgwEHy8vK4e9hQHnpowk14YlFb5sz5hL82b8ZisVQalMnYEtVx4kQ8/3v1NXJycggMrE9gYCAF+QWcOXOG1LQ0fvt1Wbn6K1eu4quFX6NUKmnbtg2OKkf2xcRgMBiY9Ogj3HXXIJvPOHLkKC//3ysYDAaaNGmCr68Phw8fJiMjk65dujBjxnM2L+nin6v0+4jZbKZRw4b41/EnOzubw4fjMJlM9OjRneeefcZa32w2M/udd4mMjMLZ2Zk2bVqTk51D7OHDqNVqXp/1Gk2bNrH5nKsZi+LW9+abbxO1a5dNeWVB2c36eXfhwgVmPv8fsrOzCQoMpF79esSfiCcpOZmQkObMeu1VHB0dr+n5q2QRQlRq48ZNls8++9ySmHimXHlaWprl6WeetQwdNtzy3nsflLu2LybGMnTYcMuYseMs586ds5bHxR2x3HPv/ZZRo8dacnJyy7XJzs62jBw1xjJ02HDLjh07reUZGRmWyVMeswwdNtxy4MDBG/CE4lYQE7PfMnTYcMtnn31uGTpsuGXS5Kl268nYEtWRmZlpGfvgeMv9I0ZaIqOibK4fPXqs3N/PnD1ruXv4vZZ77xthiYs7Yi0/e/acZczYcZbh99xnOX/+fLk2xcXFlkcnTbEMHTbcsnzFSmt5fn6+ZcbMFyxDhw23bNiw8To/magtRqPR8uC4CZahw4ZbNm/eUu5aYuIZy5ixD1qGDhtu2X/ggLV83fr1lqHDhlumTH3MkpGRYS3fvn2HZeiw4ZbJUx6zGI3Gcve6mrEo/hmW/fKL5fvFP1iionZZ0tPTLffeN8IydNjwCuvfzJ93z7/wH8vQYcMtC7740lpmNBotb771tmXosOGWH35Yci2PXi2yrkCIKvTu3YvHHptqs8zC09OTyZMmAbAzMpLi4mLrtZUrVgEw4v77CAgIsJY3b96MAQPuJC8vjw0bNpS7359/biA/P5+w0FAiIsKt5e7u7owf/yAAK1asvL4PJ24JRUVFfD53HvXr1+fuu4dVWlfGlqiOJUt+JDs7mwnjxxEWGmpz/crZidWrVmM2mxlwZ3+aN29mLa9bN4D7778Pk8nEqlWry7WJjIwiOTmZBsHBDBs6xFqu1WqZPOkRQMbV7eTs2bNkZWVRt25devbsUe5a/fr16NmzJwAnjp+wlq+49P1q/Lhx5VaTdOkSQWhoZy5cuEBUVPmZk6sZi+Kf4d577mHM6FGEhnbGw8Ojyvo36+fdsWPHiYs7gpubGxPGj7OWK5VKpk6ZjEqlYvXvazCZTDV/6BqQoEyIa9CgQTAAxcXF5OTkACUv2AcOHgRKfvBcqUtESdnu3dHlyqP37KmwTaeOHVGr1ew/cACDwXDd+i9uDT8u/Ynk5GSmTpmMUlnxVl8ZW6I6ioqK2LxlK05OTvTp07tabS6PkS42165mXDVq1Ah/Pz9OJyaSnHyxRv0Xt6bqLt1ydXUFIDk5mbNnz6JWq+nUqaNNvarHVfXHorj93Myfd6VtOnfuZDPO3d3daRESQm5uLocPx13DE1VNgjIhrkFSUhIAKpXK+oPo3LnzFBcX4+amx9vb26ZNo0YNAUg4fbpcecKpBAAaXrpelqOjI4GB9TEYDJw/f/56PoKoZQkJCaxYsZI+vXvTsmWLSuvK2BLVceJEPAUFBTRs0ACNRsOePXv5auHXzJs3n5UrV5GWnl6ufm5uHikpqQA0bNjA5n4+Pt7o9XoupqSQn59vLbeOq4a24wouj7eE0wnX4alEbfPz88Pf359z586xZcvWctfOnDnLli1bcHFxse7xOXVpfAQFBaJS2f6yqZGd8XG1Y1Hcfm7mz7vSNo2q/F522u7160WyLwpxDVat/h2A9u3bWX+7kpKaAoCXl5fdNk5OTjg7O5Obm0t+QQE6rZb8/HzyLv2A8a6gnZeXFydOxHPxYgrBwcHX+UlEbTCbzXz62ec4OzszvsySiYrI2BLVcebsGQDc3Nzsbqz/7vvFPPnE4/To0R2A1EvjysXFBScnJ7v39PLyIjs7+9IYCQIgJbXk5dnbu+JxBZByMeUan0jcCpRKJU8/9SSzXn+TDz78iBUrVlInoA5ZWVkcPhxH/fr1eGrak9ZfUJaOj4q+X1nHR8rl8XG1Y1Hcfm7mzzvrZ1Xwvczb+r3sxs76y0yZEFcpOnoPGzZsRKVSMWb0KGt5YUEhABqNpsK2Tk4l1woKCkr+t7DQeq2idqU/oErbiH++339fw/HjJ5gwfhx6vWuV9WVsierIzc0DYNfu3ezdt4/Jkx7l20Vf88WCedx99zAMBgNzPv7Emmq8dIxoNBWnfLaOq8LLY6SwsPLxKOPq9hMSEsKbb8zC38+P+JMn+fvv7Rw8eAiFQkG7tm3x8/Oz1i289O9e9fi4/D3qaseiuP3czJ93VX0v01g/p9Du9etFgjIhrsLZs2f58KM5WCwWJowfR4MGtssshKhMSkoK3y/+gVYtW1Z7348Q1WExm4GS86FGjxrJoEEDcXNzw9fXl4cmjKdrly4YjUZ+W768djsq/nG2bt3G9BnP4+3tzbvvzGbpjz8w9/NP6dO7F8tXrOTll18pl/RKCFF9EpQJUUNpaWm8+uoscnNzGTZ0CEOGDC533Ulb8luYoqKiCu9RWFhyrfTQaW2ZZRoVtSv9TU5FB1WLf5b587/AaDQyderkareRsSWqw6nMv6O9gL+0LDY2Frg8RoqKKk70Yh1XTpfv7eRU+XiUcXV7OX/+PHM+/gS93pX//vdFmjZtgpOTEwEBATz22FQ6d+pE/MmTbNiwEbg8DqseH5e/R13tWBS3n5v5866q72VF1s+xv6T2epE9ZULUQE5ODq/87zUupqTQp09vuwcQ+nj7ACXBmz2FhYXk5eXh4uKC7tI3BZ1Oh7NOR15+PqlpaQTqdDbtSu/n6+tznZ5G1Kbd0dE4Ozszd+78cuWG4pKXkfT0dF566WUApk9/Fg8PDxlbolp8fUr+HTUaDW5ubrbXfX0ByMrKBsD70rjKzc2lsLDQ7l4ee2PEx9ub3NxcUlPT7O5FLG3jI+PqtrBt298YjUbat29vN9Du2rULu6OjiT18mIEDB+BzKTlDRd+vrOPD5/L4uNqxKG4/N/PnnY+3DydPniIt1f5npVq/l/le/QNVg8yUCVFNBQUFvPra65w5c4aI8HAef2wqCoXCpl7dugE4OjqSlZVt95tJfPxJAIKDym9QDr6UXv/kpetlGY1GEhPPoFary53VIf7Z8vLyOBQbW+7PsWPHATAYDNYyg6FkOZCMLVEdpVnrDAaD3aVkubklx3eUvvC6uDjj41PyAl26z6yslJRUsrOz8fXxQVfmJcc6rk7ajiu4PN6Cg4Kv7kHELaX0e46znRddAJ1zSXlubi5w+ciY06cTMRqNNvXj7YyPqx2L4vZzM3/elbaJr/J72Y1NLCNBmRDVUFxczJtvvs3x48dp374dzz33DEql0m5djUZDm9atAdi+fYfN9R07dwIl52GU1aljyTkuO3bstGmzOzoag8FA2zZtUKsr3gAt/jlWLP/V7p8F8+cB4O/vby3z8yv57ZyMLVEdPj4+NAgOxmKxcOhQrM310rKGZfbCXh4j12dcnTx5kqTkZIICA63jV/yzuV867PdEfLzd66WHRpfOxPr5+VGvXj0MBgPR0Xts6lc9rqo/FsXt52b+vCtts3t3tM0vsjIzMzkcF4eLiwshIc2v4YmqJkGZEFUwmUy89/4HHDh4kBYtWvCfF56v8hDNocOGAPDTz8vKnYVx5MhR1q1bj7OzM3379i3Xpl+/vuh0OqJ27WLnzkhreWZmJosWfQfAsGFDr9djiX8oGVuiOoYPvxuAr79ZRHqZc8lOnjzFipUrARgw4E5r+eAhg3FwcGDtuvUcPXrUWn7+/Hl+/nkZSqXSZv9seHgYfn5+nEpIYMXKVdbywsJC5s3/ApBxdTsJC+0MQGzsYf74Y225a0ePHmXlqtVA+UN7h136frXo22/JzMy0lu/cGcmuXbupU6cOYWGh5e51NWNR3J5u1s+7pk2bEBLSnKysLBYt+tZabjKZmDdvAUajkcF3DbJ73t71pLBYLJYb+glC/MOtWrWaL79aCJS8hOi09pdMPPTQePR6vfXvX375FatW/45Go6Fd27YYjUZi9u/HYrHw/MwZ1gM2y9qxYyfvvvc+FouFVq1a4urqyv79B8jLy2PY0CFMnPjQjXlIcctITr7IpMlT8Pf3Z/68z+3WkbElqmPOnE/Y9NdfODs707x5MwwGA0eOHKW4uJj+/frx+ONTy9VfsXIVCxd+jVKppF3btqhUKvbFxGAwGHj0kYcZPPgum8+IO3KE//u//2EwGGjatAk+Pj4cPhxHRkYGXbpEMHPGdLvLvMU/09ffLGL58hUABAbWp369+qRnpHP06DHMZjN39u/HY49dHldms5nZ77xLZGQULi4utGnTmuzsHGJjY3F0dOT111+jWdOmNp9zNWNR3Pqio6NZ+tPP1r8fP34Ci8VC06ZNrGUPjLifTp0uz37drJ9358+fZ+bz/yEnJ4egoCDq16/HieMnSEpOpnnzZrw+67UqfyF/rSQoE6IKS5b8yI9Lf6qy3oL582yW6WzcuInf1/zB2bNnUalUNGvWlBEj7iekecVT4HFxcfz08zKOHj2G0Wikfv163DVoEL1797rmZxG3vuoEZSBjS1TNYrHw558bWLtuPWfPnkWhUBAUFMSAO/tX+G++a9duflu+wrpPrGHDhtwz/O5Kl4slJibyw5IfOXQolqKiIvz9/ejbty9DBt+Fg4MsyLnd7IyMZO3a9cTHx5Ofn49Wq6VBg2D69+tnPZC8LJPJxOrVv7Nh40aSkpLRaDS0bt2KUaNGEli/foWfczVjUdzaNm7cxMeffFppnWlPPmGTNfZm/bxLSUnlhyVL2LdvHzk5ufj4+NC9ezfuv+/em7K8X4IyIYQQQgghhKhF8issIYQQQgghhKhFEpQJIYQQQgghRC2SoEwIIYQQQgghapEEZUIIIYQQQghRiyQoE0IIIYQQQohaJEGZEEIIIYQQQtQiCcqEEEIIIYQQohZJUCaEEEIIIYQQtUiCMiGEEEIIIYSoRRKUCSGEEEIIIUQtkqBMCCGEEEIIIWqRqrY7IIQQQlyNYXffA8CK5b9WWXfjxk18/MmnjHxgBKNGjbSWz5nzCZv++ovXZ71G69atblhfbzUHDx7ivy//X7kyBwcHXF1daNiwIQMHDCAsLBS4/LWriSu/zkIIISonQZkQQgjxL+Xu7k6H9u0BMBQbOJN4hn37Yti3L8YaWNWpU4fevXrZtN30118ARESEo3XSlrvWoEGDG995IYS4jUhQJoQQ4rYXHh5Gs2ZN0ev1td2VW0q9unV56qkny5WtWrWaL79ayE8/L6N37960aBFCixYhNm1Lg7KHJkzAz8/3pvRXCCFuV7KnTAghxG3P2dmZevXqSVBWDUOGDKZevbqYzWYOHDhQ290RQoh/BZkpE0IIcduraE9ZWXv27OXnZcs4dSoBpVJJ69ateHDsGOrVq1fhvXr06MEPS5Zw8OAhsrOzeeH5mYSHh3HhwgU2b97CvpgYkpMvkpubi5ubntatWzPi/vupWzfA5vOH3X0Pvj4+fP75p/zy629s3bqN5ORkOnboQEiL5nzzzbfcd+89PPjgWLv9f+WVV4nZv59Zs16lTevW1/T1ql+/PmfPniMrK+ua7iOEEKJ6ZKZMCCHEv972HTuY9fobGI1GOnfuhKenJ5GRUcyc+QKnTp2y2+bc+fNMnzGT48dP0Lp1K9q2bYtKpQRg/Z8bWPrTzxQWFtGkcWM6d+6EVqtj8+YtTJ8xk4SEBLv3NFssvPXWbH77bTn+/n6EhYbi4elBn969cXR0ZOOmTZhMJpt2ycnJ7D9wgICAOtcckAEUFBQC4Obmds33EkIIUTWZKRNCCPGv98cfa3n8san0798PAIvFwrfffc+vv/7GnI8/5aMP37dps23b39w1aCAPPzwRpVJZ7lp4WCgD7uyPn59fufINGzfyySef8eVXC3l91ms290xNTcXR0ZHPP/sELy+vctciIsLZunUbu3dHEx4eVu7anxs2YrFY6Ne371U9f1k5OTkcP34cBwcH2rZtc833E0IIUTWZKRNCCPGv17x5M2tABqBQKBgzehReXl6cOnWKw4fjbNq4uekZP36cTUAG0KxZM5uADKBvnz6EhDTn0KFY8vLy7PblwQfH2ARkAAPuvBOA9X/+Wa7cZDKxadMmVCoVvXvbZkmsLoPBwPETJ3jr7dnk5eXx4INj8fWVBB5CCHEzyEyZEEKIf73u3brZlKlUKrp0iWDVqtUcPnzYJgNh2zZt0Wg0Fd6zoKCA3bujOXXqFDm5uZiMJcsOM9IzsFgsJCUl0ahRo3JtFAoFoZ07271fy5YtCAysz759MaSkpOLj4w2U7IVLS0unS5cI3N3da/LYHIqNtZ73VrYPTz81jV697qjRvYQQQlw9CcqEEEL86/n4+tgt97s0U5Senm5zzftSUGTPgQMHee/998nKyq6wTum+rbLc3NxwdHSssM2d/fvzxZdfsXHjRkaOfAC4PHPWv1+/CttVpOw5ZXn5eRw9eozMzEwWfPElDRoEExwcXON7CiGEqDkJyoQQQoiroK4geCooKOCdd98jNzeXBx4YQfdu3fD19UGtVqNQKHj//Q/Zum0bFoul2vcs1avXHXz73fds2LiRESPuJyMzkz179uLr60u7dm1r/AxXnlNWXFzMe+9/QGRkFB9+NIcPP3gfBwfZ6SCEEDeafKcVQgjxr5dyMcVu+cWUknJPT89q3+vw4ThycnKIiAhn9KiR1K9fD41Gg0KhACApOemq++ns7Ez3bt1ISUll374YNm7chNlspl+/vtb7XwtHR0emPfkEbm5uJCScZvOWLdd8TyGEEFWToEwIIcS/3vbtO2zKTCYTO3fuBCAkJMTmekVyc3MB7CbruHDhAidP2k+xX10DBvQHYN269WzYsBEHBwf69Ol9Tfcsy9nZmfvuLdln9ssvv2I2m6/bvYUQQtgnQZkQQoh/vcNxcWzYsLFc2Q9LfiQlJZXg4CBatmxR7XsFXDoYOjIystzhy7m5eXzy6WcYjcZr6muTJk1o1LAhUbt2kZycTKdOHfGqwUxeddx5Z388PDw4e/YcO3dGXtd7CyGEsCV7yoQQQvyjzZj5fIXX+vXrW60EGAMH3Mmnn33O2nXrqePvT8LpBBITz6DT6Xhq2pNVti+rSePGtGvblpj9+5n62BO0atUSgEOHYtHr9YSFhhK1a1eN7nmlAQPu5LPP5wJwZ/+aJ/ioikaj4d577+HLL79i2S+/0LVrl+v+GUIIIS6ToEwIIcQ/2rFjxyu8VppZsCpdu3alY8eO/LzsF6J27UKlVBIWGsrYB8cQWL9+jfv04osv8POyX9i+fTt79+7Dzc2N7t27MWb0aL7++psa3+9KrVu3BkqWSLav5jPW1J39+/Hbb79x8uQpoqP30KlTxxvyOUIIIUBhsZf+SQghhBC3rGXLfuG77xcz8oERjBo1sra7I4QQ4hrJnjIhhBDiHyQ/P5/Vv69BpVLR/87+td0dIYQQ14EsXxRCCCH+ATZs3EjsocPEHj5MRkYGQ4YMvu4JPoQQQtQOmSkTQggh/gFiDx1m019/UVhYwKBBAxk/7sHa7pIQQojrRPaUCSGEEEIIIUQtkpkyIYQQQgghhKhFEpQJIYQQQgghRC2SoEwIIYQQQgghapEEZUIIIYQQQghRiyQoE0IIIYQQQohaJEGZEEIIIYQQQtQiCcqEEEIIIYQQohZJUCaEEEIIIYQQtUiCMiGEEEIIIYSoRRKUCSGEEEIIIUQtkqBMCCGEEEIIIWqRBGVCCCGEEEIIUYskKBNCCCGEEEKIWiRBmRBCCCGEEELUov8HFGNDEPjHZ8IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_ground_truth = True\n",
+    "show_decoys = False\n",
+    "show_search = True\n",
+    "show_discovered = True\n",
+    "show_calibration = True\n",
+    "show_true = True\n",
+    "\n",
+    "for i in range(1, 15):\n",
+    "\n",
+    "    prediction = \"rt_calibrated\" if i != 1 else \"rt_library\"\n",
+    "    previous_df = dfs[i-1]\n",
+    "    current_df = dfs[i]\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    # Ground truth\n",
+    "    if show_ground_truth:\n",
+    "        sns.scatterplot(data=rt_df[rt_df.decoy == False], x=\"rt_library\", y='rt_observed', ax=ax)\n",
+    "        if show_decoys:\n",
+    "            sns.scatterplot(data=rt_df[rt_df.decoy == True], x=\"rt_library\", y='rt_observed', ax=ax, color='red')\n",
+    "            legend_elements = [\n",
+    "                mpl.lines.Line2D([0], [0], marker='o', color='w', label='Target', markerfacecolor='#1f77b4', markersize=10),\n",
+    "                mpl.lines.Line2D([0], [0], marker='o', color='w', label='Decoy', markerfacecolor='red', markersize=10),\n",
+    "            ]\n",
+    "            ax.legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "    # Search values\n",
+    "    if show_search:\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=prediction, ax=ax, color='black', linewidth=3, errorbar=None,\n",
+    "                    label=\"Prediction for search\")\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=\"upper_bound\", ax=ax, color='red', linestyle=\"--\", linewidth=2, errorbar=None,\n",
+    "                    label=\"Bounds for search\")\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=\"lower_bound\", ax=ax, color='red', linestyle=\"--\", linewidth=2, errorbar=None)\n",
+    "\n",
+    "    # Calibrated values\n",
+    "    if show_discovered:\n",
+    "        sns.scatterplot(data=current_df[(current_df.decoy == False) & (current_df.discovered == True)], x=\"rt_library\", y='rt_observed', ax=ax, color='green')\n",
+    "    if show_calibration: \n",
+    "        sns.lineplot(data=current_df, x=\"rt_library\", y='rt_calibrated', ax=ax, color='green', linewidth=3, errorbar=None,\n",
+    "                    label=\"Prediction after calibration\")\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # True relationship\n",
+    "    if show_true:\n",
+    "        sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true\", ax=ax, color='orange', linewidth=3, errorbar=None,\n",
+    "                    label=\"True relationship\")\n",
+    "        sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true_positive_95_bound\", ax=ax, color='orange', linewidth=2, linestyle=\"--\", errorbar=None, \n",
+    "                    label=\"True 95% CI\")\n",
+    "        sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true_negative_95_bound\", ax=ax, color='orange', linewidth=2, linestyle=\"--\", errorbar=None)\n",
+    "        \n",
+    "#    ax.set_title(f\"Iteration {i}. Precursor count: {current_df['discovered'].sum()}. Proportion Discoverable: {current_df.loc[current_df['decoy'] == False, 'discoverable'].sum() / dataset_size :.3f}\")\n",
+    "    ax.set_ylim(bottom=0, top=1200)\n",
+    "    ax.set_ylabel(\"Observed RT\")\n",
+    "    ax.set_xlabel(\"Library RT\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0adOa5577X4nXJiJln5pbRETKmYcfGkvVKlVYtz6C06dPExdnmcMzIcF6mMTatWsx/svP+fffFaxbH0F0dDRJSUk4OztTvXp16oaG0qpVSzp16ljoWu67dyTBtWuzeMlSjhw5gtFkIigoiA4dwhl8+yAOH47OdV8nJyfefusNZs/5mZ07dxEfH09SUhIAaalpue53pfvHjKZdu7YsWbKUvXv3kpiYhIeHB6GhdejRozs9e/TAycmp0I9RRERERCQ3yubWymo2z/nJ3axPreYmJTnlGlcjIiIiIteCsrm1sprNH3v0Efbu28ehQ4e4cOEiSUlJODk5ERQURMMGDejRoztt2rQu8bpEpHxwyLza5GYiIiIiIiIiIiIiIiIiIiIiIqXEsbQLEBERERERERERERERERERERHJjZpbRERERERERERERERERERERKTMUnOLiIiIiIiIiIiIiIiIiIiIiJRZzqVdQGEdPHiI7Tt2cODAAQ4cOEB8/DkA5s+ba7Ot2WwmKmovmzZtZufOnZw4eRKj0UiVgABatmrJkMG3ExgYmOu5oqKi+PXX39m3fz9Go5HatWtxyy396NWzZ677nD17lpmzZrNt23aSkpKoWrUKXbt25Y4hg3F1dS36EyAiIiIiUgYol4uIiIiIlA3K5iIiIiJyPXPIzMzMLO0iCuO99z5gw8aNNsvtBfVTp07x8COPAeDn50uDBg1wdHTMDvgeHh689uorNGkSZrPvunXr+fiTT8nMzKRpkyZU9q7Mzp27SE5OZtDA2xg9epTd84174f9ISEggJDiYWrVrcejgIU7HxhIW1pi333oTFxeXoj8JIiIiIiKlTLlcRERERKRsUDYXERERketZuR25pVHjhoTUCaFB/fo0aFCfB8c+TEZGRi5bO9CqZUuGDBlM8+bNcHBwACAjI4NJX0/m339X8NnnnzP560k4O19+ShITE5kw8SvMZjMvvjCOjh07AHDhwgVe/L+XmTd/AW3btqV582ZWZ/ty/AQSEhLo3/9WHnzgfgBMJhMfffwJEREb+O233xk69J7if1JEREREREqYcrmIiIiISNmgbC4iIiIi1zPH0i6gsIYMHszwYUNp374dfn5+eW5bvXoQb775Oi1aNM8O6QAuLi48/NBYKnl6Ehd3lr1791nt9/ffy0lJSSG8ffvskA7g6+vLffeNBGD+/AVW++zff4CoqL34+Pgw6r57s5c7OTnxyMMP4ezszKLFSzCZTIV+7CIiIiIiZYVyuYiIiIhI2aBsLiIiIiLXs3Lb3FJc3NzcqFGzBgDnzp2zWrd5yxYAOnXqaLNf2zZtcHV1ZcfOnRgMBpt92rVrazOMoq+vL03CwkhKSmLPnqhifRwiIiIiIuWZcrmIiIiISNmgbC4iIiIiZVGFb24xm82cORMHgK+fr9W6mOgYAOrWq2uzn4uLC8HBtTEYDJw8edJmn3p1bffJeayYI0eKWLmIiIiIyPVDuVxEREREpGxQNhcRERGRsqjCN7esWr2aixcv4uPjTVjjxtnLU1JSSE5JAaBKQIDdfQMuLc8K+gBxZy1fB1Sxv0/WseLOnCl68SIiIiIi1wnlchERERGRskHZXERERETKogrd3BIXd5bvvpsGwNChQ62GRExNS8v+2s3Nze7+7u7ulm1TU7OXpV3aL7d93NzdLu2TZne9iIiIiEhFo1wuIiIiIlI2KJuLiIiISFnlXNoFlJa0tDQ++PBDEhISCA9vT7+bbyrtkuxKSkomOTnZZrnBYCD2zBlq1qiOo6NTKVQmIiIiInkxm02cO3eeBg3q4+rqWtrllFnlJZeDsrmIiIhIeaVsnj/K5iIiIiJyrRUlm1fI5haj0chHH33CwYOHaBIWxv+efcZmG49LHeYA6enpeHp62myT1XHu4eGRvSyrMz09Pd3uudPT0i/t4253/ZUWLlzInJ9/yde2IiIiIlL2vPfuOzRt2qS0yyiTylMuB2VzERERkfJO2Tx3yuYiIiIiUpIKk80rXHOL2Wzmyy8nsGXrVkJDQ3n55ZfsDofo6elJJU9PklNSOBsfT7CdoB4fHw9AtWpVs5dVrVKVw4ejiT8bb/f8Zy/tU7VatXzVO2DAAHr16mWzPDY2lldfe5333n2HKlWq5OtYIiIiUgE9/RTMX2C9rGoV+PpraNO2dGqqIM6ePctLL7+Cn59faZdSJpW3XA7K5iIiIiLllbJ53pTNRURERKSkFCWbV7jmlqnffMuq1aupUaMGb7z+Kl5elXLdtk5oHSIj93D40GGCa9e2Wmc0Gjl69Biurq7UqFHDap8NGzdy6PBhu8c8fMiyvE5ISL7q9fKqlGeNVapUITAw/6FfREREKpjvvoN9XWDbNsv3bdrAH3/AFdlGrh0nJw2FbU95y+WgbC4iIiJS3imb26dsLiIiIiIlrTDZ3PEa1FFmzZg5i6VL/6Rq1Sq89ebr+Pr65rl92zZtAFi3br3Nuk2bN2MwGGjZooXVXFBZ+2zatJmMjAyrfS5cuMCeqCi8vLwIC2tcxEcjIiIikg+enjBvHlStCiNGwOrVamyRUqdcLiIiIiJSNiibi4iIiEh5UWGaW+YvWMivv/6Gn58vb735BlWrVr3qPn379sHT05MNGzeyfn1E9vILFy4wffpPAAwceJvVPg0bNiAsrDEXL15k+vQfs5ebTCYmT56K0Wik/6234Oxc4QbNERERkdISHAxbtsCPP0KOec9FSoNyuYiIiIhI2aBsLiIiIiLlSblNi5s3b+bnX37N/t5oNALw/LgXspfdfdedtG3blsOHo5k27QcAqlUL5Ndff7d7zL59+9CkSVj295UrV+aJxx/j408+5cOPPqZZs6ZUrlyZHTt2kpyczMDbBtC8eTOb4zz5xOOMe+H/WLhoMTt37aZ27VocPHCQ07GxNG7ciDvuGFIcT4GIiIiIRWYmfPMN3Hor1KxpfxuN1iLXiHK5iIiIiEjZoGwuIiIiItezctvccvFiAvv3H7BZnnPZxYsJACQnJ5OZmQnAvn372Ldvn91jNmvW1CqoA3Tq1JH33n2bX379jX379mM0Gqlduxa33nILvXr1tHucGjVq8PlnnzJr9my2bdtGRMQJqlatyl133cmddwzBxcWlUI9ZRERExEZ6Ojz6KHz/PbRrB6tWgbt7aVclFYhyuYiIiIhI2aBsLiIiIiLXM4fMrAQr5Ups7BnGPvQwU6dMJjCwWmmXIyIiIqXh1CkYMgTW55jrfORImD4dHBxKry4BlNcqEr3WIiIiImWb8lrFoddaREREpGwrSl4rtyO3SOFlZmainiYREckvBwcHHNQoUfZs3Ai33w4nT1ov/+knePhh6NSpdOoSkXxTLhcRkYJSNhcRuTaUzUVEpKCUzUVKnppbKoDMzEwSExNJSEggJSUFk8lU2iWJiEg54+rqSuXKlQkICMDJyam0y5Eff4SxYy1TEuXk6WkZtUWNLSJllslkIj4+nsTERAwGQ2mXIyIi5ZCyuYhI8VA2FxGRolI2FylZam65zpnNZk6fPs3FixdLuxQRESnHDAYD8fHxJCcnExwcrKBeWoxGGDcOPv/cdl2dOjB/PrRoUeJliUj+mEwmjh49SlpaWmmXIiIi5ZiyuYhI0Smbi4hIcVA2FylZam65zl28eDG7scXf35/KlSvj5uamYbJERCTfzGYzycnJxMbGkpaWRnx8PNWqad7qEnfuHNx9NyxfbruuZ0/45ReoUqXk6xKRfIuPjyctLQ0nJycCAwOpVKkSjo6OpV2WiIiUI8rmIiLFQ9lcRESKStlcpOSpueU6d/78eQCqVatGQEBAKVcjIiLlkaOjIz4+PgCcPHmSxMREhfSStns3DBwIhw/brnvySfjkE3BxKfm6RKRAEhMTAQgMDMz+uSoiIlIQyuYiIsVD2VxERIpK2Vyk5KkV+TqWmZlJeno6AN7e3qVcjYiIlHeVKlUCLEMtZmZmlnI1Fcgff0CHDraNLa6u8P338OWXamwRKQcyMzMxGAzA5Z+nIiIihaVsLiJSeMrmIiJSnJTNRUqOmluuYzl/gGqONxERKaqcw/MqpJcAsxnefBMGD4bkZOt1QUGwciWMHl06tYlIgeX8uanhzkVEpKiUzUVECk/ZXEREipOyuUjJUXITERERKYsyM2HdOtvl7dvD5s2W0VxEREREREREREREREQqADW3iIiIiJRFTk4wZw7Ur3952X33WUZsqVmz9OoSEREREREREREREREpYWpuERERESmr/Pxg/nzw9YXPP4dp08DdvbSrEhERERERERERERERKVHOpV2ASGkYOGiw1fcODg54engQEhJCz1496NunDw4ODqVUHfzzz7+MnzCRe+6+i6FD78le/uWXE/h3xQreefstmjdvdk3OHRt7hrEPPUyzpk159923r8k5imL79u3M+fkXoqNjSEtLA2D+vLmlXNX1J7f3oIiUgiZN4PBhS6OLiMh1SNk8d8rmAsrmIiIiUnKUzXOnbC6gbC4iUtrU3CIVWq+ePQEwm82cOn2aqL172RMVxc6du3juf8+WcnXXRnkOX3Fxcbz/wUcYjUZatmiBj49PaZckIlJ0aWnw+uvwwgvg729/GzW2iEgFoGyubC4iIiIiZYOyubK5iIhIWaTmFqnQnnrqCavvt2/fzltvv8vq1Wvo3q0b7dq1LaXK7Bs5cjhDhtxO1apVr9k5AgL8+WrieNzc3K7ZOQprx46dpKWlcddddzJ82NDSLkdEpOhOnIDBg2HjRti2DZYsAWfFMxGpmJTNbSmbi4iIiEhpUDa3pWwuIiJS+hxLuwCRsqRVq1b06NEdgIgNG0q5Glv+/v7UqlXrmgZoZ2dnatWqdU0vBArrbHw8AEGBgaVciYhIMVi/Htq2tTS2APz9t2X0FhERAZTNQdlcRERERMoGZXNlcxERkbJAHw0WuULd0FD+Ac6ejc9eNnDQYKpVrcqkSRP5fe4frFq1mtjYWNq0bs1LL70IQHp6OgsXLmLN2nWcOnUKgODg2vS7+WZ69epp91xRUVHMmv0z+/fvx8nRkQYNGzJsWO5DHuY1d2haWhqLlyxh7dr1nDx5ErPZTJUqVWjRojkD+venZs0avPzyq+yOjARgzs+/MOfnX7L3f/KJx+ndu9dV5w5dseI//lq2jJiYI5hMJqpXr063rl247bYBuLq65lqvo6MDs+f8zIEDB3FwcKBJkzBGjbqP4Nq183g1LHbt2s0rr76W/f34CRMZP2EigNUwkSaTiaVL/+Tff1dw4uRJAGrXrkXvXr248ca+ODk5WR036/mYOmUye/fuZdHixRw9egwnR0dmzZqRZ02ZmZmsXLWKP//8i5MnT5GSkoK3tzc1a9akQ4dwbr2ln832q1evYdnff3P4cDQGg4HAwGp06dyZwYNvt7nwOnXqFP/9t5Jt27cTG3uGpKQkfHy8ad68OXfdeSc1a9awqSk/79P8vE+uFBcXx48/zmD7jh2kpaVRu3Zt7rn7Ltq3b5fncyQieZg2DR5+GAwG6+VTp8Izz0CtWqVTl4hIGaNsrmyubG5N2VxERERKi7K5srmyuTVlcxGRkqfmlgrKaDRy/Pjx0i6j0GrVqoXzNZq2ITU1FQAXF+vjmzMzef/9D4ncs4emTZtQJySEyt6VAbhw4QKvv/EmMTFH8PPzpWnTJpAJe/fu5cvxEzh48CBjxz5odbxNmzbz/gcfYjKZaNCgAUFBgURHx/DSS6/QO5dQn5tz587x+htvcvToMby8vGjWrCkuLi7Eno7lr7+WUaN6dWrWrMENrW/AZDYRFbWX0Dp1CA0NzT5G9erVr3qeSZO+5q9lf+Pq6krz5s1wc3Nj9+5Ifpoxk42bNvP2W2/Y7Y7ftGkTCxctpn79erRp05ro6Bi2bNnK/v0HmDD+C/z8/PI8r5+fL7169iQ6OpromBjCwhpTPchSb9ZjMJlMvPve+2zZshVPT09atmwBmbBz1y4mT5nK9h07eGHc8zg62g5Y9fvvv/P38n9o3Lgx7dq15ezZs1d9Ln6Y/iPz5s3HxcWFpk2a4O3tzfkL5zlyJIbTp05ZhXSz2cznn3/JqtWrcXd3p379+nh5VeLgwUPM+fkXtmzdxrvvvGX13C37ezl//DGP4OBgGtSvj7OLM8eOHee//1ayceMm3n/vHerUqWNTV17v0/y+T3I6cyaO/z03Dg8PD1q0aE5c3Fn27dvH+x98yGuvvsINN7S66nMlIjlkZMBzz8H48bbr6taF+fPV2CJSASmb507ZPHfK5pcpmyubi4iIFIfynstB2fxKyubK5tnnUjYXEbkuqLmlgjp+/LhVQCtvoqOj7QaUosrMzGTT5i0ANsc/e/YsLi4uTPpqAgEBAVbrxk+YSEzMEQb0v5X77rsXFxcXwBLe337nXRYvWUrbtm1o3bo1ACmpqUyYOBGTycQTTzxGn969s8//408zmDv3jwLV/fkX4zl69BidO3fiiccfw8PDI3tdbOwZUlNTALhjyGD8fH2JitpLeHj77M7t/Fi3bj1/Lfsbf39/3n3nLWrUsIS55ORk3nnnPfZERTFr1mxGjx5ls+/CRYt5YdzzdOgQDlgC9ceffMr69REsWfrnVecBrVWrFk899QSzZ88hOiaGvn360Lt3L+tzLFzEli1bCQ6uzdtvvYmvry9gCaavvPo6EREbWLr0T2699Rab46/4byVvv/UmzZo1zddzYTAYWLx4CR4eHnz5xWcE5hju0WQysXfvPqvt581fwKrVq2nWrBnP/e+Z7IuSjIwMJk+ZyvLl/zDn51+4796R2ft0CG/PzTfdaHVsgOX//MOECV/x7Xff887bb9nUltf7NL/vk5z+XbGCQQNv47777s2+wFmwYCHffT+NX379TSFdpCDi4+Guu+Dff23X9ekDP/8M/v4lX5eIlDplc/uUzXOnbH6ZsrmyuYiISHEp77kclM2vpGyubJ5F2VxE5Ppg24opUgGZTCZOnjzJ+AkT2bdvHy4uLvTu1ctmu5Ejh9sEn8OHo9myZSsNGtRnzJjR2QEdwNfXl8cefQSApX/+lb183bp1XLyYQNOmTbIDOoCDgwPDhw21OUde9u8/wM6dO/Hx8eHxK4IXQGBgtWK5oFm0eDFgGc4wK6ADVKpUiYceehAHBwf+/GsZhiun2AC6du2SHdABnJycuGPIEAD2RO4pcm056xszenR2QAfLfKuj7rsXsFws2NOnT+98B3SAlJQUMjIyCAoKsgnRTk5Olk8gXGIymfjjj3m4u7vz/HPPWnXbu7i4MPbBB/Dz82XZsr8xm83Z6xo1amRzbIA+vXsTFtaY3bsjSU5OtlufvfdpYd8ngYGBjBgx3Kpz/9Zbb8HLy4v9+/eTkZFhtwYRucLOndCunf3GlmeegaVL1dgiInKJsvnVKZtfpmyubC4iIiLXjrL51SmbX6ZsrmwuInKtaeQWqdAGDhpss8zDw4OnnnqC6tWDrJY7ODjQvp3tXInbt28HILx9e7tD99WtWxd3d3cOHDiQvWzPnigAunbpYrO9s7MznTp1ZOHCRfl6DDt27ACgW9cueF4RvIqL0Whk3779AHTv3s1mfZ06dagTEkJ0TAzR0dE0atTIan2rVi1t9qlxaRi/c+fPF7m+uLg44uLO4uPjbbcjul27tlSqVIlTp05x/vx5m+Ec7b2uefH19SUgIIDo6Gim//gTN93Yl6CgILvbHjp0mISEBFq1bGl18ZDFzc2NevXqsXnzFk6eOkWtmjWz16WmprJp02aio6NJTErCZDQBcP7ceTIzMzl9+jT16tWzOl5u79PCvk+yhmDMycnJicBq1Th0+DCJiYn46z/kRfL2++9w772QcsWnPNzcYOpUyzoREVE2zydlc2vK5srmIiIiUvyUzfNH2dyasrmyuYjItabmFqnQevW0zNHp4OiAp4cHISEhdOzYAS8vL5ttfXx8bMIKwJkzZwCYMXMWM2bOyvVcOTt1z507B0DVqlXtbhtYrVq+H8PZ+HiAXENicUhMTMRoNOLt7Y27u7vdbapVq0Z0TAzx8eds1lUJqGKzLCsoGouhg/lqz6eDgwPVqlYlOjmZ+PhzNiG9alXb+q7m6aee5JNPP2Xu3D+YO/cPqlWtStNmTenapQtt2rTO3i7r/bF9xw67F4U5JSYkwKWQvnPnLj759FMuXkzIdfvU1DSbZbm9Twv7Psnt0xBZHewZGcYCHU+kQjGb4Y034O23bdfVqAF//AHt25d4WSIiZZWyef4om9tSNlc2FxERkeKlbJ4/yua2lM2VzUVEriU1t0iF9tRTT+R7W1c7wQfAnJkJQJOwsGsalMs8hzxWOeSxsqTkUYOLi2uBD9eiRXMmfz2JTZs2s3XbNnbvjmTFiv9YseI/OnbswIsvjAMuvz+qV69OWOPGeR6zcuXKgKXz/KOPPyEpKYm7776Lrl26UK1aVVxdXXFwcODTTz9n1erVZF46dk65vU8Ly7EsvHYi5VFSEgwfDgsW2K7r0AHmzoXq1Uu+LhGRMkzZvBgpmyubi4iIiBSBsnkxUjZXNhcRkWKj5haRIqpyqUM3PLw9gwYNzNc+WR3QcXFxdtefyWV5Xuc/ffp0vvcpqMqVK+Ps7ExCQgJpaWl2u9DPnLHUHBBQ8kPtZQ3vl9vzmXNdcdbn6elJ9+7dsoec3LdvHx9+9Anr10ewefMW2rZtk/361KpZM98XhXv2RJGYmEinTh0ZNvQem/WnYwv+WpfE+0REcnB2Bnv/3saMgUmTLFMSiYhIsVM2t1A2VzYXERERKW3K5hbK5srmIiJSfNTcUkHVqlWL6Ojo0i6j0GrVqlXaJWRr2aolM2fNJmLDhnyH9KZNmvDvvytYs3Yd/frdbLXOZDKxfv36/J+/ZUtmzJzFqtVrGD58WPawd7lxdrb8szeZzfk+h7OzM40aNSQycg+rV6+hb98+VuuPHDlCTEwM7u7uhIaG5vu4xaVq1apUrVqFuLiz7Nixk5YtW1it37x5M0lJSVSvXt1maMXi1KhRI3r26M5vv8/l6NGjtG3bhgYN6lPJ05PdkZEkJiZmd5nnJSkpCbA/tOGpU6c4fLjg/3YL+j4RkSJyd7dMO9S2LZw6BU5O8MUX8NhjeX4iRkQqJmXz4qNsrmyeRdlcRERECqq853JQNrc6v7K5snk+KJuLiJQvam6poJydnalTp05pl3FdaNSwIa1atmT7jh1MnjKVe0eOwNPT02qb6Ohozp8/T+vWljklO3XuxA/Tf2T37t38++8KevWyzGGamZnJrNlziIs7m+/zN2zYgObNm7Fr124mTZrMY489YtUhHht7htTUVOrUCQEud2ufOHGiQI/z1ltvITJyD7Pn/Ezz5s2yh5JMSU1l6tRvyczM5OabbsTVteBDFRaHW2+9hR9++JHvp03jrTffwMfHB4Dz588z7YcfARjQ/9ZiOVdcXBw7d+6iS5fOuOUYfcFgMLBr924AqlSxzEfq4uLC7bcPYsbMWXzwwUc88cRjNsNwxsfHs3PnLnr27AFAjZo1AIiIiODOO4ZkP5akpGQmTPwKo7Hg83UW9H0iIsWgRg3L9ENDhsBPP0GvXqVdkYiUUcrmxUfZXNk8i7K5iIiIFJRyefFSNlc2z6JsLiIixUXNLSLF4JlnnuLNt95m6dI/WbVqNaGhofj7+5GSkkJMzBHOnj3LgP63Zod0Tw8Pnnj8MT748CO+HD+BpX/+SVBgENExMZw6dYob+/Zl2d9/5//8Tz/Fq6+9warVq9m2fRthYWG4OLtw+vRpomNiGD3qvuzw1ahRQ3x8fFi3bj0vv/wqgUGBODg40KdP7zzntuzcqRM33diXv5b9zRNPPk2LFs1xc3Vjd+RuLl5MoFGjhgwbNrRoT2QR3DZgALt27mbL1q088shjNG/RnMzMTHbu3EVqairh4e1tuv0LKzExifETJjJl6jfUr1+PgIAA0tPS2btvLxcvJlC/fj06duyQvf2QIYM5fuIE//23kscef5K6dUMJDAzEmGHkxMkTHDt2nDohIdkhvUH9+tkXfo88+jjNmjUFYPfuSLy9vQlv354NGzcWuO6CvE9EpJh06ACHDllGchERkRKhbK5srmwuIiIiUjYomyubK5uLiEhxUnOLSDHw9fXlww/eZ9myv1m9Zg3Rhw+zd68BX19fggID6d//Frp26WK1T3h4e955+y1mz5nDgQMHOX78BPXr1+fxxx7lxIkTBQrpAQEBfPLJRyxcsJB169ezffsOHB0dqVIlgH4330S7dm2zt3V1deXVV19mxk8zOXDgAJF79pCZmUmTsLA8QzrAo48+QlhYGH/+9Re7d0diNpsJCgpkQP/+3HbbAKtu7JLm5OTEyy//H0uW/sm///7Ltm3bAahduxa9e/XipptuxNHRsVjOFVQ9iDGjR7Fj506OHTvOgQMHcXNzIzCwGncMGcJNN92Ii4tL9vaOjo488/RTdOrUkWXL/ubAgYMcPhxNpUqVqFKlCrcPGkiXLp2tzvHSSy/y62+/s3btWrZu3YaPjw9du3Zh+LBhTJv2Q6HqLsj7RETy6fhxePVVmDgRKlWyv40aW0RESpSyubK5srmIiIhI2aBsrmyubC4iIsXJITMzM7O0i5CCi409w9iHHmbqlMkEBlazu43ZbGbfvn2AZU7D4gooIiJSMen3yhXWrrVMORQbC3feCT//DA4OpV2VlCH5yWtyfVA2FxGRkqbfKyIFo2xecSibi4hISdPvFZGCKUo2178uERERkYL65hvo2dPS2ALw66/wwQelW5OIiIiIiIiIiIiIiMh1Ss0tIiIiIvmVkQGPPw5jx1q+zumHHyA1tVTKEhERERERERERERERuZ6puUVEREQkP+LioG9f+Oor23U33QQREeDhUfJ1iYiIiIiIiIiIiIiIXOfU3CIiIiJyNdu3Q7t2sHKl7brnn4fFi8HPr8TLEhERERERERERERERqQicS7sAERERkTLtl19g1CjbKYfc3eHbb2H48FIpS0REREREREREREREpKLQyC0iIiIi9phM8NJLcPfdto0ttWrB6tVqbBERERERERERERERESkBGrlFRERE5EoXL1oaVxYvtl3XqRP8/jsEBZV8XSIiIiIiIiIiIiIiIhWQmltEREREcoqOhn79YN8+23UPPggTJoCbW8nXJSIiIiIi169Dh2DtWjh9+vKfs2chKQmSky1/Gwzg6AhOTuDqCv7+lj8BAfDUU9C6dWk/ChERERERkWtGzS0iIiIiOfn7g4OD9TJnZxg/Hh5+2HadiIiIiIhIUa1cCfffX/j985oydc4caNECwsJ0PSMiIiIiIuWWY2kXICIiIlKm+PjA/PmWvwGqVIHly+GRR3QjWERERERECu7MGUuz/BNP5L5No0ZFO0dAgP3laWkwYgQ0bQrVq8PQofDNN3DyZNHOJyIiIiIiUsLU3CIiIiJypYYN4eefoU0b2LwZuncv7YpERERERKS8Wb/e0kxSq5Zl2qCJE+HUKfvbNm5ctHP5+9tfvmsXmEyWr2NjLaO4jB0LNWtCx47w0UcQE1O0c4uIiIiIiJQATUskIiIiYs9NN0HfvpY57aVMMprMGExmElKNXEw14OPhireHM65Ojjg76XUTERERkVISEQGvvw7Lltmumz/fMt3plQICoHNnqFQJgoIsf6pVg8qVLcsqVQI3NzCbLc0qaWlw7pzlT3y8ZVt7tm7Nu86ICHjhBejVC0aPhsGDwdOzwA9Z2VxEREREpGy4nrO5mltERESkYlq92jI0+KxZ4OJifxs1tpRZKQYjy6NimbLyMJEnE7KXN63hzUPd69InLBBPV0VdERERESlBx4/DM8/Ab7/lvs28efabWwDWrCn+mpydLSPHHD+e93b//mv58+ijMHIkPP00NGiQr1Mom4uIiIiIlA3XezbX/9iIiIhIxTN5suWTib/9ZrlpK+VKisHIe0uieHL2dquADhB5MoEnZ2/nvSV7STEYS6lCEREREalQTCb47DMIC8u9scXdHYYPh8cfL9na7r8fjh6FAwdg6lS4+27w9s59+8REmDQJGjWCQYMs3+dB2VxEREREpGyoCNm8/LbliBTBwEGDbZY5OTnh7e1N40aNGDjoNsKKOtdxGbJr125eefU1evXsyVNPPXFNzrFw0WL++msZp06dwmg00qxpU9599+1rcq6K7MsvJ/DvihW88/ZbNG/erLTLESl/DAZ48kmYMuXyskmToGVLy7zzUuYZTWaWR8UyI+Ko1XInRwcaBnrh5eZMUrqR2RuPEh7qT79mQeV+qEWR652yefFTNi8ZyuYiAsCxYzBiBKxaZX99UBD8738wZgz4+5dsbVkcHKB+fcufBx+0XBetWAFz58Ivv8CFC7b7ZGZaHpuXV66HVTYXuf4omxc/ZfOSoWwuIhVdRcnmam6RCq1Xz57ZX6emphIdE8P6iAgiNmzgmaefonv3bqVYXfmxfn0E3377HV5eXrRv3w53N3dq1qpZ2mWJiFiLjYU77rA/1PeSJZabvA4OJV+XFIjBZGbyf4ezv/fxcGFEhxCGhQeTZjBxLsWAv6crbi6O/BMVi8FkLpchXaQiUjYvHsrmIiIlaO5ceOABOH/edl1AALz8smUKIg+Pkq8tL66ucNNNlj9ffgnz58O0abBsmaWpJcuzz+Z5jaRsLnL9UjYvHsrmIiJSUipKNldzi1RoV3Zjm81mfpoxk7lz/+Cbb7+jc+dOODvrn8nVRGzYAMAL456nRYvmpVyNiIgdW7dahtQ+dsx23YsvwjvvqLGlnEhINbLnlGVIxToBnkwf056N0ecY++NmmzlER3Wqg9lcWpWKSEEpmxcPZXMRkRJgMlmuIz75xP76Bx6ADz6wNLiUde7ulqmK7r7bcr00caJlpMvKleGuu3Lf79w5Epw8lc1FrlPK5sVD2VxEREpKRblvXv7acUSuIUdHR4YNvQcnJycSExM5etTOf4KKjfj4eAACAwNLuRIRETtmz4bOnW0bWzw8YNYseP99cHIqndqkwC6mGgBL5/mPY8KZsvIwz/+20+4cos//tpMPlkaV6zlERSoyZfPCUTYXESkBZ87A9Om2y0NCLNMTffNN+WhsuVLt2vDhh3D8uGU0FxcX+9sdPAi1a+P2v2fwTktSNhepAJTNC0fZXERESkpFuW+u1lqRK7i4uODp6UliYiIms8lmfVzcWX799Ve2btvG+fMXqFTJk7CwMO4YMpgGDRpYbXu1OTtzmwdy4KDBVKtalcmTJzFv/nyWL/+HM2fi8PHxoXu3rgwbNhQXOzcYjh49yowZs9gdGYnJZKJuaCh33jkEFxdXu481IyODv/9ezj///ktsbCzp6QZ8fXwIDgmme/fudOvaJc/navbsOcz5+Zfs78c+9HD21zkfU0JCIr/PncuGDRs5e/Ysbm5uNGhQn4G33cYNN7SyOW7W4580aSK/z/2DVatWExsbS5vWrXnppRfzrCk1NZXFi5ewes0azpyJw2w24+vjQ916denbtw+tb7jBavv09HQWLlzEmrXrOHXqFADBwbXpd/PN9OrV0+b4kZF7WLN2LZGRezh79iwZGRlUrVqV8PD2DBk8GC+vSlbb53wP3HvvCGbMnMW2S++d0aPu47bbBgCW99Uf8+axbds2zp6Nx9XVlcDAQNq3a8tttw3A09PTTi2RzJ7zMwcOHMTBwYEmTcIYNeo+gmvXzvM5EqkwTCZ46SX46CPbdbVrw7x50Lp1iZcl1owmMwaTmYRUIxdTDfh4uOLt4Yyrk6PdYRF9PCy/00Z0CGFDdDyzNh612SanGRuOEl43oNzOISpS0SmbK5srm4tImVS9umVKol69ICPDsuzuu2HyZPD1LdXSiiI7m5ucuVijPj4X0+xn8//9D1JS8PtuMis8ZrF57HNsONRI2VzkOqdsrmyubC4iUnJ039w+NbeIRVxc4ff18sp97uCzZ63n6i0IT0+oVOnq2xWz2NhYEhMTcXZ2pnpQdat1MTFHePW110lISKBmzZp07NiBuLg4IiI2sGnTZv737DN07typ2Gr59LPP2bJlK82bNaNmjZrs2bOHuX/MI/7cOZ595mmrbQ8cPMgrr7xGWloaIcHBBIcEc/LkKd56+11uvvkmu8f/7PMvWLduPR4eHjRpEoanpyfx8eeI2hNFWmraVUN6aGgovXr2ZOu2bVy4cIGOHTvg4W55L/j5+QKW7vSXXnqF07GxVK1ahfDw9iRcTGDHjp1s27adMWNGM/BSUM3JnJnJ++9/SOSePTRt2oQ6ISFU9q6cZz0mk4nXXn+D/fsP4O3tTbNmTXFxcSE+Pp4tW7bi7uZuFdIvXLjA62+8SUzMEfz8fGnatAlkwt69e/ly/AQOHjzI2LEPWp3jh+nTiYk5QkhICC1aNCcjI4NDhw4zd+4fbN68mY8+/AAPO/8eLiZc5H/PjcNsNhEWFkaGIQM3NzfAEvzffe99kpOTqVatGu3atsVgMHD8xAlmz/mZ9u3bU7duqNXxNm3axMJFi6lfvx5t2rQmOjqGLVu2sn//ASaM/wI/P788nyuR69758zBsGPz5p+26rl3ht9+gWrWSr+s6UdBgnZsUg5HlUbFMWXnYZmjEh7rXpU9YIJ6u1nHV28OZ5jW9GRYezIPTN+frPJNXHqJ3WLVyFdKlglM2z6ZsrmyubC4iZVaXLjBpEjz6qGUqnwcfLJWpTks8my9bBgsWZK8PSE3gpi9fY+eiudTp9Qgx/jXzPI+yuZQryuVWlM2VzZXNRURyV1y5HHTfPC9qbhGLovwH38SJ8Nhj9teFhVnCemG8/jq88Uahyyqo1NRUoqNj+O777wG4+eabrLqJMzMz+ezzL0hISGDw7YO4996ROFy6abFu3Xo+/uRTxk+YSFhYY/z9/Ytcz5m4OFzd3Jj89VfZgSs2NpZnnn2OlStXMfSee6hePSi7ti+/nEBaWhp3330Xw4bek32cJUuWMmXqNzbHj42NZd269VSrWpVPP/0E7xwB2GAwcPhw9FVr7NAhnA4dwnn55Ve5cOECo0eNIjDQ+r309ddTOB0bS7duXXnyicezO+f37InijTff4ocfptO8WTObEHr27FlcXFyY9NUEAvI5lG/knj3s33+ABg3q89677+DqernzPiUlhZMnT1ltP37CRGJijjCg/63cd9+92bVduHCBt995l8VLltK2bRta5xjZ4Z6776Zx40ZUynERmZGRwTfffMtfy/5m/oKF3HO37ZzUW7ZspUOHcP737DNWdSUmJvLhRx+RnJzMqFH3MvC223B0vPxLZO/effj72wbuhYsW88K45+nQIRywXKB8/MmnrF8fwZKlfzJ82NB8PWci16WoKBg4EA4csF33yCPwxRfgav+TOXJ1hQnWuR3nvSVRzIiw7SCPPJnAk7O3M6JDCC/d0tjqeK5OjvzvxoakGUzZc4heTeTJBBJSjfmqS6RMUDZXNlc2VzYXkfLhgQcso7fUrVsqpy+VbH7oELi5QXq61XYtDm3nzyNP8GmXEXzfbiAmR/tTvyqbS7miXA4omyubK5uLiFxNceXyrGPpvnnuyk8bjsg1MHDQ4Ow/9wwdzv+99DInTpxk7IMP8MD9Y6y23bV7N0eOHKFq1SoMHz4sO6ADdOrUkfD27UlLS2P5P/8WW30PPni/VSdxYGAgPbp3A2DPnj1WtR07doygwEDuvutOq2Pccks/Gja0HvYR4OJFyw+20LqhVgEdwNXVlcaNGxW5/tOnT7Np82bc3d0Z++ADVkNCNmkSxs033YjZbGbJ0qV29x85cni+AzpAwqXH1LhxY6sgDODp6Un9+vWyvz98OJotW7bSoEF9xowZbVWbr68vjz36CABL//zL6jht2rS2CuhgGZLz/vvH4OTkxMYNG+3W5uLiwtgHH7Cp6++/l3PxYgKtW9/A7YMGWQV0y2NphK+dIY27du2SHdABnJycuGPIEAD2RO6x2V6kwli1CsLDbRtbnJ0tQ4RPmqTGliLICtZPzt5ud67OJ2dv570le686V6fRZGZ5VKzdgJ7TjIgj/BN1BqPJnL3M2cmRDnUDSEov2HygCWkZBdpeREqesrmyubK5iJRJCVe5MVyKjS2lks3HPmT5QMHgwTbbuBsNvPzf9/w+4zlCz53I9VjK5iJln7K5srmyuYhI/hRXLgfdN8+P8tOGI3IN9Op5eW7IDGMGZ87EceDAAX7+5ReCgoJo0+Zy5/GePVEAdO7cGWdn2386PXp0Z31EhFV4LgpnZ2eaN2tms7xGjRoAnD9/3qa2Tp064uRk+8mYbl27sn+/9X/01qpVE3d3d7Zs2crcP+bRvXs3Aoqhcz6nPVGWulq3voHKlW2HRuzRowfzFyzMrj8nBwcH2rdrV6DzhYaG4ujoyL///Etw7dp06NDB5gIky/bt2wEIb9/eJhgD1K1bF3d3dw7YGfkhPj6ejZs2c+L4cVJSU8k0W4YRdXZ25uSpUzbbZx3P3gXHjh07Abjpxhvz9RiztGrV0mZZjZqW98a5HO8NkQqnUSPLHPeJiZeXVasGv/9uGTZcCq0gwTo81D/PuToNJjOT/zucr/PaGxrR3cWZQG+3/BcPeLvbzrktImWLsrmyubK5iJQ5mzZBv37w1Vdw992lXU22Us/moaGWa6xly2DsWDhyxGq7VqcOsOiHp3ijz1h+bd7XZromZXORsk/ZXNlc2VxE5OqKM5eD7pvnh5pbpEJ76qknbJYdPnyYl15+lXffe5/x47+gVk3LXMHnzp0DoFouw1FmLT8Xf65YavP19bUbuLPmpczIuNxJl1Vb1apV86wtJ09PTx579BG+mvQ106f/yPTpP1KjRg2aN29Gzx7dCQsLK/JjOHfufK7nz7k8Pj7eZp2Pj49VV3h+1KxZg/vuHclPM2by1aSv+XryFIKDa9OiRQt69+pJnTp1src9c+YMADNmzmLGzFm5HjPn8wwwf/4CfvxpBkZjwTofq1atYnf52XjLEKRBQUEFOl6VANvjeV56bxgzyleXpUixCgyEP/6wNLKkpUHr1jBvHtSuXdqVlXtFDdY5JaQaizw0oreHC01reNt0w9vTtIY33h6KvSJlnbK5srmyuYiUKVu3wo03woULMGwYGI0wfHhpVwWUoWx+442wezcZL7yI09eTcMzMzN62UkYaHy8dT/fDW3np5sdJcPcClM1Fygtlc2VzZXMRkasrzlwOum+eH+WrWrl2LgWWQvHyyn1dVBTkuLAtEE/Pwu1XRHXr1uWmm25k3rz5/Ln0Tx544P587XfFh1DyxZxpznWdY2EOWEDdunWlZcsWbNi4ke3bdrA7MpK//lrGX38tY+BtAxgzZvQ1PX9eD9G1gAE9y6BBA+ncuTMbNmxg+44d7NkTxYIFC1m4cBH3jxnNgAH9ATBfel82CQvLd0Det28f30/7gUqXLnCaNWuKn59f9sXEqNH3W30ywPrxFO80KA4l8P4QKbfatIHvvoPFi+Gbb0rt98n1pjiCdZaLqYaCnTstgyAfd6tlrk6OPNS9Lk/O3n7V/R/uXg/XPC4aRMocZfNsyubK5rlRNheRa2rnTujb19LYAmA2w8iRULWqpaGjlJWpbO7lhcP48axr14fq/3uceldMR9R/3xpandrHI4NeYlf1BsrmUr4ol1tRNlc2z42yuYhUVMWZy0H3zfNDzS1ikUvncpFVsd91W9YFXuqMzjlUnv+loQfjzsTZ3efMpeX+AZeHKHR2sfwTS01LtbvP2bO2ndeF4X9pftG4uFxqy2U5WDq9b+zblxv79iUzM5Nt27bx8SefMX/BQvr06U1wcHDh6/K/VFeuz5nlArEg84PmR9WqVejf/1b6978Vk8nE6tVrGD9hIj9M/5GePXvg5eVFlUvnDA9vz6BBA/N13IiIDQAMHzGcXr16Wq1LT0/nQtZNrwKoElCF48dPcPr0aerUCSnw/iIVWmZm7lf7w4bB0KGFu4MidhVHsM7i41GwGxf2hkZ0dnKkT1ggIzoE5zns44gOIVftiBcpc5TNrSibK5vbo2wuItdMdLSlgeXcFSMM9O0L3bqVTk1XKIvZvPWw/nzsUYP6H7zO8O1/Wq2vlRDHbzPHMfGrBcrmUr4ol9tQNlc2t0fZXEQqquLM5aD75vlRvqoVKSGxsbEAuLtf/gHTpIlluMG169ZhMpls9vlv5cpL2zXJXpYVnk+etJ1PMjExkcOH8zdU1dVknXPd+gjMZtuu9tWr1+TrOA4ODrRu3Zq2bdoAcPTosaLVdWmIxq3btpGUlGyz/r+VqyzbNSn6UI65cXJyokeP7tSvXx+j0Zj9WrS8NPdmxIYN+T5W1mOoYueiYu26dWQW4hMXLVu2AGDZsr8LvK9IhZWZaZnz/q67LJ+gzI0aW4pVcQTr7HUezjSt4Z2v4+Q1NKKnqzMv3RLGhKE32ByvaQ1vJgy9gZduaZxnN7yIlH3K5srm9iibi8g1ERcHN90El373ZOvVyzLdqXvuN6JLUlnN5s/f3hrvH77jnTHvcN69stX6E3fdyyOj+yibi5RzyubK5vYom4tIRVWcuRx03zw/1NwicoXDhw/z16XAlBVWAZo3a0ZISAhnzpxh1uw5VoFsfUQEEREbcHd3p0/vXtnLAwMDqVq1CkeOHGHDho3Zy9PS0vhq0tekpKQUS83NmjWlVq2anD59ml9+/c1q3Z9//cW+ffvsPs716yNs5sZMTExk//79AFQp4qcIgoKCaNu2DampqXz73XdW823u3buPP//8C0dHR27p169I58myc9cutu/YYXOhEhsby/Hjx3FwcCCgiiVgN2rYkFYtWxIVtZfJU6bafS2io6PZunVr9vc1alYH4O/ly60ey9Fjx/jxx58KVXPfvn3w9vZmy9atLFiw0Cbo79u3r1Cd7SLXrfR0GDsWHn8cfvsN3nyztCuqMIorWMPloRHz42pDI3q6OtOvWRC/PtyRiP/rzbJnuhHxf7359eGO9GsWhKuTIykGI6cvprHvdAKnL6aRYjBiNOXRGCUiZYayubJ5FmVzEbnmkpOhf384cMB6edeusGABeHiUTl12lPVs/uzXL5CxZSspHToBYOrUieDvv1I2FynnlM2VzbMom4uIWBRnLgfdN8+P8teOI1KMvvxyQvbXRqORM3Fn2L//AGazmXbt2tKjR/fs9Q4ODjz7zNO88upr/Pbb70REbKBuaChxZ+OIitqLk5MTTzz+WPYwjFnuuftuJkz8ig8+/IimTZvg7u7OgQMH8fT0ILx9ezZs3EhROTo68uSTT/Daa28we/Yc1q1bT0hwMKdOn+LgwUP063czS5daDwl75kwcH3z4EZU8PalXvz5+fr4kJycTGbmH1NRU2rVrS+PGjYpc26OPPMz/vfQKK1b8R+TuSBo1bsTFiwns3r0bs9nMmNGjqFs3tMjnAYiJjuG776fh4+NNvXr1qFy5MhcvJhAZGUlGRga33noLATlen2eeeYo333qbpUv/ZNWq1YSGhuLv70dKSgoxMUc4e/YsA/rfSuvWrQHo3as38+cvYNOmzTz62BPUr1+PpKQkIiP3EN6+PQcOHMhzKEt7KleuzLjnn+Pd997nu++nsWjxEurXr4fBYOD48ROcOnWKzz/7FF9f32J5jkTKtdOnYcgQWLfu8rK33oKWLWHw4NKrq4Iozrk6i3toRGcnR5ydHPF0dbYa1jHFYGR5VCxTVh4m8uTluU+b1vDmoe516RMWWC6700WuV8rmyubK5iJSakwmuOceuPL3QPPmsHAhVKpUOnXlolxk8yb1YdV/8PbbOD3yCCmOTizffUrZXKScUDZXNlc2FxG5uuLM5aD75vlRNqsSKSH/rliR/bWjoyOVKnnSpEkTevboTq9ePXF0tP6hUKdOCJ9/9gm//PIbW7dtY9369Xh6ehIe3p47hgyhYcMGNufo06c3Dg4OzJ+/gKiovXh5edGuXVvuHTmSadN+KLbH0qhhQz784H1mzJzJnsg9xMbGEhISwisv/x9ubu42Ib1Ro4YMHz6MXTt3ceLECfbs2YOXlxd16oTQp09venTvnsuZCiYgIIBPPv6I33//nYgNG1m/PgI3NzdatGjBoIG3ccMNrYrlPADt2rUlMTGRXbt3Ex0dQ2JiIj4+3oSFhXFLv5vp0CHcantfX18+/OB9li37m9Vr1hB9+DB79xrw9fUlKDCQ/v1voWuXLtnbe3tX5pOPP2L6jz8RGRnJpk2bqVatGsOG3cOggQN5+OFHC1V38+bN+PKLz5j7xzy2bt3Ghg0b8fBwp1q1QIYNvYeg6kFFel5ErgubNsHtt8OJE7broqJKvp4KqLiDddbQiOGhAUxeecgmRD/cvR69w6rh6eqM0WQmw2zGaMrE0QESUo1cTMvA18MFbw8XXC+F9JxSDEbeWxJlt9bIkwk8OXs7IzqElNvhF0WuR8rmyubK5iJSav7v/2DRIutlwcGwdCn4+JROTXkorWye9elOoykTZ0cHktONxKcYcs/lLi7w1lvK5iLlkLK5srmyuYjI1RV3LoeCZfPUDGOB7plD+b9v7pBZmMnupNTFxp5h7EMPM3XKZAIDq9ndxmw2Zw+r16hRI5vAKSIiUhCl9ntlxgx44AHLlEQ5eXrCDz/AnXeWTB0CWMLvP1FnrtqQkl9GkxmDyUxCqpGEtAy83V3w9nDODt+pBiMGUyYp6Ubikw1UcnPCxcmR2RuPMSPiCI2DKvPagDDqBHiRmGYkLcNIZXcXXJwcmbr6MD+tP8LF1Ixczz9h6A30axaUrwuLgspPXpPrg7K5iIiUNP1eKUY//ACjR1sv8/eHNWsgLKxUSsqvkszmBpOZE+dTqO7ryYUUA2cS0vHxcMHNxZLNo04l8L8bG2Tn8oupBgIqueHl5sTxCyncNXkD51IMeZ5f2VyKg7K5iIiUNP1ekeLO5ZB3Ns8wmQt0z/x6yuZlr91GREREBMBohBdfhE8/tV0XEgLz51umJCpncobSi6kGfDxcrZo5yrqsuTp7h1XLtSGlIHIbGhEgOd3IX5Gn+W5NtM1FwahOdVj8ZBccgE0x59kcc54+TYIwmeHw2WSqeLlyf5dQRneqw6hpG9l1IgF7Jq88lO+ueRERERG5vmSsXInzQw/hkGNZposLxt9+xaWMN7ZAyWXzVIOR5HQje04l8vTPO2yy+ehOdXi4e13WHDjLWwujaFvHn2HhwVxMzeDouRSqebuxclwP5mw8ysQVh3JtPlc2FxEREam4yvN98+LO5ZB7Ns/vPfN1h+Kvy2yu5hYREREpe86ft8x5v2yZ7boePeDXX6FKlRIvq6jK81yWOeXVkFJcsoZHnLnB/vCIz/+2kxHhITzTtwGtQ/zYcDiesT9utnle7+8SyqwHOzDsmw3sOnHR7rESUo3l4nkXERERkeKTfCAKp9tvw8Vg/YnFt+4Jon5ALAMNyXi5Viql6vLvWmdzo8lMmtHM58sPMCuXbP7cbzvZduwCj/aox8d3tsw1m4/tVpeFT3Th3u82EBOfYvdYyuYiIiIiFc/1cN+8LN0zf7J3fX5cf4SJw25gY/S56yqbl61WGxEREZHISGjXzn5jy+OPW5aX08aW95ZE8eTs7VZBEi7PZfnekr2kGIylVGHZYTSZWb4n1m5Iz2nhzpM4Ojgw+b9DPP/bTrvP67O/7ODDpXv5flQ7fDxc7B4nIS33aYtERERE5PqTZEhm3bh7cD9vnR8/7wBv1DvGiD+G8fyy50kyJJdShWVHhsnMP1Fn7Da25LRo5yk8XZ3zzOZPzdnOlJWHmD6mvbK5iIiIiAC6b55f+b1nPmPDEdYfjmfaqHZMWXn4usvmZavVRkRERCq2+fNhxAhISrJe7uICX38N999fOnUVkdFkZnlULDMirhI8I44QHup/zeayLC8MJjOTVx6+6nYjOoTwz94zzNp4tUB/lPah/owID+ar/w7ZrPd2tx/eRUREROT6YzBmMH/vAka22MkrF+CN/yyf/ltaH57ve3m7yVu+pmtIN+4IG4Krc8XNi6ZM+H5N9FW3y282n7nhKDcE+zI8PJhJyuYiIiIiFZrum+efwWRmyoqD+KVcpEryBaqkXKBK8gX8UxPwSk/By5CKlyEFr/QUWq9xItOQyu0nL3Cn2YSz2cj+qiH879ZnrY6Zlc3va1mV8REnbc5ZFrO5mltERESkbPj2W3jwQdvlgYEwdy506lTyNRUTg8nM5P+u3qwBZXcuy+JiO3eqC17uLiSnZYCDA94eziSkZrDnVEKex3FydGBYeDAPTt+cr/NOWXWY7+5rx9crD2HOvLy8aQ1vvD0UiUVEREQqCoPZwMfrPiTTEd7uARtqwSur4J47wORkve0n6z7itkYDcKXs3dQtDpezeQYXUjPwcXfBy90ZkzkTV2dHXJ0cr0k2n7Y2hqn3tmWysrmIiIhIhab75pcZTWYMiUkknk/gvIe3VTZ3c7Hk8hfGP0O3mG35PmbVHF+bHJ3sbjNtbQzfDG7MxA0ny0U2L3sViYiISMXUuzcEBEB8/OVl7dpZGltq1Sq9uopBQqrxqjeEs5TVuSyLQ15zp47qVIf2of5MXxfNLc1rXPVYDQO9SDOYCvS8pmaYaBRUmahTidnLH+5eD9fr9IJIRERERGydTz3Pjtgd2d8vqw/L6gEOtttuO72NC6nn8XKtVHIFlpCrZfNO9apw6mIqXm5Xvy4pTDZPMyibi4iIiFR0Ffa++fnzsGcPREVBVBSm3ZEYdkXieeo4i5r3YdwtTwOXs3m3hlVJSDVyztO70Kd0NpvsLrdkc2O5yebXwasvIiIi14XQUPj1V+jbF0wmGDkSpk4Fd/fSrqzILqYaCrR9QloGQT5Ff9y2o6S44u3hjKuTY4l3uGfNnZo1xKSTowMNA73wcnMmKd3Ii3N3cU+72jzaoz4Go23QvnJ7b3dnzqUU7Hm9kGKwujk/PDyYLvWrFO2BiYiIiEi5ci71vO1CO40t2dunXaCWT9Gb7ctrNjdnmm32L45sfl7ZXERERKTCqxD3zWNjYetW2LLl8t9HradhcgI8L33dMC2ednX8SEo3svd0Is//tpPh4cG82K8xG7z9C12Gs8mY67rydN9czS0iIiJSdvTsCV9+CQYDPP00OORxl7kc8fFwLdD2xTGXZV6fxHyoe136hAWWWJd7zrlTfTxcGNEhhGHhwaQZTJxLMeDv6YqbiyOzNx5j85FzdGtYlaY1vIk8mZDr9pXcnHBydMDHw4WLqRn5qsPb3YWkdCNNa3gzunMd2tXxZ9xvO/hy6A3X7XCWIiIiIgJkZkJaGnh44O/hV6Bd/d19i3x6ZXNbfp6uyuYiIiIiFdx1e998yhRYvNjSzHLiRIF2bZp+nnE3N7bK5TMijhAe6k+TVvUhwrLdRXcvjFWr4VWrOsbK3qR5VMLZzxcnH28cfLyZuPE0CWZHjI5OGB2dOe9ROddz+vh5k5R+pFxkczW3iIiISMnKzLSMzOKcSwx57LGSracEeHs4Z98QvprimMvyyk9i5hR5MoEnZ29nRIcQXrqlcYncRM+aO7VOgCfTx7RnY/Q5xv642e7w521DLN3nozrV4asVB/Pcfmy3uix5sivDv40gJj4lzxqa1vCmamU3Jg5tjZuLI7M2HOXtRVFcTM24foazFBERERH7Jk+2NNHPno1f04a0CmrF9tPbr7rbDUE34OvhW6RTXw/ZfHTnUCb+e0DZXERERESK1XV733zNGli4sHD7Hj/GPZPWYHJ0ys7lCx7vzDuL9vDJy8/S0akplWpW57uxndkYfY4f1sXYNul0q8uIp/3zn839vcpNNi9b1YiIiMj1LT0dHnnE8vV33103I7NcjauTIw91r8uTs7dfdduizmWZ85OYecnq9u7XLOiad18npBo5cSGVhY93YfLKQ8zaaP/iIecQi10bVqF7w6p8sfxArts/NWc7I8KDmT6mPbdNXJvnp0THdA5l4c6TzIg4wr7TiZgzc9RXTMNZioiIiEgZtHs3PPusZeSWDh1wf/ddnuvzHCPmj7jqrs91Goero1uhT329ZPMuDQLo1rAKX/ytbC4iIiIixafc3TdPSYFVq2D5csuHeD/91P5BOneGGTPyLsjFBWO9+hgaNCLCLZB56d4cDKhNtF8NTI5OgHUuf6V/E1KcHRl1Zxf6NaueZ5Z/8jrO5mVrHBkRERG5fp06BT16wLRplj8TJ5Z2RSXG2cmRPmGBjOgQnOd2IzqE0DusWpFuaGd9EtPJ0YGw6pVpV8ePsOqVcXK0bSSavPIQBpO50OfKr4upBkZ0CGFDdLzdwJ3TzA1H+XfvGeIT01l/+Orbz9hwlK1HzjM8PPfndnh4MB3qBvDpsv1EnbIO6FA8w1mKiIiISBmUmgpDh1oaWwAMBpyff57bE6rzUJuH89z14TaPcFujAbg6Fz4rXk/ZPOKQsrmIiIiIFK9ycd/86FGYMAH69AE/P+jXz9LU8u23YDTaP1mnTtbfe3paGl6eeAJ++AF27YLkZOIjtrD4tfGMqTeABU26syewLqmuts0kMzccZVPMOYymTO5uV5ttR8+z7dj5PB/D9ZrNNXKLiIiIXHsbNsDgwXDy5OVlzzwDTZtCr16lV1cJ8nR15qVbwggPDWDyykM2QwU+3L0evcOqFXmYv8Q0Iz0bV+Ob+9qSZjBxLsVgMz9nVqd25MmEEhla0MfDlWHhwTw4fXO+tv929WHmjO3I87/tyt/2a6KZ+UA4qw/EseuE7RCMPRpV40KKgQWPd7Z5DopjOEsRERERKaOef94ycktOTz6JZ7defGIIp1tIdz5e96HVFEWtglrxfKcXuK3RALxcKxXp9MrmyuYiIiIikreyeN88feduTG/9A0sXwtat9g+YkAAbN9o2soDl/z2efRZuuAFat4ZGjcDJyWazSu4OTFsbk6/6p62N4eam1cHBRPt6lWhcK4T4lHMEePrj6ezNnI2nmBFx3GqUlusxm5e9ikREROT6Mn06jB0LBoP1cnd3SEwsnZpKiaerM/2aBdE7rBoJqUYS0jLwdnfB28MZVyfHYhmC3NPViToBnoz9cbPNhUDW/Jz3fb8xe67Nqw0taDSZMZjMJKQauZhqwMfDtcD1+ng6c/piGntOXX3uVACjOZOE1Ix8bx95MoGENCM/jgkn3WgiNcOEi5Mjzo6O/Lg+hlfnR3IxNcPuc1DU4SxFREREpIxasAC++sp6WcuW8OGHAHi5VuKOsCHc1mgAF1LPcy7tAv7uvvh6+OLq6FakEVuyVNRsnphm5Kf7w0k1mEjLMOHirGwuIiIiIrkrC/fNF99RjwUvfEL3TctoeuZwvo5p+PMvzjVtbSebO+U+ZVEOSWn5z9nJ6UYyHdJYtH8hH637yKZB/6n2zzH/8X6M+n5H9vXF9ZjN1dwiFdLAQYOtvndwcMDTw4OQkBB69upB3z59cHCwHcJJyqYHH3yIM3FxzJ83N9/7zJ49hzk//8KTTzxO795lb9SIL7+cwL8rVvDO22/RvHkzq3Xbt29nzs+/EB0dQ9qloZXnz5vLP//8y/gJE7nn7rsYOvSea1ZbbOwZxj70MM2aNuXdd9++Zucpis2bNxO1dx/79x/g0MGDJKekXLXenbt28cO06Rw5epSAgAAGDbyNW27pZ3fb336fy6xZs/n8s08ICQkpUG1Zr21epk6ZTGBgNbvbOzg44OHhgVelSoSEhNCkSRi9evXE19c33+dzdXUlICCAFs2bMWTIYAIDAwv0GPLNaLR8UvKLL2zXhYbC/PnQvPm1OXcZ5nwpjHu6Ohf7fJUpBiMf/7mXTUfO4+XmTFj1yuyPTcJkzrSanzPnXJt5DS2YYjCyPCqWKSsP2wT+h7rXpU9YYL465l0cHUlON+X7cXi5OXM+xXD1Da+o1ckRElKNeLk54+zowIyII8zccNTq07A5n4MZEUeLPJyliBSdsvn1Rdlc2fxKyuZ5n6/EsnlFc/IkjBljvczDA2bPtjTZX+Lq7IIrLni5VqKWT61iLSHFYGT62hhWHojDnJmJk6MDpkvjfF/v2TzZYMRR2VykXFI2v74omyubX0nZPO/zKZuXrmt939xeNnfLSKfB3/MJ/PxfahzdwaPmfEwT6uSEqX17DrbowIT0Oix6/5/sVQXN5jlHWcmLj4cL0+9vybi/n2fylsk267ef3s7oBSN4qM0j/DDmHQZO3Jx97Ostm6u5RSq0Xj17AmA2mzl1+jRRe/eyJyqKnTt38dz/ni3l6uR6VpgLC4C4uDje/+AjjEYjLVu0wMfH5xpVWLqKeiHw2WdfkJySUoDzxfLWW+/g7OxMmzatOXToMFOmfoNX5cp069rFatuzZ8/y66+/ccst/Qoc0HMKC2tM9aDqdtd5eNgGt5zbp6Wncf78eXbs3MmmzZuZOWs2w4bdw+Dbb8/1BkPO/RMSE9i//wB/Lfub1WvW8v5771CnTp1CPxa74uPh7rvhn39s1/XqBb/8AgEBxXvOCs5oMnP4bAJje9RieIZ3rkMSztxwlBuCfRkeHszK/XG5Di2YYjDy3pIoZkQctVkXeTKBJ2dvZ0SHEF66pfFVg7qzkyNVvFzz/ViS0o34euZ/ewA3Z0cemL6ZqFOW0YBy+zQsWOYobV/Hn2f7NsDjGg/7LiL5p2wupUXZPG/K5tdBNq9ozGa4917LNUlOX3wBYWElUkK6MYN0Uxp9W7jSur5HrkOFK5srm4uUVcrmUlqUzfOmbK5sLgWXVzb/+6+tfLr4c5wyr9LU4ucHAwbAwIGkdO3Oe2tPFks29/HIX84e0aEWK44stdvYktOULV/TsVYXRnZoxT97z+Ll5kxlN+dLo9VcH9m8bFYlUkKeeuoJq++3b9/OW2+/y+rVa+jerRvt2rUtpcqkIN56601MJmNpl1GsRo4czpAht1O1alWr5Tt27CQtLY277rqT4cOGWq3r0CGcRo0a4u3tXZKllkkdO3akVu2a1K9XH5PJyOtvvJXn9n/Mm09GRgbvvvMWjRo1IjExkccef5Jff/3VJqR/P+0HPDzcGVbELv++ffoU6NMP9rZPT0/n77+X8+NPM/jxxxmkJKcwcuSIfO2fnJzMe+99wO7ISL77/gfefuuNwjwM+3btgoEDITradt1TT8Enn4CzIkhxSzOlsuXMn3yx4eOrDkk4bW0MU+9tS1h1b7tDCxpNZpZHxdoN6DnNiDhCeKg//ZoF4ezkmOcw6d4eLjSt4W31KdPcODs6FGj7pjW8cXFyZN/py9Nc5fZp2CxTVx+mb1N9+kKkLFE2vz4om1som1+mbH71/a9pNq+IPvnEtsl+8GB48MESOX2SIZmF+xbke6hwZXNlc5GySNn8+qBsbqFsfpmy+dX3Vza/vlwtm099+RbWL+tIl8i1Nvtm1qiBw5AhMGgQdO0KLi6WbL77lFU2d3J0oGGgF15uziSlG9kfm5TvbO7t4XzVnO3k6MDQ8Or0nzMsX4/5yw2fsHLUKga0rsT5tPOYHc8xeWTz7Eb78p7Ny95YMiKlqFWrVvTo0R2AiA0bSrkaya/q1YOoVat4hw8ubf7+/tSqVQs3Nzer5WcvffIsyM5weJUqVaJWrVoK6cATTzzG7YMG0bx5Mzw8PK66fXR0NDVr1qRRo0YAVK5cmQ7h7Tl+/AQZGZd/qe/ctYu1a9dx770j8fT0vGb155ebmxv9+9/Kq6+8jKOjI7/9Ppdoew0ldlSqVIl77xsJQGRkJAZDwYaZztXcudCxo21ji6srTJtm+bSkGluKXZIhmeeXPceo+cOtQjpcHpLwg/Wv8MOYlvh4uBB5MgGjyUyfJvaHFjSYzEz+L3/zik5eeQiDyUyKwciS3ae4c/J6Orz/Dzd9sZoO7//DnZPXs2T3KRyAsd3q5uuYozqFsvXIOUZ1qpOv7Ud3rsOsDUe5NMq7lZkbjrIp5hzDw4OtlkeeTCAh9fq6wSNyvVE2L5+UzS2UzS9TNr+6a5bNK6KtW+GVV6yX1aoF33wDJTCNRlYuHzZ3WL5yOaBsjrK5SHmgbF4+KZtbKJtfpmx+dcrm14+sbP7ozGE4b9lutS5nNm/w2lOX93H1IOme4aT/+RcOR4/C+PGWkehdLNk9Zzb38XDhsZ6hrBzXkS+GhfBMPw++GBbCf8935LGeocyIOHLVbB59NomHrpLLGwZ6kZyRwI7YHfl63NtOb+NU0knu+v02On5/A8Ff1Oa2X3pRtdo25j/eljoBln+j5TWb63+XRK5QNzSUf4CzZy8PXztw0GCqVa3KpEkT+X3uH6xatZrY2FjatG7NSy+9CFg6QRcuXMSates4deoUAMHBtel388306tXT7rkSEhKZv2ABmzZu4nRsLI6OjlSrWpXWrVtz22398ff3B/KeRzJnfd98MyV7Wc55JLt168as2bPZtWs3CQkJvPjCODp0CCc1NZXFi5ewes0azpyJw2w24+vjQ916denbtw+tb7jB6jxxcWf59ddf2bptG+fPX6BSJU/CwsK4Y8hgGjRoYLVtzuHxXn75/5g952ciIjYQHx/PLf1u5oEH7iczM5OVq1bx559/cfLkKVJSUvD29qZmzZp06BDOrbnM23ilvIYq3LBxI7//Ppfo6Bjc3Fxp1rRZrh26WQr6WmY9/5MnT2Le/PksX/4PZ87E4ePjQ/duXRk2bCgul37x7dq1m1defc1q3yw5X8MrX/Mr9xs/YSLjJ0wEyJ4rNK+5QzMzM1m9eg3L/v6bw4ejMRgMBAZWo0vnzgwefLvNxQBYXu8ZM2aydds20tLSqF2rFrfdNoCwQgzjfPhwNKtWrWbnzp3EnT1LSkoKAQEBtL6hFXfedScBl97rcHleV4DdkZFWz1Gvnj1tPjlSHJKSkvHyqmS1rJKXF2azmZSUFHx8fDCZTHzzzbeEhTWmZ48exV5DUTRv3oxuXbvy38qVLFq8hCcefyxf+wXXrg2AyWQiKSkp+2dOoZjN8Oab8Jadbv/q1S1NLx06FP74kiuDMYMF+xbke0jC4eGtmPRfNGkZZjxc7EfBhFQje05d/VOZkBV2M/h963EW7TyFl5szYdUrsz82CZM5M3soxge71uXpPg0Y1j6YWRtz/9TpiPBgbmwaSGJ6BmHVfa66/fDwYNrV8eftRVG5bjNtbQzf3NuWlfvP4Ol6uYM+IS2j2OdvFZHipWyubK5sbqFsrmwu+ZCSAsOHQ47/aMHBAWbMgBJ4Pg3GDObvLXguBwqdza/8lOje04nK5iJyzSibK5srm1somyubS9lnMGawfPm3NH5nMse2wllPqP8kmJyst5uy5Ws6DuhE6z4D+cajIcsahDP/+RtpFFTZ7nGzsnmdAE9+GNOS/44uZcDPlg+cOjk40bRaU9pUb8PARkO4s21HktON/LL5WK7ZfNg3G/n7mW6MCA9mxgb7OdvLzZkEw4UCPf645Di83S439GU18zzU5hF+GPMOAydu5mJqRrnM5mpukWJlNBtJN6ZzIe0C59PO4+fuh6+7L27Objg7lo+3W2pqKgAuV9xUMGdm8v77HxK5Zw9NmzahTkgIlb0tP9wuXLjA62+8SUzMEfz8fGnatAlkwt69e/ly/AQOHjzI2LHWw98eO3ac1994k/j4ePz8fLnhhlYAnDx5kj/mzaNx40Z06BBe5Mdz4uRJnnt+HJUrV6Z582YkJSXj7OyEyWTitdffYP/+A3h7e9OsWVNcXFyIj49ny5atuLu5W4X0mJgjvPra6yQkJFCzZk06duxAXFwcEREb2LRpM/979hk6d+5kc/50g4GXXn6VuLg4mjZtQr26dfHy8gLgh+k/Mm/efFxcXGjapAne3t6cv3CeI0diOH3qVL5Dem6W/vkXkydPwcHBgSZNwvDz82P/vv08//w42rVrZ3efwryWWT797HO2bNlK82bNqFmjJnv27GHuH/OIP3eOZ595GgA/P1969ezJuvXrSUtLy56/FsDb2/4vy5z7RUdHEx0TYzUPZGhoaJ7Pg9ls5vPPv2TV6tW4u7tTv359vLwqcfDgIeb8/Atbtm7j3XfesgrqsbGxvPDi/3H+/AWCAgNp2bIF8fHn+OLL8dzS7+Y8z2fP73Pnsn59BHVCQggLawxAdHQMS//8i4gNG/n004+zg3poaCgdO3Zg/foIfH19rd6HYU0aF/jc+VG1ahUOH47GZDLh5GRJNydOnMDV1TV7ftZFixZz/PgJPv3k41zn5yxNXbt25r+VK9m9a3e+98n6eefo6Ejlyrm///Ll6adhwgTb5eHhlsaWGjWKdnzJlcFs4KO1H+Zr2y83fMKCu/9h8sro7E+K2nMxtWCfSLiYauTudsHc3LQ651IM+Hu64u7iyN97Ylm5P47TCWlMXx9D37BqPH9zI9qE+PH92mir4Rab1vBmdOc6tKvjz4AJa6jk5szjPevzf7c0pnWIL9PWxuS6/X3fb7QaOvFKkScTcHSAb+9rx4nzqfh5uuLm4oiXW/nIJiKFcT3kclA2VzZXNgdlc1A2l3x67jnYu9d62YsvQvfuJXJ6g9nAx+sKnsvNmRQ4m/t4uDCiQy2GhlcnOSOB+JRzBHj64+nsDWRyj7K5SJmibK5srmyubJ4bZfNrT9m8Atq2DYf33mHA73NxujSaoNdFGBIFv9j2QfLlps9Y8Os/LPpwXb6yuY+HC9Pvb8l7a19m6tbJ+Ln78VLXl3iozUOkZKRwNuUsVTyr4OGWgaermaFXyebDv9vArw91pF2oP1NXHbbJ2Q91q0eQV+752p4AzwAS0m0b5K9stC+P2bzsViblTrIhmQX7F/DRWtt5y8Z1HsdtDW+jkmul3A9QBmRmZrJp8xYA6tSpY7Xu7NmzuLi4MOmrCQQEBFitGz9hIjExRxjQ/1buu+/e7G7jCxcu8PY777J4yVLatm1D69atAUu35/sffEh8fDwDBvTnvntHZu8DcPToUVxcXIvlMa1evYZbb+nH/fePyQ4fYBkmbv/+AzRoUJ/33n0HV9fL50tJSeHkyVNWz8tnn39BQkICg28fxL33jswOKevWrefjTz5l/ISJhIU1tulgPXDgAI0aNWLK5K+tOnwNBgOLFy/Bw8ODL7/4jMAcwwWaTCb27t1XpMd95swZvv9+Gs7Ozrz88v9lBz2j0cj4CRP5b+VKu/sV9LXMPl9cHK5ubkz++iv8/PwAS9B95tnnWLlyFUPvuSd7GMinnnqC3bt3k5aWlu9u6qz9Zs+eQ3RMTIHmnZw3fwGrVq+mWbNmPPe/Z7Lry8jIYPKUqSxf/g9zfv6F++4dmb3P5ClTOX/+An169+bRRx/Ofu9s3LiJ9z/I3826nG666UYefOB+fH19s5eZzWZ++fU3Zs+ew8yZs3jyiccByxyooaGhrF8fQa2aNa9Jx/mV2rRpw7Zt2/n5l18ZNPA2du7cxebNW2jf3nIxd/78eeb8/As333wTdevmfVFUWrIu1k7HxpKRkWH1MyU3W7dtAywd7PnZPk8PPgjffw/JyZeX3XcfTJ4M7mWzw/d6cT71fIGGJEzOSOCW5tXx9sg9BuYV4O2p5OaEyZzJ/dM3EROfAliC99hudfnynhswmsy4ujhxPtlAXEIa3RpU4aZmQSSmZRCXkE4lN2fcXByZteEoby+Kyr4Z/sjMrTzQJZRHetSjU70qpBpMnE8xEODlRkAlVyavPGS1fV5OXUzjvSVRbIo5n13fQ93q0qdJIJ6uisRyfbkecjkom2dRNlc2VzZXNpd8WLQIvv7aelmbNvDGGyVWQmFyeaOgyjg6OBQom9v7lGiWVkGtGNdpHP0bDuC1+btZe8gyuoKyuUjpUTZXNlc2VzbPi7L5tadsXoFs2WIZWX7BAuy9as+vhV+aAlf0YBU0m4/oUIsVR5Yydetk6vvX58/hf7LqyCoGzhlo87v+fx3/x6BGg66azS+mGAgP9ad340AS0zK4mJaBh4sTTo4OzN54lLCaQbQKamUz9ak9NwTdgIezB7vP2G/ourLRvrxlc9uJXEUKIdmQzHPLnmPY7/bnFB72+zCe//t5kg3J9g9QykwmEydPnmT8hIns27cPFxcXeveyDUAjRw63CeiHD0ezZctWGjSoz5gxo61+0fn6+vLYo48Alm7oLOvXR3DixAmCg2szetR9Nr8cg4ODqV49qFgem4+PN/fdd69VQAdIuGjp2GvcuLFVQAfw9PSkfv162d/v2r2bI0eOULVqFYYPH2bVfdupU0fC27cnLS2N5f/8a7eGBx+832boupSUFDIyMggKCrIK6ABOTk6Wzu8iWP7PvxgMBrp27WLVwezs7MwD999vdzjBwryWOT344P3ZARggMDCQHt27AbBnz54iPZ7CMplM/PHHPNzd3Xn+uWet6nNxcWHsgw/g5+fLsmV/YzabATh9+jRbt27D09OTMWNGWb132rdvR6dOHQtcR4vmza0COli6nu+5+y4CAvzZuHFT4R5gMbnpxr6EhITw88+/MHTYCN7/4EPc3d0ZOcIyFOcP03/E2dmZ4cOGZu9jMpms5hUtqPETJjJw0GCbP7NnzynU8XLOGZucnPfP2oSEBP5buZIffpiOj483Dzxwf6HOaaV5c/jxR8vXTk7wxRcwbZoaWy4xXppb8/TFNPadTuD0xTRSDEaMJnORj30u9XyBtx/aPhhXp9xjYGV3F5rWyN88xFnbTfjnANPHtM+++R55MoGn5mznoz/3goMD9/+wia4freDGL1bT7r1/mLvlGHtOJfD24j08Pnsr3T5awaT/DtncDP92TTQr9p1hZsQRHp+9lQ//3MvHf+0lLjHd7va58XZ3ISn98lyhkScTeHLOdt5bspcUQ9mdQ1SkoMp7Lgdlc2VzC2VzZXNlcymQv674WeDpCTNngqvtf4Beq2xemFzu5ebMw93r5TubZ31K9P11LzN6wQj7v+vnDmPc3+P4emQzavt5AMrmIqVF2VzZXNncQtk8d8rmtpTNK5ZiyeabN8OAAdC2LSxYYP88DnAgADxzeWsWJJuP6FiDLzZ8jJ+7H3+N+IuP1n3EmAVj7P6uH/nHSMYtv3o27/zhCjq8/y8t3vyLTTHnOJOQzsMzttDtoxV8teIQsyJO8lT75/L1dDwZ/iRTtkzBnGn/OczZzAPlL5uXvXYbKXeMZiML9l99TuGvN39Nt5Bu3NHkjjIz3GLOOQmzeHh48NRTT9iEZAcHB9rbGZJv+/btAIS3b4+jo+0PvLp16+Lu7s6BAweyl+3YsROAvn362ITn4tayRUu7gTQ0NBRHR0f+/edfgmvXpkOHDrkO77dnj2Wu5M6dO+PsbPva9ejRnfUREXaDqJ+fHw3q17dZ7uvrS0BAANHR0Uz/8SduurEvQUHFc2FiqdlSS9cuXWzWeXtXplWrlmzYsNFqeWFeyyzOzs40b2Y7nlmNS1OxnD9fsJtcxeXQocMkJCTQqmVLm5AM4ObmRr169di8eQsnT52iVs2a7ImyvN433NCKSpVsPznSrWtX1qxZW+BaEhIS2bhpI0ePHCU5OSX7osBoNJGYmEhiYmKpDfHn6urKhx+8x/Ll/3Ds2DH8/Pzo06cPVatWISoqipUrV/HYo4/g5eVFQkIiX0+ezMaNmzAajTRs2IBHH3n4qsNcXinnEJk5FfQ4WTIzM/Ncn3O+2SzVqlblgw/es7n5UGiDB8PHH8MNN0Dv3sVzzOtAisHI8qhYpqy0M6Rg97r0CStaB7S/h9/VN8oh0CuA6pV9cc4jpGeYzIzqVIfnf9t51eON7lyHWRuOMmPDUVoF+zI8PJhJ/x3KXp+1vE2IHxuizwHg5OhA7yZBPDh9M3tO2Q6PeKVpa2OYem9bvl55CHOmZf+3BjajaQ1vq+c0N01reOPm4si+04k262ZEHCE81J9+zYLyfE5EyoPynMtB2VzZXNlc2dxC2byYsnlFM368ZUrUxx6DhAT47DNo1Mhms2uZzQuay/09/Oje0IveYdXync1zfko0L5O3fE3X4G68Pag7o6ZtzV6ubC5ScpTNlc1B2RyUzfND2dyasnnFUeRsvmMHvPwyLF6c6yZJLvBNG/gyHI7kEdfzm81N5kzSTYnsiN3BS11fYmXMSqZumZrn4/x689d0yWc2d3BwoE0dfx6cvpmoU5fz8oyI48x/vB9jWz+c57XAQ20eomtwV57969k8a8pq5imP2bzspCUpt9KN6Xy4Jn/DrX209iMGNByAcxkZxihr3kYHRwc8PTwICQmhY8cO2XNb5uTj42N3+LEzZ84AMGPmLGbMnJXruXJ2qp6NPwtQrKE0N1WqVrG7vGbNGtx370h+mjGTryZ9zdeTpxAcXJsWLVrQu1dPq+Elz52z/FCtVq2a3WNlLT8Xf85mXdVczg/w9FNP8smnnzJ37h/MnfsH1apWpWmzpnTt0oU2bVrnul9+nDtnCcVVq1XNs+acCvNaZvH19bV7weXh4ZHrPiUh6zFt37HD7kVpTokJCVCzZvZzV61qbq+3/ec0L6tWrearSV+TlpaW6zapqamlOn+lh4cHAwb0t1pmMpmYMvVb6tevT58+lmaN8RMmsGvXbkaPug9fX19+mjGTt995l68nfWX3gjg3BRkiMz8SEi6HP3s/w7IuCsyZZs7Fn2N3ZCRn4uL44ssJvPH6q/m/YXCViwGey1/3cEWRYjDy3pIoZkQctVkXeTKBJ2dvZ0SHEF66pXGhb6L7efgVaEjCal5+eLjkfa64xDTah/ozrH0wszba1p5leHgw7er48/Yiy8V91o3uyZdudGe5cnnDQC/SDKZ83TwHy3OVZjBlDw05unMdHIDRnerw3FUacJwcHXi6TwNW7Y+jUVBl9scmYTJbv48nrzx01QsXkfKgPOdyUDZXNrembK5srmwuBeLgACNGQJcululSx4612eRaZ/MC5/JK/ozp4nHVc2Vl8xHhwQwNr07/OcPyVc8n6z9i5aj+ODuCMceHNpXNRUqGsrmyec7lyubK5rlRNrelbF4xFCmbx8TAq69aRmrM5f8rMv39+aqLK6+GneaCR961FCSbX0w1kGC4gJODEw+1eYjbZt+W98Ev+WRd0bL5xdQMRn2/gx/GvEun2l35YsPHNlMgPRX+FF2Du3LzzJs5n5Z346C/hx8phsRymc3LTlqScutC2oUCzSl8Ie1CmZlHtCBzErrmMq+e+dIPziZhYSUSum3Ob857aK7c6gYYNGggnTt3ZsOGDWzfsYM9e6JYsGAhCxcu4v4xo20CS24cHHJf55rHHKgtWjRn8teT2LRpM1u3bWP37khWrPiPFSv+o2PHDrz4wrh8nb+4FOW1dMzrSShFWY+pevXqhDVunOe21yognzlzhi/HTwDggfvH0LZtG/z9/bMD7bgX/o99+/ZdtWeiNPz51zKOHDnCRx99gIODAydOnGTTps0Mvedu+ve/FbBcwL/y6musXLWKG/v2LbVaDx+OBqBGjep2Pyly5UXB0WPHePnlV9m5cycLFi7k9kGDrn4SgwHS0+HsWbjK+0ksQyouj4q1G9BzKkgHtNFkxmAyk5CawfmUDHw8XPDzdOH5TuMYPvfqN7mf7zQOF8erX0xWdnfl5Xk7ee/2FnSo68+UVbbd86M716FdHX/u+35j9vDjOW905+wsv3K5l5sz51IMV60jJ4PJzIz7wzFnwqKdJxny9Tqmj2mfawOOZf7TEEZ3roPBaObkhVQmDm2Nm4sjszceY0bEEau6E1KNZXIOUZGCKM+5HJTNlc2tKZsrm19J2VzypU4deOstm8XXIpsbjCYyTJnZ2by6ryvjOo1jWD5y+bhO40g1OFLZ7eo3ibOy+UdDWpJkPFPg3/U3Ng1iya7T2cuVzUVKhrK5sjkom4OyeV6UzUuGsnnZU6RsvmIF3Hyz5f8q7DD7BxD30OO4PvUYAWeWc6GYs7mHizOGTB+aVmtKSkZKiWbzmPgUBk7czPDwViy8+1+SMxI4l3qeWj5V8PPwZemBpYyaP4ro89F51nJD0A34e/oxfUxQuczmZacSKbeu1v11pQtpF6jpXfMaVVPyqlwaliw8vD2DBg3M5z6WruzTp09fZUuLrF+49rp3z549m69j5KZq1Sr0738r/fvfislkYvXqNYyfMJEfpv9Iz5498PLywt/fH4C4M3F2j3Hm0nL/AP8Cn9/T05Pu3bvR/dIcm/v27ePDjz5h/foINm/eQtu2bQr1uPz8/Dhx4gRxZ+IIrl3bZn1cnO1jKcxrWdZlPaZaNWvm+6LU/9L8omfizthdf8bOc5eXLVu2YjQaGTRooN0Lv9jY2AIdr6QkJCQwa9Zs+vTunT1E6PETxwGon2PI0IYNGwBw7Njxki8yhzVrLUNe2hvm057g2rV58IH7+fSzz/ntt7nc2Lev3eE0syUlwcGDYDLB6dPwyy9wzz3FUfp1y2AyM/m/w/nadvLKQ/RqXA2DyYyrk6PdG+kpBiPL98TaNJqEh/ozacStPNzm4TyHO36ozSN0rX0TJnPeNxWMJjOV3Z155dYmXEgx0C7UnzZ1/EkzmDifYsDP0xU3F0dmbTjK24uisoNulvMpBrzcbCNmzuVJ6Ub8PXO/iWOPt7sznq5OHDuXSrcGVenbJJClu0/zcI96tAnx4/u10dnPS50AT2Y+0IGNMee47/uNNo05ozrVYcHjnbnv+43ExKcAkJCWQZCPe4FqEilrKnouB2VzUDYvy5TNC0/ZXIqquLN5crqRv/fE8s3qy9ncx8OFxU/dfNVc/nCbR+hQ40ae+Xkn00a3y7OJJmc2T043ctF4IV+PIcu51PMEVLLN3UXN5n6eLjg7OSibi+RB2VzZHJTNyzJl88JTNpeiKlI279ABqlWD49bvq3Rff2Z2u4tP6vUmxeyBz+Rt1ySbe7g64erqT5vqbTibUrDfM8WRzS+mZjDpv2gmr4ymTYgft99QgxZB/pxPO0c9/3p8M+AbPJw9mLp1Kl9v+tpuHhnXaRynLxh58fcd5TKbl50xZKTc8nMv2JzCvu6+16aQUtKyVUsAIjZsyP8+LVsAsHz5P1ftIAfw87c8xydPnrRZt31H/roC88PJyYkePbpTv359jEYjJ0+eAqBJkzAA1q5bh8lkstnvv5UrL23XpMg1NGrUiJ49ugNw9GjeXZt5yao5K7jklJiYyPbtts9bYV7LwnK+NCWIveezODVoUJ9Knp7sjowkMdF2zjx7wsIsz922bdtJSUmxWb9mdcHmDU1KSgIuXzDkFBkZyYULF2yWZz8/5mv7/OTlx59m4OAAI0eOsFmXbkjP/jrr4rk0P4Wwa9duVq9eg4ODA7feeku+9+vatQuhoaEkJSWxZMnS3Dc8exb27QOj8fKyhx6CLVuKUPX1LyHVWKChvU9fTGPcbztZsvsUKQaj1fqsYRqfnLPdZh77DdHnGDJpC693/4DZg2fTKqiV1fpWQa34YeBMXuz4DiO+2U5CqvWxrzzPkt2nuGvKenp+spKnf95OSrqJrh/+y+Ozt/Lhn3t5fPZWun20gkn/HbJpbAHw83QlKd32HDmX749Nwt3ViaY1vPP1/DSt4U0m8O7iKNxcHBn89Toe+mkLvh4uOJBJJpn8PLYj/zzbnT8e7cTCJ7rw9X8HeeZn2+cr8mQCz/+2k6mrDjN9THt8PCyf1PJ2z/0TWyLlRUXP5aBsDsrmhaFsbqFsXjyueTYXiwL+51GxZvN0I+8ujuLpK7LmxdQMRnyznRc6vstPg2blmstf6PgOo77fwYbocwXK5h/8ubfAv+v9PfyIT7b95GdRs7mzoyMXUjIY/u0Gen+2UtlcxA5lc2VzUDYvDGVzC2Xz4qFsXjYVKZs7uViN1JhZuTLLhj7ODaOn8FZYf1JcLXMQXctsPuW/owxqdAdVPHOfXs6e4szmwf6efHxnYzLc1tFlWmdqf16bNlPbEPZVGIN+HkR9v/psenAT9f3rW+33SNtH6FGnHyO+3cze04mEVa9Muzp+hFWvzN7TieUim6u5RYrM193X5odCbm4IuuG6C+qNGjakVcuWREXtZfKUqXZDTXR0NFu3bs3+vmPHDtSoUYMjR48yffqPGI3WPzCPHj1q1Z3erGlTAJb++RcJCZeD1uHD0cyaNadQde/ctYvtO3bYXCTExsZy/PhxHBwcCKhiCVXNmzUjJCSEM2fOMGv2HDJzjIO3PiKCiIgNuLu706cA8yDGxcXxzz//kp6ebrXcYDCwa/duAKpUKdgvhpz69O6Fi4sLq1attrqQMRqNfPf9NLvd/IV5LQvL38/SrX/ixIkiHysvLi4u3H77IFJTU/ngg4/sfuohPj6eFSv+y/6+evUgWrVqRUpKCt9P+8HqQmLz5i2sXbeuQDXUqFkDsFzM5Xze4+PjmfT1FLv7eFeujLOzM6dPn77mFzL2HDhwgOXL/2H4sGF4e18edjK4djBgmQs169/BypWrAKgdbPtJh2vNYDCwePES3n7nXcxmM3fddSchISH53t/BwYGh99wNwMJFi2z+PWI2w9Gjljksrxz/skYNcC873bpl0cXUgg3tfT7FQGxCGk/O3s57S/Zm30Q3msws35P3MI0x8Snc9NkGwvz68veIlex9NIZ1o7ex99EYFtz1L7GnWzFw4mZi4lNISLM/l3F2A83syzeds8J0WHVvok4lsinmPFGnEjHnMhxq0xreuLk4su90Yp7LTeZMZm04yqhOdfL13IzuXIdZG44yY8NRNsWcY3h4cPaN8MkrD9Oujj8JaRl8vnw/UacS+Csylhkb8r7RMzPHsZrW8MbbQwMaSvlX0XM5KJsrmxeOsrmyeXG45tlcLvvxR2jQAH7/Pd+7FFc2NxhNLNsTa3fqHbg8VPjJUy35d+QqDj95NNdcDhQomy+POlPg3/U+7r4si7T+WVMc2fz7tdHZWRpQNhexQ9lc2VzZvHCUzZXNi4OyedmWn2zunZaU/bVNNr9nGLRujenJJ1k6by1jg2/ObmrJ6Vpl8xkRx2lRrQP+Hv6lks19PFyYfn9L3l/3MqPmD2f76e1W67ef3s6YBWP4eN3H/Dn8T/zc/WgV1IoZt8/m7R4f8OAPuxjRoRYrx3Xki2EhPNPPgy+GhfDf8x15rGcoi3aeKtPZvGxVUwAHDx5i+44dHDhwgAMHDhAffw6A+fPm5rnfP//8y9I//+TYseM4OzvTqGFD7rzrjjzn1IuKiuLXX39n3/79GI1GateuxS239KNXz5657nP27FlmzprNtm3bSUpKomrVKnTt2pU7hgzG1bVgQ32WdW7ObozrPI5hv+dj3rLO43BzdiuBqkrWM888xZtvvc3SpX+yatVqQkND8ff3IyUlhZiYI5w9e5YB/W+ldevWgKXT+8UXnuf1N95k3vwFrFy1msaNGpFJJqdOnuLI0aP834svZM9f2bx5M5o1bcruyEgef+JJwsIak5CQyP79++nf/1bmzZtf4JpjomP47vtp+Ph4U69ePSpXrszFiwlERkaSkZHBrbfeQsClYRUdHBx49pmneeXV1/jtt9+JiNhA3dBQ4s7GERW1FycnJ554/LHsYRjzIzExifETJjJl6jfUr1+PgIAA0tPS2btvLxcvJlC/fj06duxQ4MeVJTAwkNGjRzF16je8+ebbNGnSBD8/X/bt209yUhLdu3fLDlc5FfS1LKz27duxOzKSV197g+bNm+Hm5oa3tzf33TuySMe1Z8iQwRw/cYL//lvJY48/Sd26oQQGBmLMMHLi5AmOHTtOnZAQevbskb3PIw+P5YUXX+Lvv5eza9duGjSoz7lz59izJ4p+/W4uULdy+3btCA6uzcGDh3j4kUdp3LgxGRkZ7Nq1m9DQOjRu3Ii9e/dZ7ePi4sINN7Ri06bNPP3Ms9StWxdnZ2fCwhrTp3fvq57z559/YfOlUUWyLgwOHT7M8+NeyN7m/158we57NjMzkylTv6FuaCg33XSj1brq1YPo1Kkj69at57nnx+Hj48O2bdupVq0a3bp2zfdzUhh/L1/O7t2RAKSnp3P+wgUOHTpEeno6Li4u3HffvdxeiGFBw8PbU69uXQ4dPsyyZX9fHgIzIwMOHwZ7n1zw9obVq6EIF9IVgY9HQYf2vtyhnXM+UYPJzOSVVx+m8WJqBi/N3cPUe9vyzKwjVHJzJik9gX2nrZtR7HVa5zbPac4w/fxvO69aQ1YTypXNL/aWz4g4woLHOzOsfXCu/zkAMDw8mHZ1/Hl7URQA09bGMPXetkxeeQhzpuVGePtQf3o1rsbNTQJpXy+AUd9vumqtOY/VOKgyrnkMOSl5Uy4vO5TLLZTNlc0LStlc2bygSjyby2VRUfDII5CSAnfcAY8/Dp98Am55/04rrmyeYcpk6qq8s3nWUOGrD8Tz7X3t+GLpMRLSbHM5FCybG4xmth5J4rmO4xjxx9V/1z/XcRxbYxIxXjHwQV7ZfER4cJ6NKDmzeS0/D6tcDsrmZYGyedmhbG6hbK5sXlDK5srmBaVsXv7klc0rpyfz9JpZ3LVzGTeP+YoTPtXsZ/N160nHkYmT1+d5rmuRzS+mZjDqux3Mf6I9/+v4P0b+cfWfT8WZzUd0qMWKI0uZujX36ZYApmyZQveQ7hx88iCOuOCAK6/Ni2TCsCb8d3QpA362boxpFdSKp/6fvbMOb/JqH/AdbZK2qQtWgSKluLX4sCFD5kOHr7AxmAD7Zr/tm8OY82Hb2GDYjA0dMCY4RYaW4hWsLdW0TRr//RFaWpIaWti5rysX7fu+57wnb0JzvyfPeZ52U1k1qS/vrj3Dm4OaVEs3v2uDW3744Ufi9uypUpuvvvqaNWvXoVQqadmiBSaziYOHDnHg4EFemj6NmJhopzY7d+7iw1kfYbfbiWrcGE+tJ4cPH+Gzz74gOSmZ0aNHObW5dOkS0196GZ1OR2hICI0bR3Lm9Bm+//4HDh8+zNtv/ReFonql8LkR5FI5AxsMrLBu2cQ2ExnQYABy6V37tisTb29vZnzwPps2/c627dtJPHuW48dNeHt7ExwURP/+/ejcqVOpNqGhoXz6ycf88usq9uzZy/5//kEhl+MfEMDDDz9Ew4YNio+VSCS88sp/WPzdEnbvjmP//n+oUSOYcePG0rdP7+uS9LZt25CXl8eRo0dJTEwiLy8PLy8tkZGR9Ovbx+n/Q1hYKJ98PIsffviJfw4cYOeuXWg0GqKj2/HoI48U10+sLME1ghkzehSHDh/m3LnznDp1Gjc3N4KCAnn0kUfo3fv+G/5/8kC/vvj5+vLzypWcPHkSpVJJVOPGPPnkcLZt2+6yzfW8ltdD//4PkJ+fz9Zt29m1azcWi4XAgIBbIulSqZTnn5tChw7t2bTpd06dOs3Zs4m4u7vj7+/PQw8OolOnjqXaBAcH8+HMD/huyVIOHDhIXNweatWqxeRnJxEVFVUlSVcoFLz/3rssWbKU/f8cYN++/fj5+vLAA/0Y/MTjvPXWOy7bTXrmab75dhGHDh1i69Zt2Gw2bFZbpSQ9NTWNkydPldpmMBhKbTObXaeY+33zZk6fPsMH77+HVOr8of3spGfQqDXsjosjJeUczZo2ZfxT4275JExCwnESEo4jkUhQqVR4enjQtGkToqKi6N7tPry9va+778GDn+Dd997nl19X0adPbxRmM5w+DSYXEdQKhWOVZBVuyv+taNVyompqnVJvuyKqphaVQopUAtF1ffFzV7Lj9GV6NQ6scprGQpMVO7A3ybmeZlmR1uXVOb3eIJSKtucazIxcuIel42KIqevL/K1nnWp8ju4YRtswX0Yu3FNcAqnoOTYM9iThkiP4allcCvc1DKBH4yByDOYqXS+L1UaPyMBy66kKykd4efVBeLkD4ebCzauKcHPh5lXltrr5PfQ5dcPk5sIjjzgCW4qYPRvuvx8GDCi36c1z88q75pELOvILLegKLcXeeu15qurmr/1yjHVTBhDbegLzy/msn9B6Iv0b9OeBz0o7Wnlu/tbaeD59oiUtQrz5ZkdShW6eazA7eTkIN7/TCDevPgg3dyDcXLh5VRFuLty8qgg3v/tw6eZ2O/1O7ODNzfMJLHDMa7/810LmPv1e2W5ecOfcPClTz5D5+/l+woMVftbfTDeXSSUMb1+TvssqDp4F+HDnhwxoOJBCkwyLzcYLvcN4b8erLgNjDqYeZPTq4cS2nsir/d/BZquebi6x26+tM3B38PPKlRQWGqkfEUH9+hGMf2oCZrO5zCj0g4cO8cYb/8XT05OZM96nZk1Huq/jx0/w6muv4+bmxoL58/DwcC9uk5eXx1OxE9Hr9fznpenF0bA5OTn85+VXuXTpEu+8/RZNmzYpda7/vPwKCQnH6d//AcaPGws46gPO/HAWu3fHMfiJxxkyZPANPf+0tHSeip3AgvnzCAoKdHmMzWbjxAlHVGfDhg1dftjcTApMBaw5uYYZO2Y4RXq91PElBjQYgLvSvewOBAKBQHDnycpylCG6tq6xRIItJIQTGRnA7flcuduxWG2sP3qJycsPVnjsJ4+34L5GAeQVWigwWvBSK/BwkyORwIUcA30+3Vbp8/40oT0zNhx3GdzyxZCW9G0S7CSkqbmFxLz/R5l9hvlpWDSmHXuTslxOdI/vXJe2Yb4M+2p3cRrHqJpaYrvUJaauH4/P31W8/Vr+O7Ax90cFY7NDoclKtt6Ej0aJm0LKsrgUlsalFAe2lHyOH248jq7QQkSABy/3i2RfUhYKmYQ6vu70/8L1RIwrNj7Xmdo+atzdbv7NaWV87V7g3+7lUP3cXHi5QCAQ3Pvc7jmfO47VCoMGwbp1pbc/9RTMd522vyQ3zc2zDfT57M66ecd6fswd0YS1J9fy4S7nz/pp7V9iQMP+TFh8lB1nMoHKuXnbMB/eGBDF+iOXGBYTWik3L+nlHm5ytCo5/zcgioPnclBeh5tveK4zwVoV3pqb/yWYcHPh5iURbi4QCASCm8m/zs1vkGvdvKYunbc2zaXnGeeMf/988xPhjw+o/m5+ai0f7rz1bh6kVYEsi5BPK1/u69SkZE5eVNCijpZNib8yevXwCtt8O2gp94cPQq1QVDs3v2vDgR95+OEqHb961RoAHn/s0WJJB2jUqCF9+vRm7dp1bN68mQdLpKb6/ffN6PV6otu1K5Xmzdvbm5EjR/DBBzNZtWp1KVE/efIUCQnH8fLyYtTIJ4u3y2QyJk6IZd++/axdt57HH38MmUxW5eddnXFXuvNo40cZ0GAAOYU55BTm4K3yxlvljZvc7Z6NPhcIBIJ7ArsdLl6ES5ec9ykUEBEBajVcCW4RVIxcJqVnZBDDY0KcUoqXZHh0CF0bBjDhu/3EJWYVb4+qqWVKj/o0reVVpfOWTNNY6jwxoWVGWueUU+dUJpWgVsp4Y3U8XRsEsODJNgCk6wrRqhSolTJOpuUhkcLCUW0xWWx4qOTIpVLWHLqA0WJlUveIMld/dqjnj8FkpefHW2gY7ImHm5x8o8Vlakhw1BQN9dPw2eBWFJgsmCw2zFYbrUJ98FDJyS4ov2ar3W5HIpEU/66Uy5CW+F1QdYSXVz+ElwsEAoHgnuO115wDW5o3h08/rVTze8nNs/QmXv75BKM69mDrqP7kFOaQbcjGR+2Dt8qb1Fwr+UYZbz/YpEpu/lSXuvi6K5nz9xnmbTlToZuX8nKjhWy9iZreahQyKafS83kyJpQCk+uV32XhpVbgphBfBt0Iws2rH8LNBQKBQCAoTZGbj2hbC8WcOby47TvczYVOx9mUCsKzTlBo6cH3ey+yZPd5cg3mf7Wbh/ppeLhd1fKW5BpzaF+3ITK5mU/jPqxUm8/iZvFw5EDk1TBQ619hTkajkcNHjgDQoUN7p/0d2rdn7dp17N27r5SoF9Wdc9WmTevWKJVKDh0+jMlkKk6rVdSmbds2TumpvL29aRwZyeEjRzh2LMEpev1eQC6VI1fKcVe6U0tb604PRyAQCASVwWqFs2cdab6vxd3dEdiiUDhncxFUiEYp55V+kUSH+zFvyxknSS2K0H54zg6nCO34izomLv2HuFd6VCmFurubnBOpeaW2xXapS4/IIJf1MQ1mC2qF8+Shl1rB8JhQhkaHUGiykqU34atRgt2OWilj5T/n+SclG7sd3N3kFBgtSCQQ4KnioZa16BUZRP9mNUnVFdK1fgB9mtQgq8DE5bxCvNVXV3++t+44ayZ3IrJGxc8xzE/D0nEx7DyTyYJryhj1b1aD//RthFIuK/N65R/ZjP7UbgIefBmJ1HGcWiEl12BBrfxXaPEdR3j57UN4uUAgEAjuGb79Fj74oPQ2f39YtcoRgF9JqoubT+hajx6RgWhc+GdV3Vynl6JVBbLxsJGD59JIyjiLWnl9br58TwpRNb2Kn5+rdO1FlOflRV82WGz2Yjc/nppHgyCP4mCZk2n5WK+JlomqqcVTpUBRDSfQ71WEm98+hJsLBAKBQFAazfFjvPneWGT//ONy//oIeLafmbN5r9Ji+Y9MaTeVVZP6MmrhoWrv5psOG0nV6ThyIYVcg4XT6flEBHoQpL1xN883WhivCa3sZQbAX+OL3Q65hTkcSjuETCIjKjAKrZsWnVFHfHo8Vru1VJsDqQfINeYS7K6p0rluB/+KWfwLFy5iNpvx8tLi7+/vtL9evboAJCUnl9qelJgEQN0r+0uiUCgICanD6dNnuHjxImFhYaXa1Kvr3Kaor8NHjpCUnHxPirpAIBAI7kJOn4Y8FxOX/v4QEgJicvGG0Cjl9G0STI/IQHQGM5dyHRlPvDUKzmfr6fXJVqeyO0VYbXa+2ZHEuM51ef77gxWea3znunip5Wyd3o3LeUa0KkWpFOLDYkLoGRlULOsWq43Nx9JoFepb6kagqAzRnsQsnlq8z2my+plu9XiuZwPMVhsFxqsC7xDvcyikEn7af47eTWqwfE8KRy7kciotj/pBrld/LotLYXTHcKb+eKjM5+alVrB4TDRz/j7N0jjn1bZpukIu5Rby94nLjO4Qxtc7Eq+e61IuGZu/Im//agBytizCp9sYnupSlz+Op9M2zJdgL1WF11dw4wgvFwgEAoFAUCV+/RXGjSu9TS6Hn3+G0KpN6sLtdfPYrvXwVMn448WuZOZfLe+zOSGNLSfT6dqg9CT6jbj5Qy1r079ZzRtyc4lEQoCniqn3N2DmxhMuA1CgYi+Pv6jjy21nee2Bxmw9dZn3HmpMsJcCo01Plj4HtUKNRq5lxZ5LxatvAZ7qUhej2YqH279iurpaINxcIBAIBALBbcdigZkz4c03kZmdvTvVHSb3hR+jgCvJtg+mHmT06uHEtp7It2PeYdDsfeQazNXOzaPDfXm9fyRPtA0lPa+Q+wuCS3n5kt3JPNSiVqXdPNBTxRsDGvPDvnMkXNJxPDWfk2n5uCu0tAhuUaoEUlm0DG6JUupBVoGJPGsOr3V5jafbPI3JaiLLkIVaoUYtV7PgnwXM3TuX7MKrJZuyDdnUroZBuf+Ku4XLGZcB8PPzc7lfpVLh7u5Ofn4+eoMBjVqNXq+nQO9YpeFfRjs/Pz9Onz5DevrlYlEvPpe/6zZFfV1OT7/u5yMQCAQCwU2lZk04edJRmqiIkBAICABRruWmUJTS0GS1kaM38dqvR1nwZBteXnm0zMnzIpbsTmb95M4Mjw5hiYvJ4yKGRYfQs3EQn/x+km2nM/Bwk6M3WYozq+QbLTz//SGGtAvhlX6N0CjlmKw25v59lm6N8hnVIYxpPx0unqyet+UMy/Y4n6/AaCGqphdbTl7m6+2JTpPr4zqF06VBIC//cpj0fBNtw3z5Yd95gDJXfy7ZncxfU+9jaLsQl+cER3rIfclZLifQwRG1HuihpEM9b5qHeNCqrp3UvEykhRJefOp1EvdvLT5Wt2cl97VvS/dG9zPm2730aBRU5nUV3FyElwsEAoFAIKg0f/0FTzzhyDRZktmzoUuX6+72drj58OhQYsJ96TTjL4K9VC7dfNeZLP7Tt1HxJPqdcnMvtYKJ94UzJLoGeouOQks6/xsRhkLi6RSAAhV7OTjc3N9DwZMdamK1G8nQp5JekI6/xh+VHH49uYRaNYJZNakHoxYeomOEP90bBWIyi2yhtxPh5gKBQCAQCG4rx4/DyJGwZ4/L3fNbw396Qk4ZyRnn759L+9qdGBbdgjl/J1YrNw/z0zDz0WbsSczipZ+POHn5qA5h/PFiV+x2O08v/adcN3+uVwRDo2tgsOjILrzA5Pt98Xari9kqZVnceVb+k86UdlMZvXp4hZd8SvRUNhzJoF+TWtRQh/O8z/Nk6DOK3VwtV7P65Goa+zdm7/i99Fnah9NZpwHwUflU2P+d4F8R3FJocNTpcnNzK/MYlcqNgoICDFdE3VB4tbZXWe1UKscKX4PBcPVcheWfy03ldqWNc+0wV+TnF1BQUOC0PSMjo1LtBQKBQCCoEE9PRzBLcrJjFWS9eo5tghvGYrVhstrQGSzkGEyoFVLahvnSOyqYQpOVY5cqTpmYazAz7KvdrHm2Ey1DfFi4w3nSekzHcHo1DiLuTAYLtiWWmRqxKEp868nL9IwMQmewcOySjgs5BlZP6sjQdiH4uCuJS8x0OXle0eR6/EUdz/9wiOHR2bz3UFPyjRaUMiljO4bz9Y7EMp9j/2Y1MFlsTOvdkJh6fsy/Jk1801paxncOZ+iXcWX2EeDhhp9WwvaLfzLpm5mOyPU0YDmQ43z8xvn/5eAT9yGTStCq/xVKXC24m70chJsLBAKBQHDb2LsXBg4E0zU17qdNg9jY6+rydrl5bJe6dK4fwENzdpCtN2OzU6abJ2YU0DDIE7lMekfcPMxPw7djmvN3ym8M+H5YqdWfLYJb8Fz0NFZN6sOohYdIytRXysuL+vVyt/NLwi98vPtjp34nt5tMdO1WLDz0GT8//SJGswKpVILazTntu+DWIdxcIBAIBALBbcFmg88+g1degULnz3pro4aMfcDCIs8zFXb1WdwsVj/xB/O2JFYbN6+Ml0/76TDDo0OZ1qchMx9phptChpvc2c2/G9eCbec30GvJTBdu/hxDYh7AaFJgs/cjtvUE5u+fV+a1im09ka51+vDFHyk81tbKynLcPKZ2DIsOLWLDsA20/bItYd5heKu9Knw97gRiJr+as2bNGlZ8/8OdHoZAIBAI7nUCAhwrIn18oJyJLUHl0ZssbE5IY/6W0jXoe0UG8tHjLUjJ0le6r6RMPafT8ym0WFnxVAwFRgs5BjNalQKtWoFMAnbgo99PVZi2fFSHMJrW8sZstZFrcHxZkGswM3LhHhaNaYeXWlHmZPXwmNAyJ9dLsiQuhRYh3uQbLXgo5TzXqz5tw3344s/TTuN5qktdujcKpMBo4bvdSQAsGtMOk8VGVoEJfw8lGqWcXIO5zC8c6vio+d/wKKZumnpV6I8BvwBlLL41Fhby6MOP8OsfO1HKROktQeUQbi4QCAQCwW1g61YYMADy80tvHzcOZsy4ri5vpZvnGy3k6M14axRoVQqkEhi5cC9JmfpiN9+bmMX76xNI0xWSb7RwMi2fRsGejO4YTpifO3KZ9La7+Tc7kpj5WEPe2/EqC/5xnhQ/mHqQUauGMbHNRFY9O5P8Qim+GiXZ+rK9HBxu/uFjjZi2aRrzXEy2H0w9yJjVY4htHcu0DtP4M+l3Gvt0J6vASIMgschCUHmEmwsEAoFAcJdw+DBMneoIcimBVSLlp+5DaPnt/7Ho64aV6upA6gEKzDoaBnuScCmvWrh55b08mRYhXpy9XMCWk5cZ3SGslJt/+Hgj3tvxSpkOPWrVKGJbx/JG1zfwctMy6/5Z3BfalRk7ZzgFrDwfPZ0eYf34cut5/vtgfaZtmlopN4+7EMeENhNoGtgMhbR6fk/0rwhuUakd0eJGo7HMYwoLHfvUakeuI/WVCPOidhqNxkWbwlJt4GpkelnnMhafR+Vy/7UMGDCA7t27O23PyMjglVdfq1QfAoFAIBAAYDY7MrOUVWooOPj2jucexmC2sDQumZX/XOBkWulJ+d8T0hm7aC+fPNGiSn1qVQqW7E6mR6Mggr3UBHtd9Q+D2UJWvokLOQbWTOpUYZT4sOgQXu4bia9GWbwvKVPP1B8P8dFjLVxOVsukEoZGhzB+0b5KjfebHUkseLINnWf8yYFzOTzXswHfjW2H3mQlNbcQH40SdzcZfxxPZ8y3e9mfnI3N7oh0N1nsPNWlLjKJhAKjBXc3OQUmS5nn+uCRKFafWOMIbLEBW648ykEmk1G76+M0q1uzODW94NZzN3s5CDcXCAQCgeCWs349PPKI84rORx+FefOuq2zqrXbzGl5qapRw89RcA3uSsvBSK1gyNpqjF3OIifCgaaiNTH0WfhpfNHItK/Zc4u21xziYksPL/RqhVSuK+7gdbt4xwpe1p1a6DGwpydx9c+kS2oXedR8kTWek0Gwt9/gPHolizck1LifPSzJ//3xiaseQZ8yjb30lTy06zFcj26Ist5XgZiLcXCAQCAQCwW2hRQtHcMvMmcWbzgfUYUrf55C2j6GO1TkTW3lkGbLxcLsa4nAn3fx6vXzeljNM/emwk5tX1qGbBzUnVNuIriED6FSnNwVmHVmGbPw1fviqfbBjI8uQzZgu3qw5sbpKbv5czHOYLUqstqrfe90O/hXBLQH+AQBkZma63F9YWEhBQQEeHh5orki3RqPBXaOhQK8nIzOTEBeiXtRfYGBAqXOdPZtIZobrc2VcaRMQGFipsXt4uOPh4V6pYwUCgUAgKJO8PDhzxhHAIoJYbhlFqc6zCky0qOND94ZBxaWAluxOLq5V/09KDhKJhKia2lIrR8siqqYWN4UUuVSCp6q0vulNjsn6FnV8Kh0lvjQuhXbhvnRrGOg0hrQ812mgGwR5VDpdOzgCaUwWG32aBLNi7zlahnjTIzKI99cfJz3PERV/IjUPm/1qm5JZZ4Z8ubt4XJE1PJkzrJXL8yjlUprVceeZb2aAEVgJnCh/bD6+vsz+ahHfpWjJM1pxVynKbyC4adzNXg7CzQUCgUAguOUoFE4rOrn/fliyBGRVK1lzO9z82vKWhWYLaTrHl/STukegdrOQav6Ll7+f5bSackq7qaya1JdRCw+xId6b7o1un5u3CvGmdxNfZuyoXCacmTtm0ql2b55ZdoTPB7v2cnC4efMQD55ZWLl+P4/7nF8H/0q2IRtvjRKdwYJG+a+Yrq4WCDcXCAQCgUBwu7D83xtI165Deiye1NGxmP7vLT7z8mBzQjr+7lULovBV+5BvdHjwnXbz6/NyKw+3qkX8Rd11ufnncZ+zZsgajFYjeqOcXp/spGGwJxGBHrzU14M1J1fx2Z5ZHEk7wtkpZ6vU76+Df8VkNfHlljQmdq1fLd38X7FMtVatmigUCnJzdS5l/cyZswCEhYaW2h4WHgbA2Sv7S2KxWEhJOYdSqaRmzZpObc6cdW5Tsq9rzyUQCAQCwS3j8mU4eRIsFjh/HnJz7/SI7kn0Jgvrj17isXm76DTjLx6bt4seH28h9rv9hPlpWD2pI2F+jok/q83OsrgURncML9WHTCohsoYnbcN8iKzhiUzqEPvRHcNYFpfCyA5hJGUUYLE6JvwtVhubE9JY+c8FfDVKRsSE8s2OpEqNd8HWs1htdkZ1CCvelm+0lMrmUhIPNzlZelOVronBZOXVfpFsmXYfBUYLCqmU345eYm9SNgmXSge2FNUmnb/lLNN+OlzqxuFkWj5KuYyomlqnc9zfOIicwhwOHTsEX1FhYAuBsObP1bTr0BUPN3nxlxqC24PwcoFAIBAIBOXSqxcsXw7SK1OWDz4Iq1ZVuXTq7XDzcZ3qIpdenYi3WG3sPpuJh5ucAE83BrcL4tW/XmL06uGlJs/Bkf579OrhfLDrNb4d05wf9p7DZr99bq6QSckx5nAo7VCl2h5IPYDeogMkqJSuvRzgwRY1yTFkV61fsx6DuRA/d6Vw89uMcHOBQCAQCAS3A73JwvrT2Ux54HkGD3mPmMABdJ+3l9jv9uOhlFHD048WwS0q1VfL4JZ4Kr04ne7IyHin3fx6vNxotjG2U11mD2l13W6eZ8rDaNHjoZITWUPLxZxCpvYO4+1trxQ/x6jAKPRmfZXdXG8uxGaj2rr5vyK4xc3NjWZNmwKwY8dOp/07d+0CoG3bNqW2t2nd2rF/5y6nNnv37cNkMtG8WTOUSqVTm71792E2l37Rc3JyOJaQgIeHB5GRjW7gGQkEAoFAUAlsNkhOdjzsJaIIzp51TvMtuCH0JgvvrU9g8vKDTqs9i0oBLdh6trg2J8CS3cm0DfNhWHQIXmoFz3SLYOv0bswe0orpfRoVy+384a2JDvfFZLHRNsyXt9Yew3QluMVktbFkV4oj5aK3Cjv2KkWJZ+lNdIzwZ2i7EMARROJqslomlRDg6Ya/R9W+VHCTSxm3eB+x3+0vjvLu1ijA5bHlZZ0p+sJhbKdwp30xdX1Z+9s6+BK4XMGAIoGx4Bnsha+HknyjxSmiX3BrEV4uEAgEAoGgQh5+GL7+GkaOhB9/BFXly5TA7XPzNmG+WEpEa5usNub9fRZfDyV/vtiZtSdXO8pmlsP8/XPZcm4DrUO9yNGbb5ube6rkZBuyqtQ+z5SLRiljWVxKqYn+IsL8NLzSL5JMQ3aV+s3UZ+Kt0pJZYBJufpsRbi4QCAQCgeCmYbfD3LmwaFGpzSXdfI00mN0hzYr3xV/UMfWnw+w8pWN6h+mVOs20DtORS5T8NbV6uLneZMXfo2qFNRUyKS/8cLA4+P563DxTn0m+uYA/EtIZ1SGM4TG1+Sv5t1IlR7VuWjL0GVXu19uterv5vyK4BWDgoAEA/PDjT1y8eLF4+/HjJ9i4cRPu7u707NmzVJtevXqi0WiI27OHXbt2F2/Pyclh0aLvABg0aGCpNg0a1CcyshG5ubksWrS4eLvVamXevAVYLBb6P9APubx6viEEAoFAcI9gNjuytVx28W2/uzuIz6GbRlH2lCW7Ky4FtDcpi2HRDiHONZgZuXAPL/dtxN9T7yPc352nFu+jx8dbSq0sLbRY8VApGN0xjJEL9xCXmIXOYAGgwGjlw8eaEVVTS4HRSmZ+xVHiJVegmi02Zm04TmzXusx6rBmNgj1LTVaXnNh/sVdD/NyVZa7SvJailJAnUvOKv0T44LcE3nmwafGXCCXHNDQ6pNysM8v3pHB/4+DiGwoArUpO0l8/MGnY01BRvFY34DHADXxU3sikEuRSSan6rILbg/BygUAgEAgEFTJqFHzzTZXvW27EzSctO8Ar/Srv5sO+2l3s5eBw8xmPNmNvYhZIzJVK/y2TyFh/ahWjOta56W5e0vv7N6tRys1nbTqJj8qnElf0KoHuvhQYLSzZnUy7cF+GR1/18qIsjOuPXMJXXbV+A9wDcFd6cCFbL9z8DiDcXCAQCAQCwQ2TlQWPPAJPP+14HD8OVN7Np/98lPvrPsCE1hPKPS629UTa17yfXp9srzZuvuDJNgR4uF2XlwNX3byKDu2n8cND4c4vB87TLtyX0Z1q82nch6WO0Rl1+Gv8q9Tv3eDm1XNUlWDfvn18/8OPxb9bLI437LTpLxVve+Lxx2jTxhFZ3qJ5cwb0f4A1a9fx3PMv0qJ5cywWCwcPHcJutzP52UlONTo9PT15dtIzfDjrI2bM/JAmTaLw9PTk0KHDFBQUMGjgAJo2beI0tsnPTmL6Sy+zZu06Dh85Sp06tTl96jSpaWk0atSQRx995FZcEoFAIBAIHOj1cPo0mFwEOgQHQ61aIKlaHUtB2RRFgcukEhoEeeDhJiffaOFkWj7WknV3gG92JLHgyTbM23IGmx08VXIkEgmzNh1naZyz5Mdf1DFlxUGGRYcQ27Ue2XrHCjddoZlgLxUqhZR5fyczskMYy/ek8EDTGmWO00utYHhMbYZE16DArCNTn4W7Rsd/H6rP4XP52O2wcFRbCs0WfN3dOJ+l5+HWtdmTmMVTi/cRf1HHM90iGNUhjGk/Ha7wuhSlhCx5CZbEpdCurh+v94/kpZ+PFF+fytQmreHlRl6hmck9IujWKIBPfjtK2m+zef2PVeUPRAk8DFxZANgyuCU+am9+3nuBkR3CnF4jQdURXi4QCAQCgaDK/Pwz7NoFs2aVfcx13LOYrDYWbDlLZA3Pcr0cSrt5iK+GOcNaYrba+XDTCZZV0s2LvBwodvNxncPJ0GeUm/7bR+XDxLYTiW0di96s51JBIn4aX/77UH1OpxmQS6V8PbItBpMFf8+qubkr76+p9cdNYWHifeEs2X0esKNVedEiuIVTWnZXtAxuiZdKi0TiCAR6Z108nz7Rkp6NA/lp/wXC/NyJS8zkzTXH2N+ic5X69df4c+xiASPaCze/GQg3FwgEAoFAcFvZtg2GDoXz5x2/6/UweDDs3o1JKq+Um+cazPznpxN8PPhDYmp35tO4D0u5ZIvgFjwXPY2udfowauEhcg1mcg3m2+7mVpsNrVrh5Ob3NQy8bi93lP6xo3Wrmpt7KD2Q2jXsT85m9cHzPNTW3ek5xqfHo1Fo7jk3v2uDW3JzdZw8ecppe8ltubmlvyAZN24s4eHhrFv/GwcPHUIul9O8eTMef/wxIhu5TnnYoUN73nv3bX748SdOnDiJxWKhTp3aPNCvH927d3PZpmbNmnzy8UcsW76cAwcOsHv3BQICAnj88cd47NFHUCgULtsJBAKBQHDDZGVBUpKjJFFJJBIICwM/vzsxqnuavEIL3RoF8uXINhSarGTpTfhqlLgppCzfc44lu5OL61PGX9RRaLLSMNiTizmFLB4bzcb4VJeBLSVZGpdCyxBvhkWHMOfvM2hVCixWG38eT0ellBGXmMlnf5zikda1iaqpdUq/Huan4dsxzfk75TcGfD/M6eZgavvpDGo2kHyDHT93FYVmC091rcd76xNKjW3J7mRWT+rI0HYhLssHFTEsOoS2Yb68vTbBad/8LWf4PrY9Her5szQuhSW7k8utTeq4CQjlyfahWGx2LmUb0FrzSFv+H/7Zv7/c64YPMAQIvLppantH6soL2QZGdgxHKf/XJDK8ZQgvFwgEAoFAUGnMZnjpJfjkE8fvzZrBk0/etO5NFhvzn6zYy+Gqm7cO9eGjx1oQf1FHXqHZZWBLSUq6uVblcImSbn48VYebuuz03xG+EWwYtoGtyVsZtGKQazdvPBCzRYqnSoXRYmXCffX4alsin/5xqniS2ZWbV+T9U9pNZdWkvny0KYnMghwmt5vMmNVjKryuk6Mnk1mgI8BTxTPdgniyfSg5Bgu+Gjde7ReJVq3gsXm7sNrsSFAwtf10hv8ytMJ+n495HpVczbYTyTzRLlS4+U1AuLlAIBAIBILbgtUK77wDb73l/F3EoUPw22+Yej9QaTf/PSGdDF0kcmN7to7aRpYhhyxDNhq5Fo1cy/K4Swxata9UG7hNbh41EIldjt5kIa/QzIT7HPPm313JSHM+23DdXj72m8NE1tCSb9Tz9n1v8+D3D2K1W8u99JOjJ5NecJnTF6w836sBYzvV5ujlI84vkd3K/P3zK+38RW6+9UQyg6uxm0vsdnv1DLsRlEtaWjpPxU5gwfx5BAUFujzGZrNx4sQJABo2bIhUWj3fhHeCQQ8+XOp3iUSCRq0mNDSUbt3vo1fPnkjuYFaDP/74k8+/mM3gJx5nyJDBxds/++wL/vzrL955+y2XKyBuBkXvrSZRUbz77tu35By3mzVr17Fx4yYuXbqExWK5p55bVVi+fAUrvv+Byc9OokeP7sXby3pfjR8fS/rly6z6deWdGG6VGPTgwwQGBPDll/Pv9FDuHHY7XLgAqanO+5RKqFfPUY7oBhCfK67JKzSz4Wgq3+5MKhVUElVTy6gOYbQL92Xkwj0kZeoB+GlCe2ZsOE50XT/GdQpn6Jdx5WYsKdnfgifbMOG7fXwf2x6Ax+ftYv6TbRi/aB/HLjlWb4b7afh6R2JxNHxarpFfJrXmvR2vlqq5eS0T20zknW4zmPjdUdqE+RLmp3GZoSXMT8OiMe3Ym5TFNzucn/PojmG0DSv9nK/ljxe68snmk3RtEEC7cF/eWhvPK30b0+PjLS7PtScxq/j6Fp5P4PKv72IryCn/gtUFHgU0VzdNaD2RGT1nUmiSYbHB8Us6ujQIQC67Ne/lyvia4N5AuPmNIdy8bISb37sINxfcKHfl58qFC/DEE7Bjx9VtajXExUHTpjfcvd5k4fdjaSzYerZSXg4ONz+Zlo9SJqF9hH+xV1dEVE0tC0e2xVMtR6N0THQXufl76xKYfL8fjeaEIZPIiAqMQuumRWfUcVF3kbjxcczYMYMF+xeU2f/ENhN5v8cMDCYbBquObEM27govNHItK/ZcKl7lWdLNf9x3npmPNazQ+2NbT+S1Tu9itOpBUsjMnTPLHUts61imdZgGdiVqmT87TmeUuveJrOHJnGGt6DZrC5E1PJk3rBU+HvDS5mnM21/+/cfMXjMxmuQYLXbh5oKbhnDzG0O4edkIN793EW4uuFH+dZ8r587B8OGwdavzPn9/+PZb9L1635Cbd24QwKyNJzh6MZcTqXmUl0Tkdrj5jJ4zSc3LJteYhb/GFxkeN+TlPiofvh64kJ51e5KWn0WBOQdftQ9KmZLP93zO3L1zyS7MdmoX2zqWN7q+gbvCnQKjgnSdnuSCOBr41yXyf5Euz7N3/N4Knb/IzXUFYLZJOJOeX23d/K7N3CIQ3Ay6d3OsJLDZbFxKTSXh+HGOJSRw+PARpr74wh0e3a2hrBuAe5Vdu3bz1Vdf4+HhQbt2bVG5qahVu9Y9eTNyr3LkyFFee/3/6N6tG1OmPHunh1M9sVggMRFyc533eXg4AlvECqhbgt5kYcZvx1lSRtryaT8dZlh0CIvGtGPg7B3kGsz4aJToTVbGdgonW2+q1OR5UX+FJitT72+IUiYlI9+I1W4vLufjpVbgpoDeTX1oVddOal4mfhpffDXeJOec4cdj35fb/9x9c4mu1Ym2YS0YEh3C+EX7XB53LtvA1B8P8WDLWnw9si1GixW9yYpSJsVNIWVZXApvr01wiqIvSbbeRJqusPj6/F//KBRyKU1rabHY7MX1PD96vAXz/j5THPGed2gTWZvmgM1SZt8AAT0DuNz+Msgcv7cIbsG0Di8xoEF/3lt7hpEdwjmVlk+PxoG3TNAFAkHVEW4u3Fy4efVHuLnguvnzTxgyBNLTS283GGDFihsObtGbLLy3PoEluyvv5QA+GiU9IgN5a80xWob4VMnNzTYbyisuqTOYi918Q3wqr/Svy+d9P2dQw0HozXoy9Bn4a/zxVfuSnJPMj/E/ltv/3H1z6VinI5mGTKZsmFK8veQqz1ELD5Vy8/kjWvLbmV/KDWwBmL9/Lu1rd2JAgwGMXvU0H/f+mPa12/NZ3GfOK0qjp9A5pDMvbHyB7x5eyox1p1l6TQZHDzc5Gfmm4p/T80288stJ5o74kM6hXflw5wynfl/q+BL96/fnk41JPNYmRLi5QFANEW4u3Fy4efVHuLngjrByJYwbB9nOgRd07w7ffYfeP/CmuPmP+89Xaki3w81jasdwPOM4729/H3D28qRMPQ/N2cmz3SOY/2RL1p8u28tLZovp8m1nJ1d+sf2LHIg9QM/venI663Tx9udjnqdf/X4YLUakUhlz/jxNbLfajFn/Or8M/sVl+aHswmz6LO3DhmEbynT+IjfPN8g4laZj+5ksnu9Vv9q6uQhuEfyrufYD/+DBg7z19rts27adrl260LZtmzs0MteMGDGMRx55iICAgFt2Dj8/X/43+3Pc3Nxu2TluJ7vj4gB4afo0mjW7OlmWlpZeVpN/HbfjfXWr+d/sz5HJ/qUfaYWFcPq0499rCQiAOnXgXo8Uv0NYrDY2J6S5DGwpScnUiFtOXsbdTc703g2x2ezFk8CVpdBsJaaeLxabjYx8U3E5n5JpDrt8O9hFTdLn2Dt+L32W9ikWYld8FjeLDUP/Is9gdZrYLyoPNDQ6pDiVZIHRgrdGjrubnBFfx1UYRV+Ej0ZJvtFSfH2iw33p1TiI78ZGk5lvKn5OO89ksmxPCnarhew/vyLvn7Xl9iuRK/jPux/x8pTR5BTmkG3Ixlftg7faG6NJxum0QsZ1rkeQVkW4vwY3xb/074ZAUE0Rbu6McPN/H8LNBfccRiO88QZ8+KFzunKlEr74AsaPv6FTFHu5i8nzklxb6jOqphZPtRyTxUZ6XmGZZTLLosBoRS6TUmi2lHLzOj5qFDIJPiofl6nNK+vmH+36iF8H/8rzG5/HZndcu4OpBxm9ejixrSfyw8T3sViVGIwON5dKLczYMaNSY/8sbhYP1O/Pw42eoO2XbZnQZgKrBq9Cb9aTqc/ET+OHWq5m/v75vLDxBeY8MIekyyanwBaAfKMFX42y1M87zmTywGd7ePvBrmwd1d/h5oWO1ajeKi+MJhmn0goZGh0m3FwgqKYIN3dGuPm/D+HmAkEJ9Hp44QWY7yILkEwGb78N06djQcLmo5dui5vLpBIaBHng4SbHYrXfUjf/dPen/Dr4V2bsmIHNbivl5YvGvsPqg5k83Ko2hSYrEomFmWV4uY/Kh43DN5aZLeZg6kFG/DKCiW0msv+p/ZzLPYenmyfeKm9MVhNz984lKjCKCM/72JeSzXCzFovdwm+nf+P5mOcZ+etIpz5PZ50u5fw2u40sQxaeSk+CPYKxWBWcSjMQrJXRJtyP6Lp+qJTV9+9G9R2ZQHAHaNGiBffd15U//viT3XFx1U7SfX198fX1vaXnkMvl1K5d+5ae43aSmZkJQFBQ0B0eSfXldryvbjX30nu2SthscPIkmK4RPYnEEdQSKFIt30pMVhvz/j5bqWO/2ZHEgifb0CjYE41SRkxdXy7lFuLvoazSOX09lFhtsHB7In2b1CDfaMHfQ8misc3LTHN4MPUgo1aNIrZ1LBuGbaDtl21dpjQEOJB6AKMtD4nEu9T2kuWBnlq8r1Qqyaa1tHw3NhqpRFKpwJaomlrcFFJOpOYBjpuQTcfS6NYokNdXHWX9EUdpra3TuzF/y1ms+lwur/oAY4pz3dCSqLwC8B70Msuy6vLDf7cwLCaEZ7s14MC5HD7/4wBHLpROfxnbpS49GwehqcaiLhD82xFuLtz834hwc8E9xc6dMHEiHHYudUloKPz0E7S58b/t1+Pl87acIbZLXdyVMrIKTOhN1iq7uVYtx2Cy8LULN3/97/+wwEVJnqq6ud6sp0lgEw6nlb6G8/fPpUtIF0z5bXlz9XEia3jy6dBQDqUdqtTYD6QeIMuQTZ96/dkdtY33t7/PrJ2zGNRoEIHugaQXpLPq+CrMNjOxrSfyQP3+jP3GtY+fTMtHpZQRVVPL8dS84p/jL+oY9c0/yKUINxcI7gGEmws3/zci3FwguMKRIzB4MBw75rwvLAyWLYP27QEwmSw37OYBnm74e5QdSOhYhFmbIdE1KDDryNRnIVNkk2+ycyilgJpe7rfNzefvn8t9oV3oUr8XTy3eh81uL9fLJ7adyJakLeWWCAJYsH8BfSP64q50Z8XRFfx2+jcOpB5gfKtYnot5nsnLEhgRU4dgT2++f/R7cgpziPSPZELrCS5Lg2YXZvP+9vfJNmTzSudXeO3P1/BUevJp7/l0fH9r8bz+3eDm1XNUAsEdpG54OH8AGRmZxduK6hLOmTObn1f+wtat20hLS6N1q1a88sp/ADAajaxZs5btO3Zy6dIlAEJC6tC3Tx+6d+/m8lwJCQksW/49J0+eRCaVUr9BA4YOLTvlYXm1QwsLC1m3fj07duzi4sWL2Gw2/P39adasKQP696dWrZq8+urrHI2PB2DF9z+w4vsfitsX1ZOsKO3gX3/9zcZNm0hKSsZqtVKjRg26dO7EwIEDUCpLTwSVHK9UKmH5iu85deo0EomExo0jGTVqJCF16pTzapTm7NlEtm7dxuHDh7mckYFer8fPz49WLVvw2OOP4VdCNIvqZBbxVOyE4p+bREUVX4ej8fGlaslem8IvLy+PX35dRVzcHtLT05HL5dSrV5dBAwc63cSVvHavvvoyy1d8z+7dcWRmZtKvbx/GjRtb4XOszOsIYDKZ2LJlK3v27iU5OYXs7GwUcjmhYWH07duHLp07Vfq6VlST1m63s3btOjZs3ERaWhqenp60bx/D0CFD8PBwL7Mvs8XMzz//QuLZsxTo9Sxd8h0eHu7Exx9j+44dxMcfIyMjA7PZTEBAANHR7Xjk4YdL9VnUH8Cff/1V/DNQKkVoebVD9+3bz+o1azh9+gwmk4mAgABiYqKdzgWl66vWq1ePJUuXcuxYAhaLhYiIeowYMZzIRo0qfW1vOVIphIQ4MrcUIZc7yhB5et65cf1L0BnMVUpbbrHa6BEZiK7QjNEiJcBThd1uL574rYiomlrUChmFZhu/HLjAQ61qI5dK8PdQsPL46kqkH59PTO0YJrSZUJw+0RUXdZnU8bz699RLrWDxmGjmbblaHqgkRy7o+HJbImM7hfPCDxVPpo/uGMayuBQ8VaVvQs7mHGfy/f5M7xPOH8eyMFusHDx0kPSf38GqK3/VUGhkC179+CtWnigoLmk0tmM9Pv79pMsxx1/UMXnFQYbHhPJKv0bVVtQFAoFwc+Hmws2vRbh5NXZzwVUOH4b333eUG3LFAw/A4sVwk74s0hksVS71+ULPBnRrFEiB0UJtbzVfPtkGrUpeZTc3lOXmLiaUS1JZN8/UZ6J107rcN2vXTFY/8QdvrXGUA8rUZ1U47tJ9Z5Otc+etrjMY2+opangEkmfKK07T/knvT0jNv0wN93CyCyTsSXLdv9Vm5/f4VJ7qUpcpKw6yLC6F0R3C+HpHonBzgeAeQ7i5cHPh5qURbi7c/J7Hboc5c+DFFx0ZGa/liSccmVy8vIo3VXXOvKSbW6x26vhoePH+hvi5K126eckM5gO+H+YiE8s0mtUZSB0f9W1z85k7Z7L6id4kXNLROtSnTC+XSWTEto5l4PKBZZ7DR+XDxLYTiW0dS6GlkEJLIcOaDSO2TWyxm+cZpLwxsB5bzm3gvkUfFl+DCN8INo/YTKfQTszaOavMkqPdF3fndNZpvh20lO92Xiy1YPVucPPqNyLBbcFis3BeV7laZdWR2trayKW35u1rMBgAUFyTDtVmt/P++zOIP3aMqKjGhIWG4ql1fHmck5PDG2/+l6SkZHx8vImKagx2OH78OJ99/gWnT5/mqadKp9ndu3cf738wA6vVSv369QkODiIxMYlXXnmNHmVIfVlkZWXxxpv/JSXlHB4eHjRpEoVCoSAtNY2NGzdRs0YNatWqSctWLbHarCQkHCc8LIzw8PDiPmrUqFHheebMmcvGTb+jVCpp2rQJbm5uHD0az3dLlrJn7z7efutNl2kZ9+7dy5q164iIqEfr1q1ITExi//5/OHnyFF98/ik+Pj6Vep4/r1zJrl27CQsNJTLSIUqJiUn8tmEju+P28NFHHxaLenh4ON27deOfAwfIycmhffsY1Co1ALVq18JT68muXbvx9vamVcuWxeeIbHxVwC5cuMj/vfEmGRkZBAYG0rJFCwwGAydOnuSdd99j1KgneejBB53GaTSZeOXV17l8+TJRUY2pV7cuHh4eFT6/yr6OAOnp6cz+3xx8fX2pVasm9etHkJOdw/ETJzh27BgXzp+/abVhF3z5FZs2/U6TqChCQ0OIjz/GunXriY+P5/333kWj0Ti12bp1G79v3kxERD1atW5FamoqEolj37eLFpGUlExoaCjNmjXFbDZz5sxZVq78hX379jFzxgeo1Y7XKrJxI7Jzsjlw4CDBwcE0jowsPkfJ929Z/PTTz3y3ZCkymYyoqCi0Wk8SEo6zcuUv7N4dx/vvvYO3t7dTu9NnzjB/wZcEBwfTsmULLpy/QHz8Mf7v/95k1oczCA0Nvb6LeSvw9oZateDCBdBoHIEt90h61OpMUXrDqmAwWflo00nGdqrL9rOZfLXtLPc1DGRUhzCm/eRiNes1jO4YxoKtZ8k1mFk4qi0r/znPqPZhSCQWPtk9s1Jj+Dzu81LpE13hq/ZBJpUW3zwMjwklLjHT5UR0EUt2J7N6UkeGRYewtJwyTcOiQ2gb5suincmsmtSmzJuQ56OnsWWjlbQl07GZXZTcKsHg4SN5cupbdG5Yk/vbGknPMxKkVeGpkuPjrsRLrSiu2+pq3NHhvvRtElxta4gK/h0INy8b4eZlI9xcuLlw82ro5ncbdjtkZEBSEuTkOFKNG42O+wmNxvHQah0PLy9HAL1MVnZ/CxfCokWwdavr/QoFvPMOTJ16U0un5hiqXurz0Ta1mfDdfmY+2pzNCel8WU3d3E/jh87o+suBA6kHKDDraBjsSb7Rgp+masFCQR5+uMs9MFgMHL+cwPg1Y5y8/MWYadTyCMdgKT9F45aTl/l0cEvGdgxHJZfSp0kN2ob7CjcX3HXc7V4Ows2vRbi5cHPh5q4Rbi6oFMnJDne/NrBFo4HZs2HUKIrfwFzfnHmh2crj7eqgM5jZdTaLRTsTsdjsjIgJY0qP+kxc+g/WK9EXXmpFJTKYDyPu/ETef+SD2+bmlfXyqMAo9GZ9qawuMomMqMAotG5avFXefNr7U7Ymb3VZQunFmGnU9qiLxs3GtN+dr8HprNO0nN+SCW0msHH4RowWI+d05/BV+5YqOZpdmM3E1hO5L6QPA1ftcznW6uzmIrjlX8p53XnCP6v4A666kjglkTDvsJver91uZ+++/QCEhZXuPyMjA4VCwZz/fYGfn1+pfZ9/MZukpGQG9H+AkSOfRKFQAA55f/udd1m3/jfatGlNq1atANAbDHwxezZWq5Vnn32Gnj16FJ9/8XdLWLnylyqN+5NPPycl5RwdO3bg2UnPFAsOOKKiDQY9AI8+8jA+3t4kJBwnOrpdlSRu585dbNz0O76+vrz7zlvUrOmQxYKCAt555z2OJSSwbNlyRo8e5dR2zdp1vDR9GjEx0QBYrVY+nPURu3btZv1vGxg2dEilxtC79/2MHze2lFTZbDZ++PEnli9fwdKly5j87CQAYmKiiYmJ5tVXXycnJ4fRo0YRFHS1REtaWjq7du2mdq1aTjVki8Y4Y+aHZGRkMHLkkzw4aCDSK5Nvly5d4o0332Lx4iW0atnSSdhOnTpFw4YNmT9vrlOEc3lU9nUE0Gq9+O9/36B5s2ZISshDWloar73+Bj/8+BPdu3cv9Zyvl7//3sKMD94nIqIe4LiRfe+9Dzh85AjLli13GVm/6fffmTr1BTp3co6EH/zEEzRq1BB396vXxmw28+WXX7Fx0++sWr2GwU88DsD9vXpRI7gGBw4cpHFkpMvXqixOnTrF0mXLUalUvPXWmzRs0KD4XJ988hk7du5k3vwF/Oel6U5t16//jXHjxjKg/wPF2776eiFr1qxl5S+/8vxzUyo9jttCcLBjktnPr/zJZsFNQW+yFJcFqgpKuZSnutTl8z9Ps+xKAMj5bAOrJ3XkhV4N2HbqMvlGCyfT8ouFHRxle57rUZ9OEf58v/cc/6Tk0DLEG7vdTucGAWQXZlQp/XhZqc0BWga3JMjTl8XbzzGqQxj/WXmEodEhjF/kWnKLyDWYGblwD0vHxRBd14/5W86UiqqPqqlldMcw2ob5MmnZP8weFlX2TcjFg4ycPAy2lf9cJFIZ/31/JqPGxfJPSg5PLNjldM5RHcJYPakjIxfuISlT77KfeVvO0CMysNpJuuDfhXBz1wg3Lxvh5sLNhZtXYze/G/jgA1i3Dg4cgIKCqrX18oLs7FKT2MUsXVp2YEvTpvDdd9C8edXHWw4GswW1omr3QJ5qBf/5+QgfPNKM//1dvd1cLVdzNP1omX1kGbLxcJPzT0oO7gotLYJblJoEL4uWwS3xd/dBZzDw7vZXyvxyYMSvw5jYZiIf9JxZbmBKqq6QgkIzz/Wqz+aEdOHmgruWu93LQbj5tQg3F25+9Tjh5kUINxdUmrAw+PRTmHA1wxMtW8Ly5dCwYalDr3fO3FOtwG6zs3B7EiolzB3RhAKzjixDJrW9Aoh7tRObjmbyy4FLRNf14a/k34rdtWRgiM6oIz49Hqvdytz9cxnYcCBNApvcNjevjJdr3bRk6DOA0hla9GY9mfpMQrxCUMqUnM4+TXJOcqm2RW7+yxO/YDAbysziXlR+aMaOGaweshqD2cDEdRM5mn4Um91Gi+AWfBo9mwfq96fQJMdHU7bjV1c3r16jEQjuEFarlYsXL/L5F7M5ceIECoWCHt27Ox03YsQwJ0E/ezaR/fv/oX79CMaMGV0s6ADe3t488/REAH7bsLF4+86dO8nN1REV1bhY0AEkEgnDhg5xOkd5nDx5isOHD+Pl5cWka8QOICgo0OmG43pYu24d4EhpVyToAO7u7sTGjkcikbBh4yZMJueozM6dOxULOoBMJuPRRx4B4Fi8ixp9ZdCsaVOnaGGpVMrgJx7Hz8+XPXv2VuUplcvevftITk6mffsYHn7owWJBB0e0/ujRI7HZbGz6fbPL9uPHj62SoFf1ddRqPWnRvHkpQXccF8Rjjz2CzWZj796bcz369etbLOgAarWa8U+NQyKR8PvmP1y+5m3atHYp6ACtW7cqJegACoWCsWPHIJPJ2BO356aMe93637DZbPR/oF+xoBed66mnxqFUKomL28PlyxlObSMjG5USdIDHH3sMqNp79qZSWE72CokEAgNFYMttwGK1sTkhjU82nyquJV8Zompq8dYoiUvMKp48d9QGDcVNLuXR1rV5Y0AUc4a1Ysu0+3imWwR1fNT8d2Bj9r/Wk0da1+ZCjoEPHm7Glmn3UWC08GibOizfk0yWwXUd0LIoL7X5lOipZObZaFJbS9cGAbzQsz6FJmulUkkmZerp9/k2utT3Z+HItuz6T3d+eboDf7zQlfkjWnP2cgEDZ++gSwO/UjchpSgEVlBhYItUraXeyA94fvJk5v59likrDjqlqIy/qGPaT4dZsPUsi8a0w0utcNlX/EUdOoOlwucnEAhuH8LNK0a4uXDzIoSb3yE3v9vZuxe2b696YAs47j1cBbYAtG7tvM3LCz75BPbvv+mBLRarjc3H0pDLpFXycqkEOkX48U9KdrV288nRk5m/f36ZK0fBkXUx32jBarOzPO4SU9pNrdR5X4yZxuFz+WxOXF9hedO5++ay/tRanu9Vr8xj0nRGtGol768/zvPfCzcXCO4lhJtXjHBz4eZFCDcXbn5P8tRT8PCVEmXPPw+7djkFttzInLm7UsaBlBxGdAgmIPAAA77vTqM5YXT4phUhn9ah15KuoNnN16OaMeG+ED6N+xAflQ+vdH6Fs1PO8v2j3/N+j/f5/tHvOTP5DK90fgUflQ8/HvuR7MKb4+YyiYw3u77J3ot7GdFsBC2DWyKTlP4upjJerjPq8Nf4E+Ebwd7xe4nwiWDQikFE/i+STt90IuTTEPos7UOEz5X9vhFO42hVoxUzdsyo8LnY7DZe//N1etfrzfePfs/usbtJee4ca574k9TUFnSftZs5f5+5K91cZG4R/KspWTOyCLVazZQpz1KjRnCp7RKJhHZt2zodf/DgQQCi27UrJXJF1K1bF5VKxalTp4q3HTuWAOBSYuRyOR06tGfNmrWVeg6HDjmiDrt07oTmGrG7WVgsFk6cOAlA165dnPaHhYURFhpKYlISiYmJNLzmg61FC+cJrJpX0gRmZVftw0Wny2PP3j2kJKdQUKDHZrNdGaOVvLw88vLy8PT0rFKfrih6XdvHxLjcH9W4MUCp17UIHx8f6kdEOG0vj+t9HY8dS+Do0aNkZmU5ZNl+9ZpevFLD9kZx9T4NqVOHsLAwEhMTOXs2kUaNSr/mrv6vlCQzM5M9e/dx4fx59AYD9isr4eRy+U0bd9H/M1fvWW9vb1q2aEHcnj0kHD9OQEDp59iiRQunNlqtJ56eHlV+z94U0tPh3DkIDQV//9t/fkExJquNeX+fxWqzsywupUppy9VyKfO3nAUctUEXjWnHnsQsxi7a57Sq8fme9Xn6vnpsTkhj2FdxLlc9errJcVPI8FVXLkVtEWWlT5zYZiKPNB5EnkFKoCdYbHbGdAovc1WlK3INZi7lFvLm6qP0bVqTUF81r/xyhBOpedjsjpWuQ6Jr0H/FUOfGGTgCW5zvm0vhWSsC74Gv8NSgDmw6llpuuSSApXEptAzxZlh0CHP+PuPyGF2hmWAvVeWepEAguGUIN68cws2dEW4u3FxQRaKjYeXK62vr5VX2vjZtrv4cGAjPPANPP33L7mFMVhtz/z5Lt0b5VfLyX/65wOiO4Tw+fxdw59xcJpFR07Mm9XzqlVppChDbOpbOIZ15YeMLZbZvGdwSjVzLidQ8AJbsPs+qSX15qtWEcgNWYltPpGPt3ijkUiZt/rBSY525YyZ/jNjCj/su4KFS4OeuJDPfiK7Qkd1mSLsQ4eYCwT2GcPPKIdzcGeHmws0F9xgSCXz5pSPIpXdvl4fcyJy5TCqhSR01b297ucxsgsNWDmVC64nM6PkBcqmcveP3llm6Z3K7yewdv5fnNj6Ht8q7Uk+xKANMbW1tgj2CkUlkWO1WfFQ+vBDzAs+0e4YCcwGZ+kze6vYW3ipvTFYT8/bN4+NdHxPmHVYpL49Pj8dD6cEfT/7Bu9veZcH+BS6f75jVY4htHcuGYRto+2Xb4iAdV2WNyuNA6gEu5V8iNe8yDX2b89ovx/nrxGWKElPerW4uglsE/2q6d3PU6JRIJWjUakJDQ2nfPsZlnUcvL69S0eVFpKenA7Bk6TKWLF1W5rnM5qtpnbKysgAICAhweWxQYOVT4mVkZgIQHBxcwZHXT15eHhaLBa1Wi0rl+o9YYGAgiUlJZGZmOe3z93OeyCoSUYvZdborV2zduo3/zZlLYTkZLAwGw02R9PT0ywB8/MmnfPzJp2Uep9PlOW0LCKj6xF1VX8eCggI++GAmh48cKfOYojq4N0pgoOv3aWBgAImJicXv55KUdw1WrVrN4u+WYLHc2ojPonEFlvH/qeh5ZV259iXxL2MViFqlJi8v/yaNsBLYbI6glsuO9yPJyaBSQSVq0QpuDTqDpTiLyZLdyaye1JGh7ULKncQdFh1Ct4aBZBlMHLukw0utYPGYaOZtOeOy3flsAw2CtLy7LsHl/qJVj4fO5fCffpEgMVYp/biH0qNU+sQWwS14qeNL9I3oz6s/n2TVoYvF+/o3q8F/+jZCJpXQIMgDDze5y/TsJfFSK/DzcOPvE+m891BTWof4kHDJ8beyQZAHBWads4CfAn4CjE7dleKBB/vz5VdfoVZ7IZVIGbdof4XPGeCbHUkseLIN87acwdWwtSrX0ekCgeD2Ity8cgg3/7TM44SbCzcX4LiHWLoUEhPh//7P9THXfhkllzsCUNRqcHMDkwkMBsjPdzzsJQRKW84qzHbtYMQIeOgh6NvXce9yCyly8ws5hkp5+fDoENrX9efQ+Wxy76CbF6Ugf7rN09jsNsa1Goe/xh+1XM3qk6sJ9gimVXAr+iztU+5q0+djprL5WKZjZbrdTq7BzKiFh/h2zLt0qNOZT+M+dJrsfy56Gl3r9OHtNaeZ/kBQlSbGCyy5fDOmMTpTLh4KD1QKDWaLDLtN6ijBuli4uUBwLyHcvHIIN/+0zOOEmws3F9xFrFgBJ0+Wff/g61tmYAtc/5x5+7r+2OxWNieuqzCb4Lz9c+lRtwfrhq7j//7+vwoDQz7t/Slebl7lunnJ0kCFlkJyjbm83e1tZvWaxc8JP/NE1BNsTd5K98XdXXj1c0xoM4Enmz3J/ouH2Hoyh4bBnpxMyy/l5R3rdOaTK15utVuJT4/nsv6yy/GXZP7++cTUjmFCmwm8v/19oHRZo7JKMl2L0WLkYnodpize7bL80N3o5iK4RfCvpip1CJUuBB3AdmWSp3Fk5C0V5WpPGVmJAacUgNdDeno6n33+BQDjxo6hTZvW+Pr64ubmBsD0l17mxIkTpebcboSitL+tWrXE28u7zOO0WucbAqVCeXMGUQ6LFn/H4SNHaBIVxZAhgwkJCcHdXYNMJuPAgYO8+d+34CZdi+tBUcY1OHHiBAu/+RZ3jYZnnp5IkyZR+Pj4FN8Ajxo9luzbFeFdzvvyZrxnbxizGc6ccUwmF2G3O7Y1bgxl/E0S3FpyDaYSP5sZuXAPi8a0o1WoN9/sSHJaxflUl7p0ru+PWiEjPc8RuTE8JpS4xMwy5b6i/UUsiUuhZYgPLUO0TOswnWErXWRDuYbpHafj5ebLiWcSuVyQha/aB41cyx8JWfyVkMsL9zfg0Pmc4mwtO89k4u4mZ8dL3SkwWsjSm/DVKHFTSFm+5xxLdieXkuKomlq81Aqm9W5IRr6JQrOVl/o24vG2tXl33XFsdjuZ+hI39nZgJ7CZ8v9mSYBusK75Wvr91IfnoqcxoMEAPnmiOcO+iqswu0z8RR2FJisNgz2LA21KjlmrEkosEFQHhJvfRISbFyPcXLj5v4r4eJg4EbZtcwSsjBgB4eHOx7VuDa+9Bu3bQ5MmUKtW2SVObTbHPUluLuh0lPsfOywMFi++KU+lMhS5eWW8fHTHMPpEBVNosdEuzI/0PMeXf7fbzSN8I9gwbANbk7fSf3l/p0ny6R2m06deX2bv+YLTWafLPN/ENhMZ0GAA2fkSejWuWezmSZl6Bs3ex7DoFqwf8hdWCsjSZ+Gj9sFH5U2u3s7rq46TV2gp7eXlUDTp76n0JL0gnbSCNPw1/njYzaTmpVPDoy6Hkwr58LFmjFy4R7i5QHCPINz8JiLcvBjh5sLNBdWM/HyYPBm++cbxe0wM3H9/lbu53jlz7BLkMhOfxlUum2Bq/iX+TPyz0oEhD0c+wvQO0xnqws1Levm1GWC6hHZh5eO/8NqfrzJvv+tsMqNWjSK2dSxvdH2DXhHdOJNuYfaQgFLz5oNm72NETAu2jtpGWkEqerOeMO8wunzjnDXJFZ/Hfc6vg39lxo4Z2Ow2dEYdge6BvNL5FWJbx6I368nQZxQHyi/4ZwFz984tFSCvlGr57po5/JLcjW5e/UYkENxlFEWqRke348EHB1WqjY+PI03t5aJsDNeQXsb28s6fmppa6TZVxdPTE7lcjk6no7Cw0GUUelHEtp+f7y0Zw/79/2CxWHjwwUEMGNDfaX9aWtpNPV9R/dZePXvSoUP7m9q3K6r6Ou7eHYdUKuXVV19Go9GU2pd6k69FevplwsJCnbYXvX99fSv/mu/eHQfAsOHD6N69W6l9RqORnJyc6x/oNfj6+pKWlkb65cuE1KnjtL9o9YhvFWr13jYKChxBLC7qsuLn55ioFtwR/D3ciKzhWZy5JClTz8DZOxgWHcKCJ9tQaLKSrTfho1GicZPhpVKw+Xgam+LTeL5nA2RSCUOjQxi/aJ/L/ivafy0LdyTyfVR7+rj3J7b1BOa7kO0iJraZyH0hfekyYzvBXqorWVh0xSWDwBExv2hMOwbO3oGPRsGiMe3463g6X29PdJl+ffWkjqUmsMd3rsuP+8/x5upjpY4d2ymcr0e2Ja/QjFlyZdWHGVgNlL2QBgB3D3cCngwgKTAJKLp5GEZs64m82fV9vhvbjv5f7ChT0IvI1pvwcHP+vzOmY3h59+wCgeAuQ7i5A+HmN4Zwc+Hmdx1mM7zzDnzwARSt9LVY4K23rk5Ul8TdHd5+u3J9S6WObC3lZWy5Q5R084q8XCmTsvXUZf731xlsdjuzh7S67W7uo/Jh4/CNzNgxo8yVpkNXDiW29URm9pxBPZ8GvL/jHacAmOejp9GlTh8enL2PpEy9Szef83ci9QK06ArNrD+SR74xG7lUwuiO4Xw+uGVpLy+HkpP+9y26z2Xa91raGjSqoWbRzmSWjY/hlV+OkKYrLDfbo3BzgeDfgXBzB8LNbwzh5sLNBbeIAwdg8GBHxpYinnwSDh92lBitAn7ubrQN8ynO+F2ZOfPfE9I4dlHH8I6elcomKJPIGNhwIAOXD6zUmD6P+5xHGz9K5zp9nMoDVeTlnUI6sf7UOpeBLSUpCqJpEtCMP4+qmPN3opObz/4rEYAOkTr83X1JzU+tUvZEvVlPk8AmHE47jNFiJNgjmAa+DcotydRnaR8SsxN5JPIRfFS+SEhGJpXcM24uvh37l1JbW5vEKYl3ehjXTW1t7Ts9hGKat2jO0mXL2R0XV2lJj2rcmD///IvtO3bSt2+fUvusViu7du2q/PmbN2fJ0mVs3badYcOGoq6g7qT8ypfi1is1NyuDXC6nYcMGxMcfY9u27fTq1bPU/uTkZJKSklCpVIS7WhV2E8i/kr3CVdq7+Pj4KsudXFF0HZzTdIGj3unmzX+wOy7utkh6VV/HgoICNBqNk6AD7Nix46aObceOHU6Sfv78eRITi17zsEr3lZ9fALh+HXfs3IndxRKCil6rsmjcOJK0tDS2bd3GsGGlI3Nzc3M5cOAgEomEyEaNqtTvLSczE5KSnFdESqWOlZBVuCkS3BwsVhsmqw2dwUKOwcScYa1QyEpnLpnz9xnmbTlDw2DPYhH8bHBL3lufwJK4FGRSCS/3i6Rvk2AKTdbiNI3X0iDIo9z91xJ/UUdmgZG31ibwev93aV/bdfrxlzq+RK+6/Xjkf/vJ1pvJ1rsOBCmqszm2UxgPtqhdZnr2ovTrJYNh+jerQcsQbwbOdvwNKiplpFHK+HLbWQ6kZDOhawRuMk8aKxtzbP4xqKBUsFugG6tWr+L+jfc7raqZv38u7Wt3ooV/73Lrghbho1GSbyyd1nV4dChtwnzQFVpQK4UWC+4cws1vHsLNhZvfDISbCze/qzAaoWdP2L7ded/ixY7U4rfob8GdoCI3d+XlHz3Wgi/+OFnstTKpBJVSdtvdfGLbiWxL3laJlaZz6VC7EyHq+/hzxFZyjDnkm3JxV3ihkHiwPO4Sg1btKw7uLsvN24T5MHC2Iwi8yM2X70nhQraBh1vVxk3mWWGa9oqCccasHsPzMc/z5n1vMuG+2uSZMph0vxueyiA0ci0r9lxiye7zToHows0F1ZW73ctBuHmp8ws3F25+DcLNHQg3/5djscDMmfDmm44g+ZKkpcGyZfDcc5Xr6oqbW2w2Xu4biZdaUSpzSUVz5tF1fXmgsHJluKICo9Cb9VUKDMkx5PDl1mxe7li6bOfEthPZkrTFpePKJDJiW8dWKYhmzZA1DI3RMG9Loks3LzRDs6Bm5BqzScypmmdk6jPRumnxUfmwfth6pv8+nfn75zsdV9LNd4zZgdFixGq3crEgkc+Hh6GRa1kWd4nvdp276928eo1GcNuQS+WEeYfd6WHcEzRs0IAWzZtz8NAh5s1fwJMjhjuJU2JiItnZ2bRq1QqADh078O2ixRw9epQ///yrOBLXbrezbPkKLl/OqPT5GzSoT9OmTThy5Chz5szjmWcmlooQT0tLx2AwFEtWUbTwhQsXqvQ8H3igH/Hxx1i+4nuaNm1SnEpSbzCwYMFX2O12+vS+H6Xy1qQWrFmrJgB/b9lCr149i59jZmYmc+Y6/yGvCO2VqPrU1FSsViuya1Iwd2jfnjp16rBly1Zq1azJww8/VKp2rN1u5/jx4wBERkZe79MqpqqvY82aNUhJOce27dvp3KlT8XGrVq/hyJGjNzyekqxbt5727WOoW7cu4IgUX/Cl4zXv0aN7cYrLylCzVg0Aft+8mdatWxXfNKacO8fixd+5bOPrc33v2X59+7Jly1bWrltPu+h21I+IABx1fBd8+RUmk4n27WOuq9brLcFuhwsXwNUqBKUSIiLAxU2Z4MYpOUGeazDhpVaiVctRyqSYrDY2J6Qxf8vZCjOX2OwUp+57plsEcYlZLIlzTKBbbXaWxaXwSKtaZOldZOS5goebvNz9rsg1mLHaYMRXB3n7we5sHTWQnMIcsgzZ+Kp98FZ5o5AqmbjkUIUpwsFRZ3PJuGg2H0urMP360rgUWoX4sHRsOzzVCkYu3APAM93CGRJdgwKzjkx9Fn4aXzRyLRkFevbH7eL8rPNQURb0CPh44cf8kf9Hccrba/ksbhbrhvRjWExomXVBwfF6uSmknEjNK/59XKe69GwcyHPfH+ClPpEEaV3XxhYIbgfCzW8ews2Fmws3F27+r8JigWPHYO9e533h4fD5544A+buIm+Xmpb28dFmhW+3mKrmMNYcyGRbzMA9FOtw8pzCHcO9QOn/TuVL9fBo3i9VP9GXUwoN8NzaGpDQl0zeeKJV18VqK3Py7sW3xUivLcXMlajczWfl2pnd4laErH3PZX3mT/kVE+EbwTNtnWH1iNR/t+sgpyH5Ku6msmtSXsd8eRimX4uEmJ0irEm4uqLYIL7+5CDcXbi7cXLi5oJpx8iSMHAm7dzvv8/CAuXNh+PDiTbfGza/OmesMZjyV3pUautZNS4a+8p8BAFmGHLxUSic393LT0vVb16WBogKjKLQUVimIJs+Uh0oqKVXa51o3n/pDPJ8MblLmPHdZ+Gn80Bl1xW7uKrCliCI333Rmk0s3n95hOr+/2I/xi45isljvWjcXwS0CwU3g+een8N+33ua33zawdes2wsPD8fX1Qa/Xk5SUTEZGBgP6P1As6Rq1mmcnPcMHM2by2edf8NuGDQQHBZOYlMSlS5e4v1cvNv3+e+XP/9wUXv+/N9m6bRsHDh4gMjIShVxBamoqiUlJjB41sljuGjZsgJeXFzt37uLVV18nKDgIiURCz549yo3E7dihA73v78XGTb/z7OTnaNasKW5KN47GHyU3V0fDhg0YOnTIjV3IcmjXti0hIXU4ffoMEyY+TaNGjTCbzRw5cpTw8DAaNWrI8eMnKt2fQqGgZcsW7N27j+eef4G6desil8uJjGxEzx49kMlkvPLyS7z537dZtnwF69b/RlhYKF5eXuh0eSQmJpKbm8vYMaNviqRD1V7HRx55mE8++YxZsz5m/foN+Pv5kZiUxIULFxg4cACrV6+5KWMC6HpfV6ZN/w9NmzZBo9Fw7NgxsrNzCAmpw7AqvuY9uvdg1arV7N27j6efeZaIiHrk5+cTH3+M6HbtOHXqlFN60aCgQMLCQjl9+gxTp02nTp06SKVS2rVrS3S7dmWeq0GD+gwdMpglS5fx0ksv06RJFFqtloSE42RkZFCzZg0mxD51XdfkpmOxwNmzjhr21+LhAfXqQRn1i//NlCfXcpm0Un3oTRaXEh4d7su8Ea35aNMJluyuXOaSoohnmVTC6I5hPPn1nlJtluxOZlh0CGZr2QKbb7Tgq6naZIevu5IZjzTDbLWx+2wWQxccwGKzXyk75Eg/Pr5zXd59qAnvr0/g9OX8clOEH0/NQyGV8M2OpEqdf+GORJaMjab7R3/j665k3ZS2/Jn0GwO+H+Yk0C3Ot2bpjMWYr10VcC0dYfy08fSO6k3bL9uWediB1APojLloZAEu64IWEdulLm4yKT/EtifA0w0/dyULdyRyOj0fncGCViX+fwkE9xLCzYWb3wyEm/+L3fxuwG6HS5ccWVus16zUlcth2jR47bXbGhxfHd28vLJCt8rNAz3dmPFIczYcvcSIr/aScElHw2BPompqmdgju4opyHVYbCCVwIcbT5bpuiVZuCORxWPa0fPjLRW6+fQO0xnQcACvd3mdt7eWLlNVmRWrlcnsMnr1cCa2nsgvk2aSlpfN5YIMann546W28ffULkgkUnw0ws0FgnsZ4ebCzW8Gws2FmwuqhpObyyX4fjUXxVtvIjEYnBu0bQvLlzu+h7jCrXDza+fMT6blo5SWn02wCJ1RR6B71col+aq9mdA1mPVHrrp5nybBvNDHVqaXa920ZOorLt9Zkkx9Jl5uMqfSPkVuPm7RPr4Y1phfElbRNaxzpZ4vQMvglqjlahIuJ7BmyJobdvOhK4cysc1Elo6fSUaBjou6tLvSzUVwi0BwE/D29mbGB++zadPvbNu+ncSzZzl+3IS3tzfBQUH079+vVJQwOGqNvvP2WyxfsYJTp05z/vwFIiIimPTM01y4cKFKku7n58esWTNZs3oNO3ft4uDBQ0ilUvz9/ejbpzdt27YpPlapVPL666+y5LulnDp1ivhjx7Db7TSOjKwwzdzTT08kMjKSDRs3cvRoPDabjeDgIAb078/AgQOqFIlcVRQKBe+/9y5Llixl/z8H2LdvP36+vjzwQD8GP/E4b731TpX7nPTM03zz7SIOHTrE1q3bsNls2Kw2evboAUDNmjX55ONZrFv/G7t37ebEiZPYbDa8vb2pWzecdm3b0qlTx5v2HKvyOt7XtSse7h788OOPJCYmkpycTEREPSbEPoXdbr+pkj5+3FiCAgP5ffNm0tLS8fDwoF+/vgwbOgR3d/cq9aXVejLrw5ksWvwd8fHx7N27j8DAQIYOHcyDgwYxYcLTLtv956XpfPPtIo4dS+DMmbPYbDb8/fzKlXSAxx57lPDwMFatXsvpU6cxmkwEBATw8EMP8sgjD+Ph4VGl8d8SDAY4fdoxMX0tAQFQp46jJJGgFGXJdVRNLbFd69IzMghNBeny9CaLIwWiCwlvE+bL5mNpLveVpKiMz7DoEOZvPUvfJsGM6hCG3W7nRFrpyedcg5kRX8WxalInompqS427iJNp+aiUsjL3X0tUTS0yqQQ3uZQv/jhVHPVehJdawfCY2rQK01Bou8z47gpU8lDUsrJThDcI8iC30FKl9Os5ehPTejdkQAs/pv8+zbkeqQUOfnWQg/sOlt+ZHEKfDOXNZ9+kc0hn+iztQ3ZhdrlNsgzZ2BU+LuuCAgyLDqF5HW8+3HSCgc1ropRLGTh7O2cz9DzQtCZBWhVatVBigeBeQri5cPObgXDzf6mb3w3YbI4yplku0uBFRMCKFdC69W0dUnVz8+ExIfx5PJ37Gwchl+Lk5XBr3Dw63BcPNwXv/5bA0hJennApDw83Oen5ZaRcKQOjTccz3eqiM1bNzXUGc/luTunJ7Rk9Z9LYvxkzdr5bPMlemRWrlcnsAjB3/1xi6sRwPOM4729/H3AE10zrMJ0BDQYw8bv9bDudKdxcILhHEW4u3PxmINxcuLmg8lzr5q3OJ/Dupv8RfDnJ+WCpFF56yVGiqER2qZvt5smZekZ2CMVuKz1nbrXZWbb7Is+1m8ao1cPK7S8+PR5/jX+VAkO8Vd7FJZCKuJxnJD2/7OAVnVGHn8a5PFh5+Gn8UEndyTdeKrX9fLYBu93O4nEtmP77VObtn8crnV9hcrvJjFk9psJ+J0dPZv7++UQGRN4cN7fD3N1zaeXXil4RvXhgaXdyjDm0qHl3ubnE7qpQm6Dak5aWzlOxE1gwfx5BQa4j1Ww2GydOOCJyGzZsiFR8OSoQCATVi5wcR8aWa2v5SiQQEuIIbqlGVJfPlfLkuojhMaH8p08jLuXq0aqVaFVy7FBiJakClULGl9sSWbI7uVSAh0wqYev0boxftK9Sk8jR4b58M6otNrudvEILGQUm/NyV2Oz24vqiJft/sVcDavmoeeEH1zL6TLcIwvw0TPvpcIXn/vSJFvSIDGRzQjrPf3+w1L4wPw3fjmnO3ym/8dmeWS5ThN8X0pdRC0uXK2ob5sNbg5rQ97NtFZ6/6DyrJnUiKTOPgxkbGLXqmhuRfOAHoPz7HYJqBLFw+UI6xXTig+0fMG/fvAoDWwCOP52Et1sgb69NYO3hqzcQji9T6nFfgwD0JgsebnKW70lh9l9nyDWYiaqpZf6I1hxIyaFvk+BKryiuCpXxNcG9gXBzgUAgENwWrFY4cwZ0OmzACYCEBBqOH4/0scdgzhzw9LytQ6pObu6lVvBCr/o82roOuQYzOoMZrVpRppfDzXXztc925OzlAiavOOi0r0ejAN58qCYhn9apsJ8iTkw6wYnLp+kS1oVBX+ytVInRCt3cBYsGLSVEfR8RQWoKzDqyDdmE+tQgQ59Os3nNXLaRSWScnXKWgcsHViobTcvglvw6+FfCPwsvlYo9tvVE/tP+HV766QQfPd5cuLngpiDcXCAQCAS3m+ryuVLSzWvoLvPitiU8evQPl8ea6tYjf/5XqLt0uuVurjOYySgw4euuxH6Nm3upFaya1Ib3d73KAhdB2UXEtp7IjJ4fsvbkaob/MrTCa7Hs4eU09b2f/l/sKrU9soYnnw8LpcH/wly2K/LcQSsGVTqIZvWQ1dit7nSdubO4hGiYn4ZFY9qRV2hk/+WrXu6j8mHv+L3M3Dmz3ECUiW0m8n6PDziZkUxtr0DS8i7R8uOWUIDjkX/1X4lBQt+Qvmw9uZX8vHww4niYABtgvfJveREhUpDJZagUKvybdGb7bz9XWzevfuE2AoFAIBD8GzCbXQe2yOWOFZd3aXT8zUhHXlH/mxOcI8NlUgkNgjyulOKxsHxPCi3qeJFdYOL+qGC2ncrgmx2JFdb/BEfmkkKTtVKBLWF+GmY+2ozfE9JYsLXi+qIAX21PZPWkjgxtF8KyPc5fAizZnVzu/iKGRYfQq3EQhWYrX249W2qfl1rBorHNeW/Hqyz4x/VKzdGrhxPbeiLfjnmHQbP3Fd+oBGlVeKsrl27QS61g8ZhoZm08Tux9tflk7czSB1wEVgAVXMp2MR2Y/uF8fjtnopnBym+nf6tUYEvL4JZo3byQS6W83C+S53s2IFtvwkejxE0hxcNNjtVm47ejqXz+xymy9VdvxkZ3DCP+oo4ekYG3RNAFAoFAIBAIbioWC5w6BQUFpbdLJPDBBzBliuPnkk3ukJsr5VJ6Rgbi564ks8DED/vO3XI3L5o83pOYxePzd1Wqb7h5bj4iJoRwfw+m/3TEaZ+XWsH/DYxAKbNUaaWpm8yNB38YQGzrWCdnd0WFbl4Gn8bNYvUTfblv5k66Ngjg3YeakphWQO0AnzLbRAVGoTfrq1hmSU+TwCYcTrsaKDR//1za1+7EKw/0Fm4uEAgEAoHgnuZ2unlk+ll++W4qKovJ5bGnHx/FtjEv0L1lXf48knrH3XzUwkN8O+ZdOtbuzCdxHzot0nwuehpd6/RhwuIjzH9yALGtJzC/nECYCa0nMqDhAMYsdA5QT8s1EuzpV6aXW+1W5u+fzwsxL/Dkr0+W+zzBkV0lNS+dP4+oigNbirx8wdYzTOhWm0/WXPXy7MJs+iztw4ZhG2hfuz2fxX12dRx6iLBF0N27F+od3nT5eCRaSw5nE5NIv3QeLK7HYMfO+gPrKxxrudjAarJSYCqgQ4BbtXZzEdwiEAgEAsGdQKFwZGdJSrq6TaNxBLYoq1bXvbpwM9KRV4TJamPe31cDORxld0IZGh1CoclKlt6E75XAhm2nLjMkOpT31iW4nIguWf9z6bgYBszeTlaBCQ83OVl61+JfkiJJnbflTIX9l6wvmmswM3LhHhaNaUerUG++2ZFU6nrV9lGTcEnHy/0audwfVVPL6I5hdG0QyJHzOQR4qpxuKIbH1Oav5N9cBraUpGgyeVh0C+b8nQhA94aBKOVS+jerQZqukHyjhZNp+VhtzqHdw2NC2ZOUxb7kbIabtaUnt48AqyhTuosYNnIstfo+zYtrkwEI8VXxYsw0Rvxa8SrTKdFTSddZ2Xg0kXlbztAw2LM4wOlEah6rJ3Xi499PsOVkBg2CPIgI9CDfaKFNqA89GgXhppDe8HtSIBAIBAKB4JZjscCJE46SpiWRSBz3D0OHOgW23Ak3r+Oj5p2HGtMq1IOcwhwyDVn4qYP44NGGnEkrpH6TGry3PoFlcZV358q4+fV6OVDKzVuH+rDwmon9yrp590ZB5OhNLif6i9z8VPaxKqcgt9ltLNi/gL71+vNSn8Z8tzvl+ty8HA6kHqDArKNliA9vDGjC53+c5vt954h7tVOZk/5aNy0Z+oxK9V9Epj4TrZvWaftncbPYOmoQ9QO9hJsLBAKBQCC4J7ndbn48IIzkoFAaXjhV+pgmjTH/7wvywprxWJB3tXHzpEw9g2bvY1h0C9YP/gsL+WTos/FUeuEm9WR53CUGrdpHbR81B1P0zLp/Fl1CuvLhrhlOgTDT2r/EgIb9yTdI2ZPkXMp1SHQtTmScLOXlMomMqMAotG5adEYdC/YtIG58HBNaT3BZ4rOI2Nax9K7XG7NZxdK4vcXbh8eEEpeYyf6UbApcePnpc6dpMb0F7VXtaWJoguK4glMnTpGTmcNpyWlOq06DCsdDCSiAcEB25SHlajYWa4lHYYmHsdyXqVy8NUo61/evtm5ePUclEAgEAsG/AX9/xwR1Whr4+kJYmKPO5V1IeenI4y/qmLz8IMNjQnmlX6MbkiKd4Wq9+ZIR4E8t3ud0Y/DeQ034MyGt3BWW4Kj/GR3uy+YXuvD19iT2JGbiq7kaYHRtVpiiyeQiSXXVf8k2/6Rksy8pi2HRIcz5+wwASZl6Bs7ecSWwJhpdoYXMfCOBWhV2u51lcSm8uy6BAc1rsuDJNhSarKUykqzYk0KHCBs//3OBx9vWcTr3kOga9F9RcXpGcEwmr37iD+ZtSWRsx3Dua+SPHSNT+wZwUZeBn8YXjVzLij2XWLL7fPEXATKphKHRIYxftA8PNzmZ+is3CzbgT2B7+eeVyeU8+cJ/6fvYiFJp3pfsPs/qZ/tU4uZhIr3rPYDZLGdpXAo2OyRcyit1jLdGwcePt6DQbKXAaCXXYCZQ64aPRolUAopqGHkuEAgEAoFA4IRUCm5upYNbZDJHULxG43T4nXDzjvX8mDuiCWtOruG5b2c6TzJ3mE6of392ni4/IKLIzT8f3JLJKw6Qb7QUu/n1eHnJdifT8jiZlsfwmBD+99eZ4v1Fbr50XDsWj2lHjt6MrtBcJTe32e2k6ZxnkEu6eUpuCnvH7+Wp1k9VmIK8c0hn3tryFq90foXY1rEUWgop9Mnj07qhVXPzSpJlyOahlrVLXcdlcZeY1mE6w1Y631fojDr8Nf5VOoefxg+d0Tn450DqAXKMOdTW1qpSfwKBQCAQCAR3A3fCzTtEBFD7y4+h3wAA8pTwejeY3e4YTRNeZJrPnXHz8ubMcw1m5vydyLwtibQO9WHGI03xVbuRmW+iV+MaDIsJL3bzD347zvQ+Xdk6qj85hTlkGbLxVfvgrfLmdFohVquSizkFLs8/JLoGQ38ZxDeDvuG5mOfQKDTEto5Fb9aToc/AX+OPWq7mp2M/8d/7/kvXsK7M2OEcRPN8zPM8UP8BsCt5aumhMr38RNIJOIkjw/klIA9wg3y/fH7X/g6eQD2gBY6f1VV51cvBhiPIJQ9HVvWSj2wgA0dpIxdYJdZqG9gCIrhFIBAIBII7S+3a4O4OPj5Oqy3vFspKR34tS3YnEx3ue0N1GnMNjsjwiiLAj6fm4e+p4uWVRyvV7/ytZ4mu60eYn4bH29TGTS4lOtyXNmG+DI0OwWSxkq0346NRoJRJWb7nHENjQhj37b5S/TgyydRmSHQNCsw6MvVZ+Gl88XTzpFtkAMv3pBSXxsk1mNFdyeQy9cdDhPppmNKjAV0//Ks4heGcv8+4zEjSMNiTh1rW5ujFXCZo6pUaQ4MgDwrMuiqt1DRa8/hubDTNQ9SsOfELM3c6fyExpd1UVk3qy6iFh0jK1NMgyAOL1caxSzoia3jip/FFWijFfY07efF5ZZ8QQAOLli+l530DGf2NI6q95M3NW2tO8+bA9+ga2pUZO13cPERP44H6/Sk0yRnxdZzL9OxRNbV4qxVsOpbG19udU2uO7hhOj0aBqBRS1NVY1gUCgUAgEAiQSqFuXUfWx6wsR1BLvXqQ4uzBd8LN6/iomfdkE6b9Ps1levCDqQcZtnIoE1yUxHTF/K1nWToumtWTOjLmm72o3WT8d2BjejYOptBkJcdgxtddiVohZdmeFAa3c/ZycO3mgR4mYurVRoKN70oEh/RvVoNQP3e6zPyLYC8VUTW1VXLzJ9qG4OWitOe1bl6Ugrx5UHM+3f0pp7Kurqb1VnlT06MmB1MP0v7r9vhr/Fl+ZDmf7f6MAvPVyXk/tR9ta0bTM7o5q/7JI6dAQYi3H1uT4UDqKRoEBCKVNkSKlCZBTYpXoManx2O1W11ecz+1D3UjA0u5+baTmXzepq/LoPP49Hg0Ck2Vyiyp5WqOpru+P8s2ZBOgDsJNIbxcIBAIBALBvcMtdXO7HbZuha5dAWc3f/H3aTzQAJK94K2ucNnD0exOuHl5c+Y9GpeeM7fZoUGQJwGeqnLdfNQ3/yCXwoMta9E7KpCaIR5c1GVilGSTZ/YnIsibZ7qFlwoIL3LzbSnbiF0by8onVrL6xGoGrRjkNP/8YvsXUSlU9KnXh/4NioJosvBWeeOt8sZkNfHTsZ/wVPryxdAejP32MDKbGV/DOWZ/MostP67CKjvFT546qAk0AjriyMJyO5ACmiuPoDKOKcQR5JIJXAbSHA8LFoxmS7V18+o5KoFAIBAI7hXsdtDrHQEsrpBIHFlb7mKuTUdeHvO2nLmhWo1eakdkeEWrMytb/7OI+Is6jBYbC3ckcvBcDlPvb8i84a3560S6y6ww4zqF4+kmR2+6WnMnzE/Dt2Oa83fKbwz4fpjL1aprJvdh+JcHcXeTM7pjGH2iavDR7yeQSiUUmq0o5VIia2hLnc9VRhIPNzm5BjMn0/JRKWVE1bza5npWapqsBTSp5cPk9c/yzaFvnPYfTD3I6NXD6Ve/P889MJkFf+mZ2KU++UbHjUGazkj2+QzCfgrj7OkK3gvBEDU5im5d7ye/0MKFHAPPdItwKi0llUipre7G5uH90Ft0ZBfm4KPyxkvlDTYF+5NzWHfkEhdzC12eJrZLXbafzuCFH5yDfOIv6pj64yGGRYcwpUd97FCto9EFAoFAIBAIkEohPNwR2BIQ4Chz6oI74ebvPNSY1SfWlFv3HmDe/rnEXFMS0xXxF3Vk5ptYf+QSi8e2QymTolUrXXr5U13qolUpkElLLxSoyM1fiJnGqkl9eHftGXo3CaZdmC9Gi41ejYOIv6jjRKquSm5+Oc9I0JWJ95LHe7jJSc1LK/79dNZp6n9RHzvOZYVyCnPIKcwp/v2y/rLL65NpyGTDmfVsOLPesUEJmXoY+iuggtQ8aL/QsetExgncle5o3bQ0CWxCrjGXi7qLmGxX08k38G1AocWE1GZ2cvPLOgszen7otGLVarey6sQqXmz/IiN+GeFynCUpWWbJFe4KLwqMVqx24eUCgUAgEAjuHW6Zm2/eDK+8Anv3wp9/QrduLt18wWCwl9Hd7XLzirx8eofprH+uL0PmHyieM6+sm9f20TCpewh/p/zG9KWznPp+Ltrh/EWLNYvmzX1UPiwYsIBpv09zmVHxYOpBRvwygtjWscy6fxb/99f/8VfSXzQPag7AobRDHE47jM1ggyRoXtgcSaaEI7lHsIZYIRR4opzXr7qgAmpfeZRgg3UDF3MzCdL6VUs3r34jEggEAoHgXsFmc6ymzMiAiAjw9r7TI7ollEx5WBHxF3XoDObrliKtWk7z2lpGxIQy5tu9ZR5Xmfqf16I3WdCq5Kw9fIlpvRsya9OJMtNFPv/DIYZHh7JkXDQPfO6ov7NobHPe2/EqC/4pe7XqxDYTWf3sTAqMMk6m5mO22hjXuS46gxkvtQJPNzmxXeoyecXBcseab7QQqHXDanOkYhzVIay4tE++0YKf5mrAVMmaofnGfAothdixk1OYQ3pBOnbs9F7am9T8VMy2sqP0AdafWsv6U2sB+G0lSCVSFJoA1G7+dHr7ONbAKytBLwHOmR+hCTAQpvV9hb+OZ9O0lg+rJ3Uss7TUqA5h1PHx4O21F6nr78GUngHYJRayTZfx8c5iyv3+/N/ACA4k5/PaL8c4l+1I1T8sOoRO9QPoNuvvcp/P0rgUWoZ4ExmspWGw53V/sSMQCAQCgUBwW5BIHJkfwXGv4YLb7eYt62hpFerBlG9mVKpNyZKYNuf4jmKy9Sb+SclmeEwoszadYGmcay+fsuIgw6NDWDY+mqFf7ubIBR1eakWFbv7kr8OY2GYiHw+eyeGUQuRyKXqTlVEdwvDRKPFQyTFZbDzVpS5TKunmJb3cSi4G2V62Ze1n/fL9pY53FdhyqzBajRgNRrIMZQe/n8w6SfMFDXBXeBLqH87KpNrMO+hDrs4PhS0Mf1U9XuzVlj9HbCXfnEtafhYB7j74aXwAKlFONJbOIZ15YeMLLve3DG6JQuLBnyfShZcLBAKBQCC4p7jpbn7qFDz/PKxbd3Xbyy/Drl0u3byswJYibrWbp2QZKvTyoS7mzCvj5pVx/lGrhhFbIkNN0bz5xLYT2ZK0pdxSoQDz98+nc2hnVHIVB1MPcvDCQTgPnAESAQnQEA5FHHKUFbpHcFe5sz/ZRGRwQbV0cxHcIhAIBALBrcBkgjNnoODKN/xnz0JkJKhvVtHE6kNRysPKkllgwt/D7bqkSC6V8N3YGPKN5d8YlKz/WVm0KgV+Hm6M6xTAlpOXK04XGZdMu3AfxnYKw2ix8Ffyby5FuiRz982lfe1O9K33EJfzChn+9fFSAR3R4b7MHd6a4dEhLHFxk1BEm1AfvNVKompqWbI7mdWTOjK0XQjL9qRwMi0fd4WWLqFd6FinI93Du7Pu5Dp2nNvBqaxTpVKaF3FOd66Cq+Mam92GkTSOG9Kg+TU7dUAKkOR4TH16Kl+pv2JwkyF0DenDf1edZmDz2ry//rjLDDzxF3VM++kww6JDeOfBpmhUVn49sZJZu5xLJk1tP511UwYwY/1ZWob60CeqBs99f6DclJpFfLMjiYUj22Ky2qqdqAsEAoFAIPiXYrdfd8nSO+Hm2YVpVSqJWWDW0TDY0ykDSkn8PJR0bRDAH8fTXU6el2RJXAqtQ334dnQ7un+0heExtSvt5l1CuhDp3Yvxi5wDrcd3rkuPyMBizy6LIjffdfY8Mo+z4P8V5/P3gcQGFetotaHAnMexjMOAI2ieK7dTqXYJ4zfW4JkNYXQNb8Ootl3xVDZn5bE1/Jywgo97f0x07Wg+i/vMubRp9BQ6h3Smz9I+ZBdmuzzvlOipLI+7xJaTmcLLBQKBQCAQ3FPcNDfPz4d33oGPPwbzNYIZFwerViHvP+CWu/mfVXTzpXuSKu3lnep04f66g0jPK2TmxuMVunllnX/+fsd8/LDoFszfmoS7QsszbZ+h39J+5bYrYuYfMxnnOQ5+xBHQUhtHiaHBQBnJ+u92/NWR1XrOXAS3CAQCgUBwsykogNOnS4umzebYFhXlSCl+D1GU8rCyaJQyzLaqS5HeZGFzQhrbT2XwWJs65R7rqlxPeUTV1OKlVtCjYSC9ooIZvGB3pcY0f+tZFo9ph8Gip/+KoZVq88nuWfSp15/pPx92ioiPS8zi4Tk7+CG2Pe3q+jF/yxknkR/dMYy2Yb4s35NcvDJ05MI9LBrTjlah3nyzI4ll++KJqRXDwgMLeX/7+5Ua101HiyNTSxPHr1+pvqJzSGe6hnbFbofRHcP583h6uV8UAKw9fIkXe4fx0u/TXa4IPZh6kOG/OOrEftj/Q347nMXlvEI2J6RXapjxF3XkGy3IpJJqmWZRIBAIBALBv4z0dMjOhnr1QF51N7ndbn4+20CHRvoqtc0yZOPhVvZzi6qpJcBTxcAWtRj+VVyl+vxqeyLdGgUyIiaEwdFBlXbzmTtnsvqJ3iRcEzgff1HHc98fZHL3CF7u14i24b58te2si0yDochUp7l/0Zv8k7mBXzZfuRbXF5tUPZHYsUguYuEiv6Xs5LeUz0vt/v3s7zzW+DF+ePQHrHYrmfpMgj2CCfII4u/Ev2n7ZdsyA1tiW0+ka50+DFq1j1yDWXi5QCAQCASCe4qb4uarV8PEiXDxoutG9etTKFOwKT71lrr5Q61qM6SSc+ZFbj40ugY9v/uwUm1m7ZpJr7r9eMnFnPm1bt6+nh9twjX0XVY55/8sbhbrhvzJvC2JxJ3NIzpCWn4AUAFwDDgOh5MOMzl8MjQFBgJulTrlbUMlV2G0GG9qdshsXQ3iDdV3zrx6jUZwU5GUWOVjtVqR3mNfpgoEAkG1JCMDkpMdqy1LIpVCrVp3dWCLrUTq85KfMVq1vEpBJAqZlFy9BbVX5TVEb7Lw3voEluxOIbKGJ7Fd6pV7fMlyPf9ZeYQGQR54uMnJN1o4mZaP9RpDHt0xjI1HL3F/k+AKs8KUJP6iDpvNjtGaV6WI+CxDdnFEvEwqcRrf4/N2sX5KZ9qF+WIwWzCabShkUtwUUpbFpfD22gSAUhlbBs7ewX1N8sFvMa/sXIvVbq3UeG4XOYU5rDm5hjUn1yBDzYCG/Th3vi12IpEgK7Pd8JjarDm5ptxU5+CoE9sltAuNg3qRUVC1VRHZehNK+d37f1Nwd1Dy76bNZhNuLhAIBAJncnMdZU0Bjh+H+vXBzfXsaXVx835NgxnQMqjSbQH83X0J9aNcN487k0HjWl5V8vJsvYnRHcPJKky97tWq17r5//4+Q2QNT3o3DqJThD9mq42LOQbc3WBz8ipe/HsS5woOV+n532sYLAYWH17M4sOL8VB64Kf2Qy6V89+ubzGo0YN80ns2n8Z96JTV5bnoaXSt04dRCw8VZ1wUXi64XQg3FwgEAsHN5Ja4eWYmTJkCS5e6bhAUBG+9hX7YCN77/TRLlh+8JW4+pmMY2O3kF5qr5OYFRgs2qa5ML5dJZEQFRqF106Iz6jicdpgcY06pLDKu3LxxDU96NQ4kQ18157fY89k2vRteGjiWcdT5IDNwAjgEnAZqAs2AhwCPSp2matiAK/oRoAlgfKvx+KiC2H5cyfPdW/LGL0l4qbx4sa8fXRe3c9nFK51fIcIngjGrxzjtkyChtrY2MqmMPGMedX3qYrVZOZN9hlxjbrlDs5tCgOrr5iK45R5GIpHg5uaG0WhEp9Ph5+d3p4ckEAgE9y52O5w/D2lpzvuUSoiIAI3m9o/rJlJwpcSSUqksJelKmbRStejBMVG9LC6FB1vWIthLScJWNgABAABJREFUVanzWqw2Nh9LKy4TVNmsLKsPXuDXZzoS90oPcvVmsvQmfDVK3BRSlu85x5LdyeQazIzrFE63RoG4yWQUmq3k6KuWO7zQbKXAUr4QXkuWIZsgrYrujYIYGh1CoclaPD6NmwyVQka2wUTnGX/xcKtajO1Ul0nL/+FEal6pyPWijC3+PmnM3PUWnx/dVqVx3CmsGPj1xM/Az0hV3nhYuuNu7YnSHlLqOJlUwpDoGpVeefvhzplsHT2A7PyqjcdHo0StKDvARiC4GUgkEpRKJSaTiYKCAry8vO70kAQCgUBQnTAaHaVMiygsdAS4REW5zOBSXdx8c0I6Mx5tSIvgFqWCF8qiZXBLgtx9eaKtR7lurpbLSM6q2qrTzHwTnm5yDNbKTboXUZGba5QON3/h+4NM61OXcT+/z/GCZRjtGVU6j0uMQBaQf+VRgGNi3cLVCW85ILvyrxuguvJvyZ9VFJcRupPkm/LJNzlk/MlVI+h15H68JK34vPdXBLr7k2XIxlftg0auZXncpeKMLUUILxfcLoSbCwQCgeBmctPdfPsfMHas6+8a5HJ47jl4/XUMKg2b4m+NmwPMfKQZner7k19orlT595LkFVowSZ2z9/mofJjYdiKxrWPRm/Vk6DPw1/ijlqvBLiNYq+JiTiHDY0KL3dxgtuKjUaCUS1HKpeQVWrigq5qLn8/NICsXbIrD1Pev69hoB5KBgzgytchwBLRMAKoWI1Q2RetPr1XcEjEjl/WXGdF8BGaLhNSLBmq7R5CUnkFkDQ1BHoEuu5VJZMS2jmXg8oEu99uxc053rvj3EK8Qto7exvLdF4muLyH+8iGOph8hrSCVxJxE9l88TK4pFQClLRyovm4uglvucXx8fEhNTSU9PR2LxYKnpydubm6l/rgKBAKB4AaxWCAxEfJc1KX08IDwcFAoHKWJ7kJsNhsFBQWkXZFpT0/PUvvlMik9GgVVWIt+WHQIbcN8eXttAk+2D6v0+Y0WG/O2XJ3oL5mVZdpPrldJhvlpWDSmHX8dT+ebnUku0oeHsX5yZ85czqNliA+bj6Xx1fZEImt48nyvhpUeG4CXRonN6F2lNv4aH957KJAdpzN4avE+p/GN6xRO98ggQnw1xF/UoZRJXdY9PZuZQZvZI8hkFXZu8furKKjmJiuETZKDTrESnWIlKmtztJYHUdlaI0FKgyAPCsxlR/hfy4HUA6TnZ1HDowbR4b7EJWZV2CaqphZ3Nzkat+on6oJ7D09PTzIzM4v/nrq7u4tVogKBQCAAq9VRwtR6TeY9f39H5scS9xHVzc1NFhv/JOcztf10hv9ScUDyc9FTmfd3CnP+TgTKdvM/jqfz4v1V83IfjRKpRIKXm3eV2lXGzdtHaEk2/ki/H5ZzWe/iS4bKYALOAylX/k0HqhKHI7vysF95WK7ZL8UR4FIy4OXaIBgN4ImjhKjnlYfiup5NhdjsNjae2QBs4IfT7+EuDaGmqiPekmguZ4Zht5eelhZeLrjdCDcXCAQCwY1ys938/VVHeGb9fPjiU9cH3n8/fPYZNGqE3mRBb7KyYOvNd/ONz3XBTSFl28nLPD5/Fza7nTnDWlfYX0nc3eR4yHxKbYvwjWDDsA1sTd7KoBWDnDL7TeswnXcf7ovdpmRvUjabj12kR2NfLBIdybosAj388JV54+6mItDdv0rjqe3lT1SQN92+fZ1v+3xLrWO1uPDnBcgAwoD+QGNuPHLCAkFeQUxsM5E3t7zpHNRSBpn6TMK8IgADWXpHVvKTafm4K7Qug5WiAqPQm/VVnjcf2KIOfT7dSrBXAB5u96M3WXCzQy8fOdmGbI5lxKOw16nWbi6CW+5xvLy8KCwsJCcnh6ysLLKyKv6SRyAQCARVwGZzrLJ0FbhSFNBScgXmXY5KpXKZCUwmhWe6RdAq1JtvdjgHk4zuGEbbMF9GLtxDbR81WnXlFMRitZGtNzmlPFyyO7lUSZ6SeKkVLB4TzbwtZ1zeNMRf1DHtp8OM71SX53rV5911CSWOs+Olqny6yKa1tMikEpRSz6pFxHv6MWPdaZaWMb7nfzjE8OgQFo1px0NzdrrMVKOXxpGl/B9Wbuyz/alWT5FrzCVQE8gXe79wfZAFWA/8g2NSXH3l4Qn4AL5XHkE4Jsqvk0LZIQplh1DY6uBpeRC18iEy9VV7fmn5mVzMUvP6A5H0n72jwuPHdAwnM9+Iv0c1WOoquOfx8/OjoKCAwsJCLpZVK1kgEAgE/z6MRkfAfEnkctDpHI8yqC5u/tovx1g3ZQCxrScwv5xSkrGtJ9KlTh8GrdpXvK0sN5dJJbz2QOMqpXF3U0hRymUobTfPzY9ezGbcz99j1HxPgeU6MrVkAcdxpDg/By7j0RWAH+APz/R9hixlFoGBgXwW/xm44whYUeI8MW6D2T1mcyTpCH/E/8Gzkc8yZdkUxzmzgFTAUIkxuuNw+ZJe7wcEcFOzwRTYUjilTwGWI3VzR2Vtg7u1IypbK6SohJcLbjvCzQUCgUBwM7lRN5/68Vq+W/4O7okuSuZ4ezuCWkaMAIkEi9XGnsRM6vi433Q3f2ddAr+/EMBHG0+wJC7lynOQoFZUnEm95POSSMBb7VPs5T4qHzYO38iMHTNYsH+BU5uDqQcZtnKoIyik6/u0CtXwZ/LfDPh+llMQzAsx03gociADGwxk9cnVFY6nZXBLNHIvdu/ZyYUlF2g9pTVmmRlaAkNwuO/1cmVB6PjW41FIFczZN4cgjyCeaPKEI7ilkgS4B+Cl9iDH4MggCY5FvsvjLjGl3VRGrx5e6nitm5YMfdXuT9LyM7EUqhjSLoSlcSkMj6nNkOgaFJh1ZOqz8NPUQiPvwIo9l2gY5FVt3VwEt9zjSKVSgoODcXd3Jy8vj4KCAqzXrgQSCAQCwfWRne3I2HJtYItEAqGh4O5+Z8Z1C1AqlXh6euLn54dM5hytq5BJOXwhm7OXC1jwZBsKTVay9SZ8rqQzXBaXwttrE8g1mPliSEuUssqthrLYbKTrjE7bcw3m4pI8194YDI8JZU9SVrnR8ABKhZRNx9JKHXc8NR+zzc7ojuFM/bHiqOcpPRqgKzSzZPdFl5LpiukdpnMx2+Jy8rwkS+JSaBniw+C2dUplqrFhJFvxNfny9RWeqyRyiRyL/drlnfBk8yd5d9u7vNv9Xcw2M/OuvenJB77HMRkPjtTpRiAHuFTiOC9gMDcU3FKEWXqOLOUXrL+8guaJz1Wpra/ahy+2p/DBw80Y1i6k3Os8PDqUrg0C0LjJkFfyPSkQ3AgymYz/Z++8w6Oq9jX8Ts1MyqSRQktCJwZpAUJLEBSkBLAXggJRCVEERIrlePQcC82GojSlSRE7oAhioUMITVpogRBKEiBtksxMpt4/hoQMUzKx4j3rfR6ee9l77bXXXsNz/Pba3/p+UVFRFBYWUlZWhtFo/LuHJBAIBIK/mytX4Nw5x2MqFcTGur3kZtLmMqkEf5WcdzedY1a/WSRF9WLWrhlOC9ATEibTq3F/Ri761WWk+Y3a3GK1sXz3uTrFuO87V0yXJiF/mDbXSw9QrPgIk/Scc0qKJyqAQ8Cv2A0mNyIDmgAtgCjsBvFrP8HDox7m9W2v8+qAV6ncWemszWuQ1jmN/m3689Kul9D6abnr7rtYXLn4+tzbgFLgIpBz7c8VN+Ot4Lrer0KC3fASYf+jiPEjoLmKInNhLRNQO1ZJBTr5FnTyLUhsPrQMTMRP8wJNwxKFLhf8ZQhtLhAIBII/gj9Km8eePUPr88edb5CcDPPnQ4MG1YeMFivfHcrngc6NHZr+Edp8eNdotpy4Um1sgd+uzZNahlbr8vTO6WzJ2eLS2FKTuXvnkhSVhFLm41LPH8w/yKPfpJB+IZ2Phy6i5fstKDY4lz+qIkgZxCM+jzKkf392bt8JkcCd2MsP/VaXhI3r6eYSSItPY2bfmQxeNRiAo5eP4qvwrZPhPlQdyqbsTfwrOQml7LqRaPnuC6wZO4DRHcewYL/93UAmkRHpH0mEX91qJ9nXzc/z36FteKBzOJtzv2fw6hSX5qE+sUOR43NTanNhbvkfQCKRoNFo0GjsX5tsNhs2m62WqwQCgUDgFqsVpk+H115zPhcRAZ9+CgkJf/24/iQkEkmt5ezkMim9Woaz43QhiTN+plVkAP4+csorzZzIL8N67T87w7tGc3tsuNeiqNxgIVDtOic7p1DH3R/u5Ok+zVk0sjM6o5nCciOt62t4YN4uj/3KpBKGJUTxxNK9DserhHpKQrRXcZEJTUK4WKp3KTJdkRafzoAWyby1Icfj+KpYtOMsCx7tRPJ72/jmqR70bVvJ0hPPYJJ6NsbUpEFAAyZ3m8zgVoPpv6I/p4tOO5wP9Q3lYtlF+q/oz77R++gZlcSbu2baRe1F7MaW2gz50cAD2Hd9uqFXVC8O5B9Ca3T/snEjlbYrvLLlRZQyJUZL7QuNdhe+hvWHjzCxb0vG39GCvnHhfLHvIt8fycdy7R9iXAMNo5Oa0qtlGEq5FF+lkMSCvw6ZTEZ4eDjh4eFClwsEAsH/OidOwJAhYDBcP+bvD9u2QSvXJXluFm0eqFY47fQrKC8mueVghrROplhfQpG+mFDfEKQ2P5bvusTQNXtdGlvcafNPdp/jx4m9SEmw7yx0R1WM+8ViPaV60+/W5ibJJYoVC9HLMr2al2qygT3AKVwmtPg28eWhEQ/x9IinuX/d/U66HBy1+d4nbtDm12gf2Z7xCeNJjEqk/4r+1Yv58/fNZ1yXcaSuTbU3lABB1/7EXbu4DDgDncs6c3TnUXQ6nfvnsXE9BSYLTFRQRAWqVioMvQxQnz+kZKlNUskJ7Y+syBxAUnTi7+9QIKgDQpsLBAKB4PfwR2rz5g8MxpQUgk/6GPsBpRLefhuefNK+mbYGWr2ZI5dKGePbDHCvzYe0GsKQ1oMp1hd7pc3d6XL4bdr8SpmR26IGMCb+SdLi0xiyakhtUwrAzJ0z+eahb5BKpFhtrqIPr5lgopP4eMjH3PPZPc4NTBB2OoyAfQFMPPMMtAZGYV/H/g3IpXIGtRjET2d+otxUDjjq8o/2f8TIdiPZem4rFpvFWZt74Jmuz2CxWXhsbSrv3DmHaHWf6o2u5ZVmXl13mtfunsFdre8hu/gEQ1oNQWfSEeYXVicDja9cw47TJ1DIjczY9aJLI321eeh8OjP7zeJmtJLcfCMS/Ol48z+2AoFAIPDAr7/Cyy87J7Z07gxffw0NG/494/qb8VXKeWFgLAlNQpm3JZvMnOsmhrgGGsb0asbtseFeGwnMFitXKyoJUCmcIg/tgj2aYQlRGIwWcot0RGpUNA/3p9xgcopjvJGWEf4YjBaX7T7adpZ7OjRkwh0tPMZFJrUIwwb4yGWU6k2MXPQrS1Jfp3vjRN7NmOXWEV9plJN5zrtSO0cvaTFbrEQGqhi44E1ybLMwSSu8uraKS2WXeOaHZzheeJwNKRvovLBz9QJ4h8gOqOVqjlw+QruIdlgsMKDZ3QxpNZh5H3/Ev6Y9h7HSOTmnJsNTU1Ely/jo0EK3bcbEpzPjjpkMfHcXpyozqZD9hE6+Axue+67CG2MLwPiESazKyMNqs+9gMFmthPqpeHFQLNPvaUuZwYTFZkMll+GrlHHfvJ2k39acAW0ib0oXuuD/P0KXCwQCwf8wlZUwbBiUljoeX7nSY2qLt/yZ2rxvbDgvJjdzu9NvSvepDG41hBBVBHqjhS7TfqpetHeFO21eqjeRuiSTlU8k0Ck6mI+2n3Ub4/7VvgukJjalqMJYJ21uqKHNbZjRyr+mVL4Km8TLBAcT9oSWDFynogA0A24DXWMdi1iE4ojCSZeDszaXSKT4mLuzdeQ2LmkLKdIXU88vhAj/IKZvn87EjRMdrp+bOZfMJzIZHT/a/Y7YAEh/LJ2ZfWcyasFetvy0ifIjP6LP3gtuPhzciOGEwV5mqQkwAAj36jKPKKVKDpxqwk9Zl4U2F/xtCG0uEAgEgj8Lb7W5j7INnDpp/8bw+ecQH+/Ul9lipURv5GRBOSqlzEmbnys5R3rndNLi07hUfoFyYzkNAxrSIqQZZouMXrO2UKxzNpyD5zXz36rNRy7ax4on3sBkK+bXgtqT0gEO5B9AZ9LRJrwNhwoOuW03c8dMtozcyqf3fMr0ndPtml8P7AHFXgVXLFe40vEKDMaeOv4bCFAG8FDcQ4ztMpZcbS4fD1nEyau5hPoGExkQwk9nfqTzws4ADlrcK20OpHdK5+7Yu1l6cCkAszPeZN1DA2kWFsC84R1o00hNhUlLrvYMt4bHktAoni+OfcHlisukd0pncvfJpHyVUutzVK2bD0toxLqT6zwmRALM3TeXntFJ3Bd7L0q5683HfxfC3CIQCAQCQV3p0AHefBMmTrx+7NFH7fGAKtXfN66bAF+lnAFtIrk9Nhyt3ozWYEKjUqBRy1HKpF4vUuqMZvacLSQ61M+hJA9ATKgvS1O7sOdsEaOX7XUQ0Q92bsSo7k1q7d/fR06RzvWCdaneRMpHGXzyWBdaRvizaERnyivN1XGRfj5yCssNSIC7P9jBsscTqs03Q+fsJSWhPese/JkKk5YifTEh6mB85RpWZeQxNeMEH43oxIn8Mq/mAUBXaaZB4+/YcHim19e4Yv6++XRt1JUxncYwbfs0AMYljGP+vvlYbVbGJ0xi4dYLlJQbKd/xCe+/+7bnDqVyQvqO4Xzre1iS1I6eTW5z+eFgcrepJLdMJv2TI1woMaKmHWprO6ymMagD9+AfspX9+Xt+17OBfedtrxp1Yn3kUh5fupesPPtcxzXQkJbUlNtjI0j5aDfDu0ZzW6tw5m3JrtOOZYFAIBAIBII/hOefh4MHHY89+STc42LX4W/kz9DmX+2/wPT7W/PCT1NcpqIczD/IsK8eJi0+nVeSpqHVSTwaW+C6NpdJJbSM8K/eyXqyoJzDF0sZtnA3S0Z1oXfrcIp1RgrLr8e4/5hVwLFLpaR0jeae36DNF17T5pWSExQq38ckzfFuck1AJrATexlPV4QDA4EYx8OudDk4a/Ndp7T0aRXJuasGpn6ZS2z9AKSSYib3D+KH7B+c4teLDcX0X9GfDSkb6NaoG7MzZjtp86k9pjKoRTITP80i80IFvq2649uqO5aKYpRndyA5+TPZp056NwdngblAZ6A3oL5+akjLoTQJbMWCzDXoOVFrV3H1evJQk1ihzQUCgUAgEPy/xWttPnOmfWOtxrn2e01dbrHanLR585DmZD6RydZzWxn66VDnddruU/j6qTsZuehXcgqdE/w86XKL1fabtHmbhoEs3XmRofEyZBIZFpvFq/kq1BWi8XGeg5ocyD9Asb6EAHryRfI3zHn/XT6a+xHl0nJMPU3QEfidvowyYxkLDyxEKpXyfM/nUcqUzN6oJ8TXgo+8iNfuvpPZ/Wfz9u63nbR4bdq8KvUl5csU7mp9F5lPZNJ/RX8qLRWoZH4U27Zw12dvYbKa0Pho0FZqUUgVTOw2kXti7+Hp75/m1d6v1mqgqVo3v2ftPtaO60Typ8O8evY3d85kSKvBKH/vJP7BCHOLQCAQCAS/hQkT4MABWLEC3noLxo93igf8X0V+TYz7KuVEBtbd7KMzmnljfRar9pxn65Te7M0p4oFOjRjWJYrvDuexLDWBeVuyXZYMKjOY0bgpY1ST8kozIb5Kt+dzCnUkv7+DlIQoUrpGo5RLCfP3QS6ToJBJMFtt3PHOVkr1JgfzTanexIebzzJvy9kaEZPa6ojJ9x5qj9FsrXWBvwobFl7a9hRfHl/h3QW18F7Ge3zz0DfM2DGDJzo+QWJUIhM3TqwWuIM+/YUzn72B4ex+j/1I/YIIu+sFVI1uIadQ5/ThoMRQQsPAemh8AtmfU8ag2Xs4X6x37ANfKktv462Bz6AOyGbOntmsPfm126hJd7SLaMczXac41ImNa6BBIZM6mIiOXtIy7tODDE+IYvljXXnx60NMGWBfQNfqzaI0kUAgEAgEgr+OjRvhnXccj91yi91A/wfzR2rzHVP7EOov54fsbz2W+wGYv28uPRonktzibq/uEx2qYsuUbtUx6qG+IfjKNXy6J4/luy/Q560tDE+I4tHuMSgDpRjNVkwWK4PbNuBCsY6+v1GbG0yVFMqWoJV/CZLaRbrEIsGWYfNsapEDfYAEQOa6SU1dbrVZSYtPc9LmQ+fs5Y2729CndT0WjriVMmMphboiyk2l/PDID7y16y3mZs51MLmcLjpN54WdGdNpDGseWoPVZqVEX0Y9vxACfQLZl1PGwHedtbnMLxhLm2Tee/VFjOf28f7sd9m6eXPtP5wNeymmw0A/aNffUZuH65NoHmmif6c8Xv7hE/TSA9gkBqduQiQ9SekaLbS5QCAQCASC/9fIZVLkFjO+Jw4R2aWL60YymVtjS80187gGGmxYq7V5sCqYjcM3MmPHDJdGh4P5B0n5ahhp8eksSX2NoXNclwytTZcfvqh10OYBoQqultvTuau0+b1zdzKkfSQ+ChOTBoSRX16IRhVK9rhsFuxf4KRhXRHqG4q20nM6O0BOXg5bV33Pwx/MQeurhb7YS3LW0Sstl8oxW81uz1cZ1Ie0GsJ7D8VRbCimUFfE5YpChrQawsAWAynUF3JVd5U7m93JvbH3ckV3hSJdEUNaDeG+W+6joLyA/PJ8Qn1DUcvVzN83vzqNce3JtaTFp/HD8B8IVvvz3y3/Ra1Q8dWDX6Ez6biqu0o933qo5WoW7F/A3kt7ebX3qzz4xYOsvm+1WwNNVWLlyEW/EhHog8FSVqcEnRJ9Mf5Kv7pN5p+MeFMQCAQCgeC3IJHYk1pGj4aePf/u0fy/wWyx8mNWAct3240rKzNyub9TY0Ys2sPS1C481KURe3KKXBpbAArLK/H3kTuVMbqRqthGT+3si+HZzNuSzcBb6/PfoXG8vPYIzw24hee/OlIt/pfvPsfasT0Y1iWqelxWG9WJIVWkJETRp3U4ZZXmWscHYKWSSs1bfHl8p8d29f3rk1ee57FNFQfyD2AwG/jqga9oE96GiRsn8s6dc+jVuD/3T1vDyUUvYi723Jcysjlhd7+IXBNWfezGDwcvDrqFMHUQr32bxScearAOT4ime/N6pC8/x685o+gZdh8XzZ9z1rAOq811POaNGC1m9mUreLtGndhRPWJYmZHr0kS0PCOX+Ohg/j04Dq3eRKvIALQG02/62CMQCAQCgUBQZ0pL4fHHHY/5+MCqVaBWu77mb+JGbf7L8QIGtK1H76UzvLr+nd2zuDt2CLc21HD4omvtGxPqy6wHWvNTzhpm7pzpvJuxyyTWjB3AyEW/8sHmbOb+gdq8Qb2r3P7JQLQK91HnNZGf9se8rhxKPTSKAO4Dwjy04XrU+n2x9zGgxYBqY0uVNh+56FeCfRW0i1LzzYkvXc7NxK4Tq3d3ni46XX2u2FDMtO3TKNaX8q+eb2DQ+xNWzztt3rNlOE9lhrPi66/oMysB+U45WZuzap8cPbAGgspCyFKHOWjzMYnxnLlSQZgxChsmDNJf0cl2oZPtxiopRYqUD+8ZjdFkEdpcIBAIBALB/290Orj3XvjlF/j+e+jd26vLXK2Zp/ZoQkIzP5I/tWvz9M7pbMnZ4jHBA+wm9G6NepKS0J4PN5+tPl4XXZ5TqOODzdlsPXWFJaO6sGjHGY7nlzN3eDzv/3yaTx5vz+bc7+m73LmE6bgu41xq2JrULNfplkpgJwx4sz+6sAoYAjT3+Oge+fnRnxn7/ViPZZDey3iPu1vfzeSfn2HRwUXVx6u0eVJ0Eu9mvMuxK8c4duUYt4TdQofIDszqO4svjn/BOxnvVCewHLl8xGmT5/x983mi4xMcuXyEpzo/6TaBZ1yXcSRFJ3Ew/yD9mvVzMLfrTDoKdYU01DTETx7M4u0XGXpNm3eOCUZbWVKneSkylNAosFGdrvmzERmPAoFAIBC4w2CAn392f16tFsaWPxijxcq8zWeq/7589zm6NQ2lR/N63P3BThoG+bJo+1m312sNZirNFkZ2j/F4H4vVxsqMXB5PbFrrmKw26HdLBAaTlef6xyIFh7qjpXoTIxbtIa1XU968vy1xDRyd9XENNMx+qD0vDIzlu0N5LN+dy2M9PZdOslLOZeVLXDZ5Nra00vRh1b2ra32GmhgtRro27I5KruKDAUsoyG9P76fnsO/9p2o1ttx134OMe3ulg7HFYdw2iI8Kpn2jID7edpbHk9zPyTsPtOO5ga0ZsWgPGWeLsNrg3OUAzEWp1NfPx998J1J3W11rkHX1KO8ffogc0wKsVJKSEEXnmBBWeFi4/2j7WWRSCTKpBH8fORrVzRWtKBAIBAKB4P8xU6bAhQuOx2bNgrZt/57xeOBGbb4vt5gKU2mdd/o93aeFy/OBagVLH2vHtB0vMuyrYQ6LtmDfXTpq7XCm7/oXS1LbEahW/CHa/N0H2xES8TPdFydw5ErtxhaFsSmy1aGYl3s2tjz82DB4nFqNLVWYLCYWDV1En5i+Dtp86Jy9FOtMLH2sHa9ue8Ht3Dz6zaO8testfnzkR4JVwdXn2ke2Z8nQFUzt9iqf7cmnTYO6afMSvYkKk5YzPmfY8M0GWr7YElp590xbfv6FmU8M4fz2r7DZrE7aXIICtbUToaanaWRYRkTlNFqrxxLuHya0uUAgEAgEgv/f6PUweDBs2ACVlTBkCGRmenWpqzXzni3qYbTaUzhkEhlp8WnMzpjtVX+zM95kWNf6SK8F0ddJlz/Wjs4xwXSOCebZvi2pNFtJv605Cx+Nx2az8dLgZkzb+SKj1g532U/q2lRm7ZzFhpQNDhq2JhO6Tqgu1+mEGcgAZgOXQPdwBYzgdxlbAKQSqVdlkLSVWrRGR+N+lTafsWMG02+fzvnS85itZg4VHCLrShZqhZp3M97lUMEhtudu51DBIZfPJpPIqO9fn3aR7Zi5cyapa1M9zmGnBp14svOTlFaWMm37NJrMbsKDXzzIcz89x9RNU6k0W5m35Wy16TypRRhhfiF1mpcQVVCd2v8ViOQWgUAgEAhccekS3H037N8PP/0ESUl/94j+J9DqzZwoKCO2fkB1Tc+RS/bw9ZM96N0qHK3B5LB4fSPZVyqw2SCpZZjDbk1XVJos3BEbXmu7lIQo2jUOYsDsbbSM8Of5AbFObXIKdQyZYy9jtODRThiMFop19nqjYRof1v16iXc3neSR7jHMWbibr57sbi+P48KAYaWCAp+XMEpPeZyryV1fYkzHySiUnraOOhPoE8TyXYW0bRTEgXP5vDFtGqXbVmDPFHeDREpQr5EUdU5hbL9bSGgZyeIdOQ7pM3ENNKT2aELfWyLYfeYqM384yfxtZ13OiY9CysqMXKTSAhJb1OPwRcdnkNvCiddM5ZV7XuP5X55m98Xdnh9KYkWr+BL/oIN0j13GiEV7XMZqVnH0kpZinZGIABWRgSo0aiGJBQKBQCAQ/AUUF8OaNY7H+vaFsWP/nvHUwo3aXCqBIr3n+PAbKTaUEB/dwqXmHt61Eb+c+96rEkdVu0tL9Zbfpc19fCq5Z9VwMgs21Dp2qc2PgNJ7Kfv4O6xlhW7b+fj48O6cuQy6vy+r3l1Za79VBKmCmf3jOQ7kljA6qSkHcwuqd88+1buJV3Mzd+9ckqKTOP7UKbKLzhOiDsZXruGnrCIOndfzWGLTOmvzzJwiCnVFxIXHoTPpOKk4CQ8DF4BNwDnPz2UzGSj+cT71i34lOWWJW20uQYbKeisVRbcKbS4QCAQCgeD/N5WVcM89jhtpy8vhvvvg1ClQKj1ertWbOZanRSmXckdsOKF+StYfukinFjqAat1WFxN6hUlLq8gAsvLK6qTLb4tO4v2UOzivvUikvwmrzcAX+/IoN1hJ69WEH3O86cde4mdMpzFM2z7N4Vx6p3QGthjIhA0THC+yAkeBn7GbyVOAhl49rld4WwapxFDC5YrLLs+50uaBPsEU6Uu8+m3iwuOQSqX8dOonLxJ47HN4Z7M7aRPeptowUzN55rXeM2kVGYBUImFUjxg6x4Qgw0T7yPZOphlXdIjsQJA6qNZ2fzXibUEgEAgEghvZvdsuNvOupVjcd5/dRR0d/feO638ACTa2TumNwWihSGck5Npiq81mI6aeLwaTC7c2dnf58K7RDEuIwmC2YjBaeH5ga7o2DWH+1jMOJoxbG2oYd3sLbm0YyBvfZTHmtmZ0jA5yadaoEn1VC7LllWYC1a53EtYsY9Qq8ro5Z9GIzqzYfY5jeWW0rq/hg5QOyCRSJt3Zii5NHMdnRUeZ/38xWtwbWxRSFQ2tkxnR5lk+2ZXDxDub1EmQBqqCmLv5V5oFy1HsmE/ptq89XuOvCSR22EtcDmzNuSI9Q+bs4Jm+LVjxeAI6o4X8UgOBagU+Cimf7sml3BjK25tOeZyTE/llWG32OV7waCfmbcl2KCFUtVvgjR0v1m5sqcGlimwGr+5FkGkEAQxF4iGksLDcSICPgvs6NkIpE2GGAoFAIBAI/gKCg+HoUXjmGfjkE/D3h4UL7SVPb0Ju1OaRGhUSWVGd+ghSBWGz2XhhYGs6x4Tw0Xa79pVJJTycUJ/kT4d51c/sjDf56ZEtaPXS36zN83WnyZO/TnbxyVrv52tORFN0DyWrpns0tvgH16P+fS+ROOAeglSyOuvyRdt/xWwFnfEUCx7txNwt2UgkdZubmTtmktxyMC2CY7lSVonRJuWO2Pp8uieXTk1C6qzNN5+4TKhvCBofDVd1V6/fqBEwEvtHhR+AWtb+j+3fTWJCJ4LvHItfa8+Jp0KbCwQCgUAg+H+LyQQPPGBPbKlJUBCsXl2rsQWgotLI0tR4Okb7U2IooVBfRKjah0BVGC8kvsCBvAOOus0LdGYttzYM5GRBed20586ZdI/qTtePO2K1WatLFj3cZCABahvvZszyqp/3Mt7jm4e+YcaOGdX9TOk+lcEtk5FIJDwY9yDz9l0zyeQAGwENcD/QoE6PWitelUG6hp/SjxJDidvzN2pzi1xOidG7DQLBqmBUcpXXCTxVZZKC3KSrVFrKWJbaBR+5jHlbsnn12yyGd23E+C6TGLV2eK39T+o+BaXUx6ux/JUIc4tAIBAIBDVZvBjGjAGj8fqxK1fgkUdgy5abduH5/wM6o5ndZ4tYcIMZJbltfZ4f2BpfpRyTxdncEhPqy9LULuw5W8ToZXurrw1UKxxMGIUVlTQK9kUhk1JUbuRCsZ7HE5uikEkJ8/dh4YhO6CstVBjNhPjZXypWZuTy6rdZ1TsNTxaUo1LKiGugcRhjTaw2yMorA+wLxP4qefU/m0U7zvLJY12Y9PlBpt1zK+EaFcsfS6DSbOGitpj07+/jfN5Rt3MktfkRon8Zm/UWfH1k7D5bzP5zYUzqNoXhX9f+AjKp2xT255ShL8rnl49ew3Qlx2P7Zi1a8flXX9EwuilGs5WKSjN+Pnb5uHDrGfbk2D9uVC2Kt4oM4O4OjZzSdWrOSU2OXtJiMFqqdwlU4e1uAVfYMFOs+Bi9dD+hxmeQ4zpqMdhXSYneSELTEORiAV0gEAgEAsFfRWgoLFsGDz5oT3K5SQ30rrS5TCph1ws96mTgwOpHl+k/0TkmhBcGtmb16G5cLjNgtlpBqq1biaPKEqZ8fp6cQvsO1bpo8wrZNoqV72FB7/E+9dRhzLpjDtG2Noy8dxCG4gK3beVB9dE88CrGoEh8fWScyNPXWZebr73e1NTFABWmus3NlYoiPvixkHOFut+tzSUS8FNoaODfgHq+9RwbSYA2QEtgB7AdsLgfm8VQztU10zGc7Ufw7U8gVapdthPaXCAQCAQCwf9LzGZISYG1ax2PBwba0+I7dvSqm4ggGbvzf2HCkpkOOrx9ZHsmJExgTPwY9GbPOhfsZW/iwuPQ+GiI9A/nlcGhjL+9JVZJYZ20p86kq04LqSpZ9N/bXmNUhxF16keChIOjj6GUqdD4BOKvVPPfdVlMurM1b/SZSZxPHP96/l+UlpfCEKC+V11XE+EXQUGFez1fhccySDXoENkBuVTu0QRzozYPC/BhfL96btvXJMwvjLLKsjrNobZSS7hfuMvz/j7++PvIyNca+XBzNgDLd19gzdgBjO44xuPae3qndIa0GoxSfvOVDBXmFoFAIBAIwC40J02C2S5csU2bwocfCmPLn4jOaOaN9Vks3+1cpqdAa+BSiYHnvjjEmrE9HRavA9UKlqUmMG9LtlPMeanexCtrj/Hfdcd4ZXAc98Q34qdjBSzYdsYpoWVk9xhi6vkx/fss3rinLa+sPcr3R/Ic0kQALFYbKzNyGdk9hslfHKI2RvWIobjCiK/SLrmOXtJSabLynyFtuFRiIDrUF6lUgs6kY/wPD5GZt8ttX3JCCav8D0pbDHENNPgq5ZzIL+NfXx/ju/GDSYsfw/x97gXpmPh0klsm0+Pp+eSv+C9Wveetlrd07cPXq1eyICOPr1bYP0gsGdWZSZ8fcjk3AP4+cop0RucTHijWGfH3uS5J67qL1x0G2QHyVOMIMz6HytoGmVRCywh//H3kRGhU+CikyKUSVAohhwUCgUAgEPwNDBr0d4/ALe60ucVq48ejRUzuPoWUr2rXauMTJrEqw64bM84WMfSDnQxPiOLp21vgI5dwtqRuKTD5ZYVEh/oCdnP1yYLyWrW5DSul8hWUKlbX2v8dTfrz4cCFVFyt5N7kO8k9l+O2rSqyGfXufQWZf3C1Nh/52WHWjfNelw+avcfheE1dXKir+9ycK9SRmXN9V+hv1ea+SjmrMvIY2Hwovgpf12YmJdAbu9FlDfaSRR4oP/QDlRePE3b3C6jCGlfr8vJKM3KpRGhzgUAgEAgE//+w2ezlRz//3PG4vz9s3Oi1saXcWMGkHya51JcH8w8ycs1I0julM/2O6W5N6MGqYNI7p5MWn4bOpKNYX4zZaqLcVEGwr5rskrqVHi3UFaLx0Tgc++HMBga1HFCnfkoMpWScUvPl/nOcyC9j+9Q+PNqtCUUl5SxfOIdZq6dhTDRC4zp1C4Cfwo9to7bx5q43PZb4cVsGyQXjE8Yzb++8Wk0wNbW5TCrhhUFNvdogcKXiCsWGuv0W7sokVRlxzLZKQv18SG5bnwKtgfJKM48tPsTHo16ne+NE3s2Y5WSYmtp9KoNaJuOv9KvTWP4qxBuDQCAQCASFhfZowJo1L6u44w57PGCI6/QHwe/HbLHyY1aBS2ML2BeuQ3yVZF+tYPGOszzWswkTP7O7l4d3jSbjbKGTsaUmASoFt7UKZ9r6LFZkOLc7eknL5C8OkZIQxStD2lBuMPHd4Ty3/S3ffY61Y3swrEuUx/umJETROSYEi9VGeaW5+ni+1sAb67OqF55j6/tSop7O7rxtbvsKUzfmq/s38K8vCsgp1JGW1JT1h+0fCs4X60n/5AjzHp1FUnQvZu2c4SRIJ3ebyqAWg+gz6mWOrH4XahHg456dSl6zwTSICOWbA3ux2mDArfUprDB6nJuq36ouBPsqHeanZYR/nXaqesIqKaHA5wWSo6cwd+hLVJqsFOmM1A9U4auUo1aIXaECgUAgEAgENalNm399II+PRw5mdPwYFngwcKTFp9OrcX+GrtnrcHzdoTyevbMVy3edo2/bwDqNrVFgPcbeLuVSWQGhviH4yjV8feAyXZuGuNTmNoxcVbyDTu5eZwPI8CHQ+Bgnjw3gqUuHOL14Emezs922b3lrPF+sWcvTX5xw0Obniuy6fO4js0iK6sWsXa51eXLLZNI/OcL5YsfdtTV1cahv3d4/Q9TBlFc6mtd/jza37+jsz75LWxnXZRypa1NdXxAGpAJ7QLlZidHg3kxjKsylcOWzzJn/Eb37JVWXoQ3xV5J9uZxbGmjcXisQCAQCgUDwj+ONN2D+fMdjvr6wfj0kJHjVhdFsYs3xtR6N0wBz984ltUMqE7tO5NFvHnU41zykORtSNrD13FaGfjqUwwWHiQuPI1gVTJcGXbg39l6ig2KQSWRYbB4i+WoQ6huK9gbtqa3U4q/09+r6KoJUQezPLahOFbxYrGPctHns+fktrF2L4XfsfawwVTBw5UA2pGygW6NuzM6Y7azNu09lUIsBlBpKuT/ufo8mmLT4NHpG9eSZjc/Ueu+a2txitbEqI48JCZMZuSbF43XFhmK3JYbc4a5M0riEcSw+sJgnOz+FSi5h8sAwLpZerX6PWn/oKnENbmfryCGUGEoo0hcTog4mSBVEcbkNmURVp3H8lQhzi0AgEAj+tzl8GIYOhbNnnc898wzMnAly8Z/LPxOjxcq8zWecjlclbWhUcvx85MQ10PDR9rPVxpLVe88zLCGKJ5buddHrdaoMMK6MLTVZkZFLx6hgOsUEe2xXqjcxYtEelqZ2oWN0EIt35DglwYzqEUPnmBBe+/YY/72rDSfyr8d+a1SK6kVrGzb2lMykqOQHt/eTWcNQFr/C9wctLE3twie7znF7bASjFu9BJpVgsdrYkV3I4PcyWTfubga3THYQpIGqIHafvEqrpAco2LvB7X0AfFRq3vlwAauvNmRC1xj25hRhttp/i0G31ud8kc7j9d5Ew9ckroEGH4XUYX78feRe71RtEtSE2HqxrD+93kMrK9+em86+uXuQa9ORoq6+d1pSU+64JaI6WUcgEAgEAoHgf53atLlUApUmBS/1fIMejRJ5x8VOvwkJk+nVuD8jF/1aXd6ziuFdo/kp6zLv/HSKu+K71anEkUQCzeY0rt4p2T6yPeO7TAJJOE/1buagzS2UckX5GpWyLI/9Rgc2o5H1RS5cDsdmNrFz/stU5Li/Rlm/Jfrbp/LpwUKX2nxHdiGDZu/htbt6sW3UYIr1xQ66fH9OGYNm73EyttTUxXENNIT7hdRpbnzlGgdNDb9Pm1ttMHLRr6wc3Rulwszo+NHuF/ulkPZUGsOnD2f86PHs37Pf7T0qdRU88cjDaLreR1DiI0ik9vE9kdhUmFsEAoFAIBD8/2HJEvjXvxyP+fjYyxMlJnrdjdFqZNbOGV61nfTDJNY9vM5BtwWrgtk4fCMzdszg86Ofk945nXUPr0MulaOSqyirLKPYUIzZZiJnQg5z985lbuZcj+khHSI7oJarnUrzHL18FJVcVScNG6QK4oej9g2O5tLLjB13F3t8tsMjXj1yNa0DezPvrv/w6ZGVzKthBDpddJrOCzszptMY1jy0BpPFhN6kJ1AVRKAqiBN5eq5UaBmw8naPJpipPabSrVE37vjkjlqTVVxp8+W7L7Dm6f6MiR/jML4b6dG4B0GqoDrNoasySWnxafSJ6YPZZmZj9gZm7XR+Z5vSfQqxDQbx7Ooszhfrr6UqFiOXSnisZ1P6xbkuJ3ozIFbyBQKBQPC/y5dfwogRUFHheNzHx+6qHjHi7xnX/xhavdmhDnygWsHwrtEMS4jCYLRQpDMik8LopKaM//RgtbGkd+swjGaLUw35msikEq8MMFUs2nGWO+MikUpwWXanipxCHUPm7CAlIYrVo7txpdxAYbmRYF8lPgopKzNyefXbLF4ZfAtKmZQAlYJSvYm4BhpUNcwcpfKVlOLecCKzhRJhfAO5LYIVGbl0iQnh2b4tuVRq4O0H2yOXSvlk9zmW7z5HTqGOr/ZdIlCt5MesAmLrazCYyvk+I5Pt816g8tJxj8/eOCqae5+bzbxcNclt63F7bDgDZ9t3ubaM8MdgshCo9lxj87eUbVqZkesw1+WVZq92qgargvnx0R+Zvn16rW0B8kw/o/A5Q7jx38ht4Ry9pGXcpwcZ3jWaFwa2FgYXgUAgEAgEfzzFxfDf/9oXtkND/+7ReIU32hxsHDqvJz+/Pesf+gUz5ZQYSlBKA/CVa1iVkcfQNXudjC01tXn1DsYukxi5dnit4xqXMI4PMz90iAA/mH+QUWuHkxafzvPdX+NSiZ7Vo7uRceEwI9aMpbI8x2OfEYqerH9oNcmzD2CzWbn67VvoctxrWGVkcyIe+C9SH99atfmIxft5dcgttKqv4btDxSjlWg5fzKVUb+ZSqcGp77SkplitNjY90wsfhZRfz2u9npua5Z9q8nu1eU6hjvGrjvLh8NbM6juL26JvY+bOmU4L4+MTxtMruhdSiZSOL3Rk/7v7YQvg4X1Ku/sLjAVnCBs6laOXYMJqocsFAoFAIBD8P2HjRnjiCcdjEgksXw63316nror1xV6nW285t4UKYwVv9n2zWrcNbDGQLTlb+Pnsz2Q+kcnB/INYrBY2ZW/ivT3vOem6cV3GkflEJv1X9Od00WmX9xmXMI75++Y7leax2CzM3zefyd0nk/KV54QSgEndpmCzKfCVS8nd8wla5Wdc7GABmVePC4Da0pVA00Po9c0x6Vowq9+bJEX3YmaNZPNiQzHfn/6eNmFtSW45mMU7LlBusGvzMoOF+Y+2wV/p72CC0Zl0FOoKCfUNRS1Xk3U1i+25293OSU1cafNSvYmRH//KZ+nTSXST8Dg+YTyJUYks3LeQ8QnjGbVmVK33mtB1gkOZpJra3IaNGTtmuDSoH8w/yLCvhpEWn84Lg15j6BzHd7ebXZvffCMSCAQCgeDPxmqFV16BV191PtegAXz9NXTp8pcP63+VUv316OqYUF+WpnZhz9kiRi/by/H8MlpG+BOhUTHj3rY80jWKT3bnMmTODqb2b0WjYF+PfbeM8Mdg9GyAqcnRS1rKKk0MvLU+3x5yX37HPm4TW05eYVhCFE+t2I/ftZrxVTsdUxKi6BgdzPbTV0lJiGL+1jNM7teSAJWcfw+O4+VN71PKKrf9h/tFMqL5Ejb+KqkWlwu2naFzkxD6vrMFq82+u3J0UlO2TumNVm9E4yNHqZDQo4U/RfoStu3cyuEPJ1OZX+DxWUJadEAyYBInTSH8Z0hTbo8NZ8wn+6p3lPr7yMnXGmgQpK5156e3ZZuGJ0ST1CKMV7913Bl7sqAcP4WmVod6eud0tuRsYeH+hQBIkeIj90Fv1ru9xiTNIc9nImHGF1FZY6vHm9AkhAFtIpHLRJkigUAgEAgEfyD/+hd8+CEsWwbTp8Njj4H05tYbddHmu7IL6THdbvge0S2Gp1ceqNbCrrhRm289WciKXkPYfdHzDsa0+DQSoxKZuHGiw3GZREZceBxHrxzm18u7CVS3YmfuXgZ/OhCjzf2OSgkSmqse46O7X6NhYDBP9m7OjFdeRHdiu9trbm3XltcXfMrMX/KqNbK32rxJWBRF+hJ66yqrY8BXZ+aTccY+xl4twklqGcY7m06yJ6eIE/llBKgUfDd+YK27O8e4Kf9Uxe/V5vtzS6g0S3jgy6Hc1fouvhv2HeXGcq5UXKle7J+/bz4XSi/QUNOQjw5+BLeBtJkU2dcyTEUm1zcEDGf3k//JJMLu/TeK4PpClwsEAoFAIPjnc/Qo3HcfmM2Ox9991368jhTp3WvaKi2s8dGgrdRy9PJRzhbnc/piIHfGDeW26AH4+chIXjmIjcM38v6e93m6y9O8sf0Nt4aH1LWppMWnsSFlA50XdnZKKalNlx/IO8BzPZ6rVcOmd0pncKtk5n21npyjoymLvwp1qKY5qMUghjYdx7It8mpt/s6Pp/ioYScuXGrLugd/xmApo9xYSj2/EAJ9ApFJlEiAYQmRDtrcRyFlYfIinvlhHNtyt/H96e+RIMFP6Ye2UsuRy0cI9Akk84nMWp/LXWlWsBvH7/1gH2vG3kXfpgMp1hdTadXiI/fBR+bD/H3zq+c184lMz8mJ1+bw7tZ3o63UktwymRB1iJM2r7re1b8VuxlpLt0a9SQloT0fbnasbHAza3NhbhEIBALB/xZaLTzyiD0C8Ea6doWvvoL69f/6cf0PE6hWXvu/CpalJjBvSzbfHc5jeNdoFjzaqXqHaEWlmSn9W/NAp8a89l0Wn+w+R0ITzztg/X3k13aXeo9Wb+K++Ea1mlsAnkhsyo9ZBRzLux4zWLMs0YhFe/DzkbPqia481rMJlWYrp69U4K85SZ70fbC67ldq86OVbDqdG93CU4n2fnIKdRy9pMVstREfHUxmTjFHL2kZ/+lBUhKieHFQLBabga+Or+PNXTM5uOEgrANqKZX62Oh0xr34KvU0vmhUCvaeK2Lg7G0OUenllWZCfJVe7fysKtv09ZM96NYslHlbsp3KNqX2aEL35qGYLFZeGXwLH20/W93GYrXx07EiJnWbwvCvXRdWlUlkpMWnMWTVEIJVwaR3TictPo0SQwkvb36Zb45/43Z8VkkJBcrnCTWNw9/SB4B5W7K5PTb8phPqAoFAIBAI/sH8+ivMu7bwWVQEo0dDdrbd5HIT81u0+Se7ziGRSMjKK0MmlRAb6X8t1trMyYJyLNfcLjW1eaBawZyUOGbtnMWk7pNIaJTgMgL8ma7P0KNxD/qv6F+9uF5T/+lMOq7qrhLuF06u4iz3fHYfRlup2+dTy31Zetcyujfoj9ZgoajChDJ7M1d3fel+UkLAmmJBqzrId+MHk/7JEXZkF9ZNm7uIAf84aRAXSi+hUauQy408N6AlJXoLpXoTgSoFKoWM/942nW5uyj9N6jaF/s0Gce+H+5xScqqoqc0TmoawYOsZt9pcJpXw6tA45tdoU6XNH+8wmuFfD2Pixom0CW9TvTB+5PIRJEg4M/6MkzbPey6PCaMnsPuX3W6n1lR4nvxlEwm7+wVUUbcKXS4QCAQCgeCfS2EhDBkC5eWOxydPhnHjflOXIepgp2OutHA933qo5Wr8lQEcOF1ImcHK06sO8d7wGBKjE9mSswVfhS9bcrZ4NEwAzN83n+6NuzOm0ximbZ8GOCaLeNLlxfpiyo3lzOo3i17RvZix0zmhZEr3qSQ1SCT5hWS2SrZA2zpMiA2QwMWyi/j6Fjppc5PFxp6zJczdfJZWkQH4+8jp1ULNo93DMEuNrDvhXpt/N+w7ThaexF/pj1quZsH+BczNnIvVZqXYUMyyX5cxs+8st9p8crepdG/Yz2Vp1irOF+vZc7aYxBZhHLqgx2iSY1Qc5J7Phjgk4fRf0d9jmaRnuj7DwBZ2g8xT3z9FiaGkVm1+47+VquebnfEmax/8iXlbzjptUrhZtbkwtwgEAoHgf4utW10bW1JT7bsqfXz++jH9j6NRy4lroOG2VuFknC1kZ/ZV1o7tUb1D9MbF18d6NmHRyM7ojBZUCqnHJJEqU0adxqNSEKBS1Lq7MSUhih7N62GyWklsHkaxzl6WKFzjwxf7LjBkzo5qIVtRaSZQreCr/Rd4+5cdnLQ9jVXiZhejTUGY8SVyC+ox+YtDpCREsTS1S3V/ZXoT7z/cgYcW7CanUAfA1pNXeH5gUyZvmsz8PfNgE+B+DRkAiVzC4/96greem4lM4sPsH0/z8fYzmF0Ybk4WlKNSytibU8QDnRrVOjc9mtdDKZditlhYPbobJXojV8oq0agU1WWbPtt7nmEJUdxxSwS3tQqnqML+kSQswAckIJcNcuuGjwuPQ2fSUWGqIPOJTLae28rQT4c6CH0JEmzustAlZgqVb2MynSfI/AhHL2nR6s03ZcyiQCAQCASCfyA2G0yaZE+MrEKthvT0v29MXvJbtPnLQ+IA+M+Q1tx+SwgVJi2FuqLqlJJP9+SxfPcFB20+vGsjfjn3Pa9te5UPMuc4RYCH+YUR6R/J9O3TmbBhQvUCevOQ5mxI2eBS/0klUqd49BtpHNgIs9VIoB9sP1XCi/M+58yy59xf4AcMh6O6owz/ehhj4tOZ+8hMBs3ew/life3a3IWWvR4Dnsbk7pNpO7ct0UHRTOo2hSGtBqNRq3nuy8N8dyiPAJWClIT2rHvwZypMWor0xYSqg6mvCWXdwSvoKmV0b1aPnMLatXlii3r0aml/b6lZUvXHrAL2nC2iY1QQLSP8WZbahRKdya02P1TgaHRvG9HWvTZPxP4RwkOZIquhjILVLxHS70mO0k/ocoFAIBAIBP9M5HJo2RLOnLl+7KGHfpe5PVgd7JBu7UkLV5k0Hu05BCk+PN2nGTpzEWnxadz96d18/dDXDFk1xKv7vrv7XTaP3Mz9t9xPmF8Yfgo/Zu2cxcSNE73S5UnRSbzd7x22jdpGsb6EUkMJ/j7+yKVyUj9OZVj2wxBWh4mwYC9XJLH/9WD+wVq1eda1zaj5pQZG9qzPJC+1eeeFnYkOiq4u0TRx40Tuu+UhEhvdybAFB0hs4Vqbm8xyPvj5TPU7gStSEqK4tWEQukoz7RsFIZdKkEh7MbrjaIc18NNFpx3KJJksJvRmPYE+gQSqApFL5Px89mdGrRlFA00DND4awL4e7s26+Y0lqCpMWlpFBlTPWRU365r5zWW1EQgEAoHgzyY5GV544frfZTJ4/3346CNhbPmbUMqkjOnVlGEJUXy57wKrnujKxiP5LNpxluP5zoJq4me/8sb6LHyVMgrLK3k8sYnbvqtMGXENNF6NJa6BBj8fOcM/2k1ar6a8dX87p2vjGmh48/62jE5qyv3zdtJz+s+MXbWfGRuOM3bVfk7kl7H+cJ6DQ/tiiZ5X1h0lQG3GFjoDq8RNWR+bhDDjZFTWNtWHVmTkkplTREpCFAAqhYzP915gaWoXAtUKAF67+xbWnFjL/G3zYAW1Glv8QvywjbCxkAVM2TQFi62SjtFBTsYWpVzKwFsjGdalsd3YEt+IEYv2kNarKW/e39bt3Ezq1wqTxUpmTgmJM39m07ECGgX7opRLyS81cG/HRrz1QDt85FJKdEb6vLWZsav28+p3xziWp2X5rnOkLj7Ef2+bwap7V9E+sr3DfeLrx1NprmTj8I3M3DmT1LWpTiWM3BpbaqCT7cCGPaFGa3AfmS4QCAQCgUBQJzZuhB9/dDz2/PMQHf33jKcO/BZtPn9LNjZJJXL/PQxe3YfWH8bQY0lHWn8Yw5DP+hAWfoA1YzthNFtRKWXc2lDDwwn1eTdjFgDFhmKmbZ9Gk9lNePCLB3nup+d4Y9sbHCo4xLTt0xx2hnrSf7UZWwBOFp5k2FfDmPzDZFr6l1K8djo2i9l1YyWQAoRcPzRv31y+Pfktr95lL3HpUZt7iCwH+67YbbnbGNNpTPUC/ZRNU7DaKkluWx+rzZ688uHmsyTN2MmkT3PZeVzFpiNGfskqxUcm55GPvdPmRrOVD37OZuHWM/gq5dQPVGEwWag0WbkjNoIT+WUs3XkOtVLOHW9v+eO0uRS4DXgYUHmYDKsZXdZmbDar0OUCgUAgEAj+mQQGwrffwrPP2v/epQssXvy7ypIqpUomdZsC1K6Fq0waU36YjBUDMnUWGp8AdCYdZpsZnUnHrwW/enXfA/kHyC/PZ3bGbJYcWIJCqkBv1nuty7ee20qnhfG8/Mt/CFGHsCF7I/2W9KPxC43ZpN3kvbGlShbKXJ/+M7V56tpU3tr1FivuXUHn8P4MnbOXwxe1brV55pliHu0e7VGXj05qyvG8UvblFtPnrS0s2HoGo1nBzL6zWHmPo84uNhTz/env2Xx2B/7yMA6e8aesXMPXewt5YukhEqN7c+LpE3x+/+dMu30aq+9bTfa4bF5KfAmzxVzrv5XUtanM2jmLDSkb0FZq8fdxbWC5GbX5zWW1EQgEAoHgr+DVV+HQIdi1Cz77DPr0+btH9D+D2WLFaLGi1Zsp1RsJVCvRqOX0vSUCXaWFD4Z3RG+00CE6mNtjI/BRSFm15zzLd59zMIusyMgloUkIKoWUHs3qMTwhiuUZzrsVLVYbKzNyeaxnEyZ+Vrt4T0tqisVm48xVHUPm7GDCHS1YPbobBVpDdTJLVfLIq99mVY+ppqs52FdJeaXjAnmwr5JDF4qYc2gqetkxt/cPNY8mPuJOpxj3xTtyWPBoJ7aduoKPQso7P56kfpCKlIQoFu/MoWO0P6NfewUWAu5LsQLgG+PLz+t/ptvn3bDarMzbN5fEqCQGt7y7eh4bB6t57e5b6BjtT4mhhEJ9EaFqG8GqYOIadSB9+T5uaxVeHU1fc26OXiylvNLEkyv2M2dYRzpGB7F4Rw6vfnusOg4yQqPijtgIejSvx/3zdlKsM1Gss8+lzniSz9K6MbpXM3acvoKhrLODGz7ML4Qw3xAsGFlzfE2tUZrukNr8CDf+Gyl+gD2xRyAQCAQCgeB3YzbbU1tq0rDh9UXum4gqba6rtKAz2nfk+frI6qTNA9UK7u0UxuQfJrlM3DuYf5BRa4eTFp/OxyNf40Selol9W1Jh0jotrltt1upUEG2llnq+9RzOp3dO9ypK3Rvm7ZzLt8+so6jwqusGEuABoIHzqTd3zWTLyGTaNdI4aPPhXaPYevIyHaP9Gb94hlfjeC/jPb556Btm7JhRR22u/k3aPDOniMeXZmK22qrfOeRSCY/1bPrnavOWwBPASqDQ+bQ8uAH1hj6PRCIVulwgEAgEAsE/F5kM3nwTOnaE224DlSd3ryPutPnQ1kMYEz+GEN8Qr7Tw3H1z6da4GycKT9Au8lbyyvLQ+Gi4qnOje91wpeIK2cXZLP11Kc1CmvF8z+dpF9GO2RmzGdhioFdjUSmULPt1KZO/mWQ3OntpagmWBOPv78/5svO1tnWnzR/pFkVuYQXxv0Obz907l56Nk7in9T0MurU+K/d40uYB5JWYKCyvdKnLV2bkciKvjBE9YrDZbLyUHMviHTnM3ZJNfHQwd3VIYOOwzegtWrSVpQQoA5Hhz6qMPPqt2U2p3kRcAw2fj+nGvZ3CWHNirdsSS9FB0fyQ/YNXJai6NupK/2bJlFe6/qhwM2pzYW4RCAQCwf8eUiksXw5FRdDEfeqH4I9FZzTzY1YB87c413lPS2rKba3CeembI6z59ZLDuZHdY1g7tgcjFu1xiPWbv/UMn47uilQi4fmBsXRpEuJQH77q+pYR/vRpHe5VmaE+rcMxmCzVpY5e+y6LfnGRvPPjSQq0BsorzZzIL3OqP1nzfj4KKSdq7GqtOpZRuBC9PNPt/buGP8TnD71NpclKkc5IyDXhW/UBwWC0MO72FqzMyMVqg8/3XmDeI/E81rMJn36+jJMzTl53s7ujHeiSdWjCNLQJb1P98eDNXTMZ3CqZqQNaM/DWSNo0VrPuxDomLHEWyJO6TeGzMYOZ9t0ZEmf87GBY6RcXSXxUMCkf2WPZh8zZQUpClEtB/2NWASarldwix6jGC8V6rDYbM74/Xv17/Xcd1fcpr9Ry9upRMv+VyOyM2bU8sJ1QdSiF+hqr6DYp9YzPo7A1qv6NNGohiwUCgUAgEPwBLFkCR486Hnv9dfD1/VuG4w6d0cyZq1pC/aVoK0urywgZbYEUlVtpFOTP5M9/ZVPW5eprXGnzqvJCrowtNZm/by5JUUnc2WwoFquEc6X5HtsfvXwUX4VvdQy7TCIjLT7N6yh1j9iAb+DC6Qvu2/QHmrs+dSD/ACWGEp4bEMvKjFwCVAoqKs2k39acR7pGU2IorNOuWJ1J95dq83mPOGvzfeeKkUjAVylDJpVgufbC83u0uUwiIy48Do2PBm2llqOSo1getxCwJoCy49ffl6Q+foTf9zIydYDQ5QKBQCAQCP5/MGxYnZp70ublepjVdxZGq5E+S73bpPvO7nf45qFv+PzY5yS3THZpHK+NUN9QtJX2dfZZO2fRK6YXpwpPseahNQSpgkhanOTxeilSJDYJT619imt7C2tFYVbwWJtnePaO0bSY40aM38CN2rxhkJqYUBX9bw2hvLKcIkPR79bmQ1oP5qnezUluW7s2T+k6mOnrT7Evt8TBTP5EUjPio4J59OMMQvyV3N2hIYtGdqbcYK7W5UazlF+yzOgqVTzUJYJHP95NVn55tTbX6k1Yqay1xFJ6p3Sm9phKsCq4Om3HSZtfPorFZuG9jPe4N/Y+ThWUOfV3s2rzm29EAoFAIBD8EVy4YF9UvvNO1+cDA+1/BH8JepOZ6d9nsWyXs7nk6CUt4z49yPCEKF67uw1nrpZz+KK2+tzkLw6RkhDF0tQuDJmzo3qX6NFLWsoNZj7fdwG1QsqwhChuj43gcpnBoYb8yoxcPtp2ljnDOlSniNxogEnt0YQezetRXmnmi30XGJ3UlPGfHqxOfunVMozJXxxyGvuNjOoRU20+qSK1RxPe3voZJbJP3V7XrWEST7d9nbRP9jmNreoDgt5o4daGgUz6/BAxob7MvK8tm48XsGTOW6xdXIvJQwL0A7ra//9CXWF1LU64/hIgsQTTNsqXST9MciuQq+qZzkqexbg7WlCsM+GrlOPvI0cigdtmba7+jewx6tnM25JdYwH8ukEosXmYUz3P4V2j+TnrsoMRyWpzTMeJrR/AVV2x1y8mhfpCXkz8N69vew2wEmIag9ravvr8mF7NUMpEtU6BQCAQCAS/k/Jy+Pe/HY+1awfDh/8943GDwWSmrLKCvQXfM3vdm06LsuO7TKJeQDKv3tWGPTnbHPR3TW1+9wc7eTihPsmferd4/+aumfSK7s/L35zk5btcRKLUwGKzMH/ffMZ1GUfq2tTq2vHe6j+P7AayPJyPB7p47qLYUELLiBa8sT6LtWN7sOdsEQ/O30WP5qEM6mip03D+SG1+pcyIWikjSK1AKpV41OaRGhW9WoZyR1wIrRuauVh+ktnDY1BJA/h0Tx7Ld1/4Tdo8WBVMeud00uLT0Jl0XNVdpZ5vPdRyNQv2L+BDzYcMP/Moyz9eBhIp9e56HkVIQ0DocoFAIBAIBP97eKPNB/kNxmIz1tmk8cWxL3i03aMopAoH43htdIjsgFqu5sjlI9X9GS1Gvj31LetPr2f1fatrHYsMGa9vf907J4IZKIWM5zNoEtiKc9psLy66TpU2X7zjLN+N78K6k+t4YuNM/JX+TLt9Wp36cqXNi/XF7DxlZlD7MO+0+aBZVBikDtpcJpWwbPdZlj7ejgqTlkJdESWVEOATSMbZUjYdu0JcA38eSqhPhUnL+bITvJPiqM1fu/sW1hyvvcTS3L1z6dKwC2M6jWHe3nketfnczLlc1RXTIsJxjR5uXm0uzC0CgUAg+P/Hzp1wzz1QVgY7dkD79n/3iP7nuLH8kJ+PnNFJzQhQKZ1KDFWxPCOX+OhglozqQp+3tjiVIeoQFURKQhQfbr4ubosqjGw7dYWTBeXcG9+Ii8V68koNLNx2xillxSFFxGTBaLYS5KvA30dOUYURi82Kn1LO8K7RYKM66WX57nOsHdvDq+SXzjEhvPrt9ZXy4QlRtGxo4PFNE0HiOu4lOrApXTSv8fxXzivsNT8gPD8glhe+sr80LEtNYPaGQyybPpmrR7Z7/jFUwP1As+uHajrvqyg2lNAqNIIvjn1Rq0Cuikvv0XAQi7af5c42kSTEhJJbVOHyt71xAbz6njqjQz1PmVTCsIQonli61+P9/X3k5JfVLUozufndbD4cwOErGQRYBlYfH941mttjw5HfhEJdIBAIBALBP4y33oK8PMdjb75pjyf/G6mpzcsMRiKDpLyy5blaywi90WcGT97WlGnfn3BoU6XNn769ucvyQu44kH+AcmMpeaUGpPjXurg+N3MumU9kMjp+NMeuHKtzlLpLcoFNHs7HAAOxm8M9EKIO5pPd5/hgWDzztmSzck8uMaG+PN6zKZW2uo3TlTYvNZTQIjScL4596bU2vy0qmavllXSICsZisZJfanCrzfVGCy8Pac7m3O8ZvNr1R5Q1Tw9AJfcldbH79Elw1ObNQ5qzIWUDW89tZeinQ536HddlHHvH7KXMUMmR8nrkllSijmkPCF0uEAgEAoHgH4TFAjNmwFNP1XkD7W/R5mPi05l2xzSHJI7aKNQVYrFZWLhvIRO7TXQwjtfGuIRxzN83H6vNWn1MW6lleNvhrD2xlssVlz1cbcdUa8T4NS4AofY/OpOOo1d+JTq4sXfXXiNEHcxne8/z1oOxTK6RatI2ou3vSqypotRQwuD2Lfj6+Fe/SZvbrDYkUiOa4L0utfeELpOZ/fBgNp35hcGrU9xq81A/ZZ1KLK19eC2PdXjMozbPfCKTUn2Zwxo93Nza/OYbkUAgEAgEv4ePPrLXsywoAJ0O7roLrlz5u0d1U2O2WNEZzeSXGjiRryW/1IDOaMZssdZ+sQt0RjPrj+Rx/7xddJ32E3e+u42eM34h7ZN9xIT6snZsD2JCXceyf7T9LDKphOEJUU7nFu/IIaVrNNIaC82+PjLKK82U6k2MWLSHJvX8aN84iBbhdqdxzQSVqp2Kj3y0m2C1gqvllbz+XRbt/vMDvWZtpsf0X0j5KIMtJ68gl0mZfGcrZj/UnkbBakYs2kNar6a8eX9b4hpoHMYV10DDW/e3Y3RSU0Ys2lNd//Kt+9vxaI+G9FiYTImbmpVquT/j2y3km/1al+erWJGRy8/HC4gO9eOZvi3Zuu8wH00eVruxJQwYjYOx5UbnfRXBqiAsNjOzdnonkN/cNZPQACnfHLjAY0v2Mu37LKJC/OgcE0znmGBi6wcgk3r+KhDsq6S80gxAoFrBS8m3IAWO5Xmej/JKMyHqEK/GWUU9vxAC6EKweSRg/93ef7gDLwxsja9S+L0FAoFAIBD8Tq5etZtbajJgANxxh9dd/NG6HJy1+TcHL/DlsTVelRH69uQ6Hk5ojCtJt3hHDsltG1CoK6rTeIr0xfgq5azcfYnxXSZ5bFtsKKb/iv680ecNXkx8kVB1aJ3u5UQ58DngZjqVYUp4AKjFi9QhsgN+cg0do4I5nq/l1OUyOscEsyw1gQ9/OUWQKoj2ke29GpI7ba6UKzGY9XXS5hpfCaOXZTJzw3GUchn+PjJi6wc4afNAtYKlj7Vj2s4XGbV2uJPBqOojysxd/8LPx8zFEr3He1dp82BVMBuHb2Tmzpmkrk112W/q2lRm7ZxFk5BGNL/tXgLaDxC6XCAQCAQCwU2JR23+2mvw4ovQqRMcqj3tu4rfqs3n7ZvL96e+Z0ynMV7fq8qk8fq21+naqCt6k56k6CRGx4/2eF1afBqJUYnM2+s4JpVcxUNxD/Fcz+cI9wv3ehxuyQPOAo0A9fUxz8l8r8562k+uod8tYaw9sc7BfFKz1Km3fbnT5iar4Tdp8/d/OoVcYeK5Hye71d4j16Yw9ccptI+M41zJOafzo9YOZ8WRuZRWltRpY4HNZmPl4ZVeafMq/gna/OYclUAgEAgEdcVkgmeegQ8+cDx+7hxMngxLlvwtw7rZ0RnN/JhVwPwtZ5zK4aT1asodsRF1EjE6o5k31mexfLfr8kPuSgzVbFOsM/Jo9xjmbsl2MKccvaTFYLRUl7GJa6BBIZNyIt+eCHL4opaHF2awdFQXJt/ZivjoYBbtOOvwXAlNQpj3SDxvbTzB8gzXYxz/6UFSEqJ4tl8rejSrR0LTUCoMZooqjPRqEUb/NvXR6k0Ullfi6yMnUqPCYrVRYTQzb3g86mt16ldm5JL2bTplnHC6TxULkxexYrMC8LxgDDB/6xlWp3Xju+83MnrkcLSlJZ4vaA3cDfg4HnblvO8Q2YEgVRCFusI6CeRifTEjujfhy/0XaRHuj9Vm450H25NXaiDkWlmoVXvOu0zriWugwUdh//1iQn1ZmtqFM1fKySs11HrvkwXl+Ck0dYrSjPAPYf4j9Sgsr6R+oAqNWoFSJr0p3ecCgUAgEAj+gcycaU+OrEIisR/zkj9al1f1WVOby6SSOpURejfjTe6OHcIt9QM4cskxhe/oJS0Gk4Uw/7oZTkLUwZRXalm++wLrxw9iTPwYj4v5tzfpi9mioGvDnjy+bmSt/T/X4zk2ZG9w1ohW4EvAOUwQALVazctzX+a5I8/Veo/xCZNYc/Ay98dHc7nMwJT+rakfqCLIV8HTd7TEaLYyufsUUr6qfZ7daXNfhS955Xm/WZuP7B5DgyA1c4fHc6Ws0kGb+yjgl3Pfs2B/7XHmiVFJpCS05cPNZ922q9Lm/+n9H7bkbGHBvgUe+52/bz63xdzGuw8mY7RAeIAKH7nQ5QKBQCAQCG4ePGnzfynO0/U//7EH/Z0+DQkJ9m8PDz5Ya5+/R5vP2jmTdQ+vY8aOGQ7a0RU1TRpWm5U7l9/JhhS7Rn6h5wt0a9SN2RmznRNEEibQM6on/Vf0d0iI6RDZAR+ZD+/ufo+Xkl5BLrN5vS7rRCn2EqFtgPrOY/7s2Gd8MPADpnSfwjAv9PT4hEmsPXiZeztFMusrR/PJjaVOa+PP0OZBvjK2ntvKkStHaBvRlqOXj2KxOZcxnbdvHgmNEhjTaQzTtjuXUtqQvYEBLQZ4df8qruqu8sOZHzy2+Sdq85t3ZAKBQCAQeMuVK9Cvn7OxBezH33nnrx/TP4AqQT1u1UEHkQ72xepxqw7yxvrj6Ixml9ebLVb0RjPllWYulejJL9Wz6ViBS2NLTVZk5JKZU0SKi3QWgMJyI2aLlVaRAU7napaxGZ3UlJUZuQ4GmMMXS+n91maW787h9thwVo/uxo7n+rBhfCK7nu/DklGd2Xbyiktjy41j/Pl4AXvPFVFpsvDcV4d4Y30Wjy7eQ/v/bOSN9VlEBqp5euV+5m7O5oej+fgp5eRrDYxZvo+kmb8wJ2MJhXzr9h7juzxL7+jkWlNKqjhysZQZs97ioXuG1G5suQ37rtMbjC3unPeTuk3hYomZQn3ddt4WG0oY0q4BWyf3JthPycMLd9Nzxi/cP28Xt7+9xWNaz6geMazMyCVApWBZagLzt5xh5sYTBKoVtd7XYrWxKiOv1t2+VYxPmMRX+wrwkUuZ/n0WcqkUX6X8phbpAoFAIBAI/kHk5cGcOY7Hhg+HNm28uvz36nLwTpu3jPCvcxmhEkMJvVu73plZoDUQoAis007IBppQ3nmgPRsmJBLq68/rfWayZOgKpz7aR7ZnydAVPNftNe6bu4Pblwzly6wva72HVCplXJdxzid+wb4z1A1vz36fJ5OfYnRHz7th0+LT6dW4P+9syuZymYEnV+xn8ue/YrHa2HCkgEc/3kOfN7fRvcGdXvTlWpuPSxjHdye/q3MZpmJDCfd3asT2Kb25UKzjoQW7uG3WZgdt3iTUl1E9GvJuxiyv+py1cybDutZ3md5ThcVq49M9eaTcmsLsjNle9Ttzh30369Mr91NmMAtdLhAIBAKB4KbBkza/cuIsLZ9NR2KrsSBtMkH9+g7t/ixtbrKaaBNe+zvGjSaN00Wn6bywM/vy9iGTyriz2Z1sHrGZ3Am5HBpziOxx2ax5aA0nCk/QeWFnTheddujvma7PUF6pY1LXl3h29VGyLxvo0qCLV+OuphLYjL1MaALg737MBeUFJLcc7LU2//ZQAcX6YpdzOTdz7u9KrPk92vzxxBhG9GhI85BmTLt9GqvvW032uGxeSHyBYFWw0zXvZbzHmE5jkEqctbG2Uou/0t/puCf8lH5OJZZc8U/T5jf36AQCgUAgqI2DB6FzZ9i82fncpEnw3XcQ7CwU/tcxW6z8mFW7EWX57nP8lHXZKQpdbzRTZjDz3eF8Hpy/i8SZv2CxwfwtZ7y6f1WJobgG9pjsuAYBxDXQ0DkmmAiNihKdyanOI1wvY5OSEEWf1uGscGFSKdWbeGvTKTq+uokHF+zknU0n+Gj7GSwWGxYbzKvDGG9tFERmThGdY0LIzCkmK68MsxW+PZSHVm/CBnyy+xxJLcOwATM3nCArr4ywoGKKFC7MVte4JaQr/7ntNYoqKr0ai81spHD9u7z20nPgwZnv6+fH029NoP1D7R1UXvvI9iweupjJ3Sc7Oe/TO6WT3DKZFbvOE1rHUj8hqiCiQ/1YtOMsy3efw2qzOZQiqkrrWbD1DEtTu1QbV1ISougcE8KKjFyGd41mT04RK/fkcrKgHJVS5lT6yRXLd1/gjiYDGRPv3UvO25uyKTeY0RrMFOmMdXpOgUAgEAgEAo9Mmwb6Gkl8Mhm8/LJXl/5eXQ7ea3N/H3mdywiVGEpQK13X6Qn2VbLh8FUmdp3sVV/PdJ3ET8eKeHntESwWG2YbjFl2hIL89qx78GeOP5nDzlEHOP5kDmsf+JmC/PYMmvMzmeWT2X/5Z6/uMTdzLv2b93fUiGeAbe6vuePuYTzy6Ajyiq083/31Ws02Ixf9SqneRGG5kQiNqtqoPenzXzl6SUup3sTIRb967MudNq9aVF9+eDn1fOt59cxVhKiDCPTTk1V4lEZhlXz4SBue6t2kWoMfvaTl4x1nuVJRVKePKAZLmcuNBzXJOFNChVFXR+NUMVGhfmgNptovEAgEAoFAIPgL8KTNJTYrb3/3NqH6G8wC06ZBUlL1X/9MbV5uLOeRto96bJPeKd2lSaPYUMy07dOIfjea/iv6s/fSAQJ9Qvkh+wfuXn03TWY3Ydr2aQ7aNFgVzNcPfs3dsXfjq/QnpzSbOzpe4NF1fVmw33NaXzVWYB/wBdAOuBW4wTidFp9GnyZ92Ja7DZlERoBKQ1EZXmtzwO2mzapSp1O6T2Hx0MV/qTbfX7CFxMWJtJrTisTFicR+EMtdq++ieXBzMp/IpHlIc4drDuQfQGfSuTQwHb18FJVcVaeNBT4yH6cSS674p2lzUZZIIBAIBP9cPvsMRo0Cnc7xuI8PfPSRfbekwCVGi5V5m70zeczbks3tseHVjl3dNdf5u5tOsXKPXejH1g/AYLR4nUJy9JIWKbBgeCfMNhuhfkrKDGaulhuRSyVEhfoy6Nb6nCwory5nE9dAg5+PnMd6NqFzTAgVRguNgtVO5W6qsNrg6KUyjl6ylzDSqBVcKaus0xgNRgubT1zh+YGxzLuhTFKxzsitDQN58rbmaNRyinUmjuVp0aglGANnU1FQ7rLfMN8wlt29mN3ZRXRvHkagWuH2GQDMZVe58vUbGPNOehyvPCiSRg//h0a39GRd139TYdKiN2up5xuMWuHLisPL6bywc7VAbx/ZnqndpzKoZTJjlh0h81wxUwY0qVOpnyB1ECazgTvbKunUXE2obwi+cg2f7slj+e4L1c+1IiOXDlFBPNO3BYEqJX1vicBgtvD5mG40ClLxwPzdgH3X58qMXEZ2j2HyF57r1ZbqTZzIMzKz7yySonsxc+cMF1Gak+nVuH/1B4iq5B+NqvZ0GIFAIBAIBAKvyM+H+fMdj6WmQrNmXl3+e3Q51E2bl1eaCfWtm5k5WBXEA/H1qKi0OpSarNLmwX4qercaUmt5obT4dBIb9WfonL00ClZXa/M9OUXsySli3paztIoMwN9HTnmllhP5ZVSSx2XlK5hlF70aa/vI9kzuPhWNTyAz+86ia6NE3vxpGke+dr+ge0vbNnz4wZtsP3WZ7s3DGDh7G4Pb2c02FSYtRfpiQtTB+Mo1rMrIY+iavdVzEOyr5LZW4WScLaye+ypyCnUMnbOXlITrfdWmzccnjCcxKpH+K/pztvgsvgrfOmlzgAZvN6jeods+sj3Pdp3M2qf7M+LjX8kp1OHvIye/rNCr+ayizFjqcuPBjVyt48eZIn0xoX5Koc0FAoFAIBDcNHjS5k/s+Zqe5xyNvObBQ5BPup4s/Wdr8yCfINI7pVPfvwFv7prptBY6uftUhrRKZunBpcSFx6Gt1DqVwbHarHRr1INmgW1JWbifuY+k0lDTkBk7HNdWh7QcwsdDP2ZT9iYSFyf+thJE2cAPMGnkJGL/Hcv7+953GvPUHlMZ0HwAp4pOsXDwQvyV/gQog5AqlQycvccrba5Wyjxu2qxKrhnTaQxrHlqDzqTDZDERrA5GLf9ztLlCqiB5VbJTCamD+QdJXZtKWnwaG1I2ONwXoFBXSIfIDmh8NA6/X1WJpcndJ5PyVUqtY5jcfbJTiSVP/JO0uTC3CAQCgeCfh9UKL70Eb7zhfK5hQ/jmG+jU6S8f1j8Jrd7MsTwtMqmElhH+1xaRzZwsKMdS08GB3eSh1ZvxVcoxW6ycvVrOsUtlDgu4/j7yOqdhFJQZiNSo2HmqkCU7c5zql47sHsPasT0YsWgPOYU60pKa4q+S0btVOCMX7yGpZTijusfw8Y6zHscPkNqjCZfLDJhdnPNEsc5IgdaAwWihVWQAWXll1ecaBKn5z5A4JIBcKkV7bZG7XoNv+OXSXrd9XtFd4fHvHmZ8l0mYrEP49+BYnv3MtZGj8mIWV75+A0tFscvzVaii21Nv6FQq1QF8uPls9ceBWxsGMunOeizedoaHEh7hzqZDq18CND6BaHx8eXD+bo5csj/X/nPlTOo2heFf117PdEqPKZwqPEXq2lSnl5LxXSaxZuwARi6yL6KDPQlndVo39uUUUWawp94YTBZK9GbmPRLPqj3nWb77HMt3n2Pt2B4M6xLl9JGgJsMTomgdqWHgu7sY3K4t6x78GYOljDJjKfV8g5Hhz/Jdlxw/QPgpidCo0KiFBBYIBAKBQPAHERkJ338P//oX7NoFSqX9XcVLqnQ5UKs2r6nLgTpr85MF5fgpNHValLVZ/Ri1LJNHu7nW5j2b12PSZ4d47Z7pJEb1Ytau2g3H/06+pVqb3/jM+3NLsFhtGKRZXFG+ilXi3pjup/Dng4EL6Fj/VoJUQQSrg1BIffCRKzhVoCU/rx2NfoniSJkbc4sKjvU+wgNrB1Zr8wl9W/DsZ4dcmm1qvkrENdCgUkjpe0sETyx1rf1L9SaX2vyHw3mMaDeCfs36UagrJNQ3FLVczfx985m4cWL14vb8ffMZ12UcqWtTa/2txiWM48PMDx0Wrw/mH+SRb1JI75TOJ4+/TvJ7mb/pI0o932ASW6jIzHH/TtKrRRj1fNV16jdEHYyvskxoc4FAIBAIBDcNNbV5TW7NO8XkrcscjuX5hyJ9by4REnsMyV+lzR9b8ivPD+jH1pGDyS8r5KqumAaaUEJ9g5GioLyykqGthtKtcTf8lf74yHxYsH8BczPnEh0U7aDNcwp17M/R0afpEAY2Tya/rJBiQwlhfqGE+QUxZdNkjwZ2t1wBfoDI8khWrFxBXKcEcstOVBtLivXFNNI0wlfhy4rDK2gyu4mDseTZrpMZ2GKw19pcLpUQpAryOJdVyTUzdszgvtj7+GDgR1itij9Fmz/b7VlmZ8z2aCyZv28+XRt1ZUynMUzbPo1gVTDpndO5NeJWXkh8gcsVl6nnWw+1XF39+83NnEvOhJxaNxakx6czqOUgnlr/VK1jreKfpM1v/hEKBAKBQFCT0lJ7Isu33zqf694dvvzSvsAs8EiZwchTvZvwcEJ9KkxaCnVFblM3ALQGE5GBKowWK6F+KhbvOOzQX3mlmRBfZZ3GEBGg4rO9F3h7k3MiSVU5m5SEKJamduGTXeeIjw4hccYvDLy1Ph+mdOT7w/n0b1Ofzk1CuFxWSYivEh+FtNokUTX+4QnR3NkmApsNjwkprqgqg1SV+FFFXAMNgWoFOqMFk8XG6StamocFYJQdYvOlj2rt92D+QUatHc6Y+HRm9Z3Ja74KinWOYys/tInCHz4Ai9ljXwGdhhLcOxWJ9HpcvNUGWXllZOWVkZbUjJ+OX+XDzY4vAY/3bErzcBsfpMRXf6T419fH+G78YNLixzDfk0DulE6v6F7EfRjn4Cyv+Wxp8eksSX2NoXPs5hL7xxgTlWYrjy/b69HMNGLRHpamdqFjdBCLdzgbnx7v2YSO0cGkfLSb88V6h48G/j5yklqoGNI+mBUZFxx2F0cE+HBPh4Yob/K6oQKBQCAQCP5h9OkDO3bYTS4nT0Ljxl5fWqo3EqhWMLxrI6+0eZUuB+qszS1WG6sy8hjfZRKj1taecjk+YRKrMvI4fNGzNn8p+Ra+//Uy93e+h8Gtkik2lFBiKCFYFYRKrmHl7uuG45ra3GQxs2VKN6dnfvbbD/nizH+w4V67S20BBJa/QuaxVtwb2xqbzUal0YpFasNqMxOgUjLr3Q8o/GG9+we8Cwh2r81rGttvZFSPGH44VkBSi7BakyFv1ObbThfRsamUB798sHpH5pHLR5wWv+dmziXziUxGx49mwT73se9VcekTN050eX7u3rkkRiWR2rM97/10ps4fUWT4M7R9MA2CVB61uQxTnfoNVgWR0EQhtLlAIBAIBIKbhlK9swnF16jnvXUzUVhrpJ8g4ZnBz/JfdQAR1479Vdp895kihn6wm+EJUTzZuzk7L9hocWs4heUVbD2/lrd3z3KR6DKZnAk5mC1SPtl1wUGb94kNr9bmgT7BqBQ+SIF1J9fV3dhSAWwG9sGddw4k/d8z6R7filK9iTB1DNO3/YvDlw+z+v7VfLz/Y17Z8orLZJNHvkmpkzYf0T2G43l6JnefQspXnjdtWm1WBrYYyqJt57m3Y9Qfrs3TO6XTM6onT3//dG2zxXsZ7/HNQ9/wZdaXrB+2nu252+m1pJfT7zeuyzgyn8hk4saJ6I0w445ZbjcWTO42leSWyeiNVqKDoinO97xpFq5pc3UQt7VU/SO0uTC3CAQCgeCfw8mTMGQInDjhfO7xx2HOHHtJIkGtRAZJCQs/wODVKV6lblTF0ekqLVRUOjvYTxaUo1LKiGugcVjsdEdcAw0qpYx3f/RcamdFRi6dY0JIv60Z987dSbHOxI7TV3m2XyvqBfjw4IJdbk0Sr317jEFtG9ClSQilOhNzN2eT3rt5ncboo5ByIr+s2uRSxeikpizfnUNukZ5n+7Xi2EUtjUOtlKrfxmbxPh1m3r659IpO4v2HezP8Y/uOT5vFTPEvH1O2b53ni2UKQu8ci/+tt3tsVmXMqVpUB0hJiKJTTDBD5uwguW19lqZ2YcicHZwv1pP+yRHmPWov9TPLRamfKd2n0q9ZX7p+3NXJ2FKT+fvm0q1RT1IS2vPh5rMAXC2rZOG2M07zf6OZacicHQyZs4OUhCgWPNoJg9FCsc5IuEaFRiVnwdYzvLLumINZqebzZeYUUz9IRUpCFB9uzgbsHyC2n75KYoswhyh/gUAgEAgEgj8EiQQGDrT/qQMhfj6sGduJzbnfe6XNa8ZE/xZtvnz3BdaMHcDojmNYsN9zGaFejfszdM31VBJP2jxAreD+eRlk5WmrDccRGiO9Wtq4q31jjlwoq9bm5QYzSI38krOemTsd49RD1CEU6T2Xt/GRhBJi+C8KWzT94iLJL9UzanEmSS3DeLZfKzLPFqEqv0TJL4vcd9IFaO14yJU2d0VKQhSdY0J4Ze1R2jQM9DjWGynWGSksr8RPoUEqkbI9d7v7toZi+q/oz4+P/EjvmN5OUfHtI9vzTNdn6NG4B/1X9PeozWftnMkPwzcz+8fsOn1EmdJ9Cp/vzWfR9txatfnwro287ndStymcLzKR0DRUaHOBQCAQCAQ3DYFqZxPKKz/Op0lxnsOxD7o9wO6otn+rNl+ekUunmBAGt61PhUnHGztedNnHwfyDpHxlN4u81ns693TwrM0PFxxm12O7ePK7dM+TVRMzkAFsA6VVScOBjxM/Ko0ecc0pM1iQSaUs3ZHH9L4zOVl0ik3Zm/j35n977LKu2nzWhuO8dk9yneayT+vIP1SbT+w6kSGthtBpYSeP2ryKA/kHMJgNbHpkE69ve92laaZmGaNFQxfxeWY+K3Zf4NW7erF1ZDIlhhJ7SSF1MIGqIPbllDFo9h6GtI/0WpuPSxjHj9k/ckezvv8IbX7zj1AgEAgEgip++cXZ2CKXwwcfwIIFwtjiJeXGCqZsmsyotcOddtVV7VqcvutfLEltR6BaQVwDTXUcnc5odhmjaLHaWJmRy8juMV6NIS2pKWsOXMSbKkELt52h0mwlt0hHoFrBstQE3tx4gomf/erWJLFw61neeagDuUUVfLnvAjuzC1mekVs9RplUQmz9ADrHBBNbPwCZVOJ031E9YliZkUts/esmF7AL5sQWYQxt3xCt3sT+c8U8nBDFyG9S0VmuevX8NZm5cybtovx46/52tNBYKfjs37UaW2T+IUQOm16rsQVwMObENdDw5v1tGZ3UlBGL9lCqN7EiI5fMnCJSEqIA2JFdSEZ2Gf1ihrJt1HbOTzjPr2MOkzvhPNtGbmNIqyHcvfpuThedrvXeszPeZFjX+lRNr9+1uHl31ByLPcY9m8QZPzN21X5m/XACX4WM4R9l8OHm7FpTeBbvyCGlazRSyfWXnKlfHubn45cxW7yrNSoQCAQCgUDwZ6P2MTN914teafOEJiEOMdG/RZuX6k2MXPQrz3d/nSVDV9A+sr3D+faR7VkydAXPdXutuoxQTWrT5lWG48ycYr49lOekzb/99RIKhZmXNz/HsK+GOT1zbcaWliGxhBlmorRFk5IQRXxUMEcvalmd1q1am8eGq0gZ9jAWU6XrTsKBvq5P2bW5P+8+2J64BhqHczdq6Xytoc7plcG+SrQGM6sy8piQMLnW9qeLTpN5aR8Dm93loM3PTzjPlpFbOH71OJ0Xdq5Vmx/IP4DerKV1ZADLd1/gtqgBjIkf4/GatPh0+jYdiASpV9q8qt/RHT33OyY+neSWyYxedkBoc4FAIBAIBDcVASq5gwYceHw7Dxz+0aHNgfqtmN3jYYc1c/id2rzH6yz9jdpcIrXxU856j4YOsJtFvj31LcculTpp84X73+fDvR9isVqoH1CfPsv6UFpZ6rG/arKAD4BN4BPow7+XvcKvX8zARyGjRFfJ9lNXWLwjh14twzh92URMYDPe2f2OV13XRZtnX61Aq5PW6T3nj9bmOpOOrKtZXq2bVyGXyPnl7C8e02DAXsZo05lNtGkQyPliPSMX76fDf7Yw7dsCdh5XU883kpQFBxi5eD/ni/V10Pz2FMjUtamsPbEOo7luyfd/ByK5RSAQCAT/HEaPhv377UYWgHr14IsvoFevv3dcNwlmixWjxYpWb74Wb65Eo5ajlEmrHbdGs4k1x9fWGilYM3Ujtn4Q8mvuBF+lnBBf146U5bvPsXZsD4Z1iXKoK3ojKQlR9G4dTs8Zvzgcl0kltIzwv1Y2x8zJgnIsVhtHL2nRGy20igygT+sIMs4WeuwfYHnGOdpHBWI0W3k4IZonltrd3WsPXuTrp7qx+4UeFOlL3Ea+V5khXv02i5eSY6tNLmP7NKNHszD0JguVZitP9m6OSiHjowPz2Zz7vccxueNA/gFKDCU05jInFzxNZe45j+07du5C0ODnyK6ofSE9roGGcI0Prw5tQ/C1sk0rM3J59dssh5ehxTtyWPBoJ+ZtyebhLlF0aVIPrd7E7E1nUPnIaBGu4Y7YMHZlF9OygYltudu8frYKk333rlQicTAJuaPmWKy264kssfUDMJgtHPEidQfsRiej2cL8R+JpGRFQbeaZtyWb22PD/xEudIFAIBAIBP9cvNXma0+s9VgOEq5r85eS+yO51rdcJv3N2jynUMfQOXtJSWjPDylbqLSWcaHkCsHqYHzlGlZl5HHPun1EaHzoHBP8h2rz4d2iWHfyq1oX313RLiyRu6PeZI+tkrSkpvSJjeDYpVI6NwnBZLmuzV98bhKnTxx33YkcuA9QuD59IP8ApZUltIr0Z+GjndBfSykJ9lUSEajii33nGTJnB6V6EzKppM7plVXaPNRPSagmit0Xxnh8NxsTn06/Zv0o01mZ98sZ6gerSEloSanexNnibKZtn1b7xF2j2FDMLQ00HMsr4/Vvs3nroVl0bZTIuxnO0fUTEibTq3F/nvviBK8MbcO7P56qVZtXfZxZkvo63Ru77ndy96kkt0gm/ZMjnC/WC20uEAgEAoHgL8EbbW62WMkpLGdk9xgmf3GIsPJiXt/4gUM/ZUo144ZMxiyTk5bU9Hdr80C1gkFtI1DIpPRr1o+hrQehrdRSYijBXxmEQuLPyt3utfnx/DJkUjPvZszyah5mZ7zJ2gcHsOFIHlIJPNy1ISarjvvj7qN5SDNm7ZzFBe0F7yb1MrABOGP/a3jPcC4nXuZfx1/g4o/nmdZ3JvpKGd2a1eNCsZ5b6mswW20U6i7za8GvXt2irtpcJpMyefUJ4qPbs+7Bn6kwaSnSFxNS4z2nqiTTn6HNP9z7IavvW+3d/AEyiYx6fvW8N/vsmMmWkcnIpWC22v+sP5zPg50aU6IzceiiozZ/bMkhvhk7i26Nu/HO7necU0ITxpMYlVidAvnmzpkMaTUYpbuXpZsEYW4RCAQCwT8HiQTefx+OHoXyclizBqKj/+5R3RTojGZ+zCpg/pYzTmV60no15Y7YCHyVcoxWI7N2zvCqz9kZb7IxZTNGk4KfjhfQq2U4vj4yzFabywXcUr2JEYv2sDS1Cx2jg1zWYx/VI4buzepx9mpFtbkiUK1geNdohiVEYTBaKNIZCblmxFi15zzLd5+jWGdEo5IzLCGq2qhSG4t35LBkVGeMZis2bDQL8+PjkW357tTXvLvHeaF1fJdJfDd+IEcuGIitr2HEoj0MblufvrdEUlReyZO9m2Ey2/jp+GU+qlFaxyg5R4FqkldjcsfyVZ/y6rMvo9fpPLbzu/UO7nvpbZrXD2LyF4dq7XdUjxi+2HeB9YfzKK80cyK/zGVaTpURZNGITrSM1GC2WNl1ppBPHF62jjO1fysiK70zl1RRpC/G30fOg50bszIjt9a0nqOXtBiufTCpWUv1no6NPKa+uKLSZOVKWSWTPj9U/e/t6CUtWr0ZX6WQwQKBQCAQCH4jZWXg729/P3FBXbT5zB3ea/MtI4cwakkmwxKi7H38Dm3eKFhN07AATGYFusoA3lp/jvJKLfmlBoYlRLPu6Z5/ijZXyMy8s3umV9fUpEvYvXz36DLK9FYe6yHFVyGjotLMxRID/1l3rPrZdKczuPKlB+PMndiTWzxQpC9m2fYrfLX/YnWJpfJKM7e3jiA61LdaV9bchftbtHmlycKiUa+7NZhM7j6VIa0G892vhUglkmptPn/rWabd04Y2UUG13rMmQapAwJ6Mc2ebSJZsv4TBVPvC/3MDvNfmNY1TN/ZbPyCU05cNDJq9h/PFekBoc4FAIBAIBH8+Xmtzi5UlO87xTN+WDOvcmN7PvUqwwXGT3kv9nuR8UCQpCVG0axzEyMWZpHT9bdq8otLMktR2bM79nuRP7eVJpRIpbcLbEF8/nqGt7qV9eDeC/VRutfmes4WUVJbUySxSYdJy9FI5E/tF8+3Jr5mxY4bX1wNgAH4BMgEr4ANvvvcmy6zLuFxwGYC5e+eS0LAnBfnt2XKykJHdYzBarIT6KcktqL1cT03qqs3vi2/E5C8OMW/L2RrttU7r4n+GNj96+Si+Cl/aR7Z3Sqh0xb2x96Kt1Nbp9ysxlNAvLpL1h/MBGN41moSmIZwrdP6uEOKnRFtZyvGrx1nz0Bp0Jh2FukJCfUNRy9XM3zefiRsnVpdQOpB/gBJ9Mf5KP6/G83ch3hwEAoFA8M9CqYRvvgG1Gvxu7v/I/lXojGbeWJ/F8t3OOyaPXtIybtVBhneN5sVBrSkx1E3sGixaXllziZ+OX7H3MbA1hRUGtwu4OYU6hszZUV2P3Waz2RcrfWSE+imZuzmb1ZnnmX5PWwBiQn1ZmtqFPWeLGL1sr9MLxsjuMawd2wMJEOrvg8Focapb6o6jl7SYLDYMJgsfDutIPQ1M2TTZpfu6KvI9vVM6ryRN47kvjzG2T3O6Na3Hv745jFoh49/JcczaeNzBXW/DxFXlLKw4R056hRXYDC9s9Rx7KJHKCOrzOAEdk/l0fx5rxzatNSFn+LX0mSr3em1Umqw0CvHl6/3nGdGjKYt35DicL9Wb2H76KomtI7x4sOuEqIPp1cKvOgnHG0r1Jvx97DI1roGGx3s2oX+bSEr1dTO3KGRSPtl9zun5tXoTkYGqOvUlEAgEAoFAUM0jj0B+Pvz3v9C3r4PJ5c/U5vllhZTqTdf7+AO1eWZOMTGhvnzzVI8/TZvbbNTpmQHkUjlPtn+ZyT0m8p91xzl9uZwgtYKFj3bmzfVZDnrYUl5M4frZ7jtrDXSq/Z7BqiCOXTpTnVJSxaUSg9OOW2/TK91pc09GEINRisUsp1fLcFKXZFZfU6o38cEvp1mV1sHrhfMOkR0IUgWRlZfjNJbaFv7rqs3tJYzOOvU75+FQXvz6WLWxpQqtQWhzgUAgEAgEfw7eavMXBrbGaoMxvZpxtbySl7T7UZ/OcGj/betETvUdyps9YugcE8KIRXvIKdSRcbaoztp84YhOBKgsTmvVVpuVQwWHOFRwiMUHF5PeKZ1Xb5vOk8sPknH2evnOKm3+SNcoLuu9L4EDoK3U8nhSY7fr5G6xAfuAn4EqH0UjuGXMLdz3wH1MeW+KQ3N7SsxPzNtylslfHCIlIYoXBramfkBoncb7e7R5zfY1+S3aXKuzUWaQetTmt0bcyvx98xnXZRypa1NrfbbUDk9wpcJzWdYbKdIXE+qnrNbl/eIiUSnkBKqdU97v7tCAgvICpm2fxowdM2gT3gaNjwZtpZYjl49gtTmXBy0ylNAosFGdxvRXI8wtAoFAILj52LYNTCbo08f1+Xr1/trx3MSYLVZ+zCpwKdBrsnz3Oe68JQI//6t16v9i6VW0BnN1Hz2bh5LUMowwf5XbBdyqeuxlBjPPD2hFhdGCTCJBgoQIjQ/ZVypQKWUkNAlh1n3tmLcl22U/Ry9pq4Xv8wNiub1VBMY61mO/VKJnxobjdGkSRHjEwVoF+9y9c0mKSuLtB+5iwdYcXv02i/JKM9+P68mmrHyncZbIl2GS5rjsS4IEGx5iSgzA18AJz88g99WwbPlKFFFtq3fcVjn946ODWbTjrNOHh9QeTegXF8Hg97d7ZWwBe4JOZk4R93eKorjC6PJDRWF5JRqfoDotoof7hTCyh5rk97d5PZYGQSqm3X0rkmuljFZm5OJ78gqJLcPqFPvurgxSzXq4AoFAIBAIBHVi7157giTAnXdCz56wdCk0bfqna/OruuJqk8E/UZsXVRgpNdftmRcmLyQh8i4GXdO1MqnEpTa32WxcXf8uVr1rnagIUmAaYgLXYTvVdIjsgEqm4bgLDekuDef3aHN3RpD5j4Qx56dTvJAci9lqddLmx/PLsVqlTEiYwMg1I2uZRZjQdQIWq5S0pGb0aR3hoM1v/FBwI79Vm9fs16M2V93csecCgUAgEAj+mdRFm3eODqZYb+SVtccAiLb6saTPIJr8/B0Apnph3PrNMhaEh7PCRcn3umrz9o01FFq3eLVWndCwJ/HR7R3MLVXa/Nm+Lbm7c4jXcwLQOLA+G7PX183Ykg+sAy7WOJYI0t5S3rj3DTZmb6RNeBuOXj6KxWYBHMvVZ+WVsSIjl4QmIfS5JaROa8s3gzZf8EgYPgop3x/O5/bYcI/a/JmNz5D5RCaj40ezYN8Ct8+W3imdrg27UWYqqXUeahKqDmbc7fWoNFtZmZGLWnmFO2Ij0KjlDtpcJpVwe2wIpSb7ptwq41RthKiC6jSevwOxsi8QCASCm4v582HsWAgIgMxMaNbs7x7RTY3RYmXe5jNetV299zxTB9WSwX0DIb7BlNcoQ/P+z6fp1TKMAJWcyXe2cisSn0hsSueYEAa+t52ca5F4cQ00PJ7YlK+f7M76w3m8MLA1GWcLPe5wBOzCt2kobRpqUCrqVos92FeJzmjm4YT6JH86zKtrZu6cyW0xA/gs8zyPdI0mtWcTlHIpE1Y77jDVSw+iVXzttp+R7Uey+OBi1ycLgVVALWv7irAYwu75F9MPKUhRV7Dqia4UVRgxmCxIJdC7dRi3tQqjRG+iuMJeb9RHIeXTPbkYzWH4+Xgn9eIaaJBIJFSarfj6yCkzGFy20xrMVJqkjO8yiVFrh9fa75TuU1DIlEigTmNRKWRsO3WVRTvOVu8cjWugIallGGm9mjJu1cFa+xnVI8ZlGaS4Bhr8xQK6QCAQCASC38rLLzv+PSsLwsKAP1+bh/7DtbmZcmbteqNO13SP6k6IjwyFVMLY3s3davOy/d9iOLvPZR8SiYQpb07h9Quv13q/Kd2nsDLjkttSmjV33P6R2vxGI0iASs5TfZpjteCy/I/FamP9ocsMujW51oXztPg0BrUYBFYZSS3DkEhuLm0ujOcCgUAgEAj+DOqizRdsO8OCRzvx33XHsNqgxC+QL6e8xdhRw1CMH0f+rPeQhoVjw64tXeGtNh+d2JRuzTUkLva+POl3D//Mwm05mCyOYurdn04xvHui12aRTvU7YbIaeGbjWK/ujQl7CaLd2BPIAQLA/0F/xj88ngkJE9Cb9VzQXmD1fatRy9Us2L+AuZlzKTYUV5err2L+1jPc1roLU7pPYdhXta/V3yza3F8lRyGVMrhtfS6XVTq1r6nNd17YSf8V/dmQsoFujboxO2O2U4mjqT2mktwiGatVQZCqbhtJg9RBPL5kP3vOFjvocqVM6qDNW0b4U1JZjFqpqnP/Nzt1ewsVCAQCgeDPwmiE9HQYMwbMZiguhqFD7fXsBW7R6s1eR4F/fySfwGupG97QIbIDkQGhmM1WYusH0DkmGKvNRqnejFopJ0AlZ+CtkXyW1o2dz/Vhw4REdj/fh6WjunCyoIyB722rXjwHu6P8mdUHWbD1DHfGRdIsLMCp9I075m/Jxl+tQCmXEddA49U1VcKzST0/dKa61a40W8vYNrU3A9pEMH19FgWlBod5tqClUPm22z7uiBnIq73eYkz8GOeTp4GF1Gps8W3Zncjhs1AERVa7+q+UVTJz43E++OU0cpmULSeuMnLxHsau3M+MDccZu2o/6cv30Tw8ACSQ2rOJV888qkcMy3blMPmLQ0xfn0W4xodAtbMB5GRBOWarhDuaDGR0RxfPVoMx8en0bHwnukobUgmkJTX1eizfHLxI9+b1HCLRj17SojWYuSM2guFdozz2kXItWnJFhvPHmVE9Yqhw8YFAIBAIBAKBoFZ27YL16x2PTZ1qN+bz12jzhkEqOscEE1s/gOP5Zf8YbV6v3mH6rujEl1mfe9W+6pl9ZD4YLKUetbnxyjmKf1nktp/RYycw9v7JrrV5DdI7pdOj0Z0s2u7Z4PNXaPOFW8+wYOsZFDIJfkqZy3Zv/nAalVzFK71eYfHQxU7/ltpHtmfx0MW83OtlTGY5T644SKXZelNp87SkpihlYnlaIBAIBALBH09dtPnRS1oMRgutIgOICfVl7dgexNTz496ixrR/9EMSjweQOPMX0j7ZV32+WT2/6jVzb7X5klFdKNIZySsrrNtata2MbVN78lTvJg5rtharjbUHLjOl+xQPPVxnaKt7SPgogWJDce2NzwAfADu5bmxpCe1eaceZd88QFxZH3+V9iXo3iu6LuhP7QSx3rb6L5sHNyXwik+YhzQlRBzsYtY9e0lJYZqJ39MC/VZvLpBJGJ3qvhxduPcP077Ow2EDlxuBfU5u/mPgiqWtTOVl4kjUPrSHrqSx2PbaLs+PPsnXkVvo3G8yET7PQm6xIJfbEF2+Y0HUCUomMvTnFjrpcb0Yukzpoc38fOYW6ouoySd4wuftklFIfr9r+nQhrvEAgEAj+fi5fhvvus5cjqsnRo/D++/DCC3/PuP4BlOqNXre1WG2UVNh4JmEyI9ak1Np+QsIkLhWZ+fqpnhSUGSiqMBLiq8TPR4bZYkUukyK/thDp5yMn3OLD9lNXSF26162bGuy7PW+PDadlRECdXzC2nb7qtm7pjaQlNcVgsvD8gFgKjdle3aeK86VXeW9TIZ1jQpjYryUXS67Xhbdho0g5B4vEdT1MqS2IKQnvMP37s0zt9zrdGiXyTsYsDuYdhF3AJvBUrQggMHE4gd0edNgJUBXlveFIPlYbbD11lZSEKOY90gmj2UKJzl6rPlClQCKBMoOJfrERDE+IYrmLheQqqhabX/02C4DlGbl0aRLCM31bVEdxVmGx2lixO5c2DTQ83/11ujdO5N2MWU7u8wkJk+nVuD9HLhhoHCwlxE9Jn9YRbiM5bxzLkDk7SGweVh1bWUWp3kSERsULA2NJaBLKvC3ZTrsfRtWoe3tjtGRV/wKBQCAQCAS/iRtTW8LD4cknq//6Z2rzZ7tNBpuC/w69lUsl+uqdh1Vy8UZtHmZWsuP01b9dm5sk5ylRfMK5ip1e9V2TcQnjmL9vPoNbPOBem5tNXF03Cyyuy18qI5oxYuwUXv8um+fufJ2ejZN4c/dMJ/06qdsUhrQazMRPs7wqpflXaPNSvYmEJiEkuSn/U1RhZFVGAfd2rEebsLZ8+/C3lBnLKNQVEuobir/Sn/yyy5hMKh79eD9j+zTnalkloTeRNu/TOqL6361AIBAIBALBH0ldtDlAsc5IpEbFf4a0cSzV6eNX3eboJS2vfZfFzHvbsmas45q5t9o8PjqYVo3UdRpbbmkuHx/4mKSo21kzdgAjF/1abVx/e1M226YOZkz8GI+lhm4Nb8tLm734zmLCvn6dSfUato+PD8//93keTn2YBpoGTP5hsst7Hcw/SOraVNLi0/jxkR+RE+hUlrKwwsjSH3PqrM1lUgktI/yvlQoyc7KgHMu1F53atLnRbCVQrUSjkiORgL7SzO2x3uvhKm3eISqYPrHhXmnzlfespMxYxvnS8zQIaEDT4KZcKS9EajXz6McHGdunOZVmC2Z9CT2jenqVxtijcQ+K9UX0i4tk/eH86nNag/3dw1cpr9bmG4/mE+obwtzMuV6VSUqLT2NQy2SU8ps/8VyYWwQCgUDw97J/P9x1F5w/73xu6lT7H4FbAtXKOrW3WCX0iRlQq9hNi0/nrtZD+DmrlGc/P+y0SJnWqyl3xEbgq7wuJYwWKzM3nvS4eF7FhiP5NAiqXcTXFK0Gk4WtJy/z8uC4WoXn8IQo2kcFM/j97TQIUvH+8JjaB1WDEHUwhy+e47O9F3i2b0vujW9Ufa5CtgmdzP3ifD3jBJrXa8Dpy/sZOmcvKQnt+fKu9Uwa9xRf/+C+jBGARKmmXvIkfFskOByXSSVMuKMFW09eoVVkACcLyqtd6fO2ZNMqMoBbGwbyn6FxqBX230Qhk3LwQgnPDYilQ5TrGEx3i83zt55hxeMJ1VGcNVm++xxrx/Zg2c5z+Cjas+7Bn6kwaSnSFxOiDsZXrmFVRh7LL+QzOqkp/io5cpmUy0U60no1daiB6mksxTqjQ2wlgOZaOSFfpZwBbSK5PTYcrd5Mqd6Ej1xKPX8lZ66WM+WLQw47k2v2/9q3x5j9cAePv4NAIBAIBAKBE9u2waZNjseeew78ri92/1naPL1TOv2bDeKJpfvJOHvdYB3XQMPopKb0vcVRlwOYrLY/XJsr5VLuiA0n1E+JzWZj+6nLvJTsWpubJZcpka+iQvYTSKxuenRPWnwaiVGJTNw4kRFt09xq85KtyzBdyXHZh0TuQ73Bk4gM8ef05XKGvL+XlIR2rH/4F0zWcrSVJagVGpSSAFZl5LHkah794iLZlHXZ49h+rzbv0iSE+VvPeKXN5289Q88W9RjVowmTPnfe3Tvn59P0j4vganEEkX6+qKQSAn1kqKR+2Cxqfj6sYkVGJslt65PUIuwv0OYm8koNBKoVhAf4eNTm3ZrWQ/haBAKBQCAQ/FnUVZsH+yrp1TLMY6nOmFBflqZ2Yc/ZIh5csMu55JAX2ry80kyob90234X6hrIvbx+LDy4mLT6dJY+9xpyfclErZBRWGDl31cxLidPoFd2LGTtnOJhFmoc0p9xYweHLtW8W5RLwFQ6J401bNKX/1P48fv/jBPgE8HXW1x7fXQDm75vPbdG30SakHykJURRWGPkx6zJGs5VgX2W1Nn+mbxd+fnQrFSYt+WWFBKqCUEkdtfmenGKGd23Ewwn1qTBpKdQVEeobgq9cw6d78vgs8xL/GhRLVp6W/m0i+THrsoM2H3hrfWbe19bhN1HIpBhMFp7q3ZyuTb3X5ot2nOW21mF11uYSmw+VRgXfHbA5aPNAXznHrxZx3+f3eCxjND5hPIlRifRf0Z8v7v+KUD/Hf9tVuhwctbnFZiA6KLrWMknjE8bTO6Y3Msk/Q5wLc4tAIBAI/j5WrYLHHgO93vG4SgWLFsHDD/894/oHoVHLXTqFXRHXQINCLmXkol/5+qlZ1xNFbhAzzyRM5q7WQ1i45SLv/3LaqZ+jl7SMW3WQ4V2jeWFg62phWJeox8MXSx1E140EqhUM7xrNsIQoDEYLRTojwX5K3rj7Vtb9eon029wvxD7Wswm3tQpHbzQT7KvgZEE5KllAnWpL+so11a7yd386xSPdoolroOFg3nGKFO4dzgHmZDpF9iEsQMXCRztRbjBTeDmPu/oP4vDBAx7vKw+uT9g9L6Gsdz3Wu2oeRvWIwWi2cqlEz/+xd55hUV3rAn6nMDADDlXABoiV2LACNmwxaizpRTSWRNGjUWPU1JObnBRjSTExtiSaYkkxiS1qEo1ix95bLFgpUgeYGabeHyPIMAWwG9f7PDw37L32Wmvv4VzfveZb3zfz2RZ4ekhZsvMCC3ecI09n5FhqPiPj6+AhvSagcpmUB6qp2XEmi8bV1fw4PI50jZ4c7bUao4uTz5dEnpfmyGUNeqOZ8Q/WZ/qfJ+3O5emMDJq/k2+HtmHvuRxGfn8Yk8V6NWpeg1wqYXDb2nRpGIyXhxTl1b8PlULOQ59uIiEmjHnPtUJvMLudi79KYZe2slF1NWrlNXUt3gGhUsgJ8lGw5dQVHp11gGfbhPHx09Eu+3/vkcYi9blAIBAIBILK89Zb9r9Xq2YrqVqKW+Hmr7R9hU7hPXl81h67AAGwOdvYHxy9HG6um9fyV/Leow/QItyHXH0uWbpsPL3y+OSZBzh6qZBRna+5+cHL58nz+Il82WqQVL4UZNnF2wi/CJduvnvbJjS7XAeP+3d5nugmjezcXCGX4qPwZHdKEXJpCAHBPgz4agdnMrX4Kj1YMbqdy0D6m+Hmhy/m0b5eEC0jAsr1YSj2cgu9Goey/0IOC3fYzytPZ2Tg1zY333Umh/XHM0jX6CkoSuVEWj5R1dS81fuB2+bmCpmUU1cKeP23Q6Tl6d26uY+nnG5RIZX58xAIBAKBQCCoMJVx8yd1KXgVRvNgo1CGfbvbaRtfpQffDY0pyeoik0qIqlalJJPI8bT8Crn5yfQCvD3UlVqrVsqVHM44jL+XP4Eqf9RKGNc9iExdNoHKEPy95PyTrseqi2HT4M1k63I4mZnCl/tm8/PRJeU/LAuw6epPqbj0wA6BbPx1IwapgR8P/8jg5oP5aPtHbruq7Veb2Q/Ppm2ttuTqc3k81kKgMpgPn6jPyVQdIWpPpj/RDH9vBRIJmE0SMrKV1PSvxxu/HWLDiUNYrLbnvXpMe/54KYa1p1fR58cEu+fVMbwjn3T/lJGdY8nR2d5RXusdwodPNGDfuQLe/O0oF3J0dGpQlXy9ye7zkMukyMwWTmXkExcZWCk3L7qJbu4plxOg9OdU9ilaf9maEa1GsPyZ5WiN2pJsjEq5krl75jL+j/Hk6G2bS7MK00vGLOvlxfcnl0kxmDyZ2HYSCb/2L7d/H4WavvX7lP+3chcgglsEAoFAcPsxm+GNN2DKFMdztWrBsmXQosVtn9a9iEImJTE+kjFL9pfbdki7CBYnn8dDJiVTA2lpzrNunM4o4u9jeU4DW0qzcMc5YmoH0LNxKHKZlNxKpHo8mV6Aj6fzF4zSEfDDv9vtELwyuG0EViTkFBocFmKD1Z5YrFa+236OPK2Rb4e2oe/MrSxJTmVsmwkMWTGg3LmNjZnAkuTUkl2uZouV5fsvMbR9LfovH4ZVond6nYclDD/jEIZ1iEQKfLHhH8ypJ/jmfy9yJcP97s8Onbsy9L+f8POh3JL7jQhUseiFWHalZDNo/k6nz2HF6HYMmr+TdnWDnKb0VinkxEYGYjZbOX2lgFd/PVjywnUiLd/tTl6N3sQLHWpTy1/FvM320evennL2X8ilY/2qxNUJpLDIjN5kJsBbQRUvD2QSWwR86fmolXJq+ivtdrS6mktxKsnSaStHxNdxGZQil0lpUzuQh5tWc9v/gNhwukYFi9TnAoFAIBAIKsfff8PGjfbHXn8dlPbZTm62mweo/LmcbeShT5Ldlsop6+XATXPzdnUCmT2wMStPrmTcN85ThkfV6EO+Po969VaTVDCTQmNBhcb18/Ljq75f0ahqI5eLt9/0W+TUzZ9p6s8fb3zism9l3Tb4RPe0c/PaQT70blqdUxkFzNp4ml0pOSTEhLFgiO2doXQQd9lA+pvl5k1r+ZKSqaXXZ5vd+nBpCopMVPdT8nqvKFqHB9zVbi6XSWkR5k/LcH8W7jjvsv/+MeF0rF9VeLlAIBAIBIJbRkXdvGpBDu9//wamRe+gmfoJRy/7QqlS8cUMiA0n+WwWvx9KZVTn2i4ziSzccdGtm5st1kqtVReX6oz0j2Rtwlo2ndtE1++6OvXyPk37MOHHY0h9kvnl9Htc0aa77riYLGzZWi6VOuYF9IGZ/53JkZwjDPxtIAdHHiRTm8mBdMeMJcV0rd2VX5/+lRUnVtDxm44Oc3w57mUaVn+EvedzyNUa6RtdnbRCA5PXHqd+SBXe6tOI3ee2lrz7WCjivU2vO2SKqRtQl/l957Pp3CaeXznU6bP4fWwflu7OcFmiXqmQ06Z2ACfTC3h01tY75ub+Sv+SQKfJWyYzZesUGgc3Ru2pRlOk4XDGYSxWW8RR89Dm+Hr58eeRa5+BuzVzhdyDvg36kthyBHP3zHHZ//AWifSo+9A9UZIIRHCLQCAQCG43ubnQvz+sWeN4rkMHWLrUVrteUCHkMindokIYEBvmEClcmtL1IeuH+JBZaGDWxrPMSTpbStw0nMooYOPEzi4j1MsyJ+k0XaOCMVksDmmq3WG2WCkymR1S+JWNgC/LkcsaXv31EOO61uPJVjUZ+8M+CopskdcFRSZmPtuCT9adJL5+Vfo2q86xVA0JMbZns3x0T4a3GMG8ve7LMcXX6kG/5fb3//Ff/9C5zR8YpCecX2iVE2SYwMCYejQP8+Pjv05SPW07Y8e+iNXsfsequs1j5HZ4gYwij5JgHaPJQs0AFR+uOcbCZOfPYeLSgyTEhPHrf9phNFtcpvRWKeSYzBaC1Z4cS8133sjZvLw88PKQ071RCO3qBZGnNTpEr7+1/Aj5eiPjH2zACx0i8PJw/TdQ+oXSYsXtXIq/7Cl+iahIUErpmqJzkk47fOEwIr4OXaOCHdKCCgQCgUAgELjFanXM2lKzJrzwgkPTm+nmWkM+854L5rVfD7gNbCmm2MsNZgsms/WmuHktfyVznmvMxL8mMtdJ6vH9afsZ8Ft/4mrEcTzrODn6nAqOKOH5ZiOwYOCpn59yuXjrys0/+uME1Xd9jrkgy2nvUm8/AnuMYUBseImbv97rAXrN2EyAt4L4elXRGkxEVavCyfR8TqbnMyA2jC82nCYlS0vfmVvtMprcTDf38pDjp/Io14dLU7r8T0Xc/OXuNjf3lN8ZN3fm5cX9N6quZsYzzYWXCwQCgUAguOVU1M3n7/sehSYPBaB6fgBz68Uyut8rGGXXvuiXSSX0jwnj/5YfZvnoVmw8v8Yhk0h0aDRj20xg+eievL/KvZsv3HGxgmvVtlKd/0v6H+ueW8eUrVOYt8cxq3ixlw9oPIBMaSZrD62t2EPaCfwFlH7dCAMeg8SuibSu0ZrWX7amlm8tLFYLGYWuN3DW9qvNr0//yqS/JjF3z1yncxz420BGtBzBtO7TGDr/INX8vErcfO/5HM5mFjCuWz3e+/0YA2Jrsj5ltUNgS5AyiPXPref9ze+7fRYjWo5kWvdpfL35EsM6RDqds1IhJ0TteUfdXCFVMCFuEgN+6w+AxWrhYLrzElIT4iaxNyUf09XsOhVZM/dReDO9+3Q6hscz7WrJquL+o0Oj+f7RRfRt0AcfhbfLPu42xFuEQCAQCG4fx49Dv35w8qTjuREjYMYMUFSuFqag/C/1h3eIJDrMr6Q+ZEGRiQCV7TmXFbeoalXQG8wVTmF+5LKG7EIDXnIZcqm0UmnYLVbo0jCYATFhJYvExRHw7tKAF5cqyiwwMO2JaKRSWLLzArtTsvH0kLL6UCqrDqaSEBPGfzrVpWktP+YknWbw/AN8M/R92tbqwKdOUr6Pi5lIfK0eDJ7v+AVCatE2Zuz82OX9NFQN590+j9A6IoCBX25j/88zyN/7u9tn4OnpyaNj3uWod7OSOqBLdp7ni/4taB7mx59H050unpdmUfJ5YmoH0KF+EFYrmMwWpzIrl0nxVXpU6vMpTmeoVMixAkcu5fH7oVQOXcoriV5vVF3Ni12aEF+/KhYrpOXpydMZ8FUqUCvlKEpFoV/Plz2VDUopXVNUozOh0RtRe3k4zEUgEAgEAoGgwqxaBVu32h97801bKVUn3Cw3v14vn7L2OK/2jLopbv7eow+w4sRKp4Etpdl+aXuF5gjQqGpTci8/x6l/WvDN0GbER3Th0+RpbDm/paRNeW5+YdPPHNq42uUYLQa8ziuDO9I6IoBB83eSkqWlcQ1f3n2kMbM3nubhJtX48rnW6K6WPw3yURAXGYQECd9fLS10K91cfZ1eDu7dPKZ2AG/1eYCIQG9yCk3k6bROvRxuvZsLLxcIBAKBQHA3UJ6bv2X+hybb/rS7Ri/3tAtsAagf4oPBaOa/ferwwdY3nAak7E/bz5AVA0hsOZI3er9HjtbAh2ucu3mezljuWvXYmLF0jujMq+tfZVzsOJJSkpwGc5Rm4eGFFXsw+cByoHTidgnU6lOLmGdj6N2wN+3D2tNjUQ/C/cL5oMtUCg2FBKmCXHY5++HZrDixwmlgS2nm7JlD+/D2fN7/EcYsOWjn5nk6Iz0aqen+QAieChPdvu9fcp2/lz8jW49kfOx4/jj9R7nPYs6e2XQI78jQdo/CXezmCrkH/Rpey67iihEtR9K7fm8enrGz0mvmPgpvnoh6nL4N+pCryyFbn0uAlx9+Sj8UUs97JmNLMSK4RSAQCAS3h1WrICEBNGUkQS6HmTMhMfHOzOtfgrvFQwnw8V8nScnSAra0414KmVNp8/GUk62teApzgCv5RQRX8WTxzgsMbhvBxKXOI4tLk9gxErWXnDOZBUzq0ZDoMD++3ZZC/5gwp1ljKlKq6Pn2tfllz4WSHYWLks/TPMyP+HpVaRBahWOp+fSbuZuEmGiSBm1GY8gjvSCLKgpflDI1S5JT6bd8t8PiuUlyhSyF68CWtjU6seSxKfyw8yL/9+N2Tv/wLkUXDru9f1mVIFoO/4DHn+7JhxH+DF2wi471g3jpwQbsPZdNns7E3KQz5T5HgLmbztAyIoDnvt/JsI6RdIsKcSq1lUmTXzadoUohp13dIFrXDrD7+/JV2gT+r2PpzE064/C5JMbbz6ci2VU6NwjmSoGeP8Z1vK7F7+KaoiqFnFBf5186CQQCgUAgEFQIsxlefdX+WHg4DBni9rKb4ebX4+VpeXo85VL0BjO/7rt0Q27+/fYUWoT7MHaBk1Ky10GYOpzEliMZ0PRZ5BI1i3ZcZuBX++nTrHJuXnTpGLmbvnU5zsChiXwweRSLk8/z7qpjJdd/ufkMPyXGUfsJb/48ms68TY7uOqRdbVaMbnfL3fxGvBycu7m/SoFKIWP9sXQmLT1YrpcX93Mr3Vx4uUAgEAgEgrsBl25uKEQZ/YxdW4t/AD8mjLcFf5TCx1OOVGplw7k1bjOtAMzdM5u4mu3pXKuPWzdPydKWrFWvfnYDJms+5/POE6QKwl/pj0wiI0uXxajWo2ga0pSOCzrenAdyAFgL6K4dqlmzJp/M+4R6LeoTqAzA19OXPH0Ba/r/jafUh3VHs6njL0flISkpoVMahVRB21pt6bCgQ4Wm8NG2j+jXoB9Tn2jq1M17N63GhJ5VS0og1Q2oy9qEtWw+txm9Sc/UrVMrNM70bVPpU783z365k+c73L1u7iy7SjHRodFMbPsKfRv0IacAfh7R9rrWzBVyDxR44KPwpqZvzQpfdzciglsEAoFAcHvYu9cxsKVqVfjlF1s5IsEN427x8KUH69O0pl/JouXi5PNOF7tL7xwtRiaVUD/Ep6Tm5Mn0Asylik76qRTkaI0s3HGOFaPb0b9NmNPMK8UkxITRrJYfgxfs4slWtQgL8Can0MBXg1pjtlgddqdWpFTRxKUHGRATxvD4OszccLpkEXvB1hS6PxCK2sumPLZdmGcZGFebhds0xEaGUi2kCgO+2sGZTK1D31ZMZHpMxSJxnpZQIfFFkT+aTtOS0KedIePXdzFrrri8dwDPGlFUfeR1Lsn9eXfVUaY+3pQVL7ZHozdyNrOQ8CBvvD1ldG4YzKVcXblp6I9c1qA3mDFZrIxZsp8BseG83quhg6hXdHemq3SGzv6+tAYTH6w+5rS/I5c1TudTkV2cPl4+bu9ZIBAIBAKB4Lbw3Xdw9Kj9sffeq1C2yRt1c2deDu7d3P9q+2yt4brdPPyqm88f3IZc/ZWSBeXrxc/LHx+FN+c153hjw6u8seHVkpTt37/Qk8HzD1TYzc26fK4snwoWs9Ox1NUjSWv4OB2nbqDU6woyqQSL1YrOYObI5TzG/3TA7n0GbO56u9z8Rr28uI/Sf1/X4+Ug3FwgEAgEAsH9gVM3/894uHzZrt3a5yfx6EPN2VZmzVxrMOHvLeHT5GkVGm9G8nQejeqLBPduXrxW/eXmFLa80p5VJ1fxn9b/YfXJ1Sw4sID2Ye0Z3Xo0efq8G/ZysoFVQJmY7bheceR3zefJnU/ayhRxNaAibhJ9GvThP98fZtuZbJIaxfHbiYWMaTOGoSuG2vXRt2FfcvW5FZ7jvrR95OhyyCrwdurm6Ro9qfmZgC1jyx8D/mDK1insuLiD2FqxlRonV59LDX/VXe/mFcmu4hNQodu2YbVCUREYDGAygdEIUqntOzlX7SWSSgxw5xDBLQKBQCC4Pbz5Jhw4AL/+avu9RQv47TcIC7uz87pPKLtoWVBkJFSt5MDFXDvJKr1z9GKOzq4MULbWQMDVupFLdl5g4Y5z1PRXovSQYrHIyNMZGTR/J98ObUOLcD8WbE1xshsywi49ePLZbPafD2dkpzq88eshJjzUwGHu7koVlWZh8nmiw/xIiAlj1sbTgE0WC4pMBPp42s1DIZMyb9MZ5iSdISEmjAVD2tB35laHxepC5TcUcczlmNWtEzmb7kXhsU1krZ6B1VTkdo4BLXrg0zkRidzDLhvNU3O3O81Gs2J0u5Jn5Y4craGkduvCHeeIqR1Az8ahDrJdkd2ZFU1naDJbWHcs3a30u5rPje7iNJktGMwWNDqTyzJIAoFAIBAIBDeETgdvvWV/rFkz6N/feftKUBE3L5vRpWyJzrJuXlyi81iqhgCV4obd/KtNZ+jRwnTd9+jn5U/DwAbsuLSDXH2O3bnSKdsXDnsfzwq4udViJvf36ZjznQeSK7xUhD76GiezrmW7KfvMMvKLaFTdl6SJnUreZ4rHuN1ufrd4OQg3FwgEAoFAcB+yZQvMnm13yNztQVq+OQaVp5wDF3LLlKSUkFdUueCNPH0uOqO5Qm5eP6QKV/JNTGg7gdf/fp2/z/7N2oS1bDq3iTf+foOhzYe6Ga0cLMA2IAkotfTtH+BPy+EtWee1ziFTzf60/ST81p8RLUfyxYCpPDxjJ0uSU6lZvRptajRneMvhdmWBgr2DydJlVWpaOfocwgODnbq52ktOTV9bEMbI1iNLSjK1D2tPpjazUuNk63II9LZtArjb3bxC2VW2b4eTJyE93faTkQFXrtg2lufnY9VoSv5bYi6zKSAuDrZtc95vYSH43BtB7SK4RSAQCAS3B6kUvv3W9g9v06bw5ZegUt3pWd1XlF60BNuiZWlpKw5mUSlkvNStHvVCqrgtA7RidDuOp+azKPk8z7QJK1l47ztzKwkxYXz3fBtMZgs5hUa8PeVIJDikBwdYmHyOVhH+9G5WHbXSvr6jTCpxWarIGQu2pjDvuVbMSTpdsltTozOSVXAt6CSxYySLks85lC8qHRQD4OmbxDnDMpdjDW06jv+2f5GOT48kc8Nit/OSSGUMnfAOb04cy+AFu9DoTCwZFsvKA5f5bf8lTqYX2LUvzkaTEBPGt0OdB96Uxl+loKDo2pcPc5JOu4wkr8juzIpgMFuYs7Fi6dndzaeyaA0m1lWwDJJAIBAIBALBdfPPP7bda6WZMsX2XnMTcOfmC3eco1VEACqFjOEdI/nkr5MVKtG54Xg6/2QU2gXFXK+bt44MwNdTX+n7kqGkU41BPNa0BaPWDHfbdu6e2XQM70iYV+dy3dySvJCC03tc9vV/H37MwIHPMvBrW/BJRcqaFger3Ck3v9e9HISbCwQCgUAguAcxGmHECPtjKhWyeXMJ8VUC8PrDUcREXlszT4gJdwjYLo/MwmyyC40Obj4gNoxFw2IwmCxc0RTh5SHD00NKSlY+h7L/5ucjP7N7+G6mbJ3CvD3zaBrSlEntJl3fvV4CVgDp9ocbdGjAiLdG8NLWl9xePmfPbDqEdeTdR+IZ+8Mhlr/YhfkHPmVS20nE1YxjRvIM9qftJ6Mwg0BlYKWm5u/lz3+XH+ZcltbBzd97pAkKuZGW1VqS2DKRvkv6AqAp0hCkCqrUOAFKf7IKrz2Au9bNDQY4dQqOH4egIOjoogzVu+/CmjUu+3SXe8VsMCBzdbLsu+9djAifFwgEAsHtw8cHNm2ChQtFYMtdQrG0LR0Rx9ZXulA/xIeXfzpAbGQgc5POMLFMTUi4trg7b9MZ2tcL4tmYWqTm6hjVuU5JG6lEgqdchs5oQWs0Y7JYkAASF6ntvtx8hi4Nq+LjKadRdXXJ8fohPugNZodSRa4oTgPeILTKtXv0lKPR2xaXB8SE0yLcn6+3pNhdt2BrCoPiIpBenZ5ceZKzpk9djuMva0I7v8EMG/gMF8oJbJEq1QQ//R7rJNF8ufksP49oy7qXO2K2Wmke7s/MZ1uQNLETozrXxbdMcM+i5PPsSskmIcZ1hqNG1dV4ekg5kXYtxP7IZQ0aneudtqV3ZtYPqUKorxcqhbxSi9wanalSn4u7+VSU4pSOY5bsd/p3OWbJfj5YfRyt4cbHEggEAoFAcJ/TtKktwOXDD8HXF7p0ge7db+mQxW4+f3Br6of4MOL7PbQI8+OnxLhy3fzjP0/SvVEoGybE3xQ39/ZO5bGfHq3w3CVIeD56BLUMX/HNEx8wb+8XFbpu+rap1A72LPFwcHRz84mNXNz0s8s+asb0ol67XnjKZSx6IYZa/kq+GxpTofeZO+3m96qXg3BzgUAgEAgE9ygzZsCRI/bH3nsPatcu+bXsmnk1X09Uct9KDRPkHcAnTzcrcfNrWQXD0RWZuZyjQ+Upx9tTxo+7LlC7qidTtk6xy1QCcCTjCCoPFZH+kRUfvAhYDXyFfWCLH0gTpPy58k++OfVNhbqavn0qLSOqUFhk5MVFR5nYdhJ70/aiKdKw/JnlHBt1jHEx4/BX+hMdGl2hPpuHNsfXy48/j6S5dPMCvYT/dX4XrVFbkjGn+FlczzjF3Gk3l1rM1Ltyjn5HNtDzh5lIH38coqLA2xsaNYLHH3fIKmRHSEiF51KW9OzCf4Wbi9B5gUAgENw8rFbbP7ytW9t+nOHvf3vnJCgXuUyKwWzhw7W2OpCjOtdl7eG0cssAFe+qLCgyUcXTgx6NQ/nvw1F0eyCEXSk5PF2JdN5HLmsoMlmQSyUMbhvBxKt1TX085WRrDQ5ju6N0GvBG1dXIpRLkUgkznommS8NgCotMDIgNt0t1eOSyBitWkl/vyq4Lxxj8+1CMWue7MaVWNYqLz5L4RG9M2RfdzsUjOJLgx95E7htc8sxaRwSQqzPw9oqjFXo2zrLRlOaF9pEsTj7vcE6jN15XWvGKkqer3Odyo/O50XTrAoFAIBAIBJVGqYRXXoFhwyA397bUIDeYLUxec60++9rDaQSrvSpQovMc0WG+N+zmVqxsT19E/LffUGR2X3KzNNMf/BSTphuXgjMpNGoqlbLdYMmnZbg/u1Jsu2FLu/mqtesY8clnLq/3CApD0m5oSWaVl7s34MPHm1SorOm/xc1vt5eDcHOBQCAQCAT3KBcvwttv2x9r3hxefNGhaek18yU7L5D8RnuiQ6PZn7a/3GGahzbHS6Zm6ILdDG5bm55NQlk3Pp6NJzKcZhUc27UeFgo4nHGYFc+uKMlUAmC2mnnq56c4k1OxbCAcBv7AvtSQHIgDOkDjWo3tAkbKo7jE0s43uqE3Wth9No9edR+m0FhAti4bnVFHTXVNsMLLcS8z8LeB5fb5ctuX2ZuSj8ly7VhZN/dVerB2XDsuFBwvaWO2mpm7Zy5j2oxh6IrySzVNiJvkMA7cPjeXm03UzbpAk7RTNEo/TZO0U0RdOYvKWM571tmzrs/dQHBLfr6WPccy7nk3F8EtAoFAILg5FBXB6NHw1VdQowbs3g2hoXd6VoIKUHph8nrLAHWY8jfHUzWMe7A+H6w+xqJkx0XO8tJ5Z2iKCA/0pm2dIPq3CWPxzvMUFJkIUCkqdT+l04AndoxEpZAxZ0BLFiWf563lR6jpr3S6WH0xR8fKw4eZd3wAV7QZzju3SlGnPELGD+9jLSp0Ow9Vww4E9hyLVGEvyl9uPsO851rxv5VHSxa93T2b0tlojqXaF0BNiAnjwQdC+N+qMrsNALWXh8Oxm4mvsnKfy43O506mWxcIBAKBQHCfExBg+7nFlA0YkEkl9Gpa/ba5ebbuCpmKT9DL9oDZyQAuSGw5kscaDKDfzN3UD/EhS5td8YuBXH0unz/bnGfm7bBz88W/b2DuK4MxGJwHb0g9van66BtIPWy+vSj5PC3D/HmocSjv/769QmP/G9z8dns5CDcXCAQCgUBwj/LSS1BYZk131iyQO35dXnbNXCpRMC5mIoOXJ5Q7zLiYCcikCo5c1vC/VUfpUD+Iz9b9w0IXXv7l5jNM6uNNo+BGTgNPki8ll39v2diytZwqdUwi4eEn+vB7zRVwNfGM2lNNpjaz/P5KkaXLYdtFCw81qsb5LB1FRiuNa3lxKusMvx5fyp7UPRQaCtmbuJfElonM3TPXZV8jWo6gT72+PDxjl8O50m6epzPy5m/HefvR6nZtZu+aza5huxjecnhJdhvn44ykb8M+PPyp47O7HW7+4ZrPeOToRrxMlQtEByAlxfW5unVtWUZDQkp+DAGBzN2fxT96KfmeKgoVSgoUKgoVXhhkHpikMkxSGQaZBxGu3Nzbu/LzvEOI4BaBQCAQ3DhpabZ0adu22X6/dMn2+99/g6fnnZ2boFxKL0xeTxkgg8lMVDU1Cg8Zfx5Nd7p4XprijC8JMWHM2ni65LjKU4ZMCsdS80iMj6RFuB/fbkuxq0taHqXTgCfEhNEqIoDCIhPDv9/Nkcu2xec8ndH5YrU0n4/3DaLAcsFl/1UvxHHlu+/LqUEpwa/jQNSxTzpN9e5uQdzVs9HojSXZaIrvc0i7CFpHBJBdWESorxc5pTLNNKquRq28tZqnVsor9bnc6HyuJ926SiFUVyAQCAQCwb1D2YCB2+nmHRvlMvvQOMzSii90R4dGMyFuAn3q92Xk90fI0xkpKDIRqKpcIJBKrubn3Rft3Dz70llmvvI8xsIC5xdJpLQY8g4RzRpTUGTiZHoBZouVv09kEFc38LrKmt6rbn67vRyEmwsEAoFAILgH+eMPWLrU/tiwYRAb67R52TXzvEIT8bV6MLzFCObtneNymMSWI+lYqwf5OjM9GocSGeRD0okrTgNbiikoMqFW+F1X4AkWYBuwEShVccYrsiUtnxjFRxO68PusFSXHNUUaglRBlRoiQOnPP+kaWocX0ad5NYpMegoM+TSq2oSOER3I0+eRWZiNzmBgevfpdAjvwPRt0+2y3ESHRjOh7QT6NejHW7+d4kKOzmGcsm6+8WQmUurbZczJ0efQY1EP1iasJa5mHDOSZziMMzHuFXrX742mUILK095Bb6qba7WgUtkdKnZznYfn9QW2VKsGDRuCyeQ06IoXXrD9lCI7T89Hk9dXqHuXbi69dwLR752ZCgQCgeDuZPduaNXqWmBLMdu2wZo1d2ZOgkpRemHyesoAFRktLBjSmqHtI/hyU8V27y3YmkJCbDjSq7EfjaqrUSlkFOhNxEQG8t22c5y5Usicga1QyCQM71CxmqJD2kWw7lg6U59oyvCOkfT/cgepeXqnC6mLks+zKyWbhJgw6leTMmz1ExRYUlz2HZYVwZX5W90GtkgUKqo+8Ra+cU85DWwpJkdrQK30IKpaFVpH+BNVrQqyqw+j7LMBCFV78fFT0SwdEcf68fHMHdiSM1cK6TtzKxn5RXaL6wAj4uuguMU7IxUyKYnxFftcbsZ8rifdukAgEAgEAsG9RNmAgdvl5v/7eybzjg8sN7BF5aHilXavsHHQRo6NOsayp5dx5MoR3k56h/cfq4+v0oOT6QV4e6iJDo2u0NjNQ5ujkqv5ZN3JEjeP9NIy5OlHMBbmubzu/95/l4X/e4KXeir5tH84GyfGMapzbQqLTOS6KC/qinvdzW+3l4Nwc4FAIBAIBPcYer0t63xpAgNh8mSXl5RdM88sNDB4/gFea/s+3/Rb5OC70aHRfNNvEa/Gvcfg+QcoLDLzRq8oRnSqw4KtKW6nV+zQ1X2qVy7wJBP4GlhHSWBLcP1gFv36G/Wf+4BUWYiDmx/JOILKQ1UpX/f18uNKvp6AKrD29G/0WtKZ+l+EEfl5GP5T/Jjw58vkFmXj7enFh1s+JNArkE2DN3F+3Hn2J+7n/LjzJA1OItArkA+3TGFMtzB8lc6zp5R28xZhfvx1OIuxbSbYtTmVfYrWX7bmZNZJlj+znGOjjrFlyBbOjj3LpsGbqe4Zz8MzdnI5T39z3Tw/H1auhDFjICoKund3aFLs5ntqRLnt6lKVqqS374Jl/Mvw9dewfTvk5MDly7ZN484CW1xwv7m5CJkXCAQCwfWzcKEtulmvtz+uUsGCBfDII3dkWoLKUVp+rqcMkIdMytebzzIwLvy6d0gmdoxk55lsmtT0JVWj57m24exKyWbE97vx8pAx45nmJaWKXDEgJoyHGoWi0RtZtOM87646Rp7OaFemqCwLtqYw+Ylw5hyfxNn0wy779kz35Py8FLf3JA+oQfBjb+IRWKvc+w9We/HZM80p0JvI1hoIUCnw9JCyZOcFFu44Z/dsGlVXY7FaGbFwNyqFnIIiEyfS8kvSppe9vwGx4ZVO+20yWzCYLWh0JvJ0BnyVCtRKOQqZ1GU/cpmUblEhDIgNK0md74zrmY8z7kS6dYFAIBAIBPcReXkwYgS8+SY0anRnplBmUfJWu7kVCzkeX3NOu7xCfeuMOtacWsPWC1vRFGk4nHEYi9VWwL5ZSAsSYqKZtfEsS5JTGR87keeWlZ+yfWzMBJYkp2Kx2tx8YowPU2aPITsjzeU18Y91Zpn/T7wz642SY9Gh0YxtM4F3Y3rhIa3cM7vX3fx2ezkINxcIBAKBQHCPsXYtnDplf2zKFFuAiwucrZmnZGnpN3M3CTHRrHz6bwqNGrJ1OQQo/VHJ1SxJTqXf8t3k6Yx4yqW8v/oYEx9qwNFUDTKphPohPvh4yu0yDwKYLVaWJKfSo04fpm+bXv79WIEdwHquZWupCnSBjIYZGGtrSVDWZ9bGsxy5qGNS20n0/7W/bSyrmbl75jKmzRiGrhha7lAT4iZx8HwhE3rU5tV1E5mzxz5rjcVq4aejP1E3sC4XNRd4f/P7AMilch5p+AjB3sFkFGaw7PgyTBbbZOsFRJW8O5SlrJsHeSsIVPcj+dIIu7Fz9DlM3jKZKVun0Di4Mc81fY4BjYfRaeo2sgptwRs3xc0vXcaybBnWZctRJG1AYiwVGCKTgUYDanXJoWI3P9GnG6yYCsAF3xAOhdThcGhdDofU4UhIHXp2bcbrvRoivQnZDe83NxfBLQKBQCCoPGYzvPoqTHciWuHhsGwZREff7lndF1xPEEJ5lJafk+kF11UGaP+FHLo9EOK2bVmBz9Ea8PGUkxATRmxkIBarFalEgtkCfWduJSEmjDkDW6E3mMkqKOK1Xg2JjQxg7qYzdnNrVF3N8+1r065uEF9s+IfN/2RyPM32clC6TJEzDqSe5rGlL3A276TLeUvSJBTNLwKz63vr/lB3VA+9zL708qOeG1VXU8VLznNfJ3Pokv19DG4bwYrR7dDorqU6H9IugkXJ50vKKpXtq/j+GlVXMyK+Dl2jgiuV8ltrMLHuWDpzkxyfa2J8JN2iQlz2p1LIeb1XFDG1A5mTdNrh+uuZjyvuRLp1gUAgEAgE9xGTJsEPP8DPP8OoUfDOO+Dn5/aSm+3mZRclb6WbS6RGinxmkG/c6LadFCkWbAEsVqwcTD/otN2M5OmseHo9m//JIirUl/iGfUlsOYK5e9ynbI+v1YN+y3cDsHfvXh7/37tkZ2W5vCayXSRJjTdAuv3x/Wn7GbJiAIktRzK9+zRiageQfDbb7b3Bv8fNb6eXg3BzgUAgEAgEdx9u3fyRR2DjRvjPf+DoUYiLgyFD3Pbnbs181sazzEk6S4PQKlfXujV2Ac/FXphVUESO1siozrV5NqYahUYNWdpsAlUBqORqftiZypKdlwhRe3LwYiZp6QtZd/YP9zdaCPwKXK2a6RviS15cHjSlpF7LjOTp/P7s39SpqiY6zB+JtJedm8/eNZtdw3YxvOVw5u2Z53Koka1G0qdBbxYnp3KqYCdf7v2SpiFNUXuq0RRpOJJxBLPVjEwiI7FlIn2X9L32eVhMLD261Gm/xe8Oc5LOljyz4ufmzM0fjArmwyemEB8ez5RtU+zKEDUNacq4mInE1+rB03P3lAS23JCbX7wIP/yA+ZdfkCUnI3eVyd1stv1d9e1rd1ilkDPquc5st/zAp1k+JJdKSNmoupq3hZvfEPf27AUCgUBw+8nJgWeegT//dDwXH29bDK5a9fbP6z7gRoIQ3FFafswWK4uTzzO4bQQTlzpfuC7NkHYRLE4+j8UKIWovp218lR4MiA2nf0wYeoO5ZDdksNqT9x9pjFIh46m52xnTpR4PNQ7FYoWa/kpmbTzNnKTTJS8JAG0iApj3nC3gJUdrINBHQXAVLwxmC7mFBrpGhfJky7CSnZb1Q3xK5leWIskprni+iznH9eI5mWD9zgruMvu1h0FTBtEkqBV9Pt9e7jN7oX1tvtx0xk7QwZbNZuLSgyTEhPFqz4YAJMSE0ToigHdXHXPaV2LHSDxkUra92vW6vkjRGkx8sPqY0x2eRy5rGLNkPwNiw3m9V0O3AS49G4fSNSoYjc6ERm9E7eVxw0FXZSlO6Thmyf5y296OskwCgUAgEAj+RWzYAPOuLuqazfDZZ5CRAUuWuLzkVrh52UXJm+nmxYHmIWovWkUq+P7kKHZe3uaiJxuNgxtzOMN1dsPS7Evbh1VSyE+JcRToTZzPKmJqt2l0iohnylb7Bejo0OiSBejB8w+QpzOiPZVM5srpWA061/OJa8LhzofcFlmfu2c2nSI68mbv7vedm98uLwfh5gKBQCAQCO4uKuTm8fGwfz/MmAHduoHUvZ+Ut2ZuscKxVOcbKou93MdLTt1gDw5m7aPPjwklTiyTyHg86nGGNn+B/3Rux9nMXAYsf5wDF3e4v9HzwFJAAwpvBRNfn8gX1i+uZW+5yr60fZgp5MEHQsgqKCK/yOrg5j0W9WBtwlra1WrHJzs+cerrjzTsy4w/zzE0via/HMvmzNgzaI1aMrWZBKmCUMqVzNs7j03nNqE1ajmQfsD9/EvNr9CoKcmOWIwrN//rWAY7p+cw5fEObB7Sh7T8LHL1uYT4BCCxerN4x7WMOcVU2s01Gvj1V/j+e9v7odWKrAL3Yvx7Ax5lglvA5uatn3+SBVcDroSb3zxEcItAIBAIKs7Ro9Cvn2MKP7DVrPz4Y/C4t1Oa3a3cjCAEV8ik8G7fRvx3xWGOpxWwcMc5VoxuR/+YMBYnu05rXby4++22FGb2b4HayzFCOCJQxbdD27DzbDbDv9vtNONKq4gAAL7eepYHG4WQrtGVvCiUfUnYlZLD7FIBL6M610WjM/L6b4ed9t2lYTBfbXZMb1go20yWx6dYJUWuH0wu8B2gdXFeDvQDmsD0HdPZNKQfCW3CWFRO6aQW4f68vfKoyzaLks8TUzuAt3o/gFrpwaD5O+3EvKSv2HC6PXB9AU1g282w7li629TlAAt3nCOmdgA9G4e6LVEkl0lRKeSE+joPcrpR7kS6dYFAIBAIBPcBWq2t1GppvL1h8mTXl9wiN1fIpLzXrxGLdp7nWKrmprj5xRwdA2Jr8mxMNXQmDRc1Zxm5+jlOZJ1wO5dGfn2Y3/e/tPmqTYXnn63L4fB5Ka/+epgBsTXpH1udLhEP0rNuLzRFeeTq8/BR+OIh8SlZgM7VGtAkLyU36Tts+dWd06J1KwxP6SHPZZMSpm6dyqYhfe5LN78dXl48jnBzgUAgEAgEdwOVc3MPmDChQv0qZFI+eqIp609kcCxVw0+7LrB0ZBz924Sx2I1jFnv5jHX/sCSxOa+UKuXj7+XPyNYjSWyZWBIkciTjMMNWDuNgRjnB7NuBvwArxD8Wz9TJU0n4I4Hc7FynzbO12Xy2JZM/jqS7dHO51Ive9R6lV91HyNblkKW1L7GUXROqqOT4eErwUfjQ74d+DkEwY9qMIbFlIhmFGRV6riXz0+WUbGiF8t08T2dkxML9fPZMNFHV/KjpU5U3fjvEhhNXHDa2VsrNL12y/U0sXw4614H2xRikcvbUjGJzRHO2REQz7Lkn6Gm2CDe/jYjgFoFAIBBUjBUrYMAAyC8TjezhAbNmwQsv3Jl53QfczCCE0n0azBbydEZytUZC/ZT8MDwOo9nCouTzjFmyn0UvxNAyzJ/5W886BI4MaRdB64gARi/exxf9WzIn6TT+3gq76HVfpQffDY1hTtJpp8J/5LKG8T8dICEmjG+HtqHvzK0UFpmIrOpDNV+lyxeF4oCXAbHhNKquptvHmxwWmEv3PbN/c/rO3EqezogVC3nyxeR5/OD2+ZCDLbDFVSY/NfAMUN326760fWRpc3j94YY0D/djwdYUJ7sE6tAyzJ+Er3Y4XRAvzdxNZ/g5MY5N/1zB29Ne125WWnGD2cKcjWcq1HZO0um7Qnxvd7p1gUAgEAgE9wFvvgmnT9sfmzwZIiKcNr/Vbu6lkPPSgw1Qe8lv2M1Hd65LVHVPNp5fY7dT1B1S5LzdcQoHjscQ6lOt3PalCVD689XhdJaPbsXG82vo/YNtTKlESuPgxrSs1pJHGjxO06pxrD6UTo6mgKy1n6E9muS2X7/ajZmz+GvaLGpWoXkIN789CDcXCAQCgUBwp7lVbm60mDBZDHh759PhAT39Wobg69mAk2k6RnWuQ2xkAHM3OWaJKfbyQfN38mTrGmy+sLYksKVuQF3WJqxl07lN9kEiFtxmJsQC/A7sgUbNGjH5k8lst2ynx289yNHnuLzMS64mq6Bibj50wQE8PWR2JZaiqqkZEBfB0PY1mfTXhJL7KM3+tP0MXTGUxJaJTOk2BX8vf7dzKk2A0p+CIs0NuXmapsihrFGlHdTPD9ascRvYkq9QsqFOa/6sF8vGyFYUeKpKzs3ZkkLXxtWEm99G7v07qCT//PMPv/22nKPHjqHRaPDy9CQ8PJyu3brQtUsXJBKJXXuz2cyq31ezft16UtPS8PLyoknjxjz77DPUqlXT5Tg7d+5i2fLlnDlj2y1eJzKSRx/tR6tWrW7p/QkEAsFNx2qF99+H//7X8VxICPzyC7Rrd/vndR9RmYXOhTvO0S0q+Gp9USO5OiO+Xh74eMkxW6x4yqVYgXVH010IeG0SYsJ5qlUtMgv0nL5SwIIhrTGZrVzO1eGvUuDpIWVx8nneXXWMAbHhJJ/NYvHO8/gqPWy7Sq8GpZQ+545FyedpHuZHQkwYVwoM+Cs9QCFj4kMNaBnufAE/Mb4OHesF8cgXW90Kb+m+Z248RJbiU7Qy9+nXyQC+B5xnlYQw4CnAx/7wZU0mG4+a6N20Gm3rBKEzmNHojVTz9aKKlwdGs4X4aRvLFXSwBedo9Ca6RYXQsX7VW5K6UKMzcTS1/DqcJfPRme4K+b2d6dYFtx7h5gKBQCC4o6xdC598Yn+sXTsYNcrlJTfi5jlaI75KD9RKD2QS8JBJMZgtt8TNa/krSexUg0l/TWSuk4VoZ0isSoIMrxGlfhKvyEJ8Pf2IDo2uUFBM89DmyCU+THhIxQdb32De3mtjWqwWDqYf5GD6QRbsX0Biy5F82PM/dHloBLrLJ9326xXRnE5jp2H0MFfoHopxcHOjGZ3BTJCPAh8vD0zCzW8Kws3/XQg3FwgEAsG9RmXc/Ld9l3jwgSD0ZiM5uhyydbYsJf5efsilCuRSW3C5zqRl5clVfJo8zSFLyaS2k6gf2of2dYNoGRGA0WRBZzSjkEntvLygyET/mGp0+34KYMvY8seAP5iydQrz9sy7NikzuK19owd+Asl5Ca/932ucbHiSRzY/gsVqcXuvzUObo5KreatvlQq5+fwh79Fvpn1ZnyHtIjAazaw8t8ppYEtp5u6ZS6eITrzd6W3Grh3rtm3x/KpVCWT+4GCq3Gk39/aG556Dzz+3O6xRqFjToB2rG7Zne1hTDHLnFQuEm99+7vyTvo1s27adadM/wmKxUCcykgceiEKj0XD06DGOHjvGgQMHeXn8SyXtLRYLU6dNZ8eOZLy9vWnZsgX5mny2bd/O7j17eO/d/1G/fj2HcVasWMnX8xcgk8lo1qwpHnIP9u3fz7vvfcDwYS/w8MO9budtCwQCwY0hkdjqzZelVSv47Teo6XrBQnBzqOhCZ0SgiqlPNOUvF4vjg9tG0LNxNeZtOs1nfzuWljpyWcOEn23ZTsY/WB+DycKsjadJOpnB3AGtmLL2OAVFJk6k5WOxgkwqoX9MGMO+3Q3YUgMOmr+Tb4e2oWW4H+3qVmXoN7sqdI8LtqYw77lWSCWgNVnI1RpQKmT0ahzKQ41D0eiMaPS2LwOqeHpgslooMlp4uGl1Fu4451Z8F2xNYcLD3mTvmoDW4j7QhovAIsBVoHYroAdODSpA6c8Pu87h7Snj7JVCjqdp+OTp5uxKyebPI2k81bpWhQS9mFydAYPJjEIuw1clJ8hHcVMFNE9nqFR7jd54S9MnVobbldJRcGsRbi4QCASCO0p6OgwaZH/M0xO++gqkrp2rMm7+0VPNXLr50Ha16dE4lHmbTjNj/c13877Rofx2dEWFA1uqeFTFJ/+/KKyRJW5usUiY1HYS/X/tX+71k9pOolAPWy+tsVs8d8bcn2ezeOUidDnun6OqQXuCer+MHg8CVQEVuo9iSrt5bqGBXk2rcylHx5yk01zJ1zOpR0Ph5jcJ4eb/DoSbCwQCgeBexJWb18m6QKuLR/mp6YNYJVLa1Qnk02ej+OXYL0zfPtUhaGVC3CT6NeyLwWjltb9fceqz+9P20//X/oxsNZLJXafS75PtPNqiBokdI3lrxRH+OpqO0WxLI9KouhqdScOB9AMAjGw9kqSUJPvAFiPgPF7i6s1h24ApA+swK3SEXv69WHpiabnPZVLbSegNErZeWl2+m++ZTdua7ZnUI46FOy5wMr2AZ1rXIqZ2AP4+MHXplHLHA1tp0HXPreOlP14qN/hmUrtJZGjMfLru5HW5eY7WgIdMglIhI8hH4d5BLRZYudJWfug//3HeZsQIW3CLXA49e3Kpz+N0OV6FIg/PCs1HuPnt5b4JbjGbzcyZOw+LxcL4l8YRH9+x5NyFCxd57fXX2bRpMw8+2I2mTZoAsG79enbsSKZ69WpM/uB9/Pz8AJvsT5k6jY8/+ZQvZn6GTHYtrO7ipUss+OZbPDw8eO/d/9GwYQMALl26zCuvvsbX8xfQokVzqlWrXGpXgUAguKN89BEcPgwbNth+HzAA5s0DpfLOzus+oSILnRUpATRx6UEOXMhlWMdIvt3uOiBkUfJ5WoT50yUqGIDjaQVIpBK0BjPHUq+lM6kf4oPeYLZ7gUjJ0tJ35lbGdauH1Wqt1O5Dg8lMkI8nr/92iNWH0jBbrEQGqVgyPI7UXB2b/rnCX0fTOX51Ab84YGfF6HYMmr+TlCyt0753pq+k94+z0FoK3U/iELAcMDk5JwV6YQtucUJxNPyJtHwWbE3hy+daIZdJmLr2OAuTzxNVrQrDO9ap0LMoxkMqJXHhHo6l5l/NVhNJt6gK1gqtAL5KRaXaq73cvW0JBJVDuLlAIBAI7igWCwwe7BjEP20aNGzo9tKKuvn3z8cwZ+NpFiY7d/OXfz7AvvM5DOsYyTfbbq6by6QSno2pRu8fyg9KAfCUefLngHX0n30BC9fcXOWhoEOtHgxvMcLtonhiy5G0r/UQPgoZnyRPcz2QBdgCbIB8q/v3BN+4p/HtkIBEIuVkegHeHupKZZEp7eY/Do9j8upjLLr6nhRVrQp+lXRh4eaCfzPCzQUCgUBwr+LUza1W/vfXbNqdO8jTB/9k5uMvMeO5eCa6yGi4P20/A36zBa282/ldfj76o9sxfzj8A8NbDmft+BiytTlc1v7D+B5Vea1XJD/sTGXhjotEVatCts5WnkcmkZHYMpG+S/pe66QIcBc3kQ18B9QDHgLkMHvXbHYN28XwlsPtg2TKUGE3L8WnydPZMGgTbetUxV+lQC6VcDxVAzJdSYBOeexL20ehoZB34t/hvxudVAG4yshWI+kc3pPuHyeTpzNel5srZFIGfJ2MVCJx7eU6HXz3HXz8MZw8acvQ8uyz4O/v2OEDD8CPP0LnzlC1KrI8PUWT11d4PsLNby//jvwzFeDixYvk5eVRo0YNO0EHqFWrJvHx8QCc+ufabpnly1cCMOi550oEHaBt2zjatGlNamoqyck77fpatXIVFouFHg91LxF0gBo1qvPkk09gNptZuXLVzb49gUAguLV4eMBPP0HdurZAl+++E4EtN4jJbEFrMJGWp+dEmoa0PD1agwmT2TGquSILnRUtAbQw+Ty7UrJJiAlzOCeTSoiqVoXWEf5sOJGBQialSQ01ZouVxcnnGdw2wq69j6ecbK3jC0SezsjqQ6lczHVdp9IZWoOZwiIT47rWJ2liJ17uXp9vh8YwY90/PD5nOzM3nMYKtAz3J6paFY6n5TNx6UHmbTrDt0Pb0DrCn9YRtnMyqQQrRrI8ZpOpmIbWWE5gyzrgF5wHtqiAQbgMbAEYGzOBJcmpWKyU7Mo9cimv5MuMk+kFeClkNKqurtCzaFRdjaeHlBNpti8sjlzWMGbJfj5YfRytwdkkK49aKa/UfNTK+yYmWnAbEG4uEAgEgjvKtGm2kkSlMPd6mLSBL9w0N9+Vku00sKU0t8rN64f4UGjUVHghushchL/SlwahVUqOaQ1mikxmLGYF07pPY+Gji4kOjba7Ljo0mu8eWcSb7d/nneWnSC/Mdj2mDlgC/A1YXc/F09OLoD4T8es4EInEtmxptlhZkpzK2DYTKnQ/Zd08V2dg74WckvPCzQUCe4SbCwQCgeBu4kbXzfse20S7cwcBaHH5BF9/8R/2zH273IyGs3fP5vd/fmdEqxF2x2USGU1DmtI+rD296/dm17BdHEg7wIML42kwK4Jmc5pSb2Y4fX/qQtXgfSwf3YogHwX+SlsQRaPgRmiN2muerMN9YEs6KH9UEvZEGDxMSZqKHH0OPRb1YFLbSSzot+DG3LwM+9L2kZafxX8W7SXhq2TWHU2nSU1fcnQ55V9ceuqF6bwU95LL+X3TbxEfdpvK67+eKAnuvxE3d+rlGg188AGEhdkyspy8Wga1sBDmuPkbeOopqFoVEG5+t3PfBLd4eFQsaqpKFduLfHp6OhcvXkShUNCqVUuHdm3j4gDYtWu33fHde/bYzrdtW+FrBAKB4J4gKAgOHoTx422ligTXjdZgYvXhVJ6cs53Yyet56NPNxE5ez5NztrP6cKrDAml5MlWcgnzB1pQKjb9gawoJseFIr36MvkoPRnWuTdKkOD7tH85LPZWMfSgIC3q+GdKSiAAVC3eco03tAPq3ubbwXlBkIkDlfHHf3TlX+KsUvLhkH10/TiLx+z10rBfE3vM5/H4olVGd67JpUmdmPtuCST0aMvPZFiUBMKFqL3yVHnz8VHTJuUWJkViD/o8C+e/uBzVgW2Tf4vx046bN6P95AoS77iKx5Ujia/VgUfLFkmP5eiOrD6WV/O7qSwhXDGkXweLk81jKLPwv3HGO9ccynL7MVRaFTEpifGSF2o6Ir4PiX1KTU3B3INxcIBAIBHeMdevg9dftDumrhjCgXSKxH/5909z8q81nKzSdW+HmPp5yLmsuV2j8YrJ1Ofh4XluULXbzjtM2MuybQ8TX6s3fz23ixKgUdg87wPlxF0gatJmONftgNHrw/mNNKDDmOu/8EjAX+Mf9HEKrV+eHFX/g/UC8w7mFOy7SKawnw1uMcHLlNZy5+ZX8Irt7E24uENgj3FwgEAgEdws3um5epaiQN//+yq6NtUYNXrX+UaHxP93xKSNajUAqkeLv5c/rHV7nzNgz/PjEj3zY9UNm9ZqFj8KHk9knOZd7zu7a/Wn7GbJiAB9uf5NB7arj7+VHdGg0ak81mdpMWyMN4G7PcDqwDOYtnEdYW8cA+FPZp2j9ZWtOZp1k+TPLOTbqGAdGHORCZdzcBXlFuTSqruZ4Wj5zN58hT2ciQOUky4kbQn1CGb5yeMn8Tr14igMjDnJiVAornvqb9LRoUq6Y0OiufY43w80X7jhH0s5TmN/5H0REwBtvQGam48WffQZFReWOIdz87ua+edohISGEhoZy6dIlkpI22Z27cOEiSUlJ+Pj4EBsbA8DZsykAhIeHIZc7RlzVqWP7o045l1JyrKCgkCtXbP9jiYys7XBN1apBqNVqMq5cQat1XjpBIBAI7hhFRTB6NBw/7rqNyNZyw2gNJj5YfYwxS/aXZPgoxtUOwPJkyll5IHccuaxBbzDTILQKEYEqlo9uRdXgffT5sQsNZ0XQ7psW1J8ZTsdvOvDn2eWsGtea13vWZ/TivSTGRzL9yaY0qq52G1V9PRHXEgnsOWeLBv8no4BQXyXHUzWsGduB2oEqhn+3m64fJ/HknO10/TiJd1YcISEmnLBAFQlfJdNh6gaemLONmBlv0eGbVlwoPOh+0GzgK+CE89OqB+Lxe2oy7/f7jG/6LXIZbf5q3HsMnn/ALpV8FS8PDl/Os2vv7EsIZyTEhNE6IoBFLnb6zkk6jeEmLKDLZVK6RYUwINb9fAbEhtM1Khi5kHTBTUS4uUAgEAjuCOfOwTPP2MoSXcUikfB8lxfZrrF3nRtxc4Ppzrp5vr6I97a8ValHE6D0p6DIdp9l3XzfhVw8ZB58ufEifooQ8jUhTPk9g+h3koiftonOHyUxe8MpQrwD7Ts1A0nA10Cu+/HrNm7ON8vW0bpNa6fvEHk6I4PnH+C1tu9X2s3VXh4l91aMcHOB4BrCzQUCgUBwN3Az1s1f2ryI4EL7bCPZk98iOfdwheawL20fWqOWXvV6sWvYLur616XfD/2I+iKK9gvaE/ZpGD0W9aCuf13b+YC6Dn3M3TObjefWYrQYmdh2EpoiDYHKQMgE3C2VXwHWAwOgVXQrNEXO3ydy9DlM3jKZ2jNq8/TSp/GSqVi6q5Bn5+1lws+H+M/ivczZ6MTNyyG0SiBjr2ZUHxgbTlahAV9PXwfvdkXz0OaoPdUczTzKmlNr2Hh2K17SIL5cb2TsonPET93GrI1n8VbIb6qbq/UFjN2ymA7dWyN7+/8gx0W2mWbNbBk8peV7tHDzu5v7Jk+OTCZj3NgXefe9D/j4k09ZvnwF1apXIy8vj6NHj1GrVk3GjnmxJAL9ytWIrsBA5//jLz5+5cqVkmOZmbb/9vHxwcvLy+V1Go2GjIwrRES42QYuEAgEt5PUVHjsMdixA/76C5KToVRaWcHNwWS2sO5YOgt3lJOefMc5YmoH0LNxKHKZ1E6mnF3rqjwQ2HaO1g/xwcfTJo0n0wswW6zkaA2Eqr14u19dPtj6BvP2Oq832v/X/iS2TOT9Lu/ToUFTEr/bR4d6Qcx7rhUGkxkpMLxjJGN/2G93bemI64lLywkywRZx/f32c1Tx8mBAbDgDY8MxW6z0ja6B0Wwhtk4gZ7O0XMzRkacz4qv04L+9GzH9jxMlpZiMkotke3yJXran3PE4DiwD9M5OSvDrNBh1m8e4kG+h38zdJMREs/LpvzFTgKYoDy+5Gi9pFZYkp9Jv+W67xfNG1dX4eMpL0pYXk6czMmj+Tr4d2oYW4X4s2Jpi96LWqLqaIe0iaB0RwKD5O+36LM2Ryxo0OpNjHdHrQKWQ83qvKGJqBzIn6bTDfEbE16FrVPBNGUsgKI1wc4FAIBDcdnQ62ztPVpbd4ekdBrI1ItrlZdfj5jla5x53u9z8uV/+gyZvXYUfTfPQ5qjk6hJ/deXmfaJroDOaqRtShfBAb6p4eZS4+ZB2kXjKjESHRrM/bT+cB9YAqeWPH9Hxcf43ZSptm9Tih52u3yFSsrQObp6tzUHl4YtKrnbp5qVLChUj3FwguIZwc4FAIBDcaW7Guvne5RsZtNe+vN2Jlh3Rd2kDp1x06IRsXTZze8/lnaR3mLdnnsP5/Wn7GbpiKIktE1mbsJbWX7YmR28fUDFt21Tah7UjpkYb2tZqy9hvxkKQu0GBncBT0Lxmc5RyJYcz3AfkWKwWDqYfRKXwZFC7EHpFe5FZmE2QdwChVQKxWiV0DO/IpnOb3PYDtvcBicWb+GkbiKqmZki72oQHqMjUpTOmzRiGrhhabh9jYsaQX5TP8qfXILF6siQ5lV7Lk2+Zm3sZ9QzZs5KRO5aiLip0PbGHHoIJE6Br10pVJBBufvdyXz3xqKgoPnj/XSZPnsLpM2c4feYMAHK5nOhmzQgJCSlpq9fpAPD0dF74rFjCdbpr34rp9Pqr17guw+Dl5Xm1ra5Ccy4oKKSw0PF/lJnO0ikJBALB9bBzJzz6KFy+mrb65Eno3x9WrgSZ7M7O7V+GwWxhzsYzFWo7J+k0XRoGc/pKAb5KBWqlnDd6RdG+bhCf/33KTqZC1F5U87VfHPJV2hai+8eEoTeYydYaCFAp8PSQsmTnBQK9FcTXD2TDuTVOF89LM3fPXGJrxuLn6cfi4d1IzbGgN5jx8ZKx+WQmbesG0T8mjMVldjMu3HGOFaPb0b9NWEkAijMSYsJoWyeITSczWDG6HTvPZvP8t7schHFw2whWjG7HoPk7ebhpdZLPZrFo5zmMkrNo5MsolCWBxOz+wVqwRcBvdX5a4ulN1T4TUdZpVXIsT2dk1sazaHRmXuxaD5WqKoPm7+R4Wr5DanKAoe1qU2Q0Oz2XkqWl78ytJMSEMe+5VugNZjR6IyFqLyxWW0DQu6uOuVw8L0ajNxLq63xBsLKoFHJ6Ng6la1QwGp0Jjd6I2ssDtVKO4upLokBwKxBuLhAIBILbyubNcMC+5vy2Jh2YHftEuZfOSTpNpwZVKSwwlDiSq4XOELUXIVXs/726nW7+j+5nNPIVlXgwMDZmAkuSU7FYb8zNz1zJo7ukF/sX7S+3BBGAQqlC/eBorFEdeW3ZMY6kavlP5zqYLFaX7xCl3fz1Xg15Z1kqqXm5nHDh5okdI52WFALnbm40W6ii9MAq3FxwnyHcXCAQCAR3ksqumzu4eY8G6P9vCDLrtYx6ermCv0e+QW9VQKXmUs2nGjsv7XQa2FKaYi8f2Wokq/5ZhdpTjaZIw5GMI+xL20ehsZCJf01ElaHiL+1frjvKBfYBPQEpTGo7ieUnlmOxlp8dsHloc5RyFd2+j2dv6t6S49Gh0bwcO5FfnvqFuK/jOJXtPrqn9PvAkcsaJvx8gLRcPY+2VtIxvCPDWw53+zwSWybSIawDBpOVaWsusfZw2g27ucFkQW80o5BJ8fSQlrh5QaGepw6t56Uti6hWkOXYUTGPPgpvvQXR0W7v3R3Cze9O7qvglk2bNvPZ5zNpUL8+L788nrCwWmRnZ7Ns2XKWLV/BoUOHmTJlcoXrjN4OVq5cyQ8//nSnpyEQCP6tfPstJCY61hnctAmOHIGmTe/MvP6laHSmSqUnT8vTM+7H/RxLzadRdTWJ8ZF0iwohvn5V8srIFNgWmY9c1hARqOLboW3YeTab4d/tdhLlXJuqVTx5qHEgPZdMq9B8Pkv+jGXPLOP3k7/TNaIvZgt8v/083+84h7/Kg++ejyG2dgBzN50pGS9PZ+S9VUeZ8kQz2tT24+ONGziaeQCTJB2TJB1vpY7qfjI2ZltY/7MUf6UPPb83cyHLigQlUrk3UqsKKSp2pqnY/as3Dz0QxnOdVWTqjjDhr/Vc9tyNSXqxYh9AAfAzcM756Xr1GzB68lz+uCh1GRl+4EIu+y/kcjQ132kfCTFhtIrwx2i1lnweZbEtyJ9mTtJpGoRWofsDIQyICSdm8t9Oxd4Zaq+b6yrFOx1UCvlNW5gXCMpDuLlAIBAIbivdu9uyVD79NFy5gqluPYZ3fRGrpPwFySOXNWRoihi9ZC9SiaTEy10tdMKdcfMvdvxICu+U34EJSLf9tFC2ZNmRZew8MZuqSgkbt1j5+xM5/mpven1h5nyBFamnNxKFEqmnN1JPFbtOqti9TcVDTWuSUMuL9AP7eOmXdWQc24Uxv2JfKtdr0ABJtwkU+VQrObYw+RytIvy5kq8nMT7S7a7Nrg1D2H0uh7+PX3HWPWBz8y4NQ1yWFAJ7N+/VpBr/69eIwiIz8dM2CDcX3FcINxcIBALBnaSy6+Zl3fyhHb8TcNA+m7dx4isMGtgFC0XXsguWQ/PQ5lT1rsr/kv5Xobl8lvwZSYOTGNhsIJnaTIJUQSjlSubtnUeePo8DOw9wQX0BXL1yaIFjQBdAAoktR9ItshcbUtwEw5TipdiX+Gj7dLvAFrBllxm4LIGRrUaSNHgTjWc1csguU8zIViOJr9WDfst32x3/ZP1JEtp24D+//4ePH/qYuJpxzEieYfcco0OjGRszlg5hHRj/x3hm9vjGZWBLZd18WIdIhnWIZOD8ZLtA9g7nDjB17WeuH8rjj8N//2srQ3QTEG5+93HfBLdcvnyZGZ99jq+vmjfffB2lUglA9erV+c9/RpKdncOu3btZt249PXv2wOvq+aKyX/heRX812lypvPaHrLwalV5U5Lw0hO26oqttlRWad58+fejSpYvD8czMTF5/480K9SEQCAQOmEwwcSJ8+qnjudq1YflyaNLktk/r306ezvW/D87I0Rrw8bT9U11cV3RAbDiv92pIqK+XnUyZzBYSO0by3+VH+G5oDHOSTjvd6VgceZ2aW59HWik5kH7AoY0ziuuN/nb8V9rX6s7I7w/zXNy13Zp9Pt/C6M51+HF4HLk6A1fyi9CZM0m6sIb2X08gJX8fOlM+lNqkVWiEDGdr0W7s5PvTtp8SKhocnQIsxRbg4oTgRm3544/fWHoom3nPhaM3mMnRGvC/uqN2cfJ5jqfmM7ZbPUxmi0PgSnEqwvZ1g3h01lYeblq93JJMFiscS83nhfa1kUolRFVzHgxTlkbV1SVfmggE9yrCzQUCgUBwR+jcGfbsgcGDufDWZArWuA6MKEuxm+9KybHzcpVC7rDQeSfcvMMDBWR4TAWj8x2eUqRY9llsu0IvAVcTHu5lD3uxfRFwucJPw8b3P8L3lbwG4Klnnsaz0wg2nXXMuPDl5jN8O7QND36cxLNtrmVUKe3mRy5p0JnMNK3hx+fPNneZJrx93SCW7DrPEy1rknw22+2cLFbo1KAqX285S/+YcOHmgvsK4eYCgUAguNNc77r5rpQc/u/rJLotmGTfoG5dqrz1OijkGExWJradRMKv/cvtd1zsOPQmPfvS9lVoHvvS9pFakMrTS5/mYLptHTg6NJoxbcawf/d+Lni6CWwxYst2GGe7ZlLbV+hdvzdSPOke+RCJLUcwd4/rrI4jW420lTxaO9Zlm9m7Z9MhrCN/DljHsFXPOwSmjIuZyKMN+/LSj0cdshWaLVZW7MvgmcbP0PrL1oxoNYLlzyxHa9SSpc0iUBWIUq5k7p65jP9jPLMenoVGh4NHX6+b1w/xQSIBqURiFyyzOaI5O2s+QJuLR+2u2dIsnpZff4yyZQu3fQvufe6bt6/Nm7dgMplo3rx5iaCXpl27tuzavZsjR4/Ss2cPqgbZip9lZTlPaVR8vGrVqiXHgoJs/11QUIBer3daP7T4uuDgqg7nnOHj442Pj3eF2goEAkGFyMqy7VZcv97xXJcu8NNP4KJusuDG8FW6Tr/rDH+VgoIik92xsnVFi5HLpHSNCkEhl5F8NsttGSCATf9coV2UT6Xmk6XN4nLBZQqNGkwWKxOXHiQhJoxvh7ah78ytvL/6OF9sOEnn6FSO5C1l3dm1WKngdsdbhRn4G9gGrqaijnsarw4JeHlXITrMwojvd2OyWPHxlFNQZEIulTCsQyRdGgZz6FIenRsG06mh81SEBrOF9vWCKlWSKTYyyLY7tWMkY37YX+4tjYivg0KkPBTc4wg3FwgEAsEdo1YtWL8eZZ4e1jh5J3JBWTd35eVw+91ca8rh9U0vYJY6rzUvsUiwfG+Bs5Ua4qaj9vXl4RFvMGHk80xecxxwnO+RyxpMZgvTnmjGJ+tOlmQ79PGUE6L2ovsDIXRuGIzRZMXTQ+o2TbjBbCFDo6f7AyEV8vLWEQH8vPsivZpUE24uuK8Qbi4QCASCO82NrJtPSvoWVUGefYMvvoCr/9Yo5B70bdCnQqV1OkV04mxO5aT5SuEV1J7qkt/3p+1n6FdDbUEtQS4ussCjskd5Z/b/8FH4EqTy59AFLVI8+WD1cbaeyuSboe8TV7MDnyZPcwhKeaXtK8TViqPb991cZmQpZtq2qfw5YCMrn/6bQqOGbF0OAUp/VHI1S5JTOR1gRKMzOb32479Os+XVPmw6t4nJWyYzZesUGgc3LinBdDjjMBarhZGtRtK7fh+8ZEp+HhF3U9y8VUQAfx5NY1iHSMb9eO3+kUj4MH4Ivy6aCMDm8GimdBpM4ktP4tE41O2zEPw7uG+CW4rl2Fulcnpe5W07XlBg29Jdu3YEAOfOncdkMiGX2z+q06dttd8iwiNKjvn4eFO1ahBXrmRy5sxZHnggyu6aK1cy0Wg0BFetisrFPAQCgeCWcvgw9OsHZ5zUrxw7FqZPB/l980/DbUetlLssVVOWRtXVeHpIOZHmWP5mTtJpukYFOyyiaw1m2tcN4qm528vtv6DIhNrTr8JzBwhUBaIpsglwcUaZRcnnaR7mx7NtajB9ywIuWZZwYH9l93zeIi4Aq7ClXHeCxMOTwJ7j8I7qAECe3khsZCBt6wSRo7Vln1F7eZRkblmUfJ6EmDCs4HSHLti+yHi9VxQxtQN5b9VR3uz9gMt06okdI+nSMASZFELUnoQFejMgNoyFO1yL/YDYcKefvUBwryHcXCAQCAR3mpvh5q68HG6fm2cUXuGw/l3MUtdZaKwrrXc8sCW4SXs8O77ANoLwVToG8Zcmu9BAbJ0AfqoTh0ZvJLvQgEohw0Nm8/L/Lj9CTX9lSXkod27+0oP12XE6i1Gd67r28vg6dG5QFb3RwoIhrVHIpNQOEm4uuH8Qbi4QCASCO831unmLi8d49uCf9g2eespWkrQUBYYCJrWdVG5pnVGrRzHtwYqVCi2m2MtLyMOWPTzSzT1kx/Pl5MWMWryXAO8iXn7Qn4s5ejILMkrK9vSbuZtx3dqQNHgzqZqskqCUKgpfpFITD8yqX25gC9iyy2Rpc3hp8TmscHVDp6ak1M/jLcJcunmezkh6rpVp3acTHxHPlK1THJ7dSzET6RLRk+TT+cTV8bw5bh7ph2XNH/R7pA+Ag5fvrRnFrNgn2BbWjC21mwsvv8+4b77B9PP3B+DU6dNOz5/65xQAwcHBAISEhFCzZk0uXrzI7t17iI2NsWu/bbttcaJ161Z2x1u1bMmatX+wbds2B0l3dY1AIBDcFn77DQYOhMIyu+MUCpgzB4YMuTPzuo9QyKQkxkcyZsn+ctsOaRfB4uTzTutTHrmsQaMzoVLY/zNeWGREozdWqD7pyfQCvGRVKlVvVClXcjjjMAFKfwpKCfvUjT+Tp/iKLMWxcvu5LWg8YJ0RXFcEQqYOJvixN1GEXHvL8FV6IJXA5NXH2HM+pyRzS+manslns+1S0DtDpZCX7CDN15voUK8qPRtXI19vi4JXK69FrJfNvlMcGOMqtXrXqGCX4woE9xLCzQUCgUBwS9FqYft26NrVZZOb4eauvBxun5t/c+BLMgy7XTfehK0U0R1CEVIHv05DUEZEA+6D+Ivx91aQV2ikitKDpbsvsumfKw5enqczOpSHcoZKIadj/aoYzBa6RYXwUKNQCopMaHQmfMt4eZVS6+/CzQX3E8LNBQKBQHCnuR43l5jNvP/nF3bnLD4+SD/+2OGajMIrdPomvtzSOpoiDV/0+uK6vBwAHXAacFMZp4m+GR+NWMKCrSnk6UzUC66CyWzh4abVeXLOtcD4PJ2R1YcyaFwjgLeWnysJSlF7yRnbQ1mhwJZisnU5eF8t41Sairi5t5ccTaGVPvUe48HavcjR55KlzcHPy68k+8vDy3eRpzPeFDf33JSErP8YOHoU/v4bOnd26uVT4wfTqLqaz4WX33fcN590TJvW/PjjTxw5cpQ1a9bSs2ePknMnTpxgxcpVALRtG1dyvF+/PnzxxWy+/e47GjZsgJ+fHwDbt+9g585dVKtWjZiYNnbj9O7Tmz/+/Iu1f/xJhw7tadCgAWCrXfrzz0uRyWT06dP7Ft+tQCAQlMJigXffhbffdjxXrRr8+ivExt72ad2PyGVSukWFlLsDsDgl9rurXAeLaPRGh+jnKl4enMl0noq8LGaLlSXJqRWuNzomZgxz98ylWUgzVHI1J9LyMZNDtsdXnCtIqtCYtxSrBA99JOa/0rHsK3BZggjAM6wJVfu9ikzlW3KsUXU1XnIZqw+lsTDZfdr44hT0HeoFka7R46tUOASryK/+d2mp9vaUE+rrqlcbpQNjnKVvdBZ9bjJbMJgtaHQm8nQGp/MRCO42hJsLBAKB4JZhNNp2a65eDVOnwssvg0Ti0OxmubkzLwfXbi6TSqgf4lMSSH0yveC63TxMHcb8/a5Tq3MY2FBudzcfmRxlZCvULfviGdYESann7y6IH2xeLkHCkl3nCQ9U8dFfJ90OtXDHOdpE+BNTOwArEpdB5DY3t/1excuDauV4OQg3F9w/CDcXCAQCwZ3metx80J5VRF1JsTufOfF1gmvUcLjO38ufHH2O29I6xczdM5eJbSeS8GtCufMu9nKL1QJmIBno5Lp9sDaY5a/8RpB3FVrV9ufxFjXJ1hqpFagku9DgEBhfUGQiQKXgWOq14JOoalUIVAWUO7fSlN2sWkxF3XzRTpubv/LLwZKSoQVFeXbB53CDbp6VBYkT4JtvrnU4Zgzs23ddXg7Czf+t3DfBLXXq1OGRR/qxbNly5sydx+o1a6hVsxbZOdmcOHESi8XCQ90fJLpZs5JrunXtyp49e9mxI5lRo8fQtGkTNJp8jhw5gkKh4KWXxiKTyezGqVmjBoMHD2L+/AW89vqbRDdrhlwuZ9/+/RgMBoa98DzVqlW73bcvEAjuV4qK4NlnbVlbytKmje149eq3f173MSqF3O0OwCHtImgdEcCg+TvJ0xld9qP28nDs21NGkE/F65Mu3HGRza/0YUTLEczZM8dlu8SWiXQI68D4P8Yzo8dMliSnki9NIttjFhZJxYJpipFJ5EjNtZBbQ5BbA5FYlUglCp6Lq8mKgylkFmqwoMUq0WJBi0WixUIhFokWK1qQWJEgI9i7KqE+NZEaa3M+rTqWI3pyVs0Hs+tnBuDbqg8BXV6gQXU/uy8UhrSLAAks2JpSofuYk3SaFuH+9JyxGYv1WpmhrlEhaA1msguLrluWSwfGOPuipDRag4l1x9KZm3TGSQrHa2naBYK7DeHmAoFAILglWK2QmAi//277feJEOH4cZs2yZawsw81wc2deDo5u7qv0YEBsTZ6NqUahUUOWNptAVQAquZofdqayYn8av8dX3M3HrhmL3lzk+lmkActxH/TtpcTqXxO5bygybz8kHp5I5QoGtK7Oqr3nyMzJw2LQYSkqxFqkxVKkxWLQ2n436ACQyhWEBFelVngE1sDanJXWQBnZEqmnY1mRgbFhdKhXlZPp+fRqEsq6YxmYLVa7YJ+h7SJYuCOFZ2PCGfatm4w0pZi76QwtIwLoMGU9UdWuebAEyLvBhWzh5oL7AeHmAoFAILgbqJSbaw10OmPviseqRuA/6kWnfft5+ZVkY7FYLRxMd53ye+v5rbzS7hWGtxzOvD2uA8lLr5kDsBmIc9kcNJDhk0Hk55FEh0YzLmYi8bV68Oqvh1g+qh0ZGke3P5legJdCZley6WR6Ad4e6kpllynerFqMTCphXNd6dKofzI4zx1DIpRhMFodA/LJubrFiF2jjjEq7uVQCixbBSy9BZqZ9Z4cPw9y5MGpUpbwchJv/m7mvPrUhgwfRsGED1q79k9OnT3Pp0mWUSiWNGj1A9wcfpGPHDnbtpVIpkyZOYNWq31m3fj27d+/B09OTuLhYnn32GcJq1XI6Tr++fagWGspvy5Zz5OhRAOrWrctjjz4iUisKBILbi0IBXk7+oR80yFaKyNk5wS3HeaSxHJDw3fYU3l11zG1gS6PqatRKJ/+EW8FfpahwfdKa/kq0RTJebfs+HcPjmbrNsWZmcb3RHot68EzjZ2hTrQNxy54nR/FXhe/Xz8uPQU2fp1tEL2r7NuHhz3Y6zkVWl6ndVExc6vrFwooVKwY+eqIVZzO1/JOez/vPNiLxxfEsWz7X7RzkHnLe/+gDRg4fidUqIaMwm4yCTAJVAfgr/ZBLPNFoHaPjXXHksga9wUyD0CocS83nyGUNY37YT0JMGMM7RvLU3B3k6Yw3TZZNZgtGswWzFTQ6I/l6IzX8VXy45pjT3QxHLmsqlKZdILiTCDcXCAQCwU3njTdgwQL7Y7/+Cq+9BnXqOL2krJvn6Ywo5BI8ZFIWJ5936+YuvRzs3LywyMQ3Q5ux8fwa+vyY4OjcbSawJLEXRUZ5hdy8+8LuSKVSDAYXwS064EfAybSD6wfzQv+RdOn8EDXqNKTH59sc2oR1rsvU/uW4udWC1WTko2evufmUJ5qx5Z8rzN1kv4AcUzuAt/o0JDxIQY4uk14tTAQqQ5jyRAM7Nw+tEkiQtz+r9pswGM3X5eYlHhwTxshOden12eaSz0+4uUDgGuHmAoFAILgbqLCbSyQMeuodnj7wJ68mfYOfvoAFCRN5R+38+w65RMG4mIkMXl5+NpZhLYZhNBt5rf1rxNWMY0byDLdr5jn6HNgDPAB4uujUACiBqzHW+9P2M3h5AoktR/Ld8x9QqLeV5SmL2WJlcfJ5BreNKHHz4qyPY9tMYMiKAeXez7iYCSxJTsVitQXcj3+wDn2bB1NozONS4UnGdg/irb51ycgzofKSkF+US5Y2+7a4+UuRUjp/+jay9eucd9a8ue3HDcLN7z/uu08sLjaWuEqU35DJZPTr15d+/fpWapw2bVrTpk3ryk5PIBAIbi4SCXz1FZw4AXv3gkwG06fD2LFOU3MLbh9lI41NZgurD6cya6PzGtelSewYSb7OiEZnpIqXB0azhTydAU+5DIVcyvPtazP+pwMV6mftkTQ++esfRnVuyabBm8nSZZJWkIafl19JvdEF+xfw3w7v0CTkAVrMaY8W92V7ypKrz6VV9Za0qBaDFanT4JuFO86xYnQ7+rcJY/FO5/1LkDAwph4PNapGocGEvkBDt4d6cCh5s/sJeIPpaROvZE9iyTeLGRczjvZh7Rm04ilOZZ8iOjSa8bET6VO/DxGBKlKytBW6rxytAR9Pe5ValHye5mF+JMSEMWvj6ZsiyzqDCb3RwvrjGSzYepYjlzWM6lyXiECV2zSdcK2EUs/GoSLVouCuRLi5QCAQCG4an38OkyfbH/PyglWrXAa2FFPazYN8FGw5dYWh3+x2mZ67mBHxdTCYLFzI1uCr9HDq5iPiI2laS8kHW99g3l7HjCz70/YzZMUARrYayf86fciAL/czMM69m9eoUoszOWecT8oKLAVySh3zAKKB1pARnEHUIw2ICm96Y24ukTKw/TU39/KQseZwKpn5Rcx7rhV6gxmN3kg1Xy98lBZWnFjBgJVTHb4YcObmL8VMxN/nxtx8YfJ5okt5OdychWzh5oJ/M8LNBQKBQHA3UFE3t0qk/BDdgx1NOzDJ/A+vvTeQLF0aWVk5BCj98ffyAzzIKTSi9lLQp34fhrccwbxysiTG1ozl/c3v82KbFwlSVmXT4M3k6nPJ1F7BW+FNqE8o3+z/htZftrYFtpwFFECwi04t2BzdScL1uXtm075WB7qE93XI0FKMMzdfuOMiy0f3ZHiLEU7fMYoZ2XIkj0b1JbMG9G5Wg1oBMladXEmX7xy9/OW4l+kQ1oHeP3TjVPapkuO3ws0lVguxy7+j/abvkJkMjh1UqWJ7txsxwvadlguEm9+fiE9LIBAI/u2oVLbyQ1FRsHYtjBsnAlvuQkrXFXVHQkwYzWr50f3TzcRO/pun5m7nr6PpeMplpGuKKDJaeKhRKAkx5fcTVycIP6WCPJ2RD1afpOOUbWw4YibStxFeUjWXNTkMaprIwn7L0BZ50u7reLTWygW2FPPxjmlUUVpZcjXSvCx5OiOD5u8kMT6S6U82pVF1td35RtXVfPp0NBMeasAbyw4x6KNfaN26TfmBLdWB4cDVx2GLih/MtG3TWJuwFn8vf/an7ee5ZQm8/vcrfDO0mdMoeWf4qxQUFJkcji/YmkJCbDjSUv8zW7jjHOuPZWAyWxzau0NrMJFfZGLaHyeY8PMBjlzWIJNK6B8TVqkSSoZKjisQCAQCgUBwT/Hjj7YA/tJIpbbjbdtWqiu5TEqb2oH0L8enB8SE0SLMn/hpG3nIjZt3i6rKpgtr3S46A8zePZs/T6+mT7NQt27+eIMENp9Pct3ReqA4Xl4KxAJjgYcpWXC/WW4+slNd3lh2iDFL9vHVpjPE1w/m4SbVSc3VoZBLqeWvxEdpYcKfE0j4tb9D2nRXbj5o+c1xc2deDsLNBQKBQCAQCO4VynPziEAV37zSjaznatPlu47U+qQW0XObEvZpLTp804HlJ37FzxvSNUV4ypRM7jKVhY8uJDo02q6f6NBoFvRbwMS2E+mxqAef7PiEbRe3EVujIyO/P4wcNV5yFb6evmiNWoJUQbbAlmzgH6CJ63toE9jGdUYXrs/N83RGBs8/wGtt3+fbfouc3s+Sx5bwYbeppFwx4SmXEhYoZ9JfE+nvwssH/jaQyVsml3h58fGb7eYRuan8sPg1/vv3V3g5C2x59FE4dgxGjXIb2CLc/P7lvsvcIhAIBPclYWFw6JBbGRDceSpVV/Rq+r4jlzVMXHqQlx+sz8C4cNYdTadzw2Be6xVFTO0Au5TgMqmEno1DeaJlDeqHqHnh21189mzzkqjvHK2Rd1Ye591Vx2kQWuVqbU0NUu/1/HH5fcxW83Xf2760feQV5ZGh0fNkq5pOd4GmZGnpO3MrCTFhfDu0DSazhUu5OkLUXig9ZCzeeR65VErqgS2s++RVrAad+0GbAn2w7VQtw9w9c4mtGcuIViOYvMW2w3f27tnE1mhPQkw0szaeddt1o+pqPD2kdrVKiylbsqiYOUmn6RoVXOFIcJPZwtnMAo5ezrd7VvVDfNAbKpcKUqMziRSLAoFAIBAI/p2sXAkDBoC1zFbOuXOhb+WyCRRTnpe/0L42LcL9Sfhqh13JorJuvv5oOt2b+PPR9qkVGnf69qn8kbCROUlnnbp5WsFhtmlGgavXumPAlqv/HYEtoKWqY7Ob4eYAczaeZuWBVAB2peQwO+l0qfcIE5883ZQ9Z9cw183uWLh1bu7Ky0G4uUAgEAgEAsG9gis391V6sHBYc97f8hpznPjm/rT9DPitPyNbjmTqg1NJPp1Jm7pVyNJlsfyZ5WiNWrK0WQSqAkuyJI7/Y7wtaAX4aNtH9Kjdh/cfq8/bm15h3t45SCVS2tZqy6LHFjG08VDmvz0fHnU99+bq5pzUnnR7fzfi5h4yLx5p+DiPPdCPjIJs0guyCFD6o5Kr+WFnKs9vtZUyeqVHPTZcWOf0OZXGmZfDTXLzS3kovpzHmgWvoXSyrm+pUQPpzJnwyCNu+wfh5vc74tMSCASCfwOXLsHIkTBrFtSs6byNCGy5JyhbV1SjM6LytH12dnVFy/D5hlMMbV+bDSeuEFcnkHPZWk6k5fP1oNYUmcx4ykHlZSVPn0t6QRoFJj0zExqx9tBlRnaKpEW4Hwu2pnDksgaLFY6l5vNAtSpUrbaG7499eFPuLUubTe9m1Rk0fyffDm1jN2YxNf2VRFb1RiqRsPVUJlmFBp5tE0bHqRvILixi3aJZrJj/qfuBJMCDQNzV/3bBZ8mfseyZZUzZOgWL1Rah/WnydFY8vZ45SWfdpqEf0i6CxcnnXbZxVrKosrJsMFsI9PZiwdZDdsd9POVka51EtbtBozcS6uu85qxAIBAIBALBPctff8ETT4CpTDa9996DF164oa4dvFxvRO3lgZeHlHmbzvD2yqNOvRyuufmJ9ALa1JNwIL38kqFgW9jWmzXMHdiST9f9Y+fmDYLl7L/yIhaVY+ZAAHKB5djSnffEVobIjQvfqJv7Kj34eURbGtXw5cvNZ+zm2qi6mmHta1MzwINnlk2p0L3fKjd35uUg3FwgEAgEAoHgXsKZmwdX8WDN6d/KDdiYvWc2HSM6EizvRI4uh7Frx/LSHy/ROLgxak81miINhzMOlzhoMfvS9oHUxJaLf5RkYbRYLWw5v4Wu33Wl0c5G0AWXged+Ej9ypbnkFuWWe3836uajOtflyVa1OH9FwedbLrH60OESN25WU03DaipeWHv9Xg435ubB+VlMX/0pVVP2Ob3mmxa96bnsa0JquartZI9w8/sbEdwiEAgE9zrbt8Njj0FaGqSmwqZNoFTe6Vnd85jMFgxmCxqdiTydAV+lArVSjuJqzc/bMZ6vSo6fSs7LPx1kzeFUl9Iok0roFhVMQZEJpUJKjtaIr5cHX2w8zZrDqSwa3pwN59YwdZtjLc2xbSZgoSdh/t4seiGGrAIDOVoDfkoPpiW/ybx9n920e1TJ1dQN96NtnaCSSPN5z7VCbzCTozXgr1Lg6SHl8KU89EYzKoWM2Mjq5BQayMrVkPX7J6w4uc3tGGpfXzR98qBu+fPZl7YPrVFL4+DGHEw/WHLMYCmgR+NQruQXUVBk4mR6AeZSDz8hJozWEQG8u+qYy75dlSyqjCxri8wUFpkcIs0LikwEqJwUaXWD2qtiKSMFAoFAIBAI7kacubLfrm149uuHxFBm8XL0aHj99Zs6lq9KTpCPAqPFwpOzt3P4suudgKXd3FclJ1uXU6nxc/U51PALt3NzldRCz8ltKAjNdX6RBVgKBABPXP2/5VBZN5fLpDzcxObmOVojOVojj83ayrJR7WhfL4g8rdHuuuOpGnJ0OZUK7LkVbu7Ky0G4uUAgEAgEAsH1cDvXzUvG0ujwHpSAcfgIfHt1J8hHgd6sY8rWigVsTN06lb8G9OZivi3riMVqKXFOV8gkMuRSGdO2OY5xat0pTslOuVyDllllmBVmzua6z3JSzI26+ed/n+KhRiHojRZe6xXFS93ql1znp5KTo8+8IS8vPn69bm6VSGmSdsphrPO+IUzsNY7ksCa0VSgJqdAMhZvf74jgFoFAILiXmT/flrGleEF3924YPhy++w4kbrbpCdyiNZhYdyyduUlnHFKQJ8ZH0i0q5KamqnM1Xu+m1Xi1Z0N+P5Tq9DpfpQcDYsPpHxOGwWQhq6CIUZ3rEuTjiclipUvDYP6vbx3e2fSa01Tg+9P2M2TFAEZcTc047Js95OmNeCtk7MydygXDCrfzjg/vQpYug8MZh8u9x+ahzfGSqUn8fg+zB7SkTe0Avtx8hjmlUpeHqL3o/kAIXRqGYDCZeaC6L31mbmFmnxrk/vAK2tQzbsdo0DCKafOn0ffP3uXOp5gsbRZqTzUA/l7+jGw9kiCVL68+LOFyfib+XtfSOO5OyePJVjUdSkOVpVF1NV4eUqQSaB3hbyf6lZFlrcHkNNL8ZHoBXgoZjaqr7f5eXNGouhq1UiifQCAQCASCexNnrtz80nEW/fxfJEVl0lkPHQozZlz3u5C794CJD9WnYajaZWCLMzd/JLoGVll2peag8lAR6i/n+QU2N1dKzaz/aQSabm4WxtfDgG6DOdhqLwcz3S/SQ+XdHImVE2n5vLPyKCtfbF/ioRdydDw+exvfDm3DmcwC/jqazqFLeaTl6Vk/Pp4LBe5TsJflZrt5cUr0UxkFRFW7Vi5JuLlAIBAIBALB9XE7181Lj9Xh1/m8mrQSVq/kz5heBM2ZQdXaXpUK2DBZCglU+ld4/EbBjdAUaRzHSAUuAP1cX/tu5yn8dHyh3UZTV9wsNx/4tS3ry/bTmWw8cYV0jZ6CIhMd6wXRq4W5wvcN9l4ON+7mV3z8mfPMy7z2zdslx/7s0I85D4/gQJ4FhJsLKoH4xAQCgeBexGiE8eNh5kzHc9u3Q2YmVHVSXF1QLlqDiQ9WH2PhjvMO545c1jBmyX4GxIbzeq+GN0XU3Y2XrtGTmqd3el1EoIpvh7Zh59lshn+32+Fl4oX2tZnxTFN+PfZLuTXu5+yZTXxER0Z26sSLPxwgxTSLfLn7wJbnm71IXOAoZD67GbJiQLn3OTZmAkuSU9lyKoteMzbz7iON+SkxDm2RiUKDGW9PGSqFnNNX8nlxyV4y8vV89kwLLh3ZyYPTpqHNz3Pbf/eevXl1yufUDZPDn+VOp4RAVSCaIg11A+qyNmEtm85tIv7beIcMN5PaTmJk5778vj+LvjO3ugxsAXihfSQqTzmfPdOCbK2BgKuR9euPpVdKllUKOQEqx3Q9ZouVxcnnGdw2golLy//yYkR8HRS3INuQQCAQCAQCwa3GmSs3SjvFtz//H6qygS3PPgvz5oH0+rynvPeALzac5s2HH3B6rSs3l0klbHm1LdGh0RVe2JZL5aw6uZKRneIZvXAPp5a+hb6HmyCRU/B2348Ib9ePrqodN93Np6w9xuC2tZm18TSAg4emZGlLdpeO7VYfvcGMh0zCqSsFVAuo+JcHcPPdfEi7CE5nFLJxYmf0BrNwc4FAIBAIBIIb4Haum5ceKyL7EuO2Li451z15NacHZZH9x5eV6jMlN40GQZEVdvNW1VqRoy+ThVGHbe35WdfXPRzxKKGyfoxtE3pb3by0l7/WK6ok60u9EB80hoxy51GaYi8Hbpqb1xs9lIyM/QTs30XGJ1/g36YD04SbC64D8YkJBALBvUZmJnTv7jyw5cEHYedOEdhynZjMFtYdS3cq6KVZuOMc649lYDJb3La70fFKp9GTSSVEVatC6wh/Wkf4893QGOYmnWHi0oMOUchHLmuYsPQgepOeT5KnVmguU7dOpUktFZ1brys3sMXfkMioFu+w5VQencJ6MqLlCLftE1uOJL5WDxYlXwRAozdRZLRgtYLWaCZPZ0RnsKWYrOGnIrOgiGOp+SxaMJcrP/9fuYEt9XoM5pvFP/LJxgv4efkRHRpdoXtuHtocpVzJJc0l/hjwB1O3TWXoiqEOLzf70/bT/9f+TPxzIr2aBaD2ci3aA2LCiG9QlRHf76Hrx0k8OWc7XT9OIvH7PfgqK5cSUeUpw9tTTqPqaodzC3eco03tAPq3CXPbx4DYcLpGBd+SUloCgUAgEAgEtxJnruxl1Gi5yK4AAQAASURBVLNg6Tuoiwrt2qZ26Ylp/gKQuSh4fx1jlaWgyISPp80DK+rmZouVhdsvM7bNhArNY0zMGObumcuUrVN4oLon4Qe/Qt9gL/i5uCAfgowT6PvMEJJOZt0SNz90Ma9k9yM4eqhMKqG6nxe7UrIZ/t0uxv24D08PGe+tOnrL3bxnU9dunhATxkONQsnRGhj+3W7h5gKBQCAQCAQ3wO1cNy89lsRqYcraz/EyXcvSYUHCL4+NJKASWVgA/Lz8WLY3g/GxEyvUvm+DvvgofK4dsAIrge6AC5UM8Qzl7U6fsemf7Dvi5gVFJjacyGD8j/v46K8TfPTXCXQGy3V5+eGMw/h7+VfOzT1leJgdg1uK3XzXpPfo/+I82u6VCzcXXDcic4tAIBDcSxw4AI88Aikpjudefhk+/BDk4v+1Xy8Gs4U5G92XvilmTtLpG5af8sY7mV6A0lPGO30foNsDoSW7DUPVXvh4yfH3VuCr9HAaDV0/xIdsXW6lUjO+m/Qun+2a7rKNVCKnhvVlpOYOqL08UCpk7DhdwLTu04mr2YFPkqc5RG6/FDORjrV6MHj+AfJ0Rh6MCmbKE83Y/M8Vnp633SHjzPPtazN/YDQtejzN/yWvcTtnpcqbaZ/P5eknn8BgtnDwkoa95wqYEDeJAb/1L/eei788SGyVSFJKEvP2zHPbfs6e2XQI78iPiX0Y9t1ex9SbHSOJjQzksVlbScnS2l175LKGcT9WbveCQiYlq1DvNNI8T2dk0HxbqskW4X4s2JriMJ8R8XXoGhV8U0toCQQCgUAgENwunLmy3sOL13qMZs5vH+BhsaXW/juyFTMensASqey6F7kq8h5wvW6+cMdFfh/bixEtRzDHTUbFxJaJdAjrwPg/xpOjzeH5IUP5/cQyt7tCq+mGoYjoVOLmyacLmPrgtJvq5t+/EMNvey+VOGmxh37/fBueaVOT4CpKCotMJVlRfJUet8/Nwzry8dNdeXLOTrt5v9C+Ng8+EMLcpDN8vuGUw7XCzQUCgUAgEAgqx+1cNy891tBdy4m5cNju/Dct+/CVMZiRVwM2KpohUSVXM2P9LlaO6VkhN28c3BgPqce1MXYDtYBqLi6ywlePLCTQ249An0w2n9Aw5S5x8+vxcovVwsjWIyvs5t18oln511J+z5bxxkOjS+Zt7+YpDtcKNxdUFvGpCQQCwb3Czz/D4MGgtf/SHE9P+PJLGDjwjkzr34RGZ+Joavm1GMEmXRqd6YYEqLzxavkr8ZBKUSsVTksPDW4bwYrR7Rg0f6dDMIWPp5wsbXal5vPpTjeBLSiY1HIuI2Kf5L1VRwnx9eL1XlEU6E1k50Pveo/ycP3eaI0aMrU5VFH44uvph0yqIK/QxMz+LfD39sDXS8EHq4+xeKeL9JVf/431r+lc/ueQ27nKfUPwe/y/TDvpx7k/jvNcXAQAb/52lN/H9iGx5Qi35ZiKvzyY+OdE9iTuoe+SvhV6RtO3TaXvkD78PCIOjc6ERm9E7eVBFS85Z68U8OAnm9yWLFq44xwxtQPo2Ti03Bc8uUxK7SAfqvp40b9NmMMzK51q8tuhbTCZLWj0JtReHqiVchQyqYg8FwgEAoFAcM/iypXX141hdN9X+GL5h+wIa8LIR16jKEN3Q25ekfeA63XzPJ2RKWvOMvmRycTUjGFG8gyHhe2xMWPpENaBHot62FKf/w0r9y6D/7ieT4y6Fz+O/sTOzfP1JrJuopuP/+kAA2LCGPdgfXQGk52TWpFwKqOQ1349bPcsnmpV8/a5+fapbBrShx2vdS3xcrVSjhRYfzzDaWBLaYSbCwQCgUAgEFSM27luXjxW3czzTNr0nd25C74hTO84kOq+XlgsHhUO2Cgu/ZOjLXbzDyvk5k81eooxbcYw9KuhcBJIcD1G/yb9KSqsS1CEJ4Pb1ibQW4G2CMKUXVj9bC9M1oI75uaV9fLxf4xHJpGR2DKxQm7e+Qx0/GQUfnkmEoDOoxOQPvGEcHPBLUEEtwgEAsHdjsUC//d/8N57jueqV4dly6B169s+rX8jeTpD+Y1KodEbCfX1wmS2pQbU6Ezk6Qz4KhUVkiR34/kqPfhuaAyf/HXSpdBOXHqwRNDK1rMsKDIRqAqo1P24xConyPAaSzZXRWo4w9QnmlFkMAGw8mAqC3eco6a/ksFtI2hfL4iL6RLaRAai0ZrI0WoJUXsRWdWLv46kYzBbnN4PQNHlE1z57X3MBe6DcrzCmxLU71VkSlvawUOX8lB62FLQX8jRMfL7w8weOI2OYfFM2z7FMSo+9iXa1WpHj0U9qK6ujt6kr1SGm1xdDjV9a6JSyAn19QJsdWAn/XLIbWBLMZXZvaBSyJEAEx9qQMtwf+ZvPWv3glLTX0lUNTUqhQyVwpNQ3wrdhkAgEAgEAsFdjztX/qNBWwY+/S57qzekyMMTuDE3L+894Ebd/FRGAVcK8zmZdZLlzyxHa9SSpc0iUBWIUq5k7p65towt+hzYA2wBngJ8HIYCQGYM4nL6EOZtsnfzVRV08zpVvfizHDcvZmHyedrUDiBDoycxPpKYyABaRwQw8+9TTq+9U25e7OVgc/MvNpyuUB93lZsbDHDpEqSlXfvJzISCAigstP1fgwGkUlsJLoUCAgJsP4GBtvLFYe7TrwsEAoFAIBBcD7dz3TxPZ0BuNvHx7x/jWarEjQUJE3qNw8NXzXdDY/hs3WkGtn2I4S1GMG+vu4ANW+mffst3A8VurqmQm8/eNZstA7fgt86P3D65Lseo4V2DaQ/ORCXzxmS2sOZwmp2X1w6qwvQ/rxAXGUDXqODb7uYV8fIJbSfQvlZ7un3fjRx9Dk1Dmpbr5nIzvL0RXtsMUkwlx6tPeBG6dQDf8HvXzQV3LSK4RSAQCO5mNBoYMABWrnQ8FxcHv/wC1VzlwRNUlsrWdlR7eaAzmvjraDpzk844lqmJj6RbVIjLKHV34w2IDSf5bFa5Qrso+TzNw/wYGBvG+uMZ+HjKKSgycfpKId4e6gqnZnSJVUpVwySUltYl48XUDiAlq5A/jqTb7VAtXtAf3jGSHp9uItTXCx9POVWreDLxoQa0qh3AsG93Ox2m4NA6sv6YCWaT0/PFVGnZF/8uzyORykqOnUwvKKk1euSyhq2ns3h4xk7efSSeTYN7k6vPJVuXQ4DSHz8vP0CKpiiXpU/+SqhPCBmF6ZV6JNn6XGr61rQ7dit3LygVcjxkFno1CaVH41DydEY0eiN+Sg/UXh4o5CLSXCAQCAQCwb+P8tx8e3gzu99vxM3LG+tG3TwlS4uPogprTq1hytYpNA5ujNpTjaZIw+GMw1isFlsH/wCrgEbAAy4GsUoIskxCivK63LxFmD+D20a4dfOyzN10hq8HtabnjE18+nQ0u1KyXT4L4eY34Obz5sGLL17ftQC//+46uOXsWQgPtwXGCAQCgUAgEFSS27lu7qtUMGr7TzRNs8/0Mb9VX5LDmjDqqpt/vfUs64+n883Q92lbqwOfOin9MzHuFbpF9uS1X05QP8Sncm4O5Ohz6D20N7mNc0Ht/F59ZD789vQ6+s/dz/gH61NQZCIiUOXg5RO616fvzK28uewwDUKr3HY3L8/L0/KMeEllbBi0kSxtdrluHp4Di3+BthednFQobEHb4eH3rpsL7lpEcItAIBDcraSlQZcucOyY47nnn4cvvrCVJBLcNNRKeckibHk0qq6mipecD34/xvc7XJTYWeK+VqSr8WRSCf1jwiostAu2pvBjYhyPNq9ZUk/T00PK6QwN49pMYPCKARXqxwGrhCDjeFSWtnaHiwV666lM5m89y8GLuSU7VH/YdYFODaoy8aEGfL/jHHvP59IizA+z2YreYnYQWavFTM7fX5O/Z4XbqUhkHgQ8NAqfJt0czpktVhYnn7ersXkhR8fgBXuRS6F7o1ACvRVkFabz55EDTHm8GTKphD3nICwgnx7N/Cv1WAK8/ByOXe/uhYoiL7WbwdtTTnWUlRpPIBAIBAKB4F7D//ffGHf+AJ+GdSi37Y26ubv3gBtx8zy9kZr+nigVFvSmIia2nUjCrwkcTD/oeOEV4GfAC+jlun+16XG8LNciXyrr5qM61yFfb0QmlVZqkdmKlcUvxFIrQMlTc3e4bCvc3AVWK+zfD0eO2DawOKNhwwrPwSkBLjJ3ms3QuDGoVNCpE3TubFtraNAAJJIbG1MgEAgEAsF9we1cN/c9doAXt/9od+yfwFpM6/icg5unZGnpN3M3CTHRrHz6bwqNmpKADZVczfpj2ag8vHm95wN2bm60mJjabSoT/prAkYwjmK1m5zdzEs5ePAtPur7fp+p+xAvzz5OnM5a4+dgf9pKWpy/x8kXJ52kR5se4bvVYfSiVgiLTHXFzd15ussBHT1bMzZ86DHNXgl+Rk5Pdu8N330FICHCXurngnkYEtwgEAsHdStWqtp1VpYNbZDL49FMYNUosQt0CFDIpifGRjFmyv9y2I+LrkJJZ4FTQS+OuVqSr8eqH+KA3OAaCuOLIZQ1X8vWMXrKXY6n5gO0lYliHSB57oB87Lo1gjptamq7wNw3B29zJ6XgWq5X/9m6E0kOGp4eUXK2BWf2bE1HVB4PJTE1/FTG1A/H0kHIuS4u/t4IzmYV2/Zh1GjKXT0F/zn3a8eCQUKQPTsCzhuvF3oU7zrFidDuHGpsmC6w+lFby+4CYcGIjA+n12WbydEYUcinPxHSocIab5qHN8VP6ORy/nt0LAoFAIBAIBAIXzJqFYvRoxkokHO0n58/6cW6b36ibu3sPuF43/2DNUf7buy6rT/3GjJ3TOZd7jl3DdjG85XDm7Zlnf5Ee+AEwAI8A3s779rBE4GdKcBivom6uVnqgkEvQGcxcyNFV6H6KydAUseXUFXo1qVbusxBuXgqNBhYuhNmz4fBh2waVfv2gShXHtg0a3NhYgYHOj588CVqt7WfpUtsPQJ068Nhj8OijEBMjsroIBAKBQCBwyW1bN9dq8Ro6BInlWrCJSSJl/MPjKfLwJMqJm+fpjMzaeJY5SWdLMqIUFGk4kZaPxQrx9UP4YM1R/q9PPc5rjlOtSggFhgKCvYP59alfkUvlzNs7j9m7ZtvKhBaTD54bPCl6zlkEh40qpt6s318DsJVPKuvmaqUHsxKas/d8Lu3qVsVqtdK4hi8BKsUddfOyXg42N28V4V9SYtWZm6sM8NkaeH6f4zhGKVjefw/PSa/ZeeVd5eaCfwUiuEUgEAjuVmQyWLzYtsj0zz+2haqff7btshLcEuQyKd2iQhgQG8ZCN/I9IDacLg2DGbJgV4X6dVUr0tV4Pp5ysrWVi2jOKjDg43ntn/UjlzWM+3E/Y7vUZdqD04mPiGfK1ikVLlHkY3qYKqZHXZ6/nKtjytrj7ErJoVF1NcM7RtKxXlUmLT3AX8cySto1qq4msWMkKoUfVX2uZRoyXEnhyi/vYspzn3a8Wr0mfPX9Ev7zW4rbdnk6I4Pm7+TboW2IiQxg3ibHdJcvtK9N90ahvLvqCHk62wuHwWRh77kCJsRNYsBv/d2OATCh7SQUUseMSZXdvaBWCgUTCAQCgUAgcMBqhf/9D95+G8nV379YNY2BT7zNjrCmTi+5GW7u7j3getw8V2vkf4/U53+bXmPe3mtB5j0W9WBtwlriasYxI3mGzc0twDIgC6gDRLvo1ColyDAeCY6LvZVx8+EdIuneKAS90eLQjzt8POWczSwks6D8ZyHcHFtQy2efwUcfQW7uteNFRbB2LTzpZPtvjRrg7W3LtFKtGoSGQnCwLRDG29v24+kJFoutjV4P2dm2n6wsCApyPpe9e50fP30apk2z/UREwKBBtp/atW/07gUCgUAgEPzLuG3r5uPHIymTyf6LuKc5VK0e4N7NLVZKNn6WJldr5P3HGmKhkEPnDjBs1WcO5YvGxYxj17Bd9FjUg1PZp2gW0gzr3xIOdtmPq0QgcnM1/IyD/5+9+46rut7jOP46E84BDksBUQG3iAMnuLe5bZdCrlK0TLumNuze9rBsWJaj0ixHllmalpaZixRcuHKUijgAFZADHODM+8cR5HAG4ADF7/Px8FH85vecNN/nez6/z9due+lsHtetPjFRoTx3B2TzRxfucprNI1NhxSpommF/j3994d9P36DXQzPsCqZvi2wuVCvid4ggCMLtzNcX1qyBuDhrK7ewsKoeUbWnVsp5cWA4UfX8mb/1pF3Qm9C9Ab3DA8grNJGYnFmua7paK9LR/XR6EzU8y1/RLJNKCNS4E+qvJrfQyIn0XExmCwBzNv9L/QBPOtUdxOaRg7iYm8mPx37ghc3POr2eytQeP8N4JDjvDuSrVpJbaCx+fVO+TSImKoSXBjcjMTmrOAQfuaBl8rdJPBYdwvMDwokI1rD7zw1cXv8BFkOBy9f14PAYctqOokm9UCC5zPchOUPH0LnxbPpPN6Lq+ZNXaCRLp8f36jJNyxNScFdcpEmQ7QKpL/34N+unDCGu7QQWuOhwM6HtRIY2GYJSbv+FQkWfXlCKtT4FQRAEQRBsmUwweTJ89pnNZoVBzxtu55gS3OWWZnNnnwN0ehM1vcq/HKxMKiHE351f/v3RprAF4N/Mf2n/eXsmtJvAmkfXoDPo+PSDT5l7bC4ogcHOr6sxPoDSUt/hvopk8ykrbbN5eSeZ3RRSjqZq8VOX73PKXZvNjUbr7+FXX7UWnTjy00+Oi1ukUkhNBU/Pm9up9Z9/yj4mOdk65ldftT5QM348PPAAKMSTs4IgCIIgWN3yefMffoAFC2z2XwlvwR8PjoN0HTKphJpebgRUMJuH+rtjkeh4cfMr9h0UgaS0JEavGc3EdhPZPW432QV5/LDsJ57VTYJGTi5skVLDMB0p9svnlM7mk29CNpdJJTQO9MTTTU6gxv3WZHPlJbo2qsGh89nF573049+snzyYlec6MWzxX7g5WL3pm5awb+bjvH7vM7dfNheqJVHcIgiCcDuwWJxPXoWHw9atYhmiSqRWyhnQPIje4QFo841oCwxo3BVoVHKUV9dwPJupq9A1Xa0VWfJ+eqMZmVSCRCIpc7LZW6UgNjqUx6JDsVgsPNI+BL+rgXRF4lmW7jpDdr6BhdtO8fmodiikCpYnnuStvf9zek2FOYQa+hlIkDk9pmhy+3iabSX8soQUWof4EBMVwmdbTtrs+2ZXCg+3rY3XkdVc+ukjp9cGQCJl9NT/0rT3Iyzcfhp3pazcE+91fFUUmsz0eO9PGgUWtaE0FrehjAjWsGh0e15f9zdX6384m5XPxG8OM++x9+gW0p33ds6yq96f3uk5hjYZgqfScY/4ijy94KiLjyAIgiAIwl1Nr4eRI2HlSvt9r7xC2MyX+N5sueXZvGQuzymwTkh7XO2OWJFsrpQbeX/nuw6PyyrI4u0dbzMrfhZh2WGc/ui0dUdPwPGS9sjNtfExDne473qz+aPt6xLXrT6Tv01y+pqKjOkcxvKEFI6l5Yps7kpCAkycCPsd9GkvEhYGjRs73+9ouaIb9corMHo0/Pmn9dfmzXD+vPPji46rWxemTLEui+zu+LOsIAiCIAh3l1s2b64ww5NP2u7w8MBz1Uq+bdioeM48t8CIp3vZnUBKZnN3hZnv/17nsLClpHl75tGxTkdaqzsw7Y1pMNb5sd7GR3Cz2Ge6m53Nra+jDsOjapFn0JKhyyRYUwM3hZGeTWuicrvJ2XxUe+ZvPVmczXMupHOyQ38ePvyX3fVylDBrRAjNps7i9dsxmwvVlvgdIgiCUNXi46FzZ2sbYWdEYUulk8ukqJVygrzdaRzoRZC3O2qlvDhc3ey1Iouuu+2fSzy6cBfztpxkdKcwp8eH+atZO6kzYf5qHl+ym47vbOah+Tvp/cFW4r7Za7P/yAUt+YUmNh05z68XZmK05Dm8plquoaZ+JlJnvRavKprcLgq5JS2OTyYmOhRpqd+y5kId/Yfcy8qFH7m8ttTdixEvz+eNmc+xPPEsJrOF5QkpjOlcvtbYRWMzmq1tKHcnZ3E0Nad4rEcuaDGYzDQJsp00jj+ZwaA5iQS7dWfb6O2kPHOWAxMOcfaZs2wfs50Hwx9wGtCLFD298Mnw1kQE2z6BGhGs4ZPhrXlxYFOHHXwEQRAEQRDuWllZcM899oUtEgl8+im8/DJyuazSsnnR5wAvdzkJpzN4ZMHOCmXzN9b/zQVtBgfSD7gcgznPzKkvTmExW6A2EO38WH/D00hw/BqvJ5sDvLbuKL2aBjKiQ4jLccZEhdA+zI9lCSmYzBY2/Z3GE10dd5BxNrZqn80NBnjhBejY0Xlhy9ChsHGjdSmgl1++8XtWVFgYjBlj7Qh79izs2QMzZ1ofpnHm7Fn48EPrksmCIAiCIAhX3ZJ5c7Ua1q2DBg2u7Zg7F3kza1YpmjPv+M5m5m89Ve5s/tYvf2O0mPhw14flGssHOz/g6YkTsAwswEn8RmlqiLfxEYf7bmY2D/NXs2ZSO2oG7GfIyl40/SyMzl+1od7HIfRb1p0aNfehlBuY0sdZexnHY3OZzc222dwoleN37pTdtXQtm5G3cxvPLzhy+2VzodoTv0sEQRCq0uefW5+CMhjgkUesa2/Lxf+a7wQ3e61Ind7IW78cLa5ePpeVz9pJnRkRFcLyBNuKZm+Vgq/HRjF/60mWJ9pXOx+5oGX6qoPERIWwZGwHhs6NJ0un55Wtr3NAu8vpGL4c+hWH/23I0gTnFdRFk9uvrzvqcP+RC1oK9CaaBHkVr3FqyErl0urXMVx2fl0Az1r1eGfe1zzcqz0mi4XVEzuRpdMTqHHDz8ONfSlZdu9FRcZWJK/QxP1tavPm+mM2289m5TN68T7kUnj93hbc36YpbhX881iepxcEQRAEQRCEq06dgoED4fhx2+0KBSxdCg8/XK7L3E7ZvH2YLxm6MtqwW4CfAS0gwbockZPnGTyNA3A3N3e473qyeZGE05lk5hXyVM+GtAn1YXF8sl1r+Se61qdveCCZeYV8OaodvmolPioFbkoZIzqEOPwsUt6xFbnjs/nJkzBiBCQmOt5/333WziktW974vW4WiQTatrX+ev112LcPFi+G5cutxWYlTZ4sliYSBEEQBKFCrjubt29vzSVPPmntdD9qlF0uB1i660y5s/mDbWtzpSCrzMLzIknrkiAHcFb/bVZQw/AsEgdfr9/MbP79nnO8+1AT3oqfabfUKViXUhqzNpaJ7Sbybt/3eLxzPb6MP+30dV1vNs91UzNlyHS+XzYDhdm6JtHR2DjqL/iAILXa5bVKE/Pmws0ivkEVBEGoCgYDPPOM7Xryf/wB06dbn4wSbns3c61Ig9FE4qkMViSeBaxraAb7uDN743FmDgqnYz0/5m87VfyBIDY6lMTkTJeTyWBtd9gmxIdn+jQiJXcvB7WLnR47uf3z+Eg6MrGHN5Ehjie3x3QOo32YH6MWJRavDepIlk6P59X27fmn93N57SzMBbkux3rPoKHMmfc5a45k0ueDreQUGGgSdK094pTejfhPn8a0ucGxwbW2lEEalcv1YSs6eV5EfjWMFz3BIAiCIAiCIDiwa5e1m8WlS7bbPTzgxx+hb99yX+p2yua5hUb81X6uB7EPKJpXbg/UcnxYLc9gZnWezbeJGTclm5d2MaeQT//8l/BaGhaObIfFYuGKzoBKIcNNIWV5Qgqv/XyEIG93m7bln8W04T99GzstirlrsvmaNfDYY5CTY78vIgLmz4cuXW7uPW+2koUu778PP/0Ec+bAzp3WP4vjxzs/19XyyoIgCIIg3LVuKJtrNPDNN2A0YjRb2Hcmi71nspBJJZjMluJs/uHvJ3hxYPnmzbPyr5Rv4JnALsBF/Bnb4iXSLkTc8mw+77HWbDz5o8PClpLm7ZlHl5BuTO5zH+HBXrckmycFN+GDrrGM3/czx2fNpeXjD+F2nR1WxLy5cDOI4hZBEITKdukSPPggbNtmv+/oUWvhi3gy6rZ3o2tFGk1m9CYz2nwDV3QGQmt4sG1GD/69mEejAE/yDSbyCo1YLNAzPJA+zQK5km8gLbuARoFePDx/p8vxFQXRzg1rcqUgi57fjMWC2eGxXep2w6PgESYs3Yu3SkFMVAgLR7ajQG8iS6fHV63EW6VAIoF7P43nbFa+y3v7qpXkFBjQ7v6JrD8XgcXxfYs8N/N/HPDvyYDPdmO2WL9AKFnYciI9l082/8vno9px+lKe3dgCvd1ZtfcsQ+fGlxnQi54GUClEpbggCIIgCEKV+f57GDkSCgpstwcFWduRt21bocvdTtn8RHouHgoNkUGRJKUl2Q/iMrDh6r97Ar2cv67/dnuV/uF1GNSyPtl5RjLyri+b5xYane5L0xbw5/FLzN96koEtavHykAhGLUrgWFpOcTYP8r52jkQiEdkc4Nw5a2chvd52u0IBr74K06bdeZ/r3dysHWUfecRa3HLiBPj4OD72yhXo1w9mzIAHHhBFLoIgCIIgFLvhbG62oLdIyM4tRK2UM3d4G9yVUv69mEfjQE8MJgt5hdbil55NXWfzo6k5+KnKsdy9GfgJuAdwc3xIpzqdeW/Af5BJlWTmGsjI01PTyw1/DyWL4k+XK/+WJ5tv++cyI6IDmRU/q+xxA7P/epchjQeTXCqb+3sqCfBy5/tyZvPWNdycZ3NlT9zy36FdoP1/L0GobKK4RRAEoTIlJcGwYZDiINRNnw5vvy3Ws76DFK0VGVXP3+VThqXXitTpjWw6ms6CrdeqysP81Sx7IprMPD3jvt5jd62xXepxT0QQf/ydTqDGnb9Tnbd1DPNXs2RsBxJPZ/L4kt1svfQaefJzDo/1kHvzWJP3eOvn8wBk5xv4bMtJ5m89aVNgcjwth3cfbMmQVsF8tuWk03tHBGswGwuJ/+JVco/86fL9kyhV1Bg0lXGTpzNpxT683K0FOSOiQijQm8jU6fFTK3FTSFmReJZCvYnNxy8yr9TYZj/UCj+1ssyADrZPA4hKcUEQBEEQhEpmscC778Lzz9vva9HCWtgS4qwHuGu3SzY3mS2sSEhlSodpjFkbazvIoknzotjaF3ARQ5/8ZRwL933KlA7T6FNvIEt2JvPvxdwKZXM3hZTjafadRUrvM1tg3cFU/tOnMRYQ2bwsdepYO5xMnHhtW6NGsGJFhYuzbksdO1p/OfPaa7B7Nzz0EPTsaX0vWrSovPEJgiAIgnBbKzObd6tP72aBFc7mTyypWDY/lpaDSu6i8LzIbkADNHa8213mzms9X+V45mFqedXCX+PPoQt5zNpwjEfbh1BgMJersLs82bxJkBd5Bm25l1Lan7afKwVXOJWRR9dZm4uz+ZM9GlBL446fh+ts7lGo47Xf59FdXYhy8hbASTb3rdgyRIJwq4jiFkEQhMqyciWMGQP5pZ6sc3eHL7+0rtMt3HFKrhWpKzSRbzChUshQu8kcPmXoaJ3QorVAP/3zX4dLDR25oOXZ7w6wP+oKU/o0IjOv0Ol4Sq8rmi/dS57bJqfHf9x/Hqt22F/PbMFu7c/F8cksHNmO+VtPYrY4vt7QRu706tmT3BOHnN4TQO5Ti5r3v4SyZihZOj0NAzyZH9uWxNOZjHfwBcLoTmH4ebrRMMCTo6k5xWOLjQ6lXg0P6tXwuO6nAQRBEARBEIRKcvw4/Pe/9tv79bN2c9FobujyRdm8TzNrNs8pNOIulzntAHKrsvnSXedYM2kA49tOYOHeEq3EE4CimvMwoFXZrykpLYkxa2OJazuR5/u9wbC5ezBbypfNx3QOY3lCisP9zvaJbF4BEybAwYMwb551eaLPPgNPz6oe1a139Ch88sm1n//8E9q0geeeg5dess5xCIIgCIJw13OVzd3/91+kUom1YPbq8pO3IpubzBaWJ6Qyo9MMRqx28v3LFSAeiHP+WvzV/vT5pk/xz5FBkUzvNIPZD9/DGz+f5JVhzV3mcih/Nvd0k5Ohy3R+IQeyC67QONCTXw5Z5/Rjo0OJqu+Pu1LusotOy9QTfLz2PcKupFo3vD8bXnihQvcWhMp2B3+CFARBuEOYTPDii/Doo/aFLXXqwI4dorClmjCaLej01paIDvebzGw6mm4XJGOjQ0k4neEwoJe0NOEMf528TKDG+WRhyWuZ0ZGhmOv02L4hMdwf/oDLLjAlHbmgpUBvokmQV/E2mVRCeC0v2of5cn/tPP47egjnyihscQ9rTdDID1DWDAXA30PJCwPCWbD1FNNXHbSZPC+67/RVB3lvwzGe7x+Ot0pBRLCGT4a35sWBTVEr5cVPA3wyvDURwbZfipQ+VhAEQRAEQagiTZvC3FL5dPx4a8eWGyxsgWvLC2XrjFzOLXRZ2HIrs3l2voHRiw7weo93WHbfCiKDIiED+OPqATJgUMVe24K989h6dgMxUXWAsrP5s30b0z7Mj2UJ9q8jJirE6T6RzStozhxYvhyWLLk7ClsAvv4ajKXa6RuN8Oab0Lq1dUkjQRAEQRDues6yudsv65HOesfaxb5vX0hLu6XZ/MvtZ7inwSAmtJ2ATCKjZWBLuoR0oWVgS6RIYT3WpUI9nFzcAudzzttsSkpLImb1CGb99RIzBzfAYjbb5HK4/myeW2jEX+3n8vWWplZ4M7xDKFH1/OyytqNsLjWbiEtYxeql068VtoD1QYRduyp0b0GobNXgU6QgCMJtLDvbWrjyyy/2+7p0gVWrIDCw8scl3DSOWiWCddI2rlt9ejUN5GJOAWqlHC93OVm5erxViuJWgDKphBFRIYxbsqdc91u47RR9wgNpUVvDofO2E81+aiVju4Tx2BeJAGQpFmOSXnJ4Hbm5FhLtSGRSCVIJLqvKSyowmGhR25t0bSGTezVkQIta5BtMfL14EW/OnIa+9JrzpXi1vxffHmOQSK3Lb0UEa/BWK/jj6MVyfEhJoUM9P7ZO74FSLrX7kqJkF53i9UDdFU6/0BAEQRAEQRCqwPjxsH8/zJ9vXaJo2jSQSG74smXl8t7hgej0JjLzCvFWKW95NtfmG5FL3CnMac+ahzbxwJKB7DFaczrRQM2KvT6ZRMYv/6zhk/5D2HL8MsfTcykwmGhZxxtPNzn3ta5Nr6aB5BuM5BWa8PdUIpNIiI0OZemuM2TnG4gI1jCmcxjtw/wYtSjRrj25yObXQaGA4cOrehSV6623rEUs06bB2bO2+44dg86dYfJk6xdWKlXVjFEQBEEQhCrlLJv3cstl/odxyIo2bNmCqX0HCg//fcuyeW6hEYtZyux+s3mz95tc1l3mYt5Faqhr8NuPvzHFNAUinVzUArj4qDJv7zyi63ShX71hPNe/Ce//dgJ3heyGsvmJ9Fw8FOVYSumq1kGtUUg8STiVwVdj2qNwkLVLZvO8o//gEfc46gQHBckeHnDxYpn3FISqJIpbBEEQbpXjx2HYMOs/Sxs/3trGV6ms/HEJN42jVolFjlzQMvnbJGKiQhjfrT73fLSNOr4qRncKY+2kzoxalEhyho7GgZ4U6E0V6p6iLTDwbN/GjP7qWrAP81ezfFw0+VevVSA9SK78V6fX8TdM4XiqgZwCI02CvOyWIHJGo1Lwv8HNeHlIBJuOpjP6y53EL/2AnH3rXJ4nkSnw6/80ns172WyP61ofy9W26uWxYNsp+jhYj7WIw/VABUEQBEEQhNvLnDnwyCPQo8dNuVxFcvnDC3YVTybf6myu05t49edjTH/tFzJ2Xi1s8Qa6l/+1+br7MrH9ROLaxqEz6EjNO82c2DDcpV54KJW8MiQCkxn+OJrO40t22xX2jO9Wn+0zenJFp0cqlbA8IYXX1x21K2wBkc0dunTJ2oV1zhxo3ryqR3N7kEjg4Ydh8GBrAcs779h2crFYrO/Xpk2wYgW0aFF1YxUEQRAEodI5y+ZKo4FnlryEMifbZvurTQeyd8n+W5bNH25bG4XCzNrj63n3r3evFYzkgfQLKYx0cdFy1OB/lDCb+5sNpX4NNd/GdcRyg9ncZLawIiGVaR1nEPtj2R3/p0RNY0VCKltPZNCnWaDTInK5VIL8y0Wop06FvDz7A6KjrR0J69Ur+0ULQhWqJo9JCIIg3GaSkiAqyr6wRS63rse9YIEobLnDOWuVWNqyhBR2J2cSExVS3MJ74bZTLBnbAW+VAk83OZk6191OSsvM09Ohnj+xUSHAtbVH1x24QGp2ARYMZCrmOT3fyzgId7N1YlZbYKBFbe9y3TciWIOvhxKDycKb648yadFW/vxoSpmFLTJPPwJjZtkVtsRGhdA3IpC8QmPFPqTkG8s+UBAEQRAEQag6hYXw++/O9yuVN62w5XpyOVBp2dyUl0XW1iXXTugPlPOjYEO/huwet5uGvg0Z9u0wwj8NJ3JBKxrPDWXod73YeOonjJYCFmw9yZSVSQ6XEJrybRLvbjyO2k3OkE928NmWkw4LW0Q2dyAjA/r0gc2boWdPOHCgqkd0e1Gr4fXXYe9eaNvWfv+RI9C+vfXBHks5W4UKgiAIgnBHc5XN/7v5c1qm/Wuz7Vi3/nzdZvAty+Z1fVW8OLghz/3+HCNWj7DthLIRzO3N4OvkYuWML/vT9pOak8qhjB2Yb1I21xtgWNOhTGg7weW949pOpHvd/ixLOOc6mx87Br17Q1ycfWGLRAIzZ8K2baKwRbgjiOIWQRCEWyE83PqrpJo14Y8/YILrQCLcGfQmM/O3nCr+ueQamuG1vJBJr5V1L45PJiY6lKJNJSfWcwuN+KkrVujkq1YikcKTPRvy4cORTO3biITTGfyYdB4/tRKt/GcM0rMOz5WZA/AxjC7+Wa2U0ye8fEtjxXWrT0ZOIZuOpvPVui2kfj2VwpRDLs9p1bY93Wd8gVutxsXbIoI1zH6oJU/2bIhUAjqDqVz3L6ItsA/8zhhNZnR6I2nZBRxP05KWXYBOb8RoMlfonoIgCIIgCEI5JSdbl2Dt3x82brzltyudy8F5Ni+dy+HWZ/OsLUswF16dQG4EhLu83LXruvuyMXYj7/71LmPXjrVrSZ6UlsSI1SN4btMMHmhXE2+Vwum1lu46w1//XmbJ2PZEBGts9ols7kRWFvTtCwcPWn++fBl69bIuqSXYatkSdu2yLjPmXqpDT2GhdYmiIUMgM7NqxicIgiAIQqVxls2nnNnGY/t/sdl+LqAumqVLkJYI5zeazf1KZfMPH2nB2uNrmb93vu2B/wKXsS4X6kwFVk7NLszmnfi3blo2H9OlHkqJG+/0eY+vhi0jMijS5rjIoEi+GraM5zu+wehFB4oLZOyyeX4+vPSSNa/9+afdWIyhYaT8sJ606S+hs0huz1wuCKVc97JE4+Mm0rlTR0aNctWvSRAE4S7l5garV0O7dnDhgnU96p9+gpCQqh6ZcJNo861PNHqrFMRGhzIiKoQCvYlMnR4/tRI3hZQViWdZuusMRy5oKdCbbJb/WRyfzMKR7fh8+ynclTIigjV21dyOtKitQSaFn/ZdIMjbjZOXc5jYvSEPzd/JifRcMgtTyVGscHq+v2EyUqzrnkcEa5BLJTQJ8mJEhxCWJzp/2jUmKoReTQPI05t469PFpC17C4uh0OVY60QP4pd1y7HIFBToTWTp9PhefW9+PZSK0Wzhr5MZhNXwKPN1l6Rxd/7hoCRna7tGBGuI616fPuHOW6gLgiAIgiAI12H9enjsMWthAFiXc0lMhEaNbtkti3I5UK5sXjqXw/Vlc5lUwoDmQchlEr7bfQ5ftYJ/L+XwZI9r2fzQvkTyDm+yniAHBpb/dU1sP5GtyVtZuHehy+Pm7ZlHVO0uxERF8tmW006PW7DtFF+P7cDCke1ENi9LXh4MHGhfyKLRgL9/1YzpdieXw/Tp1qWKRoywdrMtaf166/zIli1iXkQQBEEQqjFH2XyULI2agz6yOc7g5s6UB2byrpv6pmZzmUzCpiPpyKQSTl7OoW/zujyxYZbtwYXAeuARnLaAiGkRw7JDy8r9ugM9AsnV597UbB5/MoP6NT1IT4vk50c2k2fQkpmfhZ/KF7Vcw4qEVIat2WPT+aU4m5tMsGyZtbDlrOOHYH/tNIRpHR4jL8EMCX/cnrlcEBy47t+dFy9eJFtbvjalgiAId6VateDHH+HTT61LEanVVT0i4SbKztcT5q9mydgOJJ7OZPzXe+wmaUuuE5ql0+Ppdu2v3aKCl4YBnixPSGF0pzCmrzro9H7WDwN1eKJrXa4UXCasVgZ1vGvSom4weXo956/kYzJbGLfmGUzkO7yGh7EnKnNk8c9x3erz299pfBWfzJKxHWgT6sPi+GS71zGmcxgd69egwGDitZf/R9JX77l+cyRSfHuPQ9pmMPkmKR9u/Bt3hQyAo6k5XLiSz5qnujB/y0lW7jnLthk9y13cExGsQaOSYzSZ0ZvMaPONZOfr8VYp8HJXYDCZycgtINBbzTu/2q/tCtb3fvKKJGKjQ3lxYFMR1gVBEARBEG6UyQQvvwxvvmm7/coVGD4cdu+2tru+BbLzra3Ky5vNtfkGm1wOFcvmRbl8eFQtjJZcUrTH6djYhyAvb/w85VzUFvB3qhaL2cSTTz117cSuOG15HuIdQkr2tdwqk8iIaxvH0BVDy/UezEmYzdpH/mD+1tOYnbRPP3JByxWdgQ83Ha+UbO7priCvwIBEIsHTTc5bd0o2NxqtRVm7dtlur1vX+rSrKMxwLTzc+t69+CJ88IHtvnr1IDi4asYlCIIgCEKlKJ3ND+4+ivTBB5HobZcX2vfi23zwVOwtyeZN6vgQ5OVPLR83svKzOJBeamnJrUAEEOT4NajkKt7u/TZHLh2x657oSOug1ni7exf/fLOz+dYTGczfepomQV54usnJLdRyPC3H7tols7kh6QCqUaMc3vtKzVo80z2OLQ3a2Y3ptsrlguCE+J0pCIJwo0wmkMkc7+vQwfpLqHZ81Eq+HhvF/K0nHXY8KVonNCYqhCVjO2A2W8gttF3zsqjgZUViCj9P6uK0e0qYv5qvxrZi+7kN9PomxiZURwZFMr3TDNZMuofhS75mV8YvducDSCxqfA1ji3+OiQqhY4MaxP97meQMHUPnxhMTFWJTLR6occdNIWX9wVR2HUvhrRmT2Ldjk8v3RarSUPPe53EPaQmA0Wzhv4MjOJupK64+P3kpj4TTGcWvtTzFPUUmdG+ABPjlcKrDpz5HdwqjV9MANhxKdTh5XtLSXWeIqud39YlbsVKjIAiCIAjCdUlPt3Zq2LzZfl+jRrB48S0rbAHwVinxVinKnc2fH9DU4XXKk82LcvmWlF8ZstI+l8/oNIMWdYYQ5q/mnz9XkXrqmHWnH9DZ+Wv4oN8H/Hbqt+IuLREBEegMOvuJeCf2p+0nz6C1e+q1tKrK5r8eTrszsrnFYl1GeN062+21a1sLW8LCqmRYdxw3N3j/fejXD0aOhIsXoU4dWLHC2uFFEARBEIRqq2Q2/3zTMYY9O5Ia2Zdtjvmy3TBe1zUiZtspl9lco1KQeDqDmKiQ68rm7/V5D3lBoO2F04HjwETnr6F7WHcMJgOTO0xm7Nqxzg+8anLUZC7nXcZT6Qncumzu6lpQKpvH5zE+vDvDjm4t3m+Ry/k3djxDfXuRr3R3ep3bIpcLggvid6UgCMKNmD8foqOtbYuFu4rGXU5icqbLpXzAuk7onuQsvNUKjqfZBlB/Tzdio0PZ+Ew3pBJ4fkBTPn400matTW+VgqXjWjNr50xG/hRjVy2elJZEzOoRvPnXC2QpS60dWoKPIQYZvkQEa/j40UheHBiOBQu9wwOIjQohO9/AZ1tOMvjj7Wz/5xJ1/dQYzWbOZOiop9Ayc+x9ZRa2KGqGUWvUh8WFLQBKuYRXfz7CrA3HmLRiH08t20urOt4sjk8uPmbprjN0qOfHiA6un4KMjQ6lV9MAPvj9BJNXJNk9TXrkgpbnVx/CAiwqcX1X5m89iV6sJSoIgiAIgnB9/vzTugSro8KWBx+EPXugRYtbOgSNSs7Uvo1sJoGdWZaQwp/HLtIhzM9uX1nZ3FulYMnjrXhn50zGrI11mMtHrB7BC3/M4KN765AbX6KN+QBcPl72/B/PM6PTDBYPW0xkUCQaNw2XdZedn+BAZn6W3VOvpbkrpLy38Rj/XXOYfh9tY+LSysnmX+5w3pK9pCrP5q++Cl9+abvN1xd++w0aNKiaMd3J7rkH9u2DHj1g1SoICKjqEQmCIAiCcIuVzOZN35lJu/NHbfbHh7bkrZ7WghFX2TxQ485nI9rw0SOR5BYaeGFgUz4e3rpC2fzZ359FrSjRTd+CdTmiITjN5v0a9MNT6YnRYqRbaDfGtx3v8vXGtY2ja0hXjBYj2sJrefh2yObvdXsMvdQ6hp2hLbm4bRdTIh91WdhSpMpzuSC4IMrlBUEQrodeD5Mnw4IF1p9Hj4bvvrulTyQKtxcLsKick7SL4k/To0nN4p+9VQqm9m1EgJcbbUN9OZupw1ulwHJ13+LR7dGbzOQVGvHzkLMpeQ3z9zovXAH4Yt9Cp/ua+EXw1aA3qOXtgb+HGwaziSeWJFLbV83YzvV4tl8TIkN82Hg4jZcGNyPxdCZjv9rNkQta8k/t5fLadzEXui7gUjfpgv/AZ5CWCMcRwRpqernzbN8mZOr0+KmVeLrL0BZcW3sVIDvfwKhFiS6XRprQvQG9wwPYefIyn293/r43DvQkW2ewub4rRy5o0eYbRZtFQRAEQRCEijAYrMsQvfOOtdtFSXI5zJ5t/bxUCZ+PlDIpwyJrM+LzhHIdv2DbKRaObMe8rScxW8qfzeVSC5tT1rKgjFw+b888Ti49iVabbd3QFGjkekz/Zv5L+8/bM6HdBNYNX4dcKqfQVFiu11PET+VLbqHzDBwRrMHPQ8kj7UPwu/pk6KajF8nI04tsDtauIq++arvN3R1+/hmaNav88VQXtWtbi9/EXIkgCIIg3BWKsvk3o1/k6aQNNvvOaQKYNPQ5TNJrXfCdZXM/DyUXcwq4nGudU84pMNKghgcrx0dTYDCRbzCy/fzPLrP5kYtHUMgURAZFWotfDgA1gDDHx0uR8kbPN+i8qDPv93ufp395mg/u+YCOdToyJ2GOXWeYKVFT6BrSlakbp/LxgI85fPFw8f7KyObB2otMPf477rPeolfLOnbZ/JxPEO/0GMMpv9qkd+7B3AZN+HvNVruxOHzvxJy5cBu7od+Vp0+f5tuV313XuY8+8vCN3FoQBKHqpKdbn0LcsePatlWr4K23YObMqhuXUKm0+cYKTdJeyTfQJMiLfL2JJWM7sC/lCg8v2OmwdXdYDROvrTtCx3r+PBIVyKz4WTc0Vm99HO//9i+5hUbiujXAR61g56ksIIujqTksfyKK1Cv5fPhIa9759ShLE1KwWCxoE1dzZesSsLiq0pbg0+0xNNEPISk1YTmua31W7T3LK2v/Lt72cLs6jOwYZncVZ0sj+aqVBGjcUClk6E1m3v/tH5ev1dNNTqZO7/KY0rQFBoK8y65YFwRBEARBEICTJ2H4cNi9235fnTrWov+OHSttOHKZlAKDqULZvEBvqlA2f2P9Ed55sCnvlieXX4bfvvvN+u8KrF1byiGrIIu3d7xNq8BWNPbqQ6Nantcm4svQOqg1arnGrlNkSWM712PelpN8tuUkYH190/o1Rm+0z/p3XTZPTIQxY2y3SaXWgpfOLtaTEspHFLYIgiAIwl1DLpNi/mMzE374yGZ7vtyN8fe/RJba22Z7WdncW6UgNroOsR2D8VDncOpKCho3H2p4+JKhu4yvuy9ZBVkOx2KymFiwdwHPRD3D6G9Hw1+Ai1WGRkaO5MdjP2IwG1iwdwH3Nr23uAB9zaNr0Bl0ZOgy8Ff7o5KrWLB3AVM3TuWDez5gwd4FmK/Ood/qbK5Pv4TqvXcI/OZLJHo9poTOFLaMc5jNF7UfBkB7d8WdkcsFoRxuqLglOfkMyclnKnSOxWJBIpGI4hZBEO5Me/fCvffCuXP2+3S6Sh+OUHWy8ysWBrPy9ARp3Hl1aHPmbz3psGX6kQtapq86SExUCP8bHMG07w/Qp4WSA+kHrnuctRR9CfFoS26hkRPpuXy+/RSfj2yHVAJmi/WeOr2JNqF+/Ho4laUJKZgNhWRs+Bjd364ruSVKFTWGTEPdMMpuX2xUCK1DfBg6N95m+6Hz2agUMrvjgeKlkeZvPUmTIC883eTkFhpZPLoDXu4KLufqy/zSIrfQiJ9aWca7YkvjrqjQ8YIgCIIgCHetb76BJ5+E3Fz7ff36wdKlULOm/b5bLDvfUKHjs3QVy+avDm1Bhu5i+XL570DRnHQ3wNvFsaXEtZ3IgIaDWHcgA6kUpnSYxpi1sWWeNzV6OisSUjFbHO+PiQqhXZgvr627VnR+5IKWdzceZ35sW4fn3DXZ/Px562f8wlKdcubMsW4XBEEQBEEQKiTbywedpz91tBeLt80YMJm/A+s7PN5ZNg/zV/PV2FZsSfmVgSti7DqnTO4wmd3jdtN/WX/+zfzX4bXn7Z7H7nG7iTgUwZEeR8BJrUawZzAzu8ykwxcdbM57KOIh3t7xNrPiZ9E8oDkaNw3aQi2HLx7GbDEXL0s0dePU4mvdqmw+f/MJtB99woztS9HoruVw2Ztvor1/uMtsfkfkckEopxsqbgkKCqRp06Y3ayyCIAi3t+XL4fHHoaDAdrtKBYsWwaOPVs24hCrhrapYGPRVK7mvdW32pmQ5nDwvaVlCCq1DfLivdW3ScjOue4xuMneWP/oqSpk7/mo/1HIN3yamUmiwVsMfTbVWj6dmF9Cqrg9vrj+KUXuJS6vfQJ9+0uW1GzRsxKS35/P7BYXdE65x3RsQWdeHkV8m2H3RcCI9F3eljIhgjc15JZktFI8tIliDRmWNK+UpKCrP9UsqeX1BEARBEATBheefh1kOOpcoFPD22/Cf/1i7XVSBW53N+zYLwNPzStkXTgaOX/33GkAnx4dJJVJ+Hv4z8Wfjmbd7HqE+oTwTNZ3udftzSQuDWgYzbske3n1oAOPbTGDhPuft1ie0nci94UP5w3LFLgNHBGsY0zmM9mF+jFqU6DCby2XSuzeb63QwbBikptpunzgRJk2qvHEIgiAIgiBUI6rWkQwe+T6f//AGrVOPM7/D/fzcrLvT4x1lc2+VgiWPt+Kt+JkOs3BSWhJj144lrm0cG2I20P7z9g47uGgLtYyeN5ojp4/AI47vL5fIWR+znnxjPi0CW7DtzDayCrLov6w/mx7bRLfQbsz+a7bTZYn6L+tffO9blc3bnz3Mq5sW0OyigyVBU1ORfTYXcFwYU3Tt2zqXC0IF3NDvzPDwcCY/LT7sCYJQzZlM8MIL8N579vtCQuCnn6B160oflmDPaDKjN5nR5hvJztfjrVKiUclRyqTIZTd3ol2jklcoDAZo3Aj0DuTh+TvLdf3F8cksGt2eK4VlH+uMj7s3Pb+5NqMeGRTJlA7T8PWsS8MAz+JJ6ppeblzKKWTf7p1c+vFtzLorLq/rXq8Nqzf8xN40Iwv7BFGgN6EtMFDTyw1ftZINh1MZ8skOh0/QmswWliekMLpTGNNXHSzzNUzo3gDl1f925fnS4kauLwh3uiNHjtzQ+RERETdpJIIgCEK1dO+9MHu29fNRkcaNrUu3tGljd3h1yuar9p7nuUEByCQyIgIiip/YPHLxCCbL1ffDDPxW4qQBgOOGhZgtZmZunsn0TtNJfiaZAgN8teMcw9bsYd3TXdDmG0hMzmT0ogN8NfZNOtXtykcJ79lNqBcVxGTlQmae3tqm3GhCV2jC31OJVCLhq7+SeX3dUZfZfEznekz7vuyuNNUum7/4orU7a0m9elm7tgjCDRLZXBAEQbidVHY2D2wUyqPD3yJ2/y8sbjfU6bHOsnlsdB3+PPOryyJvgAV7F9Cpbide7vEyz258tjib+7r7MrH9RJ6IfILB/QfDQOfX6BLShSkbpjCh7QTWj1hPTmEuF3Iu4qX0xlflz5BGD9AjZBA6Qzb5Ji0aNy/kUjmf7f6MqRunklWQdcuyuWehjhf//JIRBzY6HLtFoUASF4flsZGwyHn2uO1zuSBUgCi7EgRBcCUrC0aMgA0b7Pd17QqrVkFAQOWPS7Cj0xvZdDSdBVtPOegkUp8+4YGolTfvrz2lTEpc9/pMXpFU5rFjO9fjj6MXaVHbu8zW3UWOXNCSW2BE4+5NZFCkzUR2eaXnpdv8nJSWxJi1sSRemMiLA95k24nL1PFV4aaQMu+TeaSvmAlmo8trajrcj0/3USg9vOnXTE5OgQEvNzleKhWHz2cTXkvDs9+7DshLd51h7aTOjOgQ4vJJ2djoUHqHBxR/wCrvlxZLd53hl8ldiY0KYWlC+a9/p6vMD6nC7WnmS/9DIpFc9/k/rl51E0cjCIIgVDvR0fDaazBzpvXnxx+Hjz4CT0+7Q6tbNv/rZAZ+qqaceeYMOfocLusuU0NdA5VcxcJ9C5m3ex5Zu7PgwtUTmgENXF8zKS2JmNUxTGw3kRc7v8myhHPF2fxMhnXJ2+QMHcPm7iEmKpKfH9lMnkFLZn4Wfipf1HINKxJSGbZmDyvHR9Oxvj9SwMtNztFULb4eCjq9s9lpO/QiRdk8JiqEZRXIztUim7/0Ehw9Cr9drUpq2BC+/97ajegGiWwuiGwuCIIg3C6qLptr+bLDfS6PdZTNZVIJw6NqMfjbEeW630e7PmLL6C3c2+Re1hxfw8H0g/y323/ZfHoz3Z7uxrnAc+Dl5GQLbDmzBYBtZ7Yxoe1EZnZ5k7kbC/BwM/K/IYGcydDxyIKdNAnyIlDjTvfGavpGeDOqZRxDGj18y7J5yrc/8c6vn1A755LDY40PPIj8vXehXj089UYigs+6zObXOydfHYhsXr2I4hZBEARnjh61tij+5x/7fRMnWidylRVrfy3cGjq9kbd+OcrSXfah7MgFLZNXJBEbHcqLA5vetKAul0npEx5IbHQIS3elIJNKaBzoWbwW/Yn0XExmS/Eami+vPUJtX1WF7mEwmUm7YmJGpxmMWF2+MF8e8/bMI6p2F2KiIqnr48ajI5/gz5+WuTxHIlfiP2AyHs16AKCQSvl65xme6dOIGT8cZP3BVNrX8+OVIWU/XZadb2DUokSWjO1AdH0/Fmyz/2A1oXsDeocH2Pz3Ku+XFtn5Bg6dv8KUPo2JDPFhcXxyua5/J6vsD6nC7alnjx5Qav48NzeX3bv3IJFICAsLJeBqQeali5c4nZwMQLt2bfF08MWkIAiCINh57jnYsweGD4eHHnJ4SHXL5mH+ar4a24q1x9fw7l/v2nVPmdxhMn+N/osW77XAiBGUQP/yj71kNq9f04v1B1Pp1qhm8f7sfAOfbTnN/K2naRLkdfU1aTmellM8Oe6mkPLT/vNM6N6ARxfuRCmXMaN/0zInz4uuP2pRImsndSGqvp/DPFlts3mNGvDLL/D66/Dxx/Dzz+Dnd8OXFdlcAJHNBUEQhNvDnZjNGwd6kmfQciC97M6CAPvT9nMh5wIv//kyD0U8xNjWY5n+23Tmb5sPu4EYx+dJkODp5kmOPqd42/y984iu04XwWrbZvGipzqOpOWw5fonX13HLsvm4BTtYl7IG5XfzHB6jbdgU5aef4N6vT/G28mTzoty/+snOtA/z44sd5cv9dzqRzasf8V9LEATBkZ9/hpgYyMmx3a5QwNy5MH581YxLsGM0mdl0NN1hQC9p6a4zRNXzY0DzoJtWjatWynlxYDjDO4Tg7+FGXqGRTJ0eP7USDzc5F3Py8VUreezLRFRKGX7qihVDuSmkpGWbGNJkKBPaTmD+XtdtGCtiTsJslvb7nrGxo0n4a4fLY2VeNah5/0u4BTUErMHPTWFd//NMho5ujWry84FUtPkGPNzKFy2SM3QMnRvP5mnd6VDPn7xCI9oCA0EadyQSCd4qOapSobL0ByNnYqJCCK+l4f7P4hnSKpglYztgNJnRFhjRuCuqXVV2VXxIFW5PU6Y8bfPzlStXmD79OVq0aMH4cU9Qt24dm/3nzp1j4edfkJx8hndnvV2ZQxUEQRBuZ5s3Q/fuIHOwpo5MBqtXOz21umVzb5WCJY+34q34mQ5boielJTF27Vi6Xe6GMeNqB8TugKZiY5+TMJuNMVvI18tZe+A8gd7udl1RiibUS4sI1qCQSWlQ05MzGTpio8NYFH+6Qp89kjN05BQaqOWtYuHIdsXLjt7sbP7S4GZ8H9cRbYE1+98W2Vwmg1degaefBn//G76cyOZCEZHNBUEQhKpWKdl8yRI4fBhmzQLptXNvJJt7usnJ0GVWaBgZugwu5F5gf9p+8g351nn0P4B7nJ/Tr0E/2tRqw9s7bP/evSnZPMDrurJ5SFYq73/1Dsr0k3b7zGoPcv/7CorJk3BXu9vsK28279ywBnmFBs5m5VX7OXMQ2by6qvTfocnJybw3+/3Kvq0gCEL5/fCDtWNL6cKWgADrRK8obLmt6E1m5m85Va5j5289id5kvulj+Dc9l8eX7Kb3B1t5aP5Oen+wlceX7Obfi3lYrj6qdSI9F3eljIjg8s10FwXhsUt2s3DLOWb1eY8lw5YRGRR5U8a8f/9++nbrVmZhi1vtZtQa9WFxYQvAmM5hHDmfTZdGNRn39R461PNjRIcQTqTnIpdKyv0a6/iqKDSY6TLrDyat2Ed2vp4NR9Lo9M4fZOc7Xh6p6IPRJ8Nb290nIljD7IdaMr5bfUYtSkSjUhBeS4NaKSPIW0XjQC+CvN1RK+XVJqRX5EPqH0cvYrwFv/+F29eSr7/BYDTw0swX7CbPAerUqcOLLzyPwaDn66+XVsEIBUEQhNtKXh6MGwe9e8N7713XJapbNo+NrsOfZ351WNhSzADbvt5m/fdAoGPFx7w/bT+F5hzc5VICvdz5Ye9ZRncKK9e5jrJ52xDfCn/28HZXMP7rPXSdtfmmZ/M31v3NjP5N6dqoBmo3OUHe7rdfNr8JhS0imwuuiGwuCIIgVLZbns1/+cW6VOns2fDAA9bPE6VcTzbPLTTir65YNz1/tT95+jzi2sbx/s73IQXwAWo4OcEEqTmpTGg3AanENovelGzesAYTlu6lU4Ma5c7mnZKTWPfVFFo4KGxJa9uRsc9+SWR2ONkGx+eXN5u/9vPf1KvhWa3nzEFk8+rsusuQfvrxhwodf/z4cb7//gf27tt3vbcUBEGoHH37Qng4/P33tW1t28KPP0LdulU3LsEhbb6xeD3Oshy5oCWnwIhSJr0payyWrvyVSSU0DfIivJZ1Ec8vd5xif0oWS8Z2YOjceJYnpDC6UxjTVx0s89pjO4fx+9/ptA315fej6TQM8CRE1ZOfHxnAljO/8dhPjzo8r7ZXbfL0eVwpvOL84oeANXDBeMHlGDxb9cevbxwS2bU152OiQujVNBA3uZRPN//DsNa1cVPIeGFgU6Lq+/H732mM6RTGtHK8xjGdw1i66wxGM7QJ8aVeDU+mfHsAswXrk6Le7g7PUyvlDGgeRO/wALT515769HKXYzCZuZxbyLfjO1bLavPSKvohtbqtl1puBgN89x2MGAE3sO79nWb//v1ERETg5ubm9Bh3d3ciIiLYn7S/EkcmCIIg3Hb277cuNXT8uPXn//4XevWCDh0qdJnbJZvLrhZcF+Xyo6lapq86yKPt65Y7m8ukEoZH1WLwt66XB1UdVJGvzbcuPzKU636M7Ep+FjuPF/DmL8fwVilYO6kzIzqEsDzRdVcUR9n8uQFNOZORx5jO9Zj2fdnt3OO61WdFYgpZOutM+Q1l83wDGpUcT3cFeYXWopg5w1tX+1wOIpsLrolsLgiCIFS2W5rNExKsy5SaTNaff/rJ2v1x61bw8LihbH4iPRcPhYbIoEibJUGdaR3UGpVchZfSC6PZyIELB2An8KCLk2Rw8OJBdAYdzQOaczDd9jNBVn4mdby8Sc7IYW1SGkufiKpQNk84eZll46IxmcxM79+Uc5llZ/N/aoSQp1Sh0euKt+mV7uS88Tb9c5typdD6XotsXj4im1dfN9Rjp6CggJ9/Xsf+pCSys7Px9vamTZs2DBk8qDio//PPP3z9zTIOHz6MxWJBqVQyoL+LPlCCIAhVTaOBNWugfXu4csX6ZegXX4CqfGuyC5UrO19f7mPD/NWolTJ+OZx6w2sslqz89VYpeKJLPUZEh6A3mLmSb0CjUqC5WmyReiWfx7uE8eWOZH6Z3JWYqBCWJTgOwt4qBe8+0JLODWuQnlNA89rexe0a3RRSRi9K4ILSeQe0Ec1HENcujv7L+vNv5r+2O83AZsB1sxZkMjlN759Cbv2etu9Pt/r0Dg+k0GDiXJaOxzqG4a6UsTbpPF9sP82QVsHEdgzFy03O/rNXnL5GsIb99mF+fL/nHLMfakn7MD9GLUokO986oa5xVzg9F6ytFuUyKWql3C7M+1Rw+ac7WUU/pGrzjXdfi8VLl+Dhh2HLFsjMtLacv0vk5enQ6XRlHqfT6cjLK/s4QRAEoRqyWGDOHHjuOdCXyNVGo/Vz0MGDoFaX+3JVnc1/PpDKs30bO83lyxJSSDp7hZioEJbuOuMymzcO9ERn1HIg3fkEdH3P+mgTtOSTDx2A2o6Pk0lkmCwml6/By82b1ftPA5Cdb2DUokSWjO1Am1AfFscn278/TrL5z0nn2XzsEoNb1WJg81ocOJvFN2UsGxQZ4suQT3YQEaxhTOewm5bNPcu5ZGml2LLF+rn+00/B2/uW3EJkc8EVkc0FQRCEynbLsvnx4zBoEJT+e61HD/DwuCnZfEVCKs9GT+exn2LKHPvkqMms+nsVyx9YTlpuGiQCXQAHq6wC1jnyqzUMGboMNG72HVUUMgUT1j/OgIZD+eaJAby4+hBv3d+i3Nm8cS0NMomEFXvPcfhcdnE2P3g2i6+dZPNLnn788MpnPPm/MUh1OrRhDcn9ZhkjduZxpfDae10tsnklENm8+rru/0oFBQU89/wLpKScxWKxAHD+/AWOHj3G7t27eevNN/h+1Q989933mM1mFAoF/fvfwwP334ePj8/NGr8gCMKt0bAhrFwJBw7AtGl31ZP+dxpvVfkKGbxVCr4eG8WsX4+x1MHkdUXXWCyq/A3zV7P0iSj2nsli5JeJdsF2TOd69GoawBNd63Nv69r4qBVMu6cJbUJ8WRR/2ub4vuEBzHqwFdv/ucQjC3faXeupng14rNdFHvp+t8MxdazTEYVcwae7P2VDzAbaf96erIIs684C4AfgH9fvk0ztzROvfMJ/xz1Igd5Elk5PTS83ani6cS5TxxNLdrPz1LU1TyOCNYzuFMbSJ6IYtSiR+VtP0rNpTV4f1oJ2YX58sd3Bh6Fu9eneJIBsnZ73H27F8oQUXl93tHjyPCJYg0YlgmR5VORDKriu7K+WkpLg3nvhzBnrz//5D0REWJ9EvwvUDg7m0KHDJCcnExYW5vCY5ORkDh48RF3RmUwQBOHuc/EijBljbSdemoeHtXtLBQv8qzKbr026wPrJXVzm8pioUPRGM72bBvBA2zous3nPxjUpMDmfDPV19+XhvId55+I74A30dj6+D+75gFe2vHItm5fSOqg1CokXx9OuLY2bnKFj6Nx4YqJCWDiyXZnZ3FulYGrfBgyNDKBnhILz2Zc5n2Nm+oB6PNSuNm+uP07CadscH9etPj2bBpB8OY/VEzvhppBWz2yekQGxsXD+PMTHw/Ll0PE61o8qg8jmgisimwuCIAiV7VZk85mtvFD162fNVyXFxsK77wI3ns3bhvry/Z6zLOgxlPFtx7Nw70KnY49rG0f30O4ALD+8nM7enSEXp0XnmLApevFX+6MttM38RZ1gVh1dxXd/f0dc24m8ef8bxH6RwJBWwRXK5o93CSbX4FGczacNqMdD7WvzxjrH2bx7kwAyAsyof/mZLx+eyld/ZBTn8qLj7vhsXklENq++rn9ZojVrOXMmBW9vb4YNHUJISF10+QXs27ePrVu38dbb77Bvn7WFYr9+fRn+6CP4+vretIELgiDcMIsF8vOdP4nYr5/1l3Bb06jkRARrbAKyI7HRoSQmZzoM6CUt3XWGqHp+DGge5LINnTbfyPkr+ax7ugvzt5x0GvynfX+AmKgQpvRuhMZNwe9/p/Py2iPM6N+Eb8dHc1FbSJZOj7+Hkppe7rz1y1GH7Q2PXNBy+HwWC47PcDqmS7pLNPRtSLfQbiSlJTGh3QTe3vE2XAZWABlOTwVAEVCfgPtfYkOGH7/N2kyTIC883eTkFhqZH9uWZ75L4mhqjs05Ry5Y20jGRIUUt5H84+gl9iRvt1baPxGFtsCITm/E62p1+G9/p/PMt/tJ0xZwPC0Hs8V2HBO6N0ApWgCWS3k/pBYpq7K/Wlm1CkaNsn2KxGSCkSPh5Elw0Q68uhg8eBBzP/2MmS/9jyFDBtOlS2cCatYE4OKlS8TviOfndeswm80MHjSwikcrCIIgVKrff4fHHoP0dPt97dpZv/xv1KjCl62qbJ5TYOTlIRHlzuUW4Hialn1nsnhj/VGH2TxQ405a3gVaBrZE46ZBW6jlyMUjxR1YprWdxvuPXu2oOAhwEsvUCjVeSq9r2dyB6Z1msHTnBbtcnJ1v4LMtJ5m/9aTLbB7mr+arsa3YkvIrvb6ZbdO+PTIokmejp/PF6KHkFci4mFNITS831EoZC7ee4ptd1iLg3EJj9czmFgs88YS1sAUgORm6doXt2296gYvI5oIrIpsLgiAIle1mZ/O1W44wY/orqFJKHXfPPbBoEUitmfFmZPMvRrVDq89gRqcZdKzTkTkJcziUfoiIgAg0bhqCPYMZ2GggXUK6sProasJ8wri/6f08OelJ6O7iRZQobCkqYjl88bDNIZOjJrNg7wLMFjMAC/bOo2OdLgxpFXnTsvmXo4eizZdx4Uo+tX1VqJUyVu87f3XOPJjjYSMwJ6TZDf+Oz+aVSGTz6uu6i1sSEhJQKpW88/Zb1KoVVLy9W9cuBAYEsPK775FIJEx7diqdO3e6KYMVBEG4aQoL4amn4MQJ2LQJlHfPMibVjVImJa57fSavSHJ6jEwqYURUCOOW7CnXNcuzxmJ2vp7Y6FB2lyP4L0tIoXWID+FBGq7k6wn2cedspo7cQiOTVuzD003OoBa18HCT2xW2yKQSGgd6olEpqFlzP6cSnK8T+W/mv4xdO5a4tnG80OUFoupE8c7id7CsskCh69esbtoV/4FTkCqs1clmCzaFLJdyCl22Lix6jTFRISzYdopgH3d2J2dyNkvHK4MjCPBSYgHeWn/UZVv02OhQsb5lBZT3QyrchZX9Wq19e9Tata1rAN8FhS0Affr05vyFC/z00xpWrvyOlSu/szvGYrFw373D6NPHxePmgiAIQvWh18NLL8F77zneP2MGvP76dX8+qqpsjsVSwVzuRc8mAUz7/iCNAz05ka4ltzCgOJvf17o2EcGe1A/w4/uHvudi3kVqqGugkqtYc3wNO8/uRLddR2ZGJjQDGju+n1wqZ0PMBv7753/5+r6vmRU/q3iSvMiEthMZ0HAQ3VfvdDpuV9ncW6VgyeOteCt+Jgv3zbc7Nykticd+imFC24m82v1t6vqocHeTYbFAdoGB3cmOu8lANcnmCxZY819JPXtCVNRNv5XI5oIrIpsLgiAIle1mZnOVvoAvV72G5vwx2x3t21sfMFOUKAy4Cdk83xDEmStpDFzei/9E/4dNj21CKVOSVZBFVn4Wfio/3ORurDm2hpEtR2KwGHjioyf4Tfab06JzDECJYZYuYgFrJ5iuIV2ZunGqzalzEmaz9pE/mL/19A1n8/8ujsEjK4o+C35n3cELPDegKRYLnLyUy5/HLzl9v6pFNq9EIptXX9f9Xyo1NY0mTZrYFLYU6dOnDyu/+5769euJwhZBEG4/aWlw//2w8+rk4TPPwGefVemQhOsnl0npEx5IbHQIS50UTTQO9MRoMt/UNRa9VcoKTcovjk9m0aj2PNi2Lt0b18Tfww29yYxUImFfyhU+erS1zbW8VQpio+swPKoWeQYtl3WXeWLdm+W614K9C4iqHcXJtSexLLeAxdXREny6j0IT9QASF8tv+aqV5BYaXd73+z3nmP9YW2KiQynQm8jU6fFTK3FTyJAiQamQ8cLAcDrU82f+1pN27SgndG9A7/AAsbZlBZTnQ2qRu66yf+xY2L8f5s61/typE/zwAwTZZ9fqbNTIx4iOjuLXXzdw9O+jZGZZv8Dy9fWlWbNw+t9zD02bNqniUQqCIAiV4uRJePRR2OMgvwYFwddfQ9++N3SLqsrmHu4Kvth+ulzXK8rlFgtM7dsYbYGBRgFeFBhNSCUSMvP0DI7058ejaxn9y3t2T1pO7zSdWZ1n0XRMU1Bj7drihNFs5GTWSTrV7USBsYDmAc05mH6w+FrTOs6ge0h/cvKlNu3Gy1Iym8dG1+HPM786nDwvaf7eeXQL7cawJvcjk1iz+YsDw4mqztn8yBHrspQl1ahh/b0uvfm5WGRzoSwimwuCIAiV6WZlczejns9Xv07783/b7mjUCNavB09Pm803I5sbzGZqqP3wV/szosUI1h5fy8eJHzvM5mqlmlWHVvHrhl/BWSN+EzaFLRPaTrApYokMimRK1BS6hnSl/7L+dsuJ7k/bT55BS5MgL7vO5hXJ5k0vwaavoXZOAoe8HufJt75CIZUil0mrfzavZCKbV1/X/aegoKCAGjX8He6rWbMGALVrO1vUTBAEoYrs3g333XetJTHAvHnQqhXExVXduIQbolbKXYa/FweGk28wVeiaZa2x6K2Wk64trNCkfG6hEaPZzNYTl/FQykjO0DG6UxiL4k9ToDcVX6tk68IhK2NISktCggSL6yqVa/QwdfxUruy54vIwhbsHvoOnoWrQHrjWJaaopeKJ9FxMZgsRwRrcFFKOp+U4vVaYv5p3H2zJthOXWLjtlN1/g7hu9enTLBC1Us6A5kH0Dg9Am29EW2BA465Ao5KjlElF5XkFledDKtzFlf0ffACHD0PDhtYil7ukY0tpTRo3pkljJ4+UC4IgCHeHNWusy/VlZ9vvGzgQFi+GgICbcquqyOa5BYbryuWTVuyjV9NA/r2YS3KGjnFd69Ounprnfp/O/L2On7Qc+eNInsp5ivzcfHgI8HB9v48TPuanR38iu0DLb7G/k5yVio+7Dxo3b7zd1SzecYbY6FC7pwrLk81lUgnDo2ox+NsR5Xrt7/31Lj1C+7P7dB59wqt5Ni8ogOHDrf8safFiqFXrltxSZHOhPEQ2FwRBECrTjWZzucnIpz+9TZczB2x3BAXBxo1wdYm9km5GNj+TqeOJLsH8MfIP3tz+Jgv3LrQ7tyibn3/2PDPemgFdXNzo6jOdkUGRPNf5OQY1GkSOPoefh/+Mv9oflVzFgr0LmLpxql1hS5ErBVdoF+oLcF3ZvNFl2LwEauVaf27x6ffo6rdHP+k/yGXS6p3Nq4DI5tXXDZV4uXrCG0AuFxVkgiDcRr75BsaNsy5JVJJaDf6Oi/WEO0dZ4e9yrr5C1yu9xqLRZEZvMqPNN5KdryfYR0VeGZ1MSsvS6YuXIBq1KJHzV/JZO6kzWXl6MnXW8TlrXaiQKdCbyvEargDfwpW0Ky4Pq1uvId//sJrnNl0mS2cgNjqUEVEhpTquSFmReJbGgZ4sT0jB7KS2xlul4OuxUczfetJuWSWwfkiZ/G0SsdGhvDiwKWqlvDiwu/qSQiifsj6k3tWV/QoF/PILuLtDGblVEARBEKqtuXPh6afttyuV8O67MHnyTf97sjKzud5oQqevWLFMUS7XqBTF3RjPX8nnj2e7suHkjw4LW4o0cW/CF7O+gAisv8qwP20/OoMOf1UNlv6VSWKyDm1+NsfTcvjj2e40raVBJpMyqWdDJi7bd7WDY/myeXiQJ3kGLQfSD5Q9kKtj0RZmM2/LGRJPZ1XvbD5jBhw6ZLvt6adh8OBbeluRzQVBEARBuN1cbzaXmk18tO59+pzcbbPd7OeP9PffoV494NZl8wndQ9n27zaHhS1FIgIiOHTsEBclF61dFR1o6NGQLx/8kgDPAFRyFR5KDxLOJRBeM5zlh5dzMe8ia46twWB23UkxWOPPI+29GN2pXoWzeb1M28KWItLFX7EoYhCjezYuzuXVMptXEZHNq6cb+q9VkF/ApUvO1/9ytb+mg2o+QRCEW8JohOeesz7BX1pYmHX97VatKntUwi3gKvzdyBqLOr2RTUfTWbD1WkeSqPp+fPRIZIXG56tW4q6QUmC41qVl1KJElo+LxmyxVo44a11YrsKWZOA7QOf6sM49+1JjyDR+TobvJ3SiwGBk16lMxn+9xy7gPd6lHr2aBrhsJRkbHUrC6QyHhS0lLd11hqh6fgxoHiQqoW+yu7ay32KB2bOhWTMY5GRNAJWqcsd0G9Jqc9i6dSsn/vkHrTaHVi1bcP/99wGQkpJCaloaka1a4XaXdrYRBEGo9vr1Ay8vyCnRha9JE/j2W4iMvGW3raxsHl7Li89i2lZobL5qJUq5FH8PZXEHRZlUgt5UyOyd77o8N/vPbHRSncvliEq7UnCFul4h3BvpySPtQ1iReJYLVwrIyNUzf+tJNh+7yPMDmjK9X2MGtwom8XT5srmnm5wMXWaFXntmfhaebvLqnc3Xr4dPPrHd1qKFtZirEty12VwoF5HNBUEQhKpQ0WwusZiZ9esnDD623ebYPHcPpOvXo2reHLi12TzfUMjsv2a7PNdL6cXkdyZDCycHmEHqLmXmnzPRFmo5fPEw8WPj6VS3Exn5GcS2iMVX5cvsvrNZuG8h83bPc9i5pXVQazB7MHTuDsyWimXzuleshS11SjVF31kHZMvn8Wf8FeoGXayeufw2ILJ59XNDxS07d+1i565dDvdJJBKX+39cvepGbi0IglA+mZnWdeV//91+X8+e8N131jW3hWrvetdY1OmNvPXL0eLWdSWfpPRyU1RoUt7DTY5EAimZ16pPkjN0DP54B79P7UaL2poKtRUvZgH2AL8CZteH1u05gkXLPmPKdwdYmpBCVH1/FDIJ01cdtDv2yAUtU787QExUCHNHtGbo3Hiy820r2GVSSXFFfXnM33pStPm7Re66yv78fHjiCVi+HDQaSEy0flEn2IiP/4u5n35GQUEBFosFiUSCv79f8f6MjEzeeeddpkx+mh49ulfhSAVBEIRbpnFj+OoreOAB68/Dh8PCheDpWWVDupnZvHd4IDW93Cqcy41mMxm5hcUdFBsHepKjz3bdBSUHzv9xHh7A6ZOhjgR5BvH735d5ZuUhIoI1jO4UxtpJnZEAuYVGliWk0CbEl7Fd6vHG+qMsS3DcDbF0Ns8tNOKv9rO/oQt+Kl9yC63vU7XM5unpMGaM7TZ3d2sxl3vlZeS7LpsL5SKyuSAIgnA7cpTN62RfpO+/tt/v6hRu7J+3jOj21iXub3U2v1JYRjYHUvakcLbOWecHGOFExglOZJwo3uTj7sNF3UWGfTuMg+nWOfHIoEgmd5jM7nG76b+sP6ezThMREIHGTYO2UMszUdNZkZBa3Nm8vNk8MMda2BJWanXY3cHQPxYSg0LJLTxTPXP5bURk8+rluv+U1KxRgxo38EsQBOGWO3IEOnRwXNjy9NPWNSHF/4/uGiXXWHSl5BqLRpOZTUfTiwN6mL+atZM6E+avZvzXe5i39SRPdKlXrvuP6RzGxZx8fjmUip9aabMvU6dnUXwyT/dqVKG24gAYgXXAelwWtkjkbtw79V12/vAFHioFx9OspeLzt56kRR0fpC460S9LSGF3ciYxUfbvXeNAz+KK+vI4ckGLNr9iyzkJgp2UFOjSxVrYAqDVwrBhkJ3t+ry7zLFjx3n/gw+RyaSMGT2K996bhcViu75Yy5YtUKvVTgvSBUEQhGri/vvhhRes3SyWLavSwha4udk81E/ND3vP8ngFc/nyhBS0BcbibF6uLig7gHCgabluBVif8qyhrsHr644B1jw8fdVBFm47hZ+HkrTsAgAWxZ+m0GhmRRndEEtm8xPpuXgoNEQGRZZ7LGq5pvizQLXL5hYLjB0LpbtIf/ihtdOfIFQhkc0FQRCE25WjbH7WJ4hHh7/NJbUPAIUyBd++NJc2IwZVWja/lJvh+mQ9nD1/Fnyc7M8HbKfhaR3UGpVcxXnteTRumuLtSWlJjF07lk93f0r82HhOTznNygdX8nbvt1n98GruDR+Mu1KGt8p2uVRX2dyzENYvh4alGsEkBcI9sdAg7Fo2r3a5XBBuoevu3PL55wtu5jgEQRBurjVrIDYWckstYqhUwrx51gkvoVopubZndr4eb5XSrrVcRddY1JvMzN9yCrBWnn89Nor5W08WL79zLiufn5/uQmxUCEsdPF1ZJCYqhG6NalJoNPHJ5n/pFxFkV72+dNcZHnumK2dzTji9jp084FvARXE6gExTk4D7X2K/ogG7kzPxUStsqswL9CaaBHlxNDXH6TUWxyezcGQ75m89WXwuWD9oFFXUl5e2wCAqpIXrt2OH9cnzixdtt584Ab/9Bg89VDXjug2tWvUDEomEV195mQYNGjg8RiaTUb9+fVJSXH+RJgiCINwBzFcrnaVOnmN6661KG0plZ3NvlaLCuXxZQgq5hUbclTIigjVld0HRAv8A4yv2XszoPIMj53Rk5Nl2QFyWkEK7UD+Gdwjhsy3W15+l05eZy8E2m69ISGVKh2mMWRtb5limRE2zeeIUqlk2nzcPfvnFdtvQoRAXVzXjEYQSRDYXBEEQqsr1ZvNjAfV4ZMQ7LP/xVc68/DaPxsXcPtkc4ADQzsV+Byv8TY6azIK9CxjZaiTaQtuHNRv6NeSp9k/x28nfeH/n+ySlJRXviwyKZEqHaayZNIDRiw6QnHGtM7ujbD7ux1hWfQdtU23vf6Qm9B0JWWr4sFQ2r1a5XBBuoRtalkgQBOG2YzbDG2/Ayy/b7wsKgtWroWPHyh+XcEuVXtuzSESwhrju9ekTHlgcvJUyKX3CA+jVNIDsfAPaAgM+KgUadwVKue0ai9p8Y3FHktjoUBJOZxQXtgBk5xsY+WUCS5+IokM9PxZss7//2M716Nk0gHy9kce+TCQzT8/yhBRGdwqzWQooO9/AzB8P8cp9weV70XnAAqyT7C641W1OzWHPI/PwAeDz7af4fGQ7pBKKg3OWTo+nm+tIcOSCFovFwsAWtVh38FoqD9S4U6uCoVvjrij7IEFwZOFCmDQJDLZfDuHlZe3iMnhw1YzrNnXs+DGaNmnidPK8iK+vDydOVKCwThAEQbj95OVZi/vDwyu1iMWR8mZzo8lajNO1UQ16NKlJbqERbb4Rb5Xj9c9dZfPryeVFy20WZfPnVx8qftKy5ER2sR3AvUAFou/EdhMZ2Ggwgz5KdLj/ix2nbArIM3LLzuVgm82X7jrHmkkDGN9mAgv3zXd6TlzbiXSv259ha2yXE6022fzvv+HZZ223BQXBF1+AxEWbSkGoJCKbC4IgCFXhpmTzD2Jpq1Y5zOYyqYT/9G3MyUs57D+bhUwqqZxsfhEIA5zFvDzAw3ZTXNs4uoZ05aukr4hrG8fhi4eL9/m6+7IxdiOz4mexcO9Cu8slpSUxZm0scW0n8tXYNxg2d0/xmO2y+VP92RbfmI4nbf8+P+kLfUbCZQ/H2bza5HJBuMVEcYsgCNWLs8KW9u3hxx+hdu3KH5NwS5Ve27OkIxe0TF6RRGx0KC8OtPYOLwrzR1O1NAnywtNNTqDGnUEtg+jROKC4il1vNJGnNwEgk0oYERXCuCV77O6RnKFj0Mc7eLxLGF+P7YDeaCY734CfhxJPNzlSKSSlZLH+UBoebnJkUglLd51h7aTOjOgQYlMss+XEZaQ0dh7ar5IhQ/KVBKPWdatCz9aD8Os9Dons2l/3Ry5oyS/VqcVXrSS3sOy2h7pCE7MfaslLg5qhLTCgcVfg5S7HbLFUaB1VjUrED6GC9HqYMgXmO/jCplEja7eu8PDKH9dtrrBQj8ZbU+ZxuaW7nAmCIAh3lsuXYdAgSEyEn36CiAiIiamSoZQ3m78woCnb/rnI3M3Wp0KlEmgS5EWL2t4MbBFEh3r+xdcrbzZ3lsu9VAp81QpkEkg8ncmmo+m0C/PFw03OsbSc4mz+SLu6zrugXAFUgOtVlIpFBkUyveNzDG48mIlfH+ZsVr7D40p3USxvLodr2fyKzki+wcjsfu/Rs153ZsXPsnvK9Jmo6XSv25/Riw4UT8JDNcvmr7wCBQW22xYvhpo1q2Q4glCayOaCIAhCZbuV2TzfYOKpnvUYHlULiySP89pUPhoRilqu4dvEVJbuOnfD2Xz1vovM6DSDEatH2A7eAqQCrRy/bmmhFLOHufjnyKBIpkRNoWtIV/ov68/MrjNZsHcBZsu1Yya2n8jW5K0OC1tKWrB3Hh3rdCEmKpLPtpy+9l6XyObur/6PsM22hS2X1NA/FoIaRfKOg2xerXK5INxi4k+KIAjVy4QJ1iezzpZYp2XkSFiwANxFS7fqpvTans4s3XWGqHp+KOUSJq9IKt5eVNwR5q9m+j1N+PVwGl/uOM2RC1rCa3kxd3gbABoHelKgNxU/KVpadr6BD37/h8XxyTzTuxGDWgVjNJvRFhTi4W6hTk09D3WE8T3r4S7XsCIhlUnL9zF3RBvahPqwOD6ZIxe0mMwWlu+6wDNR0xm9xvkXEqZEE1xyuhuJTI5vnwl4RfZ3uL9kp5aIYA1uCinH01y3PgfQqBS4K+QEecttWiTqjSbiutVn8rdJZV5jQvcGKEtU+QtCmS5ehAcfhO3b7ff17w8rVoCPT6UP607g5+dHSorrdcssFgspKSkEBgZW0qgEQRCEmyo5Ge65x7o8X5HHH4eGDSEqqlKHUpFs3jbEh38u5hYXR5st1mx+NDWHxNOZfBfXkV2nMoqf8ixvNi/K5R9t+od2ob6M7hRGh/r+aAsMYDETGaqmXpAvmflZ+LrbZ/Oks1doFzaICW0nMH9viaLaQ0B3569pWqdpPNX+KXIKc/Fx98Hb3Yd9yTkMmpPotLClSFE2r0guB9tsXuT+pg8wuPEQMnSZXNBm4KfyRX31NQ5bs8emsAWqWTZfvBh8fa2d/gCeftqaFQXhNiGyuSAIglCZKpLNYzYsxitPz5Ha1uxUVjbvHV6TDx9pRs2A/QxZGeNy+Z4byea6Qj11/AcT1zaOBXsXXBv0CaCF89c0Z+AcBjcfzGXdZTyVnqjkKhbsXcDUjVN5OOJhuoZ0ZerGqcXHyyQy4trGMXTF0HK9t3MSZrP2kT+Yv/V0cWf04my+biV8+J7N8WaVO2nLF7KuUzen2bxa5XJBuMVEcYsgCNVLQID1acUuXaCwEN5/3/q0v2hDXC2VXNuzLPO3nmRhqeV4wH5N0CIn0nOL1/hUK2Vk6vQurx/mr2bJ2A7sTs7ihR8OMnNwA7ak/MqcxNl2AX9Gpxl8FtufJ5fup2ujGiwc2Q6T2UK+3oinuxy5vAYquYp8o4OJcCPg4Dv+IlK1DzXvexH3Os2cHlPyidCxneuxPCHF5j1xxFX1uFIuo0+zQGKjQ1x+YIqNDqV3eIBNC0tBcGn/fhg2zLZgsciMGdZlF2Syyh/XHaJNm9Zs2LCRbdt30K1rF4fH/P77Ji5fzuD++7tV8ugEQRCEG3bwoPXL+9RSC7mr1ZDvuqDiVqhINv9ix2mb5XiKFGXzjzb9c0PZPMRPzXsPtbqubN47vCZv9HqDqDpRzEmYQ9LfSRABOImwdbzqkFuYS4BHIFnZGhTuKtq8uhmj2fHxpRVl84k9GrLpaHqZuRycZ3OlXIESBTKJG78d0rN63xmOp+U4vGa1y+YeHtaHWgYOhA8/hFmzqnpEgmBDZHNBEAShMpU3m0+OX0H4jmUAPNMlk486X+uS4iibe6sU/G9IQ57bNN22GPwqR8v3+KoVFc7mk5bt55snWvP7yY1M7zSd6DrR1mx+Ogl8cJrNuQJHs49yv/R+/vfn/zifc57DFw/TMrAlH9zzQXH3lqyCrOJTIgIi0Bl0HEg/UOb7BbA/bT95Bm1x98XibH7qlPVBgxIsUim/zJzDktQG5C53nM2rXS4XhFtMFLcIglD9tGkDX38N3t7Qt29Vj0a4hYrW9iyP0i2/ZVIJjQM9eSw6jGNpWlbusf3y3GS2FK/xuSj+NH5qpdNrlyyQWX8olbVPt+Ot+JkO17xPSktixOoRTGw3kQUj3+Kj38+wPzmTjg1rsP7gBWp5q0i68o3jwhaAPYCThzmVQQ2ped9M5BrnrbdLPhEaExVCr6YBvLbub6fHF71PLw4Md1k9rlbKeXFgOFH1/Jm/9aTdOqoTujegd3gAaqWIHkI5ffstjB1r/+WcuzssWgTDh1fNuO4gD9x/H9u2bWfOnI85feoU0dHWJ/gLCgo4deoUO3cl8OOPP6HRaBg6ZHAVj1YQBEGokO3bYfBg0JbKwiEhsHEjNG1a6UO607P5F6Pf5uj5ApAYuG/lfXQJ6cKaR9fQ9dWupHg7LuCWmCSsj1mPn1tdCgtl5OlNeKsUhNfScOh8+ZbsdFNIaRviS68mNfnj2EWnxxa9R55ucib1bOgym6sUcmKiQgnSqO6+bD5sGAwdKh5wEW47IpsLgiAIlak82Xxy/AqmXi1sAXhmx3IsFpjbLcZpNo+NrsOfZ351WNhSUtHyPWO7tOG+yJAKZ/PFY2YhkZh5bdtrpGSnMKHdBNY8uoYhLw/hYOBBh/eU6+UsGrmEAY0Ho1LIWTjkc1K1GXi7eROk8eOrpMW0/7y9TWELgMZNw2XdZZevp7TM/CwigjW22bxePXj3XfjPf8BsrXSXfPopvcaOxXL0ol1hf7XP5YJwi4g/LYIg3JkKCsBgAC8vx/sffLByxyNUiex8109slpal0xOkcadX00BGRIVQoDehN5lxV0jZOr0HKxLPsnTXmeKWgCsSU/h5UhcOnL1S/KRoyYnhIrHRoSSczmB5YgpP9azHn2d+dRjQS5q3Zx5dQ7rx4sB7Ucjk6PRGour7I5XoGPvbbMcnGYAdjnc9+MjDtB7xHCv3X7ZrN17SmM5hbDqazrsPtqRDPX8UcgmDWtSyeTIWrF8KxEaHMiIqBKPJTL7BxOVcPRqVHKVM6rCSXK2UM6B5EL3DA9DmG9EWGNC4K1yeIwgOvfQSvPmm/fa6deHHH6Ft28of0x2oRo0a/Pelmbwz611+/GkNP61Zi0QiYefOXezcuQuLxYK3tzcvvvg8PmJpJ0EQhDvHn39aC1t0OtvtzZvDhg1Qu3aVDKs6ZPNBDe/jSsFltqdsZ3vKdt7+5W3wdn6eBQt+Kj/cpXJyTReRu1/moq6AJY+34svt51i665zLbD62cz0y8woZ160+WTo97UL9GNEhxCabl8zlBXoTuYVGAjVu6E3WCXNnGfuuzuaisEW4DYlsLgiCIFSmsrL506UKW4o0b92IbTN6OszmKxJSGB5Vi8HfjnBwRXtzEmbzW+wW/jxa8Ww+qtUoannVKu6m8vaOt3n7u7chwPl5z7V/js51BlNoNJNvzCS3MI+63oFgUbH/TC41VLXsClsAtIVaaqhrlOs1FanjXYMpvf3IzNOXyOZS5JMnQ3g4PPwwxMTAhAmo4e7N5YJwC4jiFkEQ7jwXLsD9919bgkgq/vK/W3mrnD+xWfLJxtxCIyfScwnwcuONe1vw18nLjP96j90TjKM7hbF2UmcmLd9Ht8YBjIgKwWQyM71/U85l5jGmcz2mfX/A7j4jokIYt2QPMqmkQgH/vb/epWvde5j4zWFGdgyjQz0/us6fRJYxw/EJiUBuqW0SCHsoDMn9FkJCj7Om8z2MXnSA5Ayd3emxUSHcExGEtsDAsl0pfL/nHHMejSSue33ahPqwOD6ZIxe0NkssOXqf4rrXp094oMOKcvnVQK5Wygnydi/X+yAIdjQa+21dusAPP1j/3y+UW9OmTfjss7ls+n0TSQcOcPHiJSwWM/7+/kS2asU99/TDw8OjqocpCIIglNemTdauFKU7m3XtCmvXQhV+IVodsnnvsEGcz750baOrOJsB+MOlvEt8kvg/vj7wNREBEWjcNAR7BjOk8b2smVRWNg9kUfxp4v/N4P2HW/HYl7tYPKZDcTbPKzSyZGwHEk9nVjiXQzXP5haLKGIR7jgimwuCIAiVxVU2n/LXt/zHQWHLldkfcaX7/U6z+brJXdCZLlVo+Z58o5Ytxy9VOJv/8s8vPNDsgWsbiurFnX2rfQkejn6YVN0RJq1+kkPph2yy+aCGw7g3fAgvdf0vb2x/3ebUIxePoFaoiQyKtFkmyZnWQa2RSzzp/N6fxZ1YbLJ5377WpdZLPHRQrXO5IFQyUdwiCMKdJSEB7rvv2rryr7wCr71WpUMSqo5GJbd7YrP0k42ZOj1+aiWe7nI83eS8uf6oXZcSsLZGn77qIJN7NWT5uGg2HE4rDvLeKgWPdwljbOf6JKVksTTh2vmNAz0p0Jv4O1VLeC0v8gzaCq/PaTRbmL7qIEPbuHGJHx0frAfiS71+bw3vL3yfptFNqaGugUquYn/aNpaN78HAjxKLnxKNCNYQ160+7cL8eGblfv48dgmzBWY/1JKvd55hWUIKMVEhLBzZDr3BhL+nG7M2HGNZguP3afKKJGKjQ3lxYFPRMlG4NaZPh6QkWLHC+nNcHHz8MSidfzAXnFOrVAwdOoShQ4dU9VAEQRCEG7FhA9x7LxQW2m4fOtS6nJ9KVSXDKlIdsrnOkIOf2s+6oQDnxS3ZgI/1Xz2UHkQGRfJKj1fQGXRc1l0uM5uP7VyP6Pr+DJ27g1OXdcx+qCXLE1I4dVnH0LnxxESF8Pmodni5yXnnV5HL7SQmwuTJsHQpNGxY1aMRhAoR2VwQBEGoDM6y+acn19Nl+1K74ws//oR3a3dn+Sr7JX+Ksvms+1vQLDS7QuPIKrjCxZwCGgd6Viib/3ziZ8a1HXdtw2mgsZODCwBv8FP78fSGpxnYaCBrHl1jl803n/6D6Z2n06xmS979683iQhaTxcSa42t4tuOzPPbjY2WObUrUNL7564LNEkN22TwsrFyvUxCEiruLPvkKgnDH++or6xec+hIt9V5/HVq1ggcecHqaUH0pZVLiutdn8ookgOKOI46ebHx1aDM0KqXDyfMi3ioFD7atazeBnJ1v4IPf/+Gn/ef55vFo2ob58cX2Uxy5oMXTTc6VqxPVnm5yMnSZ5Rq7TCIjIiACg1lPRLCGE+m5LD70HgXyfMcn/AWUeOCzVr1a+Iz2YdzRcXDUui0yKJLJHSbjrjCxZXo30rP1uClkqJUyFDIJE5fuJeG0tfViTFQI7cP8eH3dUbLzDXy25STzt57kf4Ob4emucDiBXtLSXWeIqufHgOZBonWicPNJJPDFF3DqFIweDRMmVPWI7khnzpwhNDS0qochCIIg3Ax//um4sOXBB2H5clAoqmRYJVWHbJ5ryCPAI4AgjyDSSHN8sBm4DHjD0MZDCfIMwkvpxbBvh9k86Vkym8c/350LWYUo5DJqerqx6WgaQ+buIDNPb5PLi17fZ1tOIpVICKvhIXJ5aVlZ8MgjkJwMbdrA559bfxaE25zI5oIgCEJlssvmfirWpP2K9zcf2x2769lXSe1yH8tXJjm9nrdKQbfGNck3V2wcPu4+5BZmVjibyyQyfNx9rN1UDiVBPRcnaKF1y9b4uPuwaOgitp3Z5jSbG0x6hjQezODG/ckuuEJWwRU8FBrcZBoUUgkT2k5g/l7nyybFtZ1I97r9GbZmj8NOgnddNheEKnDdxS0ffzL3um8qQcLTTz913ecLgnCXMRph2jSYM8d+X7160NhZya5Q3cllUvqEBxIbHcLPB1L5emwU87eetJskl0kl9GkWxLgle1xeLzY6lITTGU4nkE9d1jHw4+3ERIWw7IkotAVGCgxGvNytXybkFhrxL3rS0wlfd18mtp9IXNs4dAYdhcZ84nr5MbBNAX2W/u74pDysxS1XhXQIIaVXCqmWVJvDktKSGLt2LHFt45jdbzaBGjdyC02kZOoI9lHx8fA2bDicSqDGna6NapJwKoM6vqrip0glEgn9m9di7Fe7Xb6GIvO3nqR3eIAI6sKtoVZDfDzIZFU9kjvWlGemEhYWRvfuXenWtSv+/v5VPSRBEATheuzZY+3OUrqw5dFH4ZtvQH57PLdUXbL5znPbSc9Ld37SPqCN9dwvh33JjN9nsGDvArvDSmbz9/q+Ry1fKbn5UlIy82hV15dfJnclI6+QMH8Pu1wuk0qIjQ4Vubw0sxlGjbIWtgDk5Fj/HLi5WYu/BOE2JrK5IAiCUJlssnnSBdae/hHNfPvvdf/XbyJxL03ntXJk8/h/LxPd0KtCy/f4uvtwPC2HJkFe1PR0/XdfyWxeYCwguyCbZ9o/w+iDo8FZLf9FIABmdp2JxWLh3b/eZeHehXaHlc7mIEFm8cdL5oNCIkOGhGydgff6zaZraHfe+2uWXXHMf6Km061uf0YvOoAkM4NVP7zOWz3Gsq9OuM297ppsLghV5LpnQDZv/tPhdsnVKjWLxeJ0u0QiilsEQSinjAzrU1h//GG/r3dvWLkSxITAXU2tlPPiwHCGdwghMTnT4dOfJduTOyOTShgRFVLmJHvRk5Tb/7nEF6Pa8+nmU/xvSHMigjUcS8vBQ6FxGvAb+jVkQ8wG++pxC7jnumPxstidA8BWrMsSAYOfGMy64HXgIhsv2LuAHqE9UJk6M+Xba60ki5Yn6ta4JmqlnG6NaxLdwB9tvhFtgQE/tRKj2ezyfSrpyAUt2nzj3dUCXbh58vKsHVkmTIDOnR0fIwpbbkj9evU4dfo0Z86c4euvl9I8IoLu3bvRsWM0Hh4eVT08QRAEoTzS02HAAMjNtd0eE2PtbHmbFLYUqQ7ZXIIEC05yeQrgB0jhy6Ff8tvJ3xwWtpS0YO8Cuod2J8AjgAberRn++R6y8w3F2bxeDQ+Ry8vrf/+Dn3+23dalCwweXDXjEYQKENlcEARBqGxqpZwX+zdh6o9z0Hz9pd3+l/o9yd6BjzCqAtn8dIY/UzpMY8za2DLvPzV6OnKJkvBa1mzupfCuUDb3dfclojACApzcQA94wcR2E+ke1p2fT/zssLClpNLZfNinttm8tm8gD4Y/wNAmQ7iSn0VmwRX83H3xdvdm3p8pDFuzB62ukEXr3qfd+aOsXPE8b/cYy6J2Q4u7uNw12VwQqsh1l4298fprdr8GDOiPxWKhUaOGPP74WF544TleeOE5nnh8LI0aNcJisTBwQH9ef+3Vm/kaBEGorg4dgvbtHRe2PPOMdc15UdgiYA3q9Wp4sGjHaYf7Pd3kZOr0DvcVKc8ke0mHzmvR6Y2E+HuQpdMzvlt9TGYLKxJSmdJhmt3xvu6+bIzdyLt/vcvYtWOvhXgj8BcUeBU4vlEmsBfcVe58tuQzzrY9W66/vd/9612i6muQluiMeOSClsnfJvHWL8fQ6Y3IZVLUSjlB3u40DvSihpdb8dOi5aUtqNjxggDAmTPWLyKWLrUuK3fuXFWPqFr64IPZzJ37MQ8+cD8BNWty6PBhPv1sHqPHPM6sd99j164EjEZjVQ9TEARBcCUgAP7zH9ttDzwAS5bcdoUtRe7obA7OC1sKgL0Q2SmSFfevoHf93rwb/265xvfeX+8RXjOcbWc3EhNVB7DN5nqTWeTysqxcCW++abvN3x9WrLht/ywIQkkimwuCIAiVzmRC/eQE/EoVtpiRMH3AZJa2HljhbL501zl6hAxgfBvXS4hPbDeRexoMIlNnLM7mGw5dZnqnGXbHOsvmWalZ7DDtcHoPv1w/vhnxDdM6TaPAWFChbN4isAUHL+0iNto+mxvNEjyVHtTxrkPLwObU8a7NhSwzn205TXa+gad2fkfPU3sBUJhN/G/z50zaudLmHtU+mwtCFbruT3/Nm0fY/Lxv/342bNhIXNx4BvS/x+74wYMHsWHjRhYs+Jx27dpe720FQbhbrF4NI0dan+wvSamEBQtg9OgqGZZw+9LmG51OfucWGvFTK12eX54gX5rBaObhdnVRKeX0Dg8kJiqEpbvOsWaSNeAv3Hdtfc6J7SeyNXmrbfV4DvAd4OpBwz8gpE4oi1csIrhRME9++mS5xrY/bT85hmyaBHlxNDXHZp+rtT+9Va7fp9I07s56QgqCE1u3woMPwuXL1p/T0+G++2DbNlCpqnZs1VCd2rWJiRlBTMwIjh8/zpat24iP/4udO3exa1cCarWazp060q1bN7t8LwiCINwGJBJ48UXw9YWnnrJ2r1y27LbvbnZHZvOyrIOfPv6FXp26YraYSM1J5UD6gXKduj9tP7n6XNb/8xPv9+3P/K2nMV+toXGWzUUuL2HPHhgzxnabTAbffw916lTNmAThOohsLgiCIFSqceNg8WKbTUaJlKmDp7K2WQ+g4tk8O9/A6EUH+Grsm3Sq25WPEt6zW77nmajp3Nt0KCajAm+VrDibf7z5FFtmDGF82/E2OdxhNjdd/aeThzx9M33Z8/IePt//OQv2LODLYV9WKJsXGAtoGdSMHvX8kQDf7DpHdr6hzGze4exh/rNjuc31Lnn4sLJlP5tt1TqbC0IVu2mPNnz//Q80aFDfYWFLkf733MOmTZv57vtVtGnT5mbdWhCE6sRshldfhddes99Xqxb8+CNERVX+uITbXna+88nvE+m5uCtlRARrOHLBdpJdJpXQONCTUH81QRp3ZFIJJrOTpzVLCPNXU8tHxcYj6SyOP41UImHFuCjahfry5rqTzBx8LeAfSj9EXNs4hq4Yeu0C54CVQDMg0MlNLoCfPpKt8b/i4+fF0csHnRzo2OW8TDzdHP9V72ztT41K7vB9ciQiWINGJZ6SFMrJYoH582HyZCj9ROKJE/D339BWFEDfSk2aNKFJkyY88fhY9u9PYuu2bSQm7ua33zfx+6Y/+HH1qqoeoiAIguDMxIkQGgrduoGbW1WPpkx3XDYvyx4I9ejP76mB9MYdi6WQzPzM8p8PXMq7xIXcC+QZtHYF6I6yucjlVx07Zl2aKz/fdvucOdCzZ9WMSRBuApHNBUEQhFvuvvvgm2+K5+H0UjmTh05nQ5Nry4NfTzZPztAxbO4eYqIi+fmRzeQZtGTmZ+Gn8kWt0JBwUotC4k5iSgYf/3ECuJbNNx3J5M1eb9KxTkfmJMxxns0vAHWdvK58qBFYg4ZzG2K2mOkS0oVcfa6Tgx07m32W5/94nlx9Ls9ETWfNpP6MXnSA5Ayd02we5SPhg8/eR2YxF283SaQ8PXQGlzz9irdV62wuCLeBm/an6/Tp07Rv367M42rVCmL3btdrJguCcJfKyYHHHoM1a+z3RUVZu7kEB1f+uIQ7gqsnG01mC8sTUhjdKYzpqw5ePV5BbHQoI6JCKNCbyNTpkUslxD/Xiw1H0thy/CLp2gJOpOfaTah7qxQsHxfNrF+PsTQhpXj78M93sWRsB3o2DSA7X0/PukO4L3wo+YY8Ck2F16rHk4CfATfAxXxsQGo35nyzmGNZFjr6yanlVatC74mfypfcQseT4c7W/lTKpMR1r8/kFUllXn9C9wYoZde9wqFwN9HrYdIk+Pxz+31Nmlj/v9+kSeWP6y4lk8lo164tHh5qZDIZW7ZsreohCYIgCOUxcGBVj6Dc7qhsXpZ0cNvqRtjUsfRoUpMd/1yiYwO/Cmdzf7U/2kLrxH/pAnRH2VzkciAlBfr2vdbxr8j48fBk+TpaCsLtTmRzQRAE4ZYZMgSWL8fy6KPoJVIm3vsimxt2sDnkRrL5gm3JzN96miZBXgRq3OnRxEjfCAlRDWUcuXyEkJo+fBbbnG8TUxn39R4+jWmDTCJBItXj7ebNmkfXYDJbW7TYZPMMnBe2AA/4PMA/Pv9gTrcWmfRv0J8Aj4AKvTVF2fxg+kFGr4khru1Evhr7BsPm7nGczaUSPvzjM4JzLtlc5/2usewKaWmzrdpmc0G4Tdy04haZTEZKSkqZx6WkpCC7zdvnCoJQReLiHBe2jB4N8+aBu3ulD0m4c7h6slEmlZB4OoPHokOY2rcxa/afZ9GY9iSezmT819bAWhTaH4sOpU94AFH1/HCTS1HKpaxIPMvSXWeK172PjQ4l4XSmzeQ5wKHzWnrO3soTXcJ4rGMYRpOFDK0BXw8fLuadsLZT/B3YdfWEvoCT39bNZM2ZMm0RHRsGMnRuPG/d25zu4TWIDIq0afXoTOug1qjlGo6n5Tg9RltgIMjbdgBymZQ+4YHERoewdJfzv9djo0Mddn4RBDvp6fDAAxAfb79v0CDr0gre3pU/rrtUSkoKW7dtZ/v2HVy6dAmLxYKbmxtRUR3KPlkQBEEQyumOyObloQe+h5HjH6ddrza0D/O77myukqs4fPGw0wL00tn8rs/lly5Bv35w7pzt9t694ZNPrEt2CcIdTmRzQRAE4ZZ76CH0unxeS7jMZo39g2U3I5tvO3GJV4Y2ZEvKrwz+drbdMkVTOkzj7XYDGLVoN/2aBTCqUxh96vdl/Yn1HMs4ysBGJYr4DYCrVcuT4ZlXnuGFzS8QGRTJc51mMqjxQCQS83Vl8yIL9s6jY50uxERF8tmW0/bZfMlXBG9ab3Od+NCWzIt+0GZbtc3mgnAbuWnFLRHNmrF7zx6+Xfkdjzz8EBIHHzJXrvyOM2dS6NC+/c26rSAI1clbb8Hvv197Kksmgw8+gKefFhNXQpkcPdlYusr8/JV8Hmxbh8e71GP7P5d5Y/1RsvMNhPmrWTK2A4mnM3l8yW6bSfiIYA2jO4WxdlJnRi1K5GxWPmM6hzHyy0SH48jON/D+7//w4aZ/aBrkRd9mgYzrWh/ygWXAqasHNgBaO3kxZvhy7JeEeAUz4vNdZOcb+GzrSXo07cCMTjMYsXpEme/HlKhprEhIxVUXd2drf6qVcl4cGE5UPX/mbz1p935M6N6A3uEBdl1fBMHOnj3WFqilv5QAeOEFeP116//rhVvq8uXLbNu2nW3btnMmJQWLxYJUKiUyshXdu3cjOioKd1FAKgiCUPWSk63LD332GdSrV9WjuSG3ezb3dfct3wtZD5jg2WnP4OV+/dl8ctRkFuxdQKvAVk4L0B1l87s2l1+4YC1sOX7cdntUFPz0EyiddwYShNudyOaCIAhCZZPFxhIVmcqyW5DNx3WtT1yP2kz/fToL9863u3dSWhJj1sYS13Yin4ywdkb5cNM/dG9ck5cG3cPQpkPI0GVcO0EL+Dt5IbnWfSE+oXz/4CokqJBg4cejP5Gtz2Byh8mMXTu2zPejKJubSywvBDAnYTZrH/mD+VtP22bzY8esy6yXoFVrmDpoKhaJtPi9qLbZXBBuMzftT1jsYzEcPHSIlSu/Y8uWrXTq1JGaNWsCcOnSJXbu3ElaWjru7u7Expb9wV8QhLtQWBh8/z306WN9iv/776FXr6oelXCHKP1kY8ngXVRlXqTkpPik5fv4dERb5m89yfJE+ycij1zQMn3VQWKiQlj2RDQn0rXojWb+TnW83E8RswX+Ts3h79QcWnvmMDbmYTh9dacSGOL83FqaWjTwbUq/D7aTpTMUj+NijoHBjYcwoe0E5jv4sFAkru1Eutftz7A1zpcBLGvtT7VSzoDmQfQOD0Cbb0RbYEDjrkCjkqOUSUX1uVC2ZcvgiSegoMB2u0oFixfDI49UzbjuIhs2bmTbtu0cPXoMAIvFQsOGDenRvRtdunTGx8enagcoCIIgXGMwwPDhsGsXREbCwoV39N+Vt3M2H94hBB+VL438GvFP5j/OT0oCDkD9ifXx8wy8gWweR9eQrkzdOJUP75nrsADdVTa/63L5qVPWOYHTp223N2sG69eDp2fVjEsQbpDI5oIgCMItk5kJu3fDPfc43H0rs/k/F7PJkW51WNhSUsnOKL8cSufloQ3ZkrKeuT99yOpHVlu7rhxJcl7YAnARWndqjafCm2nfHePloQ15K34mC/fNx9fdl93jdjO+7XgW7l3o9BIls3lp+9P2k2fQMrBFrWvZvLAQRowAnc7mWLcli1nTd2D1z+aCcBu6acUtIXXr8vrrrzJnziecO3eO1at/LO7eYrFYP7XXrl2bKZOfJiQk5GbdVhCE6qZHD1i6FDp0gPr1q3o0wh1GrZQzc1A4Q1oGE1bDgzmb/ilzUvyrMR3YfOyiw+NKWpaQQlR9PxrV9OTClfxyj0l34i8G9ZmDTpd3bWMvwMf5Oa/2eIvFO84VT54XycjV883OVGb1fY+uId15b+csuzaP0zs+R6fa/Ri96EBxq3ZH4rrVL3PtT/nVQK5Wyu2WLxIEp0wmeP55mD3bfl9IiHX5ucjISh/W3Wj+fOuH+Vq1gujerRvdu3ejVq1aVTwqQRAEwaFXX7UWtgBotfDoo5CbC48/XrXjugG3YzYHuKIz8Hd6Khn5Gc4PugT8AjSE/457zXU27/ce3cO6MyvePptPiZpC15Cu9F/Wn4cjHnVagD6hewOX2fyuyuW//GJf2BIWBr/9Bv6uvu0QhNubyOaCIAjCLZGaau14d+wY/PADDB3q8DC1Us5Lg8IZEBFEvZqezN387w1nc5lUwvCoWgz+tnwNDeYkzObnRzfzSIeg4qIUgAV7F9CtZjeSfJOcn3wMaGLtVv7FtnOE1/LkzzO/Fl8jqyCL/sv6syFmAx3rdGROwhyX2TyrIMvhbTLzsxjeIeRaNv/f/2D/ftuDJk7E7cH7CYLqn80F4TZ0U3sjNWrYkLmfzOHQocP8/fffZGZZ/+fg6+tLs2bhtGje3OFyRYIg3GUuXAA/P3DWZvXRRyt3PEK1YDSZ0ZvMaPONhPmr2XUqo8xJ8W93n2X6PU1YHJ9crnss2HqKleM7YsHCUz0bsnTXGacFJBaLmey/VpK9Y5ntjhAgyvk9hjQeRt+woQxbZz/p7atWsvNUBr/PSef1e7uzbfRgrhRcITM/Cz+VL74qH6QoeX/jvyRn6Bxc3So2KpTe4YGikly4+QwGGDIENm6039etG6xaBVc7+wm33qBBA+nRvRuNGjWq6qEIgiAIrvz5p3WJ1pIaNICHHqqa8dwEt1s2L0ntJiX265FkFmQ6PsAAfG/91xEvxNIjZADD1rrI5h+lM/uhXmwfM5jLusuk5abh4+6DSq5iwd4FLE5azEtdX6V73f4OC9Bjo0PpHR4gsnmRSZPg/Hl45x3rz40awaZNULt21Y5LEG6QyOaCIAjCTXfqFPTta/0nWD8/rF1r08GlZC7PztcTUdubbScu3ZRs3jjQkzyDlgPpB8o13P1p+5FJjWw6/VtxUQrA3F1zKcwpBGd1IqlAXYhrZ+1WPvq3A6x6qi19vrEtqvk381/af96eCe0msObRNegMOjJ0GdT0qEmQZxCzdsxi6sapTgtbAAI9/anl5WPN5n/9Be+9Z3tAs2aOH+gTBKHS3JKFv1q0aE6LFs1vxaUFQbjT/fUX3H8/9O9vXZZCFLwJ5VQ6iHurlMXt/vQmM5uOprNg6ymOpeWwbUZPFmw9VeY1Gwd6kqXTl9nGvMiRC1rStQV8uOkEPZoEsHZSZ177+QjaAiO5hUZOpOdiMlswF+q4/MuH5J/YaXsBd+B+wMlv+15hffi43yKHk94RwRrcFFKOp+VgtsDoxfuQS6FfRBD+Hkoy8tL57yB/Pv7jKBN6NKRZsBeL4pPt2kqO6RxGx/o12HXyMh0b1hBrgAo3l0IBLVrYF7c8+SR89JF1v1BpJBIJ+/btFxPogiAIt7PLlyE2Fiwl1qmRy2HFCtBoqm5cZbiTsnlJEcEaPtnzJqkFic4v/CtwEWKfH8Xrwz4oVzZ/ZOFu/D0U/HdwU7o2bk6uIQdtYQZPtJ7A811eJPlSIc+tOm5TgF4ym+88eZmODUQ2L/bWW3DlCuzcac2VgYFVPSJBuGEimwuCIAg31eHD1o4tqanXtun16J98isyE/Wg0KiTAlhMXmbv5JEcuaJFJJWyb0ZP5Nymbe7rJydA5LhiXSWREBESgcdOgLdRy5OIRAFQKBR/uetfmWG26Fvyc3KQQ6ijr8MaIWXSv25+Zq0+wbHwbcg2ZDotqsgqyeHvH28yKn0XzgObF91/54ErW/bPOZWFL66DW1PTw5a9/L9MxSIV61Cjbz2lKJSxfDmq102sIgnDr3bJPzQaDgZycHBQKBV5eXrfqNoIg3Em+/BImTrQ+2b9kCbRuDVOmVPWohDuATm8sniAvXawR160+0fX9+eC3EyRn6Aiv5UWB3lSuSXFPNzmXc/UVGkuWTk9uoZHTl/Po0aQmr9/bggtX8vFVK3FTSJm75i/mPDeJgovJ9icPwelyRLU8g+nu/ybD5u5x+MTpmM5hLE9IoeT8vNEMvxxKK/75qZ4NSdUWsvFwGo9Fh3JPRC2yCwzk5BvwVilQyqUsSzjDwI+3k51vIDY6lBcHNi1zEt3VlxfiCVPBzjvvwMGD1tbxCgV8+imMG1fVo7or/frrBjp0aF/VwxAEQRCcsVhg7FhrZ8uS3noL2t++//++k7L5isSzNh1dWjc+yZs733F+wSRgH7SLiiKi67QKZfOMPAPPrDyEVAJNgrz46JFIFm87Q4ivNXd/Oaq9yOblJZHA3LmQl3dbF3kJQkWIbC4IgiDcNLt2wcCBkGVbqHHKN5jH+s3k/OxtRARreLxLPdqG+pJXaASsBSs3M5vnFhrxV9tWpfi6+zKx/UTi2sahM+i4rLtMDXUNVHIVa46vIVefa1uUko3zwhagR34Plr3+I0t3XuCxjUksHRfJ8iNf0LGui9bogNli5mD6weKfM3QZaNxc58pnO05n0Y5zfPj7ST7S7ubef/+1PeC116BVK5tNd0U2F4TbzE0vbtm48Td+3bCRM2fOANCzZw8mPz0JgJ07d7Fl61ZGjxop1hQVhLuJwQBTp1onp0p69llrGOjRo0qGJdwZdHojb/1ylKW7HK8BOvnbJGKiQlgytgND58bj6SYnU1e+SfHcQiN+amWFxuPnoeSt+1oQ/+9lRi1KtJnQD8z5h7+XvUpB9hX7E1sDEc6va84cx6Idlxzui4kKoX2YH6+vO+p6bGolHzzUii0nLvHggp0cTdXSJMgLTzc5gRp3ujeuybDI2qzed57sfANLd50hqp4fA5oHOQ3bLr+86F6fPuGB4glTwZZMBt9+a13j9513oHPnqh7RXcvf3x9LqSfWBUEQhNvIp5/Czz/bbuvb1/o56TZ1J2XziGANozuFsXZSZ0YtSqRZ3QLmJj3j/GJpwHqQK5SkthzD/G1nHB5WVjY3W+Boag4qpZyJPRqy61SGyOaO/Psv7NsHDz/seL9MJgpbhGpFZHNBEAThpti0Ce6911oEXMKRgPqMevhVLnv4Wn++oGXqdwduaTY/kZ6Lh0JDZFAkSWlJNPRryIaYDWw7s41h3w4jKS2p+NjIoEj+E/0fPJWeNPRryL+Z/4IecBH35MflfPDOJ/zn279JOJ3FUz3r8eeZX1l66GuGNBlUrtdRJMgzCG2h86Keie0mMqTxEGK27gfgGa92hL7+EZGzX0aSnQ3R0TBtms051SqbC8Id5KaVjZlMJt56+x3mL1jIuXPnqFunDhaLbWAPCwsjISGR7Tvib9ZtBUG43V26ZG2PV7qwBaBXL2jZsvLHJNwxjFdbmpeePJdJJYTX8qJ9mC/htbz4dvdZdidnEhMVUqFJ8fTsQnw8FEQEl2/SNCJYQ4CXO59u/pfpqw4Wh1aLxYJ29xoS5z1LrqPClkBggPPrPtpsNHPvf9xuHBHBGt5/qBXju9Vn1KJEh0+NljzWS6Xgw00nmPrdAY5c0BZPrO9OzmLdwVSmrzrIwm2nWDK2A94q6/Iw87eeRG8yO7xm0ZcXk1ck2QR0uPrlxYok3vrlGDq90fmLE6ovi4uJWV9f2LZNFLZUsaioDhw+cgRdfn5VD0UQBEEo7cABu8lRataEr78G6e35hN+dks2LHLmgZfqqg3yx/RTfxrVle+YLZBdmO75YAbASMMAL/2fvvuOqLv8+jr/O4MBhHKaAC3CLExfg1tRyaztHpuZM04ZaWf3amqPSlqs0zVWZs1xZioqKe+FGEBdD1gHOOZz5++MocjxwwEJBvZ6Px/24b7/zOtwpby4+1+f64BNmjez+n7O5r5uC77dfENm8MGvWQLNm1i259jvYIkoQHiIimwuCIAj/2erV0KOHXWHL/ir16NdvCjfcvEslm3sqnegUGkCAp4vDbG4yW1gRc53x4RPwdvFmy8AtTN8znaHrh9oUtgAcTTrKS2tf4p2/32Hbi9toF9QOCRKQFPHwVDD6GvHz9GJEuxo0rKyiX0RFZsXMIDYlFlcnV8ICw4r9LGDdbijAPYBJLd+xuycsMIyf+ixjUuSn/LDzKt/2b2rN5hIJ76maojt8FPr0se5EIJPl3/dQZXNBeMCUWsnYxo2b2L//AM2aNeXVsWPw8vKi75NP21xTsWIgFSsGcvjwYZ579pnSerUgCOXVsWPWb/yXClnx9uab1hX9clG5KhRNbzIzd8ftPUA9lU4MjKxCv4iK5BrUpGnS8XX1wVWu4p/T6QxsGcyCXRdxUcioX0llFywLCvF1ZfHQcK5maBnSuhoTfrPfo/NOw9pUJy41m2X7b0/oW4x60rZ8R+7Jvwu/yQV4Hiji5wa5uQr6Gy9Su6U7v4xoSUq2jvRcPZW9lLg4yUhMz2XCb8eo4OGMUiHjXHIOpkJWe41sX4OE1Bx+LmQVbUHLYhJpEuTFgIggvt9h3W9VrTXaVZEX9cuLO5VkhanwEIqPt/4yYt48aNCg8GskRf10Ktwv/fq9wMmTJ/nk408ZPvxlqlevXtZDEgRBEMA6Gf3CC5CXZ3t88WIIDCybMZVAec/mMqmE2gHuuDvLyckzkqzOo194ZV4ID2Ts5pc5mergmauBDHCp3owzvm14JuB2NtebzDjLpahcnLiSoSlRNh/WpjrxqTksjRHZ3IbBAJMnw8yZt489+6y1g4uvb9mNSxDuA5HNBUEQhP9k4ULrtt9m20Lo7dWbMbrvOzirPBhTCtn8Vi7fH5/O74cuM7hVCBNXHS/0WoCl+66wbmw3FvZeSFRCFPMPzXf4MeYcnENE5QjOXz6PRVHEwrk84AY06dgENydPwqoqWDS4BVrzjfwtjeYdmse48HEMXT/U4fsAJrScRLZGwtXrjdjw/D/kGtSkazPwUXrjKlexIuY6fdZZtyMN8nW1yeZZvoEo1661ed5Dk80F4QFVar9V/vuf7Xh5eTJxwpu4uLgUeV3VKlW5EBdXWq8VBKG8+u03GDwYNBrb487OsGABvPhimQxLeLCotcb8PUBDfF35aWhjdiRuotcvA+zaGo4Pn4C7cx/a1fZjeUyiw+DtqXRiydAI5kbF8eeJ66wf25r+4UEs3190IB0QEUSXegEM/elA/jFjdho31nxG3vVzhd8kAZ6iyH1DnaQK1jz3C2GBYSyLSWTF/gME3qyIf71zbZbFXGJwq2rMfLYxKdl5+LgqcHaSsmL/ZZbuu5S/WnRARBCP1fHnow2xRY6/oEXRCcwf1Jy5UXGYLaDWGQj0tP3efecvLxyZGxVHp1B/EdIfFf/8Y20fn5ZmLWA8cAB8HGyOK5SZKVOm4uTkxOkzZ3hzwiS8vb3w86uAQuFkd60ECZ988lEZjFIQBOER9PrrcOaM7bE33oBuDlr9lQPlNZsXLLLRmrJJ16QT4O6Pj6sH689soOWiSVzNvlr0B9sJnAPfCv5s+uM3KgUG5GfzljV8ebd7KK/9cpT2tSuUKJsPjAji8foBDFl0oOh3FvDIZPNr16xFXbt22R5PTIQPPii826sgPERENhcEQRD+tS+/LHTr0g112/JGzzeo7O9ZKtm8YC5fvj8RT6VTsdk8S2tgzeFUhrfvTJuFbUr0cV79/VWy5dlFnneNdaVJnya81+49TlzOYdzKE9QNdOfNHm7518w5MIcDww8wotkIhwU1o5uPpnfdXry67BTbz6YwNyo+f6vQnDw1Z5OyKVir/shkc0F4gJVaccvVq1dpEhbmsLAFwNnFGbW66NU6giA84Mxm+N//4LPP7M9VrmxtP9yixf0fl/BAytJa9wD1VDqx+OXGTIl+l/mH59pddzTpKEPWD2T/tdF80ncKz889zNJhEUUG74GRwcTEp+Wfe2nhfhYPDadpsBeLohPs9sgc1qY64dV8SM/NY39COgDyGxdIX/MZeempRX+A9kDtok+/GfERjQLCsAASiSS/VXlNf3fcXZwIULnw/Py9duMZ3CqE9WNb8+kfp3iiQSAtQnxIztaRkJZb9MsKiL2mRqc3USfQg9PXs1G52E+mFfzlRUmeV9gKU+EhY7FYf+nw+utgMlmPXbwIzz8PmzaJTlzl0MmTtwveLBYL6ekZpKdnFHqtRHTaEQRBuD9++81a7F9Q06YwZUrZjOculMdsnpKtY/2rzdl1eXP+RL63izcHRxxk4l8fFbtylIvAduv/OXX2HAIDAmyyeWp2Hmm5emY+25j98eklyuaP1wtEbzTn/9xQnEcim//xB7z8MqSk2J8bMACmTbv/YxKE+0xkc0EQBOGuWSzW37V8+qndqcxBQxkf0AcPN5dSy+Z35vIsraHYbD6kdQhta1UgQ3u7q4pDeshWFF3YoopV8fesvzErzFTyqITK2YOv+zXip+hEfJTe+ddl6DLouqwrmwdspmWVlsyOmW1X1PNWq7foWacXJqMT289ac+it+feiPBLZXBAecKX2t0kul6M36Iu97kbqDZRKZWm9VhCE8iQry7pNxR9/2J9r1Qp+/71ct9kWyh9PpXUvn4GRVdh+aVOhAb2gOQfn0LpqW3o1blxk8JZJJbwYGWzTgSUhTUPvb6MZEBHE/EHN0elNZGj0eLsqcHOW4+wk5c1fjzK6Q01CK3qgP/UPu3+eil7v4PtebaBD0acfC+7FLzsasuKf7TaT4mOXH+adbqFM33yGZYW0MY+9pmbiquMMjAjiq+fDWBgdT+9vo9k0vi05efZ7eN7Znv1W6/QMjR53Zzn1K6lQKe3jwK1fXpRUYVXswkMkLw9eecXaAvVO167BjRvi3/dyaP68OWU9BEEQBKGgS5esrcQLcnODFSusHS7LOU+ldWK3vGTzsY/VYsWIJny86x3mHro9ltEtRpeoJTpZwCrAAjWeeInPT7jw2THbbP7FljNU83Nj6sYzhRbmFJbNtQYTWr19Lr/1eR+pbJ6RAZMmwQ8/2J9TKGD2bBg5UmxnKTwSRDYXBEEQ7orFAq++Ct99Z3/unXfQTXoP8+fbSy2by6QS+kcEMXzxQZv7isrmvu4KKni4sGj3RU5dz8TPq/CCTRtmQFH0acklCSN6jaDFktuLo8MCw5jQchKz+/XCYLQQFhiWX8RyIf0CLRa0YFTzUax7YR0ag4Y0TRq+rr64K9xRKbzR6GRoipjDL5jNu/25mO3uQewKDnt4s7kgPCRKrbilatWqxMVdRKPV4lpE8UpmZiYX4+OpU8fBMnZBEB5M585Zt6e4s702WFdofffdAzFhK5QvKqWchpVV9IuoSM+V/Ut0zxd7Z7D++b+ZGxWfH7yXvByOVm8iR2fE280Js9liV12dpTXw/Y445kbFFWhNaORsUjZ/v9GePo0rU9PXhYDYlSxeUMykVEXg+aJPu8hcWPr0fExGVX4b84mrjjMgIoifhoTzz5mUQgtbCloak0hYkBc6g5kq3kpUSifOJt2uOre2Zw+mf0QQOr2JdI3epnW6r5uCnDwjr3SoiaKQtoi3CotKSuUiqs8fWtevw1NPwb599uf69IGffwYPj/s/LqFY/v7+ZT0EQRAEoaDvv7cuCCjou++g9oMxR+Lh4lSusnndQDfWnf3dprBFJpExstlIeizr4XhgRuBXQAOPd32Cbxd9jdJZbpfNP+jdgH/OpDjcIgluZ3OV0gkPFzkWi+35Ry6b63TWougPPrAWQd8pONjaxUh0dRUeISKbC4IgCHdFIoGgIPvj06fDxIl45Bn/czb/+oUmLBsWQXquHgkghUI7khSVzecNbMZTzSpwJGUvIYrQ4gdgcXBODZUslXi196t8OftLzBYzYO08M3BNf0Y3H830LtN5t837PLvq6fzbMnQZTN09lWnR02jg3wCVswp1npq3W02mpuox6lfyttl2COyzeV50NKF/LmCoxcKJx59CpW/4cGVzQXjIlNomXx07tCc7O5s5c+ZiMBjszptMJubNX4Ber+exjh1L67WCIJQHFgv0729f2CKXW7ewWLBAFLYI/4pCJuWNLrXJNahL1tYQOJJ0BK0pmzqBHmRpDczbeRGLBaZuPMPnm89w6FIGmVr771O33GpNeCAhg9PXswnyccXH3Zn09HRqNWtbfGGLP/AyICv6Ep1Jh9Fs5Oe9lwjxdWX92NaE+Lqy8sBlZFIJi6ITSvRZF0UnMCAymFHtayCTQGhFFYDNM0csOUinL6N4du5eOn0ZxcifD1HNz40KHs48Xi+wyD0/VUprdXpJ1K+kQi6VoiliharwANu/H5o3L7yw5X//g9WrRWGLIAiCIJTU1KkwYwY43Wxt3b8/DBpUtmO6C2azpdxkc6PZQpZOw7Ro2+1s6vvXJ0efw8nUk44HtgG4CnjDzLkz+erv84z8+VCh2Xxe1MUSfdZF0Qn0DauMBNss/UBkc4vFWpCSnm4tbE5Pt/75ziqd4hw/Du+9Zy1eGTOm8MKWnj3h0CFR2CIIgiAIglCcSZPgrbes/7dEAvPnw8SJwH/P5jl5RupXVjHwhxjGLD/MigOJ3Mhx3JGkYDa/lqnD083Cp7sn8/QvT+Ln6kdYYFjRNxsoer7cAByCqyFX0Rg0NPBvYHfJnINz+OPcH3Sr1ZXVz63B28Xb5rzZYuZ48nF2J+6mZZXWtA16nNr+nsVm864ztiEbMQLJzdzbcOtqqj7enq61fMS8uSCUU6VWLvb4412I3rOHXbt2c+bMWZo0CQMgIT6BBT/8yMEDB0lOSSEsrDHt27crrdcKglAeSCTw008QGQm5udZjfn7WlVgdOpTlyIQHnFwmJbyaL6dSY4u/uIAMbUZ+68DXOtdCbzTz54nrACSrdXzbr2mJnuOpdGLJ0AgmzF3PTx+NwZiV7PiGIOAlHBa23JKuzUCjh4mrjvNiZBDLh0cyZ8cFMrWGu9qz02gy0752BWRSCSPbV+f9tbEsGRrB3Ki4IlunT/jtGEcTg3ine2iR+30qZFJGtq/OuBVHix3HkNYhLIyOJ7Siim4NAgsN/cIDaMkSGDHCuiVRQW5usHgxPP104fcJ5U7i5cts3foX58+fR63OJiI8nMGDrb9MPX3mDBcuxNGhfTs8RKGSIAjCvSWVwoQJ1p+R3n0X5sx5oLZjydLqaVHNl9NlnM2nbz7D4cQMGgQF203kq5xVfLH3C8cP2gscwzoj9jxYnKGKt5KNJ5L+czbX6E3EXsuiRYhP+c7mW7ZAVBQcOQLx8dYts3Q6++ukUmshs0oFnp7W/+3lZd2KuLD/docPtxZHF8bdHb76ytrZ9QH6714QSpvI5oIgCMJdmTrV+juXdu3g2WfzD/+XbN6wsidvdqmD3mjm5LXbWff55oV0iinCy22D2Xh+A3MPzaVRQCNyDbmMCx/H0PVD7S82Ak4OHvYPcLMnQpomDZXz7cIRmURGff/6qJxVrDm9hrZBbdEZdZx79Rwvr3uZ9efW518bFhjGaxETaV+1KwMXHGX58EhOFpPNx+1bRZ0btjl9a7u+DOtUV8ybC0I5VWrFLTKZjP+9/x4LF/3EX39tY+vWvwC4GB/Pxfh4pFIpj3fpzLBhLyMRP8QKwsOnQQPr1hRPPQWNG8PatRASUtajEh4CeQYTgR6+d3VPRQ9fvulXG2cnGZtOXOdapjb/3LnkHFwUMupXUhF7zfFE9cDIYL7+cSk/vjcOiyHP4bXP9H+WVTV/K3FPNB+lN24KNWM61qR/RBBmi4WXWoag1ZtK9oCbtHoTv+y/wNhOtegcGoCzXEZMfFqJWqdHVPctMlTLZVI6hwYwMDKIpfuKftaAiCBahPjwyR+nqeKtLLKiXXiAGI3WVSCzZtmfCwmBdeugUaP7PSrhX1q3bj1Lfl6KyWT9t0UikaDOtv23b+HCRTg5yen6xBNlMURBEIRHT/Pm1uKCB4yLk5xsraFMs/n+hHSWxSTSIsSbNE263TWXsy6zO3F30Q+5CPx18//uAwSCt9KLYW382BuXRrva/v8pmydl6Vh39BpNgrzLdzafORO2bSv+A5nN1q20srLg8mXrMXf3ootTmjUrvLjlqaes76xWrfh3CsJDTGRzQRAE4a5JJPDNN3aH/202r+Thy7s9AkhV6/51LpdJJQyMrMhjS6xdFFXOKhIyE2gX3I4RzUYw/9D82xebcfyb6F1AM+DmTj++rr6o89R4u3gzusVoRjYbicag4YbmBn6ufng4e5CqSeHzXZ/z81PLSM7O4IYmDR+lN65yFStirtNn3UGytIZis3nNG4mM3fOLzXBOBtRgTGBHZp1JEfPmglBOlerfIoVCwaiRI1j44wImTniTQYNe5MUXB/Daa+NYsGAer7wyGoXi7vYiEwThAfLkk7BqFURHi8KWR5zRZEajN5KUpeNskpqkLB0avRGjyXzXz3J2kuEk8XDc1rCAJoFN8HLx4rVfjqDWGriRo8fb9fb3HpPZwvKYRAa3CnH4HKnEwtW/f+KLt0Y6LmyRSPHtMpIqLwaX+LvqrTGOeaxWfgvENtO28+rKI4Xu4+mIk1zK9nOpqLVGXBVy2tT0K/G2RnOj4tA7+P+Jq0LO211DmflsI7tWi/UrqZj5bCNGtKvOSwv3k6U1EHtNjVorWiw+0NLSoGvXwgtbOnaEAwdEYcsD5ODBgyz6aTF+vr68/dYklixehOWO7QVC69ZFpVKxP+ZAGY1SEARBuNdKK5u7OsuQSCSlns1X7E9kfKdatAjxJrSiBzKpfeGETCqhf0QQC3fHA5CTZ8TX1cfuuktZl4oeTAbwG9YJ9nZAQ+v4PF28WL7vEsuHR/7nbO6pdOLE1azyn80jI0v6kex5ehZ9rnlz2z937gz//AO//y4KW4RHnsjmgiAIAhSezbVx8Rj3392//f82m3u6eNH5i+24Osup4OGcf+7WnPmQViGEVvRwmM3rBnqgMd7eDkmdp8bLxYuuy7oyqdUkFvVZZB2TBcfz5bGAD+B3e3xKuRKdUceB4Qeo6V2TPiv7EPpdKG0XtSX0u1Da/9QeT2dPRrcYzba47ew+m8eXGzW8tvwS7afv4fsd8WTd3PbUUTaXWMx8vvkbFObbedkokfJW11cxSWVi3lwQyrFS69xSkKenJ61bt7oXjy4VWVlZrF69hv0HDnLjxg0UCgX+/hVo1KgRQwa/ZHf9/v0HWLtuHRcvWidRalSvzpNP9qH5nT+0F5CYmMiKlb9w8mQsOp2OioGBdO7ciZ49eyCViso84QF29Cg0bAiyIvZdEdtUPPI0eiPbTiczL+qiTZV3/UoqRravTufQgCJb+hXGSSYhNdvE+PAJDFk/sNjrJ7WaxKGEbPZdzOClhftZPDQcT6WTTdX50n2XWD+2Nf3DgwpdRWnO06DdNpvZJ6Mdvkvq4oFf37cxVr/GrJiZJf5ME1pNwmJxYtqmMyyLuf3+u6mQB+vX1Fku5WxSNmqdgUBPF7J1xrtqnX4r3BflepaGi6m5zB/UHJ3eRIbG+gsJZycpy2MS+eSP0/k/MAD54xAeQDduQEQEXLxof27cOOtqWydHPUSF8mbdug04Ozvz0UcfEBgYWOR11aqFcPXq1fs4MlsimwuC8NDS66GMF/iUZjZXyKSk5eowmkqezSc6yOZXMrQMjKxCv4gAzOQwqacbKmcvXOUqVu6/ztJ9V/JzZu0Ad4wmM6euq5FJJUglErxdfHiu3nP8fvp3TJZiOqzogRWAFqgPdLAentByEheSdTzVvCqfl0Y2dyof2bxa+lWqp19BrWtXeDa/V8UtzZpZF7o8+SQMHiyKogWhAJHNbxPZXBCER1Vh2bx62hVWrvofXhY92qgolI1Llp/+SzZPzTHw4g8xrBvbJj/reiqdcHaCJxp607S6haTsNHxdfQrN5kNbh5CuzQCs2wbJJDK8XbxpGtiUyB8iGd5sOM/We5ajSUeLHsx14ApQoFHZuIhxLDm2hE0DNjEtepptB5ibjiYdZfC6wYxsNpJ32ryDvLInn/xxFrNtvWix2XzA0c00v3ra5p4fwp8kNrAmIObNBaE8K7XilkWLfqJDh/ZUK+crMS5ciOPDjz4mOzuboKCqhIe3QKvRcvnyZdav32AX0tev38CPCxchk8lo3LgRTnInjhw9yiefTmHE8GH06NHd7h1nzpzl/f99gF6vp1atWvj7V+DUqVP8uHARZ86cZeLEN8XWTMKDad48ePVVeO01mD69rEcjlEMavZEpG08X2o4v9pqacSuOMjAymMndi96z8k4KuYzqfioqePRkRNNRzD88t8hrRzcfTY/aPek+y9oKOyFNQ+9vo/n6hTBGtqvO678eo3aAO+7Ocj7+I5b/9axP02AvFkUn5P9AYci4hnr9FHKSEhyOy6lCCBWeeg+DTzzpTnNK9FkARjUbTe86vdh5Ns1m8hxsu8pMXHW82GcNaR3C8phEzBZQuViLDrK0+hKPBYoP1R4uCr7fEcfcqDjqBHrg7iwnJ8/I2aRsux8a4PY4hAeQr691/96CxS0KBcydC0OGlN24hH8tLi6OOnVqO5w8B1CpVJw6ddrhNfeKyOaCIDy0oqNh4EBYsgTati2TIZR2NpfLpFTzcydXZyTAs+TZvEch2fyNzrWoEaggKnEzPVcOsJn4DgsMY3z4BNaN7cbghcdwc5bzTre65BlMfNirLl0b+pFtyCJRfZFpXabxUYePeGzJY1zPuV704NcAKUBloC8gvZ3L8/Qydsc9HNncxaBj0p6VDN+/hlyFkrwvxwAe9g+KiID27aFlS+sWwyEhEBAASiU4O1sLs7RayMmB7OzbWxOp1eDmVvQHaNTImiXF91VBsCOyuZXI5oIgPKoKy+b1ky6w+LcP8NNkAaB+rAvs2Y2yTq1in/dfs3ncjVwWRcfzcptqfP33eX56uTFRiZtp99MLRWbzz/6Io1fjSnSpF0C6TsrktpPztw1KVCcyrcs0JEiY+NdEfov9DYr65zwH2AkUWCc9stlI2ga1ZcWJFUQlRBVa2FLQvEPziKwSSdcaPakT6MHp69k25x1lc7/cDCZFLba5PsGrIrNa97M5JubNBaF8KrXilnXrN7B+wx9UqVKZ9u3a0a5dW/z9/Uvr8aUiKyuLjz7+BL1ez+TJbxMRHm5z/ty58zZ/vnL1Kot+WoyTkxOffvIxdevWAeDq1Wu89fY7/LhwEU2bNqFixYr59xiNRr78ahZ6vZ6hQ4fQp3cvALRaLR98+DHRe/bQ7J+mdOr02D3+tIJQivR6GD/e+gtOgBkzoHFjGDCgbMcllCtGk5ltp5Md7jMJ1q4pbWr68lhdf4xmC2qtkSytHk+lApVSjkImtdt70s1ZjkTixtRO02ldtS1fxcywC9lvtX6LHrV6MmrJSS5n3N4vNEtrYObWs/w6siUxkzuRpTGQrtHj46pALpNQwcOFhYNboNGb+GvrVia9MomcrEyHn8EztA2qruPRu8SRqpgOkuJbuocFhjGx1Vv0qt0TnV7Ot//EFfn1cdRV5pYBEUE8UT+Q7rN3Ub+SCpXS+i3dU3l3Ibm4UK1SyvMr+O/8IeFOBcchPIAkEpgzB06fhpgYCAyENWv+28peoUwZjEaUSmWx12VlZiErqiPbPSSyuSAID63kZHjhBbhyBTp0gI8/hnfegfu4Gv1eZXNXhRwJoDdKmfLYtH+VzedGxbFgcCMmbp3A3EP2E/BHk44yZP1ARjcfzYZx0zGbFCSrdVTykaJI3U+3FdNt3ufp7ElWXlbRH3IHcBpQAS9AWNXbuXzCr6d5v1d95kUV0rmOByubt7t4iE+3fk9QVjIAXroc9D98Dx9/ZP8gPz/YseOuxlYi4hfSglAkkc1FNhcE4dFVWDYPv3ySH1Z9jEqvyT+mSk8hZdQYZH9tui/Z/Ifd8Wwa34aOdZsz+Z9JxWbzr16YzuaTGWw7lUzHep7U9qlNn5V9bN5X27c28RnxRRe26IF1QB9Afnt8HUI60GdFH3577jd6r+hdoq/r1zFf82TdJ1G52M5HF5fN39mxCFVers09k58Yg87JtpBFzJsLQvlUan+Thr08lKidOzl//gJLly1n2fIV1K1bl/bt29KmdWvc3d1L61X/2ooVK1Gr1YwcMdwuoAPUrm1bDfnHhj8wm8306N4tP6ADVK5ciWeffYaFCxexYcMfjBgxPP/cvn0xJCcnUy0kJD+gAyiVSkaOGMYbb05k3br1IqQLD46UFHjmGdi1y/b4sGHQtCmEhpbNuIRyR28yM3dH4ZPCBYX4utKwshebTyYxb2fJ26O7KuTIpa48W+8ZngztTaYuk0xdJt5Kb7ycPTl5VUP3WfttAvqt9/00JJwtscks2HX7fZ5KJ97oUos+YZXR5Bn54bvZfP7x+5jNjgtVnhv5Jp9/8gHPLfyNw9pPQGIo9DoJEub3WkB4pXC8ld54unhx9rqWI5c01PT3KLI9eZbWkN+y/c6uMre+RkNah9AixIc9F9IY2qYaFdydUcikaPRG5DLpXbVOLy5UK2RSRravzrgVR4t93qj2NVDI7t8vbYR7wMUFVq+GV16B776DypXLekTCfxAQEEBCfILDawwGAwmXLlGpUkWH190LIpsLgvBQ0uutPz9duWL9s9kM771n/Z46ePD9G8Y9zOZKhRwnmRmDqfBs7unsybHE3CKz+fxBTVl3ZnWhk+cFzTk4h9ZV29K1el98VRImbJ3IvELucVjYcgqIAjd3Nxat/onI5pH5uXznWTX/61UfncH0QGdzhdHA2zsWMfTQervrnWbPgvHjrB36BEEoUyKbi2wuCMKj685s3jHuAHPWTsXFaNvl73TVOnj9sPC+ZXNvVyc8XGSsP7euRNk8snIbetR5EqRGJm6dWOg9526cK7qwxQzytXJWfr+SwNqB+Lr6opQrWXd2HVEJUax6bhVao5ZjycccjuWWI0lHUOep8XV3zv8aFZfNn9Nc5OmT/9g8Z029DuwJCbM5JubNBaH8KrXilp49e9CzZw+SkpLYEbWTqKidnD59mjNnzvDDDwtp2qQJ7du3Izy8BU5O97/1Ul5eHjuiduLi4lLigHzw0CEAWrVqZXeuVcuWLFy4iAMHDtqE9Nv3tLS7p0aNGgQGBHApMZHk5BQCAspXZxtBsHP4MPTtC5cv25977TWoXft+j0gox9Ta4veT91Q6sWRoBN9tv1Doysfi2qMr5DJABihwlrpw6rKM+SeusenEUYyF1KR4Kp1Y8nIEX/11jqUF2oyH+LqyeGg4++PTeeH7nez+aQq5sdsdjl3mrCRy6Af4tu7ML0eOcEX+PmZJbpHXf9z+Cy5fCedSIqTlJrM19hhGM7QI8ea9HvUcvutWy/YBEUHMH9QcCZCs1qFycbLZs7OKt5JfRrREIgGzxcK208mcTcopcev0koRquUxK59AABkYGOVz5OzAymE6h/nZdd4RyymSColYDVqoEa9fe1+EI90Z4eAvWrFnLunXr6dOn8FUva9asRa1W06tnj/s6NpHNBUF4aI0fD7t32x7r0AFefPG+DuNeZ3N5gVWj1zNNeDkHsPecmZ3nk9h8suhs/vPLEeiMOmbsmVaiz/HF3hn0rN2bNafX5xe2yCQy6vvXR2PQcCH9QtE3XwXWgJPcib4Tv+R4dh22/3E7lwNM7l6XtrUqOBxDec7mwRnX+H7t59RPsS9ksjg5IRk1CuRihagglAcim4tsLgjCo6tgNu99Koov/vwSJ7PJ5pqYamFU+mcz3xxOuW/ZfNmwCEwWfYmz+ayYmfSp24c1p9fnF7bcyuZKuZKjSUfJM+UV/YA/wVTHxMcXPkZ1WYU6T83JlJOYLdYBzuwyky41upRoLLdk5WXxWufavNmlTrHZ/PyVDN7981ub+9UKV6Z0fNnuuWLeXBDKr1L/CTcwMJAXnn+OF55/jgsX4tgRFcXu3dHsP3CAAwcPonRxIbJlJONeHVvar3bowoU4tFot9UJDcXZ25tChwxw9dgyDXk+lSpVo3aY1vj4++dfn5OSSmnoDgOrVq9k9r0IFP1QqFSmpqWg0GlxdXQHyK/CrV69e6Diq16hOUnIyCZcSREgXyreVK2HoUOs+2wUplfDjj9CvX+H3CY+skuwnPzAymJj4NIctvcHa/juimg/dGgQWGfo0BjNqnZENx5Icvu9AfLpNYcutSfy5UXEs2XaY1DWfoU86X+QzAEKqV2fuz/OoWCOAPKOO51a9RFLOtSKv71R5FL9F1SdLa/85c/KMuDsX/+03S2vI37Nzz9uPsSfuBrHX1Px1KhmDyZJ/TbJax+y/z/FxnwbM3XGRq5naErVOv5tQ7aqQM7l7KBHVfJkbFWe3amBU+xp0CvW3K0YSyqlt22DMGNiyBUJCyno0wj301JN9iYrayU+Ll3D23Dkib24xlZmZyd59+9i3L4adO3fh7+9P9x7d7+vYRDYXBOGhNH/+7a1cb6laFX75peii0nvkfmZziUTC3J0XCfF15Y/jRWfzsR1rcCElm0q+uhKvxjyefByJxMysmBl4u3gzusVoRjYbyfb47by83n4SOl8WsAIwQKVnJrBbU5ndhUw4rzlyle4Ni++QUB6zeY+UUzR4/U08NPYt0E1t2iCbNw/qOS6qFwTh/hHZXGRzQRAeXbey+cAjG/l46xykWGzOb6kVSewX86h6Q3/fsvnkbnVxczGTmZf2n7N5am4qA9cMdFzYsguQg6WxhePJhRd+Lzm+hKfrPV2isdzi5eLF7jPp7Dx/o9hs3mnjz3hePGdz/xftXiTV3dvmmJg3F4Ty7Z7+bapZswY1a9Zg6JDBHD9xgr+3/cOu3bvZvn3HfS9uuXzF2nnC09OTKVM+J2b/fpvzPy9dxqtjx9CuXVsAbtxIBcDd3R0XF9t91m7x9fVFrVaTkpJKSEgwAKk3rMHez6/wlq++N1vBpqak/sdPJAj3iMkE774L0wqp1q1a1bqav2nT+z4sofzzVCocnpdJJfSPCGL44oMlet7cqDiHIVIhl9Kqhp/dRLFMKqF2gDsqpRMvtQxm6E8HbO67NYm/cPVmUtdOwZyb6XAcDSIbwrMWuv7VBf4qftzP13uJGZ1msvLAFZbuu0SW1nbbonPJObgoZCVuTx5aUYWbs5zwar50rV+Ryd1DWbH/cv6zMzR6FHIpaTn6/BUAJWmd3rV+xbsK1a4KOd0aBNIp1B+11ohaZ0Dl4lTofq9COWWxwOzZ8Oab1i0S+vaF6GhwcyvrkQn3iLu7Ox9/9CHTps9gz5697N27D4AjR45y5MhRLBYLVatWYfI7b+OqVN7XsYlsLgjCQ+eff2DsHfMcLi7Wn5/87/8v6O5nNlcp5RxMSOe55lWKzOaVPJUMiAjmow2x9GnheBvQgur71ydTl0GuIZcDww+w89JOnlj6BGdunCn6pjxgOZADkz+ayuhXx9nk54LOJecgl5Z866Byk80XzCNy/HgkJtsVvxZnZ8wzZiIb8wpIRT4XhPJEZHORzQVBeHR5ujjxyt5fmbRzid25VQ06MbnHeLa3rXVPs/mtXO7uLMfDxYkeYb7MPTiXyCoRJXqnTCLjydAnydJl2mTzXit6FVmsku84uF1xI/e5orugA8SmxOIkdSIsMIyjSUeLHVOTwCZ4OXtRw19PixBfh9k8/exFRu5YanP/hSq1WNrkdkGpmDcXhAfDfSkVi409RfTuPRw5euR+vK5QOTnWfzT3HziAVCpl5IjhtG7diry8PP7cuIm1a9cx++tvqFKlCtWrV0Or0wHg7Fz0hJCLi3UfN63udmcLXf59zkXcYw382ju7YTgYd26u/T/4N27+MCAIpSozE/r3h02b7M+1bQurVpXJxKzwYFAp5Q4nhWsHuKPTF72f/Z1ir6lRaw1FBkmFTEpSloaxj9WkWbAXvx68QvMQH/pHBKHTm8jOM2AyW5j7YjNWHrhMzMU0pFIJg1oG02XkByT/+iWYjQ7H0OjJxhxvcAzsF0MWqnO1Lkx9bAb/nEmilr8768e25qWF+0lI0+RfYzJbWB6TWOL25ENbV2POjji+3xEHWEP24FYh+c/2drV+n0rX3F6de2frdJ3eRIZGj7erIr89Y5JaS00Xj5J9sJtutbh0VcgJ9Cx8Aksop3Q6GDUKFi++fezYMRgyxLqaXFLUZrjCg65y5UrM+uoLDhw4yJGjR0lJScFiseDr60tY48a0bBmJ7D53EwCRzQVBeMgcOwZPPgkG28IJFi4ss4UB9zObK2RSRneozswtZ3mne2ih2VxrMJGlNfDG47XRmEr+b6bKWYVal82WgVuYFj2N+YfmO77BDKwCkmHEqyMZ9epI/j593WE2/3nfJUa0q874lUeLHU+ZZ3OLBd5/Hz77DLv0Vq8ekpUrkTVsWPxzBEEoEyKbi2wuCMIjyGLB95P3Cy1sWdisN590Gkbdip7oDHebzY0lyuaR1X3I0OjpXC8Qnd5EukZPjQpKNpxdy8/Hl9CztuOt8Ap2aLFYLGTpskqezQESIOhYEGu3rGXVpVXMOTCHDF1GoZeaLCa+P/g9k1pNov/q/sU+ekKrSRxMyGbwosOA42xeRZ1MrkKJu/729xjVwgVsbdxMzJsLwgPmnhW3XLwYT9TOnezatZuMjAwsFgtKpZKOHTrQvn3be/XaIlnM1pVBJpOJgQP60717t/xzQwa/RGpKKtF79rBm7VrefOP1+z6+omzYsIGVv/xa1sMQHgVnzkCfPnDunP25UaOsq/0Vjlf/CY82hUzKyPbVGbfiaKHn3Z3lNpO8JZGWq8fP3dmmutloMqM3mVFrDbi5OGEyW3isbgBdG1Rk2+lkRiw5mD+J76l04o0utRjWphr9w4O4kpbN22++xomV8xy+V+6kYMj/hrLANNfhdXfaFv8XT616nPHhEwgL6sbvh66weGg4vb+NtlklunTfJdaPbc2AiCCWFdgy6U4DIoJoHuLNx3+cyj8We03NxFXHrfcOiwSJhVPX1Pi42v79LNg6vU6gB+7OcnLyjJxNysZsgUEtQ+7qswkPsKtX4amn4I7VdwAYjZCXZ11ZLjy0pFIpERHhRESEl/VQ8olsLgjCQ+PSJejWDdR3TERPnFimW7ne72xeN1DFiHbO5BnNDrP5m11q8VQz/xKvxqzkXomKHhVZf3ZdySbPNwPngUYw33se+3+NKVE23zmpY7FbB5V5NjeZrN2B7tz6CqzFVUuWgLt78c8RBKFMiWxeekQ2FwSh3DOZYPRonBYssDv1ZZsBfN3qBZBIcHeWk5vneBHmndRag10RxZ3Z3GS28FhoAP8UyOUyqYSoSS2ZFj2N2JRYXJ1ci8zmNX1qsnnAZnZe2kmflX0wW8xsGbiFTec3lSybpwBrIPHFRIZuG8q48HEcGH6Arsu6ciH9QqG3zDkwh/jx8YxqNoq5h4qemx/dbDQ9a/Wkx+zb852OsvnBKvXpNGwur+1exuBDG/i1URfe+yeHOqcOi3lzQXjAlGpxS3JyMlE7d7Fz5y6uXr2KxWJBJpPRrFlT2rdvR0R4OIoy+uW4S4F2jp06PWZ3vlOnx4jes4fY2FgAlDd/yZOXV/Rkj06Xd/Pa2892cXEhJyeHvLzC95a7VaGuLGF7yV69evHYY/bjvXHjBpPffa9EzxCEYv3xBwwYYD8hK5fDN99Yi1sEoRhymZTOoQEMjAxiaSH72QMEqO7uF+iuChkGszl/Al2jN7LtVDLzdl60mSTfNL4tM7eetSkUCfF1ZfHQcPbHpzPghxiOX7hM6tqp5F0+6fCdMg9f1q77lXfPvgHJdzVcAI4mHWXI+oGMbDaat1t+ytHLmQyICMpf3QnWye2XFu5nw6ttaBrkzcLo+ELbk7cI8eGlhfvt2qcDLItJpEWID16uTpx1sNWR2QKnr9u2nqlfSYVKKfb5fCTs3WstbEkqZI/djz6C994TLesfYWq1Gjc3tzJZHSqyuSAID4X0dGthy/XrtsefeQamTi2bMd1UnrP574eSGB8+gSHrBxb7zp61nkQhUzBr36ziBxgD7AfqAn0AacmzeZZGz5iONR1uHVSm2dxohBdfhJUr7c+9/z58+KHIdILwgBPZXGRzQRAeMnq9Nb/9al+E92GnEfzUvHf+n6VSCRU8Cu9qVZQ78+Od2dxT6cSGsW2Y8udpmwLu2gHuaI1qjiUfA2DeoXmMCx/H0PVDbZ7n7eJt16FFIVXg5uTG7JjZxQ8wA1gG9AX8rLl86PqhjGw2ks0DNtNiQYtCO7hk6DLQGLRM7zKDtkHtmbF3mk3hTVhgGBNbvkXP2j0Z/fNJLmfYd/sqOpvDp52Gs6phZ5I8fMW8uSA8oErtb+ikt97m/PkLWCwWAOrWrUP7du1o06Y1Hh53177pXvCvUAGwtj309PS0P39zq5WsLOvEg5+f9fqcnBx0Ol2h+4empaXdvLdC/rEKfn7k5ORw40YaISEhRd5TocA9jri7u+Hu7laiawXhX9FoYORI+8KWChXg99+t2xEJQgm5KuRM7h5KRDVf5kbF5U/mhvi6Muv5Jrg5W4PkmaTs/D0+c/KMnEvOwWS22DyrfiUVTjIpWRojSk85Gr2RKRtP203OD4wMJvrCDZvJc0+lE0uGRjA3Ko7l+xPRJ18kZfWnmNQpDscfHtmSei9+SM0GNTi289h/+lrMOzSHllXaYNFEMCAymLlRcRT8iG7OctQ6AxdTc5g/qDkWi4XcPBOeSjkgYcneBD7543Shk+e3LNh1kQU3772brY5Gta+BQuz1+fBbtMhanKi/Y8LR3R1+/hn69i2TYQn3z/kLFzh06DCtWrUkqGrV/ON79+1j7tz5qNVqXFxc6N/vBXr16nlfxyayuSAID7zcXOjdG06ftj3etq31+2wZ/HLyTsVlc3cXeZlkc0+lE+vGdmNE01HMP+xgNWbz0TxevTs3ctM5nlJMxj0PbAFqAs8Ad3z5HWXz+pVUKORSus3eRb/wIJYNiyAnz1i+snl6Ohw+bHtMKoV582DYsGLfIQhC2RPZXGRzQRAeMYX8PGCRyTj64RccUDWFAtl89vNNUCrubt7c3cUp/8+FZfOBkcHExKfZdSYMULmgNd7+XdCcA3M4MPwAI5qNsOnGMrrFaKISomyO1a1Ql1RNan5hTJFygJ+BDkA121PzDs0jskoko5qPYupu+wUBTQKb4K5wp/fX+/mgd3t2DelFhjaDDF0mPi5eeLp4cSghmx6z9xda2HKLo2x+xr9akfeJeXNBKP9Krbjl3LnzVK5cmfbt29G+XTsCAvxL69Glonp16z9Wer0eg8GAk5OTzfmcHGt13q0w7u7uRoUKfqSm3uDixXjq1Qu1uT419QZqtRr/ChVwdXXNPx5SLYT4hAQuXrxI8+bN7MZxMe6i9brgkFL7bILwn7i6wm+/QYcOt/eIb9oU1qyBoKAyHZrwYHJVyOnWIJBOof6otUZy8gwEeiqJvnCDiGo+THumPl6uEnINatI06fi6+uAqV7Fy/3WW7ruSP2E8pHUIy2MS6RtWGS9XOdtOJdtNnsukEvpHBDF88UGb4wXDe+7pXaRtnIXFWPjKoFt8mnZlzR+/cP6GjjTN1VL5WsyOmcn65/9GpzdRJ9DDphJ8aOtqLNuXyPc74lBrDUzqVhd3ZxlGk4XwKX9zx88shYq9pkZ789m3tjoqrp36wMhgOoX627STFx4yBgNMmABff21/rnp1WLcOGjS4/+MS7rs//9zI7t3RdO92u614cnIyM2d+iclkwtvbm8zMTBYu+omQkBAaNrx//12IbC4IwgNNp7MWiUZH2x6vV8/6fbYcbfd3ZzbX6A1U9FSSqzdhNJruezYHa6eUwQuP8dPQz2hVtS2zYmbYrcZ8PWIiT4b25tS1PBae/Nzxh0wCVgFBwPMUOdNVVDYf0a46f59JIUNjQK01IpNKyNYZqahyxmguJ9nc3x+ioqBzZ4iNtW4ZvGyZtUuQIAgPBJHNRTYXBOERI5NZi97Vati8GRQKJL/8QsNevfnNZP7P2Tw3z4iLXIrBZLbL5kXlcoAOdfxROd8uvMnQZdB1WVc2D9hMyyotmR0zmxPJJxjZbCS9V/S2uVflrOKHwz84/tw6YClQD2hS+CVfx3zN2hfWMi16GmaL2ebchFaTOJ+sIz5Ny7ZTaTQL9sOg96Kayh+j2UKTj7ZhNBf+3ILEvLkgPLxK7W/ol1/M4Ltvv+a5Z58pd4UtABUqVKBaSAgWi4WTJ2Ptzt86Vr3a7Yq95s2sIXvPnj121+/ZuxeAFi2a2xy/fc9eu3suXrxIUnIywUFB5fJrJDzCWrWC77+3/t/9+sGuXaKwRfhP5DIprgo5gZ4uhPi6YTCacZFLkcr0nEjbQq9fHqPu9yG0/qkpdb8Pofevj1HB/wjrxjYnxNeVARFBtAjxYVlMIq7OMnQGM3OjLtq9p3aAOzq9iVPXb1eb3wrvC3ddJGPnEm6sn+awsEUilTF15iw6DX+Pz7ecp3mINwq5pFS+DkeSjpBrUGMwmVG5yAmt6EGLEG8mdKlNZHVfDl3K4ItnGzOsXXV6fbObkT8fIjVbT7Ngb0IreiCTFj+ODI0ed2d5/lZHYzrW5Jt+TahfSWVzXf1KKr7p14TJ3eviqhCtFR9aN27AE08UXtjSpQscOCAKWx4hZ8+eo3r1aqhUt7sobtv2NyaTiSGDX2LRwh+YMX0aEomEDX/8cV/HJrK5IAgPtM2bYds222OVK1uPe3uXzZgcKJjNAz2VbIlN5qutZ3FSGO9bNl8UnWBzbUKahj7fHiQ5KYwNz//DmVcS2DPkCOfHXmLn4F3I81ry2YYLHE5dzfzDc4r+cFlY2537A/0Ap6IvLSybT+5el86h/py4ksms58MY3aEGvb7ZTbfZuxi5tJxl88BA2LEDWre2bi0sClsE4YEisrnI5oIgPIIUCmt3/K5dYeNG6Nu31LI5WIi+kFpoNi8sl4M1m3epF4CzzIOwwLD84xfSL9BiQQvOpZ1j3QvrOP/qeQC7Di2Xsy4zLXpa0Z/XAKwAfAD7XePyHUk6gsagoYG/7Rzl6Oaj6V2nFxuOJtlk8y5f7eSjP06Rkq2nVkDJcjmIeXNBeFiV2t/S9//3IcHBQUz57NPSemSpe/LJvnz51SwW/bSY4OAgfHx8ALh4MZ5169cD0LXrE/nX9+zVky1b/2Lzlq20bduGOnXqAHDt2jV++20VMpnMrk1kZGQEAQEBxCcksG79Bvr07gVY9wydO28BAH362FY7CkK5MGwYBAdbV4JJSucX+4IAoDea+WHXRUZ1rMLEvyYy75B96/GjSUcZsn4go5qNZvWYaWjyZAz8IYYq3kqcZBK0hYRxAHdnOeka2y1Xage4k3ojnajvJqKNO+BwbFKligp932bQyyPomatH6SQjPuM6g9cN/G8fuoAMbQZ1/QKZ1a8euQY16rxM/N1d8XA2MWdgGEv3XWbWD+foHRbIwJaVMFrUvN5NWWRl/p28XRXk5BkB6y8JTlzN5In6Afmrc9U6AyoXJ1RKOQqZVFSeP8yOH4c+fSAhwf7cG2/AtGkgFz+gPUqyMjOpVi3E5tjRY8dwdname3fritGaNWtQr14oCQmX7vv4RDYXBOGB1bevdXHAK69Y/+zjA5s2QYFtJsqjW+3Ko86msvG18PuWzQubWAdrB5fvd8QzNyqeOoEeN1uvq/nxpQo0quKDTnqE4ZtGF/2BtFhXhXoAAwDn4r8Gt7L5N/3rozGqUeszychL4d2eNTiUkMML8/ei1hkZ07Fa+czmfn7WxSjiZ3ZBeOCIbC6yuSAIjyhXV2thyx357b9k84/WxzL16UbM3HqO+S82t8vaheVyuJ3NVx+5xvjwCQxZf3sOPEOXwdTdU5kWPY0XG73IyGYj7e6/lOXg+5MZazdFE/AkxbZWSNOkoXK2FpmEBYYxqdVbdK/VjRx9Nm88EcKhhByGLzlAj0YB9IuoiNaUjcaQwKz+wSXK5V3O76PuK3NxbfEi4CbmzQXhIVJqv+EwmUz4+vqW1uPuifbt23H06DH+2b6dsa+Op27dOuj1es6cOYvBYODxLl1o3bpV/vVVKldm8OCXWLhwEe9Mfo+wxo2Ry+UcOXoUvV7P8GEvU7FiRZt3yOVyXn99PP/734csXLiI3bt3U6FCBU6dOk1GRgatWrXkscc63u+PLghgscDWrfD440VPhHXpcn/HJDz0jCYzf51OJry6F+vPbig0oBc099Ac2gW3o45nZxLSNHz9QhgZuQYK6wLuqXSiR6NKVPFS2hy3ZF6j7xMvo714weG7nPyr4f/U+8g9/bmWqWXa5jPsTYgjy+19csxFB/VA90CScpIcPrugyp5+7L36D5P/mWzXbn18+ASebNqdp5r6sy3hT7qvGFDoNevGdmPwwmMkpGlsnl2/kgpnJylnk6wtggdGBtOudgUUchkKyF8FIDwCTp6Eli1BY/vfCM7OMH8+DBpUNuMSypTJbMZsut2rVavVcvFiPA0bNrBpNe7j48PZs+fu+/hENhcE4YE2ejQoldYC0q1boWHDsh6RQ0aTmW2nre3KFw9tdt+yeVET6wWZLdhs33ktU8tbG9aw/cYrWKSmIj4QsAIaVG7AycdPQgkjb3HZfPmI7kiQlO9sLgpbBOGBJLK5yOaCIDzELl4ET08o6nekd+S3/5rNezWuhFQCozvUJLOI4o4AlX3uvJXNl+67wrqx3RjRdBTzD9u+12wxcyTpCN7Ku+xIuQ5IA4bisJviLUGeQSzqswi5VI5cKuf7A98zZuMrZOgyCAsM47Xwiawc2Yu/Lm6n1y93l8uVeh2fbV+Ae0Yyc//6i69bv4BmzHgxby4ID4lSK0MLCqpKelp6aT3unhk3bixjXhlNYGAgJ0/Gcv78BapXr874ca8yZoz9iqA+vXvx7uR3qFOnDrGnTnHs+HFq1qzJe+9OpmfPHoW+I7RuXb6YOZ2WLSO5fj2JAwcO4uHhztChQ5g44U0kYiJCuN/0ehg50tr+bsaMsh6N8AjRm8z8uOsiTYPdmbHHQcvCAmbsmU6tABdejAiiWbAPmjwjnkrbRBzi68r6sa1xV8iQSCT5rQS1cQf5+/PhXCqmsMW1blsCB8xA7mltdevtqiBZk0CS80SHhS0Avz37m03bRkeaBDZBIXOi54qeNgEcblfdT93zLlK5lje2vlrkNZ/vfY+fhja2+zoMaR3C8phEQiuK1omPvHr1rJ23CqpUCXbuFIUtj7AKFSoQFxeX/+eDhw5hMplo3LiRzXUajQY3N9f7PTxAZHNBEB5wgwdbJ7JvbrNQnulNZubuuIhCLr1v2RwgJ8+Ij6virsaanhfPzhvjsUiLKIqxAKut/3vFuhWEVQ8r0XMfmGy+cye8/z6YzcVfKwjCA0NkcyuRzQVBeOicOQNt21oXFWdmluiW/5LNawcoaVzVi9FLD+Pnrig0m898tjEqF7nd9ju3snmW1sDghcd4p9Vn/NRnmd1ct5PUCX83/xLPgbMZAlMDqfdGPSjBt7AmgU3wdPFk/Zn19FnZh+BZwUzdPZUMXQZgzd2D1w/grW2TCAusz6VM2/n64nL52L2/4J+RDIDSmMdbUYuZbLkg5s0F4SFRan+Te3TvzqzZX3Pq1Gnq1QstrceWOolEwuOPd+Hxx0veoSI8vAXh4S3u6j1BQUG8/dakux2eIJS+5GR4+mmIjrb++e23oUED6N69bMclPBLUWiNVfFzJ1GXa7dFZlCNJR8jSZfJal1o8PWcvbs4yfhnRip6NKpKs1gHwxbNhzI2KY/n+RMZ0rMlLLYMZOekDMncshkLXkt4iwavdi6gin82fMKlfScX5jONEq8dglmYWO75NFzbxWsRrDF43uNhr32z1JrNjZmO2FD0xPefgHMIrhzOq+Sim7p5a6DXzDs2hZZU2DIgI4/sd8QAMjAjmifqBaPQmxj5WU7ROfNRJpfDzzxAZCadPW7u4/P473LFSTni0tGjRnDVr1jL182k0bNCA1WvWIpFIiAgPt7nu4sV4KlSoUCZjFNlcEIRyz2Jx3CnDy+u+DeW/UGuNnLqupnvDwHuezQe3CmHiquMAnEvOwUUho34lFbHX7LcmulP1QC39Vj2BUZpb9EWbgRxgIKy4sOLhyuYXLli3vcrIgLg4WLTI2olPEIQHnsjmt4lsLgjCQ+PkSejUCVJS4No16+LirVtBpXJ423/J5pm6DBbuziAmPp1P/ojllxGteK55FU5czSI5K48lQyOYGxWHt5vCJpeDfTbv8+1BBkSEseH5f8g1qEnXZuCj9MZVruJ4oo6n6j5rV/BtZxdwFHp+25NWTVsxdP3QYj/HGy3f4OyNs7z515sOr5t7aC4RVSKKzOaF5fIaaZcZeXCNzXXmDh1xfvaZYsclCMKDodSKW+rVC6VLl858+NHHdOnSmRYtmlPBrwIKReH9p8oqpAvCI+XgQXjySbhy5fYxiwX697euMLy5f64g3CtZWj2+bgrStHfX2Stdm0kVjwqYzBY61glAIoG3u9XlepaOAJULHi5yvN0UVPVWIjXlsWrmh2TuWOnwmRKFEr9eE3GtGY5MKqF2gDvuznIi6ibR97dBmCVFT6DX8qnFsqeXo5S54K30xtPFk31X9jHXQbvI0c1H06ZqG17d+Gqxn/frmK9Z+8JapkVPK3KyfXbMTNY//ze7zqcxol0NOoX646qQ4+FSgj6PwqNBpYJ16+Dbb2H6dPGLEIEn+/YhOnoPMTH7iYnZD1hXV1aqVCn/mrPnzpGenk67tm3LapiCIAjlV04OPP88vP66fYe0B0yW1toF5V5ncxcnKd0aVOTo5UyWxSRiMltYHpNoN7F+S8Fcnq69wZHMMWRI0ooezB5or2zP/H0LyJPm4fMwZfOcHOvP7xnWFausWGH9Wf6PP4r9BYkgCOWfyOaCIAgPmSNHoEsXSCuQXWNi4K23YM4ch7dmafXIpNaOh+najLt6bbouE0+lgjEda9I/IogsnYFBLUNQOsnwcnXiSoaGP09cR+UiZ/UrrRkYEcTSmEQAu2yepTXw/Y545kbFUyfQA5XSCV83E2k5V8k0xHNI86XjwcSAYreC5WuXE9q0HlU8K7P/6n6HuXxks5H0rtObnst7lujzFpfNb+XyuVHxhAZ68NP2pciNxtsXODkh/f47sbWnIDxESq24ZfiIUUgkEiwWCxs3bmLjxk0Or1+zelVpvVoQhMIsXQrDh4NOZ3vc1RXmzxeFLcI9ZzSZ8VQ6kZarx1cZcFf3eiu9uJSmYfUrrdlxNoXn5u21WelZv5KKsR1r0qOanJ69+xJ/9qTD58l9KuP/1Hv4VanOwMhg+kcEodObWHd2De9EDUNvKqLlOVDLuy5tvWdj0IRQPdCDXw4k4iTTMuPxmbQNbs+MPdPs9vx8q/VbdK/Vg2bzm+a3U3TkSNIRNAYNDfwbcDzZftL/1jVIcvl1VEucpKJLyyNNqwWlsvBztWrB7Nn3dzxCuaVSqZg960v27NlLllpNzRo1aNSooc01mRmZ9OzZgw4d2pXRKAVBEMqptDTo1Qv27oUdO2DLFmjTpqxH9a/datV9L7P5qPbV2XIyidd/OcJ7PevRJMiLRdEJLN13ifVjW9M/PIjl+xPzx1Mwl1/OSqPfsufIkKQUPZAT0MbQiXpDPuZGrjf1Knmw+tAVtIbcBz+bWywwbJh19W9BXl7g5vbfni0IQrkgsrkgCMJDJCbG2qXlzm2IwsNhypRibw9QKYia1BITOiSSu1uc5uPixbC2AWyJTWLEkoN2uXxI62psHNcGC3AwIZ3RHWoSdjOXx15TF5rNPVyceKxuQH42P5Z0htFbJpCmdVB0fgTYAnN+Xkj10I5Ucnfj1JVsPu/8ORFVIpgdM9sul4+PGE/HkI6k5qayK3FXiT5vcdn8SNIRLJJc9k/ujMfa33DeH217wRtvQGj53W1EEIS7V2rFLfXr1ROVb4JQHphM1q2HZs60PxccDGvXQljY/R6V8IjR6I38fTqZ8Gq+XM3Q4OXiRVhgWPFtDLHuuenl4oXMImP23+dYui/R7porGVpyL52kxYDn0agdT1A3a/0YPj3fRC9TsnhoOPvj0xmx5CD7kn8l3el7kBTdllxhrsF3j69j/o4b9P8hhgERQYxoV926EnXvdeoEtmfn4J5k6jLJ0GbirfTCy8WLa5lGEjMvcyH9QrGf95Y0TRoqZ8crMtN1mVTxrFLiZwoPGYsFvvwSvvsO9u0Df/+yHpHwAFAqlXTq9FiR5yMiwomICC/yvCAIwiMpLg66dYPz561/1mis27r+8w80b162Y/sXNHojLk7W9uPbTqcw7Zk6pZ7N61fyZOrGM/kT5PsTMhgQEcT8Qc3R6U2otQbe6V6XyBq+bDh6lfd61svP5SeupnBNMxqTX2rRA7kALifD+XzdEmZtT3j4svlXX8Evv9gea9jQ2r1FJiuddwiCUOZENhcEQXgI7N5t/dkgO9v2eOvWsHFjsR33cvS5bIpbz/Q90zmRfIKL4y/eXTZXejHtzzP8HGOfy2Ovqfnkj1P89UY7vt52nqUxiXgqnWxyeYZGjwSY3CO00Gx+7PoFkhRvYZI6yOYngPXw+ZffsCuvFh/P2cuAiCDGdKyJOi+dc2nnWPfCOjQGDWmaNHxdfVHKlcw7NI9fY39lctvJxX7WgorL5pm6TKq6eMCkibYnqlaF99+/q3cJglD+lVpxy2effVJajxIE4d/KyIB+/ayrCu/Uvj389huILcGEUmA0mdGbzKi1RrK0ejyVClRKOQqZFL3JzFd/nUMhl9GmVgWGtqnO4Us5TGg5iYFr+hf77AktJ5GcZeJIYkahk+cANdP28kLfTzGZjIWev6XO4wNZMP8rgn09cJJL+XzTGZbGJJApX4pa8avDe11Mjaigf4/qPpXIyUsCYFlMIs2DvWlXuwKPfRFFltaAXAqP1w+0tnjPTWZr7DEerx/IOz3vbkWsr6sv6jy1w2t8XLzu6pnCQ0SrhREjrF25AJ55BrZtA4WibMclPHBycnIAcHNzQyIK0wVBEOzt22ft2HLjhu1xhQKk5bNzXkmyeZtaFRjRrjrjVx4t9Ww+MDKYmPi0/MIW4GaL8zjmRsVRJ9ADd2fr9NOHveox64UmTNl4mmUxiZiNWq5ljcJUxcGq0MvgFvsYvr3HE+itIifP+jPAQ5PNd++GSZNsj3l6wurVomuLIDzkRDYXBEF4wPzzj/VnBY3G9njHjrB+Pbi7O8zmOpOWiVsn2GzbM+/QPMaFj2Po+qHFvn5iq0lcTNEXWthyy8DIYKLOpuZvRVRYLs/JM5KUpWPpy+E22dxIOsnO7zoubDkDrAGfx4bzTP/BjF1xGLBm8xbB3nSq78WmC5uYFj2NBv4NUDmrUOepOZlyErPFTKOARvi5+hX7WQsqLpt7u3jB//4HSUm2J2bPFnlaEB5CpVbcIghCGTt1Cvr0gQuFrEYbO9a62t/pP+z/LQg3afRGtp1OZl7URWKvqZFJJdQOcKdhZU9GtKtG4g0NAyOD2R+fzqifDzHz2UZcTM2lT93ejGw2inkO9twc1Ww0PWv3RKeXsyg6we68xWQg4+8FrD6y0eEYJXIFvt3Go6vXnvfWnWbpsAi2xCazNOYCN5xmoZHvdHh/79pPEen1IUcTc3B2knI26XYl/g+742lZw5dsnQEAoxk2nrANzttOpzD9mbp3VXWvlCs5mVL09kq3KvOFR9CVK/Dkk3Dw4O1ju3bBa6/B99+X2bCEB0fM/v1s/HMTZ86eRa+3bsOmUCioW6cO3bt3E6tDBUEQblm1Cl580X5r1ypVYPNmqF+/bMblQEmz+ZHETMKr+TB3YDNqB3rSzOW/Z3MAmVRC/4gghi8+mP/n2gHu+ZPm55JzOH39dpY+eCmDC6m51slzTSbXb4zBXCer6A+YAi+rXqH2lFc5dCnj4cvmGRnQv7+1A2tBy5ZBzZr//fmCIJQ7IpsLgiA8oLZsgb597X9W6NrVWpSsVDrM5hOeqMlf8ettClsA5hyYw4HhBxjRbATzD80v8vWjmo2mV51eDF14e2ueO7N3XGpusdncZLbk318wm5vIIsX5PYzS60V/DS4Av8ETL76GZ8vn7LL5gt3xdKjbgvHhExiyfmCh2wjFpsTi6uRaatm8SWATfM5egm+/tT3Rvbv1/1+CIDx07llxy7Vr11Crs/Hw8KBy5Ur36jWCIIC1KnjgQPtWeE5O1l98DhtWNuMSHjoavZEpG0+zdJ+1peGYjjXz9+JM1+hxd3YivIYfUzaeZnlMIiG+rkgkEjK1Bqb+Ecf07jNoF9SeGXun2e25ObHlW/Sq0xODUYHRbOLUddtqbFNuJqlrp5B35ZTDMVaqXJln3p7FjjQVWVoDZ5KycZJKmbvzCMmKd8mTnXZ4f1VFb+Z0XUJ0XDoj29Vh+5lk6woqizX4x15Tk2c0UyfQw2aivqBnm1XBYpGXeEXsuIhxzDs0D7Ol6C2SJrSahEJ6d3uwCg+B6Gh4+mlITrY/V6mS9b9LscJPKILFYuGbb79j+/YdWG7+G+Z2c8VKbm4ux44f5/iJE3Ro355x48aK1aKCIDy6TCZru+qpU+3PNW4Mf/4JlSvf/3EV426y+Z4LN1g6LII8o4lhPx3ES+nEnBf/fTa/pXaAOzq9iauZWsZ0rEa/iIrkGtSkadLxdfXBVa5i5f7rLN13hZw8I53rBTJ88UH0aZdJSnoTS3NNoc8FIANerziZ855decLPjWFtq7P68GUKzMc/2NncYoFRo+DyZdvjH3wAPXr8t2cLglDuiGwuCILwANuwwdpF+WZRYr5evazd8p2di83mSoWFGXum2T06Q5dB12Vd2TxgMy2rtGR2zOxCs3nvur3I1cnYn5COp9KJgZFV7LK3h8ITudRCts5wV9ncTA4pzv/DIC26IwwJwC9Qo+MAho55nc71AgvN5tk6E52rdWdE01HMP2xfSG+ymJh3aB5vRL7BoLWDiv3SF5fNJ0ROwGXEG2AucN7FBb75RsyZCsJDqlSLWwwGAytX/sKWrX+Rm5sLQMeOHRj36lgAduyIYt369bw6dizVq1crzVcLwqPJYoHPPit838CAAPj9d+tej4KA43blclnxLdaNJjPbTiezdJ+1aGXx0PD8vThjr1knu8d0rEk1PzeW39zPc8nQCL7fHpffonzn+RtMfaoju4b0JFOXSYY2Ay8XLzxdvJBLZCTcyOOXAwn0bGxbFJmXdIHU1Z9hynbQEhEgCLzHetGkdR6vBzVn8MJjVPBw5mjSKaIyR2KUOag8BzwNLyDRDkBrsLAwOp5jl7N4q1sdImv4sjzmMkv3XSJLa0CrN/FW17rM2HI2/7MD1K+kYkjrEFqE+DBiyWHmDepV7IrY0c1H80SNJ3hjyxtFXjOq2Wh61+mFQi66Lz1SFiyAMWPAYLA97uFh3Z6od++yGZfwwNiw4Q/++Wc7Pj7ePPfcs7Rr2xZXV1cANFotu3bu4pdff2VHVBTVqoXQu3evMh6xIAhCGUhPt3bOKGxr1yeesE5We3iU+mvvZzb/8/h1NoxtY5PLAXrM3l9ENvdELpEXmc0LcneWk6s3sm5sc3YkbqLXLwPsJuPHh09g3dhufPLHBXQGE4djoklJ+QBLW32RzyUHvC4PYcy77zB2+WEmrjrOwIhg3u5WB63BzA+74snSWjPSA5vNFy2CX+/YKrVDh8J/vhcE4YEnsrkgCEL55TCbr10DL7wARqPtTc88Y+22p1AUm81DK3rw/aBqHEs+Vuj7L6RfoMWCFrwe+TpbB25FZ9SRqcvMnzeXSWTsv5iFp6sLIb6u/DS0cZHZ++MOH/Pn+A6sOb2hRNn85PVkUhQfopfGFf0FugIshxcHjSSlQT8m/X6CgVfUhWbz3DwTWVoz77edQquqbZkVM8NuDA0rNKFnna6MujzKrpNNQaOajaJtUNsis/moZqN5ak860v37bU+8+y5Ur1705xEE4YFWasUteXl5vP+/Dzh//gKenp40a9qUg4cO2VzTsFFDZn/9DdHR0aK4RRBKw9WrMHOm/fHmzWHNGmv7bEHAvl35LfUrqRjZvjqdQwNwVTj+lqA3mZm742J+0crcKNvJ8Ttbkg+MDCYmPs3umio+Tvweu56NF9ZxLeda/p6bjQIaMT58AsPbd8dsUuTfk3sqirRNs7EYHUx+AzQHukKsJpYh6wcystlofhr6KcuP/EPPlSMwSjOKvtcixdswHJXJOnmUodHj7ixnacwlwoI8yckzEuLryvqxrXlp4X6c5FLib+Ty68iWZOuMqLUGVEo5Hi5O7DqfSu9vo8nSGhj980nHK2JbvUXvOr2QIOG77t8zfU/R17grxP6gjwyDAV5/Hb77zv5czZqwbh3Uq3f/xyU8cLZs/QtnZ2emTvmMgIAAm3OuSiVPPPE4YWGNGTf+dbZs/UtMoAuC8Og5dAiefRbi4+3PDRtm7YJ5D7Z2vd/ZvLBcfuuau83mhQnxkzPxr4mFrsw8mnQ0P5t/3GcKixcvJTnxXehSdFcUdOCdNBJVrV5o8oy4O1u/FktjLtEkyIt2tfzo3bgSLy3cT0Kaxi6bZ93M5ioXJ3aX12x+7hy8+qrtMW9v+PlnkMn+27MFQSiXRDYXBEEonxxl849zj9H0/deQ3LmFZP/+sHgxyK05tbhsrlI6ka3PdDgOX1df+jfsz5/n/mTThU122fy1iIk8GdKbZcOb8PGud/jxyALq+9enTVAb1HlqYlNiuZR5ifr+9Zm0bSLzDs2ze8ed2TwpK4cUxSfkyc4UPbAkYCm41enMm/+bwqcbrdc6yuanrqtpULkqfWo/xZN1e6POyyQzLxMPhRcy3Fi+7zpvHDnNzOdm0NZhNu/JoWuH6Vu3L4euHyI2JRaTxXT7vE8kLoOa2o63Vi2YONHh11oQhAdbqRW3rFmzlnPnztO5cydGDB+GQqGg75NP21zj6+ND1SpVOHb8OC+W1osF4VFWpQosXw49e+ZvmcLAgTB/PiiVZTs2odwo2BLxTrHX1IxbcZSBkcFM7l7X4SS6Wmvk1HU1YzrWLHRy/FZL8rPJ2dSv5MGglsEMWXQg/7yn0onFLzdmSvS7xU58z+gynRbBnmxdPBt1zCrHH1AKdANa2B6ed2gOuXoNK2OXYTQbC7sTAInFGT/9JFzNEfnHvF0V5ORZ71kUncD8Qc1pO+0f+oUHsWxYJEgsfPLHKVrX8KN2oAeBni7597arXYFP+zZgblQc0XFp9Ji9n0/6tmfn4JsrYnWZ+Lh44aX0wknqjMksIVtnpGNQL3rW7klWXhYZ2gy8b16jkDqLji2PktRU6y/ZoqLszz3xBKxYYf3FhyCUQEpKCmFhje0mzwsKCAigUaOGHD1a+OohQRCEh5LZDF9+CZMn23dIk8th1ix45ZV70sb6fmfzC6k5LBzcgqE/HbA5f7fZPKKaDzHx6XbXRVT35s/zGwp9RkHz9s/h0i8JbD63Cbo7uNAAPumv4lH5CQD8PJzJzbud5RdGxzN/UHPmbL/A4qHhTFp1HGe5tMhs3rY8ZnOTCV56CTR3bMn0449igYogPMRENhcEQSh/HGXzs5fTcftppn1hy5Ah1m7LBQqSi8vmvm4KvF2Kns/zdvFmy8AtTIuexvxD8+3OH006yuB1A4i5OppPOn5CVVVlLo6/iMag4YbmBn6ufijlSs7cOMPey3sLLWwpaN6hOTSvGM6yE0vJkx0v+sJU4GdwDWmLb9dX8fdUljyb1/SjdoC1A6bKxZ0qVEFrMLLtVApR59KIvaamx+xsB9lcgcFsoIZPdV6PfB0vFy9UzipMFhMKmcKay0eMhIw7FrR+9x04/8ftQwVBKNdKrbhl1+5oKvj5MXrUSGQOVplUqlyJM2ccVAEKgnB3une37g0/eTLMmGFd7S/2EhRuKtgS0ZGl+y4RUc2Hbg0Ci2yDnqXV23VnKShQ5YJcJmHnpI6YzRaMZgunrt+udh8YWYXtlzYVP/F9aA4tfJqR9NtK1DHbHH9AV+B5ILiIz3ViscPbK7j681PvX4hN8GfpvitkaQ3Ur6TCzVnG2aRswPpLBp3eRJ1AD5bFJNIixAcvVyfMFnB1lqE1GFE63f526qqQ061BIJ1C/VFrDWRqDahcnJDghL9rIBXdKyGXSdHojWw9mcyWU8kkq3Xk5Bm5kJLDE/UDeaFFVWr6eNs8V3gEHD0KffpAYiF/XydOtP5bL1byCndBpVIhlxf/74hMJsPjHmy5IQiCUG699JJ1i787BQTAqlXQps09eW1ZZPNdkzpiuiOXw91l8w4h7Xi/5+P0/GavzTmZVMIL4YH0XDnN4TPQAKtgs+cm6OPgOjM8V/VTzji1yM/lHi5OSAv8eHsrmx9KzCAsIZ13e9RleUzivc3m4aWczb/+Gvbtsz02ciQ8+WTpPF8QhHJJZHNBEITypbhsbpTJefH5T/h1+VtUy7i51f2oUdbiCaltRi8um+cZTHi5eBEWGGbToeSW0S1GE5UQVWhhS0F/XfyLjzt+TLBXMH1W9rHrdjKp1STaBLWhpk9NLqRfcPisN7aOI1ufXfQF6cAS8KgciV/vCXRvXAVPpRM1KrhxNjkHk9niMJurXOy/5ymdSpbNc/S5/H5qNevOrsnvYBObEkvDgIZMbDmJ3nV7o5DJoVo1ayFLXp71Bc8/D126OPzcgiA8+Ertt2YpKSk0a9bUYWELgJPciZyc3NJ6rSAIAJMmWVf0h4WV9UiEcuZWS8SSmBsVR6dQ/yIn0D2VivwVoLcmx2VSCbUD3Knp78673UOJiU9n/s6LuCpkTOpaN/9emVRCv4iK9FzZv/iBpMKYJ8eQl5Ln+LqKWAtbvEr08Qp/lSaFd3e8nr/X6OCFxxjZrjrHrmRitty+7tY2RQALdl1kwaDmNKysAmDKn2d4546VtfqbPxxtjb09OX42KZvQiirGPlaD9rX82XXhBk1DfKhXyZN0jR4fVwXOTlJW7L/MmOVH6NW4UrErdoWHyC+/WFd+aLW2x11c4IcfYMCAshmX8ECLjIwgKiqKnJwc3N3dC70mOzubEydO0r5d2/s8OkEQhDI0YIB9cUtkpLWwpXLle/bassjm0Rdu8Gzzqjb33lU2B6ZHTydqcE8GhAexrMAq1NoB7uQa1BxLdtBhIAVYAVQBejt4iQWC3YPp3z6MlpVbsnD3FZoHe5OjM9AkyJuT125PvN/K5ouiE/hlREtW7D9A/Ur/Ppu3q+XP7vMOsvmyUszmGRnw/vu2x2rWtHYSEgThoSayuSAIQvlSkmye6u5D/xemsObXyfj2ewanr2cXurC4JNn8UpqWNyInMmit7RyfTCJjZLOR9F7hKCzf7u7y7j/vFtndpf/q/oxsNpLNAzbTYkELMnQZhTzJymFhSxawBFo0C+ePNRuROsnINWRxIeM0r3etwKRu1Vm5/zpL910pMpvLpdYC8jvzs6Ns/nqXmkTWULEt7i/aBLeiaaUwm8408w/PZ+ymMexK3MWMx2fg/v770K8fjB0Le/aITC0Ij4hS+42ZQqEgtwRFK8kpybi7/8e9iQXhUZOXB6tXW79RF0YiEYUtQqFutUQsidhratRa+8B5i0opp2Fl62Svp9KJgZHB9I8IQm8w4evuzLTNZ1gWY53sDq3ogY+rIv/eEk18A5wFVkNeXjGFLQ2wTo4rHF9WEgVbrq8cNRV3Jzf2Xky1ucbH7fY2RbHX1Gj1JsZ1qsXymER+3neJ8AIra4trNX8lQ4fOaEatNTBiyUG7vVwHtwph/djWvLRwP3+fdrxiV3hIrF8PL7xgf7xKFVizBpo3v/9jEh4KAwb058yZM7z3/gcMHTKYRo0a2pw/fuIEPy1aTGBgAANfHFhGoxQEQSgDXbtaO15+9ZX1Z6l33oEPPwSne7sVZFlk89CKHoxsV8Pm3hJn85uOJB0hKTuNMY/VpEmwF4uiE4i9psbdWU6axn6ronyngTVADeBJwEGD0e41uzOn1xw0Bg0J6tMMbF0JlbMrcomFgZFVWX/sOlla6xZSt7L56evZ3MjJI9DThSGtQ/51Ns8zmlHr7lM29/aGrVth6FA4e9b639/CheDq+u+fKQjCA0Fkc0EQhPKlpNn8uqoCXQd8weaP+hJYRMf8kmTzP45f58/x3RjVbBRzD93unljfvz4ag6bYbF7S7i7zDs0jskoko5qPYuruqcV+Pjs5wBLoFNGJ5b8sZ1viJmbsmWHXJebWYlGpxDk/m6dk6/Kz+cLoeEIrqmzyc3HZ3M/dGZ1RS4Yuo9DONOPCx3Fg+AG6LuvK+rMbeCb0aRQ1a8KmTRAfD5Uq3f3nFQThgVNqxS3VqoVwIS6OrKwsPD09C70mOTmZixfjCQtrXFqvFYSH3/Xr8NRT1rbFGg28/HJZj0h4gGRp9Xd1vVpnsNmjviCFTEr3hoEEeDizfmxr9senM2LJQTrU8SfE1zW/sAXgXHIOLgoZ9SupSjbxbQF2Af84Hp9EIuHtD99hqnmKw8nxf2PeoTl0CG5H7zpP0aiKN1IJmC3WSW1/D2eSsnT51+bqjTSs7MmE36x7kt5aWQs4bGfp46agX4sgpmw8bbf3KlhD/MRVxxkQEZS/T6mjFbvCQ6JrV2jbFnbtun2sdWvr6vHAwLIbl/DAee/9/9kdk8vlxMWd44MPP8Ld3Z0KFSoAcONGKtnZOQDUrl2LqVM+55NPPrqv4xUEQShTU6fC+fPwxhvQseN9eWVZZPM7czlQfDYvxA1NBp/9nUy9iirmD2qOTm9CZzChci9kxacZ2AHsBGoBzwAO4uyH7T8kyDOo0Ans1yJeo2ftnvw5LpyBPxzBzVluk811BhMvtAiiRYgPn/xxGngAsnmrVtbtKD/6yLqQpa3o0CAIDyORzQVBEMo3m2xusSC1mDFLC9+ZIlOpQp1npKhZupJm85d+PMbasTNoWaUtX8VYC0ZUzipuaG44HGtJu7vc8nXM16x9YS3ToqdhtphLdA8AucBiCKsXxpwlc3h/1/tFdokZsn4go5qNZsbjM9DcXBSaodHzZJPK+dm8ircyPz8Xtw1UDT83agYoeHPrm0W+c+j6ofmdaV5e/zK96/RCgZO1YLx69ZJ/TkEQHmilVtzyeJcufHlyFjO/+IqJE95ApVLZnM/JyeXrb77DZDLxxOOPl9ZrBeHhtn+/dd/ta9esfx49GkJDrZNhglACnsq7a22icil6tapcJiW8mi8WC3z2p3UCWCaVMH9Qc7u9RE1mC8tjEhncKoSJq46Tk2fE19Wn8AfrgXVArOOxyV3caTnsQ1597WW2rtyCwWxA5awiU5dJbEosFiyOH1AC0/dMp2ftXuToLNQJ9OD09WyGtA7h4o0c+oUH8f2OOAAqeDjz7poT+atHb62sVSnlDttZjnusJn+fSS508rygZTGJNAnyolmwN2qtodAVu0aTGb3JjFprJEurx1OpQKWUo5BJRTHMg0ahsBayNG8Oly/D8OHw7bfW44JwF06eLPofUovFQnZ2NtnZ9r+EPHv2HJIiVh8JgiA8sI4csbal/uEH6z7sd3J2hg0b7uuQyiKb35nLAcfZvAg+Sm+S1ZfYcTaVuVFx1An0QOUiZ1a/ejSr2Cw/m6enpnNq3im4CFTHuo2og92r32nzDtdyrvFh1Id2544mHWXwusGMbDaSD9p/wJKXwziSqLHJ5u4ucvqEVaLvd9H3KZsX3k3nrrO5i4u1wMry33+GEQShfBLZXBAEoXzLz+YWC29FLaZidipv9HijyAKX0sjmcam5/BR9nXa1nmDD893INajRGbX4uDre7aKk3V1uOZJ0hDxjHk+FPsWa02swWUzF33SzsKVqcFUen/w4u6/uLrZLzNxDc2gX3I6P+rRn8KLDVPRU0rORkv4L9pGlNZClNeTn5+K2gZrdrxEbzm4ocWeaVlVbkanNwF1h/7UT8+aC8HArteKWdu3acuDAQXbt3s2IkaOpW7cOAKdPn+GzKVOJPRmLRqulY4cOtGgh2usLQrGWLIERI6wruW4xGGDQIDhzBuSl9tdXeIiplHKbVZqO1K+kQqV0/N+VQiZl44nr+RPAd+4lWtDSfZdYP7Y1/cOD+OXgZdycVIQFhtmsxiQDWAkkOx6b3KcK/k+/T5ZfCOeStGx9cSspuSkcuX6Ej6I+KpXCFrAG/1RNOjqDCndnOQMirKtAJ606zpfPhzE3Ko7Qiio8XJyIOnd76yKZVILJYnHYzlImldCtYUWGLDpQorEsik5g/qDmZGoNBHoqbc5p9Ea2nU5mXtRFu9bpI9tXp3NoQJEt7IVyyt8f1q2zdukaNarQ/XsFoTjz580p6yEIgiCUvdRUeO89WLDAWjjQsCFMmlTWowLKLpsXzOXL9ydyLjmn8GxehCaBTXCVqzibZP0lrNkCp69n46l0ssnm/0T9w0eTP4IkrIUt/XA469QuuB21fGoxdb3jdum3JrAb+jemdc26jFl2lC+fD2PX+VTkUikavYnEdA1gzdy1A9yBe5PNC+um85+yuch8gvDQEtlcEAShfFMp5dSv6MEzP89kyCFr0XueTMHb3V7FIrEtgCjNbP7Drnh6N67EnH8ucvhyxs2C8UCH2bwk3V3upDFomNllJlMem0LPFT05l3bOwcXAEmhUuxEnO55kTMsxJe4SM2PPdKIG96RxFRWeSifaTf+HtFxr0blMKkFntBbWOMrmCrmUED8FAzdMc/guDx1ku9zuTJOuy6SKZxXbjyLmzQXhoVeqf4PfeOM1qlWvxtq1azl61FpBeP36da5fv46rqysDB/Tn6aefKs1XCsLDx2iEiRNh1iz7cyEhsHq1KGwRSkwhkzKyfXXGrTha7LWj2tdAUUzlst5kZm7U7Qprd2c56ZrC26tnaQ28tHA/i4eG0zTYi79PpTM+fAJD1t/cOzoB+BVreHZAWaMFfr0mUL1SBX4a2pioxE28tmRmiSbiAUL96nH6xqkSXQuQnJ1GkMqXEe2qUzvAg5cW7ichTYNOb6JOoAdDWoeg1RupFeDBtUxd/h6qzjIpKdl5RT63doA7Gn3J9nIF64pTnd6Ej6vtCt/i9iYdt+IoAyODmdy9rgjq5Y3FAunp4Otb+PkmTaz/Iwj/kr+/f1kPQRAEoexotfDdd/DZZ5CZefv4J5/Aiy9CxYplNrRbyiqb35nLF0UnsCLmum02d2B8xARWxFzHXKCePMTXNT+bj188g2NrjsFfWLcbrYW1Y4uDKCpBwpK+S+izsk+x7wfrBPaGfhswmPI4kJCOTm9iXKdaLNmbwNPNqtAs2Jvwar70jwi6uWWSGa2h6BWqIpsLgnCviWwuCIJQvikk8N2u+YQcut3N8fkTf6F1cubDLqNsrr1X2fxAQjqLohNYuveaw2yuzlPj5+p3Nx8PZ7kzvVb0IjErkay8rKIv1ACLoWPjjtzoeYMGTg3uuktMpi6Tt7uFojOaqF7BHaM5J3/OXAokpuWSqy86m3cO9SdDl+nwnf45cOJ7+Lkx/K/jETQGDQGuFWw/isjmgvBIKNW/vRKJhKee7Euf3r24ePEiKSmpmC1m/Hx9qVmzJk5ORbftEgQBSEuD55+Hv/+2P9exI/z6K/jdXYgRHm1ymZQRbo/pAAEAAElEQVTOoQEMjAwqcj9LgIGRwSXaP/7OCuucPKPdBG9BCWkaen8bzYCIIAa2DMbduQ/7roxk3tx5sBkoZstPVcvn8GozAC83Fxa/3Jgp0e8y//BcxzcVUMOtKyufmkXj+XVLfI+fmw9eSgVHEzOZ8Nvx/PbmBfcMzcjV07K6Ly+1CsnfQ9VssfBtv6ZFPtfdWc6NnMILgYqi1hmo6HV7ZWhxe5PesnTfJSKq+dCtQaBotVheaDQwbBgcO2btzuLhUdYjEgRBEISHg9EIP/0EH34IV6/an8/JgZkz4Ysv7vfI7JRlNi+Yy+cPao7eYMLbvSoxV0cx91DR+Xp089F0CulGj3W3O5x4Kp1uZ/M9c2EtcObmyTrAczjcigiggX8DtEbtXU2aZ+uzcZFKqBPoQW6ekYaVPZnw23F6N67E9wOasuNsKiOWHCT2mprQih73JJtX9r67bC4zm0hdsoK/Q16hW8OKIpsLgiAIgiCUB0Yj8peHEvL7MpvDJomUk4E1bY6Vh2wemxKLq5PrXXVelEvknEg54fhCLbAEPCp48NWPXxH2YxhtgtrcdZeYDG0mtQNqkZqVx3cDmmIwmtkTl1bibO7rpiBNm+7wHbM2g78G3twLT50GXb0teDw3PP+8mDcXhEfHPSlNk8lk1KpVi1q1at2LxwvCw+nkSejTBy4Wsu/guHHWCVlRICb8C64KOZO7hxJRzZe5UXF27fhGta9Bp1B/XBXyYvejzNLaTgCfS85BqZA5bK+epTXw/Y445kbF8XrHEHRrdLDR8ZidXZR4PD4Ot9C2AAyMrML2S5tKXNgik8gJsAzDnN4TH6X/XQV/H6UX32+/wHc74mzOBXq60LNRJfov2MfioeFMeLw2X2w9z4/R8dZ3SiW4OPhaFFcIVJgKHs5IC3QqL25v0oLmRsWV6Acv4T5ITIS+feHIEeufX3zR2oVLKv5/I9wfmZmZbNv2N6dOnSYtPQ0AXx9f6tWvR+dOj+Hl5VW2AxQEQfg3jEb45RdrZ5azZwu/xtUV3n0X3njj/o7NgbvJ5nqjAb1ZT4Y2g3RtBj5Kb7yVXiikzijkTnedzQvm8jqBHjzXvArTu8wgskpbZsXMsMnLYYFhTGg5iZ61erHnQnZ+wTcUyObr58LvWLcaBagHPI3DwhZfpS9p2jQ8XTzvetI8TZOGp7MMd2c5fh7OvLvmBFlaA0qFDJlEwqd/ns4f57nknHuSzeUF8ltJsvlLh/7gf/8s4MTxPzH89hPyJmF39U5BEB4+IpsLgiCUMb3eOjf36682h01SGa/1eIMN9doDttlcLrWQo88tMpcDpZbNW1Zpy1d3ZPOGAQ1J16YzqdUk+q/uX+xHfKXFK/Rc0dPxRTcLW/CAhq82JNts3YL033SJ8VZ68fXf5xjUshq/7r/Ccy2q3FU2T8vV46sMKPL5Pc5Cv5O3/1wtE3J//wun51/JPybmzQXh0XFf+i4dPnyYS4mJ+Pn50TIyErnYUkUQbK1ZYw1Uubm2xxUKmDsXhgwpm3EJDw1XhZxuDQLpFOqPWmtErTOgcnGyKVwpbj/KLvUC8FTaTgCbzBbOp+QwrG11Xv/lKHB7n3t3Zzk5eUbOJedgMlswZGcw/+2POHnE8b72VaoGsXj5r2xLcWX5/kRkUgn9IirSelG3En1WJ6kTvz2zgdeXGDFbYEXMdV4Ln8DgErRcfyNyImazEz/H2FZ416+kQukko/OXUVTyUiKTSth6KgVnp9sB2GS2sDwmkcGtQpi46rjds4sL8XeqX0mFt6vCZgLd0d6kd4q9pkatNYoWi2Vt1y54+mlITb19bN06+Ogj6/8Iwj22Z89evvn2O3Q6HRbL7f0kLl1K5MjRo6z+fTVjx46hVauWZThKQRCEu5CXB0uWwOefF74w4JZ+/WD6dKhSpehrykhJsnmOPpd1Z9Yzc+/0QotO+tTtjY+bs81z7yabn76eTc0KHlxNN5OcFMaG5/8h16DOn6x3latYfTiZnKoSmgV70z88KD+bP9fcn8iXJ8AGbndibAg8CTiYH25R4SlGRfbl5Q2D/tWkua+rLy5SNwJU2UiA7WdTqV9JhUwq4Z+zKQyICOL7mwXq9yyby0qezSvkZPD67qUANIw7hqVFc/jyS+viFUEQHkkimwuCIJSxzEx46inYvt3msMXJCeOy5bz7eE9evSOb60xafjvlOJe7K9z+07z5rWyekmWhe81e9KzTlRuaG6TmpuLr6otSrmTzhc08U+8ZRjVz3HlxeNPhrD69mrNpRSwAgPzClrBaTTja9gg55pz8bP5vusR4uXhx9HI8w9tJ+XLbWQK9nO8qm287ncK0Z+oU+k4vLcz7w/b6LFcZ8tlf4yS/vRhczJsLwqOj1P7mbty4ibXr1vP6a+MIDQ3NPz59+kz27tuX/+eaNWvw2aefoFDc3QoZQXgomc3WlYYffmh/rmJF68r+yMj7Pizh4SS/OVGukEnxcpWTozNxOV2Dl6sCD2c5UzY53o9yUMtg3upa12YCWCaVUCvAHYsZXm5dDRcF9IuoSK5BTZomHV9XH1zlKmYs/YvFH03gSlqywzE6V21A1SEfU6d+Q8KayGkW7M2W0/GM3TyMRLXjloK3GMwG6vrVpU7gJU5fz2bpviusG9ut2OA/uvloetfpxYAFB21WpgIMb1sdsLZgH9I6hOUxiUSdS+XHl1qwYNdFDCbrpNTSfZdYP7Z1/uR/QcWF+DuNbFcdmQSbCfQ7VwAUR60zEOjpUvyFwr0xdy68+qp1ZXlBKhWEh5fNmIRHyvkLF/jiy68wm81ERITTsUMH/P0rIJFISE5JYceOKPbti+GLL7+ign8FatWsWfxDBUEQykp2NixaBDNmwJUrRV/XqRNMnQotWty/sf0LRWVzb1cFri4mJmydwLxCsuvRpKMMXNOfUc1GM+PxGURU8yEm3trCu6TZfOX+6+QZoHagB2azhe93xDM3Kp46gR43J9rVnE3KxmyBNYevs2xYBBOeqEOzYG827D3J8317k7Q76fagGgN9cFjYUlXRG13qcB4LDmdE01H8eGTBXU+auyvcsZiUdA4NQCGXEuTjet+y+aj2NXCW237A4rL567uX4aHX5v9ZYjJBs2bFvksQhIeTyOaCIAhl7NIl6NEDYmNtjzs7I1m9Gufu3fEzmfOz+fVMDQFeshLncpXS+V/Nm9/K5nUCPVA6G5mwdSI/HJlPA/8GqJxVqPPUnEw5idliZubemewcvJP2Ie2ZFj3NrthmbIuxrDmzhk0XNhX9ddABP4Ond1t+WfkrXxz8yC6bzzs0j3Hh4xi6fmixX9YJLSdhsTgxukNNlsckYrbAouiEu8rmeqOZw5dymNByEgPX2Ham+WYjVM62feeZd4bTpGJVm2Ni3lwQHh2lVtyyLyaGvLw86tSpk3/s8OHD7Nm7F19fXzp0aM+JEyc4f/4CW7f+Rc+ePUrr1YLwYMrOhkGDYO1a+3Ph4dZuLpUq3fdhCQ+3wrqzjOlYk2p+bsXuR7lk7yWeb1GVke2qM27lUQBqB7ijzTPxv3UnmfNiAzac20CvXwbYBOugS0FcX3Ydg95Q+INv8mjaE+/HhnEtT073r3fzcpsQGoSk8nfaEC5mxjm8907p2gzcna3f4rK0BlYfSmValxm0CWpXaJX9W63e4rGQbgxYcJQTV20rvAdEBNEkyIs5O+L4dWRL8owmPvnD2lbRbLGw+62OLNmbyNJ9l8jSGnhp4X4WDw2nabAXi6ITbFaCHkxIZ1ibagyMDHL49R4YEUSn0ABc7qgev3MFQHFULmIrszKh11tX5M6bZ3+udm1Yvx4K5CVBuFd+X7Uas9nMpEkTaHlHsWxISAgR4eHs3bePadNm8Pvvq3n7rUllNFJBEIQSmDix8O+ttzRrZu3m0rnz/RvTf1RYNl88tBlXdNsLnUAvaO6hObQLbsf7PR+n5zd7gZJl81srTHvX6cXsrRd54/FaNKys4sRVNaevZ9u9JyFNk5/NfW8cY8MHo0hPT799QVOgFyCxuzXfM6EvYEkby8FLmQxeeIyVoz6nY0h71p1dV+JJ83ER40jKTuFGRsD9z+aRwYW2LXeUzWulXuL541ttjmn7DUTZunWxn1UQhIeTyOaCIAhl6PBha2FLUpLtcVdX6zxdp0522Xzx0GbsSypZLm8b3I6nQ5++63nzsMAwJracRK86vTh4MZMk43bmH7a+73iyfQH2hfQL1P++PtsG/c2uIbvJ1GWQpknDWe6Mk9SJXit6cfrG6aIHqwOWQFCVJ6D9K4xYerrQbD7nwBwODD/AiGYjmH9ofpGPG918ND1r92R+VDwj29fgsz+t7469pr7rbL54z2W+7teLkc1G5X/NnzoFA0/YvjO2aVXqT5yRvx3ULWLeXBAeHaVW3HL16jWCgoKQFtg+YdeuaCQSCW9Nmkjt2rXQ6/UMGz6SHVE7RXGLIFy4AJs32x9/6SXran8XUTUqlC6N3siUjbbdWWRSCf0jghi++GCR9xVsl7g4OoEPetdnQEQQy2IScXeWk6UzMOXp2kzcNpH5BcO+GdgGiXuK6bgilePz+Gg8Gj+RfyhTq+OD7VPIVizDZDE6uLlwPkpvcvLU1K+kYkjrECKr+5KjhQbej7OxXzfUeVlk6jLxc/PG382HhBt6Xl1+ihNXs/KfceveFiE+vLRwPwlpGhpV8SRZrcvv7HItU8uvBy/TIsSH9WNb51/X+9toBkQEMX9Qc3R6E3qTGW9XRX47y8ndQ4mo5svcqDi7LaBu7eVaWFtElVJ+V63TVUrRWvG+S06GZ56B3bvtz3XvDsuWgdhDXbhPTp0+Td06dewmzwtqGRlJaN26nDrlYPJDEAShPBg1qvDilubN4d13oU8fkDiosChnCsvmCrmUpsHujF80rUTPmLFnOjuH9OLNLrWZ9fd5x9n8plsrTEc3H82UzlPIyEth8cuN+XHXFZbuu2LXwRAgQ53NuxNeI+foHStAWwAlmNr5pMNUXl+RmJ+vTSYnHq/eB6nEiFRqYf/V/Q47LI5sNpInajyBs9SNAKWMF3/899lcAuTkGW1azd+LbP521E/ILOb8P+sULvDZp8V/sQRBeGiJbC4IglBGNm6E556D3Fzb44GB8Mcf0KyZXTa/21w+c890+tTpRYc6/gyMCGJpcfPmNx1NOsqAm9l8epfpdF82B5lERn3/+vldW2JTYjFZTPn3ZOgyGLb+Zf4eFIXJ4Im/m4wnlj7BiZQTds+3cbNjy7OPvYC66QhkMrnDbN51WVc2D9hMyyotmR0z236xaOu36F6rJ6OXnCQ6Lo0QPzd6Na6UvxXRv83mMx+fSbvg9vy4+VPmbbDtsqP3cCNk1d+4ObvbfTwxby4Ij45S+9urVqupX7+ezbGTsbH4+fpSu3YtABQKBXXr1uHMmTOl9VpBeHA1aQILFsCLL1r/LJPBF19YV/s/QJOyQvlgNJnRm8yotUaytHo8lbcLKeQyKUaTmW2nk+1WJNYOcEenNxW6H6Wn0omBkcH0jwhCpzeRrtHj46rAAkzqWoeW1X3ZHJtEFW9nNl5YYxvQtcAqoJiGK1I3Lyr0nYxLldvfP4zc4IbiS/Jkx8Hi4OYiNAlsQkUPX77t54uzk5TVh6+ABb7dHsfy/YlIJRRouZ5FUtYZ+oUH8d2ApuiNZq5nafFSKnB2krI8JjF/JSjAvJ0XmT+oOVM3ncFsAW9XBSeuZvHrwSsMiAhi8dBwen8bTZbWwPc74pgbFUedQA+WvhyBr7tz/hjlMindGgTSKdQftdaI+o69XO9cFXqLQiZlZPvqjFtxtNivw6j2NVAU8RzhHjl8GPr2hcuX7c+98451GzqZ7L4PS3h0aTQa/Cr4FXudXwU/zl+4cB9GJAiCUIzLl8HNDXx87M+FhUG7drBzp/XP7dpZi1q6dClXPz8Vl8tvXVNYNu8c6k+mLpNjycdK9K4jSUfI0KbzXISKgS3bsvNcJlW9nfnzzmxeiDkH5xBeOZxzaefYdGETb0ROZN3YrgxeeIyENE3+dfqUeG6sn4Eh7Y6C9Uiga/FjDAsIo5LKj2/7+eVnc4sZZm65wMoDiXSsW4FPn5xK++D2TNtj31r99cjX6VGrB85yF1bsS+Lb7XH/KZvvn9yZ2gEeNmMs7WzeKuEoneIO2By7NHg0NYJsW6cLgvBoEdlcEATh/jN9/z3SV19FYjbbHLfUq4dk40YIDi40m/+bXJ6uTUchVfBW92r0i6jC4j2Jhc+bF2LOwTm0DWrL5LaTqVehHhqDhhuaG/i5+qGUK5l/eD5zDswhQ5eR/75cQxZzo9J5q1swV9QOtm0FyAOWgl8tP+bPm0dajrRE2bzFghaMaj6KdS+sQ2fUkavPpYJbBbxcvIi9oqHHrP1czrBuw7koOoH5g5ozNyruP2Vzd5kbz9R9iucmL0WutS1ukX77HW7VahX6EcW8uSA8OkqtuMXV1RW1+vYvR5OTk0lNTaVjhw4217m4uKDT5ZXWawXhwTZwIBw9at07/tdfrfvDC8JdKqydOVgrkEe2r07n0AAkwNwdF+3udXeWk66x348yxNeVxUPD2R+fzoglB+2eO6JddTrW8Seyhi8uCiOzYmbcvjkFWAmk2z3WRuMmTRn96RzWX8jLf75Guo9Ml68xWIqvsC7KhJaTkODElE3H2H4mldEdarL3Ylr+Xp5mC3Yt128F6vkvNkNvMrNifyKnr6s5k5SDyXy7wib2mhqd3kSdQA+kEgnOTlLOJlmftSwmkSZBXgyICMqvUDdbQCqRoFTYFzTIb06UuyrkJd7fUy6T0jk04F+3ThfuoRUrYOhQ0OlsjyuVsHAhvPBC2YxLeKR5e3lx8WJ8sdfFxyfgLToKCYJQVnQ62LABfvwRtm6FKVPg7bcLv/a118DbG958E9q2va/DLImS5HJXhRy9yVxoNvd1U5CmLSZE3yExK5G3/36bHH0Ob7V6C6VzT9ts7sDXMV+z9oW1TIuexqC1AxjdfDRLh39Gj9kHyNToyT60gayonzAb7/h5oTXQpWTjm9jqLYfZ/O/TqRxMyGRomzD+GhiF1qgmQ5eBl4snXi5eSJCzNy6DH3ed5eClzP+UzUMrqnB1LrzQuLSyucRiZvKORTbXZXv5UnXq/0Q2F4RHnMjmgiAI95cmLQPJx5+ivKOw5VitJlydu5QOFSvjCoVm89LI5R/07o1EImHjhXXIJDKb7iuFmbFnBtsGbaPLz104fP1w/vGwwDDGhY/jwPADdF3WlQvp1gLI69nXGdOxJj1+Hpxf9FKoPOBnGP7kcHqO7oVM6nzX2RygkkclpMg5ejmLCX8f50DCvcvmimXL4Y8/bQ8+/TTyFwcV+THFvLkgPDpK7W9vxYqBxMaeIjU1FYAtW/9CIpHQpGkTm+vSbqSJgC4IBX3+ubXARRS2CP/CrZaJ41YctWu5F3tNzbgVR5my8QwmM1zN1Nrdn5NnxMfVdj9KT6UTS4ZGMC/qIhNXHS/0ueNXHmX65jO4KmRk6jJuV7GfAX6g2MKWp557AVP3D8mSqpg/qDl/vNqCyKZrSHX+9D8VtoxsNppWlR9n9rY4/tezPl6uCvpHBLEoOsHhfZ5KJ0Z3qEmDyl40qerN8y2C+PqFpkRN7MCYjjXxVN7egzNDo8fd2dq2cXlMIgUyPIuiExgQGYy0wOLh0q4Ed1XImdw9lG/6NaF+JZXNufqVVHzTrwmTu9cttHW6cA+YTPDWW9C/v31hS1AQREeLwhahzDRpEsa1a9f4+eelmEz2kygWi4Wly5Zz9epVu8wuCIJwT+n11hbgL74I/v7WNuFbtoDFYi0KtRTRvu/JJ2Ht2nJb2FKSXK7RG8nWGQvtnJiWq8dXWUjXGgd8XX1R56k5mnSUqdFTSc5JvqsVphqDhgb+DQDritHdl7ewfGBtgg9+Q8bf8+0LW9pR4sKW0c1H07NOTw7Ep/Nhr6KzeZbWwFd/xRH+6S5G/nSR3/bCyUQn9HpncrQSvF2VTHmy0X/O5vdihead2bxv7A4aJNu2r1R89gmuPl6l+l5BEB48IpsLgiDcPxq9kSm7rvBk98lkK5T5x1fX78gzff7HK3/GO8zm/zaXA3Sv1Z1WQa1Izk3iQvp5Pu74MXHj4pjcdjLeLt5F3n8k6QgpuSkYzUab40eTjjJ0/VBm7JnB5gGb85/h6uTKoPXPsuf62qIHdbNjCzVA2kXKYzU6cuRSxr/K5ro8J7I0FpxkTnzW9x5m87g46+4GBfn7w5w5xXbsFPPmgvBoKLW/wV27PsGsWV8zfvzrBAYGEp+QgEqlokXzZvnX5OXlcSEujoYNGpTWawWh/Lt2zTpRO2RI4eflcqgq2hMLd6+oduZ3WrrvEuEh3jaV0QAyqQSpBNxdbPejHBgZTEz87YrtIp8bk0jXBoF4qjLADOwCthczaAm8+b8JDB3xHt2/3s2ymESSNOf48+rbnEsvfk/p5+u/QOdqXfju4Dd2Lctfi5hI+6q3W6nXrejBq4/VRGcofNulWwp2qXl58QG7VbaDW4XY7Avq7aqgXa0KtAjx4ZM/bMdcsEL99PXse1YJ7qqQ/6vW6cI98MUXMH26/fG2bWHVKusPX4JQRp577ln27oth9Zq17Ny1mzatW+F/87/JlNRU9kTvISU1FQ8Pd5579pkyHq0gCA89nQ527LB2rFyzBjIzC7/u/HnYvbtcFrAU5W5yeUQ1H8JD7Ce1ZVIJl9Nz8XLxIiwwzCbrFqVJYBOUciUnU04CoHJWcT3n+l2NPU2Thsr59sTvez+8R/Yv2aQkp9hf3BFoX/wzwwLDGB8xnrZBbXlx9QCerPscjZ178W73ug6zudkCGr2JYW2rsz8+nZcW7S+1bN4s2OeerdDMz+bVPXGeMcDmnKVePZxHDC/1dwqC8OAR2VwQBOH+sMnm/tUY3Xcyi1Z9yJyIZ/iy7cD8AglH2Xz72VSmPVPnrnK5h8KDn/r8xM5LO+mzso/d3HVh3VfudGc2L2jeoXlEVolkVPNRbDq/iQ93fMj2hL+LHpQeWAbUBtrCvMPzaBXUilC/Brg61yyf2Vyvh379INu26zoLFkCFCkV/1gLEvLkgPPxKrbilQ/v2xMcnsHHjJi7Gx+Pr68v48a+iVN6uioyO3kNeXh6NGjUsrdcKQvm2bx889RRcv25tn923b1mPSHiIFNXOvDC39qOfGxWHh4sTAyOD6R8RhE5vQmcwMaJddcavPIpCLuXFyGCG/nSg+IcCKw9cZmx7N/gNKK42xQV4Bsa/Ph5MTlT3c6Vj8yN8smsyBrPj7ercFR608pnEl13eZMvJa2x4/klyDWrStRn4KL1xlatYEXOdPusOkqU1ANZq8GXDIriaYd+x5pZbXWrmRsUVWswTe03NxFXH8/cFnbTqOD7uCnqHVeKlhfvz31VQhkZPw8qevNKhJp1C/e9ZJfi/aZ0u3ANjxsCyZXD8+O1jo0fDrFmgUBR5myDcD35+fnzy8Ud8+dUsEhMTWbN2HZKbkziWm10RgoODeOP11/Dz8yvLoQqC8LC6fh02brR2afnrL8jNLf4elQouXnygilvuJpfPjYrjsbotkUqsE8aeSttsnmeQMaHlJAau6V/ss8ZFjGPeoXmYLdZW6+o8NX6ud/fv+a3OLxiBvyFub1zhF3YG2jh+1usRrzOi+QiUciXzDs3jjS1vkKHLYP259YxqNprpXaaTeKPoduz3IpurdQbe61GPJkFe93SFplwmRf7zYrhy2ea4ZPp064IWQRAeeSKbC4Ig3B93ZvPd1ZrQedgcLnlXsrvWUTY3myUlzuUTWk3g/+ydd1iV5RvHP2fCYRymgIvlJNyK4ERTS81R2VQytRJtaENtj19apjZtuEqzXC13apm5TRy5Rc2JC1Q2nHM48/fHEeRwBqAMx/O5Li8973ie+31B+L7P+73vWy6VM3nbZGbunmm3v7D6SmLrRNYMWkPMrBiHrYSKtLkTpiZNZfGji9l8ejO/Jv/qPCA91ootUUC7a5s/3/45Sx9bytvrX2dSj0k3nzZ/6y3YWeK9xNCh0K+f82t1gFg3Fwhubyr0CXvokCdJGDQQjUaDj4+P3f6mTZvy2acfExISUpHTCgQ3J3PmwIgRVrcpWEtub98O0dHVG5fgtiFH67icuSMOXchBbzTRtXEN3ukTzY5TGQz/YReHLuTgo1Kw8oUOTE9oTctQX0xmS5nHXb5lLxsmflq6saUG8Bi0jG6Jr7svabmZuIV8wjsbfy/lRGgZHMPc++fx6k+paAtM/G/FEcavhEYh3ni5yckryOFoaq5NmcPCa84rMBIR6El4gAen0zV2Y5e1Sk1hX9B3+kSx72wWoxftdSjQAWr7qfhf/2gUUuEEvyPw9IRly6BNG8jJga++guHDqzsqgaCI8PAwpn7xGQcOHOTw4cNkZFoXT/z9/Ljrrrto2lRUVBQIBJXA2bPWFkK7d5fteIkE4uNh2DAYMAA8PCo3vgqmvLo8V2egd9OaHDyfXVRBsFCb31XTm59G9GVE6xFM3z3d6TiJrRPpFNqJl/94+drYlw7hofAod+WXA4cPwK+As6IvPYE412PV9q7Nzos7WffrOg5eOlhkuClk+u5pxId15r4GDzgdozK0eU0fdwK93Cpfl2u1MGGC7bZOnaB378qdVyAQ3FIIbS4QCASVxNGj0KABSKUOtbkjYwu41uY5WgO/v1g2Xd49sju/H/vdobGlOMWrr0zcMtFmX8mqjI7Yk7qHMavHsOXcFueTFFZsaQrE2J+vMWjYem4Lvx9beXNpc40GVqyw3dawIUyd6nJ+gUBw51Hh6SMKhcKhsQWgRo1AatQQznPBbY7BAGPG2P/SzcuDl1+2tigSCCqAbK2+XMcXGMxM6N+UL/8+biNK/TwUSCQSCowmPvnzKA+3KVubLO2ZfVxZ+hFndbmuD2wEPAi4wZj249h8ZgtDlg3lsia1lBkk+Bof4c2Yj6jlVRNPtytkaKzXbLZA8sVS5gVSs3WsPpDK/Kfj6D11s42wlkklDIwN5Zm5u0odB6yVYH4a3o6E7/52KtCja6nxUSlQKUR25h1FeDj89hvIZNCxlJRmgaCaaNq0SbkWy5OPHOHixYvc3bVrJUYlEAhueSwWx33Pg4MhufSWk7RvD48+Cg89BLUcLzjfCpRXl+dojSTEhVLLx8MmEzI8wINpCa3ZeiyHNzt+SPu6nfh0+xS7kuaFLX96zu9pk/FpspiYsXsGo9qOYtjyYaXG8ULbF3juw+ewTLeAI3krAXpjtyhue4iEOuo6nM05y/nc8y7nm7xtMn0a9SUu0p/tJzNs9lWWNlerFFVjOJ82zVqpqDgTJjj+/yEQCO54hDYXCASCCmTJEhg4EF58ESZOrFBtrtHJ+ODuybSr04nPkpzrcr1JzxdJX5RpvqlJU1n62FImbZ1kYwgvWZWxODKJjLtq3EVaThq/HfvN+eAGYAHQCmju+JDC1kc3nTb38LBWbXnhBfj+e2tF7EWLwMurTDEIBII7h0p5+5aens7h5GQyMq66z/39iGrcWJRUFNz+XLkCjzwC69fb7+vRAxYurPqYBLctPqrytTwJ8XFn/ZFLNsaWwvKC36y3Cveomt4kdq7nchyLxULu7hVk/v0tOBDbNnQGugBSGN5qOLsu7OTz7Z9hweLytCCPWnzW4zs6h3VmXXIabnIZ+QVG/D3Kd81+HkoW7zlHDW83BsWG8s2Ga2XWGwZ7odM77isqk0poGOx1tTKMkWNpeRy6kMOlXB0hPu5kahyL9BHx9VCKai23J/n51j9Xe6LbER9ftfEIBJXMn3+uZcOGjWIBXSAQXCMvDw4ehB07rIuOO3bAU0/BuHH2xyqVVsPnn3/abpdIIDYWHn7Y+qdu2UzVNzvl1eVqlYLQABWrDqQWafOSZb99VApe7N6WjUM2k5aXgdaYjVKmsGv5U5JpO6ex85mdpWaYDmk8hMUTFvP7YieVFCVAH6C18+uQSqR8dPcnjFv3Upmue0/qHjI0mbzfP5p7Pttss8+ZNneky01my82lzXNzYaJt5i333AOdO1f+3AKB4I5AaHOBQCBwwuefWxOKLRb46COIiMDn0cHlGsKVNp+67ji/H7jIoNgWrHp8PUZLHpm6TJQyBV5KL34/9jvjN43ntY6vsS9tX5nmK6ye0iSoCfvTrG3OHVVlBPBz92NkzEiGtxrO6BWjOXDpgPOBDcAirBUXGzs/rLD10f60/TefNvfysnZE6N7d+vzZsqXr4wUCwR1JhZpb0jMymDlzFjt27HS4PyamDcOfeVqYXAS3J/v3Q//+cPq0/b6XX4ZJk0SvbUGFolbJia6l5tCF0kugN62tRiGTMmvzKZvtJcsLHkvLw10pczquxWgg/c+vyT/wl+sJFcADwF1WF/uTzYYw78CP7L5Yeml6D1N7lOkv8MYiE9G1djOkfTgAcfUCXMZWkuhaatwUUo6m5jJr80nmDmvL9I0nitoXebnJiyrBFFK8r6pObyJDo8ffQ4mbQsrCHWfJ1hrwcnP8/zghLoxuUUGiFdHtyOnTcP/94O4OGzZY/xYIBAKB4HZEp4MTJ+C//+DYMevfhf8uWZUCICnJ+VhduljNLWo13Hsv9OkDvXpBjRqVFn51UR5dbs1YlGO2wLfFtHlJXZ6tNTDh92PcE12bqX9e4e0+DXhhzdP8mvyrw2zOQjJ1mbz8x8v8+OB8OofFM3nbJLsM0/vc72PuuLmcSznneBAJ0B9o4fw6pMiY0XsuTYLL13Y3NTedpsEhPBEbyo9J10z3JbV5abp83vYzZOTrbw5tLpVaM0w/+cTaphJg/PjKn1cgEAgEAoHgTsVksr5zKVk9/9ln8W0cVeHa/JsNp5i1+TRbX7ubCF9/NIZcMnWZRAdF07N+T7ILsssVfmH1lBYhLXi1w6u0q9OO7j92tzGv1/evz5pBa9h4eiMtp7Yk02JvbC/CAPwCdAQinB9WsvXRTavNBw0q23ECgeCOpMLetKdnZPDqq69x5Uo6bm5utGjRnKCr2c2XL11mz9697NixkxMnTjJ58kcE+PtX1NQCQfXz228weLC1L2Bx3Nxg5kzrPoGgglHKpCTGRzJq4d5Sjx1zTyNydQYbt3VhecHEH3YRVdO7yHG9MCmFIe3DGfvrfpsxjHkZXF7yAfoLR13OFRoWxtc/zCAgPBh/lR9rT67h9b/HkKfPc3mexOKGn+EZvEz3IsFavvvQhRzG/rqffWezGHNvI+ZtP8PwzpGMXlT6NQ/tEM6CpBTMFus4BqOZRcPj+HX3OQ6cz0ajN9lUggkP8LDpq1r8ASi6lpoh7cNpEORFk9o+7DydabNvRHw9ukUF4aEUBrbbjo0bra0Srlyxfn72WfjuO1FiXiAQCAS3D998Yy3l/d9/kJJizXosKzt2ON/3+OPWKi2dOoFCceNx3sSUR5cXZixeydMXaXNnuvxYWh4LklLo3KAGc7edpVf9fvx8+OdS53gw6lG+33KO+1vcx+rHe5Gjz0JnysFd6sasL2bx4cQPsZidfJ2lWE3qTZ2PL5XImdtvATJ9W2p6l8884uPuS3qenue7NaBrVBBrDqbaafOy6PLlz3fATS4lWG1rOq4Wbe7pCe+8A889Bx9/DGfOQNu2VTO3QCAQCAQCwZ2GRmNtQ7Rsmf2+oUORt21Los+VCtfmeqMZJAWsOLaKKdsmFxnImwU345eHfynXJYT6hLLgwQWYLWaSrySzJWULxzOOF+33c/fjj4Q/+GjLR8zaMgs8XAxmANVyFdp4LdR2PW/J1ke3pTYXCAS3PRX202Tej/O5ciWd+M6defrpYXh7e9vsz8vL49vvZrNhw0bmzZvP6FEvVNTUAkH1YTbDe+85zsqqVcu6SCwWtQSVhFwmpXtUMAlxoczbnuL0uIS4MGIj/TmTbmu+ahXqi1ImYcbgNjaOa3elFKVMxsC2oUXudMPFo6Qvm4g++4rLmNzDmvHr6mXM+Tedi4fS2ZH5KhcMf7o8B0BhjqCGfhwKi+PS9POSUmgbGUCbMH+ia6ttYnPEoNhQYsL9Gb8yuWhbts5AmL8nwzpE4KNS4OkmRyaVEF1LzblMrU0Z+JJcM9lk81qvxgyOCyM9X4+fh5JgH3fc5VJRseV2w2KBadNg9GgwGq9tnzMH2rSxmlwEAoFAILhZsVggLQ2Sk+HIEau55OmnHR975Aj8VUpVPmecOwcXLliffUoSHm79cwdQHl1emLGYrb2WCelMl7sppCzZc57YyACOXMylS2gvhrcawXd7ZhEdFI3aTU1OQQ6HLh3CZDEBkNh6JPF1e9J/2S7ua1qHkfP3YbZATbmWI4sm8M+WLc4vRAo8BNzl4mItcgIK3kAt6UBQgBsBHu60CGlhUx3GGS1DWuIhV5ORr6eWj4r6NbwZ1kFlo81jI/yZ8lDzUnX5oNhQXu8dxRv3RfFS94Zkam4CbR4QYG1PVB6DmEAgEAgEAoGg7KSlQd++1japJfngA3j9deQSSYVr80BPJYFqGLd2rF3rzyOXj+Ct9GZoi6HsvrjbRps7omVIS7zdvNHoNfx96m9m753N7H6zGd56ODN3zwRgZMxINpzawKxNs8DLxf0wgvcabzbM28CslFku25KWbH1U7do8I8NqEndzc3GBAoFAYE+FmVt2//svwUFBjBr1PDKZzG6/l5cXLzz/HMmHk9m9u/S2FALBTU9ODjzxBCxfbr+vXTtrNZeaNas+LsEth9FkRm8yk6M1kq3V46NSolbJUcpKX5T1UMp5o3cUsREBTN94ws45XeiMdlfI8VHZVin54rGWbDuezszNJ+3Oe+HuBrzeuzFxkf6s+HUBPyx6C73etoVPSdRt+jNo1BuE1Qnh4OotbM9+C4P0TKnXH6YcgCV7EBKULo+bsfEEPye243ymlue61icu0p8Zm+xjH9ohnJhwf56cvYNs7bUen25yKUO+30HyxdyiYyc+2JThnSM5lpZnU2rSGfOSztAi1IdcnZFVBy4SrHZn8kPNhLHldqOgAJ5/Hr791n5f48bWvq8CgUAgENwsFBTA3r3WFkH79lkNLcnJkJV17ZjGjZ2bWxo0KPtcEglERVkN/DEx1r9vszZD16vNy6rLCzMWC7V5abp8aIcIlDIpQzuGcyItl4ndpjCx+wdc0VzhUv4lAj0CUclVLD+2nBqqmsTVupunvt9PpwaBeKvkWCywb+d2/lw6EXN+lvMLl4HkUQmWhs6NGRKLkhr6N1GZWxdpc61ez7j24xi4eGCp93Z07BgWJl1kYGw47604xMr911pdFWrzt/tElUmXz09KoU2YP8cv5fL30Ut4uclvHm0uKvwJBAKBQCAQVAjFtbl2/0HqPvEw8jOnbQ9SKq3JaAOv6dGK1ubPdY0gKHivjXnEz92PkTEjSWydiNao5bmY5/BUeqKSq5j570ym7Zxm02aokBdjx7D3jIYGIR70btCHBxo/QL4hn8ndJ9MlrCuf/PMxw1sNp/nk5qUaW1gJoQ+HUiuiFmPqjKFDaAc++ecTu7ako2NH0ym0Ez3n9yyKqVq1udForZSdlwe//AJhYS7HFwgEguJUmLlFp9PRvHkzh8aWQmQyGQ0bNiTJVeligeBW4L//oH9/66JxSZ56Cr7+WjhOBWVCozfyV3IaMzbaL2QnxkfSPSq41JJ9Hko5vZqE0C0qiBytkRydAbW7wm4RXq2S21Qp+erv404d1yPm7eb9+xqz45fP+PbLr13OL5UpGDD6PT57+yUsFgvz9v7Mv7rnMUhzXZ7no/Tn4+4zmPCbirIs/x66kMOl3AK83eXc9+UW1r0ST2xEAHkFxiI3uJtCyoKkFMavTLYxtkTXUqOQSTmammsz3hPf7WDtS53pUD+Qwd+V7XfTnK2nmTm4DeNXHuaLx1qirO7Fc0HFkpoKAwbAtm32+/r0gfnzQa2u+rgEAoFAICgkL8/aNm/tWuvvq717wWBwfc6JE9YFRLkDXdmwof22kBCr6aVhQ9u/69UDlapCLuNm5Ea1eVl1OVi1eWEmpCtdPuaXfSTEhvLGfY3xjfRixdHlTC5WBh2sC9bj2o+jW0QPVh/I5IenYrFYLFzM1NI8czN/LnoXs8l59ihycB/iga6OxukhEosbNfTvoDI3L4rtUm4BKoWUPg37MqL1iFIyRa0VZV5NOsrAWFh14KLN/kMXchgyeyebX+3KuF8POI+1GN9uOcnMwW2YtvEEZgt8+bjQ5gKBQCAQCAS3C8W1uff2LUxf8iFyXYm2976+sHQpxMfbnV9R2lwmlfB4bE36LLpmnqnvX581g9aw6cwm+i/qb6fNR7Udxc5ndtJzfk+bdkMj24zk/qh+zN16kZd+OkhCXChPtotg/PL/0BlMdGrQhr8SNvDIrAfJdst2fnOMwBqgOxwyHCKrIItnlj9Dv0b92DRkE9kF2WRoM/B198XX3Re9Sc/0XdNJ16QDN4E2f/ddWL/e+u+WLeHHH+G++8o0j0AgEFSYuaVOnTpkZNi7EEuSkZlJndqlNH4TCG52UlLg2DHbbTIZfP65tc+2yNQSlAGN3siHq5Idlkc8dCGHUQv3khAXxhu9G5dqcJFfFeQeSjkhPu4Oj1HKpCTGR3I0tfQqJSFKPXPefZoN6ze4nNfD34PlS1aQp27E03O3s/XyVHIVS12eA+BFSzY++SuY/IGtpR5fSJZGT/0gL9rXC+C7zacIC/Bg9tZTRb1Pj6bmYnaQbDq0QzgLklLs9mVrDby+eD8THmha1Fe1NA5dyEGnN/Fyj0ZFpSsFtwm7dsH998P58/b73nwT3n8fpOLrLRAIBIJq4qefYPp02Lq1dDNLSQwG6zNMZKT9vubNrb/jCg0sDRpAiTbDdwIVpc3LosvBqs3f6Vu2TMiNxy7zWu9Ixq4d49BAsjd1LwMXD2Rkm5GM7/IRz87byz9HzpK+6gs0xxwYdoujAPkz/uiCMpweIrGoCNK/i7u5ic32LI2ewCAvdpzIZkLXScTV6cTnSVPsFvdfjB1LfN2eDJm9j+fvru9QlwME+7iRlqMrty5vFOJN6zB/oc0FAoFAIBAIbhOKa/OHDvzFh2u+Qmk22hyTGVQb9z9Xo2re1Ok4FaHNGwZ7kW/IYV/aPsBaseWPhD+YtHVSURuh4uxN3cuw5cNIbJ3ImkFriJkVQ5hvWJEmfmVRMvdEh7D0uQ78sO007koZZ9I1HLqQw7ojl5j4+1D+c3PRStQIrAO6AVfzDtI16dTxqcODUQ+yOHkxq/5bxYW8C+QU5HDw0kGaBTdjVNtR7Encw+4L+2kV3KH6tPmqVfDhh9c+Z2Za278fPQruzr9GAoFAUEiFmVv69e3DF1O/5N89e2jVsqXDY/bs2cvhw4d54YXnKmpagaB66NYNPvkEXnzR+jkgwFo+rWvXag1LcOtgNJn5KznNZd9PgHnbzxAb4U+vJiE3vFArl0npcVcwnerXYNC3SU6P018+zdm1U7hwtpSWQrVB86iGiz5pHEtRsj79BQoUh1yeIpPI8dYnoDY+yNQ/rzD+/vK17gpRu3Mlr4DXejVGIgGlTMaelCyXLwQGxYYSE+7P+JUOKi0BOTojF7N15YpDZzTxdKdw3BUV9mtUUN3Mm2dt11BQYLvdwwO+/x4efrhawhIIBAKBoIhTp2DDhvKdo1JBo0bWP2az42NCQuDtt284vFuZ6tLmEQFejP2l9EzICQ/cxbKjy11WRgGYtmsasbU7UleqZvHclzBmXnA9sKcKxYhADN5nnR4isXgQrP8fbuYou32F2rxxLTVuMgXB8nhWPNqLfEMOGdpM/FV+eMjVLEy6SP9lu+jTrKZLXe7lJic933Ur1JLk6Ay8dd9dtAz1LTUhoEJ5/XWraezVV2+71lwCgUAgEAgE1UmhNp//z2nGbJ7H8//8bHfMvpAGPPXQO7xLAL1M5krV5l5uctI114zgI2NGsvH0RofGluLM2D2DzmHxHH72GFiU/J2cwdhfjvBvShZrky8xKDaUMfc0QgIM7RDBK7/sJe3KmxTU3e98UBOwGegKKK9tDvQIZEqPKYzfNN6l4WZkm5G813kiA77ZTeeGNapem6ekwBNP2G6Ty2HRImFsEQgEZea6n/wvX75s8zk6+i569byXDz6YSMeOHejUsQM1rj7gX758mS1bt7Fly1Z69epJ0yZNHA0pENxajBoFe/ZY/yxdChER1R2R4BZCbzIzfcPJMh07feOJG85CLOxPqikwoTeZnTquNUe3ceX3T7EYSjF7NAf6AAr4YPP75Bs0FMhcvwyo5VWXb3r9SNIRfxbsSGHDsSuAtcx78bLvzoiupcZksdDtk42YLdbPz3etz+u9G9Mm3I/vtpyyKx8/tEM4MeH+PDl7h02bouLkFRjxcVeUOn9xAjzdhLHldsFkgtdeg48/tt8XFgbLllkz2gUCgUAgqG4eftj6Qt0REglERUFMDDRtav13VJT1d5moOlYq1aHNDWYz2VpDqZmQSrmUVmFejJ4zqUxjj5/zDpe/v4wxx/W4zWNbktavgFTDYafHSC2eBBWMx81i37qqpDbvERXEpIeas+W/y/x5OJ20HB15BTkcTc0lqqaa8f2jaV7Xt1Rd7u+hdLjPGTV93An0cqvaii3nzsGnn4Jeb62m9MILMG4c+PlVXQwCgUAgEAgEtymF2vz9tdN5Ys8qu/1/NohjdJ8xaJXuVaLN8wqMBHj4AyCTyEhsnUi/hf1KHdfP3Y8MbTqeSncu5qQTHpLJhAG18ZBHsWjHReZtP0dshD8x4f50bRiIOf+d0o0t/wCdsHmz2zKkJQEeAew4t4Pt57Yjk8gwWRy3JJ22axpxtTsyLaEn3u5uVavNCwqsz7QZJSpGTpkC7dqVax6BQHBnc91v5p4ZPgKJg9YrFouFTZs2s2nTZof7Vq9ew+rVa1iy+NfrnVoguDmQSKwLWUYjeHlVdzQ3PYXmihytkWytHh+V0mF/yzuFHK3RTizLpBIaBnsVtdg5lpaHl5ucLo2CMFsgNVt3XfeueH9SD6WMcT0bE1XT22Yeo8lE9taFZG9d6HowCXAPEHf138CR9COlxlBDHsv2p5bzS1ImifF1aBXmy9xtp/nzcBpj7mnI5D+OciwtD5OjGohXKdla6NCFHEbO/5enO0Ywsks97okO4XKujvQ8PQFebgR4Kpm+8QTjVyY7FekAx9LycFfKymWyUauEseW2IDMTHnsM/vzTfl+XLvDzzyITVyAQCARVQ0EBLFgAX3wBv/8Ojlr51qsHrVvD7t3W7Lb27aF7d+vfbdqAj0+ZphK63J6S2tyRLjeZLfioFBWmzbf8d4WH29R1Olfh9u5RQWTpsorKoDvFAuyAE2tOWP/tgoHPDOFo64OkproytqgJLhiP0lLP4f6S2nxt8iV2fLyBSQOa8tGDzcgtMJCarUPtrsDLXY7OYKLfV1srQZcrqv779sMPrcYWgPx8q9Fl5EhhbhEIBAKBQFBuhDa3p1CbL7mrK4/sX4ub6Zp+/Kt3AqOaP4rOLKkybX4sLQ9PhZpH7noElUKFzqgrVZvX96/PmkFr2HRmE52/72TXtnN02zEse74XH6w8QWy4Hz0mdOJc4B7nA5qA3UB7oMRljeswDgkS/FR+/PTQT6jkKmb+O5NpO6eRqcu0G+rzpI/ZOKQfnSdtJVNTRdrcYrG2Htqxw3b7gAEwenSpYwsEAkFxrvvtXPRdd1lf7gsEtzPbtsGxYzBkiOP9olRamShurihZWSMxPpLuUcFVW0L6JiBbe62kn49KQUJcGANjQ9HpTWRo9Ph7KPF0kyGTStlw9BKPzvjnuu5d8f6kPioFL3VvQFSIN1893qpoHoMun4ceTyBl21+ug3YHHgYcr287xiLBxzgIlfYR5BJvRnTxY/N/VwgPcGfB8JZk6bLI0l3i6yfCUUi8i1zrJRe9nbUWkkklbD1xhS6NatCsji9nruTz9YYT1PB245Uejfhmw4lSQzSZLSxISmF450hGL9pb6vEj4uuhvEMfLm87xo51bGx5/nnrSwpF+Sr6CAQ3G4cOHcLX14/atWu5PO7ChQtkZmYSHR1dtC0uNpbgoKDKDlEgEGRnw8yZ8PnncOFqC5k5c+Cttxwf//77VnN9167g7V3u6YQud0yhNnemy90UUlYfTOXe6BCSTqZXiDaPCfcjPMCDTeO62szlrpRy/FIO9YPdydNnYzLrydRlOB0TsC52rwZ2uT5MInfD976nONByGweKLbCXJEAVyMIHlrL3pF+5tHlegZEv1v2Hl5ucFnV9OV5g5I0lB5BKYOpjrVwaW+CaLh/SPpyxv7rIWr1KtejyM2fg229ttz3zDISGVm0cAoHglkNoc4FAUBKhzR1TqM0vN23FiVZfcNcrz2KRyTj34es0Skxkh7svyRe01Pb15J8TVaPNVQoZk3pMIqcgB61B6zJ+P3c//kj4g0lbJzltETR0eQKJrUcy7t536fp2W5IDDzkf0AQcBGIoSjYtZGSbkbSr044GXzYoMrK0CGnBqLaj2PnMTnrO78nxjOM25+xJ3UOWLosQH3eX5pYK1ebTpsHs2bbb6teH774T75kFAkG5ue7fjB98ML4i4xAIbj6+/dbqJrVYrC2H4uOrO6JbkuIisSSHLuQwauFeEuLCeKN34ztKrPuorCX9wgM8mDusLTtOZTD8h11FQtxHpWD16E58+dcx5idd370r7E86b3uKzTyPztxeNI8h8yI5yz8kL/WU64CDgMcA/7Jfo9SiJlA/FpW5JQAXs3T8/N9lhnSoyarjK3nmj8l2rvUXY8ey7PmeDJm9j9PpGqethUq+eMjWGsjXG2kTEcBTRgtvLDlQLmf5rtMZjIyvx8C2oSzY4by9UkJc2A2XuhTcREyaBOvXw8mrbQgUCvjmG3j66eqNSyCoIN586x263X03L7zwnMvjFi9Zyrp1f9tUVoyNbUtsbNvKDlEguHO5eNFqaJk+HUq2j5k1y9p+SCazP6937+ueUuhy5/iolE51OUBshD/TE1rzyZ9HmVdB2vzjh5uz7UQ6Mzdde5kRHuDBvGdacE63nhcXWbVys+Bm/PTQT86D1wO/AsdcX6Pcrxb+A14gs9YMDlw67fLYdO0Vxv39/A1p89wCIy1D/RjdrWG5tPm87WdYNaoTCbGhDu91IYNiQ7m7cTXo8gkTwFDsJYCbm/N2YQKBQFAMoc0FAkFxhDZ3jo9KSYd6AUx7ogkrjqXxx4O1WO1xgbXaCfD5BFqEtGBc+3GEBfbm6/XHOZ2usTm/UrT5vDJqc2BkzEg2nt7o0NhSnBnbp7Hs1yWkRqQ6P8gE7bXt0dyrsVtHfznuZdrXbU/3H7vbVGjZm7qXYcuHkdg6kTWD1hAzK8augkuWLoumtX1IvpjrMsYK0eabNtlXZ/H0hMWLy1x9VCAQCIpzZ/1WFAjKgsEAL70EX399bdtDD8GuXdae9YIyU1wkumLe9jPERvjTq0nIHWMaUKvkxEb4M+Wh5kzfeMLOUPFEXBjbTqQ7NLYUx9W9K+xP6qNS8MOwWLt5tKf2cGX5JMy6PNfBNgYeANzKfn0KcyRB+reQW65lFtX0deehmCDG/TWOmf9Otztnb+pehiwbxMg2I1n6/GS0elmRQ7x4ayFXLx6ia6kZ3jmSv16OZ8PRtDI7yx9pU5eFO86QGB9JqzBf5mw9bTfu0A7h9Iyuecc9UN7WBATAsmUQF2dtL7d4sbW1g0BwG2EprTcF1tahAoGgikhNtZorp08Hnc7xMSkpVvNl9+4VNq3Q5a5Rq+QseCaOr/4+7tDoHBPuz7ojl1wu6MKNaXMflYK5TzVn/OY3bLTyoUuH8FB40CKkhc2CNgAaYAFwzvX1qerF4NfvaS57f4BB6voaCqkubZ6tNXDwfDajuzekRahzXR4T7o9Gb8LTrQq1+YkT1spKxRkxwnEbMYFAIHCA0OYCgQCENrfDbLb+LbVeo1olZ/rgJoxdO5YZu6dDM9vD96buZeDigSS2Hsn3wybQ/6tdDisELtyRQo+oIDrUD6wabQ7IJDISWyfSb2E/19dcAByG1JYujC1mUJ5VEp8Qz4g2I9AYNOQW5FLTuyYquYpP/vmE0WtGO2w9BDBj9wzi6sQxos0IJm6ZaLNP7eZD9ygFP+9y/SCRrTVw4HzW9Wvzs2et79aMRtvt338PTZu6nFsgEAicUeGrAGlpaeRczTxTq9UEBwdX9BQCQeVx+TI8/DBs3Gi7/coV+Owza3aloMwUisSyMH3jiTuqIoZSJuWdvlEknUq3E85PtAtjRHw9Hpn+T5nGmr7xBF0a1SA/T2/TU7SwP+lzXeuTdCqdn3adJaqmN55KGYf+XMiZZdPAYnY9eBegM3a9PF2hMrUjUP8KUq617YqupUbtLmXJkVUOjS3FmbZrGu3rdOLusH50nrwBc7F1HWcPHIUcupDD6EV7GRQbyuu9orBgYe/ZLJcmoYTYMNqE+9Hvq618tf4Eg2JDmTm4DTq9iUyNHr+rpegXJKWQmqOlvnv5WwAIbmKaNIGlS6FxY6hTp7qjEQiqhcyMTNxFq0WBoHJJS4PJk63lmLUuylh36QLjxkG3bhU6vdDlrpFLJew4lWGnL6tCm3u5yckrMNItqgbrz6y208omi4kZu2cwqu0ohi0fdm1HNvAjcMV1POq2D+LVpQ+X3N/BKC3FBeOA6tDmjWt68+A3W+nbvJZTXT5+ZTK/jGhHDe9yOPBvlPHjwWS69lmlgtdeq7r5BQLBHYHQ5gLB7Y/Q5sXIyYEnnoAWLeB//wNAJrWw5PAKq7HFBTN2T6NdnY4Mim3BNxuuVSW3VhWsw+OxNdGZckm+cghfdz/8VL4opW4o5YqK1+ZXiQ6KRmPQsC9tn/PA84HDWNsMOcMMnAd9uJ6JWyYyaeskmgQ1wc/dj2WPLaPr3K7sSd3j8v4ATE2aytLHljJp6yTMV98DtAxpSYDKj3b13BgUG+pSmw+KDSWqpvr6tLlWCw88YH3nVpw33rAaXgQCgeA6qRBzy7Fj/7Fs+XL27t2HRmNbAszDw4OWLVvQr29fGjZsUBHTCQSVw969cP/91h7aJRk7FiZOtN8ucEmhSCwLhy7kkKM13jFVMeQyKeEBXoz95UDRtsKsx2OpuVzO1ZXr3l3KKeD5hf8ilUiKeopq9EZkUgmD4kJZezCVTeO6kp2Tz9iXnufIUtflE6VKFdO+ncFu322lmlGKozY8iq9xEJISbpgRnSMxmPV8/M/kMo3z6fYp9Gvc1257QlyYnSHIEfOTUmgV6gdYeK5rfVqF+jF76ymHmaTt6wXy8PRtRQ7/bzacYPrGEzQKufZAczQ1F7MFBrcLL1P8gpuMU6fA1xf8/Bzvr8DMeIGguvl7/XqbzxcvptptK8RsMnP+/Hn27d9Pw4YNqyI8geDO49IlmDLFWhXSmalFIoEHH7Q+c8TGVkoYQpe7xmi2MHOT7QuGqtDmOr2JDI0efw8lQT7Qac7jDsectnMaO5/ZyfDWw63lza8APwCuQpLKCbj3OdybNydN+SZG6cWy3QwHVIc2P5updanLAdTuiuu+pnJz9Cj8+KPttueeg5CQqotBIBDccghtLhAIHCG0+VWOHbO+j0lOhuXLoXlzePBBDGY9U7ZNKtMQXyR9zPJH1zF94ynMFquG/35YczakrKbvT4PsWvmMaTeO/o37oTdRcdq8GGo3NVc0LtznOcBBwFXhaDNwCahbbJPFzP60/TQLbkZaflqZjC0Ae1L3oDFoaBLUhP1p1gqKo2PHcPKynv/SMl1q88T4ejSv48OTs3eUX5tbLDB8OOzebRtQ797w/vtlil0gEAicccO/Eed8P5fly1cUlUyUy+V4eXkCkJeXT35+Plu2bGXr1m3069eXoUOevNEpBYKK5+efYcgQ+wVnd3f49lsYNKhawrrVydbqy3V8js5AiM+dk52Sq7v2IFM86/G/S7mM69m4XGNlavR4ucnZeTqzqKfoaz0b07tpCN5ucjzc5Ayeupot019Df/E/l2PJfUOo8eBbLEitw/e9P6BJUHPG/fUSOqOT0vkAFhkBhtF4me6221XYczNNc9G1a70Ye1L3kK3LonfTmqzcb12Il0klDIwN5Zm5u8o0xuytp/gpsR3fbz2F2WKxcZYHeCmp4e2OTALPLdhj15vVbMGu52h0LTVq1W34IHm7s24dPPIIxMTA77+DTFbdEQkElcrUqV8hkUgAkEgkJCcnc+TIEafHWywWFAoFjz76cFWFKBDcGeh01ioPn38OJRJAipDLrc8g48ZBg8pNBBG63DUlXzBUhTYv3sInqqY3XyaEO9XKmbpMes7vyZpBawgtCOW9Ye9hzDE6PBZA6uZJjQFvIwsNJFX5OiZpWrniL0l1anNHuhyqQZu///61kvkAnp7W/7sCgUDgAqHNBQKBI4Q2BxYutJof8vKubRs8GBo2JLOub7nWkPMNOTQK8eZClo65TzXnw61vOkzW3Ju6l4QlAxnReiRT7pnCQ61rl1ub+7n7MTJmJCqFisndJxMfFs+UbVOKTDQ5BTkEeQY5DjYdq7El3sUFmYEMwIl/Wu2m5lL+JRcDOJhWk47aTQ1AYuuRxNftyau/HuW7ITFOtXmQtztSCQyZs/P6tPmcOTBvnu1BDRrA/PlibVYgENwwN7QS8N3sOaxYsRKFQkHv3r2I79yZsLBQZFd/OJlMJlJSUti4cTOrVq9m+fIVmEwmnn7KvlyXQFAtmM3w9tvw4Yf2++rUgSVLoE2bqo/rNsFHpSz1GJlUQsNgL7zc5Ph7lH787UTxB5niWY9RNb3LfS/8PJTkFRiL7ufR1Bz2nctkfP8mfLT6CLMX/8HlJR9iynfcg7MQ9/CWBPYbh0zlzel0Db2/WkuG6hOXxhYPhScvt5rBv8dCnfbc1JvMXMrPKNc1XcrPYECr2kUL6A2DvdDpTeXKbLicq6NhsDeNQrx54tvtuClkeLnJebZLPULU7ihkUu5vWYu/j5T+UDAivh7K27UE6O2IxQJffgkvv2wtH//HH/D669aWEALBbcyjjzyMRCLBYrHw08+/EBERTmzbtg6Plcvl+Pv706JFc/z9/as4UoHgNkcut7a9c2RskcmsppY334SIiCoJpyy6HK5p8wDPO1eXQ+Vr85Klv73c5FzKS3c55vGM47Qa3wrj90aXxhb/oBCih03iBAWkKV/HJL3s9NjycEdr8337rC9gijNqFNSoUTXzCwSCWxahzQUCgSPKqs0LqdJqdZWNVgsvvggzZ9rvc3eHjAwyAi32+1yQoc3Ey01OQlwdh62ESjJ99zQ6hXXm7T4PMHFV2bV5ff/6rBm0hk1nNnHfgvs4k3WGEW1GsOyxZeiMOvL1+dTwrIGvuy8tQlrYVI0hFauxxVXxaDPWyi6Bzg/JKcgh0MPFAQ4I8Aigllctvu8/n/i6PRkyex+n0zVOtfnzXetT188DgEFxoSSdKn1N306bP/44rFkDv/xi/eztDcuWWStrCwQCwQ1y3eaWo0ePsmLFSgIDA3jv3XeoU6eO3TEymYyIiAgiIiLo3qMb7777P37/fRWdOnWkkSivKKhusrMhIQFWrrTf16ED/PqrKDF8g6hVcqJrqW0MD4UU732Zb8ghU5eJznKFPL1PUe/L253CB5mSWY8nLufj6eb83pUkupYad6WUbo2D+fbJmKISiiFqq6P/v80rSFv4HhaTweU4Tz/7An96dkcitRoUjWTwn/kNjNpzTs+RWnyoZRiPN62ZOTjMYc/NX3adY8YTrQlQOWkJ4wRvpQ81Q9QMbBvKgh0peLnJydCUL7MhPU/PrM0naRjszZyhben3lbU/aGxkAKqr5Ty7RwWTEBfKvO3Oy6knxIXd3v1tbzcKCmDkSGuWQHGmTIF27az9XgWC25THH3+s6N8rVv7O6dNn+OzTT6oxIoHgDkUuh0mToG+xVi4yGTz5pNXUEhlZpeG40uVg35f+Qv4xNCbbvvS3M8VfMFS2Nvf1UOKjUhS1xATIKzAS4FHKi8xUyPkhB5wUAgJwCwxl+JQ59Ozoz6OLe2PKd25saRzYmCNXnFcPKMkdrc1ff91qnC7E2xteeaVq5hYIBLc0QpsLBAJHlKbNixNdS42nm4zUbB1qlRylTHrrrk8ePWqtrrx/v/2+Zs2syQEREfhnO1+LdoS/yg+NPofHY2vSZ9HAMp3z8bbJ9GnQh1UH7Ft3OtLmfu5+/JHwB5O2TrJpRTRxy0QmbZ1Ek6AmqN3U3BvZk5fbvcRLsWN5ctnVbgBnsRpberkIyALuend0vi4qpwOHLh3CQ+Fhb55xQsuQltTyrsXH3aezMOki/ZftKnoOcabNYyL8i77Hrlubq1SwaBHUr299Lp43D6KiSo1XIBAIysJ1m1vWrPkTiUTCK6+87NDYUpI6tWszZszLvP76m6xZ84cwtwiql2PHoF8/q6AqyTPPwFdfgfLOylasDJQyKYnxkYxauNdme1l7X3opPas24ErAaDKjN5nJ0RrJ1urxUSmLHkQKH2TMFgs6vYnzWVqe61qfgbGhyKQwvFMko3/aW+ocL9xdH6VMRliAh00JRYvJiGTHj5zZ9JvrAWQKmj02ls8+/R/N3vsDswVM5HDJ7S2MUucPE0Gquqhy3qFHVEtydQYmrjrMmXQN7gqZTc/NqY+14OedKTweF1Iu4a2SqUn4djtzhralVZgvG45evu6s2flJKbQK9WPB07FE1PC06VProZTzRu8oYiMCmL7xhF31mRHx9egWFXR79ra9HblwAR58EJKS7Pf17w/dXaVICAS3FwUFBcTFxVZ3GALBnct990HnzrB1KzzxBLz1FtSrVy2hONPlILS5WiW3ecFQWJGkorU5WLXlkPbhLH++A0/O3lFU4vtYWh6eCrVzrXwZ+AGXxpYGTVth6j6Ok8bL3LfwYfKNzjMsmwW14OeHFvPY4geFNi+N9eth9WrbbWPHQkBA1cwvEAhuG4Q2FwgEhbjS5iUZ2iGcaRtO8M2GE0TXUpMYH0n3qOBba53SYoFZs6zVlfPz7XZrBg3m3Psfofb3Q6034qfyK9casodcjcUC+YaccrUzytRl8vuoTgyctd2m9Y4jbT4yZiQbT2+0MbYUYraY2Z9mNexsSdlCo8DG9G/Ul2fbPMvqP1dzeu9pLH1cV6NJaJhATL0YRq8Z7fI4k8XE8qPLGdd+HAMXl27keSluDL/tusT4lUcxlwih0rW5VGrtmJCQAHfdVWqsAoFAUFau+zfgocOHCQsLI6px2XtPRzVuTHh4OAcPHrreaQWCG2fNGnjsMWvlluLI5TB1KowYAVf74QpuDLlMaufu9VEpytX78lZeRNfojfyVnMaMjSfthF9ifCQ9ooJJjI/kx3/OkK83svz5Duw4lcHwH3ZxLlPL8uc7FGVGOuPpjhF0rB/Ih6uO2Bxn0mRzedkkClIcOOGLIfPyp8YDb5JdqxG5OgO9m9Zk+f6TXHL7Hwap83k9pXXZNHQDtdRBZOmySNdmEKAKxsfdlz1n8vh+qzWbM75BDdpGBPDL7rOcvqJndNsxDF2eUOq9e6XdWBYmXeTkFQ39vtrKSz0aMOH+Jshl0nJlNrgppBxNtfYBnb31FL+MaOdQbHso5fRqEkK3qCBytEZydAbU7grUKjlyqQSj2UJqts7OoHTLZkrcriQlWauyXLTPuuCdd+Ddd60PVgLBHUJAQACWkqsXAoGg4khLs1ZheeUVx1loEgl88421Yks5npsrA0e6HIQ2B6tmnPxQUxI7RzJq0V683OQVrs0LOXQhh7G/7mdQbChzh1mzI7O1BkxmCwuTLjrWylnAj7g0tvg3aMmm9Ws5lneI/ov6kW/Mcnpsq5A2vNTie5b/qy2zNn8xdsydqc0tFnj1VdttwcHw0ksVO49AILgjENpcIBAU4kybl2RQbCgx4f6MX5kMWLXkqIV7SYgL443ejW8Ng8uFC/D00/ZmYcDoruKLAS/xZZ32MPNfwKoZvx3ckjHtxpGwpHTzxuirOtXTTU66pvT2OTah5aRz4LTeRpcDdtpcJpGR2DqRfgv7lWncr3d+SZ+GvWmf154Zm2Zg6W8BV6+bCuBQ7iG+aPYFL/3xEmaL2eX4QR41uTuiB8NbjXDZgimx9Ug61elJ/6922RlbqlSbC2OLQCCoYK77t19mZuZ1VV+pU6c2SUk7rndageDGyc21N7YEBlrbEMXHV09MtzEl3b1dGgWUq/flQ1EDbsky6Bq9kQ9XJTt8QCl8EHmmUyQvdm9AWraOiEBPJpZYBH9y9g7mDrNmRs7ZetpuEX5oh3C6Ngrij0NpNufpL53k0uIPMGWnuYzRrVZjAh94A7mXtcxiep6evs0D+e7IU+ilDqoaXUUtD2dn4lp2Xkji48WT7bJ7x7UfxxcD7+Nc9gXU7iqQ6HiyfSijF+7n3X69ShXeI1qPpH/jfuSHS7mvaW3cFTLcFFJmbTpJRA1Pnu4UwUs/le7CH9ohnAVJKUXi/dCFHHK0RqcPfvKrottDKSfEx1o2XqM3suZQqlOD0i2XKXE7M3cuDB8O+hLl8T094YcfrNVcBII7jNjYtqxfvwGNVouHSlXd4QgEtw8Gg7XS43vvQU4OnDkDf/7p2CAfHV3l4TnDUdZdefvS367afOCsHax9qTMJcaEcTc29MW1+OM2lAQZgflIKLUN9GRQbyjcbTgAwb/s5lj1fQivnYzW2uPCOhETFsGfLSqbt+5IJm993uRjetnZbfnloBWMWneBoWq79fA4Y2WYkD97Vn/zIO1Cb79pl/VOc994DL6+Km0MgENwxCG0uEAiKU1pFjKEdwokJ9+fJ2Tts2lkCzNt+htgIf3o1Cbm5E+8WLYJnn4XMTLtdaXXrMfCeMZwIrGuz/dCFHB6ZsYNVL/YlsfUIZux2rVP7NOjDs0mHytbmswT+Kj+W7DmDv6fSRpeDrTbffn4bGoOmTFVh6vvX57t+3/HiRy8y86eZMADXxhYD4GatJJOv1/Be/P94Z8PbTg8f2WYkvRvcS36BjMk9ptAlPJ7J2ybZrc+/GDuW+Lo9GTJ7n933D1SsNv/272McO5eBTmHdLtbNBQJBZXPdP1mUSiUFBQXlPk9foEehuPUWwwS3EQ8/bC0JPmGC9XOLFtZ+jmFh1RnVbU1xd6/JoqPTnLL3vuzXqC9Kbq2fGUaTmb+S01w67wFmbT5Ju0h/EuLCWFtiEVwmlaBSynhv+SE6N6zBzMFt0OlN6Iwm1O4KZFIJ/6Xl4aaQ8d2WU0Xn5R/ZQvqqz7AYXP989mp2D/49RiIp9nLCTSHhq30voZPtdXpeo4C7+H3gCqZsm+Lw4WJv6l4GLh5IYutExrYfS7NpzQjzDWNc+3G82+9e3lx8jA8e/ID2dTvxedIUh2Xv+zXqi0Yn42KWFh8PJSqFlHnbU9j032Uea1sXTzdFqVmzJTMbCsnRGYoEeGmUxaB0S2VK3K4YjdbS8J9/br8vIgKWLYOmTas8LIHgZuDxxx/j4MGDjH9/As888xSRkZHVHZJAcOuzbh288AIkF9MYf/1lfZ544IFqC6uslMy683Az0vl7oc2ztQYemfEPS57twMVsLX8fuXT92nzzKafzFGfO1tPMHNyG6RtPYLZYYxgyex/fD7Nq5U82fcSBGQcg3fkYXbv3YNEv83l69VOsOLbC5XwxtWKQS+VsPbuOyQ/fy5DZ+2zmc6bN+zfuR55WyvksHf6ed5g2j4mBffvg9dfh99+hYUN46qmKGVsgENxxCG0uEAhKUlKbZ2sNKOUSFDIpC5JSGL8y2aExAWD6xhN0iwq6Oc0t2dlWzfTbbw53nx4wiJ5hDxSZIUpyNlPLiB8OMmPwFOLDHJs3Xo4by91hvVny72U+fbQFOr2JGt6Uu53R0dRcO10Otto8OWM3mVp7g05J/Nz9+CPhDwaNHcT2LdvhEcDFl8dd4o5OoSv6fCEnnadbjCLUJ9KlNs/Mg0u5OgK9lPSqdz/31OuNxpBDuiaTAA8/3GTefL/lPP2X7XL4/VOh2vyfM3y86nMaXjnDUwPe4bKXv1g3FwgElc51/1QJCQnmyNEjmEwmZDJZmc4xmUwcOXqEkJCQ651WIKgY/vc/6yKVhwfMnm39W1CpFLp7z2Vnlav3ZZY285Yrf643mZm+4WSZjv3sr2PERrZj+kbr8T4qBQlxYQyMDUWnN5Gh0ePvocRisbD5+BVOXc5jzL2NyMjXU2A0cTm3gMMXc7BYzGRvnk/2Pz+5nE8mk9Hw/ufJr9cdSbHs4uhaaqbunMDK/xY7PddbFsbqgX+w7dxml655gBm7ZxBXJ44RbUYwccvEq4aXkXzw4ASe+HYvfZu3YMWjf5NvyCFDm0mghx/B3gGculTA098fIOnUtTKS0bXUDGkfzsJn4pix8SQr9l9wmTWb2DmSFqF+DP4uyU7Aq93L9jKmrAalWyZT4nYlPR0efdT6orEkd98NP/8MAQFVH5dAcJPw4YcTUSgUJB85witjxuHn50tgYA2USvufhRIkjB//v2qIUiC4Rbh0ydqKZMECx/vffRfuv/+WaG9aPOvuXPY5oc2vcjpdw7Pzd/PdkzGM+cXa2rM82txkMZOtNXD4YuktesBqyNDpTTQK8Sb5Ym5RDP2/2sXobjGErQvjwMUDTs8Pjorh519+YcrOiaUaWwCeavkUZ7LPFOny74dNoP9Xu+j/1S4GxTrX5k/N2X9na/OmTWHlSti4EUwmEMlaAoHgOhHaXCAQOKK4NlfIJCR8l8TR1Fy7NjIlKa3SRrXi6QmnHBi+a9ZEN30mz57yQVeKZt56Ip0XFyXz5eMP0iWsJ7n6bHL12QR5+uPj5ktmvgW9QULPJjX563Aaxy/lck90Dca1H8fAxWVvZ2S2ONblcE2bv98/irrBpT/njWgzghdffZHtm7bDY4CL16YPNnqQRjUaMXHLxKJtfipfRi08SEx4+bT58M6RdKpfg8nrL6HRX+CdvtFE1vCmjp/KRn+XVhHoerT5qxvn8tBB67rs0h9e4amH3uFIUAQg1s0FAkHlcd2/+dq0bs3Pv/zK4sVLePjhh8p0zpIlS8nNzaN3r17XO61AUDFIpfDTT+DufkssQN9OpJfB5VycDF0WdXzqVFI0lUOO1ljmRW2j2ULO1UXw8AAP5g5ry45TGQz/YZfdwvCQ9uEM6xiBwWhm5+kMjl/Kpa6/B+YCDVdWfoz2uOuWb1KVmq+++4F12TXYedr269AoYg+f7Zri9Nza3nVZ98R6anj6MWXbpDJd29SkqSx9bCmTtk7CbDEzY/c02tXpSN/mLfhmwymmbzxFoxBvgtXuTHwgiEm/H2e+g4zPQxdyGPvrfvadzeKZzpH8sP0M/b7ayqDY0KKs2UyNHj8PJW4KKRn5BSzdc47T6RqbcaJrqVGryvZrrzwGpZs6U+J25sAB6N/f8cPyiy/ClCkgvwkf8AWCKuTgwUNF/7ZYLGRkZJKR4fj3sEToIYHAMRYLzJkDY8Y4LKcNwODB8NFHt+RzhdDmtmRpDUUGlfJo8+SLObQO8yctR+didHsyNXq83Gz1SrbWwOo5U1m5fKXT81rFtGXV6j9Yd+4PpmybXKa5ZuyeUaTNC3X5oFirLhfavAyIFsYCgeAGEdpcIBCUxpW8AhtzRWmUp9JGlSKXw8yZ0LYtmK+2zHzsMfj6a7JkHhze5iBJzQHns7RkaUwM+nYP856O4/wVd+ZsOM+qA/vwdleQEFeHhHa1iI+S0TISPBUGWnr3ZUTrEUx3kZiZ2Hok8XV70n/ZtfaTjnQ5WLW5FCnuMm+XVWGkFikXf77Iir9XwOO4NLZghlPZp/is12dF6+YtQ1ri5+7L7jMH2Xk6s1zafPSivQyKDWXMvY3o99VWp9rc30vJvrNZ9Ptqq52x5Xq0+dBdyxiZ9GvR9tq5l/nxp7fpnPgtWqX1+1KsmwsEgsrgut/69O3bh5W/r2LBwkVYLBYGDHjQaQUXk8nEb4uXsHDhIjw9PenT577rDlggKDPTp4O/PzzyiOP9or9tteCv8ivf8e6+lRNIJZKt1Zf5WC83OVkaAz4qBT8Mi2X6xhMOS3oXLiInxIbxWq/GNA5R06tJCDv3HSb1x1cwpJ91OY+iRjg1HnyLHt26sWzhvzb74ptk8PW+sU7PdZf6seLxNew+KcWjcWa5sns1Bg1NgpqwP82a/fpF0scsf3Qd0zeewmyB5Iu53N04mC3HrzgU6MWZl5RCi1Dfoh6o32w4wfSNJ2gU4o2Xm5y8AiNHU3OJqqlm5uA2fP7XfzZZDiPi66Eso5Auz0uQmzpT4nZm1Ch7Y4tSCTNmwJAh1RKSQHCzMXPGtOoOQSC4tTl6FBITrRUbHNGqFXz5JbRvX7VxVSBCm9tyvdq8fb1Afth2ml5Na5YrNj8PJXkFRpttzTR7+XX2V07P8a1TjyXLVvDhlgV8s380FkpJ671KSW1eUpcLbS4QCASVi9DmAoGgNHxUynIdX9ZKG9VC69bWdq7z5sHXX1srLwPZqWXTdGDV5jlaA98PjeWrv48X6fLwAA++H9acDSmr6b1wkI3hpF/DfszuP5v48HgmbbVvZ/Ri7Fji6/ZkyOx9NgYPR7ocrC18ujYOZsX+s86rwpjBZ60P31/8Hgbh+q2rBZDaa/MX415EJr2mjcurzecnpdCyFG0ul0qY/kQbcnX2rYrKq83rrVvBu+tm2Ww3SqS81uuFImMLCG0uEAgqh+v+ieLl5cW4sWMYP+EDFi76iTV//EmHDu2pX68ePj5qALKzczh+4gTbtv1DRkYGMpmMcWPH4OXlVWEXIBDYoddbX3rOmGE1sDRsCC1aVHdUgqv4qfzK1fvSV+Vb6TFVNMUfRGRSCQ2DvYpE5LG0PExmS9H2sAAPfD2t5c6TTqW77FUPMC/pDC1Cfajh5caixSv46u0XMGRnuTzHo1EHAnq/RNPwINwUUo6mWjMAomupeaStF2M2D0NvcrzoL7GoeLXNXFLSfEi+mMFdYWVbPC8kXZOO2k1d9HlP6h7yDTlFZR5lUgkDY0N5Zu4uF6Nco2QP1EKhXxxHpSQHxYbSoX4gBUZzmZzi5XkJAjdxpsTtzI8/Qps2kJZm/VyzJixZArGx1RuXQHATERQUVN0hCAS3JgUFMGkSfPCB9dmiJD4+1v1PPw1lbNF7syK0eR5A0bYa3m74epRPmz8SU5vjl/L5bN1/PNC6DtG11DZVXpwRXUttp827Bebx+tD3nZ4jUwfxzlfz+HTXQr7c+zIWzGW5BUUU1+YldTlQZdo8ITaUro2DMJiENhcIBHcOQpsLBILSUKvk5dKSZa20USkUFMAXX1jfu9xzj+Njxo+Ht96CwMCiTcW1uVIupXtUEAGeStLz9fyVfAmT2WKjzYN93Fl7OK1Il/uoFMx9qjkfbn2Tmf/aV2dZfmw5Db5swOx+s9k0ZDMXc9NJ12Tir/LDQ65mYdJF+i/bZdeux9NNXqTLC7cldo4kLjKARTtTaFbbh8a17mN46+HM3D3z2oQmYAlk5mRCAlCa36hYYa5CbZ7YOpEOdTuQrsmqdG3uqP1SebW5Yc0aPvn9M7vtr/d8gXX17ddlhTYXCAQVzQ399mvevBkTP5zA5198yfnz51m58ne7YywW64vQ2rVr8+KLo2hQv/6NTCkQuCYtDR56CLZssX7Waq1tK3btgho1qjc2AQBKqZIx7caRsKT03pdj2o9DKXWrgqgqFrVKTmyEP23C/RkYG4pObyJDo8ffQ4lKKeP4pTwah3hjMJnJ0RrxdlfwRFwYw77fWabxv996irj8f/jgzTfA4npB27fTE6jbPYJEIiGxcyRuMimrRnXCTSFFJoWe83pyRXPJ8ckWCUHGcYzs0IOhc3YSHuhBgCq4XPciwCOAnALbB7IMbWZRmceGwV7o9KZyZWI6EuElKSwlWbyX6IPfbKVj/UDeuC+qVLf4bZUpcbtSpw789ht07WrNnF+8GGrVqu6oBAKBQHCrs2ULPPMMHDnieP+jj8Lnn0NISJWGVVnc0drcU4mvSoFcJiG/wEhGvgFPNxlqVdm1uUwqIchbxWu/HcRktrAgKYUh7cMZ++v+Us9N7ByJp5uc30a2R+2uwKDLp0uHWPSODFWAUuVB5BPjMQXs5ot1L0EZK7YUp6Q2L67LoWq1+cyNJxjaIQKFrPRF9CrT5ibTtSqAHh7XN4ZAIBAIBALBdaKUSUmMj2TUwr2lHlueShsVitlsXY979VVrReXGjWH/flA40F/e3tY/xVCr5NzdOIgn29elVZgXWbos0rUZBKiCmfRQI6QoyNGZSM+zanOFTMqcraeLzk+Iq8P6M6sdGlsKydRl8sDPD7DggUXoctuw6oCGvIIcjqbm2lQSLOTpjpG4KaRsefVu0nJ0qN0VuCmk/J2chkxmYUCbGuhNWs7lXmJc+3G0q9OOL5K+YO+5vfAbkAs8AZRPslLDswZj2o2hSVATes7vyQ/9f6l0bV68/dJ1afPdu6n99BNIzbZVbiZ3HswvzXo4PEWsmwsEgormhq2dDRo04OuvprJ797/s2r2b06dOk5Nr/cGp9vYmPCKc1q1a0bp1K9EvVFC57N4N998P587Zbk9JgWXLrJmVgmpHKVfQv3E/EluPYIaL3pcjWo+kX6O+KOU3h/gxmszor5pRsrV6fFRK1Co5SpnUTvApZVKmJ7Tmr+Q0hv+wy8ZtX+j6VillpF7R4amUYzKbMZrNZRKqZkMB//7wOb/v/svlcRKlisA+Y/BoYHVLJ8SG0jrMnyl/HmXMvY0YMnsH2y9/T6Ziq9Mx/IzDaFnjbjR6axnw45fzmPRQo3Jl96rkKg5eOmizvZY6gGC11ZTj5SYnQ1O+TExnPVCLU9NXxRePtUTtLmf21lNFvURPp6cQGxlAryYhLoX6LZUpcSfToQP8+SfExYG7yAAQCAQCwQ1y6RJ0727NQixJWBhMmwa9elV9XJWI0OZqhneOpEujIIwmC1KphNNX8vH1UJRJmzcM9iK/4FrLnHnbz7D8+Q4MbBvqsurLoKva3GA08dHqZHJ0Rvb++CFp5844PkECz340Er9mGYxd93oZ7pA9LUNa4qHwsNHmxXU5WLV58SzWsnAj2jwswJPeTV3rcqhCbT51Krz8srUy0yefwIABINaxBAKBQCAQVBFymZTuUcEkxIUyb7tzLZkQF0a3qKAyVdmoMCwWWLkS3nkH9u69tv3IEUzfTKNg5LNl1uZfPB7F8qMrePH7yXZtg8a0G0f/Rv2wmBXIZBKytIYirS2TSng8tiZ9FpVuzAeY8s8k/hi0gfErjzg0tYB1zbxthD/PztvN54+1KNLmBQYTs4c2Z+WxxXy+42PMFjM/PfQTMbNiGNFmBD/d/xOJgxPZkL0BBgPlzAFoGdKSEK8Q5u6dy9BlQ8nUZVaJNq/lq+K9vtF4ucsJ8FSWT5sfOgT33os0L89m85zWffkm7mGHp4h1c4FAUBlU2E+V1q2tBhaBoFpYsACeegp0OtvtKhV89x08/nj1xCVwiJfSk4/v+ZjOYfFM2Wbf+3Js+1fp16gvXkrPou32C9gKvN0VGExmLufq8HZ3LprLiqNFch+VHAvwV3IaMzaetDeqxEfSPSrYphKI3mTmk7VHHT6EHLqQw6hFexkUG8rwzpEkfJvEu33voo5/6ZmBxpzLXF7yAfrU4y6Pk/vVJOjBd1AE1i1yYPe4K4S3lh4gvmENvt96mj2pu8l0+8HpGE82G8aELlMxmSxoDCbrdRnN/Hsmr8zZvaNiRzFj9wzMxarLtAxpiYfch9d7B/JS94boDCb8PMtna3fWA7WQ6Fpq5FIJw77fwdAOEegMZpsHgekbT5T6AHhLZErcKWzebO3T6yx7tkuXKg1HIBAIBLcxQUHw5pvWBdtCZDJ48UX43//A09Ppqbcy5dXmDo0l7nIkEsjRGcnSuF7QLitVpc1HX9XmI+Ij2fzfZfo0q8W5TG3Z7l0Jo3a21sCTs3cwd1hbWoX5MmfraYdmmphwf/afy2L/uWySTmVi/m8zqbv/cD7RPfB55iewsUxhOeSVdq/gLncn0i+S4xnH7XR5pkZPsNodubR8Zo4b0eazt56iZ5PSqyBViTY/edL6/x+sCTIPPwzDh1sruQgEAoFAIBBUER5KOW/0jiI2IoDpG0/YackR8fXoFhVUpHcrXZtbLLB2LZa33kayc4fDQ4z/+x+DDHex58q1JAFn2lxn0jJ27ViHxvq9qXtJWDKQEa1HMqnHZP49lU+Ij6pof8NgL/INOexL21em0Pek7kFnymHGE635/K//7O5l8TXzAa3rMHfbGZJOZeKjUrD8hTY2rY9kEhkeCg/CfMOY+PdEJiZOhHzgSeA68u1eaf8KOqOOnw//TKYus0q0eXQtNVIJvL/yEDk6I091LIc2P37cmgiSnm6zeWXjTrzf7RmnhnCxbi4QCCoDYZkT3NqYTPDGGzB5sv2+unVh6VJruwrBTYeX0pOHogbQr1FfsrSZZOiy8Hf3xVfli1LqZpMVqtEbnS5gD2kfTtsIfx6b+Q+ebnKHorksOJrDR6Vg9ehOfL3+OPOTnBhVFu4lIS6MN3o3xkMpx2gy81dymkt3PcD8pBQ6NQhk6XMd+DclCze5a5GnO3eIy0smYtZkuTwuvlt3Pv1mNmalJ34eStwUUhYkpZCRV0DDIG9iwv15b+UuriingMTkcAwfWWM27ujLMym7iK6l5oW7GxTte2vJYX4f3bfU7N7E1ol0Cu3Ey3+8bLN9dOwY5mw5x/SNp2gU4o3aXc4Xj7UqVyamm0Jq0wO1JEM7hDN322kOXci16zUK1q9bjtbo8nvkps6UuFOwWOCzz2DsWGsLiPnzReasQCAQCCqf116ztrrbuxfatIGZM6Fly+qOqtIpqzZ3pcuHdoggJtyPxB93czpd43RBuyxUtTbfevwKr9zTCHeFjNcXH+Cl7g3LFGdegRF/D1uj9ul0Df2+2sqg2FBmDm6DTm8iU6Mv0uZuMilL9pzn3iYhvPrbAQyZF0lf85XzSZoAcWUKxymJrROJqxPHR1s+Ys2gNcTMirHT5V5ucjR6IzMHx1SZNj90IYdsrQHPUiq/VLo2t1is7ci0JUxNCQnlG0cgEAgEAoGgAvBQyunVJIRuUUHkaI3k6Ayo3RV2BpVK1eZmM6xYYa1o988/OFuROx3VkhdiBnPgim31S0faXG80sOzIcpdrygDTd08jPrwz7Wvdh0wiQyaVYDJb8HKTk67JKD32YpzPvsLlXD+Hurzkmvn4lcmA49ZHJouJGbtnMLzJcJ796FnQAEMAlcNpXZLYOpG42lWvzYd2COeHf6wGHqDs2jwlBbp1g9RUm80nmsXyco+XsUgca2+xbi4QCCoLYW4R3LpkZsLAgbBmjf2+Tp3g11+t2Ze3IeUpw30zo5QrUKLAS+lJHZ86Do/R6I18uCrZaabl2F/3Myg2lLnD2tLvq612orksOJsjIS6MrcevOFw8L8687WeIi/CnZ5MQjGYzfxxMKxLdzvBRKWhWx5fP1h5j4c6zbBrX1alQzd27hoy108HsPCMSQN32Qc60fpI3V5/Cy01OXoGRo6m5RNVU80znSPq1qMWTs3dw2vglRvlFh2NILCo881/GYlGQfDGXY2l5vN47qii2s5laRv54kGlPTKFzaDxT/rHP7h0dO5pOoZ3oOb8nmbrMon2JrUcSX7cn/ZftwmyhqPfnvKQzjO7WgFmbT5JXYORYWp7Te/d0xwgWJKU4LSU5KDbU5mHEWa/RHJ2BEB/XtvryZkoIKhCtFhIT4ccfrZ8XLrS+WBw7tnrjEggEAsHtg8Xi2DSpUMD338PatfDSS9bKLaVwp2jz0nT5mF/22ehyRwvaZaGqtbmPSsEPw2L5+I+jLNiRgkwqsdG/rjiWloenm33LnGytgW82nGD6xhNFi9N5BUbkUgk/PBXLvU1CeHL2DrLyNKSvmEKBJt/xBL7A/eD0bcJV3o1/lwYBDfh428cutfnxjOM0C27G7H6zaRHU2U6XAyzckVKl2jxHZ6BWGd4KVKo2//Zb+Ptv223PPWddVxAIBAKBQHDLcLvocrCae+UyKR5KucM1zErT5nq9NcFsyhRITnYa35XQehx7biwD02u7TEYrrs31Zj1Ttk0q/eKByVsns/7JXuQUZLP5tfbM/+cCO05lEeDhX6bzC/FT+fHj9jO8tfSgjS53tGaerTW4bH30zeZvqLe6HuRhNba4KMQeVycOrUFrU2WmsrX50A7hTrV5SV0OZdTmFy9ajS0pJb7P2ren5orf+eSsRqybCwSCKkf8ZBHcmiQnQ//+8N9/9vtGjoTPPwdl+Vqd3Cq4cmRfb2bkzUp5qqC0DPVlUGwo32w4wbztZ4iN8KdXk9L7txtNZtY5mEMmlTAwNpRn5u5yeb6PSkFCXBhtwv25kldApsbAyz0a8nrvxizccZZ528847I1ZuDg/7+ri/IKkFIa0D2fsr/uLjrGYDGSsm0XenlUuY5DIlfj3fAGv6K5YsBXAAInx9dh3NovRi/ZyXv8H+cr1Tsd6tP7/OHwizCZmqUTC0A4RjPnFKsa3nkjnvi92MP7+eDYN6UOWLotMbRb+Hr54KDyYt38eMbNiiowtLUJaMK79q3Sqey8Js/YWjW29d3V4PDYYM3mM6+OJ2s0XD7maRTsuMm/7OZs4EuLCuCc6BJk0ze4FQnQtNYmdI2kV5kfCt0k25znqNap2V1AWypopIahAzp+HBx6AnTttt7/6KnTtas2iFwgEAoHgesnLs1ZnCQyE995zfEzz5tY/ZeBO0ebXq8uBm16bJ8SFkXQqnQU7rHOazBaH2twRJrOFS7lanuoYwcs/25dHL7k4PfXxlqRla3l0plWvZm2eT8HFY44HlwJPU+qqTYDxGYa3eolfk+ey7LFlaAwa0jXpBHgEoJKrmLF7Bi//8XKRNp+aNJVNQzfx4qLDNveiurS5r6psuhwqSZsfO2Y1shWnbl2YOLH8YwkEAoFAIKg27hRdDpWkzTMy4Lvv4IsvrGtzTjjtW5MvOg1izJx3mTBvD0icm8GLa/MsjR6tObNcLYUu5l3k0V8fRSqRMrrtGKbE9MJdLqNFSAsbQ7czrK1+1BxNzbXT5WBdMy+uzQFahfpisOTZx6mFrB+z2K3bDUMBL+fz9mvYjy7hXRhw14Aq1ebdGgfzy67dNrFE11LzTKdIukUFMeLH3XbPQi61eXo69OhhbUlUnFatYNUqPHx86OWjFuvmAoGgyrk9fpsL7ixWrrRWbMktUV5NLoevvrJm+9+mlObIvp7MyOuhqlzwepOZ6RtOlunYkmX0pm884bDsnX3sCmLCA3iua32bxe6GwV7o9CYOX3Qu0MMDPJg7rC07TmXw1NydDlsmLX++g7VaSrqmaJ+jxfl528+w/PkODGwbyoIdKZjys7i87CMKzh50ed3eAcF49nkdt5D6DvcPig2la6Ma/H3kEn7qKxzKmeZ0rNrKexnQ+DE+6uVfFHPDYC+y8vXEhPsVxQZwNlPLkDn/IpfCPdEhBHgqaV7HQpfGKgY0Hsy9kf3J0Gbir/LDQ67GYpbz9pLkovsQHuDB98OasyFlNX0WDbLPMG07hmXP92LI7H1X203VIy7Cn8dmbqdTg0CHpSQPns/GYrGQqbEV6SV7jUbXUqNWlf3/R2mZEoIKZNs2GDDArswlAP/7n2gzJxAIBIIbY8sWGDwYTp2yPjv0739DLYfuJG1+I7ocbl5t7sw0U1KbO2NQbCh+HkrahLuV6dhWob5YLBbe7hPF5O9+4UzSr44PlgDP4HLRHMBf/xwxQY+QW5DF6DWjeemPl2gS1AS1m5qcghwOXjqI2WK2OWdP6h4ytFn4qq4lg1SXNo+upS6z6byQCtXmBQXw+OOQX6JyzsyZ4O19Y2MLBAKBQCCoMm4WXQ63sDY/cgTGjXM6znmfYL5o9yiLm9xNgzp+6EyUS5t7KGW81Kt8PXzSNemo3dRsSdnC0OUJJLYeyftdJjK23RsMWvJIqeePjh3DwqSLTiuZFNfmc7aeRiqRMGtwG1LzTtMxtCM5BTkcunQIU54JfgR0WI0tLmRii8C7+eDuj2k6vSFj1o6pMm3eLtKfX3ef5dNHWzhsv3TwfDYfPtiUfl9ttTG4uNTmMhmo1bYXGB0Nf/wBPj6AWDcXCATVgzC3CG4tJk+2ZlpaSiiSoCD47Tfo2LF64qoCyurILk9m5PVQlS74HK3RpUguTskyeocu5JCjNdrE4ir2kovdXm5yMjR6p/MVli+fvvGEw0VsRy2TXC3OZ2sNPDl7B3OHtcVPe453XniFgsw0l9esDm/CulXLOK1RMGfraQe9VcOJCfdn4Kwk2tVTk6X6BFOO1uFYcnNNpNlPMeZqzPOfjuOtpQcI8FJyJV/P2F/2MXdYW1qF+drMZTTDmXQNdzcOIibcn4embSclQ1OszGMOR1NzWftSPKk5uqJ7N/ep5ny49U2b3qWF7E3dy9DlCYxsM5IVoyYjxZ1Tl/Po8dkmsrUGDpzPtivxXujA//jhZjbZCNG11LiX6DU6Ir4eSuEcv/n47jtr5S1DiUpHXl4wb571BaRAIBAIBNeDXg/vvmvtF1/4HGE0wtChsGPHdVV8vNO0+Y3o8sJtN6M2d2aaKa7NS+rfwhgLtfYT3+0AKNOxCd8mkakx0L+RJ2cWT3F8MVJgEFDT6eUilUj5pPt06rj1ZMepDNI1GQCYLWb2p7muNgOQpcsq+ndlavNPHm7Gi90bsOrAxaK2TG7FtPmI+Hoo5dWoy996C/7913bb8OHQs2f1xCMQCAQCgaDc3Cy6HG5xbd46BkOjaHyOHrI5/3TNCNJGjCZk+JPs+HEPxuvU5lE1vcvdUijAI4CcgmvXOWP3NDrU7Uiven0Z3mqEQ+1ayMg2I+nToA/PJtlejyNtPig2lB+HxqByk6Ix6PB0UzGx20QCPQLJy8ij1729uKK7Ym1F5OM83hY1utA18CNydHlA1Wrz7p9atfnE1Ucc6nKwfg3Kpc19feHPP6FfP1i/HurXt7bvDQws9ZoEAoGgMhHmFsGthVptb2xp1QqWLrWWDr6NKY8j21lm5I1S1S74bK1zkeyIkmX0cnSGIsdwabGXXOzOKzDi7+H8RUfJ8uXOKCz/mBAXyt9HLuHlJicswMPhA8DpdA2dR3zAxRWfYSjQuRzXq3lPfHsk8tTPx4viNprMnM/S4qu65soevzKZbK2B4wXTOJL/r+PBLDIC9eOQXm0UOj8phdgIfz54oCkmswW5VMLpdA39vtrKoNhQZg5ugxRIzdGhdlfYzQW2ZR6ja6nxdJMXCeWEuDqsP7Pa5QMIwLRd0+gU2pn+jR5k3G8HbFzljkpJgn02QmLnSFRKGSO7WLN/+zavVSn/NwQ3gMEAL79srbxVknr1YNkya1aAQCAQCATXw8GDkJAA+xyUvz52zPpiOy6u3MPeadr8RnU53JzavE2YH1qDCZlUYte7vqT+1elNZGn11PJVIZdKmbP1lI3+LX6s0WRGZzAjl0lwk9tqZYvFzLcfvMelNAeV6qTAI0A9l5fB1Htn8u0fdXm8bT6v3NMIveWK6xNK4O/uy+EL1u/fytDmhWXU2zfwxmjJo1EdFQEe/tTw9CMjz0BtXxXxjYKqV5evXg0ff2y7LSoKPvuseuIRCAQCgUBwXdwMuhxuAW2ulNHm3CG8CjRsqBdjr81XH8EQcTeTrppbkuo2YVrsADZEtgGNhEH/nL0hbX4sLQ9PhbpcLYVUchUHL9lWNf9s+8fc37gfk7pPpkt4PJO3TbKrbPJi7Fi6hPZk8e7LfPpoC4wmM1q9CYVcaqfNAS5maVG6mVh8ZAkf/zP52ng54DbfjQJtgbVii6/zeL1owUPhn/F0x8bVqs0drZnfkDb38oLff4fRo+HNN6GmCwe+QCAQVBHC3CK4tRgxAvbuhRkzrJ8HDoRZs8DDo1rDqgrK68gu6b6+UarDBe+jKl8Wbcn2M4Vl9K6nD+mMTSdxV8rs+seD8/Lljq9BQX6BkZFd6vNgyzpkaPQEeikJ9HKzKbduMZvI2vwjOdudlCa/ikQqw697It4tewNQx09FoxBvFFIJnm4KFu5I4a/kSyRfzClyZUtV+ziSv8DpmL7GJ3GzNLDZNmPTSe5uHEyWVo+74tp9+GbDCaZvPME7faMJ81fxxpIDNg5wRyR2juRKng6zxXrvHo+tSZ9FA0u9dwBTtk2mb6O+HEsrfzZCq1A/mtf1ZeS8f3m4TV02jOmCu0KK6jbpr3tbcOUKPPwwbNhgv69HD1i0CPzLl9UhEAgEAgEAZrO1X/zrr1tbj5SkY0eYM8eafXYd3Gna/EZ1Odyc2jxLa8DfU8nGsV1YuOOsTSsksFZwKdS/hRmQUx9rid5oJrKGJ3X8VEXHZ2sNbDx2mUYh3vSICkImlfDp2v+YvfUUBtM1sWzY9zsXD/5jH5wMeAho7PoaXmn3Cp6mLmRqjvDNhhPM2nySpDc7letFgbebD0dScytFmxcvo957oX0Z9THtxrH6xb7IJe64V5cuP3HCupZQHKUSFi68I9YWBAKBQCC4nahuXQ43tzYPzzhP5PTP+OK7OdS6cp4T/rXZENnGoTZ3vyueVuePsKj5veypbStKb1Sbm8wWFu1IZVz7cQxcXLr2HBU7ihm7Zzhs45Oal8GPm7OJqNGGTUM2k6XL4oomE2+lD25SbxYmXaTfsl3U8VMRpHbnnruCWbLnHFuOp7P2cJqNNq/rp2L8gw0Z++cYpu8uZijJAuZCgfGqscXPeawR3m0wXnqNWZvO8/3WC9WmzWVSCQ2DvYqqthxLy6Oun+rGtblKZW3bKRAIBDcJ4g2f4NZj6lRrD8j77oMxY0Aiqe6IqoTyOrKLu69vhMI+oWYLVe6CV6vkDkWyI6JrqW3K6EXXUqNWWX/EXW8f0gVJKQxpH85riw/YCEOpBIfly0tSvLfoozP+cVCePILlz3dg0Nd/8+/c8WhPul6Q91T7MXfBT7SIaUeWVk+Qtzv+nkqkQJbOwMVsHQ+3qcvDbeoWLc4byCDV/TNrT1AHuJtaojbeb7f90IUcLufp+HTtMZ7uGEFi50imbTxRdA+m/vUfS55rT8u6fg4rqBQyKDaUmAh/LBYY2DaUPWczyTfksC/NQfa0A/ak7iFTm8k90SGsOuAgu9UBOToDr/ZsRESgV1Ep+6RTGUXZEYKbhH37rK2Gzpyx3/fKK/DRRyAXMkUgEAgE10FKCgwZYi2dXBKFAiZMsP6ukcmue4rq0OaFujxHa8TTTVal2vxGdHnhtptdm5dshVScwgzI6FpqTBYLfb/cwuNtbSu6lNTm2RoDT7QL44l2YUXaXHPhP87/Pds+OBnWii2NXN+P+xrcx7vx77H+cDZRNa+VG1+w/UKZXxSM6zAOs1nBYzEVr83LUkY9YclARrQeyZR7plAtS1IaDTz4IGRl2W6fMgWaN6/6eAQCgUAgENwQ1b1mfjNq84iM8/Q+soX7jm7hrkunbPbVyzjPg4ZzDrW5TuHOq71HO533RrV50slMvut8H8NbD2fmbudmicTWiXQK7cTLf7zscP+V/AySUzXsTsmix121GPNzCv1b1KZbVBB5OiPdokJ4vG14kTbP1hpoVseP2IhA3ugdVaTNAb4f1prlR5bbGlsygLmACauxxUXeXdtaseguvk6tmn7Vps3vb1mbYLWSx2Nrkm/IIV2TQYCHP14KHxRyCW9veI2Zu0vR5h3eA4MneHqWaW6BQCCoTsRbI8Gth1IJ69bd0GL0rUh5syUL3dc3QmGf0D8PpfFS94YuF4xLOoMrwgWvlElJjI9k1MK9pR47tEM4C5JSiiqIjIivh1ImxWgyk6M1XFcf0uV7z7P0uQ4kvdGNbI2BDI0efw8lXu5ydAYTPiqFTWZncRz1Fi05z5hf9tGzjomLP45Be/I/l3HVioxC1nMc3x13o7MkheHx9ZBJpaw+mMqcraccLs6vfKE9CcvuJznFcSlEqcWXQP3LSHD8MFVgMPNOn7s4lpZHt6hgmtf15VJuAf4e1rZHqw9cZGSXSFqF+TJn62m7GJ7pFEnLUF8en7kdgLnD2nLqSj6Zugsur7UkmbosAjzL/v0fonZn79ksRi/aZ/P1qYreuoIy8ssv1peOGtsXR7i5wbffWttHCAQCgUBQXiwWmD8fnnsOchxovyZNYN68CnmBXdXavFCXz9h4ErPFwlePt6pSbX4juhxuDW1eshWSo7EKry1TY2DGppNsOHqJzg0Cy6TNfxnWgl53P4/RUGJcOfAoYFtI0QapRMrrHV7n1Y6vkZ4L3e4KpnnoNW3urpSilNdkROsRtgvzJRjZZiQdat/L4O928tXAVhWuzctaRn367ml0CuvMQ1EDUMpv/Lm1zFgs8MwzsH+/7faHH4YXXqi6OAQCgUAgEFQY1blmXhZtXlKXH0nNrXBtLjWbaJJ2gviTu+l9dCtRl0+7PH/U2a0oZcOrRZufzDzHuPbjaFenHV8kfWFXSWR07Gg6hXai5/yeZOoyHcbhr/IjryCHpzpGsCAphV1nssjRGVm29zyNQ7x5/u4GKOUyl9p81aiOWABfTwuTf5t0bfArWI0tFuBJIMD5/Whdsw1LH12JUupNVrF7UNXa/NWe9fj9+HL6/mRbmWVqr6n4ufs5NLYU54d/pvHu+xvx8AxCunKlMLgIBIKbHmFuEdx8FBTAqFEwYADcc4/jY+4wYwuUP1uy0H19vRTvExoT7keGxrEL3tqzMYyBsaHo9KYiEefpJsNoMt+QiUAuk9I9KpiEuFCXZR0HxYYSE+7P+JXJgLWvZ7eoIPQmM/+mZOKhKN+9yNToqV/DizH3NmL9kUvM2WZv3BjeOdJpZmdhDCV7i5ZEe2InMz+bgkVvf35x+j4wgPin3uGxDg3QGUzojWYKDGbkMjh1JZ9zmVqb4w9dyGHC78k80Hk3m1LWOR03UP8SMhc1FT2UMkxmuJRbwCNOslstSJBIYP7TsWj0JlKzdfioFAT7uPPrrrM2Lwb6fbWV//WLpk3NGi6vtyT+7r54KJ1XhylOdC01ZouF8SsPO2yVVJm9dQVlRK+Hd96xN7bUrg1LlkBMTPXEJRAIBIJbm6wsSEyEn3+23yeRWCu1jB8P7jeepQlVq82L63KgWrT59epyuHW0OdiWW/9mwwmH1/bFX//xXNf61ntcDm1+1/H5nDxewtCuAB4D6jmPyVPhxQ/9fyY+rAuaAgl7zqbz7eY9dvfg+a71mdRjCp1C45nyzySHJcc71rmXJ77dy+l0TYVr8/KWUf9422T6NeqLkio0t6xcCQtKtGu96y6YPfuOqQgrEAgEAsHtRnWumYNzbe5Ml7sppDcsO+RSCT3kOUzN+gfF+vW0P7MPn4L8Mp17qXYEtTrGVIs2zysw4qn0ImZWC0a0GcGyx5ahMWgwmAy4yd1wk7kxY/cMXv7jZafGlpYhLfGQq2kd6kdshD8Xs3RsGtcVnd5EpkZPbV8VKqWcLf9dYfzKw3YGm0Jtvvblzqw7nEbHxrJrlVLSgB+uHvgk4EImNw9uyYL7V7D9RAHfbk52qM0n95hC57B4Jm+rRG2u8mX6v1/wzoa3bbbLJDL6N+pPv4X9XJ7voYcVCyDk9GHgMPTtCytWCIOLQCC4qRHmFsHNRWqqtUTwP/9YM/t37ID69as7qpuC8mRLFmZGXi8l+4TmFRjx97B3wRcv7T38h112Ii4xPpLuUcE35ET3UMp5o3cUsREBTN94wkFrn3Biwv15cvYO6vipmHB/E7pFBSEBPliVzO4zmXz1eKtyzRng5cYb90Xx5brjTjM7Ry/a6zSzs7C36Ie/JxMT7lfU49J01W1hsVjISfqVrI0/YLWBO0YikfDq2+/x7OhX2H7S8T12Vj49vkkOEza/43RsteEBVObWTvfHRvjj7+nGh6uSXWa3JsSG8lrvKIwmMxazBQ+lDE83GblaA++tOGxzTrbWwLjf9rP77fL1HfVV+dK4Ztme+oZ2CGd+iUzhknFXRm9dQTlQKmHpUmjb9lpWfbt2sHgxhIRUa2gCgUAguEXZtctqjE9xYFwIDYW5c6FLlwqdsqq0eUldDtWnzUvT5cM6RNAm3I8nZ+8gW2sgupaaEfH1blib+3kqeaN3FF/+XTnavCTFy62bLVfvXedImtf15Y3FB5j3dKzTe+xMm0drD7Bw3g+2EymAgUCE82tXSLxo6vYxP2/15Z56Cj5xoc1Hzv+XZzpG8NI9D9KvcV8ytZlk6rLwd/fFx92XaetT6LdkV9G9qWht3jDYq9xl1LO0mXgpq3Dh/L774MMP4c03rVVc1GqrudrLq+piEAgEAoFAUKFU55o5ONbmpeny4Z0j6XHXDayZWyyounSm3xXH1bpLctnDlx0tu6Ae8RStH74Xk0TCh9WgzY+l5aGSeRPmG8bELROZtHUSTYKa8FzMcwR5BjHg5wGYLWaX878YO4ZsjYWRXephAUJ83Muly8FqfN949DKHL+ZwV9jVjReAHwEpMBgIch5DiKoxyx9dw/T1aaVo80hG93iAPg37kKXLqhRt7qHw4L2N79rtiw6KRmPQuNTmqqvGlrtPF9u4fj08+ST8+mup8wsEAkF1Id7uCW4edu6EBx6A8+etnzMzoX9/2L4dvL2rN7abgLJmSxZmRt5IZYrivTbBKjzdlTIbF3xZSnuPWriXhLgw3ujd+IYNLr2ahNAtKogcrZEcnQG1uwJvdzkGk5kreQUsGt4OtUpe9ICy6uBF5m1PQSaV2MXuiuhaaoK83ViX7FicFsdRZqePSsFL3Rvg4y7n5R4NbVz5C3ec5YdNRzm5+GM0Rza7HFutVpP47ueMfuoxlu+9wJK95zmWlmdzjLPy6RKpluVnX8VkMTocW2muj69xsMv537wvirWHU0u9B/OSUmgR6supy/n8ffQSXm5ywgI8eDQm1OHxeqOZf8/kMabdOBKWlJ7VOabdOCwWBW3D/RnYNtRlPI4yhR1RUb11BTdAo0bWrNm+fWHYMPj6a2tLIoFAIBAIrgdfX8jIsN8+eDBMnQo+PhU+ZVVp85K6HKpXmzvT5Wp3ORIJ5OiMzBzcxrqtgrR5iNqdvypJm8/bfsZhNqcEWPF8RzzcZAR5uyOVwNR1//H5oy1Yuuc8i8uhzc05aayc975tsHJKNbZILV4E6CZwUVOHGY+WTZvP2nKKRjW9OX05n5Pp+QR4KvH1yKNzQw++2XDK7viK1OZebnLSNQ7+H7ogQ5dFHZ865TrnhpBK4fXXoXVrGDgQvvsOGjasuvkFAoFAIBBUONW5Zg722rwsunz0ojLo8vx8uHIFwsLs90ml0K0b/PST01gtwcFo+95Peq9+KLrE09XLrdq1uZebnNQsI+Paj2Pg4oGYLWb2p+3ntb9eY+czO3m61dPM3D3T6Zgj24zkwbv6I5e4oTOY2XbiCov/PYvZYkEmlRSZ1121HC00vj8zdxfhgR74q4LhHFZjixJrxZZA59fVKKAxc/uuZvcpfRm0+Uka1fSqNG0+rsM45u2f59AQpHZTc0Xj3Pyk0sPKksYWAH9/qxFcIBAIbmJEXwbBzcGPP0KnTteMLYWcPg1791ZHRDclhdmSXz7ekuhaapt90bXUfPl4yxs2kgDkaI02vTZNZgsLklIY0j68aFtZS3vP236GdcmXMJpcu65LQy6T4qGUE+LjTsNgb0J83PF0k+ProaR+kPWzh1KOXCbFYDaz+dgVompaTVElY3dFYudIpBKYvvFk6QdjzewcFBeGVGJ15S9/vgNe7goenbmdbp9u5OHp/9Dt040k/rgbL0MmlhXvlGpsqVe/ARu3bOPtZxMwmi20DPPjq8dbsXFsF57rWh8flbV0t0wqIaqmN8fScjmWlktCXCgWLBR4zeBMtuP4PRSeDG38GRIX5b8Hx4VRr4YXszbbi2xn92BgXBhHU3PZeTqTQxdyHGYTF/LWksP0bdiXxNYjXI47ovVI+jbqQ0augUHfbicxPpKPH27m8Hv/44ebMbxzZFGmsCsqoreuoAK47z5rpv2sWcLYIhAIBIIbo359mDbt2mc/P2sVyLlzK8XYUkhVaPOSuhyqX5s70uUebnJUSjnB6mLbKlCbz6gEbV64PzzAw26ctBwdy/aeZ+PRNMzoSNel0buVEZ3lCg/HBjF3WBs2jSuDNjcZyPl9Crm5xb6GMuBRSjG2qAku+BA3S/3r0uaPx4Wx5mAqP25P4a/kS1WizfMKjAR4+JcpxkL83X3LdXyFcc89cPKkNZlGIBAIBALBLU91rZmDvTa/bl1uNFqTfCdMsFad9PODES70Wffu9tuiomDUKNiwAcn583jMmk7dB3sT4u9ZpM2NZjPnMrT0bhqCTCoptza3WCw3pM3PZ+npXLcXI4ppz0xdJj3n92Rc+3HM6T+HFiEtbMZpEdKCnwb8ykfdpgAWLmsuciYnmaahJmY+2YxFw2PY/KqtNlfKpWRq9GTkF/C/ftEo5VJkUgn3RgeDxYIFC+uPXubQzoNIf5SCChiKS2OLu9ydJY+spElInWrX5iNbj6R3g968t+E9h/tzCnII9HB8Mc6MLSY/X/jrL2jZ0uXcAoFAUN2Iyi2C6sVohFdfhU8/td8XHm5tXdG8eVVHdVPjNFvyambkjbjPC9EZjHYlu+dtP8Py5zswsG0oP+06W+RwLgvTN564YWd8WTCazOhNZrI1Bh6JqcsIj3q4KaQs2XOe2MiAMlX+aB3mz6Vcnd2DijMOXcjBYrHwUOs6PN+1gVNX/u6kbax5fSJmTbbL8fwbx/L33yvYeaGAN2dud1hSceULHVhzMJXezWoV9WwN9FLSLjKQHZd+5deTfzkdv67kBV6/pyvdG2YwZ6t9T9TE+HrEN6zBlbyCct0DncFEoxBvki/mciwtD0835/1uz2ZqGfnjQaY9MYXOofFM+ce+7+jY9q/St2Efpm04S9dGwUW9RwfFhjJzcJuiPqp+HkqCfdz5dfdZuxL0jqiI3rqCMmKxwMKF8PDDoHBiKGpVvtKnAoFAIBA4JSEB/vgDzp6FefOgTtVUg6hsbZ6t1RNV0xsvN7nQ5mWgrNrcVTYnQICnksEdarLu1Go6fz/FTquObjuG7hG9aVJL7VKbr5jxEZtTjlybWAo8BDRwfg1Siw/BBR/QsmazW06b11Iry93iqNpQq0s/RiAQCAQCwS1DVayZl6bNE2JDy67LLRZW/Lyee9dfgQ3rre1gckpotU2boKDAcVJY9+7W9t7du0OPHtZKLrVrO51ObzSgN+vJ1GbSPkpD31bBfPRQIw6czScswLtM2rxtRADZOsMNa/PwAA9+fPpDOofFM3mbVXsezzhOzKwY3uvyHusGr0Nj0JCpzcJd5k1NdSAWi4VlR5fyyT8f22nVV9q9Qr9G99M23I+BbetwLjOfu2p7kKXLIkN7ntaRfux+uxMWi4QMbSZXCk4wNSGcnZt380C/QZg9zdaKLaXkRXx6z2eE+tS9KbR5v4Z9OZtzjkxdpuN5Lx3CQ+Fhp809C2DFQuh62vb4bA8ZijUr8RDGFoFAcAsg3vAJqo+MDHjsMVi71n5fly7WbMtAF1bZOxj5VUFemDFZURQuQLvJZYzr2diuZPeTs3cwd1hbujaugd5oKpeIy9Eab9gd7wqN3shfyWnM2HjSoSFEJoFnu9ajVZivQ1PHsA4RxEUGMPmPIyTEOSj36IKMfD1v3XcX649ecvgQkLtnFRl/zQCzyeU4QZ0eZfuvs5ix9TTzkxwvwn+9/jjxDdsRpLbvJ9q4TiYbsj5wOn4L/z4Em3ry7vJDxDesYWsS8VQSrHZn/9lMvl7/Hw+1rmNTzrE08guMeLlZv74ms4VLuTqe6hjByz877uu59UQ6932xg/H3x7N5aF8ytZlkarPwU1n7jh69qGXrfzkkxIajM1jvW7bWwDcbTjB94wkahVx7kOzWOJiwAI9SjS1w4711BWVEq4Wnn7a2Htq+3doSQiAQCASCG0Wrtf6tUjneP2OGdeFXJqu6mKhcbV7bz4OvHm/lsJ2O0OaOKU2bF8dRufTYCH/8vCyMWfsGM3dPtztnb+pehi5PILH1SN7s8AHuMplDbV4n5xBbl8y9dqIEeACIch5PoCqIr+9ZQuvazSpcmyd2jmTUor0Oj68obf5qz4Zlb3HUfhxKaSVW77NYIC3N+tJHIBAIBALBHUFl6XIomzZf8EwcZovFqS53N+hof2Y/3U7soOuJXdTKdd42BgCNxrquFh9vvy88HC5cAImk1Njz9PksO7Kcj/+ZbGeUGNNuHM3q9mVUt/p22lwmldCrSQgPta5DVE01H61OZmDsjWvz0+ka+kzdybCOLVibsBGtMYdMXSa+7j74uvtiscjYlJzNn4c1hPnB2N4BjF07hhm7ZxSNK5PIiA6KxkvpxZRtU/jn7D9M6jEZi8XC8bPrGPG9/bW+GPsiHUM78tAvD3J8+3EkP0uw+FlgMODt+joGNxvM400GMWPTSfo0q1Xt2vxoagFBvp5O5zRZTMzYPYNRbUcxbPkwANQ6WDUfOpy1PTbDHZLmjKdbq7Zluh6BQCCoboS5RVA9HDpkLQF84oT9vhdegE8+cZ7pL6gUSluAXv58B56cvYN+X23l1Z6NqONnX77bFVlaPb4ecuRSW6d8oaEmR2skW6vHR6Ust6Neozfy4apkh31Vi2dljoiP5EKWlh+faotGbyIjX08NLze83OVk5OvRG0280bsxBcayCdNCPJVyNHqTXUlGi8lAxtoZ5O1b4/J8pZs7A16cQMuufdh9NtuhsQUo6tn6+V//2S3U1/WXcMI8gQKTzuG5wR6hrHpyLhKLigtZOmuJRouFzf9dJjVbx9COoRjMWgJ8NNzdVI/KPYcNY9uxaMdF5m0/V6pxpIaXG/kFRgASYkMJ9HIj0MuNhNhQ5jm5nrOZWtYevkJEoJqdpyx0bdyAiauTWbpnH8arFTmfiAvl1Z5RNm52swWSL+YWjXMhS1eUuezqBUZF9NYVlIGzZ+GBB2D3buvnL7+EFi1g2LBqDUsgEAgEtziHDlmN8Z07w9dfOz7Go3z69GamOrR5RejywtjLqs0vZuv4ObHd1ZLhlavNnTFn62m+ezKGWZtPYjBZeOO+hiw7styhsaU4M3ZPo0tYZ3wlnRhdYmE6RJrDwYUfXdsgAfoCTZ2PV8MjiJeaL+DkBT/iIyUVqs2D1e5EBnq61MsVoc1f+mk/v4+2llGf4eL+jWg9kn6N+qKUV+Iz/3vvWX9WLF5s/bkhEAgEAoFAcJ2UVZu/seQAL3dvaHOurzaH3ke3cvfxHXQ8sw93o77M81pkMnL2HCC1UUvH2ryMxpYxf45xqM32pu4lYclARrQeyaQekwn182D+07FkavQoZaD2gOyCLDI0aegtet6/vwF6owwflaJMSYbgXJtnaw18tvYEX/x1gsYh3txVSw1kUGC8wpu97+Ld5ckYTBa2vtqZpUeWFhlb/Nz9GBkzksTWiWgMGq5orhDoEYinwhOT2chrf73GdCfXOmTZEBJbJ/KS+iWe++k5LCEWGAS4eJTyVHjy8T0fk9A0gUv52bRvrKs0bS6TSmgY7FWU0Lku2bk2V8ql7H67k8uqidN2TmPnMzsZ3no4v2yeyR/zIOaC7TEZ7jDto4cYff+oytXmAoFAUIEIc4ug6lm2zFo2PC/PdrtCAdOmwVNPVU9cdzBlXYAuLNn94/YzxEYElGsOhVTKmF/2c090MN2jgvFQyl0+GCTGRxYd5wqjycxfyWkOYy9OYVam3mjGZAaFTEp6XgEzN51k1YGLFBqtm9ZW8+NTsU5LA5YkupYaL3cZeTrbnqum/EwuL5lIwfnDLs+vU7cuPyz8hf9t1fBpXCjDvndettJZz1a1Sk5g3TlsOXbU8YkWOZHSt1ixN5M+zVQ8PXcXGRo90bXUvNS9AQ/FBLHi6HImb7N3tI9uO4Zlz/diyOx9nE7XuLgHcuoFefF0p0juiQ7hxUX/8nDruozu3pAWoY4zcod2CKddZCAHzmcRE+HPgGnb7Ob4cXsKD7ep47IKTLbWUJS53DbCn1mb7b+fhneKpEd06d9PghtkyxYYMAAuXbLd/txz0KsX1KxZPXEJBAKB4NbFYoHZs+H550Gng4MHrWW377+/uiOrNKpDm0uAtTeoy6H82tzbXQ5YKl2bu+LQhRzMFgtbXu3KvpRsImu48eTKyWU6d/K2ySx/9F6kEopi9pZb0K/5jOysYiXCewIuujFKLWoayycTUyeauPreFa7N953N5PDFHBLjI51Wy6kIbV6mMurtXqVfoz64y5xUYKoIpk+H99+3/rt7d5g1C558svLmEwgEAoFAcNtSHm0+5pd9eLrZauaIjAt8+IcTc74DzHdFY777bnY3bM0UbTC7Uk3w+Wag/NpcbzSw7Mhyl6ZjgOm7p9EptDP9Gj3AxFVHeLV3JCuPrmCKg0ov49qP4/fRPUmYtdepHi2kLNrcbIHDF3M5XCyR8fVeUWx5tSv7U7Lx8ZTw8baPAajvX581g9aw6cwm+i/qbxPb1F5T8XP3c2hsKc6MaTOQrJFAPeARwIWfI9w3nA1PbiBfn899C+9j05lNNveiIrR5m3A/ftp5ljbhPjweW5N8g7WKTYhXAP4eflzI1BMbaa/N9UYz/57Jc1k1MVOXSc/5Pfnz3vlMHLMG/wu238OZnjKSvhvP6AdG4aV0XgVGIBAIbjbEWz5B1WE2w4QJ8O679vuCg60ZVe3bV31cdzjlXYAeFBvKjE0ncVfKyrXI7KaQsurARVbuv0hCXBiv92rM538dY9bmU3bHH7qQw6iFe0mIC+ON3o1dinW9ycz0DWXPypw9JAZ3hZSJq444zFo8cD6HWZtPuTRTFGdoh3DWJV+iQfC12oUFqce5vHgCplJKSwbWb06jIe+T7laLhYmBWMBO6Bc6ttXucp6IC2PY9zvtxgkN/Zulx35yOs/7XSbwSPQ9pGsy0FuU/PFyLN9vPcfyvak0CFG6dO8Xllz/ftgE+n+1y6ETfWiHcDQFRiYNaIbOYOLMlXze7hONv6eShUkp6Iwm5gyNwWiyFPW79XaXU2AwUWAys/9cNq/+dsCpy/2D348we0gMCXGZTr9PT6dr+PGfM4zsUo+2Ef5oC9stXS0R+ldymtP7I6ggZs60vng0lPg6entb2xMJY4tAIBAIykt+Pjz7LPzwg+32p56CNm2gTp3qiasSuV5t7uOhuCFtPuru+ry99JCdHiuPLofya/O5w9pisUj4wkFlQqgYbV4WLmRp+XnXWXo1CSFLl8W+tNLnAtiTuod8Qw6NQrxJvpiLxWLBfeccdu0uptl7ALHOx1C7+TCk4bf8+59/pWjzc+kaIgK9iAj04rd/z2GxWCpVmxeWUZ85+N6iMuoZ2kz8VX74uPvy7+lcRi1M5quBLSunouLixdafG4UYDNYKgrGx0Lhxxc8nEAgEAoHgtqWs2nzJ5qPEBMhpG+Fvt2a+t1ZDrnj4EKjJdniuqWZNlgZEsTm8JdtCm3FPj1aMurs+wz/bdOPa3KxnyrZJZbrWj/+ZTL/GfXmtdyRj1451qkcHLh7IyDau9WghN6rNH2pdh0xtJvvS9uHn7scfCX8waeskZu6eaXO8TCKjf6P+9FvYz/mgZmAt8A9YWlqsVRVLkaKns07z0ZaPGNN+DAfSDtjsqyhtXr+GF98NbcqKoyvo+9MgOzPRK3Fj6VS3p8P43lpymFUvuq6amHfmOB49euGfkmGz3RRUA8Wa3wn0C0dKJbYKFQgEgkpAmFsEVYPZDI8+Cr/+ar8vJsa6AHUbLk7fCpR3AXrm4DZs/u8yWRq9y96QxRnaIZwFSSlF2Yzztp8hJswPRSmLmfO2nyE2wp9eTUKcLnzmaMuXlak3mjl8Idtl+5p528+w/PkODIoNddoiCGBQbCjt6wXy6dqjRdmy+Yc3kL56KpZSSkwOHPI0V5o8xtHLOsb8up83ezemU4MaRft9VAoS4sIYGBuKTm9CazA57NlaINvD6rMfO52nVUgbfjuykHc2vla0rdBZvuS5fvz+34pS3fszdk+jXZ2ODIptwTcbbM1Ig2JDiQn3x2ix8NfhNJvvh8LSnG0j/HljyX7e7dOEd5cdpFkdX4a0D+fpH3ZxNDWX0tqTJp3KQKM38UbvKGIjApi+8YTDTNOYcKuLPSVDQ6MQ76ISjoVz9IyuKSq3VAZ6Pbz4orXyVkkaNLBW64qKqvKwBAKBQHCLc/gwPPyw9e+S1KkDWm3Vx1QFlNu4/WQMUTXVeCplJMZHMmrh3lLPc6TNW9T1YVBsKN9scNA2lrLpcii/NtcZTBw4l1Vp2rys+HkoOXA+m1NX8hnXt3xZixnaTLyuZujm71nJrrWLr+2MBzq4Pj/YM4iWEQW8fU/MbaXNPZRK7v9yBw1D1AR4KknPT+PPQ9daHOVojRWvzTduhIEDrRWfivP118LYIhAIBAKBoNw40+Zyk5FGV87Q/vQ+Op3eQ+zZg8xOeZbB333ElfwCmzVzi0TK+sgYHj74FwAmiZRdtaNYV78tbUYOZK93bb4p1rKnIrV5oTGkLOxJ3UOWLpN9qQdK1aPTdk2jfZ1ODIptbqdHC6kIbe4mlzIgzioeR8aMZOPpjXbGFoDooGg0Bo3zazUAS4DDQCegW9ljmb57OrF1YhnRZgQTt0y0238j2vyZTpF0bOjN2D/HOm2l9MTSQU4NNGcztYz44SAznvyYzmHxTNlmXzXx97UWglNs70t2QBCfjZ3GvF8uYTRfYvvr3cSauUAguKUQP7EEVYNUCs2b25tbnnjCmu3v7l49cd0mGE1m9CYzOVoj2Vq94x6cTijvArRUAj+PaIdCKiUi0JOEuFCX7vVCETd+ZbLN9pmbTzJzcBumbzzhcgF15qYTdL8ryO46jCYzRrOZTE3Z+5QC5OuNrDqQ6vKYwjY385+OIzbCnxmbHJRn7xxJ18ZBnLqSz8j4+gR6KZDtnM+Vvxe6DkAqp8nDLzLp0//RefL6os2L95ynV1NrZYvwAA/mDmvLjlMZDP9hF4cu5BAT7se4nrYLsgbJBa64TcJsMTucylvpzb+p9m2O9qbu5ekVT3L+lQt8llS2kutfJH3M8kfXMX3jKcwWW0PJhJWHGX9/U1qH+9uUZC9emvPt+6KRySTkFZjYezaLC9k6kouVmyyNTI2eGt7e9GoSQreoIHK0xqv9X6W4KaQsSEph/MrkIoHvaOwcnYEQH/GzpkK5dMn64nHTJvt9PXvCwoXg61vlYQkEAoHgFmfePEhMBI2D0s7PPQcff3zTPj/ciC6H8mtzC5aiRe3uUcHXrc0LTeyutPn0jSfoFmWvy+H6tXleQeVo82Af93JXsjmamkujEG/USt9yXYO/yo+8ghy0Z/aRse7bazvaA11LP/+/jP9ua21+/LLjEu0Vrs3374f+/aGgwHb7O+/AiBEVN49AIBAIBIJbhnJr8ytX4MwZ698ZGRjPp3H38t08oMvDV5uHT0EedbNSicw4h5vJaHPqXf9uxoKFRsHedmvmqxp3wM2kZ129GDZEtiFb5c2g2FAadY5k7Fdb7cK4EW1eeM0Go5kMbabjk52Qpc3i18M/l+nYT7dPYd3gjWz+L50D5ytHm0cEehKgCkImkZHYOtFpZRa1m5orGicV1POBhcAF4D4gpkyXZ8PUpKksfWwpk7Y6Xoe/Xm3+36VscqUbS2+l5MJAs/VEOpeyzTwUNYB+jfqSpc0kQ5eFn7svPu6+HGqQjHRAH0KyrC3kz/oEM/ChDzh75Vq1FrFmLhAIbjWEuUVQdbz5JuzbZzW4SKXWRekXXwSJpLoju6XR6I38lZzGjI0OFnnL0IMzW1u+BehcnZGaPtb+6HKZtEzVNJ6cvcNhGUWd3lRUwrskxSuXZOUbSNFpih5AJMCGY5f4fX8qL3VvWK74fVQKDl5wXAayOKfTNfSeupl1L3emW1QwuTrj1bLdctwVMpbuPU/HSevJ1how6fLwS5rGye0bXY4p9fClxgOv8+5Lj9tkywIcS8tDLpUSG+HPlIeaM33jCZsM1rwCI/4eyqLPJnK4pHwfoyXP4VwKqYJcvfMF6uigaDK1GeVy71sk+ax4viPuCpnNovXbfaL44Z/TDGhVx+HXs7Bsfg0vd4a0D2f21lM211IW1O7WBqjyqw+fHko5CpmEhO+SypRhWnwMQQWxZw/cfz+kOHiBNm4cfPghyGRVHpZAIBAIbmG0Whg1Cr791n6ftzd8953VVHmTcqO6HMqvzXN0RkKuanMPpbxStXmXRkGYLZCarbN5OVDV2rx7VDA5LrQ5wP/63cXTnSJ56ae9pY5dvJLNsbQ8PBVqWoS0sMl6dEbLkJZ4yNUcOJTMlaUfYTGbrDtigHtKPb0Ioc1vkGPH4J57ILvE99Lw4fDeexU3j0AgEAgEgluG69LmX3wBEyYUfVQDY8o4X1zKflLSswjxqWm3Zr6eGNbXiyma/20XuhyuX5t7u8s5nZ7H+yuSCfBS8nqf4DJGf3Vcdx/+Tf23TMcWtuf8eUQ7sjWVo83/Sr7EpIcaMSBqgMvKLDkFOQR6BNrvSAfmAxogAYgs06U5vFaNQUOToCbsT9vvcH95tblMKuHx2Jr0WTSwTDGUNNAUx0OpQClXoPw/e+cd1tTdvvEPWZAAYQq4AHHiVlBwoHXWbbdVqasq0qGtVbvH7+10dNu6WkertbbWXavV1lFRcS/ELeIAlBkge/z+iERCQoAWtdbzua736suZ33NI8D7Pub/3gxQvmSfe0iBeXX3clpoY9sR7/PTDKxRLPRj+5PtkKGvY7S/UzAUEBO41BHOLwJ3DzQ0WLYIbN6xGl1697vaI7nnUeiMfbEx1Ojuzsj04feRVLWRKyC7UkVOssxW0H2wWTI/IIPLVBq7la/BTyJymaZRFpTXQrJaSM1lFmEqpMmfJJSU0q6Xk6c71iArzY9amMw59TF3RrJYSL3cJpzMrNyuxQGMgq1BHiNINqRhC/eR8s+sin2w5YxORhuzLXF/1Llfyrrk8liy4PjUeeYORvaKczpY1mS18v/cSbw6IJPlijkM0+5msItu1Hr92gxuydzGKrpR7vppeNUlXlT9r16WjvRyuqXLYcDCPA5fybEXr0rN/uzcJskWyl2VRUhoLRkQTHqjg6OV8PGRiWtRWYjRbbO2Dyn4OSmhWS4lS7nhcuUyMyM2tUsXz8o4h8Df58UcYM8axJYSHByxcCEOH3p1xCQgICAjcu5w5YzWuHHMsFtK6Nfz8MzRocMeHVVmqQ5fDv1+bD5m3565r82Dv8rV5CZ9sOVvpVkaltbnJbGF5cgaT2k9h9Lr4Csf0QswU5v12jMyf3sasvXkdrbHOCq0C/wZtPqBlTbJUWpe6HP6F2vziRejRA7Ky7JcPHmxtRyRMphEQEBAQELjvKKvNxWYT7S+n0PvsHq4qazDx2iPOtXmgE4NEJRFZLPifOkF2DX9yinX4KmT0jAyqVAK1M/6uNh/VMZwZj7Vk7OID+Ho0rpJpW+mu5MT1E5W+5lxNHiKzH+4S0W3R5nqjmUOXChnTZoxLrZxyPQWFVGF/relYE1sUwFjg7/9qAchR56B0V5a7vqravFGwF8UGVZXM7cUGlYPhyZmulkpEXMpR29qBXvKrxbAn30fl7skNL3+7bYWauYCAwL2I8FdLoPqxWMovIHl5wbZt93SB6Z9GjVfnOLamZrmMHYeKe3Aq5ZIqFaDBjfYfbLWJ1BK3e6+mwcjEIqZvOkWRzlipGXtBSg8m9WjECz0bsXzfZZbuvQTAd2NiHJJLSki5pmLyT0cZHhPKwtHtWHXwCqM6hjN1pZMXIWV4unM9tHpTpQquJfgpZExYepD29QKoF+jJrN/P2NapzyWTvX4WFr3GxRFA0bQrcaPfYGy3xi5d+cuT03nmgfpMW3kcsciNRsFedsaPH5LTGdGhLqPXT0MnTnVyJiujW41j0dEFLsdUrqPdBb4evhy4dInUjEKns3/9FDKKdEan+6ZcU6HRm1i6N42XejdGazDy/dMx5BTpyVXr8b/50qXkc1D6/kzoWh+Zk8+uTCwioWsEE5cfqXDs5R1DoIqYTPDGG/DRR47r6tSBNWsgKuqOD0tAQEBA4B7np59g7FgodGJySEiAzz5z2obov6bLQdDmFeGnkJG4zLk2L02BxsCYxftZ+2xn2ob6sTDpYqWTbJbuvcLa5/syIWqCy4jwxKhEugR3IaHvA5gKbhormgHO09Jdcre0+cqDl3mlbxMswCt9m5BRoHWpy+Ffps2vXLEaW66UMf137mxtjykRSl4CAgICAgJ3in+jNq9TkMWTRzcz5Njv1CjOB+CCXy2+afewc21eRXNLmm9NksJbkRTWmuzYOL7oFEdHJ9q8d9PgKqfc/V1tXtKG8ptR0Ry7XMSUDtOIX11xOsiUjtMwmU1O2+6Uh5+HL+9tSKVeoNdt0+ZvrE7ltxc7k1VUfitTk8XEvIPzmNh+ImPWjYEUYBUQCjwByCt9SeUSoAhApSv/Ga2q2tzLXUKOOrdKY8jT5DmY153pame6/HxAXafHFGrmAgIC9yLCk75A9XLtGgwbBtOnQ0yM823uYWNLdUSNVxd6k5m52y9UatvyenBC1YqQozuF892eNDsBXjITdUSHMF7u0wS13lSpfu3NaimxWCx0nbmNyJpWV/m65zrx+8ksp8klZSmJ07ZgoX09f4a1D3W5z/CYUKLC/DBYLA4vDJwZSUxm63ZubnDkcj6fPdmGcUsOAGCxWFDt+Yn8v5YC5T+NiEQiXnr9/xj/7CQ8ZOIKXfnBPu6otAa6NQliwchotHqTnfFj1aEr/Hr5QzTiveWeM0jWjlGVMLc4dbS7oE1IG2oqA/j0CT9kEhEyif0sg9L9UMvDYDLzXPeGmCwW9l7I5Ztdjg8yJZ+DkQv3kZajJj42rNzPrkQsomdksF0PW2e4OoZAFcnLg++/d1zeubO15Vxw1eJOBQQEBATuc3Q6eOkla7pCWTw9Yd48GD7c6a7/RV0OgjaHqmvz8pBJRNwo1HL+RhHfjmyH2WKpVJJNgcbAu+vP8dmTM+lQJ45Pk2fa6eXWIa15uePLxNXuydBHh6DNPG9d0Qh4BPgbkvNuaHOAZ7s3ZEtKZqV0ObjW1Xdcm2dmWo0tFy/aL4+Ohg0bQF4NbzEEBAQEBAQEKsW/SpsbTeyd9xOLNi+j64VDiMrUbiPyrtEwO52zNcIctXlgoLXFdmAgBARg8fMnSyJnV46ZAg8v8j28yPH05WxAXc4EhlEg97Ydd1avFhVq86qk3FWHNs8p0jE0ZiAJUROY58K0PSEqkQENB+DmRpX0qI+HL3+kHuPPKU1vmza/nKfhw1/P8Wr/+i7HNmf/HPaN3Udseix7f94LUVjTFF1ITjfcsLio7Ze+VrlEXm6qzd/R5kU6IwEKf6fHK4/aPoEoPW6lx9vp6qQkWL0aZs4UauYCAgL/eQRzi0D1kZwMDz8MGRnwyCNw4ADUrHm3R1VtVFfUeHWh0hg5mVHxjE6wjk+lMTodV2XFTtlYwLJ8t+cSQ9rVJaFLBBN/PFLhmEr3zyztKn+1byRjFu+v1HUtSkpj/ohonvpmL4tGt6dtmC+LktJcOr/7t6xlm03qr5AxsWdD+reoidZgIlNlP1OxUbAX3++5RIMgL7R6EyczVJj1WnI2fob69C6XYxO5exI1+h2mTn2e/1ufwuaUTBoEedEo2KvcqO9ujWvgKZMQHqBgwvcH7Fr2SERuKALX8tOZ+eWeM8A9gububxHsFVThvXNwtFfAtI7TOJ2h4attF8lSaR1mGZT+fZaHQiZGZzQz+89zFc4uWDY2luNX8+nSqIbL75NCJrHrYVv2dz+ha316RAbdsQfo/zyBgdZ0lrg40GqtyxIS4IsvQFa1NgoCAgICAgKoVNZ/V8rSrJnVNNmkidPd/qu6HARtXlVt7govdwnZxXq+3n6eBX9dYNfL3fnpwOWb47GgkEmo5etBkc7ooMsDPN25oYJ+DR5mYKOB5GjyyFHnEaDwQyFRsvnYdYa9PI6/tv9p3SEC66xQcfnjebfbu2iMGubsn0OeNs9u3Z3W5j5yKcFKdz74NbVCXb5kTHumrTxmK37/K7R5dra1zfGZMrODW7aEzZvBx+efHV9AQEBAQECg0vxrtLnFAhs2IPnfu3xwwLV+ffDsHs7WCHPU5j17gsFgm5zrBij1Ro6Uc30l/Ju1+bNLj/JV/Ey6hHZl5p7pDqbtqR1eZkCjASR+f4LxXcMrnfQyteM0jqUXE1Hj9mtzgwm0ehHTOk5j2CrnY8tT5xH9cDQFSQXQB4h1PX5/uT+J0Ym8/9f7FV7rxJiJzDs4r9xUm7+jzc9kFeEpVVbJTOQu9uatgc0o0pXR5ps2Wd/HaTRgNMKnnwo1cwEBgf80wl8ugeph8WLrC0693vrztWvWf1C3bwd397s5smqhOqPGq4sCjb5K26u0BkJ8HOPcwbEIeSqz0DZbMljpQffGQUSF+5XbTqeE/60/ycJR7RyK8WVnX0aH+dEhIpAPNp6y239Zcjox9fyJDvdjX1rFsXwp11Ro9SbcpWIGzU5ieEwo80dYE0/y1Hpq+cqRikUs2X2RFfsvU8PbnX0Xcxjarg7fjWlP21A/8jV6Lueq8ZFLqenjwfJ9lzmQlsuQdnXp3iSIb/66iJe7hFy1HmNBFtd/eRfDjTSX45L41yHo0TfJ8avDlpOZjO8Swav9Ih2SWEpHffvIpYzvUp8v/ziLTCpm7lP2yS1Ljs/m3V0fl3vOYM+abBvxO6eveKB096yUMJ6zfw6HEw5XKnI9tlZvBs8+4PT3X9EDHFhFcw0vd349nlGp2QUxEQH0jgzCoxICWyGT0Ld5iK2HrUprQOkhvSvRp/cF0dGwYAGMGWM1tUyYcLdHJCAgICBwr1KjBvz4IzzwgLX1HcCoUdYkF4XC6S7/dV0OgjavrDaviCKdEX+F1XxrMFn4+cBl4mPDCPByd6nLAfo2DyHI24NPt5xBLhPzVIcw5F6B5KkNIBGxZcGH7PxtjfVEdYEnqbC68kvqL0xsP5H94/bTZ1kfzuWes1t/J7X55F4N+fPU9crp8nr+LBgRjVTshvzfoM3Vamtiy4kyM2ebNIEtW8C/ajNgBQQEBAQEBP4+/xptvnUrTJsGhw/jaupVSlAEmxp14NcmcbZldtpc7OhUvte1eXaxnv6f7+Pdh7qyc9QA8rX55Gnz8ffwxcfDl0NphUxafhK9yczP+6/y4WMDK9SjCVEJ9K7fC61BTIMgr7+lzVcevMzYuHr4KdydGtvLanMPiYwH6w9wPjY9sBIKLhXAMKBBhcOhvl99Xox9kRx1jstrnRA1gbjQOCZvnux0/d/V5iazheXJGUxqP4XR6+IrHO+kmCks3nWVJiE+9tr8p58gPt5qygL4/HNrguEHHwg1cwEBgf8sgrlF4J9hNMKUKdZ/NMuSlWU1udSrd+fHVc1UZ9R4deEjr1pKgtJD6nJ9idjpGRmEyQIqjYECjQFfhRSZ2Bqll6e2iiRnUeFgfRjQG8283j+SB5uGsP7YNYK8PRgaE4pWb6JAYyBI6Y6fQobWYGL98534IdlerM7beYH5I6KZu+N8uTMNS59fazCh9JCQqjHw9fbzzN1xnsYh3ni5S/h6eFs2HrvG0JgwHm5Tx1ZU95FLyU/PZ8j8PU7jt5+IrsPIhfs4eCmP2cPaMOXno5w5nEzGkhcxa1y70Hv16YfXgy/SIbIuT3UIw1MmZtup67y66rjLqO9X+0ZyIC2X+A5h7LuYy/jvDti2LxRvJFf2dbnnlLh50kD0PsPmnmdo+1C6NAlgasdpDC/HyV5CnjaPY5nHmdFrJl3DuzI9yYl7v+PLDGo8kN1nC6njJ7cT6c1qKRkXF0GbUN8KH+AmdInAaLGwMCnN5ZhKmLfjPF0aBiIRmyv1XZLcFOQKmcTlyyKBaiI+Hjp1+k/8fRcQEBAQuMt07gwffghvv201tYwe7XLz+0GXwz/T5udvFFO/hidmiwWd4b+pzUsK4644k1WEh0xMTD1/ejYNZmi7UP48lVWhLu/UIJCWdXzZeyGbER2t2nzM4v2kXFNhsVjI+2M+hQfXW3euBQwHl29RbnIk8whj1o0hISqBTcM30W5BO7sElxJtPrP3TOLCujJzd/naPPnc39fmYpEbg1vXZtiC5IoHjfUzEBXuz6FLeZV+IXVbtblCAYMHw7Fjt5bVrw9//AFBFSdYCggICAgICFQfd12bHztmNbVs3lzuJip3T35p3p3lrR7kTI1wh/XVrc1lEhE9I4MI8JSRU6xn2+kbhAcoELlZMJstvNY3kgEtavHln2doHerPsGrU5mWfC/LUerzcJaRmFDJq0SEkIujdLIT/DWqGmxvM336JJ2NCeaO/N2q9kfBAT/adz+GNzh/SNawr053o0Ukxk4gLjSP221h6RfTi9b4foDdU/JqxRJt3bxJEQtdwmtVWkKPO5Xx+Dn4efoT4KPlxXzoH0gp4LKqOnTZvXdcPg8lM8oUiXu/8AbF14vispHVoIbAc0ABjgRoVDgWA/df2E/ttLDtH7aRLWBdm7J7hRHtPZVDjQfxxfhthvmHkZebZrX+548v0bzSAfeeL/pY2X7r3Cmuf71sJM1EiXev2YfDaA9Txy7Fp83571yNOTLSmFpXm+HHrezupVKiZCwgI/Ce5r80tKlUhzz3/PAUFKkJCQpg3t/yXx3/88Se/bdrE5ctXkEgkNG7UiMefeIzIcqKyAVJTU/n55184feYMRqORunXr0K9fX7p363Y7LufOk5MDQ4ZYi0hl6dEDVqyAgIA7P67bQHVGjVcXSrnE1p++vL70JTSrpUQpr3g8+ptue2f9UUd1DGf9853ZnJJJvxY1b8129JThK5ciFrlhNJmRiN3ILzZQy1fOWwOa8eepLDujRunjdawfSPNaSrte7iWu8sYh3qRm2PeI95FLiY8Ns4n+XLUeP08Znz/Zhu/3ptvEfmpGIb1uxuop3CW28/vIpax/rjMfbExlWXLF8dtf/nmWnCIt/ml/MvK5dzCXzCguB58OQ3j0lfd4PDqUnWduYDZb+HDjqQqjvlc90wmZWIRKa3Bo2VMk3urS2CJykxCgfZ0r6iDA+hJh+b501k/sU2E/1cToRGJqx7H/QiEh0q6sH/IgxQYVuZo8/OXWyPXlyRnMu3aZZx5oQJtQX1QaIwaTGXepCHeJGLlMzNfbzpGWoy73PMNjQunUIJAibdW+R3lqPe4SwUV+V1Crrekszz8PonLuv2BsERAQqEYEXX6f89JL1tTH+vUr3PR+0eVQdW2ep9YTpPTAXyFDazCiMZiRSUXkq62zUe9VbT6yQyg/JXTgm10XuJavoWODADzdb93z8jCZLWw8nsHc+CgAPthYcQue1c90okhnwGg207SWj502t1gs5O9YfMvYEgTEA1WsEc87OI/YOrFMiJ7Ah7s+tC1PjE6kS/gDHLuk5srVlqwf8mc52vwKT8fVY+GodhRpq67NX+jREE0louNL3x+t3sTmlEx6NLn9ZrFK8X//BzodzJgBoaHWmkStWnd7VAICAgL/GQRtLlBZ7po2t1isScILFji+2L9Juk8w8zs8zonuA5F6eyHWGRHfRm3+68TOXM0vpmktOfnafHI0uQTIg5nxWGPEblJMGMnTZJGryaN2oB/zRrQgLUfP5BVHSL6Y63C8qmhzZ7rcXyEjSOlO/xY1OZNVRIHGgNEMl3LU4OaGRCwiNEDB+O8OcCVPw/rnOtvq2C/1rk9s40asfXItaoOaHHUOAYoA5BI5Cw4t4NtD3xLiFULS5SR2pG+iX4OHKqXNd529wedDI9lwZj0TFjmaSSa1n8L06L6MWniUI5cDWf1MJ9R6A1KJyNZO00cuZXhMa9YP+ZNjKYd4+smnyJRmWHW58/DPcjmXe45mXzdjc/xmfo//nRxNju1a/Tz88JJ5s/bwda7k1XeqzU9eVeOGBzqTim9HtqNYVzVtXqAxsPOUihm9ZtobdkrdkxdiptK1bh9GLTxKwU1TlVZvwvTRdMQ/fuF40GHDrF0WpBWbtgQEBATuVe5rc8uiRYtRqQor3O6bb75l/YZfkclktGndGr1Bz5GjRzl85AgvT5tKbGyMwz67d+9h5qyPsVgsNGvaFG+lN8eOHefzz7/kUtolRo8edRuu6A5y/Lh1ttTFi47rXngBZs4EyX/n41XdUePVgUws4rnu9TlxtdBBuJaN75vQtT6yCoqQFfVHnbryGPExoUzu3ZjEpQftRHevyCCmP9aKpPM5zN95wSaIK1NAHt8lgu/3XGLJmPYMmp1EgcaASmvAy93+8xMeoGDJmPYOqSbgONsyT21gxmOt+HBjKktLFcrjY8NIvpjjtHheQl0/Ob2b1SDACyZ09Wbai+NY/cNql/fOTepOQL8XqdWmG/1a1OKT30/jo5BhMJkrFfXdLtyf7k1qkHQuu4yxZRs5UiciteS8uPF5728IkXZnUVKa7Z7kqQ3839pzTH98Bg+Uk8jyYsxUutTtw7I9mTwSVYdBs5Mo1BpsM2uLdCq7/qANgr3pGBGAj1yKTOLGuO8OsD8tj80vdOGZBxrQOMSbRUlpDtGc3RrXIDrcn7fXpZDQteIXVqXJKdLj6S5BKsQk3lkuXYKHHoIjR0ClgjffvNsjEhAQuA8QdPl/nIMH4fvv4dNPbf3r7RCJKmVsgftDl0P1aPND6fe2Nq/rJ+e9h5vSNsyLfG02/dua8JdrGVenNm648UKPBoz7/lC599BHLqVPsxD2XcxFpTVUugVPoc5I76bB7Dx3w87YUrBrGarkX6wbBwMjqHIBvYQvkr9gzZNrmJ40nZbBLXkxZiq96/fHYpaR+MNuCjQG5u646EKbe/1tbd6pQSBX8zVVGm+eWk+WSkuuWo/s32A+d3ODjz4CHx8YOhTCwu7ueAQEBAT+YwjaXKCy3DVt7uZmTXNzYmyxNGnCsRHPsKVVdxI7RtwRbf7VtnPENVZyoWgrExbfMm008G/A1qe28lf6X3y852PnZo7HrGaOEgNEVbV5VXR5Wo6a8V0icHODGZtO2XT4s90akHwxhx/2pSMWufFw22AG/NiN49eP0zyoOUp3JQBdw7oyPmo88S3jyVZnE6gIxEvmhUxqqVCb1/WT06+lP9O2THWaUnIk8wij18WTEJXI4jHvMXj2AaLD/QkLkHPgUr5NlxdoDHy9/SKffreW7DUfYGxSBAMAx45SlSJPm0fChgTWPLmGob8MpU1IGx5u/Bjta3civximbzrjUpt/8WRrWtX1pVhndKnNl+xOw2i22CZBSERujOwYTucGgZy/riUrs3W55vbBa0u1PLJYULz9Og85M7Y88wx8+WX5ExUFBAQE/iP8d9wHVeTo0WP8uW0bD/buxebft5S73ZGjR1m/4Ve8vb2ZMf1Dat2cDXTq1Glef+NNvvhyNs2bN8fLy9O2T2FhIV/O/gqz2cwrL0+jQ4dYAPLz83nl1ddZs3Yd0dHRtGjR/PZe5O1i1SoYMQKKi+2Xy2Qwbx6MGnVXhnU7uR1R4/8UiVhEl4ZBaPRml8J11aGrFUY+Vro/anI6rUN9iQrzsxXQfeRS3hrYjFm/n+YHJ4LYFcuS02kT6otMImJ/Wi7DY0L5evt5ani7o9bfSknxkUv5bkwMc3ecr7Agv2RMe85fL2Ln2Rt2xXOxyI1hMaGMW3Kg3PF0qh/AnKeas/7Mep79/H1SvkyBKy4vAbEyiKBH30AWFEF8bBj703JZvv8yO6d1c3mu0iz46wK9mgazZHeabVmheDO50tngVk7+O9At+HUWbK7J8Jhi5o+Its3Y9VPI8JFLkbi58WDEQzwY0Z8cTR43inNtwviP1FxSr2l5ol0ow7/ZaxPIZWfkgvXebTmZRcf6gfzfhhS6Nw5ixmOtWHXoCjV95YxZtJ/ODQNYOjYGqciNAq0RlcaAr1yKTGKN5sxUafF0r9pThp9CRm6RHqWH5O4X0O8XduyAxx6D7Gzrz2+9BS1bWs2MAgICArcJQZf/x/n2W3j2WWvKQmgoTHbeq7yy/Nd1OfwzbV7XT86Hj7Tkk99P27RwdWjzIp3Rtt2d0OaldfkLix1ndU7pMI3BTQbxZv9I3v011ekx4mPDOJCWS/uIgErr8nk7L7BwVDvcJWIW3WynabFYyN++CNW+VdaNgoGR/G1jC8DhzMMAnH/uMu4SBddVJryknryw4rBNl5stt0ebb07JpENE1RJW/RQyinRGbhTqCPSS/Tu0uZsbvPba3R6FgICAwH8OQZsLVIW7qs3/9z/46Se4ds36c82a8L//4TZqFA1McD4l845ocx+5lCVPt+Lt7a8w/9At04afhx+b4zfzwa4PmH9wvsN+zswcpVvXlGhzd6mIM1mFvNynMd/vvWSnzauqy5fuuUSXhjXYmpplM7aU1eWNgr0oNqg4mnUUgGNZx2jg34BNwzex89JOBv842EGbT+s4jYGNXWvz9x5uyrrT61223wGYd3AOHep0ZnhMa7756wI/JXTgrTUpdtsUpWwj57fPoIcJOro8XKUo0earH/8Vd4mCnEIzJpPMrmZenjZf8NcFvhnZjokrD1HD24MeTey1+fM/HGZUxzCWj++ASmMgX63HVyFD6SHl4KVcXl11nDf6N+Xr7RddmtsBRGYT7/0+h5pHNzlexBtvWL8TziaTCAgICPzH+BdUJO48Op2Or+fMpW7dujz0kOsXduvWWiOHn3j8MZtIB2jSpDF9+jxIcXExW7dutdtny5atqNVqYtq3t4l0AF9fX0aOfAqAtWvXVdfl3DnMZnj7bXj0UUdjS82asHPnf9LYAreixitDVeIM/wlqvZEPf0tl8k9HHSL/SoTrgp0XGN8losK4x6r0R12UlMbw2DBEN3XSc93qcyg9z2ZsKRHEJcXgyh5vyW7rf1vUVuKrkLF4dHue7dbAFqtY2YL84fR8OtQPYN4O++tpFOyF1kX8dl0/OXNHNGfa1qnEzx5GynsVG1v8G7Sm35uLaNWqFa3qKBkXV49v/rpY4bnKYo3lNGC6OdtAJV5LruxLl8aWdx/4mA8ffIE6fnK+3n6euOl/8tzyQ/x04DKXc4txl4pIy1GTX2zEYpERqAihoV8kYos/brjTp1ktosP86ffFX+VGI/rIpTzbrQE7p3XjxZ6NyC7SMblXI9rV8+dURiHj4uqTXajleqGWwa1rs/VkFkPm76XTR3/S9/O/6PDRn4xYuI8QHw9mPd4Kuaxq3yN3qQipWESR1nU7KIFqwGKBOXOgZ89bxpYSnnvO+kJSQEBA4DYg6PL/MFotjBsHY8fe+ndk2jSrkfIfcK/r8srMDP272jw8QMGaZzvx17lsm5GkOrS5n0LGpB6N8JFbX0bcbm1up8tXD7MrnoP1RUD86mFM/X0qQ2KC+Xp4G1rUVhJZ05t24X5E1vS2afOtqderrMt1BjNGkxlPdzFNgj3J3zKnWo0tJeRrVSjdA5BLFBy6pOJGoZatqdfL3b66tLlULKKGtwe9IoMqNc4SXX46sxClh/TOavOTJ2Hfvjt3PgEBAYH7HEGbC1SVu6rNvb3h88/B09P6Uv/sWRg7FrWZaquZQ8XaPD62Dtsu/WZnbAFIbJfIjrQdTo0tpZl3cA47Lm9ieEwdu+U+cinFOj0THqhDvWA9zcNVfBkfjq/CzKdDWlLXT15lXf58j4ZIJSK7Z4OyutzLXUKO+lYqZIlJZ8buGYxZN8apNh+2ahjTtpSvzdvUVRIV5s3M3dNdjrOEz5NnMSy2JqkZKgo0BkIDFLQL96NJiBeqPSvI2fIxDKkeY0sJJdpcIVEQ7K2g7+fl18zhljaf+1Q0JrOFaX0iebGnvTbPL9bxzqCmXC/UMWTeHjp+9Cf9vthFx4/+ZMj8PVwv1PHOoGZ4eli/RyUGmv1peaRm2BtbpCYDX6yfxTBnxpaPP4Z33xWMLQICAvcN92Vyy48rfiIrK4v333sXsbj8W6DT6Th2/DgAHTt2cFjfsUMHNmz4lf37D9gJ/gMHD5a7T3RUFDKZjKPHjqHX65HJquZuvmsUFsJTT8HatY7rYmKsaS7/4R7XMrGIhK4RTFx+pMJtKxtn+E+oymzOdvX86d00GLkLsV7V/qglvT2zCnQMbR/GE/P22Nb/HWOHVm/CaLag1ZuY2KMhX287x44zNxgbF8HGiXGIRTBmceVTUHpGBnMq0/78Xu4SctXlR2WWuMfnfTsX1gMV1GwTn32Gl9/+kGKDxeq2lkvRGUxczdfQKNjL5bmcka+xRkoWSH4iX/qdy219DaN4rPFYQrzdbYktBRprrKdcJmbtkatMWnHU5iwvmZXQr0UIYrEMncHM+iPXaB3mZzcjoDSVibTUm8y4S0SVniHwQs9GPNutPs8sO1zh/RjdKZytqVn0jAwmV60n0Nu9wn0E/iZ6PTz/PMx38rDduLH17767cP8FBARuD4Iu/4+SlmZNArt5/22YTNaY5K5d//ah72Vd3ibUWpCtV8PLZSH972jzqDA/Pn68NRZgwc5bxffq0OY/H7yMv0LGtikP8OovRytMQyzN39HmNl1ewazOuQfnEBfahZ7hgxxnQt7U5sV6Y6V0uVjkRttQXx5uUxM/LzP52lye7yXlk9c+RnV4o3WjajS2APjL/RCL3FBpDBhMZrKLyx9ndWvzI5fzmf5YK/bN2l7u80AJozuF80NyOpE1rSaXO6bN9+6F/v2t/3/XLoiMvP3nFBAQELjPEbS5QFW57dp8/XooKrK2IXTGo49Ct24QYE2lq+6aObjW5mKRG0NjajLgx2H2y93EJEQlMGj5IJfHLuHz5FmsG/IHc3dcxGyxar/FY1qxPf03uiye5TTFcOMLA9EbpDz1beWMwAv+ukCH+gEU6eyvp6wuL9IZCVD4236urElnzoE5dK7rXJv7KqTkajJtaTAVcTjzMFpTIbOHtcZLbmJynxpcy8vky3e+IO/YChiDVZu7oEOdDuy5ssf1RqUo0eZag4nMAp1LjVwZbW4wmfHzlPH+r6cqVTMfFxfBCyuOOD2fh0HL3NUf8sBF++dri0hE1qwvCXnxmUpfp4CAgMB/gfsuuSUtLY21a9fRo3t3mjVr6nLbq1evYTAY8PFREhgY6LC+fv0I6zEvXbI/x8U0ACJuri+NVColNLQuer2eayWRef92ioogNta5sWXUKNi+/T9tbAFr1HjPyGDiY0NdbhcfG1apOMN/SlVmc87feQGNwYzWYHS63mgyk1/F/qh5aj1e7hIm9mxIvkbvUhBX5XjFOiMtavuwLDmdlGsqXlxxhNWHrmIBTmcV2jm+xSLnTuSUaypUWgONQ7ztlhfpjPgrnD8YyyQiWtb24MXJk2ANro0tIgh9KpTpH0+nx6d/3XJbz9vDrrPZrHuuE0oPSbnncoZY5Ia/QkKacV6FxhYfw1B8jI+hkEnQmSxM+P4Ar6w6Rm1fOSsPXiHq3S28uyGVOn5yHouqzWNRtQELr6w6zocbT2OxuPF7Sib9W9WiZjk9bksiLeftuMDUlcfKneXw8e+nUcpl1uSeSswQ2HHmOt0aBTE8xvX3aHhMKO3C/fFTyPghOf2OtBO4b8nKgu7dnRtb+veH5GSrwUVAQEDgNiDo8v8omzdDVJSjsQXghRdg+fJ/dPh7WZcvTLqIr6c7i3alVbs2f7hNHU5lqihQG6pdm3+65SwTfzzCx7+f5tMhbXDj9mlzmURE2zCvSs/qnLVnBgoPC8MXlJkJeVObf/BwCwI8y9fl1hmX9Uh6pRPfjGoOij10WRxH6Iy6dB3QnrU/3UxsqUWFxpYe9XrSMrhlpcbdJqQNMpEXJjNsO3Wdwa1r33FtvuvsDWY82sLldiW6fFlyus3kcke0+aZN0KMH5OZa/9e7N1y+fPvPKyAgIHAfI2hzgb/DbdPmubnWibaDBkFCAqSXo23c3GzGFqh6zbxYb0JTji6HirV52RY+JTQLaobaoK6SmaPYoKJxiLetzdGHu19n9Lr4clMMX94yDZnUwNV8DTKJiH4tQngqNpR+LUKQSRzvc8o1FVqDibKqvawuP5NVhKdUSeuQ1jaTzufJn1fqOsrT5u+sS+FGcW7FByiF1qhCqjhJ18VxNJwVRtc+MaxMWgHjqdDY4oYb3z/8Pa1DWlfqXKW1efKFXIJcGLmros31Rgu/Hs9wee6Smnlcw0DindTMldoivl/xloOxxSSVsea1T2HMmEpdo4CAgMB/ifvK3GI2m5n91dd4enoycuSICre/kX0DgIAA5/2oPTw88PT0pKioCLVGA4BaraZYbY0rCyxnv5LjXb9+o8rXcFfw8oJeveyXicXW2L+FC8HDeRHuv4ZCJuG1fpF8ObSNQ9xis1pKvhzahtf6NalUnOE/pcqzOQ0mruRqyCzQoNYbMZrMgDVCfde5G7g7Ebyu8FPIUOtN9G9Rk+sq+3Ylrkwkro5XpDMS6O3O66uP2zmjD6bnYrHAzmndmD20LdP6NGH20LbsmPqArW1RWVQ3U1BKcyarCA+Z2GlUZue67gweOIgbf1TwnfQERkF6/XTytfn0bhZiW5VyTcXkn4+y7sg13n+4BV4eFcdylhTTt05py9hfh3AgpyJjSzw+xmE0q6VEInLj5/2XGd2pHu3rBbDz7A2+3XWRF3s2Yt/rPfhmRDRPd47gxV6N+XF8B/a/3oNgH3d2nb3BwNa1GfTlLuRS5/ejKpGWf5y6Tn4lX5gsSkrDArzWL5JPn2jl9Hs06/GWjO8SwS8Hr9C6ri8HL+XdkXYC9yUHDkB0NCQlOa579VWrodHH586PS0BA4L5A0OX/QcxmaxRy377WYnRpPD2tppZPPwXpP38xfi/rchEwqHUtzl0vrlZt3iMyiC0nsxyMLNWpzZclp3MoPRduozbvGRlEvja/Si8C8jR51Pazd52UaPNVh64QpPRwqnnDAxSsfS6a2jVT0FsKmLZ1KsNXDefIuSOwBDhVsiEVGlu6hfXitZhvmNbxlUqNe1qnaZzJ1Nq0ef/P/8JPIbuj2nzezgt0blij3O9RiS4fuXAfA1rWpF24/53R5suXw8CBoC4VA3/lCrzxxu09r4CAgMB9jKDNBf4J1a7N16yBpk1h6VLrz4WFMH68taV2BVRVmxuMZlQaA6czVWQWaKuszcu28ClB6a4kW53tZI/yydXk4eUuKbfNUVnmHJzD7+d/55fE9hx8M45XBwTzaKyFVwcEc+DNOJaMiaKun9xuH43BhGcFutxktrA8OYNJ7af8LZOOM21+/GoBntKq1RjlUjlTtkzhSOoR+BYIAEbgOkXRUvIfCwuPLGRi+4mVOldpbR7XqAZAuXX9ymrzpcnp/HnqeoWTPMFaMzdZLLxa9ntksTBv9fu0u3rSbnudh5zsH3/hxzrthLq5gIDAfcl99Zfv1183cvbsOSY+/xxKpXeF22s1WgDcXbRj8PBwp7i4GI1Gg0IuR6PV2taVt5/HTTOI5qa4d0VRUTHFxcUOy7OzqyaO/jGzZsHx4/Dnn+DvDz//bJ3tf5+hkEno2zyEnk2DKFAbUWkNKD2kKOUSZGLRbZ8ZWkJBFWdzXsvXMH3TKfan5dGslpLnutena8MgPvgtleX7LrNzWjea1VI6OI2d0ayWErlMDFjQGkwOBezSgriyx3OXipCI3HCXiDiUnm9bFx6g4L2HWrA/LZf5Oy84jfhb91wn/rchBZXGSJHOyJmsInwUUoKV9qYrk9nCD8npjOoYztSVx2zL9TcusXrxh+RnXXE90FrAEOCmDs/V5NlmgfrIpcTHhjEsJhSt3kR2kZ6aPh6M7xLBpB+POD1cSbzk+rM/0WLuFIoNjt/z0vjpx6I0PQRYo8F/PXaNwW1q4e0hpUsjC6/8coxfJ3bm4KU8Rny7z+Feje5Uj+ExYeiNZmRiEcE+7ixMSmN8XASTSkUeikVuVYqan7fjPPNHRPO/DSft+oA6wzpz10igl4xeTYPp1iSIPLWenCI9fgoZ7lIRW1OzOHI5n0ej6jBy4T5e6t34trcTuC9ZtgzGjoVS/2YBIJfDokUwZMjdGZeAgMB9w72oy+FfpM3/beTlWWdW/vqr47pGjawtTJs1q9ZTlujyXk2DKNKayFXr7wldnqnS8sHGVPan5QFUizb38pBQpDVy4loBExT17dZXtzZvEKQk+WKOS22+MOkCZjPkFOvZmnq9Sto8wFNGjqZqszpL63Jw1OYqjcFBl5fMiP0g6XUCPf3Rm4utUevZwDIg7+aGjYAncFk5aV3jAR4L+4zGQYG0VwxkQtQE5rpoqTQhKpFe9fohwgNdKW2eU6RjdKd6TPn51suD263NC7VG+jQLpvtNXX6jUIfSQ4q7VMQPyen8fOAKz3VvQLtw/zujzb/4AiZNclzepw98/fXtO6+AgIDAfY6gzQX+KdVSM8/JsbbNdpb0uHmzNdmtb1+Xh7jT2rxsC58SVDoVgQrHVCNX+Mv9UOsLnbY5ckYD/wZE14pi9+XfmLF6htPWRb9OGsiipKtkF+rJKdbj4yHFs8yzgTNdvnTvFdY+15dsTebfMumU1eY9IoOpqQygdUhrhyQaZ7QJaYNEJOH4wePwE/AA0LaCnSxQOpZmzv457B+3n/FR4122VEqMTqRP/f6I8ECtNyETi1iXctWhZg5V1+YLky4yf0Q0c3ecd6nNU66p0BvN+CtEPFhGmwfFfYz5icGIbv5d1Ct9yP15DUOPWnipd5hQNxcQELgvuW/MLTdu3GDpsh9o3qwZPXrcO6aM9evX8+OKn+72MEAigZ9+gqefhk8+gQjH+Mj/MkaTGb3JjEpjpECjx0cuQymXEOglu2OF89L4yP/e7EuwiqUTVwvR6M22/qPOCsvlMS4uAneJiIhALzJVWmr5yisUxK4Y3SmcLSezeKl3Y7KLdKx5thMjF+4jT23guzExfLXtnMu+lPExYXw2pA1nrxdZBbq7BE93MT0jg9lwzD72b+neS6x7rhPD2ofyw7501Gf2kP3rJ1j0FTw0twQGAqV8PP5yP3KKs8rtsekjl7JxYhzDY0JZlmw//pJi+rQ/nuGnkytcntoNN2rxPBJTb8AaDd6+XgAi4Ovt51HKJYzqFM7bA5sxd/t5liY7v1dTfj7K8JhQJvVoiMUCbUP9WLr3Erte7ma7H2CN09TqTVVLBtKbaBziTWpGYYXbq7QGQnw8EJnMJJ/PpnVdP2Q+IvLV1hnBPSOD+SE5nbfWpjCwVa070k7gvsJkgldesRoWyxIaak1rad36jg9LQEDg/uJe1eXwL9Lm/yaOHLH2ur/gJP77kUespkml6zS7quJMm9fxk99RU0sJVdXlSg+pTZdD9WnzKxq9UyNLdWvzL/4461SbX8nTkJZTyANNfBj3gB856jz85cFMf6wxYjcxfZuHVKjNwWqICZBXkDNehhJdDjjV5j5yqcN5SmbEfnt4ARcnXWTg8oFwEWvxvOTRoAXwMC7zbut7d2bFY7+gkHjw1bbzhAXImdFrJl3DujJ993SHlwwvxEyla90+PPr1QTo1CLTT5iazhQ4RAXdFmxvMRtJzimkYrERrMJFZoOXRtnUYFhPKD8npvLsh9fZqc7MZXnsNpjtpRzVsGCxeXC2pTwICAgICjgjaXOCfUG01882bYfRoyHDSwsXHBz77zGp2rYA7rc1Lt/AprftSrqegkCqqZOZQSJSAxa7NkdhNTLOgZijdlah0KlKup2CymPDz8GNz/GY+2PWBU+NGSeuixOhEPur5Een5V/DxCMbbw0RWgZ5nHqjPsz8ctm1fVpcXaAyMWniUFQnj0JirZjx3ps1PZxby15kCXoiZyqi1wys8xguxL/DCxy9gWWmBx4G6rrcP8wnnUkGa3bI8bR59lvVh0/BNdKjTgc+TP3eizV+gX8N+SEUi8nW5KD08cXMTkVus45GoOna6HG6vNi/WGW3fGWNpbd6xE+fmLKHhuOGYAwJY+vY8Pt+jF+rmAgIC9zX3jbll3rwFGI1GEhMTKr2Ph9zqFtfpdOVuo9Va18nl1og3eakWPTqdDoXCMSdNe9OpXrKPKwYOHEh3Jwkp2dnZvPb6HY7lDQiwxgLeZ6j1RramZjFvh+PsxISuEfSMDL4jkeelUcolVZ59eTrTKqCcOYydFZadMTwmlDahvszZfp5X+0WiMZicCvyqHK9Ps5rkqXUU6UxIxCIUMgm/JHbkl4NXKhnxd4nWoT5cuFHM19vP06yWkrGd69GzaTDPd2vAl9vO2bYt0BgYs3g/i0e148LvS1ix+mPXN88N6A3EYuf8bhPSBh8PX5IvnGT1M52Zu+O8wzgLNAaGf7OXJWPaEx3mxze7Ltp+X/Gxdfg4+b0KjS1gdY838kjk95QbjO0cQdfGNfjl0BV85VJ+2JfOY1G1wQL703KdGltKsyw5nTahvkSGeNO5YSCH0vMp1hlJ6BpB2zBfFiWloZCJ7eLsxSI3GgV74eUusSXjmMpYzfPUeoeo+fJQeliL0gqZhLiGNfgj9TqbUzLJUmkp0hk5nVlIZE0l7z3UnB6RQXf8u/WfJi8Phg61FgzK0qULrFwJNWrc+XEJCAjcd9yruhz+Zdr838D331sjwssmgYlE8NFHMGUKuJXt6v7P+Ldp83+iy6F6tPlP+y8zoFWtco0st1ublyQSbk//jd7LhjudNTq4ySDe7B/Ju7+m2taVaPPvxsQQFebHwqSLbE29zvTHGlfpRYCPhy+/pxzFRy7luzExDtq8QGNg5MJ9LBnTnrZhvizZnWabEdssqBl6k56ja47C79iizGkH9MPuGaAsUUHdWfbIT+w4nW+nzdU6MU39erN+yIMUG1TkavLwl/uhkChZnpzB4LUHKNAYSMu5pc37Na+JVCLiqW/3smh0+7uizVuH+t0dba7RwMiR1nTYskycaG1nJhKK9gICAgK3C0GbC/xdqkWXazTWSVhffOF8ff/+MG8e1K5dqTHdaW1euoXP6HXxt5ZbTMw7OI+J7ScyZt2YCscyKWYKy5MzUMisbY78PPxIbJdIQlQCaoOabHU2gYpA5BI58w/NRy6RsyNth8tEEoA5B+bQvnZ7zuSc4cNdH5arzUvr5RJdnnJNxYAv9rFtauzf0uZ1/eT8khiLGT1hQToKtAV0qjGACVcqSjmcwNGfj7Jm8RoYA1QwT6JDnY788MhyHv5psMMYz+Weo92CdkyInsDaJ9eiNqjJ1+ZTV1kXd4k7FouFXE0ul/WX8ZX7UqAzcKP4BmPiGqA1mOxq5inXVHi5S26bNleWSsd30Oaa2jQf8Q5/SoPx0vvx3kP1hbq5gIDAfc1989dv/4EDeHp6MmfOPLvleoP1H6Pc3Fxef/1NAKZMmYyfnx81Aq0v+XJycpweU6vVUlxcjJeXF4qboluhUOCpUFCsVpOdk0OoE6FecrygoIpfInp5eeLl5VnJqxSobtR6Ix9sTLU5tUuTck3FxOVHiI8Nq1rf0GpAJhaR0DWCicuPVLjt6E7h/JCcbou+c+YwLlvwLRFsJTSrpWRMp3pEh/sxZvF+ZBIRnRsG0LquH4cu5fJEtL2TuTLHS+gSQeeGNXhxxWG2pF63Wzc2LoLhsWFM+vGWg9wVi5LSbBF/KddUvPiTNaXklb5NaBDsxfydF7iSpyE+NozBzfwZP2oYmzasc31QD6zO8PqOq6Z0mMahtEKebO+6x2ZajppBs5Os6S1jYyjUGskr1vL9qY/4cu+nlbq2PVf2MH30DJ7u3IBle9Mo1hl4uE1tRny7D4DTmYV4eUj55q+LlTreoqQ0Fo5sR1igFxYLfLfnEsuS0xkeE8qK8R0o0hkwW0ri3OswNKYmxQYVOepcAhT+KCRKftyXwdK9VyjQWNNWArxkdrMcyqNZLaVdH9CSyNIekUGoNHevzdd9Q14e7NvnuPzZZ60vDYTZsAICAneIe1WXg6DN7Zg5E6ZNc1weFAQ//gjdulX7Kf+N2vyf6HKoHm3uIRUzNCaUAS1rsnxfOquf6XjHtHnp9j7zDzkWqUtmjU6ISmRm75nU9LUmnJRo82ExoRhNJjrUD+DB5iEUag2YTG5M6TCN+NUVx7GX6HKjGeJjy9fmpXX5NyPbobdkczTrKJ1DOjNx3EQo8f66AT2BTq7P+2jk48zu8y3f7rzMqM71bC9ATmcW4uku5ZVfDnAqU0XjEO+bxW4VpzMLHeLIS7R5eKCE5cmXuJB9a5z3hTa/fh0GD4a9ex3XvfeeNc2lmg1yAgICAgL2CNpc4O9QLbr8yBEYPhxOnnRc5+trNbzEx1dJC9wNbX4grYCvOzu2pqxsW5wJUYl0rduHwWsPUMvXg9rKYPaP28/OSzsZ/ONgB+P4xPYTebTpo8Svii/3mKX5IvkL1jy5hulJ011q8/4tayGViOjaKJDezYIp0hop0BoAaZW1eR0/BT8ltmXThTXM2nOrZVID/wZsfWornUM7M2vPLMdri5rIls+3MPfEXBhNpd5eftVvNmajL9M6TmPYKscx5mnz+HDXh0xPmk7zoOZ83udzZGIZ60+v54t9Xzi9vyFeIbiLvIn/Zh8DW9Vi/oho3AC1zoinu6RK2rx0on55NKultJnOS3DQ5g+3IEGomwsICAgA95G5BaC4uJgTKSlO1+n1ets6vd76D0/t2rWQSqUUFKjIyckhICDAbp/z563R2+FhYXbLw+uFk5JykgvnLxBa1z4zzWg0kp5+GZlMRq1atarlugRuD0aTma2pWU5FemmW7r1ETD1/+jYPQSIWlRvHWJ2iQyIW0TMymPjYUJfjGx4TSrtwf97dcGuGZFmHcQmlC77zR0Sj1ZvIU+vxU8io4e3O5pRM1h29xndPx6DVmyjQGNAYTHwdH8XKg1eY8EB9O4FfcrwXezVk2dgYNAbrPkoPKT5yKX+dvUG3WdttQq+ElGsqXlxxhPiYMN4c0JT9aXkO25TFWcRfSUpJoJc7345qh6dMzA9b9xPVvi9FGRUYQWoATwIBjqsmRCUyoNEABn25n2XjYivssVmgMfD19vP8dfYGX8RHMO63p9iXsc31+UtxOPMw+dp8ruV4oDOaeWddCu8+1ML2kGW2QKHWUKU4xCKdEZPFQlzDQN7/NZUCjYEdZ24wulM9XlxxmG9HtefXSe3YenEjA1c4zr6d1H4Ka5/ry6iFR/F0l+CrkNnNciiPCV3rO/QBldz8XihkEkJ8PMrZU6BaiIiwtpd78EFr9LtUCl99BePG3e2RCQgI3IcIuvw/wMCB8O67UFhKA3ToYE1fqOTMyqrwb9Xm/0SXQ/Vp87RsNa/0bQLAbycy75g2L2nv48zYUpq5B+cQF9qFdiF9bdr8z1PX7dp6itygX4uajOwQxuAmg0iImsA8l7M6rbq8/+f7nM6yLUuJLj94KZeX+nvCDUhZnEJeWp51AwnWNkTNXF4KTzZ/kpfbz2bziWxGd66H2WIpV5tXFD9eWpv3b1WLL7edv3+0+alT0K8fXCzzbCYWw9y5MHbs7T2/gICAgIANQZsLVIV/rMuL9Yg+/ZgaH72Lm8FJzbdPH/j2W/gbn4O7pc2TzmXzvwem27WmLGmLs/WprXQJ68Ks3Y5mjmkdX2Zg44EUqkWsSIjFRy7FW27ipd//r9x2Q2PWjWH/tf18+uCn7ErfRZ42z+U9OZx5GLVBTfOg5hzLsqY7lqfNxy2xanORGzQO8aZFbR8eaV2rStr8yXmHWPJ0K97e/opDQsu53HO0mdeGCdET2By/GZ1RT2ZhDsHeflzPuk7i6EQOBB6Ah1xekjVt8abnKVARiJtJQlzdPg4Go9KYLWZ61OtB25ptmbplqsv7mxCVwPvd32d4bB0++u0sf529wTcjo/loUypfDG1bJW1eNh3IGePiIpBuWA+Z16yTEG8i1M0FBAQEnHPfmFvWrlnldHlW1nXGJ0wgJCSEeXO/tlvn7u5OyxYtOHjoEElJuxk0aKDd+t179gDQrl203fLoqChSUk6ye/ceHnigq926/QcOoNfraRcdjUxWtR6QAncWvcnM3O0XKrXt3B3n6REZhP6muL8TMekKmYTX+kXSLsyf+X85nm90p3DahfszcuE+uwJ0kc6Iv8L5Z6+k4Dt3x/lSswyNfDWsLZ0aBJJ0LtuuAF1yrrGd6+GGhbxiPfNHRCMCMlValB5S3KUiFuy8wL60XERuMKx9GAazhSk/H3V5fSXthobHhPL19vMV3g9nEX+LktJYMCIavcHM5E++Z8X0lzBrXYvJAYMG8NArjzD7mKNze2qHlxnQaACJ359ALhNXqcfmvoxttF/wKNmaG5Xa3u7aNHnsPi9lbOf6vLbmGDcKb8W+RgR6UqB2bf5xOJ5aj8Ek4UyWioGtavH19vOM7hSOyA2Gtg/DjI73d73m9GHgSOYRRq+LJyEqkcVj3uPYZQ1SkRvD2oe6bIsUHxNKtyZCH9C7Ts+e8PHH1lYRv/wCnSqYliwgICBwGxB0+X+EJk2sbYkeesj68zPPWJPAbtO9/Ddr8xJdHlMvwJYkWPpc5elyqD5tXhKJ3aK2D72ahtwRbS4Wudna+1SGWXtmsGPUAPKKzHyx9axDworZAhuOZbDhWAZvD4xkVu9ZdAntysw90x11eceXGdhoABO+O8HlPA2RNb0rrc0LNAY2rdgI8yDPePNlgAKruT3U9b5BiiDm9JvDkqQsnu5cn/d+TeGp2HDb+n+izTNVGtv9/c9r8+3b4eGHIT/ffrlSaW2V2avX3RiVgICAwH2JoM0FqkqVdXmTW7r853X7SPzmHTqmH3Pc2MMDZs2yPlf8g+S2v1szh7+vzVvV9SXpbC5aTTvWD/mTYoMKrVFDgMIPpcyLhxo/xqBGg8nX5pOnzcfPwxcfD18OpRXS/7NkFO4SlB4S3hzQhG3pmyvdbmhC9AQ+3PVhhfckR52D0t2+v48rbW62WE3aqRmF/HLoKgff6MnMXjMrpc27NQlke/qmck0mpZNUVj+xHomhJVdTjxKfOITiXsUQUsHFlDK2tAlpg9JdicRNRr4a3us+nS5hXZmx23GMk2Im0a9hP1alrqrw/s47OI+OdTsyLOZRZmw6y7i4CLJUWop1pipp89RrWk5cLXBIbyzN8JhQHjz8O9Lx48BkAm9vGDGigpsgICAgcH9z35hb/i6DBg/k4KFD/PTzSqKjo2zO8VOnTrN58+94enrSs2dPu3169erJzyt/IXnfPvbs2UuHDrEA5Ofns2TJ9wAMHjzozl6IQJVRaYxVSsNQaQysPHiFWb+fcbr+dsSkK2QSejcLplPDQArUBlRaA8FKD8wWCz8kp/PuhlQHkX4mqwgPmdhl/9ESAQtW0R/k7cGHG1NZ5iTiu6QNUHxMGC/2asjUlUf5v0HN+WBjqq1