{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HTS Course: DESeq2 Analysis Outline for 2017 Pilot Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(tools)\n",
    "library(DESeq2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>cntfiles</th><th scope=col>md5sum</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Pst-kb1_S1.csv                  </td><td>7cab95ffd43eccabd4bff0f1e6b4ac5f</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Pst-kb2_S2.csv                  </td><td>59633fe06424e54937c34a56c5e24477</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Pst-kb3_S3.csv                  </td><td>2b576a5c270e7638b5072fa3776ecc83</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Pst-mm1_S4.csv                  </td><td>072a43688309a087125c565c9f41cf54</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Pst-mm3_S5.csv                  </td><td>3e255f6af17a313b6839a1f4f2595fdc</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Pst-mm4_S6.csv                  </td><td>dcac1408050aa686c7350d6a9b70d737</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & cntfiles & md5sum\\\\\n",
       "\\hline\n",
       "\t1 & Pst-kb1\\_S1.csv                 & 7cab95ffd43eccabd4bff0f1e6b4ac5f\\\\\n",
       "\t2 & Pst-kb2\\_S2.csv                 & 59633fe06424e54937c34a56c5e24477\\\\\n",
       "\t3 & Pst-kb3\\_S3.csv                 & 2b576a5c270e7638b5072fa3776ecc83\\\\\n",
       "\t4 & Pst-mm1\\_S4.csv                 & 072a43688309a087125c565c9f41cf54\\\\\n",
       "\t5 & Pst-mm3\\_S5.csv                 & 3e255f6af17a313b6839a1f4f2595fdc\\\\\n",
       "\t6 & Pst-mm4\\_S6.csv                 & dcac1408050aa686c7350d6a9b70d737\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "        cntfiles                           md5sum\n",
       "1 Pst-kb1_S1.csv 7cab95ffd43eccabd4bff0f1e6b4ac5f\n",
       "2 Pst-kb2_S2.csv 59633fe06424e54937c34a56c5e24477\n",
       "3 Pst-kb3_S3.csv 2b576a5c270e7638b5072fa3776ecc83\n",
       "4 Pst-mm1_S4.csv 072a43688309a087125c565c9f41cf54\n",
       "5 Pst-mm3_S5.csv 3e255f6af17a313b6839a1f4f2595fdc\n",
       "6 Pst-mm4_S6.csv dcac1408050aa686c7350d6a9b70d737"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cntdir<-\"/home/jovyan/work/2017-HTS-materials/Data_Info_and_Results/2017_HTS/counts/HTS_2017_pilot\"\n",
    "cntfiles<-list.files(cntdir)\n",
    "cntdf<-tibble(cntfiles,md5sum=tools::md5sum(file.path(cntdir,cntfiles)))\n",
    "cntdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify the csv file containing the meta data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>/home/jovyan/work/2017-HTS-materials/Data_Info_and_Results/2017_HTS/info/full_metadata.csv:</strong> 'c2dddfedfd9107615ddf0d62f0c6453e'"
      ],
      "text/latex": [
       "\\textbf{/home/jovyan/work/2017-HTS-materials/Data\\textbackslash{}\\_Info\\textbackslash{}\\_and\\textbackslash{}\\_Results/2017\\textbackslash{}\\_HTS/info/full\\textbackslash{}\\_metadata.csv:} 'c2dddfedfd9107615ddf0d62f0c6453e'"
      ],
      "text/markdown": [
       "**/home/jovyan/work/2017-HTS-materials/Data_Info_and_Results/2017_HTS/info/full_metadata.csv:** 'c2dddfedfd9107615ddf0d62f0c6453e'"
      ],
      "text/plain": [
       "/home/jovyan/work/2017-HTS-materials/Data_Info_and_Results/2017_HTS/info/full_metadata.csv \n",
       "                                                        \"c2dddfedfd9107615ddf0d62f0c6453e\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metafile<-\"/home/jovyan/work/2017-HTS-materials/Data_Info_and_Results/2017_HTS/info/full_metadata.csv\"\n",
    "tools::md5sum(metafile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  library_num = col_integer(),\n",
      "  miseq_id = col_character(),\n",
      "  miseq_filename = col_character(),\n",
      "  nextseq_id = col_character(),\n",
      "  nextseq_filename = col_character(),\n",
      "  group = col_character(),\n",
      "  genotype = col_character(),\n",
      "  rna_prep = col_character(),\n",
      "  rep = col_double(),\n",
      "  media = col_character(),\n",
      "  rin = col_double(),\n",
      "  input_sample = col_character(),\n",
      "  i7_index_id = col_character(),\n",
      "  index = col_character(),\n",
      "  notes = col_character()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "metadata<-read_csv(metafile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the dimension annd first 6 rows of the of the imported file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>54</li>\n",
       "\t<li>15</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 54\n",
       "\\item 15\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 54\n",
       "2. 15\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 54 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>library_num</th><th scope=col>miseq_id</th><th scope=col>miseq_filename</th><th scope=col>nextseq_id</th><th scope=col>nextseq_filename</th><th scope=col>group</th><th scope=col>genotype</th><th scope=col>rna_prep</th><th scope=col>rep</th><th scope=col>media</th><th scope=col>rin</th><th scope=col>input_sample</th><th scope=col>i7_index_id</th><th scope=col>index</th><th scope=col>notes</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1           </td><td>1_A         </td><td>1-A_S1.csv  </td><td>JG-1        </td><td>JG-L1_S1.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>1           </td><td>kb          </td><td>10          </td><td>Pst-kb1-new </td><td>P49-E1      </td><td>AAGACCGT    </td><td>NA          </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2           </td><td>2_A         </td><td>2-A_S2.csv  </td><td>JG-2        </td><td>JG-L2_S2.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>2           </td><td>kb          </td><td>10          </td><td>Pst-kb2-new </td><td>P50-E2      </td><td>TTGCGAGA    </td><td>NA          </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3           </td><td>3_A         </td><td>3-A_S3.csv  </td><td>JG-3        </td><td>JG-L3_S3.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>3           </td><td>kb          </td><td>10          </td><td>Pst-kb3-new </td><td>P51-E3      </td><td>GCAATTCC    </td><td>NA          </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4           </td><td>4_A         </td><td>4-A_S4.csv  </td><td>JG-4        </td><td>JG-L4_S4.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>1           </td><td>mm          </td><td>10          </td><td>Pst-mm1-new </td><td>P52-E4      </td><td>GAATCCGT    </td><td>NA          </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5           </td><td>5_A         </td><td>5-A_S5.csv  </td><td>JG-5        </td><td>JG-L5_S5.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>2           </td><td>mm          </td><td>10          </td><td>Pst-mm2-new </td><td>P53-E5      </td><td>CCGCTTAA    </td><td>NA          </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6           </td><td>6_A         </td><td>6-A_S6.csv  </td><td>JG-6        </td><td>JG-L6_S6.csv</td><td>A           </td><td>Pst         </td><td>new         </td><td>3           </td><td>mm          </td><td>10          </td><td>Pst-mm3-new </td><td>P54-E6      </td><td>TACCTGCA    </td><td>NA          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & library\\_num & miseq\\_id & miseq\\_filename & nextseq\\_id & nextseq\\_filename & group & genotype & rna\\_prep & rep & media & rin & input\\_sample & i7\\_index\\_id & index & notes\\\\\n",
       "\\hline\n",
       "\t1 & 1              & 1\\_A          & 1-A\\_S1.csv   & JG-1           & JG-L1\\_S1.csv & A              & Pst            & new            & 1              & kb             & 10             & Pst-kb1-new    & P49-E1         & AAGACCGT       & NA            \\\\\n",
       "\t2 & 2              & 2\\_A          & 2-A\\_S2.csv   & JG-2           & JG-L2\\_S2.csv & A              & Pst            & new            & 2              & kb             & 10             & Pst-kb2-new    & P50-E2         & TTGCGAGA       & NA            \\\\\n",
       "\t3 & 3              & 3\\_A          & 3-A\\_S3.csv   & JG-3           & JG-L3\\_S3.csv & A              & Pst            & new            & 3              & kb             & 10             & Pst-kb3-new    & P51-E3         & GCAATTCC       & NA            \\\\\n",
       "\t4 & 4              & 4\\_A          & 4-A\\_S4.csv   & JG-4           & JG-L4\\_S4.csv & A              & Pst            & new            & 1              & mm             & 10             & Pst-mm1-new    & P52-E4         & GAATCCGT       & NA            \\\\\n",
       "\t5 & 5              & 5\\_A          & 5-A\\_S5.csv   & JG-5           & JG-L5\\_S5.csv & A              & Pst            & new            & 2              & mm             & 10             & Pst-mm2-new    & P53-E5         & CCGCTTAA       & NA            \\\\\n",
       "\t6 & 6              & 6\\_A          & 6-A\\_S6.csv   & JG-6           & JG-L6\\_S6.csv & A              & Pst            & new            & 3              & mm             & 10             & Pst-mm3-new    & P54-E6         & TACCTGCA       & NA            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  library_num miseq_id miseq_filename nextseq_id nextseq_filename group\n",
       "1           1      1_A     1-A_S1.csv       JG-1     JG-L1_S1.csv     A\n",
