integrating DESeq2 with Pandas(3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing R/Bioconductor DESeq2 on Python "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This program requires you to have both latest R and python installed on your computer. I am running R on python because I have observed speedier execution this way versus working on RStudio markdown. Make sure that R environment has DESeq2, edgeR, limma, Biobase, dply, ggplot2 and other libaries. Also have modules such as panda, matplotlib, seaborn, rpy2 and others that are shown below installed prior to you work on python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install bioinfokit\n",
    "#!pip install rpy2\n",
    "#!pip install ipyparallel  \n",
    "#!conda install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import the python modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### importing rpy2 objects in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2\n",
    "import rpy2.robjects as robjects\n",
    "import rpy2.robjects.packages as rpackages\n",
    "from rpy2.robjects.vectors import StrVector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from rpy2.robjects.packages import importr\n",
    "# import R's \"base\" package\n",
    "base = importr('base')\n",
    "\n",
    "# import R's \"utils\" package\n",
    "utils = importr('utils')\n",
    "\n",
    "# import rpy2's package module\n",
    "import rpy2.robjects.packages as rpackages\n",
    "\n",
    "# import R's utility package\n",
    "utils = rpackages.importr('utils')\n",
    "\n",
    "# select a mirror for R packages\n",
    "utils.chooseCRANmirror(ind=1) # select the first mirror in the list\n",
    "#%Rdevice png\n",
    "%load_ext rpy2.ipython\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us now load the R libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "    library(DESeq2)\n",
    "    library(edgeR)\n",
    "    library(limma)\n",
    "    library(Biobase)\n",
    "    library(dplyr)\n",
    "    library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now you have to provide the file path where the data is loaded. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExpressionSet (storageMode: lockedEnvironment)\n",
      "assayData: 52580 features, 6 samples \n",
      "  element names: exprs \n",
      "protocolData: none\n",
      "phenoData\n",
      "  sampleNames: SRX014818and9 SRX014820and1 ... SRX014828and9 (6 total)\n",
      "  varLabels: sample.id num.tech.reps gender\n",
      "  varMetadata: labelDescription\n",
      "featureData\n",
      "  featureNames: ENSG00000000003 ENSG00000000005 ... LRG_99 (52580\n",
      "    total)\n",
      "  fvarLabels: gene\n",
      "  fvarMetadata: labelDescription\n",
      "experimentData: use 'experimentData(object)'\n",
      "Annotation:  \n"
     ]
    }
   ],
   "source": [
    "%%R       \n",
    "        load(\"/home/srivastava/Documents/HTSeq_Example/gilad_eset.RData\")\n",
    "        data <- gilad.eset\n",
    "        head(data)\n",
    "        print(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### working with metadata and cpm matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "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>sample.id</th>\n",
       "      <th>num.tech.reps</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRX014818and9</th>\n",
       "      <td>SRX014818and9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRX014820and1</th>\n",
       "      <td>SRX014820and1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRX014822and3</th>\n",
       "      <td>SRX014822and3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRX014824and5</th>\n",
       "      <td>SRX014824and5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRX014826and7</th>\n",
       "      <td>SRX014826and7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRX014828and9</th>\n",
       "      <td>SRX014828and9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample.id  num.tech.reps gender\n",
       "SRX014818and9  SRX014818and9            2.0      F\n",
       "SRX014820and1  SRX014820and1            2.0      F\n",
       "SRX014822and3  SRX014822and3            2.0      F\n",
       "SRX014824and5  SRX014824and5            2.0      M\n",
       "SRX014826and7  SRX014826and7            2.0      M\n",
       "SRX014828and9  SRX014828and9            2.0      M"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R pData(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "        cpm.mat <- log(cpm(exprs(data)))\n",
    "        mean.vec <- apply(cpm.mat, 1, mean)\n",
    "        sdvec <- apply(cpm.mat, 1, sd)\n",
    "\n",
    "#%R ('cpm.mat %>% head()')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we would make a sd vs average logCPM plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R plot(mean.vec, sdvec, pch =\".\", ylab=\"sd\", xlab=\"Average logCPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us now make a dispersion plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: estimating size factors\n",
      "\n",
      "R[write to console]: estimating dispersions\n",
      "\n",
      "R[write to console]: gene-wise dispersion estimates\n",
      "\n",
      "R[write to console]: mean-dispersion relationship\n",
      "\n",
      "R[write to console]: final dispersion estimates\n",
      "\n",
      "R[write to console]: fitting model and testing\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R  \n",
    "    colnames(data)\n",
    "    dds <- DESeqDataSetFromMatrix(countData = exprs(data), colData = pData(data), design = ~gender)\n",
    "    dds <- DESeq(dds)\n",
    "    plotDispEsts(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we would make a datafame from the dds object and export it to the pyhton "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: DESeqDataSet \n",
      "dim: 52580 6 \n",
      "metadata(1): version\n",
      "assays(4): counts mu H cooks\n",
      "rownames(52580): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99\n",
      "rowData names(22): baseMean baseVar ... deviance maxCooks\n",
      "colnames(6): SRX014818and9 SRX014820and1 ... SRX014826and7\n",
      "  SRX014828and9\n",
      "colData names(4): sample.id num.tech.reps gender sizeFactor\n",
      "\n",
      "log2 fold change (MLE): gender M vs F \n",
      "Wald test p-value: gender M vs F \n",
      "DataFrame with 52580 rows and 6 columns\n",
      "                 baseMean log2FoldChange     lfcSE       stat    pvalue\n",
      "                <numeric>      <numeric> <numeric>  <numeric> <numeric>\n",
      "ENSG00000000003  38.48617      -0.636080  0.633874 -1.0034813  0.315629\n",
      "ENSG00000000005   0.00000             NA        NA         NA        NA\n",
      "ENSG00000000419  16.53149      -0.045982  0.609659 -0.0754225  0.939879\n",
      "ENSG00000000457  26.40137       0.171818  0.476003  0.3609608  0.718129\n",
      "ENSG00000000460   1.95259       0.805282  1.602610  0.5024813  0.615329\n",
      "...                   ...            ...       ...        ...       ...\n",
      "LRG_94                  0             NA        NA         NA        NA\n",
      "LRG_96                  0             NA        NA         NA        NA\n",
      "LRG_97                  0             NA        NA         NA        NA\n",
      "LRG_98                  0             NA        NA         NA        NA\n",
      "LRG_99                  0             NA        NA         NA        NA\n",
      "                     padj\n",
      "                <numeric>\n",
      "ENSG00000000003  0.999979\n",
      "ENSG00000000005        NA\n",
      "ENSG00000000419  0.999979\n",
      "ENSG00000000457  0.999979\n",
      "ENSG00000000460  0.999979\n",
      "...                   ...\n",
      "LRG_94                 NA\n",
      "LRG_96                 NA\n",
      "LRG_97                 NA\n",
      "LRG_98                 NA\n",
      "LRG_99                 NA\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%R -o dds\n",
    "dds\n",
    "print(dds)\n",
    "%R ddsDF <- results(dds)\n",
    "%R -o ddsDF\n",
    "print(ddsDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R resultDds <- results(dds)\n",
    "%R resultDds <- as.data.frame(resultDds)\n",
    "%R -o resultDds\n",
    "type(resultDds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <td>38.486166</td>\n",
       "      <td>-0.636080</td>\n",
       "      <td>0.633874</td>\n",
       "      <td>-1.003481</td>\n",
       "      <td>0.315629</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000005</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <td>16.531493</td>\n",
       "      <td>-0.045982</td>\n",
       "      <td>0.609659</td>\n",
       "      <td>-0.075422</td>\n",
       "      <td>0.939879</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>26.401368</td>\n",
       "      <td>0.171818</td>\n",
       "      <td>0.476003</td>\n",
       "      <td>0.360961</td>\n",
       "      <td>0.718129</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>1.952594</td>\n",
       "      <td>0.805282</td>\n",
       "      <td>1.602610</td>\n",
       "      <td>0.502481</td>\n",
       "      <td>0.615329</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000000003  38.486166       -0.636080  0.633874 -1.003481  0.315629   \n",
       "ENSG00000000005   0.000000             NaN       NaN       NaN       NaN   \n",
       "ENSG00000000419  16.531493       -0.045982  0.609659 -0.075422  0.939879   \n",
       "ENSG00000000457  26.401368        0.171818  0.476003  0.360961  0.718129   \n",
       "ENSG00000000460   1.952594        0.805282  1.602610  0.502481  0.615329   \n",
       "\n",
       "                     padj  \n",
       "ENSG00000000003  0.999979  \n",
       "ENSG00000000005       NaN  \n",
       "ENSG00000000419  0.999979  \n",
       "ENSG00000000457  0.999979  \n",
       "ENSG00000000460  0.999979  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us remove any duplicate rown just in case..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <td>38.486166</td>\n",
       "      <td>-0.636080</td>\n",
       "      <td>0.633874</td>\n",
       "      <td>-1.003481</td>\n",
       "      <td>0.315629</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000005</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <td>16.531493</td>\n",
       "      <td>-0.045982</td>\n",
       "      <td>0.609659</td>\n",
       "      <td>-0.075422</td>\n",
       "      <td>0.939879</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>26.401368</td>\n",
       "      <td>0.171818</td>\n",
       "      <td>0.476003</td>\n",
       "      <td>0.360961</td>\n",
       "      <td>0.718129</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>1.952594</td>\n",
       "      <td>0.805282</td>\n",
       "      <td>1.602610</td>\n",
       "      <td>0.502481</td>\n",
       "      <td>0.615329</td>\n",
       "      <td>0.999979</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>ENSG00000254122</th>\n",
       "      <td>13.566756</td>\n",
       "      <td>-0.624627</td>\n",
       "      <td>0.620136</td>\n",
       "      <td>-1.007241</td>\n",
       "      <td>0.313819</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000254128</th>\n",
       "      <td>1.354613</td>\n",
       "      <td>1.545178</td>\n",
       "      <td>1.897659</td>\n",
       "      <td>0.814255</td>\n",
       "      <td>0.415499</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000254206</th>\n",
       "      <td>4.289691</td>\n",
       "      <td>1.362863</td>\n",
       "      <td>1.013185</td>\n",
       "      <td>1.345128</td>\n",
       "      <td>0.178584</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000254221</th>\n",
       "      <td>2.276256</td>\n",
       "      <td>0.296591</td>\n",
       "      <td>1.427451</td>\n",
       "      <td>0.207777</td>\n",
       "      <td>0.835403</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000254245</th>\n",
       "      <td>3.736322</td>\n",
       "      <td>-0.029290</td>\n",
       "      <td>1.045237</td>\n",
       "      <td>-0.028022</td>\n",
       "      <td>0.977645</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8853 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000000003  38.486166       -0.636080  0.633874 -1.003481  0.315629   \n",
       "ENSG00000000005   0.000000             NaN       NaN       NaN       NaN   \n",
       "ENSG00000000419  16.531493       -0.045982  0.609659 -0.075422  0.939879   \n",
       "ENSG00000000457  26.401368        0.171818  0.476003  0.360961  0.718129   \n",
       "ENSG00000000460   1.952594        0.805282  1.602610  0.502481  0.615329   \n",
       "...                    ...             ...       ...       ...       ...   \n",
       "ENSG00000254122  13.566756       -0.624627  0.620136 -1.007241  0.313819   \n",
       "ENSG00000254128   1.354613        1.545178  1.897659  0.814255  0.415499   \n",
       "ENSG00000254206   4.289691        1.362863  1.013185  1.345128  0.178584   \n",
       "ENSG00000254221   2.276256        0.296591  1.427451  0.207777  0.835403   \n",
       "ENSG00000254245   3.736322       -0.029290  1.045237 -0.028022  0.977645   \n",
       "\n",
       "                     padj  \n",
       "ENSG00000000003  0.999979  \n",
       "ENSG00000000005       NaN  \n",
       "ENSG00000000419  0.999979  \n",
       "ENSG00000000457  0.999979  \n",
       "ENSG00000000460  0.999979  \n",
       "...                   ...  \n",
       "ENSG00000254122  0.999979  \n",
       "ENSG00000254128  0.999979  \n",
       "ENSG00000254206  0.999979  \n",
       "ENSG00000254221  0.999979  \n",
       "ENSG00000254245  0.999979  \n",
       "\n",
       "[8853 rows x 6 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us select the rows with p value less than 0.05\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENSG00000000003    False\n",
       "ENSG00000000005    False\n",
       "ENSG00000000419    False\n",
       "ENSG00000000457    False\n",
       "ENSG00000000460    False\n",
       "                   ...  \n",
       "LRG_94             False\n",
       "LRG_96             False\n",
       "LRG_97             False\n",
       "LRG_98             False\n",
       "LRG_99             False\n",
       "Name: pvalue, Length: 52580, dtype: bool"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(resultDds['pvalue'] <= 0.05) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ... and those with positive log fold change\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENSG00000000003    False\n",
       "ENSG00000000005    False\n",
       "ENSG00000000419    False\n",
       "ENSG00000000457     True\n",
       "ENSG00000000460     True\n",
       "                   ...  \n",
       "LRG_94             False\n",
       "LRG_96             False\n",
       "LRG_97             False\n",
       "LRG_98             False\n",
       "LRG_99             False\n",
       "Name: log2FoldChange, Length: 52580, dtype: bool"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(resultDds['log2FoldChange'] >= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create a data frame with p value less than 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultDds05 = resultDds[resultDds['pvalue'] <= 0.05]\n",
