AI4H_practical4a.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmprxmUxj93_"
      },
      "source": [
        "# Background to osteoarthritis case study\n",
        "\n",
        "This lecture is taken from the [5-day JADS NHS PROMs data science case study](https://github.com/jads-nl/execute-nhs-proms/blob/master/README.md). \n",
        "\n",
        "In the previous lectures we have worked on data understanding and data preparation. In this lecture we will estimate a few simple models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1bEDtlnF4a9"
      },
      "source": [
        "# Learning objectives\n",
        "\n",
        "The main objective in this lecture is to learn how to train and interpret a number of commonly applied machine learning algorithms. In this notebook we will look at the following algorithms:\n",
        "- Regression\n",
        "- LASSO\n",
        "- K-nearest neighbors\n",
        "\n",
        "In the next notebook we will consider:\n",
        "- Decision Tree\n",
        "- Random Forest\n",
        "- Gradient Boosting\n",
        "\n",
        "To get experience with both numeric and categorical outcome measures, we apply the models in this notebook to predict a numeric outcome `t1_eq_vas`. The models applied in the next notebook will aim to predict a categorical outcome. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTiI8iavVFCP"
      },
      "source": [
        "# Importing data and settings\n",
        "\n",
        "We start with importing the modules we generally need when working with data. The modules required for more specific tasks will be imported when they are needed, such that it is more obvious where and how these modules are used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pGMlZyBaqZWS"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScWI_HvYrXHX"
      },
      "source": [
        "We can define a number of default settings for making graphs, such that it is easier to make graphs later on in the notebook. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9k56KmXKr5hs"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/25/nx21zx0953z8ygknd3s94wmr0000gn/T/ipykernel_44613/1957301793.py:5: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use('seaborn-whitegrid')\n"
          ]
        }
      ],
      "source": [
        "# To plot pretty figures directly within Jupyter\n",
        "%matplotlib inline\n",
        "\n",
        "# choose your own style: https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "\n",
        "# Go to town with https://matplotlib.org/tutorials/introductory/customizing.html\n",
        "# plt.rcParams.keys()\n",
        "mpl.rc('axes', labelsize=14, titlesize=14)\n",
        "mpl.rc('figure', titlesize=20)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# constants for figsize\n",
        "S = (8,8)\n",
        "M = (12,12)\n",
        "L = (14,14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIoAg66Or9ic"
      },
      "source": [
        "Another way to make it easier to work with our data in a notebook environment is to define the display options."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vDxxDm87sNcg"
      },
      "outputs": [],
      "source": [
        "# pandas options\n",
        "pd.set_option(\"display.max.columns\", None)\n",
        "pd.set_option(\"display.max.rows\", None)\n",
        "pd.set_option(\"display.precision\", 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VqYjF2SsPnH"
      },
      "source": [
        "Finally, we can also choose to supress warnings (not errors!) to enhance readibility of our output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_95_D3ywlrNh"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "#supressing warnings for readability\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNlLtD6BsdT4"
      },
      "source": [
        "The last step is to import the data, after which we are ready to start preparing and modeling the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bmXH7wVmsbmr"
      },
      "outputs": [],
      "source": [
        "# import data\n",
        "df = pd.read_parquet('https://github.com/jads-nl/execute-nhs-proms/blob/master/data/interim/knee-provider.parquet?raw=true')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FY2C7r2szLL"
      },
      "source": [
        "# Splitting the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zfh-ikJbXEy4"
      },
      "source": [
        "We apply stratified shuffle split, which ensures a similar distribution of our outcome measure in both the training and the test set. This requires that we specify the outcome measure (which should have a similar distribution in the train and test data), and that this outcome measure contains no missing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNQCHcuHtEg_"
      },
      "source": [
        "### Question 1a\n",
        "\n",
        "Working on a data problem very often starts with a thorough exploration of the outcome measure. For now, provide the summary statistics for `t1_eq_vas`, provide the number of missings, and plot a histogram."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSuzazdqvB2R"
      },
      "source": [
        "### Question 1b\n",
        "\n",
        "The domain expert tells you that eq_vas is defined on a range of 0-100, and that values of 999 are used to indicate missing values. Describe how often the value 999 occurs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQZS0bdQtERg"
      },
      "source": [
        "To ensure that we can apply StratifiedShuffleSplit, the code below imputes the missings (value 999) with the median. Please be aware of the risks of imputing your outcome data, as described in the previous notebook! "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lZ-6ECcOj94F"
      },
      "outputs": [],
      "source": [
        "# replacing the sentinal value 999 with the median\n",
        "df[\"t1_eq_vas_impute\"] = df.t1_eq_vas.replace(\n",
        "    to_replace=999, value=np.median(df.t1_eq_vas)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCeIFQl1wfnA"
      },
      "source": [
        "We now define a temporary categorical feature, defined by the deciles of `t1_eq_vas`, which is then used to split the data (ensuring that the train and test data have the same decile distribution of eq_vas values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Bn10aUtKxBsC"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "# add t1_eq_vas deciles\n",
        "df['t1_eq_vas_cat'] = pd.cut(df.t1_eq_vas_impute, 10)\n",
        "\n",
        "# introducing 1 split, to set aside 30% (0.30) of the data as test data\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)\n",
        "for train_index, test_index in split.split(df, df['t1_eq_vas_cat']):\n",
        "    df_train = df.loc[train_index]\n",
        "    df_test = df.loc[test_index]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijMrMn7RxKIq"
      },
      "source": [
        "### Question 2\n",
        "\n",
        "- Plot the distributions (histograms) of `t1_eq_vas_impute` in both the train and the test set\n",
        "- Now that the data were split into a train and test set, ensuring a similar distribution of t1_eq_vas, please remove the variables `t1_eq_vas_impute` and `t1_eq_vas_cat` from both the train and the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJDC2qMzPDf6"
      },
      "source": [
        "# Data preparation in a scikit-learn Pipeline\n",
        "\n",
        "When preparing data such that it can be used for modeling, we have to set up the process such that:\n",
        "\n",
        "* ...information from the test set is not used, as this makes model performance unreliable (known as data leakage);\n",
        "\n",
        "* ...it is easy to try out different choices of preparing the data, such as applying pre-processing through a [parameter search](https://scikit-learn.org/stable/modules/grid_search.html#grid-search).\n",
        "\n",
        "For this purpose, the [`ColumnTransformer` class](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html?highlight=columntransformer#sklearn.compose.ColumnTransformer) is available in scikit-learn, which is demonstrated below.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvToMd6emVyn"
      },
      "source": [
        "## Using ColumnTransformers and Pipelines\n",
        "\n",
        "As in the previous notebook, we want to perform preprocessing steps on (groups of) columns. The use of `Pipeline` is always recommended, and even more so when a feature requires more than one preprocessing step.\n",
        "\n",
        "In our example, we require the following modules: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TUakSBp82199"
      },
      "outputs": [],
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.pipeline import Pipeline, FeatureUnion\n",
        "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, LabelEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-GMRM6-24wg"
      },
      "source": [
        "To make it easier to work with our features, we define groups of features with features requiring similar preprocessing steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "BcpO7yIQ3WF6"
      },
      "outputs": [],
      "source": [
        "# define variable groups, to enhance readability \n",
        "age_band = [\"age_band\"]\n",
        "gender = [\"gender\"]\n",
        "age_band_categories = sorted([x for x in df.age_band.unique() if isinstance(x, str)])\n",
        "comorb = [\n",
        "    \"heart_disease\",\n",
        "    \"high_bp\",\n",
        "    \"stroke\",\n",
        "    \"circulation\",\n",
        "    \"lung_disease\",\n",
        "    \"diabetes\",\n",
        "    \"kidney_disease\",\n",
        "    \"nervous_system\",\n",
        "    \"liver_disease\",\n",
        "    \"cancer\",\n",
        "    \"depression\",\n",
        "    \"arthritis\",\n",
        "]\n",
        "\n",
        "# create a group for the dummy variables\n",
        "boolean = [\"t0_assisted\", \"t0_previous_surgery\", \"t0_disability\"]\n",
        "\n",
        "# create a group for the five questions that make up the EQ5D\n",
        "eq5d = [\"t0_mobility\", \"t0_self_care\", \"t0_activity\", \"t0_discomfort\", \"t0_anxiety\"]\n",
        "eq_vas = [\"t0_eq_vas\"]\n",
        "categorical = [\"t0_symptom_period\", \"t0_previous_surgery\", \"t0_living_arrangements\"]\n",
        "\n",
        "# create a group for all OKS questions\n",
        "oks_questions = [\n",
        "    col for col in df.columns if col.startswith(\"oks_t0\") and not col.endswith(\"_score\")\n",
        "]\n",
        "oks_score = [\"oks_t0_score\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnkdAp1n3w3t"
      },
      "source": [
        "### Question 3\n",
        "Describe the frequencies of the levels for each of the features in 'categorical'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4U6nl5a76etj"
      },
      "source": [
        "We now define the preprocessing steps for each set of features where different preprocessing steps are required. Preprocessing depend on the imputation strategy (median of most frequent), the values to be imputed (nan, 9 or 999), and the encoding of levels (OneHotEncoding, where each level is defined as an individual dummy variable, or OrdinalEnconding). \n",
        "\n",
        "We start with defining the pipeline elements for age and gender, followed by a set of straightforward imputation strategies for different categories of features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "_vee1Bml7Now"
      },
      "outputs": [],
      "source": [
        "# define preprocessing pipelines for age and gender columns\n",
        "age_band_pipe = Pipeline(\n",
        "    steps=[\n",
        "        (\"impute\", SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\")),\n",
        "        (\"ordinal\", OrdinalEncoder(categories=[age_band_categories])),\n",
        "    ]\n",
        ")\n",
        "\n",
        "gender_pipe = Pipeline(\n",
        "    steps=[\n",
        "        (\"impute\", SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\")),\n",
        "                   ('onehot', OneHotEncoder()),\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEjRjIUW7RtY"
      },
      "source": [
        "The age and gender pipelines are now encapsulated in the `transformers` object below, along with the imputation strategies for other sets of features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "J2iri7177fGE"
      },
      "outputs": [],
      "source": [
        "# ColumnTransformer on features that should be included in the prepared dataset.\n",
        "transformers = {\n",
        "    \"age\": (\"age\", age_band_pipe, age_band),\n",
        "    \"gender\": (\"gender\", gender_pipe, gender),\n",
        "    \n",
        "    \"comorb\": ( \n",
        "        \"comorb\",\n",
        "        SimpleImputer(missing_values=9, strategy=\"constant\", fill_value=0),\n",
        "        comorb,\n",
        "    ),\n",
        "    \n",
        "    \"categorical\": (\n",
        "        \"categorical\",\n",
        "        SimpleImputer(missing_values=9, strategy=\"most_frequent\"),\n",
        "        boolean + eq5d + categorical,\n",
        "    ),\n",
        "    \n",
        "    \"oks\": (\n",
        "        \"oks\",\n",
        "        SimpleImputer(missing_values=9, strategy=\"most_frequent\"),\n",
        "        oks_questions,\n",
        "    ),\n",
        "    \n",
        "    \"oks_score\": (\n",
        "        \"oks_score\",\n",
        "        SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\"),\n",
        "        oks_score,\n",
        "    ),\n",
        "    \n",
        "    \"eq_vas\": (\"eqvas\", SimpleImputer(missing_values=999, strategy=\"median\"), eq_vas),\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wrnMKxyu9cn"
      },
      "source": [
        "The transformers object is now a dictionary containing each of the elements just defined."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRncEUZ7uuif",
        "outputId": "94d99de6-5d10-4878-f3dd-3c06d15f7c73"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_items([('age', ('age', Pipeline(steps=[('impute', SimpleImputer(strategy='most_frequent')),\n",
              "                ('ordinal',\n",
              "                 OrdinalEncoder(categories=[['40 to 49', '50 to 59', '60 to 69',\n",
              "                                             '70 to 79', '80 to 89',\n",
              "                                             '90 to 120']]))]), ['age_band'])), ('gender', ('gender', Pipeline(steps=[('impute', SimpleImputer(strategy='most_frequent')),\n",
              "                ('onehot', OneHotEncoder())]), ['gender'])), ('comorb', ('comorb', SimpleImputer(fill_value=0, missing_values=9, strategy='constant'), ['heart_disease', 'high_bp', 'stroke', 'circulation', 'lung_disease', 'diabetes', 'kidney_disease', 'nervous_system', 'liver_disease', 'cancer', 'depression', 'arthritis'])), ('categorical', ('categorical', SimpleImputer(missing_values=9, strategy='most_frequent'), ['t0_assisted', 't0_previous_surgery', 't0_disability', 't0_mobility', 't0_self_care', 't0_activity', 't0_discomfort', 't0_anxiety', 't0_symptom_period', 't0_previous_surgery', 't0_living_arrangements'])), ('oks', ('oks', SimpleImputer(missing_values=9, strategy='most_frequent'), ['oks_t0_pain', 'oks_t0_night_pain', 'oks_t0_washing', 'oks_t0_transport', 'oks_t0_walking', 'oks_t0_standing', 'oks_t0_limping', 'oks_t0_kneeling', 'oks_t0_work', 'oks_t0_confidence', 'oks_t0_shopping', 'oks_t0_stairs'])), ('oks_score', ('oks_score', SimpleImputer(strategy='most_frequent'), ['oks_t0_score'])), ('eq_vas', ('eqvas', SimpleImputer(missing_values=999, strategy='median'), ['t0_eq_vas']))])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transformers.items()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HN14kbHviM1"
      },
      "source": [
        "We can now loop over the different items in the transformers object to create the `prep` object below. Note that features not listed within `transformers` will not be dropped by default when applying `.fit_transform` or `.transform`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "pClpNAvogR0L"
      },
      "outputs": [],
      "source": [
        "prep = ColumnTransformer(transformers=[v for _, v in transformers.items()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcA832p0AIZP"
      },
      "source": [
        "This `prep` object can now be applied to df_train (using fit_transform) and to df_test (using transform)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "BzQcn38RAcMN"
      },
      "outputs": [],
      "source": [
        "X_train = prep.fit_transform(df_train)\n",
        "X_test = prep.transform(df_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJ0ak7v2AleX"
      },
      "source": [
        "As we saw in previous scripts, X_train and X_test will no longer contain the original column names as contained in df_train and df_test.\n",
        "\n",
        "For convenience, we create `X_columns`, containing the names of all the transformed columns. We therefore have to define `X_columns` using all elements in transformers in the same order, and pay extra attention to transformer elements where additional columns were created."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Nq861Wwdj94I"
      },
      "outputs": [],
      "source": [
        "# list of columns for convenience\n",
        "# https://stackoverflow.com/questions/54646709/sklearn-pipeline-get-feature-name-after-onehotencode-in-columntransformer\n",
        "X_columns = pd.Series(\n",
        "    age_band\n",
        "    + prep.named_transformers_[\"gender\"][\"onehot\"].get_feature_names_out().tolist()\n",
        "    + comorb\n",
        "    + boolean\n",
        "    + eq5d\n",
        "    + categorical\n",
        "    + oks_questions\n",
        "    + oks_score\n",
        "    + eq_vas\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO3RJSO1BH3l"
      },
      "source": [
        "### Question 4\n",
        "Show the first ten rows of the pandas dataframe containing X_train data along with the appropriate columns names."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWa5Mpv5hW99"
      },
      "source": [
        "# Modeling\n",
        "\n",
        "In this notebook you will develop models that predict the numeric outcome `t1_eq_vas`. We start with exploring regression models before we apply LASSO and KNN."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ps-NB5SwlrNd"
      },
      "source": [
        "## Regression\n",
        "\n",
        "When predicting `t1_eq_vas`, we naturally expect `t0_eq_vas` to be an important predictor. To check our expectations we can first plot the relation between these variables:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "XMUjuVUvj94J",
        "outputId": "1bb1e4ea-cdf9-4911-ab5a-7ab613a2fa19"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Gf31ugkeOvMAdB67jVXuGAHeZOxSQ2TuUxMZ9Y7SBomHi94sN91qtrimpsTwVCfD/ve82jo4vMp1W6U8F2Td8ZfujWpU9luzlo6/z89DJi8znLLrjInftGGVLYvVB1HrUKVMK85E33ciJS2kWVJOuoMTOkRQRv/eDbTX9WvH3wf5NfPR1Cf7lyDkuTBts6pd59YEt7O9vXe+sojOX1wjKImG/iGHZ7gwkNO0vHETef/cOZjIqGUUlGQrSlwziIK4at/tGOxnriaFZDgHRR3T5pXBfZO3lnTIsGo06dMrg94ueMoJIa7Z5uqg1tUcmLfLKnZ1MLy6RMRySso/+zg60or9l2emixlhfgpGuCKblQxId/JLbVzSrU6IvRULN1MwkJgI+Yj3JlmMqXdRa6ucN1c/b9vdzYX6JgmoRDYps6u7AUP2VZ0Z/MliZfdYtB8WyPGXft3eIm8a6Kek2Yb9AIhygoBrcsr2XGzZ1oxkmAVki5Bd48IWpykxdNcYSAuPpIqdm88RCMsmAjGqanJrNV/SuR6s+CkZ76YsuMVNYCbi+qEwg0kE04uftt2zhrmyBvOIQC/noS0RJFzV6k2G6YwFsBARsBFGkoBq87dat3JPXyaoqiWCQzpifbN7iVTcOc2veoGSYhGWJeEzmiUnvlRGvNrYiO4jlgOgDQRTwiQL37xvm5q0lippDJOCjKxauxOBodwzDdJAlH7IoEOgNE5op1czehkQI9kQrz7gbR7soGiYRWSIclDg9k6W3I8LrUlGWlyyxfA5Hzxa4fVc/BxSDku0QFnwEQzJPvFhguDvBYLeN4IDtAwGBiaL3FrFFRWewM8pAKortOAg+Hz4Bvn18nqLpcHA4hSwLGIZN0bD4l2dnCIf93LOrr/LQMxyb7x7PsHu4k62miWM6+CQfAUli4kTjSnwZguPnZ1+1g4szeRZVk86gxGhfjIVMiVfuDDCdTVNQHaJBH/2JFKZjc+smm3PjWQoWREXYMpwgnSvSGXJYrDJuZ0is+f9G43tuYPzOd76TL3zhC7zvfe/j537u58hkMvz3//7fufPOO7nxxhsBOHToEDfddBO//Mu/zC//8i+TTCb5wz/8Q2KxGO94xzs87713JAWySE4xiQXlhkGZadlkFYOgLBKs7K0TUQ3L8zdlSKLQ1NhDqRhD6zc+uSxU3WQyrRALydwytjLTllN1pjMq/YnQqoMNLz3Wgmr7Jav2Mtbbr1UZQb/EZV6ka3Dv7n669Fn27e5vqFewas/k1fjVq3zfcCf7hluo7CpYi+yConNmrkAqHOD9r1iZnV4qapxbKDLWE6s8+Na7TtWxds8NQ5VrrjbW1uLvar1/6pV7OXfuLFu2bCWrGC3r3Sw+RMH9bBYf1XoPprzbmJce0ZCfZnMsayn/0lMXK4PiSNV7btFwB8tfeuoibzk06nmvtdjcyx6LOYXDFzN0RIPsGRmr/HYmU+LoRJa7sgX6EtE1yV5LX1Fdp6PjixydyhMPSWztDVHMF4jEosznFJ6dznN0fJF9w51XrN9q/fxCvsTR8SxdsSDXDa2sSM5mFY5NZBlIheiIhjz7Fi/Zya6VgWuupHNmvkBvIsRgamVg/q3nppkoggykolX7ggsGF7I2Xz08wcHtXcSX9zH7Jffzcu1vLT56cTrN4UtZOiIyuwZXZq5nMyWensjy4nSa3f0p+hJR+hJNbFvl11r9VtrMXFbhyMQSW3tjbO1eycP4468/27TeZXzkS4f5+FsOevvVI6a6YmHKZveKwcfPzHF8vkQ44GNTfGUadSFX4vnZAt86OcuB0c6a3yzkVI5OZOmMBuir3ju+WOCpi0vsH+lgZ3+yUn5qOsvhiSxbu6OMVsXBnz98flW9f/wzD/Pxd95U8TfAxFKBw+MZhpJhBqv2N08suOU/uLOPka4Vb5+fz/PUeJZNnRFGulZocf/+0dOryv7tfzjCu16xi617Vvx0Ya7AU5eyjHZF2DOy0jZOz2Y5fD7LdYNJ3nj7yormC+Npzi2Z9MRFbhtYGRg9dTa9quz1xvfcVoqOjg7+4i/+glQqxS/90i/xqU99ile/+tV86lOfqrnu05/+NPfccw+f/OQn+fCHP0xvby+f//znSSRWX3qSRQHDdvfN1cNyHEzbQa4bJK32m5cbDNtBtx2CdTQnQUlCtWwMj6Xh9cDL1X4v13qtBzTLQbUcIv7a1YCIX6Zk2GjWxun2Usbaeurdany8lHqfns1d1fdrgZc9VMtBMS1iwVqbx4N+irpFXtk4m0+nVTTb3b9fjagsoFo20+nLJy1dqeyi5qBa7t7UasSDMnndJK/aa76Xl2wbUE0Hv1i7ejWnuKsWcl1OuSy4y9nj2eKGxeF8Vke1baKB2jpFAyIl02Y+qzf8plX9fD5QTItgXflMrvHe1XhhujHOW/Wr1/ULWQXDhrBUWx6SBHQbZtOlht9olo1qWg39kSQIKLqFX6x1oODzoZruvuZqNFkIqsGFIg3+tm0fim4RDtTFgSxS0i3EekICx4diWDUJ5ACZyyRaTqWNBv1s3D4hXKeHLIholo1fqJXt8zlYQKDOttdurtjFy3rG+Oabb+bkycblku3bt/P5z39+1d8mEgk+8YlP8IlPfKIlmYZlIwtCY7CAu3wk+NzEDUFc029ebpAFH37Bh2qaldkDANU0CYoCsrBxOrxc7fdyrdd6ICD6CIo+irpBQF6ZaSrqblJYQNw43V7KWKvWOxGqZrFoXe9W4+Ol1Htb7+rsJpf7fi3wskdQ9BGSRPKqQTy8YvOcqhPxi8RCG2fz/lSQgOAmtYaqdukUDJugKNCfWvuaUquyIwEfQVEkpxo1g4mcahDzS8SCwprv5SVbAIKSD92yiFQ9tnuWWSKMujGuYbsP9+FEZMPisDvhJygIFDSLSLXNNYuwJNCdaFyRaVU/x4GQJKJatUOjvrifC00G3mVc198Y56361ev6rkQIWYCSadfMqiumjV+A3lS44TcBUSAoiRR1g1hoRT/Ttgn5RTdpugq24xCUBBS9NoVNZvXB8aYIDf4WBIeQX6SkmTXbvlTDIuwXGyd+fA4hWUTRTDcjcxnJEKRXIQMZSMkNzxkBt08o1elh2BYBUUCvezlzHB8ioNXtgRe5OiaLVvE9N2O8kdBNC9WwiIekpkvnkiiQCMmohnudZTuVf3v95uWGoF9iMBUirxjkVB3dtMmpOnnFoD8ZXHVp+2rxcrXfy7Ve64FoyM9YT5R0SWOpqKEZNktFjXRJY0tXZF23UdTjpYy1ar3TRQ3Dskhfod6txsdLqfdbDo1WEmCKxsofuHuM33Lo6pN7vewRCfk5OJpkqaAykylRUk1mMiXmcyr7hhL0JZon4bQiw8vm+4Y72TcQI6eYzGYVSprNbFYhp5jc0B9raT9/q7K7YmH2DSdYyKvMZhUUza3DQl5l71CCTV2xNd/LS7YNjHVHKWlmTUxtH4ozFHEHS5mCQbFkkCkY6MCmhMDrDw5tWBzu7k9xcCTBUtFgNlOiqGjMZkosFQ32DyXY3WQvf6v6GbbNDUMJippR03+90oPRpoyPv+XgmmW36oudA0l2dYcpaQ4LuRLFkspCrkRBc7i+N8rdO3obfiNJAvuGEmRKGvN5FVW3mM+rFHWTQ8NJFMOsKbdxODiUIFsymM6UKKru58FNq694/+lP3dngb5/Px8HhJJmSXhOfWdXg4HAS07FrbGvjcGg4Qbak17TjbZdpQ7/+QwcanjOWz+bQSIJc3b1UzWJff4yCUquf5Ths6ZBQDHdLTrHkxlQ8JK4qe73xsp4xvtZQDZuh1OpMC+XvcopJSTeRBaGGOWEtdE7VKCh6Q5LDlWKtsstZ/BcWS6TzJcJBadXs/vVE2U7zeY1MUSfkF6+I3WIt8LJHQdXJKhYFVSexPNVxOb+uF9ZCm7Qev6nG7uW9a4+fWWJBydIVCnDLWEelfCNRjqlnx7Nk8hmSsRA3DCeuKtbWGudl/b57ap7nZhT2hBRu2959RXq3Gh9l/c7NF5nLqsSC8lW3MS+96+Pjcz+xn5/43NM1CXidMnzuJ/Zfsex6lPVeKuoUNZOA5NrjLQdGiQQkHj23wMSiQiIics/OHu7fM3SZOzbq12pf++s/tIff/ofn+O65NHkDYrLB7VtSV8z8sprsety93WU6efT0Iufni6RCMndt7+bu7X0Eg1LFViXdxC+uroeXbSMdERLhEhcXSiwVS0Rlt9/+zPsP8Qt/cYQzWRt9eVqtzLZTjrfpjEpG0QmKQk0cttq31Nf1F1+1CzjO147NUrIgLMLr9vYul69u23p7lPWrfy71RoKcms9xbCLH+GKRZDjA/uEUH33zAL/191MN9//tt64kcdTrV5Y9k3UHpRG/RF8ieFlfvDiZYzZfojcWZvdgnP/2jhv5yJee5ZGLBcoL/XeMRvmdt+7z1G843kcsKHN0Ist8ViMakrhtSxd7B5Mcm8zw2NlFzs4XSAVlbtvaxc6+GF97dpLvnltiLqsSDUrct6uHLYks/6/JFuv337TS74wvlVhQVYKS6+9bN3XyyJk5nrywxExGJRYSuWt7N7dv7ebCUpFnJ3JcWiqSDLm2Hdk7yIMvTvPUxTRnCwXiAbcdH9gm8ImvzjfI/s0fHqz0q89P5ZnKlEjU3eufXpwmm8+QiMm8dnc/d2zv5l+em+aBZyd5VjHoCsncf8Mgr9jRyR9//QxPXlhisagRkQTu3NrBzs2+prI3Au2BcRVOz+axBJnd/cmmTATgnTFfpnPKKgam7SAJPhIhmUTIj9BkyUrXLV6cznBmroBqOQRFH2M90VVle8G2HbKKvmbZld85DgjLn9cItu2QKelMZRV008YvCcjLNEir1bVVGc3sERJFTsxmOTVb4MykwoJ/ju29KzZfD9YNL5imzXi6yGRaQbcd/IKPwVSI4VQESWou50p+0wwFVeffnpvk8fMZFNMmJAnkFIWRVJiOVdgh1gMl1eBbL07z+IV0hYVkKV/iLQdGiUfXxn9dRqtxnlM0/uHIJR67sEQmb3K+MMl8XmHonhBd/tYO0GmVMcW2HbIlndmcimJYlHSLzojsSf93JXpHZInJbKkhPgD8EjVrrn7J3c+43nBw8Pncz5py24cguJ+Xw2p+baWvLRkGM1mFouEOVYoGzGQVVLN1RtVW/W3a7gz1dE5F0QxUw2I2q2BWUYY6uFsDypby0rtMCedlWxsHn+N+AiwpCpPZ2oXmyazNkqJ4sjOUadzW2rd41fXozAx/+cxs5bq8BX/5zCz37Ovg3uTqdJD19qjIqnsumbbNZLrE2YU8Jc1isaTTGZG4eLFxUAxwYXwc84brm/advZEgFxcKnJwtoBo2QVlAM6Ls7E2gWFaDfpZp8eePnOGJi1lUwyIoi9w8miAaLy4PilfwyMUC3zx/nh/tuKGpfoLgIx6SSYRlRAGiQZl4SMa0bc7M5jg9l6eoWyz4RbqjMtt7o/QmQnRH/PgliURAoDcR4sHvNLfnmTOujGTYj245qLpF0O8mDko+l1M5FvCDTyfm9xMLytiOw2S6xPl517bpgE5XRGJTR4Qd/QkKhuv3RMjPjv4EX3qs+QFC5y9MYu+5npxisJBXKWoWhumQU4IsBRX+7blJnpvMoVkQSINk2XR3+PibJ85zesnEAibSBkX1PNePRtjaE+N8WiGoW8T8Ilt7YjhOftV4Wk/4HOd7OLNonVAqlTh+/DhTTpJ51eVt3DfaGpVWNX1RmQBdNdzDKZrROR29uMjT42lS4cAKkXpJuyayz8/nKxQ+ZfLzvGKwvTfWlMJnPXEtZHvZYzZT4sxCgURQZm7qIj0Do2RV44ps3iquRO/1stUnv3aMr59YoDMWIB6UyKkmi3mNe3d28V9et3e9VGyKP3/oFA8en6M7Hqw6yEblvl09vP+u7S3dq9U4/9jfP8M3Ti2SCss4egGfP0q6ZHDP9k5+4803rpeKTXEt2reiGczmtYb4+NW/fqYyW1xNtN8fgsd+4/510c+rTo+enOHx80vL/i4f6qByz84e3n5L84FSq371uv6X/t8TnJjXCcrgM8GR3IOL9vQF+ftfuGdd9PbCanH+poOjTevrF33olrPmci9//9wXnmm67zQEHP/d5v5utW/xsvlqB3xc8JDdajxPLhY4MVugK7Zi24W8yt88Pe0pu3zAR/29TNNitqA1tMuxrii9yXBDnf7qu2d59Fy6oe88teCdiVY+TMbr+VMve3wuz4m5YkPs9EVlZL/ckt5ess/MZHluKktnNEgsIJPXDBYLKoJts1AyG2SMJIOEw/6Guq52eNBfvPcA3z230CDj0ePTnMvoxIMiYUmgZNrkVIvSctAGfKwcYOJA1Adb+iMNdSofZLM5KfF793Wxa9cuwuGNOSH4e3fz5AYgFpJJhQOcWyhSUFbPeq1GPb2PKPgq/84pJqZV+zZfTSXVEQkQkAU6IoFrIruaSioe9OOX3FOJYiGZ6YyKqrc+u7JWXAvZXvawLZsXpnPEg+4slCy6M8RXYvNWcSV6r5etzsxnefJils5YgP5kmEjQT38yTGcswJHxLGfmvTkxrxYT6TxPXczQHQ8uy5bpT4bpjgd5ejzDRHrtMwCtxvnJ2QxPXcrSGfHTlwoR8sv0pUJ0Rvwcmchycjazztqu4Fq0b58PzswVifilmvg4dnGxMihOBAQiAYHE8hGt0wr84zPjV62fV51KqslTl1y6tr5kmHBQom/Z30cnssxkG08sbdWvXtc/d2mJ04vuoLgjGiAcdD9DMpxaUPnO6dkG2euF1eL8yHiG07OFhvpKoo+ptOrO4FaVy4KPqYyCJPrW5O9HT85WBsV+38ofgAJ8/pGzDfVttW/xsvnfPn6hco2v6q+MP/rXEw2yW41nx3Y4cjFDIuSnNxEiFJDoTYT411UGhwA/9tmHG/STfT6encgSCcg17TIe9HN8Oo9t2TV1WsgqPHUpSypS23fOXYae4Vf+7vFVnz/VsrF9PDWRJRH218ROLCTz9GSOgCDU6L3aoBjg0Me/3iDbMCyOTmRJhgN0x4IE/aL7KYk8OZ4lunzvsox4UObweAZJEGrq+tkHvAfFAD/7+SN0RoM1MhTd4PSiTlj20R0PEwkH6Y6HEatm0lMxP5Gwn1TMjwjkHTBtq8YeU3OXPwJ8PdEeGNfhWtA5/XulkroWsr3sYTm49DdyreyXK23ZetkqnTeXqaRq7xMPShQNi3R+416EskWXoqgZjVVRt8gW155n3GqcL+YMVMsiFqzdlhQLipQMi8Xc5YiPrhzXon3j+FAtu2HZey7n0nfVb8Yqe38j6dqKholqtEbXtl4UWrM5BcuGOncTEF2GhovzjYPy9cJqcZ5XLZaKakN9RZ+AZtv46umqBB+a5SD61ubv6czqNHTN/N1q3+Jl8wsLq9v0eBPZrcazg4+SaRGpow673Ov8RKGRtkwQfE2p30KyRMm0qG+WOb3cd9ZeX7pMl3l6Tlnz88fETdKL1FHehSSXzgypte2FJjTINmynKVWcKPqWt4fU1inkd2nc6nenzV6m28pDw8mQiuG47bLOr+Vbe/VTTt2pxcpGny9dh/bAuA5XS+dUDS8amGoqqWstu5pKqhrXgkrqWsj2sofocxunYtTKvta0ZdVYTe/1slUqJi1TSdXeJ6eaRGSRVGzj0gwSEZeiKKfWxnlONYj4RRKRtXc/rcZ5Z1wmKIrk1Vqap7xqEZZFOuPepzxeLa5F+8bnEBSFhqN9e+Lu1oN63s+y99ebrq0aEVkiKLt0bdVYja6tVb96Xd8bDyEKUOduNMvl9B3tXjsjRqtYLc5jQZGOSLChvpZjExAEnHq6KttNxLactfm7P7k6DV0zf7fat3jZfFPX6jbd1UR2q/HswyEsiRS12rquzs0AQ1Ea9LNtpyn1m2KYhCWR+mYZ95f7ztrrw5fpMrf1hNb8/JFwZ3eLWl2dTJfODLO1CRsJGmTLgq9CFVcNa3m7jlpXJ0V3adzq5156L9NtxXBPBq5GSPa57bLOr+Vbe/VTvrp8iNA1zoZrJ99VYSGrkrMF9g+nKgwRa8mAL9O6lI+Ird8nV/+7MpXU0+PuaS71exCvhEpqrbLLVFKnZvMUVffMe3xuosP23tiGUklVywYa9rath2wvewiiwHX9cc4sFCiqBgtFDXmxiIXTss1bRb3NHRt8QqPNq2Ot+je25SBJ7kOjqJuV36wlNse6E9w0muDrJxbQdJOQX0TRLQqaxb07uxjrXv0R0yrLSjWGUjEOjSZ58PgcimkREgUUy6ZQMrhvV09LR6BX+9Wy7MrRuobtNI3zHb1JDo0k+MapRYq6hqkapI0squHjnu2d7OhNtqTL5VBtp+r2bZg2siRgmDYF3biqvqVab8eBsZ4Is3kNxwd+UUS3LPaOdtL5+CUWDchqdYOoELzhxis7drExW7+xjYWDEodGkjx+fgndsJAkH6bpUNAM7tnZ05SurVW/esneM9LBtk4/J+Z1Lqbd71A0ZNw9xq/Y1tuSfq3YozrOM0UV2efDcBwMC+7b1cO23mhDfU3LYSAVRLcciqpRo/dAMuQmTxlW5fpqf1u2gyj4sGyH23f08pdPTWEAet1AJgS8946tDXVfSz+8Fn+/9ZZN/Onj7tacZsO3n33VzoYyr3tV61dQDGzcWTuf6OPAaJITswVYgnBAoqSZvGp//6rbCv7sJ909xpbtVNqG4TjcMJRgtqCxkPcRkEU0wyKn6uzqjyGIAgXNqBwB3pUIcWgkwaPn3Od0LCiQV216kiEyq+wx/sQP38Lcsh6aZRMQBSRp5fmjmVbFfwgOh4YSnJgrMpkuVvbm5hWD/YNxNNvm9FQGURKwTJu3XUbvp5b3GJu2XdFDlkX2DSV4biqLohn4JR+66aBZNjcNJ1gomUwsFYkGJAqaSa5M42bbzOQUgqL7MvGT9+9YdY/xHy3vMS6VDETJh2U6hPwy2zr9nMvoTCzkCYjuy6rlW5k1nsnp5dOoXTv7QBJETs9kkEUfhuUw0BNhfurabadoD4yr8Oxkhs09SbZ0RlvOgG+V3qdMbXJuochcQSUsuwPya0El1R0O8GRhnqcvZd0BiySyfyRB9+aulmW3isvRB60HvOzRG/FzZjbH4xczzCzq9KmLHBp1/b3RqNh8POueKiSL7B92be4Va/2xENmSztmFAorhEJJ9bO2K0h8LtcSA8s5bN3FyOsuxmQK6ZeMXBfb2RXnnrZs863ulTCf1eO3eAZ4bT/PMZA7Vcg9buHEwzmv3DrRsw1hAdhlNMkqF4nAgGWpYvivjXbePcvj8AqcXdXQb/EWdbZ1+3nX71XP5luFlp7GuGJPpEscms5QMd5Z672CC7d3xlm3rpfdwdxzIcXa+WGEb2dod4cfu6+CT/7TUcJ/33tu9bvqVbV7fxl69p5/nxtMcubRUGRTsH4xz144eTxmt+tWrfb/pjq4G+i4DeMPt3smOrfrC6/o7d3bxtWfGObagYtogCbCjK8gP7Or2rG+ZVaRe78FEmKJhNlwfiIfIKYu8OJ2t+Ht3f5wP3BXgjx7SGur6gXu8+1SvfngwEW7at3j5+1de392UQutX3+Dtby97+CJ+nr6wyJFLmUqbOTCS5JXX9wEzHBnPUFqezTwwnMQtbcQoMJgIu31nXdvY0hnl6ESa4zM5SoZNWBbY1Rdn/3AH8yWNqeUTFAOCwEAqyE/evQ04zeGJLIslt9++fUuK6zsU/v5Uo+x374OAT+DMTJajE1lU0yIouQPTA6MpzszmePTCUoWh5+BwkvfduYV/fHqSI+MZ5nIqEb/I7Vs6uHdPD5/9xlmemMzVtKXVUGlLdXoc2tTBCxNpvnshXXnmHxpJ8LabR/jmi3MNtn3t3gGOXExzbDJHyTAJyxJ7B+P0AHNN5A4C1w8kePzMHN+5mK484w4NJ/jgG7bxK198gemqd4n+EPzcKzv55AOLOKzMHPuAX3hjPw88lub5yWJNW3rdJvjahVXVXze0WSlYYaV4JhtkQXW4bUsXe4ZTLWVKl/G9wGP88IkZvntugXjQX5lBzKk6t23p4s6dfVdVh7Xiarl514J6e5T1jsgi87NTdPcOUDSsa6J3tc2DfhG1yuZesVbOTvf5AMcHPnem0Ctr3Ss2Hzg6zkOn5pFEH47jHrNqWg53be/m/n3NZxBbZQrwQlm2X/BVZj10e3XZXijXSRJ9ldkQ03I86/Tpf3uRB0/MERAFCsU80UgMzbK5b2cPP/fK3S3JvlydvDLsZVHA5/hwfA6GZbO9N0Yy7L8iFoZ6vctxUJ5tKX++9vcfaXqEqgyc9mAKaFW/cl3r29hfP36Ob5yYIxyQKjN1Jc1cEyvFWv1aRr3sbR9+oCk7w2p6rxcjxucfOsWj5zOVU/BUy6KoOTUMKPX1vZze9deXmSQCsogkCJi2jWZY/OQXnvG0kRczRBn1/XCr/n71J/+Vk0tmzYyxD9jRIfEv/+VVq8quv1e5r0iE/ESCEkXVJKvo7OyNMtgZdVdK8OHDwSf4VtW7zM5Q3zbKbcZ2HCwLRNE9frlcLgu+mtn7cvlCXqvQlnXFAquycZTZGZJVjA6ZkkZUEiiYNlG/5J4sZ1gUdJM9AwnG+hJkSiqK5hAK+EiGgzx4bIJHzy0RD8nLWx4scorBYxe8d1iX9a7X4+iFBY5cyhAJSIRkCcUwKWpmlW3BxEZCwCe4x1srprudyedzaecM217V5n/0jr08dGqeaEAiKEuohklBM1nMFhnPGog+q1InyxHJFxVyJggmCCLYFtgSxCWIRUIEAz4CgohmW6iaU2ECuRasFO0Z4yr0JoI4ksMLUzmS4QCJiJ+g7G4PLx/vmFNMYkF51aXPVowaDflZr/nKtcjOFFVemMpVskcBUhGQ8wInZvLsHU6SjKz9+NQrRdAvsdFSqu1RrXdHxI+Z9zPQEWGpqG+43s1szrLNX5jKkYoGK50fuLFm2jZTaZX+ZJBIVVJPQTMq5WuJzYV8iaPjWbpiQXoTK5zFs1mF5ydz3Ly1RFestnOpzxy/nAwvXIlsLzSrE5QfFo11urCY44mLmWXZQRYWdLq64sxmVY6MZ7iwmGNT59Xtt/Wyk6abnJkv0JsIkQqvDK5yqs7EksvfHfRLa7Ktl97VcVAdH194+ExlUFzdF5i4s6ef+9ZpfuLubVelX31dy3JmsgWOXMrSHXdZKcqYyZQ4OpHlrmyhYTtFq36tRrXsz33rdGVQXD4+VsCdifLSu9U497r+3EKOw5eypCIyA1UrXzPpUoUBZUdvsqa+a9W7fH09k0QZ/+fhJtOWVfjDfz7Of3yN92Eb1f1wq/7+5vFpzi2ZBHwus0AZ6bzO+SWTbx6f5gd39XvKrr5Xs74iFQnAEhwZzzCQijBUtbL3wS96kPku4/2f/Tq//54fqLItFFWDqYxCXzJENLBSX6+2VH39WO/Ktq/PfWN1dobPPHScu3cNV/r6WEhC1y2eurTEodGOGj1msgrPTeYY7YyyqWulP7q4kOPJixk6Y0EGUytt6bMPn19V9q0f/zr/+su1ek9nijw9nqUjEqC/6l4TS0XXtskIQ1X7xRfzKi/OZNnVn6AzuvJM/C9/sbrNf+OvjvG2uzbX9PUnp9Icmykwkgoz2r1yEuKJySWmFegMwODAim3H5/NMK9ARcxjrWbn+8VPNOas3Cu3kuzrEAjIF3aK4vL+rGl6Z0t9LUAwH1bIbliljAZmiYaEY37u6rYaXUu/VZdsUVGPNWete5V6xWdSc5czqxoz5gmFS1K6eKcALVyLbC63WKVOw0FZhxMgUms2ptgbPDHufD9V09zZWIyi52e+qaV81C4NXHJy5DPvCqRZYKVq1eV5xltkZale+1pOVwguX06vZ9+vFiJEvWqi2TayOOWE1BpT1YhOaSK++7/LFDfT3+dkcFi4HbTX8ovsidr4F2V59RTggUdItHGrjfGpx9fudmWtkZ/Bi/GiVIeRybWwqozT09bIkoBgWYl2dIn6ZomE2MIGUdLctxerYKi4HlUa9dRMUwyJax0wUWbatXbdDXBB9KIaNVKf3xGWoQBagwX+O40O3bEJ1QeITXJli3UxexWxCbZ3UjWNTbYr2wLgOec0g6heJyOKaM6W/lxCSfQRFoSF7NK8ZRGSRkPy9q9tqeCn1Xl22QDQorzlr3avcKzbLS7vNMuajskQkcPVMAV64EtleaLVOyahIYBVGjGS0tQdOK3XCcQhKPvS67HfVdLPfg5Jw1SwMXnEwdhn2he0tsFK0avNYyLfMzlD7FFtPVgovXE6vZt+vFyNGLCISFATydcwJqzGgrBeb0NBlcjN2b6C/N/fGEXEPZqiGbrmrFZtbkO3VV5Q0k7BfxFc3eBu4zBk5Yz2N7AxejB+tMoRcro0NJEMNfb1h2oRkEauuTkXdICJLDUwgYb/blvJaay/wQRr19ksQkkUKdcxExWXbCnUvHbblEJIFzDq9hy5DBdIFDf7z+Rz8ooBSFyTlEzGtOhq2itnqTswMblxufFO0B8ZVWMyrLBZUrhuIM9wZRjVcjkHLdir/joek9T0q2LLRTKuByH6jkIwEuW4gzmJBZSpTIlPUmcqUWCyo7OyLXXY7wXrV96XUe3KpSLqoM7lUXLPe6yZ7scBCXmFysVCJtaFUqCHWylnrxvL/L1fuFZtdsTD7hhMs5FUmFoukixoTi0UW8irXD8abbmUoZ45fbfxXy57NKiiayWxWWVW2F1qt06bOODePJlnIq4wvFMiXNMYXCizkVQ4MJ696G8VqdXJ8Psa6o5Q0k4sLBc7O5rm4UCCvGAx1hOiJB9esR7WMgmYsM4oYnnHwuoMjFW5Qs+oP3L22a91GsZp+XnXtS0Q5MJJgPqdyYT7P5GKRC/N55nMq+4YSlW0U1W1/LTK8+orq8p+4exvl4acFNQk9Xnq3Ktvr+oFEhIMjCdJFg/GFPIu5kvtZ1DkwlGjKgNKq7DKTRF4xyBQ1Cpr7ee+ewVV9WN5GsZb+1ivWvPz9g7v62dIhoTkus8BiTmcmp6M5sLlDWnUbRT1q+qmlIktFjYmlItmSzoHhJD7Bx2JBJVvUWSyo/OfXrH5y5Z//5L0Nti0zfpjWcgJlySCr6DVtKVvUyJZ0skWt5vqlosZ8TmOpqPHWWzevKvun7trFYkFlPq+i6hbzeZWSaXJoJEnBMJlKl0gXNabSJTIljT2DcWRZZGKpwJnZPBNLBRLhIDeNJlnMq1yczzOXKXJxPs+tm1YfnT72kXsb/Bfyy+wfTrBU1BhfLDCfVxlfLKzYVvSRLmkUNZN0SUM1LXb3xVENi7mcwkJOZS6n8J9ev7rNP/aOvSzkVc7PZZlaKnJ+Lotpw96+KHnV4vxcjplMgfNzOUzbR38IVBtms0VyRZXZbBHdcRPzDNvHxGKRbEFhYrFINBRaVfZ6o73HuAp51WLPQIqDI50El5cd1sr00CrWK+v/SrB/uIPpTIlnJrKVTNQbhxLsH+7Y8Pq+lHrvG3Kzgp8azzCzYNDny3NoOMm+odTlf7xOsuuzf/cNrdB31cdambpnreVesXnHWA+zWcWVPb8i+46x1jPHW43/u7e7SY3PT+aYzJaIyhJ3be+ulLeCVuv0tptGmVze55nNQ8LSODCU4G03rR8rhVednLDMd07O8uTFTCUz/abRJDePdhBvUQ+vTHMv9oJfeWMfv/2Vxnz9D79p7QOVy+nnVde7d/Zy9OIShycyqKZNUBI4OJTg7p29LTNcxAKyJ0NCXjMayj/+9lE+/NcXG+r0O+/Y1LJ+q8ludv1P37ONuS8fa2B++bG7GunSrlS2F0vNh+7v5L890Li34Fff0HPlDCh1sebFEPKrb97Jj/3p8y5d3HKZvFzeKmr6qao+8nU3DHJiJs+LkzkUyyIkiuweiPGDPfDNJhQJrxzwtm1IFDkxm+XMXLHCkjPWE2GwK8aZhTxn5guoprviM9YdpX+ZxeKFqTyq5R4Oct1AjF/3aGO/9ZYhbtrksjudmMkznVOIyCK3belid3+cbxyfaXju3jiS5F+em+bwpUyFzeHgSJJX39DHZLrE4YlsTVvySr4T8fbfPbv6mM+pHK6y7cFl205l1Qb2jk0dER49O8+R82kUyyQkStwwHOfODni4kfCGe3rh5s2dHD63wOGLmQrzxcHhBO941RifeuAUz88WMGyXV/z63ii//sbr+aN/PseJ+RI51cYvwPU9YT74+u18+Ylpjkxkmc67dTownOB1+2X+579d/QFFa0F7YFyFO7d1E4xE0BybsOAjFQkQC8pXzOO6GrKKXsn+DfvdbRtlbsdWsv6vBPMljWQ0yD07gm4KseOuXMyXNKKR5g+89arvS6n3ucUCiAK3be5iyl9gYLALw7E5t1hgX/TayL5lazd+nw/dcTAsa1l2p2espaTWypthUdEZ7IwymIpi2O6yKIJbHvfQW1in+A+HZe7fN8zNW0sUNYdIwNfSTPHV1Gkqq7JtMMloT5Tx8QmGhwfxyxJTWZWe5PrQA3rV6a8fP8exqRz9iRDhoEhJtTg2leNfX5ji7bdsaUmPvGagWw79yWAlq1u3HIqG2fQ+X3p4odysK/ABf/vQPD928/ro54WnLixh+QRu2dxZ4W/WTJunLixx2zZp1bZfL6OaIaH6+kxJr7CyVJc/fDRHZ0hEVyxU3GVlf0jksecLvG1fa/p5yfaq63PjabYNphjrTWFhIyLgk+DcfIkhj9WJVmX7RR+hgMyeoVQNS80/PJZFpPawBBH46mMZ3npDa/2tV6zlNYOU1Hj9lx+fIR4SEWyrwl5gCyJffnyGO8daewEt91MDqSi24yD4fPgEd4CpmDa7BuIIog/bclBMq+mgGODfprxte34+z2xeoy8RrHDDz+Y1coqBYtr0JkIVNpXZvMZkusRsQaMnHqjwHp9ZKPDQcwU6QyKKYlUSPUMhkWdOqPzoQYk7d/axdzhZeYFJRoKcn8+7z91doZrzA/7+8DjHlpOzh1ISedXk8fNLjC8WScSCvHJXCJ/ow7EcrKaM0S6sVfz34nSOcNjP3Tt6kXzuuSEWDidm8vQmw+wZTFB2oOPzcWwyw2xBY6w3il8S0E2b2YLWdFAM8I1ZeOL8IkXTPRfA7xfQdZuiafG3j00SCgW4fSyEIAjYto1u2hw7V+T9d+9gMl0ipxjEQzKDqTCqKvAD1w1wYEs3mmERkEViIZnPfv04IfHabHNoD4yrkIz4QRZrM3BbZJlYC9Yr6/9K4JXdnFN1pjMq/YlQA33aetX3pdS7oOicmSuQCgdIhGS0TIDBjjBZxeDcQpGxntiGHfJRLbuj6mG0VNRqZHvFWqvl1bgSf7cqYy3oioXpWvt5HqtiLXWq9XcUqbTIlr7khvn7cuwMHVGQMr4adoa16NGcvQBP9oKrZQpYi35eWI2F5NmJLIMdUVKXYfpZjbWhnq2luvz0VJajU3niIYne/hiFXJ5oPMZ8VuHoZI7nJpfYM+i9IrYW2V51bco6A8zn1TUx3qxFtmXZVYwKK7O3335x2d8idMRW+pZ0XuP0os6/PTfLLds6r4IBpTHWynhucomnJ3IkwxK9yZXB/2ymtCabV8Orn1osqLw4mWPXQJzOKtv+2cMnVr3f//qnF/lPr919eWaPAJiOw4tTOXb1xyssMhEkDNPmyIU0Yz1RusoME0GZqcUCz88WSIRkdgx4652MBEleRr9LC0WeGM/QHw8x0OH2FbGwH8O0eWo8y73be9jUtyLj9//h2VX1/pn/8xC/+/bba/y3lFd4cSpHV7wxPo/P5OmOBeiIrbTXpaLGC1N5euKBit4AX/vOhVVlf+abL3LHruGatn92Jsvj59JcP5hkcxWzx/hikcPjGTb3JLhpywq/+kJBrfhicGBl8uKJM3OcXFAJ+2Gkc+NZs9p7jOtwLZgn1isT+0rgld0clCRUy27Ijl3P+r6UemuWg2o5DefFR/wyJcNGs74/ZV+Jv78f8FLa/ErYGbzwUjIFtIpVWUh0i5yibxgbx6Kiodk20bos/qhfRLFsZjONB2B4oVWbryfjjZdsL4aEmZyCBdSTF/hFMG24uJS7apt7XT+bWba5/+pt7tVPST4BxbIQ6s5rnk57nzwH8MJMY5x7yhAElOXT6Krhc3wopoks1dqjZDkYNkRa0NtLtm5ZaLpFpI4xIiALqKaFU6f3kuqh8DLOzDUyHNm4J46G5FoZIVmiZFjYdcNAywLVcmdqqzGXXZ0aYj5vNrR9WXZl+6W6pPCgy4hh2rV73r18kSkZmDYEpatPml4L2gPjOlwL5on1ysS+EnhlN6umSVAUGrJj17O+L6XeAdFHUPQ1nBdf1A3CskBA/P6UfSX+/n7AS2nzK2Fn8MJLyRTQKlZlIfGLxEP+DWPj6AwFCAgChbos/oLuHkXem1z7VqlWbb6ejDdesr0YEvriIUTcY3aroVvuiWGjHfGrtrnX9b3JZZvrV29zr37KdGxCoohd9yLbn1o9Geu6vsY495Rh24Qk0T2iuQqOzyEkuTPH1QiLPmQBii3o7SXbL4oE/CLFOsYIzbAJSiK+Or07LjNZOtbTyHAkYBMSBRSjVoZimIRlEYHa60URgqK7baQaPYnVV9i6Y1JD2zcMV7Zu1tGIqi4jhiTUDkG9fJEMy0gCqObV02yuBe2tFFX42tOXuHf/VnpigcqS0Uac0FZ9Xvz0Uom8bhDzy6RigRrZXifZtVqn+usHUyFOzeY5M5NDUQ1CQZneRIjtvbHK/ep/U66vZdk1J+pU17cVvS8uFEmXNFLhAL2J4Jr0vhpEQ37GeqI8PZ7mH585z4XJPJtmTnHHjgH2D6cuu6x+NXWqlv30+TnSOYNUXGZzb6JGdqv+9rq+urza37/xhSe5pMBICH7ph2+s8fd6xZrXfR58YYpzszm29Ma577qBNf3GC5miWrN3r4yFfO0+5rLNf+tvjpEGUo8c4T++bkeNzb308zqRci0nVZbZGb5xYo7Ts9nKPrlkKMA9O3saDrlYDdVtJl1U0E0ffskh5PdX2syZ+SzpvEkqJlWYAk4umUznVgbm5dPIytsovGzY6kmc1b4rMws8dGqebx07z2IJOsOwZ3M/d23vZrQzzFxeo6SbNaeOlfWol13WO1PS0QybgCwQlEUGUkF0yyFX0iv7O4e7o+wbiPHEpRznlzJu5RYydIZE7tyaqizpe/m7PnbKsnOKhmn5kEQHvyQ1rWuZdea75xb4nX9aOfzhp+4c5bYtXRX7etm23hdl2c+OLzGXU+mJB9nRl2AgGUK3HB47O8diRqUzGWTfpk62dEicXjKZqJqpFIHd3X5euad3VZu/OJ1mPqvTnfCzuz+10j8v5SmWbCJhgd5YuHJ9tZ32DHawfyjOw2fTnF1MV2TX27zetmWcnM2wmDPojMvs6E1W+qlvvzDNbE6jNx7gwJYudg/EUEyLLz99nskFlcGuIK/eM8yXn13wjMv/9Fr3ZMsLizkyBYtkVGRTZ7wi4/DJeWaLKr2RIDtGkuzud2UcvbhIQTOIBmS640FuGIozW9B49vk5lgrulqitvSmu743y4pzCd8966/3k+XnGF0sMd4a5aXP3iuzTCyyUVLrCQXYMJbh5OMmxqRzHzk6R1yHmh5G+Tg4NJ1Adm099+VkWdej0w2tuGeUzDzcmmJbx/73nLubyGpPpAkXFJhIS6IyG2D0Qd/dGn7rEYhY6E3DjSB+7+mIIosg/PTvJTLZEXyLMbdu6uW4gxpmFAicnM2Q1k0RA4gd2DPLwae/DTX7qB3fz0Kl5np1cRCkZhMIyg/EIB4YTLCoGX3nyNJkiJCOwta+Tg8NJJMnHx/7uUS4uOox2+vjxH9xX8cWffvsEE0sqQx1BXnPDMDu6ghyfV7mwqMK6HYvWHO2BcRW+dGyO33t4ij97/27u3DLKeLrIZFpBtx38go/BVIjhVARJuvrBmmPZ/MPhCzx5aeU89ZtGEvz4nds8M4kjssRktrTmOpmm3VQHHxZ/89hZjk0X0G0bvyCwtz/Kx958g+dv+mMh/KKPqYxS6dwHkiHPbGUvGIbJ5x86xeFLWVTbJigIHBxJ8Iuv2oVt+zeUseJCbp7f+trZyv/Pn87zrdMn+f13bWUfzYkx142Nw6fzR/96ksWqF+pOeYbP/cT+lv1dZiNYa7b+/33kYf78yRW5pxX4yS88w4/dDL/2Q69dl1jz0mE6m+Pn/89hLmRtLNwH9qbEs3z6vYfY3tvZkm1V1eTJCwu8MJWrZJRfNxDn+oEEj51b4Oh4tpI5vm84wYOHj/GVMyu/TwO/9bWTvHUXXP+u1zSN895IkFPzOc7MFVAth6DoY6wnylg5a72ufHd/En/9vgVgsFvioa8sUr2oGqDAj94x0HDt5SDj4+iFBZ6+lK1ke+8fSXDrWCef+9ZJnry4ovdNowlOLJkN93CAE0umpw33DaU4t1hYs35e/p4szfA3T69kRc1pcDw9zbZhm5DYz2ym1JDh3x8NcPTiYoPskVR4+SjblT7ywEiC+3b3cyldamAQ+KeT6YZ6LioWX35+gf/u0a91hvw8cmauIXZu3tzJmZksz06s2PyGoQS9ke6mdf2DvzrC4bpl7s88fJFnjlzklg+9mhenMw2/2VJhPKj1RU9C4je+9BzH50uVLP5d3WF++tWb+eN/Od9Q3szfFvDcvE5IFJvaPCQ5fPJrzzX0wz9yxyh/+/gET11ascehkQTvu3ML3z3daKcvP984OC3b/HdKBt86NdPwmz1Dcf73w+caZBzaGeVD/+8U+arJwthj4/zMazv5gwcWqWyeOAlffHSyWVOpIJNX+cJj53jiYgbNtAhIIjePJtm7JcJv/t3zTFftxOgPwUfeup2/e2ymgVXk9bd08wf/fJZM1URlUpyr+X+93r+4kOXX/vZoAwvDf7hrgP/65VNMVJ3JMhSBNx4K8m8nqoKnBCcyi/xAF3y7yrwFnVUHxVA1rqjrE7YMS3z8a+drUve+dizLh17Xxa/+7UJdnU7w86/fxF88dIFL+ZVTJEcukyMSiVj83dPTVUmgCiI5/sM++LeqLeGLGTibWURfWOT3vrlSPjfl8NQXnuF1m+BrF1bKD08W+Mpz3i9BGwGf43wPH+O2TiiVShw/fpxfenCB8xkTEfj6B+/k1GyeWEh292OaJnnFYHtvjM3dV59F9Ol/e5EHT8zREQkSC7qZqEtFlft29vCu27c2Pa9e0Qxm89qa63R+Pt9Uh7957CxHp4rEQ+5BJkXDIqdY3Lk1xc+/anfT3/TGAoQCMpLoQ/QJWI6Nabkzxq2wSXzs75/hG6cWSYX9xIICedUmXdK5Z3snP/+q3U31blWGFzZ9+AHP7y787v1Ny6szxK+mTq/4+ANMFN2ZOwl3v6eD2zF+9T/f25K/y76ov94v+irZ+tXlr/r9Rzzr9cxHWpPtFWtedvrJP3uEC3nw4z4gDNuldRpLCPztz/9gS7Z9+MQM3z23QGc0WHkJWCyoCLbNQsmkKxYkHpTJqQYLeZW/eXraU+9vebRv07SYLbgrGRG/TFE3SJc0IpJA0bQbyvcPp9g32vhSddNvPMBcky2WPQF48mPNY80LDxwd56FT83REA0QDEgXNZKmgsZgtMp416IwFiAclcqrJYl7j1IL3/su/eO+BpjbsifhBFNasn5e/V4u1r/z0LTw9niYR9Fcy/LOq7mnbUknnUkalIxIgHJQoqSZLRY2dvVEGO6OEA1KFQaCkmfzkF57xlO3l78nFAidmCw2x0xWWsIWyPSSKusvx6mWn3/qa90xaWe/632DZzBX1Bl98+/lJLmZNIn4ISQKKaVPUwbZAEN0tK5VyzUFZZXXZy+aPHp/mhVmFjohMNCBS0CyWigZR0UKQA3RE/cQCEnnNZKmgM5yQ6UxEWmpjf/SOvTx0ar7hN9OLeS5mDTojfmJBkbxqsVjUudTCnuTL4Zd+cDMPnphrkH1qqoiC+4Je7oct3H65IyQSD7oHDxU1x21PVcatZ/7wwh2jUQ5PFIhW+alQ5ScZkH1gONB4JuLVoax3R3SF1nOpoPHs1OonJIrL9TJY0VEE/IL7p9vu32r6x3zUvNRsFDYnJX7vvi527dpFOHxlDEeXQ3uPcRXKxrCAP/n28UoGqV8SiAf9xEIy0xkVVW98Q28FFxZzPHExQ1csyGBHmHjYz2BHmK5YkMPjGY5P5iqZwaLgIyiL+HxwZq5IxC+tqU71WbDl6xcKKsdmCsSDIv2pCPFokP5UhGRY4shElu+cnm34TcQvcXahgM8H0YBMyC8SXR6Y5RRzzYd0nJzN8NSlLJ0RPwMdYWLhIAMdYTojfg5PZDl2KdOgd6syvPCpB15o+fv6LO0rrdMDz04wUXTjKxkUiQZFkkERAZgowt8/NbFmf1f7ovp6SfQxlVbdJeKq8l/8vPdABeBdf/j1BtkC7rGn4cDaYs3LTk+cXeBS3u1wk1GZSFgmGZXxAxeyNv/wTKPeXratz/wP+kX3UxZ46lKWaEiiNxEiFHA/V3tgA7zlfzzcEOeSKPDsRI6oX6YjEiAgC+6gTJZ4ZjxLUJZqylPhAOcWihSU2r3EXz5yqTIojvl9lT9wZ1C/fOTSZSJmBdVMD/3JMLGQn/5kGJ/P4dhMgWhIoj8ZJhJ0y1cbFAP86OePNNgw6pc5Mp4hKIlr0s/L33/0jWOryv6dB54lFXYz3WNBma5Y0LXtRJagVGtb2Sfw5HiGeFCmPxUmEfJXPo+MZ8DGHWgGJFLhwKqDYoC7m/jbwcdTlzIkw/6a2AkHJA6PZwkJAr2JENGQu9UsGli2k1Brp9UGxQBv/OPHK4w05d8EBIEjlzJEAnKNL/Kqxrklk5AE3YkI0UiI7kQECdAAwYbueJhoOEh3PLzqoLhadrXNS4rBsekC8ZBIbzJMJBSgNxkmJLnt0i85DKQixMIBBlIRokGJYzMFRB8ttbGf/atjFYaS8m/Ax/OzBWJ+gb6UK7svFUZXVwbFEXnlrxpBceXvcvi9b56vtJlIUKY/GcYw9MqsczIsEQ1LJMPuorkDOJZFf0eYeCREf0cYp+rkykRAIBoQSAQuP2R65KI7KO6Kh4mEg3TFw4hVA8ZUVCYakUlFW1ttXQvKeg+mIu64IhUhX1gZFMtVf9VIhiUiVfYA1yad8QCxaIDOeOCyLwXlQXErfnq5oj0w9sD5eWXDMvkzBQvNbJ69nVctFhWtIbMUx4dq2Q3L2F518sqCLZRMdMsmXNfAo34R1bCZz+qNWbuS4GZWO3XHlLbIJrGYM1Ati1hdi4kFRUqaxWyhtGGMFS9Mr56N3+z79WLROLPMBFC/b6n8/4vp/Jr97eULr2z9i5nV63Y+Q5MsZlBNB79Y6yevWPOy00y2hA3463ZFyII7UzOVXru/vTL/JUFCMS0CUmu7whaX9amG4HOz0AP1Nhd9lAyLQF2iiBfDxellf9d3rkLd92uBF9OD5BPcdiy33oXX29AvC5R0q+FoWC/9vPx9crawqtzJtNLAEiIKPkq6hb/uXoIPVN0iWJdJX85mN+zWX5Tr/W1b7olowUBdtr4kUjIsRLm+rxUp6RbiFSRu1utdjqlQXZa9qrsTM8G6LP4yGcWVpFzUy9ZMB922ifprbxaQBCxoSMiNyD50y8a5gu1s9XGLz8awaXj+CBsw01gvW1+WUd9TCHWf64FQXR9yLdfl6/VWr3BO6fszLXttaA+MPbC5O7RhmfzJqEhAap69HQuKdIYCDZml+ByCoktIvpY6eWXBRsMSflGgpNXep6BbBGWB7oS/MWvXtN3Mal/d4LtFNonOuExQdJfOqpFXLcIBkd5oeMMYK67rXz0bv9n368WiMbbMBFC/zlD+/2gqtmZ/e/nCK1t/NLl63TYnaZLF7D6YdavWT16x5mWnvkQYgZUHUhmG7T6cBlJr97dX5r9pm4QkEc1sbRWnc1mfatiOm4Wu1dvccgjLIlrdYMyL4WLbsr/rn0d23fdrgRfTg+m4+yBLRutPvXob6oZN2C9i1x0e4KWfl7939K6eEDOYCjWwhFi2Q9gvotfdy3Yg6BdR6zLpy9nsstD6o6ve34LoznSrWl22vmkRlkUso76vdU8Ms66A6q9e73JMKXVZ9kG/u4St1mXxl8kormThrF52QPLhFwQKeu3NNNNGhIYX36Lh4BcFfFcwIVQftzgCskDD88fegFFYvezyC3p9T2HXfa4HlLo+ZAMJlxpQr3fwCkd5/5732LYHxlUoh7IIfOAHdpFXDHKqjm7a5FSdvGK4xPJXyU6xqTPOzaNJd79VpkRRNZjOlFjIqxwcTrJrMN5wzrvjwFhPhKJuetbJtGw008K07AobQb0OXdEge/ui5FSL2UyJYkljNlMiUzI5MJTgFdt6G35T1E22dkVxHGrqpBoW8ZC0ZpaGHb1JDo0kWCzqzKRLFBWNmXSJxaLOwaEEe0eSDXq3KsMLv3j/dS1/X2YEuNo63X/DEEMRN74yqkVBtcio7olJQxF486GhNfvbyxem5TCQCmIs/79c/qn33rFq3f7ff7y3QbYNjHVHKWnesbYWO928tYuRmLtvLV0wKJYM0gUDHdiUEPihGxv19rJtOfN/saAyn1dRdcv9NGwOjSQoKCbTmRJ5RWc6U+Jt+1c/xOJLH7yTvGKQKWoUNPfTtGxuGIpT0A3m8yoF1WQ+r1IyTG4cTqAaZk15uqSxpSvSwN7wpgMj9Cxvkc7rDkXdIb/8dtATcL9fK8pMDwt5lanlE6Km0iUcx8fevmhF76Lq6r29a3Uaq79474EGGxZ0gwPDSVTTWpN+Xv7+2Xv2rir71+6/gXRJa7TtUALVdPcPa4bNUlHDcGxuGk6SUw1mswqKZjKbVcgqOgeGkyBAuqRR1Ny9v599942ryv7Wsr+r49mHw6GRJJmSXiOjpJkcHE6g2DazWYWC4tahoC3bya6100dft2NV2V/56VtIl7Qa/TTb5sBIkqJm1PgiFgywpUNCMWE+W6JQUpnPljCBAGALMJctki8qzGWLhC6zXF2WXV3fcEhmb3+UnGIxnS6RL2lMp0soptsuddPHZLpIvqQxmS5SUE329kWxHJhYKDCXVZhYKFy2jf3RO/aykFdrbAsO1/dGyes200tF8iWV6aUi/uBKTkHRWPmrhmqt/F0Ov/SDm1nIq0ymi+RKOpPpIrLsp9w6MiWTQskkU3KHyT7At5yoWH4m+qpWzLKaTUGzyWqXHz7fMRqloDnMZYrkiiXmMkWsqoFxumBQLLp94XqjrPf5+Tzz+RLn5/PEoisHZRhVf9VIL9sjXVp5bfABCxmNbF5jIaNxuZ0RsWUdW/HTyxXtgXET/PF7dzKcirC9NwY2ZBQdbNjeG2M4tT5Hyb771i3ct7MH27aZzJSwbZv7dvbw7lu3kAi5VEw4UNJNcKAnFmB3f7JpnQYTYdJFjYl0ifGlEhPpEumixmAi3PT6j735Bu7cmsK2HWaLOrbtcOfWFB+6/zpPvXf3J5vWKdHi6WE/e88O7tneieXYTOVULMfmnu2d/Ow9Ozz1blWGFz79I9taKgfWrU7/3/sO0r98EqiB+9kfcstb9beXL4ZTkabl77+peZ1+7GZv/bxke8W/130+/d5DbHJvQ9F2PzfF4A/e4623l21v2tTFbVu6cGyH6ZyCYzvctqWL998xxoGRJNmSzqnZPNmSzoGRJPd7nEj7pm0wmAjTGwswm1c4M1dgNq/QGwtwx1gPY11RN4ltLsdiXmOsK8qrrhtoWr6zN9FUxp/8+IHK4Lj8KO0JuOWt4q5tva5+is7p2Zw7OBxJ8htv2su9O7vcPiSrYNs29+7sWvVeXjZ88/4RV7/Csn6F1fXz8t1H3tR8sPTRNw+wszfR1IavuX6Q/cMpHMdhrqDiOO6xsu++dQt3be/GsR0msyUc2+Gu7d28ef8IvbEAc1mV03N55rIqvbHVE2G9+rW3HBhtKuP9d4yxZyBBuqhzci5HuqizZyDBG28cbqqD12v3DSLs7k821e8tB0ab+uL3f+QAe/vCGJbDQsnCsBz29oX54/fvZld3EM2A+aKNZsCu7tWJbb1s/pE37uHWTe7L3kRWQTVMbt2U4Pd/9AC3b0mh6Cbnl4oousntW1L8yhuuYyQZ5PR8nqcuLHJ6Ps9IcnXZd2/va2rbj7/lBm7fnEQxTC6mFRTD5PbNSf7g3WPU9y4R4EP3dzYtXw3vvGUzt2/poKSZnFssUNJMbt/SwWd//PrKJIUGlcmJz71vV9Nn4qd/ZBvJ5RFheZyXvMwI8TfevIexDj9FHebyDkUdxjr8/MG7x1b6QmelL/yPdzd/mb0jdRklm+Ath0bY0hFiMl3i2Yksk+kSWzpCfPIdmxr2FcvAr7y+m/5QrT36Q/Dbbx2mJwQqkLfcz57V37n53AduaOonr2Pof7CneblXv30t0WalYIWV4u/Pqmwd6K3JxN4IHuNq1PMsVmOt3LKXY07w0uG5ySVmMxq9yUDD0Z2tcue2inoOy7XofbUo2+mLj57i8NlZDm7t5Z23b18Tw8TV1qks+7Ez84wvFRjuiHLrWHeN7I3gMZZEocJO8sm/f4aJEgyF4b+8+cYahomN4jEu6/3E2YUKT+bNW7vWpLcX6nlfyzIUXa/h+X3w2ASPnlvisQvZym9v3ZTgvl09vOngKHN5zV3idNytKY5DhdmjzNMtCz58gs+z/HKx8+Ujlzg9m2Nbb7ylmeJqlPVTNQPddrPEgwG5Iruax3isO8Gv/PVhvnV6iZmqGam+qMzd2zr4xNsPrmpD23JPwhKwEUTxsvp5xdr/few4U2mVgVSQH7l1F9t7YyTDfubymqcNvXh+63lwy3X1OY67Ru04OD4f/+ufn+eJS7kaJoEyt+yn3u0+ndfKY1yWoZtmDY9xOQ5sy8ZyQPSBIAprkr1WHuOy7BPTmQpf8c7+ZIUpZjxdJJfXicf8DKcifPzLz9TQbZVRzfziFc/TSwoLJYWucIj+jlClfC5XIq9YxEIiPfEws5kSZxYKyIKAgA8bB8O2GygoyxiKwHc+cv+qtp3OFskXLWIRkf5EpCLj+Mwi6bRDKuVjV18nY11RepNh/u3pCS7lFEbiIV65f2hNbDsLRQVFcQiFfHRFVvR7/Mw8U9kSA4kwt1T1w/XPxKMXF3l6PM352QwLRZuuiMDm3iR/8dBJxvO1LA1l7uiPv2U/T4+nSWdU8qZNTBJIJYMVPZ48t8B0RqE/GeKmLV08enKGx88vcWFykYIFURE2DXYiOTZpzeaFC1kKuMy9121K1PRn9Sizzgj4yt0aNg57BhKM9SX42lMXOb+osLkzxOsOjVZi6sRUltlsid5EmJ0DiQpbS07TMTUbKSAQD/j552emmzJPVLMrPXh0kovZEqOJMPftG6z49V+eO81c1qYnIfDqPdsqbDR/9+hJpnPQH4cfvn0HvdEAkiTywLMXK33I/TeM8oVHTnNspkhHSNxwVor2wJiVgXE+2E1a8+E4Dq/c3bcmgvuXGqZlM5Eu4fP5Gs63x3H39a3n4PJ7FdV2kgV44YUXuO666zBsNtxOL6WPVN3k8IUlBNFXc0RxTnVnzfaPpjbkhQ+ujd5eMi4u5Pn0N08TD/rpTwWZm52lp7eX2ayKZdu857Yt9CZCNb8paAYzaZX+ZJBIVQJLUTWYzir0JV1mgo3Qo1X9vGS/OJ3mP33haWRJoDe58tCYzZSwbJtPvXM/u/tTVyXDC6vFmmU69MQDBP3SVcnwquuz40v85leeIygL9CTDFHJ5ovEY81kF23b4g3cfaHj5b1WGVxw8e2mR3/zH5wnIIv11Nl8v2bmSzrMTaXoTIVLhlReVB1+Y4n998xwS0FHFcpApGNjAR9+wk7t29K1JD6/4X8qrfPvUHN0xl82kjG8+N8X/OezyCaeqEqqzqoUDfPo/3MD9NwytSb+lvMJDJ+fpitfKWMirLBRU7tzRS0fVC9onv/YMf3dsydOOP7y3i59/1fVr0tsrBguKzr88P40oCjWyv3tihs89Nk5QgI74Snk6r2E58K6bBrl+pLPmN/N5lcWCxp3buuiIrUy9Tizl+bOHz5OI+BlIrcTO6Zkshy8scd1Aki09K/SY//vh86x2MHOXD37yNTtqbDiTVcgUdV63d4DuxIrsrKJz7FKGvkSQZFVdxxcLfO3ZSQaTYQY7V+aAHz4+zTdOu1zh9f62gY+8djv3XjewJr9OpUs8O5Fm30iK/sSK3jM5hXNzBW7e3ElPVV2fPr/Ap75+grBf5oaBMD+2J9Sma7uW8MrEfrlivZgTvt/xUtrppZTtxU6yXgwrq+Fa6O0lo6S7qyb1DChl5pelUiPzixezh0/woVkOou/lHzvzWR3VtokGavWOBkRKpss6c7UyvLBarJVMC9W0r1qGV10zRR3NtonUHUYS9Ysols1sCxy5XjK84iBd0NEsm+gGyvZiilkquAdDeDG/XFpS1qyHV/xbDqimTaiOIWRJcWXXL9FLuFvFzjRhX/HWT0CxGmUEZJGSYVOXB8zZhVLDvatxarawZr29YlCzHFTLaWD1yGruQFCu21LhF8G0YTqjNfwmJEuUDHc1phqq4SZ7xuqYUWRRRLPsBhmrDYoBFpxG1pmIX6ZomI19vQfzkeO4jCnhuj6kuJwI68mu1CTWvPwqiS4bjVTni6AoUjLMhmTFnG64bCb+azNkbQ+M6+CVif1yxXoxJ3y/46W000sp24udZL0YVlbDtdDbS0bY7x4fXM+AUmZ+6Qg3Mr94MXs4trv8bTkv/9jpTvgJCgIFrVbvgmYRllzWmauV4YXVYi0siQQl4apleNU1GfETEASKep3eukVIFOhNrv0wHi8ZXnGQivoJiAKFDZTtxRTTEXVn4byYX0Y6QmvWwyv+RR8EJQGljiGkI+TKrt9JYeImbo01YV/x1s8mJDbK0AyLsCxQ9z7A1q7VZwq390bXrLdXDAZEH0HR18DqkQi4XO9G3WBdt0ASoD8ZaPiNYpiEZRGhjvsiKLv0gPk6ZhTDsgiIQoOMy61hd/kaWWeKukFElhr7eg/mI5/PZUwp1fUhkeXBrSe7UpNY8/KrablsNGadL1TLIixLDfR2cb/sspnUMalsFNpHQlchrxikVTcx4nthGwWsZIjP5d1Zifo9xqstT27UXt614FrLrraTatsUdIu8YoAgXNZO6ynbstwZmfKev42WXWYnOTWbR9EtBJ8P23EwLZvtvbEr3kaxFv9V660vs10IuLNfa9G7VRmwEv+JcICbRpI8em4Jy7bRVB19sUSmpHPfrh629cYaflNm9tCX+W3L5YbtMJAMNZRfizbWavve3Z/i4EiCb51eQtGy+CXQTVBMuHtbR8M2inoZrfqpWr/LxVp5j/HVtAEve4x2Rtk3EOOJSzkKpQy6Bv5CBsvn7vMtb2VY6778ZjK84mC0K8a+fle2YeQIyAKaYaMY1Mhey359r76izBQzm9fcA1WWT+/bPZBgKOIeFDRXx3IwlhB4/Q1DzOU1TNteObG0So+CZtScZFqO/6JqVGQLosB1/XHOLBSwbIeAJKKZFqN9UTplWDQgXfcCOhShso2iXu9m+gmiyO4BV4Zh2kiiD9NyUEyTXX1xBJ+PhZyKZtkERIEP/OB1/N0x7z3Gv/5D+5jLa2i6WbMPvax3tX7VMVi/D3ysJ8rT4+72gfKphb2dYUZiMJ6HyarVAAF3j/Hrbxzm6fE0Jd1EFHxYtoNmWuzqiyGIIkt5tbI/PRoMcHA0yePnl9BMi5BfQtFNVN1iX3+MtGpwbjaHX/Khmw4HLrPH+H++x91jbNlOpb6ZksaewQSyXCtbEAXGeiLMLvtCEgVMy0YQfBwYSXJitoCQ9hENShRUk85YsHK6XTN/v/nACHN5jZJqVMlY8Su4M+eKYaKYLhtNUTe5tFjEhw8HB8O22DsYx7Bt0iWtcrJlbzLE9b1Rjs0Umc8pwGUyAa8S7YFxFRwH9g+n2N2ffKmr0hLKWfw5xaSkm8jLgz2v7H7bdsgqOlnFwLQdJMFHIiSTCPkRNnAG8aWWHZElFK3A6dkCp+d1zGiWbb1RIh3rwzSyGmIBmUxJZyqjVDregWSoYdlrI9AbCfKimeHIhTSKaRKSJG4YitMbWT2rvBla9V/Z5mfmC6imQ1DyMda9us1bleEV/++4eRMl3eSpS1kWMzadaBwacZkFvH5TPkZ1reXXoo212r7fc+cmnjo3z4WMjYWbFLQpIfCeOzd5ymjVT176dYcDnrHm94vr0ga87PHOO4f41ueeq5xuhgohLN5624Bnfct+bVbeTIZXHPz8q7fzC//3COeWTCwsRGBLh8RP3zuGadqMp4tMphV028Ev+BhMhRhORRqWscG7rxjtTZAIK0xnVDKKTlAU2N4b42fuH+FX/6bxNMUPvGZk5V5pFc12Z4QHUkH6YyGm84p3eZ3sfUMp8qrBsYkcJdMkLEnsHYrzM6/r5+NfbjwB72fuH/HUuz/mJsHVy9g7mGQ6U+KZiTQl3eWMvnEowc6+GE9dWOLoRBbVtAhKIvuGEvzwDvi7JocOvuN673ge7o4zW1QbZAd8AkcvLnJmroBqOQRFH2M9Uca63P295xaKzBVUwrLA/uEUwdc02twC3nPvIFs6ozx9YZFHLyyh6BYhv8jB4SQ7+2KcmMnzwnQO1bQJSu4Lxyuv72N8schTE1n3wBlZ5NBQgjfdNMBnHjzL0els5YVgX3+M7cCpJu3iQAAOjnSSVw2em8wxmSkRkSX2DCa4dUsXz09lG2TvHUySUwxenM6hmDYhSWB3f5w37BsicnyGZyayTGUVwn6RAyNJdozS1N8/97pRQst0d81kAByfyVMySoRlkV19Mbb3RvnSU5d46mIGxXQPuzk0muTQ9f1MZVXOzhcrddraHeFDb9jFL/2/Z8iWNn7WuJ18x0ry3dCmzfR2ri1J4uWItc5OXY7FYiPxUsouZ8wHRR8XL5xjdNMWVMupYWfYKJT1lkRfzQzNtdC7nFkd9UsEJRHVtCjoZg37ylrRqv/KNg8HpMrbf0kzV7X5lcaIF0OCopqcPn+WbZu3EgpKa2LjaLV8vXRYDWuV/cmvHePrJxYIyk4l1lTDx707u/gvr2vONdyqn7z0K2egR/0yAUlAM20KusH+4RSjXdF1bQP19njz73+D52ZUfLh7XMufe/qC/NlPvKJpfcssBV5+Wmsc/PlDp3jw+BySz4fP5+A4PkzH4b5dPdy9u59Ts3liIbky05tXjMva1stO9TOwt37sAaabnATeH4J/+iWXKaDMRlGeIS3r7VVeL7vMXuAXhIptddvmV//6maasFP0h+OLP3NlU795YgFBAbpBRjp2wX0LyCZiO7VIBWjZzRZ1kOFAzC/qZhy96xsa3Pnhn03i+nOxUlYx0Sav0kfUzyTf9xgOVY9+r0ROA3/sP7qxt1C8RkkUUw+1veyJ+EN3jyMszpzlVr+gnC0Jl1tawbTTVYDKnEfCLBAUR1bbQdIt/O7HoqXeZjcMwrEqMyLJY0a9edpkBIiCLSIKAadtohlWxk6Ib6Cb4JQj5Zd772UeaxtpQBD79o7fw9Hi6QUaZjaOe8ebohQWOXHKPfQ/4RTTdIqcaHBhJsm9TV2XVqvxZZu9IBh3evs2/ocl37RnjKoT9Gz97t5GQROGyDjUtm6xiEJTFSvaoKLifOcUkFpQ3NMP+pZKt6iaTaYVYSCYii8zJAslIgKJhMZ1R6U+ENpSdoV5vEFENa8P1Lig6Z+bcDr+cJZ0A5KLGuYUiYz2xNW8batV/1TYvsxREkBAFn6fNryZGquO/WnZfPIiRCbK5J9rgb68202r5eumwGtYi+8x8licvZumMBWoYEqYzJY6MZzkzn2Wsu5abuFU/eelXUg1emM41sBfIRYHTcwX8skgkIK9bG6i2x3dOz3JyQSUkQyoaQFM1AsEA6YLGyQWVB1+c5uYt3TX1NW2bqWUWhtX8dLk4mEjneepihu54sMHmT13KkIqG6E0GK7b1S/7l79dm23o7Bf0SZev+4zPjlYFKouqY5axmM63A3x0Z577r+hvYGaYyLjtDpI6VomyPlXKRXEnnzHyhgRHjgaPjlUFxM9l/++RFXrGzt0Zv23I4u1Bgz1CqihlCJF3QmsbO1FKRRy4ssbsvQd8yS0EsJPHgM2eaeGUFv/WPR/iJH7iuJp4Bzs4X2TuU9JRd7iMDsvtZ3UeWz3X88pFLlUFxrCrrMa87zGnwfw+f5qaR/ho9JhaLHBnPcPtYd6U8GpRQTYvHzi+xqz/BUBUDxLmZHN+ZyLKzN85o98qJkl986PSqev/iX3+Hj/3QLSTDK336Ul5tsG00KGHbDscms9y8pYuu6Epd0yWNs/NF9gwm6E+u1OlrRyeYVtyXomQdK8VEEf728AUObu6p2DAadG1+fCZPdyxQw8YxnSnw9KUsHdHafmoqXeKZiSw7+5P0VbF0TC4WeepSho5okLFOGZdZeePQTr77d4Y2O8P3JzuDF7wyq6+EfaVVPa7E5teCIeH72d/pvIlqWcSDtXrHgxJFwyKdbzw6u1VbeennxV4Q8cuUdJuSbm2YTS7OFzBtqEukJyC6iWiT6caMeS8WhlbrlC3aqKZFPFjbxlwGFJNsUb1q23rV6fQy84MXU8D5+fxVs1J4MWLM59zBSf15F2XZF9JKg96SJKAYjssdXgXDdprGjk/woegWAalWytxlltMvLmoNsv2iiGLaDUc/e8n26iPLNq8fPJX/v5g2GrYHBWSBkm41JPhJPpcBIijX3k0QfZVtFdUoXobg5NyMuuZ2KUsCJcPCV+eLsp3qqSGmsu4bWDMWEoDpJX3NbBy66UMxLaJ1bBzRoExJt9DqkvgU01pmGbo2k5ftgfG/M7TZGb4/2Rm84JVZfSXsK63qcSU2vxYMCd/P/k7FJIKiSE6t1TunmkRkkVSscf6zVVt56efFXlDUDcJ+gbBf3DCbjHZHkQSoS6RHs1zqssFUY8a8FwtDq3VKRASCkkhOrW1jLgOKRCISvGrbetVp2zLzgxdTwObu2FWzUngxYnTH3VnG+pN/y7I3pUINepumTUhePnmiCrLgaxo7ju0Q8rvJftXoCa8+dBntDDTI1i2LkCQ0DHq8ZHv1kWWb1w+wy//vTMkNzBCaYRP2iw0vNqbjMkCoRu3d7OXtPWodLcXldhxt6QuuuV0apk1YFnHqfFG2Uz01xMDyjH0zFhKA/g7/mtk4/JJDSBIp1LFxFFSDsF8kUDe4D0niMsvQ+h+j3QztgfG/M5SznlXDfQOzbKfy73hIuibsDC+F7HLGfF4xyKsGpm2TVw3yiuEupW7QNgp4afUuZ1anSxpLRQ3NsFkqaqRLGlu6Ii2xr7SqR7XNc6qObtrkVH1Vm6+Xrf69+nusO8FNowkW8xrTmRJFVWc6U2Ixr3FgONGwjQJa95OXfmX2gpyqN8Tatp4o/YnQhtnkFdt62dEVRDFgqaBRUt1PxYAdXUHu293fILvMwmAs//9K6zSUinFoNMl8Tl22ucF0psR8TuXQSJJ9I8mrtq1Xnd5w4zD9yyvUWc2mqNlkNXcQ0h+CHz4w3HCvMruGaTlrskeZEaOkmTU67BvtoFP2lv3Wm0Yb9C7qJlu7ojgONTJ8gq9p7CiWxcHhJAXdYD6vouoW83mVQzuGV/XJR99woEF2STPZ2h1xZ8DXINurj3zTgZHKce953aGoO+SX+fJ6AvAjB7exWFBr6lvQDQ4MJ1FNq0aGblocGGnUT3dsDg0lyCk6M5kSJdVkJlNiz+bVc0I+9fZXrLldFnSDvYMJDMtuaifH56u5zyu299AfcvftZ1WLompVDvcYisBbD25qeM7kVL3CxlF9r5Dfz/6RBEsFjdmsgqKZzGYV0kWNG4cSBANSzfWRkMyhkSRLBZXF/Np5wa8U7eQ7VpLv+kc2MdBdG3gvJaXZRqGcoZ1TTAzbnYmIh6RrykqxVNTRLRu/KNAR8V8T2eUs6XNzeV44dZbrtm9lS0/MMzt8PbEReq81MUjXLV6czvDE2TRLikZHKMDNW132Fb+/fiF0ffUo2/zkdJ6MopMM+dnRX2vz+vqWZczltEoWek88UJGx1jZZln1hocjxU6fZtX0bm7oi19zfmmkTkK6dv5dyCn/68Gm+cXKWvGYSC0jcs6OXH79zGx3xUNPflG01nVFRLZugKNCfDHrayqsPCYkiJ2azHJ8pUNJMwgGJXX1RdvcnkSSBrKIzn9cq2frdscC6tf1zc2l+6YtP8/TMyv7D/X1Bfu+d+9nUlWxa3zLLxHxeQzEsQnJtndZq81xB40tHLvLtMwsUSibRsMQPjHXxlgOjhIPyqrb1in+v/rk++e7YxCw/+WeHa5Ki+kPw2R87yPUDPU3bazW7RjN71F8fkSUmsyUuLJYqft3UGSatFPjZ//10U9m7+7oZTxeZWFIomRZhSWSoI8RgIkzRMBvadzl2XpjKkVcNYkGZ6wbibOmMcnQizfMTWfKGTUwWuH4owbn8DL/5d5MNcfC7bx/lLXt2M54uMr5UQjFsQrLAcEe4Irtev7LsE7MFiqpJJCixszda6SPrk++Ojs/wgT89UpOA1xOAP/nxA+zs7uLJCws8P5ElZ1jEZZHrhxLsG0pxbvmo5aJiEgm5Msr6PX0xQ04ziAdk9o8m2d0f5xvHZ3jozALpgk4q6ueusS7m1Wl+718yDXr/6ht6+PFbDq7aLo9NZMlr7laPvUMJti+zdHjZqb5djKcz/Mz/PlxzBPlQBD7z/kNs7+7kxekM5xaKlAybsCywpSvCzt4EimUxnVEq5f3JEDI+Hjo9y7MTWQqaRTQgcsNQgru29WLgNPSdogP//Pwkxy7M8eZtcjv57lrh316YY0ufwU2buvD7xZeMVmyjIQg+UpEAsaD8kg36HdyVmpfircxezld3P68t1kPvVqmnHMvmOydneex8mqJhE5EFDFNnU0ek5YFxq3rousWLkxkOX8pUKJgs26I3EkQQfE31iMgSmZLOTFapDCT8ko+ILFE0zDW3SUHwkQz76UtYzIVF9+jT8LVtvw6OS6N6FR5v1d85TeO58TSTaR3Dco8Tfm48TUHXSdpBz35tc3eM/kTosly74N2HmKZNIuynK+Kn4BeIyhKJZZvbtuNSh2UVdNPGLwnIoo9YQF4Xn9iOg2HZSLjLuxLuFgTbcVatb7lOmmETkN06ecWaZxuzbUqaiapZqKaFpPkoaSa24yBJQlPb2rZDuqg19YVXXS8uFhrozy5kMg1MAdMKTORyXD/QAzS2Vy972MvbKLzat+24kWwvz6f1RCLcsrWLbx1fQLUgKMItW7voiUQq7U837Qp9VzkprFn7DizTRzqAzydUZEuCwHBnBMWwyesGMb/McGeE5+ea0CMAmWJxRbZloxo2QVmoafvN7JEI++mI+AlIApGAG7emafPidKaBxm2mVGC+buJyXoMlRUGS3PqWdIuCbhH1iwx3RvBLYkVGUBYI+5fbxvL2GBu3UuVNB7plcXGhwMRSiaJmUdRNLi4UsJx8U71LpZKnX1XVJKcYZEruCpplQU5xtyU0s1PZR/XtwrRt6nZqYNluPPj9IvtGOxnridW8ROi6xcXZAidn8hX6Nd202Nmb4LZtPQx2RMipBvGgzGhnhGBAwtCMhr7TtG3mcyo5zaRxp/P6oj1jzMqM8YvFMJMFi9u2dLFnOPWS0Yp9P+PlQNcW8YuMXzjD8KYxirp1Tena1kNvr3t5UU/9w+ELPHpuia5YkHhQJqcaLORV7tvZw8+9cveG6vHA0XEeOjXfIPuu7d3ctq2n6b3K1FBeNE9rlV2uq1+AUyePs33HLnSb7zlawlb9/dG/PcKxmRLRgI+QJKCYNgXN4eBQlD/40Vs2tP2V21gzajKgJdqyVvH6//EgL87rBETwWeCI7h7j3d1+vvrB+1qqr1esrWcbu1Lqw/q6/uQXnvG0SZm+q9U2s9Y2+TePneXoVJFkWCLqFynoFpmSyZ1bU3z0TftaupdpWswWtAbKtDKl2Hro7eW/Vuv0W19rQqCMy9jwzQ82p6krt8X68kxB5fxSic5osPLitVhQuTSb48yiSmcsQDwokVNNFvMapxaavxAAXPjd+5uWP3xihu+eW2iQsWcgwVhfYs32qKbnK7+AgrtK8NhvNJddpgutt2GZxq3VNrajK8jbd2zsjPH3x96AdUIy4qczGuSFqRwTaaVCmSMKvsq/c4qJWf/K1MaaUE9FdC3tWk1JFQvKSIJALCgTC8nuEqfemK2/XlhPvb3uJQsuYb4k+mrK00WdJy9m6Yi4VFKRoEx/MkxXLMiR8QwXFnMbpsdCvsTR8SxdsSC9iRChgERvIkRXLMixiSwXFvIN9/I5DmfmC4QDEvGgH7/k8n5G/BJnFwr4fKxJdnVdA7KI4PMRuEaxdi38LYk+ptKqO/tYVX58OsPx+RKRgI+ueJhIOEhXPEw04OP52QLfPjG7Ye2vnvat7LtYSObiQonzC8Wm361H+3vo5AynF3WContEczjofgZFOLOo89DJmTXX1yvWvGyeKeg8eam1NtZqjHjV9Z+Pnl/VLv/rX59fswyvOgnQtE0u5nSOzRSIByV6k2Ei4QC9yTDJsMQzk1mevLCw5vYtiQLPTuSI+iU6IgECskBHJEDUL3NsMou8zP9bvv7Pv/X8qnr//P97dM19pJd+siDw7ESWSECuqdPT5xcqcoSqP3BnoP/4G6cb/BQUBV6czhGQxZpyAR9PT2SJh/x0x4IE/aJLqWb7ODqdJx6UlmPKT38yzLlVBsUAP/K5BxvKMkWVF6ZydEaDNTKS4QDPTeYwTXtN9nj2QrqGni8SECo0fdOKSx1Yj3q60LINE0E/x2dy2I6zpjaWzmuVNtYV3/iJyfbAuA6xgEzRsCmoxktCt/T9jDZd29Xr7XUvLwqmvGqgWhbRYO2WiXhQpqhbZAr1OeXrp0dRc5apw5rQWOkmedVuuBc+X1NqKC+aJy/Z3y+0hF738qLWWsyoGDZuVnkVQpKAbsNMurRhNlmtjRUME0W3Nqz9XZjLY9lQvzPIL4Jpu9+vtb5eseZl86ymo5oWsQaKPO82tl7UhxMZveHe1Tg500jX1mqb8aJry6oqumUT8ddeH/W7dF9zGW3N7Vvw+VBMk2AdLZt/mVJMqNv2Np5enZ3gwnwjPZ9XH+mln8/n0oQF68pnMqsPTi8uFhtjSnRniyWhTrbPQdEtInVUajY2mmUTquMfvNzr49npxnhQDAfVshso5CJ+maJhNlCjedljrrg6PV+Zxq4aXnShAVmkZNjUEZ14trG8bjRtYxuF9sC4DnnNICILRIPyS0K39P2MNl3b1evtdS8vCqZYUCYoihTqzrbPqQYRv0gyuvY9xq3qEQn4lqnDmtBY+SViQaHhXjhOU2ooL5onL9nfL7SEXvfyotbqTAaRBVwe0ioopo1fgL5UeMNsslobi8oSIb+4Ye1vU08MUQC97kGrWyAJ7vdrra9XrHnZPBHwE5RE8g0Ued5tbL2oD4eSq7PK7OhrpGtrtc140bUlgkH8okBRr72+oLt0Xz3JwJrbt+04hCT3wItq6MuUYnbdbufh1Op7TDd1N9LzefWRXvo5jksTptaV9yVDrIbRzkhjTFnu3lrTrpPt+Aj5RYp1VGoCAgFRQKnjH7zcsHBrf2M8hGQfQVFooJAr6gYRWWqgRvOyR09kdXq+Mo1dNbzoQjXDIiwL1I29PdtYzC83bWMbhXbyXRWW8hqLJZvbtnQxlAoxt0wLUr836/uFneJao0xF9FLYtUxJdWo2j2G4CTKZko5q2mzvjV0T+q710NvrXmUKJn2Zgqlcnor4uWk0waPnlrAdh2jQHSRnShr37exhU2djZ1aN+oz5VvToioXZN5zgoVPzWLZDNChTUA3SRY27tnezqSvWcC/H52OsO8psXnOX0Zb3t1XTPBU0o+Go3HrZ1XU1cNBMNznKwrfhsbZR/jZtu0bvgVSwwd+7+pPs6g5zbKaEuVTEL4Buu3ttDw5F+YGdvRvW/qrbmKJbCD4ftuNgWnbNHmOgYY9x0C+1zABUff1dO/rY1unnxXmdhazuJnCpOibuHuO7dvStWl/LdlaODq6jFCvbycvmyaifm0bcNmbZNiG/iKJbZBW9po15taV6vzbzRXVdq+33mn2b+crz3ntt/9Orrmcur2FZds3Rz2UZa2nfZbq2+jbZGfezty/K0aki2myagCiiWRaaJXLn1hQ3bepqkO3Vvk3L5oahOLMFDbIKfklAXz5OvEwpli5pFR+9/+7reWKVPcZ/8K7bm+pd7iPns0rNkcnlOgEVGYZtc8NQgtmCxlJRqOyP3b+5i6+94B7LXL/5yAf89D3b3OeMblVsrFo2u/vjKIbFVKZUud7BYf9QgvNLJQol0004wweCw77+GGcWVSYWisRCAnnFZktXaNU9xv/3J9y99NXMJclIkOsG4nz33AKW7dQcq71nMIEkCaQLWuV6n+Brao8bNqXofOICiwYVWr4y+kMudSDQwOAx1hPl6fE0xjIDiWq6R2Tv6osjLFPCNWtj1f18KhaotLGJBZMKR+EGoT0wrkJONdkzkOLgSCfB5Sn7nGJS0k1kQaBnmcanjStH2X4vhV0HE2GyJZ3TswXGsxbBnMa23iiDiY3ZwF+N9dTb617VFEzV5e99xVYU3eLwRJbJrEu9c3AowTtu3uQpYzUmhFb0uGtbLwXV4JmJLNNZhbBf5MBIkru29RIONb9XpCNCIlxiOqOSUXSCosD23hj9sRDTeYWptIpmuzMLA6lgwxJhGbGATKakM76kMJU3iWdVhjvCntevJ9bT32U96vUuUyrVy/i1N+1uSqn0oTfs2vD21xsJ8qKZ4ciFNIppEpIkbhiK0xsJVhhQ6v06mAh7sjM0Y6vwis2PvfV6furPnm6g0Pr42/Z41rfcJ5ydL1Yy5rd2r1BMrbWN/ejtW0gXdY5MZCv3OTCU4G03jXrWNyJL+MVGv3rF53Aq0tR+v/L6bj7x1fmG63/th3pXYiejVAYrA8kQEVlqanOv9u3VJv/jq7fxc58/wkQBynOJQxGLH7t7s6fs0d4EibDScK/OkJ9HzsxxbDJLybAIyyJ7BxPcvrWbC0vFBh+9ex984Wijnd5zAE/ZZRlHJ7IVqrh9Q64MoEHGWFeMMwt5zi0UmSuohGWB/cMpfustQ3z0SxMNsj/x9lH6YyHGF4u8OJ2r3Gt3f7xCv/b0eLZCgbZ/OMGBzUkePzPP0xO5ShzsH4rzU/dt5S8fHefIRJbpwnJMDSfYEVP4apOt5e+4foWisp65ZN9Qirxq8NxkjslMiYgssWcwwa1bunh+KssL07kKc8h1/XH2DaWa2uOX3zzKh//6YoPsX37TlgotaD2Dx6aOCJPpUoNf9w93YOA0xtoyNWB9u3jzoSGOnF/k/GIRaA+Mrxnu3NZFMBJFc2zCLwNKs+9HvJRUcUXDJBSQ2TMYR8772TkYxye6y1hXSlu2Vqyn3qvdKyU1lp+f17lley97R7rQTRO/JBEOiuR1iy4PGVlFr2R1l08sK88ktaKHgcO+TV3sGkihmBYhSSTgFzHwptACmtJbpYsauuXQnwxWZoF0yyGvGaSkxoSMvGa418cD5GIS/fHAqtevJ9bT3xU96vQuGmZTGV8+nGW4O8FQt+1OaQngQ+DJMxmuH+zZ0PZ3aj7HbEFjrCdKQBLQTJvZgsap+Rz7Rjs9/bparNXDKzaPns+zb3MnRcMgu5Qj0REnIsucnCyxz+M8iJU+IeFuKnXcWU3FslpqY0cXCmwbTLK1P4VlW4iCiCA4TGVVZFlqWl+/qLcUz160b9/5kkJIBKVqjTskwsNHi7xlrxs7fclQZfZNtxwms6VK5v9a23cz2R/7+zMIcoBtnSD7bAxHQLPg756Y5OdflWgqW7Gspvc6P59HkkRu3tKFgA8bl37vwlKRUEBm71CyHM7YwNeea+7Trx2DX3htc70fOTPHc1NZOqMrDAnPTWUBGOtLNMSBgdOUguyvHz9PX1RGLxkYDsg+8Idlnj2lcvOQgmLa7BxIIAnu9gnFsPjG8RnOL5UY7QwT8UsUdZPzSyUePzPPuSWFkY4woYCIolmcW1L48pNT/MB1Axzc3ENJ1wn7/UTDEh/6y9mmen/zNIynixVGjGRARjVNTs3m6Y0FGOtLMNoZrZkpf34qy5mFAt2xICFZQjFMziwUAOhNhhvs8fkHJxGp3U4hAv/7wQm2JHoq7BM9Ide2T4+nmUyXkCSRm7Z01awizZc0NnfHGmLNq5//h8MTqI6PTV2R5o5fR7QHxlVIRgKwnLEbC8pIouD+vdQV+z7EtbZrdca1HBCJBETiYT+GTY2/NxrrqbfXvarLq7PZB1MrHUpO1ZnOqPQnQg3bSOqz0wFEwf2saRuXqV/1fcrcmOAuT6+ljQX9EsFV6tTsXs1ky4LocpMG5e85f69V77KMiXSepy5m6I67DAllTGdKPD2e4ZXpPEOp2Ia0v/oM9DLkosC5hSJjPTGiIf9l/dos1lazhyiILOZUnrqUoSMa5LpkkumgSX9/N3M5laMTWe7KFuhLRGvq26ptq1Fd7qX3UlHj9FwBvywSWaZ+K9fXtG2m0ir9ySCRquRUr3iuRrX9njw/z/OzBaIBH5viK/5eyJV4frbAIycXuGE0WaWfSEEzKrJbbd/Vsk/OZnjqUpbOiJ++1IrsmXSJp8azPHNpka09iRrZ1fpV36uedaOMdEnj7HyRvUNJ4lV9yN8/eZHM8ugsLINlgihByYBFA774nfO87uBwjez5rMLRCXdQ3Ld8vHEsJOEDnpvMMdIVoafKhtV1jYb8lKPnxek0hy9l6YjI9A6unCQ5mylxeCLL9WcW2NwTrdFjOlPi6UtZRrsiDCzbKhEJUJjK8PRkjpGOMKNdK/vgxxeKHJ7Isne0ix1VMv7pyIreMf/Kakped5jT4K+eOM9du/orsv2SH8t2ODtfZM9ggu7EykzrUl7lhekc3TGXrQIgurxSfnwmT3csQEds5fqHTs5wbskkIEBHFTNEOq9xelHnq0fHuX64s9IGAnIAw7Q4Npnlpi1d9MZX7lX//CnHmlebPDeXq7BSDHcGaNzEsr5oT4HWoc088f2Jl5Kl4KXElbBxrJetrgU7w8uRlWI90aoe2aKNajZnAinqFtnixj1QvDLQI36ZkmGjWVcfa17XK6Z7DG6sQW8/Rd0ir2xcnK+qt25T0q01s4q0Knt8seTNQmLBxFJuw2Qv5lzGm1gd400s6O6xXsitndnJq5/yiy7DRX3UjmcvwwzRhH3FsB1U02riJ/cwF9Namz3mszqqbROtY4yIBkQUzWauoDbogePGaKhuZdLBh2bZhOsmJ2IhEcWw0OuS8qaLbhJb/QajsqYXFhtll21IXbKl5YBq2oTqGDFCskTJsLDrhofT6RIW3swvUxm9wbZBSXRZRepkez1/vNpkYfngnPpY2yi0B8Z1aDNPfH/ipWQpeClxJWwc62Wra8HO8HJkpVhPtKpHIiIQlJozgUT8IonIxnX5XhnoRd0gLAsExKuPNa/rQ5I7w5Rv0Fsn4heJhTYuzlfV2y9UtipUwyv7vlXZw51hbxYSEYY64hsmuzPuMt7k6xhv8qo7AOyKr53Zyauf0i2X4aI+aocTl2GGaMK+Igs+gpLYxE8mEVlCEtdmj+6En6AgUKhjjChoFqGAQE802KAHPjdGlTraFB8OAVGgVMfjnVfcPcj+ukFrf8QdeDacSLj8uamzUXbZhtQN8EUfBCUBpW7wrRgmYVlEqHsd6U+FEfFmfhlI+htsq5runmK7TrbX88erTUYD4jIrxdrpRa8G7V0CVfivXznMf37jrTVZwfUZlpeDV2a1V/mZ+SzpvEkqJjHWnbiqe7Vap8996zSnZnNs743zE3dvu+x9rqRO1dmx1Uv2T56fZ3yxxHBnmJs2d69JRqaoohgOIdnNtG3FHtUZ1+/59CNcMGHTv3ybz/7cHWvyt5cerV7/Y//7W5yeLrGtP8yfve/uNek9ky2QVxxiIV/NcrCX7IV8iaLmuIc8xMKVbPZ3fu7JyjWfffeNNWwc9fUt22opr1E03AdHOChVbFUvw0vv8n1+9k+/zcU0jKbgf7yn1uYnZzMs5gw64zI7epOVe02k82SLNomIwFAqVrnXU2cXmM2V6I2H2TOSqtzrwmKOTMEiGRXZ1BmvXH/X7z/i3vBr3+Zff6FWtpceXvC63is27/9vD3A2DVtT8MCHak+GqtevDC89jk9mWCyodEaDbO6JVfT44mPnOT2bY1tvnHfeuplDo0kePD7HPx5b2Yt466YE9+3qqcjx6nfqZZfhFYP115cz0H/xb45Vrvno63awfzhVidF6G5b1uzhXIK1qpIIBelOhin71fUX5+q8/P810tkR/IswrdvRwaCTJ4+eX+KvDk67gF87yyp2d3LOzp1JnL9u+OJUmndNJxf1s6Y5XZHv56LGzc1xaKDLSFeHWrT2r6t2fcBmOptJFCppFNCDSEQ1Wsu+PT6fJFy1iEZH+RKQi+5f/6nFemM5xXX+c//6OWyr3/dQDL1TKf/H+67i+N8rhiQLzMyvZliHRZSG5Y4fLDPHCRJb5XJHueIStvdGK7K+/OMXkYpHBzggHN3VVZNfrV8ZfP3GhEmtvv3kTh0YSfOPUIs9MrvBEjyQD3LO9kxtHOpnLa3z22ye4MFtgU2+U99y+rSKj/vlT7qc+/BdPMq7BcAB+7a03srU7gg288b9+g2mgH/j8L9zBZ791hozlbp8AYPmzU4Z3vmIzc3mNb74wzUxWoS8R4rbtPewbSvDcVJZvHD3HTN6kLyZxYPsgewYT+CWJLz91kXOLBbZ0Rrn7uv5KXX/lb56s2PwTb7uJgyMJvnV6iRdnVw776IvK3L2tg9vGujg1m+dfnx1nckllsCPIbdv72D/isk98+7FJFvIKXbEQ+zd1s38wzrklhS88sXJAxg0DEQ4OJQgHRN72h9+hBISBT737RpJPjJOx3O0T1egJwDtu3syp2TyHT84zrxl0B2R2jCTZ2h3B8fn4w6+/WPHFj9w2xnX9cc4sFHj45BQLWY2uRIB9o13s6oshiCL/5+GzXFwqMNoR5YcODrOlQ+L0kslEZiXDVcRlfnn9vmGeHk/zL89dYCFv0hWTuHlrP3sHE5iWzW//w3eYTMNgCt5358rz54N/+VjFtv/jP9xaaZN/+u3jnJ4tsK03yrtv315hpfjn57Pc0++VHbM+aB8JzcqR0L/04ALnMyY/fYfML953T9MMy939yaaJWqtl8ec1o6HcMi3+f/beO16Oqzz4/+7M7O5s3729q8uSbMuymhu2McaUmBBTAoSQAAFCgNSX/EgnCSlvCiR5kzfkJaSQhBRISEgChBJTbGxwkSVX9XZ73z47O/X3x9zduzuzs7orXcmY6Pl89rPS3Nl5zvOc55w5c+Y83/NXD57i0fN5VNOBiB/ckOKdd2wjHZc7ulan2dtPTs/wrr98lsZnuyDwl++8jju2bmjpo07tq2WWurNjbdvgVz/zFM/MldAtCApwXX+c3/r+PYx0JVvqCAcEHh9f4tnpAqppIYsC1w4l2T/WTdW21uyPn//k5/mnFhsmvek6+NAbXtGyvrf3Jpkrqx47+mMyJxYKaz7/P58+wh982QtA/9lXpHnvHbe2tFu04b+emeLQ+GoG9b6xFPfsGmQ8q7TM/n3o9AJHJlZjas9oivf901Neo1fk1G++smUWcyos8dnDEzw2nkPVTeSgyIGxNN+ze4hD57MeHbdv7WOponmu8/cPPcBffNur90dvFfjRO+/kT+8/zmPjq9c6MJbih160ka8fXeCx87m63Qc2pNmzKcGv/vMznFzSMC0QBdjWHeJXX38t3z6e45HzOaqGSVgSuWlDmg9/1X9XsOc++DK+dmLWY8dd2weIRr1UAEXRW55fz+p2xeYXnzjEP7Rw+w/vhZ/9npfyL4fOe+x76bX9/McTUx47XnxtD3/4heM8NVtCMy1CosDugTj33tTDr/7jGYoNPXgiQNP/3fLEL76Ev3jgpKffef3BUb5wZMaj+759Izx0ctETg7dt6+GzhyY95//dV8/SKjVoA/BfPj6/djjBx756iscn8nUf7h9N8bqbh/g/Xzjp6SveeMcgv/UvJ5lpeJs+GKHp/2458kt388lvnVmzb99/7w6+fTLrqaP9W1L81r89x9EFpV6mnb1RHplSfHXXfd7gw4NjKV69b4i/eeCcx+7B3krLvuJN19Gy/2onD/zsi/ilfz7i8eEPvniY3/nX44wX6/mZjCXgA6/Zzt/cP+mx7/vv6Oc3P322vr4VIC3S9H+3/NEPbuGn//605/iPvUjiL79peO4/t3TBA8ud2ecnn/uJA/zYXz3mIbN8794gf/agd3OQd94Mn3oET1t640207L/ayd++8zre8RfPeOx7682tr7UNONmZCl/xs/sNtyVbxtS7bxP584fMphnoAPATL4nyp19VPEl27eZrP/rD23nv357wHH/9DviXY97zXzYEX572Hvc7vyab0hIfvqfnsm4JfXVgjHdgDPDZ99zccn/vvaMZ9mzo9lzDb495v32//+nh0zx0JuvZB/2lO3p4113XdHStTve9/54/erBlgAeBkz57rXdqn99e6x/776McXVCJhwP1veNLVZv9I3H++Idvbqnj1Gx+JZN4bfu8+/lj489/3jcG/Oq7Px5GkkSPHYZhMleqrvn8d7dhbh7+lZe2tPuh47N8++wyvUmZpByioGosFFS29kTpSUc9uhVFYzyn0pOQScpBCqrOYlHl00/M+Or+2vvvqGcxN5b35HSOp6YLdMVlEnKQoqqzXFLpjkiYAcGjY0d/nOHueEd2v/3gEPefWKI7FiIhO6/JlsoaPXKAsBxesdvRsVBQOT2TJ1sFOQiyCKoJqg7JIIz0xjxlenK67Kv7T9+0m2+cWPD85s7tvdzbAmHw+SMTLc/viUpYguCJzf/3gBdpVJMPvnIbXzk677FPsk0Kuu3RkS+WyWoB4rJEPCRQ0ixKqkG+2nnX/d4XjfLfxxY9/U5csjEF0WtfREQMBj0xaOo6ixVzXXw+s1TkfF4nE12Ng6yiUS5XUSw8fUWNvhDA6bN0vK+W3fKzL9nEV47N+/o2IUvEQs5mFUXVoFcO0JtJeOro3FyeZRWiIWcTBNWwUTRoVxU13V0xuW7fclnF0nXyeoCuaLDOF19WdMYbZuMuVe7ekuTb4wXiYZFYUHB2da2arCxVJYhDVNDt+oQr4UBn9n0nysYEnCs6g7lajFyZl/AOSKz9CujLJ1tTAqfyVst6/W6RKzEwvrrG2Ed+6u++7dnfOxMNc2axTKnSvO2i3x7zfvt+zxUVHhvP0xUPNe2D3p0I8/hEnmcm8mu+Vqf73v/Ho+frHYTU8AGnAX38a95n107tC9h2y73WJ5fLHF9UiYSgJxklFpXpSUaJhwM8M1fiGyfmPDp03eTIZJ50NLymfd79/PGy3/QfFAO87c+89R0PBXlqKk9QFJrsCIoCT04WiIeCTefHQkGemioQDASazv/YV9tP87z3b77msbtcNXjsfI7uuMxAOkpUlhhIR0lGQzw2kSeA0KRbEgQenciRlIP0pyJEwhL9qUjbQTHAXR95oJ4JXiuvYdg8OpEjHQsz3BUlGQ0y3BUlLgc5NJFHloQmHaloiEMTObCb7f69z/oPigH++tHpekZ7LBJ2vsMBji+qhCRW2kaQwXQUy9SYrzozWN0JmVhUpjshI+JkoVu22XR+uwEawPv+6Sl6EnKTHT0JmWemCiwWm2f/FosKRybynvNjYYnHJvLIktQUm59qMygG+NB/naxTI2rljUgCz8yViIakpuMiNuNFkFf8kYg6v7MaXqPK4urnQvLRb07QnQg39TvRkOPzcFBo0h0LSzw1WyIYFJpiMBgM8NRsiVg4eMk+D8AKVUFgqCtKIhpmqCuKGDBZ0p2be2Nf0bgxWloWicki6TUY/uGvnqUn0exzCVZ8azOw4tuBdBQ5FOBc3kLAajrftA1mKk6yUXcy4sRgMnLBQWNNt9OWnDYVFgVOLmnEgjDYFXPqtSvG9DoOigHuP10gKYv0p6LEozL9qSiBhiWlmXiQeCxIJr76lsS2m+1rfGMfkVY/lyru+896yrmic910VCIWlUhH16ZlPeyrDYpDgdXPlZJTeYsQreu1nQQbPo1yMXUUaPi8UOXqwNhHZkusObvaL5PSL/u3rFhONm+4OdySskS5arKgqJecSexXpjPL/q/8AE602O+8U/sIBFrutZ4raRgWRFtlUFswk/Xub++fSdx6n3c/f5wu+dsMkMNb30FJQNFNAnazfQE7QMUwCLrsCEsiim4gCM3HJ5bbP7OfnfdmrVc0Jws35tobPhp2spXdc2RCAFTNm/W8FnFnMWu2iap596WXpQAV0/Ks446GJRTN9Gx3OnUBnwOeLOOIEMCwICg0HzdXutmgy7xaYngLuMYFpRW5oaQblF0jnXLVRjW9pAc5KFHRTSRXUln2InRLUqAlXUAUnFfd7m1bL4UtkXTVa1gSMSw87TUqCWgrCUiNEhJENNNCDnZ+63PbbQO6BVFX3AZWEp7c4Xwpgyi3bkF0/BhyBVVYDGCCJ5O+1qW4Y/BidEtSANMCyeXDy7Hhrbu/rYWr24yA67t+/mUo05WQ/6kJVMGro7pLlqsu9JGBOGvOrvbLpPTL/o1FBSebt9rcDRZUg1hYpDcqX3ImsV+ZNne1f/WwvcV+553ah2233Gs9HQ8hCaC0yqAWYDDj3d/eP5O49T7vfv7Y0oww9Ugab33rhkU0KGIHmu2zAzYRSUJ32VE1TKJBCcs1QBztav/UvqnPm7UeCTlZuGXX3vBK1clWdt++LBvkkDfreS3izmIOBUTkkHdfetWwiaxscdpcJoNoSERyDaCGL+BzwJNlXLFsJAF0q/m4uPIgoLvMqyWGt1hSfkFpRW6IByVi4eaLxcIBZNFLelB1g0hQxHA9KGcuQrdh2C3pAqbldNLuB8BL6bgLrnqtGiaSgKe9Koaz5lZ3xbNmmYREAVXv/GnEbXcA50auuOK2tsLPHc6XMnB067ZMx4+aK6iqpo0Inkz6WpfijsGL0W0YNqIAhsuHl2Mw5+5va+HqNsN2fdfPvwxluhJyOR4yXgiiX17E7/8IuTow9pH/80M3k1WqLBRVSqrBQlElq1TZ3BMjHglhmBZVw8RYmUFLRYKousPSNC0bVTfr+37rlk1Z1VE0g7Kq05+IcmAsxXJJYyanUFad76Vilf2jKa4bTbW9VuNxVTdJRiTPLJ5fmV59cEN9BsBo+IDzGqUVnaLxWo12+JXJDjh7rStVg4KqoRkWBVVjpCvGNT0yFQ3mcwrFcoX5nEKpanNdf5w7t/d7yhsMOlt25lbqQtVMForqyj7vSSRJWJM/vvzLrddO1+QT73Hqe7lcpapbLJerlDSd3cMpdNNiqaSSL2sslVR00+KGkSQlTW86v6zp7B5Oott2k93vfsl1bXV/9K13eeyOhSUObEizVFKZXlbIKxrTywoFRePAaAobq0m3YVkcHE1TUHWmswqFivP9hr2DbXV/7f13UKzoTeWVpAAHR9PkylVmcwqKajCbUyipOvtGU6iGxUxWIV/RnG9FY99oGgI2uXKVUlUnV67ygftubKv77QeHWCprzGYVypWq8121uaZHRjNYaRs6MzkFQQzRF3Y6/aWiSqmsslRUMXGy0IWAyNSKf6aWFW4Yar870p++aTeLRbXJV4tFleuGk/Qkok3tuycRZc9oylkPm1MoVlbabdXgwGgK1TCaYvONd7ROYK3JB1+5jYWC2mRfxbC4rj+OohlMZcuOHdkyJgHGEqCu+KOoOL8TGt7Pqubq50Ly3heNslSsMpUtU1Scb0VzfF7VLSaWyuRKKhNLZcpVg90DcXTdaooDXbfZPRCnXNWZWCqTLVWZWCqv2edz+QqVqsFcvoKNkwxWqlpMLZUplFSmlsqYtkh30FkjOZcvUyhXmMuXkRqmL7OqSUk1ya7B8J99ySYWiyoTi2VyZefbgBXfBpjLK5QVlbm8gqrZbEwJWAhNdSQGJAYjzmB9qVChrKgsFSqEL/BQVtN9brHEQl7h3GKJqmmxrTtEWcexu1xhaqnMUHp9d2S8e0uSgmoyl1cordhnN4y+cyWdsqKTK60O3AMBWFyxb7FQaVoKUDFWP5cq7vvPesrGhHPdZcUgrxgsK2vTsh721UBymr36uVKyNSWgAUslnUJJZ6m0thXGesOnUS6mjuyGzwtVrg6MW8jbD8CO/hRbe+IsFaucmC+wVKyytcehDmTLVSazChPLCpNZhWy5SiIcpC8RBhuHSWhDXyLMcCpKSAwwk69wdrHMTL5CSAzwnpds56U7erAsi6l8BcuyeOmOHt55xzZSkVDLa41mYi2Pp3wwcn7X+fiP7PK8HhOBj7/jWl+fJMLBlnYMp6ItdewaTLO9PwEW5CoaWLC9P8HvvGkPO3tlVMNmoWyhGjY7e2V+/XW7fct71/YBbt3cg23ZzBQq2JbNrZt7uGv7QEf+eKOPeW+8FnYNptk7msG2beZLKrZts3c0w+1b+4hIAkenCjx6fomjUwUiksDtW/tann/X9oGWdv/EXa25mz91d8zX7tfuHePmTV0UVI3T80UKqsbNm7r4kdu3ttT95ps3sW8sTb6icXKuQL6isW8s7VunAKOZWMvyvuWWzdy9ow/LspnKKViWzd07+vipl+1g31iaXEXj1FyR3IqO+24cpT8RZq5Y4dR8iblihf5EmB/e21rv2w/A++6+hru3d2PaFtMFFdO2uHt7Nx9+817u2dnntI2cgmVZ3LOzj4/+yI1s6ZKo6pCtQlWHLV0Sf/ojN3Db5i7Kms6ZpRJlTee2zV1t7b5zW39LX92+ta9l+759a59zvqJxYq7oPAyMpfmR27e2jM03+TwLvWUPvH7fhpb2/cbrV+yoGo4dVYPbNnfxx2/dz/6RBJphMlusohkm+0cS/MGbN5NwDcrc/3fLj9y+lds2Z1A0g7PLZRTN4LbNGX77jTewdyTFslLl2HyRZaXK3pEUv3Lf9S3j4Ffuu75+/vH5Qv18P4jSGHDX9gHu3N6LbTlbEtuWzZ3be/n11+1m30jCifPlMgVVY99Igj95+2529spoOiyWLTQddvbK/MGbN9O3Mn6s3cj7LjCefNNNG53yVqocmyuyXHHK+8dv3c9tG9NYls28omNZNrdtTPN/33agZR197B372TMUxbIhV7WxbNgz1P4t3OsPjLG5K8LEcpknp/NMLJfZ3BXhN95w/ardS0rd7p98SevrvWZtRM0m+dXX7GbvUBzNMJkv62iGyd6hOH/+th1sTQnYgGI5g5itKYGP/vB2ru2XMUxYrtgYJlzbL/OLr+67kCqP/NEPbml5/N23iS3vP7elWp19cfLbP7CbCI5dxsp3BHjrvtbnv3Xf6oC2JhGc9tqpfPxd1+MOxzAOjaOV+N91O5cPv+VG+sLOLL+K890Xxvf+886bvUtnAsD77uz8Ae2P37K15fHXbm99/ksHWh+/r/VlrqhcpVKwSqX4ua8sIkjBJjKEZVqYtrMuSxCFC5Ih3CzaGs1BEgP1PdsN066f/3xwjGtl+vfHznF2scKmngjfd2CjL80BuKAda+UYn10ocmKuyPhcmYJWJRkKM9bvDM429Sba2ncpHGOgrvuDnzzMHNAPfGiF51vT7WYD134jSyKCGMAynTXPtd+slWNcoxo8/MwMWQ0yIbj1usEmCoJfPZWrBpWV9cOxsFT3uVt37Xy16qy9DosCcliqUz0aSQk/dscGbt3cwx07BlqWtyZufm1dh25S1S3CQQE5KNbbRSAA2AEI2Ng2dbLGA8eWUAEZuGOFLfvGmzcDa+cY1+rizEyBnKqTloNsHkzSnwgTCQfJFqsUNZ1EKEgmEeY3/+0wT0yX6hn4ALEg3DyW5MNvPrjiKx3NgpAA8srDX6v2XTte0TQ0I0BIsomEQvW6cMdmrawf+tRhpnUYCsIH39gca277ar5dKlUoVyxiEYHueKSu++x80cMxzsTCHo5xO/pKjYAyX1AoKiaJqEhfMlqnyChVA82wCEkC0bBUL687Dmr2VVSTqmkQFiUiskiupHJ2WWkba26Oce1ahbJOWdeJBYMkY8E6iWJiuUxe0UlFg4x2xerHn57IsVBS6Y3LXD+abktAqdFXlKqzm1goKBENi3X7jkwsMZNVGczI7BldpQ6tlWP83r9+mIfO5ZpIIalwgNs2pnnTLZt5+Mwigg22ECBg2VgB2NQVJR2XyZU0VN1ADkqk46E68ebzh84yl9foT4W4d98mMC3myxpPnVtgsazREwuxe2NvWwJKrb5PzxRYKFfojUXYMpisx85Xnp3mzFyBzf1J7rl2qB6Dz05lWcxV6ElHuHY4wye+cYKHzuaoalUCJtgihEPhtgSNmu5PPnSiiUdbi+fPHhrn/GKZDT0x7ts3Vu+nPv3AeZaBLuANd2zg8OkFTi1XWaqsvhnojohN/3dLjQRiVCtUxQBh00YKR0gGAxgBkamZPCUD4hIMD6bY0RdjtC/B8aklFgomvUmRa4a7WcwpnFpUOD61RL4CqQhcM9zNl48tXdDu/zo8WWciv/LGkbrdf/PgMU7NK2zti/LW23dw5Nwih8ZzfOaJGUych4TX7R3kP5+Y6Zhu8bdv28fDZxZZWFbrbam3SyYuCZQMiyfPzrBQNuiNSdywaZC+WAhEgSPjCyzmDXpSEnvGeuuxdnxmieWSSVdc5JrBbt9YE4E/XqHOnJnNktV1MsEgmwcydWLRXz94lMmsykhG5u2376y347998BiT2QojmQg/fPuOevx/7dgUi6UqPfEwd+0Y5ulzSzw7W0Y39Ku4tishtYHx58fh6LyKYZr8+N3b2diTaNqvu6zqzOQrDKQjxMPN+9tjw3Am4hnATmYVAoFA03X8zr8ScjFlWi87VM3g8XPLCGKgaR/5gurMVO7dkGkalK2nNOqOBUVOnDjO9u3XUNZNX93rVd7FosLHvn6G4ArNoSZzeWeG8R13bPJsLtGpz/3OX8hX+Pcnp+iOhxlo0L1QVLEtmzccGG16yGgnfjpKVZ3ZrMpgWibWkGQ0vazw5w+eIhUJMdywtn12Zebxp+7Z1rRZRDvxq4tcucpcscL1I5mmNwWPn1nklz/7JCFJpDcZoVIuE4nFWCpWMEyLD37f9ewe6VpT+/azz68uLiZu/HzbaZ/z6//6BH/9qD+J5L493fzMPdc36SgoGk9OZulPRchEVx+M/crrZ99sVuErR2fZ0B1jKLNa3+1ize9aSyWVo1MFdg4l6U6s/maxpHJsusDOwebjf/jlJ7n/eBE/uXNbmHe/+IbL0u8cmVjivX/7OKIYoD+1avdcXkE3bV66s58NPXF6G8o7nVUYXy5z9zX9DHatLkGZK1R45PQSW/tiTdeaXCrx8OlFdgylGG04/w//80nOt8mnPjAMH37ziy+pDzk2neU3//M5onKwqV4feGaOdhySV+5M8fOvunFNfcV8QeE/j0zTHQ839ZFPn1/in5+YJBMNMty92lc8fPrCKa53X9PNYHq1vGfm8hydLbB7NMOOwXT9+MRiiYmcwqt3j7B9cDXH5tRckS8+O82m7hijPau6P/fYSY63Uf+GG/p438t2rakdz+UqfPKRc2SioSbf3n/0PA+cdLybaFjL4t7Uo5X80vdc0xRrE4slHh9fZt9YF2MNdkwtlXhutsDNW3oZa4ipiWyZb51cYOdgipHu1eP/+sSpepkaZ/xrjyf7R2UObh5sqr/JpTLTeYXv3T3SdC2/djyfU3jk7BKb+xJN96vjU3n+/rFz9MZldg9FuXeMq7i2KylJ2dknfLnk3ec9IAScxIzA2vZ/96M5dLon/XrKxZRpvezQLRvNsj0UBL9909dTLkb3epXXj2rgR0GAzn3ud74f1SMRDlLWTSodJFB1TF/RDVTdJOGxO0RZMylW1q7bry4kSXBscJFDFosVdAtirhTtqCRQtWxms17yi1/77pQIczFx4+fbTvucZ2e8VJlGOTVb9OiwoCVFxq+8fvZZQEU3ibi2sW0Xa771GhComCaCK9FZEgQqhonoqovxpfZza+ML1cvW78xkVaqWRdwVa/GgQFW3mC9oJMLNbSASckguuJKEhYBDvJGl5rqQxACKbhJxHV9oDxnizCyX3IfkKgaqZZEINx+/EFjuxFxpzX2FYQaoGCYx1wNK1bYdaonU+VDF3d+GJQHdwpM8H1kh/QTs5qw1EduJZxc9KncBn59Y8Nrt144rhpNbEneVtXIJ1D53rIUkgYrmJMw2SjgkomgmIVcMSgEn1mRXPOeK7bP6Fgqqx+exFWKRO5HVrx0LgoCysgFPo1Qtk6ppEV8Lk3Id5OrA2CUF1dknvCvu3efdtpzX1qarAXVKhuh0T/r1lIsp03rZERQChISAh4Lgt2/6esrF6F6v8vpRDfwoCNC5z/3O96N6FKs6saBIpAPkVsf0laCEHBQpeuzWiIVEEpG16/arC8OwHBtc5JCeRISgAGVXirZiWISFAAMZL/nFr313SoS5mLjx822nfc61DTNerWTrQMKjQ4CWFBm/8vrZJwCRoEhFbz7eLtZ869W2iIgilov4YVgWEUnEdNXFWHfrNZT1v/eGL1u/M5iRCQsCJVeslVaWGvUlQxSrzW2gojkkF1wDBst2iDeq0VwXhmkTDYpUXMd7LzBhtnmAS+5D0hEJWRAoVpuPX2gV6vb++Jr7Ckm0iUgiZa25jsKBgEMtMdoPylqJu7+tGhZBAQ9utbJC+rFdg1aTgBPPLnpU+gI+397rtduvHUckh19fcpU1cgk5mO5Y0wyLSMhBLDZKVTOJhkQ0VwwathNrqiue04n2w8XepOzxeXmFWORGH/q1Y8uyiEoiVVechwWRsChQWkuG8TrI/1TUX0uZy1VYKmq8dEcPu0cyzBermKbzdGtbNrplM5SOoJkrxATXGmM/MsR80Xn8u9D5V0Iay1Sprq4rDQgB3zKtlx1ySGI4E+HEnPPKs3GHtNq+6ZdLGnXrusMIzikaqmH56m78jWXaSJKAYViUNaOj8taoBt84sYCmm8ghEVUzKag6d27v9SyjgM597nd+jerx9HSeim4QFALolk1VN7l1c8+al1G001Gjk7jbRVSWODCW5ttnl1fQZhIV3UCpGty9o2/NyyjAP3bKmsGWnji27bymra1/3zqQ5Lr+OE9Ml9AWStg6BJQSGs4a4xdt7Vtz+/azr7EuGteHN5Z1qaBRNQzCkkQoGPCNGz/fdtrn/Opr9/LXj/qvMf7V773Ro8MCtvbGmStWnQ1mWrRLP/sa68LEZu9oirPLCtU5w2HoWTZVy2qKNfd69tq1TMuZtdZMZxZt11CCimFSULW6jqpusmvQOT6fUxAEAcuyeMOBrdx/3H+N8a+9+qa2/Y5frsBa8jz2jHazZyjBQ+dylCv5mtmYOGuMX7ZrgIfPLGJZNrGQRFlzaD03jqSwAjCTVbACNoIdwMLmhpEkc6UqVrZcj62qZbFvLM18WWNquUwoKKDpFq/cv6HtGuM//xFnvaumm/Wtny2ox467LmoxWNUMZzbbthlIx9g/luKhszks0yImi5RVk+50mHKbNca//foDLXXX2tJysYKFgIBFSJK4YaWfKio6BhYSAsGQyM7eKKeWq4wvFggHAlRte81rjIH6zoVlzeS6/jiabjGxWCIii1RWdlncP5JClOC5iRymbSIGRMKywIHRFKcWFayFEpGgQEW32NDfzfGs/xrjX3j1buaLVRRVq9sniGK9Hc9my015DftGUxwaz2HbNnJYQq0aDCSSRChTYW3LJ2pSW2PcGGtl3eDAaJqSbjCdVer9cFEz2DeaRjdNprIKIdHhlmuGWY+16axCJCRR0Qx2DfbUl1K4PS8Cb79tO984sYBl2cRlydmds+IQiwKCs+ytdg9tbMdLRRVRCGBaNrpts3skxVypynJZqO/sKoUC7B1O8uxsmcmlooOTuYxydWDcINmKxrUDMX74ts0kwkFyisZ0rlLvMIfSEYZTUcq6QaFioGgGQUG4IBkCWPP5V0IioshcTuHZmQKqYSFLAtcOJtnQ5lF4vewYzTjrjGZyKrmKhiwKbO9P1I9fThlORckrGifnSkzkTeRClW39cYZT/nbXfnN6sVRPrtrS0/43reS2Lb2cnS/y+EQOZeVJff9omtu29Pr+plOf+53fHenm2cksD53KouomclDk4GiK64Y6TwP301HbDtl9/Ht2D3FytsChyTwVwyIiCewbSXHXjv6OdfvFzmAiwkyxwvTKK+2wIDCUkfmZe7fzvr9+gpnaW3YdBiPwU6/c3nH79rMvEQ6SLVfJV3QMy0YSAqQiQRIhkSfPLfLoRL7J5wfG/CnHnfrWLw7efZvIxx7yDhrefZvoqyPWFSMVVTy+HU5FW9pXi3/3+ZHhJMe+dpJvTq7afWAkxa7BJIZhMZEtM5WtoFk2ISHAcCZCf0wmn9A4vVCux8iW3hjbe5PMlVWPju5IiAdPzfPEZB7FMIlKIrtHUrzzZviLb3v98Z7bg76x0x+TOXJ+iVPzJVTTRhYDbO2Ls6M/RcU0vfW6Uhfu42++Y4SvHc82JUtFsHnLnaPsGeimqOo8PVVgMqcQC0pcP5ziwMYuvnZsjicm8/Xk2r0jKe68po9vHJ/n8ORqX3HjSIrbd/XzxadnONTQh+wbTbNbgKdaTKgeiDhvbSrVEqcWSqiGw5ff2hsnnIxwdqHYsi4qVd1z/rtfspX8557jyEyR6aKT2LtnMMG2cJX757y679vqr3uwO86RySzPTReomBYRUWDXUJJrhxP89zPTPDFVqCcP7x1O8p5XbOI3/+Uo5/JWPTltY0qg3eqZt9ziJPYemsgxlVOIhUTu2dHHK24Y4O8fOs/jk/mVZT8i+0dSvO6mYf7ya2c9ut9x1yY+88iU5/w3Xgufetar9+0HVu+vbvt2DSa5/+gsT4znqRjOspi9Yylu395LuWp46vXmN2/mf/3DGX8jW8j+sfaxdngy3xRT9VibyKLoJtGgyI2jKe68dmAlBpvP//9emeH3/8u7wPrnv6+fO7f1U1J1Dk/mmc5XHDvG0tyza5DxrOK5h26uxcHM6r1h12CSPRu7ObNU4sximfmSSjQosHc0w82bU/zcPx5hfFkHLu/A+GryHavJd88WIhjBEHtHM2zoiV8UhcFPOj3/csqR80s8MZElKYfqT48FVWPvaIY9G7rb/na97PCjIFxOqWVc26bJsWPH2LFjBwFRXBONw01baPebVlLzeVAQ6jMnumVdFp+7z68RMeJhZ2mDqpuUqkYTEaNTWSsxpaZbDopIUgDDcGY+L0W3O3ZqdVR781Gb/f2nh0/z0JkskmhRKlWJx8MYptBEnem0ffuRQ9wUi688NclDZ5bJxML1GbZsuco9O/v4kTt9+EUd+tZPrv35z7dMiooBz/7OvW2v5edbPwqP+/xPffsM9x+bJxEJIUvODpjFisbdO/o4uKWXE3NFEpFg06xtjSpSaxeNs5qtdNTaUiwUJLzy2rWs6Xzkc8db2t0dhEO/cW9L+2rXykTD9dmprOKgOfvTUV86ifv4L/zTYzw7pyICkgCG5cyq7RmK8v9+5FZn5tQwMMwAkmgTklZpMclIiFhQWnkg0+qkAFmS6rN4qmEQkwTKhkVQFAkFAmi2jW6afOhzx31joUbjiIal+my8UjXqRAB3XdSOu8+vkQI0zUa1DGRBIhQKXJTu2rXc95+TUzmemyuRkINEQxKKZlBUdeSAjWo7SyokQcCwLHSLtluQn1uJ83NLBXIlk3RcZGN3sl7fpgmCZWEJAqJInXiQjgTr7TVX0dnaLTPWnyQoCquzmqbVdpb+s++5ueX9VVE0xnMqmViYuBykpOpky9U6tcHGoR4JYoAANl88MsGhySKGUcW2nQl8SVobCcQdazXqjAAEAgK27bwpqtVFNCjV7VN0g/54GEkSEWzqbw6sAPzGvx1msoXbt6YE/vknX+K8idZ0NANCEkRCQV9iUa1M4aBYr9eq7k98+r3PPcV/H1tkQ0rgx/YmLmvy3dUZ4wYZ64uTrQY4OV8iKIrEI8GGzFJnQFGoGCTkIJIodOS8Ts+/XFKqaJyaL5GJhulaGdjFZadRnFkss7Uv0fQq0S3rZYccklj7S/xLF8O0yFd054YWFomFRZLRELpFU536/cad1e33m1bSyucAy+XqZfF54/mLRYUjE3l6ErKHiPHMVIGbtigtl3J0ouNK626MHb86OjGb47HxPJl4iMF0hOXlZbq6MszlVR4bz3PT5DIbe5Idt+/G4610i4LIXL7Mo+dzdCdkhlcyzbtiTgLVExM5XpYtNqG/2ulYy/FG+buHztQHh425UlULyit//6HbNvte60K+FVe26q75qfH82XyJQ+N5epMyA+lmCsnj4zkioRA9qXCdDBGSQlimzenFEtePZEg24g4b6qJRh19b+sKR+brd0SCYBogSKDos6fAvj53n9Qc2XPBa4WAY07I5OlOkLxlpstuwLKZXiAqNx584v8TxRZVICHqSq3G+VKhwdF7ha8dn2behh3R01b6FvMKTE3m6Equ0mBQhLMvm0ESOW7f01mMHYCqncHgiy56RNIMNb9d+/98fa1GLq/L/ffJB/terD9R9HkPCtGyeWyECNNaFrpk8N1Ngx1CqTieJIaEZFofOZdnaF6e/d9W+j97/dFvdv/7vh/jRu65t0q1rJoem8mzpTdTJCXFZIlvSeHwyz2Ay0kROODWb47mZIrsGUmxtWD//yUcm2uq+9ec/z8O/cy8bu5OwMu/gFztHp3I8MVWgLy4zsqI7EwdrvsiRmSKjfSm29K3q/ueHT7TV/ZH7n+S1e7Y13V8VVeex8Rxb+xJ1+kQyEsSynfoeysSaqA1Hp7I8MVkgHQkx3J2uH/+vZ1pMzzfIq3/vv/nkT764Kdby5SqnFkoe6sxCvsKhqWW29DYTIGbzFZ6aLHDz5m56G47f/8w0k2VnI7BMfDXJLlfSOZe3+M8jk9y5Y6BJd1nVmc55aRz5isap+TIDKZl0rJmEM5NTGUxFiEdC1CLh1EKeR8/n6U6EGe25/COHq8l3LnFmDUwqhvUdRZNYL6maNqppeygFsVAQRbc8iQnfLfJ80jieT59fDBHjhaTbr46KiunodmWUJ2UnS3qpaFxyvfrpVjSHBJIIN2dQJ2Wnb8mXL9+erSfn2lMpLvT3Ruk0/osVx+5GJBo4FJLiyi6Ya6WK+Onwa0sX2uGrld1+14oEJRTD9NAq/IgK8/kKhgURV8K8LAXQLJhcrnh8aNgBKqaXFiNLDinAtbs6YUFA0UyCLjrDbN7XZADOL7UgfvgQASTRmS12b+1eI2WE3brbzFwCnFsoe3QHAgEqupc6YFoGqmERc7WZkBhAtyDYQZIwwHSLY371bdSJB832RcIiVdPCdiXMzSvtY216uerRIQqO3RHX29HoCrXBdm2zoRimQzrpkMIwUfVSSAgEWlJnbGhZF2FJRDEMz851S2UVAHdVBAVnE5WJrDfO/Wgc2AFU00JyxZQfKSZbNFbuJVdmevHqwNglZU0nFhKJSMJ3FE1ivSQsBpDFgIdSUNZ0okHBg7L5bpHnk8bxfPr8YogYLyTdfnWUiIqObldGeUF1sqS7E9Il16uf7mjIIYEUq83rfAuq07ekYpev293W355KcaG/N0qn8Z+IOHYXVK3peEHVSIQlknJozVQRPx1+bak73jwQcUsru/2uVdENopLooVX4ERX6UhEkAdy5YKphExJgpCvi8aEUsImIXlqMajjrOS3Xs1PVsoiGRHQXnWHgAmkCG7pbED98iACG6azzNFzKa6SMqlv3Bbav3tgb8+i2bZtI0EsdEAUJWRIou9qMZtoEBdA7wEoCDLU45lffUp140GxfpWoSFgUCrkFdX7R9rA11hT06TMuxu+Kibigr1IaAaxgalUSHdNIhhWE07KWQYNstqTMBaFkXVcMkKkmeHfG6V5Jn3VWhW87Sg9GMN879aBwEbGTRScRrFD9STCYhrdxLLscG4l75Tni7/x0jy0WVnOEs9B7KRL6jaBLrJfFIiK19cZ6YcBbQN66r2zuaaftK/4UsjVn/mm1RNSzKqo4VEC47jaPR504H2byu+2J9vpY1p41EjOrKjEVlZe2eHxFjvaRRt26sZrPnK9q66faro75klANjKR46k6WiG5hljZxZRK1aK9SZrkuuVz/dqajMwQ1pHjqzjGYYhEWRqmlSrBjcs7Ov7TIK8F9/73e8MQ5+6LbN/M5/HqWMs3yiUWLAD922uSPdNfsMy/KsxXb7aSAVZ99YivuPzaNqFnJQQNUtiqqzxvj60RQn5opoK7OStTWFNarIWqgbfm3pmuEMscPOcor6hN7Kd3cQXn9gg8fWxmtphtW0XnnnYIKAEFgTnWTXUJpremSenVOZzVYQcdYX66azxviuawY8BJRgUOKGUYfCAKv9cKmqs280TdU0mcophAVnllrVDW4cSVE2TCaz5fp60Nfeeg3PtFnn+/tvuZ0Tc0XKqoFtQUBwBro1IkAj8UM1LXYNJqnozccN06qTMsjbyJKIapi8fPcGHhn31/2r37ePE3NFqlUTG2cgpts2Nww71IGzCyas/EWUYP9IiufmSowvFYkGgyi6TkW32NkbpWIYTcdvGIq1XWP88O9415T73ftCIaFOPBCWFRKyQFG1KFQN9gwmCARsFopqPfHy+k19PDThv8b4/Xff4IlPA5sDY2nGcyqTS2ViskS5idoQYCFfqfspGQ2xdyTJocki9mKJuCxSUk3G0u3XGP/HB17qiTU7EKhTZwI2dTKEZln1ulgoBuplLWs6u0eSaJbVRJLYMZxiJAaTZVgo6Uisbre9NSXwvXtGPEQTOxBoSdWxbdjaF3PWPYsXJuFs7U1xcEOK/z62yKSoX6VSXEmZyqlsHsiwvTeJvDJl/51Ek1gv2bWy648763NXw25A341SIxFMLFeZLhokC1VGu6IeIHqjrBeNY0d/ikJF5+hMEcVQiEoiOwcT7OjvnAxhWTb5iubJjE9FQggtmKw1IsZjE7l69vuBCxAx1ktu39rHXL7iZFwvrGZc3761b910+NXRD71oI89N5Ti6UEbVQFbK7OyN8oabxtatXv2uc9++EZ6bynFkKl/Pct8zmOAV1w/6XsuP2lCjbriP1wga7jh4zyvSfPiLOc/13/tKfyJGO90hMeAhfvi1mbt3DnBsOs9jLirF3TsHiIdDTCyVnWz9laz8XYMJtvcmqdrWmuui3pZmiyi6QjQosnMgwf96zSC/8W/eXf9+5j7/JM/tvUmmsgpPTRZQDIOoJLF7JMne0S507DUTQj7wfdfwrj9/kmLDhFYiAO//Xn8CSq0NPD1VcMgJboLARLaZSrG9ly8+PcND55br7Xj/aJp7N8DnW4zTXrsdeqNhHi0t8MTEKlVh72iKm64dIqvqvoQXPxLIU1P5Or1g93DKl4Dy4y+W6Y/JPGfkODS1XNd9w3CKG0ZT/MO3zvHoeB7VMJElkYNjKd56x0b+5oFzKwQbpU6w+cEXjfH33xz3HD8+XUZtUafd+Mfz9l7nzYH73veKXf18/OuneHwyz5LilPW2zRnececWziwoHJstMlOoEAuK3Lq5Bzm0xB/9d8mj+2dfkfaNzx0DCT735JSnL/ye3UMcOp/1+Ol992zjD75wzEMCUXJVFlvYvQl8Y220NwkUfMkQ7vvSnpEMZ5ZKnvN/803X8p6/fJYK9edOIsCH3nidL4Vkwwrh5VJJOK89MMK3Ty0wma1ylUpxBaRGpSgGe6gKwXpWJHxn0STWW/zYnd+tUsuwF7E5duwoO3bsxCSwJsLEpcZBTbdlWpg2iAEQRKFjukXjtfxIAW6pEzECQp37qttrI2JcqpxdKDocaNuZqRICAYQATW1svcRdR3/1jRN85eg8QcmmVCgQTybRjUATGWK92rf7OjU6Qzgo1rOyq7rJ3Tv6eOPNrWdta77yIwX40RzccfCqP3qQVqsgw8DxlZm0tequ6XATP/xi7YFjszx8ZpFQIIAgClimhWbb3Lq5h9HuGCfmisiSWJ/xVI3VLPS11sVqWzKbOLHv+PiDLTPmNybg67/U3u6QINRn6zTLumCZ3Md/4VOP87WTywi2jiSCYYIVCHLXti4+8KrrWxJQanGh6waGHUAKODPJczmFU4slZzmHJKAbFophgmkxX9aIh6SVjVRMSprRlpBQ49om5VADP13j1s093LFjYM1vJ2p+kkQBIRDAsm0M02pLKfjwmw+2pIcs5hROLSoOjSMsUa46NI6tPVF60lFM08IyQRBBFIU6jcN9fC1EDHc8+xEPajG1WKySr2ikIiF6EuF6nOfKan2AmI7JvOg3Pt/S7pEY/Of/emnL+KxRGGzTrtNXAmKgToZw+6lmt6pa6KZBUJSQ5fZ216gUfrHmJkPUjtfada2d+51fI/1YZhVLAMECQQzz0h09fP9Nm1pSSC7UltZKwvn3x8/x0JllxhISb9sduUqluFKSjoVQkOpZkXJI+o6hSVwOacz6/G6XJiqF4GwPGg1LbakUjXIpcdCU3R9tnXm/1kHZWkgBjde6VCLGpYiqGUxlKyQiwaaErMbM4/VE9TXW0WS2yGPnc/QmZfpTMvOCRl+/Q6VoJEOsV/tuvE47OsORyTx35kuezU1a+SokhdANi+emCuwcSvrSHBrj4DPfOl8fFDfO6eo4W/j+5TdO8Y47t65Jt2nZnF4os3skTawhgdIvbnNllWenC3TH5TpxAGChqPLUVL4+uG8XCxeqC7+29MWnp5gsO4zbVETEMEwkSaRQMZkoOn9/xfXDF7R7rWVqrO/nZrI8Pp6nKxakP736Bmgup3BoIseh8SW29aWaCCilql4nXKQbmOjLRZVnZwr0Jpp9OJUt882zy1w7mGK4gV7w6UdOtvXXR79xjLt3jqxeKwbBosCx2SK7R9OkY3JLOlAjvcPPT/cf8acUnM1bfOaJcfZu6Gnqd84u6Dx2Ps/Gvhhj3U4b6AHGl4o8PpHnleko2wfSDXav0DhG0wz2rq4T//DnDrW1+/2ffICfffXBpngGWhIPGmNqa//qw3pjnKdjMrVSff7JSSbLzsA21ZAcl1dNJsvwr4+P89Lrhpri048MMb9Chtjal6C/gWgynbM5PJ5lz1iGLQ3bG374C+0pJD/zT9/k1++7uSnW/MgQjTEYWwNJ4tRc0SH9xMIMd62+fZrJKTw6nmNT/zLDXdEmCokoBC7YltZCwplZLvPo+TxdMZnR3jAO0PHyyXfXFOg6iF9W5FV5Yct6ESaeb92dXuv5JGLolo1mebPir0Qby5etFUKCl4hxuckQ7egMZc2kWPHa7ecrKSBQMU0EV4KmH83hzLL39W6jtKIz+OkOiSIVw/LcgvxiraLbqKblWWaRCAcpq6bDpL3EWPCL/+msAnhneiSc9b6nO7C70zIt5DVUyyLuIirEwyKKYbGQ1zzl9SNcmDaohkUkKHnOr+heKsVypTnR0S2zBa11feimEz9rED8/Lakra+tbUApMYC7rpTMEcOI55rIvFgyh6CaCa0gSEgUU3fRMHEzn27ffiWXWXK+d9qmnVmKpVawBjLegM/iRIRAcMoTsOh4SHLtFV7ufvwBU5uycumYyhF8M+p2fr2iopknCRe9IyhJK1WSxpF629l3SzRXdV6kUz4v4ZUVelRe2rBdh4vnW3em1nk8iRlAIEBK8WfFXoo2lYsIKIcFLxLjcZIh2dIZYSCQR8drt5yvDtoiIIpbrAcaP5rC5q/07oFZ0Bj/dmmkSkdxDFf9YiwQDyKJAsdrs82JVJyaLJOTgJceCX/zX2LDunHUDZxZ5Swd2d1qm3lQIWRAouYgKpapJVBLoTYU85fUjXIgBkCWBim54zo8EvVSKrgu87RlIhlrXR1B04mcN4uenbtmZ+WxFKRCB/oyXzmDjxHPZZV9Z14gGRdyPYZppEQ2KGO76TrVvv6NdrLleO+1Tt67EUqtYAxhrQWfwI0NgOWQI1XVcsxy7TVe77/OGcZNs6pfXTIbwi0G/81ORELIoUnTROwqqQTQs0hOXL1v7jgfFFd1XhkpxdWDcIGXNWYc0mJav2G5sV+XKSI0goOomVd3Esp01n6pukoxIl3X9eKNuVTedtZUXqbvTa9UysbNKleVylapusVyuklWqbO6JXda15XJIYjgToVjRKagammFRULUr0sZGMgkObEizUFCZyVao6Doz2QoLBZW9o+kLkiEuRWp0hoWCymxOQVENZnMKCwWVPSMpzzIK8PeVqpvsGkqgrhAEasfLmtFEc6jFwb37R+pLKPSGDzhrjN3LKNrpVqoGW3pjWLCmWEvHZK4dSrJUUlkoqqiayUJRZamksns4xfb+xCXHgl/837yll5GYM1OZr5gouvNtAKMJPMso2tndaZl2DWbYP5Ziuawzl1MoV6rM5RSWyw5hYt9Yt6e8NcKFvvL/2nFBFLh2MElB1Zraq2qY7B9LU9T0Jt/uHmufQPveO3e0rI8dAwnSsbVtlODnpx0bHEqBjrN8oqzo5Eo6GrApJfC6vWOefscCDmxIkS9rTW0jX9bZP5rCBpfdBjeOplAMs+n4a27a1rbMH3nLHWuu10771HtvGGEk5rzMz6umQ9pRTSycNcav3T/muVaNDKGs8LxrZdJXyBClqt5kX0VfsVs3mu0+cE1bu//wTS/y6NYtm6F0BMO01xSDfuf3JMIcGEuRLVeZySmUVY2ZnMJSscrBsTT7N3ZdtvadScgc3JBiuayyWGjPz14PuZp8R0PyXbiHDf1djGZiHvD0C0E6TSR6PrZlrsnzkdRYozlMZxWOPPsce67dxVAm6ktzWM/y1nQvFKv1zOPelcz7C+n2u1ahYqBbzqxGMiL5XkvTTJ6byfHMdImyqhGTQ1w3FGfXYJpQqDOAfKdSyw6fyamopoUsCgym5SvSxgqlKv9y6DxfO7HAzGKOwZ40d23v5fX7NpCMd5bw2KmUyhqff3qSwxN5ilVns48bR1Pce/0I8VjrhxE/X9VIAZPLFRTDJCqJjHStUinccfD0zCzv+otnaLx9hIG/fNf1vGjLWEe6/XT4xZqqGjx6bpFD53MUVY2EHGLfhjQHN/YgScJFxYK77dXif7msoZkWIVGgKxZiKpfnxz/xGOeKq7/dmICPvv0gu4ZaDyAvNj7dZZrPlfnDLx3l4dOLlHSTeFDk1i09/MzLd9KTjJKvaMzkKii6RTQoMJiO1AkXbjsiosixuTwn50somkU0JLCtb5UgcGy2SFk3iQVFdgwkyBl5fvKTpzxl/PO37eCOjRt49Nwiz04XKGsWsZDAtUNJDm7sQZalNScX1vx0flGhpBvEgxIbeqKUtQo//beHONWwtGFrSuD/vu0Am7szPDeT4/hskZJmEg+JXDOQYCwT5SvPzXjaxj27BhnPKjw1kSWnmqRlkd2jGbb2JDi1WOSZqTx51SAlS1w3nOKB08/xB1/2ri34/16Z4d233cxEtszEsoJqWMiSwGhXtF6v7uS7WkzN5lXKVYNYWGIgJdfj3O2PZ6bm+bG/eqwpAW8kBv/vRw6wa7C3Hp9VwyIsOfUaC0pM5RVPrPXHZE4sFHhyskBJ1YjLIW4YSa7aPV0kr1RJRcNcN5TgW+Mn+d3PL3ns/sVX9/HOm/e31N0Ya62Ou9u33/mmYfJXD57iwZNLlDSdeCjI7du6eecd20hGw23b0lrHG37t2zYt/uHbZzkxk+Wt18lXk++ulLxQHxE6xXf5oWyuxGCl07KupxiGxfnFEkdnCpxZ1hEmcw6HtM0Acb3Ka1m2g9HJV6jqFuGgQFAMkAgHO7ZbEAJkYmEScrCzwbplOUtS3bsHXEaRJIFNvQkGU5Er/hBm2TZK1aCimxiWs5ZPqRpYV6ChRyNBXnb9MFv7U+RUjbQcYnNfnGjEHw3o5yvLsklHQ2iGVb/Jp6MhJEkgE/LGwcZ0moMb4jx8voSJ81r74IY4Yyl/NGC7egqFxI5jLYANgUDTxgWdxoJf26utmbVx+uyahngoxGhXnJliiSrOw8BoV5x4qM126+tUJgA5KCKJIpLp+EgOigiBQL3fOT5bpGI4m2hohllHNbrtkCSBDT1xQkGxjmurJYzdsWOA3aPpJkLCw2d0IkCloZyRFX+EQiLXj2bIxMOUqgbxsMRIJookCS2RWLUBkft4bU2whU3Adr7BWW5T1pr7k7JmoZkmghAgFQ3RHQ8T1gziIYlUNERcDrVsG0ECTGUVTi+WKWsmSyGR7niYsUyUQkVnqVClZFoYmkmhSyfkU6/hcBhBCDhtxrRRNRM5JJKOhjAMi2emspyaL6GaNrIYYGtfnK09CaeO5kr1NlbV4+xYQY25/bGWPsTGoTrYK77yizVVNShUdHJKlWLVwLBsChUdw7IoVHQWiyqKZqKbNoWKzGA6DXgHxmOZjK9uv+N+9xJrZXmF33XMgE0AETNw4fZtGBZnF4odjzfc7SIkimzqS1CoVHx/s15ydcaY1RljNdpHyRQvC0rqckqn+C4/NNOVsLvTsq6n1LBlKTnI/PR5+oY2kFf1ttiy9Srv8+nzmt2ZaNizocvlxrU9n/J/v/wcXzk2T1csjFHJIkUyLJer3LOjjx9/2a7Lqns947zTa/3Qn32DxydLxMOBOuqtVLXZPxLn795z53qZ2FJquLbuuFwfZC2V1DoerBPxs7uGknIf/+A/H+KpWYVoKIBg2FhSAEVbX7v9yvSJb5zgobM5umMhErJIUTVZKmvcvb2b79s31rL9be2J05+Ortk+v/re+fOfp9VQIQI8vILvWqsOv+M11Ji7//rFTx1maWWtTm3DB4DBCPzDe1sj0/wwg0fOLXJoPEdPQiYpBymozqCwJyphCYInptph6g772F1D4bnrooZGW2sdve1jDzJTcdai1uyuLaWo4drWWn9+bSYuCZQMa13s7jSm/OK8hmvrToRJyhIF1WCpWOWlO3r4wKt2tyxTp/c+P921+NiQkri5R7+sM8YvvPUCl1FiIYlEJOi8CtCuzCLvSxU33kQUAvV/FyqGJ2nBjd4JSQJJOXRF7O60rOspjdiyTCxMUBTJxMJkomHOLJYptcjuXq/yPp8+d+PawkGBrgvY/d0g55YKPHLeuckOZiJEQiEGMxF6EjKHJnKcW7pAevclyHrGeafXevTsAs/MOYPinmSUWFSmJxklHg7w7FyJR88urLe5dXHj2uSQSG9Cpjsuc2y2SK7cajuG1uJnd1AIMJ2rIImBpuMnpgocXXAGxT3JCFEZepKRdbXbr0wzuTKPj+fJxIIMZKLEImEGMlG6YyEeG8/z0Il5T/tLyiGOzhaxTHNN9vnV9ycePF0fFIcCqx9wZpA/8fWTa/ahtLKRS1BoPi4ApxZKRFe29a71X8+cW6oPilNhgVhYIBV2hhQzFfjUo+OePi8Wkji9WCIQoElHVTM5NOGgwPpTESJhif5UhFhY4rGJPBFJaoqpbz7rPzgEeP+nvumx27Jtnp0ukpJDTXURDUkcHnd0NB5PySGOzhawbLvpOt88NsdMxWFep2SRmCySkh0/TZbh3w5NrLm9+rWZaEjisYkcUUlsOv7U+Fxbu//ky895dPvVq1+Z/OJ8oaA4uLZ4mMF0lJgcYjAdpTsR5tBEnlMLeU95Or33+elWq0Y9Prou8zI4uDow9sgLDdfWKWrm+URoPZ/ItIvBlq1XeZ9Pnz+fuLbnU3Ilk2obXFuu5N2ta73k+cTzTSwp6BZEXK8oI5KAZjl/v1zSFtfWAR4M/O32Q0nNF0voFkSDl89uvzLlywaqZZEIN7fvhCw6SwIU09P+IkEJRXc2gGgUP/v86rsVfq/p70tedFin+C4LWqLGZkvOqNi9CK3mhallxYsf9MEMVgwn2Sruaq9ySKKimwRcSi6ELTsz78WWmSaopkk42HyxoCCgGCZBF6EnHBRRdAs3SGIm7zyKuBdF1XFti6U1t1e/NhOSBCqaSdjVjhdLrbbuWZVjs8U1owH9yuQX5wXVQaYlw15cW1k3yRa9Ezyd3vv8dFdXZo3d8XG55OrA2CUvNFxbp6iZ5xOh9Xwi0y4GW7Ze5X0+ff584tqeT0nHRcJtcG3p+OVLOnw+8Xyj3VGCAlRcWK+KYRESnL9fLmmLa+sADwb+dvuhpPoScYICKPrls9uvTKmYhCwIFKvN7buomsRCIt1R0dP+KrpBNCgisDb7/Oq7FX6v6e/dXnRYp/guAVqixgZWNvVwP2LWvDDcFfXiB30wgxHJmSEsudqrqhlEgiK2S8mFsGWb+7zYMlEEWRSp6s0X0y2LqCSiuyYJqrpJNCjgRg8PppyNONxD1DqurSe+5vbq12Y0wyISEqm62nFPvP3AcMdAYs1oQL8y+cV5UnaQaYWqF9cWC4pkEt61+Z3e+/x0h1fW7Lvj43LJ1eS7BvnPw+c5uGOM7f2JegLGWrfMrEmn9AL3VpOdSg1vMl+sYlhW0zaQfYmwpww19M6JuSLPTmTJlnUysSBjPfEmu/1krds6+h2vlTWnaPUkNDkoNpV1rZnSneiuYcuemMjyqUeOcu68zsbxp3jZ9ZvZO5qpY8vc2cq18gKetVnt6tddpprPv3xkitmCykBS5pZr+pp83mnsrMUfjXb/zKefqp/zwVdd09buTuv1Qsff9Ymvc2KmzPbBGB9/24ub7PDT7dc2JrNF8mWLVExowq41Xmdjd5KbNqT5yrF57j++kqhyZoobhmLcs6OPjd3JtrovtR3X4uaPvvQkpxcqbOmN8GMvuW5Nce7XZgqVKoYZQBJtQpJUv9ZiUaFctYmFAxzc1Mt1/XEenyyxMLuaMh8RYf9InIObetv69tRCnmzRIJOQ2Nrrn6zXyuc1XNvDZxb5yBeOowIy8LY7NnDr5p66Hr8Y8fPh2YUCecUgFZUYTMcYSkfQTJsT0wWWKirdEZntw0l29kZ5albh+NzK7HBR8dg9my9RrNgkIoEmbN6RiSVmsiqDGZk9o90XLNPJ+Tz5kkEqLjGaibN/LMVDZ3M8ObX6qnssHebu7d3ctr2PJyayLBdXOSEBEXYOJBBEkROzOYqKSSIq0peM1u179OwCy/kqXakw2/tT9fr+2P0nOD5X4Jr+JO++ezu/+/ljVADNNSEfAd724m3MF6ucWyiQqxikIxIDDT78xtF5ZvIlBlNxbtiQZigjo5k23zw5x2KuQk86wnXDGbb2xpkrVnnk2Byzis5ANMj1m7rpfmScJR3yrsHSYATeeHCME3NFnjy/zFKxSncizOa+RB0z+MXD00wXSwwl4tx8TQ/7RlMcGs/x7ePnWK5CVxiuGRnkwGiKimHwia89yXwR+hLwoms38EybtbYfeeOLmC9WefC5+bqOA9u6uXYowanFEg8dn2OhqNGbCHHdhgw3jqUoGwaPnppD0WyioQDdqQg7B5IIgQD/eXiC2ZXd4G7f0c/gN04wU4Gs2jzIHonBa/aNMl+s8tjZRWbzFQZSEa4fydTr7z8OT3ByrsC2/iSvvnG03ma+cvgM8yWdvniQvduGODCapmSYfObQKfJlg1RMYvdYPw+f87f7J162i/lilSPjyyzlK3SnIuwYTNXr9T+eGGdiqcxod4w7dgzUy3RuqUCuZJKOi2zsTtbj/MRMnuVyla5YmLGeOAfGUjx0Jssj53J1ndt7Irx0R0+9v3D3z7V73+nZIoqqEZVD9KXk+r3P3fZquudzFcq6QSwokYyF6vFx/9OT/O49Pb4+WA+5mnzHavLdz35lkbM5g4/+8HZetn1LS3JDDZ3kPl7DGq2VXlDDGj07XaijTRoxOp1IjTIxnVWpWs7T4VDGHzl0am6Zn/nkIU4uaZgWiAJs6w7xh2/Zx9b+rpY6/LKxawiatfopIwf50rPTHBrPoxomsiSyb8zBWEUjQd8sdL9M6U50f/XMSX7j32Y8tv3a64Z58w3X89xMzpOtXMtKXiuuyo/4cT63xLv/8tmWCK1bN412RL7otC5+4WMP8O0WG6LdmoRP/OwrWtq9vTfJXFlds2/9jn/umSf5yJe8a8/+v1dmeNctN7XUXcNSudvGjoEEn3tyisfO5+qxc2BDmlfvGWEyV/Fc5+1/9m2yLWK5B3j4Q63t9stC77QdnyrM8KF/nfac/9tvGONNe67rqP66IyEePDXPk5N5KoZJRBK5YSTFTZu6eeTsEkcm8qims3vWntEU7/unpzx6a3Ls117esrxb+2J88uGzPHp+9VoHN6R45x3b6GrYqrYmNQSg239//vlv84Vxr97v3QR/+I5XtmwbvdEwT0wse8q0sSfCn91/kscn8vXj+0dT/OCLxvj7b457jv/jYf/1l8/8yj18/ulJT79z48YUf/RfxzkyXaz3nXuGEvzsvTuZXK56yjTWLfN/vnScJyYL9fP3jiT5t2cWfXU//gt38af3H+fx8TwV0yQiiuwfS/HGW0b51LcmeGx81ecHxlJ87/4B/vDzJ3hmzlkeEhTguv449xxI8+v/Mtk0QyvinbFtlEd//sX84ZeO8vh4HtWykAWB/WMp7t3fx29+5ihnlo06uWRzl8RPf+8W/vxL5zm+qGJYIAlwTY/Mm+8a5H//y9n6mmKA7iBN/3fLF3/qJgefl7fqOjamBH7sezbwR/9x1oM6e83BCH/yNW8a4X1b4bNeGl1b+cJPHuS9f/0o40VnKYgAjCXgzXf18bv/Me/x4S++ZpDPfnPJc098+z0j/P6/nWGmoViDEZr+75b//plbWt5f/a71mgMhPvqAN9fjh26Evzvcmd1ff/9t/Nw/HeboglKPnZ29Ud545wC/9c9nPPX3+z+0i+fOVXjkfI6qYRKWRG7akOYl1/Xyf790kiMzRaqmRVgU2DOY4AvHW/Wojjz1yy/lXw6d9/TPN2/L8JHPH+Op2VIdv7Z7IM6779nMJ7523tP2fvzl2/jqMws8Mp6jqjtLX24aS3P87Fn+8yxsSkt8+J6ey5p8d3VgjHdgDPC197fOqK1lea8107bTTNRLyd4OCgECQgDbstEt21f3G//kaxyZVogEQZZANaCiw56hKJ/6ibva6lhrtrKfn6aWShybKzlJD7KEojoA8zu393Lrtr6OMmo71f3uT/r3Mp99z81tyQ1rnUH0y8D9yU8ephWWPAZ80yeTuNOMYT9/XIzd/fEwkiSu2bfr6XNMi/my5mkb+aLKuaxKb3I1a32hoLKjL8ZoX8JznQ997njHuv2y0DttxxeTOe5Xf7mSytllZaWsEmXNIKtUESyLRcXwZPF/+gnvw19N/vZt+1qWd3yuwKkldc3Z5n6kk3Y+9+tTa/a5y3RyKsvJpSqZWJBEWKJYNciWdUK2jhYIkomuEiCyisZ4zh/8/7v37eT+Y/MrsROioGosFFTG5/PMlJ2NHOJBgZJuUagYbMmEObi931OmZ88vrZRJWq2LslFfb9tKPvjKbXzl6DypaIhYWKJcNcgrGpauk63aZOIhkmGJQtUgW9IolisUDIjLYp2WUFJNyhfxFvntB4e4/8QSmViIRFigWLXIljWWclVUICw6n6rpfEwgHIBICCJSgIphU9Gg2jBKuNBgvCbX94Z4bkEjJKzq0KzV34o463T1NV6vE9maEjiVtwgCIcHReyH3RUSIBkEOCqi6haJDZaVggYayXmjAtHdA5ulZFTkIsgCqBarerL+R4LGecvsG521RLBQgEhSo6BZlza7bAY4tNRtE4LqhmKcPKZcrZLWA0y7CAqWq0y6WKv41VYtzd/+cLZRZqNgkZYlYOEC5alNQDTBMDCHgaXu9coDeTILuuExcliipBksllW+dcyZYrsTA+OoaYx/56H8f9WRSykGR56aLyJLYdDwalji9UHbWYl1CJuqlZm/H5CDRkERMDvrq/tbp+ZXsbehJRYjHIvSkIkRDcGxB4Vun59vqaLQvEIBT82VioeZsZVkSeW62gBxs9pNtw+Pnc6QiIQZXNtYYzETpScg8NZnn/GJ5zRm1vrpFgedmCoRduv/xEf8bNsDvfO5wW3KDJAqEJfGCyydaZeA+enKuPigOC6sfgDLwt984teYsZt+6sO2WmePtBqYA9/3Ztz12x0MST03lkUSh6VrhoMhzM97496vvj9/fXvcvfMqrOywIHBrPEQsFm9qGFBB4fDJPIhJcyYh2vlPREI9N5glYgabrtBug+dmdlEMcnSlimdYltePHT0601f2hf3/cU39+mf9CIMAT43lScoj+VIR4JEh/KoIsijw+nie2kr1fy+JvNygG+OFPHPKUN2DZHJkpkpSDa8o29yOd/OEFfP66jzzgaRsiAZ6YzJOUQ01lskybp2ZLJGWRoUyMRDTMUCaGLNmcy1tEJBjqijrHu6JtB8UAP/fZo/QmZQbSUaKyxEA6ihWAM8sGsmTTn3IIHv2pKLGgwPFFFVW3mspkGvDMXImELDCQdso0kI61HRQDfOi/TtKblNnQE6cn4XyHJDi6oBALiwyv2DeciSEKFjMVZzDXn4wSj8r0J6NUGlSIDZ8LyV8/Ok13LMRQJkoiKjOUiRKwdcorv+9KhIlFw3QlwvXVzgHbIXo4RJMIjUtvU2GBeAN9op08vaAhi9CdDBOPhelOhptWVKejErGoRDq6/is6T+UtQkAmHiQWDZJxrc11EzwALBP60jGSsQh96VjTuub0Cn0iLV/Y60/MqkSC0J2QicVkuhNy06DYTfC4kHRS3w+ed4g0vSkndnpTUYwGOxKhAPFQgMSK4SagqEpTnyoF4FzeQg7C4ErcDGaibQfFsBrnTdcSVtpYEAa7oiRjEQa7ogQDJks6hIXmthcJBjiXtwCL4S6nfx/uitYHxVdKrg6MfeTUQtmbURsQqJgmgithKSSKVAz3Lu+dZ6KuZ/a2n+7xxTK65SRTNIosBdAs5+9r1YEdQDUtz3INQQxQ0S0kV9azZdlUDJOYq3NJykGKmkmhqq85o9ZPtyQ6M5eS0Hx8Otf+YWMyV71kcoNfBu5Etv1N+9SyN3O804xhAoGWmeNrEbfdsiSi6CaCKylDEgQqhonoqgu/+p5Ybq93JufVLYkCiu4grBrFFmxUwyLmyvyPhSVn61Db3fouLC1JAYaJu7o7bcdzhfZlOT7vrW+/zH/sABXDm0kvSU4m/cVsluIur0mAqmkhh5rr1S/b3I90UqS9LIOnbZi2TUUzibjsMADNtIi5KBOSJGAC0kXkTibl5g0hApbzij/q8q0cCmBYUNWb7dYtc4V80blyNx0lLIroFp7k11rTcq+mu5QbdcLV39or7dqdd1tX6Tp+Kem57n2TrmSqb7BDp7l7+Uvxedhl95UcaLmJNBfSbVquBDjBaWPyRSRmu+NcCASca3n6L+fakpsEIjiDdSHw/A5Nrw6MfWRrb8ybUWtbREQRy3Xn1EyTiCR4nNlpJup6Zm/76R7riREUnJtwo6iGTUhw/r5WHQRs5JXtQhvFMm0iQQHDNVgRhAARSaTsSlgoqDqJkEgyHFxzRq2fbsN0dpYyXLu7DaXbJzaOpMOXTG7wy8AdzbTnLm7t8maOd5oxjG23zBxfi7jtVg2T6Ar3s1EMyyIiiZiuuvCr79HWy9XrMpj26jZMi2hQRHVljgesALIkUHZl/perhjPzehEdaUtSgCR6BgydtuP+ZPuyXNPnrW+/zH8CNhHJm0lvGE4m/cUwsN3lFbEJiwKqK3vLL9vcj3RyoW1qusDTNsRAgEhIpOKyQwJCokDZRZkwDAsRmmbB1ioFtXkdpy0IiIDi8q2q2UgChIPNdgcFcYV80blyNx2lapoEBTwP3bWmpbqq9VII70VXfxtYadfuB8C6StfxS1lrqblcdSXXbeodOs3dy1+Kz6suu6/cXqNeIs2FdIuCa/LFctqYehEoT3ecW7btXMvTfznXNtwkEMuZGbcuYqJjPeXqwNhH3vvSnRQrOgVVQzMsCqqGqpvsGkqgGia5cpVSVSdXrqJUDbb0xpxZH93EtGxU3eEyJiOS5/V7LXt7qaSyUFRRNZOFospSSWXHQKIjOkWNSlHTdyHdt2zpY2dvFEWDxXyFUrnCYr6CosGO3ii3bOlbsw7bhq19Mcqa0ewnw2TXQBJVN1ksqeQUjcWSSiAA+zekyVc05vIVKlWDuXyFxaLK7pEUG3piHh2GaTOUkdFX/n9B3abFrsEkVd1sOv4DN13T1o8//6obySpVlstVqrrFcrlKVqmyuSfWRCtwi2FaVA0Tw7Tq9Al33Bzc1k9taFy1Vj/grDH+4Tu3rrn+fOsiEGBrbxyl6qw/La98f+wtN7a1+7Pvudljd0kz2D2cwjCtJjuqusmuQSf+/eq78fi77m6v+3+/0au7alnsG0tT1vSmtmHYFvtHUhQrOjM5hbLqfOcVjQMjKWzBbrrOB1/Vvr5b2V1QNXYOJhBE4ZLa8f5to211f/D79nvqz4J6/TX60LJt9o6lyKsas/kKxYrBbL6CaprsH0tRrhqML5ac9bKLJd6wd7Ct7r992z6WSmrTtWwhwJ7BBAW15luNmZzCUrHKvtGUh05RI51klSoLRZWSarBQVPmZC/j8M++/w9M2TGz2jqQouOwTxAC7B+IUVJPJxRK5ksLkYgnVCLAxJVAxYGqpTL5cYWqpzFi6/cPn7963k4WCysRimWzZ+RZsJ+FMNQLM5soUyyqzuTJl3eKaHhk5KDTVqyg5SXBF1WIup1CuVJnLKXVsmW99v3IbCwWVySVH9+RSGc1wkqLKVbPJ56YlMBhx1sTO5RQK5QpzOScfpCZmw+dC8vaDQyyVNaazCkVFZTqrYAeCxFZ+v1ysUlaqLBer9cGAHYDFQoWyorJYqDQ9KOarFqWq5aFQtJLre0OoJiwXqpTKVZYL1aYBR1YxKCkGWWX9V9tuTQlowHJJJ1/SWXYtd9Hs1U9NBBHmcwrFcoX5nNLETs6pJiXVJKde2Ot7B2QqOiwVVcqKylJRbWIe56sW5TX6EDqr79s3xClVbRYLykr9KU1vV4qaTUmzKa4YLgJROdrUpxq2kySp6jCz7LSxmeUy3ZH2b0pqcX52ochsXuHsQhHDWmljOkwtlsgWy0wtltBtke4gVK0Ac3mnrHN5hYpuszElAAJT2TIFRWMqW+aWjRcm5KynXB0Yt5A/f9sORjMxtvcnwIJcRQMLtvcn2D/WTX8izFzRyYKfK1boT4TZsYLTwQZFM8CGvkSYlM+g6uDGHm7d3INt2cwUKtiWza2bezi4sXMMSSoS6kj3/37jHq7tlzEsyKpgWHBtv8z/fuOejnXsGky39NPe0S4iksCx6QKPnVvi2HSBiCRw342j3Lm9F9uymcor2JbNndt7uWv7gK+O0UysI90HN/a0PP4rr2k9aPi11w2zazDN3tEMtm0zX1KxbZu9oxl2DaZb/saybLLlKpNZhYllhcmsQrZcZTgVban74++8DndufwT4+I/u7rj+/M7f0Z+iPxFmPq9ycr7IfF6lPxHm5njLy3BbCl+779o+0JFv/Y7/5EtaJ0f89Evjvrpfv29Dy7bxMy/fyT07+7Asi6mcgmVZ3LOzj/fdfU3L6/S2NpsB/O0+uLFnXdqxX6z95vePdtyWXnndMNcPpciVNU7M58mVNa4fSvGWWzcxlpY5vVDk0bOLnF4oMnaBNyP7x7pbXusXX30dL93R4/g2X8GyLF66o4d33rGt5XV29KfY2hNnqVjlxHyBpaKTuHjvhtZ6v28Lvn2qn32/8Oprua4/ypJS5cRCmSWlynX9UX7vB/ewbyRBQdU4s6RQUDX2jbSfr375tUPsHkoyX6zwzGSe+WKF3UNJPvyDN7J/JIFmWMyWqmiGxf6RBL/3Aze2rNff+v493LWtC9OymClWMS2Lu7a1fzVy342j3DiSZL6k8txMgfmSyo0jSX7rDTe09Pmfvn0v1/VFqegmcyWDim5yXV/UN6bayXtesp3bNqVRNZ3z2QqqpnPbpjQfe9f1bOuSME0oaM7mF9u6JP7fW69x7g0mLFdsDNO5N3z4BzbRvTK6qw3Quts/D/D7b76RTSlng5W87gz2N6UEfu9NGxmMODOZVZzvwQi+fcWrN3dsNr/3g3voDjqz4FWc7+4g/Ny93Z6NOYI494CdvTKqYbNQtlANm529Mn/w5s30OU21nnjXd4GN137vB25kR28I3YScCroJO3pDfPgHNtV/W3sU6AvDT9zlpb4AvHVf53b/1vfvYf9IHNOyWVadB+/9I3H+6Ae31Our9izQHYSP/8gu7tnh6lN39PFHP7yPPUPOxNNkTqWsGewZ8r5NbpRX3TDMxozMucUST4xnObdYYmNG5kNvuI6tXWGyFZ1zyxrZis7WrjB/8vbd3LYxjWXZzCs6lmVz28Y0f/zW/dy2uYuyqnN6oUhZ1bltc9dFxcHFylUqBatUiiNLId7xsuub/uZmbtaIAIEAzqN1wMZeubFlYuErzjFulLXqrtnw3HSO+XyFvlSEXUNp38z7tehw+6lGZ5AlJ8HItGxUw6zvj97IXu1JRNek41IZyrVM+i8/eYbzUzobhoO87IbNdfIE+HNt/XzoRy/wi5svH5ni7FKJTd1xXrZnuMnnl8oxrukQWEUUWVCnHTQm4n3sLTc27VV/uTjGnz8ywTdOLPCtZ2bIapAJwS3XDXLn9l7u3TPaVvelcIzjkVCdGNFIiPixFaZujfxyuTjGtfj/h8ePM7OkMtgt8+b91zT5fK3xXKtXXTcw7ABSwCYYlJjLKZxaLCERqLcxA5vHT87zzJzSlJBWY+r+5MsdzqlhWHUMkyQJ9ThcK8e4VqYaAadGxKnF2s998jA5IA38rivW/O0z68eDQbFuX6VqoZsGQVEiEhbqxJS8oqHozrKfVDTEH37haU4vG02JTkHg+gGZj/zgQU7MFTFMG8O0kEQBSQzUaUJnFopkS1Uy8TCbexN1f/jF4HMzWRbyGr2pELsGM3XSTyO9IRxwSD+/86b9nJgrUtVNdMMmKAUIB8W6T9w+r8XO5LJKqaISj8iMdMl18svXj00ytVhluCfMi3eMtE2wrRFQZnPlJo5xjfRz+Pwy83mFvlSUGzd01Y8/O5llIV+hNxXh2pFMvV6fPptltqQyEJe5flOGH/vk4ZbLI4LAl1coJGemSyyqVXrkMJuH4nU7np502M5diTDXj2QwDJO5UpWvH59ivqDRlwzx4muGOTmV47m5EjPzZcoGxCQY7Ivx5LQ3H6Ymf/qm3XzjxAL5sopqWMiSQComs6M/znB3nG+fmGcypzCSjnLz9r56mcbnyxQ0jWQoxFhfrH78mXNLLCg6vdEg123s5tPfOs2R6XJTQlp3ROSOLRk++Jo9zBerPDueZ7pQZCiZ4NqxVN23DxydZTpXYSgd4Y6dA/VY+63PHuHZmQLXDib5pfv28Klvn+H+Y/PM5oqUNWezmIF0ggePLdGKFjcYgW/96r2AszX8xJLCaHeUg5t6623sG0dnmcwqjGSi3Nmg280xrsXgYqlKqaITjwTpiYf5+rPTHJoocGJxtQQ1jvHNW/t4+MwiwYBQR1/otkVcEigZFtWqhW6bBAMi4bBQ74fdHONaWXOKSqVqEwkHSEdlTs3meXo6z4NPTfGhq7i2yy+1gfH27dtJJPxnHgzTYjKrEAgEmhaTq7oJNgxnImu6iT6fciVsUDWDx88tI4iBpoSXgurMEu3dkLmopKFLkVJF44vPzCCuLEU4c+Y0mzdvIV/RsW2bl+0aaDsQbpROfXglfO6nI1/ReGo8x0BKJt3w0HMl6mKxqPCxr58hKAn0p1ZnRebyzgzcO+7Y5HkoWi/JlVX+8ZFxJEmgKxZi/Px5xjZsYLmsYVs2bzgweskPon6ynvHvV6/LxQrfOL5AT9IhTNTk2fPLfPLx83THwox0r74qmM0qGLbFz75sFxt7Y5cUh35lKigaT05m6U9FyETXFmv+9ql8/cQ8vYlm+2YLFc7Ml7hpYxd96dXYeej4LB/58ilCEnQnwlTVKmE5TLZUxbThx+/YzA1beprqI6tUmc+rXD+cItXQNjr1x7dOz/Puv3kMMQDdDcznpUIF04b33bWV68e61hQLfrGzWFI5Nl1g52CS7gZ//MF/H+GrR1tAylfk9Xu6+Yl7rm/ybamqM5tVGUzLxBqSpcqqzkze2cQi3pCgmS9XeWoq56nXTz18ir87NAs07xRWmw19y75+vmfvhjXZsZCv8K2zi2zpTTDQ0FecmMnz6UPjDCYjjPWsxvMXDk2w3GYFxuYEvGLvpqZ+Z2q5zFRO4XtvGGaka/VaS4UKR2cL7BhK0RNfLdNSUeXoTN5z/PC5Zf74/mPEw0EGu1ZjcC6nYFo2v3Tv9WwfTKzJ536xNpsv8QdfOklQCjDQEOcPHpvh0XGnvhupFrVlGX/8xt28+sbmpVyd3n/8YvDoTJY///ppMrFQk/9mcgpV3eDGDRkGUtGm9jqxUOTxiSw3jnWxsaH+Fopqy37Yr6wLeYX/ODJNVyLMcEJgiNxVXNt3inRKgPhOlCthQ6f7o18J8cuk75Q8AZ378Er4vFNyyJWoi3LVRjVNT6ZyUg5S0g3K1cunez3JL53Kesa/X71aCFRMi4grQUy3nZngmCstPiGLKFWTuUL5kuPQv0ytyRrt7Pa7lmmDanjtk0URRTc8m60sl6srmfTN1w+LoBswXdY99VGjCeFKquzUH21JPybM5atrjgW/2PEjwkwvtSfeHJstrpn0ExACVE3bm8jqQ7yZyLYn/ZxZVNZshw1UdGeDiUZx3jRaxFyYjsoFliUvKF5CQjQsoWgmlovC4EcyEoVAy+OKWkWzLGLh5uvEQyIV3WImt3af+8VaseK8YXWTVPSVbEZ3omDNOyfnCril0/uPXwxqGk6f6nq4TcoSpapBoWx6+ttgUHCoMy7dfv2wX1kNO0DFND3378slVwfGHUinBIjvRLkSNnS6P/qVEL9M+k7JE9C5D6+Ezzslh1yJuoiFA8ii6MlULqg68aDkubGsp6wn+aVTWc/496tXAYuIKFBxIcWCAYGwKFB2pcUXVZNoWKQ/GbvkOPQvU2uyRju7/a4lBkCWvPappkk0KGG5BpRdsfBKJn3z9asmBCUYigU99VGjCeEeGHToj7akHxH6U+E1x4Jf7PgRYYa62y992zGQWDPpx7acJUUe9KEP8WY00/6Ny+ae6JrtCACRoEjVhRsRhRUajQvTEbnAC5feqJeQoFQNoiERwUVh8CMZmZbd8nhUDhMSBM+DfUkziQQFBtNr97lfrCUiAWRJ9JBUgiv8O/cjW8072/qTuKXT+49fDIZCOH2qiyBTUA3iYYlkTPT0t7puOdQZl26/ftivrFLAJiKKnvv35ZIr+z77BS41IsD8yp737rWl3+nLKODK2FCjM5yYc8imjbtc1fZHv9JSy6R/YiJLuaKzWNaQlxU022LvaGbNyyigcx82nm+almd3wvXwuV+ZavSOuWIV07aRAg5WTdXNS6qLtazB7UlE2TOa4hsnFgCadla6c3tvfRlFp+t51yI1YsTDZxbJlzXmixXM+RKGbXHr5p7LtowCmuNf1UwEAljY6Ka1Jp+7/dGqXgVRZNdQklOLzmvVSFCiohvIUYk9gwmemVPQ5nKEpACaYVPR4e7t3eweTV1U219LmWpkjbliFUO3sHEGPJrlb3fjtaqa4cze2jaCKHDtoGOfKATqu+uVqzq7h5Potk1B1ep9y8a+BJu7JE4vG8xmndljsVLFwllj/IobRzz9UY0mZAcCqLp50X1hjfRzZFqhulBBEh2cnLmyxvhl1w217Qvd69xrsaOtzFYallUnwlQMk6WiiiAGsEybN+7bxleP+q8x/qXvvdFZU25ZiAEB07bqpB/NtCkoWlM+wlA6gmbalFW93k/ViDdzK/UdEkU00+RFOwf55KFZbLy7uAWBd7z4Gk7MFTEtu/6bRjsa60+zLG4YTjFXqjKTddaAG6aFKAXYP5LiubkS44slYrKD+xzti7HcZo3x++911hiblk1cDlJSdXKKxr7RNIFAgFy5iiQ5EwY1klFFN8kq1XpZVcOsH18sqfW66EuF2T0Y58h0GX2hWF87XNFt7tiSYf+mDPPFKkVVr68pD4qCr89rsdYYBwOpOPvGUtx/bB7dsJDDImrVJB0NE6FEBTxUi8EInmUU0NzGtBUCjlt3ozT2X7pm1usiKYfYP5ri0EQByyoSDTq8+0LFaFpjDNR3iywbJgdG05R0h1zj3uXX3Q/73SuDQYkbRlM8PZ0naAUYusyQiqsD4w6llp1eqBgomkFQENpmrX8nypWwYTTjZLDO5FRyFQ1ZFNjen6gffz5kc3ecJ84t8a3xZaYXq0xUF9k3lmZztw+2oY106sNEOEhO0ZjOVeqd31A64nn1dCniV6ZwMkKhssRzUwUqpklEFNk1lGAw0Tobup1Ylk2+opGv6BiWjSQESEWCpCIhz+ttgLu2O0luz0wVmMorxINSnULS6bU6lV2DSb723AzfHM+TK+qks/McGEuxa9A7q7Le0h+Tec7IcehsloppEBElbhhN0t9mQO7nj1qMuOt1dGU969HZIoquEA2K7BxIsH9jgg/8wxHOLBjOABEHmfQDt451HLedlimYkJnKKhyaWqaim0SCIjcMp9raHQtKVKolTi2UUA1ndnJrb5y9o10kI0HOLJaZL6lEgwJ7RzNs700yV1Y9fcsH7tvOu//qufoukwbOAO2nX7XVtz8aTkUp68Yl94U/de9W3vHnT6FAHdsQsZ3jfrr7YzJHzi9xar6EatrIYoCtfXE2dzsJas9NF6gYJhFJZNdggt3DaZ6ayvHcbJ6KbhEJCuwaSPKuWwJ8/FveZR/vuT242u9kVaqWM2s5lJHpj8mcWCh4fD664lt3PzXamwQKnF4oUzGcGdYtvTF+7lU9/M7nFj26P/B9/QynouQVzfMbv/rLyEG+9Ow0hydzKJpJNCRy40iKd921hY9/7TSHJvNUlp3r7BtJMSyV+cK4ty6+fyfcua2fkqpzeDLPTL5CNCSybyzNPbsGGc8qnF4s1ZMqt/TE2dqT4NRi0VPWzd1xjkxmPXXxvpdv5X/93ROMF1YTnccS8K6XbCYcEDg1m+fIRAHVNJBFiT2jSW7b0su55bLH58FE6zi485o+zi+WeHwiX08y3T+a4uB9QX7zs7Meu99/3ybf+PRrY7Gu1vfjwUSEiaUyz80U6v7YNZjkXXdtIf/5oxyZKdaTd/cMJnjLrZvoijr90bHZIjOFCrGgyK2be+px6z7uR+Dyu1fesrmHuXyFMws59qQuYoefDuRq8h1rT75rlMsxy3Wl5UrY4EcveD6kRimIBAUW52bo6R+koltNlIJOpVMSiCQGmmZu1kICudQyNRJCajNNjYSQTuRCNA4/aUUhudhrrVVqWd1xWaKUXSSe6aGkGty9o4833nx52T81Ako8FCQkCWiGRUnTmwgobrmQP/woJJZpYiEg4Mwk/9PDp3noTJZw0NlhrWqaVHV46Y4ePvCq3UDncbvWMtXsjoWChCXn1Xj5AnbX4jMaluqzdUrVqMfnWokp7/j4g3x7vIBggmWBIIAlws1jSf7yXbe3/E1NLrUvfO9fP8xD53KIlrM5iGGBKQS4bWOaj7791pa6a77KRMP1GfGsUq1TN9xEnxohQQ6KTW9+fvqTh1tSChIBeOCXHSpFjRpSe1NVo0y4fV6jdLj7qdqsaMC267P6diDAOz7+IJMtJm43JuDfftrR7f6NH7mn5g9ZkgiJApppoRoGMUmgbFhYpo1h2khiAEEMtN32vUbjUKs6muVssS2Hg6t2uKhSfvbV/CSLQn3mVDWtOp0hLJgIoohlmlQtsWnmNBMNEwlJVDSHKb+pK0o6Lnt8XqNxuOOgZjd2ANG2MQNOef/qa8eZKjcvpwgA13RJfPEDL2/pjwu1Mb/zw0Gx6a1FrqRydllZWRLioCcCQqDpHupHclkrgcvvXlmj1CQEi83h4mVNvrs6Y3yRIonCC955V8IGOSRx+V5ar11yZZVnpwt0x2W6YiGsopNZu1zWODZbZPdo+qJer6/Fh4Zpka/oyEGxIdPW2d2tUDFIyMF1fTBpLJOqGUxlKyQiQU9W/ExOZTAV6YiQ4LZDFJzvC9nRk4jS09D/Xsq11iKz+RKHxvP0JmX6kjIzepHBnjjzBZUjk3nuzJcYSHX+pmAtUqponJovkYmG6WoY4AfLAmcWy2ztS3iW7qzVH1Kr86Or1zo5m+Ox8TyZeJjhzOpNYyancGgiz6mFPFt7Uxcdt+3K5Gf3chu7W8VnDAlRCNTjMx4J0aqmGvuWQ+cXeWqmRFIW6U5EqJTLRGIxlooVnp4tcej8Ivs29Pj2R5fSFx6ZWOLIdJFkRKI/1UApyCs8OVPkyMQSe0a7m3S38lU4GEY3LJ6aynPT5p4masNSUeW5FUJCdwMh4d8eHa8PihsTD1UTijZ88sEzvPrAmIcecmqh1ESZiCERsOH0YonrRzINVAqRUlVnuk5UWK2/Lz0zxWTZeSORjq56L68YjBfhs09M8JJdgx4SQi12LuQPgKmswuGJLHtG0wz2rs5u/uU3jratk9/9r6d4z127SDe0jbKqM53zUjf87GvlJ4BjM1ken8iv0Bky9eMzOYVHzi5TtWmiMyQjQUzL5vBknrt3Nvtc1y0OTS6zpT/RFAdV3eTwRJbdI5mmdvzgsRkmyxAGupINhJWixtllg68eneElO5t512tpY24ySqt7xnS2zOGJPBu6Yww2lGmhqDbdQ9MxmXSLOvE73ih+98rlosqzMwV6EzI9EUC90Ab0lyYvzKnOq3JVOpTnk1LwfNJMrgQh4WLsuNw+8cvqTsohyppJsXL5fH4xBJT1Ip0UVNMhgYSbjydlibJuki2ufZexTst0MXavV3xOLVfQLIuoi74SlQSqpsXUcqs51fWRmZVlCvFgs+54UEA1LWZa0Bv8fBWSBBTdWZfeKH6EhJmC0rZsZ5a8FBI/eogkCU4/aLsToloTFaaWnKli9wOFtKJjIlu55NgJiY4/3A/JC6X2NI5TsyWPbj/qhp99fn5SNduXzlDUTPIt6AxySETRTNx5jYEAVAwT2aVDFAIomknIZUNuZZfAkGslQUh0lg6dbUGl6LSN+Z0fwKmLSLBZ+XreQzul1FwuuTowvir/I+T5pBQ8nzSTK0FIuBg7LrdP/LK6C6pGLCSSiFw+n18MAWW9SCdJWXRIIK7EnIJqEAuKZBJrv7F0WqaLsXu94nO4K0JIEFBc9BXFcNZBDnd1vp5+rTKYkQkLAiW9WXdJt5BFgcEW9AY/X2mGRTQoYrm4A36EhMFk+1fJm7u9FBI/eohhWE4/GHA/hLUmKgx3OzO47kctY0XHaCZyybGjmY4/DNd1euPtl1ptHYh7dPtRN/zs8/OTHAr40hkSIZFUCzqDurJm2k3Cs22ISCKqS4dp2URDIprLhtrMvOair2im80CyqQWVotM25ne+jVMXFd1FvFnHe2inlJrLJS/01QBX5aqsSRopBUpVJ1upIiyXqejmJVEK1rI2ca0Ui8uxHrsxwzhbdDY7EAPO7FAjKWAtui+FaOJeJ3q56SiNWd15RSOfUygFCmiGxd07+i7bMgpwEVA0AykQwLBtNMP0JaCsxR9+ZIjGTPPeZJQDYykeOpNlLpvHtEAUIBSUeemOnvpudp3GbdUwPTt9un/XaPdioUrAtrADAqJEk92NutdCsFnLTpj7NvSwezDOt8cLLBVLGICULxEMOmuM921wEn06bWNrWZO8Z7SbPUMJHjqXo6oXiIgBKqZN1YDbNqbZM9rtuVajr0zLrlNFSprO7uEUummxkK80kT1akRMObuvmHx5xllO4UXWJALzl9s2eDP9GeohuWE3UlC09cWzbWV7QimKRL1fra3Bv2tzLSOwYk2VYUpoHLBsTcN/e0bYknkYfNvqjVDUIBQJoto1umtw4mqJsmJxqmA198a5hHjjjv8b45165m/lilUp11ecBIVCnbiwX1XpfKDQQIxrta6RxBGzqFItEA50hl59BD0DQBiS5aY2xZdn1NcYFVePGkRSWbTNfqKz63LK4YcShcSwUA/U4UHSDG0dSlA2D2Xylvl5/qDvGSAymyjBTWH3or60xdi+jgOZ7QCMhpLbG2N0O/NqkDdw4muLsskJ5RkcQBSzTwsBe0z10rTvatqJSNFJqMCwSlxlnfHVgfFX+x8iekQyn5go8Np5jdtFgwCpyYCzNnpHMhX/skk6JCu1oAIZhMZEtM5WtoFk2ISHAcCbCaCbm2ZjjYiQjBzk5nePR8RyqbiIHRQ6OpblpQ1fHujulGmiayXMzOU/G9a7B9GWno9y2rYcvPz3FIxMllApEc0vsHohz27bW2dDrKXUCytnlekb5hQgofv5IhINky1VPrEVEkUpV92Sa37QzwScenXYl51T4+e2JjuO2ns0+X0Y1nRnQrX0x32z2kXSEfz80zmMTeVTDRJZEDoymePX1Q766h1fW5bYiRrSyu4Z8ch9/+YEu7j+9OnjSAE2HVx7s7jjO/c6vUSzcut/7sq0894nHGC+ChV2nFPz4y7f5XmtrT4JCRefoTBHFUIhKIjsHE1w3lOJbZxY9ZI/dG7o4t1z2kBN+9O4Y/+d+bwbcu+5J+mb4D3bHHXqIi5qyuTvOUmXtFIsfvruP3/6PeY/uH3rpoK/uWFBqWa8bu2I8cW6Jb59brlMp9o2mObC9m7/75jlPTH3PGC2pFK/fARHR2VL82ZlCfUvoaweTdUKC+/iekQxnlrzUhq0riRFuisU9N3bzr4fnaFzQEabCrTvT7B3spqjqPD2VZzKnEAuKXD+c4qZN3Txydsnj81s29/DMdN43Dp6aLKAYBlFJYvdIkvfeO8YvfrrZcBv4sVduaNkmAV9CyHCq9RsHP5JKeCjBka8c5/GJfL0/2D+aYseAfxJ3O7JNq3YcC0qERG/c7B5OM5NTODlXYNOIr7p1katUCi6OSnFVXnhSy3oOBQSmJs8zPLKhzjH2y5j3k4slKrR6aq5lACciQc+MWafUiFZSozOkY2FiskRZNciVq9y9o4+DW3ovSvdas/j9Mu8bfX656Cj/98vP8ZVj88hhEaNcQIolUasm9+zo48dftmvd9LSSGgElHgoiBwVU3aFSrIWA4kefcMeaH1ng5/7xMDnTe92+MHzpAy/tKG5rsRkLSfUZs7Lmn83+V984wVeOzpOJhoiERSpVk6yicc/OPl6zf0Nb3e7ZWT+7awQB9/H7/ujBlnSGGPC599/RUZz7tckatcGt+98fP8dDZ5adGchAAMu2MW24Z0cf99442vZalmk1zV7Wsu/dZI+tPXH609E6g7b2/fI/etA3lmp0Br8Mfzc1paZjrRSLH//kYVptuRABHvbR7Vd/p2bzPD2dJxGSCIdEqppJUTPIF1XOZVVS0VC9/8orGt86l/e1+7PvuZknJrIk5VB9FraganXSg/t4jQTiR+lwUywaaRy1eoBmGoduWk0c4wv53B0HNZ+HBKHpzcGvfeYwMy0CfWMCvv5L97b0R60tuWPnQvcrd5uste9UNEREEqkYJvmV9v0jd25vq3ut7bh23C9m06LNhlDh6pbQV+WqXKo0Zj0PdUXpjocY6oqSiYY5s1imVNEufJEVcWfOikKg/u9CxfCsh2sUSRQIS2LT8onGDOCQJJCUQyQiQWZyKqp2aWuqGukMo90xumJhRrtj9CZlDp3P8cxU4aJ0u+1oJe5M83BQoCsW9vh8LdfqVM4tFXjkfI6ehMyW3gTd8TBbehP0JGQOTeQ4t+RNUlkvaSSgjHTH6ElGGOmO0R2XOTZbJFduv5Vuoz/8Yk0ATi2UiIYl56Fj5fvJc9n6oDgRCtQ/APNV+OdHzq85bhtjMx0LEw87337xMZkt8tj5nBNrPXF6EhFGe+JOrE3kODGbb6tbDkl1WoGf3UEhwHSugiQGmo5/+chUfVAcEpxXoaGVcCoDf/PN02uOc782GQtJnF4sEQjQpHu5VOHR83m64jK7RrrYMZxh10gXPQmZR8dzPHE+57lWNCxxeqGMAHQlZHqTMl0JGcu0eHamQFIOMZCKkIk530k5xNHZIpZpkoyGSEdDJKMhPvPo2Xq5Aw2fmnz0K0eRgyLxcJBIyPlu1NGfipCJhemv6ZgpYpkWMTlINCQRk4MEbLtlrD18Yro+KA4FVj8AFeCvv37So9uv/nTT4shEgUw0zHC3EzvD3XFEAhwaz5OIBtnQE6cnLrOhJ86ZKf9BMcCv/+vjZKJhehMycVmiNyETDUocnsgjB6Wm4/FwkKcmCwQFocm+xvpORUKkos6blW+dmK/TOLqjEpmoRHdUQgLGi/Cvj48jB0V6EzJDmQi9CRnLtnl2ukjKx+e2ZTfFQWP77k1F6EtF6E1FODpRYKbiDNy6V/TWdE8U4YtPT3l80diWGmNnLferxjbZ2L439MTpS0fYsNK+n5jIMZn1kiL82rEkBpjOqs6A26d9N8Vsg/+6kuuLN20lVwfGV+V/hFxMxryfrCdRYT2pEa2kHZ2hqFnkFfWy6V5Pn3cquZJJ1TBJys26k3KQsmaSK7WYUl0nWU8Cil+s+WXM1ygF7kURtV+fWyyuOW47jc182VqJNa/Pi6pBVjEumbrhRxY4s1yinZxbKF9yVr4ftaFUtVBN4QcrVgABAABJREFUk0S4uS6ScpCSapItVTzXCokiFcPCPSTxy76PBCUU3eFVN8qpOf/d3wBOznmpFG11GCaeZhkItIy1qeX2D+2nFpU1159u2KimQcS13lUQBSqWRdRV1vIFdgaezBuefkcSAyi6SdhF9pBFEcUwPKMhv/qeKTiPYO5lrjUax2QLGodpOtuZh11Eh5rP3THo177nyoqvbhM43YJKsV73q3btu6yZ5MveAbafbj8SiF98+PnvcsnVgfFV+R8hF5Mx7yfrSVRYT2pEK2lHZ0iEBFJR+bLpXk+fdyrpuEhYEimozboLqk4sJJKOX74Odj0JKH6x5pcxX6MUuG919Ve9PYk1x22nsZmKCSux5vV5QnZm1i6VuuFHFtjc1T6ZcmNv7JKz8v2oDfGwgCyKFKvNdVFQdeKySCYe8VxLM50NO9w3YL/s+4puEA2KCK6h9Nb+9juJbuv3Uina6pBEPM3StlvG2nBX+xSlrT3RNddfUAogi06iWqNYpkVEEFBcZY1dIPlqJCV5+h3DtIkGRaousodqmkQlCfdTil99D67sOOkem9doHCMtaByi6AzAqy6iQ83n7hj0a9/9saivbhHY0oJKsV73q3btOxYSScW8w0k/3X4kEL/48PPf5ZKrA+MGMS3/VwrfjWKYFlXDbPsq5btFdy3rOatUmctVyCr6yneVzT2xlqQAP6llzqq6iaqbzs5UK/9ORqS2SwJUzaCo6vXXt7UM4GJFp6BqaIZFQdUoVnQG0/Il0ylqdIaFgsrkcplsucrkcpmFgsq+DWmuG05eNt2NPl8oqpRUg4Wi6vG5XyxcSoxs7E5y04Y0i0WVqWUFpVplallhsaiybzTNxu7kZdNdI6AslVQWiiqqZrJQVFkqqewYSHREQGmMtVJVp6I53zWygFI1muruho0Z0itj/qJm1z/grDH+/ps2oOomy0WVhYLKclFtittGuzuNzZFMggMb0iwUVCaWymTLzvdCwfH59oFU2zbTqNuvjemWzVA6gmHaTcdftmeYGpBNs5yBgrZSdTHgrS/a0tYOP7tz5SqlqvNd1ow6taFRd1c8wsENKZZLKmfnC0wvFzk7X2CxqHJwLM3eDWmPbqVqsKU3hgVN9VrLvi+oGrP5Ctmy811QNXYOJBBEsUn36w6ubgNsN3xq8t57dnp82KhjLl8hW64yV9MxmEAQBcqqjqIZlFW9TmdQqs4ObuWV71u3D9VnLjV79QPOGuO3v3ibs6GHopFTNAqK1lR/C0WV6WyFhaJKUBTYM5okq1SZzVcoVhwag4nNvrEURUXn7EKRmWyZswtFNg+n2rabX33tfrJKlamswkJBZSqrOKSH0RSqbjCTd47P5BVKVZ3dI0l0y/Kt73xFI6/o5Csat2zvYyTmzNAuKQZ5xWBJMTBwEi5fu3/M016FQIBrhxLkVY3lcpWqbrFcrtZ9HhACTXXU2L4bfb5zNMlgxBnDLykGyw26RxPwiuuH2/Yhnd6v/Nr32YUic/kKZxeKLBRU9o6mGck4a/XX0o5rpBPdstfUvhv9lyuvfdnjxcpVKkWDzBWr2FLVN0P7u0U6zUz/btG9tSfB8Zk8T4znmJzXWLRz7B1L1TOPO5FOiQrtsuL9MoBrxy9VXn7tEAsFlUMTOcqaSWwl2/vl1w7VB6eXS/eO/lTLzPsd/amOs5U7jZE337wJRTN4bCLPUhG6JZPbNnfx5ps3XXbdBzc65Itjs0VmChViQZFbN/fUj3ci9ex+FylgQ3+KVLTiqbvffPM2fvzvTnqu86E3XeObrT+ajLQkBfgRI/zi474bR1ksqjw+kWc869A49o+muO/G0Y6pG7WlKK3OL1Z1z/HffctWfvKTpzxl+t0f2ubbxvzIF4OJiJPF76IR7OhPUTFNj+433byB4zN5npotopkWIVFg90Cc+/aNMJBqrXswEWGmWPHU656RDEVV99AI9o52oWN7dH/wtUN86F+nPXb/2uuGfX0+nJAdHVP5OjVl93CKPSMZh0rhIgKM9iaBgodq8Btv3MDPf+q8R/dvvXnzKtHERXro74rx0OkFjkwUUE0DWZTYM5rkpk1OMu7TUwWmcgqxoMT1wyl2Dg3zJ18+wRNTBaqmw6XeO5zkddfAZ1oQ2966b7WvPzyRrdt342iKO6/p4xvH5zl8vvn4LZt7GM8qnvre2pPg1GLRQ2X57R+4jvf8xTOUWeU4x4DfefMNvu11/1g3yUiQM4tl5ksq0aDA3tGMb0xF0lEg7/H5L71+tX03PgT94ut2tGyT0Pn