MS_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from VANE import VANE\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick a few variants from MS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A subset of 30 SNPs where randomly selected from the known 200 significant variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms_variants = ['rs35486093','rs11256593', 'rs112344141', 'rs1323292', 'rs72928038', 'rs78727559', 'rs1800693', 'rs10801908', 'rs6670198',\n",
    "'rs62420820', 'rs1738074', 'rs4939490', 'rs9843355', 'rs11809700', 'rs35540610', 'rs1026916', 'rs1014486', 'rs6589706', 'rs11079784',\n",
    "'rs11749040', 'rs631204', 'rs4808760', 'rs12478539', 'rs7977720', 'rs3809627', 'rs12365699', 'rs6032662', 'rs60600003', 'rs2546890', 'rs1465697']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing LD blocks for set of tagging SNPs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not obligatory to initiate the object with leading SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MS_variant = VANE(list_of_variants = ms_variants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We process the tagging SNPs with a LD block, to get a possible set of causal variants. You can specify the target population from 1000 Genome for the LD block,\n",
    "from here: https://rest.ensembl.org/documentation/info/variation_populations  \n",
    "If None is indicated a white european population is used. \n",
    "\n",
    "Verbose option can be set to TRUE to print the details of the LD parsing script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_positions = MS_variant.process_tag_variants(ld=0.7, population = \"1000GENOMES:phase_3:CEU\", verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chromosome</th>\n",
       "      <th>position</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>rsid</th>\n",
       "      <th>tagging_snp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85258126</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>rs12561876</td>\n",
       "      <td>rs35486093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85277178</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>rs11161583</td>\n",
       "      <td>rs35486093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85283878</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>rs6693229</td>\n",
       "      <td>rs35486093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85253927</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>rs4074156</td>\n",
       "      <td>rs35486093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85249122</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>rs12743302</td>\n",
       "      <td>rs35486093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>49319790</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>rs10414921</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>49311575</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>rs3760667</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>49384752</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>rs7254187</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>49387616</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>rs7251704</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>49333989</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>rs1465697</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1142 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   chromosome  position ref alt        rsid tagging_snp\n",
       "0           1  85258126   A   C  rs12561876  rs35486093\n",
       "0           1  85277178   C   A  rs11161583  rs35486093\n",
       "0           1  85283878   C   T   rs6693229  rs35486093\n",
       "0           1  85253927   T   C   rs4074156  rs35486093\n",
       "0           1  85249122   C   T  rs12743302  rs35486093\n",
       "..        ...       ...  ..  ..         ...         ...\n",
       "0          19  49319790   C   T  rs10414921   rs1465697\n",
       "0          19  49311575   C   T   rs3760667   rs1465697\n",
       "0          19  49384752   T   C   rs7254187   rs1465697\n",
       "0          19  49387616   T   C   rs7251704   rs1465697\n",
       "0          19  49333989   C   T   rs1465697   rs1465697\n",
       "\n",
       "[1142 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        In case that you want to use fine-mapped SNPs for any given LOCUS, you can use this function = process_list_finemapped()\n",
    "        list_of_locus_name = List for how you want to call the locus. \n",
    "        list_of_finemapped = This is a list of lists. For each locus you want a list of the finemapped SNPs in rsid format.\n",
    "        Both list_of_locus_name, and list_of_fiemapped SNPs need to be in order.\n",
    "        \n",
    "        Example: \n",
    "        \n",
    "        You have three locus, locus 1, locus 2, and locus 3. You have three SNPs rs2, rs3, and rs4, finemapped to locus 1, \n",
    "        rs42, rs32, and rs11 finemapped to Locus 2, and rs111, rs321, and rs33, finemapped to locus 3.\n",
    "\n",
    "        This how it should be formatted:\n",
    "\n",
    "        list_of_locus_name = ['locus_1', 'locus_2', 'locus_3']\n",
    "        list_of_finemapped = [['rs2', 'rs3', 'rs4'], ['rs42', 'rs32', 'rs11'], ['rs111', 'rs321', 'rs33']]\n",
    "\n",
    "        This output a dataframe identical to the one outputted fron process_tag_variants()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SNP 2 Gene querying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to map every SNP to a gene. We use OpenTargetGenetics machine learning powered algorithm from Gazal Et. Al 2022 Nat Genet. that combines multiple sources of data\n",
    "and a Random Forest Classifier to calculate the probability of each SNP affecting a gene.\n",
    "\n",
    "OpenTargetGenetics has open data availability, we download the source parquet files, merge, cleaned and re-formatted them into a single sql datafile for faster and\n",
    "easier access. If you followed the detailed installation guide, the file location should be at  \"base_files/clean_snp2gene.db\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "snp_2_gene = MS_variant.query_snp_opentargets(df_with_positions, \"base_files/clean_snp2gene.db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge and cleaning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We merge and clean our two files recently created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_to_rsid = MS_variant.merge_and_clean(snp_2_gene, df_with_positions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are returned a pandas dataframe with genes and the amount of SNPs mapped to them. \n",
    "You can further trim the list of genes using the amount of SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>rsid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000013725</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000016402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000019144</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000027697</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000067182</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_id  rsid\n",
       "0  ENSG00000013725     4\n",
       "1  ENSG00000016402     1\n",
       "2  ENSG00000019144    50\n",
       "3  ENSG00000027697     9\n",
       "4  ENSG00000067182     9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_to_rsid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have our cleaned SNP 2 Gene file. You can also trim your list down using the information here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rsid</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>position</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>overall_score</th>\n",
       "      <th>tagging_snp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rs767455</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "      <td>6341779</td>\n",
       "      <td>12</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.506640</td>\n",
       "      <td>rs1800693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>rs7970116</td>\n",
       "      <td>ENSG00000069493</td>\n",
       "      <td>9727518</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>rs7977720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>rs12709914</td>\n",
       "      <td>ENSG00000104901</td>\n",
       "      <td>49358133</td>\n",
       "      <td>19</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.400402</td>\n",
       "      <td>rs1465697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>rs9894179</td>\n",
       "      <td>ENSG00000178852</td>\n",
       "      <td>47487727</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0.367203</td>\n",
       "      <td>rs11079784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>rs9914653</td>\n",
       "      <td>ENSG00000178852</td>\n",
       "      <td>47460029</td>\n",
       "      <td>17</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.367203</td>\n",
       "      <td>rs11079784</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7258</th>\n",
       "      <td>rs11743463</td>\n",
       "      <td>ENSG00000171522</td>\n",
       "      <td>40360356</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0.099598</td>\n",
       "      <td>rs11749040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7259</th>\n",
       "      <td>rs80057400</td>\n",
       "      <td>ENSG00000155849</td>\n",
       "      <td>37365874</td>\n",
       "      <td>7</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.232596</td>\n",
       "      <td>rs60600003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7260</th>\n",
       "      <td>rs12475946</td>\n",
       "      <td>ENSG00000152518</td>\n",
       "      <td>43115709</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.106237</td>\n",
       "      <td>rs12478539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7261</th>\n",
       "      <td>rs28482033</td>\n",
       "      <td>ENSG00000155849</td>\n",
       "      <td>37394597</td>\n",
       "      <td>7</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.199396</td>\n",
       "      <td>rs60600003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7262</th>\n",
       "      <td>rs651973</td>\n",
       "      <td>ENSG00000118503</td>\n",
       "      <td>137674997</td>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.066398</td>\n",
       "      <td>rs631204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1120 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            rsid          gene_id   position chromosome ref alt  \\\n",
       "13      rs767455  ENSG00000067182    6341779         12   T   C   \n",
       "23     rs7970116  ENSG00000069493    9727518         12   A   G   \n",
       "26    rs12709914  ENSG00000104901   49358133         19   C   T   \n",
       "38     rs9894179  ENSG00000178852   47487727         17   C   A   \n",
       "44     rs9914653  ENSG00000178852   47460029         17   T   C   \n",
       "...          ...              ...        ...        ...  ..  ..   \n",
       "7258  rs11743463  ENSG00000171522   40360356          5   C   A   \n",
       "7259  rs80057400  ENSG00000155849   37365874          7   T   C   \n",
       "7260  rs12475946  ENSG00000152518   43115709          2   C   T   \n",
       "7261  rs28482033  ENSG00000155849   37394597          7   T   C   \n",
       "7262    rs651973  ENSG00000118503  137674997          6   G   A   \n",
       "\n",
       "      overall_score tagging_snp  \n",
       "13         0.506640   rs1800693  \n",
       "23         0.300000   rs7977720  \n",
       "26         0.400402   rs1465697  \n",
       "38         0.367203  rs11079784  \n",
       "44         0.367203  rs11079784  \n",
       "...             ...         ...  \n",
       "7258       0.099598  rs11749040  \n",
       "7259       0.232596  rs60600003  \n",
       "7260       0.106237  rs12478539  \n",
       "7261       0.199396  rs60600003  \n",
       "7262       0.066398    rs631204  \n",
       "\n",
       "[1120 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MS_variant.snp_2_gene_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regulome DB "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the SNP2GENE table, we need to query regulomeDB. This table tend to be pretty big so we implemented two ways to deal with it.\n",
    "First you can use the option snp_2_gene_all=None. If None, the whole tables saved in the object's instance. You can loop through the smaller chunks of DF \n",
    "if you are having troubles with querying RegulomeDB for a extensive amount of variants (usually 5k variants take about an hour to be processed). \n",
    "We can also use the save=True option, in case of memory troubles. If save=True, the DF will be saved as an instance of the object.\n",
    "\n",
    "You can track progression using verbose=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "MS_variant.regulomedb_scoring(save=True, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cellular level processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a list of Cells to retrieve the information from the main table. In this case, since we are studying a list of MS variants,\n",
    "we are interested in lymph and brain cells. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_list = ['B', 'T', 'mono', 'astrocyte', 'neutrophil', 'keratinocyte', 'melanocyte']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_specific</th>\n",
       "      <th>count</th>\n",
       "      <th>score</th>\n",
       "      <th>normalized score</th>\n",
       "      <th>gene</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B</td>\n",
       "      <td>117</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.914530</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T</td>\n",