       "2           2      2_A     2-A_S2.csv       JG-2     JG-L2_S2.csv     A\n",
       "3           3      3_A     3-A_S3.csv       JG-3     JG-L3_S3.csv     A\n",
       "4           4      4_A     4-A_S4.csv       JG-4     JG-L4_S4.csv     A\n",
       "5           5      5_A     5-A_S5.csv       JG-5     JG-L5_S5.csv     A\n",
       "6           6      6_A     6-A_S6.csv       JG-6     JG-L6_S6.csv     A\n",
       "  genotype rna_prep rep media rin input_sample i7_index_id    index notes\n",
       "1      Pst      new   1    kb  10  Pst-kb1-new      P49-E1 AAGACCGT  <NA>\n",
       "2      Pst      new   2    kb  10  Pst-kb2-new      P50-E2 TTGCGAGA  <NA>\n",
       "3      Pst      new   3    kb  10  Pst-kb3-new      P51-E3 GCAATTCC  <NA>\n",
       "4      Pst      new   1    mm  10  Pst-mm1-new      P52-E4 GAATCCGT  <NA>\n",
       "5      Pst      new   2    mm  10  Pst-mm2-new      P53-E5 CCGCTTAA  <NA>\n",
       "6      Pst      new   3    mm  10  Pst-mm3-new      P54-E6 TACCTGCA  <NA>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The group column can be used to subset the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    A     B     C     D     E     F     G     H pilot  <NA> \n",
       "    6     6     6     6     6     6     6     6     6     0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata$group,exclude=NULL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will look at the Pilot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata <- metadata %>% filter(group==\"pilot\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>library_num</th><th scope=col>miseq_id</th><th scope=col>miseq_filename</th><th scope=col>nextseq_id</th><th scope=col>nextseq_filename</th><th scope=col>group</th><th scope=col>genotype</th><th scope=col>rna_prep</th><th scope=col>rep</th><th scope=col>media</th><th scope=col>rin</th><th scope=col>input_sample</th><th scope=col>i7_index_id</th><th scope=col>index</th><th scope=col>notes</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>49            </td><td>Pst-kb1_S1    </td><td>Pst-kb1_S1.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>1             </td><td>kb            </td><td>8.4           </td><td>Pst-kb1-old   </td><td>P1-A1         </td><td>TTACCGAC      </td><td>NA            </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>50            </td><td>Pst-kb2_S2    </td><td>Pst-kb2_S2.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>2             </td><td>kb            </td><td>9             </td><td>Pst-kb2-old   </td><td>P2-A2         </td><td>AGTGACCT      </td><td>NA            </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>51            </td><td>Pst-kb3_S3    </td><td>Pst-kb3_S3.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>3             </td><td>kb            </td><td>8.7           </td><td>Pst-kb3-old   </td><td>P3-A3         </td><td>TCGGATTC      </td><td>NA            </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>52            </td><td>Pst-mm1_S4    </td><td>Pst-mm1_S4.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>1             </td><td>mm            </td><td>7.9           </td><td>Pst-mm1-old   </td><td>P4-A4         </td><td>CAAGGTAC      </td><td>NA            </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>53            </td><td>Pst-mm3_S5    </td><td>Pst-mm3_S5.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>3             </td><td>mm            </td><td>6.3           </td><td>Pst-mm3-old   </td><td>P5-A5         </td><td>TCCTCATG      </td><td>NA            </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>54            </td><td>Pst-mm4_S6    </td><td>Pst-mm4_S6.csv</td><td>NA            </td><td>NA            </td><td>pilot         </td><td>Pst           </td><td>old           </td><td>4             </td><td>mm            </td><td>7.7           </td><td>Pst-mm4-old   </td><td>P6-A6         </td><td>GTCAGTCA      </td><td>NA            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & library\\_num & miseq\\_id & miseq\\_filename & nextseq\\_id & nextseq\\_filename & group & genotype & rna\\_prep & rep & media & rin & input\\_sample & i7\\_index\\_id & index & notes\\\\\n",
       "\\hline\n",
       "\t1 & 49               & Pst-kb1\\_S1     & Pst-kb1\\_S1.csv & NA               & NA               & pilot            & Pst              & old              & 1                & kb               & 8.4              & Pst-kb1-old      & P1-A1            & TTACCGAC         & NA              \\\\\n",
       "\t2 & 50               & Pst-kb2\\_S2     & Pst-kb2\\_S2.csv & NA               & NA               & pilot            & Pst              & old              & 2                & kb               & 9                & Pst-kb2-old      & P2-A2            & AGTGACCT         & NA              \\\\\n",
       "\t3 & 51               & Pst-kb3\\_S3     & Pst-kb3\\_S3.csv & NA               & NA               & pilot            & Pst              & old              & 3                & kb               & 8.7              & Pst-kb3-old      & P3-A3            & TCGGATTC         & NA              \\\\\n",
       "\t4 & 52               & Pst-mm1\\_S4     & Pst-mm1\\_S4.csv & NA               & NA               & pilot            & Pst              & old              & 1                & mm               & 7.9              & Pst-mm1-old      & P4-A4            & CAAGGTAC         & NA              \\\\\n",
       "\t5 & 53               & Pst-mm3\\_S5     & Pst-mm3\\_S5.csv & NA               & NA               & pilot            & Pst              & old              & 3                & mm               & 6.3              & Pst-mm3-old      & P5-A5            & TCCTCATG         & NA              \\\\\n",
       "\t6 & 54               & Pst-mm4\\_S6     & Pst-mm4\\_S6.csv & NA               & NA               & pilot            & Pst              & old              & 4                & mm               & 7.7              & Pst-mm4-old      & P6-A6            & GTCAGTCA         & NA              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  library_num   miseq_id miseq_filename nextseq_id nextseq_filename group\n",
       "1          49 Pst-kb1_S1 Pst-kb1_S1.csv       <NA>             <NA> pilot\n",
       "2          50 Pst-kb2_S2 Pst-kb2_S2.csv       <NA>             <NA> pilot\n",
       "3          51 Pst-kb3_S3 Pst-kb3_S3.csv       <NA>             <NA> pilot\n",
       "4          52 Pst-mm1_S4 Pst-mm1_S4.csv       <NA>             <NA> pilot\n",
       "5          53 Pst-mm3_S5 Pst-mm3_S5.csv       <NA>             <NA> pilot\n",
       "6          54 Pst-mm4_S6 Pst-mm4_S6.csv       <NA>             <NA> pilot\n",
       "  genotype rna_prep rep media rin input_sample i7_index_id    index notes\n",
       "1      Pst      old   1    kb 8.4  Pst-kb1-old       P1-A1 TTACCGAC  <NA>\n",
       "2      Pst      old   2    kb 9.0  Pst-kb2-old       P2-A2 AGTGACCT  <NA>\n",
       "3      Pst      old   3    kb 8.7  Pst-kb3-old       P3-A3 TCGGATTC  <NA>\n",
       "4      Pst      old   1    mm 7.9  Pst-mm1-old       P4-A4 CAAGGTAC  <NA>\n",
       "5      Pst      old   3    mm 6.3  Pst-mm3-old       P5-A5 TCCTCATG  <NA>\n",
       "6      Pst      old   4    mm 7.7  Pst-mm4-old       P6-A6 GTCAGTCA  <NA>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a sample data frame for DESeq2, be sure that the first and second columns are the sample and file name respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sampledata<- mydata %>% transmute(sampname=input_sample, \n",
    "                                  fname=miseq_filename, \n",
    "                                  trt=as.factor(media), \n",
    "                                  genotype, \n",
    "                                  group, \n",
    "                                  rna_prep,md5=md5sum(file.path(cntdir,miseq_filename))) %>% as.data.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>sampname</th><th scope=col>fname</th><th scope=col>trt</th><th scope=col>genotype</th><th scope=col>group</th><th scope=col>rna_prep</th><th scope=col>md5</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Pst-kb1-old                     </td><td>Pst-kb1_S1.csv                  </td><td>kb                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>7cab95ffd43eccabd4bff0f1e6b4ac5f</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Pst-kb2-old                     </td><td>Pst-kb2_S2.csv                  </td><td>kb                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>59633fe06424e54937c34a56c5e24477</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Pst-kb3-old                     </td><td>Pst-kb3_S3.csv                  </td><td>kb                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>2b576a5c270e7638b5072fa3776ecc83</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Pst-mm1-old                     </td><td>Pst-mm1_S4.csv                  </td><td>mm                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>072a43688309a087125c565c9f41cf54</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Pst-mm3-old                     </td><td>Pst-mm3_S5.csv                  </td><td>mm                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>3e255f6af17a313b6839a1f4f2595fdc</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Pst-mm4-old                     </td><td>Pst-mm4_S6.csv                  </td><td>mm                              </td><td>Pst                             </td><td>pilot                           </td><td>old                             </td><td>dcac1408050aa686c7350d6a9b70d737</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & sampname & fname & trt & genotype & group & rna\\_prep & md5\\\\\n",
