Compare original RA to Tabula Sapiens RA cells.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rationale: Compare deconvoluted single cell datasets generated with different references (Tabula Sapiens vs Tabula Muris)\n",
    "\n",
    "To demonstrate whether deconvoluting the human right atrium (RA) with mouse reference data (Tabula Muris) is a robust approach, we compared the single cell deconvoluted results obtained using Tabula Muris with those generated with Tabula Sapiens."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as pearsonr\n",
    "import bulk2space\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the input files\n",
    "\n",
    "Loading the RA single cell data generated using Tabula Muris and Tabula Sapiens.\n",
    "The following files will be loaded:\n",
    "- RA sc data generated from Tabula Muris\n",
    "- RA sc meta generated from Tabula Muris\n",
    "- RA sc data generated from Tabula Sapiens\n",
    "- RA sc meta generated from Tabula Sapiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data......\n",
      "load data done!\n"
     ]
    }
   ],
   "source": [
    "# Right atrium data generated from Tabula Muris\n",
    "ra_ms_data = bulk2space.utils.load_data(\n",
    "    input_bulk_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Right Atrium/RA_human_bulk_cleaned.csv\",\n",
    "    input_sc_data_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Right Atrium/output/RA_human_from_mouse_sc_data.csv\",\n",
    "    input_sc_meta_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Right Atrium/output/RA_human_from_mouse_sc_celltype.csv\",\n",
    "    input_st_data_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Right Atrium/st_data.csv\",\n",
    "    input_st_meta_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Right Atrium/st_meta.csv\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell</th>\n",
       "      <th>Cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_1</td>\n",
       "      <td>coronary vascular endothelial cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_2</td>\n",
       "      <td>valve cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_3</td>\n",
       "      <td>fibroblast of cardiac tissue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_4</td>\n",
       "      <td>endothelial cell of endocardium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_5</td>\n",
       "      <td>fibroblast of cardiac tissue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>C_2895</td>\n",
       "      <td>atrial cardiomyocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>C_2896</td>\n",
       "      <td>atrial cardiomyocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>C_2897</td>\n",
       "      <td>atrial cardiomyocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2897</th>\n",
       "      <td>C_2898</td>\n",
       "      <td>atrial cardiomyocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2898</th>\n",
       "      <td>C_2899</td>\n",
       "      <td>atrial cardiomyocyte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cell                           Cell_type\n",
       "0        C_1  coronary vascular endothelial cell\n",
       "1        C_2                          valve cell\n",
       "2        C_3        fibroblast of cardiac tissue\n",
       "3        C_4     endothelial cell of endocardium\n",
       "4        C_5        fibroblast of cardiac tissue\n",
       "...      ...                                 ...\n",
       "2894  C_2895                atrial cardiomyocyte\n",
       "2895  C_2896                atrial cardiomyocyte\n",
       "2896  C_2897                atrial cardiomyocyte\n",
       "2897  C_2898                atrial cardiomyocyte\n",
       "2898  C_2899                atrial cardiomyocyte\n",
       "\n",
       "[2899 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ra_ms_data['input_sc_meta'])\n",
    "\n",
    "# Create a scanpy object for later use\n",
    "sc = scanpy.AnnData(ra_ms_data[\"input_sc_data\"].T)\n",
    "sc.obs = ra_ms_data[\"input_sc_meta\"][[\"Cell_type\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Cell</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>C_1</td>\n",
       "      <td>Cardiac Muscle Cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>Cardiac Muscle Cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>Endothelial Cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>C_4</td>\n",
       "      <td>Cardiac Muscle Cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>C_5</td>\n",
       "      <td>Cardiac Muscle Cells</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Unnamed: 0 Cell             Cell_type\n",
       "0           0  C_1  Cardiac Muscle Cells\n",
       "1           1  C_2  Cardiac Muscle Cells\n",
       "2           2  C_3     Endothelial Cells\n",
       "3           3  C_4  Cardiac Muscle Cells\n",
       "4           4  C_5  Cardiac Muscle Cells"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Right atrium data generated from Tabula Sapiens\n",
    "ra_ts_data = pd.read_csv(\n",
    "    \"output_from_TS/RA_from_TS_sc_data.csv\")\n",
    "ra_ts_meta = pd.read_csv(\n",
    "    \"output_from_TS/RA_from_TS_sc_celltype.csv\")\n",
    "\n",
    "ra_ts_meta.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation matrix\n",
    "\n",
    "Calculate 200 marker genes of RA cells generate using Tabula Muris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = scanpy.AnnData(ra_ms_data[\"input_sc_data\"].T)\n",
    "sc.obs = ra_ms_data[\"input_sc_meta\"][[\"Cell_type\"]]\n",
    "scanpy.tl.rank_genes_groups(sc, \"Cell_type\", method=\"wilcoxon\")\n",
    "marker_df = pd.DataFrame(sc.uns[\"rank_genes_groups\"][\"names\"]).head(200)\n",
    "marker = list(np.unique(np.ravel(np.array(marker_df))))\n",
    "\n",
    "# the mean expression of 200 marker genes of input sc data\n",
    "sc_marker = ra_ms_data[\"input_sc_data\"].loc[marker, :].T\n",
    "new_index = ra_ms_data['input_sc_meta']['Cell'].to_list() ## Keep commented if 'Cell' key error \n",
    "ra_ms_data['input_sc_meta'] = ra_ms_data['input_sc_meta'].set_index('Cell') ## Keep commented if 'Cell' key error \n",
    "sc_marker[\"Cell_type\"] = ra_ms_data[\"input_sc_meta\"][\"Cell_type\"]\n",
    "sc_marker_mean = sc_marker.groupby(\"Cell_type\")[marker].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABCA8</th>\n",
       "      <th>ABCB1</th>\n",
       "      <th>ABCC9</th>\n",
       "      <th>ABHD12</th>\n",
       "      <th>ABHD6</th>\n",
       "      <th>ABI3</th>\n",
       "      <th>ABI3BP</th>\n",
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       "      <th>...</th>\n",
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       "      <th>YWHAZ</th>\n",
       "      <th>ZBTB20</th>\n",
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       "      <th>ZFHX3</th>\n",
       "      <th>ZFP36L2</th>\n",
       "      <th>ZNF106</th>\n",
       "      <th>ZNF622</th>\n",
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       "      <th>Cell_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>atrial cardiomyocyte</th>\n",
       "      <td>0.030474</td>\n",
       "      <td>0.024539</td>\n",
       "      <td>0.028661</td>\n",
       "      <td>0.115384</td>\n",
       "      <td>0.005506</td>\n",
       "      <td>0.029049</td>\n",
       "      <td>0.079596</td>\n",
       "      <td>0.151969</td>\n",
       "      <td>0.022493</td>\n",
       "      <td>0.629446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440014</td>\n",
       "      <td>0.362847</td>\n",
       "      <td>0.279094</td>\n",
       "      <td>0.572129</td>\n",
       "      <td>0.234956</td>\n",
       "      <td>0.312593</td>\n",
       "      <td>0.036221</td>\n",
       "      <td>0.063303</td>\n",
       "      <td>0.380514</td>\n",
       "      <td>0.039710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coronary vascular endothelial cell</th>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.161751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088134</td>\n",
       "      <td>0.181522</td>\n",
       "      <td>0.427485</td>\n",
       "      <td>0.006027</td>\n",
       "      <td>0.775695</td>\n",
       "      <td>0.445656</td>\n",
       "      <td>0.174340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600843</td>\n",
       "      <td>1.096931</td>\n",
       "      <td>1.011121</td>\n",
       "      <td>1.395291</td>\n",
       "      <td>0.350173</td>\n",
       "      <td>0.471670</td>\n",
       "      <td>0.071205</td>\n",
       "      <td>0.032647</td>\n",
       "      <td>0.180295</td>\n",
       "      <td>0.167907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endothelial cell of endocardium</th>\n",
       "      <td>0.004572</td>\n",
       "      <td>0.029683</td>\n",
       "      <td>0.022005</td>\n",
       "      <td>0.234729</td>\n",
       "      <td>0.179079</td>\n",
       "      <td>0.349801</td>\n",
       "      <td>0.083400</td>\n",
       "      <td>0.857162</td>\n",
       "      <td>0.022130</td>\n",
       "      <td>0.372820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.531657</td>\n",
       "      <td>1.159300</td>\n",
       "      <td>0.925942</td>\n",
       "      <td>1.247538</td>\n",
       "      <td>0.412733</td>\n",
       "      <td>0.305715</td>\n",
       "      <td>0.056809</td>\n",
       "      <td>0.064929</td>\n",
       "      <td>0.132732</td>\n",
       "      <td>0.095595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fibroblast of cardiac tissue</th>\n",
       "      <td>0.339567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.122408</td>\n",
       "      <td>0.328215</td>\n",
       "      <td>0.110330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833924</td>\n",
       "      <td>0.381946</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.289264</td>\n",
       "      <td>...</td>\n",
       "      <td>1.090447</td>\n",
       "      <td>1.088631</td>\n",
       "      <td>0.975227</td>\n",
       "      <td>1.354736</td>\n",
       "      <td>1.301109</td>\n",
       "      <td>0.423467</td>\n",
       "      <td>0.280544</td>\n",
       "      <td>0.168717</td>\n",
       "      <td>0.349957</td>\n",
       "      <td>0.245153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macrophage</th>\n",
       "      <td>0.003497</td>\n",
       "      <td>0.614818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.406378</td>\n",
       "      <td>0.930856</td>\n",
       "      <td>0.052693</td>\n",
       "      <td>0.016911</td>\n",
       "      <td>0.395915</td>\n",
       "      <td>0.065669</td>\n",
       "      <td>0.308043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862151</td>\n",
       "      <td>0.593370</td>\n",
       "      <td>0.300632</td>\n",
       "      <td>1.176686</td>\n",
       "      <td>0.083150</td>\n",
       "      <td>0.986885</td>\n",
       "      <td>0.081799</td>\n",
       "      <td>0.127347</td>\n",
       "      <td>0.059714</td>\n",
       "      <td>0.559243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monocyte</th>\n",
       "      <td>0.007576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>1.129199</td>\n",
       "      <td>0.004669</td>\n",
       "      <td>0.186030</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.009644</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.335471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634562</td>\n",
       "      <td>0.819093</td>\n",
       "      <td>0.673435</td>\n",
       "      <td>1.082816</td>\n",
       "      <td>0.122342</td>\n",
       "      <td>0.720026</td>\n",
       "      <td>0.379002</td>\n",
       "      <td>0.355176</td>\n",
       "      <td>0.273451</td>\n",
       "      <td>0.246345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smooth muscle cell</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.679549</td>\n",
       "      <td>0.075119</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029376</td>\n",
       "      <td>0.003088</td>\n",
       "      <td>0.884605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955678</td>\n",
       "      <td>1.152182</td>\n",
       "      <td>1.465264</td>\n",
       "      <td>1.754855</td>\n",
       "      <td>0.475557</td>\n",
       "      <td>0.289799</td>\n",
       "      <td>1.151615</td>\n",
       "      <td>0.005210</td>\n",
       "      <td>0.127931</td>\n",
       "      <td>0.132270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valve cell</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.823836</td>\n",
       "      <td>0.342394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099026</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238051</td>\n",
       "      <td>...</td>\n",
       "      <td>1.206091</td>\n",
       "      <td>1.452141</td>\n",
       "      <td>1.316006</td>\n",
       "      <td>1.630800</td>\n",
       "      <td>0.628878</td>\n",
       "      <td>0.214485</td>\n",
       "      <td>0.280303</td>\n",
       "      <td>0.036624</td>\n",
       "      <td>0.203055</td>\n",
       "      <td>0.027706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1314 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ABCA8     ABCB1     ABCC9    ABHD12  \\\n",
       "Cell_type                                                                    \n",
       "atrial cardiomyocyte                0.030474  0.024539  0.028661  0.115384   \n",
       "coronary vascular endothelial cell  0.001489  0.161751  0.000000  0.088134   \n",
       "endothelial cell of endocardium     0.004572  0.029683  0.022005  0.234729   \n",
       "fibroblast of cardiac tissue        0.339567  0.000000  0.122408  0.328215   \n",
       "macrophage                          0.003497  0.614818  0.000000  0.406378   \n",
       "monocyte                            0.007576  0.000000  0.002723  1.129199   \n",
       "smooth muscle cell                  0.000000  0.002222  0.679549  0.075119   \n",
       "valve cell                          0.000000  0.000000  2.823836  0.342394   \n",
       "\n",
       "                                       ABHD6      ABI3    ABI3BP    ABLIM1  \\\n",