       "      <td>414</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.990338</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>astrocyte</td>\n",
       "      <td>36</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>keratinocyte</td>\n",
       "      <td>54</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.870370</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>melanocyte</td>\n",
       "      <td>18</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>astrocyte</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>keratinocyte</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>melanocyte</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>mono</td>\n",
       "      <td>10</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>neutrophil</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell_specific  count  score  normalized score             gene\n",
       "0               B    117  107.0          0.914530  ENSG00000067182\n",
       "1               T    414  410.0          0.990338  ENSG00000067182\n",
       "2       astrocyte     36   35.0          0.972222  ENSG00000067182\n",
       "3    keratinocyte     54   47.0          0.870370  ENSG00000067182\n",
       "4      melanocyte     18   16.0          0.888889  ENSG00000067182\n",
       "..            ...    ...    ...               ...              ...\n",
       "331     astrocyte      4    0.0          0.000000  ENSG00000168811\n",
       "332  keratinocyte      6    0.0          0.000000  ENSG00000168811\n",
       "333    melanocyte      2    0.0          0.000000  ENSG00000168811\n",
       "334          mono     10   -1.0         -0.100000  ENSG00000168811\n",
       "335    neutrophil      2    0.0          0.000000  ENSG00000168811\n",
       "\n",
       "[336 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MS_variant.process_cells(cell_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting cell results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to check how's absolute Gene's epigenomic state is in each cell we can plot it this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None-cell_specific', ylabel='protein_gene'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MS_variant.plot_cells()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check f data for the heatmap you can call it by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_genewise_standarize  = ((MS_variant.df_for_network.T - MS_variant.df_for_network.T.mean()) / MS_variant.df_for_network.T.std()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None-cell_specific', ylabel='protein_gene'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pxpMSQgghwqK3EdWr8ONQK1euRJs2bdCiRQsEBwejUaNGyMzMxNGjRxEWFoZ79+4BAD58+IDExERkZGTgyZMn2Lx5M9avX4+QkBDY2toCAOrWrctp+82bNwBy3jjMnWw/cuRIrFixApMnT8awYcNw/Phx7NixAwcPHlQbY367ndMQIiGEEFI+VPjf2DY2Nrh27Rrmzp2LiRMnIiEhAVWqVEGzZs0QFhbG1psxYwZmzJgBPT09yGQytGrVCtHR0ejQoUOx7mdtbY2DBw9i/PjxWLZsGWrVqoX169cXa9kHQgghpKwp7r6GFQmj4mOWOPnqEhPfih2C4CxM9MUOQXBr9t8WOwRBtW0oEzsEwT19mSx2CIJroa8QOwRB3VoVVnilcq7z5i2C3yN62FBe2um0IYKXdsqSCj1nixBCCCFEaBV+GJEQQgghpUfDiOpRskUIIYSQUmMYGixTh5ItQgghhJQe9WypRWloESUmJmLs2LGwsbGBRCKBpaUlunfvzq6ZZWVlxa4RYmBgACsrK/Tp0wfHjx/Pt73IyEg0atQI+vr6qFq1KrsOFyGEEEI0CyVbRRAXF4dmzZrh+PHj+P3333Hr1i0cOnQIHTp04CRJwcHBSEhIQExMDDZt2gQzMzO4ublh7ty5nPaWLFmCX3/9FVOnTsWdO3dw7NgxWvqBEEJIuUaLmqpHw4hFMHr0aDAMg8uXL8PIyIg93qBBAwwbNoz9bGxsDJks51X32rVro127dqhevTpmzJiB3r17w97eHu/fv8dvv/2G/fv3s9v5AECjRo2KFVPm3eulfKqy71k+q/prmpRPlcUOQVCHrjwTOwTBje/tLHYIgovZsE7sEARlO3ex2CFoBE3daocP9M0U4t27dzh06BD8/Pw4iVau3JXh1fH394dKpcLff/8NIGdrnuzsbDx//hyOjo6oVasW+vTpg//9739ChE8IIYQQkVGyVYjY2FioVCo4ODiU6Hpzc3NUrVoVcXFxAIDHjx8jOzsb8+bNQ2hoKHbu3Il3797hu+++Q3p6Oo+RE0IIIV8Rw/BTNBANIxaCjwX2VSoVOw6dnZ2NjIwMLF++HJ07dwYA/PXXX5DJZDhx4kS+c7eUSiWUSiX3WHo6JHp6pY6NEEII4QMNI6pH30wh6tWrB4ZhcP/+/RJd//btW7x+/RrW1tYAgOrVqwMA6tevz9apUqUKKleujPj4+HzbCAkJgampKaes/GtbieIhhBBCyNdFyVYhzM3N4e7ujpUrVyI1nwnbSUlJBV6/bNkyaGlpoWfPngCANm3aAABiYmLYOu/evcObN29Qp06dfNsIDAyEQqHgFL/+/Ur2QIQQQogQaBhRLRpGLIKVK1eiTZs2aNGiBYKDg9GoUSNkZmbi6NGjCAsLw7179wAAHz58QGJiIjIyMvDkyRNs3rwZ69evR0hICGxtbQEAdnZ2+OGHH+Dv74+1a9fCxMQEgYGBcHBwQIcOHfK9v0QigUQi4RxLpiFEQgghZQgNI6pH30wR2NjY4Nq1a+jQoQMmTpyIhg0b4rvvvkN0dDTCwv5vt/gZM2agevXqsLW1xU8//QSFQoHo6GhMmTKF096mTZvQsmVLdOvWDe3bt4euri4OHToEXV3dr/1ohBBCCBEYo+JjBjj56g5XgGHENgsXiB2C4HZeeyN2CIKyMJEUXqmcq1rJUOwQBGero9lr3r3QMRU7BME5OdQW/B5nx/vz0k7bpct4aacsoWFEQgghhJQe7Y2oFiVbhBBCCCk1hqGZSerQN0MIIYQQIiDq2SKEEEJIqTE0jKgW9WwRQgghpPQYLX5KMZ0+fRrdu3dHjRo1wDAM9u7dW2D9kydPgmGYPCUxMbGED164CpFsJSYmYuzYsbCxsYFEIoGlpSW6d++O6OhovHjxApUqVcLy5cs511y6dAm6uro4cuQIACA9PR0LFy5E48aNYWhoiMqVK6NNmzaIiIhARkYGAMDb25v9Q9PV1YW1tTUmT56MT58+cdq2srLK84c8f/78r/NlEEIIIRokNTUVjRs3xsqVK4t1XUxMDBISEthStWpVgSKsAMOIcXFxaNOmDczMzPD777/DyckJGRkZOHz4MPz8/HD//n388ccf+Pnnn9GlSxfUq1cPHz9+xJAhQzB8+HB07twZ6enpcHd3x40bNzB79my0adMGJiYmuHjxIhYtWgRnZ2c0adIEAODh4cEmYFevXsWQIUPAMAwWLOAuYxAcHAxfX1/2s7Gx8df8WgghhBBeiTWM2KVLF3Tp0qXY11WtWhVmZmb8B5QPjU+2Ro8eDYZhcPnyZRgZGbHHGzRogGHDhgEABg0ahN27d8Pb2xtnzpxBYGAgMjIy8PvvvwMAQkNDcfr0aVy5cgXOzs5sGzY2NvDy8kJ6ejp7TCKRQCaTAQAsLS3h5uaGo0eP5km2jI2N2Xol8c2vv5b42vJCqwIs8qqjo9lzHHZfeiV2CIKbM7iR2CEILvnqLbFDEJTWixdihyA8h1GC36K8vY3YpEkTKJVKNGzYEDNnzmS30xNC+fpmiundu3c4dOgQ/Pz8OIlWrs8z2tWrV+Phw4cYOHAgVqxYgYiICEilUgDAli1b4Obmxkm0cunq6ubbNgDcvn0b58+fh14+W+vMnz8fFhYWcHZ2xu+//47MzMwSPiUhhBCiOZRKJZKTkzlFqVTy1n716tWxevVq7Nq1C7t27YKlpSVcXV1x7do13u7xJY3u2YqNjYVKpYKDg0OhdatWrYrZs2dj5MiRGDVqFNq1a8eee/jwIVxdXYt0zwMHDkAqlSIzMxNKpRJaWlpYsWIFp864cePQtGlTmJub4/z58wgMDERCQgKWLFlSrOcjhBBCygyehhFDQkIwa9YszrGgoCDMnDmTl/bt7e1hb2/PfnZxccGjR4+wdOlS/Pnnn7zc40sanWwVZyeirKwsREZGwtDQEBcvXkRmZiZ0dHSK3U6HDh0QFhaG1NRULF26FDo6OvD09OTUmTBhAvtzo0aNoKenh59//hkhISF5NpwGcrL8L7N6ZXo6JLQZNSGEkDKCr2HEwMBAzu9JAPn+buRTixYtcPbsWcHa1+hhxHr16oFhGNy/f7/QuosWLcLjx49x5coVPHv2DPPmzWPP2dnZFakNADAyMoKtrS0aN26MDRs24NKlSwgPDy/wmpYtWyIzMxNxcXH5ng8JCYGpqSmnhK5fX6R4CCGEkPJEIpHAxMSEU4ROtuRyOapXry5Y+xqdbJmbm8Pd3R0rV65EamrejVSTkpIAAHfu3EFQUBDCwsLg6OiIsLAwzJkzBzdv3gQADBgwAMeOHcP169fztJGRkZFv2wCgpaWFadOm4bfffsPHjx/VximXy6GlpaX2tdPAwEAoFApOCRg+vLDHJ4QQQr4eLYafUkwpKSmQy+WQy+UAgCdPnkAulyM+Ph5Azu/QwYMHs/VDQ0Px999/IzY2Frdv30ZAQACOHz8OPz8/Xr6G/Gh0sgUAK1euRFZWFlq0aIFdu3bh4cOHuHfvHpYvX47WrVsjMzMTQ4YMwY8//ogff/wRAODp6QlPT094e3sjMzMTAQEBaNOmDTp16oSVK1fixo0bePz4MXbs2IFWrVrh4cOHau/v5eUFbW1tdv2PCxcuIDQ0lG1jy5YtGD9+PAYNGoRKlSrl20a+WT4NIRJCCClDGEaLl1JcuSsF5L7ENmHCBDg7O2PGjBkAgISEBDbxAnLWzZw4cSKcnJzQvn173LhxA8eOHUOnTp34+SLywaiKMyGpnEpISMDcuXNx4MABJCQkoEqVKmjWrBnGjx+P06dPIywsDHfu3IG5uTl7zbt379CgQQOMGjUKM2bMgFKpxNKlS7F161Y8fPgQhoaGcHR0hK+vLwYOHAgdHR14e3sjKSkpz+q18+fPx5IlS/DkyRPExMRg9OjRuH//PpRKJaytrfHTTz9hwoQJxeom/V/0Eb6+njIrtQK8jv3HayuxQxBUPZm+2CEI7qfOhb+AU959vC3cW1plQSXbemKHIDijWpaC3+O/4CBe2mk+Y1bhlcqZCpFsaSJKtjQDJVvlHyVb5R8lW/ygZEs9jX4bkRBCCCFfCaPZizSXBiVbhBBCCCk1Rkvjp4GXGH0zhBBCCCECop4tQgghhJQaQ8OIamlUz5a3tzcYhgHDMNDV1UW1atXw3XffYcOGDcjOzmbrWVlZITQ0lPM59zojIyM0bdoUUVFR7PmZM2eiSZMmnHudOXMGZmZmCAgIgEqlwp07d+Dp6cm29Xn7X7pw4QK0tbXRrVs3vh6dEEIIEZeWFj9FA2ncU3l4eCAhIQFxcXH4999/0aFDB/j7++P7778vcLPn4OBgJCQk4Pr162jevDn69u2L8+fP51v34MGDcHd3x4QJExAaGgqGYZCWlgYbGxvMnz8fMpmswBjDw8MxduxYnD59Gi8qwBt3hBBCSEWmccOIEomETXZq1qyJpk2bolWrVujUqRMiIyMxXM3K68bGxpDJZJDJZFi5ciU2b96M/fv3w8XFhVNv69atGDp0KBYvXowxY8awx5s3b47mzZsDAKZOnao2vpSUFGzfvh1XrlxBYmIiIiMjMW3atGI/Z+WGjYp9TXkja91G7BAEN+2/y2KHIDD1Oydoik938u4soWmSa9iJHYKguiyMETsEwd1aLvzSDzSMqJ7G9Wzlp2PHjmjcuDF2795dpPo6OjrQ1dVFeno65/jKlSsxdOhQbNiwgZNoFceOHTvg4OAAe3t7DBo0CBs2bCjWRteEEEJImcRo8VM0kGY+VT4cHBzUbvT8ufT0dISEhEChUKBjx47s8Xv37mHMmDEICwvDwIEDSxxHeHg4Bg0aBCBnyFOhUODUqVMlbo8QQgghZVuFSbZUKlWBXZxTpkyBVCqFoaEhFixYgPnz53MmsNeqVQtNmzbF77//joSEhBLFEBMTg8uXL6N///4AcnrQ+vbti/Dw8AKvUyqVSE5O5hSlUlmiGAghhBAhMFoML0UTVZhk6969e7C2tlZ7ftKkSZDL5Xj27Bnev3+PKVOmcM4bGxvj2LFjMDIyQocOHUqUcIWHhyMzMxM1atSAjo4OdHR0EBYWhl27dkGhUKi9LiQkBKamppzy+/I/in1/QgghRChibURdHmjmU33h+PHjuHXrFjw9PdXWqVy5MmxtbSGTydT2gFWqVAnHjh2DiYkJXF1di/UmYWZmJjZt2oTFixdDLpez5caNG6hRowb++usvtdcGBgZCoVBwyqRxY4t8b0IIIURwWgw/RQNp3NuISqUSiYmJyMrKwsuXL3Ho0CGEhITg+++/x+DBg0vdvpmZGY4ePQp3d3e4urri5MmTqFGjBtLT03H37l0AOfO+nj9/DrlcDqlUCltbWxw4cADv37+Hj48PTE1NOW16enoiPDwcI0eOzPeeEokEEomEc+zjx7RSPwshhBBChKdxPVuHDh1C9erVYWVlBQ8PD5w4cQLLly/H33//DW1tbV7uYWpqiiNHjqBy5cpo3749nj9/jhcvXsDZ2RnOzs5ISEjAokWL4OzszC41ER4eDjc3tzyJFpCTbF25cgU3b97kJT5CCCHka6NhRPUYFa07UC79N3um2CEIzqZnL7FDEJzvrjdihyCobQFNxQ5BcJmfNH8tMV2TvP9I1CTHrz0VOwTBubetL/g9bi5fyks7jcaN56WdskQzU0hCCCGEkDJC4+ZsEUIIIUQEGjoEyAdKtgghhBBSapq6RhYfKA0lhBBCCBFQmU62vL29wTBMnuLh4QEAsLKyynOuVq1anDauX78OLy8vVKtWDfr6+qhXrx58fX3x4MGDPPdzd3eHtrY2/vvvv0JjsbCwgIeHR543COfOnQsXFxcYGhrCzMwsTztv376Fh4cHatSoAYlEAktLS4wZMwbJycml+KYIIYQQkdHeiGqV+afy8PBAQkICp3y+AGhwcDDn3PXr19lzBw4cQKtWraBUKrFlyxbcu3cPmzdvhqmpKaZPn865T3x8PM6fP48xY8Zgw4YNhcYSHR0NHR0dfP/995w66enp8PLywqhRo/JtQ0tLCz/88AP27duHBw8eIDIyEseOHVO7xhYhhBBSHtB2PeqV+TlbEokEMplM7XljY+N8z6elpWHo0KHo2rUr9uzZwx63trZGy5YtkZSUxKkfERGB77//HqNGjUKrVq2wZMkSGBgYqI1FJpNh6tSp+Pbbb/H69WtUqVIFADBr1iwAQGRkZL7xVqpUiZOI1alTB6NHj8bvv/+u/ksghBBCSLlV5pOtkjp8+DDevHmDyZMn53v+8yE+lUqFiIgIrFy5Eg4ODrC1tcXOnTvx008/qW0/JSUFmzdvhq2tLSwsLEoc54sXL7B79260b9++WNdlfa8+Nk0Rk6H5S8AN/lYz/xWX68OzZ2KHILgXOpq9BhUA1NDJEDsEQSnnTRI7BOH9c1DwW2jqgqR8KPPfzIEDByCVSjll3rx57PkpU6Zwzi1fvhwA8PDhQwCAg4NDofc4duwY0tLS4O7uDgAYNGgQwsPDC4zF2NgY+/btw/bt26GlVfyvsX///jA0NETNmjVhYmKC9evXF7sNQgghpMygvRHVKvPJVocOHTgbN8vlcs78pkmTJnHO5e5/WJyF8Tds2IC+fftCRyeno69///44d+4cHj16pDaWy5cvw93dHV26dMHTp8VffXjp0qW4du0a/v77bzx69AgTJkxQW1epVCI5OZlT0tOVxb4nIYQQIhRGS4uXoonK/FMZGRnB1taWU8zNzdnzlStX5pzLHR60s7MDANy/f7/A9t+9e4c9e/Zg1apV0NHRgY6ODmrWrInMzMw8E+U/j6V58+ZYv349UlNTsW7dumI/l0wmg4ODA3r06IE1a9YgLCwMCQkJ+dYNCQmBqakpp2zasLrY9ySEEELI11fmk62S6ty5MypXroyFCxfmez53gvyWLVtQq1Yt3Lhxg9NDtnjxYkRGRiIrK0vtPRiGgZaWFj5+LN3eaNnZ2QByerDyExgYCIVCwSmDh9Hbi4QQQsoQhuGnaKAyP0FeqVQiMTGRc0xHRweVK1cu8DojIyOsX78eXl5e6NGjB8aNGwdbW1u8efMGO3bsQHx8PLZt24bw8HD07t0bDRs25FxvaWmJwMBAHDp0CN26dcsTy/v377FixQqkpKSge/fu7HXx8fF49+4d4uPjkZWVBblcDgCwtbWFVCrFP//8g5cvX6J58+aQSqW4c+cOJk2ahDZt2sDKyirfZ5FIJJBIJJxjenqSfOsSQgghYtDUIUA+lPlk69ChQ6hevTrnmL29faHDgwDwww8/4Pz58wgJCcGAAQOQnJwMS0tLdOzYEXPmzMHVq1dx48aNfIcBTU1N0alTJ4SHh7PJ1uexGBsbw8HBAVFRUXB1dWWvmzFjBjZu3Mh+dnZ2BgCcOHECrq6uMDAwwLp16zB+/HgolUpYWlrixx9/xNSpU4v93RBCCCGk7GNUxZlJTsqMV1cuix2C4D6U4MWD8ibwjnnhlcqxTg2MxQ5BcMM6F/7Gc3n39s5tsUMQlJ6JidghCM68fsPCK5XS/Y35LwheXA5DhvHSTllS5nu2CCGEEFIO0DCiWvTNEEIIIaTcOn36NLp3744aNWqAYRjs3bu30GtOnjyJpk2bQiKRwNbWVu2uL3yhZIsQQgghpcYwDC+luFJTU9G4cWOsXLmySPWfPHmCbt26sWtnBgQEYPjw4Th8+HCx711UNIxICCGEkFIT623ELl26oEuXLkWuv3r1alhbW2Px4sUAAEdHR5w9exZLly5ld5LhG/VsEUIIIaTMyG/XFHXrUJbEhQsX4Obmxjnm7u6OCxcu8HaPL1GyVQBvb+98uzg7duyIypUrY/78+fleN3v2bFSrVg0ZGRmIjIzkbHp98uTJfNv8ci0xQgghpFxhtHgp+e2aEhISwluYiYmJqFatGudYtWrVkJycXOpFytWhYcRCeHh4ICIignNMIpFg1qxZiIiIyLM+lkqlQmRkJAYPHgxdXV217cbExMDks9eNq1atym/ghBBCyFfE8LSJdGBgYJ79gr9c2Lu8oWSrEBKJBDKZLM9xHx8fLFu2DGfPnkXbtm3Z46dOncLjx4/h4+NTYLtVq1bl9HgV1zN9zU/OotMyxQ5BcHMd4sUOQVCfEu+IHYLgEi8niR2C4Cq3bCN2CIIK+/um2CEIbkL9r3AThp/Bsvx2TeGTTCbDy5cvOcdevnwJExMTGBgYCHJPGkYsIScnJzRv3jzPZtURERFwcXGBg0PBCx02adIE1atXx3fffYdz584JGSohhBBC/r/WrVsjOjqac+zo0aNo3bq1YPekZKsQBw4cgFQq5ZR58+YByOndioqKQkpKCgDgw4cP2LlzJ4YNU7/6bfXq1bF69Wrs2rULu3btgqWlJVxdXXHt2rWv8jyEEEKIEBgthpdSXCkpKZDL5exexE+ePIFcLkd8fM7IQWBgIAYPHszWHzlyJB4/fozJkyfj/v37WLVqFXbs2IHx48fz8j3kh4YRC9GhQweEhYVxjpmb52yx0r9/f4wfPx47duzAsGHDsH37dmhpaaFv375q27O3t4e9vT372cXFBY8ePcLSpUvx559/5nuNUqnM8yZGerqSNqMmhBBSZjA8DSMW15UrV9ChQwf2c+58ryFDhiAyMhIJCQls4gUA1tbWOHjwIMaPH49ly5ahVq1aWL9+vWDLPgCUbBXKyMgItra2+Z4zMTFB7969ERERgWHDhiEiIgJ9+vSBVCot1j1atGiBs2fPqj0fEhKCWbNmcY6NGOWPn0cHFOs+hBBCiKZxdXVFQds857c6vKurK65fvy5gVFyUbJWSj48PXF1dceDAAZw/fx6///57sduQy+WoXr262vP5vZlxNzah2PchhBBCBMPT24iaiJKtQiiVyjxrYOno6KBy5coAgHbt2sHW1haDBw+Gg4MDXFxcCmwvNDQU1tbWaNCgAT59+oT169fj+PHjOHLkiNpr8nszQ0/vXQmfiBBCCOGfWMOI5QElW4U4dOhQnl4ne3t73L9/H0DOXlDDhg3DtGnTEBgYWGh76enpmDhxIp4/fw5DQ0M0atQIx44d44w3E0IIIURzMKqCBjpJmfX8xRuxQxCcIuWT2CEILj0jS+wQCClUjSrGYocgqJM9eogdguD6XFQ/L5gvT/bt4aUd6x69eGmnLKGeLUIIIYSUHg0jqkXJFiGEEEJKjdGiZEsd+mYIIYQQQgREPVuEEEIIKT2Gln5Qh3q2CpGYmAh/f3/Y2tpCX18f1apVQ5s2bRAWFoa0tDQAgJWVFRiG4ZRatWqxbVhZWSE0NJT9vHbtWri6usLExAQMwyApKekrPxUhhBDCL0ZLi5eiiahnqwCPHz9GmzZtYGZmhnnz5sHJyQkSiQS3bt3C2rVrUbNmTfT4/2+xBAcHw9fXl71WW1tbbbtpaWnw8PCAh4dHkZaLIIQQQkj5RclWAUaPHg0dHR1cuXIFRkZG7HEbGxv88MMPnO0BjI2NIZPJitRuQEAAAODkyZMlju3Njk0lvra8UDx6JHYIgtMZKtzGp4TwJfaZZi+iXGtVpNghaISSbCJdUVCypcbbt29x5MgRzJs3j5NofY6h8WlCCCEkBy39oBZ9M2rExsZCpVLB3t6ec7xy5cqQSqWQSqWYMmUKe3zKlCnscalUiuXLl3/tkAkhhBBSBlHPVjFdvnwZ2dnZGDhwIJRKJXt80qRJ8Pb2Zj/n7p3IB6VSybkXAKRnZkJPh/74CCGElA00jKge9WypYWtrC4ZhEBMTwzluY2MDW1tbGBgYcI5XrlwZtra2bDEzM+MtlpCQEJiamnJK+LFo3tonhBBCSothtHgpmkgzn4oHFhYW+O6777BixQqkpqaKGktgYCAUCgWn+Lh1EjUmQgghhBQNjUMVYNWqVWjTpg2++eYbzJw5E40aNYKWlhb+++8/3L9/H82aNStRu4mJiUhMTERsbCwA4NatWzA2Nkbt2rVhbm6ep75EIoFEIuEcoyFEQgghZYqGrpHFB/qNXYC6devi+vXrmDdvHgIDA/Hs2TNIJBLUr18fv/zyC0aPHl2idlevXo1Zs2axn9u1awcAiIiI4Mz7IoQQQsoLekNfPUb1+WJRpNy4vXql2CEIrl6fvmKHILhD8gSxQxBUvScXxA5BcPX6DxI7BMF9TM8SOwRBjQ37T+wQBLcxsKPg93hx5hQv7dT4tj0v7ZQl1OdHCCGEECIgGkYkhBBCSOnRMKJalGwRQgghpNQ0dRNpPtA3QwghhBAiIOrZIoQQQkipUc+WehX6m/H29kbPnj3z/JyftWvXwtXVFSYmJmAYBklJSZzzcXFx8PHxgbW1NQwMDFC3bl0EBQUhPT093/YcHBwgkUiQmJjI09MQQgghImIYfooGqtDJVnGkpaXBw8MD06ZNy/f8/fv3kZ2djTVr1uDOnTtYunQpVq9enW/9s2fP4uPHj+jduzc2btwodOiEEEIIERENIxZRQEAAAODkyZP5nvfw8ICHhwf72cbGBjExMQgLC8OiRYs4dcPDwzFgwAC0b98e/v7+mDJlSrHjuV6zdbGvKW9+X3NT7BAEN7dlttghCMvaRuwIBPfy4nmxQxDcR6v6YocgKI3/7/AroWFE9SjZEpBCociz/c6HDx8QFRWFS5cuwcHBAQqFAmfOnMG3334rUpSEEEJI6dEK8upRGiqQ2NhY/PHHH/j55585x7dt24Z69eqhQYMG0NbWRr9+/RAeHi5SlIQQQggRGiVbAnj+/Dk8PDzg5eUFX19fzrkNGzZg0KD/295j0KBBiIqKwocPH9S2p1QqkZyczCkZGflPvCeEEELEwGhp8VI0UYmfKjMzE8eOHcOaNWvYROHFixdISUnhLbjy6MWLF+jQoQNcXFywdu1azrm7d+/i4sWLmDx5MnR0dKCjo4NWrVohLS0N27ZtU9tmSEgITE1NOWV/VITQj0IIIYQUnZYWP0UDleipnj59CicnJ/zwww/w8/PD69evAQALFizAL7/8wmuA5cnz58/h6uqKZs2aISIiAlpf/J8mPDwc7dq1w40bNyCXy9kyYcKEAocSAwMDoVAoOKW711ChH4cQQggpMoZheCklsXLlSlhZWUFfXx8tW7bE5cuX1daNjIzMc099ff2SPnaRlGiCvL+/P7755hvcuHEDFhYW7PFevXrlGTYrTxQKBeRyOeeYhYUFLC0tkZiYiMTERMTGxgIAbt26BWNjY9SuXRvm5uZsolWnTh0sWrSITUABQCaTISMjA3/++SeCg4PRsGFDzj2GDx+OJUuW4M6dO2jQoEGeuCQSCSQSCeeYrq4eT09NCCGElF/bt2/HhAkTsHr1arRs2RKhoaFwd3dHTEwMqlatmu81JiYmiImJYT8LPbm/RMnWmTNncP78eejpcX/hW1lZ4fnz57wEJoaTJ0/C2dmZc8zHxwfr16/H6tWrMWvWLPZ4u3btAAARERHw9vbG0aNHERsbi9jYWNSqVYvThkqlwr59+/D27Vv06tUrz30dHR3h6OiI8PBwLFmypEixdm2t+a/U9+ukK3YIgnt99YrYIRBSKKuqJmKHICjHRZo5dPW52I7C30Os+VZLliyBr68vhg7NGfFZvXo1Dh48iA0bNmDq1Kn5XsMwDGQy2VeLsUTJVnZ2NrKysvIcf/bsGYyNjUsd1NcSGRnJ+fnzz1+aOXMmZs6cqfa8t7c3vL291Z739PTM9zvLdffu3QIiJYQQQso4hp9kS6lUQqlUco7lN8IDAOnp6bh69SoCAwPZY1paWnBzc8OFCxfU3iMlJQV16tRBdnY2mjZtinnz5uU7ssSXEn0znTt3RmhoKPuZYRikpKQgKCgIXbt25Ss2QgghhFQw+b0UFhISkm/dN2/eICsrC9WqVeMcr1atmtrt8Ozt7bFhwwb8/fff2Lx5M7Kzs+Hi4oJnz57x/iy5StSztXjxYri7u6N+/fr49OkTBgwYgIcPH6Jy5cr466+/+I6REEIIIWUco8XPvKfAwEBMmDCBcyy/Xq2Sat26NVq3/r9dWFxcXODo6Ig1a9Zg9uzZvN3ncyVKtmrVqoUbN25g27ZtuHnzJlJSUuDj44OBAwfCwMCA7xgJIYQQUsbxNWdL3ZBhfipXrgxtbW28fPmSc/zly5dFnpOlq6sLZ2dn9gU4IZR4ux4dHR3O4pyEEEIIIV+Tnp4emjVrhujoaPTs2RNAzrzy6OhojBkzpkhtZGVl4datW4JOgypxGvrw4UOsXbsWc+bMQXBwMKcIzdvbm/1SP//5S+/evcPYsWNhb28PAwMD1K5dG+PGjYNCoWDr3LhxA/3794elpSUMDAzg6OiIZcuWcdr5fE0OLS0tVK9eHX379kV8fHy+93VwcIBEIskzXpyRkYEpU6bAyckJRkZGqFGjBgYPHowXL16U/MsghBBCygCx1tmaMGEC1q1bh40bN+LevXsYNWoUUlNT2bcTBw8ezJlAHxwcjCNHjuDx48e4du0aBg0ahKdPn2L48OG8fRdfKlHP1rp16zBq1ChUrlwZMpmM8+UwDIMZM2bwFmBpvHjxAi9evMCiRYtQv359PH36FCNHjsSLFy+wc+dOAMDVq1dRtWpVbN68GZaWljh//jxGjBgBbW1tTlacuyaHSqXCkydPMHr0aHh5eeHSpUuce549exYfP35E7969sXHjRkyZMoU9l5aWhmvXrmH69Olo3Lgx3r9/D39/f/To0QNXrtASAIQQQsoxkZZ+6Nu3L16/fo0ZM2YgMTERTZo0waFDh9hJ8/Hx8ZxFxt+/fw9fX18kJiaiUqVKaNasGc6fP4/69esLFiOjUqlUxb2oTp06GD16NCeR+Jq8vb2RlJSEvXv3cn4uiqioKAwaNAipqanQ0ck/1/Tz88O9e/dw/PhxADk9WwEBAUhKSmLr/PHHH2wvmYnJ/61BM3ToUMhkMrRv3x7+/v6cRdPy899//6FFixZ4+vQpateuXaRnAIBrt+OKXLe8ep2UJnYIgjPep9mbkJvZ24sdguDq9vpR7BAEdzUuSewQBHXt4VuxQxDcmL4tBb9H0sOCf98VlVk9zft7o0Q9W+/fv4eXlxffsXwVucmRukQrt465ubna869evcKePXugra0NbW1t9viHDx8QFRWFS5cuwcHBAQqFAmfOnMG3335b4L0YhoGZmVmJnocQQggpCxie1tnSRCX6Zry8vHDkyBG+YxHcmzdvMHv2bIwYMUJtnfPnz2P79u156igUCkilUhgZGaFatWo4ceIE/Pz8YGRkxNbZtm0b6tWrhwYNGkBbWxv9+vUrcM/DT58+YcqUKejfvz+nd4wQQggpbxgthpeiiUrUs2Vra4vp06fj4sWLcHJygq4ud1uVcePG8RIcn5KTk9GtWzfUr19f7Urwt2/fxg8//ICgoCB07tyZc87Y2BjXrl1DRkYG/v33X2zZsgVz587l1NmwYQPnDc1Bgwahffv2+OOPP/KsrJ+RkYE+ffpApVIhLCyswNjzW003PV0JPT3+1h0hhBBCSkOs7XrKgxIlW2vXroVUKsWpU6dw6tQpzjmGYcpcsvXhwwd4eHjA2NgYe/bsyZMcAjnb5XTq1AkjRozAb7/9lue8lpYWbG1tAeTsZfjo0SOMGjUKf/75J3v9xYsXcfnyZc5ctqysLGzbto2zQXduovX06VMcP3680F6tkJAQzr6MADBilD9+Hh1Q5O+AEEIIIeIoUbL15MkTvuMQTHJyMtzd3SGRSLBv3z7o6+vnqXPnzh107NgRQ4YMydNbpc7UqVNRt25djB8/Hk2bNkV4eDjatWuHlStXcupFREQgPDycTbZyE62HDx/ixIkTsLCwKPRe+a2mezc2oUhxEkIIIV8FzdlSq8SLmgI5G0A+efIEdevWLXDCudAUCgXkcjnnmIWFBUxNTdG5c2ekpaVh8+bNSE5ORnJyMgCgSpUq0NbWxu3bt9GxY0e4u7tjwoQJ7NpY2traqFKlitp7WlpaolevXpgxYwb27NmDP//8E8HBwWjYsCGn3vDhw7FkyRLcuXMHdnZ26N27N65du4YDBw4gKyuLvZ+5uTn09PTyvVd+q+nq6b0r1ndECCGECElT51vxoUQZUlpaGsaOHYuNGzcCAB48eAAbGxuMHTsWNWvWxNSpU3kNsjAnT56Es7Mz55iPjw8GDRrEroOVOwSY68mTJ7CyssLOnTvx+vVrbN68GZs3b2bP16lTB3FxcQXed/z48WjdujWWLFmCt2/folevXnnqODo6wtHREeHh4Rg3bhz27dsHAGjSpAmn3okTJ+Dq6lrEJyaEEEJIeVGidbb8/f1x7tw5hIaGwsPDAzdv3oSNjQ3+/vtvzJw5E9evXxciVvKZ2J3bxQ5BcLpS48IrlXNyPUuxQyCkUDUv7BE7BEE9b533H8qa5oeOToLfI/XZ/3hpx6iW5v29WKKerb1792L79u1o1aoVZ/X4Bg0a4NGjR7wFRwghhJDygd5GVK9E38zr169RtWrVPMdTU1NLtK8RIYQQQoimKlGy9c033+DgwYPs59wEa/369WjdujU/kRFCCCGk/NBi+CkaqETDiPPmzUOXLl1w9+5dZGZmYtmyZbh79y7Onz+fZ90tQgghhGg+2q5HvRJ9M23btoVcLkdmZiacnJxw5MgRVK1aFRcuXECzZs34jrFIvL290bNnT/ZnhmEwf/58Tp29e/fmGebMysrC0qVL4eTkBH19fVSqVAldunTBuXPn8txjy5YtaNy4MQwNDVG9enUMGzYMb99yNzCNioqCg4MD9PX14eTkhH/++Ydz3tXVFQzD5CkjR47k4VsghBBCSFlT4jS0bt26WLduHS5fvoy7d+9i8+bNcHIS/m2HotLX18eCBQvw/v17tXVUKhX69euH4OBg+Pv74969ezh58iQsLS3h6uqKvXv3snXPnTuHwYMHw8fHB3fu3EFUVBQuX77MWRn+/Pnz6N+/P3x8fHD9+nX07NkTPXv2xO3btzn39fX1RUJCAqcsXLiQ9++AEEII+Vpob0T1SjSMmLsw6JcYhoFEIlG7OOfX5ObmhtjYWISEhKhNZHbs2IGdO3di37596N69O3t87dq1ePv2LYYPH47vvvsORkZGuHDhAqysrNitiKytrfHzzz9jwYIF7HXLli2Dh4cHJk2aBACYPXs2jh49ihUrVmD16tVsPUNDQ8hkMiEemxBCCBEHDSOqVaJky8zMrMC3DmvVqgVvb28EBQVBS6RXQbW1tTFv3jwMGDAA48aNQ61atfLU2bp1K+zs7DiJVq6JEydi9+7dOHr0KHr27InWrVtj2rRp+Oeff9ClSxe8evUKO3fuRNeuXdlrLly4kGdbHXd3d04PGV/qfOfOe5tlTUZqqtghCK762jVihyAo58lTCq9Uzs3bqvnrCrZ0+07sEAR18JZC7BAE90NH4e+hqb1SfChRJhQZGYkaNWpg2rRp2Lt3L/bu3Ytp06ahZs2aCAsLw4gRI7B8+fI8c6a+tl69eqFJkyYICgrK9/yDBw/g6OiY77nc4w8ePAAAtGnTBlu2bEHfvn2hp6cHmUwGU1NTzl6IiYmJqFatGqedatWqsVvy5Fq1ahWkUimnbNmypcTPSQghhJCyq0Q9Wxs3bsTixYvRp08f9lj37t3h5OSENWvWIDo6GrVr18bcuXMxbdo03oItiQULFqBjx4745Zdf8j1f1AX07969C39/f8yYMQPu7u5ISEjApEmTMHLkSISHhxcrpoEDB+LXX3/lHPsySfucUqmEUqnkHNNSKvPsl0gIIYSIhd5GVK9E38z58+fz7EUIAM7Ozrhw4QKAnDcW4+PjSxcdD9q1awd3d3cEBgbmOWdnZ4d79+7le13ucTs7OwBASEgI2rRpg0mTJqFRo0Zwd3fHqlWrsGHDBiQkJAAAZDIZXr58yWnn5cuXeeZnmZqawtbWllOMjdVvTRMSEgJTU1NOWbBkadG/BEIIIURoDMNP0UAlSrYsLS3z7c0JDw+HpWXOnkZv375FpUqVShcdT+bPn4/9+/eziWCufv364eHDh9i/f3+eaxYvXgwLCwt8913OXIW0tLQ888+0tbUB/F/vWOvWrREdHc2pc/To0VIv9BoYGAiFQsEpUyaML1WbhBBCCPk6SjSMuGjRInh5eeHff/9F8+bNAQBXrlzB/fv3sXPnTgDAf//9h759+/IXaSk4OTlh4MCBWL58Oed4v379EBUVhSFDhuD3339Hp06dkJycjJUrV2Lfvn2IioqCkZERgJxhUl9fX4SFhbHDiAEBAWjRogVq1KgBIGeD7vbt22Px4sXo1q0btm3bhitXrmDt2rWc+6alpeWZxyWRSNQmpxKJJM+QYYYqu1TfCSGEEMIn2q5PvRL1bPXo0QMxMTHo2rUr3r17h3fv3qFLly64f/8+vv/+ewDAqFGjsGTJEl6DLY3g4GBkZ3MTFIZhsGPHDkybNg1Lly6Fvb09vv32Wzx9+hQnT55kF0kFchZKXbJkCVasWIGGDRvCy8sL9vb22L17N1vHxcUFW7duxdq1a9G4cWPs3LkTe/fuRcOGDTn3XbduHapXr84p/fv3F/T5CSGEEEFpafFTNBCjKuoM8RIYPXo0goODUblyZaFuUWG9/O+S2CEIzqgCrEXWaWms2CEIauu36hcV1hT/q1F2FnMWirOFZv4CzKUtEX9tSKFJLesIfo+MD/mvwVlcusYmvLRTlgj6X9DmzZvVLoBKCCGEEA1CE+TVKtGcraISsNOMEEIIIWWKZiZKfNDsvmFCCCGEEJEJ2rNFCCGEkAqCOrbUomSLEEIIIaWnofOt+KCxw4je3t5gGAYjR47Mc87Pzw8Mw8Db25tz/MKFC9DW1ka3bt3ybXPPnj1o1aoVTE1NYWxsjAYNGiAgIAAA4OrqCoZh1BZXV1cAwM8//4y6devCwMAAVapUwQ8//ID79+/z+eiEEEIIKUMETbYGDRoEExPxXuG0tLTEtm3b8PHjR/bYp0+fsHXrVtSuXTtP/fDwcIwdOxanT5/GixcvOOeio6PRt29feHp64vLly7h69Srmzp2LjIwMAMDu3buRkJCAhIQEXL58GQBw7Ngx9ljuelzNmjVDREQE7t27h8OHD0OlUqFz587IysoS6msghBBCiIhKPIyYlJSEy5cv49WrV3kWCx08eDAAICwsrHTRlVLTpk3x6NEj7N69GwMHDgSQkxTVrl0b1tbWnLopKSnYvn07rly5gsTERERGRnI20d6/fz+7N2IuOzs7duFTc3Nz9vinT58AABYWFnn2RRwxYgT7s5WVFebMmYPGjRsjLi4OdevWLfKzZf//JE+TSSw0f302XS3NXmfrfd1mYocguNa11O9rqike7d0jdgiCqtnOVewQNAQNI6pTop6t/fv3o3bt2vDw8MCYMWPg7+/PltxhtbJi2LBhiIiIYD9v2LABQ4cOzVNvx44dcHBwgL29PQYNGoQNGzZwlq6QyWS4c+cObt++zVtsqampiIiIgLW1NbunJCGEEEKKZ+XKlbCysoK+vj5atmzJjjCpExUVBQcHB+jr68PJyQn//POPoPGVKNmaOHEihg0bhpSUFCQlJeH9+/dseffuHd8xlsqgQYNw9uxZPH36FE+fPsW5c+cwaNCgPPXCw8PZ4x4eHlAoFDh16hR7fuzYsWjevDmcnJxgZWWFfv36YcOGDVAqlcWOadWqVZBKpZBKpfj3339x9OhR6Olp/grGhBBCCN+2b9+OCRMmICgoCNeuXUPjxo3h7u6OV69e5Vv//Pnz6N+/P3x8fHD9+nX07NkTPXv25LUz5UslSraeP3+OcePGwdDQkO94eFelShV069YNkZGRiIiIQLdu3fJsHxQTE4PLly+z+xPq6Oigb9++CA8PZ+sYGRnh4MGDiI2NxW+//QapVIqJEyeiRYsWSEtLK1ZMAwcOxPXr13Hq1CnY2dmhT58+7NBjfpRKJZKTkzlFmZ5erHsSQgghQlLxVIpryZIl8PX1xdChQ1G/fn2sXr0ahoaG2LBhQ771ly1bBg8PD0yaNAmOjo6YPXs2mjZtihUrVpTg7kVTomTL3d0dV65c4TsWwQwbNgyRkZHYuHEjhg0blud8eHg4MjMzUaNGDejo6EBHRwdhYWHYtWsXFAoFp27dunUxfPhwrF+/HteuXcPdu3exffv2YsVjamqKevXqoV27dti5cyfu37+PPXvUz4kICQmBqakpp/zx5+Zi3ZMQQggpD/LtYFAzipSeno6rV6/Czc2NPaalpQU3NzdcuHAh32suXLjAqQ/k5DXq6vOhRBPku3XrhkmTJuHu3btwcnKCrq4u53yPHj14CY4vHh4eSE9PB8MwcHd355zLzMzEpk2bsHjxYnTu3JlzrmfPnvjrr7/yXT4CyJngbmhoiNTU1BLHplKpoFKpChyODAwMxIQJEzjH3l0rP8kuIYQQUlQhISGYNWsW51hQUBBmzpyZp+6bN2+QlZWFatWqcY5Xq1ZN7bJKiYmJ+dZPTEwsXeAFKFGy5evrCwAIDg7Oc45hmDK3jIG2tjbu3bvH/vy5AwcO4P379/Dx8YGpqSnnnKenJ8LDwzFy5EjMnDkTaWlp6Nq1K+rUqYOkpCQsX74cGRkZ+O6774oUx+PHj7F9+3Z07twZVapUwbNnzzB//nwYGBiga9euaq+TSCSQSCScY6k0x4sQQkgZwtd2yPl1MHz5O7C8KVGy9eVSD+WBuvW+wsPD4ebmlifRAnKSrYULF+LmzZto3749Vq5cicGDB+Ply5eoVKkSnJ2dceTIEdjb2xcpBn19fZw5cwahoaF4//49qlWrhnbt2uH8+fOoWrVqqZ6PEEIIEZOqRDOu8tLPp4NBncqVK0NbWxsvX77kHH/58mWepZdyyWSyYtXnA6NS8ZWLkq/ptfya2CEITkdfX+wQBDdoc4LYIQhqy5BaYocguBenTxVeqZyz7Z/3DW5NcvjSY7FDEFz3Dg0Fv8enUkyp+Zy+kVGx6rds2RItWrTAH3/8ASCnQ6h27doYM2YMpk6dmqd+3759kZaWhv3797PHXFxc0KhRI6xevbp0watR5J6t5cuXY8SIEdDX18fy5csLrDtu3LhSB0YIIYSQ8kOsrpsJEyZgyJAh+Oabb9CiRQuEhoYiNTWVXVNz8ODBqFmzJkJCQgAA/v7+aN++PRYvXoxu3bph27ZtuHLlCtauXStYjEVOtpYuXYqBAwdCX18fS5cuVVuPYRhKtgghhJAKRqxkq2/fvnj9+jVmzJiBxMRENGnSBIcOHWInwcfHx0NL6/8WX3BxccHWrVvx22+/Ydq0aahXrx727t2Lhg2F6/2jYcRyioYRNQMNI5Z/NIxY/tEwIj9SklN4aUdqIuWlnbKkVBtRp6enIyYmBpmZmXzFQwghhBCiUUqUbKWlpcHHxweGhoZo0KAB4uPjAeRsaTN//nxeAywKb29vMAyT59579+4Fw+RsjHny5EkwDMOWatWqwdPTE48f/9+/aKysrDh1cktuu3FxcZzjFhYW6Ny5M65fv8624erqyp7X19dH/fr1sWrVKvZ8VlYW5s+fDwcHBxgYGMDc3BwtW7bE+vXrhfyKCCGEEEHlrhtZ2qKJSpRsBQYG4saNGzh58iT0PxvqcXNzK/Zq6nzR19fHggUL8P79+wLrxcTE4MWLF4iKisKdO3fQvXt3zrpgwcHBSEhI4JSxY8dy2jh27BgSEhJw+PBhpKSkoEuXLkhKSmLP+/r6IiEhAXfv3kWfPn3g5+eHv/76CwAwa9YsLF26FLNnz8bdu3dx4sQJjBgxgnM9IYQQUt6oVPwUTVSidbb27t2L7du3o1WrVmzPEQA0aNAAjx494i244nBzc0NsbCxCQkKwcOFCtfWqVq0KMzMzVK9eHTNmzMDAgQMRGxvLrpVlbGxc6FobFhYWkMlkkMlkWLRoEdq0aYNLly6xq9MbGhqybcycORNbt27Fvn370L9/f+zbtw+jR4+Gl5cX217jxo2L/bw30sr+vpSlZcpo/sKt47vWFDsEQelXMhc7BMG9cGwndgiCs0nj55X+skr1+xSxQxBeh4NiR1Chlahn6/Xr1/kuwpmamspJvr4mbW1tzJs3D3/88QeePXtWpGsMDAwA5Mw9K6mitGFgYMCel8lkOH78OF6/fl3iexJCCCFlTbZKxUvRRCVKtr755hscPPh/WXJugrV+/Xq0bt2an8hKoFevXmjSpAmCgoIKrZuQkIBFixahZs2anBXgp0yZAqlUyilnzpzJt42kpCTMnj0bUqkULVq0yHM+KysLmzdvxs2bN9GxY0cAObuTv379GjKZDI0aNcLIkSPx77//lvCJCSGEkLKB5mypV6JhxHnz5qFLly64e/cuMjMzsWzZMty9exfnz5/HqVPivga9YMECdOzYEb/88ku+52vVqgWVSoW0tDQ0btwYu3btgt5n+wxOmjQJ3t7enGtq1uQO9bi4uEBLSwupqamwsbHB9u3bOZtarlq1CuvXr0d6ejq0tbUxfvx4jBo1CgBQv3593L59G1evXsW5c+dw+vRpdO/eHd7e3monySuVyjwbVaenp3PiJoQQQkjZVKKerbZt20IulyMzMxNOTk44cuQIqlatigsXLqBZs2Z8x1gs7dq1g7u7OwIDA/M9f+bMGdy8eRPJycmQy+Vo2bIl53zlypVha2vLKblDhbm2b9+OGzdu4P3793j06FGeTaQHDhwIuVyOJ0+eIDU1FUuWLOEsqKalpYXmzZsjICAAu3fvRmRkJMLDw/HkyZN8Yw4JCYGpqSmnbPtTuJVuCSGEkOLKzlbxUjRRiXq2AKBu3bpYt24dn7HwZv78+WjSpEm+G0RbW1vDzMysVO1bWlqibt26as+bmprC1ta2yO3Vr18fQM6ct/zktwP62WtxRW6fEEIIEZqmDgHyoUTJlra2NhISEvJMkn/79i2qVq3KWUpBDE5OThg4cGChezjm58OHD0hMTOQcMzQ0hImJCS+x9e7dG23atIGLiwtkMhmePHmCwMBA2NnZwcHBId9rJPnsgE5DiIQQQkj5UKJhRHXZq1KpLDNJQHBwMLKzs4t93YwZM1C9enVOmTx5Mm9xubu7Y//+/ejevTvs7OwwZMgQODg44MiRI9DRKXFHIyGEECIqehtRvWLtjZjbUzR+/Hj2LbxcWVlZOH36NOLi4jgrqhNhPD30j9ghCE7SpGXhlcq50avlYocgqJnmN8UOQXCOPr5ihyC4D0/jxA5BUDc/6IodguBcW+adVsO3xMS3vLQjk1nw0k5ZUqyulKVLlwLI6dlavXo1tLW12XN6enqwsrLC6tWr+Y2QEEIIIWWehnZK8aJYyVbu23IdOnTA7t27UalSJUGCIoQQQgjRFCWaJHTixAn259xRSLFWjieEEEKI+DR1vhUfSjRBHgA2bdoEJycnGBgYwMDAAI0aNcKff/7JZ2yEEEIIKSdonS31StSztWTJEkyfPh1jxoxBmzZtAABnz57FyJEj8ebNG4wfP57XIAkhhBBCyqsS9Wz98ccfCAsLw4IFC9CjRw/06NEDCxcuxKpVq0q0tlVJeHt7g2EYMAwDPT092NraIjg4GJmZmTh58iQYhkFSUlKe66ysrBAaGqr28+fi4uLYezAMA2NjYzRo0AB+fn54+PAhp25kZCSnrlQqRbNmzbB7925Ovd27d6Nz586wsLAAwzCQy+Wl/CYIIYQQ8dHeiOqVKNlKSEiAi4tLnuMuLi5ISEgodVBF5eHhgYSEBDx8+BATJ07EzJkz8fvvv/N+n2PHjiEhIQE3btzAvHnzcO/ePTRu3BjR0dGceiYmJkhISEBCQgKuX78Od3d39OnTBzExMWyd1NRUtG3bFgsWLOA9TkIIIUQstM6WeiUaRrS1tcWOHTswbdo0zvHt27ejXr16vARWFBKJBDKZDAAwatQo7NmzB/v27UPr1q15vY+FhQV7HxsbG3Tv3h2dOnWCj48PHj16xC6BwTAMW08mk2HOnDlYtGgRbt68yW4d9NNPPwHI6TUrDd3P1jjTVFrPHokdguDiksSOQFgpPbqLHYLwNPN3A0eGmq3ENIVjiWcvE1I0JUq2Zs2ahb59++L06dPsnK1z584hOjoaO3bs4DXA4jAwMMDbt/wsqlYQLS0t+Pv7o1evXrh69SpatGiRp05WVhY2bdoEAGjatKngMRFCCCFi0tReKT6UKNny9PTE5cuXsWTJEuzduxcA4OjoiMuXL8PZ2ZnP+IpEpVIhOjoahw8fxtixY9njtWrVylM3LS2Nl3vm7mMYFxfHJlsKhYJdVf/jx4/Q1dXF2rVrC9y0mhBCCNEEKg19k5APxU62MjIy8PPPP2P69OnYvHmzEDEV2YEDByCVSpGRkYHs7GwMGDAAM2fOxH///QcAOHPmDIyNjTnXuLq68nLv/NYXMzY2xrVr1wDkJHXHjh3DyJEjYWFhge7dSz6colQqoVQqucfS0yEpI/tQEkIIIUS9Yo9U6+rqYteuXULEUmwdOnSAXC7Hw4cP8fHjR2zcuBFGRkbseWtra9ja2nIKX5s937t3j71HLi0tLfY+jRo1woQJE+Dq6lrqyfAhISEwNTXllBWbt5SqTUIIIYRPNEFevRJNC+zZsyc7fCgmIyMj2Nraonbt2rwlUUWRnZ2N5cuXw9rautBhU21tbXz8+LFU9wsMDIRCoeCUMYMGlqpNQgghhE8qFT9FE5UoQ6lXrx6Cg4Nx7tw5NGvWjNObBADjxo3jJbiv5fnz53nWu6pTpw7789u3b5GYmIi0tDTcvn0boaGhuHz5Mg4ePMjZjFulUiExMRFAzpyto0eP4vDhw5gxYwZb5927d4iPj8eLFy8AgF0WQiaTsW8yfkkikUAikXCOpdAQIiGEkDJEU1d/50OJkq3w8HCYmZnh6tWruHr1KuccwzDlLtlatGgRFi1axDn2559/om3btgAANzc3AIChoSHq1KmDDh06YO3atbC1teVck5ycjOrVqwPISZDq1KmD4OBgTJkyha2zb98+DB06lP3cr18/AEBQUBBmzpxZ5JgNq1Qp+gOWV1qa/z52TeMPYocgqPvxSWKHIDjrT4lih0AIKeMYVSmXa6WNqMWRFHNP7BCEVwGSrZ82PRM7BEH1amYqdgiC61L1k9ghEFKo6i5tBb/H3Qf8/H1W3y7vSgLlXYl/m4WHh6Nhw4bQ19eHvr4+GjZsiPXr1/MZGyGEEELKCdquR70SDSPOmDEDS5YswdixY9nV2i9cuIDx48cjPj4ewcHBvAZJCCGEEFJelSjZCgsLw7p169C/f3/2WI8ePdCoUSOMHTuWki1CCCGkgtHUZRv4UKJhxIyMDHzzzTd5jjdr1gyZmZmlDooQQggh5Ut2toqXIqR3795h4MCBMDExgZmZGXx8fJCSklLgNa6urmAYhlNGjhxZrPuWKNn66aefEBYWluf42rVrMXDg11v/ydvbm31wPT092NraIjg4GIMGDcrzxXxerKys2DZiY2MxbNgw1K5dGxKJBDVr1kSnTp2wZcsWTuKorq1t27YBAE6ePMk5XqVKFXTt2hW3bt1SG//8+fPBMAwCAgKE+ooIIYQQ8v8NHDgQd+7cwdGjR3HgwAGcPn0aI0aMKPQ6X19fJCQksGXhwoXFum+JVwINDw/HkSNH0KpVKwDApUuXEB8fj8GDB2PChAlsvSVLlpT0FkXi4eGBiIgIKJVK/PPPP/Dz80NQUBASEhLYOtWrV0dERAQ8PDwAgF0b6/Lly3Bzc0ODBg2wcuVKdr/DK1euYOXKlWjYsCEaN27MtvN5G7nMzMw4n2NiYmBiYoIXL15g0qRJ6NatG2JjY6H3xbpY//33H9asWYNGjRrx9l0QQgghYinro4j37t3DoUOH8N9//7Gjc3/88Qe6du2KRYsWoUaNGmqvNTQ0VLsWZlGUKNm6ffs2mjZtCgB49OgRAKBy5cqoXLkybt++zdb7GstBSCQS9gsYNWoU9uzZg0OHDiEoKIhTz8zMjPNFqVQqeHt7w87ODufOnYPWZ8sM1KtXD/3798/zVsSXbeSnatWqbL2AgAD06NED9+/f5yRVKSkpGDhwINatW4c5c+aU6Ln1a9Uu0XXlydM9O8UOQXBj3JuLHYKgPio1f1rB8hv8bG5flnkbPxE7BEHpmZiIHYJGKOtzti5cuAAzMzPONCg3NzdoaWnh0qVL6NWrl9prt2zZgs2bN0Mmk6F79+6YPn06DA0Ni3zvEiVbJ06cKMllX4WBgQHevn1baD25XI579+7hr7/+4iRanytNsqhQKNghxi97tfz8/NCtWze4ubmVONkihBBCNJFSqYRSqeQcy28nleJKTExE1apVOcd0dHRgbm7O7v6SnwEDBqBOnTqoUaMGbt68iSlTpiAmJga7d+8u8r01ZtVIlUqFY8eO4fDhw+jYsWOh9R88eAAAsLe3Z4+9evUKUqmULatWreJc079/f855qVSK+Ph4Tp1atWpBKpXCzMwMW7duRY8ePdjhSQDYtm0brl27hpCQkNI8LiGEEFKm8DVBPiQkBKamppxS0O/MqVOnFjhPm2EY3L9/v8TPNWLECLi7u8PJyQkDBw7Epk2bsGfPHnZkryi+3u7NAjlw4ACkUikyMjKQnZ2NAQMGFGvbm89ZWFiweyS6uroiPT2dc37p0qXs1j25vhzjPXPmDAwNDXHx4kXMmzcPq1evZs/973//g7+/P44ePQp9ff0ix5Vflq/KzCx1lk8IIYTwha8FSQMDAzlzvwEU+Ptu4sSJ8Pb2LrBNGxsbyGQyvHr1inM8MzMT7969K9Z8rJYtWwLIecGubt26Rbqm3CdbHTp0QFhYGPT09FCjRg3o6BTtkerVqwcgZ0K7s7MzgJyJ87n7HebXjkwmy7Mf4pesra1hZmYGe3t7vHr1Cn379sXp06cBAFevXsWrV6/Y+W4AkJWVhdOnT2PFihVQKpWcja1zhYSEYNasWZxjvwYG4rdffy3SsxJCCCFC42vVhuIOGVapUgVVirBfcOvWrZGUlISrV6+iWbNmAIDjx48jOzubTaCKIrdTJncv5KIo98OIRkZGsLW1Re3atYucaAGAs7MzHBwcsGjRImRnZwsSm5+fH27fvo09e/YAADp16oRbt25BLpez5ZtvvsHAgQMhl8vzTbSAnCxfoVBwyqRffhEkZkIIIUQTOTo6wsPDA76+vrh8+TLOnTuHMWPGoF+/fuwo1fPnz+Hg4IDLly8DyHkJcPbs2bh69Sri4uKwb98+DB48GO3atSvWagLlvmerpBiGQUREBL777ju0adMGgYGBcHR0REZGBk6fPo3Xr1/nSX6SkpLyTKIzNjaGkZFRvvcwNDSEr68vgoKC0LNnTxgbG6Nhw4acOkZGRrCwsMhz/HP5ZfmfUlOL87iEEEKIoMrDvoZbtmzBmDFj0KlTJ2hpacHT0xPLly9nz2dkZCAmJgZpaTlvGevp