{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bgee\n",
    "\n",
    "Extracting data from [Bgee](http://bgee.unil.ch/). See [this Thinklab discussion](http://thinklab.com/discussion/tissue-specific-gene-expression-resources/81#278) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import gzip\n",
    "\n",
    "import pandas\n",
    "import seaborn\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import IPython\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_groupby_counts(df, columns):\n",
    "    \"\"\"Group datagrame by columns and return the number of rows for each grouping.\"\"\"\n",
    "    grouped = df.groupby(columns)\n",
    "    get_len = lambda df: pandas.Series({'count': len(df)})\n",
    "    df = grouped.apply(get_len)\n",
    "    df = df.sort('count', ascending=False)\n",
    "    df = df.reset_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load entrez gene for ensembl conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read Entrez Genes\n",
    "url = 'https://raw.githubusercontent.com/dhimmel/entrez-gene/6e133f9ef8ce51a4c5387e58a6cc97564a66cec8/data/genes-human.tsv'\n",
    "entrez_df = pandas.read_table(url)\n",
    "coding_genes = set(entrez_df.GeneID[entrez_df.type_of_gene == 'protein-coding'])\n",
    "\n",
    "# Merge with entrez gene identifiers\n",
    "url = 'https://raw.githubusercontent.com/dhimmel/entrez-gene/6e133f9ef8ce51a4c5387e58a6cc97564a66cec8/data/xrefs-human.tsv'\n",
    "entrez_map_df = pandas.read_table(url)    \n",
    "entrez_map_df = entrez_map_df[entrez_map_df.resource == 'Ensembl']\n",
    "\n",
    "ensembl_to_entrez = dict(zip(entrez_map_df.identifier, entrez_map_df.GeneID))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and process presence of expression data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene name</th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>Expression</th>\n",
       "      <th>Call quality</th>\n",
       "      <th>Including observed data</th>\n",
       "      <th>Affymetrix data</th>\n",
       "      <th>...</th>\n",
       "      <th>Including Affymetrix observed data</th>\n",
       "      <th>EST data</th>\n",
       "      <th>EST call quality</th>\n",
       "      <th>Including EST observed data</th>\n",
       "      <th>In situ data</th>\n",
       "      <th>In situ call quality</th>\n",
       "      <th>Including in situ observed data</th>\n",
       "      <th>RNA-Seq data</th>\n",
       "      <th>RNA-Seq call quality</th>\n",
       "      <th>Including RNA-Seq observed data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000000</td>\n",
       "      <td>cell</td>\n",
       "      <td>HsapDv:0000087</td>\n",
       "      <td>human adult stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "      <td>no</td>\n",
       "      <td>present</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000000</td>\n",
       "      <td>cell</td>\n",
       "      <td>HsapDv:0000088</td>\n",
       "      <td>human early adulthood stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "      <td>no</td>\n",
       "      <td>present</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000000</td>\n",
       "      <td>cell</td>\n",
       "      <td>HsapDv:0000089</td>\n",
       "      <td>young adult stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "      <td>no</td>\n",
       "      <td>present</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000000</td>\n",
       "      <td>cell</td>\n",
       "      <td>HsapDv:0000090</td>\n",
       "      <td>25-44 year-old human stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "      <td>no</td>\n",
       "      <td>present</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000000</td>\n",
       "      <td>cell</td>\n",
       "      <td>HsapDv:0000091</td>\n",
       "      <td>human late adulthood stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "      <td>no</td>\n",
       "      <td>present</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "      <td>no data</td>\n",
       "      <td>no data</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene ID Gene name Anatomical entity ID Anatomical entity name  \\\n",
       "0  ENSG00000000003    TSPAN6           CL:0000000                   cell   \n",
       "1  ENSG00000000003    TSPAN6           CL:0000000                   cell   \n",
       "2  ENSG00000000003    TSPAN6           CL:0000000                   cell   \n",
       "3  ENSG00000000003    TSPAN6           CL:0000000                   cell   \n",
       "4  ENSG00000000003    TSPAN6           CL:0000000                   cell   \n",
       "\n",
       "  Developmental stage ID             Developmental stage name Expression  \\\n",
       "0         HsapDv:0000087            human adult stage (human)    present   \n",
       "1         HsapDv:0000088  human early adulthood stage (human)    present   \n",
       "2         HsapDv:0000089            young adult stage (human)    present   \n",
       "3         HsapDv:0000090   25-44 year-old human stage (human)    present   \n",
       "4         HsapDv:0000091   human late adulthood stage (human)    present   \n",
       "\n",
       "   Call quality Including observed data Affymetrix data  \\\n",
       "0  high quality                      no         present   \n",
       "1  high quality                      no         present   \n",
       "2  high quality                      no         present   \n",
       "3  high quality                      no         present   \n",
       "4  high quality                      no         present   \n",
       "\n",
       "                ...               Including Affymetrix observed data EST data  \\\n",
       "0               ...                                               no  no data   \n",
       "1               ...                                               no  no data   \n",
       "2               ...                                               no  no data   \n",
       "3               ...                                               no  no data   \n",
       "4               ...                                               no  no data   \n",
       "\n",
       "  EST call quality Including EST observed data In situ data  \\\n",
       "0          no data                          no      no data   \n",
       "1          no data                          no      no data   \n",
       "2          no data                          no      no data   \n",
       "3          no data                          no      no data   \n",
       "4          no data                          no      no data   \n",
       "\n",
       "  In situ call quality Including in situ observed data RNA-Seq data  \\\n",
       "0              no data                              no      no data   \n",
       "1              no data                              no      no data   \n",
       "2              no data                              no      no data   \n",
       "3              no data                              no      no data   \n",
       "4              no data                              no      no data   \n",
       "\n",
       "  RNA-Seq call quality Including RNA-Seq observed data  \n",
       "0              no data                              no  \n",
       "1              no data                              no  \n",
       "2              no data                              no  \n",
       "3              no data                              no  \n",
       "4              no data                              no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read expression\n",
    "presence_df = pandas.read_table('download/Homo_sapiens_expr-complete.tsv.gz', compression='gzip')\n",
    "presence_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Apply filters for gene presence\n",
    "present_df = presence_df[\n",
    "    presence_df['Call quality'].isin({'high quality', 'low quality'}) &\n",
    "    presence_df['Expression'].isin({'present', 'low ambiguity'})\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0000104</td>\n",
       "      <td>life cycle</td>\n",
       "      <td>7270053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000066</td>\n",
       "      <td>fully formed stage</td>\n",
       "      <td>6847848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0000092</td>\n",
       "      <td>post-embryonic stage</td>\n",
       "      <td>6847848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>6722450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HsapDv:0000087</td>\n",
       "      <td>human adult stage (human)</td>\n",
       "      <td>6430153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HsapDv:0000088</td>\n",