Pe4mAE4vc+GRImwrTTkoo3W7IlQgE5xF/KddUGExmPpr5CctnvAMWs8tjvDjtNfb5dqO+fzN2jnqcfG0+uZo8/OV++Hr4UqC20P/zfVzO09Au3I9ctb7C3poWDORJF1MoWQuaSl22A74evlzJyyanWMd7g1tQrLdep49cymv9IjFUdHPK4KeQIZOIWHnwKq/2i0SlMdAu3J+Hvkpi2dh2rD+9zmXvU4B5B+fQuW4cjzR9FJ3ezDPdGtA6tPyY+7Zhftwo1OLl7lX1GyBQvUyaBE89BQFOookEBAQE/sUIuvxfyODB1tYhtWrB6NG39VT/dm1ui41uEsSNIh03CnUEeMkI9HJnzvbzTnU5/HNt7iOX8my3emUisbW4yzx4ol0d3lh9gml9Im+LNm8U7EWxQcXRLNfGmBJKEgmv5HnYFc+d6emfDlzhweYh1HLvys5RA+x0uY+HL9cLTOw8rSLpvLV9Qcks24q0uUlTyF9zP2Dz6d23BhYIDMVpamNZPur5ESaLmct5GnKKdbzWr6mgzatKaqrVvGIsE88eGgq//grNm9/5MQkICAgIVBlBm9+/VFWXF2gN/HJTl8eknybm8gmH7a5FROK3agXyVi2qZYxla+Z5av0d0+bZhTqGd6iFWOTGFVUGfh5++Ml9cRd78PtRHYPaNODtdSn8nnIUY6lStUwiIixQRvz66ZW6xtLthswV1LwDFAGodPbX40ybyyQiekYGEeApI6dYz9bU69Sv4UlOkZ5Ri/bx7kOutfnei7lMf7xxpczvZouZd3a8wVPmsUybMwnj40aoKJyklLEFYGLMRIxmE1qjgdWHrjKqUz16hQ/igbA+GC2FpBekE6AIQC6Rs+DQAnRGHZ/t/azCsQF8tvczHm7yMC/1bETrUD/WHL7CgpFtWX96VeW1eeSjmC0iPn68FQuTLjro8nFxEfTZ9jMeL714a+cxY8DHx/qsLSAgICDgFMHcUgGtW7Vi4ID+rN/wKy+8+BKtW7XCaDRy5OhRLBYLE59/zqG3p7e3N88/9ywzZ33M9Bkzad68Gd7e3hw9eozi4mIGDxpIixZCwebfToHGMYLQFTnFenaccZ3KUTaOsTqQyyRYgJSrBWw8kcHkXo1JWHKA41edC3CT2Wp8GdUxnKkrnbjkyzC2cwTnbxQ6NbaUZmnyJdqF+/H1sLZkF+ltxeTStd22ob6YzJYqPQCVbTfkjGa1lE4j/ixGPS88M541P/3g8jwKT0/Gv/ExI4cPYdVXSYxadAiJCHo3C7kp5rNIvnCSVc90Iq5hDZvgDw9QsHNaN7R6E7lqPf4KGe5SEcv3XWbp3ktc1x4nR/YFBpHre+eKNiFt8PHwJTXjAjcKdVzKVtOirg/Nail5oHEQSeey6dAg0OVDV9l75ekuwWg2s/F4Bq/0bcLEHg15Yt4esgp1+Hm5MT2pcg9Rn+ydSY/wvlzNM/PVtnNE1lTaojkNZjM+cikysYhlyZd4c20Km1/o8rfvg0AVSEuD5cvhlVecz3RxcxOMLQICAvckgi6/S5REhUulzte//vodGca9oM1LYqODRHAlT8MnW87wSt9Idpy5UW6bzX+izcMDFCwe04rt6b85jcSe0mEaHz42iCKN223R5l7uEnLUuZXat4Q8bT5HLlur1tZe9WEMiwl10NM7z9zghkrvVJf/nnIUT3cp657rxLD2oZXW5hlHdpC7ZQ6m4lLR7Y2BRwD3iseeEJVA93rdkYplpGaoBG3+d4mMhKefhnnzbi2LjoZ166BmzbszJgEBAQGBKiNo8/uXqury3KJbujw5tAWfdxrK5F3LADDjxtzYR/m083A+IYC+JvNtqZn/ejyDlGsq5o+IviPavN9y59p8SOwgijQWLmYXY8ENq1vDyqBWNcnX5lfJOF623ZAz2oS0QS6Rc+K6o6moRJvX9ZPz3sNNaRvmRb42nxxNLgHyYD56rDHns7QU6Y1cztNUqM0n9WhYefO7CQ4vOcxh32fh0Updsp2xJSEqgW7h3ZCKZWTm6dlx9gYhvh70iAzm6YUHWfx0G749/DYHMw5y4voJmgc1J75lfJWN+U91DGPQ7CSuFWgZ3blWlbV5ZoEFrdFk186qtq8cbw8JN6a9gceXM8rcFxNs3iyYWwQEBARcIJhbKsHYsU9Tr149ft34G0eOHkUikdCqVUueeOJxIps0cbpPx44d+OD9d/np55WcPn0Go9FI3bp16N+vH927d7vDVyDwd/CRVy0CUyETOySHOKMkJr06I6AVMgmdGgTSrp4/eqOZsXERTPrxSLnbL917iV8ndiY+xnVc9fCYUHo1DWbM4v0uz19SmI6JCCBPY6BIZ2T2sLa4S24Vk/0UUr4c2obsoqo9ADlrN1SW0Z3C+SE53a5YbyzM4cbq91mT4ThbtzQSn2DaTviITr16ElHDk/o1PJFJRHi5S7iYXczmlCzbbM+RC/exZEx7ujQKpEVtX3afz2H+Tsd4yyfb1yCq1VoWHZ1D6YeUv8MLMVMwGsWkZhTip5CxePcZmtVRMrpjOB0aBDJuyQEu5qh5unM9Jv9UsTgf3Smc64UaNp3IwmyBYp2ReXsvkZajpl+LkCo/RKn0BQQra5Cp0rLt9A3m7jhPvxY1+b9BzRi5MJmTGdbZwc1qKVHKhX9ybjvbtsHjj0NODvj6QmLi3R6RgICAQLUi6PI7zI0b8MQT0KoVfPbZXR3KvaTN5VIJMfX8aRPqi95oZnyX6tfmPnIpS55uxQdJrzP/kPNI7PjVw0iMTuTDHjP4enhbpOLq1eZFOiMBCv8q7e/n4cuJa1mEByhYMqY9+y7mOm17OrpTPTttfqNQx8Xs4ptJLNZY+Mpq84caKah98GuOrV97ayBuQFfggYrHHOEXwZtd3iQuNI5jWcfoEtpL0Ob/lC++gJQU2LXLWrhftgw8PSveT0BAQEDgX4Wgze9PqqzL3e11+ewOTxBz+TjhuRlMHjCZ5FBrWsvtrpmrNEbc3PjXavNX+0Zypch1HbssztoNlWVizETmHZznNN3Fz8MXg7mYXye1Z/2Z9byweIZTU87gJoPoVD+AvRdzaRjsVa42/3F8LBnqsxUPPB/YBnQCgirYtkxaS0mLobjQOI5kHCMutCceUuuzyaKkNLo1CeKp2HAsFgldw3qy6MgiAJTuSnI1VTPm52vz2X+Of6TNA71qsHTvJd5cc4LGId60qO3DtF4NWdnlMUbtX+e440svwcyZVRqngICAwP3Gff+mMTg4qNzeoqXp0aM7PXp0r9KxIyMjefutN//u0ATuMkq5pEoz7qRix+QQZ1hj0o3V1pqohJKZogoZ9GoaTHxsKEv3li/A5VIxE3s0LDeuenSncLo1DkKtN7IvrXzhV7ow/fSS/Q7HGdUxnA3Pd0YmEbFi/2X6t6jabLwa3u4uX0wMjwmlXbg/725ItS3TXT3FjTUfYCpyLViDGkfTfMRbpBWJmfLzUY6kh7H22c7cKNSSXew42zMtR038N8msfqYTn2094/QB50DmDjb/NhuTKKvCawuUB5KtyS53fUJUIv0bDmDp3stE1rSm02w8nsGLvRrR/Wa/WgsWlu9LZ82znSr10NWlYQ10RhPLbm7nI5dy9Eo+AH2ahfwtke/nG2z77Jst8EDjGny76yInrt36PkzoWh9ZNT6cCpTBYoHZs+HFF60Of4CJE6FZM+giJOYICAjcGwi6/F/GwYPw8MNw+TJs3w5RUda2dncJQZvba/Nnu9Vj26XfnBbPSzPnwBxiancmK7M1O87kVKs2P5NVhKdUSeuQ1nZF8PJoE9IGPw9fki+cZPUznZm74zwrDlymUbAX7cL9bOkyKddU1aLNLRYLyZtXsenlbzBri26tcMea1tLY9XgDFYHMHzCftjXbMu/gPI5lHeOdB95h/ZEsQZv/U2QyWLkSvvnGmjYoFt+9sQgICAgIOEXQ5gLl8U91uVkkZuLAaRhFYgrk3rblt1+XW4/7b9Xmm1MyiWviV6VrC/EKcWg3VJqEqATiQuOYvHmyw7oSbT6msy9Tt0xl3sG5iN3EtAxuidJdiUqn4njWceJXD2NCVCJzR8ygSCdGrXWekpiWo+aVX47z3qO1XI5ZcUGB/rIe4yAjVCABvSReLHlkCU1rNCVHnWNrMTTv4DxOXD/Ba53fZtOJ63RpWMPWfjVfbaB7kyAMRjM9wvsyPmoC8w/OBQvUVdat1H0twU/ux86z14F/rs1L2lmNja1LxmPDnRtb3n8fXn3VeRK3gICAgICN+97cIiBQHjKxqEIndwnju0Q4JIe4QqU1EOJTURPJv49CJuGN/pH0igxm1u+n7VoUNaul5MNHmrPrXDZvrU1heEyoLRYvX6Onpo8cd4kIkciN9Jxil7kjPnIp342JYe6O87Y48NKkXFMxdeUx4mPCeKl3IxYnpfFoVJ0qPQAFerkzPq4+3+xynIU5ulM47cL9Gblwny1OsujYFnJ+/wpMrmfqjhr/LOOnvkWQUmEnxNuE+nL+RhFfbz9vO8+ojuGse64TIxfuo3/LWmw7fd2hUG10yyJP+i1q8e4KrwvguegXmdrxVbZd2sJnyTMdXPEvxEzlwfr90RmkfLsrjTcHRLLq0BUSH2hADS938jUGirRGZg9ti7tUxG8nMkl8oAHt6/kzz8mM1TGd6tG9SRAyqYgnv9hDgcZAs1pKvDykLBzZnmsFamr6yNFW9kN8kxBvf349lmH77DszG8XHhlX7zAuBUuh08MwzsHCh/XKjEd56y5rmIjwUCQgICAhUhWXLYOxY0GpvLRs/Hpo2tZpc7gKCNr+lzcUiN4bG1KxUL3uAz5NnsW7IH8zdcbFatHlCl/os+MuqN5cnZzCp/RRGr4uvcP8pHaZhMkt4sn0Yh9Lz8POUuWwjtDT572tzXeY58rbOR3f1pP0gagGPARUEznhIPKjnW493dryDRCTh+fbP0y28GyI3MbM2n+b1/oI2rxRZWRAc7HxdcPAda2smICAgICAgUH3IxCISukYwcfkRp+tFZhOTdy0jW+FLm1lvOdXlOZ6+Tve93bocrNr8zQGRPNS6Nu9uSOHolX+HNv/097MMbB1bJeN4sFcwL3d6mZm7HWvLJekmfZb1IU+b57D/lA7TELtJWXVqFT+lrOC1uNdIiEpAbVCTrc4mUBGIXCJn/qH5zNk/h7jQLjRU9uDxufvs7ldpbd68jg+eMh/n12AC7wPeFNYotCYoVkDH2h0Z0mII7//1Pkaz0Wa4kYgkTIqZxMNNHqFQI8ZTJuGH5HS8PazJ8qW1uZe7jJm9ZtIltAt52hw8ZZ5Vur++Hr680b8GYzrXqxZtPrJNMH3/NxHF1g1221nc3HD76ishgVtAQECgkgjmFgGBcpCIRfRoEmzXx90Zw2NC6d4kiLfWplT62EoPqd3PRpMZvcmMSmOkQKPHRy5DKZcgu+ksryxlj9M4xJsfx3fAYDJzo1CHROyGVCxCLhXzyi8nKNAY+Hr7eebuOE/jEG+ClR482qY23SODyS7SIRaJ8PcsP2oyPjaM5Is5Lu8PYCtMP9m+LsuT0yv9YmJ0p3DWHrlK3xY1aR/hj1ZvolhvpIa3OxKRiEVJF3l3QyoFGgMWs4m8P7+l8KAT13MpJFIZTR9/iW1+ndj2jbXdUkn0+W+T4ljw1wXGxkUwd8d5zJZbBp3hMaEsGdMeqVjE2CUHbMczo0Ul+QWV5BcsbhXHuvt51OCbAYuIDnkAXw8ZUTX6sHFoP1S6AnI1efjL/VC6+3BdZcJdJGfl4csMaFmTDhEBmCyQfCGHoQv2Ok3IMVvM5Kn1fDemPXqjmQKNAW+5FKWHBIPJjMlkJl9vxF0qtt3f3CIds7edIzYiAD+FO76evlUS+f5yP774Y7etSB8d7mczGzWrpWRC1/r0iAyq9lkXAjfJyIBHH4U9exzXDRoE338vGFsEBAQEBCqP2QxvvAEffui4LiDAmhR2lxC0+S1t3ijYq/K97LFGYhcbVDQO8SY1o7BatHm7ev4YTWZ0BhM1/UJJvjqBuQfLn6k6ISqRgY0HYDGJGNUxHL3RjMFkdtqSaFTHcDZOjOOjTalsO32dV/tFVlqbm9QF5O/8jqKjv+PQHjQW6EWFM0R7RfTigx4foDPq7GaHKt2V1HbvTL8WgjavEJ3OGqm+bBns3w8NGtze8wkICAgICAjcMSRiET0jg52m1Cm1RXy+fibdLhzEKJGgKxrFW8mOaYpikRuNgr3wcpfY0vtMZouDLofq0+Z6owG9WU+eJo9cTR7Bfn4sG9cGkHIpW4uHVIy79O5p8yHt6+CGlCkdphG/umKjzJQO03CzSOnfYDA9I3qSq8klV5NLXWVdZGIZnyd/zuTNk50aW0q0uQkjK1KWs3/cfnZe2sngHwc7mGQmtp/I/nH7mbx5MksfGYC7xA2d0YJY5IbZYuGnA5fJLNCyZEx7vNwlrD10xdH8fgPczrpRGF0IFXW1slhbKa07vY7MokxWD1mN2qC2S27JUeeCRcq56wW0ruvLN39dZN1zndh3MddBm8fU8+e1fr2pH+zOosOLmNh+ImPWjanw/k7rNA29QcyMzaf+tjb3K6XNx7euQe83E5Hv3G5/uRIJbt99B0OHVnhMAQEBAQErwttGAQEXiEXwbLcGtA0rP4KwXbg/xXoTdfzktvQQV5Ttb67WG9mamsW8HY4z+hK6RtAzMrhSxceKjtO9STCTfjxERr6WL4e25WTGrW3MFtAbzbz/UAv2peXyxLw9pFxTIRa5sXNaN6ezOcUiN4bFhDKuVDHZFQuTLjJ/RDSTVxwmoWtEpV5MtAv3Z9DsJGLqBTDxx0OYLeDlLuGFng1pFKykYZAXdfzk5ObmkL32I7SXjrkcg5dfDbwGvEJBLfsM8pLo8/iYUF7ua+0J/NaApny69aztd7osOZ1ujYNoWkvJyQwVIpEZhe8OzuuWoDWX31qoNIMaPs6j9d/g2z8KmHxtOz5yKcNjQhnZIRxfd3c83AIRi9wwmWDziXQOXjrDvKeiKNRaxzB3u+uEnOExoYzvEkHPT3ZQ08eDprWsPVdTM1SI3NyYEx9FZoEWL3eJ7f6azBaOXy3gpwNXGB4TyrQ+jSr9EDU5dipiNxmbXuiCt7sENzdQaY3MHxGN0kP6tx40BarAvn3WdhHXrjmue/NNeOcdEAn3XkBAQECgkhQVWdsOrVnjuK5LF/jpp/ITGO4Qgja3avMWtX2sBd0qkKvJw8v91rirS5u/3KcxF7PNTO85k7jQrszcM92hID6148sMajwQjU7MlbxiGgZ58+nWM/zgpGXPreTHUN57qAW5RTqUHpIKtbmbUYs09Tcu/vkjRm2x/UHlwGCgSeXu1dYLW8kqzrLNDj1x/QQJbROIqdkdL5mCWr6egKDNy+XSJXjiCatWBRg8GPbuBW9v1/sJCAgICAgI3DMoZBJe7RdJm1A/FiZdJOWaioY3LjF/9XvUy8sAQGI0QvwwGk/4in0a634+cinxsXUYGlOTYoOKHHUuAQp/FBIlf6bm2ulyqD5tXqQvZu2pdczaM8NBq07pMI1BjQfywa/nOXq54K5p829HtiOn0MDARgNJiJrAvAqM4wMaDSC70MD/fj3JmwMacDb7AqtPr+SG+gafPfgZjQIaEeYbRl7mLXNLaW1uMkopMt7gsz6fMT1pOvMPznc4z5HMI4xZN4aEqAQ+efATVLoCdr/SnRPX8mkQ7EGRvuDm71CGj8KMxWLh212X+H5cX/73wLtsPreJ09tOk+2RjaVjJSZKGAEJtKnZBk+ZJx/u+pDpSdNpHtTcps071+3M210+YNKPx/jiyTYM/2YPXw2LKjdZPvliLq+sSuGrp8J5e/vb7B+3n/FR451ebwkJUQn0a9gPrQ7MFgsL/rrA4fQ8pj7YiGkdpzFsVcXafFqnaej1In6b1AWlWoXH4AGI9u+328bi4YHbypXQv3/F90ZAQEBAwIZgbhG4rylxfhdqjWCx4OkhpUhroEBjsLrAPSQUavVcy9fYIgjz1Hr8bkZm/5CczrsbUomPDWNsXAQvrjhS4TnHx0VQkqOg1hv5YGOq0x6fKddUTFx+hPjYMF7r18SlUK/UcWJCeWtAMxYmXcRothBZ05szWUV4uUsYG1eP0R3r8eFvqbZ+7wAms4UfktMZ1TGcqSvtjSONgr3Q6k12Yt8VKddUaPUm6voruJhdTELXiApfTJTMMsxT61HIJOxPs4rxDzae4oexMXRpVIOa5usM+eQ1tJfSXJ6/YfM2qLu8gMQ7vsUMDAABAABJREFUoNxtlian0zrUlyKdES8PqS1SMS1HDcDvJzOp6+dBXMtz7L7xFSn5Zyt17QpxDWb3mcPZ9Ea8tfrW/S3SGdl2+joHL+US4OVO21Bf+rWoRf8v/iJPbS3cq7RGdp3NRioWVZiQsyw5nTahvgxtH8rX289zMuPWzIhZj7fkh+R0Ho+uw/guETQK9ua9DSd5e1AzW9/bZcnpdKgfwOAmg5gQ5Xr2bUJUIp3rPMjOMznWSHWJdcapXCYhWHl740MFgO++s7aH0OnslysUsGQJPPbY3RmXgICAgMC9yaVL1sSvY06Mws8+C59+ClLHWZTVTYk2N1tM4Ga0zar0l/vhJ/dFKnLn7PUCLtwovq+1ec/IYAIUOodju8Jf7keR7pbmri5tPnPzGRaObMeus7kMaPQwg5oMtPu9+Xr4cjZLy/PLUthx5gZfDWvL5Vy1U2NLaUp0+YUbxew4c4PRneo51eYhXmLaFCaTtHIeN65fdzxQfWAQ4OP6/nhJvRjTZhw707dxJPMIx7Ks34XWIa1ZOOh7utbtw/AFh/nu6RhBm7vit98gPh5yS73gOXnSapxbvVpIFBQQEBAQELiHKNHmeqMZsciNIq2RAq0BX7kUpVyKVOxGs5rezB8RjXj1KmrMnoa42N5k7HblCs97XOcpahAeoGDxmFZsT/+NgSuGOzWZmAmh5JVVdWnzIn0xU36f4tQsciTzCPGrhzEhKpHp/Wbw074sjJa7o801BhPFOhOvrU5lzlMz6VKecbzDywxoNIDE70/wYq9GqDRGBs8+wPCYcN7v9gkmilFpVQxoOJDHmj5GvjafXHUefmW0eai/nPHdAtiRtsOl0QNg3sF5xNaJpUd4L0xuBq5ot/HCj45Godfj3mDDpD6I3Cx08Ylj7rY5ZNfLrtxbSANw85Hzi+QvWD90Pb+d+w0vmRcqnQqNQcOUDi8TV6cPT8w9RFqOmqxCLQ+3qVNhsryXu4TMwhzytHn0WdaHTcM30aFOBz5P/rzcdk4ZhZn4evjySHsL4xRhKCRKinVG+tQfwPi2E5h/yLU27x3RHy+ZO7LrWdC7N6SUSRb19sZtwwbrRBIBAQEBgSohmFsE7ltKnN/rj1zjjQFN2Xcxl8W7HYu5CV0ieLJdKE/O34O7VGyLSzydWWjrl6gzmOgZGVSpGY+tQ335ZMtZJvdqyNbULKcCvTRL914ipp4/fZuHOMyyM5qscd5bTro+jo9cio9Cho9cyoSu9clXG5g9tC0KdzEysYiMAg2bUjLtBHrp8697rpPDtXm5S8hVV9yGpzR5N7f3lEkYNDvJrm+psxcTJTMz/RQyinRG23FSrqko0BoZ8sqnHPvhQ9RlHpzK8uTwp8htPYLT2RU/aCxKSmP+iGjipv/J0PbWuPNBs5PI12g5cH0jm9es4Nj1ysVMAgxr+jSdgyfhhS8/7LMWya2zFMIYFhOKVm8iV63H/+a1ZxdpGd0pnE+2WI0z11VaejYNZtTC/a5O4zD+kuh2uDXT9ucDV3imWwNWHrjClJ+P8eaASIe+t/N2nKd7kw683vkDYuvE8VmyY8/WF2Km0rVuH0YtPIqnu4TYiACMZovQeuhOYDTCyy/DJ584rgsPh7VroWXLOz4sAQEBAYF7mN27rUlgZY0BEgnMng0JCXdkGGq9kSPpeTSvK2f96fXlzqoc3GQQF7PVxE3/k8Yh3velNn/iuU4o3OVVisRWSJQ200QJ1aHNU66pMFssZKl0xE3fwaZJcWw+pudSLuQUZ/F7ylGMZuv5/BUy4hrV4Im5TtopOqG0rp3y81FbK6JBs5PILyxm1/of+GnKT1xyZnKXYW1B1K7i88iow8SW83mpazckYiP52nzytfn4y/3wkipZsvsqg9ceoEBjqH5t/sB/RJubTPD22/D++47rPDys6S2CsUVAQEBAQOCeQa03sud8Nk1CfNh9Ptt5zbxrBD0a1eD4uBeJXfa1wzFuKHyZOuQNZox5knE7LxDfMYQPkl53agooMZkkRiUyo/dMPMTyatHmRrORtafWuUxBAZh7cA5dw7vwaPRDZBboHbT55pRMDqXn0S7cz66NUnVq83y1niBvd5LO59D/8328+1BXdo4aYDWn3DSO+3j4ciitkP6f7+Nynob/DbbqcmsLpYvM3XGRpFe6s3x3LgcupbBwZDs2laPNn4yujdLdm8+TP69wzGA1nDwa+Sif7/2CN7e/4bC+SF9Em5DWrDm5kpe+fokczxxoWIkDGwERNmOLn4cffRv2RemuZN2T68jV5OIv98fPww+13o1RCw/azO4qjYHBbWozfEGyy1MU6YwEKPwBOJd7jnYL2jEhegJrn1zr0PJo3sF5TN48mb1j99J7aS870/u0jtPoGtqXNzt/SMe6rrX5yz+fYubAxki6dEV0/pz9gAIDYdMmiIqqxA0SEBAQECiL8CZS4L6kxPm9/mgG65/rXG5sXco1FRN/tLrAf0nsxISlB0m+eGsWWumZjBO+P8hnT7ahfT1/FvzlGJVYesbj5TwNz3arz9ztFyo13rk7ztMjMgjA1mM0X6PH212Cj0JGfrEeH7nUafR6eICCJWPas+9iLsO/SXYY19jO9ejdLIRpK487PXeBxsDIhftYMqa93WzOIp0Rf0VFTTLt8VPISM0oxEMmpo6f3K5vqbMXEyVjdJeK7AS/xWLmf++8zd4Fs1yeTyQSkzDtHSa/MImen+6s1BhLZrE2DvFmWXI6Lep40iTiEGsvfE36jcuVvlYP6tPa+0Vm9BwNbm629k0lv4/9F3P5cGMqWSqt7aGoSYg3ozvVY0ynCNYcvsqFbDW1feWYTJYqJ+Q0DvFG5OZm97mb3KsR645c5avt521F9Xc3pNrtbzRbyFcb6P/5fobHtGb9kD8pNqhsD1EKiZLlyRm2Aj9ATpGe305kMqZzuGBwuZ3k5sKTT8KWLY7runWztosIDLzz4xIQEBAQuHf57jsYNw70ZQzLfn7wyy/Wf1/uAGq9kTnbzzG+ax2mbpla4azKmb1nEuqv4JMtZ23JfnB/afOVibGVblczKWYKy5Mz7DQ2WLX56cxCvDwkPNgsmG2nr7Pgrws0CPKqkja/mq9h4/EM6vjJ8fKQ8MFvqQ7nApjYsyH5av3f0rWpGYUsS06nSaCUulf+4OSP35BeVE78exjwEOBX8TmCxD358+nvSb1q4K01J7lRqCXAy52cYj01vPT0agoPta7L6kMZFGgM1arNJ/ZoyJzt5+59bZ6ZCcOHw59/Oq5r0MD6t0QwXwsICAgICNwzqPVGPt1yhqdiw/lq27lya+avL06i0a6viT2ww2H90ZCGfDLhAwYOiMFgMjOxZ33WnVnlMu0CYM7BOXQO68LDTR6pHm3uaWHm7umVOs6MpBl0rvMg/b/Yjdli1b2JXSPoEVmDAG9oHhpm10bpx30ZLN17pdq0udJDikQsYkDLmmSptEzfdJYLN4roERlMgKfMwZziTJebLXA1T8OpzEJEbm4utXmnBjUo0BVwNKtykzgPZx6mQFfAL6dWOqzz8/Bjc/xmEhcn8vuJ3yG0UocEDdYWojdp4N+ATcM3sfPSTros7uJ0ssOPCQNJ/P4EV/M1NAr2Ik9tqFCbn8kqwlOqtBmQ8rR5Tlsenbh+ArPFTJuQNriL3Tlx/YTtGEcyjzBs1TASohJ5teN7FOS0rVCb5/SN5PCgkXT79M1bg6lTx1rbbVLJnqkCAgICAg4IbyEF7juMJrPN+f1Sr0YcuJRbYaR0iQt88eh2FGiMqLQGlB4SwI3v9qTx7oZUinRGDGYz+Wp9hTMeI2t6k1fFwq5KY+BUhoqZv59xKIKP6hjuENMN1lmh342JcWnemf/XBdqG+Tkdi1jkRqNga3F7ys9HaV/Pn/kjonEDslRagpXuNKultBtPeZQI7tQMlV2rI7MFUjPsneqlzzu+SwTL992awWjWqcne8DHfnnPtyBZ5eLN8xQr262qR5+TFgivy1HrcpTpU4nWM2TQOjTmj0vuKLErGtnyVevKBNAhS8vvJLLo0rMHJDBU+cilLn47hxDUVHRoE0ibUzy61Zfm+y7y74SRH0vP4/ulY3lx7Al+FlDNZRVUav95o5psR0VjA9rkb0LIm3ZoE8e6Gk8x6vKVdtHxpvNwl5Kv1do7/W+YjlcMLjpL7tePMdcICFE5nSghUAykp1nYRF5w82E+cCLNm3ZF2EQICAgIC/xHMZnjtNZjupNDbpAmsX299KX0HKNHmsRF+rDu9vlKzKuPCuvBo5KN0qB+I6j7V5lN/Ps70x/tVql1N17p9GLz2gN3ymHr++HmKmfNUc9SmGzzZ0Y2npWF2hfoSnehKm8OtJJexnSPILdY7LZ6LRW70b1GTy7lqx5UuyFPr8XKXYCzKpejwbzw9eyOG4gLnG8uA7kAMUEFIiJvFndpuz7I78X+kZBSWq83fWpvCgJY1WTKmPf+3/mS1afOBLWsSUy+AFfsv39va/NdfYfRouHHDcd2jj8K334JPBT2hBAQEBAQEBP41lGhzpYfUZc28fs5l5q96n/q5VxzWFTwxjODPv2S6XMF3e9KYvvE0f06LrbTJZNbuGQxsNIBTmf9Mm0fW9ObL+PAqmTeKDSqbsbpYZ6R1mII1p39hxm7HVMlJ7afw66R+TP/tYrVpczN6pvStwTVVtoOJpkBjQCxyI7Jm+bocKq/N4xrVIF11ulL3poTrxdfxknk5LI9vEk+v/+vFBe8LlTK2uBndsBgsdsaWEoPM9KTpTtsklZ7sMGfEDNQ6MT8kp9M6tGJHu8lsYXlyBpPaT2H0unjbcrPFbEtmKc3EmInMOzgPs8XssG7ewTl0qNMZraE1XabvdqnN89R6vm7UneBJGpp+/gE0bGg1toSFVThmAQEBAYHyEcwtAvcdepOZudsvUD/Qk9Gd6vHEvMrFYpe4wEN8PAjx8SC7UEf7D7baBEtkTW80OhPvrD/J/zacdJpGYhWg3kSH+ZFbXDXDxXWVjq+2n+dUZiGRNW8d+1RmIVNXHrOL6S4pisbHhlWq5+T1Qvt2Pa7a5ixPTmdAi5qsOnSF+jW8bCaVikjoEsGJqwWYLeW3Oip73iKdkRre7jSr5QO48e2vu7nwwzsYclybkWQ1wnn81c+I6dSFRUsPMkFR37audHG+dIxkCQa3q8w+9AYbbizBJFODo4Z1jkWCt6k/PoahXLlSi/fHNEAuFfPlH2dt7Zue694AhbuEQq2B8d8dcPkipE2oH+8Obsbu8zmEB3hWchBWPGRi3t+YyqYTmUTWVPLmgEg6RAQiErnx5oCmGExmliWnk6d2/AwW6Yz4lkrkcWY+KkvJQ1PJd0Qwt9wGDAbrrNjSyGQwd671hYKAgICAgEBVGD/e+tK5LH36wI8/3tGX0XqTmY3HrjFrSFOeXVj5gvegxgPxknmikEnuW21uNon5vwc+oktYV2bsnu6yXU1p00R4gII5TzXjt3NrnLZ/mtR+Cmuf68vzP5ykS6Ma5WrzpXsvUcdPjrtURFSoH7ERAeiNJodrEovceLBZMAaTGR+51GGdK21++fRxkr55n4zD28BsLHvoWzQB+gKV+OhKzWEE6l/mrX4P4ukhraQ29602bf7WgKb0aR5Ckc7INyOj701trtVaW2V+8YXjOokEZs6ESZOEVkQCAgICAgL3GHqTmQ1Hr/HJE23KrZn3PJvMpxtm4a3X2K8Qi+GTT/B5/nkMRXqbNu/XIoR8bX6VTCb52nybyaQyXFfpmLPjPGaLxdY6SOkh4XpRTqX2LyFXk4eXuwQfuZQlT7fivV2vlZsqOXpdPInRiXzw0HTyitxuqzZf93xffk/Jp1+LmuXq8gKNwTa5tDLaXG804yevRNRhKTylnqh0pUxHZiADZmtmY/Fz4qJxgnuxO1uf20rXxV3tzCOJ7RLZkbbDqbGlNHMPziEutAtd6w5g1eGrdG8SXKnzLt17hbXP9a2EASmBuNA4Jm+eXO42nyfPYt2QP5i746LLz2iJLp8a0YfVn9RANmwoBFduvAICAgIC5SOYWwTuO1QaI1fzNWyaFEdOsa6KMzSNtmjnnGKdnRPXy11iMzGULTqWLUhrDCb8PavW0sfbQ0L/FsF8+mQkxQaV0xjENqG+DI8J5evt5xGL3BgWE2prh1MeZdsLlY5KL6/IW9tfzrUCDeuOZjg1qZRleEwobcP8sFgszHq8JYuS0hxaHRXrjC7P20aUTsZ3kzEU5Lu8nhade7Ny+VKUXp7ka6wFcg+ZmJh6/kSH+zA0pqbD/Vu2N525+9Zw3fQrGvEBlhyvnBgHwCLC09QdX+MwJBZrBGbKNRV6o5kzmYUkdqtPsc76+x7aLpQPNqaWO1O39IuQaSuP0ad5CJ9vPcOcp6KrlJAT6CVjTKd6vNSrsW1msqdMTK7awJtrThBZ0/mMYrHIjQZBXvgqpAxoWZPfTmTavVwo73wlEZhmC3bfEYFqpHVrWLwYnnjC+nNICKxeDbGxd3NUAgICAgL3KsOGWVsSGUq9TH/hBesLacmd/Xe8UGvk3cEtyNNkV63grcnDS3bLZHC/avNxcREMbvYI3cP7UqDLR2cqxFPqg9TNy6FdTcm1r5jQltf/fNlpUbekUJ8QlciycTP4M7XApfHjVEYhucU6xnWJ4J31J/jg4ZY2HenlLrHdZzEW8jUGvOVSmtVSciVPQ3xsHafa/Lud55m7eAVFR37jkekprn8RPlhNLZVM9R7fNoFQ8Ti2pKiqrM37Nq9ZLdo8r1iP0WTmuR8OcfBS3r2nzU+ehKFD4ZiTCQ61a1tbZXbsWL3nFBAQEBAQELgjFGqN/N+g5k5r5m4WM8/vXsHkXcsc9jMHBCJa+TM88ABgr80DPGXkaMppJ1kO+dp8vNwrp2F85FJCfGV8NrQpKl2BTVd6y3wQiYz4efiRp82r+ECAv9yPIp2K+Ng6bLv0W4WpknMOzCGmdmeyMluz40zObdPmidGJvPvARzyz9AjJF2/dy7KG7DGd6lVam+cW66nt72tr1VMRbULa4CHxuNWq5wbgAdQGC5WopesAA+i8dAQqAmke1NyWmiJ2E5MQlcCg5YMqPg4wa88MBjYewIUb1rp/ZbR5gcbA+xvO8+nQWeUakCbFTCIuNI4+y/q4/MwczjyMulTKjzPK6vLcVycQ4uNRqesTEBAQEHCN8AZS4L6jQKMnPjaMU5kqvDyq1sZDpTXYRIiP3L4AXrYQXYKzgrRY5MbOad0qXRTtFRlEgBJQ7GXgillOnd8bJ/Xjm7+uMjYugrk7ztMo2Aut3lSpnpMlIvBKnqbCqPSpK49x9HIBbw1oxqDZSQ4mlbKF75K+8m+vO4HJDF0b1WD+iGjMZgsavZFeTYPp27wmFgt8+Fsqy5Ltz2uxWNizZgkbty8CJ1GApXnljbdo99BYZv6RxszHW+FlscYObjyewdfxzVh/Zj0DVwy3u39BnkFojTpUFIDY5eEdkJs64GuIR2ZxjBLMKNCw5shVwgM9Ebm58c7ApvxxKqvCFljLktNpE+pLVJgfBRoDBrPFro1TRYzpVI+VB6+w8XiGbWbysPahtKjjS/8vdmG22BfrfxgXy+urj9Oslg9DY0KxWCxczC7mlb5NeKVvE5bvu2xz/ztjdKdwfki+FYFZ+jsiUM08/ji8/ro1vnLVKuvLAwEBAQEBgb9D9+7wzTcwcqTVzPL11zBu3N0Zi8XC5XwNIveqFbxztfnU8alj+/nfpM1fjJnKr5P6svCvayQ+0IDtp6/j5sZt0eYvrDhCfEwY47rU4/WVVwlRevBqv0B2n89mx5kcOw3XrJaSDx5pyp9pG13OVgRr3HbnunFcy2rlcE9Ka8nX+kVy/nohUrGIV/pEklGg4ZW+TXi9fyQysYi/zmbzztoTNm3+Q3I6z3VrQGQtd7an/3ZLm1uAqxBwKoDiw8Voi7WufwkSIBbogrUdUSXZdy2ZjjFdWNl1AHsvZN9xbR4d6se0vk0YtmAvx69a7+s9pc0XLYJnnrEmt5Rl8GDr35XAwOo7n4CAgICAgMCdxWJxXjO3WPh8/SwGpe502OVEcH0UG9YSEd3Mtqy0Ns8p1hMgr1paha+HL8FKfYXbhQcoWDquNX+krXXaOmhax2kcTjhMz+97ci73nMtjtQlpg0Ki5Nz1IhaMbMGAH4dVaqylUzxulzYvMdFEhbW2M7eU6MinYkNZ/Wwn5BIRadnFSMUi3hrQjAKNnjcHRJarzQ9dUjGlwzTiV1d8rVM7TmXugbmYC8zWxJYalbo9Vp1//eb27tZFOeoclO7KW/ciqBlqg7rK6T49IoOrpM0fbB7C2sPZQAwbh25DYyxEbShAIVXir/BhRtJ0Jm+eXCkzlM5cyFOx4UzfdIoCjYF+p3aR6RXAoTqRgPU54Md9Qs1cQEBA4HYgmFsE7jt85DKGxYTywa+pTO7VqEr7KksJe6VcYlcAL12ILlnmI5c6LUibqlAU9ZFL+ejxJry8ZWq5Du5Ra4eTGJ3IR71m4IaEtwY0ZceZG7bZqq4oPZa0HHWFUekAS5Mv0TrUxzYTddDsJIbHhDJ/RDRavYlinZEaSndkYhEnrqqQSkS81rcpeWo9QUoPfDykaI1GCjUW1DoTHjLYfuq6g7HFbNCRu3k2xSnbXI5H7C6nzVNv0O7hEbQL9yMmIgCj2YJEJCKmnj99mvvxxjbnDvjrxdcrvEd2WEQoTHH4GB9HZgkvdzNfuYzjVwvQ6E3sOpfNQ21qM3T+3kqdYlFSGvNHRJOvNuDlLim3jVNZ4mNC6VA/gL6f/2WLovz48dZEhfsR/02yQxF8WXI63RrX4MthbfkjNavCOPaSWaSlz9enWQiLdqfhI5dSoDHYfUcEbgP/+x+88QZ4CA9DAgICAgL/kBEj4Pp1aNMGevS4a8Pw9JCy6UQaT3X2r9J+/h6+dj//m7T5yBJt3vsjLhec54v4cDylSvQGi00zlcc/0ebtwv35evt5dp7NLlebi8V6Rm6oXPunT/bOtBXqnQWGLEtOJ6aeP50bBqI3WvjrXDbbT18nS6VFInJjdKd6tAv3o124n02bp2aomPBAbaaV3L98IAU4AtyAHCoRHd8M6AX4Vuoy7Cj97PRap/cr/H1A9WjzOn5yErvWp0P9QIYtSLYZW0pzT2hzkcjR2OLhAZ9+CgkJQhsiAQEBAQGBexxPDykrD151rJm7ufFXeBsHc8vqpg/wap/n2N6wvt3y0tp8a+p1pj/WuIoJIUq6NrKw4VhGuduVtA56f9dr5eryYauGkRidyKbhm2i3oJ1L08KkmCksT86gQZAXxQZVlYwWxaVSPG6XNi9toinR5tZESmsaokqfxbWiQkJ8Q3CXmMjV5JOrzsEi9sNLquRqgZl24b522nzjsSzeHDSQhKgE5h2cV+65x7cdz5XMK8z8fSYoy93MkWyshpYy3qbaytrU8qpl+1npriRPU7l0nRJyNXkEeMoqrc2Hx4TSLtyfQbOTqOMnx9tdRueGwczblkeh9gaTelv4cNeHlT6/XGJNaFz3XCeWPvser6ybQbHUg+FPvk+rh3rQu1kwKq0/lptto4SauYCAgED1cRuaLwsI/LvxUUjQG01sSsm0FbwrQ7NaSpTyW34wmVhEQtcI28+lC9ElxMeGlVuQXrr3Eu3r+TOsfajL8854tDm/n99QKQf36lPr+OVQOl4eUt57qAXB3u629WKRG5E1vWkX7kdkTW9kEpHt530Xc2hfz5/RncJZlJRWwZ2wsigpjZEdwhG5WWP9vt5+ngnfHyAtp5iGwV78cuAK7hIxuWo945YcoMcnO3hs7h66zNjGkPl72HbqBp4eEuK/SUZnMLPgr4t2xzeqssn64ZUKjS11w+uxcesONnw8GZXGwNmsIs5kFVKgNrA0+RIfPtKUTRdWVnj/KkIqklJH1p9aurnUMEx1aWwpHTuYp9Zz7nohap3RbqZuye+jQ31/xsXVY2SHUPq1CEEmEZFyTYVWb6KGt4ywAAW1fD0Ys2g/CV0jmPV4S4fPbLNaSmY93pJX+kYicoMVCbFsn/oA85+K4uz1QgZ+ucuh+A3WB6CmtXz4aGMqk348Uu6s3Pk7L7BkTHt85FYR3qK2koWjonmtXySLki5SU+nBuuc60SsyyO47IvA3+OMP+NDFg5RIJBhbBAQEBASqjylT7qqxBaBIa2DxnjR8PayR2JWhTUgbfOW+dsv+jdp8Veoqlh1fSsPZYfRb3o2kaxtY+1w04QGKO6bNr+SpaRDkxbK9l5BLxRTo8v9Wob485u28YAsh7xkZxMt9mjA3PooFI6K5cKPYQZu/0b8RS3YuZu7subAA+AzYgjXWvCJqA2OAx/lbxpbSzDkwhz/SfmN4jDX953Zpc43exKYX4lg0uh2nMwvp+ckOjl8tcDqme0KbjxhhbUlUQosWcOAATJggGFsEBAQEBAT+AxRpDbaaeYvaSju9uqp1b5a07Q+AyU3Eu92e5sUBL1E/zFFzlNbmeqOZQ5eKmNJhWoXnF7uJeafre2w/lUenBoHEx5SvzeNj67Dz8qZK6fLdl3czIXpCudskRCXSI7wvqRlFNKulJEddxVRJTZ5dG6U7oc3DAxSsfS6aGkGHGbiiOz2/746HxJ11p9cStyiOsM/qEvNtaxrdfBY5lr0JhbuZy7lqCtQGNhy/xhsDG/LHhT+Y2nEqiwYvcngeaxXciqH1h/Jj8o9M2z0Nk5epcjekGLgKBABlHiXahLRBIVUws/dMUp9NZdfoXax4dAV1feo6HEbsJqZlcEs6h3amZXBLxG63Ytf95X74KqSV0uZfPNmaV/s2oVhntNPm3WZtZ8X+y2xOycJTqqzS86i7yJsJSw9y6M2ZvPbTdMQWM0q9ml/WvMPLtXRoDSa+2XmBegEKNk6Mw0eomQsICAhUG8JfVIH7DqlIhFpvqtIMTYCErvWRiW/5wSRiET0jg4mPDWXpXmuBvLRTeMWBywyLCWXckgNOj1egMVTY0mds53p0auRD54VVc3B3mb6boe1DebVvJN2bBBFZU8mwmFC0ehMFWgPhAQo8pGIKNAau5mnwU8jwdpegN5krjEovIeWaCrPFwo6p3chSafFTyHCXivghOR2LxcyT7SvXw/77sTEU6+2NH9orqdxY8z7m4nyXY/AIb4Nl0DTGb7hOs0P7Gd8lgpiIAG6oNOTpMzArNpG4eSNbzm+p1DU5w98jgGDxAH6Kf4tTV2VM+bniB47SceB+Chkag4lrBRqgxFEfRnxsKDIJSMUmCnT55Gvz8PMI5qPHGnP4UhFag4mavh4Max+Gj1yKu1TE6kNXsWCxOf3z1Hrbfd+amoVKq+f9jam81jeSsd8dsPX0LI/42DCSzmWzNNn1bOCSWbkbnu+Mh1SEXCYht0jP2etFPBplffA4fqWAmY+1svuOCFQBiwW++AJeeglMJoiIgCFD7vaoBAQEBATuZSwW+Phj8PSExMS7PZpyKdAY7ArelYnEntJhGljsZ779G7X5F8lfsObJNUxPsvZzH75qGBOiEvkp8UOMJhkanaM2zy3S46uQ4uUuQWOouI1RCeVp8wK1AaPJzBPRoSzde4nm4ZU7XgllC/XOzqvRm/jot1N2M2ub1VLatPmJy3lkpJ9Ftf9X4v+3kT1Je6o0BoKAbkBk1XariM+TZ7H+yT8RuYkZfhu0ebHewNwd55jYoxGdPvrTpS6He0Sbu7nBnDmwdy8MHAjTpwvGawEBAQEBgf8QBRprUt256wV893RrrhfnkqPOJUDhj0Ki5KdOtfljSj4LW/UlKbw14FgzB0dt/sbqk/w6aSAJUROY58SM4ufhR2K7RCbGTEJv1HOl4AbFRpjWrx6Pt6vFB7+esWvH06K2krFd6tJtSeVaB32691O2jtjK5vObOZRxyLa8dUhrXu70Mv0bDiCvyI3nezSkfg1PCg2VcV7fwl/uR5Huls6+3dq8JLXmg6TXmX9oLn4efhwYf4DpSdOZf3C+w35HMo/w1BpreuEH3aejN7gxpF0QGmMRb29/m/SCdCZET2Dtk2tRG9Rcyr/Ezwd+ZnnKco5KjkJlQ0f0wGWspvRyuqlPjJnIZ3s/Y3rSdJoHNae2d22+HfQtMpHMlu5T8nlIiEpAbVCTrc4mUBGIXCJn/qH5JKUn4Sf3pUsjBQ+1ruNSm3u6S1DIxDy9ZD91/RVMcqLNTWYLy5MzmNR+CqPXxVd4mSUpP8MPbuDhLfafZ5mqgOIfV3BtcgPGdbEmGh2/UkBcI6F1p4CAgEB1IZhbBO47JGIR/p7Wvp9ViZTu1rgGkjJCXSGT8Hr/SPo2r8nyfensPpfD7yezeK1fJL2bBqE3ui5Ip+Wo7Vr6iLA+RPiUFLT1JvK1OX/Lwb0sOZ2YiAC+eLI1v53IZPKKI/SMDGJo+zDyNXrSstX4yKWE+HiwfN9lirQGHo92dEi74mq+hh/3p3MpR02RzmgzU/zfoKYU6yvXw75v8xD8PG/1YS08upnc3+eA2ehy34Y9h6Jr8yRuIqtj+8S1PCasWEHz+ue4bkji2PWKDUuuCFc249l2z7NudzgTuzXlzxQNg1sHEx8T6rLgXBJx+O6GVFuCy8lrKnzlMsIDFCwZ057UaypMFi3rz2zk832zHPrBTukwjajwQfx15gYJS60PXCUx5O3r+fPUN3txl4rxcpfY7vtnQ1qTXaTDUybhWoGW1IxCl9cnFrm5fMFTlnk7L7AioQObTmSyKOmi04j0Cur1AuWh1VpfOi5efGvZ6NHQuDG0bn23RiUgICAgcC9jMsGkSfDVV9bUr7p1YcCAuz0qp/jIrTqwooJ3CROiEhnYeADXVXq8arjbrfs3anO1QU3zoOYcy7Jq07kH5xAX2gU0MRTrzAxtH0aBVk+xzoCPAowUklGUg7/FnwCFL892q8fSvVcqbJ0DzrV5ZE0lKyd04OClXFYfuUrXpmGVGnsJZQv1zsgs0JKlsm9Vc+JyDuM/2kkT/VmuHtvF+fPnq3RewDrT8wGgOVDJUJAWQa04fr3yvx8zRXSI8L8t2jxLpcXNzY1MlbZCY8u/Tpvn5EBAgPN1Pj5w5Agoq5JJLyAgICAgIHAv4O/pztqEVuzI2MoL3zvqokntp1B/1zquLjwKOepya+bgqM2n/XyamU/MpEtYV2bunm47dgP/Bmx9ait/pf9Fn6UPOpzzpdipfDNqEBqdhDy1HqWHFB+5lFxdZpV0eb42nw1DN1CgKyBHnUOAIgC5RE6OOpfNx3PIUxtt2txH7lOlNkoKiZLTmfa12NupzeNj67Dt0m/MP2R9bkpsl8iOtB1OjS2lmXNgDl3CutCpbif2XjxMo8BGtnv44a4P+WjXRyiNSgoosL45rOzbQxPUzalLVPMo1sjWlLtZQlQCcaFxTN48GbPFzLGsYwxoNIBN5zZxLu8cE9tP5INdH7Bp+CZ2XtrJ4B8HO3weJrafyOTYyVzJNfD4XKtp3pU2f7pzPbo1DuLx6Lrsu5hbrjZfuvcKa5/ry/i2E2z31fk1JNK1bh9Wvvwq725xvN+rug1hsqwrfJVkNzadwYxMbHb6XREQEBAQqBqCuUXgvkOtNyIVi2x9PyuaoTmmUz0ebBaMxmDGu9SkNKPJjN5kRqUxEugl443+kXh7SLmYXcTEHw/TMzKIYB95heMpiSacu+M8GyfGEeqv4OVVx/j1WAbDY0J5PLZq11d6duW8HefpWD+AOdvPsXhMDIfT8xgyf4/T4mfH+oGYzOYqnctPISPlmsrOSCEWudGzaUilC7M/7r/M6/0jsZiM5P25gMJDv7reQSxl1hdfs0XfgGNZx9GKjqMVH0MnOoHZrZjMq1W6BAcGNR7M2NbPc/JiLb7/4zIDWta09eNcdegqP0/oSHS4Pwv+uuBwH0d3CqdduD8jF+6jQGPgzQGR/JCczumsIuTuYn4YF8u3f11kRKcQ3v3rNadC+UjmEeJXDyMxKpGPes2grp+cy3kau7SbRaPbM2h2ku0lx/CYUFqH+rHm8BVe6t0YU0XVc6BRsBdafdVmA+cU6fh21wUH40zpsb3WL1IQ6VXh2jV45BFITrZfrtHAN9/A7Nl3Z1wCAgICAvcuGo21bcjatdafzWZrGtiOHRAdfXfH5gRvD4lNlyd+f4I5T82kS2hXZu6Z7lDInNrhZQY1GUjid8eZ+Xhru+PcCW0+Nq4eA6MqGcV9kxx1Dkp3exPArD0z2DFqJ3+mFvDiisO8PqA+29N/c2qsmNR+Cmuf68uohUedtpksjTNtfiqzEJMFFvx1kTNZRba47X9SqHd2XpVai/ZKKrr0Y2jTj6O7morFqON6hWdxQiDQCWhFpRspS9y8aOGVwOd9nqTLd+0qfars4gLqh9ThnR2vODVV3Xfa3GKB77+H556DZcus6SzOEIwtAgICAgIC/0k8zx3FNPBBVnbJ50hj+3VHMo8wel08CVGJLB33PofS1HRvEuRQMwdHbf56/0iUHlKu5Kpp4tOTnaMGkq/NI7Mwj8aBYby89WWn7YVKEkcSohKZ3nMGdT0UvPzLMfwVUh6Jqdq1ZRRm8Nqfr5GvzUfprkSlU3Hi+glaBbdy0Oans9N4IeYFRq0dVeFxS1I8ykq+26XNz10vYsHIFgz40ZpaI3YTkxCVwKDlgyp1H2YkzWDHqB2sPb2W0YrR1oUmoBgsXhYKJM5baJaHV7oXbz/4NhMemgAWWHdmg515CW4+18RMIi40jj7L+pCnzXMYe3pBOgfHH6RPgz68s+OdchNoxqwbQ2J0Ih/1rFibl0xCfeirJAa2quVSmxdoDIxaeJTFY96nU904Pk2e6XANL8RMpWvdPmyJn8aU3791OMbyXk/xapsn7Np1CnVzAQEBgepHMLcI3FcYTWa2pmZxOrPI1o6o7AzNspHSucU61h27xkOtb2XpqfVGtqZmMW+Ho8FhVMdw3h7YlP+tT6FD/XJmuznBbAGpWESB1sC560WYLZBTrMdPHlylayw9uzLlmooCjYFlY2P5cts5fnCSOFJaYL3aN5KYev52MY/lUZJKIhO78Xz3+nhIxaRmqLiUU1ylwuxvJzJ5KS6EojX/R+G5w+Vv6A6iCCV1Bvdgi/c6tpzfjtnDdaH97/BJry94fmkabm7ZvDOwGV0b1+CRr62CuEBj4JGvk1jzbCc6NwykQG2w+6z8kJzOuxtS7cTzuxtSMZktnM0qIrdY///snXd4VGXah++pyaQ30oA0WkIAgYSE3kSko9hJpAQh4LKgLmB3/eygu4oiEFAQpNkFlKKAFCmhhho6IZQUUifJ9PL9McyQyUySCc2y596L61pOP5Mx3Oc9z/t7cJdjV1lfG/MOzKN7RE/efKAXYxbfiMtcnplLhwg/UpIj2Hb6GuN7xNAx0p9Rn2eSU6ziky1n2TGjr+0lUU0kYhEtQ7xIjPSnzIUZwNW5VqGtM5beGpE+qG2YIOqusGePpbAlL89x3euvw6uv3vVLEhAQEBD4i1NSAsOGwc6d9stVKti7909X3GIwmsgpvuHlO88VM3j2Xt58oBfbxwyhTFNGibqUAIU/vu5+VKhg+e48SlR6fKr1LL9bbh4f5o23W8OKWwI9AlHWSD45lH+Ia1WlLNtziQ8ejbXFidek+guEL9LeYvic/bUmuLRt7IOXu4QADxnje0SjMxi5VqnjUkkVSrXe5uY3E7ftbPzXWFWGNv8MvhUXSXv8Q3b8/jtmvdaFT6QOmmIpaoltyE5i0u4Zz4yur+Ih9ccsqf85pjoRfmFsPLeuzrQgcN3Nn+oeQ0LUX9TNy8vh6adhxQrL38eOhSNHIDy8QdclICAgICAg8BfEbMaQkYH8mSmEavUs+x46jYczTjqpZByYR+/InvSIHMKaI1cZfo99/5n63DwpOoDnvjrO68PacuaKnhNFPzktbKl5zl6RPRnQ7AHOFlYSHeRJYAPHzAM9AinTlNkSFa04c/NvTnzFvvH7mJAwwWmRhUQkIT44nifbjaJf1GCGrtlrt/6eJj74eUjpEhPAiA6NuVqmoqDi9rh582AvqvRKW+JKfHA8Kr2qwSk2Reoivjn0DagAD6Ahtctm4AyEeIbQvGtzWrZrzbwtufx2qohV6Q8yrNVQStUl5Jbn2hJyMg5k8NzG52yFLc6u/fvs7wnzDnMpgaZ709rd/Nn7WhDgIadLsyDSvtjHpVI1c7eeY+GO87W6uVwqpnW4Dysy8+ne4l62jh5GfkWx7XnUQ+rDyj1X2Tj1n6RvX+FwTVeee5HvWwyBnFKHddZrE8bNBQQEBG4PQnGLwP8UOqOJ+VvPc6VMbdeOqPoMzVah3rbYusSIAJ7qGY2fuwyp2FJxq9IZeGddNsv21F0o8trQeBRySa0DmTWxFouUq/X4KaTEhXlTqtLh5+53S7Mr1TojucVVTgtbqrM8M5fEyABeGhTL8E931bmtr0LGrIfa4u8hZ/n4LiithR8dm+DjLkNrMNoqp+tDnXeOnt0mUXLp+vVJAH8sMeSBQDCWHp1BYBIpyeUHcs/jcjR5Q+gQ2oFGngF8NroRCrmE9UfzqNLq6dosiJxiy/VdKlXz0LxdLElL4nxRJb+eKODolXJbS6b4cB9eHRJnl+AiEYtoEezFhKX7mT+qja2yvj4ss3qH4CYVEdPIy/a9XLIrh5XjuzCqSxQFSjWiaqHjeqOZL/dctL0ksuKrkJHaOZKRyRFodEbUeqOtPZer+HvIqdTW3S4qY/t5+rUOESS9PhYvhokTQaezX+7paZkt++CDf8x1CQgICAj8dbl8Ge6/H06csF8ul8PSpZb0lj8ZOqOJ/1uTzfuPtLN5+aVSNWMWH0Qqhv7xoQR6yimuKiDQo5TnB8Yxf9t5Xh8af9fdPD7cm+TmPhjNlQ1yc4VUwbHCYw7rCiqLebBDuEtFzxkH5tGlSXdSktszd+sFu3VWx5vQIxqT2cz8JxOp1Bhs7ZS83WUYjCabm7satz0p0RK3PfSb39EVXkRfchVD6VV0BefR5p/BWF4AwDXgbL2fRB2IgRZAL6CBNRTuxgT89eM4dyYW9x5+nMpX0i7C2+WfT2JYInKJjFk7Z7p0vr+1m+/ZAyNHwoVq36/iYhg9GjZutLQ3ExAQEBAQEPh7Ul4O6elIv/rKtshXCz+uguTxUOnmuMusXbMYMnYonnIpOUVVtAr1RioRu+zmrwyORyYR0SfOn4ErZrl0me/vmsXgFkPw95BypVSFn6JhY+a1eTk4d/MBywewIWUDXZp0YXbmbLLys/B392dSp0k8nfg0epMepUaJylDG6n8msmpvHr+dLOL5gS1JiPSmTFPEg0lm/Nzd8HULwWiSIpNIb8rNra1whq/eT8sQL4pVNwq6fdx8KFIVufQZAmCCx5c8zq6iXZZxeA/Xd8UEnMJS3NIKCiQFPBAygnaNOvPqV5ZC/GGfWMbN/T0D+PzQvzmQd4BjhccwmR0T46tfu0Qk4ZH4R1xOoLG6edvG3rjLLF5+uqCSxTtz+Cq9C98duMTOs0XMGdmB0Yv2klOscurmTf0VvPVgazpGelGmKaNYXUKgQo9YBLtPG9h2Wke+8iKn8pTM2PoF6ZnfOVzLO73H8tjzL1G58qDDuuoI4+YCAgICtwehuEXgfwKD0YTeZKJcdaMy2lk7IpMZsvMqiA/3YVz3aO6PD+XHg1e4t3UIBpMZ8fXkF2eCXh1rlXBsqDcTesYwdVVWvdc4tlsU3x+8zJNdIpj9RDylmjKKVSVoDVpmdJ3ByO/rL4hwNrsyyEvOvK2u9bj/7PfzfDWhCylJESzf6/weowI9+G5SV8QiEetr6fGe3jOG9VN7kP7lAXaeK3Y4hhkjBlEhlZd/RXnuO2hjtAxoBwK+uBw/fruZ3nUGO86U8cXOXJKiAhjZORKj0cQLA2PpHBNAxnbLjANr2s+z97XgpUFxqHRG8ss1+HnICPZ243xRJTOupwKBJWZcazBiNJvtKuvrw1pJv+uFeylT6SlR6Qi4nhKjMRiYsvIge3NKSUmOYEnajcjFZXsu2hVvRQV6sCQtib0XSpiwdD/HryqRiEVsn9GnwS946oulP35ViVJtwEMu/PPiFIMBpk2D2bMd18XEwI8/Qtu2d/2yBAQEBAT+4pw4AQMGwKVL9st9fWHNGujZ84+5rlqo7uZ7c0qcernBBOuO5tu1fiyp0jKuWzSdogPuupur9FXoTOVkHMhgStIU0tak1bqvdSbnW33fYvWp1YicVGUHKvyJifPn/hXv13stALMzP2DNY5uZv+2CzfWtjncyv4JKrZFd54r4Ypdjm9XxPWJYN7UHE6+7uTVuu2vTHnyU+T5Zl7OgFCiGcF04seI4jm05RvsTrSkvKnDp+hqMF9ADS+sh93q2rUGMbxsCjaMpvBYHQE6xihnfHuHlwbF4yRVM7zqDFBeenV7p+QrXVNf+t93caIRZsyyJgUYnqUSRkZZibPcG/pAEBAQEBAQE/hrs2YN55EhEFy44rFLJwFvrvLjlUP4hStWl9G8dwj9WZDFnZAeABrl5+ya+yNwqGuRiSl0Zc0a2RamtQGvQ1uvlYHHzf/f6NxvPbaRNcBuOFx7HaLb3HmdufrbkLJ0WdmJi4kRWP74avVFPgCKA9WfXM2TlEIeWNa/2eINJffqw5tRanvliltN2o/2iB5F9VVu3m9fY7/luz9M7YiCPZxykXK2nUmsg0CPAtk2VrorG3o3pHtEdpVbp9P4AS0KLGfCEXaXXC1tcxQBuZ9xIDE1kZ4udIL1xbf1jBvP8NydtCZNWN//vo225L+Y+FmctrvWwSq2SIA9LPNDNJNCUa8pYOKoTl0rVNi9fufcS15QaVu27RHZeRZ1ufrG4inlPtmHtaec/s2eSp/PvYQMY83kWr25awNgDax2u49/90tk/NIVRwri5gICAwF1D+C0q8LfHGoX4+5kiHklsalteXzuiFZm5XClRM7xDY77al8vQexrjo5Ayf+t5l867eGcOS8YmER3kZRvIrI2U5Ai6xASCSMeGcz8wa9cNmfJ39+dQ+iEmJkysM6KxegW3lfhwHzzdpKw/5qTtiROOX1VSWKHhH32bkxQTwILt9vGRydEBZKQmoDOZ+M8vp53e0/GrSqasyiIlqSmvDg/mkc92crXqPHrRVQziK+hFeRhE+SAyQHMsf+4gHjIPUtumUlBVwOpTq2vdLj0hncEth9Br5i5KVXr25ZQy73qST4/mQaR2iXL6PVm4/Tx7cyzV8pVaA/nlGsZ2i2LRmE4oNXrKVXqCvNwoqrTEhlevrHeFUnUZa09qmLXhlG1ZfLgPE3rE8NHjHRi5cI9dHPrcredo4q8gO0/Ji4NiSY4JoFNUAHO2nLX7eRlNZlZk5jrMIq2Nsd2iWJGZ6zSWviZKjZ5QX2EA3IHiYnj0UdiyxXFdv37w1VcQEOC4TkBAQEBAoC527YIhQ6C0RvxxeDhs2PCnK5p05uauePmbP2Wz4qlkRnWN+kPc3Evuxbv3vsu8ffNqjSi3zuRMT0hHb9RTqaukmX8zzk05x4KDC5i3bx6lmlI6hHbAS+5Lmba0QYO3GmMFrUK9yc6rwFchY8X4zizeeYG0btGsPXyVH7KucLqg0m6/41eVPPNVFk8khjOjmx8pu7dyZP8FEr/9iABDEdriUsSXxJhMlpmUV6//744gBkVXBU37NOWM5AxmXBDLashMEST6p/NavzF0a96IQqXW4Xvy85GrLJ8wgPSEiXW2GkpPSCcxPJGcspwGXcPfys2vXIEnn4TffnNc5+cHCxbAI4/UfwIBAQEBAQGBvx5qNbz2Gub//heRyTFR4+MkmHEfaGW1H6JUU0ZFlRexod4o1YYGu/nyp5K5VHGxQZd9reoan+x9jTMlZ/h4wMc81Poh9l7Z63TM3OrmU5KnoDPoyC3P5auHv0IhVbjs5qWaUt79/V0WHFjAwfSDvLD5Bactcy6WXaR9WDzTfplGxoEMh/XV242+0OUtZm86w/wnExg0ewc5xSqGz9lPSnJ71j62hSq90tYKx8fNF4XUnQqNkcl9m7N4Zw4n8yvwlPnQM7In3SO6k56QjggRs/rNwl/hb7u/uXvnUqYsAw3gTcMSWqxUASege2x3fvxsDSWaSopUxbY2PdtPl5GVW8V/H2vv4ObfHbzM8A731+nlxwuPE6AIoH1oe7zkXg1LoAFK1KV8s6eUL68XVFlbXwV4udE82IvsvIo63Vwk0jHt12lOry8rP4sxq1OY2GEia09o8K5R2GJCxMv3/4OV7QfwgTBuLiAgIHBXEYpbBP50GIwmdEYTSrWBcrUOX4UcH4UUuUTc4Mi26lGIcWHepPdsZre+tnZE1hYzjydF8NZPJ5h6X0s0BiOosSW/1Mfxq0rUeiNisYhp97eym4lqxToTNTk6ABNa3vn9JQcZL9WU0u/LfmxI2UDXpl35757/Oq0i7tV0AGMWHbZVSQOkdYumpErnVKys/d2t93y6oBKjyUxJlY65W8/ywcP30L15ECqdkQKlBn9POcHebuQWV3Eir8I2GGtGh150Bb34EnrRJfSiy+jFl3j3SB7vHNVaTtawhO3bxtOdniYxLJF3fn+HDSkbeCD2AVuMpJX2oe2ZmjyVIS2H8FVmPqWqG5+fNckH4LFOEfT+cJvT70l1ogI9GN6+MZuyC/j1RAEFSg2NvN34132tHCrrXcFf4cexK/YzZo9fVTL1qyy7yvPFO3NYNaEzjydFIBbBisxc3v45m7ceaMO+nBKnL3BqziKtjdTkCDpFBfDmT9kuXbOPex1Pvv+rHD0Kw4fbR71befZZy4xZqfBPsoCAgIBAA1m71tJuSF2jFWSrVpZWIpGRt3T42+nlULeb1+flAN4K2R/m5u1C2hHkEUSpptRpRHnzgOZsSNnA77m/M3zVcAffnJI0hX3j9zFg+QCmJk/jt5OlRIeWNejzUxuUfDE2iUslKqICPcivUJHeuwmVuiLaRCnp1ToSk1rEvB938+Nv+ynPz0FffBl98WXeez+f90z2LWwc8xXvAG5AAtAePMM9qTJUcZrTDTqE1BSGr2EknsaeXM2XMHvzWVoE+zB55UGn35PUhVmW2a9NevChk9mvz3Z+lm5NuzHx54m8f59ryTlW/jZuvnYtjB1rKb6uSffusHw5RES4dHwBAQEBAQGBu8Ntc/NduywecPq0Q75giTukDYfVcfUfxt/dj2U78xjVNeqm3FxrMOKv8Hf9ugGFTMGBvAP4uPnQzL8Z7+96n2ldp5HcJNluzLd5QHM2PbmJHbk7GLBswC27eXpiOpvPb3Za2ALwXOfn2Jm702lhS3Ws7Ubl0vZsyS5k+VPJvLbmOAVKDRnbc5i/7UK1ZyEl47pHc/5aPsszc0lJjmDVhM5Uag1IRGa+e/Q71p5a6/DsEekeiVwlp8JUYXFxJ8k79VIEwZeDeePRNwhNC6VLk948Mv8gbjKJ7dqs/h0X5s2cJzo6dfPvD17hi7S36dKk9lQaN4kbL3V/ifyqfCJ9G/b86q/wp7jqhptbW18dvlTGCwPi2H66iHK13qmb+3tIOFu5uc6CeLEJkv49H+8s++VGkZgZA6fyXdt7hXFzAQEBgT8A4U2awJ8K60zOjG3nHVvd9IqhX1yIy7Fthhox5acLKnGXS5xGPYtEIoe/x4d5IxbBtwcvk96rGYFecgqUmgbdT4FSQ3SQJ1qDCS83aa0zUa+Wq/APPFBrMkv1GMTNozaj0qvIryjBW+6Lh9SHlZl5DF+9366wJSU5gs4xgegM9jGENfu7V4/T/uHgFaICPZj50D1oDSYul6kJ8pITEeiBTCJGq9dxRZXN61u+pUh2GJ34NHrRFRA5VvjfLURIkRtboTC3I+PRx3l756scKbDMdlx5dCXPdX6OPVf22MVIqvQqilXFBHoEopAqOJR3CLVWwpzfnM8usH534sLqjgn3VchYmpbM/G3n7AakJWIRLw9ujVQswlPm06B+sL7ufvxy3PmM3pqV55VaAx9vPs33B69gMlvO2zzEm/FL9jvdv1ytd9oGwIr1Bc+A+DCe/eqQ3ferNuLDffBRCP+02LFmDYwcCVVV9svd3CAjA0aP/mOuS0BAQEDgr82iRTBhgmMrkeRk+OknCAq6pcPfTi8H193cZLassxZhtwr15nRBJbGhf6ybHy88jofMw+Zx1d1SY9AQ7BnMi5tedOrzWflZpK1JIz0hne1jtqPXu/Ovr7N566HGDbp+X3c/DEYTMUGeyOUGdh9bzazv3+HskbNwBcufsgYd8rbj4emJOCwWcWwYAx/0ZfXV79AYLT+nKkNVPXvb404MXrqH8DB2R1QtN9363RGLROzLKXXYL6dYxZOfZfHjPx5kYPMhlKhLKVGXEuQZQIiXH+/9/h7PbHgGpVbJp4M+/d9yc40Gpk+HOXMcNxaLLe2JXnlFKLoWEBAQEBD4k3Fb3Ly01PJv/dy5YHacCWno0pmUoUo26E7Uez1WL/r898M8mtj0ptz8apmGFqH+DXIxhVTBscJjvNj9RX4+8zNvbX+LT/d+ajfmW64pJy4ojuc3PX9b3FwikpCekM6wlcOcrm8V2IopnafQc7FrrWCt7UYnfXmMlRO68PyAWNykYuRSS0udZXsuUq7Wk1KtaKJcrWd5Zi5P927OmqyrDGsfyL+3vGq5PzOgBPSAD1zkIoix/GkIJuA0lgr4SChsX8hhn8OkxKSQtugI54tUTnery82tqTRp3TuyfcwOrlWVUlBpSX7xlvsikxoZ9cMolj+0nB+zf0QsEt8WN1+WmUv7al5+/KrSzs2lEjGT+jQhffHMOs9hBiQ1XnsYRGKeG/IvzvUbygfCuLmAgIDAH4Lwm1TgT0P1mZw1OX5VyZSVWaR2juSlQbEuDaTrjCa7KERnUc91FXqUVGn5/uBlTGZLXFyTAHd8FQ2LIPH3kPP7mSJ8PaR0bx7ErycK2HyykAKlxlbFHB/uw9Jx7emz9LE6j2WNQfzl3C9senIrLQNCyLxQzMebj3H0iuOgZ6eoAEYtymTpuGTbSwNn/d2r7zehZwwSsZgZ3x7m1+xCzJjRiy7h538ChfcJTpbuQaW/LrJ/0G8PsdkHN1NL5KYWTO0xFJkhls92WKLTL16NZlqX5xn1YwqA05m1M3fOpE1wG3zcfAj3CmdE7AjuazaI45fVtUqo0WTm2OVyJvSMYeqqrFqvLbVzJJkXih1mWlq/e6O6RLEyM4+pSdMYuya13nud1mUGB3MqMNRRO7R4Zw4LRiUyf9s5ylR6POVS26zVliFe6AxGThVUEBfm7ZDSA45tAMRAQYUGH3cZwd5u5BRVsfbIFfrHh/JrdmG91zyxVzPkNzGT+2+Nnx9otfbLwsLghx8sLyAFBAQEBAQagtkM77xjeQFdk0GD4OuvwdPzlk5xu70c/vpubjQbyTiQwZSkKaStSbO5+cydM/nw/g/xdfOts4UoQMaBDHpH9Uam7cLB3DK85a0bNHhr0rtz38ufM7xRAct+XERedp5lEPqPxAdoDDQFWkCXxC7szztGue4AX126uUN2j+jJ8OhJfLLBC5HDfGLX3HxY+8ZsOVnE898dsc1+jQyEEUnFvPv7u7btqv9M6+Mv7+bnz8PDD8OhQ44bNm1qSWvp0aPeYwoICAgICAjcXW7ZzU0mWLwYXngBihzbvmglUj7qnkLnuW+RatjBhh9G1ntN07vO4EhuFQbTzbu5n4cMkVnKjK4zGPl9/eeckjyFjAMZiBAxIWGCrdikupe3CW7D052e5mTRSZfdvLXffZwrrMJb7uvUzeOD41HpVU7bifq7+7Np1CauVV1rULtRlUGJwWSmoFzDc19nkZ1XYWups25KD45eKSMuzIfRi/baxqufu68Fm08WoNRoWfR7BvO/nw8yoBHg69KpnaMEjgMSIB6IvbFq3v55dI/oyf8Nv5+Bs3c57GpNhr9comJa/5Y8tfSAzWutlKv16AwiNhwtZdHOC7bkFx93KdMGe/HhgA9t7Zxe6vHSbXPz6l5uMmPn5v3iginTlNX7MzOLLUlGciM8fhzMMhm5n3zGMyNGCOPmAgICAn8gQnGLwJ+CmjM5a2PZnoskRwcwsE1ovXGLSrXBIQqxetTzrnNF9RZ6PNChCd8fvEIjbzckIjE+CqnT5BdnxIf74CYT8+bPJ9gxow+bsgs4U1jJi4Pi7GaI+iikVOmLXBbgA3kHqNCV8fEvxTTydmPhqE6YzGaulqntZpxaq7qtLw3e+jnbaaqIleNXlUxdlcXIpKY8kKxndc6XlJq2YxDnkacG1I7Xckcxi5GaGyE1N0ZmisDN3BK5qSVSc4htkHv/KR8WjIpl0e9XiQvzoWWIL31aDWNCwkQWXH+AqZ56Y63gr9BWEOYdho+bH6sy85j05TEWjenE7Mfbs2C7/QyIto19mHJvC9o1sTwlpCZHsCzT8fOTiEWMTI6odSam9bt3Mq+C3hEDmdBxIgsO1v6QNSlhEkNaDmHw7L11fkzHryrR6Iy0CvXG213K8wNiGdc9miqdkRBvN3RGE9tn9HF4QVR9JkD1NgA//bM73x24zP6LpfRo3ohn72tBTLAXJhNkXSpjuZN7t5LaOZJ744Jvqk3B35qePeGTT2DSJMvfO3eG776D8PA/9roEBAQEBP6a7N3rvLBl9GhYuBBktxZzfCe8HP4ebj5v3zz2jd/HhIQJtkhyESIeiH2g1pmcNZm1cxabn9yGj7sUN4kbz3WebivMdooJyIWAE4G0fTGWytJiGtZI5/Ygkogw+5khEAjhRkFLjTqqzRc33dTx3SRujIgbQZTbQ5y7Es6kLp1o4VdwU25e3curtxmt1BoY72Efde7sZ+qMv7ybr19vSRIsK3Pc8KGHLL87/BvWFkBAQEBAQEDgznNb3Pz//g/eeMPpfllhLZk26BnOBkXw09rT/Dx1KOkJE+ts1TIxYRL3NxuMSishKtDjpt1cLhFjMom5v9kQu7FcZ6QnpNMjogfPbXyu1mITk9nE8cLjDGw+sEFuvmlUf36YnIBC6u7UzX3cfChSORYFAUzqNIkj+Ufwcfdx6XxWNEYlzRt5UarS4eVmeUVnbamTmhzJ1H4tGDF3J5dKLQPyIr2aypzNPLd2LhrfU6iCVXArHWjNwHngKhAMJFNr0ssHu2axfexQu3Fzy6SEJqR2CUdnqqBce5XwQH92v9SNJTuvsGzPZVtRTnU3t3o5WNoZNfUNZ/3ZtbZ2Tq66+UQX3Ly6l2fnVdi5uUwiplB91qWPyiiBJ0fAgBYDyez8IJ+Zm1O54qAwbi4gICDwByIUtwj8Kag5k7Mu5m8755IIlKt1TpZZop6/HJfE9Ptb8f7GU3UWeqQkR7D8qc74echs50vvFcOUlVn1XufYblGsyMylVKVHazAxb+t5TuQpmb/tnG32IMCcJzqSV1F3x3uJSEJ8cDw+bj4otUpKNWXkFFfxzYHLHLhYwqtD4pm54aRDv3m48dJg5kPtnKaKWDFSSqX0V94/vIl3j1qSUBocX3gzKIESMc0jujC671CaeMfw8jfFSM2hiKj75czxq0pEwLbpfRBd75f52Y4LfDLybXpH9mLWrplk5WfZKvg3ntvI233fpmdET9YfLeatn3dRqrKIdoFSQ9/YYLo1D6JcpadKZ6BpgAdyiZiSKh1XyjT4e8h4YVAcTyQ15Y2fssm8UGK7loFtQjGbzbX2lq0eM34yv4JXe7xD16bO+41O7/o8Q1oMYdKXx2wPMXVRqtLRtrFlgP+dddlM6t2cMB8ZcpmYHSeKHF4KWGcCrJncjdGL9pJTbEnjMZnBTSqhVKXn6d7NuTcuGEW1GR8vD46jc0wg87edczjexF7NuDcuuEHtCf6nmDgRsrJAp4N58ywtiQQEBAQEBG6G5GR47z3LzEsrL7xgSXMROaZcNJQ74eXw93Dz6qmAg5oPYvWp1VToKmqdyemMQ/mHqNSXM2dkR3acKaFHxADnRc/FwAHgCFAJm7m5opGGIBKL8Q9ujErRiHviYxnRO5HI6GYENPGj/0+9qNYZ6LYR5hXG5KTJhEuHMPvXfHap9EBJvW5+tUxDkJcbLzpxc2tCSk0vP11Q6dAi1FnS49/SzUUiKC+3v1CFAmbPhqeeui2/OwQEBAQEBARuP7fFzSdOhP/+FyorbYtM7u7M7Pw4n3V6EKPYInmXStVM+vIY80a9T++oXszcOdPBi55Jnk6vpgMY8el+ujUPujU335vLU91jmPHtSV4Z8jZdmzgfJ53RdQZJjZMYsHwApZpS4oPj7YpNqo+bh3qFOnXzmmPrxwuPYzQbOZR/iMKqQr478R2TEv5Jz6Y33Ny6TzP/ZgR7Bjvcg7Vd0bRfpvFGH+fFQ7XhKfPghUFB6PQSKrUGu3XLMi8SH6agjeQqh/d/jUqWibFxIZNPY5eqclNcg9DCUB7r+BizQ2dDs/p3OZR/iFJ1Kf3iQujWPAiV1oCvp4l1Z35i0MoUJ+48g3VTB5Ky8BA5xapa3fzctSo85e7MzpxtW+aSm3d5niEtXXNza/FQfLil+Mjq5j7uUozigPpv/joGCVR8sYDVPxWgKlYJ4+YCAgICfzDCb1OBPwXOZnLWxvGrSpRqQ70yUFsUYk6xih8PXaWxv6LWQg8ryzNzSYoKYGDbUACkEjH94kIsA+t1VOJW74nZMsSLUpXedn/VZw/+o09zDl8uIzLYz+lx/N39mdRpEukJ6aj0KopURQR5BBHuHc7zA3Q8+9URTGbwUcic9psHy0uDtMX7WD25O49m7LZbZ8aERpxFpXQDKnEmiIx1fh43TSVQgmWQvrja/y+BmIjmvPLRIh69rwv7ckrwVciQmR1jDmtDqdGzfM9Flmfm2op6hs/Zz2/ThjOk5RDyK4opUZcR6h1AgMKf306WMmZRFiKxiC7NAimu1KLUGCip0vHhr6f5v+FtKFXpaBHixbZT18hwMvj8VI9oPh/diZIqLYUVWgK95AR5uXGlrHah9lXIGNwuHJlUTMemfrjLJQxrOYIHY4eh1JZRpinDz90PP4UfmGUM/GinS4PnYInYv691CCsyc1mWmUtSdAD94kLqjCyd/u0RUpIjWJKWxLA5OylX64kP9yHE151ZD7dDLhE7PAx7yKUMbBPKvXHBKNUGlBo9Pu4yfBRSp9sL1ODTT0EsFl4eCAgICAjcOjNmQF6e5cX0hx/CM8/ctkPfCS+Hv4+bPxr/KHKJnLYhbYn2j8bf3R8fNx9e6vES8/bNo1Tj3Mmrc1VZTIem8Tw8fzcqnYEv0t6ma9Me/HfnTI5sPWIparlQ72FuGol3EFL/cGQB4cj8w5H6N0YWEE7buBYsGdeN3RdyELmdZ/WJ7awv30jWmb23tbDFTeLGfc36k9Y+DZG2Ayv2XOGwuMqpm78xvA2lVVpahHrX7ubdb7h5cZWOxv4KSiodi6mMJrPTFqHOkh6LVcU09mlMkEfQ38PNBwyA116zzNwGiI2Fb7+F+HiX7klAQEBAQEDgj+G2uHlYGLz+OkybZvn7gw9S/Pq7ZKxwTK7Yea6YVXsK6NysP2sfu58qvZISdSkBCn88pD6szMxj+Or9lKv15BTfmpt/te8SIzo2YVN2IftySklJbs/ax7bYzhnqHYifux9ag5bWc1vaPFupVRLkEeR03DzEM4RGno1sbg44HVtXSBUsOLiAefvmUawqZsO5DTT1iUarTOLlbu8wPmECYV7BVOgqKFIVEeoV6tCyyJog833293zQ/4MGtRuViqVsyVnHkJYPciq/ArPZjL7oIuqcLNRlu0nZdwpzSwPcjo6RKvDK8UKqTMY9bBhHPp9AvuoMs+fPdrq5s0KgEnUp3+2tYkzXaHw8TLywaYbTtk9Z+VmkfD+SSYmTWPPPWeQUGQj1dXfq5s0aeVKkKnUoRKrNzQM9Agn3bsypPA2DZ+91yc39PeRUag2M6x7t4OZuYi+7n5mXFrx0kO/teJwOoR3wd/fjtSFBtY6DC+PmAgICAncPobhF4E+Bs5mcdaHU6An1da9zm9qiECViESMSmtTaPqYmC3ac5774EKzD8SLg6d7N6RDhx+KdObbjS8QiBrYJ5bHEpiRG+VOu1vP56ETCfN1RagwOx7VG8qUv3c/8UW0cBLh5QHM2pGxg+8XtDF813KFKeVqXGax7Zih6vaU6vm1jH45ecf6wI5eJKajQ2B6GzOipkmxHKf0OvbjulwguY5YiM4cjMzdFVOaOatduzHkqSxGL1vku7tEJrNu8loiwRrz1czanCyqY/XiHBp3Wy01KTCMvu7SaJv4KVDozC365wAuDYgn1DOenI1corsxjREIT2jcNpEprwFchw8tNitZgxF0mISHSnzmbz/Cv+1vxzrpspw9ix68qefarw6Qkl/J07+Z8+ttZ8pUaRMC81ASn1xgV6GGL2R+/xBKzLxZBq1Bv7mniy+OdImga0IiPN59m6D0exIW546OQgQuSHh/ug69CRssQb6Z9cwSwVKW7Elm6PDOXDhF+pCRHMHfrOSb2aoa7tG7Zll6XcQ+5tN7/Bv+nMJvhP/8BqbT2F4ySOzDdWUBAQEDgfxORyDL78uGHoXv323roO+HlcHfd3Nr7vW1jXwa0CSU5OpAKze1z82Grhjm4+dSkqRyeeJgZm2Zw4toJ22xQZwR4+FNcpbO5+ZD/bCeqKItrPxdZosFvB1IgCPonD+SKqQkF4kBefqIvD/VJ5MklWbZ0EDN6dKLzqMVZ/F76FV2+uMSFsnO36SKqI6JnRG9GxD7GQ3EPs+NUBVeu6hmREEzHiEa1uvknrrj51zfc/OPNZ1DIJTw/wPm01mV7LvPTlEFMTJhoNyBvTXqcuXMmbYLbMKrdKJ5sO54z+XpahvxN3PzVV2HPHvD1hc8+A28nI/cCAgICAgICfypcdXNvbRXxBedQano6d4IpU2DHDnj6aejfH0+dgfjwQqduPviecMYv2c/JfKUt4bBSq3RIC4dbc/M3h7dBZzBdv089c7deYP62C7QK9cZHIePjxxsx+rN9zH2yDZF+kZTmW4pbjhcex8fNh0Pph9hyYYvTcfOpSVM5OukoUpGU3Vd2M+KrERzIO2C3zZSkKewbvw+5RE4z/2b8fGY1s+8fjNZg5OS1bMavTbMd96UeL/FM8jOMWT3Gdgxru6L44HjWn13vsL42nun8DBkHMlizbw1V+5RUbfyGaxWZmKMqIR5LG9BbRQPSXCl9wvqwW7ubOS/Mx6RKJjuvCrEIPGW+DrvUNslWIVXgLfemuOoKe85fo5TtTgtbqjNv/zx6RvREou3C/G3nnLq5l5uU/Arn7Z5qurm10Gb5g8v5Zr/RpcIWa1vahAh/20QHuOHmpwrKbUXvPhpYvwwC1NBrLBR62R9rWtcZyCVuhPrWnTIvjJsLCAgI3B2E4haBPwW1zeSsjlWEvdykBHjUv71cInYahdgyxAuNzjEKrzZqVr1XaY0MnfM7j3dqyoJRiej0RiRiEQGecio0Bq5VailT6fFWyDCaVHy46TRP927ucFzrdRy7qnSYQejv7s/G1I3M3DnTaX/JrPwsUn+wVEG/e+8slGoxS8cl89mOC7Ze7dXxcpNSUqXDhIpK6UaUktUYxc7l0RXcpe7E+LahsDgCdM2Qm5ohNYchQkJF1npKfs0Ak+NLg+r4JD+EX89RGGQeHLhYysHcUkZ1jsRDLmlQf1apWEz/+FDe/OmE7QHLGju/NDOX9pH+3BsXTNfmjdh3oYTxS/c7jQHv2iyIEB93Hktsyvrj+XXOMIAbg89xYT78duoaAR5yvNwdX9r4KmQsTUtm/rZzdrORrbOEs/MqWLXvMinJEUzoGYPJZKZCoye9ZwxTVmXV+xlM6BGDSAyjF+2lXK1HIhbRr3Woyy+IFu/MYcGoRCo0BqHv582iVsP48bB8uSWZJT4e7rvvj74qAQEBAYG/O2LxbS9sAde8HG64eaCna9vLJWKnfnM73Xzh6ERkYhFuUku8d7laj6ebFDOgkEs4fqWcRTsvMP3+Vg7HvVk393f3Z1CLQfSN6YvOqOOFbi/gLnXHXepumw1aPc2lQ2gHvGQ+XC7RYKwsRXlgDbmH1nFcW+XS/TvDzdMNY5gRQ5gBGgMhgC8gho+fnsczKy4yPMKfB3vGMGf7fh7qfpE3flmN0pSNTnQORDe8/ULZTV+GI2YJ7Rp1JcarN/uymzGl3b1IxSKkYk+6Nne/Y26+au8lp15uRYI7/+75Hp2dRN+3C2nHM8nTub/ZYPQGKX4K8d/HzSUS+P57SzsiIUlQQEBAQEDgL0F9bu6trWLs/jWM2/8jMrMJzdupgJMCVpkMfvzR9ldX3dyacFgbNd1cozfxwKc7eSSxSb1uvvd8CW2b+tkdzzpeGhfmTaXGwPG8CjadKOHtPjN5cct0W2GLRCThzd/fdCiyqO7mGoOGKn0VcUFxfPfod3ZunpWfRdqaNCYlTuK9fu8xMXEi/u7+eLlJ2H3lN6ZsmGzn8PP2zWPf+H1MSJjAggML8Hf355HWj9AupB1fPfwVZZoy4oLimHh5Yu2FHyZ40PtB9nyxh/mr5mNWmJkUNxH6AD51fsyuUQVcAjRAMBhaGBj5wEgSixOZvfc/bB2zjbPFF1DqrtHIM8CumL++SbYzus5gxsChiEViui2a6dLlzNo1i19StzJz/Wmnbl6pNRDoUXdrIJPZxJGCI7a/+7n7MbCNjK/3X673/GndoilX6ZjQqxmjPs90cPMrZWpWTx7Is83HMHL6FyTmWfbbtBT6jIZiT8vfJyVOYlirocildRe2CAgICAjcPYTiFoE/BbXN5ARLcUBq50hGJkeg0RkpV+sxmEyodIY6I92sUYipyREsqzYY6uUmpUTVsBmp5Wo9MomIokot3u4y1kzuxsq9l3h62QHmpiSw50Ixi36/4DAwO6FnDC8Nao1MInK4v+rXsWzPZVZPHmjr6Tmp0yS25WxzWthSnXn755HcuDsF+e3ZdrqYcd2jHXq1A5Soi1l89F0uu8/DLLr5gXMrrQJb8Xy35+jfbAgvfHOSX7MLMRv1FG+aT2XW+jr3FUnlBA6cimfrXvgqZDT2UxDs7c6cJzrSyNuNHw5eZkzXKKZ/e6TO4wCk94zhy905jOjYhFah3mTnVdjFzgN8te8S98eHkrHtvNOo++ox4BN6xqDSG/lsh2s58NbB5w3H8lg8NonLJSrGdotm2jc34hRTO0eSeaHYpZj9jhH+9IltRLlKT6foAIfvbk1SO0fSOzaYJxbstv28b+YFkdls5qXBsShkwj8JDebyZXjgAThwffaHyQSPPQb79kEzFxrXCggICAgI/Mmoy8vB3s11BiPXKrXojeZ645alEjF9Y0MYmRRh50W3y83XH8vHaDSz+5xzLx/fI4YRCU2IbuRJsLf7bXHz+gaBrbNBBywfwNkSS+z71ORpLPsli4NrlnJ5xZdgrLsg3CkSIBJoBq2SWzH9gen0ju5tdx6wFNIYzSbaNDvFZd1+Wn+6EZX5Ilg1+w7UNHvJvbg3agAXLsfRL/p+pvRpz7A5O5GiZ9HOCywcnYinXMLHm87cETf/fHQij3Vq6tTLweLPO88V8dbP2Q7R99Xj9u9fnclrQ1o32M27twjigTk3WhjdVTe3JglGRVlSnZzh4dGwYwoICAgICAj8odTm5t7aKsbsX8P4/avx0VTalmvffJP8Dz++K25ueS5ogoebgcvllym+7lObp3cm+6oas8nM9jrc/N64YETX/17z/kJ93JFKzGyb0QWVQYnGEMr3j36PVCzlZNFJ1p9d71BEcjNuPm//PJIaJ3G6+DTv/v5urYmMpZpSBiwfwIaUDfSP6U9ieCJbLmyh1xe97ApENj25ie4R3flg9weW5RrgHPjn+qM7q+OHwB8gDhgF3A4tU2JJTZcAwUCNgJSPMz/mx8d/ZObOmVytuMLYNU9ypOAIHw/8mH91+RdP/vCkS5NsR15vNfRev5nklruWAn8o/xAag5I1/+zu1M1PF1QSoPBvUDsnX3c/EqPcHL63NUlNjmRAm1DWHL7Ce+tP2SYC13TzZ2f/xlff7UWed2PftoWw7Ht4cXp7nkmezoi4YXjJPV26ZwEBAQGBu4PwJlPgD8NgNKEzmlCqDYgwM6FnDFNrVItXb+cywcmsvvReMfSLC3HsJXodD7mU5wfG0r5aFGKl1uBS8kt1vNylTFx2gH05pbZzP927GU/3bsa767KdDnIev6pk6qosxnSJ4IWBcaR1j+ZfX98QuOrXUa7WM2bRYb5Ie5tuTXtyb0xPBq8Y7NK1zc78gDWPbWb+tgs89/VhUpMjWP5UZwZ9vIMSdQlK6Q9sU65ly0G1JRvSRcQiMWFeYVypuOKw7nDBYZvU/vfxWZy72IinRqWQm7WrzmNKvINoNOIV3EKbExXowfKnOrP99DUytp8HzMxPTeS309d458G29Q4epyRH0Cc2mC/3XKRPbDBtG/syrns0naICbDMlARKjAtiUXeBScUnvVo1oFeLToMFnrd7I8qc688mWs/x8NI81k7vZBNsab+/qTM1FOy/QNzYYmVRMysI9fJGWTGJUAAt3nHf47qd1i6ZzTCDnCyv4clwyH28+zcp9l2/qBZFKa6SJv/DPQYPZuRMeeggKCuyXl5bCli1CcYuAgICAwF8GV7wcbt3NCys0pPeKoWPk7XXzvrHBfPRYe2ZtOFmrlz/zVRapyRG8OCgOiYhbdnNXBoHT1qSRnpDOhpQNdFrYiYGNBrPhvxv5avlyzCbnLYtqQyKXYIwxWqLKWwJuluWnOMVTPz1ldx7rLFOVXkWbec0wY671uLeTR1o/wn/6zae0SkxJlZboIC87Lz9+VYlMLGJLduEdc3OzGVZnXeXz3y/YeTlg5+Y1o++dxe3XdPMvx3UmKSaQjG3nHL7747pH07tVMGaziYc6NuZ0YSWbsgvvnpsbjTB5Msyfb0lmiY6GBOctUwUEBAQEBAT+3NTl5j6aSkYfWMu4/avxq1bUYsVrySIelCbi2Sbultw8LszqRwZOF1RirNGPKCrQgy/S7mFr7np6LUlxmvQRGTSITzafsZuACfZu/vLgOIcEmahAD958sAXbL/3E+7tm2R07ISyBX578hb5L+tod82bc3OrM1gKQBQcW1JvIeN+X97F73G5e3/q6Q3HN2ZKzdMjowKNhjzKifASev3mye99uTDEmSmNLoS82h78VvLXeVJRWgBeWxJc6Ul8O5R+yPBMEt6FYVYyPm2Xjf//2b/aN30d6QjqBHoEuT7LtEdGDiYkTeff3d1261lJNGacum3nzp2wHNwcQi+Q8kzydMatT6j3WM8nTwCyjSmsgvVcMCZH+LNrpWDiV1i2abs2D+P30Ne6NDaGgXOPUzUMqinh/1SvIS+xTYHTBQbT6YiVr4hNYmZlHQbgZ72CXbldAQEBA4C4hvM0U+ENQ6Qxsyi4gY5vlpb2vQuYgOLW1c7Fy/KqSKSuzSO0cyUuDYmsVdZXOyPlrVSwYlYhGZ6RUpSPEx61BrW885RI+eOQeWyJKldZA55hAfjlRUGsBhnVW65OdIylT6+kXF0JKcoQtUvt0QSXu1Vrw5BSrGD5nP8/0S8JkNnO44LDT49bkUP4hqvRKW3LJssxcWjcW0y/5NzIOzqFK7/igUxdSUxiTkyfQPDCEyRuernPbefvn0bSyKZ889yl5Vx2LYKqTmNwF7yEzOF8ps/1s5249y09H8kjt3IQnuzRGZy5hQl8ZVYZCZgyK5pFO4bzz82kyL5TYjhMf7sPYblF0igogp8iSQhPup+Bf/VuyZNdF3vwp2zaA3tDikl9PFNDEv2Fl81q9iYvFVbbv6OhFe1mSlkTHSD+2nrrW4JmaVToDv54o4HyRipSFe1j/TA+6twiiXKWnVKXD30OOwk2MSqvHx8OISazicuUVnurtz7QB0ZzKUxPi07AXRD4KIVaxwSxcCP/4B+jtW4Dh5QXLlsHw4X/MdQkICAgICDQQV7wcbo+be8il3P/RdlKSI26bmwO8/3A7Np8srLMw2lchw9dDjsFkpkJnuGU3dzVpMeNABq2kreh7vC/frPoKfU13qIcmLeK5/4kufK7/rM7B8IwDGVwqv4RYdGN27qniUw06lyuEe4ej1qvtYtrbh7bn+a7PM7DFEF754TQvDYzjwMVSpq46bNcuVXI9lt5S2F4/N+PmZWo9O85co1ytt/PyxTtzMJnNDm5ujb53RnU3L67SYzabkUtEdt/d6m4uk+oo05TRp62OhxQhvPdwK07fDTfXaCAlxdJyCCwtM4cPtyQJhoU17FgCAgICAgICfyi1ufmkaBlBn83l8cMb8dRrat1/Y4suGMWSm3Jznd6IRGwmwFvEJ6lRFFYWE+gRgIfUh1V781i25zLlaj2+ChlLxt3DOztfZsFBxxY81qSP9IRJfJH2FsPn7LdzQrjh5nqjmb6xN7zceuw3d7zk9Nh6k57CqsJbcvPOTTrbFWgcyj+E3qjnwIQDbLmwpc7Ulx9P/si6M+vsC1uMWNoBnYbSU6VkqDOgFZaElh7cljdwjUVNuL9Ff4Z3HE6kbyTtM9q7vK+1qCXQIxCl1uLB1ZNo/BX+DsVCtfH+rvdtSTAms6ne7f3c/dhzvqhWNy+vMtCr6QBbYmZtpCdMomfTASg1Rn45XsCi3y/w4z+60atVI7sxczeZmJ+PXOXctXK6tvSmTFPk1M0jSvNY+vVrRJXl2Z2n0C+YN//xMWcPyDn18y5MZhjVJcalz0ZAQEBA4O4hFLcI3HVUOgPvrMtm2Z4bg8/OBKd3q2CX2rks23OR5OgABrYJdRq16KOQsuPMNeZvO2eblTe4bZjLrW/SukXz2Y7zlKr0LElLIvWzTJamJWMGFtYyMFt9Vuu4Jfs4flVpW9Yxwp8vdl3AYDKz/VQhz/RryYLt52yV8OuOFtCqiaLe66pOiboULzcpJipRSlczev0aDDSg/ZBZjIepM16GwYzvNJjn721Gj8Xd69/vKLz89suY9XXPBh05Oo2pr7zD+5vOcb6ylNTOkey/WMLOs0WsnpzI1tz1DFzpWOX/r87T+WzMMEorRRRWaG2SuiIzlzd/yub7SV0J8XHHXSbhvv9upbjK/kGpoTHgR6+U49vAwWR/Txlzt96If88pVjFszk5SkiP499B4iiq1DTpepcbAttPXAHhlSGu2ZBfyzFdZtu9u82BPXhzUjN8uruODH2Y5fGbTuswgNnwYKUlNWb73Ur3niw/3wUch/FPgMno9PPssfPqp47rmzS39i+Pj7/plCQgICAgI3Ayuevnxq0qXWy3W5eZ+HlJ6tAgiY/v52+LmX45LAkR1ejnYu/nIhXtu2c0lIgnpCekMWzms7ostB3bAv978F2ZjA9JTJDK82vTFt+MgDn48mvuW9YKC+ndbd3ad6+doIM38mzGtyzRS2qUgEokoUZdQpinD390fHzcfTlzVUFJh6Ywjl4r5ePMZh5cYLUO8qNQa7qib+3lYZnOCvZcvGJUIZrPDNdWH1c1TO0eyN6eE574+jFjEn8fNy8oshSzbt9svv3IFfv0VRo1y/VgCAgICAgICfyjO3Dzs4mlO9X+fGYd/Q2SsPfnv51bd+LjbE5xqFGW33FU333Asj6VPtWdLzno+zHzfwWmmJk1j9eSBjFl0mMHtQvjt4vo6ixEAMg7Mo0uT7qQkt2fu1httJq0evu9CCS/9cBQR8OKgODpG+FGp1dV5bB83H4pURXbLXHbz61Rv1WMym/B39yfQI5DnNz1fZ+qLpS3Pe3TP6A55wGXgPJAL+APNgAeAxtxy+0+RWYSHvhnP9B6Nn4c3y499waLTi9hfvp+vHv6qQccK9Agk3CschVTBscJjtuVnS84y5scxLH1waYMm2WoMGtoEt+FIQd3Pbh1CO+Dn7sfJPMtzWm1u/vRyS2Jm16Y9+MjJd++Z5On0ajqAMYsOMz81gW2nrzGsfWO2nb7GtG8OV0thNBDkKefT1DasPb2WyV84d/PWl0JZv2oGnspSu+u96BdKyuNvc1nlBSpL8bswbi4gICDw50T4zSxwy+gMenQmHaXqUluvcn+FH3KxG3Kp/WCkwWhiU3aBnaRbqSk4vgoZj87fXee5JWIRLUO82HHmGv1aByOViO2iG8vVOnwVMr4cl8zCHRdYtuci5Wo9pwsqnc5IrUlqcgT940NQagJYkXmJrEtlvPVAG04VVBAT5Ol0YNY6q/XHrCvsOHMNk9mMRCwip1hF6meZvPVAG1ZO6ILeaEIqFlGpNfDK4NZ4uUlxk4nZlF1IkGcD+gcBnjIFR8sXcsV9KSZRA5JazDK8jP3wMTxI+7BY0nvG0LtVMIWVeXVLrQnYDOyk7phzsYSAfuk8+M8XiA72pVJroG1jH8b3iGbC0gP1Vvk/+WMK6QmTeKX723z621nylRpbVHh8uA9uMjF9WgVzqaSK75/uZkvWseLlJm3QAPbpgkq83Jz3snVGfLgPXu4yzhfZf+aWmPNzbD1VyKcpHV0+P1heBni5SZnzRAe6Nw/ikfm7bTNKm/orWDS2HdN/nU7GAeefWeoPI5mYMIn3B89i+5kiLpWq6zzfxF7NkNfSf1egBteuwSOPwLZtjuv694dVq8Df/+5fl4CAgICAQDVcdfOGeLnZbMZXIeOxjD11ntsVN39hYCwvDIxl5d5Lt+zmSrWefKWWqEDnXg53xs3jg+NR6VW1+7Ia2AFkAsZ6fLkaYncvvDsMxjthCO1aRDK9f0u0xgqXB5tvBQnuJDZOQGOo4nBBlsP6c6XnmLRuElkFWUzvOp0xq8cAEO4VzuAWg3kwbgRVGgl9WgVzuVTFj/9wdPO2jX1R3mE393aTIar247J6+fxt5xjQJpTp98e6fH6wuLm3u5TxPaIZuTAT4M/j5levwoABcPRojYuWW5IEH3nEteMICAgICAgI3DEcx6nl+CikyCViu2KT6m4uNhnpff4Aow/+RK8LB+s8/vqWXZnd7QlOBkc7rHPVzV8cFItMauDVrc+zoBanGbsm1ZbEIpOKGbxypEv3PzvzA9Y8tpn52y5gMlvcfNm4ZC6WVNKzlR+JMRKuVhRQaSikV6w/7jIx3Re9X+vxlFolQR5BdsvqdfMaVG/Vc6TgCBMTJ7Jyz0oW/LAAVNj/0WBxew0sWrGIr72/pthQDL5YClp6ACGAxKVT14nILKFdYFeeSkrhlwNN+OTxLryz82Xe3nXjZ3K88DgeMg/ah7a3K9yojQ6hHVBIFQxqMYiMAxmOaSsiuFJRdxp7Tap0Vbb2RnXxTPI0JCK53ZOQ1c0X7jjPmK5RjO0WjUIuYcTcAzzeqT1rH9tClV5pe471kPqwMjOP4ast6T813bx6CmNTfwVfT+xYp5t/+85IHvlBglxnXyh2LqAJIx9/iwJv+++WMG4uICAg8OdEKG4RuCUqdVWsPrmGD3Y7r4QdHjsML7mnbbnOaGL+1tpnVVYvCpibmlDnIHVq50hGJkeg0RkpUemoUBswmWBzdgEZ28877YO+ZvKNQVbrjNTOMQFOt7e2vhn6ye94ukkZ0zWKxMgA/D1l/Hv1cR7t1NThugI85Sx4sgM+HkYGtncjsbnCFt+44WgRA9qEc+yKknKVnl3nivhiV47jebtGEebt77KkRvpGMmhlP65oLoOLNTEKiS8TEibyRNwEJPjZElG+P3iZX07k06pJee07q4HvgLO1bwIg9vCl0QMv4t60DYt35jCwTRhznuiIl7sEpdpAp2jfBlX5x4W157dT12zLx3aL4tiVchKj/Bk2ZydD2oWxJC2JYXN22gpaKrUGgn1ca2Zq/U7JxCK7XrZ1kd4zhgq1vtaWWKcKKpFLJQ0akA/2cWfWw+2QS8QUVers/ht468HWrDm11qmgV2f+gXn0iOzJwtH3M+CjXbVul9o5knvjgp0mHgnUICsLHngALl50XDdtGrz7LkiFf1IFBAQEBP5YGuLmrnr5/G3nGN8jhrHdom6rm4/pGnXLbh4Z6MVPR/JqbeNyp9zc2YxRAPTAXiyFLbWnxTt+fo3CePbZZ3nw8SfRieQ2N9925jK5mn2uH6gBNPNvToeQziQ1TqJto05kXfAgNOQoY9ek1rmfNcq9R0QPW5T71ye+Zs+VPbzddyZJ0f4M+cS5m98fH4qvh2tJLDfr5iVVWqdubjLDxuMFvDy49U25eblK7/D9/0Pd/NIl6NsXztZ4KPPxgdWroXfv+o8hICAgICAgcEep2WLISny4D+m9YugXF2Lzlupu7qupZN6P7+JmdF4UbBKL0T74ECN8epAd7NgypaFu/n/DYpF67XVa2FKdjAPzGNR8CO1C2jaokKRKr6RVqDcFSi1fpCXg52XCKFZzruwc/gp/GvsGsODgAvKUebzW+7U6j+2suKNWN68NJXz91ddc++EanIRPP/wUZbnSkrYSiKVYJRAIAvyu//EFrUSLloYldNeHBHcGthjIiNgHSQ7vR+Y5DT1jGqEuy3U6bm40G8k4kMGUpCmkrUmr9/hTkqdwMO8g3SO68+zGZx3WOysWqo9Gno3oH3M/v+f+Xus26QmTGNJyKCWV9uPmlu9mE55IDkOlV1KkPcsnqVG4ib35al8+//oqm6YBlrak2Xm5ZOcpMV2vjrlVN5+6G/67EcTYF7acCI5m1KNvUORpP2FRGDcXEBAQ+PMivIkTuGkqdVVM+2Va/bPU+r9vG0RXql2LofZ0k3KtwrksVo8Vn7B0v63/6NrJ3floU7bT2Z7Hryp57uvDpCZHsPypzgz6eAc5xSqmfXOYeSkdSYwKQARcq9DieX2WprX1jXUwdvq3R0hJjuDFgZaYxCZ+N+LJfRUyJveN4bGkEH4+/RPjNzq+UJjRdQYKt8F0jPRn9qYztV7ntG+P4O3egRldZzDy+/qr4C+WO3nZXgteci+eTniWnVnJrNkOp05fxsstn0qtwZaIEhfmzSepUc4PcA1YBRTXfR55SDMajXgZqU+w7b5KVTr+8+spiiu1TLs/lieSwxiy6uaq/FOSI+gbG0KVVs+Tn++lXK1neWYuHSL8SEmOYO7WcwB0igzA30Ne7wC2LQ4zp5QxX+xj1sPtXJo53KVZEAaTicrr0ec1MZrMrMjMdTlmP71nDO7SGzM3ytU62zq5VEzHSC+mLp5Z73EAPtg1i+1jh/LJEx2Yv+2cw0P0xF7NuDcuuNbCHIFqfP01jB0LKpX9cjc3+OwzSK37JZCAgICAgMDdoKFu7qqXm8xwMLeUBzo0drr+Zt3c6tbWAoicYhWf/naWFwbGOnXzX08U8O81x1HIJZzMr2D6t0c4fKmMyX2bo6vR8udOu7nDILAJOApswdKKqC7cscz09IaAJoFIW3ZAGxjKh6pMPlu1HaOoDJWhmEpDEUZREbiY+lIfElMj3E334G5qx/r0iRSWezJny1n2l0npfX9LUjuLGLLKtRY2NaPcAebtn0fPyJ4oSxKcunlqcgTJ0YEYzeY76uaVGv1tc/OJvZrZ3DxXbe+Bf6ibX7wIffrAhQv2y0NDYcMGuOcel65JQEBAQEBA4M7hrMWQleNXlUxZmUVq50heGhSLh1xq5+alHr6sjevJw8c22+2nlrrxVbv72PfgaCaP60/27B0Ox26om0vEIu5tHcCQVa45zepT3xHt7zjhsy6UWiVP94qmV5wfP5/+idE/Obr5lKQppCekU1hVWOtxJCIJ8cHxrD+7nmeSn7ElCbpUoFEAZAMngXz4N/8GLyAKlO2V0ARoxF15W+YucSfGvxnPJL/Ixz/7MG/gvezPKWXI7IMkRPrTprFvnePm8/bNY9/4fUxImOC0jZKV9IR0hrQcQoW2gv7L+lOqKXXY5maSYHzd/Bjf4Z9E+MbU2kbo/maDweSG3qizuXlUoAdfpN3D1tz1DP0qxenz2fiegzlXfBlPuSce0khW7c1j2Z7LlKv1N+3mYpOlqGVqpuP9FHbrw2vDnqeo5MbzgzBuLiAgIPDnR/jtLHBT6Ax6Vp9c4/IstYfjHkIuldm9rK+LSq2BAA+5w3JrrPj8befsZDy1cySZF4rrHPAEWJaZS1J0AFum9SK/TIOfh4xrlTp+PpJHuya+LNxx3q7QoyY7zxahN5pwl0kQiUTEh/tQpTWwdFx7PNxNTP91Wq29OUd+P5JJiZN4q89Mfj6aV+d1zvjuGFumDWZiwkTm1/MZN4RKXSXluiKWjevC8Dn7bbF91TldUIm7xNtRak8B30N9BeoecT0JHDgFsczd/twaAyqdEaXGQKiPO1X6aw2u8h/UNoz+rUPoGxvMyr25zPntnF3bocU7c1gwKpEdZ66R1i2GhCh/VmReZFz3aJ772vm5nH2nrDOHO0b6sXin4wze9J4x9I0N5uUfjvH2iLZIxbVH5izbc9GlmH1rwY61sMVgNOFbbRZyv7hgyjRlDfrMytSlDGwTzr1xwSjVBpQaPT7uMqfxpwK18Pbb8MorjsubNIEffoDExLt/TQICAgICAjW4GTd31cvB4ua+TtJRbtXNl2fmkhwdwLop3TFjGZS8XKLmlxMFtGviy8q9ufRqGUi/+AC6thTTqomMQI8QPKQ+rNqbx/bTxcilEtxl2AomogI97rib2w0C78+CdcDVGjuJgDAsA+Wh1/8EAtVCBUsoBjbZ/l6uq7H/LSI3tcLDmIzC2BmZuSmi6wf1kYfyxZHTZOdVEBfmfVNuXj3K3cqsnbNY89hm3lt/GpPZ4uaLRnciNtSbLs2CeGLhHnq2bHRH3fzFQXGcynd8xrHiqpunJt+YqVnTy+EPdPMLFyyFLTXTBFu0gI0bIdqxJYGAgICAgIDA3aWu9p9WGlWWcnXZXjZHpzOwTaiDmy/tONhW3FKi8GFJxyEs7TiYUg9f4ry9b5ubtwzxokqvdMlp/N39aR/WHn+F6y25mwc0p1VQJNEB+jrdPG1NGukJ6czsNxN/d3+7Qgx/d38mdZpEekI6Kr2KMk0ZcUFxTLzsxM2rj2XrgGPAfm64ejjQF4gFgl2+jVtGKpaS0jaFL498icao4UTRceRSEU8mN6NKa+TXE/mYzJbnrvrcvFRTyoDlA9iQsoEuTbowO3O2Q6HI892eZ1CLQSw8sJC3d7zttLAFLEkwa06tcXmS7dTkaazMzGNgm3DaB93PuicGodSW29oI+bj5UlxhwkvmYefmvgoZS8bdwzs7X3aa4m59PktPSGd61+l0WtiBSL9IpiZNY/XkgfxwoOim3NxdDyu/hQdOOd5L5eiRBMxfxFKxRBg3FxAQEPiLIfyGFrgpdCYd7+9yfZaazmQRdF+FY8GKM04XVOIut7RzqY4zGZeIRYxMjmDxzhyXjp2x/TwGo5lALznbTl9DLhHzRHIEH206w76cUrLznBe2WB8Q3t94ime/PsyXey4yvkcMS8bdw4GCbaw9tabOammwzGj86fRaUpKb1LlduVrPC9+eYtZ97/PF8OW0D23v0r25QsaBeWy7tKHWazCazKzMzGN61xmWBWYs0eorqaewRYRf7zEEDZ3uUNgC4OUupVfLRpafrUxSq1TXhs5UwdsPtKGkSkePWb/x9rqTdoUtYJn5IAZWTehMr1ZBrM66wtLdF0mI9CclOcLpcZ19p3KKVQybs5Pz16pYMCqRzc/14tuJXfj9+T4sTUvCTSqmXKPn/jYhmEwmnurhGAFqpVytZ/SivaT3iuGjx9o7fKfjw334+PH2vDgoDqszq3QG1h/LA0S27QM95RSrSxr0mZVoypBKxHjIpYT6utMyxJtQX3c85FJB0F2lTRvHZV27wr59QmGLgICAgMCfhptxc1e9HCxu7qOQ3TE3l0nFiEUivtp7CU83KU8kR/D1/kv8e1hz8NjD0K/6Ejs3im5fdCR2bhTDvu5LcPAhlo/vwOLfL7BwxwXGdI2yDZreaTdvG9KW//z6H7zWecFn3BgsVwAdgUeA6cAEYND1ZeHYFbbcCaRiKclhfQnQTaaxeilh2v/ga3gUuTnCVtgC4OkmITEqAOCm3bxYVYyPm/33oXrsPFjc3ISZ7s2D0BmN9GzZiDVZV+6Ymw9oG0JRpcbps5wVq5tPu78VHz5al5tbZlE783L4g9w8N9fSbqhmYUtcHGzfLhS2CAgICAgI/Emorf2nzKjn/tO7WPjdG+yeO5q5q9/jy/VZ6E0mBzc/EtaSb9r04/kB/6TrpEXM7j6SUg9f4Pa6uZeblGJV/U7TPKA5+8bvw1vmjQiRS2PV/u7+bB61mS0Xtrjk5hkHMlh/dj2v937d4bzN/ZszfNVw4j6No8vnXUhcmMgL3V9g2YhltA1pa2vVA1iSFNcD/wHWYClyuQ94Fouf9+SuFLYEewaT3DgZAIPJwAvdX6BN8I1xxtmZHzCyc9hNufnZkrN0WtiJ08WnWf34ai4+c5E94/ZwavIpfnriJ5r4NOGnUz8x7ddp9R4rQBFAn+g+TEiYUOd2ExMm0StiAJ/+doEhn+xk47FrmExueEga4S+Lwc8tGLlYwZVSrYObp3Zu4rTNUk0yDmSwI3cHExMnkpWfxdg1qcza8wrjeze+KTc3iMHdSajji/fC+fdeQOruJoybCwgICPwFEX5LC9wUperSBs9SA/BRSB3E2xlGk5lNJ/JJ73mjaKA2GW8Z4oVGZ3QpVh0sg6yVGgMv/3CUbs2DCPFxw2A01bt/zQeEZXsu0r1FADsubSQhvCOzM2e7dP6ProtrHWEfAPyaXUiREoyVnXiq/T8I8Qx16fiuMLuea1i25zJDWw5lXPw4+BbY7Hw7K2I3T4Iffg3f5IcRiRwPGh/ug1Qspn98KGazmZ+P5hHqFdiga/Z190OpMdCvdQgjkyOdzlAAyFdqSPtiH09+vpdQH3eWPZXMS98f5Z99W/Dx4/YD2BKxiCc7Rzp9wCtX65m79Rw9Zm5h8sqDzNxwkvfWn0RjMDFp+UH2ni+hS7Mgpq7Kon1TP0YmOR+gB8uA/Je7L9KjRRALqw3Ib36uF5+P7kT3FkFsP1WIRCyyxZb+c2UWX+65yJiuUQAUV+kIVAQ06DMLcPdr0PYCThg+HN5448bfx4+HLVssse8CAgICAgJ/Em7GzX09XPNygNhQbyQiSO91Z9y8Qm3g1R+PMqx9OCE+bphMJl4d2ox3d73M2DWpDhHZWflZjFmTygubZ/BQoqVgIik6gJkPtWHrxQ131M115R0YVvwwi9IW8/ua30EGtAWeAKYBw4B4wMOl0982+kb35dCEQ3z32Hf4mgYgxbk3Vvdyscjy3HUzbh7oEYhS6/gzLlGX4uV2IyD2Sqma9GUHmLD0AFGBHnfUzTvHBOGrkNXp5QDdmgdRpdVzqdS+WKa6m+85X1Srl8Mf4ObXrkH//pYCl+q0aQNbtwpuKiAgICAg8CeieoshqdFAr/MHeP/nj9j/SSoZP7zDfWf3IjWbcDfoaPHbz5SrDE7HzKcPfoav7rkfTY1JhLfTzSu1BgI96nYaf3d/NqZuZNauWYxdM5a5++feKCSpg0mdJrHj4g46NsDNZ+2cRWq7VMQisd1509ak2T0TnC05S4eMDhwvPM7G1I08nfg07eUdiNsdB7OBLCxOPg6YDHTD0hr0TlKtwPrR+EfZOXYnp4tP25bVLA4/lH8IjbECd6nkpty8VFPKu7+/S/TsaIwmI/P2z+ORbx4h4qMIhq0cRnKTZCYmTKzzGOkJ6SQ3TmbIiiHM6DqDxcMXOxQutQ9tz4oRK5jZ7z3cJBJSO1smBljdfMKX+3l19THe/CnbqZunJkfwRHIYH2W+78KHaGmBOjFxImKR5dXlvP3zWHNqLWq9rsFubpDAo4/A4RDL37USGDkC3usBAQ1IIBIQEBAQ+HMhFLcI3BQl6obN7CvRlAEgl4jtxLsuQnzc6dc6hNTOlsHJ2mTcy01Kicr1WHWAMrWOe+NCkUnEqPVGqmrpy27F2QNCpdaAzqhlzekfUOlVDW6xY53RWBd7Lx/h+e2PMXnDeAqq8l06/u24hib+Ci6cz2PvW/vgeN3HUjRqSuio/6Jo1qnWbcZ2i2Lp7hw0OiOtQr35eNMZAj38XU6k6RDaAZHJk17v/0b6l5aB8TWTuxEV6PjWwMddhlJj4GR+BYt2XmDj8Xzef+QeUhbs4b64EBaN6cT2GX3Y8EwPdr/QFzPmOl++mMyQnVfBvpxSfjqSZ7uHz36/gFZvZNvpa0xecYgZA1rx8RMdnM7+/OCRdjzZJZKH5u2ie7UB+ckrD7LzbBGf7bjAp1vPWYq6qsWWLttzkaToAEYmRbApuxA/d78GfWZ+Cj+XthWoh5dfhscfh7lzISMD3O7wtGsBAQEBAYEGcjNuLhO77uUTezVDJhHTL+7uuLlUjEsz++bttyQSDr0nlLTF++jWwoefz/54x9x846bNTHtiEG+8/AqVkgroDzwHPAS0AiQunfK20ja4LYuHL2bBkAU8+PWDVOrK67yXml4O3JSbK6QKjhUec1gXoPCnstqzlb+HnErt3XPz0Yv2MrF3M2Y/7jyV5cNH25PeqxlPfr6X//56xq5YZvLKg/x+9hqf7bjA5VI1m04493Lg7rq5UgkDBsCpGnnq7dpZiq6D72KmvoCAgICAgEC9lCur6HHhIO+t/5h9c55kyTf/5pFjm/DVVjls+/DRzSg1+gaNmd9ONz9dUImnzKdOp5nUaRLbcrbZklfm7ZtHz8iedSZ9SEQSpiZPZfWp1Q13c10Vr/d63eG8NbEWd4S+HkraU+NIvCeR7D3Z0A+Low8Dmrp02gYhQ0Yz72Z0DO1IkEfQjRUiSyHI4uGLeafvOwxcMdAuNcVZcXiFrpxfTuTfkpvfE3IPYpGYL498yZGCI5jMJlvrovf6zWSJk1T49qHtWfLAEqZ3nc6A5QM4kHfAlgSz9om15D6Ty8EJBzk1+RQ/PvYjRwuPEjU7ikGretMo+BCrJycSFehRr5tPXnGI5/q3wkjlTbVAtfLBrlloDFpWZDbczSvcYfBIOBIM9z0JK9sJ4+YCAgICf3Wk9W8iIOBIQytbrbPUpNXEu66+o6mdI+nZshEecikvDYojOTqQHWeuOZXxSq2BAA/XY9UBwnwVFFfqGL90PyazmTlPdKxze+sDwqkCS196LzcpjbzdqNSVk1eZR5GqqEHnrzmjsSYmtJRLvyL1p+8xmOouvLFyX8x9/Hr+19tyDV08CujaeThVyrpflNw/YBCtR77M98dr3y4lOYJOUQG8+VM2fWOD8XKTkq2qYFVmXoP7eZrMltm90789QkpyBEvSkhg2Z6etPVF8uA/ucjH3xobw2ehOaHRGSlQ6TGYzX0/qgkpvZM/5YuZvO4ePu5Sk6ED6xDZsMLhUpcPLTcq+nFKqtEZGd4nkn/e2ZNe5IkqqtCwYlYhGZ6RUpSPQS46fhxyTycyYxXvJKVYBFukHSE2OoEuzQN746QSVWgNGM3axpdbY9CVpSXSM9ONUnpppXWaQ+kP9n9m0rjOQi+9MEYbBaEJnNKFUGyhXW9oa/OX7kZrN4CR1CACxGFasqH29gICAgIDAH8zNuHlDvNza31wqEd9xNwczKyd0dHlm3+zMD1j3xG/klqjJqyi+I25uVCsp3bKISTM3WQbIH8dSzHIX1UAikpDUOIkpyVNo3ag1FdoKAj0CUUgVZBzI4LmNz1GqKa3zXpx5OUCJStcgN5+SPIWMAxmYzCa75R1CO+Ap8+FUvsV1/wg379WyEd5uUpo18mTR6E5Uag2UqnT4e8jxdJNSoFQjqjat1jogX/3zeWfdSdb+szvjl+y3bVfTyxfvzOHgxcq74+YyGYSHw8GDN5a1bg2bN0NQ0N/TzQUEBAQEBP5qFBfD+vWwdi0tNmzgS2X9KYYqmRvnAxvTTWz+w9zc2p5+atI0xq5JdVgvEUlIT0hn2MphtmXWwokNKRvo0qQLszNn26WqtA9tzxu93kZr0N2Um19WXiatQxoKmYK+S/rWvqEW2AXmXWZ+abQRHgRac8emcz8c9zAf9P+A9vPbU24oJ/PJTDQGDRW6CopVxU7d3EptxeFBHv78cCiXliHeN+3m07tO59sT3zq4ubfcm0qNiYKC9qx9bAtaUwUqfTkyiYwARQAXyy7SaWEn23WWakqZv38+T3V8iuVHlvPL+V9QapUcKzxmO3Zpfilj16SSnjCJL9LeYvic/bZxebB3c5XOyIJRiZzMU+LuUXvrK4lIQnxwPD5uPii1So4XHneaclOsKqVVqDfZeRUNdvMrvtB+IpivfzeEcXMBAQGBvzZCcYvATeGvsFQP14wId0bNStjqBSvzt53j+NUbsh8f7sPEXs24Ny4YD7nUtv3ANqH0ax1MWZW+5uEtvSjlEuLDfeyOVRvx4T7IJGImLjuAyWxJZalv/1Afd6QSEdtn9LENyob7ulOsO4dSq7Sv0nYBy4xG5+dSi7MokX2KQZwHJqeb2NE6sAP/6f8hbUKb0fTDusvRq8timFcwKt1Zu/Vms5m40j3836SZGAx1F9UkDB/L/IUfIRJL6Nq6hMU7cxx+lmO7RdEpKoDRi/ZSrtbbZm4CzPntAmv+eT8TEyYy/0Dts3LTEybRq+kAhq/eb7d8eWYuHSL8SEmOYO7WcwD8s29z5BIJkYEeTFi63+F6JvSM4d7YEM4VVvLxlrMoNQYeaN+4zvusSfV70BtNTL8/lrd+zra1q3pj7QlahVoeSCq1Bk7lV/BEUgRzRna0FeLEh/vwVPcY+rUO5pmvDlGu1hMX5o1SrXeYYZFTrGLYnJ2kJEfQrUUQseHDSE+YSEYdn9nEhEkMazUUudR566ZbQaUzsCm7gIxt5x0+3/ReMfSLC7H9t/uXQaWCp56yzIgdNcr5NkJhi4CAgIDAn5ibdfOGerl1nzvp5vHhPpRryxo0s09nruDVIfEUqM/cVjc3m81UHf+N0i2fYQpWwijAtQm1LiOXyIkLak2x0o3SSikisy8Ssz8Ssx9R/o2ZNaIb/b7shdFsZPfl3WReyaRNcBvb4G/1wWaAcJ9AQnzsHyLq83JoiJun0yOiB89tfM5h3bQuMzhboMF0vXbkj3ZzsQgHLzeZcSiSt3psYmQAIxfuIcTHzenM5+pevmBUIgaDkQTvu+DmCgV8/z2MHQvLl0NkJPzyCwQF/T3dXEBAQEBA4K/G009bkn5NFger7/X13iat+bZNP36O7U5UdCgDfS3J1H+Umy/bc5nVkwcyoeNEh+TE+OB4p8krZ0vO0mlhJyYmTmT145Z0FmuBR7h3OGaTGxeVp2/Kzf0V/ry85WVe6/Wa82cCE5a2Q5uBIGAkEN2gU9ghl8hp4tOESYmTUOlVfJf9HUcKjtjWtw9tz7PJ03kwbihGs5Eo/yiy8rP4757/0sK/BR/v/Zh2Ie0AOFxwmKMFRzGajXbncFYc3iG0AxK8OHCx1Kmbr50ywObmzgpAjGYj6Qnp9I7qjcag4e0db9sV1Pyr83SW77nK3K0XmLf1Aqv/0RWFh56Hvh7BFeUVMp/K5JH4R+yScaxpOa9tfa3OzyzjwDy6NOlOSnJ75m69YFte083XZF3h+0NX+GhkpMMx/N39mdRpEukJ6aj0KopURQR5BKGQKnCTOhaeXKsqsSvit3PzJxNw/3whCSkjanVza2GLMG4uICAg8NdH+E0qcFPIxfJbmqVmFe9744JRqg0oNXp83GW1VrFaq9HNHjjIuNFkZkVmLmO6RjH92yPUx/geMSzeecE26Frf/lGBHrz1QFv2XyxhwfYbYhIX5s0nqVEcLzyOh8yjQS8UPKQ3ZjTa7oNySmWfUyXdUu8xAOQifzr5/4Ox7ceQFN4EmUxf6zXUlMVSdSkSiYn5T7Zl1d48lu25TLi3FOPvn7PxhxV1ntfNXcGs2fN46JFH8HCT8q+vDxMb6m2XWOLvIcdNJmZFZi5v/pRtGzR2k4lt912u1jP688N8kfY2nZv04KPM9x2q/J9Jnk6vpgMYs+iwXRW4lcU7c1gwKpH5286R1i2a7s2DeGfdSVuhSXWOX1UydVUWKckRvDQojubBXizccb7BL1+q34OXu5Sd54rtzld99qeV5Zm5JEcHsHZyN/RGM55uUgorNIiAC9cskaReblKn92j9rOZuPcf8bed4rFMT3h/8Pj0jevH+7pkOn9n0rs8zrNVQvOSe9d5PQ1HpDLyzLtvpDJLjV5VMWZlFaudIXhoU+9cR9dxceOABOHTI8tIgNhaSkv7oqxIQEBAQEGgQt+LmDfVyuLNu7iGXUFBZ7MJd3+BKeRHfZRYxsW/AbXNzfelVSjbORUMWPAZENeiS6sTXzZdJnSYxKXESGoMGrUGLQuqLm9jb5uZN/BVM6BFDchM/u3sxmU12g+2O9+LLi4OCeLZfyzq93Echs7tnV9x8avJUekT0YMDyAXYD52ApSO8ZcT9ikTtiEX8KN3fm5eDczYsqNRivv5CqK76/upe3CvWmS0wg7/d/n56RvXh/1x10c5kMli6FqCgYPRoaN/57urmAgICAgMBfkcaNbYUttXEwvBU/x/ZgXatu5Pk0si2f2KsZ8mq+/Ue4eblaz5hFFg/sFWk/3ujj5kNpLS1QrW2BZu6caVd8PX/QEr7L5KbdXCFVcKHsAlcrrjpukA/8jCVB8SFuS+G5zqjjfOl53tnxDhMTJ7L2ibWIEFGmKcdL7otM5MXmEyWYTGAwG3iu83OM+nEUK4+uZM9TexjYciCl6lK74owFBxcwb988SjWltRaHP5M8jWW7rxIX5tzNNx4r5q2+bzGu4zjCvMKo0FXYzuEl9yK/Mp9ARSA9v+jJyz1eZmLiRN79/V3gegFH7FB6rt4FWNy8WSMv8qsqbM8SNdN3jhYcdUjpqYvZmR+w5rHNzN92wTZJobqbe7pJ+f7QFbvWV9bvQPOA5mxI2cD2i9sZvmq4g0PP6DqDL4Z/wYDlAzhbYpmc62xCQrlaT8aW0zR58VlGZm3g4hfLeH/Xhjvv5k4Q3FxAQEDg7iH8FhW4KeRSGcNjb22WmlW8PeRSQn3dXTvv9f6jU1Zm2S1ftuciayZ3Y2RShNPBUyupnSPpFBXA2z9nM6htKIGecoqrdHy99xLfPt3FYX9fhYylacl8+ttZh+OeLqjEXeJN25C2ZBzIYErSFNLWpNV7DzO6zuD4FZVtAN+MmSrJFkpln2MS1T+IKxFJeKrD00xs/zx6o4IADzlmoEojchohWZ8sTu86g29HJ5HyeCr7MvfUfW6fYAJGvMJ/zwex8csDjOsezX8euYcH5+60DfDWnBlpJb1XM8pVeuLCbjxk5RSreP7bU7w6ZADbxwyjsLKEKoMSN7E3HlIfVmbmMXz1/lqLPo5fVaIzGFk0phNJUQH8ml1Q588fbgxm+yhkzH8yEblExIQeMUz9KqvO/QDGdotiRWauTdj9PeTM3nS63v0AMrafZ9GYTkxavpfsvBuzRhePtcwaDfFxx9+j7opxkxnWHS0gOsiLJ5IfZFjsUErVpZRqyghw98NP4YfILCO/TEulm+a2Rh4ajCY2ZRfUGY0Klv8Wk6MDGNgm9M8ftbhjBzz0EFy7Zvm7VgsPPgj790NY2B97bQICAgICAg3gVt38Zrwcbp+br9x7kYcTLIkdGr0JHzc/l68BLAOd2XkXcZdE3rqbGw0o935P2emV0FcPLRt0KQA09YmkdaN4Np5b57DOFTff8fxQVFoJm08WUlpprjUm3uFeus3gh4MFvLf+dN1e3jMGLzcJHz3egYxqs4Jrc3OFxJsQ7wBOF50hbU2abYDZes3WgvTUhVksHtvpL+3mS9KSmPbNYYK86m6tZS2cuVqmIdTXnVFdRjCs1R12c7EY3noL+Ju6uYCAgICAwF+VIUPglVccFl9u3oYlTZJYF9udK76OrRertxiqzh/h5jnFKpbvKWBS7wfoGzUQnUlFpbYKX3cfTDgv+rVSs/j6VtzcmnBSpimzT3zRAluBC0A/oHm9h2ow1Yt1zv7zLN+e+IbkJp3p3rQHg9s1QW80sfvy7wyPHc6rPV9l9D2jWXdmndO2TFOSpnAo/RD7r+6nfWh7h+Lw9IRJ9LyeVP7m8HgHN5eIRQy9pxFV+nKyr2Uzfu14h3M81/k5ukV0A+DjzI/58fEf2XhuI1OS/kWvpgMorRTRrXkQ97UO4b64EDZlF9AxyttWZFIzfcdosqTNNCRBs0qvtLUKqunmUrHY9hxSvfWVv7s/G1M3MnPnTLvUGCtZ+VmM/H4k6QnpbEjZQKeFnYjyi3I6IcFTq+LT1TPpfeEAAJHH93N2xFhG/Pz1nXfzaghuLiAgIHB3EYpbBG4aL7knH/T/4K5WwtbWf9RZn0Vn0Y19YoM5frWULdM7U6Ypo1hdQqAiBF/3VmRfqeIffWLs9k/tHEnmhWKn4l+9J+lzv/yTfeP3MSFhglMpszIpcRI9IgZgMsr44JF2fLp9F3tK30UjcU0aA6UJrH3yc95ZreSBnVlIxCJahnjRv3UIKckR9I60j5B0RRZTPhmJ5/eeVBVX1Xlut6ZtaPTAi0g8fAFLYclzXx8mNTmSr9O7cN+H253OjATLQHHfVo24WKxi0ehOVGoNtpmknm5SCpRqyqpEfJNZxYMdI3lmxSGHQfja0OpNtG/iS4XWQMa28/XvgGUwe8GoRHrN2kKHCH9mP96h3pcvKckRdIoK4M2fsgHLSwG1zsAxF2aVguXzqtQYMJmx3Vf11kqtr1fp1zVTNSrQgyVpSey9UMLjGXs5la+kf7ylSMtDXkGLEOgU5c+4JQfIKVbd1shDndHE/K2ufb7zt51z+nD+p2L+fPjnP6Fm+63KSjhzRihuERAQEBD4y/FXdPO+scHoTQYWjm5HmbaMUnUpAYog/Nz9+Hjgx/z7t387JITUxJq8kp1Xcctu/uGK9ez99g0M91yBCdSfKV8NsdkTL1MfRrYZyf8NeACdScObO16yi3Z3yc2/H8nEhEk83+Utfjx0ha4xgfSJHOhSK8/ekQORRLjx6W8X6vTyPrHBbDlZSEmV1i59MdBLjr+HHKPJTOrCvbQI8WZ8j2ZMWX6I/PJTPJHUhOUP/EiVXkmJupQAhb9DQfrfwc2TowPw95DXmyBjdfN9OaU8NC9TcHMBAQEBAYH/Vdq1g6ZNoagI7rvPUuwyeDABQcG0yy5k17ZzXHGhxdCtcrvc3EMiQa81YkCNCfe74uYSkYTXe73OfTH38fnBzzl57SQeMg/uCbmHw78dht+BJKA/ltQWF/GQ+pPWfixjO6Qwbu04l9Jj7gm5B7FIzFs73sJkNjExYRIvd38bo0FG20bJLDz4OdO7TmfGrzOc+nlWfhZpa9KYlDiJ/+v9fyR/lsyFMkvrnppJ5UPahdEnNpjfTtm7ud5gwtNNxL9/fbvW54ZRP46yKwARIWLjyG38cryI6d+c5M3hbZj1UDu+3n+JMrWe+dvO06cg0K5wvnpBz5PtniQ9Id31DxcoUZfi5SZ1dPNezfh6f67Ns6u3vgryDGBbzrY6n9MAMg5k0LlJZyYmTqRdSDs2Z5fYvSsIrihm8bf/R3yhvRM32/ITM2Z8zg+eUYKbCwgICPxNEYpbBG4JL7knD8c9xLBWQylTl1JSrRJWLna7I70La+s/mlOsYsa3R3htSGu+Se+CUuMY3agxqjhbsYmJX8xyGPCf1mUGcY2HYjSabSIZ7OPGYxm1p5lYxeyR1o85RPnVPP7z3Z5ncIshVKrFPLZgF0Gh29ir/hCNpO6iEgC5yA8f7XgyhvyTHSeqyC1R848+zRmZHIFGZ6REpcNoAk+ZJ2/2eY9uTXvwYeb7DGoxqG5ZPAyshSpD3dfg3XEw/n3HI5XJaBniZZsFerqgkmWZF0mK9ufLcZ148ftjDg9HY7tFMSA+lIxt5/nkt7OIRTidSZqSHME/+jRHZzDVOhjvDJlETFGlDolEzIk81wezNTojLUK82ZdTysiFe1iSlkSn6AA+2+HYE3Nstyg6RQUwetFeytV6UpIj6N6iEVVa54kytVGq0tn1BgVLa6UlaUl4yCW1zrCAGylC87edsxvoX3c0324764zTYXN23tbIQ6Xa0KDPV6k2/DkjFnU6mDLF0gu5Ji1bwpo10KrV3b8uAQEBAQGB28Bfyc3FgMqgYu3pn5y2wPlXl3+xb/w+uyhqZ0xNnsbKzDxM5pt380fnbkdy9Ct2FyzBnGqGukM77AhVtECiHsSkpNGM6RqLRmekuEqPl5v8eruannyw2/LsYe1hX99A7vzrPewHt0vGy13GkUuVvNVnZq3tgp7rPJ0+kQNJWXCIqf1auuzlAG+sPeHg5k8kRfDxEx2Z9s1h5FKxzc3nbr3A/G0Xqm2vdChI/7u4uZu0di+HO+jmVVUwezb861/g5ua4/jp/GzcXEBAQEBD4OyASwfr1EBMDCoVtsQc0uMXQrfJXc3N/d3/+r8//kdI2hSpdFRfLL7Jw2EIUUgW/H/sd09cmS8H5WKABjzKN3KP5V9dnGdLsccQiN4J9ZMzoOoOR39ffxtWaHmMyW1pNWd18WKsRLNh2iYm9J7Dm1I91Fp4DzNs/j56RPdk3fj9niy/h5+5nKwx/PvMUk/s2r9XNn+3XnIPFW10uAFnx0Ap83Hy4VlVCVGgpbz3UmDAfMeVqLeeuVaLWGTmRp+RKmdpWZFK9CN9kNnEo/xD+Cv96P5/qNPIMYELPUFqGeNu5eY/mQeQUVdq2q976yscD+i7p69LxP878mA2pG/CQebLjdLlteatrOSz+5nXCK4rsttfL3fjnoOfYoIgCkzBuLiAgIPB3RfgNKnDLyKUy5MjwknvSxLfJXTmnff9RPWVqi4z7KmSIRaDU6NHojZbKXDcJMrEYjVHNtF+mO41qz8rPIvUHyyzJmf1mMfTj3cQ39uXVIa3rFJPqYta1aQ/S1qTRrWk3Vj++GpVeRZmmjFCvUAIUgazKzGdLdjlSeQF58pfYeXGfS/d6b8TjfP7AR/ycVUFCVACLd1miJPdeKGHC0v0Og73je8QwqMWD3Bc9CE93CT0X93A8qAnYBOyq+9wSqYzQgU8TmDCI1M6RdsU0AR5y3GRiVu69xPLMXOamdGTpuCTUOiP55Rr8r68/V1jFpuxCm6RbI7xrsjwzl44RfvSNDal3lmT1+3WTiREB+UptvdtXR6nR2wazc4pVPDh3F5v/1YtuzQMxGM2UVuks3x2JmBWZubz5UzZN/BV88Eg7OkUF8Py3h/nwsQ6IRbiUMAPg7yGnUmufFHL8qhKD0YSH3DJ47WyGBVBnilB1qqfBzN16Drg9kYfl6rojSGui1OgbFJ16VygogIcfht9/d1w3aBAsXw5+fnf9sgQEBAQEBG4nfwU3xwxKbRWvbn3BblDVSlZ+Fk/+8CQTEybaZiI6myWanjCJXtfjvOHm3Lzi0gmOrxtLead8aO36PTf3TeL13q+ycrsvn47r6NTNk6MDeGlQf7aPGUp+RTHBXn70+qKXS8f/KPMDto7ZTs41Na0b+2IwiohU9OWnxwdRpS+nQldOsGcAvm5+lFaZ0etFLElL4uDFUqICPFk0phNag4kKtR53mcSpl4NzN7f6ZFJ0AAqZxM7Na3N5+Hu5uULuVquXwx108ylTYNEi+PZbWLXKUnzthL+FmwsICAgICPydiI93uvhmWwzdCn8VN9cYNMT4x/Dz6Z+5d+m9NwpqzNA0pykFZwrQddNBA4InfcXt+O/AV/E0d2Lp7lzm/bzXslwhY91UV9IQ0+kR0YPnNj5nt/yjzA94MG4YIzo0QSQy8/6u9126nlk7ZzGk5VBaN2qNSmfgUomahzpGMDK5djc/XVBJ26YeDFwxy6VzfJz5MZtGbaLv0r4czDtoW25tefqPvoMoVFqct+bPpHoh0/HC43jLb7Qtqo8OoR0I9grgh32XmfbNETs3n/HtYV4f3oaPNp2xuXlOsYrp35xk1qNNG9T6SGfUEewZTK+WbqR2juDCqjXM++EdfHQqu221fgE8MeRFDjaOq/V4wri5gICAwN8DobhF4LZjMJrQGU0o1QbK1Tp8FfI7UpGuu97L8JfjBYiAFwfFse5oHl/scoxWnPlwPEeLNzotbKnO/APz6BHRk/8b3ot1RwspV9U/+y+nWMXwOftJSW7Pmsd/5lpVCVfKSwjzCibatzHrjhTy8ebdlKi0eAVs5oJ+IWqDqt7jSk1NCNRP5uypNizcWsiLg+J4ask+Ph3Z0WGGoJXjV5U881WWLQWlsPKaoyyqgW+Bc3Wf3807gJc/XEjaiAGYgd3nip0W04zpGsWjiU2o0hpwk0l4d91JCis0VGoNnC2sZOv0Poxfsr/e+wVYtDOHvnEhTOgZw9RVWfVuP7ZbFJuyCxjYJhQ/RcNmIjfydrMbzA7xcaOkUsd9H24jIdKfBzs05t64ECo1BvrGBvNQxya4yW4Mpper9VRo9AxqG8ZPR/LqPZ91sL9mb1AApcZAqKXbk9MZFhKxiJHJES5/jot35rBgVCLzt52zPUDcauShr6IBU5gBH/fbPzP8ljh4EB54AC5dclz3wgvw1lsgkdz1yxIQEBAQELjT/Bnd/L2H4jlWstHp4Hl15h+YT/fI7rze+3WmbphqW14zzrtcfcPZXXXz4vJKyP6CS75r4X7X79Pd2I6HWzzDvEefZPySfcxNuadWN8+8UMLwT/eQmhzBlHtbUFRV2KCB3ILKEpbuLuH4VSVSsYhRXaJoEeyDrzyYK0XuLN56hXVHD+PtLiO1cxNSu4TTLtLExYpTeEh9CfYK4NiVShbvvEjWpbIGefninTl8ProTK/de/J9189pmPkvEIp7sHEnaF65NVnDZzVetshS2ABw6BB07wsqVMHSowzH/8m4uICAgICDwP8jd8nL487t5Y+8QGns3Ycav0+2LTZTATrjU7pKlBZGLdGvSk/KC4fycPvG6mx+zW1+u1pOy8BBfpL1Nt6Y9+c8ex1T3qclT6RHRgwHLB9gV7/i7+zOwxUDMmHF3r6RUo2+Q05eqS5CL5SBy53RBOT8cyqvTzVuGeFGhLW/QOQqrCjGY7Iu2q7c8ndlvFr4KGeVqvd3PZO1jW+xajrpLfPlX5+k8+WNKveed0W0GGq2YPrHBjHDi5i8NNtIq1NuhMD6vRtpKfZSoy2ji0wQPuZRXi/Yi++51xDXazBuaNefpx9/goMGn3uMJ4+YCAgICf32E4haB24pKZ2BTdgEZ2xzjo29XD0Pred5Zl82yPbn4KmSsndydOVvOOh1UPplfgZ+HiJlrZrp07A92z2LbmCF4usnxdHPtWsvVeuZuvcBDHSP476/XuFahpVJ71hbTrRddpVg+m1z18foPZpbia3gEX8OjiK7nLS7LzCUpOoCJvZq5PEPwvtYheHnVqKIvBFYBJXVfQpt7OvD5l6vILBQhl4qZvekMyzKdF9NM//YIKckRPD8glkqtnp+P3hhMjgvzRnM99tAVTuZXYDSY6NsqmJTkCJY7OacVay/PrEtlLN19kZHJkQ1KfPFTyOjY1N8m2F5uUkpUOkxm2JdTyr6cUsQ/HnPaQsnKtUotD3Vs7NIA+thuUazIzHU6k7Sm0NrPsDCgMRgRQ4Oj3as/QNxq5KGPQtqgz9dH8Sf652XlSkhLA43GfrlCAZ9/Dk888cdcl4CAgICAwB3mz+rmgV5iZq51zc3/s+s/bBuznQdiR1CuKUMu9rbFeQ9fvd9u8NxKfW6uuXqEa4XvYUpUuhxvLje2xN+QhrupDTuOw5bsApfdfFlmLv1aBzu6eT2oDUrGdY9BLhHjJhPzw8ErDm4eFejBF2n3sDV3PYNWpjgO0idN4/1HBvLmT2cb5OXHryoxmc2M7hKJVCL5n3Xzml6u1OgJ8JBjMJlur5tfuADp6fY7isXQ2nmc0F/azQUEBAQEBP4HuVtebj3XnXLzj3Z/xNYx23gs/nGUWiUmk+ym3HxwWxV4bLlR2GIGjgIGYAAgcu1eo7w6Iq58jBkJKfgqZHW6+Y2ijnv4bdR2lLpSLpVfItAjEIVUQcaBDJ7b+JxdYUvzgOZsSNnA9ovb6fVFT7zkXrx777uuXdx1cstzeWHzC1TqKl1ycy83KUWqegbva1CsKsbHzXlhx/wD8+gV2ZNn7+vE62tOApafScb2HLaeKqJ1uGW/7LyLXC07xdopriTcTKJzWH/+sSILwKmbq7RGhxaglVoDgR4BDbq3AHc/MJngtddwe/tth/X65M6UrPiazQuOuHQ8YdxcQEBA4K+P8FtU4LZRXZxrcjt7GBquV55bz1NfJHTLEC9UBmWDqp3LNWUEefrh7yFrcIucDcfybSJnxkSFZC1lsqWYRfVHc0d5JyCvmIjWEOawbuGO86yc0IVH5+926T6+PXCZF4aE3FhwEvgeqCclLzzxPsp7TuT/fitgXPdoJGIxv5+tu6J6eWYuydEBdIqyyKlELKJliBeJkf6o9UYkYhHGOvLBfRUyW9sjrclMqVLDiwPjSI4JJKPaLEmwfM5ju0XRKSqA7w5c5qGEJry2+jggYkzXKKZ/W7/Iju0Wxcq9uUzoFUOHSD8W78yhUmsgwMO+yrqu2HUATzcpQV5ujEyKqPOlhnWw/82fsh3W1Sa01WNLAU7luzZ4bqVUpXN4gLiVyEO5REx6rximrMyqd9uJvZohvwN9gxuM0QgvvQSznMR4Nm0KP/5omRErICAgICDwN+TP6uYdI/wwUtkgNy/TlCE2BtDML5QZ3x1h3dFj9baecebmJr2W4uMfomr5OzRz7f5kpmB89Wl4mLohqjbK3nA3v8ILQ4JdO+l13MTePLMqi+y8CuLDfRzc3FchY8m4e3hn58u1RsiPXZNKesIk3n7wXcpVJuLCvDldUOmSm3u5SSlV6dDq9f/Tbu6sncBtdXO9HkaOBGWNY86bB82cf1H/km4uICAgICDwP8rd8nK4c27u7+7PpE6TSE9Ip0RdTKm6jACPAGQiL77cdYXlmZedFrZYqe7mIpGIDx+PY8iq6619qrAUtrQDPFy7Tx9JBO7q0ZiuJWFG5LKbWwttLpVqeGVoY17Y/AJKrZJjhccwmU0O97wxdSMzd85kwYEFALQLaUeQR5BrF3mdQI9AlFolRwqOMHZNKhMTJvHOiHcpqTA5HTO/mQIQ6zlq4z+7/8PmJ7ey/mgBJjMkx/jxeFIYVXolxaoSAj0C8JBGsmpvHievapl13/v0iOjF+7tnOhTPV0/pySmuPZ3e31NOjxaN2Jdzo1jodEElnjKfBrU+8sPN4spffeW4wSOPIFuyhLLy+tP3qyOMmwsICAj8tRF+iwrcFmqKc22sPXyVC0VVqHQG8ss1nMpXkl+uQaUzYDCa6tzXis5oYv7W8wC2di2Ld+bUur2XmxSltszVWwGgoKqEGd8dYd6284ztFu3SPjVn/xlEhRTIX6JUvrDewhaR2Y3Wiqm81+M7fpn8BFGBjiZvMJlRqvUuzxBcfywfXzdf7gm5B7ZhSWypq7BFBK+88Qbye6cglrlx/KqS574+zH9+OcWStCR864kWz9h+Hh+FjMl9mrN9Rh/mPNGRBzo0IcBTzrbpvflHn+ZOjxEV6MGayd2ICvRgwtL9dHtvC/d9uJ1uM7dQUqVl+VPJ7H6hL99P6srm53qR8WQClVoDWZfKeCihCaMX7aVcrWfZnoskRQcwMimizuu0DmbP+e0cw+bs5Py1KhaMSmTOEx0J9nEjPrz++EK4/lAmFZP62R7Se8XwwSPtHPaND/fhg0faMaFnjO06a+Kq0DY03tDfQ24X7Q63FnkolYjpFxdCaue6P9/UzpG3FON42zCb4eGHnRe29OgB+/cLhS0CAgICAn9b/sxu/mCHJlyrathMxMvlRTz7dRafbj1H71bB9Ra2gKObqwuOcDl3FKouv0Ng/ftLjApGt36NY//IJt6/v11hC9ysm/vRPrS9S9t3CO2Ah9TH1jbHmZundm7CbxfX1xshn3FgHr9dXI+PQsKcJzq67OZPLNxDr/e30v+ju+/m81MT/nfc/PXXYc8e+x1GjYKU2uPg/3JuLiAgICAg8D/K3fRyuDNu3jygOfvG76O5f3OGrxpOxEcR3JPRjqYfNmHAit4EBR9i9eREp+PZVqq7ecsQL6r01yeiXgCuAZ1xrbDFCD0ienIgfR+h8u42R2+om687mo+HzJNKXSVHCo44FLYATOo0iW0522yFLQDHC4/jIfNokNMrpAqOFd5okzT/wDy25Kwn1M/5mHn1ApCbPYcVf3d/XurxEt899h3luhKeH+rFf5+IYGz3UH44vYwuizrQ7YuOxM6NYtjXfQkNzSIpxotnV2UT7taL7WN2kPvMJQ6nH+XSs5dZ98RvFOS3Z/ic/XUWtsSH+yASwfD24XZebjSZWZmZxzPJ0126t1daTcDj/qHOC1umT7e09VQohHFzAQEBgf8xhOQWgdtCdXGujahAD5akJbEvp5QZ3x656QhGpdpgE9WWIV71xmurdEZCvFwYwa5GsGcAVdoylu25yJrJ3Wqd/WdNKHmwQ2O6NgvinZ9PYsZMlWQLJbL5mEXqes/lZmxDoH4qVZownv/uGCnJESxJS2LYnJ12A65eblLKVPXErlTDaDJzpaAS2bdy+K2ejd1hxn+fJ6jlI5h/PWe3anlmLh0i/EhJjmDu1nO1HMAy4F6h0XNPUz8mLN3v8PMd0zWKNZO7MXrRXpv8+ipkLE1Lvt4L1f7zLVfreX3NCd5Ye4Ln+rUirXsUhZUa9EYT/eJCWJGZy2urj9s+o3K1ntGL9rIkLYmO12d8OnzHesbQo0UjZnx7o//r3K3n2HHmGtP6t6Kxv7vLVdbWh7LzRSqGzdlJSnIEC0YlojMY0RnM+CqkSMViFu28YOs1WpPqQltf392Gxhu6ycS2lyHWZbcaeeghl/LSoDiSowOZ72TW7sRezbg3Lvi2xajeEiIRDBtmSWepzsSJMHs2yBv20CMgICAgIPBX4s/q5hKxyNLepWET6whQ+FOpVdbr5nKpmH5xwQyID6VzTCAz153CbDJSdO4/qKK3Q5Rr5/PU9MHf/BRbD/jSWHrltrl5WRU813k6o1zoYf+vLtP5/mCBQyGP1c1TO0fwRHIIQ1aNdOn87++aRY+m93Pfh7uIC/tzuPn4HjF0igog5bM9tv22nb5GbJgP98UFIxGL/hA3B8sM69q8HBoePV6rm2/ZAu/WiLZv3hzmzKn3uH8pNxcQEBAQEPgf5W56OTTMzeVSMffHh1Beh9I6Sy+pTvWkwC/S3mL4nButiZy5OVg8+lLRJTgGtARcGKKTmCWEeYdxufIyO3K3s+vKJlKS2zN36wXbMWtzc4lYRGyoF3Fh1vY7FZzMr6jTzSUiCekJ6QxbOcxuudFsJONABlOSppC2Jq3e656SPIWMAxkOxTNWN5+07Bijuth7udFk5vuDhczoOoOR39fv+rWdo3pLpeGrhjsksExJmsK+8fsYsHwAZ0vOkpWfxagfU0hPmMQrQyw/y6hABa8Oiadtk1AkIjHrjuXZPvO6GNstii93X2R5Zq7NyzU6I0qNnnA/dzzdI9lzue7WRxMTJjF0/hbEmZl2y80SCfqPZiOeNAmp+Da7+S0guLmAgIDA3UP4TSpwW1BpjXi6SWqNua5roBRci2A0GE0YTCY7UbX2Yq8bM77ufg2Ku/N180MkujEou/ypznSOCSBju6Uvqq9CxpNdIhnTJQoTZqo0BjR6I1881YrU78aTW/JLvecRmd3w14/F1zyYVqE+tt7xq/ZdomOEP8/0a8G760/SrJEnXm5SGnm74efh+gt5fVk+Dw7sx8kTx+veMAieeHskM1JeQqWV8MPBPIfK68U7cywzKLedq3OmbKFSy4Lt5ziZX0FcmLftnk7mVzD92yMOhTv1RWOCJX78g19PERHkQb+4YDLPFzNuyX6n15FTfGMwe/lTySg1BsqqdAR5uwHw7YFL5BRX8frwNrw+zEyV1oiPQoqXu4wqjR6l2sC9sZYq67pmVNSMMrdEWp5j/rZzLB7TiW7Ng5BKxKh0BuLCfGjir7AbQK8ptK703W1IvGHNmcpw+yIPPeRSBrYJtbyYUhtQavT4uMscBvz/FIwdC1lZ8PHHIJVaXhKkp//RVyUgICAgIHDHUeuMmDHX2hryj3LzliFeVGoMKOTeDXJzb7mvrYe7Mzdv6q/grQdb0zHSi3JNGcXqErRmM28PMvPwgtGo2pXVex4AqSqUYOl02oUmOrj5s/1a8M2By3jIJVRqDah0hga5OYBYLKZP5EAmdJxYZ9rKxIRJDGg2GFWT2t180ZhOlGkLGtTeSW2sIC7MMuh7q27eNEDBfa1DyLxwc24uAk4VVCASw+ejO6E1GAnwlOPtLkOtM6DUGPB2l9pmQN4NN+8XF4wZWHcsr04v95BLb4+bl5ZAaqolcdCKTGaZgertXe9x4S/m5gICAgICAv+D3K0xc53RhFpndMnNrQmAqV3CURmvEejhW6ubO0svcUbGgXl0bdKdlOT2rD2c79TNN8/ozKGLlfzfiqUMnfNvaFPnIW+ghfXj1jPt12lcrrwMwCd7P+Snxzdz4GIZSo1zN/dVyHiqRwQpnZsglZgp05Ra2ikponGXeiMVSegbNcipm8cHx6PSq5y69rx989g3fh8TEibU+bmkJ6TTI6IHz218zmHdofxDaIyW5xtnXi5CTP+YwfU+N9R2DleKktLWpJGekM6GlA10WtiJUo2lfVDGgXn0iuzJtukPIJNIqdToKVPdnJtX9/JWod68Mrg1gZ5uSCVi3u//AT0ie/H+LsfWR9O7Ps+wVkMxdFCi+z0TzyuW8ynlHvzjgRcoMbUj/Vje7XXz/7VxcwEBAYG/MEJxi8AtYZVnndHEjAGx/8/eeYdHWaV9+J4+kzJpkISWhFBD6C0JVZqANHshUZqUqCuCEBR2XddOsX4qTcECYleKFAWlBQhFOqEFQigppJeZTP/+GDJkmEkyAQTLua+La5e3nncSyf2ePOf3EOilRKWQsmL3eZbtOueYNPRkolQmlbDvXD77MwqJaRzo9MO+4pf/vxzLZubgKMd2d73YryWyjg9l5RImd53GmFUJNT7T5JhpFJbZHC8L6Xk6hr2/nU3P9iamcRAmi4UAbwVGq4E8XTY5pXkEeQWSciGFp9c/RZHxco33UFnaECF7lnG9YhkZE0a50UK+zuj4/LadusxDXRpwf+e65OoKyCrJpa53EFqNhf8Nb8lbv6RV28dUf+4gBavncKmsqNpxaNtqmf3BmwyKupf7PtxH96Z13K5MPXqpmHKjhRahvtX2ufdVyxnSph7vPNzB5ZkqvicqUmAWbj3DyJgwxn+6t8bPC2DhljT6tgimRaiWh7u4X60L9snsij/TvrG/gEzoFcmBjEI+uJI8887GU9zVph6v39uGmT8c5qdDmVht9pUSX06I49kBLejQKIAlyWfdpr90DA8g4aMUl6/BI13D6BR+9XvXE6GtTd/d65nch5sfeSi/MnYvpfy6e5HeMubNg5wceOIJezsigUAgEAj+plROgcsvM/L+Ix3dejncPjf3Uckp1Bv5dX+Wx24+vVsSvx0vcEw+XuvmNpsVHy8rq0+s5plP5tgnRm3AOSAUqF/jLcAgp55kLM/0ncyouEjMFisFOhMahQy1UkpmYRkt6wfQr7WC7NI8tCp/fJV++KtlxDQOJOVszW2Woutr8VMrWLTlPDO7v0qfiN7MdjORW9HDviY3N1usjgloT9Gbi1k6pguf7jjHsl3nnBIaa+vmi7adISYykOYhN+bmUgm0CPWlTQM/ZgxsSbv/bcBstXv58sdjOZZZRGLvprRv5D79ZUz3CLpEBLptM1RbNzdarB57uZfy+gpv4Iqbt6yL/P57ITPT+YTXX4dOnar/8K/hL+XmAoFAIBD8Q7iZc+YAK3ZnMCAq2FG0W0HlBXMAHz3W2bHPnZtHBHnxydh2bM5Yx10r4jmQdYCZPWe6TSKpKr2kKt5JmcfWMdt4om+Ys5tfoW3dtijPqdgr3wPBHlxQDyihQ3gHoupGkXo5lQB1AIldEpnYaSJGWxFT7/LCXx2AVuXs5hFBXix7vANqlZnVJ7/jnZR3XLw7qVsSAxoP4YWer7u4uValpUDv3rULygsYtHwQ6+PXE9cwjndT3nW59uSYyfQM68mg5YOqdPZyczErxnfl050ZfLTtrJOX39uxITO+PcK/h75Kt0Y9eSdlrss9ZnSbQVyjOPp/3t/lHp4XJS0ktmEskzpP4vXtV9ME5+6Yw7Dmw+jw0iaMlht3c6sNOoYF0K6hv+P710fpzf1R9zG8xTAK9QXklxcSqPbHX+OPUqrCbJXw6p5cdg56nh+WTaNY5c2Y+//LqbrhcLPd/J86by4QCAR/UURxi+C6qS5tonLM9fkCfbUTpfZq8XBHkUeR3kRuqQGtRuE0ybhi93kGtw7BV61wxMydzC5FrZRVGTvnp1Ew864ozBYrd4TVvEpyYqdEejcahMUqceq7mF9m5KNtZ2nfyI+YJj58f/xb3to116PVppWR2NQEmMbS2u9ePhsXy+6z+S4tfAZEBfPG/S1YeeI75uyY4yKu0+KSWPnUQEYvOeiyitNms1Hy+xoKNi0GN71CK/PE1KdJmvkCX+3JdkRGpudV3YKoQGfER1X1PxkDooKp66vCWyWvti3RK2uO8eKI1mw+kVNjS6nKHL1UTKHeSMqZPCb2jnRarVv5PpUluuLz0RlPseixzsy/kjxjtcGaQ5lM6d+c0zmlWG1XV0q8t+kUPx3OdIpMLNAZCbjyEnr0YhFSiYQCnfNqz8d7RNIx3J/LpeX4qH0c+6oTWk/77i7bdY6YxoEMbh1abbzh2O6N6RwR4HiB+MdEHlosIJO536dQwIoVt3Y8AoFAIBDcYjz18vQ8HTKp5Ibd/I11qexJL6BNAy2+arnHbg5Qz0/Dsl0XWPmUZ24+pNlQxn1yyGl7hZt3DvenS6Q303+efjXSuhQoxuMWROr8dvSKfJnl4/ojk5rJ0+c4ite9lP4o5FZOFG9k/AZXL0/qlsT8R4dy7wfV95wHeLxHJN/9foH7OjekVG8iKuBOVj80kDJTMfn6AgI1AXjJtaxIyWTEyprdvFBnoo5XoGcPeQWV1JeXVh+jd/O6ju+JioTGzSdyMJpr5+ZGi43/rTrCC8Oib8jNUzNLSM0sYWKvJjQL8eVSYTmfjY3hg99O88XuDPw0Crdu7qdRoFJImfzlAafP/3rc/Hq8vKbo8WrdfOF8WLPG+eIDB8KUKR59/gKBQCAQCP683Kw5c3B2c6PZwunLpfhfaZkoAd7+5SRLktNpHuJTo5v7aRR8Oq4dryXPcnLwqpJIqksvcUd6YTo2m5WkX5Jc282UwaHzh6CRBxeyYC9suaJw07pNQyaRcXbyWWRSGetOrXPbZqfCzUd9fJAPE6KRyfXM+vXFKpNLRn4/ksTOibzRbw5tAgc6uXk9n2Bksqrn1k/nn6bL4i5M6jyJlQ+vRGfSYbKYUMlVqGQqFu5byNQNU6stRlfKlWw+t4mRMT24t0MDXlx11GnOfGNqDnvSC+xpOA/96vLekJajJzkjmdP5p52uW9uipPdS3uPHh39kdvJsR2uj/Vn7KSgvoH+rUJJP5/1hbq6UK1CiwEfpTUO/ho7tdjfPtLt5nUaMvv9FMgJCyfUOcBr7TXPzv/O8uUAgEPwNEf9qC6ql8grQyv3GAcekto9K7hSteG3M9bRvDlZZxFDRU9RdkUdFSkbP5nWJqOPNjuf6IpVASbmJ0d0imP7tISxWG1+kZDj+fi0JseEkn84lrmkdXlqZxr+HvUqv8F7M2+m+aKRHw4G8sjqN/w6Pduq7CLDqwEUm3dGQpF+mVdsPsio6hcaRmT6OOupGfD4uzm3cpJ9Gwb+HNWHmrzPcTvQfyDpAwg8jmeSmj6nNbCLv5w8pO/xLteOQKdV0GfVvzoT1pM+8XS4R4lW1IArwUjoV/Fw77jn3t+O1tcerjNCs+J7499BWSCXwwciOlJurL8C5lsslBno1D+bJ5fv4YGRHOkUEuhSffJGS4Yg9rHx/d8kzlQt2rl0pUTkysSKWviIO/72H27NxSi/O5esI8FLirZKjUkjpO28z6yb38vh5POm7W8GCLWkO2Xa74lQtRyKB4nIzix7r/M+JPExPh/vug//9D4YOvd2jEQgEAoHgD+VaN/f3UuKrkvPaOnshePMQH7pEBFBqMHMyu9Rt+5n6/uobcvM7WgTzdL/mGEwWDGYrpQazx24eExmEl1JGwwANo5cc5NNx1bv5HWGD0Rlk7DvnOim86sBFnuwTxnep315184tAEB6ltWgM3ngbniG26SCWj2/PyhM/OBWvB6gDODDpAC/99rpb9688If7lpNcZ+JZrql8F8TFh9G5RlzKDiae+2M+S0V149OPdHM8qruSaxQ7XrExVbq5WyPBSaGvV3slLrmXt4SOsOZTp9D0BsHRMV/LLamr36ozOaKbUYGbaNwf58Ca6+bVefm2ceWU3f/OB9rz5YDvyS42O+16Pm1+Pl1cUydTazY8chmnTnC8aHAyffgrSv7G3CwQCgUDwN+NG3by6OXOo2c3H94xkQq8mTOzdxCM3T4htyG/n1rnMOVckkWxI2EDPsJ68ufNNDmQdQKvSkqvL9fjzSOySyOqTq13dORvwxrPClkLsRS1X6h8mdppITIMYui/pzubRm3ll8ytu3fxw9mHeSH6DSyWXWPb4JM4UnOZA9u81JpfM3zufXuG9GBg5lI+3ZZBythAAnfE0Cx9rU61rF5QX8Pr215mdPJv7o+5n6YiljF01lm+OfeMoEqmKDqEdUMlU3PP1MMZ3nMiLvV/nhWGtkF0zZ2734LMs2HLW5b3BV61g5VP9XBYM1LYoaX/WfnQmHa2DW3Mo++o7XL6+gCBv5a1x86wsUCoh0F68f62b/94wyv153AQ3/zvPmwsEAsHfFFHcIqiSqqrMX7+3NW0a+DGxd1NGxblvPVOkNzliru/u0IB8nRGZVELzEB+H8GQXG2ruKfrlARJiwnn+rpbklpZTUm4hyEdJ18aBjOxqj79etuscq57q7vh7BZVXpZ7N03Fvx4ZIkNA8qAVrHllDibGEPF0eQV5B+Ch9yCrJQS6Tcn+nhi59FwFGdKjHmpOral3YopAqeLRVEs/3nE7/t7ZXGzdZ1UvGtSzYN5+e4b2YMqALL646jrk0n8s/vIrx0olqz5P5hRB877/JDGxMZhXthdxNNkfX16JSSF0KfiqYc19btp66XGOEZsX3RFxkEPvPF9AxvHarTbVqBeUmC2FBXqTn63ho4U63xSfucJc8U1GwU9UK5oqVpNeycOsZujYOYu6G4xSX2+/7y5TedG9ax1H85QnFenOtVscW6814KeXVpsFolHJCtP+QyMMtW+D++yE3F0aOhJQUiKr6ZUcgEAgEgr8y7tz8yT5NaR7ig59GydakPlW2hazcfmZPev4Nu/mMwS3IyNdR10cF4LGbP9I1jJX7LzK6WwQf/Ha6Rjf3Vsn4du8lt353d8d6lFv0vLnzTTACuUADDz5IK8Qo+7NkwnIeWXSAj8e0ZcbG6S6On9glkU1nNtXo/hUT4l+MH0jSt0eqTC6598NkejSty9IxXSnUGR0OWF3LT6jezZfvvFSr1qsrUjIdn2Xl74n8UgNHLxURExlU43Uqo1UrCPJRcbnEcNPcXGc0V7mC2Z2bf7T9DF0jA3n6y9/xUl69b23d/Hq9HKpPanRx87IyePhhMF5TSPTZZxAS4tH9BQKBQCAQ3H5uhptXzJkD1+Xmz3zluZsnxITxSEw9hn45sspnkiChRSU315v01PGq49Hn4TYtxApcAhpWdVYlTEARcOV217b2eaTNI27dvHKbIp1JR64ulwLDZZoGRbL9/FYC1AE1tvKckzyHnuE9qRt8gLmdBznS0lekZHrk2lablXta3sOes4UMajKMr45+VePjPh3zNAv3LcRqs7Jw33ziGvagfZ2BhPp6czK7hJb1tNfcw9WDi/QmRi85yCdjX+WOiN7MudJWqbZFSQB5ujy0Kud7BmoCKNBlM+mOpn+sm+/eDffcA9HRsHYtyOW3zs0FAoFA8JdEFLcI3KIzmt32G29S15thbRuw/mgWS5MP1xh7vjQ5nSWjuyCV4CL1Ad4KNp+ouSBiWco5OoT5k3a5lIVbz7A1qQ+vrDnGf4ZFc2erYNYeyeKl1Ud5YVg0HcOv9ntsHuLjqH4vKTcx8Y4GJP0ynYX7FiCVSGkd3BqtSkuxoZgjOUew2qwkdk5kzoC5lJSb6dOiLlnF5ZzMLgUgPrYeg794pFafY6A6kK/u/5GkL8rR9FPSpoG2yslamVRS40tGZebtmMOW0VvRFp9n0ujHMWZnVXu8KqwtdUfMQOblV+O1r51sntgrkvwyA1H1tC5f80m9IunRrA4PLNjp0biXJqczqHU9modoqeOtrDa2vjIVk/il5Wam9G+BVFJ18Yk7rk2eqVyw0yLUt9YtksoMZorLzY77F+iMPNylERLsKzc8qfou0tdudWxxuUn06gSw2eDDD+GZZ8B85WtaUgIjRthfiPz9b+foBAKBQCC46bhzc5lUwqNx4VgsNoxma7VtISu3n9l3zt6D/kbcvH2YH2cul7Fw6xmSZ/TlnV9O8uo9rRkUHcLX+y4wdukelozp4tbN39p4ijX/6k6vFh15YfNzLPq9ejef3X8O/holX+09z+8ZhVisNmRSCY/FNSBXl8XB4wdBhUdpLd4l3iy+7xPmbvTDT+PLswOasOnsTy6T5LWN8p6TPIfNo4a6bWlZObkkPe8cA6NDqOur8ui6FVTl5nvSi5jd2fPWqyNWOr+DVHxPSIHWDfyp66OqlZv7quXMuisKg9mKRiG7KW5us1FrLy83WrDaYE/61V9c1NbNb5mXv/giHD/uvG3aNHtLIoFAIBAIBH8JbpabfzyqC3qjmSf7NHW0BP2j3Pzzx2Mw2nLdpnkEqAPYkLCBN5LfYNG+RQ43D1AHsPze5R4lBUYHR1NuLr96/bIrfzwobPEpCGLdEz8SFhJBnu4yGoUGjVzjaO1TbCh26+ZNA5uyPn49W89tddum6OmuT7Nn/B4GLR/k0rqnMvuz9lNmLOOtlNnsvLjdkZbuaSvVxM6JDG42lClfpvLCME/cfCI9w3oydcNUx7Z3U+bx0yN34a2S0z7MH41C7pGXp+fpmPHtCeYnjGBIs6EUG4rQmfVo5LV73wjyCqLYcPVeHUI74Kf2Jz33zE1z80e6hiGXSq4ebLPBkiXwxBP2wu9Ll+C552DePDFnLhAIBIJqEcUtAheq6jfup1Hw+bgYXl+XyvKU6lvPVERclxnMeCll/HY8x6kHu0wqIXlGXz7adtajMS1JPuuI4/7+9ws81bcpPio5zUK1jPHT4KdR4KOSc2erEAa1rkdBmRGDyYLRYo/wG94+lB+PrWbnhR30COtBsaGYozlHsdgsTveZv3c+sQ160LHeIAZEd6BIZ0QqlbAxNQe9udjjOD+A0e1GszdzLxdLzvNwl/Z8kZLBv/o2q1IIm4f4UGby/B77s/bz4eJFzJr6PBZT9cI3buIT/OJ7JxKZZ//JV55sjo8Jo31YAD/uv+A0YR/qp8ZbKWfVwYsU6021ktwSvYmkbw/Sp2UIY7o3Zto3NT/zmO4RfJGSwQOdG+KtkiGXSWs1+e6tkqFVX22hVXE9qw18VHLHSglPKdAZHdfzUcmp768BYPTSPcTHhtE/KqTGfp1+GmWt7qlVK2p1/N8SgwGeego++sh1n0wGBQWiuEUgEAgEfyuqcvOOYf54K2W87kFbyAo3t1isvPVgB3ak5bHoBty8oihixe4MTmSX8NLdrcnXmfD3UjLrrih81HIKdSbuaFbXxc2L9CbWH7mEb8A+dl303M2XjO5CmcHM57sy2H02jzx9Dvd+eDcEuB+j84cIXIK1/1lLmcnAw10asGJ3Bo/3rM8dn852Ofx6orwvl+Xzr2Xp2KDa5JKv9p5n1pDaJc1V5eZvPdQei8XK7P5z6BPRm9lXVmtW0D60Pc/ETKd3I/sK1GvbJh29VIzRbMFLKWP4+9tJvKNprdy83Gjh1bWpvDyiNSqF7Ka4ufd1ermPSu604rm2bn7LvHzGDDh5Elatsv+9Uyd49dXru5ZAIBAIBIJbzs10c6PZQl2tmsZ1vJ2KYf4IN9cZLRTrC5FJZEQHRzuKyo/mHCWxSyJb07ey68IuFzf/cO+HPN31acauGlvtGLQqLaUG+wJRsgFfILimDxP6aPpx553vkJKuoG1YAP/97T/8nvW7o9gdoG1IWxc3ryjImZ08223roQNZBxi7aiwTO01kffx6uizuUm2CS4G+AK1K60hRiY9pz4ebzzqSUbo16sk7KXNdXPvZ2CSGtxzGD/su8/Ldra+6eePezE52dfPKaTSVx7M/az/FhiLkEjVjlu6hT8uQKlu9Xkt8TBjf7btE35Yh1PUNpqGfglJjWa3al2rkGo7kHHFsmxaXxO/pJWiUN8/NZVIJm45n07t5MF7GckhMhGXLnE9+802IicHvzmG1uqeYMxcIBIJ/FqK4ReBCVf3GE2LD2ZGW57awpTIV0YrjekRwd/uGvLHuuMs5zUN8KDPULl7OaLbyaGwYI9o3YH9GATN/cI39frxHYwa0CkUulSBRyPCWSQj0VjKuZxh6sx9dG3UgV5dLHa86aOQaFv2+iPl75jvJ5Dsp81j10GDmrj/O+F6RPPv1QcZ0r8fMX5M8GmsFj3d8nNObTvNuyjxWPbSJYe/t4dHYnmQXG9we76OSk6fL9+ziFuAXeG7Xs9UfJ5NTd9BTvPPOHB5atKtWCSlyqYR5D7SlS0Qgj32cQnqejnc2nnJEjYdo1fxveDRrD2fSIcyT3ypc5XKpAW+VvMrY+muJjwljYHQok788gEYhRymXYjRbPZb8ib0ikUkkTBvYkkAvJVqNAqkE3t2YDECpwUygl+cT2n4aBeFBXrz7cEfKDGaK9CYkVwrPO0cE8p8fj7L7bAEz72pZ7SS6VuNZFT7Yvy61aXn0tyQrC+67D3bscN03dKj9hciv5mQigUAgEAj+SlTl5vd0aMCvqTket4Uc1yOCAG8ls9cdZ9lNcHOT2cpPT/ck+XQuD1/jmdH1tYzt3pg7WtR1FGWoK7n5fZ3roTf3pH299h67+ex1x0m8oynNgn1oFHCKjm/2R6/R1zzYHEAGhEE933r8+9d/8+aABTz7VSoPxXi7LWC5nihvnbmYYK2azScuV3vcuiNZvHFvm1qnF1bn5qFaNXd36M62MdvJ1xdwsSiXQE0AXnItK1IyGbFyr0thi2PcBgs70/Kw2qiVmw+Krkexwcj5fB0apeymubmPWl4rLwcI8lYypE093nm4A+VGy3W5+S3z8jp14Mcf7SmEL74IK1aAsnbPKxAIBAKB4PZxs938jXWuyel/hJtPv7MFbcMacGbyGUf7njpedfBWeOOv9qfUWEq3sG6ONqEqmYpFvy9ixeEV/PLoL0zoNMFtEUkFg5oMIsgrCC5gT1SsKdA6FxYMX8Cvhb+S0K0Bz36VyoMx3nxy8BOXQ925eWKXRLakb6l2TAAL9y0ktmEskzpP4vXtr1d5XIAmwJFcUjGXv2DLWdLzdIx4fy/xMe1Z/dCvlFtK0JuL0ci1BGr8sVoVPLZ4HwaLlbWHMwnyUVFusjC62x1sGXUXl0ovOT7Tymk07gpt8vUFyGyBtZ4zj40Mwnwl3VIuuyLBNjnT4pJI+KHmhPjKLZLAnkQzrMVQHlrwOza4aW5utdk4crGE81v28Ph7M5CePOF6cp8+0KePmDMXCAQCQbWIf/UFLrjraSiTSqpsp+OOpcnpfDauK7+m5rgthqkqKUMpl9I/KpggbyV5ZUY2puZgNNvFqtxo4V99m2MDTmaXcqHAeTL76KViXlx9DC+lnO5N65BdUk6It4qfJndhfdpq5u6Y41E84f6s/ZSZitmXUUD79Hwa1M3lgR/iKbV6Vi1fQUWc36HsQ5SZign1UzPrh8O8fHcbt8eXGswEeQXWfGEd8A1Qw3Bk3gHUvWcWqgYtyS8zMrZHY579uuZVmBN6RaKSSZmf0MkpRh1c2wDNuiuKOj4qvFW1+6fE+8pq1iK9iae+2M+S0V2IjQx0SvcBu5yO6R5Bl4hAkk/n8kz/puSVGqjjY5fqbk3q1Cj5CTFhdAoPpP/bWx3PEV1fy5huESx7PIZRS3ZzMrsUtdKz1aYRQV4sfzzWZcVzxXUrR4xuSg1kcOvQKmPQlTIpE3tH8vSKAzV+ZpN6N0HpQaujvy179tj7r1686Lpv1ix46SWQ/oM/H4FAIBD8banKzfu2DGHsJ3s8uobDzY/nuBS2wHW6udmCv5eC9DydWy9/9puDjO/RmCf6NMVqtaE3Wm7YzZPTcpi3fhp7jN+CpoaHtgDpQDggv7IiUaHh29RveemOOTQK9CKzxH0BS7GhmDpedWq4gTNalR+9m3vXWNxisdpuupunZpbw24nL7JnZD5k1kLfWZlBqKHabHHMtAd5Kfthv96vauPnOM7koZFIm9LrqqDfDzccu3eOxlwMMiAqmrq/9faSm+P/q3PyWerlEAk8+CaNGgY/P9V9HIBAIBALBLeemu/kuz91co5SREBNGiFZFdrGBZSkZ6I325MPq3LzMYCYiWM6GtA3MqeTgTQObsvHRjaw8sZI3d77p4uZTY6ey8bGNTFg9gYVDFxLXMI53U951OW5aXBI9Gnaj5+s9PWpDRBq06dyGITFDeOKdJ2rt5rVtIfpeynv8+PCPzE6e7SjgqEyH0A6oZCpHcknF+0eLUF9SM0so0pv4cPNZFmw5y8ejOtOhUTMMFivLd57HJrHxfkI0ZaZi8nT5BHkF4iXXsik1n8gQMw99+5BL69WqCNQEoJLJHHPmo5bs5rNxMXSJCOSj7a5e/niPSDpFBPDgwp080785XRsHOhxVKpExvMUwJnaaxMJ9nrVIqkh97BcxmOSTJcxP6HRz3byeL//L2UnHt15EWl7uevLTT8O8eaBQoLRYxZy5QCAQCKpEFLcIXHDX07B5iE+t+ysaTdYqJ3evTcpoFKDhlXva0Ck8gGK9iUKdEX8vJXPua8e+jAL+/cNhlHIpjy5JQSqROE1SpufpAHvRwadju7L7bD4PLdpJsd7E2mdieGnr8yxwI3HVxRPm6wvwVsr436/vcsrwAQarG+Gqhmvj/PL1Bfio5Gw+aZd0d0J4MrsUb4W2+sjAbOBLoOoURQCU9ZpT956ZyH3t4m+x2ohtHER8TFi1yTsJMWF0iQjkiS9+Z9+5ghonw4v0JrxVMtS1jCFXyaWcyCohIsiL90d2IPl0LoV6o1PboyAfJf5eSqxWG6OX7sZqg68nxiGR4JiQPp5VxMTekXQM92dpcrrbJJ+O4QHEf7TLabXq0UvFTLsmCvSLlIwaV5v6aRR8NjaGDzef9qg1V9K3h+gXFVxlcYtcJqV/VAgJsWFuX2YrSIgNr/Y6f3s+/xzGj7e3JKqMlxcsXQoPPnh7xiUQCAQCwS2gKje/nhTEn49mu91/PW6ukEr5949H6N28rouXg93NE+LC2XzyMh9tO3PDbi6R5DJ6+UPo/bPsSSzVcRl7K6ImVzcldUti/p75WG1W8vX26/qr3KcPHs05ipfCq1ZR3kqpL3dGq3h5zbEaHfqPcvO8MiN1fFTojBangvSqiK6vRSKBfefsn0dt3fyzsV3RKGU31c2XjOnC9/sueJQC46dRMOf+drzmYfx/dW5+W7xcFLYIBAKBQPCX43a4eat6vrz3SDtC/eUU6AvI1xcQqAlgcv8eZBVZeHrFwSrd3E+j4NNx7XhtxywWVXLwirY+r21/rcq2Po/9+BiJnRP5aPhH9P20Lw+3fpiVD68EIKvEPoYQn0D++/XPTPgkGp1fWfUPrsM+t90EZvadydpTa2kd3LrWbn49LUR1Jh2tg1tzKNvVL6fGTXVKLoGrc/mVGdk1jBahWsZ/vo+8UgNLxrRjc8Y6hn01z7U1aNfp+GuGI5VI2Z6xvcYxdgjtgFblh8UCJ7KuerwEG5F1vVgyqgulBjMFOiMBXkq8VXKyi/VIsL8gLN52hjtbhTgcVSmTsvusnjn959IrrDdzd7q2SJrRfQZ3NbuLM/kX2Tl2vyP1ccjKPRTpTTfVzeuUFfDM+y/T+XSK64laLXz8Mdx/v2OTmDMXCAQCQXWI4haBC+76jVdVMV4dRXoTOSXui0IqJ2X4axQseLQTG1NzeGjhTtdJ0J6RrJvcC73pat/6a3uUAnw2NoYFW9Ic8vTp2E6sOrHK7eR5ZdzFE0olErYXzCTbtLVWz1zBtXF+gZoASg3FWKw2lldRRGGx2liRksnkrtMYsyrB9aKpwPeA+0RxB96t+xE08EkkcvvXMbq+Fi+VnH//cIh5D3agY1gAS5LPunzOE3tF0rdlCEP/b5vTLyaqw1ejwGrDo8KQCsZ0j+CLlAx81QqXr9lLq4852h6VGuxf7/iYMBY+2pmZPxxGbzRTx1cN2CU3rkkd3v7lJAqZ1GnyPVirxlctZ/HWM7y4+liVMewVUaDxMWEexT0mxIaz91y+x6257L8QMlfbmshLKWfmXVHENA5iwZY0l6/LpN5N6BcVXO01/raYzfDcc/Z+q9cSHm6PdG/f/laPSiAQCASCW8pNc3PdzXfztYczWXMo08nLi/QmR0HwzXLzTYc3sD5zFjZ/S/UPaQVOABE4Jbskdk7kjsZ38MHeD+gR1oN6PsGcyDqNlyLcbQGLxWZh4b6FPN31acauGlv9PYHJMdP4fMcl7usY5ljhWRV/tJvvPptXKy//fOc5rDbcfs1qcnObzeZw1Jvp5jZsdG0cWGMKzJz72rL11GWP4/9rcnPh5QKBQCAQCGriVrt5ixBfXrqnKSuPr2beD67Jh9Pikvg6cThGo9ytmyfENmTzuXVOhS3geVuf+Xvn0zOsJ2Paj+GFzS8wO3k2aU+d5+udNo5eSmdAo1945+AEbH5VJ5IAkAWogMZ2Nx/SbAhnC84ysMlAgjRBnKqfjbfSzyM3v54Wonm6PLQqrcv2xM6JdGvUjafXPe20vWIuH1zdvEBnYtW/OvNa8iwW/e6+aH/0qnj8ND+Q1C2Jkd/X3Bpocsw0cootbDiS4eTm8zfb3VwqwcXLrTZ4NDaML8bHMvOHw+iMFrxUV928fVgA8zefplN4b7aOHkpheaGjMMpf7c++9BImf3GcUoOZUsNll9THm+Xmg04k8+qGDwjSuyn+6tgRvv4amjRx2SXcXCAQCARVIf7lF7jgrqfhtRXjnl1HQanB7HafxWrji5QMnrijCb2bB/Pa2tQqV9tN+eoA8TFhPH9XFA38NZy/Eq1YuTBBIpGQcjbPcQ2lXErHcB8mL53t0VgrxxM2DWjKfd8MJdt0oVbPW0HlOD+wV157ybWOquvqiiiW7brAyqcGM6HjpKtybAW2AptruLFESkCfcfh2Ho6kosk89p725SYLzw1uhVQCYUFeLH6sM/ork80BXkpUCileSvsSWE9bDEXX16JVy0nNLOaXYzke9wHtEhHIy2tSSYgNd/qagXPbIz+NgsQ7GvNITD1M5PLMIDV6ay6lRn+UUhVKuQIvpZwpA5qzKTWHSZ/vxWy1odUoeO/hDjz6UQpHPEiSWZqczqLHOrNgSxqjluzmq4lxbmPY2zTQMr5nY0YudlNhXs11i8tNhPqpqz3WSylncOtQ+kUFU6w3U1xuQqtWoNXIUcqk/8zq8/x8ePhh+OUX1329e8M330Ddurd+XAKBQCAQ3GL+Cm5e2cs/3Jzm4nnX6+ZvbH8DbamW/+x9DqrXKSiA4LJgclrmwBUVbh/anmfjnmVEixGUGktZPGwxBfoCZDIrHya0IS2nnGe6TmO0m8Ly+Xvms2f8HiZ0mlDtpP/ETon0bjSIESv30rdlqMsKT5fjPXRztdzuf7V18093pjPn/na18nLgT+fmj360i8/HxRITGei2FeikXpH0aFaHBxbs9Ojz8dTNb6qXX7gA06fDu+9CcLDn5wkEAoFAIPjTcivdfHK/pnRrpmXaz9PctpY5kHWAhB9GMqlTInPvnEvLUF+OZZY43DwhNoxHYkIY+qVzcUVt2/rM3TGXDQkbeHHLi7QLaYdMouH73y+Sm76Y9dkrwbeGC6QBYdC+kbObK+VKzhefx2AxMKRDHRRSJVNipjNqZbzLJSq7+a4Lu2rdQrSud12KDVe/Zu1D25PULYm4RnEM+HyAIzES7HP59XyDeHmEr1s3H9I2hN/OrXNb2FKZsSvHcvJfJ5nUaVK1Bf4TOyUysMkQTCa5Y0HltW5e2cvB7uYJsQ2vcfPLLm6eeEdTNqXmkLB4Pw0CvKjrq+LpvnWJX7SbQxf/WDfXlpfy4saF3Hv0N7fXNiY+ifLtN0GlqvL+Ys5cIBAIBO4QxS0CF9z1G69cMe5p6xmtRuEUo3cty3adY/uMPvySmu3RarsuEYG8fHdrRi+92r+0QrAAxn+617G9f1QwheWFtYonLDOWEeoTSlpBGhZb9atCA9QBBGoCSStIc2xrH9qeyTGT6RnWk0HLBzmk+NnY6axIyXRUPlf0zPx0bFeXyO4ivYlX16Txxv2z6RPRm9d+fY3DCw7D8erHL1X7UGfEc2gi2jttj48Jo10jf8cq2jYNtDzdrzmNAjR8tvMsx7NLKdabkEslLHy0M1KphAm9Ipn85YEaP7Ox3RtjslhJzbRXdVf1TGD/fhjTPYIuEYGMWrKbUoOZkTFhTl+zykQEefHJ2Ipox3iXVQnTuyUxvMVwfJTeLpJbbrZgtdo8mjwH+y9ppBL4ddodqOUytBo5vZrXpVNEoGO1aYCXEh+1jJLy2kWMlhstLi+3ZosVo8VKsd5Mkd6In0bpEHIvpRwvpbzGYpi/PeXlEBcHJ0+67nvqKXjrLVAobv24BAKBQCC4DfxV3LzCyxdvO+Piedfj5qcun0JWKKPIv6jG473PejNxwEQmD52MzqQjT5dHXe+61PGqg0qmYuWJlczdMdfVKeNmck/UcHZedO1FX1BewKDlg1gfv54ejXrw1q63XOPGY6bTu9EgRi85SJHeRF1fVZW/pIDaufmChE7I5dZau/me9IJaeXmR3oRMKrkxN49LYnjLm+fmEuDrid3wUskY3DqU/lEhZBWVOxX/bDyWTU6J4Q9181A/9fV7udFoL9ROTobNm+GLL6BPn+u7lkAgEAgEgj8Nt9LNn3z+Dn44/p3bwpbKLNg3n55hvXj3kcEMeMveAmdpcjpLRnehyJDt4uDX09an3FxO25C2TI6ZxrLkDC6deA5DmyNQXW2BEe7zvY9nXn6GYJ/gGt38ue7PcW+r4ey4UL2b7zy/E1+lb61aiNb3rc/qR1ZTWF5IgDoAlVzFwr0LeXLtk06FLQBJ3ZNISSvio+QMFzef2CuSjhFeLgVD7igoL2DcynEsu3c5vcN7M3uHa2ugKTHTGdJsKOVGOY9+nHJT3HxaXBIjqnFzo9nqUWELXL+b++lL+HnJk4SU5rtc87KXPzMHPcVLr8+k3jWFLX+YmwsEAoHgb4UobhG44K6nYUXFuKcR15N6N0GCjah6VUu93mRBIpGweOsZj8a1eNsZvpoYh1wK5itJh/YepRbkUqnTxGaQt9LRr9NTEn9K5FLJpRqPuzNyCPdG/I+RXVuQU5ZNVmkWQV5BaOQaFu5byNQNUx1SPKlTIsNbDqPXyh1O10jP0zH8/WRHTKTJbCW31EBdXxUgYfmuDC5fCsSwwFRjYUujyOZExv+PdOPVMvlrJ60BnuzTlJExYZQbLWQXG5h0R1OwwbKUDFrX9+W3EzkMahVKXGQQCTFh7MsocEQdnswuxVIplzAhJoy+LYP5fFe6o2in4pmmDGjGVxPjyL4iuQ0CNEiQ8NnOdF5ek0qR3kRUPV/KjRa3k9GOXrDVRDvGfz+SxM6JzBkwFx+lN/IrldoVMYQnsjwT9ApKys00D7n6+dlsZh79OAWz1eb4DLRqOdMGtqzVdYvLTTQIuCrdOqOZjanZLNziWuE+sXck/aNCRJQigFoNjz8OSUlXtykU8OGH9u0CgUAgEPyD+KPcXCaV0DzEx+E66Xm6G3LziuKBAa1CXDyv1m5eCgM/HoilpjZEBuil68eYse9zX0xDcsqy0Zv0NNQ2RCVTYbKYeH7T826TVw5kHSD+hwf5T68XmNN/Dt0a9uTtFOdJdh+lDwezDzK8+b30bzycYsPVKO+KnvQjVu6lSG8iur6WIG8l43o0rrGgBGp2880nL1+3m1d+11gyqgtWm42LhXqCfJQE+6r5Zt95R4ENQPMQnxtz8x9urpuXGpzd3Gw18/bGk2QXlzti2DuFBxDdwK9W172lbv7MM/bCFoCsLOjfH778Eh544PquJxAIBAKB4E/BrXLz8wV6rJiYu8Oz5MN5O+ewdcwwlDIwWuxubrZYKTQUuhx7PW19CsoLeCZmCi2V7Rn/SW9M7ao/X1osZXbcbPp170eITwjecm/KLeXVuvnD3z3MC71eYO6AudW7ecvhmK1mpnebTvz3rikv1/JMzDRW7b/MgFZ1+TXzN95NeYdD2Yew2lxbKSV2TmRwk6HoTTLeeTDQxc17NK1DodG1YKgqVp1cRVZJARZdDL8kbKHcXExBeSF+an/81f4oJEp+OJDJ7HUnbpqbJ/wJ3LxI48v28Hbcd01qy/rmccwc+BT5Xn5M05upV0nnxby5QCAQCDxF/DQQuMVdT8Pq2ulUJiE2nJjGgSxJTneR+sqi3r6RP8V6U61W2xXrTdwZHcraw1mO7YU6E2qFzOlYqRSCvAJq9cxbzm2p/gCbgiivJ/ngzpfo//ZWvkop5vPHO1JsOMN7Ke/xbeq3DimuvJJTb5Tz9kPtmbvhhJOYNQzQEFnXmzKDmZdWH6W43MzQtvXxVsl5dfE35K58A2t5abVDattjAPdPeZ3EO1tjNFvJKzUS4G1PtPgiJYOX16QS4KVg1VPd2X02nwmf7XWRw3E9GtOreTDbTl1m2+lcLpeU89zgKAr1RnKKDfhpFKgUUlbsPs/e9Hwe6NyQfi1DAPh4e7rTeIr0JnxUcub/dppfT+Sg1ShY/Ghnfj6WzYebr6bcVNeLNiG2oUfRjvP3zqdHo17cE3UvGoXzCkx3/W+rQ6t2TgFRyqSM7+W8CiOqnm+tI0br+qqQS+1LGHRGM6+tTXW8+Fbm6KVinl5xgITYcGbe1VKIOsC0aXDggH2Va0gIfPcddO9+u0clEAgEAsFt4Wa6+Ss/pToirHWmYvLLC6jjFYifyg+TxczFQr1HY3Ln5gU6o72Q5RrPq5WbXwaCwCKtobDlkpRoyRSWvPQi/d7awme7M/n88Y6cLD3DG9vfoFlQM5oENKm2pRDAy1tfIqpOG6L8+/Pro8MoNRWRVZJHgCaAALU/FwrM9JqTTEm5qVKf+2KXnvRjukewJPksLUK1fD0xjqzicsoMZgK97f54q928SG/iw81pRNb1pqTczNrDmeiMFpY9HoOPSuGYPIe/hpvfGR3i5ObXE/9/y9x88WKYP995W1gYDBhQu+sIBAKBQCD4U3Ir3DxA7Y/BbCCjqPpExQr2Z+2nQF/AqG6NWbztLGCfM6/jxsGLDcW1busToA6gXkl9ui3sgLWNe2+sQJKjokPAezzS/yEOX95Dfd/6fH74c7wV3jW6+UtbX6JdSDtauXHzIE0AcpmMIV8M4tjlY+ybsK/GFqKJnRO5t9UILhfbyCuzcm/UfahlqipTVHo1GsSI9/eQnqdz6+YyCUTUqzlZsjK5ujx+3K/jPz8eY+GjnTBZAvn0eA4nss7y2bgYlDLZH+LmvcJ7cW/L+1DKnd36Vrn5q30fp2/aXgLKSyhWefPf/hP5IboPSCRXxnH1umLeXCAQCAS1QfwkEFSJu56GXko5s4ZEEdfkqrxXEF1fy6TeTejRtA73fJhMgc7ET0/3ICEmjNWHMkmIDWdkTBhGs4UCnYm6Pip81XKe7NOUZbvOOUlcVRTpTAR5O4uSv5fCMUkJdjEa16MJKqnJ43jCmlBYG1HHmER0aEcKrozzTK6Ou95NIT6mIW8OWMBLd8xxu5Lz64mxdIkI4KsJcRTqjWQVlTsi+yomuSue/URWCSPkB7j8zQvYrK7V45V5JmkmFyPuonVECBqFjLTsEpqG+PLCyqOsO2Jvg+SnUfDZ2BgWbElz+2J19FIxU78+SEJMAc/fFUWR3kTy6VweWrTT5Ws7oVckib0jWbE7g0nL9vHuw+0pKXf+msXHhNkj6isltOSVGugSEeD0clfVZLRMKuGRmHoeRTuCfVXCgMi7sNmkTmLrrv9tVdijQJ3/KXS3CuN6IkYDvJTIZVLMFisbU7PdCnpllu06R0zjQAa3DhU9QyUS+Ogj0GjgxRehYcPbPSKBQCAQCG4rN8PNNzzTkw1TY9hweo3bCOukbkn88uxdjFt6mMMXa560vdbNA7yU5JUZnTzPYze3APlA3RpuagPZobqE1HudqDZRjonfa91cq5HTa2nPGp8BYO6O19gyehALf7tASro9OrvUUERWUTk/PtmdoW3rsTwlw6nPfWXiY8KIi6zD3nP5dGjkD8D5vDLaNPT/U7h5RUpLVD1fCsuMws3/SDdPToYnn3TeplbDt9+Cv79n1xAIBAKBQPCn51a4+fRu09kzfg+Dlg/idP7pGsdUUF5IiFbj+LtaIcNH4efi4EdzjuKl8KpVW58VX63guX3PYWtmq/bY0KKGyDVzyTX7cte7KTzTvwnRwSaGtxjO8BXDa7wXwCvbXmHL6K0ubq5VX2Dew2Fsy9gGQP/P+7M+fj1xDeN4N+Vdl89vcsxk7o26FxlKzuXm06ahPy+vOk6wti2rH/oVnbkYg6UEb4UfComPUyojuHfzIxeL0Mhrlx4YqAmg1FDMI13DaB7ie8vcfE7yHIY2H4YS5+KUm+rmO88htVndunm+lx+v9RnD4BM7mDXwSTK1dd1eV8ybCwQCgaC2iOIWQbVUjq2r3NPwWnnXqhVoNXIkwKgl9upmP40CKRKmDGjOs3e2YPOJHLerE0d3i2DVU90ZtWQ36Xm6asfj56Ugr+xq5XJ0fS1KmRQkEoc8JcSGk3Imj/S8EiZ3ncaYVQk39Bl4m/sTaJqEFLWLXNpXRJ5lwZazVa7klMukrDpwiTtaBiOVSPj9XAE/HLjostrTZjZy5ru3eOXIr9WOR+3lzUdLPqFn/8FIpRK+SMlALssm1E+D0Wzlp8OZjmMTYsNJOZtX7YoBgNWHMnl2YAve//V0lRPtk788QHxMGBN6RfL+b2mUGSy0CPUlNbOE6PpaxnZvTOeIAEYt2e14CfBRycktMzL9m4N8OrYrHcP9WZqczvGsEreT0c1DfCgzFdeq52u+voBjF8vp3rSOQ2zd9b+tikm9m9i/h67B3SqM2kaMquT26xotVhZs9izif8GWNPpFBf9zJF2vtxewuEOjsRe4CAQCgUAgAG7MzRsFaPBWW0j65TkW7HMfYT3ySuvH5ePnEL94P4dr6MVe2c2j62tRKaT8ciybWUNa1c7NS7EXt9RU2KIDr+N3UKfFFCRSWZVuvvlELu8mRNTKKS8V57HpRI5LAcv3+y7w3OCWdI4I5KNtrhHZE3pF0rdlMGVXIrlfWHmU/w5rRaNAL+Hm/zQ3P3cO7rsPTNcs2vj4Y+jUyaP7CQQCgUAg+OvwR7t5/PfxTOw0kfXx6+myuAsF5dW3+QxQ+5NdbHe5Cjf/fNd5krolMfL7qwURFpuFhfsW8nTXpxm7amz1D2kB9S41M/xnQP3qDw283JEt//6VO9/ZDtjdfO3hbDo0DkStpFZOmV2a7+LmXSICyCnNc/z9dP5puizuwqTOk1j58Ep0Jh15ujyCvILQyDUs3LeQE5fPUVYWjLdagdFs5Zt9FwCYv/ksLwyLJjywLkkbTrjM01emspt/tfc8W5LialUYpFX58XiPyNvi5nm6fNQyjZPL3iw3n1VPz5RNr/Jjww683HKIWzf/ps0AvmkzwJHW4u66Yt5cIBAIBLVF/BQQXBeVxb15iC+hfmq8lHKK9GZ2X6moTogNZ3d6PgazlTd/PsGUrw+6VAQfvVTM9G8PsWjrGT4d29Upju5aoutr0aoV/HzUHnsuk0p4pn8ztpy8zC/HshnbvTEyqYSRMWEsTU5n2a4L9AkfzKROk67rGZVSNW/0+ZA6pmeQYn9BqVyFXBmrDVIzS9iTXkBq5lUZjq6vRSWX8sKqo2gUMv678ggDW4cyrkdjoupdvYa5JJeib2dRVkNhS3hEY2Yu+JaNujAmLttHrzm/8eHmNBZsOUNEkDcXKsXIV/4saiIhNpxNqTk1TrQvT8lgT3o+8TFhmKxWFiR0YsdzfVkxPpZezeogl0kY0ra+4+tY8QuH9Dwdw99P5szlMhY91pmfn+mFxWJlQq9Ip+v7qOTk6fJrHG9l8vUF/HQ4E6PlatJN5Qrymp67OiGuWIXxzaQ4dj3fj3s61Gdw63q1vm6x3lzL9ltmj479y/Prr9CkiX2Fq0AgEAgEguvGEzd/5Z5WrDqx2u3keWXm753PqhOrWTqmc63cfGz3CH45lk37Rv7sS89nfM9Iz9z8EqAAalgAKc+WE3RxGnWjpiGR2luSVuXm3io5lytNfHtCYXkhPqqraz+i62t584F2PN4rkkc/TuGOFnX5eFQXNk3tzbeT4tg0tTcfj+pMqcHM2E/20O2NX/lwcxpFehMfbz8r3Pyf5uZ5eTBwIGRnO2+fNg1Gera6ViAQCAQCwd+Dm+nmC/ctZOeFnUyJnVLtcR1COxCg9ufTHWcdc+ZbT17myIUShjYfxsROE52On79nPr0jertsd6IUArcHsjN0J/hXc3Mr+GaNwF/7Mt5qpZOb2/3Tn1xdbrXjv5YCvaubP3lHE4J9gpyPKy/g9e2v0/jdxjz07UM8t+k5Hvr2ISLfi+T17a+jVWn5dt9FFzeXSCQMaBXC3A0nneby3VHZzS1WGytSMpncdZpHz5HULQmtyuu2uXlWST7ma9Lhb9jNz56F0aPRxMUStG8XY39bTsr4tu7dXCJxKWwR8+YCgUAguFFEcovgplKkt6/crJi83XIihz3p+SxLqXlitkOYP/ExYXy4Oc3tMeN7RrL3XD7eKgUJseGM6R6ByWKloMyIv5cSP42CyLremMxWjmUWExHkhUwi5eW+LxPXKI63d73tcYsilUzF5lGbaV23I1sP7HIU5VistlqtEBzTPYIvUjIwWWysPHCRoe3qM/z9ZOJjwlj0WGfKjRa2bE/mucQZFOVkV3utoOadsA2exsdHLcBlp31HLxVTbrJQx+fqytXmIT6UGy01ymHF12r8p3trfB6ApcnpLHqsM9hsfL33PD8fy3ZUt1+bxHNtJKG9ECeNFqG+hGrVvHJ3G5fYxSCvQI/GUUGgJoDDF89RrDc7xZ+7W91ZQUUUaL+o4Br7dFZehcGVIqfaXrfivwtPKS43Oa34+Nths8H//R9MnQoWi32F65490KjR7R6ZQCAQCAR/KyocRCmX0jHch8lLZ3t03lu75jKk2VASYsL4wAM3n3NfW3o0q0OR3kRsZBD+GgW+GgXfToqr0s3fTH6TQ7sOQSQgcXsLB4rjCva/coDn1uY5pclU5ebX45T1tUG89WAzsouvthD9cncGJYYgGgZ4kV9qZMDbWyqlNZqrXOEp3Pwf5uZ6PQwfDidOOG+/8054441a3UsgEAgEAsHfl+t2851vsXn0ZpYdXsbJvJNuj5kWl0ROsYXxvZo65swLy0wMbhPC5vRfmN5tOrENYx3te4K8gpBL5LzS9xW6NermMm/evLw5F/ZeIL9XfvXLo40Sgsqm4OPX162bn8wuRSX1Itg72KNnrSDUN5C3HmzqcHO1QoqXSo7BrHebmmK1WTmU7TxX3yG0A15yLWsPH2HGoJY3zc2X7brAyqcGM6HjJBb9XnVxUmKnRHo0GkiP2Zsp0Jlui5v7q/0pLbegVjg79nW5+YUL8Mor9lRC89UCE0lxMSHzXiNk/nzgT+DmAoFAIPjbI4pbBDcVP41dEisEcWDrUB79aLdH5y5NTmfJ6C5sOZnD8axSLJVmiuNjwugXFcyEz/ay9ume7E7PZ9SS3Ry9VIyfxl7s8mhsOA0CNOSWGPHTKPh0XDte3v483xz7inEdxtEssJnHxS0Gi4F6vvWxAU/1aUri8t8d+5btOseqp7o7yaU7Knrcv7wmFYC3fjnF9hl9SDmT75BV73PbOPbNW1jNpiqvA+DbeQTefcY6Vqm6I7u4nEaBXg4h9lHJydfVLIeeynwFRy8VYzRb8FLK+L9fTztN6Fck8cTHhPHp2K4Mfz+ZL1IyeLxnJFO+OgBcTblJzSwh/qNdLrGL3gptraIdveRaTmSVuBVbd/1vK6JAlVcmxq+H2l634r8LT9Gqq14l/ZfHYIDERFi69Oq27Gy45x7Ytq3qFkUCgUAgEAhqTYWD9I8KprC8sHYR1voCxvZozOZq3Py1n47x27Q72HbqMg8u3Omxmz/c+GEuHrkITWoYiAE4AaZoE351/Xh+cDAjP0pxOsSdm5/MLq21U2L1ZtKyvXgpnQtX2jbwZ2z3CIwWKy1CfTmZ7fxZVIVw83+Im1ss9mSWHTuct0dHw5dfgqzq9zeBQCAQCAT/LG7EzS+XXWbjoxtpt6CdS3uiSZ0SGd5yGKXlUpoG+zjmzGVSCVtndOM/v/2HjKIMR/uecnM5wd7BPL/xeb46+pVLW59fv/+VF3e+iPUOaxUjsqM2avAzvopa3dyx7Vo3t1htfL7rAmN7ht6wmyfe0ZRBresyNXY6j/0YX+N1JsdMY0VKJlbbzXXzIr2J0UsO8snYV+nWqCfvpMx1eq72oe2Z0X0GsfXuJGHxAQp09nn/2+Xm+TojdXxVLvs9dujMTJg9GxYssM/ruuOXXxyt58W8uUAgEAj+aERxi6BazBYrRouVYr2ZIr0RP42y2slHrUZOdH0tXkoZepMFL5OsVhOzZquNBQmdkUhgxe7z7E3PZ2TXMPq3CiGzUM+ixzoze91xRxJMRJAXn47tyu6z+Yz7dA9Wm433H+lIQmxDfju3zlE9PW/nvFo/e1ZJPj8fMjIyJpxx3RvzcfJZwC6wo5bsdpLLa6uQJ/aKpGN4AAkfpTj6aBbpTaTnlvFU36a0beDNc0lJHNn6XbVjUKpUzH77/zBF9mLZrnOOa7kjwEvJusOZjur4imjDmvBU5iujM1jYmZZXZWRj5SQeg8lC/6hgt8VAFbGLFVJvNFsxW81M75ZE/Pc1x3dXfkmpSmyr6n97o9TmuhX/XVzblssd0fW1aDV/03+aL12Ce++FlBTXfQ0aOFX9CwQCgUAgcOV63TzIW0m+vsDNFavmclk+fsqQKt28WG9k1pBoXlub6nA8j9z8IszPmA8hNQwgFygF2tr/ml2aR4ewVjzeozEfbT/rOKwqN6+ICx+zKqHGZ50Wl8Sm1HyOXipxbItpHMh/hkYREeRDicFEoc7E+490RKWQsmL3+dvi5jKphOYhPo7kmIpCG+Hmt8nNbTb417/gxx+dtzdoAOvWQUCAh6MXCAQCgUDwV+R63LxNAy3R9bW1dvOs0ixKjCVM6jyJ17e/DtiLKKbHzWB4y2FIUfL+puNO6el3NK+DlVJHEc3r219ndvJs3h74Nn5qP0dLpIrtrQJacXnlZbIDsyGu+vGEEELy1N18saPUMWcO7t38850XGN2tEVNjp/LYj4/V+KzTurl386FtQwkL9Ca8znAmdZpUbUuniZ0S6d1oECNW2hNXbrabp+fpuPeDffyrXwwbRm5FZyohszSHQE0Aob5BpOWUM+L9vW7fF261mz8WF1nlMdU69KlTMHcufPopGKv43YGPD8ycCVOmgPrq+WLeXCAQCAR/JOIngaBKdEYzG1OzWbjljGvxRu9I+keFuMRGK2VSJvaKZP6WNOr6qsgvq13RRGahntnrj6MzWpjQK5In7miCTAIfbj5NqJ8GpVzmkHQ/jYLPxsawYEuaQ/5kUglqpYyEuPoM/sLz3uZhfmGcLzqPjaszwvW1Qcw7lkGIVs20gc1p18iPhVvtn0WFXE4Z0Izlj8egM1rIKirHT6NApZBy5GIRNpvNUZldgZdSzqSPfuPYspc4d9DNL/grIfMJJPCeWfxiasnoIC9HZGF6ns7l2Oj6WlQKKe9uOsWPT9qr47/ae94p2rAqPJX5ygT6KPn5qL2NUsUku1YtJ8hHRV6ZkWK9iU93pPP5uBiK9CbeWJvK9IEtiWviGknYMEBDVD0tGoWMdYcz+fV4Di/fM6jGaMfKLyl/drFVyqRM7B3J0ysO1HjspN5NUF7nqtU/NSkp9nSWzEzXfS+8AP/9L0j/hs8tEAgEAsFN4nrcXC6VMLFXJOuPZhGoqamaxJlATQA5xeW8uPqoi5sv2X4GL5UCb5Xc4eE1ufmg5Y/AMSAc8K7+3n7ZfhRpiiDi6rZg70D2ncvn2QHNadvIn4WVnDI9T0fSt4eYNaQlX02II6eknCK9iSbBYaRcrHniu3/jwZitSu5sVZ8ivQlvpYxAbxW/Hs9mxneHXT7vynHit8LN7Wk4DXkkph5lpmLydPkEeQXiJdfy5e5MgnyU/LD/ouMzr87NywwWjlwoYtaQqBrdfO2hTH47IdzcrZvbbPD883Alft2Bnx+sXy/abQoEAoFA8DfnetxcJrXx2ePtKTaUIpG4JmlUR5BXEO+lvMfi4R8xovlDhPoGEuQVgFyi5PD5Ys4VFDgVtvhpFLx2T1sySpzbJkqQcG/UvQz9YqjTdutlK0c+PQL9gAY1DKYMts7YSmaBhmkD6zvNmYN7N88psnBP1D1MOl+9m0/qlEj/iKrdfNo3hygzmPlk7KvENnSfmvJMzHR6NxrE6CUHKdKb/nA3P51/mro+QUT4R7DhcB6DWst4fe1JivSm2+7mM1JO8FTfWnr53r32pJbvvrM7rztUKnjiCZgxA0Jq9555LWLeXCAQCAS15c874yS4reiMZl5bm8qyXVeluELGvJQy5m9OY296Ac8Nbukk6kaLlZ7N6rL7bD4KmRStpnYxcQFeSkoNZlIzS5j85QHiY8KYdmcLQv009G4R7OhvKZNKmDKgOWmXS9h/vgCZVILFasNitfHLsWx6tsDjaEeAbx/4lsdXP+7ozdkhtAPeCi1dIwJZuPUMMZFBFJQZWf54DKUGM1lF9n6fKoWUxVvPsDs9H8ApwnzeA22Jjwnjw81pgP3l5szJo6x7ZSzmouxqx6Os34K698xC7hPoNrLw2qrvMd0j+CIlgwKdvTr+hye6ExsZyMZjWU59Tt1xbX/Pmoiur0WtkPHS3a3ZcDSLIW3roZBKUMlllBrM9pcOlRy5VAKAr0rOrKGtUMqkNUYS3tepIf5eSv638jSzhlYd7XjtS8ord7f+U4utXCalf1QICbFhTv9NXUtCbDj9ooKvO5L9T8unn8KECa5V/t7e8Nln9jQXgUAgEAgEVXI9bq4zmtlyModO4YHsTc8nQB1Q6whro03m1s39vVW1cvMujco5tOGQPYWlpi4tabDljS08tuoxFzdPu5xH81AtBWUGPh7VBZPFSrHehFohQ6WQ8kVKBit27yHUT42PSo5WI+c/Q6uf+O4XMZiX19h9/b9DW+GnkQMSp0SaytxqN48I8uKTse3YnLGOYV/Fu30GH004Qd5KnuzTlPjYsGrdPNBHQYg2ELkHbn5/54YEeAs3d+vm331nn/SvjFIJK1dC69Y3ecQCgUAgEAj+TFyPm5cay1h5fBXzds7hcPZhzkw+Uys318g1fJv6La/1ncs3u2wcvXSWTmGFPHtnC5qFaPnv6lTH8Uq5lJdGRJNXZiDYJxiZRIbFZkEmkZHQNgGbzeY8b34Q2AM8CGirH0uIPISenXsS6hNK6vkSSgxmrDYbS0Z3wWiu3s1D/dT8b9gb9A7vzewds12cckrMdPp66OYj3t9LfEx7Vj/0K2WmYkqMRYT4BCKxefPFrkxGrLyamnLr3XzEn8bNn72zhedefviwPZVwy5aqj1EoYPx4e1pLg5qqoDzjHz9vLhAIBIJaI4pbBC6YLVY2pmY7ZMJejRzOyJgwyo0W8nVGAq8UdpzNLaNFiC9ymRSd0czbv5wkWKvmucFRZBXpCPX3ciqaqCpKG66ucDyRdTVycHlKBp3CAxjWrj45xQYuFup5sk9TRsaEIQUyi8pd4sHXHzvJwtQXa/XMRosRreqqvU+OmcaqA5cZGRvO/C1plBrMfL3vAv2jQ1l/OAskNi6XGNh1Jg/lFTmt/CwAS5PTWfRYZxZsScNqg5b6owzpPxWzQV/tWLzbDCDozieQyJ0LgypHFlYUzADEx4TRJSKQl9fYX2K6N61DudnCgKgQdCYLSrmUA+cLWZ7iXg4tVhtHLhQxoVckk788UONnNaZ7BIu3nqFYb2LawBbojBY2n7jMJztc2zNN6BXJgFbOKxWqiySs3JOzpNzMwMZ3c2/UcArLC8kpy8dX6YdGpmVFytWXlL+K2Hop5cy8K4qYxq5V+NH1tUzq3YR+UcEuqzr+0pjNMH06vPOO677Gje2T/23a3PJhCQQCgUDwV+J63NxosfLa2lRWH8zkqb5NSRoUhc5gIKlbEiMrRVjLJDKig6PRqrQUG4o5mnMUi83C5JhpbErNp39UvRt28x8272Ry6iToUMODGoHLQBMoNZe6dfMBrepTUm7myz3neeWnVLbP6Mu+cwWUGEwczyomPVdH02AfSg1mfs8oxGK1sTe9kPiY9qx95DfMtlIuFOVSXxuEn8ofqw3ydIU80k1CfW0dkBrRKlVsSs11W9hSmVvh5i+sPMqn49rxWvIstyszD2QdYPTKeFIuJjLvobnsOVOKzQqbT1Xv5hXeXFNcuHDzatz87rshIQGWLbP/XSq1///evf/4wQsEAoFAILhtXI+bl1v0TPt5GgsrJZYs3LeQZ2KeYfTK0Y5tVbn50zFPs3DfQqw2Kzll+ZzL05GaWUJqZgmdwgPpF1WXE9klNArQ8Mo9regY7kNheSH5+gxk0gAypmRwOPsw0cHRaOQazhWds9+wHFgHGIDHgOqCvW3wXNxzDIkaQkNtQwDuaFGH/DITC7eeYfq3hxxuXmowUag3cfhiEaF+aqc5860nc0mIbcuGkZsxWku5WJxLqG9grd28SG/iw81nWbDlLC1CffFRyZnQK5gDGdl8uPlqm6Tb6eZ7/1A3N9E/fDj3RA2nqAo3H9aufu283M8Ptm1zv0+phFGjYNYsCA/37Hq14B85by4QCASC60b8NBC4YLRYWbD5DAARQV58OrYru8/mM+GzvS5iMaZ7YyKC7LniO9NySYgNZ/fZfMZ/todh7epzTwcvxnRvzMtrjlUbpb1s1wUm9opk3eFMl17xH28/y53RoYRoVWyc2pvNJ3LcjmV0twhefkDNyO8fosySVatnDvIKothgv54jUvv9vfRsGkKLUF8sFivvPtwGPy8rA9spKdAXEOgVwFP9wijRSzBZ7LHvX1yZxC/Smzh6qZhyo4Xmwd6U7ljBW98uqn4QEikB/cbj23EoEonE7SGVC2ai6mkZ0z2CLhGBjFqym4YBGv4zNIouEYEcPF/I+Xwdd0aHcilLzzP9m9MpPICPt5918zWMoFV9XyQSidv+npW59oXg2Ttb8P6vp6tc1Tr5ywMkxIYz866WHstnZZEH0NPW5X0AAM1KSURBVJvMnMlS8tNhG4cvnnMk40TX1/LK3a3/UmJb+SWkqir8vw15efDQQ7Bpk+u+vn3h668hKOjWj0sgEAgEgr8Y1+Pmu87ksv1ULque6u5w87HdwxnWwt6f/qujX5HYJZGJnSaiM+nI1eVSx6sOGrmG3zP30ymkN0cv6m/YzR8NP0vS9iewNbVW/5AlgA1HBHp1bi6VQEzjQF4eEYW32kKfaDkF+hJGdArBT+Xv1s0rJr63TO9DUZGMNiF+fH/sR97e7brSMalbEj0jBhER5OW25VBl/mg3n31fa347t67ayHGA+Xvn0yOsF0Ob3sMb604KN/eQG3JzudyeTqjRwOLF8PHH8MADt27wAoFAIBAIbgu1dfPIOhpWHl/lVNgik8jYem4rT3Z50gM3/532oe2ZumEqYG8dWmq4ep+Ptp9hQHQIu57vi1ppZtWJ1TzzyRwXx3027lmsNisWqwUfpQ+cBlZhT1a8G3A/FQ2AAgUxYTG8sesN3tj1huOaSd2SGNJsGIPbhNAt0u7mfaPlFJSX4K8OwE8Vhskiw2iGZbsyHHPmH/x2lvmb7W5eUCglOvj63dxqg9RMezG+zniKRY91Zn4t3Lwg38iUATffzXuF9WJ4i+FsPVnAKz+luiQ93gw3LzeZSTmTz7e/55BVVE6poZATWSVE1btOLw8Lg2HD7IsRK9BqITERJk+GevU8v9Z18I+aNxcIBALBDfHXmHUS3FKK9WaOZRbjp1Hw2dgYFmxJq3KCdNo3BzmQUcjMIS1p28CfBVvOoFbKmPdge8qNFgp0Rga0CqZHc1/Wn17jNq5vctdpbJg6FIVEjc5o5v3f0px6UnopZRTpjMhlUt7bdMptJfWRS0VM+mEORaqFWGwml/3VURHtKJfK+WTEcqdI7QKdkabBPtQLkLL6xE/M/cH15WBy12ncETaYF1elMbhNPVb/qwePfZxCep6OS5fzKFnzBjt+3VDtGKQaLXVGzEAT3q7a4yoEO/m5vviqFegNZnJKDHwxPhaAZbvOcTyzhEfjwvl67wXimgTRtpE/JeUm4poEcWd0KCXlJi6XGPBWyVHJ7RGRL69JJcBLwadjuxIbGejUJxWuynzFC0GR3sSTfZqy6XhOjatal+06R0zjQAa3Dr0uCdUo5HRvWocujQP/FmJbUxX+34IjR2DECDhzxnXf5Mkwb579FwICgUAgEAhq5HrcfHL/Znw5IZbFW886ubkcFfPunMfLfV/mp5M/MeLLES5uO71bEmqllK6Ng2jd4Drd/EIBY55/ibLWG6FRDQ+YC/gBV0ILa3LziDreTBsUwcrjqxm/oXZuXlZupntzX5795VkW7XO/2nLk9yOZ2CmRT8a+woj397pMRF/7mcMf4+bBvip+eKozPZbMreEDtDNvxxwGNx3Cl3uEm9eGG3JzqRQWLoRHH4WePf+YAQoEAoFAIPhTURs3n/HdIXq36MHcHfZWhgHqAKcilksll5h751xe6fsKa06uqcLNpyORSAjyCiLCPwJvhZYTWSVObl6sN+KrsTH9l+lORTQVHMg6wKM/PMrEThOZ2WUm/53+X/gGe1FLVPXPK7fKWZmwkqErhrpcc+T3I3mh1wtM7zG9Wjfv3/gu2jX046FKXm61cdvcfFS3cAp0Rvw0CpRyKXmlBvq0DGZgdCj5ZUZySsrx1ygdrZWux83n7JhDz/CeZJu2sPIp+/uMu+KcG3FzdW29/Pjxq8XZL7zg/qJPPmkvbgkNhWeegUmT7Ikut4h/xLy5QCAQCG4Y8dtFgQtFeiNg72OYcjav5uKFlHN0jQykZYgPj3ULd6pWb1XPl28SO/G/Tc+xoApJHbMqgcTOiTzf7VV+z9AxrkcEBrPNKc7RSyVn68nLbifPrRjIV8ynTL7RvuKzlkzrNh2VzJsfH9joFKkNEOStZNaQJiT9Mr3a8U/slMisoXbJHta2Hssej6H/f79i3H2DOHXieLX3b9oyGl3vKSj8Qz0ab05xOctTznEqu4Sn+jajTQM/Zv1wmJwSA+N7RtK7RTBl5Sbee7g9Px/LZsZ3hx1yL5XAXW3qcV/HBtTxUZHw0S7O5NrFukhvYto3B/lwZEc6RQQ6ipMCvJxlvuKXGyNjwhj/6V6PxrxgS9oNxZMLsf0LcekSxMVBaanzdqXSPvk/evRtGZZAIBAIBH9Vbqab/294K+7vHMx/fv1PlW4b//3IG3JzS1kh2YdnYuqeUX20OUAOEOy8qTo3r+urRK00M+3nqifuq3LzIe9tJ8RfyZqTP7idPK/Mwn3ziWvYg/iY9k6x5m4f4Q9y8+5NgyjQF3Aw+2ANH6Kd/Vn7ydMV0CLU17F6tYJrW8Mu2irc/KYhkYjCFoFAIBAI/kHUxs2bh/iQf8XnmgY2ZX38erae2+ooYglQB7Bvwj7mJM+pxs3jmdhpIuvj17M/8yDpl40k3tHUyc21GhkrT3zv1o8d2GDhpwv5etzXFEgKYAJQU6CyBebdNY9tGduw2lyTGAPUAYxqP4rpP9c8b/5c3Ct8viPd4eVFehOht8HN+7S0v3x4KeXEf5TitLizTQMt/+rbjLYN7ef8duKyI8Xyety8xFjCWymz2Xlxu6M4p9RgdvLyk9mlNzRvXqOXX7gA334LX34JKSn2bQEBMGMGqFSux/frZz9+6FD3+wUCgUAg+BMgilsELvhplLUuXli4JY2vJsbx+k+pLK8k9TMGN2fViVVuBbcy8/fOJ6ZBD2y6GMZ2j2TdkUzHJLxMKmFrUh8WbnFNgTBJsshVvo5RmubmqjUzqVMivRsN5l/Lj7HvXIFT7Hp0fS1+XjLWpa2ucfzXSrY88xAXPplCWUlRteeFtOvNdz98yfCF+zwec4hWzXODotAoZeiNZs4X6Jl5Vyu8VTKUcimzvj/EzCGteG3tcZcXLKsN1hzKZM2hTOJjwlg6pivD3092qnpPz9fx0MKdjn6lpQazI268guYhPpQbLRzLLMYTjl4qplhvxkspx2yxYrRYKdabKdIb8dMo/7IrPQVuqF8fnngC5sy5uq1ePfj+e4iNvX3jEggEAoHgL8rNdPOmwV5/qJuXX0olJ+cFbH301Q/Qgr0V0TWFLTW5eZCPglU1Tdzj6uZdGwfyxr2tkcvMzE6eXf3YrvBuyjxWPbSJBVvOurRmqswf5eZB3kry9PkejbWCy2X5+KiuvuL7aRRVtoY1moWbe4TNZvfahx+G8PDbPRqBQCAQCAS3mdq4uY9KTp4unwB1ABsSNjA7eTaL9l1tW5/YJZHN6Zs9mHdeSPdG3RnS7G6UUg2ZRUaHmyvlUvb9p6cjHcYtOcBaIB0K2hbAMBypiVVig3GdxzG0+VC6LO7i9pDELolsSd/i0bx5t4Y9iKgbx+nsEv4zJIqtpy4ju8VurpJLKTOYeW/TabdFSYcvFjPh833Ex4TxwrBo9p5LdloAW1s3z9PloVVpWbhvPr3D+/Luw91pGqJ28fIvd2dSUn4T3fz8eXuByjffwM6drvsLCmD1arj/ftd9Uincd1+tnlMgEAgEgluNKG4RuKDV2Psb1rZ4oVBn5PfzBY5tSrmUjuE+TF5aO0nNKzOwJPmsY8VhVYUUeukecpVvYpWUurucgzC/MLwV3qTmpjq2tQ9tzzMx0+ndaBDxi/e7jQUc2z0CqdRc/cvBNeNf+eBGZs95k//N+QTcVLRXpvld4zC1vYegAD+i62udKsWrIrq+FrVChkYpRSaRsOdiER9sTqPUYOZ0Timbp/ehS+NAkk/n1riqd3lKBh3C/ImPCePDzfbioFKDmUAvpVO/Unf4qOTk64w1jrcyeWUG/DRydqTl8tYvp1zaHk3sHUn/qJDa9QIV/Dl57TU4dAjWr4eYGHthS/36t3tUAoFAIBD8JbmZbt62kTdP/gFubrPZKDm0igK/jyCu+ihFuVFOI79GnJVdXXXpiZuP79EYJGbmXMcE+MKtZ/h2Uhx55Vm1Wm1ZZip2m4RSwR/p5nllRoI0IR6NtYJATQClBvvXJSLIi0/GtmNzxjq3rWGnd0tieIvhHMrQ8era48LN3WGx2OPY338fFi2CbduE0woEAoFA8A+nNm5eajAT5BXoKAKpXNgik8iY2Gkiw1cM9+i+b+96m/4RwygxO7t5/6hgCssL3TtuGbAV2A1IgSGA+zoVJ0K8Q3jrzreIaRjDoOWDKCgvcDmmtuN/J2Uev436jctlRXRuHEj/6LoUG/JuqZvvntWf7adrTsJ0N2d+PW4e5BVEsaGYpoFN6daoCzvOb2bKV+5bN6mVYRhNZtYfy2bhljO1c3OTyZ7KsnGjfS62IqGlOj75xH1xi0AgEAgEfwHEUiyBC0qZlIe7Nqp18cLlEoPTSsHh7epRVJVcu2F/1n7KLSWYzFa06qvXubaQwoaFQvlycpQv1VjYcl+LR3j3jk38HJ/M8SfS2TlmPxnPnGdD/Gays9oz4v29bifP42PC6N8qlBJjkefjP7+fJ8ePI/+3JdUWtvj6+vL2R8v5/fsFbE3qg0wCE3pFenSPMd0j+GjbGTYeyyHtcinNQrQ0C7ZLvcliY8XuDEa0b8DS5HSPrrc0OZ342HCkEvvfT2aXolbKiK6vrfa8iiKY2qCWy/ho21maBvtyocB5Ne/RS8U8veIAr609js5ortV1BX9CZDJYsQKefx42bxa/BBAIBAKB4Ab4s7u51VTO5d9epaDxYoiqvrAlxBbCzsQU9j+9n4xnMjg06RDnp5zn5xrcPCEmjAHRIWSXXt8E+NFLxZQZLFwqzvPo3Ary9QVOn+G1/JFuvjE1B3+1P+1D23t0bofQDnjJtZzIKsFPo+DTce14fccsxqxKcJpAh6vtp5J+nk7rhhqKK6U4gnBzAHQ6eOABe2ELwJkz0L8/XL58e8clEAgEAoHgtlIbNz+ZXYq3QssTnZ/g3ZR3nfZFB0ejM+lq57bmYhc3d5soYsRe1PIekAJogbHUWNiikqmYN2Ae+ybsI7ZhLF0Wd+F0/mm3x17P+DNLM7nvm2H0/KQnq09+j7fCh6aBTT06H27Mza02sNpsLNlefVujCq6dM78eN9fINVwsvsiGhA28tv01Rn4/0q2Xj1mVwPObkjBYy5m7/oTLAli3bm61wrvv2tsHBQba22T+7381F7aEhMDUqfaFiQKBQCAQ/EURxS0CF+QyKZ3CAwj2rV1fRX8vBeFBXkTV80UmlTAwuh55etfK7uooMxXi663n7Ydb8WSfxvhp7BmJIVp7z0gLJeQoX6JIsQIkVU+eK6QKJrV9jfjmc3jmyxP0mr2DZ744x5trdbz4wyX0BjmRdX1pGKBxOi+6vpb3Hm7P84Oj2HoihwtFHk5eFgFLYPV3P1Z7WP2wCNb9upUH772H135KZfxne3n7l5P0axlCQkxYtefGx4TRJSKQ5SkZLNiShlaj5NGPdzGxdyTzHmhLdH0tKWfy0NVyVW+50UKLUF8ALFYbX6RkMLpbRLXneVoEU0F0fS0qhZS3N55kT3o+8VU867Jd59iUmoPZUn3qjeBPQn41cZz+/vYXJbWbfq8CgUAgEAg85s/k5o0CNAxpW5+G/naHNhVkcmnLE+j77oIaFjK2ULYgJDSELh93InBOIENXDOXtXW/z+6VDqOQy2jYMcO/mj3RgyoDmvLQmleyS6y9OKSk3UccroFbn25NQ3Bd3/NFubjRb+f1cKdPikjw6N6l7EptS87HaICG2Ib+dW8ei32toP7VvPmtOruHlu6Pc7v/HunlODvTtCz/84Lz9xAnYvv32jEkgEAgEAsGfgtq4ucVqY+OxfExWk0sRiFalJVeXW6t7u3PzzhGBBGkCr9wQ+B34P+BXwAA0BSYCDaq/tlKmpFXdVnx26DOGrhiKDZvbxJYbGX9Fm54DWQd47Md4Zv76POvj1xOg9szRb8TNB7cOpVBnuu4589q6+dMxT7Nw30Imdp7oktrjjvl757P6RNVeDte4uVQK8+fDTz9BafWLf/Hzg1GjYM0ae8uiN9+Etm09eg6BQCAQCP6M/IMzhgXVoVHIqeNDrdrl1PFR8VCXMAK9lKgVUrQaBYUGU43nVkYlV3LnsgFIJVImd53GmqfvQooKb5WcRsE57Cx6Hos0u9prqCV1WRv/A10bxPDAAntfyWvb7OzP2MG/+jXlkzFdKTdZyC01EOqnRi6VopRL6D33V4K1at4Z6UFf9QzgK+xRj9XQPq43I6bMZu15Gc9GyVhzONMh5Iu3pfHc4Ja0D/NnaXK6S/TgmO4RdIkIZNSS3RTpTRTpTZQbLagUMoa/n0x8TBiLHusMNhuFutp95gU6o1PV+7Jd5/hlSi8SYsJYluI+ptFitXHkQhETekUy+csDNd5jTPcIvkjJwGqzV74veqwzC7akue2RumBLGv2igj3vIyq49dhs8M479hUB27ZBmza3e0QCgUAgEPyt+TO4+TNdp/PT5OHsOFWCRCKhTuER9qX9B4aaQFL9ddpq23Ko+BBc0Xirzcqh7EMcyj7E0gNLSez8JK/c8Tqfju2K3mghv8xIsFaFXCpBJpVw51tb6RoZSLBP3VqNv3KbHqVChkLuR/vQ9i4rJt3RIbQDdb0DkEudffhWuvm/fzjGT5OHMbHTJBbuq7pQZVKnRIY0G8qmY0XIpBIeianH0C9HenS/eTvnsGX0UORSMLupYfnHufnJkzB4sD2ppTIKBXz+Odxzz+0Zl0AgEAgEgj8NtXHzzSfy6NhE47K92FBMHa86tbqvOzffeaoEP5UPjbMbc/bbs1CxTlMC9L7ypwZXBzBajOzP2k/70PZMiZlCsHdwtd58PeOvaNNTwfy984ltGMukzpN4ffvr1Z57o27+32HR5JYaajXea+fMPXXziZ0m0jOsJ9N/ns6+ifs8bt1U2cs1+jJiMo5gk8CmpjGOY5zcvH9/e/G1O/z94e677UmE/fuDsnYJ7AKBQCAQ/JkRxS2CKlHJpUzsFcnTVRQvyKQSmof44KOSM7FXExZsSeOD3+x9KKPra5nYK5K+rQJqNYGskWs4knMEq83KmFUJJHZO5IWer/PMqoXsLpuJRVpe7TXUlrYsGvI50XWaU6w3YcNGlwh7VffJ7FJ8VHISYhvySEw9ykzFnMo/Rl2fIEL9tShlcOh8IXvSC8grM1GoN+Ot0FY//n3AT0ANixm1Xe+l3oP/YvyAtvSc/SsxjQN57+H2NA3xpdxowWixcrFQz5nLZSx6rDPlRgsFOiMBXkpUCilfpGTw8ppUiipFhpcZzYRo1aRmlvDh5jQWbEnj3o4NeHZAixo/68oEeCkdRTbR9bWM7xmJXCpl0h1N6BoZxMItaa59PntF0jHMH4PFysiuYdX2Kq2onH95TSrgXPnurkfq0UvFFOvN7nuICm4/5eUwcSJ89pn97yNGwJ49EBR0e8clEAgEAsHfHHduXtnHdUYLYMNL+ce4+ehV8ey6mEhS7MuMmjaFffoF0LP6a0gtcp7s8gQf7v+AtiFt0aq0FBuKOZpzFIvNQoA6gMQuiUzsNJFiUx65ZUX4qQMI9rO7+bGLxVhsVn78V2fKTMXU9a5dcUpFm57o+lq0ajnf/36JyV2nMWZVQo3nT++WxKHzZSx49Pa5uVaj4PAFPfPunEevsN7M3Tnb6dnbh7ZnetwMhrccxo/7LtOzeV0m92tGmam4VhHxheWF3BkdytrDWS77/1Fu/tNPEB8PRUXO2/387CkuffrcnnEJBAKBQCD40+Gpm9fxUeKvck0mOZpzFC+F1w25+c4Lk+hc3JOnH5nN2aOV2u14AfdiT22pBgkSZvWaxcjWIyk3lxOoCaSOVx10Jh1lpjKmd5tO/Pfxbs+9kfFX5p1d7/Djwz8yO3k2VlvVE+w36uabT+TwQXynGsdZmSAfpVNSjCduPjlmMj3DejJo+SDqa+t73LpJYQafXfuxpv2bn3/8jbDTR5DbrByo18ypuMXJzfv3hw8+uHqRdu3s2wYMsHurKGgRCAQCwd+Uf8AMleB6kcuk9G8VQkJsGMt2XS1e8NMoSIgNZ2RMGOVGC6UGM3V9VbSqrwUkLNt1zt4L8ssDLEzoQFK3JEZ+X/PKwYq4vsoiO3/vfM4WZrD+9E81nq813c8THWfSr0Uztp7OZkCrIN4ZGU6eLp8gr0C0Sn9kMlh3ag3Dvop3nRjulsSw5sN465dTgD2dZNOxfPfjtwDrgT3Vj0kiVxI46F/4RPfhWFYZRrOFPi3q0ik8kL3n8pnw2V6OXiomqp4v7z/S0TER3iLUFx+VnFKDmRNZJW4TTgK9lbx2TxtGLt5Fep4Oqw1C/TRorrQL8nRVb7BWxcsjWhPgpcRPo0AigWnfHuTBzg25MyqYXs3qkFdqdHppWHc4k3KzhaOXipncvxkxkYEs2nqmxsSZCq6tfL+W4nIToX6inc2fjosX7atV91T6xj97Fh58EDZsALn4kSIQCAQCwR9FZTdffTDTUbCtMxdTbCgkxCcIP5U/JXqQSmV/iJsv2DGfzYu3cDzyGDSs/nxpuZbfJqznYM5ezkw+g86kI1eXSx2vOmjkGr5L/Y67W97NlvQtjPhyhFs3H95iGD+f/pVhX/2XA1kHmNlzJk93fZqxq8bWOP7JMdNYkZKJ1QZjuzfGZLHyzsY0fnhyMBM6Tqq2ZU9FEkrP2TsoKTfddjd/edVxJvcfxvCWwyjQF5CvLyBQE4Cf2p/f00sY/8kh3rivLamZxSTEhnGuqIoVnFWQry8gyLvqye+/vZtbrfDKK/Dii/aEwsqEhcHatRAdfVuGJhAIBAKB4M9Jbdxcq8GlCMRis7Bw30KP3dbJzW3ASVi4cAELM69x2jDgfsCDTvI2bLy27TXUMjVPdHmCMlMZq06sYs6OOZwrPMee8XuY0GmC25Y6FeOfGjuVx358rHbjr8T+rP2YLCZaB7fmUPYht+feDDc/kV2KViOvnZf7qnn/kY6OOXF3bp6ru8zF4osEeQWhkWvsn8mGqRSUF9AjrEfVrZts0DoH+p+BAWnQ6xz4mADexa/SYW0zT+OnL6FI4+vY5nDzO+6AcePsBS19+0JwcI3PJRAIBALB34F/zG8iDQYD+/cfYM+evRxLTeXy5ctIpVLq1QslLi6OEcOHodG4RgQCbNr0K+vWr+f8+QvI5XJaNG/OAw/eT1TLllXeLzU1lW+++Y4TJ09iNptp1Kghd901mL5/sdVeXko5M++KIqZxEAu2pFFmMPPp2K7sPnu1MKOC6PpaRneLYNVT3Rm1ZDfpeTqSvjvCb9OHMqnTJBZ4ENc3dcNUl301FbZIbF508JnFfweMpk+LutgwYFLs4I5P5zheGgLUARyYdICXNr/udhwHsg4Q//1IJnVK5P9GvsKI9/dSpDcR4KWmb4vhznGDZcDXwLnqPzv/uqGoB89AVa+ZY1uR3sRr97Tl3U2nnNJOTmaXoq408e0u0aQy0fXtbygf/naaT8d2Zfj7yZQazDzSNYyV+y8yulsE0791/0JQmYm9Ivlu3wWOZRZzV+tQgiMCKTdZeX9kB5QyKXKZFLPVzE+HM9l26rLTS8P7v6URHxNGx7AAejWvS+eIQI8q58F5Rao7tGpFjWMX3GJ27oR774Us1xW93HGHvderQCAQCAQeItz8+vBSypl1VxRT7wxn9Un3BduTu07jjrDBvLomjTujQ2+em+cBqXA87hjIqh+n1tyCbx5dRfv6oaQVHncpXukV3ovvHvyO//z6nxrcfBLTuk3jXKFdvOfvmV/tJPvV8SfSu9EgRqzcS3xMGH1aBrNsVzoFOhOjlxzkk7Gv0q1RT95Jmevy+T0TM53eYYPQGeU0DNBw9JLptrv5f4dHO9xchor9ZyUsPJrFz0cPOloJVUSud40IpK5PYI33qkygJoC8sqpbv/6t3bywEEaNglWrXPd17Ahr1kC9erd8WAKBQCAQ3EqEm18fnrr5Xc2G8p+eL3HfN87taTx3W7ubT1k3BY4DW4FL1xwkAeKA/oCHU3QVSSP9GvfDZrPx6tZXndx80PJBrI9fT1zDON5Nedfl2dqHtmdQk0Eej9/dvD9AmUnHG/3mMfPXpD/UzaUSCWO6N2baNzUnqYzt3hijxcq/VvxO6wZ+bt08v8xIXa9gvjv2HZ8d+syRqlPBta2bVCboexaGnrT/Cau5xgYpNuIyDrG+RXfHNoeb+/vDRx/VfBGBQCAQCP5m/GOKW7Zs3coHH8wHoGHDhnTt0gWdXsfx4ydYseJLtm3bxquvvIy/v7/TeR999DGr1/yEUqmkQ/v2GE1GDhw8yP4DB5iRNJ3Y2BiXe+3YsZO5897EZrMR3aoVvlpfDh06zLvv/h/n0s8xZszoW/DENxelXMLiUZ3xVcl5Y91xlqe4tqE5eqmY6d8eIj4mzCGORXoTM745ztuPzKN3eG9m76g+rq+gvKBW44r0b8nCwV/Qsm4LfFRyyow6/rvlOZeVmIldEtl0ZlO1k/gAC/bNJ7ZhD+Jj2lOst9IvKoSfj2QyZ8BceoX15n9f/5eTi05CUbWXoUtMHKU9nkYn93XaHuSt4uCFQpc2PharjS9SMjye+B7TPYIvUjJYlpJB+zB/4mPC+O1EDuVGC29tPMWqp7p71C6ob8sQivRG7u7QAC+lDKVchu81izJzSsoZ0b4+9f3VLE1Od1TDF+lNbDl5mSZ1vRnYuh6TPk/BbLXVWDkfXV+LSiHlRJb7F5Ho+lq0mn/MP01/DZYsgcREMBqdt/v4wOef23u4CgQCgUBQC4SbXz8WDMz6dUaVRSFjViUwsVMis4baC7aHtq13425+BvsK0R41j+/RFhN5b/g7yOUWpv88ze04e4T1YPWJ1R64+QJiGsYwqfMkXt/+OgXlBTVOsj8TM53ejQbx6po0/jM0il7N6qI3mvl4ezoA6Xk6Rry/l/iY9mwdvY18fSGXinMJ1ATgJdeyIiWTESv3khAb/qd088yicto18sdosXIuT+dYbFDh5k3renNnm8BaRcT7qf35+aj7Sf6/tZsnJ8PIkZDh5uvy0EPw8cfg7X3rxyUQCAQCwS1GuPn146mbz+k/m2dipvJOyluO/Z647eSYyXQN6Uq3p7pR+HOhveD8WjTA3YAH3TDHdRjHo20fJcQnxJE0cqHoAg20DVye4XT+abos7sKkzpNY+fBKdCYdepMeP7Uf3gpvJBIJI74cweJhi+nWsBvvpLxzXfP+apmWrBwvto7eRlZpPrll+X+ImxeWGekSEeCRl3eOCEBvsrB0TBd8VQqUcnuheWU3L9AZmfLVMeY/OpF6Pg1c2hQppAqCvYO519qCx746Qf8z4G1yvV9VGGRy9jZshV5x9aZ/azcXCAQCgcBD/jE/CeUyOQPvHMCwYcNo1OhqhnZ+fj4vv/IqZ86c5eOPl/Lss1Mc+w4cPMjqNT/h6+vLnNmvU79+fQCOHz/BrH//h/f+731at26Nj8/VCa+SkhL+7/0PsFqtPDcjibi4WAAKCwt57vlZ/LhyFZ07d6ZNm9a36MlvDLPFysbUbJ5ecYCn+jQloo6328KWyixPyaDDFXH8cHMav6TmUFDain4Rw+nRaCDllhLKTIWo5EqXuL7aMLzZfYwIf5mFvxaw79xvbEvqzdYLa10KW2QSGRM7TWT4iuFVXMmZd1PmsemxLSilGqZ8uZ+fU3NoFKChj7KQ82+dB3315z/y2GgutHyEnCJnW42uryXAW8l7m065PW/ZrnMeT3x3iQjk5TWpACxNTmfRY53Zdy6ffJ2RIr2JUUt28+nYrnQM92dpcrpLws7EXpHENanDhUIdH207w6nsEr6cGOf2fl5KOQPf2Up8TBiLHnPf13Tp9rM8Fle7Fwx3hS8Ak3o3QSkTKSB/CkwmePZZ+L//c93XpAmsXCki2gUCgUBwXQg3vz6MZhMrj6+qsShk4b75xF0p2P5w89nrd3MbcARoDPhUPzaFTckT7d/hYlY7ivUmtl1c43actXXz91Le48eHf2R28mysNqvLJHu5uRyjxYi/OgA/lR/FOjBa4OW725BTosdgtvDox84tMu2FIHk80ac589bmkFVcTqmh2Kk4+2a7+WfjYugSEchH211beT7eI5JOEQFk5JexJPnsDbt5SIaSaXFJJPxQc/upaXFJ/J5e4kiAuZa/rZu//jr8+9/2lkSVkUphzhyYOhUkktszNoFAIBAIbjHCza+P2rh5t4Y9mNXjRdqHdnJKDzydf5qxq8byzsB32Dp6Gzll+ZQa7W5uLDMy+ZXJjP1qLLaSKiZSGwAPAP7Vj1Uj11BuLmfPpT2cyDtBsaGYIzlHkCDhzOQzVbp5QXkBr29/ndnJs2kd3JrO9Trz1sC3yNXlMmj5IE7nn2bA5wNIeTyFQc0GUaAvwGQxoZKrUMlUNc77dwjtgEam5fvfjzC4dQMmL0vHWyX/Q9w8t8zI9G8OejRnPvaTPbw0IprNJ3KY2LsJcjc+7KdRkpyWx5B3d/Py3b3ZOnooheWFTi1Ey40SHouZxIiXpric746SVq1Z7tOc5Ij27GnYinKFc6X739bNBQKBQCCoBf+Y4pa+ffvQt69rtGFgYCATJ0xgxnPPs3PXLkwmEwqFPdpt1crVADz4wP0OQQdo2bIFgwYNZM2an9i4cSN33z3Cse+XXzai0+mI6drVIegA/v7+jBr1KG+8MYeVK1f9dSTdYmXB5jPIpBIeiQlj/Kd7PTqvQhwXbEnDaoO8MiMN/b3oNXsHg1qH8tyQBgz7cqBLXJ9H2KT4mx/DdHkMvs19mPdAGGOX7sFXA7OTZ7scHh0cjc6k42B2zZGDYO/1WWYqxipV8HNqDjarhUM/LmD7rm+qPU8ulzN04kyO1OlG8ZXCFplUQvMQH3xUchLvaILeYOZIFX09PSlKGdM9gi4RgYxacnWC/uilYsqNFoJ8VAR6KQH7itSnvvidT8Z0pX9UCAU6I3mlVye9N6Zmk3w6l35RwcQ1qcOUAS0oLDORUa7DT6NEq5E7os+1Gnvs44eb01iwJc1tX1M/jeK6XjCuJSE2nH5RwW5fGAS3mNxcePBB+O03130DBsCXX0Jg7SLvBQKBQCCoQLj59WG0Gpm7w9V33fFuyjxWPbSJBVvOXp+bG7C34WxT871k+jrEhbxL94Y96dozkAAfW5XjvB4315l0tA5uzaFseyH1tZPs6+N/RiMLoLTcgtFsudJa08r6I9ksT8mgSG9y8vJSg5kJPSPRG8ysPpTp9r43080BJNiIrOvFklFdKDWYHQUp3io52cV6JNjwUctp3cD/ht189UENP00e5txa1Q2TOiUytPlQhry72+3+v7Wbq1SuhS1BQfDVV9Cv3+0Zk0AgEAgEtwnh5tdHbdz8nZR59Gt8FzlZ7Vn7yG+YbaVcKLKnB4b6BiKxKek1ewtxTYJ4oLmeB2bcTd6OPLuTV0UMcCc1tgxtE9wGk8XE8bzjDp+uoG1IW4/c3Gqzcij7EIeyD/G/Pv/j37/+m9P5pwF4pM0jrDm5hsdXP07r4NY82eVJgr2Due/r+2qc958SO42Nx/KY0DOSz3amc6yKlkM3y809nTN/f2QHJBII8FZhtUFWUTlFeqOTm2s1cqLrazmTnsOymfN5uW4jWvTsSJC3kryybH4+epB6fhp+mvw4lxq9RP3zrgU+6X7wSxOg/wAe+ddC7lp+lvMF7lfW/q3dXCAQCASCWvCPKW6pjsaNIwAwmUyUlJQQGBiIwWDg0OHDAHTr5rpqrltcHGvW/MSePXudJH3vvn1VntO5UyeUSiUHDx3CaDSiVCpdjvmzUaw3cyyzmKh6vhhMFo5letAMErs4Gs0W7u3YgKOXignRqlHKpUTV07LhaDYzh0QilUhrXdgitflQx5iExtqRY5kljjZIX0yIpciQ61bEtSotubrcWt2nQF+AVBVAU38JR5fPpvjIjmqP9/YLwHdIEvt92kC5GT+NgoTYcEbGhFFutFBqMBOsVaFRynh2QHM+2n7WafVoBel5Ooa/n+xYiQmQU1xOHR8VKoWUnw5lcs8HO8jXObeHsYu4AbVSRnR9LRcK9HwwshNv/nySL/dkuJ30BkiICWfqnc1JXLaPlLP5jutF19cysXck/aNCUMqkTOwdydMrDmC14bavacULxg9PdCeuSRALtqS5vGBM6t2Evi2DSTmTR8MAjdPzV+zvFxWMl1L8s3TbOXjQ3mooPd1139SpMHs2yMXXSSAQCAR/DMLNq6ZAX1Drgu0Wob61d/Mi7KktzWu+j7q0M3Vlz3EuW830bw/x7IDm3NNFU+U4r8fN83R5aFVal+0Vk+znCjI5m2mgX6tgQrRqPt5+lrc3nnQUYT/Zp6nDy/N1RoJ9VdTxUWEwW2gUoKlyAvlmuHlM40Dm3t+O+ZvT+GJ3BlIJbt08PiaMmXdF4a9RMOGzva6rSGvh5ucL9CR+foSFj82lV3hv5rppPzW92wyGtxjG4Qw9Wo0CKn0G/wg3f+YZ2LABfv7Z/vfeve3tNhs1uq3DEggEAoHgz4Zw86qprZuXGov49UQu87ecZVtSH35P03P0Ui7/GRqMSmpFmrGXT5Z+w/z0A9VfTAWMAFrVfN++DR9hwd3/o/n77sX+etw8sySTeXfOY0PaBooNxY5Uxgo3f27jc+wZv4fHOz7Oon2LqrxOYudEhjUfRoleSqC3ikAfFasPXvLIzVeMjyWvzO7cIVp1rd28pjnzym7+0MKdV93cZqOXt4HEOno6XT7DsrUb8Nq3G5XFzLvdHuZt//pO967w8qUPjoI338EigR2NYE1z+x9lm3ZM7/4cw1sMQ46KpEGqKufV/9ZuLhAIBAJBLRA/DYGsrCzAnrzh6+sLwMWLlzCZTPj5aalTp47LOU2aRAKQfu6c0/b0s+kARF7ZXxmFQkFYWCNOn07j0qVLRERE3MSn+GMo0ttlMESrxlBVVnUVGExWxvWIRK2Q4q9RIJXA84NbMGrpXlakZDK56zTGrErw+HoKawR1jbNQ2Oo5bV+eksGg6FB8ffPdnldsKKaOl+vXsDoC1P5czjjL2Y+fIeu0+zZCFQSFNWflyh/JtPiwNDmdMoOZT8d2ZffZfLcT0xN6RfLT0z1I+CiF9Dydy/WK9CY+3JzGit0ZbHq2N40CvCgxmMjKK6dXs7oMaBXCit3nWbbrnKNAJMBLSXG5mY3HshjfM5JTOaWknM1zpKi4m/QGWJZyjvZhfnQKD3Aqbjl6qZinVxwgITacmXe1pH9UCAmxYSzbVXUqS49mdVEppAxuHUq/qGCK9WaKy01o1Qqn1aa9mtcltklQlfsrY7ZYMVqsFOvNLtXxokr9D+Lbb2HUKNBd872pUsHixfDoo7dnXAKBQCD4xyDcvGry9bVr45mvL8BHZX/lq8nNn+k6ndGr4qEQ8ANq6shikhJgGI+vbCiSSgdvPXWZHlFV9zC6HjcP8gqi2FB1kX0DvzoUl8gZs3SPw8Xr+avZcCSLfw9tVaWXP94zkrWTezLp830kp+W5vfaNuvl/hkY5eXlVBSnLUzLo2jiQk9klTuOE63PzxnV9kErU3B91H8NbDKNQX0B+eSGBan/8Nf4opSqUcgVdGmv4ZlLcdbl5kLcKH7UMufQv6OZSKXz6KXTqBE88Ac89B7Ialj0LBAKBQPAPRLh51Vyvm1ttUFJupm/LEDrVhcX/9xYfLV7E+Yyq3c5BPextiGoIU5bY1ASanqRb0Ch0Jtf55wqux819Vb7svbSXSZ0n8dOpn1ySXwrKCxi0fBDr49cT1zCOd1PedSm0nhqbRM+GA7n7/b2k5+lq7eYPdGrIoQtFxDYJQmc0X7ebX+vlnS8cJe7cIQI3FpP/sYI2Obm8dDEHv/JSxx+l1ew4vnIJVr+0Pbzd0/V3HY3r+mDpOgZzm3aUD+hDY28Z8eWF/OsaLwdqnFevjJg3FwgEAsE/FVHcAqxe8xMAHTq0d0QrXs69DEBQUJDbc9RqNd7e3pSWlqLT6/HSaNDpdJRd+YVwnSrOCwoK4vTpNHJyLv8lJN1PY1e0O1oE46uu3beLSi5ly8ls+kcHcrEknxJDIREhQaTM6sGKlEv0jbiLwU0Gsy5tXc3joBdaw9NIUbvd/9Xe88wYEux239Gco3gpvGgf2t5JpKuiQ2gHkjftIiF+NObysmqP9Y7qidegyTyx8jzjekTw1YQ4wMbr646zPMX1heTopWImf3mAhJgwPhsXw7D/2+42wSUiyIvlj8ey7VQui7aecZmIH90tglVPdWfUkt14q+QEa1UsHd0Vrcb+NerRrA6Pfew+Xvxaro2pr8yyXeeIaRzI4NahzLwripjGVaeyVK4el8ukeCnlhPq5fr3kVwS7qv0V6IxmNqZms3CL6/NP6GVfuaqSC1m/qezaBQ884Lq9QQP44Qfo0uXWj0kgEAgE/ziEm1dNoCag1seXXikKUcmlbD2ZzaA2dcnT51FqKCIixJuUWT34bl829bXBqM1qyv3La7yuvDiAuvJXUcrCXPaVGsz4qvyrPPd63Fwj13Ak50iV+70VWt7ddIDjWaVYrDaGv5/ME3dE8s7DHXh9bSrLqvDyKV8dID4mjAWPduKud7dVuUr0RtzcZoOkbw/X+JwAi7aeqdLL4frdXIkCH6U3Df0aulyztm6+fFcGnSP8eCSmHnnlWZwuLKCebxBBmgCUsqsT838KSkvhiy9g/HiQuKnWCg2FkyfB2/vWj00gEAgEgr8Iws2r5nrd3GY2sWntSpZ+8gn7d2zGem2rxKroDAyixt/oqAgnyPAcClsj8sqMBGrcz5nD9bm5j9KHl7e+zPcPfU/y+WS3yS+n80/TZXEXJnWexMqHV6Iz6cjT5RHkFUSoTz1+OpjPiPf3OrUP8tTNB0QFU9dXjdFSwNhP9tTOza02/vPFniqfL+7cIZ7dvrzGz8EdrbPTCC3OJUtbxzGWCi/XKOXQri0+gA+49XIQ8+YCgUAgEHjCP764Ze/efWzcuAm5XE78yEcc28v19kldlUpV5blqtYqysjL0VyRdX351Iriq89Rqu5To9e7l7FpKS8soK3MtsMjNrV1c4PWi1chp00DLgFYhyCQSoutrXVYSumNAVDBBWsBrF8O+mudSnf1s7HSWHjzD+rT11V5HKpESLn8cS/EwpxWh17LuSBav3dvcrYhbbBYW7lvI012fZuyqsdUP3AbNU1vwSOKD2GxuZpSvIJFImDbzBf41dTpf7rnAsl3nMJhtpF0u4VROmdvClsosS8mgU3gg8TFhfLg5zWmfn0bBZ2Nj+HDz6SoLZCraMX06tiupmcVoFDJ81Vcnkov1plq1kCo3WmgR6ut2FemCLWmOyfHaVI/fCDqjmdfWprpdjVpRIBQfE8a0O1ugUkhFJOPNIiYGxo6FJUuubouLg++/t0/+CwQCgUDwByPcvHoCNAG1mnj2kms5kVXCiHb1CPaTMqxjALm6LHLKcqjjVQeL1cynRxez+fxmfvr1J2zyqv23gsCCWHzUM5DgvojhZHYpaplvleOslZsDT8c8zcJ9C6tsZzo1djpquZoPR3ZEJpM6Vmq2rOfHz8ey3Ra2VGZ5SgZdIgJ5+e7WjF7qOtF9o26eVVR+07wcbq+bbz+Vyydj27E5Yx3Dvop3ecdL6pbEsBbD8VHe5mIRmw2WL4ekJMjMhDp14N573R8rClsEAoFAIKgS4ebVUxs3bx/SnlOH09j7xbuUn9jKU0WFnt9ICQwD2tR8aPdGPVl013L+tSwNpUJKoc5EoCaITvU6sS9zn8vxtXXzad2m8dPJn9iXuQ+dSUewd3CVyS8F5QW8vv11ZifPpnVwa7QqLcWGYr6+bxXLdp13u+DTEzefc387Xl+X6rmby6X4Hj4AX3+N+bvvifduzP4hU9yP2cuvxs/AHTaZDGuPHqwZGUV+s6g/zMtBzJsLBAKBQPCP/sl24cIF3n7nXWw2G6NHPUbjxo1v95BcWL16NV9+9fVtu79SJuXZO5tTbrTww4GLTO7XjMXbzlBqMHMy274y8loCvZXMeaAlM36ZzoJ9C1z2H8g6wKM/xtd4bz+VP4GGJKzFbWtMRbdYbRSW2ZgaO53H3Fx7/p757Bm/hwmdJlTd69MITbc35autX1Z7L4nSi5lz5zNl7EM8990h7owOZdVT3VHJpYCE5793v6r0Wj7a7n5lZkJsOHvP5ddYILM8JYPO4QEMah3qIsnuXg6qo0BndETWX8vRS8UU6814KeUeV4/fCGaLlY2p2dXGrIP9+TuE+eOrVtCzWR0h6jcDiQQ+/BCOHbOnuIwbBx98YG9JJBAIBALBH4xw85pRSpVMj0si/oeRNR47OWYaK1IyadPQn1fubc73qd/x1q63nCbfmwU2o0hfRI4+p8Y2RLIyGSGGJ1BoBlZ7nMVqY0VKJtO7JRH/vftxeuTmwKROk+gZ1pOpG6a63T+xUyI9Gg7kt+O5xEYGObl5kI+KBxfsrP6hrrB42xm+mhiHXArXdmK9UTevaPPqKdV5Odw+N199MJNV/+rMa8mzWPS7+3e8kd+PJLFzInMGzL19BS47d8Kzz9r/t4KpU2HwYNBobs+YBAKBQCD4CyLcvGaUUiVJ3ZIYWYXzYgOygKNwMf0Swy70q/1NgkH6iBRrgGfpLscuH+FEwT5+/Fd/MkvyyC3LJ1eXx4aEDby16y3m75lPQblzOyVP3Xxip4kMaTaEoSuGApCny0Or1BKgDuDBVg/yXep3WGwWl/OsNiuHsg8BzgX4VVGdm8+5ry1bT132yM1jg+QMPLgJecJHcNDeNkkO9FNfRma1YJG6tqTM12irvW4FpUoNp4LCOFC/OXdPG0XAXQOQabXUAWrX5Kl2iHlzgUAgEAj+wcUteXl5/O9/L1NaWsqI4cMYNmyo0361xj45aDAYqrxGebl9n+bKJJlGfXVC0WAw4OXl5eaccqdzamLYsGH07dvXZXtubi4zZ/3bo2vcCHKZlNjIIPJKy3kkJgQrpSQN9Ub7/+zdd1zVZf/H8ReHw2EflgKSIm4RK82B21IbmiPbjnJUjoaNX9rdupt3pdl935XlqPS2HO3SzJblSE3cmnuvFEVANhwO5/z+OIHiOSxFQHg/H48exXdep4P4Phef7+fyDMTHaObTtSeYs+YYKVm5BHh7MLRDXUZ1r8/ivQtdFraUlr97Q76+7RtGfnS01Ofk2dy4uUk/xrQZ43Tvc9f67FKvi9PEfgtTCzI/zWTfrn3F3sMUUpdaA59lEw2wA8/1bUH/Kau4v0sD7mpXj7Rsa5mezLTm2Qo9mXnlFWYe6NqAwR/EleoaH648yI0tnTtq5C8nVVpBPibSc6xF7k/Nzr1kE+bns+TZmLbsQKmOzV9S6dedp+jtoshHLoCnp6NTy6JFcP/9rlu4i4iIlDNl89IxGT3o37y/y7x7rtFtxnJt5E2sy0vl4Z5R/Hv1v3lx+YtOk817E/eWWNQC4HkwhB/+bykj5hY/iZpvzppj/P6U61wOZ7P5iuEruLb+dUxaPdGpC8hTnZ/i5iZ9WXpwKfUD65Mcn1xo/5MdJ9Cl7o0M+2gLvp5GYhuGFGTzf9zUjGubhZYpl6dl5XJfl4ZMX3E2h5ZHNi/vXA6Vk82HdqjL0sM/uCxsOdfU9VPpEtmN26Nvq9glirZvh2efhQULnPcdPgxvvgn//GfFjUdEROQypmxeOiajB33On4vOL2jZAWwHkhybEzhV9htcC27d3LAZSlfY0ji4MT8O+ZEVh1fQ7X9dnfL1Ex2eYN0D67hp7k3sSzo7B144m1/LpNWTnM59NPZRrou6jnRLOjsTdvJM12e4MuxKnuryFMdSjzHx+olMun4SMzbOcFlAky+/AN/VEpz5HMXcudwQE87iP+MBx3I7IztH0blxLe6cXnwBe1jaaUat/YZe//kZk8W5C1BQdhrtj27jj/pXO+07EhjOL41jSfI2c8bbn9t7Xcm8vWnsyvEgxcuPM97+JPoEcMK/VsGcaefruhFk9i92TOVF8+YiIiI1tLglLS2NF158mVMJCfTs2YMRI4Y7HVO7Vm3AEeZdyc7OJiMjAz8/P3z+Dtw+Pj74+viQkZnJ6cREIl2E9PzrhYbWLtVY/fx88fOr3FbJVnsOK4595zrYtn+SBQ/35tmv9zDxjmasOPIT6ZYAJq6aeMH3887rQEjOkzSr3YSYiJRSLYMUE2HG38uDrJw8/tHpX3So25X/xr1ZaLx+Jj+2nNzCLc1v44aG/TmTfYakrGT2b97DY/c9RGIJLStbd+7B/z7+mHFf7WH78VRSMnM5cDqdIbGRrNibwM1X1SEps2xPZmbn2phzXyyJ6RbM3kZ8TEZSyrikUP6Tm+cyextLvYRUTIQZTw9DsRXzZq+Km5hOzSpbgVC2JY+ftsfTMzpUIb207HY4cgTq13e9v04deOCBih2TiIjUWMrmZeOGG6/2eJWO9TrynzX/ccrnz3V9nuuibsDDHdo3NrDr9C5uj7mde66+hxkbZ/DOmndIz03Pv1jx0sB7VwfCWz5D03qNiYk4U6p8WTfIG6vVxMReb9Kxblf+c14ubxXeivGdnsLf00yb0JtYPOgmUnNSSMpKJtg7iECvQDzdPUnNzKO+bycWD1paaL/ZM4BTKXkYcHSXOz+b7ziRytX1Asv0//VMVi5PXN+EEZ2jSM2ylls2L+9cDhWfzXefTGPGsJb0/bTkjkEAk1dPon+zfpiKWLqqXB04AK+8Ah9/DLYifvFz441w552XfiwiIiLVgLJ52bgb3Him4zN4H/Tm4y8/JnFzIpQuOhZxQeBqoCfgC3ZKXjYUIMgriJ+G/sTEVRNddl/ZHL+Ze7+9l7Ftx7LkniW0nt66oAAlv3gl25rN8bTjLLh7AZm5mSRmJhLiE4K30ZvpG6ZjcjdxIu0EcffHsfLISrr/r7tTxh/XfpzLAhpwFOB3r3cTAxasL/H1pGTl8trAK3m0Z1MCvD3w9zJy4kwWCek5RWbzumfiGRv3Jbf/uQTPvOKLxbsc2uyyuGVbeGMeuO15wJHNb76nDf+ZtLTYYhzNm4uIiFSsGlfckpWVxUsvv8rRo0fp2KEDDz04FjcXnQmuuCICDw8PUlJSSUxMJCQkpND+/fsdFbJR5/1yOKpBFNu37+DA/gNE1qtXaJ/VauXIkaOYTCYiIiLK+ZVdGumWDMb//GSRywuNWDiUsW3HMn3YJF5Y+gJLDi7hmohWbDm55YLuZ869k0DrUOwY+HrDMe7r0oAnPi/5Wg90bYibGwz5MI7kzFwevz6W3+5ZQXpuCik5Zwj0DCTQKxCbzYjRzQg2A37G2nz66Te8+ux4rNbiA6+5w+0kdrqHz7YkMXtke/pPWUVypoVfdpzk0V5NWbb7FJ5GA8E+ZXsy02R0Y8aKA3RtWot6IUF4GAwcTyndurL5XD25aXI3MLp7Q8bN31zi+SM6RzEv7kiRIT0mwozZu+J+VFxI6/aTqdkui3zEhawsR+HKjz/CunVQBdvKiohIzaFsXjbplgye+mUCW09u5Z6r72Hx4MWk5KQUTDz7m/zxMnrx037nwvSWoS3xN/mfLWwpyRY3gnIfwNyyPzYocza3Y2fIB5vo2qQViwctxWJLIzUnhSDvIAK9ArDkupOclke9ID9Ss6z4GU24eQbja3THHQMGN3feW7qXeWuPYHCDZuH++HkaSc9JZXd8GjY7DImNdJnNX/t+B4FlzOUB3h488fkW3NzcuLt9vXLL5uWZy6FysnnTMD8yclNL/RlvU/wmzmQlX9qliXbsgNdfh/nzIc+5/T0AjRrBf/4DffuqG6GIiEgpKJuX3vHjx/lqwdf8+39vcWTzEWyW0nVXKVItoBVwDeBc91Oise3GsvzQ8mKXFQJHl71u9bux95G9HEv9i2DvIIK8g/hl/y+0mdGG5OxkJiyZQMvQlpg9zaTmpLLt1DYeuOYBOtTtgMHNwOsrXy+ygGbkwpGMbjOaH4f8SLsP2pGcnezoytjpKTpE3MDwmVtIycot8fUEeHvw3tJ9pGTl0ufKcNo3CCGqli/7Epw/ywRmpTJu1acM3bQYk62YOf6oKGyDBvP7VV15c1PxvwuAqpvNy0Lz5iIiUh3VqL/RcnNzee21N9i7dy+tW7fi//7vcdzdnddWBPD09OSqK69kw8aNrFq1mv79+xXav/rvNbzbtWtbaHvbNm3Yvn0Hq1f/wbXXdi+0b9369VgsFtq1bYvJVLbJ1sqQY81l4e6Slxeaun4qnSM742n0xOxp5nRm8R1QXDFgIsjyCH551wEQFeLDLa0j8PX0YHD7SOatLboF+tDY+lzfIozTadlMvuNqgnxMeHoY+HDFEdYeSmLa0DaE+HmSnWtl24lUQvxM9Ji0hLxVszi6emGx43IzehLSexy+LRzvZf56lUNiIwnyMfHnXylkW/Icc6Vubvh6lvxkprvBjaZhflx5RQA+JndW709g45FkujcN5b6uUQSWsXW5q+pwo7uBXtFhDO0QWewanENiI2kXFcwri3YWjMvxSwMre06mk2ezM6Z7I0wVWNl9oa3bK7I9+2Xr6FEYOBA2bHB8fcstsGoV+PlV6rBERKRmUjYvmxxrLnsT9/BUl6fIzM3kdOZpUnJS8DP5sfzwchbsWsCUPlN4ecXLLiebt53aVrobpYFxiS8hMc/j1bQlcOHZ/IV+MQT5mMjLM/DbDivL92Qy+fYm+Jk8yXazciAhC6sder61/JziFSt3t61HoK+p4D42OwXLeJ6rqGx+JCkDs7dHqTqmuBvc6N0ynEBvD44mZ5Jns/P2kr3lls1Lm8uHliKXA5WSzf08jSRmJpXpvKTsM9QNqFv+A9q4EV57zbGMpr2I3zTUquVYomjsWMeSmyIiIlIiZfPSy8nJoUmTJmRmZl7chTyhxXUtePWJVxm5YSRncs6U6XR3N3diQmMI8griwbYPcvO8m0t13qRVk+gR1Zuv1rjx+PVh+JlMXN/oBt7r837BcqFbT24FHN1YPur/EV0ju/Ltrm8J8goqsYBm+obpXBt1LRtHbcXdAEY3PwK9fBg+awOHEov/f3ZuNo87mEiezc7B0xls+yvNKZt7Wi0M37CQh/74AnNOhsvr2d3dcevXD0aPhhtuwGAw0M5iZajXzss2m5eF5s1FRKQ6qjHFLXl5eUx+699s/fNPWrRowdP/eAoPj+JbxvUf0I8NGzfy+Rdf0rZtm4Kq8V27dvPTTz/j6+tLr169Cp1z/fW9+OLLr4hbu5Y//lhDx44dADhz5gyzZ38CwIAB/S/BKyx/uTYLk0q5vNBbq9/i27u/5Yd9P1DLp1aZ7lPbpzaNDC9zIsFRsR/g7cHHI2OZumw/q/efZvbI9lxTP5BZqw4VmpyOiTAzultDWkUG0e/d3/H0cC8ImflPcwIkplsI8fPEy8NIq3qBrNlxgBPznyXn2I5ix+XuX5vatz6LZ3jjQttnrTrEzGHtyLXZ2B2fRnKmBR+TkXlxR+jdMozhnaIY/+VWp+sFeHswtEN9BsdGkm3JIys3D5sd5j3QkaQMCzabnZxcW5lblxdVHe5jMvJMn2hiG4Qwbfl+p/93Y7o3IrZBMPfNXl9oXEmZFoL/LhDafjyVrk1qVWjbwgtt3V6RLSAvS6tWwa23wqlz1vjduhVGjoTPP6+8cYmISI2kbF52ubYctp7cyn/j/uuy/ffMATNZdWRViZPNxdoKjfc3IaL/Kxy2OIpfyzubJ2VYqOV/NpvvS0gvVLzibnCjV0w4D8wuuV05uM7mXh5GNhxO5v6uDXn8s80uzzs3m9vtdg4nZTJ9aFv8vIzlns1Lk8t7NA9l9upDReby+WuPYrHaKryluNnbSJjZixCf4DKdF+wVWP6DeeopmDSp6P2+vvB//+f4x2wu//uLiIhUU8rmZePp6Um3a7vx4+Ify36yG9AQx9JDzWGHaQeng08TFRRVKOMXpXfj3nh7eNO8VnNGtxlNZm4mWblZ2Oy2MnXZS846w7rDySSk5RDka8LP5Mtt0bdxc9O+pGSf4XRmAr4mXzzdPZm+YTrjfx7P5jGby1RAs3hQbxLT4OVF23l/SBvubh9J3EHXBdNlzuZ/pfD53Ke4On6vy+vluBtZ0uFmesycjHfTwnP7l3s217y5iIjUdDWmuGXx4h9YsyYOALPZn2nTXE/6jhgxDPPfE2Gtrr6afn1v5rtF3/PY4/9Hq6uvxmq1snnLFux2O+MeedhpXU9/f38eefgh3pz8FhMnvUnLljH4+/uzZctWMjIyGNC/H1de2fLSvthykJVr5VR6YplCcWZuJm644ePhw1VhVxVUeBfH2+jNz0OXces7Bwu2De1Qn7iDiQVPavafsoohsZHMuLct2ZY8kjMtBd1ZkjJy+HbTMQ6cLrrq2+xt5NDpdLw8jOzftYW7bruNnL/+KnZcnnVjqH3L07j7Bjrt2348lVybjV92nMRmP1sBPWfNYcZ0b0iov5fTE61RIT7MHtmetQeTGPXxeqfQPLxTFO0bBDP+iy081qsJo7s1ZNynm0v8/1dSdbiPyUjvluH0jA4lNctRpW328sDsbcTkbiA3z8bHI9vz665TLsc1ulvDEsdQ3i6kdXt0nYptAXnZ+eADeOghyD2v7ae/P9xzT+WMSUREajRl87JxLBU6vsilQkcuHMnYtmN5qvNTBHkFkZydXLYbpALfw3VR1zHj+0+4Ycrmgl2XMpsH+BgJ9C480do0zI9sS16Z1pJ3lc2f++ZPfnysm8tOM5WRzUvK5UZ3A8M6RfHLjpMux/RA14Zc3yKswtuJm9wN3BgThq+HD63CW5Xqly6tw1sT6B1Y/oPp3t11cYuvL4wZAxMmQGho+d9XRESkmlM2L5t0SwaZDcrQtcUNiAJaAs2B81ZufPTHR8mylm4pzNd7vE6D4AZ8teMrBnw6gM3xm+kS2YXXe75e+vEASVnJ+HkanbJ5ZrY7T8w/wsDWEfSMDiY19wx3txzMqNYPl7mAJiXnDGbv2qRmW0nPzuX66LByzeZzWvfh6h/eLnStHHcjc1v1YXrsrTw36no8GoW7HN/lnM01by4iIjVdjflbLT397HqM+WHdlbvvvqsgpAPcf/99NGjQgO8X/8DmLVswGo1cffVV3HnnHUQ3b+7yGp06deS1f73C5198ye7de7BardSrV5eb+/ShR4/ryu9FXSLWPBsbDidj8Egs03mJmYn4mnz59x//JsPiuhXg+d6+cQp1/Rsw6XZ/Zq06xK74NAbHRhZ6UjMlK5f3l+1n2vL9hdqV745PI7qOmRn3tuW/S/a6XP8yJsKMn6cH981ex/pfF5H4wzvk5eYUOya/1n0I7vkAbu5FVzSnZ1tZviehUAV0dB0zGTlWwI3xNzajTf0gZq46yLHkLD4eGcu05ftdtnDffjyV8V9uZUhsJM/1bcHQD+P4amynkluXd6hfqupw49+B3MdkdGo/aMmz8dYvu13eZ/vxVMZ9upmhHerzTJ/mFRbWL2RJpVdvaVmhLSAvG7m58Nhj8P77zvuaNIEFCyA6usKHJSIiomxeepYyLBXa/or23NL8FmZtnlX6G6wDlkBo91A++/Izjp8xEF3H8TSgu8HtkmXzXfHp3HxlOK/ecmWhpw/9PI0kZZZtLfmisnlOro1nb44mtkEwM34/wPbjqQWdaCojmxeXyzMtVl7/wXV79O3HU3nss4rP5eAYc8/oMH7fe4pH2z/JiIVDSzznyU4TMBkucDkgux3y8sDo4jXedJMjw+79+wndgAAYNw4efRRCQi7sfiIiIqJsXgb52XyF54riDzRAdNtoIjtG8pPHT1DMiuClLWwJ8gqiQVADnvrlqUKfDVJzUsvcST3YO4gwsw1fTyMPfLwOqw2urhvA031akGnJ45lvtmP4Fm695gru69KQKb8e4Nb2RSwJWYQz2WcICgzDaHDD3d3AGz/s4NmbW5RbNp9r78kdf/5C+7+7sy+M7sakbvdyLDC8WmdzzZuLiEhNV2OKWwYNuptBg+6+oHN79uxBz549ynROdHQ0L/zz+Qu6X2Wz5NmYH3eUcTeUbYIwxCeEhIwE3l37Lja7rcTjx7QZw91X3snYT7bQq0UYn4/uSKbFSm6e3eWTmue2K8+3/Xgq2ZY8moX7O+0DGN29EafTsjCsm8ephdOLH5DBneDrx+DfqneJYzcZDZxMzea+Lg2YF3cEmx1GdWuI0eCG0d3gaKfeIpQbY8LJtdlYsTvBZUA/19y4I7SODKTf1RHcOf0Pvnmwc7HtEXtGh2JyN5BpsZKaZSUly0KAt6lQhXlxrHk2luw8WWwQBpiz5jCxDYLp3TK8wtosltQeckTnKNpFBTNs5lr6XR1R4S0gLwunTsEdd8AKFx+2b7wR5s+HoKCKH5eIiAjK5mVhKcNSoRN+mUBiVikL1E8DC4FjQD+Y/NJkfE2+bD16ilkj2mGz2UnPseIG5ZrNkzMtDImtT/dmoQWdX0Z1a8ij53RGCTOXbT14V9l8dLeGGAxwNDGDHtGh9GoRRkpWLgYg7lBSlcrmVTmXgyObd20SSra1P6PbjGF6MYVWY9qMpX+zfpiMZWx9/tdf8L//waxZ8OyzMGKE8zEGAzzyCPzrX46ClgcfdBS4iIiIyEVRNi+9gmxuBuoAJ87Z6Q00AZoCjcEryosZd8+gwdsNSjVXXpKXr3uZ7/d+71T0vv3UdnzK2GXPx2jmumbuWKw2PhzWnvRsK0mZFizWvILOKDY7HE7MxNfTyPbjqTzgU79M4w33D+b7rSe4v2tDvIwG/u/G5lhy87giyJvPRncktZTZ3M1uKzKb/89tApZP/8Ok7sPYWqcpMRFmnqoB2Vzz5iIiUpPVmOIWKb3ULCs/bo/n6ZsblCkUbz+1nd2Ju0s8tp65Hq9e9wbdIm/iw+V/MW1oG44lZZFnt7N8TwINaxdTyu5CcqYFP0/nb+WhsZFcE+bBiKF3sOTnn4q9Ru3aoTQa9E9OeEeVeL+YCDMmo4E2kUEFFdBDYyPp2TyUTIuVD1YcYE7cEdKyc4mJ8GfW8FimrzhQqtcya9UhZtzblmnL9zNmzgb+N6Jdke0RLXk2Fm87wfTlB5yXE+rekF7RxbdGtOTZmLasdOOatnx/hQfhgvaQzUNJyrSQkJaD2csDTw8D8+KO8MX6Y/zfDc3oGR1a4S0gq7xNm+CWW+CIiw9h48fD66+Du3uFD0tERETKLjkrudTtvxMyE0o+KA9YCfwOBqMB21AbY+8cy4Dmt/C/lX9xb6cobHaYvfoQp9KyuaV13bKNt5hs3rN5KDa7HZPRvaC9d4C3Bwsf7szg9pGs3n+ayXdcjdmrbGvJu8rmPZqHkpKdy8KtJ5gbd4SMnFxuahnOi/1bMn151crmVT2XgyObm9x9mXzDZK6t352JqycW+pzYKrwV4zs9Rf9m/fAz+RZ9oXOdPg1ffw2ffw5Ll4Lt71/6zJzpurgF4IEHYPRoMJku7gWJiIiIXIBC2bwpYP37382AusA5EW1T/CYyczNpGdqSrSe3XtR93d3cGXzlYHrMdi4kyrPnMX3DdMa1H8fIhSNLvNajsU+y469MbmoZzvI9p3hv6dkCiQBvDxaP68qQ2EhW7TubzY0GN3w9zGX6XUGQdxAnko9yR5u6ZFqszPp7zjwlKxejgRKzuV9OJk8vm0memzv/vGGs62z+8hBSn76LyTUwm2veXEREair9zSZOUrIs5NnszI87UerW010iu3DXl3cVe4yPhw9Tb57KLc1vJTvHQK4NrHb4bdcp2kYFsXTXKT5aeZApg64p03hD/Eyk51gLvo6JMHN/lwbUMyRzbddO7N+3r9jzwxtGM/Kl92nVvBHjvyz5g8bIzg1IysjhgW4NeXXRDl7qH0O3prV49LNNLN2VUKgFu9XmaN3u6mlXV8592jXuYBIpWVbCA7yc2iNmWqy8trjo1ojj5pfcGjE1y1qmcaVmWSs8DOe3hzQZDdTyM5GenUdSpoV7O0bxcI/GpepQU+N89pnjlwFZ57U09fKCDz+EIUMqZ1wiIiJyQZKyksvvYsdwdGs5BRFXRBBwXwD/vOOf3NykLxk57mTk2li+J4H6wT689cseouv4M7xTgzLdoqhsfn2LcOw2O5+uO8LXm/5iz0lH+/uUrFyGzVzLJ/e1Z/yNzXjzp90E+ZoY3imq3LP5/oQMUjKrXja/HHI5OLK5n7svt0bfRt9m/TiTdYak7GSCvQIJ9A7EZPAsuWNLcjJ8+60jsy5Z4liC6HwrV8Lu3dCsmfM+r7J19REREREpT4WyeXeghJWUEjMTMXuaiz+oFF7o/gIZlowii96nrpvKugfWMarNKGZsmFHkdca0GUu/pv0wGbz4asMxvtt6HJvdjrvBjTybnZSsXIZ8uIZP7mvPkzeczeb3dowq0+8KJnSawF9JuYy5rhE/bY9n/JdbnebMi8vmXQ5u4o0f36FuqqN4/4dmnfiDq5XNz6F5cxERqalU3CJOArwdT8HNWXOMBQ/3ZtQ1Y5ixsejW0x3rduLdte8We00Pgwdj2oyhZ/0BdJ+4muTMXGIizAzvFEWb+sEE+HjwwYoD7DmZjpfJvUxPatb292LKoGtIzrQQ4mci2MfETz98z3UjhpGe5twO/Vxdb7qFQy2Gsmi/hVG9gxncPrLYNohDY+tzU8twkjNyyM2z80L/GJbsPEm21eY0eQ7g52ksNLlfGuc+7Zqaneu05md5tUZMybKUaVyuxlJR8sO6l4eRWv6elTKGKi8vD557Dt54w3lf3bqOXyK0aVPhwxIREZGLE+xdDssIWoBfgbWAHaKaR7Hwu4VE1WvArJXH6PLVauoGeTO8UxSt6gVhMhowuFFu2dxgcCPXZiMjx0qryCB6NA/D08PA/LVHmbPmMIcSM1mw+ThXBHozb+2RQt1cakI2v5xyOYDJ6IEJD/xMvtQNuKLkE/bsgUWLHP/8/jtYS/EeLFgAEyZc/GBFREREylGhbF6KuoEQnxBSslNKff0u9brw9k1v89Wur5i6bir1A+vzaOyjXN/weg6nHC7yvOTsZG6aexM/DvmRjnU78nbc205d9p7q9BR9m/UjJcMNg6edzo1rER1hJtjHVGw2D/YxsWhcFzJzrPRs0KfE3xWMbTuWm5v2ZdbK46zat4t/39XK5XGusrl/TgZPL53J4C2Fu7BP/OEdbho5RdncBc2bi4hITaPiFnFi9j7bBnz4zC38b+S/6FSvK/+Ne7NQKL467Gqu8I9k8b7vir2el9GL13pMYmDTIQz5YCPJmbmAo7J5/JdbGRIbydO9o/E2uZNnszMv7kipn9Qc3a0h2RYruXk2IgK98HB3Y9LEN3j9lRex2+1Fnufm5sbL/3qdVd4dMcSnFTwxOntke66pH8isVYdctCxsRNv6QTz26UbOZFlJz7GyOz4Nmx16RYfR58o6LNp6otA5D1/XmDBz2UJlkM/Zp13NXs5PPpZXa8T8IqbScjWW0rLm2bDk2cq8xqmUwbx5rgtbOneGr76CsLCKH5OIiIhctCDvoFK3/3ZpD/A98Pecer029Vi2eBkenkEMeHcdB05nAo4OKvnZ/B+9m9OmfhDrDiVfVDY3GQ1Y8uws23GS/612ztfDO0Xx3SNd+Hl7PHe3j+TOaX8UjKUmZfOKzOVQQdk8IQFee81R0FJCJ80C/v5w991w333Qvn35jENERESkHJUlm7cOb02mJZM/T/1Z6uuvPLqS+767j/GdxnPw0YOkW9J5b917fLTxIz7o/0Gx5+5L2ke7D9oxpu0YFty9ADfcOJOdQqBXIIFegRxMyCElw431h5OZscJ5uZ7zs/nIWet46LoGDIqtQ4b1FK0bpYObF5NveJNu9bsx+Y9JTgU0T3SYwHX1byr0OcNut5cqm3c5uIlJP7xNRNppp9cWkpnClfH7CPK5SdlcRESkhlNxizgxuRsY3b0h4+Zv5lBiJgOmrOexXu1ZPvx3TqQmkpSVjL/Jj5dW/JMvd84v9lpdI7sxrffHLNqcwYAp60nJynU6Zm7cEWIbBHNL6ytYdyiZOWsOl+pJzSGxkVxdL5CxczcCYM3JInfp+3y/4Otix+Tp48/cefO4pvN1fDh5WcH2Q4mZ9J+yiiGxkcy4ty3ZljySMy0E/V297mNy54dt8fyyM8Hpmpk5eUy+4yqeu7kFqS7W+CzL066eHgZ2x6cRE2HG7H3pWiOeW8RUmnG5Gosr5wbyTIuVUH8vftt1kukuPjSVtMaplMGQIY4ilgULzm4bNQrefRdMZftQJiIiIlWHyWDiyY4TGPrN4LKdmAL8AOw6u6nnHT35fNaXfLXhNFOWrikym7cvh2wO8NYdrZi2fL/L8/IL3YfGRjL+puYkpOUUyrg1KZtfqlwOlZjNPT0dOdTVskPn8vaGvn3hzjuhTx/w8bn4e4uIiIhcImXJ5r0a3sD1c64v8Th3N3fy7Gcz0+b4zQz5egij24xmfKfxTFs/jdScVHw8fEosrEnOTub1la/z8/6fWT58BXNWpXE0+SRxB3bwzYNdeG9p6bJ5coaFSXc0Y9mRH+j32ZBC9+xWvxv/ufG//D7id5KzznAm+wxB3kH4GP35cMUxbv52XaHPGSVl89a1POn36buM3LDQ5WtaHXkVT/Ueh7lFU2VzERERKU3zPKlpjO4GekWHMSQ2EnA8Obn4z1PsPmHhsXmHmfR9Ij0+uqfEwpa7WgyjX5336Pv2Nt5fdtDl5Hm+6SsO0LN5GAa3s09qju7ekMl3XEVMROF1SWMizLxzdysevLYxw2auZd2hZP7Ysou4dx4usbDFGFyXkCGTCWnejjQX40nJyuX9ZfvpOvE3Hp6/kYk/7uLh+RvpNmkphxMzuaFFOAY35+uavT3w8nCs79k0zL9gvU+ju6GgWKg0RnSOYl7cEWx2GNO9EaZyWk7IlbKMq6ixnC/TYmXxthPcMe0PbvzvCowGA68t3sm4Tzc7fSDIX+P0tcW7yLSUrT28uGAwwCefQEwMGI0wdSpMn67CFhERkcucyehBTGgLPAylfCIwD1gFvEehwpYXX32J51/5hJ5vreVfi3cXm81nXEA2H9WtYUE2X3comfYNQog7mFhsQQzAnLgjbDqSVKOz+aXI5XCJsnluLmzaBDNmwP33w+efuz7ObC56SUxPT7jlFpg/H06dclzj9ttV2CIiIiJVnsnowYDm/RndZkyxx7UOv4Y3V08kKSup2OOMBmOhwpZzTd8wnd+P/M6YtmPIs+cxfcN0nujwRKnG+WTHJ5m75i+mrzjI4j/jubt9/VJn8z+PJWP2sfH66mcZsXCoUzHNisMraDPjGib8/BQmNzP/+SGT9Ax/8vI8mbbc+XcAxWbzbX8ye9rDLgtb0k3ePHvDgwy5+1WOBoYrm4uIiAigzi1SBDfgwWsb0zrS0QY8PcdKsI+JbSf+4pTnS1gMu4s9PyB3KG/1ep/7P17vtNa9K9uPp5JjzSO6jqMqurgnNX09jfh6utNt0lKSM3PJPrKVhG/fwJZVfDW1d6N21Oo3HoOnD19u+Itn+0QXeazNDjtPpBXaZvbyIDs3j2bh/oX2lVShnV8sNDQ2kjlxxT/t2i4qmFcW7WRIbCTXNbu0ywkVjKtDZLHrkA7tUL/IpY3OlWmx8trinQXXeui6xqX70FTMGqdSRv7+js4tx45B9+6VPRoREREpJ82CWtC5XmeWHV5W/IGHcSxBdOrsJoPRg5aD/sEDD43nvtnrCpYILc7246lk5VpLzOYhfiZC/b3YeSKV/lNWFUxkuxvcGBwbyQOz15fq9dX0bF7euRzKIZvX8cC4fx/s3ev4Z88ex7937oTs7LMn5OY6uq64ct11sHat47/r1IGbb3Z0aenZE/z8SnwNIiIiIlWREU/evP5NukV2580/JhYq/oipHYPVZmNT/MaiL3AOq634woV34t7h27u/5af9PxFTO4a+Tfsyqs0oZmyYUeQ5Y9uOpeMVN9D/a0cWL202dze40TTMDzt2Vh/9nY82Fb8M0tQN7xNbtzPNw1vh4W4oWza32eDf/8b47LOYLc4FKSvrX80/eo/jWIBjmfUan801by4iIlJAxS3iUkqWlT7v/F5oAjvLFk+y7z+w2IoJXXYDIbmPEBt2G2ZvD/adSi/1PbMsecwa0Y7Zqw8zZ83hgic1py3fT7Nwf/w8jaTnWBl7bWOa1PYlKcNC2sZFJP/6AdhtxV7b3PEuArsOwc3NEQJ/2BbPG7deWeaW5PEp2fh5Fv5jU5oKbR+Tkad6N6fV38VC57cZHNE5inZRwby6aAfP942mXVQwCenZ+Hk5T/qWZ2tEH5ORZ/pEE9sghGnL9zuNa0z3RvSMDi2x/aE1z8aSnScLAnpZf6FR1Bqn4kJmpqN4pWlT1/sbNXL8IyIiItVGSnYeqSeG42FYTa7NxdOIGcAvwBbgnMJyN5M3tW59jrptu+LvZSxTNs/JtZWYzcd0b0SYvxen0rILPaHZNMyPbEteqVuCK5uXXy6Hsmfzjoe30O7YDqKSj9Mg6TiN3z2OMTPN5bFO1q0ret+ttzq6sfTtC61aOToNioiIiFzmzmRZuWPaWl65pTsrhvflTPYZUrLPsCn+T5746VFOZzkvm3muIK9gkrOL7+iSb1P8JtxwY/Hgxbwd9zb//uPffHb7Z3Sq24nFexdzPP04qTmpbD+1nSvDruTxDo9za/Nb6f/u2aWBSsrmAd4eDO1Ql0GxdcjMTSXbdoowYyP2j9vPjI0zmLpuKsnZyS7PfTtuMt8P+o28vDJk86NHYdgwWLrU6Xo5Hib+1X0EH1/TF9zcamQ2P5/mzUVERApTcYu4lJJlKTSBHVH7FDssT5NhO1nkOW52T2pZnsLH1p5R3RqSZ7Oz9Mlrmb/2aMGEeHE83A28/N0OujetzcKHOzNs5loOJWYWelJzaIf69GweSnZ2DkEbZnLk12+KvaabhychfR7Ht3mXQtvzbHaSMiyM6NyAJ7/YUuL/j/y2h7ddU5f0nLMV9WWr0M7jQEKGo1goN4+MHCu1/Tzx8zKSnGEhz2bnhf4xzIs7wiuLdvLTY91cXie/NeK4+ZtLvGdpJ/d7twynZ3QoqVlWp7VPS/PaLHk2pi07UPB1WX+hUdQap3Kew4cdLdxPn4b16yEsrLJHJCIiIhUgJctCYkotfIwDSfH4rPDODcASIKvwZoNvIGF3vIQprNElyeZDYyPp0DCE3Dwb3ZqEFuqE4udpJCmz9C3Blc0dyiOXQ+FsbrLm0sN4BvuaOHacyHB5fP8dyxm09edSXdvJrl2QkgIBAc772rd3/CMiIiJSjaRkWTianMXwWRsxGqBXi9pkeX3JJ9snY6f4FuYhHlcxZ+Bkes+/odT3S85O5pEfHmHF4RUEeQUx/8/5jG47mh4NepCUlUSQdxCBXoFY8ixMXTeVlJxUbm1dl8m/7AGKz+ZRIT78b+TVLDvyA/0+G1KoC02r8FaMaz+OdQ+s46a5N7EvaZ/T+ZviN2GxpfHbDivdmtQuOZsvX+6Y2zxzxulaJxtGw5xPGNa4GbdX02wOmjcXERG5WPobUVw6t4Vfpts2Vqe+gt3N9WQogMHuR6jlBTxt0QyJjaRVvUC6v7mMukHeDO8UVWhC3JX8py8X/3mCRVtPMCQ2ktkj2xe0N4+JMHN/l4b0ahHKrxt386/H72dL3JpiX4O7OZTQ257DFOp6jcw8m52ODUMY3D6y2BaA+W0Pv1h/jMGxkeyOTytzhTaAv5eR5XsSCj3tmpFjxc3NEZbTc6zsjk/DZi++cvxStEY0/h3IfUyO9U/LKjXLWiiQl/UXGuBY4/RC7l1jrFgBt93mKGwBuP12+PVXMJWt3aaIiIhcfvKzudl6J2m2H7F5pkA8sAg45ny8MagOoXe+gkdgeLln87pB3ozu1pAe0WG8t3QvDWv7c2NMGI/0bFLQCSV/SdOyUDY/e70LyuW5ubBtG2zZgm3znzz+/UoaJx4l8sxJ3O02MhZdBX1fc3nqoeCI0t8nX6NG0K6dildERESkxjl33jzHdobZe54n231TieeZc2/j+c4v06pOCF0iuxR0XMmz5xV7XqBXIGeyz9A4uDE/DvmRFYdXcMOcG1wWogy+cjBplnTubl+f8ECvYrN5gLcHs++7mtdWPcuMjdOc9m+O38zIhSMZ3WY0Pw75kXYftHPZwSU1J4VDp+H6FmElZ/PGjcF43jY3N3Kf+D9GRdzEnwvjaRaecfln83No3lxERKR8qbhFXMpv4bcu/icSTJPAregnO91ttQi1vEzrOi0L2gQOm7mWlKxcUrJyGf/lVqdilfPlP31p+7u4fW7cEWIbBPPTY12x46i6nht3mAW/rGDUvYPIOlN8e0fPyCupPeAfuPu4eIIQRwj2MBq456M1zBrRnmvqu25JPrJzA9pGBTFs5lqeuL4pHgYDq//RE7O3EaPBDavNTnxKNilZFgK8TcVWbp9bOX7u2qOulKZyvLxaI5aHlKzCgfxCfqHhao1TAex2mDYNxo0D6znr8K5cCU89Bf/5T+WNTURERCpEfjZf/c3/sB1NgdrAWsDFypym8MaE3v4iVzWJLPdsvmz8tXi4G9hwKJkDCemsO5TM+8sOsPlIJA9e15jjZ7KYcW9bLNY8avl5lqkluLJ5GSUlOVq5r14NcXGwcSNkOdr3+AHXn3e498H9jlzp5uZ0qYNBV7i+h9EIDRtCkyaOJTHz/92qFYSElN9rEREREbmM5GfzDfGrOW2aRJ5b8UsMGex+dAr6J1NuG0nD2p6cyT7DpF6TCPIOwtvoXezSP63DWxPoFchfqX+x9oG1TFw1kRkbZjgdd24hyqTr3+S9JftxdzcUm82HdqjL0sM/uCxsOdf0DdPpULcDY9qO4fWVrzvt93L357pmXqXL5uF1MH7yCfTu7Ti5Xj34+GPcunbjvm0nLt9sXgzNm4uIiJQvFbeISyZ3A1GRq1mc9Dq4uZg1/1vT4Gjev/FLogIj8fQwFLQGPH+SfG7cEVpHBjIkNpL3l+0vtC//6ctXFu0stH36igPENgzhX9/voFuT2uTtWcH9jz9MTk5OsWP3v6YvQT3ux8296G/vEZ2jmL/2CJ4e7rywcDvdm9Z2tCS35JGcaSHEz0RtPy+mLtvHy4t20O/qCHq1CCsIvZkWKz9uj2f68gNOAXl094b0ig5zCsjlXTleXq0Ry8O5TywA7DmZjpfJvVzWOK3RLBZ45BGY4fyhlaZNYcyYih+TiIiIVDijG5jWz+XMspmODftdH9f12h5Mnj6bkMDAS5LN2zcIYfjMOIZ2iOKa+oEF+W1O3BFaRQaSnWuj68TfaBbuz6ejOvBA14Y89tnmEl+fsnkp/fknfPEF/PwzrFsHtqI/p53PkJlBF79cVmY4T6TvqRXJiqjWHAyO4FBQBHmNm/D0uH54N3XxZK2IiIhIDWc0QK06P3AyaWKx8+YAV9Zuw7Ten9Ayoh4Ldy1k+Pdvlmnpn3Gx48jNy+WFa19g+aHlLgtbzjV9w3SujbqWTo27M2zWxoIuhZ+O6sCorg159O9s7m5wY1BsHfp+Otjlddzd3IkJjcHsaSY1J5X31r7HV3d9xcRVE7HZz77m1uGtCfAK5IqGvqXP5j164fOPf8CBA44H+oKCMMLll81LSfPmIiIi5Ut/K4pLCZknmb3jlWIDupetBT8O+Y33f41n2/GNBa0BizJr1SFm3NuWacv3F7QQPP9p0nNtP55KeraVPSfOMPs/r5C27ttix+zh4cEbb73N6Su6FNvK/P4uDbi2WS06Nvan/zXeJGYmEeJjwGbP5ve9SSzfk8jg2Ei+XH+MZXsSeLl/DNfHFJ48f23xTpdBe/vxVMbN38zQDvV5pk9zp0n08q4cL4/WiOUh/4mF/NeTZ7MzL+4IwztFMf7LrSWeX1LFfY108qRjGaJVq5z39ekDc+dCYGCFD0tEREQq3vp1a/n2k+nFHuMXcy1ffLOAyb/sZ9vxA5ckm2fkWLHk2Rn/5Va2HE3hn31jWH/I0f3l3OvtPJHGydRsOjYqeZkhZfMymD0b3nrrgk5NCwnl3kY+rNxqddp3KPgK7r3rlYKv3x3UGo9m4aB8LiIiIuLk0+3z+HjH6+DcEK+Q0W0eJsQyksigcMb/PL7MS/+MbjOarpFdmbZ+GmPbjaXH7B6lGt+kVZNYPrwvRgNYbRRk8w7nZPOmYX5k5Kay5eSWQucGeQUxtt1YRrcZTWZuJqczT1PLpxbeRm9M7iY61evEyiMrC45/suME/D298fYonM0/X7kfo80KJu+CYwtl8xdewsfTo1BXwcsum5eS5s1FRETKl4pbxKU6/nX44o4v6P9pf6w25wnQUI/OzOw3By93M19sWF+qa24/noob8OOjXfH7u5XenDWHXT5Nmu/Q8ZNs/vAfpO1cV+y1Db6BfPHV1yyM9+Wf3RsW2cr8iV5NaN/Ij693fMN/1zpXyj/a/klev70vfh4+NK7tx4jODfD1dMf779BszbOxZOfJYivI819XbINgercMd6oEr0qV4+Xl3Lbu+easOczChzuX+AuN0lbc1ygbNsAtt8CxY877nn4aXnkF3N0rfFgiIiJSOTp16sTbb7/No48+6nJ/1LV3Mv3d/2Jzc+eLDS7ygwsXks2TMy34eeZ3azlMq8iAgu4v24+nkm3Jo1m4PztPpOHn6cEz32zln31jlM3Ly513llzc0rAhtpYt2V+rHlNPmtgXUo/9wXUxBgaw8MbODPY6oGwuIiIichGGXDmEWZtnsezQMpf7jfjyTMe3GRM7mDlrDrLk4OIyLf3zw74feDT2UbpGduWmuTcR7hvOvVff61SIUpRN8Zs4k32GG2LCWfxnPIBTNl97MInEzMLLKTUObsyPQ35kxeEVDPh0gFM2H99pPHNvnUvPj3uyL2kfY9uOpX/zfnh7ODqT5GfzX37ZyOffvMaRwHDG9RvvtCxm4WxeeN9llc1LSfPmIiIi5UvFLVKk3k1681H/jxj27bBC2wfHDOe9m6fibTRyMDGzTNc8mZrN1xuP0a1pbTYfOePUBv1cloTDjBw4jlOHDhR7zaD6zWk18lWu69aFx1/+mfWHVjEkNpKPhrXDZrdz/EwWQT4mfDzd8feyMf6XJ5m2wXWl/IiFQxnbdiwTe03iVFo2YQFeBZPnAJY8G9OWFT+efNOW7y8yfFaVyvHy4qqte0pWLsNmrmX2yPZF/kKjotc4vSzMmwf33QfZ2YW3e3vDzJlw992VMy4RERGpVOPGjePEiRO88cYbhbY/99K/mDBhPCZ3t0uazQGCfEyk55wtfD+/+0t+8UtMhBk/TyNLdyUom5fF7t0wZQq8/jr4+Tnvb9cO6teHw4cdX3t5Qdeu0KkTxMY69teqhQG4wmLFZ/FOtuYX/iibi4iIiJQLd4M7826dR+vprTmZcbLQvpa1r2burZ/RLKQRBxMzi13653zvxL3D8uHLeTT2Ud6Oe5snfnqC5OxkmgQ34VTGqTKNMTkrmdr+ngAus/mEG5uTZUsoOD7IK4ifhv7ExFUTXS59tDl+M0O+HsLoNqNZcs8S1h3fwE2Nb8TP5FtwjCXPxoqZC1g0+1lqZ5yh1Yk9/BF5FfNb3eR0vcsim5cTzZuLiIiUL/3NKMW69+p7OZl+kglLJgDwfLfneenal3Bzc8OaZyPQ26NM1zN7ebD+cDIbj5xhxr1tmfr3RPj5MveuIen7t8jLySr2erfdNZjJb79Laq4BS24eNrsjHL6/bD8f/H6AlU/14PP1R/nzrxT+0bspx7KXupw8P9fU9VPpEtmN26Jvw/O8NeZTs6zsOFHyWpjgeBo2NctaYwKoq9aRhxIz6T9lFY9f34S598eSnZtHarb1sq+4vyTy8hxdWd5803lfZCR8+y20bl3hwxIREZGq47XXXuPkyZPMmjULd3d3Zs6cyb333gtwSbM5OCZYPT0M7I5PK9h2freWKwK9ufmqCIJ9PMhRNi+9NWtg0iRH3rPb4eqr4f77nY9zc4NHHoH4eLjxRujSxVHg4oKyuYiIiMilU8e/DvNum8f1n1yPzW4DYEybMfznpv9gdDORa7MR4mMiKedkmTquJGScwdcYwFWhV1E/sD4hlhCm951Onj2vTOML8g5iXI9a+Hl60CzMD6vNViibz1x1kHXPdaVVeCs2x29mbLuxLD+03GVhy7mmb5jOtVHXMrD5rXgaTYX25b4/jdemPI7pnC7wL/w6g00RzdgV2qDQsVU6m18CyuYiIiLlp2akB7koT3Z6klMZp2gQ1IAH2z0IONbP/HXnSdpFhRRaM7I4506I2+wUmgjPZ7fbSFn9GSkr5xZ/MTcD0QMe5D/vv8GDczdyf9eGNKztx/XRofyy01HJnptn5+M/DtMuKphvNx/nmvp+PDprYqle8+TVk+jfrB+eFP4FQUqWpVTn50vNzq0WFealVVLryEAfE+EBlT3KKig5GQYPhh9/dN7XrRt88QWEhlb8uERERKRKcXNzY8aMGWRmZjJs2DB69+4NXLpsfq4RnaOYF3fEqfjl3G4tbm5umL2MXNc8FGuevdBYlM3PY7PB4sWOopbffy+8b8YM18UtAP/3f6W+hbK5iIiIyKXTo0EPXuz+IhNXTeSDfh8w6MpBZFqsLN55gg9WHGDm8PYkZyeX6Zon0xOZ/usJJt3Wn2vr98bH5MbTvz7NU12eKihEKUnr8NYEeAVyzwfrGNapAT2iwzC4USibZ+fa2Hg4nSc7TmDYt/cwus1o+s/vX6oxTlo1ib5N++HJ38UtOTnwyCMEfPCB07HxfiHYzluWKF+VyuYVQNlcRESkfKj0U0rk5ubGmze8Waiw5bXFO3lk/mY+WXOY4Z2iSnWd8yfE8yfC89ksWZz+9o0SC1sCAoNoO2YSLz83gflrj/LnX6k8+ulm3vhhF5Nuv5qAc55YnbPmMO0bBPNivxacyT5TtrVJs5w/fAR4m1wcXTSzV/FPz1rzbGRarMSnZLM7PpX4lGwyLVasebYy3acqObdtZNMwf8IDvPAxGVVpXpx33nFd2PLgg7BkiQpbREREpIDRaOTTTz8tVNhyKbL5uYbERtIuKpi5cc7rwecvVTSicxQf/3GIJz7fwsQfdmEyGni4R6NCxyqb4yhq+fprR0e+fv2cC1sA1q2DTZvK5XbK5iIiIiKXzrPdnuXPsX8WFLa8tngn4+Zv5s+/Uvn+zxOE+4WU6XrB3kGcTM3mSFI2v+1K4ptdi3h//ftM3zCdce3HleoaT3aawMZDaWw5lsoTn2/hjR92AThl8+e+2UG/pv14sfuLZOZmXlg2P3ECrrsOXBS2LG9wDf2H/Yc9taNcXqdKZPMKpmwuIiJy8fS3ppSJNc/Gkp0nC9aHzJ+gHtw+stjzXE2Ihwd4EWZ2VGfnnoknfs54MvesLvY6fuFRfLdkOTdef4PT9easOczKfaeZdPuVBdvy16+8qWU4SS4mxIuTlH3GaZvZ2/FUamnERJgxexfdHCnTYmXxthPcMe0POrz+Kzf+93c6vP4rd0z7g8XbTpBpsRZ5rlQzTz/t6NCSz8PD8cTue+85/ltERETEhUuVzfPFRJiZfMdVjOrWkGEz15KSleu039PDQJvIoELXmxN3hN92neLapqEM7XB2LDU6m9vtjmWHrrkGbrsNtm51fVxAgCMbRkRc3P1ERERE5JIzuBloENTAKZcDvLNkLyE+QbQKb1Wqa7UOb42P0czu+DTCA7zoFR3Mf9ZMAmDquql0q9+NUW1GFXuNsW3H0rdJX57/dmfBtjlrDvPrTudsfjQ5i7GfbOOJjk+QmZtZhlf9dzZfswbatIE//nDaPzX2dkbc/gKpXn4uz6/0bC4iIiKXLRW3SJlY8mxMW3ag4Ov8CeqHrmvMO4NaO00uFzUhHhNhJtDHxAv9WvB6LGR8Np7chEPF3tu7SQemfvYDAWF1eaCICfZpy/fTtXFt3j1nLIcSM4k7kEiwd1CZXmuwV6DTNpO7gdHdG5bq/DHdG2Eqour63Er+89vGbz+eyrj5m3lt8S4F9ZrCZHIsPRQZ6ejSsnQpPPBAZY9KREREqrjyzuYv9ovh9wnX8f0jXfj1ie5Mv6cNBxIy6D9lFYcSnSe8R3ZuQFJGjstsPm35fuzAM32ia3Y2t9th4ULHxP/AgbCliCdir7gCJk+Go0fhtdcgLKzs9xIRERGRSnF+LgdIyrTw2dp4JnSaUKprPBr7JPPjThBdx0ywrwmLLa2gm0pydjI3zb2JCZ0mMGvALKeCmVbhrZh/63wm9ZrE2E+2cTQ5q9D+orL5qv2JrN6XSqhv2bpG1/38B+je3dG55RxWL28e7j+BidcOx2ZwL/J8zZuLiIjIhSq6PFbEhdQsKztOnA2VAd4e3HxVBB7ubvRoXptuTWqRmG4hOdNCkI8JTw8D8+KO8MqinYUmu0d0jiIxLZtbHnyOLV9NwW7LK/a+AZ0G8dATT3FL+0bMXHWQj1YecipsAUfATc22Oq1fGRHojZ3sMq1NGugd6LTd6G6gV3QYQztEFqrEP9/QDvXpGR3qsqWgq0p+V+asOUxsg2B6twxXa8KaIDQUFi2CwECoV6+yRyMiIiKXgfLM5knpOYz/cgse7gamDL6GJTtPMnPlQXafTCcvf+2icwyNjeTGmLAis/n246mkZlkJD/CqmdncboeffoLnnoMNG4o+LjoaJkyAwYMdBc8iIiIictlxlcuHdqhPn6uuwM8znDFtxjBtw7Qizx/dZizd693EgAXreb5vNGlZufyVepqrwq7C7GkmNSeV7ae20+6DdoxpO4YFdy8gMzeTxMxEQnxC8DZ6YzR48NLCfazan+h0/fLK5h5WmLOiFsErXBTsREWR+8WXBB4zQVXL5iIiIlJtqLhFyiQly1Lw31EhPswe2Z61B5MY8b91HEvOYuHDnfn+zxP8vjeB9Bwru+PTOH8ufEhsJFeG+/DkI2PY/MXcYu/n5uFF63ue5ZkHh9OhYQj9p6zkwOni2ySmZucSHuBVaA3L+JRsdp9M58mOExj6zeASX+eTnSZgMni63OdjMvJMn2hiG4Qwbfn+QhXkMRFmxnRvRM/oUHxMrv94uarkL8q05fuLDPtyGTp0CKxWaNzY9f4rr3S9XURERMSF8srm7aKCATtv3n4lwf4GkrNO0azuGaYOa4CHwZ+5fxxnzppjpGTlEhNhZmTnBqXK5ufm8hqXzTMz4Z574PRp1/ubN4cXXoA77gD3op9qFREREZGqr6hc/sDs9WTkWJl937/oXr87E1dPLFRA0iq8FY/Fjqd7vZsYPnMLfa+qQ2yDYLxMbrQIbcRnt3/G6czT1PKphbfRmxkbZzB13VQmrppIy9CWBYUv205tY9eDB9h2XqeTc11sNg9LczSe7nrERb7t1Qs+/RTvkBCeucpa9bK5iIiIVBsqbpEyCfA2/f1vDz4eGcu05fuZt/ZsJfWwmWuZPbI9EYFezFp1qNDkeUyEmRGdo4j0zKFrt2tJOLCt2HvVjYxi9vzPaXdNK6Yu28/Li3a47NZyPrOXh9O2lCwLz32zg+8f7cfoNmOYXkyl/Jg2Y+nfrB8mo/N18vmYjE5V7mYvD8zeRkx/f0AoyvmV/MXJr6ovKvDLZWTpUscvL2rXhrg4MJtLPkdERESkGOWRzdtFBTNj+X6e7tuIBbu+Y/Ifk5wm3Cd0msCKp/qRk2vEYrUxL+5IqbK5q1wONSSb+/rCP/8J48YV3t6smaOo5c47VdQiIiIiUk2UlMv7v7ueh65rw4rhv5OUlUx8WhKBXoH4GM3MjzvBU3G7ebhHYzo2DMHNYGHxvoVMWu2cy8e1H8e6B9Zx09yb2Hpya8G+1uGt8XI3szs+rcgxXlQ2t+cXtri4wJNPwuuvg9GRkatkNhcREZFqQ3/zS5mYvY3ERJi5tlkocQcTC4V0gEOJmfSfsoohsZHMuLct2ZY8snPz8Pf2wOAGb8z+junPP4g1PanY+wQ3uYbH35pOhzZXk5JjZfmehFIVtsREmDF7O39bB3ibOJqcxdhPtjH1njfpFtmdN/9wrpQf3/Ep+jbti8XiASV0BT+/yr20zq3kL438qnq5TNnt8N578NhjkJcHiYkwZAgsWAAGPVkgIiIiF+5is/m8uCO8vWQvCx5px/ifn3TZKn1z/GYGfz2YsW3HMun6N3l10W4WbjlR4tiKyuVQg7L56NHw9tuwfz80beoodrn7bhW1iIiIiFQzJeXylKxcXlu8h6nLDjKuZxP6XtUcm93GydQcbrsmksGxBr7eeIw8cnj992eKzOUjF45kdJvR/DjkR9p90I7k7GQAHo19kvlxJ5y6NOa76GxepxVpb96FffjLuGVlOTZ6ecFHHzmW1zxPlczmIiIiUi2ouEXKxORuYEz3hlxTP5gHZq93eUxKVi7vL9vPtOX7aRbuz5VXBPB/NzTjysFPk/DjFMizFnsP/7YD8LtuJO+sOkmjuqdoVS+Q+7o0YPyXW2ka5ofZ24MQXxOJ6TmkZlvZczKdvL+T+5jujTC5qP7O/4Cxan8iN7+9lldu6c6K4X05k32GpKxkgr2DCPAKZOOhNB77dCdTBre++P9ZRciv5C+toqrq5TKQkwMPPeT4oHeuRYscv+h4/PHKGZeIiIhUCxeTzTu/8StWG0wf2pqf9i9yOYF+rqnrp9KlXjde7D+AhVtO4G5wKzabF5XLoRplc5sNPvkE+vaFkBDng00m+O9/ISHBsUSRUR+/RURERKqj0uRygOTMXF76bgevLNpBnyvr8EK/GIbNjGNXfBpjr23A8iM/lJjLp2+YToe6HRjTdgyvr3yd0W3G0j3yJsav2UVsw+BLms2nD8tjzLTnIDISvvkGrrnmov6/nU/z5iIiIlISza5JmRjdDfRqEcap1JwSWwTa7LDzRBo7/jpDws8zSPj+/eIv7m4k5MaH8buyV8GmGb8f4PPRHendMpzuTWuTYckjI8dKgLcHfp5Gcqx5WPJszIs7isVqK3KdTZO7gdHdGzJu/maOJmcxfNZGjAa4ISbcEfgzTvLz9i1YbfDuoNZFhv3ykP+BYXsxa6DmK66qXqq4Eyfgttvgjz+c9/XvD/fdV/FjEhERkWrlQrL5zhNpjO7WiJta1uG6ZrXp3CSALrMmlup+k/+YRP/m/fhgaBti6gZgtdldZvNtf6XStUmtIluOV4tsvnYtPPKI499jx8L7RXzW6du3HEcrIiIiIlVRWXI5OLL5oq0neLxXU+w4cub9Xetx3cfOXVBceSfuHb4b9B1Xh13N9Q37kGv14K07W136bD5mBHmta+M+cKBj6fVypnlzERERKYn+9pcy8/YwYrNn0y4qiPScwp1TzpeXlcrpBW/wv8NbXe7PFxoWzgcfz2e3vQ5z1hwuWIIoI8eK3Q6/7DzJ9BUHCgXbmAgzwztF0alRLVrVC6BTo1pFrrFpdDfQKzqMoR0imbPG0RbSaoPFf8YXOm5oh/pFFsiUl3M/MJSkuKp6qcLWrYOBA+Gvv5z3Pf88vPiiliQSERGRclGWbJ4vN8/GP/u14PutJ4hPT2TLyS2lutem+E2cyT6Dh9HIAx+vLzKbG92Kv85lnc1PJ8Dzz8HMmWd3TJ8Oo0ZBq1aXbJwiIiIiUrV5exjJys0kuo4/fp7GUmVzi9XGh/e2xejuxpns02XK5Xbs9G96K8t2JzBl6f5yy+bNEg6xN6Rekdnc/epRpRrjhdC8uYiIiJREf/tLmVjzbGRarHgZ3ZlwU3OmDLqG5eOv5aHrGhPgXbgNoCXhEPGzHye7hMIWU51meNw2kem73IkK8WHhw52JCvEhwNuDj0fG8voPOxn36Waniu3tx1MZ/+VW3l+2j6Zh/vx3yV4yLUUveeRjMvJMn2jeHdSamAhzoX0xEWbeHdSaZ/o0L7JApryc+4GhOBUxmS+XwCefQNeuzoUtPj7wxRfw8ssqbBEREZFyUZZsfi5PDwPDZ65l8Z8nOJWeWKZ7xqcl8t6y/cVm88Zh/vznl+qVze9pX48bV36LsUV04cIWcCxP9MgjYC++qEhEREREqi9rno26QT5MGXRNqbO5l8mdfy3eybj5mziRdrpM9zuTdYZP1hxh7NxN5ZPNezfnOzaxaPZj/N/vcwr2VcVsrnlzERGRmkudW6TUMi1Wluw8yfTlrjuoLHy4M8NmruVQYiaZu1dz+vt/Y8/NLvaavi17EXLjg7gZTQWhe0hsJLNHtmfh5uPEHUxkbtyRYq8xN+4IrSMD8XB349edp+jdMrzIYOtjMtK7ZTg9o0NJzbKSmp2L2csDs7cRk7uhwgJx/mR+bIMQpi13rqwf070RPaNDL/kHBilHViv84x/w1lvO+6KiYMECuOqqCh+WiIiIVE9lyebniokw4+FuYFd8Gs3C/QnxCS7Tff1NAaTnnClyf3XM5k9eYaXb5DG4u1puEqBpU3j2WXAr4bFYEREREamWLiSbO3K5Gz9ui7+wXO4ZwDebDhZ7TKmzeVISPvfdx5XffgvAQ2u+YOD9/THcfnuVy+aaNxcREanZlACkVDItVl5bvLOgbfi5zi1KmTW8LR3uGEvC8rnFXs/d3Z0m/R8ks8kNuJ03CTw37gjXRAYyODaSez5aW6rxzVp1iBn3tmXMJ+tLrNo2/h3GfUxGwgO8SnX9S6GqTOZLOUhKgrvvhl9+cd533XXw+edQq1bFj0tERESqpdJm89kj29N/yqqCJT8BRnSOYl7cEWx22HMyHV8PM63CW7E5fnOJ920d3hofo5nd8WnFHldtsjl5hPx3EsYnJ+OWm+t8kp8f/POf8OijYDJV/KBFREREpNJdaDa/2Fzu4eZfYi6HUmTzVatg0CA4erTQ5ohHx0D3DtC4cYn3KG+aNxcREZGiKAVIibIsVn7eftJlQD/XJyt2cdcdt3OqhMIWDx8zc75cQE6zG50KW/It3Z2AxWpjx4lUl/vPt/14KtmWPKw2O6lZRbdYrGrOnchvGuZPeIAXPiajAvrlZPt2aN/edWHLI4/ATz+psEVERETKTWmz+dy4I6w7lMSQ2LMtvYfERtIuKrigM2Kezc6vO5KY0GlCqe79aOyTzI87ga2E1XeqRTbfvo7wLu3weON114UtQ4bA7t0wfrwKW0RERERqqAvN5q5y+fy4EzwWO75U953QaQJz/jheYi6HYrK5xQIvvgjduzsVtgDw8MOObtSVRPPmIiIi4oqSgBQr02Ilw5LHjBUHij0uN/k48XOeZN3yn4s9zq9OA/4773uOeDUqNnyfTM3mTJaLSeRiJGda8PM0kppdtvNELspbb8H+/YW3mUzw0Ufwzjvg4XpNXREREZGyKm02zzdr1SGGdKjPlVeYmXzHVYzq1pCRs9YREehFu6ggouv4E+TjRc+oPoy6Zkyx1xrbdizd693E3Lhjpbr3ZZvNT5+G4cOhZ0/Yt895f6NGjqLmOXMgIqLChyciIiIiVcOFZPNhHaOKzOXz1/5F3yZ9GdOm5Fzeu0nfUudycJHNN22Cdu3gpZcgL6/wwbVqweLF8MYbYFTjfxEREalalE6kSFkWK2sOJFI/2LfYDipZBzdyesFEbDkZxV7vupv6MmXah9QLC+Z/qw4R4O1RqEX6udJzrAR4l60oIMjHRHqOFbOXigmkAr37LmzcCFu2OL4OD4evv4aOHSt3XCIiIlKtlDabn2v78VTcgA+HteWzdUdZuPk4H98fS7Ylj6RMC6H+ngT7mvhm41880/lfdKrXlf/GvVmoFXqr8FY8HjuegdH96ffOuiLz+/kuy2x+8iRceSUkJDjvMxodXVqefx68vSt+bCIiIiJSZVxoNrfZ7ViseU65PNjHhNnbAwNuPN35NTrUdZ3LH4sdz8Dm/UnPMpQ6l8M52dzN5sizr7/uXNQCjuXVVcQtIiIiVZiKW8SlTIuVTEseP/wZz53t6rk8xm63k7buW5KXzQK7rdjrBXQZwv6r7qLP1PXERJgZ3imKhQ935uVF20nNspKeY2XPyXTy/m7nsudkOmYvIzERZrYfL/kDQkyEGU8PA0aDG2ZvfVtLBfL1hW+/dTzt0KABfPMNXHFFZY9KREREqpHSZPOiJGda+H7rce5qF8nag0mM+nh9oXydn80NeOKb15kfBt1MWm4KpzOSCPYOwsdo5rO1J3Br7omvZ+ly9mWbzcPC4PrrYd68wts7dIAZMxyFLyIiIiJSo11MNk/KtHB9izos233KZS4f0SmK2IbB5Ka1Y/GgpWTkppKYmUyQVyDeRjPz406QVBe8PAxlnjdvt3sdoV0egL17nA9yd4d//hOefdbx3yIiIiJVlJYlEifWPBtrDyaSkpnLtuMpBPs4ryFvy80h8ft/k7z0o2ILW9xM3tS+9TkCOw/Czc3x7bb9eCrjv9zKBysO8t+7WvN0n2imDLqG5eOv5aHrGhPg7UHzcH9wg1HdGpZqzCM6RzEv7gijujXCpHU3paJFRcHSpbBihQpbREREpFyVJpsXx9/Lg4eva8KMFQcY/+VWpwnw/Gw+fflBboyJ4PgZG15utWgU1Jw/9trp/+46lu5OrDnZ/J13IDTU8d9mM7z3HqxapcIWEREREbnobB7sa+KztUeKzOVPfrmVacsPcHe7BmTnGMnODqCuXxMCPEP5bO0pNhxOwcvDyKm0rFJn84cauJPZdwAvT30Sg6vClquugrVrHcUtKmwRERGRKu4ym2mUimDJs/H91niSMi3sOZmOl8mdmAhzwX5r2mlOzv8HGduXFnsdY2AdwodOxqdJB5f758Qd5qft8SzZcZKe/17O6E82EBXiw8KHO/NEryZsPnKG1pFBDG4fWex9hsRG0i4qmNw8Oz2jQzFebhPocnlYuhT++qvo/S1bgpdXxY1HREREaoSSsnlxYiLM+Jrc+f7PE8yNO1Lssedm866TljJi1jp8Te58+1ANy+YhITB1KvTvDzt3woMPguEyew0iIiIicklcbDb38nDn30tcFJicY27cEX7aHs8XG47R992VdJ64lJH/W0/9EB+mDW2Dl4eBZbsTSp3NO67/jaarlzjvNBrhhRdg3Tq45ppSvQYRERGRyqZZOnGSmmUtqDzPs9mZF3eE4Z2iAMg+tpP42Y9jObG32GuENGtL+L3/xlS7frHHzVp1iCEd6mNwO/vU6IwVB2jfMISnv/6Tez+KY3T3hky+4yqnDwoxEWYm33EVD17bmP2n0nn8+ib4mC6jtudyebDb4e23HS3qb70VsrMre0QiIiJSgxSXzUsyonMUBoMbs1YdKtXxNSKb2+3w2Wfw9ddFH3PrrY5lJyMiKmxYIiIiIlL1XUw2H92tIQs2/YXNXvKxM1cdLMjlcDabv/XzHlKzrXy08lCps/nmW+/F1qRp4Ru0a+coannxRTCVrfuMiIiISGWqgrONUtlSsgpXns9Zc5iFD3emZdp6vp//KtisxZ5//9hH+NmvF26GktsYbj+eSrYlj2bh/uw8kQY4qtNbRwbS7+oI3l+2n/5TVjEkNpIP7m2Lm5vjQ4TZ24ifp5E8mx2T0UC3prUvv6dCperLzoYxY2D2bMfXa9c6vp41C9zcKndsIiIiUiMUlc2HxkYyp5huLENiI+nYsBZnMizsOJFa5HHnqvbZ/PRpR5b76isIDobOnSEszPWxynoiIiIicp6LyebXNQ+ly8TiO6Hnc5XLwdFtsVVkAENiI0udzbu2rIvh3XfgppugVi144w0YMULdCUVEROSypAQjTgK8C1een0nPovddI/j+/ReLLWwxeXrSa+zLjJrwYqkKW/IlZ1rw8yxcZ3XuU6MpWbm8v2w/XSb+RlK6hd92neRYciYe7m4E+pjwMRmr5uS5XN6OH4fu3c8WtuSbPRsWLqycMYmIiEiNc342T8nK5eVF23mqd/Nin9Ic1a0hb/y4k9MZljLdr9pm8++/dywj+dVXjq+TkhxLDtlL8eisiIiIiAjFZ/P/3NnKKZtfeYWZD+5tw+O9mnIiJYv0nOIfGj2Xq1wOLrL50n08O2oS7i+/XHQ2v/FGmD4d9uyB++5TYYuIiIhcttS5RZyYvY0Fleez7m4KP/yLndvXFXtOUO1wWo98FVNEU4J9ytbKMMjH5BTstx9PJTu3cHW6zQ5eHu4s3HKciT/uZmiH+jzTp3nVbHcul7c1axzt6E+ccN734ovQr1+FD0lERERqpnOz+cKHOzO4fSR7T6Wx91Q6BxIy+ODetgCcTsvB39uIj8nI4j9PMPD91YSZPZXN09PhyScdk/nn+/prWLkSunat+HGJiIiIyGWnuGx+NDmDz0Z1JDU7l/TsXGr5e+BhzCMxM5n9Z3YSYa7FsvEd+XTtCeasOUZKVm6x93KVy+Gcri5hfoSuXs5jK+fR+sRuAHa4NWSiMcJ1Nh81qlz/X4iIiIhUBpXoihOTu4HR3RqScHg3nTp24EgJhS3XtGvP3O9/Y8YTdzH9njYE+5qcqtSLEhNhxtPDwO74NKd9GTnWQtXp5x87Z81hft15CmuerQyvTqQEs2Y5OracX9ji6+v4BcgLL+jpBhEREakwbsCobg1Jycpl2My1jO7ekFHdGhLk48H7y/Zz8zu/s+FQEk3C/PAxufNXchbdmtRm4cOd6Rkdhp+XseZm8zVroHVr14UttWrBZ59Bly4VPy4RERERuSwVlc1DfD3IsVpJt6RjMKYQFmjnpwPf0u1/XWnwTiQdZrYm8r/16P95D2qHbmLBw22JCvEp8j7F5XLsdvJ++JG333mI2V+8UFDYAjDoyylgt1fNbC4iIiJSDvQbWnFidDfQMggS5/+DnOSTxR5bt8PNnO72NGO/OkDPfy9n9Ccb+OtMJqO7NSzVvUZ0jmJe3BFsLrqB1/bzJOOc6nRXx05bvh+LQrqUh9xcePRRGDkSLOe172/QAP74AwYOrJyxiYiISI2VmZNHj+ahDG4fyaHETPpPWcXmo2eo7e/F9dGhfPtQZ3Jtdm6f9gexr/3GrVNXF+TyJqF+mD2N3N+lQanuVW2yeW4uPP88dO4M+/Y57+/bF7ZtgzvvBDe3ih+fiIiIiFyWXGXzQ4kZhPi7ERa2mbu+HkBmbiYTlkxg8NeD2Ry/udD5m+M3M2LhUN744zn+N/JqArw9XN7HVdZ2s9votTeOr+c8SdPhd9D04Han8zod3kqnw1uAKpTNRURERMqRilvEJZtXAA8/9kSR+90M7gRfPwZDtzG4Gc+G8O3HUxny4Vo6NAxhaGxksfcYEhtJu6hg5sYdcdoXE2HGz8tYMNdc1LHbj6eSmlX6tUpFXEpMhJtugnfecd7XsyesWwdXXlnx4xIREZEaL8Ni5eDpDEZ3b8jkO66ibpA37y3dz/9WHWTS7VczffkBnvh8C9uPpxY6b/vxVB79dDNv/bKHXi3CGNy+hmTznTuhY0d49VWwnTeZ7+sLH3wACxdCWFjljE9ERERELlvnZ/Pm4f7cEBPE+J+fZNiCIXSJ7MLyQ8uZsWFGsdeZvmEqy4/+yJDYuk77zs/aJmsud275mV8+fJAPv36Fa47vdjoH4GhQHZ7s8xhxkY45zCqRzUVERETKWRVbEF2qCl9Pd34xdaZl5xvYturnQvv8A4Lw6TMer8irXJ6bkpXLndP/4OsHO9OmfjAfrjxQaLI9JsLMiM5RtIsKZtjMtS7XFx3ROYrkDAsxEQGM6Nyg2GNTs3MJD/C6yFcsNdbWrXDLLXDwoPO+xx6DN98Eo35UioiISOXwMRnJzbPTf8oqhsRGMuPetmRb8jAZDfy+N4F5a52LUc714cqDtI0K4vHrm3JN/UBmrTpUPbO53Q7vvQfjx0N2tvP+jh3h44+hceOKH5uIiIiIVAvnZ/Op97Tmx/3fMG3DNNzd3BndZjT95/cv1bXejpvMwrt+Zdryg9jszrncnpzMmM0/MmLDQsLSk4q8Tm79KN5qfwcf1u+M1b3wHKbmzUVERKS60W9sxSWztwf1Qnz5s/0DeOzZQ27CIQBatLyS2gOf45DFt9jzDyVmMnbOBqbf04brW4RxJstCQloOYWYvPNwNzFp1kFcW7XQ5IZ5fne5hNPB/NzRl9urDRR4LYPZy3b5RpERffQX33guZmYW3m0wwfToMH14pwxIRERHJ5+PpjtVmp26QN+8v28+05fuJruPPzOHtmbb8QKmu8e5v+/hgWFvOZFj4bFTH6pfNExIcS0suWuS8z2iEl16CCRNUsCwiIiIiF+XcbD59xQEGdwhj0qqJAMSExpCZm8mWk1tKda1N8ZvALYOfH++Gu8FAbT8TK/edZsibPzLq51ncuv03/CxZRZ5vrR/FLwPv5zn/1iRaXKwriubNRUREpPrR7J645OHuxgNdG/LY8VRq3/oc8bMfJ7R5Gz79+lP6TV9fqmvEHUwiy5LHlxsOs3jbCfw8jZi9jPyzXwwNa/tSN8i70KT4udXpry7awSu3tKTzG79hLWZp0JgIM2ZvfRvLBbDbYcYM58KWOnXgm28gNrZyxiUiIiJyDpO7gcSMbIZ3imL8l1ux2cFmh/RsKztOpJZ8ARwtybMsedx8VQSvL97JxqPJ1Subb93qurAlOhrmzIFrrqn4MYmIiIhItXNuNp+56iAZuakFxSxmTzOnM0+X6XqnMpL4Zm0y6w8nk5Obx6wR7Xn8llZcN/WPIgtbrDEt+W/rAcyo0w6LwQhFFLZo3lxERESqI6UbcclkdOf6FmEMbh/JvLUQPuw/dLy6OdluZav2TsqwsGJvAjtPpBVsW3+4cEv15EwL4QFeeHu489FKx1OjL/eP4eM/Dhc7eQ4wpnsjTO6GC3mJUtO5ucH8+dCuHRz4+6nn2Fj4+muIiKjcsYmIiIj8zehuoEEtP2r7ef2dzY/g52kkKdNSputk5Fg5kpjB3POWMaoW2bxnT0dnlkmTzm579FF4/XXw9q748YiIiIhItXRuNk/OsJCYeXa5oNScVGr51CrT9Xw9Alh/+HDB3Hn+ckfXjxkNk14vdOzhq2NJGDMO7769mTJldYnX1ry5iIiIVEcqbpEi5eTaeLpPc9o3COaD3w+QYckj2MdUpmv4eLqTnmMttC0lK7egpXqzcH/8PI2Emb14uk8005bvZ3BsfXq1CGPb8ZRirz20Q316RodiVEiXCxUcDAsXQocOcPvtMHUqeGkdWhEREalafExG7HZ48sZmtKkfxNLdp8qcy2v7eTLdxTJG1Sabv/IKLF0Khw/Dxx/DjTdWzjhEREREpFrLz+Z3tY/kTHZ8wfbtp7bj4+FDq/BWbI7fXOT53ha4bSfcmFobn3FmdseffSg0P5t/k9GMlQZ33Ox2NrfvQd3XX+DDlECe7tMcgKEdIpmz5khRt6j8bC4iIiJyiai4RYqUkJ7N/bPX88otLflsdEdy82yY3A3ERJjZfrzkFugxEWY83A2FAvq5bHYKdXT5vxuaMmt4O9o1CMbHZOTx65tyVd1Api3fX+h+MRFmxnRvRM/oUHxM+haWixQTA5s3Q8OGjm4uIiIiIlXQ8TOZ/LAtnns7RtGnZTg2KFMu9/My8sO2E0Uec9lkc7vddWYzmeDzzx2FyuHhl34cIiIiIlJjHT+TyY/b4xnR+YqCYpY8ex7TN0xnXPtxjFw4stDxBht0OQJDt8Jd28BsAUhgxjdrsLlYVeiEXy0ev/kJ1tVtwQlzbVa2uoZn/Ex4ezjy9jN9ooltEFL52VxERESkginhSJECvE0cTc7ixYXbmT2yPesPJ5OencvwTlGM/3JrieeP7taQeXFHXAZ0VyxWO50b1yqoKPcxGendMpye0aGkZllJzc7F7OWB2duIyd2gynMpHbsdPvgA7rgDgoJcH9OoUcWOSURERKSM/L1MfL3xLwa0uoJ1h5LJyCl9Lr+vSwPSsq2lzuVQBbN5ejqMG+dYUnLsWNfHREVd2jGIiIiIiODI5l9t+ItB7evxZMcJDP1mMABT101l3QPrGNVmFB+sm0Gno3Dndrh9B0Sku7jQ/z6BTkNc3mNhi+4F/52Zk0fdoLO/yqn0bC4iIiJSSZRypEhmbyOxDYL5eGQs05cf4P8+38K/f9lL+wbBDG4fWey5Q2Pr06N5KHPjim6PeL4Abw+n4G10N+BjMhIe4EXTMH/CA7zwMRkV0KV0srLg3nth9Gi4+26wWks+R0RERKQKMnsbmfdAB6YvP8CTX5Qll0dyQ0w4dnsZKluoYtl8yxZo0wZmzYInnoBt2y7t/UREREREipGfzacuPUjnK25kVJsxAKRlJPPPF7szcZGFtKmBrJwF49YWUdgC9N38CwZbXinu5+G0TfPmIiIiUhOpc4sUyeRu4J/9ook7mMi8tY4ilZSsXIbNXMvske25pn4gs1Ydctn68LpmoeBmp26QNylZuSXeKybCjNlb345Sjo4dg4EDYf16x9c//wxPPw1vvlm54xIRERG5AEaDG2sPJpUpl4/u3oiezUPJyrUS6GMq0zJGVSKb2+3w4YeOji3Z2Y5t2dmOouV168Dbu3LHJyIiIiI1Un42/2jVQdZu3MvM8Nb844+OhPy+FnPmX8D/ij3f7ubGxmbtmNX0WkpaJL3KZHMRERGRKkCpSIpkdDcQFeLH+C/+LLT9UGIm/aesYkhsJDPubUu2JY/kTAshfiZC/b3wNDpaH1qseTzQtSGPfba5xHuN6toQo6GkKC9SSqtXw623wsmThbe/9RYMGwYtW1bOuEREREQukNVmZ8aKA4W2lZTLPdzdMBnd8TQa2H0ytdTLGI3p3ghTZT/xmZ7uWH5ozhznfUeOOLq5dOhQ8eMSERERkRrv3Gz+yNw3qL13DbVLc+JVV5F3513su3EABzCz6HLJ5iIiIiJVhFKRFCst28qOE85Pd6Zk5fL+sv10nfgbD8/fyMQfd/Hg3I0kZ1rItdkAR8hvF1Vyq/QhsZG0jQrGaitbq3QRlz78EK691rmwxc8PvvlGhS0iIiJyWUrNKlsuP5WWzer9p8m0WLHk2Xhp4c5SLWM0JDaSHs1DK7ed+fbt0L6968KWdu1g0yYVtoiIiIhIpTk3m//WsG2xxx6s04DkfzzH2sW/k7luAznjJ/D8+tTLJ5uLiIiIVCHq3CLFSsmyFLvfZoedJ9IKvo5PyeaHbfEM7RBJapaVIR+uKbZV+ojOUbSLCmbIh2v4dFRHfEz6lpQLlJsLjz8O773nvK9xY/j2W4iJqfBhiYiIiJSHsubyxHQL7y3dz7a/0ri9zRWsPZRU4jJG+dk805KHr2cl5fKPP3Z0bMnMdN736KMwaRKYTBU/LhERERGRv52bzX9r1M5p/7awRixp3J7vm3Vhb+36fDm6I2/+uIvuKw9dXtlcREREpIpRKpJiBXiXbeI4yMfE1xuP0aKOmVp+piJbpQf5mPD0MDAv7givLNpJSlYuqdm5hAd4XaJXItVaQgLceScsW+a874Yb4NNPISiowoclIiIiUl4uJJen51hZvucU1zZzNEkvbTb/YkxHavt7XoqXUbSsLHjkEfjoI+d9ZjPMnAm33VaxYxIRERERceHcbH7KP4S4ujGkefrwW+P2/NawHfHmWoWOv+yyuYiIiEgVpeIWKZbZ20hMhLlQ5XhRYiLMeHoY2B2fxvy1R3ju5mjgbKv0acv30yzcHz9PI+k5VnbHp3HuSkRmL49L9TKkOtu8GW65BQ4fdt735JPw+utg1I86ERERubxdaC5vFu6Pr6d7wb7SZPMKz+V79sAdd8DWrc77WrWCL75wdOITEREREakCzs/mdw1+A9zcXB572WVzERERkSpMizVKsUzuBkZ3b1iqY0d0jmJe3BFsdvhhWzx+Xh7ERJgL9ue3Sl93KJmdJwoXtsREmDF7qwBByujzz6FzZ+fCFk9P+OQTePNNFbaIiIhItXChuXzPyXRMRvdCuRyKzuYVnss/+wzatHFd2DJmDPzxhwpbRERERKRKccrmRRS2wGWWzUVERESqOBW3SLGM7gZ6RYcxtENksccNiY2kXVQwc+OOAJBns5ORYy31BPyY7o0wuevbUUrJZoNnn4W77oLMzML76taFlSth6NDKGZuIiIjIJXAxuXxe3BFGdauCufyvv2DYMEhPL7zd1xfmzYOpU8FLy5aKiIiISNVSLbO5iIiIyGVAyUhKZHI38HTvaN65u5VTVXlMhJnJd1zFqG4NGTZzLSlZuYX2lybkD+1Qn57RoRgV1KW0LBb45Rfn7Z06wbp10LZtxY9JRERE5BLzMRl5pk807w5qXaZcPmfNYXo2r4K5/Ior4N13C29r2RLWr4dBgypmDCIiIiIiF+BC58yrbDYXERERuQyop50UK9NiZcnOk8xdc4Q29YOYcW9bsi15pGbnUtvfk0BvD+avPUL/KasKhfSYCDP+XsaCCfjYBiFMW76/YB3S/GPGdG9Ez+hQfEz6VpQy8PKCb75xFLHExzu2PfCA45cjnp6VOzYRERGRSyTTYmX5nlMkZuQU5PLkTAshfiYCfUzYbHaGz1rLocTCne3qBnnjbqBq5vL774cVK2DOHBgxAqZMAR+fih2DiIiIiEgZXOicOVTxbC4iIiJSxSkdSZEyLVZeW7yTOWscbRPjDiYxbfl+moX74+dpJD3HSpvIIB7o1pApS/cXOvfclok+JiO9W4bTMzqU1Cwrqdm5mL08MHsbMbkbVH0uF+aKK+Drr+H66+HNN2HMmGLXtxURERG5nJ2fzV/+bkehXL47Po1B7SOZMvgap0n0Md0b4fF37q5yudzNzbH8UP/+cMcdFX9/EREREZEyuJg5c6ji2VxERESkilNxi7hkzbOxZOfJgpCez2aHnSfSCr7eeSKNVpGBDImN5P1ljrDuqmWi8e9A7mMyEh7gVTEvQqq/jh3h4EGoXbuyRyIiIiJyybjK5ufncoC5cUdoXUI2r5RcnpwMS5YUXbzi56fCFhERERGp8i5mzhyqSDYXERERuYyp/FdcsuTZmLbsQKmOnbXqEEM61OfKK8y8O6g1z/RprpaJUj6ysuD55yEzs+hjVNgiIiIi1dxlnc03b3YsJXnXXfDzz5U3DhERERGRi3Qhudzg5lhqqEpkcxEREZHLnJKUuJSaZWXHidSSDwS2H0/F4Aafj+mIh0EtE6WcHDkCAwfCxo2wbx/Mm6dlh0RERKRGumyz+ccfw+jRkJ3t+HrwYNiwAerXr7wxiYiIiIhcoLLmcjdg7TO98PF011JDIiIiIuVAaUpcSsmylOn4tGwr3h5GBXQpHytXQrt2jsIWgE8/hUmTKndMIiIiIpXkssvmOTkwdiwMG3a2sAUgMREefLByxiQiIiIicpHKmsvTc6zU8vfEx6R5cxEREZHyoEQlLgV4m8p0vNnL4xKNRGqcGTOgRw84darw9jfegKSkyhmTiIiISCW6rLL50aPQrRtMm+a8r0MHmD694sckIiIiIlIOLqtcLiIiIlINqbhFXDJ7G4mJMJfq2JgIM2ZvrXAlF8licTzhO3o05OYW3te0KfzxBwQHV87YRERERCrRZZPNlyyBa66BtWud9z30ECxfDnXrVvy4RERERETKwWWTy0VERESqKRW3iEsmdwOjuzcs1bFjujfCpLaKcjFOnYJevVw/4du7N8TFQfPmFT8uERERkSqgymdzmw1efx1uvBFOny68z9sbPvkEpkwBU9medBURERERqUqqfC4XERERqeaUrsQlo7uBXtFhDO0QWexxQzvUp2d0qNYMlQu3cSO0bQu//+6876mn4LvvIDCwwoclIiIiUlVU6WyekgK33grPPOMocjlXo0awZg0MHVpx4xERERERuUSqdC4XERERqQHUF0+K5GMy8kyfaGIbhDBt+X62H08t2BcTYWZM90b0jA7Fx6RvI7lAn34KI0dCVlbh7d7e8NFHMGhQ5YxLREREpIqpktl82zYYOBD27XPe178/zJ6tImURERERqVaqZC4XERERqSGUsKRYPiYjvVuG0zM6lNQsK6nZuZi9PDB7GzG5G1R9LhcmLw+eew7eeMN5X7168O23cM01FT4sERERkaqsSmXzb791dGTJyCi83WCAV191dOAz6LOCiIiIiFQ/VSqXi4iIiNQgKm6REhn/DuQ+JiPhAV6VPRy53KWkODqy/PCD876uXeHLLyE0tOLHJSIiInIZqDLZ3N3dubClVi2YPx969aqcMYmIiIiIVJAqk8tFREREahCVEItIxTtwwHnbmDGwZIkKW0REREQuB/36wUsvnf26XTvYsEGFLSIiIiIiIiIiInJJqHOLiFSsgABYsABiYx1dXIxGmDIFRo+u7JGJiIiISFk89xxs3AjBwfD+++ClJ1ZFRERERERERETk0lBxi4hUvGbNHC3rR46Ezz93LEckIiIiIpcXgwE++wxMJnBzq+zRiIiIiIiIiIiISDWm4hYpNYs1F4vNQnJWMklZyQR7BxHkHYjJ4InJ6FHZw5PLTe/esH8/+PhU9khERERELjsVks3z8uD556FpUxg+3PUxnp7lcy8RERERkcuUNc+GJc9GapaVlCwLAd4mzN5GTO4GjO6Gyh6eiIiISLWh4hYplXRLBgt2LWTyH5PYHL+5YHur8FY82XECA5r3x8/kW3kDlKrn8GGYNAn++1/wKOIXLCpsERERESmzCsnmyckweDD8+KOjgCUmBtq1u7hrioiIiIhUM5kWK0t2nmT68gNsP55asD0mwszo7g3pFR2Gj0m/hhEREREpDyoblhKlWzJ48ucnGfrN4EKT5wCb4zcz9JvBjP95POmWjMoZoFQ9y5dD27bw/vvwxBOVPRoRERGRaqNCsvn27Y5Clh9/dHydkwO33gonT174NUVEREREqplMi5XXFu9k3PzNhQpbALYfT2Xc/M28tngXmRZrJY1QREREpHpRcYsUy2LNZcGuhUzfMK3Y46ZtmMrC3d9hseZW0MikSrLbYepU6NULTp92bJsyBT78sHLHJSIiIlINVEg2X7QIOnRwLB95rvh4WL267NcTEREREamGrHk2luw8yZw1R4o9bs6aw/y68xTWPFsFjUxERESk+lJxixTLYrPw5uqJpTp28upJWGyWSzwiqbIsFhg9Gh58EKznPY3w7rvO20RERESkTC5pNrfbYfJk6N8f0tML7wsLg6VLYeDAMoxWRERERKT6suTZmLbsQKmOnbZ8PxYVt4iIiIhcNBW3SLGSs5LZcnJLqY7dFL+JM1nJl3hEUiWdPAk9esAHHzjvu/lmWLECjFpbVkRERORiXLJsnpMD990H48c7ilzO1b49rF8PXbqUcbQiIiIiItVXapaVHSdSSz4QxxJFqVl68E9ERETkYqm4RYqVVMZilaTsM5dmIFJ1rV8PbdvCqlXO+555BhYsgICAih+XiIiISDVzSbL5qVOOJSVnzXLeN2QILF8OdeuW6b4iIiIiItVdSlbZOpinZl/AkqEiIiIiUoiKW6RYwd5BZTveK/DSDESqpjlzoGtXOHas8HZvb/j0U/jXv8DdvXLGJiIiIlLNlHs2//NPR2eWlSsLb3dzg9dfh08+AS+vsg1SRERERKQGCPA2lel4s5fHJRqJiIiISM2h4hYpVpB3EK3CW5Xq2NbhrQn0Dryk45EqIi/P0bb+nnsgO7vwvvr1YfVquOuuyhmbiIiISDVVrtl84ULo1AkOHy683dcXvv4a/vEPR5GLiIiIiIg4MXsbiYkwl+rYmAgzZm8t2S4iIiJysZSoLpGcnBy+/OprVq5cSULCafz8/LjmmtYMGTyIkJCQyh5eqZkMJp7sOIGh3wwu8dgnO03AZPCsgFFJpUpOhrvvhp9/dt7XvTt88QXUrl3x4xIREREpgrL5ed5+Gx5/HOz2wtvr1YPvvoOrry6H0YqIiIiIOKs22dzdwOjuDRk3f3OJx47p3giTu54zFhEREblYSlSXgMVi4fl/vsDnn39BVlY27du3o1atWvz66288/sT/ER8fX9lDLDWT0YMBzfszus2YYo8b02Ys/Zv1w2RUe8Vqbc8eR+t6V4UtDz0Ev/yiwhYRERGpUpTNXbDZnAtbOnaEdetU2CIiIiIil0x1yuZGdwO9osMY2iGy2OOGdqhPz+hQjCpuEREREbloSlSXwOdffMnu3Xto1qwZU9+fwoTxTzL5zYmMHDGclJRU3n33vcoeYpn4mXyZfMNk5t4636kNeqvwVsy9dT5v3vAmfibfyhmgVJyAAOdliDw84IMPYMoUx3+LiIiIVCHK5i489hg88sjZr++9F5YuhbCwSzJmERERERGoftncx2TkmT7RvDuotdMSRTERZt4d1Jpn+jTHx6QG+iIiIiLlQamqnOXm5rL4+8UAjBn9AN7e3gX7Bgzoz29Ll7Jt+3b27dtP48aNKmuYZeZn8uX26Nvo36wfZ7KSSco+Q7BXIIHegZgMnurYUlOEhcE330DXro4il7Aw+Oor6Ny5skcmIiIi4kTZvAhubvCf/8DRo9CuHTz9tGObiIiIiMglUl2zuY/JSO+W4fSMDiU1y0pqdi5mLw/M3kZM7gZ1bBEREREpRypuKWc7d+0iIzOT8PBwGjZs6LS/U8eOHDp0mHXr1l1WIR0cbdBNeOBn8qVuQN3KHo5UlrZt4cMP4b//dRS61NX3goiIiFRNyubFcHd3FCkbNNkuIiIiIpdedc7mxr+LWHxMRsIDvCp7OCIiIiLVlmYyy9mhg4cAaOQioAM0auTYfujw4Yoakkj5GzIE/vhDhS0iIiJSpdX4bL5iBaxcWfR+FbaIiIiISAWp8dlcRERERC6aZjPLWcLp0wCE1ApxuT8kxLE94VRChY1JpMx++w3uvx9stqKPMarxk4iIiFRtNTqbz58P118PAwbA7t2VPRoRERERqeFqdDYXERERkXKh306Xs+ysbAA8PT1d7vfycrQlzMrOKtX10tMzyMjIcNp+8uRJAE7//aFApHzYYfZseOUVyLNBaBg8+mhlD0pEROSylJ/T8vLyKnkkNVfNzOZ2mD4d3pgI7kbIynYUuHz9NYTUquzBiYiIiFQKZfPKVzOzuYiIiIic72KyuYpbqrjvvvuOTz/7vMj9zzz7XAWORmqMntc7/r1jJ4weU7ljERERucwlJycTEVGnsoch5eCyyuY33Fj462eq0NhEREREKomyefVxWWVzEREREXFyIdlcxS3lzMvbUWGek5Pjcn92tqNC3dvLu1TX69evHz169HDabrFYOHnqFFdE1MFgcL/A0UpJTp8+zTPPPsdr/3qVWrX0pGtNofe95tF7XjPpfa95Kvo9t9nySEpKpkmTxpf8XuKasnn1op/bNZPe95pH73nNo/e8ZlI2r3mUzasX/eyumfS+1zx6z2sevec10+WUzVXcUs5q//2GJ55OdLk/MdGxvXZo7VJdz8/PFz8/X5f76tWrewEjlAtRq1YtwsJCK3sYUsH0vtc8es9rJr3vNU9Fvud16uip0MqkbF496ed2zaT3vebRe17z6D2vmZTNaw5l8+pJP7trJr3vNY/e85pH73nNdDlkc0M5j6PGi2oQBcD+Awdc7t+/37E9qn79ihqSiIiIiEiNpGwuIiIiIlI1KJuLiIiIyMVScUs5i27eHF8fH+Lj4zlw4KDT/tV//AFAu3btKnpoIiIiIiI1irK5iIiIiEjVoGwuIiIiIhdLxS3lzMPDgz439wFg+owZBWuFAixYsJBDhw7TMiaGxo0bVdYQRURERERqBGVzEREREZGqQdlcRERERC6WsbIHUB3decftbNmyhV27djNm7EO0aBFNQkICe/bsJSDAzCOPPFTZQxQRERERqRGUzUVEREREqgZlcxERERG5GOrccgmYTCZefeVl7rzzDjw9PYmLW0tCQgI9elzHv9+aTHh4eGUPUUrJ19eXu++6E19f38oeilQgve81j97zmknve82j97xmUjavPvRnuGbS+17z6D2vefSe10x632smZfPqQ3+Gaya97zWP3vOaR+95zXQ5ve9udrvdXtmDEBERERERERERERERERERERFxRZ1bRERERERERERERERERERERKTKUnGLiIiIiIiIiIiIiIiIiIiIiFRZKm4RERERERERERERERERERERkSpLxS0iIiIiIiIiIiIiIiIiIiIiUmWpuEVEREREREREREREREREREREqiwVt4iIiIiIiIiIiIiIiIiIiIhIlWWs7AGIXCo5OTls2rSZdevWs2PnThISEjAYDNSpE07Hjh0Z0L8f3t7eLs/99dff+OHHHzl69BhGo5FmTZtyx523E928eZH327lzJ1988RW79+zBarVSr15d+vTpTY/rrrtUL1FKITU1jYcfeYSUlFTCw8OZPu39Io/V+375S0lJ4euvv2HtuvWcPn0ak8lEaGhtrrrqKkYMH+Z0/Nq16/h2wQIOHDgIQKOGDRk4cABt27Yt8h5Hjhxh/qefsW3bdrKzs6kTHk6vXj3p2/dmDAbVjFakvXv38s03C9ixcyepqal4eXpSv359evbqQc8ePXBzcyt0fF5eHou+X8yvS37lRHw8Xl5eXNmyJYMG3U29enWLvM+FfJ/Ihdu3bz+bt2xh79697N27l8TEJAAWfPt1sedV1M/w06dPM3fefDZt2kx6ejq1a9eia9eu3H7brZhMpgt70SI1gLK5gLJ5TaNsXrMom1dPyuYi1ZOyuYCyeU2iXF7zKJtXP8rlZ7nZ7XZ7uV5RpIr4+ZdfeO+9qQDUrVuX+pGRZGZlsmvXbrKysqhb9wr+9eorBAYGFjrvww8/4rtF32MymWjdqhWWXAtbt/6J3W7nqQnj6dAh1uleq1f/wZuT38JutxPTogX+Zn+2bv2TjIwMbhnQnxEjhlfAKxZX3n77XZYuW4bdbi82pOt9v/zt27efF196mbS0NCIj6xEZGUlWZhZHjx7ldGIi33z9ZaHjFy78jo9mzsLd3Z2rr74KD6MHmzZvxmKxMOqB+7n55j5O99i1azfP//MFLBYLTZo0ITS0Njt27CA5+QydO3Vi/Pj/cwqGcmnk//mz2Ww0atiQ8DrhpKamsmPHTvLy8ujWrSv/98TjBcfbbDYmTnqTNWvi8PX15aqrriQtNY3tO3ZgMpl49ZWXadq0idN9LuT7RC7Oa6+9QdzatU7biwvqFfUz/MSJE0x46mlSU1OpHxlJ3Xp12b9vP/EnTxId3ZxXXn4JDw+Pi3r9ItWVsrmAsnlNomxesyibV1/K5iLVk7K5gLJ5TaFcXvMom1dPyuXnsItUU7/++pv9vffetx85crTQ9sTERPtjjz9h7z9goH3y5H8X2rdp82Z7/wED7UOG3mv/66+/Crbv3LnLfuttd9gHDR5qT0tLL3ROamqq/e5BQ+z9Bwy0r179R8H25ORk++gxD9r7Dxho37r1z0vwCqUkmzdvsfcfMND+3nvv2/sPGGgfNXqsy+P0vl/+zpw5Yx96zzD7HXfebV8TF+e0f/fuPYW+PnrsmP2WgbfZb7v9TvvOnbsKth879pd9yNB77QNvvd1+/PjxQufk5ubaHxg1xt5/wED7twsWFmzPzMy0j5/wD3v/AQPtS5b8Ws6vTFyxWq32e+4dbu8/YKB92bLlhfYdOXLUPmToPfb+Awbat2zdWrD9p59/tvcfMNA+ZuyD9uTk5ILtq1attvcfMNA+esyDdqvVWuhaF/J9Ihfvy6++ss+ZO88eF7fWnpSUZL/t9jvt/QcMLPL4ivwZ/tQ/nrb3HzDQPuODDwu2Wa1W+2uvv2HvP2Cgfd68+Rfz0kWqNWVzUTavOZTNaxZl8+pN2VykelI2F2XzmkG5vOZRNq++lMvPUi8oqbZ69LiOBx8c69QyKzg4mNGjRgHwx5o15ObmFuxbuOA7AO6843YiIiIKtjdv3oybbrqRjIwMlixZUuh6v/yyhMzMTGLbt6djxw4F2wMDAxk27B4AFixYWL4vTkqUk5PD+1OnUa9ePW65ZUCxx+p9v/zNn/8pqampDB92L7Ht2zvtP7+yeNF3i7DZbNx04w00b96sYPsVV0Rwxx23k5eXx3ffLSp0zpo1cZw8eZIGUVEM6N+vYLu3tzejR90P6D2vKMeOHSMlJYUrrriC7t27FdpXr15dunfvDsC+vfsKti/4+8/5sHvvLfTkUadOHWnfvh0nTpwgLq5w5fOFfJ/Ixbvt1lsZMngQ7du3IygoqMTjK+pn+J49e9m5cxcBAQEMH3ZvwXZ3d3fGjhmN0Whk0feLycvLK/uLFqkBlM1rNmXzmkXZvGZRNq/elM1Fqidl85pN2bzmUC6veZTNqy/l8rNU3CI1UoMGUQDk5uaSlpYGOELd1j//BBw/tM/XqaNj27p16wttX79hQ5HntG3TBpPJxJatW7FYLOU2finZp599zsmTJxk7ZjTu7sYij9P7fvnLyclh2fIVeHl50bNnj1Kdc/b96+S070Le80aNGhEeFsbhI0c4efJUmcYvZVfaFnb+/v4AnDx5kmPHjmEymWjbto3TcSW/56X/PpGKVZE/w/PPadeurdP3YGBgIC2io0lPT2fHjp0X8YpEaiZl8+pP2bzmUDaveZTNJZ+yuUj1oGxe/Smb1wzK5TWTsrlA9c/lKm6RGik+Ph4Ao9FY8EP8r7+Ok5ubS0CAmVq1ajmd06hRQwAOHT5caPuhg4cAaPj3/nN5eHgQGVkPi8XC8ePHy/MlSDEOHTrEggUL6dmjBzExLYo9Vu/75W/fvv1kZWXRsEEDPD092bBhIx/NnMW0adNZuPA7EpOSCh2fnp5BQsJpABo2bOB0vdq1a2E2mzmVkEBmZmbB9oL3vKHzew5nvxcOHT5UDq9KihMWFkZ4eDh//fUXy5evKLTv6NFjLF++HD8/v4I1Iw/+/d7Vrx+J0ej8ob2Ri/fuQr9PpGJV5M/w/HMalfgz4LDL/SJSNGXz6k3ZvGZRNq95lM0ln7K5SPWgbF69KZvXHMrlNZOyuUD1z+VFl2WKVGPfLfoegNatWxVUkiWcTgAgJCTE5TleXl74+vqSnp5OZlYWPt7eZGZmkvH3D+daRZwXEhLCvn37OXUqgaioqHJ+JXI+m83GlPfex9fXl2HntMAqit73y9/RY0cBCAgI4LXX3iBubeEWeZ/MmcsjDz9Et25dATj993vu5+eHl5eXy2uGhISQmpr69/tXH4CE047AVqtW0e85QMKphIt8RVISd3d3Hnv0EV559TX+/Z//smDBQupE1CElJYUdO3ZSr15dHh33SMEkTP57V9Sf84L3LuHse3eh3ydSsSryZ3jBvYr4GVCr4GeAnkQRKStl8+pL2bzmUTaveZTNJZ+yuUj1oGxefSmb1yzK5TWTsrlA9c/l6twiNc769RtYsuRXjEYjQwYPKtienZUNgKenZ5Hnenk59mVlZTn+nZ1dsK+o8/J/uOefI5fW998vZu/efQwfdi9ms3+Jx+t9v/ylp2cAsHbdOjZu2sToUQ/w8exZfDBjGrfcMgCLxcLb77zLgQMHgbPvn6enqchrFrzn2Wffv+zs4r9X9J5XrOjoaF771yuEh4Wx/8ABVq5cxZ9/bsPNzY1WV19NWFhYwbHZf78nJb93Z/9sX+j3iVSsivwZXtLPAM+C+2S73C8irimbV2/K5jWPsnnNpGwuoGwuUh0om1dvyuY1i3J5zaVsLtU9l6u4RWqUY8eO8Z//vo3dbmf4sHtp0MC5ZZZcvhISEpgzdx4tY2JKvY6kXP7sNhsAeXl5DB50N3369CYgIIDQ0FBGDB9G506dsFqtfPPtt5U7UClXK1b8zpPjn6JWrVq8OWkin306j6nvT6Fnj+v4dsFCnn/+BXJzcyt7mCIiUgxl8+pN2bxmUjavmZTNRUQuf8rm1Zuyec2jXF5zKZtLdafiFqkxEhMTeemlV0hPT2dA/37069e30H4vb0fFWU5OTpHXyM527PP29nb8+5yWW0Wdl1+1ln+OXDrTp3+A1Wpl7NjRpT5H7/vlz+uc/8euPpzlb9u+fTtw9v3LybEUec2C99zr7LW9vIr/XtF7XnGOHz/O2++8i9nsz3PPPUPTpk3w8vIiIiKCBx8cS7u2bdl/4ABLlvwKnP0eKfm9O/tn+0K/T6RiVeTP8JJ+BuQU3Md1O04RKUzZvPpTNq+ZlM1rHmVzyadsLnL5Ujav/pTNax7l8ppJ2Vyg+udyY7ldSaQKS0tL44UXX+ZUQgI9e/ZgxIjhTsfUrlUbcIR5V7Kzs8nIyMDPzw+fv//g+vj44OvjQ0ZmJqcTE4n08XE6L/96oaG1y+nVSFHWrV+Pr68vU6dOL7Tdkuv4SzYpKYlnn30e/r+9e4+Lus73OP7mJkcx8TbgBQQtL6CI5gW89ViRm6ZZlrfKbatz6+o5Z8vafZyze0rPtm1Za5rHSiutk+XdtM0bWLkKIoYXQBEFUzMJUpBBbByY8wcySDMoF2WGmdfz8eAPvt/f7zvf33yHH295fPz+JD333H+oQ4cOrLsLCDBUvce+vr7y9/e37Q8IkCSVlFyUJHW+uuZGo1GXL1+2+1xIe+tn6NxZRqNRRUU/2X0ebPU5Btb8ltu16+8ym80aPHiw3X8YjRo1UvvS05WVna3x4xNl6NxZUt0/59a1M9SsXWM/J2hezXkPN3Q2KC8vXz8V2X+tIus9IKDxFwS4CbK5eyCbuyeyufshm6Ma2Rxomcjm7oFs7n7I5e6JbA7J9XM5O7fA5ZWXl+ull+fp9OnTGhEdraeefEIeHh42x3Xv3k0+Pj4qKblo9wf+xIk8SVJoSEit9tCeoZKkvKv91zKbzTp16rRatWqlbt263YSrwY2UlZUpMyur1texY7mSJJPJZG0zmaq2XWPdW75evaq2STWZTHa30zMaSyXVVJC2besng6EqtFU/U/RahYVFunjxogIMBrW55he4dc3zbNdcqvkshIaENu5CUG/VP6t+dgKWJLXxq2o3Go2SpJ5X1+67707JbDbbHH/Czto19nOC5tWc9/Dqc07c8B4QYrcfQBWyuXshm7sfsrn7IZujGtkcaHnI5u6FbO5eyOXuiWwOyfVzOcUtcGlXrlzRn/70Z+Xm5mrw4EH67W//XV5eXnaP9fX11cCICEnS7t17bPr3pKRIkoYNG1qrfeiQIVX9e1JsztmXni6TyaTIgQPVqlWrJl0LbmzjhnV2v959Z4kkqUuXLta2wMCqKkHWveUzGAzqGRoqi8WizMwsm/7qtl7XPCu4Zv1uzprn5eXpXEGBQnr0sH62cOu079BBknT8xAm7/cdzj0uq+R8IgYGBCgoKkslkUnr6fpvjb7zm9f+coHk15z28+px9+9Jt/ihQXFys7CNH1LZtW4WF9WvCFQGujWzuXsjm7ols7n7I5qhGNgdaFrK5eyGbux9yuXsim0Ny/VxOcQtcVkVFhV6f/4YOHT6s8PBw/e7FF+Tj43Pdc+6ZPEmStGr1Gp09e9bafvRojrZu3SY/Pz/FxsbWOicuLlZt2rTR3rQ0paSkWtuLi4u1fPlHkqTJk++5WZeFW4B1b/nuu+9eSdIHHy7X+fPnre15efna+PnnkqTExARr+8RJE+Xp6aktW7cpJyfH2n727FmtXr1GXl5eNs8Xjo6OUmBgoPJPntTGzzdZ2y9fvqwl77wniTVvLlHDh0mSsrKy9eWXW2r15eTk6PNNmyVJI0eOsLZPvvpzvnzFChUXF1vbU1JSlZa2T127dlVU1PBaYzXmc4Lm11z38D59eissrJ9KSkq0fPkKa3tFRYWWLHlXZrNZE++eIG9vnvoJ2EM2R32x7i0f2dy9kM1xLbI50DKQzVFfrHvLRi53P2RzVHPlXO5hsVgsN200wIls2rRZS5e9L6nqF2yb1va3v3r00UfUrl076/dLly7Tps1fyNfXV4MiI2U2m3Xg4EFZLBa9MOd5RUdH2YyxZ0+KXnt9viwWiwYM6K/bbrtNBw8eUllZmSbfM0mPPfborblI1EtBwY/653/5V3Xp0kXvLFls9xjWveVbsGChknfulJ+fn/r16yuTyaSjR3N05coVxcfF6amnnqh1/MbPN+n99z+Ql5eXBkVGytvbWxkHDshkMumf/vFxTZx4t81rHDl6VH/4w3/LZDKpT5/eMhgMys4+ogsXLmjkyBGa8/xzdrdvxc33wYfLtWHDRklSjx7BCg4K1vkL55WTc0yVlZVKiI/Tk0/WrHllZaVe/ctrSk3dq7Zt22rgwAhdvFiqrKws+fj4aN68l9W3Tx+b12nM5wRNk56ers9WrbZ+n5t7XBaLRX369La2TZ82VUOH1lSWN9c9/OzZs5rzwu9UWlqqkJAQBQcH6XjucZ0rKFC/fn01b+7LN/yDIOCuyOaoRjZ3D2Rz90I2d11kc8A1kc1RjWzu+sjl7ods7prI5TUoboHLWrnyU3362aobHvfuO0tstkRLSkrWF3/7UmfOnJG3t7f69u2jadOmKqxf3dsmHTlyRKtWr1FOzjGZzWYFBwfp7gkTFBMztsnXgqapT0iXWPeWzmKxaPv2HdqydZvOnDkjDw8PhYSEKDEhvs71SEvbp/UbNlqfCdqrVy9Nue/e626Zd+rUKX2y8lNlZmbp559/VpcugYqNjdWkiXfL05MN0ZpTSmqqtmzZphMnTujSpUtq3bq1evYMVXxcnO66a4zN8RUVFdq8+QvtSErSuXMF8vX1VUTEAM2cOUM9goPrfJ3GfE7QeElJyXpr4aLrHvPsM09r3LgYm/Oa4x5eWFikT1auVEZGhkpLjTIYDBozZrSmPnA/W+oC10E2RzWyuXsgm7sfsrlrIpsDrolsjmpkc9dHLndPZHPXQy6vQXELAAAAAAAAAAAAAAAAnBblcgAAAAAAAAAAAAAAAHBaFLcAAAAAAAAAAAAAAADAaVHcAgAAAAAAAAAAAAAAAKdFcQsAAAAAAAAAAAAAAACcFsUtAAAAAAAAAAAAAAAAcFoUtwAAAAAAAAAAAAAAAMBpUdwCAAAAAAAAAAAAAAAAp0VxCwAAAAAAAAAAAAAAAJwWxS0AAAAAAAAAAAAAAABwWhS3AAAAAAAAAAAAAAAAwGlR3AIAAAAAAAAAAAAAAACn5e3oCQAAmmbyvVMkSRs3rLvhsUlJyXpr4SLNmD5NM2fOsLYvWLBQyTt3at7clxURMeCWzdXZHD6cqf/8rz/UavP09NRtt7VVr169ND4xUVFRwyXVvHcN8cv3GQAAAK6NbN54ZHMAAADcTGTzxiObA3BWFLcAANxe+/btdefgwZIk0xWTTp86rYyMA8rIOGAN2l27dlXM2LE25ybv3ClJGjEiWq3/oXWtvp49e976yQMAAAAuhGwOAAAAOAeyOQBnQ3ELALiR6Ogo9e3bR+3atXP0VJxKUPfumj37mVptmzZt1tJl72vV6jWKiYlReHiYwsPDbM6tDumP/uY3CgwMaJb5AgAAoOUjm9tHNgcAAEBzI5vbRzYH4Gw8HT0BAEDz8fPzU1BQECG9HiZNmqigoO6qrKzUoUOHHD0dAAAAuBiyef2RzQEAAHArkc3rj2wOwJHYuQUA3Ehdzw691v7932r1mjXKzz8pLy8vRUQM0KyHH1JQUFCdY9111136ZOVKHT6cqYsXL+rFF+YoOjpKP/zwg7766mtlHDiggoIfZTQa5e/fThEREZo2daq6d+9m8/qT752iAINBixcv0tp16/XNN7tUUFCgIXfeqbDwfvrwwxV64P4pmjXrYbvz/+MfX9KBgwc1d+5LGhgR0aT3Kzg4WGfOfK+SkpImjQMAAAD8Etm8YcjmAAAAuFXI5g1DNgfgKOzcAgCw2r1nj+bO+x+ZzWYNGzZUHTt2VGrqXs2Z86Ly8/PtnvP92bN67vk5ys09roiIAYqMjJS3t5ckadv2Hfps1Wpdvvyzet9xh4YNG6rWrdvoq6++1nPPz9HJkyftjllpseiVV17V+vUb1KVLoKKGD1eHjh00LiZGPj4+SkpOVkVFhc15BQUFOnjokLp169rkgC5J5eWXJUn+/v5NHgsAAABoCLJ5bWRzAAAAOArZvDayOQBHYecWAIDVl19u0VNPPqH4+DhJksVi0YqPPta6deu14K1F+uub823O2bXr77p7wng9/vhj8vLyqtUXHTVciQnxCgwMrNW+IylJCxe+raXL3te8uS/bjFlUVCQfHx8tfnuhOnXqVKtvxIhoffPNLu3bl67o6Khafdt3JMlisSguNrZR13+t0tJS5ebmytPTU5GRA5s8HgA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+      "text/plain": [
+       "<Figure size 2250x750 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_ground_truth = True\n",
+    "show_decoys = False\n",
+    "show_search = True\n",
+    "show_discovered = True\n",
+    "show_calibration = True\n",
+    "show_true = False\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "for j, i in enumerate([1, 2, 5]):\n",
+    "\n",
+    "    prediction = \"rt_calibrated\" if i != 1 else \"rt_library\"\n",
+    "    previous_df = dfs[i-1]\n",
+    "    current_df = dfs[i]\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # Ground truth\n",
+    "    if show_ground_truth:\n",
+    "        sns.scatterplot(data=rt_df[rt_df.decoy == False], x=\"rt_library\", y='rt_observed', ax=ax[j])\n",
+    "        if show_decoys:\n",
+    "            sns.scatterplot(data=rt_df[rt_df.decoy == True], x=\"rt_library\", y='rt_observed', ax=ax[j], color='red')\n",
+    "            legend_elements = [\n",
+    "                mpl.lines.Line2D([0], [0], marker='o', color='w', label='Target', markerfacecolor='#1f77b4', markersize=10),\n",
+    "                mpl.lines.Line2D([0], [0], marker='o', color='w', label='Decoy', markerfacecolor='red', markersize=10),\n",
+    "            ]\n",
+    "            ax[j].legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "    # Search values\n",
+    "    if show_search:\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=prediction, ax=ax[j], color='black', linewidth=3, errorbar=None,\n",
+    "                    label=\"Prediction for search\")\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=\"upper_bound\", ax=ax[j], color='red', linestyle=\"--\", linewidth=2, errorbar=None,\n",
+    "                    label=\"Bounds for search\")\n",
+    "        sns.lineplot(data=previous_df, x=\"rt_library\", y=\"lower_bound\", ax=ax[j], color='red', linestyle=\"--\", linewidth=2, errorbar=None)\n",
+    "\n",
+    "    # Calibrated values\n",
+    "    if show_discovered:\n",
+    "        sns.scatterplot(data=current_df[(current_df.decoy == False) & (current_df.discovered == True)], x=\"rt_library\", y='rt_observed', ax=ax[j], color='green', label=\"Precursors identified at 1% FDR\")\n",
+    "    if show_calibration: \n",
+    "        sns.lineplot(data=current_df, x=\"rt_library\", y='rt_calibrated', ax=ax[j], color='green', linewidth=3, errorbar=None,\n",
+    "                    label=\"Prediction after calibration\")\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # True relationship\n",
+    "    if show_true:\n",
+    "        sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true\", ax=ax[j], color='orange', linewidth=3, errorbar=None,\n",
+    "                    label=\"True relationship\")\n",
+    "        # sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true_positive_95_bound\", ax=ax[j], color='orange', linewidth=2, linestyle=\"--\", errorbar=None, \n",
+    "        #             label=\"True 95% CI\")\n",
+    "        # sns.lineplot(data=rt_df, x=\"rt_library\", y=\"rt_true_negative_95_bound\", ax=ax[j], color='orange', linewidth=2, linestyle=\"--\", errorbar=None)\n",
+    "        \n",
+    "#    ax.set_title(f\"Iteration {i}. Precursor count: {current_df['discovered'].sum()}. Proportion Discoverable: {current_df.loc[current_df['decoy'] == False, 'discoverable'].sum() / dataset_size :.3f}\")\n",
+    "    ax[j].set_ylim(bottom=0, top=1200)\n",
+    "    ax[0].set_ylabel(\"Observed RT\")\n",
+    "    ax[j].set_xlabel(\"Library RT\")\n",
+    "    ax[j].set_title(f\"Iteration {i}\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "number_name = \"Number of precursors\"\n",
+    "proportion_name = \"Proportion of precursors\"\n",
+    "\n",
+    "optimization_batches = pd.DataFrame({\n",
+    "    \"Batch size\": [\n",
+    "        8000, # 1\n",
+    "        24000, # 2\n",
+    "        56000, # 3\n",
+    "        56000, # 4\n",
+    "        56000, # 5\n",
+    "        24000, # 6\n",
+    "        8000, # 7\n",
+    "        24000, # 8\n",
+    "        24000, # 9\n",
+    "        8000], # 10\n",
+    "    proportion_name: [\n",
+    "        0.0005, # 1\n",
+    "        0.003, # 2\n",
+    "        0.005, # 3\n",
+    "        0.006, # 4\n",
+    "        0.010, # 5\n",
+    "        0.030, # 6\n",
+    "        0.020, # 7\n",
+    "        0.024, # 8\n",
+    "        0.04, # 9\n",
+    "        0.05 # 10\n",
+    "        ],\n",
+    "    \"Optimization iteration\": [\n",
+    "        1,\n",
+    "        1,\n",
+    "        1,\n",
+    "        2,\n",
+    "        3,\n",
+    "        4,\n",
+    "        5,\n",
+    "        5,\n",
+    "        6,\n",
+    "        7,\n",
+    "    ],\n",
+    "    \"Number of batch steps tried\": [\n",
+    "        1,\n",
+    "        2,\n",
+    "        3,\n",
+    "        1,\n",
+    "        1,\n",
+    "        1,\n",
+    "        1,\n",
+    "        2,\n",
+    "        1,\n",
+    "        1,\n",
+    "    ],\n",
+    "    \"taken_for_calibration\": [\n",
+    "        False,\n",
+    "        False,\n",
+    "        True,\n",
+    "        True, \n",
+    "        True, \n",
+    "        True, \n",
+    "        False,\n",
+    "        True, \n",
+    "        True,\n",
+    "        True],\n",
+    "})\n",
+    "\n",
+    "optimization_batches[number_name] = optimization_batches[\"Batch size\"] * optimization_batches[proportion_name]\n",
+    "optimization_batches[\"Requisite proportion\"] = 200 / optimization_batches[\"Batch size\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0:02:06.498150 INFO: Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
+      "0:02:06.501695 INFO: Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1050 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line_colour = \"#ff7f0e\"\n",
+    "line_width = 1.5\n",
+    "\n",
+    "fig, ax = plt.subplots(3, 1, figsize=(10, 7))  \n",
+    "palette=sns.color_palette(\"viridis\", 3)[::-1]\n",
+    "sns.barplot(\n",
+    "    data=optimization_batches, \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=\"Batch size\", \n",
+    "    hue=\"Batch size\", \n",
+    "    palette=palette,\n",
+    "    ax=ax[0],\n",
+    "    )\n",
+    "\n",
+    "sns.lineplot(\n",
+    "    data=optimization_batches.loc[optimization_batches[\"taken_for_calibration\"] == True], \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=proportion_name, \n",
+    "    c=\"#ff7f0e\",\n",
+    "    legend=False,\n",
+    "    lw=line_width,\n",
+    "    ax=ax[1]\n",
+    "    )\n",
+    "\n",
+    "sns.scatterplot(\n",
+    "    data=optimization_batches, \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=proportion_name, \n",
+    "    legend=False,\n",
+    "    hue=\"Batch size\", \n",
+    "    palette=palette,\n",
+    "    zorder=10,\n",
+    "    ax=ax[1]\n",
+    "    )\n",
+    "for i, val in enumerate(np.sort(optimization_batches[\"Requisite proportion\"].unique())[::-1]):\n",
+    "    batch_size = np.sort(optimization_batches[\"Batch size\"].unique())[i]\n",
+    "    ax[1].hlines(val, 1, 7, color=palette[i], linestyle=':', lw=line_width, label=\"Requisite proportion for batch size \" + str(batch_size))\n",
+    "\n",
+    "sns.lineplot(\n",
+    "    data=optimization_batches.loc[optimization_batches[\"taken_for_calibration\"] == True], \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=number_name, \n",
+    "    c=\"#ff7f0e\",\n",
+    "    ax=ax[2],\n",
+    "    lw=line_width,\n",
+    "    legend=False,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "\n",
+    "sns.scatterplot(\n",
+    "    data=optimization_batches, \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=number_name, \n",
+    "    hue=\"Batch size\", \n",
+    "    legend=False,\n",
+    "    palette=palette,\n",
+    "    ax=ax[2],\n",
+    "    zorder=10,\n",
+    "    )\n",
+    "ax[2].hlines(200, 1, 7, color='red', linestyle=':', lw=line_width, label=\"Target number\")\n",
+    "\n",
+    "\n",
+    "sns.lineplot(\n",
+    "    data=optimization_batches.loc[(optimization_batches[\"Number of batch steps tried\"] == 1) &\n",
+    "                                  (optimization_batches[\"Optimization iteration\"].isin([4, 5]))], \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=number_name, \n",
+    "    c=\"#ff7f0e\",\n",
+    "    linestyle=\"--\",\n",
+    "    lw=line_width,\n",
+    "    ax=ax[2]\n",
+    "    )\n",
+    "\n",
+    "sns.lineplot(\n",
+    "    data=optimization_batches.loc[(optimization_batches[\"Number of batch steps tried\"] == 1) &\n",
+    "                                  (optimization_batches[\"Optimization iteration\"].isin([4, 5]))], \n",
+    "    x=\"Optimization iteration\", \n",
+    "    y=proportion_name, \n",
+    "    c=\"#ff7f0e\",\n",
+    "    linestyle=\"--\",\n",
+    "    lw=line_width,\n",
+    "    ax=ax[1]\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "for i, optimization_number in enumerate(optimization_batches[\"Optimization iteration\"].unique()):\n",
+    "    ax[1].vlines(\n",
+    "        optimization_number, \n",
+    "        optimization_batches[optimization_batches[\"Optimization iteration\"] == optimization_number][proportion_name].min(), \n",
+    "        optimization_batches[optimization_batches[\"Optimization iteration\"] == optimization_number][proportion_name].max(), \n",
+    "        color='#ff7f0e', \n",
+    "        lw=line_width,\n",
+    "        linestyle='--')\n",
+    "    \n",
+    "    ax[2].vlines(\n",
+    "        optimization_number, \n",
+    "        optimization_batches[optimization_batches[\"Optimization iteration\"] == optimization_number][number_name].min(), \n",
+    "        optimization_batches[optimization_batches[\"Optimization iteration\"] == optimization_number][number_name].max(), \n",
+    "        color='#ff7f0e', \n",
+    "        lw=line_width,\n",
+    "        linestyle='--')\n",
+    "\n",
+    "\n",
+    "ax[0].set_xlabel(\"\")\n",
+    "ax[1].set_xlabel(\"\")\n",
+    "ax[0].set_xticklabels([])\n",
+    "ax[1].set_xticklabels([])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "alpha",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 3201478794ec3806b1dfbffe52d596f40bb965cc Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 13:21:12 +0200
Subject: [PATCH 213/250] review comments