6eHYsWMIDQ1FamoqLC0t4enpid9++61Y962wyRYAtGrVClevXsW8efPg5+eHxMREGBkZoXHjxli6dCmGDRvGqT906NA8bYSEhGDq1Klq7zFmzBgsWbIEUVFR6NOnD+/PQAghhJQFZX3pBwAwNzfH1q1b1Z63srLiJI2WlpY4depUqe/LqMpDKkryiN25XewQBGdYreQLyJUXVzMtxA6BkEJVPf6X2CEI6lXH/oVXKue6d1A/esKX89dieWnHpWnBc6PLowrds0UIIYQQfgi9r2F5RskWIYQQQkqNxsnUK/dvIxJCCCGElGXUs0UIIYSQUisPE+TFUuF7try9vdnl/HV1dWFtbY3Jkyfj06dPnHonTpzA999/jypVqkBfXx9169blLFiaa926dWjcuDG7ZY+zszNnm4E7d+7A09MTVlZWYBgGoaGhX+MxCSGEEEGpVCpeiiaq8MkWAHh4eCAhIQGPHz/G0qVLsWbNGgQFBbHnV61ahU6dOsHCwgLbt29HTEwM9uzZAxcXF4wfP56tt2HDBgQEBGDcuHGQy+U4d+4cJk+ejJSUFLZOWloabGxsMH/+/GJtD0AIIYSQ8omGEZGzaGhu4mNpaQk3NzccPXoUCxYsQHx8PAICAhAQEIAlS5ZwrmvUqBHGjRvHft63bx/69OkDHx8f9liDBg041zRv3hzNmzcHgALX5yKEEELKE3obUT1Ktr5w+/ZtnD9/HnXq1AEA7Nq1CxkZGZg8eXK+9RmGYX+WyWQ4deoUnj59yl4vlBNaNoK2XxY02L9N7BAE5zJ5utghCCrq5AOxQxBcR+daYocgOKtAzd6H9dEOzV5HLIfw62xRrqUeDSMCOHDgAKRSKfT19eHk5IRXr15h0qRJAIAHDx7AxMSEM+S3a9cuSKVStty6dQsAEBQUBDMzM1hZWcHe3h7e3t7YsWOHYHsvEkIIIWUFzdlSj5ItAB06dIBcLselS5cwZMgQDB06FJ6enuz5z3uvAMDd3R1yuRwHDx5EamoqsrKyAOTsAH7hwgXcunUL/v7+yMzMxJAhQ+Dh4VGqhEupVCI5OZlTMjLSS9weIYQQQr4eSraQsxm0ra0tGjdujA0bNuDSpUsIDw8HANSrVw8KhYKzAbVUKoWtra3aocKGDRti9OjR2Lx5M44ePYqjR4+Wam+lkJAQmJqacsq/uyNL3B4hhBDCt2yVipeiiSjZ+oKWlhamTZuG3377DR8/fkTv3r2hq6uLBQsWlKi9+vXrAwBSU1NLHFNgYCAUCgWndPnRu8TtEUIIIXxTqfgpmogmyOfDy8sLkyZNwsqVK/HLL79g8eLF8Pf3x7t37+Dt7Q1ra2u8e/cOmzdvBgBoa2sDAEaNGoUaNWqgY8eOqFWrFhISEjBnzhxUqVIFrVu3BgCkp6fj7t277M/Pnz+HXC5ne8vyI5FIIJFIOMd0dfWEenxCCCGE8Ih6tvKho6ODMWPGYOHChUhNTcXYsWNx5MgRvH79Gr1790a9evXQtWtXPHnyBIcOHYKTkxMAwM3NDRcvXoSXlxfs7Ozg6ekJfX19REdHw8LCAgDw4sULODs7w9nZGQkJCVi0aBGcnZ0xfPhwMR+ZEEIIKZXsbBUvRRMxKk2d+q/hTl6KETsEwb1O+lR4pXJOU+cn5OrqaCZ2CIK7mqgUOwTBGUfvEDsEQWkbGIgdguCajP9F8HscOHmbl3a+dxV+mYqvjXq2CCGEEEIERHO2CCGEEFJqGt5RXyqUbBFCCCGk1DR9WkRp0DAiIYQQQoiAqGeLEEIIIaVG79upRz1bAF6/fo1Ro0ahdu3akEgkkMlkcHd3x7lz5wAAVlZWCA0NzXPdzJkz0aRJE86x5ORk/Prrr3BwcIC+vj5kMhnc3Nywe/du9v+Iu3fvRufOnWFhYQGGYSCXywV+QkIIIURY2Sp+iiaini0Anp6eSE9Px8aNG2FjY4OXL18iOjoab9++LVY7SUlJaNu2LRQKBebMmYPmzZtDR0cHp06dwuTJk9GxY0eYmZkhNTUVbdu2RZ8+feDr6yvQUxFCCCFfj6aukcWHCp9sJSUl4cyZMzh58iTat28PAKhTpw5atGhR7LamTZuGuLg4PHjwADVq1GCP29nZoX///tDX1wcA/PTTTwCAuLi4Esdto3hc4mvLizpKzV9n6+DHGoVXKsd2yYv3D5byyFXnhdghCE7v23ZihyCo3S/0xQ5BcE3EDqCCq/DDiFKpFFKpFHv37oVSWfLFCbOzs7Ft2zYMHDiQk2h9fh8dnQqf2xJCCNFQtDeiehU+2dLR0UFkZCQ2btwIMzMztGnTBtOmTcPNmzc59aZMmcImZrll3rx57Pk3b97g/fv3cHBw+NqPQAghhIguW6XipWiiCp9sATlztl68eIF9+/bBw8MDJ0+eRNOmTREZGcnWmTRpEuRyOaeMHDmSPS/kWxhKpRLJycmcokzPEOx+hBBCCOEPJVv/n76+Pr777jtMnz4d58+fh7e3N4KCgtjzlStXhq2tLaeYm5uz56tUqQIzMzPcv3+f99hCQkJgamrKKau2b+f9PoQQQkhJqVQqXoomomRLjfr16yM1NbXI9bW0tNCvXz9s2bIFL17knTCbkpKCzMzMEsUSGBgIhULBKaP79i1RW4QQQogQaOkH9Sp8svX27Vt07NgRmzdvxs2bN/HkyRNERUVh4cKF+OGHH4rV1ty5c2FpaYmWLVti06ZNuHv3Lh4+fIgNGzbA2dkZKSkpAIB3795BLpfj7t27AICYmBjI5XIkJibm265EIoGJiQmnSPR0S/fghBBCCPkqKvzrcVKpFC1btsTSpUvx6NEjZGRkwNLSEr6+vpg2bVqx2jI3N8fFixcxf/58zJkzB0+fPkWlSpXg5OSE33//HaampgCAffv2YejQoex1/fr1AwAEBQVh5syZvD0bIYQQ8rVo6uR2PjAqTR0g1XDPX7wROwTBKVI0f52t9IwssUMgpFA1qhiLHYKgTvboIXYIgutz8azg9/hz/zVe2vmpe1Ne2snP3LlzcfDgQcjlcujp6SEpKanQa1QqFYKCgrBu3TokJSWhTZs2CAsLQ7169Yp83wo/jEgIIYSQiiE9PR1eXl4YNWpUka9ZuHAhli9fjtWrV+PSpUswMjKCu7s7Pn0qeodAhR9GJIQQQkjplYdhxFmzZgEAZ2mngqhUKoSGhuK3335j53Fv2rQJ1apVw969e9lpQIWhni1CCCGElJomriD/5MkTJCYmws3NjT1mamqKli1b4sKFC0Vuh3q2CCGEEFJqfPVsKZXKPNvnSSQSSCQSXtovjtxVAqpVq8Y5Xq1aNbUrCOSnQvVsvX79GqNGjULt2rUhkUggk8ng7u6Oc+fOAQCsrKzAMAwYhoGRkRGaNm2KqKgo9vo7d+7A09OTrRcaGlrg/ebPnw+GYRAQEMA5/unTJ/j5+cHCwgJSqRSenp54+fIl349LCCGElDv5LeQdEhKitv7UqVPZ393qihALjhdHherZ8vT0RHp6OjZu3AgbGxu8fPkS0dHRePv2LVsnODgYvr6+SE5OxuLFi9G3b1/UrFkTLi4uSEtLg42NDby8vDB+/PgC7/Xff/9hzZo1aNSoUZ5z48ePx8GDBxEVFQVTU1OMGTMGP/74I5v0EUIIIeUNXwuSBgYGYsKECZxjBfVqTZw4Ed7e3gW2aWNjU6JYZDIZAODly5eoXr06e/zly5do0qRJkdupMMlWUlISzpw5g5MnT6J9+/YAgDp16qBFixacesbGxpDJZJDJZFi5ciU2b96M/fv3w8XFBc2bN0fz5s0B5GTS6qSkpGDgwIFYt24d5syZwzmnUCgQHh6OrVu3omPHjgCAiIgIODo64uLFi2jVqlWRnuf2xDFFfvbyyiVkXuGVyrkEbX2xQxDUpmOPxA5BcIPd6oodguDMNHwN5SZbdogdgkbgayWp4g4ZVqlSBVWqVOHl3l+ytraGTCZDdHQ0m1wlJyfj0qVLxXqjscIMI0qlUkilUuzduzfPWLA6Ojo60NXVRXp6erHu5efnh27dunEm1OW6evUqMjIyOOccHBxQu3btYk22I4QQQkjxxMfHQy6XIz4+HllZWZDL5ZDL5ewOL0DO7+Q9e/YAADsVaM6cOdi3bx9u3bqFwYMHo0aNGujZs2eR71therZ0dHQQGRkJX19frF69Gk2bNkX79u3Rr1+/fIf60tPTsXjxYigUCrYHqii2bduGa9eu4b///sv3fGJiIvT09GBmZsY5XtzJdoQQQkhZUh6WfpgxYwY2btzIfnZ2dgYAnDhxAq6urgByttBTKBRsncmTJyM1NRUjRoxAUlIS2rZti0OHDkFfv+gjExWmZwvImbP14sUL7Nu3Dx4eHjh58iSaNm3KWW9jypQpkEqlMDQ0xIIFCzB//nx069atSO3/73//g7+/P7Zs2VKsP4TCKJVKJCcnc0p6Fq08TgghpOwoD0s/REZGQqVS5Sm5iVbOc6g4c8AYhkFwcDASExPx6dMnHDt2DHZ2dsW6b4VKtgBAX18f3333HaZPn47z58/D29sbQUFB7PlJkyZBLpfj2bNneP/+PaZMmVLktq9evYpXr16hadOm0NHRgY6ODk6dOoXly5dDR0cHWVlZkMlkSE9Pz7NFwMuXL9mJeF/K782M7Xfvlej5CSGEEPJ1Vbhk60v169dHamoq+7ly5cqwtbWFTCYDwzDFaqtTp064desWOwYsl8vxzTffYODAgZDL5dDW1kazZs2gq6uL6Oho9rqYmBjEx8ejdevW+bYbGBgIhULBKX3rO5bsgQkhhBABZGereCmaqMLM2Xr79i28vLwwbNgwNGrUCMbGxrhy5QoWLlzILsFfmPT0dNy9e5f9+fnz55DL5ZBKpbC1tYWxsTEaNmzIucbIyAgWFhbscVNTU/j4+GDChAkwNzeHiYkJxo4di9atW6t9EzG/NzP0tLWL+xUQQgghgtHQPIkXFSbZkkqlaNmyJZYuXYpHjx4hIyMDlpaW8PX1xbRp04rUxosXL9jJdACwaNEiLFq0CO3bt8fJkyeLHMvSpUuhpaUFT09PKJVKuLu7Y9WqVcV9JEIIIYSUA4yKr4UxyFf16bOhT02llVG8JTfKo+1nnogdgqAUaRlihyC4rq3qiB2C4GS6mv1CzukvFtDURB7btgt+jz+2XeKlnbH9WvLSTllSYXq2CCGEECKc8rD0g1go2SKEEEJIqVGupV6FfxuREEIIIURI1LNFCCGEkFKjYUT1KNkihBBCSKnR0g/qafQwIsMwBZaZM2eydR0cHCCRSNTuT3jixAl8//33qFKlCvT19VG3bl307dsXp0+fzrd+Qe25urqyMejr66N+/fq09AMhhBCioTQ62UpISGBLaGgoTExMOMd++eUXAMDZs2fx8eNH9O7dm7NBZa5Vq1ahU6dOsLCwwPbt2xETE4M9e/bAxcUF48ePz1O/sPYAwNfXFwkJCbh79y769OkDPz8//PXXX/x+AYQQQshXkt+egyUpmqjCrLMVGRmJgICAPHsSAsDQoUMhk8nQvn17+Pv7IyYmhj0XHx8PW1tbjBkzBkuWLMlzrUqlyrOtT0HtATk9W02aNEFoaCh7zM7ODs2aNStywvXp9csi1SvPXl69KnYIgvvhX0nhlcqxdcOsxQ5BcI7mmr+bw8fXr8UOgZRSlabfCH6PBZvO89LOlMEuvLRTllT4OVsfPnxAVFQULl26BAcHBygUCpw5cwbffvstAGDXrl3IyMjA5MmT873+y0SrsPbUMTAwQHq65i/iSQghhFQ0Gj2MWBTbtm1DvXr10KBBA2hra6Nfv34IDw9nzz948AAmJiaQyWTssV27dkEqlbLl1q1bRW7vS1lZWdi8eTNu3ryJjh07CvOQhBBCiMBoGFG9Cp9sbdiwAYMGDWI/Dxo0CFFRUfjw4QN77MveK3d3d8jlchw8eBCpqanIysoqVntAzjwwqVQKAwMD+Pr6Yvz48Rg1alS+MSqVSiQnJ3OKUqks1XMTQgghfMrO5qdoogqdbN29excXL17E5MmToaOjAx0dHbRq1QppaWnYtm0bAKBevXpQKBSctwqlUilsbW1Rp06dYreXa+DAgZDL5Xjy5AlSU1OxZMkSaGnl/8cREhICU1NTTvl92XKevw1CCCGECKFCJ1vh4eFo164dbty4AblczpYJEyawQ3+9e/eGrq4uFixYwEt7uUxNTWFra4uaNWuqTbJyBQYGQqFQcMok/3Elf3BCCCGEZ9kqfoomqrAT5DMyMvDnn38iODgYDRs25JwbPnw4lixZgjt37qBBgwZYvHgx/P398e7dO3h7e8Pa2hrv3r3D5s2bAQDa2trFaq+4JBIJJBLuW2uflB+L3Q4hhBAiFE2db8WHCpts7du3D2/fvkWvXr3ynHN0dISjoyPCw8OxZMkSjB07Fo6OjliyZAl69+6N5ORkWFhYoHXr1jh06BCcnJywa9euIrfHh7MP3/PSTllmWNVO7BAE9+coPbFDENS75fPEDkFwcY2cxA5BcPGOrmKHICjLasZihyC4Kl/hHpraK8WHCrPOlqY5dv6+2CEIzlBf8/8tYGxIyVZ5Z0HJVrlXEZItJ4fagt9j1vqzvLQTNLwtL+2UJZr/24wQQgghgqONqNWjZIsQQgghpUa5lnoV+m1EQgghhBChUc8WIYQQQkqNJsirV6Z6thiGKbDMnDkTcXFxYBgGcrkcANjP2traeP78Oae9hIQE6OjogGEYxMXFlap+bjE2NkaDBg3g5+eHhw8f5nmG9PR0LFy4EI0bN4ahoSEqV66MNm3aICIiAhkZGQCA06dPo3v37qhRowYYhsHevXt5/R4JIYSQry1bpeKlaKIylWwlJCSwJTQ0FCYmJpxjv/zyi9pra9asiU2bNnGObdy4ETVr1uSl/rFjx5CQkIAbN25g3rx5uHfvHho3bozo6Gi2Tnp6Otzd3TF//nyMGDEC58+fx+XLl+Hn54c//vgDd+7cAQCkpqaicePGWLlyZZG+F0IIIYSUX2VqGPHzzZ5NTU3BMAznGAC8efMm32uHDBmCiIgIBAYGssciIiIwZMgQzJ49u9T1LSws2FhsbGzQvXt3dOrUCT4+Pnj06BG0tbURGhqK06dP48qVK3B2dmavtbGxgZeXF9LT0wEAXbp0QZcuXYrylajV1CyzVNeXB3fTtMUOQXAmcbfFDkFQDgt/FzsEwb2/f0/sEARXFalihyCobmsSC69Uzv23VPilHzS0U4oXZapnqzR69OiB9+/f4+zZnHU+zp49i/fv36N79+681P+SlpYW/P398fTpU1y9ehUAsGXLFri5uXESrVy6urowMjIqyaMRQgghZR5t16OexiRburq6GDRoEDZs2AAA2LBhAwYNGgRdXV1e6ufHwcEBANj5XQ8fPmSPEUIIIYQAGpRsAcCwYcMQFRWFxMREREVFYdiwYbzW/1Lu4vsMw3A+802pVCI5OZlTlP9/SJIQQggpC1QqFS9FE2lUsuXk5AQHBwf0798fjo6OeTaELm39L927lzNXw9raGgBgZ2eH+/f530YnJCQEpqamnBK6bj3v9yGEEEJKioYR1dOoZAvI6a06efJkkXupils/V3Z2NpYvXw5ra2t2jtaAAQNw7NgxXL9+PU/9jIwMpKaWbJJpYGAgFAoFpwT4Di9RW4QQQogQykOyNXfuXLi4uMDQ0BBmZmZFusbb2zvPUlQeHh7Fum+ZehuRD76+vvDy8iryl1jU+m/fvkViYiLS0tJw+/ZthIaG4vLlyzh48CC0tXPemgsICMDBgwfRqVMnzJ49G23btoWxsTGuXLmCBQsWIDw8HE2aNEFKSgpiY2PZtp88eQK5XA5zc3PUrp33jRGJRAKJRMI5lqmn2RsYE0IIIXxLT0+Hl5cXWrdujfDw8CJf5+HhgYiICPbzl7+TC6NxyZaOjg4qV67Me303NzcAgKGhIerUqYMOHTpg7dq1sLW1ZetIJBIcPXoUS5cuxZo1a/DLL7/A0NAQjo6OGDduHDtMeeXKFXTo0IG9bsKECQBylqOIjIwscuyEEEJIWVEeFiSdNWsWABT7d61EIsmzFFVxMCpNnY2m4SavPCV2CIKbM7S52CEILv6f/WKHIKhDWVZihyA4D+04sUMQnF7LDoVXKsfSL50QOwTB2fbuK/g9/Jed5KWdhSNbQ6lUco7lN8JTGpGRkQgICEBSUlKhdb29vbF3717o6emhUqVK6NixI+bMmQMLC4si30/j5mwRQgghpPzK76WwkJAQ0eLx8PDApk2bEB0djQULFuDUqVPo0qULsrKyityGxg0jEkIIIeTry87mp53AwEB2ek2ugnq1pk6digULFhTY5r1790q8Dma/fv3Yn52cnNCoUSPUrVsXJ0+eRKdOnYrUBiVbhBBCCCk1vt4kLO6Q4cSJE+Ht7V1gHRsbm1JGxW2rcuXKiI2NpWSLEEIIIZqvSpUqqFKlyle737Nnz/D27VtUr169yNeINmfryzUrviwzZ85EXFwcGIZB1apV8eHDB871TZo0wcyZM9nPrq6u+baTmZmZ57y+vj7s7OwQEhKSZ7XaPXv2oFWrVjA1NYWxsTEaNGiAgIAA9nxkZGS+91m/PmeR0YSEBAwYMAB2dnbQ0tLiXJufbdu2gWEY9OzZs8TfJSGEECK28rCCfHx8PORyOeLj45GVlQW5XA65XI6UlBS2joODA/bs2QMASElJwaRJk3Dx4kXExcUhOjoaP/zwA2xtbeHu7l7k+4rWs5WQkMD+vH37dsyYMQMxMTHsMalUijdv3gAAPnz4gEWLFrGvbKrj6+uL4OBgzjEdHZ0855VKJY4fP44RI0bAzMwMo0aNAgBER0ejb9++mDt3Lnr06AGGYXD37l0cPXqU06aJiQknVgAwNTUFkLO1TpUqVfDbb79h6dKlBcYbFxeHX375Bd9++22B9QghhJCyrjys/j5jxgxs3LiR/Zy7KPmJEyfg6uoKAIiJiYFCoQAAaGtr4+bNm9i4cSOSkpJQo0YNdO7cGbNnzy7WUKdoydbn61WYmpqCYZg8a1jkJltjx47FkiVL4Ofnh6pVq6pt09DQsMB1MD4/P3ToUKxYsQJHjx5lk639+/ejTZs2mDRpEnuNnZ1dnl6n/GLNZWVlhWXLlgEAu8l1frKysjBw4EDMmjULZ86cKdLrp4QQQggpucjIyELX2Pq8d83AwACHDx8u9X3LxZyt/v374+jRowgODsaKFStK3Z5KpcLZs2dx//591KtXjz0uk8mwdetW3L59u9j7JBZXcHAwqlatCh8fH5w5c6bY109zU590aoq05/8TOwTBjb9R9AV4y6NezbTFDkFwRlVrih2C8P73QOwIBKVbowL8GX4F5aFnSyzlYp0thmEwf/58rF27Fo8ePVJbb9WqVZBKpWyZOHFivuclEgnatWuH7OxsjBs3jj0/duxYNG/eHE5OTrCyskK/fv2wYcOGPIurKRQKzn2Ku6rs2bNnER4ejnXr1hXrOkIIIaSsUqn4KZqoXPRsAYC7uzvatm2L6dOnY+vWrfnWGThwIH799Vf285f7Heaef//+PYKCguDi4gIXFxf2vJGREQ4ePIhHjx7hxIkTuHjxIiZOnIhly5bhwoULMDQ0BAAYGxvj2rVr7HVaWkXPWT98+ICffvoJ69atK/K2QkqlMk/Cp0xPh4T2RySEEFJGlIftesRSbpItAJg/fz5at27NmVP1OVNTU85ehQWd37FjB2xtbdGqVSt238NcdevWRd26dTF8+HD8+uuvsLOzw/bt2zF06FAAOclVQfcpyKNHjxAXF4fu3buzx7L//0pwOjo6iImJQd26dTnXhISE5Hk5YIrfaEwdO6ZEMRBCCCHk6ylXyVaLFi3w448/YurUqaVuSyqVwt/fH7/88guuX78OhmHyrWdlZQVDQ0OkpqaW+p5Aziult27d4hz77bff8OHDByxbtgyWlpZ5rslvNd2PT5/wEg8hhBDCB5qzpV65SrYAYO7cuWjQoAFnSYeS+vnnnzF79mzs2rULvXv3xsyZM5GWloauXbuiTp06SEpKwvLly5GRkYHvvvuuyO3K5XIAOetzvH79GnK5HHp6eqhfvz709fXzTL7PHe5UNyk/v9V0s2kIkRBCSBlCyZZ65WKC/Ofs7OwwbNgwfPr0qdRtmZubY/DgwZg5cyays7PRvn17PH78GIMHD4aDgwO6dOmCxMREHDlyBPb29kVu19nZGc7Ozrh69Sq2bt0KZ2dndO3atdTxEkIIIaT8YVRCL9dKBHFJ/ljsEARn/uCy2CEIzsTaWuwQBBV6RVl4pXLOppqB2CEIblCbvNMbNMnjvXvEDkFwDX4eJfg9fpp3nJd2/pzWkZd2ypJyN4xICCGEkLKHum7UK3fDiIQQQggh5Qn1bBFCCCGk1GiCvHqUbBFCCCGk1GhRU/VoGJEQQgghREAVOtliGKbAMnPmTMTFxXGOmZubo3379nk2j545c2a+bTg4OLB1XF1dERAQkCeOyMjIPFsLEUIIIeVJtoqfookq9DBiQkIC+/P27dsxY8YMxMTEsMekUinevHkDADh27BgaNGiAN2/eYO7cufj+++/x4MEDVKtWja3foEEDHDt2jHMPPhZfJYQQQso6TU2U+FChMwGZTMb+bGpqCoZhOMcAsMmWhYUFZDIZZDIZpk2bhm3btuHSpUvo0aMHW1dHRyfP9UKpp5f2Ve4jpmSpVOwQBOf2Z4rYIQhqm5+d2CEITpb+TuwQBJcc/1TsEARl99MQsUPQCJRsqVehhxFL4uPHj9i0aRMAQI+2zCGEEEJIISp0z1ZxuLi4QEtLC2lpaVCpVGjWrBk6derEqXPr1i1Iv+iNGTRoEFavXs1+XrVqFdavX8+pk5mZCX19feGCJ4QQQgRGLyOqR8lWEW3fvh0ODg64ffs2Jk+ejMjISOjq6nLq2NvbY9++fZxjJiYmnM8DBw7Er7/+yjm2e/duzJs3T+29lUollErutifK9HRIqGeNEEJIGUHDiOpRslVElpaWqFevHurVq4fMzEz06tULt2/fhkQiYevo6enB1ta2wHZMTU3z1KlatWqB14SEhGDWrFmcY5NHj8IUv9HFfApCCCGEfG00Z6sEevfuDR0dHaxateqr3C8wMBAKhYJTAnyHf5V7E0IIIUVBSz+oRz1bJcAwDMaNG4eZM2fi559/hqGhIYCcuVeJiYl56n6+PERJSCQSTg8aAGTSECIhhJAyRFMTJT5Qz1YJDRkyBBkZGVixYgV77M6dO6hevTqn1KlTR8QoCSGEECI2RqWi9wfKo8TEt2KHILit0Q/EDkFwze0qix2CoD6ETBE7BMF1XLtG7BAE9/DNR7FDEFS9ygZihyA4/cpVBL9Hl2nRvLTz77xOhVcqZ2gYkRBCCCGlRsOI6lGyRQghhJBSo2RLPZqzRQghhBAiIOrZIoQQQkipUc+WeuW6Z8vV1RUBAQF5jkdGRsLMzAwAMHPmTDAMwxZTU1N8++23OHXqFOcaKysrhIaGFni/Xbt2wdXVFaamppBKpWjUqBGCg4Px7t079r6599HS0kKtWrUwdOhQvHr1im1j7ty5cHFxgaGhIRsjIYQQUt5lqfgpmqhcJ1tF1aBBAyQkJCAhIQEXLlxAvXr18P3330OhUBS5jV9//RV9+/ZF8+bN8e+//+L27dtYvHgxbty4gT///JOtZ2JigoSEBDx79gzr1q3Dv//+i59++ok9n56eDi8vL4waNYrXZySEEEJI2VQhhhF1dHQgk8kAADKZDMHBwYiIiMCDBw/QvHnzQq+/fPky5s2bh9DQUPj7+7PHrays8N133yEpKYk9xjAMe68aNWpg3LhxmD59Oj5+/AgDAwN2253IyMhSPVPiXxGlur48GNHbS+wQBJelTBc7BEFpR24SOwTBZYodwFfgUEuzl0ZwmX5J7BAE999S4ZdToGFE9SpEz9bnlEolIiIiYGZmBnt7+yJds2XLFkilUowenf9ehAUNBxoYGCA7OxuZmRXhr2RCCCEVVVnfricuLg4+Pj6wtraGgYEB6tati6CgIKSnF/yP3k+fPsHPzw8WFhaQSqXw9PTEy5cvi3XvCtGzdevWLUilUgBAWloajI2NsX37dpiYmBTp+ocPH8LGxga6urrFuu/Dhw+xevVqfPPNNzA2Ni523IQQQgjhx/3795GdnY01a9bA1tYWt2/fhq+vL1JTU7Fo0SK1140fPx4HDx5EVFQUTE1NMWbMGPz44484d+5cke9dIZIte3t77Nu3DwDw4cMHbN++HV5eXjhx4gS++eabQq8vziL7CoUCUqkU2dnZ+PTpE9q2bYv169eXOHYgpzdOqVRyjqVnZkJPp0L88RFCCCkHyvowooeHBzw8PNjPNjY2iImJQVhYmNpkS6FQIDw8HFu3bkXHjh0BABEREXB0dMTFixfRqlWrIt27XA8jmpiY5DvJPSkpCaampuxnPT092NrawtbWFs7Ozpg/fz5q1qxZ6NuHuezs7PD48WNkZGQUWtfY2BhyuRy3b99GamoqTp8+DTs7uyI/U35CQkJgamrKKRuOHS9Vm4QQQgif+HobUalUIjk5mVO+7HDgi0KhgLm5udrzV69eRUZGBtzc3NhjDg4OqF27Ni5cuFDk+5TrZMve3h7Xrl3Lc/zatWuFJjja2tr4+LFo+30NGDAAKSkpWLVqVb7nP58gr6WlBVtbW9jY2MDAgJ9JpYGBgVAoFJwyzK0jL20TQgghZUl+HQwhISG83yc2NhZ//PEHfv75Z7V1EhMToaenl2dudrVq1ZCYmFjke5XrcahRo0ZhxYoVGDduHIYPHw6JRIKDBw/ir7/+wv79+9l6mZmZ7JeSO4x49+5dTJnC3ST3+fPnkMvlnGN16tRBy5YtMXnyZEycOBHPnz9Hr169UKNGDcTGxmL16tVo27Yt5y3FgsTHx+Pdu3eIj49HVlYWez9bW1t2XtmXJBIJJBIJ5xgNIRJCCClL+BpGDAwMxIQJEzjHvvwd+LmpU6diwYIFBbZ57949ODg4sJ+fP38ODw8PeHl5wdfXt3QBF0G5/o1tY2OD06dP49dff4WbmxvS09Ph4OCAqKgozrjsnTt3UL16dQCAoaEh6tati7CwMAwePJjT3qJFi/KM2/75558YNGgQFixYgGbNmmHlypVYvXo1srOzUbduXfTu3RtDhgwpcswzZszAxo0b2c/Ozs4AgBMnTsDV1bW4XwEhhBBSJvC1IGl+HQwFmThxIry9vQusY2Njw/784sULdOjQAS4uLli7dm2B18lkMqSnpyMpKYnTu/Xy5Ut2maeiYFTFmf1Nyoz/PXstdgiCk5kbiR2C4H5aXPQx//IoeICj2CEIzrq6aeGVyrmXlzV7HapnR46IHYLgWs2bL/g9mvhH89KOfJlwa4I9f/4cHTp0QLNmzbB582Zoa2sXWF+hUKBKlSr466+/4OnpCQCIiYmBg4MDLly4UDEmyBNCCCGEFMXz58/h6uqK2rVrY9GiRXj9+jUSExM5c6+eP38OBwcHXL58GQBgamoKHx8fTJgwASdOnMDVq1cxdOhQtG7dusiJFlDOhxEJIYQQUjaU9X0Njx49itjYWMTGxqJWrVqcc7mDfBkZGYiJiUFaWhp7bunSpdDS0oKnpyeUSiXc3d3VvjCnDiVbhBBCCCm1sp5seXt7Fzq3y8rKKs/amvr6+li5ciVWrlxZ4nvTMCIhhBBCiICoZ4sQQgghpZaVzYgdQplFyRYhhBBCSq2sDyOKiYYRCSGEEEKEpCKkCD59+qQKCgpSffr0SexQBKHpz6dS0TNqAk1/PpWKnpFoJlrUlBRJcnIyTE1NoVAoYGJiInY4vNP05wPoGTWBpj8fQM9INBMNIxJCCCGECIiSLUIIIYQQAVGyRQghhBAiIEq2SJFIJBIEBQUVayf28kTTnw+gZ9QEmv58AD0j0Uw0QZ4QQgghREDUs0UIIYQQIiBKtgghhBBCBETJFiGEEEKIgCjZIhVKfHw8Kto0xdjYWBw+fBgfP34EAI18/vT0dMTExCAzM1PsUAghJA