Example_analysis.ipynb

{
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  "nbformat_minor": 0,
  "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": "3.6.8"
    },
    "colab": {
      "name": "Example analysis.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pachterlab/SBP_2019/blob/master/Example_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sW3QOyQQQ20E",
        "colab_type": "text"
      },
      "source": [
        "# Example analysis notebook - single cell studies database\n",
        "\n",
        "This notebook produces figures in the manuscript `A curated database reveals trends in single cell transcriptomics`\n",
        "\n",
        "Notebook repository: https://github.com/vals/single-cell-studies/\n",
        "\n",
        "The database is available at: https://www.nxn.se/single-cell-studies/\n",
        "\n",
        "Running this notebook will save the following figures in the current folder:\n",
        "```\n",
        "all_studies.pdf\n",
        "biorxiv_over_time.pdf\n",
        "cells_per_month.pdf\n",
        "clustering_over_time.pdf\n",
        "clusters_per_cells.pdf\n",
        "pseudotime_over_time.pdf\n",
        "scrnaseq_studies.pdf\n",
        "studies_over_time.pdf\n",
        "techniques_over_time.pdf\n",
        "tsne_over_time.pdf\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boo2E4M2Q20G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# %pylab inline\n",
        "# %config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import io\n",
        "\n",
        "import matplotlib.font_manager\n",
        "from mizani.breaks import date_breaks\n",
        "from mizani.formatters import date_format\n",
        "\n",
        "# Only pandas >= v0.25.0 supports column names with spaces in querys\n",
        "import pandas as pd\n",
        "import plotnine as p\n",
        "import requests\n",
        "import numpy as np\n",
        "\n",
        "import warnings\n",
        "import matplotlib.colors as mplcol\n",
        "import colorsys\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")  # plotnine has a lot of MatplotlibDeprecationWarning's"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzjSc7VNQ20L",
        "colab_type": "code",
        "outputId": "23a6e432-9578-4f60-bccb-e341259b8a6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "url = \"http://nxn.se/single-cell-studies/data.tsv\"\n",
        "df = pd.read_csv(url, sep='\\t')\n",
        "print(df.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(729, 25)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3SNQFzwQ20P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# converts string to date format, can only be run once!\n",
        "df['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d')\n",
        "\n",
        "# converts string of reported cells total to float, can only be run once!\n",
        "df['Reported cells total'] = df['Reported cells total'].str.replace(',', '').map(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "7cq6OoEwQ20T",
        "colab_type": "code",
        "outputId": "948ed0fc-5bd7-4a43-a813-3f05cd21ac35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# plot number of studies over time\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(p.aes(x='Date', y='DOI'), data=df.resample('1M', on='Date').count()[['DOI']].reset_index())\n",
        "    + p.geom_bar(stat='identity')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Number of studies')\n",
        ")\n",
        "fig.save('studies_over_time.pdf')\n",
        "print(fig)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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A3phS2l7ffzxwBXAicCfw8pTSdRVyS5IkSVpBlaLg54EXNhYEADnnnSGENwLvr5BhjqIo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\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "<ggplot: (-9223363263912146965)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPA7JpKsQ20X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tidy_split(df, column, sep='|', keep=False):\n",
        "    \"\"\"\n",
        "    Split the values of a column and expand so the new DataFrame has one split\n",
        "    value per row. Filters rows where the column is missing.\n",
        "\n",
        "    Params\n",
        "    ------\n",
        "    df : pandas.DataFrame\n",
        "        dataframe with the column to split and expand\n",
        "    column : str\n",
        "        the column to split and expand\n",
        "    sep : str\n",
        "        the string used to split the column's values\n",
        "    keep : bool\n",
        "        whether to retain the presplit value as it's own row\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    pandas.DataFrame\n",
        "        Returns a dataframe with the same columns as `df`.\n",
        "    \"\"\"\n",
        "    indexes = list()\n",
        "    new_values = list()\n",
        "    df = df.dropna(subset=[column])\n",
        "    for i, presplit in enumerate(df[column].astype(str)):\n",
        "        values = presplit.split(sep)\n",
        "        if keep and len(values) > 1:\n",
        "            indexes.append(i)\n",
        "            new_values.append(presplit)\n",
        "        for value in values:\n",
        "            indexes.append(i)\n",
        "            new_values.append(value)\n",
        "    new_df = df.iloc[indexes, :].copy()\n",
        "    new_df[column] = new_values\n",
        "    \n",
        "    return new_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0K2Fb1iQ20b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# alias for pd.Timestamp\n",
        "ts = pd.Timestamp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooP83dzoQ20f",
        "colab_type": "code",
        "outputId": "0dcceaa0-15c8-459b-a354-21d97dacfd53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "#tdf is a temporary dataframe\n",
        "tdf = tidy_split(df, 'Technique', ' & ')\n",
        "# technique dictionary for top 5 techniques\n",
        "t_dict = {k: k for k in tdf['Technique'].value_counts().head(5).index}\n",
        "\n",
        "t_dict"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Chromium': 'Chromium',\n",
              " 'Drop-seq': 'Drop-seq',\n",
              " 'InDrops': 'InDrops',\n",
              " 'SMARTer (C1)': 'SMARTer (C1)',\n",
              " 'Smart-seq2': 'Smart-seq2'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YflvBM8Q20l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# maps remaining tecnquines to `Other`\n",
        "tdf['Technique'] = tdf['Technique'].map(lambda s: t_dict.get(s, 'Other'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cv1yTT2Q20p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# list of techniques in alphabetic order for the plot with `other` at the \n",
        "techs = list(\n",
        "    tdf['Technique']\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        "    .index\n",
        "    .difference(['Other'])\n",
        ")\n",
        "techs.append('Other')\n",
        "\n",
        "tdf['Technique'] = (\n",
        "    pd.Categorical(\n",
        "       tdf['Technique'],\n",
        "        categories=techs,\n",
        "        ordered=True\n",
        "    )\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB_WXLObQ20v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def desaturate(color, prop):\n",
        "    \"\"\"Decrease the saturation channel of a color by some percent.\n",
        "    Parameters\n",
        "    ----------\n",
        "    color : matplotlib color\n",
        "        hex, rgb-tuple, or html color name\n",
        "    prop : float\n",
        "        saturation channel of color will be multiplied by this value\n",
        "    Returns\n",
        "    -------\n",
        "    new_color : rgb tuple\n",
        "        desaturated color code in RGB tuple representation\n",
        "    \"\"\"\n",
        "    # Check inputs\n",
        "#     if not 0 <= prop <= 1:\n",
        "#         raise ValueError(\"prop must be between 0 and 1\")\n",
        "\n",
        "    # Get rgb tuple rep\n",
        "    rgb = mplcol.colorConverter.to_rgb(color)\n",
        "\n",
        "    # Convert to hls\n",
        "    h, l, s = colorsys.rgb_to_hls(*rgb)\n",
        "\n",
        "    # Desaturate the saturation channel\n",
        "#     l *= prop\n",
        "    l = 0.8\n",
        "\n",
        "    # Convert back to rgb\n",
        "    new_color = colorsys.hls_to_rgb(h, l, s)\n",
        "    \n",
        "    hex_color = '#{:02x}{:02x}{:02x}'.format(*map(lambda c: int(c * 255), new_color))\n",
        "\n",
        "    return hex_color\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipaJSRM9Q202",
        "colab_type": "code",
        "outputId": "0b28cff4-5043-4dd6-996e-128744c156cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# lighten matplotlib default colors\n",
        "clrs = list(map(lambda c: desaturate(c, 1.2), ['C0', 'C1', 'C2', 'C3', 'C4', 'black']))\n",
        "clrs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['#a7d2f0', '#ffc899', '#afe9af', '#efa8a9', '#ccb7e0', '#cccccc']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "-w69yINnQ208",
        "colab_type": "code",
        "outputId": "75c9cbad-f44a-45ed-d896-fb6a9fce53d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#### Plot number of studies per month by technique\n",
        "per_month = (\n",
        "    tdf\n",
        "    .groupby('Technique')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()['DOI']\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes(x='Date', y='DOI', fill='Technique'),\n",
        "        data=per_month.query('Date > @ts(\"20130101T010101\")')\n",
        "    )\n",
        "    + p.geom_bar(stat='identity', color='grey')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(\n",
        "        breaks=date_breaks('1 years'),\n",
        "        labels=date_format('%Y')\n",
        "    )\n",
        "    + p.labs(y='Number of studies')\n",
        "    + p.scale_fill_manual(clrs)\n",
        ")\n",
        "fig.save('techniques_over_time.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cPxEREREREZkgksyAAhAGm2UNOFOp1DxgPbCxs7PzjHDbCcC3gBOBh4F3dnZ2/q6c1xUR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\nSY0wAZUkSZIkNeL/AK6TkxgPHbQjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363263942730678)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmevkeHxQ21F",
        "colab_type": "code",
        "outputId": "dba3cbb7-ce27-42dc-c186-fba1d63599c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# plot number of studies doing clustering over time\n",
        "\n",
        "tdf = df[['Date', 'Cell clustering', 'DOI']].dropna()\n",
        "tdf['Date'] = pd.to_datetime(tdf['Date'], format='%Y%m%d')\n",
        "per_month = (\n",
        "    tdf\n",
        "    .groupby('Cell clustering')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()['DOI']\n",
        "    .reset_index()\n",
        ")\n",
        "per_month['Cell clustering'] = (\n",
        "    pd.Categorical(\n",
        "        per_month['Cell clustering'],\n",
        "        categories=['Yes', 'No'],\n",
        "        ordered=True\n",
        "    )\n",
        ")\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes(x='Date', y='DOI', fill='Cell clustering'),\n",
        "        data=per_month.query('Date > @ts(\"20130101T010101\")')\n",
        "    )\n",
        "    + p.geom_bar(stat='identity', color='grey')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Number of studies', title='Perfomed cell clustering')\n",
        "    + p.scale_fill_brewer(palette='Greys')\n",
        ")\n",
        "fig.save('clustering_over_time.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X6xal8fkYwRBbCC7k3QRcH74eJTiZRERERERkinLOfdLMricYZnoBMAu4H/gt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\nGT4lSZIkSc0ZPiVJkiRJzf0JpdCjTCS9+BcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911895016)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "s4r5ohzbQ21N",
        "colab_type": "code",
        "outputId": "3dca6957-f91c-4dee-ccbe-908a76a2ffff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "# plot number of studies using tsne over time\n",
        "\n",
        "tdf = df[['Date', 'tSNE', 'DOI']].dropna()\n",
        "tdf['Date'] = pd.to_datetime(tdf['Date'], format='%Y%m%d')\n",
        "per_month = (\n",
        "    tdf\n",
        "    .groupby('tSNE')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()['DOI']\n",
        "    .reset_index()\n",
        ")\n",
        "per_month['tSNE'] = (\n",
        "    pd.Categorical(\n",
        "        per_month['tSNE'],\n",
        "        categories=['Yes', 'No'],\n",
        "        ordered=True\n",
        "    )\n",
        ")\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(p.aes(x='Date', y='DOI', fill='tSNE'), data=per_month.query('Date > @ts(\"20130101T010101\")'))\n",
        "    + p.geom_bar(stat='identity', color='grey')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Number of studies', title='Performed tSNE')\n",
        "    + p.scale_fill_brewer(palette='Greys')\n",
        ")\n",
        "fig.save('tsne_over_time.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MERERERGRhlDqJOxvgD9nfF4zvu9vBz6RsWiV53l3Ai8neObYDOAzvu/vBv7b87zLgPMJ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\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363263942731958)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnHrFbWdQ21c",
        "colab_type": "code",
        "outputId": "f4235a77-df31-496c-db23-e3a753873f41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "#plot number of studies performing pseudoanalysis over time\n",
        "\n",
        "tdf = df[['Date', 'Pseudotime', 'DOI']].dropna()\n",
        "tdf['Date'] = pd.to_datetime(tdf['Date'], format='%Y%m%d')\n",
        "per_month = (\n",
        "    tdf\n",
        "    .groupby('Pseudotime')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()['DOI']\n",
        "    .reset_index()\n",
        ")\n",
        "per_month['Pseudotime'] = (\n",
        "    pd.Categorical(\n",
        "        per_month['Pseudotime'],\n",
        "        categories=['Yes', 'No'],\n",
        "        ordered=True\n",
        "    )\n",
        ")\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(p.aes(x='Date', y='DOI', fill='Pseudotime'), data=per_month.query('Date > @ts(\"20130101T010101\")'))\n",
        "    + p.geom_bar(stat='identity', color='grey')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Number of studies', title='Perfomed pseudotime analysis')\n",
        "    + p.scale_fill_brewer(palette='Greys')\n",
        ")\n",
        "fig.save('pseudotime_over_time.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PHLtPFbHNj650fwyf9wH8qKmvjPZRjp/iz6kWth/YJuMX+C8KTsF/uTCIH+X2A/gRVfMZ\nxJ87r8HfqzgZ/8XA7cA3nXN3lBmniIikzIZ/gSgiIvVkZsfipyZ5j3PusvpGIyNddMW2Dz8f4on1\njkdERBqH7lkUEREZ3d6Hn0v0wnoHIiIijUXdUEVEREahaACiPfDdgf9eq27cIiLSvNRYFBERGZ3m\n4e+j/Ct+kCgREZHt6J5FERERERERGUb3LIqIiIiIiMgwaiyKiIiIiIjIMGosioiIiIiIyDBqLIqI\niIiIiMgwaiyKiIiIiIjIMGosioiIiIiIyDBqLIqIiIiIiMgwaizWQV9f3/i+vr65fX194+sdS6mG\nhoZ2Hxoamjs0NLR7vWMpVbPlWTlOh/KcDuU5Hc2WZ+U4HcpzOpoxzyLlUmOxPsYD3dFzs9gdH3PT\nfIjTfHlWjtOhPKdDeU5Hs+VZOU6H8pyOZsyzSFnG1juARhF9k5XKB9SUKVMmDQwM0N7eftjQ0NCm\nNPaZgIMyz0NDQ3UNpFRNmGflOB3KczqU53Q0VZ6V43Qoz+lopjy3trbeXe8YpDmZc67eMTSEoaGh\nufhvh0RERERERozW1lardwzSnNRYjKR5ZfG5556bNDAwsKi9vX322LFjG/qbqJiDgMuBU4B/1TmW\nkjRhnpXjdCjP6VCe09FUeVaO06E8p6OZ8qwri1IpdUONtLa2rgZWp7GvtWvXTgZ4+umnl3Z2dm5I\nY5/VinUJ+VezfOA0W56V43Qoz+lQntPRbHlWjtOhPKejGfMsUi41FkVERESk4fT09OwMTMxRNNjd\n3f1k2vGIjEaJjIZqZvuY2WvNbGoS9YmIiIjI6BU1FNcAj+R4rInKRaTGyr6yaGb/B4xxzp0VvX4L\ncAXQCjxtZq93zt2VbJgiIiIiMopMBFpOPvlk2tranl+4ceNG5s+f30LuK44ikrBKriy+Bbgz9vqr\nwO+BmcAdwJcTiEtERERERrm2tjYmT578/CPecBSR2quksbg7sArAzKYDBwJfds7dC3wfeGly4YmI\niIiIiEg9VNJYXA/sGv38OmBdrNvps8AOSQQmIiIiIiIi9VPJaKiLgC+Z2W7A2cA1sbIDia46ioiI\niIiISPOq5Mrix4DHga/hG4afj5W9E/hLAnGJiIiIiIhIHZV9ZdE59xjwmjzFbwA2VxWRiIiIiIiI\n1F1V8yyaWZeZvcLMJgE45zY457YkE5qIiIiIiIjUS0WNRTN7v5k9hp8Y9S/4exUxs6vN7KMJxici\nIiIiIiJ1UHZj0czOwk+R8XPg9YDFihcCJyUSmYiIiIiIiNRNJaOhfhg41zn3ZTMbk1W2nOgqY6mC\nIDgTOA2YAVwdhuHbY2UPA7sBW6NFj4RheEgFMYuIiIiIiEgZKmks7gHclqdsCGgrs74+4MvAa4Gd\nc5S/JQzDBWXWKSIiIiIiIlWo5J7FR4Cj8pQdDawop7IwDK8Kw/Aa4MkKYhEREREREZEaqOTK4iXA\nXDNbC1wVLWs1s+OBT7L9vItJ+FkQBC3AfcDnwzC8NeH6RUREREREJEvZVxadc98CLgUuBtZGi28F\nrgV+4Zy7ILnwOBXYB9gL+DXwhyAI9k6wfhEREREREcmhkiuLOOc+YmbfAV4H7ASsA25yzj2QZHBh\nGC6OvfxREARvB44DLkxyPwB9fX3jgfFJ15tHe+a5r68vpV1Wp6OjY9KYMWPYunXrpLVr106udzwl\naqo8K8fpUJ7ToTynownzrBxXYdGiRVMHBwcnZi+fOHHi4OzZs9fFFjV9nru6utp6e3vzrt/V1dXW\n19dX73O+afLc2dm5od4xSHOqqLEI4Jz7N3BRgrGUYhvbT9WRpM8C3TWqO59HU95fxfr7+zM/Lqpn\nHBVqijwrx+lQntOhPKejifOsHJdp8+bNrFixAufcsDIz46ijjmLChAnZRU2b51mzZjFv3ry868+a\nNeu+FMIqVTPkuVb/P8sIV1Jj0cyOAO53zj0T/VyQc+7uUgMIgmBsFMdYoCUIggn4qTJ2B/YG/hat\n+m7gZcB7S627TOcB59eo7mzt+A+WPYGBlPZZlY6OjsPGjBmzaOvWrbP7+/uX1jueEjVVnpXjdCjP\n6VCe09GEeVaOK7Rw4cI9nXP3nXzyybS1vTDw/MaNG5k/fz4LFy48ZM6cOZlGS9PnefHixXvix6vI\nafHixfHjrZemy7NIuUq9sngncAxwR/Tz8K+1PIvKsudfLOQLbH9F7yTgZ8A3gO8D+wFbgH8CbwzD\ncGUZdZess7PzWeDZWtSdLdZVYaBZugUMDQ1tAmhpadnULDE3W56V43Qoz+lQntPRbHlWjivX29u7\nEaCtrY3Jk4f3vuzt7d2YiW8k5DlzvPnEj7demjHPIuUqtbH4anxjLfNzYsIwnAvMzVP8kiT3JSIi\nIiIiIqUpqbHonLsl188iIiIiIiIyMpU9dYaIiIiIiIiMfKUOcLON/PcpDuOcK+eeRREREREREWkw\npd6z+HFeaCyOBc7CDzpzDbAGmAacALQC3044RhEREREREUlZqfcsfifzs5l9HVgCnOCc2xZbfjZw\nLX7KCxEREREREWlildyzeBpwQbyhCBC9vgA/H6KIiIiIiIg0sUoaizsA++Qp2weYUGkwIiIiIiIi\n0hhKvWcx7hrg62b2DHCNc269me0IvAU4LyoXERERERGRJlZJY/FDwETgUuBSMxvCD2xjwNXAmcmF\nJyIiIiIiIvVQdmPROTcAvM3MDgZehh/QZjXwd+fc/QnHJyIiIiIiInVQyZVFAKKGoRqHIiIiIiIi\nI1DZjUUzm11sHefcosrCERERERERkUZQyZXFhYDD36OY4bLWGVNpQCIiIiIiIlJ/lTQWD8+xbArw\nBuCtwBlVRSQiIiIiIiJ1V8kAN0vzFC00s0F8Y/HPVUUlIiIiIiIidVXxADd53AZ8MuE6UzE0NLQ7\nfmTXmpsyZcqkgYEB2tvbDxsaGtqUxj4TcFDmeWhoqK6BlKoJ86wcp0N5TofynI6myrNyXLk5c+ZM\nW7BgQaHyQ4eGhnaGkZHnco63Xpopz62trXfXOwZpTkk3Fk8A1iVcZ1rOALrT2NHYsWOZMmUKQDMO\nBHR5vQMoVRPnWTlOh/KcDuU5HU2RZ+W4ctOnTy9Wfn3m55GQ53KOt16aLM9WfBWR4SoZDTXMsXgc\ncCCwF/CpaoOqk4uAXMeWuOeee27SwMDAovb29tljx45t6G+iYg7Cf4CfAvyrzrGUpAnzrBynQ3lO\nh/KcjqbKs3JcuZUrV04D8jaQVq5cefwRRxzxOIyMPJdzvPXSpHkWKUslVxYnM3z0083AjcCVzrk/\nVh1VHbS2tq4GVqexr7Vr104GePrpp5d2dnZuSGOf1Yp1vflXs3RlaLY8K8fpUJ7ToTyno9nyrBxX\nbsGCBXsVKb/36KOPXgUjI8/lHG+9NGOeRcpVyQA3x9YgDhERERERGQHMbC7b3971JLAM6HbO/aUu\nQWUxs2uAjnLaNmbWAZwFzHfO/TO2fB/gIeAk59yVyUZaXy3lbmBml5rZvnnK9jazS6sPS0RERERE\nmtgzwMujxweBnYCbzOzQukZVnQ58I/jFWctX44/z5tQjqrGyG4vAacAuecp2Bt5dcTQiIiIiIjIS\nbHPO3R49rgTehO/V