       "      <td>human early adulthood stage (human)</td>\n",
       "      <td>5094102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HsapDv:0000091</td>\n",
       "      <td>human late adulthood stage (human)</td>\n",
       "      <td>5051786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HsapDv:0000090</td>\n",
       "      <td>25-44 year-old human stage (human)</td>\n",
       "      <td>4821586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HsapDv:0000092</td>\n",
       "      <td>human middle aged stage (human)</td>\n",
       "      <td>4311395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HsapDv:0000093</td>\n",
       "      <td>human aged stage (human)</td>\n",
       "      <td>3371418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HsapDv:0000094</td>\n",
       "      <td>65-79 year-old human stage (human)</td>\n",
       "      <td>3018341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HsapDv:0000089</td>\n",
       "      <td>young adult stage (human)</td>\n",
       "      <td>2604138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Developmental stage ID             Developmental stage name    count\n",
       "0          UBERON:0000104                           life cycle  7270053\n",
       "1          UBERON:0000066                   fully formed stage  6847848\n",
       "2          UBERON:0000092                 post-embryonic stage  6847848\n",
       "3          UBERON:0000113            post-juvenile adult stage  6722450\n",
       "4          HsapDv:0000087            human adult stage (human)  6430153\n",
       "5          HsapDv:0000088  human early adulthood stage (human)  5094102\n",
       "6          HsapDv:0000091   human late adulthood stage (human)  5051786\n",
       "7          HsapDv:0000090   25-44 year-old human stage (human)  4821586\n",
       "8          HsapDv:0000092      human middle aged stage (human)  4311395\n",
       "9          HsapDv:0000093             human aged stage (human)  3371418\n",
       "10         HsapDv:0000094   65-79 year-old human stage (human)  3018341\n",
       "11         HsapDv:0000089            young adult stage (human)  2604138"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find genes present per developmental stage\n",
    "stage_df = get_groupby_counts(present_df, ['Developmental stage ID', 'Developmental stage name'])\n",
    "stage_df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0001062</td>\n",
       "      <td>anatomical entity</td>\n",
       "      <td>613385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000061</td>\n",
       "      <td>anatomical structure</td>\n",
       "      <td>613367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0000468</td>\n",
       "      <td>multi-cellular organism</td>\n",
       "      <td>612546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000467</td>\n",
       "      <td>anatomical system</td>\n",
       "      <td>580567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UBERON:0000064</td>\n",
       "      <td>organ part</td>\n",
       "      <td>452218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UBERON:0000990</td>\n",
       "      <td>reproductive system</td>\n",
       "      <td>414926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UBERON:0005156</td>\n",
       "      <td>reproductive structure</td>\n",
       "      <td>414926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UBERON:0001016</td>\n",
       "      <td>nervous system</td>\n",
       "      <td>411851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UBERON:0000475</td>\n",
       "      <td>organism subdivision</td>\n",
       "      <td>410747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UBERON:0001017</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td>410621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Anatomical entity ID   Anatomical entity name   count\n",
       "0       UBERON:0001062        anatomical entity  613385\n",
       "1       UBERON:0000061     anatomical structure  613367\n",
       "2       UBERON:0000468  multi-cellular organism  612546\n",
       "3       UBERON:0000467        anatomical system  580567\n",
       "4       UBERON:0000064               organ part  452218\n",
       "5       UBERON:0000990      reproductive system  414926\n",
       "6       UBERON:0005156   reproductive structure  414926\n",
       "7       UBERON:0001016           nervous system  411851\n",
       "8       UBERON:0000475     organism subdivision  410747\n",
       "9       UBERON:0001017   central nervous system  410621"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find genes present per anatomical entity\n",
    "anatomy_df = get_groupby_counts(present_df, ['Anatomical entity ID', 'Anatomical entity name'])\n",
    "anatomy_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1: Number of genes present by developmental stage and anatomical entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l8ftOwNlAXaBrjrIntw8G2gNnACOAXsCJwGXx+M+Atu7eALgHaBXTHx7TnQCc\nbmb7xPhXgfqIiIiIiGwnGqIvUj4KgRfj9oeE3u1syb+o19z9G+BT4O3Ya/0JoQEL8BGhR3wOoee9\nZtz/qrtvcvd/A//JkUeudAY8B+Du6wmjCLIV9Xe0xbeAu/8BmEHo5Z6aI91/gB+Z2XzCqIEfxDIc\nCTwZYx4HMLO9gVrAIjN7FDiNcAMjaS/C9cx4zd03uvt/gK/ZWrI8f3b3zwnX+EN3/8Td/8vmmwDv\nAZPM7GagAZtvOvzJ3b90903A+2x+T94n3NwQEREREZEyoga+VBS5nsPfAOwXnyU/tphYAMxsV2Ao\ncD7QgdBozjS0t5VutyLSvQ7UiTG7A0ckyxa3f5njlJvI+vsys6OAXdz9DEJv++RE7A7A6cCP3b0N\nodf+e7EMr2XKQBh9ALCe8MjAWbEXfQzwcFYZ/gHsnvi9yGucox7FzYswE2gXH0t4L1HXotLtHssj\nIiIiIiJlRA18qShyDRW/DlgOLAM+JveQ+C223f0z4AngKeBewjPrtYpJh7t/mpXuLaCWuy8B3jez\nZ4A5wAcxyU1ALzN7iNBAzi7bC0DX+Kx6xh+B+ma2hvBMemb9qieBeYSRAoeY2SpgIvBMLPsY4Ky4\n/3LC4wyFQA9guZk9AXQk3IxIWk0YVp8pV676TwKmmtkDhO+DYq9xfL0NWGtm9xNuNuS6xkknsvUN\nCBERERGRMlNQUFCqP5WBJtmTcufu8xLbGwiT4OHuNxOeZc+2Jh5/kzDBHu7+CXHWenc/v4is1iTy\n2S/7YFHp3H2rBT7d/QXCxH7Z+/eLr8sJNyeSx/5LeFY9O80gNjf2T8g+bmZNgcHu/ryZNQL2jeke\nAh7KVeZoFTDQzGq6+xpy1N/dVwArcqStE4//7/3ISlfUZHl1ErEnJfafRxiVICIiIiIiZUQNfJGK\n7x1gjpl9TRjK3y1NIncvNLNhhJUBJpZh+bbJzI4mPJv/l/Iqg4iIiIhUP5VlYrzSpAa+SAUXRyrU\nKTYwd9rVhKH65cbdXyXMoi8iIiIist1Ux2XyCgoLi5tLS0Rku9CXkYiIiEjlU2Fb0Red2KlU/315\n6zMzKmxdM9SDLyIiIiIiIlWOhuiLiJSjC46/PHXsguduyitNJr7NCR1S5zH/2Vl5pcnET2g5MnUe\nve4aCEC/065JFT/6wTF5xSfT3HLp71LFX3zzbwGY2Cp9PXosCvW4tf24VPEXzemTV3wyzaTzRqWK\n735nmNd2LA7OAAAgAElEQVSxf+N+qfO4duVoAG67LF252s4OZRpxxqBiIjcbdP+IvNJk4m++5PrU\neVw67yoAprS+NlV8l4X9AVjYcULqPFrP7JVXmkz86LOHpM6j35JhANx1ZbppRFpO7QHArLZjU+fR\n4barAVjcbVKq+OaTu+cVn0xz7Vnp6t7/vlDvJd0nFxO52dmTuuWVJhN/X4/0eZw1cfvlMbb50FTx\nVy8OcXPbpf/7aDf3qrzKlSnT9vj7KEke2+P9WNrzxlTxZ97QFYD7e6WLBzhjQtftlsfaYTeliq87\n5PK84pNplveZkir+9HFdAFjx26mp82j6uysBePia6aniG43pnFd8Mk2+10oqDi2TV0WYWTszG13e\n5Sip0iy/mbU0syL/FWVmQ82sU9zuWtr5x/P92sxyrQCQK7ajmZXJzTYzu9DMWpjZJeX1+TCzJmbW\nvjzyFhERERGpTtTArzoq+/PL27P8yTXhByT2lZd+hNnxS5WZfR+4yN3vLe1z58PdHwBamtku5VkO\nEREREalevu2699k/lYGG6Fcttc1sJbA3MM3dZ5nZOuAId//KzMYAbwDrgP7ABuAAYDphPfljgInu\nPt3MWhKWV6tJaPy2AI4Grgb+S1gbfaG7bzH+08zqASOBb4C3gU7AzsBNwG7AfsCUmMdq4ENgD2BB\nTN8BONzd+5rZDsDvgV+7+1fx+K7ALOCHWeeqA9wAfBLr9YKZHRjLeFJM+xTQOha1wMz6A3uY2Y3A\nc0Vcv1OBEfGc/wTau/unZjYO+E0813x3n2RmBswB/hNj/511bfYG7iBMRLIT0Bn4NWFd+wXxms8E\nfgzUAu5z90FmdhgwF/gK+AtwkLs3MLNWQK94rR939+xxyBcCKzP1LaJ+69g+n4/lQDsg/bhAERER\nEZFvoTo+g68e/KqjANjo7o0Jja2ecX+yZzq5vT9wDnAFMBBoCzQlNMgBDgeauXtd4HWgcUz/k5iu\nNtA3WQAzKyA0UFu4e33g74RG3aHAgli2xkDvRHnmu/uphEYqhIZ+czOrATQBVmUa99GhhIZj9rmm\nARe6+2mkW5KtMDY+P3b3rhR9/WYk6rMGGGhmzQiN7NrAyUAbMzsKuB4Y7O6NgIdz5Hk8sJ5wnbsA\n33f32cAHhBsPBwBPuXsT4ETCDQDieUe6e0PgCaDQzHYHhgIN43u0v5k1ysqvHvBK4vfy/Hy8AtTP\ncU1ERERERKSUqIFfdRQCL8btDwm95tmSt7Bec/dvgE+Bt939a0Lv907x+EfAPDObA/yC0FML8Kq7\nb3L3fxN6qpP2JvQ8LzKzR4HTCA2+DwmN9lsJQ+KTI0c8eQJ3/4LQkG5MuDmQPcPHP4o4177u/se4\n/ViOumfXP5ctrp+Z7QV85u7vJ877c+CnwNpY3q+Bp4GfAUYYCVBUGVYQGuhLgOHApqzj/wKON7Pb\ngPHAjnH/kcCTcfvxWI/DCNd7RbzWPyP0miftFesC5f/5+ADYM0eeIiIiIiJloqCU/6sM1MCvWnI9\nR74B2C/2rh9bTCzwv2HwQ4HzgQ6EhlrmE72tZ9XXA38DznL3BsAY4BGgD6Fn+iLgLrb83GUaucm/\nmFkx373d/bWsPHoXca6/m9nP4/ZJ8XUDsI+Z1TCzHwIHJ85TkPW6Vd3cfT2wq5ntG3fVJ9yQeIPQ\nc4+Z1QTqAH8k9GSfnFWGpPrA+7EXfRSQGb6+ifAMfjvgE3dvS2jgfz8efy3mAaFnHOAd4F2gUbzW\nU4GnsvL7B7B7op7l+fnYPZZHRERERGS7qFFQUKo/lYEa+FVLruHW1xGef14GfJzYX9TQ7EJ3/4zQ\n0/wUcC+hUVurmHS4+yagB7DczJ4AOhIavUuBLvH57zOBz81sR7b0v4nv3P1ZwlD823PUMde5agKX\nA7PN7GFCD3uhu38IPEToVZ9JaIRnl/31OBogOfFe8ngH4B4ze5zwHPoId18GvGNmT8ZrtMjdf094\nHr6vmT0CNMi+PsDLwOWxx/06Njfw1xLen4eBJmb2EHAN8LyZ1SI8135NrNuZhKH26wk3AR4zs6eB\nU4E/ZeW3mjDUf4vrm1W/7fX5OJHcjy2IiIiIiEgp0SR7VYS7z0tsbyAO13b3m4Fcy7WticffJDRc\ncfdPCEO9cffzi8hqTSKf/XKU4yFCozppNWECtmwNcpU/Pn//BXHivazzF3WuF9ncu52M75wjdlji\neMMcaZLX7xHCKITsmKty7FvHNp4zd/ePCY8tZO9vl/j12OzjZtYGuMzd3zazy4n1dPfbyX0TJOMO\nYDHwuwrw+WgKtNpGWUVERERE5FtSD75UKGZ2MPACYSK9L8q7PBXEu8BCM1sDXEBYpaBY8frdYmbn\nlGXhimNmpwN36f0UERERke1Jy+SJlDN3fwc4rrzLUZG4+1rCDPwlSXtLKRenJGVYXt5lEBERERGp\nDgoKC7c1Z5qIyHajLyMRERGRyqfCdm13rtu9VP99OX3tpApb1wz14IuIiIiIiEiVU1mG1ZcmNfBF\npMKoc0Sz1LFPvrUsrzT5xifTnGxnpIp/3O8HoGfDPqnzuGHVOAC61uuZKv7GNTfkFZ9MM+KMQani\nB90/AoDRZw9JnUe/JcNKlEfa+GSasc2Hpoq/enGIK8n7Mf7cEanie98dyt/31L6p87juoevySpOJ\nn9L62mIiN+uysH9I23xYMZFB38XhvZ7YKtUUHwD0WDQwrzSZ+OHN0r/ng5eF92FCy3R59LprYF7x\nyTT51qMk12pQ0/6p4kesCO/1pPNGpc6j+50D8kqTiZ98fvo8ut2RX5p845NpRp45OFX8wKXDS5xH\nvvWYeeHY1Hl0vP1qIP/P1Yw2Y1Ln0Wn+NQDc3+vGVPFnTOgKwLLeU1Ln0Wx8F2D7vOf55jHtgtGp\n87hiQT8A7usxOVX8WRO75RWfTDPn4utSxbe/Jfw/4OZLrk+dx6XzwhzPd3a+IVX8edN75hWfTLO4\n26RU8c0nd099btk+NMleFWdm7cws/Tdg2ZVjXY6l8ZLHW8Ql4Uorv1eLOf5+fD3azOqmKWMJyrDU\nzA5MEXeAWcoWZP5l+K6Z3WJmBaVdvzzKUGBmc81sp+2dt4iIiIhUXwWl/F9loAZ+1VdRnmsurhzd\ngV23R0GynEtc+o1QxtL+y01z/f8P+E0p55vRE7jD3Qspm/oVK+Y9H0jfxSkiIiIiInnTEP3qobaZ\nrQT2Bqa5+ywzWwcc4e5fmdkY4A1gHdAf2AAcAEwnrIF+DDDR3aebWUvgSqAmocHYgrAu/dXAfwnr\nqy9095xjSc3sKGAcsAOwF3AFsAdh/fd5sTe9M2E5uMJ4rslZ58hVhn/F8v6CsKzcrjF2LrDA3Vea\nWRPgfHe/NB7bD2gHbDCzF+Ppp8Wl+ojn/ZKwTvzBsczj3f1OMzsOmAR8E69XB3d/18yGAc2A9+M1\nzK7/lcDFwCbgOaAXcA3wPTN7EvgMGEy4+fYDoI27/9HMBgHNgY+AnYFBwEvA7Hj9ALq7+2uJvAqA\ntvHaZmTXrzlg7t4v9rC/4e4Hm9nqeP6jgC+AtUBj4IfAabH8NwG7AfsBU+LnYzXw+5huV6CVu/8V\neAQYDwzPviYiIiIiIlI61INf9RUAG929MaFBl3lwN9mznNzeHziH0PAeSGggNgU6xeOHA83cvS7w\nOqHRVwj8JKarTdE9tQWE3vI+7t4IGAtc6u7LCI3Ji+P5zyP0aJ8CNDezI7LOk6sMzYGd3b12LPtu\niboVJrb/x93fIzTex7v7c3H3Te7egHCz49RY7w/d/TdAI2Ckme0JzAK6uHt9YCowPjb6G7j7r4FW\nhAZ6tnYxXR3CTZUCYDRwu7svjdenbSzDPUArMzsGaAL8OtYz8yhDf+Bhd28Yyzktx3X61N2/SezL\nrl9RIwwKgWfi+/Rd4Et3P41wvesBhxJunDQmXP/eWelOBR4i3KghluEfZnZ0EfmJiIiIiJSqGgWl\n+1MZqIFf9RUCmd7pDwm9v9mSH9fXYmPsU+Btd/8a+ATIPD/9EaGnfQ6ht7xm3P+qu29y938D/9lG\nWd4DBsWe9ZZsOYqkgNDzeyCwCniY0Dt9WNZ5cpXBCD3iuPt6QuM5W67Pe/af6gvx9QPCtTqS0HuN\nu39BaOAeCtRy91di7Frg5zH2hRi7IZYn+/yXAl1jT/eB8XhBIu49YJKZ3Qw0iHU7EnjW3QvjeZ+P\nsUcB7c3sUWAmsHtWXnsR3vNt1W9b1yLzufkk1hvCSImd4nmbm9mtwAC2fB9/H1/fZfPnBsKohj0R\nEREREZEyoQZ+9ZCrl3YDsF8cxn1sMbEAmNmuwFDgfKADoSGfaRSmeda8AJgIDHH3dsCrbP4Mborb\nbwJ/cPcGsaf5ViDTkMbMdiuiDK8DdWLM7kCm138DYQg5wC9zlOkbwtD7jOx6vAFkJuHbhfA4wjvA\ne4ne6HqAxzKcaGY14mR2x+U4Xwegc+z5Py6W+ZvEdZgJtIuPEbwX6/YH4Pg4Wd13YzritZoQr1Nb\nYF5WXh8ShtQnZZdnA5tHBGRfn229p32Ap9z9IuAutvwuKSrd7mx9w0FEREREpEwUFBSU6k9loAZ+\n9ZBrOP51wHJgGfAxuYexb7Ht7p8BTwBPAfcSGrW1ikmXve82YJGZLSd8/jLpnyQ0UN8FHjGzx83s\necIz/e9lTuLun2aV4S1Cb/oS4H0zewaYQ+ihhvCceC8ze4jQ0M+u5wuEHvX6OcpdSGhw72lma4FH\ngaHu/hGhoX6jmT0GdAN6ufvLwBLgWWAxsD7HdXgVWGtmjxAau0/HfWeb2fnx+qw1s/tj+lrxufrl\nMfYeYCPwFTAKOC/24N9H1qgFd38b2MfMMn/nuer3AHBQrF8rwsiN4hQCS4EucW6HM4HPi5ihvxAg\nlmF/d881skJEREREpNTVKCgo1Z/KQJPsVXHuPi+xvYHQYMbdbyY8f55tTTz+JmGCPdz9E+JM8+5+\nfhFZrUnks1/2QXc/JG5OiD/ZxwcRJo4D+F38KapOOcvg7lstDO7uLxAmCczev198XU5oPEO8NnF/\nv0R4uxzpXyL03GfvH0uYW6Coss8mTIyX9BJhGD7AHdlpzGxv4F/ufmLswX8NeNfdPybMq7AttwNn\nAYsT70F2/ernKGeDxPYFie1eibBcz9Mn081I7G9CjrqJiIiIiEjpUQ++SMW3njBE/1ngMWCWu/8t\nZdobCBP1ldstx5j3BeS4sSMiIiIiUlaq4xB99eCLVHBxHfn2JUy7AbiwdEuUdxkKgYvKswwiIiIi\nItVBQWFhmrnRRETKnL6MRERERCqfCtu13bNhn1L99+UNq8ZV2LpmqAdfREREREREqpwaFffeQ5lR\nA19EKox2J12ROnbuU9PySpOJv6xOl9R5zH5ySl5pMvEz2oxJnUen+dcAMLzZoGIig8HLRuQVn0xz\nZ+cbUsWfNz3MVzn5/FGp8+h2xwAAFl2RLo9W00Ied3eZmDqPc6f0AGBK62tTxXdZ2B8o2bVa3G1S\nqvjmk7sDMO6c4anz6HPP4LzSZOLntrs+dR7t5l4FpP8sZj6HS7pPTp3H2ZO6AXBfj3RpzpoY4ie2\nGpk6jx6LBgLpPyeZz8ici69LnUf7W/oC6eueqffSnjemzuPMG7oCMP7cEanie98dPrP390qfxxkT\nuuaVJhNfknos6z0lVXyz8eG784GrpqbOo8n1VwL5/w3edtm41Hm0nd0HgAf7TksVf9p14f8zd12Z\n/vuq5dTwWUx7fTPXtiR/g6sGzCgmMmg4qhMAjw5MFw/QYGRIk++1Wt4n3WcE4PRxXUqUR9p6w+a6\nPzlqTqr4OgPa5xWfTLN26KxU8XWHdsgrPpnmkX7TU8X/3+jOecUn0zw+PHtO6NxOHnxZ6nOXh8ry\n3HxpUgO/ijKzmoTl4g4EvguMdPelZnYcYYmzP8bQae5+ZxHnaAN0dfc6iX01CEvrLc6aJX27i0vb\ndUrO8h733wgscvc1iX1zgQXuvnK7FjIFM2sBPO3u75fBuesCxwEvA52zr9X2YGZHAee4e/oWkIiI\niIiI5E2z6FddFwIfufsphCXKMreQfwWMd/cG8aeoxv1x5J7YbSTwQyrG89JFlSHX/opQ3qJ0B3Yt\n7ZPG2euHAFMpx2ej3P014DAzO6TYYBERERGRUvJt173P/qkM1INfdS0C7orbNYCNcftXwBFmdjah\nF7+nu3+RTGhmewKjgJ7ArMT+lsA3wANkNRjNbDfgBeBwdy80s7HA88CbwMQY/0/CTYMvgRnAj4Fa\nwH3uPij2su8B7Amc7u6fJs5/KjAC2JA4T0HieGegI/AP4PuJuid1MrO+wG7AFcBHhF79k+I5ngJa\nA5cChwJ7xbJMAc4FjgAucfdnzGx0vJZ7Ai+7e3szGwocBOxDGDnRy90fTJRxJ+BOQmN+Z2AAUBM4\nFpgXe9uH5zjvXsB8YEfAgYbufriZ1SPccPkGeJswmuHrRH1PBf7g7l+bGcDhZrY8lm+puw8zs9VA\nR3d/K17DHwFzYzn/GuuzEDiKMBJgmbsPiHkPJny2fgC0IXzGFsR0hwLPuvuVsSx3Al2APjneFxER\nERGRUldJ2uSlSj34VZS7f+nuX5jZLoTG7sB46Bngt+5eD/gzoYf3f8xsB2A20Bv4IrH/KMJa5oPJ\n0RscG+OPA03iOZoAiwk3CK509wbACqAvoWH/lLs3AU4EOsfTFAKPuPtvshr3BYQbAi3cvT6wJtan\nMB7fm3Az4kTg9Lg/V4/98+7+f8BkoF0RMZly/NvdmwJ3E242nAWMAVrHa/qxu58GHA/UNrP9YroN\n7n460APolXXeQwkN9zPjtfyOuy8HXgIuBnYq4rwDgHti3Rex+cbcrMQ1+XusU1I94JXE7zsBZwN1\nga6JupJj+2DCTZQzCDdWehGub+ZBq58BbeP7eg/QKqY/PKY7ATjdzPaJ8a8C9RERERERkTKjHvwq\nzMwOIDS+prj7wrj73kTj+V5gspmdS2jwFQLXAIcB0wgNwp+Z2QTgK2B/YBWhV/crM3sn2UNNaHB2\nJ9w4esjdN5rZT4FpsQe5JvAW8DFwvJk1AD4jzBGQ4bHsXYCWsUztgM8Sz6ivJYwwuD/+fhjwhrtv\njGmfIPeQ9Bfi64eEHvRsyTQvxtdPgD8ktncC/gP8yMzmE26C/CDWDeD38fVvMXZzxdz/YGYzCL3c\nNYHsGYSKOu+RwM0x5vFYx70Jox8WxWv7PeDBrPPtBTyV+P21eI02mtnXbC15w+/P7v65mW0EPnT3\nT2K+mZsA7wGTzOwLwufi8bj/T+7+ZYx9P3EN3ifc3BARERERkTKiBn4VZWY/IjT4rnT3RxOHHjCz\n7u7+HNCI0Kt9N6GnOuOoeI4DgYXuvkVPtJkNAd7Patzj7k+Y2URCL++AuPtN4CJ3/5uZnUJo5LUD\nPnH3zmZ2GGFofUZhPNcUwtD4TA/+rma2r7t/QOiZ9kSaPwI/N7PvEYbwn0AYLVCcDcA+ceLAXQm9\n1tkK2PpmQVPgx+7eOja0W+SI2UocBbGLu59hZrWAJwgTFm4CdiCMPsh13teAOoTe+NrxdOuBd4Gz\nYkO8OeHGSdI/gN0Tv+casbAB2I9w4+WXhBsTRcUmzQQOcfcv46MVmZsDRaXbPZZHRERERGS7qCzP\nzZcmNfCrrv6EZ80Hm9nguK8pYTj8lNgz+z5bNq6zFZD/5HS3Ay3d/Y34+xXArWb2nXiu9oTG+Xwz\n+xXwF+D5OBSdXPnFZ/o7APeY2SZCQ7YdcDRQ6O7rzWwkoRf5YzbPN5CtMPFa6O4fmtlDwHOEZ9j/\nWFRs1vazwCAzWwV8QHjsIVf5s+vyR2CImZ1HaBBn1u56EphHGD4/MOu8tQiPBtwa070HbIzXpAew\nPN6g+JQwzD9pNeEmwa1s/dhCZnsSMNXM/koY5l+Ydbyo7duAtWb2HuEmTq0i6pxxIvBwEcdERERE\nRKQUqIFfRbl7D8Jz4NleBk5OeY51hJ7j7P3DtpFsBxIT87n7i0CDHHHH5th36TbK8gjwSNbuNfEH\nd78FuGUb6S9NbK8EVsbtzjnChyViZyS2lwBL4q8n5Ej3ZCL2TaBhVhn+S3hWPbtsg9jc2N/qvGbW\nFBjs7s+bWSNg35juIeChHOXIWEW4YVAzLhn4v2UD3X2/+LqC3KMd6sTjG4BDcqQrarK8OonYkxL7\nz2PzqA4RERERkTJXUH4LSZUbNfCl1MSh2vsSJpGT0vMOMCc+N78D0C1NotjLPwy4krCSQbkws6MJ\nz+b/pbzKICIiIiLVT4GG6IuUnLu3K+8yVEVxNMBWIylSpl1NGKpfbtz9VcIs+iIiIiIiUoYKCgvz\nfcRaRKRM6MtIREREpPKpsN3kA5v0L9V/X4584NoKW9cM9eCLiIiIiIhIlVMNR+irgS8iFUfz4y5K\nHbv497fmlSbf+G+Tx8gzBxcTudnApcMBGHHGoGIig0H3j8grPplm8vmjUsV3uyPMhzjpvHTxAN3v\nHFCiPKZdMDp1Hlcs6FeiPEpyrWa0GZMqvtP8a/IqU7Jc+dZjfofxqfNoM6s3AFNaX5sqvsvC/gDc\ndNF1qfO4/Na+eaXJxJfkPc/3/ZjVdmzqPDrcdjUAMy9Ml6bj7SF+zsXpr1X7W0Ld833PS5JH2jSZ\n+JJcq3zzKMln9+ZLrk8Vf+m8q4CSfXbTlitTplvbj0udx0Vz+uRVrkyZSvKerxk8M1V8veFh4aS1\nQ2cVE7lZ3aEdAFjYcUKq+NYzw8rKc9ule/8A2s29qkR53HVl+umFWk4N807f12NyqvizJnbLKz6Z\nJt/Pbtr4ZJpHB84oJjJoMLJTXvHJNEu6p6v72ZNSTQ1VLZhZTWAOcCDwXWCkuy+Nx9oAXd29Tvy9\nA2E1s69j3LK45PdtwN7A58AlcbWw2sANMfZBdx++rXLU2NZBqR7MrJ2Zpf8XVzkxs0vMbKsJ/Mxs\nq+e7zezXZnZz3P5ge5QvRxlamtmQlLFdy7Ac/czsV2Y21Mw6lVU+xZShk5k1LD5SRERERKRSuhD4\nyN1PAZoANwKY2XGEpcKJv+9LmDS7DtAYGG1mOxKWF385pr8FGBiTTAcucPeTgRPNLNdqZP+jBr5A\nJXn22d3nZe6C5aky1K9MlpAzswOAo939Bcr3OtwEDDAzfeeIiIiIyHZRo6CgVH+KsQjIDOOsAWw0\nsz2AUUBPNs9VcALwhLtvdPfPgD8BvwB+AzwQYx4AGpnZLsCO7v5O3L8SaLStQmiIvmTUNrOVhCEh\n09x9lpmtA45w96/MbAzwBrAO6A9sAA4g3FFqCBwDTHT36WbWkrA0W01Co7IFcDRwNfBfwrrqC919\ni7GjsSd+DeED/ibwIXBKTHM64S7W+8BMYEaMexfYNaY3wrCY/wD/BL7MOv/RhOXiCuLx9vGPKnP8\nx8BUYCegFjDQ3ZeY2RnAMOBT4F/AK+4+LI56OJmwdN14d7/LzOoQhtB8Eq/RC1llOAK4GdhI+MNv\nA1wC7GFmNwL9gNnAbsB+wJR4TU8g3AX8HPgHsMHdLzWzbsAF8TovdPfs8VRXEL5sMs42s1bAnsAg\nd7/fzD5w931j+RYC04CDCcsdZq7FROBs4Cjgt+5+Xxx10AL4PrA+bl8Y36vvAYcCY+ONmW/M7PdA\nM6AkN2lERERERCosd/8SIDbKM439OUBvQrsgY1dCuyLjc8K//XcFPtvGvsz+Q7ZVDvWmCYQG70Z3\nb0xopPWM+5M9vsnt/YFzCI3HgUBboCmQGf59ONDM3esCrxOGnhQCP4npagN9c5TjB8DtcVhKXcKd\nrXrAjsDPE2VoAezs7rVjGXaL+68HBrt7I+Bhtp7RcxZwpbs3AFbkKIMB49z9NMIzMV1ij/NEoIm7\nNyTcPMDMmgIHxTo2JPRO70ZoHF8Yz5FrabhGwNPxdQiwm7uPAj52967AYcCC+F40JnwhQLiRcom7\n/x/wNlBoZj8FziPc7TsFaB5vICTVA16J2wXA3+L16RmvHeR+nwuBH7h7M2AscIW7nxOvy6VmVgDs\nATSK78N3gONjul3d/UzgLOCaxLlfAernuCYiIiIiIqWuoJT/K04cPbuKMMT+j4R/208DFgA/M7Px\nhMb9LolkuxA6Bz9L7P9/9s47TKpi6cPvopjFgAnMGMoAV71cAygKBsSIWUFFMCAiKuq9mLNecwIV\nFVGCmMCAIopelSBmMYDAz4BZQVEBEx8o+/3RPe5hmNk9s+zsLm69PPPMmXOquqvPmV22uqqrc52D\n4PDPKs8Gd/AdCE7ZhHg8A1ghh0zyGz1J0p+EL+cnkv4gfNGWi9e/Bwaa2b2EKHv9eH6ipAWSfiM6\nyjnI2DGLMDkAIWq+XELGgDcBJM0kZBYsdB4Yl6PtLYG+ZvYS0IUQIU8yHTjZzAYB3QhO65rAHEnf\nZ7XbFGge23omym4ErCPpoygzNocN/Qn37VmgB6FYRpLvCI76YELafibLppGkzDjHEZ5HU0IRjxcJ\nExqrE36JJFmD8EwhPOdMRkGa5/xOfJ9N2T2eBSwnqZSQhfCgmd0DrEfZc343vn/Fws/tW0LmgOM4\njuM4juP8rTCztYHngF6SBkh6U1LTGFw8Cpgs6SyCv9LKzJaNAcItgUnAeEImLITg6VhJPwPzzKxJ\nDLC1JbeP8Rfu4DsZcq3Pngs0jl+mbSuQBcDMGgCXAkcCJxEc+YzTmGYNeBqZyYSiFJjZasDmifO7\nxOOdcuhNBY6NP2Tns2iq+OXAIEmdgNGEn4/vgJXNbI0o0yLR1kuxrb0IaTifAF+b2dZZsknaA+Ni\nFH0YYdkClN2js4FXJR0br2d+Rr+MEftkuwI+kNQm2jGYsmh9hu+AVXPYkaS+ma0Yi3tsnThf3nNu\nBrSXdBRwerSzoue8erTHcRzHcRzHcYpONa/BP5+QWXyxmb0UX5lgVwnxb2RJ04HehKDdC8D5kv6P\nEOnf2szGAScSlghDCDwOAV4HJkjKBDRz4mvwnQy50rSvA0YS1t3/yMLp2zn1JM0xs/HAqwRnToQ1\n3O3cwYIAACAASURBVJ+Wo1feuUVk4rr4Nmb2OvANIfIOcCYwwMzOJkSP/8xq9xRgsJktHc8dz8IM\nBW4wszMIafSrSyqNa81HmtlsgiMrSU+ZWWszG0tYWvCYpF/M7ESgv5n9Qljn/0FWH28RshvmEdbu\nZ5ZDTI6ZA/cCfczs4Kj7c9xyoztwb2x3HiHV/n0ze8HMXiZEyl+L9yPJaMJkx7Cse5E8viXqTiM8\n6+zrpTn0PgZ+jeOfSci8aJyll328I2WFQxzHcRzHcRynqFTsk1cdks4Azshz7TNigDJ+vodQhDop\n8zth+W227uvkDhzmxB18B0kDE8dziYUbJN1HKAiXzZh4fSph/TmSZgFbxeMj83Q1JtFPdno8kpok\njlskjg+Oh68nzvUki/iD0zrH+cbxfQLQJo9tSHoIeChxKjNrti2wSyw2OJhQ2A9JZ+doYwK5swcy\n16cR6gtkn09uIdcs+3ossndA3AvzCkLhQSTdANyQrz9C3YEbgGGSMuPJfnZXAldm6SWf1ShCxU4k\nvUtZ6tAe5fS70HcpTqpsS8hQcBzHcRzHcRynCHiKvuNUzM/AazFSDvBwDdgwA3guRsy3AW5PoyTp\nC+B9M2teTONScBLw37h233Ecx3Ecx3GcIuARfMepAEm3k9KhLqINjwKPVlI3Ozpf7UjqW9M2OI7j\nOI7jOHWLkurM0a8llJSWekDNcZxagf8ychzHcRzHWfKotV70lQdcXKV/X1741OW1dqwZPILvOI7j\nOI7jOI7j/O1IUfn+b4c7+I7j1BoO/Wfn1LKPThhQkE6h8ovTx5UHXJy6jwufuhyAK/a/KJX8RSOu\nKEg+qXP7Uf9NJX/qQ+cD0O+Ya1P3cdL951Sqj7TySZ17jr0ulfyJg3sBcOMhl6fu4+zHwrN7pNst\nqeSPuDPU+7y6/SWp+zhv+GUF6WTkK/M87u2U7l4dPyjcq6GnpBs3wOF9exakk5Hvc+RVqfs47eEL\nKtVH2nFD4WMvdNxJnbRjz4x7WPdbU/dx2B1nFKRTqHx191HovXryjD6p+zjw1tMKsitjU3X0MeLM\n21L3sf/NPQAYfdHdqeRbX9G1IPmkzuM9eqeSP/i20wuSr4zO4vTx+fARqeQ3bL9/QfJJnQ8HDatA\nMrB5p8MA+GhI+hWYmx19KADP/ueOVPLtru9ekHxSp9B7VVupg/69O/hOzWNmnQGTdF5N21KVmNlT\nQA9Jn1cgtz6wjaT0/4ukt2FZQiX94whbFW4uaV5V91OBDSWE3Ri6xcr6juM4juM4juMUAa+i79QG\n/s5rr9OMbQ9g5yL13xN4OFavL6UG1kjFvh8AelV3347jOI7jOE7dpV5JSZW+lgQ8gu/UFnYys1HA\nmkBfSf3M7DNixNnMrgGmAJ8B5wNzgfWBOwn7uW8D3CrpTjM7DOgO1Cc4tQcT9pY/h7B/fBPgIUkL\n5Qbn0fuRUEG/OTAd2Bg4AFgA3AUsD/wOdJX0lZldBuwHfBvtI6uP7kCnqP8mcCZwLrC8mb0CzAEu\nJky+rQR0lPSRmV0EHAR8D6wAXAS8C/QHVo/Nny5pUqKvEuAYwv7zGfqa2cbx+ODYpkk6z8yWA6ZI\n2tjMRsf2mwK/AOOAvYFVgbbR/nuAVYDGwO3x3o8G3ol6DYDD41Z9LwA3AelzpR3HcRzHcRzHKQiP\n4Du1gRJgvqS9CU5nz3g+Gf1OHq8LHAKcAlxIcGL3AU6O1zcD9pPUCphMcExLgQ2i3k7kjibn0jsQ\nWF3SjsAJlDntNwC9JbUBbgSuMbPtgDaS/gUcTnDQs+kMnCqpJWHCogS4Ghgi6SlgK+CY2O5jwOFm\ntg3QDvgXwSFvFNs6H/ifpN3j2LO3otsMmC3pz8S5e2LbnwF7kT/DoBR4XdKewLLAr5LaxvuyG7AJ\n8GB8ZnsDZ2Xp7QU8D3QAiDZ8Z2bN8vTnOI7jOI7jOM5i4g6+UxsoBSbE4xmECHU2yZyYSdFhnA18\nIukPYBawXLz+PTDQzO4F/kGIyANMlLRA0m+EqHs2ufS2AF4FkDQTmBplmwLnm9lLhGj6WlH27Sg7\nlxChz87l6QL0iJHuDeP1koTcN0BvM7sPaJOw4Q1JpbHdtxI2HB9tuBtYLauvNQj3M8nb8X06i97n\nbFszz2QWwbEH+Ilwn2cAB5nZYOACFs4Geie+f0nZM4GQ1dAQx3Ecx3Ecx6kGSqr435KAO/hObSFX\nJHku0Dimmm9bgSwAZtYAuBQ4EjiJ4MhnfhrL01slj94koEWUWQ3YPKpMBc6J0fAewMMEJ3hHM6tn\nZssA2+Xo8yRCsbnW8XpL4E/KfhbvBjpL6kJw9kuAD4DtzawkFs3bLmHDzdGGY4CBWX3NIKTUJ8m2\nZy5lGQH/rEA2ydnAq5KOBYax8O+SfHqrseiEg+M4juM4juMUhZKSkip9LQm4g+/UFnKl418HjASe\nJqyFLy1HFqBU0hxgPCHq/jggyhzYfHpImp2l9yHQSNLTwEwzG09Yc/4bMA/4N3BJjMT3J2QVvAcM\nB94AngBm5hjnRGCcmb1AcHZfi+fam9mRwP3x+oio3yiuqx8ZZR8D5kcbrgKOiBH8Jwkp/8kxfQKs\nZWaZn/Nsx7sUeBbYyMzGEZYVzM5hczalwFPAqbFuwgHAz3FSI5cs0YZ1JU3JIeM4juM4juM4ThXg\nRfacGkfSwMTxXEIRPCTdR9heLZsx8fpUQoE9JM0irF9H0pF5uhqT6KdxDjsW0TMzA8ZJ6mFmDQkR\n/ZmS5hPWxWe3cS2Qd7NqSf0JEwJJ3iWk4UPIBMi2YU3gJ0k7xgj+JOBLST8SahaUxxBCHYEnJDVJ\n2JHckrB1DjvbJI47JI7PTIjlWk+f1Lsrcb4dOcbmOI7jOI7jOMWi3pIRdK9SPILvOOXzJdDBzF4F\nngF6Ree+OplJSNF/AxgL9JP0VUrdWwiF+mrs11vsuwNwc03Z4DiO4ziO49Q96mKKvkfwHaccYkG+\ng2rYhlLg+ErqzgWOrlqLCrahFDi2Jm1wHMdxHMdxnLpASWlpeXW0IKYE/xsw4HTgDOBqSfOKb57j\nOHWI8n8ZOY7jOI7jOLWRWhvavunQK6r078uzHr2o1o41Q5oU/dsJ+3k3B/4ANmXRNcSO4ziO4ziO\n4ziOU2vwFP3cNJe0nZm1k/SLmXUiFPlyHMepUs5te05q2Wueu7YgnULlF6eP6w66LHUfvZ64BICR\nZ9+eSn7fG08tSD6pc8+x16WSP3Fwr4LkK6OTkb//hBtT93FM/7MBuLdTuj6OHxT6+Hz4iNR9bNh+\nfwCGn94nlXz73qcB8MJ5d6buY4+ruxWkk5GvzPO477jrU8l3GfgfIP24oWzshd6reXN+SN3HMg0a\nAjCs+62p5A+74wwg/bihbOyF9lGZe5V27JlxV8fzSDtuKBv7k2ek6+PAW08rSD6pU+i9GnHmban7\n2P/mHgXZlbGpMn0U+jye69U3dR9trzsFgHGX9ksl3+rSkwqST+o81TPd2A+4pUdB8pXRychX5l6l\n/f8g839BZf7/mDFudCr5tVu1BuC78WNT97HWzrsC8Ox/7kgl3+767gXJJ3UKvVdO7SGNg78ga/ur\nNYAFRbLHqULM7DzCFmb1gdskDTSz7QhbnH0UxfpKeiRL70FgnfhxY+AVSR3N7CzCfutzgT6SHqyO\nceTDzFoDJyervMfztwFDJY1JnBsAPChpVLUamQIzOxh4TdK3RWi7FbAd8B7QLfteVQdm1hQ4RNLl\n1d234ziO4ziOU3fxKvq5uRX4H7COmd0KvE2ozO3UYqLz20JSS8I2aJkt0poDN0lqE1+PZOtK6hC3\nSTsY+Ak4MzppnYCdCFuhXWBmaxd/JOWSb01NrvO1eX336UCDqm40Vq+/BLiDGlwbJWkSsKmZNalQ\n2HEcx3Ecx3GqCE/Rz4GkQWb2NsGpqwfsL+n9olvmLC5tgYlm9gTBefxPPN8c2NzM2hOi+D0l/ZKn\njcuB3pJmmNmuwOhMcUUzm0Rw9ofHz6sQJn82k1RqZtcCbwFTCZNEJcAPhGrwvwJ3AesBjYAnJV0U\no+yrAw2BfSXNzhhiZnsBVxCyBzLtlCSudwO6At8BKwLDcoznZDPrBawCnAJ8T4jqt4htvAocBXQB\nNiFkqzQk1KE4FNgcOE7S62Z2dbyXDYH3JB1vZpcCGwFrARsCZ0p6LmHjcsAj8XmsAFxAyK7YFhgY\no+2X52h3DeABYBlAwO6SNjOz3YArgT+BTwjZDH8kxrsX8IGkP8wMYDMzGxnte0rSZWY2Gugq6cN4\nD9cGBkQ7v4jjeQhoSsgEeFrSBbHviwm/E1YCOgLzgQej3ibAG5K6R1seAU4Fzs7xXBzHcRzHcRzH\nqQIqjODHKvqbAD8Ds4Ht4jp8p3azJsFRPAzoBgyJ518H/i1pN2AaIcK7CGa2FrA7wdkDmAjsamYr\nmVlDoCXBSQUgOuMvA+3MbCmgHfAE0A/oHjMCngF6ERz7VyW1A3aM9kGIsr8gaecs576EMCFwsKTW\nwBjgwiiPma0J9Ixt7RvP54rYvyVpD6AP0DmPTMaO3yTtAzxKmGw4ELgGOMrMVgZ+lNQW2B7Yycwa\nR725kvYl7DZxZla7mxAc9wMI+8IvLWkk8C4hO2K5PO1eADwWxz6Usom5fol78nUcU5LdgORk3HJA\ne6AV0CMxVnIcb0yYRNmfMLFyJuH+nhCvbwUcE5/rY8DhUX+zqLcDsG/8HkH4/rTGcRzHcRzHcaqJ\nkpKqfS0JpFmD/0x8/zzr/KAqtsWpWmYCU2JE90MzmxsjwY8nnOfHgT5mdihlDt9Zkt4hTAwMiXuY\nI2lqXNv+LCFC+3rsI0k/Qrp5PeB5SfPNbEugb4wg1wc+BH4EtjezNsAcYNlEGwIws1OjDaUEx3VO\nYo36OOAqIFP9Y9M41vlRdzy5U9Lfju8zSExOJEjqTIjvs4APEsfLAb8Da5vZA8AvhAh2/SjzTnz/\nKsqWDUz6wMzuIkS56wO9s/rP1+4WwH1R5uU4xjUJ2Q9D471dHnguq701gFcTnyfFezTfzP5gUZIT\nftMk/Wxm84EZkmbFfjOTAN8Avc3sF2DdjF3Ax5J+jbLfJu7Bt4TJDcdxHMdxHMdxikQaB7+hpG2K\nbolT1bxMiCLfFKPAKxAc6/FmdrqkN4E9CVHtRwmR6iR7ECK3AMTJgQaSdonp+K8AryUVJI2PdRpO\nIESdIaToHyvpq5jm35DgsM+S1M3MNiWk1mfITCjcTkiNz0TwG5jZOpKmEyLTSuh8BGxtZssTUvh3\noGxiqjzmAmuZWT1C2vzGOWRKWHSyYB9gPUlHRUf74BwyixDrGKwsaX8zawSMB54mFK1cipB9kKvd\nSYSMifcJyyIgTK58CRwYHfGDCM83yXfAaonPuTIW5gKNCRMv/yRMTOSTTXI30ETSr3FpRWZyIJ/e\natEex3Ecx3Ecx3GKRJoiey+a2V7RCXKWECQ9DbxjZm8ATwKnSlpASIe/2cxeAloQ1nDnwggp/Jn2\nZgIW2xsF9JL0cw69IcDakqbEz6cAg81sXOxrIvACIZX/eeBc4K04CQE5HMSYRXAS8JiZvUxYOpCZ\nfCiNtl1JmNR4jrAWPBelifdSSTOA54E3CQ7rR/lks47fAJqY2YuE+gKvE5zkbPuzx/IR0NrMxhDW\npF8Uz78CDIx2ZLfbiLA04MB4/kRgfrwnZwAjY8ZCV2ByVn+jCWn12eNI2tYbuMPMniX8PijNup7v\n+H5gnJmNIEw2NMoz5gw7Eop1Oo7jOI7jOE61UK+kpEpfSwJpIvhfEBw6YiowBOdoqWIZ5VQNkhbZ\nvFvSe8AuKXSb5jh3SopulyKk6md0JhAKNGazbY5zXcqx5wXCxECSMfGFpEGUs2xEUpfE8Sjid1pS\ntxzilyVk70ocDycWFSRkCWTzSkJ2KmEiImnD/xHWqmfbdhFlzv4i7ZrZPsDFkt4ysz2JWxhKep4w\nQZGPF4ELzax+3DLwr20DJTWO78+QO9uhZbw+l7IdGJJ6+YrltUzItkicP4KyrA7HcRzHcRzHcYpA\nGge/J7CRpC+KbYyzZBNTtdchFJFzqo5PgXvjuvmlgNPSKMXdDC4DuhMyAmoEM2tGWJufXcfDcRzH\ncRzHcYpGSc3tFF1jpHHwv2LRtb2OswiSOte0DX9HYjZAywoFc+uOJqTq1xiSJhKWZjiO4ziO4zhO\ntbGEZNVXKSWlpeXX0jKzoYQtu8YD8+LpUknHF9k2x3HqFhUV9nMcx3Ecx3FqH7XWje7b4eoq/fvy\nlAfPq7VjzZAmgv90fCXxP8Qdx3Ecx3Ecx3GcWsuSUhivKqnQwZc0wMwaAisSZmeWIvd2Yo7jOIvF\nRfucn1r2imf+W5BOofKL08f9J9yYuo9j+od6hYOPT6dz7L2FySd1njitdyr5g/qcDsDTZ92euo/9\nbjoVgOGn90kl3753KCXx5Bnp5AEOvDXoPH9O31Tye10b6oJW5l6NOPO2VPL739wDgAdOuil1Hx37\nnVWQTkb+7Zvy1hFdhOZndQLgxQvuqkAysPtVJwOVe+ZpdTLylblXk/o+kEq+6SkdgfTjhrKxf3D3\nQ6nkt+56VEHySZ2Hut6cSv6ou88EYEr/h1P3seUJRwIw9b5HUslv0eUIADRwaOo+7LjDC9IpVD6p\n89KF6Z5hmyvD83vlqntT99HyguMLsitjU2X6eObfd6SS3+eG7kDlvrvjLrsnlXyrS04sSD6pM+2R\nJ1LJNznioILkK6Pzl/zQ4RVIJnQObw/A58NHpJLfsP3+BckndcZd2q8CyUCrS08qSD6pM/GOdL8T\nm3XvWJB8UufLESNTya+//76p23aqhwq3vjOzqwnbpU0lbEP2MXBeke1ynFqJma0RtxjMd721mT0Y\njw+O+92nabdHVdmYo+3zzKy5mV1qZicXq58KbDjZzHavWNJxHMdxHMdxnMqSZm/7DsAGhH27WwN7\nEKp6O45TPmcADVLKFmULOTNbH2gm6W1qdmnNPcAFZpbmd47jOI7jOI7jLDYlJSVV+loSSLMG/1tJ\ns81sIrCtpEfN7KpiG+bUXcxsCDBE0kgz2xK4HjgIGEBYHrIUcJOkR8xsNNBV0odm1g1YO8o9BHwB\nbAK8Iam7ma0BPAAsAwjYXdJmWX1fDTQHGgLvSTrezNYGhsR+Pyc6ymb2GbC5pHlmdg0wBfgsXtsX\n2AYYaGatJM2P5zcH7gPmEybYOgLHAaub2W2E7Jj+wCpAY+B2SXea2Q7AbcDPwHfAXEldzOw0wiRc\nKfCQpOx851OAZA5iezM7PI7vIkkjzGy6pHWifQ8BfeN9PgBYDmhE2GavPdAU+LekJ2PWwcGE5Tsz\n4/HRwL7A8vHeXytpoKQ/zewdYD/gKRzHcRzHcRynyCwhPnmVkiaaNtvMjgUmAEebWQtgreKa5dRx\n+hGcXoDjCdHfbsAMSTsDewJXxtoQyah08nizqLsDsG900i8AHpPUmuD0LjTBZWYrAz9KakvYOWIn\nM2sc9R6U1Ibg6Gd+VeTrG0kjgXeBThnnPrIn8Fp8vwRYRdJVsd8ewKaxr72BvYGzot6dwHGS9gA+\nAUrj5McRwM7ArsBBcQIhyW7A+/G4BPhK0p5AT4Lzn28cpcBKkvYDrgVOkXQI0BXoYmYlwOrAnpJ2\nivdy+6jXQNIBwIHAuYm23ydkATmO4ziO4ziOUwTSOPgnAGtJeomQmn8ncGFRrXLqOmOArWLEfS9g\nBLAFMA5A0i/AZEKEOEny+/yxpF8lLQC+JUSitwBeiddfztHvXGAtM3uA8D1fCagPGPBmlBmXx+a0\n84P9gdnAs0AP4I+s698RHPXBhImFzCREI0lTEjaUEKLpGwIvAv8jONybZrW3BjAjHpcCb8fjGcAK\nFYzjnfg+m5CdADALWE5SKSEL4UEzuwdYj3CvIExsAHxFuO8ZviVkDjiO4ziO4zhO0amLKfoVOviS\nvpZ0Yzw+W9I2ktKXjnWcAonO42CgDzBK0h8EB7MV/BVpb0aYcJpLSGUH+GeimVzrzScBLePxTjmu\n7wOsL6kjwblenuDwTgZ2yaE3F2gco9nb5mhvASGtP0l7YFyMog8DzonnM78xzgZelXRsvJ75Gf0y\nRuwBWsR3AR9IahOzCwZTFq3P8B2wag7bktQ3sxXNbBlg68T5vGv2zawZ0F7SUcDp0c5cmQ1JVo/2\nOI7jOI7jOE7RqVdSta8lgQrX4JtZZ+AGwh/nGUolZTsujlOVDACuIDjyAHcD/cxsHMHxvlTS92bW\nG7jDzL4AvmbhFPMkpcA1wGAzOwL4hhCBTvI6cKGZvQhMj58bRTsyelMTbV8HjCSsu/8xR9+vAIPM\nbC9Js+K5twjr8ucRnP+e8fxkMxsE3Av0MbODgQ+An82sPtAduNfMfgHmEVLt3zezF8zsZUKk/LU4\nriSjCZMSw3Lcl8zxLVF3WhxL9vXSHHofA7+a2VjC+vsJlE205Fu6sCMhc8FxHMdxHMdxnCKQpsje\nJYR1sx/EyKrjVAdLAWMlfQgQ17F3zhaS9AzwTA79lgmZFgBmtg9wsaS3zGxPYJ2stmYQ1uznYr8c\nfd9HKJiXzZh4/SLgoiydacRMhKzzyS3kmmVfj0X2DpA008yuAP4v6t1AmIDLR794fZikyxL9TQV2\nj8dXAlfmGkO8PgoYFY/fJRTRg7CjRl4kzQWaRPuXJmQ5nF2ejuM4juM4juM4lSeNg/+VpElFt8Rx\nImZ2CHApUNV7tn9KiIL/QZhAOK2K2y8mM4DnYgR/FmVFCMtF0hdm9r6ZNY9b5dUUJwH/9UlCx3Ec\nx3Ecp7pYUtbNVyVpHPy3zWwY8BwxakhI0R9UPLOcuoykx4DHitDuVBKR/SUJSY8Cj1ZSNzs6X+1I\n6lvTNjiO4ziO4zjO352S0tLyA2pmNoAcRbMkdSmSTY7j1E08uu84juM4jrPkUWvD5AM6X1+lf192\nHvCfWjvWDBVG8CV1rgY7HMdxHMdxHMdxHKfKqOcp+o7jODXHmIvvTi272+VdC9IpVH5x+njj2gGp\n+9jhnM4AzJvzQyr5ZRo0LEg+qTOp7wOp5Jue0hGA924bkrqPbXocXak+0sondSb3S7dT61YnHQVU\n7l5NvufhdH2ceGSl+yj0mVfmXn05YmQq+fX3D7Uz044bysZeHfeq0D7SjhsKH3uh407qFPrMq+N5\nTOmfvo8tTyhMp1D5pE6h9+qrZ9Jv0rLePu0KsitjU2X6+PyJEankNzxo/0r38eGgYRVIBjbvdFhB\n8kmdQseRVr4yOovTR3X8X5v2GWaeX2We+bShw1PJNzm8fUHySZ1C75VTe3AH33FSYmaXAt8Stsrr\nJqlDBfLbAAdKuqKINq0PbCMp/f9yhfexLHCMpP7F6sNxHMdxHMdxqpq6WGSvXlpBM1utmIY4zhJA\nQWt4JL1XTOc+sgewc5H7aAScWOQ+HMdxHMdxHMdZTCqM4JvZtsBDwIpm1hIYDRxRw1tuOQ4AZrY5\nYS/6+YQJq46SvjKzq4FdCNvh3QQ8DowFLgPeA14A2gH3A10lfWhm3YC1JV0W9ZsDDYH3JB2fo++9\ngJMkHRE/jwcOlTQ9fm4NnCypg5l9DIwHNo99rwLsAEhSp1jMch6wIbAs4WfuAGADoD3wGXA3sB7B\n4X4SuAQ4F1jOzF4BziJkFigzFmAAMAKYCYwEngVuJRRD+QE4XtKcxJh2Bm6MtvwGHAZcAGxlZhfG\ne90XWC7acaGk4Wa2f7y3s4GfgPcT9/Gv5yApfV6g4ziO4ziO4zgFkSaC3wc4BJgp6UvC3uS+5ZVT\nW9gTeC2+XwKsYmb7ABtJagXsTnBQVwQ6Epz9wcDZkr5i4ah8KYCZrQz8KKktsD2wk5k1zu5Y0vNA\nMzNb1cy2Br7POPc52DDa0Qo4Hbhd0o7ALma2Suz7U0l7A1Oi/fsRtsY7AFgfeFVSO2BHgiO/ALga\neEDSU8kxZI1rbWAvSdcD/YDuktoAzwC9suxsT5hc2I3wc74acCUwOW63twVwY7w3XYFTzaweYdKg\nnaTdgd/jfVzkOcSxOo7jOI7jOE7RKSmp2teSQBoHfwVJkzMfJP2PEGF0nNpAf0LU+FmgB/AH0Axo\nbmYvEZzYpQmO5ufAy8CakkblaCvz8/A7sLaZPQDcCawE1M/T//1AB6AzcE85dv4g6StJfwC/Spoa\nz88mRMMBJsT3WUDmZ+6neP1HYHszu58wSZH5GSwh99YkyZ/tT2O/EBz0vvHedAGyJy7+C6xLyDI4\njJAZkWx/OnCymQ0CuhHu7ZrAHEnfR5lx8b0piz6HDXPY6jiO4ziO4zhVTklJSZW+lgTSOPg/xDR9\nAMzsaIKz4Ti1gfbAOEl7AsOAcwgR8JdilHovYCgwzcx2ArYGxprZ2VF/LmVO7j/j+z7AepI6EqLu\ny5N/f8/7gCMIkfnyyjYXugdndn+dgVmSjiE4+CvE839S9nOcaywACxLHAo6N9+Z84CkW5hhgQIzE\nTyZE6ZN9XA4MktSJsFynHvAdsLKZrRFlWsT3qeR4DhUN3HEcx3Ecx3GcypGmin53YCCwtZnNBj4C\nji6qVY6TnreAgWY2j7DOu6ekd82stZmNJUTfHyM4ovcABwFfAq/HyHJv4A4z+wL4muCIvwFcZGYv\nEiLWr1PmOC+UAi/pGzObA7wSU+aTlJLbsV9kWUCK4xeAB8ysOfA58JaZNQImElLf384zluy2TgEG\nm9nS8Xx2bYE3gHvM7FeCY9+V4MAvY2bXEJz0G8zsDMLSiNUllZpZD2Bk/B1RL9waPZX9HCT9kuN+\nOI7jOI7jOE6Vs4QE3auUCh18SR8DO5vZisBSyYJcjlPTSJpGiJ5nnz87h3jTxPG2ieNncsjukOPc\nK4njMVnXFtlCTtKYjJykRonzjRPHmUh7l8S58xLHt+axOcO3hLT7DLnG0jLR3gSgTQ6ZzPU3KIvA\nJ9kucZzchPyyhG27SJpnZoMJkyj5noPjOI7jOI7jFJ16ddDDT1NF/yVCpK8kfl5ASAWeDPxXh7+S\n3QAAIABJREFU0k9FtdBxailmtjxhvfkLcaKhLvMz8JqZ/QZ8Cjxcw/Y4juM4juM4Tp0jTYr+FMKW\nWfcSnPyOhK26viVELQ8pmnWOU4uR9Dvwr5q2ozYg6Xbg9pq2w3Ecx3Ecx3HqMiWlpeXX/jKzCYk0\n4sy5tyT9y8zekbRdPl3HcZwCKLQQoeM4juM4jlPz1No8+Ie63lylf18edfeZtXasGdJU0V/azP5a\nuxyP65nZCsAyRbPMcRzHcRzHcRzHcSrJ4u57n/1aEkiTon86oTr2d4QJgdUIW2ldAgwqom2O49Qx\nXr58kVqFednl4hMK0ilUfnH6ePO6Aan72L5XZwDmzfkhlfwyDRoWJJ/UmXxPutIIW514JACT+j6Q\nuo+mp3SsVB9p5ZM6U/qn09nyhCBfmXulAUNTyVvnwyvdR6HPfNKdD6buo2m3DgDMGDc6lfzarVoD\n6ccNZWPXwJT36rjK36tC+0g7bkiMvcA+0sondQp95pXpo9BxfDhoWOo+Nu90WEE6hcondQq9Vz9N\nfDt1H6s1a16QXRmbKtPH50+MSCW/4UH7V7qPaY88kUq+yREHFSSf1Cl0HGnlK6OzOH1Ux/+1aZ9h\n5vlV5pl/Omx4KvmND2tfkHxSp9B7VVtZUvaur0oqjOBLGg00AU4i7MW9uaTxwLmSri2qdY5TCzCz\nzmZ2dY7zD5pZfTMbYGZ757g+MWX7rcysWVXYmqPtzczsv2a2oZm9Wow+Utiwlpn1qYm+HcdxHMdx\nHKcuUaGDb2ZbADcBPYAzgf5mNlaSr5d16go5v+uSOkiaT/797tNyAtC4QqnKcT1wAzW4NkrSd8DP\nZrZrTdngOI7jOI7j1D08RT83DwNPALsAA4B9yb3XtuMs8cSt7+4DNiDUmOgRL+1kZqOANYG+kvqZ\n2WeAJXRXAO4H1gA+AZbK0f59wCbA8sCthO0m9wa2NbPJQHvgYGBFYGY8XpqwHKYRYX/5XSWtG6P+\ntxKc9x+A4yXNSfRlQD1JP5pZA2BNM3s8tvO+pK5mNgB4UNIoM2sHHCmpi5l9DIwHNgdeAFYBdgAk\nqVOsxXFjHOMawCmSXjWzj4CX432ZARwqaQHwAHAZMDb903Acx3Ecx3GcyuMp+nlkJF0CjAImEByQ\nRdKRHedvQjdgmqSWwFHAjvH8fEl7ExzunvFcMmpfEnU/kLQrcA1ZRSjNbGWgVWyjHfCnpAnAs0Av\n4CtgdWBPSTsRHPvtga7AJ5J2AS4F1o5N9gO6S2pDmHTrlTWW3YD3E58bEJbZtAD2MLM1WTT7IHO8\nIXBBtPd04HZJOwK7mNkqwFbA2ZL2BK4FukS9jYEL4/1bM9oPYbvNXXAcx3Ecx3Ecp2ikieD/ambL\nAh8CzSW9bGZrFNkux6kpNidmqEj6GLjVzI4jTG5BiEqvkEfXgJFRV2b2ffKipJ/NrCfBMW9AiPYn\nr5ea2XzgQTP7BVgPqA9sQZgEyG53S6BvCNRTn/AzmqRhtDfDNEmzAWLRzOxxlFCWyv+DpK+i7K+S\npsbzs4FlgW+Ai8zsd2DleB5gpqSv4/GXwHLR7j/j2BzHcRzHcRznb4mZ7QhcI6lNXOp+DyGA9iFw\nYvx7/yRCAO8P4EpJT8cs4vsJAbKfgeMkzTSznYBbouxzki6vyIY0Efz7gRHxdbqZPUv4495x/o5M\nIUadzayJmQ2O59OssZ8M7Bx1NyGkrv+Fma1DmCQ7BNgfuM7MlgIWAEuZ2T+A9pKOIkTN6xEc7kmE\nqHt2u1OBY2ME/3zgqSx7vgNWTXzONYa5lK3//2cFshlKCEsDLpHUGZhI2e+SnHpmVkL4xeQ4juM4\njuM41UJ1rsE3s16EQN6y8dSlBAe+VTy3X/QHTgNaErLirzazZYBTgPdiJvAg4MLYxp1Ah5jJu6OZ\nbVvRmNNU0b+NsI72e6A1cDchxdhx/o7cBTQxs9GEmhM3x/O50tizz90JrGtmLxPWm/+YbFjSdGAd\nMxsPPAdcL+lP4HXgaoID/KuZjSVMrE0grJfvD2xkZmMI21POjU2eAgw2s3HAlQRHO8loypYYZNub\n+XwPcKaZPU9w9PONLfv4fmComY0k/B5pRG4y8s2AV/LIOI7jOI7jOM6SzsfAIZRlxP4ONIyBrpWB\neYSaVuMlzY+1sz4G/kEIEj4b9Z4F9ozLe5eR9Gk8PwrYsyIjKkzRN7NXJbUAkPSlmX0DvEv4g91x\n/lZI+j/g6KzTExLX5xK2jURSk3i6S0I2Wze7/VNynLubMHEGsEf2dTNrAfSX9LyZbUaM5sf1+23K\n6esTM1tgZmtJ+owwU5i51iIefgFsk0O3cZ7jTJT/ZsomP5J6jRLHHRKXjgFuy2er4ziO4ziO41Q1\n9aqxyJ6kx8xso8SpPoSg3oXALGAMcDhlS1shpOOvQli+O6ecc5nzTaiAvA6+mb1EKNKFmS1IXPoT\nGF5Rw47jVBnTCOvyLyGstT+1AN1ewFnAucUwLA1mtjawsiSP4DuO4ziO4zjVRg0X0b8faCVpipl1\nJ+xANYoQzc+wMsH5n5M4n+scBId/VkWd5nXw47pezKy3pNPTj8NxnKpE0gxg90rqTqUGnftowwzC\ncgLHcRzHcRzHqSusQIi6A3xLyKZ9A7gqFrFfjlA0exJhe+p9gTeBfYCxsUD3PDNrAnwKtCWs6y+X\nktLS8muHmVlDYDtJ/zOz84HtCMW1Jhc8RMdxnPykKWToOI7jOI7j1C5q7Wbzw0/vU6V/X7bvfVq5\nY40p+g9IamlmexLqZM0F/g84SdIXZnYioYp+PeAqSY/HKvoDCXWt/g/oKOm7WJX/FmApYJSkiyqy\nMY2D/xyhOvcU4LrYwYmxwp/jOE5V4Q6+4ziO4zjOkoc7+LWICovsAatJ6mNmfYCBkgaZmafsO45T\n5Yy/on9q2Z0vOqEgnULlF6ePt24cmLqPf519HACfPzEilfyGB+1fkHxSZ0r/h1PJb3nCkQB8cPdD\nqfvYuutRAEy975FU8lt0OaIg+aTOty+9kEq+UZtQs/Lr555L3ce6bdsC8NGQR1PJb3b0oQD8NPHt\n1H2s1qx5QToZeQ0cmroPO+5woPB7lXbcUDb2Tx58PJX8Jh3CBjzz5vyQuo9lGjQE4OMHHkslv2nH\nQ4D044aysRfaR1r5pE7asRc67mQfhT6PaY88kbqPJkccVJBOofJJnULvVWW+V4WOozJ9fD1qVCr5\ndffeG6jc75Jvnn8+lXzjvfYqSD6pUx3Pozr6SPv/Qeb/gsr8/1Ed46iNz9ypPVS4TR5QYmbNgYOA\nEXHvvTQTA45TLZjZYbEAXb7rl5rZydVs02pm1qFiSTCzpmbWqkh2NDSzO+Px9GL0kcKG5c1sQE30\n7TiO4ziO49RdFnff++zXkkAaB/8c4HrgRkmfAHcQqnI7zpJCTaR+bwMcmFL2MGCrItlxJWXb09VI\nCryk34FXzKxTTfTvOI7jOI7j1E1KSkqq9LUkUGEkXtILwAuJzy3LEXecKsXMGgD9gFWBxsDtku40\ns5aEehCzCIUr3oryZwNHAn8Qqk+em9Xe1cAuhEIVN0kaFret6AQsAN6UdEbcb/4ewrZ0vwFHAcsD\nd8X33wnFMZYGHiTsJ78J8Iak7sAFwD/M7ERJ9yT6vwpoHfUeJWyf0RmYa2YTgA2B7rHfUuBg4Efg\ndqA5MB3YGDgg2ruQPZK+yrp3/5I0KZ5a1syGABsAPxAmFi4EvpV0l5ltAfSV1MbMJhL26vwHMBWY\nAexKKPqxL7AOYbJvOUIxkAslDTez94HRUa8UaC9pDvAI8CwwaJGH7DiO4ziO4zhOlZAmgu84Nckm\nwEOS9gb2pix7pC9wtKS2wETCUpJmwOFAizgRtZmZ7ZdpyMz2ATaS1Iqw7dwFZrYKwcE+NepMMbOl\ngBsIVS1bArcSdo+4Hugdt5C8EbiG4MRuBhwP7ADsG/d9vxJ4MencRzoCHYBWwCxJ3wD3ESYb3oxt\n7RdtnBzHfCCwuqQdgROA9WNbN+SwJ8lOgBKfVwLOi22vEseUL6q/EjAkFtNsBYyXtBuwDLA1YISs\nnraEiY5To97KhMqhrYGvCdt8IGkWsIaZrYzjOI7jOI7jVAN1MUXf19I7tZ3vgJ5mdggwh7Lv7DqS\nPorHYwnOrAGvSfoznh9HcEYzNAWam9lL8fPSwEZAF+DfZrYx8Cph4mvzeIykpwDM7BbgfDM7h1At\ndF5s52NJv0aZb4FlyV9N9GjgWkIE/JnE+Yz898BAM/sF2CLakHlH0kwzm5oYTy57MjQkRN4z/Cjp\ni3g8nbA3Z5JsmyfE91mEyQaAnwhR++mECZITCJMEyd8l78T3L6NshhnA6pTtB+o4juM4juM4RWNJ\nSauvSjyC79R2zgJelXQsMIyy7+zXZpZx3lvE96nAjma2lJmVEFLKP0y0NRV4KUa89wKGAp8AJwHd\nYtR5u9jeFEJEHjPrYGanxnPnRP0eQKYkeXYUvAT4k6yfLzNbBjhcUgdCBkFnM9uAkGpfL2YTXEpY\nYnASIe2+BJiUGaOZrUaYfMiMJ5c9Gb4jLG3IkCtaP5eQYg/wz6xr5a3ZvxwYJKkTISU/OdZ8eqsS\nJjAcx3Ecx3EcxykCeR18M1tQzuvPfHqOU8U8BZxqZqMI685/NrP6wIlAfzP7H7AlUBrXmj8CjAde\nBz6VlNkDpzRG4n8xs7HAG8ACSb8QUvzHmdkLhCjza8B/gPNitP9oYEg8d4mZjQb6ExxvWNShLSVM\nHDRLbikpaR7wo5m9BrwIjIoR9bcJDvp20fZXgccJkxONJD0NzDSz8YS6AL8RovX/zmNPhtcIxf6S\ndmXb+TBhWcFLlJ+yn603FLjBzJ4hrOlfvRxZzGxVwpKE31K07ziO4ziO4zhOJciboi/Jo/tOjSNp\nNNAsx6UJhLT8bPmbgZuzzl2WOD47h05/goOc5BNgz6xzs4B2OWz5q/CkpBaJ84tUxpd0BXBF1rmR\nwMj4cXS2jpkZME5SDzNrSHDkZ0qan8eeTLu/mtmbZratpHclNU5cS27ht0MO3Y1zjUnSwfHwDSC5\nSfplOfTOS1zvSCgU6DiO4ziO4zjVQh3M0K94DX4sGHY0sCIhXXgpYOOYmus4TvH5ErjWzHoSfv56\nRec+DRcDVxEK4dUIZrY80FLSMTVlg+M4juM4juPUBdIU2XsM+JiwBvhxoC0LFwdzHKeIxLT2gyqp\n+z016NxHG34H3Ll3HMdxHMdxqpW6WGSvpLS0/CW3ZiZJZmY3EtbdTgGGSdqrOgx0HKfOkGb9v+M4\njuM4jlO7qLVe9LP/uaNK/75sd333WjvWDGnW2f8Y3wX8Q9JsYI3imeQ4juM4juM4juM4TqGkSdF/\n0cyGEip2P2dmzYH/K65ZjuPURXruvkgNxLzc8uKNBekUKr84fTx66q2p+zj09jMA6HPkVankT3v4\ngoLkkzrDT++TSr5979MA+PiBx1L3sWnHQyrVx1M9b0vdxwG39ADgk4ceTyW/yVGhJmRl7pUGDk0l\nb8cdXuk+Cn3mnw4bnrqPjQ9rD8BXI59NJb/evqFe57Sh6ftocnj7gnQy8pW5V4X2kXbcUPjYCx13\nUqfQZ/7Zo0+m7mOjQw8E0n9PMt+RyvSRVqdQ+aROofdqxstjUvex9i67FWRXxqbq6GPaI09UIFlG\nkyPC6r2vn3sulfy6bdsWJJ/UmT76xVTy67TevSD5yugsTh/z5vyQSn6ZBg0Lkk/qpH2GmedXmWf+\n08S3U8mv1qx5QfJJnULvVW2lXh1M0a8wgi/pAuA8SZ8TKmELOKTYhjmO4ziO4ziO4ziOk540VfQf\nlXQogKS3gbfjfuF7FNu4vxtmVo+wj/nmwALgpFjgYFNgQDw3CThVUo2uRzazz4DN497tmXP/ItjW\npYr6uA0YKin9lHjl++oMWNbWbbUCM2sKrCZpXBHabghcJambmU2XtE5V95HChuWBvpI6V3ffjuM4\njuM4Tt2lDgbw80fwzexxM/sU2N/MPk28vgSWqz4T/1a0BVaUtAtwOWH7MoCbgPMl7UooUtG+huxL\nUh0TDNU5iVGbC7gdBmxVpLavBDI50DVyD2IV/VfMzLfWdBzHcRzHcZwiUl4EvzOwGtAbOI2y6oh/\nANOLa9bflt+BVcysBFgFyETH/ylpbDx+hjAR8NeCHDMbAgyRNNLMtgSuJ2ybdhewKWGi5kJJY8zs\nMKA7UJ/g0B0MNAOuJdROuEvSkETbqwL3AysTvg8XSnopcd2Ae6PtPwC/JQcUsxLuBtYDGgFPSrrI\nzAYAc4GN4vnOkt4xs26Ebdu+A1YEhmW1t1Hsb6lo/+nAhsDBko6PMm8D7YDWwJnAn8DLks4zs0uB\nlrHtEyRNTTS/k5mNAtYkRJT7JTMVzOwawi4RnwHnR/vXB+4Edge2AW6VdGc59/mceJ+bAA9J+m/W\n+K6Kdi8NPBrvfWdgrplNiGPNbvdH4HagOeFnb2PgAELGx13A8vH5dJX0VaKvBsC/JE2Kp5aN36UN\nCM/yMOBC4FtJd5nZFvG+tDGzicAY4B/AVGAGsGsc277AOsAdhMm+RoTvzXAzex8YHfVKgfaS5gCP\nAM8Cg3Acx3Ecx3EcpyjkjeBLmi3pM0kHAisRHJ0NCI5Ly2qy7+/GeIJDNJXgFPeO55PJI78QnP8k\n/YDj4vHxhDT/k4DvJe1GcPZvj9c3A/aT1AqYDOxNcLSWlbRr0rmPXAiMiu0cDvRPXCshTCZcLGlP\n4H85xrQ+8KqkdsCOQLd4vhT4LJ7vA3Q1szWBnlFu3yiTHVW+Abg52nNGtOdpoIWZrWBm2wOfECaa\nLgV2j2Nd18z2jO19IGnnLOe+BJgvaW+C09wzYSc5jtcl1Jo4Jd6jY4B9gJPj9Xz3eYOotxPQK8f9\n6gh0AFoBsyR9A9wH3CTpzTztHgisLmlH4IR4zzP3qrekNsCNwDVZfe1EqJmRYSVCPY1WhO/YduSP\n6q9EmFTaNdo6Pj6TZYCtAQNulNSWMGFzatRbGXhAUmvg63jPkDQLWMPMVs7Tn+M4juM4juNUKSUl\nJVX6WhJIswb/dkK0cBoLOwNtimXU35heBEfpAjNbj7BDQTNCJDbDysCsLL0xQB8zWwPYCziPMDmw\ni5ntGGWWiuutvwcGmtkvwBbAq/G6AOJ6/37x3OAocz+ApG/MbI6ZrZXo24A34/FYFp3c+QnY3sza\nAHOAZRPX3onvXwI7E7INpkiaH20Zz6L7Zm4R+0HSe2a2vqQFZjaM4Di3iPZvSojEPxOSDFgZ2CS2\n8SGLUgpMiMczgBVyyCRtmSTpTzObDXwi6Q8zm0XZ8pR893mipAXAb2b2e44+jiZkU6xDyNbI7jtX\nu3+1L2mmmWUmLpoC55vZOVF/HgvTMI41w4+SvojH03Pcg+xnkblfswiTDRCe93JR/wIzO4Fwb5O/\nS5LPPbmcZwawOvAzjuM4juM4jlNklhCfvEqpsIo+IV3cJLWW1CbzKrZhf1NWJDjBEByl+oRU9HfM\nbLd4fh+ig5shFtwbTIiEj5L0ByGV/MH4LNoTUqDnE6LaRxIi/L9T5rQtiG19nHiO98Z2WgGY2brA\nqoT07QyTgV3icYscY+pMiEQfQ6glUJ7j/BGwtZktH5cp7MCiEeQphFRwzGxb4Nt4vj/QCdhB0vOE\nNPovgT3jPbiDMic7OWGSJFe0ei7QONqzbQWyRLsakP8+l6e3DHC4pA6ElP/OZrZBtLeema2Sp91J\nxHtvZqsRijRCyAQ5J46/B/BwVpffEZ5neWOaS0ixB/hn1rXy1uxfDgyS1ImQkp/8XZJPb1XCBIbj\nOI7jOI7jOEUgjYM/LaWcUzHXE9aBjwNeIKRL/wacDVxmZq8QIqHDcugOIESwMyn0dwFbmNlogoP1\nRVzrPJ7g6D5OiNpnnLd8Ttd/gd3NbEzU6SrpT8rS588EesWdE9rkaOd/QDszex44F3jLzBpn9VkK\nlEqaSSj69jLwHGFCIpt/A6dFe+4gpKQj6bPYzvD4+XvChMJYM3uNkNnwUQVjzZWOfx0wkrAM4Mcs\nm3PqFXCfF7Ij7kjwY7T3RcJkzRfA2wQHfbusdj8EGkl6GpgZMx7uIdRBmBfv1SXxO9CfMBGQ5DVC\n3YCc9sTPDwP7mtlLlJ+yn603FLjBzJ4hLEtYvRzZTK2HWfH77jiO4ziO4zhFp6ReSZW+lgQqTNEn\nRJonR+dzbjxXmil45qQnrkM+OMf5jwiF18pjKWCspA+jzjzK1uUn2zoyj37Oregk/ZTHpibx8LPy\nbJM0mYUj3xm6JGRGAaPi8SDKKbQm6XNC1kiua3tnfR4CZNcUuCyP7sDE8VxCLQkk3UdYA5/NmHh9\nKiHannl+W8XjCu+zpMbZFyVdAVyRdW4kYZIBwmTNQsRCh+Mk9YjLMCYBM+NSh3Z57EDSr2b2pplt\nK+ndpD0xiyDDDjl0N04ct0gcZ74rbwAPJVQuy6GX3JKwI2V1IhzHcRzHcRyn6NTFFP00Dv6z8ZWJ\n7JWQLsrnVBFmdgghdfvkCkSdvydfAteaWU/CRE+vTB2DFFxM2I6xa7GMqwgzWx5oGZdxOI7jOI7j\nOI5TJCp08CUNMLONCZWzRwHrS5pWdMucv5D0GPBYTdvh1Awxrf2gSup+Tw0699GG3wm7EDiO4ziO\n4ziOU0RKSkvLD8ab2VHABYTiaTsTKmT3kjS4+OY5jlOH8Mwgx3Ecx3GcJY9amwg/+qK7q/Tvy9ZX\ndK21Y82QpnjeOQTHfo6k6YRK2+eVr+I4juM4juM4juM4TnWSZg3+n5LmxL3GkfStmf1ZXLMcx6mL\nvHZ1rnqHudnpvC4F6RQqvzh9vHNL+gSn7XoeC8C8OT9UIBlYpkHDguSTOh8OyrVBx6Js3ukwAD64\n+6EKJMvYuutRleojrXxldDLylblXHz+QblXUph0PqXQfhT7zKf2zd8LMz5YnhDqgP30wIZX8aluH\nXTLTjhvKxv7Jg4+nkt+kQ6jRWR3PI+24ofCxZ/qozL0q9JlXx/NIK18ZncXpo9B7VZlnXug4KtPH\n58NHpJLfsP3+le7j2xdfSCXfaPc9CpJP6hQ6jrTyldFZnD6q4//aQn/vVuaZzxg3OpX82q1aFySf\n1Cn0XtVW6mKRvTQR/A/M7DRgGTPb1szuBt4tsl1OLcDMzjOzV2Il9kUq9ifkdozbrGU+b2tmY83s\nJTN71szWqh6L82Nmn8V96JPn/mVmFXpuZtbazB7Mcf5mM1vfzC41s0UKIJrZxJS2NTWzVmlkC8XM\nGprZnfF4ejH6SGHD8mY2oCb6dhzHcRzHceouJSUlVfpaEkjj4J8KrAv8DtwLzAG6F9Mop+Yxs9ZA\nC0ktCdvkNckj1wvoByybOH0L0ENSG0JxwHOKamw6Fmf9TU5dSWdK+nIx2wY4jLj9XhG4ErgtHtfI\nGvdYZO8VM+tUE/07juM4juM4Tl0hTRX9X4Bzq8EWp3bRFphoZk8ADYD/5JH7GDgESOYkHylpRjyu\nT5gc+gszGwIMkTTSzLYEridUib8L2JQw8XShpDFmdhhhQqk+/8/encdpVdb/H3+NJmopqKgJ7uvb\nDMwWN1xBcyvDjRQ0xVxyyzT9aW6lpaZ9XcolNXcT0VzKcqMyUcQ9NTTg7RaJCSKCoiahMr8/ruuW\n4809M+ceZoYZ+Dwfj3nMmXOu61zLmbnhOteWGqi7A32Bc4H/AVfYHla49zLAjcDSpN/vU20XRxeI\n9KLqA+At4L9VeWsALgY2BroBPwHeAdaVdA+wIvAn22dIGklh68Ic9wpgQ9LWdt2rK0vSWaQXJp8B\nbs95HQrMlPQ0sHqN8k4j7SH/VWAysCawKzA7p7dkLs+htl8rpNUd+Jrt5/OpxXPdr5bLvhdwKjDJ\n9hWS1gcus90/jz54MJdlPPAGsHWu812AlYBfA0sAvXI93ylpDDAyx2sEBtqeAfyOtN3mDdV1EkII\nIYQQQnvoIp3ubarFHnxJx0iaJml24Svm4C/4ViA1KPcCDgOG1QqUt/D7qOrcGwCS+pFGgFxYFe1K\noDLk/7vAVcAhwJu2tyE19i/N19cFvmF7K2AssCOp4bi47a2LjfvsVGBEvs8g4OrCtQbSy4Qf294e\n+GuNIu0G9LS9KdAf+Fo+vwQwENgKOCqfq+4R3x34rO3NgMOBHjXuPwQYnO/ztu3XgWuBC2w/2UR5\nvwUsl/N0ELBqvtd5wEV5pMT5wDlVaW0GuPDzUsBJ+d49gC/XKEMx7DDbW+e8js512o20ZaaA823v\nQNqG78gcb2ngJtvbAv8Bdgaw/TawvKSlm0gvhBBCCCGEMI/KLLJ3LLCR7VfbOzOhU5kKjLP9EfCC\npJmSVmVOD+xfbJ/dVGRJewMnA7vYrl6l40HgYknLA18n7cpwEbClpE1zmEUl9QTeBK6X9B6wPvBo\nvu6czjqkFwaQRhGsT+oVx/brkmZUrQEg4Ml8/BDQrypv61XSyI3SH+fpCs/b/hD4UNJH1PbJvW1P\nlTSuRph9SaMPVgLuLZyvvF+sVd5Pyp3vOz6H7QOcLOnEHH9WVVo9ST3vFdMKf8eTSVtfFlW/46ys\n+vI26WUDwHTSy47JwCmSDiK9JCh+ljyTv0/MYSveAJYD3iWEEEIIIYTQ5srMwR8LTGnvjIRO52Fg\nJwBJvYHPAa/Z7p+/mmvc70fq0d3W9oTq67YbSY3xi0m97R8B44DhuTd6IGlI94fA6cDepB7+D5jT\nCJ2d7/VSIU/X5PtslfOxMrAMaTh6xVhgy3y8eY3sjyMNz0dSjzwsv5Fy89fHkl8YSFqW9LKgWC/d\ngEG2BwMDgKGSVstlWURSjybK+3wlr1X3HQ+cmOvsKKB6ie0pufwVtcowkzTEHtIWmEXNlfmnwA22\n9ycNyS9+ljQVbxnSC4wQQgghhBDaX0ND2351AWUa+L8izcX+raRr89c17Z2xMH/Zvht4RtITwB+B\nI3LDvCmNAJIWJf3OLAXckVfSP71G+OtIc/crQ+ivANbP89pHAq/mudujSb3Xvyf12leZyOp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gghhBBC6Cw6eI29l4A9SDtHAexje3I+Xoy0LfcmwOi8yPWHkl4CNgS2AM7NYe8jLf69NNDN9r/y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\nY8BnJM3vu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7kbYLUaZBdJmpGPxwOnFsINIw0Z3wcQTU8RaKk8cx3bvlvStpIeAboBt9h+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\nFf2xj7JvjKFlWoxztr8uacD2oKQLgGbVuDu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HRERExN2N/fQRETF+JcGPiIiIiCckXQTc\nAAxSlugfWnNMERHRpiT4EREREfGz6jZ7NyMixrHswY+IiIiIiIiYAJaoO4CIiIiIiIiIGLsk+BER\nERERERETQPbgR0RERExSkr4wwo9SZC8iYhxKgh8RERExeZkU1ouImDBSZC8iIiJikpM0FdgUmAoM\nAKvaPqveqCIiol2ZwY+IiIiIGZTvhatRajRdByTBj4gYZ1JkLyIiIiJWtL0DcC2wCbBMzfFEREQH\nkuBHRERExOOSBoBlbf8dWLHugCIion1J8CMiIiJiBnAEcKOka4Gnao4nIiI6kD34EREREXEbcJnt\nQUkXArPqDigiItqXKvoRERERk5ykX9qeVnccERExNpnBj4iIiIhBSTOAO4EFwKDtQ2uOKSIi2pQE\nPyIiIiJOBQaGPM4Sz4iIcSgJfkRERERsavugxgNJZwKn1xhPRER0IAl+RERExCQl6SDgMGB5SW+p\nnh6gFN2LiIhxJkX2IiIiIiY5SYfZ/lzdcURExNgkwY+IiIiY5CQtB+wILNl4zvYZ9UUUERGdyBL9\niIiIiPgx8ABwf92BRERE55LgR0RERMSA7T3qDiIiIsYmCX5ERERE3CRpM+B6qhZ5tufXG1JERLQr\nCX5EREREbA3svMhza9QQR0REjEGK7EVEREQEAJJWAB61nS+IERHjUBL8iIiIiElO0lbAicAU4AfA\nfbZPrTeqiIho1xJ1BxARERERtTsG2Ap4GPgKcGC94URERCeS4EdERETEAttzAWzPA+bVHE9ERHQg\nCX5ERERE3CXpi8AKkj4F3Ft3QBER0b4k+BERERFxADAbuBJ4DNiv1mgiIqIjSfAjIiIiYmNgSdsf\nBF4NrF9zPBER0YEk+BERERHxdeDC6v6ngeNqjCUiIjqUBD8iIiIi5tu+C8D2PcDTNccTEREdeFbd\nAURERERE7e6T9HngWmBT4IGa44mIiA5kBj8iIiIi9gIeAXasbveuN5yIiOjEwODgYN0xRERERERE\nRMQYZQY/IiIiIiIiYgJIgh8RERERERExASTBj4iIiIiIiJgAkuBHRERE35C0vqQFknYfwzF2lvSR\nLsSyqqQLm79y2LELuvD+z5M0Y9FYunV+EREx8aRNXkRERPSTvYDzgAOAH3Z4jI2BMVcRtv0g8Kax\nHmcMXgC8cphYunJ+EREx8aSKfkRERPQFSc8C/gBMA64GXm3795JmA2cA2wPPAd5r+zpJWwHHAMtQ\nkuGPA7cCl1ES4E8C5wLfAjYEFgBftn2mpPdREuZVgdWArwH/BmwDzKW0i1sFmGn7pZJeAnwHWAn4\nO7Cv7Zslfa4aszwwB9jd9h8lLbD9jJWSkpYFTgTWA6YAX7J9ThXLDtU5rAn8j+0DJf2kOucLgEOA\ny6u4LqvO5XDgCGA727MkPQe4HVjb9vwh7/tQ9TlsAfwDeLvt2ZLeVh136eqffW3/UtJM4Dpg2+r5\nDwEHAy8HjrX9tZHOpcm/4oiI6LEs0Y+IiIh+8SZgtu1ZwI8os/hQkvU5tl8DfBM4tHr+IGAf2xsD\n+wJH2r4dOAk4yfbpwFHAI7Y3oCTiR0naoBq/KSWBngZ8BfiZ7VdUP9t+yHsDfAM4tzrOUcDhktYC\nZHtz2wLuAt49yvkdDvzW9ibAVsBhktaofrY5sDvlQsTOktajJNYP2n4LMAAMDjm/b9qeDpwO7FEd\n4y3AT4cm95UXARfb3gi4AjhI0gDwfuBNtl8JfAn42JBzHrS9IXAmcAKwW/U5HdnCuURERE2yRD8i\nIiL6xV5AYxb4B8B3JR1ePb6our2VkghDSWx3lvR2YDPK7D6UZLjh9cDeALbnSvoxsDUwD7jK9mPA\nY5IALqnG3As8f5HYtgT+szrOz4GfA0j6qKT9AVGS9LtGOb9tgaUl7V09XoYyAz4IXG378eqY91BW\nBDw+wnGGnt9pwC+ATwN7UlYtDKfx+d0CbGl7UNJuwC4qJ78VZXa/4efV7X3AtbafBO6T1PhchjuX\nlwO/H+H9IyJiMUiCHxEREbWT9EJgJ2BjSQdTktjnU2alAZ6sbgdZmOBeSUnKZ1a3Zw05ZGPmfQme\nmRAvwcLvP8+Y6bY9WmG8p4YeR9LLKcvXz6LM/p9LSZAHhh298L3fbfuG6hgrU7YDvGvI+TViH+04\nQ2OeLeneqijhC23/ZoTXDT3XgWo5/28pKwBmAjdSVkQ0DH390MS/2blERESNskQ/IiIi+sEewC9s\nr257DdsvBT7PwmX6zyDpBcDLgE/bvoiypH5K9eOngKnV/UuBfaoxKwK7UvawN0ugF/35FcA7quO8\nETiZMqs/0/YplL3v2w2JYTiXAh+sjrEKcD2w+iix/IPhJ2OeWuT56cBxlDoFrVoHeBr4AiXB34nR\nY1/UcOeyWhvjIyKiB5LgR0RERD94H2Wf+1DfoOyTX3LIc4394X8Gvg3cKukq4DFgSUlLU5Lxd0s6\nEPgMsLykmyhF6o6pZp0HeWYl+kWrDg8u8pqDgLdIup6yHH4/4PvAK6rnzqMsa19jyPhFHU1Z1n4z\nZcXBx23fM0wsDQ9TlsVfsshrhp4fwAzKkv4zhznGorE0jnMjcAPlwsTlwE2UIoPDjR3ucxruXLI8\nPyKiZqmiHxERETFOVcXydgT2t/0fdccTERH1yh78iIiIiPHrWEr3gR3rDiQiIuqXGfyIiIiIiIiI\nCSB78CMiIiIiIiImgCT4ERERERERERNAEvyIiIiIiIiICSAJfkRERERERMQEkAQ/IiIiIiIiYgJI\ngh8RERERERExAfwfjbGfkc5TeygAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f498e644cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of present genes per development stage -- anatomical entity pair\n",
    "pairwise_df = get_groupby_counts(present_df, ['Developmental stage name', 'Anatomical entity name'])\n",
    "rect_df = pairwise_df.pivot('Developmental stage name', 'Anatomical entity name', 'count').fillna(0)\n",