+aIE/yOHHiBK5du4ZWrVqhTZs2WLNmDebOnYuPHz+iZ8+eWL58OQwMDMQOs0S0tbWRkJCAqlWrih2K4N6+fYu+ffvi+PHjYBgGDx8+hI2NDYYNG4ZKlSph8eLFYodYamlpaRg7diw2btwIAHjw4AFsbGwwduxY1KxZE1OnThU5wtI7c+YM1qxZg0ePHmHnzp2oWbMm/vzzT1hbW6Nt27Zih8eb169fIyYmBgBgb2+PKlWqiBxR6SxfvrzIdceNGydgJKRMEG+nIFIWrV27VqWtra2ytbVVSSQS1bx581RGRkaqkSNHqkaPHq0yMTFRTZkyRewwS4xhGNXLly/FDuOr+Omnn1Tu7u6q//3vfyqpVKp69OiRSqVSqQ4dOqSqX7++yNHxY9y4capmzZqpzpw5ozIyMmKfce/evaomTZqIHF3p7dy5U2VgYKAaPny4SiKRsM/3xx9/qLp06SJydPxISUlRDR06VKWjo6NiGEbFMIxKR0dHNWzYMFVqaqrY4ZWYlZVVkYq1tbXYoZKvgJItwtGgQQPV8uXLVSqVSvXvv/+qdHR0VJGRkez5HTt2qOrWrStWeKXGMIzq1atXYofxVVSrVk0ll8tVKpWKk2w9evRIZWRkJGZovKldu7bqwoULKpWK+4wPHz5UGRsbixkaL5o0aaLauHGjSqXiPt+1a9dU1apVEzM03owYMUJlY2Oj+ueff1QKhUKlUChUBw8eVNWtW1c1cuRIscMjhBc6YveskbLl8ePH6NGjBwDAw8MDDMOgRYsW7PmWLVvif//7n1jh8WL69OkwNDQssM6SJUu+UjTCSU1Nzfc53717pzGLKb5+/TrfIeHU1FQwDCNCRPyKiYlBu3bt8hw3NTVFUlLS1w9IALt27cLOnTvh6urKHuvatSsMDAzQp08fhIWFiRccITyhZItwfPr0iTMfSyKRcH4xSySScj8J+datW9DT01N7XhN+SQPAt99+i02bNmH27NkAcp4rOzsbCxcuRIcOHUSOjh/ffPMNDh48iLFjxwL4vz+79evXo3Xr1mKGxguZTIbY2FhYWVlxjp89exY2NjbiBMWztLQ0VKtWLc/xqlWrIi0tTYSI+DFhwgTMnj0bRkZGmDBhQoF1NeEfd6RglGwRDoZh8OHDB+jr60OlUoFhGKSkpCA5ORkA2P8tz/bs2VMhJsgvXLgQnTp1wpUrV5Ceno7Jkyfjzp07ePfuHc6dOyd2eLyYN28eunTpgrt37yIzMxPLli3D3bt3cf78eZw6dUrs8ErN19cX/v7+2LBhAxiGwYsXL3DhwgX88ssvmD59utjh8aJ169YICgrCpk2boK+vDwD4+PEjZs2aVa4T5uvXryMjI4P9WR1N+ccdKRi9jUg4tLS0OP/x5yZcX37OysoSI7xSq0hvIwKAQqHAihUrcOPGDaSkpKBp06bw8/ND9erVxQ6NN48ePcL8+fM5zzhlyhQ4OTmJHVqpqVQqzJs3DyEhIWwvj0QiwS+//ML2WJZ3t2/fhru7O5RKJRo3bgwAuHHjBvT19XH48GE0aNBA5AgJKT1KtghHUXsD2rdvL3AkwtDS0kJiYiIn2Tp37hy++eYbjZnHlCs+Ph6Wlpb5/ss5Pj4etWvXFiEqUhLp6emIjY1FSkoK6tevD6lUKnZIvEpLS8OWLVtw//59AICjoyMGDhxYbpeYIeRLlGyRQmlSMrJx40b069eP8ywmJiaQy+UaMwcml7pevLdv36Jq1arltnfyc5r+jMOGDcOyZctgbGzMOZ6amoqxY8diw4YNIkVGiiM1NRXz589HdHQ0Xr16hezsbM75x48fixQZ+Voo2SKF0tRkJJexsTFu3Lihcc+npaWFly9f5lkc8unTp6hfvz5SU1NFiow/+fVUAsCLFy9Qt25ddtX88kpdMvnmzRvIZLJy/7JKrocPH+LEiRP5JiIzZswQKSr+9O/fH6dOncJPP/2E6tWr5+lt9vf3Fyky8rXQBHlSKMrHy5fcN58YhsmzzEVWVhYuXbqEJk2aiBQdP3JX52YYBuvXr+cMq2VlZeH06dNwcHAQK7xSS05OhipnHUT2hZVcWVlZ+OeffzRm3uG6deswatQoVK5cGTKZjJOIMAyjEcnWv//+i4MHD6JNmzZih0JEQskWqfDWrFmT76vn5VXum08qlSrPMhd6enpo3LgxfvnlF7HC48XSpUsB5Dzj6tWroa2tzZ7T09ODlZUVVq9eLVZ4pWZmZgaGYcAwDOzs7PKcZxgGs2bNEiEy/s2ZMwdz587FlClTxA5FMJUqVYK5ubnYYRAR0TAiKdTWrVvxww8/wMjISOxQeBcbG4tHjx6hXbt2MDAwyPP2ZXk2dOhQLFu2DCYmJmKHIpgOHTpg9+7dqFSpktih8OrUqVNQqVTo2LEjdu3axflFraenhzp16qBGjRoiRsgfTZ+mAACbN2/G33//jY0bNxa6oDLRTNSzRQoUGxsLCwsLaGlpAci7FER5pW6TZh8fH43ZpLldu3bQ1dUVOwxBzZgxQ+MSLeD/3vZ99OgRrKysNOK/OXW8vLxw5MgRjBw5UuxQeOXs7Mz5c4uNjUW1atVgZWWV57/La9eufe3wyFdGyRbJl6YnI+PHj4eOjg7i4+Ph6OjIHu/bty8mTJhQ7p8PAKZOnQp/f394eXnBx8cHLi4uYofEOw8PD9SqVQtDhw7FkCFDYGlpKXZIvOrYsSOGDh0Kb29vjV2qw9bWFtOnT8fFixfh5OSUJxEZN26cSJGVTs+ePcUOgZQhNIxI8jV48GC8evUK69evh6OjI/u23uHDhzFhwgTcuXNH7BBLRSaT4fDhw2jcuDHnbcTHjx+jUaNGSElJETvEUsvMzMT+/fsRGRmJf//9FzY2NmxSIpPJxA6PF2/evMGff/6JjRs34s6dO+jYsSN8fHzQs2fPArdkKi9CQ0MRGRmJ27dvo0OHDvDx8UGvXr00YhmWXNbW1mrPMQxDyyIQjUDJFsmXpicjxsbGuHbtGurVq8d5vitXrsDd3R1v374VO0RevXz5Eps3b8bGjRtx//59eHh4wMfHB927d2eHiMu7a9euISIiAn/99RcAYMCAAfDx8WFXJS/Prl27hsjISPz111/IysrCgAEDMGzYMDRt2lTs0EgxXLlyBffu3QMA1K9fH82aNRM5IvLVqAjJh1QqVT148ID9+dGjRyqVSqX677//VObm5mKGxosuXbqofvvtN5VKlfN8jx8/VmVlZam8vLxUnp6eIkcnjIsXL6pGjBihkkgkKisrK5WpqanKyspKdeLECbFD483z589VQUFBKolEojIyMlJpa2ur2rZtq7p9+7bYofEiPT1dFRoaqpJIJCqt/9fefUdFda1hA38GVHqzgFgAUVAQUAx2Ayo29IottmBBLF8siBU1XCNg515LEhKxEYGoUYM9Rv1AsWCNhAFEQYqC2CVoEAsy5/vDxXxMZkRzOcPO7Hl/a7mW7DOSZzTiyznv3q+OjtCuXTth+/btgkwmYx1NFDKZjJv3UlVhYaHQo0cPQSKRCBYWFoKFhYUgkUiE7t27C4WFhazjkVrAx7e0RHSffvopYmNj5R9LJBLIZDJERESgV69eDJOJIyIiAlu2bIGPj498SLOLiwvOnj2LtWvXso4nmocPH+K///0v2rZti549e+L58+c4evQo8vPzUVRUhFGjRmHixImsY9ZIeXk5fv75ZwwcOBC2trY4ceIEIiMj8fDhQ+Tk5MDW1hYjR45kHbNGysvLsXfvXvj6+mL+/Pnw8PDAtm3bMGLECHz55Zfw8/NjHbFGYmNj4erqCgMDAxgYGMDNzQ1xcXGsY4lmypQpKC8vx40bN1BcXIzi4mLcuHEDMpkMU6ZMYR2P1AbW1R75Z0pPTxcsLS2FAQMGCPXq1RM+++wzwcnJSbCyshJycnJYxxNFSUmJsGLFCmHkyJGCj4+PEBISIty7d491LNH861//EurWrSu0bdtW2LBhg/D06VOl1zx8+FCQSCQM0olj1qxZQoMGDYT69esLQUFBQnp6utJr7t+/r7Hv8dq1a/L32KhRI2H+/PnCjRs3FF6Tnp4u6OvrM0pYc+vWrRMMDQ2F4OBg4dChQ8KhQ4eEhQsXCoaGhsL69etZxxOFvr6+kJKSorT+22+/CQYGBgwSkdpGuxGJSi4uLsjOzkZkZCRMTExQWlqK4cOHY+bMmbC2tmYdr0bKy8sxYMAAREVFISQkhHUctbG0tMSZM2fQtWvX976mUaNGyM/Pr8VU4srMzMS3336L4cOHv7dpvGHDhjh9+nQtJxNHx44d0bdvX2zatAlDhw5VeZRHixYtMGbMGAbpxPHtt99i06ZNmDBhgnzN19cXbdu2RWhoKObOncswnTiaN2+O8vJypfWKigpuzksj1aMGeaKkajHi4ODAOo5aNGrUCBcuXOD2/RE+3LlzB7a2tqxjqJW+vj4yMjLQqlUrhfVbt27B1dUVr169YpRMPIcOHcKqVavw3XffwcPDA8C7ZvnAwEAsWrSIjonQAtSzRZTUrVsXaWlprGOo1bhx47B9+3bWMdRq9uzZ8hmCVUVGRmLOnDm1H0gNVq9ejejoaKX16OhoLnrvHj16hMuXLyutX758Gb/99huDROJr1aoV9u7dq7S+Z88ebr4Z8vf3R2pqKjp37gw9PT3o6emhc+fOSElJQUBAAOrXry//QfhEd7aISnPnzoWenh7WrFnDOopaBAYGIjY2Fg4ODvjkk0+URhGtX7+eUTLxNG3aFIcPH1baXp6SkgJfX1/cvXuXUTLx2NnZYdeuXUoHtl6+fBljxozR6EekANCpUycEBwfjs88+U1jfv38/1q5dq7IQ0zTx8fEYPXo0+vTpIx/UnJycjMTEROzduxfDhg1jnLDmYmJiPvq1mr5hhahGPVtEpbdv3yI6OhoJCQlcFiMZGRnyM4qys7MVrvEyGuXp06cwMzNTWjc1NcWTJ08YJBLfgwcPVPYQNmrUCPfv32eQSFyZmZkqz9Jyd3dHZmYmg0TiGzFiBC5fvowNGzbg4MGDAAAnJydcuXIF7u7ubMOJhAooQsUWUYn3YkRTG6b/jlatWuH48eOYNWuWwnrlafI8aN68OZKTk5VOIU9OTuai8VhPTw8PHz5U+vO6f/8+6tTh58v3J598gh9//JF1DLWqqKjAwYMH5Yeatm3bFr6+vtDV1WWcjNQGfv62ElFpQzFSqfJxWrNmzRgnEde8efMwa9YsPH78GL179wYAJCYmYt26ddi4cSPbcCKZOnUq5syZg/LycoX3GBwcjPnz5zNOV3P9+vXDkiVLcOjQIfldypKSEnz55Zfo27cv43TievToER49egSZTKaw7ubmxiiReHJycjBw4EAUFRWhdevWAN71GzZv3hy//PILWrZsyTghUTu2J08QTVBYWMjdKccVFRVCWFiYYGpqKujo6Ag6OjqCmZmZEB4eLlRUVLCOJ5rvv/9eaNq0qSCRSASJRCK0aNFCiImJYR1LNDKZTAgODhb09fXlf46GhoZCWFgY62iiuHv3rmBvby+YmZkJPXv2FHr27CmYm5sLrVu3FgoKCljHE8Vvv/0mtG3bVtDR0ZH/f1r5Q0dHh3U8Ufj4+AgDBgxQOOvuyZMnwoABA4SBAwcyTEZqCzXIE5VkMhlWrFiBdevWyecgmpiYYP78+QgJCdH4eXpLlizB9u3bERYWJm/KPX/+PEJDQzF16lSsXLmScUJxPX78GAYGBjA2NmYdRS1KS0tx48YNGBgYwMHBgatBzS9evMDOnTshlUrlp6uPHTtW5Zlbmqhdu3Zo2bIlFi1aBCsrK6U2BR6OvjAyMsKlS5fg6uqqsC6VStG9e3eNnzVLPoweIxKVQkJCsH37dqxZs0apGHn16pXGFyMxMTHYtm0bfH195Wtubm5o2rQpZsyYofHvr6rHjx8jKysLANCmTRs0bNiQcSLxGRsbyxvleSq0gHf/UE+bNo11DLXJy8tDfHy80jlbPNHT08Off/6ptF5aWop69eoxSERqm2bfniBqU1mMTJ8+HW5ubnBzc8OMGTOwdetW7Nixg3W8GisuLkabNm2U1tu0aYPi4mIGicT34sULBAQEwNraGp6envD09IS1tTUmT56MsrIy1vFEIZPJEB4eDjMzM9ja2sLW1hbm5uZYvny5Uu+PpsrNzUVgYCD69OmDPn36YPbs2cjNzWUdSzTe3t6QSqWsY6jVv/71L0ybNg2XL1+GIAgQBAGXLl3CF198ofANH+EX3dkiKvFejLRr1w6RkZFKh35GRkaiXbt2jFKJa968eThz5gyOHDmicHdy9uzZmD9/PjZt2sQ4Yc3xfgf2xIkT8PX1Rfv27RXOoGrbti2OHDnCRZP8tm3bMHHiRGRkZMDFxUXp8SgPxcg333yDiRMnomvXrvL39/btW/j6+uLrr79mnI7UBurZIip17twZnTt3VipGAgMDcfXqVVy6dIlRMnGcOXMGgwYNgo2NjXx24MWLF1FYWIhjx47h008/ZZyw5ho2bIiff/4ZPXv2VFg/ffo0Ro0ahcePH7MJJqImTZogKipK6R/kQ4cOYcaMGSgqKmKUTBzu7u7o37+/0uHCixcvxsmTJ5GSksIomXiOHDmC8ePH4/nz50rXJBIJKioqGKQSjyAIKCwsRKNGjVBUVCQ/+sHJyYnrR6dEERVbRCVtKEaKiorw/fff4+bNmwDeffGbMWMGF+czAYChoSGuXbsGJycnhfXr16+jU6dOePHiBaNk4tHX10daWhocHR0V1rOystC+fXu8fPmSUTJx6OvrIz09XWlsTXZ2Ntzc3LiYG2hnZ4d//etfWLp0KaysrFjHEZ1MJoO+vj6uX7/Ozfgh8vdRzxZRycvLC1lZWRg2bBhKSkpQUlKC4cOHIysri4tCC3g3zmblypWIj49HfHw8VqxYwU2hBQBdu3bFsmXLFP5BfvnyJcLCwuQFtKarfBz8V7w8Dm7UqBFSU1OV1lNTU2FpaVn7gdTg6dOnmDt3LpeFFgDo6OjAwcEBT58+ZR2FMEQ9W+S9KosRHv3www8wNjbGyJEjFdb37duHsrIyLsZrbNy4EQMGDECzZs3khYdUKoW+vj5OnDjBOJ04IiIiMGjQICQkJKi8A6vppk6dimnTpiEvL08+/zE5ORlr167FvHnzGKcTx/Dhw3H69GmuD/Zcs2YNFi5ciE2bNsHFxYV1HMIAPUYkKvFejDg6OmLz5s3o1auXwvqZM2cwbdo0+VEJmq6srAw7d+5UeFTq5+cHAwMDxsnEc+/ePXz33XdcPg4WBAEbN27EunXrcO/ePQDv+tQWLlyI2bNnczE6a+XKldi4cSMGDRoEV1dXpQb52bNnM0omHgsLC5SVleHt27eoV6+e0t8/HjYdkepRsUVU4r0Y0dfXx82bN2FnZ6ewfvv2bTg5OWl8r095eTnatGmDo0ePKvVsEc1UeU6TiYkJ4yTi+utcy6okEgny8vJqMY167Nixo9rCWNO/eSUfRo8RiUoFBQUqvwja2tqioKCAQSJxWVpaIi0tTanYkkqlaNCgAZtQIqpbty4XzdMfo6SkBFeuXFE5V2/ChAmMUomPtyKrUn5+PusIaufv7886AmGMii2iEu/FyNixYzF79myYmJjA09MTwLu7dkFBQRgzZgzjdOKYOXMm1q5di23btqFOHT7/qh85cgR+fn4oLS2Fqampwt0DiUSi8cXWw4cPsWDBAiQmJuLRo0f464MITT8W4a+Sk5Ph4eHB3RQAXV1d3L9/X2lTw9OnT2FpacndnyNRxudXYFJjvBcjy5cvx+3bt+Ht7S0vRGQyGSZMmIBVq1YxTieOq1evIjExESdPnoSrqyuMjIwUru/fv59RMvHMnz8fAQEBWLVqFQwNDVnHEZ2/vz8KCgqwdOlSWFtbc9GjVR0fHx+kpqbC3t6edRRRva9b5/Xr1zSuR0tQsUVU4r0YqVevHvbs2YMVK1YgNTUVBgYGcHV15WLobSVzc3OMGDGCdQy1KioqwuzZs7kstIB3p+GfO3cO7du3Zx2lVvDWQlx5KLREIsG2bdsUBsFXVFTg7NmzKid1EP5Qgzyp1q1bt7gtRqqqqKhAeno6bG1tYWFhwToO+UjDhw/HmDFjMGrUKNZR1MLZ2Rk7d+6Eu7s76yi1wsTEBFKplJs7W5V9r3fu3EGzZs2gq6srv1avXj3Y2dkhPDwcnTt3ZhWR1BK6s0Wq5eDgAAcHB3kxYmpqykUxMmfOHLi6umLy5MmoqKiAl5cXLly4AENDQxw9elRpxI0m6t27N/bv3w9zc3OF9efPn2Po0KE4deoUm2AiGjRoEBYuXIjMzEyVxwZo+ly9jRs3YvHixdi8ebNS/ySPNm/ezNXhppXN/7169cL+/fu5+NpJ/jd0Z4uoxHsx0qxZMxw8eBAeHh44ePAgZsyYgaSkJMTFxeHUqVNITk5mHbHGdHR08ODBA6Wm3EePHqFp06YoLy9nlEw8OjrvH4LBw1y9quczGRoaKhWTPJ3PlJOTg9zcXHh6esLAwACCIHDfo0a0B93ZIir9/PPPGDduHIB3O77y8vJw8+ZNxMXFISQkROOLkSdPnqBx48YAgGPHjmHUqFFwdHREQEAAvv76a8bpaiYtLU3+88zMTDx48ED+cUVFBY4fP46mTZuyiCa6vx71wJuNGzeyjqB2T58+xejRo3Hq1ClIJBLcunUL9vb2mDx5MiwsLLBu3TrWEWssICCg2uvR0dG1lISwQsUWUYnnYgQArKyskJmZCWtraxw/fhybNm0C8O7E9ap9FZqoffv2kEgkkEgk6N27t9J1AwMDfPvttwySkb9LGw67nDt3LurUqYOCggKFA3hHjx6NefPmcVFs/fHHHwofl5eXIyMjAyUlJSr/jhL+ULFFVOK5GAGASZMmYdSoUfLt9H369AEAXL58WeN3B+Xn50MQBNjb2+PKlSto1KiR/Fq9evVgaWmp0X+G33zzDaZNmwZ9fX35bq/34WHUS6VXr17hzZs3CmumpqaM0ojn5MmTOHHiBJo1a6aw7uDggDt37jBKJa4DBw4orclkMkyfPp3rmZDk/6Nii6jEczECAKGhoXBxcUFhYSFGjhwpP0RRV1cXixcvZpyuZip3jPL6iG3Dhg3w8/ODvr4+NmzY8N7XSSQSjS+2Xrx4gUWLFmHv3r14+vSp0nVN70kD3r1HVUd3FBcXc3e4aVU6OjqYN28eevbsieDgYNZxiJpRsUVU4rkYqfTZZ58prfH02CYmJgYNGzbEoEGDAADBwcHYsmULnJ2dsXv3bo09xqPqeBfeR70EBwfj9OnT2LRpE8aPH4/vvvsORUVF2Lx5M9asWcM6nig+/fRTxMbGYvny5QDeFckymQwRERFKs1l5k5ubi7dv37KOQWoB7UYkhFOtW7fGpk2b0Lt3b1y8eBHe3t7YuHEjjh49ijp16nBxgnx4eDgWLFigdGfk5cuX+M9//oOvvvqKUTJx2NjYIDY2Fj179oSpqSlSUlLQqlUrxMXFYffu3Th27BjriDWWkZEBb29vdOjQAadOnYKvry+uX7+O4uJiJCcnc/GYbd68eQofC4KA+/fv45dffsHEiRMRGRnJKBmpLVRsEcIpQ0ND3Lx5EzY2Nli0aBHu37+P2NhYXL9+HT179sTjx49ZR6wx3mfOGRsbIzMzEzY2NmjWrBn279+PTp06IT8/H66urigtLWUdURTPnj1DZGQkpFIpSktL0aFDB8ycORPW1taso4nir3fodHR00KhRI/Tu3RsBAQHczi4l/x/9CRPCKWNjYzx9+hQ2NjY4efKk/LtrfX19vHz5knE6cbzvLCapVIr69eszSCQue3t75Ofnw8bGBm3atMHevXvRqVMnHDlyROmwWk1UXl6OAQMGICoqCiEhIazjqM3p06dZRyCMUbFFCKf69u2LKVOmwN3dHdnZ2Rg4cCAA4Pr16xp/GrmFhYX8eAtHR0eFgquiogKlpaX44osvGCYUx6RJkyCVSuHl5YXFixdj8ODBiIyMRHl5OdavX886Xo3VrVtX4Vw4nr19+xZJSUnIzc3F559/DhMTE9y7dw+mpqYKMxMJn+gxItEaz58//+jX8rClvqSkBP/+979RWFiI6dOnY8CAAQCAZcuWoV69ehp9JyEmJgaCICAgIAAbN26EmZmZ/FrlzLmuXbsyTKged+7cwbVr19CqVSu4ubmxjiOKuXPnQk9Pj5uGf1Xu3LmDAQMGoKCgAK9fv0Z2djbs7e0RFBSE169fIyoqinVEomZUbBE53osRHR2djx7/oem9PtrizJkz6Natm9IYG6I5AgMDERsbCwcHB3zyyScwMjJSuM7DHbyhQ4fCxMQE27dvR4MGDeTDtpOSkjB16lTcunWLdUSiZvQYkciZm5tzXYxU7Zu4ffs2Fi9eDH9/f/kdkIsXLyImJgarV69mFVEtysrKUFBQoHQgJg93Rry8vOQ/5+XQzw8d1FqVpp8jBrzbjdihQwcAQHZ2tsI1XmYjnjt3DhcuXEC9evUU1u3s7FBUVMQoFalNVGwROd6Lkar/MIeHh2P9+vUYO3asfM3X1xeurq7YsmULF+dtPX78GP7+/jh+/LjK65pYMP9VWVkZgoODuTr0s7qDWqvi4dBWQDuax2Uymcr/F+/evQsTExMGiUitEwhRoXfv3sKuXbuU1nfu3Cl4eXnVfiCRGRgYCNnZ2UrrWVlZgoGBAYNE4vv888+F7t27C1evXhWMjIyEkydPCnFxcULr1q2Fo0ePso4nihkzZghOTk7Czz//LBgYGAjR0dHC8uXLhWbNmgk//vgj63jkbyosLBQKCwtZxxDdqFGjhKlTpwqCIAjGxsZCXl6e8Oeffwq9e/cW/P39GacjtYGKLaIS78WIo6OjsHDhQqX1hQsXCo6OjgwSia9x48bC5cuXBUEQBBMTEyErK0sQBEE4dOiQ0L17d5bRRNO8eXPh9OnTgiC8e4+3bt0SBEEQYmNjBR8fH4bJxPX69Wvh5s2bQnl5OesooquoqBDCwsIEU1NTQUdHR9DR0RHMzMyE8PBwoaKignU8URQWFgrOzs6Ck5OTUKdOHaFLly5CgwYNhNatWwsPHz5kHY/UAnqMSFRq3rw5tm7dioiICIX1bdu2oXnz5oxSiWfDhg0YMWIEfv31V3Tu3BkAcOXKFdy6dQvx8fGM04njxYsX8sM+LSws8PjxYzg6OsLV1RUpKSmM04mjuLgY9vb2AN71ZxUXFwMAevTogenTp7OMJoqysjIEBgYiJiYGAOS72AIDA9G0aVMuRmeFhIRg+/btWLNmDbp37w4AOH/+PEJDQ/Hq1SusXLmSccKaa9asGaRSKX766SekpaWhtLQUkydPhp+fHwwMDFjHI7WAii2iEu/FyMCBA5GdnY1Nmzbh5s2bAIDBgwfjiy++4KKYBN6N68nKyoKdnR3atWuHzZs3w87ODlFRUdyczM37oZ9LliyBVCpFUlKS/OgOAOjTpw9CQ0O5KLZiYmKwbds2+Pr6ytfc3NzQtGlTzJgxg4tiCwDq1KmDcePGsY5BGKGjH8h7FRYWKhQjTk5OXBUjvPvxxx/x9u1b+Pv749q1axgwYACKi4tRr1497NixA6NHj2YdscY2bNgAXV1dzJ49GwkJCRg8eDAEQZAf+hkUFMQ6Yo3Y2tpiz5496NKlC0xMTORHBuTk5KBDhw5/67iWfyp9fX2kpaXB0dFRYT0rKwvt27fnZtrBrVu3cPr0aTx69AgymUzhmqbP8CQfRsUW0Vrnzp3D5s2bkZeXh3379qFp06aIi4tDixYt0KNHD9bxRFdWViafldiwYUPWcdSCt0M/DQ0NkZGRAXt7e4ViSyqVwtPTE8+ePWMdscY6d+6Mzp07Kx15ERgYiKtXr+LSpUuMkoln69atmD59Oho2bIjGjRsrHGkhkUi4eaxP3k+HdQDyz3Xu3DmMGzcO3bp1k58FExcXh/PnzzNOVnPx8fHo378/DAwMkJKSgtevXwN4NxB31apVjNOJLzk5Gbq6uujQoQM3hVZ5eTm8vb0VDoS0tbXF8OHDuSi0AMDDwwO//PKL/OPKf6S3bdvGzQn5ERERiI6OhrOzMyZPnozJkyfD2dkZO3bswH/+8x/W8USxYsUKrFy5Eg8ePEBqaip+//13+Q8qtLQDFVtEJd6LkRUrViAqKgpbt25VOH28e/fuXH7x8/Hx4e7wRG2Yq7dq1Sp8+eWXmD59Ot6+fYuvv/4a/fr1ww8//MBNL5OXlxeysrIwbNgwlJSUoKSkBMOHD0dWVhY+/fRT1vFE8ccff2DkyJGsYxCGqNgiKvFejGRlZcHT01Np3czMDCUlJbUfSM147RYYN24ctm/fzjqG2vTo0QOpqal4+/YtXF1dcfLkSVhaWuLixYv45JNPWMcTTdOmTbFy5UrEx8cjPj4eK1asQJMmTVjHEs3IkSNx8uRJ1jEIQ7QbkajEezHSuHFj5OTkwM7OTmH9/Pnz8qMEyD/f27dvER0djYSEBG7n6rVs2RJbt25lHUNtfvjhBxgbGyvd+dm3bx/Kysq4mObQqlUrLF26FJcuXYKrq6vSLE8eJgGQ6lGxRVTivRiZOnUqgoKCEB0dDYlEgnv37uHixYtYsGABli5dyjqe6DZv3gwrKyvWMUSnDXP1AODRo0cqd7Hx0Ju2evVqbN68WWnd0tIS06ZN46LY2rJlC4yNjXHmzBmcOXNG4RovY5dI9ajYIirxXowsXrwYMpkM3t7eKCsrg6enJ/T09LBgwQIEBgayjieqnJwcNGjQADo677oGBEHgphDhfa7etWvXMHHiRNy4cUPpUbBEItHI2Y9/VVBQgBYtWiit29raoqCggEEi8eXn57OOQBijYouoxHsxIpFIEBISgoULFyInJwelpaVwdnaGsbEx62iiefr0KUaPHo1Tp05BIpHg1q1bsLe3x+TJk2FhYYF169axjiianJwc5ObmwtPTEwYGBtwUlAEBAXB0dMT27dthZWXFxXv6K0tLS6SlpSndRZdKpWjQoAGbUGqUnJwMDw8P6OnpsY5CahOzQUFEI7x+/Vq4fv26cPnyZeHPP/9kHUc0MTExQmZmptL6y5cvhZiYGAaJxDd+/Hihf//+QmFhoWBsbCzk5uYKgiAIx48fF5ydnRmnE8eTJ0+E3r17CxKJRNDR0ZG/x0mTJgnz5s1jnK7mjI2N5fMeeRUcHCzY2toKp06dEt6+fSu8fftWSExMFGxtbYX58+ezjic6ExMT+f+nRHvQbkSiUmxsLG7cuIF69erB2dkZnTp1grGxMV69eoXY2FjW8WrM398fnTp1Uho99OzZM0yaNIlRKnGdPHkSa9euRbNmzRTWHRwccOfOHUapxDV37lzUrVsXBQUFMDQ0lK+PHj0ax48fZ5hMHN7e3pBKpaxjqNXy5cvRuXNneHt7w8DAAAYGBujXrx969+7NxTEzfyVwujOYVI8eIxKV/P39YWRkhB07dmDEiBHy9cpiZMKECQzTiSMsLAzjx49Heno6QkNDWccR3YsXLxQKkErFxcXcPMI4efIkTpw4wW1BuW3bNkycOBEZGRlwcXFR2sVWdZ6gpqpXrx727NmDFStWIDU1FQYGBnB1dYWtrS3raISIhoot8l68FyOVp+MPGzYMGRkZiIuLYx1JVJ9++iliY2OxfPlyAO/61GQyGSIiItCrVy/G6cTBe0F58eJFJCcn49dff1W6xkuDfCUHBwc4ODigoqIC6enpMDU1hYWFBetYouN1ZzCpHj1GJO81btw4nDp1Cps3b8Znn33GzUBY4P8fC9ClSxdcvnwZOTk56NatG27fvs02mIgiIiKwZcsW+Pj44M2bNwgODoaLiwvOnj2LtWvXso4nisqCshJvBWVgYCDGjRuH+/fvQyaTKfzgpdCaM2eO/GDaiooKeHl5oUOHDmjevDmSkpLYhhOZqp3BRDvQIGqikq6uLu7fvw9LS0sUFBTA19cXEokEUVFR6Natm8Z/odfR0cGDBw9gaWkJ4N2QZj8/PyQmJuLFixca//4qPXv2DJGRkZBKpSgtLUWHDh0wc+ZMWFtbs44mioyMDHh7e6NDhw44deoUfH19cf36dRQXFyM5ORktW7ZkHbFGTExMkJqaqvHvozrNmjXDwYMH4eHhgYMHD2LGjBlISkpCXFwcTp06heTkZNYRa+x9O4MDAgK42xlMVKM7W0SlqjW4jY0NLly4ADs7O/Tt25dhKvEsW7ZM4ZgHQ0NDHDhwAHPnzlV5cr6mqRzS/OjRI4SEhGDv3r04duwYVqxYwU2hBQAuLi7Izs5Gjx49MGTIELx48QLDhw/H77//zkWBMnz4cO7PEnvy5AkaN24MADh27BhGjRoFR0dHBAQEID09nXE6ccydOxd16tThdiMH+TDq2SIqva8YWbZsGc6ePcswmTiWLVumcj0sLKyWk6iHNgxpBt4datqrVy+EhIQoXfvuu+8wc+ZMBqnE4+joiCVLluD8+fPcjnmxsrJCZmYmrK2tcfz4cWzatAnAu7vNurq6jNOJg/eNHOTD6DEi0RqHDx+Gj48P6tati8OHD7/3dRKJBIMHD67FZOoxd+5c6OnpYc2aNayjqI2FhYV8LmJVX3/9NZYuXYrnz58zSiYOVSerV5JIJMjLy6vFNOoRGhqKjRs3wtraGmVlZcjOzoaenh6io6OxdetWXLx4kXXEGjMxMUFKSgocHBxgYmICqVQKe3t7/Pbbb+jfvz+ePn3KOiJRM7qzReR4L0aGDh0q79MaOnToe1/Hyy4vbRjS/J///Ac+Pj44e/Ys2rRpAwBYt24dwsPD8csvvzBOV3PaMOYlNDQULi4uKCwsxMiRI+W7SHV1dbF48WLG6cShDTuDSfXozhaRq9o0XrlbRhVeihHeVfdFXCKR4NSpU7WYRn0iIiLwzTff4Pz589izZw9WrVqFY8eOoXv37qyjiYrGvGgu3jdykA+jYosQovEWLVqE7du3o6KiAr/++iu6dOnCOpLoTE1NkZqaCnt7e9ZRyP+A953BpHr0GJFojW+++eajX8tD43FVd+/eBQClBl1NpOrPsWnTpjA0NISnpyeuXLmCK1euAODrz5G+L9ZM5eXlGDBgAKKiolRu5CDage5sETnei5Hqmo2r4qXxWCaTYcWKFVi3bh1KS0sBvGvUnT9/PkJCQqp9VPxPpm1/jpWqNlYTzdKoUSNcuHABDg4OrKMQRqjYInLa+o8Yr5YsWYLt27cjLCxM3r90/vx5hIaGYurUqVi5ciXjhOTv2LVrF4YMGaK00YH882nDzmBSPSq2COFUkyZNEBUVpTSs+NChQ5gxYwaKiooYJVMPnhvIc3JykJubC09PTxgYGEAQBPnIKU30d47kMDU1VWOS2hEYGIjY2Fg4ODhwuzOYVI+KLaK17t69i8OHD6OgoABv3rxRuMbDFz99fX2kpaXB0dFRYT0rKwvt27fnatYlwGcDOa9jXnR0dD66WORh57O27Awm70cN8uS9eC5GEhMT4evrC3t7e9y8eRMuLi64ffs2BEFAhw4dWMcTRbt27RAZGanUixcZGYl27doxSqU+PH7fWHXMi5OTk3x99OjRmDdvnsYWW1VHEN2+fRuLFy+Gv78/unbtCgC4ePEiYmJisHr1alYRRcX7yCXyYXRni6j0oWJE078T69SpE3x8fBAWFiZvPLa0tISfnx8GDBiA6dOns45YY2fOnMGgQYNgY2Oj8I9YYWEhjh07hk8//ZRxQnHx2EDeuHFjnDhxAu3atVN4f3l5eXBzc5NvfNBk3t7emDJlCsaOHauwvmvXLmzZsgVJSUlsgqkJTzuDycfTzO1IRO2WLFmCBQsWID09Hfr6+oiPj0dhYSG8vLwwcuRI1vFq7MaNG5gwYQIAoE6dOnj58iWMjY0RHh6OtWvXMk4nDi8vL2RlZWHYsGEoKSlBSUkJhg8fjqysLO4KLQDYvHkzrKysWMcQ1YsXLxQGF1cqLi7mpjft4sWL8PDwUFr38PCQH+Gh6WQyGcLDw2FmZgZbW1vY2trC3Nwcy5cvh0wmYx2P1AJ6jEhUunHjBnbv3g1AuRgZMmSIxt/5MTIykj8atba2Rm5uLtq2bQsAePLkCctoomratKlW7DrMyclBgwYN5MdZaHoDeSVtGPPSvHlzbN26FREREQrr27ZtQ/PmzRmlEldISAi2b9+ONWvWKO0MfvXqlVb8HdV2VGwRlXgvRrp06YLz58/DyckJAwcOxPz585Geno79+/dzc/r4Dz/8AGNjY6U7kfv27UNZWRkmTpzIKJl43tdAPnnyZI1uIK8UEREBb29v/Pbbb3jz5g2Cg4MVxrzwYMOGDRgxYgR+/fVXdO7cGQBw5coV3Lp1C/Hx8YzTiSMmJgbbtm1T2Bns5uaGpk2bYsaMGVRsaQF6jEhUqixGAMiLkZUrVyIgIICLYmT9+vXyL+xhYWHw9vbGnj17YGdnh+3btzNOJ47Vq1ejYcOGSuuWlpZYtWoVg0Tiq9pAXvVx2+jRo3H8+HGGycTh4uKC7Oxs9OjRA0OGDMGLFy8wfPhw/P7779zM0xs4cCCys7MxePBgFBcXo7i4GIMHD0Z2djYGDhzIOp4oiouL5YPSq2rTpg2Ki4sZJCK1jRrkiUp5eXkoLS2Fm5sbXrx4gfnz58tPQF6/fj1sbW1ZRyQfoK+vj5s3b8LOzk5h/fbt23BycuLi6AeeG8irjnmhk8c1W+fOndG5c2elncGBgYG4evUqLl26xCgZqS30GJGoVHVHl5GREaKiohimUa/S0lKlJlUeDlK0tLREWlqaUrEllUrRoEEDNqFExnMDed26dZGWlsY6Rq04d+4cNm/ejLy8POzbtw9NmzZFXFwcWrRogR49erCOV2MREREYNGgQEhISVO4MJvyjx4jkg0pLS/H8+XOFH5ouPz8fgwYNgpGREczMzGBhYQELCwuYm5vDwsKCdTxRjB07FrNnz8bp06dRUVGBiooKnDp1CkFBQRgzZgzreKKobCCvxFsD+bhx47h5rP0+8fHx6N+/PwwMDJCSkoLXr18DAJ49e8bN425t2xlMlNFjRKJSfn4+Zs2ahaSkJLx69Uq+XrnLS9NPde7evTsEQUBQUBCsrKyUdq55eXkxSiaeN2/eYPz48di3bx/q1Hl3E1smk2HChAmIiopCvXr1GCesuYyMDHh7e8vPfvP19VVoINf0viZtGPPi7u6OuXPnYsKECQqPgn///Xf4+PjgwYMHrCMSUmNUbBGVeC9GjI2Nce3aNbRu3Zp1FLW7desWUlNTYWBgAFdXV+767Z49e4bIyEhIpVKUlpaiQ4cOmDlzJqytrVlHqzFtGPNiaGiIzMxM2NnZKfXdOTs7K3yzp6m0YWcwqR71bBGVpFIp18VIx44dUVhYyO37q8rBwQEODg6oqKhAeno6TE1NuXhUWrWBPCQkhHUctdCGMS+NGzdGTk6OUm/h+fPnuZkGsHr1amzevFlp3dLSEtOmTaNiSwtQsUVU4r0Y2bZtG7744gsUFRXBxcUFdevWVbju5ubGKJl45syZA1dXV0yePBkVFRXw8vLChQsXYGhoiKNHj6Jnz56sI9aINjWQA/yOeZk6dSqCgoIQHR0NiUSCe/fu4eLFi1iwYAGWLl3KOp4oCgoK0KJFC6V1W1tbFBQUMEhEahsVW0Ql3ouRx48fIzc3F5MmTZKvSSQSbnrSAODnn3/GuHHjAABHjhxBXl4ebt68ibi4OISEhHBxKGZlA/maNWtYR1ELmUyGFStWYN26dfJjLExMTDB//nyEhITIT8zXZIsXL4ZMJoO3tzfKysrg6ekJPT09LFiwAIGBgazjiUIbdgaT6lGxRVTivRgJCAiAu7s7du/erbInjQdPnjxB48aNAQDHjh3DqFGj4OjoiICAAHz99deM04nj7du3iI6ORkJCApcN5Now5kUikSAkJAQLFy5ETk4OSktL4ezsDGNjY9bRRFO5M9jExASenp4A3g2K52lnMKkeNcgTlZydneHk5ITg4GCVxYimN1kbGRlBKpWiVatWrKOoja2tLbZu3Qpvb2+0aNECmzZtwqBBg3D9+nX06NEDf/zxB+uINcZ7A3mTJk0QFRWlMOYFAA4dOoQZM2agqKiIUTLxxMbGomPHjnByclJYf/XqFfbu3SsfGK/JtGFnMKkeFVtEJd6LkcGDB8Pf3x8jRoxgHUVtQkNDsXHjRlhbW6OsrAzZ2dnQ09NDdHQ0tm7diosXL7KOSD5AX18faWlpcHR0VFjPyspC+/btuZgCoKOjAyMjI+zYsUPh7+PDhw/RpEkTjb+LXhXvO4PJ+9FjRKJS7969uS+25s6di/T0dLi6uir1pP31ToImCg0NhYuLCwoLCzFy5Ej5ieq6urpYvHgx43Ti47GBvF27doiMjFQa8xIZGYl27doxSiW+sLAwjB8/Hunp6QgNDWUdR2143RlMPozubBGVtmzZghUrViAgIIDLYqS6xmIeetK0Be8N5GfOnMGgQYNgY2OjcswLD6eP6+jo4MGDB8jLy8OwYcPQvXt3xMXF4fnz59zc2eJ9ZzD5MCq2iEpUjBBNsGTJEmzfvh1hYWFKDeRTp07looG8qKgI33//PW7evAkAcHJywowZM9CkSRPGycShq6uL+/fvw9LSEgUFBfD19YVEIkFUVBS6devGxdeaZs2a4eDBg/Dw8MDBgwcxY8YMJCUlIS4uDqdOneJiZzCpHhVbROuUl5fDwMAAqampcHFxYR2H1IA2NJDzrvLOlqWlJQCgrKwMfn5+SExMxIsXL7gotvT19ZGTk4NmzZph2rRpMDQ0xMaNG5Gfn4927dpxMW+WVE+z77ETtSgvL0edOnWQkZHBOopa1K1bFzY2Nlx8Edd2xcXFaNOmjdJ6mzZtUFxczCCRuH744Qfs27dPaX3fvn2IiYlhkEh8y5YtUzjmwdDQEAcOHMDcuXPlxyRoOisrK2RmZqKiogLHjx9H3759AbwrLHV1dRmnI7WBii2iRBuKkZCQEHz55Zdc/IOszSobyP+Klwby1atXo2HDhkrrlpaWWLVqFYNE4lu2bBkMDQ2V1sPCwrgZVzRp0iSMGjUKLi4ukEgk6NOnDwDg8uXLKr9ZIPyhx4hEpe3bt2P//v2Ii4tD/fr1WccRnbu7O3JyclBeXg5bW1ulwzBTUlIYJauZv/M4wtTUVI1JagfvDeT6+vq4efOm0snjt2/fhpOTk8Ye/XD48GH4+Pigbt26OHz48HtfJ5FIMHjw4FpMpj4///yzfGdw5Y7ZmJgYmJubY8iQIYzTEXWjYouoxGsxUiksLKza68uWLaulJOLS0dH56NPweblzyXMDuY2NDSIjI1X2pM2cOVN+3IWmqdqnRZtxiDagc7aISkOHDmUdQa00tZj6kKqPXW7fvo3FixfD399f4a5PTEwMVq9ezSqi6Jo2bcrFrkNVeB3zIpPJVP6cEF7RnS2i1a5du4YbN24AANq2bQt3d3fGicTj7e2NKVOmYOzYsQrru3btwpYtW5CUlMQmmIh++OEHGBsbY+TIkQrr+/btQ1lZGSZOnMgomThozAshfKBii1SL12Lk0aNHGDNmDJKSkmBubg4AKCkpQa9evfDTTz+hUaNGbAOKwNDQEFKpFA4ODgrr2dnZaN++PcrKyhglE4+joyM2b96sNCPxzJkzmDZtGrKyshglExdvY17+eiJ+dWbPnq3GJITUDiq2iEq8FyOjR49GXl4eYmNj5QNwMzMzMXHiRLRq1Qq7d+9mnLDmWrdujSFDhiAiIkJhPTg4GIcOHeKiEOG1gfx9Kse82NraavSYlxYtWnzU6yQSCfLy8tSchhD1o54tolJgYCD+/PNPXL9+XakYmT17tsYXI8ePH0dCQoL8vQGAs7MzvvvuO/Tr149hMvFs2LABI0aMwK+//orOnTsDAK5cuYJbt24hPj6ecTpxWFpaIi0tTanYkkqlaNCgAZtQIuJ1zEt+fj7rCGqnbTuDSfWo2CIq8V6MyGQypXmPwLszxnhp2B04cCCys7OxadMm+U69wYMH44svvkDz5s0ZpxMHrw3klX7++WeMGzcOAHDkyBHk5eXh5s2biIuLQ0hICI15+QczNzfXup3B5P3oMSJRycTEBOfOnUP79u0V1n///Xd4eXlp/HiJIUOGoKSkBLt375YfEVBUVAQ/Pz9YWFjgwIEDjBOSj8F7A7m2jHm5e/cuDh8+jIKCArx580bh2vr16xmlqpkzZ87If/6hncGavpGDfASBEBV8fX0FT09PoaioSL529+5dwcvLSxg6dCjDZOIoKCgQ2rdvL9StW1ewt7cX7O3thbp16wru7u5CYWEh63iiOXv2rODn5yd07dpVuHv3riAIghAbGyucO3eOcTJxZWdnC3v37hWOHDki3L59m3Uc0djY2AgnTpwQ3r59KzRv3lw4evSoIAiCkJGRIZibmzNOJ46EhATB0NBQcHFxEerUqSO0b99eMDc3F8zMzIRevXqxjieK3r17C7t27VJa37lzp+Dl5VX7gUito3E9RKXIyEg8f/4cdnZ2aNmyJVq2bIkWLVrg+fPn+Pbbb1nHq7HmzZsjJSUFv/zyC+bMmYM5c+bg2LFjSElJkZ/urOni4+PRv39/GBgYICUlBa9fvwYAPHv2jJtRL5UcHBwwcuRI+Pj44I8//sAff/zBOpIotGHMy5IlS7BgwQKkp6dDX18f8fHxKCwshJeXl9KRHprq4sWL8PDwUFr38PDAlStXGCQitY0eI5L3EgQBCQkJCidzV36xJ/987u7umDt3LiZMmAATExNIpVLY29vj999/h4+PDx48eMA6Yo3x2kBeFe9jXkxMTJCamoqWLVvCwsIC58+fR9u2bSGVSjFkyBDcvn2bdcQa04adwaR61CBP3ksikaBv377yCfW8SUxMRGJiIh49eqTUFB8dHc0olXiysrLkTeNVmZmZoaSkpPYDqYE2NJB/9tlnSms89fgYGRnJ+7Ssra2Rm5uLtm3bAgCePHnCMppotGFnMKkeFVvkvXguRsLCwhAeHg4PDw9YW1t/9K4hTdK4cWPk5OQoHYtw/vx52NvbswklsidPnqBx48YAgGPHjmHUqFFwdHREQEAAvv76a8bpyMfo0qULzp8/DycnJwwcOBDz589Heno69u/fjy5durCOJwpt2BlMqkfFFlGJ92IkKioKO3bswPjx41lHUZupU6ciKCgI0dHRkEgkuHfvHi5evIgFCxZg6dKlrOOJwsrKCpmZmbC2tsbx48exadMmAEBZWRl0dXUZpyMfY/369SgtLQXw7utOaWkp9uzZAwcHB43diahK8+bNueuVJB+Pii2iEu/FyJs3b9CtWzfWMdRq8eLFkMlk8Pb2RllZGTw9PaGnp4cFCxYgMDCQdTxRVDaQV35DwGMDOe+q3mU1MjJCVFQUwzTqc+7cOWzevBl5eXnYt28fmjZtiri4OLRo0QI9evRgHY+oGe1GJCrxXoxMmTIFu3btYh1DrSQSCUJCQlBcXIyMjAxcunQJjx8/xvLly1lHE01oaCi2bduGadOmITk5GXp6egAAXV1dLF68mHE68neVlpbi+fPnCj94oE07g4lqtBuRqLRo0SIYGxtz87jpr4KCghAbGws3Nze4ubkpnSbPw+OL2NhYdOzYUWEKAAC8evUKe/fuxYQJExglI9XRtjEv+fn5mDVrFpKSkvDq1Sv5uiAIkEgkXJyurg07g0n1qNgiKvFejPTq1eu91yQSCU6dOlWLadRDR0cHRkZG2LFjB0aMGCFff/jwIZo0acLFP2I80tHR0aoxL927d4cgCAgKCoKVlZXSe/fy8mKUTDyGhobIzMyEnZ2dQrGVl5cHZ2dnhSKT8Il6tohKaWlp8lE9GRkZCtd4aJY/ffo06wi1IiwsDOPHj0d6ejpCQ0NZxyEfoer/mx8a88IDqVSKa9euoXXr1qyjqI027Awm1aNii6ikLcUI78aNG4du3bph2LBhyMjIQFxcHOtI5AOq3skJDw/H+vXrMXbsWPmar68vXF1dsWXLFi7O2+rYsSMKCwu5Lra0YWcwqR49RiSEU7q6urh//z4sLS1RUFAAX19fSCQSREVFoVu3blw8guKdoaEhpFIpHBwcFNazs7PRvn17lJWVMUomntzcXHzxxRcYN24cXFxclFoW3NzcGCUTjyAIWLVqFVavXi3/M6vcGczThhXyflRsEcIpHR0dPHjwAJaWlgDenT3l5+eHxMREvHjxQmOLLW1qINeGMS+XLl3C559/rjCWRyKRcNUgX+nNmzfIyclBaWkpnJ2dYWxszDoSqSX0GJEQTi1btkzhi7mhoSEOHDiAZcuW4ezZswyT1Yy5ubnWNJBrw5iXgIAAuLu7Y/fu3Sob5HlQdWews7OzfJ12BmsPurNFCNEoZ86ckf/8Qw3kPPQ0FRYWKox5cXJy4mrMi5GREaRSKVq1asU6itrQzmBCxRYhHDl8+DB8fHxQt25dHD58+L2vk0gkGDx4cC0mUw9vb29MmTJFoYEcAHbt2oUtW7YgKSmJTTDy0QYPHgx/f3+FIoQ3Ojo6+O9//4t///vfCA4Olu8MpmJLe1CxRQhHqvZp6ei8f0AEL70w2tBAzvuYly1btmDFihUICAiAq6urUoO8r68vo2Tiqfx7mZeXh2HDhqF79+6Ii4vD8+fPqdjSElRsEUI0Fu8N5PHx8Rg/fjz8/PwQFxeHzMxM2NvbIzIyEseOHcOxY8dYR6wxbfimgHYGEyq2CCEa69ixYxgxYgRatWqlsoF84MCBjBPWDI154QOvO4PJx6PdiIRw5Jtvvvno186ePVuNSWrHwIEDkZ2drdBAPnjwYG4ayLOysuDp6am0bmZmhpKSktoPJLLy8nIYGBggNTUVLi4urOOoDa87g8nHoztbhHCkRYsWH/U6iUSCvLw8NachNWVvb48tW7agT58+Cne2YmNjsWbNGmRmZrKOWGP29vY4cOAA2rVrxzoKIWpDd7YI4Uh+fj7rCLWO5wZybRjzEhISgi+//BJxcXGoX78+6zii0badwaR6dGeLEKKxeG8g14YxL+7u7sjJyUF5eTlsbW1hZGSkcD0lJYVRsprRtp3BpHpUbBHCsbt37+Lw4cMoKCjAmzdvFK6tX7+eUSrxaEsDOc9jXsLCwqq9vmzZslpKQoj60GNEQjiVmJgIX19f2Nvb4+bNm3BxccHt27chCAI6dOjAOp4oeG8g14YxL1RMEW1AxRYhnFqyZAkWLFiAsLAwmJiYID4+HpaWlvDz88OAAQNYxxNF48aNkZOTAzs7O4X18+fPw97enk0oEfn7+6sc8/Ls2TNMmjSJi2Kr0rVr13Djxg0AQNu2beHu7s44Uc1o285gUj0qtgjh1I0bN7B7924AQJ06dfDy5UsYGxsjPDwcQ4YMwfTp0xknrDltaCAPCwvD+PHjkZ6eLh/zwpNHjx5hzJgxSEpKgrm5OQCgpKQEvXr1wk8//YRGjRqxDfg/2rBhw0e9TiKRULGlBajYIoRTRkZG8j4ta2tr5Obmom3btgCAJ0+esIwmmsWLF0Mmk8Hb2xtlZWXw9PSUN5AHBgayjieKcePGoVu3bhg2bBgyMjIQFxfHOpKoAgMD8eeff+L69etwcnICAGRmZmLixImYPXu2/BsGTaONO4PJ+1GDPCGcGjp0KAYNGoSpU6diwYIFOHToEPz9/bF//35YWFggISGBdUTR8NpArg1jXszMzJCQkICOHTsqrF+5cgX9+vXjoveOELqzRQin1q9fj9LSUgDvHkWVlpZiz549cHBw4GInIsB/A3nV74VtbGxw4cIF+Pn5oW/fvgxTiUsmkykNnwaAunXrQiaTMUikHrzvDCbVoztbhBCNpaOjo7KB/OHDh2jSpInG3/kJCwvDwoULYWhoqLBeOebl9OnTjJKJZ8iQISgpKcHu3bvRpEkTAEBRURH8/PxgYWGBAwcOME5Ycx/aGXzq1CnWEYmaUbFFiBYoLS1VuktgamrKKI14dHR08N///hf//ve/ERwcLG8g56XY0gaFhYXw9fXF9evX5fMsCwsL4eLigsOHD6NZs2aME9Zcp06d4OPjI98ZLJVKFXYG87BZhVSPii1COJWfn49Zs2YhKSkJr169kq8LgsDNqdWVp3Tn5eVh2LBh6N69O+Li4vD8+XONLba0ccyLIAhISEiQDxN3cnJCnz59GKcSj4mJCVJTU9GyZUtYWFjg/PnzaNu2LaRSKYYMGYLbt2+zjkjUjHq2COHUuHHjIAgCoqOjYWVlBYlEwjqS6CrfU5cuXXD58mX4+vqiW7duiIqKYpzsfzd06FD5mJehQ4e+93W8FMzAu/fSt29frnrRqtKGncGkelRsEcIpqVSKa9euoXXr1qyjqA2PDeRVH/fy1CBencTERCQmJuLRo0dK7zk6OppRKvF06dIF58+fh5OTEwYOHIj58+cjPT0d+/fvR5cuXVjHI7WAii1CONWxY0cUFhZyXWwtW7ZM4ZgHQ0NDHDhwQN5ATv75wsLCEB4eDg8PD1hbW3N5B1YbdgaT6lHPFiGcys3NxRdffIFx48bBxcVFaXu9m5sbo2SkOto25sXa2hoREREYP3486yiEqA0VW4Rw6tKlS/j8888Vmm8lEonGN8jz3kDeokWLj3qdRCJBXl6emtOoX4MGDXDlyhW0bNmSdZRawevOYFI9KrYI4ZSzszOcnJwQHBysskHe1taWUbKaqdyBaGlpCR0dnfe+TpMLSm2yaNEiGBsbczPLUhVt2BlMqkc9W4Rw6s6dOzh8+DBatWrFOoqotLGBnGevXr3Cli1bkJCQADc3N6XH3Tz0NGnDzmBSPSq2COFU7969IZVKuSu2tA3vY17S0tLQvn17AEBGRobCNV6KEm3YGUyqR8UWIZwaPHgw5s6di/T0dLi6uirdMfD19WWUrGa0qYH8Q2NeeMDDyKEP0YadwaR61LNFCKd47WfSpgZyGvPCB9oZTKjYIoSQfyga88IHXncGk49HjxEJ4VB5eTkMDAyQmpoKFxcX1nHI/4jGvPAhICAA7u7u2L17NzXIaykqtgjhUN26dWFjY6MV3zHz3EBOY174wOvOYPLxqNgihFMhISH48ssvERcXh/r167OOoxa8N5DTmBc+0M5gQj1bhHDK3d0dOTk5KC8vh62tLYyMjBSup6SkMEomHmogJ5pgy5YtWLFiBQICArjaGUw+HhVbhHAqLCys2uvLli2rpSTqo00N5DTmRXPxujOYfDx6jEgIp3gopj6E9wZyGvPCB5p0QKjYIoRz165dw40bNwAAbdu2hbu7O+NE4uG9gZzGvGg+2hlMACq2COHWo0ePMGbMGCQlJcHc3BwAUFJSgl69euGnn35Co0aN2AYUAe8N5DTmRfNp085g8n7Us0UIp0aPHo28vDzExsbCyckJAJCZmYmJEyeiVatW2L17N+OE5EN69eqFkJAQ9OnTh3UUUgPbt2/H/v37ud4ZTKpHxRYhnDIzM0NCQgI6duyosH7lyhX069cPJSUlbIKpCY8N5DTmhQ/asDOYVI8eIxLCKZlMpvSPM/DusQYvDbu8N5A/fvwYubm5mDRpknyNxrxonqFDh7KOQBijO1uEcGrIkCEoKSnB7t270aRJEwBAUVER/Pz8YGFhgQMHDjBOWHPdu3eHIAgICgpS2UDu5eXFKJk4nJ2d4eTkhODgYJXvz9bWllEyQsjfQcUWIZwqLCyEr68vrl+/jubNm8vXXFxccPjwYTRr1oxxwpozNjbmuoHcyMiITh7nCM87g0n16DEiIZxq3rw5UlJSkJCQgJs3bwIAnJycuGq27tixIwoLC7kttmjMCx+0YWcwqR7d2SKEaCzeG8hpzAsfaGcwoWKLEI4lJiYiMTERjx49UmqKj46OZpRKPJcuXcLnn3+uMJaHpwZyGvPCB23bGUyU0WNEQjgVFhaG8PBweHh4wNramsvTxwMCAuDu7o7du3dzecI6L7tGtZ027Awm1aM7W4RwytraGhERERg/fjzrKGrDcwM5jXnhhzbsDCbVe/89akKIRnvz5g26devGOoZaVTaQ84jGvPAjMjISz58/h52dHVq2bImWLVuiRYsWeP78Ob799lvW8UgtoDtbhHBq0aJFMDY2xtKlS1lHURveG8hpzAs/BEHgemcwqR4VW4RwKigoCLGxsXBzc4Obm5tSIcLDoGbeG8hpzAshfKAGeUI4lZaWhvbt2wMAMjIyFK7x0kjOe3MxjXnhB+87g0n16M4WIUQjUQM50RQf2hlMDfL8oztbhBCNpE0N5DTmRbNFRUVhx44dXO8MJtWjO1uEEI3FewM5jXnhQ4MGDXDlyhW0bNmSdRTCCBVbhBCNxXsDOY154YM27Awm1aPHiIQQjcV7A/nx48eRkJAgL7QAwNnZGd999x369evHMBn5O169eoUtW7YgISGB253BpHpUbBFCNNayZctYR1ArGvPCB23YGUyqR48RCSEaj9cGchrzQggfqNgihGgs3hvICwsL4evri+vXr6N58+byNRcXFxw+fBjNmjVjnJAQ8jGo2CKEaCxtaCCnMS+EaD4qtgghGsvMzAwJCQno2LGjwvqVK1fQr18/lJSUsAlGCCFVUIM8IURjaUMDOY15IUTzvX+KKyGE/MP17t0bQUFBuHfvnnytqKgIc+fOhbe3N8Nk4ggLC0O/fv2QmJiIJ0+e4I8//lD4QQjRDPQYkRCisXhvILe2tkZERASNeSFEw1GxRQjRaDw3kNOYF0L4QMUWIYT8Q9GYF0L4QA3yhBCNxnMDOY15IYQPVGwRQjRWWFgYwsPD4eHhAWtra+5Gn9CYF0L4QI8RCSEaixrICSGagI5+IIRorDdv3qBbt26sYxBCSLWo2CKEaKwpU6Zg165drGMQQki1qGeLEKKxqIGcEKIJqGeLEKKxevXq9d5rEokEp06dqsU0hBCiGhVbhBBCCCFqRD1bhBBCCCFqRMUWIYQQQogaUbFFCCGEEKJGVGwRQgghhKgRFVuEEEIIIWpExRYhRGP5+/tj6NCh8o979uyJOXPmMMvzd/w1a1lZGUaMGAFTU1NIJBKUlJTAzs4OGzduZJaRECIOKrYI4Zi/vz8kEgnWrFmjsH7w4EEaZMzY/v37sXz5cvnHMTExOHfuHC5cuID79+/DzMwMV69exbRp0ximJISIgYotQjinr6+PtWvX4o8//mAdhVRRv359mJiYyD/Ozc2Fk5MTXFxc0LhxY0gkEjRq1AiGhoYMUxJCxEDFFiGc69OnDxo3bozVq1e/9zXx8fFo27Yt9PT0YGdnh3Xr1ilct7Ozw6pVqxAQEAATExPY2Nhgy5YtCq8pLCzEqFGjYG5ujvr162PIkCG4fft2tdlkMhkiIiLQqlUr6OnpwcbGBitXrqzR5/xY33//PRwcHKCvrw8rKyt89tln8ms9e/bErFmzMGvWLJiZmaFhw4ZYunQpqp4B/fr1ayxYsABNmzaFkZEROnfujKSkJIX/RnJyMnr27AlDQ0NYWFigf//+8qK36mPEnj17Yt26dTh79iwkEgl69uwJAEqPEUtKSvB//s//gZWVFfT19eHi4oKjR4+K8vtBCFEfKrYI4Zyuri5WrVqFb7/9Fnfv3lW6fu3aNYwaNQpjxoxBeno6QkNDsXTpUuzYsUPhdevWrYOHhwd+//13zJgxA9OnT0dWVhYAoLy8HP3794eJiQnOnTuH5ORkGBsbY8CAAXjz5s17sy1ZsgRr1qzB0qVLkZmZiV27dsHKyqpGn/Nj/Pbbb5g9ezbCw8ORlZWF48ePw9PTU+E1MTExqFOnDq5cuYKvv/4a69evx7Zt2+TXZ82ahYsXL+Knn35CWloaRo4ciQEDBuDWrVsAgNTUVHh7e8PZ2RkXL17E+fPnMXjwYFRUVCjl2b9/P6ZOnYquXbvi/v372L9/v9JrZDIZfHx8kJycjB9//BGZmZlYs2YNdHV1a/R7QQipBQIhhFsTJ04UhgwZIgiCIHTp0kUICAgQBEEQDhw4IFT+9f/888+Fvn37Kvy6hQsXCs7OzvKPbW1thXHjxsk/lslkgqWlpbBp0yZBEAQhLi5OaN26tSCTyeSvef36tWBgYCCcOHFCZbbnz58Lenp6wtatW1Ve/5jPWfX9CYIgeHl5CUFBQdX9lgiCIAjx8fGCqamp8Pz5c5XXvby8BCcnJ4X/9qJFiwQnJydBEAThzp07gq6urlBUVKTw67y9vYUlS5YIgiAIY8eOFbp37/7eDH/NGhQUJHh5eSm8xtbWVtiwYYMgCIJw4sQJQUdHR8jKyvrg+yOE/LPQnS1CtMTatWsRExODGzduKKzfuHED3bt3V1jr3r07bt26pXAXxs3NTf5ziUSCxo0b49GjRwAAqVSKnJwcmJiYwNjYGMbGxqhfvz5evXqF3NxcnDt3Tr5ubGyMnTt34saNG3j9+jW8vb1V5v3Q56yJvn37wtbWFvb29hg/fjx27tyJsrIyhdd06dJFYRNB165d5b8n6enpqKiogKOjo8L7OnPmjDxb5Z0tsaSmpqJZs2ZwdHQU7XMSQmpHHdYBCCG1w9PTE/3798eSJUvg7+//t3993bp1FT6WSCSQyWQAgNLSUnzyySfYuXOn0q9r1KgR6tWrh9TUVPmalZXVB3uvPvQ5a8LExAQpKSlISkrCyZMn8dVXXyE0NBRXr16Fubn5B399aWkpdHV1ce3aNaXHeMbGxgAAAwODGmX8K7E/HyGk9lCxRYgWWbNmDdq3b4/WrVvL15ycnJCcnKzwuuTkZDg6On50P1CHDh2wZ88eWFpawtTUVOVrWrVqpfCxg4MDDAwMkJiYiClTpvxPn7Mm6tSpgz59+qBPnz5YtmwZzM3NcerUKQwfPhwAcPnyZYXXX7p0CQ4ODtDV1YW7uzsqKirw6NEjfPrppyo/v5ubGxITExEWFiZKXjc3N9y9exfZ2dl0d4sQDUOPEQnRIq6urvDz88M333wjX5s/fz4SExOxfPlyZGdnIyYmBpGRkViwYMFHf14/Pz80bNgQQ4YMwblz55Cfn4+kpCTMnj1bZVM+8O5IikWLFiE4OBixsbHIzc3FpUuXsH379v/5c36so0eP4ptvvkFqairu3LmD2NhYyGQyhSK0oKAA8+bNQ1ZWFnbv3o1vv/0WQUFBAABHR0f4+flhwoQJ2L9/P/Lz83HlyhWsXr0av/zyC4B3zf9Xr17FjBkzkJaWhps3b2LTpk148uTJ/5TZy8sLnp6eGDFiBP7v//2/yM/Px6+//orjx4/X6PeCEKJ+VGwRomXCw8Plj/+Ad3eQ9u7di59++gkuLi746quvEB4e/rceNRoaGuLs2bOwsbHB8OHD4eTkhMmTJ+PVq1fV3pVaunQp5s+fj6+++gpOTk4YPXq0vA/sf/2cH8Pc3Bz79+9H79694eTkhKioKOzevRtt27aVv2bChAl4+fIlOnXqhJkzZyIoKEjhgNEffvgBEyZMwPz589G6dWsMHToUV69ehY2NDYB3BdnJkychlUrRqVMndO3aFYcOHUKdOv/7A4X4+Hh07NgRY8eOhbOzM4KDg1XubiSE/LNIBKHKwTGEEELQs2dPtG/fnkblEEJEQXe2CCGEEELUiIotQgh3/nrUxF9/EEJIbaLHiIQQ7rx8+RJFRUXvvf7XnZGEEKJOVGwRQgghhKgRPUYkhBBCCFEjKrYIIYQQQtSIii1CCCGEEDWiYosQQgghRI2o2CKEEEIIUSMqtgghhBBC1IiKLUIIIYQQNaJiixBCCCFEjf4f/w1l0wlgqfwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cell_genewise_standarize, cmap='vlag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tissue/Organ specific "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "organs_list = ['brain','spleen','thymus', 'lymphoid tissue', 'lymph node', 'skin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>organ_specific</th>\n",