+IE6x5U459yz0XE260CfeVXSWITh9yxm7A88VWGdIiIiIiIyAjnnVgFrgX3N\nrMPMLjGzx8xss5n1mtkV8fXNbE8z+6WZPWlmz5jZIjM7MmsdZ2ZnZy07y8xc1rKDzeyWaF8rzSzn\nxS0zm21mt0X7ezLqUTk1KtsH39UU4DfRvp2Z7RM9nJm9LVbXw2b2gyieXjMbMLPLzGy8mb3EzG41\ns01mdoeZzciKw8zsbDNbYWbPmtm/zexj5eQ7KSXds2hmH8RfPgbfUPyVmT2TtdoEYB/gN4lFJyIi\nIiIiTc/MJuO7ovYB5wPHAZ8BHsbPdX5cbN0pwGJgI/BhYH30fLOZ7e+ce6KM/U4A/gRsAt4ZLf4S\nftDOB2LrHQncACwETgJ2A74GHGJmr8B3NT0RuAr4HPDnaNPV5J+r/c3Avfgp+l4UHfcWfJfV84E1\nwNfxjc8XO+e2Rdt9F3gv8BXgb8ArgK+b2TPOuQtLPfYklDrATR9wV/TzocBy/DcDcVuA+4GfJBOa\niIiIiIg0KzPLtDX2BP4PGANcGf38K+fcz2Krx68snoW/P/CoTMPQzG4CVgBnU95UfacBncBBzrkH\norqW4NszD8TW+zzwOPBG59xQtF4v8EfgP51z10XbATzgnLs9dpyF9v9m59yWaL1jgfcBxznnFkTL\nWoDrgBnAUjObDpwJfMA5d3FUx41mNhHoNrOLY43Kmiupseicuxa4Fp5PxrnOuX/XMC4REREREWle\nk4Ch2OungTOdc380s1OA08xsNbDAOXdv1ravx1+5WxdrcG4FbgFeVmYcRwP3ZhqKAM65B81sadZ6\nrwTmZRqK0Xp/MrN+YBa+QVeuWzINxcgKYBvbD4SzInruApYCr41e/zZ27OCnKfx0tN4jFcRSkUqm\nznhP9rKopdsJrHTO5bufUURERERERodngNn4W9ieBHpjV8Q+DKwDPgF8M7qCd55z7kdR+c7AMWzf\n2MxYWWYcuwO5uq2uAXaIvZ4SLcu13tQy95nRn/V6C/BMVgMy8/OE6HlnwPA5y6WxG4vRTaSTnHM9\n0etXAiG+3+9DZvYG51y5b6KIiIiIiIwc25xzd+YqcM6tx3c1PSsa3OWjwAVmdm80D+M6YAHwxRyb\nP5v187is8ilZr1cDR+SoZzdgQ+z1OmDXPOulOcrpOnwDexYvNCTjlqcYS0Wjob4XeDT2+nzgPvwN\nnE8CX00gLhERERERGeGcc8uAzEifB0fPN+LnMrzfOXdn1mNZbPNHY9tkvC7r9R3AoWa2X2ZB9PNh\nWestBk6Id/00s9fh751cHC3KvgpYCzdFzzvlOPY7nXMDNdz3MGVfWcRf+nwQwMz2AI4EXuWc+0uU\n3B8V2lhEREREREYvM7sVuBo/UuhW4F34hthfolXOB04BbjGz7wKr8PO8Hw30Oee+Ha13Jf7q5N/x\nV9xOBfbI2t1lwBeA35lZ5krll/CD2cR9BbgtWu/7vDAa6h3A76N1Hsd3Lf1vM3sIf2XzH5VlITfn\n3Aoz+yHwCzP7Jn401FbgAODVzrkTktxfMZU0Fp/BdzkF+A/8kLa3Ra/7gR0TiEtEREREREamW/EN\nxH3xA74sA97knLsfwDn3lJkdA3wZP7XETvj7Dm/HNzIzzsV3He2O6rkIP+3E/2VWcM49Y2avx1/Q\n+iXwWLTdm/FXDTPr3RWtdx7wW/xUGyHwCefc1midbWb2HnxPypuA8dExJO0j+MbvGcA5+PbWcuow\nRWEljcU7gM+Y2Tbgk8AfMgkEpuPfABERERERGYWcc3OBuQXKP0WR6S+cc4/jb38rtM4m4PQcRedn\nrXcffrCduJ/mqO8W/JyGhfZ5DXBNjiLLWm+fHNvOJSsvzrmHc2zrgB9Ej7qqpLF4NvA7/PCxj+Dn\nJMn4L164yliyIAjOxM+BMgO4OgzDt8fKDgV+DMzET9p5ZhiGN+eoRkRERERERBJS9gA3zrl/Oude\nBOzinNvXOfdgrPgT+MZkufrwl5kviS8MgqAV3ygN8SMb9QBXB0GQa6QiERERERERSUglo6ECvi9x\njmXLnHNry60rDMOrwjC8huHziRwLTAS+Fobhs2EY/hp/I+xJFYQsIiIiIiIiJaqkG2qaDgWWhWG4\nLbbsnmi5iIiIiIg0mJ6enja2n/C+WoPd3d2bEqxPStTojcU2/Aircf3A3knvqK+vbzx+RKM0tGee\n+/r6UtpldTo6OiaNGTOGrVu3Tlq7du3k4ls0hKbKs3KcDuU5HcpzOpowz8pxhbq6utp6e3sLlvf1\n9WXia/o8l3m89dI0ee7s7NxQfK1k9PT0TGlpaXlk27Zt7cXXLs3YsWMfAfZJqj4pXaM3FjcyfCqO\nHYFaTEb5Wfywu2l6NOX9Vay///k2+6J6xlGhpsizcpwO5TkdynM6mjjPynGZZs2axbx58wqV35dj\ncdPmucLjrZdmyLMVXyUx7du2bWs/+eSTaWtrq7qyjRs3cvXVV09NIC6pQEmNRTP7CHCFc+4JM9sL\nWO2cG6ptaIC/P/HTQRC0xLqivgTI/+lRufPIGma3htrxHyx7UpuGb+I6OjoOGzNmzKKtW7fO7u/v\nX1rveErUVHlWjtOhPKdDebEj2/sAACAASURBVE5HE+ZZOa7Q4sWL9wTyNpAWL158yJw5czKNlqbP\nc5nHWy9Nl+c0tbW1MXlyvS/+SrVKvbL4beCv+MkwHwJejp9vMRFBEIyNYhkLtARBMAHYCiwEngE+\nFQTBt4EAP73GiUntO6Ozs/NZ4Nmk680l1lVhIM1uAdUYGhraBNDS0rKpWWJutjwrx+lQntOhPKej\n2fKsHFeut7d3Y7HyTHwjIc/lHG+9NGOeRcpV6mioTwHTo58NcAnH8QV8o/Dz+JFOnwEuCcNwCN9A\nfAv+XsUvASeGYfhEwvsXERERERGRmFKvLF4P/NzMvoZvKF5jZvmuwjnn3PQ8ZTmFYTgXmJunbBlw\ndDn1iYiIiIjIyGZmIdDnnPtAbNmRwM3AkVnzwUsFSm0svh9YDBwMfBx/8/HjtQpKRERERESkiDOA\ne81svnPuZjNrBS4FvqiGYjJKaixGg9n8BMDM3gp8zTnXDDfSi4iIiIjICOScW21mHwV+YmYzgE/g\nb137vpm1RK/fD+wE3Aac4Zx7LCr7FnAKfj7Ix4D3O+f+Uo/jaGRlT53hnNu3FoGIiIiISOV6enp2\nBiZ2dXW1zZo1i8WLF+8ZDRQz2N3d/WSx7XIUFdxOpBE4535pZicBvwJeCbzUOefM7Cz8WCivBfqA\nc6N1XgUcB7wVOAxYg5/DMekxWUaEUge42Y6Z7WFm3zSzv5rZ8uj5G2a2R9IBioiIiEhRU/D/9D7S\n29t737x58+jt7b0PeARYEzUIh4mWr4nWy37k3U6kwZwBvAb4inNuZbTsg8DnnXOPRL0kzwFeYWad\nwBZgAnAIMMY595Bz7uE6xN3wyr6yaGaH4u9ZbAVuAO4BdgM+APyPmc12zjXSRKkiIiIiI90OQEv2\nROgbN25k/vz5LeS+cki0vJLtRBqGc+5xM3sSP0d7xt7Ab81sW2zZc0CXc+4GM/sy8DVgfzP7A/Ax\n55zGZMlSdmMR3793JfB659zTmYVmNgX4U1R+XDLhiYiIiEipKp0IXROoywjUC5zmnFucq9A59338\nvY1T8WOznAe8J8X4mkIl3VBnAV+ONxQBotdficpFRERERETq5UfAV83sRQBmNjW6txEzO9rMjolG\nTx3Ez/G+tX6hNq5KGovPAePzlI1HiRYRERERkfr6NnAV8HszGwCWAK+PynYELgaexg9+swPwuXoE\n2egq6YZ6I/AVM7vHObcis9DM9sePMnRDUsGJiIiIiIgU45zbJ+v1NuA70SN73T8BM9OJrLlVcmXx\n4/hG5j/N7B4z+6OZLQHuj5Z/PMkARUREREREJH1lNxadc6uAGfhG4YqojhXAx4CZzrneRCMUERER\nERGR1FXSDRXn3Ebge9FDRERERERERphKuqGKiIiIiIjICKfGooiIiIiIiAxTUTfUkWhoaGh3YPc0\n9jVlypRJAwMDtLe3HzY0NLQpjX0m4KDM89DQUF0DKVUT5lk5TofynA7lOR1NlWfluHJz5syZtmDB\ngrzlhx9++IFLliwptP2hQ0NDO5dbb77tamC7PDdQXHk10/nc2tp6d9r73LhxY0PVI5VRY/EFZwDd\naexo7NixTJkyBWBRGvtL2OX1DqBUTZxn5TgdynM6lOd0NEWelePKTZ8+vWD5jBkzLivUWJw+ffr1\nldSbb7saujzab8GV6hDXME12PluK+3p8ypQpp912221Tk6qwvb39saTqkvKU1Vg0swnA/wJ/cs7d\nW5uQ6uYiIExjR88999ykgYGBRe3t7bPHjh3b0N9ExRyE/wA/BfhXnWMpSRPmWTlOh/KcDuU5HU2V\nZ+W4citXrpwG5G0gLVu27DTgsgLbH3/EEUc8Xm69+barge3y3EBx5dWk53PNdXd3bwF+Vu84JBll\nNRadc5vN7MvAXTWKp25aW1tXA6vT2NfatWsnAzz99NNLOzs7N6Sxz2rFut78qx5dGSrRbHlWjtOh\nPKdDeU5Hs+VZOa7cggUL9ipUvmTJkuVFtr/36KOPXlVuvfm2S1p2nhslrkKa8XwWKVcl3VDvAV4M\n3JJwLCIiIiIi0uR6eno6gEkJVjnQ3d2tBnkdVNJY/ChwuZmtBX7vnBtMOCYRERERySF7sI96D/7R\n09OzMzAxR9Fgd3f3k6VuO2fOnGnTp09n5cqV0xYsWFDXgWukOj09PVPM7BHnXHtSdba0tDwC7JNU\nfVK6ShqLNwPjgF8DmNkg4GLlzjm3YwKxiYiIiIg3AWD+/Pn5ysenF4oXNfbWkHsqtm09PT275Wsw\nZm8bG/n0emAbcETiAUta2p1z7Ycddhjjxo2rurItW7awbNmyxAbLkfJU0lj8P7ZvHIqIiIhIbW0G\nOPjgg2ltbX1+4dDQEPfffz/As3WIaSLQcvLJJ9PW1vb8wo0bNzJ//vwWcl9xLHXbHWoUs6Rk3Lhx\nTJgwod5hSJXKbiw65+bWIA4RERERKaKtrW27f8A3b95cx2i8trY2Jk+enPq2IlJ7uboNlMzMuszs\nFWaW5A2sIiIiIiIiUmcVNRbN7P1m9hjwCPAX4MBo+dVm9tEE4xMRERERERnGzBaa2XNmdkBs2UFm\nplvmElJ2Y9HMzgK+D/wceD1gseKFwEmJRCYiIiIiIlLYeuDcegcxUlVyZfHDwLnOuc8Cf84qW050\nlVFERERERKTGvg/8p5kdnl1gZpPN7Cdm9riZPWpm55tZ6iMHN7NKRkPdA7gtT9kQ0JanTEREREah\nzHx6XV1dbbNmzWLx4sV79vb2bqSEufiS2G+OopLnAMya/2+vUrZtUFN6enpyLe8stmGBPE6pNiiR\nBDwOfA/4KnBcVtn3gGnAQfjpZ64FzgE+n2aAzaySxuIjwFH4+RazHQ2sqCoiERERGTHi8+n19vYy\nb948gPui4oJz8SW13xzFJc8BmDX/X01jrrElbH/rUKmmAA+RJ49VRSSSnG8CK81sNvAEgJmNAf4b\nOMo51x8t6wYuQY3FklXSWLwEmGtma4GromWtZnY88EmUfBEREXlBNXPx1Wu/9Yq5liz7eADWrFnD\n9ddfn2cTwM93WCgXInXnnOs3s68D5wH/Ey3eGRgHPBxb9WFgdzMz55wGwSlBJfMsfsvM9gIuBi6K\nFt8aPV/gnLsgqeBERERkZKjXfHqaA/AFuY5n48aNFW8r0mC+D3wUeGP0+klgC7APsDRatg+wWg3F\n0lVyZRHn3EfM7LvAa4GdgHXATc65B5IMDiAIgsuAd+Df7IwXh2G4Kul9iYiIiIhI83HOPWNmXwK+\nEr3eamZXAOeZ2TuA8UA38Is6htl0KmosAjjnVgIrE4ylkPPDMPxMSvsSEREREZHm8xPgbPzFLICP\nAN/Bz9iwFfgN8KX6hNacKmosmlkrcBp+QJvdgdXA7cDPnHNDiUUnIiIiIiKSg3Pu2KzXzwH7x16v\nB96TclgjStk3JpvZAfjW+Y+Aw/Ejax0OXAisMLNazLP4/iAI1gVBsDQIgtNrUL+IiIiIiIjEVHJl\n8SL8/YMHRl1RATCz/YDr8I3I1yQTHuDnRzkb6AdeCfwmCIL1YRj+NsF90NfXNx7flzkN7Znnvr6+\nlHZZnY6Ojkljxoxh69atk9auXdssd7g3VZ6V43Qoz+lQntPRDHnu6upq6+3tLVje19eXN/ZFixZN\nHRwcHDb66MSJEwcB8pV1dXVNLLTfnXbaac9LL7102NzQpWxbTcyzZ89el7fiAttOnTq1Y926/Jt2\ndHRM6u/vL1R1Raqpt1Ceip0Xmf1mD8CTeV3sPUjJsM+Mat77Wurs7NxQr31Lc6uksXg08M54QxHA\nOfegmZ0D/CyRyCJhGN4de/nnIAh+CJwEJNpYBD6Lv+k1TY+mvL+Kxf5QLKpnHBVqijwrx+lQntOh\nPKejGfI8a9aszNyK+crvy1e2efNmVqxYQb6BC80sZ5mZ8Za3vIVCjZF169bd+tRTT1W0baUxmxlH\nHXUUEyZMKHvbYmbPnn1jGIZlb1fLegvlqdh58f/+3/+78frrr2f+/Pk5y4855pi8ddfBo1Dde5+C\nSubYFKmosdgH5PsUc8DjlYdTkm3U5oQ/Dzi/BvXm0o7/YNkTGEhpn1Xp6Og4bMyYMYu2bt06u7+/\nf2nxLRpCU+VZOU6H8pwO5TkdzZDnxYsX7wnk/cd+8eLFh8yZMydnA33hwoV7OufuyzPHH8458pXd\nfPPNrwVuzLffaratJuaFCxdWvG0hixYtKhhzpaqpt1Ceip0Xt95662uBGw8++GBaW1ufXz40NMT9\n99/P7bffnrfuFG33mVHNey/SqCppLPYA55rZPc65f2cWmtmLorKepIIDCILgZOAPwCbgFcCZwIeT\n3AdAZ2fns8CzSdebS6x700CzdAsYGhraBNDS0rKpWWJutjwrx+lQntOhPKejGfLc29tbcCK/3t7e\njfliz2xbaI6/fGX9/f2bisVW6bbVxFzt8eZTyvFWopp6SznWYvtta2vb7mrc5s2bi9adluzPjGre\n+5Foy5YtxVdKsR6pTEmNRTPL7n/QASw3s3uBJ4BdgUOBNcBbSbYr6pnAxcAYYBXwhTAMr0iwfhER\nERERScaAmQ0sXbq0vfiqpWlpaanb/Z6jXalXFiezfdfTFdEDYBx+8JnF0evETgyAMAxnJ1mfiIiI\niIjURnd399M9PT0dVDDrQj7btm3bllRdUp6SGovZc5iIiIiIiIjk0t3dvQ0/zog0ucRa/CIiIiIi\nIjJyVDLADWbWBZwAdAHZYwA759xHqw1MRETS19PTszMwbI4wYLC7u/vJtOMRqVa+efqKlRUxpacn\n53h+gwnUXZV67VdERqayG4tmdjLwC/xVySeA7CGKHKDGoohIk4kaimvI3etkW09Pz25qMEoTGQ8U\nnHKiQNn4InUvIfc0XtvwI7cXqrtWE+0VPV4RkXJVcmXxq8A1wPudc+sTjkdEROpnItCSZ46wFnJf\ncRRpVM8C5Junr0hZsam0rMDviRWpe3NVR5Vf0eMVESlXJY3FXYCL1VAUERmZKpnjTaRR5Zunr1hZ\nKfUW+j2ppu5q1Gu/IjIyVTLAzQLgmKQDERERERERkcZRyZXFDwC/NrOJwE34ORa345y7u9rARERE\nREREpH4qaSy24+9b+Szwmawyww9wM6bKuERERERERKSOKmks/hzYC/gwsILho6GKiIiIiIhIk6uk\nsXgU8A7n3DVJByMiIs0rPkfjnDlzpk2fPp2VK1dOW7Bgwc7FptwoML8jvDB3Xc75HwuUQYPODzkS\n57MscExTqq27RnMl1kvRORprJVduNm3alLMsqTzW8rxoVE14TorkVUlj8YEKtxMRkREqe47GBQsW\nZIqup8gcjUXmdwQ/dx15yguVUWzf9TAS57MsdkxVVD0BajZXYr0UmqPxiFruuA55nAI8RPLnRaMq\ndr7Wao5NkZqppNH3ceBbZnavc+5fSQckIiJNqZo5GnNum7U9BeoeVlbGvuthJM5nWeyYKrUZajZX\nYr0UmqNxh1ruODtXAIODgzz44IO1yuMO1Oa8aFTFzlfNYyJNp5LG4neAacC9ZtbH8NFQnXPusKoj\nExGRplPNHI2lzFuXr7wZ54ZsxpiLqdUx1WquxHqp13ufnatCZUnmcSSe64U04zkpkk8ljcW78COe\nioiIiIiIyAhVdmPROXdaDeKou6Ghod2B3dPY15QpUyYNDAzQ3t5+2NDQ0KY09pmAgzLPQ0NDdQ2k\nVE2YZ+U4HcpzHnPmzJkWu9cwV/mhQ0NDO6e9bbUK7TuuGfKcQ0Ocz9W8h7U6Nw4//PADlyxZom2r\n1KjHU+bvSU1kf2Yk/LudqNbWVs2BLhXRQDUvOAPoTmNHY8eOZcqUKQCL0thfwi6vdwClauI8K8fp\nUJ6zTJ8+vVj59fXYtlqF9h3XDHkuoK7nczXvYa3OjRkzZlxWaUNltG1bq3pruW2FvyeJyv7MqNHv\ndlJyDaokUlTZjUUzu7TYOs650ysLp64uAsI0dvTcc89NGhgYWNTe3j577NixzXQ15nLgFKApBjZq\nwjwrx+lQnvNYuXLlNPzopfnKjz/iiCMeT3vbahXad1wz5DmHhjifq3kPa3VuLFu27DTgMm1bnUY9\nnjJ/T2oi+zMj4d9tkYZQyZXFw3MsmwJ0AU8Cj1UVUZ20trauBlansa+1a9dOBnj66aeXdnZ2bkhj\nn9WKdW/6V7N0ZWi2PCvH6VCe81uwYMFeRcrvPfroo1elvW21Cu07rhnynK1Rzudq3sNanRtLlixZ\nrm2r16jHU87vSa1kf2Yk+bst0igquWcxV2MRMzsYmAd8otqgREaTnp6efYHdDj/88ANnzJjBsmXL\nZixZsmQcsKa7u/uheseXSybmjo6OSbNnz2bRokUv6+/v30SRmDPb5Smu6ngL1F11HmtZd6VqOal7\no04oXc7E7FnLunp6ejpzVFnT96/YZOSFjqfQOQcMZOqdM2fOtOnTp7Ny5cppCxYs2Lne8zOW8x5l\nHe+om7i9kEb9HWwwU3p6evKVVf05KCJeYvcsOufuN7OvA98GXpJUvSIjWfQP0krAlixZQnR/xmVR\nsevp6dm10f7gxWPu7+8nDEOAG6PivDHHt8tTdcXHW6TuqvJYy7orVcNJ3Rt6QukKJxQHWJxnec3e\nv1ImqS8Q8+7AX8lzzkWPFoDYYBrXU917X61i506x472N0TNxeyHjoej5LN4S8v89qefvgsiIkvQA\nN+uB/RKuU2QkmwhYngl8jcacnLvSmHNuV+K2tYqp3nVXE1MtJnVv6Amly5mYvVh5Cu9f0UnqCxyP\nUficKzSpe70+M4qdO8WOdzRN3F7Is1A4j/K8Yb8H0BC/CyIjSiUD3EzNsXgccDDwVeDeaoMSGW2a\ncQLfSmPONSl0Usdb60mlG+09qscE6PVUzsTsxcrTOqZC71GxmIodbyNOcl7Oe5TreBvxmOqhUX8H\nG43OGZHaq+TK4pP4LjDZDOgFTqgqIhEREREREam7ShqLpzO8sbgZeBT4m3PuuaqjEhERERERkbqq\nZDTUy2oQh4iIiIiIiDSQ0XbjuIiIiIiIiJSgpCuLZvYQue9TzMU556ZXHpIkqdhcbLWaq60R9tvV\n1dU2a9YsFi9evGdvb+8TxfYbPZdblilvJ/+caBQo25qnzroqMsdbsZjzzWmXb37FEa1ILslXVsr8\nf5XOxVYgpjHVblvh3IE5t823LE3VzrFZq/nyqnjv6/LZXIpGnFuw0nkjq6m3UVWTi1rlsVbq+Xsg\n0mhK7YZ6LcUbizOBV5ewnqSkhLnYDgbuL1Be0RxFjbLf3t5e5s2bB3BfKfuNnssty5Qb+edEo0DZ\n4XnqrJti8wpSPOZ8c9qNOiXkknxlReb/q3g+xBJjymcK+ec2c0ViKjZ3YMPNLVflHJtF5x2sRhXv\nfeqfzSVoxLkFi8ZUoGx8scqr2bYOqsnF5Cq2rUsuajiPrUhTKqmx6Jw7K1+Zmb0EOAc4Fv9H9bxE\nIpMkTARaDjvsMMaNG/f8wi1btrB06dIWYGqR8mrmvGvG/VKorMh8ToXmcStUtkOFx1pLxeYVLBpz\nrjnvBgcHefDBB5OOtdEVy2Wl8/9VMx9i0ZgK2KHC44HicwcWnSuxDqqZY7PovIPVqOK9LzSf4dQi\n5bWat64R5xYsGlOBsmeLVV7NtnVQTS42VLFtvXJRq3lsRZpSJaOhAmBmL8U3Eo8HVgDvBn7lnNtW\ncENJ3bhx44bNP1ZO+Wjab6GyYvM5VTO/WCOqJuZcc96NZrU6Nxpx22rnDqzVHJzVqFeeaxlTsc+y\nesxd14ifk434u1svaf3eN0ouNIejiFd2Y9HMjgG6gdfju/e9A5jvnKtZ99MgCDqAi4HjgAHgG2EY\nfqdW+xMRERERERntSh4N1cxmm9kNwG3ArsDbnHMznXO/rmVDMfIDfN/1PYA3AJ8LguC4Gu9TRERE\nRERk1Cp1NNSFwCuBO4A3Oud+X8ug4oIgmAScBBwZhuEGYFkQBJcApwN/SCsOERERERGR0aTUbqiz\no+dDgSvMCo6B4JxzO1YV1fYOAFrCMLw3tuwe4MQE9yEiIiIiIiIxpTYWe2oaRWFtwPqsZf34ee0S\n09fXN570hmnOxN7e19cHwB133DF53bp1w+6knjp16gaAfGVHHXXUhnzb7rLLLm1r165ly5Yt2y3P\nvO7o6JjU39+ft3yXXXaZdumll7YBzJw5c9+99tqLVatW7fvoo48+kdZ+0zze7J+zXxebA66SeovF\n1NHRMf273/3u/tn7bW9v75szZ85jCxYs2GNgYGDYnIbt7e19APnKCm274447uvXr1yeaR/Aj3OUr\nK+V48x1PiTE/X+/++++/34EHHsjy5ctf9sADD0wplquuri7r7e2t6D0qtm32z/HXXV1dbZdeeunB\nAwMDnRMnTtzhpS99KXfeeefswcHBZ8o53iRjquZ3qNLzptTfz0q3jedqJOQ5ft5kx5U5nnzz2mX2\nW6C84Y63VvWOlm0h/2dzMxxPob/L1f49KeX3oNzPjK6urra+vr66jJrT2dm5oR77leZntb/dsDpB\nEBwO/C0Mw3GxZW8Dzg3D8OCk9tPX1zcXP3BPXdx1113cfffdw5YfccQRAHnLjjzyyLzbzpw5k2XL\nlpHrPTYz3va2t3HllVfmLZ8xYwb/+Mc/RsV+66GUXOyyyy488cQTw8qmTZvGm970Jq677joef/zx\nnOVA3rJC2+66666sXbs29TwWO17IfTylxJyv3mJ1T5s2jde97nX88pe/rOg9KrRtIWbGqaeeyg03\n3FDRe1SrmCp97xvx9w+KnxvNmOdi502+YwU46aST+M1vfpO3PN/29TreWtU72rZtxJiSOJ5C5ysU\n/ntS6PekUHmxz4xTTz21biOFd3Z2FpsaSSSniqfOSNEKwAVBcEgYhvdFy14C3Ftgm0qcB5yfcJ35\ntAOPAnviR3dl69atk7u6uoZ927R169YNAAXKNuTb1sw2HHDAAWMHBweHzQk0ceLEwY6OjnUHHHDA\n1HzlZvZcpt6ZM2cevNdee125atWqtz366KN/S2u/1RzvjjvuOGnGjBl3LFu27KihoaG1xfYLkK9s\nYGCgfXBwcNfssqj8ifb29oFK6o3HFM/xP/7xj/snTpw4ODg4uENHR8ewbz9bW1v7gMdaW1v3KFBO\nJduOHz++74ADDnimlJjjOV6/fv2mQscLsGXLlvHjxo3LOXdWCceb83hKiTleb3Rl8cLly5d/4IEH\nHniwWK4mTJjwWKHzplDMxbaF/OfGhAkT1mXeo+jb6+vuvPPONw0ODj5TzvEmGVM5v0Px8/nBBx+8\nq9Lfv1LiqmbbeK5GQp7j5032tuPHj+/r7Owcm+uzbOLEiU90dHQ80tnZuXe+8jFjxjxX7fHm+syo\n5rO5UFk19Rb7bE5jv9Vsm9Rnc5rHU+5nRrG/y4XOVyj896TQ70m83nI/MyZMmLAu1z5FGlnDX1kE\nCILgcmAS8E5gb+BG4D1hGDblADdRF4T1wI7N0i1gaGjoCOAu4MjW1tbhl/UaULPlWTlOh/KcDuU5\nHc2WZ+U4HcpzOpoxzyLlKnnqjDr7EDAErAZuAL7WrA1FERERERGRZtAM3VAJw7AfP32GiIiIiIiI\npKBZriyKiIiIiIhIitRYFBERERERkWGaYoAbERERERERSZeuLIqIiIiIiMgwaiyKiIiIiIjIMGos\nioiIiIiIyDBqLIqIiIiIiMgwaiyKiIiIiIjIMGosioiIiIiIyDBqLIqIiIiIiMgwaiyKiIiIiIjI\nMGosioiIiIiIyDBqLIqIiIiIiMgwaiyKiIiIiIjIMGosioiIiIiIyDBqLIqIiIiIiMgwaiyKiIiI\niIjIMGosioiIiIiIyDBqLIqIiIiIiMgwaiyKiIiIiIjIMGosioiIiIiIyDBqLIqIiIiIiMgwaiyK\niIiIiIjIMGPrHUAzC4JgPPBD4D+AnYFVwFfCMPxVVH4o8GNgJvAwcGYYhjdHZbsDFwEvBXYHDg7D\n8F+xus8APgnsAmwG/gB8JAzDDakcXAOpZZ6z9vNn4FhghzAMN9fwkBpSjc/n04CfAM/EdnlGGIaX\n1/aoGkutz+UgCGYC3wGOwuf6kjAMP1f7I2ssNT6X/wC8Mra7VuDZMAwn1/iwGk6N8zwefy6/BZgA\n3BVtf38qB9cgapzjduB8IMCfx78APhGG4XOpHFwDqTLPxwOfAWYAW4CbgLPCMFwTq/9c4APAOOA3\nwIfCMHw2lYMTqZKuLFZnLNCH/3CZDJwB/CgIgpcHQdAKXAeEwBSgB7g6CIJdo223AQuAE/LU/Sfg\nqDAMdwT2w3/AfK1WB9LgaplnAIIgeDcwpjbhN41a5/nvYRi2xR6jqqEYqVmOgyCYCtwA/Az/z87e\nwBW1O5SGVrM8h2F4XPw8Bq4B5tf0aBpXLT8zPgHMAg7Hf2l6FzCvRsfRyGqZ4/OBacD+wIvx+R51\nXy5Fqsnzjvj/z/YAXgRsBS7LVBwEwXuBU4Cjo/KDgC/V/pBEkmHOuXrHMKIEQfB7/LdK/wB+Cewe\nhuG2qOxW4FdhGP4waxtH4Stek4ELgF3DMHx9LeNvFknmOQiCnYDbgVOj51F5ZTGXpPIcXVn8QBiG\nx6QVe7NIMMdfBvYNw/CU1IJvIjX6bJ6K/wfzNWEY3lbL+JtFgufzJcDTYRh+Knp9CHAPMC4Mw1H9\nj0uCOV4LvDlz7kZfmn4pDMO90zmSxlZJnqOyo4CbwjBsj607LwzDH0SvXwdcHobhrtnbijQidUNN\nUBAEk/DdPb4LHAosy3ywRO6Jlpda3/HAr/Dfcg0Cb00u2uaVdJ6Bb+K7O60ptuJoUoM8z4z+OVkP\n/BaYG4bhM0W2GdESzvHLgXuDIFiM/+Z6Cb7r+qjqtpdLDc7ljHcAD6uh6CWc5x8D3wmCoAt4Ajgd\n+KMaionm2KJH/PVeQRDsGIbh+iTibVZV5vlVwL2x14dG68e33SUIgt3iXVVFGpW6oSYkCIIWfLeD\nv+O7kLYB/Vmr9QPtuB9WBAAAByRJREFUpdYZhuH1UTfUvfGNmX8nEmwTSzrPQRDMxt9n8KPkomx+\nNTifF+H/YO4GHI+/N/QbCYTatGqQ4y7gPcDZQCdwK3BtEASj+kvBWnw2x5wO/LSa+EaKGuR5Of7e\nsFXAJvy9ix9OINSmVYMcXw98PgiCKUEQ7AF8JFo+sfpom1c1eY6uKn4B/zmckb195udKPnNEUqfG\nYgKCIDDgQvw/aP8VffO5Ed+PPW5HYKDc+sMwXIW/72C03n8EJJ/n6D6EC/A3mm8rtv5oUYvzOQzD\nf0ePbWEYLscPBnBSgmE3lRp9ZgwC14ZheHsYhluAc4G9gAOTibr51PKzOQiCw/CDXfw8gVCbWo3y\nfAH+n+ldgR2ArwM3B0EwKhsyNcrxWcBa4J/AQuDXwBCjuJdNNXmOBhi7DnhvGIa3xoqyt8/8XPb/\ngyL1oMZilaIPlh8CLwGOC8NwY1R0LzAj+oYq4yVs3zWhHGOB6RUH2uRqlOc98N31wiAIHsd/iwjw\ncBAEc5KJvLmkeD5vY/vuT6NGDXP8D2BUd9GLS+FcPh1YEIbh6qqDbWI1zPNM4LIwDNeGYTgUhuFF\n+IFuXpxQ6E2jVjkOw/DpMAzfHYbh7mEY7o+/ReDvo/XL02ryHATBDPxVyI+HYfibrKrvjdaPb7tW\nXVClWYzq7kkJ+QFwDPAfWdNaLMQPXf+pIAi+jR+aegZwYmaFIAgmxNYfF71+NgxDFwTB6cAfwjBc\nHQTBi4AvAzfW9lAaWuJ5BnqBPWNlXcAd+BHLHq/BMTSDWp3PxwH3xM7nrwFX1/ZQGlZNcoyfmiQM\nguCl+HtiPovvxre8dofS0GqVZ4IgGIcf3fD9NT2C5lCrPP8NeFcQBDfjGzGnRes9WKPjaGS1+lx+\nEf7q1lPAK4AvAu+q5YE0uIryHA2+dAPw2TyjfF8GfDoaMGc9cA7qvi5NRI3FKgRBsDfwv0QNjyAI\nMkVfDcPwq4Ff8GOgG/9P24lhGD4RqyI+uMfS6HnfaN2jgK9EI6E+Bfwe/8/fqFOrPIdh+DCxRmHs\nj+qa0Tj/UY3P59cAPw38vF5PAlfi/2COKrXMcRiGtwRB8AngKnw3pzuBE8LROWdaLc9lgDfhr+Je\nV4v4m0WN8/xJ/L369+PnWXwg2j773rERrcY5Phz4Hn46iIeAj4VheFNtjqSxVZnns/Hdpb8fBMH3\nMxuGfmodou32xvdeamWU/v2T5qWpM0RERERERGQY3bMoIiIiIiIiw6ixKCIiIiIiIsOosSgiIiIi\nIiLDqLEoIiIiIiIiw6ixKCIiIiIiIsOosSgiIiIiIiLDqLEoIiIiIiIiw6ixKCIiIiIiIsOosSgi\nIpjZXDNz0WObma03s2Vm9gMzO7iC+o41s8/VIlYRERFJhxqLIiKS8QzwcuAVwNuAnwKvBe4xs1PL\nrOtYQI1FERGRJvb/27uDEKvKMIzj/0eGNDLaSAjOQgySSG2VSJtEsFUbFy1yEUKSqzDaSLnIokWL\ndtIsQiHctJRJXKQJYjIFLaSwrMEGo5EYghHMUKf0bXHOyGXOqLeaO+Pi/4ML597vnI/3Wz587/3O\n0FIXIEl6YNyuqq97vp9MMgIcBw4nGauqiSWqTZIkLTJ3FiVJd1VVN4DXgYeA3QBJXklyNsl0kitJ\nTifZPPtMkgPAO8AjPa2tp3vGn0oy2ra6/pnkeJInFnVhkiTpvtxZlCTdU1X9kOQyTYsqwFrgCPAz\nTYh8GTiTZFNVjQOHgGFgJ7CtfeYqQJJ1wBhwHtgF3Ab2A6eSrK+qm4uxJkmSdH+GRUlSP34FVgNU\n1XuzPyZZBpwENtOEv7erajLJJN22Vmh2HKeB7e2uJUnGgAngVWBkwOuQJEl9sg1VktSPAAV32kiP\nJpkCbgF/AeuBJ/uY5wXgM+DvJENJhoArwDng2YFULkmS/hN3FiVJ/RgGxpM8CpwAfgfeBH4BbtC0\nnq7oY55VwBvtZ66ZhSlVkiQtBMOiJOmekjwNrAE+ofnf4jDwYlV923PPY8BkH9NN05yuOl+76R//\nu1hJkrRgDIuSpLtKsgI4CNyk2T18ph2a6bnnOZpDb77veXQGWD7PlF8AG4BzVXVrACVLkqQFYliU\nJM1almRLe70S2Ai8BqwDdlXVpSTXgWvAR0k+oNlxfBe4PGeuC8BQkr00p59eraqfaA64+Qb4PMnH\nwBTNwTnPA19W1acDXaEkSeqbYVGSNOth4Kv2+hpwCTgF7KiqHwGqairJS8CHwCgwDuwB9s2Z6xhN\nq+lbwOPAGWBrVV1s38n4fju+EvitHf9uYCuTJEn/WqpqqWuQJEmSJD1gfHWGJEmSJKnDsChJkiRJ\n6jAsSpIkSZI6DIuSJEmSpA7DoiRJkiSpw7AoSZIkSeowLEqSJEmSOgyLkiRJkqQOw6IkSZIkqcOw\nKEmSJEnqMCxKkiRJkjoMi5IkSZKkjn8AJ3jUP6Nu8IIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911893077)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejeQ1ZLbQ21m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# uncomment the line below to print all authors and number of studies to easily search\n",
        "# print(tdf['Authors'].value_counts().to_string())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR4DiAh6Q21t",
        "colab_type": "code",
        "outputId": "22c8797b-a682-43bd-f3ed-f092dfe6dc52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "# check top 20 authors\n",
        "tdf = tidy_split(df, 'Authors', ', ')\n",
        "tdf['Authors'].value_counts().head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Aviv Regev                   52\n",
              "Orit Rozenblatt-Rosen        22\n",
              "Rahul Satija                 20\n",
              "Sarah A. Teichmann           19\n",
              "Fuchou Tang                  16\n",
              "Sten Linnarsson              14\n",
              "Danielle Dionne              14\n",
              "Alex K. Shalek               13\n",
              "John C. Marioni              13\n",
              "Itay Tirosh                  12\n",
              "Berthold Göttgens            12\n",
              "Ido Amit                     11\n",
              "Amit Zeisel                  11\n",
              "Alexander van Oudenaarden    10\n",
              "Karthik Shekhar              10\n",
              "Cole Trapnell                10\n",
              "Florent Ginhoux              10\n",
              "Rickard Sandberg              9\n",
              "Peter Lönnerberg              9\n",
              "Dominic Grün                  9\n",
              "Name: Authors, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5fLQHz_Q211",
        "colab_type": "code",
        "outputId": "639a3db2-5e7e-48ae-d539-10f988b6afd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# check total unique authors\n",
        "np.unique(tdf['Authors']).shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN-hs_XUQ22A",
        "colab_type": "code",
        "outputId": "d00650ef-ee6e-4bce-bcc1-1b3a8201f40f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "# check top 20 journals\n",
