diff --git a/docs/src/contents.rst b/docs/src/contents.rst
index ac89dcf..bfef1ff 100644
--- a/docs/src/contents.rst
+++ b/docs/src/contents.rst
@@ -43,4 +43,4 @@ NetworkCommons: Table of Contents
    :maxdepth: 2
    :caption: Additional resources
 
-   vignettes/6_moon
\ No newline at end of file
+   vignettes/A_moon
\ No newline at end of file
diff --git a/docs/src/vignettes/6_moon.ipynb b/docs/src/vignettes/6_moon.ipynb
deleted file mode 100644
index 7d30f92..0000000
--- a/docs/src/vignettes/6_moon.ipynb
+++ /dev/null
@@ -1,531 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vignette 6: recursive propagation with MOON"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this vignette, we are going to use MOON:\n",
-    "\n",
-    "> Dugourd et al., Modeling causal signal propagation in multi-omic factor space with COSMOS. *bioRxiv (2024)*. https://doi.org/10.1101/2024.07.15.603538\n",
-    "\n",
-    " to iteratively compute enrichment scores for a prior knowledge network, taking metabolic measurements and signalling cascades as inputs. \n",
-    "\n",
-    "This is the python version of the MOON workflow detailed in this [R vignette](https://saezlab.github.io/cosmosR/articles/NCI60_tutorial.html) within the package CosmosR. For more information, please check the [MOON section](../methods.html#moon) in the Methods details and the [original CosmosR paper](https://doi.org/10.15252/msb.20209730).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkcommons as nc\n",
-    "import decoupler as dc\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Input preparation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first import the COSMOS network, including signalling, gene regulatory and metabolic networks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meta_network_df = nc.data.network.get_cosmos_pkn(update=True)\n",
-    "meta_network = nc.utils.network_from_df(meta_network_df, directed=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we aim to remove self-interactions, calculate the mean interaction values for duplicated source-target pairs, and keep only interactions with values of 1 or -1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meta_network = nc.methods.meta_network_cleanup(meta_network) # equals R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook, we will use data from the NCI60 Human Tumor Cell Lines Screen. We will use the cell line 706-0. To have an overview of the cell lines, we can run `nc.data.omics.nci60_datasets()`. For more information, please check the [NCI60 details page](../datasets.html#nci60)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell_line</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>786-0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>A498</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>A549_ATCC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ACHN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BT-549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell_line\n",
-       "0      786-0\n",
-       "1       A498\n",
-       "2  A549_ATCC\n",
-       "3       ACHN\n",
-       "4     BT-549"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nc.data.omics.nci60_datasets().head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This resource contains three different types of data: transcriptomics, TF activity estimates and metabolic information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>data_type</th>\n",
-       "      <th>description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>TF_scores</td>\n",
-       "      <td>TF scores</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>RNA</td>\n",
-       "      <td>RNA expression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>metabolomic</td>\n",
-       "      <td>metabolomic data</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     data_type       description\n",
-       "0    TF_scores         TF scores\n",
-       "1          RNA    RNA expression\n",
-       "2  metabolomic  metabolomic data"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nc.data.omics.nci60_datatypes()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can get measurements for different subnetworks within the cosmos network:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sig_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='TF_scores')\n",
-    "rna_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='RNA')\n",
-    "metab_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='metabolomic')\n",
-    "\n",
-    "sig_input = sig_df.set_index('ID')['score'].to_dict()\n",
-    "rna_input = rna_df.set_index('ID')['score'].to_dict()\n",
-    "metab_input = metab_df.set_index('ID')['score'].to_dict()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the metabolites, we add the compartment they are located in. We also remove those genes that do not apear in the PKN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metab_input = nc.methods.prepare_metab_inputs(metab_input, [\"c\", \"m\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meta_network = nc.methods.filter_pkn_expressed_genes(rna_input.keys(), meta_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We filter out those inputs that cannot be mapped to the prior knowledge network."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sig_input = nc.methods.filter_input_nodes_not_in_pkn(sig_input, meta_network)\n",
-    "meta_network = nc.methods.keep_controllable_neighbours(sig_input, meta_network)\n",
-    "metab_input = nc.methods.filter_input_nodes_not_in_pkn(metab_input, meta_network)\n",
-    "meta_network = nc.methods.keep_observable_neighbours(metab_input, meta_network)\n",
-    "sig_input = nc.methods.filter_input_nodes_not_in_pkn(sig_input, meta_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Network compression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we will compress the network to reduce the dimensions of the problem and thus increase computational efficiency. For more information about how this is carried out, please check the dedicated [section](../methods.html#network-compression)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meta_network_compressed, signatures, dup_parents = nc.methods.compress_same_children(meta_network, sig_input, metab_input) # equals R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We clean the network again in case some self loops arose."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meta_network_compressed = nc.methods.meta_network_cleanup(meta_network_compressed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. MOON scoring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now it is time to compute the MOON scores from the compressed network. The network has been compressed by around a third of its original size, which increases computational efficiency. We will use the metabolic inputs and the signalling inputs to compute the MOON scores. After each optimisation, we check the sign consistency of the MOON scores, and remove those edges that turn out to be incoherent (the real TF enrichment scores are compared against the computed MOON scores and the sign of the edge). If there are incoherent edges, the function computes the MOON scores on the reduced network. The loop continues until it reaches a maximum number of tries (in our example, 10) or there are no incoherent edges left (more details [here](../methods.html#moon-scoring))."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can get now the GRN from DoRothEA, filtering by levels of confidence A and B."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_regn = dc.get_dorothea(levels = ['A', 'B'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moon_network = meta_network_compressed.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moon_res, moon_network = nc.methods.run_moon(\n",
