main_work/proteins_lognormDist.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "4e158002-7422-422c-89ca-fbaacb381b27",
   "metadata": {},
   "source": [
    "# Distribution of the lengths of the proteins\n",
    "**for the different species (reference proteomes)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8046a185-31f3-48b1-83f7-947911a96d15",
   "metadata": {},
   "source": [
    "## Import python modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51b0a90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from scipy import stats \n",
    "\n",
    "import sys\n",
    "sys.path.append('../gl_lib/')\n",
    "import gl_constants as c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b6d3b5",
   "metadata": {},
   "source": [
    "## The protein length distributions for the different species"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d509543a-9bbf-4428-bb98-a287201ef79d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Retrieving the statistical descriptions of the length's distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86b76b58-6c34-4b8a-82b3-f117e835fad0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of proteins in this study:  55650809 proteins\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>superregnum</th>\n",
       "      <th>species</th>\n",
       "      <th>proteome_id</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>uniprot_fasta_file</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>division_both_dbs</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8008</th>\n",
       "      <td>bacteria</td>\n",
       "      <td>Tardiphaga sp. vice352</td>\n",
       "      <td>UP000319639</td>\n",
       "      <td>2592816</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>5026.0</td>\n",
       "      <td>310.757859</td>\n",
       "      <td>45131.523247</td>\n",
       "      <td>bacteria</td>\n",
       "      <td>2.401842</td>\n",
       "      <td>0.083230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>bacteria</td>\n",
       "      <td>Paraburkholderia xenovorans (strain LB400)</td>\n",
       "      <td>UP000001817</td>\n",
       "      <td>266265</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>8591.0</td>\n",
       "      <td>320.591666</td>\n",
       "      <td>46426.060717</td>\n",
       "      <td>bacteria</td>\n",
       "      <td>2.425923</td>\n",
       "      <td>0.072088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     superregnum                                     species  proteome_id  \\\n",
       "8008    bacteria                      Tardiphaga sp. vice352  UP000319639   \n",
       "1503    bacteria  Paraburkholderia xenovorans (strain LB400)  UP000001817   \n",
       "\n",
       "       tax_id                                 uniprot_fasta_file   count  \\\n",
       "8008  2592816  /ftp.uniprot.org/pub/databases/uniprot/current...  5026.0   \n",
       "1503   266265  /ftp.uniprot.org/pub/databases/uniprot/current...  8591.0   \n",
       "\n",
       "            mean           var division_both_dbs  log10_mean  log10_var  \n",
       "8008  310.757859  45131.523247          bacteria    2.401842   0.083230  \n",
       "1503  320.591666  46426.060717          bacteria    2.425923   0.072088  "
      ]
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     "text": [
      "(9913, 11)\n",
      "division_both_dbs\n",
      "bacteria         7997\n",
      "fungi             772\n",
      "archaea           330\n",
      "vertebrates       248\n",
      "invertebrates     226\n",
      "plants            184\n",
      "protists          156\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# statistics on length distributions for different species     \n",
    "stat_file = c.STAT_P_FILE\n",
    "if 0:\n",
    "    print(\"The statistical descriptions of the protein distributions for \" + \\\n",
    "        \"the different species is in:\\n\", stat_file, \"\\n\")\n",
    "\n",
    "# retrieve data and diminish the number of columns\n",
    "stat_df = pd.read_csv(stat_file, low_memory=False, sep=\"\\t\")\n",
    "stat_df = stat_df[[\"superregnum\", \"species\", \"proteome_id\", \"tax_id\", \"uniprot_fasta_file\", \\\n",
    "    \"count\", \"mean\", \"var\", \"division_both_dbs\", \"log10_mean\", \"log10_var\"]]\n",
    "\n",
    "# Calculate the total number of proteins in this study\n",
    "total_p = stat_df[\"count\"].sum()\n",
    "print(\"Total number of proteins in this study: \", f\"{int(total_p)}\", \"proteins\")\n",
    "\n",
    "# visualize data\n",
    "if 1:\n",
    "    pd.set_option('display.max_columns', len(stat_df.columns))\n",
    "    display(stat_df.sample(2))\n",
    "    print(stat_df.shape)\n",
    "    print(stat_df[\"division_both_dbs\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7864e7f-66e1-4115-9a45-c2d020171688",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Python functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af6c0ad2-9985-4285-887a-c413505d46e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# species' stats for length distribution\n",
    "def get_df_for_taxid(df, taxid):\n",
    "     return df.loc[df[\"tax_id\"]==taxid].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1371569f-3901-448e-871a-828de23bd9d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Function to plot the log-normal distribution\n",
    "# blue: Observation\n",
    "# red: Theoretical/Simulation\n",
    "##########################################\n",
    "def plot_log_norm (log10_len, mu, sigma, N, title, subtitle, xlabel, ylabel, dist_of): # N is number of \n",
    "    print(\"µ:\", mu, \"\\tσ:\", sigma)\n",
    "    my_normal_dist = np.random.normal(mu, sigma, N)\n",
    "\n",
    "    c_hex = c.COLOR_FOR_DIST[dist_of]\n",
    "    count, bins, *rest = plt.hist(log10_len, 50, density=True, \n",
    "                                  alpha=0.8, color=c_hex, edgecolor='silver') # plot 60 bins\n",
    "    \n",
    "    plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n",
    "             np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
    "             linewidth=2, color='r', alpha=0.5)\n",
    "     \n",
    "    # mean\n",
    "    plt.vlines(x = mu, ymin = 0, ymax = 0.2, colors = 'white', label = 'mean')\n",
    "    plt.xlim([1,7]); #plt.ylim([0,2.0])\n",
    "    plt.suptitle(title, style='italic'); plt.title(subtitle + \" (\" + str(int(N)) +\")\")\n",
    "    plt.xlabel(xlabel); plt.ylabel(ylabel)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee7bf25c-d0a0-4028-9af0-2e8f7979a15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def does_it_fits_normal(data_observed): \n",
    "    # Kurtosis and Skewness\n",
    "    # =====================\n",
    "    print('Kurtosis:', stats.kurtosis(data_observed))\n",
    "    print('Skewness:', stats.skew(data_observed))\n",
    "    # D'Agostino's K-squared test\n",
    "    # ===========================\n",