        "df['Journal'].value_counts().head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bioRxiv               116\n",
              "Nature                 63\n",
              "Cell                   61\n",
              "Cell Reports           44\n",
              "Science                43\n",
              "Nat Commun             40\n",
              "Genome Biol            20\n",
              "Nat Methods            20\n",
              "Cell Stem Cell         18\n",
              "Nat Immunol            17\n",
              "Immunity               16\n",
              "Nat Biotechnol         16\n",
              "Development            13\n",
              "Nat Neurosci           12\n",
              "Sci Rep                11\n",
              "eLife                  11\n",
              "Developmental Cell      9\n",
              "J. Exp. Med.            9\n",
              "Cell Systems            9\n",
              "Cell Res                8\n",
              "Name: Journal, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBzaoQprQ22L",
        "colab_type": "code",
        "outputId": "bec9c6c2-d7fa-4eae-81bd-cd524f80ca3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "# check top 20 most common tissues\n",
        "tdf = tidy_split(df, 'Tissue', ', ')\n",
        "tdf['Tissue'].value_counts().head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brain             135\n",
              "Culture            73\n",
              "Blood              28\n",
              "Embryo             21\n",
              "Pancreas           20\n",
              "Heart              18\n",
              "Lung               16\n",
              "Skin               15\n",
              "Whole organism     14\n",
              "Bone marrow        14\n",
              "Tumor              12\n",
              "Organoid           11\n",
              "Testis             10\n",
              "Kidney              9\n",
              "Spleen              9\n",
              "Breast              8\n",
              "Eye                 7\n",
              "Liver               7\n",
              "Colon               7\n",
              "Intestine           4\n",
              "Name: Tissue, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IT0dwgxnQ22R",
        "colab_type": "code",
        "outputId": "78d3910b-85f1-490b-ec21-2b4f05171641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "# number of studies with preprints in the biorxiv\n",
        "df['did_bioRxiv'] = (df['bioRxiv DOI'] != '-').map({True: 'Yes', False: 'No'})\n",
        "print(df['did_bioRxiv'].value_counts())\n",
        "cluster_fracs = df['did_bioRxiv'].value_counts()\n",
        "\n",
        "print('Fraction of studies in biorxiv:', cluster_fracs['Yes'] / cluster_fracs.sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No     409\n",
            "Yes    320\n",
            "Name: did_bioRxiv, dtype: int64\n",
            "Fraction of studies in biorxiv: 0.438957475994513\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-WTH1adQ22c",
        "colab_type": "code",
        "outputId": "578a3cc7-a864-4596-aa38-1c3c511c2642",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "# plot studies in the bioRxiv over time\n",
        "\n",
        "tdf = df[['Date', 'did_bioRxiv', 'DOI']].dropna()\n",
        "tdf['Date'] = pd.to_datetime(tdf['Date'], format='%Y%m%d')\n",
        "per_month = (\n",
        "    tdf\n",
        "    .groupby('did_bioRxiv')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()['DOI']\n",
        "    .reset_index()\n",
        ")\n",
        "per_month['did_bioRxiv'] = (\n",
        "    pd.Categorical(\n",
        "        per_month['did_bioRxiv'],\n",
        "        categories=['Yes', 'No'],\n",
        "        ordered=True\n",
        "    )\n",
        ")\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes(x='Date', y='DOI', fill='did_bioRxiv'),\n",
        "        data=per_month.query('Date > @ts(\"20130101T010101\")')\n",
        "    )\n",
        "    + p.geom_bar(stat='identity', color='grey')\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(\n",
        "        breaks=date_breaks('1 years'),\n",
        "        labels=date_format('%Y')\n",
        "    )\n",
        "    + p.labs(\n",
        "        y='Number of studies',\n",
        "        title='Published studies with pre-print deposited to the bioRxiv',\n",
        "        fill='bioRxiv'\n",
        "    )\n",
        "    + p.scale_fill_brewer(palette='Greys')\n",
        ")\n",
        "fig.save('biorxiv_over_time.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pwNuAC4L1IiIiIiIikkCSStpHgUnAFcAVZjaMnzDEgBuAs6sXnoiIiIiIyOhS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\nMeXTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911653864)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXwi40tKQ22i",
        "colab_type": "code",
        "outputId": "d25c5e86-a865-4336-8513-9388973145c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# display breakdown of deposited studies in biorXiv over the last 10 months \n",
        "display(\n",
        "    per_month\n",
        "    .pivot(index='Date', columns='did_bioRxiv', values='DOI')\n",
        "    .fillna(0)\n",
        "    .sort_index(ascending=False)\n",
        "    .head(10)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "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>did_bioRxiv</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>17.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-31</th>\n",
              "      <td>24.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "did_bioRxiv   Yes    No\n",
              "Date                   \n",
              "2020-01-31   16.0   0.0\n",
              "2019-12-31   27.0   0.0\n",
              "2019-11-30    7.0   0.0\n",
              "2019-10-31   16.0   0.0\n",
              "2019-09-30   21.0   0.0\n",
              "2019-08-31   16.0   1.0\n",
              "2019-07-31   19.0  19.0\n",
              "2019-06-30   17.0  23.0\n",
              "2019-05-31   24.0  18.0\n",
              "2019-04-30   11.0  12.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECUNWoTkQ22n",
        "colab_type": "code",
        "outputId": "60593ec8-93ff-4d2f-eafd-a429a62bf298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# check what kinds of measurements there are\n",
        "df.Measurement.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['qPCR', 'Microarray', 'RNA-seq', 'In Situ', 'RNA-seeq', nan,\n",
              "       'RNA-seq & In Situ'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlqtaqFhQ22s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove nan from measurement entries\n",
        "df.Measurement = pd.Categorical(\n",
        "    df.Measurement,\n",
        "    categories=['RNA-seq', 'In Situ', 'Microarray'],\n",
        "    ordered=True\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaT4u5XcQ22y",
        "colab_type": "code",
        "outputId": "a77e99ed-d801-4133-f4f6-469be2109652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "# plot kind of measurement over time vs number of cells reported\n",
        "m_idx = df['Measurement'].value_counts().index\n",
        "\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes( x='Date', y='Reported cells total', color='Measurement'), \n",
        "        data=df.query('Measurement in @m_idx')\n",
        "    )\n",
        "    + p.geom_point()\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.scale_color_manual(['black', 'C1', 'C0'])\n",
        ")\n",
        "fig.save('all_studies.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X9q5poXWNheYahmEYxvZlzpw5HHPMMYTDYSKRCMFgkKOOOor3339/S3GbnZUn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\ntfo/WvSMjxNcCn0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363263943157181)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPF9RcAhQ225",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make list of technologies\n",
        "df['Tech'] = df['Technique'].map(lambda s: t_dict.get(s, 'Other'))\n",
        "\n",
        "techs = list(\n",
        "    df['Tech']\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        "    .index\n",
        "    .difference(['Other'])\n",
        ")\n",
        "techs.append('Other')\n",
        "\n",
        "df['Tech'] = (\n",
        "    pd.Categorical(\n",
        "       df['Tech'],\n",
        "        categories=techs,\n",
        "        ordered=True\n",
        "    )\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MQuamaSQ22-",
        "colab_type": "code",
        "outputId": "fc1dbdc2-358f-4e81-d44b-606156016d4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# plot reported number of cells vs technology used over time\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes(x='Date', y='Reported cells total', fill='Tech'),\n",
        "        data=df.query('Measurement == \"RNA-seq\"')\n",
        "    )\n",
        "    + p.geom_point(alpha=1, color='none', size=1)\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(\n",
        "        breaks=date_breaks('1 years'),\n",
        "        labels=date_format('%Y')\n",
        "    )\n",
        "    + p.scale_fill_manual(['C0', 'C1', 'C2', 'C3', 'C4', 'black'])\n",
        "    + p.labs(fill='Technology')\n",
        ")\n",
        "fig.save('scrnaseq_studies.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dQy79NtgnuMBFJ9fFHq889uM/p2Y4nFVa1AxgzshOUwK81O7WrVu/nJSUVBtg\n7NixM2bOnPn7zkMp/qt3/1d7YLta+2oF+WT9888/fsnJyf/LrWvfvn2LgQUA48aN67Js2bIHQm5/\nzepduxnpcEpimmt9bOLy5OTka88U66b9J24DBgJkOpwOh9M12mqx6qeidnT9YcfR/tUDvffPHdV5\n7rIdR2baazT0c2WmcvTzJ/D19T0xePK2iUDW4Pf+eDT+RGZ7gJ9jjv22ZFyP74EXnE7nbP92195p\n8TF6WP8N6ak3vvnMyEBfuxMgxxjKOwlQ/t7WI5Xs1knTp0939u7du0bDhg3TWrdunZa/vfkNHz78\niYSEhFZetZsT+ZcVtm3Cz8t65NnrWr56fYfasjS1EJSDIb7m0N5n8j08NyoqalR4eHhnjHmlHTC2\nt9kCTIyKitocHh7+CsY81QDgP+BL4OmoqKhzfjgUh8PheBWQIb5CCCFEGcjJyeHFF19kz549jB8/\nnk6dirWGzynUgbVY512PcuWggxuSc9eqcjHMtzi2b9/O+vXr6d27N/Xr1+dA4gGid0Qz97G5jAze\nzoNdjN1ndEBtcsadeevPLzfF8dg32/Puf3rHJTSrbKFGjZMr3I4fP54JTzzHje+tJSEtm2Y1/Pni\n7i5U8rLx9NNPM23aNBo3bsx33y/hl3g4ke7g2ZsuIT09nVqj38arRsNTrmmzKGoEelM/2Jc3bmlH\nVf8zT8VMznBw6+x17D5mfN27p2dDxvRoQPdpv5DjMr7TPjGgOdvjkln01yHSd6+ljvMIbz1+L107\ntAag/1u/5Z1/SYPKfDa2CwDq4Homz/uNr9JaAdCkuh9LxvU45frrYo/zz9E0+raqQTW3dqampuLr\n64vVakVrzfx1B9h9LI1bL6lLi5oBeeVGjRrF/PnzCbp8GJV73Jb3uLfNwtf3XEazkABKm91ulyGC\nolwpkQRVKdUDaAmsMleAu+hIgiqEEEJchA5vQR3diW7cB/xKZr5nUcTExODn50fdunWLXcf27dvp\n1q0bmZmZ1KhRg+9WfMfoX0ajAhX73tjH6/Ud3NHRTFB9KpPzcMFbiFo2zeHvZbO4Pv0psrFR2dfO\nknHdqebvzbBhw/jiiy8ICAhg2bJl7KUmT3x7crHaBWMvpXMDY3irw+HAbrfzyvK/+d+qWLKP7OHQ\nnPEAeNVuQcNbHqdhg/rYLBYSMxw8ck1TwtoWfgmRwe/9wdY4Y12fJtX9mHfHpXSf9iP26ktQtiRC\nrTew64DfKee0CPFn8QPdAVix4ygPf7mFe6yLuI7V3JnzKE1VHO9aX0ahecTyKNsDL+feXo3o3+bU\ndjkcDp5++mn+/vtvxo8fT/tGwXkAACAASURBVM+ePXn66aeJjIykZs2aLFu2jG3pgTy+yEjygyvZ\nee2mtsz4eS81Arx5pFdtZkx/mXhHJTYE9sDl9jX8ufCW3HZpPUqbJKiivCnyEF+l1HwgS2s92rx/\nDzDTPJyllBqotf6xBNtYXnyCsdx4kV3MQ67yk1g9LpQLbHjZeQhFYvUYea+WilAk1hLjdDoZNmzY\nNXFxcSETJkz4YdCgQUklEmvNduia7QpbOpQSjPPaa6+98eeffx6slHLedddd77z99ttrCyp3ruf1\nlVde6ZWZmXkXGHNFx780/ifVT/UGqD2qNu/OO36kawNnpabVvTOt7W77mIK+z7icWJY+Oqeldtm/\n9nqKlf4D9/cfMbl+NX/vJ4DYjz/+mH79+tVt06bNiY4dO6b+uzGunkUx1aWxe9ssSZW8bI8CqQBa\na9WnT5+b9zUMv0rVahnoyjo5mDc7fhc9kn+e0abVpKSZv+ydoDXWPw+cmBnWtubGwsQK4OdtuxEY\nDBDoa/+uqp/XZy1b/X7Pv2n/XpG+7y526dN7YGOOpDo27T8xvlP9yknXtKrBT2MqB9Wau2BmhGMs\ne5zVuMm2gu2uBixy9uBLZwdIT2XCwi2OkECf8Z3qV87bKueWW24ZEB0dPQxgyZIlGRs3bhwXGRk5\nG+Dw4cOMGTNmeaOxb6UDNwAkpju459M/U7Od2h8g7kTG8q9feWWuy+XyXbUz/v17Fu7MyHFpX7tV\npZt/a9lupgQppX4GugIOjPVq9gHfAi9rrVPKsGniLIrcg6qU2g88orVeaN7fBywDJgLvAg201j1L\nuqFlzeFwTOH0ochCCCEqqrRjWLZ8hg6qh241qKxbc9GbOXMmEyZMAKBOnTrs2bMHi8VSxq06P/Xq\n1ePIEWPKa9++fVm8eDEAOU4XSRmOsw51dbd//366d+/O0aNHadGiBe/+713uXHknPg18cKY5md1r\nNl2adTlnPbYZnVBJ+wFw9piI68rHz1o+5nAKW+KS6NG4KrUr++Y9PnfuXO68806867Wlxk1PY7H7\nEBrzGbv++JGrr76a999/n+FzNrE+NhGAxtX9WJpvKO25/LH3ODkuzeVNqhpDamPm8+KyLWT/16fg\n2CyKHJdmct9m3HhJEGOXDueTHet5P3swbzsH0Zg4/qUWLk59TX3/QHeauq36+9RTTzFt2jQAlFJM\nnjyZV155BafT2HL2tdde48bhY7n9ww0cSMxgdPcGfP1nHEkZOQDcdmldngs3hhC/tCSGD9fsA6Bt\nnUC+vqdrkf4GJeVi7kE1E9QFWuv3lFK5e9hOA2oCXbXW6fnK27TWOaXfUuGuOIskVcfYdBilVGug\nHsZGzKlKqbnAFyXYvvLkG+Dv4px4Mf+inZ/E6nGhSK+iR1WUWOW9ev5s73WLVJkn6gG4YqI/cA56\n/6d8RUK5SGIthFA8HOunn346HOgHxpYz8fHx9wUGBs4sL+/VMWPG9IyOjh5QvXr1uKeeeurLgQMH\nHvHz83OdrTI/P7+JQGeA7Ozsr4Gv/jqYFDh6zsanU7JyatWv4rtx6bger1sVPmd7XuvXr8/ixYv9\nV6xYUefWW2+NbdCgQdbwP4a3/HHVj137dun7R5dmXfIWeTxx4oS1cuXKzoLa4+z1WB3r728N1L6V\nE11d798EPFtQrLma1wyguTm/8sddR2vUDvJNb1krIHXx4sV9gRFZB7Zy8O1hvP/pFxHDn59zwP3c\nxLTs+4AeABnZzi0YSQMTJkzo+dVXX93tcrn2z5o16/WBAwcW2KNYp7KPz5EjR7wbNrzk/vgjR1s1\nGPtqsq2RK8joJLNgt6rU0KqVNg25pO6y93/bN+RQUmZbgDd+3J0UZ/vt03/+s983POtJwtVa2tv+\nzf7PWtvLlXUyOVXgalkrYGXTEP8Pcx+bPn160wMHDtQKCQm56sSJE7W6du26IjIycnDu8SZNmqy9\n++673/by8tIrJlzO8bRse/UAb4eX1dLu8w0Hb6vkbU0c1LH2e0Cyy+Xy3X0k+f3cc/8+kpoMlMze\nSaJAWmsX8KdS6mYgBhitlEoD7gF+AUZjfN+/Wyk1EngMI5HdBNyntd4FeUnv70AvoC2wDhiltT5A\nAZRSlwDvYEyHzAKWa62HmceaAm8BlwJJwGta65nmMR9gBsZogQTgTeBtrfVF+4NCruL0oB4EIrTW\n88x9QSdqreuYx/oCC7XWQSXf1LIlPahCCCHyOB3YI09uheHsOALXgOll2KCLX0xMDP369SMuLo5J\nkybxwgsvlHWT8iQkJFC3bt28XjSALl26sGLFCnx9fc94XkZGBvPmzSMgIIBbb70VpRSf/LGfpz7+\ngeyje/Fp0J7vHulLq1qBZ6zD5dRYrOf+vnrw4EH69u3L7t27eeihh4iMjCyw3M5DKew5lkqvZtXy\ntl9JSEjg9ddfx8fHh4kTJ562vU/k0hg++G0fvnYL/7u9E21DjDmrmzZtolGjRgQFBTFu3Dj69OmD\n06WxWhQpmQ7e+Xkv2U7N/b0a5vUWV6tWjeRkY37piBEjePvtt1m1ahWhoaE0a9aMRYsWMf7VOXj1\nvIv0Xas49t1r+IR2JOTWqXntGdalLhP6NKVyJaP9uXNhAVrXCuTaDjm8s3YF2cd7kHXoH4598yK2\nwGpUHfAQjsQ4KjW+FC9y+Or+K/IWOJo3bx5jxowBYMCAASxatIhDhw7RpEkTHA4HAO+//z4jRow4\n53ORa8O+RO6a9yeW7BQe7t+O27o2PPdJHlBRelDzPf4J4IWxU8j7GMnoGxidd5dhJKr9MZLTScAY\noJXWOsuss6V5fAdG4thCa93rDG34HfgOeMm8Zmet9W9KqUrALvPx94FGwArgDq31CqXUC8DVwHVm\nVd9i9PpetM9XruIkqLOBAcB8YBTwidZ6onnsYWC41rpDCbezzDkcjlpAwRtznUNOTo5fSkrKrwEB\nAT1tNptHVhk+gxbAp8AwjDeAx0msHlfqcYLEWgoqyusXLqJYbW93iFDJB2/WypLm6jjiflf/V7fm\nK3LRxFoIpRJrUlKSZffu3d6dO3fOKE/v1eXLlwcOHDhwBflGpt10000PzJ8///eiXGDA+GmDfpw9\n9Umdk40tKETP+GjBwBH9LkuZNWvWry+99NLv9erVW/37778vAPhsyvqJKQmZQ612y95ew5re26Rz\njYQz1du2bdt7Y2JixubeX7BgwdWDBw9OdC/z6NfbOn3zZ/y7gC3I137g98m96tmtlmEhISEPJCYm\ndgOoW7fu13v37j3l14FmTy3/Q4MdoLq/1/drJl/5FECXLl2Gbd68eSKAxWLJuDpyyayYBMcDdqtK\nuKdnw/vH9W7yb/52BgQEfJ+VlRUCEBoa+tnRo0c7pqentzDvz4+Li7u66pDIGt61m5O5fwtHPnsc\nW3Btat/xDsrYw9Y1unuD4Y/3b573/KRkOixj5m4afCAx/dKENMfVWfExZMRGUbn7JA7PjyDrwDYA\nrIEh1L33g5Nt8VLrujSq9tl7wzr+Wq9eveePHDnS3zzkzM7O7gJw/fXXX7V27drB1apV27lu3bp3\nK1WqVKgv1rmv3+DPBy6wJ8QM0Rb7MVfPyXe5ekzYX5jzS5Ldbi/WGiseNSWoFkaiuJYpSYeKW81Z\nEtRpQCeM9/OLQB1tJkVKqfeBFK31Q+Z9C3AQI8f50axzo9b6YfN4ZeA4UF9rfbCANvyC0WP7nPtx\npdQtwMNa68vcHnsKaKi1HqOU2guM11ovNo9dDyyqCAlqcSZvPIIx57Qf8D2n9ioOApaWQLuEEEKI\nci3nwc2RzqufD3cO/WpAAcmp8ICgoCBX586dS3PocqFce+21yX369HnK19f3H4xt8VBKZXXt2rXI\nyUbiukXeOicbgJykIypmVVRQZGRk44kTJ3L06NFuGzdunDR69OhLYv44Uj0lIXMYoJwOV+M/lx24\nIX9dP+46Wjk2Id0LoFq1anl7bNpsthOtWrU6bRvSLQeTLsVMspMyHPWOpWQBkJ6enre0bEpKymnL\nzFbysuYlg9UDvPNuZ2Zm5k3edLlc3n8nOO5HOWwErgn5NGbOk6nZqad9D500adJj/fv3p3HjxovG\njBmzMDc5BYiNjR1it9uTHAnGd3yf+u2ofc3Ybb45KX+59vy+y8+uNndvXOWJx/s33+VwnhxdHeBj\ndyUdOxSYlJbZDsCRGE/Sql9I3fYTFu+TvcHOlGM4Ek9uRZqSrbv8uOvY6zfPWnuT3W53/3tZvvzy\nyyoA33777crDhw8/uG3btpmFTU5zqexU7AkxQwCUy1HdsvmTm4ty/kXLSE43Y/Ribjbvl7TaGEkl\nwAF9ao9dHdyGtZtDg/ebj+fa73b8BJAM1FFKPa6USjX/LTGLjAEqAZuUUtuVUmPMx0OBjkqpE7n/\ngEcxhhXnttH9M2RfsaO9wJT5PqgXChniK4QQQhSP2jQXy6E/cbW9FV2/W1k3x6NWrVrFypUr6dev\nH126nHthovx27tzJ5ZdfTmpWDo17D+GV554kMCWWPn1OLv6zcOFCBvQbyPyn15GdYQwrvvL2ZjS7\nLCSvTO6w2+BKdj4efQnNQ/x555132LlzJ2PHjqVjx46nXfuvg0kM+2A9WTkuOtYL4rOxXbBaFPPm\nzePuu+/Gy8uLhQsXcs0115xyXlKGgy83xVHd35vw9idziePHjzNkyBBiYmKYPHkyX2W15ajvB9gD\njX1YhzUfxsOdH+aLjXH8tieBvq1qnLKtS05ODg0bNsxbSKpmzZr88MMPPDP1BZJCOtLn6ms59NPH\nfPjzTqr2vR8wtpO5u2dDHl+0nUb2RO6qd4il+xRLMpubtWpcWRm4Du/CWrctzvRkEld+iJcrg1ce\nuZMd/8ax6biN/ZU75G0BM6hDLerF/8zDDxu7DSql2Lt3L3XquOcrxeByYpvRAZVidBA6+72Mq/OY\nc5xU8srdEN8pQTdgJKe5BjElaVFxqiqoB1UpFYjRo/k8kAbco7Xu6na8oB7UA8CI4vSgutWrMOat\nLgdaAZeY177yDOXz96CGA99WhB5USVALSYb4Fo7E6nEy7NXDKkqs8l4tFRIrYP186FWW3ctfBdDK\nkum88aMw3TzsxPle8Hxitfw6rSGpRwJcA6ZvKeJlS/w5TUpKslxzzTVDT5w4ETJu3LjPHnjggfgP\nPvggZPo/wW9l2/2bAFxSL/B1//UfPDR//vykqlWr/rZ169ZngoKCXL99sbt17JaEQZUCvf4ZNKnj\n57l1xiake13zxuq8ocV1Kvss/PnhntPO1Aa1f42/5deXe+pqzfd9FHRf4uYDSXUf7988o3qA9xxg\n2F9//RU7bdq0Dr169Yq9++67DxclvoMHD9refffdhtdff33cluwaVd79d9R8bU32A7Aqa0J7y1Mv\n/7LNJ69tlbyse74c3bZxw5pVetpstrQlS5YE3nXXXRFUbxxU47qHs6sEB+/5aGTnWXWDfR0AISEh\n76guw7oGdOiXW4W2W9XRRq59Ib02TOKJH9Lwa9ObamET89pUO8jni/ikzFN6K7s2DGZUg+QHZ86c\naRs9evSWJzf7vpaW5ewAUDfY99N3B1R/5+qrr342KSmpRdu2bT9bs2bN5xRBREREs08//XRkYGDg\noejo6Fl169b1SklJ+bXyP18Ota+fNUD7VdvvHL74K4xhyqWq3A3xPdmDWgNj250OxR3mm28VXwW0\nAyIxFnntAtzC6QnqVcDXQF/gT4zRo3dhzDPNnYPaAmM06S7gdaCN1vqKM7RhBLBUa31UKdUBWGue\nfwzYBkwF5gFOjLmtPlrr9Uqpl4ArgXCzqkVAd0lQcwsptRVzyEphaK0LvaFYRRAfHx+IsTJXUO3a\ntZNL67oOh6MTsBHoXFofPhKrZ5VFnCCxelpFef2CxOpp5TLWKUHjMRYfydWSKUnnndwVO9YpQcMw\n9jZXwEymJN1f2FM98ZwqpSIwFkkB2NNg8ncPYmzbV99sI3arWvjL/R1uoZCxhkZEK2A3xqIrAA/E\n+gxdDFTD+KLdE5jLlKRpTAmyYCwE0x5jCdyBTElakhvr7t27u7Vq1eo98/gJjEVaYgoZmzfwM8Y+\nlHFA1zZz2twCvJZbJjvx0r+zDt/YzP282zvX4L4edfJiDY2IDsDYQcLPLPJsbGTYFPMag7xrt1hY\n45bnbOZw3feA68Zao+t8/eEs/jzsQtm8qD7oCbzrtSHr4Lr9fi1DJ7sy630GoLWLqr7Wpbe39us3\naei1GS5njq/Vr/LBOvd+lKIs1pbm9TbERoZdWpiYz/K3OMTJoZvPxMXFvUEZvFcvGCU7BzX/PqiL\nMfZBTVJKjSJfgmqeNwaYDIRgJKn3a613uNX5B8b7qC2wHmMV3wKH9JsLMl2L8fo9BERqrT8wjzUF\nXgW6Yczj3gU8afbU+mKs/jsI+I8KtIpvYbeZ2UgREtSL0fn0oAYHB/ulpKQQEBDQ3uFwlPYv2gAt\ncleY8zSJ1eNKPU6QWEtBRXn9gsTqUeUxVjVk4V/Wr0btxJHegsA63+Q8+FcljOTnvBQUq3XeDQPU\nwXX3YvOJcw588zHd4rrE/OfZfKvcpTKOKwCtrMNyHI4P8pc5i/N6Ti3LIlpZti4ch9Ur2dn/lZd0\ni+sS69at2+XgwbxRgaF2q/rQ4dR541wVZF/XtuY6m812S2Gf13+mXstn6w48+NGafTdW8fOK+7zy\nDKv+W+1RaPfvfZHO5c/8q2+a+5/ty5Htc5uogxvdmuNwHMmNdeXKlVdhJKcAlTt37nyHw+FYUNB1\nExISrI8++mj7tm3bHp0wYcLBm+98qMcXs1/P/eJf57LLLrt31dBVX/X8oufGVEdqZwAfvwMBdi/r\nptRsZ95rwuGE4ODg9g6HI+1wUqbNy6rmZzt1bnJKgI9tgMPhiALIzs7e9+233/bdFp9SZa9f/dCD\niRnN9v6XVucPV0t6hnrx5+FMdE42+qc31tQc0GRn1oCsWzMTenyWlVlHZ+7bktNx43OWvxqN7vfi\n+kpg9/GtPWoa9qr16lpcjkQXVgBqV/bZ68j3mk3JdFgCfOxn3UbI/e+ilKqW2ylUv3791sHBwe3L\n6L16mnLXgwqYSWmxhvW6O9PwWbfjc4A5BTz+IfBh/sfdxGqtIwrZhuFnOfYPcP0ZjmVgzF8dA6CU\nalFQuYuRDPEtJJmDKoQQQpwHVw5YirP9eiE5HdheaYByGgsMOS+7D9fVz51WzLJuFtYVTxhNahGO\n88azfQctuhkzZvDYY49Rr149Fi9eTONgUPt/R9fvjm3BrahEY+FaV7uhOK97i61btzJgwACOHj3K\nc889x4/2rvybaCxO1KdFdaZc15KagT5nv2gBf9vU1FR2795NixYt8FvyIJYdJ6f0LXL24H85YTRt\n1prnb7kM6yfhVDq8Dm3zwTn0K3S9vEVFOXz4MF26dOHw4cN4eXnx66+/0qnT6b8vxJ/IoG9YODHr\nf8HLy4uoqCie/yOTNdNG4MpIxmK1surXX7n00kvZkbCDR1Y9QmJiTU7sG0KOE7xtFrJyjHzvymbV\nmD3cuMa+