    "rect_df = rect_df.loc[stage_df['Developmental stage name'][:30], anatomy_df['Anatomical entity name'][:50]]\n",
    "IPython.core.pylabtools.figsize(14, 7)\n",
    "seaborn.heatmap(rect_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>CL:0000000</th>\n",
       "      <th>CL:0000015</th>\n",
       "      <th>CL:0000019</th>\n",
       "      <th>CL:0000021</th>\n",
       "      <th>CL:0000023</th>\n",
       "      <th>CL:0000064</th>\n",
       "      <th>CL:0000066</th>\n",
       "      <th>CL:0000083</th>\n",
       "      <th>CL:0000115</th>\n",
       "      <th>...</th>\n",
       "      <th>UBERON:0016526</th>\n",
       "      <th>UBERON:0016529</th>\n",
       "      <th>UBERON:0016538</th>\n",
       "      <th>UBERON:0016548</th>\n",
       "      <th>UBERON:0016887</th>\n",
       "      <th>UBERON:0017672</th>\n",
       "      <th>UBERON:0018254</th>\n",
       "      <th>UBERON:0018260</th>\n",
       "      <th>UBERON:0018674</th>\n",
       "      <th>UBERON:3000961</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18992</th>\n",
       "      <td>105378753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18993</th>\n",
       "      <td>105378933</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18994</th>\n",
       "      <td>105378966</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18995</th>\n",
       "      <td>105379030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18996</th>\n",
       "      <td>105379175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 667 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Anatomical entity ID     GeneID  CL:0000000  CL:0000015  CL:0000019  \\\n",
       "18992                 105378753           0           0           0   \n",
       "18993                 105378933           0           0           0   \n",
       "18994                 105378966           0           0           0   \n",
       "18995                 105379030           0           0           0   \n",
       "18996                 105379175           0           0           0   \n",
       "\n",
       "Anatomical entity ID  CL:0000021  CL:0000023  CL:0000064  CL:0000066  \\\n",
       "18992                          0           0           0           0   \n",
       "18993                          0           0           0           0   \n",
       "18994                          0           0           0           0   \n",
       "18995                          0           0           0           0   \n",
       "18996                          0           0           0           0   \n",
       "\n",
       "Anatomical entity ID  CL:0000083  CL:0000115       ...        UBERON:0016526  \\\n",
       "18992                          0           0       ...                     1   \n",
       "18993                          0           0       ...                     0   \n",
       "18994                          0           0       ...                     0   \n",
       "18995                          0           0       ...                     0   \n",
       "18996                          0           0       ...                     1   \n",
       "\n",
       "Anatomical entity ID  UBERON:0016529  UBERON:0016538  UBERON:0016548  \\\n",
       "18992                              1               0               1   \n",
       "18993                              0               0               0   \n",
       "18994                              0               0               0   \n",
       "18995                              0               0               0   \n",
       "18996                              1               0               1   \n",
       "\n",
       "Anatomical entity ID  UBERON:0016887  UBERON:0017672  UBERON:0018254  \\\n",
       "18992                              0               0               0   \n",
       "18993                              0               0               0   \n",
       "18994                              0               0               0   \n",
       "18995                              0               0               0   \n",
       "18996                              0               0               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0018260  UBERON:0018674  UBERON:3000961  \n",
       "18992                              0               0               0  \n",
       "18993                              0               0               0  \n",
       "18994                              0               0               0  \n",
       "18995                              0               0               0  \n",
       "18996                              0               0               0  \n",
       "\n",
       "[5 rows x 667 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter to human adult stage (human)\n",
    "adult_df = present_df[present_df['Developmental stage ID'] == 'HsapDv:0000087']\n",
    "\n",
    "# pivot dataframe so each column is an anatomical entity\n",
    "adult_df = adult_df.pivot('Gene ID', 'Anatomical entity ID', 'Expression')\n",
    "adult_df = (adult_df == 'present').astype(int)\n",
    "\n",
    "# convert to entrez GeneIDs\n",
    "adult_df = adult_df.groupby(ensembl_to_entrez).max()\n",
    "adult_df = adult_df.reset_index()\n",
    "adult_df = adult_df.rename(columns=({'index': 'GeneID'}))\n",
    "adult_df.GeneID = adult_df.GeneID.astype(int)\n",
    "\n",
    "# save dataframe\n",
    "with gzip.open('data/present-in-adult.tsv.gz', 'wt') as write_file:\n",
    "    adult_df.to_csv(write_file, sep='\\t', index=False)\n",
    "\n",
    "# see tail\n",
    "adult_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16278"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coding genes in entrez dataset\n",
    "len(set(adult_df.GeneID) & coding_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41120029857725282"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency of gene presence \n",
    "adult_df.drop('GeneID', 1).as_matrix().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and process differential expression data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene name</th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>Differential expression</th>\n",
       "      <th>Call quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000015</td>\n",
       "      <td>male germ cell</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>low quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000738</td>\n",
       "      <td>leukocyte</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000007</td>\n",
       "      <td>pituitary gland</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000029</td>\n",
       "      <td>lymph node</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene ID Gene name Anatomical entity ID Anatomical entity name  \\\n",
       "0  ENSG00000000003    TSPAN6           CL:0000015         male germ cell   \n",
       "1  ENSG00000000003    TSPAN6           CL:0000738              leukocyte   \n",
       "2  ENSG00000000003    TSPAN6       UBERON:0000002         uterine cervix   \n",
       "3  ENSG00000000003    TSPAN6       UBERON:0000007        pituitary gland   \n",
       "4  ENSG00000000003    TSPAN6       UBERON:0000029             lymph node   \n",
       "\n",
       "  Developmental stage ID   Developmental stage name Differential expression  \\\n",
       "0         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "1         UBERON:0000113  post-juvenile adult stage        under-expression   \n",
       "2         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "3         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "4         UBERON:0000113  post-juvenile adult stage        under-expression   \n",
       "\n",
       "   Call quality  \n",
       "0   low quality  \n",
       "1  high quality  \n",
       "2  high quality  \n",
       "3  high quality  \n",
       "4  high quality  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read simple differential expression by anatomy\n",
    "diffex_df = pandas.read_table('download/Homo_sapiens_diffexpr-anatomy-simple.tsv.gz', compression='gzip')\n",
    "diffex_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filter differential expression\n",
    "diffex_df = diffex_df[\n",
    "    diffex_df['Differential expression'].isin({'over-expression', 'under-expression'}) &\n",
    "    diffex_df['Call quality'].isin({'low quality', 'high quality'})\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Differential expression</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CL:0000738</td>\n",
       "      <td>leukocyte</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>12406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000473</td>\n",