       "Cell_type                                                                    \n",
       "atrial cardiomyocyte                0.005506  0.029049  0.079596  0.151969   \n",
       "coronary vascular endothelial cell  0.181522  0.427485  0.006027  0.775695   \n",
       "endothelial cell of endocardium     0.179079  0.349801  0.083400  0.857162   \n",
       "fibroblast of cardiac tissue        0.110330  0.000000  0.833924  0.381946   \n",
       "macrophage                          0.930856  0.052693  0.016911  0.395915   \n",
       "monocyte                            0.004669  0.186030  0.013158  0.009644   \n",
       "smooth muscle cell                  0.000000  0.000819  0.000000  0.029376   \n",
       "valve cell                          0.000000  0.000000  0.099026  0.009438   \n",
       "\n",
       "                                      ABLIM3     ACADM  ...     YPEL3  \\\n",
       "Cell_type                                               ...             \n",
       "atrial cardiomyocyte                0.022493  0.629446  ...  0.440014   \n",
       "coronary vascular endothelial cell  0.445656  0.174340  ...  0.600843   \n",
       "endothelial cell of endocardium     0.022130  0.372820  ...  0.531657   \n",
       "fibroblast of cardiac tissue        0.000000  0.289264  ...  1.090447   \n",
       "macrophage                          0.065669  0.308043  ...  0.862151   \n",
       "monocyte                            0.000000  0.335471  ...  0.634562   \n",
       "smooth muscle cell                  0.003088  0.884605  ...  0.955678   \n",
       "valve cell                          0.000000  0.238051  ...  1.206091   \n",
       "\n",
       "                                       YWHAB     YWHAQ     YWHAZ    ZBTB20  \\\n",
       "Cell_type                                                                    \n",
       "atrial cardiomyocyte                0.362847  0.279094  0.572129  0.234956   \n",
       "coronary vascular endothelial cell  1.096931  1.011121  1.395291  0.350173   \n",
       "endothelial cell of endocardium     1.159300  0.925942  1.247538  0.412733   \n",
       "fibroblast of cardiac tissue        1.088631  0.975227  1.354736  1.301109   \n",
       "macrophage                          0.593370  0.300632  1.176686  0.083150   \n",
       "monocyte                            0.819093  0.673435  1.082816  0.122342   \n",
       "smooth muscle cell                  1.152182  1.465264  1.754855  0.475557   \n",
       "valve cell                          1.452141  1.316006  1.630800  0.628878   \n",
       "\n",
       "                                      ZFAND5     ZFHX3   ZFP36L2    ZNF106  \\\n",
       "Cell_type                                                                    \n",
       "atrial cardiomyocyte                0.312593  0.036221  0.063303  0.380514   \n",
       "coronary vascular endothelial cell  0.471670  0.071205  0.032647  0.180295   \n",
       "endothelial cell of endocardium     0.305715  0.056809  0.064929  0.132732   \n",
       "fibroblast of cardiac tissue        0.423467  0.280544  0.168717  0.349957   \n",
       "macrophage                          0.986885  0.081799  0.127347  0.059714   \n",
       "monocyte                            0.720026  0.379002  0.355176  0.273451   \n",
       "smooth muscle cell                  0.289799  1.151615  0.005210  0.127931   \n",
       "valve cell                          0.214485  0.280303  0.036624  0.203055   \n",
       "\n",
       "                                      ZNF622  \n",
       "Cell_type                                     \n",
       "atrial cardiomyocyte                0.039710  \n",
       "coronary vascular endothelial cell  0.167907  \n",
       "endothelial cell of endocardium     0.095595  \n",
       "fibroblast of cardiac tissue        0.245153  \n",
       "macrophage                          0.559243  \n",
       "monocyte                            0.246345  \n",
       "smooth muscle cell                  0.132270  \n",
       "valve cell                          0.027706  \n",
       "\n",
       "[8 rows x 1314 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sc_marker_mean)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate 200 marker genes of RA cells generate using Tabula Muris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_ts_meta.index = list(ra_ts_meta[\"Cell\"])\n",
    "ra_ts_data_new = ra_ts_data.T\n",
    "new_header = ra_ts_data_new.iloc[0]\n",
    "ra_ts_data_new = ra_ts_data_new[1:]\n",
    "ra_ts_data_new.columns = new_header\n",
    "# ---------------------------------------------------------\n",
    "# Added this line to fix code breaking at groupby\n",
    "ra_ts_data_new = ra_ts_data_new.astype(float)\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "# Extract mutual marker genes\n",
    "common_markers = list(set(ra_ts_data_new.columns).intersection(set(marker)))\n",
    "ra_ts_data_new = ra_ts_data_new[common_markers]\n",
    "\n",
    "ra_ts_data_new[\"Cell_type\"] = ra_ts_meta[\"Cell_type\"]\n",
    "ra_ts_marker_mean = ra_ts_data_new.groupby([\"Cell_type\"])[common_markers].mean()\n",
    "\n",
    "sc_marker_mean = sc_marker_mean[common_markers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LRRC8A</th>\n",
       "      <th>ANGPTL2</th>\n",
       "      <th>FABP3</th>\n",
       "      <th>PTGR2</th>\n",
       "      <th>EBF1</th>\n",
       "      <th>EGR1</th>\n",
       "      <th>LRRC32</th>\n",
       "      <th>TCN2</th>\n",
       "      <th>TNS1</th>\n",
       "      <th>NEURL2</th>\n",
       "      <th>...</th>\n",
       "      <th>PRDX5</th>\n",
       "      <th>CYBB</th>\n",
       "      <th>MKNK1</th>\n",
       "      <th>RARRES2</th>\n",
       "      <th>PEX16</th>\n",
       "      <th>FGFR1</th>\n",
       "      <th>PAPSS2</th>\n",
       "      <th>PLOD1</th>\n",
       "      <th>PDGFRL</th>\n",
       "      <th>DBNDD2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiac Fibroblast</th>\n",
       "      <td>0.642087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002779</td>\n",
       "      <td>0.409128</td>\n",
       "      <td>2.424644</td>\n",
       "      <td>0.929596</td>\n",
       "      <td>0.137403</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>1.137689</td>\n",
       "      <td>0.145932</td>\n",
       "      <td>...</td>\n",
       "      <td>1.336749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280167</td>\n",
       "      <td>0.642622</td>\n",
       "      <td>0.140606</td>\n",
       "      <td>1.538802</td>\n",
       "      <td>0.146972</td>\n",
       "      <td>0.721047</td>\n",
       "      <td>1.295602</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardiac Muscle Cells</th>\n",
       "      <td>0.244964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.595781</td>\n",
       "      <td>0.477265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008865</td>\n",
       "      <td>2.150835</td>\n",
       "      <td>...</td>\n",
       "      <td>2.756900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.309882</td>\n",
       "      <td>0.440219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.042416</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Endothelial Cells</th>\n",
       "      <td>1.541499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127884</td>\n",
       "      <td>0.523833</td>\n",
       "      <td>0.005849</td>\n",
       "      <td>2.557961</td>\n",
       "      <td>1.065241</td>\n",
       "      <td>1.968689</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>...</td>\n",
       "      <td>1.815320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.106353</td>\n",
       "      <td>0.005149</td>\n",
       "      <td>0.369120</td>\n",
       "      <td>1.499487</td>\n",
       "      <td>2.329499</td>\n",
       "      <td>0.731393</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macrophages</th>\n",
       "      <td>0.073864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625936</td>\n",
       "      <td>0.003968</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.148904</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.735379</td>\n",
       "      <td>0.174928</td>\n",
       "      <td>0.485021</td>\n",
       "      <td>...</td>\n",
       "      <td>1.128564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.011558</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.530364</td>\n",
       "      <td>0.146176</td>\n",
       "      <td>0.218220</td>\n",
       "      <td>0.339090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smooth Muscle Cells</th>\n",
       "      <td>0.455208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154149</td>\n",
       "      <td>1.966114</td>\n",
       "      <td>0.046159</td>\n",
       "      <td>0.741066</td>\n",
       "      <td>0.027998</td>\n",
       "      <td>2.225939</td>\n",
       "      <td>0.138347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322661</td>\n",
       "      <td>0.390666</td>\n",
       "      <td>0.188524</td>\n",
       "      <td>0.310384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.758711</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1312 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Unnamed: 0              LRRC8A  ANGPTL2     FABP3     PTGR2      EBF1  \\\n",
       "Cell_type                                                               \n",
       "Cardiac Fibroblast    0.642087      0.0  0.002779  0.409128  2.424644   \n",
       "Cardiac Muscle Cells  0.244964      0.0  4.595781  0.477265  0.000000   \n",
       "Endothelial Cells     1.541499      0.0  0.000000  0.127884  0.523833   \n",
       "Macrophages           0.073864      0.0  0.625936  0.003968  0.000012   \n",
       "Smooth Muscle Cells   0.455208      0.0  0.000000  0.154149  1.966114   \n",
       "\n",
       "Unnamed: 0                EGR1    LRRC32      TCN2      TNS1    NEURL2  ...  \\\n",
       "Cell_type                                                               ...   \n",
       "Cardiac Fibroblast    0.929596  0.137403  0.000636  1.137689  0.145932  ...   \n",
       "Cardiac Muscle Cells  0.000000  0.009582  0.000000  0.008865  2.150835  ...   \n",
       "Endothelial Cells     0.005849  2.557961  1.065241  1.968689  0.003605  ...   \n",
       "Macrophages           0.148904  0.000252  0.735379  0.174928  0.485021  ...   \n",
       "Smooth Muscle Cells   0.046159  0.741066  0.027998  2.225939  0.138347  ...   \n",
       "\n",
       "Unnamed: 0               PRDX5  CYBB     MKNK1   RARRES2     PEX16     FGFR1  \\\n",
       "Cell_type                                                                      \n",
       "Cardiac Fibroblast    1.336749   0.0  0.280167  0.642622  0.140606  1.538802   \n",
       "Cardiac Muscle Cells  2.756900   0.0  0.151037  0.000000  0.309882  0.440219   \n",
       "Endothelial Cells     1.815320   0.0  0.106353  0.005149  0.369120  1.499487   \n",
       "Macrophages           1.128564   0.0  1.011558  0.000262  0.530364  0.146176   \n",
       "Smooth Muscle Cells   0.996334   0.0  0.322661  0.390666  0.188524  0.310384   \n",
       "\n",
       "Unnamed: 0              PAPSS2     PLOD1    PDGFRL  DBNDD2  \n",
       "Cell_type                                                   \n",
       "Cardiac Fibroblast    0.146972  0.721047  1.295602     0.0  \n",
       "Cardiac Muscle Cells  0.000000  1.042416  0.000000     0.0  \n",
       "Endothelial Cells     2.329499  0.731393  0.000000     0.0  \n",
       "Macrophages           0.218220  0.339090  0.000000     0.0  \n",
       "Smooth Muscle Cells   0.000000  1.758711  0.000064     0.0  \n",
       "\n",
       "[5 rows x 1312 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ra_ts_marker_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transpose both datasets prior to correlation calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_marker_mean = sc_marker_mean.T\n",
    "ra_ts_marker_mean = ra_ts_marker_mean.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the correlation matrix between RA single cells generated from different references"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:00<00:00, 1999.55it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeffmat = np.zeros(\n",
    "    (sc_marker_mean.shape[1], ra_ts_marker_mean.shape[1]))\n",
    "for i in tqdm(range(sc_marker_mean.shape[1])):\n",
    "    for j in range(ra_ts_marker_mean.shape[1]):\n",
    "\n",
    "        corrtest = pearsonr.pearsonr(\n",
    "            sc_marker_mean[sc_marker_mean.columns[i]],\n",
    "            ra_ts_marker_mean[ra_ts_marker_mean.columns[j]],\n",
    "        )\n",
    "        coeffmat[i, j] = corrtest[0]\n",
    "\n",
    "tm_ct = list(sc_marker_mean.columns)\n",
    "ts_ct = list(ra_ts_marker_mean.columns)\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(coeffmat, cmap=\"RdBu_r\")\n",
    "ax.set_xticks(np.arange(len(ts_ct)))\n",
    "ax.set_xticklabels(ts_ct, fontsize = 12)\n",
    "ax.set_yticks(np.arange(len(tm_ct)))\n",
    "ax.set_yticklabels(tm_ct, fontsize = 12)\n",
    "plt.xlabel(\"Tabula Sapiens reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.ylabel(\"Tabula Muris reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.setp(ax.get_xticklabels(), rotation = 45,  ha=\"right\", rotation_mode=\"anchor\")\n",
    "plt.colorbar(im)\n",
    "ax.set_title(\"Expression correlation\", fontsize = 13)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polishing the figure\n",
    "\n",
    "The correlation matrix can be improved by formatting the cell names consistently and order them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reordering  \n",
    "reordered_cells = ['Cardiac Muscle Cells', \n",
    "                   'Endothelial Cells',\n",
    "                   'Cardiac Fibroblast', \n",
    "                   'Macrophages', \n",
    "                   'Smooth Muscle Cells']\n",
    "ra_ts_marker_mean = ra_ts_marker_mean.reindex(columns= reordered_cells)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatting cell names\n",
    "formatted_names = ['Atrial Cardiomyocyte', \n",
    "                   'Coronary Vascular Endothelial Cell',\n",
    "                   'Endothelial Cell of Endocardium', \n",