hHSufmP1Kdcad1UjNHA4OYuxPUJpVN2PvcfSuG7m72TnnMwZG3GQoNifGDLgWm649Xam\nrpvKoj1RpO56HrByw+YHWLB6H5Zb3kJ5+ZK8IYqqV9+Vd/49PRtSv0olBnWohc1qrOeUnp3DHR9v\nYuP+EwxoU5PpN7XFYjl3h9asWbN47LHHqF+/PosWLSI0NPSc55SWcjcHtZw708rApXDdFsDOitCD\nKglqIckc1MKRWD1O5mV6WEWJVd6rpUJi9aDTYs1MttimN1mptMsfQFdt9r+ce9bMKuhc6zd3XkZG\nYpAzfOZP+NfIKcJlzxpnQkKCtVatWr+D0fV2dbs60csHp/VS2uWvlSUVZclUrpxqANqvxpKcCTue\nBEhPT1f79+/3ql69ek6TS3p9V6nLzTVcWak0z4l98efvvvjqTM/rnmOp3n99OO7tm3IWd3JZvBIt\n3R+829UrYu/XX38dPHLkyLlZWVl1KlWq9M+hFzt8F5i47SGABBV8pEvGWyFOs3fQvuWbJfuXf3Bt\n17pWa5VaofMX/rozd/hti8TExE+bNm16ODk5uabNZvtv1KhRj8ycOTNvxeoff/wxYPbs2W379+//\nz7S/q8ze/cqNeSv81q0f+o31thk3OI7HKf79i571Ou8ae2XXl986sK3B5kTbE7hczgA/3y2pDnKH\nzmrMIc03tK3OS4Pb9rTZbGmHkzJt1765el6Gw9UU0EG+tj+qVPLa8W9C+h0AXlbLwe1Trr7+rnl/\nXrky5lje0GGLIl0p5ejfOmTyyG71t3+/7Uj9P9Jf7nMw688xqbsfRTuq8ET8BF5cvIfjzQYTfNVo\nctISsVaqjFIKiyLj5Rvb3nB9+1r/vfXWW3WqVKmSdfvtt/837IP1A9fFJj6be51q/l5LPhzR6fmW\ntQIzi/A6Ksv36mnKZQ9qOSYJqucVdg7qW0WoU2utxxe/SRefcjknyEMkVs+SeZmeV1FirejvVaVU\nLeAhjJUXp2uts0v6uuUl1tJQbmKdEnQl8DiwF5jIlKTTtlJhSlBr4Hhx5rWdK05zEZY4zB+0n7jC\na/7zvX2GuhV5GrgVSABGMCVpX77zq5jHcr2qtZ50pue1YcTiyF3eoyZ7q7wc+0OmJN3hZVW3O1x8\n4lbP1fqZwEpAzb5ZkRtjdP0NmMlg3P/u+jsnMT53Dmic1rpubqyLFi3aeMstt7g3MdxtRdFAjLmr\njVHWE/UnLQo8+M4Iiystb1T1Xw0mf7dHaQaPSvF2/eWdY/nb7uRE6jGXLbC6BSAnJSHFFlA1wCy/\nDtjqY7NkLh7b5n4/L6v7HNRAoDewM9ZnaNINWc8t2qyb5HbzZgM+QACwCmiXun1lbEL068erDpjg\n49f6qhbmKqyA1ragjUo7AtJykkMrNU7eoB/NeMs17vt0m778XhwtB+BWZ6/YyLA/lFKPAy8AOcBt\nDSZ/dxz4kVO9FRsZNl4pFQp4F2aObnx8fKBL6ySLUjIHVYh8CjvW5roi1KkBSVCFEOIiopSyaa2L\n0tNU3n2DsQAHQBWMvefEhW5K0s8Yi/Kc4XjQO8B9QBZTgq5jStKKkry81lorpa7FWPVz302t7O8C\nPYAGGPsqzmRK0tSznH9cKTUHGIWRqH581uthqR6vq9JQHcl9aB/AuwN9OtwfnUmWEwK9oXs96/7Q\nzPm7gfsx5tvl9sC4nKnHvwIeM+//7lY9bdu2xWKxZLpcLh+M7Ti2uR8GGgMou1dlXM4sr2r1vTPN\nBNWrVvPaQFtfDRlKWzZ5G9vhOFMTLLbA6gBkH9mTZguoOhxjv8d5sZFhKWYyfsrCVbGRYcnAIqYE\nWYGdr9rfazoyezKHqJrjwvJwbGSYBpJDI6IvzT6yt0nCd69ttgZUDfVv0zvfX0ypnKRLcGYk+x2e\nN479x+PUqkqBlpx0TeUTqQSdLOgF5K5WfJ/5vw24MzYyrG9oRPQI4H8YiTFAbaXU7cBcwKKUelJr\n/YJxRdXQ/Fv9au6XSWhEtN1mUZ87XRqrRX2e4/ozPDYyrPQWWxCinCtUgqq1bujphgghhCh/lFIB\nwDKgq1JqPjBcXxxzQ5qc4bYoJWav1h0YK5u+p7U+VgqXzZ1g6A2MAEo0QQXQWm/DSDANU4I6YCQo\nW5iSlFSI80crpaYACVrr1HMUf2ms45Er7rB+X7+mSvyut3XzS0qpSi/18R6z5g4/ft2XQ9/GNlpW\ntyaFZjIaeDvf+SeqDhj/5H/fTvsLowfSvdeVxo0bM2nSpJHTpk2rCfyotf7XXFn0fozk7CBQ1xZY\nI0NZbb6BXQaTFbcLnZOlKzXr9h0wOl3BUevJeaGu7ExS/lqWjTMnO23HL8OPfvnsD+f6m7gJBpo2\nscTzm894nFp9b1VaM4UApiSlxEaGZSulagJ2V1YGrpxsLDav0yrJ2LuRnONxAOSkG52XqVtW4N/u\nGqfVL9hqFtsQGhF9JbAByN3wdANAbGTYJ6ER0RpjxeAEjP003wDMnlrGAi8opdpgrPbqB8QopTpp\nrdOBvplJ//VL+P4NXBnJ/Xzqt5sEYS8W4e8gxEXNg6sVXFxkFd/CkVg9Tla29bCKEmth4+zRo8f1\nv/32Wzfz7rAHHnhgicPh2Hkely4XsXbp0mX2unXrHrVarWmjR4+Oyr9CZwkpF7GWkiLH2qZNm9Hb\ntm17AMDf33+Iw+E440qXBSlOrDavgG0qO6UDgKtWxzhn0Z/3oj+nT/wHRu9jYwp5TnZ2NkDV3GsU\nFOvBxAz7P1OvdQBDYDQADmj7tH4+9LVXpwZfGWrljo5e/HQ4YE2TJ2LrNnpzdbe9/50c8Rzka1sz\noE3N/z03dWoHmPqP+XBrt7haAEydOtU2derU1YC3w+HoVKtWrecOHToUBlC9evUVHTp0mFG5xxC9\nMZPHfRt1Dmrw4CeJQ5rywO3X9Ym9dfa6TqlZzvabgll7qZff3/9kZbYOtKTureWKWV+lapUTi0/s\ne6tKlSpJkZGRz44ePfromWJ1/1v+cl+DdXM2Jne54ZJazptqHw0HwrV34Mgch+Oepv1GvRnS647L\nr3QFsHrHYgJWfUl2z5sSnDYvi0tTGVA2nIeU1X7a97mcE4dI/+aZwwG3v5WbjFYNrVrp4W82bXr5\nrrvu2lWpUqX0qKio6NzPin+mXrsDY5sRrF+NvmJSd5/Lpq8xpqGGhITEOByOTp06dbpt06ZNuasP\nN3/ooYeudzgcMXf0aBAw7Yk3yIz9EwBX4sFHHA7H0kK9ODxA5qCK8qZYiyQppaphDF+5FGOj20Fa\n6+1KqfHAWq31HyXbzLInq/gKISqiFStWEBYWBoC3tzc7duygXr165zjrwpCeno6Xlxc224X1W+2B\nAwd48MEHSUtL49VXX6V9+/bnPqmceW/LezzzwDMk/mEMB/Xx8SE5uRSm4WWlYNm6EO1fE90izPPX\nO1+pR7BsnocOrItuiT8jMgAAIABJREFUdysAcScyGP7hBg4kZnDn5aE82veULUTJzs6mV69ebNy4\nkcDAQH755Rdat27NoaRMhn+4gX3H07mjRwMi+jUvUlMOJKbz9sq9LHh8KIf3GutDdejQgXXr1nH5\ny79wJMVYeXhol7o8e12rkyFk5eBrt/LQwi18OX8u/y2dgQKC/Lw5kWaeM3Qoc+bMOWcbsrKyqN+g\nAd4DHmd26DIGWtcCoO1+7Br8I33/t5XaTivDs4Pzzul+cyPa9KzD2piDjHjxY1z1LyF963KOff8W\ntso18arZlPRdqwBoXvdSvIdMIclqfDd+aUBLbup27s8727td0Al7WLAth5Sg5gx7axXe3t5s3bqV\nK664gvT0dJo0acL69evx8zPy1bBbR/yfvfMOjKLq+vBzdze9NwKhLUVIgEhHEKRErJGoIIgFguWV\nKFiAV42fDRU12BWDwVdQxI6KRAIIGgQRFEGBAKGTAAlppPct9/vjbsIS0kmwzfMPszO37myGOfee\n8zusX6Gy9bRv355jx4412E9roan4avzVaPL/ykKIAajg8AJgIzAa5SoDygViFkoA4J/GIiC+ORX/\n1aqKF45/y1w1ZdtW5t8y18bO84orrmDkyJHXHT16tN/w4cPXdezYcdt5dv2Xmaurq+t5tfvD8R98\n3t3zboSfs9+phWEL19VSpFXmOnTo0Jezs7PDAEaNGnUoPz9/ctW1v8MzeMOJDd7v7nn3B+8wbwp2\nFmAtt2I0Gt9F/T/baJo1VycPrIPuako3Nbmgv19D7KAvhLmsG0BRXgaG4TNG3vHBjmkn8sruBPjf\n5hSGGH3CRvcMqHYddnR05P3333d66aWX+oSFhaX27t07B6CdlzPfzxrBybwyhw4+LvVu5U5899cJ\nyacKZgZ4l3lm5rkU63W6TECWm6zdTb3C4dhBibRavL29nwFWZxdXbJRSuldmHCJhU8baZ8b1eryq\nLXcnA7OX7x68Zm9mXOkxtWP47jhnXvipgnxbmQ0bNqzCtglQ23297u0t045kl9zqZC4+UmjSDenQ\nPpgvLeVcofsdJ2ECN/+Pjx49+p60WjacdnEjq7iQNo6eVJrLK8yVlolA2oMPPhBpHRz1AIBT1yEY\n3LxK/K+PdjMXZlcbqL7uAUQUO7HX0YyfVUdQWuXtQPKUJduvzSmu6DDt0s4rbx7UoTrgt5rirPd1\nQlx8a6gD0r1ktdnJ6UlQMbxz587tsHHjxu4zZ87c4ebmVlRVZUbk5PamvIz4X375Jb1z586LaYGc\nnxoa/xSavIMqhNiIclW5HiWIVAkMklL+LoSYgFJD7NziI/0b869XVbwA/Fvmqinbtj7/lrlqf6vn\nT+jS0CSgj+3jvUmRSWelHGituQohVgIRto/7pJS9q679He5r6NJQNyAd8LSUWajMqHz08NzDLzW1\nzwsxV5vAzQCUwE32Bf39zvUyoN6xBEBJ8E0UjHzO69K3/piMzZi/Tre1cIHDggIhyAImMreg3m04\nY3SCAfBMiQnPraeMK8hC5w4f6svTJ4HVpepSCSqWEktx3tqTsVNuk1Lm2urcKFY++nHK/r0uBh1W\no7duwqHTlm/s2uwN7C7Zt1GXs+pV0ma5kppv5b/rK9ALsn86bukrpTzFXK+OUuewsKLDpdeJyuIB\nTlGJfxijEzoAJ6rayvrmxXXel06+wrFNFxEgc83hhm13zn1+wTIAvyvvnanz8H+q6NtXAzoF9CQj\nL5WisrxJUsrlTkHBtwWMf/wjg7svAOUn9sxyCgqOLju6IzD7a6Vb5ezgyv3jXjEHeAYZXv3m/pzM\n/BOe3qPv2OB1yYSrbN0fAS6yiTLZ3ysjKg61BPg/5hacpgEeeuih9p9//vnJjIwMUArBI6SUvzZU\nT0Pj34Cu4SLnMBh4S0ppQhmo9mQDbc57VBoaGhoaGn9hQpeGOnDGOAXofwG7n4NKp/E7SmTob0VS\nZFIJcDXwkd5F/38uXVxe+bPHVBtCiB7ALuBLYLsQwvuCDmBugRklvIOEgtLgiVVX/gfcDyx8wyEW\nIegIDASeqbUdG8bohK7AMeC0MTrhHftrQoj/CCGOCSHWZHz8qKvQF5c4eCQj9GdtTCei0jKl6N19\n/q/zo6smGaMTFhijEy4eHH/DqpT9e10AzFZ0nTzFs3b9hnlbTj/SJ3P1tg7de673C58VXVwpVwzr\naODnO93YdIfb00CxEOIiKeVCYTVd53x8I46ZOxfamigHKqraa3PDY8sNngHewJRs4fvRB5arg43R\nCd4Ap9e983bO1/MurzCVcSh9J0VleQCOxuiEAW69x7xXsucHSg9upWjXumMZHz/6htAb7nMI6JSP\nwUkClJtKrS9/fd/Mxz686dHM/BP+gKPQO1QZp6BUi92oydyCFOYW3M7cgumNMU4Bfvjhh8424xSU\nR+PQxtTT0Pg30JzAmxLAs45rnTg7f5eGhoaGhsY/jqTIJFPo0tCFqBQUhcD7F6pvKeVhbOIsf1eS\nIpO2UiOlyYUkNipRzIgLa8iF7DKUsi2o95tQ1DvQhWNuwWzmer1WNHCm1dTm4jQA2+7d2+q69VrO\nvJM1pBB8J9DBdhxljE6YC+SY8jOmI3SxSCtuvccYA2685oTQ5+Vg8nF06bDMuTLncqyV/qutFUG3\nps6/zgm4z/fK+2Z59L+2StRqctH1b3b2K3yJ02kqratHt37SGJ3gAYxGWpa7fj3DKeFoMS4GiOzr\nkPhJkW7C3NH6MUCReKawGJW31n9nhjVrxykLD64tx9dFDDg5V4RIKZON0Qk3AVFAAPCAztm9HTCd\nM+q612JbJJJSJgkhHkepNP8EfA7E6F09nd1CrgNAWi1djNEJU1Niwj8Evn468MT4U6dOffXee+9Z\nXUNGxaE37C9JUuLCxUnfl3sMvK5UCJ0v8E5KTHhDysqNYs6cOQfeeust/vjjD1Dvzgkt0a6Gxj+B\n5hio3wFPCCF+gOrwAWmTHX8QWN1Sg9PQ0NDQ0PirkhSZNCN0aegrQF5SZFJ+gxU0zsEmuvh/gAl4\nXkrZZFfd5cuXOz300ENXAkeklEfqKxsbldgHZQgExEYl3j8jLmxxPcV/xOZCjNp53I0t72drERuV\n6AEMA/bOiAtTeVDmFpwsVu7MtTEetXOaCTzRQPMH7I4zUe9wn+ndvG8SegekuYJ2U4NxDvzQGeig\nLxl84JGRY3om5SRFvTT6sUUAYj5rgBGW0rNsYf/9srO366RXYP9P6D3bsN8YfADYCXT1LE1j61Fl\n05WZIW6H6UVg3DMbK8ZKKct4RjwH+ANM/aaszZFcq7XMjK7UJB1R+VmnpsSEr7K5J6+w9RlaY269\n7D9IKV8AqtO2GKMTtlflXtVLKMtJxVJatLTznPL5wsH5891PPrbs9lsm4RY61uB3zYMAwQbvtrkF\nP3201pR19D0hdNsA75SY8LQGvuMGEUJ0At42GAyuCQkJ3H///WEHDx5MklLmnG/bGhr/FJpjoD4K\n/AwcAjag3HznoR4OkoYfkBoaGhoaGv8IkiKT/jzpzX8GHwLX2I7bA7c3pbLVamXOnDkrUUZdhRDi\ncinlz/VUeQy1GwrwGlCngSqlPCKECAX6AT9LKQvON11Q6NLQ0UBX4KukyKSzrLzYqERXVM7MXkBB\nbFTiJTPiwg6c24odcwv+4Ew8cr3Ycnfqbe0vASzATToHZwJueIzC31bg4HlG90k6HfOb3HMRk3tO\n/s2umT4AxbvW4dH36lK9u48r8FZKTHi6MTrhG/eLr7wBsJQrAamJAAUuQXQLcCGn1I2Ckmob7FJg\nLPAtylUdgD1Z1uNCCDPIrrZT6XZ919zxPsWZ9H+fVJ20bZj8B+UavERKaU6JCf/MGE2luSDrGYvZ\n1Cfn29dod+dbCKFrCzw4/eM/sgcOHMjm0jP2p/elk329L50clRITXiVs1FK75wuAcWazmejoaDZs\n2LDjQsaLa2j8HWhyDKqUMg31sF6AejAcAfxQqnaDpJRZLTpCDQ0NDQ0Njb8MQogxQogNQogPhRB1\n7ew1ls51HDeKnJwcLBZLVZ5eJ6Ch3DFpdRzXipTyBMpw/srmNtpsQpeGTkQt7C9GxXPWJATola63\nssq10utrt4pXbQZli5ESE/5BSkz4Iykx4ftTYsLNKBdYXLoNInDy8/vA8auqsh28Pb+tpYkYAEtR\ndsnJhZHjALeUmPAHbdduAi5HGbGLgSIAoTMUT4v4ZOnMa+ejE9XTMaPeH5FSrgDGAdHAZYMGDZo4\nadIkXF1dXwXm2vUdD7yMEql6BOiNisG+IiUm/A67ch8Cb6KEpF62m/vXJ9+5I7R9WqpZWk0IceYV\neOvR3Gc+Nw+hnafzl1ZTRZULbxygN0YnvGuMTlhujE4IBhBCnG9KFicXAwxsp0NvKTvPpjQ0/pk0\nK/mblDIfJQeu5QXV0NDQ+IchhBgEXAdskFJu/LPHo3FhiI1KfASYiTIAbpsRF1Zas4zt5fxroEow\n6BTKs6q5PAF8hDJYnrH10RXwk1L+Vl9FAD8/P3Q63S6r1doXtSP4gxAiDIhFuefebovZreJpW1+B\n2Iyt+hBCXAxUCQqNvuWWW/I//fTTRk+uBvZxwwNCl4a6JUUmlRijE0KAp1w8KZxe6HT8S7eKThbM\nmHWGcNT9eLO5HTaCq4EbgeMpMeE/QTihS0OvBCzfRHyZB9gbfkgp5wshlgBl0mopBjBGJwwFhgDf\npsSEVxvexuiEwahUhBt9DJ6b8fVk4sS3+DWojcWq1xcIg6OfXburgFUA6enp+QCvv/76s0FBQeUA\nQgg96vcRDDwhpVxr62MpMMEYnRCREhNelQpwgN2Q7Y8RQgSM7jP+gKfZ2js3cTEe/a6uNPgEGYQQ\n+lKTFUPw1aN1VhkI+KTEhKcZoxPeRe3GYinJ7y2EKAH6CSHelFL+t+aXaYxOuAXoAryfEhN+qrYv\n/PIu+ifeHecysquPzqXU0QQbov257UNtB1VDw47m5EHtCARIKc+RV7flSM2SUp5sicFpaGhoaFxY\nhBBGYBPgAjwuhBgspdz5pw5Ko9WJjUo0AvNtHzuidqYW1FJUAI52n53Pp18p5QrbLqyUUlqFENej\nVHMNQohFUsqo+urr9XpmzZp17auvvjoCOCil3CmEOIoyEkAZoTdVlZ8RF1aGinltLGclyy0pKXGp\nq2AjWA7cjfrOvrGpGQN8Zco7FZK+4nnmSl3Zg8OuYdLBRDJdfYkZfFtwVeV9+/YRGRl5VXp6+kop\nZWXVeWN0QiCQlxITXkkTSYkJLwU+FkK8LubzFWqH93YppcmWUuccpJTZdn0PBTYDeuBNY3TCNuCa\nlJjw3JSY8APY4l5joxJ/Ba492tGINEi9UJ53McDwRg71bqBqB/sKIUSQlDIPtXjwH9tYnkuJCX8K\n9bt9DbVgsbBGO0/8uOdrlZbptxUlRb+tCOj86KrTqOcdEgyp86+zAK+J+QxtN+2tIsdA5W1ctHNt\ne84IUs0RQrwtpUyx+y7u4Uwu36koY/ocbrj11pu6Gla6ALhWZmM5+fNIlEiURisghPgR+ExKGddA\nuWmonf8ywIqK0f4ZeLk2m0ejdWlOmpl3gCl1XLsVtWqpoaGhofH3pDu2lzXUImavespq/HOoQO0s\nVlFrvJ2U0op6+T4ArAeeP9+OpZQWW7sAt3Bm8XxqY+rPnj27WEr5hd1Civ3O73n5UEopf0G5iaYC\nS5YtW/Zjc9tKikzaBPRApROZEBuV6BQblRhhPp0WlPHxI5iyU7DmprhMPvgDOiTtSk/z7NbFVoBB\ngwZdc9VVV5Genv4FKm4TIcTVATc8VghkSIs51Rid0KXOzutBCDEEeAilkDsJm0F/MLOoMYsPg1HG\naRVDUMZkTSYC95Xp2G13LrMJw/SxO3bmzDMqzO58GICU8g1UnHEHKeXnNdqx2B2b2t210MwZwU86\n+bosByJR30OnnG9f7i3NlYeBU+bcE/bxyiWcq5rc1+64pzE64ZzvzxidELzMfMXDhVIN36xzwuzV\neZcxOqE57+MaLc9vUkp3KaUnMAgljLbF5pVxDkKIZnmiajRMc/4gLqH22AlQK2/D6rimoaGhofHX\n52fOpP/YC6z5E8eicYGYERd2CrXI/APwEvBBXWWllF9JKYOllFfu6xmsTw4OGVOxf/957aTa8VMd\nx03hVlTGgS+Ac9ww7YmNSrw5Nirxs9ioxDvqKiOlfERKaZRS3uXl5WWtq1xjSIpMOpEUmfRrUmSS\nFaUmvFK/7l0va0keAOVWK3lmc3UfXpUlewDy8vKuslqrT18phHA0eAW+7xY8wgNA6A1tqXvzAAAh\nxFAhxH+cO4V2MEYn9LWlgQGVJkkCuPUaTbtpb000RiesC3976883vLOV31Ly3OtpNh44S3tEWq2Z\nNpfcambEhZXOiAt7p0gnr0btUr2LShNzDjN/ndlr3u55hH8b/qrQi++EEMXARajfZg7wFHC9EOIW\nKeUndlWrj6WUJ6WUtRnAz6F+F1uBiY7+nQKBdkHk0EFkI5SHQPUCh+n0CcvxV8dfnhITHlS898fZ\nwCxswl62HVx73ueMsftOSkx4eS39d8vEV3dD5XM8aZrG8tBF9EqdswAwG6MTPtUM1dZDCDFaCJEh\nhLhfCHFKCJElhHi4rvJSyiwp5Yuo+zrfrh0phLhPCLEf2/0WQvQQQqwXQuQJIQ4KIe6yKz9NCPGL\nEOI1IUS+EOKIEGKS3fWrhRBJQogi2/heRqNZMajuKDn42rByJmeYhoaGhsbfDCllmRBiJCpfYrq9\nK+E/FSHENShxl3gp5aY/ezx/FjPiwpaj3FAbRXJwSE/gV8Dr5NTIg11+2oTOyalZfQshIlG7i4tR\nOS0DUfkra5abiBKvyb344ounrFlz7vqJlHI3KrayXmwpZz5BLdbfHBuVeHBGXFh9CsAtRmxUojPq\nN4er9YzmjgnyVhQW3H2nr98VKCXcOABvb+91WVlZd9iM1PVSykqdo0uhpSSvrd6tenNxX1392XaA\n1guDo8537PQKP4twusikz//vzPWTpZTfCSEi3S++8iG/ax4YgIpJBWBvehGvrD90xYoegUeAQill\nkX27KTHhqcbohB6oHdKxxbvW5Z5e+9ZrwCIhxN1Syo9qlD9F7Tus1RwuPPzZocJD5G3Pu5szywF3\nouJZe6Lcz4cAHH9p3FOdH101GKhIiQlPqq9dGwEocaZ4KeX3xugE3T36b5MfNXwWArBfP8Kxj/pN\n9EZtyCyWUh6vqmzbna2VlJjw7cbohM6o+NXUOop1ADgqgzhqCWKqyR+LzKlyc56Mck/e0oh5/OMJ\nXRraDnUPfk2KTKo1nrcZ+KNCGDqjBF83CyFW1IhTr8mXwHQhhJuUssqzZBIqprxICOGAip/+DCXS\ndjGwVgiRKqX83lZ+EGoxJwAYA6wUQvwhpTyEMoAfkVIuE0K4o357/3qaY6Amox5ea2u5dj1n59nS\n0NDQ0PibIaU0Ayl/9jguBDZBqFUoI2WmEKKXlFKLB2sEWWZTxGOnTnntKitnordXj7dTUnDq2fOc\ncra8j7cD+6SU39Ry/W7gf7aPtwNdpZSWmuVsvAX4Ar7JycnR5zmFNpztSdb2PNtrNDPiwspjoxLX\nA1dMHD7D8kb8rOSisvwi4L6Xs7J2ooSoqtmxY8fqPXv2MG3atJtPnTr1DYA0lU/KWf3mQt+rZvQy\nuPuVCZ1uoDE6YWVKTLhJCGGw/R1XMQLQOQZ2x9e/i9NthU44IbytyFWxUYn9pJTLjNEJQdQQFQLY\nFvvQYJSYVaEQ4mop5Vb76ykx4QXAe8B7Qly3GnV/AF5ECWBhjE7wAxzrEg6ypYbpAhzp80EfXwCD\nx1mvqBJYc2OwwWVTqoXTZdUZZ4akxIQ/V9f3nBwcorONvV+mybTYNs62tj6llPIT69O3Fm12deaR\nNv5Ijt0eEjf2m9JjD72XEhP+WF3tCiHGAA+jUi4+IqWssH0Xhagd6brYhdrM0QH5e36M98bzUjVB\ni1kKvSGjnrr/GmzG6U7U32hW6NLQfi1kpFpRIluVwDbbLmg/oD4DNR21s+7NmdCHmKqsJUKIESgX\n9Gdsz63tQoj3UK7iVQZqrq2OFVgnhFgL3IxK01kJdBdC+Nty4f7aAvP829McV4I3gP8IIRYKIQYJ\nIYJs/8aiAtVfb9khamhoaGhotBoXceb/QifOiOtoNMCDaWm+W0tLKZVWlublcbjk3LBVIYQjSnTr\neWCFvWubHfZxzp1Qnlp1US3Qo9frs+spV9tYOgohpgghuttO/YhKkVeO2t2Ir6tuKzEOGBfo3WlA\n53c6TO3zQZ8X+nzQZ29dhfv06cP27dvXVnk1SCl3BU6cm+PgGeAtdLp2QLQ0Vz7u6uSRCJiE0K0X\nQlRtaX8DFJry0vEtN1mcULu2OoQBteMDsIwzL+rftfd2/uLVm/qQtm/7WNs5T+CeBuaUWvPYGJ0Q\ngUrpk2aMTphds4IQwh8V67cX2OJW6Rbt6eCJV1+vfU7tnJ5FuRALwOWVK525d5ADAAYdEpXPtT6m\nodR/bxSC5ULo2l5/yT3cf90rXDsocgKATrDtbR9vSnQ6iio76EpTo5YDh43RCe/X1qDtN70Slb/3\nAZTbb6NIiQn/BbWDFt2nresVRYe2kbPqNYp3ryf7mxcKU2LCtcUxxSUo4xTbv5e0ULu5NbyCSqn/\neQMQhFogybc7Z/87bw+crLGolmI7X0WaXZx9Vf2q6zcCocAhIcRvQojrGpzFv4Am76BKKT8UQgSi\npNrt4wfKgGgp5dKWGpyGhoaGhkYrswqVVmUgSl+huXGP/zp2lZfvqDoWQpi8AwMdainmw9n5TfsB\nX8RGJXbanbJl9k97491QsZi3otx6Y6WUNcVn7JmAev/InT59+nwacBe1G18g6j4HoNzy+kspjwC3\nCyGigMuADjPi5LHGtNcSzIgLqwBWhS4NnYByrRbAahrO5WpPm7M+7Ns27nhlsW0XVI5FuUuvkFLu\nFkL0tJbmG8sqS8wlwmOlmxRBKHfX9QApMeHpxuiEnoBnSkx4vk3Fd5Krq+uR0tLSHrYu7EWOamMO\ncBrw9Bh8Y5wxOuFz4ArU4g/AbJTCrj1XosTZAAacmnfqma1btwIMC4oMKhRCDEYZg5wqsvJcmDNT\n+jpgsfJFSGzxigbG4wfwcV4eW0tLXC7vc+ORK/rd3A2gR1C/iNioxKAZbZllcXS9Asw9TfkDwOpc\nFT87zRid8KBtV9QeB85Wd25SLuCUmPBNwKb09HTPn9rdzfeTJ5eU7E10xZZmSQNQu4hZ2HZQ+XN3\nFScAO+zce8EWs20jDegghNDbGalGzs6z3F4IobMzUjsBvwPYFILH2+K2bwa+FEL41ejvX0dz86C+\nLIRYhBJE8kM9jLZKKbU8ThoaGhoafxuklEU2FVNf4LSUUjZUpzURQriiRFyGAouklI83UOVPQ0r5\npU1k5JKIiIjN7du3Pyc+T0qZKYT4FKXOmwN8HBuV6JhdkLblgx+eb19pLkcI3VQprf2BnCq3uXr6\nPIRyAyY9Pb0phsHFKOMUlFbGEOCIbTfsJ5ThXOoc5Hz1RS9e1BvITYpM+qIJ7Z8P4UBVIOo1oUtD\n9UmRSXW5ONckWkhr/NWp2zz9ygpK8g2di3YIHVJaEQgcDE7pVQWllBlABkBsVGI3lPfAEft8t6nz\nrwsCZor5pGVnZ2/18vJiyZIl902ePHkYcBKodxNCSlmKcqnFGJ3wDipWz55dtVTbjXJzdASKe/To\ncajG9XtRsZmGeT9VfPnJeH1YDz99NmqhAltfAhUH6wR8mhITbhZCuHZ3dDwe5eu3//mszGCAkVZz\n16o6NgVWR+YWmGembLh3a8bWxK8LS7fm5FWLfR4CimuZY4kQ4j5b/wc5D8/Byy67jCeffLL7c889\nVymlzG1uO/80kiKTToUuDe1Hy8egNhohRAAqD/Cd1L9o9Ctqd/VJIcQLQB9UnPRtdmV8gUeEEK8C\no1ALLo/Znj83A6uklHlCiHyU8dvYv/9/LM2WR7YZo9+14Fg0NDQ0NP5hJAeHdEa5L20O2Z+c31D5\nPwPbqnbOnz0OG5GceRn6PyHEh1LKv6y2g5TyFYC6cmYmB4eI3T16vr88P//L57Iy10spi2KjEttl\n5B9vX2kut7VhdQRCpJRfteJQf0PlmuyKSm9SJYbVBWWcAri2vaXtBmxpU0KXhnZLikx6sRXHVEVC\nv7TLpwWUdBTHfJJ+W/HfDxr9cpoSE74xOTjkFeBZwG23b//2H7n6UlCSQzvfLvnpuUe31VZvRlxY\nOVCbqNBabCItY8aMefv3339n/PjxeVLKeU2dlOn0Ce/c79/FUlaIf/jsUseAznOxiT7VIBn1wj4Q\nWLt06dI0gOfXp/olJP/xn86PrjqKEi4amAy/+8SE1+bW+yLwqO04rNOsL57Tu/v+cLg41/jfjFNm\nh4AuBIx/nOOOruJUSeGedg6e7sDCGXFhKQAj2o8oGNF+BA8PZOZFT67rgjLel6bEhNeq2iylfBel\nRHzeREVFlT777LPaBk8NbEbpOTHrrcxgm2q0FRVL/DMwQkq5va4KUkqTEGIcKs1mFioM4XEp5Tq7\nYttRu8HZqHjUO6WUB2wG6q3AmzaxpRRgkpSyNgXofxVa/h4NDQ0NjVYhOTikF2p12R04kBwcMrB7\nUkMegv967FNXmKhlB6c+evXqdW+vXr3Yu3fv/QUFBeedo7QxxEYlRrl5O15x+R0htO3qiRBiKCrm\nNOu3i3qUu+l0027x8eEWH5+ZQOyMuLBTMZF5X7f16Tw+Iy8Vg84h1Ww1bWit8dmEsEwoA2gAkCSl\nrIpfTUUZSCEALl1d7NOjDG3pscRGJV6CSuOTC0TNiAvLjNr6piO2HdSup/v1jY1KDJwRF9aUHKFV\nMaRsP/x9x4IStdaSnnvUGzWvanVfY3SCASUUNBL4MCUmfG7VNSGEQCkpA5Cbm2vvmo0QwrPTI/HX\nC6F7xzb+cSmWAUMWAAAgAElEQVQx4bXtiAKQsey/0lqhvBRPLX2wUppN56TPEEK0BzYC3YClUsqX\nq3bG1+zPTbCN3567jdEJXVJiwmsudo2wOx5prSzfZinO9XMMCkbv4Wvw6Ht1sYN3W3cJLHOR3qnz\nwzrWNe6UmPDWXCjRuMBIKUfbfWxb49pQu+MPqCe9ll05Ucu5/dhUueupNxvl4m5/rhKb+7rG2WgG\nqoaGhoZGa3E5ZwQoegLBnB27o3Eun6Ne1i8B3pdSpjVQvhqbYRhji9+bJ4T4qSlpc2KjEkOBhSj3\nsntmxIUdbESdScA7JfmVrFm4h7F3BnuixHY6AFiUy2cVEahdBvw3zH7js7b+Zb+4iRPRJ1Oek2eX\nazE6BfRcCky1fXxwX8/gxcCzycEhbYDnpZT7hRDDUUZc27yNeQSEB4AyaD9ohSF9gtrFBeUWeAd2\ncaQC4YSKacwUNpnZRrS5EPWS62aVLMfmAo3aAapp6N6M2qUHeNoYnbDCzshsixIq6gfkT5gw4XNg\n3M6dO52HDBnyC3CJtbzEonfx0ANuOskTsVGJuSiXxWUz4sLOWhCxVpScSUljMdd1f6eifu8AkUKI\nZ9PS0nIqzFas8hzjFJSSaiBnC9aAcj2+FGXor9e7+0z3HDYJn5Hq1lvNleXYnkVCiHrdyDU0NP58\n/hIGakRExEyU0loosCI+Pn5yI+tNR8l8B6BU+NYAD8THx2uuEhoaGhp/Pj+iVBJdUWIsB7DbodE4\nF5tB0tydT9can92aWP9dzuwavo0Sr2mITlUHpgoLuemlnpwRxGFnWdnJke7uVfd8FYCHXr9wTZeu\n9/pZS4lwcyaiZ/B+GohtbA6xUYkXV5hKq4xTHA3OkSjBpqpdjCFAT1vs1yXAfazOOvl9aOUwD6s1\nwFXK1oh7sxeScrT9+z5wAypX4sKZiy4/MnMRnwE3CSG+B65PS6t7nSJkf/KG5OCQ9oDrq9lZp14T\nYg0wGPhESnm6RvEyu2OJeneqYhFn3J23v/rqq8kAY8aMWYSKq8NSlKPXu6h090MqDO7ATbby82Kj\nElfNiAuz31F9ApV3MhCoTglkc/v3CdmfvBMVw1lFMfB1ly5dDiclJeGoFx9VWuTttvN5qPyVn9ao\nA0BKTPj/jNEJG1Hf6REhdIO9h99a7XauMzj6oxZHvIC5Neu3BDaRm1dRhvJiKeWi1uhHQ+PfwF/C\nQEXlGJoHjEU9zBrLOmB5fHx8bkREhAfq4RoD3NfyQ9TQ0NDQaAoh+5OTkoND+qJcEDeG7E8uNplM\nf/aw/slscHFxifP19Y06ffr0e+Xl5bXlK29pPgCmAKF9+rkIn7hZYVN9fOI+zMu7Cch85FT61F8u\n6tEHKArZn7xZCOHlr9ff62c46/Wjey3ttgQ9erTvT1bBSQBCfTuncMYAA2U4ASClPA5EM9drHhbL\nrbbT/Znr1Ya5BXXuYtrixh5ECUa+IaVsyDU3EpWu7zTwGMCMuLBCVOoRAGYuYgxqpxPgKlSO+Vrv\npTE6QQ/04oZXTlblGJVSfoLaqa2NFaj3rcuk1fpR6kvj7OObfWseSykpKSmpTgOU/fU82kx85jsH\nvw67BlUYdgJXAwxw+4pBbss/ZG7Fl8wteC42KrHz29N/eBhlTH4KnAJIDg65HvgSMCQHh7wtpbxf\nCDHVyeAypNJcHiWRfSsrK/suWrSIH+fMmXHpW3/MQ8XtFQBeKTHhdQoJpcSEVxuuxuiE4UJviAJe\nQcUUr0iJCZ9ZV90W4lbUbwFgkBDie5tStMa/mMa6DmuczV/CQI2Pj/8aICIioh81DNSIiIjBqBWp\nUJT63ONV5ePj4+3l4AUqqLm1/qPT0NDQ0GgiIfuTD1N/EnSNFkJKKdPT0x8FooA5QUFBTXWnvgfl\nLmoGGvUyPyMuLAfoW5hdODhrys3bKlNSHoxuE0h0m8CpIfuTl9mKnagqf+PQqIr4be9lfJyX1/Y2\nHx+KLZYcd73+vdraTg4OGQh8BjgDd4TsT/6+tnL1sHbS8Ad29Gw/YKChsujAAxmrB6N24SxACXB/\nLXW87I49UDly6xMteo4z4jyjULtndTIjLmwD0LeBcWfY+qyKh02vrZAxOkGHyt16LZBrjE64LCUm\nfF9tZatIiQmXQoi5KAM2TrzMTcD1UsoK4L+ovLB6bLk9hRC0bdv2u4yMjGsAaS7IzE9/L+ok8H8u\n03+wAn07Ov4RPszjoz6ohaiLmeu1GVa8XmOeZbFRicPClPtx1bvnNOB+KeWy1+5KuPPRpTcazJaz\nF7BSYsIPxEYleqBEoAyxUYnzZ8SFNah2mxITXg68YYxOWIvKY7kRIDYqsa1tjh1RuUyjgNKQ4W0f\nuWzyRQ012xD28cuixmcNDY0m0CgDVQjxVFMalVI+27zhnE1EREQ71KrhXcC3KPeXNREREcnx8fHJ\ntjLhqAetJ8qVbEJL9K2hoaGhofFvYkZcWBIqH2iTEccPOZlSUuxPDULFolYTG5XocHnfiev6dhne\nduOelcUr0rY9/2WA60sh+5NrVUpFeURVLTq/BfSqo1ytzIgLK46NSrykf9eRAaM3PnCxsGUeKHP2\n05v1zv8dsuP7ZbVUi7GNvTMQzdyChhR17V3WezZlfHUhpUwWQtwEjAfWSyk3fzH/l4sBclJLv5PW\n/ffNiAv7AxXTfa2tmi8qzUpjcmmO5Ez6l6tQscHLpZS/cCYetFqZef/+/U96e3t/grqfPqh3ssQZ\ncWGfANHMvXEtYC9y5YjKA2mPCyom/WfOuAX/DBAblRjg5OAyOjLs//juj49xdnQ9On369K52dd/h\nTMqOENt4EUJ4oWKd99vlnzyLlJjw/cB+u1OPA2G24zkoQ9L96B8501vAQP0IGI5KwbhYStlgDLeG\nhkbtNHYHdVaNz46ohw2o+AVn23EZUIFa6WoJpgDfx8fHV8lM/xoREbEC9RB+FiA+Pj4B8IqIiOgE\nTEfJyLc46enpTtjF1TQRj6p/09NrXQhtFby9vd30ej0Wi8UtOzu7SYmkzwNtrq3InzRP0Obaqvxb\nfr+gzbUxlL4Y4289ccLffWHs/oZLn8OfM9eQEOE+7jqKv10FUKDr0uXrmnlKvQKdLi7IrLjM3zOI\nCZfe664z3NfpJ4N4eO/Lv6669LaOJ2q2WeTZOcijMBWASidPUUve0wbneuNTwQClpS+NPWT6bl3m\noa43Bp7oeDlI+ea+x35Ku/L+bpvPqnBPchHKaFPU6LPmPe3atWvc0aNHLwfcvby8YpqYm7VO0tLS\nEoFENYR0z9yTZa9aTBJgqBAsCXlyzQbUu1n1Tmugh8Oeqv6XLFnism7dujZxcXGp3t7eZ7U9cODA\noh07dlTX69atW35t466aq4ODg+ugQYOSt2/fLrGpDQcGBuqr69yT/HvbJQNeF5aK66Te6buMO3/f\n6vjyoacryywvo3ag9UCpV1unX7wSf9hTePPNp6ioDHCaOnX5tvX7/RH8iIT+XUfSv+tIi3c7p2fc\n3d2XYruvQnBRtVSUoHt6errn+PHjuwoh1ksp/Q0GQ+IPP/wwISQkpK6FDgA2fZDaTacXHayWascC\nC7b3YAdnfZ7Vaj2v55ItTnhO1edG/BY8KkotbHj32GwpDx+8Zlb3Vc3ptyUICgrStFs0/lKIpuYk\nt8m1f4Fya/nSluTcA2U0PgHcLKX8rTmDiYiImAsEV4kkRURELEQlyLUP4jcAy+Lj4++tpf5lwJvx\n8fG15mM7HzIzM18RQsxpuKSGhoaGxp/B1q1bycvL48orr8RgaLkIlnnz5vHuu+/Sp08fPv74Y3x8\nfFqs7ZqYdu+m8L8PQ3k5Ttdeg3t09FnXpZRUJm5AlhTjdNVVCKfmrpu2DpYTJxDe3ug8PM65Vllm\nYe1bB6koOXuzy9nDwNUPXISD09kekQnPbKPT3q/QWc0UX34Ll9w76LzGZs05zdcL0nEsyydk/zJc\nHcpp8+QcHPopT9TKzT9T/NJLCFdXPJ59BkOPxul5VVRUYDKZcHd3b7hwM9n8USqnDipRXJdAJ56t\nOCNi27+9O3cNa8+QTsoeSklJISIigszMTK655hqWLFmCTqc7q73Vq1ezevVqRo0axcSJExs1hqVL\nl7J8+XIuueQSnnjiCVRWmrqxWiSmCgunj5fi2cYZd19HSgtMJH2fgRCCi68IpLLcwncLzkQAXDTM\nj37XtAOU6NaO+HTy0kopLTQDcMmEDnTo7cWCBQuYN+9MatYtW7bQrVs3auP48eM88/DLXHHRXeh1\nBpzcDHi1caLbEF8yjxRjcNTRa3QbHJwvvEfuuoWHKchQr7iDrg+iy0DfBmq0Dm3atKn/ZmpoXGCa\n8z/428DLUsr3q05IKYuAJUIIF5RK2pAWGt9x4LP4+PhpjSxvwM49pSXx8fEpa+hhrKGhoaHx57Bo\n0SLuv1+FFE6cOJGPP/64RdpNTU1lwYIFAOzYsYOvvvqKhx9+uEXaro2sHzdCuXphrVi9hg7z5iEc\nzgi/5i56l+K33wbA+ttveI2fQOWBA7hfew2OnTvX2uYFpT7j3QdumONG6u7TFGSVkLxFZfsoLzJT\nflrQpvfZdX0uCmK/ZQoAA/p2PP+FAR8f/DsVE7DmA7wLDrMvJJIdX5QSUlTAkAgjKbFvI/Pzkfn5\nmD5cRkDs2+fX33lSuHIlOS+9jCEggGHPvMIvUofFbGXohG4s+uJ3MgsrALi+fweu6lstpkxcXByZ\nmUqrac2aNRQUFNC1a9ez2r7tttu47bbbaAoPPfQQDz30UJPn0bb9meMtHyeRdqAAgLS9hegddbj7\nOFGcV4HeQUefER3x8VGLGztWp3IiSZXV6WHqi0NxdFF/CyNGjEAIgZSSoKAgQkJCcHM7W7RaSknK\nrtM88+QLOJQHotepV96KEjPXRA3CydWB0BH8aVjM1mrjFKAkx9qqi18aGn8nmmOg9gWO1XHtCDYp\n8qYQERFhsI3FAOgiIiKcUa4XHwE7IiIixqFSyOiA/kBhfHx8ckRExJ3Amvj4+FMRERFdUcp0TRVR\naBRCiC9RybybjNVqdSkuLn7P3d39bp1OV9ZwjRbDiEpX8DiQciE61Oba6hi5wPMEba4XACP/0N9v\nbGxs1yVLllzeuXPnk8uXL1+j1+uNtMJcX3/99dnAQICVK1eWoAR/gPOba3FxsZter19gsVicAA4c\nOPAOsLmBalUYaeJcZXnZqKqx67y9U4WDw//ZX89funQ+thyjZZt+Mpdt3GQAyFu8uKDTqm9n6du0\n0VXN9dC2LP/TaSVt+l3Rcberp6Pp+N5c/5wTxQG9RwYddHI1NBRb2WikxYLl9OkehjZtnq5rrtbS\nUl32vHkjkFL0efLJnw48tSwcOXwytoXfbV8kb+v0zIg37euMmNTdZevXR8c4OutL+1/VaSM1cug2\n576Ouu0it5OJFY9lthnUJavNQAB2rj+Bp7/zY05l5XdjW+Q2nzq1CZUZoCZGLtDfatbTc5dgsThV\nFhbCc9E/j1yyeHjVXJ+4Njhw8eaUK9p6OWdMHtzxrPeegoKCfqgUfLi4uOQ7ODjMRoVfNRUj9czV\nYpVELNw6/VBm8fBAT6f9X06/5JVAT+fK+ho8fbL4+Vyd1VgqJB1MYDZZsZis2X1GBy1r283rZEAn\nj8yq+5pxrDAeW7yp1YrZbJJ3O7pgAggLC+Opp57qtWPHjs7Tp0//zc3NLadmX9++uXtSxpHC60+n\nlpNfsoMxoePR6fS4eDgcdnA2PN2UubYGQodL14G+7x3dkYvQiQpPf+cYakmho6Hxb6Q5Lr7JqFx2\nN9onkBZqe3ElcJGUsrbkynVic+2t+bBYGh8fPy0iImIg8BJKGl4Cu4HZ8fHxOyMiIuJQ8uueKMn2\n1cBj8fHxeU2aVCMwmUyvYBdboKGhoaFRP6WlpRiNRvLzlTtiXFwcd955Z6v0tWzZMu6++26klNxx\nxx0sWtRyKQg3btzI4sWL6du3L7Nnz27QtfF8KdmwAVN6Oh7XXYfey+usaycmTaIiWYWmVjp74Vhe\nUH2t8+rVOHTsoMol57E2bg/SCm27ejLk+i4kvJ2ExWQlqIcX4TNDW2Qe5pwc0u64A1NKKh4REQQ+\nP6/WclnPPUfhF8sB8LzpJpyCe7LyeyfKnf0A6DPEi0unXHze42nUmLOz2f3sErbbBHeFgElPDsK1\n/DSnY2PRubni98CD6L1aJkQ6KyuLlJQU+vXrh6OjY71l09LSWLduHf3798f70WjMttha7zvuwH92\nTTmQulm1KoGjPxXj69SRnkOCGHyd8XymUCu7ThZw06Jfqz/PH9+b8f3b11MD0g/lc++Hf2Asgd4m\ntUfSxuiBMdSP5K0ZtA1yoJ/bfgy+PjiNvpxNnx6iIKuM/ld2pGv/gCaNb+VrO8k8VsTXW+NI3L2c\nDn7dGRg6lE++/R9rvkvgu+++47LLLuPmm29u9b/p+sjLKMXF3QFnd4eGC7cSDg4Omougxl+K5uyg\nRqNyWB0SQnwLZAFtgHEo1bub6qlbK/Hx8XOpI3FyfHz8DpTyW23XolAS4RoaGhoafzGKi4urjVOA\nEyfO0cFpMaZMmcLFF19MXl4eo0aNatG2R40a1eJt1ofbmDF1XvO8+Way5yqh1qMD7qLL9sU4VRZQ\naByMocMZ4+DEvlykTTIm42ghR3/PxmJSJ9IPFlBebMLFo35jqTEUxcdjSkmtPva55z+1uhpXJCVV\nH5cnJRHwWDTDdrxCco4/nt3aM+RmZSyaTpzg1KzZWHJy8I+OxuPqq85pC6CoqIipU6eyb98+Zs+e\nzfTp0xs9ZkNAAAMWPAprj5NxpICLhgTiFeACdKBtzItNmH3D7N27lzFjxpCfn8+IESNYt25dnfHR\nBQUFDB8+nPT0dBwcHPj5009pu3kzhoA2+N7btFedPp2Gkl66j9JSC398d4JOvX0J7NI8gzvnRDFO\nrgY8/JzPOu/j6oBBJzBb1V5FGw/n2qqfRdBF3oQMCGDV9nRMAnycDFx7nZHVb6vfR9cfXuF0nnJW\n83voIcbedfaCVqXZyq/Hcmnn5Uz3Nu6U795N1rPPIZwcCXzuORztXJm7D2pD5rEiThdlAHDy9GGK\n/shmz77dTJw4ESkl//vf/1j67qfMfeh1/Dt40G1A04zg86Wo3ESqqZKLHM7/b1FD459Ekw1UKeVK\nIcRglKF6PdAOlYB5G3CTlHJnyw7xL8My4PfmVNTcXi8IRv4dczWiub22Kv+WuV6IebZp04bhw4ff\nvGXLluu8vLxOhYWFvQB400pz7du39vSS/6TnkteECWAydbHk5bnknwyesPWSucEOpmL0AYEHBgjx\nbNVcnVwNryF4EIne1ctxv8FRtxJ4BBBOboZUB2fD49RwmW0O5qysQdiU/nUeHggHh+c4O60HAA4d\nO42tSN4/DcChU6elwtFx/UUv/R81E3uceuDBqMrDhy8DyHzssVKPq6/6T2393nLLLRHr1q27GeD+\n++/Hzc3tkdtvvz2tKWMfcPWZuM2cV1/rV/DJJ/ei01X6zpzxpk9kZF25e4004Z4+8sgj1+fn508C\n2Lx5M/Hx8bPHjx+fWVvZTz/9tEt6evo8AJPJxIsff7z0888/XwfqN1xYWPieu7v73aveShqXlVp0\nuaun45HrZ/V9w9HDwRSz9uBAg1nqbu/Z9nDHYJ/czGOFfVH3G4DTJ0seD+zi2eB4a8518+eHn9+3\n+RQILKGj278xbHzX6vegTr6uRA7r1H/jwZxhwW09kkd099tQX2NVRA7t7L77ZOHt+00Wt5ljun1u\nMVkqgNcB4Vl4ZoiF33zzu89dd75qX3fsG5sfO1VQ3keAZfrILq/c+MyMydaCgs4A6VH37jau+25+\nVdneI4NwdDG0z3C9pvveBVunmM1m52HDhn324YcfZkgpZwEY9A6EGaexa7366eSml3w2+Drjt038\nnprFrhP5gTM/2/laRmElHs6GU19Nv+TJLv5uF/L5pKHxl6XJLr7/Vkwm01zOdUPW0NDQ0GgAq9V6\njoro3x1pMpExew6lW7bgNnYsgS++gGjCHE0nTpL51FNYi4sJeCwalwFNF5/PPVXCT58pO+qyyd3x\nbed2zvWi0+W07+mDwUFHVmoRBVlldO7ji6NL/evTiYmJrFy5ktGjR3PjjTfWW7YwPp6KffvwGBeB\nc++6U5WaTp4ESbUbcm1kz59PwUdK4MrQvj2+9/yHvKUf4nTRRbR59hl0rq4ALFiwgDlzzkTdjBs3\njq+++qrecdZH6rgIqvK4ugwZTPvFiwGw5OVRefQoTiEh1X03hU2bNnHllVditVoxGo3s2rULFxeX\nWsuWl5czcuRIdu7ciZ+fH5s3bz5HmTY/s5Qv5u2o/jx8Yje+Kipk9c8nuLnYCTcp6DE0kNG39WB7\nQippB/Lo0i+Ai8Pqd72ti8Wzf67eee86wJ+xd5wdwSWtVrKeeILi79bhMnQo7V5/DdGAG3NtHPw1\nkwO/ZGA8sAKXn74Gg4F2r716ljdBSYWZfvMSqz/ffklH7vz4OSr27AHAbfRo2i14q9b2S0tLKSsr\nw8/Pj/LyckaNGsUff/yBl6s/z0/5vLrcgKs7Mijc2OTxN4dvd59i9vIzngVLIgdwWXf/C9J3TTQX\nX42/GudloAohOgIdgV1SypIWG9VfEJPJ1Bfo3Zy6/6TV+4bQ5trqGNF2FVuVf8tcz3eeRQmrg4rW\nrOnnOnToAe/bbzvShKpGWmmu0mQSJ6dMnWo6eTLYuX+/TUELFqyB1rmn2S++OLjgk0+rJU197o16\nwe+++/Y+9dRToXv27Onw4IMP/jJq1Cgv6phravh1D5qOHx8CoHN3z+i6dcscgILlyztbCwqdfe6+\n60BzxtUSc42Pj28zadKkl61WqwGQ8+fPnztr1qzqHcWcV17tX7Zje6jrsEv/8Hvg/qo3bCMtcF9N\nJ086Z8yec4u1pMTL4/rr1+QuWPAkttybriMv+yQoNjYBID8/X9+xY8d3Kioq3AA6deq0+/Dhw/Pr\nabpejl0+9glLVlYIgEOXLls6x6+MLd2yxe/UAw/OkxUVnjoPj7SOXy5/0iEoqF1j52mqsOgsZqvu\n3cXvdN62bVvnGTNm7Bg2bFhBbWWP7coJ3PTJoVklJSVe2fq966+bOmL9wIEDi6quV93XkmzrnE9i\nfnrpf2uf0ucXnOCp0AFHloXd79O7zM13SMWZGMYJj/af7tfBvbi534cNY0Js0vNp+5WbvrGv39Ir\n7+61zr5A7rvvBucuePvJqs+ekya91ebJJ37lPChct66LpVv3eV5djOf8hi+d/+NT2cWVPQF556Wd\nX51pOZR1+s23pmAwmNo8+cQHrpdeerqx/cTcddfodrt2jREDHnQpdzK2d/V0ZMzUnq+17+m9o+Ha\n58+6vRnG2V/ueb7CbMVBL0qWRA58dGgX3xbXUGkMDg4On/wZ/Wpo1EWzDFQhxD2o3cR2KBehwVLK\n34UQK4AfpZRv1tvA3xBtB1VDQ0NDYc7O5vj1N2AtKgKDgY6ffYpTz55/9rAoWruWzIervRrpuOJr\nnLp3b5W+yv74g7SpkQBUCsHa6yPYk5bGRx99BIDRaCQpKQmnOvKUZjzyCMVr1gLg2K0bnb5ZQcHy\nL8l+9lkAvG67lYAaOVAvFJs2bWLs2LHVn5cuXcott9wCQPm+fZy85VawWsFgoNNXX+HYtUurjMNS\nUMix0aPBrHJg+j/yMN5Tppw1znvvvRcXFxfef/99QkNDa23HlJ6O6eRJXPr3Pytljz3mzExOxy5E\n5+iI7/0z0Xt5Ufj112Q9Pbe6TPsP3sdl4MBGjT39UD7fvbsPi8nKyFsuosclgfWW/3HZAQ5uU2l3\nHJz13PHypXWWfejeR1i4+A0Wte/AZe7ufNJzLDu6hRNeqnYu3X2cmPz0YHT6FhDBqrRweEc2rh6O\ndOpzbo7OyiNHOD5+gvo9AO3fX4LLoPPLV1sf5SYLGw/m0MHHhd5BzRexklKSevU11SJUbv99goBb\nb8LgcGE9PQ5lFrP9eB7Duvpi9HNruEIroe2g/nMQQkwH+kgp72+h9tYDz0kpN7VEe42lyTGoQoiH\ngPnAa8APgP1q2o/AROAfZ6Ci5Objm1PRbDa7FRUVbfLw8BhpMBgu5E5zMPAxcBu1xAS1BtpcW50L\nPk/Q5noB+Fv9fk+/8eYAa1HR/2wNkR0z/+kO7y9Z1cjqrTbXwhUrrgSqVW6K1627yal792OtcU9d\n+vfHeeDAcabU1KHTDx/2/nX+/KH211NSUtiyZcv9Y8aMWUAtc3W99NKA0q2/PIbF4uY6/NLXgf2n\n33gjFhgKUPD5F9kB0dFXN3VcLTHX3r176319fV/Lzc0d4e7untSrV68ooBwg9+23w7BaX7Z1Ru7C\n2JltX3llK61wX/VenrgOGXJF2a5dt+o9PA67hYW9BJjMOTmGsl9+9R429vKyzZs3bzIvW3Z36RNP\nDsns3Xt74AvPn7X7lfXsc6GFX365CCmddB4e240/fB+lc3E5Z2XeEBhI4LPPnHXOlH6qK0J8hJRO\nGAynk3eVzD62/vdpxr5+Y0LHtJ/q6GzYW9fY17+X/Lap3DIMYNOnhzJ6XBIYXt9cM44WzgJuB7CY\nreko4clq7O/rL39suh6Y08dZiRLdeuB7Qiuyfz8x8oHNbSt1Tr1HtovX6UWGff0f9md5V5itumv7\ntM2tbxw1CDY46j8OHta2znvq2K0brpcOu7wief/lDp06/eoyaNDKJrRfK/X9hp0d9FzVu35jvzGY\njh1zMqenb6nuc82KdYbISVdygZ/B/o6mTRP7t7vQ70v/KoQQl6LslqoVrMPAU1LK1UIIIyp15iEp\nZQ+7Og5AGhAAuEgpy+2uPYSKmb5RSvmN3fmqtqruZR7wKRAtpbQKIfaixGQBnFAbfFVpmT6SUp63\n6KsQwhF4Etv/I7ZzApiJSl/WFSgAdgDPSyl/EUIMAGJQadp8pZQ1FyteBF4ALmjW4Oao+N6PsqTn\nCSH0NTO3FAgAACAASURBVK4dAP78ZXQNDQ0NjVbDe1rkvuLv1yfL0rIQ4eCQ7jX55i0N16qdwvhv\n/XNjY+8SDobSwPnz33Pu3bvZLrjt3nhj/fEbx19syc3t72g0Jvjdd985ObuLViX4m9LT3Xzv+U9q\nc/uposMH738LfJvs7/+e3WkroPP39984cuTIs4yBjKOFbr+vSR3u294tZegNNxx0HTrskYLPPzd+\nkJmhb/fJJ/4jgoJ+qSwsHAqg9/X9JW369Csrjxwd6tyr14Z2b7350/mOt7H4+flZMjIyHtyxY4fL\nwIEDz7offrNmbSn//Y+d1pKSfjpPz1/9//vwb605lqBFceuB9VWfi1Yl+Gc9/fRiWVnZQf+q/0/u\nsW+z5s23Fv5QVOg4ZNfOu2/28Z7i//DD+yv273c+9cCDD5lPnx6KlE4A1qKiQSUbfvT1uPaaRrmB\n+s2ccRS4vey33/p4hIdv+3GL7j2rpaTd6bQSMo4URoTPDK3TQHVw0p+sKFU7vwYHXYPiTZffERz3\n/eJkg9lk9e85rO3/6iubkJDw+ahRo3w2VlRefoPB0BkwD+3is+zGhwfXusMR+cH2q7YcyX0W0H2w\nJfXFL+655OuGxtMUgt555wfUhkWt7Nt8qu2eH9Oud/F0PDnugYsT7K+VbtvmLk0mndvw4YUtOaba\nOPJ7tu+u709e7e7rdPzKu3ttdujUaanp+PFIDIbTXrfd+j1wZWuPQePCIoTwBBKAh1ALaHpgCOcK\nxEkhxKVSyqr/y8KBXJSBWpM7bNemAd/Uct1fSlkuhOgJbEDltn1PSlkdJiiE+AzYL6Wc28x5CUAn\npayZz3q8rd2TducWAlcA04GfbefCgRuAX1A5kj8D4oDaAvk3AJ2EEH2klHuaM97m0Jw8qOXAtVLK\nRJuBagIG2Vx8LwdWSSlrVwD4G6O5+GpoaPzTKM6r4OCvmXgFuNBtYMPpFUp/+43SzT/jOmI4Ln37\nUpmaikP79s0Sj6niZOQ0yn9XwqCeE8bTZu7cZrfVECU//UTGQ7OQlZV4T4vEf07LpLZesWIFU6ZM\nwWw2M3/+fMaOHUtISMhZwlAVpWa+mLedsiITQgfX3N0D+cIsXtiQyOLcXBwdHVm+fDmjvH2wlpag\n9/WtdiFGr6fT11+dlULjfCnbuYuib77BqVcIXpMmVZ8v3fb/7d13eFRV+sDx75sCoYQOUgICgiT2\ngqKoyEbXsrpXF9HfinUVNRpdK66997JWIIpdwbLYrgUbsSAgIoqgJlRBIaETSAhJJsn5/XHuwBAS\nkkzmZgbyfp6Hh5m57byZmTv33HPOe75n/bPPktijB53+858a39vK4uIGve91Vb5iBStGXU/5ypV0\nuvZayleuZM3DD29Zvub8iznmpmsJGIMAH917L8eOGsWaxx6n4IUXttlXsz33pOdbbyLxVe+t185U\nGp6/ZiqVFfaaKXVwV4acWTUHcUi5yyr4+YtlBEor2P/YlIhM6VOdsiVLiGvZkoQuXWpc5/RnZzD7\nTzv0dY/Orfjk30f4UpbqmErDG3f9QOFa2wA15Mz+pA7uCkDRF1+w4vr/QGUlXW67lTbDhvlajrfu\nncWGVfZ+yx4Hd6J4Q4De/Zqzz3F9a+z6DVC2dCkrb7oZU1xM51tvCSuhWazbVbv4ishA7NDD1jUs\n741t9bwZ6G2Mudh7/T1gBrblcEsLqtfaOBPbW/RNoIcxZlWVfYWu/z9glTEms8pxq62gisjJwF3Y\nls6FwOXGmO+8Zd9he63+BdvaeYQx5qcq278G/GaMuc97vjcwB1tP22bdav4WqUBONS2oiMir3n4j\nOw/XDoTTgroUe/chu5plg7B3CnZF2sW3DjRW32m3V581lVg3bypNfv+xn77atD4AwG/f5t/+9yv3\nq7Gb7rqxWb3XjRnzJpBQ8MIL5e0vzfhndS2UdbBNrKVz534AdAco+vyL6V3uuOPyMPa5Q8H3tOCO\nO780ZWV/ASh45dXiTtdee1Qk9v+Pf/yDb84978hWP/yQkTjx7dVd9trrrri9915PSKxv3fPDuZsL\nA8cDmEr4+LZHv05f+MvRb3jzxJaVlTFy5MjJy5cvvx4g79JLjwEeAqCignXPPJvR9cEHam2trMvn\nd9PUqW3yL8v8iMrKlrwNmyZn39D9mazPKwoL4/IuuvhLKitbbwY2//TTc7u77tjq9lFN5dSXz/Cy\nc869uXzFimEAK64ftbnVsX+9DhgNQHx80deL41sHvBvtBnj644/vPXbUqHeKJk26EW9e9qVlZWX3\nl5TMrFy+bOanlZWvJoZRQZU4oUP3VmeuWVZ0dXL75vE9BrS7DtuyUK3ff17baf73K8+Pi4/b3O+Q\nLs+3SG5WUtO6dVHT+9qsd+9at125sfQm4DSADZsDnwE31vGwdX5PKwoK4vP/feXJiJhuTzz+UXy7\ndhUAa5YVtShcW/JtcL2fPvtzQurgro8CrLrl1ucJBA4AWH3f/UvaDBt22o5ibYjl8wuSN6za/FXw\n+aJZawDIXwgbNsWfd+QZ/cprinXZOec+Wrl+/VCAvIsvWbLHDzNPi0SZoni9tFPISU3rhq1XzEjL\nzckPczfzgRIRmQBMAGYYY1ZXs95rwCwR+TeQDAzGTqd5X5X1/gVkG2PeEZEl2G75/63uwCKyF3AU\ntntxrUTkUOBFbPf+77EtnB+ISH9jTHBC8fOxrZ+/YVuDq9oPCO0h8VdgUW2V0zrIAQ5o4D7qJZwK\n6jjgDhFZzdY/QqKInASMwt6F2OUkJibmY+d7rbfVq1e3AVi/fv3P3bt3970bS1AgEAg+zE1MTAxr\nDtf60lj9FY04QWP1WzRizX7v157ByilA/sINbXd07HVjxqSw9TcjYf3YrEDXK6+sd1mrxmoCgWuB\nF4Diyo0bR/kRf/A9rdi0KRt79xkqK2fU9Vg5qWnx2OEry9Nyc6rNwtpmypSPgK6Vmzenrbjm2v9L\ny825ODTWzYWBLb+3M+Z/Zj7+5qkhB/fpy75JSXxXXBws52fBMhV/O/U3bJfDY4D3iz7++LnE/z5a\ntTtXjbHu6PObn3FpP2BLDbN42rTKxMTEHxcOPiIR2NKUFPh9ycZgeW7ZrWure1etPBRYaoxZXHWf\nO/oMi0g34J/YO/Cf1hZDqPIVK34HyAsEeG39+sqXnnoy7fKOnZ4b0b590U/JyZ/3TNnro316HcYv\nf3xHSsc9ij+eOvX+xMTETeX5+dcAPSuN6Tlsye/NNxtzBPl5R7Rq1WqxMeap+pQh6P9uPvTHzZtK\n3khoFv8D8OWOPj9fvjLva2AIwDsP/hTIzEpv0PiyhpyX8jeUjMCOP2u2pqhsbGJiYp260dfnvLTw\nqCHPeMfg96OG7JaWm3MpQPc9OoC9oZAJrClcW/JAYmLiXIDKTZtm4F30mtLSH4PHWL16dZvS4nLc\nB3P6l26qmJGZlV5jV+q66rNvF7AtXv8HFBPy+f9tSv4fR57RL/g33S7WyvXrt1RoTGnpxkido6J1\nvbQz8Cqns4EuwKqc1LQDwqmkGmM2emNQr8d+DlNE5BvgImNM6FzHq4Dp2EphV+BdvHH3QSLSHBgB\nXOO9NB5bYa1aQV3hjQVtga34jq5jcTOAMcEWU+AdEbkW+zvwlvfaC8aYYOb06n4P2gOhn6WOQF4d\nj78jhd6+G029K6jGmEdEpBfwLLZVEbb2aR5jjBkTqcLFkkAg0A2btbje2rdv36qwsJDk5OT9A4FA\nY7cqAqSG/ND4SmP1XaPHCRprI2jUWJ9//vnd0tLS6F3WjiU/FiBxFOx/TMr3gUCgxr5rPd95e82y\ns8+Za4qL95WWLeemvPbqmh2tvwPbxNpv7pz5lZs3HxVMXBPmPnco+J72/nbK1yszM2+s3LCxfeeb\nbvygLseq3LxZ4tq2faJyw4YjiI9fv2rs2Avajxz5R9X1AnFxLRK9TKaInLfIOWVzr7cnBqfbSN1z\n0G6vT/ls5qCFeXNbTp77xqq1FRVdR/yxlKNaty7ttHvv/23u3u3XSZMmfREsU7+5cwD+U1FYGBef\nnFwJ7L+jz8att97ab8qUKftccMGFPw8+IJ2SAk7s3DmwoLp1+82dw5Ljjv9feX7+cElKWrjbAw/M\nCgQCB/WbO4flF11855IlFTfFBzYnxnXt0Xvtm2+esPre+57JLirsZUOTsvPOO++y5557ruod+Wo/\nwytWrEho3rz526WlpSkA//j76Tfd9u+H56Yd1S0vPr72jKkp4197b8U11/Y9+7vpJ64oK2sFXP70\n2jU8W7gxP7Bo4WXtcs/iCufB/Iva3rbygKP7Pnz4sL4DvM8VwGW5ubnNN++335Yx0r179x4cCASm\nApQUBeIXzFzVtfd+HVcld0yq0xcvoVl8MM/GDr+rcfGyZ7A7cPOWCfs29HPdkPPSgruPAwj+DdLq\ncY6p83lJkpL+Ykrs9by0aDE0NN6LnzrqhQUzV7kde7Qq+vGTP4Z8nDXn6L9emDZt908mvZh/1dXF\nVFQkdrn7rjeC28SVJ3X45oXfKN1U8TwQ+GpCzoVHnN7v14WzVnX8ZsKCp8vLKvs0b5XwXaeU1pNO\nuHTvT+vyOQK48PEjHvrpkz8mduqVvHba/xZdWlxYdkSrts2/OvOOQzZWF+uKG248pHTu3MEtBg/+\noSwnp5OpqGjR9owzHovUOSqK10vbacybwHU0CFs5xft/ENWP96yVMWY+MBJARHbH1l9eBQ6vsupL\n2JssXYFLq9mVAySxdZzma8DtIjLQGPNDyHpdscmPzgFuA1qxNRnSjvQG/ikiV4a8lgiETmJcW+6E\n9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lvWueSSS3jqqe2n4Vy8eDHr1q1j4MCBDYjSKikpYfbs2SQlJVFaWsrAgQOJj7epISoq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\npVRM+H+DzRa6NRTusAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911537889)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JsWJ2xjQ23E",
        "colab_type": "code",
        "outputId": "2c02923f-ab4d-4e77-a3cb-da9a9d991ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# display data for last 10 months \n",
        "display(\n",
        "    df\n",
        "    .query('Measurement == \"RNA-seq\"')\n",
        "    .resample('1M', on='Date')\n",
        "    .median()\n",
        "    .tail(10)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Reported cells total</th>\n",
              "      <th>Number of reported cell types or clusters</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>15354.0</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-31</th>\n",
              "      <td>15021.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>15399.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>14932.0</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>15928.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>40000.0</td>\n",
              "      <td>24.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>46770.0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>95601.0</td>\n",
              "      <td>405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>6202.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>13382.5</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Reported cells total  Number of reported cell types or clusters\n",
              "Date                                                                       \n",
              "2019-04-30               15354.0                                       10.5\n",
              "2019-05-31               15021.0                                       14.0\n",
              "2019-06-30               15399.0                                       12.0\n",
              "2019-07-31               14932.0                                       15.5\n",
              "2019-08-31               15928.0                                        6.0\n",
              "2019-09-30               40000.0                                       24.5\n",
              "2019-10-31               46770.0                                       24.0\n",
              "2019-11-30               95601.0                                      405.0\n",
              "2019-12-31                6202.0                                       14.0\n",
              "2020-01-31               13382.5                                        7.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNBa5adkQ23K",
        "colab_type": "code",
        "outputId": "bd2693af-cd5a-4961-d63f-a2ec8e907f0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# display data for number of studies using RNA-seq for the last 10 months\n",
        "display(\n",
        "    df\n",
        "    .query('Measurement == \"RNA-seq\"')\n",
        "    .resample('1M', on='Date')\n",
        "    .count()\n",
        "    .tail(10)[['DOI']]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "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>DOI</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-31</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            DOI\n",
              "Date           \n",
              "2019-04-30   22\n",
              "2019-05-31   41\n",
              "2019-06-30   40\n",
              "2019-07-31   38\n",
              "2019-08-31   17\n",
              "2019-09-30   18\n",
              "2019-10-31   14\n",
              "2019-11-30    5\n",
              "2019-12-31   21\n",
              "2020-01-31   14"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3M36pQCQ23Q",
        "colab_type": "code",
        "outputId": "fffe1f22-2474-444b-b020-65a6d1dc084f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "# query number of cells reported in 2019\n",
        "df.query('Measurement == \"RNA-seq\" and Date > @ts(\"20190101T010101\")').median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Reported cells total                         14585.0\n",
              "Number of reported cell types or clusters       12.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8f5EmhjQ23Z",
        "colab_type": "code",
        "outputId": "1a1570e6-84e4-402c-d31a-39dbf7c5654a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "# display median for last 8 months\n",
        "df.query('Measurement == \"RNA-seq\"').resample('1M', on='Date').median().tail(8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Reported cells total</th>\n",
              "      <th>Number of reported cell types or clusters</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>15399.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>14932.0</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>15928.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>40000.0</td>\n",
              "      <td>24.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>46770.0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>95601.0</td>\n",
              "      <td>405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>6202.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>13382.5</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Reported cells total  Number of reported cell types or clusters\n",
              "Date                                                                       \n",
              "2019-06-30               15399.0                                       12.0\n",
              "2019-07-31               14932.0                                       15.5\n",
              "2019-08-31               15928.0                                        6.0\n",
              "2019-09-30               40000.0                                       24.5\n",
              "2019-10-31               46770.0                                       24.0\n",
              "2019-11-30               95601.0                                      405.0\n",
              "2019-12-31                6202.0                                       14.0\n",
              "2020-01-31               13382.5                                        7.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZupEHZ9AQ23g",