---
 alphadia/workflow/optimization.py | 82 +++++++++++++++++--------------
 1 file changed, 45 insertions(+), 37 deletions(-)

diff --git a/alphadia/workflow/optimization.py b/alphadia/workflow/optimization.py
index 7fd86ea0..b65d0fa5 100644
--- a/alphadia/workflow/optimization.py
+++ b/alphadia/workflow/optimization.py
@@ -101,23 +101,27 @@ def __init__(
             self.parameter_name
         ]["automatic_update_percentile_range"]
 
-        self.try_narrower_values = workflow.config["optimization"][self.parameter_name][
-            "try_narrower_values"
-        ]
+        self._try_narrower_values = workflow.config["optimization"][
+            self.parameter_name
+        ]["try_narrower_values"]
 
-        if self.try_narrower_values:
-            self.maximal_decrease = workflow.config["optimization"][
-                self.parameter_name
-            ]["maximal_decrease"]
+        self._maximal_decrease = (
+            workflow.config["optimization"][self.parameter_name]["maximal_decrease"]
+            if self._try_narrower_values
+            else None
+        )
 
-        self.favour_narrower_optimum = workflow.config["optimization"][
+        self._favour_narrower_optimum = workflow.config["optimization"][
             self.parameter_name
         ]["favour_narrower_optimum"]
 