    "                   'fibroblast of Cardiac Tissue',\n",
    "                   'Macrophage', 'Monocyte',\n",
    "                   'Smooth Muscle Cell',\n",
    "                   'Valve Cell']\n",
    "\n",
    "sc_marker_mean.columns = formatted_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run again the correlation matrix and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:00<00:00, 1599.58it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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HBDiCIAiCIOQ4YgyOIAiCIAg5jmjBEQRBEAQhxxEBjiAIgiAIOY5Y6E8QBCENKSkp/Pvvv+TOnVvnJfYFIaNIkkRcXBwFChT46Kap+kpMTFRZlVsbxsbGmJqaZnhdMpIIcARBENLw77//Zshmm4KQER48eEChQoUy9JqJiYnkNbPkNck6PS9fvnzcvXv3iw5yRIAjCIKQhty5cwNwuF09LI3Vl93/HHo7dc+SclP7tqRDVleBMVbqe1Z9Tu2vFsmyst8lxnN4VAv585iRkpKSeE0yARTEWMtRK0mk8OuTRyQlJYkARxAEITtSdktZGhtlWYCTy9QiS8pNzcTcMqurgJWFeZaWb2SW9e9DZnaTmikMMVZoF+AYSor3m5184USAIwiCIAhfOQMFGGoZPxmACHAEQRAEQfjyGSoUGGrZQmRI9hhwLwIcQRAEQfjKGerQgmOYuVXJMCLAEQRBEISvnGjBEQRBEAQhxxEtOIIgCIIg5DiiBUcQBEEQhBxHgfZ7N2WP8EbsRSVkE87OzgQGBur8vIiICBQKBatXr87wOmUGTfUdP3682CZAEIRMpWzB0fbIDkSAI2SohQsXolAoqFKlisbzV65cYfz48URERHzeimkpMjKSIUOG4OHhgbm5ORYWFnh6ejJp0iSio6OzunpCKrt27WL8+PFZXQ1BEL5QIsARMlRISAjOzs6cPHmSW7duqZ2/cuUKEyZM0DnAuX79OsuWLcugWmp26tQpSpcuzYIFC/Dx8WH27NnMmjWLChUqMHXqVFq3bp2p5adl9OjRJCQkZEnZX7Jdu3YxYcKErK6GIOQIykHG2h7ZgRiDI2SYu3fvcvz4cbZs2ULPnj0JCQlh3Lhxel9PkiQSExMxMzPDxMQkA2uqLjo6mhYtWmBoaMg///yDh4eHyvnJkydnWID1+vVrzM21X3Y+V65c5Mol/lQFQcg87wMXbQcZZw+iBUfIMCEhIdjY2NCoUSNatWpFSEiIyvnVq1fj7+8PQI0aNVAoFCgUCg4fPgy8H2fTuHFj9uzZQ6VKlTAzM2PJkiXyudRjcF68eMGQIUMoU6YMlpaWWFlZ0aBBA86fP69X3ZcsWcKjR4+YPXu2WnAD4OjoyOjRo+XH27dvp1GjRhQoUAATExNcXV356aefSE5W3ZHXz8+P0qVLc+bMGapXr465uTkjR44E3gdVgYGBWFtbkydPHjp37qyxG0zTGJx3797x008/4erqiomJCc7OzowcOZI3b96o5FO+pocPH5Zf0zJlysiv+ZYtWyhTpgympqZ4enryzz//yM9dtWoVCoVCJU1pypQpGBoa8ujRIzktLCwMT09PzMzMsLOzo2PHjirnla5du0br1q2xt7fHzMwMd3d3Ro0aBcChQ4dQKBRs3bpV7XmhoaEoFApOnDhBYGAgCxYsAJA/R6lfo5SUFObOnUupUqUwNTXF0dGRnj178vLlS7XrCoKQM1twRIAjZJiQkBBatmyJsbEx7dq14+bNm5w6dUo+X716dfr37w/AyJEjWbt2LWvXrqVEiRJynuvXr9OuXTvq1KnDvHnzKF++vMay7ty5w7Zt22jcuDGzZ89m6NChXLx4EV9fX/7991+d675jxw7MzMxo1aqVVvlXr16NpaUlgwYNYt68eXh6ejJ27FiGDx+ulvf58+c0aNCA8uXLM3fuXGrUqIEkSTRr1oy1a9fSsWNHJk2axMOHD+ncubNW5Xfr1o2xY8dSsWJF5syZg6+vL8HBwbRt21Yt761bt2jfvj1NmjQhODiYly9f0qRJE0JCQhg4cCAdO3ZkwoQJ3L59m9atW5OSkgJAq1atMDMzUwtU4f177efnR8GCBeXXo3Xr1hgaGhIcHEz37t3ZsmUL3t7eKkHbhQsXqFKlCgcPHqR79+7MmzeP5s2b8/vvvwPvA8LChQunWaarqyteXl707NmTOnXqAMifo7Vr18p5e/bsydChQ6lWrRrz5s0jKCiIkJAQ6tWrx9u3b7V6jQXha5ITBxl/Urt3TEwMv/32G5cuXSI+Pl7+FQxQuHBhMfPjK3LmzBmuXbvG/PnzAfD29qZQoUKEhITwzTffAFC0aFF8fHz4+eefqVOnDn5+fmrXuXXrFn/88Qf16tX7aHllypThxo0bGBj8f4zeqVMnPDw8WLFiBWPGjNGp/levXqV48eIYGxtrlT80NBQzMzP5ca9evejVqxcLFy5k0qRJKl1qT548YfHixfTs2VNO2759O0eOHGH69OkMHToUgN69e1OjRo10yz5//jxr1qyhW7ducrdZnz59cHBwYObMmRw6dEjlOtevX+f48eN4eXkBULJkSerVq0f37t25du0aRYoUAcDGxoaePXty5MgR/Pz8yJ07N82bN2f9+vVMnz5dfq3/+ecfrly5Itf77du3DBs2jNKlS3PkyBFMTU2B95+Bxo0bM2fOHHmsTL9+/ZAkibNnz8rlAkydOhV43xrTsWNHZs+eTUxMDNbW1gA8e/aMvXv3yi09Xl5eFC9enH379tGxY0eV1+fYsWMsX76ckJAQ2rdvL6fXqFGD+vXrExYWppKe2ps3b1RawWJjY9N9PwQhJ9B5s81sQO96hoeH4+LiQq9evZg/fz6rVq1CkiQqVapE0aJF2bt3b0bWU/jChYSE4OjoKH+xKhQK2rRpw4YNG9S6bT7GxcUl3eAGwMTERP7CTU5O5vnz51haWuLu7s7Zs2d1rn9sbCy5c+fWOn/q4CYuLo6oqCh8fHx4/fo1165dU6trUFCQStquXbvIlSsXvXv3ltMMDQ3p169fumXv2rULgEGDBqmkDx48GHj/t5layZIl5eAGkGe41axZUyXIUKbfuXNHTgsICODff//l0KFDclpISAhmZmZ89913AJw+fZqnT5/Sp08fObgBaNSoER4eHnJ9nj17xpEjR+jSpYtKuYDKj6GAgADevHnDpk2b5LSNGzfy7t07tWBGk7CwMKytralTpw5RUVHy4enpiaWlpcq9fCg4OBhra2v5KFy4cLrlCUJOkBNbcPQKcC5evEirVq2IiYlBkv5/z/RcuXLRpEkTUlJS2LFjR4ZVUviyJScns2HDBmrUqMHdu3e5desWt27dokqVKkRGRnLgwAGtr+Xi4qJVvpSUFObMmUOxYsUwMTHBzs4Oe3t7Lly4QExMjM73YGVlRVxcnNb5L1++TIsWLbC2tsbKygp7e3v5y/fD8gsWLKjWMnTv3j3y58+PpaWlSrq7u3u6Zd+7dw8DAwPc3NxU0vPly0eePHm4d++eSvqHwYSyVeTDL29leupxKnXq1CF//vxyl1FKSgrr16+nWbNmckCoLE9T3T08POTzysCpdOnSH70/Dw8PvvnmG5VuqpCQEL799lu1e9bk5s2bxMTE4ODggL29vcrx6tUrnj59muZzR4wYQUxMjHw8ePAg3fIEISfIiWNw9OqiCg4O5s2bNygUCipUqKAyCPGbb75h1apVHD9+PMMqKXzZDh48yOPHj9mwYQMbNmxQOx8SEkLdunW1ulbqlpGPmTJlCmPGjKFLly789NNP2NraYmBgwIABA+QxJLrw8PDg3LlzJCUlpdtNFR0dja+vL1ZWVkycOBFXV1dMTU05e/Ysw4YNUytf23vSlbZdwIaGmuc8pJWe+keLoaEh7du3Z9myZSxcuJD//e9//Pvvv1q1pHyKgIAAfvjhBx4+fMibN2/466+/+OWXX7R6bkpKCg4ODhrH8QDY29un+VwTE5NMn7EnCF8isRfVfw4fPoxCoWD06NHUrVsXHx8f+ZzyV6E+Az2F7CkkJAQHBwd5VktqW7ZsYevWrSxevBgzM7MMG5e1adMmatSowYoVK1TSo6OjsbOz0/l6TZo04cSJE2zevJl27dp9NO/hw4d5/vw5W7ZsoXr16nL63bt3tS7PycmJAwcO8OrVK5VWnOvXr2v13JSUFG7evKkyQDsyMpLo6GicnJy0roc2AgICmDVrFr///ju7d+/G3t5epRtRWd7169epWbOmynOvX78uny9atCgAly5dSrfMtm3bMmjQINavX09CQgJGRka0adNGJU9anyVXV1f2799PtWrVMi24FIScJifuRaVXF9Xz588B8PX1VTun/PUrVn39OiQkJLBlyxYaN25Mq1at1I6+ffsSFxcnd1laWFgAn/75MDQ0VGlpgPdjLzRNS9ZGr169yJ8/P4MHD+bGjRtq558+fcqkSZPkskG1pSMpKYmFCxdqXV7Dhg159+4dixYtktOSk5PlQdrpPRdg7ty5KumzZ88G3o99yUhly5albNmyLF++nM2bN9O2bVuVdXkqVaqEg4MDixcvVhmgu3v3bq5evSrXx97enurVq7Ny5Uru37+vUsaH76WdnR0NGjRg3bp1hISEUL9+fbXANa3PUuvWrUlOTuann35Su5d3796Jf5sEQQNDdOii0rOMBQsW4OzsjKmpKVWqVOHkyZMfzR8dHc33339P/vz5MTExoXjx4vIYRG3o1YJjZWXFixcvuHnzplp/unJasI2NjT6XFrKZHTt2EBcXR9OmTTWe//bbb7G3tyckJIQ2bdpQvnx5DA0NmTZtGjExMZiYmFCzZk0cHBx0Krdx48ZMnDiRoKAgqlatysWLFwkJCZFbCXRlY2PD1q1badiwIeXLl6djx454enoCcPbsWdavXy8P1K1atSo2NjZ07tyZ/v37o1AoWLt2rdqX9Mc0adKEatWqMXz4cCIiIihZsiRbtmzRavxQuXLl6Ny5M0uXLpW7y06ePMmaNWto3ry5VjOxdBUQEMCQIUMA1LqnjIyMmDZtGkFBQfj6+tKuXTsiIyOZN28ezs7ODBw4UM77888/4+3tTcWKFenRowcuLi5EREQQHh7OuXPn1MpUTtvXFKwo35/+/ftTr149DA0Nadu2Lb6+vvTs2ZPg4GDOnTtH3bp1MTIy4ubNm4SFhTFv3jytlwMQhK+FgQ4tOAZ6tMRv3LiRQYMGsXjxYqpUqcLcuXOpV68e169f1/jvf1JSEnXq1MHBwYFNmzZRsGBB7t27R548ebQuU68Ap2LFiuzbt4+xY8eqzA5Zvnw5U6dORaFQyP/4CDlbSEgIpqam8pokHzIwMKBRo0aEhITw/Plz8uXLx+LFiwkODqZr164kJydz6NAhnQOckSNHEh8fT2hoKBs3bqRixYqEh4drXIdGW1WqVOHSpUvMmDGD8PBw1q5di4GBASVKlGD48OH07dsXgLx587Jz504GDx7M6NGjsbGxoWPHjtSqVUurGWDw/nXZsWMHAwYMYN26dSgUCpo2bSpvDZGe5cuXU7RoUVavXs3WrVvJly8fI0aM+KSVoz+mQ4cODBs2DFdXVypXrqx2PjAwEHNzc6ZOncqwYcOwsLCgRYsWTJs2TeUfpHLlyvHXX38xZswYFi1aRGJiIk5OThq3wWjSpAk2NjakpKRoDKBbtmxJv3792LBhA+vWrUOSJHkdoMWLF+Pp6cmSJUsYOXIkuXLlwtnZmY4dO1KtWrWMe2EEIYfQaQyOHj1Us2fPpnv37nLMsHjxYsLDw1m5cqXGf7dXrlzJixcvOH78OEZGRsD7hUt1oZB0+dn5n40bN9KuXTuNfeCSJKFQKPjtt9/kaaSCIGRvUVFR5M+fn7Fjx+q8xpC+3r17R4ECBWjSpInaWKvPJTY2Fmtra053boylsVGW1CHI5fssKTe1aqUds7oKTLK+mKXlt7icsWPbdPE2IZ79g+sSExODlZVVhl5b+RlfY+eOuYF2nU+vU5LpHHWdBw8eqNQnrUH6SUlJmJubs2nTJpo3by6nK1dv3759u9pzGjZsiK2tLebm5mzfvh17e3vat2/PsGHD0pwg8SG9xuC0adOGdu3aIUmSSrO88v/btWsnghtByEFWr15NcnIynTp1+mxlbtu2jWfPnhEQEPDZyhSEr5U+08QLFy6ssm5UcHCwxmtHRUWRnJyMo6NqoOzo6MiTJ080PufOnTts2rSJ5ORkdu3axZgxY5g1a5Y8FlIbeq9kvG7dOry9vVm5ciVXrlwBoESJEnTt2pVevXrpe1lBEL4gBw8e5MqVK0yePJnmzZvr3ESsj7///psLFy7w008/UaFCBY2TGQRByFg6zaL6L5+mFpyMolzuYenSpRgaGuLp6cmjR4+YMWOG1l3xegc4CoWC3r17q6zEKghCzjJx4kSOHz9OtWrVtJrhlREWLVrEunXrKF++PKtXr/4sZQrC185AodB68LAyn5WVlVZdZnZ2dhgaGhIZGamSHhkZSb58+TQ+J3/+/BgZGal0R5UoUYInT55otV4Z6NlF9fbtW2JjYzXu06JMFxvaCUL2d/jwYZKSkjh06JC8sWZmW716Ne/eveP06dPprnosCELGUBgqdDp0YWxsjKenp8qq9ikpKRw4cEBlG5nUqlWrxq1bt1QWTr1x4wb58+fXes9AvQKcvn37YmNjQ/369dXONWjQABsbG6321BEEQRAEIesZGCp0OnQ1aNAgli1bxpo1a7h69Sq9e/cmPj5enlUVEBDAiBEj5Py9e/fmxYsX/PDDD9y4cYPw8HCmTJnC999rP+hery6qI0eOAO+nhn4oMDCQEydO8Oeff+pzaUEQBEEQPjdDAxQGWrZ5KHSefE2bNm149uwZY8eO5cmTJ5QvX54//vhDHnh8//59eQNleD+Aec+ePQwcOJCyZctSsGBBfvjhB4YNG6Z1mXoFOMoN6DQtqqbcLPHhw4f6XFoQBEEQhM9MYaB915NCz60a+vbtK68n9qHDhw+rpXl5efHXX3/pVRboGeAop4NHRESonVOm6bPhoSAIwpeo+dumGCjMs6TsBiO7Zkm5qRU6djCrq4Bh4awdjzUkv+bBsJ9D/Ks49mdyGbp0PRnk5L2oihQpgiRJTJs2TWUO+5MnT5g+fTrw/5tuCoIgCILwZVMYGOh0ZAd6teDUrFmT69evc+fOHdzd3alSpQrwfv2KuLg4FAoFtWrVytCKCoIgCIKQOXJiC45eAc7gwYNZs2YNCQkJxMXFyVO/lF1XZmZmKhvsCYIgCILw5dJl+re+Y3A+N73amYoWLcrGjRvJnTs3gMqWDblz52b9+vW4ubllXC0FQRAEQcg07wMcAy2P7BHg6L2ScaNGjbh16xa//fabvFVDyZIlad26NXZ2dhlWQUEQBEEQMpfoovqAnZ0dffr0yai6CIIgCIKQBRQKBQoDLbuoUr6CAOf169fcvXuXFy9eqOwqrlS9evVPubwgCIIgCJ+BgaEBBobajVoxkHLwLKqEhAQGDx7MihUrePfuncY8CoUizXOCIAiCIHw5dBpkLOXgFpyBAweydOnSjK6LIAiCIAhZICcGOHq1M23ZsgXFf9ulm5ubU7BgQZycnFSOIkWKZGhFha+Hs7Ozxn3OsrPDhw+jUCg0Lkeur4iICBQKBTNnzsywa2qqZ2BgIM7OznpdLyvfy4y+F0HIyZRdVNoe2YFetXz16hUAffr0IS4ujgcPHnD37l21Q1+3b9+mZ8+eFC1aFFNTU6ysrKhWrRrz5s0jISFB7+vmdGXLlpVXmU5LtWrVcHR0FN2H/1EoFGkevXr1yurqAbBr1y7Gjx+f1dXIdImJicyZM4cqVapgbW2NqakpxYsXp2/fvty4cSOrqycIOdt/LTjaHOTkaeJly5bl1KlTNGrUSG7JySjh4eH4+/tjYmJCQEAApUuXJikpiWPHjjF06FAuX74susfS0KFDB4YPH87Ro0c1DvCOiIjgxIkT9O3bl1y5Pml8eY5Sp04dAgIC1NKLFy+eBbVRt2vXLhYsWJBlQc6yZcsyfW+5qKgo6tevz5kzZ2jcuDHt27fH0tKS69evs2HDBpYuXUpSUlKm1kEQvmYGCgUGWs6iMsjg7/3Mote33KhRo2jWrBmhoaE0aNAgwypz9+5d2rZti5OTEwcPHiR//vzyue+//55bt24RHh7+yeVIkkRiYiJmZmaffK2MFB8fj4WFhd7Pb9++PSNGjCA0NFRjgLN+/XokSaJDhw6fUs1sJTExEWNjYww+sndK8eLF6dix42esVfZiZGSU6WUEBgbyzz//sGnTJr777juVcz/99BOjRo3K9DoIwtdMuYifVnlTcnAX1T///IObmxuhoaGULVuWAQMGMHHiRLVDV9OnT+fVq1esWLFCJbhRcnNz44cffpAfv3v3jp9++glXV1dMTExwdnZm5MiRvHnzRuV5zs7ONG7cmD179lCpUiXMzMxYsmQJAHfu3MHf3x9bW1vMzc359ttv1YIoZV/+b7/9xuTJkylUqBCmpqbUqlWLW7duqeQ9evQo/v7+FClSBBMTEwoXLszAgQPVutYCAwOxtLTk9u3bNGzYkNy5c9OhQwfGjRuHkZERz549U7v/Hj16kCdPHhITEzW+foULF6Z69eps2rSJt2/fqp0PDQ3F1dWVKlWqcO/ePfr06YO7uztmZmbkzZsXf39/tR3i3759y4QJEyhWrBimpqbkzZsXb29v9u3bp5Lv2rVrtG7dGnt7e8zMzHB3d1f5Ukpr7MP48ePTbQV88eIFQ4YMoUyZMlhaWmJlZUWDBg04f/68Sj7l+7RhwwZGjx5NwYIFMTc3JzY29qPX14afnx+lS5fmypUr1KhRQx57ptxcNrWHDx/SvHlzLCwscHBwYODAgWqfSaWwsDA8PT0xMzPDzs6Ojh078ujRI/l8YGAgCxYsAFS70z60dOlS+e/gm2++4dSpU2p5rl27RqtWrbC1tcXU1JRKlSqxY8eOdO9d03s3c+ZMqlatSt68eTEzM8PT05NNmzaley1N/v77b8LDw+natatacANgYmKiNs5I33sRBEEz5UJ/2h7ZgV4tOMovJUmSuHz5MpcvX9aYb+zYsTpd9/fff6do0aJUrVpVq/zdunVjzZo1tGrVisGDB/P3338THBzM1atX2bp1q0re69ev065dO3r27En37t1xd3cnMjKSqlWr8vr1a/r370/evHlZs2YNTZs2ZdOmTbRo0ULlGlOnTsXAwIAhQ4YQExPD9OnT6dChA3///becJywsjNevX9O7d2/y5s3LyZMnmT9/Pg8fPiQsLEzleu/evaNevXp4e3szc+ZMzM3N8fLyYuLEiWzcuJG+ffvKeZOSkuRft6ampmm+Jh06dKBHjx7s2bOHxo0by+kXL17k0qVL8nty6tQpjh8/Ttu2bSlUqBAREREsWrQIPz8/rly5grm5OfD+vQ4ODqZbt25UrlyZ2NhYTp8+zdmzZ6lTpw4AFy5cwMfHByMjI3r06IGzszO3b9/m999/Z/LkyVq9lx9z584dtm3bhr+/Py4uLkRGRrJkyRJ8fX25cuUKBQoUUMn/008/YWxszJAhQ3jz5g3GxsYfvX5iYiJRUVFq6VZWVirPffnyJfXr16dly5a0bt2aTZs2MWzYMMqUKSO3ZCYkJFCrVi3u379P//79KVCgAGvXruXgwYNq11+9ejVBQUF88803BAcHExkZybx58/jf//7HP//8Q548eejZsyf//vsv+/btY+3atRrrHxoaSlxcHD179kShUDB9+nRatmzJnTt35NaXy5cvU61aNQoWLMjw4cOxsLDgt99+o3nz5mzevFnts56eefPm0bRpUzp06EBSUhIbNmzA39+fnTt30qhRI52upQxMOnXqpFX+jL4XQRB0nEWV0xf6Uw5kTWtAq65jc2JjY3n06BHNmjXTKv/58+dZs2YN3bp1Y9myZcD7Qc8ODg7MnDmTQ4cOUaNGDTn/rVu3+OOPP6hXr56cNnDgQCIjIzl69Cje3t4AdO/enbJlyzJo0CCaNWum0rWRmJjIuXPn5C89GxsbfvjhBy5dukTp0qUBmDZtmkrXV48ePXBzc2PkyJHcv39fZXbZmzdv8Pf3Jzg4WOXevLy8WLdunUqAEx4ezsuXL9P9EmjVqhX9+vUjNDRUJcAJDQ0FkLunGjVqRKtWrVSe26RJE7y8vNi8ebNcTnh4OA0bNvzouKd+/fohSRJnz55Vub+pU6d+tK7aKlOmDDdu3FB5Lzp16oSHhwcrVqxgzJgxKvkTExM5ffq01l2QK1asYMWKFWrp69evp23btvLjf//9l19//VV+bbp27YqTkxMrVqyQA5ylS5dy48YNfvvtN/z9/YH3n6ly5cqpXPvt27cMGzaM0qVLc+TIETlo9fb2pnHjxsyZM4cJEybg5eVF8eLF2bdvX5rdaPfv3+fmzZvY2NgA4O7uTrNmzVSC3B9++IEiRYpw6tQpTExMgPd/L97e3gwbNkznoODGjRsqr2/fvn2pWLEis2fP1jnAuXr1KvD+fdZGRt9Lam/evFFpbcuI1j9ByA5EF9V/OnfunO6hadDmxyj/IVFu4JmeXbt2ATBo0CCV9MGDBwOodTO5uLioBDfKa1SuXFkObgAsLS3p0aMHERER8h5bSkFBQSq/6H18fID3LQxKqf/Rj4+PJyoqiqpVqyJJEv/884/affTu3VstLSAggL///pvbt2/LaSEhIRQuXBhfX1+1/KnZ2NjQsGFDduzYQXx8PPA+CN2wYQOVKlWSB86mrufbt295/vw5bm5u5MmTh7Nnz8rn8uTJw+XLl7l586bG8p49e8aRI0fo0qWL2tIAGTUA3cTERA5ukpOTef78OZaWlri7u6vUValz5846ja9q1qwZ+/btUztSB8jw/rOROsgwNjamcuXKKu//rl27yJ8/v0rwaG5uTo8ePVSudfr0aZ4+fUqfPn1UWuQaNWqEh4eHTmPN2rRpIwc3oP65fPHiBQcPHqR169bExcURFRVFVFQUz58/p169ety8eVOlW0wbqV/fly9fEhMTg4+Pj8b3Iz26/O1nxr2kFhwcjLW1tXwULlxY72sJQnZiYKhLN1VW11Y7erXgrFq1KqPrgZWVFQBxcXFa5b937x4GBgZqu5bny5ePPHnycO/ePZV0FxcXjdeoUqWKWnqJEiXk88qWGUDtC1z5pfLy5Us57f79+4wdO5YdO3aopAPExMSoPM6VKxeFChVSK79NmzYMGDCAkJAQxo4dS0xMDDt37mTgwIFaBQ0dOnRg69atbN++nfbt23P8+HEiIiJUxi8lJCQQHBzMqlWrePTokUpLXOp6Tpw4kWbNmlG8eHFKly5N/fr16dSpE2XLlgX+/0s09euU0VJSUpg3bx4LFy7k7t27JCcny+fy5s2rll/Te/0xhQoVonbt2lrl+/D1t7Gx4cKFC/Lje/fu4ebmppbP3d1d5bHy8/lhOoCHhwfHjh3Tuv7pfS5v3bqFJEmMGTNGrbVL6enTpxQsWFDrMnfu3MmkSZM4d+6cSouHPkFt6r/9PHnyfDRvZtxLaiNGjFD50RQbGyuCHOGroDDQYS8qLfNltS9mrrCVlRUFChTg0qVLOj1P239QM2LGlKGh5rBVGRwkJydTp04dXrx4wbBhw/Dw8MDCwoJHjx4RGBioNtU2dctEajY2NjRu3FgOcDZt2sSbN2+0nunTuHFjrK2tCQ0NpX379oSGhmJoaKjS3dKvXz9WrVrFgAED8PLywtraGoVCQdu2bVXqWb16dW7fvs327dvZu3cvy5cvZ86cOSxevJhu3bppVR9I+31KHaykZcqUKYwZM4YuXbrw008/YWtri4GBAQMGDNA4fTmzZsel9/5nlfTqpXyNhgwZotaKqfThD4WPOXr0KE2bNqV69eosXLiQ/PnzY2RkxKpVq+SuUF14eHgA78eJKVuf0pLR9/IhExMTudtLEL4mBgY67EWVnD26qD4pwFm9ejXLli3j0qVLxMfHExcXx8yZM5EkiT59+mBnZ6fT9Ro3bszSpUs5ceIEXl5eH83r5ORESkoKN2/elFtcACIjI4mOjsbJySnd8pycnLh+/bpa+rVr1+Tzurh48SI3btxgzZo1Kl10H8440kZAQADNmjXj1KlThISEUKFCBUqVKqXVc01MTGjVqhW//vorkZGRhIWFUbNmTfLlyyfn2bRpE507d2bWrFlyWmJiItHR0WrXs7W1JSgoiKCgIF69ekX16tUZP3483bp1o2jRogDpBqY2NjYar/1hS5smmzZtokaNGmrjZKKjo3X+jGU2JycnLl26hCRJKkHdh58z5Wfr+vXr1KxZU+Xc9evXVT57n9rVp3yPjIyMtGqpSs/mzZsxNTVlz549KsGAvi27TZo0ITg4mHXr1qUb4GT0vQiC8J5Og4yzySwqvcOwgIAAunbtyl9//UVcXBySJGFmZsbWrVuZMGGC2iwmbfz4449YWFjQrVs3IiMj1c7fvn2befPmAdCwYUMA5s6dq5Jn9uzZAFoNdGzYsCEnT57kxIkTclp8fDxLly7F2dmZkiVL6lR/5S/p1L/oJUmS66yLBg0aYGdnx7Rp0/jzzz91XqelQ4cOvH37lp49e/Ls2TO1tW8MDQ3VWh7mz5+v1qLy/PlzlceWlpa4ubnJ3RL29vZUr16dlStXcv/+fZW8qa/v6upKTEyMSnfO48ePtfqcaKprWFjYJ421yCwNGzbk33//VZky/fr1a7VB2pUqVcLBwYHFixerdPHs3r2bq1evqnx+lWsjaQoQteHg4ICfnx9Llizh8ePHauc1LUnwMYaGhigUCpXPSkREBNu2bdOrfl5eXtSvX5/ly5drvEZSUhJDhgwBMv5eBEF4TznIWNsjO9CrBWfNmjWsW7dO47kGDRpw7tw5du7cSffu3XW6rqurK6GhobRp04YSJUqorGR8/PhxwsLC5H1typUrR+fOnVm6dCnR0dH4+vpy8uRJ1qxZQ/PmzdUGiGoyfPhw1q9fT4MGDejfvz+2trasWbOGu3fvsnnz5o8uDqeJh4cHrq6uDBkyhEePHmFlZcXmzZvVxuJow8jIiLZt2/LLL79gaGhIu3btdHq+r68vhQoVYvv27ZiZmdGyZUuV840bN2bt2rVYW1tTsmRJTpw4wf79+9XGtJQsWRI/Pz88PT2xtbXl9OnTbNq0SWWG188//4y3tzcVK1akR48euLi4EBERQXh4OOfOnQOgbdu28gyX/v378/r1axYtWkTx4sXTHZjauHFjJk6cSFBQEFWrVuXixYuEhITIv+Y/1Y0bNzR+nh0dHeWp8Nrq3r07v/zyCwEBAZw5c4b8+fOzdu1aedq9kpGREdOmTSMoKAhfX1/atWsnTxN3dnZm4MCBcl5PT08A+vfvT7169dS6G7WxYMECvL29KVOmDN27d6do0aJERkZy4sQJHj58qLam0Mc0atSI2bNnU79+fdq3b8/Tp09ZsGABbm5uKgGsLn799Vfq1q1Ly5YtadKkCbVq1cLCwoKbN2+yYcMGHj9+LK+Fk5H3IgjCewoDAxRafudpmy+r6RXgKKdlOzs706FDB5W1TpT96cpuHl01bdqUCxcuMGPGDLZv386iRYswMTGhbNmyzJo1SyVoWr58OUWLFmX16tVs3bqVfPnyMWLECMaNG6dVWY6Ojhw/fpxhw4Yxf/58EhMTKVu2LL///rvOU13h/ZfW77//Tv/+/QkODsbU1JQWLVrQt29ftWnC2ggICOCXX36hVq1aGhc+/BgDAwPatWvHjBkzaNKkidoMlXnz5mFoaEhISAiJiYlUq1aN/fv3q41r6N+/Pzt27GDv3r28efMGJycnJk2axNChQ+U85cqV46+//mLMmDEsWrSIxMREnJycaN26tZwnb968bN26lUGDBvHjjz/i4uJCcHAwN2/eTDfAGTlyJPHx8YSGhrJx40YqVqxIeHg4w4cP1+k1SYty1tSHfH19dQ5wzM3NOXDgAP369WP+/PmYm5vToUMHGjRoQP369VXyBgYGYm5uztSpUxk2bBgWFha0aNGCadOmqQy2bdmyJf369WPDhg2sW7cOSZJ0DnBKlizJ6dOnmTBhAqtXr+b58+c4ODhQoUIFnderqlmzJitWrGDq1KkMGDAAFxcXpk2bRkREhN4Bjr29PcePH2fhwoVs3LiRUaNGkZSUhJOTE02bNlUZIJ+R9yIIwnu6bKKZXTbbVEh6jJC0trbm1atXrF27FicnJ3x8fOQm64MHD1K7dm0sLS3FGhKf6Pz585QvX15l7RVBED6f2NhYrK2tKdR+KQbG5uk/IRM0WJ3121SUOKa+UOXn9n3h+Cwt/8ibfOlnyiTxr+JoWvF9N79y1mFGUX7Gz3ZvTm5j7bZliUt6S8Vl2zKlPhlJrxYc5aZ39vb2aueUU4y1mR0jfNyyZcuwtLRU614SBEEQhIykMNBhob9s0kWlVy2VS+Nv375dbYaHcpqopvVdBO38/vvvTJs2jaVLl9K9e/dP2oBTEARBENKjHIOj7ZEd6NWCU6NGDVauXMmiRYs4evSonO7j48P//vc/FAqF2tRXQXv9+vUjMjKShg0bMmHChKyujiAIgpDDvZ8dpd0SxQrD7NFDo1cYNmTIEHl5+UuXLsmtOMePHwfA1NRUZRaIoJuIiAgSEhLYtm2b1ltXCIIgCIK+Psc08QULFuDs7IypqSlVqlTh5MmTaeZdvXo1CoVC5fjYRtOa6FVLDw8PQkNDsbCwQJIklcPS0pKQkBB5zyNBEARBEL5sBgYGOh262rhxI4MGDWLcuHGcPXuWcuXKUa9ePZ4+fZrmc6ysrHj8+LF8aLMwbGp6r2TcvHlz7ty5w8aNG+VNKUuUKEGbNm00Dj4WBEEQBOHLpNNu4nq04MyePZvu3bsTFBQEwOLFiwkPD2flypVpLvmhUChUVuDXlc4BTnx8vLzJXZ06dfj+++/1LlwQBEEQhKyXmQFOUlISZ86cYcSIEXKagYEBtWvXVtlJ4EOvXr2St2WqWLEiU6ZM0XrLItAjwLGwsGDBggW8e/dOq9WCBUEQsjuDXMYY5DLOkrInz/fPknJTi/PI+lZ56V3Wlv8oKjHLyk54lfllKxQ6rGSseJ/vw7Xu0tqsNioqiuTkZBwdHVXSHR0d01wU2N3dnZUrV1K2bFliYmKYOXMmVatW5fLly1rP0tZrDI5yifys3kVZEARBEIRPp88g48KFC2NtbS0fwcHBGVYfLy8vAgICKF++PL6+vmzZsgV7e3uWLFmi9TX0CnB69eqFJEmsXr1an6cLgiAIgvAF0SfAefDgATExMfKRugsqNTs7OwwNDdU20Y6MjNR6jI2RkREVKlTg1q1bWt+TXoOMra2tKVasGNu3b6dy5cq0bNmS/Pnzqy36FxAQoM/lBUEQBEH4jPTZi8rKykqrrRqMjY3x9PTkwIEDNG/eHICUlBQOHDigsnHzxyQnJ3Px4kUaNmyoVX7QM8Dp0qULCoUCSZI4c+YMZ86cUcujUChEgCMIgiAI2YDCQKHDbuKK9DN9YNCgQXTu3JlKlSpRuXJl5s6dS3x8vDyrKiAggIIFC8rdXBMnTuTbb7/Fzc2N6OhoZsyYwb179+jWrZvWZeo9TVxJjMMRBEEQhOwts6eJt2nThmfPnjF27FiePHlC+fLl+eOPP+SBx/fv31dZX+fly5d0796dJ0+eYGNjg6enJ8ePH6dkyZJal6lXgFO9enW17ihBEARBELKnzA5wAPr27Ztml9Thw4dVHs+ZM4c5c+boVY6SXgHOhxURBEEQBCH70mea+Jfuk7uoBEEQBEHI3hSGhhhovdmmdvmy2icFOIcPH2bZsmVcunSJ+Ph4Ll68SFhYGAAtW7bE0tIyQyopCIIgCELm+RxdVJ+b3gHO6NGj5dHOkiShUCgwMzNj8uTJ3Lp1C0NDQzp06JBhFRUEQRAEQdCWXmHY77//zpQpU+QdxFNr0qQJkiTx+++/Z0gFs4vAwECcnZ0z9Jrjx49HoVAQFRWVYdfUVE+FQsH48eN1vtbhw4dRKBRZNiYrI+8lLa9evaJbt27ky5cPhULBgAEDMuza+oqIiEChUGTLhTY/x3smCILu9Fno70unVy1//vlnAGxtbenSpYvKOeVGWBcuXPjEqn261atXo1Ao0jz++uuvrK4iAFOmTGHbtm1ZXY1Md/v2bXr27EnRokUxNTXFysqKatWqMW/ePBISErK6ehpNmTKF1atX07t3b9auXUunTp3SzOvs7JzmZ61+/fqfsdaCIAi6URgY6HRkB3p1UZ05cwaFQsHUqVMpUaIEK1eulM8pN8F6/PhxxtQwA0ycOBEXFxe1dDc3tyyojbopU6bQqlUreYXHzy0hIYFcuTJ3vHl4eDj+/v6YmJgQEBBA6dKlSUpK4tixYwwdOpTLly+zdOnSTK2DPg4ePMi3337LuHHjtMpfvnx5Bg8erJZeoECBjK5ajvE5Pn+CIHycGIPzn/j4eACNQcObN28ASEzMup1XP9SgQQMqVaqU1dX4Ypmammbq9e/evUvbtm1xcnLi4MGD5M+fXz73/fffc+vWLcLDwzO1Dvp6+vSpTgtLFSxYkI4dO2Zijb5siYmJGBsbqyzYlZ7M/vwJgpA+hYFC+wBHj5WMs4JeYZhy5cEjR46ondu5cyeA1htofQmUYxpmzpzJ0qVLcXV1xcTEhG+++YZTp06p5d+2bRulS5fG1NSU0qVLs3XrVo3XjY+PZ/DgwRQuXBgTExPc3d2ZOXOmyrglhUJBfHw8a9askbszAgMDVa4THR1NYGAgefLkwdramqCgIF6/fq1W3rp16/D09MTMzAxbW1vatm3LgwcP0r3/D8dA3Lt3jz59+uDu7o6ZmRl58+bF39+fiIiIdK+lyfTp03n16hUrVqxQCW6U3Nzc+OGHHzLkXrT19OlTunbtiqOjI6amppQrV441a9bI55Xji+7evUt4eLj83uj7GqQWGBiIpaUljx49onnz5lhaWmJvb8+QIUNITk5Wyat8762trcmTJw+dO3cmOjpa43UPHjyIj48PFhYW5MmTh2bNmnH16lW1fI8ePaJr164UKFAAExMTXFxc6N27N0lJSQC8ePGCIUOGUKZMGSwtLbGysqJBgwacP39e5TrK12jDhg2MHj2aggULYm5uTmxsLKD938mHn7+0xrMpx6R9+Ny+ffsSFhZGyZIlMTMzw8vLi4sXLwKwZMkS3NzcMDU1xc/PL0PeP0HIiUQX1X+8vb3ZsGEDU6ZMURnH0rFjR0JDQ1EoFPj4+GRYJT9VTEyM2kBdhUJB3rx5VdJCQ0OJi4ujZ8+eKBQKpk+fTsuWLblz5w5GRkYA7N27l++++46SJUsSHBzM8+fPCQoKkrvmlCRJomnTphw6dIiuXbtSvnx59uzZw9ChQ3n06JG8QuPatWvp1q0blStXpkePHgC4urqqXKt169a4uLgQHBzM2bNnWb58OQ4ODkybNk3OM3nyZMaMGUPr1q3p1q0bz549Y/78+VSvXp1//vmHPHnyaP16nTp1iuPHj9O2bVsKFSpEREQEixYtws/PjytXrmBubq71teD9oPSiRYtStWpVrfJn5L1okpCQgJ+fH7du3aJv3764uLgQFhZGYGAg0dHR/PDDD5QoUYK1a9cycOBAChUqJHc72dvbf/Tab9++1Tgo3MLCAjMzM/lxcnIy9erVo0qVKsycOZP9+/cza9YsXF1d6d27N/D+M9SsWTOOHTtGr169KFGiBFu3bqVz585q19+/fz8NGjSgaNGijB8/noSEBObPn0+1atU4e/asHDD8+++/VK5cmejoaHr06IGHhwePHj1i06ZNvH79GmNjY+7cucO2bdvw9/fHxcWFyMhIlixZgq+vL1euXFHrbvvpp58wNjZmyJAhvHnzBmNjY63/TjLC0aNH2bFjB99//z0AwcHBNG7cmB9//JGFCxfSp08fXr58yfTp0+nSpQsHDx7M8DoIQnanMDBEYaDlOjha5stqegU4AwcO5LfffiMlJYX9+/fLv6rWr18PgIGBgdov8qxUu3ZttTQTExO1brT79+9z8+ZNbGxsAHB3d6dZs2bs2bOHxo0bAzBs2DAcHR05duwY1tbWAPj6+lK3bl2cnJzka+3YsYODBw8yadIkRo0aBbzvjvH392fevHn07dsXV1dXOnbsSK9evShatGiaXRsVKlRgxYoV8uPnz5+zYsUKOcC5d+8e48aNY9KkSYwcOVLO17JlSypUqMDChQtV0tPTqFEjWrVqpZLWpEkTvLy82Lx580cH2n4oNjaWR48e0axZM63yZ/S9aLJ06VKuXr3KunXr5KUMevXqha+vL6NHj6ZLly44OjrSsWNHuWVC226nvXv3agyCgoODGT58uPw4MTGRNm3aMGbMGLn8ihUrsmLFCjnA2bFjB0eOHGH69OkMHToUgN69e1OjRg216w8dOhRbW1tOnDiBra0tAM2bN6dChQqMGzdObp0aMWIET5484e+//1bptp04caLcslimTBlu3Lih0s3UqVMnPDw8WLFihVzn1Pdy+vRplQBO27+TjHD9+nWuXbsmB3E2Njb07NmTSZMmcePGDXLnzg28DyqDg4OJiIhIc8bjmzdv5G52QG6NEoQcz8Dw/aFt3mxAr3amb775hp9//lneUTz1YWBgwLx58/D09MzouuptwYIF7Nu3T+XYvXu3Wr42bdrIwQ0gt0LduXMHeD9w+ty5c3Tu3Fn+RxugTp06auM0du3ahaGhIf3791dJHzx4MJIkaSw/Lb169VJ57OPjw/Pnz+V/fLds2UJKSgqtW7cmKipKPvLly0exYsU4dOiQ1mUBKl9Ub9++5fnz57i5uZEnTx7Onj2r07WUdVR+yaQno+9Fk127dpEvXz7atWsnpxkZGdG/f39evXrFn3/+qfe1q1SpovZZ27dvn0pZSpreV+VnTVnPXLlyyQEPgKGhIf369VN5nvJzGRgYKAc3AGXLlqVOnTrs2rULgJSUFLZt20aTJk00jklT/lAxMTGRg5vk5GSeP3+OpaUl7u7uGt//zp07q3xmdPk7yQi1atVSCViqVKkCwHfffafyuVOmp36NPxQcHIy1tbV8FC5cOMPrKwhfJAMD3Y5sQO+pC3369MHPz49Vq1Zx5coVAEqUKEFgYCClS5fOsApmhMqVK2s1yLhIkSIqj5XBzsuXL4H3rQsAxYoVU3vuh//437t3jwIFCqh9sZcoUULlWtr4WL2srKy4efMmkiRprBcgd69pKyEhgeDgYFatWsWjR49UxgzFxMTodC0rKysA4uLitMqf0feiyb179yhWrJjaQFh93psP2dnZaWwx/JCpqalaS4+NjY38WVPWI3/+/Gorgru7u6s8Vtb3w3R4f0979uwhPj6eV69eERsbm+7fZ0pKCvPmzWPhwoXcvXtXZVzQh926oD7ZQJe/k4zw4d+HMqj6MDhRpqd+jT80YsQIBg0aJD+OjY0VQY7wVVAYGmq9BUOO2qpBudbNqFGjcHV15ddffwWgYcOGzJgxI/Nq95kZpvGmfbiY4eeWXr1SUlJQKBTs3r1bY15dt8zo168fq1atYsCAAXh5eWFtbY1CoaBt27akpKTodC0rKysKFCjApUuXtMqf0ffypUrrPf0STJkyhTFjxtClSxd++uknbG1tMTAwYMCAARrf/9StNxnhw4HESh8OwFZK67XU5+/ZxMQEExOTdGooCDlQDuyi0irAUS6Y161bN1xdXQkMDEShUHD06FHs7Owyu45fDOXYgZs3b6qdu379ulre/fv3ExcXp9KKc+3aNZVrQdr/oGvL1dUVSZJwcXGhePHin3QtgE2bNtG5c2dmzZolpyUmJqY5eyc9jRs3ZunSpZw4cQIvL6+P5s3oe9HEycmJCxcukJKSotKKo+m9yUpOTk4cOHCAV69eqQR2mj5rmtLh/T3Z2dnJg5ytrKzSDTY3bdpEjRo1VMZ9wfsZXdr8vevyd6KJjY2Nxs/ap7SsCYKQDgMDHQKc7NFFpVMt9f2Cyyny589P+fLlWbNmjUpXzb59++RuOqWGDRuSnJzML7/8opI+Z84cFAoFDRo0kNMsLCw+6bVt2bIlhoaGTJgwQe3XqSRJPH/+XKfrGRoaql1n/vz5af6CTs+PP/6IhYUF3bp1IzIyUu387du3mTdvHpDx96JJw4YNefLkCRs3bpTT3r17x/z587G0tMTX1/eTy8gIDRs25N27dyxatEhOS05OZv78+Sr5Un8uU3+OLl26xN69e2nYsCHwfvB/8+bN+f333zl9+rRaecrXW9P7HxYWxqNHj7Sqty5/J5q4uroSExOjshr648eP05xmLgjCp/tqp4k7Ojry9OlTOnfurNJ/369fP5VBhKkpFAoOHDiQMbX8RLt375Z/nadWtWpVihYtqtO1goODadSoEd7e3nTp0oUXL14wf/58SpUqxatXr+R8TZo0oUaNGowaNYqIiAjKlSvH3r172b59OwMGDFCZCu7p6cn+/fuZPXs2BQoUwMXFRR4QqQ1XV1cmTZrEiBEjiIiIoHnz5uTOnZu7d++ydetWevTowZAhQ7S+XuPGjVm7di3W1taULFmSEydOsH//fo3jL7StX2hoKG3atKFEiRIqKxkfP35cnqKdGfeiSY8ePViyZAmBgYGcOXMGZ2dnNm3axP/+9z/mzp2r9YBoTR49esS6devU0i0tLXVeqbpJkyZUq1aN4cOHExERQcmSJdmyZYvGcVAzZsygQYMGeHl50bVrV3mauLW1tcoaM1OmTGHv3r34+vrSo0cPSpQowePHjwkLC+PYsWPkyZOHxo0bM3HiRIKCgqhatSoXL14kJCREp78Vbf9ONGnbti3Dhg2jRYsW9O/fn9evX7No0SKKFy+e4eN3BEH4j0KHLipFDuqiqlSpEuHh4bx48UJe3E+SJM6dO6cxv3J38S/F2LFjNaavWrVK5wCnfv36hIWFMXr0aEaMGIGrqyurVq1i+/btKptOGhgYsGPHDsaOHcvGjRtZtWoVzs7OzJgxQ20p/9mzZ9OjRw9Gjx5NQkICnTt31inAARg+fDjFixdnzpw5TJgwAXg/yLJu3bo0bdpUp2vNmzcPQ0NDQkJCSExMpFq1auzfv5969erpdJ3UmjZtyoULF5gxYwbbt29n0aJFmJiYULZsWWbNmkX37t0z5V40MTMz4/DhwwwfPpw1a9YQGxuLu7s7q1atUltkUVfnzp3TOI3eyclJ5wBH+RkaMGAA69atQ6FQ0LRpU2bNmkWFChVU8tauXZs//viDcePGMXbsWIyMjPD19WXatGkqg4ALFizI33//zZgxYwgJCSE2NpaCBQvSoEEDeX2jkSNHEh8fT2hoKBs3bqRixYqEh4erTHNPj7Z/J5rkzZuXrVu3MmjQIH788Ud5DaibN2+KAEcQMksOHIOjkLQYQXvx4kXq16+v0/5SCoVC7y4NQRCEL0FsbCzW1tYUCViNgbFuC1xmlNPlTmZJuanFtdP8I/FzKvjuWZaWv+HRp8/g1FfCqzh6+pYiJiZGnpmaUZSf8afbFmBlod2Egdj4BByaf58p9clIWrXgKBf++vvvv3n48KE8yHjEiBFpTucVBEEQBCGbyIEtOFqvg2NhYUHNmjWB93vFSJJEw4YNtV5+XxAEQRCEL1QOnEWl10J/uq6FIgiCIAjCl+urXegvLbdu3WLVqlVcunSJ+Ph4du3aJW+++e2332JsbJwhlRQEQRAEIRPpsgVDTm7BAViyZAn9+/fn3bt38qwpY2NjOnbsKO9O3KJFi4ysqyAIgiAImSEHjsHRKww7duwY33//vRzcpNa0aVMkSWLHjh0ZUkFBEARBEDKXwsBQpyM70CvAmTFjBikpKRgbG8urpCqVK1cOQONKqYIgCIIgfIEUOuwkrsjBXVQnTpxAoVAQHBzMN998w65du+Rzyp19dVkzRxAE4Uv27z/7UBhmzZjCrpV7ZEm5qbX10G3h0cxQ+OTRLC3/UcyLLCs7Mf5NppehS8tMdmnB0SvAUS4VX7ZsWbVzyhlW6S3HLgiCIAjCFyIHThPXq5a2trYAGrdqUG7l8DXtMi4IgiAI2Zq23VO6zLbKYnrVskqVKkiSxNixY1m2bJmcPnr0aGbNmoVCoeDbb7/NsEoKgiAIgpB5lOvgaHvoY8GCBTg7O2NqakqVKlU4eVK7bUg2bNiAQqHQfT8/PepInz59AEhISGDt2rXyxprBwcG8e/cOgN69e+tzaUEQBEEQPjflNHFtDx1t3LiRQYMGMW7cOM6ePUu5cuWoV68eT58+/ejzIiIiGDJkCD4+Prrfks7PAOrWrcuPP/6IJEkq08SV///jjz9Sq1YtfS4tCIIgCMLnlskBzuzZs+nevTtBQUGULFmSxYsXY25uzsqVK9N8TnJyMh06dGDChAkULVpU5zL1Xuhv6tSp+Pn5sWLFCq5cuQJAiRIl6Nq1Kw0aNND3soIgCIIgfGYKAwMUWo6tUeaLjY1VSTcxMcHExEQtf1JSEmfOnGHEiBFymoGBAbVr1+bEiRNpljNx4kQcHBzo2rUrR4/qPotO5wAnMTGRvXv3AuDq6kpYWJjOhQqCIAiC8AVR6NAyo3ifr3DhwirJ48aNY/z48WrZo6KiSE5OxtHRUSXd0dGRa9euaSzi2LFjrFixQuNkJm3pHOCYmJjQsmVLJEli7dq1lCpVSu/CBUEQBEH4AigU2i/g99+42wcPHmBlZSUna2q90UdcXBydOnVi2bJlnzQjW+cAR6FQ4OjoyJMnT8RUcEEQBEHICRQ6rFD8Xz4rKyuVACctdnZ2GBoaEhkZqZIeGRlJvnz51PLfvn2biIgImjRpIqcp19jLlSsX169fx9XVNd1y9Rpk3KpVKyRJ4sCBA/o8XUjl1KlTVK1aFQsLCxQKBefOnWP8+PHyzDQlhUJB3759P0udnJ2dady4cbr5Dh8+jEKh4PDhw5lfqQz07t07fvzxRwoXLoyBgYHOUw+zQmBgIM7OzippCoVCY3NwVouIiEChULB69eqsroogCFqSFAY6HbowNjbG09NTJWZISUnhwIEDeHl5qeX38PDg4sWLnDt3Tj6aNm1KjRo1OHfunFrXWFr0GmTcrVs3/vjjD2bPno0kSbRs2ZL8+fOrfSkrt20QNHv79i3+/v6YmpoyZ84czM3NcXJyyupqfTEWLlyIubk5gYGBGXrdlStXMmPGDAYMGEDFihW1+pxu3bqVpUuXcurUKWJjY7Gzs8Pb25tevXpRs2bNDK3fl2j8+PFMmDAh3Xy+vr4isBGE7EiPFhxdDBo0iM6dO1OpUiUqV67M3LlziY+PJygoCICAgAAKFixIcHAwpqamlC5dWuX5efLkAVBL/xi9Apxy5cqhUCiQJIlZs2Yxa9YstTwKhUJeE0fQ7Pbt29y7d49ly5bRrVs3OX306NEMHz48C2v2ZVi4cCF2dnYZHuAcPHiQggULMmfOnHTzSpJEly5dWL16NRUqVGDQoEHky5ePx48fs3XrVmrVqsX//vc/qlatmqF11EZCQgK5cuk9EVInLVu2xM3NTX786tUrevfuTYsWLWjZsqWc7ujoiJOTEwkJCRgZGX2WugmCkAEUCnlsjVZ5ddSmTRuePXvG2LFjefLkCeXLl+ePP/6QBx7fv38fgwxeIfmT/nVUttikXgtHGfgI6VMucKSMTJVy5cqVoV9c8fHxWFhYZNj1srunT5+qveZpmTVrFqtXr2bAgAHMnj1bpZVy1KhRrF27NkPeq8TERIyNjXX6Azc1Nf3kcrVVtmxZlb3noqKi6N27N2XLlqVjx45ZWjdBEDKALlsw6BmI9O3bN82hFukNddCnZVjvcEm5yN+HwYwIbrQTGBiIr68vAP7+/igUCvz8/AA0jsFRCgkJwd3dHVNTUzw9PeW9v5SUz71y5Qrt27fHxsYGb29v4P3Yk59++glXV1dMTExwdnZm5MiRvHmjeafavXv3Ur58eUxNTSlZsiRbtmxJ976OHj2Kv78/RYoUwcTEhMKFCzNw4EASEhJU8j158oSgoCAKFSqEiYkJ+fPnp1mzZkRERADvxwFdvnyZP//8E4VCofL6pCU+Pp7BgwdTuHBhTExMcHd3Z+bMmfJnUjk25NChQ1y+fFm+blp/WAkJCQQHB+Ph4cHMmTM1viedOnWicuXKALx48YIhQ4ZQpkwZLC0tsbKyokGDBpw/f17lOcqxSxs2bGD06NEULFgQc3NzeU2Jbdu2Ubp0abmZduvWrRrr9+EYnHv37tGnTx/c3d0xMzMjb968+Pv7y69patHR0QwcOBBnZ2dMTEwoVKgQAQEBREVFffQ11oamMTjpvd8Ap0+fpl69etjZ2WFmZoaLiwtdunSRz6c15iutMT/Xrl2jVatW2NraYmpqSqVKldixY8cn358g5ESZOQYnq+j101M5mlnQX8+ePSlYsCBTpkyhf//+fPPNN2prBHzozz//ZOPGjfTv3x8TExMWLlxI/fr1OXnypFq/pL+/P8WKFWPKlCnyF3y3bt1Ys2YNrVq1YvDgwfz9998EBwdz9epVtS/Rmzdv0qZNG3r16kXnzp1ZtWoV/v7+/PHHH9SpUyfNOoaFhfH69Wt69+5N3rx5OXnyJPPnz+fhw4cqayZ99913XL58mX79+uHs7MzTp0/Zt28f9+/fx9nZmblz59KvXz8sLS0ZNWoUwEdfH0mSaNq0KYcOHaJr166UL1+ePXv2MHToUB49esScOXOwt7dn7dq1TJ48mVevXhEcHAy8X6BSk2PHjvHixQsGDBiAoRZ7r9y5c4dt27bh7++Pi4sLkZGRLFmyBF9fX65cuUKBAgVU8v/0008YGxszZMgQ3rx5g7GxMXv37uW7776jZMmSBAcH8/z5czkwSM+pU6c4fvw4bdu2pVChQkRERLBo0SL8/Py4cuUK5ubmwPvuJR8fH65evUqXLl2oWLEiUVFR7Nixg4cPH2bK7Mj03u+nT59St25d7O3tGT58OHny5CEiIkKroFqTy5cvU61aNQoWLMjw4cOxsLDgt99+o3nz5mzevJkWLVpk8B0KQjaXyWNwssLn6cAX1Hh5efHmzRumTJmCj48PrVq1Svc5ly5d4vTp03h6egLQtm1b3N3dGTt2rNoXQbly5QgNDZUfnz9/njVr1tCtWzd5g9Q+ffrg4ODAzJkzOXToEDVq1JDz37hxg82bN8vjK7p27YqHhwfDhg37aIAzbdo0zMzM5Mc9evTAzc2NkSNHcv/+fYoUKUJ0dDTHjx9nxowZDBkyRM6bepXL5s2bM3r0aOzs7DR2gXxox44dHDx4kEmTJskB0ffff4+/vz/z5s2jb9++uLq60rFjR5YvX46hoWG617169SoAZcqUSbd8Zb4bN26odDN16tQJDw8PVqxYwZgxY1TyJyYmcvr0aZXXa9iwYTg6OnLs2DGsra2B9wN369atm+4A9EaNGql9jpo0aYKXlxebN2+mU6dOAMyYMYNLly6xZcsWlS/60aNHZ0oLrDbv9/Hjx3n58iV79+6lUqVKcvqkSZP0KvOHH36gSJEinDp1Sl6bo0+fPnh7ezNs2LA0A5w3b96otGh+uFKrIORYOTDAyR61FID3QZEyuIH3s9SaNWvGnj17SE5OVsnbq1cvlce7du0C3o9kT23w4MEAhIeHq6QXKFBA5UvAysqKgIAA/vnnH548eZJmHVN/WcfHxxMVFUXVqlWRJIl//vlHzmNsbMzhw4d5+fJluvetjV27dmFoaEj//v1V0gcPHowkSezevVvnayq/3HLnzq1VfhMTEzm4SU5O5vnz51haWuLu7s7Zs2fV8nfu3Fnl9Xr8+DHnzp2jc+fOcnADUKdOHUqWLJlu+amv9fbtW54/f46bmxt58uRRKX/z5s2UK1dO45d8Wl2jn0Kb91s5Jmrnzp28ffv2k8p78eIFBw8epHXr1sTFxREVFUVUVBTPnz+nXr163Lx5k0ePHml8bnBwMNbW1vKh7XRUQcj2lAGOtkc2kD1qKQBQrFgxtbTixYvz+vVrnj17ppLu4uKi8vjevXsYGBiozIQByJcvH3ny5OHevXsq6W5ubmpfdsWLFwfQOKZD6f79+wQGBmJra4ulpSX29vbyWKOYmBjgfSAwbdo0du/ejaOjI9WrV2f69OkfDZzSc+/ePQoUKKAWjCi7nz68P20oF7CKi4vTKn9KSgpz5syhWLFimJiYYGdnh729PRcuXJDvPTVN7xFofp/d3d3TLT8hIYGxY8fKY5CU5UdHR6uUf/v2bZ2mWn4qbd5vX19fvvvuOyZMmICdnR3NmjVj1apVaY4P+5hbt24hSRJjxozB3t5e5Rg3bhxAmjsYjxgxgpiYGPl48OCBfjctCNmMpFDoMAYn438IZQYR4ORQqX/Np5YZv9CVkpOTqVOnDuHh4QwbNoxt27axb98+efBn6rFbAwYM4MaNG/KaB2PGjKFEiRJyK8+XwMPDA4CLFy9qlX/KlCkMGjSI6tWrs27dOvbs2cO+ffsoVaqUxnFrab1H+urXrx+TJ0+mdevW/Pbbb+zdu5d9+/aRN2/eLB83l977rVAo2LRpEydOnKBv3748evSILl264OnpyatXr+Q8mnzYeqm81yFDhrBv3z6Nx4eBvpKJiYm8Oqu2q7QKQo6QA1twxBicbOTmzZtqaTdu3MDc3Bx7e/uPPtfJyYmUlBRu3rypMqg2MjKS6OhotfEdyl/Bqb9Ubty4AaC2oq7SxYsXuXHjBmvWrCEgIEBO37dvn8b8rq6uDB48mMGDB3Pz5k3Kly/PrFmzWLduHaBbMObk5MT+/fuJi4tTacVRbuSmzwKK3t7e2NjYsH79ekaOHJnuQONNmzZRo0YNVqxYoZIeHR2t1cBdZR01vc/Xr19P9/mbNm2ic+fOKutSJSYmEh0drZLP1dWVS5cupXu9jJbe+w3w7bff8u233zJ58mRCQ0Pp0KEDGzZsoFu3btjY2ACo3c+HrXNFixYFwMjIiNq1a2fuTQlCTpHJ6+BkhewRhgkAnDhxQmUsxYMHD9i+fTt169ZN98u3YcOGAMydO1clffbs2cD7Aaqp/fvvvyozq2JjY/n1118pX768xr1DALkOqQeqSpLEvHnzVPK9fv2axMRElTRXV1dy586t0iVhYWGh9mWWloYNG5KcnMwvv/yikj5nzhwUCgUNGjTQ6jqpmZubM2zYMK5evcqwYcM0DsBdt24dJ0+eBN7f/4d5wsLC0hzv8aH8+fNTvnx51qxZo9KltG/fPq5cuZLu8zWVP3/+fLUWju+++47z589rnH6eGYOMtXm/X758qVZ2+fLlAeQ8Tk5OGBoaqi2NsHDhQpXHDg4O+Pn5sWTJEh4/fqxWnw+7cwVBQLTgCFmrdOnS1KtXT2WaOKDVEvrlypWjc+fOLF26lOjoaHx9fTl58iRr1qyhefPmKjOo4P14m65du3Lq1CkcHR1ZuXIlkZGRrFq1Ks0yPDw8cHV1ZciQITx69AgrKys2b96sNrD0xo0b1KpVi9atW1OyZEly5crF1q1biYyMpG3btnI+T09PFi1axKRJk3Bzc8PBwSHNbRGaNGlCjRo1GDVqFBEREZQrV469e/eyfft2BgwYoNXGbJoMHTqUy5cvM2vWLA4dOkSrVq3Ily8fT548Ydu2bZw8eZLjx48D0LhxYyZOnEhQUBBVq1bl4sWLhISEyC0K2ggODqZRo0Z4e3vTpUsXXrx4wfz58ylVqpTcVZOWxo0bs3btWqytrSlZsiQnTpxg//795M2bV+2eNm3ahL+/v9wN9OLFC3bs2MHixYspV66c7i/UR2jzfq9Zs4aFCxfSokULXF1diYuLY9myZVhZWcnBubW1Nf7+/syfPx+FQoGrqys7d+7UOJ5mwYIFeHt7U6ZMGbp3707RokWJjIzkxIkTPHz4UG1tIkH42umyvk2OXgdHk1evXnH8+HESExOpVauWWDk3E/j6+uLl5cWECRO4f/8+JUuWZPXq1SorzH7M8uXLKVq0KKtXr2br1q3ky5ePESNGyAMvUytWrBjz589n6NChXL9+HRcXFzZu3Ei9evXSvL6RkRG///47/fv3l8datGjRgr59+6p8aRYuXJh27dpx4MABeSVgDw8PfvvtN7777js539ixY7l37x7Tp08nLi4OX1/fNAMcAwMDduzYwdixY9m4cSOrVq3C2dmZGTNmyDPF9GFgYMCvv/5Ks2bNWLp0KTNnziQ2NhZ7e3t5sKxys7iRI0cSHx9PaGgoGzdupGLFioSHh+u07Ub9+vUJCwtj9OjRjBgxAldXV1atWsX27dvTXelz3rx5GBoaEhISQmJiItWqVWP//v1q75mlpSVHjx5l3LhxbN26lTVr1uDg4ECtWrW0Wm9HV9q838qAe8OGDURGRmJtbU3lypUJCQlRGYw9f/583r59y+LFizExMaF169bMmDFDbdB0yZIlOX36NBMmTGD16tU8f/4cBwcHKlSowNixYzP8HgUh21PosJJxNglwFJIebdKhoaGsW7cOOzs7fv31Vx4+fEj16tXlvvDChQtz/PhxtYXNBEEQspPY2Fisra3JVaYDCkPjLKlDwz49sqTc1NqO7JJ+pkxW+OTRLC3/aMSLLCs7Mf4VE5pUJCYmJsMHvis/488ibmBlpd2SGLGxcdg7F8+U+mQkvcKwLVu2sGfPHvnxvHnziIiIkLduePDgAVOmTMmwSgqCIAiCkIly4BgcvWqp7L+uXr068H53ZoVCgZeXF/ny5UOSpDRnzgiCIAiC8IURAc57ykF9ylU+r127hkKhYPfu3cyYMQNALJAlCIIgCNlETlzoT69BxsqdoQ0MDHjw4AEJCQk4OTlhZWVFwYIFAbGruCAIgiBkGzlwLyq9Ahw7OzsiIyNZsWIFRYoUAf5/KXll646trW0GVVEQBEEQhEyVAxf60yvAqVy5Mjt27CAsLAx4v+Ksj48PAHfu3AHU99kRBEEQBOELlQNbcPSq5fDhwzExMZFnTTk6OtKtWzcAtm/fDkC1atUyrpaCIAiCIGQa7cffaL8gYFbTqwXn22+/5dy5c+zcuRNra2uaNWsm74U0aNAgUlJSqFKlSoZWVBAEIav8vvYnLHJrt0ZITnRkw7asrgIVgrtnafmXfEZmWdlvEz6+inmGyIEtOHqvZOzu7i6Pu0nN39//kyokCIIgCMLn9X4WlXZja3LULKr79+8D7zcDNDIykh+nRzkAWRAEQRCEL5ckvT+0zZsdaBXgODs7Y2BgwJEjR6hatSrOzs4o0ongFAoF7969y5BKCoIgCIKQeVIkiRQtIxdt82U1rbuoNK1roylNoVCINXAEQRAEIRuR/ju0zZsd6B3gpBXEiOBGEARBELKXFOn9oW3e7ECrACclJeWjjwVBEARByL6Uy75omzc70HkW1evXr1m+fDkAnp6eYr0bQRAEQcjmvtoWnNTMzc0ZPHgwKSkp/Pbbb5lRJ0EQBEEQhE+i12o9yl3Ezc3NM7QygiAIgiBkDUnLI7vQK8Dp1KkTkiSxZcuWjK6PIAiCIAifmbKLSttDHwsWLMDZ2RlTU1OqVKnCyZMn08y7ZcsWKlWqRJ48ebCwsKB8+fKsXbtWp/L0WsnY29ubYsWKsXLlSmJiYmjZsiX58+dXWxunevXq+lxeEIRszs/Pj6ioKC5dupTVVREEQQuZPch448aNDBo0iMWLF1OlShXmzp1LvXr1uH79Og4ODmr5bW1tGTVqFB4eHhgbG7Nz506CgoJwcHCgXr16WpWpV4BTr149eb2bzZs3s3nzZrU8YqE/Qfh/q1evJigoCICjR4/i7e2tcl6SJIoUKcLDhw9p1KgRO3fuzIpqCoLwlUr579A2r65mz55N9+7d5X8HFy9eTHh4OCtXrmT48OFq+f38/FQe//DDD6xZs4Zjx45pHeB80o5ZyhYbZeT34SEIgipTU1NCQ0PV0v/8808ePnyIiYlJFtRKEISvnXKrBm0PXSQlJXHmzBlq164tpxkYGFC7dm1OnDihRd0kDhw4wPXr13XqGdKrBadIkSLpbtUgCIK6hg0bEhYWxs8//0yuXP//5xcaGoqnpydRUVGfrS6SJJGYmIiZmdlnK1MQhC+TPtPEY2NjVdJNTEw0/kiLiooiOTkZR0dHlXRHR0euXbuWZjkxMTEULFiQN2/eYGhoyMKFC6lTp452lUTPFpyIiAju3r2b7iEIgqp27drx/Plz9u3bJ6clJSWxadMm2rdvr5Z/5syZVK1albx582JmZoanpyebNm3SeO1169ZRuXJlzM3NsbGxoXr16uzdu1c+7+zsTOPGjdmzZw+VKlXCzMyMJUuWAHDnzh38/f2xtbXF3Nycb7/9lvDwcJXrHz58GIVCwcaNGxk5ciT58uXDwsKCpk2b8uDBA411unLlCjVq1MDc3JyCBQsyffp0lfNJSUmMHTsWT09PrK2tsbCwwMfHh0OHDqld6/nz53Tq1AkrKyvy5MlD586dOX/+PAqFgtWrV6vkvXbtGq1atcLW1hZTU1MqVarEjh07NNZREIS0e2I+1kNTuHBhrK2t5SM4ODhD65Q7d27OnTvHqVOnmDx5MoMGDeLw4cNaP/+TuqgEQdCNs7MzXl5erF+/Xk7bvXs3MTExtG3bVi3/vHnzqFChAhMnTmTKlCnkypULf39/teBjwoQJdOrUCSMjIyZOnMiECRMoXLgwBw8eVMl3/fp12rVrR506dZg3bx7ly5cnMjKSqlWrsmfPHvr06cPkyZNJTEykadOmbN26Va1OkydPJjw8nGHDhtG/f3/27dtH7dq1SUhIUMn38uVL6tevT7ly5Zg1axYeHh4MGzaM3bt3y3liY2NZvnw5fn5+TJs2jfHjx/Ps2TPq1avHuXPn5HwpKSk0adKE9evX07lzZyZPnszjx4/p3LmzWv0uX77Mt99+y9WrVxk+fDizZs3CwsKC5s2ba7wfQRD+fwyOtgfAgwcPiImJkY8RI0ZovLadnR2GhoZERkaqpEdGRpIvX74062RgYICbmxvly5dn8ODBtGrVSqcgSq8uqvv372uVr0iRIvpcXhBytPbt2zNixAgSEhIwMzMjJCQEX19fChQooJb3xo0bKl1Iffv2pWLFisyePZtGjRoBcOvWLSZOnEiLFi3YtGkTBgb//7vlw7Fwt27d4o8//lAZpDdw4EAiIyNVBj93796dsmXLMmjQIJo1a6ZyzRcvXnD16lVy584NQMWKFWndujXLli2jf//+cr5///2XX3/9lU6dOgHQtWtXnJycWLFiBQ0aNADAxsaGiIgIjI2N5ed1794dDw8P5s+fz4oVKwDYtm0bJ06cYO7cufzwww8A9O7dW2Nz9Q8//ECRIkU4deqU3Fzep08fvL29GTZsGC1atEjjnYE3b97w5s0b+fGHTfCCkFNJaD+2RpnNysoKKyurdPMbGxvj6enJgQMHaN68OfD+R8uBAwfo27ev1nVMSUlR+ftMj14tOM7Ozri4uHz0KFq0qD6XFoQcr3Xr1iQkJLBz507i4uLYuXOnxu4pQCW4efnyJTExMfj4+HD27Fk5fdu2baSkpDB27FiVQARQGyvn4uKiNgNh165dVK5cWWVml6WlJT169CAiIoIrV66o5A8ICJCDG4BWrVqRP39+du3apZLP0tKSjh07yo+NjY2pXLkyd+7ckdMMDQ3l4CYlJYUXL17w7t07KlWqpHKPf/zxB0ZGRnTv3l1OMzAw4Pvvv1cp88WLFxw8eJDWrVsTFxdHVFQUUVFRPH/+nHr16nHz5k0ePXpEWoKDg1Wa3JWLmgpCTpciSToduho0aBDLli1jzZo1XL16ld69exMfHy/PqgoICFBpAQoODmbfvn3cuXOHq1evMmvWLNauXavyb0p69GrBURIzpQRBd/b29tSuXZvQ0FBev35NcnIyrVq10ph3586dTJo0iXPnzqn8ckkduNy+fRsDAwNKliyZbtkuLi5qaffu3aNKlSpq6SVKlJDPly5dWk4vVqyYSj6FQoGbmxsREREq6YUKFVILsGxsbLhw4YJK2po1a5g1axbXrl3j7du3Gut679498ufPr7Z6upubm8rjW7duIUkSY8aMYcyYMWr3BPD06VMKFiyo8dyIESMYNGiQ/Dg2NlYEOcJXQZdVivX55m/Tpg3Pnj1j7NixPHnyhPLly/PHH3/IA4/v37+v8gMtPj6ePn368PDhQ8zMzPDw8GDdunW0adNG6zL1DnDSCm6U6+MIgpC29u3b0717d548eUKDBg3IkyePWp6jR4/StGlTqlevzsKFC8mfPz9GRkasWrVK41RzbXzOGVOGhoYa01P/+7Bu3ToCAwNp3rw5Q4cOxcHBAUNDQ4KDg7l9+7bOZaakvB8dMGTIkDTXyvgwKEotrVkggpDTfY7NNvv27Ztml9SHg4cnTZrEpEmT9CvoP3oFOMp/RFKLiopix44d9O/fH1dXV44ePfpJFROEnKxFixb07NmTv/76i40bN2rMs3nzZkxNTdmzZ4/Kl+6qVatU8rm6upKSksKVK1coX768znVxcnLi+vXraunK6ZtOTk4q6Tdv3lR5LEkSt27domzZsjqXvWnTJooWLcqWLVtUWnvGjRunVsdDhw7x+vVrlVacW7duqeRTdo0bGRmprLkhCEI6dFnfJpu0YWTYLCo7Ozu6dOnC4MGDuXTpEmPHjs2oSwtCjmNpacmiRYsYP348TZo00ZjH0NAQhUJBcnKynBYREcG2bdtU8jVv3hwDAwMmTpyo9uNDm9bUhg0bcvLkSZUFt+Lj41m6dCnOzs5qXV+//vorcXFx8uNNmzbx+PFjeeCwLpStPKnr+ffff6st/lWvXj3evn3LsmXL5LSUlBQWLFigks/BwQE/Pz+WLFnC48eP1cp79uyZznUUhK9BCpJOR3bwSWNwNLG2tkaSJMLCwpg7d25GX14QcgxNU5xTa9SoEbNnz6Z+/fq0b9+ep0+fsmDBAtzc3FTGsbi5uTFq1Ch++uknfHx8aNmyJSYmJpw6dYoCBQqkO61y+PDhrF+/ngYNGtC/f39sbW1Zs2YNd+/eZfPmzWoDl21tbfH29iYoKIjIyEjmzp2Lm5ubygBgbTVu3JgtW7bQokULGjVqxN27d1m8eDElS5bk1atXcr7mzZtTuXJlBg8ezK1bt/Dw8GDHjh28ePECUB2TtGDBAry9vSlTpgzdu3enaNGiREZGcuLECR4+fMj58+d1rqcg5HS6rFCcXUah6BXg/Prrr2pp79694+HDh8yZMwdA/odHEAT91KxZkxUrVjB16lQGDBiAi4sL06ZNIyIiQm2g7sSJE3FxcWH+/PmMGjUKc3NzypYtK0/R/hhHR0eOHz/OsGHDmD9/PomJiZQtW5bff/9dnoqe2siRI7lw4QLBwcHExcVRq1YtFi5cqDYAWBuBgYE8efKEJUuWsGfPHkqWLMm6desICwtT6ZM3NDQkPDxc3o/GwMCAFi1aMG7cOKpVq4apqamct2TJkpw+fZoJEyawevVqnj9/joODAxUqVBAty4KQhs8xBudzU0h6jAg2MDBIc6sGSZJQKBSUL1+eM2fOfHIFBUH4Mhw+fJgaNWoQFhaW5qyvz23btm20aNGCY8eOUa1atQy/fmxsLNbW1uw+dweLVFPjvzZH7mb9D9ZWG9U3ZPycfvIZmWVlv014xW89axATE6PVujO6UH7G/7r+AMvc2l37VVws37oXzpT6ZKQMn0UF7wf4TZkyRd9LC4IgqFEujKiUnJzM/PnzsbKyomLFillYM0HI/nQZW5Ojx+BUr15drQVHoVBgY2ND8eLF6dmzJ87OzhlRP0EQBAD69etHQkICXl5evHnzhi1btnD8+HGmTJkiNgwVhE8kxuD8R5fNrgRBEDJCzZo1mTVrFjt37iQxMRE3Nzfmz5+v01LvgiBopssKxfqsZJwVMnwWlSAIOZOfn1+WLuLZvn37NLe0EATh0ySnvD+0zZsdaB3gHDlyROeLV69eXefnCIIgCILweX3VLTh+fn5pzpzSRKFQ8O7dO70qJQiCIAjC55MiSSR/rQGO0odN1JqCHrEXlSAIgiBkH+/XwdE2wMnkymQQnQOcDzfTFMGMIAg5XecJWzAwypqZWvbOTulnymTmuY2zugoMblk/S8vfHvxblpUtvXuT6WXkxDE4eu1FZW1tzYABA7h58yYpKSkaj9T75wiCIAiC8OVSjsHR9sgOtA5wfv/9d3l33piYGObNm4e7uzuNGzdmz549mVZBQRAEQRAyV/J/Y3C0PbIDrQOcRo0asWfPHq5cuULv3r2xsLAgJSWFXbt20bBhQ9zd3Zk/f77KLsOCIAiCIHz5Uvj//ajSPbK6slrSuYvK3d2dBQsW8PDhQ2bPno2rqyuSJHHz5k0GDBggb7YpCIIgCEL2kJwi6XRkB3qNwQHInTs3Li4uFCpUCIVCoTb4WBAEQRCE7EHSYfxNdvmu13kW1cuXL1m2bBmLFi3i/v37wPsXxtbWlm7dutGtW7cMr6QgCIIgCJknWXp/aJs3O9A6wDl//jzz589n/fr1JCYmyhFcuXLl6NevH+3bt8fU1DTTKioIgiAIQub4qlcyrlChgtwNZWRkRMuWLenbty/VqlXLzPoJgiAIgpDJdBlbk13G4Oi10J+RkRF//vknf/7550fzPXr06JMqJwiCIAhC5vuqW3BSS0hIICEhQeNAI2Urjy77VgmCIAiCkHVy4hgcnWZRSf+NnpY+Moo6u4yuFoScYPXq1fIsxmPHjqmdlySJwoULo1AoaNy4cRbUMPNcuXKF8ePHExERkdVVEYRsLyeuZKx1C86qVasysx6CIHwCU1NTQkND8fb2Vkn/888/efjwISYmJllUs8xz5coVJkyYgJ+fH87OzlldHUHI1lJSJFK0HFujbb6spnWA07lz58yshyAIn6Bhw4aEhYXx888/kyvX//9Zh4aG4unpSVRUVBbWThCEL12KDl1U2SS+0X+hP0EQvhzt2rXj+fPn7Nu3T05LSkpi06ZNtG/fXi1/fHw8gwcPpnDhwpiYmODu7s7MmTPVupgVCgV9+/Zl27ZtlC5dGhMTE0qVKsUff/yhds1//vmHBg0aYGVlhaWlJbVq1eKvv/5SyxcdHc3AgQNxdnbGxMSEQoUKERAQQFRUFK9evcLCwoIffvhB7XkPHz7E0NCQ4OBgVq9ejb+/PwA1atSQu+kOHz4s59+9ezc+Pj5YWFiQO3duGjVqxOXLl7V+TQXha5ITu6hEgCMIOYCzszNeXl6sX79eTtu9ezcxMTG0bdtWJa8kSTRt2pQ5c+ZQv359Zs+ejbu7O0OHDmXQoEFq1z527Bh9+vShbdu2TJ8+ncTERL777jueP38u57l8+TI+Pj6cP3+eH3/8kTFjxnD37l38/Pz4+++/5XyvXr3Cx8eH+fPnU7duXebNm0evXr24du0aDx8+xNLSkhYtWrBx40aSk5NV6rF+/XokSaJDhw5Ur16d/v37AzBy5EjWrl3L2rVrKVGiBABr166lUaNGWFpaMm3aNMaMGcOVK1fw9vYWY3YEQYOcuNmmXrOoBEH48rRv354RI0aQkJCAmZkZISEh+Pr6UqBAAZV8O3bs4ODBg0yaNIlRo0YB8P333+Pv78+8efPo27cvrq6ucv6rV69y5coVOa1GjRqUK1eO9evX07dvXwBGjx7N27dvOXbsGEWLFgUgICAAd3d3fvzxR3lJiRkzZnDp0iW2bNlCixYt5DJGjx4ttx4FBAQQEhLCvn37qF+/vpxn3bp1VK9enSJFigDg4+PDzz//TJ06dfDz85PzvXr1iv79+9OtWzeWLl0qp3fu3Bl3d3emTJmikp7amzdvePPmjfw4NjZWm5deELK9nDgGR7TgCEIO0bp1axISEti5cydxcXHs3LlTY/fUrl27MDQ0lFtAlAYPHowkSezevVslvXbt2ioBT9myZbGysuLOnTsAJCcns3fvXpo3by4HNwD58+enffv2HDt2TA4UNm/eTLly5VSCGyXl0hK1a9emQIEChISEyOcuXbrEhQsX6NixY7qvw759+4iOjqZdu3ZERUXJh6GhIVWqVOHQoUNpPjc4OBhra2v5KFy4cLrlCUJOkMz/TxVP99CzjAULFuDs7IypqSlVqlTh5MmTaeZdtmwZPj4+2NjYYGNjQ+3atT+aXxMR4AhCDmFvb0/t2rUJDQ1ly5YtJCcn06pVK7V89+7do0CBAuTOnVslXdm9c+/ePZV0ZYtJajY2Nrx8+RKAZ8+e8fr1a9zd3dXylShRgpSUFB48eADA7du3KV269Efvw8DAgA4dOrBt2zZev34NQEhICKampvK4m4+5efMmADVr1sTe3l7l2Lt3L0+fPk3zuSNGjCAmJkY+lPUWhJwus8fgbNy4kUGDBjFu3DjOnj1LuXLlqFevXpp/j4cPH6Zdu3YcOnSIEydOULhwYerWravTAsKii0oQcpD27dvTvXt3njx5QoMGDciTJ88nX9PQ0FBjemaueRUQEMCMGTPYtm0b7dq1IzQ0lMaNG2NtbZ3uc1NSUoD343Dy5cundj71LLMPmZiY5Mgp9YKQHl3G1ugzBmf27Nl0796doKAgABYvXkx4eDgrV65k+PDhavlTt+ACLF++nM2bN3PgwAECAgK0KlMEOIKQg7Ro0YKePXvy119/sXHjRo15nJyc2L9/P3FxcSqtONeuXZPP68Le3h5zc3OuX7+udu7atWsYGBjIXT2urq5cunQp3WuWLl2aChUqEBISQqFChbh//z7z589XyZPWaunK7jQHBwdq166t070IwtcqRYe9qJRjcD4co5bWD4SkpCTOnDnDiBEj5DQDAwNq167NiRMntCrz9evXvH37FltbW63ywycGOE+fPuXUqVO8fPlS/tWUmrZRliAIGcPS0pJFixYRERFBkyZNNOZp2LAhS5cu5ZdfflH5B2fOnDkoFAoaNGigU5mGhobUrVuX7du3ExERIS+6FxkZKS8+aGVlBcB3333HxIkT2bp1q9o4nA+3eOnUqRM//vgjJiYm5M2bV61eFhYWwPtp56nVq1cPKysrpkyZQo0aNTAyMlI5/+zZM+zt7XW6R0HI6fTZbPPDMWrjxo1j/PjxavmjoqJITk7G0dFRJd3R0VH+YZWeYcOGUaBAAZ1+tOgd4AwaNIhffvlFbSqnkkKhEAGOIGSB9BblbNKkCTVq1GDUqFFERERQrlw59u7dy/bt2xkwYIDKgGJtTZo0iX379uHt7U2fPn3IlSsXS5Ys4c2bN0yfPl3ON3ToUDZt2oS/vz9dunTB09OTFy9esGPHDhYvXky5cuXkvO3bt+fHH39k69at9O7dWy1QKV++PIaGhkybNo2YmBhMTEyoWbMmDg4OLFq0iE6dOlGxYkXatm2Lvb099+/fJzw8nGrVqvHLL7/ofI+CkJPpE+A8ePBA/vECZFr37tSpU9mwYQOHDx/G1NRU6+fpFeCsWrWKuXPn6vNUQRCymIGBATt27GDs2LFs3LiRVatW4ezszIwZMxg8eLBe1yxVqhRHjx5lxIgRBAcHk5KSQpUqVVi3bh1VqlSR81laWnL06FHGjRvH1q1bWbNmDQ4ODtSqVYtChQqpXNPR0ZG6deuya9cuOnXqpFZmvnz5WLx4McHBwXTt2pXk5GQOHTqEg4MD7du3p0CBAkydOpUZM2bw5s0bChYsiI+PjzwGQBCE/5ecgg4Bzvv/WllZqQQ4abGzs8PQ0JDIyEiV9MjISI3j5FKbOXMmU6dOZf/+/ZQtW1ar+ikpJD1GCvr5+XHkyBHs7OyIiopCoVBQunRpHj58SHR0NO7u7jg6On50OqYgCEJ6WrRowcWLF7l161aWlB8bG4u1tTUOLWZiYGSWJXWwd9ZtTFRmMM9tnNVV4HClu1lavn1w1nwGAaR3b4g//jMxMTFaBRS6UH7GR28/g6mFpVbPSYx/xaRmnjrVp0qVKlSuXFkeS5eSkkKRIkXo27evxkHGANOnT2fy5Mns2bOHb7/9VrsbSkWvaeKXLl1CoVAwbdo0OU3Z71+tWjViYmJYuXKlPpcWBEEA4PHjx4SHh2tsvREEIWMpu6i0PXQ1aNAgli1bxpo1a7h69Sq9e/cmPj5eblENCAhQGROoXIF85cqVODs78+TJE548ecKrV6+0LlOvAEc5ctrFxUUeFPju3Tty587N0KFDefz4sd5N3YIgfN3u3r3LunXraNeuHUZGRvTs2TOrqyQIOV6KDsGNPisZt2nThpkzZzJ27FjKly/PuXPn+OOPP+SBx/fv3+fx48dy/kWLFpGUlESrVq3Inz+/fMycOVPrMvUag2NhYUFsbCwGBgaYm5vz+vVrLl26RPXq1YmJiQFQ2fROEARBW3/++SdBQUEUKVKENWvWpNtHLwjCp0uWdBhkrOcaWH379pW3d/nQhzFDRuwZp1eAY2dnR2xsLHFxcRQtWpSLFy8yfPhwDh8+zIEDB4C0FwcTBEH4mMDAQAIDA7O6GoLwVdFnFtWXTq8uqlKlSgHw77//0rBhQwDi4+PZvHkzL1++RKFQ4Ovrm3G1FARBEAQh02T2GJysoFcLTtu2bTEzM5PH3GzdupUbN27I511cXJgzZ06GVVIQBEEQhMzzLkXCUMvA5V1OD3Datm0rPz537hxbt27l/v37FC1alMaNG2NmljVTKgVBEARB0E1O7KLKkL2oTE1NadeuXUZcShAEQRCEz0yfvai+dFoFOEeOHNHr4tWrV9freYIgCF8S929LkcvUIkvKHtuoRJaUm9o3+bPm3lN7/sYrS8vfHJl140rjk5NpmcllZPZu4llBqwDHz88vzZ1706JQKHj37p1elRIEQRAE4fP56ruo9NjVQRAEQRCEL9xXG+AUKVJE5xYcQRAEQRCyh682wMmIFQUFQRAEQfgyJUspJKekaJ03O9BroT9BEARBEIQvmV7TxO/fv69VviJFiuhzeUEQBEEQPqOvdpr4h5ydndMdkyNmUQmCIAhC9pCcImHwNY7BSYuYVSUIgiAI2d+7FFBovVVDJlcmg+gd4KQV3CgUChH4CIIgCEI2khNbcPQaZJySkqJ2PH36lOXLl2NmZkaZMmV4+fJlRtdVEL5oCoWCvn37ZnU1Mt3q1atRKBRf/OzK8ePHq3WlOzs7ExgYmDUVEoQvWE7cTTzDZlHZ2dnRpUsXBg8ezKVLlxg7dmxGXVrIgS5evEirVq1wcnLC1NSUggULUqdOHebPn5/VVfuo48ePM378eKKjozP0uhERESgUChQKBZMmTdKYp0OHDigUCiwtLTO07C9FZGQkQ4YMwcPDA3NzcywsLPD09GTSpEkZ/noLgqBKBDhasLa2RpIkwsLCMvrSQg5x/PhxKlWqxPnz5+nevTu//PIL3bp1w8DAgHnz5mV19T7q+PHjTJgwIdO+cE1NTVm/fr1aenx8PNu3b8fU1DRTys1qp06donTp0ixYsAAfHx9mz57NrFmzqFChAlOnTqV169ZZXUVByNFSdAhucvQsql9//VUt7d27dzx8+JA5c+YA8OLFi0+rmZBjTZ48GWtra06dOkWePHlUzj19+jRrKvWFaNiwIVu2bOH8+fOUK1dOTt++fTtJSUnUr1+fgwcPZmENM150dDQtWrTA0NCQf/75Bw8PD5XzkydPZtmyZVlUO0H4OiSnSFoPMs7RLTiBgYEEBQWpHN27d2fChAnExMSgUCgoWbJkRtdVyCFu375NqVKl1IIbAAcHB5XHynEtYWFhlCxZEjMzM7y8vLh48SIAS5Yswc3NDVNTU/z8/DSOCwkLC8PT0xMzMzPs7Ozo2LEjjx49Ust38OBBfHx8sLCwIE+ePDRr1oyrV6/K58ePH8/QoUMBcHFxkbuUPixz27ZtlC5dGhMTE0qVKsUff/yh9Wvj5eWFi4sLoaGhKukhISHUr18fW1tbtecoFArGjx+vlv7heJO3b98yYcIEihUrhqmpKXnz5sXb25t9+/apPO/atWu0bt0ae3t7zMzMcHd3Z9SoUenWfffu3fLrlzt3bho1asTly5fTfd6SJUt49OgRs2fPVgtuABwdHRk9enSGlCUIgmaSJCGlaHlkk4lEendRSZKk8QAwMjJiypQpGVZJIWdxcnLizJkzXLp0Sav8R48eZfDgwXTu3Jnx48dz9epVGjduzIIFC/j555/p06cPQ4cO5cSJE3Tp0kXluatXr6Z169YYGhoSHBxM9+7d2bJlC97e3irdTPv376devXo8ffqU8ePHM2jQII4fP061atXkAKZly5a0a9cOgDlz5rB27VrWrl2Lvb29fJ1jx47Rp08f2rZty/Tp00lMTOS7777j+fPnWr8+7dq1Y8OGDfLfU1RUFHv37qV9+/ZaX0OT8ePHM2HCBGrUqMEvv/zCqFGjKFKkCGfPnpXzXLhwgSpVqnDw4EG6d+/OvHnzaN68Ob///vtHr7127VoaNWqEpaUl06ZNY8yYMVy5cgVvb+90ByPv2LEDMzMzWrVqpdV9fEpZgiBolvJf15O2R3agVxdV9erV1WYnKBQKbGxsKF68OD179sTZ2Tkj6ifkQEOGDKFBgwaUL1+eypUr4+PjQ61atahRowZGRkZq+a9fv861a9fkz5SNjQ09e/Zk0qRJ3Lhxg9y5cwOQnJxMcHAwERERODs78/btW4YNG0bp0qU5cuSIPH7F29ubxo0bM2fOHCZMmADA0KFDsbW15cSJE3IrSfPmzalQoQLjxo1jzZo1lC1blooVK7J+/XqaN2+u8TN+9epVrly5gqurKwA1atSgXLlyrF+/XusZVu3bt2fKlCn873//w9vbm99++w1TU1OaNm2qU2vQh8LDw2nYsCFLly5NM0+/fv2QJImzZ8+qrEQ+derUNJ/z6tUr+vfvT7du3VSu3blzZ9zd3ZkyZcpHy7x69SrFixfH2Ng43Xv41LLS8+bNG968eSM/jo2N1ftagpCdpG6k0CZvdqBXgHP48OEMrobwNalTpw4nTpwgODiYPXv2cOLECaZPn469vT3Lly+nadOmKvlr1aqlEkxUqVIFgO+++04OblKn37lzB2dnZ06fPi23yKQenNuoUSM8PDwIDw9nwoQJPH78mHPnzvHjjz+qdAGVLVuWOnXqsGvXLq3vrXbt2nJwo7yGlZUVd+7c0foapUqVomzZsqxfvx5vb29CQ0Np1qwZ5ubmWl9Dkzx58nD58mVu3rxJsWLF1M4/e/aMI0eO8MMPP6hts/Kxlcv37dtHdHQ07dq1IyoqSk43NDSkSpUqHDp06KP1io2NVXkfP+ZTy0pPcHCwHPQKwtdE2f2kbd7sIENmUUVFRan8YyMI6fnmm2/YsmULL1++5OTJk4wYMYK4uDhatWrFlStXVPJ++GVrbW0NQOHChTWmK9dgunfvHgDu7u5q5Xt4eMjnP5avRIkSREVFER8fr9V9adp/zcbGRud1odq3b09YWBi3bt3i+PHjn9w9BTBx4kSio6MpXrw4ZcqUYejQoVy4cEE+rwzCSpcurdN1b968CUDNmjWxt7dXOfbu3ZvuwHErKyvi4uI+S1npGTFiBDExMfLx4MGDT7qeIGQXoosqlaioKEaNGsWmTZvksQzW1tb4+/szadIklXEJgpAWY2NjvvnmG7755huKFy9OUFAQYWFhjBs3Ts5jaGio8blppWdl82lG1aldu3aMGDGC7t27kzdvXurWratzXZKTk1UeV69endu3b7N9+3b27t3L8uXLmTNnDosXL6Zbt246X18pJeX9uu1r164lX758audz5fr4PzMeHh6cO3eOpKSkdLupPrWs9JiYmGBiYvJJ1xCE7EhKeX9omzc70Otfg4cPH1KtWjUePnyo8g93dHQ0y5cv548//uDYsWNqv7AF4WMqVaoEwOPHjzPkek5OTsD7MTw1a9ZUOXf9+nX5fOp8H7p27Rp2dnZYWFgAH++qyUhFihShWrVqHD58mN69e3/0i9vGxkZtXZ6kpCSNr6Otra088/HVq1dUr16d8ePH061bN4oWLQqg9eBvJWWXnIODA7Vr19bpuQBNmjThxIkTbN68WR7EnVllCYKgWU4cg6NXF9XQoUPTbLqVJImHDx8ybNiwT6qYkHMdOnRI4x+IcqyLpq4ifVSqVAkHBwcWL16sMnB09+7dXL16lUaNGgGQP39+ypcvz5o1a1QChUuXLrF3714aNmwopykDnc+xsu6kSZMYN24c/fr1+2g+V1dXjhw5opK2dOlStRacD2dyWVpa4ubmJr829vb2VK9enZUrV3L//n2VvB/7B61evXpYWVkxZcoU3r59q3b+2bNnH61/r169yJ8/P4MHD+bGjRtq558+fSqv7vypZQmCoJnoovrPnj17UCgUWFhYMH78eLy9vYH3U2QnTpxIbGzsJ832EHK2fv368fr1a1q0aIGHhwdJSUkcP36cjRs34uzsTFBQUIaUY2RkxLRp0wgKCsLX15d27doRGRnJvHnzcHZ2ZuDAgXLeGTNm0KBBA7y8vOjatSsJCQnMnz8fa2trlTVmPD09ARg1ahRt27bFyMiIJk2ayIFPRvL19cXX1zfdfN26daNXr15899131KlTh/Pnz7Nnzx7s7OxU8pUsWRI/Pz88PT2xtbXl9OnTbNq0SWV2188//4y3tzcVK1akR48euLi4EBERQXh4OOfOndNYvpWVFYsWLaJTp05UrFiRtm3bYm9vz/379wkPD6datWr88ssvadbfxsaGrVu30rBhQ8qXL0/Hjh3l1/ns2bOsX78eLy+vDClLEATNcuIgY70CHOUvp6lTp9KnTx85vXLlypiYmNCvXz+Nv64EAWDmzJmEhYWxa9culi5dSlJSEkWKFKFPnz6MHj1a4wKA+goMDMTc3JypU6cybNgw/q+9+w6L4lzbAP7M0kSqdEFAEFGpKnYFREXB3jF2oyaWRLFF1GA3dond2HvvFU1QY0fsAeyIXQQVKdL3/v7w2zk7AkoVy/O7Lq8DM7P7vjuQMzdv1dLSonbt2tHMmTMl5TRp0oSCg4NpwoQJNH78eFJTUyNPT0+aOXMm2djYiNfVrFmTpkyZQsuWLaPg4GCSy+X04MGDYgk4edW/f3968OABrVq1ioKDg8nd3Z3+/vtvaty4seS6IUOG0P79++nYsWOUlpZG1tbWNHXqVHHxQiIiV1dXunDhAgUGBtLSpUspNTWVrK2tP7lVQteuXcnc3JxmzJhBs2fPprS0NLKwsCB3d/c8BdbatWtTeHg4zZ49mw4dOkQbNmwgmUxGVapUoYCAAEkIK2xZjLEc5CPg0FcScAQUoDOtcePGdPLkSTp48CD5+vpKzh0+fJhatmxJHh4ePJ2cMfZVS0hIID09PXKfeYRUS5VMiB3fokqJlKusZtmSC/AK8WlZn76oGEXU/XRranFJzsqi9veu09u3b0lXV7dI31vxO14tYDep5PF3PCs1ma7OaJ/v+ixevJhmz55NL168IFdXV1q4cCHVqlUrx2sjIiJo/PjxdPnyZXr48CEFBQWRv79/nssiKuAYnOnTp5O6ujqtWLGCMjMzxeNZWVm0cuVKUlNTo8mTJxfkrRljjDH2mRX3Vg3btm2j4cOH04QJE+jKlSvk6uoqrh6fk3fv3pGtrS3NmDEjxxmTeZGnLqoPl78nej+wcd++fWRjYyMusHbx4kV6+vQpVahQgdatW0ceHh4FqhRjjDHGPp/iHoMzb9486t+/v9iNvGzZMjp06BCtXr2aAgICsl2vWD6EiHI8nxd5Cjhr167NdXrs06dPac+ePUT0PgEKgkD37t2j+/fv06pVqwpUKcYYY4x9PnI55Xk38f9fjirbVia5rSOVnp5Oly9fpjFjxojHZDIZNWnShM6fP1/wSn9CnruoPra55odfM8YYY+zrkdsz/mPPfktLS9LT0xP/TZ8+Pcf3jouLo6ysLDI1NZUcNzU1pRcvXhTbZ8pTC47yqrKMMcYY+7YUZCXjx48fSwYZf2mrgHPAYYwxxr5zcjny0UX1/jpdXd08zaIyMjIiFRUViomJkRyPiYkp8ADivCiSzTYZY4wx9vXK8wyq/KyX8//U1dXJzc2NQkJCxGNyuZxCQkLERTyLQ4F3ppPL5bRnzx66cOECvXnzRtwET0EQBB5kzBj7Jlw9cIwE1ZJpfr9du+T39KtrULJr0BAR7YxMLtHym9a1KLGyk9IziO5dL9YyinsW1fDhw6lXr15Uo0YNqlWrFv3555+UnJwszqrq2bMnWVhYiON40tPTKTIyUvz66dOndO3aNXGLmbwoUMBJTk4mLy8vunz5co7nFbOpOOAwxhhjXz45QEIeJwnJCzCZyM/Pj2JjY2n8+PH04sULqlq1KgUHB4sDjx89ekQy2f86lZ49e0bVqlUTv58zZw7NmTOHPD0987yIcIECzuzZs+nSpUs5nvtcuy0zxhhjrGh8jr2ofvnlF8m2K8o+DC3ly5cv9KzsAo3B2bdvHwmCIC7CIwgC9ezZkxo3bkwAyMPDg8aPH1+oijHGGGPs8yjulYxLQoECzv3794mI6LfffhOP/fTTT/T3339T37596dy5c1S/fv2iqSFjjDHGihXkIHke/30tu4kXKOCkpqYSEZGxsbHYZ5aSkkJERJ06daLMzEyeWs4YY4x9JQqy0N+XrkABR19fn4jeb66pp6dHRERHjhwhIhLH5ly/XrwjvhljjDFWNIpzmnhJKVDAUSzMk5iYSA4ODgSAgoKCyMTEhAIDA4mIyNDQsOhqyRhjjLFik9fuKcW/r0GeA86pU6fo1KlTlJCQQC4uLgSAoqKiqEuXLuI1cXFx4hTxjh07FkuFGWOMMVa0IM/K17+vQZ6niTds2JBkMhmdOnWKhg0bRu7u7uTq6ko1a9ak0NBQ2rhxo3itn58fTZ06tVgqzBhjjLGilZ/g8rUEnHx1USkGFrm5udHPP/9MderUIRUVFVq/fj09efKEzp8/TzExMbR582bS1NQslgozxopHdHQ0CYJAa9euLemqFIm1a9eSIAgUHR0tHmvYsCE1bNiwxOrE2JcKcnk+WnDyuCtnCSuyvajMzc2pdu3aZGxsXFRvyRjLRevWral06dKUmJiY6zXdunUjdXV1evXq1WesWcElJCTQpEmTyNXVlbS1tUlTU5OcnJxo9OjR9OzZs5KuHmPfNGRl5evf1yDfAYdXKmas5HXr1o1SUlJoz549OZ5/9+4d7du3j3x8fL6KAf9RUVFUtWpVmjJlCjk4ONDMmTNpwYIF5OXlRatWreJWF8aKGZCPMTj4OgJOvrdq6NixI2lofHrTOUEQxAUBGWNFq3Xr1qSjo0ObN2+mnj17Zju/b98+Sk5Opm7dupVA7fInMzOT2rdvTzExMXTy5Elq0KCB5Py0adNo5syZJVQ7xr4P3/0YHCKi58+fU3R0dK7/Hj58KH7NGCsempqa1L59ewoJCaGXL19mO79582bS0dGh1q1b0+vXr2nkyJHk7OxM2trapKurS76+vp9cq2rOnDkkCAI9fPgw27kxY8aQuro6vXnzRjwWGhpKPj4+pKenR6VLlyZPT086e/bsJz/Lrl276Pr16zRu3Lhs4YaISFdXl6ZNmyY5VtCyGGM5+xZnURXZGByFr2WFQ8a+dt26daPMzEzavn275Pjr16/p6NGj1K5dO9LU1KSoqCjau3cvtWzZkubNm0ejRo2i//77jzw9PT86tqVz584kCEK29yci2r59OzVt2pTKlClDRETHjx8nDw8PSkhIoAkTJtAff/xB8fHx1KhRI7p48eJHP8f+/fuJiKhHjx55+tyFKYsxlrNvMeDku4uqT58+ZGVlVRx1YYzlQ6NGjahs2bK0efNmyQ69O3bsoIyMDLF7ytnZme7cuSNuq0L0PkxUrlyZVq1aJS7O+SErKyuqU6cObdu2jUaNGiUeDwsLo6ioKJo4cSIRvf+jZsCAAeTl5UVHjhwRx+n9/PPP5OjoSL///jsdO3Ys189x8+ZN0tPTI0tLy09+5sKW9SlpaWmUlpYmfp+QkFDg92Lsa6KYRZXXa78G+Q44ffv2pXr16hVHXRhj+aCiokJdunShoKAgio6OpvLlyxPR++4pU1NTaty4MRGRZMxcVlYWxcfHk7a2NlWqVImuXLny0TL8/PzI39+f7t+/TxUqVCAiom3btpGGhga1adOGiIiuXbtGd+/epd9//z3bjK3GjRvThg0bSC6XSwKWsoSEBNLR0cnTZy5sWZ8yffp0mjRpUoFey9jXTC7PIspjwJF/JS04Rd5FxRj7fBStNJs3byYioidPntDp06epS5cupKKiQkREcrmcgoKCqGLFiqShoUFGRkZkbGxMN27coLdv3370/Tt16kQymYy2bdtGRO9bUHbs2EG+vr6kq6tLRER3794lIqJevXqRsbGx5N/KlSspLS3to+Xo6up+dLq7ssKW9Sljxoyht2/fiv8eP35c4Pdi7GvCXVSMsS+Km5sbVa5cmbZs2UJjx46lLVu2EADJ7Kk//viDAgMD6ccff6QpU6aQgYEByWQy8vf3J/knmprNzc3J3d2dtm/fTmPHjqULFy7Qo0ePJLOaFO8xe/Zsqlq1ao7vo62tnWsZlStXpqtXr9Ljx48/2U1V2LI+RUNDI0+zRBn71nyLs6jyHHCsrKxIEAQqVapUcdaHMZZP3bp1o8DAQLpx4wZt3ryZKlasSDVr1hTP79y5U1xPRll8fDwZGRl98v39/Pxo0KBBdPv2bdq2bRuVLl2aWrVqJZ5XdF3p6upSkyZN8l3/Vq1a0ZYtW2jjxo00ZsyYj15b2LIYY7nIyiLI8hhcvrWF/qKjo+nBgwdUvXr14qwPYyyfFK0148ePp2vXrmVb+0ZFRSXb7MYdO3bQ06dP8/T+HTp0IBUVFdqyZQvt2LGDWrZsSVpaWuJ5Nzc3qlChAs2ZM4eSkpKyvT42Nvaj79+xY0dydnamadOm0fnz57OdT0xMpHHjxhVJWYyxnPFCf4yxL46NjQ3Vq1eP9u3bR0SULeC0bNmSJk+eTH369KF69erRf//9R5s2bSJbW9s8vb+JiQl5eXnRvHnzKDExkfz8/CTnZTIZrVy5knx9fcnR0ZH69OlDFhYW9PTpUzpx4gTp6urSgQMHcn1/NTU12r17NzVp0oQ8PDyoc+fOVL9+fVJTU6OIiAjavHkzlSlThqZNm1boshhjOYNcnudBxt/sLCrG2JenW7dudO7cOapVqxbZ2dlJzo0dO5aSk5Np8+bNtG3bNqpevTodOnSIAgIC8vz+fn5+9M8//5COjg41b9482/mGDRvS+fPnacqUKbRo0SJKSkoiMzMzql27Nv3888+ffH87Ozu6du0aBQUF0Z49e2jv3r0kl8vJzs6O+vXrR0OGDCmyshhj2SEfs6i+ljE4AnhlPsYYy1FCQgLp6emRdoNhJKiWzODj2TNKPrT1ttcs6SrQssjkEi2/6Ur/Eis7KT2Dam0Oprdv34qzF4uK4ndcx314nn/HkZlGiafnFUt9ihK34DDGGGPfuW+xBYcDDmOMMfad44DDGGOMsW+OXJ5FAgccxhhjjH1LkCUnEvIYcLJ4FhVjjDHGvgJAPrqoeB0cxhhjjH0NIM/KewsOd1Exxhhj7GvAAYcxxr4jimXCkJlWYnVISc7bTuvFKSEhs6SrQKnJJbsOTlJ6RsmVnfH+/hfnsnXISM17cMkquXuRH7zQH2OM5eLJkyef3OGcsc/l8ePHVK5cuSJ9z9TUVLKxsaEXL17k63VmZmb04MGDL3oDbg44jDGWC7lcTs+ePSMdHR0SBCHfr09ISCBLS0t6/Phxiaz4WtLlfwl1KOnyi6IOACgxMZHMzc1JJsvzHtl5lpqaSunp6fl6jbq6+hcdboi4i4oxxnIlk8mK5C9mXV3dEl3SvqTL/xLqUNLlF7YOenp6RVyb/ylVqtQXH1YKouijIGOMMcZYCeOAwxhjjLFvDgccxhgrJhoaGjRhwgTS0CiZnchLuvwvoQ4lXf6XUofvEQ8yZowxxtg3h1twGGOMMfbN4YDDGGOMsW8OBxzGGGOMfXM44DDGGGPsm8MBhzHGGGPfHA44jDHGio1cLie5XC5+n5X1dexEzb5+HHAYY+wbpQgWmZmZnz1YvH79mojeb3chk8koOjqaiIhUVFQ+az3Y94sDDmOMFQNFoHj37l2JlS+TySgqKorGjh1LW7dupYSEhM9S9qtXr2jKlCk0evRoIiK6c+cO2dra0pgxYz5L+cqUW49KguL3IDU1lZKTk0u0Lt8b3myTMcaKWFZWFqmoqNC9e/coICCAqlSpQoGBgaSurv5ZypfL5aSiokIRERHUokUL0tDQICMjo8+22aQgCJSZmUnz58+nZ8+e0cGDB6lFixbUsWPHz1K+guLnQER0+/ZtMjIyIkNDw89e/r1792j69OlkaWlJ/fv3JwsLi89Wh+8ZBxzGGCtCinARGRlJTZo0ISsrK7K0tPws4QYACYIgdgn5+PiQk5MTjR07ltzd3Yu9fAUDAwOaMmUKPXv2jDZt2kTly5en5cuXU9myZT9bHZTDTf/+/en27dvUpk0bGjJkCKmpqRVLmYr7TyT9PWjWrBkZGhqSg4MDh5vPiLuoGGOsCMlkMnry5Am1b9+eXF1daeHChRQUFFRs5c2ePZsuXbpERO9bThRdImvXriUNDQ0aP368GG4SExPpzp07dO7cOXFMTHHR1tamZ8+ekYWFBUVHR9OMGTPEc8U9HgiAGG5atmxJwcHBVK9ePercuXOxhRui9/dfsfuR4vegZcuW5OjoSEuXLqURI0YUW9ksOw44jDFWQAMHDqTjx4/Th1v6nThxgl6/fk2DBw+mmjVrEhHRs2fP6OLFi7Rs2TIKCwujjIyMQpd/8uRJGj16NM2YMYNu3LhBRP8bxBsbG0tyuZxsbGyIiGjfvn30888/U+XKlcnT05N8fX3pwoULha6DMsV4l6ysLFJVVaU///yTNmzYQD/++CMtXLiQhgwZItbxw89flNsiKlpRxo4dS5cuXaJ58+bR2LFjydLSUrwmMzOzyMr97bffaMuWLWLZivc+ePAgpaen0+jRo6lu3bpERPTy5UsKCwujzZs3U2hoaKHLZrnjLirGGCuAhw8f0sGDB+nYsWO0fv16qlevnvhgjY6Opnfv3lGFChWIiGjHjh20bt06Cg4OJrlcToaGhvTHH39Q//79C1WHhg0b0tq1a6l3794EgCZMmEAuLi5ERFSuXDl6/vw5+fv7k1wup6NHj5KdnR3Nnj2bNDQ0aOrUqbR69WqqVq0aqauri3UvKEWXUHR0NG3evJkqV65Mbdq0IRUVFbKxsSEAtGjRIiIiWrBggdiS8vjxY7K0tCx0+R9KTU2lc+fOUZ06dahjx44kCAK9fv2arl+/Tnv37iUA1LFjR/Lw8JB0LeVXREQELViwgFxcXEhTU5Patm1LqqrvH62JiYmUmJgojvvZtm0bbdiwgQ4fPkxE78PQli1bqHPnzkXzoZkUGGOM5VtWVhZu3LiBGjVqoHz58jh9+jTkcjkA4MaNG9DR0YGDgwNq164NVVVVeHh4YOnSpXjw4AHs7e3h7u6O1NTUApV99OhRyffr1q2DIAho3749rl69Kh7v27cvKlWqhHLlymHu3LmSczVq1EDz5s0LVP6HsrKyAAARERGwtbWFg4MD5s2bJ7nm/v376Nu3LwRBgL+/v3i9h4cHWrRoUST1UJaUlIQGDRqgUaNGePnyJcLDw9GsWTMYGRlBS0sLOjo60NTURGhoaKHLOnnyJGxsbFCjRg3s2bNHPL5jxw6YmZmhevXq8PLygqamJtzd3bFixQr8/fffcHNzQ926dfHq1Svxd4cVHQ44jDGWDytWrMDjx48BvH+wX79+HdWrVxdDTmZmJgAgODgYDRs2hIeHB9auXYvbt2+L79G3b184OTkhISEh3+VPmzYNgiDgr7/+khxXDjmXLl0Sjz958gTx8fHi93K5HKGhoXBycsKIESOQkZFRJA/X+/fvw8zMDL6+vjh27FiO19y5cwf9+/eHIAhwcnKCk5MT9PX1cfHixUKVrQhYH5o0aRK0tbVhbGwMQRBQs2ZN/PHHHwCAv//+G4aGhpg0aRIA5PserFmzRvL9yZMnYW1tjRo1amDXrl3i8dmzZ8PHxwfOzs5YsWIFIiMjxXPt27dH9erVxd8ZVrQ44DDGWB4tXboUgiDg119/xbNnzwDkHHIUD8vU1NRsrTSXLl1CjRo10LVr1wK14Jw5cwYtW7aEsbExli1bJjmnHHIuX74sHld+eIeEhMDX1xdly5bF3bt3813+h+RyOTIzMzFixAg4ODjgwoULYnnv3r3Dw4cPcenSJaSkpAB4H4TmzJmDhg0bwtfXFxEREYUqPyMjQ/z62bNnuHv3rhhAAWDlypUICAjAokWLEB0dLR4PDw+HmZkZ/vzzz3yXuXbtWgiCgOHDh0uOK4ecHTt2iMeTkpLEz69w6dIl1KlTB7169cp2jhUNDjiMMZYPP//8M4yMjPDLL798NOTk9Fd5cHAwvL29YWpqilu3bhW4DhcvXkSLFi1gaGj40ZBz7do18XhycjKaN28OV1dXlC9fHjdu3Chw+Tnx8/ODg4OD+P2hQ4fQt29faGpqQldXF7Vr1xZbrDIzMyGXy5GYmFioMpXv8Y8//ogqVapAVVUVtra2GDVqVK6ve/ToEcaPHw9TU1OEhITku9zo6GiMHDkSgiBg2LBhknPKIWfnzp051vXQoUNo2rQpTExMCvV7wD6OAw5jjOWBcktB//79YWBg8MmQo5CUlIRevXrBxMQE9vb2BQ4Xyi0xoaGheQo5169fF4/37NkTAwYMwP379wtUfm51UrTgWFhY4JdffsHAgQNhYGCAKlWqYOzYsRg5ciR0dHQwdOhQ8fqi1Lp1a5iYmGDo0KFYunSp2A3WrVu3bN2Ap06dwoABA1CqVCnMmjWrwGU+fvwYI0aM+GTIUR6Tk5SUBB8fH1SuXLlQvwcsbzjgMMZYHik/mPMTchISErB27VpMnTpV0k1SWHkNOcqDiwvbHZLbeJcXL17Ax8cHlpaW0NPTw6RJk8SxNRkZGbC3t0f37t0LVXZONm/eDBMTE2zatEkMM7t27YIgCOjbty9ev34tXrtnzx6oqanB3t4eCxcu/ORn+pSChJwJEyZg3LhxePDgQYHKZHnHAYcxxvIhvy05Z8+eFa8vTMuFcuuN8gP5/PnzHw05MpkMTZo0KZLWAkX9nz9/jhMnTmDt2rV4+vQp3r59CwBITEzEo0ePEBcXJ3ndhQsX4OjoiPHjx0MulxfpjKEJEybA1tZW7O46fvw4SpcujZ49e+LRo0fZrt+4cSMuXLggfl/QcKOQ15CjPPA4PT29UGWyvOGAwxhjn/Cxh2C/fv1yDTm1atWCjo4Ozp8/X6jyFcHizZs3ePr0Ka5evYrk5GTx/NmzZ3MNOX/99Rd0dHQkA28LQnkqeJUqVVC6dGkIggATExOMHz8+15apkJAQNG/eHBYWFoXuGsspGI0aNQp2dnYAgNOnT6N06dLo1q2b+LMA3s9kWrBgQZ7eL7/lA+/H5Hws5FSoUAEVKlTA/v3781UeKxwOOIwx9hGKcBETE4NTp05h27ZtuHXrFt69eyde07dv3xxDzuXLl9GwYcNCzVZSlB8ZGYk6derAwMBAnPI8c+ZM8boLFy7kGnKUp4nnx4cP9Lt376Js2bJo1KgRVq9ejXv37qFfv37Q19fHTz/9hIcPH4rXJiQkoFevXnBzc4OlpWWhW5CUQ6by4OQlS5bA0NAQ06ZNg5aWFrp3744nT56I569du4aaNWti3LhxhWo5UfwcYmNjce3aNRw+fBgPHjwQW/SioqJyDTl///03nJ2dERUVVeDyWf5xwGGMsVwot1oo1mxRU1ODpqYmhg0bhnPnzonX5tZdlZaWVuh63LlzB8bGxmjYsCGmT5+OAwcOwMPDA4IgYOTIkeJ1itlVpqamCAoKEo/nt6Xi6NGjSEpKkrw2KSkJXbt2RaNGjSSL402cOBGCIEBfXx/9+/cXW4pevHiBOnXqoE+fPoWejq4cbjp06IDx48cjJiYGAJCWlgZnZ2cIgoDGjRtLQtbz588xevRoWFtbZ1scsSDlR0REwNHREbq6uhAEAebm5ujZs6cYdh8+fJhryFEOxOzz4IDDGGMfERUVJbZabNiwAZcuXcKUKVMgCAL69Okj6Qrp378/TE1N0bt3b7x48SLfZSkHEcXXGRkZGDBgANzc3CRjRwYNGgQ9PT1s2LBBsp5OaGgoGjRoAFtbW7x58ybfdQgICIAgCFi2bJnYDSaXyxEbG4vmzZtLpl8HBARATU0N69atw48//ghBENC/f3+xKyopKUnSlVYQymOekpOT4eHhAWNjYwQFBeH58+cAgLCwMDg4OMDS0hILFixAXFwcTpw4AX9/f6ioqGRbVbkgFAsZent7Y9WqVbh58yYGDhwIQRDQunVrsYVHMSZHJpOhf//+4ut5peLPjwMOY4zlQPHACgwMhLOzs6S1ZurUqVBVVcXGjRuRmpoqGTzcpUsX2NraFijg5FaP6tWro1u3buKxESNGQFVVFStWrBAH+Cp3Q126dCnHAbZ5Va9ePZQpU0YSct69e4d///1XvGbJkiWQyWTibKS0tDTY29ujfPny8PPzK5LZYsotNz/++CMaNmyIGjVqQBAE6OrqYtasWXj16hUA4OrVq6hatSoEQYAgCFBRUYGVlZUk3OQlZOQ2mHvChAlwcHDAmTNnxGv8/f2hp6eH5cuXS1poHj58iJ9//hm6urp48eIFh5sSwgGHMcbwPkikpqZma3Fo1aoVGjduLH4/cuRIqKqqYvny5eK05JSUFMkDTrlVJ69CQ0MxefJktG7dGuvWrROPx8fHw83NDYGBgQCA4cOHi+Urlzl69GicOnUq3+UqU24tqVu3LvT09LBs2TKxu0p5PJK7uzt69eolTsNOTU2Fk5MT7OzsYGNjU6B7kJsuXbrA2NgYs2bNQnh4ODZt2oS2bdtCTU0NM2fOFENORkYG9u/fj2XLluHgwYOShQ7zM1sqp25FHx8feHl5id8rfg+UQ+abN2/Ee/TkyROxG42VDA44jLHv3smTJzF06FBUqVIFCxcuRGxsrHjO19cXvr6+AP7XJfNhuOjQoQMOHz5c4PI3btwIS0tLVKxYEb169cLBgwclYaNnz56wsLCAv78/1NXVsXTpUkn5q1atQtmyZfHPP/8UuA4KOYWcpUuXSoLfq1evYGFhgZ9//lk8FhISAk9PTzx//jzbNPHCiIyMhKGhIUaMGCEGj6ysLMTHx6NXr15QV1fH7NmzPxqo8tqC8u+//2Lo0KFwdXXFhAkTxOMZGRno2LEj2rdvD+B/LWgf/h4MGDBAEk5ZyeKAwxj7rq1fvx7GxsaoXbs2+vfvj7CwMMlD/q+//oKxsTF8fHygqqqKVatWiX+xA8CBAwdgbm6ebfPFvNq6dStUVVUxaNCgbJtOKloDzp49iwoVKkAQhGx7J126dAnNmjWDh4cHXr58WaA6fEh5tpFyS47iYR4TE4OqVauiatWq2LZtG/bu3QsfHx/Y2dkVWR0UwsLCIAgC5s+fn61ur1+/RtOmTVGmTBnMmzevUGWvX78eJiYmcHNzw/Dhw3H48GFJWdOmTUOpUqXQrVs3aGho4K+//pKEm61bt8LKygpbt24tcB1Y0eKAwxj7bu3cuRNqamoYMmRIrtOYb968CTc3NwiCgN69e0vOXbx4Ed7e3qhevTqePn2a7/IjIiJgb2+P3r17S2b/fNjikJaWhhkzZsDIyAhVqlTB4cOHcfPmTaxcuRKenp4wMjIq0KaVOQ1qzkmdOnXEkKOYon3u3DmYmZlBQ0MDWlpasLa2LpatB2JjY2FmZoaePXuKx5THPAUFBUEQBJQpUwbz588v0GylHTt2QF1dHb/88otkawvlsp4/fw4vLy8IgoDRo0dLurEuXboEHx8f1KxZs0i75ljhcMBhjH2XHj16hOrVq6Njx46SZfNzGqtx4sQJlC9fHmXKlMHw4cPxzz//YNq0aahbty4MDQ0RHh5eoDrs2rULmpqaOHjwYK7XKIJHSkoKli5dChcXF3EgraGhIapVq4b//vuvQOUD7x/giof4q1ev8N9//+H69evZ9nCqXbu22F2lOHfr1i2sX78eGzduLNSgZiDn+y6Xy5Gamgp/f38IgoAlS5ZI6g28Dyfe3t7o1q0bNDU1xW66vI65iYqKQtWqVdGlS5dP/h7s3r0b1atXh5GREWbMmIGQkBDMnj0b9evXh4GBQYF/D1jx4IDDGPsunT59GhoaGtiyZctHr1MEjDNnzqBTp07Q09ODIAgwNjZG48aNC/VQ+/XXX6Gnp5etrA8pt1ikpaVhx44dWL9+Pc6cOSMZL5RX7dq1w9ixYyXHIiIi4OrqCn19fWhpaaFChQrYsGGDpGVJ0ZKzdOnSQu8Erky5SzAqKgqXL18WBw4D7xcxrFWrFvT19bFo0SLxeExMDH766Sd07twZz58/R9WqVeHi4pKvqemnT5+GmpoaNm/enOs1ip+LXC5HSEgI2rdvL4ZMIyMjeHh4cLj5AnHAYYx9VxQPq3nz5kEQBHHNltzChfJf8ikpKXj48CGOHDmCe/fuFWidGWVDhgyBpqYmnj59+sk9ml6/fl2ogcwKDx48QN26dSEIAmbMmAHgfVCwsrJC7dq1MXnyZIwfPx716tWDmpoahg4dijt37oivr1u3LoyMjBAUFFQki9cph7eePXuiXLlyEAQBzs7O+O2338RzR48eFevdunVrDB48GK1bt4ZMJsPSpUsBAL/99hv09PTy1V03a9YsCIIg/ixza/n5cB+x0NBQ/Pvvv7hz506BV4pmxYsDDmPsu7Ry5UoIgiDutP2xcJGSkoL9+/eLC8sVlY0bN0IQhDy1Hjx58gTu7u64fPlyocu9evUqWrduDUEQMHv2bHHVYeV1buLj4zF48GAIgoDAwEBJl1WVKlVgbW1d6ICnfM/btm0LU1NT+Pv7Y+vWrfDz84OxsbFkB/JLly6Jm2tqa2ujcuXKkhWbhw0bBmNj43ytnKwYw6PY+T23gKOYubV+/fp8fkpWUjjgMMa+S8ePH4cgCOjbt6+4zsuHlMOFs7Mzjh8/XqR1uHjxIgwMDFChQgVcunQpW7nKAgICYGFhIem6yS/l97169SpatmwJQRBQt25d1K1bV3y4K3fJ/PjjjyhdunS2cT7K41UKa8aMGahUqRI2btwozlDbu3cv1NXVoaamhs6dO0s+Q3JyMl68eCEZ2H3x4kU4OzvD09MzX8Hr/PnzUFNTk0wL//D+K75/9+4d7O3tceDAgQJ8Sva5ccBhjH236tWrBwMDA2zfvl2cEpxTuFiwYAFMTEwQGRlZ5HWYPn06BEFAy5YtJQvTKXeJXLp0SVxYryj3NLpy5QratGkDXV1dVKpUSQwMirLlcjkuXrwozjCSy+VFsreWsqSkJPj6+qJ58+ZiePv333+hpaWFjh07om/fvpDJZOjVq5f4mg9bWdauXQtvb2+UKVMm3wOuHz16hLp160Imk2HPnj3ZylD+fZgzZw5MTU2LNNyx4sMBhzH23VE8wENDQ2FhYQEnJyccOHBA3NNJeW+na9euwcvLCy1btpSsf5MXKSkpuZ5TfkgruoLq16+Pv//+W3LdyZMn4evrCxMTE9y+fTtf5edG+aEdFhaGNm3aQBAEjBkzRjyuCHxZWVmwsLBAnz59iqTsnJw+fRphYWEAgNu3b6NMmTLo3LkzkpKSEBMTI64BpFhoT9nKlStRs2ZNVK1atcCzyXbu3AlBEGBrayuZ0fZhi1ejRo3Qtm3bfP8esJLBAYcx9t1KTU3FunXrYGpqChsbG8yYMUMyO+jQoUPw8fGBgYFBvltvOnTogOHDh390tpHyA1SxyaWqqiratWuHoUOHok2bNrC3t4elpWW29Vny62PTpsPCwsTuqokTJ0rOnTlzBgYGBhg2bBiysrLyta/Sh9d+OFD3QykpKejXrx+cnZ3x33//ibOrOnXqhGrVqkFTUxOHDh2SvCY5ORlnzpwp9OamixYtEqfeL1u2TNJSFRwcDB8fHxgaGhZLKx4rHgIAEGOMfaeSk5PpxIkTNGrUKLp9+zaZmpqSo6MjvXz5kuLi4khLS4t27dpFLi4ueX7PlJQU+u2332jZsmUUGBhIw4cPJ21t7U++bvv27XT48GE6cuQIZWVlkYWFBXl7e9OgQYPI1ta2wJ8xKyuLVFRUKDo6mjZt2kTh4eFkZWVFNWvWpA4dOpAgCHTp0iWaOHEiHT58mH744Qdq2LAhJSUl0YEDB+j69et04cIFqlixYr7Kff36NRkYGJBcLqesrCxSU1MjIqLjx4+ThoYGWVlZkaWlpXh9ZmYmeXl5kZaWFgUHBxMR0YMHD6hbt27Uu3dvcnV1pdq1a4vXy+VykslkBb4vH77H2rVraezYsfTixQtydnam8uXLU1xcHD19+pRUVVVp9+7d+fo9YCWspBMWY4x9CeLj4xEYGIi2bduiRo0aaNGiBYKCgiTrwOTH27dvMW7cOMhkMkycOPGjLTkftq68evUKL168QGZmZqF3ola8PiIiAmXLloWZmRnKlSsHHR0daGtro0uXLuI1YWFhaNWqFVRUVFCqVCn07dsXvXv3LtAaL5s2bYKdnR2uXLkiOd6uXTuULl0agiDAxcUFs2bNEs8lJiaiW7dusLCwQGhoKO7du4cJEybA2NhYMnssPxtn5oXy+124cAEzZ85E9erV4eDgAE9PT/zxxx9Fsjs6+7w44DDGvkk5BYO8rHUDoMjGWLx9+xZjx47NU8jJrY6FDTjA++0OnJyc4OnpiVOnTiE5ORn3799Hly5doKGhAV9fX0nI6dixo2T/p4KYMWMG9PX1UaNGDXEq/i+//AJzc3NMnjwZGzduRP369VG2bFkMGzZMfN2OHTtQpUoVaGhowNjYGGpqauJ6PQWR15/5h9elp6dL9qJiXx/Vkm5BYoyxoqboknn16hU9f/6cYmJiqHr16qSvr5/j9R92c+jo6BAREQASBKHA9dDV1aXRo0cTEdHkyZOJiGjEiBEf7a76sLzClK/ofrl9+zY9fPiQfv31V3J3dyciIltbW1q8eDGZmJjQsmXLaMyYMTRjxgyqUaMGjRw5klRVValJkyYFLnv06NGkqalJM2bMoD59+tDmzZtJXV2dfv31Vxo6dChpampS7dq1adasWbR27VrKysqi+fPnU8eOHcnQ0JAuXrxI8fHxVKdOHWrTpo3k8+SV4vcgPj6e3rx5Q69evSIbGxsyNDQkmUwmnieS3mcAYnea4vvC/BxYCSnhgMUYY0VK8Zd5REQEatSoAX19faiqqsLCwgJz584VVy4ubsotAgVpySkMxWBeRTkHDx6EIAg4cuSIWDfFfXrz5g1q1qwJJycnScuV8kyy/FJuHZk3bx7MzMzg6OgIIyMjcZCwYgDxo0eP0L9/f5QpUwa//PJLnt4zLxT3ICIiAm5ubihTpgwEQYCjoyMCAwPz+5HYV6hwo7MYY6yE4YN5EjKZjO7fv0+NGjUibW1tmjp1Kh0+fJgaNWpEI0eOpCVLllBiYmKx1EUul4tfK//Fr6urS6NGjaKAgACaPHkyzZ07l5KSkoqlDgBIRUWFrl27RlWqVKHr16+TqakpEREdPnyYkpKSSBAEkslklJ6eTvr6+jRgwACKiIigO3fuiJ9BQ0OjwHWQyWTi+wwbNoxGjhxJKSkplJCQQG/fviWi9/dHLpeTpaUl/f7779SxY0fatm0bDR48ONf3zA8VFRW6d+8eNWzYkHR1dWnChAl09OhRsrOzo6lTp5K/v3+BPx/7OnAXFWPsq6Z4UCo/VBcuXEjlypWjmTNnUq1atYiI6Ny5c6Sqqko1a9Ys1MM7N4rujsePH1NwcDDduXOH3N3dqVq1amRpaUn6+vo0atQoIsp7d1V+Ke5DQkICjRkzhkxNTSkhIYHc3d2pe/futH79emrUqBG1bNmSVFVVSV1dnYiIHj16RLq6umRmZlboWUn4/+4cmUxGGRkZpKamRiNGjCBVVVUKDAwkf39/qly5MlWrVo3kcjnJ5XKysrKi8ePHU1ZWFi1dupQ6d+5Mnp6ehb4XixYtInNzc5o+fbo4+2r37t2kq6tLTk5OlJ6eLt4DcDfUt6dkG5AYY6xgfvrpJ4waNUr8XrkLw93dHR06dBC/HzFiBNTU1LB8+XKxGyYlJaXIZuMod4tZW1tDX18furq6EAQBrVq1wrFjx8Rr37x5I3ZXTZkyRbLHU1F4/vw5zp49i6pVq2LHjh1iV1BERATq168PQ0NDrF27Fs+ePQPwfqsCT09P1KlTB69fvy5U2cq7ggPItupxUFAQTE1NUbVqVXF2VVZWlnj/oqOj8c8//+SrzE6dOmHt2rU51qV27dro1KmTeGzkyJFQVVXFypUrxd+D4u4uZCWHAw5j7KsTExODOnXqwNraGlOnThWPp6enIyMjA1WrVsXAgQMBAKNGjYKqqiqWL18u2eagd+/e2aYwF8aDBw9Qvnx5NGnSBAcOHEBKSgr27t2L0qVLw8vLS7JA3Zs3bxAYGCju6F0UM6UAICEhAZaWljA3N0eVKlWQnJwsnpPL5bhw4QK8vb0hCAIqVKiAunXrwtraGoaGhgVeBVhBOSyOGDECjRs3hqOjI8aPHy8JLXPmzEHZsmXh6uoqzq5SDjk5vV9unjx5Ai8vL2hoaGD79u2Sc+/evYOnpycGDx4M4H/h5sPfg19//bVIdmlnXx4OOIyxr4oiDERFRaFVq1awsrLC5MmTJdeMHz8e5ubm6Ny5s/hQU24p2blzJwwNDbF79+58l798+XJERERIjqWlpcHf3x81a9bEuXPnxOPTpk2DIAjQ19dHrVq1cPToUfHcq1evMGXKlCJdGTcjIwNr1qxBuXLlIAgCVqxYkW314MTERMycORPNmzeHl5cXfvnllyLbAgIAWrduDR0dHdSoUQOenp5QVVWFlZUV/vzzT/EaRchxc3Mr9O7oN2/eRKdOnaCmpoZt27ZJzv34448oV64cBg4cCHV1dfz111+S0LdmzRpYW1tzwPlGccBhjH11FH/dR0VFoUWLFrCyssKUKVPE8yEhIbC1tYVMJoO/v7/ktWFhYfD29kadOnXyvbz/7t27IQgChgwZIpll9O7dO4wYMQIDBgwQjwUGBkJVVRXbtm3Djh07xF27lVtyCttFllPLT1JSErZt2wYDAwPUr18/1wCRkpKCzMzMbN1K+aUcoE6dOgVHR0ds3rxZ7AIKDg5GtWrVoKenh4ULF4rX/vnnnzA0NET58uURGxub71Ys5esjIyPRoUOHbCEnMjISjo6OYkuZcpfZ5cuX0axZMzRo0AAxMTH5/tzsy8cBhzH2VVI84O7duyeGnEmTJonnV65cCQMDA1haWmLx4sW4cuUKFi1aBHd3dxgaGmZrhcmr2bNni600ygvB3bx5U3zYr1u3DmpqaggKChJbjtq2bQsjIyNUq1ZNMianIORyuVhWUlISYmNjxZ24gfctShs3boS2tjaaNWsm6X761H5QBTVu3DhMmjQJrq6uiI+PB/C/AHfmzBk4OjqiYsWKOHv2rPia6dOnY8WKFQUuUznkhIeHZws5KSkpWL58OaysrODg4ICNGzciIiICS5cuhaenJwwMDAq0SjP7OnDAYYx9VRQPzfT0dLEVRTnkKG8WuWHDBnh4eEBNTQ0ymQxGRkaoV69evsebhIaGZjsWHh6OYcOG4ebNm5K6paamokuXLvD29paEDh8fHzRo0ABVqlTBgwcP8lU+AAwaNCjbGjI3b95EkyZNYGtrCwcHB3Tq1Al37twR71FuIaeoRUREQFNTE4IgwN7eXvy5KIepvXv3QiaTYdGiRTm+R35bcBSfUblVRjnkbNmyBcD7ALh9+3bUqFEDgiBAEAQYGBigZs2axXpPWMnjgMMY+2ooHpj379/HuHHjMHv2bHHmj3J31YQJE8TXPHz4EGFhYdi0aRMuX76MuLi4fJU5btw4CIKAvXv3So7PnTsXgiBg4MCBuHfvnng8PT0d9erVg4eHh3js/PnzqFOnDq5du1agBfSOHz8OTU1N2NvbIyQkBMD7e2BiYoLKlSujR48e8PHxgb6+PiwtLbFt2zakpaVBLpeLIadFixbioN6ilpWVhX///VcMEatWrRKDhyKMZWZmwtbWFj4+PpIWqIJQvDYqKgq//fYbli5dKp5T7q5ShBxFeAoODsauXbtw5cqVfP8esK8PBxzG2FdBeSq2paUlXF1d8ccff0iuUQ45yi05hbF//354eHjA3Nw8W8iZPn06BEFA//79JSGnX79+0NLSwpgxYxAUFIQmTZrAwsKiUBs2rl27Fra2trCzs8PJkydx6tQp1KtXT2xdyszMxJkzZ1CrVi2YmJggODgYwPvxQVu2bIEgCOjQoUO2qdv5ldu4odTUVJw8eRJ2dnaoWLEigoODJWHuzp07sLCwwM8//1wk5UdERKBcuXJwc3NDnz59JNcoh5ytW7cWqjz29eKAwxj7ajx9+hQVKlRA48aNcebMGfG48jTj+/fviyFn2rRpRVLusWPH4O7uDjMzs2whZ+rUqWLIUZ6N1LJlS+jo6EBLSwuOjo64ceNGvsvdunUrYmNjxe/XrVuH8uXLo2LFimjcuDGaN2+e7TU3btyAi4sLnJ2dxZaO1NRU7NixQ9KdVhDKA5LDwsJw4MABnDx5UhxQLJfLcfLkSZQvXx4VKlRAUFAQ3r59i8uXL2P8+PEQBAGbNm0qVB2A99PD7ezs0KRJE1y4cEE8rtwqpBxyduzYUegy2deHAw5j7Iun6GLYsGEDypQpg507d4rHlFsUlGdXtW7dGlpaWpg9e3ahywXyHnKUQ8Tp06cRGhqa79laALB9+3YIgoBJkyZJBjOvWbMGlSpVgqGhobiIXUZGhmRs0pIlSyAIAtavX5/vcnOjfJ/9/PxgZGQkjmmxsbHBgQMHxNahkydPwt7eHoIgwNraGpUqVUK1atUKtSu4svXr10NXVxd79uzJsX4KkZGR8PPzgyAIkmvZ94EDDmPsqxEYGAgtLS1xZpJyAFE84BTHbt26BT8/P9y5c6dQZRYk5Ny6datQZQLvW6vmzp2L8+fPA5C2nqxevVpc6+bAgQPicUUQevz4MQRBwLx58wpdjw917doVpqammDJlCs6fP48ZM2agZs2a0NLSwrJly8SwdfLkSVSrVg2lS5fG+vXrJa1bhZ0eP3ToUOjq6ub4fh+O7bl27Rp69uxZpOsNsa8DBxzG2Bcpp/AyceJECIIgDpZVPMyUr924caM4gFS55aMoygeAI0eOfDLk/PDDD4iKiipw2Zs3b8aUKVPEWVgRERGYNm2aZFbWxo0bUbZsWZibm4vjbRQOHjwIDQ0NLFmyJNtnKYwrV67A0NAQkydPFhfMy8zMRHh4OFq1agVtbW1x1WLFmBxra2u4ubnh5MmTBQo2OdV91qxZkMlkku6pD69V/tkUdtwR+zpxwGGMfXEUweX169eS2S4RERHQ1taWDCpVbtnYtGkTGjduLAaggj7YFeUnJCTg1atXuHfvniQsHT58ONeQM27cOGhra4t7PeXX2rVrIQgCxo0bh6SkJADAjBkzIAgCxo8fL9kvat26dbCysoKmpiYWL16M0NBQbN++HU2bNoWJiUmBpqMrU9wHxf8GBwdDEAT8/fffAKTB4dq1a3B0dISTk5O4FYJidlX58uXh5OSEU6dO5Wv2lOLa+Ph4PHr0SDy+f/9+CIIAf39/cZE+5Z/1pk2bYGlpWWyzxtjXgQMOY+yLonio3bp1Cx4eHvDw8BAf1AkJCfD394cgCBg0aJDkdWFhYWjWrBlq1aqFly9fFrr8yMhIeHp6wtTUFIIgwNvbG8uXLxevCw4OzjXkFHQK8po1ayAIAkaMGIGnT5+Kx+Pj48XWq3HjxklCzoYNG8Q6lilTBi1atICHh0eBBjUrUw4MM2bMwNWrVxEZGSmuCqyg3DU4atQoaGhoSMYhyeVy/Pvvv6hYsSIsLCwkC/19jOLncPv2bTRp0gTdunWTDCwfPHgw1NXVMWXKFMnstMuXL6N58+aoV6+eZIA2+/5wwGGMfTEUD8vIyEgYGxujSZMmki0YgPeL+vXu3RuCIKB27doYNWoUBgwYAAcHhyJbmfb27dswMjKCu7s7AgMDsXr1ari4uEBNTQ3Tp08Xr1OMyVGsPaNQkJYjRbjx9/fP1vqjaD0KDAyETCbLFnLWr18PR0dH6Ovr4/Dhw4XqmgOyDyg2MTHB4cOH8eDBA9SoUQN2dnY4fvy4eI2iFW3VqlWQyWS4fv265P3kcjn+/vtvuLi4SKbTf6r8iIgImJqaom7duliwYIHkmpiYGHTv3h2CIMDDwwOzZ8+Gv78/qlevDgMDA17Ej3HAYYx9WV6+fInq1aujWbNmuHTpkuScIji8ePECf/31FxwcHFCmTBlYW1ujdevWRTKQNC0tDT179kStWrVw8eJF8XifPn2gr6+PTZs2SdZ3OXr0KJydnVGpUiUkJiYWKNz8888/EAQBvXv3lmwKCgB//fUXvLy8kJKSgqSkJEyYMCHHkLN48WJUrVo1TwHiY5TDzdOnT9GuXTssWbJE7HZS7Kvl4eEhdlUB738mPXr0gL29vaQ7SUEul0s2uvyUmJgYuLq6onHjxggLC8v1ukmTJsHW1haCIMDExATNmjUr8DYc7NvCAYcx9kW5dOkSDAwMsGzZMvHYzZs3sWPHDvzwww9YunSpOIA3JSUFt27dQkxMjDhepbDevXsHBwcHDBw4UDw2cuRIqKqqYsWKFeI+S4q1X4D3Kw0XdBE/uVyOVatWwcrKCra2tpL3WblyJWQyGUaPHo2UlBQA73cDVw45b968Ea9X1K0o9OjRA56entDX1882pmnVqlVQVVWFhYUFAgICsHDhQvTs2ROqqqqSXcMLIyQkBIaGhuJqxABw9+5d7N27FyNGjMDcuXPF48+fP8eDBw/w6tWrIvs9YF8/DjiMsRL1YYvHoUOHIAgCNm/ejMzMTKxZswb16tVDqVKlYGJiAkEQ0KdPH8mDvSjFxMSgcuXKCAoKAgAMHz4campqWL58udiKAQATJkzI1sJUUOnp6di8eTPKlSsHW1tbvHz5Eps2bRLH3CiHKeB/IUdDQwP+/v5FGmyA9604Hh4e0NPTg5GRkdgiojyoeN++fWjcuDHU1NQgCAIqVaok6UYqzO7gwPuWMR0dHSxevBjA+1BVt25dyGQy6OjoQBAEdOzYsaAfkX0HOOAwxkqM4qEWFxcn+cvbx8cHgiCIa720bt1aXHJ/1KhRUFdXx927d4ulLoryXVxcEBAQAHV1dSxdulQSbhYvXoxy5crlecBsXsrNyMjAxo0bYWFhAQMDAwiCgClTpkiCnHIdExMTMXLkSBgYGBRqUDUgXTtG8TkzMjLQtWtXCIIAX19fsR7K43vi4+Px9OlT3L59WzIoOr/TwRXXx8bGimEuKioKNWvWRJkyZWBubg4VFRW0adMGu3btQlpaGgYOHAgTExMea8NyxQGHMVaiYmNjYWxsjFGjRokPtzdv3mDixIn46aefEBwcLBl0O2/ePJQtW7bA68woQoLioa54oCu3Thw+fBhly5aFIAiSmVPA+9laTZs2RePGjYtsw0blkLNhwwa4ublBW1s722DdDyUlJRV6ppDyNPugoCBMnz5dss6Qn58f1NXV8fPPP4shR/GanFppCjo1/9mzZ6hUqRJ++eUXcWzR9evX8fvvv6Nnz544dOgQHj9+LF4/efJkmJub5zjehzGAAw5jrIQlJCRg6NCh0NDQwIQJE5CYmCie+7Al4PLly2jcuDE8PT2zddt8yqxZs8SHr6IV4tatW2jRogUaNmyI7t27i1Or37x5gzFjxkBLSwvu7u64cOECnjx5gg0bNsDLywvGxsZFvjKuct3Wr18vjslRBLmiWqxPmfL9bdOmDczNzdGrVy88fPhQPJ6ZmYkOHTpAW1sbAwYMEENOYVcjzkmzZs1gbm6O0aNHi4saZmZmZivr0qVLaNSoEby9vYu8e459OzjgMMZKXEJCAsaOHQuZTIaJEyfmGF42btyIJk2aSMaE5NW2bdvEMRuKoHDnzh0YGxvDzs4ONWvWhLm5OUqXLo2DBw8CAF69eoUZM2bA0tISgiBARUUFhoaGcHJyKvQaM7n5sLtKMSbn/v37kvNFrW/fvjA2NsamTZvEVim5XC5Z6E8RcgYPHlzk45+Uu706deoEY2NjjB49OluYysrKwvbt2+Hl5QUjI6MiWRKAfbs44DDGPhvFA/PDfaOA97OSlEOOoiXn3bt3GDNmDEqXLo3q1asXaMzF8+fPMWnSJKirq6Ndu3YA3nfHNGjQQBwofOrUKbRo0QIaGhrYvn07gPfbDbx8+RKrV6/G4sWL8ffffxdo48z8yC3kFFdLzq1bt1ChQgX88ssv4j1XLkPRHZWZmYlOnTpBTU0NPXr0KNT2B4rfA+X3+DDkmJiYICAgQGyhSUtLQ9u2bWFsbIxq1apxuGGfxAGHMVasFA9IRai5d+8efH19c1xi/+3btwgICIBMJsPUqVPFlpyrV69i69atBd7+AHi/vs6kSZMgk8nQrVs3DBs2DL/99pvkmoiICLRt2xbq6urYuXNngcvKjfKO3x8LCB+GHBsbG+jr6xd664WcnDlzBoIgiKFO+efxYZjKyMiAt7e3OLOpIBT34NatW+jRo4dkjRtFyJHL5ejYsSM0NTUxduxYMeSEhYXhr7/+kgxoZiw3HHAYY8Xm0KFDGDt2rDhoNDMzE/v27YOqqio8PDzEAbLKD9IXL17Ax8cHKioqmD17tmRMTkHI5XIxZMXFxWHixIniNOPff/8dgLT1IDw8HG3btoWGhgb27NlTqLKVKVot7t27B39/f0ycOFEMebnVG3gfKlatWgVHR8cinzkGvG+5Ut55XHnQscKJEydw+fJlSb0+/PpjNm3aJFm3BvjfgoEdOnSQ7Bml/LNwc3ODjo4OAgICxJlixdVNx749HHAYY8Vi9+7dEAQBZmZmmDx5sjieIikpCbt27YKpqSnq1auX44NrwYIF0NTUhCAI+OOPPwr0UHv16pVkVeCoqChERkYiMTERkyZNgpGREWrXri0GKOWp0uHh4ejQoQMEQcCBAwcK8vEllLceKFeuHKpVqybZ8iE3yiEnv4Oqc3uvD2VmZqJu3bqoXLmyWMaHga958+ZYs2aNJPzk9WeiWKVZEIRsIWfz5s0QBAFt27aVhBzFzLalS5dCV1cX+vr6mDhxIrKysjjgsDyTEWOMFQNVVVUiInr9+jXNnz+fFixYQPHx8aSlpUU+Pj60aNEiioqKorZt21JcXBwJgiC+NiYmhjp16kTz5s2jtm3bSs7lRVxcHM2cOZMmTZpEAOjBgwdUs2ZN2rp1K2lra9PPP/9Mv/76K125coV69epFAEhFRYWysrKIiMjR0ZHGjx9PXbt2JTs7u0LfC5lMRi9evKD27dtTxYoVacmSJRQQEEBERAByfZ0gCASAVFVVSVdXt8DlZ2Vliffw7du39PbtW0pNTRXP9+vXjx4/fkyNGjWi169fk5qaGhERPX78mHbs2EGXL1+msmXLij9TRd3y4sqVK0T0/p6OHTuWpk+fLp774YcfaMOGDbRv3z6aNGkSXbt2jYiINDU1iYjoyZMn1KJFC+rQoQN16dKFZDJZvn8X2HesZPMVY+xb9eLFC/j5+eGnn36Cm5sbZDKZpCUnOTkZO3bsgJmZGerUqYPw8HCkpKTgxo0baNy4MX7//fdCTUXu168fBEFAly5doKurCx8fH8kAZcWYHBUVFXTo0CHb+jgACjWQVkHxvrt27YK+vj42b94sniuOqdYfUv48w4YNQ506dWBra4tu3brh3LlzAN7PYpsyZQp0dXVRvnx5DBs2DBMmTEDTpk3FrsKCksvlqF69OhwcHNC5c2eoqKhIdiMH3s+QEwQBbdq0wcmTJwG8H2/j5eWF+fPnF7hs9n3jgMMYKzaTJk1CxYoV8fjxY7Ru3RqCIGQLOQcOHICdnR2MjY1Rs2ZN2NnZQU9PD7dv3y5QmR+uSKyhoQFra2ucOHFCPK4IFnkJOUVl1qxZUFNTEwdKK9ezOIKOXC6XvG+rVq2gr6+PNm3aoFu3bnBycoKuri5CQkIAvF8ZeevWrWjatCl0dHSgp6eHunXrSvYEy289FfdxyZIlqFevHhYsWICWLVtCEATMnDlTcu3WrVtRpkwZmJmZoXbt2rCxsYGRkRFu3rxZ0FvAvnMccBhjRU55DEeVKlXEjSsbNWoEFRUVScjJyMjA/fv30adPHzRs2BCtW7cu9G7QipYXc3NzaGtrQ0VFBYMGDRIH9SqP5VCEnFKlSsHb27vYxnjMnz8fgiDg33//BfC/h79yeatWrSrwTDHF+ynfe4VRo0bB1tYWW7duFbfECAoKEtf3CQ4OltQlPDwc0dHRkinxeQ03H86aA4DIyEhYWFhg9uzZePz4MXx9fXMMOSdPnsSAAQPg4eFRJL8H7PvGAYcxVmhbtmzB3Llzce3aNfFYVlYWsrKyMGPGDFSpUkVcHbdBgwZQVVXF5MmTs61CK5fLC90tpBwYoqOj8eDBA3Tt2hUymQwDBw4UA4TygNm4uDj89ttvMDAwkGwHUNjylT1+/BhmZmbw9fUVjymHkW3btsHDwwPnz58vcJkvX75E27ZtsWPHDvHc3bt34eLiItl5/PTp0yhdujR8fX3F7kNF11BOnyGvoe/o0aOYMmUK7t27l+1cUFAQdHV1ERUVhVu3bqF58+Y5hpz09HTI5XIkJyfnqUzGcsMBhzFWKMqzZDw9PdGzZ088f/5cfEA9evQIJiYm+OWXX8TXuLu7iyFHMXOnsC0nyqvdAtnHz/j5+UEmk2HQoEF48uSJWObjx4/x9OlTpKWlFXpfJ0UrSlJSEp48eYKYmBikpKQAeN8FNHHiRAiCgHbt2iE1NVV8XWhoKHx9fVGtWrUCLySYmJgIa2trCIKAevXqYe/eveK5vn374uLFiwCAmzdvQkdHBz/88APi4uKwfft2CIIADQ0NHDt2rKAfXZw1J5PJYGxsjLlz50rCWmRkJFxcXDBlyhQAwMWLF9GiRYtsISenFijGCoIDDmOswORyOVatWgVnZ2eoqqqib9++qFGjBsqWLYuffvpJfKguX74clpaWknEwnp6e0NTUREBAgGQ6d0EogkVUVBQCAgLQuXNndO7cGYcOHRLDDAB06dIFKioqGDx4MB4/foyoqCh06NABHTp0kASOwtTh5s2b8Pb2hoWFBcqXLw8/Pz9xq4UnT55g2LBh0NDQgL29Pfr164fu3bvD3t4eBgYGhd4Zu06dOhAEAeXLl0eNGjWyrePz9u1bNG7cGA0aNMCdO3ckr1Ps3P7w4cMChc2DBw9CRUUFpqamaNq0KczNzeHo6Ih+/fqJCxQqpucrwm9YWBhatGgBNTU1TJw4scCfm7GccMBhjBVKeno6NmzYgMqVK8Pa2ho3b97ErFmz4O7uDnV1dYwcORITJ05E7dq18eeff0oenq6urjAyMipUy4ni/SIjI2Fqaopy5crBzs4ORkZGEAQBXbt2xYULF8Tru3btCjU1NVSsWBEuLi4oVarUJ3ftzonyGBNFHW7dugUTExO4urpi+PDhGDBgACpWrIhy5cqJi/Q9f/5c7I4qW7Ys7O3t0blz50INplWEq927d6NevXro1q0bTExM4OLiIlnH59GjR7C2tpaEibCwMJibm2PBggWFXvPn0KFD0NTUhJ+fH5YsWYJFixbBxsYGdnZ26NevH86ePYvy5ctj5MiR4muuXLmCBg0aQF9fH3FxcbzODSsyHHAYYwWmvAP2hg0bYG5uDicnJzx69AgpKSlYsmQJqlatCldXVwiCADc3N3GQq0J0dHS+y/1wtd24uDjUrFkT7u7uOHXqFADgwYMHmDp1KgRBQKtWrSQLyf3+++9o3rw5fH19CzSQVdH9pTzbKjY2Fh4eHmjatClCQ0PF4506dYIgCDA2NsatW7ck7xMdHY34+HhxYbvCevDgAZycnDB16lQEBwfDwMAALi4u4gaicXFxMDc3R/v27QG8Hxc0depUVKtWTbKDeGFmde3evRsaGhrifUhOTsaUKVPE1iVBEODr6yvpirt+/ToePXpU4DIZywkHHMZYoXy4b5K5uTkqVKggDjS9ffs2Dhw4gBYtWmDYsGFia01BpmJPnDhRHJis/PobN25AX18fc+bMEY8pHtJ//vknBEHA+PHjJe+VkZEhjo/JjwEDBsDe3j5bKDl69ChMTU3FPZ0AYMyYMVBVVcWvv/4Ka2trmJqaFnrTzA83LFV8rfh+7dq1UFNTw7179xAcHAxDQ0O4urqKIScwMBBqamqoUKECXF1doaKikm2gb2Ht3bsXpUqVQv369cXWsczMTCxatAje3t4YN25ctgHmjBU1DjiMsULLaQfs8uXLS2bTZGVlFeqhtmvXLqioqMDb21scmKxoyTl06BAEQcCGDRsAvG9hUQ4QisX+lFspCiItLQ2DBw+Gvb19ti6ltLQ0DB06VCx37ty5UFFRwYoVK5CSkoJp06ZBEARYWloWuDtK8d7Pnz9H165dsXr16mwbcD569Aienp4YNGgQgPcz3IyMjODi4oJ///0XaWlpWLlyJby9vdGxY0esWbMm2/sXhb1790JTUxPu7u44ffq0ePzFixeFHnPFWF5wwGGMFYmcQo6NjY04wLawUlNTMX36dFhaWqJJkyaSvZliYmJgbm6Ojh07iscyMzPFVo3ly5dDJpPh+PHjBS5f0XLy7t07ceDyw4cPc+xeunr1KiwsLDB69Gi8evUKAPDs2TNUrFgRenp6sLGxEadD51dycjJcXFwgCALs7OxgaWmJv/76S9L9NXHiROjq6ordQJs3b4ahoSGcnZ0l90B5KnZxLDaoCDmenp7i+j+MfS4ccBhjRSankGNra1vobhnFw1fRElKuXDk0atRIDDkJCQkYPny4uFLyh/78808YGBjgypUrBSq/b9++OHz4sGTsT2xsLBwcHFCnTh0xKCimOB87dgzq6urieCC5XI4lS5bA2dkZu3fvztbqkh9v377FsGHDoKurCzc3NwwcOBC6urqoW7cuRo0ahbS0NKSkpKBOnTr44YcfxNdt27YNZmZmqFy5Mnbt2iV5z+Ic2Ksccs6ePVts5TD2Id5skzGWJ3K5nIiIMjIyKD09PcdrlDeH9PPzoxkzZhAAql69OkVHRxd4o0SZTEbp6elUqlQp8vf3p59++onu379PnTt3psTERNLR0aFevXqRl5cXzZgxgwICAig9PZ0yMzPp4sWLtGvXLrKxsSErK6t8l/306VM6deoU/fTTT3Tq1Clxc0xtbW1q3bo1PXv2jDp06EDv3r0TN6nU09OjzMxM+vfff4mIKDQ0lA4fPkxOTk7UvHlzKl++fIHuAxGRrq4uTZgwgQYPHkxXr14lS0tL2r17NzVs2JDWrFlDrq6uNHbsWKpcuTK9ePGCrl+/TkREnTt3pqCgILp7965ko02ivG+cWRBt2rShLVu20NWrV2nQoEEUGhpabGUxJlHCAYsx9hVQdM/cu3cP/v7+mDhxorjtQU6UW3JWrVoFR0dHcZp0YcoPDw/HyJEjUbt2bZibm4szchRje8LCwtCmTRsIgoAKFSrA0dERtra2MDQ0xI0bNwpc/s2bN9GgQQNYWFggJCREbMl59+4dJk+ejLJly8LHx0dsyXn27Bl69OgBVVVVWFhYwMzMDIaGhoVe50bZ27dvxVariRMnIjExEa9evcKgQYPQsGFDccbS1q1bJa8ryM/hwz26MjMzsy3I96lWoO3bt6Ns2bIFmjXHWEEIwP//OcIYYzmQy+Ukk8koMjKSmjVrRsbGxtS5c2cKCAj46OsAkCAIlJmZSe/evSNdXd1C1eP+/ftUt25dcnV1pfr161P16tVp8eLF9O+//1KDBg1o165dpKenR9HR0XTx4kXatGkTpaWlUZUqVWjQoEFUsWLFApcNgG7dukU///wz3b17lzZt2kT169cnDQ0NSklJoTlz5tDSpUvJxcWFdu3aRVpaWnTnzh06efIknThxgsqWLUsDBgwge3v7Qt2DDyUkJNCkSZMoKCiIhg4dSkFBQQSAnj17Rjt37qQbN25Qnz59qEGDBuLPUfFzUXz/MZs3byZvb28yNjamjIwMUlNTozt37tDcuXPp2bNn1LJlS3J3dycHBwfxPn2sNSgpKYm0tbWL9B4wlqsSDFeMsa/E8+fPUalSJXh5eUmW3//UX+1FNbZDLpdj9OjRKFOmjGSwanp6On7//XcYGxtLZlcVZR0UrTUJCQm4dOkSrK2t4ejoiJCQEHE9HOWWnKZNm4pr/SjGDhXH7uQKb9++xciRIyEIAoYNGyYZ9JzT/cgrxRpCv//+O+Li4gC8X0zR0NAQxsbGsLGxgSAIaNy4MY4cOSK+jhfqY18KDjiMsVwpHla7du2Cvr4+Nm/eLJ4rjlk3H9O5c2dYW1uL3yu2VkhJScGgQYMgCAJatGghTkFWdKEU5oGr+Izh4eHw9vZGjRo1YGtrK85gCgkJEctRDjktWrTItqBhcVIOOSNHjkRiYqLkfEHvQcuWLSGTyTBmzBjExsZi1qxZ8PHxEbfgOHToEExMTFCzZk3JthAcctiXgAMOY+yTZs2aBTU1NXEnbuUHWHEHHUVZo0ePRqlSpRAWFiaeU7SMxMfHw9bWFlpaWnBzcytUy8WHHj58iLJly8Lb2xvr1q3Do0ePMHv2bDg7O6NcuXLZQs60adOgoaEhmbL+OSiHnNGjRxdqN27l2WKKDTHHjRsHLy8vjBkzRnLtiRMnYG1tnW3vq88dgBn7EAccxtgnzZ8/H4IgiN1DimChHHRWrVolBqDCyO3BuGfPHgiCAH9/fzx//lxyLiMjAw4ODvDw8ICrq2uRDmRdsWIFNDQ0sH//fvFYWloawsLCUKNGDVhaWiIkJERsUUpOTsasWbMkm1l+Lm/fvsVvv/0GQRAwaNCgbFta5Ify5qM+Pj4QBAFWVlYIDg4GIF1M8cSJE7CyskKNGjWwb9++wn0IxooIBxzGmCi3roXHjx/DzMwMvr6+4jHlWTSKzSOVx+cUhCI4vXjxAmFhYbh06ZLk/JAhQ6Cqqorx48dL1pIJDQ1FvXr1cPLkSbx+/bpI6qDoYpo0aRJkMpm4KrNy19fx48ehr68PJycnnDhxQhyTU5Levn2LQYMGYf78+fl+raJrSxGMlPeHateuHQRBgLe3tziDLjMzUxJyKlSoADs7Oxw6dKiwH4OxQuOAwxgDIH2wP3nyBDExMeJeTYmJiZg4cSIEQUC7du0kf92HhobC19cX1apVk2ygmF+KB2V4eDisra2hqqoKQRDQpUsXMTglJCSgT58+4uDWxYsXY86cOXB3d4eFhUWhyv+wDqNGjcLz589x7NgxyGQyrF69WrxOefBwkyZNIAgCdHV1v5jVegsStGJiYjBq1CisXbsWwPsNMAVBwLJly8RrFCEnICBAHHisHHKOHTsGZ2dncWFHxkoSBxzGmBhubt68CW9vb1hYWKB8+fLw8/MTt1p48uQJhg0bBg0NDdjb26Nfv37o3r077O3tYWBgUKg1XhQPyNevX8PBwQGNGzfGnDlzEBQUBB0dHVSvXl0yU2f69OniOjiampqwt7cv1Do3ynWIjY2FmZkZmjVrhv/++w937tyBm5sbrK2tcfLkSQDSbrRu3bqhb9++aNeuHW7fvl2oOpSkp0+fomvXrhAEAZMmTUKZMmXQpEkTXL9+PccxOWPHjpWEHMU9KczYH8aKEgccxr5Dyg9oxYP91q1bMDExgaurK4YPH44BAwagYsWKKFeunLg43PPnz8XuqLJly8Le3h6dO3cu8OaRynWJj4/H8ePHUbNmTTFIAO9bEsqUKQMHBwdJ18eDBw9w9epVXL16VdyhvLB1eP36NS5evAgvLy9xmwXg/Q7dZcqUgZubG/7++2/x+JUrV1CjRg0sWrSoWKeCfy5RUVHw8vKCTCaDi4uLZFFA5S7J5s2bZws5invIM6jYl4IDDmPfGUX3hfIDOTY2Fh4eHmjatClCQ0PF4506dYIgCDA2NpZs5ggA0dHRiI+Pz3GzyU8JDw+XTGV+9uwZzM3N0bx5czRo0CBbXW/duoUyZcrAyckJBw4cyHd5efHo0SNUrlwZNWvWhIODQ7YBukuWLEHZsmWhqamJXr16oUePHqhWrRqMjIy+mS6Zt2/folq1atDX14dMJsPKlSsBSFemVmjevDnU1NQwdOhQcUNRxr4kHHAY+44MGDAA9vb22ULJ0aNHYWpqiu3bt4vHxowZA1VVVfz666+wtraGqalpoTfNBN7v7C0IAk6cOCEeS09PR9u2bSEIAkqXLi1pEVI8VBUhp2rVqpLpyEUlNjYWjo6O0NLSQuXKlcWuFuXxLEeOHMFPP/0EQ0NDmJubw93dHeHh4UVel5KSlJSEAwcOYN++fWjTpg1kMhlWrFghnpfL5ZJgXL9+fejr6+Ply5clUV3GPooDDmPfibS0NAwePBj29vbZupTS0tIwdOhQMbjMnTsXKioqWLFihbiDtyAIsLS0LFR31Jo1a6CiooLBgwdnm1KuvGCfYoCvgiLk3L59G4IgoF69ekWykN6HLROxsbFo3LgxBEHAzz//LB7/cNDumzdvkJyc/FkX8ysOH1ur5tKlS2jdunW2kAO8H6+joDzTirEvCQccxr4Dir+63717hydPngB4v4BdTt1LV69ehYWFBUaPHi12PTx79gwVK1aEnp4ebGxskJ6enu9WnDVr1kAQBAwfPjzX9XJSU1PRo0cPqKioYOLEiZJZUYqwcefOnWzdZfn1sQd7XFwcPD09oaOjg3Hjxon3TlH+tzLGRPG5oqOj8eeff6Jfv36YPXu2pAtQsXmp8iyyW7duoUmTJggICCiRejOWVxxwGPvG9e3bF4cPH5aMn4iNjYWDgwPq1KkjdsUoBpEeO3YM6urq4iBbuVyOJUuWwNnZGbt375asP5NX69atE8ON8l//wPvg888//4jfp6WloXv37h8NOYWheLA/fvwYy5cvx4wZM7B+/XrJNS9fvoS7uzsMDAzw+++/S3bR/hYoAl5ERATKlSsHAwMDGBoaQhAEaGhowN/fX7w2LCwMbdu2hUwmg7e3N9zc3FCqVKlsaxQx9qXhgMPYN+zJkyfiTKiQkBCx9SElJQUBAQGwsrKCj4+PZGpvaGgoZDIZpkyZAgA4f/48WrZsiR9++EGy/k1e/f333xAEAd27d8+2AvHq1ashCAImTJggCQ/p6eliyJkyZUqRrJAMSB/slpaWKFOmDDQ0NKClpYV69epJZmMpQk6ZMmWy1e9bEBMTgypVqqBRo0b4559/kJmZiTNnzsDPzw+CIKBfv37itf/99x9+++03VKpUCY0aNfqmxh2xbxcHHMa+cTdv3kSDBg1gYWGBkJAQsRVEeXNI5ZDz7Nkz9OjRA6qqqrCwsICZmRkMDQ0LtM5NVlYWtm/fLu7AHRkZKZ5buXIlBEHA+PHjc9w7Kj09Hb169YIgCJgxY0aRBYyoqCjY2NjA29sbBw4cQFxcHDZu3AiZTIYGDRpIxpS8fPkSXl5eEAQB06ZNK5LyvxSnTp1C6dKlJQsYAu+n3yvGQs2YMUNyLi4urkj3+WKsOHHAYewbJ5fLERkZCXd3d5iZmUn2TVIOOc2aNRMHzd6+fRt//fUXunTpgmHDhhVqAbuUlBTs3bsXlpaWcHZ2RnR0tNhyM27cOMkD88OxMRkZGejfv78kGBVGamoqhgwZggYNGkjW2hk+fDjU1dVRunRpuLq6SkLOixcv0Lx580KP+/nS7NixA4Ig4OrVqwCk69zcvn0bLi4uqFq1Kt68efPNjDti3xcOOIx9wxStNQkJCbh06ZLYkhISEiLODFIOOU2bNhVDjvJ2BIWVmpqKPXv2oFy5cjA1NYVMJkNgYKBkLRzlcPP8+fNCz865e/cuDhw4gPnz54vTmJOSktCiRQuMGDFCvO7333+HqqoqlixZgvXr10NFRQXu7u6S8r/FnbFPnz4NQRAwcuRIsfVOeRr43LlzIQhCiWwaylhR4IDD2DdK8VAODw+Ht7c3atSoAVtbWwiCADs7O4SEhIh/tSuHnBYtWhTL9OfU1FTs3r0b1atXh56enmRjTuUAERERgebNm8Pf379AY34AYPv27XBzc0Pp0qXRokUL7Ny5U2yFiI6OFj/3unXroKGhgQULFiAjIwPp6elwc3ODIAgoX768OOPsa/axcObp6QlTU1Ps378/21T4cePGwdDQsND7ezFWUjjgMPYNe/jwIcqWLQtvb2+sW7cOjx49wuzZs+Hs7CwOPFYOOdOmTYOGhgY6duxYLPVJSUnB7t27YW1tDRcXF4SHh0u6PyIjI9GqVSuoqKiIXSf5tWHDBqipqaF3797Yv38/gP9N7VYuKzU1FV26dEHDhg0lM7t8fX3RtWtX1KlTR9xB/GulaI15+vQpdu/ejT/++ANHjx4Vx1Pdv38fdnZ2qFChAjZs2CAG27CwMDRo0AD169fnMTfsq8UBh7Fv2IoVK6ChoSE+6IH307DDwsJQo0YNWFpaSsbkJCcnY9asWcXaLaHorrKysoKTk5M4vubmzZto0aIFtLS0cO3atQK996lTp2BiYoKBAwfi4cOH4vGcxpAkJSWhUqVK8PX1FY+FhoaievXq2LFjh7iT+tdKecaYnZ0dtLW1IQgCBEGAk5MTNm7cCAC4ePEiHBwcIJPJ4OzsjGbNmqFChQqF3kCVsZLGAYexb4jiL3bFX+KTJk2CTCYTWyIUrTVyuRzHjx+Hvr4+nJyccOLEiWxdFMVJOeQ4Oztj3759aNWqVYHDjSLABAQEwMbGRrKf1scGyAYGBkImk2HevHlYvHgxmjZtCktLy2+iawp4v0yAtbU1GjdujJ07d+L+/ftYtmwZ7O3toa6uLu41lZSUhCFDhsDLywu1atXCjz/++M0NqmbfHw44jH0jFA/y8PBwcauDY8eOSVahBaSDh5s0aQJBEKCrq4t///33s9ZXEXLs7e0hCAJ0dHQK3HKjeD8nJye0aNEiz6+JiIgQp6KXLl0adnZ2uHHjRoHr8KXZsGEDdHV1sW/fPsnxEydOoG7dutDX18fBgwfF4+np6UhJSSmSBRUZK2kyYox99QCQIAgUFxdHTZo0oRs3blBcXByVL1+eqlWrRpMmTaJ///1X8hoVFRUyNTWlH3/8kRo3bkxmZmaftc4aGhrk6+tLkyZNIk9PTzp79iy5uroW+P0yMjIoMzOTZDIZAaDMzMyPXv/69WuytramtWvXUlhYGB0/fpzOnj1Lzs7OBa7Dl+bx48eUmJhI7u7uRESUnp5OREQNGzakwMBAysrKol27dhEAksvlpKamRqVKlSJVVdWSrDZjRYIDDmNfOblcToIg0Js3b+jBgwdUpUoVGjduHDk5OVHFihXp119/pYSEBBoxYgT9888/JJO9/8/+6tWrdPv2bapWrRrt2LGD7O3tP3vdNTQ0qG3btrR///5CBwttbW0yNTWlu3fvEgBSVVUluVye7TrFsdDQUBo7diy9e/eO3NzcqHbt2mRiYlKoOnxpDAwMiIjoyJEjlJWVRerq6uLn9/X1pdatW9ORI0coOTlZ/L1g7FvBv9GMfeVkMhk9fvyY6tWrR4MHD6aYmBiqW7eueL5Xr140bdo0evbsGbVu3Zp69+5NPXv2pL59+1J0dDQ1b96cVFRUSqz+pUqVIh0dnUK9BwAiImratCndvn2bxo8fT0Tv782HIUfxIF+2bBmFh4dT6dKlC1X2lyCnIEdE1KlTJzI3N6eVK1fSo0ePiIgkQSYrK4u0tLRIQ0Pjs9STsc+JAw5j3wBNTU1SUVGhyMhIksvlYleE4n8HDhxIq1evph49etDBgwcpJCSEtLW16eTJk2RjY1OSVS8SgiAQEVH37t3J0tKSFi9eTKtXryai9w/0rKwsSQg4deoUPXr0iBo3bkxE/wtIX6OsrCwx5O7cuZN27dpFycnJRPS+BWfWrFl08eJFGjJkCF2/fl183dWrV+nOnTvk7OxMcrn8q74HjOVEAP9WM/ZVUoy7yczMJFVVVYqLi6MuXbrQ8ePH6aeffqJFixaRqqoqpaenk7q6uvi6+Ph4UldXJwCkpaVVgp+geJw7d46aNWtG2traFBAQQEOHDpWcv3LlCgUGBtKtW7coJCSEypcvXzIVLUKRkZHUpEkTevHiBRER2dvb07Jly8jDw4PS0tJozZo1NG7cONLV1SV3d3fS0dGh8+fP06NHj+js2bNUpUqVEv4EjBU9DjiMfWXkcnmu4yVevXpFHTp0oCtXrtCQIUNo0qRJpKKiIoYgRSj61v3zzz/UsWNHSkhIoHbt2lGXLl3I0tKSDh06RCEhIXTnzh06fvw4ubi4lHRVCy0lJYX8/PwoMzOTunfvTu/evaPFixfTkydP6M8//yQ/Pz8SBIEuXrxIAQEBFBUVRWpqauTg4ECzZs0iBweHkv4IjBULDjiMfUWysrJIRUWFnjx5QkeOHKHXr1+Tubk59ejRQ7wmNjaWOnToQBERETRo0CCaOHEiqaioiK/9Xty6dYt+++03Onv2LL1584aI3nfZ1K9fn2bOnEmVK1cu4RoWnHLITU5OpsaNG9OgQYOoZ8+eREQUERFBQ4YMoStXrtCiRYuoXbt2VLp0acrMzKTY2FhSVVUlLS2tb2L8EWO54YDD2FdC8VCLjIwkHx8fSkpKonfv3pGqqiq5urrSvn37yMjIiIj+F3LCw8NpyJAhFBgY+F2FG4WkpCRKSkqi69evEwBycXEhXV1d0tbWLumqFZgiqL58+ZJiYmIoNjaWtmzZQgsXLqRSpUqJrXR3796ln3/+ma5du0YLFy6k1q1bF3owN2Nflc+77A5jrDCioqJgY2MDb29vHDhwAHFxcdi4cSNkMhkaNGgg2QH75cuX8PLygiAImDZtWgnWmhUVxWKOERERsLW1hYaGhrj9QlBQULZVm+/cuQMvLy+YmJhg1apVePfuXUlUm7ESwbOoGPtKpKWl0Z9//kkWFhY0btw4atmyJRkaGtKVK1dIVVWVrly5Qq1ataLHjx8TEZGxsTFt2bKFfH19qUOHDiVce1ZY+P+WmcTEROrWrRvZ2NjQuHHjaPz48aSrq0sbN26kEydOSF5TsWJFWr58OZUrV46mTp1KGRkZJVR7xj4/7qJi7At07949unXrFkVFRdEPP/xAxsbGlJycTH5+flS5cmWaM2cOEREFBgbSjBkzaMGCBaStrU19+vShevXq0aZNm8jS0pKIPj4omX0dFOHm5cuXFBERQYGBgTRlyhTy8vIiIqLDhw9Tly5dyNHRkf744w/xuML9+/dJVVWVrK2tS6L6jJUIDjiMfWF27NhBM2fOpJs3b5KXlxf16dOH2rdvT4Ig0MOHD8nc3JzU1NRo/fr19NNPP9Hs2bNp4MCBBIDq1q1LV65cIWtrazpz5gxZWFiU9MdhBaQYa4P3ewbSq1evqGLFiuTg4EBpaWl0+fJlyXXHjh2jjh07kqOjI02fPp0aNmxYsh+AsRLGf9Yx9gXZuHEjdevWjZydnWnr1q108OBBMdwAIGtra1JTU6O0tDQ6cuQI1a1blzp06ECqqqqkpqZGJiYm9MMPP5CZmRmlpqaW9MdhBXTlyhWaNWsWnTlzhgRBIJlMRsbGxuTn50cXLlygq1ev0sWLF4mIxBDUtGlT2rlzp9jCc+zYsRL+FIyVLA44jH0hTp8+TSNGjKB+/frRpEmTqFWrVpLzyuvXZGZm0tWrV0lTU5PMzc2JiOjixYsUExND7dq1oxMnTlCFChU+a/1Z0di5cyd1796dFi1aRIcOHSKi9600RER//fUXBQYGEhHRrFmz6O7du0REYgBu2rQp7d69m86ePUtz5syhd+/elcyHYOwLwFvGMlbCFOMrDh8+TFpaWtS7d2+ysrKSnPuQlpYWde7cmaZNm0ZBQUGkoaFB+/bto9jYWKpbty6VKlXqc38MVgS2bNlCvXv3pk6dOlGvXr3I29ubiEiyjtGkSZMoOTmZ5s2bRzo6OjRu3Diys7MTQ06TJk0oJCSEzM3NeZ0b9l3jMTiMfQHS0tKoRo0aZG1tTQcPHszTayIjI2nWrFm0fv16sSVn9+7dhd6Vm5WMS5cuUefOnalFixY0YsQIcQuJD7faUBg2bBjNnz+fevXqJYYcotxDMWPfG27BYewLkJGRQZmZmSSTyQgAZWVlkapq7v95vn79mqytrWnt2rX066+/UmZmJtnY2JCJiclnrDUrSv/99x8lJyfTDz/8INkfSxFuIiMjCQBZWVmRjo4OBQUFEQBasGABqaio0KhRo6hSpUocbhj7fxxwGPsCaGtrk6mpKd29e5cAkKqqao7TuxXHQkNDKTg4mKZPn05ubm4lVGtWlC5fvkxpaWlUr1498diLFy/o3Llz9Mcff9CVK1eIiKhdu3bUo0cPatu2Lf3555+kpqZGc+fOJQ0NDfF7xhgPMmasxCl6iZs2bUq3b9+m8ePHExGRTCYjuVwuuVYReJYtW0bh4eE8xuIb4uzsTO/evaNp06YR0ftB44MHD6Zu3bpRXFwctWrVijp16kQHDx6kqVOn0oULF4iIaPbs2TR27FgaNGgQhxvGlPAYHMa+EI8ePSJ3d3dKSEiguXPn0o8//khE72fQKKYKExGdOnWKhg4dSp06daKxY8fymItvxKNHj6hTp0507do1MjY2ppiYGMrKyqLu3bvT2LFjyc7OjlRVVWnBggU0bNgwWrx4MQ0YMKCkq83YF4u7qBj7QlhZWdGWLVuoWbNmNG7cOEpMTKShQ4dKNsm8cuUKzZw5kxISEqhr165ERBxuvhFWVla0detW+uuvv+jy5cvUvn17qlOnjvhzVmjYsCEBoP/++6+EasrY14FbcBj7wvzzzz/UsWNHSkhIoHbt2lGXLl3I0tKSDh06RCEhIXTnzh06fvw4ubi4lHRVWTGQy+WUlZUl6W7KyMgQv9+4cSMNGTKE5s+fTz169CipajL2xeOAw9gX6NatW/Tbb7/R2bNn6c2bN0REZGBgQPXr16eZM2dS5cqVS7iG7HNRDjfXr1+noUOH0qtXryg4OJi34mDsIzjgMPaFSkpKoqSkJLp+/ToBIBcXF9LV1SVtbe2SrhorAbt27aKVK1fSxYsX6eTJk7zeEWOfwAGHMca+YOfOnaPAwEC6e/cumZqa0tq1a8nR0bGkq8XYF48HGTPG2BfM3NycTE1NqU2bNtSxY0dx7zHG2MdxCw5jjH3h0tPTSUVFRTKjjjH2cRxwGGOMMfbN4ZWMGWOMMfbN4YDDGGOMsW8OBxzGGGOMfXM44DDGGGPsm8MBhzHGGGPfHA44jDHGGPvmcMBhjDHG2DeHAw5j7Ks2ceJEEgSBBEGg3r17F1s55cuXF8s5efJksZXzpendu7f4uSdOnFjS1flivHv3jsaMGUN2dnakoaEh3iP25eCAwxgrdsrhIC//vqcAoSw+Pp7Gjh1Lzs7OpKWlRRoaGmRmZkZVq1alHj160IYNG0q6iuz/BQQE0IwZM+j+/fuUnp5e0tVhOeC9qBhj7Avw5s0bqlWrFt27d09yPCYmhmJiYuj69ev04MED6tGjx2et17hx46hfv35ERGRlZfVZy/6S7dmzR/z6999/p6ZNm3ILzheGAw5jrNjt3LmTUlNTxe9Xr15Na9asISIiMzMz2rFjh+R6Z2fnz1q/L8H8+fPFcGNlZUWBgYFka2tLKSkpFBERQfv37yeZ7PM3ulesWJEqVqz42cv9HJKSkkhbW7tAr3369Kn49Y8//kg2NjZFVS2JwtTxe8ddVIyxYlejRg1q0KCB+E+5JUBDQ0NyrlSpUjR48GBydnYmY2NjUlNTIx0dHapatSpNmDCBkpKSPlpWaGgoNW7cmLS1talMmTLUpUsXevz4seSahg0bit1ha9eulZxT7iqLjo7+5GfbsmULtWnThuzs7EhfX5/U1NTI0NCQPD09afXq1ZTX7f4uXrwofj1ixAjq168fNWrUiFq0aEG//fYbnTlzhg4dOiR5zaVLl6h79+55vlcfjlc6c+YMNWzY8KP36mNjcN69e0ezZs2iWrVqka6uLmloaFDFihVp+PDhFBsbK7n25MmT4vuUL1+eHj16RD169CBDQ0PS1NQkd3d3unTpkuQ18fHxNHLkSKpcuTJpamqShoYGmZubk6enJ40aNYrevXv3yfv6Yf03btxI1apVo1KlSlH37t3F6548eUL+/v5iWdra2uTm5kZBQUGUkZGR7f2Uf662trYkCAI1bNhQPPb69WsKDAwkV1dX0tbWJk1NTXJ0dKSJEydm+7msXbtWrGPDhg0pLCyMvL29SVdXlywtLT/b/SYiSktLowULFlCDBg2oTJkypK6uTubm5tSyZUs6f/685NqbN29Sv379yNbWlkqVKkW6urpUv359Wrt2bZ5/74sVGGPsM5swYQKICEQEa2trybmlS5eK53L65+bmhoyMjBzfy8HBARoaGtleU65cOcTExIiv8fT0FM+tWbNGUr7y6x48eCAet7a2Fo+fOHFCPO7n5/fR+g4dOjRP96RLly7iaypVqoStW7fixYsXH31NYe5VxYoVoaam9sl71atXL/HchAkTxOOxsbFwcnLKtWwLCwtERUWJ1584cUI8p6urCxMTk2yvMTIyQkJCgvgaDw+Pj36+58+ff/K+Kte/YsWKkte3adMGAHD+/Hno6+vnWo6XlxdSU1Ozvd+H/zw9PQEAd+/eRbly5XK9zsnJCa9evRLruGbNGsl909TUFL/X09P7bPf71atXqFatWq5lBAUFidfu2bMHpUqVyvXabt26QS6Xf/LnU5y4BYcx9kVxcXGhuXPn0p49e+iff/6hEydO0I4dO6hmzZpERHT58mXJ+AdlkZGR1KxZMzp48CAtXLhQbNp/8uQJjRs3rljq27p1a1q2bBnt37+fTpw4QSEhIbRq1SoyMjIiIqJFixbRixcvPvk+LVq0EL++ffs2denShczMzKhcuXL0ww8/0P79+7P9VVyYe3X37l3y9fUt8L0aPHgwhYeHExFR1apVacuWLXTkyBHq0KEDEb3vwunVq1eOr01ISCAtLS3avHkzrVmzhvT09IiIKC4ujjZv3ix+ferUKSIisrS0pK1bt1JISAht3LiRRo8eTU5OTvke83L37l2qX78+bdu2jQ4dOkRdunShtLQ08vPzo/j4eCIi6tChAx06dIh27txJLi4uRER04sQJmjZtGhG9H5N0+vRpyfvu2LGDTp8+TQsXLiQiou7du9OTJ0+IiMjLy4v27NlDBw4cIE9PTyIiCg8PJ39//xzr+PTpUzIwMKAVK1bQsWPHaPLkyURU/PebiOiXX36hq1evEhGRuro6jRo1ig4dOkRbt26lvn37koaGBhERxcbGUo8ePcRu5wEDBlBwcDBt2LCBrK2tiYho06ZNYjd0iSnReMUY+y59rAUnIyMDCxcuRP369VGmTBnIZLJsfx0OHz48x/cyNzdHWlqaeG7OnDniOX19fWRlZQEo2hacuLg4jB49Gs7OztDS0oIgCNnqu3///jzdl8GDB+f4esW/Nm3aSP4q/hz3KqcWnDdv3kBFRUU8vnnzZpw+fRqnT5/GiRMnJC1Dt27dAiBtUSAiXLx4USx7wIAB2eqbkpIiluHs7IzLly8jJSUlT/dRmXL9LSwssr3HgQMHxPPGxsY4deqU+FkWLlwonitbtqzkdbn9nvz333/icTU1NRw9elR8v507d0rOJSYmApC24AiCgOvXr0vK+hz3Oz4+HqqqquLx+fPn53pPle+Lk5OTWJfTp09j3Lhx4rk6derk++dVlHiQMWPsi/Ljjz9+cjr0mzdvcjxeu3ZtUldXF79v0KCB+HV8fDzFxcWRiYlJ0VSUiFJSUqh+/fp0+/btj16XW30/tGjRIvr111/FFoHQ0FB6+/ateH7fvn20bds26tKlCxGV3L26c+cOZWVlid937do11/LDw8OpUqVKkmM6OjpiKxMRkaGhofj169eviYioVKlS1KtXL1q9ejX9999/5ObmRjKZjKysrKh27drUp08fatasWa7l5qR58+ZUqlQpybHIyEjx69jYWPLw8Mjxtc+fP6dXr15J6poT5ffLyMjItY4ZGRl0+/ZtcnNzkxy3s7MTW44UPsf9vnPnDmVmZorH27dvn2sZyp8xPDyc3N3dc61LSeIuKsbYF+Pp06eSB7a/vz8dO3aMTp8+TT179hSPy+XyQpWj3LWh/H/qHw7U/JQ9e/aI4UZLS4sWLFhAJ06coNOnT0tmguWnvpUqVaLff/+djh49Sq9evaLg4GAqU6aMeD40NJSIPt+9KqycBoUbGBhIvldV/d/f2lDqhlu+fDlt3LiRunTpQk5OTqSurk7R0dG0bds28vHxoX379uWrLmXLls1n7aU+NcC9KN6vOOqY1/td1Ir6fuUXBxzG2BdDeQaPoaEhBQUFkbe3NzVo0EAyLTc3Fy9elMx4OXv2rPi1np6eOC5GOTAoxkoQER04cCBf9X306JH4tY+PD/3666/UsGFDcnFxkbxvXpw4cUIcB6KgoqJCzZo1o9q1a4vHFIHlc92rnNjb25OKior4/e3btwlAtn9JSUm5jgvJC5lMRt26daMtW7bQf//9R8nJyTR79mzx/JYtW/L1fjmN2alSpYr4tZWVFWVkZOT6WRTjSz5G+f00NTUpPj4+1/dTjMn5VB0/x/3+sIycxm4pwpDyZ6xXr16OdVHUpyRxFxVj7Itha2srfv3q1SuaNm0a1ahRg3bu3EkhISGffP3Tp0+pc+fO1K9fP4qOjqZJkyaJ5zp27j9CwgAABEdJREFU7CiuI2Nvby8eDwoKIh0dHUpISKA5c+YUuL4hISG0YcMG0tPTozlz5uS5W0ph1apVtHv3bmrZsiV5eXlRhQoVSBAEOnPmDP3999/idXXr1s1WdnHeq5zo6+tT+/btxfWLmjdvTqNGjSI7OzuKj4+nhw8f0qlTp+jWrVt069atfN0HZXZ2dtS8eXNyc3Mjc3NzysrKEgceE5FkbaWC8vb2JktLS3r8+DE9evSImjVrRv379ycTExN6/vw53b9/n44dO0YVK1bM06BZZ2dnqlmzJoWFhVFKSgo1atSIhgwZQpaWlhQbG0sPHjyg48ePk1wup3/++SdPdfwc91tPT486depEW7duJSKiUaNG0dOnT8nT05OSkpIoJCSEXF1daeDAgeTn50djx46lpKQkOnfuHHXs2JG6du1Kenp69PTpU7p9+zYdPnyY2rZtSxMmTChQfYrEZxnpwxhjSj42yFh5urTin4qKCtzd3cXve/XqleN7VahQQTJQUvHPwsJCMuU6KioK6urq2a77cBrupwYZJycnw9bWNtv7mJmZoXLlyrkOZM5Jt27dch1crPjn4eGBzMzMIrlXuU2p//Be5TZN/OXLlx+dtvzhz1Z50OuHP3PleinXN6f6Kf/btWvXJ+9rbvVXdu7cuY9OE/+wXkDug4wB4M6dOx+dJk70vynlgHSQsfJxZZ/jfsfFxcHFxSXX91eeJr579+6PThP/2P3+XLiLijH2RVm5ciX5+/tTuXLlSFNTk2rXrk2HDx+mRo0affK1DRo0oL///pvc3d2pdOnSpKenR507d6azZ8+SqampeJ2NjQ3t3buXqlatSurq6mRmZka//PJLtum/n1K6dGk6fvw4tWvXjgwMDEhPT49at25NZ86ckZSXFxMnTqT58+dT27ZtqUqVKmRgYEAqKiqkr69PdevWpblz59KxY8ck3QiFuVc1a9akf//9lxo1akRaWlq53qvcGBsb08WLF2nOnDlUp04d0tPTIzU1NTI3N6c6derQuHHjaNeuXfm6Bx+aPn06tW7dmsqXL0/a2tqkoqJCxsbG5OPjQ4cPH/7oQNj8qFu3Lv333380fPhwcnR0pNKlS5OmpibZ2NiQt7c3BQUFidO186JixYp048YNGj9+PFWrVo20tbVJQ0ODrKysyMPDg6ZNm0bLli3LVx0/x/02NDSk0NBQmjdvHtWtW1cso2zZstS8eXNJV2m7du3o6tWr9NNPP5GdnR2VKlWKtLS0yM7Ojlq2bEnLli2jQYMGFao+hSUAX8Jyg4wxxorbxIkTxa6oXr16ZVvFmbFvCbfgMMYYY+ybwwGHMcYYY98cDjiMMcYY++bwGBzGGGOMfXO4BYcxxhhj3xwOOIwxxhj75nDAYYwxxtg3hwMOY4wxxr45HHAYY4wx9s3hgMMYY4yxbw4HHMYYY4x9czjgMMYYY+ybwwGHMcYYY9+c/wMrvjm127GtWQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeffmat = np.zeros(\n",
    "    (sc_marker_mean.shape[1], ra_ts_marker_mean.shape[1]))\n",
    "for i in tqdm(range(sc_marker_mean.shape[1])):\n",
    "    for j in range(ra_ts_marker_mean.shape[1]):\n",
    "\n",
    "        corrtest = pearsonr.pearsonr(\n",
    "            sc_marker_mean[sc_marker_mean.columns[i]],\n",
    "            ra_ts_marker_mean[ra_ts_marker_mean.columns[j]],\n",
    "        )\n",
    "        coeffmat[i, j] = corrtest[0]\n",
    "\n",
    "tm_ct = list(sc_marker_mean.columns)\n",
    "ts_ct = list(ra_ts_marker_mean.columns)\n",
    "\n",
    "#plot again\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(coeffmat, cmap=\"RdBu_r\")\n",
    "ax.set_xticks(np.arange(len(ts_ct)))\n",
    "ax.set_xticklabels(ts_ct, fontsize = 12)\n",
    "ax.set_yticks(np.arange(len(tm_ct)))\n",
    "ax.set_yticklabels(tm_ct, fontsize = 12)\n",
    "plt.xlabel(\"Tabula Sapiens reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.ylabel(\"Tabula Muris reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.setp(ax.get_xticklabels(), rotation = 45,  ha=\"right\", rotation_mode=\"anchor\")\n",
    "plt.colorbar(im)\n",
    "ax.set_title(\"Expression correlation\", fontsize = 13)\n",
    "fig.tight_layout()\n",
    "\n",
    "# Save the final figure\n",
    "plt.savefig('Tabula Muris vs Tabula Sapiens.png', dpi=600)  # Save the figure as PNG with 600 dpi"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}