        "colab_type": "code",
        "outputId": "95c49495-74af-4f65-f234-35831f049259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "# plot total number of cells in studies published each month over time\n",
        "rna_df = df.query('Measurement == \"RNA-seq\"').resample('M', on='Date').sum().reset_index()\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(p.aes(x='Date', y='Reported cells total'), data=rna_df.query('`Reported cells total` > 0'))\n",
        "    + p.geom_bar(stat='identity')\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Cells per month')\n",
        ")\n",
        "fig.save('cells_per_month.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5jna7nfk9qS4ZVMdSddqnJf5tX2bdunV3JlkudaPBe3++MeYg4CPAhZ3JV3f+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\nYII=\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911636477)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjrcCcGcQ23o",
        "colab_type": "code",
        "outputId": "9f829e39-7fc0-45db-dc7a-e0c85a62a272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "# plot median number of cells in studies each month\n",
        "median_rna_df = df.query('Measurement == \"RNA-seq\"').resample('1M', on='Date').median().reset_index()\n",
        "p.options.figure_size = (9, 2)\n",
        "fig = (\n",
        "    p.ggplot(p.aes(x='Date', y='Reported cells total'), data=median_rna_df.query('`Reported cells total` > 0'))\n",
        "    + p.geom_bar(stat='identity')\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    + p.labs(y='Median cells \\nper study \\nper month')\n",
        ")\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gTblCyY2IiIiIiExNRZ7udRFwgpmtAi4KISwzs42BdwD/mq7P8ixgcZt1M0jG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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 900x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363263942793966)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhfT09ssQ23v",
        "colab_type": "code",
        "outputId": "374a8b99-bec6-4431-fc03-df974d4fb87d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        }
      },
      "source": [
        "#take a look at the last 5 entries in the dataset (newest studies)\n",
        "df.tail().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>724</th>\n",
              "      <th>725</th>\n",
              "      <th>726</th>\n",
              "      <th>727</th>\n",
              "      <th>728</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Shorthand</th>\n",
              "      <td>Volden et al bioRxiv</td>\n",
              "      <td>Rubenstein et al SReports</td>\n",
              "      <td>Gillotay et al bioRxiv</td>\n",
              "      <td>Massoni-Badosa et al bioRxiv</td>\n",
              "      <td>Cho et al bioRxiv</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOI</th>\n",
              "      <td>10.1101/2020.01.10.902361</td>\n",
              "      <td>10.1038/s41598-019-57110-6</td>\n",
              "      <td>10.1101/2020.01.13.891630</td>\n",
              "      <td>10.1101/2020.01.15.897066</td>\n",
              "      <td>10.1101/2020.01.15.908350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Authors</th>\n",
              "      <td>Roger Volden, Christopher Vollmers</td>\n",
              "      <td>Aliza B. Rubenstein, Gregory R. Smith, Ulrika ...</td>\n",
              "      <td>Pierre Gillotay, Meghna Shankar, Sema Elif Esk...</td>\n",
              "      <td>Ramon Massoni-Badosa, Giovanni Iacono, Catia M...</td>\n",
              "      <td>Bumsik Cho, Sang-Ho Yoon, Daewon Lee, Ferdinan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journal</th>\n",
              "      <td>bioRxiv</td>\n",
              "      <td>Sci Rep</td>\n",
              "      <td>bioRxiv</td>\n",
              "      <td>bioRxiv</td>\n",
              "      <td>bioRxiv</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <td>Highly Multiplexed Single-Cell Full-Length cDN...</td>\n",
              "      <td>Single-cell transcriptional profiles in human ...</td>\n",
              "      <td>Single-cell transcriptome analysis reveals cel...</td>\n",
              "      <td>Sampling artifacts in single-cell genomics coh...</td>\n",
              "      <td>Single-cell transcriptome maps of myeloid bloo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>2020-01-12 00:00:00</td>\n",
              "      <td>2020-01-14 00:00:00</td>\n",
              "      <td>2020-01-14 00:00:00</td>\n",
              "      <td>2020-01-15 00:00:00</td>\n",
              "      <td>2020-01-16 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bioRxiv DOI</th>\n",
              "      <td>10.1101/2020.01.10.902361</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.1101/2020.01.13.891630</td>\n",
              "      <td>10.1101/2020.01.15.897066</td>\n",
              "      <td>10.1101/2020.01.15.908350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Reported cells total</th>\n",
              "      <td>3000</td>\n",
              "      <td>3479</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66136</td>\n",
              "      <td>35173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Organism</th>\n",
              "      <td>Human</td>\n",
              "      <td>Human, Mouse</td>\n",
              "      <td>Zebrafish</td>\n",
              "      <td>Human</td>\n",
              "      <td>Drosophila</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tissue</th>\n",
              "      <td>Blood</td>\n",
              "      <td>Skeletal muscle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blood</td>\n",
              "      <td>Lymph gland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Technique</th>\n",
              "      <td>R2C2</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Drop-seq</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data location</th>\n",
              "      <td>PRJNA599962</td>\n",
              "      <td>GSE130977, GSE130646, GSE138707</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GSE141275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Panel size</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Measurement</th>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>RNA-seq</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cell source</th>\n",
              "      <td>PBMCs</td>\n",
              "      <td>Mononuclear muscle cells</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PBMCs</td>\n",
              "      <td>Hemocytes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contrasts</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Healthy vs CLL, preserved after 0h, 2h, 4h, 8h...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Developmental stage</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72h AEL, 96h AEL, 120h AEL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of reported cell types or clusters</th>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cell clustering</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pseudotime</th>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNA Velocity</th>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCA</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tSNE</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H5AD location</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Isolation</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>did_bioRxiv</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tech</th>\n",
              "      <td>Other</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Chromium</td>\n",
              "      <td>Drop-seq</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                         724  ...                                                728\n",
              "Shorthand                                                               Volden et al bioRxiv  ...                                  Cho et al bioRxiv\n",
              "DOI                                                                10.1101/2020.01.10.902361  ...                          10.1101/2020.01.15.908350\n",
              "Authors                                                   Roger Volden, Christopher Vollmers  ...  Bumsik Cho, Sang-Ho Yoon, Daewon Lee, Ferdinan...\n",
              "Journal                                                                              bioRxiv  ...                                            bioRxiv\n",
              "Title                                      Highly Multiplexed Single-Cell Full-Length cDN...  ...  Single-cell transcriptome maps of myeloid bloo...\n",
              "Date                                                                     2020-01-12 00:00:00  ...                                2020-01-16 00:00:00\n",
              "bioRxiv DOI                                                        10.1101/2020.01.10.902361  ...                          10.1101/2020.01.15.908350\n",
              "Reported cells total                                                                    3000  ...                                              35173\n",
              "Organism                                                                               Human  ...                                         Drosophila\n",
              "Tissue                                                                                 Blood  ...                                        Lymph gland\n",
              "Technique                                                                               R2C2  ...                                           Drop-seq\n",
              "Data location                                                                    PRJNA599962  ...                                          GSE141275\n",
              "Panel size                                                                               NaN  ...                                                NaN\n",
              "Measurement                                                                          RNA-seq  ...                                            RNA-seq\n",
              "Cell source                                                                            PBMCs  ...                                          Hemocytes\n",
              "Contrasts                                                                                NaN  ...                                                NaN\n",
              "Developmental stage                                                                      NaN  ...                         72h AEL, 96h AEL, 120h AEL\n",
              "Number of reported cell types or clusters                                                  3  ...                                                 32\n",
              "Cell clustering                                                                          Yes  ...                                                Yes\n",
              "Pseudotime                                                                                No  ...                                                Yes\n",
              "RNA Velocity                                                                              No  ...                                                 No\n",
              "PCA                                                                                      NaN  ...                                                 No\n",
              "tSNE                                                                                     Yes  ...                                                Yes\n",
              "H5AD location                                                                            NaN  ...                                                NaN\n",
              "Isolation                                                                                NaN  ...                                                NaN\n",
              "did_bioRxiv                                                                              Yes  ...                                                Yes\n",
              "Tech                                                                                   Other  ...                                           Drop-seq\n",
              "\n",
              "[27 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "egpqo0iQQ231",
        "colab_type": "code",
        "outputId": "07315816-67a0-42d9-83db-56dd9412c6ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "# plot number of reported cell types/clusters vs total number of cells\n",
        "# draw some arbitrary lines according to the orders of magnitude mentioned in the paper\n",
        "reflines = pd.DataFrame(\n",
        "    {\n",
        "        'x': [1e2, 1e3, 3e4, 2e5, 1e6],\n",
        "        'y': [4e0, 6e0, 2e1, 1e2, 1e3],\n",
        "        'Tech': ['Other'] * 5\n",
        "    }\n",
        ")\n",
        "\n",
        "p.options.figure_size = (6, 4)\n",
        "fig = (\n",
        "    p.ggplot(\n",
        "        p.aes(\n",
        "            x='Reported cells total',\n",
        "            y='Number of reported cell types or clusters',\n",
        "            fill='Tech'\n",
        "        ),\n",
        "        data=df.query('Measurement == \"RNA-seq\"')\n",
        "    )\n",
        "    + p.geom_point(alpha=1, color='none', size=2)\n",
        "    + p.scale_x_log10()\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal(base_family='Arial')\n",
        "    + p.scale_fill_manual(['C0', 'C1', 'C2', 'C3', 'C4', 'black'])\n",
        "    + p.labs(fill='Technology')\n",
        "    + p.geom_line(\n",
        "        mapping=p.aes(x='x', y='y'),\n",
        "        data=reflines,\n",
        "        color='r',\n",
        "        size=0.75\n",
        "    )\n",
        ")\n",
        "fig.save('clusters_per_cells.pdf')\n",
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Slc59SqwyFq0ijuxgv85ZP45+imfXwfxi6FUfdg+gua2CRVdMH5xkdRnXp9jMsUknvtcc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\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772912061658)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dRT65T8Q236",
        "colab_type": "code",
        "outputId": "218b644d-aaf2-48cb-b5e5-d592b37d9724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "# plot number of clusters vs technology over time\n",
        "df['clusters_per_cells'] = df['Number of reported cell types or clusters'] / df['Reported cells total']\n",
        "\n",
        "p.options.figure_size = (9, 4)\n",
        "(\n",
        "    p.ggplot(p.aes(x='Date', y='clusters_per_cells', color='Tech'), data=df.query('Measurement == \"RNA-seq\"'))\n",
        "    + p.geom_point()\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/bqoNum35t81Y89rBA2RP8UVl4ZQ9fY/P5ztzEEd5APg15j2bh/n8XYJZMSCd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\njZsjWNmj/8RyKsTCyZZ7pEf/3PDbnJ5pA5qVcE5T2nTc7pOXvLH0kYv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VBayJLFvDKlYB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\ntKjEiQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911796025)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTm9q17UQ24B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# calculate number of authors in each study\n",
        "df['num_authors'] = df['Authors'].str.split(',').str.len()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Stvo9LVdQ24e",
        "colab_type": "code",
        "outputId": "ec9b0627-891a-4666-c37e-34eac5370c51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "# plot total reported cells vs number of authors and technology\n",
        "p.options.figure_size = (6, 6)\n",
        "(\n",
        "    p.ggplot(p.aes(x='num_authors', y='Reported cells total', color='Tech'), data=df.query('Measurement == \"RNA-seq\"'))\n",