       "      <td>testis</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>10267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0002369</td>\n",
       "      <td>adrenal gland</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>9079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000955</td>\n",
       "      <td>brain</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>8485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UBERON:0002369</td>\n",
       "      <td>adrenal gland</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>7989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Anatomical entity ID Anatomical entity name Differential expression  count\n",
       "0           CL:0000738              leukocyte        under-expression  12406\n",
       "1       UBERON:0000473                 testis         over-expression  10267\n",
       "2       UBERON:0002369          adrenal gland         over-expression   9079\n",
       "3       UBERON:0000955                  brain         over-expression   8485\n",
       "4       UBERON:0002369          adrenal gland        under-expression   7989"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate counts per anatomy--DE pair\n",
    "de_anatomy_df = get_groupby_counts(diffex_df, ['Anatomical entity ID', 'Anatomical entity name', 'Differential expression'])\n",
    "de_anatomy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2: Number of differntially expressed genes present by anatomical entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VdZyIrAdcjFWIvB0b6H0P2EpVVxWRbpgj3kyswNqLftwLKFB/2zUw12Niv47A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\n7UrTBoLeLfhaomfx5Q13JPaPv38WOA2P11RCqANfCLpgiK9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4Wmp8pY3myFEJAtv5VTO7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\n1eUwm4FZkvbEU+gnpQzTw8CpeNp4eby6d2v8R2yLNo5dKel5m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49bbcd7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot DE counts\n",
    "rect_df = de_anatomy_df.pivot('Anatomical entity name', 'Differential expression', 'count').fillna(0)\n",
    "rect_df['differential expression'] = rect_df['under-expression'] + rect_df['over-expression']\n",
    "rect_df = rect_df.sort('differential expression', ascending=False)\n",
    "IPython.core.pylabtools.figsize(1, 16)\n",
    "cmap = seaborn.cubehelix_palette(15, start=2.2, rot=1, gamma=1.6, hue=1, light=0.98, dark=0, as_cmap=True)\n",
    "seaborn.heatmap(rect_df.drop('differential expression', axis=1), cmap=cmap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diffex_pivot_df = diffex_df.pivot('Gene ID', 'Anatomical entity ID', 'Differential expression')\n",
    "diffex_pivot_df = diffex_pivot_df.replace({'under-expression': -1, 'over-expression': 1, numpy.NaN: 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def round_diffex(value):\n",
    "    if value <= -0.5:\n",
    "        return -1\n",
    "    if value >= 0.5:\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "diffex_pivot_df = diffex_pivot_df.groupby(ensembl_to_entrez).mean().applymap(round_diffex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>CL:0000015</th>\n",
       "      <th>CL:0000738</th>\n",
       "      <th>UBERON:0000002</th>\n",
       "      <th>UBERON:0000007</th>\n",
       "      <th>UBERON:0000029</th>\n",
       "      <th>UBERON:0000044</th>\n",
       "      <th>UBERON:0000057</th>\n",
       "      <th>UBERON:0000082</th>\n",
       "      <th>UBERON:0000178</th>\n",
       "      <th>...</th>\n",
       "      <th>UBERON:0005363</th>\n",
       "      <th>UBERON:0007227</th>\n",
       "      <th>UBERON:0007318</th>\n",
       "      <th>UBERON:0007371</th>\n",
       "      <th>UBERON:0007808</th>\n",
       "      <th>UBERON:0009834</th>\n",
       "      <th>UBERON:0009835</th>\n",
       "      <th>UBERON:0010414</th>\n",
       "      <th>UBERON:0011876</th>\n",
       "      <th>UBERON:0013554</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18615</th>\n",
       "      <td>105378753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18616</th>\n",
       "      <td>105378933</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18617</th>\n",
       "      <td>105378966</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18618</th>\n",
       "      <td>105379030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18619</th>\n",
       "      <td>105379175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Anatomical entity ID     GeneID  CL:0000015  CL:0000738  UBERON:0000002  \\\n",
       "18615                 105378753           0           0               0   \n",
       "18616                 105378933           0           0               0   \n",
       "18617                 105378966           0           0               0   \n",
       "18618                 105379030           0           0               0   \n",
       "18619                 105379175           0           0               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0000007  UBERON:0000029  UBERON:0000044  \\\n",
       "18615                              0              -1               0   \n",
       "18616                              0               0               0   \n",
       "18617                              0               0               0   \n",
       "18618                              0               0               0   \n",
       "18619                              0               1               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0000057  UBERON:0000082  UBERON:0000178  \\\n",
       "18615                              0              -1               0   \n",
       "18616                              0               0               0   \n",
       "18617                              0              -1               0   \n",
       "18618                              0               1               0   \n",
       "18619                              0               0               0   \n",
       "\n",
       "Anatomical entity ID       ...        UBERON:0005363  UBERON:0007227  \\\n",
       "18615                      ...                     0               0   \n",
       "18616                      ...                     0               0   \n",
       "18617                      ...                     0               0   \n",
       "18618                      ...                     0               0   \n",
       "18619                      ...                     0               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0007318  UBERON:0007371  UBERON:0007808  \\\n",
       "18615                              0               0               0   \n",
       "18616                              0               0               0   \n",
       "18617                              0               0               0   \n",
       "18618                              0               0               0   \n",
       "18619                              0               0               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0009834  UBERON:0009835  UBERON:0010414  \\\n",
       "18615                              0               0               0   \n",
       "18616                              0               0               0   \n",
       "18617                              0               0               0   \n",
       "18618                              0               0               0   \n",
       "18619                              0               0               0   \n",
       "\n",
       "Anatomical entity ID  UBERON:0011876  UBERON:0013554  \n",
       "18615                              0               0  \n",
       "18616                              0               0  \n",
       "18617                              0               0  \n",
       "18618                              0               0  \n",
       "18619                              0               0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffex_pivot_df = diffex_pivot_df.reset_index()\n",
    "diffex_pivot_df = diffex_pivot_df.rename(columns=({'index': 'GeneID'}))\n",
    "diffex_pivot_df.GeneID = diffex_pivot_df.GeneID.astype(int)\n",
    "\n",
    "# save dataframe\n",
    "with gzip.open('data/diffex.tsv.gz', 'wt') as write_file:\n",
    "    diffex_pivot_df.to_csv(write_file, sep='\\t', index=False)\n",
    "\n",
    "# see tail\n",
    "diffex_pivot_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16184"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coding genes in entrez dataset\n",
    "len(set(diffex_pivot_df.GeneID) & coding_genes)"
   ]
  }
 ],
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