       "\\hline\n",
       "\t1 & Pst-kb1-old                      & Pst-kb1\\_S1.csv                 & kb                               & Pst                              & pilot                            & old                              & 7cab95ffd43eccabd4bff0f1e6b4ac5f\\\\\n",
       "\t2 & Pst-kb2-old                      & Pst-kb2\\_S2.csv                 & kb                               & Pst                              & pilot                            & old                              & 59633fe06424e54937c34a56c5e24477\\\\\n",
       "\t3 & Pst-kb3-old                      & Pst-kb3\\_S3.csv                 & kb                               & Pst                              & pilot                            & old                              & 2b576a5c270e7638b5072fa3776ecc83\\\\\n",
       "\t4 & Pst-mm1-old                      & Pst-mm1\\_S4.csv                 & mm                               & Pst                              & pilot                            & old                              & 072a43688309a087125c565c9f41cf54\\\\\n",
       "\t5 & Pst-mm3-old                      & Pst-mm3\\_S5.csv                 & mm                               & Pst                              & pilot                            & old                              & 3e255f6af17a313b6839a1f4f2595fdc\\\\\n",
       "\t6 & Pst-mm4-old                      & Pst-mm4\\_S6.csv                 & mm                               & Pst                              & pilot                            & old                              & dcac1408050aa686c7350d6a9b70d737\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "     sampname          fname trt genotype group rna_prep\n",
       "1 Pst-kb1-old Pst-kb1_S1.csv  kb      Pst pilot      old\n",
       "2 Pst-kb2-old Pst-kb2_S2.csv  kb      Pst pilot      old\n",
       "3 Pst-kb3-old Pst-kb3_S3.csv  kb      Pst pilot      old\n",
       "4 Pst-mm1-old Pst-mm1_S4.csv  mm      Pst pilot      old\n",
       "5 Pst-mm3-old Pst-mm3_S5.csv  mm      Pst pilot      old\n",
       "6 Pst-mm4-old Pst-mm4_S6.csv  mm      Pst pilot      old\n",
       "                               md5\n",
       "1 7cab95ffd43eccabd4bff0f1e6b4ac5f\n",
       "2 59633fe06424e54937c34a56c5e24477\n",
       "3 2b576a5c270e7638b5072fa3776ecc83\n",
       "4 072a43688309a087125c565c9f41cf54\n",
       "5 3e255f6af17a313b6839a1f4f2595fdc\n",
       "6 dcac1408050aa686c7350d6a9b70d737"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampledata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data as a DESeqData object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dds<-DESeqDataSetFromHTSeqCount(sampleTable = sampledata, directory =cntdir, design=~trt )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Estimate and inspect size factors for the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dds<-estimateSizeFactors(dds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Pst-kb1-old</dt>\n",
       "\t\t<dd>1.55592063666617</dd>\n",
       "\t<dt>Pst-kb2-old</dt>\n",
       "\t\t<dd>1.3861043989566</dd>\n",
       "\t<dt>Pst-kb3-old</dt>\n",
       "\t\t<dd>1.03775079182795</dd>\n",
       "\t<dt>Pst-mm1-old</dt>\n",
       "\t\t<dd>0.50339693701456</dd>\n",
       "\t<dt>Pst-mm3-old</dt>\n",
       "\t\t<dd>1.45624118576326</dd>\n",
       "\t<dt>Pst-mm4-old</dt>\n",
       "\t\t<dd>0.677278949795447</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Pst-kb1-old] 1.55592063666617\n",
       "\\item[Pst-kb2-old] 1.3861043989566\n",
       "\\item[Pst-kb3-old] 1.03775079182795\n",
       "\\item[Pst-mm1-old] 0.50339693701456\n",
       "\\item[Pst-mm3-old] 1.45624118576326\n",
       "\\item[Pst-mm4-old] 0.677278949795447\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Pst-kb1-old\n",
       ":   1.55592063666617Pst-kb2-old\n",
       ":   1.3861043989566Pst-kb3-old\n",
       ":   1.03775079182795Pst-mm1-old\n",
       ":   0.50339693701456Pst-mm3-old\n",
       ":   1.45624118576326Pst-mm4-old\n",
       ":   0.677278949795447\n",
       "\n"
      ],
      "text/plain": [
       "Pst-kb1-old Pst-kb2-old Pst-kb3-old Pst-mm1-old Pst-mm3-old Pst-mm4-old \n",
       "  1.5559206   1.3861044   1.0377508   0.5033969   1.4562412   0.6772789 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sizeFactors(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate gene specific dispersions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n"
     ]
    }
   ],
   "source": [
    "dds<-estimateDispersions(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conduct differential expression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using pre-existing size factors\n",
      "estimating dispersions\n",
      "found already estimated dispersions, replacing these\n",
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n",
      "fitting model and testing\n"
     ]
    }
   ],
   "source": [
    "ddsDE<-DESeq(dds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MAP): trt mm vs kb \n",
       "Wald test p-value: trt mm vs kb \n",
       "DataFrame with 5842 rows and 6 columns\n",
       "             baseMean log2FoldChange     lfcSE        stat       pvalue\n",
       "            <numeric>      <numeric> <numeric>   <numeric>    <numeric>\n",
       "PSPTOA0001  51.383556      1.1074926 0.2955247   3.7475462  0.000178573\n",
       "PSPTOA0002  15.437549      0.4056040 0.4615707   0.8787473  0.379538281\n",
       "PSPTOA0003   5.172905     -0.5142773 0.6847216  -0.7510751  0.452607434\n",
       "PSPTOA0004  35.440383      0.9753153 0.3390883   2.8762869  0.004023838\n",
       "PSPTOA0005  12.558815      0.5673048 0.4668771   1.2151051  0.224326014\n",
       "...               ...            ...       ...         ...          ...\n",
       "PSPTO_t59   0.3895036     0.57087886 1.1528334  0.49519631 6.204616e-01\n",
       "PSPTO_t60   1.9232968    -1.93855730 1.0311783 -1.87994381 6.011574e-02\n",
       "PSPTO_t61   0.1606037    -0.06597916 1.0382954 -0.06354566 9.493320e-01\n",
       "PSPTO_t62  19.3487956    -3.66099758 0.6020517 -6.08086929 1.195327e-09\n",
       "PSPTO_t63  19.2699434    -3.15820294 0.5531821 -5.70915614 1.135377e-08\n",
       "                   padj\n",
       "              <numeric>\n",
       "PSPTOA0001  0.001019702\n",
       "PSPTOA0002  0.519773470\n",
       "PSPTOA0003  0.588250598\n",
       "PSPTOA0004  0.014614985\n",
       "PSPTOA0005  0.353660766\n",
       "...                 ...\n",
       "PSPTO_t59            NA\n",
       "PSPTO_t60            NA\n",
       "PSPTO_t61            NA\n",
       "PSPTO_t62  1.535772e-08\n",
       "PSPTO_t63  1.303432e-07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myres<-results(ddsDE)\n",
    "myres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphical over of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CZBsQMAADAJih0AAIBJUOwAAABMgmIH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\nAED+kNflTm6SlZW1evXqefPmse4JAABAPmG72FldlTc9Pf3KlSu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tWrWmTJnixKiA4124oLAwzZ8vqy9B/vLLnMGcOUpPv41peaUygOuV\nK6cnnlDdukbncLDPP9eRI5K0dKk2bDA6TWFh/VJsQkLCfffdd/LkyeyPK1eujI+PX7t27c6d\nO3v16tWqVaujR49+9dVXI0eOrFmzZq9evZwYGHCknj3122+StGaNZsy4+Vt/f23fLkm1a6to\n0bzOOXy4Pv9cNWvqp5/M/99xALiqfPlr43LljMtRuNxyuZOTJ08OHjx49OjRkt5///0xY8Zk\nZGRMmjRp1KhR2fv85z//ue+++yZPnkyxg/1lZurXX1WihNq2VVaWxo9XZKQeflijRzvwoFlZ\n+vPPnHF2vbvJjBmqUUMpKRo5Mq9zxsVp2jRJ2rdPH36o2bPtERQAbImIUEKCevVS6dKGZQgN\n1ZEjiolRSAh/rXUa68UuMjKyatWqM2fOdHNzkzRr1qxff/01ISHh6aefvrpP/fr1W7RosT37\nBAZgX337atEiSXrtNdWvr3fflaQ1a9S0qTp1ctRBixRR+/aKiJBk/SjlyunDD29vzhIlro1Z\nLgeAc3zyiV5+WZImTdKOHXKz/rve4Tw99cUXxhy6ELN+j118fHxgYKDb//5VcHNzCwwMlFSh\nQoXrd6tQocK5c+ccHRGFTmamli7NGS9erP/dEiBJiYmOPfTSpZo9WwsW6JNP7DNh9eqaMUMB\nAerZU2PG2GdOAMjdr7/mDOLidOiQkUngdNaLXWZmZsmSJa/fctPHbLe1jjGQV66uato0Z9yi\nhfr3l7+/JDVr5vClAUqU0L//rb597fkX3KFDFRurpUtVsaLd5gSAXLRpkzOoVUs1ahgaBc5m\n0OlZIHc//aSZM+XpqWeflaendu7UiROqVEn8XQIAbBo1SrVr68gR9e0rd3ej08CpKHbIlypW\n1Pjx1z66uqpyZWOSbNyof/1Lp07prbdk7V3JAJDvFCnCW2gLrVsWu71793788cfXf5R0/Zar\nGwEze/NNHT4sSaNH69lnVaqU0YEAwJavvtLEiapaVV9+KV9fo9PAqW5Z7LZv3x4aGnrTxn9u\nAUyuWLGcgbu7YU+WAUDenT2rIUOUkaH9+zV2rMLDjQ4Ep7L+i2pa9spbAD74QElJSkzUG2/c\nsHZJ3l24oCNHVKeOitzeG/wA4E5kZFx7f9elS4ZGgQGsF7sXXnjByTmAfKpOnWurFt+BmBi1\na6czZxQUpN9/v3b+DwAcpHx5TZyot99WlSp6802j08DZOIUAONKcOTpzRpI2bNDatUanAVA4\n/L//p7Q07d2rxo2NjgJns17sRo4ceerUqdua6OTJkyNGjLBHJMB+jh3T998rPt6wAFdXkHJ1\nVdWqhsUAABQO1otdSkpK7dq1R4wYsXXrVovFksvPWyyWLVu2vPzyy76+vhcuXHBMSOCOHDyo\ngAD16aO6dbVzpzEZnntO48ape3fNn8+rEgEAjmb9HrsvvvhiwIABI0eOnDp1at26ddu2bRsU\nFOTv71+uXDkvL6+UlJQzZ87s3r17/fr1f/zxx549e1q0aLFixYrWrVs7OT2QmyVLdP68JF28\nqJUrVb9+Xn9w40bNn6+GDRUScrdPPLi76+2372oGAADy7JbLN7Rp02bTpk0bNmyYOXPmkiVL\nZs2a9c99KlSo0LVr12+++aZly5aODAnckRkzro1btLjhqzNn5OkpDw8rP3XsmDp0UFqaJGVm\n6tlnHRkRAAB7ym1dLhcXl1atWrVq1SorKysmJmbbtm2JiYlJSUlly5b18fFp1KhRYGBgEVZw\nQP6Umqr9+3PGfn7X3pwoacQIffyxSpXSokXq2PHmH9yzJ6fVSfr7b8cHBQDAbvK04GqRIkUa\nNmzYsGFDR6cB7MbTUx07atUqSXr66Wvbz5zR1KmSdO6cJk2yUuyaN5efn/bulbu7