-    "    meta_network_compressed,\n",
-    "    sig_input,\n",
-    "    metab_input,\n",
-    "    tf_regn,\n",
-    "    rna_input,\n",
-    "    n_layers=6,\n",
-    "    method='ulm',\n",
-    "    max_iter=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Decompression and solution network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the MOON scores are computed, we need to restore the uncompressed nodes that were compressed in section 2. For this, we will use the signatures that we obtained when we compressed the network to map back the original nodes to the compressed ones. After that, we can retrieve a solution network that contains the nodes (with the subsequent MOON scores) that are in the vicinity of the signalling input(s) and are sign consistent in terms of signed interactions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moon_res_dec = nc.methods.decompress_moon_result(moon_res, signatures, dup_parents, meta_network_compressed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we perform the decompression of the network, mapping the compressed nodes to their original components."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "FInally, we reduce the solution network by removing incoherent edges and filtering for nodes with moon scores higher than 1. We retrieve a networkx.DiGraph that we will visualise, and an attributes dataframe with the moon scores."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res_network, att = nc.methods.reduce_solution_network(moon_res_dec, meta_network, 1, sig_input, rna_input) # equals R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualise the 10 nodes with highest (absolute) score:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Kjo6Wr6+v8uXLp1GjRqWpAwAAAPD3ed3pAeDWioyMVO/evXXffffJzPTFF1+oQYMGWrt2rUqUKJGmfvz48erWrZtGjRqlKlWqaNu2bWrRooUcDoc+/vhjZ12JEiU0Z84c53cvr/R/lQYMGCCHw5Hh+EaPHq169eo5v2fNmtX5n4cOHaq4uDiNGDFC999/v1auXKm2bdsqW7ZseuKJJyRJb731lsaNG6cRI0aoaNGi+umnn9SwYUMtXbpU5cqVc7nWqlWrNHz4cJUuXdrleFRUlA4dOuRy7LPPPlPfvn316KOPZnq8S5YsUbNmzdS/f38FBAToxIkT+uyzz5QvXz5VqlRJDRo0cN5DWFiYJk+erDx58mjPnj1aunSp3n33XY0aNUrHjh1T586dFRgYqOeff16+vr6qW7eu2rRp45z3p59+WgUKFFBcXJyyZ88uSQoICNCoUaNUr149lSlTRgMHDlTNmjU1fvx4HThwQO3bt1dKSooCAwP17LPPqnjx4ho5cqTmzJnj8ruwf/9+1a9fX2+++aYGDhyovXv3qmvXrqpVq5Y6dOigMmXKKDIyUsWKFVPXrl2VK1cuzZgxQ82aNVNISIgef/zx6/rZXOt3KTExUfXq1VO9evUUFxenv/rtt9+Umpqq4cOHq1ChQtq4caPatm2rs2fPql+/fpKk06dPq06dOqpVq5aGDRumDRs2qFWrVsqaNavatWsnSVqwYIEaN26sKlWqyM/PTx9++KHq1KmjTZs2KU+ePJKkwoUL69NPP1WBAgV07tw59e/fX3Xq1NGOHTuUM2dOHTx4ULVq1VKjRo306aef6vTp0+rUqZNatGihyZMnS5Li4+MVFhamcePGKSoqSkuXLlW7du3k6empDh06OO/rueee05EjRzRy5EgVKlRIhw4dUmpqapr7BwAAAHADDG4nW7Zs9vnnn6fbFhsba4888ojLsc6dO1vVqlWd37t3725lypS55nXWrl1refLksUOHDpkkmzp1qkt7eseuFBMTY6+//vpVx5I7d2779NNPXWqefvppe/HFF12OnTlzxu677z6bPXu2Va9e3Tp27HjVsZctW9ZatWp1XePt27evFShQwOXYwIEDLU+ePGZ2ad6bNGliBQoUsAsXLrjUXTnvlSpVstjYWOe9pqSkWEREhNWsWdM579e6hxdeeMEk2YkTJ5zHpk+fbn5+fnb69GnneCXZ2rVrXc6Ni4uzihUruhy7fG6vXr0sS5YslpCQkOaa9evXt5YtWzq/Z+Znk9nfJTOz+fPnp7mnjPTp08fy58/v/D5kyBDLli2bJSUlOY917drVihQpkmEfFy9etCxZstgXX3yRYc2pU6dMks2ZM8fMzIYPH25hYWGWkpLirFm/fr1Jsu3bt2fYz8svv2wPP/yw8/uPP/5oISEhduzYsavfKAAAAIAbwuszbiQlJUUTJ07U2bNnFRMTk25NlSpVFB8f73xt4Pfff9fMmTNVv359l7rt27crIiJCBQoU0Isvvqi9e/e6tCcmJuqFF17Q4MGDFR4enuGYYmNjlSNHDlWqVEmjRo2SmTnbkpKS5Ofn51Lv7++vlStXKjk5+ao1v/zyS5rrPPbYY6pVq1aGY7ksPj5e69atU+vWra9rvDExMdq3b59mzpwpM9ORI0c0efJk1atXzznvu3fvVkxMjGJjY5UrVy6VLFlSH3zwgR544AHFx8dryZIlio+PV8mSJZ3z7uHhoVq1amnfvn3OeV+xYoWGDRumbNmyqWTJkoqLi1NiYqJzLJfn56/zcv78ecXHx7scf/LJJxUWFqYHH3xQ06dPz3BOz58/r+HDh+v5559XYGBgmv5PnTql0NBQ5/fM/myu9bv0d/x1LMuWLVO1atXk4+PjPFa3bl1t3bpVJ06cSLePxMREJScnu/RzpQsXLuizzz5TSEiIypQpI+nSPfv4+MjD4//+q9Xf31+S0tz31cY7ffp0VaxYUX369FGePHlUuHBhvf766zp37lwm7h4AAABApt3hUAa3wfr16y0wMNA8PT0tJCTEfvjhh6vWf/LJJ+bt7W1eXl4mydq3b+/SPnPmTPvmm2/s119/tVmzZllMTIzlzZvX+QSCmVm7du2sdevWzu9K5ymLnj172i+//GJr1qyx3r17m6+vr33yySfO9ri4OAsPD7fVq1dbamqqrVq1ynLlymWS7ODBg2Zm1rhxYytevLht27bNUlJS7OeffzZ/f3/z8fFx9jNhwgQrWbKknTt3zsyu/ZTFSy+9ZMWKFUtz/FrjNTP75ptvLCgoyDl3np6eLvNepEgR8/X1tVatWtnq1att4sSJFhoaaj169HDOu6Q0896lSxcrXLiwc95feeUVK1asmOXOndtGjBhhefLksYYNGzrr+/TpY5JsxIgRdvHiRdu/f7899NBDJsnGjx9vZpeeYvHz87Ply5fbypUrrWvXruZwOKxHjx7m4eFh48ePT3OuJFuxYkWaufn666/Nx8fHNm7c6DyWmZ9NZn6XLsvskyLbt2+34OBg++yzz5zHateube3atXOp27Rpk0myzZs3p9vPSy+9ZAUKFHD+3lz2/fffW2BgoDkcDouIiLCVK1c62zZu3GheXl7Wp08fS0pKsuPHj9szzzxjkuyDDz5I9zpLliwxLy8v++mnn5zH6tata76+vvbYY4/ZihUr7IcffrDo6Ghr0aLFVe8dAAAAwPUhFHEDSUlJtn37dlu9erV169bNcuTIYZs2bUq3dv78+ZYrVy4bMWKErV+/3qZMmWJRUVHWs2fPDPs/ceKEBQcHO1/JmTZtmhUqVMjOnDnjrEkvFPmrt99+2yIjI53fExMTrWXLlubl5WWenp4WERFhb7zxhkmyw4cPm5nZ0aNHrUGDBubh4WGenp5WuHBhe/nll83Pz8/MzPbu3WthYWH266+/Ovu9WiiSmJhoISEh1q9fv6uONb3xbtq0yXLnzm19+vSxX3/91b7//nsrXLiwPfnkk855z5s3r0VFRdnFixed53300UeWLVs2y5Url/OVll69ernMe5cuXaxSpUou179y3ufOnWuSbMeOHWb2fwFClixZzNPT0wICAqxXr14mySZOnGhmZqNHj7aQkBCXPps2bWoPPvigffTRRxYcHJzm3KioqDTzMG/ePAsICEjzmsm1fjbp+evv0pUyE4rs37/fChYs6BLImV1/KNKrVy/Lli2by+/NZQkJCbZ9+3ZbtmyZtWrVyvLly2dHjhxxtn/11VeWK1cu8/T0NB8fH3v99dctV65c1rt37zR9bdiwwXLkyGHvvfdemvH6+fnZyZMnnce+/fZbczgclpiYmOH9AwAAALg+hCJuqGbNmmn+QLzswQcfTLOOx5dffmn+/v4u6yT8VcWKFa1bt25mZtaxY0dzOBzOpyQ8PT1Nknl4eFj16tUz7GPGjBkmyc6fP+9y/MKFC7Zv3z67ePGiDRkyxLJkyZJmLOfOnbP9+/dbamqqvfHGG1a8eHEzM5s6darLExuXx3J5fFeGE2ZmY8eONW9vbzt69GiG48xovE2aNLF//etfLjWLFy92PtlSs2ZNCw8Pt5o1a7rUzJw50yTZq6++aklJSebp6WlTp051mfdmzZrZk08+mWYMl+c9ISHBJNmsWbPM7P8ChOPHj9uBAwcsMTHRNm/ebJKcTzakF4p8+umnFh4ebmZmqampznNXr15tkqxz584u9QsWLLDAwEAbPnx4hvOU0c8mI1f+Ll3pWqHIgQMH7L777rOmTZum+f1o2rSpNWjQwOXYvHnznHN0pb59+1pISIitWrXqquO8rFChQuk+BXL48GE7c+aMJSQkmIeHh33zzTcu7Zs2bbKwsDD773//m+bcZs2aWcGCBV2OXf75bdu2LVPjAgAAAHBtrCnihlJTU5WUlJRuW2Jiost6CJLk6ekpSS7rZ1wpISFBO3fuVO7cuSVJ3bp10/r167Vu3TrnR5L69++v0aNHZziudevWKVu2bPL19XU57u3trcjISHl6emrixIl6/PHH04zRz89PefLk0cWLF/Xtt9+qQYMGkqSaNWtqw4YNLmOpWLGiXnzxRa1bt855b5eNHDlSTz75pHLmzJnhODMa77XmLjU1VTlz5tSOHTtcdhHZtm2bvLy8nFv4VqhQQXPnznWem5KSorlz56ZZB+bKeb88x5d/Bpc5HA5FRETI399fEyZMUFRUlMqXL3/Ve7rcx5Xn9uzZU5LUtWtXZ+2CBQv02GOP6cMPP3Tu4JKejH426fnr71JmHThwQDVq1FCFChU0evToND+HmJgYLVq0yGWtldmzZ6tIkSLKli2b81ifPn303nvvadasWapYsWKmrp3RP0+5cuVSUFCQvv76