    "    k2, p_value = stats.normaltest(data_observed)\n",
    "    print(f\"D'Agostino's K-squared test(statistics={k2}, p-value={p_value})\")\n",
    "    #\n",
    "    # Mathematicaly it ends up beeing a theoretical log-normal when.\n",
    "    #    - Kurtosis o Skewness: $\\leq -1$ o $\\geq 1$ \n",
    "    #    - D'Agostino's K-squared test: p-value $ \\leq 0.05 $, becuase it rejects the null hypothesis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bcc7a1e-2408-46a8-af94-026a08044e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The next function performs the whole analysis for a tax_id\n",
    "def species_distribution__retrieve_plot_and_fit(stat_df, tax_id, dist_of):\n",
    "    species_df = get_df_for_taxid(stat_df, tax_id)\n",
    "    species_df.drop_duplicates(subset=['tax_id'], inplace=True, keep='last')\n",
    "    display(species_df)\n",
    "\n",
    "    # retrieve lengths \n",
    "    lengths_f = c.GENES_PROTS_LENGTH_PATH + species_df[\"uniprot_fasta_file\"].item() \n",
    "    lengths_f = lengths_f.replace(\".fasta.gz\", \".length.tsv\") # I get the \n",
    "    # retrieve data\n",
    "    lens_df = pd.read_csv(lengths_f, sep=\"\\t\")\n",
    "    lens_df = lens_df[[\"length\"]]\n",
    "    \n",
    "    # visualize data\n",
    "    pd.set_option('display.max_columns', None)\n",
    "    if 0:\n",
    "        display(lens_df.head(2))\n",
    "        print(lens_df.shape)\n",
    "    \n",
    "    # plot the distributions: observed and theoretical\n",
    "    lens_df[\"log10_length\"] = lens_df[\"length\"].apply(np.log10)\n",
    "    title = species_df[\"species\"].item().capitalize() + \". \" + species_df[\"division_both_dbs\"].item().capitalize(); subtitle = \"Proteins\";  xlabel = \"$log_{10}(length)$\"; ylabel = \"Probability density function\"\n",
    "    plot_log_norm (lens_df[\"log10_length\"], species_df[\"log10_mean\"].item(), math.sqrt(species_df[\"log10_var\"].item()), int(species_df[\"count\"].item()), \n",
    "                   title, subtitle, xlabel, ylabel, dist_of)  \n",
    "    # fit to normal\n",
    "    does_it_fits_normal(lens_df[\"log10_length\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2111fd8f-02db-4b6b-b70d-50d386b34b0c",
   "metadata": {},
   "source": [
    "## Distributions for different species:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d86cba-90c3-420a-a342-effebd787c95",
   "metadata": {},
   "source": [
    "### _Homo sapiens_ (tax_id = 9606)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0866ec4e-b56f-45c2-9858-b517c8481d26",
   "metadata": {},
   "source": [
    "#### Retrieve its statistical description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cd3536d-1829-4625-9dbf-234606f1c6e2",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>superregnum</th>\n",
       "      <th>species</th>\n",
       "      <th>proteome_id</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>uniprot_fasta_file</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>division_both_dbs</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8585</th>\n",
       "      <td>eukaryota</td>\n",
       "      <td>Homo sapiens (Human)</td>\n",
       "      <td>UP000005640</td>\n",
       "      <td>9606</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>20614.0</td>\n",
       "      <td>552.845105</td>\n",
       "      <td>354913.157154</td>\n",
       "      <td>vertebrates</td>\n",
       "      <td>2.601146</td>\n",
       "      <td>0.125239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     superregnum               species  proteome_id  tax_id  \\\n",
       "8585   eukaryota  Homo sapiens (Human)  UP000005640    9606   \n",
       "\n",
       "                                     uniprot_fasta_file    count        mean  \\\n",
       "8585  /ftp.uniprot.org/pub/databases/uniprot/current...  20614.0  552.845105   \n",
       "\n",
       "                var division_both_dbs  log10_mean  log10_var  \n",
       "8585  354913.157154       vertebrates    2.601146   0.125239  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "µ: 2.6011463573824893 \tσ: 0.3538905693139241\n"
     ]
    },
    {
     "data": {
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y2fGxY8fe8Lm7d+8un9upUyezFlcjY6vupk2bhBBCfPvttwKA2Llzp8W5Wq1WrFq1SsTFxQkPDw+z1woODpbP02g0QqVSiZkzZ5o93prPo7o/3+T42LJDNSYxMRFNmjSpcOTI8ePHodfrzX5LO378OCIjI+Uhy6aSk5Pl/hYajQYnTpwwG/EDGK7HG6/Jmzp69Ch0Op38WsYOoC1atKjwdSRJsvgt2JRKpcKePXvwyy+/4LvvvsPatWvxzjvv4J133sG0adOg0+kwcOBAHDx4EGPGjEG3bt0QFBQEFxcXDB8+3Ow9HzlyBIBlJ20AOHjwIG677Taz9zxs2DCzxz700EP473//W2GdDRs2BACcO3cOhYWFFp+LTqfD0aNHMWjQIPlYs2bNcOrUKXz77bf44YcfMGvWLMybNw9bt25Fr169Kv1M0tLS0KRJE4vjxpaR6187LS0N2dnZFp9FRe9l3759ct8joLzF8PrnrGyag8OHD0OSJPk5Ll68iC5dusDDwwMTJkxAy5Yt4e/vj7S0NIwdO9aib1D79u0tWnAOHz5sMd9RWloaEhISMHXqVIv3UFVRUVHw8/OTf0YXLFiA4uJivPHGG/I5xp+Z77//Xm6dup7x7yIxMRERERFo3LixxTmJiYnw9va2uO/IkSO4/fbbMWfOnAqf29jPRqfT4dixY3L/HVPG1h/jZ5mYmAilUin3AzI1atQobNiwAaNHj8Zzzz2H4OBguLm5YcqUKWYtZCdPnkRZWZnFvxVrPo/q/nyT42PYoRqTmJhY6X8gR48eBWD+JS9JUoXBaNeuXUhJScHMmTMBGJr41Wq1xX96xi+7648bv3CN//Eam9uvfy2dTodPPvkEt99+u3zpozIKhQIDBw7EwIED8eabb6JHjx5Yu3Ytpk2bhm3btmHPnj3YtGmTWTjZu3cvcnJyzOoz1mbaGRUAfv75Z5w7dw4LFiwwe8+mX8SFhYUIDw9H3759b1ir8TWu/1xOnTqF0tJSi8sC3t7eGDVqFEaNGoWTJ0+iXbt22LBhww2/DDQajXx5raLXvv41ru9snZaWhoKCAnmuGaODBw/i3LlzGDlypMVjK/r7d3d3twixSUlJiI2NlUP0e++9h5ycHFy8eNHskpnxMtH1fz+PPPKIxfuq6HLKZ599BiGE3Lm5ulq1aoUTJ07g0qVLWLp0KcaPH282dYNxdFHPnj0RFBR0w+dKTEysdGqHxMREtGvXzuIybGFhIZo0aXLTn6vk5GSUlpZa/OwCwLJly9CxY0c5SCUmJqJZs2YWYeTYsWNYt24d3n33XTz33HPy8cuXL+PUqVNmQaqiDvrGeoGqfR5A9X6+yfFx6DnViPT0dFy5cqXSuVUqCjstW7bEuXPnkJaWJh8rLi7GSy+9hEaNGslfeJV9eRtH6FT0xRoQEIDo6Gj5dQDgzz//NDvvnXfewdmzZzF9+vRK31dBQQE0Go3ZMXd3d+j1enkG2YsXLwIo7y8AGIaqG/usVNSy89dff8nHSkpK8Morr6B169Z46KGHzN6z6WNjY2OxZcuWCucqyczMlLeTkpKgUCjMWkeA8nBofM6Khkt7eHhAp9PddHbcwMDACofsJyUlVdoPR6FQyP1wjO/B9DmMI/IAmNWelJSEsLAwhIWFmT1nYmIi2rRpY/Hle30wuXjxIgICAsxGS504cQKvv/46XF1d5cCVlpaG3Nxci58zvV6PI0eOmD2nRqPB0qVL0bt3b3Tu3Nns/PPnz+P06dMWn01lWrdujRMnTmDevHlQKBQWLSyxsbEAgG+++cbisfn5+VCr1QDKW14qCzupqany6Kvrn/+PP/7AlStXLO4zPWb8mdyzZ4/ZOV9++SX279+P2bNnm51bUR0V/VvRaDQYP368WWussV4AFjVX9fO4lZ9vcnxs2aEaYfwivVHY8fb2Nrv08cwzz2Dx4sUYOHAgnnrqKajVaqxcuRKXL1/Grl274O7uDsDw5eXh4WExUeHhw4cRGxsLHx8fs+PG32CNmjdvjnvvvRevvvoq8vLyEBsbi+3bt+Prr7/GzJkzK+xka/Tll1/i1VdfxcMPP4yWLVtCo9Fg3bp1OHv2rDz/Tffu3aFQKDBq1Cg89dRTyMrKwqpVq+QOrsb/9PV6PY4ePYr27dvjiSeewPPPPw8XFxesXLkSFy9exO+//y5/cScmJsLT09Ps83ruuefw1FNP4bbbbsPo0aPh4+ODc+fOYdu2bXjggQfklrCkpCR5Xp3rPxfTz/HZZ5/FkSNHcN999yE2NhaXL1/GihUr0KBBA0yYMKHSzwQwtEb88ssvFseTkpIqnLAuMTERTZs2lX/Tb9iwIYKDgzF37lwUFBRArVbjs88+kwPN9ZexKvriPHz4MPr37292zPiFP2TIEPlYz5498eWXX2L48OHo378/Tpw4gS+//BKBgYHw9fWVOx9XFqqTk5NRXFxs9r5WrFiBtLS0Cufeuf322+Hr6ysH/Jtp3bo1Vq9ejY8//hgzZsxAeHi42f1DhgxBkyZNMHnyZBw8eBCdOnVCXl4ejh49ih9//BEZGRkAgNOnT6O0tLTSsBMbG4udO3fivffeQ2BgIG677Ta0aNECU6ZMwc8//4yuXbvKw7NTU1OxZ88eNGvWTJ42ISkpCT4+Prh06RJGjhyJfv364cCBA1i+fDn++9//4v777wdgCLBnzpzBmDFjLGro2LEjPD09MXXqVKSkpKCkpASfffaZ/HNvWrsx1Dz//