multi-omics-state-field / Git / [1bd6b5] /notebooks/Overview.ipynb

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multi-omics-state-field
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aims**:\n",
    " - Explore best ways to present the data\n",
    " - Prepare the publication-quality figure for the manuscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run notebook_setup.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported `literature` (904B0F94) at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import literature",
       "finished": "2020-09-25T18:08:08.680033",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "904B0F94",
          "sha256": "A2EFC068A287A3B724AE4B320EE5356E1E99474BD08A2E2A3EBA34CD0194F23B"
         },
         "subject": "literature"
        }
       ],
       "started": "2020-09-25T18:08:06.456660"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import literature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>multi-omics</th>\n",
       "      <th>pan-omics</th>\n",
       "      <th>trans-omics</th>\n",
       "      <th>poly-omics</th>\n",
       "      <th>cross-omics</th>\n",
       "      <th>multi-table omics</th>\n",
       "      <th>multi-source omics</th>\n",
       "      <th>multi-view omics</th>\n",
       "      <th>multi-modal omics</th>\n",
       "      <th>multi-block omics</th>\n",
       "      <th>...</th>\n",
       "      <th>has_doi</th>\n",
       "      <th>year</th>\n",
       "      <th>term</th>\n",
       "      <th>has_url_in_abstract</th>\n",
       "      <th>PMC</th>\n",
       "      <th>has_pmc</th>\n",
       "      <th>has_full_text</th>\n",
       "      <th>full_text</th>\n",
       "      <th>article_type</th>\n",
       "      <th>abstract_clean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31312515</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>multi-omics</td>\n",
       "      <td>False</td>\n",
       "      <td>PMC6616462</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>\\n    \\n\\n      \\nIntroduction\\nToday’s system...</td>\n",
       "      <td>brief-report</td>\n",
       "      <td>Computational tools for multiomics data integr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32636387</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>multi-omics</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Computational tools for multiomics data integr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          multi-omics  pan-omics  trans-omics  poly-omics  cross-omics  \\\n",
       "uid                                                                      \n",
       "31312515         True      False        False       False        False   \n",
       "32636387         True      False        False       False        False   \n",
       "\n",
       "          multi-table omics  multi-source omics  multi-view omics  \\\n",
       "uid                                                                 \n",
       "31312515              False               False             False   \n",
       "32636387              False               False             False   \n",
       "\n",
       "          multi-modal omics  multi-block omics  ...  has_doi    year  \\\n",
       "uid                                             ...                    \n",
       "31312515              False              False  ...     True  2019.0   \n",
       "32636387              False              False  ...     True  2019.0   \n",
       "\n",
       "                 term has_url_in_abstract         PMC has_pmc has_full_text  \\\n",
       "uid                                                                           \n",
       "31312515  multi-omics               False  PMC6616462    True          True   \n",
       "32636387  multi-omics               False         NaN   False           NaN   \n",
       "\n",
       "                                                  full_text  article_type  \\\n",
       "uid                                                                         \n",
       "31312515  \\n    \\n\\n      \\nIntroduction\\nToday’s system...  brief-report   \n",
       "32636387                                                NaN           NaN   \n",
       "\n",
       "                                             abstract_clean  \n",
       "uid                                                          \n",
       "31312515  Computational tools for multiomics data integr...  \n",
       "32636387  Computational tools for multiomics data integr...  \n",
       "\n",
       "[2 rows x 65 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_doi = literature.doi.dropna()[literature.doi.dropna().duplicated()]\n",
    "with_duplicated_doi = literature[literature.doi.isin(duplicated_doi)]\n",
    "with_duplicated_doi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all duplicates are identical:\n",
    "assert all(with_duplicated_doi.groupby('doi').agg(set).apply(len) == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove all but the first occurrence of duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[32636387]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uids_of_all_but_first = with_duplicated_doi.reset_index().groupby('doi').agg(list).uid.apply(lambda x: x[1:]).sum()\n",
    "uids_of_all_but_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "literature = literature[~literature.index.isin(uids_of_all_but_first)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series, to_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from search_terms import primary_terms, secondary_terms, descriptive_terms\n",
    "terms = [*primary_terms, *secondary_terms, *descriptive_terms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "literature = literature.replace({float('nan'): None}).infer_objects()\n",
    "literature.date = to_datetime(literature.date, utc=True)\n",
    "%R -i literature -i terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "library(ComplexUpset)\n",
    "source('helpers/plots.R')\n",
    "source('helpers/colors.R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Publication figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported:\n",
       "\n",
       " - `predicted_article_types` (3D39430E)\n",
       " - `reliable_article_types` (5D584CB5)\n",
       "\n",
       "at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import predicted_article_types, reliable_article_types",
       "finished": "2020-09-25T18:08:13.452197",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "3D39430E",
          "sha256": "C434CF669D09A80085574C5EAF7D4B6154FF04EC1A2143DA15E42E464E3314E9"
         },
         "subject": "predicted_article_types"
        },
        {
         "new_file": {
          "crc32": "5D584CB5",
          "sha256": "585366F3E5A11FC007CC4DFF5AF9C7AFBCBEBA3A15B65333657C632F2218A1AC"
         },
         "subject": "reliable_article_types"
        }
       ],
       "started": "2020-09-25T18:08:12.064800"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import predicted_article_types, reliable_article_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported `code_repositories` (5FF4AA2D) at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import code_repositories",
       "finished": "2020-09-25T18:08:14.316622",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "5FF4AA2D",
          "sha256": "92B28FE95EA205C4311BD4E9D6360D87087D0C5D452CCF9567829CFFD27EE1E5"
         },
         "subject": "code_repositories"
        }
       ],
       "started": "2020-09-25T18:08:13.482162"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import code_repositories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_article_types = concat([\n",
    "    predicted_article_types,\n",
    "    reliable_article_types\n",
    "]).loc[literature.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported `omics_features` (32CBB0C4) at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import omics_features",
       "finished": "2020-09-25T18:08:15.090675",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "32CBB0C4",
          "sha256": "5341315C160BE59D98DC80F7B2F5F2FB982F24900AC029B6301F92EE639DECB8"
         },
         "subject": "omics_features"
        }
       ],
       "started": "2020-09-25T18:08:14.377725"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import omics_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.text_processing import prefix_remover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "omics_columns = omics_features.columns\n",
    "\n",
    "omes_or_omics = (\n",
    "    omics_features[omics_columns[omics_columns.str.startswith('ome_or_omic_')]]\n",
    "    .rename(columns=prefix_remover('ome_or_omic_'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "literature['omic_terms_detected'] = omes_or_omics.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported `domain_features` (9CBD2CED) at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import domain_features",
       "finished": "2020-09-25T18:08:15.858447",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "9CBD2CED",
          "sha256": "69E41B5E85F3320A8BED275B947ECA40F456F11EC6734F3E3BCDE4BD64EA9255"
         },
         "subject": "domain_features"
        }
       ],
       "started": "2020-09-25T18:08:15.181707"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import domain_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (\n",
    "    literature\n",
    "    .drop(columns=['full_text', 'abstract'])\n",
    "    .join(combined_article_types)\n",
    "    .join(code_repositories)\n",
    "    .join(omics_features)\n",
    "    .join(\n",
    "        domain_features\n",
    "        [domain_features.columns[domain_features.columns.str.startswith('mentiones_')]]\n",
    "    )\n",
    ")\n",
    "data['is_type_predicted'] = data.index.isin(predicted_article_types.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_names = {\n",
    "    'is_review': 'Review',\n",
    "    'is_method': 'Computational method',\n",
    "    'is_other_research_article': 'Other research article',\n",
    "    'is_other': 'Other'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_type(t):\n",
    "    if len(t) > 1:\n",
    "        return 'multiple'\n",
    "    if len(t):\n",
    "        return type_names[list(t)[0]]\n",
    "    return 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other research article    2335\n",
       "Review                     505\n",
       "multiple                   205\n",
       "Other                      145\n",
       "unknown                    139\n",
       "Computational method       126\n",
       "Name: chosen_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['chosen_type'] = combined_article_types.apply(lambda x: get_type(set(x[x != False].index)), axis=1)\n",
    "data['chosen_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "from helpers.stats import bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "oo = []\n",
    "\n",
    "for predicted in [True, False]:\n",
    "    for type in combined_article_types.columns:\n",
    "        d = data[(data[type] != False) & (data['is_type_predicted'] == predicted)].dropna(subset=['omic_terms_detected'])\n",
    "        boot = bootstrap(list(d['omic_terms_detected']), stat=mean)\n",
    "        oo.append({\n",
    "            'is_type_predicted': predicted,\n",
    "            'type': type_names[type],\n",
    "            'mean_omics_detected': d['omic_terms_detected'].mean(),\n",
    "            'mean_omics_detected_ci_lower': boot['lower'],\n",
    "            'mean_omics_detected_ci_upper': boot['upper']\n",
    "        })\n",
    "\n",
    "oo = DataFrame(oo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i oo -r 110\n",
    "(\n",
    "    ggplot(oo, aes(x=type, y=mean_omics_detected, fill=is_type_predicted))\n",
    "    + geom_col(position='dodge')\n",
    "    + geom_errorbar(\n",
    "        aes(ymin=mean_omics_detected_ci_lower, ymax=mean_omics_detected_ci_upper),\n",
    "        width=.2,\n",
    "        position=position_dodge(width = 0.9)\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + scale_fill_manual(values=c('FALSE'='darkgreen', 'TRUE'='green'), name='Article type predicted?')\n",
    "    + xlab('Article type')\n",
    "    + ylab('Mean number of omics detected')\n",
    "    + theme(legend.position = 'bottom')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i oo -r 110 -h 160\n",
    "\n",
    "plot_counts_summary =function(...) {(\n",
    "    ggplot(oo[oo$is_type_predicted == FALSE, ], aes(x=type, y=mean_omics_detected))\n",
    "    + geom_col(\n",
    "        position='dodge', fill='darkorange',\n",
    "        ...\n",
    "    )\n",
    "    + geom_errorbar(\n",
    "        aes(ymin=mean_omics_detected_ci_lower, ymax=mean_omics_detected_ci_upper),\n",
    "        width=.3,\n",
    "        position=position_dodge(width=0.9),\n",
    "        size=0.3\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + ylab('Mean number of omics detected (95% CI)')\n",
    "    + theme(legend.position='bottom', axis.title.y=element_blank())\n",
    "    + coord_flip()\n",
    ")}\n",
    "plot_counts_summary(width=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "integ = 'integrated omics*'\n",
    "multix = 'multi-view omics**'\n",
    "other = 'other terms***'\n",
    "data['term_group'] = data.term.replace({    \n",
    "    'integrated omics': integ,\n",
    "    'integrative omics': integ,\n",
    "    'integromics': integ,\n",
    "    'multi-block omics': multix,\n",
    "    'multi-modal omics': multix,\n",
    "    'multi-source omics': multix,\n",
    "    'multi-view omics': multix,\n",
    "    'cross-omics': other,\n",
    "    'pan-omics': other,\n",
    "    'poly-omics': other,\n",
    "    'trans-omics': other,\n",
    "    'multiple': 'multiple terms'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R -i data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 700 -h 400 -r 100\n",
    "(\n",
    "    ggplot(data, aes(x=year, fill=term))\n",
    "    + geom_bar()\n",
    "    + scale_fill_manual(values=terms_colors)\n",
    "    + theme_bw()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>abstract_clean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28624903</th>\n",
       "      <td>Early Diagnosis of Sepsis: Is an Integrated Om...</td>\n",
       "      <td>10.1007/s40291-017-0282-z</td>\n",
       "      <td>Sepsis remains one of the leading causes of de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30006342</th>\n",
       "      <td>Integrated Omics: Tools, Advances, and Future ...</td>\n",
       "      <td>10.1530/JME-18-0055</td>\n",
       "      <td>With the rapid adoption of high-throughput omi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32186712</th>\n",
       "      <td>Adaptive multi-source multi-view latent featur...</td>\n",
       "      <td>10.1093/bib/bbaa028</td>\n",
       "      <td>Accumulating evidence has shown that microRNAs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32675233</th>\n",
       "      <td>A mechanism-aware and multiomic machine-learni...</td>\n",
       "      <td>10.1073/pnas.2002959117</td>\n",
       "      <td>Metabolic modeling and machine learning are ke...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      title  \\\n",
       "uid                                                           \n",
       "28624903  Early Diagnosis of Sepsis: Is an Integrated Om...   \n",
       "30006342  Integrated Omics: Tools, Advances, and Future ...   \n",
       "32186712  Adaptive multi-source multi-view latent featur...   \n",
       "32675233  A mechanism-aware and multiomic machine-learni...   \n",
       "\n",
       "                                doi  \\\n",
       "uid                                   \n",
       "28624903  10.1007/s40291-017-0282-z   \n",
       "30006342        10.1530/JME-18-0055   \n",
       "32186712        10.1093/bib/bbaa028   \n",
       "32675233    10.1073/pnas.2002959117   \n",
       "\n",
       "                                             abstract_clean  \n",
       "uid                                                          \n",
       "28624903  Sepsis remains one of the leading causes of de...  \n",
       "30006342  With the rapid adoption of high-throughput omi...  \n",
       "32186712  Accumulating evidence has shown that microRNAs...  \n",
       "32675233  Metabolic modeling and machine learning are ke...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "literature[literature[list(terms)].sum(axis=1) > 2][['title', 'doi', 'abstract_clean']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 700 -h 400 -r 100 -i integ -i multix\n",
    "terms_group_colors = c(\n",
    "    terms_colors,\n",
    "    c(\n",
    "        'multi-omics'='#59A14F',\n",
    "        'multi-view omics**'='#EDC948',\n",
    "        'integrated omics*'='#4D79D7',\n",
    "        'multiple terms'='#b3b7b8',\n",
    "        'other terms***'='#c85200'\n",
    "    )\n",
    ")\n",
    "\n",
    "term_counts = (\n",
    "    ggplot(data, aes(x=year, fill=term_group))\n",
    "    + geom_bar()\n",
    "    + scale_fill_manual(values=terms_group_colors, name='')\n",
    "    + theme(legend.position = 'bottom')\n",
    "    + theme_bw()\n",
    ")\n",
    "term_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 400 -r 110\n",
    "\n",
    "term_counts = (\n",
    "    ggplot(data, aes(x=year, fill=term_group))\n",
    "    + geom_area(stat='count')\n",
    "    + scale_fill_manual(values=terms_group_colors, name='')\n",
    "    + theme(legend.position = 'bottom')\n",
    "    + theme_bw()\n",
    ")\n",
    "term_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Imported `all_articles_by_journal_and_year` (AB6E261E) at Friday, 25. Sep 2020 18:08"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {
      "text/markdown": {
       "action": "import",
       "command": "from pubmed_derived_data import all_articles_by_journal_and_year",
       "finished": "2020-09-25T18:08:28.543971",
       "finished_human_readable": "Friday, 25. Sep 2020 18:08",
       "result": [
        {
         "new_file": {
          "crc32": "AB6E261E",
          "sha256": "343D4005442B93F41397AF04892D839174F38A2128ED5A08201A581D7FAF0201"
         },
         "subject": "all_articles_by_journal_and_year"
        }
       ],
       "started": "2020-09-25T18:08:27.812039"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%vault from pubmed_derived_data import all_articles_by_journal_and_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2002    60303\n",
       "2004    69921\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_articles_in_popular_multi_omics_journals = all_articles_by_journal_and_year.groupby('year')['count'].sum()\n",
    "total_articles_in_popular_multi_omics_journals.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2002.0     1\n",
       "2004.0     2\n",
       "2005.0    10\n",
       "2006.0    24\n",
       "2007.0    18\n",
       "2008.0     7\n",
       "2009.0    10\n",
       "2010.0    21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_groups_summary = data.assign(one=1).groupby(['year', 'term_group']).one.sum().rename('count').reset_index()\n",
    "total_by_year = term_groups_summary.groupby('year')['count'].sum()\n",
    "\n",
    "present = list(term_groups_summary[['term_group', 'year']].apply(tuple, axis=1))\n",
    "for term in set(term_groups_summary.term_group):\n",
    "    for year in set(term_groups_summary.year):\n",
    "        if (term, year) not in present:\n",
    "            term_groups_summary = term_groups_summary.append({\n",
    "                'term_group': term,\n",
    "                'year': year,\n",
    "                'count': 0\n",
    "            }, ignore_index=True)\n",
    "term_groups_summary['count_adjusted'] = term_groups_summary['count'] / term_groups_summary.year.map(total_articles_in_popular_multi_omics_journals)\n",
    "total_by_year.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_with_year = total_by_year[total_by_year <= 3].idxmax() + 1\n",
    "term_groups_summary = term_groups_summary[term_groups_summary.year >= start_with_year].copy()\n",
    "start_with_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_groups_summary['yearly_fraction'] = term_groups_summary['count'] / term_groups_summary.year.map(term_groups_summary.groupby('year')['count'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "SEARCH_DATE = date(year=2020, month=7, day=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_groups_summary['complete_year'] = (term_groups_summary.year + 1).replace({2021: SEARCH_DATE.year + SEARCH_DATE.timetuple().tm_yday / 365})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 400 -r 110 -i term_groups_summary\n",
    "\n",
    "(\n",
    "    ggplot(term_groups_summary, aes(x=complete_year, fill=term_group, y=count_adjusted))\n",
    "    + geom_area(stat='identity')\n",
    "    + scale_fill_manual(values=terms_group_colors, name='')\n",
    "    + theme(legend.position = 'bottom')\n",
    "    + theme_bw()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 400 -r 110 -i term_groups_summary\n",
    "\n",
    "term_popularity = (\n",
    "    ggplot(term_groups_summary, aes(x=complete_year, fill=term_group, y=yearly_fraction))\n",
    "    + geom_area(stat='identity')\n",
    "    + scale_fill_manual(values=terms_group_colors, name='')\n",
    "    + theme(legend.position = 'bottom')\n",
    "    + theme_bw()\n",
    ")\n",
    "term_popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 400 -r 110 -i term_groups_summary\n",
    "\n",
    "term_counts = ggplot(term_groups_summary, aes(x=complete_year, fill=term_group, y=count)) +\n",
    "    geom_area(stat='identity') +\n",