a/n5+al27drOtk2bNunhhx9W8+bN9f7776c5t2rVqjp48KDLVr7btm2Th4eHIiMjMzUuAAAAAJlwp1MZ3FrdunWzhQsX2q5du2z9+vXWrVs3czgc9vPPP5vZpX+DfuW/le/evbtlyZLFJkyYYL///rv9/PPPVrBgQXvuueecNa+99potWLDAdu3aZUuWLLFatWpZjhw5rvp0hf7y+sz06dNtxIgRtmHDBtu+fbsNGTLEAgIC7J133nHWbN261b788kvbtm2brVixwho1amShoaG2a9cuZ83y5cvt22+/tZ07d9qiRYvskUcesfz581/1FYuMXp/Zvn27ORwO+/HHH9O0ZWa8o0ePNi8vLxsyZIi1b9/ePv30UytVqpSVLFnSOe/jxo2zLFmyWJEiRaxdu3Y2Y8YMCwsLs4cfftg57wMHDjRvb28LCwuzevXqWbt27Sxr1qzWvn17W7BggS1YsMDatGljlSpVstDQUBs7dqwVKFDAqlWrZocOHbK1a9faiBEjTJKNHj3aJk+ebN26dTNvb2+bOnWq7dmzx9auXWsNGzY0Pz8/mzJlik2ZMsXeeecd8/DwsFGjRlmfPn1s/fr1tnHjRuvZs6c5HA6XXX8uvzITFxdnhw4dcn6u3C0lMz+bzPwu/fWeFi1aZGvXrnVea//+/VaoUCGrWbOm7d+/32U8l508edJy5cplTZs2tY0bN9rEiRMtICDA5QmX3r17m4+Pj02ePNmlj8uvgSUkJFhcXJwtW7bMdu/ebatXr7aWLVuar6+vy1oqgwYNsvj4eNu6dat9+umn5u/v77L2zIYNGyxnzpzWpEkTl+tcec9nzpyxyMhI+9e//mWbNm2yhQsX2n333Wdt2rRJ/5caAAAAwN9CKHKPa9WqlUVHR5uPj4/lzJnTatas6QxEzC4FBM2bN3d+T05Oth49eljBggXNz8/PoqKi7OWXX3b5Q7ZRo0aWO3du8/HxsTx58lijRo2ca1lk5K+hyI8//mhly5a1oKAgCwwMtDJlytiwYcNcXnvYvHmzlS1b1vz9/S04ONgaNGhgv/32m0u/CxYssGLFipmvr69lz57dmjZtagcOHLjqWDIKReLi4iwqKird14QyM16zS4uXFi9e3PmqjoeHh2XPnt1l3pcuXWpZsmQxDw8PK1CggL3//vt2/vx5l3nPli2bBQUFmbe3t1WqVMmWL1/uMu9XfgoVKmRdunSxU6dOWffu3Z0Lol75KVCggM2cOdPMzJo3b55uTdGiRW3SpElmZvbwww9bSEiI+fn5WZkyZUySy+9NRn1c+XpUZn42mfldyuieRo8ebWaXwqj02v+a+f7666/24IMPmq+vr+XJkyfNGh/R0dHp9tG9e3czu/QaUMOGDS0iIsJ8fHwsd+7c9uSTT7ostGp2KWgMDQ01Hx8fK126tI0dOzZT9xMdHe1St2XLFqtVq5b5+/tbZGSkde7cmfVEAAAAgJvMYZbBOxEAAAAAAAD3MNYUAQAAAAAAbolQBAAAAAAAuCVCEQAAAAAA4JYIRQAAAAAAgFsiFAEAAAAAAG6JUAQAAAAAALglQhEAAAAAAOCWCEUAAAAAAIBbIhQBAAAAAABuiVAEAAAAAAC4JUIRAAAAAADglghFAAAAAACAWyIUAXDPGjp0qEqXLq3g4GAFBwcrJiZGP/74Y4b1Y8aMkcPhcPn4+fllWN++fXs5HA4NGDDA5fiaNWtUu3ZtZc2aVdmzZ1e7du2UkJCQbh/Hjh1TZGSkHA6HTp486dK2YMEClS9fXr6+vipUqJDGjBnj0n7mzBl16tRJ0dHR8vf3V5UqVbRq1SqXmoSEBHXo0EGRkZHy9/dX8eLFNWzYsDTjWLZsmR555BEFBgYqODhY1apV07lz51xqfvjhB1WuXFn+/v7Kli2bnnrqKZf7qFevniIiIuTr66uoqCh16NBBp0+fdtb88ssvqlq1qrJnzy5/f38VLVpU/fv3d7lGZn5m58+fV2xsrLJnz66goCA988wzOnLkiEvN3r179dhjjykgIEBhYWHq0qWLLl686FLz1VdfqUyZMgoICFDu3LnVqlUrHTt2LM3cAAAA4N5FKALgnhUZGanevXsrPj5eq1ev1iOPPKIGDRpo06ZNGZ4THBysQ4cOOT979uxJt27q1Klavny5IiIiXI4fPHhQtWrVUqFChbRixQrNmjVLmzZtUosWLdLtp3Xr1ipdunSa47t27dJjjz2mhx9+WOvWrVOnTp3Upk0b/fTTT86aNm3aaPbs2fryyy+1YcMG1alTR7Vq1dKBAwecNZ07d9asWbM0btw4bdmyRZ06dVKHDh00ffp0Z82yZctUr1491alTRytXrtSqVavUoUMHeXj83/9EfPvtt2ratKlatmypX3/9VUuWLNELL7zgbPfw8FCDBg00ffp0bdu2TWPGjNGcOXPUvn17Z01gYKA6dOigRYsWacuWLXrrrbf01ltv6bPPPnPWZOZn9uqrr+r777/XpEmTtHDhQh08eFBPP/20sz0lJUWPPfaYLly4oKVLl+qLL77QmDFj9M477zhrlixZombNmql169batGmTJk2apJUrV6pt27bp/pwAAABwjzIAcCPZsmWzzz//PN220aNHW0hIyDX72L9/v+XJk8c2btxo0dHR1r9/f2fb8OHDLSwszFJSUpzH1q9fb5Js+/btLv0MGTLEqlevbnPnzjVJduLECWfbG2+8YSVKlHCpb9SokdWtW9fMzBITE83T09NmzJjhUlO+fHl78803nd9LlChhPXv2vGpN5cqV7a233srwfpOTky1PnjwZzltGPvnkE4uMjLxqTcOGDa1JkyZXrbnyZ3by5Enz9va2SZMmOdu3bNlikmzZsmVmZjZz5kzz8PCww4cPO2uGDh1qwcHBlpSUZGZmffv2tQIFCrhcZ+DAgZYnT57M3yAAAADuejwpAsAtpKSkaOLEiTp79qxiYmIyrEtISFB0dLSioqLSfaokNTVVTZs2VZcuXVSiRIk05yclJcnHx8flKQt/f39Jl14fuWzz5s3q2bOnxo4d61J72bJly1SrVi2XY3Xr1tWyZcskSRcvXlRKSkqa13v8/f1drlOlShVNnz5dBw4ckJlp/vz52rZtm+rUqSNJOnr0qFasWKGwsDBVqVJFuXLlUvXq1V36WLNmjQ4cOCAPDw+VK1dOuXPn1qOPPqqNGzdmOI8HDx7UlClTVL169Qxr1q5dq6VLl2ZYk97PLD4+XsnJyS5zU7RoUeXNm9c5N8uWLVOpUqWUK1cul7k7ffq08+cZExOjffv2aebMmTIzHTlyRJMnT1b9+vUzHC8AAADuPYQiAO5pGzZsUFBQkHx9fdW+fXtNnTpVxYsXT7e2SJEiGjVqlKZNm6Zx48YpNTVVVapU0f79+501H374oby8vPTKK6+k28cjjzyiw4cPq2/fvrpw4YJOnDihbt26SZIOHTok6VJw0rhxY/Xt21d58+ZNt5/Dhw+7/FEvSbly5dLp06d17tw5ZcmSRTExMXrvvfd08OBBpaSkaNy4cVq2bJnzOpI0aNAgFS9eXJGRkfLx8VG9evU0ePBgVatWTZL0+++/S5J69Oihtm3batasWSpfvrxq1qyp7du3p6l56623NGPGDGXLlk01atTQ8ePHXcbYuHFjBQQEKE+ePAoODtbnn3+e5t4iIyPl6+urihUrKjY2Vm3atHFpv9rP7PDhw/Lx8VHWrFnTzM3hw4evOneX2ySpatWq+uqrr9SoUSP5+PgoPDxcISEhGjx4cLo/DwAAANybCEUA3NOKFCmidevWacWKFXrppZfUvHlzbd68Od3amJgYNWvWTGXLllX16tU1ZcoU5cyZU8OHD5d06SmFTz75xLkga3pKlCihL774Qh999JECAgIUHh6u/PnzK1euXM4nQuLi4lSsWDE1adLkhu7tyy+/lJkpT5488vX11cCBA9W4cWOXJ08GDRqk5cuXa/r06YqPj9dHH32k2NhYzZkzR9KlJ18k6d///rdatmypcuXKqX///s6A6MqaN998U88884wqVKig0aNHy+FwaNKkSS5j6t+/v9asWaNp06Zp586d6ty5c5pxL168WKtXr9awYcM0YMAATZgwwaX9en5mf9fmzZvVsWNHvfPOO4qPj9esWbO0e/dulzVQAAAAcO/zutMDAIBbycfHR4UKFZIkVahQQatWrdInn3ziDDquxtvbW+XKldOOHTskXfpj/ujRoy5Pd6SkpOi1117TgAEDtHv3bknSCy+8oBdeeEFHjhxRYGCgHA6HPv74YxUoUECSNG/ePG3YsEGTJ0+WJJmZJClHjhx688039e677yo8PDzNjipHjhxRcHCw83WcggULauHChTp79qxOnz6t3Llzq1GjRs7rnDt3Tv/97381depUPfbYY5Kk0qVLa926derXr59q1aql3LlzS1Kap2eKFSumvXv3SlK6Nb6+vipQoICz5rLw8HCFh4eraNGiCg0N1UMPPaS3337b2Yck5c+fX5JUqlQpHTlyRD169FDjxo2d7Vf7mYWHh+vChQs6efKky9MiR44cUXh4uHMMK1euTDN3l9skqVevXqpataq6dOninJfAwEA99NBD+t///ucyXgAAANy7eFIEgFtJTU1VUlJSpmpTUlK0YcMG5x/ITZs21fr167Vu3TrnJyIiQl26dHHZFeayXLlyKSgoSF9//bX8/PxUu3ZtSZd2cvn111+dfVx+xWTx4sWKjY2VdOmplblz57r0N3v27HTXQwkMDFTu3Ll14sQJ/fTTT2rQoIEkKTk5WcnJyWnWLPH09HQ+/ZEvXz5FRERo69atLjXbtm1TdHS0pEvBhK+vr0tNcnKydu/e7axJz+VrXG2+M/PzuLKmQoUK8vb2dpmbrVu3au/evc65iYmJ0YYNG3T06FFnzezZsxUcHOwMdhITE9OdF+n/QioAAADc+3hSBMA9Ky4uTo8++qjy5s2rM2fOaPz48VqwYIEzwGjWrJny5MmjXr16SZJ69uypBx54QIUKFdLJkyfVt29f7dmzx7nmRfbs2ZU9e3aXa3h7eys8PFxFihRxHvv0009VpUoVBQUFafbs2erSpYt69+7tfLKhYMGCLn38+eefki49nXG5pn379vr000/1xhtvqFWrVpo3b56++eYb/fDDD87zfvrpJ5mZihQpoh07dqhLly4qWrSoWrZsKenS9sLVq1dXly5d5O/vr+joaC1cuFBjx47Vxx9/LElyOBzq0qWLunfvrjJlyqhs2bL64osv9NtvvzmfZAkODlb79u3VvXt3RUVFKTo6Wn379pUkPfvss5KkmTNn6siRI7r//vsVFBSkTZs2qUuXLqpatary5csnSRo8eLDy5s2rokWLSpIWLVqkfv36uazPcq2fWUhIiFq3bq3OnTsrNDRUwcHB+s9//qOYmBg98MADkqQ6deqoePHiatq0qfr06aPDhw/rrbfeUmxsrHx9fSVJTzzxhNq2bauhQ4eqbt26OnTokDp16qRKlSql2WYZAAAA97A7ufUNANxKrVq1sujoaPPx8bGcOXNazZo17eeff3a2V69e3Zo3b+783qlTJ8ubN6/5+PhYrly5rH79+rZmzZqrXuOvW/KamTVt2tRCQ0PNx8fHSpcubWPHjr1qH/Pnz0+zJe/l42XLljUfHx8rUKCAjR492qX966+/tgIFCpiPj4+Fh4dbbGysnTx50qXm0KFD1qJFC4uIiDA/Pz8rUqSIffTRR5aamupS16tXL4uMjLSAgACLiYmxxYsXu7RfuHDBXnvtNQsLC7MsWbJYrVq1bOPGjc72efPmWUxMjIWEhJifn5/dd9991rVrV5d7GjhwoJUoUcICAgIsODjYypUrZ0OGDHHZvvhaPzMzs3PnztnLL79s2bJls4CAAGvYsKEdOnTIpWb37t326KOPmr+/v+XIkcNee+01S05OdqkZOHCgFS9e3Pz9/S137tz24osv2v79+9P+gAAAAHDPcpjxnDAAAAAAAHA/rCkCAAAAAADcEqEIAOCmGjp0qEqXLq3g4GAFBwcrJiZGP/74Y4b1U6ZMUcWKFZU1a1YFBgaqbNmy+vLLL11qjhw5ohYtWigiIkIBAQGqV6+etm/f7mzfvXu3HA5Hup8rtw1Or33ixInO9kOHDumFF15Q4cKF5eHhoU6dOqUZb40aNdLt5/IOP8nJyeratatKlSqlwMBARUREqFmzZjp48KBLP8ePH9eLL76o4OBgZc2aVa1bt1ZCQkK6c7Rjxw5lyZLFZccdSRoxYoQeeughZcuWTdmyZVOtWrXS7LyTkJCgDh06KDIyUv7+/ipevLiGDRuW7nXMTI8++qgcDoe+++67NO1jxoxR6dKl5efnp7CwMOfCwFee369fPxUuXFi+vr7KkyeP3n//fZear776SmXKlFFAQIBy586tVq1a6dixY+mOBwAA4FYjFAEA3FSRkZHq3bu34uPjtXr1aj3yyCNq0KCBNm3alG59aGio3nzzTS1btkzr169Xy5Yt1bJlS+fiqmamp556Sr///rumTZumtWvXKjo6WrVq1dLZs2clSVFRUTp06JDL591331VQUJAeffRRl+uNHj3ape6pp55ytiUlJSlnzpx66623VKZMmXTHO2XKFJfzN27cKE9PT+eis4mJiVqzZo3efvttrVmzRlOmTNHWrVv15JNPuvTz4osvatOmTZo9e7ZmzJihRYsWqV27dmmul5ycrMaNG+uhhx5K07ZgwQI1btxY8+fP17JlyxQVFaU6derowIEDzprOnTtr1qxZGjdunLZs2aJOnTqpQ4cOmj59epr+BgwYIIfDke59f/zxx3rzzTfVrVs3bdq0SXPmzFHdunVdajp27KjPP/9c/fr102+//abp06erUqVKzvYlS5aoWbNmat26tTZt2qRJkyZp5cqVatu2bbrXBAAAuOXu6IomAAC3kC1bNvv8888zXV+uXDl76623zMxs69atJsllYdeUlBTLmTOnjRgxIsM+ypYta61atXI5JsmmTp2aqTFUr17dOnbseM26/v37W5YsWSwhISHDmpUrV5ok27Nnj5mZbd682STZqlWrnDU//vijORwOO3DggMu5b7zxhjVp0sRGjx5tISEhVx3LxYsXLUuWLPbFF184j5UoUcJ69uzpUle+fHl78803XY6tXbvW8uTJY4cOHUozT8ePHzd/f3+bM2dOhtfevHmzeXl52W+//ZZhTd++fa1AgQIuxwYOHGh58uS56n0BAADcKjwpAgC4ZVJSUjRx4kSdPXtWMTEx16w3M82dO1dbt25VtWrVJF16ekOS/Pz8nHUeHh7y9fXVL7/8km4/8fHxWrdunVq3bp2mLTY2Vjly5FClSpU0atQo2Q2uNz5y5Eg9//zzCgwMzLDm1KlTcjgcztdfli1bpqxZs6pixYrOmlq1asnDw0MrVqxwHps3b54mTZqkwYMHZ2osiYmJSk5OVmhoqPNYlSpVNH36dB04cEBmpvnz52vbtm2qU6eOy3kvvPCCBg8erPDw8DT9zp49W6mpqTpw4ICKFSumyMhIPffcc9q3b5+z5vvvv1eBAgU0Y8YM5c+fX/ny5VObNm10/PhxZ01MTIz27dunmTNnysx05MgRTZ48WfXr18/U/QEAANxshCIAgJtuw4YNCgoKkq+vr9q3b6+pU6eqePHiGdafOnVKQUFB8vHx0WOPPaZBgwapdu3akqSiRYsqb968iouL04kTJ3ThwgV9+OGH2r9/vw4dOpRufyNHjlSxYsVUpUoVl+M9e/bUN998o9mzZ+uZZ57Ryy+/rEGDBv3t+1y5cqU2btyoNm3aZFhz/vx5de3aVY0bN1ZwcLAk6fDhwwoLC3Op8/LyUmhoqA4fPixJOnbsmFq0aKExY8Y4z7uWrl27KiIiQrVq1XIeGzRokIoXL67IyEj5+PioXr16Gjx4sDN0kqRXX31VVapUUYMGDdLt9/fff1dqaqo++OADDRgwQJMnT9bx48dVu3ZtXbhwwVmzZ88eTZo0SWPHjtWYMWMUHx+vf/3rX85+qlatqq+++kqNGjWSj4+PwsPDFRISkunQBwAA4GbzutMDAADce4oUKaJ169bp1KlTmjx5spo3b66FCxdmGIxkyZJF69atU0JCgubOnavOnTurQIECqlGjhry9vTVlyhS1bt1aoaGh8vT0VK1atfToo4+m+5THuXPnNH78eL399ttp2q48Vq5cOZ09e1Z9+/bVK6+88rfuc+TIkSpVqpTLuhlXSk5O1nPPPScz09ChQ6+r77Zt2+qFF15wCS+upnfv3po4caIWLFjg8lTNoEGDtHz5ck2fPl3R0dFatGiRYmNjneHJ9OnTNW/ePK1duzbDvlNTU5WcnKyBAwc6nzCZMGGCwsPDNX/+fNWtW1epqalKSkrS2LFjVbhwYUmX5qdChQraunWrihQpos2bN6tjx4565513VLduXR06dEhdunRR+/btNXLkyOuaHwAAgJviTr67AwBwDzVr1rR27dplur5169ZWp06dNMdPnjxpR48eNTOzSpUq2csvv5ymZuzYsebt7e2su5oZM2aYJDt//nyatmutKZKQkGDBwcE2YMCAdNsvXLhgTz31lJUuXdr+/PNPl7aRI0da1qxZXY4lJyebp6enTZkyxczMQkJCzNPT0/nx8PAwSebp6WkjR450Obdv374WEhLiskaJmVliYqJ5e3vbjBkzXI63bt3a6tata2ZmHTt2NIfD4XItSebh4WHVq1c3M7NRo0aZJNu3b59LP2FhYfbZZ5+Zmdk777xjXl5eaa4vyX7++WczM2vSpIn961//cqlZvHixSbKDBw+mO48AAAC3Ek+KAABuuctPEdxofUhIiCRp+/btWr16td577700NSNHjtSTTz6pnDlzXvM669atU7Zs2eTr65vpsV02adIkJSUlqUmTJmnaLj8hsn37ds2fP1/Zs2d3aY+JidHJkycVHx+vChUqSLq0fkhqaqoqV64s6dK6IykpKc5zpk2bpg8//FBLly5Vnjx5nMf79Omj999/Xz/99JPLGiWXx5GcnCwPD9e3ZT09PZWamipJ6tatW5rXf0qVKqX+/fvriSeekHTptRdJ2rp1qyIjIyVd2lL4zz//VHR0tLPm4sWL2rlzpwoWLChJ2rZtmyQ5axITE+Xl5fp/PTw9PSXphtd2AQAA+DsIRQAAN1VcXJweffRR5c2bV2fOnNH48eO1YMEC5xa7zZo1U548edSrVy9JUq9evVSxYkUVLFhQSUlJmjlzpr788kuX100mTZqknDlzKm/evNqwYYM6duyop556ymWxUEnasWOHFi1apJkzZ6YZ1/fff68jR47ogQcekJ+fn2bPnq0PPvhAr7/+ukvdunXrJEkJCQn6448/tG7dOvn4+KR59WfkyJF66qmn0gQeycnJ+te//qU1a9ZoxowZSklJca4TEhoaKh8fHxUrVkz16tVT27ZtNWzYMCUnJ6tDhw56/vnnFRERIUkqVqyYS7+rV6+Wh4eHSpYs6Tz24Ycf6p133tH48eOVL18+53WCgoIUFBSk4OBgVa9eXV26dJG/v7+io6O1cOFCjR07Vh9//LEkKTw8PN3FVfPmzav8+fNLkgoXLqwGDRqoY8eO+uyzzxQcHKy4uDgVLVpUDz/8sKRLC8WWL19erVq10oABA5SamqrY2FjVrl3b+TrNE088obZt22ro0KHO12c6deqkSpUqOe8bAADgtrrTj6oAAO4trVq1sujoaPPx8bGcOXNazZo1na9PmF16LaV58+bO72+++aYVKlTI/Pz8LFu2bBYTE2MTJ0506fOTTz6xyMhI8/b2trx589pbb71lSUlJaa4dFxdnUVFRlpKSkqbtxx9/tLJly1pQUJAFBgZamTJlbNiwYWlqJaX5REdHu9T89ttvLq+FXGnXrl3p9iHJ5s+f76w7duyYNW7c2IKCgiw4ONhatmxpZ86cyXBe09uSNzo6Ot3rdO/e3Vlz6NAha9GihUVERJifn58VKVLEPvroI0tNTc3wWkpn6+JTp05Zq1atLGvWrBYaGmoNGza0vXv3utQcOHDAnn76aQsKCrJcuXJZixYt7NixYy41AwcOtOLFi5u/v7/lzp3bXnzxRdu/f3+GYwEAALiVHGY8rwoAAAAAANwPW/ICAAAAAAC3RCgCAAAAAADcEqEIAAAAAABwS4QiAAAAAADALRGKAAAAAAAAt0QoAgAAAAAA3BKhCAAAAAAAcEuEIgAAAAAAwC0RigAAAAAAALdEKAIAAAAAANwSoQgAAAAAAHBLhCIAAAAAAMAtEYoAAAAAAAC3RCgCAACQSUOHDlXp0qUVHBys4OBgxcTE6Mcff7zqOZMmTVLRokXl5+enUqVKaebMmS7tCQkJ6tChgyIjI+Xv76/ixYtr2LBhLjU1atSQw+Fw+bRv396l5q/tDodDEydOdLb/8ssvqlq1qrJnzy5/f38VLVpU/fv3TzPewYMHK1++fPLz81PlypW1cuVKl/bz588rNjZW2bNnV1BQkJ555hkdOXIk3Xs/duyYIiMj5XA4dPLkSefxKVOmqHbt2sqZM6dzHn/66ac05x84cEBNmjRxjrlUqVJavXp1utdq3769HA6HBgwY4HJ827ZtatCggXLkyKHg4GA9+OCDmj9/vssY69Wrp4iICPn6+ioqKkodOnTQ6dOnnTWHDh3SCy+8oMKFC8vDw0OdOnVKc/0xY8akmX8/P790x3q18QIAbi9CEQAAgEyKjIxU7969FR8fr9WrV+uRRx5RgwYNtGnTpnTrly5dqsaNG6t169Zau3atnnrqKT311FPauHGjs6Zz586aNWuWxo0bpy1btqhTp07q0KGDpk+f7tJX27ZtdejQIeenT58+aa43evRol5qnnnrK2RYYGKgOHTpo0aJF2rJli9566y299dZb+uyzz5w1X3/9tTp37qzu3btrzZo1KlOmjOrWraujR486a1599VV9//33mjRpkhYuXKiDBw/q6aefTvf+W7durdKlS6c5vmjRItWuXVszZ85UfHy8Hn74YT3xxBNau3ats+bEiROqWrWqvL299eOPP2rz5s366KOPlC1btjT9TZ06VcuXL1dERESatscff1wXL17UvHnzFB8frzJlyujxxx/X4cOHJUkeHh5q0KCBpk+frm3btmnMmDGaM2eOS+iUlJSknDlz6q233lKZMmXSvVdJCg4Odpn/PXv2pFt3tfECAG4zAwAAwN+WLVs2+/zzz9Nte+655+yxxx5zOVa5cmX797//7fxeokQJ69mzp0tN+fLl7c0333R+r169unXs2PGq45BkU6dOva6xN2zY0Jo0aeL8XqlSJYuNjXV+T0lJsYiICOvVq5eZmZ08edK8vb1t0qRJzpotW7aYJFu2bJlL30OGDLHq1avb3LlzTZKdOHHiqmMpXry4vfvuu87vXbt2tQcffPCa97B//37LkyePbdy40aKjo61///7Otj/++MMk2aJFi5zHTp8+bZJs9uzZGfb5ySefWGRkZLptGf0sRo8ebSEhITc0XgDA7ceTIgAAAH9DSkqKJk6cqLNnzyomJibdmmXLlqlWrVoux+rWratly5Y5v1epUkXTp0/XgQMHZGaaP3++tm3bpjp16ric99VXXylHjhwqWbKk4uLilJiYmOZ6sbGxypEjhypVqqRRo0bJzDIc/9q1a7V06VJVr15dknThwgXFx8e7jNfDw0O1atVyjjc+Pl7JyckuNUWLFlXevHld7mnz5s3q2bOnxo4dKw+Pa//fzdTUVJ05c0ahoaHOY9OnT1fFihX17LPPKiwsTOXKldOIESPSnNe0aVN16dJFJUqUSNNv9uzZVaRIEY0dO1Znz57VxYsXNXz4cIWFhalChQrpjuXgwYOaMmWKc16uR0JCgqKjoxUVFZXuE0TXGi8A4PbzutMDAAAAuJts2LBBMTExOn/+vIKCgjR16lQVL1483drDhw8rV65cLsdy5crlfHVDkgYNGqR27dopMjJSXl5e8vDw0IgRI1StWjVnzQsvvKDo6GhFRERo/fr16tq1q7Zu3aopU6Y4a3r27KlHHnlEAQEB+vnnn/Xyyy8rISFBr7zyisv1IyMj9ccff+jixYvq0aOH2rRpI0n6888/lZKSku54f/vtN+f9+Pj4KGvWrBneU1JSkho3bqy+ffsqb968+v333685p/369VNCQoKee+4557Hff/9dQ4cOVefOnfXf//5Xq1at0iuvvCIfHx81b95ckvThhx/Ky8srzT1e5nA4NGfOHD311FPKkiWLPDw8FBYWplmzZqV5Dadx48aaNm2azp07pyeeeEKff/75Ncd9pSJFimjUqFEqXbq0Tp06pX79+qlKlSratGmTIiMjMzVeAMDtRygCAABwHYoUKaJ169bp1KlTmjx5spo3b66FCxdmGIxcy6BBg7R8+XJNnz5d0dHRWrRokWJjYxUREeF8IqNdu3bO+lKlSil37tyqWbOmdu7cqYIFC0qS3n77bWdNuXLldPbsWfXt2zfNH+CLFy9WQkKCli9frm7duqlQoUJq3Ljx3xp7euLi4lSsWDE1adIkU/Xjx4/Xu+++q2nTpiksLMx5PDU1VRUrVtQHH3zgvKeNGzdq2LBhat68ueLj4/XJJ59ozZo1cjgc6fZtZoqNjVVYWJgWL14sf39/ff7553riiSe0atUq5c6d21nbv39/de/eXdu2bVNcXJw6d+6sIUOGZPq+Y2JiXJ4YqlKliooVK6bhw4frvffey9R4AQB3wB1+fQcAAOCuVrNmTWvXrl26bVFRUWnWjHjnnXesdOnSZmaWmJho3t7eNmPGDJea1q1bW926dTO8ZkJCgkmyWbNmZVgzY8YMk2Tnz5/PsOa9996zwoULm5lZUlKSeXp6plmXpFmzZvbkk0+amWW4PkjevHnt448/NjOzMmXKmIeHh3l6epqnp6d5eHiYJPP09LR33nnH5bwJEyaYv79/mvu/3Gfr1q1djg0ZMsQiIiJsyJAhljt3bpPk/Fy+joeHh0VHR5uZ2Zw5c8zDw8NOnTpl33zzjRUpUsR8fX3Nx8fHmjdv7tJ3amqqvf322xYeHm4+Pj4myX755Zc046pevbrFxsZamTJlTJKtXbvW2bZr1y6XMV3+LFu2zPr3728Oh8M8PT3N4XC4tGfJksXOnTvncp1PP/3UoqOjzdfX1ypVqmQrVqxIM46/XufKtWouGz16tJUqVcp8fX0tZ86c9vLLLzvbzp07Z82bN7eSJUuap6enNWjQIM35Bw8etMaNG9t9991nDocj3fVU0huLJKtfv76z5ttvv7XatWtbaGhomnm77NChQ9akSRPLlSuXBQQEWLly5Wzy5MkuNf/73/8sJibG/P39M1zDZc6cORYTE2NBQUGWK1cue+ONNyw5OTndWgBgTREAAIAbkJqaqqSkpHTbYmJiNHfuXJdjs2fPdj5RkJycrOTk5DTrbnh6eio1NTXDa65bt06SXJ50SK8mW7Zs8vX1zdTYfXx8VKFCBZfxpqamau7cuc7xVqhQQd7e3i41W7du1d69e5013377rX799VetW7dO69atc76GsnjxYsXGxjrPmzBhglq2bKkJEyboscceSzO2qlWrauvWrS7Htm3bpujoaEVGRurjjz/WjBkz9P3336tt27by8PBQzpw51aVLF+f2vpfXXVm5cqXLLkBBQUEaN26cyy5Affr00cCBAzVs2DDnlsiNGzfW+fPn04xtyZIlV905Zs6cOdq/f78KFiyodu3aqUKFCmratKnWr1+v999/X97e3urdu7fCwsL03HPPyc/PT//973+d52dmFyDp2jsSffzxx3rzzTfVrVs3bdq0SXPmzFHdunWd7SkpKfL399crr7ySZu2byzKz886UKVNcxrFx40Z5enrq2WefddacPXtWDz74oD788MMM561Zs2baunWrpk+frg0bNujpp5/Wc88957Ir0YULF/Tss8/qpZdeSrePX3/9