    "#(resultDds['log2FoldChange'] >= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### select rows with p value less than 0.05 and log fold change more than 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000129824</th>\n",
       "      <td>35.864006</td>\n",
       "      <td>6.103900</td>\n",
       "      <td>3.085011</td>\n",
       "      <td>1.978567</td>\n",
       "      <td>0.047865</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000138131</th>\n",
       "      <td>365.293870</td>\n",
       "      <td>5.045004</td>\n",
       "      <td>1.393825</td>\n",
       "      <td>3.619540</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.388019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000129824   35.864006        6.103900  3.085011  1.978567  0.047865   \n",
       "ENSG00000138131  365.293870        5.045004  1.393825  3.619540  0.000295   \n",
       "\n",
       "                     padj  \n",
       "ENSG00000129824  0.999979  \n",
       "ENSG00000138131  0.388019  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05[resultDds05['log2FoldChange'] >= 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### select rows with p value less than 0.05 and log fold change in negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <span>StrVector with 279 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            ...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "          </tr>\n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<rpy2.robjects.vectors.StrVector object at 0x7fe65894af40> [RTYPES.STRSXP]\n",
       "R classes: ('character',)\n",
       "['ENSG0000..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000..., ..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000...]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geneID = resultDds05.index.tolist()\n",
    "%R -i geneID \n",
    "%R gene.list <- as.character(geneID)\n",
    "%R gene.list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame with 279 rows and 5 columns\n",
      "              query         _id X_version      symbol  notfound\n",
      "        <character> <character> <integer> <character> <logical>\n",
      "1   ENSG00000002834        3927         5       LASP1        NA\n",
      "2   ENSG00000003249       79007         3      DBNDD1        NA\n",
      "3   ENSG00000006210        6376        10      CX3CL1        NA\n",
      "4   ENSG00000008323       55200         2     PLEKHG6        NA\n",
      "5   ENSG00000008517        9235         5        IL32        NA\n",
      "...             ...         ...       ...         ...       ...\n",
      "275 ENSG00000215256       55449         1   DHRS4-AS1        NA\n",
      "276 ENSG00000216588      147710         1      IGSF23        NA\n",
      "277 ENSG00000242220      140290         4      TCP10L        NA\n",
      "278 ENSG00000242574        3109         2     HLA-DMB        NA\n",
      "279 ENSG00000244734        3043         6         HBB        NA\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "    library(mygene)\n",
    "    geneName <- getGenes(gene.list, fields='symbol')\n",
    "    geneName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000012124</th>\n",
       "      <td>11.487811</td>\n",
       "      <td>-1.723722</td>\n",
       "      <td>0.844196</td>\n",
       "      <td>-2.041850</td>\n",
       "      <td>0.041166</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000013588</th>\n",
       "      <td>19.013981</td>\n",
       "      <td>-1.724493</td>\n",
       "      <td>0.866386</td>\n",
       "      <td>-1.990444</td>\n",
       "      <td>0.046542</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000019549</th>\n",
       "      <td>20.535126</td>\n",
       "      <td>-1.797608</td>\n",
       "      <td>0.616859</td>\n",
       "      <td>-2.914131</td>\n",
       "      <td>0.003567</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000024422</th>\n",
       "      <td>61.266958</td>\n",
       "      <td>-1.464572</td>\n",
       "      <td>0.517943</td>\n",
       "      <td>-2.827669</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000044574</th>\n",
       "      <td>600.087194</td>\n",
       "      <td>-1.059411</td>\n",
       "      <td>0.376333</td>\n",
       "      <td>-2.815092</td>\n",
       "      <td>0.004876</td>\n",
       "      <td>0.999979</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>ENSG00000198805</th>\n",
       "      <td>361.350697</td>\n",
       "      <td>-0.665147</td>\n",
       "      <td>0.339123</td>\n",
       "      <td>-1.961373</td>\n",
       "      <td>0.049836</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198899</th>\n",
       "      <td>3085.649983</td>\n",
       "      <td>-1.426550</td>\n",
       "      <td>0.613479</td>\n",
       "      <td>-2.325346</td>\n",
       "      <td>0.020053</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000205038</th>\n",
       "      <td>8.542856</td>\n",
       "      <td>-2.005153</td>\n",
       "      <td>0.905479</td>\n",
       "      <td>-2.214465</td>\n",
       "      <td>0.026797</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215256</th>\n",
       "      <td>47.778264</td>\n",
       "      <td>-1.224522</td>\n",
       "      <td>0.518542</td>\n",
       "      <td>-2.361473</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000244734</th>\n",
       "      <td>94.976908</td>\n",
       "      <td>-2.935069</td>\n",
       "      <td>0.704277</td>\n",
       "      <td>-4.167490</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.080981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000012124    11.487811       -1.723722  0.844196 -2.041850  0.041166   \n",
       "ENSG00000013588    19.013981       -1.724493  0.866386 -1.990444  0.046542   \n",
       "ENSG00000019549    20.535126       -1.797608  0.616859 -2.914131  0.003567   \n",
       "ENSG00000024422    61.266958       -1.464572  0.517943 -2.827669  0.004689   \n",
       "ENSG00000044574   600.087194       -1.059411  0.376333 -2.815092  0.004876   \n",
       "...                      ...             ...       ...       ...       ...   \n",
       "ENSG00000198805   361.350697       -0.665147  0.339123 -1.961373  0.049836   \n",
       "ENSG00000198899  3085.649983       -1.426550  0.613479 -2.325346  0.020053   \n",
       "ENSG00000205038     8.542856       -2.005153  0.905479 -2.214465  0.026797   \n",
       "ENSG00000215256    47.778264       -1.224522  0.518542 -2.361473  0.018203   \n",
       "ENSG00000244734    94.976908       -2.935069  0.704277 -4.167490  0.000031   \n",
       "\n",
       "                     padj  \n",
       "ENSG00000012124  0.999979  \n",
       "ENSG00000013588  0.999979  \n",
       "ENSG00000019549  0.999979  \n",
       "ENSG00000024422  0.999979  \n",
       "ENSG00000044574  0.999979  \n",
       "...                   ...  \n",
       "ENSG00000198805  0.999979  \n",
       "ENSG00000198899  0.999979  \n",
       "ENSG00000205038  0.999979  \n",
       "ENSG00000215256  0.999979  \n",
       "ENSG00000244734  0.080981  \n",
       "\n",
       "[173 rows x 6 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05[resultDds05['log2FoldChange'] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [1] \"LASP1\"     \"DBNDD1\"    \"CX3CL1\"    \"PLEKHG6\"   \"IL32\"      \"CD22\"     \n",
      "  [7] \"GPRC5A\"    \"SNAI2\"     \"ALAS1\"     \"EHD2\"      \"HSPA5\"     \"H6PD\"     \n",
      " [13] \"PLEKHH1\"   \"TMCC3\"     \"CREB3L3\"   \"PFN2\"      \"CDH19\"     \"IRAG1\"    \n",
      " [19] \"MKRN2\"     \"MCAM\"      \"FDFT1\"     \"PTPRH\"     \"HSP90AA1\"  \"IRAK3\"    \n",
      " [25] \"EFNB1\"     \"RFFL\"      \"BAMBI\"     \"TREM2\"     \"EFNA2\"     NA         \n",
      " [31] \"CDHR5\"     \"OSM\"       \"MMP9\"      \"OXT\"       \"MYOM1\"     \"CHRDL1\"   \n",
      " [37] \"SRPX\"      \"F9\"        \"FLT1\"      \"NECAB2\"    \"CRISPLD2\"  \"MEFV\"     \n",
      " [43] \"TOX3\"      \"MAZ\"       \"RASL12\"    \"RETN\"      \"CLEC4M\"    \"PLA2G4C\"  \n",
      " [49] \"JAK3\"      \"CLDN15\"    \"PSMA2\"     \"OGN\"       \"ASPN\"      \"MINPP1\"   \n",
      " [55] \"ACTA2\"     \"TNFAIP1\"   \"CRYAB\"     \"APOA4\"     \"RNF141\"    \"POU2AF1\"  \n",
      " [61] \"MGP\"       \"IFNG\"      \"ENO2\"      \"LDHB\"      \"VNN2\"      \"ACVR2B\"   \n",
      " [67] \"IGFBP5\"    \"GNLY\"      \"MMP8\"      \"TCF21\"     \"FGF23\"     \"ZC2HC1C\"  \n",
      " [73] \"PPP1R3C\"   \"GNA13\"     \"TMPO\"      \"HS3ST2\"    \"SRGN\"      \"ADGRE5\"   \n",
      " [79] \"LRP1\"      \"NFE2\"      \"FAM124B\"   \"KCNS1\"     \"UNC5CL\"    \"TREM1\"    \n",
      " [85] \"ARMC7\"     \"TNFSF9\"    \"TNFSF14\"   \"TICAM1\"    \"METTL16\"   \"FGL2\"     \n",
      " [91] \"DLL4\"      \"CDO1\"      \"RPS4Y1\"    \"SHC2\"      \"ANGPTL8\"   \"CNN1\"     \n",
      " [97] \"GADD45G\"   \"IQCN\"      \"GMFG\"      \"DNAJB1\"    \"RAMP1\"     \"MTSS2\"    \n",
      "[103] \"IGHMBP2\"   \"MYH11\"     \"LYVE1\"     \"EMP1\"      \"APLNR\"     \"NAA35\"    \n",
      "[109] \"DTX1\"      \"AVIL\"      \"SPRY2\"     \"EDNRB\"     \"CHAD\"      \"GATA4\"    \n",
      "[115] \"RPL35\"     \"MYC\"       \"ABCG5\"     \"LOXL4\"     \"CH25H\"     \"OIT3\"     \n",
      "[121] \"MMRN1\"     \"CXCL9\"     \"CLSTN3\"    \"CBLN3\"     \"RAB15\"     \"BCL2A1\"   \n",
      "[127] \"TSPAN3\"    \"CYP1A1\"    \"ZNF710\"    \"OSGIN1\"    \"GAS8\"      \"SCRN2\"    \n",
      "[133] \"ZMYND15\"   \"SLC16A3\"   \"CCN1\"      \"S100A8\"    \"FEM1C\"     \"SLC17A4\"  \n",
      "[139] \"PLA2G7\"    \"ENDOD1\"    \"SERPINH1\"  \"ESAM\"      \"TAGLN\"     \"TM7SF2\"   \n",
      "[145] \"PRSS23\"    \"UBC\"       \"GLT1D1\"    \"BMP6\"      \"KCTD15\"    \"GAL3ST2\"  \n",
      "[151] \"MS4A1\"     \"HPD\"       \"H2BC5\"     \"NBL1\"      \"STC1\"      \"ACTC1\"    \n",
      "[157] \"TFF2\"      \"S100B\"     \"SCGB3A1\"   \"NAGS\"      \"RPS6KA4\"   \"DHRS3\"    \n",
      "[163] \"ADGRL4\"    \"B3GALT2\"   \"TNFAIP8L2\" \"S100A11\"   \"PROK2\"     \"IGFBP7\"   \n",
      "[169] \"IHH\"       \"TGFBR2\"    \"RYBP\"      \"PPBP\"      \"S100P\"     \"LEAP2\"    \n",
      "[175] \"CITED2\"    \"KCNK5\"     \"MID1IP1\"   \"CLDN2\"     \"IFI27\"     \"CKB\"      \n",
      "[181] \"ZNF319\"    \"GABRB3\"    \"ANPEP\"     \"SLC27A4\"   \"C15orf39\"  \"TMIGD2\"   \n",
      "[187] \"PLIN4\"     \"IGFBP6\"    \"VWCE\"      NA          \"CAVIN2\"    \"MUCL3\"    \n",
      "[193] \"NMD3\"      \"ADRB2\"     \"CLIC4\"     \"MT1B\"      \"GP9\"       \"OTUD7A\"   \n",
      "[199] \"PDGFD\"     \"FPR1\"      \"FUT3\"      \"SYNPO\"     \"ID4\"       \"CES2\"     \n",
      "[205] \"MYEOV\"     \"SYT12\"     \"PPP1R3B\"   \"LRFN4\"     \"RCE1\"      \"ATP5MK\"   \n",
      "[211] \"MSRB3\"     \"TMEM167A\"  \"RESF1\"     \"FUT1\"      \"CA5A\"      \"DDIT3\"    \n",
      "[217] \"MLXIP\"     \"FIBIN\"     \"PNPLA2\"    \"STAP2\"     \"TMPRSS9\"   \"APOLD1\"   \n",
      "[223] \"KLHDC7A\"   NA          \"WDR97\"     \"H2AC6\"     \"SSTR2\"     \"FAM83H\"   \n",
      "[229] \"IZUMO1\"    \"ADGRG3\"    \"C19orf71\"  \"PCDH9\"     \"ALDH1A3\"   \"XPOT\"     \n",
      "[235] \"H2BC21\"    \"SV2B\"      \"SRL\"       \"IFIT1\"     \"CYP4F3\"    \"RTN4RL2\"  \n",
      "[241] \"GJA4\"      \"ISG15\"     \"TMSB4XP8\"  \"H1-2\"      NA          \"C15orf62\" \n",
      "[247] \"HES4\"      \"GJB3\"      \"PRTN3\"     \"MMP1\"      NA          \"HSH2D\"    \n",
      "[253] \"TRPV1\"     \"ZNF165\"    \"PIGN\"      \"S100A10\"   NA          \"GPAA1\"    \n",
      "[259] \"S100A6\"    \"TPM2\"      \"SSBP3-AS1\" \"COX2\"      \"SLC5A3\"    \"COX1\"     \n",
      "[265] \"PNP\"       \"ATP6\"      \"SPIRE2\"    \"PKHD1L1\"   \"CCDC85C\"   \"DPP9-AS1\" \n",
      "[271] \"LINGO4\"    \"CEACAM16\"  \"TSPAN4\"    \"PLIN5\"     \"DHRS4-AS1\" \"IGSF23\"   \n",
      "[277] \"TCP10L\"    \"HLA-DMB\"   \"HBB\"      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%R listGeneName <- geneName$symbol\n",
    "%R -o listGeneName\n",
    "print(listGeneName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-174-2c4f64a33c02>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  resultDds05['GeneName'] = listGeneName\n"
     ]
    },
    {
     "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>GeneName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000002834</th>\n",
       "      <td>36.797449</td>\n",
       "      <td>0.963482</td>\n",
       "      <td>0.470134</td>\n",
       "      <td>2.049375</td>\n",
       "      <td>0.040425</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>LASP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000003249</th>\n",
       "      <td>65.893079</td>\n",
       "      <td>2.988309</td>\n",
       "      <td>0.744210</td>\n",
       "      <td>4.015411</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.124832</td>\n",
       "      <td>DBNDD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000006210</th>\n",
       "      <td>195.905358</td>\n",
       "      <td>0.962956</td>\n",
       "      <td>0.359309</td>\n",
       "      <td>2.680021</td>\n",
       "      <td>0.007362</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>CX3CL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000008323</th>\n",
       "      <td>14.800023</td>\n",
       "      <td>1.280345</td>\n",
       "      <td>0.605227</td>\n",
       "      <td>2.115480</td>\n",
       "      <td>0.034389</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>PLEKHG6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000008517</th>\n",
       "      <td>273.532925</td>\n",
       "      <td>1.508017</td>\n",
       "      <td>0.666956</td>\n",
       "      <td>2.261044</td>\n",
       "      <td>0.023757</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>IL32</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215256</th>\n",
       "      <td>47.778264</td>\n",
       "      <td>-1.224522</td>\n",
       "      <td>0.518542</td>\n",
       "      <td>-2.361473</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>DHRS4-AS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000216588</th>\n",
       "      <td>23.506956</td>\n",
       "      <td>1.712557</td>\n",
       "      <td>0.676110</td>\n",
       "      <td>2.532956</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>IGSF23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242220</th>\n",
       "      <td>6.314970</td>\n",
       "      <td>2.081923</td>\n",
       "      <td>1.000790</td>\n",
       "      <td>2.080281</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>TCP10L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242574</th>\n",
       "      <td>31.791989</td>\n",
       "      <td>1.221171</td>\n",
       "      <td>0.536003</td>\n",
       "      <td>2.278291</td>\n",
       "      <td>0.022709</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>HLA-DMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000244734</th>\n",
       "      <td>94.976908</td>\n",
       "      <td>-2.935069</td>\n",
       "      <td>0.704277</td>\n",
       "      <td>-4.167490</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.080981</td>\n",
       "      <td>HBB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000002834   36.797449        0.963482  0.470134  2.049375  0.040425   \n",