       "      <th>count</th>\n",
       "      <th>score</th>\n",
       "      <th>normalized score</th>\n",
       "      <th>gene</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>brain</td>\n",
       "      <td>612</td>\n",
       "      <td>516.0</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lymph node</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lymphoid tissue</td>\n",
       "      <td>45</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skin</td>\n",
       "      <td>405</td>\n",
       "      <td>381.0</td>\n",
       "      <td>0.940741</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spleen</td>\n",
       "      <td>54</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.981481</td>\n",
       "      <td>ENSG00000067182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>lymph node</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>lymphoid tissue</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>skin</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>spleen</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>thymus</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSG00000168811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      organ_specific  count  score  normalized score             gene\n",
       "0              brain    612  516.0          0.843137  ENSG00000067182\n",
       "1         lymph node     27    1.0          0.037037  ENSG00000067182\n",
       "2    lymphoid tissue     45   44.0          0.977778  ENSG00000067182\n",
       "3               skin    405  381.0          0.940741  ENSG00000067182\n",
       "4             spleen     54   53.0          0.981481  ENSG00000067182\n",
       "..               ...    ...    ...               ...              ...\n",
       "283       lymph node      3    0.0          0.000000  ENSG00000168811\n",
       "284  lymphoid tissue      5    0.0          0.000000  ENSG00000168811\n",
       "285             skin     45    0.0          0.000000  ENSG00000168811\n",
       "286           spleen      6    0.0          0.000000  ENSG00000168811\n",
       "287           thymus     11    0.0          0.000000  ENSG00000168811\n",
       "\n",
       "[288 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MS_variant.process_organs(organs_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None-organ_specific', ylabel='protein_gene'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MS_variant.plot_organs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from VANE import VANE_network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### T cell analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to create networks for a few an analyze "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_cell_network = VANE_network(network_folder = 'base_files/cell_networks/', df_for_network=MS_variant.df_for_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The python package already comes with a dictionary between regulomeDB cells and Human protein atlas. A custom python dic can be easily made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B': 'B-Cell',\n",
       " 'T': 'T-Cell',\n",
       " 'mono': 'Monocytes',\n",
       " 'astrocyte': 'Astrocytes',\n",
       " 'oligodendrocyte': 'Oligodendrocytes'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_cell_network.cell_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ATP1A1', 'BACH2', 'BCL10', 'CAVIN1', 'CD58', 'CD6', 'CLEC2D', 'CXCR5', 'DDX6', 'DIPK1A', 'EFCAB13', 'ELMO1', 'GDPD3', 'GFI1', 'IFNGR1', 'INO80E', 'INTS8', 'MAST3', 'NPEPPS', 'PHLDB1', 'PTGER4', 'RGS1', 'SHC1', 'SLX1A', 'SP140', 'STAT3', 'TAGAP', 'TBKBP1', 'TIMMDC1', 'TNFRSF1A', 'TREH', 'UBLCP1', 'ZFP36L2']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x7fcef64da140>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_cell_network.create_cellular_network(cell = 'T', epigenome_threshold = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The number of nodes in our subgraph can be called this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_cell_network.graph.number_of_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The number of edges in our subgraph can be called this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_cell_network.graph.number_of_edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute CI intervals and P value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We bootstrap the original cellular network to create an empiric distribution. We can estimate the one-tail P value using the following formula:\n",
    "\n",
    "pval = (1+sum(s >= s0))/(N+1)\n",
    "\n",
    "Davison and Hinkley (1997), Bootstrap Methods and their Application, p. 141. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "CI, pval = T_cell_network.calculate_p_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024975024975024976"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Edge Count', ylabel='Frequency in bootstrapping'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_cell_network.plot_bootstrap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Astrocytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Astrocytes_cell_network = VANE_network(network_folder = 'base_files/cell_networks/', df_for_network=MS_variant.df_for_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't have in our original dictionary the specific cellular method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ATP1A1', 'BACH2', 'BCL10', 'CAVIN1', 'CD58', 'CXCR5', 'DIPK1A', 'EFCAB13', 'EVI5', 'INO80E', 'INTS8', 'PTGER4', 'RGS1', 'SHC1', 'STAT3', 'TBKBP1', 'TIMMDC1', 'TNFRSF1A', 'UBLCP1']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x7fcef4476440>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Astrocytes_cell_network.create_cellular_network(cell = 'astrocyte', epigenome_threshold = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Astrocytes_cell_network.graph.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Astrocytes_cell_network.graph.number_of_edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P value calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "CI, pval = Astrocytes_cell_network.calculate_p_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000999000999000999"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Edge Count', ylabel='Frequency in bootstrapping'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkZFasGCB+vXrp127dnm7VgAAAJ8xdKToqaeekt1u1/Lly9WqVSu3bdu2bdPw4cP19NNPa9GiRV4pEgAAwNcMHSnatm2b0tLSPAKRJLVq1UppaWnatm1buYsDAACoKIZCUa1atRQeHn7J7eHh4apVq5bhogAAACqaoVCUlpampUuX6sSJEx7bjh07pqVLlyotLa3cxQEAAFQUQ3OKnE6nqlatqptvvlndu3dXw4YNJUn79+/X+vXr1aBBAzmdTi1YsMD1GpPJpHvuuccrRQMAAHiboVD0/PPPu35+7733PLb/8MMPbm0kQhEAAAhshkLR+vXrvV0HAACAXxkKRfXq1fN2HQAAAH5l+I7WknT69Gn961//crujdadOnVSzZk2vFAcAAFBRDIei2bNnKzMzU4WFhW7rQ0NDNWzYMD300EPlLg4AAKCiGApFc+fO1dy5c/WHP/xBgwcPVqNGjSRJ+/bt0+LFi/XKK68oJCREI0eO9GatAAAAPmMoFC1btkxdu3bVvHnz3NbXr19fKSkpGjFihJYuXUooAgAAQcPQzRvPnDmjLl26XHJ7SkqK8vPzDRcFAABQ0QyFonbt2v3ms822bdumdu3aGS4KAACgohkKRU8++aS2bt2qqVOn6sCBA3I4HHI4HDpw4ICeffZZffPNN5oyZYq3awUAAPAZQ3OK+vXrJ6fTqUWLFmnRokUymy9kK4fDIUkKCwtTv3793F5jMpn01VdflbNcAAAA3zAUinr06CGTyeTtWgAAAPzGUCh67rnnvF0HAACAXxmaUwQAAFDZlOsxH0ePHtWOHTuUl5cnp9PpsX3AgAHl6R4AAKDCGApF58+f17hx47R27Vo5HA6ZTCZXKLp4rhGhCAAABAtDp8/+/ve/a926dUpPT9eiRYvkdDr13HPP6fXXX1dKSoqaNWumd955x9u1AgAA+IyhUPTRRx9p4MCBuu+++9SkSRNJUt26dXXDDTfo1VdfVWRkpBYvXlyuwvLz85WSkqL4+Hh99913bttWrFihHj16KDExUf369dOnn37q8fq8vDxNmDBBHTp0UNu2bTVmzBgdP368XDUBAIDKy1Ao+vnnn9WqVStJUpUqVSRJBQUFru09evTQunXrylXYyy+/LLvd7rH+gw8+0KRJk5SamqrMzEy1adNGo0aN0jfffOPWLj09XZs2bdKTTz6pF154Qfv27dPw4cNVXFxcrroAAEDlZCgU1a5dW6dOnZIkRUREKCoqSvv27XNtP3PmjM6fP2+4qL1792rJkiUaPXq0x7aXXnpJvXv3Vnp6ujp27KinnnpKiYmJmjt3rqvN1q1btXHjRj377LPq1auXbrrpJr344ov64YcftHbtWsN1AQCAystQKGrVqpW+/vpr13LXrl01f/58vfvuu1q9erXeeOMNtWnTxnBRzzzzjAYNGqTY2Fi39YcOHdL+/fuVmprqtr5Xr17avHmzCgsLJUnZ2dmyWq3q3Lmzq01cXJwSEhKUnZ1tuC4AAFB5GQpFQ4YMUUxMjCuEPPTQQ4qMjNSjjz6q8ePHKzIyUo8//rihgtasWaNdu3Zp5MiRHttycnIkySMsNW7cWEVFRTp06JCrXWxsrMddt+Pi4lx9AAAAXMzQJfnt27dX+/btXcvXXnutPvzwQ+3atUtms1lxcXEKCbnyrgsKCvTcc89p7Nixql69usf23NxcSZLVanVbX7Jcst1msykyMtLj9VFRUdq+ffsV1/WfQkK8e89Ls/mX8GaS3x6fUvK+Fgv387yckn3Evgp8jFVwcNh/HR+L2SSzl//Gwvsq63fripNLQUGBHnnkEd18881uD301m81q1qxZuYqZN2+eatWqpVtvvbVc/fiS2WxSzZrVfNK3xWxWSIjFJ31f9r1/+cW2WiP88v7BiH0VPBirwGY/9+vfvUhrhCy/XMCDwFfZvltXHIoiIiL0r3/9SykpKV4t5MiRI3r99dc1d+5c5eXlSZLOnj3r+nd+fr6ioqIkXbjcvk6dOq7X2mw2SXJtt1qtOnr0qMd75ObmutoY5XA4ZbOdLVcf/yk01KLq1avI7nCouNjziruKYLc7JEk2W4HrZ5TOYjHLao1gXwUBxio4OC66MCfPViBngX/+DqLsgu27ZbVGlOmolqHTZ9dff722bt2qO+64w8jLS3X48GEVFRXpvvvu89iWlpam1q1ba8aMGZIuzBmKi4tzbc/JyVFoaKjq168v6cLcoc2bN8vpdLqdjtq3b5+aNm1a7lqLi737C+AaKKdKfVxKRSh5X7vd4fXPV1mxr4IHYxXYHBeNjd3hdFtGYKts3y1DJwMnT56sr776SjNnziz1iIwRCQkJWrhwods/jz32mCRpypQpeuKJJ1S/fn01atRIa9ascXttVlaWOnXqpLCwMElSSkqKcnNztXnzZlebffv2aceOHV4/wgUAACoHQ0eK+vXrJ7vdroyMDGVkZMhisbgCSQmTyaSvvvqqzH1arVYlJSWVuq1FixZq0aKFJGn06NF6+OGH1aBBAyUlJSkrK0vbtm3Tm2++6Wrftm1bJScna8KECRo3bpzCw8M1c+ZMxcfH6+abbzbwiQEAQGVnKBT16NHDb1dJ9enTRwUFBcrMzFRGRoZiY2M1Z84ctW3b1q3drFmzNG3aNE2ePFnFxcVKTk7WxIkTDV0VBwAAKj+T01+TWIKU3e7QyZP5Xu0zPDxEVmuEXlz6tQ4fz/Nq32VVr051pd95vU6dyq9U54d9ISTErJo1q7GvggBjFRwc589rz8j7JUnNXs2UwxLq54pwOcH23YqOrlamidaG5hTNmTNHu3btuuT23bt3a86cOUa6BgAA8AvDoeiHH3645Pbdu3e7PYsMAAAg0PnkVpSnT59WaCiHPwEAQPAo86zjL774Qlu2bHEtr1u3TgcOHPBol5eXp6ysLK/cDwgAAKCilDkUbdmyxTVPyGQyae3atVq7dm2pbZs0aaJJkyZ5p0IAAIAKUOZQNGzYMA0ePFhOp1M33HCDpkyZ4nHPH5PJpIiICIWHh3u9UAAAAF8qcyiqUqWKqvzykL7169crOjpaERGV60FwAADg6mXoTob16tXzWFdQUKAPPvhAhYWFuvHGG0ttAwAAEKgMhaIJEyZo27Ztev/99yVJhYWFuuOOO7R7925JUmRkpP7xj3+oefPm3qsUAADAhwxdkr9lyxb98Y9/dC2///772r17t1544QW9//77ql27NjdvBAAAQcVQKPrpp5/cTo99/PHHatmypfr06aMmTZrojjvu0LZt27xWJAAAgK8ZCkURERHKy7vwjK7i4mJ9/vnnSk5Odm2vVq2aazsAAEAwMDSnqEWLFlq+fLmSkpL0ySefKD8/X926dXNtP3jwoGrVquW1IgEAAHzNUChKT0/XsGHDdOutt8rpdKpHjx5q1aqVa/u6devUrl07rxUJAADga4ZCUWJioj788EN9/fXXslqt6tChg2ubzWbTnXfeqaSkJK8VCQAA4GuG5hR98cUXkqTu3bu7BSJJslqt6tu3L3OKAABAUDEUitLS0rRp06ZLbv/ss8+UlpZmuCgAAICKZigUOZ3O39xeWFgoi8ViqCAAAAB/KPOcoh9//FFHjhxxLefk5LhOo13MZrNp2bJluu6667xTIQAAQAUocyhatWqV5syZI5PJJJPJpFdeeUWvvPKKRzun0ymLxaIpU6Z4tVAAAABfKnMoSk1N1e9//3s5nU6lp6dryJAhat++vVsbk8mkiIgIJSQkqHbt2l4vFgAAwFfKHIoaN26sxo0bS5KmTZum9u3bq379+j4rDAAAoCIZuk/RLbfc4vo5Pz9fR48elST97ne/U7Vq1bxTGQAAQAUyFIokadu2bfrv//5vff3113I4HJIks9ms66+/Xo888ogSExO9ViQAAICvGQpF3377rYYMGaLQ0FDddtttrtNqe/fu1QcffKC77rpLixYtcnv0BwAAQCAzFIpmzpypunXrasmSJapTp47bttGjR+vPf/6zZs6cqQULFnilSAAAAF8zdPPGb7/9Vn/60588ApEk1a5dW3fccYe++eab8tYGAABQYQyFIrPZLLvdfsntDodDZrOhrgEAAPzCUHJp27atFi9e7HaH6xI//vijlixZonbt2pW7OAAAgIpiaE7RX//6Vw0ePFipqan64x//qEaNGkmS9u3bp/Xr18tisehvf/ubN+sEAADwKUOhqHnz5lqxYoVmzpypTz75RAUFBZKkiIgIdenSRenp6WrSpIlXCwUAAPAlw/cpatKkiebOnSuHw6GTJ09KkqKjo5lLBAAAgpLhUFTCbDarevXqrp8BAACCkeFQ9OOPP2r27NnasGGDTp06JUmqWbOmbrzxRo0aNUr16tXzWpEAAAC+ZigU7d27V3feeafy8vJ0ww03uO5onZOTo3feeUeffvqplixZori4OK8WCwAA4CuGQtGMGTNkNpv19ttvKz4+3m3brl27dM8992jGjBmaO3euV4oEAADwNUOTgL744gsNGTLEIxBJUtOmTTV48GB9/vnn5S4OAACgohgKRcXFxapSpcolt0dERKi4uNhwUQAAABXN0OmzhIQErVixQrfffrsiIyPdtp05c0YrV65U8+bNvVIgUFHMZpPMZtNl21ksZrd/e4vD4ZTD4fRqnwCAsjMUikaPHq3hw4crNTVVAwcOdLuj9dtvv63Tp09r8uTJ3qwT8Cmz2aQaNavKcgW3lbBaI7xag93h0OlTZwlGAOAnhkJRp06dlJGRoenTpysjI8NtW0JCgv77v/9bHTt29EqBQEUwm02ymM1asmanjp88+5ttTSaTLBaz7HaHnE7vBJhroqvqzp4JMptNhCIA8BPD9ym64YYbtHr1ap04cUI//vijJOm6665TnTp1vFYcUNGOnzyrIyfO/GYbk8mkkBCLiovtXgtFAAD/K/cdrevUqUMQAgAAQc9wKDp58qQyMzO1YcMGHTlyRJJUr1493XjjjRo6dKhq167ttSIBAAB8zdDlM7t371bfvn21YMECRUZGqmfPnurZs6ciIyO1YMEC9evXT7t27fJ2rQAAAD5j6EjRU089JbvdruXLl6tVq1Zu27Zt26bhw4fr6aef1qJFi7xSJAAAgK8ZOlK0bds2paWleQQiSWrVqpXS0tK0bdu2chcHAABQUQyFolq1aik8PPyS28PDw1WrVq0r7nfDhg2666671LFjR7Vs2VI33XSTpk2bpry8PLd2n3zyifr166fExET16NFDb731lkdfhYWFev7559W5c2e1adNG9957r3Jycq64JgAAcHUwFIrS0tK0dOlSnThxwmPbsWPHtHTpUqWlpV1xv6dPn1arVq00ZcoUzZ8/X/fee69Wr16thx56yNXmyy+/1KhRo9SmTRtlZmYqNTVVjz/+uNasWePW1zPPPKMVK1Zo7Nixmj17tgoLC3XPPfd4BCwAAACpjHOKFixY4LGuatWquvnmm9W9e3c1bNhQkrR//36tX79eDRo0MFRM//793ZaTkpIUFhamSZMm6dixY6pbt67mzZunVq1a6amnnpIkdezYUYcOHdJLL72knj17