    "    scale_fill_manual(values=terms_group_colors, name='') +\n",
    "    theme(legend.position = 'bottom') +\n",
    "    theme_bw() +\n",
    "    ylab('Articles in PubMed')\n",
    "term_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from repository_detection import (\n",
    "    source_code_platforms, mixed_publication_platforms, data_only_platforms,\n",
    "    all_platforms as platforms\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "mostly_code_platforms = list(source_code_platforms) + ['bioconductor']\n",
    "mostly_data_platforms = list(set(mixed_publication_platforms) - {'bioconductor'}) + list(data_only_platforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "platform_id_to_name = {\n",
    "    '.git': 'Other git',\n",
    "    'bioconductor': 'Bioconductor',\n",
    "    'bitbucket': 'Bitbucket',\n",
    "    'cran': 'CRAN',\n",
    "    'dryad': 'Dryad',\n",
    "    'github': 'GitHub',\n",
    "    'gitlab': 'GitLab',\n",
    "    'osf': 'OSF',\n",
    "    'pypi': 'PyPI',\n",
    "    'sourceforge': 'SourceForge',\n",
    "    'zenodo': 'Zenodo'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "platforms_counts = code_repositories[[f'mentions_{platform}' for platform in platforms]].sum().to_frame('count').sort_values('count')\n",
    "platforms_counts['platform_id'] = platforms_counts.index.str[len('mentions_'):]\n",
    "platforms_counts['platform_type'] = platforms_counts.platform_id.apply(lambda p: 'code' if p in mostly_code_platforms else 'data')\n",
    "platforms_counts.loc['mentions_zenodo', 'platform_type'] = 'both'\n",
    "platforms_counts['platform_type'] = Categorical(platforms_counts['platform_type'], ordered=True, categories=['code', 'data', 'both'])\n",
    "platforms_counts['platform_name'] = platforms_counts.platform_id.map(platform_id_to_name)\n",
    "platforms_counts['platform_name'] = Categorical(platforms_counts['platform_name'], ordered=True, categories=reversed(platforms_counts['platform_name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>count</th>\n",
       "      <th>platform_id</th>\n",
       "      <th>platform_type</th>\n",
       "      <th>platform_name</th>\n",
       "      <th>nudge_y</th>\n",
       "      <th>label_top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mentions_dryad</th>\n",
       "      <td>1</td>\n",
       "      <td>dryad</td>\n",
       "      <td>data</td>\n",
       "      <td>Dryad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_gitlab</th>\n",
       "      <td>5</td>\n",
       "      <td>gitlab</td>\n",
       "      <td>code</td>\n",
       "      <td>GitLab</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_osf</th>\n",
       "      <td>5</td>\n",
       "      <td>osf</td>\n",
       "      <td>data</td>\n",
       "      <td>OSF</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_pypi</th>\n",
       "      <td>7</td>\n",
       "      <td>pypi</td>\n",
       "      <td>code</td>\n",
       "      <td>PyPI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_zenodo</th>\n",
       "      <td>14</td>\n",
       "      <td>zenodo</td>\n",
       "      <td>both</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_bitbucket</th>\n",
       "      <td>17</td>\n",
       "      <td>bitbucket</td>\n",
       "      <td>code</td>\n",
       "      <td>Bitbucket</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_sourceforge</th>\n",
       "      <td>46</td>\n",
       "      <td>sourceforge</td>\n",
       "      <td>code</td>\n",
       "      <td>SourceForge</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_.git</th>\n",
       "      <td>56</td>\n",
       "      <td>.git</td>\n",
       "      <td>code</td>\n",
       "      <td>Other git</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_cran</th>\n",
       "      <td>82</td>\n",
       "      <td>cran</td>\n",
       "      <td>code</td>\n",
       "      <td>CRAN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_bioconductor</th>\n",
       "      <td>116</td>\n",
       "      <td>bioconductor</td>\n",
       "      <td>code</td>\n",
       "      <td>Bioconductor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mentions_github</th>\n",
       "      <td>245</td>\n",
       "      <td>github</td>\n",
       "      <td>code</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count   platform_id platform_type platform_name  \\\n",
       "mentions_dryad             1         dryad          data         Dryad   \n",
       "mentions_gitlab            5        gitlab          code        GitLab   \n",
       "mentions_osf               5           osf          data           OSF   \n",
       "mentions_pypi              7          pypi          code          PyPI   \n",
       "mentions_zenodo           14        zenodo          both        Zenodo   \n",
       "mentions_bitbucket        17     bitbucket          code     Bitbucket   \n",
       "mentions_sourceforge      46   sourceforge          code   SourceForge   \n",
       "mentions_.git             56          .git          code     Other git   \n",
       "mentions_cran             82          cran          code          CRAN   \n",
       "mentions_bioconductor    116  bioconductor          code  Bioconductor   \n",
       "mentions_github          245        github          code        GitHub   \n",
       "\n",
       "                       nudge_y  label_top  \n",
       "mentions_dryad             0.0        1.0  \n",
       "mentions_gitlab           -0.5        0.0  \n",
       "mentions_osf               0.5        0.0  \n",
       "mentions_pypi              0.0       -1.0  \n",
       "mentions_zenodo            0.0        1.0  \n",
       "mentions_bitbucket         0.0       -1.0  \n",
       "mentions_sourceforge       0.0        1.0  \n",
       "mentions_.git              0.0       -1.0  \n",
       "mentions_cran              0.0        1.0  \n",
       "mentions_bioconductor      0.0       -1.0  \n",
       "mentions_github            0.0        1.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platforms_counts['nudge_y'] = 0\n",
    "platforms_counts.loc['mentions_gitlab', 'nudge_y'] = -0.5\n",
    "platforms_counts.loc['mentions_osf', 'nudge_y'] = 0.5\n",
    "platforms_counts['label_top'] = 0\n",
    "for i, platform in enumerate(platforms_counts[platforms_counts['nudge_y'] == 0].index):\n",
    "    platforms_counts.loc[platform, 'label_top'] = (((i % 2) - 0.5) * 2) * -1\n",
    "platforms_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i platforms_counts -r 110 -w 800 -h 190\n",
    "\n",
    "code_and_data_platforms = function(text_size=3, point_size=3, scale_name='Used for') {\n",
    "    (\n",
    "    ggplot(platforms_counts, aes(y=nudge_y, x=count))\n",
    "    + geom_text(\n",
    "        data=platforms_counts[platforms_counts$label_top == 0, ],\n",
    "        aes(\n",
    "            label=platform_name,\n",
    "            x=count - 0.5\n",
    "        ),\n",
    "        hjust=1,\n",
    "        size=text_size\n",
    "    )\n",
    "    + ggrepel::geom_text_repel(\n",
    "        data=platforms_counts[platforms_counts$label_top != 0, ],\n",
    "        aes(\n",
    "            label=platform_name,\n",
    "            nudge_y=label_top * 1.3\n",
    "        ),\n",
    "        min.segment.length=0,\n",
    "        segment.curvature=-1e-20,\n",
    "        segment.color='grey40',\n",
    "        point.padding=2.5,\n",
    "        size=text_size\n",
    "    )\n",
    "    + geom_point(\n",
    "        aes(group=platform_name, fill=platform_type, shape=platform_type),\n",
    "        color='black',\n",
    "        size=point_size\n",
    "    )\n",
    "    + scale_fill_manual(\n",
    "        values=c(\n",
    "            'code'='#d62726',\n",
    "            'data'='#1f77b4',\n",
    "            'both'='#9467bd'\n",
    "        ),\n",
    "        name=scale_name\n",
    "    )\n",
    "    + scale_shape_manual(\n",
    "        values=c(\n",
    "            'code'='circle filled',\n",
    "            'data'='square filled',\n",
    "            'both'='diamond filled'\n",
    "        ),\n",
    "        name=scale_name\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + scale_x_log10(\n",
    "        sec.axis = dup_axis(breaks=platforms_counts$count, name=element_blank()),\n",
    "        name='Detected use of code and data versioning/distribution platforms'\n",
    "    )\n",
    "    #+ annotation_logticks(sides='b')\n",
    "    + scale_y_continuous(expand=c(1, 0.5, 1, 0.5)) # bottom mult, bottom add, top mult, top add\n",
    "    + theme(\n",
    "        legend.position='right',\n",
    "        axis.title.y=element_blank(),\n",
    "        axis.ticks.y=element_blank(),\n",
    "        axis.text.y=element_blank(),\n",
    "        panel.grid.major.y=element_blank(),\n",
    "        panel.grid.minor.y=element_blank(),\n",
    "    )\n",
    "    )\n",
    "}\n",
    "\n",
    "code_and_data_platforms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5788712011577424"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20 / len(data) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.features import number_of_articles_mentioning_feature, eval_features_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_features_py = eval_features_frame(domain_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>rank</th>\n",
       "      <th>term</th>\n",
       "      <th>count</th>\n",
       "      <th>kind</th>\n",
       "      <th>proportion_of_features</th>\n",
       "      <th>proportion_of_articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>cancer</td>\n",
       "      <td>786</td>\n",
       "      <td>diseases,clinical_findings</td>\n",
       "      <td>0.234627</td>\n",
       "      <td>0.227431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>carcinoma</td>\n",
       "      <td>132</td>\n",
       "      <td>diseases,clinical_findings</td>\n",
       "      <td>0.039403</td>\n",
       "      <td>0.038194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank       term  count                        kind  proportion_of_features  \\\n",
       "0     0     cancer    786  diseases,clinical_findings                0.234627   \n",
       "1     1  carcinoma    132  diseases,clinical_findings                0.039403   \n",
       "\n",
       "   proportion_of_articles  \n",
       "0                0.227431  \n",
       "1                0.038194  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import concat\n",
    "top_n = 10\n",
    "features_counts = concat([\n",
    "    number_of_articles_mentioning_feature(\n",
    "        domain_features_py,\n",
    "        Series(['diseases', 'clinical_findings']),\n",
    "        exclude=['disease']\n",
    "    ).head(top_n),\n",
    "    number_of_articles_mentioning_feature(\n",
    "        domain_features_py,\n",
    "        Series(['species'])\n",
    "    ).head(top_n)\n",
    "]).reset_index()\n",
    "features_counts.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i features_counts -r 110\n",
    "plot_features = function(names_padding=0.05, counts_padding=0.05, expand=expansion(mult=0, add=0)) {\n",
    "    (\n",
    "    ggplot(features_counts, aes(x=kind, y=rank))\n",
    "    + geom_tile(fill='white')\n",
    "    + geom_text(aes(label=strtrim(term, 25)), hjust=0, position=position_nudge(x=-(0.5-names_padding)), size=3)\n",
    "    + geom_text(\n",
    "        aes(label=count, color=proportion_of_articles),\n",
    "        hjust=1, position=position_nudge(x=0.5-counts_padding), size=3.4\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + scale_y_reverse()\n",
    "    + scale_x_discrete(\n",
    "        position=\"top\",\n",
    "        labels=c(\n",
    "            'diseases,clinical_findings'='disease/clinical finding',\n",
    "            'species'='species'\n",
    "        ),\n",
    "        name='Top diseases and species mentioned in the abstracts',\n",
    "        expand=expand\n",
    "    )\n",
    "    + theme(\n",
    "        legend.position='bottom',\n",
    "        axis.title.y=element_blank(),\n",
    "        axis.ticks.y=element_blank(),\n",
    "        axis.text.y=element_blank(),\n",
    "    )\n",
    ")}\n",
    "plot_features(names_padding=0.05, counts_padding=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from omics import omics_by_entity, omics_by_entity_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "omic_entities_columns = Series(list(omics_by_entity.keys()))\n",
    "omic_entities_group_columns = Series(list(omics_by_entity_group.keys()))\n",
    "omic_entities = data.rename(columns=prefix_remover('entity_', enforce=False))\n",
    "omic_entities_groups = data.rename(columns=prefix_remover('entity_group_', enforce=False))\n",
    "%R -i omic_entities\n",
    "%R -i omic_entities_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Dropping empty groups: clades (meta_* & pan_*), protein modifications, exogenous factors, clinical data\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -r 100 -i omic_entities_columns\n",
    "\n",
    "omics_upset = function(...) {\n",
    "    upset(\n",
    "        omic_entities,\n",
    "        omic_entities_columns,\n",
    "        min_size=50,\n",
    "        width_ratio=0.2,\n",
    "        min_degree=3,\n",
    "        base_annotations=list(\n",
    "            'Intersection size'=intersection_size(\n",
    "                counts=FALSE,\n",
    "                mapping=aes(fill=term_group)\n",
    "            )\n",
    "        ),\n",
    "        themes=upset_modify_themes(\n",
    "            list(\n",
    "                'overall_sizes'=theme(axis.text.x=element_text(angle=90))\n",
    "            )\n",
    "        ),\n",
    "        labeller=function(x) {\n",
    "            c(\n",
    "                'endogenous molecules'='metabolites²',\n",
    "                'genes'='genes',\n",
    "                'transcripts'='transcripts',\n",
    "                'proteins & peptides'='proteins¹',\n",
    "                'epigenetic modifications'='epigenetic³'\n",
    "            )[x]\n",
    "        },\n",
    "        set_sizes=FALSE,\n",
    "        ...\n",
    "    ) + theme(axis.title.x=element_blank()) & scale_fill_manual(values=terms_group_colors, name='')\n",
    "}\n",
    "omics_upset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Dropping empty groups: clades (meta_* & pan_*), protein modifications, exogenous factors, clinical data\"\n"
     ]
    }
   ],
   "source": [
    "%%R -r 110 -w 800 -h 800\n",
    "\n",
    "upset_plot = wrap_elements(\n",
    "    omics_upset(matrix=intersection_matrix(geom=geom_point(size=1.75)))\n",
    "    & theme(\n",
    "        legend.position='none',\n",
    "        plot.margin=unit(c(0, 0, -0.3, 0), \"cm\"),\n",
    "        panel.background=element_rect(fill='transparent', colour=NA),\n",
    "        plot.background=element_rect(fill='transparent', colour=NA)\n",
    "    )\n",
    "    + theme(panel.border=element_rect(color='grey20', fill=NA))\n",
    ")\n",
    "\n",
    "TITLE_SIZE = 10\n",
    "common_theme = theme(\n",
    "    axis.title.x=element_blank(),\n",
    "    plot.title=element_text(size=TITLE_SIZE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HnemjoqJoLfTt25eqfKhlYWFx5swZzeYNDg5esWIFQohGo128eJHSvHSCTJhCRjv2vLw8Go1WXV3d8k+U5Gy0AwetGjx4MLlrMjEx6d69+8KFCxsaGlTP9f7779NotCtXrsi1x8fH02i0OXPmqJ5d4a5GR2uRoVbOVuNqs79VP4ocXez3VHSuYs+jjhkzZsh+dXK53LCwsJKSEhWzqIio5UorR6dLsn2hU9XRlStXampq2Gz2qVOnhgwZos4sXl5eu3fvlm1hsVhU5aO9srIyc3NzZ2dnqjqcPXt2t27dqOqtfWmPY9c4Z9k1pz0OHJACAwO//fZbhJBYLM7Pz//uu++ampp++eWXVmdMT08fOXKkbEtbKwY9rEX6XDn1v1F0+E3vgw8+2Lt3L0IIw7Di4uKEhITg4ODHjx+bm5tr1qH2Ky2QQ1mBdfLkyb59+3788cdpaWlbt25VZxYOhyP3cRqVr7/+ulevXjt27KCqw59++omqrtqd9jh2jXOWXXPa48ABydnZmdxHjRkzpqqq6ueff261wOrbt++ZM2d2795No9GIlrKyssePH/fp00f90HpYi/S5cup/o+jwm561tTW5co4ePbpHjx5jx47NycnR7FuVkpUWyKHmFKFEIsnIyIiMjAwPD3/27NnDhw8p6bapqYmSftQkkUj0GQ60OzpdQ2D1M362trY2NjatThYcHPz27dv79++TLWfOnPnoo4+cnJx0mZ2R0vNuXIWOvYkNGDAAIVReXq7Z7LDS6gI1Bda1a9eqq6sjIiICAgI4HM6pU6e06c3KyurGjRtffvllv379EEJCoXDlypUffPABm8328/O7desWMVlVVdWUKVO4XG6vXr1Wrlw5e/bsFStWSKVSGo1248YNsjcnJ6dff/1VRT82NjYHDx7s27cvg8FwdHT817/+hRAaPHhwZmZmYmKiq6sr2VVsbKyPjw/59urVqyYmJs+fP5fL/9WrVxMnTrS3t/f09ExMTMRxnGhnMBjENQFXr179+OOP2Wy2s7PzwoULm5ubiQnq6+vnzp3bvXt3Z2fn6dOnv3v3DiGkYkTK+lHWrmwJKEtYVmNj44IFC3r27GlraxsYGPj48WNy6f3444+enp4WFhY+Pj537tw5fvy4t7c3m80ODg6uqamRG7vCMarIWZayebXMDSFUUFDg7+/P4XAGDBhw4sQJuZwVriEIoYqKiqioqO7du7NYrP79+58+fbrlmkN2oiJJhZ2rs0CA3kgkkr/++ispKSk6OrrViYlDCxkZGWTLmTNnwsLCyLcqNmqCsrVIjjFslQq3AvS/u3Flw1EWl6Rsl6VOYq1uv8r2ewqDXr161czMjKw/pk+fPnToUKlUqs1OFSna86j5LdNSbm4uQqhbt26q1y6F+zqkxkqrbMmoP9jOCKfCnDlzBg4cSLz+4osvPvjgg1ZnmTp1astkXr58ieM4m80eMmTIzp07nzx5guP4hAkTevfuvXv37j/++GPlypXW1tYPHjyQSCR9+vTx9vY+derU2bNnictRly9fTvyPcv36dTKQo6NjSkqKsn5wHO/SpYuNjc2OHTsePnxIXJp34cKFpqamMWPGzJ8/n8/n4zjOZDIzMjIePHiAECosLCR6njlzZkBAgNy4RCKRu7t7375909LSzpw54+PjY25uvnz5chzH6XT6hQsXKioqLCwspk+ffuXKlZ9//tnc3HzDhg3EvMOGDevXr9/Zs2ePHTvWt2/fwYMH4ziubETK+lHRv8IloCJhWYGBgW5ubidOnPj999/Dw8NZLNbr16+JpWdvb3/8+PEbN274+vqyWKzhw4ffvXv30KFDdDp93bp1xOzE2JWNUUXOshTOq2VuxF7J2dl59+7dv//++9y5cxFCmZmZsjkrXENwHB86dKi3t/fx48cvX7785Zdf0ul0Ho8nt+aQnahIsmXnai4QoFODBg2S20ENGDCgrq5O9Vy9evX6/vvvk5KSvLy8iJbq6mo6nf7kyZNRo0bNnj0bV75R4//sapStRXKMYatUuBXg/7sbVzYcZVs0sRBwJbssNRNTvf2q2O8p+6ZYtGiRt7e3SCQ6d+4ci8UqLi5WNrE6O1Vlex51vmVwHI+Ojvbw8Lh+/fr169evXr26f/9+Z2fnHj16NDY2Klu7VOzr1FlptRlsp0VBgSWRSOzt7Tdt2kS8PXnyJELo0aNHqueaOnWql5fX7/9LKBTiOM5msxcsWEBMRvxPVlpaSs4YHh4+YcKEEydOmJubv3jxgmisqKhgMpkqCixl/eA43qVLl/nz55Nj4XK5RA0+duzYxYsXE+3kBk+siDiOCwQCa2vrQ4cOyY3r8OHD5ubmRKWI4/jz588ZDIZsgXX9+nWE0L1794gJrl69euvWLeKFqakpmeGtW7f8/Pzevn2rbETK+lHWrmwJqEiY9OeffyKE/vzzT+KtWCx2dXVduXIlsfTIjz45ORkhVFZWRrwdOXJkdHQ08ZoYu7IxKstZlrJ5tcyN2Ons2bOHDBQcHDxixAj8f3fQCteQLVu25ObmEu2lpaUIofv37+P/u+YQnahOsmXn6iwQoGuDBg0KDAwk907Hjh0bOHCgl5dXY2OjirmIXURlZaWJiQnxNXn48GHif071Cyxc0VokF8gYtkpc+VYguxtXOBxlccmFoGyXpWZiqrdfZfs9Fd8UfD7//fff/+abb7p167Zv3z5cu52qij1Pq98yOI7LHUm1tLT09/fPz8/Hla9drUZUvdJqM9hOi4KL3K9fv87j8WJjY2NjY8nGU6dOeXt7q55RxUXu5P+OxIfq7u4u99fc3Nx+/fqRPxJxdHRUfYsHZf0QLz766CPihampqa2trYp+IiMjT506tWbNmrNnz2IYNnHiRLkJ8vPz+/Xr17VrV+Jt9+7d5ZbDRx99NGLEiE8++WT06NHDhg0LDg4mDgjn5ub26NHDzc2NmGzo0KHZ2dkIIalUqjATZf0oa1e2BFpNmJjX2tqaXFx0Ot3f3//vv/8m3vbo0YN4YW9vb2pq2rNnT+Ktg4ODXD/KxqgsZ3XmPXfunPa5BQYGkq9DQkLWrVsnF13hGrJ48eLz58+fPXu2rKwsJycHKad6AbbsXJ0FAvRA9iJ3hNDQoUN79OiRlpY2bdo01TO+9957Q4YMSU9Pj42NbXmqRTOy/Zw6daqhocHgWyVSuRW0PASoTlySsl2WBltHy01M2X5PxTeFpaXloUOH/Pz8Ro8eHRMTo2JidXaqBIV7nla/ZchARJHdJir2dapXWu0H2wlRcA3WyZMn33///fsyxowZk5aWpk2fVlZWxAsOh8NgMOrr6xtl/PHHHyKRiPyxA8HERPFYxGKxin6IadT/XWtERERubm5xcXFKSsrEiRNb3leCTqfLJWZpaSn7lslkXr9+PScnx8/P7+rVqwMHDly+fDlCqLm52dTUVJ0ciBEp60dZu7Il0GrCCCEMw1oubYVXjMpNJkfZGJXlrM68VOUmO3vL9aFli1Ao9PPzW7NmDUIoJCTk+PHjKvpUnWTLztVZIED/XFxcrK2tX758qc7EYWFhGRkZIpHo4sWLrRZYxEatWlBQ0Mt/hISEGMNWqXorIHfjbYpLUrbL0mDraLmJKdvvqf6mqKiowDDs+fPnQqFQxcTq7FRbIvc8rX7LtJWytavlvk7FSkvtYDsJbQssqVSanp4eHh7uKyMyMvLx48eFhYXa5+ft7Y1h2IMHD1gsFovFYjKZX375ZXJycu/evfPz81+9ekVM9vbt2/z8fHIu8l5qz549I14r66et+fTr18/T0zMpKSkrK2v69OkKE5ZNrKamJi8vT3aCGzduxMbGfvjhh6tWrbp06dKmTZv279+PEOrbt29ZWdmLFy+Iyf78808nJ6eioiJlI1LWj7J2ZUug1YSJeevq6v766y/irVQqvXHjhgZ3hVU2RmU5qzMvJbn9/vvv5OusrCx1DhdlZ2ffvXs3MzMzLi5u4sSJHA5HxcRtTVKdBQL078WLF/X19eTBIdXCw8Pv3r175MgRFotFHkGR1XKjVo3JZHb9h4WFhTFslW3aCtSMS06gbJdFydahbL+n4puisrIyJiYmKSmpubmZuFJem50qQeGep9VvGXUoW7tU7+tUrLTaD7YT0vYU4R9//PH27dvQ0FDZxs8++8zExCQtLW316tUHDhwoLS3dsGGDZv1369YtOjp68uTJW7du7d69+y+//HL58uWNGzf26NHju+++++yzz/7v//7PzMxs7dq1OI4jhExNTV1dXTdt2mRnZ0en05cuXUoU18r6URGauD6gvLxcbn8aGRn5/fffd+3a1d/fv+VckydP/te//kUmlpCQIPcvApPJ3LJli4mJSVhY2IsXL06ePDl06FCE0JgxY/r16zdhwoSEhISmpiYiBHGDXYUjUtaPsnZlS8DNzU11wgihDz/8cOTIkZGRkZs3b7a1td29e3dlZeU333zT1k9T2Rhra2sV5qzOvDQaTfvcYmNjaTSap6fniRMnzp8/L3eqQiEul4sQSk5OjoiIKC0tXbt2LULo6dOn/fv3b7nmtHUBKvsQgZ5VVlaSv8bi8XgbN260t7cfN24cQqjVPVuvXr169+4dGxs7ceJEuePrynZTctMo3P+QjGGrVLEVtDocZXHJWZTtsurq6rTfOpTtqFV8U8yePXvo0KFz58794IMPRo8eHR4ePnToUI13qgRlex7V3zKqqV67VO/rVKy02nyDdF5aXsM1f/58LpcrlUrl2ocOHUr8rnDSpEmOjo4tZ5w6deqQIUMU9kncDp5829zcvHLlSnd3d+KnoX/88QfRXlpaGhISYmNj8/7772/fvj0oKIi4sC4nJ6d///4MBgMhFB0dPXDgQOLqUWX9dOnS5dixY2Q48oemaWlpjo6Offr0wWWuPMVxnLjKIS4uTtkyefnyZXh4uJ2dXa9evTZv3rxgwQLZi9xxHD9w4ICnpyeTyXRycpoxYwZxXSeO4zweLyoqytHR0c7OLiIigrxsUNmIlPWjrF3ZElCWsKz6+vqYmBgXFxcbG5vAwEDyRwyySy8zM5NOp5OzfP7553KX06oYo7KcZSmbV5vccnNziQ930KBBVlZWgwcPJq8mlr1IVuEasnv37u7du1tbW48aNeqvv/6Kiopis9klJSWyaw7ZiTpJynauzgIBOiV3CRGbzR42bBjxgzJc+Z6NvEIZx3Hi3BnxQy1c5nphXPlGTe5qFK5Fcoxhq1S2FcjtxhUOR1lcciEo22Wpk1ir26+y/Z7CoPv27evSpQvxI00cx+fNm+fh4dHU1KTxTlXFngdX41smOjp60KBByv6qcO1SEVHNlVabb5DOiYZ3lFtWBAcHe3t7b9myhXgrkUjq6+tVX7GumQcPHgwaNKioqEj2ny090N2IAAAGARs1UIiSbxlYuwyOskflGBs6nU75iiWVSgUCwYYNG/z8/PRcXSHdjAgAYECwUQM5FH7LwNplcB22wNKFx48f9+/f38rKKisry9C5AAAA6GjgW6Yj6TinCN+9e1dVVaXTA0sSiSQ/P9/Ly8vCwkJ3UQAAAHRO8C3TkXScAgsAAAAAwEhQ87BnAAAAAABAggILAAAAAIBiUGABAAAAAFAMCiwAAAAAAIpBgQUAAAAAQDEosAAAAAAAKAYFFgAAAAAAxeBO7nry9OlTje/Mi+M4hmGmpqbUptSSVCql0WhyD1GnHIZhOI53mOHApyOLw+HMnDmT2pQM4vz586WlpYbO4r+kUqke1jFtSCQShBCdbtTfKUa+GHEcl0gkpqamut7MtSGVSk1MTGg0mqETUUoqlWIYRjzrmkLh4eHdunVr0yxGvTF0JP/v//2/qqqqYcOGaTCvRCKRSCRmZma63uqEQqGpqSnl66UcsVgslUrNzc11uoniOC4Sieh0uq73+B1sOM3NzRiGMZlMzWb/5ZdfOkaBdf36dV9fXzs7O0Mn8h8CgYDJZBrztxqfz0cIsVgsQyeiFIZhIpHImO+QLpVKm5qamEymrnfC2l1imdgAACAASURBVCB2RMZcpwoEAolEYmVlRWGfmZmZFRUVUGAZKRMTEw8Pj+DgYA3mbWpqampqsrGx0fU6zePxmEwmm83WaRQ+ny8QCGxtbXVaL+I4Xl1dbWFhoes9fmNjo1AotLOz0+mXH4ZhNTU1lpaWlpaWuouCEGpoaBCJRPb29prNfuTIEWrzMaCPP/64e/fuhs7iP+rr69lstjEf2Hj37h1CqEuXLoZORCmpVMrn862trQ2diFJisbiuro7NZmv8H44e8Pl8MzMzYy4B6+vrxWIxtf8d5efnazCX8W6uAAAAAADtFBRYAAAAAAAUgwILAAAAAIBiUGABAAAAAFAMCiwAAAAAAIrBrwgNZvzqp22cg6f+pL9t8Gxj5wCA/5JIJPX19cQv44yBVCqtr683dBaqSKVS9M9vCY0Tccs6I88QIdTU1CQUCg2di1IYhjU3NxvzHUOkUimO49R+0CKRiFjD2wQKLAAAkEen062trY3npgNwmwbttZfbNFhaWsJtGrRB3KaB2lXR3Nxcg9skGe/mCgAAAADQTkGBBQAAAABAMSiwAAAAANBxzNhWZegUEIJrsJSpqqr68ccfCwsLzc