       "ENSG00000003249   65.893079        2.988309  0.744210  4.015411  0.000059   \n",
       "ENSG00000006210  195.905358        0.962956  0.359309  2.680021  0.007362   \n",
       "ENSG00000008323   14.800023        1.280345  0.605227  2.115480  0.034389   \n",
       "ENSG00000008517  273.532925        1.508017  0.666956  2.261044  0.023757   \n",
       "...                     ...             ...       ...       ...       ...   \n",
       "ENSG00000215256   47.778264       -1.224522  0.518542 -2.361473  0.018203   \n",
       "ENSG00000216588   23.506956        1.712557  0.676110  2.532956  0.011311   \n",
       "ENSG00000242220    6.314970        2.081923  1.000790  2.080281  0.037500   \n",
       "ENSG00000242574   31.791989        1.221171  0.536003  2.278291  0.022709   \n",
       "ENSG00000244734   94.976908       -2.935069  0.704277 -4.167490  0.000031   \n",
       "\n",
       "                     padj   GeneName  \n",
       "ENSG00000002834  0.999979      LASP1  \n",
       "ENSG00000003249  0.124832     DBNDD1  \n",
       "ENSG00000006210  0.999979     CX3CL1  \n",
       "ENSG00000008323  0.999979    PLEKHG6  \n",
       "ENSG00000008517  0.999979       IL32  \n",
       "...                   ...        ...  \n",
       "ENSG00000215256  0.999979  DHRS4-AS1  \n",
       "ENSG00000216588  0.999979     IGSF23  \n",
       "ENSG00000242220  0.999979     TCP10L  \n",
       "ENSG00000242574  0.999979    HLA-DMB  \n",
       "ENSG00000244734  0.080981        HBB  \n",
       "\n",
       "[279 rows x 7 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05['GeneName'] = listGeneName\n",
    "resultDds05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENSG00000012124         CD22\n",
       "ENSG00000013588       GPRC5A\n",
       "ENSG00000019549        SNAI2\n",
       "ENSG00000024422         EHD2\n",
       "ENSG00000044574        HSPA5\n",
       "                     ...    \n",
       "ENSG00000198805          PNP\n",
       "ENSG00000198899         ATP6\n",
       "ENSG00000205038      PKHD1L1\n",
       "ENSG00000215256    DHRS4-AS1\n",
       "ENSG00000244734          HBB\n",
       "Name: GeneName, Length: 173, dtype: object"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listNegativeExpressedGenes= resultDds05[resultDds05['log2FoldChange'] <= 0]['GeneName']\n",
    "listNegativeExpressedGenes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseMean            365.294\n",
       "log2FoldChange       6.1039\n",
       "lfcSE               3.08501\n",
       "stat                3.61954\n",
       "pvalue            0.0478648\n",
       "padj               0.999979\n",
       "GeneName             RPS4Y1\n",
       "dtype: object"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05[resultDds05['log2FoldChange'] >= 5].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseMean          float64\n",
       "log2FoldChange    float64\n",
       "lfcSE             float64\n",
       "stat              float64\n",
       "pvalue            float64\n",
       "padj              float64\n",
       "GeneName           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05[resultDds05['log2FoldChange'] >= 5].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make volcano plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bioinfokit import analys, visuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = resultDds05\n",
    "visuz.gene_exp.volcano(df=df, lfc='log2FoldChange', pv='pvalue', theme='dark', color=(\"red\", \"grey\", \"green\"), markerdot='*', dotsize=25, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we would use an R library called mygene to check convert geneid into the known gene names. Often time gene names are not available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>GeneName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000002834</th>\n",
       "      <td>36.797449</td>\n",
       "      <td>0.963482</td>\n",
       "      <td>0.470134</td>\n",
       "      <td>2.049375</td>\n",
       "      <td>0.040425</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>LASP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000003249</th>\n",
       "      <td>65.893079</td>\n",
       "      <td>2.988309</td>\n",
       "      <td>0.744210</td>\n",
       "      <td>4.015411</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.124832</td>\n",
       "      <td>DBNDD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000006210</th>\n",
       "      <td>195.905358</td>\n",
       "      <td>0.962956</td>\n",
       "      <td>0.359309</td>\n",
       "      <td>2.680021</td>\n",
       "      <td>0.007362</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>CX3CL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000008323</th>\n",
       "      <td>14.800023</td>\n",
       "      <td>1.280345</td>\n",
       "      <td>0.605227</td>\n",
       "      <td>2.115480</td>\n",
       "      <td>0.034389</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>PLEKHG6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000008517</th>\n",
       "      <td>273.532925</td>\n",
       "      <td>1.508017</td>\n",
       "      <td>0.666956</td>\n",
       "      <td>2.261044</td>\n",
       "      <td>0.023757</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>IL32</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000214063</th>\n",
       "      <td>114.527296</td>\n",
       "      <td>0.779046</td>\n",
       "      <td>0.389764</td>\n",
       "      <td>1.998765</td>\n",
       "      <td>0.045634</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>TSPAN4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000214456</th>\n",
       "      <td>173.946616</td>\n",
       "      <td>1.060871</td>\n",
       "      <td>0.380995</td>\n",
       "      <td>2.784473</td>\n",
       "      <td>0.005361</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>PLIN5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000216588</th>\n",
       "      <td>23.506956</td>\n",
       "      <td>1.712557</td>\n",
       "      <td>0.676110</td>\n",
       "      <td>2.532956</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>IGSF23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242220</th>\n",
       "      <td>6.314970</td>\n",
       "      <td>2.081923</td>\n",
       "      <td>1.000790</td>\n",
       "      <td>2.080281</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>TCP10L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242574</th>\n",
       "      <td>31.791989</td>\n",
       "      <td>1.221171</td>\n",
       "      <td>0.536003</td>\n",
       "      <td>2.278291</td>\n",
       "      <td>0.022709</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>HLA-DMB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "ENSG00000002834   36.797449        0.963482  0.470134  2.049375  0.040425   \n",
       "ENSG00000003249   65.893079        2.988309  0.744210  4.015411  0.000059   \n",
       "ENSG00000006210  195.905358        0.962956  0.359309  2.680021  0.007362   \n",
       "ENSG00000008323   14.800023        1.280345  0.605227  2.115480  0.034389   \n",
       "ENSG00000008517  273.532925        1.508017  0.666956  2.261044  0.023757   \n",
       "...                     ...             ...       ...       ...       ...   \n",
       "ENSG00000214063  114.527296        0.779046  0.389764  1.998765  0.045634   \n",
       "ENSG00000214456  173.946616        1.060871  0.380995  2.784473  0.005361   \n",
       "ENSG00000216588   23.506956        1.712557  0.676110  2.532956  0.011311   \n",
       "ENSG00000242220    6.314970        2.081923  1.000790  2.080281  0.037500   \n",
       "ENSG00000242574   31.791989        1.221171  0.536003  2.278291  0.022709   \n",
       "\n",
       "                     padj GeneName  \n",
       "ENSG00000002834  0.999979    LASP1  \n",
       "ENSG00000003249  0.124832   DBNDD1  \n",
       "ENSG00000006210  0.999979   CX3CL1  \n",
       "ENSG00000008323  0.999979  PLEKHG6  \n",
       "ENSG00000008517  0.999979     IL32  \n",
       "...                   ...      ...  \n",
       "ENSG00000214063  0.999979   TSPAN4  \n",
       "ENSG00000214456  0.999979    PLIN5  \n",
       "ENSG00000216588  0.999979   IGSF23  \n",
       "ENSG00000242220  0.999979   TCP10L  \n",
       "ENSG00000242574  0.999979  HLA-DMB  \n",
       "\n",
       "[106 rows x 7 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultDds05[resultDds05['log2FoldChange'] >= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us make the volcano plot to compare p value to the fold change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HFvhBbG9vz4gRI9Dr9Va794P3VBQFo9EImJJyYd2aDyt/lBUrVnDw4EHatm3L2rVr8fPz49ChQw+9/sH6C+syflTsJeHs7PzI8yNHjuTo0aMsWLCAAwcOEBUVhZeXF1lZWVaNSwhR/khCLIWrV6+SnZ2dryw7O5urV6/aKCJTa/Dy5ctcuXLFXHb69GlSUlLMXZ4ODg7k5uYWqb6mTZvy5ptvcuDAARo1asS3335b4Bq9Xk/16tU5fPiwuSw3N5fIyMhSfpo/16RJE/bt21fg3yHPvn37mDx5Mj179iQwMBBHR8cC3a5/xsHBAaDIXzMhRPkkCbEUzpw5A8DTTz/NpEmT8Pf3z1duC926daNJkyYMGzaMY8eOcfjwYYYPH06nTp1o0aIFAHXr1iUuLo6oqCiSkpLIzMwsUE9cXBxvvvkmBw8e5NKlS2zfvp2zZ88+9DnipEmTeO+999i0aRNnzpxhypQpJCcnF7tVWlyvvPIKBoOB559/niNHjnDu3Dm+/vpr87/BU089xddff01MTAzh4eEMGzbsT1uVD6pTpw6KovDjjz/y+++/k5aWZo2PIoSwMUmIpeDv78+AAQMYPHgwVapUYciQIQwYMMCcGG0hb9qCp6cnHTt2pFu3bjz55JOsXbvWfM3AgQN59tln6dy5M9WqVWP16tUF6nFxcSE2NpaBAwfi5+fH2LFjeeWVVxg3blyh950xYwahoaEMHz6cNm3a4ObmRo8ePXBycrLaZwXw8vJi165dpKWl0alTJ5o3b86yZcvMXaBffvklycnJNG3alBdffJHJkyfj7e1drHvUrFmT2bNn849//IPq1avzyiuvWOOjCCFsTFHLy/IqxWQwGNDr9aSkpKDT6fKdy8jIIC4ujnr16ln9B3ZlZTQaCQgIYPDgwbzzzju2DqdMk+9HIazrUfngfjLtQlhEXrdqp06dyMzMZPHixcTFxTF06FBbhyaEEEUiXabCIjQaDStXrqRly5a0a9eOkydP8uuvvz5y7qIQQpQl0kIUFuHr68v+/fttHYYQQpSYtBCFEEIIJCEKIYQQgCREIYQQApCEKIQQQgCSEIUQQghAEqIQQggBSEKsUEaOHJlvh3pLKGyH+v3799O4cWPs7e3p168fYWFhKIrC7du3LXpva5k1axbBwcG2DkMIUcbIPEQLUFWVPZf20KlOJ6svZv0oCxcuLHTLpdLw9fUlISGBqlWrmsumTZtGcHAwW7duxc3NDRcXFxISEqy65dWDVq5cydSpU8tNEhZClH3SQrSAbee30XlVZ3658ItN49Dr9Rbf0V2r1Zp3rs9z4cIFunTpQq1atfDw8MDBwQEfHx+b/jIghBClJQnRAr47/V2+V6ve67vvaNy4Mc7Oznh5edGtWzfu3LkDFOwyTU1NZdiwYbi6ulKjRg0++eQTQkJCmDp1qvmaunXrMm/ePEaPHo27uzu1a9fm888/N5+/v8s07/3NmzcZPXo0iqKwcuXKQrtM9+/fT6dOnXBxccHT05MePXqQnJwMwLZt22jfvj0eHh54eXnRq1cvLly4UOCeGzZsoHPnzri4uBAUFMTBgwcBCAsLY9SoUaSkpKAoCoqiMGvWrId+zf71r39RvXp13N3dGTNmDBkZGfnOG41G5syZQ61atXB0dCQ4OJht27aZzw8cOJBJkyaZj6dOnYqiKERHRwOQk5ODu7s7v/xi+oUoJCSEyZMn88Ybb1ClShV8fHweGZ8QomyQhFgCRtXIksNLmLt3LnP3zuW7mHsJMa9syeElGFXL7riekJBAaGgoo0ePJiYmhrCwMAYMGPDQbtJp06axf/9+Nm/ezI4dO9i3bx/Hjh0rcN38+fNp0aIFkZGRTJw4kQkTJhAbG1vgurzuU51Ox4IFC0hISGDIkCEFrouKiqJr164EBgZy8OBBfvvtN3r37m3eYPfOnTtMmzaNiIgIdu7ciUajoX///gV2qH/rrbeYPn06UVFR+Pn5ERoaSk5ODm3btmXBggXodDoSEhJISEhg+vTphX4N1q1bx8yZM5k7dy5HjhyhRo0a/Pvf/853zcKFC5k/fz4fffQRJ06coEePHvTp04dz584BpgQXFhZmvn7Pnj1UrVqVPXv2ABAREUFGRgbt2rUzX7Nq1SpcXV0JDw/ngw8+YM6cOezYsaPQGIUQZYRaQaWkpKiAmpKSUuBcenq6evr0aTU9Pb1EdRsyDGqV96uozEJVZimqdrZWZRaqdrZWVWYpKrNQq7xfRTVkGEr7MfI5evSoCqjx8fGFnh8xYoTat29fU4wGg2pvb6+uX7/efP727duqi4uLOmXKFHNZnTp11BdeeMF8bDQaVW9vb3Xp0qWqqqpqXFycCqiRkZHma/R6vbpixQrz8e7du1VATU5OVlVVVUNDQ9V27doV+XMlJiaqgHry5Ml891y+fLn5mujoaBVQY2JiVFVV1RUrVqh6vf5P627Tpo06fvz4fGWtW7dWg4KCzMdPPPGEOnfu3HzXtGzZUp04caKqqqp64sQJVVEU9ffff1dv3bql2tvbq++++646aNAgVVVVdd68eWrr1q3Nf7dTp05q+/btC9Q3Y8aMQmMs7fejEOLRHpUP7ictxBJwd3QnclwkbWu1BSBXzc332ta3LVHjonB3dLfofYOCgujatSuNGzdm0KBBLFu2zNwN+aCLFy+SnZ1Nq1atzGV6vb7QzYubNGlifq8oCj4+PiQmJpY4zrwW4sNcuHCBoUOH8uSTT6LT6ahXrx4Aly9ffmhcNWrUACh2XDExMbRp0yZf2f3HBoOBa9eu5WvdAbRr146YmBgAGjVqhJeXF3v27GHfvn0EBQXRp08fcwsxLCyMTp06PTT2vPhL8zUVQlifJMQSqq2vze6Ru3Gxd8lX7mLvQtiIMHz1vha/p1arZceOHWzdupWGDRuyaNEi/P39iYuLK3Ct+kc36oMDXdRCulfzdpfPoyhKge7L4nB2dn7k+d69e3Pz5k2WLVtGeHg44eHhAGRlZT00rrzPUZq4HqWwr1NemaIodOzYkbCwMPbs2UNISAiNGjUiNzeXkydPcuDAAUJCQh4ae14d1opdCGEZkhBL4fDVw9zJvpOv7E72HQ5fPWy1eyqKQrt27Zg9ezaRkZE4ODiwcePGAtfVr18fe3t7Dh++F4vBYDA/F7OmJk2asHPnzkLP3bx5k5iYGN5++226du1KQEDAQ1u5j+Lg4GB+JvkoAQEBHDp0KF/Z/cc6nY4nnniC3377Ld81Bw4cyLeXY95zxLCwMEJCQlAUhQ4dOvDRRx+Rnp5eoIUphCh/JCGWwpYzWwDo93Q/zk86T1//vgBsPrPZKvcLDw9n3rx5HDlyhMuXL7NhwwZ+//33QjfhdXd3Z8SIEbz++uvs3r2b6OhoRo8ejUajsfr0iDfffJOIiAgmTpzIiRMniI2NZenSpSQlJeHp6YmXlxeff/4558+fZ9euXUybNq3Y96hbty5paWns3LmTpKQk7t69W+h1U6ZM4csvv+TLL7/k7NmzzJw50zw6NM/rr7/O+++/z9q1azlz5gz/+Mc/iIqKYsqUKeZrQkJCiI6O5uTJk3To0MFc9s0339CsWTN0Ol2xP4MQomyRifml0Me/D0E+QYQ2CkVRFDYO2cjqU6upo69jlfvpdDr27t3LggULMBgM1KlTh/nz5/PXv/610Os//vhjxo8fT69evdDpdLzxxhtcuXIFJycnq8SXx8/Pj+3bt/N///d/tGrVCmdnZ1q3bk1oaCgajYY1a9YwefJkGjVqhL+/P59++mmBLsc/07ZtW8aPH8+QIUO4efMmM2fOLHRqw5AhQ7hw4QIzZswgIyODgQMHMmHCBPMUCYDJkydjMBh47bXXSExMpGHDhmzevJkGDRqYr2nUqBFVq1alTp065uTXqVMncnNzCzw/FEKUT4pa2EOlCsBgMKDX60lJSSnw23tGRgZxcXHUq1fP6smhLLlz5w41a9Zk/vz5jBkzxtbhiD9U1u9HIR6XR+WD+0kLsQKLjIwkNjaWVq1akZKSwpw5cwDo27evjSMTQoiyRxJiBffRRx9x5swZHBwcaN68Ofv27cu3LqkQQggTSYgVWNOmTTl69KitwxBCiHKhzIwyzZuLlmfhwoW0bduWtm3b8vHHH//p38/MzMRgMOT7I4QQQhRVmUmI3bt3N7//+OOP+fjjj3nxxRcZPnw4ixYtYv78+Y/8+++99x56vd78x9f3zyfGV9DxRKKcke9DIcqGMjPK1N3dndTUVAAaN27M8uXLad26NQBHjhzhxRdfNC+lVZjMzEwyMzPNxwaDAV9f30JHFeXm5nL27Fm8vb3x8vKywqcRouhu3rxJYmIifn5+aLVaW4cjRIVT7kaZ3j9ZPCEhId8anC1atODq1auP/PuOjo44OjoW6V5arRYPDw/z2pIuLi6yl5947FRV5e7duyQmJuLh4SHJUAgbKzMJMTMzM9+KJbdu3TK33lJTU/NtUGsJPj4+QPEXixbC0jw8PMzfj0II2ykzCXHo0KHmNS179+7NlStXzAlx27ZtNG7c2KL3UxSFGjVq4O3tTXZ2tkXrFqKo7O3tpWUoRBlRZp4hWlpR+4yF9STdTcLTyROtRn7gCyFsp6j5oMyMMhUVS3p2Og0WNWDBoQW2DkUIIYpEEqKwil8u/MLtjNt8feJrW4cihBBFUmaeIYryLyE1gcHfDSY1M5XradcBOH7jOEH/CUJBYUTQCF5t86qNoxRCiMJJQhQW4+bgRnp2OsdvHM9XfuLGCew0dvi4yUhKIUTZJV2mwmLcHd05OOYgw4OG5yt3tXcl4uUIQhuH2igyIYT4c5IQhUXZa+2xU/J3PNzJvkM1l2o2ikgIIYpGEqKwuJ/O/YSrvSvLey+nR/0egGmQjRBClGXyDFFY3JKeS2j+RHPqetRlVNNRfHf6O9r6trV1WEII8UgyMV8IIUSFJhPzhRBCiGKQhCjKnT3xezh786ytwxBCVDCSEEW5kp2bTd81fZmydYqtQxFCVDAyqEaUC3ey7pCek85vl38jJTOFX+N+Jf52PG4ObugcdThoHWwdohCinJOEKMq8jJwMvD/y5m72XQA0ioYcYw71FtYDoGOdjuwZuceWIQohKgDpMhVlnpOdEx91/wh7jT0ARtVoPlfNpRpzQubYKjQhRAUiLURRLkxoOQGdo44XNr6Qrzzi5QjqeNSxUVRCiIpEWoii3Dh89TAAWuXehsMR1yJsFY4QooKRhCjKjYhrETxV5SmOjj3K+kHr0TnqOHLtiK3DEkJUELJSjSg3bqXfwtXeFUc7RwBSMlJw0DrgbO9s48iEEGVZUfOBPEMU5UYV5yr5jvVOehtFIoSoiKTLVAghhEASohBCCAFIQhRCCCEASYhCCCEEIAlRCCGEACQhCiGEEIAkRCGEEAKQhCiEEEIAkhCFEEIIQBKieEyMqpGJP00kNinW1qEIIUShJCGKx+LAlQMsPbKUJYeX2DoUIYQolKxlKqwmKzeLX87/QmZuJmtOrQFgbfRaOtXtBECbWm2oqatpyxCFEMJMEqKwmhM3TtBnTZ98Zb/f/Z1B6wcB8Hrb1/mg+we2CE0IIQqQLlNhNS2eaMH3g7/H3cE936a+AFNaT+Gdzu/YKDIhhChIEqKwqgEBAxjbfCy5aq45KdZwq8EnPT4x72sohBBlgSREYVWqqvLd6e8ACKgWgIJCQloCkdcjbRyZEELkJwlRWJWKit5Rz7wu84gaF8WekXto7N2YtKw0W4cmhBD5KKqqqrYOwhoMBgN6vZ6UlBR0Op2twxFCCGEjRc0H0kIUQgghkIQohBBCAJIQhRBCCEASohBCCAFIQhRCCCEASYhCCCEEIAlRCCGEACQhCiGEEIAkRCGEEAKQhCiEEEIAkhCFEEIIQBKiEEIIAUhCFEIIIQBJiEIIIQQgCVEIIYpm+3b4y1/AaLR1JMJKJCEKIURRLF4MO3bAwYO2jkRYiZ2tAxBCiDLrwAFTy1BVYds2U9k//wkdOoCLC7zyiulVVAiSEIUQ4mE2b4b33ze912pNr7t3w65d4OwMgwdD3bo2C09YlnSZCiHEw8ybB598AnZ2kJt7rzwwEI4dk2RYwUhCFEKIh9FoYNw4U0IEUBRT95h7y9kAACAASURBVGmvXvD007aNTVicJEQhhHiU7dshIwM8PaF/f1PZ2rW2jUlYhSREIYR4lOrV4cUXIToavv8eVq+Grl1tHZWwAkVVVdXWQViDwWBAr9eTkpKCTqezdThCCCFspKj5QFqIQgghBJIQhRBCCEASohBCCAFIQhRCCCGAEq5Uc/DgQTZv3kxUVBTJycl4enoSHBxMr169aNeunaVjFEIIIayuWAlx165dTJ8+nZSUFDp37kz37t3R6XQYDAZOnz7N8OHD0el0zJ8/ny5dulgrZiGEEMLiipUQ//Wvf7FgwQI6duz40Gv27dvHvHnzJCEKIYQoV0o9D/H69eu4ubnh5uZmqZgsQuYhCiGEgMcwDzE8PJw6depQu3ZtfHx8ALh69Sq3bt0qaZVCCCGEzZQ4IU6YMIG33nqLjIwM7O3tAYiLi2P48OEWC04IIYR4XEqcEOPi4hg7diwajQZFUQBo0aIFR44csVhwR44cIff+LVeEEEIIKylxQqxfvz5RUVEA5D2GdHJy4u7du5aJDOjTp490wQohhHgsSjQPEWDGjBkMGjSIr776ytxC3Lt3LzVq1Ch2XVWqVCm0PCUlBT8/PxRF+dPEmJmZSWZmpvnYYDAUOw4hhBCVV4kT4qBBg7h58ybPPvssaWlpdO/enYiICBYuXFjsuqpUqULt2rV58803cXBwAEytzoEDB7J8+fKHJsz7vffee8yePbvY9xZCCCHAAtMuUlJSCAsL4+bNm7Ro0YImTZoUu46MjAz++c9/snXrVv7973/ToUMHAGrUqMHx48fx9vb+0zoKayH6+vrKtAshhKjkijrtosQJcffu3QQHB+Pp6VniIB907Ngxxo4dS3BwMB9++CGBgYFERUUVKSE+SOYhCiGEgKLngxJ3mb788svEx8dTq1YtmjZtSnBwsPm1du3aJaqzWbNmhIeHM3/+fIKDg0lJSSlpeEIIIUSxlKrL1GAwEBkZSWRkJNu2bWPHjh04OzuTlpZW6sAuXrzIvn37eP7553F0dCxRbNJCFEIIYfUu08J88cUXGI1GXn75ZUtVWWKSEIUQQsBjWLqtMGPGjOHbb7+1ZJVCCCHEY1HiZ4hDhgzJ9+ywevXqpKenEx8fb8HwhBBCiMejxAmxZcuWREZGsmrVKs6dO4eXlxdGo5G//OUvloxPCCGEeCxKnBCnT59ufn/37l2io6MxGo20atXKIoEJIYQQj1OxEuKWLVsICgoqMK3CxcWFli1bWjQwIYQQ4nEqVkKcM2cO0dHRODk50aRJE4KCgggODiYoKIjAwMASTY8QQgghyoJiT7vIzc3lzJkzREVFERUVxfHjxzl+/Di3bt3Cz8+PoKAgvvnmG2vFW2Qy7UIIIQRYcaUarVZLw4YNadiwIUOHDjWXX79+3ZwghRCiXPr6a/D1hZAQW0cibKDEg2pOnDjB9OnTSU5OpmHDhjRr1oxmzZrx97//3ZLxCSHE45GRAePHQ2AgHD5s62iEDZRqLdPGjRvz2muvMWDAAG7fvs2rr76KRqMhJyfHkjEKIYT1xMRAUhIcPQp370JEBPz0E+h0ULeuqcUoKoUSJ8TTp0+zb98+HBwccHR0ZNOmTbzzzjv4+PhYMj4hhLCerCxo1szUOgTQasFohF69TMctW0prsRIp8dJtOp0Oo9EIgJOTEzk5Obz66qt8+umnFgtOCCGsysEBfv4ZvL1BUSA3F/LGGTZuDP/9r23jE49ViRNi+/bt2bFjBwABAQEcOnQIrVZLXFycxYITQgir69wZPvvMlAgV5V75Tz+Bn5/t4hKPXYkT4pdffkmzZs0AePXVVxkyZAjt27eXCfpCiPLn559Nr3o92Nub3m/dart4hE2U+Bmiq6srrq6uAPTq1Yv169cTGRlJaGioxYITQojH4to16N4dvvoKbtyAoUNNZaJSKfF+iLt27aJLly6WjsdiZGK+EKLIHuwufViZKJesvh/iqFGj6NWrF7GxsSWtQgghyobCEp8kw0qnxAnx7NmzdOjQgQ4dOjBhwgQSExMtGZcQQgjxWJU4ITo6OjJjxgxiY2NxdHSkYcOGzJ07l4y8+TxCCCFEOVLihJjHy8uLBQsWEBERwZEjR/D397dEXEIIIcRjVeJRptHR0QX+nD9/Xgaw2MDtjNuM2jSKRX9dRC1dLVuHI4QQ5VKJE2JwcDB+fn40btyY5s2bM2rUKJo0aYKvrPv32G2M2cgPsT/Q6olWvNnhTVuHI4QQ5VKJE2JaWppsCGxDyenJbIzdSK4xl+XHlgOwImoFVV2qoigKzzV4jhruNWwcpRBClB8lnodY1lX0eYibz2ym75q++coUFFRM/5yf9fqMsc3H2iI0IYQoU6w+D1HYVh//Pnw74Ftc7V3RKloANIoGB60DS59bysvNXrZxhEIIUb5IQizHQhuH0vXJruSquWgUDblqLkHVgxjfYjyKTCoWQohisUhCvHz5siWqEcV0J+sO285vAyCkTggKChHXIrhquGrjyIQQovwpdULMzMykXr16lohFFFNmbibNazRnw+AN7Byxk1+H/0qbWm3Iys2ydWhCCFHulHpQTWZmJs7OzubNgsuKij6oRgghrC4hAQICYMsW6NDB1tGU2GMdVCPPq4QQogL6/ntISTFti1UJlHgeohBCiAro/HmYORNycuDgQVPZ6tVgMJjeT5wInTrZLj4rkoQohBDinvR0Uxdpauq9sjt3YN060GqhAm8CL9MuRJm0IWYD8bfjbR2GEJVP48Zw6hQEB4PmjxSh0YC3Nxw6BP362TY+K7JIQqygi90IG7mVfovB6wcze89sW4ciROVUuzZUrQp5gyWNRlBVaNbMtnFZWakTolar5bnnnrNELKKSS7qbxOWUy/z3xH/JVXPNrcTLKZfJzMm0dXhCVB5pabB7Nzg7w7vvQo0a8PvvcOSIrSOzKlnLVJQJ11KvUevjWua1WO9flxVgUMNBrBu0zlbhCVG5ZGfD3Lnwwgvw1FNw6xYsXAh//7up67ScKWo+kIQoygRVVfnk0CfM+HUGOcacfOf8vfzZMGQDDas1tFF0QojyrKj5QEaZijJBURSmtZmGRtHw6i+vmssdtY4cGXsENwc3G0YnhKgMiv0MMTs7m2XLlrFkyRLi4uKsEZOoxPZe2guYEiGYlqc7eu2oLUMSQlQSxU6IY8aMYcaMGSxevJimTZty9Kj8sBKWc+LGCdr5tuPcpHP8t/9/cXNw48SNE7YOSwhRCRT7GWLVqlU5cuQIdevWZcmSJWzfvp1NmzZZK74Sk2eI5VNqZiquDq5oFI352MXeBa1Ga+PIhBDlldXWMs3KyqJu3boAvPTSS0RFRZU4SCEe5O7obk6GeceSDIUQj0OxE6KqqqSnpwPg6OhIWlqaxYMSQgghHrdijzLNzs5Gr9cTGBjIM888Q2ZmJlFRUTRs2BAHBwdrxCiEEEJYXbFbiGlpaURERDB58mTs7OwICgqiffv2uLm50bBhQ55//nlrxCmEEEJYVbFbiHlJMCgoiFGjRgFgNBqJiYnh2LFjREZGWjxIIYQQwtqK1UIMDQ3l9OnTBSvRaAgMDOTFF19kzJgxhFbg7UGEEEJUTMVqIfbt25fevXvj5eVFly5dePrpp9HpdBgMBmJjY9m9ezdJSUnMmzfPWvEKIYQQVlHseYiqqvLzzz+zefNmoqKiSE5OxtPTk+DgYHr37k3Pnj3RaGy/zaLMQxRCCAGyuLckRCGEEIAVJ+YLIYQQFVGpd7uIj49nxowZnD17FlVV0Wg0NGjQgPfee48nn3zSEjEKIYQQVlfqhDh69Gg++OADWrRoYS47duwYY8aMYffu3aWtXgghhHgsSt1lmpmZSfPmzfOVBQcHk5WVVdqqhRBCiMem1C3EUaNG0bZtW0JCQvD09CQ5OZm9e/cyYsQIS8QnhBBCPBYWGWWanJxMeHi4eQpG69at8fT0tER8JSajTIUQQkDR80GpW4gAnp6ePPvss5aoSgghhLAJq027+Mc//mGtqoUQQgiLK3ULccOGDQXKVFVl27Zt/Otf/ypt9UIIIcRjUeqE+NJLLzF16lQefBQpGweL8uSH2B9IvJPI2OZjbR2KEMJGSp0QGzZsyLhx46hevXq+8sJ2xRCirJqxYwbX0q4xMngkDlrZ6FqIyqjUCTEsLAw7u4LVrF27trRVC1GoszfP4uflV+p6TiWe4mLyRW7evcnZW2cBmH9gPoHegVR3rU7rWq1LfQ8hRPlR4mkXq1evJigoiKeffrpM7G7xIJl2UTbEJccxdstY1g5aSxXnKqWub0/8HkJWhbBv1D7a125fqrqC/xPM8RvHAdAoGhQUctVcADycPEh6PQmtRlvqmIUQtmX1xb3/85//0KxZM9zc3GjRogVjxozh008/5dChQwWeJ4rKa9XxVfwa9ysbYgoOviqJtdGmnod10etKXdcPz/9Aq5qtADCqRnLVXBQUnqryFGEjwiQZClHJlLiFOHPmTC5dusSwYcO4efMmP/zwA99//z1PPPEE6enpzJ49mwkTJlg63iKTFqLtXLp9iQ0xG1BR+TT8Uy6lXKKpT1NeaPICWkXL0MZDqeZarcj1/XT2JyZtnUSOMYfradfJNmZjr7HHx80HO40d/37u3zz7VMnmwZ69eRb/xf4AKCioqGwbto0eT/UoUX1CiLLH6hPzFy9eTGJiIlqt6bfo559/ngULFqAoCm3atGHEiBFotVrGjpVRe5XNzridTNs+DTB1RQIcv3GcyO2RAPhX9S9WAnuqylPkqrlcMVwxl2Ubs7liuEIdfR3qe9YvcaybYjcB4Obghou9C4l3Etl0ZpMkxLLk+eehcWN46y1bRyIquBJ3mVapUoULFy7kK5swYQIff/wxrVq14ptvvmHRokWlDlBYj6qqrDm1hsycTIvWO7rpaL7q9xXOds4oKICp9eXp5MlPQ38qdmvOv6o/JyecpEWNFuYEq1E0tKrZipMTTtLAq0GJY83IySCkbginJpzi9MTTDAwYSEZORonrExZ24wasWweLF4PRaOtoRAVX4i7TxYsXs2zZMr744gvz1k9RUVF07tyZ5ORkAHQ6HQaDwXLRFoN0mf653XG76fJVF77o8wWjm462eP15g1byuiJ7+/Vmc+jmEtWlqio1P65JQlqCucxX58ulqZdQFMVSIYuyQFVh3jxTMrx4EX76yVQ+YgTodNCsGYwcadMQRfli9UE1r7zyCiNGjKBbt240atSIDh060K5dO/NzwwsXLuDk5FTS6oUVpWamkpKRYh6gsubUGlIyUkjJSLHYPeJvx3P8xnE0iobe/r0B2HZ+G2lZJVuw4czNMySkJdDUpynbhm2jSfUmXDFc4ULyhT//y6Ls+Oc/4bvvHn1NTg589RUsWmRKhooCGg2sWmUq27Ll8cQqKp1S73aRmprKrl27SEpKolGjRrRubZq7dfjwYU6cOMFLL71kkUCLS1qIhfvx7I/0Xt37oec/ffZTJrWeVOr7XLp9ibE/jmVOyBxa12rNL+d/4ZNDn7D2b2vRO+mLXV92bjY/n/uZvzb4Kw5aBzJzMtl2fhs9G/TEXmtf6njFY3DrFnh7Q2AgHD/+6GtTU2HcOFi9On/5zJnw9ttQyNxnIR6mqPmg1AkxMzMTR0fH0lRhFZIQC5eWlcbIH0byfcz3Bc61r92e9YPW4+PmY4PIRIX1ww9w8yZERsKSJaayhQvB1RUaNYLWD1kAYeVKGDUqf1lkJAQHWzVcUfFYvcv09OnTBAUF4eLiQrVq1RgwYIAs11YOuDm4sX7QegYEDMg34KVZjWaEjQiTZCgsKz0dhg+Hl14yJUONBrRamDLFVPaokaPf//FLW58+0LSp6f3GjdaPWVRaJU6I48aN429/+xvx8fFs376dgIAAOnfuzPE/6wopRE5ODvPmzWPUqFEcO3aMq1ev0r17d+rVq8eECRPIyJBRf5YW/r9wVFQ0igYVlVOJp0jPSbd1WKKicXaGw4dN3aSKYhopmmtaDYjBg+8lvcL85S/wxRemFuahQ/DOO6YBNUJYSYm7TD08PLh9+3a+sq+//pqVK1eyc+fOYtU1bdo0oqKi0Gg0nDhxgvHjx9OkSROysrKYO3cuvXv3LvZWUtJl+nA30m7gM9+HgQEDWdxzMXP3zWXx4cUcHHOQZ2o9Y+vwREW0ejUMHWpKink/cv73P6hZ07ZxiUrB6s8Qn376aX766Sfq1783KTorK4tq1aqRklK80Yq+vr5ERUVhNBqpXr06MTEx+PubVg85efIk/fv35/z584+sIzMzk8zMe/PpDAYDvr6+khAf4qrhKk+4P2GesvA/w/+o6V5TpjAI6xg4EDZsgIAAuH4dkpNNI0ZfecXWkYlKwOrPEKdOncrf/vY3YmJizGWxsbF4eHgUuy6DwYCXlxfVqlXDzc3NnAwBGjduTGJi4p/W8d5776HX681/fH19ix1HZVJTlz/51dLVKnPJ8HbGbaZum1riqRqiDKlaFaZNg2PH4NQp6NsXZFqWKGNKPHZ5/PjxGAwGnnnmGXx9ffH09CQqKor58+cXuy69Xk96ejrOzs689cBD9pSUlCKNYn3zzTeZNm2a+TivhSjKr9UnV7MwfCHNajRjeNBwW4cjSuOzz+69f+IJ03NBIcqYYiXELVu2EBQURO3atQF44403ePnllzlw4ABJSUm0bNmShg0bFjuIfv36cfnyZfz9/ZkxY0a+cxs3bqRp3gizR3B0dCyT0z9E8dzJusP+K/tRVZWVx1cC8GXkl1R3rY5G0dC+dnuc7Z1tG6QoOqMRwsOhTRtbRyLEnyrWM8SWLVsSHR2Nk5MTTZo0ISgoiODgYIKCgggMDLRKQrpz5w6KouDi4lKsvyeDasqnz49+zrgfxz30/Nf9v+aFJi88xohEqaxZA6GhcOQING9u62hEJWWVZ4gRERGkpqby22+/MXbsWBwdHVmzZg09e/bE3d2dRo0aMWzYsFIHfz9XV9diJ0NRfo1uOpq3O7yNgpJvIW+NouHdzu8S2ijUxhGKYln3x76Vf7Zcm6X8/DNcvvx47iUqnFKvVJPn+vXrREVFcfz48QLdnrYgLcTyretXXdkVtwuNosGoGku1MLh4zL78EqZONU2vuHPH9KrRgIsL2NubkmOXLpa/r8FgGrwzdKhplRsh/mD1Uab3u3z5Mj4+Pjz77LNlIhmK8i0lI4W9l/YCUM+jHgA7Lu7gbvZdW4YliqpdO6hWDdLS7s05NBpNx35+pr0NLclggMREWLsWsrNN0zsSEkxl2dmWvZeo0EqdEDMzM6lXr54lYhECgKS7SXi7erO893LOTTrHkp5LqOJchVvpt2wdmigKf3/T4t15q9OA6bVnT/jtN1OytJQbN8DLC6pXh7FjTfdJTTWNZK1e3fT8Uogissji3s7OzhjL2Oad0mUqhA1lZECVKqa1TPP4+0NsrGXvo6rwwQfwf/9XcAPhOnVMa58WYZS6qNgea5dpWZvQLYSwsaNHTcmwTx+IiICWLeHMGejVy7RjhaUoCsyYAQ/Of7a3h6goSYaiWCySEIUQIp/WrWH/ftME/BYtTO8XLjRt+Lt0qeXvd+iQ6TVvn8TsbFNCFKIYZJdNIYTl2dlB27Zw9y58+y1kZpqSIcD69RAUZHrfrZupK7W0jh0ztQbXrTM9p5w40dRKDQkpfd2i0rDIM0QXFxdy87Z0KSPkGaIQZcDx46Ytm+5/vqfR3Dt++23Ttk6ldfMm6PX3Woi3boG7u6nrVFR6j/UZooWmMgohKpqgINi1C3x8TBsDgykZajQwdy7MmmWZ+3h53UuGYBrQI8lQFFOpE6JWq+W5556zRCxCiIqoUyfTZsC5uaZECKYE9o9/3EuSQpQBpU6IdnZ2bNmyxRKxCGFz52+dJyE1wdZhVCyqem8Jt06dTC2333+/NxBGiDJCRpkK8QdVVen+dXde2vySrUOpeBo1guXLYdUq6NzZNNhFujRFGSOjTIX4w4kbJ4i/Hc//DP/DkGlA5yiDsSxCUWDHDtP7d9+F7dvhv/81zU0UogyRhCgqtRxjDgFLAriWeo0cYw4aRUOOMYfqH5n2X+xQuwPbXthm6zDLt+vX4ZdfTF2nq1aZyv79b9NcQa3WNHlfr7dtjEIgCVFUcnYaO15o/AKz98xG5d5o6YycDBy0DrLdlCV89x1MmpS/7MAB0x+AH38EGZgnygB5higqvZkhM1nZb2WB8n2j9jEieMTjD6iimTjRtLSand29UaZarWk7qG++kWQoygxJiEIAl1MKbipbWJkoAY0Gpk0zbfuUNwcxN9eUCIcOtXV0QphJQhQC2B2/G52jjg2DN/Dps59ir7Fnd9xuW4dVcVy5cm9R72eeMb3++GP+3TCEsDF5higE8Fmvz3B3cKe6W3UAnvN7Dmc7ZxtHVYFkZJgW+X7vPdP6pd9+CwsWmNY4da5gX+foaNPWU25uto5EFFOp1zItq2QtUyHEY3fnDnh7w9//btqnUZQJj3UtUyGEEMDWraYdPlavNk0zEeWKJEQhhCiNa9fA19c0l3LYMNNCBP/7n+lYrzcNKBLlgiREIYQoDW9vaN8eDAbIyrrXMkxNNb3v0cO28Ykik4QoyrTf7/zOxpiNtg5DiIezszMNEpoxI3+5kxOcOiUJsRyRhCjKtA/2f8CAdQO4knLF1qEI8XCKAgkP7JKSkWFqJYpyQxKiKHNyjbkk3U0i6W4Sa6PXAvDtyW9JupuEIdPw2OLIMeZgVI1/fqEQADt3Qo0apoXMX33VVLZrl21jEsUi0y5EmTPxp4ksPbK00HMaRcPJCSdpWK2huSw7Nxv/xf7MDpnNi0EvWiyOFp+34NmnnuXdLu9arE5RgR07BvXqgaen6fjoUahfHzw8bBuXkGkXovya0noKAVUDCj03vc10nq76dL6ysPgw4m7HsTxyucViiPk9hqMJR1kRtYIK+jujsLRmze4lQ4DmzSUZljPSQhRlUkZOBk/Mf4LkjGRz2Vsd3jK31pLTk3ljxxvczbnLyRsnOZl4EgWF5xs9j6Io9H+6P39r+Ldi3TM7N5txP47jVvotLqdcJvK6aamxHvV74GTnRMc6HZnWRobQC1HeFDUfyNJtoky6cOuCORlqFS25ai4Hrhwwn8/KzeKncz+RkJZ/IMPqU6sBaObTrNj3NKpGjl8/zrHrx/KV/3LhFwAaVGlQ7DqFEOWHdJmKMulYwjHsNfZ80uMT4qbE0b52e3OLDaC6W3VOTjhJ9ye7m8tUVJzsnNj8/GZea/tase/paOfIgTEHGNd8XIFz3wz4hg//8mHJPowQolyQFqIok4Y1GcazTz1LNddqAISNCONW+q1813i5ePF01afZcXGHuSzHmEP72u1LfF9HO0ee9HyyQHlgtcAS1ymEKB+khSjKJI2iMSdDAK1Gm+84z4aYDWgUDTPazcDfy58cYw5bz28t1b03xW5CQWF6m+l0q9cNgM1nNpeqTiFE2SctRFGuTWo1iY51OtLGtw3/7PRP5h+YT7BPcKnqnNByAnO7ziWkbghG1ciKyBU08m5koYgrjszMTFJSUvD29rZ1KEJYhIwyFZXO0WtHaVqjKRpFOkhK45dffuHo0aO8/vrr2Nvb2zocIR5K5iEKUYjj14/TYlkL1kWvs3Uo5Zqqqpw6dYrs7GwunD5tmoO3Y8ef/0UhyjDpMhWVynenvwNgXfQ6nm/0vI2jKX+2bt1KVFQUqqqSnZ0NQOSHH/J0ZCRnXn+dmr/9hpvsFC/KKUmIosLbe2kvg9YPIjs3m9Qs02LLP8T+QJX3q6AoCh91/4hRTUfZOMryoUGDBhw/fhxNSgodDhzALieH2pcuAfBUTAyaf/7TtNB1v37QoYONoxWieKTLVFR4T1d9Gl+dL8kZyeQYcwDTnMXkjGRc7V1pVbOVjSMsP5566ikmTJiAW2YmrQ8dos3Bg9S4dg0AbW4uyiefwMcfw8GDNo5UiOKThCgqPG9Xbw6OOUjPBj1RUMzl/l7+nJxwkkBvmWNYHOnp6SR5ePCf8eO5Xr36va9obq5pD8D//hfeeMOWIQpRIpIQRaWgKAoXky+icm9QddztOLKN2WTnZtswsvInLi4OgIDevXHp2hUF7n1V7exg0CBbhSZEqUhC/BP7Lu3j7M2ztg5DlFKzz5oRmxSbrywrN4tqH1bD831Ptp4r3WT+yqRp06aMGzeO7t26odu+HYD0oUOhShVIS4Pdu20coRAlIwnxEXKMOfRd05fJWyfbOpQKS1VVLiZftPp93un8Ds52zgXK7TX23M2+y7t7Zc/DonJycsLHx8c0eGbwYNi+HZdvvoHTp2HsWKhZ09YhClEiMjG/EOnZ6aTnpLP/8n76rOmDncaO85PO4+7ojruDO/ZamYRsKRtjNjJw3UBO//10gX0OLS3qehRNP2uar6y3X2+2nN2CgsKKvitwsnMi0DtQVqYRogKRifkllJmTifdH3nh94EWfNX3QKBpyjDnUXVgXrw+86PpVV1uHWKGsPrUaFdU8P9CajiUcK1C25ewWNIoGRVEYuWkkz3//PK9tf42ku0kMWDuAG2k3rB5XWZednS2bJItKQRLiAxztHPmg2wfYa0ytQKNqNJ+r6lKV2SGzbRVahbHm1BoafNqA+p/WZ0PMBgDe++096n9an4AlAYT/L9wq9w3/Xzgejh5sHLKRz3p9hlbRAqZ/47x/595+vfl2wLesi17HxtiN5v0V/4yqqmyK3URWbpZVYreV3NxcFi5cSEREhK1DEcLqZGJ+ISa0nIDeSc+wDcPylYe/FF7o1kCieOp71ic5I5mb6TfNZXez73Ix+SJ+Xn7U1FnnGdSHf/mQf3X7F57OnuaycT+OQ6NoMKpGFBS61uvKxtiNLDu2DIAvjn2Bm4MbCgq9/Xvj7Vr4QtaHrx6m39p+fNbrM8Y2H2uV+G3h4sWL3Llzh6ioKFq1kvmaomKThPgQ/bHQmAAAIABJREFUR64dATD/sMwrk4RYei1rtiR6YjTtV7Tnwq0LqKjmZLQpdBMu9i5Wua/OMf+zg5/O/QRAPY96XEu9RnpOOlN/mZrvmlO/n+LlLS8D8JX2K14MejHf+azcLFRVZf3p9YBpSbgRQSMAU28DQFJSEu7u7jg6Olr+Q1nB3bt32bx5M1lZWSQnJwOQkJDAqlWrUBSFwMBAmjdvbuMohbA86TJ9iIirETzp+SQRL0ewftB6dI46Iq5Kt5GleDp7cj3tunleoIrKrYxbVkuG97uYfJEj146Qa8xlfIvxnJhwghMTTtCxdkdGBo3Eyc7J3J2qVbQ4aZ1Y3ns5LzR5IV89Oy7swOldJ5zmOjH/4HwAdsbtxGmuqeyTg5+Qm5vL8uXL2bVrl9U/l6VoNBqSkpKIi4vj9u3b5vL4+Hji4uIKf56Ynm6amC9EOSYJ8SE2hW4iemI0zWo0428N/8alqZeY3VmeH1pKxNUI0rLSaFOrDVtCt1DXoy7HEo6RnJ5s9Xv3XdOXrl915VrqNZY+txQXexfSs9P5qv9XrOi3go61O5Kr5qJRNOSqubSu1ZoxzcagKEq+ep6p9Qx9/PsUeo+mPk0ZEDCAixcvkpmZyalTp8rNwBQnJyfGjRtHw4YN85Xb2f1/9s47LKpj/eOfs7v0jgIqFiwYFRWx99gToyZIRI3GFmNs0RhNbspNu7m5iYnRxPiL0URjicaCRrHE3rvBAiJFFBFFRRAE6ezu+f0x7sJKERQE9Xyeh4fdmTkzc1Y5787M+35fDaNHj6Z169amF+j14OUFM2c+xlkqKJQ9StiFQoWQkZvB9ovbefk5EdZyN/suu6J34dvIt1zzFF5KukSDeQ2M7y+/c5k6DnWo9UMtmrg0Ya3/WlxmuaDVa+nu0Z19MftQSSri34unqnXVAv3JsszQ9UNN0knVtKrJDPMZZGdno9Vq0WqFfqqFhQWSJNG0aVP69etXbvdYVmzdupWgoCCTshkzZhTMZnH0KHTqBPXrQ1SUiE9UUKhElNQeKGeIChWCtZk1fo39jO/tLOxM3pclsizjvcCb0FuhJtJtAHXn1jWeE9+4e4P4tHha12jNv7v8m/4N+7MtahtfHvySLG1Wkf2fun7K5H1cZhyW1S1JiU4xKc/Ozkaj0VC/fv2yu7lyJDIyErVaTY8ePQgPD+fatWtERUXh4+MjFGlGjYKUFIiNFRdcugTduwv5tv79Ydq04gdQUKhkKCvECiYxI5EcXQ417GpU9FSeauYcm8N7O98rYBANSEgmdV1qd+HgmIMP7Dc5Mxnn75xpXb01dhZ27IsRsmX9PPvhb+1PTHCMsa1KpWLy5Mk4Ozs/2s08Js6cOYO7uzuurq7o9XrOnDlD3bp1xfwzMqBjRwgOLvzi776D998v/0mGhMBnn8GaNfCEOC0pPH6UwPwnhFEbRvHCihcqehpPPdM7TGfDkA1F1uc3htZm1rzT7p0S9WumNmOA5wCCbgQZjSEID9alwUtN2ur1erKyil5pVjZ8fHxwdRVhJiqVilatWuUZc2trOHECxtyXR1Kjge3bH48xBFi4EAIDYc+exzOewlONsmVagdzJusPO6J1o9VouJl2kgXODB1+k8NDE3IkpUbsjbxyhRbUWJWo7fst4NkdtLrRuP/vx1ngzyX8SV65c4ejRo0RHR1OjxlOyG2BhAW5upmVarThLLE8uXIB7ouKsWSN+z54N0dFgbg7DhsH955wKCiVAMYgVwMurXuZE3Amytdlo9VokJFr/2hoLjQV1HOpw/M3j5epY8iSSlJlE619bs9JvJR1qdXioPvbH7Ecjif/yQ5oOYc35Nej0ugLbqBGJESU2iN/0/Ibo5GiOXzteoO4F9xf4etjXWFtb07BhQ5o2bVqpt+8fisBAsSr8z39g716xUtuypXzPD9euhU8/Fa9V9/5O9u0T40sStGwJ93vCKiiUAOWpWwH0rNuThPQEUrKF04WMTEp2CrfSb9Gzbk/FGBZCYEQgl+9cZunZpQ/dx48v/sihMYcIfzucFX4riJ4aTcvqLXG0dGTXiF0sfWUplmpL3v77bXZH7y5Rn7UdanNg9AEsNZYF6q7mXMXaOi+usnr16tjY2Dz0/Csl//632Dr9+GOxavv9d+jdu3zH/PhjmDUL1GowuECoVODqKgyyYgwVHhLFqaaCGLZ+WAGdzCWvLGF0i9EVM6FKSFxqHN8d+Y5cfS57L+8l8nYk9hb2DG8mJPVGeY+iXc12jzyGjbkNjpaOACw9u5QxgWMY7DUYGzMbzFRmLBywsNg+Dl05RNelXQGw0liRqc2kpn1NZFnm6rtXC8QvKpQBsgw1a8L168IY6vUwYQL88ktFz0yhEqKEXVRCjl49yvQd09HqtZxPOF+g/vjV44pBzEdCRgK/nf6NTG0mEsKopOWk8UuQeOi1c2/3yAbR3d6dpMwkPtj1Adm6bA7HHgZgU+QmcnQ5SEgMajKI3vWLXvVcSr6Ek6UTi15eRDePbrwR+AbB8cFET41WjGF5ERQkjKGZGTz/POzeDQEB8H//J1aOCgoPgbJCfIyEJYTRfVl3bqXfKrS+Q80OHB179DHPqnITmRjJwDUDibwdiV7Wo5JU2JnbsdZ/LX3q9ymTMaJuR+Gz0If03PRC6xtXbUy7mu1o6NyQj7p8VKBelmW0eq1JnsxcXe5TnzdTr9cjSVLFGP0zZ4Qn6+zZ4O0Nf/wBy5cLD1fFICrcR0ntgWIQHzOJGYm8uOJFztw8Y3zAezp7svSVpdha2CqJaQth4OqBbIzcaHzvauPKjRk3yvSs9WLSRQatHURwfBFxdUDver3ZOWJnmY35pLN48WJq1apFnz5l88VEQaG8UOIQKylVrauSkZthzKChl/XczblLu5rtFGNYCFnaLLZd3IZGpWFau2k4WTpxK/1WmQutN3BuQJsabYqs/7DTh/w9/O8yHfNJJikpiWvXrhEcHPxgjdbNm+GHHx7PxBQUHgHFID5m4lLjCE8Mp5Z9LVYMXIGnsyfX714v9ExRQWSbGN9qPCfePMEPL/7A+UnnebvN21S3q16m48iyzPrw9UhIvFC/oFBClzpd0Kie7SN3vV7Ppk2bWL16NevWrQNEqqit//sfa1es4MiRI4Vf+Mkn8OGHQu5NQaES82z/hVcALjYuLBqwiEFNBuFg6cDAxgNZdW6VkmexCMzUZsztO9f4vrpddea9NK9cxhrebDh+jf34I+QPADwcPKjtUJuDsQfZEL6BlzxfKpdxnxRkWebWrVvExcUZy2xTU3nphx/Y1acPqe/kU/c5d04E0N+9K+TVQGTD8PEBFxfo2vUxz15B4cEoZ4gKCvexJnQN6bnpjGkhZMkWn1mMg4UD/l7+AOj0On468RNvtnwTOwu7ipzqY0en07Fjxw7++UdsWbc5eZKX/v6bdC8vbEJD8xp26iSyYIAIi5CkvHyJNjaQlCRUZRQUHgNK2IWCwkMypOkQk/dvtnzT5P3u6N1M3zkdM7UZb7d9+3FOrcJRq9W4JyfT5fvv0Wi1mOXmIgM258+Dk5MwfB98AOvWwfDhQkFGL87LkSSoV0+ERyjGUKESopwhKiiUAFmWuZh0kYjECJaHLAdg5bmVRCRGEJEYgU7/7GSLD01L45arK1ZZWWh0OoxBF3fuCBm355+H6tVh2bK8iyRJBNN/953YNlVQqIQoBrGSERIfUmLZMIXHR2BkIJ7zPGn8c2P+PPcnAMevHafxz41p/HNj5hybU8EzfHw079gRaccOGD3aWCaDkE47fx7atxeFgYHit40NuLuL1xs3oqBQWVEMYiVjyrYpDAkYglavreipKOSjb4O+xpRQEqaB6H6N/RjXalxFTKtCaNasGfUaNIBr14xlEkBCQp62KEBurkgYfO6cMJQjR5rWKyhUMhSnmkpARm4GtzNuk5KdQvNfmiMjs85/HW3d22JnYWfU2VSoeIb/NZxV51YhIyMh4VPdh6BxQc+eRJssi/RPtWrBkiWwaJFQi1m7Fvz9K3p2CgomKEo1T5BBbPtbW/65Lrz2JCRUkgqdLM6kbMxsSP4g+amXAXsS0Mt6qn1fjYSMBKzNrMnIzUAtqUl4PwEnK6eKnt7j59gxaNYsL/fg0aPQooVIHqygUIlQlGqeIOa8MIdqttUAkQrKYAytNFYs7L9QMYbF0OePPsw9PvfBDR+S1OxUhv81nJtpN0nKTOJO1h0mtZnEqOajAJAkiUvJl8pt/MpKWloaUVWrmibi7dhRMYYKTzSKQawEdK7dmSNvFFT5WOG3guHNh1fAjJ4Mom5HsSt6F/P/mV9uY/wV/hd/nvuTFSErsDO34/T400xrN42/Iv4C4F8d/4WjpSNxqXEP6Onp4sCBA6xatYqMjIyKnoqCQplRaeIQd+/ezeHDh/H29mbgwIEmdZMmTWL+/PJ76FUGDGmHADQqDVq9liOxR/Br7FeBs6p8aPVaPt/3OclZyUQkRgBwIekCYwPHYqGxoHPtzgxrNqzQay8nX6aGXQ0sNBbFjpGancrfUX+j0+v49dSvACw5u4QtF7Zw4MoBk7ZfH/6arw9/DUDYpDAauzR+1FustOj1erKzswEICwtDlmVCQ0Np1qwZarUacyW2UOEJp1KcIS5ZsoQZM2bQvXt3Tp48yXPPPceGDRuwsxMqIPb29qSmphbbR3Z2tvGPFcSeca1atZ6IM0SA93a+x/Lg5fwx8A/c7d0ZtHYQ7nbu7Bm1p6KnVqlIz0mn6fymxKTEGMtUksoolj6u5Th+HfBroddVm12NGR1m8EW3L4zlKVkpTNsxje97f08V6yoArA9bz6CAQSWaj4RIf/RV96/4sPOHT7VzTWBgIGfPni20TqVSMWXKFBwdy8ABTK+HjAzT7VgFhUfgiTpDnDVrFtu2bWP9+vVERUVRrVo1evXqZTSCJbHZ33zzDQ4ODsafWrVqlfe0y5T/9fgfV6Zd4YUGL9DUtSmhk0IJfC2woqdV6bAxt+HMhDP4PudrLNPLeiQkfnjhBxb2Lzy7/faL20nLSTPGEBoICAtg6dmlrApdZSzza+zHogGLsNRYopZEbr38oRa2ZnkPahmZz7p+xkddPnqqjSFA+/btcXIq3HmoU6dOODg4lM1Ac+eCp6cI21BQeIxUihWiwXLnZ8KECQQFBbFr1y7q1Knz1K8QFUrHnGNzmLFzhknZpamXTETSr9+9Tu8/epOanUpKVgp3c+4CUMOuBtnabLp7dOdq6lVOxJ3Ap5oPH3UWRq1XvV44Wjriu8qXwAtFfymxUFuQrcuma52uHBh9oMh2TxPZ2dnMnj2b3HzGqnv37nQtS7Fub28hCL5jByi5FhXKgCdqhVilShViYmJMyhYsWEDbtm3p0aMHWu2Dg9QtLCywt7c3+VF4elkfvh4QGSq8XLwA2BC+waSNs5Uz7nbuXEu9ZjSGIAzl7czbrAtfx4m4EwCcuXmGwesG4x/gz/qw9aTnpLMlakuxc8jWZdO2RlvjWaaBxMREvv32WxITEx/5Pisbt2/fNhpDw4o4f/aLh2b+fOjWTci+GbJjjB8vyvr3h9u3H32MsiQyMk+8XOGpoVIYxF69erF06dIC5fPnz6djx45kZWU9/kkpmLDz0k78A/xLtH1dXmRrs/nq4Ffczb7LwEYDWTtoLSv8VhD0VhBfPP8FPtVNNTItNZZsf307k9tMNim3NrPmky6foFFpjFuiakmNRqXhmx7fIEkSvwT9UqIt0I1DN3L5ncsmZatWrSIrK4tz584Ved3Ro0cLfAl8Erh+/TpqtZq+ffsyZcoUqlevzvXr1x+9Y7UaDh4UPwZiYuDAAfHbrJKFHk2eDK+8kpfBQ+GpoFJsmebk5KDVarEuIoYpNjaW2rVrl6rPJykw/0ngpZUvse3iNk6/dbqA4Xlc/BX+F6+ufZUF/RYwvvX4El83euNolgUvMym7NPUSU7ZN4e+ov41OOd09ujP3xbl4L/BGpmR/Fv08+9G+ZnucdE7UuFOD3NxcwsPDAbC2tqZ+/fpIkkSHDh2oVk3EmmZkZPD999/j4eHByJEjS3wflQG9Xk9mZiY2NjaFvn8kDh2C3r0h39EHw4cLFRxLy0fv/1HJzRVJjlNToX59YQy3bYN27cT8rKwqeoYKRfBEpX8yNzcv1mW7tMZQoWw4GXeSg1cOIssyu6J3AfD5/s/pWqcrNmY2jG05FnN1+bra6/Q6zt06h06vY+nZpQD8EfIHrWu0RpIkmrk2e6BwwY5LO3C2cmbRgEVsjNjI8pDlbIrcxK5L4p6auzXn7M2zHLxykJr2Ndk7ai9D1w0lMSPRKJJQFFujtrI1aitN1U3x15uuoDMyMjh37hySJNGiRQtiY2PJysoiNjYWWZaJiYkhPDwctVpN1apVcXZ2frQP6zGgUqlMjN/97x+JunVNjSGIlFJlYQxlWaSi6t5dZN54GJ5/XqjzGFCpoG9f8dreHhITK99KVqFUVIoVYnmgrBAfnclbJzM/SMR/qiU1OlmHhISMjIXagotTL1LTvma5zmF16GpeW/9akfVzX5xLr3q9+HD3h6wZtAYrs4Lf0vdd3kcTlya42boBsPfyXlysXRi8bjAfd/6YEd4jWHt+LZ/v/5xtw7fh4ejBezvfY/ax2cb7LY46DnV41+5d7ly7U2h9vXr18Pf3Z9asWejv5QaUJMnEeNavX5/XX3/9gZ9HRbFhwwYaNmyIl5dX4Q0uXoTPP4fffxcapw/Dr7+Kc8OuXcV25EcfQbVqcOXKw0/cwObN8PLLsGsX9Or1cH3s2gVDhkBysmm5mRnMmyfmrlApeaKcahQqJ3P7zuXz5z9HQjKulCRJ4rkqz3HqrVPlbgxBhEC81+E9Mfa90AfD77E+YxnrM5ZfT/3K5gub2XlpZ6F9dK/b3WgMAXrU7UEzt2aETw5nhPcIAAZ7DSZ8cjgejh7Issya82sA8HT2RCWJP5OqVlUL7T9Tm4mdmx3ZDtnkYhoqoFKp0Gq1WFpaMmLECONqKr8xrFevHr6+vlRWkpKSCAkJ4Vj+1dE9UlJShFrNwoXw55+wezfk5DzcQD17CoO6dy9Mnw5nzsBXXz3i7O+xbp3p74ehd284frxg+YoVijF8SlAM4hPKb6d+K1cNTxCKOe93fN+4LWo4axvebDherkWsFMoYc7U5s/rM4tXGr5qUt6reihbVWrDk7BJjDOEPx3/g/07+HwuDFpKabRqmE5kYybwT80o8roejBwC96vfi7PiztK3RlsTMwr1Gb6XfYuypsXyT8g3LWc4+9hnr9Ho9sbGxLF68mFq1atGzZ0+TayVJwt/fH9tKFoSu1+s5efIkhw4dYtcusbUcFxfH/v37OXToEJcvX0a+epWTkydz7r33xDkfwNdfi4D6r76C9PTSDVq/PowZIxxsAJo0gREjHv4mVq8W263m5rBcJHVm4ULx3soKNmwo/vrCCArKe6269/g8derh56hQqVC2TJ9A9LKe6rOrk5WbReK/EstV/Nug2mJvYU+bGm3Yc3kPTVyacH7S+XIb08DNtJtUs61Gri6XqrOqkpqdirOVM0mZSSbqNIbX+bc3j75xlA61Ohj7GrNxDEuDl3Jl2hVqOxR9Jp2QnkCmNpOVISv5eO/HuFi78M+4fzhw5QCTtk5iRPMRbI3aytXUqwWurUtd4ojDDjsmMQk1amOds7MzKpUKtVpNfHw89vb2pKWlodfrGThwIM2bNy+rj61MyMzMZO7cuSaxvfm3eZs3b07vkyexnTkTEAmCC5zMHToEnTs/ngkXxrVrYpv0zJmCdW3aiATG1auXrs/p02HxYpHyqlYtkerKwwP27y+LGSuUE0+UU41Cydgfs5/o5Ghu3L3BrfRbAHx54EvqOtWlln0tetfvXeZjOlk5MajxIOa8MIea9jVZeGohx64W3Dora45fO07HxR058eYJatrXRKfX8WnXT/mk6yfMOzGPz/Z9xuQ2k/nhxA/o9GI7VyWpcLJy4k+/P+lQqwN3su5w4fYFZFk2inHP/2c+fo39sDGzoYlLE5PQiqjbUTT8v4Ym80jISMBjrgcAI71H8kv/X2j7W9tCDWI22eSQQy96GY2hIW9iUlKSsZ2XlxcDBgzgzp07BAYGkvOwW4zlgF6vJzc3FysrK8aPH09AQAA3btwA8rZ5bWxsiIqKQrp8mfZubrjFxxuvNxpGjQb+9z+xGvvgA5EJ43FTs6bY4uzeXfzW68WqrmtX2Lnz4Rxg/vtf+OwzMEjUhYdDZmbZzvtx8q9/ic+jf/+KnkmlQFkhPkF4zfciLCEMwBg/Zzjbq2VfiyvTrjyx8mGXky8TdD0Ify+RXHba9mnMPTGXGR1m8H2f79HpdahVeSsunV6HSlLh8aMHsamxxlXiWJ+xLHpZbN+9/tfrrDy30njN/Q4yF6dcpL5zfeN7WZb55vA3fLrvU+Pq04BhpapWqY1fRgpDQsIaa3zxxRPPAvUWFhZ06tQJgEaNGuHi4lKaj6nc2b9/P6dPn2batGmoVCqOHz/Ojh07TNr4+Phw5swZOh86RM89Qmu30BUiiC3L7duFh2ZFoNWCq6upI4ybG1y/nrfl+axy4wa4u0OHDnCkYLadpwnFqeYJZXPkZsITwgut2zNyDz3q9gCEITQYw441O3L4jcMlNoZB14PYe3lv2Uy4jPjywJcMXTcU7wXetFjQwphl4pegX2ixoAVtF7Vlf8x+Y3u1Sk1IfAixqbFoVBrj57I+fD1avVA2+q73dzxfJ+9BbDCGGpWGJa8sMTGGILYEP+7yMf/p9h+TcltzW5b7Lkcv64s1hoYx0klnLWvRoy9Qn5OTw969e9m7dy/R0dEl/HQeD7IsExwczN27d4mNjSUhIYGge2dmdevWxepenN2dO8Kb9nCXLmx/4QWgEGOoVoswiuDgijOGABERwhj26wehocLDND4eLj17OSwBEX6yfr3Y9v38c/H+2DH45RdR9oyfhypbppWIjNwMhqwbQq96vdj02qYC9dVsq/Fdr+9o/Vtro6eljMxXPb4q9lzsfiZsmcC11Gtcn3Hd6EFZEWRps7hx9wZ6Wc9f4X+hR8+lpEuk5+Y5Y2TkZhAcH4yLtQtVrU29PFWSijY12vBBpw94tcmr/HnuTxafWWxc3dWwq8GuEbtw/NaRjNy8vH2OFo6MbjG6yHkZUjwZtErTctJwsnIidFIoLRa0ICEjwdi2c63OHL56uEAfDWiAClWB8ApZltFoNPTr1w9vb+/SfWDlxJEjRwgPD0eWZaOx27hxI9nZ2WRlZdGtWze6dOnC4cOH2bdvn1GqTdLrcUxKKnx1qNOJrUTPgqvkx4qXl3CEadlSxB/u2CHOFBs0qNh5VRQpKcJRybDNa1glT5okfr/yCmzcWDFzqwQoK8RKwO2M28TciWF16GoytZlsv7idiMQIYu7EmDzIIU/D097C3hj28Ff4Xw8c4272XWLuxPBP3D+cunGK+PR4tl7YSsydGO5kFR4/V96M3DCSej/Vo8G8BqTmpCIhmRhDEEavhVsLwiaH0dS1qUldU9emJGUm8c3hbwAY1mwYe0buMRELOHjloPEz1KjE97/EzERGbBjB2MCxLA9eXmBe52+dp7tHdy6/c5lFAxZhpbEiJD4EjUpDYoapp6lBC/X+OVtgQbI6mXHjxtGkSROTehsbG7y9vSvN9raZmRlxcXEmEmwpKSlGycRz584xf/589u0T3rOGM09ZpeKmuzsSkF6YSsvNm3DhQskmsWyZ8AotayQJWrXKC8ZXqfLer18vPFvvFwN4mnF0hBMnoGFD8Vno9eIHYORIEULyDKOsECuYu9l3qTa7mnGbTyWpyNXn0vhnkWi2Z92e7B6529je1tyW7h7d2Rezj5k9Z/LP9X+ws7B74DgdFnfgfILwDJWQUEkqXl79MgBOlk4k/ivxsa8Wv+z+JaG3QglPFFvEhQXA62U9qTmpVLGqYizT6rXk6HKISIzgUrLY+rqYdJEadjUwV5ujUWmMZ44xd2KM54uGzxhgRYj4w7c2t2akt6l8WsTbEdiZ2yFJEmNbjsXfyx9bc1vWha1DRmZwk8G82fJN3gh8g2t3rxU657OcpU6DOri6uhIVFQVAkyZNiIyMJCUlhfj4eKOUW0XTtm1b3NzcWL58uVE4ID+3ixLWlmVuVK/OuaZNqX+/4fv1VyHIXRIvTp1OeG9aWYnA98f1ReG33yA6WsRO9uv3eMasDDRrBu+/D+PG5RlFlQrmzHnmc1AqK8QKxs7CjgX9FmChFuoe+Z05PBw9+K73d8b3yZnJvNXqLeP2aOCFQL7t/S0zOsx4oOj2zy/9jKuNKyAMj+H80c7cjkUvL6qQrdNGVRvxz7h/jPeen/pO9Vn16irqOdUjOjmay3eEgLYsy9SdWxebr21o9WsrVJIKCQnPeZ7YfG2Dz0IfopOjcZjpwMm4k4xtOZbb/7pdII5RQmJBvwX89OJPAIQlhNHsl2bEp8UTlxpnshK0t7BHJano26Ave0buYdWrqwhLCOPTrp/S1KUp9RzrcT/ebt78p9t/kGWZ+vXrM2zYMPz9/Rk/fjzPPfccZpVM4svW1rZQY2igqNVsppUVTUNDsc7vKWtlBaNHw4cfCkmzojh1CjZtgp9+gqQkiIuDBQtEWRGJiB+ZLVtErOOYMXDPIYiPPhLvJ02qfFk1youAAPHbywvs7IRRDFTyryoGsRIwtuVYFvRfYFImIXHizRO0rN4SEPGAzt854zLLxShUvf3idlxmuVB1VlV+Cfql2DGe93ievSMLOtIE+Afg19ivjO6k9Pxz/R+ydWLLymAYfar5EDwhmKFNhxI8IZglryzB3c4dEA/m/3b/r8kXCMPK0sbMhs+f/5y159eSnptuXAU6WjrS1r2tybgyMr3r9zY+6JedXUborVACwgLourQr03ZMKzBXOws7etTtQcitEKbtmMaBKweIvhNN9J2CzjHgfrAsAAAgAElEQVQxd2KwMrNCo9EwZMgQPO+dpbm4uDB06FCqVKlS4JqKxLCK9fT0pHv37gXqC9UTliTu2tuTkN9T1spKnE8FBxc/oCwLV/9XXhGrQ7Va/EyaJMoGDXqU2ymaS5dg6VLxY8hUERoq3q9d+2SHUJQGV1fxRSAoSNz/Sy89vOTeU4SyZVpJOHTlECCUWXJ0OcjIHL16FN9GQtKrT/0++DfxJyAsoMC1ver1YrDX4AePESvGkJBQq9Ro9VoOxR7ihQYvlOGdlI7QW6HYmtvya/9f6VKnC6+tf43rd69jYy4kzmzNbQs4wIxuMZpqttXou7KvSXk793asCl3FkVjhQr4seBlxd+OQkIzbsmN9xhJ0PYjg+GAWBC0whmIYzhK/PfItiRmJBJwP4D/P/4cGVYTzhSzLRCVFka3NZvHpxQAcu3aM2b1nM/HviQXuq5mqGQ2rNCxQXlnx9PTE1tYWLy8vIiMjjeUqlQq9Xm9M+6RSqTA3Nyc9PR1zjQZ1aiqpDg64JtxzNOrQQQS913yArJ8kCYm2QYMgLMw0jZKPjzBO5cE77wjv16FD84yfLEOXLmLV5OZW/PVPC3/8kfe6dm3YurXi5lKJUOIQKwmtf22NLMus9V/L+YTzjNo4iuntp/Pp858a28iyjO8aXzZHbjYGfLd1b8vRsUdLtOU5eetkNkRsYNWrq3C0dGRQwCAaVW3E5tc2l+etFYtOryNLm2U0gHpZT0ZuBrbmxZ9lzDw8k4/2fGQSW2jwCr0fc7U5I71H4uXixbT208jWZjP72GyytFn89+B/ixzDXGWOSlLRv2F/ZnScQYfFHYpsmx833PDHn88nf07VqoXrn1ZmDh06xLFjx/D19WXr1q2kp6djY2ODj48PkiSRmJhIaGgoAB2OHsWyc2fcOnXiuRkzROLc2Fgo6X0fPiyMkSQJwwRw+rQwiuVFWhpUqWKquTphggg9UHgqKak9UAxiJSEtJw0rjZUx+DwjNwMzlZmJLJte1lPt+2okZCQYs09Yaiy5/a/bWJsVnksyP9nae1uTGrE1YnBOKcm1lQ3f1b4cu3aMP/3+JCM3g5EbR9Krbi8iEiMITwzPEyNH4vT40xyIOcD0ndOJeSeGWg61jP1si9rGa+tfIzU7tcisFlPaTuHHF39k5uGZfLrvU2RZLtBWjZrGlo2JzIokl1w+4ANqVa2Fra0t1tbW+Pn5oVarC+2/spGamsrdu3dJT09n1SqhEztw4EC2b99O5r1VlWGr2fD4aNCgAcOrVRNxfv/7H3z8cckGe+89mD1bGCidDu7cEfFxX3xR5vdl5K+/4NVXheTaoEHCmaRKFbhVfIypwpOLIt32hHH/iqgwI3X97nUSMhIY0XwEc16Yw1cHv2LuiblEJkaWKGmvwRAa0Kg0xlCEJ43FLy/GTG2GvYX4z23IWt9qYSuTHIYyMguCFrDlwhZjvOM77d8x1vf17IubjRsp2SlFjnUx6SIqScWGiA20rtGak3EnC7TRoyc0KxQJiXa0wxxzEhMTSUxMfOLyef7++++kpIjPw2D4NtwTwlar1eh0OhMnrjZ169LH0xN+/lkUrFgB9wL28fIqPp9hdrY4S1y0SKjKvPEG3Av3KDeaNIF//1tIytnZgZ+fkHJTeOZRVohPGPFp8SapjO5//6Ry9OpRsrXZdK9b0KGjpCSkJ+D6vWuxbcxUZlibWWOpsWTH6zuw1FjS6OdGWGosaejckJBbIQWuUUtq6jnVIyopChWqQhVo1JKaqpZVGZA5gJrknZ+1b9+e3r17o3qCZMJiY2MJCAggLS3NpNzGxoaWLVty6NAhk/LJ8+ZRtSjvzM8+g//8p/A6BYXHhCLd9pRyv/F7GowhwJub3mTkxpEPDB8pDjsLOz7q/BH9PIuOKcvV55KSnUIz12bUc6qHnYUdI71HEjwhGI1ag525XYHVuU7WEZUkvDD16DFTiW1sg56soY2nk6eJMQQRzvAkGUMQHqVDhw4tUD5kyBBu3dtWzH9P4VOnijPA+0MzBg8WHqQKpty8CW++Wfr0WArlzpP1l6pQ6dHpdUzdNpXo5OJ1OnN0OSRnJhN1O4r9MfsJTwznWuo1/o76m6jbUdzOKH08mKXGkq97fs3kNpOBvETC9zOi+Qh2jNiBnYUdNexqsMx3GfNOzMNGY0PL6i1JeC+B/p5Fq//n6kUS4PxbsypJxdHrR8kkk0aNGtG1a1cALpRUqaWSYdBZNaSsAjhzL41SgwYNjDGLNWrUILVVKy5PnpznFCMaCQHte5kyFPKxbJnQDd1ccc5sCoXzZB4gKVRaDsUeYt7JeVioLZjVZ1ahbTaEb2BM4BicLJ2ISYkBMAbY918lDJGLtQvx78U/lLyZQd6uVfVWpOemG0MuDFxNvWrilZuZm8n8oPlGUYSI2xHk6Eqekql19dbM7DWTX0/8il9jP9p4t0GSJBo0aFBg2/FJITk5mSpVquDv748kSQQEBBAeHm6UczP8u1y/fp3r168TYW3NdEBydhZB9hcvip8lS+Dbb4saRPzUu0/YIDoaHByEo0tZ8O23EBICK1c+uG15cfu2yJkoy8IggkhWrNGI+Ms+fcDGpuLmpwAoZ4gKZUCOLoe9l/eSo8thefBy1oevx93Onfn95gPQvmZ7o0oOwCurX2FT5CY+7PQhi88sNhHLBiElt9JvJX09TeMMCyMxI5FlZ5fxbod3jUZu7vG5pGSn8FHnj9gQvoEh64fQzr0ds/vMZkzgGKKTo8n+JJtlwcvYe3kvZ2+eNcragUildTX1KhZqCxwsHR6Y4UItqZnUehLtarZjePPhJf7cKjN6vR5Jkky8SVNTU9m4cSMxMTEmbau7ueH/1Vc4vfQS1Kkjgr03bRLqJ9WqwZQpouGgQcLIrVghysaOFamh4uLyRKZlWSTezcgQItP3VtoPjSyLmMgbN8RWpWvxZ8zlxk8/iRjIotiwAXx9H998njGUsAvFID42jl49SqffOxVZ/1Hnj2jr3pbFZxaDDDsu7SBXn0tth9rUcahjFAwwsG/UPrp5dCvR2F8d/IpP933KkTeO0LFWwSS0mbmZnLl5xliXkZtB8M1g2tdsj8NMB+7m3C35jT6AgY0G8teQooXWZVlm8eLF1K9fv1A1mMqOTqfj5s2bLFq0yKR8xIgR1KtTR+TU69ZNGKH8GpkGSbgFCyAhAT79FA4cEN6ld++KWMROnUSg/OnT0LmzaP/aa/Dnn6WbZEaG0Ch1cBCrwtRUsT0J8PLLQszbzU0kxn2c4uo6HXz9tQgpkSTxmRjUeX76Cd566/HO5xlDcapReGx0rNWR5b7LsdJYGVdphvO7yW0m89nzn5GlzWJb1Da2RG0xnsHFpsQajaEKlTEE5EDMgWLHy9ZmExIfQkh8CH+eEw/M38/8Tkh8COEJ4SaOOVZmViaG0trMmg61OiBJEj+88EOR54wGVOTdT3FtP+nyCWv9i1dXiY+PJy4ujiNHjjyS81B5IMsy27Zt49q1gmLlIOa+YcMGAu5pYFpbWxsfLBEREeLB3rUr7NsnDE7+h7uVFcycKQLwv/9elPXqJYyh4bVKBdbWecYQYNUqEbLRqJHI0FASVq2CadPEduQPPwhjqFKJn02bRNlvv4kQj8eJWi2+CLRunWcMdTqxKhw/XjGGlQTFICqUCSO8RzCu5Tj0sh6VpEJGprZDbeb1nYelxpKhTYdydOxRnC2dTa6rZluNGrY1ODjmIEHjgvB09iQsIazYsf578L94L/DGe4G38Xxw8ZnFeC/wpsn8Jsbkx5m5xetSjm05lp/6/lRkvZOlE3r0RjUcQzB+/vRSBgZ7DS40plOr1bJ37142b95M4D3xZJ1OR0BAAJs3b+b8+fMFrqkIEhISOHnyJMeOHStQp9VqWbx4MWFhYaSkpNCgQQMmTZrExIkTad68uangwPPPC6Om0+WthCwthdC3l5fIxweQm5t3TVaWqUNOfrKzhfrN4YI5J43o9eK8Mioqb0Xp7p6X4y9/iqNBg4SoeEWIq1+/LraTAZreS2W2deuzlX6qkqM41SiUmszcTCw1liYOL7IsG51ZPJ09ibwdSWxKLGEJYXi5egHQzLUZGVrT/I4danbATGXGrKOz2Dh0I+GTw0nJTuFO1h0cLR0LHX96h+mExIew+UKel55h9fbZ85/RzaMbOy7uwG+tH1FToqhhV6PQfgIjAnlv53tF3mdyVrK4t3yqNBISNmY2BZxuJm6diF9jP95t/67J55KdnV0gbg8gPFwY8pCQEJo0aVIhuRH1ej0XLlwgNzeXixcvAsIrNiQkBEmSqFKlCitWrMDS0pLcfAYsNTWVgIAAHBwc8PX1NZ17ZmaeLuagQeIcMDlZrB4PHix6Mq6uhSvFmJuLVd7rrxd97fz5eeeUBtavL7xtv35iO7UiSE0VeQhnzhQC5osWCZWc9HRFWLuSoKwQnxK+PfwtbX5rU+5bcTm6HOr9VI+f//nZpFyr12JjZsNX3b/i/KTz7Hh9B57OnibJhw9cOUCWNouGVRoyq/csLDWWbIvaxoaIDWyN2sqdrDuoVWqmbZ/G80ufL3IOzlbOBA4NxNM5Lxu7jMyE1hP4otsXqFVqVoSsICM3o9jkyY2qNjLJkVgSZGSjoTTklQQ4cvUI807O41DsIaJuRxnb29jY8OqrrxYai2hmZsaQIUMqLFFwSkoKa9as4a+//iIkRAgSaLVaNmzYwF9//cXixYvJzMwkOTnZ5Lpbt25x5coVsgtb2ciyUKnZsUMIdJ86JR7+v/9efJb6omTTzMyEEHdxjBwJ/v5F13t55eU7LMpQPg4aNYKICLFNKkkiH2F4ODg7P/hahceC4lTzFCDLMg3mNSA6OZpzE88VyCxflmy/uJ2+K/vSqnorgt4KKtW1calxrApdxaQ2k/hs32cEXQ8i5k4MV1KuACJbhZOlE7uid6GTdVx4+wKeVTwL7etqylVq/ygk0RwsHEjJTsHNxo0W1VoAeca3mk01vKt5Y6Yy4/s+3/Nc1eeMfciyTPXZ1YlPjzcRCQeMSYVntJ9BQHgA11KvmeSqvJ9XnnuFRQMW0ejnRrSq3oodI3aY1G/evJnTp0+blA0bNsyYFqqiuHz5MuvXrycjI8Pky5Sbmxt37twp3OgB9erVo1OnTtS7P2QiP6NGiS3U7GxhkOrUEeUGIW9HR5FIeMSIgtuGderAFfH/gn37hLNOcciyWIUeOSJeS5LIutG3r0hzZG4uzg7T0+Hddx/wqSg8bShaps8Ai04vYl/MPnJ0OcZA+LGbxtLAuQGOFo788OIPhZ53lZbbGbcZt3kcaTlpxgz1p26covcfvZGQGOwlMsg/CHd7d97rKLYo41LjOHDF1HnmRJyp40TP5T1xsHTA3c6dLcO2mJzRXUq+hJXGijkvzGGk90im75jO0rNLjZ+HgZvpN7l56SZVrasaV3PrwtYREh/Cnaw7xKfHAwW3RVtUa8HpG6dZE7YGGzMb49moXtabZNUwGNIXG7zIqRunuJ15m70xe7machV7C3uszawxU5uZpFQycPr06Qo3iHXr1qVdu3bs3bsXSZKMRjE+vvgY0Js3b7Jp0ybeeeedwttFR8Py5SLF07Vr8OOPwkh5eYn8e+3aCS/QAQPgyy+Frmh+Fi4URmzhwpKlZEpPh5MnhTG0thbephcuCAOpuff/5q23SvqxKDyjKAbxCSYiMcLoZWl4MJ+MO8nJuJM0rNIQrV5bJgZRrVITeivUKF9mYHf0biSkh0ow/Oerf9K6Rmve21X0Gd7V1KtcTb1KU5emJjJpAN08upHyYYoxG8iC/guY13cekbcj6fNHH26m3TSmyGpfsz2BQwNxsRGJbBedXsSOS2IFZ8gaYsDwOa5+dTUrz63km8PfGA1sW/e2nL15liytCE5vXLUx7vbu7I7ezcStIieiSlKh1WuNq9dOtTpx+I3D2NjYIMsyr7/+Ojk5OaxatarI1dfjJixMODG5uLiQmJiIXq/H2tqajIyMIq8x1F28eBEnJyesra2x1mhE5gq9Hgznpgav1f/9TxiriAjxXqMRXpcLF4oVoCSJUIiEBLG9unWrCEcoaXjK5cvCkeezz0Q///43/N//idjDB+VmVFC4h7Jl+gQjyyKx7ZjAMSYrnNeavsZvA34z5hgsCzJyMxi9cbRJgmJLjSXbh2/neY+iz/uKY2PERgauGVhsm5k9Z/KvTv8q8TmbVq+l6ndVTbJX9Krbi10jdxnfZ2mz+GDXB/x00tTDtH/D/lxLucblO5fxbeTL3Zy7xjPI+k716Vy7MxZqC45fO07jqo1Z4rsEC40F3ZZ243Ds4QIpoZwsnVgzaA296/dGp9MhSRIqlQqdTsf8+fPp0aMHXl5eJbqv8mT58uXUrl2brl27kpCQwJYtWwCKDMEwYFhR1r5yhUF//YXttGlIpRXyVqlEKISPjwjLAGEMnZ1FsuHSkJEhVocG0tMV9RcFQNkyfSaQJImOtToWeBA3d2tepsYQRPyei7WLSVm2NtvkTK60bIrcBMDwZsOxMrNi0elFBdqYqc1K5XRy/NpxUrJTqO9Un6ntpvLpvk/Zf2U/6Tnpxs8kS5vFsGbDjAbRsCrsXa8372wXaiLLgpeZ9Hsp+RKXki/R3K05p8afKhBicf+/QSMaMZKRdKvdDcAkNCE6OpqkpCTOnj1bKQziyJEjja/d3Nzo0aMHK+/JnGk0GrRFxOwZvku3CgnBLiVFKNCAUJq5evXBA9vbw99/i6D8/PQrWpy9WKzvS5mmGEOFUqJ4mT7hGIxKW/e2vN3mbSBPy7OsCYwMxExlxre9vqV9zfbIyGyL2vbQ/bWp0QaApq5N+W3Ab7zc8GUApradyqzes9CoNGy7WLr+G1VtxOw+swmeEMzUdlMJnRjKd72+w1KTl5Ov5/KejN00FoCGVRoas1sYjGF+ndP8jPUZy/Gxx9GoNJy5cYbfz/zOwqCFHI7Ni5EzhH+0pCU5WTkcOHCAyMhIUlNTWbNmDYGBgezdK+Iko6Oj2bRpE5s2bSIqKqrQMR83siwbcx+2bt0aiyLCAWpfucLLgYG8sWgRzU6dEoWXxPky16+XbLBBgwoaQwWFCkTZMn3COXb1GMevHWdKuyloVBp2XtrJpaRLTGwzsczH+v3M77R1b0tT16Zo9VoWnV5Er3q9aOBcjDt9ISRnJpOrz+X7o98z6+gsmrs1Z9eIXYQlhKGX9fSo2wOAkPgQkjOTH3pLtjCik6Op/1N9zFRmTGwzkW96fsPGiI2M2jAKrVx8CMaiAYsY21IYUr81fmyIEIbD4GzjiSdNER6+TWmKmrxVobu7O3FxcUX23bt3bzp2LCg997jIyckhKyuLpKQkli1b9sD2zYOD8d2wAQmQ4QF6P/dhUGlxdBRnhhplo0qhfFG0TJ8Rg1iRLAhagKuNa6mcao7EHqHzks5F1v+7y7/5qsdXZTE9Izq9jv5/9ic2NZbU7FTiUuOQkanvVJ+0nDQcLR1xs3Xj4JWCgeNqSc1gr8GsCl3FS54vsXWYCDpPy0lj8t+TWR683Ni2DW3oh9juMzj0GDAzMyM3N9fEkxNEeiVPT89C8w8+LmRZZs6cOcbMHPfPsSg8oqMZEhCARWZmQYPYoAHExAinmF27Cl7873/DuXMiLlAxiArljKJlqlAkUbejWHb2wauA4sjWZvPezvf4cPeHpbqujXsbJrYufPXapkYb3mpV9q7xKkmFi40LYQlhXEu9Zjzvu5R8ifj0eCJvRxZqDEHkPBziNYSfXvwJFSqjx6ksy8zuPdu4vaqSVPzDP1yzEo4o9+ueGpRe7jc0er2eixcvFnlO9ziQJIm+ffsaxQNK+h35iocHklpdeKW7u/AcvZdXkUGD4MUXxesGDaB3bxEXqBhDhUqEYhCfUnR6HUMChhB0vWDw/FcHv2JM4Bji0+JL3e/xa8fZGLGR2cdmk56bTlRSFItPL2ZjxEZC4kMeeL252pz5/eYzvtV4k3IPRw+OvHGE2g61Sz2n+/nz3J/4rs5LpSNJEst8lzGnz5wCbdf5r2NGhxlF9qWSVPiu8WXq9qlsidrCxoiN5Opyee7/nmPy35PRy3pszW1p4tIEgAOZxQuTF4ZOp2PRokXs2bOnQPD+4+K5557DuZSKKe7XrmGRloZsa5tXaGYmxLwPHBB6pYZzxXXrRKonELqj3bqJ+EKDI87DkpQkxlJQKAOUr2dPKUeuHmFt2FqszaxZ4ruE9Jx0rqRcQS/r2RCxARmZxWcW49vIF1tz2xIZIp1ex0srXzJKl6klNTIyb24WQfnebt6cnXC2xPMD0Kg0aPVaYu7EsPb82jLJJ/jj8R/55/o/RCRG0KhqI0AYRbWq4GrGUmOJm03Rgd/3q9MMWz+Mjzp9xI20G2yK3ES3Ot1Y4ruEGnY1eH/r+1w8dxG04ODggIWFBbeKkiS7j/j4eOLj43Fzc8PHx+exy7mp1WqaNm3K/v37S3xNqoMDRzt0wHzyZHzMzSE5GfUXXwitUEO8YXH07i1SMj0KX30F8+aJeMOySiis8MyinCE+RWTmZrI6dDVZ2iy2RG3h76i/sbewZ2bPmSw6s4jTN/JWH/dLlcVOi6WWQ60HjhESH4J/gD8Xbl8wKe9QswOrB60ukWHN1eVi/bU1L9R/gcUvL+bXU7/y2f7PALj67lVq2pc+kHrHxR0cij2EVq/l2yMiQ3vfBn1pWb0l9hb2vNv+XQasGsDu6N183fNr4tPimXN8DuNajuNI7BHCEkVwugoVeoqWaLsfD0cPatnXwr+JP1PaTeH27dsEBwfj4eFhlESztLQ0ZpovDldXV3x8fKhatSoNitP9LCcCAgKIiooyEfIuDR2PHqX3zp0la2xmJuIO9+wp/UCyLJxyZBk8PIRX66JFQipOkoTTjoJCPhSnmmfQIJ6/dZ7mC5qbrGoMHpAAbjZuRqkyEEbRTG3GTy/+xPjW4wv0VxQ7Lu7gxZUvmhjVyLcjaVilYYn7SEhPoIpVFbJ0wlA0+bkJV1KuMKfPHMa3Ho9aUmOhKXkGgHGbxxnjGA3qM4b5OVs5EzUlivO3zmNvYY93NW/+jvqbfn8+ZLxbIYxsPpLv+3zPnet3CAsL48qVKyai2MXF891PlSpVePvtt8tsbiUlJyeHn376ifT09FJdZ5WRgWNyMq4vvcSA2FjU06YVnc4pPyqV2FJ1cABb25KnZJo4USQbNmDQRgWRNSI8HOrWLdU9KDzdKE41zyBerl4cGH2A6rbVjVJnelnk8/v8+c+JmhKFhTrPyMjIzOw5s1TGEITCDICrjSt25naASKVUGlxsXJi+czo2X9tg87WNUeDbUGb7je0D8yLmZ0G/Bfyvx/+QkIxSbDIyHWt1JHhCMM5WznSp04Wmrk1ZfHoxraq3wlxVvKydt5s3s/vMLtH4y0OWszJoJadOneLs2bMFMkSU1BjWrVuX0aNHl6htWaHT6Thx4gRHjhzBobSpkWSZ/lu20POff/D19UU9dSrY2Zm2KcrQ6fXCcDk7C2HukjJ2rHDayTcHI+PGQe1HP4dWeDZRDOJTRufanRnebDg6WWf0gHS0dOTTrp8SdD3IKEptay4cIXZf3l3qMXL1uQxqMoiwyWGcm3iOHh49jPqepeHNlm9S36l+oXVvtXqrVCtOtUptIjBu8PL0fc7XZAt2x6UdvLn5TYauG0qOPqdAP/mJSIxg1blVJRp/StspWCVbcSTySJFtrO9XUimE7t27Y5vfSeUxcPPmTbZv387Bgwe5XtKg+nu0P3aMJmFh1AkN5daiRWLllpoqKj08xO8HbcE6OAiB75LSurUI2bhfiebTT8V5orJlqvCQKE41TxmyLLPm/BoAutTuwrFrx0jOSuZw7GEu3L6Ao6Ujv7/8O51qd2LUhlFEJhbMwvAgFr2cJ7HmbOXMnlEPcQ6EUKgJnhBM7R9rk5SZZCx/v+P7TO8wnda/tmat/9oSG8bAiEBkZGrY1aB19dZsurCJgLAA3u/0PrfSb3E74zZvbhJGc/+V/cX2JSGRrcsm6EbJUlxZaaz47sJ3qGxUNEtvhjfeBdpkZGQUunVqZmaGtbU1KSkphIWFUavWg89yy4KcnBwyMzPRlDb0wZBeCXBMSUEG1FotruPGiXorK5H41sxMOLv8/LNYDcYX4dV84gQ8V0oJwKtXhVZpfkJDS9eHgsJ9KAbxKaSxS2M+7vIx41uNJywhjHe2v4O52py3Wr3FGJ8xxgwY217fRra2YjMuxN2NMxpDw5nfmRtnCDgfQHB8MH8E/8F/e/y3RH3VdarLxNYT8XT2xNHSEd9GvkQkRhBwPoDB6waXal73a5M+iO+PfW88q73IRWpTG0ccC8QjFrZ1qlKpmDBhAjt37sTKyqpU4z4KS5cu5caNG6W/UJKoce0ayc7OXHN3p13+OpVKrAhjY4VRbN1aOL2kpECNGkKA+35OnSq9QTx6VKwEv/xSJCAePBgOH37wdQoKxaA41TyDaPVaztw4Qxv3NsW2++HYD5y5eYblA5cX2+5R+CP4D8YEjuGLbl9wMOYgp26c4m7OXeo61eXC7QvUsKvBKO9RSEiM9B75QDHxP4L/YOymsThbOXN9xnVUkoq0nDTGbRrH6vOry+0+7qc1relIR8wxx5ait0AN4RUvvvgitra2NGnS5HFNkStXrhAQEFBqJxoDbY4fp8f+/ZhnZ6MyPEasrCAzU4RA3L4tVpJbtogV3YQJph20ayccYN54Q2S8KA3Z2XDjRt627P3vFRTyoXiZKgaxSBadXsS4zeOImBxRpIGRZZk6P9bhWuo1br53E1cb13KZi1av5cbdG6Rmp9L0l6bGcoN3bP58hatfXc2QpkMK7edw7GGuplxlxs4Z3EgTq57ven1HTfua1LSvSesarXlt/WtsitxU6tXf/WgkzQN1T/MzkYm4UTDW0czMjIkTJ2JlZcXcuXOxsLAoOpXzcUgAABgGSURBVOFuOZGUlMS8efNMytzc3IgvanszH6N//506sbFkWlpiVYKwEiRJrOgGDICRI4WOaViYyGW4a5fIY6igkJ/ff4dt2yAg4MFti0FJ/6RQJGtCxRnj+vD1fNzlY5O6tefXcvrGae5m3+VqqkjhM2HLBBpWaYiLtQvTO0wvswd2ri6XJWeXkJaTZswYoUKFjbkNaTlpxnaOlo6s9FvJS54vFdrPnug9+K31IzU71VgmIfGv3eIBa29uj7W5NZm5mQ9tDPs26MvJuJPczrzNx10+5suDJXMC6UY3XHAptE6v17Nq1Sp8fHzIysoiKyuL06dPY2dnh4ODA24lyRT/iMTGxhpfq1Qq9Ho9SUlJxVyRh5lOx4m2bQlq3ZrJ8+cX37hlS5Gw15Dj8J9/IDFRCHz//DMsXgx+fkLWTUHBwI8/Cgeq6GioV6/ch1NWiM8IP5/8mY/3fowsy9zNuQsIlRgrjRUWags2D9tM+5rtGbh6IBsjRViFWlKjl/VGI1LHoQ4XplwwnkE+KjfTblJ3bl2ytFnG88P7M9iDcL45N/Fckf0MDhhskrg4P2pJjUbSkK0XZ6WlWd3lj+GsZlsNT2dPsrRZ1LCtQeCFwAf29zzP052SZXw3fMkw/Dk6OzszZcqUEl37KGzfvp1z587h5+eHra0tAQEBWFpacvfuXVJTU4u99rkLF4hs2BBbMzNmxMeLDPdFceMGVKuW9z4pSawOZVmo1dy5A++8IzRPra1FwuDHrNajUEnYskWcEefkwOx7YU/9+kHz5uIL1IwZpfYkVrZMFYNoQkh8CC+tfIm4uwVTEHWp3YXAoYE4WTmRq8vls32fMfPITJM2AxoOYKnvUpytSqd3+SDCEsIYtHYQEYkRJqs3CYlR3qNYFbqKbF02l6Zeop5T4d8Qs7XZfLj7Q3488WOBOnc7d+M9a1Qa6jvVJy41jrTctAJtDeM+6paqgdrU5g3eKLSuuEB9Nzc3/P39qfIYpMi0Wi2yLGN2L1ZQr9ej1Wo5ceKEMW9jUai0WvQaDQ4pKbSKi0OKi6PZuXM45DekhlRPv/8OY8bklY8ZA0uXFt15ZCQ0LHnYjcJTxKhRsPye34JaLTyUQXx5cnUVWrj3x7o+ACUwX8GE5m7NCZ0USj1HU6MyqMkg9o3ah5OVEyAy1L/e/HVjvcFL0q+xX5kbQ4AmLk0Y6T0SGdkYNykhsWfkHpb4LuH0+NMMazYMK03R3pcWGgve7/R+oXX5vwBo9Voib0cWMIZmKjNcrcUZaVHG0ExlxsxeM00SDefH1cYVC7UFMzvOZBKT8MCDWxStY1pcoP7AgQMfizEEYZjN8gXOq1QqzM3NTcqKQn8vXCPVzo69TZqwp3dvrucPmAd4+23w8ioYcvHdd9C3r2mZJAmnnGXLFGP4LLN4MXzxhfj/YJDok2WRSiw4uNTGsDQoBvEZQiWpiE2NNSmLuRNTQPTakPi2UdVG9KnfB4B1YevKbV6GuMmudbriYOGAjGyUmGvi0oSVfiupble92D4M6jkWagvau7cHKNJ4OVgINZaXG75MFasq5OpzmdpuarE6rLn6XIY3G87Z8QXFy5u5NuPKO1dIeD+BGd1n0Kt5L0Yxind516SdIb1SURjiASMjSx8bWtakpKRgV9IHj1qNRXY2Q//8k8bh4aLslVdEdotPPhHxgR98YHqNiwts3pz3cDPIr33yiXC4UXh20WiEGpFh89Kwde7ra7rtXg4oBvEZ4tjVY2j1Wvyb+HN87HG8XLwIuh5Eeo6p230TlyZ82OlDTr91mm3Dt/F/ff+PLrW7lNu8vN28mfviXPaO3EvopFBebfxqqVej1WyrMcp7FNemX+PI2CPM6j2L/p79C7RzsHBgWNNhLPNdxsahGwmbHMboFqPpUqcLG4dsLNB+dIvRtHMXkXabIzez4+KOAm0u37mMpZkldhZ2aDQaBg4ciE8LHyzIk8mztbXlgw8+oGvXrqgLOf9o164db7/9Nh4eHmRnV2xsKEDv3r15++23jXPNr7Jjbm5u4lglyzKu8fE8d8FU8J0XXoCqVcXrws4Djx2Du+I8G9d7XsybN5fZPSg8wWwQX8qpUwdeuudM94iepiVBMYjPEN3rdufIG0dYM2gN7Wq249Rbpzj6xlGszUwlxfwa+/FNr2+wMrNCkiQmt53MB50/KKLXR2ep71KmtpuKJEnUtK/JusHrjCvTkuLX2I+lvkupal0VlaTivY7v4eHoYax3tHQEICU7hYltJjLSeySSJOFq48qSV5bQtU5XdlwSxs5CbYFGJVZrNe1qcviNw8zrO48udboQcycGgPY127Pj9R04WTqRlpNGXKrp2axBAs1gONLS0sjNzcXHxwedztRpCCAqKgoHBwdGjRpF7969S3Xv5YFKpSIjIwO1Wk3Pnj2ZMWMGNWvWRJIk/Pz8TJx/JOBqrVqkd+smzgUdHcXqUP+ArCE3bkD16qLtxYviXDEhobxvTeFJoEEDmDoVQkLEl6RffxU5NMsZxalG4anlaOxR/Nb68WbLN/mi2xfMPT6XWUdn8f/t3XtQVPX7B/D3AgHS7rogiiILklfwAgh4T4WJTM0b/jS1fzK1TE1nSmZMHW/FNFnm3RQZnSxTv4YXLEzF0TCQ8IYCjmFSSl5AQC7KRVie3x/EGRAQzYXV3fdrhhnO+ZxzeD8Hdh/O7tlzdv/fbgztMLTO8rN+noW463H434T/4SWrlzDpx0nwbu2N70K/U5YpN5Qj7nocgjyDYKWywkPDQ5y6fkqZBqreHwwPD4enpyfGjBmDM2fOID4+HlOmTKm6tN6ePWjbti1CQ0MRGxuL9PR0+Pv7Y8SI+j9WYkoiUudosLi4GNHR0Rg0aBD0ej0yU1Lw2y+/YPT77+NlrbbqyjQnTgBvP8G9LWtcBq7eaSIj4FmmbIj0lKrvDFLzIxA1T/Z5GtV/d9Xbqvl3WFZWBnt7e+Vn1JwmIuPjB/OJntKjjU+lUtW5FumTevQ2SjWnazY/lUrFZkj0nOB7iERERGBDJCIiAsCGSEREBIANkYiICAAbIhEREQA2RCIiIgBsiERERADYEImIiACwIRIREQFgQyQiIgLAhkhERATAjK9lWn3N8sLCQhMnISIiU6ruA43dy8JsG2LRvzce1ev1Jk5CRETPg6KiojoX3q/JbG//VFlZiVu3bkGj0dS6n1tNhYWFVfdzy8y0iFtEsV7zZUm1ApZVryXVCjRNvSKCoqIiuLq6wsqq4XcKzfYI0crKCm5ubk+0rFartYg/tGqs13xZUq2AZdVrSbUCxq/3cUeG1XhSDREREdgQiYiIAADWy5YtW2bqEKZkbW2NoUOHwsbGbF89roX1mi9LqhWwrHotqVbAdPWa7Uk1RERET4MvmRIREYENkYiICAAbIhEREQA2RCIiIgBsiERERADYEIleeEVFRbh165Zy/V4yT2fPnoXBYDB1jCZXUlKC4uJik/xsNkSiF1BlZSU+++wzeHp6QqfTQa/XQ6fToUOHDggPD0dlZaWpIxpVbGwsli1bhv3799cZmzVrlgkSNb/Ro0cjLy/P1DGMKiIiAunp6QCArKwsvP7661Cr1dBqtXjjjTeQnZ3drHks6nOIpaWleOmll2BtbQ0ASEhIQHR0NABgzJgx6N+/vynjNZtJkyZhy5YtT3RtvxdNZWVlvRfv/eeff5742rYvgrlz5+LUqVNYtGgRfH19odPpUFBQgAsXLuDzzz/HoEGDsHbtWlPHNIrt27fj448/RlBQEJKSktC1a1fs378fGo0GQNU1L83pNm9OTk71zi8oKIBWq4VKpTKbxujm5oa0tDS0bNkSkydPho2NDZYvXw4RwYoVK1BaWoo9e/Y0Wx6Laoh9+vTB1q1b4ePjg++//x6zZs3C2LFjAQDR0dHYtGkTpkyZYuKUxrNu3bp65y9ZsgRhYWHQaDSYO3duM6dqGjdv3sTEiRORlJQELy8vrFmzBsHBwcq4uT1pOjs7Iy0tDS4uLnXG7ty5g+7duyM3N9cEyYzP29sb27dvR9++fVFaWorp06fj6tWrOHbsGLRaLTQajVm9XNypUye4u7vjk08+ga2tLYCquzWMHz8ekZGRcHJywpAhQ0yc0jjUajWKioqgUqng6uqKP//8Ew4ODgCqXjr18PBo3qNEsSBarVb5vmfPnhIfH69MJyYmSrdu3UwRq8moVCrp1auXDB06tNaXra2tDBgwQIKCgkwd0WgmTJgg7777rly6dEnWrVsnOp1OfvjhB2VcrVabMJ3xOTk5SVZWVr1jd+7cEUdHx2ZO1HRqPm6rvf/+++Lv7y95eXmi0WhMkKrplJSUSFhYmPTo0UPi4uKU+W3btm3wd/6i8vX1VWrs1q2bZGZmKmM3b94UZ2fnZs1jUQ3R2dlZ8vPzRUSkVatWUl5erowZDAaze9LcvHmzdOzYUb799tta883xgeXi4iLFxcXK9Pnz58XFxUWp3dyeNGfPni1+fn6yb98+uXbtmuTm5kpGRobs27dP/P39Zc6cOaaOaDSenp7y119/1Zn/wQcfiK+vr7Ro0aL5QzWDc+fOib+/v0ybNk3y8vKkXbt2Zve4PXjwoOj1eomMjJSvvvpKfH19Zfv27bJ9+3bx8/OT+fPnN2sei2qI06dPl9mzZ4vBYJBp06bJpk2blLEtW7ZIYGCgCdM1jdu3b0toaKgEBwfL1atXRcQ8G6Kjo6M8ePCg1rxLly6Ji4uLbN261ewaYnl5uSxZskT0er2oVCqxsrISlUoler1elixZUuufvRfdjBkzZOnSpfWOzZo1S1QqVfMGakYVFRXyxRdfiLu7uzg4OJjd41ZE5PDhwxIYGKj8DatUKvHw8JDw8HAxGAzNmsWi3kMsKCjAm2++iczMTHh5eeHo0aNwc3ODSqVCSUkJfv75ZwQEBJg6ZpOIiopCWFgYpk6dig0bNiAlJQVt2rQxdSyjGTJkCBYsWIDhw4fXmp+amorXXnsNOTk5qKioMFG6ppWfn4/79+9DrVZDp9OZOo7RPXz4EBUVFcp7S4+6ceMG3N3dmzlV88rIyMCpU6cwadIk2NnZmTpOkyguLsa9e/egVqtNdsKfRTVEoOrN6ZiYGMTFxSEvLw9qtRo+Pj4YN26cWZ51WVNBQQHCwsJw7NgxnD17Fq1atTJ1JKOJiYlBYWEhJk2aVGcsLS0Nq1atwrZt20yQjIheFBbXEB9lCf9d1mRJ9VpSrUT07Cy6IZaVlcHBwcEirv4AWFa9llQrERmHxV+pxtL+H7Ckei2pViJ6dhbfEFUqlakjNCtLqteSaiWiZ2fxDZGIiAhgQyQiIgLAhkhERASADdHiTrywpHotqVYienYW3RCtra0xcuRIU8doNpZUryXVSkTGYdGfQyQiIqpm0UeIRERE1dgQiYiIwIZIREQEgA2RiIgIABsiERERADZEIiIiAGyIRHUEBATg4MGDTbb93bt346233jLa9hrLGx8fD71e/1Tb279/vzGiGVVISAhiY2NNHYPMGBsiUQ0VFRVIS0uDj49Pk2y/srISCxcuxOLFi5V55eXlsLe3x8svvwy1Wq18JSQkGCVvampqrfGysjKsXLkSPj4+0Gq1aNu2LUaNGoXTp08r2+vevfuzFdoEFi9ejLCwMFPHIDNmY+oARM+TP/74A3Z2dujQoUOTbD8mJgZOTk7o2bOnMi8tLQ0PHz5EYWEh1Gr1U23vSfLWbIglJSUYPnw4ysvLERkZCX9/fzx48AA7d+7E0aNHodVqAQAdO3Z8+uKa2ODBg5Gfn4/4+HgMHDjQ1HHIDPEIkaiG5ORk9OrVS5nevHkzunbtCq1Wi4EDByItLa3W8hs2bECnTp3g6OiIJUuWYPDgwdi5c2eD24+OjkZwcHCteefPn4eHh8djm2FDOR7NCwARERHo0qULHB0dsWrVKqSkpCgNcdGiRcjJyUFsbCwCAwNhZWUFjUaDmTNnYunSpUhOTka3bt2wfv16uLu7Q6vVIjw8XNn2/fv3MXHiRLi6ukKtViMwMBDJycnK+N69exEQEIDly5dDr9fXWf9J9tnevXvh7+8PnU4HX19fJCYmAqi6v2VwcDCio6Mb3E9Ez4INkaiG5ORkpXmsXLkS33zzDQ4dOoT8/HwMGzYMkydPVpb9+uuvsWXLFhw6dAjZ2dm4fv06EhMTH/vyZXXDqen8+fPo0aNHg+s8LkfNvACwZs0arF27FlFRUcjOzkZKSgpOnz4NHx8f5OTkYNOmTfj000/RokWLBvOlp6fDyckJ165dw48//oilS5eirKwMAJCfn4+3334bV69eRU5ODnr16oX58+fXWv/y5ct45ZVXkJGRUWf9xvbZ5s2bsWDBAkRERCAvLw8zZ87ExIkTlQu1e3t712rAREYlRKQICQmRyMhIyc3NFY1GIykpKcrY9evXBYAUFxdLQUGBaLVaSUxMVMaPHDkidnZ2Ul5eLgkJCdKvXz8ZPHiwjBgxQu7duyciIp06dZK9e/fW+pn9+/cXOzs7admypfL1999/i4g0mqM6r4gomc6cOaMse/z4cXFwcBCDwSC7d+8WOzs7qaioeGz9H330kTJ969YtsbKykrKysnqXP3z4sPTu3VuZHj58uMybN6/e9RvbZ/n5+aLRaOS3335Txg0Gg9jY2MjNmzdFRCQiIkICAwMbzE/0LHiESFTDxYsX4ePjg4SEBLi5udU6csvJyYGTkxNatGiBEydOoH379ujbt68ynpubC29vb9jY2MDDwwPHjx/Hr7/+ilGjRmHjxo0AAEdHRxQWFirrGAwGXLx4EVFRUcjPz1e+PDw8AKDRHNV5AeDEiRNo06YNAgIClGWzsrLQs2dPWFlZ4fbt22jfvj2sra0fW/+4ceOU6ZSUFHTu3Bm2trYAgJ9++gnBwcFo164ddDodQkND4e3trSyfnJyMsWPH1rt+Y/vsxIkTuH//PkaOHAmdTgedTgcnJyfY2NgoLycXFhbC0dHxsb9Dov+KJ9UQ/ev27dvIzc1F9+7dkZqaWueJNyoqCq+++ioA4O7du3XGDxw4oDQnV1dXZb6NjQ1sbKoear6+vrhy5YoyduXKFRQXF6NPnz71ZsrJyWkwR8281Zlat25da9ndu3crmTp06ICsrCyUlZXBzs6u3vrv3r0LX19fZd65c+fg5+cHoOrjG++99x527dqF/v37w9bWFqNHj0bv3r0BANnZ2bhz546y/KPrN7bPcnNzERQUhOPHj9e7LwDg8uXLtfIRGZWpD1GJnhcxMTHi5eUlIiKpqani4OAgycnJUlpaKjt27BBnZ2e5dOmSiIicPn1a7O3t5fz581JSUiKrV68Wa2trWb16da1t5uTkiJ+fn9y9e1dERKKjoyUgIEAZ37Fjh3h4eDSY6XE5auYVEYmPjxdbW1tJSkqS4uJi+fLLL0WlUsnGjRtFROTBgwfSuXNneeeddyQ7O1sMBoNkZGRIeHi47Nu3T2JiYqRLly61fv748eNl5cqVIiKyfv168fX1lQcPHkhRUZGsWLFCAMjJkydFROSXX36RTp06Nbh+Y/ssOTlZNBqNnDx5UiorK6WkpETi4uIkMzNT2Z6np6fExcU97tdI9J/xJVOif9V8+bF79+5YtWoVRo0aBWdnZ2zbtg1HjhxRPi7Rr18/zJs3D0FBQejcuTNEBO3atat1pFdcXIwJEyZgw4YNcHZ2BgCMGDECOTk5SE1NBVB1Qk1DR4eN5aiZFwAGDBiAefPmISQkBF5eXrhx4wZcXFyUZRwcHHD8+HGUl5fDz88PWq0WwcHBSE9PR58+fXDx4sVaR3dA7SO8yZMno2XLlmjdujX69u2LNm3aQKVSKeMXLlxQjhbrW7+xfebj44O1a9dixowZ0Gg0cHd3x+effw57e3sAwKlTp6DRaJSjdCJj4w2CiYwgKSkJI0aMQFZWFqytrVFRUYHQ0FBMnTq11ntyALBr1y4cOHAAe/bsMVHa58Oj+6wxw4YNw/z58xESEtIM6cgSsSES/Qe///47WrVqhY4dO+LChQuYMmUK5syZgzlz5gAAvvvuO3z44YfK+10jR460+KusNLbPiEyNDZHoP9i6dSsWLlyIkpISeHh4YM6cOZg5cyZUKpWpoz23uM/oeceGSEREBF6phoiICAAbIhEREQA2RCIiIgBsiERERADYEImIiACwIRIREQFgQyQiIgLAhkhERASADZGIiAgAGyIREREA4P8B8Sbq34w6yZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = resultDds05\n",
    "visuz.gene_exp.volcano(df=df, lfc='log2FoldChange', pv='pvalue',  color=(\"red\", \"grey\", \"green\"), markerdot='*', plotlegend=True, dotsize=25, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let us make the massachussettes plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srivastava/anaconda3/lib/python3.8/site-packages/pandas/core/series.py:679: RuntimeWarning: invalid value encountered in log2\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "<__array_function__ internals>:5: UserWarning: Warning: converting a masked element to nan.\n",
      "/home/srivastava/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py:85: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/home/srivastava/anaconda3/lib/python3.8/site-packages/matplotlib/ticker.py:586: UserWarning: Warning: converting a masked element to nan.\n",
      "  s = self.format % xp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visuz.gene_exp.ma(df=df, lfc='log2FoldChange', ct_count='baseMean', st_count='stat', plotlegend=True, \n",
    "    color=('#00239CFF', 'grey', '#E10600FF'), show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us check the names of all the geneIds that we have queried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <span>StrVector with 279 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            ...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            'ENSG0000...\n",
       "            </td>\n",
       "          \n",
       "          </tr>\n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<rpy2.robjects.vectors.StrVector object at 0x7fe6588c82c0> [RTYPES.STRSXP]\n",
       "R classes: ('character',)\n",
       "['ENSG0000..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000..., ..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000..., 'ENSG0000...]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R queryGeneName <- geneName$query\n",
    "%R -o queryGeneName\n",
    "queryGeneName\n",
    "#print(queryGeneName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.7201211 , 3.70187149, 2.1923488 , 1.86838281, 3.36989703,\n",
       "        3.02927173],\n",
       "       [      -inf,       -inf,       -inf,       -inf,       -inf,\n",
       "              -inf],\n",
       "       [2.84465236, 1.8047515 , 2.12781027, 2.37920843, 2.60264188,\n",
       "        1.81624909],\n",
       "       ...,\n",
       "       [      -inf,       -inf,       -inf,       -inf,       -inf,\n",
       "              -inf],\n",
       "       [      -inf,       -inf,       -inf,       -inf,       -inf,\n",
       "              -inf],\n",
       "       [      -inf,       -inf,       -inf,       -inf,       -inf,\n",
       "              -inf]])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R exprsData <- exprs(data)\n",
    "%R cpmMat <- cpm.mat\n",
    "%R -o cpmMat\n",
    "cpmMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>SRX014818and9</th>\n",
       "      <th>SRX014820and1</th>\n",
       "      <th>SRX014822and3</th>\n",
       "      <th>SRX014824and5</th>\n",
       "      <th>SRX014826and7</th>\n",
       "      <th>SRX014828and9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>56</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000005</th>\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>ENSG00000000419</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>LRG_94</th>\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>LRG_96</th>\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>LRG_97</th>\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>LRG_98</th>\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>LRG_99</th>\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>52580 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRX014818and9  SRX014820and1  SRX014822and3  SRX014824and5  \\\n",
       "ENSG00000000003             60             60             16              9   \n",
       "ENSG00000000005              0              0              0              0   \n",
       "ENSG00000000419             25              9             15             15   \n",
       "ENSG00000000457             32             19             21             31   \n",
       "ENSG00000000460              1              3              0              5   \n",
       "...                        ...            ...            ...            ...   \n",
       "LRG_94                       0              0              0              0   \n",
       "LRG_96                       0              0              0              0   \n",
       "LRG_97                       0              0              0              0   \n",
       "LRG_98                       0              0              0              0   \n",
       "LRG_99                       0              0              0              0   \n",
       "\n",
       "                 SRX014826and7  SRX014828and9  \n",
       "ENSG00000000003             56             37  \n",
       "ENSG00000000005              0              0  \n",
       "ENSG00000000419             26             11  \n",
       "ENSG00000000457             28             28  \n",
       "ENSG00000000460              1              1  \n",
       "...                        ...            ...  \n",
       "LRG_94                       0              0  \n",
       "LRG_96                       0              0  \n",
       "LRG_97                       0              0  \n",
       "LRG_98                       0              0  \n",
       "LRG_99                       0              0  \n",
       "\n",
       "[52580 rows x 6 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R exprsData <-  as.data.frame(exprsData)\n",