9yk9edcMwJ2cLPD6Zr/cf+0dSvOqG4bZ9aivdfu3Y7/joykz92dkso5eZkXCVSsEqlSLZP4olyRfM1Hyhy8USFV7oumuEhLAgMD01ztDwGFXr4qgUNVkrUWEt5InLwTFu1G2YJoYZQBJtJFG8Irpr9e3OuO5LOHXdSbZypzFS0z1fLPP0sye5/tpt9CViV0R3TXJltX6jvVhWds0ONynAj+bwM598hAdOZzE1k4AAtgViSOSOLRneevvWltn6tcz4tRIj/KQWa1XNomoZhAWJcEhoirW1UjdqutfKP/31fz3M/SeWMNQqRRUSMkhymLu3d/Orr70R8Mb5hTLm3TQCvzLVyC8hSSAsOrvt1ZjZb7x5c1vdfgQIN42g5kO37nd8/EG+PV6g8Q23vMJv/st33d7SV7V6CopCE8e4RmHwI0kEbLuJ8/t//usZHhkvYKgmVgAEGyTZibWffPkuTswVPaSHXEnl7LJCJhquc36zSpXrh1JsHUihr8wcBoUAwaDIkXOLHBrPERIFBEnAMiw00+LTT8y0jMEI8I8rVIpoQxKrYph1KkVEkpDEAIZpUzGMevy767tWF24qy5PnFjk8WUDVq+gmBEWQg+Em+opfe3Uz3f3i2Y8k8Zv/dpgnpkuYOoRwsISiq7795FIJQDXajoSAIASwLBsDi1s393D9aGZd2vGF/KEpZbTs9GWlUlydMW6QkCTCSqZmQg6ua5b8d4q02otcFJzvy23386m7kZCQigSpZIMMZqLkKzpnFsts7Ut0tJyiJpIoXLARtdp7PiStztTW9qqXQxIXN3Raq+7VWb6Cql123U31HV31raqbLJc1bPDEgmFZTGdV5xX3JcRIo+4tPUnUHpktPUl0i8uuu1HSMZl0R7/wt6NWJnB8WCtTY909PbXME5MF0lGJ/qHV9YZzOYVDU3k2HJtnQ2+crpVBflx2ovfoTJHehFyvJ7fda4mPxlgbzqzWtzvWGtvMWvuEVm2s8fjxuRyPjefpjoXoG06Sy+ZIZ9LM51QOTeY5Ppfjmv50kx1+uhvjINaQaNTo80bdjeSXgfTqjXo2p3BkMs+d+RIDqfgFdQMUFI1TCyWHXBBdfRDz+HDl+KHzizw1UyIpi2zpW9W9UFB4erbEofOL7NvQ01TeVv0RQFapcnqhzPXDKeLh2nGRsqoznaswkI40HIcnx5c4Ml0kGZUYdMXaE5N5Hjo5z+b+ZF1HDIlq1eSJiTwbumP0p5wZwGQkiI0zU7yxJ0FvanWXwtmswqGJPJlYmKHMqn1femqi/u9gACwbhADo9goR48EzvHjXQD3OAWayZQ5P5Ng9mmm61kJR5ehMkb5khHTDjnr5isap+TIDKZl07TphWCqrPDqepy8ZZrSra/X6OYVDEzmuHc4wkIn6ttd4JESrFfEXahcAj55d4Jm5EglZpK9NffvJWu5XftJI2+lNrPYGC0XneDoaJhULXVI79jvuvpcsZi/SiDXKd9/I7xLlYsgCLyRZT6LCC0n380lIuNzkie9U3e3qW1uZUVtrtnKnMfJ86l5P6bTNzOWqVC2LeKg5uTAeElGrFvPlqqcN+NEIOrX7YmJtvfqEpYLukCHkZrsTsoiimywVvBiDTjPm/crUjvxS1kyKlbXb7Ucj8PPh1HIFzbKIujYpiUrOrPXUshd461dPNVIGrmQsP1JAtqj5xlpFt5jLe4k3FqxsWtJ8PBYKUtYNqq41/dWVWce4i4RQrLZfZzqZq3jiPCg5sRBy+dyPDIEdQDUtzwYwhgmqYRIPeekMBdUkW9EvOZ794mM5X0W36Ki+10va0XZKmklZNy/bvd3PH5dLXrAD409/+tPce++97Nmzh1e+8pX8/d//PY2rQs6cOcOP/uiPsm/fPm666SZ+8Rd/kULhwuzSiyELvJBkPYkKLyTdzych4XKTJ75Tdber75AoEJKENWcrdxojz6fu9ZRO20x/OkxYEChpzdnbJc1EDgv0xcKeNuBHI+jU7ouJtfXqE7qTQYcM4doXuag660i7k16MQacZ835lakd+iYVEEpG12+1HI/Dz4XBXhJAgoBjN11EMZy3ucFcEt/jVU42UgWsQ40cKyCRCvrEWCQr0p7zEGwGIBEUPEaOs6cSCEmHXwCcsCshBkZKLhJAIt3+7N5KOeOJcN5xY0Fw+9yNDELCRRWf5RKNIIsiSSEnz0hmSskgmErzkePaLj65UmKBAR/W9XtKOthMPicRWeNSNsl59p58/Lpe8IAfG//zP/8yv/MqvcMstt/Bnf/ZnvOIVr+A3fuM3+Ku/+isACoUCb3vb21heXub3fu/3eP/7389XvvIVfvqnf7rtdTWj80zNF5qsV4bqC013IyEhW66imybZcvWiqBSdyuUmT3yn6m5X312xEN2x0JqzlTuNkUbdVd3Esm2qV0j3ekqnbeb64S72jiTJKQZzOYWyUmUup5BTDPYNp3jxjj6ySrVlZrwgCpdk98XE2nr1Cdf0pzkwlmKprDGbrVDRdGazFZbKGvtGUlzTn16z7k7joJH8MptTUFSD2ZxDPdgzkmIg5X1x7qfbjzbi58N9G3rYPRinoJosFBTKispCQaGgmlw/EG/5Wt2vnmqkDDvQTEjwIwVs6EmwZyhBoUWs7R1Jcdu2Po8OE5u9oykKqtZEbMkpVa4fTiJJzTEohyX2jabq5IxK1WAuX2GkAc+n2w4hooYHjwBvv32zJ84Vw3SoFIaxJjKEbcPWvhhlrbku5KDEwbEU2ZLGTE6hrOrMrNT3vtE02weTlxzPfvGxvT/Fdf1xih3U93pJO9rOtUNJRrujl+3e3ugPzbj8yLYX5Brjz3zmM+zdu5df/uVfBuCWW27h3Llz/P3f/z3veMc7+Md//EcKhQKf/exn6VpZA9Tf38+P/uiP8vjjj7N///6W1/2Trz3Fr7/29qZMzdl8iWLFJhEJNHVwfgvG/RbW+x2/mOQcv0QYPx3u82v2/d7nnuHkfIltfXE+8KrrmuzudKH8Wsta0/H/vnKU4/NlrumL8WP37Lwk3X7+cJ+/azANwH1/9u2VM57hs++5uX4c/OvjUuu1lhX/U594gPElGOuG//O2O5qy+/1iza9Mx+dyLBV0upPBphv/qYU82aJBJiGxtTdV13HXRx6on/O19zfrnswWyZctUjGhjt0BZwe5XMkkHRfriLN2ZVosKpSrNrFwgJ5EtF6vN/7Gf9fPOfwrL22q71NzJZYUle6ozNb+eD0r+VtnFpjLVelPh7llc2/9N4+eXWBiSWG0O8rBTb316xyZWGImqzKYkdkz2u3V/fkHPbqPTRdYVKr0RMPsGErWdU8uVyhpOvFQkJGuSP03fnXk579dP/95FCAKPPc7zdu1+v3GXX813Q8em2MyW2YkE+P2Hf314x//2klOzBXY3p/kXXdt4+fuvRY+/yz/9sxi/Zqvua6Hn7v3WrpizozSl5+eZbZYYSAR4WXXD9Qz4x89u8h8TqUvLXNwU09dh7tea+KOwbXE2jeOz3JuvsjGvgR3XjNQ1/HsZJb5kkZfPMS1I5n68W+dnmd8scxYT4xbtvTVr/ORzz3DszMFrh1M8v5XXcf77r4GOM5fP1ojNJR4+8GhleMrIfDkJKfmCmztT3LvDSN1HX/x1ROcWlTY2hPlnS/ZXo+Dh08vMJ+r0JeOcOuW1Rh0X+fe60cA+LnPHq3r+t37dtaPA3z16Axn5wps6k/ykp2D9Wv9x5FJJrMVRjIRXr1nhFhXjFRU4ac+8QBnlmCzq694459+ntMzsGUQPvW+e/n1197Ar/7rk9x/uoAzRIS7tyT59dfeUNf9xaenOD1XYEt/8v9n773DLEurev/PDifHyrk6x5nu6enuyYmJhMERuCiowEWQiwNIEsNFhSt69XpFRSSIiCJigOtPQKIMYRJhUk9P6tw93V05nrxz+P2x65w6aZ+u0101w2Cv56mnunbv8653pXe/591rfRcv2TVUGeufHjzJyXmVTd0Rfun6TQylopRMi7+79zjHZ0ts6Y3xphdtqejjD//jAEemC2zvT/C7d+3ld392F3/6jUNNfa3sJ//nG09wel5jfXeY337ZZfRs6ObA2CJ//p0nmF606O+Uee9tl7F/tAvddfjvf/MdzszAuj74h/9xG3fuGiYeDvDOf30SGw+v96Ov3c0dl6X5tb8/XNMGPAJ85n/srqzpn73/JGeyGuvSYd5446YKysSH73mSiazGUDrM+27fXfH//++Rs4wtKox0RvlvV4xWbPGp7x/j9ILC+q4ob715K1eMdvDlx8f40DeX0V8+8NItvHrfuspz4CuPjzOZURjsiPKzlw/7+k6ZXv/pezg5ZbBpIMg/vuX2yv2//sUfcmpaYWN/lD/7+Wv53z+3h9//0pMt7f3g8RnOzBVZ1xPn+i19let+seS3ptbfX0bVecNnH6vc87k37uPK9d0El1Jqvn9kmomMylBHhJu3L8d3fdz7+aYf7/I4r/qrH/Hh29fuCwC8QFEpXvWqV9Hd3c3f/M3fVK596EMf4stf/jIHDhzg9a9/PcFgkM985jOV/3cch/379/O6172O9773vTXjlVEp3nfPPM9mLX7jpR389/37+fpT4zx2Nodm2YRliX2jKV566RC2QAMkSUSSODKT48RsEc12CUsCm3ur4V5qr2/sinNwPMMzk/kKDMwlg0muXN9NONz8+4of5FdfLMyxuXwDj609SWZKWsP9j02O8b5/ebZh/I/80ibuumTbqsBY+c31+MIsb/3skRqYGQH4zJt28qLN69viHQvITOSUBh7lxb3+/rd95Dv8qLFrJdcm4e/e+2IePj3fYI89wx2cWihesF0fPv0MH7u3sRXwu2+L8yvXXNPU127d0c+hqXzDWBt7onzmvpM8cjaHZtuEJYkrRlP8wrWjfPnRCR4+s3z9ynUpPvHgWKPQS/Tk797Gvz12hkfOZCu8r1iX5rZL+viPAxM8dCaLbtmEZImr1qV59RWjHJspNszp0sEUPzo1z8GxZd57RlJ88l+f5OkmfC8PwD/8lsf7oTNZVMMmEvR4XLu1k49+6xiPT+QrcEeXDyV5w4tG+fi3TvL0TBHTgYAIl/bFeftLNvG5e89ycLJQuX/PYIJvHPWv0Dj4O7fy+R+d4sdVvK+uku/piTyK5eVuXjqUZOdAku8enm6w0Q1be/jWU1MN+vvWfcd5WGnke0MHfPztzXV+y85evvjQ2Qb73XFZL3/2taM8PVPEsCEoeXK/+MoOfv//jdWA/QdoBP+vpvvfdz2/8/8O8tR0sVJNv6s/zq+9dDP/8uA4j40vQ2LtG07ya3ds5fBkscGuu4aT/P39pxp8cHlT2kjfec81vOfzj3F8wcB2QBJhS1eQ3/tvO7n36QUeOpPzTgkDEletS3Hb7h7+5CtHODynVOy9oyfKLXtj/PFX51pI2Uhf+7Ur+NW/e4TxqkZxwzH477f18eGvzKBX3RsC/uC167n38RyPTxYq+rh8MMErrunjQ//vSMM44y0a0H373Vfzzs89yqlFq7Kx29gp87aXreejXz3F6ZyDhXdKtT4lctV6h396onGcncChtqSGb7zzSt729w9ztrCMbDCagDfeMcSf/fsEhaqFOCHA3S/t5C+/sdigjzdcI/DpH7W3VfjwL2xo+pz5xd3wz0823n9TF9y30Hg9ATRZtlvS59+yizd/+qkGOd58Y5BP3N+Yn/zeO5J88Qf5Bru+/pYe/u9X56g+o5SAVmeW33rXVbzjs4802PVXX7aOj/zHsw08btzUXB/Xp+HB7Lllrabvvfda3vfPBzg6r2E5IIuwrTvMO162kU/95+mGWHr7Szfy6XtON6ypb7l9PR//5qmG+x+aaLKoLdG9v34dv/Wvj3N4VsFwvfbgO3qjvOWOdfzl1042xP1bXjzKn3/lRINv/uYrt/IP3x1v4P3whIILbEjLfPj27jVFpXhBboy/8pWv8P73v58/+qM/4pZbbuHgwYO8853v5BWveAUf/OAHufbaa3nZy15WOVEu01133cX69ev56Ec/WnO9fmMM3rf97x6ZpScZJhkOeq9+8hpXb+jkum39DZAkM1mFE/PFpR7rAUqGSUbRK/Aw9dexHWZLBl3xcGXjt1DUuHZjNzdu76cZ+UF+WZbNTFFv4NEXDyHLUsP9b/384766ffz3blsVGCu/ufrxFoADbfIuQ+nU8yhDDtXf/+KPPOAr9+feuI8fnppvsEdvLAiSeMF2/ev7G7E+y+Tna0PJEKFwoGGs4xMZji/odMWCJMJeXuVCySAq2jhigK5EiGRYJq9ZLBR0js37F2R84KVbuOdwmXeAvGYyl9eQXZu86dKdWL4+X9DY2BmhrzPWMCfRcZhXrIb7/SCVWvGeyxSY01ySEZl4QKRoOuRVC0t3cSSIhwQisohqORR1F9EGOSQ03J/T/Ze2992ygXuOzK5YPl0zmcjrDTaSXYeM7jTI8KPTubbldkyTjO422C9bUClYjXKr5/FG8YZ1cR4dLzaMJTsQDEukozLxoETRsMkqFutSATYPdjToaWqhwJmc2eCDZ7O6L+9dPUEOzRmEJW9zb9ig2RAVYKAr0iD3Qk6laEE06OXdapaLYkALs/pSefMq4m1ULBoanTVQqoVPtTPO9k6Zo4sWIWFZbt1dxqAN4j38TceD3lpNWp+A0wVvruX5tm7k/NNBIcDfE/2pHbv60eaUyImc42vX1eDhR3v7wzw1rREJQEgC3QbV9HgFhcZYclwvZ7p+PbBsD+Wjndi7aijKwUml6WckaIj78vLVzDdDLXg/FxvjF2Qi7Utf+lLuuusufvM3f5P9+/fzK7/yK+zdu5f3v//9gJdjHIs1AtDHYjGKxeKKePz51w/TFQ/RmwwTDor0JsN0RIM8NpalUNIJiIDrEBDBMi2enswTD8qkIgFkicop8oGzOUKiWHM9IIg8eiZLNCDRGQsSkAU6Y0E6okEOTeZZKJSwbbvmp6TqnF1UiC0luUuCSywgIQIHx3JEZamGR1SWODieQ4Sa+7/8yMmWcv/JV58kKFIjn+g6jC0qSLg114Oih7GqG+aK5vq9Z5Y3h0LVD3gPiY9/+5kG3hIuY4sK4tLf5euubXN8pkhEFmt4RGSR47NFXNuuub/Vphi8V0Md0WCNPWIBiUfPZgkIQo1uQ6LIgbEcEUlakV2/dcB/UwzwB19u9LVYWOaR8RwBUagZyzItnpwuEg+J9KbDRMIB73cATi1aBGWXvlSYSEimLxVuuSkG+NA3j9MVDy19RqIvFSYkCzw9UyQSFGuux8MyBybyiHbtnEKSxMNnc0QDAt2JEMGASHci1HJT7MdbwOV0ziEkuvQkI0QiIXqSESRcSoDkQmfCu96ZiCC7eNepvb/Vphjgw997ls5YLe9EyJPPddwa+WTgkfEc8aBcY6OQLHJwqkAsKNWM02pT7Cd3UIbDc0qVzuUlfZjM6l6sVcttVm2Kpaqfc9EDZ4pEg0KtDgUouCAJNj2pCJFIkJ5UhGhI5Oi8hulYNXYVcHh6xttcV/tgq00xwFNzBiEJ0vEg0UiQdDyIhMcb26yT22LB9HCvOxNhopEQnYnweW2KwdsUC0AyLBINiyTDtY8+ueqnTLpe61Outczcb5xmdGTRIihAOh4gGgmQjgdq3pilYjLRiEwqtvqZjacLnl8kIxLRiEQyUuslIWn5p5qa6eOFRGVPbFcOP7uKVT/nohM5hwD+dm3Hd9qlA9Ma4QB0xENEIyE64iFEvOer49bGkuh6b5cEp3Ztwfaui23G3kMTCpFg7WfK9asCtXFf/Z2+mW9a7urE/fnSC9Lv7777bg4cOMBv/MZvsHv3bo4ePcrHPvYx3vWud/Hxj38cAKFJFaTruk2vN6MZE9T8HFPqcjWzoptMFhyeOqqTSVbjHVqcntSwUxL5KhiajGIyPmvQLeRQM8vjLJR0phYMuoUCdmE531G3LRYVl0elDN3xWtMohsPReZ1ESEAWlwMqq5mcnTKI6QGKC9X5xhZnF016WSRbBXVzZKw1Msdjp2Y5NuQgVulJtxwmCxb5hEyoCibGcV00y2U2JhOoKmn3m+tTpxcr/27m54+emOKKbnNFvIuGzfE5AzUpEwksX1dNh/G8hVwINsAInYsycxMoi9X2M5iaN5kIltCz1XifJuMzBt3kKC2e265j5/guVqTR10qKRjbnMD1tIirLObxTcyUU1cENqGQzy2c/hmpiAIaisbi4SDtklBaYNar8vKChaqAXC8yLy2cdekmnUIKp2XEC5vI39ZmSzmJWJxtUkfRzI7+04l3Iad6pgQmlwrLirKUnneOApiy/zit3cbf02vtXQpaaYd5efq9pKp58MzNThI1s5fp0TiWbMykGVabs5Ze6hZxCUQGlsMgs/q8Ym1G93MW8hmaAWSqxuLicDKErno0ds1bu6lO/dk+dRMutGctdGszSoJhfls/VbXQL5mfnmKjK5JxfKKJqYIU1spn2jq0FG4wqqK3yOqCUrBq/1YqeDgQHNLUxBel8SIAGhIEyNbtqUOeDVaL6jePL2wXDaJ7kYppre4bryd3cTo6P+Z6b2v+1p3blaMc/WpGAv13b9Z12SbBA15a/pJZN7FIbS+Xrjlu7tpRj0jmP2BOs2s+UJbWojftqauabNqsX9+dDL7iN8YEDB3jwwQf5wz/8Q37u534OgCuvvJKRkRHe+ta3cu+99xKPx5ueDCuKQn9/8zSFeuoLQCTZQ396Gf5kclFhMGyxa9sGBjuXNwaZos6sNE93PEh3FfD1TFZl3s0yOJRioApQPLSoMKDN092fYKhz+WR7vqCRdmD/3iHS0dpCPM2wcM5mkQRIVG10Z/Mas+4i/T0xequA0WdzKqVwifXrO+lNLo+1fewZnj3qv3nZt7GXrdu2EaoCFS9pJsm8zmAqTDS07DK6aeMCQ+lIQzFcs7nuWjjKoYz38Kv+elIOxP2bB9i6bXMNb0W3SOY0BpKhGrD9vGJgxfP0JUOkqppH5BSDSEFnx2CSZNV1vnavr8xl6ugZqrGfvFhiwC0wNNxVY6epjMI8OYaH0/RV+YefXUcOPcXpFnu2OI2+ps8XSetF+vt7GO1ZLswqSYtEZ6cQQiHSHVUFemaeYFEnGA3T2ZleHvzs9DnlDsa66O1Y5p1zckRyWULxBN3dywV3JYokDJWB3kFGB6t6cs6X6CzMk+6KM9SzfD8Hj7TNe87IIGfyEIBYYlk+WcmDDqII4arXZ2JJAQfkUO39ZM69QZcjHXRX8VZcT76+vn5GB1KV6/ZMnnRhnngyzkDvMo+SkCeezxBNJOntq9LHsdZvCJrJnXWyhLM5ArEYnZ3LY80o86BYiIFaueWCUtkcV587rWSb6sgC4egyb0FVwAI5DPHkMu+cVSJkmnT39jA0vNwZsihmiMzNIIfCpKuKBpk8d96vK0GwCmpLWII4i8bkSqE0wIyyAJqJK0I4UrUWls7/YelCLSZt1QalWoflq0HqfDCfryjYbxxf3gIEq3FvqyCvAtWYvsbqb5I9uasOCapeN4jVZwdVztNMHy9EaleOdvyjFbn427Vd32mXXBlC4eWDO0nVsfCeudWxJC3FkijUri1Cwdski+cRe65c+xmxpGHjbTSr454qaMNmvimxenF/PvSCS6WYnPSKO/bu3Vtz/YorrgDg+PHjbNiwgbNnz9b8v+M4jI+Ps3nz5hXxee+dO1go6szmNTTDYTavkVEM9o2kScRCaEtNITTTQQ7IXDqYpGgsFXzZXnGeatvsHU2hO07NddN12L8ujbLUAcy0XBZLBhnFYOdgkq5EDEmSan5ikRCjnVFKS0DatitQWoL32TOSQrHsGh6KZbNnOIUDNfe/4opNLeX+rZ/ZjeF4eVEIIqYDjiAy0hnFRqi5bjjQEQsRCgZWNNdbLllX4eNW/YAXtG+/45IG3jYCI51RnKW/y9cFSWJLXxzVcmp4qJbDlt44giTV3P+f727dKvNzb9xHRjFq7FEybfaPpjFdt0a3Xhtpr4p5JXZ9yd51LXn/3isafa2kWVwxnMJ0XOYLOiXdZr6gIwdkdvfHKeoOs1kNVTO936ZX0GNYAjM5bQnWSGNrd2tcyw+8dAsLRZ2prEpRtZjKquiWy6V9cVTDA4zPlUwmFlWKmsXeoSSO5NbIp9s2V46mUExvrobpMF/Q+fm9AyviPbmoklMMJhdVXATWp0R0R2Aur6KqOnN5FRuBGGALMJ9TKCo68zkFS8C7jsBMVqWoaMxkVVKh1m+G3nfLBhZLOmNzJebzGmNzJQq6J58gCswVdBTdYa7gPVSuGE5RNKwaG+mWw56BBCXDXtK59/ua9amWvMtyV3/GsLwCE9VwmM6qlFST6ayKS4DekBdr8wUVRdWZL6hUfXfErvo5F92wLo5iuMxnFQqKxnxWwXK9wivblZjJqpRUg5msiqI7bOsOExBlZvMaiu7J7yJyaV+cou7W+OBounXL7F09QXQbMkUDRTHIFA1sPN5IASYXVQqKvuQHMl0BsF1YKGgoisZCQeMcZvWl4Zi31uQ0h5LmkNNqNyQWjfm3oZDAbM6DxJrNKQjyMnO/cZrR9k4Zw4Vs0URRTbJFs+ZgIFuyUFSLbGn1N8XrE55fZFSbkmqTqUtM1+3ln2pqpo8XEpU9sV05sppDUXPI1tnVqfo5F21OiZhApmRRKFlk6uya0xyUFfpOu7S3P4xmwmJRR1F1Fos6Dt7zVRSWYknVWShoOIJXrOuKsJBXvBjLKyB51x0XFpfuX1xB7F01FEU1aj9Trst3qY376u9juSXfzFX5piy0x3u16QW3Md64cSMAjz76aM31AwcOADA8PMx1113HI488UvNq7oEHHqBUKnHdddedk8fbbgxy565hbt3ei+O4TGQVHMfl1u29vGrvKEFJYCqn8ux8iamcSlAS2D/axd6RDlzXZbao4boue0c6uHPXcNPrr963jms3duM6LlN5FddxuXZjdwUOpRmNdMTY2pcAB7KqAQ5s7Utw89b+pjxu3trf9P7/85rmG7UP/8IGUpEgvYkQuKAYFrjQmwgx0hFrer0a+molc/3o65p/Mfn4G7a2zXvnQLopj50D6ab37/WBKt4XgivXdze1x6v3rVsVu/7q9c1fzrzjRWFfX7v7lq3sGvQwPI/P5smUdHYNpvjgK3dz69YubNdhMq9huw63bu3io2/Yz23bu3Ech4mciuM43La9NazNq/aOct3GTkqaycm5AiXN5LqNnXzwVbvYO5xiUdE5OptnUdHZO5zi11+6o6l8v3LjFm7a2oPruEzkFFzH5aatPWz14XsJ8IrLR7h8OMlsUePQVJ7Zosblw0k+8oZ9XLc+jeO4zComjuNy3fo0n3rLLnb0hNEsmC/ZaBbs6Anz8Tdfwu7+GIphMpk3UAyT3f2NNQbV9OorRtnYGWEsq/DkZI6xrMLGzgi/doen82zJ4NhsjmzJYNdgirtv2drURr/7s7u4fUevp/OsguM43L6jl8sb+0kAcHUcXr1vXdPP/PFr9nDdxg4Uw+LZxRKKYXHdxg4+8abL2d0fxbJc5hUby3LZ3R/lQ68epp6ND9sKffBVu9jcGaRkwlzBoWTC5s4gH3/zpVw1mkS3bCbzGrplc9Vokv/z2j3sG02TUwyOzRTIKQb7RtP8wasva+qDrejPfmkvWzplbBvyJtg2bOmU+eSv7GLfSJKsqnNsrkRW1dk3kuTjb7qMS/rCWDYsal4qwyV9YX7zZZ0t+TSjT/zyfgYi3gPaxPs9EIH339VL/XY+BPyf16xj/3ACw3KYLnoYtvuHvfWrK1A7Ttc5lP7RN+xnW6eM60LJ8vpnbOuU+cgvbapsXEuO93t9An5hV/NxNrQtNXzkDfvo9ZZC9KX59obgt1/e3dR33n1bvCFXXQLe2BzhtCV96NXDTa+/ymdRuLpZn+TzpD97/Zam1/3keMeLwqSXBC9vY9MSvOvW1utIM/rw6y6nN+SNoy2N1xvy9FH2QYNlH3z19ubj7DqPzeD//YXL2d4TxLQhq3kHsNt7gnzqjdu5pC+MacOi6mIuxdKn3rST3f1RTBvmVQfTht39UT795kvYMxjFcSGruzgu7BlsXej2J6+9vOln/uaN25vG/Yd/YQMDXkozOt7vgQh89HWb2+a92vSCRKV45zvfyQMPPMDdd9/NZZddxokTJ/irv/orBgcH+cIXvkChUOBlL3sZfX19vOMd7yCbzfKnf/qnXHbZZXz6059uGK+MSvFb98xTtARu3tLJH7/Gi6B63NJMSWe2oCNLApIgYrsOlu1W0BleCDjGZcSILz1+iqlFnYHOEK+8fCNb+xJsWHptv1Y4xvcfmeaHp+Y5NL5ARrXoiMjsHO6qQeNYKxzjv7vvGPccnq0pjrpmfYrbd/Typpu2trTHhdq1LPc3HzrDnA49IXjpVetq5PbzNdN2MC2XgCwQkMSKr60Ux/g1f/V9Dk4qNQUMIcFbbP76TdcyW9DJKhqq7hIJCaSj4Qrih6pZGK5DUBCJhOWKj6wUx/gLPz7Fd4/M8u0jyzhMd2zv4tbtvVy5qYdjMwV0067IFwpIFR71uMRlv52YV8joOh2hEEPd0Uo19VRGo6TpxMIhBjrC/Na/PE62yTFqbwg+/AseConoCF4CqCvgiC67BlNs7k9hWU4FpkuWl3W+Uhzjrx8c475jczUFiD+/d4CbtvZw556Rpp8p23umoFBSHGJRkb5EtILKcnQyy1xepScZYdtgujKnehzj9b/99Uahl+jLd1/NgbEMmZxO0bKIyzIdqRCbu+P0paOMzRVZUHW6IiFGeuIV3qphYFgCQdklEgz6+uBKfO3g2UWmcioDqQh7RjsrvlYomeiWSUgOkIgFKugyhyazFSzhnYPpyv1ff/wM41mV4XSEOy9ftyK0nR+dmGN8schwZ5xrNvdU5PjMfSc4PpNnS1+SN9+0uWKLZ+cLLBQMuhJBNnQnKuhDp+ayZAuQTsDGnjQf+trRpnwDwPEl/Op6HOMyj4dPzDOVVxhIRrlyc3dF57/3/37AqTmHjT0if/Bz1/GVR0/zg1OLPDNZqsCAXTIY44lJf6y4sr1PzxbJqQapSJD1vfEKes6J6UWKmks8LLC5v5MNnVHS8TAPHpmtYPBev723gnz0nSfPMF3Q6U+EuG33Ol+5AT7+2t3cd2yOQ2enyakuqYjAztF+RtNhotEg33n6NNNZg/50kNsuXV+Z05ceOMO8C90CvPKGdRUUnuriztF0qGWxZxl1plBS0U2HUEAkEYuwdzjF1Vv7+OrBMxW84p/Zs45sUePZRYWpXIFCERJxGEglKmg0s7kiedUiGZHpTcVr1rN6KiMczeQ1SopBLBqkLxmurC0PHpmp+P/12/s4eHqex85muf+JKRZt6JTgxssGKsgvc1kdHe8LW8855C77+ZNnFysxs3u0s+JTh8eyzBQU+hJRdoykl9eW6RxzeY2eZJht/alKXNRjCb/n8w9x/8kMC1Wnu10RiRs3dfAXr7sKaMQfLvv542cWmc0p9KaiXL5uOe6PnM0yq+j0RkNsH01X1v/6ccrPkuPTuYtwbc3IMAw++clP8pWvfIXZ2VkGBwe57bbbePvb315Bozh27Bh/9Ed/xOOPP04sFuO2227jN3/zN4nHG7+WljfG/3LM5NC0iu04/MUv7mXnQEfNfZbtMJ5REASBcNW7TM20wYWhjsjz0h2rHdIMi0dPLyJKAsnw8kYur3knrnvXdaxZN7RsSeNfHjqLLIv0VOXyzhU0XMfl568YWfGXg3ZpPFPgf3/1MAHZq/qfnZmht6+PmZyG4zj89p07aposrCadj9yr5Ws/OjnLW//hESQBupLLaRULeRXbhQ/+7KXsX99dwyOvGDwxnqEvFaEjunye1q6PTOeK/Pl/HicgC/Snlxew6ayCaTncsLWbrkRkRX7o57cLRY3DE3l2DCbpqtLtNx87y8d/6KVTxYNgWSDLUFxKbXvptk72buqpscd0TiVbMnj57kF6qvL129X5fEHhU/eeWvK15XFmct4J+5tv3FDTJAP87V3STKZyKv3pCPHQ8hmf35ze8tl7ueeI/0bp0g548+276ayCWJwvaMwXNW7c1ldzvaibTGc0BtLhmvx+P94tfc2B//XKS9m3rtbXciWdJyeyDb6WUXRmcxq7hlKkqubk55u/8Y8PcjjrKzb7+uHPX/+iFcWSny0WCyr3HZ2jOxmu8ZsvHjjO15/wmFdHRfnk8XdevJW33Fx7gunHw0/nJ2dz/Pm3j5EIywxVNUv5/EP+GOUAncDv/XytvScWS/zgxBw7BlIMd9XWTpxZLHHrjn4GquJ1Nq/y45MLbO6N1/jzB//tEU63ABjuAX7upg01nzk7X+TEXIHbdgwwWsV7crHEAyfn2NlfO6dDExm+9Pg4vYkwo93L6/M3n55pKTfArdu6auQYmy+xqOq86dqNbKmqIZjMlPje4RnWdcVqaoGencrz3ROzbO9Psq57ed/whR8cb4lbvb0D/ts121a0tsxkVT7/0Gk6okEGq3g/PbHId56ZZiAdZV2V3N95eqYlTvmeAfjL171oRWtIu/H91MQi7/jHx5Algb4qvc4svUH76Ov2sWuo9m2On5+3+4ypfpZs6AhweVJb043xC674DiAYDPKud72Ld73rXb73bN26lc9+9rNtjx0PSUwVLOZyBtSlSNqui+W4ROvQDgKSiGJY2K77E69Q03ExHJd0qPZFWliWyaoGpuOyNltTUE0XzXYYqMM9ToQCTOVVVNMlvUa8cyUHzbLpitfyToYDTGQVciWH4Q6fD18gnY/cq+VrZ+dLmA7E6pK0wrJARneZzKgENtVu9hxAs1yCklT3mfZ8pKC6SzqvXbyS4SDPzhfJqQ5DHbVS+PHw81tZEFFtG1GqlW+y6J2q1L+NFJfkmy3pJOrGigU9XzCd2rOCdnVe0l0028fXct6JenfddzA/ewuigG67SEKtjfzmdGyqxRMbmMx4clZTKCChmA523em6JIjojoNQ18DHj3dLX9NcxhdVrt5Y98VCEJr6WlCSUC0H6lCE/HxzPNtSbE7MevNeiRx+tnAQUW2HSKDWCxYyrTNYj800FoL68fDTeUnz1q/BUOuagXpapNHesiSimF7TnmoKBSQUw/YqBatIcAVUyyIg1+pv5hxdN+bwfL6awiEJ1WjyGkcQUA27pvAawLEFDNshUo8ntwKq552ISEzkbQy7NrdXwNNHpI63K3stoiN1esq2RsBkJs+K1xbV8tomx+vmKrhebUw0UKvzVptigDPTjX7ut4a0G98zWR3dcUjXpU/GgxIzJYOZrE5V4zqgVSy194ypfZas/VnuT/bx5vNARd0mKov0pBoTUiVBQBYFzLrAMm2HgCgirRAK7vmkgCgQFAU0q3Yx1yyLsCQSaNLFbrUoEhAISyIFvTa8C7rpYRAH1o53KiYSliXyWi3vvGYSC0qkYmsXCucj92r52mh3jIDoLULVpFkuQREGOyINPES8zYxRt1Nq10cSEWFJ57UwPXnNIBmWSUXEFfuhn99arkNEknDsWvkGlzal9UtoWdLeWKjBHiXDJBaQG3i3q/NYSCAsNfe1eEBu2DiCv71dx0vTsd2V+cHWgdY5kYMdnpzVpJs20YBI3TMK2/W6CLp1D3M/3ufyteHORl/DdZv6mmHbRGTRS8itIj/fHE63EBrY3MuKY8nPFiIOEUlErYPh6upo/XVpa1+y4ZofDz+dx8Le+lXvs+eiThrtbdkO0YCEXgeTpZu2t4ERanm7gktEljHrEBT6zvGCrQcaYkDTvS6TDeS6RIISulk7J1FyCUoian114AqonndB9Ta/wbqNo4unD7WOt2B5TaXUOj2lz/HdpC/JiteWiCwRDkgU6+bqCh7+vmLW6vxcNQTr+hv93G8NaTe++9IhQqJIse6LTdGwiUgifU2Kb/1jqb1njN+zZK3oJ/2A8zmlmazCYsni5i2dlTSK+jzVVCTAbME7jarvAPeTnkYBEA7KDHVEODZTwLAcZFHEchx002ZrX2LV0yiq9ZeOhblkMMkPT81jO26lY1xW0bl2Y/eapVEADHckuGJdmnsOz2JbDoZhMrmgsKgY3L6j97zTKFaSc10td141CUneN3jTtn3lXi1fu2ZTLzt6vI5E6pxaaWfqLuV93rK9n9mCjm17Jweu4+IAm3vizBR0JFGo6SrYjo/0p+LsG03x3SOzFBSDoCxiWA6a5XDr9l4uHerg2NKx07l4VPutaTnIgojlevrYOZhAtWwyik5QkjBsm90bu0g/dJasvZw+UW4/1RuCX7hmQ1M/3DWUIhCQ0Ez7vHXenYiyZyTFfcfmWMhruLgICLgC3LS1pyGNAmrtrRuWd1LquriCwGA6gmG75FTDM5zg4i4VlMqSWJNj/+k3vqhljvEfvtbLObUdl0hARjUt8prBjv4koiBQ1M2a2onBjjCG7frqo9r/q33NzKiVzlum7fnazdsafc0VhKa+pugWm3piuIJAXjEqLWOrfdNx3IofvPvll7fMMf7bt9zWVLfNdBheatRUf78oSewcTHJivogkChW/2TfSV0mlqD87DkAljcLvWZIrGaiWdzIZCkoVnedKeoV3VzzClaMpfnBqkYKiExBFTMfhsnPkGP/dUo6xudR6XLNsNNtm32ia2ZLBZEYhEpRRDc8PLh9O4bguU1ml4muO63LZcJKZoo6VcSr+8YabtrXMMf5fSznGmmERCsjopkVRt7hiJI3p2IxnSkgI2Ljots3+EW9OxyazuIKA4LqIosDu/jjHF3TOzOaIBL0T55XmGKuGTTQoohgOJd1k/3CKaFhmNqeWVYsLXD6S4tlFBTLeyblu2ug4XDGcYiKvM51VKh0vd450Md4ix/j9P+vlGOtLJ+C6aVM0zMraUu3PoaDEvpEUj53NUlCNSlwIrsClfXHO5EzGF4rEQiIl3WHgHHL//a/c1vDMMB23soaUNLPCw3KW47s+7pvF966hTvYOJ7n/ZAbdLBIUXQxHQDUdbtzUUUmj8Isly1n2neo4BirrtqJbTdf/6meJYGtcnjzXV4QLo4sb4yrKKibrUkHeestmHMd7EOVUE8txkUWv+1n5FUletVAMi4AotkRn+EmkgUSEsYUShybzlQV550CCgUR7r+lakZ/+9o50UtBMnprIM5FViAVkdg2l2D/auqJ9NehVe0dRdItHz2aZyTv0yRbXbezkVXtH2x7LT75UJIjY5ET10sEUPz4xy49PZyo6v2I0xaWD/tBeZZ+6UF+7+yUbePNnnqmB85Jc73oiFCCrGExm1UoR4WA6wrq+FKmoylRWI6sahCWRrX0JRjraq9K+YWsP9x2e5tGzuUox256BBDds7aE36Y21Uh4Vv53Io9o2EUli52CCPcMdnFoocnKuhGo5RGSRTT0x/uiXtvK2zx1rGOePfnEH+0e6mvrhTVv6MHEvWOe7hpP8649O8fRMEdPxujle2hdn17A/XGIsIKPqRU7MFdEs7yR1c0+coe4EJ+YLnJgtodkOYUlkc2+MUDLCs3MFJjIqhuMSFAWGOiK8YjN8+UTj+K/aCtv7UuRVk8NTBRRLISpL7BhIsHekkzlFZzKjoTveSdJgR5ihVJSSaTXoIxEKkCnpDf7/P1+xg7v/7jGmVCgu7RIHIvA7r9zZtq/1xcIcm8s36GNjV5y8ajb4wU1dcF+T/cqtff669dNhX8wrQK2/f+9IJ8lIgFPzJWaLGtGAyN6RDt73kjQf/la2gfd77+zyXSskF6/waiyHZtqEA94m6fadA5zNKA28X3Z5P987NMXhRQsbDy1iY6f/I7wD2NqTZCKj8OREDsW0iQYkdg+luGJ9J98/MsPj4zkUwzspvnw4xU3bernv6CwHzuYq69Te0RQ3bO3hW09N8eCzi6hLaQf7R9NcHYcfN8Fov6kLrtnYzTPjGR49m0OxbKKyxP7RFC+/fJDvHZrlh88uVua0bzTN9du6+OR3TvD4RL7i55cPJXnLbRv4i68d5/CcUomlHT1Rzjaqe9nX949waCLLwalCzbrzS9et4+hUicfGFyvyXTac4ubtfdBCHwfHc946EZS4dXsvcWeBf29cWnjtpbBnuIMTM3keO5utke+qDV2MZ9UGu161qZP7Dk83zPVdL9vC333vNAenCkzml6+n8zpPNkF6uy7l/8yIBWQmckpD7A0kIkwV1Ia4jwXkpvH95ps38PiZec7m7crmfjQBb7llI5blMJYpNcTSQCJCUBIaeIz0JIF8w7o9lGqeN3zdlm6+/dQEhyY1XrmxfWSadugFWXy32lQuvvvmmEPekLhpaw/XbulltqATDkgNJyUdsVDb6Aw/SVSu7g/LEpIoYDte/k41KsWFUrkStV5/5SpY07SwXAFZcAkE5Ipe15LKc8oUFZ46dIxdO7fSEY+eF28/+fzGKqMUJEISwYCMYVoUdLsGpcCPLtTXykgBoguCCK4DTh1SgB/Kih/ix0qpjAQSj8iEJBHddiiqVg0SyEp5VPutKAk4tue3ZfQCwXVrTgM/e98xfvBsFkPX0VUIRSAYCnHr1i7e+eKdHuKHjx9eqM5//98f57vHFkgERSIhEVV3KBgepNkHX3V5S/miIbnmBKWMuhELysiyiGU5lIzl64lIoObEfSXoDK7jVnQuiEIlLst/l//fTx9+/n/Pk+P84NQiNiaCLeFKNhIBbt/Ryyv3r2vL186lj3o/aCX393/9xpZj1euwfL3+/vIaWY9Gs+/3vs5CkyyHrgB857dva6qrMhpBRyxEPBygqJlkSjrb++IMdcUbeH/lkdMcmCgSCUA4IKKZDqoJ00X/9Iqy3LIkIgoCjuti2U5FPtGlEjOOQAWdIRUOVk47c5pBXBYpWg6JYICALGJaDgXD5K/v929mU0ZniMgSoijgOC6qZVfGigcDBAMihulQNExmFkucWlRJhoOEgwKa4ZLXDLojElIgQEAUvaIBF0zHaYkMUUbEkCUB0fXWO8t2K4gYsVCAsCSh2TYl3aQvHkKWJa/ratVJedne9Wg0K0F+iUhSRbeqbVd41Nv1x8dmODCeIxYOEA16ed4lzWRnX5wtQ2l0zUF3bEKiRCgstjylP72EgOIXr/Wx5xf35ev1PvuvPzzJD05lCLoWQkjC1W0MQea27d383FUbODZTaIil8vrsx6P8Jqj82+8Z+rFvH+KeI7NsSAd4/SXhi8V3zxVt6kkylrd5YjzHUGeMjlioUkkpLbUIyqsWiXAAWRJfkMrTDIuJjEoiEmhAA5jKagykIhecTmHZXkOTcECq0Z/lOEwuVcGmq74VaqZdo9e1oOo5jXYlKHSGGe1KYDq0zdtPPmg+1nxB4eBYju5EuAGl4OmJPFdtUpq+Xi/Thfjaj07OcnhOIRpsRAo4PKfw/Wdm2Lepq6piWKqxRzgon3cx5nimwCNnsvQkwzXV4VNZhQNjWe7IFBjuSKyIh5/fZhSdk3OlBvSCw5PeSVVHLED/UJJsJku6I81sVuPRsSwHxhbZ3JMkXdUdsd4Pz1fnR2eyPHI2R1csSH9Vpfl0RuGx8RxHZ7I18Hp+8sWQcWyXQ1M5tg+mSJflC4HtLF8v3x+Ug/x/Dz3bcm4f+/YzvOHGbQ1ICJM+1enN9OHn/7NZlYfOZumKhxmq6mI3lVV4bCzLJcNp+tOxFfnaSvTRFV/2mo/d+2RLuf/46wd54w3ba8ayHZdDk3l29CdqdGgaNoem8mwfTFUq5mPISKJQWSPjkSBlnIJ/e+RMZVMcq3rDWzJhwYR/fvAUL98/WqOrbFHjwNkcHbFQBY0gGQnguC6PjWUZ7IjV8D67UOTgVIFUVGawquPl94/Ot5T7f/zN/fz+a6+siZn5oubJPVCL5DK1WOLAeI51nbXoDOacwyNji+wf7WSoCjHic/c+3ZL3n339AC+/YmsNOsPYfJFHzi5y+WhnDfrEsYksBybyDHdEWdezLN+p2QJPTue5akMX2wbTlesf+/bxlrzf969P8stNEDEeGcty+/YB+pPV6zA8OZHjqo3d9FZdr34m9qfi9C+93PvLbxxqyfuj3z3Ez+xZX4MEMp1XeXLCk6Parot5g0fGcnTFQoxU6ePMXJHHxnNs6k+zc2RZ7r/61oGWvH/rCw/xJ6+56pzxCpJv3Jc0k8msh2JR7bNjC0UeGfN8dqhzuVJ9Kqvw8NksG3rnGeqK18SS7biV9TlW5YPVPOrRdpo9Q08v5HnoTJbuRJjBzhBr3Y/xhXXU+RxQMhygqNvkNbNpFbPpeN/EXqhUru4Py41oANpSPtKFUrkStV5/rapg11qvfnM6H97tjlVGKaivkk6GAxRNi5K+dnKXkQLCcq3OvcIHGM8W18zPy0ggzeQuGTa50soXNz+/9UMvyJUsNMchEaq9PxH2EBgWC8aayb2QN9Fsm0S4tsjI422zkG884fOTTxIF1KVagJVcn8wqLed2bFa94Lj08/+SaaGbNvFwrQzJcICCZpFRrBXrvF19jM+1hgp4dkFrGEsWRVTLbvhCLEtiUx5+a+TxJqgT1XQ606hzw/EQCerRCKIhGcWwcevwVDTNw9WOB9r7ujZWwFduqc7eLqAaNpG6g5GALKIaNsE6VIrZUuuCuKm824DOIEke+kS4/hBCAN12iNXFazQgYthOQzGWf5atRyqNqBSRkIxq2Dh1xYVB2UOlEKlfI5vb+5np1vaeyugNSCBhSUIxrfplCtUy0SybeJ3csYi3rpl1xWkzhdY6f2bKHwFlpXHvh2KR16yl9bz2ejIsU9JtFkrWitfnVmg7zdaDbNFGb/IsWSu6uDGuo7xmEg9JJMOBFzT6hB89F6gU7VZcPxd6XU1EkXbHOh+UgtWilkgBEgyn42vm56uJBOLnt37oBamYTFgUKei19xc0D4GhMxFcM7m7kt5r2oJW+xDzeEt0NSkc8ZPPdlwislcgu5Lrg+nWrxa39jYiQ7Qbl37+HwvIhAISRa1WhrxmkgjLdETlFeu8XX0M97Suj9jQFW5ENHEcIrKEVTencqpBPQ+/NXJLE9SJalrfBPklKHqIBPVoBIpuEQ1KCHV4KuGwSEgSKZqtoeHqaSSBr9x2nb0FWCpuq73ftBwiQQmjDpWiN9YaQm0gKTSgM9i2hz6hNaCTQEgSKdXFq2I6BCWxYXN6rsS3CI2oFKpuEQlKiHVwdIbloVI41K+Rze19SX9rew90hBqQQDTbJhqQ65cpInKAsCxRrJO7pHrrWqAOKqYv0VrnlwxcOAKKH4pFMiwvree11/OaRSwk0RWTV7w+t4u2k45LhJo8S9aKLm6Mq2i+oDFf0LhsOMW6rhia6WEM2o5b+XcyIq/q637NsChoJprR3oJ3vlSu7i+oJhlFp6RbZBSdgmoykA6vCipFueK6Xn/lKndz6e+11GurOeUVg5Lu/T4f3n7y+Y1VRimYL2hMZRUKqsFUVmG+oHHpULJlGkUrsmwH3bIbHuzVVEYKUAyYy6rkCypzWRXF8ApYbr6kb838vIwEMpfXODNfZDavcGa+yFxeY+9I+pxIINXyVfttXvNa9OY1owa9oFqGgXSM/aMpMiWTZ2dyzBe83wslg/0jafaOdLaUeyW69aNtfWmuGE2xUDKYypQoKDpTmRILJYN9w6mGNArAVz7Nstk5kEQ3bbIlnaJuki3pNder779jd/M2vGV6xx2XtIzLom6iGjZF3fT1Az//T8aCXDWaZqGocXIuz8RiiZNzeebyGvtG0mztT61Y5yvRR/X69crL/IsaAf7nnXsaxtJNm50DCTTbqeVhO015VK+R1XN99RXrKm2hS+byD3g5xr94/UY0067RbTgUYO+o1+59MqOQV00mM4rXcnskjSAKzOVUZnMqczmV7niYPQMJcorNVHbJp7Il+uOtT9D+5n/c6C+3Ves7jgB7h1PkNYPpnEpBtZjOqSiWhyRRMi3GF4os5FXGF4pcua21r/36nXtZKGrMFTQ0w2auoFEyvbE002J8ocRsXmV8oYQjwt6hJEXN5MRMnrH5PCdm8pR0k939cQzL4dnZApOZEs/OFrhpe+ti7Q+/djfzBY2ZnIqqW8zkVAqayRUjaQzXZiankCvpzOQUiobJ7qEUpu342rv6Of2ul+1syfudt+4ko+gslnR002GxpHtyDCUxnVpfC4dErhhJkVV0prKK14wjq5DXvLVClLzmIJmSZ5OX7dvckvefvMbrPlftn+0+j8soFpZde70nGeGKEc9nz8zmmcmWODObZ6Ggc+Vomv0bule8Pvvx8Ftz1ncluWpdmvmCxmxWa6mD1aAXYprsmlFJ92BsbtrSRzSytugTfhWcIx0xZHltv68MpaLkFGPF1aDnQ37VsYlQgIJuPi+oHuXK9GOzRU7MGxiTebb2xol1toe0AO0jRty0pY+iZnJwPMdkViUWlCq+1i61i4jx+6/exTs++wincw5506tmX58S+f1X71o15As/evllQxyfzvPoeA7NcgjLIvuHU7z8siHfz/jJV/bPevQCP+SEN79oI0enHufovI5uQUg12NYd5vXXr2/pn82qsf1060d337IVzTzMgfEcC4pBVBa5bkOau2/Z6vuZMhqHHzrDyflipQ33pu44W3uSzJS0hvt/52f7+N9faewM9nuvHDh31Xpd5Xj9q/Ay+Y3zin3DHhrARC0KyUt2+fP207mfvSv6qFu/3ntHkj//duOr5N94aYevbstV+SvlMZCINJ3rH//Sdv7HZ4808P6T1+1YRuOo0+2LLxkE4PHxHFM5lejSmnDz9j4eOb3IYxPLCBCXDaV43507+KtvH+PxiTxZ1SQsiVyzPsXxp+dplu17ddzfp/zk7lrfxQMnZhvQWvat6+AbT07y2Fi2gtqwbyTNr1wNf/vjRt5vvynEleu7ATgyXWAqrxILSFy7sZvt/Qm+9sREw1gv293D//7SIQ5N5mrQJ956+0a+9NAUj41nK7bYN5xiXwgea5JTcWMn3Ly1H4CnJ/JM5BTiAZmbtvZw1YYuHnp2oQGl47pNPZxeLDXYuy8Wbopa8ruv6OcPvzzdwPsDrxpk50AaoAG5xC9e942k+deHTvPYWLaCfHH79l5etX+Ex89meXI8j2JZRGWZ3cNJ3nZjkE/c34jn+65bY22jafk9j/2u//KNGzm78CRPThcxbO80f3d/nF+8Zj39qea+1grZpp29wC9evQHFsBhbaJ3Kshp0EZWCZVQKUn3E48maqsi1Qp8oV1zXV3CuJjKEH5UrVOur+NcCGcJPf88HqscyqoHI2dMnGF2/Gc1yLkjnK5WjrHPVMDAsgaDsEgkGnxNEjPuPTPPDU/MUigaK7RCVRBLxINdu7ObG7f1tydEulXnLrgCSALaLJbg1vNuVzw/Fol6GMiJGWBLJ5zMkkx1otlODiLFStIV27VQeZzJXolCyScQkBlOxFY1TL18lXgUacIyb6eP1n7yPR8eLqFWZHBEJ9g/H+ce7b2optx8qhR/Vj/OFH5/iu0dmCQe9Zgam46IZFrdu7+U1V288L5376qNu/XrfPz3EkTmjpjtYANjVH+bf331rU92eU+d1PM5Vra+bKpYNsgShQITbtnfzlpu3NdVteSxNNzEcL70iHAowk1U4MV8kFgwQkr1GHCXDZHN3nL50lPHFItmiSToeYLgzzos/8oCvfcooBe3KXY/WUp5TqArJSLdsPvrNo2SbpL0OROBHH/R4Z0ta5QtdOhbm4JkFDoxlvLHKOMaWzfGJLIdmioQCIiFJQrdtdNNhKBEkHgsTlEVCsohuORiWwxcPTJ1T7vmC12kyFhLoTkQrz4B6lI4yckI9QoKqm8wU9Ibn9OcfOM6T0yXUqvqQSEjguvVpPvHL1wI0IJf46bwy54U82aJNOi6xvitZmWtAEBAEAdd1MV2XT33nMIfntKbx/dE3XH1eaForvV5GV5IQEAUXx/XsV42utNL1+VzX66mCKpXJE1BmLqJSPFfUHY9AQKqtxF4D9IlmFddB2fu9WsgQftS8QnXtkCH89Pdco3pU6zwWkJiVJdLRICXTviCdr0SOap2no8sV1+ej83YRMbIljWcm83TFw2xfOskAmCtoHJkusHskTToWXhN7VPOurkyv592ufH4oFtUyVCNi9KXCzEoqvX1JZnJaDSLGStAWqnmvxE7V45QbBcHK7V0t30ritfr+h5+d4+mZIvGQwPrk8kNjPq/wzEyRh5+d48oNPSuoWl/ZfKvHmc4Veexsjp5kmP6qXOfprMLB8Rw35Yr0p+Jt63wl+njw2AzHFwzCMgwmQuiaTigcIlPUOTav8eDxGa7f0ufrOyvh4VetP76o8MjZ5tX6j57NcuVEhg1dCd+x+qtwuzNFnWem8vQk6mNG4PBUgd5khEuHlxE/Pvjlh5rapUxllIJ25a5Ga1ksaE3n9O0nxiqb4lRo2T9yusOUCv/x+Bh3XT5COhautLwvqgYnZot0REM1qA0nZgo8ejZHfzrCuu5aVIqDUwVetC3CljIsBPClh5sAdVfRX3z9Gd5z5yV0J6KV9usrQbZJVs0prxicmCvSl4rUPKdPzBSXYizAlv5lP5/JKTwxVeDg2AJ7RrpqkEuqyc8W67uS0NV6rg+dmuPovEY0AOt7lv1mPq/w9EyR+w/PsWdDR9toWiu5vlJ0pZWszyu5Xk3VvjnQEWG+dY3xBdPFHOM6ei4QEp4LZAg/Wk10hhcS/bTovN2xVNNFs52GV+KJUICSaaOaayf3+fBeLV2dDyLGavF+Pu09tuA1QYjUpWNFZBHD8f5/reZbUN0lnde+Ck2Gg5QMm4J64fb2u386p2A7EKqrTQpJYDoe/NVKyY+HXyV9TjWWUEgaq/WLpk2m2Ihw5DeW6bholkOkDn0iEpBRLLthnTox23qH0AylwI/85LZdms4ps3RkWb+JKN/VDK1Dt100221AbTBtG81xiNYZMBbysM/rkRPOVYTVTO52kW0cykXKtXPKqzqmA9Fg7f3xgIhmO0xlLjwH1m+u+aKO5UAk0CS+bZjIFdbs2f58oiv5+eZa0cWNcR09FwgJzwUyhB+tJjrDC4l+WnTe7liRgEBYEhuqwwu6SSwgEQmsndznw3u1dHU+iBirxfv5tPdIV5SAiPegryLVcgiK3v+v1XwTEWFJ57X5j3nNIBaUSEQu3N5+9/enokii14K6mnTby1WtxsY9F/nx8KukT0WCSygkjdX68YBER7wR4chvrIAoEJZF1Dr0CdW0iMpSwzq1ubf1q+RmKAV+5Ce3JNB0Th0Rb8NY/xWzfFcztI6QJBCWhAbUhoAkERZFlDoDlnQvT70eOeFcsF3N5G4X2UakDGtZO6dkJERABMWovb9oet36BjrOF/n93HNNxkPIIqhmk/iWYCiVWLNn+/OJruTnm2tFF1MpqkjVLQTsSp/wtaJyxfWxmYL3d12O8VqlUcByRXl9P/VyHtILrYvfSqla56Zpo1k2WcWo5Bg/Vzqv7hdf3ZP+fMaCc9svHQtzyWCSH56aRzfsSsesomFy7cbuhlSG1aR63uVOWq14r5Z/lhEx7jk8i6JbKAWNklCkpHtd98qIGPX5bavBu3oce+m0qzpn91zjtDun6vuv3NDDpX1xHh0vUpouIYtgOeAu5SBeuaEHaMwDXA25+1Nx9o2m+O6RWVTDIihLGJZNUfNyjPtT8QuWz+/+S4c72NIV5MicwVhG9/JEVR0JL8f4+jaKXKt5KJqJ7XqbQ1ESGUxHMGyvwKmc792dCHHFaIofnMqQUxRcx+sw6bgSt23v5rKhjqa+UB5LM+2KHIIocMlAkhPzRWzHrckx3jGQQBAFSppZGec9d1zOtw755xiXUQr8qD4PtpluRUlcnpPrVjrGbRpMkX5imqztpU9U00AE7rp8pCmPzb1xDoxlAIgFA5QME0F02T+a4tBMkYlFhURYpqBZlHSTPQMJLNvl7HyRaFBGMSy2DnZxcNo/x/g9d14CNPp5s+duPXJCWW4H2NwTZ6agI4lC5f6+ZIhL++I8OV3i7FyOUEBENx1028sx3jPSGjHDj1Yy1+GOKNu6wxye0xifLxGUwLDBcr34vnFHz5o928voSvcdm8O0va6Qim6RUwxu2tpz3uhKK6GauH8OELwuboyraL5kMBSN+lZiryb5VQuPdLSPkNAurTUSwU8qldE4js8UGcvZhPM6W/riq4rG4Ud+lenn42vt2m/PcAcnZvINVeB7hjua3r+adD68V8s/X7JrgEdPzXNwKkdRgXgpV0FIaLd6u13eFXtn1cqmYDAdaWnv86kob4aQ8Jbb1/Pw3z7tNUFY2q+EbHjrHRt80XCGUlF6Excu983b+zh4ZpHHxnM1CAI3b+9bNfn87r/7pet52+eOLetz6ed/vHhdWzIARCSJmazCM1P5CprKJQNJ9gx3cGqhyInZEprtnRBu7o1x+55uvvTYZE0hWlqCW3Z1+/qCX7X+UCJMQTMb0Aj2DHewoDaO87o98PmDjTK8Ya+/fIZhc2gqy4nZIprtEpYENvfG2d6XojcR8p/TRB7FtIgGZHYPJXn/z63nN//1dMP4v/PqLb48Ni8l/dajNrzskn4+94NTPDqWY66kEQ1IXLexk1dfOcI3Dk7xaNUasn8kza9eL/PXDzZukt59W9zXzwcSXm7sSpETYp0xUlGl4f7f+JntvPMfHuVsARwcRGA04fKOF29ZuZMtUbtz/e1XbOfuzxz0ui0uid8VgPfcuXXNn+03bO5lJqc2rOc3bO5dlfFbUTmOJooGrVHLL5wuboyrqCcexLBdCrpJh7y66Az1JMsiG3oSDKQiTSs415JEUaAjFiIRDjznyBDPJ5VMi0gowK6hJIFCkO1DSQRJomRaBIOtgdMvlAq6iWG7DKTDldOe8/W1du13aqEIksg1m3uQBQHLdTEsm1MLRfbE19bPy7yv29iDJAnYtovmtOa9Wv75wLE5LEFk32iKQnaBRDqFbnnX79gVrFRvR4MSpu1UTlpWg3fZ3v3pSOUNwbnsnVONtuZUjeZQff/Hv3kKUYIEFSAQLOBT336WoVd3VNBw0qEAmmVVTqU29CQuWO5HTi9iCyJXbugiKHldywzL4ZHTi1y7RV4V+fzu/+jXTlV6l7lQ+fdHvnqSl+xojf9aT0dmcpyYL9KTCBMJyKimxYn5IgXNRJYl+lNhZFnEshxmCjp/8Y1DlGyv8UT5FK9kwx996TCfeUtXU18omVZTOZ6dKyDLEldt6KxBIzi1UCQSCjSM808Hm8vwTwfgQz/f/P8OTWU5MJahIxqiN+Kd2pZPcfes62oxp64ySAem4/Cv35kmIoFtLyM5SBJ84f4phuNdLXls7k3UnCRnSjo/u389N27TKJq2l4aSCDO+qBCNBrllWx+SCLYDFi4f/17zk8PPfr/Iz15WaunnzZ67waDU1P+b3f+xb4+RSsS4POm9HXAd79T2e0/PsXOwpy1fG8u0N9fPPXCCWCxEEgdBdHEdARuRrz46zd7RgTV9ti+oBkNdcQY7YrgICLgIosCCapBc42dJeU3tjgUptW50ecF0cWNcReGg3IBK8VzwXLuX2a3puUaGeD6puqo1EJKIhSSS0SCm0x7iwIXyXk0kkJXYz68KfLGkc2q+xObeRA2U0GrShfK+EP+sRkjoTYaZclUG+tPM5jUeH8uxuT9NdyLUdvX2Sqi5vaWW9l4pIsa50BwOnl3k8JxCLCjQk6pFpXh6psi3n5hm14Z0SzSc85W7VdX6E+M5hjpjdMRWpvN29fG9w1OcWrQICZCOBzAMk2AwQLZo8uyixfcOT3HLjoEVydHMb+NhGcdxeXIix1Ubu0mX/TkEh8eyHm8ZupPLq/liQePonMZ3Dk9x5Yaelr5QlsMPjSBb0jk5X2TXcAfxylsHic/ee6zSr636q7299POp7x7jrbfWYmc3ky8U8H5Xx+W55vTQiVkPISEIvalGhIT/fGqCrYMdLXmUM7+r7b2hKjd5saTzzGSB3mSI7ipEjK89dZry3igsg2OBKINmQdaGzz94mlt397f083aQE6rvP72Q56EzWboTYQaq0FemsgqPjWU5vZD3ECZWQCtFqCrzPjqT5ZGzObpiQfo7qpBfMgqPjec4OpNlW1/6OUPTAq+G4LlE0wKZ0ppwWaaf/mPCNumnHZ3hvyo9n2gczydvvyrwWDCAYjro9k8n71YICQXdJqvoa2aP87H3aqEzLORUD5WiWdW6BTOKumbILC2r1nWbvNaIztCufH73PzuTx8Y7ra2moOSdlj/bBCHBj/z8NiiLKKaNSG2R0byqYQNhH0SMsUV1xXL4oRHIsuihuNS1Mz55Dtyqo20gQ/jFpd+csqqJ5UBYrhW87GuzeWfFPHwRMWyvnXIoUMtjIds6z/Rstrhmfp4t2ugtEG+yxSbAzj7ULlrSQt5cQkCp1UciLKGYNgv5tWuZ/JOI7LRWdHFjXEc/7egM/1Xp+UTjeD55+1WBlwyTaEAkJP108m6FkJAISaSjoTWzx/nYe7XQGbpSEQ+VolnVugx90ciaIbO0rFoPSSTDjegM7crnd/+GviQSXgpDNRm291p0QxOEBD/y81vDcogGJBxqNwDdkTASoPkgYox0RlYshx8agWU5HoqLUMt7U3fr+ohtbSBD+MWl35zSkQCyCJpVK3jZ13qT4op5+CJiSBCWvKLdaupKtz6ZHE3H18zP03GJUAvEm3R85Wl57aIldSUDSwgotfooaF4Xv67k2tVH/SQiO60V/Vd5k74ienoiy+ahnpoKznY7grXb3aW+K9BzSV9/YpwTM3k29yW587Lavver1bHO7/5DUxnmcgY9qWBNE4Tz4e3Xaadet+Wq1t/4fw9yaMJl5+P38Qc/d/2KKnb97LTS7kZl3v/44HFOzBTZ3Bfn567aWMO7XV+o7+rkN6dyFfh7vvhk5Z4PvHwbe0c6KnP20+14pkCu5JCKiRUkh1ZyV49TzftjX3mSKR0GQvBLt9fyXmlXrjL5+c6JuRyZgkVHQmZzT6qCkPBPD094Nzxzkju2d3Hr9l429sSYLegYpl3T6apsj+lckYLqkogIFTSFVjby87X3/OO9nJmFdb3wx6+9oeXaUv5MUTUr7ZRlWax85uhMloW8SVcywLa+dOX++4/OMJVVGUhHuHJjNzt6ojw5rTA/vfzCsdwZ647L+jk2U+DkZAHF0IkGQ/R2hCvILE9NLDKT1elLh9g1tNxIYiV+UF21/on7nq3c8/N7B7hpaw/rujydZxUD3XQIBUTCAclX52X5jk7nyJYM0rEg67rilft/dHKWs/MlRrtj3LJjgI2dMkcXLaYLSxsW3UQAtnXKlTQKv5g5OLbAVEZjoCPMnpGuit/mFBPXdREEAQeH3UMpTNvhiTMLFAyTRDDAaF+cjZ0yJxctTi8uY9iWu+7dtmOA2YLOD4/PMplTGExF2bu+qyJHvT+X0Qi+/vhZphY1BjrD3Lh9gE3dcVwX/u77Rzk5p7CpJ8qrrtrAX/9wDBcvdaKaJKikUdR3VSvLd3wqS1FziIdFulORSlzW66M8p2fGMmRKJh2xAKPd8QpCwuEmvvbiXUMcGMtwZCpLQbdIhGT6qnh866kJTs7k2dSX5CW7hir2/sy9hzk1q7KxN8IvXbeVSwYTnJgv8uDJSeYyJj0dAXYNdRNhDhUvfcILKO9XWoLXXb+eYzMFpjNapVgwFpYqfl4v37n8vD4urlqX5p4js3z36ELlnssGY9y+vbeSRlG/HpXJz96PHZ9jUbPoDMtsHU5X5vq9w1M8O5NnQ1+SW3YMcMVoiu8eW+DxiUJlzNF0iFu3drGtL90Ql9Xrc70flMkvLvyQMj70bw8znoHhDnjPnZfXIDv5rZF+ul3pWlv2j3/7wQlesn5tt64XW0Kz3BL6f907TzQY5E9eezmjXammFdSpSBCxyTejVhXXBd1suB4SRB49u8Azk/lKdfMlg0muXN9NOLy2Rn96YpZf/btHGK9K1BmOwV+/6Qp2DvS0JUe7+tANk7/89hEePZtDcxzCosj+0RTvefEOupPRtnjHAjITOaWhmrcnGuLA2GKDbh969mk+fp/eMNd33RrjPbe/qKmuNM3i4dPzDWMtV6fXVlxv7UkyU9Ia5vTM7BTv+MfjDeP/9X/fxos2rG/Kw88XFMXk+8emOTiWQ7NtwpLEnpEU123q4fRiqWFOb/vkj5lsItsIcN8fvaypzgXH5d8PnOWRM1k0yyYsS1yxLs1de4YZz6pNq9lV224Y56+/930+9YPGV4u/er3M+15yu2819lRBbbgekuCv7jna4Dv//cb1/Mdjkzx8ZlkfV65L8YkHx5raFODYh17iVczPFdEsl7AssLknzmhHlHsOTfHY2VxF7n2jKW7d0c+hqXyDjXYPpXlyIttw/YcnnmhaMf+OF4V57x23NNV5AIH7js9wcHyZ957hFLuGk/z9/ad45OyyfFeMprh5dycf+MLTS5XxLFXGw+lCA9sKPfgbN/DBf3+CJ6eLGLZDUBLZ3R/n7js28U8PjHFgPF9BTdk7nOTu2zbz4NGFFfvB+z/5Yw414btLgH/53dv5+lPjDbq9aVsv9x2dbbh++foUH/nmUQ5OFipz2jOY4HU3jfCXXz/B4TmvmUlAhB09UR6a8E8rOPSBO5rGzLaBGH/+jSMNPN52x2a+8fg0D1fN6crRFC/Z08dffvMYT04VMRyHoCiyeyDOd0/6p2p8611X8Y7PPsLpnIONt2FdnxL5wM/t5JuPzTX482Vbw/zPL5ypOZcWgLe/KMzH7m2vecTB37mVz//oFA+dyaJbNiFZ4qp1aa7cmuIDX3yaU4tWZU4bO2V++5Xb+OKDkw36eO0NQ/zpV45xfMHAdkASYUtXkKfmDF/efnK/7c71fPSrpxr89tXXpfnwt7IN49x9Q4C/ecCs2fhLNH4RqKaHf/tF/MV/HubRsVwlLvePpHjNtcP8zXdONcj3zpds5YfHFhv8/PptXXzyOyca4uJLT8/78j7w/lv42/uPN6xHd+0b5B/uP91g7xfv7eZ/ffGZBn28+65N/PU3zzTY6MiifxpJZV2ri8vBVJgvPnymwQ9etX+Ex89mG+Lihs29LKhGwxr8zz98gE//qHHL+NbrJN5z+21Nn2Pb+xN87YmJBt2+ZNcADxybW/Fae7o4wwf+bZwNaZkP3969pi2hL26MWd4Y/8H98xydt1bUc7yeqiuoq+8PSgKG7TZcPzGd46nJHF3xcGXjt1DUuHZjNzdu719Tea//g68zXvICUMb7ou3gbY6/+t7b2pKjXX389XcO86PTOTpjAeIhiaJus1gyuXlLJ7/58l1t8fbrY58tajy7qDTo9q/vP+Ork9P/586m1+8/Ms0PT803jNUbC4Ik0hENVbA4M4pOXzyELEsNc3rr5x/35f25N+5rysPPF8r96rsTYZLhAHnNZL6gMZoOE40GG+b0oa8d9eX9+O81t/c9T47zg1OL9CSXeczlNbb3xhjpTTTw2Nwdpy8dbRjnxR/xx1j9/q/fWKnGrtZVRBZRLafh+lceOc2BiWKD78QlGzEQoisRIhmWyWsWCwWdY/P+pctl3tGQTFCSMGwbRbc4Ppnlycn8ktxB8prBXF5jKBkiFA402CguixQtpy1f89N5eU1IV+k2q+gcn8hwfEGnKxYkEfZeoy6UDGayOjreyWRQAMOFc2UYvvLSbu4/mSEZ9gD5S7pLXrOQHBtblEhHZeJBiaJhk1UseiICHcnYiv2gla99/LW7ue/YHJ2xENGwjKJZLJZ0JNdhQbUadD61UGCyaJOKSMSCktexULXRVBtLgGjQa8CgWS6KAa0ab5V518fM4bEFzuZNkhGZeECkaDrkVYuBmMRob4pkJEg0LKFoNnnVqMwpGZaJhkQU3fH8TfXfpm1fOskOsoxYYQBBoCMeaPDn6eLq5Ym+75YN3HNktkHuqYUSOQMiMgQCYJqgWiC6EAwJDfowdBdHgEjAK3bTLFDN1jrf2x/mqWmNkOTlW+u291OWTmb5+bPayLRvu36E7xyZJx0NEg+JFHWHrGKgqCoFq1G+nrBAT0eiwc8z+RJzqtsQF63sXeZdvx45pk7RlhrsnSmanj8IEBTBcLxYLiOrhIRlv9GXrvvRl+++uoIEUh2XxyeyHJopNvjBUCJIPBZuuL69L85QV3xVnmO5gsbpjNag246QiCWIK15ry2vqc7ExvphjXEUd8TDxkMDTM0XuPTJTqYiWRKHy77xqYdXludRXUJfvlyWByYzmvYaoum5YFk+M5+iIhuhJhAkHJXoSYbriYY5MF8iWLrylpB99/YnxyqY4FZaIhSVSYQkRGC/Bvz8ysWI52tXHWKbIgfE86ZhMXzpKLBKiLx2lMxbgsfEcB04vrJi3IMCJ2RKxoEwyHCQoiyTDQQTg8bEcqXCwRrdff8R/owLw+k/f03AtW9J4ZjJPVzxcM1YiKPPYWJaAKNEZCxEKiHTGQsRCAZ6cyBMQxZo5ffup0y15f/L+Zxp4+PlCfeV/ZOn1ZDIc4NGxLAFBrJlTq40KwOV/8J0GnecUjYfPZOlaqrqOhQMMpKOko0EeHc/hurU8kuEgh6cKuI5bM85v/qv/phjgbZ++v1LhXNZVWJY4NJUnFJBqri8WTA5OFUhFan0nEoLTOYeQ7C7NNchAOtpyUwxw8595vDuiIWIhmY5oCMuBh85m6YiF6E9HiYZl+tNRkpEgj4znCEpSjY1iQZlHxrJEZbnm+lcfbO1rb/pMo84ty+HguLcp7k9FSERk+lMRbMflyakiibBEf4cnt1eNblB+99ERDxCLBeiInzu/8EtPz5OOygx0RknGIgx0RglKNrM6BCXb0210SbcBgVOLFrLo1vhBKuL5ATZt+drb//VJr5K/wxtjoCNKSBZ5dDxHbEnXZZ2LgsDxBYNoEPo7YiRiYfo7YkiOTQmQBehKRohFw3QlIy03aNW8q2PGdOwlHgJ9qSixaJi+VJRIAE4tWriOy7qeOD2JCOt64oiuN6dYEAY6o6RiYQY6oy03SQBHltAyOpNB4rEgnckgIqADgmvW+LNeWt4Uy1U/1SRW/ZyLPvy9ZyvoCWX7ua7DrO6dtHenI6RiEbrTEQQXVEDCrdGHJLioeOnN3akI8ViE7tS5dX5gWiMSgO5UmEQ8THcqXHPKm4rKxKIyqejK3pAGheWfc9EnHhyjKxFiuCtGOh5huCtGQLQYL0E4UGfvoMjpnIOAXaMnSfD8IBIUauLiXPYu865ej4KywOmcQ1i2a+zt2CY6no07E0v+kQhWNr8u0JEIEosG6ai67kev+OSPK2gj5bi0bXh03HsmVssXC8kcnCoQlMSauEhFAjw2lsVxqVmDP/wf/ptigN/4h8canmOyIPLo2RypaC3vaNjjHQlINXGfiAR4dCxHQBRrxnnklH8zl7WgixvjOorIIobjwZ9caAW1JIjoTmOfd8sWUC2bWB20SSIUoGTaXvXxGtGJpSrl+qWo/PeZxfyK5WhXH7mihe44xOvkjockFMNhJq+tmDeugGY7yHLt/QJe5Xikvor5HLiHJ6caXwmqpotmOw1NGYIBCcWwG4oNwpKEYlrU11ZNZFqfh0xljAYefr7gV/kfDsgohs351D/U61wxPESHRKiu2jwso5o2rlP7RSgSkFEsu6EqeWymNd8zeRoqnCVRQLUcZLF2TiVNQ7cd4qG6mBRF7zXjeQhez9uyHHTTJlKXvhIKiGim3ZAyFBBFVMNGlutQCs5RH3JmulHnuu2gWXZDFb/jChiOQ7QebmGJx/kkXcXrxgosFULV6zwS8HRbj8IQC0uopt1QhLYSqvfbQEBEsxwidbZwcbEdiNQhHpT/lFde3+TL27Y9TNywVDtYWJa8DVxdoZsrenMKy+0/NuvN5zd9ew3ql+rlFpfW63odBpfEqnNnJKf2/9uh0ArlXgtK1sWxuBRzgboFOiwKnp/XXRcEltBG2he8nndI8sYKNax3S3Or+/yFuEH9GmJbNprlEK1DsogEJXS78dkaD3vPEqfuuT55DmCXGZfGBkai4KWN1KHkhEUR3XYI1F2PBmRUy0aqK86cK6wd2kYzurgxriPVcgiK0N8RveAKatv1cpLq+7zLkktElijVtTYs6CaxgORVH68RbV6qUq7fqpX/XteZXLEc7eojFZcJiSLFOrmLuk00KNKXDK+YN4JLWPIA9qvJxascV+urmM/RKmfTQCOebiQgEJZECnpddbrpdfyp3whqtk00IFOfnDTU0Xr7MtARbODh5wt+lf+aaRENSpwPYk69zqNBD9GhoNdVm2sWkYCEULeBUk2LqCw1fFEYOUcX3nVJGiqcbcclIotYdZvvWDhMSPJeidbM3XG8fMPzELyetyyLhAISqlZ7XTcdwgEJp97/HYdIUMKy6lAKzrGqrutv1HlIEgnLUkMVvyi4BEURpR5uYYnH+byCLtaNZS5BZ9XrXDU93dZvgEua98WzfsO8Eqr3W9P0usqpdbYQEJBEUOsQD8p/Wq0P7VbEW5K8jYlm1w6mWba3gauDRhMcb06adY5vPk2o3nx+05fW4EykXm5nab2u16GxJFadO2OLtf/fDukrlHstKF8Xx85SzJl1C7TmuJ6f1113XZbQRtoXvJ63bntj6Q3r3dLc6j5/IW5Qv4ZIskRYFlHqkCxUwyYkNT5bi5r3LKn/ojB4DmCXPoGG5xiOS1gU0epQcjTHKy42664rpkVElrDr4Px6EmvfjbiaLm6Mq2ixoFHUXS7ti/Oi7X1opo1m2tiOW/l3MiI