PNyK87gwYOr/Hncys83OQF7dxoi5/Tmm28KACIxMbHC+2NjY806Ohr9+uuvokuXLsLNzU2EhoaKkSNHWnSuHDx4sFknX6P27duLYcOGWRwPDQ0VkydPNjuWnZ0tHn/8ceHn5ye8vLxE9+7dxYYNG276vn777Tfx0EMPiejoaKFSqURUVJR48MEHLd7n6tWrRaNGjYSHh4fo3r27+OGHH8STTz4pGjRoIJ9z+vRpAUBs3rxZzJgxQwQHBwtPT08xaNAgceTIEYv3fNttt1nU8/XXX4tu3boJPz8/4ePjI1q1aiUmTZpk9pm1bt3arIOr6XOafo6rVq0Sd999twgJCREqlUo0bdpUTJkyRVy5cuWmn8u6deuEJElmHXb1er3w9vY26/B7o5q2b98uWrZsKdzd3UWXLl3EN998I2bNmiXc3NyERqO54XMaOy0vXrzY7PixY8cEALFx40b5mFarFVOnThVBQUHC19dXDB06VBw7dkzExMSIxx9/XD7vzTffFJIkiYKCArPn3LBhgwAg/x3l5OSIkJAQ8dRTT1m8T2Ndxs7iVWEc1h4cHFzpEOnLly+LCRMmiJiYGKFSqURkZKTo16+f2VD86+u83tGjR0XPnj3lTsHff/+9fN/OnTtFnz59RFBQkPD09BRNmzYVY8aMEYcOHZLPuf/++0WXLl3EsWPHRI8ePeSfmXfffddsGPrevXsFAPHjjz9WWMePP/4o/723b99erFmzRrz66qvCxcXFbHCDVqsVTz/9tAgKChIAxAMPPGDV53ErP9/k+CQhhLBTziKq1zZv3oz7778fp0+fNmvGd0SFhYWIiIjAihUrzPrX1Afjxo3Dn3/+iQMHDlTYb4mI7I99dojsJCkpCe7u7hWOlHE0Pj4+GD16NFavXm3vUmrV999/j2+//Rbfffcdgw5RHcaWHSI7GT58OE6cOCH3byIioprBlh0iO0lKSrIYak1ERLbHlh0iIiJyamzZISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQUa05d+4cJEnC2rVra/21L1y4AHd3d/z555+1/tq1RaPRIDo6Gh9++KG9SyGqUxh2iBzc2rVrIUmSfHN3d0fz5s0xefJkZGRk2PS13njjDWzevNmmz1lb5s2bh27duqFXr17ysW+//RbDhw9H48aN4enpiRYtWuD5559HXl5ehc/x/fffo1OnTnB3d0fDhg0xZ84caLXaCs/dsWMH7r77bvj5+cHHxwedO3fG+vXrzc5Zv349Hn/8cTRr1gySJKFPnz5Vei+vv/46JElC27ZtzY67uroiPj4er7/+OkpLS6v0XET1AWdQJnJwa9euxZgxYzBv3jzExsaitLQUf/zxBz777DM0atQIR44cgaenp01ey9vbGw899FC1W2aEECgrK4OrqyuUSqVNaqqKzMxMREVF4ZNPPsGIESPk48HBwYiMjMSwYcPQsGFDJCUlYfny5WjcuDEOHjwIDw8P+dyff/4Z9957L/r06YMRI0YgKSkJS5cuxVNPPYVly5aZvd6aNWswbtw49OvXD/fddx+USiVOnjyJqKgovPDCC/J5ffr0wYEDB3DbbbchISEB7du3x+7du2/4Xi5evIgWLVpAkiTExMTgyJEjZvfn5eUhLCwMy5Ytw9ixY2/hUyNyIoKIHNqaNWsEALFv3z6z4/Hx8QKAWLduXaWPvXr1qlWv5eXlJUaPHl2dMu1q0aJFwsPDQxQWFpod37Vrl8W5n3zyiQAgVq5caXa8devWokOHDkKj0cjHXnnlFSFJkjh+/Lh8LCUlRXh4eIgpU6bctK7U1FSh0+mEEEK0adNG9O7d+6aPGT58uLj77rtF7969RZs2bSo85z//+Y+44447bvpcRPUFL2MROam7774bAJCSkgIAePLJJ+Ht7Y0zZ85g8ODB8PHxwWOPPQYAKCoqwvPPP4/o6GioVCq0aNECb7/9NoRJw68kSSgqKsInn3wiXzJ78skn5fvT0tIwduxYhIWFQaVSoU2bNli9erVZTRX12THWlZaWhmHDhsHb2xshISF44YUXoNPpzB7/1VdfoXPnzvDx8YGvry/atWuHJUuW3PSz2Lx5M7p16wZvb2+z4xVdNrr//vsBAMePH5ePHTt2DMeOHcNTTz0FFxcX+fjEiRMhhMDGjRvlY8uXL4dOp8O8efMAAFevXjX7HE1FR0dDoaj6f8N79uzBxo0bsXjx4hue169fP/zxxx/Iycmp8nMTOTOGHSIndebMGQBAUFCQfEyr1WLAgAEIDQ3F22+/jQcffBBCCNx333149913MXDgQCxatAgtWrTAiy++iPj4ePmxn332GVQqFe644w589tln+Oyzz/Df//4XAJCRkYHu3btjx44dmDx5MpYsWYKmTZti3LhxN/1iBgCdTocBAwYgKCgIb7/9Nnr37o133nkHK1askM/Zvn07RowYgYCAACxcuBALFixAnz59btrhWKPRYN++fejUqVOVPrf09HQAhktcRocOHQIAdOnSxezcyMhINGjQQL4fMPTVadmyJbZs2YIGDRrAx8cHQUFBmDVrFvR6fZVqqIhOp8Ozzz6L8ePHo127djc8t3PnzhBC4K+//qr26xE5Fbu2KxHRLTNextqxY4fIzMwUFy5cEF999ZUICgoSHh4e4uLFi0IIIUaPHi0AiOnTp5s9fvPmzQKA+N///md2/KGHHhKSJInk5GT5WGWXscaNGyciIiJEVlaW2fFHH31U+Pn5ieLiYiGE4RIPALFmzRr5HGNd8+bNM3tsXFyc6Ny5s7w/depU4evrK7RabdU/HCFEcnKyACDef//9Kp0/btw4oVQqxalTp+Rjb731lgAgUlNTLc6/7bbbRPfu3eV9X19fERAQIFQqlZg1a5bYuHGjGDlyZIWfvambXcb64IMPhJ+fn7hy5YoQQtzwMtalS5cEALFw4cKbvV2ieoEtO0ROom/fvggJCUF0dDQeffRReHt7Y9OmTYiKijI775lnnjHb37JlC5RKJaZMmWJ2/Pnnn4cQAj///PMNX1cIgW+++QZDhgyBEAJZWVnybcCAAcjPz8fBgwdvWv/TTz9ttn/HHXfg7Nmz8r6/vz+Kioqwffv2mz6XqezsbABAQEDATc9dt24dVq1aheeffx7NmjWTj5eUlAAAVCqVxWPc3d3l+wHDZavc3FzMnTsX8+bNw4MPPogvvvgCAwcOxJIlS1BYWGhV/cb3MHv2bMyaNQshISE3Pd/4XrOysqx+LSJn5HLzU4jIESxduhTNmzeHi4sLwsLC0KJFC4v+IC4uLmjQoIHZsfPnzyMyMhI+Pj5mx1u1aiXffyOZmZnIy8vDihUrzC47mbpy5coNn8Pd3d3iSzwgIAC5ubny/sSJE7FhwwYMGjQIUVFR6N+/Px555BEMHDjwhs9tJG4y8PT333/HuHHjMGDAALz++utm9xlHZZWVlVk8rrS01GzUloeHB4qKisxGfQHAiBEjsHXrVhw6dAh33nlnlWo2mjlzJgIDA/Hss89W6Xzje5UkyarXIXJWDDtETqJr164WfUqup1KprOoQWxXGfiiPP/44Ro8eXeE57du3v+FzVGUYemhoKBISErBt2zb8/PPP+Pnnn7FmzRqMGjUKn3zySaWPM/ZZMg1O1zt8+DDuu+8+tG3bFhs3bjTrhAwAERERAIDLly8jOjra7L7Lly+ja9eu8n5kZCROnz6NsLAwi/pvVkdFTp8+jRUrVmDx4sW4dOmSfLy0tBQajQbnzp2Dr68vAgMD5fuMr2Ha74ioPmPYIarnGjVqhB07dqCwsNCsdefEiRPy/UYVtRSEhITAx8cHOp0Offv2rdFa3dzcMGTIEAwZMgR6vR4TJ07ERx99hFmzZqFp06YVPqZhw4bw8PCQR6Vd78yZMxg4cCBCQ0OxZcsWixFbANCxY0cAwP79+82CzaVLl3Dx4kU89dRT8rHOnTvj9OnTSEtLQ+PGjc3OBVCly1Cm0tLSoNfrMWXKFItLjQAQGxuLqVOnmnUEN75XY+scUX3HPjtE9dzgwYOh0+nwwQcfmB1/9913IUkSBg0aJB/z8vKymF1YqVTiwQcfxDfffGMxwR1guMxlC8a+N0YKhUJuMaro8pKRq6srunTpgv3791vcl56ejv79+0OhUGDbtm2VBpE2bdqgZcuWWLFihdlw+GXLlkGSJDz00EPyseHDhwMAVq1aJR/T6/VYs2YNAgMD0blz5yq823Jt27bFpk2bLG5t2rRBw4YNsWnTJowbN87sMQcOHIAkSejRo4dVr0XkrNiyQ1TPDRkyBHfddRdeeeUVnDt3Dh06dMAvv/yC7777Ds899xyaNGkin9u5c2fs2LEDixYtQmRkJGJjY9GtWzcsWLAAu3btQrdu3TBhwgS0bt0aOTk5OHjwIHbs2GGT+V7Gjx+PnJwc3H333WjQoAHOnz+P999/Hx07drxpC8bQoUPxyiuvoKCgAL6+vvLxgQMH4uzZs3jppZfwxx9/4I8//pDvCwsLQ79+/eT9t956C/fddx/69++PRx99FEeOHMEHH3yA8ePHm73+0KFDcc8992D+/PnIyspChw4dsHnzZvzxxx/46KOPzDo579mzB3v27AFgCIVFRUX43//+BwC48847ceeddyI4OBjDhg2zeE/GlpyK7tu+fTt69eplNu0AUb1m17FgRHTLKptB+XqjR48WXl5eFd5XWFgopk2bJiIjI4Wrq6to1qyZeOutt4Rerzc778SJE+LOO+8UHh4eAoDZMPSMjAwxadIkER0dLVxdXUV4eLi45557xIoVK+RzKht6XlFdc+bMEab/RW3cuFH0799fhIaGCjc3N9GwYUPx3//+V1y+fPmG79tYm4uLi/jss8/MjgOo9FbRMPBNmzaJjh07CpVKJRo0aCBmzpwp1Gq1xXmFhYVi6tSpIjw8XLi5uYl27dqJzz//vNL3WNFtzpw5N3xPlQ09z8vLE25ubuLjjz++8YdCVI9wbSwiqhfGjRuHU6dO4ffff7d3KTVq8eLFePPNN3HmzBmzUWJE9RnDDhHVC6mpqWjevDl27txptvK5M9FoNGjSpAmmT5+OiRMn2rscojqDYYeIiIicGkdjERERkVNj2CEiIiKnxrBDRERETq3ezbOj1+tx6dIl+Pj4cN0YIiIiByGEQGFhISIjI61e9qbehZ1Lly5ZrG1DREREjuHChQsWCxrfTL0LO8a1fy5cuGA2kyoRERHVXQUFBYiOjjZbw6+q6l3YMV668vX1ZdghIiJyMNXpgsIOykREROTUGHaIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmoMO0REROTUGHaIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJyai70LIKrL1Go1tFpthfe5uLjAzc2tlisiIiJrMewQVUKtVuPo4QToNZoK71e4uqJNh44MPEREdRzDDlEltFot9BoNYkqz4SH0ZveVSAqcQxC0Wi3DDhFRHcewQ3QTHkIPT+jMDwr71EJERNZjB2UiIiJyamzZoXqBHY2JiOovhh1yeuxoTERUvzHskNNjR2MiovqNYYfqjco6GuuFQElJicX5JSUlEOyJTETk8Bh2qF7TQIJGo0HKieOQJMnsPr0Q0GrU0OsFu/ITETkwhh2q17SQIAmBRiXZ8JLMW3HyJBeclzzZukNE5OAYdogAuAsdPK8LOyVCAUiVPICIiBwGG+eJiIjIqTHsEBERkVNj2CEiIiKnZtews2fPHgwZMgSRkZGQJAmbN2++4fnffvst+vXrh5CQEPj6+qJHjx7Ytm1b7RRLREREDsmuYaeoqAgdOnTA0qVLq3T+nj170K9fP2zZsgUHDhzAXXfdhSFDhuDQoUM1XCkRERE5KruOxho0aBAGDRpU5fMXL15stv/GG2/gu+++ww8//IC4uDgbV0dERETOwKGHnuv1ehQWFiIwMLDSc8rKylBWVibvFxQU1EZpREREVEc4dAflt99+G1evXsUjjzxS6Tnz58+Hn5+ffIuOjq7FComIiMjeHDbsrFu3DnPnzsWGDRsQGhpa6XkzZsxAfn6+fLtw4UItVklERET25pCXsb766iuMHz8eX3/9Nfr27XvDc1UqFVQqVS1VRkRERHWNw7XsfPnllxgzZgy+/PJL3HvvvfYuh4iIiOo4u7bsXL16FcnJyfJ+SkoKEhISEBgYiIYNG2LGjBlIS0vDp59+CsBw6Wr06NFYsmQJunXrhvT0dACAh4cH/Pz87PIeiIiIqG6za8vO/v37ERcXJw8bj4+PR1xcHGbPng0AuHz5MlJTU+XzV6xYAa1Wi0mTJiEiIkK+TZ061S71ExERUd1n15adPn36QAhR6f1r164129+9e3fNFkREREROx+H67BARERFZg2GHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh2qvzQaKC6mQVKr7V0JERHVILsuBEpkN0IAu/6E6nIWwr0TIfW5DQjys3dVRERUA9iyQ/XTmXPA5QwAgLKoGK5bfwUuZdi3JiIiqhEMO1T/lJUBBxLNj2k0wM49QHKKfWoiIqIaw7BD9c+hI0BpKQBAFx2Fkuhow3G9HvhzL5BwxHCZi4iInALDDtUvWTnAqTOGbVcXaLp2RvZdfaBr2bT8nMNHgb/2ATqdXUokIiLbYtih+kMI4N8D5a027dtAeHsBCgV0t3UCunQoPzc5BW47f+NILSIiJ8CwQ/WGdPqsoWUHAPx9gVbNTO6UgDYtgd49AaUSAKC8nIGgPXvsUCkREdkSww7VC1JpKZQHD5cf6NZZDjVmYqKBfr0BlRsAwP1iGlBUVEtVEhFRTWDYoXrBd99+SGXXLknFNgLCQys/OSwEaNVc3lVcvFzD1RERUU1i2CGnJ6WlwfPUScOOq6t535zKREfKm4oLF2uoMiIiqg0MO+Tc9Hoot24t3+/YFvD0uPnjAvwNnZcBKNIzAXZUJiJyWAw75NwOHICUng4AEAH+gOkQ8xuRJOiiowzbej1nVyYicmAMO+Tc/vpL3tR16wwoqv4jr2sQVb6TmmbLqoiIqBYx7JDzyssDcnMBAOrwCIiwEKserg8Lgd7NMCoLaZc5ySARkYNi2CGnoVarUVxcLN9Kjh2DRqOBRqNBaWS49U+oVKLU2LqjVgNXsmxbMBER1QoXexdAZAtqtRpHDydAr9HIx/x3/waP9MsQAijqHAe9Xlgd70uio4Hk44adC2lARJgNqyYiotrAsENOQavVQq/RIKY0Gx5CDwgBl9TTkDQlKHF1RWpQMIQotPp5S6OiDP18hA64cAm4Lc4w2zIRETkMXsYip+Ih9PCEDp4F+XArLoIr9JBCgiueLbkKhJsb9OHX+vpcLQLy8m1YLRER1QaGHXJO6eVDxfXht3bpSc9RWUREDo1hh5xT+hV5U3+L/Wz0JrMp48KlW3ouIiKqfQw75HyEANIzDduurtAHBtza83l5AUHXniM7BygqvrXnIyKiWsWwQ84nrwAoLTVsh4VYNZFgpRqwdYeIyFEx7JDzMemvg4gbrG5ujWiTfjsXGXaIiBwJww45H5P+Ogi3UdgJ9Ae8PK89fwag1tzwdCIiqjsYdsi56PXl/XXcVUCAv22eV5LKW3d0ekiXLtvmeYmIqMYx7JBTkXLyDEs7AEBYqG0nADQZlaW4wCHoRESOgmGHnIqUYXoJy7qFP28qLARwdTW8zsVLXBiUiMhBMOyQU5FMOyfbqr+OkVIJNIgwvI5aA7eMjJs8gIiI6gKGHXIeOh2kjGv9dTzcAT9f27+GyRB099RU2z8/ERHZHMMOOQ3XrCxIGq1hJzysZhbsbBAhz9vDsENE5BgYdshpqC6nl+/Yur+OkZsbEBwIAFAWFgLFnE2ZiKiuY9ghp+F22WSyP1v31zEVFChvSunpNziRiIjqAoYdcg5abXmHYS9PwMe75l4rqHytLYYdIqK6z65hZ8+ePRgyZAgiIyMhSRI2b95808fs3r0bnTp1gkqlQtOmTbF27doar5PqPiktDZJxKHhEDfXXMQpk2CEiciR2DTtFRUXo0KEDli5dWqXzU1JScO+99+Kuu+5CQkICnnvuOYwfPx7btm2r4UqprpPOny/fCauh/jpGfj4QLkrD6zLsEBHVeS72fPFBgwZh0KBBVT5/+fLliI2NxTvvvAMAaNWqFf744w+8++67GDBgQIWPKSsrQ1lZmbxfUFBwa0VTnSSdO1e+U5P9dQBAoYAI8AfySiDl5hpWWHd3r9nXJCKianOoPjt///03+vbta3ZswIAB+Pvvvyt9zPz58+Hn5yffoqOja7pMqm0aDaRLhs7Jwtcb8Paq8ZcUgQEQAtBoNChJSUFxcbHZTW1csoKIiOyu2i07arUaV65cgV6vNzvesGHDWy6qMunp6QgLCzM7FhYWhoKCApSUlMDDw8PiMTNmzEB8fLy8X1BQwMDjbFJT5f46IjzsJifbhiYoEPrTF5CXlYWC3/egKLet2f0KV1e06dARbm5utVIPERFVzuqwc/r0aYwdOxZ//fWX2XEhBCRJgq6OrRekUqmgUqnsXQbVpJQUeVMfVsOXsK7RBAYCAvDTFiPo0lnompSHrBJJgXMIglarZdghIqoDrA47Tz75JFxcXPDjjz8iIiICUk2OerlOeHg4Mq5bjygjIwO+vr4VtupQPWHSX0dE1E7YEf5+EEoFXDQCqpxsACYhX9RKCUREVEVWh52EhAQcOHAALVu2rIl6bqhHjx7YsmWL2bHt27ejR48etV4L1RF6PXBtRJTW1xfw8IBZ8KgpSiU0/v5AZjGQXwhoNPKK6EREVLdY3UG5devWyMrKssmLX716FQkJCUhISABgGFqekJCA1GtrDs2YMQOjRo2Sz3/66adx9uxZvPTSSzhx4gQ+/PBDbNiwAdOmTbNJPeSAcnIArWE9LG1g4E1Oti1NYJBhQwggN79WX5uIiKrO6rCzcOFCvPTSS9i9ezeys7NRUFBgdrPG/v37ERcXh7i4OABAfHw84uLiMHv2bADA5cuX5eADALGxsfjpp5+wfft2dOjQAe+88w4+/vjjSoedUz1gcllTU8thR22ybARycmv1tYmIqOqsvoxlHPp9zz33mB2vTgflPn36QIjKOzhUNDtynz59cOjQoSq/Bjk5O4YdTVBQ+U42ww4RUV1lddjZtWtXTdRBVD0mYUcbEACgrPJzbUwTEAAoJMNlLIYdIqI6y+qw07t375qog6h6roUd4eYGnY8PUFp7YUe4uED4+QJ5eUB+AaDTAUplrb0+ERFVTbUmFczLy8OqVatw/PhxAECbNm0wduxY+Pn52bQ4ohsqLTUEDQAiNLRmF/+shAgMMNSg1wO5eUBw0M0eQkREtczqDsr79+9HkyZN8O677yInJwc5OTlYtGgRmjRpgoMHD9ZEjUQVM7mEJUJrZ36d6+lNVkDnpSwiorrJ6padadOm4b777sPKlSvh4mJ4uFarxfjx4/Hcc89hz549Ni+SqEKmE0yG1c4yEdcTQSZhJyfPLjUQEdGNWR129u/fbxZ0AMDFxQUvvfQSunTpYtPiiG7o+padrMxaL0H4+xsun7GTMhFRnWX1ZSxfX1+zuW+MLly4AB8fH5sURVQlpmEnJMQ+Nbi5Ar7Xfu7z8gydlImIqE6xOuwMHz4c48aNw/r163HhwgVcuHABX331FcaPH48RI0bURI1EloQArlwxbAcEAPZc7NXYb0enN4zKIiKiOsXqy1hvv/02JEnCqFGjoL02Tb+rqyueeeYZLFiwwOYFElUoNxdQqw3bduqvIwvyB1LOG7azc4HAYLuWQ0RE5qwOO25ubliyZAnmz5+PM2fOAACaNGkCT09PmxdHVKk60DlZFshOykREdVm15tkBAE9PT7Rr186WtRBVXV0KO2YjsthJmYiorqlS2HnggQewdu1a+Pr64oEHHrjhud9++61NCiO6oboUdtzcAB9voPCqoWVHr7dvPUREZKZKYcfPzw/StdlpfX195W0iu0lPN/zp6mrooFxaat96AgMMYUerBQoKAS87BzAiIpJVKeysWbNG3q5oJXKiWlVWZuigDBhadRRWDyq0vSB/4PwFAICUkwtE2LccIiIqZ/W3xN133428a+sRmSooKMDdd99ti5qIbsw45Byw/yUsI5NOyhInFyQiqlOsDju7d++G2jjk10RpaSl+//13mxRFdEN1qb+OkWnYYSdlIqI6pcqjsRITE+XtY8eOId3YZwKATqfD1q1bERUVZdvqiCpSF8OOhzvg5QkUFRtadoSwd0VERHRNlcNOx44dIUkSJEmq8HKVh4cH3n//fZsWR1Shuhh2AEPrTlExJI0GysJCe1dDRETXVDnspKSkQAiBxo0bY+/evQgxWYvIzc0NoaGhUCqVNVIkkUyI8rDj5we4u9u3HlNBAcCFNACAa1aWnYshIiKjKoedRo0aAQD0nEOE7Ck/3zAaC6hbrTqA2eSCrtnZdiyEiIhMWd1Bef78+Vi9erXF8dWrV2PhwoU2KYqoUnX1EhZg1kmZYYeIqO6wOux89NFHaNmypcXxNm3aYPny5TYpiqhSdTnseLgbbrgWdthJmYioTrA67KSnpyMiwnLGtJCQEFy+fNkmRRFVymQUIMLD7VdHRSQJ8PcDAChKS4HiYjsXREREQDXCTnR0NP7880+L43/++SciIyNtUhRRpYwtOy4uQGCgfWupiL+vvCmxkzIRUZ1g9arnEyZMwHPPPQeNRiMPQd+5cydeeuklPP/88zYvkEim0QA5OYbt0NC6sUzE9XxNwg777RAR1QlWh50XX3wR2dnZmDhxojyTsru7O/7v//4PM2bMsHmBRLIrV8r7wdS1/jpGJi07yMy0Xx1ERCSzOuxIkoSFCxdi1qxZOH78ODw8PNCsWTOoVKqaqI+oXF3unGzkx8tYRER1jdVhx8jb2xu33XabLWshujFHCDvuKgiVG6DnZSwiorrC6rBTVFSEBQsWYOfOnbhy5YrFJINnz561WXFEZhwh7EgShJ8vkFsMqbDQMAEiWz2JiOzK6rAzfvx4/Pbbb3jiiScQEREBSZJqoi4ic6bLRPj4AJ6e9q3nRvz9gNxrw84zM4EGDexbDxFRPWd12Pn555/x008/oVevXjVRD1HFCguBkhLDdl1t1blG+PkAuDbnVFYWww4RkZ1ZPXY3ICAAgXVxfhNybqaXsOraZILXEX5+5TsckUVEZHdWh53XXnsNs2fPRjFnh6XaZDpzcp1v2eHwcyKiusTqy1jvvPMOzpw5g7CwMMTExMDV1dXs/oMHD9qsOCKZI3RONvLyhHC59u+Cw8+JiOzO6rAzbNiwGiiD6CauXDH8qVQCQUH2reVmJAnaa2tkITfXMPPzdb8UEBFR7bE67MyZM6cm6iCqnF4PGOesCQoyBJ46TuvnDxQVGUaRZWfX+X5GRETOrA4uLkR0ndxcQKczbIeE2LeWKtL6+5fv8FIWEZFdWR12FAoFlEplpTcimzMNC8HB9qvDCpoA//IddlImIrIrqy9jbdq0yWxfo9Hg0KFD+OSTTzB37lybFUYkc8CwozUdfs6WHSIiu7I67AwdOtTi2EMPPYQ2bdpg/fr1GDdunE0KI5I5YNjR+fpCGFs62bJDRGRXNuuz0717d+zcudNWT0dUzjTs1PWRWNfoJQlqHx9oNBqo09NRfPUqiouLUVxcDLVabe/yiIjqlWqvem6qpKQE7733HqKiomzxdETlhCgPO35+gJubfeupAg0kaDQaZJSUwCPdsGzEld/3QHft0pbC1RVtOnSEmwO8FyIiZ2B12AkICDBb/FMIgcLCQnh6euLzzz+3aXFEKCoqXxPLQS5haSFBEgJBngp4aQwzjQekn4Vwa4ASSYFzCIJWq2XYISKqJVaHncWLF5vtKxQKhISEoFu3bggICLBVXUQGDthfx8jV1xuu0Bu283MBRADCvjUREdVHVeqz88ADD6CgoAAAIEkSHn30UYwePRqjR4/GE088gYEDB1Y76CxduhQxMTFwd3dHt27dsHfv3huev3jxYrRo0QIeHh6Ijo7GtGnTUFpaWq3XJgfgwGHHsPr5NfmF9iuEiKieq1LY+fHHH1FUVAQAGDNmDPLz823y4uvXr0d8fDzmzJmDgwcPokOHDhgwYACuGJcGuM66deswffp0zJkzB8ePH8eqVauwfv16vPzyyzaph+ogRw47vj6A8ZJvXoF9iyEiqseqdBmrZcuWmDFjBu666y4IIbBhwwb4+vpWeO6oUaOq/OKLFi3ChAkTMGbMGADA8uXL8dNPP2H16tWYPn26xfl//fUXevXqhZEjRwIAYmJiMGLECPz7779Vfk1yMKZhx0FmT5a5uAA+XkDBVaCgwNDZWrr5w4iIyLaqFHaWL1+O+Ph4/PTTT5AkCTNnzjTrpGwkSVKVw45arcaBAwcwY8YM+ZhCoUDfvn3x999/V/iYnj174vPPP8fevXvRtWtXnD17Flu2bMETTzxR6euUlZWhrKxM3jdejiMHYQw77u6Al5d9a6kOX19D2NFogaJiwLviXxKIiKjmVCns9OzZE//88w8AQyA5deoUQkNDb+mFs7KyoNPpEBYWZnY8LCwMJ06cqPAxI0eORFZWFm6//XYIIaDVavH000/f8DLW/PnzObOzo9JogLw8w3ZwcPklIUfi7wtcvGTYzi9g2CEisgOrJxVMSUlBiJ0uJ+zevRtvvPEGPvzwQxw8eBDffvstfvrpJ7z22muVPmbGjBnIz8+XbxcuXKjFiumWGFc6Bxyuv47M17STMlsViYjsweqh540aNbLJCwcHB0OpVCIjI8PseEZGBsLDwyt8zKxZs/DEE09g/PjxAIB27dqhqKgITz31FF555RUoFJbZTaVSQaVS2aRmqmUO3DlZ5m+yRhZHZBER2YXNlouwlpubGzp37my2xIRer8fOnTvRo0ePCh9TXFxsEWiMK60LwQlMnI4zhB3T4ed5thnFSERE1rHJchHVFR8fj9GjR6NLly7o2rUrFi9ejKKiInl01qhRoxAVFYX58+cDAIYMGYJFixYhLi4O3bp1Q3JyMmbNmoUhQ4bIoYeciOkCmo4adtzcAE8PoLjEcBmLoZyIqNbZNewMHz4cmZmZmD17NtLT09GxY0ds3bpV7rScmppq1pJjHAU2c+ZMpKWlISQkBEOGDMHrr79ur7dANcnYsqNQAI48O7efryHslKmB0jLA094FERHVL1aHnTlz5mDs2LE267szefJkTJ48ucL7du/ebbbv4uKCOXPmYM6cOTZ5barD9PryDsqBgYAjt9z5+wKXDX3TpPwCINDO9RAR1TNW99n57rvv0KRJE9xzzz1Yt26d2Rw2RDaTnw9otYZtR72EZWQyAafEfjtERLXO6rCTkJCAffv2oU2bNpg6dSrCw8PxzDPPYN++fTVRH9VXjjxz8vX8TebW4aSWRES1rlqjseLi4vDee+/h0qVLWLVqFS5evIhevXqhffv2WLJkic3WzqJ6rJKRWGq1GsXFxRa3kpISiLq6pLifacsOww4RUW27pQ7KQghoNBqo1WoIIRAQEIAPPvgAs2bNwsqVKzF8+HBb1Un1TQVhR61W4+jhBOg1GovT9UJAq1FDrxd2nFChEu4qQOUGlKkNfXaIiKhWVSvsHDhwAGvWrMGXX34JlUqFUaNGYenSpWjatCkA4P3338eUKVMYdqj6TMNOUBAAQKvVQq/RIKY0Gx5Cb3Z6nuSC85Jn3WzdkSRD686VLEjFJZDYz42IqFZZ/Ttwu3bt0L17d6SkpGDVqlW4cOECFixYIAcdABgxYgQyTedIIbKWMez4+BgWATXhIfTwhM7sprou/NQ5JjMpu/AyLxFRrbK6ZeeRRx7B2LFjERUVVek5wcHB0Ovr+JcP1V3FxUBRkWHb0UdiGZnMpOzCEVlERLXK6pYdY9+c65WUlGDevHk2KYrqOWdYAPR6Jp2UXYwruRMRUa2wOuzMnTsXV69etTheXFyMuXPn2qQoquecYZmI65mFnVw7FkJEVP9Uq2VHkiSL44cPH0ZgIKeGJRtwhgVAr+flCbgarhq7smWHiKhWVbnPTkBAACRJgiRJaN68uVng0el0uHr1Kp5++ukaKZLqGWcMO5JkmEk5Ow/Kq1fLZ4cmIqIaV+Wws3jxYgghMHbsWMydOxd+fuWjS9zc3BATE4MePXrUSJFUzxjDjpub2VILDs/XB8jOM6x8npPjXO+NiKgOq3LYGT16NAAgNjYWPXv2hKura40VRfWYVgvkXuvTEhxsaBFxFiYjsqTsbCAmxn61EBHVI1UKOwUFBfC99ltoXFwcSkpKUFJSUuG5vvxtlW5FTo6h5QNwnktYRqbLRpiOOCMiohpVpbATEBCAy5cvIzQ0FP7+/hV2UDZ2XNbpdDYvkuoRZ+yvY3R9yw4REdWKKoWdX3/9VR5ptWvXrhotiOo5Zw47Pt4Qxl8UcnLsWwsRUT1SpbDTu3fvCreJbM6Zw46Li2EIepmAlJVluFznTH2SiIjqKKvn2dm6dSv++OMPeX/p0qXo2LEjRo4cidxcTpZGt8gYdiQJcMJ5m8S1S1mSWg1UMDknERHZntVh58UXX0RBQQEAICkpCfHx8Rg8eDBSUlIQHx9v8wKpHhGiPOwEBBhaQpyMMO3Ab9qKRURENcbqb5OUlBS0bt0aAPDNN99gyJAheOONN3Dw4EEMHjzY5gVSPVJQAKjVhm1nu4Rl5Hdd2ImNtV8tRET1hNUtO25ubiguLgYA7NixA/379wcABAYGyi0+RNXizP11rhGmYYcjsoiIaoXVLTu333474uPj0atXL+zduxfr168HAJw6dQoNGjSweYFUj5iGnZAQ+9VRg4Rv+fBzXsYiIqodVrfsfPDBB3BxccHGjRuxbNkyREVFAQB+/vlnDBw40OYFUj1SD1p24OEO4epm2GbYISKqFVa37DRs2BA//vijxfF3333XJgVRPWb65R8UZL86apIkQet/bV25/HxAowG49AoRUY2q1nAXvV6P5ORkXLlyBXq93uy+O++80yaFUT1kDDuenoabk9L6+QFXi8oXBA0Ls3dJREROzeqw888//2DkyJE4f/48hHENo2u4XARVW2kpUFho2HbS/jpGWj9/Q9gBDAGPYYeIqEZZHXaefvppdOnSBT/99BMiIiIqXCeLyGqmI5Octb/ONVo/XyAtzbDDfjtERDXO6rBz+vRpbNy4EU2bNq2Jeqi+MvnS1/j5QXNtegNTJSUlEBAWxx2N1t+/fIdhh4ioxlkddrp164bk5GSGHbKta1/6Op0OpzOvoCThkMUpeiGg1aih14tqjCOsO7Q+PuULgjLsEBHVOKvDzrPPPovnn38e6enpaNeuHVyvG0nSvn17mxVH9ci1L329Xg+1lxdiSrPhIcw7v+dJLjgveTp+646LC+Dvb1gbKzubC4ISEdUwq8POgw8+CAAYO3asfEySJAgh2EGZqu9a2BEuLtB5e8OjLBueMP9ZKhEKwEkygQgKMoQdtdrQMdt0zSwiIrKpaq2NRWRTOp1hCDZgWOlc4cDXqKpIBAYC588bdrKyGHaIiGqQ1WGnUaNGNVEH1Wd5eYbAg2stHvWAMB1xlpUFNG5sv2KIiJxctX6F/uyzz9CrVy9ERkbi/LXfThcvXozvvvvOpsVRPWHSSbe+hB2zGaLZSZmIqEZZHXaWLVuG+Ph4DB48GHl5eXIfHX9/fyxevNjW9VF9UA/Djtn75OrnREQ1yuqw8/7772PlypV45ZVXoFQq5eNdunRBUlKSTYujeqIehh14egIeHoZttuwQEdUoq8NOSkoK4uLiLI6rVCoUFRXZpCiqZ+rDAqDXk6Ty95qfbxiVRURENcLqsBMbG4uEhASL41u3bkWrVq1sURPVJ0KUhx0/P8DNzb711CbTTsq8lEVEVGOsHo0VHx+PSZMmobS0FEII7N27F19++SXmz5+Pjz/+uCZqJGdWXAyUlBi2nXxNLAvXh52ICPvVQkTkxKwOO+PHj4eHhwdmzpyJ4uJijBw5EpGRkViyZAkeffTRmqiRnJnpJaz6HHbYb4eIqMZYHXYA4LHHHsNjjz2G4uJiXL16FaGhobaui+qLehh29EKgpKQE8PCAq0ZjOJaWBl1xMVxcXOBWny7lERHVgmqFHSNPT094enraqhaqj+pZ2NFAgkajQcqJ45D0ekRkZABCD41ajazYGChcXdGmQ0cGHiIiG6pS2ImLi4NUxYUKDx48eEsFUT1Tz8KOFhIkIdCoJBtekoCLuwSpoBgiuwzeJVk4h2BotVqGHSIiG6pS2Bk2bJi8XVpaig8//BCtW7dGjx49AAD//PMPjh49iokTJ9ZIkeTEjGFHpQK8vcs7Kzs5d6GDpyQAP2+gIB/Q6uFx9Srg4fyBj4iotlUp7MyZM0feHj9+PKZMmYLXXnvN4pwLFy7YtjpybhqNYV0swNCqU8XWQ6fi5wNc+2cjFRQAIfYth4jIGVk9z87XX3+NUaNGWRx//PHH8c0331hdwNKlSxETEwN3d3d069YNe/fuveH5eXl5mDRpEiIiIqBSqdC8eXNs2bLF6telOiAnxzDPDlAvLmFVyNdH3pTyC+1YCBGR87I67Hh4eODPP/+0OP7nn3/C3d3dqudav3494uPjMWfOHBw8eBAdOnTAgAEDcOXKlQrPV6vV6NevH86dO4eNGzfi5MmTWLlyJaKioqx9G1QX1LP+OhUyCTvIL7BfHURETszq0VjPPfccnnnmGRw8eBBdu3YFAPz7779YvXo1Zs2aZdVzLVq0CBMmTMCYMWMAAMuXL8dPP/2E1atXY/r06Rbnr169Gjk5Ofjrr7/g6uoKAIiJibH2LVBdwbAD+PnKm1IBww4RUU2wOuxMnz4djRs3xpIlS/D5558DAFq1aoU1a9bgkUceqfLzqNVqHDhwADNmzJCPKRQK9O3bF3///XeFj/n+++/Ro0cPTJo0Cd999x1CQkIwcuRI/N///Z/ZoqSmysrKUFZWJu8X8Aul7mDYAdxVhltpGaQCXsYiIqoJ1Zpn55FHHrEq2FQkKysLOp0OYWFhZsfDwsJw4sSJCh9z9uxZ/Prrr3jsscewZcsWJCcnY+LEidBoNGadqE3Nnz8fc+fOvaVaqYYYw45CAQQG2rcWe/L1MYSdohJIXBCUiMjmrO6zY096vR6hoaFYsWIFOnfujOHDh+OVV17B8uXLK33MjBkzkJ+fL984YqyOMF0ANCAAqKRlrl4w6bfjwn47REQ2d0szKN+K4OBgKJVKZGRkmB3PyMhAeHh4hY+JiIiAq6ur2SWrVq1aIT09HWq1usKJ2FQqFVQqlW2Lp1tXUGAYeg7U30tYRib9dlzy8+xXBxGRk7Jby46bmxs6d+6MnTt3ysf0ej127twpT1Z4vV69eiE5ORl6vV4+durUKURERHDGWUfD/jrl/MpbdpRs2SEisjm7XsaKj4/HypUr8cknn+D48eN45plnUFRUJI/OGjVqlFkH5meeeQY5OTmYOnUqTp06hZ9++glvvPEGJk2aZK+3QNXFsFPOly07REQ1yerLWLt27cJdd91lkxcfPnw4MjMzMXv2bKSnp6Njx47YunWr3Gk5NTUVCkV5HouOjsa2bdswbdo0tG/fHlFRUZg6dSr+7//+zyb1UC1i2Cnn7QkoFYAOcMnPt3c1REROx+qwM3DgQDRo0ABjxozB6NGjER0dfUsFTJ48GZMnT67wvt27d1sc69GjB/75559bek2qAxh2yimVhnXB8q8awo7JZVoiIrp1Vl/GSktLw+TJk7Fx40Y0btwYAwYMwIYNG6DmkFmyhjHseHkBHh72raUuuNZJWdLpytcLIyIim7A67AQHB2PatGlISEjAv//+i+bNm2PixImIjIzElClTcPjw4Zqok5xJaSlQeG0CvfreqmMU4CdvSpmZdiyEiMj53FIH5U6dOmHGjBmYPHkyrl69itWrV6Nz58644447cPToUVvVSM4mO7t8m2HHwHTZCNNLfEREdMuqFXY0Gg02btyIwYMHo1GjRti2bRs++OADZGRkIDk5GY0aNcLDDz9s61rJWbC/jiV/hh0ioppidQflZ599Fl9++SWEEHjiiSfw5ptvom3btvL9Xl5eePvttxEZGWnTQsmJMOxY8vWBkCQADDtERLZmddg5duwY3n//fTzwwAOVzkwcHByMXbt23XJx5KQYdiwplYCPN1CiM3w+er1hzTAiIrplVv9vOmfOHDz88MMWQUer1WLPnj0AABcXF/Tu3ds2FZLzMYYdFxfAz+/G59Yjwt/wWUhaLUdkERHZkNVh56677kJOTo7F8fz8fJtNNkhOTKcDjD8/QUFsvTAhTPrtgCOyiIhsxupvGiEEpGt9C0xlZ2fDy8vLJkWRE8vLMwQegJewriNMW7kYdoiIbKbKfXYeeOABAIAkSXjyySfNLmPpdDokJiaiZ8+etq+QnAv761SKLTtERDWjymHH79pvnUII+Pj4wMNk1ls3Nzd0794dEyZMsH2F5FwYdirn5wsYW00ZdoiIbKbKYWfNmjUAgJiYGLzwwgu8ZEXVw7BTOaUSWp9rrTuZmYAQ5eGHiIiqrVqjsRh0qNpMw05QkP3qqKO0Af6GDY2GI7KIiGykSi07nTp1ws6dOxEQEIC4uLgKOygbHTx40GbFkZMRovzyjJ8f4OZm33rqIK2/P5BxxbCTmQkEBNi1HiIiZ1ClsDN06FC5Q/KwYcNqsh5yZoWFhkVAASA01L611FEWYad5c7vWQ0TkDKoUdubMmVPhNpFVMjLKtxl2KqTx9y/fYSdlIiKb4IxuVHuuXCnfDguzXx11mNbfX14jy+zzIiKiaqtSy05AQMAN++mYqmh2ZSIA5l/ebNmpmIsL4O8PXL1q6MzNEVlERLesSmFn8eLFNVwG1QvGsCNJHHZ+AyI42BB21GogP98QfoiIqNqqFHZGjx5d03WQs9Pry/ugBAUZWjCoQiI4GDh3zrCTmcmwQ0R0i6r0jVNQUABfX195+0aM5xGZyc0FtFrDNi9h3ZAwbfXKzASaNbNfMURETqDKfXYuX76M0NBQ+Pv7V9h/x7hAqM64yCORKfbXqTIRElK+w07KRES3rEph59dff0VgYCAAYNeuXTVaEDkphp2qCw429GsynYSRiIiqrUphp3fv3hVuE1UZw07Vuboa+unk5nKNLCIiG6hWL9Hc3FysWrUKx48fBwC0bt0aY8aMkVt/iCwYw46LC8Cfk5sLCTGEHbUaKCgwLK9BRETVYvWkgnv27EFMTAzee+895ObmIjc3F++99x5iY2OxZ8+emqiRHJ1WC2RnG7aDgwGFAmq1GsXFxRa3kpISCAj71lsXmPbb4aUsIqJbYnXLzqRJkzB8+HAsW7YMSqUSAKDT6TBx4kRMmjQJSUlJNi+SHFxWlmHoOQCEhUGtVuPo4QToNRqLU/VCQKtRQ68X9Xt+7+s7KTdtar9aiIgcnNVhJzk5GRs3bpSDDgAolUrEx8fj008/tWlx5CSu66+j1Wqh12gQU5oND6E3OzVPcsF5ybPetu7ohUBJSQkkHx+4XAuD+osXoSsuhouLC9y4UjwRkdWsDjudOnXC8ePH0aJFC7Pjx48fR4cOHWxWGDmRSjonewg9PGE+VUGJUAD1tC+uBhI0Gg1SThyHQqtFePplAIBap0N2gygoXF3RpkNHBh4iIitVKewkJibK21OmTMHUqVORnJyM7t27AwD++ecfLF26FAsWLKiZKsmxcSRWlWghQRICjUqy4SUJuKgkSFeLIDLT4FOShXMIhlarZdghIrJSlcJOx44dIUkShCi/tPDSSy9ZnDdy5EgMHz7cdtWRczCGHZUK8PUFSkrsW08d5y508JQE4O8NXC0ENGXwKCoCPLieGBFRdVQp7KSkpNR0HeSsysqAvDzDdmgo54uxhp8vcNFwKUvKyweYdYiIqqVKYadRo0Y1XQc5K9Nh07yEZR3/8rl1pPx8OxZCROTYqr309LFjx5Camgq1Wm12/L777rvlosiJsL9O9fmXL6or5d14AV4iIqqc1WHn7NmzuP/++5GUlGTWj8e4OCgXAiUzDDvV51sedpDPsENEVF1WT9s2depUxMbG4sqVK/D09MTRo0exZ88edOnSBbt3766BEsmhMexUn5sr4OUJ4FqfHVE/5x4iIrpVVrfs/P333/j1118RHBwMhUIBhUKB22+/HfPnz8eUKVNw6NChmqiTHJUx7Hh5GW5kHX8/oKgYkloDRXGxvashInJIVrfs6HQ6+Pj4AACCg4Nx6dIlAIZOzCdPnrRtdeTYioqAq1cN22Fh9q3FUZn023HJzbNfHUREDszqlp22bdvi8OHDiI2NRbdu3fDmm2/Czc0NK1asQOPGjWuiRnJUvIR16/zKw45rXq4dCyEiclxWh52ZM2eiqKgIADBv3jz85z//wR133IGgoCCsX7/e5gVS3aZWq6HVaiu8zzUtDa7GHYad6jEZfu5inK+IiIisYnXYGTBggLzdtGlTnDhxAjk5OQgICJBHZFH9cKPVywHAf++/iNHpDIvGMuxUj0nLDsMOEVH1VHueHQC4cOECACA6OtomxZBjudHq5SWSAoWZWdArFIawExJipyodnHFEVlEpXHJzOSKLiKgarO6grNVqMWvWLPj5+SEmJgYxMTHw8/PDzJkzoankN3xybsbVy01vHnodXHOv9THx9zesi0XVE+APAFCo1eVLbxARUZVZ3bLz7LPP4ttvv8Wbb76JHj16ADAMR3/11VeRnZ2NZcuW2bxIckBFxYBaDY1GA72fH3Qmw6ZLSkogwBaKKgsKAC6mAwCk9HQgKsrOBRERORarw866devw1VdfYdCgQfKx9u3bIzo6GiNGjGDYIQCALq8Aer0OeVlZuFqQj8KE8vmX9EJAq1FDrxfVaFushwID5E0pPd2OhRAROSarv2pUKhViYmIsjsfGxsLNza1aRSxduhQxMTFwd3dHt27dsHfv3io97quvvoIkSRg2bFi1Xpdqjj6vABCAn7YY0Z5Aq5JM+RZVlgcIsHWnqgL95U0pI8N+dRAROSirw87kyZPx2muvoaysTD5WVlaG119/HZMnT7a6gPXr1yM+Ph5z5szBwYMH0aFDBwwYMABXTOdoqcC5c+fwwgsv4I477rD6NanmKa71LXERAh7+3mb9eVTXdWamm/DyhHA39HmSLl9mJ2UiIitV6TLWAw88YLa/Y8cONGjQAB06dAAAHD58GGq1Gvfcc4/VBSxatAgTJkzAmDFjAADLly/HTz/9hNWrV2P69OkVPkan0+Gxxx7D3Llz8fvvvyPvBp02y8rKzIJZQQEXVKwNknG2X4VkNnyaqkGSIAIDgMwCSMXFhlmpr81iTkREN1elsOPn52e2/+CDD5rtV3fouVqtxoEDBzBjxgz5mEKhQN++ffH3339X+rh58+YhNDQU48aNw++//37D15g/fz7mzp1brfqomvR6KPILASghfH0ApdLeFTk8EegPZF4L6pcvM+wQEVmhSmFnzZo1NfLiWVlZ0Ol0CLtu3aSwsDCcOHGiwsf88ccfWLVqFRISEqr0GjNmzEB8fLy8X1BQwHmBalrhVUCnAyQlhL/fzc+nmxKBAQBSDTuXLwPNm9u1HiIiR1LtSQUzMzPlhT9btGiBkFqYNK6wsBBPPPEEVq5cieDg4Co9RqVSQcU5XmpXXr68ybBjG8JkRBYuX7ZfIUREDsjqsFNUVIRnn30Wn376KfR6Q0dTpVKJUaNG4f3334enp2eVnys4OBhKpRIZ140wycjIQHh4uMX5Z86cwblz5zBkyBD5mLEGFxcXnDx5Ek2aNLH2LZGt5TLs2JyvD4TrtdGODDtERFaxejRWfHw8fvvtN/zwww/Iy8tDXl4evvvuO/z22294/vnnrXouNzc3dO7cGTt37pSP6fV67Ny5U56w0FTLli2RlJSEhIQE+XbffffhrrvuQkJCAi9P1RV55Z3A9Qw7tiFJ0AQFGrbz8wGTSRqJiOjGrG7Z+eabb7Bx40b06dNHPjZ48GB4eHjgkUcesXpSwfj4eIwePRpdunRB165dsXjxYhQVFcmjs0aNGoWoqCjMnz8f7u7uaNu2rdnj/f39AcDiONnRtZFYQqkEvL3sW4sT0QQFAdk5hp3LlwG2YhIRVYnVYae4uNiiQzEAhIaGorgav20OHz4cmZmZmD17NtLT09GxY0ds3bpVfo3U1FQoFJxm12Go1UCBYSSWJiAAUCgATh5oEww7RETVY3XY6dGjB+bMmYNPP/0U7u7uAAxrHc2dO7fCS09VMXny5EonJNy9e/cNH7t27dpqvSbVkKxceVNdxU7kVDWa4GDg1GnDDvvtEBFVmdVhZ/HixRg4cKDFpILu7u7Ytm2bzQskB2NseQCgDmHYsSWtnx+Ey7V/sgw7RERVZnXYadeuHU6fPo0vvvhCngtnxIgReOyxx+Dh4WHzAsnBZJmEnaAgOxbihBQKiLAw4MoVICcHKCsDOK0CEdFNWRV2NBoNWrZsiR9//BETJkyoqZrIkV1r2RGurtD6+QG6/Js8gKwhwsMNYQcA0tOBRo3sWxARkQOwquevq6srSktLa6oWcnTFJUCRoZO6CA4EJMnOBTkfYTo4gJeyiIiqxOphTpMmTcLChQuh1Wproh5yZCb9dfTGOWHIpoTpZJsMO0REVWJ1n519+/Zh586d+OWXX9CuXTt4eZnPo/Ltt9/arDhyMCb9dfTBDDs1IiTEMJxfr2fYISKqIqvDjr+/v8Wq50QAgOzyYed6dk6uGS4uQGioob9OZiag0QCurvauioioTrM67NTUCujk4IQAsrIN2+7uEF5VXyONrBQRYQg7QgAZGUCDBvauiIioTqtynx29Xo+FCxeiV69euO222zB9+nSUlJTUZG3kSAqvAmVqwzY7J9esiIjybV7KIiK6qSqHnddffx0vv/wyvL29ERUVhSVLlmDSpEk1WRs5EpNLWGB/nZrFsENEZJUqh51PP/0UH374IbZt24bNmzfjhx9+wBdffAG9Xl+T9ZGjMOmcDI7EqllhYeUtZww7REQ3VeWwk5qaisGDB8v7ffv2hSRJuHTpUo0URg7G2F8HYMtOTXNzA4zrjl25Auh09q2HiKiOq3LY0Wq18sKfRq6urtBoNDYvihyMXg/k5Bm2fbwBdy5hUOOMl7J0uvIZlYmIqEJVHo0lhMCTTz4JlclaPKWlpXj66afN5trhPDv1UH4BYJxkMijAvrXUFxERQGKiYTs93bwfDxERmaly2Bk9erTFsccff9ymxZBjkngJq/Zd30k5Ls5+tRAR1XFVDjucX4cqI5ksE4FgTiZYK7hsBBFRlVm9NhbR9RTGkViSBAT627WWesPdHQi81oqWnm7oN0VERBVi2KFbo9UCuXmGbT9fLl1Qm4yXsjQaIDv7xucSEdVjVi8XQWTKNScHkl4Ydthfp0bphTCbtVwREAClcTRkaipcQ0LsVBkRUd3GsEO3xDUzs3yHkwnWGA0kaDQapJw4DunahIKqnGwEphv66xT/+ScatmsHNzc3e5ZJRFQnMezQLXHLzCrfYctOjdFCgiQEGpVkw0u61pLmKeCqKYZGkqDOyIBWq2XYISKqAMMO3RLXrGthR6kAAvzsW0w94C508DSGHQ9XwMsdKCqFa3Y2OykTEVWCHZSp+kpL4ZKfZ9gO8AeUSntWUz+FGIb6Sxo1pIwMOxdDRFQ3MexQtUmm87twfh37CAuVN6Xz5+1YCBFR3cWwQ9VmFna4TIR9hJePwJJSU+1YCBFR3cWwQ9Umma54z87J9uHnC3Ft4VUpNZX9doiIKsCwQ9VmDDvC1dUwoSDVPkmCuHYpSyorA9hvh4jIAsMOVU9hIaTCQgCACAowLBVBdiFMLmXh3Dm71UFEVFcx7FD1mFzCEryEZVd6k07KDDtERJYYdqh60tLkTcGRWPbl7we9u7thOzUVEMK+9RAR1TEMO1Q9pi07XCbCviQJ6rBww3ZJCfvtEBFdh2GHrKfTARcuAAD07h6Al6edC6KyiPDyHV7KIiIyw7BD1ktLA8rKAABlkZHsnFwHqCMiyncYdoiIzDDskPXOnJE3y6Ki7FgIGWkDAiCM/XbOn2e/HSIiEww7ZD2zsBNpx0JIJkkQjRoZttlvh4jIDMMOWaekRB6JJYKDoffysnNBZCQaNizf4TpZREQyhh2yzrlz8iUSfWysfWshM3rTsMN+O0REMoYdso7JJSzRuLEdCyELYWGAh4dhm/12iIhkDDtUdUIAycmGbaXS/LIJ2Z8kAcZ+O8XFwJUr9q2HiKiOYNihqsvNBfLyDNsNGwJubnYthyoQE1O+zUtZREQAGHbIGiaXsNCkif3qoMoZW3YAdlImIrqGYYeqjmGn7gsLA4zz7Zh0Jiciqs8YdqhqdDogJcWw7ekJhIff+HyyD4XCvN9OZqZ96yEiqgMYdqhqTJaIQOPGXCKiLmO/HSIiM3Ui7CxduhQxMTFwd3dHt27dsHfv3krPXblyJe644w4EBAQgICAAffv2veH5ZCNnz5Zv8xJW3Wbab4dhh4jI/mFn/fr1iI+Px5w5c3Dw4EF06NABAwYMwJVKhs3u3r0bI0aMwK5du/D3338jOjoa/fv3R9q1WX2phrC/juMIDwdUKsM259shIrJ/2Fm0aBEmTJiAMWPGoHXr1li+fDk8PT2xevXqCs//4osvMHHiRHTs2BEtW7bExx9/DL1ej507d9Zy5fVIaSlw8aJhOyQE8PW1bz10Y6b9doqKgKws+9ZDRGRndg07arUaBw4cQN++feVjCoUCffv2xd9//12l5yguLoZGo0FgYGCF95eVlaGgoMDsRlZKSSlvHWCrjmNgvx0iIpldw05WVhZ0Oh3CwsLMjoeFhSE9Pb1Kz/F///d/iIyMNAtMpubPnw8/Pz/5Fh0dfct11zvOfAlLoUSPu+6GT78HAIXS3tXYDsMOEZHM7pexbsWCBQvw1VdfYdOmTXA3zi1ynRkzZiA/P1++XbhwoZardALGsKNUmnd+pbrLtN9OSgqg19u3HiIiO3Kx54sHBwdDqVQiIyPD7HhGRgbCbzKPy9tvv40FCxZgx44daN++faXnqVQqqIz/6ZP1cnIMy0QAXCKiDtMLgZKSErNjyqgoKE6ehKKwEMrz5wGuUk9E9ZRdw46bmxs6d+6MnTt3YtiwYQAgdzaePHlypY9788038frrr2Pbtm3o0qVLLVVbT5kOOecq53WSBhI0Gg1SThyHZDL/kbtCQkD6ZUgKJYIPHYIrww4R1VN2DTsAEB8fj9GjR6NLly7o2rUrFi9ejKKiIowZMwYAMGrUKERFRWH+/PkAgIULF2L27NlYt24dYmJi5L493t7e8Pb2ttv7cFrO3F/HSWghQRICjUqy4SWZDDMPdgegRp7eDeLoUWDoUMOlSCKiesbuYWf48OHIzMzE7NmzkZ6ejo4dO2Lr1q1yp+XU1FQoFOVdi5YtWwa1Wo2HHnrI7HnmzJmDV199tTZLd356fXnLjqcnEBFh33rohtyFDp6mYcdVgbIGkcDFLEilpYa/y2bN7FcgEZGd2D3sAMDkyZMrvWy1e/dus/1zHFlSe64tEaHT6aCNioLuuj4hJSUlEOCEdXWZPqYhcPHaPDtHjzLsEFG9VCfCDtVRZ85Ap9PhSno6cps0RknCIbO79UJAq1FDrxcOPq7PeYmoCAjXa53Kjx8H/vMfwIX/7ImofuH/elS55GTo9XoIvQ7hge5wLzFfQTtPcsF5yZOtO3WZUonSRg2Bq0WGhVyTk4GWLe1dFRFRreLv41Sx7Gx5iQhtQADcvTzgCZ3ZTSU4d4sjKDEdRXfkiP0KISKyE4Ydqtih8ktWxezn4dDKIiMhjJNunjwJqNX2LYiIqJYx7JAlvR44fBgAIBQKlDRtaueC6JYolRDGS1caDXD6tH3rISKqZQw7ZCk5GSgsBACIZs2g9/Cwc0F0q/StW5fv8FIWEdUzDDtkyeQSlr5DBzsWQrYiGjUCvLwMO6dPGzorExHVEww7ZK6oyNCvAwC8vSE4a7JzUCiANm0M21pt+d8xEVE9wLBD5hITy1fI7tjR8CVJzsEYdgBeyiKieoXfZFROCODgwfL9jh3tVgrZjnFF9OLgYKjd3aHRaKA+cQLF2dlQc2QWEdUDnFSQyl26BGRemziwYUMgOBgoLrZvTXRLrl8R3VflBq/TlwEAed9/h7K2bdGmQ0e4ubnZuVIioprDlh0qZ9IxGXFx9quDbMZ0RfRWJZloEOmHEE0xQjTFiD6VBL1GA61Wa+8yiYhqFFt2yECjAZKSDNtubub9O8jhySuiB/kBPp5A4VWIS+lQXLe4KxGRM2LLDhkcO1Y+HLlNG0PgIecjSUCjaMOmEHA/d86+9RAR1QKGHTLgJaz6IzZa3vQ4e9aOhRAR1Q6GHQJycqA7c8YwSsfHB8VBQSguLkZxcTFKSkq4qrmzCfAH/HwBAG7p6ZDS0uxbDxFRDWOfHYJm3z5kpadD6HUoaBCFosMJ8n16IaDVqKHXC0ZjZyFJQOvmwN+GaQYUv/0GcLFXInJiDDv1nV4PcegQhF4Hf10p/BsEACWZ8t15kgvOS55s3XE2TWIgkk4CZQKKlBQgNdUw3QARkRPi7+r13dmzkK4t+qmMioCnpxs8oZNvKqG3c4FUI5RK6NqbjLjbvdtupRAR1TSGnfrOZMZkfdNYOxZCtU00bgSdj49h5+xZ4Px5+xZERFRDGHbqs4sXDUPOAejdPSCiIuxcENUqpRKFHU1G3rF1h4icFMNOfSUEsHWrvFvYsQOgVNqxILKHkqZNIAIDDTspKQDn3SEiJ8SwU18lJhpadgCIkBAUt2pl54LILhQK6Hr1Kt/ftcsQhImInAjDTn1UVgbs2CHv6vr1AxT8UaivRNu2hkVfAUO/HbbuEJGT4TdcffTHH8C1EVho2RIilh2T6zWFAujdu3yfrTtE5GQYduqbnBzgr78M20ol0L+/feshu9ILgZKSEhTHxkLt5weNRgPNmTMoOXoUarXa3uUREdkEJxWsb375BdDpDNs9egCBgUBxsX1rIrvQQIJGo0HKieOQJAnuIcEIOJIEAFB//jlyH7gfbTrGwY2LwhKRg2PYqU/OngVOnDBs+/gAd9xh33rIrrSQIAmBRiXZ8JIEEO4NFy9XSHn50Fw+j6vnzkHbth3DDhE5PF7Gqi/0euDnn8v3+/YFVCr71UN1hru4NmO2pIdbx1ZwhR6uQsBn335Aq7V3eUREt4xhp77Yvx/IvLbmVVQU0L69feuhuqlhAyDQHwDgmpMN5c8/s7MyETk8hp36oLjYMMLGaNAgw8rXRNeTJKBXVwgXwwSTisREYN8+OxdFRHRrGHacnRCGTsklJYb9Dh2ABg3sWxPVbYEB0PXsWr6/dSvXzSIih8aw4+x27wYSEgzbbm6GvjpENyFiG+Fq23aGHb0e2LAByM+3b1FERNXEsOPM/voL+O03eVfTvz+KlUoUFxeb3UpKSiDAfhlkrvC2LtDHxBh2iooMgYcdlonIAXHoubM6eNBw+eoaTd++OCL00CccsjhVLwS0GjX0esH4S+UUCujuvx/44gsgNxdISwN+/BEYOpR9vojIofCrzRkdPQr88EP5/l13QdOpE/QaDWJKs9GqJNPsFlWWBwiwdYfM6IVAiSSheMgQqAHD7Mr79qH09985uzIRORS27Dib5GTg22/Lhwv36AHceafcQdlD6OEJndlDSoQC4C/qZMJiduWmTRBgHNH36afIzc1Bs0GDOeEgETkEhh1nkpoKrF9fvhxEXJxh7SteciArWcyuHOEDRYuGUB45AY0kQfz0E3TBIUDPnvz5IqI6j5exnEVamqFvhUZj2G/dGhgyhF9EdEvk2ZWhg3tcG7hGhcFVCEg6HVy2bAG++QYoLbV3mUREN8SWHUenVhuGl//zj2GIMAA0bQo88ACgYJYlG1IogLt6QbcvETh7yXDsyBFD0H7oIcPM3EREdRDDTg1Tq9XQVjJc18XF5db6PJw4AWzZAhQUlB9r2BB45BHAhX+1VAOUSui7d0FuzFUEnzlrCNi5ucDq1YY5nLp3Z2siEdU5/EasQWq1GkcPJ0BvvLR0HYWrK9p06Gh94MnLMyzqefJk+TEXF+DOO6Hu0sUQrq4LWJxLh2ypOCYGV3v0hNfWrVCkpRkun/74I/QnTkAzaBAUvr4VPu6WAz4RUTUw7NhAZa03JSUl0GnUiC3NgYfQm98nKXAOQdBqtVX/z1+rBf7913DZyjRANWkC3Hsv1N7elYYrzqVDtmIcqXX28iVI7drCR62Gd1IiAEBcvgzdH79D3aQJilu2hCYszKylp9oBn4joFjDs3KIbtd4YA4ZKp4OnwjzsVLmRRacDzp41zJ1z4gRQWgqdTge9Xg/h5QVd//4QrVoBkoSSwsJKw1We5ILzkidbd+iWWYzUahcDKVAF5R//oKxMgzy4IuJEEtxOJEEE+EPfvAn0jWNQolJZH/CJiGyAYaeKqtN6U+2AodMBKSmGgHP8uNloF51OhysZGbjavDkKO3eCUJcBhxMA3DhccS4dsjV3oYOndO1nOyoUuK8fSo+egf7MRbiUlMJVEkBuDvBvDnDwEJSxsXBt2hYl0Q0rfD5e4iKimlInws7SpUvx1ltvIT09HR06dMD777+Prl27Vnr+119/jVmzZuHcuXNo1qwZFi5ciMGDB9dYfdVtvalSwFCrDR08r1wpv126BN3Vq9DrzZ9PuLmhrGlTXOnSGQ18FIjSFwAl5fez9YbsysMDmi5xuNS5J4KTk6BKTgauZBnu02ghTp1BQPJFlP2yHcW+PlCHhEITEgx1SAg0QUFQeHjwEhcR1Qi7h53169cjPj4ey5cvR7du3bB48WIMGDAAJ0+eRGhoqMX5f/31F0aMGIH58+fjP//5D9atW4dhw4bh4MGDaNu2bY3UqNVq5aUWbtp6I4RhhIpWC0mjhqumDFJRJqAuAUrVhlaaMjWUxWUILdbB1csbcHU1e06dTocr6ekQeh2EiytKGzZESeNYlEVFQa9UGsKVJp+tN1Q3KZXQN4kBmjUCcnKBU2eAs+eh1whAAH7aYrjlFAM5GcC1PvYahQLZvsEQ+w8AQUGAjw/g62v408cHcHcH3NzKb66unFqBiKrM7mFn0aJFmDBhAsaMGQMAWL58OX766SesXr0a06dPtzh/yZIlGDhwIF588UUAwGuvvYbt27fjgw8+wPLly6v8umXr1qHMw6N8WQUAQq8vzwpCyDe9Wo3ASxfhXVYIlV57LdAIQOgh9ApE6F3gprkK6LSGS1DXntMdSoRJbnDVlwKSeWuLAgooXDyhcVNZ1KZ2cUFRTCMERQVCFRFaPoxck4s8LVtvyIEEBgDduwCdOkCdlonCnGIEZ6TCNSenfKZvANADiqtXoUtJgebiRYunEUJAum5Iu3B1BVxdoXB1hYtKZQg/SqXhZtyWJMO2JFneAPNh8pVtX68mhtYrlcCDD9r+eYkIgJ3DjlqtxoEDBzBjxgz5mEKhQN++ffH3339X+Ji///4b8fHxZscGDBiAzZs3V3h+WVkZysrK5P38/HwAQOquXfB2K29RMTTI6KFUWv62aLhPh1y9Bq7XhYxSSYFiuCBHqHF943upJFAIwEVoLe4rk/TIhRY5GjW0Xl7Q+ftB4+8Prb8/tO7u0Gk1UGqL4FGmBsrKF10slFxQJAlk6spQdF2Aypd0KJIk3mfFfQVKwPXaPEXFRWWAXnfLz8n7KrkvLAyXwj3g3qIhPPVaIL8AiuxcSNk5KM0rQF6JDlcvXbJonbzRv00AgEKBgMAgKJXKiu93AApXVyj79bN3GUR1WsG1/6uFsP6XfbuGnaysLOh0OoSFhZkdDwsLw4kTJyp8THp6eoXnp6enV3j+/PnzMXfuXIvjXTZsqGbVREQ1YMECe1dA5BCys7Ph5+dn1WPsfhmrps2YMcOsJSgvLw+NGjVCamqq1R8WmSsoKEB0dDQuXLgA30omkaOq4WdpG/wcbYefpe3ws7SN/Px8NGzYEIGBgVY/1q5hJzg4GEqlEhkZGWbHMzIyEB4eXuFjwsPDrTpfpVJBpbLsF+Pn58cfOhvx9fXlZ2kj/Cxtg5+j7fCztB1+lrahqMbgBLsOZ3Bzc0Pnzp2xc+dO+Zher8fOnTvRo0ePCh/To0cPs/MBYPv27ZWeT0RERPWb3S9jxcfHY/To0ejSpQu6du2KxYsXo6ioSB6dNWrUKERFRWH+/PkAgKlTp6J379545513cO+99+Krr77C/v37sWLFCnu+DSIiIqqj7B52hg8fjszMTMyePRvp6eno2LEjtm7dKndCTk1NNWuy6tmzJ9atW4eZM2fi5ZdfRrNmzbB58+Yqz7GjUqkwZ86cCi9tkXX4WdoOP0vb4OdoO/wsbYefpW3cyucoieqM4SIiIiJyEJyClIiIiJwaww4RERE5NYYdIiIicmoMO0REROTU6k3Y2bNnD4YMGYLIyEhIklTpWlp0c/Pnz8dtt90GHx8fhIaGYtiwYTh58qS9y3I4y5YtQ/v27eWJxnr06IGff/7Z3mU5hQULFkCSJDz33HP2LsXhvPrqq5AkyezWsmVLe5flkNLS0vD4448jKCgIHh4eaNeuHfbv32/vshxOTEyMxc+kJEmYNGlSlZ+j3oSdoqIidOjQAUuXLrV3KQ7vt99+w6RJk/DPP/9g+/bt0Gg06N+/P4qKiuxdmkNp0KABFixYgAMHDmD//v24++67MXToUBw9etTepTm0ffv24aOPPkL79u3tXYrDatOmDS5fvizf/vjjD3uX5HByc3PRq1cvuLq64ueff8axY8fwzjvvICAgwN6lOZx9+/aZ/Txu374dAPDwww9X+TnsPs9ObRk0aBAGDRpk7zKcwtatW832165di9DQUBw4cAB33nmnnapyPEOGDDHbf/3117Fs2TL8888/aNOmjZ2qcmxXr17FY489hpUrV+J///ufvctxWC4uLpUuwUNVs3DhQkRHR2PNmjXysdjYWDtW5LhCQkLM9hcsWIAmTZqgd+/eVX6OetOyQzUnPz8fAKq1OBsZ6HQ6fPXVVygqKuLSJ7dg0qRJuPfee9G3b197l+LQTp8+jcjISDRu3BiPPfYYUlNT7V2Sw/n+++/RpUsXPPzwwwgNDUVcXBxWrlxp77Icnlqtxueff46xY8dCkqQqP67etOxQzdDr9XjuuefQq1evKs9iTeWSkpLQo0cPlJaWwtvbG5s2bULr1q3tXZZD+uqrr3Dw4EHs27fP3qU4tG7dumHt2rVo0aIFLl++jLlz5+KOO+7AkSNH4OPjY+/yHMbZs2exbNkyxMfH4+WXX8a+ffswZcoUuLm5YfTo0fYuz2Ft3rwZeXl5ePLJJ616HMMO3ZJJkybhyJEjvKZfTS1atEBCQgLy8/OxceNGjB49Gr/99hsDj5UuXLiAqVOnYvv27XB3d7d3OQ7N9HJ/+/bt0a1bNzRq1AgbNmzAuHHj7FiZY9Hr9ejSpQveeOMNAEBcXByOHDmC5cuXM+zcglWrVmHQoEGIjIy06nG8jEXVNnnyZPz444/YtWsXGjRoYO9yHJKbmxuaNm2Kzp07Y/78+ejQoQOWLFli77IczoEDB3DlyhV06tQJLi4ucHFxwW+//Yb33nsPLi4u0Ol09i7RYfn7+6N58+ZITk62dykOJSIiwuKXllatWvGS4C04f/48duzYgfHjx1v9WLbskNWEEHj22WexadMm7N69m53ubEiv16OsrMzeZTice+65B0lJSWbHxowZg5YtW+L//u//oFQq7VSZ47t69SrOnDmDJ554wt6lOJRevXpZTMlx6tQpNGrUyE4VOb41a9YgNDQU9957r9WPrTdh5+rVq2a/maSkpCAhIQGBgYFo2LChHStzPJMmTcK6devw3XffwcfHB+np6QAAPz8/eHh42Lk6xzFjxgwMGjQIDRs2RGFhIdatW4fdu3dj27Zt9i7N4fj4+Fj0GfPy8kJQUBD7klnphRdewJAhQ9CoUSNcunQJc+bMgVKpxIgRI+xdmkOZNm0aevbsiTfeeAOPPPII9u7dixUrVmDFihX2Ls0h6fV6rFmzBqNHj4aLSzWii6gndu3aJQBY3EaPHm3v0hxORZ8jALFmzRp7l+ZQxo4dKxo1aiTc3NxESEiIuOeee8Qvv/xi77KcRu/evcXUqVPtXYbDGT58uIiIiBBubm4iKipKDB8+XCQnJ9u7LIf0ww8/iLZt2wqVSiVatmwpVqxYYe+SHNa2bdsEAHHy5MlqPV4SQgjb5C4iIiKiuocdlImIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYIaIqeeGFFzBs2DC71pCdnY3Q0FCcO3fO7HhdqM3oZrU8+uijeOedd2qvICJi2CGiqklISEDHjh3tWsPrr7+OoUOHIiYmxuy4vWqbNm0aHnjgAYta2rdvX+ljZs6ciddffx35+fk1XR4RXcOwQ0RVcvjwYbuGneLiYqxatQrjxo2zuM9ete3duxddunSxqKVDhw6VPqZt27Zo0qQJPv/885ouj4iuYdghopu6ePEisrKy5C/xI0eOYPDgwfD19UV4eDief/55qNVqs8f8+++/uP322+Hh4YGOHTtiz549kCQJR44cqVYNW7ZsgUqlQvfu3W9YGwCkpqZi5MiRCAgIQGBgIB577DHk5uYCAM6cOQNJkvDjjz/innvugaenJ1q0aIF///23yvWr1Wq4urrir7/+wiuvvAJJktC9e3e5FgDo169fpc89ZMgQfPXVV9X6HIjIegw7RHRTCQkJ8PPzQ2xsLA4dOoSePXuiU6dOOHjwIL766it8+eWXWLhwoXz+kSNHcM8996BPnz44dOgQZs2ahYcffhgqlQotW7asVg2///47OnfufMPaACA5ORmdO3dG06ZN8c8//2D79u1ITk7Giy++CMDQ8iJJEhYtWoRZs2bh8OHDaNiwIaZPn17l+l1cXPDnn3/Kr3/58mVs3boVCQkJAIClS5fi5ZdfrvC5AaBr167Yu3cvysrKqvVZEJGVBBHRTbz22mvizjvvFEII0blzZzFx4kSz+19++WXRtWtXef+uu+4SI0aMMDtnyJAhIi4uTt4fNmyY8Pf3Fw8++KDZeT/88INo3ry5aNq0qVi5cqV8fOjQoWLs2LE3rE0IIfr16ydmz55tds7GjRtFbGysEEKI2bNni4CAAHHlyhX5/vfee0+0adPGqvo3bdokgoKCLGoJDAwUmZmZlT63EEIcPnxYABDnzp2zeD9EZHsu9g5bRFT3JSQkoEOHDjhx4gQOHDhg0d/Ezc1NbqU4f/48du3aZXG5SqVSmV1qmjp1KsaOHYtPPvlEPqbVahEfH49du3bBz88PnTt3xv3334+goCCUlJTA3d290tqMr719+3b88ccfZiOedDodoqOjARhadoYOHYqQkBD5/pSUFDRt2tSq+g8dOmTRNychIQFDhw5FcHBwhc9t5OHhAcDQD4mIah4vYxHRTRlHOx09ehSurq5o3ry52f3Hjh1Du3bt5HPd3NzQpk0bs3OOHz9uFg769OkDHx8fs3P27t2LNm3aICoqCt7e3hg0aBB++eUXAEBwcLDc76ai2gBDkAkMDERiYiISEhLkW1JSEnbt2iWf06NHj0qfo6r1m4Ys02PX9ymqaKRYTk4OAJgFLiKqOQw7RHRDhYWFOHv2LDp27AgfHx/odDpoNBr5/pSUFGzatAmPPfYYAECpVEKr1aK0tFQ+Z+fOnTh69OgNRykBwKVLlxAVFSXvR0VFIS0tDQAQFxeHY8eOVVobALi6uqKwsBCRkZFo2rSp2S0qKgr5+fk4d+4c4uLizJ7HNJBUtf6kpCSzEGOs5UbPbXTkyBE0aNDArAWIiGoOww4R3dDhw4ehVCrRpk0bdOvWDf7+/pg+fTrOnj2LX3/9Fffeey8effRRDBw4EADQuXNnuLq64sUXX8TZs2fxww8/4KmnngKAm4adGxkwYACOHj1q1rpjWhsAdOvWDb6+vhg1ahQOHz6M5ORkbN26Fc899xwAIDExES4uLnIrFGC4bJWbmysHkqrWr9frcfLkSVy6dAn5+flyLTd6bqPff/8d/fv3r/ZnQUTWYdghohtKSEhAy5YtoVKp4Ofnh82bN2PPnj1o06YNJkyYgFGjRmHNmjXy+REREVi9ejW+++47tG/fHmvWrMHo0aPRtGlTBAYG3vC1IiMj5ZYcAEhLS0NkZCQAoF27dujUqRM2bNhQYW0AEBgYiC1btiA7Oxt33nknOnXqhFdeeQWNGzcGYAhHLVq0MOv7c+jQIfj7+8sTFVa1/v/9739Yu3YtoqKi8L///Q8JCQk3fW4AKC0txebNmzFhwoSq/hUQ0S2ShBDC3kUQkfPS6/Xo06cPbr/9drzxxhtm9+3evRsffPABNm7cCMDQQblVq1bYvXu33EH5r7/+QlBQEADgp59+wosvvogjR45Aoaid39VuVH91LFu2DJs2bZL7IhFRzeNoLCKyqT179iAzMxNxcXHIysrCW2+9hfPnz2Pz5s1m5/Xt2xeHDx9GUVERGjRogK+//ho9evTAO++8g7vuugt6vR4vvfSSHHQA4N5778Xp06eRlpYmj66yV/3V5erqivfff98mz0VEVcOWHSKyqa+//hrTp09HWloawsLC0LdvX7zxxhsICwuzd2lV4uj1E5Elhh0iIiJyauygTERERE6NYYeIiIicGsMOEREROTWGHSIiInJqDDtERETk1Bh2iIiIyKkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIic2v8DE+xadrdMG78AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis: 1.163832716732534\n",