    "%R -o exprsData\n",
    "exprsData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us check the 23rd entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>SRX014818and9</th>\n",
       "      <th>SRX014820and1</th>\n",
       "      <th>SRX014822and3</th>\n",
       "      <th>SRX014824and5</th>\n",
       "      <th>SRX014826and7</th>\n",
       "      <th>SRX014828and9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000080824</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRX014818and9  SRX014820and1  SRX014822and3  SRX014824and5  \\\n",
       "ENSG00000080824             15              5             20              1   \n",
       "\n",
       "                 SRX014826and7  SRX014828and9  \n",
       "ENSG00000080824              5              5  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R exprsData[rownames(exprsData) == queryGeneName[23],]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "    dataFrameNew = 0\n",
    "    x = length(queryGeneName )\n",
    "    i = 1\n",
    "    while (i <= x ){\n",
    "    dataFrameNew = rbind(exprsData[rownames(exprsData) == queryGeneName[i],], dataFrameNew)\n",
    "    i = i +1\n",
    "    \n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>SRX014818and9</th>\n",
       "      <th>SRX014820and1</th>\n",
       "      <th>SRX014822and3</th>\n",
       "      <th>SRX014824and5</th>\n",
       "      <th>SRX014826and7</th>\n",
       "      <th>SRX014828and9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000244734</th>\n",
       "      <td>201.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242574</th>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000242220</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000216588</th>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215256</th>\n",
       "      <td>102.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>25.0</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>ENSG00000008517</th>\n",
       "      <td>64.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000008323</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000006210</th>\n",
       "      <td>90.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000003249</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000002834</th>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRX014818and9  SRX014820and1  SRX014822and3  SRX014824and5  \\\n",
       "ENSG00000244734          201.0           31.0          260.0            9.0   \n",
       "ENSG00000242574           17.0           22.0           16.0           63.0   \n",
       "ENSG00000242220            4.0            0.0            3.0           14.0   \n",
       "ENSG00000216588           13.0           11.0            8.0           13.0   \n",
       "ENSG00000215256          102.0           59.0           34.0           18.0   \n",
       "...                        ...            ...            ...            ...   \n",
       "ENSG00000008517           64.0          104.0          245.0          652.0   \n",
       "ENSG00000008323            7.0           11.0            7.0           16.0   \n",
       "ENSG00000006210           90.0          133.0          162.0          229.0   \n",
       "ENSG00000003249           14.0            6.0           23.0           89.0   \n",
       "ENSG00000002834           22.0           29.0           21.0           59.0   \n",
       "\n",
       "                 SRX014826and7  SRX014828and9  \n",
       "ENSG00000244734           15.0           48.0  \n",
       "ENSG00000242574           44.0           27.0  \n",
       "ENSG00000242220           11.0            6.0  \n",
       "ENSG00000216588           25.0           81.0  \n",
       "ENSG00000215256           51.0           25.0  \n",
       "...                        ...            ...  \n",
       "ENSG00000008517           99.0          422.0  \n",
       "ENSG00000008323           19.0           32.0  \n",
       "ENSG00000006210          381.0          218.0  \n",
       "ENSG00000003249           22.0          258.0  \n",
       "ENSG00000002834           57.0           34.0  \n",
       "\n",
       "[279 rows x 6 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%R dataFrameNew = dataFrameNew[1:x,]\n",
    "%R dataFrameNew\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R -o dataFrameNew\n",
    "#type(dataFrameNew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a heatplot just because...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0kwSJIeiayJuPgai55ppojwAId/58tEcA9JSQ0PtmBgDA5TXglSbTNGWapqqrq9XV1aW8vDxJUlNTk2w2mzIyMiRJhmHIsiwlJCSE+va1e60/bX6/X4FAILQbz+fzqa6uTpJ09OhRfetb31JnZ6c6OjrU0dGhc+fOKSYmRh9//LH+9m//Vtdee62mTJmi8+fPKyEhQenp6RE/z7Istbe3hx2dvNsNAAAAAACAQTJsMuUu7NfY2Kg77rhDjzzyiGpra1VTUyPzgrJCbre7z/lFypT71Xky5QAAAAAAwGXS3T3yjhFu2GXKBYNBTZ8+XT6fT++//77sdrtaWlpUWFionTt3Khi8+Ht/kTLlvmOSKQcAAAAAAIDBMWwy5c6fPx/qZ7PZJEmdnZ3Kz89XWlqali9fLofDIcMwVFlZqR07dmjfvn2h120vFClTrisQo65LfMgAMBIEAtEeARAu5ly0RwCEY0MAhqKOjwkrxtAzM9oDAK4QI9ifrWPQv9pujPYQAADAAMRQowlDDItyGIr4Xomh6OHOxmgPITo6fhXtEVx5cd+J9giiakA75crLyxUXFyfTNFVRUSGv1yuPxyOn06mSkhKlpqYqPT1dmZmZqqqqUnx8vCSpqKhIpaWlstlsWrRokRobG+X1emUYhoqLi/vd1traKrfbrbq6OjkcDvl8PuXm5mrz5s367W9/G+q3dOlS5eTkKCcnR/n5+YqLi9Mbb7yhDz/8UG63W88995zGjRunWbNm6Z577ukxT8uyZFlW2LnOYLfsBn9iAQAAAAAA4IsbloUegsGgkpKS1NbWJsMwtHHjRt1yyy2SJJvNppMnT6qlpUWTJ0+OOL9IhR5+HaTQAwAAAAAAuEyC3SPvGOGGZaEHSVq3bp3mzJmjDRs2aMuWLdqyZYt27dqlQ4cOae7cuVq5cqU2bNgQ8d6RCj08ZFDoAQAAAAAAAINjWBZ6OHHihNauXaujR49q6dKl+t73vidJamlpUXJysn7605+qvr5es2fPjjjHSIUeeHUVQ4kj9uLXAFeaOeA/UYDL6yyFHjDEkN2FoYisQwCIngH/aGCapkzTVHV1tbq6upSXlydJampqks1mC1UzNQxDlmUpISEh1Le3XW/9bfP7/QoEAqHdeD6fT3V1dT2uT05O1sKFC7Vnzx5NmjRJ9fX1evHFFzVt2jRNmDBBLS0tuu666yJWXpU+zZRrb28POzrZVgkAAAAAAIBBMiwz5T7fVlNTI/OCLRwX5s1FQqYcAAAAAAAALqdhmSm3Z8+esLaWlhYVFhZq586dCgaDYXlzkZApBwAAAAAArqhoF12g0MMVNywz5W677TbddtttobZAIKDly5fL4XD0yJuLJFKm3JhYQkAwdJzvivYIgJ74ugSAvpEph6GIr0sAiJ5hmSn30Ucf6dVXX1VbW5uampp08uRJ2e12JSQkKDk5WZ2dnRo3bpymTJky0OkDAAAAAAAAX9iwzJSLi4vT9OnT1dTUJLvdroaGBpWVlWn79u0KBoNyu919zo9CDwAAAAAAALichmWmnCTdddddcjqdOnjwoObNm6eKigpZltXnjrzPRCr08M55Cj0AAAAAAABgcAzLTLkjR46otrZWR44cUVFRkU6dOiWbzaZ58+ZJkiorK7Vjxw7t27cv9LrthVwul4qLi8POvTv+Vn1CXhIA9IpMGgw13WxyBwAAVxPe0BtxjOBnW8vQpxrjxmgPAQCGNBblMNSwKAcAwNXp0WBjtIcQHf63oj2CK2/8I9EeQVQNaKdceXm54uLiZJqmKioq5PV65fF45HQ6VVJSotTUVKWnpyszM1NVVVWKj4+XJBUVFam0tFQ2m02LFi1SY2OjvF6vDMNQcXFxv9taW1vldrtVV1cnh8Mhn8+n3Nxcbd68Wb/97W9D/QoKCrRmzRodPnxYTz/9tBoaGnTs2DFt2rRJ1dXVKi0tld1u15NPPqkbbrihxzwty5JlWWHnOtUt+6VF8AEAAAAAAABhRkShh9zcXN11112aM2eODh48qDvuuEOPPPKIamtrI84vUqbcb0SmHAAAAAAAAAbHiCj0IEm1tbV6+OGHNX36dPl8Pr3//vuy2+0R7+1yueT3+8OOB5XYvwcEAAAAAAAwQMFg14g7RroRUejh3LlzCgaDGj16dGgenZ2dys/PjzjH2NhYxcbGhp3j1VUA6Bv5XRhqyDkEgIvjeyUARM+AvwWbpinTNFVdXa2uri7l5eVJkpqammSz2ULVTA3DkGVZSkhICPXtbddbf9v8fr8CgYAKCgr00Ucf6dVXX1VbW5uampp04sQJBQIBdXV16dprr9Xx48c1evRo2e12vfXWWxo/frwqKirU0dGho0ePyrIs7d+/f6DTBwAAAAAAAL6wqzZTbuzYsWG5cQ0NDSorK9P27dvV2NgYlht3YZvNZtPJkyfV0tKiyZMnR5yfZVlqb28POzrFFhAAAAAAAAAMjqs2U04Kz42bN2+eKioqZFmWbr311rDcuAvbWltbNXfuXK1cuVIbNmyIeF8KPQAAAAAAAOByumoz5R544IGw3LhTp07JZrNp3rx5ivm/YITPcuP+9Kc/hdrGjBmj9957T/X19Zo9e3bEObpcLhUXF4ede3f8rZfweAEAQLSQcwgAF2cO+DdCAJcNP7yMOEbws3Kl6FONcWO0hwAAAAAAg+qaa6I9AqCn71qN0R5CVARPr4v2EK44I/H70R5CVA3o70XKy8sVFxcn0zRVUVEhr9crj8cjp9OpkpISpaamKj09XZmZmaqqqlJ8fLwkqaioSKWlpbLZbFq0aJEaGxvl9XplGIaKi4v73dba2iq32626ujp1dnZqzZo1Onz4sJ5++mn9/ve/V3Nzs0zT1BNPPKHnnntO48aN06xZs3Ts2LFQ2z/8wz+otLRUdrtdTz75pG644YYe87QsS5ZlhZ3rVDcVWAEAAAAAADAohmWhh88Xc+irCEQkZMoBAAAAAADgchqWhR4+X8yhryIQkbhcLvn9/rDjQSUO9FEBAAAAAAAAEQ3LQg+fL+YQDAZ7LQIRSWxsrGJjY8POjTJ5dRVDB/mfGIpi+DYJAH0iUB8A0Kcgv+iNNAP+Fco0TZmmqerqanV1dSkvL0+S1NTUJJvNpoyMDEmSYRiyLEsJCQmhvn3teutPm9/vVyAQUEFBgSZMmKBRo0bJ4XDIbrersbFRlmWpublZEyZM0Fe+8hWZpqk777xTBw4ckCTt3LlTp0+fVmdnp5qbm3XmzJmIn2dZltrb28OOTv7lAAAAAAAAwCAZlplywWBQbrc7NDa73a7jx49r3LhxSkxMVFlZmbKzs+Xz+SLOL1Km3IZuMuUAAAAAAAAwOIZlptzn+5w6dUoej0eWZSkYDKqhoUGHDh1SVlZWxPtGypSbHUOmHAAAAAAAAAbHsMyUk6TKykrt2LFD+/btU0xMjJYtW6ZgMKgTJ07oscce0/z587V//35NmzatxxwjZcoZXTE6f4kPGQBGArIOMdSQc4ih5pNPoj0CAAAwlAybTLkLc+N8Pp/OnTunpKQktbW1qb29XWPHjtV9992n5OTk0C6/66+/fqDTBwAAAPD/2Lv/6KjqO4//z5u5w0wgQpKaXxzj91hXyIq4mUpbrdaFoy3sVys/TkVUPBsibvYUpSQhazIMe2YFlJEv7uyp/fJNtuaLpKvdSCu6fttsrcg5Log/UGjEMVDShpqYoPxIiMCdYXK/f7g7y5AJJBiYMXk9zrmn7f3MvXl/piE/Pvnc11tERIaf3Tf6jlFuxGTKnZkbN3HiRJYtW0ZeXh7Tp08nJyeHkydPEg6HAWKLdQMtBCZs9IA+WUREREREREREZHiMmEy5s3PjOjo6KCgowDAMlixZQk1NDVu3bgVg06ZNzJ07ly1btiS8b6JGDy+jRg8iIiIiIiIiIjI8Rkym3O9///tYbpxhGDQ0NLBw4UIANm/eTCgUIi8vj66uLjZu3EhbWxvLly9POMeamhoqKirizr044YYLeHtFREQkWZRzKCIiIiKpzLBt2052EV8FzxmTk12CiIiIiIiIyIh3n92S7BKSwv6sPtklXHLG5aXJLiGphrRTzu/3k5GRgWmaBAIBQqEQwWCQ6upqqqqqKCwspKioCI/HQ11dHZmZmQCUl5fj9XpxOByUlJTQ0tJCKBTCMAwqKioGPdbV1YXP56OpqYlIJEJ9fT2tra0sXbqU1157jZ6eHtra2qipqaGxsZGdO3dSVlbG0aNH+fDDDzl9+jRLly5lw4YNHDx4kIqKCiZP7r/YZlkWlmXFnYvQh/PCIvhERERERERERM5NjQ9GnVHR6GHevHl4vV6OHDlCdnY2K1asYObMmXR0dCScnzLlRERERERERETkYhoVjR5s22b16tU88sgjAOzZs4cDBw4wY8aMhPetqamhu7s77riL7CG9TyIiIiIiIiIiIgMZFY0efD4fhw8fZuvWrYwfP54HH3yQBQsW0NzczNSpU/vN0eVy4XK54s6NG6NHVyV1pOnTUVKQQvUl1ehzUlKNvn9LKtLXShGR5BnyjwamaWKaJg0NDUSjUUpLvwjla29vx+FwUFxcDIBhGFiWRVZWVuzac+16G8xYd3c34XCYsrIycnJySE9Px+1243Q6ycjIID09nXA4zPHjx3G73axbt44DBw7wN3/zN3z00Uf87d/+LXl5ebFdflddddVQpy8iIiIiIiIiMvzsvtF3jHJD3ikHA2fKzZw5kxkzZvDkk0/i8/liu83ef/99pk2bxsyZM3n66afp7e1lzZo1rFixIpYbN5ixmpqafplyb7zxRr9MubPHzsyiA8jNzeXo0aMDLgQmbPRg9+E09OdNERERERERERH58kZsptyZY2fbtGkTc+fOZcuWLQnvm6jRw4tRNXoQEREREREREZHhYdi2bQ/2xX6/H7fbjWmaHDp0iJUrVzJlyhT27dtHMBjEtm0ikQiLFi2itraW/Px8Ojs7eeyxx/B6vaSlpcVy40KhEIZhUFFRMeixU6dOUVlZyR133NEvU+7Xv/41n3/+OceOHWPJkiVxY5FIJHbdggUL2LhxI21tbSxfvpyvf/3r/eaZaKfcixNuwHlhzWpFREYF84L2XotcPKdPJ7sCERERuRD32S3JLiEp7EN1yS7hkjNy/y7ZJSTVkBblRrPnjMnJLkFEJKVpUU5SjRblREREvpq0KDd6jPZFuSH9CuX3+8nIyMA0TQKBAKFQiGAwSHV1NVVVVRQWFlJUVITH46Guro7MzEwAysvL8Xq9OBwOSkpKEu6GG8xYV1cXPp+PpqYmIpEI9fX1tLa2snTpUl577TV6enpoa2vjJz/5CatXr6a3t5e1a9fy2muv8d577xGNRqmurmbWrFnMmjWLxYsXk5GR0W+eCTPl6NNOORERERERERG5ONQOedS5oFWmgRo9XHvttdx222387Gc/w+fzUVlZSWVlZaxhQ3V1NZs3b+bNN9+kqqqKo0ePDmnszIYN/9PMob29vV+jBwCfzxer7dZbb+XYsWNEIhHg/I0eEmXKvYwy5UREREREREREZHiM2EYPZ3I6naxZswan0wmcv9FDTU0N3d3dccddZJ//zRERERERERERERmEIScABYNBTNOkr6+PsrIypkyZQjAYZP369di2zaRJk1i0aBGrVq0iPz8fl8tFcXExjY2N7Nq1K9bMYd26dWRnZ+PxeHjhhRcGNdbc3MyOHTuora3t1+jh97//PatWrYotANbW1vL222+ze/dudu/eTVtbG2lpaXR1dcU1ekjE5XLhcrnizunRVRGRc1N+l6Sav5o68B/2RJJhT7OinCX1ZIxLdgUiIqPXkFeaTNPENE0aGhqIRqOUlpYC0N7ejsPhoLi4GADDMLAsi6ysrNi159r1Npix7u5uwuEwZWVl5OTkkJ6ejtvtxul0kpGRQXp6OuFwGIBPP/2Ub33rWxQXF1NYWMjYsWNxOp3k5eURiUQYP358ws6r8EWmXE9PT9wRQc92i4iIiIiIiIjI8BiVmXJnjiWiTDkRERERERERuaTsvtF3jHKjMlPufJQpJyIiIiIiIiIiF9Ooy5QD4sb+53HbMyXKlMsap0w5SR3qlC2pKBxJdgUi8doOKr9LUktuTrIrEOnv1KlkVyAiMnoZ9tnbyiShlzMmJ7sEkRgtykkq0qKcpBqFl0uqGTMm2RWI9KdFOUlFd/a0JLuEpLA/+b+TXcIlZxT8KNklJNWQdsr5/X4yMjIwTZNAIEAoFCIYDFJdXU1VVRWFhYUUFRXh8Xioq6sjMzMTgPLycrxeLw6Hg5KSElpaWgiFQhiGQUVFxaDHurq68Pl8NDU1EYlEqK+vp7W1laVLl/Laa6/R09NDW1sb//Iv/xJ3XXNzMwcPHuT111/npZdeYtasWcyaNYvFixeTkZHRb56WZWFZVty5iN2H09BuORERERERERER+fKG/PgqDNzoYebMmcyYMYMnn3wSn88XewT0/fffZ9q0acycOZOnn36a3t5e1qxZw4oVK2LNHAYzVlNT06/RwxtvvNGv0UOi63bt2hVbJMzNzeXo0aMD5tU98cQT/NM//VPcuQXObO4dc/mFvF0iIiIiIiIiIuemxgejzohs9JDousbGRubPnw/Apk2bmDt3Llu2bEl430SNHn7oVKMHEREREREREREZHiOy0cPZ1508eRLbthk7dixdXV1s3LiRtrY2li9fnnCOiRo9WCfSsBK+WkRERFJR7+fJrkAk3unuZFcgIiIiqWTIO+VM08Q0TRoaGohGo5SWlgLQ3t6Ow+GIdTM1DAPLssjKyopde65db4MZ6+7uJhwOU1ZWRk5ODunp6bjdbpxOJ9/5zndwOp184xvfoLe3l97eXk6ePElaWhrPP/88EyZMIBAIEI1GcblcHDlyhLa2toQfz7Isenp64o4I2kYqIiIiIiIiIiLD44I6FwyUKXfttddy22238bOf/Qyfz0dlZSWVlZWxjLfq6mo2b97Mm2++SVVVFUePHh3S2C233NIvU669vR2n08lzzz2HaX6x8W/fvn3ceOON3HvvvTQ2NrJnzx5WrFjBW2+9RUFBAcuWLSMvLy92r7M98cQTTJgwIe54mSMX8laJiIiIiIiIiIj0M2Iy5To7O1m2bBnvvPMOHo+Hjo4OXn31VZxOJ/fccw+BQADLsjAMg46ODgoKCga8d6JMubtQppyIiIiIiIiIXCR23+g7RrkRkym3cOFCVq9ejdvtxuFwABCJRFi8eDF/+MMfcDgc3HPPPQA0NDSwcOHCAeeYKFPOeWGbCkVERCRJTp9OdgUiIiIiIgMbMZlyubm5uN1upk6dSk9PDwcPHqS7u5ve3l5uvvlmsrKy+PDDDzly5AinT59m1apVtLS0DHX6IiIiIiIiIiIiX9qQd8rBwJlyM2fOZMaMGTz55JP4fL7YbrP333+fadOmMXPmTJ5++ml6e3tZs2YNK1asiOXGDWaspqamX6bcG2+8EcuUu/LKK4H/zZSbNGkSjY2NfPvb3+b6669n//79ZGdns2LFCn71q1/R0dHB5MmT+83PsiwsK77XaoQ+7ZYTEREREREREZFhMSoy5bZv38727dt599136e3tZc+ePRw4cIAZM2YkvK8aPYiIiIiIiIjIJdXXN/qOUW5UZMrl5eUB0NnZyeeff86DDz7IggULaG5uZurUqf3mWFNTQ0VFRdy5IeLxgAAAIABJREFUFyfccCHvr8hFkaZNm5KC9D1VUo15Qc8DiIiMLvr+LSKSPIZt23ayi/gqeM7o/5irSLJoUU5SkX6ol1SjRTkRkfPT929JRQuiozP/3f5zMNklXHJG4bJkl5BUQ/px1e/3k5GRgWmaBAIBQqEQwWCQ6upqqqqqKCwspKioCI/HQ11dHZmZmQCUl5fj9XpxOByUlJTQ0tJCKBTCMAwqKioGPdbV1YXP56OpqYlIJEJ9fT2tra0sXbqUTz/9lG3btlFYWMj999/PrFmzmDVrFosXL+a3v/0tBw8e5PXXX+ell16io6ODefPmsW3bNtxud795KlNOREREREREREQupgtaZRqo0cO1117Lbbfdxs9+9jN8Ph+VlZVUVlbGGjZUV1ezefNm3nzzTaqqqjh69OiQxm655ZZ+jR7a29tjjR7MM/4knpuby9GjRzEMg3nz5vHd736XuXPnEo1Gef7555k1a9aA81OmnIiIiIiIiIiIXEwjstGDbdts2rSJuXPnsmXLFgAaGxuZP38+77//PsePH2fnzp1s27Yt4X1ramro7u6OO+4ieyhvk4iIiIiIiIjI4Nl9o+8Y5UZko4dDhw6xceNG2traWL58OSdPnsS2bcaOHcu0adOYNm0afr8/tuvubC6XC5fLFXdOj65KKlH2h4jI+Z0+newKROIp51BERETONOSVJtM0MU2ThoYGotEopaWlALS3t+NwOCguLgbAMAwsyyIrKyt27bl2vQ1mrLu7m3A4TFlZGTk5OaSnp+N2u3E6neTm5uJ2u5k6dSp5eXk88MADvPfee0ycOJFAIMDEiRP53e9+R0dHB8FgkI8++og333xzqNMXERERERERERH50kZkptzZuXE5OTmcPHmScDjMxIkTWbZsGXl5eQPulLMsi56enrgjgrYmiYiIiIiIiIjI8BiRmXLvvfdeXG7ckiVLqKmpYevWrQB0dHRQUFAw4L3V6EFERERERERERC4mw7Zte7Av9vv9uN1uTNPk0KFDrFy5kilTprBv3z6CwSC2bROJRFi0aBG1tbXk5+fT2dnJY489htfrJS0tLZYbFwqFMAyDioqKQY+dOnWKyspK7rjjjn6Zcvv27eO9997j1KlTrF27NlZvdXU1r7zyCqFQCLfbTVVVFYFAgPvvv58rrrgi4Twty8KyrLhzL064QblyIiLnkKYvkZJilL8pIiLy1XSf3ZLsEpLCbvu/kl3CJWf8H8uTXUJSDWlRbjR7zpic7BJERFKaFuUk1WhRTkRE5KtJi3Kjx2hflBtSDyi/309GRgamaRIIBAiFQgSDQaqrq6mqqqKwsJCioiI8Hg91dXVkZmYCUF5ejtfrxeFwUFJSknA33GDGurq68Pl8NDU1EYlEqK+vp7W1laVLl/Lpp5+ybds2CgsLuf/++1m9ejW9vb2sXbs29hhrYWEh9957b9w9r7nmmn7zTLRTLkKfdsqJiIiIiIiIiMiwGJGNHgB8Pl/sv585dvY9E1GmnIiIiIiIiIiIXEwjstHD2U/knjl2vnsC1NTU0N3dHXfcRfY5rxERERERERERERmsIT2+ChAMBjFNk76+PsrKypgyZQrBYJD169dj2zaTJk1i0aJFrFq1ivz8fFwuF8XFxTQ2NrJr165YM4d169aRnZ2Nx+PhhRdeGNRYc3MzO3bsoLa2tl+jh4ULF7J69WrcbjeGYVBbW8vbb7/N7t2748bOvmciLpcLl8sVdy7d1KOrkjrMIf/LFbn4lN8lqeb06WRXICKS+pQJK5JC9AP1qDPkL8GmaWKaJg0NDUSjUUpLSwFob2/H4XBQXFwMgGEYWJZFVlZW7Npz7VAbzFh3dzfhcJiysjJycnJIT0/H7XbjdDrJzc3F7XYzdepUAD799FO+9a1vUVxczHvvvUdmZia33347J06c4Pjx49i2TVtbW8KPZ1kWPT09cUfE1j8OEREREREREREZHqMiUy4nJ4eTJ08SDodxOBx89tlndHZ2cuWVVyacX6JMuS19ypQTEREREREREZHhMSoy5ZYsWUJNTQ1bt26lq6uLu+++myeeeIItW7YkvG+iTLk5acqUExERERERERGR4TEqMuX+8Ic/EAqFyMvLY9y4cbzyyivs3LmTOXPmJJxjokw5I5qGomkkVSgnSURE5KtH2V2SihRhJSKSPCMmU+7M3DiAH/zgB5w4cYKioiLcbndssS4SiVBcXMzHH39MJBIZ6vRFRERERERERIZfnz36jlFuxGTKnZkbF41Gef7555k1axZA3D0LCgpYtmwZeXl5sXudLWGjB/QnJBERERERERERGR4jJlPuzNy4999/n+PHj7Nz5062bdsWd0+Ajo4OCgoKBrx3okYPL6NGDyIiIiIiIiIiMjxGTKbc5s2bY7lx06ZNY9q0afj9fqZPn87p06dj9zQMg4aGBhYuXDjgHGtqaqioqIg791LmDaQNvD4ocsmZQ/7XKyIyuoxNT3YFIv0pv0tSTViJPiIiSWPYZ7cqlYR+4Zic7BJEYrQgJyJyflqUk1SjBTlJRVqUk1Q072RLsktICvtAINklXHLG1Y8mu4SkGtKv9n6/n4yMDEzTJBAIEAqFCAaDVFdXU1VVRWFhIUVFRXg8Hurq6sjMzASgvLwcr9eLw+GgpKSElpYWQqEQhmFQUVEx6LGuri58Ph9NTU1EIhHq6+tpbW1l6dKlNDQ0kJmZyXXXXcftt9/O6tWr6e3tZe3atQA888wz7N+/n7Vr19LR0cG8efPYtm0bbre73zwty8KyrLhzEbsPp6GWWSIiIiIiIiJyEeivN6POiGz0AODz+WK1bd26leuvvx6gXxOIRBJlyr1kK1NORERERERERESGx4hs9HC27du3s337dt59913efPPNhE0gzlRTU0N3d3fcMdvIPt9bIyIiIiIiIiIiMigjstEDQG1tLW+//Ta7d+9m5cqVAHR2dnLLLbdwyy23xJpAJOJyuXC5XHHnHHYafUrfkxTx3xtCRUTkHPS1UkRERERS2ZB3ypmmiWmaNDQ0EI1GKS0tBaC9vR2Hw0FxcTEAhmFgWRZZWVmxa8+1620wY93d3YTDYcrKysjJySE9PR23243T6eSDDz7gsssuw+Px0NfXxx//+Eduuukmxo0bB3yRKQfQ19dHdXU1kUiEP//5z0OdvoiIiIiIiIjI8OvrG33HKDciM+XOvu7MTLmzxxKxLIuenp64