SpKOHj2qlStX6oknntBtt90mSUpMTFTXrl21bNkyDR8+3FB9AACg8ipTKHr++ecvue29997zWPfDDz/o+eef1z333GO4sBI1atSQJBUVFamwsFBbtmzRww8/7NamV69eev/993X48GHFxMRo48aNcjgcrpBU0k/nzp2VnZ1NKAIAAB7KFIrWr1/v6zrc2O12FRcXa8+ePZo7d666deummJgY7dmzR0VFRYqLi3Nr37hxY0lSTk6OYmJilJOTo1q1aikqKsqj3cqVKyvscwAAgOBRplBUr149X9fhpmvXrjp27JgkqUuXLpoxY4YkKTc3V5JktVrd2pcsl2y32WyKjIz06NdqtbralEdIiHefju56MrvpwiMk/KHkfb395PdgUfK5TSbT5cfA9Ou/TfLOeF3t+99XSvYn+zWwOey/jo/FbJLZy39j4X2V9btV7sd8+EJGRoYKCgq0Z88ezZs3TyNGjCh1XpM/mM0m1axZzSd9W8xmhYRYfNL3Zd/7l19sbz/5PdhYLGUfgxCL98aK/e9b7NfAZj/363cp0hohS5UqfqwGV6KyfbcCMhQ1a9ZMktS2bVslJiaqf//+WrdunZo0aSJJHleQ2Ww2SXKdLrNarTpzxvOhnjabzeOU2pVyOJyy2X77KepXKjTUourVq8jucKi42O7VvsvKbndIkmy2AtfPVxOLxSyrNUJ2exnGwHQhEBXb7ZKXngd7te9/XykZV/ZrYHOcP+/6Oc9WIGeBf/4OouyC7btltUaU6ahWQIaii8XHxys0NFQHDx5Ut27dFBoaqpycHHXp0sXVpuT+QyVzjeLi4vTTTz8pNzfXLQTl5OR4zEcyorjYu78AroFyym9PXS953wuhIPB/wX3F6XRedgxcp8y8OF7sf99ivwY2x0VjY3c43ZYR2CrbdyvgTwZ+++23KioqUkxMjMLCwpSUlKSPPvrIrU1WVpYaN26smJgYSVJycrLMZrPWrl3rapObm6uNGzcqJSWlQusHAADBIaCOFI0aNUotW7ZUfHy8qlSpou+//17z589XfHy8unfvLkl64IEHlJaWpieffFKpqanasmWL3n//fc2cOdPVz+9+9zvddtttmj59usxms+rWratXX31VkZGRGjRokL8+HgAACGABFYpatWqlrKwsZWRkyOl0ql69err99ts1dOhQhYWFSZLat2+v2bNna9asWVq5cqWuu+46PfPMM0pNTXXra+LEiapWrZpmzJih/Px8tWvXTgsWLCj1qjQAAADDoWjv3r166623dPjwYeXm5nrMrTCZTPrHP/5xRX3ed999uu+++y7b7qabbtJNN930m23CwsI0btw4jRs37opqAAAAVydDoWj16tWaMGGCQkJCFBsb63HfIMl/E4YBAACMMBSK5syZo4SEBGVmZio6OtrbNQEAAFQ4Q1efHT9+XLfeeiuBCAAAVBqGQlF8fLyOHz/u7VoAAAD8xlAoGj9+vFauXKmvv/7a2/UAAAD4haE5RZmZmYqMjNTgwYPVpEkTXXvttTKb3fOVyWTSvHnzvFIkAACArxkKRbt27ZIkXXvttcrPz9eePXs82vjrae8AAABGGApFn3zyibfrAAAA8KuAf/YZAABARSjTkaIff/xRknTddde5LV9OSXsAAIBAV6ZQ1K1bN5lMJn377bcKCwtzLV/Ozp07y10gAABARShTKJo6dapMJpNCQ0PdlgEAACqLMoWigQMH/uYyAABAsGOiNQAAgAhFAAAAkghFAAAAkghFAAAAkghFAAAAkgyGouPHj3u7DgAAAL8yFIr+8Ic/6C9/+YtWr16ts2fPersmAACACmcoFI0ZM0bHjx/X+PHj1blzZz388MPKzs6Ww+Hwdn0AAAAVokw3b/xPI0aM0IgRI7Rjxw699957+uCDD/T++++rVq1a6t27t/r27avExERv1woAAOAzhkJRiebNm6t58+Z69NFH9dlnn+m9997TqlWrtGjRIsXGxqpfv37q168fD4YFAAABzytXn5lMJl1//fW68cYb1bp1azmdTh04cEBz5sxR9+7dXafbAAAAAlW5jhRJch0hWrt2rc6cOaOmTZtq3Lhx6tu3rywWi1atWqVXX31Vjz76qN544w0vlAwAAOB9hkLR999/r3fffVcffPCBjh8/rtq1a+u2227TgAEDFB8f79Z26NChCg8P1/PPP++VggEAAHzBUCgaMGCAqlSpoptuukkDBgxQ586dZTZf+kxckyZN1KZNG6M1AgAA+JyhUDR16lT16NFD1apVK1P7jh07qmPHjkbeCgAAoEIYCkUDBw70dh0AAAB+Zejqs4ULF2ro0KGX3D5s2DAtWbLEcFEAAAAVzVAoWrlypRo3bnzJ7U2aNNHy5csNFwUAAFDRDIWiQ4cO/WYoiouL08GDBw0XBQAAUNEMhaLQ0FCdOHHiktuPHz/+m1ejAQAABBpDyaV169Z6++23debMGY9teXl5WrVqlVq3bl3u4gAAACqKoavPRo0apbvuuksDBgzQ3XffrSZNmkiSdu/erX/84x86ceKEZsyY4dVCAQAAfMlQKGrdurVeeeUVTZ48Wc8++6xMJpMkyel0KiYmRvPmzVPbtm29WigAAIAvGX72WefOnbVu3Trt2LHDNam6QYMGatGihSskAQAABItyPRDWbDarZcuWatmypbfqAQAA8ItyhaI9e/bo0KFDys3NLXX7gAEDytM9AABAhTEUig4ePKhHHnlE27Ztk9PpLLWNyWQiFAEAgKBhKBRNnjxZu3bt0oQJE9S+fXtZrVZv1wUAAFChDIWir7/+Wvfff7+GDBni7XoAAAD8wtDNG2vWrKnIyEhv1wIAAOA3hkLRoEGD9O6778put3u7HgAAAL8wdPqsUaNGcjgc6t+/v2699Vb97ne/k8Vi8Wh38803l7tAAACAimAoFI0dO9b18/PPP19qG5PJpJ07dxqrCgAAoIIZCkULFy70dh0AAAB+ZSgUdejQwdt1SJI+/PBDvfvuu/r3v/8tm82mhg0basiQIbr11lvdHh2yYsUKvfbaa/rxxx8VGxursWPHqmvXrm595eXladq0afr4449VVFSkLl26aOLEibrmmmt8UjsAAAhuhiZalygsLNTWrVv18ccf6+TJk+Uu5o033lBERITGjx+vefPmKSUlRZMmTdLcuXNdbT744ANNmjRJqampyszMVJs2bTRq1Ch98803bn2lp6dr06ZNevLJJ/XCCy9o3759Gj58uIqLi8tdJwAAqHwMP+Zj4cKFmjNnjvLy8iRJr7/+ujp16qSTJ08qNTVVjzzyiG677bYr6nPevHmKjo52LXfq1EmnT5/WggUL9OCDD8psNuull15S7969lZ6eLknq2LGjdu3apblz5yozM1OStHXrVm3cuFHz589XcnKyJCk2Nla9evXS2rVr1atXL6MfGwAAVFKGjhS99dZbmjp1qrp06aJnn33W7VEf0dHR6tixo7Kysq6434sDUYmEhASdOXNGZ8+e1aFDh7R//36lpqa6tenVq5c2b96swsJCSVJ2drasVqs6d+7sahMXF6eEhARlZ2dfcV0AAKDyMxSKFixYoJtuukkzZszwmMsjSS1atNDu3bvLXZwkffXVV6pbt66qV6+unJwcSReO+lyscePGKioq0qFDhyRJOTk5io2NdZuHJF0IRiV9AAAAXMzQ6bMDBw785iM+atSoodOnTxutyeXLL79UVlaWxo0bJ0nKzc2VJI9nrZUsl2y32Wyl3nE7KipK27dvL3ddISHlmorlwWz+JbyZ5BHkKkrJ+1os3v1swaLkc5tMpsuPgenXf5vknfG62ve/r5TsT/ZrYHPYfx0fi9kks5f/xsL7Kut3y1AoslqtOnXq1CW379mzR3Xq1DFclCQdPXpUY8eOVVJSktLS0srVlzeZzSbVrFnNJ31bzGaFhHjeBLMilPxiW60Rfnn/QGGxlH0MQkq5YWl53ldi//sK+zWw2c/9+l2KtEbIUqWKH6vBlahs3y1DoSglJUXLly/XnXfe6bFt9+7dWrFihW699VbDRdlsNg0fPlw1atTQ7NmzZTZf+A9GVFSUpAuX218cumw2m9t2q9Wqo0ePevSbm5vramOUw+GUzXa2XH38p9BQi6pXryK7w6HiYv88OsVud0iSbLYC189XE4vFLKs1QnZ7GcbAdCEQFdvtkvO3m5bV1b7/faVkXNmvgc1x/rzr5zxbgZwFPEIq0AXbd8tqjSjTUS1DoSg9PV133HGH+vTpo65du8pkMmn16tV66623tHbtWtWpU0cPPvigka517tw53X///crLy9M///lPt9NgcXFxki7MGSr5uWQ5NDRU9evXd7XbvHmznE6n26mQffv2qWnTpobqulhxsXd/AVwD5ZTbpPWKVPK+F0JB4P+C+4rT6bzsGLhOmXlxvNj/vsV+DWyOi8bG7nC6LSOwVbbvlqGTgXXr1tWqVavUpUsXffjhh3I6nXrnnXf06aefqnfv3lq+fHmpV5JdTnFxsdLT05WTk6PXXntNdevWddtev359NWrUSGvWrHFbn5WVpU6dOiksLEzShSNZubm52rx5s6vNvn37tGPHDqWkpBj4xAAAoLIzfJ+iWrVq6dlnn9Wzzz6rkydPyuFwKDo62nWqy4gpU6bo008/1fjx43XmzBm3GzI2b95cYWFhGj16tB5++GE1aNBASUlJysrK0rZt2/Tmm2+62rZt21bJycmaMGGCxo0bp/DwcM2cOVPx8fE8pBYAAJTKcCi6mJGjQqXZtGmTJOm5557z2LZ+/XrFxMSoT58+KigoUGZmpjIyMhQbG6s5c+aobdu2bu1nzZqladOmafLkySouLlZycrImTpyokBCvfGQAAFDJGEoIc+bMuWwbk8mkkSNHXlG/n3zySZna3X777br99tt/s01kZKSmTp2qqVOnXlENAADg6uT1UGQymVwTnK80FAEAAPiLoVD0/fffe6xzOBw6cuSIlixZoi+++ML1HDIAAIBg4LVbUZrNZtWvX1/jxo1Tw4YN9cwzz3irawAAAJ/zyf25/+u//ksbNmzwRdcAAAA+4ZNQtH379nJdmg8AAFDRDM0pWr16danrbTabvvzyS61du/ayV4cBAAAEEkOhaPz48ZfcVrNmTd13331ceQYAAIKKoVC0fv16j3Umk0lWq1XVq1cvd1EAAAAVzVAoqlevnrfrAAAA8CtmQwMAAMjgkaJmzZrJZDJd0WtMJpN27Nhh5O0AAAB8zlAoGjlypD7++GPt2bNHycnJio2NlSTl5ORo06ZN+v3vf6/u3bt7tVAAAABfMhSKrrnmGv3888967733FBcX57Zt7969uvvuu3XNNdfojjvu8EqRAAAAvmZoTtH8+fN11113eQQiSWrcuLEGDx6s1157rdzFAQAAVBRDoejo0aMKCbn0QaaQkBAdPXrUcFEAAAAVzVAo+v3vf68lS5bo2LFjHtuOHj2qpUuXqmnTpuUuDgAAoKIYmlP02GOPadiwYerRo4e6d++uhg0bSpL279+v9evXy+l0avr06V4tFAAAwJcMhaL27dtr+fLlevHFF/Xxxx/r3LlzkqQqVaooOTlZo0ePVnx8vFcLBQAA8CVDoUiSmjZtqrlz58rhcOjkyZOSpOjoaJnN3A8SAAAEH8OhqITZbFZ4eLiqVq1KIAIAAEHLcIr57rvvNHToULVu3VpJSUn6/PPPJUknT57UAw88oC1btnitSAAAAF8zFIq+/vpr3XnnnTpw4ID69esnh8Ph2hYdHa0zZ87on//8p9eKBAAA8DVDoWjmzJlq3LixsrKyNHbsWI/tSUlJ+vbbb8tdHAAAQEUxFIq+++47DRw4UGFhYaU+GLZu3br66aefyl0cAABARTEUikJCQtxOmf2nY8eOqWrVqoaLAgAAqGiGQlHr1q310Ucflbrt7NmzWrVqlf7rv/6rXIUBAABUJEOhaMyYMdq+fbvuu+8+ZWdnS5J++OEHrVixQgMHDtTJkyf14IMPerVQAAAAXzJ8pCgjI0MHDhzQuHHjJEnPPfecJk2aJIfDoYyMDDVr1syrhQIAAPjSFd+80el0Kj8/X+3atdNHH32knTt3av/+/XI6napfv75atmxZ6uRrAACAQHbFoaioqEgdOnTQ2LFjNXz4cCUkJCghIcEXtQEAAFSYKz59FhYWptq1ayssLMwX9QAAAPiFoTlFt9xyi9555x0VFhZ6ux4AAAC/MPRA2Pj4eK1fv159+vTRLbfconr16qlKlSoe7W6++eZyFwgAAFARDIWiv/71r66fX3zxxVLbmEwm7dy501hVAAAAFcxQKFq4cKG36wAAAPCrMoeiv//97+rVq5eaNWumDh06+LImAACAClfmidYZGRnavXu3a/nUqVNKSEjQ5s2bfVIYAABARTJ09VkJp9PprToAAAD8qlyhCAAAoLIgFAEAAOgKrz47cuSI/v3vf0uS8vLyJEkHDhyQ1WottX2LFi3KWR4AAEDFuKJQ9OKLL3rcl2jKlCke7ZxOJ/cpAgAAQaXMoWjatGm+rAMAAMCvyhyKbrnlFl/WAQAA4FdMtAYAABChCAAAQBKhCAAAQFKAhaIDBw5o8uTJ6t+/v5o3b64+ffqU2m7FihXq0aOHEhMT1a9fP3366acebfLy8jRhwgR16NBBbdu21ZgxY3T8+HFffwQAABCkAioU7d69Wxs2bFDDhg3VuHHjUtt88MEHmjRpklJTU5WZmak2bdpo1KhR+uabb9zapaena9OmTXryySf1wgsvaN++fRo+fLiKi4sr4JMAAIBgc0X3KfK1bt26qXv37pKk8ePHa/v27R5tXnrpJfXu3Vvp6emSpI4dO2rXrl2aO3euMjMzJUlbt27Vxo0bNX/+fCUnJ0uSYmNj1atXL61du1a9evWqmA8EAACCRkAdKTKbf7ucQ4cOaf/+/UpNTXVb36tXL23evFmFhYWSpOzsbFmtVnXu3NnVJi4uTgkJCcrOzvZ+4QAAIOgFVCi6nJycHEkXjvpcrHHjxioqKtKhQ4dc7WJjY2UymdzaxcXFufoAAAC4WECdPruc3NxcSfJ41lrJcsl2m82myMhIj9dHRUWVekruSoWEeDdLms2/hDeTPIJcRSl5X4slqHKy15R8bpPJdPkxMP36b5O8M15X+/73lZL9yX4NbA77r+NjMZtk9vLfWHhfZf1uBVUoCgRms0k1a1bzSd8Ws1khIRaf9H3Z9/7lF9tqjfDL+wcKi6XsYxBi8d5Ysf99i/0a2Oznfv0uRVojZKlSxY/V4EpUtu9WUIWiqKgoSRcut69Tp45rvc1mc9tutVp19OhRj9fn5ua62hjlcDhls50tVx//KTTUourVq8jucKi42O7VvsvKbndIkmy2AtfPVxOLxSyrNUJ2exnGwHQhEBXb7ZLTO+9/te9/XykZV/ZrYHOcP+/6Oc9WIGeBf/4OouyC7btltUaU6ahWUIWiuLg4SRfmDJX8XLIcGhqq+vXru9pt3rxZTqfT7VTIvn371LRp03LXUVzs3V8A10A5JafTS/+VvUIl73shFAT+L7ivOJ3Oy46B65SZF8eL/e9b7NfA5rhobOwOp9syAltl+24F1cnA+vXrq1GjRlqzZo3b+qysLHXq1ElhYWGSpJSUFOXm5mrz5s2uNvv27dOOHTuUkpJSoTUDAIDgEFBHigoKCrRhwwZJ0pEjR3TmzBlXAOrQoYOio6M1evRoPfzww2rQoIGSkpKUlZWlbdu26c0333T107ZtWyUnJ2vChAkaN26cwsPDNXPmTMXHx+vmm2/2y2cDAACBLaBC0c8//6yHHnrIbV3J8sKFC5WUlKQ+ffqooKBAmZmZysjIUGxsrObMmaO2bdu6vW7WrFmaNm2aJk+erOLiYiUnJ2vixIkKCQmojwwAAAJEQCWEmJgY/fDDD5dtd/vtt+v222//zTaRkZGaOnWqpk6d6q3yAABAJRZUc4oAAAB8hVAEAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAICkAHsgLICrm9lsktls8lp/FovZ7d+X43A45XA4vfb+AIILoQhAQDCbTapRs6osZu8fwLZaI8rUzu5w6PSpswQj4CpFKAIQEMxmkyxms5as2anjJ896pU+TySSLxSy73SGn87eDzjXRVXVnzwSZzSZCEXCVIhQBCCjHT57VkRNnvNKXyWRSSIhFxcX2y4YiAGCiNQAAgAhFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkqQQfxcAAEAJi9kkc4j//n/d4XDK4XD67f3hX4QiAEDAiLRGyFKlit/e3+5w6PSpswSjqxShCAAQMFZ8vEtHbUV+ee9roqvqzp4JMptNhKKrFKEIABAwTpw6qyMnz/u7DFylmGgNAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAICkSh6K9u7dq3vvvVdt2rRR586dNX36dBUWFvq7LAAAEIAq7X2KcnNzdffdd6tRo0aaPXu2jh07pueee07nzp3T5MmT/V0eAAAIMJU2FC1btkz5+fmaM2eOatSoIUmy2+2aMmWK7r//ftWtW9e/BQIAEGDMZpPMZtNl21ksZrd/e4u/nz1XaUNRdna2OnXq5ApEkpSamqonnnhCmzZt0sCBA/1XHAAAAcZsNqlGzaqymMsedKzWCK/W4O9nz1XaUJSTk6Nbb73VbZ3ValWdOnWUk5NjuF+z2aTo6GrlLc+N6ZdQPrR/S9n99Itg+eX/DKKivPsLHmyGDUj0yxiw/3/FGFx9nM6qinr1ZUlS68gasvvpQAG/A5LZbNbZc0VyOC8/CCaZ5JT3BstsMqlqlVDVrFlVZXj7K+u7DEe/pEocimw2m6xWq8f6qKgo5ebmGu7XZDLJYinbzr1S1auG+aTfK2G+gv9DqIz8PQZX+/6XGIOrleV3gTOl4Wr/HahaJdSv7+/P/X91jzwAAMAvKm0oslqtysvL81ifm5urqKgoP1QEAAACWaUNRXFxcR5zh/Ly8nTixAnFxcX5qSoAABCoKm0oSklJ0b/+9S/ZbDbXujVr1shsNqtz585+rAwAAAQik9Pp7TnegSE3N1e9e/dWbGys7r//ftfNG/v27cvNGwEAgIdKG4qkC4/5ePrpp7V161ZVq1ZN/fv319ixYxUW5v+rvAAAQGCp1KEIAACgrCrtnCIAAIArQSgCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASociv9u7dq3vvvVdt2rRR586dNX36dBUWFvq7LFzCgQMHNHnyZPXv31/NmzdXnz59/F0SSvHhhx/qgQceUEpKitq0aaP+/ftr5cqV4u4jgWnDhg2666671LFjR7Vs2VI33XSTpk2bVuqzKxFY8vPzlZKSovj4eH333Xf+LscrQvxdwNUqNzdXd999txo1aqTZs2e77rh97tw57rgdoHbv3q0NGzaodevWcjgc/Ec2QL3xxhuqV6+exo8fr5o1a+pf//qXJk2apKNHj2rUqFH+Lg//4fTp02rVqpWGDBmiGjVqaPfu3Zo9e7Z2796t119/3d/l4Te8/PLLstvt/i7DqwhFfrJs2TLl5+drzpw5qlGjhiTJbrdrypQpuv/++1W3bl3/FggP3bp1U/fu3SVJ48eP1/bt2/1cEUozb948RUdHu5Y7deqk06dPa8GCBXrwwQdlNnOAPJD079/fbTkpKUlhYWGaNGmSjh07xt/CALV3714tWbJE48aN0xNPPOHvcryGvw5+kp2drU6dOrkCkSSlpqbK4XBo06ZN/isMl8R/TIPDxYGoREJCgs6cOaOzZ8/6oSJcqZK/i0VFRf4tBJf0zDPPaNCgQYqNjfV3KV7FX3k/ycnJUVxcnNs6q9WqOnXqKCcnx09VAZXTV199pbp166p69er+LgWXYLfbdf78ef373//W3Llz1a1bN8XExPi7LJRizZo12rVrl0aOHOnvUryO02d+YrPZZLVaPdZHRUUpNzfXDxUBldOXX36prKwsjRs3zt+l4Dd07dpVx44dkyR16dJFM2bM8HNFKE1BQYGee+45jR07tlL+TwZHigBUWkePHtXYsWOVlJSktLQ0f5eD35CRkaFly5bpmWeeUU5OjkaMGFHpJvFWBvPmzVOtWrV06623+rsUn+BIkZ9YrdZSLznNzc1VVFSUHyoCKhebzabhw4erRo0amj17NnPCAlyzZs0kSW3btlViYqL69++vdevWqWfPnn6uDCWOHDmi119/XXPnznX996tknt7Zs2eVn5+vatWq+bPEciMU+UlcXJzH3KG8vDydOHHCY64RgCtz7tw53X///crLy9M///lPRUZG+rskXIH4+HiFhobq4MGD/i4FFzl8+LCKiop03333eWxLS0tT69attXz5cj9U5j2EIj9JSUnRK6+84ja3aM2aNTKbzercubOfqwOCV3FxsdLT05WTk6PFixdzSXcQ+vbbb1VUVMRE6wCTkJCghQsXuq3buXOnpk2bpilTpigxMdFPlXkPochPBg0apEWLFmnkyJG6//77dezYMU2fPl2DBg3ij3iAKigo0IYNGyRdOIx85swZrVmzRpLUoUOHUi8FR8WbMmWKPv30U40fP15nzpzRN99849rWvHlzhYWF+a84eBg1apRatmyp+Ph4ValSRd9//73mz5+v+Ph4133BEBisVquSkpJK3daiRQu1aNGigivyPpOT2/L6zd69e/X0009r69atqlatmvr376+xY8fyRztAHT58WDfddFOp2xYuXHjJPxaoWN26ddORI0dK3bZ+/XqOPgSYjIwMZWVl6eDBg3I6napXr57++Mc/aujQoZXy6qbKZsuWLUpLS9PKlSsrxZEiQhEAAIC4JB8AAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQhAgDl8+LDi4+O1atUqf5cC4CrDYz4AeNWqVav02GOPXXL7P//5T7Vp06biCjJg586dmj9/vr744gv9/PPPqlq1qpo3b66+fftqwIABslgs/i5Rr7zyipo0acKjMAAvIhQB8IkxY8aU+kiNBg0a+KGasluxYoWeeOIJ1apVS/3791fDhg2Vn5+vzz77TI8//rhOnDihESNG+LtMvfrqq+rRowehCPAiQhEAn0hJSQm6ZyF98803euKJJ9SmTRtlZGS4PXvrnnvu0Xfffafdu3f7sUIAvsScIgB+Y7PZNH78eF1//fVq3769xo0bp7y8vFLbfvjhh+rVq5cSExPVp08frVu3TuPHj1e3bt3c2jkcDr3xxhvq3bu3EhMTdcMNN2jy5MnKzc29bD1z5syRyWTSCy+8UOrDSBMTEzVw4EDX8tmzZ/Xcc8/pxhtvVMuWLdWjRw/Nnz9fFz9S8rfmSMXHx2v27Nmu5dmzZys+Pl4HDhzQ+PHj1b59e11//fV67LHHVFBQ4Pa6s2fP6u2331Z8fLzi4+M1fvz4y34+AL+NI0UAfOLMmTM6efKk2zqTyaSaNWtKkpxOpx588EF99dVXGjRokBo3bqx169Zp3LhxHn39z//8j8aOHaumTZvqb3/7m3Jzc/X444+rbt26Hm0nT56st99+WwMHDtSQIUN0+PBhLV68WDt27NDSpUsVGhpaar0FBQX67LPP1L59e1133XWX/XxOp1MPPPCAtmzZottuu00JCQn63//9X02fPl3Hjh3ThAkTyrKbSpWenq6YmBj99a9/1Y4dO7RixQpFR0frkUcekSRNnz5dEydOVKtWrXTHHXdICvzTkkAwIBQB8Il77rnHY11YWJi+++47SdL69ev1xRdf6JFHHtGwYcMkSX/+85+Vlpbm8boZM2aobt26Wrp0qapVqyZJ6tSpk4YMGaJ69eq52n355ZdasWKFXnjhBfXt29e1PikpScOGDdOaNWvc1l/swIEDKioqUtOmTcv0+davX6/PPvtM6enpeuCBByRJgwcP1pgxY7Rw4ULdddddhoNKQkKCpk6d6lo+ffq0Vq5c6QpF/fv315NPPqn69eurf//+ht4DgCdCEQCfmDx5smJjY93Wmc2/nrHPzs5WSEiI/vznP7vWWSwW3XXXXfryyy9d644dO6Zdu3ZpxIgRrkAkSR06dFDTpk2Vn5/vWrdmzRpFRkaqc+fObkepWrRooapVq2rLli2XDEVnzpyRJLf3+C3Z2dmyWCwaMmSI2/q//OUv+uijj5Sdna277rqrTH39p0GDBrktt2/fXuvWrdOZM2dKPa0HwDsIRQB8olWrVr850frIkSOqU6eORwj5zyD1448/Sir99FDDhg21Y8cO1/KBAweUl5enTp06lfqeP//88yXrKQkbF4es33LkyBFdc801HiGlcePGru1G/efpO6vVKknKzc0lFAE+RCgCUGk4HA7VqlVLL7zwQqnbo6OjL/nahg0bKiQkRLt27fJqTSaTqdT1drv9kq+5+IjaxS6ewA3A+whFAPyiXr16+uyzz5Sfn+92tGjfvn1u7UqOmhw8eNCjjwMHDrgtN2jQQJs3b1a7du1UpUqVK6onIiJCHTt21Geffab/+7//07XXXnvZ+jdv3uxxSisnJ8e1XZKioqIkXbjS7mIlR8AABA4uyQfgFykpKSouLtbSpUtd6+x2u9588023dnXr1lXTpk21evVqt1Nbn3/+ucdRndTUVNntdr388sse71dcXOwRTP7TyJEj5XQ69eijj5Z6Gm379u16++23XfXb7XYtXrzYrc0bb7whk8mklJQUSRdOy9WsWdNtnpQkLVmy5DdruZyqVate9vMAuDIcKQLgE9nZ2a6jJhdr166d6tevr27duqldu3aaMWOGjhw5oiZNmmjt2rWl3qdo7NixevDBB/XnP/9ZAwcOlM1m0+LFiz0mWnfo0EF/+tOf9Oqrr2rnzp3q3LmzQkNDtX//fq1Zs0aPP/64evbsecma27Vrp8mTJ2vKlClKTU11u6P1559/rk8++UTp6emSpG7duikpKUkzZ87UkSNHFB8fr02bNmn9+vW6++673eZA3X777crIyNDjjz+uli1b6ssvv/Q4InalWrRooc2bN2vBggW65pprFBMTo9atW5erT+BqRygC4BMvvfRSqeunTZum+vXry2w2a968eZo6dareffddmUwmdevWTePHj9eAAQPcXtOtWzf9/e9/1+zZszVjxgw1atRI06ZN0+rVqz3uMP3UU0+pZcuWWrZsmWbOnCmLxaJ69eqpX79+ateu3WXrHjRokBITE/X6669r9erVOnXqlOvZZ9OmTVO/fv0kyVX/Sy+9pKysLK1atUr16tXTo48+qr/85S9ufY4cOVInT57URx99pA8//FApKSl67bXXLjkhvCzGjx+vyZMna9asWTp37pxuueUWQhFQTiYnM/cABKn+/fsrOjpaCxYs8HcpACoB5hQBCHhFRUUqLi52W7dlyxZ9//336tChg5+qAlDZcPoMQMA7duyY7r33XvXr10/XXHONcnJytGzZMtWpU8fjRocAYBShCEDAi4qKUosWLbRixQqdPHlSVatW1Y033qiHH37Y9Sw1ACgv5hQBAACIOUUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSpP8HbU0IsMCWVtsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Astrocytes_cell_network.plot_bootstrap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "27540ef028084b4bf8ce8bb7c992ab07d5f9e30bb60d6dc44ceb8fb68897341e"
  },
  "kernelspec": {
   "display_name": "Python 3.10.9 ('streamlit': conda)",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}