        "    + p.geom_point()\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+6IpUkvSZ36bCNM0bqL3mN+Us+OtF+mffghFN2yIayiNO1ma+Qfs2vWs8T5VM\nQJW/h8L+w2PhwkobhZV925YFZK0HvRKMTqDsrmS3nHQPXxV8RyRctdXuPV9OwjO012Y9zOrV67n7\n3vf59dflZLdO59QxezFyhN0k5INnP+G+859ARzRKwRmTTuK4K45AlVyGKn+/St2MRRpW1jWcsuMs\nVhUWJTzOHR9fT/89+21WjDVR625Elb2Gimk9bJGGlXkmVqutVc7eMC9PeYsXJ71O2Kxcn6mU4q5P\nb2CH3fomMTLRXBmrR6Cs/xKOR3KeB9cuSYhINJTL5ZKaX5EyJPmtp5pmfsPhcFZJScns7OzsfZ1O\nZ0p9IjdW7zsZveqg2FX52lJh5drlFt3muYJqT9IrnUbRfnNUXCsrCyyd++nuGO3Dr9/9brfv3p87\nMD0zfeOYG31zeg/aLr4f7Q7YZSijgYUAxpoTjif8yxUKHBaqHOfwm3Sbp2Yek3vGi6UbyqpmkIrw\nsFGDJ9709pUNbp21rLAo7bwLnn25vDzSlcorG9bw4b3Gd1ixeuV7T3z0TPw5ww4efMWkp9/1Yq0a\nVTX5VSbOXW49sX+rvYpXrt0PK6bGVamymwquPHjoQYPWvvtowP3xi5/vCTDi5L2/PPxcbyj2/hsy\nRoziY04lErwgbuY3jHPYJN3m2ZSY+T2xyzlT165at2/sMaVUmWfX3ndP/eym1+pxFwnjoyVL5feQ\nJEl8/1g17EXY6FFx6zp0q/872so8e0kSYtymmsMYkZlfkUok+d1CqVyLpUOelz95q+0JD1/XhbVF\nTrr3KeWqB5aU9+5fer7hDj5VwzkZVN0yOlarUV0GHYbdVaOi7m4JsHdA+5dX3Ci+Zk+HPAdj18jG\n/uHSwO6jugy6FDieqjuGWcCQgPY3eDHUSO+UA4H3AQNt2Y9oZ/+fOj76fiVwbDWnzXm/cN5k4O2Y\nGDX21sI7juoyqAz4BNgx+rw1cFxA+9/1Gr69o4/niDlvVED7P6u48wbW/GZh1/z2i8ZiAV9h1/ym\nRI2A1/A9CpxJ1bIpEzg5oP2v6ZCnI3b5UmtgtuEOVmkDIjW/VaXye0gy1FDzexD22g8V/c8EXgXG\nbOlahaZAxogQjSsVan6bhEat+d1GXn909+KnbtqAZdn53D9/pDP+2D5p17yw/4ZBo8zqrxXmzcdY\nvdv3WOsGqU21dYRRrX/++utHdlfqjmmWVVl3h6JHTrvWL5umOS7mXqrU7BlG19PQy7SK6fNrocI4\nep8+7vFzn7/vgidG6XCkjQUWFs7OvTs9/+SCqY5NdckNsMfuvfvP+Wi+Zfy8CNZvBMPA6tWZNE+3\njhltszLWFSf+iNJbpbWP5DnChO4AACAASURBVM1fZqw5ZRLheVcodKaFK2RlHDfByrqm04wy+Pfv\nFWOfue6VIRtLNrbaI3/YglFnjPjPNM1dHC7HGxEzkkll0mw5nI43TNMcVXH/DRojefMh/Pt5xrqr\nj8Za0xkjb7HOvu9tjPYD6llPu9WdfvMJM5+9/tVTLW0pwIHCTM9M+/uRH+/4J7z20XyF41nQWYAG\nKy2y8rjbdJuXYtugSc1vjFR+D0mSxPGRN3+lWnf1Oap89klgZmN0naNznnkBI7u+deZNWnMYI/JB\nV6QSmfmtp1Su+bUsi19+WcbSZavp2DGHXYb0RCnFbWPu51P/nCr7oDnTHFz84Fl4x+yLKnsJtfFJ\nsDZgOXfDan0DGG1Br8IouRAV/tm+f8fO6JwHWfD1aq7Y/4aEx2/nbssLix+qMT61/g5U6QsoKpsV\nWKRhtboIK3Msa1eVMGva55QUraPfrn0YfuiQTbdbvuhfvnjzG8JmhF28A+usA166eCXnDLgcq8ys\nMs08/PSRDOjn5smJLyack3/hKM67+7SKFxMwQaUl3C5e6fpSjml3ZrXfe23lk7TKbgobDm6efxYu\n48Vb32Dl0tX0HdqLU649llY5rTDWjIXwtygq64EtDHTuLFBtURumosrs6g0r/XCsVuPq9VoLIZo2\nqfkVqURmfuvvUSChTjbZtVjhcITTz3zy6hUrS45WinLLwpWTk/nZM0+OvWLux/OnYHFA7O0jpi5/\n/+mPbxt15KMG4XkTN9X2mgFTFX0R1LmzTsfI0zrneVT5jPYAVtqhK1Eu5n8xpzuVLb8qWOvXbvgd\nOCnmWHzNXj8IP2+BoUBZoME0wXE8sDQnL5ujLjkk4bk99H/P9J/+aOAxLMsAxXPXv+oaPKL/Nbe+\nd/V7Nb0ez02Ytgdl5n3xNcs/v/7lT5f9du9Z0ya9/mzp+rJNW2S70pzLvKftdzxg1y0rhd3MIVFR\nqNi5ctlqV9+hvTZGX3sFfFfdbbXWw4h+7Ej2GNkauu/Qlaueq2YBXvi79xWR9rGHFBq14b6xyvzy\nUHToyIorCpQ+H1Hl7xfo3FmTtknQKao5jo8tJDXhcWSMCNG4ZOZ3CyW7Fmukd8pR2G3eYrcONoFL\nHB99vw67TVzs98qA/u8XzpuB3Vc53mDDHay21tZr+BT2NtInYNfoWtH/Dgto/6aEtIaaPS/wGNAF\n+As43XAHq24Tlvh4C7BrX2MT2TLsfsBh4GbsGt5y4CHgQeAQ7A8pDqr6JqD9u3kN38XAvVSWKUwO\naP//6ojDCdwJXBS935+Ao4EQdl1wdbID2r8Okj9GtiUd8swBhhP34QN7C/KFJP5cNJCRKvXMydCS\nxkd9SE14IhkjQjQumfmtp1St+e3Rvd0h//yzOmJVTSocHTpke59Z99wtF+068dElvy47DzAMQ63f\n6+jhV0188dI21qrBORWtzGLp9BMGR0wzPkEBYEbZi6xfs+Heqw68edW/S1bs4XA61u86avBLlz91\n/n9x9bnV1eytIm6xWaSOml6l6GNZCUlU+hm3nOR96/6Z5xb9W3xgTAeGe3oN7NnnjFtOfOv6o+5Y\nryO6NZUJWKTPLtvPvv2MB8ZSNfEFuGrCIZOKbi646qOa4ujWr8u5S38rPJPK17i/K805+7Gf7zr2\nnIFXLMUq7ZKVow2A9WsNjUpf/vba5zwVzz3ZY2RbUuknPqzKXhkKlsZ+vSyM7tN0zkMdHMVHVDeu\njEibt3aNmGZ8x5AWoyWNj3qSmvA4zWGMyAcZkUpk5reeUrXm95135/LIY58QDlfWWDqdBsf7hnPa\nmL0AuzZ1zcoS8rrk4nRF16mtuwlV9kZML18Fqg069wNQWawrLeenpXbHrgHdOpGdkU69RBZD5E9Q\n7cE5oKLTwmY5o9+l/Pv3iirHDIfBk79O5QzPpQm3z27XmleWP8afc//mxmPuZOWy1ShDcexlh3PG\npBN5afKbvDLlbcwys8r9HTJ2JBfeX33tLsCZ/S4lFBcH2L2IW2WGyOZC2nawN7ErXpHGWu6n+057\nU/RvMRO8k1j2RwiH0+DAMfty0QNjUVvwmtSmtNTk14WFhE1Nv35ucnKSVHMc/gNV5gdrA7iGY6Ud\nDkphrDkJwgs21X5bOMGxI7rty8mJcxuxLIvgd39SFFpDjx270qXPVtnTRoiUJjW/IpXIzG/9pWTN\nb98+nVopxUvY20G7gLBlUbJdz/bHA6sBNpRHnH8sK27dplPbNU6XXYdqZZyUocrfu72sbN1eZeUO\nWmdFisg47BJU1oKHZ329/ZOzv380rHUugMMwisfuM/TcCw7YbVFtsUR7+V4JSoPlwOj+nW7z0gUY\nuZHazqtJv+F9dvtv8YoHlGFhWShLK3oN6nnXN9N/+AR4N/7264rWlwF79h68HU/9dg/fvT+v7fY7\n91jfabsOJsCct78bHS43LyKmqFdHdPiHj35+EXtGuForlq7+AMiLP/7R87NPufDa+27CKu+plD0r\nnNuxPJKrLllSsuYz35jtL5qlIzon+jjMfGIW37439/Xn/nzgVgC14ZHtVHhuL8vosNrK+t88VGa9\nPomWbihTy35fnh7bX3nGez91eujhWY+bZqQLoA1DbTxk1ICLLrnY+3N97nNzvPfkrE5/L1jabrdD\nhywdcsCAkk3fcPbBck5MuL2VfnwHFZ70AIT7AKBaLbUyfGcDibsXNBNrV5U4zh14xW1rVpaMwC7V\ncfTdZfs77p1zyysVt0n2e0gKkprfODJGhGhcMvO7hVKhFmukd0o74FpgZ+BP4KZZgQmF0e9dBtyG\n/UFnJeCbFZjwyUjvFBdYDwBn2/tzWUELdcSswIRg/4lTfwX6oi0HlgUOIwL8OX/yuBq3M9MhzxDs\nOr1Nn+4tCCu42nAHb4/eZnugB7DIcAf/qbid1/C1B8Zg1/J+E9D+d6K3H/X7T5nTP3o9V4VNpYYf\nsJbhB5RcNqrLoAeAxdgJf8WldBP4NKD93ppi9Bq+rsB8ICv6eujoeUMC2v9rLeeFdJqjU1nvtmiX\ng7RlJbhWbWT7HTYOf2RW8Jvqzjlh4E4nFK90vVLNt8oD2p+uQ57/A+7GToic2H2Qj66t9jVac301\n9s86DfgDOCag/T+P9E75BNiTyn7JFvbPu+uswIRGvXYcjeM+7BposGuuTw1of3XPtwod8jh05iX5\nVvpBb6Da7+pKb1ftgsHmwmv4rgBuJbGP9bCA9v8AqfEekkqk5jeRjBEhGpfM/DYDswITVgPj4o+P\n9E45HriDytrXPGDGSO+UnYBzQJ1BNFm1UL2AwK5n3DFY5aod2k7/nYw/VgNQ2ifXUXxYX0//iVNb\nz588bl0NYQzHXoyWUXEguqp/bx3y3IGdAEyIfsvSIc94wx28K5qQfgfkRr/n8hq+qQHtvwJ4uO/A\njUbfgRtjL5fd+X7hvCejm218AHSIHg8Cp9b2OgW0f5nX8O2BPYvfD3uDjgtrS3wBwtlpetXoAURa\nu0Bb4DBoO/13Qov/zcBOnl3xp5QUO3NquDuHDnmGYie+Kubcg4ArgMm1hHIOcAOVCf/2wEdew9eP\nA4buFheHwn5temB/IGpM5wDnxXydBkzzGr6fA9q/oLYTDXcwEjHNxdEvdW23bSb2IXF8lGL/vkhi\nJ4QQSSDJbzPgNXw7Yid0O2DPiF4Y0P5vgOOouupeRb8+ADiRqn+UnUCP1oURT/oXi3TGomJDRS8K\nZPxZTNt3f7eKjt2xpp3fAIqIW8lvgVawKhrH+Lg47tAhzw8waCx2Uh4by2Vew/f8+4V0IW470+hj\nuAPaP9dr+HoBA7ET0LkB7a9zhjOa6O5b1+2qPLEj+9mJr8PY9AyLD+2D+UPrEPxyG3ZSX/G7FAZu\nj4TVzBru7m9gGHEfFLCf/551hHIqcQsbsWfL9wTWxt1fheI67nNzjCLxvcME9gZqTX5boJVU7oZY\nwcnW+bkIIYSoB0l+6ylVuz0Envu0g+EwXtMRnYn9BzZPKT579vqXT8pt2yqnqHiDRUwCqRRG//5d\nu/+2cHmaGU6ceDviwP79Zl71fZUOC0pbZP5RpF4+84RhZk3Lr9u8tdRYc0IhlHVR4LKbNFhaZ4x9\nT5XPOA5dWGWZl4UycfQ6Ib1V+rCyDWVVZ8YUkWGjBnstFi6Dsu1ie/ZaqHLdtqB9xDSzZ5Sc3dYo\ne7u/hcu0Ms9xmVupY4DVLSeN+PIgh8Go8w7cO9LuxjeNNSdnEll0hH18+3d1m+ffmFGW2enSPa+5\n/ffvF11ZcYphqA0XPzj2Iss5bQjhrzKqvh6AapdR2652mdmZWRtLEj9/7Hnkrr1Xduv0xK8Ll0+g\n8rUKt2/fumDac+f2NE2z52Y+9Wq1c7dNXx0qrjKuUDgG7bdTx3ruytdiVvOfcNWR779y+9tj7BWl\nGChMV7pr+d2zb1xS8Vol+z0kBbWY8VFfzWGMSAmLSCVS81tPqdrt4Y17pvPsda9gllXunuZ0OTjy\nooPZ+Zg9uP7GNzflbUopMjJcPPnYGdz8f8/x64oN9rYTAFpjlIeZ9vIFnNLj/Cq7wtknw1vFz5CW\nUctuXGYQo+R8sFYAGeis8ZDhQ62/G1X6bDU7vF3KLWcWM6fgOyJxifg9X06i78B1TL/nMgqebkPY\nVOx58BpOu+ViXDn5YM7FKDkXrI2ABYYbnfMsOLps/otZgxMffoXflq9IeEnevvQUeua1rfXcfxev\n4OvpP5CTl82eR+1KWroLSqdjrL+S+OTXcnmxcu6p8b5mPP4RD136NDpiv1aGw6B1bhaP/3IX2bmt\nCQTm88a0LzDLTPY9ZBCjR++JwxHfKW7LLZgT5MqRN6K1/Yo4nA7atM/m4bm3k53butEfr6n748e/\neObaV1i1bDV9dtmes24bTZv2NVXFCNE8SbcHkUpk5rf+UrLbwwfPfnpWuDw8lpgOBmEzEvns9a/f\nGDtl9JT+/bvmL1hQeLnWVmun0/hnlLf/xLy81r/++dZX0+nW0U2PTnYCvHYD/LKIlya9cVp2buuz\nS4rW7baph67CzM7N+iYtI+2SGgMxf8wy1p7xKpjtFTgtNmCsv1Fbkd9Ox+i8FiKvWOBU4LAgAuH1\nGO19O+3RKf3Lt7+bhiKDaE/f9l3bveMZ2uvmi4ZPHLPop46XVBx/8/EO4Vlvz/zoxb/3vM5Ye+p7\nEGmrKmqWdWHYWHP0L7rd12M397WcPu+39o98/M3ojeVm+7zWrX6/b/ThL3Zq0zrcq0PurguXr3gI\n+7EUEOmYnTW9Z17bGwGeu/7V3nPe+W4/gN0PHzr7tJtO+KPiPjv17MCh5xyQVVJSMttwqH2B9UbZ\ntGOBy4FN/eMUQPibzy1I7OEWddDp+1Hw4HtnLVm47GwsnA6n459Dxo68Iju39R/fzvyxzcOn3H9/\n6brS/gCvvfPVv8bi0IWnXHvcX5v7etRkpz087H3sbrt//e4Pl5lmOC8jKz3oG59/c3Zu68J63kWL\nWs3fZ8j2THp3Qo3fT/Z7SApqUeOjPmSMCNG4ZOZ3CyV7Fa7X8O0DfErV2lgNnBDQ/tcqDoz0TnHO\nCkwIx5z3G+CxL7fbew5HDcGuS30Re7c0gPeAkwLaX2Odog55TgKeJaZ2N1rzO81wB0+NdoO4D9gO\n+A24wHAHg9FYugFnE+32ALwQ0H7La/hWAO2Jc/HkpcMOP21VdV0CSg13MFOHPHnAA8Be2DXH1xvu\nYMIHl1j9J07tCswFcrA/SJQDXwDe+ZPHRfpPnLoHdneDHOBD4P75k8dpr+E7GPtDkbafMk7gyID2\nz6i47/gxokOePaL3HfszKwcmG+7gDbXFCeA1fA4gs2IHueixAuxa3IoPQRFgKdA7oP2b1Wpua5HV\n/FUl+z0k1cj4SCRjRIjGJTO/TVxA+z+Lbtl7H5X1npNiE1+A2MQ36j7gXgWO6PX8cuxte38JaH8Y\nONRr+DKjj1HbQrcK2dgJ16bkN1qr2wbAcAd/xF75Xt1zWEr1JSVZ1d3+m1nZkcNPW1Xdt0p0yJMG\nfIzdzSEN6A68pUOewwx3cOZI75R04FzsrZ2XAA/NCkxYh71orU1M/GnReA8H3p4/edwcYE7sg0Vb\nfr1A4mr+572Gr31A+6v9ZGm4g3N0yHMzdsuysuj53wJTqrt9vGgyG9914wBiZv+x6797Ynd7aPTZ\nXyGEEKKpkuS3GQho/4New+fHbn21NKD9yyq+F50ZvgN7sd5c4ILo9x/CTrquxE4yPwbOiia+Ffdb\nn6S3wpdUTb6wIKKgxm2DY+mQJxe73dk/Mb1uZwMjqUwuNRD6OtDmF+wylDPjvnc9dseB/sQukrNA\nKcaP9E75EMuaxeqSXdXGUoeVnqZp3+aMkd4puzIsvSfVtCwDutYSdhbVbH6BPYPdGiip5nsAGO7g\n9TrkmQ4MAkLATMMdjP+A0hClQKtqjm/YgvsUQgghmh1JfuspVbs9VJhR9iLY7aY6mabZCeCRy5/b\nEbsUwcC+xN7Fmeb8bt7s+cfPKHtxPXZyOTvmbnqYptmj1gcyv25tlIy/Gqt4NzA24tzhWZ3z/Kvk\nzcdYc/oNhL+9HnAoAEffLyJtXvoiUlsHAF1iGGuOGa/geAALx5pw0f/GWa1vnDdh2iV3Tj37kd5l\nG8r7ADicjuKjLz30sjNvPWlgRJc8YRQf3h2raE8ggtHjDZ377tfG2gv3s8o+wYhZ56UUKqIzu/bv\n3+XqBa9+uYcKrVIYCqUtg3Y5/brn73ZbRp5rxZJVxaZVNQFOO2xQv3BNHQwK1j3HUTmnrdfaqjJD\nrQy14e21z/atWKle4xjJmx/GvrwLMDCyBSvbe+zY9eUlvy47l4qWWgozJy/7i5eXPdrVNM3aEvhk\nkNX8MVLlPSSFyPiI0xzGiJSwiFQiNb/1lKrdHmpz19iHmfXi51i68mfsdDkY98R5jDhxr4bfoRXB\nWDsGwgtQ2H+ULBxYWVdjZZxg30avhMjfYLQHx3Z13qXa8CRq432bOkHYHaFaoXNngpFH2Azz189L\nCJeH2X5ADzKyMqLnPYHaeC92ma0CLHT2ffy7rCPt008kLb3yOZvlinnfDGfGJ/vzxcPvVelkYSnF\nTkftxk3PnsdpT7zO3yuLcCiFGdEcO6w/Vx+xf63xz37tK24bcz9GNNvWWnPlsxey3/F1tewFrPUQ\nWQpGnv16bQGtNf4732H6IwHCZpjdjxjGOXeOIaNVet0nCyHEVibdHkQqkeS3nmqa+U3lVbgndj33\nrrUrS/aPPaaUKus3vM+dd8++8Y3azr328FsP+m9x8AiADj0870ya/r8P1IZ7+hgbH0/YwtYibYnO\n+/HoolCx84Zj7jzOLDN33e2wXfbf7bBdzt9ht77Vbv9bwVi16zOKDQOq3h8RXCMv1zn3f1btSeZP\nrYy1J31atf8vgOvf684afXvvvjPvOO3Kf42KxsIrCp1cePCgoJXbcdmaP5ePUDEfBiwgs2vez28s\nuv/0N5+Z1fm5B6ZPMDeU57XOa71w6rTLpnTu0aHOUoR7z3980LyP5x8AMHC/nWb936PnzI39fnVj\nxFh7/gjMz25RWOkAltFtmm777t2o+MqLZklW88dI5feQJJHxEac5jBGZ+RWpRJLfLZTKq3APch53\nmaW5i4SOsmpgQPt/rum8Mz1HTLj09qWTB+xuv8f+8nUr7r2y+zWPf/pbAPi6mlOWjuoyqCcwAxgB\nuJxpTmVpqygSjvQPaP/ymh5LhzyfkrjjWgQ43HAH36vhnN7AH9V8y7z+tO18Ex5a8lZ6pt5U+mCW\nw4wX8v548NpudwMPKqvyBbEUFkpNVtp6ELsmug2V3R4+Bw6qrVuCDnkygfuBk6KHXgEuNNzBTfXS\n1XR78ADzqVp2FI6e91hNjxV9PAewK3Zd8U+GO7g0elxhb3F9YTT+N4ErDXdwq2z8sSVkNX9Vqfwe\nkgwyPhLJGBGicTV+B3yRMobtXzJCVXOhaeQxRYNqOsdr+IwLb1l6yw67VK6T6jdkAxfesvSmZYvS\nfsXerjV2R4py7KT3ECo7DqhweZhIOJIN/K/ihjrkydUhz8465Int8P9IJIz+aU4WX8zMYfniNBNY\nhp141qTaVg+AvuaxxaSlayu25teVBgf6isLK4nksfrfsRNOeYbb4T2nrHmAi0JbKRXtpwH7AYbXE\nAfAYMAZ7sVkr7Nmqx+s4Z18gfkbZSWVrueqfnJ1oB7C7ThQAi3TIc2L021cBtwG9gG7YHS2eryOO\nFkuHPKfqkGeODnl+0CHPtTrkkfUPQgjRQsgbfjO2bo1jmGVVzX7T0i2Usg4FXoi26toOuzPB7wHt\nLz3k5FVtBu+13lBxyePgvdcbd1/eLf2yu5YeCkwHOkS//TFwGXAq9oK72DHlxE7G0CHPk9jdGYh+\nPcVwByeO6jKoIM9d/tfqf129DQdYGlcPT+m9jy94N76VV6w8gNXFmSz4oyNOZ4SB/UK0ygwbrnRr\nQ3UXMzKz9IqA9q/zGr7hHbuV3+zuXj5kRaFr4fLF6dcGtH+F1/BtR2K3h3LsRJKrfzqmymt1y8A3\nSnXI4wJOpuqHyDTgZB3ynGm4g+U1xF9K4gdPi7o7M9yE3bsY7IVtDuB5HfJ8BVxB1dc+DThOhzwd\nDXfwvzrut0XRIc/52H2gK34GO2O3hDs7aUEJIYTYZiT5bUJ0yOPG/kO9GphruIO6ttvntAuvTW8V\nce/uXUu7DmH+WpjBz1+1Jrtt5D+v4WsFvEblbOMKr+E77P3CpT9pXbVQAux2YZfdtXS94Q5+q0Oe\nnkBf7F6zfxnuoAW+hcTsWAaAwsTiFx3yjCMm8Y2aoEOeH2HQ7qtCad0BItG50L8XZt7uNXxvBbR/\nUQ1PLfTTwk6R/915kMMMG1iWol3bjdwxYea/D1+e9/uFtyyjY9dyXNE5XK3h+Ts7LT/jbni/cN7x\nwAXYieNe2D1wbwV+Bg6iaru2dGDh1T8dk/BaXf3TMYfd3JGfqf7qiaKi60L1ZgBF2El8xe+ghT2L\nXJsRcfFVnDcEyKzhnGp7JbdwN1D15+YCztIhzwTDHazpqoIQQohmQsoemggd8hwDLAbex66Hm6lD\nnozazhlxVNENd7/5B1fet4QzJi5n8kuLuOKeJXrvw9ZMAu7ELlOokAfMfGBi1+x1awzM8sr01yxX\nlBQbEB0vhju40XAHfzLcwUV24gvAJ8CTQEQpVe5Mc5KW7lqGnVieWkOIZ2GXFsQndBq7/221Dhxz\nZvlVt48qKy1zEok40NpgdXEml9x4eMn3n+QMuOLoPpG5n2dTUuwgtMTFHZf04MV73L11yLMn8AiV\niakCbtYhz1HAZCCIPdtbil13/Dj2zHa1r9W1/+3sjD7v2H5M5cCnsTW/8Qx3cDV24v0ldh1fEDjS\ncAc/remcqFVU6VUB0edSHI0jdqY5AvwT/U9UlVPD8bbbNAohhBBJITO/TYAOeboDL1P1svz+2JfB\nr6zpvBFHr9nJsggrhdPpsqLHipVS7IRdyxqbdBpA3jvPtu9z0eRlP5VtVAN0xJ4AjkSwcnL1fMMd\nrHGVcXQ74nOBd3rs2PXgIy8+5IIdduszuu/gXmt0yFPTDHUEWIGd7MZ+EHNiz4zWpLtpOqts6KC1\nQcm69N4O2Lj6P5dxzSm94s9Zh71hRjkQ+6HBArwB7X/La/iGAycAbuyZ4BkB7beu/umYal8r7Fn4\nE4C3gD2i3/uRaM/i2hju4J/YiX9D3AocWLTSybpih+rUvbw8Ld2ah10f/Qv2NtQVPYn/Aw7dwo0z\nmquvgT2p/H2ysGvZlyQtIiGEENuMJL/1lMxNLgzX/vthfhK/di3NIvMI0zRfrvE81XZfRXHVn7FS\n5ZZzwCFpGS6rvDSxgfxxlx/RU7c6+Jo07n5EYXYBSM90Ltet/u/qWjerYNNGG8uAL7BLC7YzTbPU\ncA6eQXjuLvE9JyzXATNHnTmg5P2nPvZS0bBXYWa2zvj16d/uXV/T5hJXXzYq95a730/8RjjieGL+\n3esuGHrVP+VlZlesypKC4YcOKbAc5d2ILDQUVVqdWRjds03T3CUa/0/R/wCGmKaJU7mssJX4Wu2V\nl98zkvdKebh840ULPp3WG2Cn/Ub/6UzL7BYxzW4Vt2usMbLe+e36/x10wRe/zy3fByCjlVbeU4c+\nd/694+1WcbrkXLXxid5Q6rQyTliEo1daXT+zJEnqJgYq64bb1PpbH4PyroAFxgYr3Tfean3dgC3Z\naGRzNYcNDBqZbHIRpzmMkW3ZuUMpVZ82VmdYlvVMIz3ewcBMYIBlWb80xn2KrUtandVTUje5ML/G\nWDuWuKQNnLuh2zxV42lq3c2oMj+KSMx5DnT2VGY8Cw9d+jQ6Yk/KOl1Oeg3uyV2f3ojDYYBVBuHf\n7JOc/UDFlPOWf4QyvwGViZV+NDh61vkU1Po7UaVPR7ejUOhW4yBzLADzv1jIS5PfYs2KtfTfqx+n\n33zCps0sqrP8z385/ai7oGtHMKIptWXh/H0p0/+4h9WhYu49/3EWfv072W2zGHODz950IrIco/hI\noBRFJLqhhhPdxg/OvjU+3ndFAaaHnsKKNrkwcNI5YzvGbnczJSvXcZPvbn6dEwRgx937ct1rl9Om\nQ01X1jffK7e/zQs3vUbEjP48FWS0SufJX+8ht1ObRn+82qwrLefNHxawat0G+nbK45ABHgyjCfWw\nt0ohPBcsE5wDwJCKByG2pm25yYVSave4Q3OwW1K+GHPsT8uyVjTS40ny28RI8ltPSd3kQq90GkUH\nPwulvRWWy4rmvlbaIRda2XdW13cXgD/mPOjp2unRF9NbRZTLZdfuLvur1ZrOQ2d4MdpGxo+40ffn\nvMWn64jOzMlr/c34Zy68ZcgBA0oA3ntyVqePXvhsd4ADTtnnq4PHjvwXwCg+4hwii84GNCgL0FbG\nmDOtrKtim9FX36Q+EnIqc06ulbZPEUb7zb4cv/CbP1qN2+e62Vb3jsrq2Ba0hVq2krSitf8VrHu+\n1nZhav3Uvqr0xWugivJguAAAIABJREFUtCekhaz0wydbrW+cV9s5ESvMU39f71vyz19nhjdYmW27\ntf76uO0vntQ7a2CJr+NZj6xfu2HIpllmRTgrp9Vc/39PnFtxfmONkWPzznx247rSnePD2/3woVdc\n9/rls6s9aSv44vfFOQ++/9zLZw37pkPH1uvUgv868PJP+3z8ysUXjHc5alvnt4lsYhCjOWxg0Mhk\nfMRpDmMkmT2bozPB4y3LunMr3b8kv02MJL9baFs1H9chT1vgbmAf7IVPN9S0CUQFr+G7q0OX8ouP\nPW+Fq73b5M9fMnnt0Q4Rs8w4IqD9M2s5bw/sfrIVZQNh4KD3C+f9Q2JdZAT4xnAHN+3nuy2a1HsN\n3zXAjdj1txU1xb6A9te6c1097jcTu543FND+cPRYBnbP3OOiNysEDsX+w7yRxOYYAJkB7S+Fho+R\naO3xg0BP4DfgvID2z/cavk9IrBOOAIcFtL+aOpCt4/QHr3jqofzpZzgdGqdhUR4xWFzUhnPfOuKI\nT6655t26zpdNDKqSDQyqkvGRSMbIlqkt+VVK5WGvpzgSe9Hrz8CVlmV9HHe7I7H7wQ/EXhT9I/B/\nlmX9HJP8jsAu+TsUex3BPZZl3bPVnpjYbFLz20QY7mAxie3C6uJZUZjmeuS6rrHHyoHt6zjPj906\nq2IRmit6rLqFXI563F+jixww9AG15N8jWbN+F5TStMt+y+rS/p2K73sN327AcOy2cG8FtL/KbMlI\n7xTHrMCEKju3eQ3fBGAS0Q4KXsPnC2j/h9ibR+TH3LQT8AHgoaJWuSqLqhuB1JvX8O0AzMZ+zQ0g\nF/jSa/j6YyfE+1D5czGBpdS+IUijGz34pwMqEl+ANIdmu9xiTh0y70igzuRXCCFSgVIqA5iF3QFm\nArAcOAN4Tyk10LKs36K3OxV4Fvvv4GTsCaG9sa8Gx+6W+nj0do8DPmCqUmquZVmfbJMnJOpNWp01\nb79Rtf0V2F0Lauqfi/f/2TvvOKeq9A8/5ySZAgy9BEQExIgCA4gVG84aG1bcoK69rL2uDeSnq7si\ns7j27tpWXFtc7AIbxLa6ig1RESJIh4DUGYZhJsk5vz/eGyaTyRSGoXqfzwed3Jt777k39ybvec97\nvl8daq49dpdzblyqx02dwbipMzjv5qVae2yXzya2XE5Nqa0EItW11SgKFitgvO3WqdD266lt3x5e\n26X9SUCxcw43IDVeY4FngWlBHWrvbHtYUbB4HhAvChbHioLFJzrbnAGMpkoGrRXwdlCHeiAZgXS1\nBw/QERmefZnq17gSeDliwrUZXNTH+chzmXo2vYje8GkREw4j8nBLEEOMT4HDMwP7LU3ngnXxVOCb\nImk1Pduurtia7XBxcXHZTM5HvseD1trnrLWTEKv6GcAoAKWUF7gbeMtaO9xa+6a19l1r7Uhr7X8y\n9veStfZOa20EuAz5rv49LtsdDcr8KqXe2oR9WmvtSY1sj0vTMgY4FejivFaILFddQ+TrL/vL4sSx\nZ67y+nIkwPn9Zctp0SqZvOOCHnMmLfnueuAeqgK+CuQhB8DEAjmq2cguNu8UUFX+CkEdOhp4wmnL\nXOC8iAn/r74T6DPyPoW4yeUAS34cc51BMq9HZLzVB1wU1KF/IEGvokrSrBswtihYXOyce46zvhPw\nelGweLAHhlG9M6icf7+jdsOKCiQYrQRSNsOvIMNejaU52d3fmgNETPhZJKDfZnQuWPdSPKn/z+ep\nSm7nepIM3m3h89uwWS4uLi6bylFIic08J8hNMRk43vm7L5LseLoB+9sYDFtrk0qpWTguoS7bFw0t\ne2hJzYyfy3ZOxIRXHuk7fQC5vltQ7Ia1n6r1FY9ETLjWz3LSku+0MWidYW98/Dkr9QnnrdTaH73P\nxALTEJ3hdcBL2h9dBGBigWOAV/T6MS3t+mLQ3S8xZu6lR3fpPxCxRNZIQNkLmBLUoX4RE55dW1v6\njLyvJRJMHuMs+qnPyPuGdpJscza8wF7I+nRN5BxE//Zk5/jpwaVBAtc4NUsYlLOv2q5XecSEy4Hz\ngzp0AYjecW3n00CmkNaZcMhFzDZSZid3Am2R7PYlW9u+uE3+hr9WJvVgazkiaZXxKOsxVt2S02XW\n1K3ZDheXHQ0TCxQAibpMcFy2Ku0RjfZsmnqpEbV2zv+XNGB/azJeZ+rKu2wnNCj4tdYO2cLtcNkC\nFAWLPQwZ8CDisGaR4ZcyoHZ9NMjRumY5jNIoJIhMaH/0A5xgLIWJBXoiWeVcAIXFmrkXIS5m/ZAg\nM5VBTQXBpyL1tLXxBNYWedZUoJKGRJu8PfDo99Z18fRtv3LDjCvO+rz3Qfss1Imk4p0pvZMvvtn/\nLaRmK/O+TiIT9bxkD2Q9wPNUZW9T26xHjCMerqV9XZHSkqYIenH2Mz6oQ3+lSlbPANdETPgTEwsc\nj9ScpT6f44CPTCwwUPujG5ri+A1B+6OVObFAEDjcq6wf+MHXedb39W3n8tvBxAI+4E9IXeSvwL3a\nH/3NzoI3sUBn4HXgAOf1q8D52h9dv00b5rIK+IqaCQeomreRsjzvkuU9Ljso7oS3nZsrgD84fysk\nyPtHUbD4qymREdODOnQ+UuTfAlF3uDpioiVLvt9nRfs2Ze29XonnEgnFitXNV3bp901dX9RHkBFY\nKgnSTkSML7JNCvNRB6o8fkLbf8/MyV1cKu1ometdOXzv3mVd8rtOuf2lxTm+5F6pNp598jR92tDv\n553cs88XSBB+vLP/BNKrH+Wc/18yDuMB3oqY8OSgDp0N3I9kVaPAHyImHAvqUG218Vvk+YmY8O1O\n+UZXYF7EhJc5q66jetY6B5l0dyjy+W01tD9qyOgAubgAmFhAAf9GRmx8SEfyDyYWOFD7o9O2aeO2\nAc71eAdJAqQ4GUlEbOokZpemZTLym7DAWlvbCNoPwDKkPnhTSkBdtmMa9eOtlNKIVWyALCl9a+29\nm9kul6bhOLJ/xgcHdWgQ8BRVwdQZwO6/K7ypqGP3ojZ/GzGJrj1l/tKyhTncVDykzbL1xd4pkRG1\nlRxUJhPkeNKOZg0kEsogJQ9XZrw/h7prj2n97uycnCWlG197SipoF57BkL97WzfLTwTT3+vxWJXv\nSVwaMeGRQR0aDlwNHAbEgAciJvwTQFGweDiS5W2ODEldMSUyYjJAxIT/BfwrqEMqI5MbI7uixRYr\nN4iY8GKk05BONueMJE49sIvLdsKBSOcz1eFNjfjcSVUd5W+JXaiyHU+Rg6jnuMHvtuUpZN7Gh0qp\ne4HZiMLOPsj8pdustQml1M3Ac0qplxGjjDhwMPBxlklvLjsAmxz8KqX8wIfUlHlKDxZ2uuB3W9ob\nN5Ycn2fPyngyc7H2+1sNXpvnO6RyQzwzi3jISUfuPfytJyZ7Lp4UYLeABL/zo7kkvMv1E9Nu3D8e\nj2+4bXxk4PTZiwfn5vrWnTNk33eH9t9zxbj7T2837Nywbl6QJBUAW2DcvX0K3qt48ddrD7l1bPTL\nOTcAHqWo7D+kzx13TRyVrM3CGCB/7up0UzvxY1tbwSHePgOlmqI6FpUfj8f3Cd144h5vPPjen+IV\niV2AZNc9u+jy9eWPe31eJr13/bySkvIjvvp6bpsBA3Zb07ZN80RmG96reJF0W9VeA7uPn/3tvGup\n+hFP5DbLmffMT/f76mp/isbcIx++8lm7mVNndxxwRJ+lBx4/aA2A1j2+wcwdoJznVi6NMrb5X8u3\nUxvj2nDta9PYnr9DGoP2HT6Y+EdJVf33xWPJ26Mhzws72f2hWtzVXq+7pcZyi1YNvB47xT2yPWo2\nW2vLlVKHI5rxtwF+RJ/3a9LK3ay1/1RKlSI6v68iJXFfAy9t9Ua7NAmbbHKhlHoREd8fDixEapiW\nAWchtaVDrbVzmrid25xtam9cD9Za/vf5HBYtWkXHji059JAAHo/m+htf5ocfM5OHcPFFh/Pmjc+x\nYtGqGuvGTLqFkUfflfU4/175DM998CVPTJ0GSQMocrTmxStO58fXv2biY09w88Pz2W3PDaxd5eWJ\n27uwfOVB3PP+bQBsWF/B6tga2nVpQ05eTtZjpHN8s7M22i+n89zP9+MvOBvMEpRTlmXxge9gSsxY\nrtj3Gn5/8Wz67l/K2lVeXnqoM4cMv5wTLjuq3mNmw1rLq2Pf5LV736GyvJI+B/fmxueuaBJL4WTS\niJ10Gi/dNZ5xf3kNLGiP5rL7zmXoJUEw69FrhqKc0TkLmPwroVm2cjUXl21Ech56zfEZduw+bO6J\n2BaZVUe/AaxFl5wFiR9QzlxduR7DsC1u28aN23psTXtjF5f6aEzZw2HIkPJS57Wy1i4A7lJKKaS3\nVKfF7A7KE2Sp92lK28lEPMEtx9x14oKZiw/XWsX7HbrXmyNfvKZOObANG+Lq3AueKl6zZv0RSpGw\nFs+DD0W+e+Lx8y5fu7b8j8B5VP+c7dx5Ky62lqEohkrUKMu1R68DdTyK9zfa9aZQJMd//O0pT0yd\n9hZKgVeSoJVJw9nF/4z1+HDZN6ujecddftSeVQcCdIt5axC5MPKa5dK5Z6cGXw+TNF+SRYv6v+O/\nPOnUi4d5VPljj4BxsvE535u806555IJHC2955Lv79ygsx5djMQYGHPIzd178+C8nXHZUqMEHTz91\npTjt5pM57eaTG7N51nvkHze90PudJyJj4hvi3bRHl+59UODese/f9tbtp9w9ZOp7396dOm+TNDxy\n9bP252/mXvSn4rcOwC6/gLTPU5c/UmG1/ySbd0qjPOo3rK9Qtx5ffMLy+Sv65eT71p501bGvHX9J\nMNaoE204rn1tGjuDdW01PN3Bs9cZNjnjelCVYD2gF9mco84HGuJOtnPdH0ph8i9qrdfdcpe1JfsD\nBt32bZt/YTHZVQZqsNPdIy4u25jGZH5LgeOstZ8opdYAZ1lr33HWFQFvWmsLmr6p2ydNaTsZ1KG/\nALcgw+upD+aMiAm/Uts2RcHi04AXqB7gVgI3IfVMHyMTLRKIEsODUyIjrju13SktBg0p+WntKm/X\ninJNp10rk8sW5Jxw/xcTJgR1aJSFO5QzzG8hqeCONUf3XLx+gL+G1qEuq0x2fO2XV1Rs1R9qzGpr\nkb9icsnzHRpzPY7MPcOoeKJGtsB09+/x/i8PzTaxQA4imxYH5mh/1Lw46pAbh1+xfGy6VJsxMPPr\nZmv6njCtTWPasblk3iNBHfIjP+otqCqlsMjkwKORmcfp2sIbgDGTlnx3ArBvxu7jwHDtj76xqe0K\n6pBClD/OQoLthHOs/SImvMWMS1z72ursrNa1JhYYhNyvq4F3GqpssDPfH44KhtH+aI16tLrYWe8R\nF5dtRWMyv3Opqn39ETibKkvTUxDpEJdNJKhDLYH/o6qGOvX/exGt29rYC5n0lP5ZeoG9p0RGlBUF\niwcjBg5+YPqUyIj3AU6+6Ncx4+7xd7UWsIqZ3xjPoCGlL3w7fmBH6HWXEoezM52GvAjc6VtWdgyZ\nGIMurUyoRPIrqpQlACd69+hGl8DYnl0UsxZUFZUrhe3cFrv7LgUgkluIE89GioatXmySkB78ag2d\nu1eWsv1wNDJRND3AVcizNJ/qsnCpdeuBbOfgoUqPclMZhIwMpMhBguBi5J5xcWk02h/9GgliXRy0\nP7rjFzG7uOwENCb4fRdxRXkVmb37plJqOZKB8gM3N13zflN0oKYcGIgId10sybJdAkcpYPK4Z9oC\n/RGNQm1iz3yi/dHKD99o80dr2LhpMqGZOrlV294D1w9FArPrkaAaRK/z6+bTlv070TZ/Zdmgzu3A\nSnRrocWXS54+NvjL9Ikvta3WFAXsMzA2r2GnXxPbtYPBozWLfgVrsB3aYLv7AWoViO+4S/wza0iS\nFjwm4tjW7ROvN7YdW4DaHOM8wHPANUgArJHPoALpAM0EDqeqFKQSsar+pJHt6IY8t+mSc16gZyP3\n5+Li4uList2zycGvtXZk2t8TlFKDkYxvPhCx1k5owvb9lliEZPbSS0YSiMZgXYxrVVB+9VknT+u9\n2y5rPEuXF5h/vTng1+UrWzxkYoFdgG+RLG4OcBpwnIkFjipds7c3W6y9arnPj/iYa6rX2z4JvFLw\n30XzfYtL21V2a4WKJ2k2fTnessRXyT7qLF+OJV5ZfZ8VZerQTboK1ZlmO7fbx3Zul76sFAn4snJ0\nl/6rgqetXHft3YtaWQMeL0SnNVN3Xtx91stLa9tKMLHAnsCtwK5IxurP2h/dEhnj95HgNl0txQD/\njpjwjKAOHQ48ikwsnQVcEjHhhcBCEwsMRzqdbYDPEYe3xhpcRKmptRyn/nvOxcXFxcVlh6UxUmfd\ngKXW2jiAtfYrxCEFpZRPKdXNmQDnsglETLjC0ad9AwmIFFLjdXZd200e94zXGPItCo+2JPeKcezh\n0VytyUUMLFpTFeDkIJnDoR13qVxQusbTI5GmdubNMSyak/sJ0BIsbTrIzOTVv3oBVWALmh2mS9fv\n02zWSprNWrlxO5vjvd+bY19KmprBtC/X1qYL3BCGIzXLnZBAsRI4cUpkRGVtG/hyzCGTw21bTf+0\ngJ59yyld7WHGV83Jb2YuRQLKrJhYYA8k4M1FnosDgSITCxyg/dGKzTiHGkRMeH5Qh44DXkOsM5PA\nyIgJv+Ss/5yauqAAaH/034iBQFO044egDt2J1JlXIvfcSuDGpti/i4uLi4vL9khja34PAqZmWVfo\nLK9tWNelbmYjhgq7Oa+jVKlq1MbZWtNVZlSDR1uNmCFcDnSnZmavEuia28wWtWqb/GHNCtVceyzG\nKAYesu7mL6e0nNm6fXzln5+e127v/WR+yoyvmvGXC7uvXunN76NKs8xZSSQLFtL9raQuvwSVQNmq\nlObagl02TlhxnNJaAWsaYgc8JTJiTlGweC/EPS4H+HRKZMSStH39CezvgQpQj0VM+OXDT1zTZ8rr\nbVi2KIdli6rk1Np1ruxWz+FuoCrwxTleH+AEJEjdZEws4AGu7qS8x23gEHzMORDeTwmiG6rsM9P/\n3qpETPjWoA59gkgWrgJejJjw6m3Rlq3JqOnDWiDWuznA56MLx28xwxIXFxcXl+2LxgS/dWn15SL1\niS6biBPMvYe4AaXYF3iauicfdbQWM+2/LVj0Sy4dd6lkv6JSr9Z0Ar5H6rPTRXVzgZn3/HfCvFDH\nkzv26ld+usdr269d6X37rsmTfgJY+f3ei1u0SmysNdijsJzHJkcXDf9d+3W1NeK7WGAXNai8Us1Y\nkGPXrYccH8k9ujK7srV2zu9q4B7knqsM6tAFjqNanUyJjCgB3sxcnt88+UTFBn2hSSq5H5U97Pjm\np7a9/61fl34xuSVlJR6Mk4n2eCxFp6zZWL7gqBy0QYLwVNDZmZrPQwKpxW4sTwFnKpXw5dmPADvB\nxALHHN2l/yxgIlXuiD5gbFCHFtWl7LGliJjwf4DfjEvRqOnDuiNGPV2Rflr5qOnDjhtdOP6/27Jd\nLi7bEseG+QJEmrIEeFL7ozuV2oaLS4oGBb9Kqd7A3mmLhiiluma8LQ+xyK21HtOlTroCe2Qsy0GU\nAQAoChbnIFnh1VMiI1YAJJNMu/+GrrmRcFt8OZZEXDHo8FJ16z/mfZebb8chmcs9kaF1HxKQfQCw\nZoVv6JoVvsuQTHHXoA6NmLTkO9O6PYX/ebktkddEGezIU1dz9Bmr+nXK+3XWr2Qzp1ArgDKLQilA\nbewfWaAkqEMnAw9knNcLQR2aFzHhT+u6KCYW6AOc7mwzQfujH55YMKxNeZnnompvtAqtzd979tnQ\nt/jVOeaOC3ro5Yty0Npy6qXLk6ddtfwtgKAOHY9Iw7UC1gd16NKICY9DRiyOpmZH4du62ldHu3uR\npqSglMVaFDAWeBCpp07vSGogRN3KHi5NwzhkAmhqhKoF8Mao6cO6jC4cX2tJjYvLTs4DVMksGuBC\nEwscof1Rt1O4GSilzgMutdYeuA3bsA7Yx1q7xSQsdzQamvk9jSp3M4tIIWVjDdWlk1waTi0Zc6mt\nLgoWH4JkQNs6r/8BXOadUqhQYI2icoPEUl9/VMApgX5qYvy1UhML7I98fn5gOhJA2qAOnY5ImKUC\nsO7AnvOjucf/b1JL+8+xnZVJyqofpzZn9QqvPeOqZdMfuHnXH4G9lbOdBaOMuVtFF36qYqu8JJyy\nh0QSPX2Osrt0+AypPc7GzYi2bVZMLHAYEElbdKOJBS4ZcnLX7yf8q12N9yfiKl/7o7/0InD581/8\n9FgygdUelNZEgRFBHepn4XXA65x0Mwv/DOrQwklLuBsoQkxc4kjg+2ftj35eW/sAgjrkAUZRVZv9\nAnDnpCV0zHyvUiikfrm2EodN0v7MaIcCrgSuQNr+OlJH7I7E1GRfqpcDKaT2eldgp3OndHGpDxML\n9ACuSluUCoDvp6a2uEsWlFIhRCWpLyJNOQMZ7dzmWGtbbOs2bG80NPi9H5FgUkhmdxg1M2KVQMxu\nqmuGS4rlgf7rl/wyI69LahKax2sYNKT0m6JgcUefNznhjBO+a9Fvz2WsKcnj1ff6nf/zvPa/WKvy\nsFRQNYSOSWJBFQJof7Qc+ewyuZXqmccc4KiLh/Te0+OxdmM5AWCSiufv9luTVBVKhuv7pNYp+YKc\noBcuP9Qxw9BU7diqxb8egiiBZKM5gIkFugKXIIH9VOB57Y9aJEvtpbrqxCNnXLOs/6fvtaJ0jQdr\n1cZrtXvfcmNiAQ0MU4qE14cPaV8voLeFIAqtMu5Qq1RI+6MfmlggiAz5+YEfGjjkNxb50UgFU6OQ\nSYajkWdiYybZWuJK8Q1SYlDhbJM6N4sEzo3lBuAuqp7pK5Ds5umbsc+dlbWkPS9p7PS1zi4utdAl\nyzJN9TI8l1pQSl2D/KZeDkxAgt8hiPb9JklRKqW81m7WRHGXBlDDOjYb1tq11tr51tp5QA/gPed1\n+r+lbuDbeCYt+a7d2NfmdDn8pDU0b5mgZdsEw69Yzu3Pzuvv9SYP/OufIi3OOGE6A/ss5bAD5vLQ\nn9/29g3E/ggso8bnqBLIxLm6aFnL8g7JpKpxXxhZtifSs03HC7wN5KkqV7qNDQGaIbXM2RhvYoHd\nkdrkm5AA+CngKaf+rBs171Ffp67x5IhH5/+c19zg8Vq0x9K2Y4Krihe9gUzGDFIVjGoki3EH7Vr2\nhYxzUyhat+gLoP3RpPZH/6P90ecbEvg6Wd+rqZ5F9AFXHd2l/xrgHCBpLRXWegEVQ2TLliJZ5lSJ\nUAnwx4gJv13fMevgJqp3ZnOA04I6tDk1yzsr/0f17HsceGx04XjXoMflt8rP1Bx5iiOjhS51oJRq\niSQ7rrDWvmqtLbXWJq2171trL0x732il1Eql1GKl1Jlpy59TSj2ulHrdKU8IKaVylFJ3K6UWKaWW\nOe9plbaNVUpdqpSaqZQqc7bvoJR6VylVqpT6LL001Xl/b+fvD5VSl6atO0YpNS/t9Tyl1I1KqW+c\nfY9XSrVRSj2vlCpRSn2vlOq7pa7n1qIxOr/zlTAUmS3dFpkl/gkwwQ2AG02L/OaGmx5cmLm82TGH\n/fy7QX2XbCyl9WhIWjjr5Gm7jnqswzjgWqRsIQf5wloFPFLP8T5EpMTSa1zXIRn9BUjgmc5CRBEg\nG7sizmTZCoLXItnQQ4GD05a/jkiPvYrUXKbfixcAjzv77EX1ADgOLB50+LrfPfPJzM9nTWvWxeuz\n7L1v2dfNW5pzkAl+CaoHpBrobHbtOFevKq0mrosFu0v7xmb8csj+DHmAXO2PvmJigWmWVkVr7XWP\nepl/YKvOI5cAREz4a2CPoA75IibcYNenoA6dg+j8tga+BM5zNICb17JJAfDrJpzTTs/owvFPjZo+\nbB1wKZIBHs92Mjzp4rIt0P7ochMLXAz8Axmx0shIyKV1brgDE9ShXOAixNTnF+CpRpaJDabqe6Q2\nBgH/QsreTgSeV0q9ba1NWVWfhczPGebs6xZkFHJ/xMFzHPJ7eWbaPk9Ckj0tkd/t/ZEE0jQgDNwG\nXNyI8wEZMTwBsbv/H/AFkug5H7gPcZ49qpH73i5ojM5vGySTdwBS47sM+UBvBj5XSh1nrV3TpK38\nbbAQcWXzUzURpxL4uPsuq+PJpMLrrepXeDyW9m3KbMR8uC6oQwc0a5H8q8drByYT6qf16zy3Rky4\nPummq4AA8sAYxDXt5IgJrwnqULZgLAHUts8E2d3CAFpGTNgEdehQJADeHZgRMeEvAJzMb+Z9WOns\n70JgsrNMOfu+TPujZSYW+HvbTokOBx290eZ+IFJ3O5maUntJ4HPatfrW9O8V1zPm+qhMYL0ebO9u\nCdup7Ve1nFedREy4PKhD05Aar9Q5JJzzKwPQ/uis2JIlS4FHK2Bdq5r72JTA9/dUlR+BXM8PgzrU\nD/gIySanOiBJJPvvam5nYXTh+JeBl7d1O1xcthe0P/qMiQWmId8r64A3tD+6sp7NdkicwPcTxP1U\nIaOW5wR16LBGBMDtgBUp74NaWGytfcj5e7xS6jnk9zf12/OOtfYD5+9ypdRZwPXW2iUASqmbgB+U\nUudba1OTcv9mrV0NrFZK/RdYYq390nn/q4gza2N5yFq72NnXRCBgrZ3ovH4ZeGsz9r1d0Bips78j\nAczR1tqNk5GUUkGkZvHvSG/KZRPQ/mjSxALHIfWgnZzFs4Bz2rctG+LxVE+oJw3k5SZKACYt+S5E\n1SzdwYgW8111Hc8Jcg9GeqQtgO8iJrwiqEPNkM83kx6Io9haRCkhnReRgDUz62+RniOOru/Hzr90\nZiDBY2bpwGztj35nYoGByIS9HGCi9kc/NrFAG2pmJDTwF0QtYb1zTik8iIbyONq1vMwc2r8vSePD\no+OIs95DNJ5hSMCdsgReiDgebgmupHqdtg9R/zgUmWj6H0RrG8SsYmjEhN3aMRcXlwbhlHv9FuTN\nLkIC3/TRygFIwqVWM6RaWAm0V0r56giAM8sQM3+j5mes3wWYl/Z6HvI75qcqoZG+z/VZXm/OJLct\nue/tgsYEvydPTf0pAAAgAElEQVQCN6UHvgDW2ohSaiTwN9zgt1Fof3S6kwnthwST32t/NH7IvoEN\nmcUkHg0d2patMrHAYGN4/JMvu+tFsVZ0bLdOHXHgL3/1xgIztD/6Rl3HcwKjLzIW19XrXYvM/P0A\nkWYDCTYvAVqBLVWKNtZWq6t9GsDEAp2AkTiZX+Au7Y+uRWpVi3BULJCA7m7tj37nXJOfgNsz2tGm\nlvYVIMNA2SbYhaZERtzjqGZcjUfviXzh3D8lMmJtHedcJxETnnvBn04dqHJEizlZyuvPPfLvRu+v\nHrKdlwVyIya8LKhDg5DgNwf4PpV9dnEBMLFADjKcuRtS4/mq9ke3ibmKi8s2pic1PQsUVUmMTeEz\nJMlzClLG1xgyE0eLkVLG75zX3ZER2vrm8jSEdchcnBT+JtjnDkdjgt/mSKlDNmLUXnvo0jCCwFAk\n+B2HZFu7qIzH1FrweGibSKrf3fFAEVO/2xWtDdYq3p7SW/3tpklHNxer5E2lNmUGgGYRE54N7BrU\noTZAecSENwCYWGDt7O/zFz5wU9e282bl0apdgj/eGqs4/MTVv5hYoD2STeiABLdHASebWGAf7Y8u\nMbFAP6TmqS0wVfuj79bTxoXACuf9qUA7jgwh1VZzbgCmREaUIx20JmHU9GE9O5/HZCQzDvDnUdOH\nHTm6cPyWkMx6AynvSGXJU5n1qbCxM7PJWRsTC+QjX/qrtT+6pGma6rI9YWKBXGAKsB9SEuMFzjKx\nwEnaH220xJ6Lyw7KL2QfqdxknwJrbYlSahTwsFLKIIpI5cicqLOAOrXsa+FfwK1KqalIzW8x8Epa\nycPm8C3we6XUk0gi6ap63r9T0pjg91vgSqXUJGvtxi9NpZRGLuJvYchki2BigesR6SyQB/FSEwuc\niAR61eZpOcFwydOv7rvnF9N21cZqkkbiwJlz2qsnXtqv959G1n28oA41B45EhjA+j5jwHORBW4nU\nMaWzGukxApDFAndor37lfR+a8HP6XDIfUgu+FGhPVdCWg/RkzwEec+rKHiALQR06BhlJyEV0jp+O\nmGjcuS4TkR6sRnrKZyF16Cuc9qfu7yTZ5d6agvHIhL8UXZ1l/Tdnp47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JVvWdd210bFe2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\nstWau3nppRX1UR8EjZpKTxTflH92Zepz9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B1krFoZPT/qsDvMkQ6Wv/0CcD71c34nt2Cs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\n",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363263943330588)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-MebN7rQ24m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# calculate number of cells reported per capita\n",
        "df['cells_per_author'] = df['Reported cells total'] / df['num_authors']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELMeqTUrQ243",
        "colab_type": "code",
        "outputId": "02c94b75-bad3-4c2c-acc2-488e286623ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "# plot cells reported per capita\n",
        "p.options.figure_size = (9, 4)\n",
        "(\n",
        "    p.ggplot(p.aes(x='Date', y='cells_per_author', color='Tech'), data=df.query('Measurement == \"RNA-seq\"'))\n",
        "    + p.geom_point()\n",
        "    + p.scale_y_log10()\n",
        "    + p.theme_minimal()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V7ojXAEb7xljT0GpEFb8NuohTBmgWfpRc1kUdHO7rvOetm+/Dmm9ZHyItY/4C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\nYZEqSZIkSaoNi1RJkiRJUm38PxVN42R8nwXbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8772911944070)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "Szzj43RGQ25C",
        "colab_type": "code",
        "outputId": "d0a1093d-8f01-4e3c-a9b2-8c7c7b9e9310",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        }
      },
      "source": [
        "# query dataframe for most recent studies doing tsne\n",
        "df.query('tSNE == \"Yes\"').sort_values('Date').head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shorthand</th>\n",
              "      <th>DOI</th>\n",
              "      <th>Authors</th>\n",
              "      <th>Journal</th>\n",
              "      <th>Title</th>\n",
              "      <th>Date</th>\n",
              "      <th>bioRxiv DOI</th>\n",
              "      <th>Reported cells total</th>\n",
              "      <th>Organism</th>\n",
              "      <th>Tissue</th>\n",
              "      <th>Technique</th>\n",
              "      <th>Data location</th>\n",
              "      <th>Panel size</th>\n",
              "      <th>Measurement</th>\n",
              "      <th>Cell source</th>\n",
              "      <th>Contrasts</th>\n",
              "      <th>Developmental stage</th>\n",
              "      <th>Number of reported cell types or clusters</th>\n",
              "      <th>Cell clustering</th>\n",
              "      <th>Pseudotime</th>\n",
              "      <th>RNA Velocity</th>\n",
              "      <th>PCA</th>\n",
              "      <th>tSNE</th>\n",
              "      <th>H5AD location</th>\n",
              "      <th>Isolation</th>\n",
              "      <th>did_bioRxiv</th>\n",
              "      <th>Tech</th>\n",
              "      <th>clusters_per_cells</th>\n",
              "      <th>num_authors</th>\n",
              "      <th>cells_per_author</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Zeisel et al</td>\n",
              "      <td>10.1126/science.aaa1934</td>\n",
              "      <td>A. Zeisel, A. B. Munoz-Manchado, S. Codeluppi,...</td>\n",
              "      <td>Science</td>\n",
              "      <td>Cell types in the mouse cortex and hippocampus...</td>\n",
              "      <td>2015-02-21</td>\n",
              "      <td>-</td>\n",
              "      <td>3005.0</td>\n",
              "      <td>Mouse</td>\n",
              "      <td>Brain</td>\n",
              "      <td>STRT-Seq (C1)</td>\n",
              "      <td>GSE60361</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Somatosensory cortex, hippocampus CA1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Adult</td>\n",
              "      <td>47.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IFC Capture</td>\n",
              "      <td>No</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.015641</td>\n",
              "      <td>14</td>\n",
              "      <td>214.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Macosco et al Cell</td>\n",
              "      <td>10.1016/j.cell.2015.05.002</td>\n",
              "      <td>Evan Z. Macosko, Anindita Basu, Rahul Satija, ...</td>\n",
              "      <td>Cell</td>\n",
              "      <td>Highly Parallel Genome-wide Expression Profili...</td>\n",
              "      <td>2015-05-21</td>\n",
              "      <td>-</td>\n",
              "      <td>44808.0</td>\n",
              "      <td>Human, Mouse</td>\n",
              "      <td>Eye</td>\n",
              "      <td>Drop-seq</td>\n",
              "      <td>GSE63473</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Retina</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nanodroplet dilution</td>\n",
              "      <td>No</td>\n",
              "      <td>Drop-seq</td>\n",
              "      <td>0.000870</td>\n",
              "      <td>16</td>\n",
              "      <td>2800.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Grun et al Nature</td>\n",
              "      <td>10.1038/nature14966</td>\n",
              "      <td>Dominic Grün, Anna Lyubimova, Lennart Kester, ...</td>\n",
              "      <td>Nature</td>\n",
              "      <td>Single-cell messenger RNA sequencing reveals r...</td>\n",
              "      <td>2015-08-18</td>\n",
              "      <td>-</td>\n",
              "      <td>238.0</td>\n",
              "      <td>Mouse</td>\n",
              "      <td>Organoid</td>\n",
              "      <td>CEL-seq</td>\n",
              "      <td>GSE62270</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Intestinal organoids</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.063025</td>\n",
              "      <td>8</td>\n",
              "      <td>29.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Macaulay et al CReports</td>\n",
              "      <td>10.1016/j.celrep.2015.12.082</td>\n",
              "      <td>Iain C. Macaulay, Valentine Svensson, Charlott...</td>\n",
              "      <td>Cell Reports</td>\n",
              "      <td>Single-Cell RNA-Sequencing Reveals a Continuou...</td>\n",
              "      <td>2016-01-21</td>\n",
              "      <td>-</td>\n",
              "      <td>652.0</td>\n",
              "      <td>Zebrafish</td>\n",
              "      <td>Kidney, Heart</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>E-MTAB-3947</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>cd41 cells from kidney and heart</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>0.009202</td>\n",
              "      <td>8</td>\n",
              "      <td>81.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Tirosh et al Science</td>\n",
              "      <td>10.1126/science.aad0501</td>\n",
              "      <td>I. Tirosh, B. Izar, S. M. Prakadan, M. H. Wads...</td>\n",
              "      <td>Science</td>\n",
              "      <td>Dissecting the multicellular ecosystem of meta...</td>\n",
              "      <td>2016-04-07</td>\n",
              "      <td>-</td>\n",
              "      <td>4645.0</td>\n",
              "      <td>Human</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>GSE72056</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Melanoma</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sorting (FACS)</td>\n",
              "      <td>No</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>0.004090</td>\n",
              "      <td>37</td>\n",
              "      <td>125.540541</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Shorthand  ... cells_per_author\n",
              "50              Zeisel et al  ...       214.642857\n",
              "57        Macosco et al Cell  ...      2800.500000\n",
              "72         Grun et al Nature  ...        29.750000\n",
              "98   Macaulay et al CReports  ...        81.500000\n",
              "107     Tirosh et al Science  ...       125.540541\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lc8xV9A1Q25Q",
        "colab_type": "code",
        "outputId": "6ff4e911-992c-4c43-c930-d026c148a530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        }
      },
      "source": [
        "# query dataframe for most recent studies doing pseudotime\n",
        "df.query('Pseudotime == \"Yes\"').sort_values('Date').head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shorthand</th>\n",
              "      <th>DOI</th>\n",
              "      <th>Authors</th>\n",
              "      <th>Journal</th>\n",
              "      <th>Title</th>\n",
              "      <th>Date</th>\n",
              "      <th>bioRxiv DOI</th>\n",
              "      <th>Reported cells total</th>\n",
              "      <th>Organism</th>\n",
              "      <th>Tissue</th>\n",
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              "      <th>Data location</th>\n",
              "      <th>Panel size</th>\n",
              "      <th>Measurement</th>\n",
              "      <th>Cell source</th>\n",
              "      <th>Contrasts</th>\n",
              "      <th>Developmental stage</th>\n",
              "      <th>Number of reported cell types or clusters</th>\n",
              "      <th>Cell clustering</th>\n",
              "      <th>Pseudotime</th>\n",
              "      <th>RNA Velocity</th>\n",
              "      <th>PCA</th>\n",
              "      <th>tSNE</th>\n",
              "      <th>H5AD location</th>\n",
              "      <th>Isolation</th>\n",
              "      <th>did_bioRxiv</th>\n",
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              "      <th>num_authors</th>\n",
              "      <th>cells_per_author</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Trapnell et al NBT</td>\n",
              "      <td>10.1038/nbt.2859</td>\n",
              "      <td>Cole Trapnell, Davide Cacchiarelli, Jonna Grim...</td>\n",
              "      <td>Nat Biotechnol</td>\n",
              "      <td>The dynamics and regulators of cell fate decis...</td>\n",
              "      <td>2014-03-23</td>\n",
              "      <td>-</td>\n",
              "      <td>270.0</td>\n",
              "      <td>Human</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>GSE52529</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Primary myoblasts</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IFC Capture</td>\n",
              "      <td>No</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>0.011111</td>\n",
              "      <td>10</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Macaulay et al CReports</td>\n",
              "      <td>10.1016/j.celrep.2015.12.082</td>\n",
              "      <td>Iain C. Macaulay, Valentine Svensson, Charlott...</td>\n",
              "      <td>Cell Reports</td>\n",
              "      <td>Single-Cell RNA-Sequencing Reveals a Continuou...</td>\n",
              "      <td>2016-01-21</td>\n",
              "      <td>-</td>\n",
              "      <td>652.0</td>\n",
              "      <td>Zebrafish</td>\n",
              "      <td>Kidney, Heart</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>E-MTAB-3947</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>cd41 cells from kidney and heart</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>0.009202</td>\n",
              "      <td>8</td>\n",
              "      <td>81.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Petropoulos et al Cell</td>\n",
              "      <td>10.1016/j.cell.2016.03.023</td>\n",
              "      <td>Sophie Petropoulos, Daniel Edsgärd, Björn Rein...</td>\n",
              "      <td>Cell</td>\n",
              "      <td>Single-Cell RNA-Seq Reveals Lineage and X Chro...</td>\n",
              "      <td>2016-04-09</td>\n",
              "      <td>-</td>\n",
              "      <td>1529.0</td>\n",
              "      <td>Human</td>\n",
              "      <td>Embryo</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>E-MTAB-3929</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Preimplantation embryos</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Smart-seq2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>152.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Lacar et al NCommunications</td>\n",
              "      <td>10.1038/ncomms11022</td>\n",
              "      <td>Benjamin Lacar, Sara B. Linker, Baptiste N. Ja...</td>\n",
              "      <td>Nat Commun</td>\n",
              "      <td>Nuclear RNA-seq of single neurons reveals mole...</td>\n",
              "      <td>2016-04-19</td>\n",
              "      <td>-</td>\n",
              "      <td>201.0</td>\n",
              "      <td>Mouse</td>\n",
              "      <td>Brain</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>GSE77067</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Neurons, dentate granule</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>0.014925</td>\n",
              "      <td>18</td>\n",
              "      <td>11.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Treutlein et al Nature</td>\n",
              "      <td>10.1038/nature18323</td>\n",
              "      <td>Barbara Treutlein, Qian Yi Lee, J. Gray Camp, ...</td>\n",
              "      <td>Nature</td>\n",
              "      <td>Dissecting direct reprogramming from fibroblas...</td>\n",
              "      <td>2016-06-07</td>\n",
              "      <td>-</td>\n",
              "      <td>405.0</td>\n",
              "      <td>Mouse</td>\n",
              "      <td>Embryo</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>GSE67310</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RNA-seq</td>\n",
              "      <td>Embryonic fibroblasts</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>SMARTer (C1)</td>\n",
              "      <td>0.007407</td>\n",
              "      <td>11</td>\n",
              "      <td>36.818182</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Shorthand  ... cells_per_author\n",
              "31            Trapnell et al NBT  ...        27.000000\n",
              "98       Macaulay et al CReports  ...        81.500000\n",
              "108       Petropoulos et al Cell  ...       152.900000\n",
              "112  Lacar et al NCommunications  ...        11.166667\n",
              "118       Treutlein et al Nature  ...        36.818182\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    }
  ]
}