3IBiUK8rr77dsl8GOMObS3+XrQVnmsuEUGUVnrqChGTZzBY2Fosb2/sSaolPcedkwwzEvELOaTUmzyWp2Jcf4VVcMrViOdvUx0hFn73CSbMliJqtQUnVmsgqLJZN9wyn2ru9aMW/Xhc29MUqGRV4zMCyHvGbgApePpMhpRo1u77yidVvYf3zL7YCXBqJbNpbtkI6FuWQwyUJRqxmrYFjsG0ljOjaLJR3ddFgs6ZR0k91DSUzHqZnTHbvWt+R9942XNPDw84Vy5f98QWMyo5BXTe+3ZrJ/JI3pOjVz+sDLt7Xk/fjv3dag81Q0zJXr0iwUNKayCiXNZCqrkFUM9g+nEIRaHnnNYMdAAkEUKGkmimFR0kz+72tvaMn7E2+5kYJq1uhKs2x2DiTRTbvmemciwJ6BBDm11ndU3Svo0S1haa4GU1mFrd2tvwl9/9cbecsiXDWaJlPSmc4qKJrFdFYhrxpcMZzCsO0aG5UMiytG0iiWVXP9Z65v7Wt/9+ZGncuyyJ7hFFlFZzqnUlAtpnMqkiiweyBOQbOZzJQoKDqTmRIQpJzZnymalBQvV/Fc9MpLu8kqSzpUPB0atkRvCAxbYiqjkC+qTGUUVNNlY6eM5Qg1fpBTPT9Aoi1f+/hrdzNf0JjJqai6xUxORbcc9g+nKC3puqxzx3XZ0hVEMWByoUS2qDC5UMIWJWJ4m7f5vEpJ0ZjPq4TOsUcv857KePOfyigERGmJh8tUVqGgeP6uml6RkyAKTC4q5BSDyUUFR/DmVDJgaqFItqgwtVCkK9L6HHR7p4zuwmLBoKQYLBYMHLwuea4QYHKxRL6oMrlYIhRb3gBYNM+/dap+zkXvu2WDJ3eV/QRBpDfkYSzPZVVyBZW5rIorQASwEZjOefqYzinYrkAE73vCXFalsHT/uXS+tz+MasJCXqNY0ljIazUnxlnFoqhYZJWVfb0z3OWfc9Hbrh9hoaAzsRQzE5kSpiMzHAPNdJnJKZQUjZmcgmo4rE+JuEg1erJdzw9Uw62Jl3PZuxlvw3JZnxLRLInpTMnTbaaEKAUI4dk4s+QfmYJR+cop+Fz3oy/ffXVlX1HUvHVJkmD/sPdMnFhUyCve75JusWcggWE7NTGZU032jaQRBWrWx/fddXlL3n/63/c1PMcs12H/aIqcYjA2XyJT0hibL6FoHm/VtGvivqCa7B9JYTpOzTp4xcaVNedZLbq4Ma4i1YB1KZnfe+WlpCJBehMhcEExLHBp2XPc7/6RjljT6zdv7efajd24jstUXsV1XK7d2M2V67vXXM5P/PJ+BiLet1ID7/dAxLverhzt6uO37ryEm7d0YjsOUwUd23G4eUsn73nxjrZ57xzwIG1wIKsa4MDWvgR37hpuqtu33dh8ru+8JYrjuGRKOuMZhbFFhfGMQqaks3+0q+lYr963jr0jHbiuy2xRw3Vd9o50cPPW/qZz+ujrmrek/cQbtnLl+u62fOGmLX3sG02TUw2Oz+TJqQb7RtP84tUbms7Jr5RzqIWdXnfNRm7f0YvjOExkFRzH4fYdvbz91m1Neewf7SIoCUzlVJ6dLzGVUwlKAm+9rvlD5O4bAox0xJrq6sr13U2v/+7P7mrqOx974xXctr3bm2tOxXEcbtveOo78eL/umo3cur0Xx3GX5Ha5dbsndzMbve6ajU2v/8rVzfn+6vWyr85v2tLHrsEU2ZLBsVkPomzXYIrfe8UurtuQRjMszmQUNMPiug1pPvXmS1jviUDJ8X6vTzTnW6bfuvMSbtzUgeO4zJQMHMflxk0dfOJNl7NnMEZJNxnLaZR0kz2DMf78dXvb8oMdPnwvBW7e2s9NW3twHZeJnILruNy0tYf3vHhHU53/yS/sYWdvhJxmcDajk9MMdvZG+PibL+GSvjCmDYuqi2nDJX2tDxPKMZNVDU7MFMguxcyf/uLl7B9OYFg20wUdw7LZP5zgz1+3l6s3dJLXDE7OFshrBldv6OSPX3vZ0pzMpTmZ7Oxt/SXsI6/fx6aUiONCwQLHhU0pkU++aSd7h+IUdJPTWZWCbrJ3KM6HXj3cdJy339R+y93XXbOR27fX2W97Lx9942WMJEVKJsxrUDJhJCnyV7+8w9OHuaQP09PHx9+0kw0dMtrS/ZoJG87xJuz//sLlbO8JYjmQ08FyYHtPkI/80qbK4YzOcuH3u2+LNx3nLdcIDRvCc20Q33TDZq7b2IFqWDy7WEI1LK7b2MEnfnk/161P4zgus4qJ47hctz7Nx954RVM//+gb9jeNl/Ph/ZE37OOa9Sk002Y8p6GZNtesT/HpX7mUbZ3em8aS5Z1Ub+uU+djrtzS93oq296XY3B33io9n8ywUvMLoX7t9G9dt7KRkmJxaKFIyTK7b2Mn777q0aUy+et+6puvjW65prvm3Xif5Psd+7fZtXD6cZL6kcWgqz3xJ4/LhJL/9M5c0jfu7b9nadB30i4u1oIuoFCyjUnxvHOxAqAYN4KcRx7iMGPHA0Rmmsyr96Qg3bOurQZj4acQxfnauwLGZAn/1rcc5NQ8bu+HXXuJhMKajwZYoJBeKY1zm/e2nzjKR0RnqCHHHrlG29iXY0JNoOt9z2U/TrQrebTgkV+ZaP6cyisUXDywXMJSxZe/cM9JStyvFMS7PSZYEJEHEdh0s260gh/zl1x5nLAsjaXjXyy+vkXutcIy/8ONTfPfILN8+sow1WsYxfs3VG1vyuFAc47LO73t8igUXugS46fLWOi/r0DTtypwCAamCyjKVLZFTLFJRmYF0rHL9xyfnmMmq9KUjXL2phzd/+oEaKMYyrU/Avb/joa/U47KW/XM2p6LoJtFQgN5UpGKnlfrBSnTuh5lar/ODZxY4MJYhXzTRLZOQHCAZD1QQUA6PZZkpKPQlouwYSfP2v/8BJ3O1iR9lHON/efuLvJgx7RoM5bIOT88UmFc0uqNh1vclKtdLuoVq2ESCErGQzExW4cR8kaJmopku4YBAPBzgnx84zpmcU4MKUsYx/sxbrme2oPPos/MVO+3f0F2JjcWiTkm3iIVkOuMhLMtmpqjz6OlpFvM2nUmJ/ev7K3J/6cenOTFfZHN3nFdevb4l8ksZbacev7aMtlNUTEqGTSwoEY8G2DWYYnN/ipNzeTJ5g45kkE09yYrcc3mtMteeZJjP3XeUsQINUGo7e4J87ldvZLag89R4hpmcSl8qwq7hjopuHzo1x8RCiaGuGFdt7Kmgshw8vUBGMeiIBtmzvquC/HJyJkemaNERl9nUl/JFfgkADy8hv8zmFQqqTSIi0ZuMVtbIC8UxfvOnH+DHZ/NU1UoSC8DVo0k+/ItXMlvQmSkolBSHWFSkLxGtyD2eKZErmaRiAYaXDn06YqEGvOIy1V//v197ku8cma8pMN7aHeG27d285eZtzBZ0HNvBdkESvNzqMu9MQa/gb3ckQhXeK8UxLq8Tf/4fjzOeh+EkvPeuy1s+x8qf0XUbw7YJShKhkFT5TH3c+62D5bl+9GuPcd0AF+Ha1prKG+NJ0kyXXFzH5eevGHnOG248F2TZDuMZBUEQlvqOe6SZNrgw1BFZ0Yb3hUaaYfHo6UVESSAWkDh27Chbt26jZNrYlktvMuT1pF8DnVTzru8xjwN713WsuMd8u/abLyh86t5TBGSv8rhMMznvG/2bb9xQsxCeD/nNKa8YPDGeoS8VoSO6fNp1PnK3S9O5In/+n8cJyAK9yTBTU1MMDAwwm9dwHJd33b6lZsO7mnQ+OvfTYVE3mc5oDKTDxKqKqEqayVTO+1Ibryp4+cYT4/z+148iAemqav+cYuECH/+lPbxk11AN79Xyz2qd96erNrxLp0Ht6LyoGnzr6SkkyausL9NcQWOhoHPj1h46E8vr8/eemuL3vnoISfBQOgzDJBgMkC2aOC588OWXcP2OngvS7WJJ597Ds/QmQ3RX8f7R8Wn+9sExIhJ0pZavLxY0bBc+8DM7uXpz74piYzav8uOTC2zujdf4znxBY76oceO2vhp9/M13DvOZh5uhlHv06zdt5NdeWnuOny1p/MtDZ5Flr/K/TDM5lUzJ4M7LBuhNLttvsaBy39E5upPhmvt/cHyGv33wLCEBulLLc8oUdGwH/tcrd3LNplq5/XQ7l1P5yhMTdMU9VJYynZkv8PjZDHvXdTLatew7X378NPceywEs5cF7r7/LG/Rfu36UV1+zcU3W88fOzPOr//AIsiTQU6WnubyCZTt84Gd3sXu4c0W+1u6cTszl+O0vPklAFhmoirGprILtOPzazVsZ7YmvSOft8j6fdaLdz6zk+aYqJY4dO3YRru25pOcCGeL5JD/ECD+EiZ8WatXnXVmq2l0rnaxmj/l27eeHYpEMByiaFqVzYS5dwJwcQLNcgg1V/+3L3S4VVA9Zo3oxBg9+qGTYFNS1430+Om8X2UYQBXTbRRJq75/OeqdI9aUqMt6m4eRMY2n5avnnaupct100222osC8joNTV5jBZULyq/3qkBcnL3zybK1ywbm3bK9QL1SHeFHQXGwjUfXcISWDaMJFRVxwbgiugWhaBOuSLUEBCMR3q6gQ5Oe/f1ATgUBN7+6HtxIIyJdPCqoPGcBBRbYdInYBZ1cQB6r8zBSUvB/zsYqPcfro1HbcpKktY8gpP68fJFvwbiwAcmSmu2Xo+sah6SDF1NorKIrrjMp1ZObpSu3PKFKyltaVW6cmwTEm3mSvpK9Z5u7zPZ51o9zM/KfuTixvjOnoukCGeT/JDjPBDmPhpoVZ93qNLVbtrpZPV7DHfrv38UCzymkk84DV5uFDym5OI14DBaKj6b1/udikR8ZA18lrtAzSvGcSCEonI2vE+H523i2zjOl76gu3W3t+f9k7b6svwLLyTtU19jaXlq+Wfq6nzkCQQloSGCvsyAkodmhODiahX9V+PtGB7XwpGU4kL1q0kedBueh3iTSIkIAFmXQ2ZbkNA8k65VhobruASkWXMOuQL3bSJBkTq9tFs6m59Yrazib390HZKhkUsICPXQWOIOEQkEbVOwHQkgAjUgQx5OhdgtLNRbj/dBkShKSqLZntQhfXjpBPN60XKtL0vvmbr+VBnxEOKqbORYjmERIH+jpWjK7U7p46EvLS21Co9r1nEQhI9sdCKdd4u7/NZJ9r9zE/K/uTixriKsiXjOUGGeD7JDzHCD2Hip4XKfd4LqklBM7Ech4JmUlBNhjsj9CbDa6aTat7VVb4F1WQgHW4rnaBd+1WjWFRXHs8XNC4dSl5wGkWrOTnA5p44im5dsNztUn8qzr7RFHN5jemsimaaTGdV5vIae4ZTa5ZGAeen83aRbUzHZTAdwbJrr1+7pZfhmHc6nFMsSopFTrGwgJEEDWkUsHr+Wavz5Urz89F5PBJkc2+cjKI3RUARJbFG7r2bulifEjFcD6VD0b3f+hKywMsuH7hg3YqCwCWDCXKaUTOn3nTUQxywvfSJkqKxWNBQTdjWHeaOSwdXHBum7XDZcJKiYdbwyGkGO/qTiIJQM87PX7uppR7r0ygAX7SdjKKzayhJUJZr5ZYkdg4mydfJ3Z+OMJrwUCIyBZ2SopMp6Gg2bOkO8uJLGuX2020gIFVQWarnpJo2+0fSKEYt8sv2/nRFHhuvgLz8FSMAvOnmrWu2nu9b183ugTh5zWYu76FbzOUV8prN7oEE12/uvWBkJz/a3JPiynUpFgp6DQrPQkFn/0iKS0fSK9Z5u7zPZ51o9zM/KfuTiznGLOcYP1OIsr6/gyvXdxMOr91D+0Kp3QK4enIcl5xqkFctTMf7JpaMeNXy9U0MfprIshzGMiXOzJc4dOw4O7duYV13jJGOGKIorKpO6m20zFuhaFrEAzLruqOMdMQampSci8r2WywZ6JZDSBbpjAV956ooJt8/Ns2BsxlymkUqLLN31EPQiEZXBx/Sb06xgMxETmEqq1X63g+kw+cl97moXufFksHXnxrngePzjM3NMdLTww1burlz1zDxWOsTp5Xy8Lte1vnTE/mKvS8dSrbUuV9cltui+l1fLBkYtkNQ8nQ+kc3xjs8+wunC8tjrE/CJX76SnYM9zeVa8s8LtVNZ54+czVWKtK4YTdXofKU6NAybQ1NZjk4XKBo28aDEtv4E2/tSqLbd4Gsz+QLv/sfHOLK4fDq1vVPmo2/Yz+bezqb+WdbhbF5Hs2zCskRvMlSj2+r7I5LEkZkcp+ZLKKZDNCCysTtGOOjy/i88weFZDdPxusrt6A3z4V/cy/rudIV3tZ38YqMvFubYXJ7Hz2bJ6ybJUIDLR9MVuev94KGzY/zqPzR2HfybN27nju3NN86aZvHw6XmOTBcomTaxgMT2/gT7R7vQXadhrn5yBwMOv/UvBzk8Z2Dhnc7v6AnyF6/bx8aejpb+XK/bkCDy6NkFnpnMUzIdYgGRSwaT7B5K8+RElqcm8hR0i0RIZtdQEo0Sb//skYaCx8/8yqVcv3GUnGowV9BRlxo+9SyhKJ3Pel5fhDa+kOeD//4EBybyGCYEA7B3KMnvv+oyBjsSbcVxeU4rfa4v5lX+9v7jPHBigZJuEwtJ3LC5i1+5cQvpeHhNea9knfB79q10bTnX/iSby3HyxIk1zTH+yd39PQ902yW9DPZ0nfvG54nKDpNTTSzHRRYFUpFA28EuigIdsRCJcOCCNtgvNBJFgXQ0iJqwmIl4Va7pqKe71dKJn41iS/l5Di6C29g04XzIxUUQvN+tSJZFhjqizBZ1UppNIiwx1BFd9Y1psznJssiGngQDqUhT9IfVID+dC46HKpAzLQwTcqZFSbcagPwvhEf5gdMsJu/cM8JVm5pXezejVj7YITdeLzcccfGgnMpSRQMB+pJRJgoKJt5moS8ZJRrw/xK0WnYKh2Su3NRDJBgkp+mkwiF2jaQIh+S2dRgLyKSiQbriIUKGRTzo/S2KXkuyel8LL7XmPr1YRAPC4LWVrcpvrP+M47hkFYPpnFp5aAdloRKv9fcHgxJ71nWxuTdRg8ZRLBm85uoNfO2JCRY1i86wzMsvG6I3FqviXWsnP50rislERuHsokJRs4iHTXoTQbb2JJv6x2gqxfoEDV+EhpP+HRnCYZkbt/ezeyRdgyDgOC66ajTM1U/u6UyRzX1JxvPzGAYEg14TqXgw6OvPy37bqNtdIx10xMMUNZN4OMBwR4RoQGakK4ZiOpXrI10xTDvArv4wB6a1ily7+sMMJ5MVu07m1AoKSUASSIQCbT0ry5u6iYyK4bgERYGhjgiJoMzl67qYL+nkNYdkWOTydV0t5Qb/OM6U9BU/10VBIBqUCcgiQdshIItEgzKi0Po5thq8W60TrfYn7awtrfymjFix1nTxxJjlE+OtW7eSSJwDCPR5pLJT+EGKXaTWVNZfUIRjRw+zddsODIdV1Z+fjcrQTIlIwCs8sCwKqlkDc3OhPPzkKMNedURDxIIBSoZJRtHZO9LBnnWr80Xw+fRNP973PDnOD04t0hUPYZQWCMa6WCjq3L6jlzfdtHVVeJRhkH6S5P6f//oIz8xoRINeHqtmuSgG7BmM8oVfu3lN51SGZmrm536QiH469IuZvkSISCjQcP8H/t9jPDmtEA0KiJaLIwsohsv+4TgffcPVbcWlHw8/u5Zh6nqSYZLhIHnNYC6vcev2Xu7YNdRWbJSh/roTYZLhAHnNZL6g1UD9VdOL/vfXOV3wTrlklpuCVMPzrZTajeP3fP4h7j+ZIR2ViQclioZNVrG4cVMHf/G6q9ri0a4f/MU3nuLkokU4AIIFruzhK+/qD/Nnv3Slrx+2s976+fOPj81wYDzXYKPbt/fyjjt2rqnOP/btQ9xzZPZ54f1cjdVqfNHSyM+MXUSluEje64mcahIOSIQDEpIoVP6dVy2s82hb+V+JqvUXCnjtLkOrrD8/Gwmuy4m5ItGQTDIcJCiLJMNBEpGA93qpvnrlPHj4yVFUDU7MFumIhuiMhQgFPPirjmiIU/Mlimrr6u61mNNqkh/vgmLy0NksXfEwAx0RIoEAAx0RepJhDoxlGc8Uzj34OXjIksBkRvNOQH5C5H5mPMPReY1IELqSEWLRMF3JCNEgHJlT+NHJ2TWbk2ZYTGRUEpFAg5+PL6rM5rUV61CEpjETDcmcnCshuG7N/UencxyeU4gFoTsZIRr2fsdDAk/PFPn+0ZkVx6UfDz+7TueKPHY2R08yTH86SjQs05+O0pMM8/hYjlNzxRXHxnxB4eCYt+HqS0WIhGT6UhG6E2GensgzX6hFofjWUxOcXdoUp6IysahMKiojA2MF7/9XSu3G8VMTixwYz5OOyvSlo8SiIfrSUdJRmYMTeZ6aWFwxj4AoMJlVkaVaPxAEODFbIhastdHp2QKnFi3CMnTGQ0TD3u9IAI7MaXzrqemmftjOeuvnz4pu8shYjnQsyEA6SiwcYCAdpTsR5rGxLKcXGpFAVkvnpxfyPHQmS3ci/Jzzfq7GOtf4Qbl158HVoIsb4xcI/aTAmLxQ6bnQnx8PBGHVYMvalcMP9ioWDKCYDno97tV50PPpm368C4aJbtrEG2CNApQMm1xp5Qt1u1Bqz6fcczkVy4GIXDunsCxgOHB2vknnj1Wi84FE9NOhH5xZUJJQLQfqqtPn8l5ub6QuPSgiixgOzGSUFcelHw8/u7aCqSvoNlnVWDN4xZMzeRwacyJbwfP5UbtxPJPV0R2HeLBWf/GghGo7zGQbX3n78fCDFMMV0GynIe1rUTGx8aDhqqkCkZctXjD8oJ8/G5Zn73iw0UYlwyZbrINGaUHt6jxbtNGt5v6x1ryfq7HaGX+t6OLG+AVCPykwJi9Uei7058cD11012LJ25fCDvSoZJtGASKge9+o86Pn0TT/eiWCAUECi2ABrZBILSqRiK1/62oVSez7l7klFkEVQrdo5aZZLUITR7hhrRecDieinQz84M8O2vc1v3YO2JxkmIOJtaKtItRyCIvR1RFccl348/OzaCqYuEZJIR4JrBq+4qS+JiJc6UU2t4Pn8qN047kuHCIkiRaNWf0XDJiKJ9KUbX5/78fCDFENwCS8VcFVTZzSAhAcNV00ViLx0/ILhB/38OSh79i4ajTaKBSXS8ZWfaLar83RcIiQ394+15v1cjdXO+GtFF4vvXiBUhjEpJ57X5/D8VyieuxCq1p/mOBQNm4Jqgiiumv6qedi2dwrmOi6uILC5J87Mku2CkoRh2yi6xda+xDmLnOqrfJvxMB23qRxl2KsDYxkWizquA4Lo/ewd6ahpY70acsPa+2Z9G+JmvBPRAFeNpvnBqUVU06RU0CgKBRTd5vYdvTXtXs9XvjIEk2G7FHWzphV2We5W1d4X0nbdzw8uGe5gW3eYZ2Y0xudUJJbgrAQvx/iaTb0tZb0QxJsyNNOxmQKG5SCLIpbjoJt2TY6xnw41065cL8OZNYuZTT0x3CXYsvL92/pT7OiJ8uS0QnFG8TqhFRQECfYPx7l5W18D71ZxWeZR0symMVatpzJM3XePzLJY0L0+1K53ynXr9l429sRbxkb1WGWov/uOzWE7LvFwgKJmkinp3LS1h+5EtOb+l+waYjRxkNMFWFBqN3DrfeD5Wtm77FOW4zT152p0hl1DnewdTnL/yQyGnScsSmiOjaLDjZs62DXU2ZJH/fo1mI40xJLrwubeGDMFHdtxESUBx3ZZ35tgY6fMyUWLiYyOCwiqjoCXY/ySXf0cm/HSpepzjMNBeUWxV+3P1eNEQwGuGElxYDyHZRWIBEVUw6Gom9y+vZf1XSv/MlKtD92wvLcUrvfMaOYf67uSXLUuzT1HZjEsh1hQpGQ45FVjRbz9bOHnm35Uj9Kx1s+AmvHbSD08b35rzuEirRqlljYxedVCMSwCS5u61Cpsbv4rUCwgo+pFjs8UOT5nYMVzbOmLE+tcvVO0RCjgVUNn1crmbTAdYaQnCeQ5OVdCtRwissimnhhDKf/igVYIF0GpkUd9J6syDacjfOWxszwynkMzbcIBiSuGU9y1a3DV5H4ufLMM33Vitohmu4Qlgc29cbb3pehNhBp437V3iMfPLPDkeBZFhWg+y+7+OHdc2r9q8pUhtyYzGrrjnX4OdoSJBWTfam+gbYSLZhXifr72qy9Zz1s/e6QGq0Rw4a0vXu8r32oh3gwkIowtlDg0mUe1bCKyxM6BBAOJCMGl9971OqyGkqq+HklHgVxDzFTDllXf/5Y71vGWvz9cK7cNv3zrqK/9/Hhs7UkyU9IadOtn130b0nzhx89ydF7DckAWPQzjKzZ2+PJOhAJNx7phcy9FzeTx8RxTOZVoUGLfaJobNvc2vf+dP7OR9/7zqQZbvPsuf4zjc64tdf4ckSSenSs0oDO85ZaNHJ48wLOLFhY2MrChU+bu2za3vX4NJCJMFdQG3kPdCfKqyaHpHKrpEAmI7OxP8raXreednz/RINuvvmQ9Ix3emj6V1ciqBmFJZGtfgqFUtKkO/WKvvD7Xj7OjP8bMV5/h4FQB3XYISSJ7BhK8an9jceS5qPxcOjFXRLO8txibe+JE0s3n+qr9I5ycLfD4RL6CpHL5UJJXXzHatr3Lz4yVrtt+KB0jHbE1fwaU17uJokHk3LdfEF1EpeCFg0pRpgvFMf6vSuUK47AkcOb0Kdat34hmu+eFDOFH5cpZWRJqTlzKFdciXv5k+Xerit1zVW/X8/Ab6+/uO8Y9h2dJR4NEwzKKZpFVjPNCZzgXraVvngtdo553We6wLKAWskQSaTTLvSC563mUbRQQhZrTr1ZoFcCqIFz4+dr7/ukhjsx5r/XLJ8bgnaT9+7tvbSrXalWUL8eYWDnt0mynJsZWelJeqUKneczU3/8zf3YPh+YMxCWZpaX7d/YE+eqv396Sh+C6Nad1fjHmZ6O//s5hfnQ6RywoEAkIqKZLyXC5eUsnf/ya/S15+/mBbtiVLxehoOTL++1//wNO5moBIAVgW6fMt37zxW3Zu8yj3p/9kCGeOD3P4+N5JNGt6Ml2BG7f0csr969ra/06F+9wQEIWRCzXG+dPv/oEz+a8twvuksxinb3rTzbPF12mfpwycogkCEgC2EtvCPyQQ1YSM9GQXPPWwg8Z5cR0jqcmc4gOOKKA6Lg4Ily7sZsbtzf/0n+u+F7put0KdeZc8X2hVJbB1VVK8+MXcYwvUi3JknjRcG1SdYVxLCAxGxBJx0KUTJuprMZAKnLB+Lr1lbkeSZQ0k8msSn86QrzqVFczvVOvRDjQ9DV7/ViSKGHbTpOxJN+xxjMFHjmTpSfpVTEDEIdAVuHAWJY7MoW20grORWvlm/XoGgChgPf71HyJzb0J4pFghXe13H2pMLOiRm9fipmcdkFyV8vX3N5Q1E0mM5rX2anKdgCLJQ8jtt6uluP4fqaZXf187f4j0xxfMAgHoCux3L1zsaBxbF7jweMzXL+lr0YmP1/z4+1H9VX8ZcprRk2M+fnISnRb7+fl++87uiS3BOl4EEM3CIaCZIsGJxYM7js6zU3b+lfEo3m8Sr52PTmT58BEnnQ0wEDH8oN6JqtwYDzHoakMOwc6zsm7Pr7T6eVzMT/eDx6d4XTOIShAZ1Wb5EzB4NlFi+8dnuKWHQM1evbjXe2DsarirlxJ58Rckb5UpGLXoBxkLqfxyNkcvfEww1W561NZhUfPZtgxnGIwHffnUaXbap3HQs15d0SXv6D94NC0J7foye1n73BQphwFK9G5n/9Xj1OPHFKxd07l6Yk8V21SVtxRtFnMxJARXDg5X2TXcEfNnAqKzhNjOToTIfqreM8VNI5MF9g9km7o2rvS+D7Xut1srkHZ+72S+L4QqpYBZNauhNiji8eNF+m/BLWqmG8XGcKP2q24blWxu1pj5UrOUsV88yrmdtAZnk9qF13juZD7fNAqDNvBaAOdwc+ufrxnsyq2A+Fm1foOnJkrrliOdivKVzPG2p3T6dkCttOIUhCUwHK8/18pD78Y87NRRjHQbYd4qC6/PyShWA5zuUZIxNXiPZ1VsfHs2yA38GwTVIp2/dYPvcOyXVTLJhapvZ4MByjpXoytlIcvKoUP7zlV85d7ley9WsghrcgvZmRZRDVdrzigiixXQLXthnUwEQpQMm3vM3X0kxjf7dJFVIqLdJHWgFpVzLeLDOFH7VZct6rYXa2xUjFxqWK+eRVzO+gMzye1i67xXMh9PmgVQUkk2AY6g59d/Xj3piNIImjNqvVFWNcTX7Ec7VaUr2aMtTun9b0JJLERpcCwvXzf9b2NbwfajTE/G3VEg4QkkaJehyGu20RlkZ5UY47lavHuT0eQ8OzbIDewoQkqRbt+64feIUsCEVmipNZez2smsZAXYyvl4YtK4cO7JxL2l3uV7L1ayCGtyC9mLMshEhC84oAqkgWXiCQ1rIMF3SQWkLzP1NFPYny3SxdRKZ5HUgyTn/wM44t0PlRdYWyaNpplk1UMNMtZETLESsivMre64jqvGA35ks1eU69krOqqfL/q3+GOBFesS3PP4VlMyyEalFGM5Rzj1UyjaEX1SBLtUjW6BtCQYxyPBGvyAKvlti0HwzCZXFBYXEW5/SrsLWcZaSFX0hsqzYEVozP42bWmmt2yvZMlwWX3aCdbuoIcmTOYWtQICGC6nq/t6g9X0ihWqzq9mmpizLAbcozbibGVoK9Uy3DTtn62dAU5NGewmDe8pFPdwHC9nNObtjXmXrYbY34oJEOdMfYOJfnR6RyuW6jEWE51uHlLJzsHOs6Ld041KnZ1XSq8M0W94uf7N3azPiVyMucwnTdqcsq3dcqVNIr6HNlzoaxUy12N3qFpVgV1IxyUuGI0xePjecxZm1BAQjdtCprF7Tt62d6fboq20IyHn86reUuiUMlrXT+QYH1K5Nmcw1T5RF4zEKi1d7uIGCuJvWrkENWwCAdktKV282XkkJWSH/JFybDY1B3HdamZUyAgc9lIiqcmcyiKCbIAlouBw7UbuxvSKFr52oXEt2bYiAg4uJjnEd/t0kVUiueRfnQyw/qiy86BdKWC+iL99NBQKkpOMTg+U2QsZxPO62zpi7dEhmiXfKvfJYkjM7mGyuNWiBitqtmbVfH7Vf/etWeYsYUSj47nUE2bSEBi/3CKu/YMr5rcfuSHJHE+MbZzIA14OcWzRY1oQGTvSAdbe5JNK+bv2jOMols8ejbLTN6hT7a4bmMnr9rrX73dLvkhQ/TFwhybyze1d7lZwYXatVLNPluqVKdv7o3x63dt4e7PPIMKlN/qRoD33bV11arT/aiCSjGVr6A87BxIMpBov47cT7d+yBB//NrLeMdnH+FsYalYz4XRBPzJL+zx5dFujPmhkLzrxdvIf/kpnpouMl3QCUkiu/rjvP02/yLPc64VdXYd6IpzcDzDM1N5NMshLItcMpDk3Xdt4h3/eByoxTN+389u8UURGEpF6U2sXG4pHuLoVI4DY8tryN6RFK+6Ypip7PEGhISX7R70RVtY54Mq4qvzzhipqNKADPG7r97BWz/zDNVtRELAb71iW9uIGEOpKCXTWrH/X7Oxm2fGMzw6lkMxbaIBif0jKa7Z2L0Cz66lVggazebUEe7k0VPzPFiHMrRzwB+qbbUQI/piYQ5ZWR57NoNqW0QkmctGkvQ12ZCvNl1EpXiOqYxKMemmmdPcSpX7RfrpokpVq21z5MgRtm/fjiBJq9bLvZrqTyr8Ko9XgohxIXi3sIzmgA2O6yAKIkg8J35+LiSJ86H602e/SulyVXemqPDUoWPs2rmVjnh0Ve3thwxRrqRvZe8LtWtZ7lhQRpa9Jgglw+KLPzrJwckSkmuXD+qwBYkbN3XwgVfuWZXq9HPNKRSQGnCM20V+ORfCS70MM1mFE/NFJhbynByfYtPwAENdyRX5WrtIGfXICWXeRd3EMkEOQDwUOC/efna1LJuZok4yHCQSkFFNi7xm8KWHnuXEogE2iAI4LrCE3/yhV+9tiSKwUrnvPzLND0/Nk4wEiQZkFNMirxqIjsO84m20ZNHL7zUdh5u29rBzKN1yzWvX/+tPvd/z+Ye4/2QGCRtDg2AYbFr7+bkQfVbq/2V9hJZSowzLQbedlsgQ56J6+fz08YUfn+K7R2ZJRoOEJRHNdsgrBrdu7+U1V29syeNC47u8nseDgYrcRcN8Tp4lF1EpnidKRAJYolBT5X6Rfjqouqo1EJKIhSSS0SCm017l/UqpujLXr/JYEoUVIWKspIrfj5qhOQAslvQ19/OVIkm0S/FIkHKmrF+ltO24nJwrsXs4zWhXgkJnmNGuxKra2w8ZIq8YDZX0zex9IXZtigARglPzBZ6cLpKMyAx0LJ8gzWQVHp/I8fDpebb3py+oOr2tOdGISrESWgnCS7UMimHxzGSB3mSI9VsHGZBVNm4cJKeaK/K1C0HKWCxoPDOVpycRZlPPss5XGmPnWisIgWE5PDa+yKa+BD1LaCPxsMzpmTzHFwxiAejtWX77NJ9XeHqmyHeenuSS0c7WKALnmFO2pPHMZJ6ueLjCO00Q07D5wZkMlw6kWFeV0zuTUzkwlgVEOhLBlmteO/5fjQzx1MQiB8bzpKMyPakExXyBeDLBXE7lwESeh07NsWOwwxd9ohWiz7k8tJk+oDUyxEqoWr5qqp7TdK7IY2dz9CTD9KeXN4XTWYWD4zluyhXpTzXWETQbq13ye5YESuKaP0suolI8z+RX5X6RXti01r3cW9HzWc3bLprDC423n26DkoRqeRin1bSa9vbzKQeaVtKvpr395FY1G8N2iAVqeceDnj5ms/qaxcBzgUrhhyBg26DZXp5rNa2mr/nNyXZBsxwigVq5z4e3nw4FAVTLJlznUyXD8VBIgrWFTxFZxLBgpmBesD1U00WznYYGQrIsoJg2wUAjKkVRsyjoF87bj2ayOrrjEA82+rlmNvfz80EHakZ++miFDLFaVFDdJbSd2g1oMhykZNgU1Bf2eu5HF1Epnmfyq3K/SC9sWute7q3o+azmbRfN4YXG20+3hm0TkcWGBW417e3nUyI0raRfTXv7yR0JSwQlkZJZy7toeProTYfWLAaeC1QKPwQBSYKw5BV/VdNq+prfnCQBwrKIatbKfT68/XTouhCRJbQ6n4oFRQ+FxKjdlKiWQ1CGvkTggu0RCQiEJZGCXhvHluUSDUgYZiMqRTwskwhdOG8/6kuHCIkiRaPRz8OB5n5+PuhAzchPH62QIVaLEhFhCW2nFgIwrxnEghKJyAt7Pfeji6gUzyN97oHDXH/ppkqVO6x9l7mnJhaZyer0pUOV3vJl8ss58qN2c7Y+/f3jHJvJs7UvyVtu3rKi+a6WPr53eIpnZ/Js6Es2AND7kZ8+siUN1XSJBISaV1h+ldgv/8gDFIDE1+7l3959Q01lrt9Y7eq2fpxyNe/v/vPDTJRgKAa/9crLa6p525VvPFMgV3JIxcQalIX6HNwymsN7vvhk5Z4PvHxbjZ/78ZgvKJR0l1hIqKm29rv/9EKebNEmHZdY35VcEe9DUxnmcgY9qWBNBf90rkhBdUlEhJrXgw8en+HMXJF1PXGu39JX0e3DR2eZU3R6oiF2jHawqSeGA1z7J/d6H/zavfxnnb390DL85KufU9mnprIqBc0kEQ7QEQtWKulf/5lHKp/91Otq7e0nn58+fnRylrPzJUa7Y1yzqbci948OzzBdMuiPBbl0fSe7++McnCzx7GKm8tmuiJd7eeX6bmYLOkenc2SLBul4kHVd8YpO/HjX29XvenlOv/jph33l9tPtibkcmYJFR0Jmc0+qotvvPTPF9NIr8Gu39lYQBL7/9Azj+QLDyQSXb+zgksEEJ+aL/Pq/PeUN+MBjDb722Jl5JhZVhjoj7Fu3XCh1zzOTnJrJs7Evye2XLLdKr9d5eU7feWaS8YUSw10xrt/axyUDSU7MF3lfmTeNfn50JstC3qQrGWBbX7py39efGOfETJ7NfUnuvGy4osOPf+8gEwsGQ11B3nDNJVw2nGKmqPORrz/NZBYG0/AzV25jS1eQE4sGh6eXXzBHlnKMb7t0kGMzBY5MZPn/2XvvMEmu8t7/U6m7OvfkvFkbpdVqgyIIhBD5YmyTERhfG5y5Dtg4AL7YcC/m+l7bXBtH7Gsb24Ax6WeSQQYEEkLalVbSStocJoee6dxduX5/1HRPp+qd3p3ZlWC/zzNPz9RUn/e84bx1quq835PTLeJBmZHuSNUfn/r+eU7N5bhuIM4bbtlU/f7D5xaYWCwx1hPm5s197BmO8+DZFIfPzZMrmcTDCpv74hwcS5AqWfzuF5+ufvf1+4eW1xjHOTmXZ2qpiGE4BAIi8VCgKrvRthX83688w9NzOXYPxPmll++qHn/fZw7z1EyOPUNxfv+1B9k/Guf+M2nOLGa8E1KZapzfsqWP+bzOVLpAsewQCYn0REPV2Hng5BwL2TJ9iRB7Rruq8e/no8a4qdjjQ18+UT3nZ+/cWMcM4Zefj04sMpPWGOpS2Te2si7Xzx6N8XFgQ4L7js/zTw9PVc95yc4e7t7ZX80jfuPVTz+/8xt1qOTzD3z6CTJAEnhXQ5z75VQ/e/jlwcY8URl7f/jZw/zsLZ0XOXaCa8V3rBTfvfvrKc5lLH7lnhi/dNfzW1a1JkIBxDV44jObLvAHX3qKRydz1erm/aNx3vPKPfTGwr77kVcq2mvRrtK81f7vj0/P8I6PP0XtfZ8CfPynr+fObRtb9tdPRqf2ODm7yLv+4TBnl6zqtq1bumU++raDbB9svXjfr7K6Lxzk0YklnppeqYjeMxxn/1g3CyW96fwPf/J+vjbV3P7Lx+CPfuqlPHw+1dTWwQ096K6zatsGBZHD44tN7dz35BH+/kiz7J+6BX7rv7y8I/12Dsb498eneORCBs2yUWWJQxuTvHrfKJOZchMDxGv+/CFffxz/7631vn44wffOpjg6kUWzvVe4+5arro9NZ5vO3z4Q5TOPjPP9Cxl0yyYoS9yyMclX/vMcT7WQeyAIf/4rL+SPvvYMh8ezaI6DKooc3JDgHXdt5ZGzaY6MZ6v6HdiQYNdohP/+mWOcSGlYjsdXuqNX5W13j/AHnz3DTHml/aEQdX83xeHvvawlW8aWSuV/g367h+Lc98xsU59u29bDJx+6wMMXVux088YEDx+e4LDWLPf5XfDnv3gPX3pysqmtQ1u6+Otvnmmyx4/eMsT//v9O8sxCCdPx+Ih39YV5wwsG+dC/nmWxZiD3KNT93YgHf+NOPviFJzk6na/mnX3DMd754i186sHJJtk/+YLN/McTc01+fdVNw/z7Y9NNx//wP891HGvb+iN84sFzTTa8dWeC3/6XY0zWLCgcjcAvv3ozf/HlC5zPOtUcsikhcjrr/zTpu7/+fH73s4/zxEwBw3EIiCJ7h6K86c4RPvzZE01tvfs11/F335hosvmbXjjE//jMGeZrqBD6g9T93YjDv3UXf3bfCR4ZX9Hv0IYEL7ihi/d+8qkm/V5xo8hfPdisy90DcN+cv5xW+Nav3cF7PvlYkx6vu3OAD376HJmaB65JCd7/hi187oF5js0VqudfPxDlpTd38YF/nWi6ZrQJNR7+zeXxPZGt+vvgmH8833Mozgc/P9vUztv2wz882pnej/72i/ib+081xdSLbujlf3zumaYc8puv2clXHp1v8tGPHBri/371VFPc3HemeeOUCp5474v5zJELTfn59u3dfPSrJ5vG3r0vGONPvnS6yR5vfdEIH/7sqab4mGyzwPbo79zNJ753tmlcvuzGQf7pgQtN+r3ljo189fHZpvNfvX+Ebzw116TD0ROn+OLZZrmv2wUfelPrnDqaDPHFo5NNbb3shiG+c3KhKQ/evWuQp2dyTXniW08f4W8fhs1JmT+8p3ddi++uTYxpnhgDPPa+F7et3r5cVCpqk2GZaECiYNhkShZ3bu3iXS/dfdH9yGvR6f7vr/jj72C36JMCnPrwK1v292J7ra8WL/vI1zixZBEUvF2KDNujlNrRLfPV33hpy+/4sQ5kChrnlkr0RNXqRHWxoLG5O0wyqjad/zOfeMy3X//w9gM8eDbV1NYNwwm2DSZWbdvKPvaN7fzF/Rd8ZX/z1+7sSL9sXuN8WqMvrhJXFXKayUJOY2d/hLH+WBMDxO/9+wlf2X56V6rNe2MrMlJ5jd6wjCOKTefPLRU5u1RuOv/xaf8s/qabBvjmqSW6IwrRoERBt1kqmgxHJXqTkWX9AuQ0g4WcxpMXFkmVIaR4O17pNpTNlYuzV5bhUVa1iu9afP7nbm3JloHtMF80mvTTNZOpnN7UJ9s0SZVtuiMqUVWioNksFdvr/Qev2cV9x+eb2kplikwX7CZ7FIsmBhAOeEs0NMulZKxQsVV0v5jOAD96fS/3n0mTCMlEgiJF3SFbtghg40pKk+xkwCWoBpv8qgoumit05G+/WBufy3F6UaMnFiSuyuQ0i8W8zoVUGX1Zt8okrKKjAASElTgwXI+62A93b43z0HiOuCoRlkVKlkNOsykuB08Ab0JiOlB5SR0U1sbmP3nzMPedXKQnEiCmSuQ1m8WiwVRGx8ZbelOJ27V+UVzROxqUCMsCJculoK/o3QohCaJBgZAsUrYcCrpLeTWKNuDnnzfGN46nSEYUYkGZvG6RKZosZssUrPa2vVy8+0Wb+frxebqjKzRwSwWdmcUiWQNCAQjJAmXLpWxASIR4LNjkI62sU3TwjZtWeP/Lr+Prz8w35eeFdJ4FzSUekokqIgXTIVe2MHQXp02sdRIfFb0bx6VjmmRNoUm/hOIiKkrT+XFFwBKkJh2+dz7rK9svp07M5zk+X2xqqysoYgliUx4ciQcJqorvNfRKTIyvrTH2wc///X3V6mNJFKq/58oW1mWuc6mtqB1IhomEgwwkwyTDMo9OZXng5GK1Kjkgi8TVALGQwkxGQ2sgt26slK70VREFpjNlZEmoO/75I+PVZC7X/IB34fnrb55q6q+fjE7t8Z/PzHB2eVLcFQsQCQfoigUICnBuyeI/n5lp+k5jlXbFHoIg8OhElriq0BdTUQMSfcuD+7HJLIIg1J3/rjaTYoC3/b8j1SrjSltd4SBPTuUwLGtVtjUsi8cns3SFg3Xt/Nt3/SfFAD/5p/c36SeyrF8oUNeWIogcnswSUxWGkmEiy5+JcIDDk1lcy6U7EiSoiHRHgm0nxX56ByWJw+NZYkGZgUSI0PJnVJV5ZCJLUBLrzhcdgUenck19ajdJAviXx+bojijeGAh5YyASFDiR0pBEgcFkmLAqM5gMo5k2M2XvZqo7phIJq3TH6mu4k2GZaFgmGb74sqPX/PlD1erqiq1UWeLweIZooD6mAoLA4YksMTVQ16eALPLEbIFIUGakO0wiHGSk++J6v+fzz1SryittiTicSGlEgmKdPSTBIu96Y7QnHiISVumJh6it70kERaJBkUTw4un8c8dSJMMyg11hYmGVwa4wQclZtq1TJzscEDi7ZCFL1Pk1pIg8s1BCVeSO/N0q1lwHjs7kiauVtgIMJcPYhjcpFpb9Gmnwqwt0L+eQ7lig7aQY4L4z3qS4Lx4mElbpi4cRa1JpMqoQCSskoyuFRa5bb3P7Em3+dw9P0xMJMNjl2XawK4xl6dU8nFAlIqpEonEf7zVARe+BRJhYJMRAIoxdM6kLySs/Fdg29C7bqTcepnZZsyqt/FwMH/vuBD2xIKPdURIRldHuKCI2i6b3pLbWtms5KQb4w/88R29MZaQrQjwcYKQrAo7FvO7lkN5l2b3xEIoEGRsk16jzkSJ45wcl6uKm3aQY4Pe+coq+uFo3NhAczme93ewGEl5bA4kwkutSBqSGWKutTewkPip618oOyILHXBIQ6vSLKN7xgCzWnR8JyhybK6A2HG83KQZ4e4uc6roij0xkSYYD9TJUiaMzedSgXJcH4yGFw5NZZKH+GvP5B9pfQ9ca1ybGPjg3v34MBu0qasuGw1y+uOpq3k6rty+k2l+8Ts41vyJaK0aHc3M5bLzEVIuA5N0Nn2sh269KG6Bs2kQCjZXgMqXGfWGBNm86q2isMo4EZIqmhWXXLxXxs61lC5St5j4tXOS+YUqnST8Xl7JpE26odHdw0SyHcLAhdoIyZdPmUp45NeodkARKlo2s1OsXkCXKpk2gaW25i247hIOdlyxEG/QIyRKWQ5MajuMdUNYwYzVWV0uCQNm0CTYIkWTRYwRQmuPfsB1CSucTmsaqckHw9A7J9bFWWaXUGP6XY4bGvFMZ13JD8UxQFrABRZAajkuYDgTkzpeUNcZaJXYiDRPMypBby6liuHEZmlj30YTG45dj81jDpEZcTplXosS7Se8OcTlXu7jawK6xrPGV2D8rrtbHmrtcXNeYQyo9FBviv/L3pZTSNMqWXBEbUBtkVIaQ3GCPy4mLRtkKIrbj5fVaBGXveGMhZFASvCUdHY7vDM051XUcNMsm1BAHIVlCtx3khoLHoOJdY+SGPi1cwhuLy8G1ibEPNvevH4NBu4raUEBkIBZZdTVvp9XbG3v9d1oD2D7QvHvOWjE6bB6II+Etn6iFYXvJaXML2X5V2gAhRaJoNFaCW4RbZN3VLPZorDIuGhYRRUaW6i8NfraVJZeQ3NynvouMspEgTfoJCIQUiVJDpbuIgCqLlPSG2NGt5Qla50O6UW/DdgnLEpZZr59heTteGU1vCASCkkhJ73yrzkKDHmXLRhZpUkMUvQPmGr5rbqyutl2XkCKhNwixLcdjBDCb4z8gics3JJ2hsarcdT29y1Z9rFXugxvD/3LM0Jh3KuPaaqBb0i0XCTBdu+G4jSKCYXU+ZWqMtUrsFPUGpoflptfyeliyGu+26j6a0Hj8cmye1+o1cZZT5pVYx9ikd4e4nKtdTmtg11jWuMWzizVHTquPNWH5AU5jDqn00GmI/8rfl/KCuFG2LThIgNYgozKErAZ7XE5cNMo2cZBEL6/XQre8440P23Tb9ZYVdTi+kzTnVEEUUWWJckMclC2boCRiNTxU05d3VbQa+tR3hTcivjYx9sHHfuJuNNNGM21sx63+Hg/Jl81OccNIN/tH42RKFnOZEsWSzlymRKZksX8kwR3be8iXTXKagWE55DSDfNlkKKk2sVNU9hBv7Gtl/3dref/3yvHXHNhQfQpj1fyAt46vFTuFn4xO7fGiXUNs6ZbRXUjnDYolg3TeQHdhc7fckp2isj97oz1c12X/WIKcZjKXLZMrL39qJjeNJnBdt+78j957U9u+/cPbD7BY0FjIa2iGzUJeI13SuWEkTkCWV2XbgCxz42iCdEmva+fHn9e6oLGCv/vFO5v0c1jWr2zUtWW6DgdHE+Q1k4mlAumCzsRSgWzJ4OBoAkEWWMhrFDSLhbzG+1+1o2O9ddvm4IYEed1iLlumvPxZ0CwOjSXQbYfpdImlos50uoQjuuwfiZPXTKbSRXIlg6l0kRuH29+EvemmAZaKpjcGyt4YKOouO3pVbMdlIlUkXdSYSBVRFYmhkHdBTeU08kWNVK6+ui1TsiiULDKli0/QP/9zt5Iu6SwVdXTTYamoo1k2BzckKRhmnT0M1+XgWIK8ZjCbKVHSLGYzJQzLYe9glKJuMZMpUdRMZjKli+r9B6/ZxUJOY2qpRK5kMrVUwkFkR69KUXeYSZfIl3Rm0iVsVyYmeGM0lStTLGqkcmVqGaGyuuOtFdYvfgX/0et7yZQsppcKZAplppcK6La4bFuRmUzRk50pUjJctnTLWDZ1fi2bDrv6wmhmZ3pXYm06UyJTNJjOlBBE2DcUI6dZTKQKpHMlJlIFpECIIN4EIV2yyJUs0jV+FYClvEGxaLCUNy46ebt7a5ycZjObLpApFJlNF3BqUmmmYFIsmWQKKxd2Qai3uXSJNv/Jm4e9NcWLRXJFjanFIrIcrObhjGZT1Gwy2trPFit6z6WLZIsl5tJFpJqHemVr5acCSYL5XIlCSWM+V6KWOlmzV34uhp9/3hiLeZ3JxSKZgsbkYhEHiR7F2yFvIVumUCyzkC0TXONH5+9+0WZSea0uPhFl+oNeDlnMlSmWNBZzZUzbKzy0hQBTSwWyBY2ppQKm652v2zCzlGcxV2RmKU+k8aVHA97/8utYyDXIdkU2JUTKpstMpkS+5P3fFgRCeG9IUjnPHqlcmdrnX2nNpqDZpFdh9FZ6G5bLdT0BiobLbNrLtbPpEkXTO25YTt35Rd3i+oEoWsPx2zYl2sr+fy1yqiA4HBpLkCkZ9TI0m31DMTTdYmKxSLpgMLFYJFc2OTiawHKduhz8mjvaX0PXGtcmxi3w7pclSYQC9MeC4ELJsMDlkvYW98N7XrmHO7d24Tguc0UDx3G5c2sX73nlHsa6PDodHMiUDXBg+0Csuqd6I/z6OtYVaXn8L39yV9OFRAD+8r/u9u3vWtnjo287yI5uGdeFouWt49uxzErhBz97vPz6EW4YTpAu6pyaz5Eu6twwnODl14+0PP8lw63bf9ko3Lypl9u39OI6LjO5Mq7jcvuWXu7aPtiRbe/aPtiynbf6zMt/8pC/fq+8YbRlW790zw5uGo2zmNd5ejbLYl7nptE473jhNrb1Rlks6Jycz7FY0NnW678LEvjr/dN3XscLtvfhOi5T2RKu4/KC7X28/Xlb2dwdZnypyNHxNONLRTZ3h/mVl+3kji3dFHWLs4sFirrFHVu62eUjd38AfuWlu7jrum5sx2Emr2M7Dndd182H37iPvcNx5vNljk1mmc+X2Tsc54/ediMbEyK6BUs66BZsTIh8+A0b6Q96Txd1vM/+i7wi2D2UZP9YF67rMl/Qlm+0unjtgY0t7fELd+/g7p39OI7LVKaE47jcvbOfD/zYjdyzsx/HcZaPO9yzs5/9PsPi9ji8/PoRbt3cTV4zOD2fI68Z3LrZ0/u2TQk002IyW0YzLW7blOAv37GXPQMqlg1Lmotlw54BlT9+y1aGQl67lXlN5W8//MrLdrK7P0S2bDKe1siWTXb3h/iTn1iWbdiebMPmtk0J/s+9+1v69X++YV9Lvdth/1i3FzuLRR4dX2J80YudX33FTvYMRlgoaDyzUGChoLFnMML/fut1JCXvSa2J95mU4IOvG2NrQsRxIW97T9W3Jtpfyn7r1XvYkgyQ11xmsg55zWVLMsCfvvU6tiVEXKDkeBPxbQmRj71t+5rZ/Gfuuo4DozGymsGZxSJZzeDAaIw/f/tOhkKeTAPvcygE77it9SzxhZfAUPU7P3I9O3pUCobDbM6mYDjs6FH58Bs2EmsQExPgI2/cxN7BMJblkirZWJbL3sEwH3njppbnt8Pbn7eVA2NxMmWdU6kCmbLOgbE4//cn97K9N4BuwWLJG8fbewP85qtaK/iGPZ3rfe9tW1rG51/99AH2DYdxXMjoLo4L+4bD/MVP38iB0Rj5ssnZpSL5ssmB0RgfuXcPQ2GRdBmm8w7pMgyF28faaw9s5J5dDbJ39fPRnzjIwdEYhmUzm9cxLJuDozH+/Kf2ePYwl+1hevb4wzdtruaxyu3axfKan95/dO8B7t7eg+06TOc0bNfh7u09/NG9B1qe/6HX7Wupwyt95qc/tt0/p/7C3TtatvWb/2UPe4fjzOXKHJtKM5fz8vzP3HVdyxz8k4c6i4HLwTVWClZYKT51Ms9H3vTCuv/9IPIYV1gevn18hpklnaHuIC/YOeTLerEaGZ1iLXiMK0wZpu1gWi6KLKBIYpUpo/H8it6/9InHMPAq0f/vMsdqRe/14jG+//gsD55N8W/3XyAF9AI/vsx7eefOwZb6+bVV0UMzLSzbW5+mKjIDsSChoIJj2ziIiDiIksRvffIRnprT6gpcgoJ3QfjUL93VVu9GHuOKbFEQcB0QRHBctyp7sVCu4w2tsHTUsnL8bIPejdy5FRmW7WLZjreNqSRgWTZzBZ3ZTImCZhNVJQaTYSKySNFyGF/Iky5adEVkNvTF+KMvnSDfIrv1B+HhD3jsK5fLY1xBIw/ol45O8O2TC3z60ZWC0gq/6+3X9TOf1ymWjSqtUSQUqDKdTC8WWdIMutUAwz2R6vGnpjKksiV6E2H2jCSrcf7FxyaqXLSvvmmMTb/5pWall1GpHM+WDEzDRglIJMIBtvVGGUiGmVwqkCmYJKMKo93RquxGv1ZkN+r9U3/9HR4az9UVKEUUuHVDnPe+Zp8XOy7enbjrLSmo+NW0HEzbRZEEFFmkVDIYz2hkCmU0y0WVBZLREAc2JNm3qZcj5xeZzpYZToQ4sKmHl/7xd3z1rjBiFDUT3XIIyiIRVanGYSOPcSW3rNbmB973pZY0eT0KfOZdHutMtmBSNgxCgQCJqFIdM989Oc9MtsxQIsTztvdT1k3m8jr/9OApJpaKjHVHeMvt13FqOsMT0zmOTSySK0I8AteP9fAfxxcv6u+lnEHJNAgrAbrjK/7+xhPTTGTLjCVCvHjvcNXfx6czpLJlehMhdg6v6P3P3ztX1fvNt21eld7Fsl2VHQlJVbaLiYUSS6US3eEwY33h6vH7nphhPF1gQ1eUu/cOMbVY4PhcgSfPz7BUhO4I3LBpqG5sNeL8MruSHz9vI2dwJeekSzqG7hAIinSFg1Wb267j3ZmJIAki4/NZZoo22ZqkmggK3LEpycd+8nagmbe3ElPnF4qk8iV6Y2E29UWq/r4wnyetWXSpMhv7Y1V7PDmRZqGg0xcNcsNYF7/8icdoxUQZE+DJ/9le78vlMf7UQ2e57/h8XcxVOJTfcOsWYPU8xhWbO7a3IVNAkhAlqtdjv+veP3/jSd71/Gt0beuOysR4+/btxGLtJ4bPdWiGxeHzS4iSUFcAlNO8J5X7N3atahJ+tWHZDpPpEoIgLO+f7kEzbXBhpCtUN1Gt1TuiSJw8eYLt23d4O4Sts96Zosa/fH8cWfYqbStYyGu4jsvrD43VTb7awc9/maLOXL7MDaNddU/xHzw5x3s/fwxluRK7gsVcGceFv/iJQ3Vk8pciO13Smc9q3DCSIFFD3beQLfHFo9N0x4IMJlZkt9PbT8Zcrsz3zyyyrT/CQGIlGc6kixydyLB3rIuRrpXjX3nyAp88PA9ANOCt0ZVlKCwv7f2j193Ajx7YsCq9O0UqX+Ivv3UWRRYZqNF7LlvGsh1efv0w3bFgXdwWNZOZrLeJRbSmQK2gm8ymNYaSKpGaohq/OP+1f/ke//b4km/fbhyRePsde+iu9VNeYzGvc+f2vjqmD78++ck+ciHFz/79I8iSQF98xRcLuRKW4/Azd17H7tFknV/ns2UeOpdiW3+MgZr4HE8V+frxGa7rj7GhZ+UGZCZTIlsyeMPNYwwlV47/t3/8Ng9N+C+h2RCBt75gx6rGX6e55TOPXODd/3YMoO41e+Xm4B23j3DXnpFVjZls2eCJ8QyDCZVkzfHzC3n+9fA4g/EQozV1Ip/4z1PMtVk5tKMbfvbFe9fF3xfV+5YN3LV3sE7vxbzGM7NZdg0l6Imq9cdnsuwcTtAbrd3EqMQXHptgrCvMSPeK3n/yxafxj3I4NAL/+kutqUcb4ZdzLszn+dSRcQYTIcZ6VmQ/Mb7It48v0R0VGe1ZmS/MZb03SX/21oN1G3eAf0z5+TtV0Dg+nWPXYIyemnHx2UfO87cPTQL1zCCVVRb/40d28+bbNq9K704xmy3wf752CkX2GIOqx5ffoP23e66re1jQDp3OQ2rPD2NhZ6av0bVdw9rBj+Vhrfawv1LolCnjaupdNl0022mqyI8FFYqmTdlcvWw/PWTZW7+GW/9+cz5Xbsl2oMoChuNNQC5XdkCSKFuOtyizBpYrULbtpkrldnr7yRAFgbJloTaUbyuyRKkFU8bSMh9549veylmnWjCgrBWKuotm203V4XFVIa/b5A1r1SwykiCiOw5CQ9GtX5w/NdNer9l0sz9CikzJsmmozfHtk5/sqaUyhuM0sSCEZRHdclkoGM3sMqLHLqNK9X4VBNAMu+lmNRqUKRo2hlVvj3Pz7deVzxebGTH84rDT3HKxWDq/oK16zOAKaLbTtJGT7Syz8DSwW2Quspx+eqmZKWCt/H0xvS9km9mVREmgbDrIjXEuCpQtB1msP+4s13GoDYw37SbFAGf8HyY3wS/nWK7HTR9tYFRwHNdjmGjwUVQR0WyHmXTzzj5+MeXnb1n0mHAa38rO5trzK61nXsuXXTTLbmLUiasBioZNvnz51zG/63E7dqr1wLWJ8Q8Z/Fge1moP+yuFTpkyrqbeIUVAlcSmivy8bhJRJELK6mX76WFZHkcmQn1C6Y+HWrIdaJZLQIQNF2EpWY1sw7YJyaK3YLwGsuASkqSmSuV2evvJcFyXkCyjNZRvm5ZNuAVTRnfSS6CNqbpy1nUtGFDWCpGggCpJTdXhOc0kFpSIBeRVs8jYrrdDltt4ofCJ8z1D7fUa7Gr2R9m0CMsSUuMczadPfrJHukMERLGJBaFkOQRlgb5ooJldxvHYZTS73q+uC2pAauJtL+gWkYBEQK63x+b+9hfM/kgzI4ZfHHaaWy4WS5v61FWPGQQXVRKxGmwoicssPA0FWMmLzBOGu5uZAtbK3xfTe2OimV3JsV1CiojVGOeOS0gWsZz64+IyV7zWwHhTv+iwGVtXtzoP8M85siCiKhKFBkYFURQ8hokGHxVMb5e2oa7mt39+MeXnb8vxmHAa9wgYjLdfZLyeeS0WElBlqYlRJ6cZRAISsdDlX8f8rsft2KnWA9cmxjUoGS0WS3UIy3bQLfuyNwFZ67YqqGV5yBR1Crr36cd6sV64XN06Zcqo1Xt6qcxMwWB6qbxuetfql4yo7BmOs1jQODOfZ3KxwJn5PIsFjZ2DsVUvo2jUo9Z/RcNia28U16XOHrtHu9jRq1I2YD5dJp0vM58uUzJgZ1941csoGmXXMmiUdIutfRFcQaiTrSgyN44lyJR0nhpP88T4Ik+Np5v01gyLvGaiGVadjHRJp6hbpEs6lu1w42icgmExly2RLerMZUuULJubxhJolseQMJ8vM5MpsX2wq1ocVDBAc1aWUfQHqS6jWI8x1hsLs28sQSqvMZ4qsLD8mcpr3DiaYFNfBM20KegmZcP79GM6sWyX4S4V03FZymss5DSW8lpdnBfKBosFnULZ4H+/6ba2ffvAqw+RLumk8hr55V2ucprBrqEYoiSSLujM5zTSBd23T35j7MDGXvYORclpNuMLeebSecYX8uQ0m72DMe7ePdgUt6bjcONIgoJu1lWzm67NzWNJ8mXTY+koG8ykSywVdPaNJggFAuRKBpmSQa5k8IEfb6/3B1/nMWLMZsvkyxaz2bLv+FtNbqmNm9ce2kjP8kPZornyA95a2zfftrUpnmvHTG0cuC5s649QNCwWc2WyRZ3FXBlFFjm0IUm6VGF58D5v3NZDO/zBG26tsuRUmGpW4+9a216y3s/b1KS3ZtnsHoyjmXZdDtEsm91DcXTT9nb3LBosFjQEXA5tSJIpGYynCszny4ynCrx4f/uZ78WWUdTq4ZfXZEXk5g1JMkWd6XSRfMlkOl1EQGBLt0zZdJlcyrOYLTK5lCdXtrhxKNa0jKJdTNX6u9ZOummzeyiGZjt14+WWbX1UFlY0MoTEBNZtGQXAYCLKgQ0JFnIaZ+ZzTC+VOTOfYyGnsW80seplFIBvnve7Htf5qGz4tLp2ePYvJr2C+N6ZNJsKLruHkgQ6ZCB3HJds2SBbNrEcF1kUSIQUEqEAYodPI9eyrVYYSYTJlgzOpArVxe1be6OMJNZnvU4t1lK3ylraXNmiZFgootiWKaMnFGBqscDh8QwzqTLH81Mc3JDktk3tLy6dwE+/3UNxHjo9z0Pn05Qtm5Ds7VV//XB7CpxW8PPfzoEEZdtussfvve4GfvH/PcJEHmzL2zRhLAa/++PXdyy7wowyk9HIlA1USWT7QIyRRJiiaTXJDslxPvvIeZ6YLWAs8/7uHYxy7y0bsCyHiXSRqXQZw3EJiAIjXSEGIirZmMGZhSJlyyEki2zti7CpO8IDZxZ4YipLyfSeFO8dSXBoUzffPD7HYxNpSoZNOCBx02iCX//xUd7/mckmHX7zx7es+xi7Y2sf5+bzPDKRoWzYhAISh8aS3LG1j2hQIVMymE5r6I73RHi4S/W1YVAQOTy+yFMzOTTLQZVF9gzFGYoGOXphkdPzhWoR37b+KD+yFb5wprlPP7Yddg4kyJVNnpnNUTIdworIrsE4e0eSPDGVaZJxcEMPuuuseoz90suu48m/OcK8TnVv5f6gd9wvbrf1xjidynM2VWS+oBFWRPaPdbGhK8zXn57hscks09ky4YDEgQ1J7tk9xHi6xOmFQrUob1tflFduhC+12CDr1Vvg4IYe8prJk1M5pjIlIorMDSMJDm5oPfb9ckssqJAu6k1x8zuv28Kv/vPZpnbe9/qtK3o3xPP2vjhzRa05Dnpj5MomT8/kqufvHorz2kMb4JHxppgqzS7y3UyzDi8erPH3TJ6SVSIsS+waivn6e99oF2cXC022DSXDLfX+rddu5t3/cq5J9u+8bstF9W7MIX3hII9OLPH0VI6ybROSJHYPx3jF3mFKLfS+OQwPl5r1vrPN42S/cV+59jX26dCGLsKPTfDoRIbZrEYkIHHr5m7eedcG3vupJzm7ZGHjcRRv6Zb55Zf702P6xVQoGQayTXaqjIvG8fK+12/gtz893tT+775x/SbFFdy1c4CjF5Y4PJmtxs3B0QR37RzouC2/+PCbhwzFQkwsFjk3W+L6dS4Fu1Z8x0rx3bSbZEHzKEb2bexsslSpOFUVyVuTZXvrkyrVvFerrXbtCwLemlTBxV2mG1uL9lcjey11Wy1TRoUpIK5KZJYWSHb3kdNsXrC9j1fuG7tUlergp9/R8ymOjGeIBSUCioxhWuT1S5N9Mf812qOid9mwquwdoYB8WXr7MWg0yv7AZx/jvpOLxIMSkaC3kUNOt7l7ew9ve/42Ts7liYUUb22ZZZEvm9VqfZFqITgOVKu3ZUlEFAQc12OtqLAahBSZgChiOA5l0+Kfv3OKC1mH2vdACnDDoMrH3/G8dR1jRy8s8uhEmoAg4QguoitguDb7x7rY2BtlPq+jiAKCKOA6Lqbj+vqv0lZcDRBSZMqm5b2+XGbj6AoHiQQUiob39KXdNuCPve/FzOd1HNfFtj3OWlEQmMuUOJ0qNMmo5MLVjrGKvxXBRhQFHMfFdCXu3t7Du166u23cNlazV+K8bJgYFgRkCAWUahyEgzIBScKwbUq6xc+02fa9ordpWliugCy4KIp8UX836u03vt//r0d4YraEbVcJN5AkODga5aNvu9XT23W9NcWuiysIVQaIxjio6BdUJGTRW16gm3aVpcCynarvZElcld6O7WC7IAkgSqKvvweiQWRZarJtZUw26l1hvHFcT2cBb7fGfcNh/uK/3s58Xm8axxdjDVJlCVESvPXFll0d34q48rDKdOy2cV5hpWjExa4/fsxHeU1HNyAYgJga5IETszx0bgkHB8cVEAUXEbGOnaHTmPKLj8bxUok1y16xqbwca//4cy9oK/tyUbmWqIpIQJEwTBvNdC7rOuYXH42oxEfQNYkZqXUtvrv2xLgGsZCCJQqcTRXZ1h+roxppB8t2yJZNVEWqVpxKy4M4V7aIqcqqqc3Wsq3Vtg/e6/e1aL9T2WuhmyyJFw3kVL7E0YksvTGV3lgQt5RhuDtKIK9zbCrHLVs9+pzLgZ9+mZLBkfEs3ZEgQzXMCXPZcseyV+u/ij1q9W5kSLgcvdWATKsFILWyT8xleGQ8S08kwOCy3t3AbLrEIxNZtp9eZGN/pFrMEZADOLbLmVSBG0a7iNeMv1zJ4PRCgYFEiK7wStKcz5Q4Mpll60CMwZrq7YdOzHE+66DKMBwLoms6QTVIuqBzIqVx37E5Dm3rWZcxVigbnJ4v0BUO1rEBLBV1Ts8XCMgSEVVZlf9atRVVZUzH4bELaW4c66oeDypB/u3h02379j++cJRffOn1dbKXijpPTefpjwfpXWYpiKoyUkMuvNgYa+Vv8Px9eDLLE+MZNvVHffWOhgJUXsbWxnky3D4OIsh84fD5tn376NeO8/YXbKtrazU5r9YXfuP7sfFFnlkoEQ4K9NewcaRyJY7NFfjW8Tn2b+qp07ugm0y3YBvJFvWWcZ4qaDxdYSmo4bP/X195vK3ev/PZ7/OeVxxArdHbz9+O4/LEVJabt/TW2RbgzEKRvaPJOr0Pn09xIqURCjQz3jyzUOKbT89yYEuvr79rc4hmWEyly8RCShMbzZHz6SY2mr/69vG2en/o80f5ndfsqzu2mutPbZ/qY3DlEeXEUp5HxjP0xIIM1/hiNlPi6GSWF2QLbZcVXCymwGMImc40M4R8//S8b6w9NVfg4XML3Ly5r61tLhVreS3pdB5SGx9hBOx1Xk1xbY1xAyIBhZLpoDeW7LZBp1XMV6qtq9H+s1V2O6aAgmlR1C9ftp9+uuntFx9uqG6+FNmd2vBK6O2HxZyJZtvEGirpY6pEWbdZLJZXza7hsFww2MBeIIoCZauZ1SBr2NhAsGFFVFAC04bxTH7d4lC3XTTbbWIDiAQUiqZDybRXLduvLUUUl9k46u00k29ftX5ioVlv2wbNtgkq9cbqNBe283dJt5krlFatt1+c+8XB5GIzE0AtTqcKl+1vvz4tZjRMhyY2jpAsYjjejUHjd/zYRhCElvr5sRRMptuzypyaa5bt5++g7MWU2FBkV2HQaFyFn8q0Ybyxvb5dLmuQHxvNXLYVk+8KWrGzdJo7ffO54U3gYsHLZ2fwk+HHELKQK7eNtYnFFmtL1ghreS1ZK1ap9cK1iXEDioZJWBEJNpbstkGnVcxXqq2r0f6zVXY7poCoIhNZgz1J/fQLKt5+8aWG6uZLkd2pDa+E3n7oiSuokkS+oZI+r9mEghI9kdCq2TVEKhfc+rYcxyUkN7MaJAISEt52rrXQbVAk2JCMrVscBiUBVRKa2ACKhklEEQkr0qpl+7VlOs4yG0e9nYZi7ZeB7Ohr1luSQJUkdLPeWJ3mwnb+DgclBqLhVevtF+d+cTDa076AdVtv9LL97dennqSKItLExlG2HAIiDHY16+3HNoLrttTPj6Vg1Gc31AquG2iW7edv3fJiymmYlFQYNBonC73JNow3kte3y2UN8mOjqX1i2Qqt2Fk6zZ2++TwAqiKR1y+fncFPhh9DSF881DbWxnrWr05oLa8la8UqtV64NjGuQaVCcktvZNXLKKBzhoQr1dbVaP/ZKruWKWA+W0azTOazZVJ5jetH4pe9jAL89VMViQMbEiwVdeayZcq6xdwlyu7UhrV6X67sTrFjIMmhDQkWiwaz6RLFss5susRi0eDQWIJbt/U0VYL7sWs4wLa+KCXdqjvfdF1uHE1QbGA16OsOsamyfXRBp6R5n2UTdvSq3H39wLrFYTQUYFt/lHRJr+tTuqSzrT/KUDK0atl+bZUNi5vGEpQtq+74i65vv87vt39kX5NsURDYM+xtWdzY305yYTt/HxxNsHdDctV6+8W5Xxzcc8No276966U7L9vffn3aNZRkV1+Yku6SypUoljRSuRIF3eX6gSgv3Nkca7VsI3UsBYLQUr9aloLa42++dVvbPn/ox25Ztb8LhsnekQRWg4wKg4ZD/Zi8fmSF8WYxV6ZY0ljMeYw3u/rC3LV78JJYg2pl17LR1Pb17t3DbfVuXEbRzn+dxmBMDXJoQ5LFvLcLZ0mzmM2ULomdwU+GHyPMjmH/WNszEF23ZRSwtteSy2GVKhrrPzm+VnzHSvHdhJVg02DXZbFS5MoWpuPd9cRD8mWxUqxFW1ej/dXIXioaVZaC7kjgisgulUy+eXKWR86luTA1y8aRQQ5t7uKu7YOEw8rFG1gF/GyrIPDtU3Mcm8pRMC2iisz1I/FLkt2p/yp6f+/sEvmySSykcNuW7jXV2w+pbIk/u+8Ej0xkKevek+JDYwl+4e4dJCMqE+kiMxkNzV7mAE3WszPU6hdRZKaypabzByIqJxdynJovUDRsIgGJ6/qjqAGX3/7U4zy9vB12UIDdAyp/+Ob9bOpNtrVhp1ufN55vGDZPz2Q4mypW2R+29EbYPZRElsWO/OfXVi2bQ+3xpxenW1at/+GbNvNjN+xqKTskSRyfy7bsbyAgrdoe7fzdHQuRLRss5PUqu0DfMsNFK7394rwSBxNLpWpl/Fh3mGcWZvn5fzjZ1M5fvX0nL96+ZU387denbKnM+z7zOI9PF9AtCMpw43CUD71uH6Pd8ZbfiQUV8rrZlAsr+h2fyZMt6STCQXYOxRiKhZjJlzk5mydvWMQCMtsHY3zz3Gl+77PTTX39vdeOcu/+vW39fXq+QNF0iCgi2/qjVcaIRttWxuR8zqNcU2WJ/niQdLHEb33qKMdmS5jLb2OuHwzzB2+8iQ09iY5sXmGpOTNfIK9bxIIyW/uj1fF9fLZAQbeIBmV2Dkbbxvlrb9zd1n+rvf74nS+58JVjU3zvzCI5zSauSty2tYdX3jBKNLL6h2rtYqoSH36xdmyuUC1KvX7Ai7UNvZ2zHLVDo48q15KrcR2rxMfZ6SWS1rXiuyuGzb1hNvRGm3ahWQ1EUaArEiSmKh1dUNe7ravR/mrg4nHbX8m7MlkWGekKM5stU87IjHWrjHSFL8nffmhn21fuG+OWrSWKukskKFzy09pL9V+l6lm4gvfCyYjK256/jR1nl0jly/TGQtyypZtkREWWRTb3xRhKhJoYLgIBqaV+rc53HJeNvVGU5bWQIVlkuCuEbdncuq2PnDZFKuvQmxC5dVsfSVX1taHjuC1pqS524Wx1/r6NPWzrj9UxLVTQif8CAcm3rX2R5uPdEZnnb1ziOxcK1TaevzHKzSMjbWOnlYxO7VHx93WnF1gqGnRHAty2rY9kRMVxXI+mLlvGsBwCsogiCcSCSsu22vkoGQ5g2O7y7nhegd71/f3cvXWW/zyTqzIkvGhrnN19fWvmb792BMfllq19LBk2pbJDOCRyy9Y+4sFgW9nQnAsN0+bpqQyHx1foyRzHJrFFZmKxyMnZXHUyG1JEdvf10R+c9ijyltEfhL0DA21lb+yNEljeOTKsSAwlQwQCEkm32bYAmZLBbLZcvSkNyAJJVeWle0ewxRmyJZtEWOKl1w/RHQp1bPOQJJEtGSwUdEq69/SwNxpgIKKSCAfoiQVQFZGIKpMIB7hZHeGWkRTfn1pZV3vLSJiDw+2fJreyeafni4JAVFWIBBUsFyJBhaiqNK3PXg3ajckuufl4SJJ4x107+PLT0ywsGvT1BHjF7mH6o6vfrOliaJfXrtZ1zHFcsiWDdNkgub7Pc649MYaVJ8bxgTEcWb0itGU/rFhvKrp2qNBeJVSF+ekL9A9vJKuZl0TP91xChWKnN6YSVxVyyxs7rCVNnR8qFDuNlGzbB2Js7lsbMkq/mPrkg2d44GyarkgAR88iBhOkiwYv3tnLb7xqb0dt+cXn1YxnP7z1z7/N4ckC0aBQpfgq6O4l0Tl1ql87fwNrEgt+ffrg5x7j0ekC0aCEaNo4ikRBt7l1Q5yPv+P5a6KfH/70P57m68fnm8bYPTv7+cWXtH56eTFqx8a2esMyjijSE1WrTxMXCxr/eP8FKiV4Fcor8CbHD3+gM9qyCkVY4/EKhVyj7x4/n+KxyRx98ZW+LuQ07tnVz399wfaOZFco5BrpBysUco2y//RrT3EyZRAKgGCBK0PZ8KjiPvVLd62Jvzv10ZXIqfcfn+XBs6mmOLh9Sy937hxcExnPxrxWuX73qQLDQmZdnxhfW2Ncg4DsUYfkytaa7oR1DR4aKVokUaj+vt42r6W96ooEUSSJrkiQrnCQs6kihSuwm87VQCPFTigoM5AI0RtTOTaVI5VfvyrmRgqmgCwSVwPEQoq3HGIN1or5xdRcvsQj41m6owGGukKEAt5nTyzIkYkspxeyq27LLz6vZjz74eFzCxyb8ybFvfEwkbBKbzxMNChU6ZxWi071a+fvc6kS5xaKlx0Lfn06O1tY1luiLx4irAr0xUPEVYknZwscuZC6bP38cH4xx/cveJOkoWSYiKowlAzTG1M5MpHh/GIzQ4KfbE03eXQ8S1ckWDdeI0GZwxNZQrJEX0xFDXifk4uF6qQ4FhCIBARiAe+p5bwOnzvSvNTAT7YiCkxnysjL2zBXjgsCnJ4vEgnIdb7TdZtHxrN0hYN1evfFVR6dyDCZzq9atuu4PDWTI64G6I4ECSoi3ZEgkaDCE1M5FEGok30hVeDUojcp7omphNXlzwAcXyjxvTPzl+1vv/PLhsGj41m6o8ErnlMzRY2npnP0RNW6OOiJqhyfzZMptmdnWQ2ejXmt9vodC63z42KuTYybcCWow35YcTXp2tpRaHVKz/dcwtWka/Oj2FFlGc12MBsr8i8BfjFVLDkedViwkSJPpmjapPPNE7G1onO6mjlkYrGE6Xj0TbW4FDqntaJUUmWZsmFRsuzLjgW/Ps3lC5gORJT642FZRLcdppaa6b3Wyn+Zgo1utR5jRcMmU7CbvuMn23CgbNlEG9pSl6nUpAaGkFzJi+PGF/iVVk/NrZ62zI8iDFdAs52mJWem43h9DdXX41T0zhabJ1B+sk3HRbMcQkpDfEgSJdNqWtaSLpnYDk2MKaosYDgwnmqmsVur8W1YHkVktCm3rH9OLZsumu0QC9bHRyyoUDRtj+ryMvFszGt+1+/1wrWJcQOuBHXYDyuuJl1bOwqtTun5nku4mnRtfhQ7mmWhSiLKGhRb+sVUJCx61GF6I0WeRUSR6Io1l1esFZ3T1cwhYz1hFNGjb6rFpdA5rRWlkmZZhAIyYVm67Fjw69NALIoiQtGsP16