+vRxUloA\nAOwhr+fb0tLS4uPjz58/n/uzFEA+8n//pyVLFBmp11+/trFkSZUsmTOuWNHKT5UsqS1btGKF\ndu9W+/aStHChatVSkybassXxoQEAuHO5nbFbvXr13LlzN2zYcOLEiXPnzmVvLFGiROXKlbt3\n7x4SEtKgQQOnhATuSNGi6tXr5o3FimnRIr3/vnx8NGmS9R/09FTXrjnjjAw984xSUyVp5Ej9\n/vvNO1ssWrhQu3erf3/eyQgAMJb1YmexWIYMGRIWFibJ29vb19e3bNmy2c/DJicnHzhwYOrU\nqVOnTg0JCQkLC3N1dXVuZuDudOmiLl1s7BMXp9deU1KSnn762st5MjKs7Dl9uoYPl6TPPtO+\nfSpVyq5ZAQC4Dbd8V2xYWFjz5s0nT54cHBzsduO7zzMzM6Ojo8eOHTtnzhx/f//Ro0c7JSrg\nLAkJatIk5x2LkZEaM0YzZ6p0aX3wgZWd16/PGZw6pX371LSp83ICAHAj6/fYzZ8/v0qVKpGR\nkW3atLmp1UlydXVt2bLlypUrGzdu/OWXXzo+JOBcmzff8ObsevV0+rT2/IAblwAAIABJREFU\n7VNwsCStWaNevfTCC0pKkqQePXJ28/NTvXpOzwoAwDXWz9jFxsZ27ty5WK4vLHdzc2vbtu2M\n65cKA8yhRQt5eubcV1etmrp1u/bVpUt65JGcdY/T0/XFF+rbV76+2rNH3bop1//LAADgaNaL\nXWBg4IYNGy5dupRLt8vMzFyzZk21atUclg0wSJUq2rZNy5bJx0ePPXbDOsZnz+a0OkmHD+cM\nmjVTs2bODgkAwD9YvxTbv3//hISENm3aREZGZvzjhvHMzMxNmzZ16dJly5YtgwcPdnxIFHAJ\nCWrZUiVLKjTU6Ch5VquWQkPVr9/Nb6fw8dGAAZJUvLhefNGQaAAA3Ir1M3bDhg2LiYmZOXNm\n27Ztvb29/fz8sp+KTU1NTU5O3rdvX1JSkqSBAweOGjXKuYFRAH30kTZtkqSPP9bAgfnu8YLM\nTB06pGrVVLRonvb/73/1xhsqV05lyjg4GQAAt8d6sXNxcZkxY8bw4cOnT58eERERFxeXmn2/\nkVS8ePHKlSsPHDgwJCSkUaNGToyKAsvd3fo4Pzh3Tg88oB07dO+9WrtWVark6adYrw4AkC/l\ntkDx/2fvvuOiONo4gP+OIr2IIE1QVIIIIqIooIgdK9iwG0ViNMYee6ImvHaNvcVeYtTYsEfs\nKCIoRUEQRDqCiihIF27fP269g/PoB8fB8/28n0+G3dnZZ30pc7Mzz5ibm+/atYtXzsrK+vDh\nQ+PGjdXU1DgcTq3ERuqLBQvw7BnCwvDjj6hrSa2vXEFoKADExeH4cQwYgObNoakp6bAIIYSQ\nqqjQXrEAVFVVVVVVazQUUm81bYobNyQdRCmaNROU9+/H0qXQ0MCdO3XufTEhhBBSARXdK5aQ\n+snJCTt2wNkZU6YgJgYAMjJw9KikwyKEEEKqgjp2pMGbORP//Ydly8DPxU1T6AghhEinir6K\nJaSea9UK587hxAlYWeGnnyQdDSGEEFIVojt2mpWZPP7p0ycxBUOIRLm4CPYHI4QQQqSQ6I7d\npk2b/vrrr6dPnwJo0aKFhoZG7UZFCCGEEEIqTXTH7ocffpg8efLgwYNv3LixZcuWoUOH1nJY\nhBBCCCGkskpdPCEnJzdz5szaDIWQGhEZidWrcfGixALIzMTatVixAqmpEouBEEJIw1DW4gkb\nGxsVFRVZWdlai4YQMUtLg709Pn4EgKNH8f33Eohh6lT8+y8AeHvj8WMJBEAIIaTBKKtjZ2Bg\nwN9JjBCpFB7O9uoAPHwomY5dUBBbCA4GlwsZyjFECCGkptDfGFKvWVsLtn8dPLhWbx0aChsb\nNGsGMzP2yLBhyMmp1RgIIYQ0MNSxI/WaujqCg3HkCJ4+re1UJsuWITgYycnw9sbNm/jxR5w9\nCx0d/P13rYZBCCGkISk/QbGdnV1pp9TV1Zs2baqrqztq1KguXbqINTBCxERHB5MmSeC+DCMo\ndO6MYcNQVISiIqxahQkTJBAPIYSQBqD8jp2cnNzHjx/Dw8N5X6qoqGRnZ/PK6urqWVlZXC53\n8+bNrq6u586do5UWhLBWr0Z8PFJT4ekJdXVoa4M3Y7Vp0/KvZRhwODUdICFEmvz5Jy5cgIMD\n1qwR7H9IyDfKfxV78eJFeXn5Dh06XL169fPnz1lZWVlZWf/995+NjU3//v1zc3NjY2Nnzpx5\n8eLFnTt31kLEhFTRhw/4/Ln2bte+PUJD8f49u0HZ6dPo1w8uLti/v6yrIiJgZgYFBSxcWDth\nEkKkwIMHWLAAvr7YuBFHj0o6GlKnld+xW7Bgwdu3b2/dujVw4EBVVVUAKioqzs7ON2/evH//\n/sqVK1u0aLF9+/bu3bufO3eu5gMmpEr++ANNm0JXl808Uvs6d8aNG7h4UbCWQkhwMIYMwYAB\niIrCly/YtAmvXtVuiISQuurdO9FlQr5Rfsfu1q1bPXr00NLSEjqupaXVs2dPLy8vABwOx8nJ\nKSwsrEZiJKSavnzBmjXgcpGbi7VrJR1NKcaOxZUriI9nv5SRgaKiRAMihNQZAwaga1cAMDXF\n5MkSDobUbeW/p+dwOO9K+Xzw9u3b9PR0Xjk/P58m2JE6Sk4OTZogJQUA9PREVCgsxKlT+PQJ\n48ejcWMx3PH1a3z8iI4dKzFVjr8vhbIymjXDnDkwMhJDJISQekBZGQ8fIjUVTZtSLkxStvK/\nPxwdHe/du3fp0iWh41euXLl//3737t0BZGdnnzlzpm3btjUSIyHVxOHg3Dk4O2PUKOzeLaLC\nokWYOBGzZqFvXzHcbv9+fPcdbG0xfnwlrvrtN8jKolEj7NyJyEjMmCGGSAgh9YmeHvXqSLk4\nDD8pQymSkpI6der09u1bFxeXXr166erqvn379t69e15eXurq6iEhITk5OUOGDImNjfXy8nJ1\nda2duGvTX3/9NX369M+fP/OmGJJ6qFMnBAay5cxMqKlVqzU7O/j7AwCHg4yMSrT24QPk5KCh\nUa27E0IIqWEFBQUKCgq+vr4ODg6SjkVY+a9imzVrdv/+/aVLl164cKH4uF3v3r03b95sYmJy\n7949Lpd76NChetmrIw2CszPbsevWrbq9OgDm5mzHzsgIlfow0KRJdW9NCCGkYatQLhwzM7Pz\n588nJia+fPkyISFBX1/fzMysZcuWHA4HgKOjY2xsLIfSbhHptWoV7OyQno6RI8uvnJ6OP//E\njRswNcX27dDREa6wdSsMDJCWhrlzKR0dIaQ2BATgwgXY2MDNTdKhEAmrRJJDIyMjdXV1AwMD\nfX394otkac0EkXocDoYMqVDN+Hh06ICPHwEgMBBaWti1S7iOhgZWrxZzhIQQUpr4eDg5IS8P\nAE6fxqhRkg6ISFKFpmFmZmauXLlSV1dXU1PT0tKySZMm2tray5cvz8zMrOn4CKlbrl5le3U8\nX1eFlyoiAnfvorCQ/TIyUrD6lRBCxCIsjO3VAQgIkGgoRPLK79jl5OTY29t7enpyOJxhw4bN\nmDFjxIgR8vLyq1atcnBwyM3NrYUoCakrLCwEZS0tLFjAlh8/xtWr+PKlROVDh9C2LXr1woAB\nADBlCtq0gbEx5s9HVFSJmp8/Y9s2bN0K+rBECKkse3sYGACAnByGDpV0NETSmPIsXLgQwMKF\nC3Nzc/kHc3Nz58+fD2Dx4sXltiDt9u7dC+Dz58+SDoTUDWfOMD/+yBw7xhQWskc2bmQABmD6\n9BFUi49ndHXZ4wDz8iXD4Qi+lJNj7twRVHZ1ZY8PHlyrz0IIqR/S0pizZ5moKEnH0VDk5+cD\n8PX1lXQgIpSf7qRDhw5FRUXPnj0TWh7B5XLbtWunqKgYyM8TUU9RupO65cMHbNuGoiLMmiU6\n23Dts7fH48ds+eNHaGoCgI0NgoPZg1paePMGxsYl9gKaPRvbtrFlXV32VOPG5b/eJYTUZXPm\n4NAhWFnh7Fno60s6GlIj6nK6k/JfxUZFRVlbW3+76FVGRsbGxiYyMrJmAiOkFN9/j//9D2vW\n1KEJwra2bMHMTJCFrvhOry4ucHFBRgYMDSEnJ3wVIFi6QTmDCJFqT55g+3ZkZeHRI2zdKulo\nSENU/qpYExOTiIgIhmGE+nYMw0RERLRs2bLGYiNElOfP2UJISNUb4XKxdSuCgzF6NAYPFl3n\n3Dk8eQIXF5T7gWzDBpiaIi0NU6cK8ptMmYLt2wFARgbq6jhyBACSk7FhA9LToaGBtDQEBcHG\nBgD27YOrKxgGgwZV/aEIIRJXPE0EpYwgklD+iJ2Tk9PTp083btzI5XL5B7lc7qZNmwIDA3lb\nihFSe8aNYwsTJlS9kQMH8Msv+PtvDBtWYmiN78IFjByJ9evRsyeio8tpTVERs2bhjz/QrJng\n4LZtuHULq1cjOBjm5oLjZmYYPRorVmDePNjZ4dkzAJCRwZAhcHFBYiL69IG5OdsRJIRIkWPH\nMG8eOnaEri6cnTF/PnJz8fgxPn2SdGSkASl/xG716tVXr15dvHjx0aNH+VuK3b1798WLFy1a\ntFhN+bpILVu/HsOGoagIXbtWvRFedwpAYSFev4apqXCFp0/ZQkEBnj1D69bsl1wuXryAgUGF\ndono3Ru9ewOAqSnu3sWDB3BxweDB2L2bXT/75QsePED79oJLfvsNt28DwNSpcHVF48ZVfEBC\nSC2Ljoa7O3gjIJ6eWL4c6emwtERMDLS04OeH776TdIikQSi/Y6epqenn5/f7778fOnQoPDyc\nvUxObtq0aStXrtSgfS1J7bOzq9bld+7g4EG2bGEBR0cRddq1g4wMuFxoaoI/LF1UBGdn3L4N\nZWVcuYKePSt6RyUlnD4t+NLJCY0aoaAAjRqhR48SNXNyBPcqKKho+4QQiXv/Hvz3Wm/fAsDN\nm4iJAYD0dJw+jeXLJRYbaUgqlKBYX1//r7/+ys7OjoyMvHv3blRUVHZ29t69e/VpvQ+RRocP\nIz+fLR88iPh4XLqE7OwSdQ4dYn9HMwz4+6yEh7PDaTk5OHCg6gG0a4fAQOzYgaAgWFqWOLV8\nOUxMoKCAP/6Arm7Vb0EIqWWdO7MTdvX18dNPAEq8CjAzk0xUpOERPWKXlZUl8riBgYGBgQGA\ngoKCgq/DCZQEhEgZ/ntVJSVERmLKFBQVoW1bBAZCUZE9xf8RyMtDURE7CVpfH0pK4CXl5jdS\nBTk5+OcfREejTRtYWCAoCJs2QUcHy5ejQwfExIDLhUyFPnQRQuoKWVlcvow3b6CjA3l5ALCx\nwZkzuHgR9vZ1aBU/qe9Ed+zU1NQq3kS5mfAIqYr0dNy7BwsL8X/SXbwYXC4CAzFpEi5dQlER\nAISHIywMnTqxdTw9MW4cPn/Ghg1o1Ig9qK2NK1ewbx9MTbFkSdUDWLsWa9cCwJUriIvDoEHs\nPmMfPuDvvwFQr44QacXbAYJv5EiMHCmhUEgDJbpjN378+FqOg5ASPn1C+/ZISoKcHG7cQK9e\n4mxcURGpqbh6FTduwM2NPdi4cYlBuD598PYtCgvZT958vXpVKBguF6dPIy4OEybAyEj4LG/a\nDYDcXMTEsNNxih8nhBBCqkR0x+5v3rABIZISEICkJAAoLISXV4X6Uu/e4eFDdOgAE5MSx798\nwbt3MDAQZJjLzWVnyBUWIioKBw4gMhITJ7I7RvBxOMK9um99/gxPT8THY9o0tGkjuMvGjeyQ\n3p49ePUKCgolrpo6FRcvIjsb/frB1hZTpuDgQcjLY+bM8h+TEEIIKR298SF1koUFlJXZcufO\n5ddPTYWlJUaMgLk5iu9xFx2Nli3RrBl69RIsmFBUFLwuad0aHh7YsAHt2lUosLQ0HDmCBw/Y\nL1euxKZNOHMGffuiWTP064e8POzbh/372QqJiYiPF26kRw/ExyM0FNevQ1YWBw7g5UvExwtS\n9BFCGpTjx6GjAxMT3Lsn6VCI1Ktcxy4lJWXo0KGPHj2qoWgIYRkawscHS5bg1KkKJSL28cH7\n9wCQn49LlwTHDx5kR/7u3RP8xuRwcPUqxo/HrFnYsaMSUeXmwtYW7u7o3h1HjwLFXp7yZpre\nuoX58zFtGl6/Zo+3bQuRu7M0aQJLS8FcOjMz2lOSkAaKy8XPPyMtDXFxWLhQ0tEQqVe5jl12\ndvbFixffvHlTQ9EQItCxI9auxejRFarcvr1giUPxEb7iGUP09ARlKyv8/Te2b4eOTol2uFxc\nvoyzZ9kEwkJevkRcHFu+fh0Apk+HklKJOomJgrKnJ/z8BJvDEkKISPzfEvTrglQbfQ+ResHM\nDLdv4/JlODiU2G71p5+QkICgIIwbV2KDh9LMno1duwBg+HCcOyd81tQU+vpISQEAJycA6N8f\nCQlITMTRowgJwfffQ1sb16+jqAiWlli0SHh2HSGECJGRwcGDWLAAqqrYtk3S0RCpRx07Ul90\n64Zu3YQPKihg8+ZKNHLtGlu4ehUMI1hvwaOqiseP8e+/MDWFiwt7UFsb2tro0EFQ7cULvHqF\nXr2oV0cIqZBhwzBsmKSDIPUELZ4gpBj+/rPdugn36niMjbFgAVxdRZ/lMTPD4MGCxR+EEFLv\n5efD3R2mppg3D5TdVqIqN2JnYmLy/v37SqUvJkSa7NuHrl2Rlwd3d0mHQggh0uPYMRw5AgBb\nt6JPnxJTYkjtqlzHTlZWVltbu4ZCIUTylJQwfbqkgyCEEGmTkyO6TGodvYolREISEjBsGLp2\nxdWrwqcKCyURECGEVNXkyXB0hIwMhgyBq6uko2nQqGNHGranT9GlCywtBcsmas3ChfDywqNH\nGD0aeXnsQS4XkyZBURFWViUypxBCSF2moQEfHxQU4NIlQfIpIgnUsSMN26xZCAjAixeYPLm2\nJ/ymp7OFnBxBx87PD8eOoagIoaHYvr1W4yGEkGqSlZV0BIQ6dqSB4/eo8vNru2O3bBkaN4aM\nDJYtE2xTq6IiqKCqWqvxEEIIkX6Ux440bOvXY9Ik5ORg2zbBBl/fys/Htm14/RoeHhXau7Yi\nevbE27fIzy/RgbO2xpo1OHoU7dtj7lzx3IgQQkiDQR070rD168fuJFG29euxciUAnDqF+HjB\nAFt1XL6MjRthaIgtW0psd7Z0KZYuFUP7hBBCGh7q2BFSAeHhbCEzE4mJYujYZWVh1Cj2RbCi\nIg4fLrXmixe4dw9OTrC0rO5NCSGE1HfUsSOkAiZOxPnz+PIFDg4wNxdDg1lZgul979+XWu3F\nC9jYoKAAjRrhyRNYWYnh1oQQQuovWjxBSAUMGoToaDx4gHv3ICeOj0N6epg1CxwOGjfGokWl\nVrt/HwUFAFBQgHv3hM+mpKBvXxgaYvVqMYRECCFE+lHHjpCKMTZGt26Qlxdbg9u3Iy0Nqano\n3r3UOvw7ysvD0VH47IYNuHULb97gt98QESG2wAghhEgt6tgRIjlaWkhIwOTJ8PBAXJyIClZW\n8PfH5s3w80OHDsJnv3wRlGmzCkIIITTHjhAJGzsWT58CQFQUHjwQUaFDBxFdOp6FC3H3LqKj\nMWoU2rWrwSAJIYRICRqxI0SiYmPZQkxMpa/NycHr1ygowIkT2LBBvHERUj/FxeHnnzF3boXy\nHBEihahjR4hEzZkDABxOVdIR+/khPx8AGAZLl+L5czHHRkj94+aG3buxbRsmT5Z0KITUCHoV\nSxqA2FicPQszMwwZAg5H0tGUtHw5JkyAjAyaN6/0tY6OkJNjZ9dxuXj1ivKhEFKOyEi28PKl\nROMgpKZI64hdTk5OQkJCZmYmU8v7exKpk5kJOzssWgRXVxw8KOloAACJiejYEQoKmDEDDAMT\nk6r06gCYmuLiRSgpAUDr1ujdW7xhElIP/fgjW5g2TaJxEFJTpKljd+fOnUmTJpmZmWlqaqqo\nqDRv3lxDQ0NVVdXU1HT+/PmhoaGSDpDUSS9f4t07tnz3Lvz88OmTRAMCtm5FUBAKCrBnD9q3\nx/r1VW9q4EAkJeHhQzx/Lp6Nzgip3zZtwvPniIjAsmWSDqVeePeOzbVJ6gzp6NgxDPPjjz/2\n7t372LFjb9++bd26dd++fYcPH963b9+2bdump6dv2bLFyspqypQpRUVFkg6W1DEWFjA2Zsu3\nbsHBAS1bCl7HSISCgqAcGoolS0QkH644LS107cqO2xFCytWuHdq0kXQQ0o9hMGECdHVhaIgn\nTyQdDRGQjo7djh079u/fb2tre//+/bS0tKdPn3p7e587d87b2/vJkyfv3r17/Phxnz59Dh8+\nvGnTJkkHS+oYFRU8fYqDB/G//7FDdx8/4vTp2g7jwwdcuMAuff3lFwwejMaNBWe/3VUsNhb7\n9iEoqPYiJISQiouMxIkTAJCWhm3bJB0NEZCOjt0///xjaGjo4+PTvXt3uW82dJKVle3Spcv1\n69etra0PHDggkQhJnaajgylTMGiQ4IiZWQ3eLj8f69fjp58E3bK0NLRrh+HD0aYN/PzQpAku\nX0Z4OBtG164YPLhEC0lJsLbGtGno3Bk+PjUYKiGEVE3jxoKdeHR1JRoKKUE6Onbh4eH29vaK\niopl1JGTk3NyckpISKi1qIiU6dABZ85gwgTs2oVRo2rwRqtXY8kS7N2L3r3x+TMAPHrEJs36\n8gVeXmw1PT2Eh+PtWzx8KPwi9fFjZGYCQFERvL1rMFRCCKkaXV2cOAFHR3h4YPlySUdDBKQj\n3YmlpeXjx4/z8vLK6NsVFRU9ePDAyMioNgMjUmbkSIwcWeN3efGCLXz6hORkmJqiTRsoKLA5\n5zp3FtSUkUHTpiJasLWFigqyswGgR48aDpcQQqrEzQ1ubpIOggiTjhG78ePHJyUlde/e3cfH\np/CbPTGLiooCAgL69+8fFBTk4eEhkQgJEZg4EbwJA46OiIyEtjasrDBzJhYtwr//YsSIsq4N\nD8eJE1BUREAANm2Cjw/69Cm18sOH6NwZtrZ4+FDMj0AIIUQ6SceI3YwZM8LCwvbu3evk5KSh\noWFqaqqlpaWmppaVlfXx48fo6Oj09HQAEydOXLhwoaSDJQ3e0KGIikJSEuztYWvLZlfZuxcZ\nGZCVLetCHx/07o3CQujoICwMv/wios7Vq7h5Ez17wtUVU6eySVY9PCS8zpcQQkjdIB0dOw6H\ns2fPntmzZ+/cufPmzZsvX77MysrinVJSUjIwMJg4caK7u3v79u0lGychLBMTmJgAgLo6