-        if self.favour_narrower_optimum:
-            self.maximum_decrease_from_maximum = workflow.config["optimization"][
-                self.parameter_name
-            ]["maximum_decrease_from_maximum"]
+        self._maximum_decrease_from_maximum = (
+            workflow.config["optimization"][self.parameter_name][
+                "maximum_decrease_from_maximum"
+            ]
+            if self._favour_narrower_optimum
+            else None
+        )
 
     def step(
         self,
@@ -286,10 +290,10 @@ def _unnecessary_to_continue(self):
     @property
     def _just_converged(self):
         """Optimization should stop if continued narrowing of the parameter is not improving the feature value.
-        If self.try_narrower_values is False:
+        If self._try_narrower_values is False:
             1) This function checks if the previous rounds of optimization have led to a meaningful improvement in the feature value.
             2) If so, it continues optimization and appends the proposed new parameter to the list of parameters. If not, it stops optimization and sets the optimal parameter attribute.
-        If self.try_narrower_values is True:
+        If self._try_narrower_values is True:
             1) This function checks if the previous rounds of optimization have led to a meaningful disimprovement in the feature value or if the parameter has not changed substantially.
             2) If not, it continues optimization and appends the proposed new parameter to the list of parameters. If so, it stops optimization and sets the optimal parameter attribute.
 
@@ -302,24 +306,31 @@ def _just_converged(self):
         if len(self.history_df) < 3:
             return False
 
-        if self.try_narrower_values:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+        feature_history = self.history_df[self.feature_name]
+        last_feature_value = feature_history.iloc[-1]
+        second_last_feature_value = feature_history.iloc[-2]
+        third_last_feature_value = feature_history.iloc[-3]
+
+        if self._try_narrower_values:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             min_steps_reached = (
                 self.num_prev_optimizations
                 >= self.workflow.config["calibration"]["min_steps"]
             )
 
-            feature_history = self.history_df[self.feature_name]
             feature_substantially_decreased = (
-                feature_history.iloc[-1] - feature_history.iloc[-2]
-            ) / np.abs(feature_history.iloc[-2]) < -self.maximal_decrease and (
-                feature_history.iloc[-1] - feature_history.iloc[-3]
-            ) / np.abs(feature_history.iloc[-3]) < -self.maximal_decrease
+                last_feature_value - second_last_feature_value
+            ) / np.abs(second_last_feature_value) < -self._maximal_decrease and (
+                last_feature_value - third_last_feature_value
+            ) / np.abs(third_last_feature_value) < -self._maximal_decrease
 
             parameter_history = self.history_df["parameter"]
+
+            last_parameter_value = parameter_history.iloc[-1]
+            second_last_parameter_value = parameter_history.iloc[-2]
             parameter_not_substantially_changed = (
                 np.abs(
-                    (parameter_history.iloc[-1] - parameter_history.iloc[-2])
-                    / parameter_history.iloc[-2]
+                    (last_parameter_value - second_last_parameter_value)
+                    / second_last_parameter_value
                 )
                 < 0.05
             )
@@ -334,24 +345,20 @@ def _just_converged(self):
                 >= self.workflow.config["calibration"]["min_steps"]
             )
 
-            feature_history = self.history_df[self.feature_name]
+            feature_not_substantially_increased = (
+                last_feature_value - second_last_feature_value
+            ) / np.abs(second_last_feature_value) < 0.1 and (
+                last_feature_value - third_last_feature_value
+            ) / np.abs(third_last_feature_value) < 0.1
 
-            return (
-                min_steps_reached
-                and (feature_history.iloc[-1] - feature_history.iloc[-2])
-                / np.abs(feature_history.iloc[-2])
-                < 0.1
-                and (feature_history.iloc[-1] - feature_history.iloc[-3])
-                / np.abs(feature_history.iloc[-3])
-                < 0.1
-            )
+            return min_steps_reached and feature_not_substantially_increased
 
     def _find_index_of_optimum(self):
         """Finds the index of the row in the history dataframe with the optimal value of the feature used for optimization.
-        if self.favour_narrower_parameter is False:
+        if self._favour_narrower_parameter is False:
             The index at optimum is the index of the parameter value that maximizes the feature.
-        if self.favour_narrower_parameter is True:
-            The index at optimum is the index of the minimal parameter value whose feature value is at least self.maximum_decrease_from_maximum of the maximum value of the feature.
+        if self._favour_narrower_parameter is True:
+            The index at optimum is the index of the minimal parameter value whose feature value is at least self._maximum_decrease_from_maximum of the maximum value of the feature.
 
         Returns
         -------
@@ -364,13 +371,14 @@ def _find_index_of_optimum(self):
 
         """
 
-        if self.favour_narrower_optimum:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
+        if self._favour_narrower_optimum:  # This setting can be useful for optimizing parameters for which many parameter values have similar feature values.
             maximum_feature_value = self.history_df[self.feature_name].max()
             rows_within_thresh_of_max = self.history_df.loc[
                 self.history_df[self.feature_name]
                 > (
                     maximum_feature_value
-                    - self.maximum_decrease_from_maximum * np.abs(maximum_feature_value)
+                    - self._maximum_decrease_from_maximum
+                    * np.abs(maximum_feature_value)
                 )
             ]
             index_of_optimum = rows_within_thresh_of_max["parameter"].idxmin()

From a501226a5bd4cba1fc0782d2a05b45b5fbf42cbf Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 13:38:43 +0200
Subject: [PATCH 214/250] review comments

---
 alphadia/constants/default.yaml               |  2 +-
 docs/methods/search_parameter_optimization.md | 14 ++++----------
 2 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 847b43a4..0e1c84b4 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -120,7 +120,7 @@ calibration:
   norm_rt_mode: 'linear'
 
 search_initial:
-  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
+  # Number of peak groups identified in the convolution score to classify with target decoy competition
   initial_num_candidates: 1
 
   # initial ms1 tolerance in ppm
diff --git a/docs/methods/search_parameter_optimization.md b/docs/methods/search_parameter_optimization.md
index 28b2a329..24519f69 100644
--- a/docs/methods/search_parameter_optimization.md
+++ b/docs/methods/search_parameter_optimization.md
@@ -2,11 +2,11 @@
 
 ## Calibration and optimization
 ### Overall process
-The first step of every alphaDIA run is the optimization of search parameters and the calibration of the empirical or fully predicted spectral library to the observed values. This step has two main purposes: 1) removing the systematic deviation of observed and library values, and 2) optimizing the size of the search space to reflect the expected deviation from the library values. For DIA search this means calibration and optimization of certain parameters: retention time, ion mobility, precursor m/z and fragment m/z. The process of iterative calibration and optimization is illustrated below.
+The first step of every AlphaDIA run is the optimization of search parameters and the calibration of the empirical or fully predicted spectral library to the observed values. This step has two main purposes: 1) removing the systematic deviation of observed and library values, and 2) optimizing the size of the search space to reflect the expected deviation from the library values. For DIA search this means calibration and optimization of certain parameters: retention time, ion mobility, precursor m/z and fragment m/z. The process of iterative calibration and optimization is illustrated below.
 
 <img src="../_static/images/methods_optimization.png" width="100%" height="auto">
 
-Optimization can be performed either in either a targeted or automatic manner. In targeted optimization, the search space is progressively narrowed until a target tolerance is reached for a given parameter. In automatic optimization, the search space is progressively narrowed until an internal algorithm detects that further narrowing will reduce performance, at which point the optimal value is selected for search. Automatic optimization can be triggered by setting the target tolerance to 0.0 in the GUI, or by setting it to any nonpositive value in the command line. It is possible to use targeted optimization for some parameters and automatic optimization for others; currently, it is recommended to use targeted optimization for precursor m/z, fragment m/z and ion mobility, and automatic optimization for retention time.
+Optimization can be performed in either a targeted or automatic manner. In targeted optimization, the search space is progressively narrowed until a target tolerance is reached for a given parameter. In automatic optimization, the search space is progressively narrowed until an internal algorithm detects that further narrowing will reduce the confident identification of precursors (either by directly assessing the proportion of the library which has been detected or using a surrogate metric, such as the mean isotope intensity correlation for precursor m/z tolerance), at which point the optimal value is selected for search. Automatic optimization can be triggered by setting the target tolerance to 0.0 (or a negative value). It is possible to use targeted optimization for some parameters and automatic optimization for others; currently, it is recommended to use targeted optimization for precursor m/z, fragment m/z and ion mobility, and automatic optimization for retention time.
 
 AlphaDIA iteratively performs calibration and optimization based on a subset of the spectral library used for search. The size of this subset is adjusted according to an exponential batch plan to balance accuracy and efficiency. A defined number of precursors, set by the ``optimization_lock_target`` (default: 200), need to be identified at 1% FDR before calibration and optimization are performed. If fewer precursors than the target number are identified using a given step of the batch plan, AlphaDIA will search for precursors from the next step of the batch plan in addition to those already searched. If more precursors than the target number are identified, AlphaDIA will check if any previous step of the batch plan is also likely to yield at least the target number, in which case it will use the smallest such step of the batch plan for the next iteration of calibration and optimization. In this way, AlphaDIA ensures that calibration is always performed on sufficient precursors to be reliable, while calibrating on the smallest-possible subset of the library to maximize efficiency.
 
@@ -18,7 +18,7 @@ The process of choosing the best batch step for calibration and optimization is
 If enough confident target precursors have been detected, they are calibrated to the observed values using locally estimated scatterplot smoothing (LOESS) regression. For calibration of fragment m/z values, a fixed number of up to 5000 of the best fragments according to their  XIC correlation are used. For precursor calibration all precursors passing 1% FDR are used. Calibration is performed prior to every optimization.
 
 ### Optimization
-For optimizing the search space, tolerances like retention time, ion mobility and m/z ratios need to be reduced. The goal is to cover the expected spectrum space but reduce it as much as possible to accelerate search and gain statsitical power. Search starts with initial tolerances as defined in `search_initial`.  For targeted optimization, the 95% deviation after calibration is adopted as the new tolerance until the target tolerances defined in the `search` section are reached. For automatic optimization, the 99% deviation plus 10% of the absolute value of the tolerance is adopted as the new tolerance, and search continues until parameter-specific convergence rules are met.
+For optimizing the search space, tolerances like retention time, ion mobility and m/z ratios need to be reduced. The goal is to cover the expected spectrum space but reduce it as much as possible to accelerate search and gain statistical power. Search starts with initial tolerances as defined in `search_initial`.  For targeted optimization, the 95% deviation after calibration is adopted as the new tolerance until the target tolerances defined in the `search` section are reached. For automatic optimization, the 99% deviation plus 10% of the absolute value of the tolerance is adopted as the new tolerance, and search continues until parameter-specific convergence rules are met.
 
 The optimization is finished as soon as the minimum number of steps `min_steps` has passed and all tolerances have either 1) reached the target tolerances defined in `search` if using targeted optimization, or 2) have converged if using automatic optimization.
 
@@ -40,7 +40,7 @@ calibration:
   min_steps: 2
 
 search_initial:
-  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
+  # Number of peak groups identified in the convolution score to classify with target decoy competition
   initial_num_candidates: 1
 
   # initial ms1 tolerance in ppm
@@ -56,18 +56,12 @@ search_initial:
   initial_rt_tolerance: 240
 
 search:
-  channel_filter: '0'
-  exclude_shared_ions: True
-  compete_for_fragments: True
-
   target_num_candidates: 2
   target_ms1_tolerance: 15
   target_ms2_tolerance: 15
   target_mobility_tolerance: 0.04
   target_rt_tolerance: 0
 
-  quant_window: 3
-  quant_all: True
 ```
 
 ## Calibration using LOESS

From 4bbc022270cfc634134e01c67962421f3d52909c Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 15:30:37 +0200
Subject: [PATCH 215/250] make automatic default

---
 alphadia/constants/default.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 8fba4bab..12cdbd31 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -93,9 +93,9 @@ search:
   # target ms2 tolerance in ppm
   target_ms2_tolerance: 15
   # target ion mobility tolerance in 1/K_0
-  target_mobility_tolerance: 0.04
+  target_mobility_tolerance: 0.0 # default is to optimize automatically
   # target retention time tolerance in seconds if > 1, or a proportion of the total gradient length if < 1
-  target_rt_tolerance: 60
+  target_rt_tolerance: 0.0 # default is to optimize automatically
 
   quant_window: 3
   quant_all: True

From 45eb17b7a5d8d26d743b6729154390c2a676320d Mon Sep 17 00:00:00 2001
From: odespard <odespard@gmail.com>
Date: Fri, 30 Aug 2024 15:33:36 +0200
Subject: [PATCH 216/250] use try_narrower_parameters for ms2 by default with
 maximal_decrease=0.5

---
 alphadia/constants/default.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 12cdbd31..9ca9c6fe 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -268,8 +268,8 @@ optimization:
       targeted_update_factor: 1.0
       automatic_update_percentile_range: 0.99
       automatic_update_factor: 1.1
-      try_narrower_values: False
-      maximal_decrease: 0.2
+      try_narrower_values: True
+      maximal_decrease: 0.5
       favour_narrower_optimum: False
       maximum_decrease_from_maximum: 0.1
   ms1_error:

From e90126ba4bec1ebefe4c29e3a7330586b052b0f3 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Sun, 1 Sep 2024 01:58:16 +0300
Subject: [PATCH 217/250] change config attributes to private

---
 alphadia/outputtransform.py        |  15 +-
 alphadia/transferlearning/train.py | 225 +++++++++++++----------------
 2 files changed, 111 insertions(+), 129 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 8b67a0bd..adeee463 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -395,7 +395,20 @@ def build_transfer_model(self):
 
         device = utils.get_torch_device(self.config["general"]["use_gpu"])
 
-        tune_mgr = FinetuneManager(device=device, **self.config["transfer_learning"])
+        tune_mgr = FinetuneManager(
+            device=device,
+            lr_patience=self.config["transfer_learning"]["lr_patience"],
+            test_interval=self.config["transfer_learning"]["test_interval"],
+            train_fraction=self.config["transfer_learning"]["train_fraction"],
+            validation_fraction=self.config["transfer_learning"]["validation_fraction"],
+            test_fraction=self.config["transfer_learning"]["test_fraction"],
+            epochs=self.config["transfer_learning"]["epochs"],
+            warmup_epochs=self.config["transfer_learning"]["warmup_epochs"],
+            batch_size=self.config["transfer_learning"]["batch_size"],
+            max_lr=self.config["transfer_learning"]["max_lr"],
+            nce=self.config["transfer_learning"]["nce"],
+            instrument=self.config["transfer_learning"]["instrument"],
+        )
         tune_mgr.finetune_rt(transfer_lib.precursor_df)
         tune_mgr.finetune_charge(transfer_lib.precursor_df)
         tune_mgr.finetune_ms2(
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index a498c17d..b9aa2426 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -42,20 +42,22 @@ class CustomScheduler(LR_SchedulerInterface):
             The number of warmup steps. Defaults to 5.
         - num_training_steps: int
             The number of training steps. Defaults to 50.
+        - lr_patience: int
+            The patience for the ReduceLROnPlateau scheduler. Defaults to 3.
     """
 
     def __init__(self, optimizer: torch.optim.Optimizer, **kwargs):
-        self.optimizer = optimizer
-        self.num_warmup_steps = kwargs.get("num_warmup_steps", 5)
-        self.num_training_steps = kwargs.get("num_training_steps", 50)
-        self.lr_patience = kwargs.get("lr_patience", 3)
-        self.reduce_lr_on_plateau = torch.optim.lr_scheduler.ReduceLROnPlateau(
+        self._optimizer = optimizer
+        self._num_warmup_steps = kwargs.get("num_warmup_steps", 5)
+        self._num_training_steps = kwargs.get("num_training_steps", 50)
+        self._lr_patience = kwargs.get("lr_patience", 3)
+        self._reduce_lr_on_plateau = torch.optim.lr_scheduler.ReduceLROnPlateau(
             optimizer,
             mode="min",
-            patience=self.lr_patience,
+            patience=self._lr_patience,
             factor=0.5,
         )
-        self.warmup_lr = LambdaLR(optimizer, self._warmup)
+        self._warmup_lr = LambdaLR(optimizer, self._warmup)
 
     def _warmup(self, epoch: int):
         """
@@ -66,7 +68,7 @@ def _warmup(self, epoch: int):
         epoch : int
             The current epoch number.
         """
-        return float(epoch + 1) / float(max(1, self.num_warmup_steps))
+        return float(epoch + 1) / float(max(1, self._num_warmup_steps))
 
     def step(self, epoch: int, loss: float) -> float:
         """
@@ -85,16 +87,16 @@ def step(self, epoch: int, loss: float) -> float:
             The learning rate for the next epoch.
 
         """
-        if epoch < self.num_warmup_steps:
-            self.warmup_lr.step()
+        if epoch < self._num_warmup_steps:
+            self._warmup_lr.step()
         else:
-            self.reduce_lr_on_plateau.step(loss)
+            self._reduce_lr_on_plateau.step(loss)
 
     def get_last_lr(self):
         """
         Get the last learning rate.
         """
-        return [self.optimizer.param_groups[0]["lr"]]
+        return [self._optimizer.param_groups[0]["lr"]]
 
 
 class EarlyStopping:
@@ -104,11 +106,11 @@ class EarlyStopping:
     """
 
     def __init__(self, patience: int = 5, margin: float = 0.01):
-        self.patience = patience
-        self.best_loss = np.inf
-        self.last_loss = np.inf
-        self.margin = margin
-        self.counter = 0
+        self._patience = patience
+        self._best_loss = np.inf
+        self._last_loss = np.inf
+        self._margin = margin
+        self._counter = 0
 
     def step(self, val_loss: float):
         """
@@ -124,27 +126,27 @@ def step(self, val_loss: float):
         continue_training : bool
             Whether to continue training or not based on the early stopping criteria.
         """
-        if self.last_loss != np.inf:
+        if self._last_loss != np.inf:
             if (
-                val_loss > self.best_loss * (1 + self.margin)
-                or abs(val_loss - self.last_loss) / self.last_loss < self.margin
+                val_loss > self._best_loss * (1 + self._margin)
+                or abs(val_loss - self._last_loss) / self._last_loss < self._margin
             ):
-                self.counter += 1
-                if self.counter >= self.patience:
+                self._counter += 1
+                if self._counter >= self._patience:
                     return False
             else:
-                self.best_loss = val_loss
-                self.counter = 0
-        self.last_loss = val_loss
+                self._best_loss = val_loss
+                self._counter = 0
+        self._last_loss = val_loss
         return True
 
     def reset(self):
         """
         Reset the early stopping criteria.
         """
-        self.best_loss = np.inf
-        self.last_loss = np.inf
-        self.counter = 0
+        self._best_loss = np.inf
+        self._last_loss = np.inf
+        self._counter = 0
 
 
 class CustomCallbackHandler(CallbackHandler):
@@ -162,8 +164,8 @@ class CustomCallbackHandler(CallbackHandler):
 
     def __init__(self, test_callback, **callback_args):
         super().__init__()
-        self.test_callback = test_callback
-        self.callback_args = callback_args
+        self._test_callback = test_callback
+        self._callback_args = callback_args
 
     def epoch_callback(self, epoch: int, epoch_loss: float):
         """
@@ -181,7 +183,7 @@ def epoch_callback(self, epoch: int, epoch_loss: float):
         bool: continue_training
             Whether to continue training or not based on the early stopping criteria.
         """
-        return self.test_callback(epoch, epoch_loss, **self.callback_args)
+        return self._test_callback(epoch, epoch_loss, **self._callback_args)
 
 
 class FinetuneManager(ModelManager):
@@ -217,15 +219,14 @@ def __init__(
         instrument: str = "Lumos",
     ):
         super().__init__(mask_modloss, device)
-        self.lr_patience = lr_patience
-        self.test_interval = test_interval
-        self.train_fraction = train_fraction
-        self.validation_fraction = validation_fraction
-        self.test_fraction = test_fraction
-        self.epochs = epochs
-        self.warmup_epochs = warmup_epochs
-        self.batch_size = batch_size
-        self.max_lr = max_lr
+        self._test_interval = test_interval
+        self._train_fraction = train_fraction
+        self._validation_fraction = validation_fraction
+        self._test_fraction = test_fraction
+        self._epochs = epochs
+        self._warmup_epochs = warmup_epochs
+        self._batch_size = batch_size
+        self._max_lr = max_lr
         self.nce = nce
         self.instrument = instrument
 
@@ -233,7 +234,8 @@ def __init__(
         self.early_stopping = EarlyStopping(patience=(lr_patience // test_interval) * 4)
 
         assert (
-            self.train_fraction + self.validation_fraction + self.test_fraction <= 1.0
+            self._train_fraction + self._validation_fraction + self._test_fraction
+            <= 1.0
         ), "The sum of the train, validation and test fractions should be less than or equal to 1.0"
 
     def _reset_frag_idx(self, df):
@@ -298,47 +300,6 @@ def _order_intensities(
             ]
         return reordered
 
-    def _log_training_progress(
-        self, epoch: int, current_lr: float, epoch_loss: float, val_loss: float
-    ):
-        """
-        Log the training progress.
-
-        Parameters
-        ----------
-        epoch : int
-            The current epoch number.
-        current_lr : float
-            The current learning rate.
-        epoch_loss : float
-            The training loss value of the current epoch.
-        val_loss : float
-            The validation loss value of the current epoch.
-        """
-        logger.progress(
-            f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
-        )
-
-    def _log_test_metrics(self, val_metrics: pd.DataFrame, data_split: str):
-        """
-        Log the test metrics.
-
-        Parameters
-        ----------
-        val_metrics : pd.DataFrame
-            The dataframe containing the test metrics.
-        data_split : str
-            The dataset label used for testing e.g. "validation", "train", "test"
-        """
-
-        logger.progress(
-            f" Model tested on {data_split} dataset with the following metrics:"
-        )
-        for i in range(len(val_metrics)):
-            logger.progress(
-                f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
-            )
-
     def _accumulate_training_metrics(
         self,
         metric_accumulator: MetricManager,
@@ -427,9 +388,17 @@ def _evaluate_metrics(
                 "value"
             ].values[0]
             continue_training = self.early_stopping.step(val_loss)
-            self._log_training_progress(epoch, current_lr, epoch_loss, val_loss)
+            logger.progress(
+                f" Epoch {epoch:<3} Lr: {current_lr:.5f}   Training loss: {epoch_loss:.4f}   validation loss: {val_loss:.4f}"
+            )
         else:
-            self._log_test_metrics(val_metrics, data_split)
+            logger.progress(
+                f" Model tested on {data_split} dataset with the following metrics:"
+            )
+            for i in range(len(val_metrics)):
+                logger.progress(
+                    f" {val_metrics['metric_name'].values[i]:<30}: {val_metrics['value'].values[i]:.4f}"
+                )
 
         return continue_training
 
@@ -473,7 +442,7 @@ def _test_ms2(
 
         """
         continue_training = True
-        if epoch % self.test_interval and epoch != -1:
+        if epoch % self._test_interval and epoch != -1:
             return continue_training
 
         self.ms2_model.model.eval()
@@ -557,10 +526,10 @@ def finetune_ms2(
         self._normalize_intensity(psm_df, matched_intensity_df)
 
         # Shuffle the psm_df and split it into train and test
-        train_psm_df = psm_df.sample(frac=self.train_fraction).copy()
+        train_psm_df = psm_df.sample(frac=self._train_fraction).copy()
         val_psm_df = (
             psm_df.drop(train_psm_df.index)
-            .sample(frac=self.validation_fraction / (1 - self.train_fraction))
+            .sample(frac=self._validation_fraction / (1 - self._train_fraction))
             .copy()
         )
         test_psm_df = psm_df.drop(train_psm_df.index).drop(val_psm_df.index).copy()
@@ -639,26 +608,26 @@ def finetune_ms2(
         # Train the model
         logger.progress(" Fine-tuning MS2 model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_psm_df):<10}"
+            f" Train fraction:      {self._train_fraction:3.2f}     Train size:      {len(train_psm_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_psm_df):<10}"
+            f" Validation fraction: {self._validation_fraction:3.2f}     Validation size: {len(val_psm_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_psm_df):<10}"
+            f" Test fraction:       {self._test_fraction:3.2f}     Test size:       {len(test_psm_df):<10}"
         )
         self.ms2_model.model.train()
         self.ms2_model.train(
             precursor_df=train_psm_df,
             fragment_intensity_df=train_intensity_df,
-            epoch=self.epochs,
-            batch_size=self.batch_size,
-            warmup_epoch=self.warmup_epochs,
-            lr=self.max_lr,
+            epoch=self._epochs,
+            batch_size=self._batch_size,
+            warmup_epoch=self._warmup_epochs,
+            lr=self._max_lr,
         )
 
         self._test_ms2(
-            self.epochs,
+            self._epochs,
             0,
             reordered_test_psm_df,
             reordered_test_intensity_df,
@@ -699,7 +668,7 @@ def _test_rt(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.test_interval != 0 and epoch != -1:
+        if epoch % self._test_interval != 0 and epoch != -1:
             return continue_training
 
         self.rt_model.model.eval()
@@ -740,9 +709,9 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         """
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.train_fraction)
+        train_df = psm_df.sample(frac=self._train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.validation_fraction / (1 - self.train_fraction)
+            frac=self._validation_fraction / (1 - self._train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -771,24 +740,24 @@ def finetune_rt(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning RT model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self._train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self._validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self._test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.rt_model.model.train()
         self.rt_model.train(
             train_df,
-            batch_size=self.batch_size,
-            epoch=self.epochs,
-            warmup_epoch=self.warmup_epochs,
-            lr=self.max_lr,
+            batch_size=self._batch_size,
+            epoch=self._epochs,
+            warmup_epoch=self._warmup_epochs,
+            lr=self._max_lr,
         )
 
-        self._test_rt(self.epochs, 0, test_df, test_metric_manager, data_split="test")
+        self._test_rt(self._epochs, 0, test_df, test_metric_manager, data_split="test")
 
         metrics = test_metric_manager.get_stats()
 
@@ -824,7 +793,7 @@ def _test_charge(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.test_interval != 0 and epoch != -1:
+        if epoch % self._test_interval != 0 and epoch != -1:
             return continue_training
 
         self.charge_model.model.eval()
@@ -892,9 +861,9 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         )
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.train_fraction)
+        train_df = psm_df.sample(frac=self._train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.validation_fraction / (1 - self.train_fraction)
+            frac=self._validation_fraction / (1 - self._train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -925,25 +894,25 @@ def finetune_charge(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning Charge model with following settings:")
         logger.info(
-            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self._train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self._validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self._test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.charge_model.model.train()
         self.charge_model.train(
             train_df,
-            batch_size=self.batch_size,
-            epoch=self.epochs,
-            warmup_epoch=self.warmup_epochs,
-            lr=self.max_lr,
+            batch_size=self._batch_size,
+            epoch=self._epochs,
+            warmup_epoch=self._warmup_epochs,
+            lr=self._max_lr,
         )
 
         self._test_charge(
-            self.epochs, 0, test_df, test_metric_manager, data_split="test"
+            self._epochs, 0, test_df, test_metric_manager, data_split="test"
         )
         metrics = test_metric_manager.get_stats()
 
@@ -978,7 +947,7 @@ def _test_ccs(
             Whether to continue training or not based on the early stopping criteria.
         """
         continue_training = True
-        if epoch % self.test_interval != 0 and epoch != -1:
+        if epoch % self._test_interval != 0 and epoch != -1:
             return continue_training
 
         self.ccs_model.model.eval()
@@ -1032,9 +1001,9 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
             psm_df["mobility"] = ccs_to_mobility_for_df(psm_df, "ccs")
 
         # Shuffle the psm_df and split it into train and test
-        train_df = psm_df.sample(frac=self.train_fraction)
+        train_df = psm_df.sample(frac=self._train_fraction)
         val_df = psm_df.drop(train_df.index).sample(
-            frac=self.validation_fraction / (1 - self.train_fraction)
+            frac=self._validation_fraction / (1 - self._train_fraction)
         )
         test_df = psm_df.drop(train_df.index).drop(val_df.index)
 
@@ -1062,24 +1031,24 @@ def finetune_ccs(self, psm_df: pd.DataFrame) -> pd.DataFrame:
         # Train the model
         logger.progress(" Fine-tuning CCS model with the following settings:")
         logger.info(
-            f" Train fraction:      {self.train_fraction:3.2f}     Train size:      {len(train_df):<10}"
+            f" Train fraction:      {self._train_fraction:3.2f}     Train size:      {len(train_df):<10}"
         )
         logger.info(
-            f" Validation fraction: {self.validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
+            f" Validation fraction: {self._validation_fraction:3.2f}     Validation size: {len(val_df):<10}"
         )
         logger.info(
-            f" Test fraction:       {self.test_fraction:3.2f}     Test size:       {len(test_df):<10}"
+            f" Test fraction:       {self._test_fraction:3.2f}     Test size:       {len(test_df):<10}"
         )
         self.ccs_model.model.train()
         self.ccs_model.train(
             train_df,
-            batch_size=self.batch_size,
-            epoch=self.epochs,
-            warmup_epoch=self.warmup_epochs,
-            lr=self.max_lr,
+            batch_size=self._batch_size,
+            epoch=self._epochs,
+            warmup_epoch=self._warmup_epochs,
+            lr=self._max_lr,
         )
 
-        self._test_ccs(self.epochs, 0, test_df, test_metric_manager, data_split="test")
+        self._test_ccs(self._epochs, 0, test_df, test_metric_manager, data_split="test")
 
         metrics = test_metric_manager.get_stats()
 

From de3ae18cd20f0fcb0dbe2fd71b4291bc4c5ad850 Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Sun, 1 Sep 2024 05:07:59 +0300
Subject: [PATCH 218/250] feat: save stats of transfer learning to disk

---
 alphadia/outputtransform.py | 29 ++++++++++++++++++++++++-----
 1 file changed, 24 insertions(+), 5 deletions(-)

diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py
index 2620169b..ee269d8f 100644
--- a/alphadia/outputtransform.py
+++ b/alphadia/outputtransform.py
@@ -302,6 +302,7 @@ class SearchPlanOutput:
     LIBRARY_OUTPUT = "speclib.mbr"
     TRANSFER_OUTPUT = "speclib.transfer"
     TRANSFER_MODEL = "peptdeep.transfer"
+    TRANSFER_STATS_OUTPUT = "stats.transfer"
 
     def __init__(self, config: dict, output_folder: str):
         """Combine individual searches into and build combined outputs
@@ -377,9 +378,17 @@ def build(
             _ = self.build_transfer_library(folder_list, save=True)
 
         if self.config["transfer_learning"]["enabled"]:
-            _ = self.build_transfer_model()
+            _ = self.build_transfer_model(save=True)
 
-    def build_transfer_model(self):
+    def build_transfer_model(self, save=True):
+        """
+        Finetune PeptDeep models using the transfer library
+
+        Parameters
+        ----------
+        save : bool, optional
+            Whether to save the statistics of the transfer learning on disk, by default True
+        """
         logger.progress("Train PeptDeep Models")
 
         transfer_lib_path = os.path.join(
@@ -400,14 +409,24 @@ def build_transfer_model(self):
         tune_mgr = FinetuneManager(
             device=device, settings=self.config["transfer_learning"]
         )
-        tune_mgr.finetune_rt(transfer_lib.precursor_df)
-        tune_mgr.finetune_charge(transfer_lib.precursor_df)
-        tune_mgr.finetune_ms2(
+        rt_stats = tune_mgr.finetune_rt(transfer_lib.precursor_df)
+        charge_stats = tune_mgr.finetune_charge(transfer_lib.precursor_df)
+        ms2_stats = tune_mgr.finetune_ms2(
             transfer_lib.precursor_df.copy(), transfer_lib.fragment_intensity_df.copy()
         )
 
         tune_mgr.save_models(os.path.join(self.output_folder, self.TRANSFER_MODEL))
 
+        combined_stats = pd.concat([rt_stats, charge_stats, ms2_stats])
+
+        if save:
+            logger.info("Writing transfer learning stats output to disk")
+            write_df(
+                combined_stats,
+                os.path.join(self.output_folder, self.TRANSFER_STATS_OUTPUT),
+                file_format="tsv",
+            )
+
     def build_transfer_library(
         self,
         folder_list: list[str],

From f7751796d9a19595952466e6e900c7e0de12e30d Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Sun, 1 Sep 2024 05:52:04 +0300
Subject: [PATCH 219/250] fix: incorrect rt and mobility columns

---
 alphadia/outputaccumulator.py              |  7 ++---
 tests/unit_tests/test_outputaccumulator.py | 36 ++++++++++++++++++++++
 2 files changed, 39 insertions(+), 4 deletions(-)

diff --git a/alphadia/outputaccumulator.py b/alphadia/outputaccumulator.py
index 4d0ef135..16b8b808 100644
--- a/alphadia/outputaccumulator.py
+++ b/alphadia/outputaccumulator.py
@@ -137,15 +137,14 @@ def parse_output_folder(
         psm_df["raw_name"] = foldername
 
         # remove decoy precursors
-        psm_df = psm_df[psm_df["decoy"] == 0]
+        # assert that  decoy is int
+        psm_df["decoy"] = psm_df["decoy"].astype(int)
+        psm_df = psm_df[psm_df["decoy"] == 0].reset_index(drop=True)
 
         self._precursor_df = pd.DataFrame()
         for col in psm_df.columns:
             self._precursor_df[col] = psm_df[col]
 
-        self._precursor_df["decoy"] = self._precursor_df["decoy"].astype(int)
-        self._precursor_df = psm_df[psm_df["decoy"] == 0].reset_index(drop=True)
-
         # self._precursor_df.set_index('precursor_idx', inplace=True)
         # Change the data type of the mods column to string
         self._precursor_df["mods"] = self._precursor_df["mods"].astype(str)
diff --git a/tests/unit_tests/test_outputaccumulator.py b/tests/unit_tests/test_outputaccumulator.py
index 5e51d99f..5b4c683e 100644
--- a/tests/unit_tests/test_outputaccumulator.py
+++ b/tests/unit_tests/test_outputaccumulator.py
@@ -210,3 +210,39 @@ def test_keep_top_constraint():
         ), f"{len(built_lib.precursor_df[built_lib.precursor_df['precursor_idx'] == precursor_idx])} != {keep_top}"
 
     shutil.rmtree(temp_folder)
+
+
+def test_default_column_assignment():
+    """
+    Test that col [rt,mobility,mz] columns are correctly assigned where:
+    col = col_observed if col_observed is in columns
+    col = col_calibrated if col_observed is not in columns
+    col = col_library if col_observed is not in columns and col_calibrated is not in columns
+    """
+    # Given:
+    config, temp_folder, raw_folders, psm_dfs, fragment_dfs = prepare_input_data()
+    keep_top = 2
+    config["transfer_library"]["top_k_samples"] = keep_top
+
+    # When:
+    output = outputtransform.SearchPlanOutput(config, temp_folder)
+    _ = output.build_transfer_library(raw_folders, save=True)
+    built_lib = SpecLibBase()
+    built_lib.load_hdf(
+        os.path.join(temp_folder, f"{output.TRANSFER_OUTPUT}.hdf"), load_mod_seq=True
+    )
+
+    # Then: The columns rt, mobility, mz should be correctly assigned
+    for col in ["rt", "mobility", "mz"]:
+        if f"{col}_observed" in built_lib.precursor_df.columns:
+            assert built_lib.precursor_df[f"{col}"].equals(
+                built_lib.precursor_df[f"{col}_observed"]
+            ), f"{col} != {col}_observed"
+        elif f"{col}_calibrated" in built_lib.precursor_df.columns:
+            assert built_lib.precursor_df[f"{col}"].equals(
+                built_lib.precursor_df[f"{col}_calibrated"]
+            ), f"{col} != {col}_calibrated"
+        else:
+            assert built_lib.precursor_df[f"{col}"].equals(
+                built_lib.precursor_df[f"{col}_library"]
+            ), f"{col} != {col}_library"

From 3827452babcace31c263efacdf81d2ecbb0d84ef Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Mon, 2 Sep 2024 20:58:47 +0200
Subject: [PATCH 220/250] fix installers

---
 .gitignore                                  |  2 ++
 release/macos/build_gui_macos.sh            |  1 +
 release/macos/build_installer_macos.sh      |  5 ++--
 release/macos/build_package_macos.sh        | 29 +++++++--------------
 release/windows/alphadia_innoinstaller.iss  |  4 +--
 release/windows/build_installer.ps1         |  2 +-
 release/windows/build_installer_windows.ps1 |  4 +--
 release/windows/build_package_windows.ps1   |  1 -
 8 files changed, 19 insertions(+), 29 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7da601ba..6d8cadbb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -41,6 +41,8 @@ MANIFEST
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
 # *.spec
+dist_pyinstaller/
+build_pyinstaller/
 
 # Installer logs
 pip-log.txt
diff --git a/release/macos/build_gui_macos.sh b/release/macos/build_gui_macos.sh
index cd4a64f9..be861a7e 100755
--- a/release/macos/build_gui_macos.sh
+++ b/release/macos/build_gui_macos.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e -u
 
 # Build the GUI for MacOS.
 # This script needs to be run from the root of the repository.
diff --git a/release/macos/build_installer_macos.sh b/release/macos/build_installer_macos.sh
index 4401eea8..4ce33d3e 100755
--- a/release/macos/build_installer_macos.sh
+++ b/release/macos/build_installer_macos.sh
@@ -1,12 +1,11 @@
 #!/bin/bash
+set -e -u
 
 # Build the installer for MacOS.
 # This script needs to be run from the root of the repository.
 
-pip install build
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install pyinstaller
-pyinstaller release/pyinstaller/alphadia.spec -y
+pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/macos/build_package_macos.sh b/release/macos/build_package_macos.sh
index 5e3df255..76de8a49 100755
--- a/release/macos/build_package_macos.sh
+++ b/release/macos/build_package_macos.sh
@@ -1,9 +1,9 @@
 #!/bin/bash
+set -e -u
 
 # Build the install package for MacOS.
 # This script must be run from the root of the repository after running build_installer_macos.sh and build_gui_macos.sh
 
-
 # Set up package name and version
 PACKAGE_NAME="alphadia"
 APP_NAME="alphaDIA"
@@ -15,31 +15,23 @@ rm -rf ${BUILD_NAME}.pkg
 
 # Cleanup the package folder
 echo "Cleaning up the package folder"
-
 rm -rf $PKG_FOLDER
 
 # === Prepare structure ===
 echo "Preparing package structure"
-
-mkdir -p $PKG_FOLDER
 mkdir -p $PKG_FOLDER/Contents/Resources
 mkdir -p $PKG_FOLDER/Contents/MacOS
 mkdir -p $PKG_FOLDER/Contents/Frameworks
 
-
-
 # === Backend ===
 echo "Copying backend"
+BACKEND_BUILD="dist_pyinstaller/$PACKAGE_NAME"
 
-BACKEND_BUILD="dist/$PACKAGE_NAME"
-
-# Check if the backend build exists, otherwise exit with an error
 if [ ! -d "$BACKEND_BUILD" ]; then
   echo "Backend build not found at $BACKEND_BUILD"
   exit 1
 fi
 
-# Copy the backend
 cp -a ${BACKEND_BUILD}/. $PKG_FOLDER/Contents/Frameworks/
 
 
@@ -56,27 +48,24 @@ echo ARCH=${ARCH} KERNEL=${KERNEL}
 
 ls ./gui/out
 
-# Set the path to the GUI build
 GUI_BUILD="./gui/out/${PACKAGE_NAME}-gui-${KERNEL}-${ARCH}"
-
-# Check if the GUI build exists, otherwise exit with an error
 if [ ! -d "$GUI_BUILD" ]; then
   echo "GUI build not found at $GUI_BUILD"
   exit 1
 fi
 
-# Copy the electron forge build
 cp -a ${GUI_BUILD}/. $PKG_FOLDER/Contents/Frameworks/
 
 # === Resources ===
 echo "Copying resources"
-cp release/logos/$PACKAGE_NAME.icns $PKG_FOLDER/Contents/Resources/
-cp release/logos/$PACKAGE_NAME.png $PKG_FOLDER/Contents/Resources/
-cp release/macos/$APP_NAME $PKG_FOLDER/Contents/MacOS/
+CONTENTS_FOLDER=$PKG_FOLDER/Contents
+cp release/logos/$PACKAGE_NAME.icns ${CONTENTS_FOLDER}/Resources
+cp release/logos/$PACKAGE_NAME.png ${CONTENTS_FOLDER}/Resources
+cp release/macos/$APP_NAME ${CONTENTS_FOLDER}/MacOS
 
-cp release/macos/Info.plist $PKG_FOLDER/Contents/
+cp release/macos/Info.plist ${CONTENTS_FOLDER}
 
 # change permissions for entry script
-chmod +x $PKG_FOLDER/Contents/MacOS/$APP_NAME
+chmod +x ${CONTENTS_FOLDER}/MacOS/$APP_NAME
 
-pkgbuild --root $PKG_FOLDER --identifier de.mpg.biochem.$PACKAGE_NAME.app --version $PACKAGE_VERSION --install-location /Applications/$APP_NAME.app --scripts release/macos/scripts dist/$BUILD_NAME.pkg --nopayload
+pkgbuild --root $PKG_FOLDER --identifier de.mpg.biochem.$PACKAGE_NAME.app --version $PACKAGE_VERSION --install-location /Applications/$APP_NAME.app --scripts release/macos/scripts $BUILD_NAME.pkg --nopayload
diff --git a/release/windows/alphadia_innoinstaller.iss b/release/windows/alphadia_innoinstaller.iss
index b3aff4e5..f06063eb 100644
--- a/release/windows/alphadia_innoinstaller.iss
+++ b/release/windows/alphadia_innoinstaller.iss
@@ -24,7 +24,7 @@ LicenseFile=..\..\LICENSE.txt
 ; Uncomment the following line to run in non administrative install mode (install for current user only.)
 PrivilegesRequired=lowest
 PrivilegesRequiredOverridesAllowed=dialog
-OutputDir=..\..\dist
+OutputDir=.\
 ; example for BUILD_NAME: alphadia-1.7.2-win-x64
 OutputBaseFilename={#GetEnv('BUILD_NAME')}
 SetupIconFile=..\logos\alphadia.ico
@@ -41,7 +41,7 @@ Name: "desktopicon"; Description: "{cm:CreateDesktopIcon}"; GroupDescription: "{
 [Files]
 Source: "..\..\gui\out\alphadia-gui-win32-x64\{#MyAppExeName}"; DestDir: "{app}"; Flags: ignoreversion
 Source: "..\..\gui\out\alphadia-gui-win32-x64\*"; DestDir: "{app}"; Flags: ignoreversion recursesubdirs createallsubdirs
-Source: "..\..\dist\alphadia\*"; DestDir: "{app}"; Flags: ignoreversion recursesubdirs createallsubdirs
+Source: "..\..\dist_pyinstaller\alphadia\*"; DestDir: "{app}"; Flags: ignoreversion recursesubdirs createallsubdirs
 
 ; NOTE: Don't use "Flags: ignoreversion" on any shared system files
 
diff --git a/release/windows/build_installer.ps1 b/release/windows/build_installer.ps1
index 126891ed..feb8e965 100644
--- a/release/windows/build_installer.ps1
+++ b/release/windows/build_installer.ps1
@@ -1,4 +1,4 @@
 # TODO remove with old release workflow
 
 
-&  "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" .\release\windows\alphadia_innoinstaller.iss
+&  "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" .\release\windows\alphadia_innoinstaller_old.iss
diff --git a/release/windows/build_installer_windows.ps1 b/release/windows/build_installer_windows.ps1
index b227514e..50c6b4a9 100644
--- a/release/windows/build_installer_windows.ps1
+++ b/release/windows/build_installer_windows.ps1
@@ -9,5 +9,5 @@ python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install pyinstaller tbb==2021.13.1
-pyinstaller release/pyinstaller/alphadia.spec -y
+pip install tbb==2021.13.1
+pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/windows/build_package_windows.ps1 b/release/windows/build_package_windows.ps1
index 1b66cdab..0f2a2881 100644
--- a/release/windows/build_package_windows.ps1
+++ b/release/windows/build_package_windows.ps1
@@ -1,4 +1,3 @@
 # Build the install package for Windows.
 # This script must be run from the root of the repository after running build_installer_windows.ps1
-
 &  "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" .\release\windows\alphadia_innoinstaller.iss

From 3e5b0b9f0145d403dd69e1ea7bd0b0c393c3f37a Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Mon, 2 Sep 2024 21:08:04 +0200
Subject: [PATCH 221/250] fix installers

---
 release/windows/alphadia_innoinstaller.iss | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/release/windows/alphadia_innoinstaller.iss b/release/windows/alphadia_innoinstaller.iss
index f06063eb..65544d64 100644
--- a/release/windows/alphadia_innoinstaller.iss
+++ b/release/windows/alphadia_innoinstaller.iss
@@ -1,6 +1,9 @@
 ; Script generated by the Inno Setup Script Wizard.
 ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES!
 
+; Note: apparently, ISCC uses the directory of the .iss input file as the working directory,
+; so all paths are given relative to the location of this .iss file.
+
 #define MyAppName "AlphaDIA"
 #define MyAppVersion "1.7.2"
 #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs"
@@ -24,7 +27,8 @@ LicenseFile=..\..\LICENSE.txt
 ; Uncomment the following line to run in non administrative install mode (install for current user only.)
 PrivilegesRequired=lowest
 PrivilegesRequiredOverridesAllowed=dialog
-OutputDir=.\
+; release workflow expects artifact at root of repository
+OutputDir=..\..\
 ; example for BUILD_NAME: alphadia-1.7.2-win-x64
 OutputBaseFilename={#GetEnv('BUILD_NAME')}
 SetupIconFile=..\logos\alphadia.ico

From 4272ae646f8af9f6b725536c26832572fd36a8b7 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 4 Sep 2024 16:56:14 +0200
Subject: [PATCH 222/250] get rid of most pip installs in installer building

---
 release/macos/build_backend_macos.sh | 2 --
 release/windows/build_backend.ps1    | 2 +-
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/release/macos/build_backend_macos.sh b/release/macos/build_backend_macos.sh
index 5afe4671..b0395d5a 100755
--- a/release/macos/build_backend_macos.sh
+++ b/release/macos/build_backend_macos.sh
@@ -2,10 +2,8 @@
 
 # navigate to the root directory
 
-pip install build
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install pyinstaller
 pyinstaller release/pyinstaller/alphadia.spec -y
diff --git a/release/windows/build_backend.ps1 b/release/windows/build_backend.ps1
index b227514e..e59e98fc 100644
--- a/release/windows/build_backend.ps1
+++ b/release/windows/build_backend.ps1
@@ -9,5 +9,5 @@ python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install pyinstaller tbb==2021.13.1
+pip install tbb==2021.13.1
 pyinstaller release/pyinstaller/alphadia.spec -y

From fb653fc05fbec25f3d876f1720e3f9243922cc94 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Fri, 6 Sep 2024 17:04:46 +0200
Subject: [PATCH 223/250] adapt to new release workflow

---
 .github/workflows/create_release.yml  | 19 +++++++------------
 .github/workflows/publish_on_pypi.yml |  2 +-
 2 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml
index cb800352..49cba5c2 100644
--- a/.github/workflows/create_release.yml
+++ b/.github/workflows/create_release.yml
@@ -1,27 +1,22 @@
 # Create a draft release and build and upload all installers to it.
-name: Create Draft Release
+name: Create Release
 
 on:
   workflow_dispatch:
     inputs:
-      commit_to_release:
-        description: 'Enter commit hash to release (example: ef4037cb571f99cb4919b520fde7174972aae473)'
+      commitish_to_release:
         type: string
-        required: true
-      tag_to_release:
-        description: 'Enter tag to release (example: v1.5.5)'
-        type: string
-        required: true
+        description: 'Enter commit hash or branch to release (default: main).'
+        default: "main"
 
 jobs:
   create-release:
+    uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     secrets: inherit
     permissions:
       contents: write
-    uses: MannLabs/alphashared/.github/workflows/create_release.yml@v1
     with:
       package_name: alphadia
-      commit_to_release: ${{ inputs.commit_to_release }}
-      tag_to_release: ${{ inputs.tag_to_release }}
+      commitish_to_release: ${{ inputs.commitish_to_release }}
       build_nodejs_ui: true
-      test_app: true
+      test_app: false
diff --git a/.github/workflows/publish_on_pypi.yml b/.github/workflows/publish_on_pypi.yml
index e89d5b47..4a538d6f 100644
--- a/.github/workflows/publish_on_pypi.yml
+++ b/.github/workflows/publish_on_pypi.yml
@@ -14,7 +14,7 @@ jobs:
     uses: MannLabs/alphashared/.github/workflows/publish_on_pypi.yml@v1
     with:
       # see the documentation of the workflow for more information on the parameters
-      package_name: alphdia
+      package_name: alphadia
       tag_to_release: ${{ inputs.tag_to_release }}
     secrets:
       test_pypi_api_token: ${{ secrets.TEST_PYPI_API_TOKEN }}

From 3d55bd1089f1660dcb35cc4ee7013ba442dfedec Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 09:06:27 +0200
Subject: [PATCH 224/250] add linux installer

---
 release/linux/build_gui_linux.sh       | 13 +++++++++++++
 release/linux/build_installer_linux.sh | 18 ++++++++++++++++++
 release/linux/build_package_linux.sh   | 21 +++++++++++++++++++++
 release/linux/control                  |  7 +++++++
 4 files changed, 59 insertions(+)
 create mode 100755 release/linux/build_gui_linux.sh
 create mode 100755 release/linux/build_installer_linux.sh
 create mode 100755 release/linux/build_package_linux.sh
 create mode 100644 release/linux/control

diff --git a/release/linux/build_gui_linux.sh b/release/linux/build_gui_linux.sh
new file mode 100755
index 00000000..be861a7e
--- /dev/null
+++ b/release/linux/build_gui_linux.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+set -e -u
+
+# Build the GUI for MacOS.
+# This script needs to be run from the root of the repository.
+
+# Cleanup the GUI build
+rm -rf gui/dist
+rm -rf gui/out
+
+npm install --prefix gui
+# Build the GUI using electron forge
+npm run make --prefix gui
diff --git a/release/linux/build_installer_linux.sh b/release/linux/build_installer_linux.sh
new file mode 100755
index 00000000..7d546577
--- /dev/null
+++ b/release/linux/build_installer_linux.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+set -e -u
+
+# Build the installer for Linux.
+# This script must be run from the root of the repository.
+
+rm -rf dist build *.egg-info
+rm -rf dist_pyinstaller build_pyinstaller
+
+# Creating the wheel
+python setup.py sdist bdist_wheel
+
+# Setting up the local package
+# Make sure you include the required extra packages and always use the stable or very-stable options!
+pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+
+# Creating the stand-alone pyinstaller folder
+pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
new file mode 100755
index 00000000..118a26da
--- /dev/null
+++ b/release/linux/build_package_linux.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+set -e -u
+
+# Build the install package for Linux.
+# This script must be run from the root of the repository after running build_installer_linux.sh
+
+PACKAGE_NAME=alphadia
+
+# BUILD_NAME is taken from environment variables, e.g. 'alphadia-1.2.1-linux-x64'
+rm -rf ${BUILD_NAME}.deb
+
+
+# Wrapping the pyinstaller folder in a .deb package
+mkdir -p dist_pyinstaller/${BUILD_NAME}/usr/local/bin
+mv dist_pyinstaller/${PACKAGE_NAME} dist_pyinstaller/${BUILD_NAME}/usr/local/bin/${PACKAGE_NAME}
+mkdir dist_pyinstaller/${BUILD_NAME}/DEBIAN
+cp release/linux/control dist_pyinstaller/${BUILD_NAME}/DEBIAN
+dpkg-deb --build --root-owner-group dist_pyinstaller/${BUILD_NAME}
+
+# release workflow expects artifact at root of repository
+mv dist_pyinstaller/${BUILD_NAME}.deb .
diff --git a/release/linux/control b/release/linux/control
new file mode 100644
index 00000000..fb917c73
--- /dev/null
+++ b/release/linux/control
@@ -0,0 +1,7 @@
+Package: alphadia
+Version: 1.7.2
+Architecture: all
+Maintainer: Mann Labs <opensource@alphapept.com>
+Description: alphadia
+  AlphaDIA is an open-source Python package in the AlphaX ecosystem.
+  AlphaDIA was developed by the Mann Labs at the Max Planck Institute of Biochemistry and University of Copenhagen and is freely available with an Apache License. Additional third-party licenses are applicable for external Python packages (see https://github.com/MannLabs/alphadia for more details.).

From 5b4c4b66e8620f1ecb650d9fbdca180c3874e4ed Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 09:35:12 +0200
Subject: [PATCH 225/250] add gui to linux package

---
 release/linux/build_package_linux.sh | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index 118a26da..a00055e9 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -9,9 +9,31 @@ PACKAGE_NAME=alphadia
 # BUILD_NAME is taken from environment variables, e.g. 'alphadia-1.2.1-linux-x64'
 rm -rf ${BUILD_NAME}.deb
 
+mkdir -p dist_pyinstaller/${BUILD_NAME}/usr/local/bin
+
+# === GUI ===
+echo "Copying GUI"
+
+# TODO find out where this contract from ARCH & KERNEL is used in GUI building
+ARCH=$(uname -m)
+if [ "$ARCH" == "x86_64" ]; then
+  ARCH="x64"
+fi
+KERNEL=$(uname -s | tr '[:upper:]' '[:lower:]')
+echo ARCH=${ARCH} KERNEL=${KERNEL}
+
+ls ./gui/out
+
+GUI_BUILD="./gui/out/${PACKAGE_NAME}-gui-${KERNEL}-${ARCH}"
+if [ ! -d "$GUI_BUILD" ]; then
+  echo "GUI build not found at $GUI_BUILD"
+  exit 1
+fi
+
+cp -a ${GUI_BUILD}/. dist_pyinstaller/${BUILD_NAME}/usr/local/bin/
+
 
 # Wrapping the pyinstaller folder in a .deb package
-mkdir -p dist_pyinstaller/${BUILD_NAME}/usr/local/bin
 mv dist_pyinstaller/${PACKAGE_NAME} dist_pyinstaller/${BUILD_NAME}/usr/local/bin/${PACKAGE_NAME}
 mkdir dist_pyinstaller/${BUILD_NAME}/DEBIAN
 cp release/linux/control dist_pyinstaller/${BUILD_NAME}/DEBIAN

From 06bf4f29528c8b524b1beb65b2b64051687c1c51 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 09:40:47 +0200
Subject: [PATCH 226/250] fix linux installer

---
 release/linux/build_installer_linux.sh | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/release/linux/build_installer_linux.sh b/release/linux/build_installer_linux.sh
index 7d546577..4e0aced4 100755
--- a/release/linux/build_installer_linux.sh
+++ b/release/linux/build_installer_linux.sh
@@ -7,11 +7,7 @@ set -e -u
 rm -rf dist build *.egg-info
 rm -rf dist_pyinstaller build_pyinstaller
 
-# Creating the wheel
-python setup.py sdist bdist_wheel
-
-# Setting up the local package
-# Make sure you include the required extra packages and always use the stable or very-stable options!
+python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder

From a99e65082ad61e940cbf03a447630496a5cd5e11 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 09:54:57 +0200
Subject: [PATCH 227/250] rename alphaDIA -> alphadia

---
 release/macos/{alphaDIA => alphadia} |  0
 release/macos/build_package_macos.sh |  2 +-
 release/macos/build_pkg_macos.sh     |  8 ++++----
 release/macos/distribution.xml       | 10 +++++-----
 release/macos/info.plist             |  6 +++---
 release/macos/scripts/postinstall    |  4 ++--
 release/pyinstaller/cli_hook.py      |  2 +-
 7 files changed, 16 insertions(+), 16 deletions(-)
 rename release/macos/{alphaDIA => alphadia} (100%)

diff --git a/release/macos/alphaDIA b/release/macos/alphadia
similarity index 100%
rename from release/macos/alphaDIA
rename to release/macos/alphadia
diff --git a/release/macos/build_package_macos.sh b/release/macos/build_package_macos.sh
index 76de8a49..497c05ad 100755
--- a/release/macos/build_package_macos.sh
+++ b/release/macos/build_package_macos.sh
@@ -6,7 +6,7 @@ set -e -u
 
 # Set up package name and version
 PACKAGE_NAME="alphadia"
-APP_NAME="alphaDIA"
+APP_NAME="alphadia"
 PACKAGE_VERSION="1.7.2"
 PKG_FOLDER="dist/$APP_NAME.app"
 
diff --git a/release/macos/build_pkg_macos.sh b/release/macos/build_pkg_macos.sh
index 99a39d59..4d1ac816 100755
--- a/release/macos/build_pkg_macos.sh
+++ b/release/macos/build_pkg_macos.sh
@@ -15,7 +15,7 @@ KERNEL=$(uname -s | tr '[:upper:]' '[:lower:]')
 
 BUILD_NAME="${PACKAGE_NAME}-${PACKAGE_VERSION}-${KERNEL}-${ARCH}"
 
-PKG_FOLDER="dist/alphaDIA.app"
+PKG_FOLDER="dist/alphadia.app"
 
 # Cleanup the package folder
 echo "Cleaning up the package folder"
@@ -68,11 +68,11 @@ cp -a ${GUI_BUILD}/. $PKG_FOLDER/Contents/Frameworks/
 echo "Copying resources"
 cp release/logos/alphadia.icns $PKG_FOLDER/Contents/Resources/
 cp release/logos/alphadia.png $PKG_FOLDER/Contents/Resources/
-cp release/macos/alphaDIA $PKG_FOLDER/Contents/MacOS/
+cp release/macos/alphadia $PKG_FOLDER/Contents/MacOS/
 
 cp release/macos/Info.plist $PKG_FOLDER/Contents/
 
 #change permissions for entry script
-chmod +x $PKG_FOLDER/Contents/MacOS/alphaDIA
+chmod +x $PKG_FOLDER/Contents/MacOS/alphadia
 
-pkgbuild --root $PKG_FOLDER --identifier de.mpg.biochem.alphadia.app --version $PACKAGE_VERSION --install-location /Applications/alphaDIA.app --scripts release/macos/scripts dist/$BUILD_NAME.pkg --nopayload
+pkgbuild --root $PKG_FOLDER --identifier de.mpg.biochem.alphadia.app --version $PACKAGE_VERSION --install-location /Applications/alphadia.app --scripts release/macos/scripts dist/$BUILD_NAME.pkg --nopayload
diff --git a/release/macos/distribution.xml b/release/macos/distribution.xml
index c549e8d1..d760dfd6 100644
--- a/release/macos/distribution.xml
+++ b/release/macos/distribution.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8" standalone="no"?>
 <installer-script minSpecVersion="1.000000">
-    <title>alphaDIA 1.7.2</title>
+    <title>AlphaDIA 1.7.2</title>
     <background mime-type="image/png" file="alphadia.png" scaling="proportional"/>
     <welcome file="welcome.html" mime-type="text/html" />
     <conclusion file="conclusion.html" mime-type="text/html" />
@@ -8,10 +8,10 @@
     <options customize="never" allow-external-scripts="no"/>
     <domains enable_localSystem="true" />
     <choices-outline>
-        <line choice="alphaDIA"/>
+        <line choice="alphadia"/>
     </choices-outline>
-    <choice id="alphaDIA" title="alphaDIA">
-        <pkg-ref id="alphaDIA.pkg"/>
+    <choice id="alphadia" title="alphadia">
+        <pkg-ref id="alphadia.pkg"/>
     </choice>
-    <pkg-ref id="alphaDIA.pkg" auth="Root">alphaDIA.pkg</pkg-ref>
+    <pkg-ref id="alphadia.pkg" auth="Root">alphadia.pkg</pkg-ref>
 </installer-script>
diff --git a/release/macos/info.plist b/release/macos/info.plist
index 8aa19f72..d7b99b0e 100644
--- a/release/macos/info.plist
+++ b/release/macos/info.plist
@@ -3,9 +3,9 @@
 <plist version="1.0">
 <dict>
 	<key>CFBundleDisplayName</key>
-	<string>alphaDIA</string>
+	<string>AlphaDIA</string>
     <key>CFBundleExecutable</key>
-    <string>alphaDIA</string>
+    <string>alphadia</string>
 	<key>CFBundleIconFile</key>
 	<string>alphadia.icns</string>
 	<key>CFBundleIdentifier</key>
@@ -15,7 +15,7 @@
 	<key>CFBundleInfoDictionaryVersion</key>
 	<string>6.0</string>
 	<key>CFBundleName</key>
-	<string>alphaDIA</string>
+	<string>alphadia</string>
 	<key>CFBundlePackageType</key>
 	<string>APPL</string>
 	<key>LSBackgroundOnly</key>
diff --git a/release/macos/scripts/postinstall b/release/macos/scripts/postinstall
index a30d6b75..ddb11a73 100755
--- a/release/macos/scripts/postinstall
+++ b/release/macos/scripts/postinstall
@@ -1,6 +1,6 @@
 #!/bin/sh
 
 # make sure this file itself is executable
-xattr -dr com.apple.quarantine /Applications/alphaDIA.app
-chmod -R 577 /Applications/alphaDIA.app
+xattr -dr com.apple.quarantine /Applications/alphadia.app
+chmod -R 577 /Applications/alphadia.app
 echo "Postinstall finished"
diff --git a/release/pyinstaller/cli_hook.py b/release/pyinstaller/cli_hook.py
index c412fa6c..1ac98459 100644
--- a/release/pyinstaller/cli_hook.py
+++ b/release/pyinstaller/cli_hook.py
@@ -6,7 +6,7 @@
     from alphadia import cli
 
     # catch pyinstaller bug
-    # subprocess.CalledProcessError: Command '['/Applications/alphaDIA.app/Contents/Frameworks/alphadia', '-sS', '-c', 'import platform; print(platform.mac_ver()[0])']' returned non-zero exit status 2.
+    # subprocess.CalledProcessError: Command '['/Applications/alphadia.app/Contents/Frameworks/alphadia', '-sS', '-c', 'import platform; print(platform.mac_ver()[0])']' returned non-zero exit status 2.
     # [36481] Failed to execute script 'cli_hook' due to unhandled exception!
     if len(sys.argv) >= 3 and (sys.argv[1] == "-sS") and (sys.argv[2] == "-c"):
         import platform

From 9aed2861293c87bf85c080dfe66797b4f1248baf Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 10:00:23 +0200
Subject: [PATCH 228/250] rename alphaDIA -> alphadia

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 08974b35..7407154d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "alphaDIA"
+name = "alphadia"
 requires-python = ">=3.10.0"
 dynamic = ["version", "dependencies", "optional-dependencies"]
 

From ce1d0f16163754cf26b053d4d0fd0eee2ab04ec0 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 10:25:44 +0200
Subject: [PATCH 229/250] rename bundle_name AlphaDIA -> alphadia

---
 release/pyinstaller/alphadia.spec | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/release/pyinstaller/alphadia.spec b/release/pyinstaller/alphadia.spec
index 7862707c..b887430b 100644
--- a/release/pyinstaller/alphadia.spec
+++ b/release/pyinstaller/alphadia.spec
@@ -21,7 +21,7 @@ block_cipher = None
 location = os.getcwd()
 project = "alphadia"
 remove_tests = True
-bundle_name = "AlphaDIA"
+bundle_name = "alphadia"
 #####################
 
 

From 224bf07ef97578014197990e2808b0412faf69a8 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 11:17:44 +0200
Subject: [PATCH 230/250] fix linux installer building

---
 release/linux/build_installer_linux.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/release/linux/build_installer_linux.sh b/release/linux/build_installer_linux.sh
index 4e0aced4..441b628f 100755
--- a/release/linux/build_installer_linux.sh
+++ b/release/linux/build_installer_linux.sh
@@ -12,3 +12,6 @@ pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
+
+# to avoid 'no space left on device' error
+rm -r build_pyinstaller

From b3d819712ec749cb9efddf5aa575357c865ee3e6 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 12:50:22 +0200
Subject: [PATCH 231/250] limit linux installer building to cpu

---
 release/linux/build_installer_linux.sh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/release/linux/build_installer_linux.sh b/release/linux/build_installer_linux.sh
index 441b628f..bb25fb4b 100755
--- a/release/linux/build_installer_linux.sh
+++ b/release/linux/build_installer_linux.sh
@@ -1,6 +1,8 @@
 #!/bin/bash
 set -e -u
 
+CPU_OR_GPU=${1:-CPU}
+
 # Build the installer for Linux.
 # This script must be run from the root of the repository.
 
@@ -10,6 +12,10 @@ rm -rf dist_pyinstaller build_pyinstaller
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
+if [ "${CPU_OR_GPU}" != "GPU" ]; then
+    pip install torch -U --extra-index-url https://download.pytorch.org/whl/cpu
+fi
+
 # Creating the stand-alone pyinstaller folder
 pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
 

From 0a0284871cbf4e860887bf37997dfdaa48b01e12 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Tue, 10 Sep 2024 17:23:14 +0200
Subject: [PATCH 232/250] adapt bumpversion

---
 misc/.bumpversion.cfg | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 3a4123c0..0e8ab656 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -21,6 +21,8 @@ serialize =
 
 [bumpversion:file:../release/macos/info.plist]
 
+[bumpversion:file:../release/linux/build_installer_linux.sh]
+
 [bumpversion:file:../release/macos/build_installer_macos.sh]
 
 [bumpversion:file:../release/macos/build_package_macos.sh]

From a8597e4becc97827d41a32d5085d8aa1a754fc4a Mon Sep 17 00:00:00 2001
From: Mohamed Sameh <mohamedsameh2210@gmail.com>
Date: Wed, 11 Sep 2024 00:31:31 +0300
Subject: [PATCH 233/250] test: add noise to test data.

---
 tests/unit_tests/conftest.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py
index 3fa6b592..e6c8ecf3 100644
--- a/tests/unit_tests/conftest.py
+++ b/tests/unit_tests/conftest.py
@@ -35,7 +35,6 @@ def mock_precursor_df(
         A mock precursor dataframe
     """
 
-    precursor_idx = np.arange(n_precursor)
     precursor_mz = np.random.rand(n_precursor) * 2000 + 500
     precursor_charge = np.random.choice([2, 3], size=n_precursor)
 
@@ -61,18 +60,18 @@ def mock_precursor_df(
         for __ in range(6):
             sequence += chr(np.random.randint(65, 91))
         sequences.append(sequence)
-    return pd.DataFrame(
+
+    df = pd.DataFrame(
         {
-            "precursor_idx": precursor_idx,
             "decoy": decoy,
             "mz_library": precursor_mz,
             "rt_library": random_rt,
             "mobility_library": random_mobility,
-            "mz_observed": precursor_mz,
-            "rt_observed": random_rt,
-            "mobility_observed": random_mobility,
-            "mz_calibrated": precursor_mz,
-            "rt_calibrated": random_rt,
+            "mz_observed": precursor_mz + np.random.rand(n_precursor) * 0.1,
+            "rt_observed": random_rt + np.random.rand(n_precursor) * 0.1,
+            "mobility_observed": random_mobility + np.random.rand(n_precursor) * 0.1,
+            "mz_calibrated": precursor_mz + np.random.rand(n_precursor) * 0.1,
+            "rt_calibrated": random_rt + np.random.rand(n_precursor) * 0.1,
             "charge": precursor_charge,
             "proteins": proteins,
             "genes": genes,
@@ -85,6 +84,10 @@ def mock_precursor_df(
         }
     )
 
+    df = df.sample(frac=1).reset_index(drop=True)
+    df["precursor_idx"] = np.arange(len(df))
+    return df
+
 
 def mock_fragment_df(n_fragments: int = 10, n_precursor: int = 20):
     """Create a mock fragment dataframe as it's found as the individual search outputs

From 2ac12c725e7449223358412a474868063b4b8136 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 09:59:51 +0200
Subject: [PATCH 234/250] trying to fix linux GUI

---
 gui/src/main/modules/engine.js | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/gui/src/main/modules/engine.js b/gui/src/main/modules/engine.js
index 7e2e92ce..a73e543b 100644
--- a/gui/src/main/modules/engine.js
+++ b/gui/src/main/modules/engine.js
@@ -12,9 +12,12 @@ const path = require('path');
 var kill = require('tree-kill');
 
 function getAppRoot() {
+    console.log("getAppPath=" + app.getAppPath() + " platform=" + process.platform)
     if ( process.platform === 'win32' ) {
       return path.join( app.getAppPath(), '/../../' );
-    }  else {
+    } else if ( process.platform === 'linux' ) {
+      return path.join( app.getAppPath(), '/../../../../local/bin/' );
+    } else {
       return path.join( app.getAppPath(), '/../../../../' );
     }
   }

From 1829373b7e53f7fa762f76be9da81f3568edd831 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 11:46:44 +0200
Subject: [PATCH 235/250] trying to fix linux GUI

---
 gui/src/main/modules/engine.js       | 2 +-
 release/linux/build_package_linux.sh | 9 ++++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/gui/src/main/modules/engine.js b/gui/src/main/modules/engine.js
index a73e543b..894e60ba 100644
--- a/gui/src/main/modules/engine.js
+++ b/gui/src/main/modules/engine.js
@@ -16,7 +16,7 @@ function getAppRoot() {
     if ( process.platform === 'win32' ) {
       return path.join( app.getAppPath(), '/../../' );
     } else if ( process.platform === 'linux' ) {
-      return path.join( app.getAppPath(), '/../../../../local/bin/' );
+      return path.join( app.getAppPath(), '/../../' );
     } else {
       return path.join( app.getAppPath(), '/../../../../' );
     }
diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index a00055e9..99dd4bba 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -9,7 +9,8 @@ PACKAGE_NAME=alphadia
 # BUILD_NAME is taken from environment variables, e.g. 'alphadia-1.2.1-linux-x64'
 rm -rf ${BUILD_NAME}.deb
 
-mkdir -p dist_pyinstaller/${BUILD_NAME}/usr/local/bin
+BIN_PATH=dist_pyinstaller/${BUILD_NAME}/opt/${PACKAGE_NAME}/bin
+mkdir -p ${BIN_PATH}
 
 # === GUI ===
 echo "Copying GUI"
@@ -30,13 +31,15 @@ if [ ! -d "$GUI_BUILD" ]; then
   exit 1
 fi
 
-cp -a ${GUI_BUILD}/. dist_pyinstaller/${BUILD_NAME}/usr/local/bin/
+ls ${GUI_BUILD}
+cp -a ${GUI_BUILD}/. ${BIN_PATH}
 
 
 # Wrapping the pyinstaller folder in a .deb package
-mv dist_pyinstaller/${PACKAGE_NAME} dist_pyinstaller/${BUILD_NAME}/usr/local/bin/${PACKAGE_NAME}
+mv dist_pyinstaller/${PACKAGE_NAME} ${BIN_PATH}
 mkdir dist_pyinstaller/${BUILD_NAME}/DEBIAN
 cp release/linux/control dist_pyinstaller/${BUILD_NAME}/DEBIAN
+
 dpkg-deb --build --root-owner-group dist_pyinstaller/${BUILD_NAME}
 
 # release workflow expects artifact at root of repository

From 704a27d10130168276a4adc9575f728eddded59e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 13:45:20 +0200
Subject: [PATCH 236/250] trying to fix linux GUI

---
 gui/src/main/modules/engine.js       |  2 +-
 release/linux/build_package_linux.sh | 14 +++++++++++---
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/gui/src/main/modules/engine.js b/gui/src/main/modules/engine.js
index 894e60ba..c75c3259 100644
--- a/gui/src/main/modules/engine.js
+++ b/gui/src/main/modules/engine.js
@@ -16,7 +16,7 @@ function getAppRoot() {
     if ( process.platform === 'win32' ) {
       return path.join( app.getAppPath(), '/../../' );
     } else if ( process.platform === 'linux' ) {
-      return path.join( app.getAppPath(), '/../../' );
+      return path.join( app.getAppPath(), '/../../../' );
     } else {
       return path.join( app.getAppPath(), '/../../../../' );
     }
diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index 99dd4bba..0a67fd16 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -9,8 +9,8 @@ PACKAGE_NAME=alphadia
 # BUILD_NAME is taken from environment variables, e.g. 'alphadia-1.2.1-linux-x64'
 rm -rf ${BUILD_NAME}.deb
 
-BIN_PATH=dist_pyinstaller/${BUILD_NAME}/opt/${PACKAGE_NAME}/bin
-mkdir -p ${BIN_PATH}
+BIN_PATH=dist_pyinstaller/${BUILD_NAME}/usr/local/${PACKAGE_NAME}
+mkdir -p ${BIN_PATH}/gui
 
 # === GUI ===
 echo "Copying GUI"
@@ -23,7 +23,9 @@ fi
 KERNEL=$(uname -s | tr '[:upper:]' '[:lower:]')
 echo ARCH=${ARCH} KERNEL=${KERNEL}
 
+echo ls ./gui/out
 ls ./gui/out
+echo
 
 GUI_BUILD="./gui/out/${PACKAGE_NAME}-gui-${KERNEL}-${ARCH}"
 if [ ! -d "$GUI_BUILD" ]; then
@@ -31,9 +33,15 @@ if [ ! -d "$GUI_BUILD" ]; then
   exit 1
 fi
 
+echo ls ${GUI_BUILD}
 ls ${GUI_BUILD}
-cp -a ${GUI_BUILD}/. ${BIN_PATH}
+echo
 
+cp -a ${GUI_BUILD}/. ${BIN_PATH}/gui
+
+cd ${BIN_PATH}
+ln -s ./gui/alphadia-gui ./alphadia-gui
+cd -
 
 # Wrapping the pyinstaller folder in a .deb package
 mv dist_pyinstaller/${PACKAGE_NAME} ${BIN_PATH}

From 67daff6aff77da943344e1dd005701d3e3362cb1 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 14:39:29 +0200
Subject: [PATCH 237/250] trying to fix linux GUI

---
 release/linux/build_package_linux.sh | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index 0a67fd16..71fa8994 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -10,7 +10,7 @@ PACKAGE_NAME=alphadia
 rm -rf ${BUILD_NAME}.deb
 
 BIN_PATH=dist_pyinstaller/${BUILD_NAME}/usr/local/${PACKAGE_NAME}
-mkdir -p ${BIN_PATH}/gui
+mkdir -p ${BIN_PATH}
 
 # === GUI ===
 echo "Copying GUI"
@@ -37,10 +37,11 @@ echo ls ${GUI_BUILD}
 ls ${GUI_BUILD}
 echo
 
-cp -a ${GUI_BUILD}/. ${BIN_PATH}/gui
+cp -a ${GUI_BUILD}/. ${BIN_PATH}
 
-cd ${BIN_PATH}
-ln -s ./gui/alphadia-gui ./alphadia-gui
+cd dist_pyinstaller/${BUILD_NAME}/usr/local
+ln -s alphadia ./alphadia/alphadia
+ln -s alphadia-gui ./alphadia/alphadia-gui
 cd -
 
 # Wrapping the pyinstaller folder in a .deb package

From 8473998d4f48d43722263628a1e0954791fd09ab Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 14:59:28 +0200
Subject: [PATCH 238/250] trying to fix linux GUI

---
 release/linux/build_package_linux.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index 71fa8994..a9e327db 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -32,16 +32,16 @@ if [ ! -d "$GUI_BUILD" ]; then
   echo "GUI build not found at $GUI_BUILD"
   exit 1
 fi
-
 echo ls ${GUI_BUILD}
 ls ${GUI_BUILD}
 echo
 
 cp -a ${GUI_BUILD}/. ${BIN_PATH}
 
+
 cd dist_pyinstaller/${BUILD_NAME}/usr/local
-ln -s alphadia ./alphadia/alphadia
-ln -s alphadia-gui ./alphadia/alphadia-gui
+ln -s ./alphadia/alphadia alphadia
+ln -s ./alphadia/alphadia-gui alphadia-gui
 cd -
 
 # Wrapping the pyinstaller folder in a .deb package

From 703aba065ecd4ff74dacc08d531a8f5a6ecde155 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 15:41:55 +0200
Subject: [PATCH 239/250] trying to fix linux GUI

---
 release/linux/build_package_linux.sh | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index a9e327db..c680fb35 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -11,6 +11,7 @@ rm -rf ${BUILD_NAME}.deb
 
 BIN_PATH=dist_pyinstaller/${BUILD_NAME}/usr/local/${PACKAGE_NAME}
 mkdir -p ${BIN_PATH}
+chmod 755 ${BIN_PATH}
 
 # === GUI ===
 echo "Copying GUI"
@@ -39,9 +40,9 @@ echo
 cp -a ${GUI_BUILD}/. ${BIN_PATH}
 
 
-cd dist_pyinstaller/${BUILD_NAME}/usr/local
-ln -s ./alphadia/alphadia alphadia
-ln -s ./alphadia/alphadia-gui alphadia-gui
+cd dist_pyinstaller/${BUILD_NAME}/usr/local/bin
+ln -s ../alphadia/alphadia alphadia
+ln -s ../alphadia/alphadia-gui alphadia-gui
 cd -
 
 # Wrapping the pyinstaller folder in a .deb package

From 5a6a45f87b2f93e40b8627ec8d733ffd37a05831 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 16:00:32 +0200
Subject: [PATCH 240/250] make windows script bash scripts

---
 release/macos/build_installer_macos.sh      |  3 +++
 release/windows/build_gui_windows.sh        | 11 +++++++++++
 release/windows/build_installer_windows.ps1 |  1 -
 release/windows/build_installer_windows.sh  | 12 ++++++++++++
 release/windows/build_package_windows.sh    |  3 +++
 5 files changed, 29 insertions(+), 1 deletion(-)
 create mode 100644 release/windows/build_gui_windows.sh
 create mode 100644 release/windows/build_installer_windows.sh
 create mode 100644 release/windows/build_package_windows.sh

diff --git a/release/macos/build_installer_macos.sh b/release/macos/build_installer_macos.sh
index 4ce33d3e..4cd80b02 100755
--- a/release/macos/build_installer_macos.sh
+++ b/release/macos/build_installer_macos.sh
@@ -4,6 +4,9 @@ set -e -u
 # Build the installer for MacOS.
 # This script needs to be run from the root of the repository.
 
+rm -rf dist build *.egg-info
+rm -rf dist_pyinstaller build_pyinstaller
+
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
diff --git a/release/windows/build_gui_windows.sh b/release/windows/build_gui_windows.sh
new file mode 100644
index 00000000..c872f473
--- /dev/null
+++ b/release/windows/build_gui_windows.sh
@@ -0,0 +1,11 @@
+# Powershell script to build the GUI for Windows
+
+cd gui
+# delete old build using powershell
+rm -rf ./out
+rm -rf  ./dist
+
+npm install
+npm run make
+
+cd ..
diff --git a/release/windows/build_installer_windows.ps1 b/release/windows/build_installer_windows.ps1
index 50c6b4a9..9802814e 100644
--- a/release/windows/build_installer_windows.ps1
+++ b/release/windows/build_installer_windows.ps1
@@ -4,7 +4,6 @@
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./build
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
 
-pip install build
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
diff --git a/release/windows/build_installer_windows.sh b/release/windows/build_installer_windows.sh
new file mode 100644
index 00000000..55d20cde
--- /dev/null
+++ b/release/windows/build_installer_windows.sh
@@ -0,0 +1,12 @@
+# Build the installer for Windows.
+# This script must be run from the root of the repository.
+
+rm -rf dist build *.egg-info
+rm -rf dist_pyinstaller build_pyinstaller
+
+python -m build
+pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+
+# Creating the stand-alone pyinstaller folder
+pip install tbb==2021.13.1
+pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/windows/build_package_windows.sh b/release/windows/build_package_windows.sh
new file mode 100644
index 00000000..92fe755f
--- /dev/null
+++ b/release/windows/build_package_windows.sh
@@ -0,0 +1,3 @@
+# Build the install package for Windows.
+# This script must be run from the root of the repository after running build_installer_windows.ps1
+"C:\Program Files (x86)\Inno Setup 6\ISCC.exe" ./release/windows/alphadia_innoinstaller.iss

From e1056f27f05b6b9dd4a2202b4fdceb3ebb9984d6 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 16:52:10 +0200
Subject: [PATCH 241/250] remove windows bash scripts

---
 release/macos/build_installer_macos.sh     |  2 ++
 release/windows/build_gui_windows.sh       | 11 -----------
 release/windows/build_installer_windows.sh | 12 ------------
 release/windows/build_package_windows.sh   |  3 ---
 4 files changed, 2 insertions(+), 26 deletions(-)
 delete mode 100644 release/windows/build_gui_windows.sh
 delete mode 100644 release/windows/build_installer_windows.sh
 delete mode 100644 release/windows/build_package_windows.sh

diff --git a/release/macos/build_installer_macos.sh b/release/macos/build_installer_macos.sh
index 4cd80b02..dee5251d 100755
--- a/release/macos/build_installer_macos.sh
+++ b/release/macos/build_installer_macos.sh
@@ -7,6 +7,8 @@ set -e -u
 rm -rf dist build *.egg-info
 rm -rf dist_pyinstaller build_pyinstaller
 
+export EAGER_IMPORT=true  # TODO check if this can be removed with newset peptdeep version w/out transformer dependenc
+
 python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
diff --git a/release/windows/build_gui_windows.sh b/release/windows/build_gui_windows.sh
deleted file mode 100644
index c872f473..00000000
--- a/release/windows/build_gui_windows.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-# Powershell script to build the GUI for Windows
-
-cd gui
-# delete old build using powershell
-rm -rf ./out
-rm -rf  ./dist
-
-npm install
-npm run make
-
-cd ..
diff --git a/release/windows/build_installer_windows.sh b/release/windows/build_installer_windows.sh
deleted file mode 100644
index 55d20cde..00000000
--- a/release/windows/build_installer_windows.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-# Build the installer for Windows.
-# This script must be run from the root of the repository.
-
-rm -rf dist build *.egg-info
-rm -rf dist_pyinstaller build_pyinstaller
-
-python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
-
-# Creating the stand-alone pyinstaller folder
-pip install tbb==2021.13.1
-pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/windows/build_package_windows.sh b/release/windows/build_package_windows.sh
deleted file mode 100644
index 92fe755f..00000000
--- a/release/windows/build_package_windows.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-# Build the install package for Windows.
-# This script must be run from the root of the repository after running build_installer_windows.ps1
-"C:\Program Files (x86)\Inno Setup 6\ISCC.exe" ./release/windows/alphadia_innoinstaller.iss

From a728094959742c3d03e2207f518b46bf464ee99e Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 17:01:13 +0200
Subject: [PATCH 242/250] revert some old changes

---
 release/macos/build_backend_macos.sh | 1 +
 release/windows/build_backend.ps1    | 5 ++---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/release/macos/build_backend_macos.sh b/release/macos/build_backend_macos.sh
index b0395d5a..90b04769 100755
--- a/release/macos/build_backend_macos.sh
+++ b/release/macos/build_backend_macos.sh
@@ -6,4 +6,5 @@ python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
+pip install pyinstaller
 pyinstaller release/pyinstaller/alphadia.spec -y
diff --git a/release/windows/build_backend.ps1 b/release/windows/build_backend.ps1
index e59e98fc..f05c8cae 100644
--- a/release/windows/build_backend.ps1
+++ b/release/windows/build_backend.ps1
@@ -1,5 +1,4 @@
-# Build the installer for Windows.
-# This script must be run from the root of the repository.
+# TODO remove with old release workflow
 
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./build
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
@@ -9,5 +8,5 @@ python -m build
 pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
-pip install tbb==2021.13.1
+pip install pyinstaller tbb
 pyinstaller release/pyinstaller/alphadia.spec -y

From 5f92b78ca3dcbae9651ed782e32d033e9b58cb86 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 17:41:52 +0200
Subject: [PATCH 243/250] fix linux installer

---
 release/linux/build_package_linux.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/release/linux/build_package_linux.sh b/release/linux/build_package_linux.sh
index c680fb35..229275c2 100755
--- a/release/linux/build_package_linux.sh
+++ b/release/linux/build_package_linux.sh
@@ -40,6 +40,7 @@ echo
 cp -a ${GUI_BUILD}/. ${BIN_PATH}
 
 
+mkdir -p dist_pyinstaller/${BUILD_NAME}/usr/local/bin
 cd dist_pyinstaller/${BUILD_NAME}/usr/local/bin
 ln -s ../alphadia/alphadia alphadia
 ln -s ../alphadia/alphadia-gui alphadia-gui

From efe62f98df04a39cc04b536133fa4c8f1fa618d3 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 17:50:57 +0200
Subject: [PATCH 244/250] update bumpversion.cfg

---
 .pre-commit-config.yaml | 2 ++
 misc/.bumpversion.cfg   | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index cfefafb7..b64f17a2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -13,3 +13,5 @@ repos:
     - id: ruff-format
     - id: ruff
       args: [ '--fix' ]
+
+exclude: .bumpversion.cfg
diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 0e8ab656..fe7b1e95 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -23,6 +23,8 @@ serialize =
 
 [bumpversion:file:../release/linux/build_installer_linux.sh]
 
+[bumpversion:file:../release/linux/control]
+
 [bumpversion:file:../release/macos/build_installer_macos.sh]
 
 [bumpversion:file:../release/macos/build_package_macos.sh]

From 3999fa4f098e722bf94a5e0ba37388b5387ab4e1 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 17:52:04 +0200
Subject: [PATCH 245/250] update bumpversion.cfg

---
 misc/.bumpversion.cfg | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index fe7b1e95..0c226792 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -37,6 +37,8 @@ serialize =
 
 [bumpversion:file:../release/windows/alphadia_innoinstaller.iss]
 
+[bumpversion:file:../release/windows/alphadia_innoinstaller_old.iss] # TODO remove with old release workflow
+
 [bumpversion:file:../release/windows/build_installer_windows.ps1]
 
 [bumpversion:file:../release/windows/build_backend.ps1] # TODO remove with old release workflow

From c9cf591be02b21ed65d53dc878e633b3db6308cc Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 18:26:38 +0200
Subject: [PATCH 246/250] add missing file

---
 .../windows/alphadia_innoinstaller_old.iss    | 54 +++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 release/windows/alphadia_innoinstaller_old.iss

diff --git a/release/windows/alphadia_innoinstaller_old.iss b/release/windows/alphadia_innoinstaller_old.iss
new file mode 100644
index 00000000..4a74b198
--- /dev/null
+++ b/release/windows/alphadia_innoinstaller_old.iss
@@ -0,0 +1,54 @@
+; Script generated by the Inno Setup Script Wizard.
+; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES!
+; # TODO remove with old release workflow
+
+#define MyAppName "AlphaDIA"
+#define MyAppVersion "1.7.2"
+#define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs"
+#define MyAppURL "https://github.com/MannLabs/alphadia"
+#define MyAppExeName "alphadia-gui.exe"
+
+[Setup]
+; NOTE: The value of AppId uniquely identifies this application. Do not use the same AppId value in installers for other applications.
+; (To generate a new GUID, click Tools | Generate GUID inside the IDE.)
+AppId={{alphadia_Mann_Labs_MPI}
+AppName={#MyAppName}
+AppVersion={#MyAppVersion}
+;AppVerName={#MyAppName} {#MyAppVersion}
+AppPublisher={#MyAppPublisher}
+AppPublisherURL={#MyAppURL}
+AppSupportURL={#MyAppURL}
+AppUpdatesURL={#MyAppURL}
+DefaultDirName={autopf}\{#MyAppName}
+DisableProgramGroupPage=yes
+LicenseFile=..\..\LICENSE.txt
+; Uncomment the following line to run in non administrative install mode (install for current user only.)
+PrivilegesRequired=lowest
+PrivilegesRequiredOverridesAllowed=dialog
+OutputDir=..\..\dist
+; example for BUILD_NAME: alphadia-1.9.0-win-x64
+OutputBaseFilename={#GetEnv('BUILD_NAME')}
+SetupIconFile=..\logos\alphadia.ico
+Compression=lzma
+SolidCompression=yes
+WizardStyle=modern
+
+[Languages]
+Name: "english"; MessagesFile: "compiler:Default.isl"
+
+[Tasks]
+Name: "desktopicon"; Description: "{cm:CreateDesktopIcon}"; GroupDescription: "{cm:AdditionalIcons}"; Flags: unchecked
+
+[Files]
+Source: "..\..\gui\out\alphadia-gui-win32-x64\{#MyAppExeName}"; DestDir: "{app}"; Flags: ignoreversion
+Source: "..\..\gui\out\alphadia-gui-win32-x64\*"; DestDir: "{app}"; Flags: ignoreversion recursesubdirs createallsubdirs
+Source: "..\..\dist\alphadia\*"; DestDir: "{app}"; Flags: ignoreversion recursesubdirs createallsubdirs
+
+; NOTE: Don't use "Flags: ignoreversion" on any shared system files
+
+[Icons]
+Name: "{autoprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"
+Name: "{autodesktop}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; Tasks: desktopicon
+
+[Run]
+Filename: "{app}\{#MyAppExeName}"; Description: "{cm:LaunchProgram,{#StringChange(MyAppName, '&', '&&')}}"; Flags: nowait postinstall skipifsilent

From 90912b93c40570e2b69f7d5b762ae795f167e809 Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 11 Sep 2024 18:27:14 +0200
Subject: [PATCH 247/250] =?UTF-8?q?Bump=20version:=201.7.2=20=E2=86=92=201?=
 =?UTF-8?q?.8.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 alphadia/__init__.py                           |  2 +-
 docs/index.md                                  |  2 +-
 gui/package.json                               |  2 +-
 gui/src/main/modules/profile.js                |  2 +-
 misc/.bumpversion.cfg                          | 13 ++++++-------
 release/linux/build_installer_linux.sh         |  2 +-
 release/linux/control                          |  2 +-
 release/macos/build_backend_macos.sh           |  2 +-
 release/macos/build_installer_macos.sh         |  2 +-
 release/macos/build_package_macos.sh           |  4 ++--
 release/macos/build_pkg_macos.sh               |  2 +-
 release/macos/distribution.xml                 |  2 +-
 release/macos/info.plist                       |  4 ++--
 release/windows/alphadia_innoinstaller.iss     |  4 ++--
 release/windows/alphadia_innoinstaller_old.iss |  2 +-
 release/windows/build_backend.ps1              |  2 +-
 release/windows/build_installer_windows.ps1    |  2 +-
 17 files changed, 25 insertions(+), 26 deletions(-)

diff --git a/alphadia/__init__.py b/alphadia/__init__.py
index 7cde780c..c2f02751 100644
--- a/alphadia/__init__.py
+++ b/alphadia/__init__.py
@@ -1,3 +1,3 @@
 #!python
 
-__version__ = "1.7.2"
+__version__ = "1.8.0"
diff --git a/docs/index.md b/docs/index.md
index f26ef4d4..818e5ed6 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,6 +1,6 @@
 
 # AlphaDIA Documentation
-**Version:** 1.7.2 | [Github](https://github.com/MannLabs/alphadia)
+**Version:** 1.8.0 | [Github](https://github.com/MannLabs/alphadia)
 
 Open-source DIA search engine built with the alphaX ecosystem. Built with [alpharaw](https://github.com/MannLabs/alpharaw) and [alphatims](https://github.com/MannLabs/alphatims) for raw file acces. Spectral libraries are predicted with [peptdeep](https://github.com/MannLabs/alphapeptdeep) and managed by [alphabase](https://github.com/MannLabs/alphabase). Quantification is powered by [directLFQ](https://github.com/MannLabs/directLFQ).
 
diff --git a/gui/package.json b/gui/package.json
index 1e95ec91..4856a5c2 100644
--- a/gui/package.json
+++ b/gui/package.json
@@ -1,7 +1,7 @@
 {
   "name": "alphadia",
   "productName": "alphadia-gui",
-  "version": "1.7.2",
+  "version": "1.8.0",
   "description": "Graphical user interface for DIA data analysis",
   "main": "dist/electron.js",
   "homepage": "./",
diff --git a/gui/src/main/modules/profile.js b/gui/src/main/modules/profile.js
index d2d5f2f0..5b9c864a 100644
--- a/gui/src/main/modules/profile.js
+++ b/gui/src/main/modules/profile.js
@@ -3,7 +3,7 @@ const path = require("path")
 const { app, shell, BrowserWindow} = require("electron")
 const { dialog } = require('electron')
 
-const VERSION = "1.7.2"
+const VERSION = "1.8.0"
 
 const Profile = class {
 
diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 0c226792..f51e7b54 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -1,9 +1,9 @@
 [bumpversion]
-current_version = 1.7.2
+current_version = 1.8.0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
-serialize =
+serialize = 
 	{major}.{minor}.{patch}
 	{major}.{minor}.{patch}
 
@@ -29,19 +29,18 @@ serialize =
 
 [bumpversion:file:../release/macos/build_package_macos.sh]
 
-[bumpversion:file:../release/macos/build_backend_macos.sh] # TODO remove with old release workflow
+[bumpversion:file:../release/macos/build_backend_macos.sh]
 
-[bumpversion:file:../release/macos/build_pkg_macos.sh] # TODO remove with old release workflow
+[bumpversion:file:../release/macos/build_pkg_macos.sh]
 
 [bumpversion:file:../release/macos/distribution.xml]
 
 [bumpversion:file:../release/windows/alphadia_innoinstaller.iss]
 
-[bumpversion:file:../release/windows/alphadia_innoinstaller_old.iss] # TODO remove with old release workflow
+[bumpversion:file:../release/windows/alphadia_innoinstaller_old.iss]
 
 [bumpversion:file:../release/windows/build_installer_windows.ps1]
 
-[bumpversion:file:../release/windows/build_backend.ps1] # TODO remove with old release workflow
-
+[bumpversion:file:../release/windows/build_backend.ps1]
 search = {current_version}
 replace = {new_version}
diff --git a/release/linux/build_installer_linux.sh b/release/linux/build_installer_linux.sh
index bb25fb4b..80c88012 100755
--- a/release/linux/build_installer_linux.sh
+++ b/release/linux/build_installer_linux.sh
@@ -10,7 +10,7 @@ rm -rf dist build *.egg-info
 rm -rf dist_pyinstaller build_pyinstaller
 
 python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.8.0-py3-none-any.whl[stable]"
 
 if [ "${CPU_OR_GPU}" != "GPU" ]; then
     pip install torch -U --extra-index-url https://download.pytorch.org/whl/cpu
diff --git a/release/linux/control b/release/linux/control
index fb917c73..50796ad7 100644
--- a/release/linux/control
+++ b/release/linux/control
@@ -1,5 +1,5 @@
 Package: alphadia
-Version: 1.7.2
+Version: 1.8.0
 Architecture: all
 Maintainer: Mann Labs <opensource@alphapept.com>
 Description: alphadia
diff --git a/release/macos/build_backend_macos.sh b/release/macos/build_backend_macos.sh
index 90b04769..ea713320 100755
--- a/release/macos/build_backend_macos.sh
+++ b/release/macos/build_backend_macos.sh
@@ -3,7 +3,7 @@
 # navigate to the root directory
 
 python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.8.0-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pip install pyinstaller
diff --git a/release/macos/build_installer_macos.sh b/release/macos/build_installer_macos.sh
index dee5251d..f529f977 100755
--- a/release/macos/build_installer_macos.sh
+++ b/release/macos/build_installer_macos.sh
@@ -10,7 +10,7 @@ rm -rf dist_pyinstaller build_pyinstaller
 export EAGER_IMPORT=true  # TODO check if this can be removed with newset peptdeep version w/out transformer dependenc
 
 python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.8.0-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pyinstaller release/pyinstaller/alphadia.spec --distpath dist_pyinstaller --workpath build_pyinstaller -y
diff --git a/release/macos/build_package_macos.sh b/release/macos/build_package_macos.sh
index 497c05ad..be8ce536 100755
--- a/release/macos/build_package_macos.sh
+++ b/release/macos/build_package_macos.sh
@@ -7,10 +7,10 @@ set -e -u
 # Set up package name and version
 PACKAGE_NAME="alphadia"
 APP_NAME="alphadia"
-PACKAGE_VERSION="1.7.2"
+PACKAGE_VERSION="1.8.0"
 PKG_FOLDER="dist/$APP_NAME.app"
 
-# BUILD_NAME is taken from environment variables, e.g. alphadia-1.7.2-macos-darwin-arm64 or alphadia-1.7.2-macos-darwin-x64
+# BUILD_NAME is taken from environment variables, e.g. alphadia-1.8.0-macos-darwin-arm64 or alphadia-1.8.0-macos-darwin-x64
 rm -rf ${BUILD_NAME}.pkg
 
 # Cleanup the package folder
diff --git a/release/macos/build_pkg_macos.sh b/release/macos/build_pkg_macos.sh
index 4d1ac816..1f2a7bd6 100755
--- a/release/macos/build_pkg_macos.sh
+++ b/release/macos/build_pkg_macos.sh
@@ -3,7 +3,7 @@
 
 # Set up package name and version
 PACKAGE_NAME="alphadia"
-PACKAGE_VERSION="1.7.2"
+PACKAGE_VERSION="1.8.0"
 
 ARCH=$(uname -m)
 if [ "$ARCH" == "x86_64" ]; then
diff --git a/release/macos/distribution.xml b/release/macos/distribution.xml
index d760dfd6..12e28534 100644
--- a/release/macos/distribution.xml
+++ b/release/macos/distribution.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8" standalone="no"?>
 <installer-script minSpecVersion="1.000000">
-    <title>AlphaDIA 1.7.2</title>
+    <title>AlphaDIA 1.8.0</title>
     <background mime-type="image/png" file="alphadia.png" scaling="proportional"/>
     <welcome file="welcome.html" mime-type="text/html" />
     <conclusion file="conclusion.html" mime-type="text/html" />
diff --git a/release/macos/info.plist b/release/macos/info.plist
index d7b99b0e..87ae0263 100644
--- a/release/macos/info.plist
+++ b/release/macos/info.plist
@@ -9,9 +9,9 @@
 	<key>CFBundleIconFile</key>
 	<string>alphadia.icns</string>
 	<key>CFBundleIdentifier</key>
-	<string>alphadia.1.7.2</string>
+	<string>alphadia.1.8.0</string>
 	<key>CFBundleShortVersionString</key>
-	<string>1.7.2</string>
+	<string>1.8.0</string>
 	<key>CFBundleInfoDictionaryVersion</key>
 	<string>6.0</string>
 	<key>CFBundleName</key>
diff --git a/release/windows/alphadia_innoinstaller.iss b/release/windows/alphadia_innoinstaller.iss
index 65544d64..8191350e 100644
--- a/release/windows/alphadia_innoinstaller.iss
+++ b/release/windows/alphadia_innoinstaller.iss
@@ -5,7 +5,7 @@
 ; so all paths are given relative to the location of this .iss file.
 
 #define MyAppName "AlphaDIA"
-#define MyAppVersion "1.7.2"
+#define MyAppVersion "1.8.0"
 #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs"
 #define MyAppURL "https://github.com/MannLabs/alphadia"
 #define MyAppExeName "alphadia-gui.exe"
@@ -29,7 +29,7 @@ PrivilegesRequired=lowest
 PrivilegesRequiredOverridesAllowed=dialog
 ; release workflow expects artifact at root of repository
 OutputDir=..\..\
-; example for BUILD_NAME: alphadia-1.7.2-win-x64
+; example for BUILD_NAME: alphadia-1.8.0-win-x64
 OutputBaseFilename={#GetEnv('BUILD_NAME')}
 SetupIconFile=..\logos\alphadia.ico
 Compression=lzma
diff --git a/release/windows/alphadia_innoinstaller_old.iss b/release/windows/alphadia_innoinstaller_old.iss
index 4a74b198..58f0ef86 100644
--- a/release/windows/alphadia_innoinstaller_old.iss
+++ b/release/windows/alphadia_innoinstaller_old.iss
@@ -3,7 +3,7 @@
 ; # TODO remove with old release workflow
 
 #define MyAppName "AlphaDIA"
-#define MyAppVersion "1.7.2"
+#define MyAppVersion "1.8.0"
 #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs"
 #define MyAppURL "https://github.com/MannLabs/alphadia"
 #define MyAppExeName "alphadia-gui.exe"
diff --git a/release/windows/build_backend.ps1 b/release/windows/build_backend.ps1
index f05c8cae..82543623 100644
--- a/release/windows/build_backend.ps1
+++ b/release/windows/build_backend.ps1
@@ -5,7 +5,7 @@ Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
 
 pip install build
 python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.8.0-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pip install pyinstaller tbb
diff --git a/release/windows/build_installer_windows.ps1 b/release/windows/build_installer_windows.ps1
index 9802814e..e950bb12 100644
--- a/release/windows/build_installer_windows.ps1
+++ b/release/windows/build_installer_windows.ps1
@@ -5,7 +5,7 @@ Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./build
 Remove-Item -Recurse -Force -ErrorAction SilentlyContinue ./dist
 
 python -m build
-pip install "dist/alphadia-1.7.2-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.8.0-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pip install tbb==2021.13.1

From 3e0b742493da7d45bdb48542e9b18d98258580e9 Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Tue, 10 Sep 2024 18:10:04 +0200
Subject: [PATCH 248/250] Update GUI for release

---
 alphadia/constants/default.yaml               |  8 +-
 alphadia/libtransform.py                      |  2 +-
 gui/src/renderer/components/ParameterInput.js |  3 +-
 gui/workflows/PeptideCentric.v1.json          | 85 ++++++++-----------
 4 files changed, 42 insertions(+), 56 deletions(-)

diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 45c3f0ec..14917790 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -89,9 +89,9 @@ search:
 
   target_num_candidates: 2
   # target ms1 tolerance in ppm
-  target_ms1_tolerance: 15
+  target_ms1_tolerance: 5
   # target ms2 tolerance in ppm
-  target_ms2_tolerance: 15
+  target_ms2_tolerance: 10
   # target ion mobility tolerance in 1/K_0
   target_mobility_tolerance: 0.0 # default is to optimize automatically
   # target retention time tolerance in seconds if > 1, or a proportion of the total gradient length if < 1
@@ -143,10 +143,10 @@ search_initial:
   initial_ms2_tolerance: 30
 
   # initial ion mobility tolerance in 1/K_0
-  initial_mobility_tolerance: 0.08
+  initial_mobility_tolerance: 0.1
 
   # initial retention time tolerance in seconds if > 1, or a proportion of the total gradient length if < 1
-  initial_rt_tolerance: 240
+  initial_rt_tolerance: 0.5
 
 selection_config:
   peak_len_rt: 10.
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index c35c496e..c10ceb1c 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -328,7 +328,7 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
             logging.info(f"Loading PeptDeep models of type {self.peptdeep_model_type}")
             model_mgr.load_installed_models(self.peptdeep_model_type)
 
-        if self.peptdeep_model_path is not None:
+        if self.peptdeep_model_path is not None and self.peptdeep_model_path != "":
             if not os.path.exists(self.peptdeep_model_path):
                 raise ValueError(
                     f"PeptDeep model checkpoint folder {self.peptdeep_model_path} does not exist"
diff --git a/gui/src/renderer/components/ParameterInput.js b/gui/src/renderer/components/ParameterInput.js
index 25376942..bb1a63a8 100644
--- a/gui/src/renderer/components/ParameterInput.js
+++ b/gui/src/renderer/components/ParameterInput.js
@@ -16,8 +16,7 @@ const SingleFolderSelection = ({parameter, onChange = () => {}}) => {
             console.log(err);
         })
     }
-
-    const folderName = parameter.replace(/^.*[\\\/]/, '')
+    const folderName = parameter ? parameter.replace(/^.*[\\\/]/, '') : ''
 
     return (
         <>
diff --git a/gui/workflows/PeptideCentric.v1.json b/gui/workflows/PeptideCentric.v1.json
index 31bf8e47..f9b459be 100644
--- a/gui/workflows/PeptideCentric.v1.json
+++ b/gui/workflows/PeptideCentric.v1.json
@@ -35,13 +35,6 @@
                     "description": "Number of threads to use for parallel processing.",
                     "type": "integer"
                 },
-                {
-                    "id": "reuse_calibration",
-                    "name": "Reuse Calibration",
-                    "value": false,
-                    "description": "AlphaDIA will save the calibration parameters in the project file. If this option is enabled, the calibration parameters will be reused for subsequent searches of the same file.",
-                    "type": "boolean"
-                },
                 {
                     "id": "reuse_quant",
                     "name": "Reuse Ion Quantities",
@@ -153,7 +146,7 @@
                 {
                     "id": "max_var_mod_num",
                     "name": "Maximum variable modifications",
-                    "value": 1,
+                    "value": 2,
                     "description": "Variable modifications for in-silico digest. At the moment localisation is not supported. Semicolon separated list \n Example: Oxidation@M;Acetyl@ProteinN-term",
                     "type": "integer"
                 },
@@ -241,7 +234,7 @@
                 {
                     "id": "peptdeep_model_path",
                     "name": "PeptDeep Model Path",
-                    "value": "",
+                    "value": null,
                     "description": "Select a custom PeptDeep model for library prediction. This can be a DDA or DIA trained model. Please make sure that you use the same instrument type and NCE for prediction as the model was trained on.",
                     "type": "singleFolderSelection"
                 },
@@ -265,6 +258,34 @@
             "name": "Search",
             "hidden": false,
             "parameters": [
+                {
+                    "id": "target_ms1_tolerance",
+                    "name": "MS1 Tolerance",
+                    "value": 5,
+                    "description": "MS1 tolerance in ppm. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
+                    "type": "float"
+                },
+                {
+                    "id": "target_ms2_tolerance",
+                    "name": "MS2 Tolerance",
+                    "value": 10,
+                    "description": "MS2 tolerance in ppm. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
+                    "type": "float"
+                },
+                {
+                    "id": "target_mobility_tolerance",
+                    "name": "Mobility Tolerance",
+                    "value": 0.0,
+                    "description": "Mobility tolerance in 1/K_0. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached. Set to enable automatic optimization.",
+                    "type": "float"
+                },
+                {
+                    "id": "target_rt_tolerance",
+                    "name": "RT Tolerance",
+                    "value": 0.0,
+                    "description": "Retention time tolerance in seconds if greater than 1 or as a proportion of the gradient length if less than 1. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached. Automatic optimization is enabled if set to 0.",
+                    "type": "float"
+                },
                 {
                     "id": "channel_filter",
                     "name": "Channel Filter",
@@ -293,34 +314,7 @@
                     "description": "For every precursor in the library a number of top scoring candidates will be extracted. This number is the maximum number of candidates that will be extracted per precursor.",
                     "type": "integer"
                 },
-                {
-                    "id": "target_ms1_tolerance",
-                    "name": "MS1 Tolerance",
-                    "value": 5,
-                    "description": "MS1 tolerance in ppm. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
-                    "type": "float"
-                },
-                {
-                    "id": "target_ms2_tolerance",
-                    "name": "MS2 Tolerance",
-                    "value": 10,
-                    "description": "MS2 tolerance in ppm. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
-                    "type": "float"
-                },
-                {
-                    "id": "target_mobility_tolerance",
-                    "name": "Mobility Tolerance",
-                    "value": 0.04,
-                    "description": "Mobility tolerance in 1/K_0. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
-                    "type": "float"
-                },
-                {
-                    "id": "target_rt_tolerance",
-                    "name": "RT Tolerance",
-                    "value": 100,
-                    "description": "Retention time tolerance in seconds if greater than 1 or as a proportion of the gradient length if less than 1. Search windows are optimized and calibrated during processing. The window is reduced until this tolerance is reached.",
-                    "type": "float"
-                },
+
                 {
                     "id": "quant_window",
                     "name": "Quant window",
@@ -332,7 +326,7 @@
                 {
                     "id": "quant_all",
                     "name": "Use all MS2 observations",
-                    "value": false,
+                    "value": true,
                     "description": "Use all MS2 observations for quantification. If disabled only the best scoring observation is used for quantification. Recommended for synchro-PASEF data.",
                     "type": "boolean"
 
@@ -374,13 +368,6 @@
                         "heuristic"
                     ]
                 },
-                {
-                    "id": "competetive_scoring",
-                    "name": "Competetive Scoring",
-                    "value": true,
-                    "description": "If enabled, only the best scoring candidate per target decoy pair is retained.",
-                    "type": "boolean"
-                },
                 {
                     "id": "channel_wise_fdr",
                     "name": "Channel wise FDR",
@@ -405,7 +392,7 @@
                 {
                     "id": "initial_num_candidates",
                     "name": "Number of Candidates",
-                    "value": 2,
+                    "value": 1,
                     "description": "Initial number of candidates to extract per precursor.",
                     "type": "integer"
                 },
@@ -426,14 +413,14 @@
                 {
                     "id": "initial_mobility_tolerance",
                     "name": "Mobility Tolerance",
-                    "value": 0.08,
+                    "value": 0.1,
                     "description": "Initial mobility tolerance in 1/K_0.",
                     "type": "float"
                 },
                 {
                     "id": "initial_rt_tolerance",
                     "name": "RT Tolerance",
-                    "value": 240,
+                    "value": 0.5,
                     "description": "Initial retention time tolerance in seconds if greater than 1 or as a proportion of the gradient length if less than 1.",
                     "type": "float"
                 }
@@ -445,7 +432,7 @@
             "hidden": true,
             "parameters": [
                 {
-                    "id": "multiplexed_quant",
+                    "id": "enabled",
                     "name": "Enable Multiplexing",
                     "value": false,
                     "description": "Quantify and score identification across non-isobaric labled channels.",

From e9346548598f43c7174d326b24b70a0344519bcf Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Tue, 10 Sep 2024 18:17:53 +0200
Subject: [PATCH 249/250] fix #234

---
 gui/src/main/modules/engine.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gui/src/main/modules/engine.js b/gui/src/main/modules/engine.js
index c75c3259..4034e8cc 100644
--- a/gui/src/main/modules/engine.js
+++ b/gui/src/main/modules/engine.js
@@ -263,7 +263,7 @@ class CMDExecutionEngine extends BaseExecutionEngine {
                                         "--no-capture-output",
                                         "alphadia",
                                         "--config",
-                                        path.join(workflow.output_directory.path, "config.yaml")
+                                        `"${path.join(workflow.output_directory.path, "config.yaml")}"`
                                     ] , { env:{...process.env, PATH}, shell: true});
             run.pid = run.process.pid
 
@@ -423,7 +423,7 @@ class BundledExecutionEngine extends BaseExecutionEngine {
             // use binary location as cwd and binary name as command
             run.process = spawn(prefix + binaryName,
                 ["--config",
-                path.join(workflow.output_directory.path, "config.yaml")
+                `"${path.join(workflow.output_directory.path, "config.yaml")}"`
                 ],
                 {
                     env:{...process.env, PATH},

From 25dfebbdb5c0a901b379a1c1a08adef1f4e6177b Mon Sep 17 00:00:00 2001
From: GeorgWa <wallmann@biochem.mpg.de>
Date: Tue, 10 Sep 2024 18:22:45 +0200
Subject: [PATCH 250/250] fix bug

---
 alphadia/libtransform.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index c10ceb1c..2dbd5021 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -324,11 +324,11 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
         model_mgr = ModelManager(device=device)
 
         # will load other model than default generic
-        if self.peptdeep_model_type is not None:
+        if self.peptdeep_model_type:
             logging.info(f"Loading PeptDeep models of type {self.peptdeep_model_type}")
             model_mgr.load_installed_models(self.peptdeep_model_type)
 
-        if self.peptdeep_model_path is not None and self.peptdeep_model_path != "":
+        if self.peptdeep_model_path and self.peptdeep_model_path != "":
             if not os.path.exists(self.peptdeep_model_path):
                 raise ValueError(
                     f"PeptDeep model checkpoint folder {self.peptdeep_model_path} does not exist"