3NR44c+cUXX9BotIaGhr179+bl5Tk4OMycOdPHxwchpLARAAAAAPo3deNrhND41U8N/mMvOIKlgFQqXbVqlZWV1bZt25YtW/b777+fOXMGIbRr1y6xWLx169YxY8YkJCTU1dUpawQAAABAZwYFlgLFxcXv3r1buHCho6Njnz59wsPDb926xePx7t27N3fuXGdn5+DgYA8Pj8uXLytsNHT6AAAAQGfX9nshUQxOESrA4XBmzZpF/gy1sbERIVReXm5ra8vlcolGLy+vsrIyV1fXlo3KusVxnLjNiR5oE0g/Sep6aZCd6204+um/YwwHAAB0weBFlSwosBRwcnJycnIiXhcWFp47d27OnDk1NTWyN1CxtrYuKChQ2KiwT7FY3NTUVF1drdPMSRoHEgqF+rnHXW1trR6iCAQCgUCgh0A1NTV6iNLU1NTU1KSHQBqvPxrciw8AAHTEsFdiQYGllFgsTk1NPXv2bExMjL+//6VLl+TuXSuRSHAcb9mosDcTExMGg2FhYaHDjGVoFkggENDpdF3fQU4sFkskEiaTqet7AetnOM3NzVKpVNefLI7jQqGQwWDQ6brdZrUcjjHf3xkA0AkZsMaCAkuxysrK9evXs9nsLVu29OzZEyFkY2Mj+6iKhoYGW1tbhY0KOzQ1NTUzM2OxWDpO/D80C0RUJLpOks/nSyQSS0tLnd6WGsdxgUDAYDB0PRwcx6VSqaWlpU5rCwzDiALL0tJSd1GIQFKpVOOFZsy3GgcAdGxGdX4QUX6RO47jZ8+eXbVq1bx58xBCN2/ebI+nDKRS6bp16wYOHLhhwwaiukIIubq6VldXk2eCCgsL3dzcFDYaJGcAAAAAtGSowovKAovP548YMWL8+PGJiYlJSUkIoejo6GHDhhnzwzUVevDgQW1t7ahRo968eVNZWVlZWcnj8ezt7X19fQ8fPiwQCG7fvv306dOAgACFjYZOHwAAAAAGRmWBFR8fn5eXd+HChfz8fKIlKSmptLR07dq1FEbRg2fPnjU1NS1YsGDuPzZs2IAQWrp0qUAgmDVr1vHjx+Pi4uzt7ZU1AgAAAEBvVB+mMshBLCqvwTpx4kRsbGxQUFBpaSnREhAQ8NVXXx06dCgxMZHCQLoWERERERHRsp3NZq9atUqdRgAAAAAYD/1f7U7lEaz6+npXV1e5Rg8PDx6PR2EUAAAAAABl2J4rDZ0CQtQWWD4+PllZWXKNly5d6tOnD4VRAAAAAADaSs8nCqk8RRgXFxcSEmJmZubv748QysvLS01NTU5OPnr0KIVRAAAAAABIspUTcfiK7bmy8elGw2WEELVHsIKCglJTUy9fvhwVFYUQ6t+//759+xITE6dMmUJhFAAAAAAA1RSeKNTnQSyK74MVERFRWlr65MmT8+fP379///Xr19988w21IQAAAAAAWmXYGovKAmvnzp23bt2i0+menp7BwcG+vr6WlpbFxcWTJ0+mMAoAAAAAAKHl+UEjQWWBtXjx4hEjRuzcuVO2saam5tSpUxRGAQAAAABQhwEPYlH8LMLPP/98+fLl9+/f379/v94ebNxO6bjQztBl5wAAAED7YKgL3im+BmvevHkXL17Mysry8/N7/vw5tZ0DAAAAAJCM9vwgorzAQggFBATcv39fIpH4+vpeu3aN8v4BAAAAANRnkNqL4lOEhJ49e966dWvGjBmBgYFffPGFLkK0OxKJRCAQ6O251xoHam5u1nWSGIYhhOrr63UahSASicRisU5DSKVShFBdXZ1OoxCEQmFzc7NOQxDD0XgdIGbXs8uXL+/evVu2xdTUND09fc+ePRcvXiQbFy1aNHr06IaGhr179+bl5Tk4OMycOdPHx0fv+QIADED/Jwp1UmAhhFgs1okTJ/7973/HxcXpKET7QqfTLSwsunTpop9wmgXi8XhmZmZsNpvyfGTx+XyBQGBtbW1iQv0BVBKO49XV1ebm5iwWS3dREEKNjY1CoZDD4dBoNN1FwTCspqaGyWRaWlrqLgpCqKGhQSQSabyimpqaUpuPOoYOHerh4UG+zcrKIsr3ioqK6dOnDx48mGi3tbVFCO3atQvH8a1bt+bl5SUkJBw4cIDD4eg/ZwCA9tp6flDPNRaVBVZJSUnXrl1lW1atWjV48OC7d+9SGAUAAGSx2WzyvwIej/fnn39u27YNIVRRUTFt2jQXFxdySh6Pd+/evZ9++onL5To7O+fk5Fy+fHnSpEmGyRsA0KFRWWC5u7u3bBw1atSoUaMojAIAAMr8/PPPEydO5HA4YrGYx+MdO3asoKDA2to6JCQkNDS0vLzc1taWy+USE3t5eZWVlRk2YQCAPunzIJa2BVZ8fLyLi8ucOXOI18omS0hI0DIQAACoVlJSUlJSsnz5coRQZWUlQsjb23vevHmlpaU7d+60srLCMMza2pqc3trauqCggHz73XffVVVVEa+bm5sbGhr0c2mdOiQSSX19vU5PQ2tJnxcjagbHcalUauQZIoQEAoFIJDJ0LkpJpVKxWGwMq2LUpgrydZuuYSdqrDatCSKRSIMLTLUtsJKTkwcPHkwUWCkpKcomgwILAKBrqamp4eHhdDodIdStW7e0tDTiNZfLLS4uvnr16ogRI+S+GCQSCfm6srKSKMsQQjY2NhiGET/IMBI4jhNfwMbMqJaYQsacIfH5GnOGBAzDjKHA0gbbcyWGHVR/es02PW0LrBcvXpCv4WA7AMBQeDxeXl7e4sWLibc0Go2orgjdu3e/ffu2jY2N7M9XGxoaiCvfCUlJSeTrb7/9lsPh2NjY6D5xtdTX17PZbJ3+LkRLxC9P9fY7Hg1IpVI+ny97CNPYiMXiuro6FovFZDINnYtSfD7fzMyMwWAYOhGE0BttZm7T1s1kMjX4BQ/Fm2tFRUVeXh5CSCQSrVu3LioqKj09ndoQAADQ0rVr13x8fKysrIi32dnZW7ZsIf9aUVHh5OTk6upaXV1dU1NDNBYWFrq5uRkgVwCAdoz5/qIkKi9yv3Llyvjx4yMiIg4ePJiQkLBhwwZPT8+UlJTjx49HRERQGAioT6dPXPptg6fuOgegTe7fv//xxx+Tb93d3bdv356amjpq1Kjy8vLMzMzly5fb29v7+voePnw4JiYmNzf36dOnX3/9tQFzBgB0YFQewVqzZo2Xl9eyZcsQQsnJyd9++21BQcHUqVO3b99OYRQAAJAjFAqLioo8Pf9b8Ts7O69fv/6vv/5auHBhcnJyTEzMwIEDEUJLly4VCASzZs06fvx4XFycvb294bIGAHRkVB7Bevz48fbt2729vUtKSl69ehUZGYkQCgwM/O233yiMAgAAcphMZsurEXr37r1582a5RjabvWrVKn3lBQCgnk7PzFCIyiNYLBaLz+cjhC5cuMDhcPr164cQam5ulr3UFAAAAACAEkZ7ARai9gjW8OHD9+zZw+FwduzYERISYmpqWlJSsmvXLqLSao+ePXt2//598kbP8GgzAAAAAKiDygJr48aNgYGBs2bNsrW1JR5B+Omnn9bV1e3Zs4fCKHojFApTUlLMzc3JFni0GQAAAGBA7eX8IKL8UTmFhYVFRUXdunUj7jXyww8/DBo0SO4Bhe3CoUOHzp4929zcPGzYMLIRHm0GgC6cOXNGnclCQ0N1nQkAoB0x5vODiNoCCyFEp9N79+5Nvm2/O8TIyMgJEyYcO3aMuHseQggebQaAjoSFhamewMLCgsVitd/9CQCgE4LLzxVjMplMJlP2/KAGjzaTJZVKm5ubiR8B6IF+AmkWRSwWI4Sampr08LAFsVis60VBDkenUYgHNehhOMSjYzSOotlTPng8Hvn6+fPngYGBgYGBc+fOdXV15fF4Bw8ePHPmTHZ2tmYpAQCAQUCBpS4NHm0mC8MwsVgsEAj0kStC+gmkTRShUEhhJspIJBJlnwi19LPAxWIxUc/pmsbD0eyJXXZ2duTrGTNmBAYG/vrrr8RbFxeXgQMHCgSCBQsWZGZmapYVAKBjaBc3cCdBgaUuDR5tJovBYFhaWsp+keiUTCCequkoi9IGTU1NAoHAxsZGpw9Ww3G8pqbGwsLC0tJSd1EQQnw+XygU2tra6vSAHIZhtbW1lpaWFhYWuouCEGpsbBSJRBqvqBo8rkvOzZs3d+zYIdcYEBCwaNEiLXsGAAB9Mt5Hhxob7R9tRvtfOs1Wz1HaRJt5jTAQDKfl7Nqws7N78OCBXOP9+/fJKx0BAKBdoLjAEolEf//9d14L1EYxCHd391u3bqWmplZVVd2/fz8zM3Ps2LHko80EAsHt27efPn0aEBBg6EwBaMcmTZr0ww8/bNu2jbgOrKmpKTExcdeuXRMmTDB0agAAQ2pf5wcRtacIs7OzQ0NDa2trW/5JsyszjArxaLPk5OSMjAwHBwfZR5vt3r171qxZXC4XHm0GgJbWrVtXXFy8fPnyFStW2Nra1tTU4Dg+efLktWvXGjo1AABoAyoLrCVLllhZWW3atKn93rpdzrRp02TfwqPNANA1BoNx8uTJP//88+bNm69fv+7Wrdsnn3zi6+tr6LwAAKBtKCuwcBx/9OjRjh075syZQ1WfAIBOCMfxysrKN2/e1NfXf/311zdv3pRKpdpfPg8AaL/a3flBRGGBJZFIMAyDC1EBANrg8/nBwcHZ2dlMJlMoFO7bty86OtrBweH8+fNdunQxdHYAAKAuyi5yZzAYUVFRW7ZsaWhooKpPAEBnEx8fn5eXd+HChfz8fKIlKSmptLQUrsECALQvVF6D5ebmdu7cOXd395EjR9rZ2cn+Znv37t0UBgLq0/Gh1Axddg46oxMnTsTGxgYFBZWWlhItAQEBX3311aFDhxITEw2bGwDAINrj+UFEbYF1/Phx4hTh48ePKewWANB51NfXu7q6yjV6eHjIPk4HAACMH5UF1qNHjyjsDQDQCfn4+GRlZU2ZMkW28dKlS3369DFUSgAAoAHqH5Xz8OHDu3fvvnjxomvXrkOGDIHfVwMA1BcXFxcSEmJmZubv748QysvLS01NTU5OPnr0qKFTAwAYgOz5wfaFygJLIpF8+eWXJ06coNFotra2tbW1GIZ9/vnnR44ckX2KHwAAKBMUFJSamrpixYoDBw4ghPr378/hcBITE+WOaQEAOqF2dAEWovZROQkJCenp6T/++COfz+fxeHw+Pykp6fTp0//+978pjAIA6NgiIiJKS0ufPHly/vz5+/fvv379+ptvvjF0UgAA0DZUHlhKS0tbvnz5vHnziLdMJnPu3LnPnz8/efJkfHw8hYEAAB0bnU739PT09PQ0dCIAAENqv+cHEbUF1rNnz/r27SvX2K9fv507d1IYpZ3CMEwikQiFQv2E008gzaJIJBKEkEgkkr2RB+WIx19KpVJdLwqpVIoQEgqFehiOHlYhcjiaza7ZU0eHDh2qzmS3b9/WoHMAQMfQvs4PImoLLA8Pj2vXrsldKnHt2jX4N5SA47jeHnqtn0DaRNFbhroORPSvnyh6CKTPKKQ7d+4ghFxdXfv166fTOhUAAPSGygJr7ty58+bNs7CwiI6OdnZ2fv369aFDh/bt25eUlERhlHbKxMSEwWBYWFjoJ5x+AmkWBcMwsVjMZDJNTKi8BFAOjuN8Pp9Op+t6UUilUolEYmFhodPKAMOwpqYmPQxHIpEQw9Fsds0WQmxsbHp6elFREY1GCw8PDw8PHzp0qE5Xj1ZJpVI+n288z6WQSCR8Pt/QWaiCYRiO48azxFrCcVwikRhzhhiGIYSEQqFYLDZ0LkoRuwj9bJ5f/vuV7jpv05rQ3NxMfDptQmWBFRMTU1lZuXnz5l27dhEtlpaWa9eunTt3LoVRAAAdzMaNGzdu3FhQUJCRkZGenr5t27b33nsvNDQ0PDw8ICDAzMxM/ymZmJgwmUxLS0v9h1aosbFR1/+TaKm+vp5GoxnPEmtJKpUKBAJjzlAikYjFYjMzM3Nzc0PnopRAIGAwGPq/MwDl5wfbtCYwGAwN/nukeBl99913CxcuzM3NffXqVdeuXX18fOzt7akNAQDokHr37t27d+/Vq1e/ePEiIyMjIyNj3LhxlpaWn332WXh4eHBwMJvN1lsyNBrN1NTU1NRUbxFVI/Ix5gKL+PoxniWmELEYDZ2FUsQxEhMTE2NOkkajGXmGamrTEGg0muELLISQnZ3dyJEjKe8WANBJdO/efdGiRYsWLaqpqTl79mx6enpUVBSNRhMIBIZODQAA1KVtgRUfH+/i4jJnzhzitbLJEhIStAwEjFzbf0zbhkfL/bYBfifRGdXW1lZVVb19+7a5udnGxsbQ6QAA9KedPuBZlrYFVnJy8uDBg4kCKyUlRdlkUGABANT06NGj9PT006dP5+XlOTs7h4eHf//998OHDzd0XgAA0AbaFlgvXrwgX5eVlWnZGwCgc8Jx/N69e6dPnz59+nRJSUmvXr0mTJiwb9++jz76CG7cAABoj6i8BuvKlStDhw6VuzK/pqbm77//HjZsGIWBAAAdycKFCzMyMl69etWvX7+pU6dOmDCh5S2LAQCdRwc4P4ioLbBGjRr19OlTDw8P2cYHDx6EhYUZ+R1cgPZ0vA1k6LJzYGB79uxBCLm6unK53JycnJycHIWTXb58Wb95AQCA5igosE6dOrV+/XridVhYGJPJlP1reXl5r169tI8CAOio/P39iRfEY5QAAKADoKDAcnR09PPzQwjl5ub6+vrK/dhn1KhRUVFR2kcBAHRU165dM3QKAABj0THODyJKCiw/Pz+ywNqwYYOLi4v2fRqJZ8+e3b9/f9KkScTbhoaGvXv35uXlOTg4zJw508fHR1kjAEBjAoFg//799+7dq6io6Nmz54gRI6ZOndoBbmwIAOhUqLwvcHZ2dkeqroRCYUpKiuxPI3ft2iUWi7du3TpmzJiEhIS6ujpljQAAzVRWVnp5eS1evDg/P9/CwuLhw4fR0dG+vr719fWGTg0AoHNtv6Wi8aL4wQsHDhz48MMPq6qqEEJXr1718fE5ePAgtSH049ChQ1OnTr137x7ZwuPx7t27N3fuXGdn5+DgYA8Pj8uXLytsNGDaALR3ixcvrq2tzc7OfvTo0blz53Jzc2/duvXs2bPly5cbOjUAAGgDKgusEydOzJ49G8dxCwsLhJCbmxuLxZo5c2ZycjKFUfQjMjLy4MGD48aNI1vKy8ttbW25XC7x1svLq6ysTGGjAdIFoKO4cePGihUriKsOCEOHDo2Njb148aIBswIA6F+7vgALUXubhsTExEmTJp08eZJ427Nnz1u3bkVGRu7YsSM6OprCQHrAZDKZTKbsI81ramqsra3Jt9bW1gUFBQobFXYoFov5fD6P14bnw2hDP4GMfzgCgUA/D7Crrq7WQ5SmpqampiY9BNJ4gUulUi1DMxiMrl27yjV27dpVJBJp2TMAwMh1pPODiNoC6/Hjx/PmzZNrHD9+/Llz5yiMYig4jsvdUVoikShsVDi7iYkJg8GQu4eF7ugnkJEPRygU0ul0Op36J5rLEovFUqlU14sCx3GRSGT8w9H+ruvjxo1LSUmZNm0aeVU7hmFHjhyRPaYFAADGj8qdtbu7+6NHj+QaHz161DHug2VjYyN7mW1DQ4Otra3CRoWzm5qampmZsdlsnSeKEEJIP4GMeTg4jguFQgaDwWKxdJESqbGxUSqVslgsnT7RBcMwkUhkZmYm96QEyjU0NEilUo0/WRMTTa46kP0fbOTIkWfOnBk8ePD06dO7du36+vXrw4cP5+fn//bbb5qlBABoF+QOX7X384OI2gJr5syZS5YssbW1nTZtmpOTE4/H+/XXX7dt27Zp0yYKoxiKq6trdXV1TU0NUUIVFhZ6e3srbDR0pgC0M2PHjpVrefXq1V9//SXbEhwcjOO4HpMCAACtUFlgLVq0qKKiYt26datXrzYxMcEwzNTUdP78+UuWLKEwiqHY29v7+voePnw4JiYmNzf36dOnX3/9tcJGQ2cKQDtTWVlp6BQAAIBiFF/PsWHDhgULFty5c6e8vNzZ2fmjjz5ydXWlNoQBLV26dPfu3bNmzeJyuXFxcfb29soaAQDqe++998jXb968MWAmAACD6HjnBxHlBRaO4w8fPrx//35tbe3SpUtv3rzp4uLSfm/BPG3aNNm3bDZ71apVctMobAQAaMbR0VHZn+AUIQCgHaGywOLz+cHBwdnZ2UwmUygU7tu3Lzo62sHB4fz58126dKEwEACgo8rMzJR9W1tbe/bs2fT09L179xoqJQCATnWwuzOQqCyw4uPj8/LyLly44Obm9sEHHyCEkpKSpkyZsnbt2sTERAoDAQA6qs8++0yuZerUqTExMWvWrImOjtb1XSoAAAbXMc4PIsrv5B4bGxsUFET+VDsgIOCrr746c+YMhVEAAJ1NWFjY27dva2trDZ0IAACoi8oCq76+vuUl7R4eHnq73zcAoEP6888/ORyOg4ODoRMBAFCsQ17eTqDyeLuPj09WVtaUKVNkGy9dutSnTx8KowAAOrD169fLtRQUFKSlpQUFBamYa8+ePbIPK1y0aNHo0aMbGhr27t2bl5fn4OAwc+ZMHx8fhJDCRgAAoByVBVZcXFxISIiZmZm/vz9CKC8vLzU1NTk5+ejRoxRGAQB0YC2v1zQ1NR01atSePXtUzFVRUTF9+vTBgwcTb4kb/+7atQvH8a1bt+bl5SUkJBw4cIDD4Shs1NFYAACqddTL2wlUFlhBQUGpqakrVqw4cOAAQqh///4cDicxMVHumBYAAChTVVWlwVwVFRXTpk1zcXEhW3g83r1793766Scul+vs7JyTk3P58mV/f/+WjZMmTaIufQCA5jrS+UFE+X2wIiIiJkyYUFJSUlZWxuVyvby8dP3oNABAx/D06dOioiIbG5v+/ftbWVmpP6NYLObxeMeOHSsoKLC2tg4JCQkNDS0vL7e1teVyucQ0Xl5eZWVlrq6uLRupHwkAAFBeYFVUVLx9+9bHx8fV1XXjxo2JiYkTJkwIDw+nNgrotHR6PPm3DZ666xyoUFdXN2XKlKysLOKtg4PDkSNHVF90JYt40o63t/e8efNKS0t37txpZWWFYZi1tTU5jbW1dUFBQU1NTctG8u2lS5caGxuJ101NTSKRSCgUajk0qhBP+9bpA8W1hOM48YR1QyeiFIZhGIYZc4ZSqRQhJBaLDZ2IKhKJBP2TqpYi/u+Z7Fs9H75q05ogkUg0uNExlQXWlStXxo8fHxERcfDgwYSEhA0bNnh6eqakpBw/fjwiIoLCQACAjuS7777LysqKiYkZM2bMixcv1q9f/8UXX5SUlNjY2Kgze7du3dLS0ohbZHG53OLi4qtXr44YMUKuHCF2kS0bydd79+59+fIl8bp3795NTU1kvWUMZFM1Wka1xBQy/gxFIpFIJDJ0FqoYeQmopjatCWKxWIOaksoCa82aNV5eXsuWLUMIJScnf/vttxs3boyKitq+fTsUWBKJRCAQvHv3Tj/h9BMIhoP++U+urq6O6nQUEAqFzc3NOg1BDEfjBa7Z/7Vnz56dPHnyvn37iLcffPDBmDFjbt++HRISos7sNBpN9gak3bt3v337to2NTX19PdnY0NBga2ursJF8u23bNnLxJiUlWVtbG88jKPh8voWFBXmLQSPU0NCAEGrTuV09wzBMIBCwWCxDJ6KURCJpbGy0tLQ0MzMzdC5KCQQCBoOh/S1/J/yrRPat/q++atPWbW5ursFD/6gssB4/frx9+3Zvb++SkpJXr15FRkYihAIDA3/77TcKo7RTdDrdwsJCb/tr/QQyxHAq9RKlDRobG4VCIYfD0enpGwzDampqmEymri9qbGhoEIlEGn+ymj14tLy8fMmSJeTbESNGIIQqKirUnD07O/vOnTsrVqwg3lZUVDg5Obm6ulZXV9fU1BAlVGFhobe3t8JGsh93d3fytZmZmampqfHcOJ4oIo25wCLWf+NZYi1JpVK5WtzYECehTExMjDlJExMTo9o0NNamIZiYmGiwh6dyc2WxWHw+HyF04cIFDofTr18/hFBzc3MH+CQAALojlUotLCzIt0wms02zu7u737p1KzU1taqq6v79+5mZmWPHjrW3t/f19T18+LBAILh9+/bTp08DAgIUNlI9GgBAKzrwzUVlUVn6DB8+fM+ePRwOZ8eOHSEhIaampiUlJbt27SIqLQAA0AVnZ+f169cnJydnZGQ4ODjExMQMHDgQIbR06dLdu3fPmjWLy+XGxcXZ29srawQAAMpRWWBt3LgxMDBw1qxZtra2cXFxCKFPP/20rq5O9R0CAQCgrq7uzZs3qlvee+89ZbP37t178+bNco1sNnvVqlXqNAIAAOWoLLDc3d0LCwuLioq6detGclMGBAAAIABJREFU/Bb6hx9+GDRoUNeuXSmMAjozHR9JztBl50CVZcuWEb+PUdGiwc+kAQDGppOcH0QUFlh8Pv/YsWNjxozp3bs32RgaGkpV/wCAjur77783dAoAAEAxygosFov1/fffm5mZRUVFUdUnAKAzWLNmjaFTAADoQ+c5fIWo/RVhbGzs7t27jf82bgAAAAAAOkXlNVh8Pp/FYrm5uX366aeOjo6yt2zZsWMHhYEA0LW2P5OHp/6k8EweAADo2IevELUF1s8//4wQsrOzy8/Pz8/Pl/0TFFgAtPTF4TDddX502n+v2Yd6EQBgcDp9mKwRorLAevLkCYW9AQAAAAC0UxTfYx3H8czMzFu3btXW1u7bt+/mzZtDhgzR7OkZRmjPnj0XL14k3y5atGj06NENDQ179+7Ny8tzcHCYOXOmj4+PATMEAAAAjFCnurydQPE1WMHBwdnZ2UwmUygU7tu3Lzo62sHB4fz588bzzFRtVFRUTJ8+ffDgwcRb4nFmu3btwnF869ateXl5CQkJBw4c4HA4Bk0TgPZNJBKVlJRIJBK5dvjvBQDQjlBZYMXHx+fl5V24cMHNze2DDz5ACCUlJU2ZMmXt2rWJiYkUBjKUioqKadOmubi4kC08Hu/evXs//fQTl8t1dnbOycm5fPnypEmTDJgkoATc0dRQsrOzQ0NDa2trW/4JbjQKQMfQGQ5fIWoLrBMnTsTGxgYFBZWWlhItAQEBX3311aFDhzpAgSUWi3k83rFjxwoKCqytrUNCQkJDQ8vLy21tbblcLjGNl5dXWVmZYfMEoF1bsmSJlZXVpk2b4BmmAHQYne3ydgKVBVZ9fb2rq6tco4eHB4/Xhl8kGa3KykqEkLe397x580pLS3fu3GllZYVhGPFQIIK1tXVBQYHC2TEME4vFAoFAP9nqJxAMx5gDGWQ4Wh5kwnH80aNHO3bsmDNnjtZ5AQCMUSc5fIWoLbB8fHyysrKmTJki23jp0qU+ffpQGMVQunXrlpaWRqfTEUJcLre4uPjq1asjRoyg0Wiyk7W8cIQglUqbm5v5fL4+ckVIP4FgOMYcSDaKTvdofP7P5GsMw7TpSiKRYBhGHhIGAHQAnfPwFaK2wIqLiwsJCTEzM/P390cI5eXlpaamJicnHz16lMIohkKj0YjqitC9e/fbt2/b2NjU19eTjQ0NDcSV7y3R6XQLCwsbGxudJ4oQQkg/gWA4xhzIIMPR8ifDDAYjKipqy5YtQUFBVlZWWqcGADAunefwFaK2wAoKCkpNTV2xYsWBAwcQQv379+dwOImJiXLHtNqp7OzsO3furFixgnhbUVHh5OTk6upaXV1dU1ND1FWFhYXe3t4KZ6fRaCYmJnq7Y4V+AsFwjDlQOx2Om5vbuXPn3N3dR44caWdnJ3uEePfu3RQGAgAAnaKywLpy5crYsWMnTJhQUlJSVlbG5XK9vLyEQmF2dvawYcMoDGQQ7u7u27dvT01NHTVqVHl5eWZm5vLly+3t7X19fQ8fPhwTE5Obm/v06dOvv/7a0JkC0I4dP36cOEX4+PFjQ+cCANBWpz0/iKgtsEaNGvX06VMPDw9PT09Pz/88PePmzZthYWF6u7pFd5ydndevX5+cnJyRkeHg4BATEzNw4ECE0NKlS3fv3j1r1iwulxsXF2dvb2/oTAFoxx49emToFAAAOtGpzg8iSgqsU6dOrV+/nngdFhbGZDJl/1peXt6rVy/toxiD3r17b968Wa6RzWavWrXKIPkAAAAARqszH75ClBRYjo6Ofn5+CKHc3FxfX1+5S2tHjRoVFRWlfRQAQEcVHx/v4uJC3JohPj5e2WQJCQl6TAoAQKXOdvgKUVJg+fn5kQXWhg0bZG90DgAArUpOTh48eDBRYKWkpCibDAosAEA7QuU1WNnZ2SKR6O+//4aHiAEA1PfixQvyNTwLAYCOQfb8YCc8fIUoL7DgIWIAAAAAAFQWWPAQMQAAAADA4StEYYEFDxEDAAAAACBQVmDBQ8QAAB0GjuPNzc0ikcjQifwHhmHNzc1yTz41KsR1IMazxFrCMAzDMGPOUCqVIoQkEomRJykWi7V86qgxaNNClkqlGlzpRFmBBQ8RAwB0GDiOE9/Hhk7kP4h8jL/AMp4l1hKGYcRiNHQiShG5GdWK15I6q2JkQjn52mjPD7ZpIWt2HTmV12DBQ8QAANqrqKh4+/atj4+PSCTauHFjSUnJhAkTwsPD9ZmDiYkJk8m0sLDQZ1AVxGIxk8k0MTExdCJKEccDjGeJtSSVSqVSqTFnKBaLhUKhmZmZ3P26jQqGYWZmZgwGw9CJaKtNawKdTtfg3xsqCyx4iBgAQEtXrlwZP358RETEwYMHExISNmzY4OnpmZKScvz48YiICENnBwBoBVzeTqKywIKHiKmA47g+z6zrJxAMx5gDGWQ42t+QZc2aNV5eXsuWLUMIJScnf/vttxs3boyKitq+fTsUWAAYs07+YJyWjPeAcweD4zh5VYceTrHrOQoMR7NA+onSvgI9fvx47ty53t7eJSUlr169ioyMRAgFBgY+efJE+84BADrSsrrq5IevkPZHsG7cuKHOZCNGjNAyUHtnYmLCYDD0dvpfP4FgOMYcyCDD0f4qbBaLxefzEUIXLlzgcDjETfWam5vpdCoPtwMAKATHrhTSdp/l7++vzmRwJ/eWfvA03h/iAmAow4cP37NnD4fD2bFjR0hIiKmpaUlJya5du+D2xQAYJ6iulNG2wDp16hQleQAAAEJo48aNgYGBs2bNsrW1jYuLQwh9+umndXV1e/bsMXRqAAB5yqorOD+ItC+wJk6cSEkendCtmsW663zsPy++4H6luygAUM7d3b2wsLCoqKhbt27W1tYIoR9++GHQoEFdu3Y1dGoAgP8Bx65Ug8saAADGxdTUtLS09MiRI7W1tfv27bO3t3d0dDR0UgCA/1JdWsHhKwIUWAAAI8Ln84ODg7Ozs5lMplAo3LdvX3R0tIODw/nz57t06WLo7AAAaOJ3pYZOoX2A2zQAAIxIfHx8Xl7ehQsX8vPziZakpKTS0tK1a9caNC8AAEIITVn/QvUEcPiKBAUWAMCInDhxIjY2NigoiHwsTEBAwFdffXXmzBnDJgYAaPXMIFRXsqDAAgAYkfr6eldXV7lGDw8PHo9nkHwAAAS47qqt4Bosg3H4aYwOex/TMW88BjcP6/B8fHyysrKmTJki23jp0qU+ffoYKiUAAFRXGoACC1BDP/eD0M+9LYABxcXFhYSEmJmZETcxzsvLS01NTU5OPnr0qKFTA6AzavVeDFBdKQMFlrYaGhr27t2bl5fn4OAwc+ZMHx8fQ2cE2g29HZBrR7dDCwoKSk1NXbFixYEDBxBC/fv353A4iYmJcse0AAB6ANWVNqDA0tauXbtwHN+6dWteXl5CQsKBAwc4HI6hkzIAOOOpAb0dkGtfn05ERMSECRNKSkrKysq4XK6Xl5elpSW1IQAArYLTglqCAksrPB7v3r17P/30E5fLdXZ2zsnJuXz58qRJkwydV4elt0LhG53+Zm2aDvvuGOh0uqenp6enp6ETAaCTgupKe1BgaaW8vNzW1pbL5RJvvby8ysrKDJuSnPZ16KKzgU+HsGLFCnUm27Jli64zAQAgqK4oAgWWVmpqaojHpRGsra0LCgoUTikWi/l8vt5+aq6fQDAcYw5kkOFIpVINesjIyFBnMiiwANADqK6oAgWWVnAcp9Fosi0SiUThlCYmJnQ63dzcnGzpnVyvZhSJRCKVSs3MzORiqUP9KAghkUhkampKp2uyVnTy4ch+srqIguN4c3MznU43NTXVaSCxWIxhmGbDQQhp8JkihIqLizULBwCgFlRXFIICSys2Njb19f/96mpoaLC1tVU4pampqbm5uZWVlQZRmpqampqaWCyWZl+u6hOJRAwGg81m6zQKn88XCARsNpu8Vbcu4DguEonMzMxYLJbuoiCEGhsbpVIpm83WrLZQE4ZhNTU1ZmZmur7cu6GhQSQSabaiIoQo+UwPHDiQlJR07tw5BweHq1evLlmyZPHixTNmzNC+ZwCAClBdUQvu5K4VV1fX6urqmpoa4m1hYaGbm5thUwKgXTtx4sTs2bNxHLewsEAIubm5sVismTNnJicnGzo1ADopeAaOZqDA0oq9vb2vr+/hw4cFAsHt27efPn0aEBBg6KQAaMcSExMnTZr0559/EkdSe/bseevWrYiIiB07dhg6NQA6MmWHr6C00hgUWNpaunSpQCCYNWvW8ePH4+Li7O3tDZ0RAO3Y48ePx40bJ9c4fvz40tJSg+QDQGcA1ZUuwDVY2mKz2atWrTJ0FgB0EO7u7o8ePZJrfPToUa9evVTMVVVV9eOPPxYWFpqbm48cOfKLL76g0Wh79uy5ePEiOc2iRYtGjx4Nj14AQA5UVzoCBZb+HD9+/Pfff9dgRgzDMAwzNTXV6WXUCCGJRGJiYqLTa8/RP8PR7Md9bQLD0YBUKsVxXOPhCIVCLROYOXPmkiVLbG1tp02b5uTkxOPxfv31123btm3atEnZLFKpdNWqVX369Nm2bVt1dfXWrVtZLFZYWFhFRcX06dMHDx5MTEb8AAUevQCAOqC60h4UWHoycuTIjz/+WLN5cRxHmv4A3ggDwXCMOZCWUbSv/xYtWlRRUbFu3brVq1ebmJgQ/1rMnz9/yZIlymYpLi5+9+7dwoULGQyGo6NjeHh4Tk4OUWBNmzbNxcWFnBIevQCAnJaHr6C0ogoUWHrCYDAYDIahswCgHdiwYcOCBQvu3LlTXl7u7Oz80Ucfubq6qpiew+HMmjWL3L4aGxsRQmKxmMfjHTt2rKCgwNraOiQkJDQ0VPWjFxobGzEMI15jGIbjOFFuGgljy0chY86QyK1dZKi3JEPXFMq1dJ7qSg8LGQosAIDR6dq168SJE9Wc2MnJycnJiXhdWFh47ty5OXPmVFZWIoS8vb3nzZtXWlq6c+dOKysrDMNUPHph6tSpL1++JF737t373bt3RvWQ6ebmZkOn0Lrq6mpDp9AK48+Qz+fz+Xw9BJq5XU8PezBObVoTBAKBsruIqwAFFgDAwOLj411cXObMmUO8VjZZQkKCik7EYnFqaurZs2djYmL8/f1xHE9LSyMuJuNyucXFxVevXh0xYoSKRy8EBga+e/eOeF1eXm5ubs5kMrUZF4Wam5sZ/5+9+45rIukDBj6B0INAKNJEsWBDEWnqWU9BsALWs+GJgop3eiqePrYXFU89QWxYsIByIiqKigVBz3IqZ8GCCiio2Ai9hkBCsu8f+zz75k0jgQkE+X0//JFsdmdmJ2V/zMzOaGg0Q090o5Hxn6amZksXRCqCIHg8niqXUCAQkG+0sueUlqbtNF8hhBT6dtPp9EaMf4AAq5k8fvz4+PHjjTsWRvmockZwOsJMTU1DQkIUPSo6OtrFxYUMsGJjY6XtJiPAYrFYoaGhDAbjzz//7NSpE0KIRqMJD9Xv0KHDw4cPZS+9sHjxYurxqlWrdHV1lb2qgfwqKyv19PSUfYtDU5CxqerUmDg+n89ms1W5hDwej8vlNk9kD0OvFPokaGhoQIClukpKSvr16zd9+vSWLggAShQYGNiIoz5//kw9Fh4UJSc+nx8SEuLq6jp37lwqNLx3715aWlpwcDD5ND8/38LCglp6gYyrsrOz7e3tG1FgAFo1iK6aBwRYzUdLS0t4/AcA35+mN7DdvHlz4MCBIoOfSktLX79+PWTIEImHPH36tKysbNSoUQUFBeQWOp3epUuX8PDwuLi4UaNG5eXlJSUlrVy5klp6ITAw8Pnz51lZWb/++msTCwxA6yJ7wUGAEQRYAAAVMmrUqKysrO7duwtvfPr0qbe3t7SRvx8/fqypqQkKCqK2dO3aNTw8PDQ0NDo6OjEx0dTUNDAwsH///gih5cuX792719/f38zMDJZeAABB85XSQIAlQWFh4fz58xMTE1V5xAMA35Nz586FhoaSj729vUXGoOTl5cmYyX3q1KlTp04V396rV68dO3aIbISlF0BbBp2DzUnVA4gVK1ZkZTV3e6a+vn5gYKCMzo5mKxWLxZo2bZqRkZG6unqHDh0iIiIacadogwiCOHXqVNeuXTU0NPT19UePHp2Tk4M9F4TQ58+fvb29DQwM6HR6x44dDx06RE07hBFBEEePHrW1taXT6QYGBuPHj8/Ly8OeC0Kopqbm+fPnaWlpaWlpjx49ou7wx4vP5+/bt8/GxkZdXd3IyGjy5Mlfv35VRkbZ2dmjRo1iMBgaGhrdunU7c+aMMnKRxtzcfPDgwYMHD0YIOTk5Df7/+fv7nzx5sjnLA8D3BzoHm5mqt2BVV1eLhxQ8Hk+pk3bS6fSxY8cqWirs3r175+TkVFVVRT798uXLb7/9lpCQcOfOHbxNa35+ftTVq7q6+saNG3369ElNTf3hhx8w5vLy5cuBAwfW1NSQTz99+rRw4cJLly5duXIFYy4IIR8fn4sXL5KPKysrk5KSbt68+c8//5A9RLiUl5dnZ2cbGRlRo4VKS0tLS0v79u2LMReCIDw8PG7dukVlmpCQcP369cePH/fs2RNjRrdv3x4xYgT1NCcnZ9q0aampqYcPH8aYiwxUdPX8+fOtW7cKT78OAGg6idEVNF8plUq3YK1du5bFYm3ZsuXWrVtVVVVTp07NyMgICAhIT09/+fLlihUrpk6d6ufnFx0dTRBETU3N5MmT//77bz8/vylTpmzevJnD4SCEEhMT58+fP2XKlFWrVlEtGe/evVu1atW0adOWL19OriwrnH5KSsqECRMEAkF6evrvv/++d+/e6dOnz507NyYmhs/nC5dKWvpYLFq0iIquKP/88w/epoV///1XvG2gtrZ2zpw5GHNBCPn7+1PRFeXq1atXr17FmMvNmzep6IrC4XDmzp2LMReE0Nu3b42MjIS3aGtr19fXl5aWYswlMTGRiq4obDZ7/vz5GHNBCM2ePVt8Y1RU1LNnz/BmJBubzZ47d25LTQIEQJsC0ZWyqXSAFRoaam5uvm7duh9//BEhxOPxbt68uW3bNkdHxy1btgwcODAyMjI4OPjq1asvX74kd7h3797u3bvDw8Nzc3OTk5MzMzNjY2N//fXXyMhIc3Nz8t/xioqK9evXDx8+fP/+/cOGDQsJCSFXqKXSd3Nzo8qQmZlZW1sbERERHBz8zz//nD9/XrhUEtOXSNFZ+fl8/u3btyW+dOrUKYWSki0xMVHi9vfv33/69AlXLtXV1U+ePJH4Unx8PK5cEEJnz56VuD0jIwNj6FNXVyexB5nBYBQWFuLKBSEUFxcncXtaWlrTl1Wm5ObmSuvflPbxUBI9PT3qXxcAAC4w9KpFqHoXoTA+n+/j48NkMvl8/s6dOzt06IAQqq2t1dHRIScPJAjCz8/P0NDQ0NDQxcWlsrKSnFyYzWYbGBgsXbqUfHr37t3OnTuPGTMGITRx4kRdXV02m62pqUmlLzyDPp1ODwoK0tXVNTc3nzNnTmxs7JQpU6hXJaYvkaKDjdhsNp/Pl/gSdS86FuSKIhKVlJTg6qmhJsgWl5+fjyULkozToWY/ajoejyetlQXveibSwjWBQFBRUYFrNkIZS0bgfXfkQbYZ+/j4qPKEkAC0IjD0qqW0pgALIWRqaooQUlNTe/HiRVhYGELIzMxMuC2BuumanMTZwcEhICDgwoUL4eHhtra2kyZNcnNzKywstLS0pA5xd3dHCJGdcWT6wphMJjXIxsbGpqioSPhVielLLLmio6b09fV1dXXF+9QQQnZ2dgolJZvI/fDCMI6DMTMz09DQ4PF44i/16NEDVy5kauJdhAghdXV14Te9ibS1tXk8HkEQIu1YAoFAR0cHVy4Ioe7du9+5c0d8u6amprGxMa5cZLzReN8debDZbD09vc6dO48YMcLc3Fz4i7Nr165mLgwArR0MvWpBKt1FKI78tX327Nnp06c3bty4e/futWvXCjdLiFzwWCxW9+7dd+zY8ddff3l4eOzatau2ttbY2Fi4EejcuXMfP34UTl9YWVkZ1Rfz5csXkauaxPQlllzRCRhpNNrMmTMlvrRw4UKFkpJt+vTpEhfnGjFiBMZLuKampre3t8SX/P39ceWCEPLz85O4fcyYMRgX7qXT6ZqamuLdvuXl5RjDOISQtLFWU6dOFV4HponMzc0HDhwovl1TU1Pi9AdKdeTIERaLZWxs/PLlyxs3blwX0swlAeC7BNFVs2kFAVZNTY3IlaympkZNTY3L5dbV1SUnJ3/48EFaWPPmzZuQkJCcnBwOh8PhcAQCgZqa2pAhQ7Kzs1NSUsrLy69du3b69GkZnRE8Hi8yMrKoqCg7OzsmJmbkyJHCpZKYPq4T37lzZ79+/UQ2rl27Fu/NfZ06dYqMjBSJsTp06ID9rvgDBw6It5Zt377dwcEBYy49e/aMiIgQ2dilS5ejR49izAUh1KtXr/LycuE2ufLycgsLCz09PYy5uLi4bNq0STzrvXv3YswFIRQXFycSGmppaR05csTa2hpvRg3KlK6ZSwJAawdDr1qWqgdYQ4cODQsLExn0OnDgQBcXl2XLlgUFBZWVlc2cOfPo0aMSR+kOHz58wIABISEh/v7+f//997p168i+lY0bN16/fj0gIODq1atr1qyRMZuzlZWVlZVVcHDw1q1bBwwYQA7AokolMX2J6TRiwqd27do9ffo0OjqaHO7Tq1ev9PT0LVu2KJpOg/z9/TMzM+fNm4cQotFoEREROTk5VlZWeHMxNjZ+/fr1oUOHyMa8QYMGvXr1atWqVXhzQQgtXbo0MzPT09MTIUSj0Q4cOJCVlSXe+dtEWlpabm5uenp6NTU1lZWV9fX1vXv37tixI95cEELr169/8eKFk5MTQkhNTe3o0aMZGRmGhoZ4c+nYseP79+937txJPg0KCsrKypJ4a2Hzy8nJ2bZtm7Ozc0sXBIDWBIZetTiaone3tSnp6elHjhyJjIxselJXr14tKiqS1oElm66uLofDWb58OTnsTElqamr09PTU1NSkDa7HRUtLi8vlHjx4sHELA8vpxo0bo0ePptPpEgd+tTo7d+4MDg7W0tLCePOgRGpqagRBNPpnYcaMGbjucn379u25c+fOnj37/PlzhJCxsXFxcTGWlOWxatWqX375hbyTRhVUVlYyGAxVXluCvJEFe+iPEZ/PZ7PZqrwgLI/Hq6ioYDAYTb9/BYZeNejUHAVukQ4PDx8yZIiLi4tCWbSyQe4AgO9bVlYWGVeRc6906tRp6dKl3t7e0lZ6BgDIA6Kr5gcBlizGxsYDBgxo6VIA8P178+YNGVe9evUKIdStW7exY8deuXLlw4cPLV00AFoZGHqlIlS3wVkVdOzYEfuE5gAAEb169erdu/fGjRvpdPqmTZsyMjLevn27aNGili4XAK0PDL1SHdCCBQBoYZmZmXQ6PTg4eOXKlbgmgwWgrZEWWkHzVUuBFiwAQAtbt25d586d//jjD3Nz8zFjxsTExFRUVLR0oQBoNSb8JwuiKxUELVjNhCAIgUCg6A16AoEgNjaWXH3l+vXrM2bMEJ8ZC4v3799v27aNLOeuXbsWLlwobb6JpuDz+ceOHSNv64uJiRk0aFCvXr2w54IQqq6uJiePJQjiw4cPHTp0UHSiV3nweLxDhw5duXKltLS0f//+y5cv79atG/ZcEEIZGRmnT58mczxy5Iifn58y7iarq6vbv38/ef9gUFDQ8uXLO3XqpGgijbv9cPPmzZs3b37x4sWZM2fOnDkzd+5cLS0tcta0oqIi7FNsAPA9kdEnCNFVy4IAq/nweDyJS99IU1VV5enpmZGRQT598+aNs7NzcHDw+vXr8RbsxIkTy5cvJ8M4giCWL18eHh6ekpKCdyqs0tJSd3f3d+/ekU8fPnzYt2/fzZs3L126FGMuCKGvX79WVVVRE53X1NQ8evSoe/fueENGFovl7u6el5dHPn369GlMTMyOHTvI6cQw2r59e2hoKPlYIBAsWLAgPDw8OTkZ7/3wnz9/HjVqFLXyYGRk5JEjR/bt2zd9+nSF0mnKtC8ODg4ODg6hoaHPnj2Lj48/c+YMQsjCwmLo0KGTJk3y8fHBO0s+AK0dDLdScdBFqLrWrFlDRVeUP//8My0tDWMunz59+u2330SWKP7y5Qv2SaqWLl1KRVeU9evXi59jU7DZ7KqqKgMDA2qLhoYGk8nEfjPawoULqeiKxOVyly1blpubizGX9PR0KrqiZGZmBgcHY8wFITR37lyRdZ25XO6SJUu+fv2KNyN5ODo6btu27f37948fP16+fPn79++XLFnS/HPKA6DKGoyuoPmqxbWtFqycnJwDBw58+/atf//+6urqnTt39vb2LiwsPHjwYGZmpqWlpbu7u6enZ01NzZw5c4KCgqKjo2tqavr27bty5UodHR3xPRFCiYmJSUlJFRUVtra2QUFB0ubyVvQ/e4Igzp49K/GlY8eOYZw84uzZsxKn4rx7925paSmuEcdcLvfq1asSXzp58uSOHTuw5IIQKioqEp9IkJw9FeNCRlVVVRLXYEYInT59eu3atVhyQQhFR0dL3H7hwoUDBw7gWo6woKDg8ePH4tu5XO6FCxeWLFmCJZdGcHZ2dnZ23rFjx6NHj+Lj41uqGACoFHkariC6UgVtqAWrpqZm48aN7u7u+/fv79Kly+3btxFCfD5/w4YN1tbW+/fvnzdv3pkzZ+7evYsQ4vF49+7d2717d3h4eG5ubnJyssQ9MzMzY2Njf/3118jISHNz88OHD0vLXdGlcqqrqzkcjsSXxNuBmiInJ0faS58/f8aVS1FRkbQZ1bOzs3HlghDi8XgSh1tpaGjU1dXhyuXbt2/ShtPhPR1p7w6Xyy0tLcWVi8Rlpkhv377FlUtTuLq6KnUZAwBaBRmD2YVBdKUi2lALVlpamqWlJdns5Ovrm5KSghB69uwZm82ePXs22ZGtlbGlAAAgAElEQVTk6+ublJTk7OxMEISfn5+hoaGhoaGLi0tlZaXEPWfOnIkQYrPZBgYGS5cuFeloE6boIGtdXV06nV5fXy/+kpmZmWJnLlP79u2lvWRsbIwrFxkDhszNzXHlghCS1kZVX1+voaGBKxcjIyNpL+E9HWnju9XU1DCu+CGjnRLv6QAAGg1GXLU6bSjAKiwsFA4myMcsFqu6uvrnn3+mtlPhC7UCNNkRI3FPBweHgICACxcuhIeH29raTpo0yc3NTWLuinZOqaur//DDDxL7ocgFp3EZP358eHi4+PbOnTtjHPWip6fXv3//9PR08ZcmTZqEKxeEEJPJLC0t1dXVFdkuEAhwdaghhMzMzLp16yaxKdHb2xtXLgihyZMnX7hwQXy7q6tr01cro9ja2lpZWUkcbjV27FhcuQAAGkf+0ArarlRKGwqwjI2NhS/wRUVFCCEjIyMTE5OoqChyY2lpKdUxJ9LmJHFPFovVvXt3Dw8PLpd79+7dXbt2RUdH47ryRUREDBkypLq6WnjjwIEDfX19saRPcnJyEl+gV0tL6+DBgxhzQQjt37//xx9/FOn3HD16tIeHB8ZcjIyMCgsLRXoDS0tLbWxsMOaCEDp8+LCnp6dIRlOmTBk0aBDGXMaNGzds2DCROFtXV3fv3r0Yc0EIRUVFjRkzRmTj3LlzHRwc8GYEAJAfhFatWhsagzVgwID8/PzU1NSKioqLFy+yWCyEkKOjI5fLPXPmTFlZWVZWVnBw8PPnzyUeLnHPN2/ehISE5OTkcDgcDoeDcRg1QqhLly7p6em+vr5kqMdgMLZt23blyhXscyAdOHDgyJEjtra25NORI0empaVhX4Sxd+/eT58+pVpEjIyMIiIilDFy2c7OTltbu6qqinxaVVXVpUsXfX19vLk4OTk9fvyYig4tLS0PHjx45MgRvLnQaLTExMQ///yTui9y4sSJ6enp5BxRGA0ePPjJkyfDhw8nn3bp0uX48eN79uzBmwsAQH5yDrci/5qhPEBRbagFS19ff+PGjfv27Tt+/Li7u7u9vb2+vr6urm5ISEhUVFRCQoKenp6Xl9fYsWMlzlYlcU+BQJCTkxMSEsLhcGxsbNatW4d3siVzc/Po6Ohr165xOJy5c+cuXrwYY+IUGo02derUcePGmZubq6mpSeyTwsLa2jouLs7ExITL5f6f//N/hPtbMaLRaObm5tTtnHZ2dsrIBSHUqVOnc+fOGRkZ8fn8v/76y8nJSRm5qKurBwYG1tXVkZ+ukydPKiMXhJCdnd2lS5cMDAwIgnj27JmScmkt6uvrKyoqGAxGSxfkvwQCgYrPbk/ex1NWVtbSBZGKIAiCIFS8hAghNps9ddPHBneGoKqJFPok1NbWKjpPOGpTAVZJScmnT58iIiLIp4sWLSLH9nbq1ElkqiFdXd1Lly5RT6kZocT3VFNTCwgICAgIUG7RAQDNi06nGxgYyLiboZlVVlYyGAxlzOCPS3l5OZJ5O0uL4/P5bDYb490h2PF4vEkbG55ID0IrLBT6dmtra6urqyuaRRsKsOh0emRkpJqamqur6/3796urq/v06dPShQIAAABg4tDvUBsKsAwMDFavXh0bG3vs2DEbG5v169djvK0MAAAAaAQIrb5XbSvCICeGbulSAAAAABBafefaVoAFAAAAtDjZoRXEVd8H1R0y2YIKCwsnTJig6OI2CKG///4be2Hev39Pzuf+7t07ibc3YsHj8ZKTkxFCBEHgXX1ZRE1NDXmnTGVlZSNqWE51dXUPHz5ECBEEkZmZqaRcEEKvX78mT+fevXsy5vFvIg6H8/r1a4QQedeqknJBCFFzW5SUlCi6eiYAoEHkWjcyoiuYc+F70kZbsFasWLFgwYIePXpIfFVfXz8wMFChxW0KCgrOnTv38uXLESNGSNxBXV1dU1NTobu+CYJYuXLlrl27yKfJyck9e/Y8derU6NGj5U9EHunp6d7e3uSCdARB/PDDD9OmTYuOjtbS0sKYC0EQWVlZVIzI5XIzMzO7deuGaz1pyoMHDyZPnkzOcyYQCNzc3H7++edDhw7hHXLH4/H8/f1jY2PJpxs2bDh48OD58+ddXV0x5oIQunnz5vTp00tKShBC9fX1/fv3DwoK2r17N94byvh8/uvXr6n7kCsrK1ksVs+ePRW95UqVb3MDoAVBb2Ab9J0HWDweT+IKdNXV1RKX+SPp6OgoukIIg8EYPXr0q1evZOxDo9EUCtrCw8Op6IpUVlbm7e2dmZlJTQradJWVlT/++GNlZaXwxvj4eCaTGRkZiSsXhFBOTo5AIKAu2OSSju/evXN0dMQYyRUVFXl4eIg09R0/ftzExGTHjh24ckEIrVy5koquSN++ffPw8Pj48SPGe/s/f/48fvz42tpa4Y379+83Nzdft24drlwQQpmZmRoaGjo6OuRTbW1tbW3tN2/euLi4wL0gADQFhFbNY1+POvGNS7JwNhMoqnX/u5menv7777/v3bt3+vTpc+fOjYmJ4fP5VVVVU6dOzcjICAgISE9Pz83NXbNmzbRp0xYvXnz9+nWE0Nq1a1ks1pYtW27duoUQKiws3LRp008//bRixQpyh5KSErKLsKamZvLkyX///befn9+UKVM2b95MrvSSmJg4f/78KVOmrFq1Ki8vDyGkp6fXtWtXRVd0lm379u3iG+vq6vAukxIXFycSXZGOHDmCsUeSIIiSkhLxWVgNDQ0/f/6MKxeE0PHjxyUWe9++fTweD1cudXV1hw8fFt9eWVl54sQJXLkghA4ePCgSXZHCwsIwduHV19fX1NSIB1Lt2rXLz8/HlQsAbY3s3kAEHYJNs69HnfCf7H2auWykVv+/aWZmpomJSURERElJSUREhK6urqenJ4/Hu3nz5rZt2zQ0NBYuXDhx4sTg4OAPHz6EhYUxmczQ0NDAwMBffvnF3t6ez+dv2LDB1dV1yZIl+fn5YWFhurq6vXv3ptLn8Xj37t3bvXt3VVXV+vXrk5OTu3fvHhsbu2HDBgsLi5MnTx4+fFhk9lGJFJ0Els1mk6slivv3338VSkq2J0+eSNzO4/FycnL69u2LJRculytxljY1NTU2m40lC9KjR48kbudwOF++fMHV8peXlyeyCiEF77uTlpYmcXt5eXlJSQm1HnkTsdlsiSsQaGhoUKOyAAByknOJm2YoyXemKXESeWwzN2i1+gCLTqcHBQXp6uqam5vPmTMnNjbW09OTz+f7+PgwmczU1FRjY+OpU6cihJhM5tixY1NSUoRHyTx79ozNZs+ePZvssfL19U1KShIOsAiC8PPzMzQ0NDQ0dHFxqaysJMcys9lsAwODpUuXyjm0WdHGLYk9myS8Q6NkLE2NcdkfGo3WPIOmZVQOxtORkQuupb4bTA3j6aipqUl7d2BMFQDyg9AKO7yNT1RqzRNptfoAi8lk6urqko9tbGyoVh9TU1OEUHFxsaWlJbWzpaXl48ePhQ9nsVjV1dXCi+KZmZmJZEG1E5B9KA4ODgEBARcuXAgPD7e1tZ00aZKbmxu5w3/+8x9p5VT0QqWpqdmjR4+sLAlf1zFjxiiUlGyjRo3at2+f+HYDA4Nu3brhykVTU1PiPYM8Hg/vaiReXl6nTp0S325mZmZlZYUrFxsbGyaTWVpaKv6Sp6cnrlwQQmPGjLl69ar4dltbW4wrfjAYDIn/J3A4HAsLC1y5APBdkieoIkFoJadGBFUPSpfJeHUQM6LpWTRCqw+wysrKamtryX/0v3z5YmxsTG4nAxoTExPhThYWiyXSq2JkZGRiYhIVFUU+LS0tJUdZCRNpfGKxWN27d/fw8OByuXfv3t21a1d0dDRZAGtra4yntnfvXnd3d5GN7du3X7hwIcZcxo8f37dv35cvX4psDwsLa8TSSzJ06tTp69ev+vr61BaBQFBVVdWzZ0+MuUybNi0kJER8LoM9e/ZgzIVGo4WHh8+dO1dku729va+vL8aMfv755z/++OPr168i2/GOw6PRaBYWFuXl5dQgd4SQQCDgcrni/28AAOQPqhDEVXJTNOiRHVRJ21M82FKeVt/+z+PxIiMji4qKsrOzY2JiRo4cKfyqm5tbYWFhQkJCRUXFixcvkpKSqB3ICZkcHR25XO6ZM2fKysqysrKCg4OfP38uO8c3b96Ql3AOh8PhcAQCgTytU43oIBs1alRycrJwC9zo0aMfP36s0FwPDVJTU7t9+/bs2bOpLYaGhidOnPD398eYC0LI3NzcysqKavWpqampra11dHTE2wmloaFBTtNAbTE2Nj579uy0adMw5oIQ8vPzi4uLE25+++mnn27fvo33njtdXd1Hjx4J39Pavn37y5cvK3qXa4M6duxobGxMvTulpaV8Pt/R0RFvLgC0XtQUVgo1WUF01SD5x6E/KF0m/Ne47Jp4uEJafQuWlZWVlZVVcHAwQRDDhg2bMmWK8E1k+vr6mzZtioqKio+PNzY2njVr1sCBAxFCQ4cODQsLCwgIGDlyZEhISFRUVEJCgp6enpeX19ixY8k5h6QZPnx4Tk5OSEgIh8OxsbFZt26dPKNhGjcCycPD4/Pnz+/fv8/Pz+/ZsyeuQc0ijIyMTpw4sW3bNisrKzU1taKiIiXdmW9ubt6+fXuyRbBDhw79+vVTRi6mpqZnz55NSEiYPHmyurp6QUEB3qY4yvTp0ydPnqylpSUQCFJTU0WCe1wsLS2TkpI2bdq0ceNGTU3Nb9++KWlclLW1NdWL6uzsDLMzAKBQS5UwiKvk0WBQ1TxhkPK0+t9QGo02bdo04fYJfX39S5cuUU+7du0qPt/BzJkzZ86cST7u1KmTyG2AxsbGZAq6urrCSQUGBpIPAgICAgICsJ6HVGpqal27du3atauyMzI0NCQfKPXKSvW3KinooZB9kTQaTakZ0el08oyEuz6VgRxoSKPRlDrqnHp3ILoCbVajgyoShFYNkh1XtfagShj8jAIAAGjrmhhXIQitGtJ24ipK6w6wjI2NBwwY0NKlAAAA0CpBXNUMpIVW32VQJax1B1gdO3acM2dOS5cCAABAa9L0uApBaNWQNhtXUVp3gAUAAADIada2bwh9a2IiEFfJIKMfsO3EVZTvM8BKT08/fvy4PHMFlZeXz5kzJyEhoaysbP78+YmJiY0bRPzx48fIyEgtLa0uXbqIT4/UFARBkDMaMBgMvHO4i6Bu0edyuRhnCRdB3U0pY7FtLCoqKsjspC34jQWPxyPPqLy8XElZkMgFhQiCqK+vV94IdGq60dLSUiaTqaRcAGhmWNqrEIRWypyq6rvUWgOsFStWLFiwoEePHrgS1NfXDwwMJO+iakTiFy5cmD17dp8+fdavX//x48dOnTqJ7EBe6RVdPrmkpOTLly9aWlrq6upcLpdGo3Xu3BnvYiwIobKyst9+++3s2bMIIYFAYGJisnPnzlmzZuHNBSFUVFT07ds3MiL58uXL06dPu3Tpgj2YKyws/PXXXy9fvowQ4vP57du3j4iIEJ4ZC5f4+Phly5aRM9SPHj3a29t7z5491FS3uHz79m3RokWpqakIIS6Xa2FhsX///nHjxuHNBSF0/Pjx1atXk++OsbHxTz/9FBYWZmBgoFAizbMgEgDygLhKUbjmN1e1uMo0StICG55K/7FqrQFWdXU13iYQHR0davLGRiROrldYVlZWXl5ubm4ucR+BQKDQks9lZWWFhYXUBZucZTsrK6tXr14Ypx7g8/mjRo0SXpOnqqoqMDCQw+HMmzcPVy4IoaKiooqKCup0dHV19fT03rx507t3b4xTD3C53OHDh+fl5VFbysrK/Pz8eDweuSQlLidPngwKChLekpiY+ObNm7S0NIwtTDU1NYMHDy4oKKC2FBcXT5s2LS4uDu9co/v371+zZo3wlri4uFevXt27d0+hdwcCLNDiWmNc1WBkg33tvFbdFiU5YFI9LRxg1dTUzJ49e8qUKRcvXqTT6T4+Pu3btz9x4kR5efmPP/5IzjtVWFh48ODBzMxMS0tLd3d3T0/PtWvXslisLVu2BAQE/Pjjjy9fvoyJifn8+bOOjs6IESP8/PwQQgRBJCYmXr58ub6+fujQoX5+fnQ6PTc398iRI+/fvzc2Np4wYYLwsnElJSU///xzYmLi+vXrhRMXzx0hlJiYmJSUVFFRYWtrGxQU1LFjRyaTef369dTU1F9//RVXC9O3b9/Eu2kMDQ3z8/Mxrslz9epViSserl+/3s/PD2MkV1BQINK6o6amZmBgUFBQgHHBu4SEBOHoirJ69WqMARZBEOvWrRPf/vbt28TERIytZSdOnBCOriirV6/GGGDxeLzNmzeLb8/IyEhJSRk9ejSujADAC1csJaLZQivhKEdGBDOIGSEtHpIn8JJzkvQG92kGrSVyklPLt2DxeLxv374dPXr03r17+/fv79+/f1hYWEZGxh9//DF27FgLC4sNGza4urouWbIkPz8/LCxMV1c3NDQ0MDDwl19+sbe353K5W7ZsmTp16po1a1gs1qZNm8j1Pb58+ZKbmxsaGlpSUhIREaGjozN+/PgNGzZMnDgxODj4w4cPYWFhTCbTzs5OpDzCifP5fPHcTU1NY2NjN2zYYGFhcfLkycOHD4eGhl65ciU3N3f79u3SIhKJSx3LIG1/Op1Oji7C5c6dOxK3V1VV5ebmitdP4/B4PIk1o6GhUVVVhTHAIrvSxBUXF3/79k143aGm+Pz5c1lZmcSXbt26hTHAknY6eXl5VVVVuGY3ffv2rbTO67t370KApWziUcKlrdgGP7RqSoqfhDV/95+MoIoKL4oWXBfeLi36kRF4SaQKUdR3FkLJ1vIBFkLop59+0tXVdXd3j4yM9PHxYTAYAwcONDAwqKqqYrFYbDZ79uzZGhoaTCbT19c3KSlp6NCh1LHq6uo7d+7s0KEDQqi2tlZHR6eyslJPT49OpwcFBWlra5ubm8+ZMycmJsbMzMzY2JhsxmAymWPHjk1JSZEdQDx79kw8d3IKeDabbWBgsHTpUnJc8IMHD9hs9tq1axFCQUFBZHmENaLrRGSR6aYkJUNtba20l6ghz01HEIS008Grrk7qzw2Px8OVi4yakVGARpCRGsZ3R0aHuIyPRytVVVUVGRn54sULU1PTefPmOTg4NHMB5AkaFAosWnU0hjeEUuXBUlQkJC2oanCjSNQlnlQTtanQp3moRIBFDqRVU1NTU1Ojltsj7/xisVjV1dU///wztbOZmZnwsWpqai9evAgLCyNfoq7iTCaT6qqzsbEpLS0tLi4WbsCwtLR8/Pix7IJJzN3BwSEgIODChQvh4eG2traTJk1yc3MTWWxHnKJ9bWpqahIbsQQCATkYCxdnZ+cTJ06Ib9fQ0OjSpQuuXDQ0NCRexQUCAd4x+25ubsKrG1F0dHSohfaazsbGRktLS2L04+LigisXMjWJTYyGhoYY7/Lr0qULnU6X+AY5OzvjykVF7NmzhyCInTt3vnjxYvPmzUePHlV0IL/8mqEBRnYuqhN7tdlF/ci46kHpsgf/vUu78XGMnFGXoikA5VGJAEsGIyMjExOTqKgo8mlpaSmHwxHe4dmzZ6dPn967dy95vVm+fDm5vaysrK6ujpzX4MuXL+3btzcxMUlLS6MOZLFYDa6dLDF3FovVvXt3Dw8PLpd79+7dXbt2RUdHY7+zDyHEZDKpU6CUl5fjXZdw8uTJGzZsEJ9lYN68eRgjORqNZmBgID4BRFlZGa5eSNLMmTO3bt1KTmogbPHixRjHnmtqas6fP3///v0i2w0MDH766SdcuSCE5s+fv2/fPvFmpN9++w1jiyCDwfjpp59Onjwpst3Y2HjixIm4clEFxcXFjx49ioqKMjMzs7S0/Oeff1JSUnB16TYlnCLDiOqsbVhKQpFWJOUFXgpVQmsPnqShgiqEEBlXKS+ygZhJlal6gOXo6Hj48OEzZ864u7sXFBT8+eefvr6+ZFNETU0NOUeUmpoal8utq6u7ffv2hw8famtr9fT06urqDhw4MGvWrOLi4piYmDFjxri5uR07diwhIWHUqFEfP35MSkpasmSJtHzJxCXmrqOjExsb+5///MfU1JTD4QgEAiWtv2tpaZmXl1dWVqavr09O01BVVWVtbY13NiwGg5GUlDRjxoxPnz5RG6dMmdJgm5yirK2tP3z4QEVyXC63uLjYxsYG7zQNTCbz8uXLM2bMYLFY1MY5c+ZIHJPeFJs2bSovL//rr7+oLVZWVqdOncLbHGJpaXn+/Pk5c+YUFxdTGxctWrR06VKMuSCEwsPD2Wz2+fPnqS2dOnWKi4sjF5n+buTl5TGZTKoVvGfPnh8+fMCSsjyBhYx4YunFiwghhC7uljuibUo01jxNa6i5QqgZZovk2W0QMwJXjrL75pQdVIHWQtUDLF1d3ZCQkKioqISEBD09PS8vL/LmqaFDh4aFhQUEBAwfPjw9PX3ZsmUMBmPUqFEzZ848evTozJkze/TooaGh8dtvv2loaIwcOXLChAlqamqbNm2KioqKj483NjaeNWvWwIEDJc4PSSU+cuRI8dwFAkFOTk5ISAiHw7GxsVm3bp3ypuXs2LFjbW1tWVkZl8vV09OzsbFRRjDXt2/f58+fp6amZmRkMJnMQYMG9ezZE3su5CReVAOktrY23gkaKM7Ozq9evUpJSXn9+rWpqengwYO7deuGPRcNDY0DBw4sXbr0wYMHJSUl9vb2o0aNUsaMpoMHD379+vVvv/126tQpOp3+9OlTW1tb7LloaWlFR0evXLkyLS2trKzMwcFh5MiRGO8hVRGlpaXt2rWjnrZr1+7NmzfU040bNxYVFZGPyf9nGnFDiUhI8b/ICSGEUHbDh/9/+zdA/j3/q9vvTbrkKzpTgPj463fbFejPUoRcVfEOX36mSEknApqPQt/uuro6hWZZItFg3prmceXKla9fv+LtP2qlTExMuFzu7t27hQe3Adn27NlDhvLCTVkqaMGCBadPn27pUkh148aNq1evRkT8tyXj0qVL//zzz44dO8ins2bN+vbtv+uodOjQYeXKlbhuO226ZrtNpClUv5CtooRI+h1OqqBt1uGhQ4dGjBgxcOBAhY5S9RYsAADAxcjIqLKyknpaVVUlfK9AbGws9XjVqlWGhobY5+VvtMrKSgaDoaTRCFiQvQGGhoYtXRCp+Hw+m80WbsJUNTwer6KigsFgKGNQLy5sNltTU1N56481XWVlJY/Hw/vl1dbWbsQoXtX9ugIAAF62trYlJSXUspvZ2dmdO3du2SIBAL5XEGABANoKExMTJyenEydOcDichw8fZmVl/fjjjy1dKADA9wkCLABAG7J8+XIOh+Pv7x8fH79u3boG52oBAIDGafVjsAoLC+fPn5+YmKjs0QmyM3r37t2JEyfU1NQcHR29vb2x59484wpVf/SiQprtdKDeWhEGgyGyrDUAAChDqw+w9PX1AwMDlXc9WLFixYIFC3r06CE7o5SUlIULF1paWvr7+0sMsNTU1NTV1RWd0KGuri43N7e2tpbMl8lk2tjYKONk8/PzCwoKyCururp6586d9fT0sOfy6dOnZcuWkWu8bNiwQSAQBAQEYJ8LgCCIb9++FRUVkfeSaGpqdu7cGe/096SampoPHz5QS9a0b9/ewsIC+7tTX19/4MCB7du3I4S4XO6MGTMiIiIwrvZNqa6u/vDhAzmfO41GI09H0US+48gMAAAU0uoDLB0dHXJmLCWprq4mLzmyM1q8ePGXL1/279/fr18/iTvQaDQ6na7QHKHV1dWvX782MjKiYh0ej/fq1SsnJye8l7GMjAyCIIyMjKgtb9++tbOzw7gYC0Lo+fPngwYNoubBqqio+O23365du3b9+nW8p/Ps2TM6nS58OllZWT169MB7f1NZWdnbt2+Fc6murs7Ozsa7th1BEB4eHnfv3qW2JCUl3bp169GjR71798aYUXFxcU5OjvA7XllZyWazFc0FAiwAACCpboBVWFh48ODBzMxMS0tLd3d3T0/P9PT0+Ph4a2vr+/fva2trjxgxYtasWeXl5T///DPZc5eTk3PgwIFv377179+fbIbx9vYWT6empmbOnDlBQUHR0dE1NTV9+/ZduXKljo6O+J5r165lsVhbtmwJCAhwcHCgMnr37l1UVFReXp6VldXPP//cp08fhBCTyRwwYEBMTIz4+jaNk52dLXKjqYaGhkAg+PbtG8Zl9crKyng8HoPBEN7IZDLfvn07YMAAXLkghPz9/UWWOUII3bhx48qVK+PGjcOVS1FREY1GE6l/8nRcXV1x5YIQEomuEEJaWlrV1dUlJSUYbw8+f/68cHRFqqmpWbBgwYMHD3DlghDKzc0Viae1tbXJmTaVt1QfAAB8x1Q0wOLz+Rs2bHB1dV2yZEl+fn5YWJiuri6DwcjMzDQxMYmIiCgpKYmIiNDV1aVuAqqpqdm4cePs2bNdXV1v374dHR3duXNniek4OzvzeLx79+7t3r27qqpq/fr1ycnJ48ePF98zNDQ0MDDwl19+sbe3LykpITOqqKhYv379nDlzXF1d79+/HxISEhsbGxISsnr1amdn5/j4+PLy8vbt24uckaITuhIEwePxxLdraWkVFxdjDLAKCwtFoisSnU7ncDi4etaqq6vT09MlvnTmzBm8AZa01V14PB6uuVvq6uokDsVjMBhFRUUYAyxpk3ampaXV1tbimiyHzWZLnOKFwWAUFha22QCroKBAdeadqq6u1tXVVZ3yiCMnGBNfBlR18Pl8DodTVVXV0gWRqr6+vqqqSldXF+96aHjV1NRoampiXNoVO7LfSXwJ1yam2YijVLSOnj17xmazZ8+eraGhwWQyfX19k5KSpk+fTqfTg4KCdHV1zc3N58yZExsbSwVYaWlplpaWnp6eCCFfX9+UlBRp6Tg7OxME4efnZ2hoaGho6OLiUllZKXHPoUOHipft7t27nTt3HjNmDEJo4p0PdL8AACAASURBVMSJurq6bDbbx8dnz549NBrNxcVFPLpCCAkEAoVqgM/nS/sxJbsscamvr5cYdpCrH+IKsGQsSiC8aGDT1dfXS/zm0+l0LpeLK8Di8XjSho5RQ7KwoFZuEUEQREVFBa4AS9rp0Gg0iVF+W9CtW7djx461dCn+H/IHQZV7YMkAS5Wn8SQIQiAQqPICUK0iwBIIBDQaTZU/ilVVVXw+H/uct434V1NFAywWi1VdXS28lAq5PiuTyaTaJ2xsbIQvP4WFhcKRDflYWjoIIer2bPJ6LGNPEYWFhcILaLi7uyOEjI2NZfdAKfqvJ51OlxaT4f3uaWtr83g88eJhjK4QQqamphoaGhKv1r169cKVC0JIW1tb4k1wXC4X4+TIZKWJbxcIBHhH0/fo0ePOnTvi27W0tDDOL6Cjo8PlcsVb/rCfTiuyYMGCli5CKzN9+nR1dfXIyMiWLkgr9vjx40WLFm3cuHH8+PEtXZZWbOXKlY8fPz516lRLF0RVAywjIyMTE5OoqCjyaWlpKYfDKSgoKCsro3pGvnz5ItwXY2xsLNwJRcZeEtMhH4tcg2XsKcLY2Pjp06fU03Pnzjk7O3fq1En2GTUi3m/Xrh2fzxf5f6uqqgrv3NOWlpYZGRkiwb5AIKDT6RgXsdbU1PT19Y2Pjxd/yd/fH1cuCCFLS8s3b96In462tjbG/1zpdDo5Hk4kMCWXScaVC0JowYIFhw4dEt8+bdo0jKejpaUl8R+AiooKvKcDAABth4r26Ds6OnK53DNnzpSVlWVlZQUHBz9//hwhxOPxIiMji4qKsrOzY2JiRo4cSR0yYMCA/Pz81NTUioqKixcvkh1P0tKRP0eEUE1NjfAIqiFDhmRnZ6ekpJSXl1+7du306dMSxzA1nZ2dXXV1tXBHMjniWGRsdRPp6OhYWFiQ64iReDxeWVkZ3pvUEEKRkZHijVVhYWHkLQK4MBiM9u3bl5WVUVu4XG55eTnedjKEUO/evcvLy4XbsSoqKqytraWNAGscJyenP/74Q2Rj37599+zZgzEXhFDv3r1LSkqo0yEIoqysrEOHDqq8JhoAAKgyFW3B0tXVDQkJiYqKSkhI0NPT8/LyGjt2bHp6upWVlZWVVXBwMEEQw4YNmzJlChUZ6Ovrb9y4cd++fcePH3d3d7e3t9fX15eYTk1NjZw5IoSGDh0aFhYWEBBAzb9gbGy8cePGY8eORUVFtW/ffs2aNUqaDJpOp7u4uHz9+rWkpKS+vl5LS6t79+7KGOLQoUMHY2PjvLy8mpoadXV1Q0PDXr16Ye9iZzKZGRkZ0dHRFy5cKCwsdHR0XLZsWY8ePfDmghDq2LGjiYlJXl4eh8Mh52vo3bs39tPR0tJyc3P79OlTWVkZn8/X0dHp3bs33uiKtHr16vHjx+/evfv58+cWFha+vr6zZ8/GPthZR0eHPJ3y8nI+n6+np9e3b9822z8IGsHMzEyVhze1ClpaWlZWVsqYg7BNYTKZwsN4WhBN0bvbWlB6evqRI0ek9fGXlJS8ePGCGvO+aNGigIAAR0fHZiygLNevXy8oKPDz82vpggCgRDNmzFCFoQ8AANDiVLQFqxHodHpkZKSamho5e0J1dTXevicAAAAAADm1pgDL2NhYxtSXBgYGq1evjo2NPXbsmI2Nzfr161V5og4AAAAAfMdaUwjSsWPHOXPmyNjB2dnZ2dm52cqjqJycnNTU1JYuBQBKhHeSNqAiioqKDhw4kJ2draWlNXLkyBkzZtBotKqqqsjIyBcvXpiams6bN0/4htOPHz8+efJk8uTJ5FMZe7YpTazG/fv3JycnU6/+8ssv5CRBbYr8dahobStDawqwWrXOnTu/e/cuMzOzEccSBEEQRDNM4tw8M8jB6TROqzgdjJPyAxXB5/PXrFnTu3fvsLCwkpKSnTt36unpeXt779mzhyCInTt3vnjxYvPmzUePHiVnYqytrY2NjRWerk/anm1K06sxPz/fz8/PxcWFfIp3rdhWQf46ZDAYCtW2kkCA1Uzs7Ozs7OxauhQAAKCYd+/elZeXL1myRENDw9zc3MfH559//hk8ePCjR4+ioqLMzMwsLS3/+eeflJSUyZMnx8TEXL58mcvlDhkyhDy8uLhY4p4te1LNr4nViBDKz8+fM2eOjY1NC55Fy5K/Du3t7eWvbeUVWEXnwQIAAKAKDAwM/P39qWWmyEXZ8vLymEwmtdxFz549P3z4gBCaNm3a8ePHhScil7ZnW9PEauTxeMXFxadPn54+fXpAQEBiYmIrmgEAF/nrUKHaVh4IsAAAAEhlYWHh5eVFPs7Ozr5y5cqYMWNKS0uF5+Rr164dObuvtra2vr6+cMeWtD3bmiZWIzl1tr29/Z49e37++efTp0/funWrec+g5clfhwrVtvJAFyEAAIAG8Hi8uLi4y5cvBwYGDh8+/MaNGyLDAaXd3yC+NmhbvhOi0dVobW2dkJBA3hpvZmb27t27W7duCa9l0nbIX4eNrm1cIMACAAAgC4vFCg0NZTAYf/75J7nuqpGRUWVlJbVDVVWVtDHX8u/53WtKNdJoNOGJhzp06PDw4UMll1cVyV+HTaltXCDAAgAAIBWfzw8JCXF1dZ07dy7VAGBra1tSUlJaWkpeorKzs+3t7SUeLv+e37cmVuO9e/fS0tKCg4PJp/n5+RYWFs1TctUhfx02sbZxgTFYAAAApHr69GlZWdmoUaMKCgpYLBaLxSouLjYxMXFycjpx4gSHw3n48GFWVha1TJkI+ff8vjWxGrt06fLgwYO4uLiioqInT54kJSW1wSlR5K/DJtY2Lq1pLUIAAADN7MyZM7GxscJbunbtGh4eXl1dvXfv3oyMDDMzs3nz5vXt25fa4cSJEwUFBVRzi4w9246mV+ObN2+io6Pz8vJMTU2nTJkybNiwZj0BFSB/HTaitpUBAiwAAAAAAMygixAAAAAAADMIsAAAAAAAMIMACwAAAAAAMwiwAAAAAAAwgwALAAAAAAAzCLAAAAAAADCDAAsAAAAAADMIsAAAAAAAMIMACwAAAAAAMwiwAAAAAAAwgwALAAAAAAAzCLAAAAAAADCDAAsAAAAAADMIsAAAAAAAMIMACwAAAAAAMwiwAAAAAAAwgwALAAAAAAAzCLAAAAAAADCDAAsAAAAAADMIsAAAAAAAMKO3dAFa0oT/ZGFJ59LWHljSAbLNOOGNK6lTcxJxJdUalT7ywpUU0/UarqSAopKuJ2NJZ5znaCzptBH/TqJhScctgcCSDlBZ0IIFAAAAAIAZBFgAAAAAAJhBgAUAAAAAgBkEWAAAAAAAmEGABQAAAACAGQRYAAAAAACYQYCldHl5eTQajc/nt3RBZNm+ffvs2bObnk6jT9bJyanpubeggoICGo1WV1cnvBHjW98qPkVYNLEmW7aicH3+2+DXQeIhQJqKigoajcZms1u6IA2T/dlu7R912SDAUjomk7lv3z41NalV7erq+uDBA+z5KilZ2Ro8WXFJSUkeHh6lpaUTJ048ePCg8srWbKiab0RtSIMxqVakETXZshXV9M8/fB0USrOtwXviyq5G2Z/t7++jLq5NTzTaPPT19YOCgmTsUFZWxuPxGp1+XV2dlpYW9mQbp8GTFTdu3Dg2m71s2TI3N7eFCxcqqWDNiar5RtSGNBiTakUaUZMtW1FYPv/wdZA/zQZJ+3lspfBeLJRdjQ1+tr+zj7q4tvUPcYv4+vUr2TBeUVGho6Nz8uRJS0tLPT298ePHV1VVjRgx4v379xMnToyJiUEIffz4cdy4cUZGRq6urocOHSJTePLkiaurq5GR0U8//TR79uywsLCSkhJ9ff3bt2936dLl+vXrt27dcnV1ZTAYlpaWq1atIghCnmSvXbvm4OBgaGg4YcKEoqIi8ZKnp6cPHTq0Xbt2PXv2JI+qqKjQ1tbetGmTkZFR+/btd+zYce7cuW7durVr1+6XX34RPlmE0OPHj3/44Yd27dq5uLjcvn2bTDM8PLxTp056enqDBg3KyMggNz569OjZs2d5eXkCgUCZb4VcGjzH3NxcDQ0Nav/Zs2dv376deipc88K1IazZKrZlKbsmx48fv2LFCvKxQCCwtLQ8e/as8J7iH/uBAwdGREQghKqrqzU0NBYtWkQe3rFjx9jYWJHyt9Tn/7v/OiQnJw8ePHj+/PmGhoZWVlarV6+ur68XzlTi75XENCXuLPzzGB8fL7v8SCW/O0jSx0/kxBFCqampffr0YTAY5NUENVQb169fp9JvnmqU/dlWqY+6MkCA1azq6uri4+OfPXv2+PHj9PT0qKiov//+u3PnzhcvXvTz8+PxeB4eHt27d3/9+vXOnTtDQ0Pj4uIqKys9PT39/f1fv37t6OhIXQbq6uqOHz9+7949Dw+PCRMm+Pj4ZGVlxcXFRUZG3rp1q8Fk37596+3t7e/v//Lly3Hjxu3evVukqKWlpe7u7qNHj87KygoPD1+zZs2lS5fIfN+9e5eXl7d58+bff//9yJEjjx49iomJ2bdvX1bW/1t6qKioyN3dfebMmW/evJkxY8aYMWOqq6vv37+/bt26Y8eOZWZmdu7ceenSpeTOYWFh5ubmhw4dUpEuMDnPUSLhmpe4Q3NWbItTak3OnDnz3Llz1M5cLnfChAnUqxI/9mPHjk1NTUUI3b9/X09P7++//0YI5ebmfvnyxdPTUzjxFvz8t4Wvw/3796urq58+fRoXF3fmzJkdO3ZQL0l846SlKW1n6udx4sSJssuvmt8diR8/8co8cuTIzZs3nz179vbt26ioKHlqg8qieapR9mdb1T7q2H2fZ6WyCIL4448/2rdv36tXr3Hjxom0G6WkpFRUVGzdutXS0nLo0KHBwcF79+69cOFCt27dAgMDydYpOzs7cmcej7dy5UpLS0s6nf7vv/+uXr3aysqqffv27dq1Ky4ubjDZ2NjYUaNG/frrrzY2NgEBAWPHjhUp6sWLF62trdeuXWtpaenl5RUUFHTs2DHypY0bN7Zr187f319NTS04ONjIyMjHx8fMzKykpIQ6PC4url+/fosXL7a2tv7tt9/27t1bUVHB4XAQQhUVFWZmZsePH7948SL2GsZFnnNsnLZWscqryQkTJpSUlDx69AghdOrUqVmzZgn3Ykj82I8ZM+bOnTs8Hu/OnTuLFy/++PFjfn7+rVu3Bg4caGJiIpx4W3ubZMP+Jmpqah4+fLhLly5Dhw7dunXr8ePHqZckvnHS0pG2M/Xz2GD5VfNNkfHxE7Z161YzM7Nu3boNGzasqKhIztoQ971WY4uDAKu5dejQgXygqakp8lJubm5ZWZm1tbWpqampqemmTZu4XG5eXp6trS21j/Djjh07IoTodHpqaqqjo6Ojo+Pq1atpNNGFSCUm++XLl549e1L79O3bV+Soz58/d+vWjXpqZ2f36dMn8rGpqSlCSF1dXU1NzdraWuLpfPz4Ufhwf39/KyurUaNG7dmzZ+fOncbGxsOHD79161ZDtdVi5DlHEkE0vGJrdHS0jo6Ojo6Om5tbW6tY5dWkrq6ut7f3uXPn6urqzp8/P2/ePOE9JX7sHR0d9fX1Hz16dOfOHS8vrwEDBty+ffvWrVvjxo0TyaitvU2y4X0TEUIWFhbt2rUjH/fu3ZuqWyTljZOWjoydyZ/HBsuvmm+KjI+fsE6dOpEP1NXVkdy1Ie57rcYWBwFWcxMPgCjm5ubW1tZF//PixYtTp05ZWVl9/PiR2kf4a0Z+qZKTkzdt2nT16tXnz58nJiaK/5siMdkOHTpkZ2dT+3z+/FnkKGtr69zcXOrp+/fvqW+UPKytrYWLvW3btpcvX+bm5g4YMOD+/fslJSX+/v5z5sxpFbcZixMIBNR4oPz8/Ab3nzt3LofD4XA4//77L1SssKbUJPpfL+GVK1e6desm8k+CxI89jUbz8vK6dOnSq1evXF1df/zxx7///vvvv/8WD7DgbZKfom8iQojFYlVXV5OPs7OzraysqJckvnHS0pGxM/nz2CDVfFPk/PiJ9Kw1uja+12pscRBgqYSKigqCIEaPHs3hcEJDQ1ks1oMHDwYOHJiSkuLj45OTk3Ps2LGioqJdu3a9f/9eJESrrKxUV1evra2tqak5fPjw8+fPqV8uGcnOmjXrxo0bBw4cyM/Pj4uLo8ayUCZOnJiXl7d9+/aioqLU1NS9e/fOnTtX/jOaNm1aWlra0aNHCwoKDh48uGnTJiaT+c8//3h5eT19+rSysrKqqorP58v57VUpTCaTIIiDBw9yOJzTp09T45eFkTUv8XCoWEoTaxIh5O7uXlVVtW7dOn9/f5GXJH7sEUJjxow5cOCAk5OTlpbWyJEjT58+raWlZW9vL3I4vE1yatybWFdXt3Dhwk+fPqWlpa1evVq4bqW9cRLTlHNnGVTzTZHx8ZPxjWhEbXzf1djiIMBqedOnT581a1ZMTEy7du2Sk5Nv3rxpZ2c3ffr0wMDAoKAgJpN59erVffv29ezZk8ViDRs2jMlkCh/u6+s7btw4R0fH3r17s1isTZs2rVixIisrS3ay3bp1S0xMPHjwYI8ePU6ePBkaGipSKmNj4xs3bly+fLlz585BQUGbN2/29fWV/6SsrKyuXr166NChLl26REZGJiQkWFtbz5o1y8fHZ8yYMZ06dYqNjb106ZK2tjaGGmxeRkZGu3fv3rx5M4PB2Lx58/Tp00V2oGpe4uFQsZQm1iRCiE6nT5069ePHj+LHSvzYI4Tc3d1ra2uHDRuGEHJ1dSUIQrz5CsHbJLfGvYl2dnbdu3cfOHCgj4+Pt7f3mjVrqJekvXES05RnZ9lU802R9vGT/Y1QtDa++2pscTQ5u8y/SxP+08AtMHK6tLUHlnQk+vr1a2pqKnXbSI8ePfbs2ePh4aG8HFXWjBPeuJI6NScRV1KtUekjL1xJMV2v4UoKKCrpejKWdMZ5jsaSjpySk5OXLVuWmZnZnJli9O8kqcM8FOKW0HYvvm0ETDSq6jQ0NBYuXEin08ePH3/27NmysrLhw4e3dKEAAAAAIAt0Eao6MzOzc+fO7dy5087O7tSpU5cvX5Z48w4AALQKVlZWPj4+LV0KAJQOWrBagbFjx4rPUwUAAK2Rvb391q1bW7oUACgdtGABAAAAAGAGARYAAAAAAGYQYAEAAAAAYNamp2kAAAAAAFAGaMECAAAAAMAMAiwAAAAAAMwgwAIAAAAAwAwCLAAAAAAAzCDAAgAAAADADAIsAAAAAADMIMACAAAAAMAMAiwAAAAAAMwgwAIAAAAAwAwCLAAAAAAAzCDAAgAAAADADAIsAAAAAADMIMACAAAAAMAMAiwAAAAAAMwgwAIAAAAAwAwCLAAAAAAAzCDAAgAAAADArPEBFkEQe/fuHTJkiJGRkaWl5YQJE549e9bgUd26daPRaDdv3hTZvn79ehqNtmDBgkaXp0W8ePGCRqOVlJQoNZe3b9/27t3bwsICb7I0Gi05ORlvml5eXsHBwXjTbAoZ5fH09Hz06BHGvK5du1ZaWipjB9lfmdDQUH19fYTQkydPvLy8MBasbZo9ezbtf+h0er9+/X7//fe6ujrZR/n4+NCkYLPZuMpGfSyV8R38jrWW6lJ2OSX+rDU6UyMjo4SEBBzl+q/muSy2Co0PsCZOnPj7778PGjTo6NGjISEhBQUFP/zwQ0ZGhjzHXrhwQWTLxYsXG12S797+/fv5fH5qaireZOfPn29tbY03zQZ9+PDh27dvzX+siPj4+KqqKldXV4RQVVXVL7/80rFjR3Nz8+nTpxcXF5P7REVFiVxiyX8M9uzZ06FDBwsLi02bNlEJvn79evXq1YaGhjIylfMr4+zszGaz4+PjsZxpW2Zvb5+ampqamnrhwoWJEyceOHBg+PDhsmOskJAQ8hDyWrV06dLU/9HW1sZewhb5DjbC58+fx44dq6+v369fv/v377dUMVS5uoR/nVqknAplivG3FAuM5VGtUyMa5cyZMxoaGmlpadSW+vp6JycnT09P2Qd27dq1T58+1tbWAoGA2vj+/Xsajda7d+/58+c3rjwt5fnz5wih4uJipeYyffr0efPmKTULXDw9PVeuXCljh3Hjxi1btqxxiTfiWGnlcXBwOHfuHPnY3d3dzs4uISEhOTl5+PDhvXr1qq+vJwji999/d3R0PC2ExWKlpKSYmZldvXo1NTW1Y8eOp0+fJhOZOHHixYsXZZSkwa/Mli1bGAwG+fj8+fMODg4KnSkQMWvWrMGDBwtvefHihYaGxoEDB+Q5vL6+HiF08OBBZZStwa+JqnF3d/f09Hzx4sWiRYssLCyEf70BqSm/bIpq+udHuLSGhobUjyEWjbgsYqy95nwjGtTIFqyNGzf+9NNPbm5u1BZ1dfXff/9dTU2N/GGSwcvLq7Cw8MmTJ9SWixcvurm5YekCazB3OfdpBjU1NfLvrKYGo+WwSUtLKyws9Pb2Rgg9evQoJSXl9OnTvr6+Hh4ely5dYrFYZHtqbm6um5vbNCHt27e/e/eur6+vl5fXyJEjZ82adffuXYTQw4cPCwsLJ0yYICNThb4yEydOLC4ufvjwIf6Tb8P69u07efLkPXv2tHRBWp+XL1/Onj27b9++CxcuzM/Pp1p52zIs1xGlXoxU5ErXaApdIlVTYy7bbDY7Kytr4sSJItunTJly5coVOp0u+/B27dqNHDkyMTGR2nLx4kXyakepra1dvXq1nZ0dg8EYPHjwgwcPyO35+fmzZ8/u0KGDnp5ev379zp8/T243MjI6fvx4nz59NDQ0zM3NN2zYIJ6vxH34fD6NRrtz5w61m4WFxV9//UXuf+DAgR49eujo6Dg4OKSlpcXHx9vb2zMYDC8vL+HRNm/evBk+fLiBgYGjo+OZM2dkn4K+vv6dO3dmzpzZt29fkRJWV1cHBQV16tSJyWR6eHi8evUKIeTu7n769OkjR44YGRnJs7/8JdfQ0CD7QSorKwMCAjp06GBpaenn51deXk7ucOvWrUGDBjEYDEtLyyVLlnC5XPFa/fr166RJk0xMTHr06BEREUEQhIzTd3FxSUpKioiIsLW1lVFFEssj57HSyiPs+vXrQ4YMUVdXJ987dXX1fv36Ue9Onz59UlJSEEK5ubldunSpqKjIzc0VCATkDk5OTpcvX37y5MmrV68SEhKcnJwQQmvWrPnjjz/EM6Io+pVRU1MbMmTI9evXZaQJGmHEiBFv375t3IVH4teNHG5y7do1W1tbPT09Z2dniaMdpH0sqe+gtO+aor+EiqYjz3ccITRmzJi4uLj6+vo///zTzc3N1NRU4m4SU5NRRQr9AghXV1NOR86fR2lZSLyOiPw6UeWU8RMt8YLVlJ81KtMGr4YipUUIsdnsuXPntm/f3tzcfN26dbILI0za5xBJuSxKfINEyiNyiZSWhTyXCTk/3krUiFav9PR0hNCTJ08acWzXrl23bNly6NChnj17kltKSkrodHpmZuaoUaOoLkJfX99evXrt3bv37t27q1evbteu3dOnTwmCGDhwoL29fXx8fEpKysyZM+l0OtkOaWhoaGRktGvXrvT0dHL03/Xr10WylrgP+VN7+/Ztajdzc/PY2FhyfxMTk/j4+Dt37jg5Oenp6Q0dOvTff/+NiYmh0+khISHE/9pCLS0t9+7dm5qaGhAQgBBKSkqScQoMBmPAgAG7d+/OzMwUKaGHh0fnzp3PnDmTmprq4+Ojp6f37ds3DoczadIkPz+/6upqefaXs+QEQdDpdLKWhgwZ0rdv38uXL58+fbpPnz4uLi4EQeTn5+vo6Pj5+d28efPIkSNaWlpbt24VKUBdXV2XLl369OmTkJBw8eJFBwcHLS0tsu1a4unX1NR4enouXryYzWbLqCKJ5ZHnWBnlEam30NBQ8jE5su3Nmzfk09raWgsLi4kTJxIE0a5du759+5JxmL6+/p49e8h91q1bp6urq62t/csvvwgEgmvXrnl5eUn4rAuR5ysj3EVIEMS2bdtGjRolO1kgg3gXIUEQV65cQQh9/PixwcPFuwglft3IXwAmk7l79+7U1NTAwEAajXbr1i3hpGR8LMnvoIzvmkK/hIqmI893nFRaWspkMq2trX18fEpKSiTuIy01GVWk0C8AIfST1ZTTkfPnUVrZJF5HRH6dqHLK+ImWeMFqys8alWmDV0OR0pIVsmvXrmfPnm3ZsgUhdOPGDRk1IEzi51DaZVHaGyRSHpFLpLSLfoOXCfk/3srTmADr33//RQiJ17U8yACLxWKpqallZ2cTBHHixAk7OzuCIKgAiwzzc3NzqaN8fHx8fX0Jgvjzzz+fP39ObszNzaUuWoaGhosXLya319fXm5mZkWG+MIn7yA6wtm/fTm6Mjo5GCH348IF8OnLkyLlz5xL/++3Yv38/dbiXl9ewYcNknAKDwQgKChKvmcePHyOEHj9+TD7l8Xi2trarV68mCGL69Onio9Nk7C9PyYn/fSFv3bqlrq5OFfXBgweDBw8uLCy8ffs2QujRo0fk9lu3bj148ECkDCdOnNDS0vry5Qv59NOnTxoaGitXrpRx+lQHubR9pJVHnmOllUek2Pb29lFRUeRjLpfbp08fBweHpKSk1NTUMWPGaGhojBw5sqSkhMFgzJ07Ny8vr6SkhGygunDhAnlUfX09j8cjCEIgEPTv3//Zs2fUu0BIIs9XRiTAOnbsGPVPCGgEiQHW1atXGxdgSfu6kb8Au3fvpg4cM2aMh4eHcFIyPpbkd1Dad03RX0JF05HnO04QRGlp6fjx4/v3729oaBgeHk4QxOvXrzMyMkR2k5aatCpqxC8AWV1NPB15fh5l1Ly0a43w0B+ynLJ/osUTaeLPmnCA1eDVUGQM1sKFC6mXrKys9u3bJ6MGhEn8HEq7LMp4g4TLI3KJlJiFPJcJOT8PStVAd55EPXv2RAi9f//+/7J33nFNXV8AP0kgBEjChgACIig4ylBAqDd01gAAIABJREFU1CpYGeKiKsNqHdRBq1XUinuhqHVRwYq27lFxIgjiwvFDWylOFEGWKIgyA4EQQiDJ749nY0xeIEAgKvf7yR/v3dxz7rn3nvfefffd0b9/f/Hwd+/eRUdHz5gxw8rKqnkNRkZGrq6uFy5cWLZsmfT3QaxqJZQ4OTkBwMKFC5OSkhISEgoKCu7evSseQTS6hUQi6erq4qYrTxxxLCwssAN9fX0SidS9e3fsVKKH3MvLS3Q8atSosLCwZrIgfiBORkYGnU4X/aWiouLu7v78+XNZtjUfX07LAeDJkycWFhY9evTATgcNGnTnzh0AGDhwoJub25AhQzw9PYcOHerj42Nvby8h+/TpUzs7O1NTU+zUzMysX79+0GwNituPG0eWPfLIyrJHAi6XSyaTsWNVVdUrV64sWrRo+vTpVCo1KCiISCTS6XRdXd3a2lqRyPLly69evXrs2DHMV7FuLQA4ffq0jY2Ng4PDs2fPpk+f/uTJE3t7+6NHj0p8/23DJaOmplZfXy9tPKI9FBcXk0gkkYfIT/OXm/gdwMfHZ/PmzeKyLbqlrGuttXfC1uqR5xoHgDFjxhgYGKSmpiYnJ/v6+tra2p45c4bP5x87dkyeXMgqojbfAdqZHZDj9tj8HUzO50jzPiOtpJ23NXFa+6QbPHiw6FhdXb0ZYyQEm3kiSz8W5a8g8YRwk5DnMSF/ch1HW8Zg0Wg0S0tL6aEG169fDw8Px9byaZFvv/02Li6uoaHh6tWrEg0sLS0tVVXVmpoathgpKSlcLvfrr79etWoVAIwaNUpiHruamlqLicoTp7GxETecQCC0KAsARCJRTU1NVhawOLhFJBAIJJJofsaA/PGbt5zH44maC+JQKJTbt2/fvXv366+/vnnzZv/+/ZcsWSIRR0VFRUK5hoYGyK5B8Ziy4siyRx5ZWfZIYGhoKD6EzsTE5PTp0xUVFa9evVq3bl1hYSHuA9jW1ra0tFQ8pKmpacOGDdhiDZMmTRo1alRxcfHo0aMDAwMlZNtwyVRWVjIYjOZKAdF6/ve///Xq1avFcaLSyH+5SYe36JayrrXW3glbq0eeazw7O/uff/5Zu3atqqqqj4/P+vXrJ02adP78+WHDhknElEebeBG1+Q7QnuxIg3t7bP4OJs9zBFryGWkl7bytiSOnhc0olOce3vwTWRzssSh/BYluibKSkOcx0TZ/UCxtnJu2du3amJgY8eKuqanZsWOHs7OzoaGhPBrGjx//77//Hj9+XFNTU3xqFQD069dPIBA8fPhQU1NTU1OTQqFMmTLlyJEjd+7c+ffffxMTE1evXj1x4kQtLa22GS+NaEm0V69etWF5NPEVqi5fvmxvby8rC80o6devH4vFEq08yefz//e//3311VeKii+Lr776qqCgoKioCDu9f/++sbFxTk7O//73v2XLlrm4uKxYseLatWtbt279888/pW14+vRpcXExdspkMtPT00F2DUrI4saRZY88srLskTY7NzcXO66urvb09MQ6kwHg+fPnT58+DQgIiI+P7969e15enkjqwYMHEv1SBw4ccHd3t7a25nA4WVlZS5YsMTY2Xrp0aU5OTk1NjUSirb1k8vLy+vbtKx2OaDPPnz8/c+bM/Pnz2yDb/OUmfge4cuWKxGXYolvKutZaeydsrR55rnGsr1e0eNiyZcuMjIwaGxsnT54sEbN5bdJF1OY7QHuyIydtuIHjKmntLb09tzXFIk8JNP9Eln4stqGCZCUhz2NCgf7QZtryiRAApk+ffuzYMU9PzwULFgwePPjNmzdHjx598eKFaGL5wYMH8/PzJbrKxbG2tu7Tp8+yZcsmTpwosQZBt27dZsyY4e/vv2PHDjMzs0OHDl2/fv3XX3/FLvIjR44EBATk5+evX78eAF68eCGaAtYGSCSSpaXl1q1b9fT0VFRUFi9eLGdPlTjLli0jEAhYz3lSUtKdO3dkZaEZJS4uLiNGjAgMDNy2bZuuru7u3btLSkpCQkIUFV8WI0eOtLOzmzBhwsaNGzkcTnh4uKmpac+ePauqqrZv304kEr/99tuioqKzZ88OGjRIQtbf33/t2rWjR4/esGEDmUzeuHEj9ubUTPaxD+evX7+2sLDAjdOrVy9ce+SR7dGjB649EowYMWLr1q3YMbY06Jw5czCRZcuWffvtt05OTjY2NkQicdKkSYsXLzY2Nj5y5EhWVtbZs2dFSjgczo4dO7B+aQ0NDQcHh7Vr186dO/ePP/7o27cvnU6XSLTFS0aC1NTURYsWtbY2EeKwWCxsgjCHw3n06NH27dsdHR2DgoKwf1u8R4kj63IrLy8HgHXr1pFIpD59+pw7dy4hIUFiQW1Zl4kICoWCe6219k5oaWnZKj0sFqvFa7x79+4DBgyYPn36tm3bVFVVd+/ezWazTU1NN2/ejA2IbjEXGNJFJMuqZu4AGO3Jjpy04QYufnfCQlp7i5aVqJy3tVYhba2cxojHwd4Mpf0QGxEh/VgkkUiyKkiWPbKSCAgIaPEx0bxDdhJtHr3V2Ni4ZcuWAQMGUKnUbt26jRs3TnzYo5+fH4PBkJbCBrljx1i/HzbnTig2yF0oFPJ4vOXLl1tZWWETRFNSUrDw3bt3m5mZ0el0Dw+Px48fT506lUql5uXlaWtri5Z8FAqForms4siKc/fuXQcHB1VVVQCYMWNG//79RYPcRfETExNVVFREspMmTRINcqdQKHFxcU5OTjQazdnZWTRfQ1YWqFSqrFXdampqgoODzc3NdXR0vLy8ROWJO8i9mfjyWC4UGxRZUVExdepUBoOhp6cXEBAgGk158OBBW1tbCoVibGwcFBSEDSGU4M2bN+PHj9fT07O2tt62bdu8efOw0Zeysn/+/HkGg9G3b99m4siyRx5ZWfaIw2azdXR0nj9/jp2WlZUFBgZqa2v36tVr2bJlooHqFRUV06ZNMzc319XVHTVqlCg+xpYtW7Dxqhi5ubnu7u5UKtXNzQ2bvSFN85eM+CD37OxsHR2d2tpaXD0Iefj+++9FdzkSiWRnZ7dkyRIulyuKIOsehSE9ixD3csPG8yYlJWGzwR0cHM6fPy+tTZZbiq5BWddaa++ErdUjzzVeWFg4ceJEXV1dHR2dwMDA169f37p1i0AgSOcUV1szRdTaO4CouNqTHTlvj7KSkPUcEb87ieyU5xYtrqQ9tzXxQe4tPg3FrZVYaNTa2vr3339vxhhxcP3w/Pnzsh6LsipI3B6JR6QsV5fnMSGPP3QoBCHeihpdkKamppqaGnnGAyK+AEJDQ1ksVuf3GMvJ3LlzqVTqtm3blG0IogXS09MdHBxKSkqMjIyUbcsnCioiRJeljZ8IvzxUVFRQ66rrsGrVqv79+5eXl8taMlGJMJnMa9euiW91gEAgEIjPDrQBC6IrgvWKs9lsZRuCQ01NzZkzZ5rfNxrx6dCGOYldDVREiK4J+kSIQCAQCAQCoWBQDxYCgUAgEAiEgkENLAQCgUAgEAgFgxpYCAQCgUAgEAoGNbAQCAQCgUAgFAxqYCEQCAQCgUAoGNTAQiAQCAQCgVAwqIGFQCAQCAQCoWBQAwuBQCAQCARCwaAGFgKBQCAQCISCQQ0sBAKBQCAQCAWDGlgIBAKBQCAQCgY1sBAIBAKBQCAUTLsaWLW1tVu3bp08eXJISEh6erqibPpMefXq1blz55RthZIpLy/fsGHDlClTfvjhh7/++qvLbiX+/PnzxYsX+/v7BwcHX716VdnmKJ/i4uLc3FxlW6Ewzp8/HxERUVZWNm7cOIFA0MmpV1dXR0REyPpr3LhxjY2NzdvWBstFmiXCb9++HRQUlJKSkp6eHhwcLKc25RYgorWgmmob7WpgRUVFNTY27tixY+TIkRs3bmSxWIoy67ODy+WeOHGioKBA2YYoEz6fv2LFChqNtnPnzl9++SU5OTk+Pl7ZRikBDoezefPmIUOGREdHT5w4MTo6+sWLF8o2SplwudwNGzbcvHlT2YYoGBqNFhwcTCAQOjndf//9d+DAgc3Had42BVp+/vz5oKCgYcOGmZqafvfdd62SVVYBIloLqqm20fYGVkVFRVpa2pw5c0xMTHx8fGxsbK5fv65Ayz4jjh49+v3336elpSnbECWTm5tbXV39888/MxiMvn37jh8//p9//lG2UUogOztbXV194sSJBgYGXl5elpaWmZmZyjZKmfz5559MJlPZVigedXX10aNHd/5T58GDBwMGDGg+TvO2KdDympoaExMTANDX13d3d2+VrLIKENFaUE21DZU2S75+/VpXV9fQ0BA77d27d5ftvwkMDJwwYcKpU6eqq6uVbYsy0dLSmjlzpqqqKnbKZrOVa4+y6N2797Zt27Dj2tra6upqCwsL5ZqkRO7du1dYWDhkyBBlG9JeHj58ePTo0bKysn79+mFNisrKyqCgoLi4OCKRGBcXl5iYyGKxLC0t582bh9V4WVnZvn37srKyTExMPD09R44cCQBPnz49evRoUVGRurr68OHDp0+fTiAQ5BfncDgkEolCoQBAbm7u/v37X79+bWpqGhQU9NVXX4msFdnG5XKnTZs2b968I0eOcDgcOzu7JUuWcDgckeW4SqSNxC2T0NDQ6urq1atXz5s3j06nR0dH//HHHxwORzpFdXX1ZgoQ10h1dfW8vLy9e/e+ffu2f//+JBKpR48e3377bUdX9BeAtDs9evTo9OnT3bp1+/vvvykUyvDhw7///nsSiYTrY9IuIe7quCK4Doxoew8Wk8mk0+miUzqdXlVVpQiTPj8oFAqNRlNTU1O2IUrG2NjYx8cHO87Ozr506dKoUaOUa5JSoFAourq6fD5/yZIls2fP/vrrr1vsb/hSYTKZ+/fvX7RokYpK29/lPgWKi4s3bdrk6em5e/duZ2fnhIQE8X+zsrJOnDixYMGC6OhoBoPx559/AgCfz1+7dm23bt327Nnzww8/nDlzJiUlhcfjhYeHDxo0KDo6OjQ0NCkp6enTp/KLA0BaWpqLiwsAsFisNWvWuLu779mzx83NLSwsjMvl4hrf2Nh4586dyMjIiIiI/Px88UGBuEpwjcTVvH37dm1t7fDw8KFDhzafYvMFiCvC4XDWrVvn6em5Z88eKyur27dvt7LSuii47oSFc7ncXbt2hYaG3r17NzY2FtfHmvcrXBFZKSLaftcTCoUSHYZNTU3ttgfx2dPY2BgTE5OQkBAcHNzaTwZfEkQicdq0aRkZGZcvX3Z0dOyCbSyhUPjbb7/5+/ubmpoq25b2cvv2bQcHh7FjxwKAt7f3gwcPxP/l8XgAUFdXp6WlFRISgp0+fvy4rq5u6tSpqqqqurq6EyZMSExMHDJkyI4dO8zMzACAy+Wqq6vX1NRgb6ryiA8bNiwtLe2nn34CgJSUlB49emDvML6+vhoaGnV1dSQSSdp4oVA4ffp0bW1tbW1tZ2fnmpoa0V+4SrS1taWNbFVxSafYfAHiiqSmppqYmGAdJBMmTOiyQ1BaC643AoCKisq8efM0NDQYDMa0adNOnDhhaWkp7WMsFkvaJUTKcd1yypQpuCki2t7A0tHREb/qamtrdXV1FWES4jOmpKRk06ZNVCp1+/bt3bt3V7Y5yqG2thYAaDSanZ2dnZ1dZWVlcnJyF2xgJSUlCQSC4cOHc7ncpqYmAoHA5XKxb1ufHZWVld26dROddu/evbS0VHRqb28/Z86cCxcuREREWFpaTpw4ceDAgSUlJWw2OygoSBTN0NCQSCSmp6fv3LkTO8XeUeUX5/F4PB6PRqMBQFlZGfahDcPT0xMAZI1S0NfXxw4kuhJxlQiFQmkjW4tEis0XIK5IWVmZkZGR6F/xY0Qz4LoTAOjq6mpoaGBxzM3Ny8vLcX0M1yUqKyuxU1wRWSki2t7AsrS0rKysZDKZWLsqOzu7X79+ijMM8fnB5/PDwsJcXFxmzJjRlYdDJiUlZWZmhoWFYae6urpdc3BeXl7es2fPAgICRCHJyckXLlxQokltRl9fPz8/X3RaUVEh/m9JSYmNjY2XlxePx0tJSfntt9+OHDmio6Ojr6+/f/9+LA6Tyayvr3/8+PGpU6d2796N3TYXL17cKvFHjx45OjpiIXp6eg8fPhTZcO7cOScnJ21tbVz7ZV2PuEqYTKa0ka1FIsXmCxBXRE9P79GjR6LT8vLyNpjRBcF1JwCoqqoSveG8efNGT08P18fu378v7RJYmx4AcEVwU/xMX6UUS9vHYOnr6w8YMODYsWP19fX37t178eLFN998o0DLEJ8dDx8+rKqq8vDwKC0tLSkpKSkpwb2NfvG4urpmZmbevn2bxWI9ffr0ypUrX8AQ7zYQEhJy8T+8vLxGjx79mbauAMDd3f3x48eXL1+uqqpKSUn5+++/xf/F2tN5eXn19fX19fUCgYBIJDo6OvJ4vDNnzlRVVb148SI0NPTJkyccDodIJPJ4vIaGhqtXrxYUFHC5XPnFU1NTXV1dsUSHDh2anZ19/fr16urqy5cvnzp1ikqltjZfuEpwjezQAsTF1dX13bt3ycnJLBYrPj6+pKSknTZ0EXDdCQAaGxujo6PLy8uzs7OPHj06YsQIXB9r3q9wRWSliGjXyNPFixfv3r175syZhoaGq1evFnXwIromr1694nA48+bNE4VYW1vLWhHxC8bCwmLlypUxMTHR0dF6enoBAQHo3eNzx8TEZNWqVUePHj169Gjv3r2nTp0qvm6qu7t7Xl5eWFhYfX29ubn56tWryWQymUwOCwvbv3//+fPnNTU1fXx8Ro8ezefzHz16tHDhQiqV6uHhMWXKlIMHD27bts3V1bVF8ZEjR96/f19PTw9LVE9Pb926dYcOHdq/f7+RkdGKFSv09fVb21eKq2TQoEHSRhobG3dcAeJCo9HWrVv3+++/Hz582NPTs1+/fqJ+FEQz4HojAJiampqamoaGhgqFQjc3N39/fxKJJO2iACDtEqJPhBoaGtIiAoEAN0UEocuutY1AIBCIT5bKysr09HTRy8lPP/00Z84c0RdSRKt49OjRgQMHoqOjlW1I1+LznjuNQCAQiC8SFRWV6OhoIpHo4uLy999/s9ls8YW+EIhPH9L69evbI8/n89+9ewcAaBWo0tJSDoejqampbEOUDIvFqqyspFKpXXmcOwBwudzS0lIVFRXRyqtdluLiYj6fr66urmxDui7V1dWf3VVJoVB69Ohx5syZs2fPVldXL1iwwMDAQNlGfa7weDw+n29vby9PzJKSEhKJhD7ztZ/2jkQrKyvz9fU9efKkQqz5rJk3b15oaKiyrVA+Bw8e9PX17bLLuIt49OiRr6/vnTt3lG2IkhEKhb6+vpGRkco2pEvzxx9/+Pr6tn+seifj5OS0a9euY8eOhYeH9+rVS9nmfMZYWFhMmzZNnpiZmZm+vr5oi3qFgIb6IxAIBAKBQCgY1MBCIBAIBAKBUDCogYVAIBAIBAKhYNq1TENNTU1AQACdTldRUcHdA6vTEAgEBAJBueM3GxsbAUC5I5qFQqFQKFTuIm9NTU18Pl/pkx74fL7SfbKxsVFVVVW51aH0cgCAhoYGEokk537PjY2NDg4Oy5cvb3Ny586dO3nyJJpuIs4nclUiPn2EQiGPx1P6M/1Tg8VihYWFtXaVkPYu02BhYbFp0yYNDQ3RJkdKoa6ujkwmK7dxU1VVRSAQZG1V0TnweLympial10V9fb2enp5y27vV1dVKr4uamhoajabcpxqLxaLRaMpt5FVUVKipqcm5SmROTk47J83weLygoCBsX2EEBpvN5nK5Sr8qEZ8+jY2NLBaLSqWivW7EiYiIaGpqaq0U+kSIQCAQCAQCoWBQAwuBQCAQCARCwaAGFgKBQCAQCISCQQ0sBAKBQCAQCAWDGlgIBAKBQCAQCkYxmz1r7FXyjOhPYUK2jrINAAAygNK3j9L8NKpDmRMIAQCADKCvbBsAQKtDtf/S9kVeEJ0JlUqlUqnKtgLxGaCqqqqv/yncur4EUA8WAoFAIBAIhIJBDSwEAoFAfMKcPw8EguRv/HgAgOpqmDMHjI1BXx8mTYK3bz9InTsHgwcDnQ7Dh8Pz5/KnVlpaqtwlFRHtp/FaAnvKqBoXy7oZ3/LzXmCBQlZV/eoFNW59az0c68OXQyMPABqvJ7L6Gkj8OAumK8QMxXwiRCAQCASiQxgyBBITP5zy+TB7NnzzDQDAnDnw8CEcOQLq6rBwIfj5wT//AACcPQszZkBEBPTsCb/+CmPGwIsXgBay7xo0Xr1Yv/JnyrKNRIseDQeiOHMn0xJTQUWl7qfJIBBo7DwAjTzu1jWcVQs0tu1TcXTRjP6wsrFQwK9fu0hl4FCFWIIaWAgEAqF4BAIBn89HfSEKgMGA0aM/nO7ZA1ZWMG8eNDZCfDzs3Ane3gAAK1eCvz+8fQsmJhAWBmvXQnAwAICNDcyYAdnZYGenHPsRnUtD9Ha1uUvIAdMBgNjdun7VfP6rPGE1k5/+gJb8mGjcDQA0du6vHe8mWLyGyDBVcfMUyfJiDhLNLcnf/aAQS9AnQgQC0SVISUlxcnKi0+lubm4ZGRlYYGJiooODg4aGhrm5eVRUFACUlpYaGxtHREQwGAw6nT579myBQCBLPDs728PDQ1tb293dPSEhAQCKi4vNzMxiY2PNzMwyMzOVlNcvl3fvYN06OHAAsN2fSCQQNWGx3ScJBMjKgufPwd//fbipKVy/3trW1dmzZ3v37k2n02fNmiUQCLKzsw0MDET/Ojs7X716tbS01MjIKCQkREtLy9zc/PTp02FhYaampo6OjrGxsVhM+R0MoRAEL3P4eS9UvX2xU6KRseaBc6RefQQFeQQtHax1BQBEKxuCGoX/6F9xWWF5Kff3bRobfgMF7S2GGlgIBOKLgs/nSwcWFxePGTMmNDQ0Nzd38ODBfn5+AFBbW+vn5/fzzz8XFRVFRUUtXLiwvLwcAMrKyh49epSfn3/79u3jx4/funULV5zL5Xp6enp6ehYUFKxYsWLmzJmPHz8GACaTmZSUlJ6ebm9v37lZ7wKEhcG4cdCnDwCAqipMnQqRkZCWBs+eQXg4DBsGxsbw9i0QCPD4MTg4vB+D9eRJqxJpampKSEh48ODBrVu3jh49mpKSIitmWVmZiopKaWnprFmzJk2axOFw8vPzR44cuWTJEmiNg7WrTBBiCMpKgEDgZz1jT3B/PwbrRQYAEBkmwppqYWX5+2hvi4ScOkFFubgsN3q76nBvopUNvubWt4PRJ0IEAvFFgXsfjImJcXd3DwwMBICwsLDu3bs3NDRQKJT09HQbGxs+n29kZEQikZhMpra2tkAg2Lp1q6amZv/+/V1dXSsqKnDFr1y5QiKRQkNDiUSit7e3v7//4cOHly1bxuFw1q9fj+a6K55Xr+D4cXj27EPIli3Qpw8MHAgAoKHxviFVXg4AsHw5bN8ODAb89ht88w1kZ4NYF1SLbNmyRVNTc8CAAQMHDqysrDQ2NpYVc+XKlRQKZfLkyWFhYaGhoRQKZcqUKfv27QMA+R2sTcWBwEHIrAQAbsQGypL1RH3DhmP76oLG0y6lqri6EbtZcEKDKQtWCHkN3J0bgEQCTp1IUFBc1HjxDDX+jizNbWhgoR4sBALxRYE77KmwsNDKygo7JpPJwcHBampqJBIpLi7OxsbGwcFh165dJBJJFN/U1FRcG674y5cvS0tLLSwszMzMzMzMLl682NTUBABEIlEkjlAkERHg5QU9erw/ra0FFxfw9YV376CyEn75BYYOhaIi0NAAoRAOHIBvvwVXVzh6FIhEOHNG/nRIJJKEA0ggFH5YAU5PTw+LRiaTsVa1SER+B0MoDHV1EArVN+xSHTGKZO+ksXkPEImNV+NATU3zwDmgUOp+nFS//hey/zSiviFB78NbUMPRaJUhw4ndLGQpVlFpdYcU6sFCILoi7Ab4+xV44/eFdwb//POPpaVlMx0DisXExCQtLQ07bmpqCgsLCwkJSU1NjYyMTE1NNTc3BwAajdYqcQaD4erqevPmTSw8JydHXV0dAIhEIoFA6Nj8dEF4PDh5Ev7440PI1atQUgLR0YA1XDZsgDNn4MwZGDIEAOCrr95Ho1CgRw9480b+pHCrD2s9Y5SWlsqjJykpSU4HQygKoq4+AJB69X5/rqZG7GYhKHkHAMRuFh8mDPJ49RtCiYb/3X8aeY2XYtXX71SwMYpVhwMDnKOBsOT9T2Up9NgK0Rmt0UCHfCLQVneUgQhEF+RdLfySoEwDtm/fjo1Y6hwCAwOvXbt24cIFJpO5cePGI0eO6OjoVFdXq6mpUSgULpe7ZcsWNpvN4XDkF/fx8cnIyNi3bx+Lxbpx44azs3NBQUGn5ajLcfkycDgfTScEAD4fGhreHwsEwOUCgQB2dqCtDf81iIHNhtxcsGnXy4Surm51dXVsbGxTU9OuXbveyNdck9/BEIqCaNOXQNPiP3t/bxFy6gSvXxItrYVVzLrgQGw8FgA03rpC0NAgDfz6/emdG8DlqA7zxFfaZmMUq04Wv06Cij+g4g8ojIJFPjDvCNxnyS3MB0Mt2KeYWZMIBKIrYmlpGRsbGxYWZmZmdu3atfj4eBKJFBAQMGjQoJ49e9rb22tra8+aNWvcuHHyi+vo6CQlJZ04ccLU1PTHH3+MjIwcNmxYJ+erC3HtGjg7A4XyIcTbG4yN3699lZYGQUFQWwsBAaChAXPnwpw5EB8P//4LkyaBtjYEBLQncQMDg8jIyNWrV2toaMTGxn799dfySMnvYAhFQaCok78Lql+3uPHmZf7Th5wlswl0uupIX4KOrpBdW79ucdM/txuvJ9ZvDFWbMZdAUcekmv65TernqPCV0jrlE6EQqGqg1wBABKDCfG+Iugqpr8FZzmmz9UBTgSnOAJUdayYCgfiC8fLy8vLyEg8hk8knT56Ujik+wub69euyxAHAycnp7t274iGmpqaNjY2KsRghzs195XOTAAAgAElEQVSb4Ov7UQiNBjdvwrJlMGECNDaCqyvcvg3dugEAhIcDkQi//AJVVTBsGNy+DRoacqZjZGQkXoOiKX4LFixYsGCBRGSRq1hYWNTX12PHNjY2VVVV0EoHQygKyoKVQCRyt60V1rBUnAZpHonHGlIaEQe4G5ZyFgYRTc3V5ixSm/ajSKQp9Y7qNz4Kt6QTx2DxAACAC2AMBAIY6wDQIbsG5h2GBwXgYAG/+MDY/jBmJzhZwvrBAOpQQQKTeZC5GQi14LASasPxRVzXwezhMHMg5LDAZgnsmQFzB8GreuixCN5uB0bn5RCBQCAQHUNWFk5g9+5w+jROOIEAGzfCxo0dbRTiU4RAoMxfQZm/QiKYaGSisecErgQt8Z+OMKRdDSyhUCjXxEUCcHjAUgUgApcEcTdAIAR3a+CSwXMLzPOEsyGQlg9T98LVpRDoCtsTYf1gAC04fxNcrcHaCPKZ743FEVkGI+3hZibMdIc7aUBXh/+9gLkj4H+PoX93YFAAuO3JIgLxxcJthOTcdmmoT5BrGBePxyORSOJTqACgoqKivr6ezWZLROZwOLgLWSEQCMTnRXt7sORcGWJpDCyN+XB67CfQ14K4x0AiQqgPEHngbQf+A+FwCoQHwJwDkNsAPQlwOhVmuAGIUtCFK3gikwfDHzcBCHDnBSzwhv23AIhwOxNGOaDWFQIhk9oG2HqzXRoaX7R90k1hYWFjYyOXK3mJ8ng8tLA1AoH4AmhXA4tAIMi5MsTvM2CeJwBAPQ8O/Q9++BNG9IWXZVDKAotFH6KN7Q90dfCxh9hnMF0bHryEePFP3iR8ERcr4DVBVjHczYGbq+Dgbch+B//Lgr/mtidzCMQXjgEVrge3S4NwsVwrUFdWVqqpqVGpVPHACRMm0Gg0bA0hicif+MpA+fn5Dg4OtbW1yjakK3L1xKGhvn4aNDp2yqqsSI45WpT7gkAgmtv08Zg0laajCwD8psabZ/56mZFeV8NiWPQY7j/ZuHuPZhW3AuQAnzj8psZ/Tx0pevqovqZav7vVwMAZBpbWbQhvP50yi1AIIAR4B1AK6nz4cQRokCH7HTC0wdUaina//91YCSvGAgBMGgSx9+FcGnzrBLT6jzThihAJ4NkPTvwNTXww1wP3PnDsDrDqwQUNv0IgOhKCfOBGbkZcqXlqGUNDQ2ydbkQnU5id+ejW9UYeNp4XhEJh7J6I+jr2+J8Wen//Q8XbNxf27sL+itsX9eJhmrvfd5MWr1RTVz8dsaWuRv6J6y2AHOAT50b0jpf3/3EJmDYqNIysoZm0fV09q7oN4e2nc1dyFwBUA4kIelRgccDHHjLewL5kYNXCjefgvAYKygA4MKY/ZBbD7mswYxiA+GAMrgwRgJH2sPsqDLUFEMLwPrD7Koy0A1KdDDMQCASiJQQCAe58QBqNNmXKlM63pyuT9/RxzM7NJ3dsEg+sfFdc8rrAd858C9u+tk4Dh4wZX5yfW89ms6urch4/GB30o01/F5Me1r7BCwAg5/EDRRmDHOBThlPNfPUw1X3WAkunQYZWvUb89AsAvHr0b2vDFWKMcrbKsTWBU/dARxOSQuHE32AaAj8ehMhpMMwCoAY0yDDGEep54G75sVitDBEeeNtBLReG9gJoguF9oJYLPmiXVQQCIUZKSoqTkxOdTndzc8vIeL/eYGJiooODg4aGhrm5eVRUFAAUFxebmZnFxsaamZllZmZKS+Xn52NLcpeWlhobG0dERDAYDDqdPnv2bGz02L1791xdXWk0mr29fWJiIhbTyMgoJCRES0vL3Nz89OnTYWFhpqamjo6OsbGxsixBiKBp6/RxGfT12AnigapqFI/vpmHfBAGArK5OUlFVJZM57FojcwuTHu+/8qioqpLV1evk7pNosbJEDgAynCo7O9vDw0NbW9vd3T1BvokgCEXBra3RM7c0tOqFnZJUyWR1jXpWdWvDFWJMZzSw7m+EecPFzgWQtBROzQeoBqcecHcdsA9BbgTMcAWoARAC8CHmZyiMAiLWp1sHVkZQexCgSYYIC4y1QfgXzBkGUAPWRiD8C7536YScIRCfKxQV6GOkTAOsrKy0tLQ6Lbni4uIxY8aEhobm5uYOHjzYz88PAGpra/38/H7++eeioqKoqKiFCxeWl5cDAJPJTEpKSk9P19fXl5YSp6ys7NGjR/n5+bdv3z5+/PitW7cqKip8fHyCgoIKCwvXrFkTEBCQm5uLxVRRUSktLZ01a9akSZM4HE5+fv7IkSOXLFnSjCUIDCPz7vZDh9s6DRQP1NLTd/bwAYDXL56nXbt05diBwaN9Vchkw27mP6z7lfLfwlevsjJYFeXmNr1x9Mqg+coSgetUXC7X09PT09OzoKBgxYoVM2fO7MwdCxC6Zt0nbtxF1tDETosz02sryhi2fVsbrhBjOn4drBKpEPFNnOql/gWAso9PGwDeicWXFmkSiwAfHyMQCDzMtOHMVGUasGPHjs5MLiYmxt3dPTAwEADCwsK6d+/e0NBAoVDS09NtbGz4fL6RkRGJRGIymVQqlcPhrF+/Xl9ff8eOHdJS4moFAsHWrVs1NTX79+/v6upaUVERHx9vZ2cXHBwMAH5+fufOnYuJicFOV65cSaFQJk+eHBYWFhoaSqFQpkyZgo3mwbXEwMCgM4vo8yUv/XHB86c8bj1VW0fir2f/pFw5ftB+6HBzmz6t0tlMZYnAdaorV66QSKTQ0FAikejt7e3v73/48GFHR8d25hHRBnLu3rx7dK+tm6eJbb/2hLcZtNkzAoH48iksLLSyssKOyWQy1uIRCARxcXGHDh0ik8l9+vQRrdRFJBJNTU1lSUmAxQQAbPJjcXGxSAQArK2tRfvWYVMmVVVVyWSyvr6+SAQASCQSriUIeRgR+D0A5D97cmbXVrOetnrGJgDAqqy4dGjfu1cvh0/8zmmEd2t1NlNZInDd4+XLl6WlpRYWFqJoY8eObWPGEG2FXVF2+0BUeUGeS8D0fh6j2xzeTlADC4FAfPmYmJik/bf7b1NTU1hYWEhISGpqamRkZGpqqrm5OQCIBtYQiURsMiOuVPMJmZqa3rhxQ3T68uXLXr16tWheUlISriWIZih+mVtdXtZ34BDs1OorB026VlHuCz1jk3evXsbs2GTZ1+7HzRGaWtodZACuezAYDFdX15s33y8xl5OTo66u3kEGIHApL8i7tHVNt68cA7ft1dDSaXN4+1HOIHcEAoHoIHAXgg8MDLx27dqFCxeYTObGjRuPHDmio6NTXV2tpqZGoVC4XO6WLVvYbDaHw2lRqvnUx44d++TJk0OHDrHZ7Li4uPj4+EmTJrVoc4uWIKSpeFt87cShpv9mevK49VxOnQaNLhDwz/++86vBQ8f/FNJxrSuQ4R4+Pj4ZGRn79u1jsVg3btxwdnYuKCjoOBsQEgj4/GtRm3t9/Y3HvKXiraXWhuNobv0CyKgHC4FAfFEQiTjvjZaWlrGxsUuXLs3NzbWzs4uPjyeRSAEBAUlJST179mQwGAsXLpw1a9a4ceNSU1Obl2o+dUNDw0uXLi1atGjBggVWVlZnzpyxtbUtLS1tXgrXkqKiojZkv+vQy8HpTtzZxIPRLt5jhALB3wmxNB1dyz5fvcrMqK1imlr1epWVIYqsY8jQ0tNXrAG47qGjo5OUlLRw4cIlS5YYGxtHRkYOGzZMsekimqE4M72OWWlobVOcmS4K1DI0rnr3plXhVH1DCc1tWKKPIL6td2upqakJDQ3dtGmT/lE0GBOB6Hr8Itfdo6KiQk1NTc7PXjk5OSdPnly/fn2bjTp58iSNRkMDX748Kt6+2b8mdO623aKmUlVZyY1Tx4sL8ghAMOtlO9x/sra+4f3ky8kxxyRkh/t95+ozrtNNRnQ2GdcS/vnrgESgS8A0FVVyq8IdRk8UD4mIiBg6dKizs3OrjEE9WAgEAoH4DNA36bbiYIx4iI4hw29BqEQ0Zw8fbPkGRBekn9fYfl74L1etDW8/aAwWAoFAIBAIhIJBDSwEAoFAIBAIBaOYT4Scn+o0/ls2VynU1dWRyWTpdUo6k6qqKgKBoK3dgZNWWoTH4zU1NSm9Lurr6/X09JS7a291dbXS66KmpoZGo6mpqSnRDBaLRaPRcMd9dykuXboUHh5+7949ZRuCaBeXTxx2852oQaM3H87lcG6cPZmb/pjPb7Ls088z8Hua1BqkbSY/P9/BwaG2trZt4riu2E6dCFyunjg01NdP5BX8psabZ/56mZFeV8NiWPQY7j/ZuHsPAKivY98881f+syckkkpPh/4jAqeSVBTTNOrqt10EAoFoPy4uLllZWcq24gvndXbWw1vJjTxei+GXjh54lfV87A/B/vMWMUtKzkdHKtAMQ0NDiSXdO0EncrDWUpid+ejWdXGviNsX9eJhmrvfd5MWr1RTVz8dsaWuhiUUCs/u2lZRXDT+xwWjg+YUZmddOqSwykWD3BEIRFdHIBDw+fz2dIEXFRXxpB78nU/7M/Jpkvf08b/Xr7x6kSlPuIDPz3ny0CNwilU/OwAYMtr3/N7I2uoqRXVi0Wi0KVOmtBitVXXRos5PxME+C/KePr5//fLrF8/FA9nVVTmPHwQuWtGjnx0A+AYv+P2XuTmPH+gaMYpf5s7bvpuuqw8A3/644MC6Ze5+39F19dpvCerBQiAQXQKhULhq1SoGg2FpaYnthFhcXGxmZhYbG2tmZpaZmZmdne3h4aGtre3u7p6QkIBJJSYmOjg4aGhomJubR0VFYYHh4eHdunWjUqleXl5v37718PAoKyvz8vK6evWqRKIpKSlOTk50Ot3NzS0jI0OWztLSUmNj44iICAaDQafTZ8+eja1qiCsubadERjq8KDsdqrZOX5dBw8aOlzOcQCSKViwjq6lBaxYxKi0tNTIyCgkJ0dLSMjc3P336dFhYmKmpqaOjY2xsLADk5+eL1hyRriCJurh3756rqyuNRrO3t09MTMSkpF1RXKe0iISD4fokQgRNW6ePy6Cvx04QD+Swa43MLUx6WGOnKqqqZHX1OlY1s+SduiYVa10BgL5JN1VV8pvcbMWYImwHLBZrzpw55eXldXV17dHTfthsNo/HU64NTCazqqpKuTY0NDR8CnVRXl4uEAiUa8anUBfl5eVcLle5ZlRXV/P5fOXaUF5eXlNTI2fk7OzsdevWtSe5v/766+LFixKBiYmJRCJxwYIF7969u3z5so6Ozrlz5968eaOhoTFz5szy8vL6+nozM7Nff/2VyWReuXLFwMDg0aNHNTU1ampq+/fvr6iouHDhAoFAKCsru3Hjho6OzsOHD9+8eePp6Tl37lyhUMhgMJ48eSKR6Js3b2g02qlTp0pKSpYvX25jYyMUCnF1lpSUEInEKVOmsNnshw8fqqmpJScn44rj2imekfYU3SdOWXHRxh8mV1dI5lE6/NLRA3tXhRa/zCstKjy8ef3RXzfIn0pJSQkALF68uL6+PiwsDACWLl1aX1+/fPlyS0tLoVCYl5dHpVKFMupXvC7Ky8u1tLT27dvHZDLPnj2rrq6ek5OD64oinbgiQjEHw/Wf9pbsl0h5cdHmHyZJewtGQeazzT9Mev3iee6Th5tnfsdmVWPh1RXlm3+YlHYtSSL+zp0709LSWmsD6sFCIBBdAi0trR07djAYjJEjR86fPz8mJgYAOBzO+vXr9fX1r1y5QiKRQkNDdXR0vL29/f39Dx8+TKFQ0tPTZ82apa2tbWRkRCKRmExmQ0ODQCAoKSkxMDC4fPnyrl27ZKUYExPj7u4eGBhoZGQUFha2aNGihoYGXJ0AIBAItm7dqqmp2b9/f1dX14qKClxxXDvFM9Jp5fkpM3xiYH0d+1D42j/XLS8tfDVmxuzWali5ciWFQpk8eTKRSAwNDaVQKFOmTKmqqhKPg1tBIFYX8fHxdnZ2wcHBOjo6fn5+48aNw7wO1xUxZImIkOU/CPl59k/K2ajt9kOHm9v06d7nKx0Dw/g/dxfn575+8fzC3l0EIpHXwFVIQmgMFgKB6BKYm5uLBsTY2tpev34dAIhEoqmpKQC8fPmytLTUwsJCFH/s2LEkEikuLu7QoUNkMrlPnz7YVycfH5/NmzevW7fO399/yJAh4eHhLi4uuCkWFhZaWVlhx2QyOTg4GAAEAoG0TgzMEgDA7MQVx7VTPCMIHpd7KHytjeOAYb4TSSoqadevHP11w8w1G1s1qkZPTw8AVFVVyWQy1myVHk2FW0EgVhfFxcWiCABgbW395s2bAQMG4LoiBq6IeKK4PomQE1ZlxaVD+969ejl84ndOI7wBQEVVddLilddPHjn926+adLrryHF3Lp7TlJqm2jYU08DS2KupED1tRsnJAwCAIvfgbitkALKybdD8NKpDwSs0yLcnDOJTpqioqLGxEXuw5ebmdu/eHQCIRCI2OofBYLi6ut68eROLnJOTo66unpSUFBkZmZqaam5uDgDYKJmXL196eHjMnTuXxWJt27YtODj48ePHuCmamJikpaVhx01NTWFhYSEhIampqdI65RfHtVM8I4j8jKdsVrXP90EEIhEA3L71y7yfmnn/X1fvUYpNCLeCQKwuTE1Nb9y4IYr/8uXLXr16gQxXxJAlIgLXJxHy8O7Vy5gdmyz72v24OUJ8F3BtA0P/kKXYMb+p8crxg1QdXYWkiD4RIhCILgGTyVy+fHllZeXNmzcjIyODgoLE//Xx8cnIyNi3bx+Lxbpx44azs3NBQUF1dbWamhqFQuFyuVu2bGGz2RwOJzk5edy4cbm5uXw+n8/nNzY2YhrYbLZEioGBgdeuXbtw4QKTydy4ceORI0d0dHRwdeIajCuOa2dHFNdnjVAgaGpqen8sFDY1NnZE4xO3gsQjjB079smTJ4cOHWKz2XFxcfHx8ZMmTYJmXVGWCPznYPL7D0IcgYB//vedXw0eOv6nEPHWVT279vRvv5YWvsZOc588VFVTs7Dto5BEUQMLgUB0Cb755puysjIbG5s5c+Zs3rzZ09NT/F8dHZ2kpKQTJ06Ympr++OOPkZGRw4YNCwgIGDRoUM+ePe3t7bW1tWfNmjVu3LigoKChQ4cOGjTI3Nw8NTX15MmTABAYGDhq1KjLly+L67S0tIyNjQ0LCzMzM7t27Vp8fDyJRMLViWswrjiunR1XaJ8jVv3sqFra56J3vcnLeVuQn3DoDx6X29vZVeEJ4VaQeARDQ8NLly7t3buXwWCsW7fuzJkztra20KwryhIROZj8/oMQ51VmRm0V09Sq16usDNGPVVmhTqU11HMuH9tfkPks+2HaleOHBnqPViUrZnVoglDY9m8fNTU1oaGhmzZt0j9qoBBrEIhPlNZ/IkQruYuoqKhQU1OT81tGTk7OyZMn169f3+bkTp48SaPRsJFJiC+J8rdv/lizbP62SC09/ebDqyvKb5479To7S8Dnm1pZfzNxkmE3M2WYjFAaFW/f7F8TOnfbbswr7idfTo45JhFnuN93rj7jaquYV48ffJ2dpa1v8NUQNxcvnE/JERERQ4cOdXZ2bpUNaJA7AoH4osBWkEJ8eRiYdFt98C95wrX1DSb8OL+z7EJ8iuibdFtx8MMETGcPH2cPH9yYNB1dvwWhLSpsQ28U+kSIQCC+KPh8vrJNQCAQXxptuLGgBhYCgfiiQBPXEQiEwmnDjQV9IkQgEF8Uyh1whug0nj96cHLvbonAPo4DHFwH44ZPmbugs0xDfIrU17GvnonJzUgnkVRs7B1HBk4hqagAwL3rV66c/ugT85p9h1VUJFtHbVgGBTWwEAjEl09+fr6Dg0Ntba2sCDExMaGhoXv27MnIyHj16tX+/fs70zxxWjQVgWFu1XPa/MWiU4FQcOHooR62vWWFy6n20qVL4eHh9+7dU7C5UqCK7kyEQuGJyJ1CoTAg+Gd+E//y6RNxh/dPnP0TAFSVl/Xo3XeQ50hRZEX1gqNXPQTiI0pq4XlJC3HKyspEO+8iPgsMDQ337dvXTISdO3dGRkb6+vp2mkkSuLi4ZGVlgRymIjBoWlo2dvaiH4tZqWdo6DrcQ1Z4pxkmqsrmQRXdmbzKznrzMi/wp/kWvWx79OkbEPxzxv1UFrMSAJjlZWZWPXvZOYh+ilqzt1MaWHTIaILxp8B8CxhuAO+jcLNCKo4GlGqA6rLOMKejUHgW6JBPBNpqGf9S4bUqqK9QXHIIAAC4lQfbb7cQJyUlZcuWLZ1hDUJB0Gi0KVOmNBOhsrJSfEHtzqeoqIjH44EcpiKkqWVV37h4Yfy0HyQejbLCOxRRVTaDQCDA9jfsHJMQFSUl6ppUrf+2SzIwMVVRJRfm5QBAVXm5joFhI4/HkVoruJ10fANLHy7nwcC10NsE4hfDrVXgZQdjI+D3ZwAAYAwu0ZDV8EV0pSkqC6Iy4YOhFuz7QUY0NJAXgZCb/Px8bCGu0tJSY2PjiIgIBoNBp9Nnz54tEAjGjRtXVFQ0atSoixcvikslJiY6ODhoaGiYm5tHRUVh4kZGRiEhIVpaWubm5qdPnw4LCzM1NXV0dIyNjZUnAq5aDw+PsrIyLy+vq1evikwFgHv37rm6utJoNHt7+8TERFn2d2JBfqLcTIjrbe9oaCK5G6Os8OYRCoWrVq1iMBiWlpY7duzAAqVrDQBSUlKcnJzodLqbmxvWqy1eldnZ2R4eHtra2u7u7gkJCQBQXFxsZmYWGxtrZmaWkJAgqmhc5QgFoqWrw+XUsWtY2Gl1ZQWvgctmsYRCYXVFWdqt65vmzdq68KeoVaEFLzIVlWgHt2tUoV4Icw9DeABsDgBHBvRlwC+j4PQCWH4K3vIBAIqYwGvr/UEghEa8iZOywhVFB+kXqX1fJvVAE8CU1i1shkAgWqCsrOzRo0f5+fm3b98+fvz4rVu3Ll68aGZmlpSUJL4odm1trZ+f388//1xUVBQVFbVw4cLy8nJMXEVFpbS0dNasWZMmTeJwOPn5+SNHjlyyZIlIfzMRcNUmJycbGhpeu3bN29tbZEBFRYWPj09QUFBhYeGaNWsCAgJyc3Nx7e/U4vv0qKqseHzvb/fRkp93ZYW3yP3799ls9pMnT/bu3bt58+bz58/j1lpxcfGYMWNCQ0Nzc3MHDx7s5+cHAKKqdHNz8/T09PT0LCgoWLFixcyZM7E9K5lMZlJSUnp6ur29PZacLE9DKJAevfvp6Buc+zO6KD+v4EXmmb27iUQir6GBzaoGAsHMqueSHbuX7Nxtbt3r5O7fsE+H7aeDG1jacDcbWBz4eThACUAVQDlALYxxBGsjuJAFHpuhjAVe2+Hqy/cSZwuhdwTQV8OsRBAIAeiQDeBxGLTXgvtBSHgLoA7FFDDbDLFvwGwzZIp2YZII50kJAgADwv+FbpuAugq8jrxv4eEkAQAGkFgCDlGgsRLMt0BUOo7+FBY47QH6anA7ABliPYuSWRCnRbUCsTIpgfxaoP0EAAB6MpOTmQUEAoGHQCDYunWrpqZm//79XV1dKyqkhywAAFAolPT09FmzZmlraxsZGZFIJCaTif21cuVKCoUyefJkIpEYGhqKfeupqqoSyTYToRm1EsTHx9vZ2QUHB+vo6Pj5+Y0bNy4mJkZ++7sOf1+70rPvV7oGkhuKyApvES0trR07djAYjJEjR86fPz8mJga31mJiYtzd3QMDA42MjMLCwhYtWtTQ0CBScuXKFRKJFBoaqqOj4+3t7e/vf/jwYQDgcDjr16/X1/+wGL38LoFoMyqqqtMWL1Mlk0/s2p5w/PAAt+FULS1NOp2mrbNm76FR302lamnRtLTHTg1SUVXNevxQMYm2R1goFLbQO60CmcVgawKqVWKBbAAa2FvAi7eQvBKM58GVZWBvAqV10MSHhEfwIBxevAXXdfD9QHA1AM8tMM8TzoZAWj5M3QtXfwFDPWCyIekJpG8BfQEA1sYiAvA/hFMp0OuXjwWXQlUmRCRB8kow0oKgfbDpOuwZA1yyVBKLwVoH/CLh9+kw3hnuvIAJu+C7AQAqH/Q3NMHQJbB/Nrj3hl1XwO8PeLEOAHCy4C66ulWhtqkFtfpUsTIxhXzszqkOxXUwZvvHyW34UMi4WXCktqduuzTV9fDwzUch9Xfvip++ePGioaGhrq6uGSXYqnQNDQ2iTWeVAp/P53A4nTkABZempqbmi0tEfX19J6wUamr6/puRqqqqrDgkEikuLu7QoUNkMrlPnz7iE4v09PQwWTKZjD0pJfQ0E6EZtRIUFxdbWVmJTq2trd+8eSO//V0EflNTetq9b6cGyRkuD+bm5qKCtbW1vX79Om6tFRYWiiqITCYHBweLK3n58mVpaamFhYUoBNu+iUgkiqoPQ36XQLQHHQPDKQt+wY6bmpoSjx+ha+tIxCGpqOjoG7BZ1QpJsb3LNMjz+R/33q5CBL4AQAggBGgEqH8fviUQNBtggCUMtIJKLlxJBxIRQn2AyANvO/AfCIf/hmXjgMOD9RNAnwfw8VDC9+EaEPdYSvAO+NiDQAglVdDPBC4vBYEAgAhXpGP+Azu/h/QtYKMPfAAjLSARgckFKvU//VTYkQTufSDQEYAEYROhuz6I3lwksvABPlBUm1XLAygBYPxXJqLnshbESCcn6hvTxc+Co5ecdYiQJKMEll/6KKTx8UdzDcrLyy0sLOrr66ElWhzr2glwudyWI3UwfD5fnuICgIaGhk9kUFFSUlJkZGRqaqq5uTkAyLmXogLVmpqa3rhxQ3T68uXLXr16KcSGL4mcjKeNDTybr+zlDJeHoqKixsZGrI2Vm5vbvXt33FozMTFJS0vDRJqamsLCwkJCQkRdUwwGw9XV9ebNm+/tyclRV1cHACKRKPHC00GehhCHw649f2Cf58QAhpkFAGQ/eURWU7O07ZP58P6NC2d/WLZak0YHAF5DQ2VZiauHd0v65KJdDSwCgSC9GNdHCKC3KWQVQyMfVD9ulKcXwk9S02ZJRDAVAtQB0N/Hf2S2kWwAACAASURBVFkGpSywWPQhztj+AABEApiCZOvqQzgJX9DHHjYHwLpY8I+CIb0gfDy42OLHJBEg7j4cSgEyCfp0AxLxY/0AhZVgZQjABCACmQ7BbgA8AAFOFsRLowW1sp/FOMmJXvJlZBbRZr62hCOTPgoRLv5omMu5c+fi4uKwXgpZ8Hi82tpaKpWq3M2ea2pqqFSqctferKysVFNTo1Ll6lOtrKz8RHplqqur1dTUKBQKl8v97bff2Gw2h8PR1tbuCLXYX+yPJzGNHTt28eLFhw4dCggISE5Ojo+Pf/hQMV8uviRyn2eYWlqqSPmMrHB5YDKZy5cvX7lyZXp6emRk5KlTp0pLS6VrLTAwMDw8/MKFC25ubpGRkUeOHBFtUs5ms318fBYuXLhv377vvvvuwYMHEyZMSEhIEO+SFNGMSyAUhQaV1lDPuXj00IgJ/g319Yknjgz29lElky1te3M5nHN/Rg/xHkVWo6QkXaTStfs6uSgk0Q5eaLQehtoAjQJ7nsJCx/8CDeBKOmS/A0+phd8IBMlGBkMbXK3h5qr3pznvQF0VAIBIBAJec0QUjiNIhpel4NEP5noCiwPbEiH4GDzejB8zKR0ir0LqBjDXAwCgzfxYPwFMtCEtHwAASNBEhrBYCPkGQAUnCx8waEmtLHCTG9FSKTXKVohoJRJvnNhp89/dRHGU/nnuU7AB5F4H+VMwFSMgICApKalnz54MBmPhwoWzZs0aN27cgwcPOkJtUVFRYGDgqFGjTp06JeqmMjQ0vHTp0qJFixYsWGBlZXXmzBlbW9vS0tJ25+yL4