e0RV\nFTLlDZBfu8ZuI/H+PR49wtChwhX8/NhlFtu24d495OSwx/kFQgghDZt0dOx4zM3Nd+3axStn\nZWV9+PChcePGampqnOptEhUWFpbPm/xUClqQQapu1y7Mno3sbKxZU/5uZnZ2bEFWFn5+cHUV\nvuTZM0E5JASbNrFp9P/8U4whE0IIkV4c6dqSKysrKzY21sjISFNUkv2UlJT8/PwWLVpUvMHX\nr1+bmppW5B8hMzNTTU2t4i0TUhXr12PJErY8axZWrkSTJoKzcXGwscHHj1BXR2AgWrcGlwug\n/LFAQggh4lNQUKCgoODr6+vg4CDpWIRJzd+DyMjIHj16qKurW1lZaWlpjRw5Mjk5WajOsGHD\nTHjvvyqsVatWmZmZ6WXavHkzgGqOCxJSIcWXde/YgXbtcP8++B88WrTAy5e4fBmRkWjdGgBk\nZKhXR0idExWFo0cRHS3ONnNz4e4OCwv88Yc4myX1jnS8ik1ISOjUqVNWVpaDg4OxsfHdu3fP\nnTvn7+/v6+trzN8tqqpUVVXLrqCsrFzNWxBSUYMGwcJCkDAlJQU9ekBFBRMnYudOyMqiadMS\n2YwTE/HsGRwcoKUlkXgJIcJevECnTsjLg5ISgoPFlg593z4cOQIAv/+OXr3g6CieZkm9Ix2f\n9ZcuXZqVlXXs2DFfX9+TJ0++efNm7ty5SUlJEyZM4PJeRRFSP2hoICQEa9eWOJidjb17ceGC\ncOWQEJiZYcgQWFiwu6URQiTu9m3k5QFAbi5u3xZbs7xs5zy8BOaEiCIdHTs/P79u3bpNnDiR\n96WMjMyff/45cuTIBw8eHOF9giGk3pCTw+LFOHMGVlYljvP+VBR38SJycwEgNRV379ZSeISQ\nstnbs5ks5eRgby+2ZqdOBS/zg6sr+vUTW7Ok3pGOjl1ycrLQK1cZGZkdO3aoqaktXbr0Ey9P\nGCH1BoeDkSNx/z5++QXNmkFODoMHi8jw3qEDW5CXR7t2tRwjIUQ0W1vcuwdPT/j4CH5Iq09X\nFyEhyM2Fl5dgk1ZCviEdHTtDQ8Nv95zQ09Nbu3btu3fvJk2aRC9kiXTbvh1NmsDKCs+fCw5q\namLTJiQm4ssXXL4MBQXhqxwc0KsXdHUxfTratq3NeAkhZenaFcuXi3O4jq/MPdMJgbR07IYP\nH56UlDR69Og3b94UPz5jxowBAwZcunRpwYIF2byNNQmROp8+Yd48pKcjNBQrVlTiwlmzcOcO\n3r7Fjh3Yvl1klaysrNOnT/v7+4snVEKIry+7ld+DB5IOhRARpKNjt3z5cgsLi/PnzxsaGhoY\nGERFRfGOczicY8eO2dnZbdmyxcjI6CVveyVCpEvxlCWVyl1S/Bv+xo1vzxcWFtrb248ZM8bO\nzu7QoUPViZEQwpo6FU+e4OlT0NbkpE6Sjo6dhoaGn5/funXrbGxs8vPzc4ptoKStrX3nzp3l\ny5crKipmZGRIMEhCKiorC3Fxgi/V1bF7NwwNYWuLNWsq0c6cOYKtKdLSsHRpiXVzwOvXr8PC\nwnhlLy+vasVMCOGR4FZ+CQnYuRP379f2fYlUkY6OHQA1NbXFixcHBgZ++PDB2tq6+CklJSVP\nT8/ExMSYmJg7d+5IKkJCKsTPD0ZGMDGBiwv4c0OnTkVSEgICoKCALl2grY3//a/UFj5/Rmgo\nCgsxeTJCQrBkCVRVERCAdeuwbFnxis2bNzcwMOCV62B6dEKk0qZN0NSEpqborfyePIGtLSws\ncO2amO+bno6OHTFrFnr0wLlzYm6c1CPSkaC4ImRlZU1MTCq78wQhtSc3F0pK2LsXvHXcly8j\nLEw4p8maNQgIAIAVKzB+PFq2FG4kLAxOTuyveB8fWFmhSROsW8eeffWqeF1FRcWHDx8eP37c\nxMRk3LhxNfNUhDQwI0di+HCglIkTs2bh6VMAcHdHamr5O0RX3LNnSEtjy7duYcQIsbVM6hep\nGbEj9cfZs2jZElZWbA+mIXjzBpaWUFaGqyv09dmDjRqhaVPhmsX/DIj8k3DsGNLTASAwED4+\nAGBoiKFDAUBeHj/+KFTdxMRkxYoVEydOlJWVFcODEEJQ5lZ+/HyT+fkQ71bs7dtDW5st9+kj\nzpZJ/UIdO1K7uFz88ANiYxEairlzJR1NSZGRWLAA27YhP1/MLe/bx+4SdukSOnfG7NkYMABn\nz0JPT7jmr7/CwQG6uli7FrzhZ6FUPvwxaTk5NG/Ols+fR1AQ4uLYgYRvhIaGjh49evLkyQkJ\nCeJ6JkJqQkZGxv79+y9fvsyIt1dUQW/f4sWLanXINmyAnh40NLB9u5j3cdbSwtOn2LoVd+/S\ncB0pQ/15FUukA8MIeipFRRINpaSCAvTogdRUAEhNFd7Uq5o0NASYuctuAAAgAElEQVRlPT1s\n21ZqzebN4evLliMiMHQoYmMxa5ZgNs/UqfjwAUFBGDsW5ubsQQ6n7Dyow4cPj46OBvDmzRtv\nb++qPwghNYlhmB49eoSEhADw9PRcvnx5rd7+0iWMGoX8fAwbhvPnq9hIv35ISRFrWMU0b445\nc2qqcVJf0IgdqV2ysti9Gzo6aNkSmzbV3n0/f8b27di+XWjdqEBqKturAxAcLOa7T58Od3dY\nWmL1ahRfxMDllrXH64YNiIrCly/YvBmhoTh+HKdOgWFgb49WrZCejorlbuRyuUlJSbwyjdiR\nuiwtLY3XqwMggU8gf/3FjtZfuIDExNq+OyFiQiN2pNZNmIAJEyRw00uXAOD2bVy8KKKCkRG6\ndcPDhwAwdqyY766khG/TyL1/jx49EB6Ozp1x+zZUVYUrKCuzBRkZLFzIZqrr2VOwLeyWLXj+\nHI0alX1zGRmZX375ZfXq1XJycvPnz6/ekxBSg7S1tS0tLXk5enr16lXbt2/Vii1oaqJJk9q+\nOyFiQh070jA8esQWeF23b3E4uHMHt2/DyAgWFtW9XVISgoNhby+Y7Fycry8ePEBaGsLDASAg\nAF5eIjq7v/2GuDi8fo3Zs7F4MXuw+IqTyEhERLD7gpdp1apVP/74o4KCgq6ubpWeh5DawOFw\n7t+//88//+jq6o4Qmkbm7w93d3z6hHXr8P33NXL7VaugqIikJMyaJfhYRYi0oY4daRgGD8aR\nIwDg4lJqHXl59O8vhnuFhsLODjk50NHBs2eCZbA8vr7o1g0A5Ir99H27PBaAvj6uXmXL16/j\nyhXg6/7iPE2aiMiHUgpjY+OKPwEhkqKlpTVz5kwRJxYtQkQEAPz0E8aOhby8+O+tro4NGypa\nmbccqvofAgkRN5pjRxqGAwdw4QK8vHDgQM3eKD0dkyezKenfv8etW8IV+GsjCgsxcCAcHLB6\nNfr1K1EnKgorVuDwYcH6ktOnsW8fDh/Gf//BxwcTJ2LmTDx8CDW1Gn0aQuqKb7P/vHmDnj2h\nr49Vq2o7mBUrYGkJS0v8+mtt35qQ8tCIHWkYZGXZZG81zdMTQUFsWU5OxHvSPn0gL48vX6Ci\ngu3bBdN6+LKz4ejILqp4/x6LFgGAsjKmTmUrODrC0VH4Ki4XublQURHfkxBSl2zcyL6KXb+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Vnzx5InTRf//9p6+vr6+v/1ONdpoJqRoZ\nGUyZwu4WTQgpBY3YEekhJ4d793DgAAwMMHWqpKMBAGRnY8gQZGUBQFERdu6sYjtDhuDGDQQG\nwsUFzZohIgIbNwLAs2eYNQtXrwLA5cts5Rcv0KkTunXDli3w98f584KdxPhmzoS3N4KC+mtr\n3168mH+4c+fOurq6p06dAlB8MSzPli1bsrKyAOzdu/ePP/5oyt97gxBCiJSgjh2RKi1bYs0a\nSQdRTHo626sD8Pw5evRAQQHWrxeMwFXQ4cP4/XcUFaGoCG3bolEjwalr1/Dnn/jlF9jb4+JF\nADA0ZNfq8rx4gY8f0bgxAGRn4/lzdj+0x4/fv39/u2TnrEePHr/++uukSZOUlZW7d+/OP+7r\n67tnz56Ur8ty1dTU1NXVK/cIhDRwyck4dAja2vDwKPEjTEgtk3S+FSlAeewanPXrGSsrZvJk\nJju7/MpjxjAAo6zMfPcdm7CqRYvK3e79e0ZWVpDv6vBhhmGYX38VHOnbl2EYprCQOX6c8fRk\nYmMZhmFWrWLPOjoyXC6zZAnTpg2jqsoATNOmTFwcwzCFhYXGX1eB2NvbHzly5MuXL9/ePzMz\nk9+N09PTc3BwuHXrVuUegZAGjssV/AaYN0/S0ZAaR3nsCJEeT5+C9+7y+XOYm2PRonLqnzwJ\nT0/o6AiWSlR2I6O8PHblBE9EBAD88QdOn2ZTrvToAQCysoKEJvn5SEqCqSk6dsShQ/jvP6xb\nJ2jh3TucP49582RlZe/cubN37149Pb0ZM2Yolcz7lZSUFBAQYGdnl5ubm/k15tTU1E+fPhnx\n5zISUmf5++PuXfTqhc6dJR0K8PEjoqLY8uPHYmjwzRvMmIG4OCxciPHjxdAgaTCoY0dISRkZ\nostl4GVeWL0aU6agsBDFUsRVSLNmmD8fW7aAYaCszO5HKSuLhw9x8iQMDTFihPAl+/dj714A\nePUKP/8sIk4TE95/W7VqtXHjRgBHjx4NDAwcPnx4jx49AERGRnbs2DE7O1tdXT0oKGjgwIHX\nrl3jXZKXl+fr6/sdPz0yIXVQYCC6dUNhIeTkEBBQ6kLyWqOlhe7d4eMDAMOHi6HBFSvYqRfu\n7ujfH02aiKFN0jDQqlhCSnJywrBhAGBuXrmMKiNHIj0dGRnw8KjoJQEBGD4cHh5YsACfPsHH\nB3FxghV/urqYOxduboJM+nwfP5You7qyyR1atMCoUejeHcOHo0ULvHjBq3Ly5MnJkyfv2LHD\n2dmZl+vkv//+420Im5mZ6e3tfeXKlePHj8vJyQFQVFTs1q1bJR6ckNr36BGb8aewEI8eSToa\nAIC3N86dw8OHWLBADK3xP619+YKcHDE0WHcEBuLIERTLmk7Ei0bsCClJTg7nzyMnB8rKVbm2\n4rhcuLiwv90yM3HmTCWWXPzwA06eREQEnJ3h7Aw/P/Byl8jIYMEC9s1UfDz+/BOHDgF49uwZ\n77qCgoLw8PDWrVufOXOGd0RGRqZTp04cDmfChAnm5ua+vr59+vQxLSP7KyF1Qa9eUFBAfj4U\nFdGrl6SjAQAoKIhnrI5nyRL4+SElBfPnoz5NjbhyBUOGAIC+PsLDoakp6YDqIerYESJKFXp1\nlZWXh/fv2XJCQuWu5f1O/PyZTXF87hy7u2VMjGCiDwANDd5/R4wYsW3btry8PGNjY0dHx3fv\n3vl+TfXM5XI5HM769eu9vLzs7e03bNggV6nuKSESYWGBZ8/w8CEcHVEvpw107IjERLbnWp98\nnfKBlBQEBdWVTnn9Qq9iCZEQZWV2C9pGjaqY8p6/cQV/gpGSEpycsHMnrKzg5pY1d+4ff/wx\nderURo0aRUVFXbt2LTQ0tHHjxklJSbLFtiTv27fvkiVLHj9+vGXLliNHjvCPMwxz+fLlbdu2\nvXnzpkpPSAg+fvwYHh7OMIzIs9HR0XPnzv3jjz8yK7vkCICZGTw8hHt1z57B3R2LF1d0gmxd\nxuEI9+pevkRIiISiERP+ewlNzYru5EsqS9LLcqUApTshNSgujklLK6tCbi5TWFhOI1wuc+gQ\nM2cO8/AhM3QoAzAyMoy29vG+fXk/5jo6Ovn5+fzqvPUTIrVo0WLatGm87/adX/MtGxkZ5eTk\niOFhSQNz584dFRUVAAMHDiwqKvq2Qsuvu7JOnjxZDPcrLGR0ddmcIx4eYmiwTlm/nn20GTMk\nHUr1XLnCrFnDvHwp6TiqpS6nO6ERO0Ikqnnzsta7/f47VFSgq4t790SczcnBzp3YvBkZGXB3\nx9atKCiAlxcAcLlISxt365YOAOD9+/evX7+eOXOmi4vL7du3c77OxZb5uiyDw+HwCnFxcX/9\n9RdvIe3du3d5BxMTE3lLLgiplH379vHW6Fy7du3ly5dCZ7Ozs2NjY3nlsG93TylXSgpu3cLn\nz4IjGRmCKfnF5yTUDwcOsIWDB8HlSjSU6hk0CEuXwsxM0nHUW9SxI0SseCv1xCIzE56e4HLx\n4QNWrRJR4YcfMGsWfvkFI0eyR3j7T3wlwzAasrIABg4cuGvXrl27dl2+fNnV1fW3337T1dVV\nUVFZuHChqamphobGtm3bbty4wb/w3bt3APp+HfAzMTGh5RSkClq0aMErKCsr6+rqCp1VUVEZ\n+fVbd9KkSZVrOigIrVqhb19YWiI9nT2opYVRowBATg4//ljlsOsoc3O2YGYmYqU8IXySHjKU\nAvQqllRIcDDTvDkjL88sXcowDLNyJaOpydjaMjExVWwwL49RUWFfvnTvzvz2G3P7dokKzZuz\nZ5WUmEuXmO3bmdRUZsMGRlmZPT5lSkxMjJ+fX1FRkYuLC/+nPu7rvhTFGyssLHR1dQWgr68f\nGhoaFxeXlZV18+bNvXv3vnv3roqPQBq2rKysRYsWDR8+/ObNmyIrcLnchw8fvnjxotJNL1sm\n2Jrl/PniLTKBgUxCQlVDrsPevWMWLGBmzKj6rxQiPnX5VSx17MpHHTtSIW5ugr80Dx8KylOn\nCtdMS2OcnRldXWbhQtFNRUYy06czixczJ04wtraMoyPblJwcM2UKo6fH9OnDvH3L/Pwze7xD\nB7agrc2kp7O3yMoq3uTAgQN5vboBAwbwjkRFRQ0bNszZ2dnf359fLTU1NS8vb+jQoQA0NTXr\n5q8tUp98+PChf//+Kioq8vLynTp14n3qKN/Jk+z3vLy8tM/WItKIOnbSjTp2pELc3QXdr8BA\nQcdu2jThmitWCM4+fiyiqdat2bNjxjAMw+zcKajP/9/ChUxREXP5MnP+POPqKjju4PBtexG8\nbcoAAN9//z3DMMnJye2+LknT0NDYsGED/zu8+Gyn8ePHi/OfiJBvrFy5svhLpBkVXxlw6BDz\n00+Mt3dNRkeIaHW5Y0fv6QkRE09PDBwICwscOAAbG6xdCz09ODri11+FaxbfGbZ4mSc/H19n\nlLNph/v1g4oKABTf7LWoCDIyGDwYw4bh62Q4AAgI+HZitZqaGn+dROPGjV+9etWmTZvQ0FDe\nkYyMjEWLFtnY2KxatSo3N1dHR0deXv7r/Z8wpSSqIEQsikr+CPC/98rn7o7du0t88xNCaPEE\nqbv++QdDhmDFCjb1bgUVFsLbG8HBYgiAYZCfX4n6zZrh6lWEhYE3DXzJEqSkwMdHRNb42bPh\n6Ag1NcycCXt74bMKCujUiS0nJCA5GaamiIjAiRN4+RLz50NdHQ4O+OUXwSU//4yvb1rRq9e3\nE6uDg4O5X3t71tbW169f/1x8LSEA4NWrV8uXL581a1bTpk3t7Ox4B6OioqLq3+pCUpfMmjXL\n0dGxUaNGKioqffr0WbJkiaQjIkS6UceO1Enh4Rg/Hleu4H//w19/VeLCQYPg7AwbG3zNwVZF\njx/DwAAqKvjtt2q1I1LTpvDxQWYmduzA1zwjJfATDn/5gtevAcDICOPGwdgYf/6JjAz4+sLA\noMQlXl74+2/s34/z579tLyUlhV/Ozs7uUGzH9K5duxaveeXKFQCdeZuSAYqKitra2uU+0Jkz\nZwYNGrRo0aK8vLxyK5OGpqCgYNu2bfPmzXvxdfPi4po2berj45Ofn89brKOnp1f7ERJSr0j6\nXbAUoDl2EnDjRonJZBWUnl72VLNKGDJE0FTNrQn99Inx8GAcHJjjx0sc9/FhFBUZgLGyYkrL\nDBwaymzdyjx5Inw8OZk5fJi5cYOZNIlxdS148GD79u2Kioq8jcIsLCzS0tIYhrl8+fLcuXOv\nX79eWFi4b98+fh67Dh06MAyTkZExc+bMQYMGXbt2rdyHiImJ4e9jsXr16qr8O5B6bcWKFbxv\nD21t7ezsbEmHQ4gY1OU5drQpJKmTundHly7w94eODtzdK3qVpiZMTNgJajY21QpAXZ0tKChU\naK/GoiKcOIHYWEyYgFatKnqXdetw8CAA+Puje3cYG7PHHR3x+jWio2FjA3d3XLkCJyf8+y87\n0w6Anx+6dQOXCxkZ+PmBN8CWmorYWLi64v17cDg5DDMEuHPxIofDYRgGQIcOHZ48eRIWFnbj\nxg1nZ+fBgwcDSE9PnzdvHq+CkpLSgQMHAKirq+/YsaN4pAzDHDx4MCQkpG3btnFxcR07dhw9\nejTvVEpKCn+aVGJiYkWfnTQYIV93wUpLS0tKSvquXm7tSkjdIeGOpTSgETvJKCxkIiKYyn6+\nj4lhFi5kNm4sdaCrguLjmcGDGWtr5vTpCtXn7/ajp1eJmD08BOOCwcEiKpw5I6iwa5fguLOz\n8Ijmv/8yjRoVXzZ7RNTPe/PmzXmja82aNcvIyIiNje3Xrx//LIfDKT6gEhsby5uNxzDMoUOH\nhJo6deoUr1pBQUHv3r0BNGnSJFjkU5CG7eTJk7zvOjs7u8Jy98eraampjLs7M3Agc/euhCMh\n0oxG7AipPFlZtGlT6atMTLBhgxjubmyMy5crUd/fny2kpvsbphEAACAASURBVCIuDm3bVuiq\nWbNw9SpSUzF6NNq3F1Hh6ytOAJAr9tOqpSUo8ybM7dmDgoLilzYXdcP4+HheISkpad++fUuX\nLi0stlWGjo6O0teFtz4+Pn379i0oKFBTU5OVldXQ0BBq6unTp7xBO3l5+Zs3b8bExOjr6ysr\nK5f9xKQBGjNmjLW1dXx8fM+ePWWLf0t/lZiY+PPPPycmJi5ZsoQ/ElxTFi7E8eMA8PAh3r2D\ngkLN3k6krCwoK4tz94jHj3H7Nnr3xtdlT6RBk3TPUgrQiB0p35Ej7FCZhQVTUFCJCwsKmPfv\nSz1bWMj88AOjr8+MHs3k5rIHY2KYM2eYVq0YGRlm9Gj24JQp3+a6m1D68ihFRUV1/utmAICJ\niUlERAT/zmPHjhW6hPcnmTdXT15evm5+VCXSaOLEibzvsUaNGmVkZNTszXr3FvyMpKXV7L2+\nxeUykyYxAGNoyISGiqfNp08ZOTlBBk1SK+ryiB2tiiVSKy0Nq1Zh0yZ8k7lDAiZNQkAATp2C\nnx8qmIgrPR39+6NZM6xejdJyxcnKYv9+vHmDU6fYqX4nT8LUFG5uMDVFfj5OnWJrbtyIOXPg\n6Fj86uPALf42siWpqallZmbyyhwOx9bW1sfHp02bNgCSkpJGjhx55swZoUucnZ1v3LgRExNz\n4cKFly9fOjg4VOgxCSlPRkYGr1BQUJCbmyt8+sABjBpVYnV8RgZWrsScOeya8UqZN4+dqzpj\nBpo0qWLEVRYWhqNHASA5GVu2iKdNPz92i+rCQjx6JJ42iVSTdM9SCtCIXR3F32jLzU3SoYgS\nGMhMmMDMm8d8+CC6wu+/C0YOHjwQUSE3l5k9m3FyYg4cEBzs109wVUICw+WWaD88nJGVLT5o\nl7B8Of/lV9++fTU1Nb/9JbBnz57itx0zZsy3dWRkZIrvPEZIxX369Kl79+5ycnKurq75+fnf\nVnj8+LG+vr6srOxS3j7Lxd28Kfh+5q/RnjiRPdK6dVk3zspi5s9nBg1iLl0qcTwjg0lOrvrz\nVEd8vOAntOLr/csWHs4uoldUZMLDxdMmKU9dHrGjOXZEavF2ZQBw754kwxCpqAgDBuDdOwD4\n+BGHD4uoUzzlW3Y2goMREgJnZ0GCup07sX07ANy/Dzs7WFgAgLk5vL0BQFsbHA4sLREejm7d\ncOMGlJVhbo6zZ7FtG//fxEhF5ejRo7t3727Tps3mzZs1NDSmTp3KW/oKwMzMbNKkSdOnT+cH\nUlBQ4OPjw/9SVlZ2wYIFmpqaLVq0ePDgAcMwXbp0Ecu/EGkI/P39c3NzQ0JCeN9UFy9evHTp\n0shvRpG7dOmSnJycn5+v+O0K9OJjcvwyPx/e69fIyUFpMzvXr8fmzQBw8yZiYwU/WerqKDkP\nofYYG+PgQezeje++w9Kl4mnT3BzPn8PXF127wtRUPG3WV6mpAFDvcyVKumcpBWjEro4yNGQ/\n+MrKVncNrNh9/CgYZujWTXSdjRsFdebNYws6OoIpdwsWCCrcvs0ezMpi/viD+eEHJjiY2bRJ\nUGHqVObOHbZOYSEzdizTqBHTvTuTns4wzJMnT3bu3BkVFcUwzKNHj1RUVAB07NgxLy+PH05u\nbi7DMAd56VcAAH379k1MTGQY5sWLF40aNQIgJyf34MEDNzc3ZWXl/v37Z2VlMQyTmZnp5eUV\nGRlZA/+ORColJiauW7du+PDhvG+k4h8GLl++XLm2UlIYY2N2Uhp/mG3bNvbbfuzYsq6dPFnw\nAxIUVMWHIfXGxo2MjAwjI8P8+Wf1G6vLI3bUsSsfdezqqD592F/ZcnJMGf/v/P03Y2DAtGnD\nPHpUi8ExzPffMwAjL8/884/oCqdOCf7qFM+HfPUqWyEyku289u0rekHG4cPCCyYOHhSqEhkZ\nacEb6gNUVFRiY2MZhnnw4MHhw4cLvrYZGxtrZmYGYOzYsWvXruX/DR43bhyvAn+ED8D48eP5\n5V27dmVlZbVu3RqArKzs9evXq/2vRqRefn6+MT8jIwBAXl5+4sSJJiYmc+bM4XK5lW4xO5sJ\nDBTOIvTiBfPoEVN2a35+jKYmAzADBjAST7NCJE5XV5CUqtrqcseOXsUSqbVqFaKj2SUUqqqi\n6xQVYfp0ZGXhzRssWABf39oL7+hRLF4MFRWEh2PBAuTkYPp0WFkJKri54elT3LyJAQNga8tm\nV1FTg7U1W6FVK8TG4t07JCVh5Eioq2PNmhI7z06YgNBQeHsjLIw94uUFbW3Y2kJfn3dg+fLl\n/H2csrOz/fz8/v3338WLFwO4ePEibxeK5s2bR0ZGAjh58qQ37z0vACAgIADAjRs3Nm7cyMty\nLC8vb21tfeLECV4FOTm54ODg6OhoAEVFRefOnevfv7+Y/xmJtElISEhISCh+xNra+tixY1Vv\nUVm5RL5xLhdr1sDPD66uIrZaLs7ODgkJePsWrVqJ3ruPNCjNmuHtWwAi9u+uZyTds5QCNGIn\nxQoLGVVV9lNa1661euvISObsWaZjR8Fwmra2IGXJt65fZ9asYV68YBiGSUlhbGwYGRlm7Fjm\nyxdGX18wsPet7GzBW2neHGp1dSY6mneS/zoMgJKSUkxMTJuv2QH524iJXFEBwMXFJTg4uHji\nscaNG3/48MHDw0NfX3/06NHv37+3KfYXV2gRBmmYvnz5YlTsD+fo0aOTRa1UqPpv1GPHBD9T\ntTwMT6Tay5fMmDHMmDGMOOaN1OURO0p3QqRQcDCuXEFF9puXlcW+fWjWDG3b4s8/az6yr27d\nQtu2GDkSgYGCg2lp7OdFkfr3x9KlbGbjPXsQFAQuFydP4tYtvH/P1omLg5cXkpPZL5OT0bkz\nmjRBnz74809Mm8b+m2Rm4vp1XpVly5aZmZmpqKi4uroGBgaamJjw38zKfc14zOVyvw1HUVFx\nxIgRHTt25G8XBuDjx4+Wlpbr1q178+bNqVOnvL29g4KCeKdGjBhhYWHBz1tBGiw5OTk3Nzf+\nlzo6Ogb8VQsAgISEhO+++05NTW3o0KHF82NXVPFt62gLO1JxZmY4eRInT6K+b2pHHTsibQ4f\nho0NhgyBkxNE9UiEjR2LxES8eIHSlnMmJODmTWRnizPICxdQrD/E6tMHJecelYq/JywADQ0s\nWAAOB40aIToaw4bB3BzR0QCwdev/27vvuKau9w/g5yZhiyAKKCiIIi60bmXUvau1zlr3rHvV\nqnX+1Fpbt3Ug1IV11om1Yh0obvwKDoobFRyACigSNuT5/XHjzSVMFQhcPu8XfyQnJzcnN+vh\njOew69dZcjLbsYO5ubEhQzT3atyYMXbq1Kl27drx0+zMzc1jYmIYY15eXrNnz548efKKFStM\nTEyMjY0XL15crlw5rSa4ubktXrw4a8wXGRl59uxZ/rKlpaVQ7ufn17JlSycnJ61hOJCM5OTk\n69ev5yd256ds8kzEb2bGGGOenp6PHj1ijB09evT8+fMf3Y6BA1nlyowx9sUXrEsXxhh79Yqt\nXs0GDGCDBjE/v/wehyhfn/qEBBYT89GNBNAhXXcZlgAYii1eunbVDMQ8fvy5R7twQb3FqpNT\nbiswPtbWrZpGchxNnUrjx1OPHpky0uUiPp6++45q1qSFC9UlERGZ1sCuW0dENGeOpmTSJCIi\nX18aPZqmTaMbN86cOaNQZJpEa2xsvGzZsl27dqWlpX14nPgNGzYsXbr07t2720U5WSwsLO7d\nu8flMC1p27ZtQkt/+eUXV1fXTp06Cbf+/vvvBXYaodh49+6dk5OT8N7IvfLr16/5XjoTE5Pg\n4GCtW5ctWya8W4I+baeEpCS6f1+9HiI1lapX13wQDA0pMpKePaMePahZMzp8OPsj3L9P9vbE\nGA0enNsKDB8fMjEhjqOsCfagdCvOQ7EI7PKGwK54mT9f/Q1uY0OibB2faNIkzU9Cp040eHCB\nTL8glYqWLSNra7KwoN9/p4MHNY9y4cInHvPqVe2pRWFhVLlypuW0b96QpSWfAmZ+zgnnpk6d\nyh9ywoQJfEn9+vWJaMGCBQqFwtbWNjAw8Jqw+20Ww4cPf/LkSevWrR0dHTdv3kxE4n6XU6dO\nFcAJhGLm0KFDwku8YMGCPOu/ffvW19c3IiIi600JCQkjRoyoX7/+qsxZJ9LT0+/cuaP1TRsd\nHZ3HQtrHj7XXhv/vfzRggCbOUyqzude4cZr6gYE5HtzFRZNTKdvjQGlVnAM7DMVCSTN/PvPw\nYD/9xPz987WBd2QkS0vL8dZ69dQXZDJ28iTbuZNlt+/CR+M4NnMmi4piMTFs8mT29KnmJvHl\nj9KiBTt5kk2fzv75h929y5YvZ6NHsxcvNBWiotj16+oJeRkZLT8MM8lkMr3Mu5z5f0hfHBAQ\nwF8IDg6Oj4+/ceNGenr669evw8PDv/jiC2dnZyZaYyGoXbv2/Pnz/f39Q0NDx44dGxMT07Jl\ny927dw8ZMmTHjh0dOnT4xCcIxVj16tWFy06iKUrR0dGdOnWysrIaP368uL65uXmXLl0qfVid\nLRYZGenn5xccHHzu3Dlhjl1SUlKLFi3q1q3r4OBw9+5dxlhycnLr1q0rVKhQu3btyMjIHFtm\nZ8dEI7/M3Z01aMDevVNfTU5mWfcoY4yJFwyZmeV4cGtrTf2syZMBiiddR5YlAHrsSqqUFGrb\nlhgjO7scB20zMsjLiyZM0KQ4Mjcv+JY8e0Y2NuoB35x2GCOiGzfor78oz03Qp0xRN1Um0/Q6\n1K9Pb9/Sy5dkasqXxHl7jxgxomPHjidPnoyIiFizZo3xhwT9P/30E3+kRYsW8SXt27cXD8V2\n6dKFiC5fvrxy5crp06fzhTVq1Bg6dOiyZcuSkpKExbAcx/G/0+7u7l27dkWaYgnbv3//gAED\n1q5dK+5CmzdvnvC2GTJkSH6O8+OPPwp3OXfuHF/477//CoUzZ84kosOHDwslS6ZM0T7K9eu0\nYIF6r7CYGPL0pH37KChIPT578SJVqEAyWY5DqG/f0sCB1LAheXrm1tbwcOrXj9q3J3///Dw1\nKD2Kc48dAru8IbArqf79VxP35DlFZs0adZxUIJNpLl6kRYvo/HlNSUICBQfnNnYs5Ct2csot\nKwoRNW2qPfZUvjz9+Sc1aUKmpjR6NC1dSllyBQsrE+3s7Lp37+7o6Ni0adMJEyYcOXLk4MGD\nCxYsEP+/N2XKlCVLlvCXBw4c6Ovru3379jdv3vTp08fc3FzosOE4bs6cOSqVqkKFCnxJhw4d\niEilUv3666/du3ffmiVhMkjGkiVL6tatW0/o82ZMT09PmL7Ji4yMXLt2rY+PT3p6enh4OL9R\nrDgJ9n///cfXFLItMsY8PDyISLy1nRdj9MsvmuM+farO7MMY+fhk37709GK3IQ1ICAK7kg2B\nXUl186Ym9Fm7Nu/606YRx5G+Pnl7f9bjBgaSQqHeEuPatWwqrFpFLi40ZQqJN0QXZgUxRrnP\nKF+4UDuw4+cACZdFc9sfP358/Pjx+Ph4U1NT/jdSK2vd8OHDicjd3V0c1cXHx9fhE68wplAo\nfHx8WrRo0bBhQ5bFrFmzypYtKwzXNm7cmIiEDMaMsatXr37WyYRi6erVq8JLLPQEOzg4iOsk\nJydXrVqVv4kfybWxsXnw4EFCQsKYMWNatGjx66+/dunSxcnJaf369US0d+/e7t27L1y4UIgO\nly1b1kImm8JYKmNUtSqpVPT779S+PQ0cqHm3z5qlg+cPpR4Cu5INgV0J5ulJX35J06ZlCqGy\nlZFBRkbqn4o6ddSF6ek0dSo1aEBz5+axeZHYxo25BZTiNRAbN2rKhe0vLS21R2MzMujoUTp0\niITukFOnaOdOqlUrmwiPMXr4kIh27dpV/8NGF0KPmtZlxljDhg1TU1Pnz5/PX3V2dn737t3S\npUuFwM7a2jprxgpenTp1hIW3HMeZmpryO4EuXbpUqLNv3778njcoOU6ePCn+T2D48OF9+/a9\nffu2uM79+/ezvmecnJxmzpzJ7zIsTMvjOC48PDzro8ydO9eA42ow9h+/LZh4Fz6+x06hoEuX\niug5A4ggsCvZENiVFg4O6t+MDh3UJTt3an5I8r95+YMHZGxMjJGREd29q33r339rjilkMyGi\njAzatYsWLMhmWe6YMer6336bqTw+npYtU//C1a5NrVuTqSlVqUIzZhznNyjLmXhJhJubW1pa\n2r59+9avXx8dHf3dd9/lVNnMzEy47OLicvPmTaGOvb29sPlsWFgYP/Lr7Owc9yFIzcjIyO8J\nhGIvLS2tZ8+e/Euc7dJXIkpJSeH3Ec6Kn+U5bNgwoYSfnXn8+PHmzZt/9dVXp06d4ne0431X\nsyZFRdGSJZrPTrlytHs35ZV4BaCQILAr2RDYlRZBQdSjBw0YQE+fqkvWrdP8kPz550cc6skT\n+vPP7FdsJCdTmzbEGDk60vPn+TqakNPExCSbW6Oi6OJFSk6mf/4RWvtbXmt7hw8f3kDYlJax\nhw8fCserUaNG7vflGRoailOi9O3bl4g8PDx69uzp5eWVmJh4584dYUzNy8urTJkyVlZWJ0+e\n/IjTCMVbYl6T2KKjo728vE6dOrVt2zZhWJYx1q9fPyK6d+9ezZo19fT0fvzxRyJKTU0VJgww\nxuRyuUymztswYsQIIqKHD9WTHBijzp1ze+AHD8jbmx49ylR48CB17EgTJxZkxkoorRDYlWwI\n7Eqv2Fhq1IgYoy+/pISEvOvHx9OwYdS4Ma1fn0fNV68o/z1Y336rybSXix07hMDu/ty5ZqIk\nDllTltSrV89OtA3Gq1evjh075ujoWLdu3datW4trGhkZ5RTbhYWFDRw4kDFWpUqVe/fu+fr6\nCjcJ2ex+/vlnV1dXId9Kw4YN8/usoXhISEgYNmyYs7Pz0qVLtW46f/68i4tLmzZtbt26lftB\noqKihChNoVCcPXtWuEnoylUqleKNiRljVatWrVGjRocOHZ49e6au/eABDR1Ks2fntro8JETd\njW1kRPfvqwtfvCA9PfUHZO7cjzoDAFkhsCvZENiVdvl/6Rcv1vTwhYRkuikggK5f/7jHvX+f\nXrwgIkpIoA0baM2aTHPvMi8/VLezeXNijL74gmJiXr9+ferUKX68LKv27du7uLgIV48cOVKx\nYkX+sng/qNzxSxrfv3/P/zZv2LBBuMnT05OIjh8/rnUXNze3jzsJoFMqlWro0KHCyxcQEEBE\n79+/nzx5cuvWrS0sLPjyFi1a8PWPHTs2c+ZMIYmJIDIyUvjvonfv3jk9nLAWm9euXbtPabQw\nXVU8jVW8lGr48E85LIAIAruSDYFdqaBU0oABVKMGzZnz6QeZNk3z43HxoqZ88mR14YwZ+T3U\n2LHqta49epCHR6Y8KUlJ1KULcRy5udG7d0REKhWtXEl9+9KePRQdLT5MlSpVsgZkfCecuEuv\nZcuWwqKKOnXqaPXwOTg4CCUymYwfL+vVq1dGRkZERMTdD/MIIyIi+F7AqlWrvnr1iojEufHk\ncnnz5s1v3rz56acXitwff/whfiecPHly/PjxQt+boFatWkR0+vRp/qpCodBaSEFEK1assLCw\naNiw4X2hFy07Pj4+QrzIGPP19f3oRv/vf5pl6TduqAszMujrr9W5gT72XyyALBDYlWwI7EqF\nVas0MZk4/9xHefyYqlUjxqhPn0wjreXKqY9csWK+jpOUlCl9CWP0ww+aW8VrA5cto9mzyd09\np1QpWt1v+vr64iWrgpEjR/7111/W1tZ2dnbbtm3TurVXr17CGJm+vr5SqeTXMO7du5cfYx08\neDD/cImJif7+/mvXrl2xYoWbm1vnzp3Fi3DPnDnDVwsMDOzatWvPnj1z/40HnRN317Vq1Uq8\nXIanp6dnamp66NAhypygbvv27UR09erVJk2a1K9f//Tp01pHXr9+fe/evbPNdPj3338Lx+Gn\n39HLl3TsGL1+nd92X7pEixerd94TCw9XJ7eLjKRr17Lp9s7q/Hlq3Zq6dSuYzQZBKhDYlWwI\n7EqFn3/WxEZZsvtmT6UiT08aPjzTglmVKpuh25Yt1Ufu2DG/R7azyxTYiUcwxetq27fXznVy\n4IBQMTAwUOtnmOO4o0ePClf79OnD5yt+LfrJTE9Pd3V1Fd9LPKW9f//+oqfVUiiPiYkhouTk\nZAcHB/F9y5Qpw18wMDC4devWhAkTBg0aJEzvc3d3z9cJgaIVFBTEj7Pv37+ff6Xs7OyUSmVo\naKjWO2rSpEnCEpng4GC+M9jCwuL58+dEJOQ+rFKlivj4Pj4+whGyjttGREQI3cknT56ku3ep\nTBl1Z1s+1xvl7swZ9SQ8V1f6sJQ7eyoVVayo/mR92rgwSBQCu5INgV2p8Po1NWtGcjl9+616\nV6I8bdig/sZXKOhDAv3sRUbSrFk0eza9epXf9gQH08CB6o3IGCPxdukZGTRtGtWsSSNHUufO\nmaK62rXVg7NERPTzzz+LQzrG2Pfff09ES5YsadGixfTp0y9dunQ3a0IWop07d2YdbuMNGTJk\n1KhRN27coMzdOSYmJk5OTtnuDSr8tAsT/oSDOzo65veEQFGZMmUK/+osWrSIiK5du+bt7R39\nYYh/3bp11apVE17WQYMG8eU3b948dOjQnTt3Dh06FBkZyRfWqlWLr1ahQoW7d+8uXbr0+PHj\nRLR69WrhCNl22j158mT9+vVX+C635cs17/Bt2wrgGQ4ZklMPt7a0NM0WFw0aFMBDg1QgsCvZ\nENiVIvkM6YjozRt1sros/WQFKTmZDh+mXL47Dh8mfX1ijLp2paAg8VS8jIwMrTBr3rx54rv2\n7t2bL9+wYYPWUYXMxlqEmXYcx/n5+QlHyA83NzfhsDKZTC6X6+vr8wN2UKwInax2dnbZVhBW\nVFhbWwcFBRHRnj17+LvUrVs3RZQM/NixY5aWlubm5hs3bixbtixfZ//+/c+ePeMzHVarVu3V\nq1exsbG5Nej0afWnTC6no0fzaH1yMn33HdnY0KhROX6chXx4xsZ5D+8uX04KBZmY0JEjedSE\n0gSBXcmGwK7UuXyZTp7MLcjbvFk9NsT/lS1Luf8yFaqXL+nGjawbY8TGxmqFVq1atbp9+/bT\np0+JKD4+XojSmjZtqnXfDh065BmoiddeaOE4bufOnZ07dxZKbGxsOI4zMDDg++rc3NyaN28+\nZcqU9PxH0lBUmjVrxr9qXbp0ybbC8+fP+f2Cu3btymel/uabb4TXOuuyCZVKJd6pYtKkSUSk\nVCpv3br15MkTvleP34P4+++/F75pMzIyEoQcQ4sXq5OVKBT0YaZm9rZs0XwwDx7Mvk5yMi1Z\nQoMH53c27fv3ue3yDKUSAruSDYFd6fJ//6f+VejTJ/sKqamazcf4vz17iraJ+aXVoyZEcosX\nL1apVMJkOHX2V5HHjx8Lg2hyudzNza18+fJ5hnqC+fPnE9GOHTuyPrSBgUHfvn2F8m0FMrIG\nBSo8PHzy5Mk//vhjVFRUthXmzZsnvIJHjx5t3769cNXS0lL4qrx165ajo6OhoeGiRYtiYmKs\nrKz4OidEc1h/+eUXrTcPP/5748YN/p+B8ePHU0YGWVhoPm5jx+bW+k2bNDX37i2wkwKQGQK7\nkg2BXelSp45m3EdrYnV0NP3zD4WHk6mpuk716uTnV9QtTEqi/v3J1pbGjqX0dPL3J3//bLey\nValUhw4dEobABBYWFkT06NGjyZMnL1y4ME5ra1oiIvLw8BDqe3l5ZQ3ssiY95pmYmNy6dWv4\n8OFyudzc3NzBwWHcuHFCgmItv/32W6GfLsjZw4cPvb29xfuO5MfYsWOFV1CcUrhly5bCMufE\nxMTatWsLb5WIiIiIiIht27YFZZ7Txn+7ik2cOJGIBgwYwF9tylhiixbEcZpw7Y8/cmucUkmd\nO5OhIfXunfcO0QCfCoFdyYbArnQZPFj9+9GoUabyiAiyslLv67V6NTk6UpMmmixZRcnTU/Mj\n17Wr+sKECVkrXr16lV+laGlpaW9vL/x2KhSKvn37JiUl5fQId+/ebd68uVB/6NChWmtdDQwM\nsg3UsuJ/p8WhgMDJyUmYZQ9F7+7du4aGhowxQ0PDPXv2TJw40dPTM88tfd+/f5/TZiTrRRuu\nzJ8/X/x+i86cXlGQmpo6aNAgITq0sbHhQ8Px48fzJY/EIZ1cTmPGfMSWLQCFpjgHdop8fjsD\nlBYeHqxePRYfz8aNy1Tu58dev2aMsYQElpjIHj3SSetYXBw7fFhz9eJF9YW//mLr1rH169nt\n2+y771iHDoyxvXv3JiUlMcbevHmzefPm/fv337x5Mzo6Oj09/cCBA506dRo5cqTW4YnIxcVF\nvAksY+zKlStbt26dPXt2YmKiTCZTqVRhYWH89xqP4zgiyra9GRkZo0aNevz4sVb5vn37+vbt\nm9PaWygCZ8+eTU5OZowlJycPGzYsNTWVMZaenj5hwoRc7tWtWzf+TaXFxcVl9OjRwlXxKz5/\n/ny+x/fGjRv37t3r0qWLkIJYT09v586dq1ev3r9/P/8v9PPnz2vWrPl///d/b968efz4sd3D\nh0ypZIwxOzt24wb7mCkBAKWUriPLEgA9dkBEdOuWZgPy48d11oxRo9Rt4Djq04c6dFBfdXHR\n3KSvTw8fUni454fhVH19fX7NxN69e4XP/ubNm4WjpqWl8Z1nAQEB2X5R1KlTJykpafny5Tl9\nk7Rp00arxMjIyMDAwNraOmtlLy8v/nFVKlVCQkJ4ePjDhw8vXLjg5eUVERGhi9NaGgUGBioU\nCpZ5OHX06NH8rdeuXdu2bZvWNLuUlJSchuBnzZolVPvhhx/Kly/PH7ZOnTr88OvhD/+Q2Nvb\nZ/06bdCgAX+rVsY72riR9PTI0JB27vyI55aYSHPmUN++9GHPYoCCVZx77BDY5Q2BHaidOkWT\nJ9P+/bpsQ6VKmpGpmBiKjqaFC+nbb7XTFFeuTIyl1KzpYG3NGGvZsiW//jQ1NbV///5mZma9\nevUShmIfP37MZwxu167djRs3xL/W4s2dGjduPHXqtB76NgAAIABJREFUVOGqsHaSpzUJj09m\nkRMTE5N79+7duXOH36BWHCtUqlQp2zl/UBguX768ePHis2fP8vPh9PT0/Pz8iOjIkSPCy/FO\nlBmRiMRbDDPGjI2N+QvCvq7e3t7CrfxQr1wuP3HixPDhw4Vyf39/rZYIi3UsLS3fv3+vVCo1\nt8XHq7eLyL8FC9QfBEPDj0geCZBvCOxKNgR2oGMZGXT5MoWGEpE6ax0/1U8gTrjKb1z24bIw\n1Jp1TyciWrJkiZ2dnbBckTHm4ODg7Owsk8nKli179uxZrUWLfBzGGGvatOn69etzCd3y9Msv\nv3z11VfZ3nRRvM0uFIm4uLiDBw/6+Pi8f/+eiMSDqn5+flu2bJk0adIXX3xRuXJlrQl2wlVL\nS8vIyMjAwEDhTSI2ceLEzZs385cNDQ3lcrm9vX1wcLDQgH/++cfKysrc3Lxly5YKhcLAwCDH\nHIdPn9L69XTpUm7PZ9Agzcfh1q0CPFEAPAR2JRsCO8jeo0fUsyd16kQBAYX7QF26EGMkk9HO\nndSsmfrnqnt3TQUPD3VhuXLk60t//SX8qnX58MsakKWR4mFZLUIWkpSUFH60Lqv69evnNCon\nyKXC9u3bxes5BJaWlm/fvi3c8wmZnT592tzcnD//VlZWISEhQmprExOTWbNm5fISCz1tbdu2\n1dfX13r1hUU2+/btU6lUBw4cmDRpklBBLpfLZDKO49q2bZuWlpaRkfHFF18Itzo4OGTT1qgo\nTbKh5ctzfEr+/mRiot5zD7kSoRAgsCvZENhB9qpXV//A2Np+9H2VStq1i/79N++aERGavod2\n7ej5c/rhB5ozh968UVfYtIkGDKAJE+j//o/4ZBMqFf3yC7Vpk7JkyeDBg+vUqfPLL79oHVWc\nUTYrjuP4dGJv377NpdrnqFOnTtaVE/r6+sdEG+9GRETwW9BCoWrVqpX4VRg4cKBw2djYuEaN\nGjm9iNbW1kKPna2trdatQsmkSZPevXt39OjR0NDQly9fiqf0CVasWBEWFiYuadasWTZtPXJE\n83HQmo2nJSaG/vsPS2ihkCCwK9kQ2EE2bt/W/MAoFB/RK6BS0Z9/aqbKLVmSR/3UVLK2Vlee\nPJmISKmkzZvJw4MSEujoUU0zch+cElm0aFFuMdcHwcHB6enpeXbLFSy5XD5+/Pj09PQFCxZw\nHKenp4ckxoUqLS1Na+u5X3/9VbicU39tVkL/K8dxVlZW4sHcdu3a8ZM49fT0zp07t3XrVvHo\nP69SpUq7d+8WrlapUiUkJCSb5j57pslp17gxEVFGBp04QX5+2aZyBCgkCOxKNgR2kI2AAE1E\n9eWXH3HHHTsyzYdzccn7LsHBNGYMLV5M/IzyFi3U9y1ThubM0Rwq3/uuCsNnuStTpsy+ffvc\n3Nzy+dOeH/kME3/44QehK6h27dofcXrhI504cUI47VWrVt20adPbt2+z3S/O2NjYxMQkp1dw\n165dHTt2FK7WqVNH2HNWjN9PLCYmpmvXruXKlROOZmpqumTJEvHRMrXy0SM6flz9/l+/nqys\nqF49unuXSDTBdMqUoj97UGoV58AOSaQAspOeznJIzKbWrBkbPZopFKxePfZhB/R8ybzslLVs\nyRhjDx6wrVvZgweMMebhwczMmIMDu3pVXadePebpyebPZyYmLDGRCRlJlEqWksL4zg8zMzZm\nDHN0ZMHBOT1yaGjohAkTZs+e7ejoKC7P6adaqVROnz7d29u7Y8eODRs2nDhxonBTTilqs502\nJ0a5n9UPLl++LMzBr1y5cn7uAp8mKipKuDxp0qTg4GALC4u4uLisNRMTE8Uvn9bbZsSIEXwm\nPN7du3eVfP65zOzt7efNm7d8+fItW7bExsZ6eHgYGBgoFIpFixb179+f78lzcnLq2rWr5j4n\nT7LatdlXX7EmTVhSEps4kb16xYKDGb+zhZDWcccO5uHBEhM/4SQASIquI8sSAD12pc66dWRo\nSBUq0MmTedT8hNGf8+fV25mXK0deXpSWRnfukIEBMUYGBhQUpFn3amOj2aR83z6ysaEaNWjc\nuEwdfj/9RM2bU8OGmpK+fWn2bGrUiH78UWuCkdBRV7FiRRcXF2dnZ/6q1nBbkyZNhMivfPny\ntWrV6t+/f1xcXEZGxtChQy0sLExMTHL6PnFycvrjjz+EmfifTCaTWVpafvnllwMHDuQz8MGn\nUalUx48f9/Lyio2N5UsWL15coUKF5s2bP3369L///uNXPHAcZ21t3adPn8984fLzyvIX3Nzc\nli9fLpfL9fT0hC0r4uPjb9y4ob0nysiRmrd31g6Stm0zfSL69i30cwpQvHvsENjlDYFd6ZKa\nSoaG6h+Jpk0L5SFCQ+noURLSg23YoPlZWrOGjI01Vw0NKTycVCoqV05dwgeF/N+oUZq5esLE\nI/F26R82QQ8LC/vmm2+0uliWLVtWoUIF/rKQjczIyCg2NtbT09PExIRPQsZbvHgxf6hBgwbl\n8rPNcZxwqM9nYmLSu3fvK1euTJ8+fdCgQf/73/8K5eWQtBUrVvAns3bt2unp6eINIapXr964\nceOCerE+lkKhEAZ8q1WrRkTJycnZPwfhA2JqSq9fa98aE0O//qr5zFpb05UrhK9rKGTFObDD\nlmIAmcnlzNiYJSczxpipaaE8RPXqrHp1zVVXV6anx9LSmELBWrZkS5cyIQ9wcjLr35+Jf30V\nCpaWpr48Zw4TUpbIZMzWlj1/zmJjNZU/XJ46daqPj49WK8LDw83NzaOjoxljaR+OmZSU1L59\n+7S0tISEBHFlpVIZHx//9ddf+/v75/LMiCix4MbCEhISDh06dOrUqfj4eMbYiRMnIiIitHJq\nQO6EVMP37t178eKF+Kas+7wVpfT0dCsrK37MNyIiolGjRjdv3nR1dT1x4kTZsmUzVR03jhkY\nsDt32KBBzNJS+0AWFuynn1hwsPqz8P49c3VlNjYsMJBlXhQCUEpgjh1AZjIZ27WLNWzIWrVi\n69YVxSM2bMguXWJLl7JLl1ijRpl+ujiOXb3KNmxgLi7M3p7VqsW2bWPVqzMzMzZvHnNwYKNG\nqWtmZLAyZTLNC2zcmA0YwF/ko7csD9vw999/53NPpKenC+U3btwQspwYGhpyHCeTyU6cOLFu\n3brcozqxbFNafBo+qmOMxcTEREZGFtRhpeHJkyd16tRRKBRt2rQRh9QZGRlRUVGHDx+++mGm\npkKhOHr0aLVq1Xr37q2jxjKO44Q3hqGh4b59+/hpAMnJyTdv3mSMXblyRVgbm5iYOG7cOBcX\nl01eXmzUKLZmDculf/HPP9nBg2zUKMbvYxsRwf7+u5CfDUBxpesuwxIAQ7ElUkgIPXqk60Z8\nkvBwMjVVZyQWBlV79sy+8rlzmjrCaKxcTufOiWsdP34826RxWtuC8TiO8/b2trCwMDMzEwby\nWJY9xFiuuTC0O10KAp/wVrzFLYwZM0Y4P3369OELo6KiatasyRjT09MTn0CZTBYVFbVy5coC\nf2k+TWxsbNb5mlWrVuX3CxZnXfnvv//ydTpOntR8HLLsWgZQgIrzUCx67ECKpk1jzs6sRg0m\niktKDDs7dvs2++MPduAA43/29PSYaEVqJi4umvFiobvO3p61bi2u1bVrV3EuCV5qaur//ve/\nbI/at2/f169fh4eHi3cCCAoK0qqWnp7u6Ohomt2A9fv377Nv8GcgopSUlDFjxjRr1szX17fA\nj18SiXtG7927xxi7du2as7PzgwcPmGiEnadSqfr165fnyuWi0aBBg1q1amUduA8LC9u4cSNj\nLCYmRijUdDlv3cq+/JJNmJD96teOHdn27WzQILZzJ8ucdRmgFNF1ZFkCoMeuhFGpNOsPqlfX\ndWuyk5xM06dT+/Z5ZJ4bPFjT/ZBL7+N//9GECZokxjIZHT2aQ8X/hgwZYmxsnHsyufr16+/e\nvVvc2aPV8ZNVEScx5v3zzz+f9SpIQmRkpOWHsfsePXoQUdu2bbOeK/E6ZWNj46zdt0VJT0/P\n29v766+/Fkrc3NzEi3L4jVJCQ0P5GLRNmzZpaWlERPfuaT4RWTZTyUZKCu3ZQ3v3Umpq4b4M\nqank5UWLF9Pz54X7QFBsoMcOoAhxHBM2QXJy0mlTcrBxI1u1ip05w4YPZ3fuqAtfvmSHD7OX\nLzXVhJ4wfj1HTpyd2YYNbP9+Vrcuq1mT+foy0U9m5orOO3bsSEhIuHHjxrp167Idh5XL5cHB\nwQMHDhR39mh1/GRF+ctOV7Dmz59f9A9a3FSsWHHfvn385aNHj9aqVevWrVtadYyMjFQqlXA1\nMTFRfLXopaWlGRgYCEti9fX1hw0b5uzsPG3aNHt7+8aNG3fo0IEx5uDgwO9XceHChUOHDjHG\nWGCg5iivX+f9SEOGsAED2HffsREjCv5piM2bx8aMYQsWsNatmU7PLQBj6LHLB/TYlTyhofT9\n9zR5MkVG6uDR/f2pZk1ycMip54x+/FHT8XDmDBHRw4dUpox6M4mHD9XVoqKoRQsyMCBbW7p4\nscCbmZiY6OXl9bHfGAW4KuLzrV27tsBPS7F1//794cOHjx8//uXLl0JheHi4q6tr7meJD490\nQk9Pr3LlylnnYo4ePbpBgwb8ZaE30cbGxsDAgDGmr69/+/Zt8aLdbt26ERH17Kn54OQn942Q\n+sfSstBeFiIiatVK07CoqMJ9LCge0GMHULSqV2deXuz339mH3QuK1JQp7MED9vQp+/777CuM\nGqVOxNCmDfvyS8YYO3WK8Wn6lUr277/qatbWLDycpaSwly9znGMnWL+eVazIGjRgO3awyEh2\n9izr3Zv9+CP7sKRUy6NHj/7+++/69evnOYqqlZeuYcOGebSkCB04cGDVqlWjR48+ffr0xYsX\nV69ezc8zk6RevXpt377dw8NjlLAUmrGxY8deuXIl9zs+e/askJuWo9GjRzdu3LhRo0a7d+8W\n7w/79OnTkJAQ/vK7d+/4CxEREfyPZWpq6tmzZytVqiTkWaxXrx5jjAnvVbmcZd49JXvCTNPG\njdnPP7OjRz/7CeWgRw/1BVdXZm1dWI8CkE+6jixLAPTYwcepV0/9v7uVVY51kpPVmYd5V69q\n/uO/ckVTzdJSXVi3bjYH8fentm2pZ0+6eTPTElpDQzIyUl+ePl18j8TExBcvXly9epXvRBGn\nIJbJZOKlEoyxChUqzJ0798GDB1WrVhUKs+3+MTMz08k0u6z09PScnJzGjh2bmJgo7LUgASqV\nStjGzdLSkl8lmp6ebl1cw4jy5cuPHTu2f//+/FUDA4PvRf/nfPvtt8JOccI7p2rVqvxsToVC\nERgYSES3b98eP378b7/9pt6L4s4datiQrK1pw4Z8nbXERNq8mVavprJl1R+HPXsK6xW6dIkO\nHCCtPTNAuopzjx0Cu7whsIOP4+dH1aqRrS0dOpRHzSdPyNdXnSX/1CmaMUN7E7O//iIrK6pS\nJZvNzTIyqEIF9c9Vx46ZAjvxX69ewj2uXr1arlw5xhifCyOrn376SXzV2dm5b9++v/zyyz//\n/MOPl7Xkd7bNrFgNzgr4KVyDBw9WfcK2b8WSuteKMcaYQqEYP358DWEuabFUrly59u3bC1f1\n9PTKli1rZGRUpkyZJk2a/PXXX/y7UdCpU6fAwMAVK1Zcu3atIE/c6dOaj8OECQV5ZCjFENiV\nbAjsoFCcPaveH6xGjU/ZASk5WbO9WNOmtH69Js7jOwsZI2NjOn1auMfAgQNz+RmWyWS55B/u\n2LHjixcvPDw8hBK5XF6jRo1mzZqJ+/OKoa5du2Zk3jO3pIiPjxdHpeKhzJJi5cqVFhYW2d7U\nuHFjrX8JatWqpX0KYmKoVSsyNqbhw+niRVqwgP79N1OF5GQ6doxu3sztPMbGqnfek8vpxImC\nenWglCvOgR3m2AHoyMGD6s3BHj1i169/1F1TU1MHjRy50tCQOI6ZmLC5c9nEiezDpCXGGKtd\nm929y54/Z6Iuk0q57rBUt27dhg0b5jSieurUqd27dw8ePFjIgsZx3NChQ4OCgsLDwwsjHXFB\n8fX1PXbsmK5b8XGSkpJq1KhhampqYWFx7tw5V1dXS0tLJT8Ls+QwNDTs1KnT+fPns31TvXv3\nLiMjQ7jKcdygQYN+/vnn7du3a/ZB8fRk58+zxES2fTtr0yZu8eLFnTvP6t9fvTeaSsVatWLd\nu7OGDdm2bTm2o1w5dusW27GDBQWxzp0L9CkCFEu6jixLAPTYQaH44w9175qxMYnWOeaHt7c3\n//k1Z+zAn39qbnB3Vx9z3bqs93r37p143j1jrHz58sLltWvXxsfH57KZhFwuF1JU8IShNN3m\nRcsTx3FWVlaXLl36zFes8KhUKl9f3507dyqVyrS0tGnTpgmNr6iTBUCFSSaTmZmZHT582PHD\nAgiZTHbt2jXhf4b58+erz8tvv4nnFXz74QgNGjQgIgoL09zaubMOXz4ohYpzjx0Cu7whsINC\nkZFBW7bQ5Ml09erH3nXz5s3Cz+TOnTs1NyQnk48PBQTkcl9bW1vhvnPnzj1z5gwfn1lYWISE\nhGzdutXOzk6Yp5/LkghxUPgJjIyMin5yXsWKFUNCQj7htSpsCxYs4FtobW3Np/yQpNq1a/fo\n0SMwMDA9PZ2IEhMTJ0+e3Lt376CgIPHS3fbt26vPS1wcff01VapEI0eSmVntDxUUCkVaWhql\npJCNjTqwmzdPl68flD4I7Eo2BHZQ3CQmJnbr1s3IyKhnz57Jyckfdd/AwEB+MaxCobh06ZJ4\nmPKnn37i6/j5+VWpUsXMzMzV1TWndZe59O3lU/PmzT/zCJ+A4zg9PT1zc/OaNWv6+fkVwovz\nKYrt4tYCNGnSpFzOQEZGRuPGjfmaG7Jd9Prixc99+vAVBg0apC58+JBmzaKNGyklpRBeFoAc\nFefAjiNdpIwvWby8vMaOHRsfH1+mTBldtwWgALx48cLPz69x48bOzs4hISHCcsvNmzeLx2r7\n9u178OBBxpihoWFycnKBN0Mmk+l2CwTG2IIFC0aPHm1lZaWvr19kD/rkyZNnz56Fh4e3b99+\n9+7d8+fPT01NLbJHL2zZvqxmZmbR0dG5/zOQmJh47NixKlWq5JJyOSgoSKlUtmzZspik14FS\nKzU11cDA4PLly3lmCC96JTWwS0xMjI6ONjc3NzU1LexPOAI7kLYDBw7s37+/SZMmM2bMEM+W\nc3d3v3z5MmNMJpONHj362LFjZcuWvX//ftYjFIcQ7XPI5XJLS0tXV9edO3c+ePCgWrVqWrMJ\nC8qzZ89Gjx596tSpwji4zmm9DeRyubA8Yv/+/X379tVRuwAKXnEO7ErSUKyfn9+QIUOcnJzE\n37nGxsaOjo7Tpk0LDg4upMfFUCyUTj4+PsbGxhzHzZgxgy95//59ixYthI+e8DEU5tt95sS7\nYoLjuLJlyy5evPjzz2FERMTu3bsDAgKCg4OdnZ11/cwKgEKhEBLcaPXAafV69uvX78iRIwMG\nDDh27Njnn0mAYgVDsZ+LiMaMGcNPGDczM3N0dLSwsDA1NY2Pj3/79u2TJ09iY2MZY8OHD9+8\neXOBz8hGjx2UWvHx8UlJSeIMaqmpqWvWrHnw4MGIESM8PT13796tr6/v7e2tVCrlcnm/fv1m\nz5599uzZypUrX79+/d27d8I3jJubW2BgYEpKCsdxHMeVoB4+hUJhamr61Vdfbd++Pc+ZhQkJ\nCfv27Tty5MjFixf5/waLppGFSi6X29jYNG/efMyYMXzO4ZMnT165cqVz585BQUFXr151dXVt\n3bp1hw4dIiMjhXvt2LFjyJAhums1QCFCj93n+v333xljTZs2PX/+fFpamtat6enpAQEB/NfN\nb7/9VuCPjh47gJzcvXs3Kuddz/fu3dusWbPu3btHRkYS0YMHD7y9vR8/fpyenl6tWjX+K6iE\nTpbiMtN1cwoGx3FVq1aNjo7es2ePv7//hQsXpk6deu7cuXy+Ga5du8b/96tQKEaNGlVC80ID\n5Ad67D5XixYtXrx4ERoaKt7aUkt6enrTpk2VSuWjR48K9tHRYwdQ4FJTU1esWPH8+fP+/