VfXr11e9evW0du1aff3115o+fbq6deuW4XUBuLk7ncoAAADcLbp162YLFy60Xbt22fr1661bt27mcDjs559/NjOzpk2bWrdu3Zz1S5YsMS8vL+vXr59t2bLFunfvbt7e3rZhwwZnTfXq1a1EiRI2f/58+/3332306NHm5+dnQ4YMMTOzHTt2WM+ePW316tW2a9cumzZtmhUoUMCqVavm7GP69Ok2YsQI27Bhg23fvt2GDBliAQEBLk9mfPrppzZ9+nTbtm2bbdu2zT7//HPLkiWLyy43EydONF9fXxszZoxt3rzZ2rVrZ1mzZrXDhw87a9q3b2958+a1efPm2erVqy0mJsZiYmIynLP58+enebrkq6++Mi8vLxs8eLAdOnTI+Tl58qSzZuXKlebl5WXvv/++bd++3b766isLCAiwcePGpXudbNmyWWhoaJrdZ7Jnz2558uSxhx56yLZu3Wqvv/66eXt7W6lSpezf//63/fDDDzZy5EjLkSOHxcXF2YwZM6xYsWJWuXJl8/X1tQkTJpiZ2dq1a23t2rVWqFAhCw4OtsmTJ7s88fDuu+/aF198YZJs/Pjx9vzzz5ufn59t2rTJZZyxsbH2yCOPmJk5d5/p3LmzVa1a1VlzrV2AzK69I9Hx48fN39/f5syZk2HNlZo3b57ukyJXyswuSGZm/fv3tyxZslhCQkKatstP06T3pEhgYKCNHTvW5VhoaKiNGDEiTW1Gu/3ExcVZxYoVXY5Nnz7d/Pz87PTp09ccOwD3QygCAACQSa1atbLo6Gjz8fGxnDlzWs2aNZ2BiNmlPxr/+lrGN998Y4ULFzYfHx8rUaKE/fDDDy7thw4dshYtWlhERIT5+flZkSJF7KOPPrLU1FQzM9u7d69Vq1bNQkNDzdfX1woVKmRdunSxU6dOOfv48ccfrWzZshYUFGSBgYFWpkwZGzZsmKWkpDhrBg4caCVKlLCAgAALDg62cuXK2ZAhQ1xqzMwGDRpkefPmNR8fH6tUqZItX77cpf3cuXP28ssvW7Zs2SwgIMAaNmxohw4dynDO0gtFMnrd4q9z9/3331vJkiXN19fXihYtap999lma/i9evGgTJkwwHx8fy507d5rXlVatWmV+fn4WEBBgWbJksQceeMBmzpzpfI1p3rx5Vq5cOZNkPj4+dt9991nXrl3txIkTVq1aNXvllVfMzNId75V/3Hfq1MkiIiKcr/D4+PhY2bJlbdq0aS7j+eqrrywkJMRWrFhh0dHR9uabb1rRokXt/fffN7PMvcZ0eQ5z5Mhh2bNntxIlSli3bt3s7Nmzzvavv/7afH197YsvvrCiRYtanjx57Nlnn7W9e/em+3O6maFIyZIlrW3btum2XS0UqV27tj322GN27NgxS0lJsQkTJlhAQIBt3749TW1GoUjnzp3TbOU8e/Zsk2Tz58+/5tgBuB9CEQAAANx11q9fb4GBgebp6WkhISFpwqYreXt72/jx412ODR482MLCwszs0hM9kuzgwYMuNc8++6w999xzZnZpzZF69erZe++9Z2bp/3H/xx9/2EcffWTLly+3lStXWteuXc3hcKQJRj755BPz9vY2Ly8vk2Tt27d3th04cMAk2dKlS13O6dKli1WqVMn5ffjw4TZr1ixbv369jRs3zvLkyWMNGzZ0tvfq1cu8vb2tSJEiNmvWLFu2bJnVrFnTihQpYklJSWnm6GaFIitWrDBJadZAuexqociJEyesTp06Jsm8vLwsODjYfvrpp3T7ySgU+emnn8zDw8PGjx9vFy9etP3799tDDz3kfIIHAP6K3WcAAABw17nZuwBdy6BBg3TmzBnFxcVlWJMjRw517tzZ+f3+++/XwYMH1bdvXz355JOSpAULFuiDDz7QkCFDVLlyZe3YsUMdO3bUe++957KD0LVca0ei1NRUJScna+DAgapTp46kS+u4hIeHa/78+S5ri9xMI0eOVKlSpVSpUqXrPvftt9/WyZMnNWfOHOXIkUPfffednnvuOS1evFilSpXKVB916tRR37591b59ezVt2lS+vr56++23tXjx4jRr9wCAJPHfDAAAALjr+Pj4qFChQqpQoYJ69eqlMmXK6JNPPkm3Njw8XEeOHHE5duTIEYWHhzvbLx/LqGbevHlatmyZfH195eXlpUKFCkmSKlasqObNm2c4zsvBx2Vvv/22mjZtqjZt2qhUqVJq2LChPvjgA/Xq1UupqanKkSOHPD09rzqWjK4jyXmty4vwXhkS5cyZUzly5NDevXsz7OdGnD17VhMnTlTr1q2v+9ydO3fq008/1ahRo1SzZk2VKVNG3bt3V8WKFTV48ODr6qtz5846efKk9u7dqz///FMNGjSQJBUoUOC6xwXg3kcoAgAAgLvejewClD9/foWHh7vUnD59WitWrHDWDBw40GVXnZkzZ0q6tFPM+++/n+G41q1b57JLUGJiYrq7DUmSmWVqF6CMriP9XxhStWpVSXLZwef48eP6888/FR0dnWE/N2LSpElKSkpSkyZNrvvcyzsFXe9OTBlxOByKiIiQv7+/JkyYoKioKJUvX/66+wHgBu70+zsAAADA9bgVuwD17t3bsmbNatOmTbP169dbgwYNLH/+/Hbu3Ll0x5De2hhjxoyx8ePH25YtW2zLli32/vvvm4eHh40aNcpZ0717d8uSJYtNmDDBfv/9d/v555+tYMGCzrVLzK69C1BmdiQyM2vQoIGVKFHClixZYhs2bLDHH3/cihcvbhcuXHDWbNq0ydauXWtPPPGE1ahRw7nLzpUuH6tQoYK98MILtnbt2jS76piZPfjgg9aoUaN05+vYsWO2du1a++GHH0ySTZw40dauXetcpPfChQtWqFAhe+ihh2zFihW2Y8cO69evnzkcDpf1Yvbs2WNr1661d99914KCgpxjO3PmjLOmT58+tn79etu4caP17NnTvL290yxcezf54IMPrGLFihYUFGQ5c+a0Bg0a2G+//XbVcy5cuGDvvvuuFShQwHx9fa106dL2448/pqn79NNPLTo62nx9fa1SpUpp1oJJb1Hkf//73872P//80+rWrWu5c+c2Hx8fi4yMtNjYWJeFoC8vtvzXz5ULNHfv3j1Ne5EiRZztx44dsw4dOljhwoXNz8/PoqKi7D//+Y/LjlVmZv/5z3+sfPny5uPjY2XKlElzv5f/uf3rZ9myZS5133zzjRUpUsR8fX2tZMmSadYsSq8PSdanTx/ndVq1amX58uUzPz8/K1CggL3zzjtp1vNJTU21vn372n333Wc+Pj4WERFh//vf/5ztixcvtipVqlhoaKhzIe6PP/7YpY+/8/sBV4QiAAAAuKvcil2AUlNT7e2337ZcuXKZr6+v1axZ07Zu3ZrhGDIKRYoVK+bc4adSpUo2adIkl/OSk5OtR48eVrBgQecfdy+//LLL7jxmV98FKDM7EpmZnTp1ylq1amVZs2a10NBQa9iwYZrdZ6Kjo9P94+5K6bVHR0e71Pz2228myeXncKXRo0en20/37t2dNdu2bbOnn37awsLCLCAgwEqXLp1mi97mzZun28+VO8s8/PDDFhISYn5+fla5cmWbOXNmumO6W9StW9dGjx5tGzdutHXr1ln9+vUtb9686W55fNkbb7xhERER9sMPP9jOnTttyJAh5ufnZ2vWrHHWTJw40Xx8fGzUqFG2adMma9u2rWXNmtWOHDnirKlevbq1bdvWZevsK3/Pjh8/bkOGDLFVq1bZ7t27bc6cOVakSBFr3Lixs+ZyKLJ161aXfq7c+ap79+5WokQJl/Y//vjD2b5hwwZ7+umnbfr06bZjxw6bO3eu3XffffbMM8+43Pd//vMf+/TTT61p06ZXDUXmzJnjcq0rg8IlS5aYp6en9enTxzZv3mxvvfVWmhD1ynMPHTpko0aNMofDYTt37jSzSzuCtWjRwn766SfbuXOnTZs2zcLCwuy1115LM94iRYrYtGnT7Pfff7fVq1e7/DO0Zs0aGz9+vG3cuNF27dplX375pQUEBNjw4cOdNX/n9wOuHGZmN/vpEwAAAADAzffHH38oLCxMCxcuVLVq1dKtiYiI0JtvvqnY2FjnsWeeeUb+/v4aN26cpEvr0Nx///369NNPJV16TSsqKkr/+c9/1K1bN0lSjRo1VLZsWQ0YMCDT4xs4cKD69u2rffv2Sbq0uPDDDz+sEydOKGvWrOme06NHD3333XfO18AyY9KkSWrSpInOnj0rLy/X/UMy6m/37t3Knz+/1q5dq7Jly6bbb6NGjXT27FnNmDHDeeyBBx5Q2bJlNWzYsHTPeeqpp3TmzJk0r+ldqW/fvho6dKh+//13SdKWLVtUunRpbdy4UUWKFMnEHV/y9NNPKzAwUF9++WW67Zn5/YAr1hQBAAAAgLvEqVOnJEmhoaEZ1iQlJcnPz8/lmL+/v3755RdJ0oULFxQfH69atWo52z08PFSrVi0tW7bM5byvvvpKOXLkUMmSJRUXF+dc/yU9Bw8e1JQpU1S9evU0bWXLllXu3LlVu3ZtLVmyJE379u3bFRERoQIFCujFF1+85oLAp06dUnBwcJpAJDOefPJJhYWF6cEHH9T06dNd2pYtW+YyL5JUt27dNPNy2ZEjR/TDDz9cc4HhU6dOufzMvv/+exUoUEAzZsxQ/vz5lS9fPrVp00bHjx/PsI+1a9dq6dKl6c7vldeRrv77AVeEIgAAAABwF0hNTVWnTp1UtWpVlSxZMsO6unXr6uOPP9b27duVmpqq2bNna8qUKTp06JAk6c8//1RKSopy5crlcl6uXLl0+PBh5/cXXnhB48aN0/z58xUXF6cvv/wy3YV0GzdurICAAOXJk0fBwcH6/PPPnW25c+fWsGHD9O233+rbb79VVFSUatSooTVr1jhrKleurDFjxmjWrFkaOnSodu3apYceekhnzpxJ9/7+/PNPvffeey5bU2dGUFCQPvroI02aNEk//PCDHnzwQT311FMuwcjhw4evOS9X+uKLL5QlSxY9/fTTGV53x44dGjRokP797387j/3+++/as2ePJk2apLFjx2rMmDGKj4/Xv/71rzTnR0ZGytfXVxUrVlRsbKzatGmT7nUy+/uBv7jT7+8AAAAAAK6tffv2Fh0dbfv27btq3dGjR61Bgwbm4eFhnp6eVrhwYXv55ZfNz8/PzMwOHDhgkmzp0qUu53Xp0sUqVaqUYb9z5841SbZjxw6X44cOHbItW7bYtGnTrHjx4vbSSy9ddXzVqlWzJk2aZNh+4sQJCw4Ots8//zxN26lTp6xSpUpWr149l7VArtS9e/d01xRJT9OmTe3BBx90fvf29rbx48e71AwePNjCwsLSPb9IkSLWoUOHDPvfv3+/FSxY0Fq3bu1yvG3bts61Vi6Lj483SWkWSv39999t/fr19tlnn1loaGia8V2W2d8PuOJJEQAAAAD4h+vQoYNmzJih+fPnKzIy8qq1OXPm1HfffaezZ89qz549+u233xQUFKQCBQpIknLkyCFPT08dOXLE5bwjR44oPDw8w34rV64s6dKTD1cKDw9X0aJF9eSTT2r48OEaOnSo86mU9FSqVClNH1fKmjWrChcunKbmzJkzqlevnrJkyaKpU6fK29s7wz4yq3Llyi7XCQ8Pz/S8LF68WFu3bs3wyY2DBw/q4YcfVpUqVfTZZ5+5tOXOnVteXl4qXLiw81ixYsUkKc2rQ/nz51epUqXUtm1bvfrqq+rRo0eaa13P7wdcEYoAAAAAwD+UmalDhw6aOnWq5s2bp/z582f6XD8/P+XJk0cXL17Ut99+qwYNGkiSfHx8VKFCBZeFQVNTUzV37lzFxMRk2N/lhUtz586dYU1qaqqkS+uaXK2fq/WRkJCgnTt3utScPn1aderUkY+Pj6ZPn55mzZS/669jiYmJSbNg6uzZs9Odl5EjR6pChQoqU6ZMmrYDBw6oRo0aqlChgkaPHi0PD9c/vatWraqLFy9q586dzmPbtm2TJEVHR2c43tTUVJe5vZHfD1xy/avSAAAAAABui9jYWI0fP17Tpk1TlixZnGtbhISEyN/fX5LUrFkz5cmTR7169ZIkrVixQgcOHFDZsmV14MAB9ejRQ6mpqXrjjTec/Xbu3FnNmzdXxYoVValSJQ0YMEBnz55Vy5YtJUk7d+7U+PHjVb9+fWXPnl3r16/Xq6++qmrVqql06dKSpJkzZ+rIkSO6//77FRQUpE2bNqlLly6qWrWq8uXLJ0kaMGCA8ufPrxIlSuj8+fP6/PPPNW/ePP3888/Osbz++ut64oknFB0drYMHD6p79+7y9PRU48aNJf1fIJKYmKhx48bp9OnTOn36tKRLT8V4enpKuvQES0JCgg4fPqxz5845Q5zixYvLx8dHX3zxhXx8fFSuXDlJ0pQpUzRq1CiXNVA6duyo6tWr66OPPtJjjz2miRMnavXq1Wme9Dh9+rQmTZqkjz76KM3P7HIgEh0drX79+umPP/5wtl1+4qRWrVoqX768WrVqpQEDBig1NVWxsbGqXbu28+mRwYMHK2/evCpatKgkadGiRerXr59eeeWV6/r9wDXc6fd3AAAAAADpk5TuZ/To0c6a6tWrW/PmzZ3fFyxYYMWKFTNfX1/Lnj27NW3a1A4cOJCm70GDBlnevHnNx8fHKlWqZMuXL3e27d2716pVq2ahoaHm6+trhQoVsi5dutipU6ecNfPmzbOYmBgLCQkxPz8/u++++6xr16524sQJZ82HH35oBQsWND8/PwsNDbUaNWrYvHnzXMbRqFEjy507t/n4+FiePHmsUaNGLuuWzJ8/P8N52LVrl8s8XK1mzJgxVqxYMQsICLDg4GCrVKmSTZo0Kc28fPPNN1a4cGHz8fGxEiVK2A8//JCmZvjw4ebv728nT55M0zZ69OgMx3ulAwcO2NNPP21BQUGWK1cua9GihR07dszZPnDgQCtRooRzvOXKlbMhQ4ZYSkqKsyYzvx+4OoeZ2W1JXwAAAAAAAP5BWFMEAAAAAAC4JUIRAAAAAADglghFAAAAAACAWyIUAQAAAAAAbolQBAAAAAAAuCVCEQAAAAAA4JYIRQAAAAAAgFsiFAEAAAAAAG6JUAQAAAAAALglQhEAAAAAAOCWCEUAAAAAAIBbIhQBAAAAAABuiVAEAAAAAAC4JUIRAAAAAADglghFAAAAAACAWyIUAQAAAAAAbolQBAAAAAAAuCVCEQAAAAAA4JYIRQAAAAAAgFsiFAEAAAAAAG6JUAQAAAAAALglQhEAAAAAAOCWCEUAAAAAAIBbIhQBAAAAAABuiVAEAAAAAAC4JUIRAAAAAADglghFAAAAAACAWyIUAQAAAAAAbolQBAAAAAAAuCVCEQAAAAAA4JYIRQAAAAAAgFsiFAEAAAAAAG6JUAQAAAAAALglrzs9gNspNTVVBw8eVJYsWeRwOO70cAAAAAAA9zgz05kzZxQRESEPD55L+Kdxq1Dk4MGDioqKutPDAAAAAAC4mX379ikyMvJODwN/4VahSJYsWSRd+mUMDg6+w6MBAAAAANzrTp8+raioKOffo/hncatQ5PIrM8HBwYQiAAAAAIDbhiUc/pl4oQkAAAAAALglQhEAAAAAAOCWCEUAAAAAAIBbcqs1RQAAAAAA+KcxM128eFEpKSl3eij3BE9PT3l5eWVqHRdCEQAAAAAA7pALFy7o0KFDSkxMvNNDuacEBAQod+7c8vHxuWodoQgAAAAAAHdAamqqdu3aJU9PT0VERMjHx4ddam6QmenChQv6448/tGvXLt13333y8Mh45RBCEQAAAAAA7oALFy4oNTVVUVFRCggIuNPDuWf4+/vL29tbe/bs0YULF+Tn55dhLQutAgAAAABwB13tSQb8PZmdU2YeAAAAAAC4JUIRAAAAAADglghFAAAAAACAWyIUAQAAAADgLnP48GH95z//UYECBeTr66uoqCg98cQTmjt3bqbOHzNmjLJmzXprB3kXYPcZAAAAAADuIrt371bVqlWVNWtW9e3bV6VKlVJycrJ++uknxcbG6rfffrvTQ7xuycnJ8vb2vu3X5UkRAAAAAADuIi+//LIcDodWrlypZ555RoULF1aJEiXUuXNnLV++XJL08ccfq1SpUgoMDFRUVJRefvllJSQkSJIWLFigli1b6tSpU3I4HHI4HOrRo4ckKSkpSa+//rry5MmjwMBAVa5cWQsWLHC5/ogRI5zbCDds2FAff/xxmqdOhg4dqoIFC8rHx0dFihTRl19+6dLucDg0dOhQPfnkkwoMDNT//vc/FSpUSP369XOpW7dunRwOh3bs2HHzJvAKhCIAAAAAANwljh8/rlmzZik2NlaBgYFp2i+HEx4eHho4cKA2bdqkL774QvPmzdMbb7whSapSpYoGDBig4OBgHTp0SIcOHdLrr78uSerQoYOWLVumiRMnav369Xr22WdVr149bd++XZK0ZMkStW/fXh07dtS6detUu3Ztvf/++y5jmDp1qjp27KjXXntNGzdu1L///W+1bNlS8+fPd6nr0aOHGjZsqA0bNqh169Zq1aqVRo8e7VIzevRoVatWTYUKFbop8/dXDjOzW9LzP9Dp06cVEhKiU6dOKTg4+E4PBwAAAABwj7va36Hnz5/Xrl27lD9/fvn5+WWqv5UrV6py5cqaMmWKGjZsmOlxTJ48We3bt9eff/4p6dKaIp06ddLJkyedNXv37lWBAgW0d+9eRUREOI/XqlVLlSpV0gcffKDnn39eCQkJmjFjhrO9SZMmmjFjhrOvqlWrqkSJEvrss8+cNc8995zOnj2rH374QdKlJ0U6deqk/v37O2sOHjyovHnzaunSpapUqZKSk5MVERGhfv36qXnz5pm+Vynzc8uTIgAAAAAA3CUy+1zDnDlzVLNmTeXJk0dZsmRR06ZNdezYMSUmJmZ4zoYNG5SSkqLChQsrKCjI+Vm4cKF27twpSdq6dasqVarkct5fv2/ZskVVq1Z1OVa1alVt2bLF5VjFihVdvkdEROixxx7TqFGjJEnff/+9kpKS9Oyzz2bqnv8OFlr9hzEznUhM1tmkiwr09VK2AG85HI47PSwAAAAAwD/AfffdJ4fDcdXFVHfv3q3HH39cL730kt5//32Fhobql19+UevWrXXhwgUFBASke15CQoI8PT0VHx8vT09Pl7agoKCbeh+S0n39p02bNmratKn69++v0aNHq1GjRhmO92YgFPmHOHUuWd/G79cXS3drz/H/S+6iQwPUvEo+PVMhUiH+t38lXgAAAADAP0doaKjq1q2rwYMH65VXXkkTLJw8eVLx8fFKTU3VRx99JA+PSy+IfPPNNy51Pj4+SklJcTlWrlw5paSk6OjRo3rooYfSvX6RIkW0atUql2N//V6sWDEtWbLE5ZWXJUuWqHjx4te8v/r16yswMFBDhw7VrFmztGjRomuecyN4feYfYOG2PxTTa67em7FZe4+7Psq093ii3puxWTG95mrhtj/u0AgBAAAAAP8UgwcPVkpKiipVqqRvv/1W27dv15YtWzRw4EDFxMSoUKFCSk5O1qBBg/T777/ryy+/1LBhw1z6yJcvnxISEjR37lz9+eefSkxMVOHChfXiiy+qWbNmmjJlinbt2qWVK1eqV69ezrVA/vOf/2jmzJn6+OOPtX37dg0fPlw//vijyxsOXbp00ZgxYzR06FBt375dH3/8saZMmeJczPVqPD091aJFC8XFxem+++5TTEzMzZ28vyAUucMWbvtDLUev1LnkFJmkv74ddvnYueQUtRy9kmAEAAAAANxcgQIFtGbNGj388MN67bXXVLJkSdWuXVtz587V0KFDVaZMGX388cf68MMPVbJkSX311Vfq1auXSx9VqlRR+/bt1ahRI+XMmVN9+vSRdGm3l2bNmum1115TkSJF9NRTT2nVqlXKmzevpEtrgwwbNkwff/yxypQpo1mzZunVV191Wcz0qaee0ieffKJ+/fqpRIkSGj58uEaPHq0aNWpk6v4uv+bTsmXLmzNhV8HuM3fQqXPJiuk191IgkomfgsMh+Xt7allcTV6lAQAAAIC7wM3efeafqG3btvrtt9+0ePHim9Lf4sWLVbNmTe3bt0+5cuX6W32w+8xd4Nv4/Tp3IXOBiCSZSecupGjKmv23dmAAAAAAAGSgX79++vXXX7Vjxw4NGjRIX3zxxXVvmZuepKQk7d+/Xz169NCzzz77twOR60EocoeYmb5YuvtvnTtmye5Mb8MEAAAAAMDNtHLlStWuXVulSpXSsGHDNHDgQLVp0+aG+50wYYKio6N18uRJ5+s8txq7z9whJxKTXXaZySyTtOd4ok4mJitboM/NHxgAAAAAAFfx151sbpYWLVqoRYsWt6TvjPCkyB1yNuniDZ2fcIPnAwAAAADg7ghF7pBA3xt7SCfoBs8HAAAAAMDdEYrcIdkCvBUdGiDHtUtdOCRFhwYoawC7zwAAAAAAcCMIRe4Qh8Oh5lXy/a1zW1TNJ4fjeuMUAAAAAABwJUKRO+iZCpHy9/FUZvMND4fk7+Opp8tH3tqBAQAAAADgBu7aUKR3795yOBzq1KnTnR7K3xbi762hTSrIIV0zGLncPqxJBYX48+oMAAAAAAA36q4MRVatWqXhw4erdOnSd3ooN6x64Zwa3bKS/L09L4Ujf2m/fMzf21NjWlZStcI5b/8gAQAAAAC4B911oUhCQoJefPFFjRgxQtmyZbtqbVJSkk6fPu3y+SeqXjinlsXV1DtPFFfe0ACXtryhAXrnieJa/t+aBCIAAAAAANxEd10oEhsbq8cee0y1atW6Zm2vXr0UEhLi/ERFRd2GEf49If7ealk1vxZ0qaGwIF9JUliQrxZ0qaGWVfMr2I9XZgAAAAAA7uXChQu3tP+7KhSZOHGi1qxZo169emWqPi4uTqdOnXJ+9u3bd4tHeOMcDoc8PC69ROPh4WCXGQAAAADAP87kyZNVqlQp+fv7K3v27KpVq5bOnj0rSRo1apRKlCghX19f5c6dWx06dHCet3fvXjVo0EBBQUEKDg7Wc889pyNHjjjbe/ToobJly+rzzz9X/vz55efnJ0k6efKk2rRpo5w5cyo4OFiPPPKIfv311xu+D68b7uE22bdvnzp27KjZs2c7J+VafH195evre4tHBgAAAADAzfP54t/1+eJdt+16bR7KrzYPFch0/aFDh9S4cWP16dNHDRs21JkzZ7R48WKZmYYOHarOnTurd+/eevTRR3Xq1CktWbJEkpSamuoMRBYuXKiLFy8qNjZWjRo10oIFC5z979ixQ99++62mTJkiT09PSdKzzz4rf39//fjjjwoJCdHw4cNVs2ZNbdu2TaGhoX/73u+aUCQ+Pl5Hjx5V+fLlncdSUlK0aNEiffrpp0pKSnJOFgAAAAAAd6sz5y/q8Onzt/V61+PQoUO6ePGinn76aUVHR0uSSpUqJUn63//+p9dee00dO3Z01t9///2SpLlz52rDhg3atWuXc3mLsWPHqkSJElq1apWz7sKFCxo7dqxy5ry0ruYvv/yilStX6ujRo84HH/r166fvvvtOkydPVrt27f72vd81oUjNmjW1YcMGl2MtW7ZU0aJF1bVrVwIR3BAz04nEZJ1NuqhAXy9lC/Dm1aXbgHkHAAAA0sri56Xw4My9IXGzrnc9ypQpo5o1a6pUqVKqW7eu6tSpo3/9619KTk7WwYMHVbNmzXTP27Jli6KiolzW+yxevLiyZs2qLVu2OEOR6OhoZyAiSb/++qsSEhKUPXt2l/7OnTunnTt3XtfY/+quCUWyZMmikiVLuhwLDAxU9uzZ0xwHMuvUuWR9G79fXyzdrT3HE53Ho0MD1LxKPj1TIVIh/ixye7Mx7wAAAEDG2jxU4LpeZ7ndPD09NXv2bC1dulQ///yzBg0apDfffFNz5869Kf0HBga6fE9ISFDu3LldXrG5LGvWrDd0rbsmFAFutoXb/tBL4+J17kJKmra9xxP13ozN6vfzVg1tUkHV2Q75pmHeAQAAgLufw+FQ1apVVbVqVb3zzjuKjo7W7NmzlS9fPs2dO1cPP/xwmnOKFSumffv2ad++fc6nRTZv3qyTJ0+qePHiGV6rfPnyOnz4sLy8vJQvX76beh931e4zf7VgwQINGDDgTg8Dd6GF2/5Qy9ErdS45RSbJ/tJ++di55BS1HL1SC7f9cfsHeQ9i3gEAAIC734oVK/TBBx9o9erV2rt3r6ZMmaI//vhDxYoVU48ePfTRRx9p4MCB2r59u9asWaNBgwZJkmrVqqVSpUrpxRdf1Jo1a7Ry5Uo1a9ZM1atXV8WKFTO8Xq1atRQTE6OnnnpKP//8s3bv3q2lS5fqzTff1OrVq2/oXu7qUAT4O06dS9ZL4+Iv/QH+17/K/8Ls0h/pL42L16lzybdjePcs5h0AAAC4NwQHB2vRokWqX7++ChcurLfeeksfffSRHn30UTVv3lwDBgzQkCFDVKJECT3++OPavn27pEtPl0ybNk3ZsmVTtWrVVKtWLRUoUEBff/31Va/ncDg0c+ZMVatWTS1btlThwoX1/PPPa8+ePcqVK9cN3YvD7Fp/ntw7Tp8+rZCQEJ06dUrBwcF3ejgZeuCDuTp8+rzCg/20/L/pL1CDv2/UL7v03ozNaZ5SuBqHpHeeKK6WVfPfqmHd85h3AAAAuKOr/R16/vx57dq1S/nz55ef3+1bWNUdZHZueVIEbsXM9MXS3X/r3DFLdsuNMsSbinkHAAAA8E9EKAK3ciIxWXuOJ17X0wrSpVc59hxP1MlEXuX4O5h3AAAAAP9EhCJwK2eTLt7Q+Qk3eL67Yt4BAAAA/BMRisCtBPre2C7UQTd4vrti3gEAAAD8ExGKwK1kC/BWdGiAHNd5nkNSdGiAsgZ434ph3fOYdwAAACBjrKF382V2TglF4FYcDoeaV8n3t85tUTWfHI7r/bMeEvMOAAAApMfb+9K//EtMTLzDI7n3XJ7Ty3OcEZ5Jh9t5pkKk+v28VeeSU5SZ8NDDIfl5e+rp8pG3fnD3MOYdAAAAcOXp6amsWbPq6NGjkqSAgAD+heANMjMlJibq6NGjypo1qzw9Pa9aTygCtxPi762hTSqo5eiVkkNX/QP98n8fDWtSQSH+vMJxI5h3AAAAIK3w8HBJcgYjuDmyZs3qnNurIRSBW6peOKdGt6ykl8bF69yFFEly2S72cjbr7+2pYU0qqFrhnLd9jPci5h0AAABw5XA4lDt3boWFhSk5OflOD+ee4O3tfc0nRC4jFIHbql44p5bF1dSUNfs1Zslu7Tn+f+/x5Q0NUIuq+fRMhUgF+/Gkws3EvAMAAABpeXp6ZvoPedw8hCJwayH+3mpZNb9aVMmnyu/P1dGEJIUF+WpBlxq8y3cLMe8AAAAA/gnYfQbQpUfWPDwu/THu4eHgD/PbhHkHAAAAcCcRigAAAAAAALdEKAIAAAAAANwSoQgAAAAAAHBLhCIAAAAAAMAtEYoAAAAAAAC3xJa8AOBmzEwnEpN1NumiAn29lC3Am51/AAAA4JYIRQDATZw6l6xv4/fri6W7ted4ovN4dGiAmlfJp2cqRCrE3/sOjhAAAAC4vQhFAMANLNz2h14aF69zF1LStO09nqj3ZmxWv5+3amiTCqpeOOcdGCEAAABw+7GmCADc4xZu+0MtR6/UueQUmST7S/vlY+eSU9Ry9Eot3PbH7R8kAAAAcAcQigDAPezUuWS9NC7+UvDx1zTkL8wuhSMvjYvXqXPJt2N4AAAAwB1FKAIA97Bv4/fr3IWUawYil5lJ5y6kaMqa/bd2YAAAAMA/AKEIANyjzExfLN39t84ds2S3LLNJCgAAAHCXIhQBgHvUicRk7TmemGYNkWsxSXuOJ+pkIq/QAAAA4N5GKAIA96izSRdv6PyEGzwfAAAA+KcjFAGAe1Sg743tuh50g+cDAAAA/3SEIgBwj8oW4K3o0AA5rvM8h6To0ABlDfC+FcMCAAAA/jEIRQDgHuVwONS8Sr6/dW6LqvnkcFxvnAIAAADcXQhFAOAe9kyFSPn7eCqz+YaHQ/L38dTT5SNv7cDckJnp+NkL2nc8UcfPXmB3HwAAgH8AXhgHgHtYiL+3hjapoJajV0oO6Wp/h18OToY1qaAQf16duVlOnUvWt/H79cXS3dpzPNF5PDo0QM2r5NMzFSKZbwAAgDuEJ0UA4B5XvXBOjW5ZSf7ennJIadYYuXzM39tTY1pWUrXCOW//IO9RC7f9oZhec/XejM3ae0UgIkl7jyfqvRmbFdNrrhZu++MOjRAAAMC9EYoAgBuoXjinlsXV1DtPFFfe0ACXtryhAXrnieJa/t+aBCI30cJtf6jl6JU6l5wik/TXh3QuHzuXnKKWo1cSjAAAANwBhCIA4CZC/L3Vsmp+LehSQ2FBvpKksCBfLehSQy2r5lewH69w3CynziXrpXHxl4KPaywdYnYpHHlpXLxOnUu+HcMDAADA/0coAgBuxuFwyMPj0ks0Hh4Odpm5Bb6N369zF1KuGYhcZiadu5CiKWv239qBAQAAwAWhCAAAN5GZ6Yulu//WuWOW7GZXGgAAgNuIUAQAgJvoRGKy9hxPTLOGyLWYpD3HE3UykVdoAAAAbhdCEQAAbqKzSRdv6PyEGzwfAAAAmUcoAgDATRTo63VD5wfd4PkAAADIPEIRAABuomwB3ooODdD1Ll/rkBQdGqCsAewCBAAAcLsQigAAcBM5HA41r5Lvb53bomo+dgMCAAC4jQhFAAC4yZ6pECl/H09lNt/wcEj+Pp56unzkrR0YAAAAXBCKAABwk4X4e2tokwpySNcMRi63D2tSQSH+vDoDAABwOxGKAABwC1QvnFOjW1aSv7fnpXDkL+2Xj/l7e2pMy0qqVjjn7R8kAACAm2OJewAAbpHqhXNqWVxNTVmzX2OW7Nae44nOtryhAWpRNZ+eqRCpYD+eEAEAALgTCEUAALiFQvy91bJqfrWokk+V35+rowlJCgvy1YIuNVhUFQAA4A7j9RkAAG4Dh8MhD49LIYiHh4NABAAA4B+AUAQAAAAAALglQhEAAAAAAOCWCEUAAAAAAIBbIhQBAAAAAABuiVAEAAAAAAC4JUIRAAAAAADglrzu9AAAAABwbzEznUhM1tmkiwr09VK2AG+2oQYA/CMRigAAAOCmOHUuWd/G79cXS3drz/FE5/Ho0AA1r5JPz1SIVIi/9x0cIQAArghFAAAAcMMWbvtDL42L17kLKWna9h5P1HszNqvfz1s1tEkFVS+c8w6MEACAtFhTBAAAADdk4bY/1HL0Sp1LTpFJsr+0Xz52LjlFLUev1MJtf9z+QQIAkA5CEQAAAPxtp84l66Vx8ZeCj7+mIX9hdikceWlcvE6dS74dwwMA4KoIRQAAAPC3fRu/X+cupFwzELnMTDp3IUVT1uy/tQMDACATCEUAAADwt5iZvli6+2+dO2bJbllmkxRkipnp+NkL2nc8UcfPXmB+ASATWGgVAAAAf8uJxGSXXWYyyyTtOZ6ok4nJyhboc/MH5mbY9QcA/j5CEQAAAPwtZ5Mu3tD5CUkXCUVuELv+AMCN4fUZAAAA/C2Bvjf279eCbvB8d8euPwBw4+6aUGTo0KEqXbq0goODFRwcrJiYGP344493elgAAABuK1uAt6JDA+S4zvMcuvRqR9YAXun4u9j1BwBujrsmFImMjFTv3r0VHx+v1atX65FHHlGDBg20adOmOz00AAAAt+RwONS8Sr6/dW6LqvnkcFxvnILL2PUHAG6OuyYUeeKJJ1S/fn3dd999Kly4sN5//30FBQVp+fLld3poAAAAbuuZCpHy9/FUZvMND4fk7+Opp8tH3tqB3cPY9QcAbp67JhS5UkpKiiZOnKizZ88qJiYmw7qkpCSdPn3a5QMAAICbJ8TfW0ObVJBDumYwcrl9WJMK7IZyAy7v+nO90caVu/4Adzu2oMbNcletbrVhwwbFxMTo/PnzCgoK0tSpU1W8ePEM63v16qV33333No4QAADA/VQvnFOjW1Zy2QXlyj9PLmcl/t6eGtakgqqxC8oNYdcfuDO2oMbNdlc9KVKkSBGtW7dOK1as0EsvvaTmzZtr8+bNGdbHxcXp1KlTzs++fftu42gBAADcR/XCObUsrqbeeaK48oYGuLTlDQ3QO08U1/L/1iQQuQnY9QfuauG2PxTTa67em7FZe68IRKT/24I6ptdcdlrCdbmrQhEfHx8VKlRIFSpUUK9evVSmTBl98sknGdb7+vo6d6u5/AEAAMCtEeLvrZZV82tBlxoKC/KVJIUF+WpBlxpqWTW/gv34t7c3A7v+wB2xBTVulbsqFPmr1NRUJSUl3elhAAAA4AoOh0MeHpf+ZPfwcLDLzE3Grj9wN2xBjVvprglF4uLitGjRIu3evVsbNmxQXFycFixYoBdffPFODw0AAAC4rdj1B+6ELahxK901ocjRo0fVrFkzFSlSRDVr1tSqVav0008/qXbt2nd6aAAAAMBtxa4/cBdsQY1b7a5ZZWnkyJF3eggAAADAPwa7/sAdXN6C+npduQU1uy3hau6aUAQAAACAq8u7/kxZs19jlrhuUZo3NEAtql7aopRFbnG3Ygtq3GqEIgAAAMBd7PKuPy2q5FPl9+fqaEKSc9cfFlXF3Y4tqHGr3TVrigAAAADIGLv+4F7EFtS41QhFAAAAAAD/SGxBjVuNUAQAAAAA8I/FFtS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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "top_nodes = att.sort_values('score', key=lambda x: abs(x), ascending=False).head(10)\n",
-    "nc.visual.lollipop_plot(\n",
-    "    df=top_nodes,\n",
-    "    label_col='nodes',\n",
-    "    value_col='score',\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an optional step, we can translate the HMDB identifiers to more readable names (e.g HMDB0000122 is Glucose)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapping_dict = pd.read_csv(\"../../../data/moon/hmdb_mapper_vec.csv\", header=0).set_index('HMDB_id')['name'].to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "translated_network, att_translated = nc.methods.translate_res(res_network, att, mapping_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The resulted network can be used now for visualization purposes, or further studying of the topology can be conducted, as shown in Vignette 1. Since the network is quite big, it will not be shown in this notebook."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "networkcommons",
-   "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/src/vignettes/A_moon.ipynb b/docs/src/vignettes/A_moon.ipynb
new file mode 100644
index 0000000..fdff0e8
--- /dev/null
+++ b/docs/src/vignettes/A_moon.ipynb
@@ -0,0 +1,521 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vignette A: recursive propagation with MOON"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this vignette, we are going to use MOON:\n",
+    "\n",
+    "> Dugourd et al., Modeling causal signal propagation in multi-omic factor space with COSMOS. *bioRxiv (2024)*. https://doi.org/10.1101/2024.07.15.603538\n",
+    "\n",
+    " to iteratively compute enrichment scores for a prior knowledge network, taking metabolic measurements and signalling cascades as inputs. \n",
+    "\n",
+    "This is the python version of the MOON workflow detailed in this [R vignette](https://saezlab.github.io/cosmosR/articles/NCI60_tutorial.html) within the package CosmosR. For more information, please check the [MOON section](../methods.html#moon) in the Methods details and the [original CosmosR paper](https://doi.org/10.15252/msb.20209730).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkcommons as nc\n",
+    "import decoupler as dc\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Input preparation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first import the COSMOS network, including signalling, gene regulatory and metabolic networks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_network_df = nc.data.network.get_cosmos_pkn(update=True)\n",
+    "meta_network = nc.utils.network_from_df(meta_network_df, directed=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we aim to remove self-interactions, calculate the mean interaction values for duplicated source-target pairs, and keep only interactions with values of 1 or -1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_network = nc.methods.meta_network_cleanup(meta_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will use data from the NCI60 Human Tumor Cell Lines Screen. We will use the cell line 706-0. To have an overview of the cell lines, we can run `nc.data.omics.nci60_datasets()`. For more information, please check the [NCI60 details page](../datasets.html#nci60)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_line</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>786-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A549_ATCC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ACHN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BT-549</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cell_line\n",
+       "0      786-0\n",
+       "1       A498\n",
+       "2  A549_ATCC\n",
+       "3       ACHN\n",
+       "4     BT-549"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nc.data.omics.nci60_datasets().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This resource contains three different types of data: transcriptomics, TF activity estimates and metabolic information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data_type</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TF_scores</td>\n",