      "Skewness: -0.26039124808735764\n",
      "D'Agostino's K-squared test(statistics=717.1684257602484, p-value=1.8571844920422435e-156)\n"
     ]
    }
   ],
   "source": [
    "tax_id = 9606\n",
    "species_distribution__retrieve_plot_and_fit(stat_df, tax_id, \"proteins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cc3497-9902-4714-a7c1-76bc14dc0a95",
   "metadata": {},
   "source": [
    "## Fig. 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cea303e-22c0-4353-b1d3-8609aacffb0c",
   "metadata": {},
   "source": [
    "### _Danio rerio_ (tax_id = 7955)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00572d37-eca3-4dd6-b056-6b109b2054f0",
   "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>superregnum</th>\n",
       "      <th>species</th>\n",
       "      <th>proteome_id</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>uniprot_fasta_file</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>division_both_dbs</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8528</th>\n",
       "      <td>eukaryota</td>\n",
       "      <td>Danio rerio (Zebrafish) (Brachydanio rerio)</td>\n",
       "      <td>UP000000437</td>\n",
       "      <td>7955</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>25706.0</td>\n",
       "      <td>538.158329</td>\n",
       "      <td>315453.463435</td>\n",
       "      <td>vertebrates</td>\n",
       "      <td>2.599115</td>\n",
       "      <td>0.114029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     superregnum                                      species  proteome_id  \\\n",
       "8528   eukaryota  Danio rerio (Zebrafish) (Brachydanio rerio)  UP000000437   \n",
       "\n",
       "      tax_id                                 uniprot_fasta_file    count  \\\n",
       "8528    7955  /ftp.uniprot.org/pub/databases/uniprot/current...  25706.0   \n",
       "\n",
       "            mean            var division_both_dbs  log10_mean  log10_var  \n",
       "8528  538.158329  315453.463435       vertebrates    2.599115   0.114029  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "µ: 2.599115440779346 \tσ: 0.3376817874330292\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis: 0.8752018347236281\n",
      "Skewness: -0.138739152539118\n",
      "D'Agostino's K-squared test(statistics=494.0354527107476, p-value=5.267014471006957e-108)\n"
     ]
    }
   ],
   "source": [
    "tax_id = 7955\n",
    "species_distribution__retrieve_plot_and_fit(stat_df, tax_id, \"proteins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c988c165-1b89-46ca-86e6-772ad1391fc1",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30adfb77-8a42-4281-9777-e94085d70820",
   "metadata": {},
   "source": [
    "### _Shewanella loihica_ (tax\\_id = 323850)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68cb9451-b048-49eb-929b-bb239739f7bd",
   "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>superregnum</th>\n",
       "      <th>species</th>\n",
       "      <th>proteome_id</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>uniprot_fasta_file</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>division_both_dbs</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>bacteria</td>\n",
       "      <td>Shewanella loihica (strain ATCC BAA-1088 / PV-4)</td>\n",
       "      <td>UP000001558</td>\n",
       "      <td>323850</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>3855.0</td>\n",
       "      <td>339.778729</td>\n",
       "      <td>60661.644073</td>\n",
       "      <td>bacteria</td>\n",
       "      <td>2.440595</td>\n",
       "      <td>0.079555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     superregnum                                           species  \\\n",
       "1734    bacteria  Shewanella loihica (strain ATCC BAA-1088 / PV-4)   \n",
       "\n",
       "      proteome_id  tax_id                                 uniprot_fasta_file  \\\n",
       "1734  UP000001558  323850  /ftp.uniprot.org/pub/databases/uniprot/current...   \n",
       "\n",
       "       count        mean           var division_both_dbs  log10_mean  \\\n",
       "1734  3855.0  339.778729  60661.644073          bacteria    2.440595   \n",
       "\n",
       "      log10_var  \n",
       "1734   0.079555  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "µ: 2.4405946049463645 \tσ: 0.28205457432966796\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis: -0.09329483526783733\n",
      "Skewness: -0.04857619394836839\n",
      "D'Agostino's K-squared test(statistics=2.9239278013247283, p-value=0.23178063331967275)\n"
     ]
    }
   ],
   "source": [
    "tax_id = 323850\n",
    "species_distribution__retrieve_plot_and_fit(stat_df, tax_id, \"proteins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba26e0e-2bf0-4c37-8028-591e62c69dd8",
   "metadata": {},
   "source": [
    "---\n",
    "### _Orchesella cincta_ (tax\\_id = 48709)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55d37c9c-fea1-4037-83a8-3249ed29916a",
   "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>superregnum</th>\n",
       "      <th>species</th>\n",
       "      <th>proteome_id</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>uniprot_fasta_file</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>division_both_dbs</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8785</th>\n",
       "      <td>eukaryota</td>\n",
       "      <td>Orchesella cincta (Springtail)</td>\n",
       "      <td>UP000094527</td>\n",
       "      <td>48709</td>\n",
       "      <td>/ftp.uniprot.org/pub/databases/uniprot/current...</td>\n",
       "      <td>20218.0</td>\n",
       "      <td>424.12019</td>\n",
       "      <td>156494.930996</td>\n",
       "      <td>invertebrates</td>\n",
       "      <td>2.495977</td>\n",
       "      <td>0.115681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     superregnum                         species  proteome_id  tax_id  \\\n",
       "8785   eukaryota  Orchesella cincta (Springtail)  UP000094527   48709   \n",
       "\n",
       "                                     uniprot_fasta_file    count       mean  \\\n",
       "8785  /ftp.uniprot.org/pub/databases/uniprot/current...  20218.0  424.12019   \n",
       "\n",
       "                var division_both_dbs  log10_mean  log10_var  \n",
       "8785  156494.930996     invertebrates    2.495977   0.115681  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "µ: 2.4959771728410467 \tσ: 0.3401194732115712\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis: 0.07804696175054904\n",
      "Skewness: -0.07578260990627901\n",
      "D'Agostino's K-squared test(statistics=24.185233843939038, p-value=5.600711459410769e-06)\n"
     ]
    }
   ],
   "source": [
    "tax_id = 48709\n",
    "species_distribution__retrieve_plot_and_fit(stat_df, tax_id, \"proteins\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}