uWLzN4OOC+UssLl4ZtvvikrK7OxsdHW1t68ebOnpyePx8OttdjY2KVLl+bm5trZ2cXHx2Nf90RVmZSUtHDhwiVLlhgbG0dGRg4bNqy4uFg6OVku0TbjEbII+HF+4okjp6MjtfUNho0eh60sqq5JDV6z4fKpExcO/wkAPXr3/TZoNqn5niO56eAGFhvUjSA6CAKioLIOApxBTQ2u3IAVp2D3DDD/79WCLbtt4WMPC4/DvmT4biA8KIQJv0HCYrAybildLp7gL/DiHUQkQcIi0NMCvgAa+TJiLoHqOlBTBQoPuDz47QqwucDhwYcXcD4EDoLwOLgwBNx6Q2QcHEmB9T5Q0fzQEVJLasVLTrxMcJMbA+/HtOBmYTGYSX5cRiC6LlZWVtiS2UZGRkLhh7kn169fxw4KCgqwg1Wr3r+pkMnkkydPSqsSiYt/JraxscHGsIvrx40gS+2uXbt27dqFHYtW9/7666/v378vHk2W/V2WhRt/bVV4i4wePXr06NESgbJqzcvLy8tLcjSGeFXe/Xj4pqmpaWPj+3dfkU/KUo5QLHQd3cliS/yLhwf+1CHbKHXwhwMBABtGO8K9MHhWBD47YOBaOPcvnF4AP7gCNmopcBCM2gmX8/AV6GhCUiic+BtMQ+DHgxA5DYZZy5FuLZ5gTwhyg6G2MGgDmC+A1Dw4OU9mzABXGNQTeq4B+xWgrQGzhsO4XWL6q8DSAGIXQdgFMFsA155C/C9AEso05z28ltT+x/syES0F3HxyuFmwwFGLQCAQCASicyCIvwy1lpqamtDQ0E2bNukfbXYerDqAFoB4x389ADZIXx8A68fiAlAAAOAdAAAwAAgADQD1AOJjHngALAADsZgiVD4OV5cSrAQw+LjPrhaAjRezCkDvv5hY8Ygbj+mnAoiPRKwF4H5sgCgLovm2RDnUipdJA4Daf39JJ8cD0PvvX9zMItrE1Wy49xrWS7yU/vLRZXLjxo1bt26Fh4c3o4fH49XU1NBoNOWOwWKxWDQaTbljsCoqKtTU1OQcupuTk3Py5EnRWJY2cPLkSRqNhs3YQiAQiPYTERExdOhQZ+fWrUvZKZs913+YJCiJrAVcxDeDk5Z9JxUCAE0fh+Mmirt4m/wxxWEDSK+qL26A9H52AjnUgowyaT65ZkoY0Uq8bcDbpoU4I0aMGDFiRAuREAhEx/Ps4f0716+WFr8xtejuO3mqkYkpANRzOJfPnc56+oTP51v37jsm4Dt6u2cnIL4ApL0l49GDE3t/l4jW17H/1LkK+Gj4BexQg0AgEB9oT6988wQFBe3cuVN8Kxtl8fr1a2zOfxfn2YP7Zw7tHzBoyLR5ISQS6UjUb9iyc7HHD+e9yPQPmjX1p/kVpSUn9kk+QRFdEFxvsbDqOWP+ItFv2s8hVBrdyraPtHgbbiyogYVAIL4oOnoZLUNDw3379nVoEgg5SU6I8xjrO9BtuJVtb/+gWXqGRuUl7/h8fuaTx197ePfq+5VlL5vho8YU5ufVVCtm6UgMFxeXrKysluMhPiVwvYWmpWVrZy/6sZhMXUPDQcNxPlC0oYHVKZ8IEQgEorNQ1ELYAoGAz+dLL8pFo9GmTJmikCQ6GllZ+DIoe/e29G3xVwPeL1lE19aZtTgUAPh8PpFAFLkBWU0NZKx33TzNlF5RUdGnsIYwQn5keYs4tazq6xcv/Bi6EnexmDaMZEU9WAgEokuQkpLi5OREp9Pd3NwyMjKwwMTERAcHBw0NDXNz86ioKAAoLi42MzOLjY01MzPLzMyMjY3t06ePrq7utGnTsN1+xD8R3rt3z9XVlUaj2dvbJyYmAsCYMWNEI/QrKirIZHJeXp50KgAQHh7erVs3KpXq5eX19u1bWUZmZ2cbiO2m5+zsfPXqVfF8tZiFDipPpVNTXU0gEN4WvY7csGbd/B//3PHr26JCACCRSI6DBv9943pRwcuS4jc3Ey9a9rKhack7Bkui9KSL18PDo6yszMvLS6IiEJ8ysrxFnOSE+D72joYmJopKFDWwEAjEl09xcfGYMWNCQ0Nzc3MHDx7s5+cHALW1tX5+fj///HNRUVFUVNTChQvLy8sBgMlkJiUlpaenk0ikyZMnr1y5Misrq3fv3mfPnhXXWVFR4ePjExQUVFhYuGbNmoCAgNzc3MDAwNjYWCzC+fPnXV1djYyMpFO5efNmRETExYsXs7OzAWDTpk2yjGyeFrNgb9+WvWI+C+rYtQBw+fxZj7HjZy4KpdLo+3dsrautBYCRE/w5dew9mzfsWr/6bVHhxOk/tEqzqPR69OghXbzJycmGhobXrl3z9lbMhiqITqAZb8Goqqx4dO/vb0aPU2Ci6BMhAoH48omJiXF3dw8MDASAsLCw7t27NzQ0UCiU9PR0GxsbPp9vZGREIpGYTCaVSuVwOOvXr9fX19+5c+f48eO///57AFixYsVff/0lrjM+Pt7Ozg7b4tfPz+/cuXMxMTELFy6cM2dObm5uz549T58+PWPGDNxUsC0XS0pK+vXrd/nyZWzcGK6RzeerxSx0UHl+CqiSyUKhcOL0oB69bAEg4IfZm5cuevogrf+gIXs2hfVx6O85bjxJReXv5Kv7tm6av2q9lq6unJpFpdfY2ChdvOIdiojPBVneIhpudefalV59++kqtHJRDxYCgfjyKSwsFG0DRyaTg4OD1dTUSCRSXFycjY2Ng4PDrl27RKN2iESiqakpABQXF9vYfFi0o2/fvuI6i4uLxbeWs7a2fvPmDZ1O9/HxiY2NLSkpefDggb+/P24qPj4+mzdvXrdunY6Ojo+Pz+PHj2UZKZERiZG2LWbhC4ZKowMAw9QMO1VRVdXVN2BVMXOeP6utYX07ZRpNS0tDU9PTd4K6hsbTB2nyaxaVnqziRXx2yPIW7JTf1PQkLdXR9f/snXdck1f3wA8khDASEmbYIipDBKyiUEVpBRRfoQ4E6qjllRbrtooVKwoO/NUqglbBUdyIWBFEUBFwtFXf4kDFAUhQlsywQlgZvz8eTSMZhBCM4v1+8seT+9x7zrnn3iQnz3Ofcz+Xr1IUYCEQiIGPkZHRq1evsGM2mx0WFlZXV5eRkRETE3P16tXHjx+fOXOGv5xZWVkZW+VqYmJSVFTEF1Ja+s6iDWNjYzqdzn9Lp9ONjIwAICAgIDk5+Y8//pg+fTqJRBKphU6nu7u75+bmVlZWOjk5YZfBRBqJHfO1dNuLsMcuDGAMTUzV1NXLS94MQWdHe31NtR7NELD16W+dxuPx2F1d0Btn8L0nzr2Ijw4JswUACvIfdXV0Wo+Q8/10+dwiZP3Qqq6uLhdRstHa2kogEBQ7+xsaGpSUlCgKTWfX2dnJZrMVPhZtbW06OjqK/X5vbGxU7FggPij8/f23bt16/vz5iRMnxsTEHD16NDw8vLGxUVVVlUgktre37969m8lkslgsTc1/dwedM2fO6NGjvb29J0+enJCQcPfuXcF1Ud7e3j/++GN8fLyfn19WVlZqauq9e/cAYNq0aQsXLty7d29sbCwAiNSSm5sbFRWVlpamo6PD4XCw/elEGsnj8RobG5OTk318fH777bfy8nLBfvXYhQGMCoHg7Dbp3IkjX309j6SllX3xAlFN3X70GC6XS9ainIz97ctpPsrKyrevZXe0t9uPHiODCpHuxU4xmcKpnxEfLuJmC3a26MkTEwsLvLxDCHQFC4FADHwsLCySk5MjIiJMTU0zMzNTU1NxOJyfn5+Li8vQoUMdHBwoFEpQUJCPzztLXO3s7E6dOrV58+bBgwffunVr+fJ3kjvr6+unp6fHxsbSaLRNmzYlJSVZW1sDgLq6+rRp09ra2tzc3ABApJbAwEBXV1cXFxczM7M7d+5ge/2KNFJPTy8mJmbDhg3q6urJycnjx48XtKHHLgxsPKfPHOUyPj0p8UhMFA6H+z4kVIVAUCUSv1vzkyqReGL/niMxu1qZLd+HhGpRpV2AJYg49/r7+0+dOvXSpUvy7hCiHxE5W7BTL54/NRsszT7HvUM+exGqq6sr/KoJuoIF6AqWAAq/goX2IuSD9iJEIBAfNbLtRYiuYCEQCAQCgUDIGRRgIRAIBAKBQMgZlAcLgUAgEB8rj+/d/fPqlaqKcmPzQdPnzDMwMgYAHo+XlZZ6//YtNrtrxGejp872x+PRjx0CHt/LFZgt87HZwmazM84mFuQ/ZrY0G5sPmjrLz2SQhVzUoStYCATikyMwMHDXrl2Cm94Ik56e7uLi8j6tQvSWR3dzz8QfGuUybsGS5Tgc7sieaCylRU562t/ZV6f6+vkuCHyS9+BC4qkeRb1PJE+8HhE5M/so81NAYLaswOFwR/bsxmZLwsH9j+/f9Zrlt3DVGqKa2u/RO5nNzXLRKJ+gXj1WQy5yZEbB6gEAgKpoAwCAAEBQtA0a73M4Vsv+iAYCoa+vHxcXJxdRY8aMOXbsmI2NjVykIaQkKy3V3fursRPdAECXRjsbf7i26rW+odHt6zmTp88cMWo0AHw1Z27CgbgpM3zVNeT/zSTbuMtx4vWrzAFGVlqKu/dXYyd+AQKzRUOT9OTB/YUrVw8bPgIA5nz3w7aQlU8e3MOq9RF0BQuBQHyikEikuXPnykVUWVlZZ2enXEQhpKTmdWV1ZYX9qDcPdmlRqEE/hhiamNZWvW5parIaYY+VD7UdzuGwy0ro4iXJjmzjLuXE43K5WII0Ocr8ZHk7W94kvuLPllZmi5GpmanFmx0U8CoqqkS1FjldwUIBFgKB+CRITk62tbXV1tb+5ptvWltb4d27KhcvXnR0dFRXVzczM9uzZw9WyOPxfv75ZxqNZmFhsXPnTqywoKDA3d2dQqG4ubmlpaUBgLu7e01Njaen55UrV4TPAsDWrVtNTEw0NTU9PT0rKyvfd88HKM2NjUpKShVlr6I3b9y47IcDO3+pLCvFygGAn/hKRYWgrqHZ0twkveQ///zTwcFBV1d38eLFrq6u2dnZBQUFglsQOjk5XblyRXDcBZtXV1cbGBisWLFCS0vLzMzszJkzERERxsbGI0eOxDYCF5x4N2/eHD16NJlMnjhxYn5+PgBUVFSYmpomJyebmpo+ffr09u3bzs7OJBLJwcHh4sWLWCvhmSkoU7hJjyaBqIk9kBCYLWEbly06sPP/sNliaGK6YuNmtbe5jV48e9pQXzd4mJVEYdKCAiwEAjGgEPmnPz8/f86cOevXr3/27JmNjc3Zs2cFz7a0tPj6+i5durSsrGzPnj0rV66sra0FgNzcXCaTmZeXFxsbGxkZee7cufb2dg8PDw8Pj5KSktDQ0IULFz548CArK0tfXz8zM3PixInCZ3NycqKioi5cuFBQUAAA27Ztez9+GPC0MlsA4NK5sx7e04NWrdEkkQ/u3NHa0sJiMvEqKoL7BqoSia0tLVKKZTAYX331VVhYWEFBgZqa2l9//SWuJn/cJ0+e3O1UTU0NHo+vrq4OCgoKCAhgsVjFxcVTpkxZs2aNYLWKiopp06aFhIQUFRV9/vnn/H0CGAxGRkbGw4cPjY2Nvby8AgMDS0tLw8LC/Pz8sL2bhGcmX2ZdXZ3IJpJNEjmxpfTYR4HAbJkRtCpEk0Q+uPOXbrPi3q2/ju6Ndho/YbCVtbAEwR2rpAQFWAgEYkAhMqvqqVOnZsyYMW/ePAMDg9DQ0G7bNhOJxIcPHwYFBVEoFAMDAxwOx2AwAEBLS2vnzp00Gm3KlCnLli07ffr05cuXcThcSEgIlUqdPHny7Nmzjxw5wpcj8mxHRweXy62qqtLT07t06VJ0dHR/e+ATQYVA4PF4vgv+O3zkZ2aDLf3/G6SkrPTo7j9qGhrsri4Oh8Ov2dHeriZ1+uXk5GQnJydfX18dHZ1t27apqanJZt769euJROKcOXOUlZVDQkKIROLcuXMbGhoE65w+fdrNzc3f39/AwCAiImLVqlUdHR0AwGKxwsPDdXV1U1NT7e3tg4ODqVSqr6+vj4/P6dOnQdTM5MsU10SySZIn9gDg7WwJfDtbvsNmC3a2ob7+4K5fUk+f9Jo1e9Y3gSIlyJCuGQVYCARiQCF46YJPRUWFldW/l/27BVg4HC4lJcXKysrR0TE6OpovwczMjL8/hLW1dWVlJZ1Or66uNjc3NzU1NTU1vXDhguD/WpFnvby8IiMjN23aRKVSvby8BtiFAQWiSSIDAM3YBHuLV1HR1tVrbGggaWkBQEtTI1bOZrPbWK0kLWn3dSgtLTU3N8eOiUSigYGBcB1pdkDR0dEBABUVFQKBoKurix0L67K0fLP6h0AgBAcHYxs/KCsrGxsbA0BFRQW/AgAMGTIE24xSeGby64hrItkkyRN7APB2tphib9/OFgYAlL8siY7YoKGpGbL1l3GTPMTtQYICLAQCgRCBiYkJdqMEo7S0VPBsRkZGTEzM1atXHz9+fObMGf6vTllZGf+GY1FR0aBBg2g0mrOzc9lbsrOzQ0ND+XJEnqXT6e7u7rm5uZWVlU5OTsHBwf3f3U8CQxNTNXV1/ur1zo6O+ppqPRqNZmyiSSYXPnmCldMLnispKQ0aIu1Oc4aGhvzp0dnZWVNTgx0LBhzV1dVy6YKRkdGrV6/48sPCwurq6gBAWVkZ+5k3Njam0/9dnk+n042MjEDUzOTXEddEMpIn9gBAaLa019dU69EMuVzO8f17R7mMmxu8BAvN5QgKsBAIxMBnzpw5586dS0xMbGho2Ldv3927dwXPNjY2qqqqEonE9vb27du3M5lMFosFAAwGY926dfX19Tk5OTExMYGBgV5eXvn5+XFxcU1NTdnZ2U5OTiUlJZgQJpMp8mxWVpaPj09RURGHw+FwONI/F4aQjAqB4OL2ZfKJo0/zHpSW0E8djCWqqTuMHqOsrPz5F5MyU5NLCgvKSugXEhM+cxmnpi5tjoaZM2fm5uampqY2Nzdv3Lixs7NTSUlJW1u7sbExOTmZzWZHR0fzrwkBAJPJlLkL/v7+mZmZ58+fZzAYW7ZsOXr0KJX6TsIfb2/vvLy8+Ph4JpOZkpKSmpoaEBAAomZmj00kI2FiDwxUCAQXt0nJJ448zbtfWlLMny1FT582NTDMLIe8ePaU/2qor5eLUpTcFoFADHzs7OxOnToVFhb2ww8/TJ06dfny5YJn/fz8MjIyhg4dSqPRVq5cGRQU5OPjExcX9+WXX9bU1FhZWVEolMjISA8PDwDIyMhYuXLlmjVrDA0NY2JiJkyYAAD+/v5Tp05NTEwUPuvi4pKbm+vi4tLe3j569OiEhATFuGAg4jl9ppKS0sWkxDZWq8Uwq+CQdSoEAgB8+R9vDoeTdOR3Nrtr+MhR3v5fSy/TwMAgOTn5hx9+YDAYP/30k4mJCZVK1dPTi4mJ2bBhQ0BAgLOz8/jx47HK/HH38vKSwX4LC4vk5OS1a9cWFRXZ29unpqZ2u8Gtr6+fnp6+atWq5cuXW1paJiUlWVtbFxcXC8/M4uJiCU16vORGpVJFTuyBhNBsCVUhEGqrXwPA6UPvpBDzmjXbbcp/+q5RSZp7yeJobm4OCQnZtm2b7jG9nmsjPnkYLHjVACON5SdRfKLRxsZGCkXaVReC5Ofn6+npiVx40Ss6Ozubm5tJJBK2qEJRNDU1kUgkGVYPyJG6ujpVVVUp00wXFhYmJCSEh4fLrC4hIYFEInl7e8ssAfEpU1ZW9tdff3399dcA0NHRQaVS6XQ6jUZTtF0IRRIVFeXq6urk5NSrVu/la5cM+WyYkQhm20F/M0w+Bjl1QnXUoVodVH56H+YgFEVeJay/pGgjeuL//u//bt68qWgrEAiEYsDhcIGBgRcuXGhqaoqMjBw9ejSKrhCy0f8Bli5cegFjN4KNEaT+CNd+Bk978I6C3x4DAIAhjNkPzzrQYjAEAoFAKB4jI6OkpKTNmzc7Ojo+efLk2LFjirYI8bHSz2uwVKCNB4uPwFY/WDUFoAMAD8NNwMoQAvbCTFswAihjQCdXRvFcHnC4oCLioWwEAoFADHD+vJqZlnRasCQy9hAej2e1tl48m1iQ/xiHw9s6OHr7BeDwvfix8/Hx8fHxkbexCMXz+N7dP69eqaooNzYfNH3OPAOjdxasnD913NNnhob89szu5wtHFPirAJpYsPQLgCqABoBagBaYNhKGGMD5Z+AeCTVN4PkrXHn7VOnZUrCJAvIGCLoIXB4AGQoA3I8AZSO4/Q5plQBqUEEE00hILgfTSHjK6t8eIBCIT4pXr17JnFvy41I6AKivqxliYxu4bCX/hcPheDxe/J7d1ZWVc7//Yfa3/6UXPk86+nsfFaEBGgA8upt7Jv7QKJdxC5Ysx+FwR/ZEv5PEruD5nevX5LujaJ+uYPF4PC5X4tUnPDytAGsjUBHMXssEIIGDOTyvhKz1YLgELv8EDkZQ3QpsDqTdh7tb4XklOG+CeWPBWQ88tsMSDzi7Av4phvmxcGU16OsAgwkZefBwO+hyAVCM9fHQ2gn3ynuuJiVt4nex6OrqEk7rJw319fUdHR3YXnV9AUsk3dHRodh8fRwOh8ViiUud995gs9lSurStrU0wB/eAYcyYMceOHbOxsVG0IQMKRm3tIMshNvYOgoXFz5+V0ovX/7KToq0DAHO/X7w7Isxr5myKtvZ7Ng8N+gdFVlqqu/dXYye6AYAujXY2/nBt1WtDE9Pnjx7+lZVZXPBc7hr7eouwhwALAABEfrfjlYHDBeAB8AC6ANrelG/3B40OGGUBYy2hvh0uPwScMoR4gXInTLaH2WPhyN/wkw+wOiF8Juh2AqAN7D8qSuoh+A+5SWNfWyo3WW+pq6vr7Oxsa2vruaoUyPf/kGy0t7cr2gTgcDhSuhTbWKa/7ek7XC6Xw+FIH8SXlZV9CJNBMr3tlMJh1Nbaj3bq6uzs6uxU19TECmurq9Q1NLDoCgAMjIzwKiovXxQ5jhn7ns37KAb9E6HmdWV1ZYX9qDePAWpRqEE/hmDHZArFYczYQUOHXb2QIl+lfQqwlJSU8JJvbHPBxhieVUAXp/tKqYel8IN79+o4ZTDmAbQCkN/Up9dAdROYr/q3jvdnAADKSmAMKLr6+LAzhEtBcpPGWXlP3Knm5mYymSyDzG+++UZDQ6Nbuj8Z6OrqYjKZGhoaBAKhj6L6QktLi4aGhmLTNDQ0NBAIBA0NqTI9ksnkHr5VZOXmzZs//vhjYWHhyJEj9+3bZ2dnBwC3b99etWrVkydPBg8evG3btmnTpgk2ET5bUVHh7OwcExOzbNmyjIwMIpG4ZMmSu3fvOjo6rl69GssNIazI3d29pqbG09Pz+PHjkydPvnjx4oYNGwoLC3V1ddesWdMtKVePNgs3r66udnR0DAkJ2bFjB4vF8vf3P3DggLKyssjmBQUF3Wzu1ikHBwdx9nxQ8Hg8Rl3trWvZZ4/G83g8XX2DmfMXDLG20aJqt7FYzOZmTTIZABrq6zs7Olqam6QUm56evnv3biqVevnyZX19/VWrVi1d+s4fOSn97+npKTjoW7dujYuLa2xs/Pzzz48ePSpNdnWEHGlubFRSUqooe3U8di+jttbYfJC3/9dGpmYAYGRmbmRmXl1ZIfcAq5+/dtvA1QpIRNj3SKBQDy4/hILX4CF03VRJqXvMRKOA8xAo2/vmlb0eQr0BAJSVQQlFV588OPEoKSlJOCu5obKysmxtBcFiGrmI6gsy+0GOAID0ZvA3CZEvFRUV06ZNCwkJKSoq+vzzz319fQGgrq7Oy8srMDCwtLQ0LCzMz89PcDsdcWcZDEZGRsbDhw+trKw8PDw8PDxKSkpCQ0MXLlz44MEDkYqysrL09fUzMzMnT57c0tLi6+u7dOnSsrKyPXv2rFy5sra2VnqbxTWvqam5f/9+cXHx9evXT5w4ce3aNZHN29vbhW0W7NTHEl0BQEtTEygpmQ8esuHX3Rt27h40dOjR32IaGYyhNrbaunoJh+JeFb8ofv7sZNw+ZWXlzo4O6SVnZ2cbGBgUFRXt379/48aNGRkZ/yqV2v+Cg56TkxMVFXXhwoWCggIA2LZtm9y9gZBMK7MFAC6dO+vhPT1o1RpNEvngzh2tLS39qrSfnyJkgpoB7A8Evz1Q3wp+TqCqCpezITQR9n4LZm+vQzPFh0peDrDyBMRlwddj4W4pzNwNaT+CpWH/Wo1AIAYYp0+fdnNz8/f3xYY3yAAAIABJREFUB4CIiIhBgwZ1dHSkpqba29tjmwP6+vr+8ccfp0+fXrBgAdZE5NmFCxeyWKzw8HBdXd2UlBQcDhcSEqKsrDx58uTZs2cfOXLEzMxMWJFgslkikYgFZxwOx8DAAIfDMRgMIpEopc0im1MoFC6X+8svv2hoaHz22WfOzs51dXUim1++fFnY5p9++onfqX4fCflBplAi9x/kv501b8Gzh3n5D+6Nn+Tx3ao1qYmnfo+J0iSR3aZMvXohRbM3j4aRyeRdu3apqqrSaLRly5YdP378l19+wU5J739BgdiN76qqKjs7u0uXLn0UN8EHGCoEAo/H813w38HDrADA/79B29b++OjuPy5fTOo/pf0cYHEBmPCfkXA7Ajb+AUduQGsHjDCFM8thmi0AAwDA3wWm7oLEJfDZYBECqBqQEQIrT8CaBDCkQMw3MGEIVPR1/TECgfi0KC0ttbS0xI4JBAIWNlVUVPALAWDIkCGCe8yJO6usrGxsbAwAdDq9urra3NycX8fb21ukIkFwOFxKSkp8fDyBQLC1tcXhxKaZESmKy+WKa45ZBQDYIiqRzUXaLNipjxccHq+tq4fdCtTW0wtcthIrZ7PZySePkSm9uOlvamrKj4mtra2vXr36rxbxw9fN/4J4eXlFRkZu2rRp9uzZ48aN27p165gxY3rfRYTsaJLIAEAzNsHe4lVUtHX1GhsaJDbqK/2/MqMFoBHsTSHlRyj/DRoOwc2NMM36TXQFbIieD02HwcsGDLSg6/jbVjy4tgFmOQA0wujB8NcmYMZDURR86wzQBMZUgZqIjwc1FaDJLcNIf6Gvr6/5drUsYsBgZGT06tUr7JjNZoeFhdXV1RkbG9PpdH4dOp0uuDJG3Fn+TUwajebs7Fz2luzs7NDQUJGKBC3JyMiIiYm5evXq48ePz5w5I2FFuUhRfWwu0mbBTn1EPL5/99ewUGZLM/a2s6OjrqaaZmTcymT+HhNVWVaKlT/Ne0BQVR1i3YtH+crLy/mL01+8eCE4K6T3vyB0Ot3d3T03N7eystLJyUk47Eb0N4Ympmrq6mUlbz7RnR0d9TXVev2co/+9LH1tA6gCeC3wanx7qu5tScPbAwysPgOg7d2G9QDsd2siPh5czOGIv6KN6ImoqCjZ9m1FfCCIzIvh7++fmZl5/vx5BoOxZcuWo0ePUqlUb2/vvLy8+Ph4JpOZkpKSmpoaEBDAbyL5LAB4eXnl5+fHxcU1NTVlZ2c7OTmVlJSIVITVZzKZANDY2KiqqkokEtvb27dv385kMlks0ZlmRIrqY3ORNsvmZ4VjaWXTxmIlHDpQkP/45Yuik3H7SWQt+1FOGpqa7W1t544fLXr65PH9u+dPHZ/gOUWlN8+aNDU1rV27tq6u7tq1azExMfPmzeOfkt7/GNigZ2Vl+fj4FBUVcTgcDofT1dUlc68RsqFCILi4fZl84ujTvAelJfRTB2OJauoOo3txHVGG9DFohxoEAjGgEPkQooWFRXJyckREhKmpaWZmZmpqKg6H09fXT09Pj42NpdFomzZtSkpKsra25jeRfBYAqFRqRkbGyZMnjY2NFy1aFBMTM2HCBJGKAMDf33/q1KmXLl3y8/NzcXEZOnSog4MDhUIJCgoSlzRcpKg+Nhdps+y+VijqGhorNoSrqaknHf39RNw+NQ2NRSHrsIzt84IXk7S0jsf+lpV24cup3u7TepeW3dHREY/H29raBgYG/vzzz9OnT+efkt7/IDDogYGBrq6uLi4uZmZmd+7cSUhIkLnXCJnxnD5zlMu4i0mJR2KicDhccMi6XoXdEu7mi0OJx+P1tg2f5ubmkJCQbdu26R7Tk1kIAiE7q8XO3sbGRgqF8j5t6UZnZ2dzczOJRBJc4Pz+aWpqIpFIik3TUFdXp6qqSpJulXFhYWFCQkJ4eLjM6hISEkgkEra0CIHoLenp6eHh4bm5uYo2BPEBERUV5erq6uTk1KtW6AoWAoFAIBAIhJxBARYCgUAgEG8YPHiw4KIrBEJm+jlNAwKBQCAQHw82NjZo90CEXEABFgKBQAAABAYG2tnZrV69WtGGvKG4uNjR0bGln5NNf9TcvJqZlnRasGR77CE8Ht9QX3/hTAK9qFBJScnSytrH72utPm9+JSXV1dUmJia9fU5Q8liLlImmh8xUv34dG/XrytAN2P7fbDY77Y+k5/n5Lc3NJubm3r6zTc0HyUURCrAQCATiQ0RfXz8uLk7RVnzQMOpqhtrYurp78ktwOByPxzu2fy9BVfWb4MWtra2ZF86fOLB/6bqfFWhnj8gw1mh6yAaHzT55+GBzUxP/Cb8TB+NevSyZGTBXi0rJuZRxIDrqp4itJJm2su0GCrAQCARCkXC5XA6HI5yykkQizZ07VyEmfSzU19aaWw6xsX9n/8TqysqK0lcbfo3SolABgMPhJByKYzGZ6h9qAmEul0skEns71mh6yEZGSjKX+29Gq+bGxsd5D75fscp6uB0AzP8uOHzt6vy8By4TJspBGa8PNDU1ff/997W1ta2trX2R03eYTGZnZ6dibWAwGA0NDYq1oaOj40MYi9raWi6Xq1gzPoSxqK2tbW9vV6wZjY2NHA5HsTbU1tY2NzdLWbmgoGDTpk19UXfq1KkLFy4Il6elpTk4OKipqZmamsbExGCF586ds7GxoVKp8+fPnz179s6dO3k83vPnzydNmqSlpTVx4kRMVFVVFY1G27Vrl4GBAYlECgoKwrx669atsWPHampq2tvbp6WlYTJv3LgxatQoEok0YcKEx48fi9NeXl5uYmJy7tw5IyOjvLw84VYvXrzQ1NTslfaqqip9ff3ly5eTyWRTU9PExMTw8HAjIyNHR8dz585J8MNHyo4Nobl//9nZ0dHa0sIvZNTV3czK5L99+jBv3aKgzs4O6cUKTwAej7dlyxZjY2MNDQ0PD4+KigqsUHjUqqqq8Hg8ljWNRCItXLgQGyxhmYIT4Ny5c9hYSy+TPz14A2tM+5XCZ083rVlV9Pz5qu/+y6ir4/F4FWVlOzeHswR+N7esW3slLbVbw127dv3zzz+9VYcCLLmBAiwMFGBhoACLz4cQYDU3N6uqqh46dKiuru78+fNKSko1NTWPHz9WVVU9ceJEVVVVZGQkAOzcubOtrc3U1PT//u//GAzG5cuX9fT07t+/X1VVpaysPHfuXCaTee/ePVVV1aysrNraWi0trbi4OAaDcfbsWTU1tcLCwvLychKJlJiYWFVVtW7dOisrK3Hay8vL1dXVFy5cWFtbK7KVYIAlpfaqqioA+PHHH9va2iIiIgBg7dq1bW1t69ats7CwEGdJX7ytQLhc7rpFQdFbw0O+C1wT9O3/rf+p6NlTwQovnj29kXl5S8iqzAsp0osVOQGys7OpVOq9e/fKy8s9PDwWL17M4/FEjho2BPPnz2cymXfv3sXj8deuXRMpU3AC8Mdaepn8JgNpTPuVViYzPGT100cPX1dU8AOsbhQ+e7rqu/8WPX/erRwFWCjAQgHWv3wIY4ECLIwPIcDq7Ox8/vw5j8djs9m3bt3C4/HPnz9ft25dQEAAv87w4cN37tx5/vz5QYMG8Z22ePHiZcuWYb9w5eXlWOHEiRMTExMPHz7s6urKb+7v7x8REfHrr796e3tjJR0dHXFxce3t7SK1Y1tHl5WV8Xg8ka0EAywptWM16+rqeDxeUVGRsrJybW0tj8d7/PgxhUIR54e+eFuBNDU0rPvhu/MJJ5sbG5saG84cOfzzkuCG+np+hQtnTu/c9POGZT/cuXlderEiJ0BGRoaWllZ6enpHRwebzcZ+bkSOWrfBGjdu3B9//CFSpuAE4I+19DL5TQbSmPYrR2L3nT15nMfjiQuw/rn199rFixKPHRFuK1uAhdZgIRCIgQ8Oh0tJSYmPjycQCLa2ttiuFxUVFVZWVvw6w4cPBwA6nV5dXW1ubs4v5yeFNzY2xg6w9VIVFRWWlpb8akOGDCkvLycSifxCAoGAbevL5XKFtQOAsrIyJrO0tFS4VTek0Y4d6+joYNUIBIKuri6/iTg/fKSQKZTt+w/y386at+Dpw7z8B/fGT/LASrz9AgDgef6j32N2Wwwdpk8zlEasyAng5eUVGRm5adOm2bNnjxs3buvWrWPGjBE3ajgcrttgiZtU/AnAR3qZfAbSmPYfd2/fel1evnpjuMizDfX1p4/Gl716OW3WrPFfTJKXUhRgIRCIgU9GRkZMTMydO3fMzMwAANu3x8TEpKioiF+ntLR0zJgxNBrN2dk5JycHKywsLFRTUxMp09jYODs7m/+WTqcPGzaMSCT+888/WAmbzY6IiFixYsWdO3eEtQOAsrKykpISABgZGQm3ktwjkdpl88PAAIfHa+vqtTQ3vaIXM2prR451xsqt7exJZHJJUaGUAZbICUCn093d3RcvXtzU1LRjx47g4OAHDx6IGzVsTHuUCQITgI/0MvkM4DGVIy/pxbU11euW/sAv2RK6doTjyMDFS8tevYyN2mllO3z91kgSWUuOSlEmdwQCMfBpbGxUVVUlEont7e3bt29nMpksFmvOnDnnzp1LTExsaGjYt2/f3bt3AcDLyys/Pz8uLq6pqSk7O9vJyamkpESkTG9v77y8vPj4eCaTmZKSkpqaGhAQ4O/vn5mZef78eQaDsWXLlqNHj1KpVJHaBUWJbCW5RyK1y+YHqb34YfH4/t0dYaHMlmbsbWdHR11NtYGRcU1l5fmEE+y3WaM62ttZLJaGprRhh8gJkJWV5ePjU1RUxOFwOBwOlpJK+lGTflLJMBMG0pj2H57/8V4bvgV7BS5eCgDBK3+cHjCHy+Ue2f+bk8vnC4J/kG90BSjAQiAQnwJ+fn4uLi5Dhw51cHCgUChBQUE+Pj52dnanTp3avHnz4MGDb926tXz5cgCgUqkZGRknT540NjZetGhRTEzMhAkTRMrU19dPT0+PjY2l0WibNm3CHvKysLBITk6OiIgwNTXNzMxMTU3F4XAitQuKEtlKco9EapfND1J78cPC0sqmncVKOHSgIP9xyYuiE3H7SWQth1FOwx1HElRVE+MPl5XQXxW/OHkglkKlDrMdLqVYkRMgMDDQ1dXVxcXFzMzszp07CQkJ0JtRk35SyTATBtKY9h9kCoVmZIS99PQNAEDfgEbV1i589rSxocF8sGXR82f8V0N9vVyUKvHe5tqSgebm5pCQkG3btuke05OLNYiPhtVip01ra2tbW5uOjo6Ea9rvgcbGRgqFokADOjs7m5ubSSSSqqqqAs1oamoikUjKyor8K1VXV6eqqirlnYvCwsKEhITw8HCZ1SUkJJBIJP7CKcQApqmhITUx4WVxEQAMtbad5heA5Yesq6lJSzpdSqcrKYHF0GH/8fXT1kU/Uog3VFVW7ggPC9u+g6qjczM7K+XM6W4Vps30/XKKl2BJVFSUq6urk5NTrxShNVgIBGJAweFweq6EGBBoUanf/LBEuFxXXz9waQ+L2BCfLDQjo6iDv2PHEya5T5jkLk0rLpfbW0XoFiECgRhQyPA9iEAgEJJBARYCgfjUEd5zBoFAIPoIHt/rO37oFiECgRj4VFdXm5iYdL19suyD4tWrV9bW1m1tbfwDydXes3l8iouLHR0dW1paFGWABKpfv46L2rEiNIyirf3o/r1jcfu6VbBz/Ax7dkyO9NEhCh/QTw1GfX3ymUR6UZGSstKQYVYz/Pwp7z6hefbUSS+frzTll+cCBVgIBAIhI2PGjDl27JiNjY2iDXkf6Ovrx8XFKdoKEXDY7FOHDzQ3NWHPbFkMGRK0bCX/LJfLTTp+dKgUj1j2lg/WIQhheDze77H7VVUJgYsWtTJbL11IPXIgbtW6UH6FFwUFf924Pundte19BAVYCAQCISNlZWWdnZ2KtuIduFwuh8Ppy31ScRJIJNLcuXP7Zl2/kJGSzOH++2QDiaxlM8Ke//bvazm6enrj5Jee+19FH6pDEMJUV70uL321ecevWhQqAHA4nOOHD7a2MjU0NJ88fnQjK6uo4LnclaI1WAgE4lPh7NmzNjY2ZDI5KCgIW7JaUFDg7u5OoVDc3NzS0tIAID093d3dffbs2SQSydLS8rfffsPaXrx40dHRUV1d3czMbM+ePQDg7u5eU1Pj6el55cqV27dvOzs7k0gkBweHixcvYk1u3rw5evRoMpk8ceLE/Px8rFBYozAFBQV6ev+mFXBycrpy5YpgBWFjKioqTE1Nk5OTTU1Nnz59KlhZpBk9ShBuVVxcjOXaqK6uNjQ0jIqKotFoZDL5u+++w5wp7ITq6moDA4MVK1ZoaWmZmZmdOXMmIiLC2Nh45MiRycnJ4izpFUXPn97/3+2ZAaIDneampisXUvwWBPYqa4ywSSK7LOiQPnZz69atJiYmmpqanp6elZWVvXUCokcIBNWZ/gFYdAUARDUiHo8nEAgAoKVF+czJafI0+Sd2QQEWAoH4JGCz2WlpaXfv3r127dqxY8du3rzZ3t7u4eHh4eFRUlISGhq6cOHCBw8eAEB2draBgUFRUdH+/fs3btyYkZHR0tLi6+u7dOnSsrKyPXv2rFy5sra2NisrS19fPzMzc9SoUV5eXoGBgaWlpWFhYX5+fkVFRRUVFdOmTQsJCSkqKvr88899fX0BQJzGXiHSGABgMBgZGRkPHz50cHDgVxZpRo8SdHV1hVsJUlNTc//+/eLi4uvXr584ceLatWt1dXXCTsBq4vH46urqoKCggIAAFotVXFw8ZcqUNWvWSLBESlitrafjf/f7JlBDzLqZzLTU4Y6OBoZGfXevcJe7OUTmbubk5ERFRV24cKGgoAAAtm3bJr21CCnR1tGZOMkdAIoKnl+7mpl08oSH11QVFQIAmJiZOY93dfxslNyVoluEnyjMDnhaDWPMFKAa2yPCwsJCAboRnzbbt2/X0NAYNWrU2LFj6+vrL1++jMPhQkJClJWVJ0+ePHv27CNHjkyePJlMJu/atUtVVZVGoy1btuz48eMeHh4PHz60srLicDgGBgY4HI7BYPAvMqWmptrb22P78vr6+v7xxx+nT59WV1d3c3Pz9/cHgIiIiEGDBnV0dIjUuHr16l71gkgkChujqanJYrHCw8Ox3Z35nD59WtiMHiXs3LlTuJWgWC6X+8svv2hoaHz22WfOzs51dXUinYC9Xb9+PZFInDNnTkREREhICJFInDt3LrZ6SaQlglfvJHP2xNHhDg42I+yrKiuEzzLq6+7dubVm05a+u5dCoQh3edCgQYINe9tNIpGINezo6OByuVVVVXZ2dpcuXUJ5RvqVJ48ePX/6pL2tXav/M1G/lwBLG+5WwPokyHsFXC44D4EdM8FW/X1olhYyFDPBcT20bO2bHE141QHWa6Btu3zseoM6VAOYLIOuX3rZ0BACD4CdMawWCs1LGLA4Ge6uFNWqnzl58iQAhIWFKUA34hMGh8MZGxtjx9gCIzqdXl1dbW5uzq+D5X83NTXl59+3tra+evUqDodLSUmJj48nEAi2trbddi+pqKiwtLTkvx0yZEh5eTmRSOQXEggELNQQp1Ey3fbbEGeMsrIyv4N8SktLhc3gcrmSJYhs1Y1uzhTpBOxYR0cHq0YgELD4j7/AS7JjJXP39q3K8vLVGyPEVbhxNXOYrZ2O1OFajyZ163I3ZO6ml5dXZGTkpk2bZs+ePW7cuK1bt44ZM6ZXNiOkZ/psPwB4mv/4wJ6YwUOHGdBo/aer/wMsbfj7FXhsh00zYe8C6GTDvqvgHgWP1oNuf9+fNIQxYXBsIdiI26qEX4ED+loQ998+a+zFl4MU8M3TBEB/aRCIviG8CodGozk7O+fk5GBvCwsL1dTUHj16VF5e3tnZia3PePHihZGRUUZGRkxMzJ07d8zMzACg27Y/xsbG2dnZ/Ld0On3YsGFEIvGff/7BSthsdkRExIoVK0RqFHnFgs1m84+rq6sFT4kzRllZWbiPRkZGwmbcuXNHsgSRrUR6VbITJDeR0BdpeEV/UVdTHbp0Eb9ka2gIPx0Dh81+8L87vvMXSC+w7ybJJpNOp7u7uy9evLipqWnHjh3BwcEy3DhGSOYlnV5fVztqzFjsra3dCBKZTC8q7NcAq9/XYPEIsOI4hM+Cn6aCFRlGGEHcf8FACxKf9LdmAIAyBnRKDE3eVGgDEhfm9m6XofdBj/YjEAiZ8fLyys/Pj4uLa2pqys7OdnJywu5fNzU1rV27tq6u7tq1azExMfPmzWtsbFRVVSUSie3t7du3b2cymSwWCxPCZDK9vb3z8vLi4+OZTGZKSkpqampAQIC/v39mZub58+cZDMaWLVuOHj1KpVLFaeyGtrZ2Y2NjcnIym82Ojo7mXwrCkGCMMCLN6FGCyFaSnSnSCT0OQa/60g2P//iEhG/BXlhQ9f3K1dMDvsbOPst/3NnZaSvwOKGU9MUk2WRmZWX5+PgUFRVxOBwOh/NhZmv72Kl6XXk24RTftx3t7SwWS44pr0TSzwGWBrysg/sv4Qc3gBqAZoAaAB5cDYWvxwJow+0WcI4D0gZw2AMXKwHUoVodDDbDiqugFQZm2+HMS4i4DcbbYOReSC4D0If0KnA/ArOTgLQBLHfAb48ByFAAoMe/TkwDp31wpRbcI6GmCTx/hSvVAHpwsQoc94D6ejDbDnseAhgKVKiC4hYg/QAAYqxSA8OtEPUAaFuAvAG+uwhcniiZIumxU/CmC+5HgLIR3H6HtMp3zaO/kXS2FGyigLwBgjADhE0FAH1ILgPb3aC9Cb6JhdYOMVYhEJ88VCo1IyPj5MmTxsbGixYtiomJmTBhAgA4Ojri8XhbW9vAwMCff/55+vTpfn5+Li4uQ4cOdXBwoFAoQUFBPj4+AODv7z916tR79+6lp6fHxsbSaLRNmzYlJSVZW1tbWFgkJydHRESYmppmZmampqbicDhxGruhp6cXExOzYcMGdXX15OTk8ePHC54VZ4xIRJrRowSRrSQ7U19fX9gJPQ5Br/rSDTKFQjMyxl66+gYAoG9Ao2rrYGcLnz4xHTQI3/t0FX0xSTaZgYGBrq6uLi4uZmZmd+7cSUhI6KM6hDAjHB1VVVVPHYl/VVLykl589OABCpVqZWPbr0r7dIuQx+P1sBxPHZ7RwYgCJME/AHWgiwfAQ10neP0KvwSAnzNkPwG/3+DhViBrQk0z4HFQHQs70iDgN1g7DYp3Q0QyrEmAmU4AANlPYIkHFO2Ch6Xw9W8wWA8sRV3ky1oPhkvg8k/gYAAtbPCNgd8WwAwn+PM5zIyGr10EKhhDcR0AAKiJt6oJ7r+E4t1Q8Bo+D4eAMTBGW0imyPvmygBciZ36GdoJ4LEdlnjA2RXwTzHMj4UrOgLmGUF1K7A5kHYf7m6F55XgvAnmuYAdScjULdDxGubsh8NB4DEC4m/A+jMwdrDowelgw71y0ad6pO2vv8SdYrPZHA6Hv4RFmFevXhkaGra2tsqoWzq4XG5/q5AMtuVwR0eH4L0ehZjBYrF69Yx6f8Bms6Ucjra2tv7YrdnAwEDwwgD/EbDRo0f/JTSZ8Xj8zp07d+7cyS8hEAgif/aio6Ojo6Ox49zc3G5nPT09PT09uxUKazQ3N8fSefMPAGD58uXLly/v1hY7K84YcVc+hM2QRoJwK0tLSyxruYGBgeCysKtXr2IH48eP7+YEwZqCvbOysmpoaJBgSd8pev7MznGkDA3FmSSyy8IO6VU3sZoqKiqHDh06dOiQDNYipERDQ3PZ6pDzZ88c+m0PKClZDh26ZNVqgvjfKbnQ1zVYPQRYOOBwQUkJQPALkw3ABtCH1D/B3hSCJwJ0ge8Y+ON/cPp/EDwJAGC9NxCVYc44iEiGkP8AURnmjoO4rDcCyGqwaw6ocoFmD8smw/G/IKL7Q8QAvLevLoBGIOrCw+1gpQscAAMtwCkDowH0yG8r8H8BSWKt4vLglwDQ6IDPBoHzEKhrBaKKkMw2IIofL7Gd0obLDwCnDCFeoNwJk+1h9lg4cgNGmr817+1WCtv9QaMDRlnAWEuoZ0HqPSFT/4G2LpgxGuaNBVCCUB849bdYe2pbIfgPSaMnAfY12TedqK6u/vbbb9/DBhEfwh4UH0IWyvb2dkWbABwOR8rhwJ6o6m97EAMMmpHxroPxgiU/bUbJDhDvoKuv/92SZeLO0oyMYg4elq/GPgVYSkpKPWx/yAZrI6hogBYekPh/oQ1gXxYUVIGuJlgaAGDZQNRgCA3KGW+q6HQA4EEFBwQ86HYBdIKKwPVpUx1QbQTgANDA2hCuPu5mFvDeLQAu4JQgJRfibwIBB7YmgFMGEPmXHgcVDLFWGfMAWgHIb4wRIVPi74LYTuGAXgPVTWC+6t/K3p8Jmabc3QCRprZ3gZUhAHZBzhCGm4i1x0RL9qcIOSvviTvV1tbW3t5OoVDEXTXZunUrAPS4pKOPNDc3k8nkflUhma6uLiaTqaGhga2VVhQtLS0aGhrKyorMeNfQ0EAgEDQ0NKSpTCaTZdhUVY4MHjx43rx5CjQAgUAMDPr5i6wFLA3AyhB+y4XQt7fPuEpw8BpMHw3G2pDNX+rOA3oNDOPf6RO8qdIJ8G5Oh3IGdAIQAEAJXlSDERUAgC1wkay66V0z9CDjIcRcgTubwUwHAIC0UKzJkqx692KE9DJ77BSNAs5DIOfnN28LX4Oa0MoBJaXuBog0tZMNRVX/1imtgzH9kHBKwoIMLK7C4XDiAizsx75Xz2PLgJKSUn+rkAx2n0tZWVmxZmB+UGyABb0ZDpGPwr1PbGxsPpG9BREIRL/Sz1+7HaCsBL99C5vOwdY7UMCGBx3wTSy8boSl7uD9GeS9gvjnwCRDyj1IvQsBLlJJbWLB2stQpwrXnkLMZZg3DrQ1oJEFyWXA1oXoy/9ecwIAZicADhpbQVUFiJ3Q3gnbLwCzHVhsgQp8OqW2SlmSzN7RDl4OkF8OcVnQ1ALZT8ApDEpqRJn3LiJ1aGT4AAAgAElEQVRNnTMOzuVCYgk0aMK+q3BXxCNKCAQCAODVq1dqamogsAmMSNLT011cun8L8NsiPgSqX78OD/mxkfHmq5/NZp9PTNi+Yf365Uv37/q17NXL92kMmhsfJmw2+4/E05s3/ByyfPmeXbtK384KRn39of37161aFbr6x/gDBxobGuSlsf//1zbDpOFwNRSu5oPzZpjyC7S0wc0w0APQJ0N6CMTmAG0ZbDoHScvBWkcqkY7mgMeB7c8QeAB+ng7T7UGPDDHfwIY/QH0hJP8D463e1PR3gam74FIe+DmDy1AYGgYOoUBRh6AvwCdaoMKzt6KbpLaKJ0pmlEwuagGqBmSEwMm/wXgFLPodYr6BCeYC5r0Q3U6EqVpgZwKnFsPmFBj8I9wqhOWTZTIJgfhokWGNvL6+PpZxu18ZM2bMs2fPeq6H6CUcNvvk4YPNTU38leYnDsY9vH/vPzN9g1f9qKamdiA6qqW5uT9UC44pGt8PnCMHD+bdv//VzFlLVq1SU1PbFx3d0tzM4/EOx+5vbWUuXBTsP2fu68rK+AMHRDaXYW1o/691aAUAmGgNNwQTdzcBsACUYPwwyOVvY9AO0AwGesA7BfAaoA3MdaHt6JtjK0NoOATY6io8DnbOhZ3Y5p5dAHUAVFju+W4w0QbAhuj5ED3/zSLxhCUA8EbCmwVYXW8rdACoQsvvAK8BmiVaBQBcuBoK0CFGJry1Gf6V0EOnXgM0wujB8Nemt006AeoBCG/NawcgQtfxt2J5cG0DQIcoUxsAAGaNgVn85xl5AOy3S7IEIODBWEtosN4L/b36CvGJI8P9UBKJNHeu6N2C5UhZWdmH8NDDwCMjJZnL/Teqbm5sfJz34PsVq6yH2wHA