I4I+WUREREREREREZHiMyEy5sz/emZlyx48fP+c9IXGjh5dRowcRERERERERERkeIzJT7uyPd8011wD/mynn9XpjY4nU1NRQUVERd+7FCTdcwNsrIiIiyZJ2Qc8DiFw8+pyUVKSnx0REksewbVvtCwbhOWNysksQERGRIdACiKQafU5KKtKinKSiBdGWZJeQFHbLmmSXcMkZk1cku4SkGtJOOb/fT0ZGBqZpEggECIVCBINBqqurqaqqorCwkKKiIjweD3V1dWRmZgJQXl6O1+vF4XBQUlJCS0sLoVAIwzCoqKgY9FhXVxc+n4+mpiYikQj19fW0traydOlSrr76ap555hn279+Pz+fj0UcfZfz48dx222243e7YdUeOHKGxsZGdO3dSVlbGjBkz+s3Tsiwsy4o7F6EP54VF8ImIiIiIiIiInFuf9kyNNiOm0cOZzRwcDgefffYZnZ2dXHnllXHXTZw4kWXLlpGXlxc7dzZlyomIiIiIiIiIpJb29nYWLlzI1772NcaOHUtxcTG7du2Kjdu2jd/vZ+LEiaSnpzN9+nT27t0bdw/LsnjkkUe4/PLLGTduHHfddRcff/xx3GuOHj3KAw88EFsTeuCBBzh27Niwz2fENHo4s5nDgQMHuPvuu3niiSfYsmVLv+s6OjooKCgY8N41NTV0d3fHHXeRfd73RkREREREREREht/Ro0e5+eabcTqd/OY3v+HDDz9k/fr1sac0AZ588kmeeuopnn76ad555x3y8/P53ve+x/Hjx2OvWbZsGS+++CK/+MUv+K//+i96e3u58847iUajsdfcd9997N69m6amJpqamti9ezcPPPDAsM9pSJlyfr8ft9uNaZocOnSIlStXMmXKFPbt20cwGMS2bSKRCIsWLaK2tpb8/Hw6Ozt57LHH8Hq9pKWlxZo5nP2I6mDGTp06RWVlJXfccUe/Rg9FRUUAVFdXU1lZSVVVFZdffjlz5sxhwoQJset+/OMfEwgEuP/++7niiisG/UYpU05ERERERETk4rvPHqWZcqHVyS7hkgt/vapffJjL5cLlcvV7bXV1Ndu3b+eNN95IeC/btmNPRz766KPAF7vi8vLyCAQClJWV0d3dTU5ODg0NDdxzzz3AFxu3CgsL+fWvf83MmTMJhUJce+217Ny5k29/+9sA7Ny5k5tuuomPPvqIyZOHb31oyDvlTNPENE0aGhqIRqOUlpYCX2whdDgcFBcXA2AYBpZlkZWVFbv2XLveBjPW3d1NOBymrKyMnJwc0tPTcbvdOJ1OAJ555hkAcnJy6Ozs5IorrqC4uJhDhw7xve99j5MnTwIQiUR4+umnB/x4lmXR09MTd0RQAqqIiIiIiIiIyHBJFB/2xBNPJHztyy+/zLRp07j77rvJzc3F4/Hwr//6r7HxP/7xj3R2dvL9738/ds7lcvHXf/3X7NixA4Bdu3YRiUTiXjNx4kSuu+662GvefPNNJkyYEFuQA7jxxhuZMGFC7DXDZURmygHk5uZy9OhRDMPg1ltv5dixY0QiEQB8Pt8556dMORERERERERG5pPr6Rt2RKD6spqYm4dvT2trKhg0buOaaa/jP//xP/v7v/56lS5eyadMmgFi8Wl5eXtx1eXl5sbHOzk7GjBkTt4Es0Wtyc3P7ffzc3NzYa4bLiMyU6+3tZdOmTcydO5ctW7bgdDpZs2ZNbEfd+ShTTkRERERERETk4nK5XIwfPz7uSPToKkBfXx/f+MY3ePzxx/F4PJSVlfHQQw+xYcOGuNedvY400JrUuV4z0DrV+e4zVOZQLwgGg5imSV9fH2VlZUyZMoVgMMj69euxbZtJkyaxaNEiVq1aRX5+Pi6Xi+LiYhobG9m1a1csN27dunVkZ2fj8Xh44YUXBjXW3NzMjh07qK2t7Zcpt3LlSuCLFc3PP/+cn/70p7S1tbF8+XI2btxIW1sbaWlfrEHW1tby9ttvs3v37tjjtmdK9PzyZe4L2lQoIjJqmI5kVyAS75R1/teIXEpjBvf3YZFLSl8rReSroqCggGuvvTbu3F/+5V/yy1/+EoD8/Hzgi3WhgoKC2GsOHToU2z2Xn59POBzm6NGjcbvlDh06xHe+853Ya7q6uvp9/E8//bTfLrwva0iNHkazX6Wr0YOIyLloUU5SjX7RlFSjRTlJRfpaKaloQXSUNnrY+1iyS7jkjCn/OOjX3nffffz5z3+Oa/RQXl7OW2+9xY4dO2KNHsrLy/mHf/gHAMLhMLm5uf0aPfz85z9n/vz5AHzyySdcccUV/Ro9vPXWW3zrW98C4K233uLGG28c9kYPQ9op5/f7ycjIwDRNAoEAoVCIYDBIdXU1VVVVFBYWUlRUhMfjoa6uLtaWtry8HK/Xi8PhoKSkJGGH1cGMdXV14fP5aGpqIhKJUF9fT2trK0uXLuVPf/oT7733HtFolIcffphHH32U8ePHc9ttt3Hq1KkB73nNNdf0m6dlWf26f0TsPpyGdsuJiIiIiIiIiFxq5eXlfOc73+Hxxx9n/vz5vP3229TV1VFXVwd88cjpsmXLePzxx7nmmmu45pprePzxxxk7diz33XcfABMmTODBBx+ksrKSr33ta2RnZ7N8+XKmTp3K7bffDnyx+27WrFk89NBD1NbWAvB3f/d33HnnncO6IAcjqNHDmc0cHA4Hn332GZ2dnVx55ZXnvGciiRo9/Oq0Gj2IiIiIiIiIyEWSAo0XLvkxBN/85jd58cUXef7557nuuutYtWoVwWCQ+++/P/aaf/iHf2DZsmX86Ec/Ytq0abS3t/Pb3/6Wyy67LPaaf/7nf2bOnDnMnz+fm2++mbFjx/If//EfOBz/++jPv/3bvzF16lS+//3v8/3vf5/rr7+ehoaGL///8VmGnCn38MMP43a7qa6u7tfo4c0336SsrIyrr74a+KLRw8svv8y999570Ro9pKens2/fPq688krWrFnD2rVr6erq4u677+aWW26JdeEY7D3hi0YPFRUVced+k3vDOa8REREREREREZGL58477+TOO+8ccNwwDPx+P36/f8DXuN1ufvKTn/CTn/xkwNdkZ2fz85///MuUOigjptHDmc0cxo0bxyuvvMLOnTuZM2cOx44dG/CeiSRq9KBHVyWVKJNGUtGECcPbiUjky2rvUGyuiMj5uBM3ORQRkUtgyCtNpmlimiYNDQ1Eo1FKS0sBaG9vx+FwxLqZGoaBZVlx3SzOtUNtMGPd3d2Ew2HKysrIyckhPT0dt9uN0+mksLCQsWPH4nQ6cbvdeDweIpEIBQUFHDp0iHA4TDQa5dixY1x22WUcPnyYvgG2SlqWRU9PT9wRsYe2rVJERERERERERGQgIzJT7uyxPXv2sGLFCt566y2ysrJYsWIFM2fOpKOjI+H8lCknIiIiIiIiIiIX05AX5R5++GF+9KMfcfnll/fLlBs/fjxlZWWx177xxhtUVVVh2/ZFy5Srrq5m3759OJ1O1qxZg9Pp7Dd2zz33EAgEsCwLwzDYs2cPBw4cYMaMGQnvW1NTQ3d3d9wxz8we0vskIiIiIiIiIjJoffboO0a5EZkp19raGjd2+PBhHA4H99xzD11dXTz44IMsWLCA5uZmpk6d2m+OiTLl7Ega4Qt/n0WGVVifjJKCTpzUN1URkXPR7x4iIiJyphGTKTd+/HgcDgfRaBS3243b7aa7u5tPPvmEm2++maysLD788ENycnK4/fbbOXr0KG63e6jTFxERERERERER+dJGTKbcvHnz8Hq9HDlyhIkTJ7Js2TLy8vKYPn06W7du5frrrwfod89E1OhBREREREREREQuphGTKWfbNqtXr+aRRx4BoKOjg4KCAgzDYPv27Wzfvp13332X48ePn/OekLjRw0u2Gj2IiIiIiIiIyEXS1zf6jlHOsG170OkWfr8ft9uNaZocOnSIlStXMmXKFPbt20cwGMS2bSKRCIsWLaK2tpb8/Hw6Ozt57LHH8Hq9pKWlxXLjQqEQhmFQUVEx6LFTp05RWVnJHXfc0S9TrqGhgcOHD+PxeCgrKyMQCHD//fdzxRVXxOqvrq7m8ccfj7vnNddc02+elmVhWVbcuZcyb8BpXNDGQpFhZw45DVLk4jt9OtkViMQbMybZFYjE0+8ekorS9CuOpKB5J1uSXUJS2O+vTHYJl5zhWZXsEpJqSItyo9kvHJOTXYJIjBblJBVpUU5SjRblJNVoUU5SkRblJBVpUW70GO2LckP61d7v95ORkYFpmgQCAUKhEMFgkOrqaqqqqigsLKSoqAiPx0NdXR2ZmZkAlJeX4/V6cTgclJSUJNwNN5ixrq4ufD4fTU1NRCIR6uvraW1tZenSpezZs4cPP/yQ06dP4/f7CQQCmKbJvHnzaG9vH/C6q6++ut88E+2Ui9h92iknIiIiIiIiIiLDYkQ2evjggw/Yu3cvlmXhdrvPeV0iypQTEREREREREZGLaUQ2egiHw0yaNInFixdTX19/zusSqampobu7O+6YbWSf970REREREREREbkgffboO0a5ISdTBYNBTNOkr6+PsrIypkyZQjAYZP369di2zaRJk1i0aBGrVq0iPz8fl8tFcXExjY2N7Nq1K9bMYd26dWRnZ+PxeHjhhRcGNdbc3MyOHTuora3t1+jB5/Nx+PBhtm7dykMPPcSzzz7LU089xezZs895XSIulwuXyxV3zmGn6fNFUoYyaUREzk85h5JqlN0lqSht4D0QIiJykQ35RwPTNDFNk4aGBqLRKKWlpQC0t7fjcDgoLi4GwDAMLMsiKysrdu25dr0NZqy7u5twOExZWRk5OTmkp6fjdrtxOp3ccMMNXHHFFXzyySd8/vnnfP3rX+fkyZPk5uayf/9+br31Vo4ePcrll1/Oxx9/zNixYwd8fNWyLHp6euKOCFoFERERERERERGR4TEiM+XONbZv3z5uvPFG7r33XhobGxPOL1Gm3MsoU05ERERERERERIbHiMyUO9fYN77xDTo6Onj11VcH3CmXKFPuLpQpJyIiIiIiIiIiw2NEZso5HI4Bx6655hoAIpEIixcvTjjHRJlyl7kVAiKpQzlJkoq+edOYZJcgEmfPrnCySxARSXmno8muQERiFB4+6hi2bat9wSD8Kn1ysksQidGinKSiG76tRTlJLVqUExE5P60BSCr6odWS7BKSwn7Hm+wSLjnjm48nu4SkGtJOOb/fT0ZGBqZpEggECIVCBINBqqurqaqqorCwkKKiIjweD3V1dWRmZgJQXl6O1+vF4XBQUlJCS0sLoVAIwzCoqKgY9FhXVxc+n4+mpiYikQj19fW0traydOlS9uzZw4cffsjp06f5x3/8xwHvWVpayoYNGzh48CAVFRVMntx/sc2yLCzLijsXsftwGtotJyIiIiIiIiIiX96IbPRwrntmZWWxYsUKZs6cSUdHR8L5JWr08KvTavQgIiIiIiIiIiLDY0Q2ejjXxwPYs2cPBw4cYMaMGQnvm6jRwzxTjR5ERERERERERGR4DClTzu/343a7MU2TQ4cOsXLlSqZMmcK+ffsIBoPYtk0kEmHRokXU1taSn59PZ2cnjz32GF6vl7S0tFgzh7MfUR3M2KlTp6isrOSOO+7o1+ihoaGBw4cP4/F4eOihhwa85wMPPMAdd9zBggULmDlzJlOnTh3U3H/hUKacpA5lf0gqStMT/pJi9LVSRETkq+k+e5Rmyr1VnewSLjnj22uTXUJSDflXKNM0MU2ThoYGotEopaWlALS3t+NwOCguLgbAMAwsyyIrKyt27bl2vQ1mrLu7m3A4TFlZGTk5OaSnp+N2u3E6ndx0001cffXVHD9+nM7OTvLz82ltbeXjjz/m3Xffxel0UlxcTF5eXmyX31VXXZXw41mWRU9PT9wRsfWTvYiIiIiIiIiIDI8Rkyl35nUFBQUsW7aMvLw8pk+fTk5ODidPniQc/qILW25uLkePHh1wITBRptxLtjLlRERERERERERkeIyYTLkzrwPo6OigoKAAwzBYsmQJNTU1bN26FYBNmzYxd+5ctmzZkvC+iTLlZhvKlBMRERERERERkeFhDvWCYDCIaZr09fVRVlbGlClTCAaDrF+/Htu2mTRpEosWLWLVqlXk5+fjcrkoLi6msbGRXbt2xTLe1q1bR3Z2Nh6PhxdeeGFQY83NzezYsYPa2tp+mXI33XRT7DrDMGhoaGDhwoUAbN68mVAoRF5eHl1dXWzcuJG2tjaWL1+ecI4ulwuXyxV3zmGn0Tfo9D0RkdFH+V0iIiIiIiKDN2Iy5c72+uuvs3nzZnp7e8nPz+e1117jkUceYdy4cRw8eJAJEybQ2to61OmLiIiIiIiIiAw727ZH3THajchMOdu243LjzrzO4XDw2Wef0dnZyZVXXplwfgkbPaAtICIiIiIiIiIiMjxGbKbcQLlxXV1d3H333TzxxBMDZsolavTwMmr0ICIiIiIiIiIiw8Owh7Bf0O/343a7MU2TQ4cOsXLlSqZMmcK+ffsIBoPYtk0kEmHRokXU1taSn59PZ2cnjz32GF6vl7S0tFhuXCgUwjAMKioqBj126tQpKisrueOOO/plyv3hD3+IXffAAw/E5cZ9/vnnsevuu+8+qqqquPzyy5kzZw633HJLv3laloVlWXHn/r/Lb8BpXNDGQpGLQvldkmrG9E8SEEkqZcGKiJxfXu7AmyBEkuXmto+SXUJS9O18NNklXHJpNwaSXUJSDWlRbjTb7Jqc7BJEYrQgJ6lIi3KSarQoJyJyflqUk1Q0ahfldlQlu4RLLu0765JdQlINqfuq3+8nIyMD0zQJBAKEQiGCwSDV1dVUVVVRWFhIUVERHo+Huro6MjMzASgvL8fr9eJwOCgpKUm4G24wY11dXfh8PpqamohEItTX19Pa2srSpUsJhUKx60pLS9mwYQMHDx6koqKCvXv38uGHH3L69GmWLl0aNzZ5cv/FtkQ75SJ2n3bKiYiIiIiIiIjIsBiRjR6ysrJYsWIFM2fOpKOjg3nz5uH1ejly5AjZ2dlxY4kkypR7MapMORERERERERERGR4jttHDnj17OHDgADNmzMC2bVavXs0jjzzSbyyRmpoauru74465juxBvDsiIiIiIiIiIiLnN6THVwGCwSCmadLX10dZWRlTpkwhGAyyfv16bNtm0qRJLFq0iFWrVpGfn4/L5aK4uJjGxkZ27doVa+awbt06srOz8Xg8vPDCC4Maa25uZseOHdTW1vZr9HDTTTfFrjt06BAPPvggCxYsoLm5mV/84hccPnyYrVu3Mn78+LixqVOn9pujy+XC5XLFnTOiaZy+8PdZZFgpU05E5PxO6xu3iMh5tR1UAKeknpuTXYDIJTLknXKmaWKaJg0NDUSjUUpLSwFob2/H4XBQXFwMgGEYWJZFVlZW7Npz7XobzFh3dzfhcJiysjJycnJIT0/H7XbjdMani+fl5TFnzhw+++wzpk6dyg033EBRURG//vWvycvL4+6778YwDDIyMoY6fRERERERERGR4dfXN/qOUW5EZsrZto3P54vVNm/ePL773e8yd+5cPvjgA/bu3YtlWbjd7oTzsyyLnp6euCNi65NFRERERERERESGx4jNlDtbY2Mj8+fPJxwOM2nSJBYvXkx9fX3C1yZq9PCSrUYPIiIiIiIiIiIyPEZkppxhGNTW1vL222+ze/duJk+ejG3bjB07luLiYp599lmeeuopZs+enXCONTU1VFRUxJ37j+wbSBvS8vn0AAAgAElEQVR4fVDkkhozJtkViPSXc7m+SEpq+fPHykmS1KLv35KKwuFkVyAiMnoZ9kBbyyROo3NysksQiTGHvJwucvFpUU5SjRblJNVoUU5SkRblJBXdZ7cku4Sk6PuvymSXcMml3bI+2SUk1ZB+tff7/WRkZGCaJoFAgFAoRDAYpLq6mqqqKgoLCykqKsLj8VBXV0dmZiYA5eXleL1eHA4HJSUltLS0EAqFMAyDioqKQY91dXXh8/loamoiEolQX19Pa2srS5cuJRQKxa5bvnw5q1evpre3l7Vr1/KrX/2KgwcP8vrrr/Pzn/887rqrr7663zwty8KyrLhzEbsPp3FBEXwiIiIiIiIiIufWpz8ojjajrtHD2dclkihTbkufMuVERERERERERGR4jLpGDwNdd6aamhq6u7vjjjlp2QPWISIiIiIiIiIiMhSjrtFDa2tr3HWJuFwuXC5X3LnL3Hp0VVLH6WiyKxDp7/hxbbeX1JKdlewKROKl6cdJSUFOpzJhRUSSZcg/GpimiWmaNDQ0EI1GKS0tBaC9vR2Hw0FxcTEAhmFgWRZZWf/7E/G5dr0NZqy7u5twOExZWRmZmZlEo9HY+KFDhwiHw0SjUXp6emhubuav/uqvuOyyy0hPT2fy5MlUV1fz9a9/HcMwmDRpUr+Ft/9hWRY9PT1xR8TuG9obJSIiIiIiIiIiMoCvbKZcdnY2K1asYObMmXR0dLBnzx5WrFjBW2+9RUtLCzfeeCP33nsvjY2NbN26leuvvx6ADz74gL1792JZFm63O+H8EmXKbY4oU05ERERERERELpK+vtF3jHJf6Uy5PXv2cODAAWbMmME999xDIBDAsixuuOEGOjo6ePXVV3E6nWzfvp3t27fz7rvvcuTIESZNmsTixYupr69PeN9EmXI/dCpTTkREREREREREhsdXNlNuwYIFPPjggyxYsIDm5mZs28bhcHDPPfeQ9t+BHZFIhMWLF5OXlwdAZ2cnt9xyC7/85S956qmnmD17dsI5JsqUs06kYQ31zRIRGUVOnUp2BSLxlN8lqUYbAiQVpaUpE1ZEJFkMe6B2pRLnOWNysksQERGRIdCinKQaLcpJKtLXSklFC6ItyS4hKfq2LUt2CZdc2vRgsktIqiHtlPP7/WRkZGCaJoFAgFAoRDAYpLq6mqqqKgoLCykqKsLj8VBXV0dmZiYA5eXleL1eHA4HJSUltLS0EAqFMAyDioqKQY91dXXh8/loamrixIkTbNiwgYMHD1JRUcH27dv55JNPME2TJUuW8OijjzJ+/Hhuu+02enp6+PDDDzl9+jR+v5+Ojg7mzZvHtm3bEubKWZaFZcXvi4vQh/PCIvhERERERERERETijMhGDw6Hg88++4zOzk6uvPJK5s2bh9fr5ciRI0SjUZ5//nlmzZo14PwSNXp4GTV6EBEREREREZGLJNlNF9To4ZIbkY0eurq6uPvuu3niiSfYsmULtm2zevVqHnnkEd5//32OHz/Ozp072bZtW8L7Jmr0cBdq9CAiIiIiIiIiIsNjRDZ6GDduHK+88go7d+5kzpw5+Hw+Dh8+zNatWykrK2PatGn4/f7YrruzJWr0oEdXRUREvlr0x1cRkfPT10oRkeQZ8kqTaZqYpklDQwPRaJTS0lIA2tvbcTgcFBcXA2AYBpZlkZWVFbv2XLveBjPW3d1NOBymrKwMp9PJ3Llz2b9/P2PGjKGlpQXLsvjkk0/Iycmhs7OTK664guLiYq6++moKCws5duwYAIFAgMsuu4xPPvkk4cezLIuenp64I4K+W4mIiIiIiIiIyPAYkZlytm2Tm5vL0aNHMQwjbqy5uZm9e/diWVbCJg+gTDkREREREREREbm4RmSmnGEYbNq0iblz57Jly5a4sUgkwqRJk1i8eDH19fUJ76tMORERERERERG5pPrs0XeMciMyU66rq4uNGzfS1tbG8uXL+eSTT2JjxcXFPPvsszz11FPMnj074RyVKSciMnRp+jIpKUY5SSIiIiKSykZMptzevXsBeOedd0hPT+fPf/4zLpcL27axbZtXXnmF+fPn09nZyVVXXcWf/vQnTp06NdTpi4iIiIiIiIiIfGkjJlPO6XTy8ccfM378ePbt28eNN97IvffeS2NjY9x1EydOZNmyZeTl5Q3YfVWNHkRERERERERE5GIaMZlyhw8fJhgMYlkWHo+Hjo4OXn31VZxOZ7/rOjo6KCgoGPDeavQgIiIiIiIiIpdUX9/oO0a5EZMpl5aWxqpVq2LZcgCRSITFixfHXffjH/+YhoYGFi5cOOAca2pqqKioiDv3H9k3YA68PihySSm7S1KRPi8l1Zw+newKRERSn34nFhFJHsO2bbW7GIRG5+RklyASo8UPSUX6vJRUo0U5EZHz06KcpKIF0ZZkl5AUfb9dkuwSLrm07/802SUk1ZB2yvn9fjIyMjBNk0AgQCgUIhgMUl1dTVVVFYWFhRQVFeHxeKirqyMzMxOA8vJyvF4vDoeDkpISWlpaCIVCGIZBRUXFoMe6urrw+Xw0NTVx4sQJNmzYwMGDB6moqGDv3r0cPHiQ119/nYaGBrxeL06nk4cffpiGhgYyMzO57rrruP3221m9ejW9vb2sXbs24Twty8KyrLhzEbsPp6HfOEVERERERERE5MsbMY0e5s2bx3e/+13mzp3br9FDTk4OJ0+eJBwOA+Dz+c45v0SZclv6lCknIiIiIiIiIiLDY8Q0egBobGxk/vz5fOMb34hr9LBkyRJqamrYunXroOZYU1NDd3d33DEnLXtQ14qIiIiIiIiIDFmymy6o0cMlN2IaPfzFX/wFtm0zduzYWK3/0+hh8+bNhEIh8vLyAKitreXtt99m9+7dFBcX95ujy+XC5XLFnTOiaSiaRkREREREREREhsOQd8qZpolpmjQ0NBCNRiktLQWgvb0dh8MRW+QyDAPLssjKyopde65db4MZ6+7uJhwOU1ZWhtPpZO7cuezfv58xY8bwm9/8hokTJzJ79mx6e3tpa2vDsiyam5txu9243e7YfX7wgx9w4sQJioqKEn48y7Lo6emJOyJoBVdERERERERERIbHiMyUczgcfPbZZ3R2dnLllVfG3TMajfL8888za9asAeeXKFPuZZQpJyIiIiIiIiIiw2NEZsp1dXVx991388QTT7Bly5a4e7733nscP36cnTt3sm3btoT3TZQpdxfKlBMRERERERERkeExIjPlxo0bxyuvvMLOnTuZM2cOx44di93zm9/8Jt/85jfx+/2xXXdnS5Qp50q7oE2FIiIikiTmkH/KEbm4TiugWFKQvlaKpJA+O9kVyCVm2Lat/9cH4ReOyckuQURERIZAv2hKqtGinKQifa2UVPRDqyXZJSRF36//PtklXHJp/+f/c/4XjWBD+hLs9/vJyMjANE0CgQChUIhgMEh1dTVVVVUUFhZSVFSEx+Ohrq6OzMxMAMrLy/F6vTgcDkpKSmhpaSEUCmEYBhUVFYMe6+rqwufz0dTUxIkTJ9iwYQMHDx6koqKCvXv3cvDgQV5//XVeeuklAoEApmkyb948urq62LZtG4WFhdx///1xY1dddVW/eVqWhWVZcecidh9OQ7vlRERERERERETkyxuRjR4++OAD9u7di2VZuN1unnvuOcz//hPQ2WOJJGr08JKtRg8iIiIiIiIiIjI8RmSjh3A4zKRJk1i8eDH19fV0dnaybNky3nnnHSzLihtLJFGjh9mGGj2IiIiIiIiIiMjwGJGNHoqLi3n22Wd56qmnmD17NlOnTmX16tW43W48Hg+bNm2KjSWSqNGDHl0VEREREZGRpiB/4E0QInKJ9fUluwK5xIa80mSaJqZp0tDQQDQapbS0FID29nYcDgfFxcUAGIaBZVlkZWXFrj3XrrfBjHV3dxMOhykrK8PpdDJ37lz279/PmDFjCAQCTJw4kd/97nd0dnZy1VVX8ac//YlTp04xbtw4xo0bR2ZmJmlpabS0tJCfn8/UqVMTfjzLsujp6Yk7Irb+cYiIiIiIiIiIyPAYMZlyOTk5nDx5knA4zMSJE1m2bBl5eXlMnz6dW2+9lWPHjhGJRADIzc3l6NGjAy4EKlNOREREREREREQuphGTKbdkyRJqamrYunUrAB0dHRQUFGAYBk6nkzVr1uB0OgHYtGkTc+fOZcuWLQnvq0w5ERERERERERG5mEZMplxLSwuhUIi8vDwAGhoaWLhwIQAbN26kra2NtLQ0urq6Yv97+fLlCeeYKFPOYafRZw/13RIREZFkCYeTXYGISOprO6hfciT13JzsAkQukRGTKffBBx9w2WWX4fF4Ytf9+7//O3/84x/505/+xIQJE7jpppuIRqO4XC6OHDlCW1vbUKcvIiIiIiIiIjL8+vpG3zHKjchMuQ8++IC9e/diWRZut/uceXOJJGz0gD5ZRERERERERERkeIzITLlwOMykSZNYvHgx9fX158ybSyRRo4eXUaMHEREREREREREZHoZt24MOEfD7/bjdbkzT5NChQ6xcuZIpU6awb98+gsEgtm0TiURYtGgRtbW15Ofn09nZyWOPPYbX6yUtLS2WGxcKhTAMg4qKikGPnTp1isrKSu644w4WLFgQ+8+ZM2fGrnO73VRWVlJeXk56ejqzZ8+mvb09NlZVVUUgEOD+++/niiuuSDhPy7KwLCvu3IsTbsB5YRsLRUREJAnS9G1bROS89LVSUtH8SEuyS0iKvpcXJ7uESy7trp8lu4SkGtKi3Gj2nDE52SWIiIjIEOgXTRGR89PXSklFo3ZRbsuDyS7hkkub80yyS0iqIXVf9fv9ZGRkYJomgUCAUChEMBikurqaqqoqCgsLKSoqwuPxUFdXR2ZmJgDl5eV4vV4cDgclJSUJd8MNZqyrqwufz0dTUxMnTpxgw4YNHDx4kIqKCp5//nkyMzO57rrruP322+no6GDevHls27YNt9vNM888w/79+/F6vXi9XpxOJw8//DBXX311v3km2ikXoU875UREREREREREZFiMyEYP0WiU559/nlmzZgGwdetWrr/+egD27dvHjTfeyL333ktjY2PC+SlTTkRERERERERELqYR2ejh/fff5/jx4+zcuZNt27axfft2tm/fzrvvvsukSZPo6Ojg1Vdfxel0JrxvTU0N3d3dccddZA/1rRIREREREREREUloSI+vAgSDQUzTpK+vj7KyMqZMmUIwGGT9+vXYts2kSZNYtGgRq1atIj8/H5fLRXFxMY2NjezatSvWzGHdunVkZ2fj8Xh44YUXBjXW3NzMjh07qK2tZcGCBTz44IMsWLCA5ubm2GOveXl5TJs2jWnTpuH3+5k+fXpsx1xnZyfjx48HIBKJsHhx4hBFl8uFy+WKO6dHV0VEzk2ZNJJq+vqSXYGISOrT10oRkeQZ8q9QpmlimiYNDQ1Eo1FKS0sBaG9vx+FwUFxcDIBhGFiWRVZWVuzac+16G8xYd3c34XCYsrIynE4nc+fOZf/+/YwZM4Yf/vCHTJw4kcOHD9PR0UEwGOSjjz7izTff5LXXXmPdunWxjLtwOMypU6fIy8sb6vRFRERERERERIZfX9/oO0a5EZMpd2Zu3MSJE1m2bBl5eXlMnz6dW2+9lWPHjhGJRADw+XznnJ9lWfT09MQdEfTJIiIiIiIiIiIiw2PEZMqdmRvX29tLR0cHBQUFGIaB0+lkzZo1A2bInU2NHkRERERERERE5GIaMZlyK1euBL7IjcvIyOCnP/0pCxcuBGDjxo20tbWR9t+BR7W1tbz99tvs3r079rjtmWpqaqioqIg79+KEG4b85oqIiEjymEP+KUfk4jp9OtkViPQ3ZkyyKxARGb0M27btZBfxVfCcMTnZJYiIpDQ1epBUo89JSTValJNUpEU5SUU/tFqSXUJS9P1qUbJLuOTS5v2/yS4hqYb0N2S/309GRgamaRIIBAiFQgSDQaqrq6mqqqKwsJCioiI8Hg91dXWxxgrl5eV4vV4cDgclJSWxTqmGYVBRUTHosa6uLnw+H01NTZw4cYINGzZw8OBBKioqmDx5Ms888wz79+9n7dq1BAIBTNNk3rx57N27N3bP0tLSftedzbIsLMv6/9m7/+ioyzvv/6/JfIYMECBBIcB349mWGrMgngRzFHaBhcU2bG0FsquAlXPHmMpZBc0PUmaG4ZwoiRA8stN76XrICkvJVtqIllrvbu6lTdlVJP4AdIGOCVtpKAkJv8KEFPjMMJnvH95MGTPBBAOTTp6Pcz5Hz+ea65P3NXJCcnnN6x1xL6AuOrACAAAAAICbIhTkzNRgE5eNHg4fPqwjR47INE3Z7faIZ6akpETMi4ZMOQAAAAAAANxMcdno4dy5c0pPT1dBQYG2bt0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1+ZXJV155RUVFRZo/f36glgoGMjKXg4ODqqysVHp6upKSkvzKpcPhUHFxsZYvXx6opYJBjP6sTEhIUFlZmWJjY/3K5NGjR/XYY48FaplgIKMzyXkOgJsJ8Xg8nkAXAQAAAAAAANxN+KYcAAAAAAAAYDA25QAAAAAAAACDsSkHAAAAAAAAGIxNOQAAAAAAAMBgbMoBAAAAAAAABmNTDgAAAAAAADAYm3IAAAAAAACAwdiUAwAAAAAAAAzGphwAAAAAAABgsP8D38Y3RNibveIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfHm = dataFrameNew\n",
    "visuz.gene_exp.hmap(df=dfHm, rowclus=False, colclus=False, cmap='RdYlGn', dim=(16, 16), tickfont=(4, 4), show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This looks unconclusive. So let us select only the genes with average count values greater than 120. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfHm['mean'] = dfHm.mean(axis=1)\n",
    "dfHm120 = dfHm[dfHm['mean'] >= 120]\n",
    "visuz.gene_exp.hmap(df=dfHm120, rowclus=False, colclus=False, cmap='RdYlGn', dim=(16, 16), tickfont=(4, 4), show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a principal component analysis graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "    rld <- rlog(dds, blind=T)\n",
    "    rld_mat <- assay(rld)\n",
    "    pca <- prcomp(t(rld_mat))\n",
    "    df <- cbind(pData(data), pca$x)\n",
    "    ggplot(df) + geom_point(aes(x=PC1, y=PC2, color = gender))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}