+       "      <td>TF scores</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RNA</td>\n",
+       "      <td>RNA expression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>metabolomic</td>\n",
+       "      <td>metabolomic data</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     data_type       description\n",
+       "0    TF_scores         TF scores\n",
+       "1          RNA    RNA expression\n",
+       "2  metabolomic  metabolomic data"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nc.data.omics.nci60_datatypes()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get measurements for different subnetworks within the cosmos network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='TF_scores')\n",
+    "rna_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='RNA')\n",
+    "metab_df = nc.data.omics.nci60_table(cell_line='786-0', data_type='metabolomic')\n",
+    "\n",
+    "sig_input = sig_df.set_index('ID')['score'].to_dict()\n",
+    "rna_input = rna_df.set_index('ID')['score'].to_dict()\n",
+    "metab_input = metab_df.set_index('ID')['score'].to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the metabolites, we add the compartment they are located in. We also remove those genes that do not apear in the PKN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metab_input = nc.methods.prepare_metab_inputs(metab_input, [\"c\", \"m\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_network = nc.methods.filter_pkn_expressed_genes(rna_input.keys(), meta_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We filter out those inputs that cannot be mapped to the prior knowledge network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_input = nc.methods.filter_input_nodes_not_in_pkn(sig_input, meta_network)\n",
+    "meta_network = nc.methods.keep_controllable_neighbours(sig_input, meta_network)\n",
+    "metab_input = nc.methods.filter_input_nodes_not_in_pkn(metab_input, meta_network)\n",
+    "meta_network = nc.methods.keep_observable_neighbours(metab_input, meta_network)\n",
+    "sig_input = nc.methods.filter_input_nodes_not_in_pkn(sig_input, meta_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Network compression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we will compress the network to reduce the dimensions of the problem and thus increase computational efficiency. For more information about how this is carried out, please check the dedicated [section](../methods.html#network-compression)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_network_compressed, signatures, dup_parents = nc.methods.compress_same_children(meta_network, sig_input, metab_input) # equals R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We clean the network again in case some self loops arose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_network_compressed = nc.methods.meta_network_cleanup(meta_network_compressed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. MOON scoring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now it is time to compute the MOON scores from the compressed network. The network has been compressed by around a third of its original size, which increases computational efficiency. We will use the metabolic inputs and the signalling inputs to compute the MOON scores. After each optimisation, we check the sign consistency of the MOON scores, and remove those edges that turn out to be incoherent (the real TF enrichment scores are compared against the computed MOON scores and the sign of the edge). If there are incoherent edges, the function computes the MOON scores on the reduced network. The loop continues until it reaches a maximum number of tries (in our example, 10) or there are no incoherent edges left (more details [here](../methods.html#moon-scoring))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get now the GRN from DoRothEA, filtering by levels of confidence A and B."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf_regn = dc.get_dorothea(levels = ['A', 'B'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moon_network = meta_network_compressed.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moon_res, moon_network = nc.methods.run_moon(\n",
+    "    meta_network_compressed,\n",
+    "    sig_input,\n",
+    "    metab_input,\n",
+    "    tf_regn,\n",
+    "    rna_input,\n",
+    "    n_layers=6,\n",
+    "    method='ulm',\n",
+    "    max_iter=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Decompression and solution network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the MOON scores are computed, we need to restore the uncompressed nodes that were compressed in section 2. For this, we will use the signatures that we obtained when we compressed the network to map back the original nodes to the compressed ones. After that, we can retrieve a solution network that contains the nodes (with the subsequent MOON scores) that are in the vicinity of the signalling input(s) and are sign consistent in terms of signed interactions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moon_res_dec = nc.methods.decompress_moon_result(moon_res, signatures, dup_parents, meta_network_compressed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we perform the decompression of the network, mapping the compressed nodes to their original components."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FInally, we reduce the solution network by removing incoherent edges and filtering for nodes with moon scores higher than 1. We retrieve a networkx.DiGraph that we will visualise, and an attributes dataframe with the moon scores."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_network, att = nc.methods.reduce_solution_network(moon_res_dec, meta_network, 1, sig_input, rna_input) # equals R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualise the 10 nodes with highest (absolute) score:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_nodes = att.sort_values('score', key=lambda x: abs(x), ascending=False).head(10)\n",
+    "p = nc.visual.lollipop_plot(\n",
+    "    df=top_nodes,\n",
+    "    label_col='nodes',\n",
+    "    value_col='score',\n",
+    "    label_gap=0.03\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an optional step, we can translate the HMDB identifiers to more readable names (e.g HMDB0000122 is Glucose)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_dict = pd.read_csv(\"../../../data/moon/hmdb_mapper_vec.csv\", header=0).set_index('HMDB_id')['name'].to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "translated_network, att_translated = nc.methods.translate_res(res_network, att, mapping_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulted network can be used now for visualization purposes, or further studying of the topology can be conducted, as shown in Vignette 1. Since the network is quite big, it will not be shown in this notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "networkcommons",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}