/O+Cw9euzs974DJhotxVC44pGt8PmabGxkd5DxavWGkzfDgAfPvddxvWhjzKe2A5dGhZaemWHb9SKBQA4HA4Rw8fam1lamhodpMgw9KF97IyoxWgCuC1wIslqrwBgP32GAA6BY47BI4BgCfQCgsdGoRUNALUCRzXvj2uEqjJr8AQkC/ZKgCofttEpEzBmhjSdKrt3eb1ACBgXsO7YqveGiBsKgA0vyuqSkR0BQBWepAaKNNo9pmlS5cuXSr7Q4gIhGTEfQ/++eefDg4Ourq6ixcvdnV1FUw7LniL8Pbt287OziQSycHB4eLFi1ghj8f7+eefaTSahYWFYPoGjIsXLzo6Oqqrq5uZme3Zswcr3Lp1q4mJiaampqenZ2Vlpbu7e01Njaen55UrV4RVVFdXGxgYrFixQktLy8zM7MyZMxEREcbGxiNHjkxOTpagBVH0/Nm9/92ZEfBvfMxkMo1Nzcwt3uSnwauoEIlqLc1NYgSI4ObNm6NHjyaTyRMnTszPz8cKhf0vOKaCx4KiCgoK3N3dKRSKm5tbWlpaX3uLkBUmk2liajro7R64KioqRCKxubmZQFCd5R9AebsvIVGNiMfjCQQR6QA+1ABLjnBgsD7MG99zRQQCgeDDYDC++uqrsLCwgoICNTU14dxXGHV1dV5eXoGBgaWlpWFhYX5+fkVFRQCQm5vLZDLz8vJiY2MjIyPPnTvHb9LS0uLr67t06dKysrI9e/asXLmytrY2JycnKirqwoULBQUFALBt27asrCx9ff3MzMxRo0aJVFFTU4PH46urq4OCggICAlgsVnFx8ZQpU9asWSNOy/tw3IcNq7U1If53/2++FUzJbWRisjpsk5r6m8eIip4/Y9TXDR5qJUZGdyoqKqZNmxYSElJUVPT555/7+vqCGP/zx3Ty5MmCx3xR7e3tHh4eHh4eJSUloaGhCxcuRHvgKApjE5OfwjbyZ0XB82eM+vohQ4dp6+i4TZoEAEUFz3OuXk08edLTa6rIvSZl4GMLsFrAxgBWuCvaDAQC8VGRnJzs5OTk6+uro6Ozbds2cWuQU1NT7e3tg4ODqVSqr6+vj4/P6dOnAUBLS2vnzp00Gm3KlCnLli3DCjGIROLDhw+DgoIoFIqBgQEOh2MwGFg2r6qqKj09vUuXLvGTkUpQAQDr168nEolz5sxRVlYOCQkhEolz587FMlWK1NKP/vpISDpxbLiDg434/XByb986vHfP2PGuQ6ykDbBOnz7t5ubm7+9vYGAQERGxatWqjo4O2fx/+fJlHA4XEhJCpVInT548e/bsI0eOSNs3RL/xv9u3Duzd6zJ+/FCBWZH/6NGdW3+3t7Vrvb2a1XcUmW9GFtoBFJ80EYFAfGSUlpaam5tjx0Qi0cDAQGS1iooKS0tL/tshQ4aUl5ePGjXKzMyM/6fW2tqan8gbAHA4XEpKSnx8PIFAsLW1xbJReHl5RUZGbtq0afbs2ePGjdu6deuYMWMkqMCOdXR0AEBFRYVAIOjq6mLHErR84ty9fet1efnqjeEizzbU158+Gl/26uW0WbPGfzFJerGlpaX8ASIQCMHBwQDA5XJl8D+dTq+uruZPPADw9vaW3hKE3GHU1588erT01cuvZs2a8MWXgqdmzPabAfA0Pz92T4zl0KFy2QT6YwuwEAgEovcYGhr+888/2HFnZ2dNTY3IasbGxoJrs+h0+rBhwwCgrKysq6sLC3eKiooGDRrEr5ORkRETE3Pnzh0zMzMAwNZy0el0d3f3xYsXNzU17dixIzg4mH9vSJwKyYjU8onzkl5cW1O9bukP/JItoWtHOI4MXLy07NXL2KidVrbD12+NJJF79ziPkZERf6qw2eyIiIgVK1bcuXNHBv/TaDRnZ+ecnBzsbWFhIUrfoEBKX738LSrK2nb4xq1byW9nxUs6va6udvSYsdhbWzs7EplcXFQklwDrY7tFiEAgEL1n5syZubm5qampzc3NGzdu7OzsFLlk1dvbOy8vLz4+nslkpqSkpKamBgQEAACDwVi3bl19fX1OTk5MTExg4L9PiDQ2NqqqqhKJxPb29u3btzOZTBaLlZWV5ePjU1RUxOFwOBwOf48/JpMpToVkRGqRk28+Vjz/4702fAv2Cly8FACCV/44PWAOl8s9sv83J5fPFwT/0NvoCgD8/f0zMzPPnz/PYDC2bNly9OhRKpUqwf9MJpPfVvAYALy8vPLz8+Pi4pqamrKzs52cnEpKUGZCxcDlcg/t3z/GxeW/wcFkgVlR9boyKSGB/wntaG9nsViapO6PEMoGCrAQCMTAx8DAIDk5OTQ0dNiwYQYGBiYmJiLThejr66enp8fGxtJotE2bNiUlJVlbWwPAl19+WVNTY2Vl9f3330dGRnp4ePCb+Pn5ubi4DB061MHBgUKhBAUF+fj4BAYGurq6uri4mJmZ3blzB9vo19/ff+rUqffu3ROpQjIitcjPPR8lZAqFZmSEvfT0DQBA34BG1dYufPa0saHBfLBl0fNn/FdDfb2UYi0sLJKTkyMiIkxNTTMzM1NTU3E4nDj/Y2N66dKlbscYVCo1IyPj5MmTxsbGixYtiomJmTBhQj94AtEzz589bWxosBhsWfD8Gf/FqK8f4eioqqp68kj8q5KSEnpx/MEDVCrV2sZWLkqVBHcI7y3Nzc0hISHbtm3TPaYnF2sQHw2rxU6b1tbWtrY2HR0dxW540tjYSJHfWkUZ6OzsbG5uJpFIqv28YbtkmpqaSCSSYrfKqaurU1VVlfKuSmFhYUJCQnh4uMzqEhISSCRSt8UuZWVlf/3119dffw0AHR0dVCqVTqfT5HEXAPEhUFVZuSM8LGz7DqqOzs3srJQzp7tVmDbT98spXgqxDfEhcD07+9yZxG6FPjNneUyZUltTk3w26RWdDkpKlkOHTp/lq6Or261mVFSUq6urk5NTr5SiNVgIBGLgg8PhAgMDNTQ0Jk6cGBUVNXr0aBRdDSRoRkZRB3/HjidMcp8wCT1qjngHt0mTsHQMwujp6wcv6ZfUjPIJsFg/tKqrq/dcr99obW0lEAjyyl0hGw0NDUpKSgq/asJmsxU7FgjEB4iRkVFSUtLmzZvr6+tHjRp17NgxRVv0XmEyme3t7Qq/roz48Onq6mpqatLU1CQSiYq25aMHXcFCIBCfBD4+PmjdEgKBeG+gRe4IBAKBQCAQcgYFWAgEAoFAIBByBgVYCAQCgUAgEHIGBVgIBAKBQCAQcqavi9wbGhr+/PNPAoGg2GQ/HR0deDxesftztba2AoCGhoYCbWCz2RwOR+Fj0dnZqampqdjnlVpbWxU+Fm1tbUQiUbEPt7JYLDU1NcWORUtLCx6Pl3KTkMrKyj6q43K5jx8/xuPREzz/0tXVxeFw0HNhiB7hcrmdnZ0qKipov0tBXr58OX78+N626tN3EIFAcHV1LSkpweFwis1kyOFwlJWVFfsrwmazAUCxX+s8Ho/L5Sr2g8HhcLhcrmKjCgBgs9kKHws2m63wj4bC/QAAXV1dysrK0k/LcePG9UWdtbV1TU3Ns2fP+iJkgMHlcgFAsVMR8VHA4/F4PJ6SkhLK6CHIoEGDxO0QL4E+ZXJHIBAIBAKBQAiD/tAgEAgEAoFAyBkUYCEQCAQCgUDIGRRgIRAIBAKBQMgZFGAhEAgEAoFAyBkUYCEQCAQCgUDIGRRgIRAIBAKBQMgZFGAhEAgEAoFAyBkUYCEQCAQCgUDIGRRgIRAIBAKBQMgZFGAhEAgEAoFAyBkUYCEQCAQCgUDIGRRgIRAIBAKBQMgZFGAhEAgEAoFAyBkUYCEQCAQCgUDIGRRgIRAIBAKBQMgZOQRY8+fPVxJFfX29lK3weLyjo+NPP/3U0dHRd3vki5eXV0hIiAINKCwsHD58uKGhoXzFvs9+SaNL4X6WBiUlpStXrrzPhhizZ8/Oz8/vVsj3WF+EY20fPnzY4we2RyGyte0RNTW11NRUKSv304cFgUAgZAAvFyk2NjZ79+7tVkgmkyW3srOzi46OBgAWi3X37t3du3ffvHnz+vXrqqqqElqVlJSoqqoaGRnJYGdf2iqKffv2cTicrKwsRRuiYD6EsQsKCjIxMXmfDQGgq6ursLDQzs5OZuESXIe1ZbPZvbVKUGZfetd3BC1BHxYEAvHhIJ8AS0tLa9KkSb1tRaFQ+K28vb1nzZo1evToI0eOLFq0SEKr5cuXDxkyZPfu3TLY2Ze2iqKmpmbcuHHDhw9XtCEK5kMYu0OHDr3nhgBw/fp1Nze3vgiX4Dqs7cOHD3trlaDMvvSu7whagj4sCATiw+EDWoNlb2/v6+u7Z88eRRvywaGs/AENk0hkuATyYcJiseQip0eHSO+xtLQ0b2/vPlvUHXn1tFe8h3ki/YdFIR5AIBCfDh/WL/cXX3xRWFiIfQu3t7evW7du2LBhmpqa48ePv3XrFgA4OTldvHgxOjrawsJCXB0AaG5u/v77701NTY2MjBYsWNDY2Ch924qKilmzZunq6lpbW0dHR/N4vG5GcjgcJSWlGzdu8EsMDQ1PnToFADk5OZ9//rmmpqaRkdHSpUs7OzuxCuJ0CcJkMpcsWTJo0CBtbW1PT09szY2Hh0diYuLhw4epVGq3+iL7KE6OhH5JY9vr16/nz59vamqqoaHh6OiYnJyMlVOp1CNHjowYMUJFRYVGo23cuFFKH0qoI1JXt7ETZw+fGTNmCF5SffDggZKS0u3bt8V1lkQi3bhxY+7cufb29hLGUUVFBVtsJM7J4hzCbyiuQktLS2BgoJ6e3vDhw0+cOGFjY3PmzBns1M2bNydOnCjBY3zhIs3u5rpuPeW3BYCnT5+6ublpaWmNHDkyKSkJxE/1bjJldgsfbBHYpUuXLCwsNDQ0Ro8eLbzuqseJ0e3DIs6Ybh6gUqmxsbHW1tZqamoODg537tw5c+aMnZ2dpqaml5cXg8HAWombEggEAiEWXp+ZN2+esFhra+seW40fP75bYXp6OgC8fPmSx+PNnDnT1tZ27969N2/eXLduHZlMvnfvHovFmjJlyuLFi1tbW8XV4fF4rq6u9vb2aWlpiYmJI0aMcHJy4vF40rTt6OiwtLQcMWLEuXPnUlNTHRwcVFVV16xZI2gkFv9dv36dX0Kj0U6ePPn69Ws1NbUFCxZkZ2cfPnxYVVU1MjISqyDOTkE8PT0HDx6clJSUlZU1Y8YMDQ2NysrKtra2WbNmLViwgMlkdqsvso/i5EjolzS2ubi42NnZnTlz5urVq3PnzsXj8XV1dTwej0KhUKnU3bt3379/H1twffnyZWl8KKGOSF3dxk6cPXySkpJwOFxNTQ32NiQkBJuQ4jqrqanp7OwcExPz7NkzCeOIx+MvX74szsniHCLYUFyFyZMnOzo6Xrp06fz580OGDMHj8YmJiTwe7/Hjx35+fpI9hgkXZ3Y31wn2lN82Ly8PAIyMjPbu3ZuVlfX9998DwMWLF8VN9W4yZXYLH8wAbW3tmJiYrKys4OBgJSWlnJwcHo9HJBJTUlKkmRjdPizijOnmAQqFoqure+bMmRs3bowaNUpDQ2PChAn/+9//jh07hsfjIyIieDyehCmBQCAQ4pBPgGVjY5P1Lrdu3eqxlXCAlZGRgQVY2N/N4uJi/qkZM2bMnDmTx+NNmzZt5cqVPB5PXJ2cnBwcDscvv3Xr1vjx47Hf2h7bHj9+XFVVtby8HCssLS1VUVGRMsC6fv06APzzzz9YYU5ODuYECX3hk5ubCwC5ubnY266uLgsLi3Xr1vF4vICAgKCgoG6OEtdHcXLE9Usa23g83q+//pqXl4cdFxcXA8Ddu3d5PB6FQlm8eDHfLfr6+tHR0dL4UEIdcbr4YyehDh8Wi0UikQ4ePMjj8bhcrpmZ2a+//iqhs5qamkuWLMEKxY0j720kIWGwRDqE926AJVzh3r17APD8+XNBA7AAKzIy8sSJE5I9hgmXYLag6wR7yns3wNq3bx+/3MvLa+LEieKmejeZMruFD2ZATEwMv2Tq1Kmenp48gQBLmonB/7BIMKabBygUyi+//IIdHz16FABKSkqwt5MmTfr22295EqcEAoFAiEORi9yFqaiowOFwxsbG586dAwBLS0vBs6NHjxZ8i/1eCtfJy8szNzcfPHgwVuLi4vLnn392UySu7aNHj+zt7Y2NjbESU1NTCU9vdWPs2LETJ04cN26ch4eHq6url5eXg4ODBF3d7CGTyfxCPB7v5ub25MkTcbrE9TE9PV2kHDabLbJf0tgGACtXrszIyEhLSyspKfnrr7+69Ro7wOFw2traACCNDyXUkaBLGnsw1NTUZsyYcfbs2e++++7vv/+urKycP38+9jMprrP8A3HjyEfyYAk7pBvCFfLy8vT09KysrLByFxcXJSUl7DgjIyMlJUWyx6Q0W7jL3fD09OQfT506NSIiQmQ1cfTRLd0M8PLyioyMFDwrzcSQ0phuHjA3N8cOdHV1cTjcoEGDsLd6enp846X0LQKBQPD5sNZg3bhxY9iwYXg8XktLS0VFpbm5mSnA/7N3n3FNZN/DwG8KkBASOgakCoJYgFWwLGJBVFBgRUUUy9oLdlZdXcuu4s+1YMGKfVddQVGwUBV7RRQLFpoIAkonAaSEJPO8mP/myZIEA4QkwPl+fJFM7tx75s6dcJy5mbl//75wYUllOBwOiURquiFJ65LJZMHfNpy6uvp3w25oaEAIUSiUu3fvPnz4cPDgwbdv3+7bt++qVauaaEu4Bj6f36hdIpHYxIxgSdsoqR5J2yVNbHV1dYMHD16/fj1CaMyYMYK5QTjRe2pI04eSyjTdljTxCPj7+9+5c6esrCwsLMzT07NLly5NbyydTsdfSNqPAk3vrKZvMiK2QENDg3CF+M3hEEIlJSVkMllXVxdJ0avfDVtAsKVNIBKJYjcEH+pitbJbRAMQHv9S7nQpg2miBxqthZO+bwEAQECJEqx3795dvHhx6dKlCKHevXvz+fwXL17QaDQajUahUKZOnYqfwBeQVKZPnz6fPn3Ky8vDiyUnJxsaGmZkZEizbu/evd+8eVNQUIAXKy8vl/QLdsFdGXNycvDX9+7d+/XXX/v3779u3bobN27s2LHj2LFj0m8Lm81++fIl/pbH4927d69Pnz6S+krSNkqqR9J2SRPbgwcPkpKSoqOjN2zYMGHCBE1NTUlRCbblu30oqYw0bUkZj5ubm46OzqVLlyIiImbPni3lxiLJ+1E4+GbtrO/q3bt3cXFxVlYW/jYpKYnP5yOEYmJixo4dKyjTdK9+N+zvEr59VFxcnOAkjehQl7QVrewW4QDi4+OF123BIJThPmp93wIAOiHZXCKsrKwU/qkRztbW1sDA4OTJkx8/fmx0th/HZrPxtWpqalJSUnbt2vXDDz/MmjULIWRsbDxz5kxfX9/g4GATE5NTp07dvHlz+/btCCF87lFubq6ZmZnYMtbW1nZ2duPHjw8KCqqpqdm6dWvXrl27d+8uzbrdunXbtGnT2LFjt2zZoqqqGhQUJPo/bxKJZGFhsWPHDl1dXTKZHBgYiP+vl0Kh7Nq1i0gkjhs3Li8vLyIiYtCgQU1vi0D//v1HjBjh5+e3c+dOHR2dAwcOFBYWLl++XFKHu7u7i91GAoEgth49PT2x2yVNbAYGBgihv/76a9KkSR8/fvzjjz8QQmlpaQ4ODmJj8/X1/W4fSirTRFuCfdd0GeHdNGnSpI0bN5JIJA8PDyk3ton92OKd9V3Ozs6jRo2aMmXKn3/+WVdXFxQUhJ+vun79uuDA+W6vNhG28LBvIoxff/2VQCD06NHj4sWLsbGxDx48kDTUxdbZ+m75/fffSSRSz549L126dP36deG7w0szMIS3Trb76LtDAgAAxGj9NC6xvyJECOGTYSdOnMhkMptei0Qi2dnZrVq1qq6uTlCAw+GsXbvW0tIS/0X9/fv38eWXL19mMpm9evVqokxpaen06dOZTKauru6kSZMEU4OlWTc/P9/Hx0dXV9fKymrnzp2LFy9uNEEbw7CHDx86ODioqKgghGbOnNm3b198Y0+ePNmjRw8KhWJoaDhr1izBr9gktSWssrJywYIFpqam2trao0aNSk1NxZeLneTexDZKqkfSdkkT24EDB0xMTBgMhpub28uXL6dPn66hoZGVlaWlpYXPxcYJbh8gTR9KKiOpLeF9J6lMoybwuzD8+uuvgiWSNlZDQ+PSpUuCYpL2o2CuuqROltQhwpPcxRZgs9nTpk3T1tbu16/f48ePiURiZGRk3759pekxQeWSwhbuukZbKpjkjs8ld3R0pNPpTk5Ogl/5SRrqwnW2uFsE8EnusbGx+K0QHBwcLl++jH8kmOQuzcAQPlgkBdOoB4Rji46OJpPJgo8mT56MT3Jvom8BAEASAibuHkVAGlwut7KyUtKMXQCkVFFRcf78eT8/Pz09PYRQWlqara1tVlZWo/n4CtTWQ/3169cODg6FhYVdunRpoyYAAEDOIMECQMG4XK6ZmdmwYcM2bNiAYdiKFSva9PHJSggSLABAx6NEk9wB6JzIZHJcXNzXr1/79+8/evRowYMBOhUyWTbzQQEAQEnAGSwAAAAAABmDM1gAAAAAADIGCRYAAAAAgIxBggUAAAAAIGOQYAEAAAAAyBgkWAAAAAAAMgYJFgAAAACAjEGCBQAAAAAgY5BgAQAAAADIGCRYAAAAAAAyBgkWAAAAAICMQYIFAAAAACBjkGABAAAAAMhY+3iCfU5OzvPnzydOnKjoQEBHs3jx4ry8PMHbESNGLF++XD5N37x588CBA8JLSCRSVFSUfFpXEpIO7YKCgpqamu7du7egHuhYORPeWRiGnTt37t69exwOx8vLy9fXV5oaDh06lJCQIHi7dOnSkSNHtqwqmcjNzT127FhmZqaWltb48ePd3d0RQiUlJUeOHElPT1dTUxsxYoS/vz+BQGiL1sUeFI0WKrBzFKLpzhcegWLHkgIiRgi1iwSrrq7u3Llzampqig4EdEAVFRX9+vXDv0ARQubm5nJretCgQTY2NoK3cXFxlZWVcmtdGUg6tOvq6rZs2dK3b18pE6xG9UDHylOjnXXmzJmXL1+uX7++pKRk586dZmZm/fv3/24lX79+/fnnn52cnPC3Ojo6La6q9TAMCwoKsrOzW7RoUXZ29t69e42MjHr16rVu3bpevXrt3r27rKwsODiYRqONGzdO5q2LPShEFyqqcxSCx+M10fmNRqDYsaQoyp5g/f3339evX+dwOC4uLoqOBXRAtbW1zs7OAwYMkH/TGhoaGhoa+OvS0tLk5OTdu3fLPwxFaeLQPnbsWHl5eYvr6eQdK2fCO4vD4cTFxW3dutXCwsLCwuLnn39ms9nSVPL169cZM2aYmpoKlrS4qtYrKysrLi6eNGkSk8k0NjaOjY398OGDqqoqi8VasmSJiooKk8n08fF5+PChzBMssQeF6EIFdo5CZGZmNtH5jb4uRMeSAil7guXn5zd+/Pjw8HAWi6XoWEBHU1NTw+Px/v777wMHDhCJxH79+v32229EogImJp44cWLChAmampryb1pRJB3aT548+fz5s7OzcyvrwXXCjpWnRjsrPT2dRCJZWVlhGIYQ8vLykqaShoaG0tLS8PDw9+/fMxiMMWPG/PTTTy2rSiZ0dXWNjY2jo6MnTZr06dOn7Ozs6dOna2pqzpkzR0VFBS9TXV3dFk2LHcyiCxXYOQrRROc3GoFix1IbXcmVhrInWBQKhUKhwPVB0BbevXuHEOrevbuvr29SUlJUVNShQ4eWLl0q5zCysrKysrJWrVol53YVS+yhXV5efvz48aCgIOmnTDXxFdE5O1ZuRHdWRUWFpqbm8ePHb926hRAaPnz47NmzBX8XJSksLEQI9e7de+HChR8/fgwJCaHT6SoqKi2oSiYIBMKqVatWrlx57do1hNC4ceN69eqFEDI0NMQLpKenx8TEzJs3T+ZNix3Mogtb1s/tl6GhodjOFx2BYsfSiBEjFBW5sidYALSdfv36Xbx4kUKhIIRsbW3fvXv3+PFj+SdYYWFhPj4+ZHJnPxgxDNu7d6+vr2/Xrl1lUiF0bNsRu7Oqq0i8bs0AACAASURBVKvz8/MHDBhw8uTJ8vLyzZs30+l0f3//pqsyNja+fPkyvpsMDAwyMzNv377t7OzcgqpkoqKiYvPmzfPnz3d1dc3Pz9+5c6e5ubmrqytCqKGhISws7Pr16wsWLBg2bJgcghGrZf3c3jXqfLEjUOxYUmCCBbdpAJ0XkUjEsyuchYVFfX29nGMoLS19/fr1kCFD5NyuEoqNjeXz+cOHD6+rq+NyuVwut66ursW1Qce2KbE7i06n0+n0GTNm0Gg0ExMTNze3pKSk71ZFIBCEk2ATE5Py8vKWVSUTycnJurq6Y8eOpVKp3bt3Hzdu3I0bNxBChYWFgYGBHz582LVrl5ubm3yCEUuBnaMoop0vdgSKHUuKixrOYIFO7Pjx40+fPj158iT+Nj8/n0ajyTmGO3fu2Nvb0+l0OberhLKyslJTUydNmiRYkpiY2OLbK0DHtimxO2vPnj08Ho/P55NIJIQQjUaTZnbHgwcPnj59unr1avzt169fDQ0NjY2NW1CVTPB4PHxuE47P5/N4PB6Pt3nz5v79+8+cOVOBc3pwCuwchRDb+WJHYGBgoOhYUkDE/4IzWKDzcnJyKikpWb9+fVpaWlhY2Lt37+Q/XfT58+d2dnZyblQ5LV++/Nq/Ro0aNXbs2NbcvAo6tk2J3Vn4j9pCQ0NLS0vfv38fFRU1fPjw71ZlaWn5+PHjsLCwkpKS58+fR0dHe3p6tqwqmXBycvry5UtkZGRFRcXbt2+joqKGDBny4sWLiooKNze3oqKiwsLCwsLC0tJS+cQjSoGdoxBiO1/sCBQ7lhQYOZzBAp2Xg4PDwoUL//777zVr1qioqHh4eAj/f0gO6urqMjIyZs+eLc9GOwPoWEX57bffDh48uGzZMg0NDW9vb8Ed5ppgZGT0v//976+//rpy5Yq+vv6CBQv69u3bsqpkQk9Pb/PmzadPnw4PD9fS0vL09PT09IyIiKipqVm8eLGgmJWV1Z49e+QTkihFdY5C5OTkSNn5ksaSohCEz4UCAAAAAIDWg0uEAAAAAAAyBgkWAAAAAICMtYMEKyQkxNHRUbE/tgQd2NSpUx0dHQsKChQVwKRJk6ZPn66o1pWEr6/vjBkzWl/PxYsXHR0d8VvIArl5+vSpo6NjbGxs66sqKytzdHTcv39/66uSCS6X6+joGBQUpMAYDh8+7OjoWFxcrMAYlJaHh0dAQICioxCvHSRYAAAAAADtCyRYAAAAAAAyBgkWAAAAAICMteo2DVVVVdOmTZNhNGLht6wlk8kKv38ukB6fzycS20f6zuVy8QGmqIB5PB5CCL8pswLx+XwCgaCoo0xWnYBhGD72OsPXhfIcZXi3EwgEmcTD4/FkVZVMyDCelh1lfD4fwzCFf0Uop7b+/sQwDN9fQUFBzb13catuNIphmKGhYWhoaGsq+a6GhgY2m62hoSH82Dig5FgslpaWlqKjANLi8XgVFRU0Go1KpSo6FiCtyspKDQ0N5UlEQNP4fH55eTmVSpX/I7lAi1VVVamrq4eEhLTgSbVwZAIAAAAAyBgkWAAAAAAAMgYJFgAAAACAjEGCBQAAAAAgY5BgAQAAAADImMISLFtbW/zXqkQi0dra+pdffuFwOLKqfNasWbt375ZVbQAAaSQkJBBELF26VLatwNHdLKWlpfPnzzcxMdHS0hoxYkR4eLjcmu7Tp0+jwbB69Wq5td6058+fjxo1ysDAQE9Pz9PT8/379/hyJycnQbRkMrlbt26HDx8WXnHlypUEAiEiIkKwBL+Jw8KFC4WLjRw58tChQ3LYkA4mJSXF29vb0NBQU1Nz0KBBkZGR+PKPHz/S6XTR8vr6+gkJCcJLfvjhh5MnT4qtPCYmZtCgQTKPuQmKPIN15MiR0tLSL1++HD58+ObNmxMmTFBgMACAVnJ1dS0VEhkZSSAQ3N3dFR1XpzZ9+vT8/Px//vnn48ePv/zyy4oVKy5fviy31g8cOCA8JDZv3iy3ppvw6NGjIUOGjBgx4sGDB3fu3DE2NnZzcystLcU/3b59Ox7t58+fV65cuXjx4uTkZPwjDMMiIiLMzc0vXrzYqM7jx48/e/ZMrpvR4WRkZAwePNjIyOjKlSspKSlTp06dNm3a2bNnEUIGBgaCG0L179//w4cPCo1UWq26D1Yr0el0XV1dhBCTybx586aZmdnjx49//PFH/FM+n8/j8VRUVBQYIQBAeioqKvgRjRCqqKhYvnz5smXLxo4dq9ioOrO6urobN24kJyf37dsXITRmzJjAwMDTp0+37H+zLfhOFnzJt10TzYVh2PLly//44481a9bgS0JDQ5OSksLDw5csWYIQ0tDQEMS8dOnS/fv3P3361MnJCSH08OFDFot1/vz5yZMnf/v2Tfh2VtOmTVu0aFFycjLclqzFZsyYMW/evJCQEPztkiVLCATC77//Pn36dDqdPnXqVHx5Xl6eDK93tSllGQpdunQZNWpUYmJiQUGBiYlJZGSkiYnJ+/fvAwICVqxYgZfJyclRU1MrKyuLjo52cHBQV1c3NTUVPHQ9MjKyZ8+eOjo6M2bM+Pbtm+I2BQCA5syZo6Ojs2PHDvxtenq6m5ublpbWsGHDrl+/jhAqKioyNDTcs2cPk8lkMBjz5s3j8/kIoSdPngwcOJBOp9vb20dHR+Oriz26xZYEwigUirm5+bFjx6qrq/Ela9asEfSVaAemp6fr6+sLVndyckpISGj0nXz//n1HR0cGgzF06NC3b9/iJUX3bxNE223UxIMHD+zt7fX09AICAlxcXG7dutXcJpqQk5OTkpKyaNEi4YU3b96cMmWK2PIEAsHQ0BB/feHCBW9v7zFjxlAolJiYGOFi69atY7FYR44caXFgnVxubu6zZ882bNggvHDOnDkRERE8Hk9widDNza24uHjUqFGNrgw2InYkI4QwDFu/fj2TybSwsAgODm6bTRGCtQKbzV6wYEHL1u3Ro8e5c+eElyxdunTmzJn5+fnq6upz5swpKSnBMCwxMbFr164lJSW1tbXbt2/39PSsrKxUU1M7fvx4aWlpVFQUgUAoLi5OTU1VU1M7e/ZsYWHhtm3bEELBwcGt2TTQShUVFYoOATQDl8stKSmpqamRSW2HDx9WV1f/8OED/ra2ttbExGT79u3l5eXx8fH6+vopKSmFhYVEInHq1KnV1dUvXrxQU1NLTEwsKSnR1NQMDQ0tLy+PiIigUqkZGRlij26xJWUSfDvCZrN5PF7TZW7evGllZUWhUNzc3Hbs2JGfn48vF9uBaWlpenp6gnUdHR3j4+OFv5Pz8/PpdHp4eHhhYeHatWttbGwwCfsXw7DevXsL/63BaxbbrnATZWVl2traERERpaWlgYGBCKHExERJTbRATExM165dJX3q6Oi4c+dOFovFYrEKCwtDQ0MtLS3xP0Y8Ho/JZEZFRWEYNmPGjAkTJuCrcLlchFBOTk50dLSWllZRURGGYW5ubgcPHmxUOY/HKykpqa6ublnkHdv169dNTU0Fb6urq1n/4nK5WVlZGhoa+EdMJvPVq1f4az09PdGs5sSJE2JHcnR0NJFIXLZs2devX+Pi4rS1tS9duvTdwCorK7lc7u7du589e9bcjVKiBGvZsmWzZs3Kz89HCOXl5eELGxoadHV18QPMwcEhLCyMw+GkpaVhGMblch8/fkwmk9PS0tauXTt58mRBVb169YIES7EgwWpfZJhgpaamUiiUkydPCpZERUWZm5sLUoGAgIClS5cWFhYihAR/74cOHRoeHn7ixAkXFxfBin5+fps3bxZ7dIst2frg2xdpEizs36/KLVu29O7dW0ND49SpUxiGie1ASQmW4Dt5165dXl5e+Kf19fWhoaF1dXVi9y+GYb179xaeg1VWViapXeEmjh8/PmrUKPzT2tpaKpWamJgoqYkWuHbtmrGxsaRPHR0dG/21PnPmDP7R7du36XR6VVUVl8u9dOkSlUrFUyVBgoVh2Lhx42bMmIFBgtV8J06c6Nmzp+Ct8NzNhw8fNpFgXbhwQXieX+/evZtIsLS1tTkcDr5w06ZNgiy5Ca1JsJTlEiFCKCcnx9zcHCFEJBK7du2KLySTyV5eXjExMZmZmVlZWd7e3iQS6cqVKzY2Ng4ODvv27cMf8VhQUGBjYyOoqlevXorYAgA6u9ra2smTJ3t7e8+ePVuwMDs7u6ioyMzMzMTExMTE5Nq1a/jfJISQ4EjHp90UFBRYWloKVrSyssrPzxd7dIst2ZZb1i59/vz59u3bJBJp0KBBGzduTE1NXbVq1eLFi7lcrjQdiGEY/kLwnfz582fBWqqqqgsWLFBTU2ti/+JzsHA6OjpI8o4TbsLMzAz/lEKhdOnSBTU5hJqrR48eBQUFVVVVwgsPHTq0bNky/LUgMaqpqTl48ODs2bO/fPmCELpw4UJVVRWdTieTyRMnTqytrRW9MB0SEnL58uX79++3LLbOzMbGJisrq6GhAX8bFxeH7wUTE5OmV9TU1NQVQiaLmVkuGMmmpqaCGX49evTA92zbUZYEq6Sk5MaNG25ubgghIpEo/LBxHx+fuLi4iIgIHx8fdXX12NjYkJCQmzdvpqamXrhwAe8sY2PjzMxMwSqfP3+W/yYAAFauXPnt27djx44JL2QymQMHDsz7161bt9atWyd29a5du2ZnZwveZmdnGxkZiT26xZaU8ca0f+np6VOmTBF+SK2Hhwefz+fz+ZI6UDhxKSoqwl8IvpONjIxyc3PxhVwud+PGjaWlpdLvXyR5xwmaMDQ0FHyBczic4uJi1Jwh9F2WlpY2NjYHDx4ULOHz+ceOHdPW1m5UkkqlLly4UF1dPT09ncvlXr58OTw8XHBywtfXV/S3hKamphs2bAgICBAkCkBK/fr1MzAwOHDggPDC58+f5+XltaxCsSM5Ly9PsGsyMzPxczptqLmnvIS18hLh0aNHWSxWaWnp3bt3HRwcPD09MQzLz88nk8nCJaurqxkMhoGBQXx8PIZhZ8+eNTc3Lyoqqq2txSdkpKSk4LM0wsLCysvLDx48SCaT4RKhYsElwvZFJpcIL1++TCQSY2NjWUJqamrKy8v19fWPHDnCYrESExMZDMa9e/fwS4SCdd3c3MLDw4uKihgMxsmTJ6uqqqKiovCJXGKPbrElW90N7cx3LxFyOJyePXt6enomJycXFhY+e/Zs8ODB+DUssR2IZzOXL19uaGjYu3cvQgi/RCj4Ts7OzqbRaJGRkWVlZZs2bTI2NuZyuWL3L4ZhvXv3/uuvvxqFJLZd4SYKCwt1dHSuXLnCZrN//fVXMpl869YtSU20TGJiooqKSlBQUFpaGn47AH19/eLiYgzDHB0dG13as7CwiIqKSkhIUFdXF766d+HCBSqVil8xRP9eIsT7vEePHkQiES4RNtfVq1dpNNrGjRvfvHmTm5t79uxZOzs7JpMpeonw4cOH+Gs9PT08MRBwcHA4ceKE2JGMn3EMDAwsLS29deuWjo7OjRs3vhtVe52DJUjyrKysAgMD6+vrMXEJFofDmThxor6+PpfLxTCsvr5+ypQpDAbD2tr68OHDc+fOxS+oX7p0ydbWVktLy9/fPzAwEBIsxYIEq32RSYI1bdo00f/CLVy4EMOw5ORkZ2dnGo1mZWV1+vRpDMPEJlgYhj148MDR0ZFGo9nZ2UVHR+Ofij26xZbsVKSZg8Vms+fNm2djY0OhULp167Z69WrBH3ixHRgSEmJra6uiouLi4jJ48OBGCRaGYQkJCfb29urq6gMHDnzx4gW+UHT/YhISLLHtNmri7t27tra2Xbp02bNnj7m5OT6fXWwTLXb37t0hQ4ZoaWkZGBh4e3sLsnPRBMvDw8PPz2/OnDm+vr7Cy6urq6lUalhYWKMEC8Mw/GePkGC1wLNnzzw8PPT19U1NTadMmZKXl/e///3vzZs3wgnW8uXLGQxGbGwsJjnBwsSN5OjoaFdX12nTpunq6lpaWoaGhkoTUmsSLAL277XJFqisrFyzZo3g9l9tpKGhYeHChVQqVfikLlByLBZLS0tL0VEAafF4vIqKChqNRqVSFR0LkFZlZaWGhkYHu/FSXl7ew4cP8Zsm1NfXa2trZ2dnM5lMRcclA3w+v7y8nEqlCt9ACyi5qqoqdXX1kJAQFxcX/HZo0lPkjUalUVtbm5mZefny5djYWEXHAgAAoG2RSKRZs2bRaLShQ4fu2bPH0dGxY2RXoBNS9v/6PHnyZOjQoQMHDmzzyWgAAMVJSkqC3161a7dv3379+nXr6zEyMrp48eKWLVscHBzevXv3999/t77O1sAwLCoqSjC1HygVDodz8eLFkpISRQcinrInWK6urk+fPjUzM0tLS1N0LACAtnL37t3ExERFRwFaLjY2VlYpsre39/Pnzz99+nTp0iULCwuZ1NliVVVV8fHxL168UGwYQKwvX77cunXr3bt3ig5EPGVPsJDQHSxAJ/H27VsfHx9TU1MDA4PRo0ffvn1b8FFpaen8+fNNTEy0tLRGjBgRHh4u+KhPnz6E/1q9erXY+nk8npmZmY5k+CPJgJzBkd6uddTd11G3qyNR2n2k7HOwQGcTFxc3ceLE5cuXb9q0SVVVNT4+3svLa8eOHXjSM336dAKB8M8///Tq1SspKWn27NkqKiqCJ9ceOHBA+IFikuZrc7ncz58/G846QFQVU+Dbh/sFBQVtsGUAAAA6EUiwgBKpra0NCAjYunXrypUr8SW9evWysbGZPHny+PHj8duWJCcn9+3bFyE0ZsyYwMDA06dPCxIs/LbRUrZF1jQgqon5LQ+JykCoUhZbAwAAoPNqB5cIQefx8OFDNpvd6Aqdp6enlZVVVFQUhUIxNzc/duxYdXU1/tGaNWtEn1YBAAAAKBwkWECJvH//vkePHoJnRQnY29vjv3I4evTorVu39PX1R44cuXPnzkbX8mbOnCmYgKWvry+/uAEAAID/ggQLKBfhx1AKkMlkHo+HEHJzc0tLS7t9+/aQIUPOnj3bo0eP06dPC4odOHBA8Ez19PR0+QUNAAAA/BckWECJ2NrafvjwQfQ5qa9fv+7Xr9/nz59v375NIpEGDRq0cePG1NTUVatWLV68WPBQT3wOFk5HR0fu4YOW43A4ws8kBgCA9g4SLKBEXFxc6HT6oUOHhBfGx8enp6ePHDkyPT19ypQpwn+GPTw8+Hw+n8+Xe6RAlsrKykJDQ0+ePAm/3wQAdBhK+ivCL1++fP36FX+dk5NTWlqakZGhqamJL9HW1u7WrZviogNthUqlHj58eNKkSWVlZZMmTVJTU4uPj1+3bt2BAwdMTU0NDQ319PQmTpz4+++/m5iYfP78+ZdffvHz81NVVVV04C3R0NBw7ty569ev5+Xl0Wi0QYMGzZ0719LSUtFxyVtZWZmbm5uxRi91Hfrw4cPv3LnTtWvXFtRTUVFx/Pjx27dvl5aW6unpjRgxYt68efBAzLaGYdj169cvXrx4+/ZtKpWalpY2c+bM5j6yDVdfX3/mzJmYmJiCggINDQ1nZ+f58+ebmprKPGYp4UdoZGRkUlLSo0ePkpOT5X+EFhUVHTt27N69eywWi8lkjho1as6cOfAoQ4TQt2/fTp8+HRUV9ebNm+fPn//000/z5s0zNDRUdFz/oaQPex4yZMjjlLcEFTUxn/G5WqpIaW+ND3CtedjzmzdvNm3a9Pz582/fvvXp02fNmjWenp74R5WVlatWrbp//35ubq6RkdGECRN+//13/OumT58+q1at+vnnn79bf319PYVC0R27kkgWM8Bqc1+5mVOioqJaFryUMjIyxo0bR/iU7cPQNFVVqebzH32rSeByt27dumLFijZtWixFPey5oqJi1KhRGIv+8/B1RCLpatLxT9XP79y5Y2xs3Kx64uPjp06d2qe+fhSdbkAmF3EbblRVv6NQwsLCRo4c2UbBK5zCH/ZcVlbm5+eXdv++r6amtZoaF2Eva2svsdgzFi3at28fmdyM/8Djtxem5uf/pMkwVVFh8/gPvn27zefv2rVr4cKFbbcJkkg6QoOCggQ3kWmu5j7s+eLFi/PmzRuIYcM1aHokckFDQ1xVVb629qVLlwYOHNiyGDqGZ8+eTZgwwbC8bCydYaRCLufx7lZ/e4TQ0aNHhW+FKBMd8GHPXC5XZ/Ri9e5ixlBDeUFNxK/yDwnIjZ2d3ZUrV8R+xGAwjh07Jvaj1NRUKetXUVHx8fGpqkpFYuf86KEhQ4ZIWVXLFBYWjhgx4qe62gAzM8GUfg86fXp9/cLVq9XU1BYtWtSmASiJRtkVQuinAfOuJqHhw4ffvXtX+vNYjx8/9h079k9D5gh9PcHC8ZqaN6uqJ06cmJCQ0Mn/GrURDofj6emp+/5dtIW52r+/TXHT0JiprR1w8uQyPv/w4cNSVpWXl+fm5jadz59lZio4IsYy6G/r6hYtXUKlUqX5j5MMFRUVSTxC16xRU1MLCAho6xhiYmLmTpmyv6vRAHV1wcJJWpqRbLaHh8ejR4969uzZ1jEop/T0dHd39+WqqpNMTAQLvRmM5JrahdOn02g0b29vBYYnTEnPYP34449ZhiOaSLAqKipk3iiQodacwerw5syZU3bp0p+GTNGP3tTVza9gZWRkdOnSRZ4hyf8Mlmh2JXA16XjOtxdSXivk8/n29vY+JcVTxI23fypY15nMV69eif11anun2DNYISEhJ9auPW9qQhbp21Iu1zMnN/7Bg0GDBklT1ZQpU/jxcX+IG/PJNbUrqqs/fvyora0tg6ClM3fu3OKIiB3ijtDUurq55RWZmZktOEKlP4NVV1dnbW0dyOOOptNFPz1QWpZub3/r1q3mBtAxjB492vzF8xV6eqIfJVZXb8dQVlaWDL/HWnMGCya5AyBXNTU14eHhAXri7zhvR6H8wONdvHhRzlHJWRPZFULIe8DcrpQ+w4cPl2bOe1JSUlFa2iQJ2fxkLc2C9++Sk5NlEDT4r5MnTy7Q1RHNrhBCemSyn6bmqVOnpKmHxWJFRUUFSHgGg5M61bq+PjIyslWxNkdNTU1YWNgSCUdoHwqlH8a/cOFCm8Zw48YNteIisdkVQmiOjvbju3c+ffrUpjEop7y8vHs3b86V8CNxNw0NellZXFycnKOSBBIsAOQqLS2NzuGYiNxMVcBRnZqSkiLPkOSMxWK5uroSK7V/dv1NNLtCCBEQYcKPAV3V+gwfPrywsLDp2l6+fPkDlSKmFoQQQiQC4QcK9eXLl62OGvxHQ0PD27dvHSWfJ5B+GL99+5aJYQaSJ2w5UuV6RKSnp2s0fYRS1ds6nlevXvWT3LfqRGJPNUrnHNUvX760UVPTkHzW1lFdiY53SLAAkCsOh6Pa5OUqNQKxY98Rqri4+OPHjzZd+xIJEr9/CIhgY9w3Pz8/Pz+/6do4HI6q5HoQQmpEQl1dXQtjBRJwOBwMw5oYyaoEgpTDmMPhqDV5RKjKdw9+Nx41qTetxerr678zqgmddFRzOBw14nf2jvL0DCRYAMhVt27dirjcKsn37kqvr+/evbs8Q5Iza2vr+Pj46y+PJmXckFTmfd6zsMc7L1y44Ojo2HRtVlZWmU3+tUuvr7e2tm5hrEACGo1maGiYUc+RVCBD6mFsaWn5uYFTx5c4GzhDvntQGY7Qpkc1H6FMDqdzjmorK6uP9Rye5AIZ9UrUM5Bggc6Ox+NVVlbKrTkDA4Mfhw69xGKL/bScx7tRVTVhwgS5xaMQP/74Y0xMjKQc633es3MP/zx37pyXl9d3q3J1dS2h0VJqa8V+mlxTy6Izhg0b1sqAgShfX98wFkvsRzwMu8hiT5w4UZp6zMzM7BydoirFHxFFXO7d6m8+Pj4tD7SZ9PX1nYcNi5B8hCa0/RE6duzYt0RiloT8NbGqim5i0rdv3zaNQTnZ29vrduuWUFUl9tNsDucVgSC4rY/CQYIFOikMw8LDw52dndXU1DQ1NTU1Nf39/d+/fy+Hprdv336krOyFSE5Qx8dWffk6YcYMOzs7OYShWJJyrGZlVwghdXX1oKCgtV8LC/99YpLA1wbuusLCrVu3yvnmXp3E2rVrn6ipRbIbJyJ8hP5XXKLr4DB58mQpq9q5c+e+ktJUkSs73/j8X758/XnhQjmfk9i+fXuohCN09Zev46dPt7e3b9MADAwMVq9everr1wpe45M12RzOlqLi4OBgBd7/TIEIBMKuXbv+V1Qsmn2yeLxVX76uWLFCeW43qrx7qPplbFnCQdF/7IfnFB0aaJWUlBRvb29DQ0NNTc1BgwYJfh/08eNHurhfzVCp1Hv37n232piYGCl/E44Q4nK5/v7+q2fMGJudfdvc7JV19390ddQTEgYNGnT+/Hnpt6VlBgwYcOzcuUX5BftKS3M4HAyhaj4/oarKNzdXa8gQ6e8e1N6J5ljNza5wAQEBk5cvn5iTe57FKuVyEUIlXO4/FSzf3NzpgYELFixok+g7PUNDw6tXr+7j8tYXFn6or+dhWD2GPampmZ2X/9bYODIykkSS9NuDxoYOHbrv+PE5efmHSss+cxoQQlV8fkxllW/uZ9PRo/fu3duW2yFG//79JR2hmkOGHDlyRA4xbNiwYfDUqRNyciPZbDzN+trAPVle7v85b+2ff44fP14OMSinn376aeOuXVM/fz5eXv6loQEhxOLxotiVE3M/9/fz++OPPxQdoBCsFdhs9oIFC1pTgySXLl369V+rV69etmzZL7/8Ilhy5MiRtmgUyFBFRYXY5enp6VQqdcGCBU+fPs3Kyjpw4ACVSj1z5gyGYZWVlefOncOLOTk5vX//Hn9NoVDu3r373Rajo6MHDhwoZXgrV67sQ6Ekdbd6b2Mt/O+cqYkGkfjo0SMp62mN1NTUSZMmqaur4zM27e3tjx49yuVy5dC0KC6XW1JSUlNTI/+mHz16pKWlNX34rwFj/mQwGNeuXWtZPXFxccOGDSMSiQSESCSSq6trQkKCbENVNmw2Yw/WgwAAIABJREFUm8fjKTaG/Pz8hQsX6urq4sPYwsJiy5Yt1dXVLagqJSXFx8eHQqHgVfXt2/f06dMK3ED8CKXRaLI6Qnk8XklJSbM6JyIi4scff8RHtYqKioeHx8OHD1scQEfy+PHjMWPGqKmpERAiEokDBw68cOFCWzRUWVnJ5XJ379797Nmz5q6rpAmWMA6HU1JSUltb29YNARmSlGANGDBg2bJlwksOHjxoYWHRqBiTyXz16hX+WuYJVlZWljqRmNjNolF2hf9bZ6D/448/SlOPTJSXl0+bNu38+fNya1EsBSZYGIY9fPhQS0uLTqfHxsa2sqqkpKRp06a14KuwPVKGBEuguLiYzWa3vp4vX75Mmzbt8uXLra9KJng83tevX799+yaTqpqbYOFu3bo1bdq0tLS01sfQwTQ0NHz9+rVN04PWJFjKe4kQdDy5ubnPnj3bsGGD8MI5c+ZERETweDzBJUI3N7fi4uJRo0YlJCRIqio6OtrBwUFdXd3U1HT//v34QgzD1q9fz2QyLSwsgoODJa0bGRk5hEYzknCfm4mamilPnuTl5bVkC5uPSCSqq6urqYl77Gan4ezsfPPmzZiYGA8Pj1ZWpaKioq6uriL5Jkagjejr6zMYjNbXQyKR1NXVlecJ7kQikclkqgs9r0b+8FEt/SXXzoNMJjOZTAqFouhAxFPSZxGCDik1NdXExERfXx9/++3bNy6XixCysrISLpaYmGhoaBgfHy9pJmlVVdXEiRMPHjzo4+Pz4MGD8ePH4w/4TE5OHjBgwKtXr169euXv729hYSH2xz6ZmZk9JCc0VCLRVFU1IyPDROhBV6Ctffd2DAAA0L7AGSwgP0VFRRoaGoK3EydO1PrX06dPpa+HQqG8fv167ty5WlpaXbp0IZFI5eXlCCFNTc3g4GAmk+nu7r506dKwsDCxq6uoqDQ0+QjOBgyDUyAAAABaAxIsID82NjZZWVkNDQ3427i4OPxCdXPPFZFIpCtXrtjY2Dg4OOzbt09w5tzU1FSQGPXo0ePLly9iV7e3t5d02ySEUBmX94VA6N27d7NCAgAAAIRBggXkp1+/fgYGBgcOHBBe+Pz58+ZOeIqNjQ0JCbl582ZqauqFCxcESVVeXp4ge8vMzDQ3Nxe7+oQJE96rqLyUkGOFlpd5eXnpSHiYKAAAACANmIMF5IdKpR46dMjf35/FYvn6+mpqat6/f3/Xrl1MJlO0cHV1tfBrttD9DFkslpqaGoVCqaur27t3b3V1dU1NDUKovLx87dq1v/322+vXr0NCQsLDw8WGoauru2vXrpUBAUeNu9r8dzLWPxWsBBI5edcu2WwwAACAzgoSLCBX3t7ed+7c+f3330NDQ6lUqrOzc0xMzJkzZxr9/sjPz2/MmDHh4eH4z8qEH32goaFRVlYWGxvbvXt3JpO5YsWKuXPnent7h4aGurq6FhcX29jYaGlpbdu2beTIkZLCmDdv3rdv3/x/+cWTTnfRoGkSSbkNnKvsylIDgxtRUZJOfQEAAABSggQLyJuTk1NsbKzwkt9++w1/UfXvE6b27du3b98+/HWtuGt5Yu+3PnbsWOnDWLFihaenZ2hoaNjjxxUVFWZmPX8eO3bOnDmK/T02AACAjgESLNB5WVlZNXG7LPlQV1dnMpldu3ZVbBgdhoGBgY6OjoGBgaIDAS3EYDAMDAzgiBDGZDJ1dXV1dXUVHQhoHkiwAFAkFRWVzZs3KzqKjqNr165//vmnoqMALUehUIKCghQdhXLp3r37tm3bFB0FaDb4FSEAAAAAgIxBggVAG+rTpw/hX1paWuPGjSsoKGhBeS8vr40bN8oranlTUVERbDWTyVy8eDGHw0EICZ6eJCwmJmbQoEEtbuvevXt9+vRpVbigSW/fvvXx8TE1NTUwMBg9evTt27dFyxQVFbXFvXzFDpi206xx20ZmzZq1e/du+bQFmgsSLADa1t69e0tLS0tKSm7duvXx48eFCxfKtnzHEB0dXVpaWlRUFBkZGRMTs2fPHoSQgYFBaGgoXqB///4fPnyQZ0jyb7EDiIuLGzBggK2t7dWrV+/cuTNq1CgvL6+DBw/in7Z1l8p/wHx33CohGNhyA3OwAGhbdDodn52qp6e3dOnSlStXYhhGIBCaVV5+4SqIlpYWvtUGBgbjxo17//49QohOp0+dOhUvkJeXh58ekBv5t9je1dbWBgQEbN26deXKlfiSXr162djYTJ48efz48UZGRq3pUj6fz+Pxmj7vJf8B891xq4Sk6Rlpeht8F5zBAkB+SCQSj8fDMCwgIGDp0qX4wpycHDU1tbKysibKyzdMhcEwLCsrKyEhwdvbGwldanFzcysuLh41alRCQgJebP369Uwm08LCAv8daHp6uuAh4gghJycnvOT9+/cdHR0ZDMbQoUPfvn0r3FZ9ff3o0aNnzpyJYVh6erqbm5uWltawYcOuX78u2iKQxsOHD9ls9pIlS4QXenp6WllZRUVFiXZpRESEra0tg8GYO3cun89HCInuiIKCAhMTk8jISBMTEzx9EXjw4IG9vb2enl5AQICLiwt+xlfsgGlrksYtQujJkycDBw6k0+n29vYxMTH4QrHDUrhkdHQ0QqioqMjQ0HDPnj1MJpPBYMybNw/vpcjIyJ49e+ro6MyYMePbt2+oOeNfuGdEW2yit0FLYK3AZrMXLFjQmhqkweFwSkpKamtr27ohIEMVFRWKDkEp9O7de//+/SwWq6Ki4s2bN3Z2diNHjsQwLDEx0djYGC+zfft2T0/Ppst7enpu2LCh7eLkcrklJSU1NTVt10QTyOT/nEp3d3fncrkYhmVlZWloaOBlmEzmq1evMAyLjo4mEonLli37+vVrXFyctrb2pUuX0tLS9PT0BBU6OjrGx8fn5+fT6fTw8PDCwsK1a9fa2NhgGHb37t3evXvX19ePGTNm6tSpPB6vtrbWxMRk+/bt5eXl8fHx+vr6KSkpwi0qLTabjeffSmLfvn2DBg0SXT5jxowlS5ZgQl1aWFiIEJo+fXp1dfXz58/JZPKdO3fE7oj8/Hx1dfU5c+aUlJQI11lWVqatrR0REVFaWhoYGIgQSkxMFDtg2s53x21JSYmmpmZoaGh5eXlERASVSk1KSsrIyBAdlqIlMzIyCgsLiUTi1KlTq6urX7x4oaamlpiYmJqaqqamdvbs2cLCQvynhcHBwdKPf0HPiG1RUm93ZpWVlVwud/fu3c+ePWvuunAGC4C2tWzZMi0tLW1tbScnJ319/ePHjyOEhg4dWltb++LFC4RQeHi48AUFseU7PHwuS2lpaVZWFkLI39+/icKamprBwcFMJtPd3X3p0qVhYWFii4WFhQ0bNszPz69Lly6bN29euXJlfX09QojD4fj6+iYnJ58+fZpIJMbHx5NIpNWrV2tra48ePdrX1/f06dNtsY2dgdhr32QymcfjiS7/888/aTRav379BgwYUFZWJmlH1NTU/PHHH3p6esLrRkZGOjk5TZw4UVdX93//+x+VSm2jLWpa0+P26tWrdnZ2CxYs0NbWnjhxopeXV2RkZEREhOiwbFTS29sbH9V8Pn/Hjh00Gq1v374DBw4sLS39559/fHx8pk2b1qVLl3Xr1vXq1UtSbJLGv9jYBC2K7W3QMjAHC4C2deLEiTlz5jRaSCaTf/rpp6ioKDqdnpWVhV9ZaKJ8hyeYy6Krq7t69eqxY8cK/zFoxNTUVDA7pEePHjdv3mxUAMMwhNDnz58tLS3xJaqqqgsWLMBfZ2RkODs7q6mpnTp1asGCBdnZ2UVFRWZmZoLVvby8ZLdlnYitre2HDx8aGhoazd15/fr1okWLGhUmkUiCu4ni5SXtCCKRKHrf0c+fPwtKUiiULl26yHRTpNX0uC0oKBCMQISQlZVVXl4em80WHZaiJfPz8/HXjXqpoKDAxsZGUFJsgtX0+JcUG96i2N4GLQNnsABQjAkTJly5ciU8PNzHxweezyOMRCLV1dU1NDRIKpCXlyf4NDMzE392JJfLFRQoKipCCBkZGeXm5uJLuFzuxo0bS0tLEUKWlpYnT57ct2/funXriouLmUzmwIED8/5169atdevWtdnGdWQuLi50Ov3QoUPCC+Pj49PT00UfDCp6rkvSjiASiaKFDQ0NP3/+jL/mcDjFxcWy3JIWER23Xbt2zc7OFrzNzs5mMpmGhoaiw1K0pJGRkdhWjI2NMzMzBW8FnSD9+JcUG96i2N4GLQMJFgCK4ebmlp+ff/jwYWX+wZHcVFVVsdlsNpudm5u7a9euQYMGaWhoNCpTXV2NvygvL1+7dm1ZWdnt27dDQkJmzZqlo6PDYrEiIyO5XO6+ffvw/4v7+fnduHEjKiqqvLw8KCjor7/+0tbWRghRqVQCgTBhwoQBAwasWbPGw8Pj7du3oaGhbDb71q1bTk5Onz59atQikAaVSj18+PD69es3btyYmpqakZGxf//+CRMmhISEmJqa4mWa6NImdoSo8ePHJycnX716tbKyctOmTRwORzQtkMPua3rcenl5vXr16tSpU9XV1VeuXLl27ZqPj8+ECRNEh2WjklevXp08ebLYFv39/S9fvhweHl5RUXHo0KHnz58jhJo1/vGekb5F0HKtmfwFk9yBJDDJHde7d+8TJ05I+nTq1KkGBgb4xNimy3eeSe5UKnXo0KHp6enYfye5L1++nMFgxMbGRkdHu7q6Tps2TVdX19LSMjQ0FC8QEhJia2uroqLi4uIyePDg+Ph4DMMSEhLs7e3V1dUHDhz44sUL7N9J7vgqWVlZVCr13r17ycnJzs7ONBrNysrq9OnTjVqUb2c0g7JNcse9fv36p59+6tq1q5aWlouLy/Xr1wUfCbq0sLCQTCYLlg8bNuzSpUsYhonuiPz8fOGSwu7evWtra9ulS5c9e/aYm5unpKSIHTBtt6XSjNsHDx44OjrSaDQ7O7vr16+XlJRUV1eLDstGJaOjo7F/fwogaM7NzS08PBzDsEuXLtna2mppafn7+wcGBgYHB2NSj3/hnhFtsYne7rRaM8mdgLXiF+CVlZVr1qxp6zuqNTQ0sNlsDQ0NCoXSpg0BGWKxWFpaWoqOQtktXryYTCaHhIQoOhDE4/EqKipoNJqiJguDFqisrNTQ0CASO+OFiLy8vIcPH06ZMgUhVF9fr62tjV+AU3RcTeHz+eXl5VQqlUajKToWIK2qqip1dfWQkBAXFxcnJ6dmrQuT3AFQgNra2k+fPp0/f150gjYA4LtIJNKsWbNoNNrQoUP37Nnj6Oio5NkV6IQ64399AFC4J0+eDB48eO7cuY6OjoqOBYD2x8jI6OLFi1u2bHFwcHj37t3ff/+t6IgAaAzOYAGgAK6uruXl5YqOAoB2zNvbW/j+JgAoGziDBQAAAAAgY5BgAQAAAADIWGsvEWZnZ69du1YmoUjC5/MbGhrIZDKJRGrThoAMid7NGSgzDMM4HA4cZe0L/sUIt4VsL/CjjEQiNXqIIVBmXC6XRCJlZma6uLg0d91W3aaBz+d//fq1xatLKTMzc9euXdOnT2/B5gFF4fP5nfPX4+3Uly9f/vjjj/Hjx7u7uys6FiAtOMraFxaLtWbNGnd39/Hjxys6FiAtwVGmp6enpqbWrHVblUfL56FFxcXFRUVFampq8IAkANpIfX19UVERmUyGowyANqKmpoY/xAaOsk4C/vcDAAAAACBjkGABAAAAAMhYq+ZgyQeLxXr+/LmtrS2cVgWgjXz79u3JkydWVlbm5uaKjgWAjonD4dy/f9/c3NzKykrRsQB5aAcJFgAAAABA+wKXCAEAAAAAZAwSLAAAAAAAGWsfCVZOTs6lS5cUHQUAHVCjg6uqqmrHjh3+/v7Lly9//fq1AgMDoF2T5siCw61jawcJVl1d3blz5z59+qToQADoaEQPrv379zc0NAQHB7u7uwcFBbHZbAWGB0A7JeWRBYdbx6bsCdbff/89bdq0Z8+eKToQADoa0YOrtLT02bNn8+fPNzIy8vDwsLGxuXnzpgIjBKA9kvLIgsOtw1P2BMvPz+/06dNeXl6KDgSAjkb04MrNzdXR0TEwMMDf2trawpljAJpLyiMLDrcOT9kTLAqFQqfTm/sAIADAd4keXOXl5QwGQ/CWwWBUVFQoIjQA2jEpjyw43Do8ZU+wAAByg2EYgUAQXsLlchUVDAAdhtgjCw63Dg8SLADA/9HW1q6srBS8raqq0tHRUWA8AHQMYo8sONw6PEiwAAD/x8LCoqysrLy8HH+bnp7erVs3xYYEQAcg9siCw63DgwQLAPB/9PT0+vXrd+bMmdra2idPnqSlpbm6uio6KADaPbFHFhxuHR4kWACA/y8wMLC2tnbOnDkXLlzYsGGDnp6eoiMCoCMQe2TB4daxwcOeAQAAAABkDM5gAQAAAADIGCRYAAAAAAAyBgkWAAAAAICMQYIFAAAAACBjkGABAAAAAMgYJFgAAAAAADIGCRYAAAAAgIxBggUAAAAAIGOQYAEAAAAAyBgkWAAAAAAAMgYJFgAAAACAjEGCBQAAAAAgY5BgAQAAAADIGCRYAAAAAAAyBgkWAAAAAICMQYIFAAAAACBjkGABAAAAAMgYJFgAAAAAADIGCRYAAAAAgIxBggUAAAAAIGOQYAEAAACy9PTpU0WH0FrtaxOUM1pIsAAAAIDvKy8vnzRpko6OjoODw61btxBCfD5/zZo15ubmHh4enz9/xoslJSXt3r1boZH+f2/evNm+fbvgrdJuwufPn8eOHaurq2tiYrJx40Y+n6/M0UoJEiwAAADg+2bPnl1XV5eUlLRw4UJPT8/i4uJz5849fvz4zp07AwYMWLhwIV5s06ZNmzdvVmyouOrq6o0bN75+/VqwRDk3gcvlDh06VFdXNzk5+Z9//jl9+vTevXuVNlrpETAMU3QMAAAAgFLLy8szNzfPzs42MzNDCA0fPnzUqFEFBQV9+vRZsGABi8Xq3r17SUnJvXv3Tp48eebMGUXHi9auXRsSElJXVzd58uSwsDCkxJvw5MmTESNGVFRUqKmpIYT27dt38eLFCxcuKGe00oMzWAB0Olu2bPnnn38UHQUA7cnbt28NDQ3xP/YIIWdn5zdv3tjb21++fLmsrOz06dN2dnYIoT/++OP3339XaKT/Z8OGDfn5+cuWLRMsUdpN0NfX37NnD55dIYQqKioIBILSRis9sqIDAKB5amtrnz59Wlpaqq2tPWjQIBqNpuiI2pnAwMAzJ8JqOd84HM6sWbMUHQ4A7cOXL1/09PQEb/X09B4+fDhr1qzk5GQrKytra+uzZ8/Gx8dbW1tbWloqME4BDQ0NDQ0NdXV1wRKl3QQrKysrKyv89dOnTw8ePLh//36ljVZ6kGCBdoPD4WzevDkkJES/ocGQTC7icr+SSIsXLw4KCqJQKIqODrHZbH9/fw6HI6nArFmz/P395RmSqHXr1p05EbbMa/e3usoli5bX19cL5jEAAJqAYRiBQBBe0tDQQCaTjx07duzYMXzJ7Nmzw8PDFRGdVJR8E+rr6/Fv+IMHD06dOvXEiRPKHK004BJhU1xcXAgi3r17l5iY+Ntvv0laa/jw4YLCFAqlb9++sbGxCKGysjICgVBWViZpRXt7+7i4OPx1VFRUt27d1q9fL/ONaiMXLlxwcnISXS6rDamvr3d3d4/fu/cffb1YC/OTJsbRFuYRzC4PDh92dXWtqalpZf2iduzYMX36dOnLl5SUxN1IfE1zFPvvcV6t9D8kbm7TUlq3bt3xQ38t89qtS2ea6lsvGbvzlxVrjh492sQqCQkJffr0aW5DbDabQCB8+/atFcECoFyYTKbwt3d5ebmhoaFwgaioKCcnJ2Nj48ePH/fp00dXV3fRokU8Hk/ukUqkzJuQnZ3t6Oj46NGjJ0+e4GfWlTlaKUGC9R0BAQFZ/9W9e3c3N7fbt28XFhZ+d624uDgrK6sJEybk5OR8ty0Oh4P/NhUhtHv37hUrVgQFBUkTZF1dnXRbowCy2pD169dXPX162sTY+t/r9AihbqqqJ4y7kt68+eWXX2QQa6sRSGT1HoPF/lPRNZFVK/3793/8+HFz1xLOrvAleI4VuHx10zlWWwcGQLtgb29fUFDw5csX/O3Tp09/+OEHwad8Pn/Hjh1r165FCPn7++/fv//jx48FBQUnT55UTLjiKO0mcLncMWPGuLu73717F59ZpczRSg8SrO/Q0dGx/C9VVVWE0Lx585pIGgRrDR8+/Pz586qqqnfv3hUu8Pjx48GDBzMYDH19/fnz5zc0NAwZMiQjI8PPzy84ONjDw+Pp06fr1q3D5+59+vTJw8NDS0vL2toa//Eq+vckwYsXL3r16vX3339/+/aNQCDs3btXR0dHV1d39erVz549++GHHzQ0NLy8vGpra4VbLygoIBAI9fX1+NuNGzcK5uKcOHHCyspKXV3d0dFRcMbl69evEydO1NPT++GHH4RvqfLs2bMBAwZoamq6ubnl5uaK9kMLNkRsf5aXlx8+fDiI2UXlv2eMEUIkAmELs8tfR48KjsMOr6KioqGhoVmriGZXONnmWC0ITBkMGjRIcMq5S5cu3t7e6enp+EctO1ctH4IT3kVFRW16vlAmJ6Fv3bplYWEhulxwXr9lWyG3TkAImZiYuLu7r1u3rqqqKioq6smTJz///LPg0wsXLri6unbp0gXDMA6HQyaTyWSyioqK4P/MykBpNyE2Nvbr16+zZs3Kzs7++PHjx48f8/PzlTZa6UGC1UKTJk06depUo8RFLPwqsvAfHj6f7+XlZWdnl5KScvHixYiIiPPnz9+/f9/a2vrChQurVq2Ki4v74YcfTp06FRQUxOVyR44cyWQyX7x4sW/fvq1btwpnIWvWrDlw4MDUqVPxt9euXUtNTd27d29wcPDUqVPPnz8fFxd38+bNS5cuSbNRqampCxYs2LJly5s3b3r27IlnXXw+393dncFgPH/+/MiRI2fOnNm5cydCqLS01M3NbeDAgc+fP/f19d24caNohS3ekEbu3LnTHcPMVFXFfsokk+2p1MTEREnblZyc7OzszGAwnJycBJluSkrKkCFDGAyGra2tIL2Ii4uzt7fX0tLy9vYuKSnBF+bk5Hh6empra/fv319WJ3tEiW369u3b/fv319DQMDIyWrNmDYZhw4cPz87O/umnn/DeEy0gWrOk7Ar33RwLw7Ddu3ebmZkZGRkFBgbik8xEe69RYAihxMTEPn364Cl+VVWVLDqprSxbtiwrKyszM/P8+fM0Gs3JySktLQ0h1Ppz1W1xdhmvU3DCm06nBwcHq0o4OlqvWSehW6xlWyG3TsCdO3euqqrKzMxsy5YtV69eNTY2xpfzeLy9e/euXr0aIUQgEMLDwxcuXGhmZqavrz937tw2Dam5lHMTUlNTKysre/XqZfUvHx8fpY22GTAg2eDBgxt1F4VCEXxqaGh448YN0bWGDRu2ZMmSnJycnJycBw8eTJkyRVVVNT09vbS0FCFUWlpaW1sbGhrKZrPx8qNHj966dSuGYT169IiOjsYXOjo6hoeHYxh27do1XV3d2tpafPn27dsHDBiAYRiLxUIIRUVF4curq6sRQvHx8fhbAwODbdu24a9dXV23b98uHGF+fj5CqK6uDn+7YcOGmTNnYhgWFRXFYDDKy8sxDCsrK8Nru3XrFo1GY7FYeOErV67o6+tjGLZ79+4ePXrw+Xx8+ZQpUxwdHUV7o7kbItaBAwfc6fT3NtaS/k3Q1BRsbyPFxcWampqHDh3Ky8vbs2cPlUqtqqoqKyvT0dHZunVrQUFBbGystrb21atX09PTVVVVQ0JCcnNzjx49SiaTp02bxuFwunfvHhgYWFBQcO/ePRMTk/Pnz4ttKDMzk6imbvZrtNh/mj9OXrp0qaQNFNt0TU0NjUbbtm1bXl7e3bt3aTRaYmIihmFWVlZ3797FMExSAWEXL14kEcm/Tz5zcMGtJv7NH72FQCCkpaU1Wj0+Pp5EIvn7+3/8+PHevXsWFhabNm0S23vCgeH71NPTs6ioKCMjw9raevfu3U3sX8UaOHDgH3/8IbzEw8Nj0qRJ+OsTJ04EBASIXXHYsGEbNmwQvG1oaGAwGKdPn8Y3//nz5z179gwNDc3OznZ3d9fU1OzevfuePXvwwomJiX379l2+fLm2tnaXLl2WLl1aX1+PfyS2fKM6XVxciEQijUbbtWsXnv9VV1dLWhf/crh8+XLXrl2pVOrw4cO/fPkiujli13V3dyeRSOrq6sJb2kT8kr5bEhMTzc3Njx8/bmVlhZ+2r6mpwTBM8K0ovBVZWVkjR45kMBj29vZXrlzBq3r06JGzszOdTtfT05s3bx6Hw5FVJxw/ftzS0pJKpfbr1+/JkyfiRwkAzQe/IvyOgICAwMBAwVvhHzV07949IyNj5MiRomsdPHjw4MGDCCEikWhpaXn+/Hlra2vBfD0KheLn5xcREZGampqSkvLo0SMXFxdJAWRkZNjZ2Ql+JTdgwIBdu3YJPhVcrsb17t0bf8FgMASXqxkMhpQb6+rqampqamZmNm7cOA8Pj3HjxiGE0tPT6+vrBb+h5XK5LBaroaEhIyNj8ODBgg5xdnbOzMxsovJmbUgj2trarCanLpbzeDo6OmI/CgsLc3BwCAgIQAitXLmSwWCw2ewbN24YGxvjVz2MjIwWL1586tQpOzs7Nzc3/LYx8+fPxy/33Lx5k81mb9u2TU1NzcjIaPXq1QcOHJgyZUoTwbTAuXPnRJsmk8lJSUk9e/ZECFVXVzMYDPyvkcB3CyCEPD09hw0fevHRgfmjt6iQxP//nl1TdjXp+KJFi6ytrUU/VVVVPXr0qIaGRrdu3bZt27Zu3Tpzc3PR3vP29m604rZt2wwMDAwMDIYOHSo4J9cuLFy4UHAyddKkSUuWLAkODqZSqU2v1ehcNX5Stm/fvo6Oji4uLi9evEhPT58+fbqOjg5+pePAS70AAAAOB0lEQVTly5f45IHy8vKZM2eqqqoGBwfjJ3rFlhfU2b9//wULFtja2gYHB48dO7aoqAj/tOl1t27deuPGjYaGhokTJ27fvj0kJEQ4eEnrxsXFOTk5rVq1ys/Pr9H2io2/if7Jz8//66+/Ll++jJf/5ZdfDh8+LFqMw+G4urr6+voeOXLk2rVrPj4++Al1Ly8vPz+/v/76Ky8vb/z48c7Ozvfv3299J+Cn7c+ePdu/f/8tW7bMmjXrw4cPTe9oAKQECdZ34LOpxH5kbGxcXl4u9qMNGzY0cUa9rKxs4MCB3bp18/b2njBhQqNvukaw//6wlkgkcrlcwdsm7gJFEJmuJImgQgaDkZKSkpCQEBMTs3z58q1btyYnJ9PpdCcnJ9HJyyQSSfj3GmpCc89luyEIIRcXl7m1tSweT4tEEv30G5+fXFNzaMgQsevm5OR0795d8HbOnDkIoby8POGF1tbWMTExOjo6tra2goV2dnafPn36+PFjRUWF4NQ0Qkhw4zsZys/PF22aTCYnJibif+nNzc1Fd+h3CyCEqFTq9evXvb29j8VvnD96iwq58W6qqq04EL3a29f94MGDYmswMjLS0NDAX/fq1evLly9ie090RXNzc/wFSdxeU2aWlpbV1dXFxcUGBgZ0Ol1bW/vhw4di/yvFYrHw2Yd5eXmHDx+uq6sbOnQo/tHSpUtdXV2vX7/OYrGOHDlCoVAsLS3XrFlz5MgR/O89hmEnTpzo1asXQmjPnj0///zzzp074+LiJJUX1Ckp7KbX3bx5M56LT5kyJTU1tVnriiU2/ibKc7nckydP2tjY4OXxicmixaKioohE4s6dO4lE4sqVK9PT0/Py8qysrLZt2zZlyhQGg2FlZTVgwAD8VFnrO+Hjx48aGhoeHh7a2tr79u1LTk5uYhMAaBaYg9VyeXl5urq6LVjxxo0bNTU1sf+vvXuPieLa4wB+ZhQWFFALiLcsDWoXwRpaLphWo7S3LwOX1kpppAQbTd9ACkhIA01vUu6lBWssrVIecTVGSxO1DYGGsuCWG1sWFZpaLYqk5bFIWQtc1wUBecy5f5zd2WUZltcKXfv9BM0w+5szv5ms+OM3Z2YrKhITE7du3SpONpcUEBBw6dIlMaa+vl6yzTAL4rSYK1eusIWampqSkpKoqKiCggI2s6S+vj4oKOjy5cuswU4IOX78+Ntvv00IUSgUGo2Gmib9XLhwwfbu5nIgDzzwwD+jo/d3W7dnmLyeni3PPmtZoFiSy+WW02JycnIuXbokl8t/++03cWVLS4tcLvfz8xNnNxNCOjo6CCGrVq2Sy+XdJj///HNJSck0054+yV2rVKqsrKyKioqLFy+Wlpbef//9VltNGcC4urqWlZX5Bi4vVv1rZHTcm61v8Oan5WnPxTxbVFQ0WUXe1dUlPgWjqamJta8mnr2JG/K8o/54YadCPCGsVy0ZeejQIX9/f39//8cff7yhoYH1qtlLrCk7sXH766+/smU3NzdWnRBCtm7d2t/f//vvv9uIJ1M1em1vK7a3ly1bNtNtJUnmbyN+xYoVrLpi8Xfu3GHvcytXr14NCwsT3zyFhYURERFi1z8lJSU8PLy6unqyvcz0JIht+1deeUWlUoVP8nsawCw46k/AecN+Q7Uk1iXNzc2BgYGzGNPNza23t/fChQu3bt06dOjQt99+yzphHMfpdDrLvg4hJCIiwt3dPSkpSavVVldX5+TksKtdc+Hl5SWTyfbt29fb2/vFF1988803bL1er3/nnXdUKlVnZ+eRI0cIIQqFIjQ0NDg4ODY29tq1a5WVlWlpaeyod+3apdVq09PT29raTpw4cerUKds7neOB5Ofn/7h8+b9v/DFgcZPIkED3/dFd47pEfO7cRDt37jx37pxSqbxx40ZhYWFWVtZ99923ffv29vb23Nzc7u7uM2fOHDx4cPfu3fHx8VVVVQUFBV1dXV9++SW7M2Dbtm2Dg4PZ2dk6nU6j0WzatMnGD3dKhVG9TvJLGOq3cXSSuzYYDIsWLRoaGhoYGCguLr548aJY5rIJfDYCrIg1VpHqfbHGmk51RQgZGBhISEjo6OjQaDQZGRlvvPGG5NmzTMzGkToE1tXw9vZm39ruVbPJFmNjY83NzS+++KL4EmvK2mjcWq0nhIyOjs6l0Wt7W9sTwG1vK0kyf6uYyQZh8ZIpjYyMTGx59vb2bty48eTJkwqFIjs7m82AnsWBTNwja9uzmxuSk5PDwsLuxkP14K8JBdYUxN9QRexDJW/dumUwGDZv3jyLMaOiol5//fXIyMgNGzZotVqlUqlUKsvLy+Pi4lJTU62uGDo5OVVXV2u12uDg4MTExPfee0/8z2zWZDJZcXFxSUmJl5dXRkZGUlISW79jx46kpKQ9e/YoFIpjx46dPn2aNUVKS0tdXFw2bdr06quvvvXWW2yqkKen55kzZ77//vvg4GClUjnl7XVzPBAfHx+NRtOzcePTLa3vdukOdPdkdOmebmlpe/jhuro6P79JnzLl6+tbUVFRVFS0du3azz///KuvvpLL5Z6enlVVVeXl5WvWrGHPgo+OjlYoFKWlpYWFhYGBgcePH8/OziaEeHh4qFQqtVodEBAQGxv75ptvJiYmSu7I3d3da5n7wMl0ya/FrbWrVkncxMdI7jo6OjoqKiokJOShhx7S6XRZWVlpaWlNTU2xsbHx8fHHjh2bLEByF6zGkgeuYDXWNKsrQsjmzZudnZ3DwsJ27twZFxeXkpIiefYIIWJiNkZzCMXFxZGRkeK3s+5VE5uN276+PnG6T21traurq5+f31wavfO8rWT+4ktsQeyOE0Ju3rwpTtOsra318PCQ7LkGBAT89NNPYpn+/PPPHz58ePpd/5keiGTb3vaBA0zXvE+rv0cUFBSkpqYudBZzZTAYhoeHFzqLmWloaMjJydm7d++HH354/vz5hU7Hwdy+ffupp55a5/t3n+V+CQkJ4k2gf2WPPfZYSkpKW1tba2vr2bNnd+3a5e7ufvXqVTHAx8dHrVZP3NDqLkIRu+NPp9NRSoeHh1evXv3aa6+1t7dXVVV5eXkdPXqUUsqeKrJly5bGxsYffvhhzZo1ycnJNuItx2SCgoIOHz48MjIi3kA32basr3n9+nW24f79+1944QWrnCfbllrcCGxpsvyHhoZkMll6enpPT8+JEyd4nhfvIiSEhIeHX7lyhcVnZmZSqbsI+/v7fXx8MjMz29vb8/PzFy9e3NjYWFZWJpPJNBqNXq8/ePDgokWL9u7da5eT8PXXX3t4eFRWVl6/fv2TTz5xdnbu7Oy0/YYBmCZMcp8NSumRI0fm86GCd4m7u/tCpzBjoaGhoaGhC52Fo1qyZElZWdlLL7304INb8vLypn8nxL0tLy8vLy+PEOLt7f3oo4/W19eLs4Xm0qsmpsZtQkJCcHDwypUrLRu3vr6+MTExkZGRg4ODMTExubm5tuOtsIa3Xq+Pj4+fcl9zyXMykvmz7nhmZubHH3/s5+eXlJRkMBhYfEhIyCOPPPLMM88IghAXF/fBBx9IDrt06VK1Wp2YmPjZZ5/5+vqWlJSsX78+KCiIdf3d3NxefvllpVKZnJz8xBNPzP0k7Nixo6GhYc+ePXq9ft26dWLb3kpvb2+VqlKn0/GEcJRwhHKEcoTwhLJ/QmyBI5SjbIHFEI5SnlBCCEcITwkxbWj6m21iHpAtE0J4SolFDCGEI5SnxqHMm1DxVfPePddtuN3ZPtxnMCVDiTGScFTc0bitiPGlcSMbVxojzSMQapEtpRxha0zJC+KpIISaTgilxBTMCVQcmaPU6R8Ro+oKnlIiGGN4QSCUEIFyVOAFSsYoJ1BujHJjAhklnCKQ5OfbfnP+SXDU8SdMzL/vvvuurq7OgT4oEABmp7CwsLm5+cCBA/YdVq1W7969W3KWt0OYMv++vj4XFxcnJ6f5zOru0el0p0+davzlMk8oR1k5RXlWV1FjpcVblDhiACuwOGN5ZFGZmWoU3qLA4i0KIN6iEuLMI5hrF96iwBILKRawNiL6jx/rBm50isWNOTGrYalF3UapMUk6fmRxENMIxBTAG1eyMHP9xFnUT6aXBGMApZxg8ZJAl777n8GPMnmBcgIllHIC5QWBEygRKCcI/JjAjVFujHKjAjcqcMOUC9lIzp9f6HfEtKCDNRtPPvmkjZulAeDecM/0quefI3bHbRCvQjqE/zX9MtxvWOgspmvkv6qFTuFuQYEFACCtpqZm+/btXl5edh/Z29t727Ztdh923jh6/jM1Ojra09PtKBfU9S3X2LVFhzBy7qyjpDpTuEQIAABgS2tra27OR+IVvT/5JULOHOYAlwjZwj15iRCPaQAAAACwMxRYAAAAAHaGAgsAAADAzlBgAQAAANgZCiwAAABbXF1dg4LWL3QW0+X/9HNLVv5tobOYLtfU9xc6hbsFBRYAAMAUJn4E9Z8W7+zsQB/SwMlcFjqFuwWPaQAAALDlzp07HVrt2NgYMT9filo+a8py5bg1dMIa65jx31LjH6uY8SMQbvIwjhB+sTMdG6WCwBHz/+8ctd67eZlKJEPohF0bw6zGtAqwyIqaBrQY3zyIaRhe5kKHBs2jmT6ixzgCNY3APmxHoMRjGXGQT0tDgQUAAABgZ7hECAAAAGBnKLAAAAAA7AwFFgAAAICdocACAAAAsDMUWAAAAAB2hgILAAAAwM5QYAEAAADYGQosAAAAADtDgQUAAABgZyiwAAAAAOwMBRYAAACAnaHAAgAAALAzFFgAAAAAdoYCCwAAAMDOUGABAAAA2Nn/AR5xG3qqbnlRAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -r 110 -w 800 -h 800\n",
    "figure = (\n",
    "    (\n",
    "        (\n",
    "            (\n",
    "                (\n",
    "                    upset_plot\n",
    "                    + labs(\n",
    "                        title='A. Frequently discussed omics combinations'\n",
    "                    )\n",
    "                    + common_theme\n",
    "                )\n",
    "                |\n",
    "                (\n",
    "                    term_counts\n",
    "                    + common_theme\n",
    "                    + labs(\n",
    "                        title='B. Multi-omics articles indexed by PubMed'\n",
    "                    )\n",
    "                )\n",
    "            )\n",
    "            & theme(legend.position='bottom')\n",
    "            & plot_layout(guides='collect')\n",
    "        )\n",
    "        /\n",
    "        (\n",
    "            (\n",
    "                plot_counts_summary(width=0.8)\n",
    "                + scale_y_continuous(\n",
    "                    expand=c(0, 0.02, 0, 0.1),\n",
    "                    position='right',\n",
    "                )\n",
    "                + shadowtext::geom_shadowtext(\n",
    "                    aes(label=type, y=0.1), hjust=0, size=3,\n",
    "                    bg.r=0.05, color='black', bg.color='orange'\n",
    "                )\n",
    "                + theme(\n",
    "                    legend.position='right',\n",
    "                    axis.title.y=element_blank(),\n",
    "                    axis.ticks.y=element_blank(),\n",
    "                    axis.text.y=element_blank(),\n",
    "                    axis.title.x=element_blank(),\n",
    "                    plot.title.position='plot'\n",
    "                )\n",
    "                + common_theme\n",
    "                + labs(title='C. Mean number of omics detected (95% CI)')\n",
    "            )\n",
    "            |\n",
    "            (\n",
    "                plot_features(names_padding=0.035, counts_padding=0.035, expand=expansion(add=0.0))\n",
    "                + guides(\n",
    "                    color=guide_colorbar(\n",
    "                        barheight=0.5,\n",
    "                        label.position='top',\n",
    "                        title.vjust=0.5,\n",
    "                        title.theme=element_text(size=9),\n",
    "                        order=0,\n",
    "                        #label.vjust=-5,\n",
    "                        label.hjust=0\n",
    "                    )\n",
    "                )\n",
    "                + common_theme\n",
    "                + scale_color_gradient(\n",
    "                    low='grey40',\n",
    "                    high='red',\n",
    "                    name='D) Proportion of publications',\n",
    "                    breaks=c(0.0, 0.1, 0.2),\n",
    "                    limits=function(automatic_limits) {\n",
    "                        automatic_limits[[1]] = 0\n",
    "                        automatic_limits\n",
    "                    },\n",
    "                    labels=scales::percent\n",
    "                )\n",
    "                + labs(\n",
    "                    title='D. Top diseases & species mentioned in the abstracts'\n",
    "                )\n",
    "            ) & theme(legend.position='bottom')\n",
    "        )\n",
    "        /\n",
    "        (\n",
    "            code_and_data_platforms(scale_name='E) Platform used for')\n",
    "            + common_theme\n",
    "            + labs(\n",
    "                title='E. Detected use of code and data versioning/distribution platforms'\n",
    "            )\n",
    "            + guides(\n",
    "                fill=guide_legend(\n",
    "                    keywidth=0.1,\n",
    "                    title.theme=element_text(size=9),\n",
    "                )\n",
    "            )\n",
    "        ) & theme(\n",
    "            legend.position='bottom',\n",
    "            legend.box='horizontal',\n",
    "            legend.direction='horizontal',\n",
    "            legend.margin=unit(-15, 'cm'),\n",
    "        )\n",
    "    )\n",
    "    + plot_layout(\n",
    "        heights=c(3, 2, 0.9),\n",
    "        guides='collect'\n",
    "    ) + plot_annotation(title='Multi-omics literature overview')\n",
    ")\n",
    "figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Saving 7.33 x 7.33 in image\n",
      "\n",
      "R[write to console]: Saving 7.33 x 7.33 in image\n",
      "\n",
      "R[write to console]: Saving 7.33 x 7.33 in image\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%R ggsave(filename='figures/overview.pdf', plot=figure, scale=1.1)\n",
    "%R ggsave(filename='figures/overview.tiff', plot=figure, scale=1.1, dpi=300)\n",
    "%R ggsave(filename='figures/overview.png', plot=figure, scale=1.1, device='png', dpi=105)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compress the PNG if trimage is available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shutil import which\n",
    "from contextlib import redirect_stdout\n",
    "from io import StringIO\n",
    "\n",
    "if which('trimage'):\n",
    "    f = StringIO()\n",
    "    with redirect_stdout(f):\n",
    "        !trimage -f figures/overview.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Details for captions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion full-text articles among all articles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4399421128798842"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.has_full_text==True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion full-text articles among PMC articles (this is because non-PMC are NA, and are not accounted for when calculating the mean below!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.779087647360328"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.has_full_text.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative UpSet plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -r 100 -i omic_entities_group_columns\n",
    "\n",
    "upset(omic_entities_groups, omic_entities_group_columns, min_size=50, width_ratio=0.1, min_degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Dropping empty groups: clades (meta_* & pan_*), protein modifications, exogenous factors, clinical data\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -r 100 -i omic_entities_columns\n",
    "\n",
    "upset(\n",
    "    omic_entities,\n",
    "    omic_entities_columns,\n",
    "    min_size=50,\n",
    "    width_ratio=0.2,\n",
    "    min_degree=3,\n",
    "    base_annotations=list(\n",
    "        'Intersection size'=intersection_size(\n",
    "            counts=FALSE,\n",
    "            aes=aes(fill=chosen_type)\n",
    "        )\n",
    "    ),\n",
    "    themes=upset_modify_themes(\n",
    "        list(\n",
    "            'overall_sizes'=theme(axis.text.x=element_text(angle=90))\n",
    "        )\n",
    "    ),\n",
    "    set_sizes=FALSE\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}