ft7\nenq+ePGiZcuWRNS4ceO3b9/OmTMnOjqar8lxmb6mtK5C7qpXrx4dHR0XFyeTyb755ptWrVp9\n88039evXj4uL4ytwHOfp6ZmSktKtWzcHBwfdthagpCjOPXYl4yuybNmynTp1OnDgQO7Vpk6d\numnTJj6OzqenT582b95ck+g8OykpKYmJiUql0sTEJP9HBoBPlpqaGhERYWpqqlQqraysYmNj\nly9f/vDhw++//75Vq1bHjh0LDg4ODw+/cuUKYywmJiYtLU1fX/+jPvslmp6eHp8KrkKFCnFx\ncWlpaRzH8YvJypcv37Fjx1q1anXt2jUxMdHe3p7juLdv35qZmQkrY9LT0/v16+fn51exYkVv\nb28XFxfdPh2AEgeB3edydXV9/vz5o0ePcumxy8jIaNasWVxcXGhoaP6PrFKpLly4kHtgd+fO\nnalTp6akpBRlQgQA+ATJycnnz59PSEh4/PjxxYsXDQ0NX7x4ERkZGR8fHxsbS0RCh19x6/nj\nOI6fzGdpaVmhQoU3b96UK1cuIiKiUaNGHMeFhoaOHj26efPmFhYW5cuXF2/AGhsbe+/evYYN\nG4qXswBAoSrOgd3nphgtGgMHDpw4cWLLli1Xrlzp6uqqNX85IyMjKCho7ty5N27cWLp06Ucd\nWSaTtW7dOvc6+LoEKCn4/Un5yzNmzPjYu0dERBBReHg4Yyw0NNTLy0tPT+/JkyevX78Wp5rL\nNiLUmmknl8vLly9ftWrV7t27v379mk+nYm9vb29v//m5ncUsLCzc3NwK8IAAUKIVr/9Zc0JE\n48eP5xcxmJmZ1ahRg18Vq1Qq3759Gxoayq+KHTx48LZt2wr2S5MxduXKFTc3N/TYAQAAAEOP\n3efjOG7Tpk2TJ0/esGHD6dOn79+/r1Qq+ZuMjIxsbGwGDx48fPjwL774QrftBAAAANChkhHY\n8WrXrr1x40b+slKpjImJKVeuXBHsPAEAAABQIpSkwE6sTJkySD4CAAAAICbLuwoAAAAAlAQI\n7AAAAAAkAoEdAAAAgEQgsAMAAACQCAR2AAAAABKBwA4AAABAIhDYAQAAAEgEAjsAAAAAiUBg\nBwAAACARCOwAAAAAJAKBHQAAAIBEILADAAAAkAgEdgAAAAASgcAOAAAAQCIQ2AEAAABIBAI7\nAAAAAIlQ6LoBJYC+vj5jzMDAQNcNAQAAgOKCDw+KG46IdN2GEuD27dvp6em6boWkeHt7+/r6\nLlq0SNcNkaxHjx4tXrzY29tbLpfrui3SRERDhgyZO3durVq1dN0WyVq4cGGTJk26deum64ZI\n1q5duxISEtauXavrhpQ8CoXiiy++0HUrsoHADnRj5cqVf/311/Xr13XdEMm6fPmyu7t7amqq\nnp6ertsiTSqVSi6Xnz9/vmXLlrpui2S1aNGiZ8+es2bN0nVDJGvq1KnPnj07fPiwrhsCBQZz\n7AAAAAAkAoEdAAAAgEQgsAMAAACQCAR2AAAAABKBwA4AAABAIhDYAQAAAEgEAjsAAAAAiUBg\nBwAAACARCOwAAAAAJAKBHeiGvr5+8dxlTzL09fUVCoVMhs94YeE4Tk9PD2/jQoUvisKGMyw9\n2FIMdCM5OTk2NtbGxkbXDZGyJ0+eVKtWTdetkLKnT59WrVqV4zhdN0SyIiMjzc3NjYyMdN0Q\nyXr//n1qamqFChV03RAoMAjsAAAAACQCwzQAAAAAEoHADgAAAEAiENgBAAAASAQCOwAAAACJ\nQGAHAAAAIBEI7AAAAAAkAoEdAAAAgEQgsAMAAACQCAR2AAAAABKBwA4AAABAIhDYAQAAAEgE\nAjsAAAAAiUBgBwAAACARCOwAAAAAJAKBHRQLBw4c4Djun3/+0XVDJOX9+/czZ850dHQ0NDSs\nXr364MGDnz9/rutGSQEReXh4uLm5lS1b1tXVdcOGDUSk60ZJCt66RQzfwFKCwA50782bN+PH\nj9d1K6QmPj7ezc1txYoVKSkpvXr1qlix4q5du+rUqfPgwQNdN63EGz9+/IQJE8LDwzt27Bge\nHj5p0qQxY8boulHSgbduEcM3sMQgsAPdmzhxYnR0tK5bITUeHh4hISE9e/Z8+vTpnj17Ll++\nvGPHDqVSiRDkM927d8/T07Np06ahoaEHDx4MDQ1t0qTJ5s2bz507p+umSQTeukUM38ASg8AO\ndOzQoUP79+93dnbWdUOkZv/+/YyxDRs2KBQKvmTIkCGurq4XLlxQKpU6bVrJtmHDBsbYypUr\nDQ0NGWNGRkarVq1ijHl7e+u2YZKBt25Rwjew9CCwA12Kjo4eN25chw4dhgwZouu2SE1YWFil\nSpVsbGzEhXZ2dkT09OlTXbVKAk6fPm1mZubq6iqUuLi4mJmZBQQE6LBVUoK3bpHBN7AkIbAD\nXZo0aVJSUtLmzZs5jtN1W6TGz8/P399fXKJSqc6dO8dxXJUqVXTUKCmIjIysUaOG0JnEGNPT\n03N0dHz16pUOWyUleOsWGXwDS5Ii7yoAhePIkSP79u3btGmTvb29rtsiQQ0aNBBfValU06dP\nf/XqVa9evczNzXXVqpJOqVQqlUoLCwutcgsLi7i4uKSkJCMjI500TErw1i0a+AaWKvTYgW7E\nxMSMGzeuTZs233//va7bIn1RUVH9+/dfu3atra3tunXrdN2cEiwmJoYxZmpqqlXOl7x580YH\nbZI0vHULCb6BJQw9dlC4MjIyxNNiTExMKlWqxBibMmVKfHz8li1bZDL8d/FZcjrDPCLatGnT\n7Nmz379/7+7uvmvXLltbW100UyLKlSvHGMs6hT8+Pp4xhv6kAoS3bqHCN7CEIbCDwvX27dsa\nNWoIV3v06OHj43Py5Mndu3evW7euWrVqOmybNGR7hvnLMTExQ4YM8fX1tbKyWr169bBhw+Ry\nuY6aKRGmpqaGhoZv377VKn/79q2xsXHWnjyLN6BCAAAKGUlEQVT4NHjrFip8A0sbh4TpUKhS\nUlKOHTsmXLWxsXF1dV27du20adNyusumTZvGjh1bJK2TgmzPMGMsKSmpbdu2AQEB3bp127lz\nJzqTCkr16tXfvXv3+vVrIdTIyMiwsrKysLB49OiRbtsmDXjrFjZ8A0sbeuygcBkYGPTp00er\nsG7duiNHjhSXBAcHX79+vUOHDnZ2drVq1SrCBpZ42Z5hxtivv/4aEBAwderUVatWYbSlAHXv\n3v33338PCgpq1qwZXxIYGBgbG4uEEQUFb93Chm9gaUOPHRQLK1eunDFjxrFjx7p166brtkhB\nRkaGnZ1dUlLS8+fPTUxMdN0cSbl161bDhg07duzo6+srl8vT09O7dOly5syZ4ODgevXq6bp1\nJR7eujqBb2ApQY8dgAQ9e/YsIiLCzMysXbt2WW89cuSIeIEFfJQGDRoMHDhw9+7dLVq0cHd3\n9/f3v3Xr1tChQxHVFQi8dQE+EwI7AAkKCwtjjMXFxV27di3rrSkpKUXdIGnZvn17rVq1tm7d\nunHjxipVqixdunTGjBm6bpRE4K0L8JkwFAsAAAAgEZiXCgAAACARCOwAAAAAJAKBHQAAAIBE\nILADAAAAkAgEdgAAAAASgcAOAAAAQCIQ2AEAAABIBAI7AAAAAIlAYAcAAAAgEQjsAAAAACQC\ngR0AAACARCCwAwAAAJAIBHYAAAAAEoHADgAAAEAiENgBAAAASAQCOwAAAACJQGAHAAAAIBEI\n7AAAAAAkAoEdAAAAgEQgsAMAAACQCAR2AAAAABKBwA4AAABAIhDYAQAAAEgEAjsAAAAAiUBg\nBwAAACARCOwAAAAAJAKBHQAAAIBEILADAAAAkAgEdgAAAAASgcAOAAAAQCIQ2AEAAABIBAI7\nAAAAAIlAYAcAJZunpyfHcStXrizUR3n//v3MmTMdHR0NDQ2rV68+ePDg58+f51I/MDCQy9nE\niROFmkR07NixwYMHN2rUyMTExN7evkOHDj4+PkRUqM8IACRJoesGAAAUd/Hx8W5ubiEhIZUr\nV+7Vq1d4ePiuXbt8fHwCAwNr1qyZyx1tbW1btGiRtbxBgwb8hbS0tGHDhu3Zs4cxVrVq1S+/\n/PLZs2d+fn5nzpz57rvvdu/ezXFcYTwjAJAqBHYAAHnw8PAICQnp2bPn/v37FQoFY+zPP/8c\nOnTomDFj/P39c7mju7v7vn37cqkwb968PXv21K5d+/Dhw7Vq1eIL79y5M2LEiL1797q6uor7\n9gAA8oShWACAPOzfv58xtmHDBj6qY4wNGTLE1dX1woULSqXykw8bGhq6cuVKGxubgIAAIapj\njNWtW/fw4cMKhWLjxo0YkAWAj4LADgCkLy4ubuLEifXr1y9Tpkzjxo1nzZqVlJQkrvDy5ctB\ngwZVrVrVzs5u+PDhMTEx7u7uwihqWFhYpUqVbGxsxHexs7MjoqdPn35yqzw9PVUq1U8//VS2\nbFmtm2xtbadOnVq9evU3b9588vEBoBTCUCwASFxUVJSLi0tYWFijRo169ux58+bN5cuXHz9+\n/MqVK3xE9eDBg9atW7969ap169bW1tYnTpy4efNmWlqaqakpfwQ/Pz9jY2PxMVUq1blz5ziO\nq1Klyic37PTp04yx/v37Z3vrihUrPvnIAFBqIbADAIlbtGhRWFjYb7/9NnPmTI7jVCrVzJkz\nV61atXr16oULFzLG5syZExUV5ePj06NHD8ZYbGxs+/bt796927x5c/4IwloHnkqlmj59+qtX\nr3r16mVubp7LQ1+6dKlPnz5aha1bt544cSIRPXz4sEyZMhUqVCjAJwsApRwCOwCQstTU1C1b\nttSpU2fGjBn8ClOZTLZkyZJdu3Zt2rRp4cKFz58/P3z48Ndff81HdYwxCwuLJUuWfPXVV9ke\nMCoqavLkyQcOHLC1tV23bl3uj/7y5ctDhw5pFZYpU4YxlpKSkpyc7OTkhHWvAFCAMMcOAKQs\nPDw8PT29bdu2Mpnm687Q0NDV1fX169fv37+/f/8+Y6xt27bie3355ZdZD0VEHh4eNWvWPHDg\ngLu7++XLl21tbXN/9G+//Zay8Pb2ZowZGBjo6elFRkZieQQAFCAEdgAgZS9fvmSMWVtba5VX\nqlSJMfb8+fNnz55lrWBqampiYiIuiYmJ6dat24QJEwwNDbds2eLv729vb/85DeM4rlq1avHx\n8Tktjzhw4MA333xz5MiRz3kUAChtENgBgJTxnWqvXr3SKudLKlWqVLFixawVEhISEhIShKtJ\nSUndunXz9fXt1q3bgwcPRo4cKZfLP79t7dq1Y4zx2Ymz2rRp09GjRy0sLD7/gQCg9EBgBwBS\nZm9vr1Ao/P39VSqVUJicnHzlypXy5ctbWFjwCeTOnz8vvtelS5fEV3/99deAgICpU6cePXo0\n99USH2Xs2LGMsaVLl8bGxmrdFB4efuHCBWNj42w3rgAAyAkCOwCQMn19/REjRoSEhKxZs4Yv\nUalUc+fOjYyM5OOqatWqtW3b1sfH5/jx43yFt2/fzpkzRzhCRkbG1q1by5Urt2TJEvFEvc9X\nr169CRMmvHnzpmnTpkFBQUL548ePv/nmm4yMjCVLlhgYGBTgIwKA5GFVLABIwY4dOwICArQK\n3dzcpk2btnDhwpMnT/7444/79u2rXbv2zZs3Q0JC6tSpM3PmTMYYx3Fr1qxxd3f/+uuv+Tx2\n/v7+7u7ujx8/5rPcPXv2LCIiwszMjB851XLkyBF+ut6nWb16dUpKypYtW5o0aWJra1uvXr2Y\nmJjAwEAi6tOnz5QpUz75yABQOiGwAwApCAkJCQkJ0SrkdwCrVKnSrVu35s6de+HChUOHDjk5\nOc2YMWPhwoVCzuH69evfunVr1qxZFy5cePHixeDBg3/++WdjY2MrKyvGWFhYGGMsLi7u2rVr\nWR83JSXlc5qtr6+/efPm/v37e3h43Lt3z9/f39bWtnPnzuPGjevWrRsyoQDAx+Kw0h4ASrOM\njIyLFy9aWlrWrVtXKAwLC3NwcPjhhx9WrVqlw7YBAHws9NgBQKkmk8mGDRsmk8n+++8/PsUJ\nEfHxXL9+/XTdOgCAj4MeOwAo7bZu3Tpq1CgHB4d+/frp6+tfuHDh/PnzPXr08PHx0XXTAAA+\nDgI7AADm4+OzatWqu3fvqlQqZ2fn3r17T5o0qUCS1QEAFCUEdgAAAAASgTx2AAAAABKBwA4A\nAABAIhDYAQAAAEgEAjsAAAAAiUBgBwAAACARCOwAAAAAJAKBHQAAAIBEILADAAAAkAgEdgAA\nAAASgcAOAAAAQCIQ2AEAAABIBAI7AAAAAIlAYAcAAAAgEQjsAAAAACQCgR0AAACARCCwAwAA\nAJAIBHYAAAAAEoHADgAAAEAiENgBAAAASAQCOwAAAACJQGAHAAAAIBEI7AAAAAAkAoEdAAAA\ngEQgsAMAAACQCAR2AAAAABKBwA4AAABAIv4fIBdrNqMHSukAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(myres$log2FoldChange,-log10(myres$padj),pch=19,cex=0.3,xlab=\"Log2 FC\",ylab=\"-log10(BH Adjusted P-value)\",col=ifelse(myres$padj<0.05&abs(myres$log2FoldChange)>2,\"red\",\"black\"))"
   ]
  },
  {
   "cell_type": "code",
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       "R version 3.3.1 (2016-06-21)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 8 (jessie)\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       " [1] tools     parallel  stats4    stats     graphics  grDevices utils    \n",
       " [8] datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] DESeq2_1.14.1              SummarizedExperiment_1.4.0\n",
       " [3] Biobase_2.34.0             GenomicRanges_1.26.4      \n",
       " [5] GenomeInfoDb_1.10.3        IRanges_2.8.2             \n",
       " [7] S4Vectors_0.12.2           BiocGenerics_0.20.0       \n",
       " [9] dplyr_0.5.0                purrr_0.2.2               \n",
       "[11] readr_1.0.0                tidyr_0.6.0               \n",
       "[13] tibble_1.2                 ggplot2_2.2.1             \n",
       "[15] tidyverse_1.0.0           \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] locfit_1.5-9.1       Rcpp_0.12.8          lattice_0.20-34     \n",
       " [4] assertthat_0.1       digest_0.6.10        IRdisplay_0.4.3     \n",
       " [7] R6_2.2.0             plyr_1.8.4           repr_0.7            \n",
       "[10] backports_1.1.0      acepack_1.4.1        RSQLite_1.0.0       \n",
       "[13] evaluate_0.10        zlibbioc_1.20.0      lazyeval_0.2.0      \n",
       "[16] uuid_0.1-2           annotate_1.52.1      data.table_1.10.4   \n",
       "[19] rpart_4.1-10         Matrix_1.2-7.1       checkmate_1.8.3     \n",
       "[22] splines_3.3.1        BiocParallel_1.8.2   geneplotter_1.52.0  \n",
       "[25] stringr_1.0.0        foreign_0.8-67       htmlwidgets_0.9     \n",
       "[28] RCurl_1.95-4.8       munsell_0.4.3        base64enc_0.1-3     \n",
       "[31] htmltools_0.3.5      nnet_7.3-12          gridExtra_2.2.1     \n",
       "[34] htmlTable_1.9        Hmisc_4.0-3          XML_3.98-1.9        \n",
       "[37] crayon_1.3.1         bitops_1.0-6         grid_3.3.1          \n",
       "[40] xtable_1.8-2         jsonlite_1.1         gtable_0.2.0        \n",
       "[43] DBI_0.5-1            magrittr_1.5         scales_0.4.1        \n",
       "[46] stringi_1.1.2        genefilter_1.56.0    XVector_0.14.1      \n",
       "[49] latticeExtra_0.6-28  IRkernel_0.7         Formula_1.2-2       \n",
       "[52] RColorBrewer_1.1-2   survival_2.41-3      AnnotationDbi_1.36.2\n",
       "[55] colorspace_1.3-1     cluster_2.0.5        memoise_1.0.0       \n",
       "[58] pbdZMQ_0.2-3         knitr_1.15.1        "
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