yHIKSyEh3M73XWvkvGZUIyq3HWCQgkYw2F3L7yQ6IEJIlCg1tactUanbDDXw87MVx41Sl0up1A6unLfOjCENwUZeL42qhiKLX13L9xL+idyLSPL3wk62IAqosUjYb4sO2CStydT12BV1hBUmk6YGGZrkERNjQ27zedq3Gd0D2KCILTbll/XNqSBFQJZG8Xh8fed0kokge1eVl4tmY1/yu3+uFa8V3NTAsGwdvHc2VLkZ7rqDTav1aVCha5vM6luMgCSK262DZ7rrbvEJ79ehEmpJmki6ZzKRL6I7D/rGujuj5LgeXY79LQYVi59snF9ANCyUgYRo2ed3iBdv71pWurUKxc3Iuj2bYiAg4uJi2w/aBWN1W0peK2pgCquvhBmJhDm1I8MDZNLppY5YN8naBom7z4p29bOtrrt6ubcu2nabtmv3onBplV9biyZK4pv7224a7Fjdv7uP6gSiHJwuU5osEJDBscARvy9hO6JxWo18tav1dNuy6bbtr1xgDTWuM28VCow1b9WnLYJTrB6I8Ol3AWCyAAWgFDAdu3RDnwMbetm3phlW3He/F8lGtLzb1xLllY5KvH5/HtlyiIW+ymC7r3LOzn009zZNTv1hTgwr7NyQ4Mp7Bsh1CAe9pe1G3ODiWoGzZzGXL1eOjPVEiLFIE8kb9JLE/CD96YMOqZVcowgzbK7yq3YZ4W3+EubyOKAlV3wWDEoc2JHhsMoeT8iaLZcsmWzK4Z1c/o13N68b9/CeIAnuG4pxOFZBEobrGuKib7B2JY7ou6ZJe3aZ6Y2+U63oCnEwZzC5pOA6IZQ3L9dYY37a1v61sv+vPamItFAhUfWTaDmpARjO8WF7vnJqMqOwZjvPg2RTm8hP2ynbet2/pJRlpz+e9GnSaB68Eaq/fsiMQW+e5+bWJcQ1cvH26E1dokvRcQrsq1U5o1mJBxatMT2vojkdyP9ylNr0aWg9s641xYibLoxNZJucMUmTZP5ZgW+/aFIG1w1rZ71Jw25ZenppMexRapk1I8Si0btvSu65yAQYiKk9bGY6cS1O2LUKSzI1jcQbWIIFXUBmvubJFybBQRJH+WJCfvHML44tP8MRsgVIZwnqJvYNR3nzbJt+2qvGZKVfZGYaTId/49JMdCyodsR20Q4WmaCpdxnBcAqLASFeIsa5IS0aVX3jZVn7qLx+nCFTeZkeWj3cKP/38cmTV3+fTlC2LkCxz46jn7woF5kxGI1M2UCWR7QMxxnw2qfAbMxVfNPbp1//LTn7+7w4zWXmLrsNoBH7lFTt82wpJEmXd5PRCAc3yNtfY1hcl0t26T36+eO2hDSwWdA5PZpnMeWPs4GiCN96yyde2frF2965BFnIahycylAybcEDi4FiSHzs4xmPjGZ6cyjKZKRFRJG4YSfC7b9zEb3zyfFP773/9dR3LrlCjnZ4vVtkntvVH2N4XJxHWmnx3w3CM9NdPcGQyW2WEOTCa4FU3jvjK9oupjckw8ZDC2VSR+YJGWBHZP9ZVpSU8s1CsytjaF+E9r9nBz/3Nk+QrDzYdiAnwa/+lddFfnd4N15+IIrccr36xds/uIRZyGo9MrDCHHBpLcsfW9eMRrmD/WDczmRKPTWar8XHTaIL9Y91rJqPTPHglsHsoCcD4fKZ57dAa49rEuAb9sSDJa2wULZEtG9Uq1XDA27mrcifdSZVqXjcxbJehpFq9EzVsl7xu0iWvr+1Pp/IULYd9o0l6yTI6mqRo2ZxO5dkXWV9WirWy36Xg2HQWRxS5Y0sviiRg2t6642PTWe6Mt99B6nJxciHHXEFn20CUgCxiWA5zBZ2TC7k1YwLxo/158NQ8PYkIdycjpBZm6e0bwHHh8Yksoy2e4sFKfA4mQ9UnSu3i0092bVX35fp7Il2ssjkkgwqaZVWfvLZic/iHb40jBwV6cZEEsF0wEfiHb41z6+b2G2GsVj8/VP3dHyUoi+gN/vaj52uFi42Zxj790wOL9CQi9CahmC8SiUVwXfjWUylGkomWbVXYFgYSoerTyLm8TiJcamlbP1+EZJFbtw9ww4YeTMtBkT1asbxh43f76RdrT8/kCIcDvGjHAKIAjgsWLqfni2wbTLCxL4ZpuSiygCKJfPBzZ4ko4JgrrBSiAp97cI5X7Wk9SfSTfXIhx1xeZzDh0SlalrNsD62l7+4/PksipvKSXSFEScSxHSxcjs/m6U+2vrnolDIwXdQJBRX2jiY93ZZ1/PvvzBCJKMQcE1MHJQiOqPC5789w86bWE3O/689UtlRl41hNrB29sEg4HOCeXUNVakDDsTm/VGRfbO1u+lthoaSTjKrcvVNFEERc19uue6Gkd8yh7IdO8+CVQIW2cigqMHk+s66yrk2MayCJ15ZPtEJjlSqAJHqfubJFTFVWvQlCYzvg7abUSTuXgtqq1kRIobikMJAMkS2bnE0V2dYfW7flFGtlv0tBYxVzBQt5jeOzefaOJdfk9Vsr1Nq8u2YyqBTFdbG5LInVhFbLxtEbCxKx84wMJUjldY5N5bhla6nplWfr+JRWFZ+1stfS341MDwAB2fucyWgMJUJ1k8ujE4scnc4TD8kMJFb0m8uWeHwmz9GJRfaNdX5DUqufH1brbzUgc7GIW60NK32qZYYYSKikUha9vUnmst6T1wPTOYa7w3Vt6ZbN6fkigwmVZNjrbwQZSRRa2tbPF6bp8PRMlp3DCUZqnn7nNKNlO376gUS6oPPUTI6+WP14TeU1npnN0ZcI1R0/cm6pysYx0L/i74VcqcrGUVlGcjHZ2bKxYo+K/4IgSvX2qEi/3NziF1PRUIDK3nF+14wnp5Z4dDJHV0SmPxEjl80RT8RZyGo8Opnl6Zk0u4e6VqE3FDWT6UyZwWRoVbHmF+dLRX3dryWtYhDax1qnuJw8eCUQvgIFeNdmgtdwUaxVleoPKyvF1dT7SlQx++Fq2vxS2DiejXHeKbPHzPIr4mgDO0NUEdFsh5n05dM5+WEt/d2pDdsxQxQ0m1RJa2rLj23Bz7Z+vpBEgbLlIIura6edfn7sDEFFomQ62A0EFwu54pqxcXRqj6vJkJAtWOiOQ6RhEhgNSpQsh4VsM/1mp2wcfrF2NfPa1WT6+WFi7Lo2Mb6Gi2KtqlR/WFkprqbeV6KK2Q9X0+aXwsbxbIzzTpk9hrpUgqJIoYGdoWAub6HdtX6vedfS353asB0zRFSV6A2rTW35sS342dbPF7bjEpJFLGd17bTTz4+dQTdtwoqI1EBw0RePrBkbR6f2uJoMCYmoTFAUKTbwXxd0m7As0pdofmLbKRuHX6xdzbx2NZl+fpgYu64tpajBZKbIrlj9urJOq8o7PX81learRaeyn5xaYi6jM5AMcsOI/8L92ipVw7Tr1nldrErVr8r37GyOJc2gWw0w3BNZl2rXWtm1Va3//sQ5zo/n2bRwkju2DdexUvjZ8HLjoKL3N56cZiZbZigR4nk7B1alt5/s1cRObRXzH3/5BEW8Qqy33rnxsqqYM0WNsukSUoS6Ngplo259YMXm//Cdp5nJWgwlZP7Lga11NvfTw0/vRhkVzGYL5MsusZDAYCJaZeP42LfPeSccPc7r9w/VVY43yq6yFFh2XVV+xU9+evv5+6/ve4bTqQLbeqO89c7tdf5ejd61TA+pjIZh2wQkiWBQqrI5nJjLsJgz6Ykr7BvrYd9wjAfOZ5jIrGxikggK3LEpWV1G4WfDTuO/0R4Vf//Pzz3BvAn9CvzsS3fU+dvPhpPpPNmiQyIiMtoVq9owlS+hmaAqEFWDVRum8ivb0tYyQ9x3YtFr8OwUNw5HuGdnPzuH48zndfIlo2pzRZGqbAtlw8YRXETXY06p2LaxrxVfZPIGhuMQEEUkWWD3UJyyafOq//vdqj5/ee9NdYwbjTav6LeYK6PZLqokEAkFquwM5+ay5A2HWECkJxFi12AcURA4PZcnWzZIhAJsHVhh45ibWtlQI6LUs3H4xfnTU2mWcjrd8SBb+uNVe2TLOpYtIEsusrQSa0cnFplJawx1qewb66nmlg99+URV9s825JbGcVnB+cUcmYJNMirVMXecXsiSzlt0xWS29SXq+5rX6Y55fd0/Gud757M8M7fkfXFuicGowl3XdVeXUfiO7wYWkgobh2baLdlX/PLax77+BDNpGOqCN91RH+d+Y8zvuF8+8IvBX//bh5myYUSC97+pPtZW21YrrIa943NHxjk1l+O6gXhL5pNW8OtTp/Z4ajrDei+muLYlNCtbQv/tE2U29sX56TuvIxlVO2IR6JR1oNNK83boVPZsusAffOkpHp3MVStz94/Gec8r9zDYFW0hAQzD5umZTFP19u6hZLXafDV9KusG/+vLT3cku1P4yX7g/AV+8R9PNZ3/sbdt52U7t/lWwOd187Lj4MJSmp/72yPM1LzZHArBX/7UQfaODnSkR0SRmcqWVh07v/mJL/HJY83tv/F6+PC9rbeM9YOmWTx8PsVT07lq1fqe4Tj7Rrs4u1jg9HyhepHf1h/lgfMn+F9fSTf36VW9/PSth1qOgZFEmKJptWQQOD6XbZKxoSvM15+e4ch4Fs2yUWWJAxsS/M3nn6HZ27AH+MIHX95Stl9V/paeKEcn0016H9zQg+46TX395pmz/Oo/n22S/cdv2cqrdm3vSG/bsvnb75zm4YksmmmjKhI3jyV4zcER/uXBcR4Zz6LZNqrk0Wf93cPTvv47+Xsv88Zxgw13DiQo2/aq4z8oiBweX2yyx5cPH2kZa/fug/e+5qUtY2fnYIx/f3yKRy5kqv47tDHJS64f5D+OzXL4QoayZROSJQ5uTPKKvcMcuZDm6MSK3vvGEvzCJ5/w1fvp97+Eb56c5ejkSozsG01waFM33zw+x6OT2Sq7wP7RBHfvGuTpmVxTX7cPRPnMI+M8PJ5Z8cWGJF/8z3OcbCF3rwif+e+tbT6aDPHFo5NN+u3dFOW9n3qSs0sWNiABW7pl3v/a3Xz9aKojf5/2iXMBm9///JM8MVOoTvD3DkX5lVfs4FtPpZr0e9H1ffzp105xdDpfzdv7hmN8+UTz2AaIAd973z186cnJpnF5x3W9fP7IJN+/kEG3bIKyxC0bk7xi3xCfeXiChy+s6HfzxgQv2zfAn3zlJE/MFjBsh4Aksncwyn1nmjcvuZje1fHdcB2rxH+ubGE63pPReEj2zTlfO3aEP/9OM6fuL75Q5V0vemFLf1fYNRqPb++LM1fUmvraFw7y6MRSUwx+6ZEjfOqpZp3fciN84HWt9fZr6+ZNvahq80OVyvykkb1jsZTn5//2Meb1lXP7g/BXP32AfWOtt6P2m+us5NrV2cN1LX73354gp2m8/87edd0S+trEmJWJ8d8f0zi+oPPinb28464da7Knut/55xby1ermRk7PVtXQ7dCp7F/5xPe5/0yaZFgmGpAoGDaZksWdW7v4o3tvaSmj0t9wUK5Wb5d0y7e/fn36i288w/fOZ0mGFaJBiYJukymZ3HVdN//zDQc70rtTe7z0j7/j+53H3vfilt8JSEK1Wvly4uDtf/kdZspe9bICmHj0gKMR+O77Wk9O/dqqVNKvNnY2/eaXfPU+/+HOJsb3H5/lwbMpeqJqddK0WNDojwRAEukKB6scpOmSzu/9+wnftr75a3e2HAMDsSChoNKk91ymxOlUoUlGKlPidKpEX1wlrgbIaQYLOY3/OL7YseyQLFK2vLWLlar8omGRKWicWyo16X3DcIJtg4mOYq1Tvb/+xCQPnF2iKxxc4cgt6eiaTkpz6YkEiKkSec1msWgwntF9ZX/+527l0Yl0kw239UYZSIZXHf+nZ7M8OZ1tssdf3H/BV/Y/vP1Ay9jJ5jXOp7Vl/ynkNJOFnEZXUMQSRHpiKrGgTF63WMxr9IRkbEGkN7Zyfiqv8elHZy4qO1mjd6akE5VFCpZDPBQgosgUTYtc2ageb+yrrplM5XSS4SBRVaagWWRKettY87N5JW67oyoxVSGvmSwVNJ6eWGShBCEZAgoYJpQtiMrQkwgtsySI5DWHdFHnZKp5HfHFYu0T3znFE7NFoiGZqOItvSmULYYiEhv6E3RFg0SCMkXdIl3QGZ/PMlO0idecnytbZNvs8vYHr9nFfcfnm8albZqkynaT/yTHpmAJ9MSCxFWZnGaxmNcplcvkLaGpr+1kX2x8+13HGt+MHL2w2NJ37fKan78jskjRcpqOD0SDyLLU1Fe/nNNujPnp7dfW7Vt6uXNn84S2cu1RRKGOx/jNf/YdFpfvBypvjcGbHD/8gdbXEr+5jmXZzBX0VdvjL7/xDM8saOzsVXjvnT3rOjG+tsa4Br1xlZ5YkMMTWY5Npi97T3W/8xsrSyv7v8dCCjMZDc1o3q7WD53KrlTzJsMyA8kwkXCQgWSYZFjm6FSOJ6eWmmTU9rcr7CXMyp7lrfrr16fpxeKybIWhrjCxcJChrjDdEaVaSXy58JP92e83P72rxZ99/emm78iSwHRa817lXEYcPHAiVZ0UJ1WJiCqRVCVEYKoIX3p8ctV6CAKcni8SCcirip2X/0//SfFq/l+Lxip0NSDRF/Mu6kfGMwREie5IkKAi0h0J8qUj/hcPgN/5zPebxkAkIHMmVUAQqNPbcV2ems6TUAN1MkTgkQtZEpEAg8kwYVVmMBluO1EBuOt/398kW5Ulnp7NoSoSyUiQaFAhGQkiCgKPTmSJq4E6vbvCQZ6cynok/zV9/cf72+v9ka883iQ7HJQ5s1BEcN26trIljYcvZOiJqYz1RuiKeJ8BSeRESiOiwGBXmEgoyGBXuO2kGOA1f/5QtZq+YsO4GuCZmTxOgx6KKDCdKSNL9fFv2g5HJ3J0hYN19viXNhdsgJ//f0eaYkcSBA5PZEmEFYaSYSKq9xkNyhydyaMGJIa7wsTCAYa7PP8enswSlCUGEiFCQZmBRKjtpBjgbcuyBxMhYiGZwUSIsCzzyESGiCIznAyTiAQYToaJyBKPTGRQFbmur7Ig8shElnhI8XwRDTLWG7lorLWyOY7AIxNZkuEAI91h4mGFke4wtmkzWfR2wOtNhohHQvQmQygCLJogYC+frzLSHW47KYbWcT6dLnlMFqrEUCJMLKwylAgTCgicXbJwcBjtjtAVCTLaHcERXM4uWYQUGEiEiYRVBhLhthNTgPd8/hn64mrduAxKEk/MFggH5Dp/BxSBEymNUFBaPh5gKBlGER0mixCU3Lq+Xky27/ieyRFUJN/rmCyJBGWpunyiln2i4rtPfav9+H73PzX7OxyQeWw8S0iW645HAgpPTOYIiGJdX0VB4NHx5pxzsTH2lj9p1tsvf/VEVY7P5skU6wtya689EVUhHJCJqArfemq2OimOBQQiAYFYwHtrOq/D546MN/XHb66jiCKPT2aJBJU6e0QDCk9MZZGlenuMzxU5kdIIKwLJ6PrTxV2bGDcgrsoUdZuFgnHZe6r7nb+WlaWdyp7L6F7VesPyh2hAomw7zLW4sHbaX78+LWmGJzvYILtNJXGn8JN9aq7o843l/8+Xm74jCSK64+38U4tO42A6W/C+1yBTxntqfHqu+ZXgWlWOn8nSFhf7fy38qtCDskTJtGlcxTGTad/ehZTeFFOyLHrV7G69zW3b2x42qNTHjoBI2baJKJ2vz2+ULUoCZdNBbqhOdx0omzZqw5gJBWSKpo1p1dv8dKrUVu6FVKlJdkCSvG2cGwpbSoaLZtnEGsaMLLhYDpdUl9BYTR9SZEqWTWMxvV+1vmm5aLa3zXMtLhZKBWiKHVHw4lmV6/ULLLMqNBd8yWiWg3wJhV2NshVZpGzYBBpiSl4+rjaMPVkU0KzmGFwNGm0u4nib7QTrbWgs55RGt1bC5VJqqxpjraSbHpNFg82DkogN0MB8IboCNt447xS1lGIAsgiG7RBqsGFQlLAcmgrXpGWFg5dApXq57CF+7BMzhfZyp3LN/lZEkZJlozToF5QlSpbVtKU3Lt7ymoacc7ExNm016+2Xv/zYQ/yuPVNZL681eqLy96kW1zG/uYMgePqpDdWkQVmkZHq7ZtYiZxhYDutaLF6LaxPjBuQ0i0hQoi8auOw91f3OX8vK0k5lDySDXtW6UZ/9CoZNSBIZSDbfjXXaX78+dasBT7beILtNJXGn8JN93UBrsvnq//tDTd+xXW9tldtI29RhHAwvF5w0rkiz8J4ibxto3mxirSrHtzbvfNzR/2vhV4WuWzZhxbuw1WJ5oyJfbOwNNsWUZTle8hPqbS5JoEoSulkfOy4OIUmiaK7+LUsFjbId2yWkiFgN1emCCCFFQmsYM2XDIqJIKHK9zbf1tn+9t7E33CTbsG1CsggNN1vhgIAqS+QbxozlCsgiHb1dqqCxmr5sWoRlicZier9qfUUWUCVvS+JaXCyUotAUO47rxbNm1etnLLMqNFOEWaiyiHUJVGCNsk3LIRSQMBpiylo+rjW+EXJcVLk5BleDRps7iIQUibJeb8PAck5pdGslXC6Fjasx1sJBxWOyaLC5bjtI4C1qru2r4CLhjfNOkdPqH3ZYDgQkkXKDDXXHu7FupDqzlxXWGxg/VoPLZQ/xY58YukgpzEi82d+m4xCWJcwG/XTLJizLzRu5CXhbbDfknIuNsWG5WW+//OXHHuJ37RlZ5kVv9ETl7+taXMf85g6u6+mnNfAP6pZDePkNYS3igQCyyLrSi9bi2sS4BvOZMot5nYNjCa4f7UIzbTTTxnbc6u/xkNzEIlCp4lzt+ZVK83zZJKcZGJZDTjPIl02GkmpHT4E6lX3DSDf7R+NkShYzmRL5ks5MpkSmZLFvJN6SnaLT/vr1abgnsizbZC5ToljWmcuUWCqa7B9NNBGyXwr8ZP/YLVvafu8X7tnd9B3LdhnuUjEdl1zJIFMyyJWMVcVBUTMpGRZFzeSOHb0MhbynwxnNpqDZZDSP3WMkAq+8sXk3Mj89XBe29UcoGhbpku6tASzpvr74ym+1X0N8sf/XosJwsVjQWMhraIbNQl4jr5kc2JDEcGyWijq66bBU1HnlgR1t2/vQa29piqmiYbG1N4rrUqe3KAjsGY6R1Yw6GQ5waGOCbNFgNlOipFnMZkq8ZGf7TSy++Wt3NsnWLJvdg3GPyL7muOO67B9LeGska/ROl3RuGEmgSCIF3aRs2BR0k7fe2V7v33j5jU2yS7rF1r4IriDU6Z0Iq9y8McliXmMmU6KomcxkShi2w45elaIJ00sl8iWN6aUSG1rc2Nbi8z93q7e+ddlvqbxGTjPYNRRDlMQ62abjMpwMYS1X61eOK5LIvrE46ZJeZ4833bmxreyPvf1AU+zYrsvBsQTZklmnX0G32DcUQzPsOr+WNIuDowl0y2Y6XSJXNplOl3j9/qG2sv9hWfZctkyhbDKXLVOyLA6NJSmaVl2fipbNobEkWsNxy3U4NJYgVzaZWiqRK3mfF4u1is0X8hoFzWsT0eXQWIJMyWAiVSRd0JlIFZEUidEIGA6kcmWKJY1UrozpQo8CLhJTS17enloqsb23/c6VreJ8uCvM9QNRCprNTLpErqAxky5RNly2dMuIiHU2F12BLd0yZdPbKKZY0pjLlki0oDysxR+8ZhcLOa3OVrpts3cwSsmw6uPZdNnRq1LW7Tr9TEdkNAK6LTCTLpLNl5lJFy8qu6J3bY7ULJvdQ3H0hvHtlzsr7BPpkl6Xc97wwvbj+w/fcmvTd0qGxU0bEpQtq+540TDZOxrHcJzmnLOhOedcbIz9039r9rdf/losaOwcjDWxU/hde164Z5Ce5QfhecOlaLjkDW+i2h+kJTuF39zBdBxuHE1Q1M06exQMk70jCSy73h4bBiLs6FUpmS6ZQvvlYmuBaxPjGuQNiwNjcd7+vK0kQgH6Y0FwoWRYsEzZlPDZ0abT88e6ImwfiIEDmbIBDmwfiDHW1f7J5lrI/vVX7OaWDXEM02Y6p2GYNrdsiPPrr9jtK6PT/vr16T2v3MNd13VjOw4zeR3bcbjrum5+5aW7OtbbD36y/+InWie0v3r7Tt/vDMVClHWTxyfTPHJ+iccn05R10/fVfSyoEJAEZrJlzqWKzGTLBCSBP/mJfcsXtpXCux4FPvoT+zvWY+dAgoFYkPmsxqn5PPNZjYFYsHpH34g3Xt+6fb/j7XDzpl5u39KL67jM5Mq4jsvtW3p57YGN7B/rwnVd5gsaruuyf6yLX39565ud33xVr29M7R5KttT75k29LWX89J3XcffOfhzHZSpTwnFc7t7Zj9+t0E784/nmTb0tj7/yhtGWer/gugECksBsWuNsqsBsWiMgCfzhmza3lP3Hb9nasd733raFe3b14zjOsn4O9+zq5yNvuok7NicpmyYX0mXKpskdm5Nt/bdzIMG23iipghc7qYLGtt4oBzf0tJQ91hVpefyu7YMt7fHmva3lvm2/f+z8ykt3tdTvvT9yQ0u//reX7OTAhiTZssGpuRzZssGBDe31PrihhxuGE6SLBifmc6SLBjcMJ3jzrZtb9une27a0PP5zL9rOrZu7yWsGp+dz5DWDWzd3s9VH7u4amy8WdE7O51gseMWOb7tjC3uH48znyxybyjCfL7N3OM5Hf2I/ewZULBuWyi6WDXsGVP78p/Zxx5YuSqbJuaUiJdPkji3tHyb4xdoHfnwvewcjlAyT6bxOyTDZOxjh/9y7v6XNP/q2g9yxKYnjuMyXTBzH5Y5N/jYPAS+/fqSlrX73R/dyz84Gf+9cjucW+v3x225iZ2+IvGYxmTPIaxY7L3JDMJIIt8yRBzf0dHQd2z2UbJlzfuEFrW9A3/WisO93XnnDaMvjd20f7CjnvGFPa53fcqO/v/3aunlT6w3L/a49f/2O/fQvq15bePdXP33A1xd+fbpr+2BH9vjDN+/n4GgUp3nhyZrjGisFK6wU41aMcCRaV93/g8hjXKk4Pb+YJ5M3ScYUNvXEfJkWLqe/fn16eibNQtagLxFYkyfFq5Fd0fvTD53l0dNT7N82wutv3VKnd+N3LpWNQ5aEOv7HCqPCmfkc2bxJIqawtT/O/rEu9m1s/8TJT49KVXAtp3Qr/1WYJD55/wUyQBJ44zLXaKuK5NVgtTzGlaruLz56gtkMDCbh1ft31Om9XjzGf/ofT/P14/M8Pr2yvrzCa/uLL9ndVvZqeUD9qrcrbA6f+O5JTs0XuK4/yr3P217no071buT5rciezZTIlE2SIYXBZJjf+uQjPDWnUVufFBRg33CYv/ivtzOf13FcF9v2lqiIglDt1+XyGFf69PN/8x3GC7AhCh/76efX6b1aHmM/v1ZkaLqJ4XiFampQqTLezBZWXmNXeG1/41U3eDzsllXl5w3IcrVffn3y06+kWRRNi4giE1ZlHjgxy0PnluoK8V6ys4e7d/bzkhtGPJvbNg4iIg6iJFXZZSzLwXZBErz1zRV2kqcm0yxky/QlQuwZ7arGVCpbJmdYxAMyvYkQXzh8ngfOLnUU55W8NrtUpmgYRAIBBrtD1bzmxz3cyGPcboy95Y6tzOd1imWjjqe5YvNGHuOKbRdzOmlNp0sN0hMPVnNnIWdQcGyiokQ0HmBiPs/x+SLfO7+y8va2TQnu2dXPjx7c2DZHdnoda8w5lb6+71+/y/l5l039Ar//uufVxfl68RhXfPfBTz/GlAEjAXjv62+quyZdDo9xLfzG/dXkMX7oxARKYf4aXdt6ozIxlpLDlJDBgf0buy57ovpshGU7TKZLCIJQt1+8ZtrgwkhX6Krug75eqNVbEeGpp55iz549mA6+emuGxeHzS4iS0LQvfasY8bPtUlHnW8/M0x8P0htT6467rstLdg/WJYTV6rEa/2WKGv/y/XFkWaSvRvZCXsN1XF5/aOySN/m4GAplg68em0GSlivxl3EpeneK84s53vfZYwRkkYGESiqVore3l7mshuM4/O5rrq/bVOBS4OeLomYyky0zmAwRrSn4Wssx5if7gVNzvO9zx1Ak6I2vPFVbzJWxXfjvr7qBA1t71mXsX4nc4ifjyaklfv8LTxEOSvQnQuSyOeKJOAtZDdNx+I2X7WT7YPKy+uUne3Ipz8fvP+exWnStXKhnMyVs2+W1B8boS4TqvpMtGzwxnmEwoZKsGRvpks58VmPvaJJ4uGYypZvMpjWGkiqRmm2vz87n+N//cYJY0GO1qGBm+UlsqzjvNK/5oXaMDSXrZVuOwzvv3MJod2xVNvezbbqg880Tc/TF1Lr8dXouy+cem2KsO8xod5j5uTn6BwaYy2pYjsPbbtvIUDL6nI1zP6yV757LyOfznDx58hpd25XEWu45/mzED+s+6Jei91qxcfgxKkQCCiXTaSo4WUs9/Jgk/CqS1xJ+Vd2XonenyBRsdMsmrtbLjqsKRcMmU+i8kKgRfr7wY3NYyzHmJzuVKXvV2w0FgaosYNgwmSms29i/ErnFT0a2YKE7Hv90LaJBibLhMd5cbr/8ZGumV2Efa2CYiKsBcrpNVrNWzS5TYSdpLHDyY8gp6LbHWqLW55Z2cb5WrEhtx5hmky7al83sZDoumuUQali65roeQ0ikyeYKBc0iW3Ke03Huh7VktLoGf1ybGDdgLfccfzbih3Uf9EvRe63YOPwYFYqGSVgRmyiK1lIPPyYJv4rktYRfVfel6N0pklGJoCyR0+pl5zSTSEAiGe2ceqoRfr7wY3NYyzHmJ7s3GfKqtxso5DTLJSDBaDK6bmP/SuQWPxmJqExQFCk20DkUdJtQwGO8udx++clWFa/CPt/AMJHTDOJBiYQqr5pdpsJO0nhh9mPIiQYlj7VEq88t7eJ8rViR2o4xVaIrIl02s5MiCqiySLmBdUYQPIaQYpPNTaKqTCIsPqfj3A9ryWh1Df74wX7m3iGKhkXBduv2HP9Bw2r2Qf9BRK3eecskU7ZYKmoosuKrd6Wi9uRc3vu7YZc5PzaO+bxXNVvZKazCqHA6VUAUhbpdfvaPdXW0nMBPRmU3vkY9KkwSD55NYdoOYUWmtLy71+1betdtGQWsVHU/OpHGXN4SVrNsCobVsd6dYlNPnFs2Jvn68XnKhoVe0im4BYq6xT07+y97GQX4+6LC5mDYLgXdXJcx5id7z0gXO3pVnprTmF0qE5RBt8B0vDXGd+0ZWHXs1GI19Qu1fSppZnXdrCiJq9J7teubKzJs26mu6x7rirJ/NM73zmfRzTyWZpE185QMuOu6bg5u6r2knOcnu9Z+UTXIwY1JHjq3hGHZ1TGWL5vcvbOfrQOxpu9U2GXm8jqiJFRzS4WdxIGm2BnuUjFsl6JmVvXujqrcvCHBA2eXyOs6AVHEcBxMy62L81o9Os1rfqgdY7ppV3fKy2sm9+zsZ/dw16pjzc+2giiwZyjO6VQBqSZ3CqLAobEEx+eL5LUypbzGor2IbUvcs6ufHYPJS4rz1aDTHLyWqPVdrmjgIiDgIojCs2be0mmd1bMRV9+KzyK47qUzQzyXEAsqZEpG0z7oja/bf9AQkiTmMiWOTec4P62xIC1y/XCcjUn/dUqVWJjJaGTKBqokXpSNAyBXtigZForoTQo2JsPEQwpnU0XmCxphRWT/WBe7L0b224EMPxaS/WPdzGRKPDaZpWTYhAMSN40m2D/WTM231tjeF2cqXeKJqSwl0+M73juSYHvf5U9ML4YfOzjG01MZjs7kKZQgWi6ybyjGjx0cWzMZfr6IKDJT2dK6jjE/2e//8T383N8eYaYMlTfpQyH4nR/d3XHsOI5LtmyQLZtYjossCiRCColQALHF06nKGHtqJodmOaiyyJ6h9mPMT0Zl69rG4xFFJiAZTGfK1aKd4WSIn7l7K6f/+TFOLRpeUZ5mcl1PgJ964ZaOc167PrWy36tuHGFiscgjk1k000ZVJA6NJrh716B/jHRHSIRLTbllKBZiJl9u6mv1eIPerz4wzHdOzHFs1sB2QBLhup4AL7tx0FePCoPNavOaH157aANn5vM8NpXzNmqRRG4aifPaQxs6jrVOc+ftW5P89qce50RKQ7cguFRgR6/KC3f1dSy7U6x3++3QEwowtVjg8HjG2yRGkTi4Icltm9oXca83Os0Vz2ZcmxjXYNdgjJ7uZqaBHzTkdRPDdhlKqtUnD4btktdNuuT1327xauH4XJbTqQK90QB2QqI3GuB0qkA8pPgyQ8iyyOa+GEOJ0KqqmEVRoCsSJKYqTXfN+zb2sK0/1rICtxO0k9EKCyWdZFTl7p0h3OVnDA4uCyWdaGR9E/lcUUOWJW7e0osoCDiui2U7zBU1NofX90bssfEM0YjKC7eppBZm6e3rw1o+PrxG49zPF+mivu5jzE/25w7n2DSQYJPt4AguoiuAJHL4TJa9owMdxU62bDCf11EViXDAezVeeVLWigGlMsb6YiohRaZsWhcdY34yMiUDw3abjgck7/hgMlR9omrYLt94cp5oJMSBWIhSLks4nsBx4P7jKXoORjryx8X0brTf/cfTBFWFu3b0I4veJhKm7fD0TI47E2Ffm7fKLX6xM5Mvt9T77+8/T9YU2NSnEhQFdMclq7v80wMXeNdLIy316I+1lt0pTs4VGOiO8IquqLdbkettCHJyrsBgV7SjWOs0d37gs4+R0lw29qiYJQ0lrJLSXP7xu+f53R+7qSPZnaLTHLyW+M7peY7PFRjpChMOypR0i+NzBb5zep5X7lu7m/5O0WmueDbj2sS4Bo1bg/4gonYf9MaK2lzZIqYqz9nXH+1Qu+99IqSQU2R6YypK2eRsqsi2/ljbiaoakOlk0YEsiS0HVzQU4CIbJ122jFo07lVfQU4zmMloDCVC6/b67WrKTuVLHJ3I0htT6Y0FCTl5RoYTpPI6x6Zy3LK1RG9s7Sqaa31xpcdYrezJdJ5HLmToi6tNTAGPTmR4STrPaFdsVbHTSg9J9D5b6VE7xiosJFFVRhIF3zHmJ8NyHKaXWRhqj9u2w3SmkfFDYiJV5PvjGXqiKkNdKvOyTv9AkrmsxuGJDLtHEy1ZClrpsVq9K/bLFDWems7RE1WbmF+Oz+bZO5YkGVF9bV6bW/xip6iZLfU+MZPj8HiWrnCA4RpWitl0icOTWR6/kGbzQMxXj07zWi0uV28/rCZ3npjL8Mh4lp5IgP6kSiZtk+yKMZ/RODKZ5cRchh0DyY5ld4r1br8RtXltIOGxznRHgiiSuC55bbXoNFc82/Hc6ek1rAl+WFkpriZDwtXE1axivpqyi7qLZreumC+YFkV9/WRfzTGWLTpobdg4ssXVb63bqR6XMsb8ZPixMPgxfuQNE920iaqXz1JwNZlfOmU6SZd0NMchptYfj6kSJd1mvqCtWxxeTcabxZyJZjezccRUiZJps5gzfb753MbVzGvt8IM2r7g2Mf4hww8rK8XVZEi4mriaVcxXU3YkKKBKrSvmo4pM5CJbyl4OruYYS0RE1DZsHInI6lN+p3pcyhjzk+HHwuDH+BELKAQViYJ2+SwFV5P5pVOmk65wEFUUyWv1x/Oa7XE6R9V1i8OryXjTE1dQpWY2jrzm1TH0xH8w62WuZl5rhx+0ecW1pRQ1sJ3VP015ruJqVtReTdQyJGiGSU6zmMuWKFvOujMk1MJvlx9Yn2re2irmpbxeLc5RZHHdq5hrZRuWU117qZv2qmRfzq6TvbEw+8YSfPvkAumCxuJSiZKUxXbhBdv71vx142qYE0xn/ZlfRrtiHNqY5OvPzDOf0XBcG1GQEES4Z1d/3Y5ya61H7RgzbAdVktBsm6Ju+o4xPxmWs8LCUMfOUMP4oZl2NX/Fwgq3bEjywNklFgsl8nmNeWMJEOtYCnTDAkEA14vNY8gAAQAASURBVMVd3vFPlsRVsU/U5sja82uZX+bTGoZrERBkJIU65pfV7LZWK7uxrxW9s2UDXAEEl4GEysENCR44lyFbLOMKLoIr4AgSd2/v4caNXW3ZOPzy0WoYQmr1Lureza5mO2imtSrGGz8Zq7HTjoEkhzYkuO/kIiXTwC6bZKwcZQPu3t7DjoFkW9lrhSvNwFCb13Jlg4AsYFguhuXU5bUr3a8ftHnFtYlxDebyOq6sPyerKDvB1ayovZqoMCQcncgyvmhSDpTYN3ZlGBIMw+bpmQyn5wvV7VG39UfZPZRElsV1rebtUhVOTWd4pKaK+dCGJLdsXH9WiqFYiInFIk9P5yhbNiFZYveQV3nvh06rm/3Ov3EswZcem1hmpXCIZhbYNxTjpg3JNdPPT7Yfc8KVYH45uDXBX3/rFDPllWNDITj4quvWXY8VFpIcJdMirMjsHYm3HWNVxogGGX7sDCOJMEXTaspfL7q+j88fucD5rIMFyIt5NiVEbt++k4giU9YLnF4ooFkuqiywrS9KKBkmXdRXzT4RCyotz9/Qo/LX31zk2FwB0wFFhOsHorz55jEsy2EiXWQqXcZwXAKiwEhXiLGuSNMGH4BvX0d6Y5xO5Tk9X6wyQGzrj/DyA3188bE5irVtYPHSm/p82TiCgsjRC4tN+WjnQIKyba+aIeT64QRPjC9xeHypynhzcCzJ3pHkJcXaVLa0aju95Y6NHD6X4lRqmYWkqHNdT4C33LHRV/Za4WoyMBza1M13T8zx/XPpKvPLwdEEhzZ1X9V+/SDNK65NjGsgwnO2irITXM2K2quJCkPCrZt76BPSbN3Ug4V7RRgSnp7J8OhEmq5wkP6Qx8X56EQagI290XWt5v3aU9M8MZ1jMB4iokoUNZsnpnN87alp3nDrlstuvx1m8mXKlsOuoTiSKGA7LmXLZiZfZrPa+ullp9XNfud/4fB5pvIGm3oi6GqWYCTCVN7gs4cn+MWX7F4T/fxk+zEnXAnmlw997mmWNIhKEJRAt2FJ845/6pcG1lWPyhi7ZVM3oijgOC6m236MVVhyGmVUWBga2RmKptUyf/3Op0+xoLn0RAVc3UUICixoLh/96kl+/VU3MJfXGUiECEgShm0zl9eBLKGgsmr2iXRRb2mnv/jGCU4t6vRGgoQDEiXD5tSizp/fd4p3vmgHJ+fyxEIKyaCCZllVDuHNfc1jYCpbatnXXNmkbDkMJlRk2dscZC6v87/+v6fRAAXvgm4BGvChzz7DP/xsX0sbHh5f5HSq0JSPcmWTgWR41Qwhp2ezFCyHA2NdKIqEadoULJsnpjLcuXOwo1gr6wXm8vqq7fTVx2cRFYUbRmSKhRKRaBjDFvjq47P84kvW96b/ajIwfPP4HItlixtGk1Vu+MWyyTePz/GSG0auWr9+kOYV1ybGNQjIEijSc7KK8lJwpStqryZqGRIiisSSKtMbD1I07XVnSGhVrR9UvM/T8wUCskREVdalmnc2W+DIeJa+uMrgMktBVxTkjMDRySwvyBYYTKwVT0Y9LoWVotPqZr/zp9IFHh7P0h0NMpQMkRI0enujzGU1jkxkOL+Yu+xNPlbDqBCpYRC4Eswv3zszzzMLJcIB6ImvPJVfzJU5vlDie2fmuW1r/6r08GOA8NNjrfwNEgXdXLFhTaFRo+xKa0cnFjk6nScekumLhyjmC0RiURZyZY7O5PnWM/PsGIlX+xVZ/uaZhSJ7R5OrYp/ws9PZ+QKPTuZIhhWGurwx1g3MZTxmiF0nU2wbjFZlB2Tvs5VNWtkwgoxjuzw9m2XXUIJkZYIThGPn05zPOgRE6ImvTHzSeZ1TiwZfPTbN7dv66xgulvJlnp7O0RtX6/KR47g8M5ujLxFaFUNIXjM5OpGjNxasMiRAMyvFxfwtiRKGaXN6ocBAIrQqO51fzPH9C5lldgaVVMqmt7drTce3H64mA0OrfA4wmynx2ESWrQMx+uLhq8oM8YMwr/jBnvldAp6rVZTX0B5XkyGhXbV+0XQoLa+VrMVaxWG+7C6zFNS/zoqrAYqGTb787GKl6LS62e/8ouaxMzS+8q+wM2QK9UU7l4JOGRWuRG4ZTxUxHVDletmqLGA43v8b0SkTgp8ea+nvTm04s7xUIKo0rHtWRDTDYb6gNfUrIEmULYfGypJOY22puCw72CA7KFHWHRYL5VXbxM+GoiRQNh3kBl+kdB0b781AvW5gOTCZLjf110GkbDuElHoZQUWiZDrYDUPDzxem5aLZFqGGm512rBR+NnSobFter4ifnTIFG70N+8pajG8/XE0Ghnb5PK9bZMvrdy35YcK1iXEDnqtVlNfQHleTIaFdtX5EEQkr0rpV88ZCwjJLgVF3PKcZRAISsdCzi5Wi0+pmv/MjqsfO0FgxX2FnSEYvn7O8U0aFK5FbNvRGUERvklELzXIJiN7/G9EpE4KfHmvp705tONSlEhRFCmZ9OwXTQQ2I9EfVpn4Ztk1IFpsugp3GWndkWbbeIFu3CQVFeqKhVdvEz4aO7RJSRKwGX/QGg0h4y2XqdQNZhNGuUFN/RRxCkkjZrJehmzZhRaRhburrC0UWUCWZslHfTjtWCj8biizfvDXMyv3slIxKBNuwr6zF+PbD1WRgaJfPY0GZRGj9riU/TLg2Ma6BYdlopk08JP/AL6P4YUOFISFfNslrJpbjkNdM8mXTe0W4juwMlWr9dElnqaijmw5LRZ10SWdbf5ShZAjN9GLPdtzq72sRh4OJKAc2JFjIacxmSpQ0i9lMiYWcxr7RxLoto4B6m+c0A8NyyGlGW5tXqptXaw+/83uiIW7ekGCpoDOTLlM2DGbSZVJ5jQNjyTV5zeon27I9RgVz+e+19mk73La1n119YUqGt3yiWNJYzJUpGbCzL9y0jKKdHuYyA4Rlr06PtfR3pzbcN9bDvuEYubLFXLZMSXeYy5bJlS32DcV44a7+pn6VdIutfRHvaeVlxNpwV4j9o3EyJZO5TIliWWcuU2KpaHJwNMHzt/eu2iZ+NtQsm92DcW8pSc3xzcMxNiVEDMdbPlEs6aTzOpq9vC309cNN/RUlid3DcXKaUZePsprBrsE4oiCsyheKJLJvLE66pLOQ19AMm4W8xmJBY+dgrCUrhZ8NHWBbX5SSbq3KTpt64tyyMUkqr63b+PZDpzlqLdEun980lmBrf/yq9OsHDYLrXnu+XiqVeOaZZ4j0jTLQk1xVBeeVpkO50lgNXc9zTe9Kdfi5hQLHTp7m+u3b2NwX9a16XkvUslIUTYeIIjaxUiwVDXTLISiLdEcC1Ti8XJsXigZfenKShy9kKGgWUVXm5o1JXnnD6JpvCd3Y14rNzy96STysymzqCbe1eaWyOle2MB3vaUc8JF+UlaLxfNd2+OeHzvH9Cxlml9IMdndxy8Yk9962hWSsPZXUaiij2smuVPEvFQ0M2yEg1ft0PXF+IcN7PvkYj0+VMIAAcONImD94401s6ksCzX66mB6r9UXF32cWihTKJtGQwta+yCX5+2KyG3WYXsrzwS88ycPn0+R1iAXh5k1dvPdHbqA/HmEiXWQmo1UZHYaS9QwXlxNrumHyJ/9xnMOTWcq6QyjoMQX8ykt30R0NtZTtZ5OKDRvPr7B0TCyVKJsOIUVkrDuMbun8+j8/xokFw2PjAHb0Bfijew+wpa+rZX9DksTxuSxnU0VKpkNYEdnSG6myUqzWF0FB5PD4Ig+dXSJXNoiHAty6pZubN/WiLm+2stpYq7BSrNZOmbzGJ753lgfPLzGXyjLQm+D2Td1143u9rmMVHa7G+K7k8wfPLlLUbCKqxO1benjlDaOEQ0pHufO5iEw2y5nTp9m1axfh8Prs8vdcXyO9pljNMtOrSYdyJeCnnx9dz3NRb9t1wXWv6JorWRbZ2BslIHs7M4UViaFkqC7hu7gebSlev9Yq1gKKxI6hBHnDIaOZJFWFHUOJNd0CvR0FE4DjuiAuf14EnVY3+51vWQ6vvGmM/qTKseMa1+8c5NDmvrac1Z1Sa/nJdpaTiQu4LlzJpw8BSaInGkQNlBBMCCrQEw0SkKS2MeVn8y559b5wHJdsyWA+p1E2bUqmTW9EYSThfwFr5+9Wsh3HbUmZJgoCiXCAgAyKDgEZEuEAsigiyyKb+2IMJUJNNzyBgHT5sRZUeOeLdnD9qRQLBY2+qMrt1/XSHQ21ld0Kfuc7jksyHMCwHTTTQVVEkuEAri3z0htGcJ6cJFe2iYckXnrDCL3hcFvb7tvYw7b+WBOPsYq8al8UigYzmRLTmRIFw6agW8xkVCzbuaRY68ROqiKzd0M3Wd1GtYpsHUqwd0M3qiJfsevY1RjfoiAQVRWiwQCOaxANBoiqXvz/IDFDNKLi0wrLxnri2sS4BookXJTa5GrStFwJ+OnnR9cDzx29J9JFTs7liQQk+iISIUVqSwe0lqjYNaIqJCPBOvsBNTYX19zmFaq4nmiQDd3ROqq4fRt71lS/dhRMXeHgRSmYatFpdXPj+RV/Dycj2INBhpORi8qufGe1lFF+suvtIV/R8fLBLzzJA+czxMMKQ4q37vaB8xk++IUn+dDrD7TNX342X60vamkJe6NqR7G2Wtl+sfYX33iG753PkoyEiCtlCIT45qkl4Bn+5xsOAt5ShVbvCtYq1jYPRNk1kmwZN36y/dB4/oreMjF1JU984fB5Hji7xGB3nO2qQk4z+frxeYAqLaGfftFQgFaLqVbriy89Ocl9x+fpi6sMJcPkNIP7lmVfjDrMT8Zq7fTw+RQPnk3RGwsi9QXoigV58GwKgBvGutb1OnY1x/c3T87y7ZML9MZUxrrD5DSTb59cAOCV+8aAHwxmiEZUbH4lpvk/GLcSa4SA7NGv5MoWlt1Yq9xM0yKJQvV3v+88l+CnnywJTKc17w7+Oap3LQ1STFWQRZGYqhALKd6ru4YCkrVEu7hZKhosFvWm/ymiwHSmjCxdns0bqeKCikh3JEhXOMjZVJFC2bh4I5eonwicXigQDsrE1QABWSSuBq6IzS/F341UWZfa36uZJ2ppywYSYSJhlYFEmHhI5uhMngfPLKxbv65mrE2kPcq0rojMQDJEKOh9dkcUHp3M8vRM+rJl+2Gt4qYd/PReKpR5+EKW7qg3MY2oCkPJML0xtUpbtl5opA4LqzKDyTB9cZXHJrKcXSiuW6xlihpPTefoiar0xlSCkkxvTKUnqvL0dI6JxWbZa5VTr+b4TuW9Dao8mrrlOE+E6I2pHJvKkcqX1k321UStzQPy+hVWVvCcnRgfPXqUt771rezbt4/bb7+d97znPSwuLlb/f/bsWd75zndy4MABbrnlFn77t3+bXO7iSaIdtcnVpGm5Eng20k+tFa4mXVu7uNEtB9O+fKosP7SjiiuZDrp9+XqvFQXTWuJS/L1WMXI180Rb2jLLZiHTTN+1Vv26mrGWLVjojkOk4dV7NChRshwWspc/KffDlcgtfnoXdAfNtok18LVdCdqy9tRhNpmSvm6xVjZdNNtpomOMBRXypk3RcNYtp17N8V3UXTS7NU1dwbQo6s/+a/GlwM/m64Xn5MT42LFjvO1tbyMcDvOnf/qnvPvd7+aBBx7gF37hFwDI5XK8/e1vZ2lpiY985CP82q/9Gl//+tf55V/+5Yu23Y7a5GrStFwJPBvpp9YKV5OurV3cBGURRbp8qiw/tKOKCysiQeny9V4rCqa1xKX4e61i5Grmiba0ZbJEX7KZvmut+nU1Yy0RlQmKIsWGp7MF3SYsi/Ql1m9b2iuRW/z0jgZFVEki38DXdiVoy9pTh0kkw8F1i7WQIqBKYhMdY143iSkSkeUlabVYq5x6Ncd3JCigSq1p6qKKTCT47L8WXwr8bL5eeE4uQ/nIRz7Crl27+NjHPoa0/DQqGo3yoQ99iImJCb785S+Ty+X4/Oc/T3e3tzXkwMAA73znOzl8+DAHDx5s2e5Xj07ywn2b6Y8FqwvWG6vTEyGF+bxOSTNwEBFxECWp+p1Oq10n03myRYdERGS0q34No19lvJ+MTFGjbLqEFKGOKsfv/I9/+zSn5nJcNxDnp16wrUpDM5/XsRynuj1rha7HsFdoekzbQTPtS9b7M49cqMp+7aHL29vez06N9hjpCnFyLs8v//XDzAP93/gev3/vTWwfiFW/16keq616rtj1w186zJn5Mlv7Q/zyS26iP+atR5vP6+iGhVd95+IKAsPJEIbtUtDNOl9UbH56IUs6b9EVk9nWl6jKns0WyJddYiGBwUSUbf1RHp1I8yuffqJ6zvtftYP9Y13Vghs/GxbKRlNxTjv9ZpaKFEybqCLRFVPZ1hdlLq/zax9/xLM5NNncL2799DtyIcXUUpmR7hAHNvb6nl/x95v/+mHvhG98j79skN2oX+U7uYKBg4uIgCAJ1e/42ckvT7z2f32LBaAP+MQvP78ut/jp7ZcTHj63wMRiibGeMDdv7qse/9yR8epY+tEDG9g3HOOB8xkmMtnqOYmgwB2bkty+tY/5vM6J2SyZokEyEmBjT7Tar6dn0ixkDfoSAXYPdVW/v1ofrSbW/PTwO94oo2Lb47MZMnmTZExhU0+M/aNxvnc+yzNzS94X55YYjCrcdV13VRc/2x6dWGQmrTHUpbJvbGUttN/5jXZqijVoirVUvkRRd4kEBXpjK8WIfnH+1SenODOXY+tAnJfdMFLV+zsn55jLlBlIhji0uZebNyZ44OwS3zy58sb0xuEI9+zsr9KW+enhJ9uvryfmMizmTHriCjsGkhzYkOC+4/P808NT1XNesrOHu3f2s6UvwnxeZ3KpRLZskAgF6I0Fq7HWmKcq+PaJWc7P59nUH+MFO1a2lf7iYxPVOH/1TWPsGY7z4NkUH/ryieUAepKfvXMjt2/pZawn0jLOKzn17HyOgm4TDUp0R9Vqn/zivDFPVHzxlj/8FpMujArw8f9WP779xpKf3qs9f99Ygm+fXOBj3z5XPef1+4d4wfa+qq86zeedzh1+99+O8NRMjj1DcT7w4weoRacy/OzRGIMVm3/6gRO8YtP6ruN+ztG1pdNpbrvtNj784Q/zmte8puU5b33rWwkEAnz84x+vHnMch4MHD3Lvvffyq7/6q3XnV+ja3v31FJMZi4//9PXcvmmsZXV6XzjIoxNLPD2d83YPkkR2D8c5uKEH3XVWXe2aK+h85sgFHrmQQbNsVFni0MYkrz2wkbCqtJRdoRZqlFGhzHlqOleludnTpk9Pzszyjr85Rm1tZxD4+Dtu4NaNo0yki0wvv5INiiLDXf60Rp1W+T46PsM7/vpRFmtueHsU+Ot37Gf/hqFOQsGXQaDio0Z7fPa7R/j86eZ2XrsTPvK2V3TEANFp1fNnjz3F739upqmd9//YMPfu2+tRuS0U0CwXVRbY1hdle1+cuaLW5IuIIvL/vnuGhy9k0WwbVZK4eWOCN966ke+dXuTIeLYaUwc2JHjP55/xteHpD768pQ0HIionF3Kcni+g2S6qJLCtP1qlc2rUz7Zs/vY7p5v6dGFigi9daG3zD77xpTx8PtXkp239ET7x4Lmmtl62b4A/+cpJnpgpYDgOAVFk71CU//by7Xz16FzT+R/77oSv3id/72VV+rxa/UaTIb54dJLD4xnKpk1IkTi4Iclrbhojq1tNdqpQaDUe/6PP3c8XzzbLfc02+PC9rfXeORjj3x+fasoJN29L8gdffIZjcwVMBxQRrh+I8rrnD/LBfz3NfM1A7g9S93cjHvyNO/ngF57k6HS+GlP7hmO888Vb+NSDkxwez6I5DqoocnBDgp+7+zrOp8qr9tHXvjvBmRZyrwc+9u7n8Tv/erRJj595yWb+8j/ONR3/tVft4D8en2+S8YqbBvnYf5xu0uHLJ/zXET/x3he3zLe3b+/mo1892dTWu162nQdPLjWdf+fOXj7+rbNNdvqXx+Z8ZT/9/pfwzZOzHJ1Y0WPfWIIbxxL88/fON+l3+64k7/3kk4znveVIIrAhBu/6L1v42JfOcz7rYAMSsCkhcjrr/xTNT+8X7e7n098fb5L95ts28fhEtqmvN4zG+bv7z/LI+MrxQxsS/N3D076yD//WXfzZfSeavvMTz9/M988sNeWpHSNh3vfpJzm1aGA7IIkeF/NP3jPK//rcWWbKK20Phaj7uxF+cf7zL9nGlx+b5eEa2TdvSHDv7Zs5PV9sivN9o12cXSw05Yl//M+H+LcTzXJfvxve/eoX8kdfe6YpRt5x11YeOZtu0vvQli7++ptnVn3+WubzLT1Rjk6mVz13+NLxE7z3X5vz6v94/QZee/3uljnVT8buoTj3PTPbpN9dOwd45PxSUwzOGSl+77PTbE7K/OE9vetK1/acmxg/9NBD/MRP/AR/8zd/w+c//3n+8z//E4C7776b973vfSQSCW6//XZe8YpX8N73vrfuu69+9avZtGkTH/3oR+uO106Mz2UsgsBXf+3OanW6KstolkW+bGJZNnMFnbgaIKTIlE2PkHxbb5SBpLdHeeMT1VZVqn/77ZN8/Rmvmje+XEm8kNO4Z1c/d+0eail7IBYkFFSaZJyezfLkdJaeqFqdmC0WNG4YTrBtMNF0/qv++DuYTT3yJscPve/FzOd1FFFAEAVcx8V03KoejXeQ6aJerc5djd4H3veluklxBT0KHPn9V642DAA4t5BvaadMQePcUqnJHn9xf4sZ2jIeW9Z7tXr46R2QhGrVc+3xl/7xd3xlf3M51sJBmYAkYdg2Jd2q+rvRF5988AwPnE3TEwsSV2VymsViXmcgIhEKBZdjKkBOM1jIafzH8cWLyvaL865w0Nu62jC9DUl84rxSGd8dUYmpMnnNYqmo8fh08xbEFfzD2w/w4NlUk5/G53KcXtSa9MsXy+QsiKsSYVmkZDnkNJu4DLFIqOn8kyn/K+fnf+7WKntCrX6lksF4RiMRDhAJyhR1i2zJYOdAlJGeaJOdQrJI2XKajv/MJx7rWO9sXuN8WmvKCRMLWRY1iAaFqryC7lKueXsuQtPWxq3wo9f3cv+ZNImQRCQgUTRssmWbADaupNAdUYgGJQq6zVLR5LqeIHs29qzaR+1s/vyNUQ5PFpr0cGwQpWb9YjIkY81+NfQyeUsiHpaJBiQKhk2uZLFY9l9P+/6XX9cy3y6k8yxoLvGQTHSZwSNXtuhTBfq6Yk3nF4plFspuk51mC60yqoc/e+PeKotApa1UXmMxW2QiazbpdyFVRsd7nSsD1vIPgAAEBG8LaN0G4yJUYX56O6ZJWnebZI8lFHoSkaa+zizmuZA16YkEiKkSec1msWgwnvG/C/v5543xjeOpZfowkbzmkC7656mj5xZZ1EGVvC2tDRs0G2q9WrHHxeAX510Bl0gktJynPD2WihpbukMMdEea4rw/EgBJbMoTv/fvLWbFy3jTTQN889RSU4wMRyV6k5EmvVOZItMFe9Xnr2U+x3aYLxqrnju0u4755VQ/GbpmMpXTm/TrCcnYgtgUg59+1HuwdCUmxs+5NcZLS95rst/+7d9GVVU+9rGP8Ru/8Rt8+9vf5p3vfCeO45DL5YhEmrc+jUQiFAoF37YrLxx0vIlrJCARUSQkwSWiSMjA0YksYVmiOxIgFPBIvaMBmaemc1imhSICroMiQkD0JlC6YWLbdvXnwmKWhy9k6IkGGUiohIISAwmVnmiQh85nePTcUpNsVRY5NVfAte06GZqhc3QyS0JV6I4EUGTBIxpXFR6fzKLrZt35//7wheqkWK75qej9N/c9Q0AEdXlNoKqIdXoIuMgCCLjohslSUScgsiq9P/39c9VJcVhZ+QFYNOHT3z9Xd367n2JZZ3yp1GQnHJcj41miAbnOHn/fZlIM8KoPfmPVevjpLboOE0slJNy64//n60fbyn7f575HJCARD8qosrD8ueLvWl/MZYo8Mp4lGVKWY0dmIKESCUgcmysgSS79cdXbBjeutk2iAHf97/ubbCgBj096cZ4IKcgSJEKKb5ynciUevpAlGQow1KUSDckMdaltJ8UA//X/HaErHKjzk+M4HJ3OE1XlOv0E0WGmDEEBemIhQqEgPbEQAbwnR7Lo1p3fboIG8Jo/f4iEqtTpJyHwyHiGWNDjmI6HFIaSIRKqzJGJDLbp1NkpKAo8NZ1DEag7/httJsUAP9VCb9kVODyZJVLV28sJjm0wWfQmQt3LenfHQjg1s4VoAMIB7/Ni+NyxFPGQTH8yTCQcpD8ZJiDZnm1Fm76ESkhV6EuoRAOC9xTXtOv66jouR2fyRJf7uFqbf+dCgXBAqNNDFry849r1+imC9+RbFO06GaJkM54HVYGBRIhIKMBAItR2Ugzwe1851ZRvBeB81iEou/TFQ8uTtRCq7HI+6yBA3fmy6HBq0SAcEOrs1G5SDPALn3yC7oi3jCCgiB69GC5PzBaIBMQ6/QzTmxQLQDwkEQ5JxEMr64RdoCuqEA4pdEWVi/LnttJbkQSeWSgRCgr1OSQo8sRsAVFw6vrquA7H5grEggL9SU/v/qTadlIM8LHvTtAVqeQE7zMSEDk2VyAginV5qqwbzOvetSgZDRAOBUhGA3WT4nhQIBwUiK9iHW2rOA8u+1UWWO6T4vUpqPDoVA5cty7Oo4rM4fEMMkJdnvhom0kxwL88NkcyLNfFSDggcCKlIVCfnxEcTqQ0wgGx7vzI8vlw+fm8dt5Sq4cievmuMp+52NzhTy9yHfsf/96cU2UEHrmQIarUX4+9XJslqsp1+oWDMocnswRl6mLwybPNb1vXE8+5Ncam6SWhPXv28KEPfQiA2267jXg8zq/+6q/ywAMPACC0WADvum7L4xXUPnF57Nwsu+Masrhy75AuW4zP6IQNhcLiiunKpsVE1qbPTpEIrRx3XBfNcpmPyCg1hSdnF3XmFnP0hWHeWKkuNUyT2bzL03IBoy9YJ1uzbCayNko+QKSmAjlVMBmfLkNCRMustFU0LCZyNk8FsvSGV44fObvQUt8KHjk9xd4uHbHGTn56mLbLXNFClYVVnf+do7PV3+0Wt/3fOfo0W+WlFr1qRslwOJHSiQWFOjstFk2m5jWitoSZW5kptL9kw5QLJ088c1l665bDdN4iF5MJ1mwG8dSpfFvZJ8+ZTGw6vSp/H18okckXUaOwtKSt9KlkUNYgk15iBo1OMHG+XnZas7gwraPqq4vz04tllrIF1JhAKrV6uiALSC9MUVpaaWtmsUihbGMF8ywtrVx49YJX4GMbUC6uTLiF5TgqFLTqTfNqMT99gXQNY8ZsQWd+SaNbLEI5Uz2e00ym0xanz5cpLq7EVF63OZcyCJYDLNb4aGVlb2uYtNA7UyaXNzGCGvPOin65vOdLxwSttGLb2uFjdcgGJpg6hdzKl+xlMxuaQy67wtzjGBZlDabmponaK8enF4sUSjaWUmBpqTO2B9Fy6/VY7oZLvX7O8vFS0ajzq1E2cABHtynk2o+rRhjFxbp8m8tpnh0NKOZXHpg4hmffXD7L/NxKDGZzGoYDtmbU2Wk1yKdnMfMrslOLRUplF1MtsrS0MrHWlrvhApbVerJvGO0n4k3nN+hdymtoBljFEkvSShyYmkGpDKn5BSL2SgwuLRYpa2AHNTLpzlguHD3L0tJK9jXKlTy1wIywImMxveJ7Q28dU5bV2cvtxjh3dAcLKBaKpFI18V/WyRdhdmaaoJ6pHk+XDGZSJtOBIka2Zq3saoQbZXLZFT+5hoVuQS69xIy0kp9z6RK6BY6uk8uu2NbWl8/P1J+/GjTlc595S6poMJvS6Rdz2IWVt6J+c4cnzrYfb2cmYH6sPqcuFAxmF3X6pDxmfsWGC7kymbxJIaAxY660W8zr5PI2i6k5AsbK8blMRya4bDznJsaVJ8F33XVX3fHnP//5ADzzzDNEo9GWT4ZLpRKDg4NNxyuofXx+0+ZBxjaNEquhRUnlNBZYYqgvQn8itHI8ryHHDXZd10tXdCXAdNPGBUaSobqF672ZAvfNnkAWBfq7VtqZSZcZVF12XzfAQFKtk50pGag5nZ0jceLhmgDLlzhrzdEdVur6NJ8tI0ZN9uwYoje+stD9wNJZHpwdb9K3Mkk+tG2E7Tu2EKzZFc1PD8t2mMyUEWFV5z/fGuf/O/c0AFJt5C3nj+fv282+fRtYDTTDwhnPIAnU2Wk2U+asPk93d4ihrpXXLKGHn2w7OR4RYPuOXZeld1Ezied0hhPenW8Fe84dYfIp/6SyfbPC2KZtq/J3cC7H1y6cQFQkurtX9MtbBUJagWRXN0M1hRs81WrFZz0aZS/kNFIsMdy7ujgPL+S5b/IUUkCit6fm1dbZlYKcVpCBrr4Remu2Zy4HMkTn/3/23jtMkqu89/90VXWOk/PM5qhdrXZXGSSEAiDJBIlkLAwGDBeMuWBsjDG2cSA4XJvLD5NsDLYxwRebJDDRBBmEpN3VorBa7a42TY6du3LV74+e7u1Uvd0z3TOS0Pd55pndM9XnPW84b52uOu/3TCP5/HR2XnjrM6vEIaciesBf8jZoKZcBHUIhH52dsQudn7/wJcwJvYNjZdtk3ItZetOzdHYFGeq6UAQiLGUZlBS2bBhkqES/eEZF9afZ0Beiq0SH6Pfvrbs4dtfQ2/SmicRn8ISC9PZckD0jz0FCRnCDr+S1oZTOFZ+mlbGENbBOtd1eQpELfl1UEqCDxycQiUaK7Wk9g9+nMdQ3yOjwhZhSPElC81NIPh+dnSXHQzRgc0ty4QtckC3JOTDyT0hL9ROW2wNBT7GAGmBOXUJIaQhekVCkpFh5IXFR2Z5gV1m+ndcSSEtJ8EAwfEGPhJZC0iESjtLbFyu2LxlLeJbSiD4PkZIiIWYv/oUs3NFfNpdyQoLA0hxur5/Ozgt6+JIzkMzbQyrla9UvLJo8pXR46sUXyZV6x80kvkQCKRigs7PE32aagF+mu7eHoZIcknTF8c/PIvp8xEoLxKcuPGRxguCN0lkiO22k8ctZYh09DAxckD2Zm4NEPkd6vCWvPkoYLySp5KFWA9R/lXG+pKaQNItgKEh3d6zYnpnPEg7m6B/oZ7T/QgGieynHgJ1icKiL4a4LOSd2z+GLL449fiIl/k7qGbySRqQiPy9ZS3gX4gheL5HoBRkpPYNX1YjEVp/PndYt3niWfnWRrp4wQyX6Oa0d9p45zLk697HNI9U51bOYl9HbG2Gg5H5lzKaJJeYJdYYY6L4wlxQpQ0RO0tXdwVDvBV/0nT3OUuKiqrcMT7mtFBs2bABA08rvAMbyowefz8fGjRs5f/582d8ty2JiYoItW7Y49l34DukFXnf9NrKaSVY3MW0XWd3EAPaNRMkZy8VHJiRlnYxmsHswguSW0C3AJaBboFn5U3C8HjeiKBZ/xrqiXDEWYzGjMptUkFWT2aTCYkblqg0x9m/srJKtGBZb+0K4RLFMhs/jZd9wlKSis5TV0A2bpaxGUtG5dDiK1+suu/72K8Zwl+hrVOj9hht3olk0pIfX46Yz6G34+pdfuZGuZeE5/cIP5PcYv/zKjWXX1/sJ+r2Mdgaq7ITg4sBolIxmlNnjNdfVZ764+703rVpvyyUw0hnAxFXW/js376sr+89fcnXD/u6LBbl8NEpC1pdjx2A2qZDVTC7pC2GaLuZSCopmMZdSuGVH/dPGfvjO66pkm8Clw43HeXckwBVjURKyVhbPlw5Wb2cqxT+99gDxnMZ8WiWnWsynVYTlIpmMYjAdV8jKGtNxBdsSGPCDasNiWkaWVRbTMhr5QhzDci1frzMdV9jW7a8r+6tvvqo4Z2TNYimrYWJz+WiMtGoyl1TIqAZzSYWkYnBgJIboFsrspFo2uwcj6DZl7X9112V1ZX96We/S+DRcNgeHo2QVo8yGguhhOJjfT7qQzJHJKSwkcwgla6aMduHnYnjJJd2kZIPZhExWVplNyGimmLetJTKblMnKOrNJmYxmc0lfCPfyQTSFsboEF/sGwmSWx1iIwYvZ/NljIXKazUJKJierLKRkDDufd1wiLKZy5HIqi6kcup0vJLQskal4jnROYyqewzJFRsOg6DCflJFljfmkTFfJdoNa+OMXbGUxozK5JJPMaUwuydjki9dUw8VsMkc6pzCbzKEYLjZEBWwo84VhCWzt8pDTbOaTCrKiM59U6A+568r++1fuZSmrsZBW0XSLhbSKiYu9/SGymlVmQ4/bj5f8E+OkbJKT83tjC3AB8YxOLqcTz+hcbFNBQe/puEJazs8p3bTZ2RNAVm2m4wqZ5TmWVS329oewbKFsrIJL4JK+EGnVZnpJJpVVmV6SGY3VZwZ4y7NGiGc1phZl0jmVqUWZrGZxSV8IzbLK8pTf66HXu/wWKaORkzXiGY1Sr6ZUm5xqk2qAq7cQ56Uxoi771bDL/ZpVdfYPRcDlKstFGd3g4GgMA5v5tJrvL63yttu315X9q5f1kcgZZTGS02y2d/uwKc/P2ALbu33kNIuZeI5UVmEmniO7fD2sPp87rVt0K5/vcobZ0NrhrRe5j73n9nxOjWc1cppJPKthYHP5WIyMXn4/zufaKBnFYCapkF3+nVMNDg5HUQ3KYnDPpuYK81eLp9zCePPmzQwNDfHNb36zrP0HP/gBAAcPHuTaa6/lgQceKHsFd88995DNZrn22msvKuPvf2MnIx1BtvWFwYKErIEF2/rC3LCtn/0jHdi2zVxGwbZt9o90cMWG7jz1lg05zQAbesNeov7aG/9eemCMm3f2YlkWk4kclmVx885eXnpgzFH2roFYTRk3bOvnmk3d2JbNdErGtmyu2dTNDdv6a17/6TdcQmVKK7BSRP2epvRo9vqPv34f4YpsHnbl25uFk51u2zNc0x4v2ly7nzu3N6+H0/UjHcGa7e95YW/Nfv7wRX1N+/u3btzOTTu687GTlLEsi5t2dPP+l+3jxh29WJa9HFM2N+6oLfdiNmw2zt9w3VZu3lERzzt6uc3h+8id2+HgaBd7BqMkshon5vK0SnsGo7z7V3Zz7aYOcrrOmaUcOV3n2k0dfPx1B7hqNIJp2SzIOqZlc9VohI+/7kDJ9dni9fWwoy/Klu5QvmBsLsViOl9Y+MorN3BgNEZC1jg1myYhaxwYjXHH/tGadrpiQ3fN9lsdXnzcvgGu2NBdMz7f8bydNXPCR197gJ09PhQDFrIWigE7e3z8xctGqFyK1l+awu/duosrRyOohslUSkU1TK4cjfD3v7GfqzdEUTSTiaSMoplcvSHKn7/00ppjfe+L9tSMQYcpxiXAn7/0Uvb2B9BNm4WciW7a7O0P8MnX715uhwXZRDdhb3+Aj/7GPg6MREjkNE7MZ0jkNA6MRPjIaw5y3eYOLMtmNqthWTbXba7v7xdfNsJlwxEWMgrHplMsZBQuG47wN3ddxt7+ILJqMJ3SkFWDvf1BPvzrB2r64u/uOsANWzsxLYvptIppWdywtbOu7Bu29XP9th5sy2YymcO2bK7f1sOf3nFpTRv+wxsuYTiYLzpTyP8eDsLf/OpGNkcFLBvSJlg2bI7Wv4XfsX+Uazd1klE1Ts+nyaga127q5P0vv7Q4Z86WzLE/ecne2mO9cy8HhsOkFI0zSzlSisaB4fpHur/2WZvz/lNUTsxnSSgqB0Yi/Omde2vmqU+8fj9bOyUME1IaGCZs7ZT4q1duoHf5hlV4iNNbf03O79+2u2aMfPS1l9fMU793666aueiF+4bpC3k5PZ/h8PlFTs9n6At5ecnW2nJfvgve8bydNWPkb161v6beH3jFpezu87OU0zi9mGMpp7G7z88HXnFp2/P5Sw+MNbV2+IuXjdSU+4GXjxZz6kJG4eRcmoWMwpbuEHfsH60p483P3cZVGztJyxon59KkZY2rNnbyv2/ZUTMG/+gla7c4fsqxUgB8+9vf5u1vfzvPf/7zednLXsbp06f527/9W5797GfzkY98hKWlJW699Vb6+vp461vfSiKR4K//+q+59NJL+Yd/+Ieq/gqsFF88nqOro4NrNnVz3Y78lovV8rteDE8mHuNStIrPtxIFJolDZxeYSar0R70cXF5cbOypn2id0CiP8dFzixwZj/N/7n6cLBAE3rnMsbpvrKulele2/+T4DD87vcC9x84xl4HeEFy9a6yhWHOS0SiPcUF2KStHge+z1XF+djFFImMSC4ls6IoUbf6Rux8nAcSAty3bfKw7xFxaRdcNDNuF5LJxu6Uis8d8SialGER8Ej0Rf5EhpJLHuMAQspCUSWkGEY9Ed9TPH/+/wzw0kytjb/CLcHA4xEd+/Srm0mqR6aPA/FGQregmqm7hdQv43GJRdqM8xt88Os6PT8wXK6nhAtfobftGasZnAZU5oTBnJhZzJBSNmM/DcFeAycUMx2czLCRlMppJyCPSHfWXySxFKevM+YUMS1mVzqCX0e5QUe+JpQyJjE4s5Ga4M1TUu1Ee44K/S6v2/7jC3yem08wlM/RGQ2wbCBdlH59OspiS6Yr42TEQRVZ1ZtMqKVlD0yw8HoGI31PMFQ9PLjGbUOmLedkz1MnVf/rNmhReA374/Fvy1fqqZqFaBl5BwusRiiwYMymZTE4nFHDTH/EXZTTKY3zHh3/AwzNKGduPG9jT7+M/334j0DiPcSGe7zuxyEQqzXAkzJXbuop2Onx2kamkzGDUz4ENXbz64/fUpOgb8MO3fjfv71RWJWdYBCSBSNBb7GshrVZxDHcEvVVjLcRgMmcgaxp+j4doQOJrD5zlyGSmrACxwB39xuduz3OC53Q03cTjFokE3EXbVuapgt5Hzy4xk8rRHwmwb0Nncaw/OTbDVEJmMObnul393PHhe6hV3ht2wcMfzDMcVcZIAZV5qiBb183iPHa7xWIMul0uBEHAsix02y4yBr3h/7uHcRNGRPjH3352GYtRo7zEhTmTzRiotonXJRIMScX7UuX1BUare89e2LB19YYoN+/s5XXXbwPWh8e4YEPLtjFNEEUQXC7HHFK4PqOoKHq+oDbku3gM/uiR07x0q/8ZurZa+OEPf8jf//3f8/jjjxONRvmVX/kV3vGOd+Dx5J194sQJPvCBD/Dggw8SDAa56aabeNe73kUoFKrqq7AwniLGTNbGtmxefvlIWYA8g9VD0QwOnV1CEF1lx4imlPw32/1jHWWTuJXIyBrffmQacfkwitOnn2DTps0kZR3btrllV39ZomglElmFL9x3HkkS6CnZVzqfVtoea+spu9TmnSX7zpayKoZpsXsoSnCZfrCArKIznZTpj/kJlRz3qugm2DDUUWO/dzyHy+Uq6+e+U/O896sP4RZddEf8KLkcvkCApbSMYdm87/a97NvYuSrZTlhI5/jkj07jlgT6Svb0zSbzT0tef93GsoVRPTjNmYmFDF9/eJKhaKBs/+N3HjrPz87m6ytKX/AXli2/de0IL79mc5neGVVnJq4wEPMR9K1c74v7O0bQKzUkO5lVeWgyQV/UT0fgQl9OueLrD47zti/lDxWJeF0Yho0kuYqv3d9yzSjP2t1fZsOFjMLxqRQ7+8N0lexFbTYf/c/JWd70z4cQXdBZMseW0gqWDZ94zUGetbXvov2Aczw72enuoxP8+fIBF1HvBR8l1XzVyB/cvIXn7xteVZw7xeCj4wn+738fJ+x1M1haD5DIoZsWv37VRjYPhBvK9c3q/e8/PcP/+XGeKNwrUqT8KxwA+IEX7eJVV2+8qL3ryU7lNH4xEa+KwURWZTYts2e4o+yNYrPzBerPmVr3pYl4mvd/4zHcksBA7EIOmV5++v3u23ZWPWBbCzjZsNm83UgMBjAwE1NtXRg/5YrvCrjhhhuqCvBKsW3bNj772c823W/Y62Y6JSPrNrGVD+8Z1IBu2WiWTazifHufJJGQNXTLpl1fRVTTRjFtev3lsoMeN3MZBdW0qf7K1BrIuo1iWgxU8CGvRaytp+x6Np9OyuQ0k1igfFwuwYVq2oiu8huLWxTIaQambZclLdO2MSybgKd8j+l8KoduQdRX3o9fEliUTSYTGS4Xu8v+1qxsJ2RVG8U06QqV6xbxuZlM5p+CdDd473KaM6ZtI2smQV/5iJJyfYqKk/My7oqbtugSUC0LV8VBNs3qXdffKZmcahALlH/5dJKNy4Vi2HjEcr865YqTs3mWiMpxFnhvzyVkbpLK/yoJArJhVi1ims1H5+YzGFaeMq8UXjG/JeDcfKbhhbFTPDvZaWIx/yWocod1Qe/z8Wp/NxvnTjGY0lRU02LAWy495BWZShgs5hR2S+VbXJxs26ze55P12W8K8dAInGRbUDMGJUlA1m2wVzdfoPn7UjJroRgOuSWRI5m1GK6/q6gtcLJhs3m7oRhsloZnBXjK7TFuN9KqTtAt4nc/Pc8cX0+4BRcewYVSEdiKYeATBdw1TphrFbyiC5/oIltBdZTVdALLPMHtgt/twicKpCsqyNci1tZTdl2bewQCHhHdLCcNtK38az7TLm/Xzfxpi2IF3aLociEJrqp+eiIB3ALk9PJ22bDwiDAUC61athOCXhc+USSllOudUnRCbolgAzysBTjNGdHlwu8RySrl7aU0erWwtcdfpbdp508Gs63yl4fN6n2xORbwSg3Lxs6fAKmZ5fRgTrlia1+e4aDylln4/1jMX2VDw7LwSyJGxZiazUdjPSEk4cLTygJUM89zPtbT+Fdup3h2stPwMntKJYlaQdPRjmp/NxvnTjEY8XjxigKZCsUzqonfI9AV8DWc65vVezRa/0lhIR4agZNsAWrGoGFY+bzpWt18gebvS9GggE+qnVuCHpFocH2WdE42bDZvNxuD7cIzC+MSLKYVFjMKO/rDK369bJgWqmFWJVsnKJpBWtFRtLVx+HrC55EY6vDnT6fLqmTU/O+0rDMQ87VtGwVAyO9hS2+IeC5fiR/P6cu/VTZ1B9u2jQIgFvSxezDCYkZhJimTlg1mknJVrDUbO83KPjmV4MRMipNTiSrZ7YjDUpvPpGQSWY2ZVN7mW3tDDET9KLpJRtWRtfxv3bIZjPkxTJusopPTDLKKjqKbRPxS1dM9aXlrjKKbKLqJaeX3B28fjLKzJ0BOs5lL5EhlYS6RI6PmmRau29lb9ZlS2aVjcpLthO5wgH0jURbSCpPxLPGsymQ8y0Ja4ZKhSMPbKMB5zrhEFwdHYiTlPFtDStaZiucYLqH+0kt+YJl15rnbq2xumDaDHT70ZTsU7FGqt1NslraX+vvMXJqz8xnOzKVL/O1rWLbtyh+LnlMNFjIKiZzGQkYpyxWlMfvCy0YYWN4NkVJtcibFbRQDfnjZ1RtIy/mTuLKqQTynouomuwbCKKZFStHQjPzvZvPRs7b2sb3bh6zDbFwhnlKYjSvIOmzr9hWfFjcyv53iudROpfPiWdt6i4VoSdUq/hT0fvlVG1B0s+wzpXHeiL9LY7DUfrGQm30DYZKywdn5FLPxDGfnUyxldQ6ORHnW9h7Sst6QbZvV+5bLhihs3lDNfIwX1udhFxfdRlGqn5NsC4oxWKpDVjPY3B3CtvNb0qbiMvNppek8AeU5cimrouoWS1nV8b403BHm8rEY8ymFiYUsS9n87/mUwv6R2LpsowBn/zWbt52uL43BhczFTiVogT5tl/AUQlox2TPYwcHR+pQotWBZNklZqzpbPOr3INR48mAYVs0zzUc6gkjS0/f7ylA0QDKn8cRCprgRf3N3iKGLPAFoBbZ0h3l8OsmR8wkm5jQW7AT7R6NsafS99ipwcLSLtKLz8GSKyUSOoFtiz1CUg6NdTcdOs9jWF+Irh85xZDKFalp4RYH9QxG2Xb2h7XG4rSfCZDzHQ5MpcrpBwC2xdyjCtp4IHo9IIqcxFVdQrfyTocEOHwNhP9NpmamEXCwUGYz5CXtr02IV9vmlZIOcZuAWBHrDXv7kzkt42z8f4mwyT+wvqXl6rj988W7HzwTdEpPJXNWYnGQ74dlbeplNyhwZT5DVTIIekf0jMZ69pX5VeS04zZnLRzv43rFpHpxIMp2UCXhEDozG2LHBxZ/951RVP3/+yg2Eve6aNh+KBsjqRpU9wl438axaFZuFo10r24djfr778CQPnE8iGyZ+SeTy0Sije4ealu2N+EnJixybShX72jUQpifg5cx8uipmf/+Ozbz936p5Xv/wpVsv2HA+i2xY+CWBzT1BtvVEmM0qTCcUErKGTxTY1hdmpKM+1WAl/vjO3bzxHw8zp0JhN0uvN9/e7Py+aGxWzIt337mJ3/n86ap+/uDOLRdsXvGZZv09EPbXtN9bn7eV3//CUU4uapgWiAJs7fLw+udsKtqwUds2q/cH79rC2z53qqqfv3z1Nkc/OfmiML+rZHcGiQZyVTp0+T3cc2qOo+MpFNPAJ0rsG4kwEnE+J8EJuwZiAJxeyDKXUQi4BfaPdBTbK/Hiy0ZYSCscGk+SS5gE3CIHR6K8+LLajBFrBSf/1WN2aub6noCX+zPznJ5NcMtwe3Qo4JmFcQmu29qDLxhEtS2aXaYlZY25tIrPLRZfEc+l86XCHcFqbpnxeLZ4pnnM60YxDE7M5smzV8rO8FRAVjfwe93sGe7I79Fy2dh2vt3jqdwp11qcWkiTNSz2DkfpdiUZHIqSNSxOLaTZF2z+y1AzUG2LLf1RxrqCZQwMqm2hNhk7zeLLD5zn9JLMaEcQn8eFotmcXpL58gPnue2ykbbG4WxWQZJErtzYhcsFtg26ZTGbVYjZHjTTZiDmwyW4sC0bzbSZTstopk1/zI/oEjBtC820Sas6HVK1PQTBRUfQS9jnLqug/tlJhf2b+9iuqSwsLtHd1UnQ4+WJWZkdA7U/E8+qNcfkJNsJi7LGUFeIwc4AliUgCBYul8CirBEJNedTpzmTVA32behmx0Cs+IXH55X4q29MEvW6UFUbmzz3rdfr4kcPJrl5q15Tv6xuONqjVmwmchqaaVe1//TxGU4t5BjrDhLwSuRUg1MLOb53bJpb9gw1JfvMfBrZsNjZHy4+xZQNiyPjS8iGVRWz//KDSfwisPwE0Q0gwpd+Ms21G8byNhyKUghE2+VCtS029oQZiPprVvE3iiOnU2weiDJmWWBZIAhIgsCR0ynGOjuamt9O8VyIzcp58bn/nsLrAsPOvwK2AMkF//bDKZ69cUPNzzTrb4/owu91s3c4hsUFOd97aIJQ0M8VYT+iy4Vp2+gW/OT4Aluu72zKts3qfe+jaUZjXnRdJZOFUBDcbi8PHM9w667aMi52n66UDdTU4SfHZ3h4Kkl32IvfE0DWDB6eShL2uYtMP43C4xHZN9bFlt5wTcaIKh1Ug0s3dLNjqBPdsHBLAl63QFI1qE8c2F44+a9V1x8ZX+LMUo7+iBeaPN21WTyzMC5BLOgBt0hKNgj73A2/EjFMi6Ss43OLxQpLcZmBv1ZfimYwGZcJ+93Fil2PlP89nVAYiPrbuq1gvVDLTpCvRG3W5s0iI2ucmsvQEfAS9buR424GOgIkZZ3TC1m29Ibbtp2iVO/S4iNFzxOr29gNx06zOLuY4r5zCbrDvqoq5vvPxxnoCDLQGWhLHNaKc8hXpk8syWhG/jVtZcX8VKKyYl5sKEYkUSgmtIV0jqPjSbrDPnaEo0xKGkPDvSykVR6ZTHHl5hzd4UDZZ1oVn/X0btauTmPKqDpTy9X6sZITHh88u8BD0xlCfomt/RfaZ5M5jk6n+dmpeS4Z7qirXz17iIKIYVlF2aXtC+kch84l6Ar7GCyMKQwziRwPjifZ3BemJxJoSLaTDRczCscmU+wcjJTF7KPn4zy+oBDw5A+cKWUheWQ2w4+Pz3DZhm5H2T6PtOLC34l4mgfOJeiJVM+xw+MJdg910t/hb3p+Xzw2RX56YpbHFxT8HuguPc0wJfPYfI4fPjrHgc2dZZ9Zjb9LmSHOLaS4f9nfQx3leh8ZT3BLPM1wR7hp2zai9+OTCQ6dT9IR8NDfESERTxDriDGXUDg8keTx2QTbS04trKcfUNMepSjVIZFVeHQqRVfIV2T6ifjdCIKL4zNp9o7EVrQVM+T3XLQAvHReDHWsLre0C042XM31pTbvDrpo98L46fvOfoVwiwK6lefuaxSFCsvK6l+nvgoVlr6KKmmfJKGYFnplIcrTBM3aqZUoVP8GPdXVvzndQm3giNGVop7eqmGhm+2zSSJjohomEV+53hGfm7RispTT2xaH9eI8Z+RP92umYr4ZexSYIWrpndENsjVOz2pVfLZyfjuNyalafzGjolkWIXf59SG3gGJYzCWqWQqc9GtWtqKDbJiEveV6R3we0qpBUjZXnSMll4BsmggVRUkpRcewwC+Vv3XySwKaCdM12BlaNccKTAG1Yi2lmMQVbdWynXwxn5SX9S63R75oDCaS6bb5O6fZKIZJuIKVIuJzk9VMktnV10o4jSmh6iiWRdhXLjvsE8npJoup6qOyWzW/C0w/ldurwl43Wd3MM1a0Cb+sawcnm7cLzyyMK7CSytJWVViuBTvDeqJZO7US68lKUU9vryTgFttnk1hIxOtQxRz2iXQG3G2Lw3pxHpBEfJLwpGKGaFV8tnJ+N1ut3xXy4hEEMhVsHBndwicJ9MaqWQqarRx3ku1z5xemabVc75SiEfZKRP3VLCTN5kjDtvCLIlbFF9mIz40k5BfmpSiwkAzUYGdo1RyrxxQQ8Yl0+Dyrlu3IvhL1L+tdbo88zRgMR8Nt83fA48IniaQrWClayZDgNKaY141PEEgr5bLTSn7PbVekegHVqvm9nkw/v6xrByebtwvPLIxLoBnOVZH1sJoKy9VUQ68W7WBBqIdm7dRKlFb/LqYVcprJYlpZE1aKUr0rmRY6gx66gt622WRDV4Qrx2IspBWmE7k8uX8ix0Ja4YrRDvZv6GxbHNaL8+FOP70RX11miGYZEkpRygwxvpBmIaPmf1cwQzhVpzfCiNGI3ospmWRWZTElr8iupWNqhM1hY2+EvQMhMrLBxEKKxWSGiYUUKdlg30CYa7b0rLpy3El2yOfl4FiMxbTCxFKejWNiKV8xf9lIlM29kYZt68TGoegmuwbDKIZZFlOjPSG2d/vIaTCzlCORzv8usJBcv6N/1X51QilTwPmFDAvp/O/5lMKBkRjbBsKrnt9Ovtg93JFnxNBgNi4TT8nMxmVyGuzsCXDD7mr2lUb83UisRQM+rlj29+RSjmROY3Ipt2qGhEYYI/o6AhwcjRLPaZybS7GU1jk3l2Ixq3FgOFq1jaKeDZv1RSnTz8RihoWUzMRiZtWMVo2gdF7MpWQWUgpzK8wtq8Farx1Kbb6UrnHMY4vx9NvIugrYULcqsh6arbBstmK3lWg3C0I9NGunVmJHX5SUrPPoVIqJlIE7qrF7MMKOvujFP7xKOFWHh73uos3bZZO7rt4EwOHxRJ4RwyNy845e7rp6U/ELQbvisF6cO+ldYDxotGLeKW6v3tTNoxNxHhhPshjX6dLTXD4S5epN3Y5zIOiW8Ii1/dQMBsJ+xhezHJtOFav4dw1EGAj7L/7hCjTL5vB7t+/k7f96mNNLBiYmIrCpU+LtL9jesspxJx+9+LIRcqrB4RI2jgMjMZ63e5BQnTlQC05sHE5MEu+9Yxe/9ZkjF46F1vOUZb/3Kzvqzr9W4IX7hhlfzHJo/AIbx8GRKC/cN9yynOfUz/teeglv+cwhJrIUCYyHg/BHdzizrzjJbjbWXnVVnhbt0HiCuXSeGeXaTZ3csX+0aRs2yxjx+uds4vHpB3l8QUE1wCurbO/28epnbWjahs36Yt9wB6dmUxweT5DTzDwjzEiMfWtwukZf0McxI8HhM3Fk08AvSlw6EqFvDU7qXc+1Q4HZ6fRMHNrMT/DMwrgEvWEvsRWyADRbYSlJQkuqoVeCZhk0Wolm7dRKyKZJXyxAV9BDt7XArq3dSG4J2TTxtXkqpFXdmWkh6G2rTWJhH2+9ZRdnF1MkMiaxkMiGrgsE+O2Mw4vFuZPeHVLjFfOFfirxyFQSSxC4amMXc0GV3t4udMvikakke0YEh+p7rSlGDCdMp2Vkw2LHYBRJEDAsC1k3mU7LbPQ1l9ULsdMom8N3H55ktDfKSK+reFauC5sHzybZ1t/VssrxWj46M59m62CMjX1hDNOFJNpIokhc0bEFV1O2dWLjcGKS+OZ3x+ntCNIbM8lkFUJBH7hEfnJskZGOWEv86oSJhMxIb5iRrjCmZSEKAoj59u5ooCXz28kX3304y66RLjZbBpYOghu8gsSj4xkuGeptSnazsRbPqty8d5irtvSR1nTCHjfhgBt7BYukZhkjvnLoHF6flwOjHtLpFOFwBN1y8aPH5tnUW3uB2qr7z+nFDIgCV2/pQXK5MGwbzTA5vZhhX5OsM83ixHyK2YzKlr4QHklAMyxmMyon5lPsG2svu9J6rh0KzE5DYQEzMdNWWc8sjEsgCqtfjDRbkbmaauiVoFkGjXahWTutFqV6u70iMZ9ER8iLbrVfb6fK6qrq8DbbZENXBBzyZrvjsF7/Tno3UjEPtf1XWsXcGfQg5LyM9oRYymocm0rREfQSCXicq++bZMQoxVqwUjgxC8wkMxw+n6Qn4qO/hCFhJpHj6ESS65MZ+qOhllWOOzNJXHjjkFI0xpdyRXq3enOgGb1LY6qUfaUv6mNhYYHu7i5mkwqHxhPsn0wx1BVoSHazKGW86SxZHCxl1TLGm1bN73b5u9lYK2Pb6fY4Xt8ImmWMKGUC6Yv6mJvV6O3rYDaplDFiOGE1vmjU3+2Ak2x3VlhTdqW1XjuUyQ76WEi0RUwRz+wx/iXDejJDrCfWU+9fVpu3Es3asF7leEa3yGhGw9X3T0ZWCqcxpWV7mSGh/OYY8XnIaiZpeZ0q5g0LRWuclaJZvS/GvrIoq22bf+vJeNNKfzdr81bmtWb7qscE0ipGDCesp7+frOxK63UPbRdaLuXUqVOcOXOm1d0+gxZhPZkh1hPrqfcvq81biWZtWK9yPOQWCHmkhqvvn4ysFE5jCvtdywwJWll7StEIekTC/nWqmJcEfJ7GWSma1fti7Ctdfm/b5t96Mt600t/N2ryVea3ZvuoxgbSKEcMJ6+nvJyu70nrdQ9uFVUXPF7/4Rd73vvcV//+2t72NX/mVX+HWW2/lLW95C5qmOX/4SQjTWhujryfWkxliPVGqd1rWyWj532uh95PF5omswnRCJpFtLzl6LSiaQVrRUTTj4hfXQLOMEaVVzNNLOeI5jemlHIsZhV2DEYY7Aw2zLayGlWK1bB/Nxk5/NMSB0SjzKYUnZlOML2Z4YjbFfEph33CU/mj9IwRWU21eqnc8p5JVDeI5lbSsM9IZoDfcOPtKs3qXsq+cmE4xlVQ5MZ1iIa1wcCTGzqFIS+dfqZ1KGW+m4jkWUjJT8dyaMN6U+vvsfIaZZI6z85mG/V2KZm1eev1SVmU+pbK0zBzSDmanUpuXMYHMZ1nKqpyfz66aEaMRlPp7NimzlM3/Xq2/G5l7pbLn0woZxWB+HdiV6sWHkx6ryS2lsrUKSsZ2YMVbnr7yla/wvve9j23b8ueS/+hHP+K73/0u+/btY8OGDXzta1/jM5/5DG9605taNth2YzatYkvqmlRYrifWkxliPRF0S8hqhpOzGU7OaxihJFv7QgQ7288Esp42VxSD+88u8OhUCsW08IkCuwcjXLGhG5+vvTu9DcNiPJ5lMi6jWTYewcVQh5+RjiCS1NyCpFlmgULl+KHzCaYXdAbsFAdH85XjoSbZFpr1UytZZ5qNneu39/LzU/McOR8vHhW9fyjC9dt7HWW0qtq8yCQxny2ycWzuCTIUDRT9vVpGDKfrb903wH8fm+axuWyepSCZZnu3j1v3DbRs/jnZaVNXiMenkzw4ES+yFFw2HGVLd5vL54Ebd/ZzfCrJAxNJFN3E5xa5fDjKjTubO5oYmre5XxSZTeR4dCqNYpr4RJHdg2HGSvY7r1a2ExvNrXsHOTmT4tBEklTaJqJlODgc5fZLh5qW3Sy29USYjOd4aDJJTs9zJ+8dirKtJ3LxD1eg2blXYFd6bDpNzsgRkER2DoTXhF2pXnzUYxVJq/qqc0tB9ny8/Q92VnxX/OIXv8ju3bv5t3/7NwC+853vIEkSH/vYx+js7MTlcnH33Xc/pRbGAqxZheV6Yj2ZIdYTk8kcs2mV3rAHOSLRG/Ywm1aJBnJs7GnvDWw9bX7/2QV+dnqBrpCPgaCXtKrzs9MLAFy3o/mbZzMYj2c5MZsm7HcT8+YPEzkxmwZo2uZ1mT1qMAsUKsev2tTFlCfL4HAXhm0vV447szPUYltoFq1knWk2dn78+BxZw2bfaAyvJKEaBlnV5MePz/GKq2o/QWxVtXmRSWIoCi4X2Da2y5VnNfA0x77SrN5fvn+cjOFia48fJSvjC/rJ6Pn2d93e0ZL552Sn2USOrGFx6WgHbiG/5zKrGZxaSLMv2F6mgGPTKbw+N8/d3lfCJGFybDrFddHmFqjN2vz4bJJTCxl6I168bhFVNzm1kCHidzfNkOAk24mN5tRMkmjYxy07vMzMTNLf34vpyh/L3Btr78OO2ayCJIlcuakbl+3CdtnopsVsVmFjoDkKwGbnXoFdqSfsw7RBdIEgCmvCrlQvPpz8lMhpxcLb1eSWgmyX4SU92xb1ilixFU+ePMk73/lOfL58XfDPfvYzdu/eTWdnJwCXXXYZ3/rWt1ozyjWCRxLBLa4pO8N6Yq2ZIdYTpRXzQbfInFsgFvSS1c01PWN+rW1eys7QE87PVZ8nX0l8fCbN3pFY2wjpa7EzeKQLvMmrZ2dwZhYord6O+t1oCQ/DnUGSst4QU0Cr/NRKto9GxtQoS0EpWlVt3jCrQYsYMUpxaj7J/eeSdIW99EV9LC2ZdHZGmU0qHB5Pcmo+yZae6Kr86mSnnJJnOumOXJhjsDYsBbXmN8B8WlnV/G7ETrUYEsK+PC/7avRuhI0mndM4OpGkK+SlJ+xFUhOM9kdZymrrktegdawz9eZe2fWB1TGBrAaV8eGkRynTT6uYLFrBHnYxrFiCy+XC7c5/Mzp9+jSzs7NcddVVxb/Lsozf3zyZ/XrjGaaApyeeOWO+mp0hq5vI+jqxFLSZnWE9q7fXEythKWhVtfl6Vq3H0waKaRKp2BoU8UlkdZN4emV720vhpJ+FgGxa+N3lstci1tZzfq/FHHOyuW7l47xS9tM9rz1ZGY6cxtUqpp+1xooXxhs2bOD+++8H4Bvf+AYul4tnP/vZABiGwbe+9S3GxsZaM8o1xDNMAU9PPHPGfDU7Q9At4nevE0tBm9kZ1rN6ez2xEpaCVlWbr2fVekdYwieKpJTyWEspBkG3SEd49c//nfQTsPCLArJeLnstYm095/dazDEnm7uFfJxXyn6657UnK8OR07haxfSz1ljxwviOO+7g7rvv5vbbb+dTn/oUGzdu5ODBg5w8eZI777yThx56iF/91V9t5VjbDs14+rMz/LKitGI+regYlkVa0df8jPm1Rik7w3xaQdFM5tMKixmFHf3htr1uhPVlZyit3o5nVXTTJJ5V16R6ez1RylIwk8iRUwxmErm6LAWtYk1ZT/aVLT1RrhiLsphWmY7LyJrGdFxmMa1yYCTKlp7VFyY56SeIIrsGI6QUjaWsiqpbLK1RrK3n/C6dY+3S28nmbrfIvuEoiZzKQlpBNQ0Wfgny2pOF4ahRPVrF9LPWWPFq4Nd+7dfQdZ3/+I//4JprruE973kPkH9aPDk5yTve8Q5e/OIXt2qca4KppMzeDdE1ZWdQNGPNj4QuwOmI4HowTKslxWML6RxZ1SboddEdbr6CeSVjKjAC3H3kPA+eSnKZvcDt+0cbYgpold73PjHH+YUso91Brt7szBLQCBqNnSs2dAPwV998kKm4xWCHwLtuu6zYXg/N6l15fcG2n/rRMc7MKWzs9fHG5+wqs7mTHhlZK7JPhPye4rycSSpkVYOgV6I/6iu2V16/ayAGwB/82885mYKtkUf44K9dVWyv9ZmLtTvZw0mHrz84zsnZFFv7IrzwspEyWzl9JpFVkHUbv9tVdoN3aq/s57Y9wwD8/lcfK17zly/eWWyv1VfBhhNLMhlNJ+RxM9zpL7Y7ya60R+H6j3//BKfm02zpCfPmm7atKqc62akyh7zhuq0AfOx/xpevSPKWZ40U26F5f1eioMd0QianWwTcAgMxP2OxABG/m7/53i+YWFAZ7vbyuzdfWhZrTno0G1OVvijM41//7OHiNf/y2gNl89tJRrPzu1J2Qb8P3H2EybjGUIeH99y+v6E55qRfZbsTE8JIpJ+wz83vfvYwc0AvJ/mbCr2d0KwvKlHIX5//6RmemE+zuSfMq67d2NC9pFH9Cu1O17/3P+/n+EyaHf1h/uKOK8rmWLMx1aydjo4vMh1XGOjwsW8kX2RZkL+U1ciqBl6pMaafZmPzifnURce9Wrhsu7WbPEzTxDRNPJ6nzhOZXC7HY489xqcfktnQE+EN122lM9Le/dGtpLFqFom0wufuPc195xKoholXErlyLMZdV28iFq79TbtVdE65nM4PT8xwdDxZpPfZNxLlhm39BJqs5m12TCdmFnnbvxzi9JKBBniATZ0SH/n1g2zrr11B3Sq9z84n+P0vPshj8zl0C9wC7OwJ8JevvIwNPbGm9G42dv7x54f5i69Wny3/3hf384arDtSU0azeTtc/ODnFb/7TMUqZJ0Xg06/fzbM2jtbUoy/o48R8ilNzGRTTxie62NIbYkt3mFMLaR6fzaAYFj5JYHvfhfbK63986lH+7nvpqrH+7vNjvPGaqzg2nXCUUdm+oy+KbJpV+gXdEpPJXJUOcTnDb33mCNPyBbkDfvjk6w+yq7+npt49AS9HxpeqaPX2DsV4aDJR1b5/pJP5nFrVzxv/z084WcOn24Gvve95Nan7CjIemUyRMywCksAlQ86yD452odpWlT0OT0zym595jNKbigv49Ot28dxtG2vGmhOc4rzL7+GeU3NVOeRz33yIe6vdzXWd8I9vf35T/t41EMPjEav60jSTY9MJHp9JF+notveHOb40zbu/dK7q+r991SZeuGt7TT2GogGyutFwTDnFx9//v8Pcl63W+9oo/Ovv39oSCi0nyscTicmaueXPXjrMK/deUtPm23oizGaVKv0Gwn6m07JjXqtcKH3wG9/mkz+t5rR907Uif/Arz28qppx84WSP+89O8vpPHiVdEuhhF3z6Tfu4YkNturiL5e1K/Zyu/+/TJ/nzr0xX9f++O4f49QOX1vS3U0w53TOcZIsuiw9941GOTqVRrfw2iX2DYd77oj30x0IkZY3FrIpu2rhFF11Bb9GGlfo1S+8mqxp//a1jzCazvPOqKDt37iQQWN1DNSe0fGFciiNHjrB///52dd8yFBbG//yIwvF5lZt2dPOu2/e2VeaZ+XSRxip/ZKpBWtbZ1hduO3XYR797jO8dn6M77CPic5NSdBbSCjfv6OWtt+yq+ZlSKha3KKCbFopu0hv2NkW58s2j4/z4xHyV7Ou39XDbvpGLd7CKMT3/r77D40sGHhe4bLBdoNmwvVPi2+96Xlv1fsX/90OOTuUIeMAnuVAMm5wG+wYDfOm3b2hK72ZjZ8O7v+nY19kP3VazvVm9na5//ofvoVaCEYHvv/O6mnoYhslsRqUj4CXocZPV8gdGBCWBrGE13P5ndz/uqPdX33wVR8bjDfe1pTtEXyxQpZ+s6sym1Sod3vOlB1lc3v4oAYXdiAN++PxbauudyCicWcrRFfIVbw6LGYWQJJAxrKr2jZ0BYiFfVT9v+tyDjnr/y2sPFKn7GpHh1L5nMMqW/miVPZ734XtqynUBZxxizQlOcT65mOH4bKYqh/z7kerFwkr9vX+koybd2NFzizX7qRdrP3SI876wF7/X3XBMOcXHJ35SvSAv4ME/uqnmvPSIriKFViPz+yfHZ2rGTT3ZTjbvC3mRJLFKP78kIBtWW/OaU0w5+cLJHnv+4Jtli+ICwi54+IPNyXbSz+n6evPbyd9OMdWs7M/dc5KHZrJE/BIht0BGt0jJBtduiPH+lx9oyT3DKTb/7389wn3nU+zs9fKOKyJtXRiv+NGkZVl86lOf4kUvehE333wzN954Y/HnOc95DgcOHODXfu3XWjnWtqM74qMr7C3S+7QLlXQvHkkg4vMQ9ruZTigrPh2sEZxdTHHfuQTdYR8DsQBBn5uBWIDusI/D4wnOLla/pqikYhEFV/HfKdlo+CSbhXSOo+NJusM++qJ+/F6Jvqif7rCPRyZTLKRzDevR7Jj++7FpTi8ZeF3QEXIT8OZ/e11wZsngvx+rvqm2Su97n5jjsfn8orgr4icY8NEV8RPwwPH5HPc+Mdew3s3Gzju/cG/d/mr9vVm9na7/2qFzxUWxWPIDYAKf/OHxKj3cosAvJlKEPG46g168boHOoBefJPHgRJKAJJa1BySRB8eT+NxSWfuX73VeqAC8+0s/L9JMFfvySDx4PolfKu8r6vPw2EwKy7bL9HO54NRclqBHKtPhoXPx4qI46hUIegWi3nyqnZbh/917tkpvwYYj40kifg89YR8+j0hP2EfALXFoPEHALZW1R3weHpxI4nK5yvqpd9OE/Ov2ArVXUYYk8sB4goCnXIbfI3HofIKAVN4eC3h5eDKFYVhl9vjPn58uyhFKfgBs4OPfq++TUjjFuW1bHDqfIBb0lOWQeotigBd/vIa/HWKnI+Dl9EKWjFxewFhJT1a4/vvHztSV/cG7j1bpEfRIPLGQweWioZgScHFkojo+6i1MAQ7++fer5qVbcDGVkJFEV0Pzu5ISriD7Zycn6+v99cNVtgp53Dw0mUQShTL9vG6RY9NpfBXtTnntpR9xXhQ7/d0ppgJeiSfmswjQkD0+f++Z4qLYJ174AUjb+b83KttJP6frP3fvI3X1/sMvH6rytwCcms8Q8Eqrkj2blHlkNkPIJ9IXDRAM+OiLBoj4JY5Op/nZqflV3zOcYvPsYpqjU2miAYnOUPvPmFjxwvgTn/gEf/u3f8vp06fJZrNMTk5iWRaLi4vMzMxgWRZvfOMbWznWNUEr6X2csJ7UYYmMiWqYRHzl2xYiPjdZzSSRqX411SqKmKxqL1MqVcvO6AZZtX00N2dmU5hA5dtRj5h/mndmtvoLQav0Pr+QRbfyT4pL4ZNcaFb+742i2dh5dLr+fqxaf28VhdCpuUxd2WcWclV6uGwXsmHgrni95xEFcpqJWyp3oFsSyOkm3gpuy5mLfK+dWqKK6sktCOQME3dFNb3XLZLTLczKqWG7UEyr6lXk3PKR25XJtaDpuYRSpTcukHWTQAXll1tykdNMPBW2DXhEctrKjkatpPbySgKyZuKp0MMr5m3rqYjboMdNVjdQK252TyzI1MPxGnPMCU5xbllC3k4rqMeo8rdD7DjRjTnRk80ly5kRKnFuqdrfkiTkKcXsitf0DjFluWxkzSTobk5vE6rmpUtwoZo2oqux+e1ECTefrH+PHE/qVbbyLttcqGAjkAQB2TCr9vU65bUn6n8Pqvl3p5jyiCKyYVH5mMPJHicvEse1/t5s3na6fmKxfqydmM/UoBMExbDxiOW5s1nZ6ZyObkHAXd5PyC2gGBZzCXnV9wyn2EykdVTLIlhje1M7sOKF8Te/+U22bt3Kz372M77whS/gcrn43Oc+x6FDh3jXu96Foijs3dve7QjtQCvpfZywntRhsZCIVxJJKeUTLKXoBD0isVB14LWKIibodS1TKlXLDrklgt720dxs7IsgApVrCc3ML1g29lUXH7ZK79HuIG4hn5xKoRg2HiH/90bRbOzsHqhfVFnr762iENrSW/uUtQI2dgeq9LBdNn5JQjfK+9JMK39qklHuQN2wCLhFVKv8+v6LkBAMdlJF9aRbFgFJRK9cEOkmAbeAWDk1XDa+5b2ApehdLk6rvNEWNB2L+ar0xga/WyRXQfmlGzYBj4hWYdvCscMrQSW1l2pY+D0iWoUeqpm3rVYRt1lNJ+iW8Fbc1DZ316/L2FFjjjnBKc4FwcrbaQVv1ar87RA7TnRjTvRkvdH6tRFjndX+NgwrTynmqvhy7RBTgu3C7xHJ6s3pLULVvLStfCGcaTc2v50o4Xqi9e+RI1F3la3UZZtbFQslw7LwS2LVk0WnvLZ5oK7omn93iinNNPFLQtViyMkeWy8Sx7X+3mzedrp+uKt+rG3rCdWgE1x+CFPxzb5Z2eGAG7cAOb28n4yer/fojflXfc9wis1Y2I1XEMiu8GFAs1jxwnhiYoIXvehFhMNhxsbGCAaDPPjgg0iSxOte9zquv/56/uVf/qWVY207FlJKS+l9nNBKupdmsaErwpVjMRbSCtOJHFlFZzqRYyGtcGAkVpOdolUUMd3hAPtGoiykFWaTMrJqMJuUWUgrXDIUuSg7hWFaqIaJYVpNj+m5OwfY1Cmh2hBP6+TU/G/Vho2dEs/dWZ1JW6X31Zt72dkTIKfBQkomm1NYSMnkNNjRE2iKnaLZ2Pk/v3p13f5q/b1VFEIvOjhGIR2aJT+Qv2G/6YYdVXropsWlwxEyml5GAaUYBpcNR8kZJgtphfTyvtKcYXLZSBRFN8quf+nV2+vq/aFXXFVFM5XTDC4bjSIb5X0lFY2d/REEl6tMP9uGLb1BsppBIquSUXUSWZW9Yx0U7l9J1SKrWiTVfKIf8MPLrt5Qpbflgv0jUVKyVka7ldMNDo7EyOlGWXtK0bhsOIpt22X9fPKuy+rq/S+vPcBiRmEqniORVZmK58gZJpePxMhp5TJkzeDgaIycYTCTlEnL+d+JnMqeoQiSJJTZ446rNhXlWCU/kN9j/Oab6/ukFE5x7nIJHByNkchqZTnk5fvrr5S++uYa/naIHSe6MSd6spt21S8q/IPb91XpkdUMNneHsG0cYyqeU8mq+d8WNvuHq+Pjf11X/6yAQ390U9W81C2bwZgfw2yMQsuJEu6arbWLzIp6v/AA8ZzKfFoho+RjK6Pp7B2KYphW2ZxRdZNdA2EU02oor335bfX3q9f6u1NM5VSDzT3B/JPVBuzxqqs3El5ObIp54Qfye4xfdXV1PDSbt52uv+vqS+rq/f6XHqzytwVs6QmRU41Vye6L+rmkL0RGMZlN5sjmFGaTOVKywb6BMNds6Vn1PcMpNjd0hdk3GCaZM1hMK3Vt0Aqsiv6go6Oj+O/R0VFOnDhR/P+zn/1szpypv/fqyQYbm5t2dJfR+7QLIx1BtvWFwYKErIEF2/rCDdG9rBZ3Xb2Jm3f0YlkWk4kclmVx845e7rp6k+Nnon4PvWEv2OSf1tiUUa40ihu29XP9th5sy2YymcO2bK7f1sMN2/odP2NZNvGsykQ8x/hSjol4jnhWJex1NzWmD7/6AJujAiagkF+kbY4KfPjVtZkZWqn3B1+xj919PnQTlmQb3YTdfT4++Ip9TfUDzcfOH/xKT1Pt0LzeTtd//DW1F0Mff+0ORz1u2NbP/pEObNtmLqNg2zb7Rzp4wSVDbOkOsZBRODmXZiGjsKU7xAsuGap5/W/fUPsJ5ttvyrMO1PrMbXuGa7ZfsaG7pn47+qL0hb3MpmVOzWWYTcv0hb18/PX76F3eCld47tLrhY+/7oCj3rftGeaaTd3Yls10Ssa2bK7Z1M1dV2+q2X7bnuGa/Wx28OlWYP9IJxs7A5xfynLkfJzzS1k2dgZ45ZUbasp41VUb2TMYJZHVODGXJJHV2DMY5fqtfTXt8anX7qgp+x9+Y6fDqJzhZKeXHhirmUMud3hgfU2Epv1dSjdWCqd+/uJltQuH/+qVGxz12DUQqxtTc8l8nM8lFfrCXl5wyVBNH13pcMu4Nuo8L0c6gk3N7ys2dNeU/Ycv6qt5/fvuHGJHX5Qt3SEW0yon5lIspvNFrM/e0ltzzhwc7Woqr73hqprNju3gHFNOvnCyx6fftK+4OC6gwErRrGwn/Zyud7L5H71kwNHfuwZiLZH9/pft49oNMSzLZi6nY1k2126I8d4X7WnZPcMpNt/zwku4cjRS9ZS5HVgxK8Xzn/98brnlFn7nd34HgLe97W1omsYnPvEJAL7whS/woQ99iF/84hetG22bUGClCPQOsXPEeYHWDjzDY3xxHuOLMSQ0OqZCPw+cmuPBx05w2c5tXL6ltyGGidXqXZD92HiS2XSGvnCInSPRptktStFo7BQq6e8+cprppM5A1M3t+zc5Vt6XYrU8xgXZh07PsJAx6A5JHNzUXya7UR7jgg0t28Y0QRRBcLmKNqy8vlBJ/717z3Feh1E33Hz1GNds6ua6Hf01ZTjJdtKvMCaXi/x+UZeNbVOsrP7JsRmmEjKDMT/X7eov83e7eIwLFeWlhXifvOuy/I0OODGbxlXyatO27WJ1eqWMgn768tMct+DC7RYd517h+v+47zSn52Q29fq588pNbYnzyhxS8HdpQdr/um51/nZC5fUFm3/jyDjnFjOMdYX4lf0jZVX/jXLnFmPKtsHlAtvGLolzpzjY8e5vogA+4HgFK0O7eIwLY/3oDx7i5GyGrX0h3nrj3vzCBphLq9iWXdTbJbiKc6NyzhT0azavffbbj3PegFEJXvv87Q3ltdXyGBfw+XvPFHnKaz0pbkZ2o9cXYu1zPzvBxFKW4c4gd12zrSzW1oPHuIDV3jOc2guxNj63SIe+9OSka/vABz7AV77yFT74wQ9y44038o//+I987GMf4z//8z8ZHh7mda97HXNzc3znO99p9ZhbjsLCeNu2bYTD7aVKewbNwTAtJuI5XK58dWoBim6CDUMd/oYnX6EftwCPPvoou3fvRrdoqp/11GElyMga335kGlHMV4UXsJRVsW2bW3b1t+1krlbKbtaGiazCF+47jyQJdAY9nD93jtGxMZayGrZl8/LLR1Z9OpbTmLKKznRSpj/mJ1RSsLQW/lY0g0NnlxDEPFtFASlFQ9EssG18XrHqb1iwf6yjapHQjM3XM87Xwt9OqGfzWnath/W0YbOoN1bDtLCxcYtiW+ZGaW6J+t2cPv0EmzZtJinrbc9r64lWxtpTCaWxhq6wMHn2yUnX9qY3vYlwOMxv//ZvE4/HednLXobH4+H222/n6quv5tChQ0+5k++ewZMPrWKGaFU/K8F6ynaqpHeqvH+yym7Whk6V9GGvm6xu5hkBVolmK6vXwt/1qt9lzSBnmA1XxreKnWQt9F4LfzuhlSxD62nDZlFvrKphLR/y0J65sZ55bT2xnoxW6wmnWGsXViylq6uLr3/967znPe+hs7OTWCzGv/7rv3L55ZczPDzMW97yFt70pje1cqzP4JcQrWKGaFU/K8F6ynaqpHeqvH+yym7Whk6V9GlVJ+gW84wAq0SzldVr4e961e9+j0RAEhuujG8VO8la6L0W/nZCK1mG1tOGzaLeWL2SgFts39xYz7y2nlhPRqv1hFOstQsrfuZumiahUIhXv/rVxbZt27bx2c9+thXjWheY1toY/cmCVu0XbicK1atzaRWgao9xo+Mu7ccSwLJtVN1Es2iqn9XqYFgWokvAtC0M02677EIl/ZHxOLpp4RNFFNMko+rsH+lo6+vGUtmmZeN3S8h6vjK6WdnN2rBQSf+z0wtomkFa1ZiJ50hrBtds6m7Ja3Wn2CxUVmumTUbV19TfhYryE7NpphZzxSPfwwF32R5joOoELJ9HqsoJzcy99YzzUn+nMiozSRlrNo2OXebvVu1/LEWpzXUtz8VrmBaKaRXt2ihWk+8m4mmSWYtoUGC4o3xLYDtyfelYc5pRte8fqDs3FN1ccT4vy2uGiawbLKQVMppRllva4e/1RGmsmVaem1gzTXKq0XSsrQZrXRtVGmtCBWVnW+St9IPXX389L3rRi7jzzjvZtMmZzeCphNm0ii2pjuejP13gdEb5k1XvQlVrSjbIaQZuQVgRM0Th+nhWRTFsbFbGMLEShL1uEjmNqbhSPGN+sMNX9eq3HdjWE2EynuOhiRQ5wyAgSewdjrCtp3Fe2ZViR1+UlKzz2EyanJ4j4BbZ2R9mR1/zdIjN2nD/SCfTiRxHzieZWDQZFmX2j0bZP9K5WrWKcIrNoFtiMplbF38H3QI/enSKQ+eTKJaFTxA4OBrlsuGddIby1A3TCYWErOETBbb1hRmKBohn1aqcUBhvo3NvPeN810CEHx6b5n8mkiSSOrH0PJcPR9k1EGk65zV7/UDYz/hilmPTKWTDwi8J7BqIMBCuz+1cC83mu1RG5cuHz/HAuQSKYeKTRC4fi/HSA2OEAp625nq/KDKbyPHoVBrFNPGJIrsHw4zFAniWebYr9SgcKb3afF7ILY9OpRhPmkgRjd2DEXb0Rdvu7/XEUDRAMqfxxHy2GGube4IMRduz37YUhmExHs8yGZfRLBuP4GKow89IR7DqUJpWoxAf8/H207WteGHc0dHBpz/9af7pn/6JSy+9lDvvvJNbb72VYLD9dGPtgsCFb7grraB+KiApa0WWh4BHRDetJ7XeguCiI+gl7HOv6tt8oZ+AW2AuKDEU8+P1tP+GDflXupppMxDz4RJc2JaNZtqkVZ0Oqb02n80qSJLIlZu68kFu5Q+zmM0qbAy0V3/ZNOmLBegJe7EQELAQRBHZNPE1mX6ateF8TiUW8nHDTjdnPGk2bu7G5RKZz6mEgq35MuQUm/Gsum7+/uz/PMHh8RQdAS9hv0Batjg8nuKz//ME77p9Lxt7wgxE/WVPfEqZX2rlhEbn3nrG+Q8em2EypbKtO0TGIxOKhJhMqfzgsRlu2TPUVM5rNkdOp2Vkw2LHYBRJEDAsC1k3mU7LbPQ1V9DdbL778uFzfO+xOXoiPrpCXlKKzvceyx8z/5KDY23N9cdnk5xayNAb8eJ1i6i6yamFDBG/m31jXY56dEirz+eF3NIV9NBjLrBzazeSW0I2TWTZbKu/1xNZ3cDvdbN3OIZFMaWT1Y3il5F2YTye5cRsmrDfTczrRjGM4huoAiNGu1CYFy7DS3q2raJWvjD+xje+wWOPPcZXvvIVvvWtb/FHf/RHfOADH+D5z38+d955JwcPHmzlONcEHkmE5bO9wz73k/JVympReUY5gChc+Gb/ZNZbEoWVB2xFP25x7V6V1bI55Cux223zynPvC0gpGtMJhYGov22vwsr0DlyQvRK9m7Vhqd5Bt490yM1ANEhWN9uid2lsrqe/T80nuf9ckq6wl4FY/glSNAhiwsXh8SSn5pNs6Yni80gUNpM0mhMuZq311HsmmeHw+SQ9ER+9ER/TZpqB3hBzKYUj4wk29YXojQQbynnN5sh2zbFGbD4RT/PAuQQ9EV/R30Ff/svu4fEEl4x00Rf1tSXXZ2SNU3MZOgLeIutM2OdGEFycXsiypTdMyO9x1GM1+bzUR26vSNQv0RHyolvkmUiwG/bfU+me+GS7l3ik/O9230tKIQrt98WqJOzcuZP3vOc9/PjHP+YTn/gE119/Pf/1X//Fq1/9am655RY++clPtmqca4YnY/VvK/FUqnp+umA9bb6eVcyt1LvZvp4uejeLeNpAMU0ivnK9Iz6JrG4ST1cfKfx0YH5JyzaKYZYtTAEiPg8Z1SQtt49dYz1jLZm1lvUuf/MT8blJKyZLWaVt/lhPZoiVMGI8GdlUmsUv671krdGSpbcoijznOc/hwx/+MN///ve56aabOH/+PB/+8Idb0f2a4slY/dtKPJWqnp8uWE+br2cVcyv1bravp4vezaIjLOETRVJKud4pxSDoFukIVz/ReTowv4T9LnySmOdzLUFK0Qh5RcL+9rFrrGesRYPCst7l7AwpRSfsE+kM+trmj/VkhlgJI8aTkU2lWfyy3kvWGi157m2aJvfccw9f+9rX+NGPfoQsywwPD3PHHXe0ovs1g2aYWDRXHftUw3pWjj9ZkFE0krJJRtGIBpsvjmkWrWLWaBSlldVlFfO6hSi48ufPG+aqqpgbqd5uZayV9mWaVnH/qm7V7qtU78WUzGRSxbeYweOW2l693exYG0UjJ0Rt6YlyxViU7x9fIJFVsC0bl+DCtFzctKObLT35osfKqvJWM7+ohll1qtnF+lnNaWT90RAHRqP84PgcyaxGMp4jQwrNtLhxRy9beqMrYtdo5PrSWLNMG0kSMAyLrNYYU8BqmBOGO8JcPhbje4/NoWgGfreArFukFYObd/aytS/U0rxTKruUGcKy7OIe42QF60y7GTHOJbIcn8/hnU3RFws6MmI04m+n+dqqk+RWgspTFlczx1aD1TJitCoONP1JzEoB8PDDD/O1r32Nb33rW8TjcbxeLzfffDN33nknV11V58DyJynWkqVgPbGelePrCU0zOTad4MRshlOTMgueObb1hdg1EGt70UKrmDXqwamyui/oY1zKcmw6ueqK+Wart1sZa8W+EnLxRjEY8zv21eFzc3IqwX3nEizEc3QvTXDlWIwrx1rHStGqsdaDk80L1f2V7c/d082X7htnseRBXpcbbtrb41hVPhQN0BtefXwG3RKymuHUXBbFtPCJAlt6gwQ7nYuy640pqxsNx9q1W7v57sOT3Hc+Q06BQGKRvQMhrt3a3fT8a/b6IlPAQqZ4ZPLm7lBdpoBWMSc8f88AD4/HeXAyVbT5ZUMRbt072LK84yR7W09kmXUmRU63CLgFdvavjBmiWZiGyRd/9gQPnE+SSGf5zrnHuXw0ym/duJ3O5dzWNJtKxXwNuqWabC0F1pl2sjMU7len5jIopo1PdLGlN8SW7jCyqjc1x1qFlTBitCoOCnliYj5Nu/k3VrwwfsELXsDZs2exbZs9e/bwtre9jdtvv51QKNTK8a0pesNeYk+yCtR2YD0rx9cTx6YTHBmPE/W56fC7EAU4Mh4HYN9Y10U+vTq0ilmjHpwqq2VVb1nFfLPV262MtUJf/TF/8elzvb6+8+gUD02l6It4iSDij3h5aCrFdx6d4hVXtZdistmx1oOTzRM5Dc20q9o/8JXHyBjgd4FXBNWEjAF/+bXjfOiVobpV5auNz8lkjtm0Sn/UV3xyOptWiQZyjlXrTpXuyZyG3+tuONa+eniCBdlkS28IJZPCFwqxIJt89fAEb71lV1Pzr9n5WmAK2DPcUfYUrx5TQLNzyen6o2cXcHvcXLu1G7dLRLdNdMPm8Lk4t+0LtSTvOMn2iK4860zUX8ZjvBJmiGbxT/ec4qen48T8bnwhENwiPz0dx+85xbtu37siNpXK+TqZzNWcY7KaYTattpWdoXC/6gh46fW7yWo6R8bjTMZzSJLY1BxrFYpxPhQFlwtsG9vlammcO6GQJ0LtfYYFrGJhnEgkeM1rXsNLX/pStmzZ0soxrRvWotpxvbGeVa3ridIK6qjfTVwU6Qh6EQShrIK63WgVs0YlnCqrVc3g1HyGvqifjsCFJLSSivlmq7dbGWu1+xId+6piKdBSDHQFmUspHJ1Icn0yQ3+0PV/imx1rs32JgohpWkwlZPpj/rL2Q2cXeHxBwe+B7siFNwKLKZljczm+8/AUe8c661aVrzQ+a7IzeEEQXY6x5lTpbpk2Tyxk2DPc0VCsnV1Mcd+5BN1hH31RHwuCSnd3mNmkwuHxBGcXU2zoijQ9/9rFxtGKuSQKIomMwpHzSTqCXgY7LjxHm03KPDKZ4srNObrDgZYxQJTKNiyLqbjCQMxH0FfOOtMsM0SzKGVf6Yv6WFpS6OwMICaVMvaVlftPJKPqRf2ccmq72BlqMX543V400+KhyRRXbui88BDvInOsVViLOHdCaZ4I4MLMtVCxGlhxZN5zzz28+93vbnhRvLS0xI033siDDz64UpHPoAV4KlXgthLrWUG9FnA8S97lQjHy+8FKsZJK4mZjZz1ZKeqxFGS1PEtBu7AWersEF6ppI7rK2xcSMoYFfqn8FaVPcqGZMJNU2lZVvpKqdafPSJKArNv5p68lcLJhImOiOrAzZDWTRKZ9+xJX4u9WzSXNAtkwCdXQO6MbZNXVx7mTbNEloFr5PbmVOjTLDNEsVsK+4oRm9WtlTnWC0/3KJ4rkdKNqC8KTlW2nVbnQKU+0CyteGEtNDtCyLCYnJ1GU9p9a8gyc8VSqwG0l1rOCei3geJa8bS8visoXBiupJG42dtaTlaIeS0HQk2cpaBfWQm/byu+DNO3y9u6YH0kA2Si/4eRv5NAf9bWtqnwlVetOnzEMC787vyWhFE42jIVEvA7sDEGPSKyN719X4u9WzSWPAH5JJFND75BbIuhdfZw7yTbtfM2AbVX7qFlmiGaxEvYVJzSrXytzqhOc7leKaRJwS1hW5fx+crLttCoXOuWJdmFtlt9PEaxFteN648nCSrHWZ62XVlDrhkFOM1lIy2Q0s6yCut1opd6VVb61Kultl4stPSFm0yrxtIZmGnhECa9HaKiSuFn2Aqcx5RSt7OS71bBSNBK3pSwFWVknlZZRhDQ53eTGHb1t20ZROdZERkGz8gsYn9fdUjaOwZgfzbTJKnqx/ZKhDrZ3+3h0VuH0vIwImIDbBfsGAzxvz2CeocSwkFwChp3330qZOpwYUFTVxAZcgGZZjv3XY3TY3B3CtvOvai/GLLChK8KVYzG+d3yOpbRMNq0yr8SxcHHzjl42dEWqxtssA0Qj7CvJrIZsmPglEa/HOc4bYb5wmnulceDzutk/GuXw+QRzaRlTtxDdAm5B4PptPXSHAyvSu5GxGqbNYIcPzbR5Yi5JVrEI+gS6Qv6GmCGaZXooZWcoZV+RVQM1p5EyMuQ0s4x9pVX6pXJa8ZS50pxqmXbLmH5KUXq/gvybzaymk1V19g5F0G2beE5tmhliNVgJu1KrGHrKGDFcxpO3+O7piGMzaYZNcU3O/V5PrCcrxXqetb6jL0pK1nl0KsVEysAd1dk9mK+gbjdaqXc9lgKorsQ2/RLffXiS+88nUQwTnyRyxWiUy0c7mh6vE3tB2OuuWb3tdQnMJnIcm0ohmxZ+UWDXYISxWPOprdm4vX57Lz8/Nc+RiQTprE04nWD/UITrt/c2LbtZuHFx9OwCR84niwul/aNRbtsz3HRfThXzA2E/02m5qv21Nw3zts+dAvJHxQKoNrzmxmEGwn7GF7Mcm0whmyZ+UWTXYLhphhKnGOwJeDlmJDg8uYSsm/jdIpcORekL+hz7cmJ02NEXRTbNhpkFnrO7my/ed4aJ7HJDOsdwMN/eKgYIp+sL/j48nkTRTXxukQMj9f3txBgRdEucmU9Xzb2BsB+P6Kry96VjET730yc4uahhWiAKsLXLwxuv39AyRgCnsZqGyT/dc6oqt7zhuq3EQr6an3HKFU5MD31BHyfmU1XsDC8+OMThM4s8Np9B0cCXy7CzJ8DLrxxtWK+L6ecXxfyx1/MZFCP/pHhLT4hNXSFSst4Sph8n7BqIAXB6IctcRiHgFtg/0sGW7jCnFtJNMUO0CithOWkVQ08hT5yblQm0uQDvmYVxCQQXa3bu93piPVkp1vOsddk06YsF6A556bYW2L2tB1ESkU0TX5unQiv1vliVb2Ul9j/9+AQPTqToDfsI+UQyismDEym++uA4r7t+24rGWykjnlVrjmk2kePUQobuiA+/W0LWDU4tZIj43U0zgTQbtz9+fI6sYbNvOEYqMU8kFiOrm/z48TlecVV72XN+fHKWw+cTdAS9DPvcZBSdw+cThHxubts30lRfThXz02m5ZvtffeVUzX7+6isn2PnGfmTDYudgBEF0YZk2stE8Q4lTDM4mcsxmVDb3hPFKIqphMptROTGfcvS3E6ODbJpNMQv83bceJ65BpxfQATfEtXz737wq2hIGCKfrS/0datDfTswXZ+bTdVk6Kv3913c/xvmUTkdQwCeKKKbJ+ZTO337rOO9/+YGWMAI4jfWj3z3GT08v0RnyMuj1k1Z1fnp6iYDnjCMTiFOucGJ6OGYkmM2oVewM43NpBLeby0ZjZNMJguEYmmHz38fmeN31sYZ1u5gvZtMqfVF/8ensbFolJbeO6ccJHo/IvrEutvSGy3iM41kVv9fN3uFY8Sm2RX0GlFZhJexKrWLoKeSJLb1BlMXEKjWpj2cWxiUIeiRcSGt67vda45f1rPVSvd1ekZhPIhb0oFutqZKuh1bq3WiVb6G3iXiaB84l6In4GFh+StsRBEl0cWQ8wS3xNMMd5Ym80fEWZDiNKadoHJtK0R3x0RPOPz0K+SREwdU0E0izcVvFSmFlGOgLrwkrxUI6x9Hx5DJDQv4JUsTvRhRcZUwBjaCRivlg8cmLyDd/McFEFkQgFrgQU8mcwfk0fOWB81y1vbesKLFZhhJHf2sGj06l6Y146Q5feEK8lHVmfmnUrxcb1eFzCzw0nSHiE+kK+5GzWfzBIItpmYem0/zPqTn2DneuigHC6frV+rtUv0ZYOkIl/j56Ls5D0xlCPqk4vwFmkzmOTqW49/Q8uwcbY/ZoBKVjLWUCKciOBDwILpcjE4gje45hcmouS3/UV6a3ohgcnkiyuS9cxs6QyOo8MJ5kuCPASFeAOUGhty/KbFJxzGvN6lfLF0EkLNPm2HSSHYNRukMX4nwlTD+NIOT3UMhUTxZWqUZZTlrF0FPaD7hpd6Xa03e/wArxdDz3uxS/rGetP130blaPZNZaZmeoXa2fzFYU661gvE5jshDy2yfc5f2shAnkqcRKkVXt5Yr51TMFNFsxP5uQAah8SSmRf6o0npBXHYdOYzLNfHGQ113+1Kqev1s1LyeXZDTLIlCxLSkgCaiWzUxcaRubSiv93SxLx0I6h2ZZhDzlYw25BRTTYjahti3nrYQJxJE9x3ahmFbVtjJBcCEbJr4KBgjTMlEMk6C3kpXCOa81CydfiIIL2bCQKuhdn+73sZWgVeN1jJs24ZmFcQWejud+l+KX9az1p4vezeoRDQrL7Ay1q/WjweoU0Ox4ncYkkN9TLOvl/ayECeSpxEoR9LqWK+ZXzxTQbMV8Xyz/xLJcMhjkk/1IzL/qOHQakyjm6aTUiiLmev5u1bwc6vTjEQRyRnk/OcPCK7jo7/C1jU2llf5ulqWjOxzAIwhktPKxZvT8iWh9MW/bct5KmEAc2XNcNj4xX3hZCsuy8Uv57SFl/QgiPkkkq1ayUjjntWbh5AvTsvFL+e0TpXi638dWglaN1zFu2oSn316BVeBLDzzOcy7ZVlbd6VQ522y7E2aSGdKyTdjvavjVbrPVvE7VzX/ytQc4t5BlrDvIH9y6v6xKtFVnw1ee816oLP3BL8YZT+mMRNxcsa2/zOZOMlql9/Ufvid/wd0/4jtvf3aZ3omsUiwAipUUDFXqcTHZC+kcWdUm6HXRHQ4U9f6ru+9nchGGuuDNN17WkN6VMVLQ49xClnguv/euL+or6nF0fJHpuMJAh499I11cPhbje4/N8fWHZot9Xr0hys07e4uvGytlFMa7lFaLBT1u6QKThdOYcoqOaYPoAkEU2TUY4dRChj/+8sPkgADwu7dvL2MCcbLt2cUUiYxJLCSyoStSlDGfVEhrOmGPm3DgAtPDvU/McX4hy2h3kKs39xZZKf7t/sl8h48+wS07uspYKZxkO8VBo/7eNxLlxyfm+diPzxSvefn+gTKmgIl4mmTWIhoUyl77Vsou6P3AE/PMpmT6In72jHYWK+Y/f88TnEnIbIz5ufXACMPBx5nIwmKu/Ia+IQwvuXyUE7NpHj4XZymn0RnwsKE3VPTrfz82zZnZFBv7Ijx354CjfoUxLaaUYnFh0O9m92CYUwsZ3vnlh4uf/eMKfzv5dTElF4urgn5P0a8PTy4xm1Dpi3nZM3ThOO//OTnLufkMYz0hnrW1j70DIX5+PsV0OpO/IJkh6IarRiM8a0svc2mVqXiGjGoR8gp0LrMnSKJQFQfFWEvnUDXweiDs8xavr4z/RvzdaJwX5t7Rc0ssZlS6Ql4294aLLB0/fmyO6WSGgWiIS8di7B0IcWgizaFzyWKfUa+LazfEuHpTD3NplXhWRjNceCQbv+eCbZ1i8NR8knjaoCMslTE8HJuOM5/U6Il62DXQUWQC+cHji8VrLh0MljGBVMqonSvyxxrPplUen0mRkDVifg/DnQEuHY4ym1H5ybEEcVmjw+9hqCfI5SNRjs9l+fax+bzgE+eq8pqTzSv1cIrzYh5MaSimgU+UcLtd7BqIIOsmv/3p/2EqB4MBePcd5fncyYZOuaUyDgp4fDbBYkqnK+Jme1/swn3sr39UvKbyPuZ0r2w2rzm1/8Y//TenpmW2DPj5zOueSymcmImc2ISc1kBOOedT33uYV+1qbz2Sy7bX5tn7wsICz3rWs/jMZz7D1VdfvRYiG0Yul+Oxxx7jd7+3wJmEwYdeMcbLL93tWPmfVvWG252qfzNZjW8+PMHhkmreA8tV66Fg7X2XThXGTtW8har1yvbvPH6MD929UNX/e17YyxuuOtiUDCdWBadz3pfUFG/+p2OoJdd6gU//5h6uGht2ZELI6saq9X7j//kJJ2vYdTvwtfc9j/vPLvDoVKp4/vzuwQj7hjs4vZip0mNbT4TZrFIlo8vv4Z5TcxwdT6KY+VeA+0aiPDZzgo/+qHpn1DtuDvNb1z+rpt4dPjffeXSqKkYu39TBP/zwCQ6dT6JYFj5B4OBolFdcM8ynvn+ao1PpImvDvsEw33o87hD58Mgf3VwzDq/d2s1XD0/wwPlEkV3g8tEYLz4wzE9PLlRdf+POfo5Np3h0OoViWPgkgd0DEb78k8N8/XS13Du2wYfuen7NGBmM+vj3+89x37kEqmHilUSuHItx+2WD3P3gFPedT6Dq+Vf2V47GeNbOTj741cd4bD6HboFbgJ09Ae6bdD4e6cSf1Za9qSvE0Yl4VRzsH+lkPqc27O+v/Oghvj9TLfcFI/CXr7mJLx8+xwPnEkUbXj4W4/ZLhzg+k66S3RkW+IMv/qKKdeDO63r44H9MVs0ltVpsEd99+1W87V8OcXrJwCS/F3lTp8Rbb9vIR795pqr9Q796KZOLepV+B8Y6+NZDUzxwPlFkYbh8NMa//NdJztaQux340h/eyOfuPV3l1xfuH+L7j85y6FyiuMg+OBbjWdu7+Pj3T3FkIlWM5/3DEV52zSB/8/UTPL6gYFggCbC928eRGeedh0fe81z+8Scnuf/cBT2uGIvy69duYialVsXBcMzP149OVOn3/D0D3HNivir+//qrj1GdUWEQ+JFDrDnF+cGtEd77xYc5m7SKvtgQFXjbr2ziY986W+Wj40vO3K6F+V3JjvLsbT18++Hpqhh87q5e/v2+81V2euGBQf75J2ercs4XHpx1lP3Qey8S5xW5ojsi8t5/f6jKr79+4xB/+Z9PMC1f6HvAT9n/K+E0v7tDbv7++yeq9HjTc7fw6GS6Ks4vHYny+XvPVrFuJFLjfP6harmvvxJ+68basXbXNRs5NZetmt/b+kJ8+YHzVXHw/Ev7+befnuOB8xf6uXw0ymfun3LU+/QHbq15//a6BA6dX2w4rzndQ7/+0IP83ffSVXLf+bwov3X9tXXXDpVsQk73txt29PHA2aUqX5xaPMPffS/NxpjE39zczc6dOwkE2sPE8czCmOqFMcCDf3RTsXK2lK/PI7qKZ6c30t4b9tas/v3Sz0/zg+Nz9ETyxQYpRWM+pXDjjl5ecdWmmuMsreYtlSGrerGa1ydJKIZBWtbxSwKyYVW1v+lzzqcPOuntJGNbX7gmq8LRc4vFc94LHIzxnMoH73686jUvQBC4+53XFauxS2X0hb34ve626v0vrz3Az04v0BXyFb/kLGYUeoMeEIUqPfpCXiRJrJIxuZjh+GyG7rCPiM9NStFZSCv8+5FpR9k/dND75FSCh6ZSVTGSTMtMZ006Ah7CPoG0YhHPaeiqSs5yEfFLhNwCGd0iJRsk6+xx/MsX76wZh0HJRdaw83r7RLKKSTynFtsrr9/SHaA7FiDi8xTZJ1KKxp/d/bij7K+++aqaMXJyMsGxGjaMuF0YLpGukI+QTyKjGCxmFM7OJllSIODJn/CmGDY5LU9R1qxsTIu5rFYVBxs7A8RCvpb4+49fsJXvPVawef4z8ymFDR0+omFflexvP3iOyTT43OATQTFB0au3SzSC/f0+Hp5R8EngFUE1QTHyfbkArws8Imhm3n49XnjO7oEq/UTbYlE26Az5CPvcpBWdpYzCd48vOsr+3edu5HvH56r66vaLiG53VV/Ti2mmMiaxgETII5LRTBI5g5xsYgB+9wUd5IvYoyC7M+Qj7BVJqyZLGYVdfSG2DsWq4mAhkePUQo6usJew10Na1VhMO8d/Pb2bjfNzs1nSZp772iuAauVPuCsshr3iBb1VM9/uhL9/5V5+fGKezpCXkFcioxosZVR0TWcmo1fFoKXrxFWbrrCXiE8ipRgsplUsXSVjinQG3YS8IhnVZCmb78MJTnG+ozfISG+4Kld85b4znFrSCLjB5xZQdIucDvKyghL5vfM6+a1B9eBk858+Ns2js3KVHmNRN2N90SpfFOzUEfQS9EpkVYN4VuXes0lH2W951gjfP75QZcMtXT5G+yJV83t2KcvpJblKtqXrJHUXXUEPYZ9IWjFZzGqcTzh/9XW6f5+aSfLwVLLhvNbKtUNhbeQWXGU8xj99fIafn1mqmktdfgnTJTjm1LVYGK/ZHuNAIMBb3/pWRkaaoypaL3zg6w8VKylFwZV3tpDnkJREV0PtPrdISjYwKvbFlFbM98cCBHwS/bEAPREfRyeSzCQzVeOprO4syHC54NRclqBHIuLz4JEEIj4PPlHg2HQKr1ssa//MT+ofyf32L95bLcO2OTWfIeAtlxH2u5lOKChaeZqqPOfd6xboDHp5dHypePPyChd+ALLAP//PE8UK4IKMoEfiiYUMLhdlYxKg5ph8bpFjU2l8Urne9SY0wK9/9jBdoTx7gs8j0hP2EfJIHB5P4JHEMj2CXjcPTaZwu1xlMmwbDp1PEPV76Iv68Xsl+qJ+fvqo8yIJ4Hf++SdVehu6xf3nE8SC3rIYcQsij83nCLldDHYGCAd8DHYG8AgW0zJ4BZu+aIBgwEdfNFB3UQzw+199rCoOAx6JBydTBHwSw91BOkI+hruDBLxujkym8HnFsuujQTeHxpO4IG83n0RP2MdH6iyKAe76+M+rYsTUbQ6PJ4n6PQzEAgR9bgZiAQJuiUdmM/g8AkOdAaIBD0OdAWzbZFoGtwhdET/BgI+uiL/uohjgxTVkeyUxT7HlcZfFQdTn4cGJJEKlvy077+9Aub/rLYoB/uy/ThZZQgr6hQNuDk0kEV1Cmex4VuN8Or9Y7Qr78vqFfWWLQI/rws/FcGRGwe+GroiPUNBHV8RHwVQ20BH2EAx46Ah7cANzKqRktUw/jyRwaCJJyCcx1BkgEnAz1BmouzgE+Jv/PlNkMCjq7RF5aCaDRyjvS7AFTi5q+N3QFwsQDHjpiwUQLBOZ/AKxc9kenRX2qCd7qCNAJODN//Z7ODyRxDZdZXEg4uKBc0kiAQ+DHUHCATeDHUECHpEHJ1MEC/Nk+ffF9K4Va6Zpc2iiOs5N2yBh5m/MXREvoZCXroiX0jtIZ9hLMOClM+ytuygG+K0vPlS0eXhZllt0cWQyRcjvLpPt9Qg8Np8j6BWX2/PXe9w2Z5PWBV/4876otyiG2nEeC3h4YCIJdnmuWEjJnFzML4p7Y0EiQT+9sWDZqr8z5CYcctMZujj/bS2by1mDh2YyRPximR5BX15vy7bK43zZThG/m5GuIJ0hLyNdQQ7XWRQDfPx/xukKe8tsGPFJHJ1Og0HZ/BbIywj7yn3hllycXNQIel30d+TH2t8RqLsoBrjsz79fdf/WdYNfjCeJBbwN5TWvW+TYdBqfKJS1f/K/H64r+y3//JMq2ZLoYiqu4BZcBH1uAh6JoM9NWlF54HyCrnD5/S3kzedBvySU+eK+i9xDW40V7zH+6le/2vRnhoeHGR5unuB+PXBiLlNVAekSXKimjehqrN0tCuQ0A9O2ywxdqJjvCpV/24n4PEwmcqRlm/6KMycKVZmBSp5Ch2peSRRqVs5OOr9VB+DsQra68rPO2fAJWUO3bErp+wvnvPf6yxPYYqq8EKoS5xcyDVdjW1BzTJJLQDZNhBUc8VxJOO5zi+Q0E6miQMDpvHrTtJF1k4CvfEwXyWWcj1Old+FEspCvvF1wWegWeL3lPhKX9fVIzetdydzgWa5od1fEjkdyoZpW1ViDbg85PY1Q8T07cRG5GfKMBaUwbRvZtAhW2NDncS1vkyhvL5x87F3BV/xK2SIucrqJx13emXc5Dipj0MaV97e3+TRaWckf8IgohkVFCiGnGVjkF8atgreir4LIyshxC/knlbJacQyxJKAYFl6p+UFV6u1xi2imhVusKC4UTEwL/J7KvLY85hXYo1J20CciL1loZvkXe5fLhWyaVfHhlZbnxQoq4yv7skwbxbAIVjpjOcYqv+QI5NeIK3mSVam3tDyPAxX+c7uEfG6pyCFuREzyT7BXKzvgzVN1VdStkVMtTCtv41I4xWYjqLR5zjbRTItQxWQKuPN6V747FwVhOd+VS69/F8t/wYxU5O2AV0Q1LWyh8njpZV9UXO8WXJgWeITmjV4Zn4btEM8OeU0SBGTDrKqROr9Y/zn92TmtYfYcVctTzIW9wfKxSy4Uw0KsiIOL3UNbjRUvjN/97nfjKlkwFHZkVLYV/l/494tf/OKVilxTbOsNoZtWkfcRwLbym/hN2yL/zKJ+eyMV86UTol7FfGlVZumYyqp5S3ZsGKZVs3J2qAPGq7cIFbGhO1gto+Rs+GBJyDhV4Zae8+51XxhUV6Q+Z+1odwjFMIqcueBcjS1AzTEZtoVfFLGaoAIrIK3q+EqSpqKbBDwiRkXGdDqvXhRd+N0iOcWks2S+x7yQrTOxRzuo0ltyCfjcIhnFKNuKY9kCbgHUisWKuayvZjSvd2UcassV7XpF7GiGjVcUqqq0s7pGwC1iUX59jPqL4xBUxYjocuEXBbKKSVdJLaqi2bgF0K3yZ2SFe1aFORpCpWwTm4BbRNPLO1OX46AyBl3YeX+rRpFjtVGkFJ1gyaIhp5n4JAG7Qo+AR0Igv7WhVVDN/NalAgoiKyOnYAZ/xbcObXlfqGo0P6hKvTXdxCMK6GbFDdUSEQWQK9gWCiKtFdijUnZWMfFLAh6x/DZo2zZ+USSr6XSXJFXVWJ4XK6iMr4w1QXThkwSyqklX6U605RjTKpxhVfxuBilFx1/y5c1Ynse5Cv/ptpXPLRU5RMdEJP8laSWyy+Jcze/XrlzvBbwCopC3cSmcYrMRVNo84BLxiAIZzaT0uVNOz+tdSZBgWtZyviuX7qH+4tgFpBSDYMkDh5xq4hUFXFaFENeyLxQDSuJAt2xEIX+cerOovH9LrgvxHPZfiAOnvGZYFn5JrHrTPdolsTDlvDje0Oupku3EnuP15B88pVWNcOBCfOiGjU8SMCviIOaF5Boujle8MP7Xf/1X3vve9yLLMq95zWvYunUrPp+Ps2fP8q//+q+Mj4/z9re/nWi0/cfttgPveeHeqjPBdctmMOZHM20U3bxou9MZ4v3RULFiXjdtgj6JrGKQyKplFfOlcDqn3LYpVvPmk21+P5BiWsXK2ZSiFdt/47rL+HmdbQUffuXV1TJKzoYXBVfVHuNKdgqnc953j3Ty9V/Mo1O9kAkCr3nW5uLpagUZWc0oVmOX2taC4piA4qlEim6yazCMbJhlZ8l/8q7LGtpjDBT3YGU0gwMjMTTDZCmr1jyvvtS2LhccHI1xfDaDOykU90ddu3ug7uv1v33NdVV6S26BK0ZjPDSVwi26inuwdMtkZ0+A6azJTDxX3HumWQIDfshZLmaTueIe46jX1dAeY92wCfklMstHfV42FCGrGMwkckXZOVVn/1CErGqWtSezOgdHothQZqe33b697h7jzy3vA7QsG687T/Mlul0cGIlybDbDdCJXtGFON7ikL4SiWWXtLpfIgB+WFFhMycU9xl5XY3uM4UJ8qobJgdEYc1mN+bRyoaBW0bhsOIpl2ySyKpKU/yLqElwX/C1e8PfL99f3d2HvpWVR3CudzukcHI5i2laZ7I6gh9EwTKZhMa0U9xgX9lpC9SKqHgp7jBdSSnGfauFW7QLiaa24x1gHer0Q8XuZWMwW85RmWBwcjrIol8fHLTu6GtpjDBRtldZM9vaH0KzyviyXxdYuD1MZk+l4jqDHRVazsQQRP/k9xk72aFR2StY4MBzFJdplcWtic/lYlFMLOSaXcsV9zznNrDkvLqb3V2vFueji4HB1nIsuiZiokjZhKa0W9xgXnhgDLKXUoo9EGttjXKq3btrsH4owk9HLZKuaxc6eAHHVZDKeJeKVSKkGmu5iQ1Qgo8NsIlfcm9sfcje0x7hUdiKncflwFFzluaI74mdrl4dTSxoLqVxxj2upgvGMjtsFegPxXmt++4MSe/tDPDorI5XokVXyegsugal4rnhyoeZgpwMbonX3GL95eY9xXu/8HuOUYrBvIAwSZfPbIi/j9JLMZDxbbNcNm61dHpKqzeRShpBHIqMZjMa8De0xhgv3b7db4tKRKA9PJREFV828VnofU3WTXQNhZMMqa3/Tc/fUvYd+7DXXVck2TLvInpNV9OIe47DPy+WjMX5+Zqm4lSOlaGTUfB6UDYvZpFy0+ZW7Bzh3kS1qrcSK9xh/5zvfwbIs7r77bl7/+tdz3XXXccUVV/Dyl7+c//iP/2Djxo088cQTvOQlLyn7eSrgQ68YI7pMF4Sdf52JDb1hLyMdwabanc4Qf97uQfYORphLyTw6kWAuJbN3MMLzdg86jstpTLsGYmzrC4MFCVkDC7b1hbliQ3fN9t97QUfN/n//tq6mZYx0BGv2tWsgxv6RDmzbZi6jYNs2+0c6+PQbLqHyE0Hg02+6lJGOYE0ZuwZiNce0oy9KX9jLXFLh5FyauaRCX9jL/pHOmu0bHOy6GbhiQzfXbOrGtmymUzK2ZXPNpm5eemCsph43LFPMVY71pQfGuH5bD7ZlM5nMYVs212/r4S3X1Y6D/31j0FHvu67exI07erEsm8lEDsuyuXFHL3/5ysu4cVsXpm0xlVIwbYsbt3Xxydcf5NoNMSzLZi6nY1k2126IOcYTlMRhWuaRiQRz6XwcvvMFO2vK/v3bdtdsf8N1W2va6YW160h5yVbY0RdlS3eIhUzeTwsZhS3dIf73LTu4eUcvlmUty7C4eUcv73/ZPm7eWdG+s5dPvv4g+wYDWDYkVBvLhn2D9YsynOLzpQfGasbBCy4Zoi/sZTYtc2ouw2xapi/s5Y79ozX9fWNfbbnPH4Y79o9y7aZOMqrG6fk0GVXj2k2d/O9bdtSU/dHXXs6OHg+6CQkFdBN29Hh4351DVD6nvthz6w+84lLGogKKAUtqvvBuLCrwkbu2sL1TwrYha+RfK2/vlPjY6y5jR1+IqWSOX4zHmUrm2NEX4rdv3l4zDjY6yN0O3HX1ppp+/dM7Lq3Z11+/6jL2DQbJqDrjCYWMqrNvMMjHX7+bHT0ejGV7GMv2qAcn2e943s6acfC6Z2/hqo2dpBWNU3Mp0orGVRs7+b1bd9Uca7eD3EGaj/NP/eY+tnZKmCak9PzBKVs7JT5y1xY2RgU0C5J6/gnuxmj9W/gN2/prxufv37a75lz64Cv2ce2mDnKawZmlLDnN4NpNHXzkNQe5YWsnpmUxnVYxLYsbtnbWlf3SA2M1ZfzWjdtr2vzv7jrAweEQpmWzpJiYls3B4RCfeM12NuTTIzk7/3vDRdi6nOb3n95xaU09PviKfRwYjZGUNU7OpkjKGgdGY/zerbtq6vBrl9aW+7or4A3XbeWmHd35zyRlLMviph3dvPdFe2rO79+7dRfXbuokqxqcXsyQVQ2u3dTJX/7qPg4Mh0nLOqeXsqRlnQPD9RV3un/fsK2/puzb9gw3tXb43zfWvt+/4+awo+yhaACP6GI6KXNmIct0UsYjurhj/2jNufSO5+2sGbO/c0ukpux2YMWsFNdccw2ve93reMMb3lDz7//8z//Mxz72Me67775VDXAtUGCl+PKJDM/Zs7WMaaFdPMZn5tOcmE1jmqCZJh5RRBRxZHkoxWr5fH9yfIafnV7gx8fOMZ+Cnghcvyu/ILhuR/+KZDjBiUfyX396mpOzKbb2RXj1teWrp0Z5jAssHYWz4gu/C1WwLtvOvx+zbWyXi6NnFzh8PlH2NK/ANXrbvnxRaLt4jAs2//bRcyyloDMCz99XbvNGeYwLqOS3LKCSx/gPvnSIH55cKnu60x9yc8PWTt743O2cmE1j6Ba6ld/6I7mFYhw6yXZqr7RTQe9/+Mm5YlX9b16X13vPSAdzaRXLtjHN/AERgstVZHJx4vV04l6t5DH+6HeP8b3jc/xiKlu8psCx+tZbdtX1a2UcFGLN5SK/J89lYy8n/o6gt8rfBdaZ0ieJBQ7lW/YMMZdWSWVVckb+tLZI0Fvsy0n2w+fjzKZy9EUC7BntKMb5N49OcH4xy2hXkNv2DfO8Ak93DRSepM0vySQ0k5hHpKfTz/6RDvaNdVXxGBfylG3axTnmEl2O8VFgoyl9U1DgMd431gU487VW9lWQPZeUySk6AZ+b3qi/yFLz0Pkl5pIyvVE/e0c7edXf38NijQeYvV64/09vqyu7Mg4KNs/KWhW3ckfQWzXWbx4d58cn5mvmlmu29jYV5wXZR88vMZOQ6Y/52TfaWWThOTWRIq5pdHg8bBmO8Nff+AVnlqndChCBXT0evvHOm4HqfOQ0lwqy51I50jmTcECkNxIojrWS/7debvngKw7WlOFk8wLuPzPP+GKOka4AV2zsKY7p/tMLTCdlBqJ+rtjUzV994xccmkiXvREr8Dd/7DeuqSujUo+CDEXV0az8fmqf113Uu1KHQpz/43cfZ0aDfg+84ZbyOG+Ux7ggezEjk5Utgn6BrpC/6O9EVkXRTHwekVjQSyKjcGYpxyd+cq7Y5/+6rrH792p5jAtz8hM/eIiJRZPhLpH/dePeumumgn6S6KriMa41lwpwitk//NJPuGNL8MlJ17Z//37e/OY385u/+Zs1//7Rj36Uz3zmMxw+fHhVA1wLFBbGYmyQHBJYsH+so6XnnZdC0QwOnV1CWH5FXkBK0douO5FV+MJ955GkfPV7AfNpBduyefnlI2UT5skKw7SYiOdwuVxlZ8ZnVJ2ZuMJAzFe2t206keELPx+nI+ShN+JjcmKCoeFhFtIqtmXz+us2lk2+VmI9bX5sOs7//twR3JJAX+yCfrOJHLpl8ZqrNrGpP9SWOKynt2FYPGtbLxG/u8x/im6CDUMd/oYOyHHC2cUUf/Sfj+CRBPqiPhYWFuju7mY2qWBZFn/y4kvKFkb14BRrTmOdSWb42++cxC256C+x+Uwih2navPTACD1Rf0N9NRvnH/6vR/nCgzUIlJdx7ZiXl165vWxP9FJWxbZtbtnVX/Wlr5k8lZE1vv3INKIoNNR/PTjJTmRVZtMye4Y7yt7GfeWBc3zoe6cACHnAMECSILO8GfTvXraHlxwYbUh2s/5eSOf45I9O5+dY1F9sn03KGKbFCy4ZpDPsXZW/k1mVhyYT9EX9dAQu2PbHj8zwdz88hUeAzsiF9nhaxbLhE685wPXb+9uid73cYloWf/eq/WWHZ6wETmM6fG6BP/nKw3g8In0RP9l0hmA4xHxKxrJs/v7VB9k30tUWvVsZ506yUzmNX0zEq/w9E8/xveMzjHUGGey4YPO1uJesZN3SrG0bQTqd5sSJE09OurZLLrmEz3/+8ywtLVX9bXJykn/7t3/j8ssvX9Xg1gNrcd650xnsayFb1m0U06piYAh73WR1M88A8RSA09npTlWwmuFCNkxCFQwCEZ+bjG6QvRi/1yqwnjafT2oolkWoovo95BWRVYuFrNK2OKyvt0VG0av8514u+jNXSa+eyJiohllVFR/xuclqJolM49VbTrHmNNYC60wl20fE5yGlmiQVo+G+mo3zJ+arqR5LMZVWq6rTgx43Od1CrShYbTZPFdhoGu2/HpxkO7HUTCXypz1U3tAK/z85m2pYdrP+zqo2ilk71tKqSVpbvb+dmIGWVDXPGFFBbuERwbDg7FydSusKNKt3vdySMyzmkxfjb1j5mOIpDc2yCFUwyISWGXWm486HvTQqw0nvVsa5k2wn1iXTtpE1E3/FAnQt7iUrWbc0a9snC1b8OOgtb3kLr3/963ne857Hi170IsbGxgA4deoUX//61wH4nd/5ndaMcg2xFuedl57BXspEsBay/W4XPlGoYmBIqzpBt5hngHgKwImlw6kK1iPZ+CWRjGqU0WulFJ2QWyLofXravCfqwScIZFST4IWHWWRUE79XoDvoa1sc1tdbIORzV/nPicmlWcRCIl5JXK7IL3kSo+gEPSKxUON8X06xthLWmYhXJOqTGu6r2Tjf3BPi/gnnxfFg2FtVrZ/VdAJuAW8FxWGzecqJjcap/3pwku3EUjMYywd3ZQ1/4f9b+xrfn9isv4NeFz6xEGvluSXsFQl7Vu9vJ2agTq83zxhR8T1PM/Mnx23obfzo3Gb1rpdbApJAT7Sxp6YrGVNHxINHyBcYl8leZtQZ6Gj8qWmzercyzp1kO7EuiS4Xfo+IrBl0lJyQuxb3kpWsW5q17ZMFK35ifNVVV/F//+//JRAI8LnPfY73v//9vP/97+dLX/oSQ0NDfPazn2Xbtm2tHGvbkdXyTAsDMV/btjIA+DwSQx1+0rJOStHQlqs/10J2LOhj92CExYzCfFpB0Uzm0wqLGYUd/eGnxDYKuMDSoeh5JgrTyjOCFKpg9eX/F9r9Hg/7R6MsZVTmkjKKoTOXlFlIK1wyFGnbNgpYX5vvGujg4GiUpazObCJHVlaZTeRYyuarf6/d1t22OKyn9+7BCMMd/ir/KbpJxC+tahsFwIauCFeOxVhIK0zHZWRNYzqe9/eBkVjD2yjAOdacxlpgnZlPKcwkcuSWWQzmUwqXjUTZ3BduuK9m4/y112+tq8vv3XoZ8ZzKUlZF1S2WsirxnMqm7mDV699m81SBjabR/uvBSXYlS01B7xt2D9C1/AAvo4FiXdhG0eul4W0U0Ly/u8MB9o1EWUgrzCZlZNVgdjm3XDocZUNPcNX+LjAD5VSjzB5bhsJsiAqoVn77RDanEk+rKCZs6fI0vI1iJXrXyy37h6Or3kZRb0ybeyLsHQiRkfO2zql5FoOUbHDpQLjhbRQr0buVce4ku8C6VOlvE5v9w9H8KXFrfC9ZybqlWds+WbDqI6Ft2+aRRx5hYmICgE2bNrF9+/aWDG6tUNhjLPt7GeqJMdIRrDowo9UwDIvxeDZ/ctzy2eUDMd+ayFYUg/vPLvDQZIqMYhDySewdinDFhm58vvYtykvhVBTRDCzLJilrpGSjWDgW8UtFOprKdjcufnxylqPjCc5OzbBhsJ99IzFu2NZPIOC+uMBVoGDz4zNpsrpJ0C2yoz+8JjafS2T5u+88xqGJJLJq4fcKHByO8o7n7aQz5G9rHBb0vu/0EklFI+rzcOWmTq7Y0I3HI9b0X9TvqTo8ZSVIpBU+d+9p/ufUIrNLKfo6IzxrSxd3Xb2JWLj+DaSygMQp1pzGmslqfPPhCY6cT5BUTaJekf2jMW7bM0zA726qr4vF+VJWQzMtPKJAZ9DDD584ze98/nRVPx/+tc3cun0rx6YTnF7IktMtAm6BTd1Bdg3E8NQ4ReRiearSTppmcmw6weFzSRKySszv5cBY1LH/ejZ3kj0UDZDVjSq9zywu8b8+fYS5EjarXi986g0H2DfS+AKxns2dfJTL6fzwxAyPTKbI6AYht8QlQxFu2NaPzye1xN9Bt8RkMldlD9VQ+b3PP8jJRQ3Dyj8p3trl4e/uOsCWvvrMEauN80JueeB8nJxmEfAIXD7awTuetzN/cl0NGReD05gq/Z1VVD7w9Uc4MpkknbMIBwT2D0V574v2MNjZ+JPyejZ30rsQ5yfnMkW9t/aGGorzRmU7+bsn4OXI+BL3nYmTzKlEA16u3NixJveSlaxbSv2nGhZeKe+/leb5RDLJE6dOtXWP8aqt6HK52LJlC6FQiP7+frze5ojun0zY2R+mq8kJtVJIksDGnjADUX9TLA+tkj3SFSRnWGRkndDykZftXpDDhYRyai5TrPbessKEIgguOoJewj53VeLtkKrbLcvmmq299EU9HLWT7Lukl829sTX5MuDzSVy3o5+9I7GaVcHtRGfIzxufu51dJ2dZzOh0hdw8a2sfnSF/2+PQMPO8w1MJmYxqkFVMphM5DNPCJ0iO/msFJEGgJ+KjI+gmm4WOoJueiK/qNMhSFJJ4UtYxLBtJcBH1u4n6PU2N1eeVuGJzDwGvp/iF4JLhKD6vVDdua8Hp+sIBMzZ5erXCE47rxsZ4y7N0vnTvOCkTIiK84uoRrhsbw+MR2TfWxZbecENfTJ3iw7Js4lm1yk6iDY9PJ3lkMk5WMwl6coQ8sKU77Di/69ncSTZ6td7burr4k5fs5QsPnGFiOs3wQJhfvXwj27oaf3p4MZs7IRBwc9u+Ea7cXLuSvhX+BmrawzCCfOTXr+LuByaZyskMBvzcfvmQI5UmtC7OYwEfr7hqIwOdYRazCl1BH9dt6yYW8NWVUe8LQeX1hRqFSn/HfD5uu2wEl8fF+OQSI0Mxbt09TMzXfF5t1t+SJDDWHcKzfHpcwCMyEPWv6B7arL8zWY3pRI6JRJbc8h720YS36kCOdmA19wsbO08StaLjWi7ER4EruZ1Y1R1wfHycv/iLv+CnP/0plmXx6U9/GpfLxfvf/37e9773ceDAgVaNc03gcTe3MGsFfB6Jtd68MB7PcmI2TcTvpjfsRzGM4gETF6OKWy2OTSc4Mh6nI+Cl158nXi8QsRdobpqFJAo1A7myvTCpuoJ+RjrcdAX9xUnW0eTJZStFLOgjtiaSLqDg7819UXYPSTX93a44/ObDE/zg+Bw9ER/9MT8pReMHywctvOKqPE2fk/9aJbsr5MXb5cYf9FbJrkQhRnxukYBHRDetshhpdKwFm3dFvAx1BmvavFm9neI5P1apONYv/uwJfno6zrbRWPGAgcKBA++6fS+Qfx1cfYyQMyrjw8lOP318hp+fWcr7O1rb35W4mM3ry76gd4GO8ZLBTsb8GuGOzuLhFgU6xmbRrI+6wwG6HVLoav1dQKU9CrF2YHsX10q153clWhXnhXw+0hVgx0C0LJ+PdYfqymh0TImchmbajv4eiYWJ2VnCsfCa+bsw1qDXTSzgvahuq5Fd6e/SnDoYDTQ0x1qNZu4X5X4VVmyrQj9rsflixTKmp6d5+ctfzr333sv+/fuLR0JblsWZM2d4wxvewPHjx1s20GfQGiiawWRcJux3E/F58EgCEZ+HsN+dfz2i1T8PfTXIyBqn5jJ0BLx0Br143Xm6m46Al9MLWTLy6quYnWCYFklZx+cW8bpFBJcLr1vE5xZJycaafNteD6ynv2eSGQ6fTy4vigMEfBL9sQA9ER9HJ5LMJOszKLROth+f201/zF9XdmmM+Nxi/pTHFcTIWtjcaazzKZkHxpN0BL0MxAIEfR4GYgG6wl4Ojyc5Ne98YtdqZacVlQfOJ+gKexv2d7M2d7pe0U0On0/SGfTSG/Xjk/Kcx91hH49MplhI51at95MRK4m1VsV5vXx+ci7DdFJetV8l0cVUXMk/mSz1t2pweDnO19rfrbLfSrCeOXUlaJWtSvvxSO1/gLnihfFHPvIRVFXlK1/5Ch/+8IeLC+NrrrmGL3/5y3g8Hj7+8Y+3bKDPoDVYT6q4VtLcNIunKm3MarGe/q5HW5bVTNLyk0t2q2JkLWzuNNaUYizrXd4e8UlkdZN4evWLcifZqpYviAt722dzR9m6hWKYZSwgedntp2NcT6wnhVbdfK5Z5DSzbbSEqmmh6CahGhR57fb3et5L1jOnrgStspUjjWGbsGIp99xzD7/6q7/K5s2bcVVQbuzYsYNXvvKVHD16dLXjewYtRinlSinWgiqulOamFCuhuWkWpbQxpXiy08asFuvp71LaslKkFI2gRyTsf3LJblWMrIXNncYa8UnLepe3pxSDoFukI7z6TStOsr0e8k+O1fbZ3FG2W8AnieSUcpuvBR3jemIlsdaqOK+bzz1CcTtEIzKcxuRES+gVBXxukYxSLnst/L2e95L1zKkrQats5dRPu7DiLJlIJIrcxbUwODhIPB5faffPoE0oUK4U9qD5lvekpWWdbX3hthYAFmhuCnvQgp78HuN4TmX/SMeK2SkaQYE2Zi6tYglg2TaqbqJZ+WN914o2ptkK7dWi1N9Z2cgXQODCwm67vwu0ZT84PodlQdgnkVYMFjMKN+7oLTv+sxk0YsNS2ZphkpV1zKUsyZzuKLs0RmD5qcbyk6lmYqTU5rphIbkEDDvfT6ts7jTWnoify0ei/PR0HNXQkUQXhmmTUy1u2tFddjRtq2WHfV4uH43x8zNLWDaEPBIZzSCeUVdl80p/F65XDbN4PLfPLXJgNMrh8wlyqk4iraLPpVEMi+u39ayYjnGt52sjKB1TaawlczqWbSO4XAguymLNyYammX8aa1s2umU3Feel+dyybLxuEVU3SSoa+0c6GIj6G55LTnFQoCXUzDzNV6Hd55U4MLLsb0VnKaWgzaZRzdX5uxGUjtWwrKpjjtsZJ6V5TTdsQn6RjGySyDnPsXag0XnRqpxa2o9gNH4400qx4gzd39/PqVOnHP9+9OhRent7V9r9M2gjCtXK0wmFhKzhEwW29YXrVjG3CrsGYgCcXsgyl1EIuAX2j3QU29uJwhGy8ayKYuRrY3vD3rKjZduFZiu0W4megJf7M/McGU8i6yZ+t8j+kSg9G7vbKhfgBZcMkVUNDo8nmEnJBDwiV23s5AWXDDXdV7M2fN7uQeZTCg+cTzCXMOgVVC4fjfG83YOOMgqxkJINcpqBWxBWFCMDYT/ji1mOTaaQTRO/KLJrMMxA2H/xDzcIp7H+2rVj/OL8Eo/PZov0Xdu7fbz0ipUVJDUj+8WXjZBTDQ6NJzizXK1/cGRlNg973TWZL/yiiKzqnJrLFimjtvQGefa2Ho5PJXngfIJEWieWTXD5aJQrNzZf1Lue87XZMXX43EwuZjh0PlGc3wdHY1y9ocvxM0G3hEfUmErIRXaSwZi/6pTKi2FHX5SUrPPYTKpI/7ezP8KOvmiRhaTRuVQvDgr0m6Xt12/v5ei5JX5+Nk4qbRBJL3JwOMrlG+pT1LUCYa+bRE5jKq6gWvmn2oMdvqbttxIU8tqh8QRnFxubY63CSuZFq3Jq4fr5Jk41XClWvDC++eab+eIXv8jtt9/OyEg+4Ra2VHzrW9/i61//Oq961ataM8pn0FKsJ1Vcs3RRrUSBFifgFpgLSgzF/Hg97U9kcPEq8HbiyPgSZ5ZyjHUHCbglcrrBmaUcR8aXuG5Hc/yuzcJ0wbXb+7l0rBNFt/G5XYR8HswVrC2atWFc0dk6GGNDd4QTZzW2bRjF7RGIK7ojj3GztE1OmE7LyIbFzsEIgujCMm1kw2Q6LbPR1xrmF6ex/ttPn8AU8gtxryigmhaaYfGto9O89ZbVH7pQT/aZ+TRbB2Ns7Atjmi5E0UYSxRXZPJ5Va/pbVnVm0yr9UV+eS9mwmE2rHDm7SNaw2T8WJb6g0tEdJavb3Hdmkdv2NfcEcT3na7NjOnp2geOzGYZifkI+iYxicHw2wz2n5rhma2/Nz3jEPNNDf8xffOKpmTZpVadDalw/2TTpiwXoifoxTRBFEFwuZNNsmo6xWfrNLz08yaJssHsgQiagEYpGWJQNfnh8tu3sDGlVRzNtBmK+4hP3ldhvJSjktY19UQzTyrNZiK66c6xVWMm8aFVOLfTjMrykZ1elxkWxqiOhf/SjH/GqV72quM/4ox/9KO9///s5deoU/f39vPnNb27lWJ9Bi7EeVHEFNEsX1UpIooBbXLtXo5WVuUDxeMyUbBD2uds2lkRW4dGpFF0hHz3LSTOGB7cocHwmzd6RWNv4lEv1jgUueFvRzab1btaGpdX6wYiIvORlpDtAVjeZTigMRP11vwyuhkKukimggJSiNSS7WZSO9exiivvOJegO+xiIXVgMTidyHB5PcHYx1dSpf83ILtf7wtunRvUu7cvJ35pucmo+Q1/UTyywfCP2QkYxeGA8wXAswEDMj1tNMdQTYSGt8shkiis35xp+vb6e87XZMcWzMkfOJ+kMeYv+jgXBnZT5xUSSoc4QHUFP2WdM02IqIdMf8xMqPuEUm56XtcYE1fO7VTR1pe2l7Ay9ER/TZoaB3jBzKYWjE/8/e28eJklW1vt/MiIyM3LPrLWrqqv37ulleqane6ZnhoEZh2EfFUQU5IKirF7RqyAqCBdRES+LchVEQZQfiix6Ba5yBdmEAWaYtWfpmd6ml9qXrMo9Y4/4/ZGdVZmVGdmZVZlZ3T31fZ56svtkxHnP+573vOdkxHm/J8MdmXzHthU0q3cn0O3YUom1jot20XKKDXjo24VVS4hEInzxi1/k537u55iZmcFxHB588EEmJyf5qZ/6Kb74xS/S09P5Vxob2MDljvXMYlYMB9Wya17xRfxeCoaFYlwZ2dut1rWebBzrKTudt9BMi2idbP2CbpHOd25/Xjv1dutvG1BNB59YTdlkOw6qbhH0r52V4nJksHFrk256UEyLcD29dYusotfc4xE8aJaD6OkMU8DVzs6wnnqvZ2y5HMdFp7CmBXw0GuW9730v733ve1lcXMS2bXp6ehDqrOg1TeM//uM/ePazn01fX+f3Nm5gA5cLKjNqy7+woTtZzAGvB1kUyGkGcsXJYznNIOQVCXi7k729Vr1brasyWz9U+VSnC2wclbJ90vLE3Q3Z8bCIXxLJqgahisVxVjUI+UTi4c5xgLZTb7f+FgBZ8qBbFqGK6UvweJB9IkXNJBao1rtVloL1HK+ttsknOQQkkbxmEglUPkE0CPtEogFfzT2OXdrCZjk2sHr91tNOlewMsm/5jVc32BnWU+/1jC2X47joFNr2TLqnp4e+vr66i2KAXC7HO9/5Tk6fPt0ukRvYwBWBckataliohoVlO0v/jgakjr6WjYdkDgxHWcirzGQUcorJTEZhIa+yd1Oko8dSV+qd1wwUvfS5Gr1btWE5Wz+nGGSKOophkynq5BSDobjc0T31lbKzqo5u2mTV5mWblo1mWi2R35ev39Yb5eatcZI5lbFknoWcwlgyTzKncmQ03tZtFCtRqXeqqFHQTFJFbVU2d+tvG9jVH6aomVW29Qhw02icTFFncrFAVit9JnMq145EW2IpWMt4zSs6C3mt7YcVubUp4PNxeEuMxbzGbEZB0UxmMwrJnMr1m2Ns7Q3W3GPYDsPxAOZFpofVxqPKNmUUnUzRIKPoXYlrZXaG+azKTFpBNQxm0grzWZVDm2Nt30ZROcYq9S6oBkXdpKDWxrW1jGM3rDW2rAXrOY9VwrK7cPR1xyVUwLmKHrVvYAOtoF2ZuavBjVt6yakGj09mmUwXCXklDo7EuHHL6o7gbgXtzN5u1YYjsSCZos7puTwTWZNATmP3QJiRWOeonMpYDfNLqxnfbte/4qYtTKaKPDSRQTFtApLAkc0xfv6oO71mu1C2+dPzhSXZO/tDq7K5W3+HekLEgsUa2960JcFXHhnnobE0U4sWw4LKkS1xnrOrdXakVn1N1y2enE5zZi6PajnIooddA2H2D8WX2BnWCrc23X1wM2HZyxOTWSYzRcJeiTv29HPnnk3IFw88aZbpodV4FPJKKFq+hiEk1NN5hqO7D24G4JHxDPNFGI3AXXsHlsrbgdWwerRrHLtdv56sUus5j5XtVE726yS6ujDewAaeqWhXZu5qoDk2uzbF2NobXmIh8XpFNMem00vEdmZvt2rDgmES8Hs5OBxFyvnYNxzFI4oUDLNtixU3rIb5pdWMb7frZ9NFdo/E2TEYx3RMJI+EKMFURmUg3tnJc8nmIzHweMBxcDyeVdm8UX/Xs+25+RwjvWGGYkGePqewc/swgiSwoOhEw531tSen0zw8niIR9DMQKPGzl/naD21tzw/QRm26+9AoN+8sUtAcQn5P1RPyVpgeWsVkpliXISQWLLK9vz3sK24Ih3y88pYdPPuaLPc/pHL0yE5GEu19I+I2xhqxegBtGcdu168nq9R6zmNlO3VD2sbCeAMb6CLalZnbLKqZISr2pHUhg7pT2dvN2LBSttcvlvZbBn0YdneZBZplfmk149vt+qJqcHw6S39kmYUEYLGgcTZZYNdApGP0iN3u70rbVrGQeEXySR/DPc2zkLQquxJ5RefMXJ5E0E/PxYWM31v67ITN3drUFwnS57IWbYbpoVXUZUjwgyB6Os6QUIlN0RBbEj42Rdv7o89tjJm2zVRKZSguE1rB6rFY0HFw1jyO3a6vxHqySq3nPFa5L75TuDyO8tnABjbQEaxnJvEzVfZq0Gp73a63HFBNm4C3etoK+bwUDRvNujptvp7Z+prloFoOoRWc6N2w+XpiPW3eDbj5s+gR0OzSiYGV8IoCmmljWGsfx5drnFovuNmpU9hYGG9gA1cx2nVW/YbszqLV9rpdL3pAlgQUw6wqL+gGQa+AX7w6bV6ZrV+JbmTr+0UPsuihoBtV5d2w+XpiPW3eDbj5s+WUciWcFQt/w7LxSyWO/LWO48s1Tq0X3OzUKWxspajAXK5IJNLZfVEroepmx/cJuZ1r/uR0ivmMTn/Mx/6h5k7EavaM9EthIpUjU7CJhQQ2J9Zm81bb9MMzc9xzMkUhPMft1wx1Vfax8QWmUypDCZlDo2vbe+gme2V5+Yz5f3twjDMLeXb1hnn+9SNNnVW/Fv+slP3wuQXmskUGokH2b443JdtNv7yi1z010U3vt33hHk7NwJ4H/4s/edVzmpLthlbt8fXHJ3l6NsvOwSgvOlh9DLZbezXdrNqfW27vSr3rXS+IAgeGopxJ5nnnvzyOAXiBd/7kNRweTSzZy00PN5u7Xe/Wph+enmM6rTAUD3BkW++afK0ZPy9n65+azfGH/+d+zi7Cjofv5bdecgN7BiNL9TU7ZlpBOOBj10CYh8dT/Oj0LAtZjd6on30j8Sqbu/mtm96tlr/+77/L6ekiu4eCfPqX76xqY7qgohgOAa+niommVXus1KFs88eeTpHSNRI+P9uHw2uyeas++Ltf+jH3n0xx9NSP+fAvPKuZLnPFShllfzZte2kvsWk5DCdkdMvhgXNJFrMqPVGZ3YNRBiKlLTRzOQ3LspfyKgzbWRoDbuMeLj4ptmxUw3K9voxvHp/i7GyWHYNRnt/kcdDtWm+4+fJq0Gx/L9lJNxvU1h54nC5RRSSTSZ797Gfz93//99x6663dENk0isUiTz31FP/6tMXOwTivOLK15WSNVmGaNuOpApMpBd128AkeRhIBRhMhJKk9D/Ldsl013eB//+cJHhzLoNo2siBw45YYv/XCfa7JOas5I70esnmNf3noAg9cSKOaFrIkctPW1dm81TadnUvx2//0MCeTKpoJfgmu6ZP58KsPs2OgtaNyW5U9tZjjj7/6OMemckvsDIeGI7z7pQcZ7mnth4Gb7HKm+cry4zOzvPFTj6NU1BEAPv2m63jWjtG6Mtrln5U2N22QhEvb3E2/gChyYjZTk/m/dzCGYlk11//dD37IX35Xqan/N54b5G0vuLOOZHe0ao8np+b5739/P2O50qEUArAlAn/1y0fZu6mvsX7zeVTTQZY87OoPs6svwplkrkbvpfIV13/sy/fxrZlaHV44Ah//1RfX1WMkFqRgmHWz7yczxZrrB0Myp+azNW0SRJO3/cPDnM/YWJR2A26LCXzsdTexd6g+f72bbd3a5Obnn/yv7/GJe4ya+v/77T5++0XPa2nMtBrXfnR2nNd/8rG6Y+zGzcN1GSv29EeZLag1eg9FAkznlKbL/+3xY/zZf2Zr2vTbL4rzhltu5v7zSY5PZZdYIw4MR7lxSy+aYzdtD7exJ0kWv/25Rzi7aC71944eib/4xRvZNdDTks3dyt188FtnTvL+r9aeDfyenxni9TcfbrrvwN0HyzZfyarjOCbv/T+P8cRsHsMGrwDXDoZ5/88dYjgeYTxVqGGsuJQ/ZxUTwy49KY4GJFd7TGey/Mb/92Bbxlir8byd7CuNGD/q9feS/8+nCShz7Nu3j2CwM+njGwtjlhfG//eczamkxvP3DfArd+zpqMxz8zlOzeaIBLylPVmmSU4x2DMYaVs2b6qgLWW7Vv4S/etvPcW95zP0hLyE/SJ5zWKxYHDn7h4+8MobW6prIOJ3PSO9Hv7ue6f45lNz9EdlorKXrGown1VXZfNW2/Tyj36bx2dUZC94THAkUA04uEnmX3/zro7K/u9//yN+eD5NNCAR9grkDZusYnLbtjh/9cutPeVwk+0TPeiWU1P+so/eQ+3yEELA8T+9u66MdvnnK//yuxybKhLwQUDyoJgOig6HhoN88dfrL07d9JtNFzmTLCU5hXylzP9UUWNXX5jBeLDm+hd+9B7Xdp130dsNrdrjJ97/Nc7nSq/kJMC8+LctAl/+zefV1U/RDGZzGkG/hE8U0S2LomZimhazea1G78GwH0kSa65/8z8+4qrHd99+e109BiN+An6va5tWXu/Wpr/77kkmC+DzgF8AzQbdgWt6JL7+Oy9sybZubXLz80b9/ch76tvcra5W49qh3/8a6TqHCsZF+MybbllirKjXfyv1DkgCimk3Xd6ovz/7uiP86GyS3rC8tNBayKscHI6xa1OsaXu4jb1/uuc0FzI2sgR+ETQLVLMUUz/9xme3ZHO3cjcfbKR3u8Z32Qe9gqfq6e8ff/kRHp7KE/GLBL0CRcMmp1ncsiXKh199lLmchiR6qp4yX8rXVj45dYuDb/70PZzPgY/SgtywQQd2xQS+9c4Xt6Rfq/H82IWFur58eDTRMvuKm35u/V3uC0dTKCQnOrow7uoeY89lvl+mL+KnPyrz8HiaiVSuY3JWZvP6JIGo7CMS8DKdVlHb8KpgZbarKHiQvSITqTwPT2ZJBCUG40FCAT+D8SA9IS8PT2R4cjrVdF2yVySrmE0TmE+kcjxwIU1/VGYoHiQkexmKB1dl81bb9IPTs5xMqgS80BP2E5RLnwEvnEqq/OB07ZOHdsk+Nr7Asakc0YDEYCxIKCgzGAsSDUg8Op3j2PjCmmV7BQ9TaQVJ9FSV/8fD40uLYr+w/AdQAP7hh2drZLTLP+99eo6n5osEfdAXDRAKyvRFAwR9cGK+yL1PzzWtn21ZPDmVJSr76An58XsFekJ+orKPp2Zy2JZVdf27/sV9kQTwmk/+Z1M6rMYeX398krGLi+JYUCIUlIgFJSRgLAdffWiyRj+PB87MFQj5pNKk4y99SqLAoxNZwj6pSu+wT+KxyQySKFRd/+sNFgsAL/7I92v0CPolnp4v4HGcqjYJwJn5PEG/VHW91+Ph0YkMIb+3qk1TySIThdKE3RPxEQr56In48Hvg3KLJd56abtq25TYJ0JSf/97nG/f3r3zyWzU2l0QPUym19Kp2DXHtiz8+v7QoDkjLfwBpC/7hvqeXGCuW+8+71H+VesuSyJPTWfxesarc7xV5cjqHLFWX//V/Nu7v9375IXrDJYYS2SfSH5GJB/08PpnFNO2m7OHYDsena8fefEbjfMbGJ0JvVCYckumNygS8cDKp8u0nZpuOU26yPY5T1wc/+4PHG+r9ji/c11TfwaV90OM4hGQvQZ9ESPZyZi7LE7N5In6BgViQcFBmIBYkKos8NpPnR6eTyF6RsN9LwFf6bMbXJFHAL4lL2yfqxcEHziYZy5W2R8XDXkJBL/GwFx9wIWPzzeNTTevXajxfyb5S9oNE0M/ZZKGlQ23c9HPr78q+CPg7vwO4qwvjK+GAj6jspaBbZAqd2+TdjWxetyzOTN5Es2xC/urXHmG/SNG0mc/UOne7MmczBfvi+fbV2dursXmrbbown8e0S081KuEXS7+4L8znOyZ7+uJruLC3+vqwV0C1bKZT6pplewQPmuUgeqrLx1L1nhUv4/Rs7SvYdvnnWLKAYZeO8K2ux4Nul75fCTf9bAQUq5ZtIeCVKBoW9opQNn6J3zlPT9e+cndDq/Z4ejaLTW0Ch0RpW8WFVK42u9rxoFp2zWtNweNBMU1kqdpxZUmkaFgIKx42XGpqUi+2uxI+UUQx7dI+5QrYgGo6+MRq2YLgQTEt5BXlKa0k3btijPnE0tPycy34WrlNKyOCm59fmK/VtRLn5miJXaCVuFZvDFViOqXVMFb4JaFu/4mCB8W0kVacICsJAoppIa5o68Ri47bNZqg5TCfk81IwSvNAlWwXexi2U5fpJK2oWIC8wtH9IhgWjKVr/dyt/9xk4/HU9cGJVOPF3PHpxn1SiUv54MpxkcxpGDYEV4zVoFRipZisM75b9TW3ODiVKWID3hVm8gqlMXa2g/G8newrriwTLv3t1hedQtcWxn19fZw4ceKy20axElnVIOQTiYU6Z5puZPO6ZXHGwhJ+UaCgVb/3y2sWQUmgP1a7kb5dmbOxkHDxfPvqRclqbN5qm7b2h5GE0qu+SmhWKahs7W/+CNFWZQ8lZPxCaftEJfJGac/fUKJ5Nko32Y5d2stmOdXlWxKBhvXtHqwlxG+Xf27pC+EVSour6nocfELp+5Vw00/AJiDWsi0ohknQKyKsWEKNDjZu286h5k/ea9UeOwejCJQmqkqYlALu1kSkNrva4yCLpQMSKmE7DgFJQjWrHVc1LYJeEXvFhHqpNBj5YrsroVsWAUmAFXUJXPwRY1XLtm2HgCSirihP+EvSjRVjTLdKPwq2t+Br5TatjAhufr61v1bXSmwfoCV2gVbiWr0xVImhhL+GsUIz7br9Z9kOAUnAXHHsrWnbBCQRa0VbN/c0bttgjKVDJ8oo6AYhb2keqJLtYg+v4KnLdBIPyIiUtk5U6WaVfhxtidf6uVv/ucnGcer64OZE46eGB4aaP+jjUj64clz0Rfx4BSiuGKtFs8RKMVJnfLfqa25xcDgWRACMFWYy7NIY29HBeN5O9hVXlgmX/nbri05hg66tAvMZlfmsyuHR+JqZEhqhG+edu51rvjkR5vBIlFTRZCZVIFdUmUkVWCwYHN4cq8tO0a4z0jcnIty0Nc58VmUyVSBb1JlMFVZl81bb9Ozdg1zTJ6MYsJDTKCilT8WAPX0yz959iZXUGmQfGu3l0HCErGIymylSKKrMZopkFZPrhyItsVO4yTZsh+F4ANNyqspffHiU8tJYs5f/oLTH+LW37aiR0S7/vHXnAPv6gxR1SGYU8gWFZEahqMPe/iC37iwd1WtaNpppLb1OrKefIIrsH46SVXUWCxqaYbNY0MiqOvs2RRBEser6P3nFcxq27R/f9IKG31e2qVV7vOjgCFsipYVwpmhSKJpkiiYmpQS8lx4ZQTUs8pqBopc+HQd2DYQo6CapokZBK32als31m6PkdbNK77xuct1IDNOyq9r0l6+5oaFe//H222v0KGomO/tDOB5PlQ1tYFd/mKJmVl1vOA7Xb45R0Azmcyp51WQ+pzLcF2RzqPTUOpXTKRR1UjkdzYHtPRLP3VfLAONm23KbbHD184JqUNRNCqrBn/5C4/7+uzc9r8anyuwChu2sKa698uZtxC8+4FLM5T8o7TF+7S07SRW1Ff1n1O0/1bTYPxRFM6yqcs2w2D8UQTWry9/ygsb9/b6fOcJCXmU+p6LqFvM5lXRR4+BIFEkSmrKHR/BwYKh27PXH/GyLCegWLORUCkWVhZyKYpQSbO+6drDpOOUm2/F46vrgLz77YEO9P/SqW5rqO7i0D64cF7sGolw7GCan2cxnS/F8Plskq1pctynMs3b3NfS1Sr918zW3OHjTjj62RMAAUnmDfMEglTfQga0xoS47RbvieZl9ZaUvp4oaO/pCLbFTuOnn1t+VfaGbdTbztxkbC+MKKIbFbTt6ePnhLR2XNZoIsWcwAjakFR1s2n7eeSzgK9HHOFDUTXBgIOLnHS/Zz63bYqiGxURGRTUsbt0W43+8YG/LdbV6RvrLD2/hth09FFSDp+dzFFRj1TZvtU0f/IUb2Nvvw7RL+2tNG/b2+/jgLzSeXNoh+90vPcht2+LYtsNc0cC2HW7bFufdL20c4FuRPZoI1S3/xOsPsPL5qBf4xBuudZXRLv/8wCsPcWBQxrQhpZZsfmBQ5gOvPIRtO6QKGhOpIuOLRSZSRVIFjYjfW1ePo9v6ODyawHEc5vIqjuNweDTB0W19da9/60/UfxL/63e6P0V3a9NILNiSPf7ql4+yLQIOpe0LDqXEu7/65aNE/F58ooeZlMrZZJ6ZlIpP9LCnP8pgxM9cRuX0XI65jMpgxM8duwfr6n3nnk112/QCF+amF21279f9Q/G6Ntw/FK97/R27B9nVF2Yhp3FqLstCrpQE+Ve/fCPX9Eg4DhTM0gOeay6yFLih1TaNxIL4RA/TGYVzyQLTGQWf6OFXn1P/LcCv3eFvecy0Gtc++cZDRFY8MIt4SuX7h+It9d/RbX0tlf/m8+q/7XrbC6Ic3dbHs3b04dgO01kFx3Z41o4+7tyzqSV7uI29v/ilG9nb78OwIK2W3haUY2qrNncrd/PB339p/YcZ7/mZ1ik4W/XB9//cIW7ZEsWyHZKKgWU73LIlyvtefr2r3m5+u3KrSxlu9fzFL93I5lBpcVx0Sp+bQ/DRXzzSsn6txnM3X94/FG+pnkb6ufV3uS+68cy4aVaKd77zna1X7vHwJ3/yJy3f122UWSmMcD+JWLzljOS1YD14jMvZoBOpPJm8SSwssTkRbkrvtfIYl2WniyqK5hDwe4gH5TXZvNk2lWU/OrbAw8dPc/jAbq7f0tsV2WWsB49xOZP4xHiamZzKpojM3tF4U5nEa/XPss2fnEozl1EYiAXYPxyv4vtsNku7jGZ5jMuZ2H/xH49wdgF29MJvvPiGhpnYl2IbaQePcVnGykz3cta6x3FqeIwTIX/TPLhlvd/2j49QoPRm4M9eU633WnmMyzrYlo3llA4WEURhqa3feWqac7NZtg9G6z4prodm21SW7Zb1/67P38OTs7B/EP7kF55TNb47wWNc2affenSS8bTCaDzA8y5yhZdld4rHuNzff/3tx5hctBjpEXnLXddV9XeneIzLej82tshcWmEgHuC6LT1rsnmrPMa//fkflXiMr0m0ncf4Um166EKSyUWFkZ4AR7ZWU6U167eXmn/c4trpiRyzxQKDwRC7N0eaYpi4knmMy0hnMjx95szlQdf2spe9jBMnTuDxeJpOovN4PDz11FNramA3UF4Y941sA68MDowkAms6wOJyhWnZTKSKeDyepbPZofS6stN6Xy6yvQIcP36cAwcOYNhc1f2dV3S+/sQ0oljKIC5jsaDhOA4v2L9pzYHNDY3627RsHErJJ53wBVU3efD8IoLoIeQVOXXqJHv2XEPBsMCGw1sTNRNDN/zTTUZBNZjOKGyKBwhXPEFqVXal3lF5uV+zqu6qd7t0WM8YktcMZlJq6dWwV+jq+F5Pe3Sjv92wnnpXwrIsjh07xqFDhxBXJG1dLmiXrdazvy8X5HI5Tp061dGFcdMW/Jd/+Rfe8Y538B//8R+85S1v4ed+7uc60qD1hlcUKOomluNclccClrNBgyvIuLuh92UjuyLx42rv73Im8UCgNpN4Lq+iWQ7Npx22hkv1t+NAcEUQb1d/lDOx434vVLx8kyWJtKJj2A4rN1p0wz/dZLhl67cqu1rvZTTSu106rGcMaZT1fzXHtW70txvWU+8rDe2y1Xr29zMJTfutJEl85CMfYX5+ns985jP87M/+LKOj9U/NupJxtZ9RXpkNKgrLg7Qbel8usitZ0672/q7MJPZ7l58YryaTuFU06m+fKOBc/HcnfKEyEztU+YSmQSZ2N/zTTUZ1tv7qZVfq7ZMqXtd3iPHmcokhVVn/FT59tce1bvS3G9ZT7ysN7bLVevb3MwktvecQBIEPfvCDOI7Dn/3Zn3WqTesG3Ww9I/lKQDNZ/93Q+3KRXVANNNNumBXcKVT2xVqh6iY51WhI0N7OTOJWsdLmlZnYPSEfvSFfx3yhMhM7U9RRDJtMsXEmdjf8s1VWkVZlV+qdzKukizrJvNoVxpv1jCGVWf+aYWE7pc+rPa5V9vdcViGZVZnLKm3tbzdU6p0paGSKOpmC1vWYquomRd1uy8FYnUK7fKQbjFYbaOGJcRnDw8O8733v4+TJkxiGgdfbPB/o5Q6H1WUkX65wO4u8nAWbVUyKuolXELqmd1nGesiO+L2kizrjixpTOZNoVmO0J+iaFdxOuPVFLOBDaPFXfqvn3pczhs8mC8zlVYJeYdWZxK2ibPOptLKUrDEcDxDxe5f07pQvjMSCZIo6p+fyTGRNAjmN3QNhRmLu+9K64Z9uMspH9q5V9lAkwPhCgSensiimRUAS2T8UYSjSmNO6HTqsZwwp2y9V0FBNp6vxfD3tMRiSedJM89C5FIplEhAlrh+NMhjq/Ev1kFdC0fKcmc+jmiUO2l39YUI97WNXckM5Do4tFjmZ1LDH0mzpCbrGwfVGu3ykzCQxnVZJKzqyKLSd0eqZjlX9vHjZy152yWssy2JiYoKtW7euRsS6YCDiJ94lNopuIKPoSxn2QZ+IYdnM5TQAEiE/Edm7pkzs1UAQPOsmO6cZ6JbDcEwmG5EYjsnolkNOM0hIne33S/VFKxhPFZbOvY/7vaimyanZ0nHa9bKSfT6RQ1t72TUQaVsmcbMo23xTPLCUib1k85C/o75QMEwCfi8Hh6NIOR/7hqN4RJGCYeLz1U/S6YZ/NpKRkNYuezqnoJg2+zZFlo6XVUyb6ZzCdrk9/OzrOY4vZb+gV2AuJDESD+D3defBzXra49R8ltm8xq7BMD5JQDdtZvMap+azl2SdWSsmM0VmcxqDsQA+UUS3LGZzGrFg8ZIMCWtFOQ6GfCIRvwfRQ8M4uN5ol49IksD2/ghDsUDHGa2eqVj1yN23bx///u//7vr9l7/8ZV7+8pevtvp1gShcfr8yVwu3s8jdzmbvNrotu9IeQb+EXxII+qUqe3RDtltfNIu1nHsfDvjoDfu7tiiu1Dvs9xLwlT5X6t0JX6iUHQ36CPtKn83avBv+6SZjLbIr/aM3GiAW8tMbDTTlH6vB5RhDJFHAK3ZvYdpMmzqFvKJzZi5PIuhnMBYgEbr4GfRzNlkgr1zqkPDVo9LXEkE/Ib9EIujvmK+5yY7IXiRBICJ7uyJ7rWiXj8g+iYjs3VgUdwBNW3R2dpZ777136f+O4/DAAw9gmrUOaNs2//Zv/4Ztd26xsYHG2MgYrsZ6slK0sy+upKzky4a94BnEQnIl+ccG1o71ZJ1ZT19bDevMBjbQLJqeGxKJBH/5l3/J1NQUUOIo/tKXvsSXvvSlmmvLPMc//dM/3aZmbqBVbGQMV2M9WSna2RdXUlby5cJe8ExiIbmS/GMDa8d6ss6sp6+thnVmAxtoFk0vjH0+Hx/72Mc4ceIEjuPwrne9i5//+Z/nhhtqj9MVBIG+vj5uuaX588o30F6Us2DL+1hXnuJ1NbFuNINKe9gCS1nruk3H7dHOvihnJZf30smShGqa5BSDPYORy+q12nr64Hr293riSvKPDawdZdaZh8dTQOlJcUE3SBU1Do8mOrptaj19rVK2bdmYtk1ONSjo1oafb2DNaMl79u3bx759+wB44IEH+Nmf/Vmuv/76jjRsA2vHemZKXwprPYJ1NSjrPZ9Vyes2huUwEJW7mrW+WNApaCZ+afV9sdqsZLdjYaFz/dGM3u2SvbKesowLyRzTWZNBRWdrX6Qjsi8nlP3giYkMGTVHTPZx7eZYlX904yj69UJe1ckoFnlVJxaqZuK4Gvp7pQ5ldpnTc3mms0pLrDNrtUfZp84vFEnliwT90poZEpptU1nGhWSBlGIzZDk1sq+G/nbDeup2NdsVVslKAfCBD3ygne3YQAewnpnSbmgnbdmq4Vnx2UU4OHg8pc/VotWsZFU1uf98kuNTWVTLRhYFDgxHObqtD59P7Ep/1NO7Xb7gVo/fI/D4eIpHJzKcn1PJnV4gq5hd1Xs9oGom9z89z/3nUxQMm5BXoKjp9B/0IyO1RPV3JUHXLZ6cTnNqNs+ZSYWkb449g2H2D8WRJOGK7+9G9Jtb+8L4vBJFzSTolxiKyQ37s91x2HZKI9t2Vh/XWm2TIHiIB30oEZPZgFhilQqWrr0s5pkOYT11u5rtWomr61HBBupCEoXLpqPbSVu2Wtk+wUPYV9qH1m3ZJb2Ftugt+6SmEkzuP5/kR2eT9IZlhkJ+cprBj84mATg4muhofzTSG2iLbDefOjOT4fGpDImgj/6wgE/ydE3v9cTXHp/g2yfm6I/KDEQDZFWdb5+YA+Dozv6WqP6uJDw5nebh8RQx2Usi4EEUWNpisLUvfMX3t5ufp4s6uuUQ8kvEg76mdGtXHK6kjkwE/Wvyp1bbVL4+4BOJB0QCPrHtseVyxOUwh16Ndq3E5bJe2sAzACtpy4ClpKysYpZodzr0RLtStlcAwePB7xUR7O7K7rbe6YLK8aksvWGZ/khpGS1fZIk4PpUlEfYTDfg60q5Gei8WdBycNdvETUauqPHoeIaeiJ++iJ/iokRfREYQBJ6cypII+Zfo29qt93piJpPnobEM/VGZTfHSQSZBuRTmH76QJuDz0heTicql7STlpKnptMpQLHDFbquopC2LBbykRJFEyI8gCJyey+MVRcIB7xXb325+blk2U2mFTfFA07q1Kx6tpI6E1ftTq21qFM9LsYV1ibedxuUyh15tdl2Jq0OLDVwRKFNoeVcMHq8oYNilPUsbstsLxXBQLbvmdL+I30tBt8lrZsfa1UhvzSzt8V6rbDcZpuNBsSxCvlq984ZNXu+c3uuJnOKgmtbSQqWMqOwjo1tkVQNZql6syJKEatkY9pWrd5m2bGV/l5LRLBTTvqL7283PPYIHzXIQPc3r1q54VKZMa4c/tdqmRtfrlo1+hfe3G56p81i3sbEw3kDXUEmhVYlu03c9k2QHvB5kUSCnGVXlOc0g5BMI+6WOtauR3n6pdAjDWmW7yZA8DgFRpKDX6h32CoR9ndN7PREJeJAlkaxafbBDVtWJ+USicmn7RCWuBoqrStqyShR0g5BPJCAJV3R/u/m5Y5dOsrSc5nVrVzyqpEyrxGr8qdU2NbreJwr4rvD+dsMzdR7rNjYWxhWwNg4k6SjKFFqqYaEaFpbtLP07GpC6Qt+lGhZFzUQzbYqa2XXZ3dY7HpI5MBxlIa8yn1NRdYv5nMpCXuXAcJTNiSCqYZHXDBS99FnZLtOy0UxrVScDNtK7J+SjN+Rfs03cZHi9EtePxkgXNZI5Fc0ySV7Ue/9wlM09wa70x1rstxoZm2JhjmyJMZ9VOTOTZXwhz5mZLPNZlcNb4xwcjZNTDLKqjm7aZFWdnGIwFJev2G0UsExblipqzGYUcprBbEYhVdTYPRBmOBFANSyyRZ10USdb1Nvm582iE2PJsB2G4wFMy2E+ozCVKjKfURr6crviUZkyLaeU6OEKmkmqqDXtT5X2aLVNldfP54ok8wbzuWJFbPGtS7ztNCr1LqgGRd2koBpd0a1Sttt8cbXgyo2EHcBsTsORtKsuw/JywnpSyEX8XtJFnfFFhamcSTSjMtoTrNlm0Amsp95Ht/UBcGImx3RWIeQVedaOPo5u60OSBNJFnamUimbb+AWB4YRMyCuRKmhrzj5uRu+12sRNxmh0ExHZy5NTWRaLDnGbJb19PrHuPe3qj25kb7vJeM6efu47M88j46klFpIbRqLccc0AfZHSvuNWqf6uBOzpjzKZKnJsPMNY0qToLXBoNMae/iiSJKBoec7M51FNB1nysKs/TCAebIufN0K7fMHNz714+N7pWY5NZFBNC1kSObQ5xmh0U8t1ter/I7EgmaLO0/MFFNMmIAns7A8xEgu63tOIXaOVNvk9AmdmMhybyDA2pXDWnF3SW764r/5ypCpdK8rz2FRaQbNKbwyG44GuzGNLslfMF92Q3U1sLIwrIMBVmWF5OWE9KeRymoFuOQxF/WQjEkNRP7rlkNMMElJn+3s99ZZlidv3buK60XgNj3GqoJVsEpfxCB4c20G3HCYzRXTLWXP28aX0bodNGsm4fe8mDoxEeFBMcePhEXojywvATvZHN7K33WT88OQMBdPh0GgcnySimxYF3eJ7J+d45S07WqL6u5IwW1CRJJGj2xP0k2L79gQ2HmYLaun7nMZgLIBPFNEti9mcBmQI+L3r0k+tynDz8++fmOHxqQy9Yf/SAR+PT2WIyF5u31t/cdyueFQwTAJ+LwdHYpT4GB0cj4eCYS79+GzVHs226cGxBR6fytAT9EJMoCfordL7cqMqbRfK89imeADRI2A5dtfmsaU5dMV80Q3Z3cTVERHbBJ8kgle86jIsL0d0m0KuOotZxC8JhGQvRhdYKSqxntR58ZBMvOL/9bKMAQqq0XKm+6XQSO922cStnnhQpi8sEQ/Wktt1oj+6kb3tJmMhr/HAhTS9YZnhnuWndjPpIscmMtyRybMpFm6a6u9KQSVDQsgrkg5IDEQDFAyL8wtFbMepYk8IXez1p+cLXLc53vV+WouMSp+txzoTCUiIgocTMzmuG43XHObjVlercIsfqmGtmRHjUm2q1Lsn5ENNexmIBZAksUrvy4mqtB2ob3Oxoc07K7txf1+puDq0aCOuxgzLDTyzMmqbRTsz3TewjG74mpsMRbdQTYuQXL0ciMo+CrpFTrk6+64RQ0JRM1F0q+Y7nyiimDYrd/x2o5/aJaMh64xhoRiXF0tBu+yxnnqvJzZYKbqDK35h/Na3vpXnPve5VWVnz57lTW96E0eOHOHmm2/mXe96F9lstqn6rsYMyw08szJqm0U7M903sIxu+JqbjIBPRJZECmo1U0BW1Qn5RCKBq7PvGjEkBP0SAZ9Y851uWQQkoWYS7EY/tUtGQ9YZr0jAe3mxFLTLHuup93pig5WiO7ii3zJ89atf5Zvf/CYjIyNLZdlslte97nUMDAzwwQ9+kIWFBT70oQ8xMzPD3/3d3zWs78x8ls39XgYi/qVXAqputrQXz+0M8byiL22UD1ckAMxk8uQUh0jAw6ZYuFUTNAW3Nn3xx+c5PZtl92CUV968bU0y3PRzk/3QhSSTiwojPQGObO1rqr1ufeEme2V5LOBlLqfx5k/fw6kc7Pnef/G/X/+cqv5uFW5tTRfUmv28AO/80v0cn85yYCjKB37+aFVdbvq5lbvJSOaKFDSHkN+zlGwFsO33vrb07/N/evdSlvFcTiNX1JdkeL0iw/EAuuVQUI2lvWSG7SzZyk2Gmz3e8YX7lvT+0KtuaUpvN/2OjS8wnVIZSsgcGu1dKj85m2Yha9Ab9XLNYHypfOfvf730j3/+Ouf/9O4q2a36mlv5ynrKdn3Xv/yAC/MmW/sl/udLb20qtri16cx8hlTOJBGR2NUfW5JxdibHoqLSE5AZ7g1y09Y4951b5PMPTi7d+4K9vdy1d2ApxrjZys3mbuVubf21f7iHp6az7BuK8vHXPqfK5m51udnDzdfOL2RJ5y3iYZFtvVFGEgFOzeZ49afuL13wrXv5m9fcwJ7B0glsp2ZzGKaN5BEwHRvVsNjZH8IGZlIFdBt8Asj+5TnALba44e2fv3fJzz/yC7dW+YJp20t7Qk1reSy52dBN75XlB4aj/Ohskvf/v5NL17zl9q08a0ffkn3d9Gg1pq68vqzb//jMf3F+Hrb1w//6b89p6OflezTTAscDHgfH4ZI2XzlX1uh9/+M1erc6H7td71b+T/eeW5pDX33r9ipfaFVGszGnbL+3/t1/cWEBtvbCh19bbfNW9WgGlb78w9NzTGcUhmIBjmzrbWoOXYvsShyfStPpVD+P41yZz79nZ2f5qZ/6KQKBAKIo8p3vfAeAv/mbv+ETn/gE3/nOd+jp6QHge9/7Hm9605v43Oc+x4033lhTV7FY5KmnnuIj92UYjIX43bsP0BcJMp4qMJlS0G0Hn+BhJBFgNBGqewa9W6ZtQBQ5MZvhzFwe1XKQRQ+7BsJsSQT55pPTPDS2nEl8ZEuMuw9uJhzqbGb86fkkb/rUMdLW8rVxEf72TTdw49bhlmTousWT0+ka/fYOxlAsq0Z2TlF535cf47HpPLpt4xMErhsK876XX89wIlK3vSGvxGSmWNMXgyGZU/PZGtm7+iKcSeZqyr/ywH185sFaHV5/M7znZ+6u/WIVtvV7BB4cW+D4VHaJEeDAcJSTqQne/9XZmnr+4GdHeM0N19X1taFIgOmcUlPeH/Tz8PhijYxrh2PcezbJsfEMqmUhiyKHRmP82hcec9Xjyf/5Ar57aqYmo/05uwZYUGozn3sDPu45M1cj447dgxg4Nfb4P48/wR9/ZaZG7nt+ZohfOnKort5u+m3u8fPhrz3FsancUkb0oeEIb3n+Tr58/xQPjC236aYtMf7+/qm6OnuBk3/ykpZ8za0vRmJBCoZZU8/nHniQD389XSP7HS9O8Obbbqmrt1tdlmnxd/ec4f4Ly/od3RrjJw8P8anvnOXhySyaZeMXBQ6PRPnyE0nX/n7wnXfy8W+frLHV6+/Yydn5Yo3NrxuJ89hkuqb8xi29aI5d09YvPPwI/+trCzVyf+8n+3jdjUe4/3yypq7Doz3MF7Uae7j52g1b4vzrg+P8+EIazbTwSyI3b43z4e+cc9X7xB+8kPvPJ3lyKodiWQREkf3DEfYPRfn2UzM8PJZBMS0CksjhLTGev3+IsVSxJobsH4rXTSr72/sequvn737ZJl534w2Mpwo1mfxu/b3EMrFC75u39/Ljcwstje9Tf/iiuvF5T3+U2YLadEx1u/7z9/6AT/6oln7uLc+W+O0XPb+uny/LKCz5wa6BkGvcdpsrP/GVpzhfR+c9wNddxrfbfOw2X0X8XnKaUVN+cm6eN37yUXIVq6eIBz795kMcGt7U0pzYasz5px/dw6furV22vfk2kd+9+4WujB/19GiVGeXEdJK3fuYBzmdsLEAEtsUEPva6m9g71Ff3nnaxsowlM/z+Px8jq6r8z9v72LdvH8GgO/vJWnDFLozf+MY3Eg6H8fv93H///UsL49e+9rX4fD4+/elPL11r2zY33ngjr3nNa3jb295WU1d5YfyxB3M8Na9z5+4e3vTca5bOgJclCdU0ySkGewYjdc+ATxW0pUxbr1giF1cNi9l0kTPJ0lGl5YzhVFEjmS5yJlmkP1o6njWr6sxnVe7aO8Arb9nRFhu5tekV//ueqgFdRlyEY+9vbYF47MICD4+navTb1RdmMB6skf3HX36Eh6fyRGWRoCRQNG2yqsUtW6J8+NVH67ZX0Qxmc1pNX5imxWxeq5EdkgQKpl1T/of/ftJVj5VPEldr2zMzmYsZ4vJSMFrIq/z19y+41vXdt99e19cCkoBi2jXl6bzKucVijQzBtkkWTfoiMlHZS1Y1SOZUvvTwtKvsz77uCD86myReYat0UePgcIxdm2JIoqfqKdex80keGkvXyDiyJc6hbX019njhR+9pWW83/e4/NcvTKY1oQCLsFcgbNlnFJCg4eP1+ekM+IrJITrVYKOiMpTVX2Y+853kt+ZpbXwxG/EusBmvV262uL/zoaX54NkVvxE9UlsiqJgs5DdvQyFsi8aCXsF8kr1mkiwYzecNV9i8fHebbpxZqbLW718/ukUSNzcOSQN60a8rL/tGK3mVfW1nX9p4g8bBcY4/JhTwnZvM1vpYvqEzm9JryR6cKl7S5LIkIogfbKp0QWJaRCPkJy17yqkGqoLElLhMM+mpiyOHRBIe29tbUX/k2ZiXKvuYVPFVvX3yiZ4n5pdKGbmMsJHkomE5L4/srv3pL3fg8GPYjSWLTMdXt+jf/4yOXtLnbWAr5JCRJwDRtCrrpKtttrvzPE7U/wFbavNn52G2+cuujl3/0Hup5W8QD//CW+jZ3k9FqzGlkcze93fQYiPhbYkZ50Qe/wclFEx/gE0G3QAeu6ZH4+u+8sO49bnNlq7Jf+4nv8eBEnr19Xt59e29HF8ZX5B7jf/7nf+b48eO85z3vqfnu6aefZvv26lcagiCwefNmzp8/37DenrCfeFDiwfEMPzg1T8gnEvKKiB6HkFck5BOZWCxSUDQsy1r603SDxYKGTwCvADg2XgFMw+T4VJbIxdcfkgixgBfB8XD/hQxhWWIgKiP7BAaiMr1hP4+MZ5hMZavqX82fW5u+eWxiaVEsS8t/AGkLPn/v2aZlZAoKp2bzxGRvlX4Rn8TxqSymYVbJPjGV4onZPGGfQG8kQCDgpzcSIOwXeWwmz/efmq1pr2NZnJ7NE5CEqr4QgWPjGYKSWCU7IIo8Mp7Bd/FXabn8k19zXxQDvOzPvrZm2+q6ybHxDFFZoifkwyt56An5+OGpsYay3/3P99X4ml/wcHwqi9dDVTmWw4NjGSL+ahk+QeD+8Qwhr0BfxI/v4mejSRPgFz/zEImgj/6In6BfoD/iJyZ7eXQig6aVgrNPhIAkUFR0HhrLEA94q2TEA14eGc9QVPQqe3zw/z3QUPbv/8u9NXpjOzw8niHiq9avqBicTKoEvR76oyXf6Y8GkByHaaX0CnwgLhOQvQzE5YaLYoAb/uhbdX3t1FweeYWveS/2hc/jqSoPSAKn5/I4llVVzx9+9UcNZf/WP32/Rm9ZEjg9W1vXTKbAA2MZ4kEvgzGZgF9iMCbjE23OZ2xkCQbjMqGAl8G43HBRDPD390+RCHqrbBWWS+PPNK0qm/slgQfG0gRFsao8Jnt5dDyDqmtVbf3j/9u4v//oKyVfq6wr7JN4eCwDtlNlD9u0uX8sTUSWqnxN8Dgcm8oRumiHgF9kMCY3XBQD3PmRks3jQS9Rv0Q86MU0LR4YSxOVS3WFLtYV8YvcP5ZGpDqGxGQvZ+byZApKVTx42z/9sKHst3/h3tIWDa9QOqHPKyDiML5YRLhou7INC6rKQ+O1Y0yWBB4YyyBLnpbG98s+cV9NfA56RR6dyCA61bFFdC7GVK/Y1PUf+qr7Ag3gLZ+u9XOf5+JYEjxEZImAJBCRJUTg0YnaeO7YDvePZYjK3qq5stGiGOqPb7f52G2+Ehyb8cUiIk5V+dcfHltaFPvF5T+AnAOf+cGZGpuHfSLHp7JYZvX4xrY5PZtHFqtji9cDx6ey+IXq8j/7SmObv+ava/V208MnlBatmm40Ne996/gkZxdNfB5IRLyEgl4SES8+D5xdNPnW8cmm58pWZd/39CxPzOYJ+jzE2/RGvRGuuD3Gk5OTfOADH+ADH/jA0laJSmSzWUKhWrL6UChEPp9vWHexWABdI63C46fOIBVCSELF/hzbJqc5sOgn6FsuNyyH2YKJLHkQKjagp1WTc5MqRlQiU/H6bSqrsphR6fUWmDZyS+WqYTBfhPsfUtmSWFvnu7XpwTPzS/+2zdr77nn0Kfb4Uk3JyCgWZyYVEgEPKXFZv6JuMZE16bOTxCuy44+dz6KoIPttlMLyRCYYDnkDHjx+ElmJVrU3r1ucntdRohIB77LNU4rJ2LRGUPeSX5Aqyg3GZ3V6yKCmlncizVzivcjxOTh27FhTervZNlk0GJtWcKIiWnq5TeOXEH56TGf8/JkqX8tpFueSOn7Fx4J/2bbJvMHUnErEEtEzyz4yW9BYTGksehUELdOUHmWk5icpLi63t6CbjGctjvsy9AUrbJjXmZwr4okKWIXl8rxuMJG1eeJkgWR4uU1PnVUb633BqNF7vmgwMasSMCT07LKMyZkcmgkYNoXc8jj2XPRhVdFIp9It6X3q5FNV/VfQLM4kdbSYyJy0bPO8bnJu3kAu+kjKy+WKYTORNZFyPsIV4/vkuYo9SnXw9DQ1equmxXjGwpvzEaro7xNzCulcAX8YFheX7akUdUzA0gyymeYSi8tw9Dzp1PIPB1s1KCowNT1FyFyuK1nQmFvUmfUVsQrLT3aKhsFYxuZJb4a+8HIfnbxEf4+na30tq+lMLFic9efIhpbrms3pzCYVIrYIxWWfWkwXySs2ej5D0tNYXo38FTafzunMJBXCtohTKaOoM79oMD6pYmSW9TYsi5Ti0KPNEgss99EDJ9MN5T51tlDja5ppM5UzyUYk/BVb82ayOpOzRYRY9RhL51TSOZNUUsOrt9bfc1MXquJzXjO4kDTpt1OkgpWxsyKmVsTtnGpyYcGgz0mRCiyXn12euuriTLrW5pVxLVnh5ynV5MKUhqxVx/OpjMpiWmVAKjBttKb3Spu7zcdu85VbHz30dMUcWmeonxhPck1Ea2pOrJzf5rz17RSpsNPpxksYTs/X6u2mh+04qKbDXEjCK156S8M9xxfQAdkBveLodY8DGnDPQ8dJqNXbidzmypZlP51BUSHiddA0Dah9a99OXFELY8dxeNe73sUdd9zBC19Y/7E9gKdOdqTjOHXLKxEMhiAnEJccDu7Zzmh/iIi8HJxyqoHjwOEt8ZpkmYm0ggD4K/j9UnmNpJCkL+yjN7KcZCIlC/QsThGKBhjqW064m0krjEbg6JGdbIqu7SQqtzbdmL/Af02U9uIJlb1/cYHxnOv3cejQ1qZk5FWdpG8OUagmql/IqXhjOvt395EIV0yowSRfOfMEtigQCC2/AslbCkHJ4cYD17BnS7yqvdmijhnOMhj1EwsuT17zWZV5FhnqDzEQCyyVz6YVFpw0wyNRhitO89r0g4caLo4PDMChQ4ea0tvNtgs5jbPaFImQxGDFyU+jY0+QbiB89xYfo9t2VflaKq+hBXJsGwxX+U40pXBWn6O3N8Cm+LIMZz5PT36Bnt4II5VbfY6duKQ+if4R+ipkzGUUhLDBgWuG6Isul/elFR7PjhMLSGxKVHDkpop4AibXXjPKYHy5L/aduo+pU+6Ll91bvTV6R9MKF7Q5+hOBKhkpZwH/9BR4BUKR5TGTLGRBBzngJ56ILlc+tTx5uWHPNftqfE2fyjIY8ROv8LWFvIYq59g2GKEnsuzPmaJOIKexbzhKtOL6a47fQ7LB4njnEDV6p4s6clZj70h1Xb7+LN8YO4noE+mp4CVe1FJIOQ3R7yUaq+jv2cVL6u3xhYknlvspZxcIGirDQ8NsGUksX7iQZ0BLMziYYKRveSzNZRQ8QYP9+wfpr0gKu+bJ+5hrsDgejdf62nSqyGZRYcf2ATZV+E54scgT+SmiMZmRnmXZBTFNeCGJLxyiryJ2cnY50dBV/gqbBxcLPJWfJhaTGa6QwUKefrvA6Mggo73L5amCRr8DR/YPEJaX++imk/cx/ljaVe6+HaEaXytqpZM3h6J+QhVt6s0UeLwwSVSuHmPGXI54IU2iL8bIQIWfNzG+B4a3VsXnuaxC0Vdg+9Ye+mPLfTGfuRhTB0IMRANV1yv+AjtWXL/jx/dypsHieFe81uaLuVJc274pQk/F3DCfVUmyyHBfdTz3zOfoSc8QjAYZqvBBjj99Sb1X2txtPnabrwqqQTSrMRyTCfqXJ8sj2XN8f7K0NU6o3G5+ccjvHe1jYHhTlc2TOQVvzODAnv6qJ545xcAMZxiI+IivmEO1QJ4dmyJVbdp9372cbLA43t1fq7ebHpph4QAj8UBTyXApeYbPP3kMxwM+33KfFnQDnwPPOXKAQysOlHGbK1uVbcST/NOTD2J7PPj9nT9I5IraSvG5z32OkydP8q53vQvTNDFNk/IWadM0sW2bcDhc98lwsVgkEmn8KyNV0EgXTW4cjfHsPf0UdIuCYWE5HgpG6fSozT1BQgE/oigu/fl9XnpCfnQbDBvwCBg2SF6JA8NRcvrFBAsLMoqB7XE4ujVGXjWZy6qous1cVmUhr3HDaIyRRLSq/tX8ubXp+Yc2E7n4+0A1l/+gtMf4F27d0bSMWCjAnsEwGdWo0i+nmxwYjiJ5pSrZe4cTXDsYJq/bzOcUiorGfE4hr1lctynM7fsGa9rrEUV2D4ZRTLuqLyzg0GiMomlVyVYsixtGY+i2U1X+pruvadj3X3nb3Wu2rc8ncWg0RlY1SeY0CppFMqdx254tDWX/8c/dUuNrmu1wYDiK4VBVjujhxi0xcprJYkHHMB0WCzq6bXN0NEbBsJlOKWQVg+mUws8fHmoo+7OvO0KqqFfVlVENrt8cw+/3VukXDPg4siVGWjFI5jR0wyaZ00grBjeMxggGfCimjW6BYtr8zktuaij7/a+4tUZvBA+HR2Pk9Gr9ggEv1/TJFA2H+ayComjMZxVMj4ehAOg2zKRUCorBTEplS7xx8HzkPc+r62t7BsKoK3zNuNgXuuNUlSumze6BMB5RrKrnf770WQ1l//mrb6/RWzVtdg/W1rUpFuKmLTHSRYPplEpe0ZlOqeiWwLaYgGrCbLqk92xaZVO4cb72Lx8dJlU0mEurKGrpM6+Wxp8kiVU210ybm7bEKVpWrX+MxpB9flTDRrMcVMPm3T/duL/f87JaX8vrJoe3xEDwVNlDkASObomTU02m0wo5xWA6rWA7Hg4NRyhoJrMZFUWzmM2oXD/c+EHCd99ea3NJErlpS3xpvJb9OadZHN0Sx6I6hmRUg10DYWKhQFU8+LNX39ZQ9kdedWuNr1l4GO0JYl/8f7k8JMscGS2NsemUQrZY+lRNm5u2xFBNp6qtlxrfX/nVW2ric9GwuH5zDMtTHVssz8WYalhNXf+Ol97QUPZfv77W5rpzcSzZTk08v35zKZ6nCjpFvfTpETwc3RIje/HQoWyx9PmCvbX7vCtRb3y7zcdu85XtERjtCWLhqSp/0eEtlL1Ns5b/oLTH+HXP3lVj87xucWA4iiiJVTESQWD3YBjVqraH4cCB4SjaCju97WWNbf6Pb6nV200P3S490PL7vE3Ne887MMKOHgndgVTeoKgYpPIGugM7eiSed2Ck6bmyVdm37Bzk2sEwRd0hXWi8Ta4duKIWxt/4xjdIpVI8+9nP5sCBAxw4cICvfOUrTE5OcuDAAT7+8Y+zfft2xsaq93Tats3ExAS7du1qWL/twJ27e/itF+5jNBEq0fzYkFZ0sGHPYITRRP0AHAv4GIj4wYGibsJF+pmj2/o4PJrAcRzm8iqO43B4NMEbbt/NXXsHsG2HyXQR23a4a+8Adx/c3DZ7ubXp46+/lpXLBj/wsV852LKM/UPxuvod3dZXV/YfveJ6Dg+HMQyLuYKBYVgcHg7z3p+5zrW9+4fidfvizj2b6sq+++DmuuW/dKS+Dr9ytH55I7i19Y7dgxwcjpEqaJyey5IqlBLZfvfu+oH83S/b5OprR7f11S2/++BmnrWjD8d2mM4qOLbDs3b08bpn72TvYJipTJFHx1NMZYrsHWxMAXh0W1/duu7cs6mufncf3Mwde/px7NKx0Y7tcMeefl587Qg+0cNMSuVsMs9MSsUnenjXTw+0rLebfh9+9WFu2xbHth3miga27XDbtjif+JUj3LY9jmIYXEgVUQyD27bHXXUWGvSfm6+59cX+oXjdet72gmhd2W9/YcxVb7e6fvW5e7htR4KiYXB+sUjRMLhtR4KP/uIRbt0WQzVMJjIKqmFy67ZYw/7+tbuu4a49vViOzVRWxXJs7trTy/tefn1dm7/m1h11y+/YPYhP9DCdUTiXLDCdUfCJHt7x4kRdub97d6+rr919cHNde7zshtGSP6eLHBtPMZUu+fPv/dQBnr93ANu2L8ZOm+fvre9nZbjZ/BVHttb159fcuqNuDNk/VN+v3Pz8XT894Opro4lQ3fIXHhheHscTy+P4zXfurtvWRnCLz3fu2dRSTHW7/vU315f7xls9Lce1O3YPsqsvTDKvcnouRzKvsqsvzC/etoPrhqPMZhQen0wzm1G4bjjKSH3R7KT1+dhtvnLro0+96TpWrgRCwKfedL2rzW/c0ls3Ru4djLVkpzfcUkdp4E3PElr2tVgTFISV+Ohrj7AzJmA5kDPBcmBnTOCjr3WZXBv0Rauy/+gV13PdpiBmLQlK23FFsVKcPXuWQqE6yeLjH/84TzzxBJ/4xCcYGBjgn//5n/n0pz/Nt7/97Rq6ti984QvccEPtL64yK4W3d4jrtlXTlV2NPMbfPzHDj84mOT+XJa+ahGWJbQNRnrWjj9tXvAppFs1yNpYzVE/PZFjIqvRGZXZvilVlqHaKx/jcfI5Tszn+178+wtki7AjC7778BlemkWbgpp9h2Rimg1fy4BWFpWzo/3fsJLMZGIzBSw5dU5XlvlYe47J+juVgU1oAekTPUrZ3JSvH//zJatmt8tSu5FIt6+2Wef/nX3+MM/N5dvWH+a0XXVfV32vlMS7Lnk0VSWsGcb+XwURwyeaN9O4Uj3GZreUrD5xkahGGe+BlNzXX324+lcxpZBSdWMBXSsC6aNvpRYVkUaEvGGCoJ8A7v/AAx2dVtIrI7vfAoeEgX/z1O4G18xiX27SStaTcpj/6t/sYmzPYMuDlPT91S1V/N8tjvOTPNpjYSAh4BJbG60oe44/955N888RcVSLe9cMhnr93gLe+YH9Dm7txBjfLY1xu62fuOcZkymEk4eF1zzlUFVua5ZZd1tvBwYMHB4/gWaprZVub0btTPMblueSr915gzoABL7z01q1Vc0mzY6nsU7bjYFkgiiB4PEusDaZlYVkeRNFBEkUePZ/kkYks955fzqm4dVuM5+8b4Ffu2NPQ5mvlMS639VuPTXFuMc/2njDPu264ys9XynCLkeV7mrVT2T8+8tVHmMzBSATe/tIbVuVrraKswyNnU0xkc2yORrhhR6Iphol2yT49MceQJ7NB19YIv/d7v1dF17a4uMhLXvISBgcHeetb30o6neZDH/oQ119/PZ/61Kfq1lFeGO/Zs+eS2y2udKQLKp//8RiSJNBfsc9qPqfi2A4/f9No1WTVTpiWzUSqiMfjqTlrHQdGEs3tN1oNVN3kwfOLCBezf0+dOsmePdeUXt/bcHhroqkfPo3gpl8qr/Hdk7P0R+Qqmy8WNBzH4QX7NzV1eEAjVOoXrdj/OJdRuO9skp2DETZV7Btsp2w3vfNaaVvDUFyu2kfZzv52k71Y0Pivp+YYiPpJhHycPfs0O3bsJKMYbdPbDXlF5+tPTCOKAj0Vk8VqbO6mX0E1mM4obIoHCFcci3vfqSTv+sqjeEXoq+jvhayC7cBf/9JN3Lqz8dPVdrdpNf3t5s9ZVa87Xs8vZHnPvz6BTxIYjMkkk0n6+vqYzajYts17X3Yt23rrP8VfK1ptazvrqtR7qCLnYPrik/RO6t3OucTNpzKKzmNjaTbF5Ko9uGfnsvzDvecZiMgM9wSYm51lYHBwqb9/7+59bE50Zi5fzTzWrrmvnb7WKtZz/q6UjaGSnDy/QdfWCnp6evjsZz9LIpHgt3/7t/nzP/9zXvSiF/Hnf/7n6920ywLrecb8ep61btgOuu0gS9VBQ5YkVMvGsNcu200/w3ZQTZuAt1p2yOeleHGP5lrhpp/HA4pZOgygU7Ld9BY9Appt41lB4t7O/naTbVmgWlZVwge0V283aJaDajmEfNVjbDWy3fTzCB40y0H0VJdPZ3OYNgSkapvLkgfdhrFkY1qzTrRpNf3d6nhN5y000yIqV9s8Knsp6BbpfGOmkLWgnbHlStK7nXOJm0/heFAtu+ZQLcN0UEyLkFw9vst6Zwqde9++mnmsXXNfN+YxN6zn/O3qHx3CFcVKUQ9/+qd/WlO2Z88ePvOZz3S/MVcAKs+Ylysoa7pxxnzlWetiRTpvN85a9woefIIH1TRLfLkXoZomsijgbeEEHje46ecVPMiSgGKUtq2UUdANghe5TdeKSv180vKTBMeBgCSiWtUTZDtlu+ltOaVTvpwVwbqd/e0mWxRBFkU0wyJY4eft1NsNftGDLHoo6AZ+b0WW+ypku+nn2KVXtJZjUzp/qoShaARJAMV0qvZAqqaDT4AtfWtju1lNm1bT327+7DZe42ERvySSVQ0CFfRWWdUo8ReHa0+raxdabWs766rUu/KtTDf0budc4uZTeBxksXQISGVijFfyEJBECqpFBQHQkt6xUOcWUKuZx9o197XT11rFes7flbK7gSt+YXylo13nhzcrIx6Sl86Y1w0Lr1fAMGxymlF1xnwnUHnWOlBzCk6n9AeQfRIjiQCnZnMUiwYpxWQ+o2J5SnsW2/H6yU0/j+DhwFCUM8k8umnjEwV0y0Y1TQ6PJtrySr9SP2DppCTDtrl+c4zZvMbYgoWABxsHw7abkt2Mf1bqbdp21Z7T4YRc2gebzqObHnySQ8Dna1t/u8kWPB4ODEc4k8yTLxrMZjW88wUswWmbzStRaadwwMeugTAPj6ewbIeAV0IxTLKq3rJsN58ybIfheADdclANa6l87+Yo1/TJHJ9VmV5Q8EpgmGA6pT3G5W0UreZOtNqmjKKD4wGPg3Mx2aaV/nbz5/LpoyvbvK03ys1b43zzxBypnEKhoJFUU1h4eP7egY5tJ1jZ1kzRwHYcBI8HYUVsaWYsrUXvrKLhFUUMy0I3nY7oXek3lXOJcfGNWNnPW51LKn3KuhgzHbvkO7sGQszmNATRs2QPv7d0lPkjE1lsx0Yp6ugLBdJFg+fvG1jaRtGJ+bVRvHPz83bNfZX+YVtO1cmB7ZrH3LAavSuxlr6olC2YnXsLsiSv4xI2UBftOj98NTKuG4lzZjbLQ+NpinrpidqR0TiHNtfPKG8nypmoWcWkqJt4BWFVGaqrwWBI5kkzzYPjGcamNeZZ5NBojME2/hhw028o7Gc6XeSRidSSzW/YHGNXX/v2wZUZU6bTKmlFRxYF9gxGiPhEvvDj89w/lkY1LGSvyNEtcbb1uD89bNU/I34v6aLOVEpFs0tPiocTMgnZyzeOT/HwWAbFtAhIIoe3xNrKvuIme/9QlMfGFvnxWJrkokpfYZabL6F3q3Cz057+KFnF4KnpHEWzSFAS2TcUYe9gY9aIenDzqfKxyivL3/Uz+3jz3z7CgkHpvFag1wu/+9K9mKbNeKrAZEpBtx18goeRRIDRRKjmdfVq2hQQRU7MZjgzV0C1bGRRYNdAiNAqbO7mz27MQM87OMBXHrrA+YyNCUiZIttiAs87uLY91c2gN+BjciHPg2NpFMMi4BW5cUucW7f1tjyWWtX7Rddv4huPT/L4jI5lgyjA7l4fL7p+dYnU9eDmN9eNxC/GtUxVXDs8Wnv41qWwNI7TylLS2nA8wNbBGLGgUmOP60eiKN89zYPjGVJZm4SlcONojJ8+tLnj86tbzFm5raQS7Zr7RmJBMkWdp5P5pSTWnX1hRmKd2W9bidXo3a6+KNtpPtXawT6rwcbCeJ2QUfSl88ODPhHDspd+TbZyfvhqZMymiyAK3LqzH6/Hg+E46JbF2YU8h8Ltke0GQfCQCPmJyN6OPylfiVPzWWbzGjsHQgR1L0MDIWbzGqfms0tMAWuFm37HLixQMG0ObY7jlUQM06JgWpxJ5jgUao9sSRLY3h9hKBaoehr4tWPjjKVVrhmI4POK6IbFWFrlh0/Pc/eh0bp1teqfOc1AtxyG4vLS0x7dcvjG8SkeGkvTE/Yz6pfIayYPjaUJy15X2a3CTfa3n5phLK2ypz/EoJgn1hO6pN6tws1OPtHDYDxIf0TGckD0gCAKKJaF3GLYbTRmElJt+Xe+m6Q3FqAfE8cJ2k8bAADUcUlEQVTjweM42Eh867F5EoEwp2ZzRAJe4n4vqmkuPZ1shZnFrU3n5nPM5jQ2xeSlp1mzOY1YsNgy84ubP7vhb779NCndw2BUxNYsBL9ISi+V//lr+lqS3SruOTPHidk8I/EAYVkir5qcmM1zz5k5nrV7oKWx1Kren/vhBTKGh+29Mj4JdBMyhsPnfniB97689QVqPYynCnX9JiAJxMMyz9sbWGLCsXCYL2qEWzy6tzyON8UDS08jdctBsay69jh2YYHRgQjD8RDjU2OMDg8j+gQm0gqiJHZ0fnWLOTnNICHVr79dc1/BMAn4vRzcnKh6K1MwTHy+zm2dgdXp3a61Ttl+HtNPbrYt6rhiY2G8DjAtm4xiIHvFpezO8p6drGISkb1tydavJ0PRTI5PZ+syJJxNFtg1EOlYtn4lJFHoqvPlFZ0zc3kSQT+xQOnI04FogIxidETvSv0qZa9kKeiEbNknUe7ZZK7IsfEMfRGZwYoTpbwZhScms9y8s1hFTwWt+2e966F0It5D4xkSIf9Sxnwk4EPweFxltwo32dPpPA+PZegJ+xmIyqCkGekNk8xpHZUtCiKmbTNVZuMIVtCRGdaaxrfbmKksPzOf4f4LGXojfobiy2+AptNFHhhLs31ggZHe0FJGe3mf4nRaZSgWWNW2ivIdqm4ymVKIBLzLGfN+EETPquuHan92w+OTizw8kSUelOiPBchnc4SjEeYzCscmszw+ucjBkfYsElei3hiLh0pj7NGJDCM9YRIhX8uxvhm9T86meWAsQ2/IV3Ma5UMTGU7Opqvo+FaDev3qk3wYhs2TMxn2DcXoDS+3NKvqLfd3/XEsVo2ZSntUxfNeLx4lwI6hKBnF4PRcHq8oEg54OzK/usWcZsf3Wua+tcpeC1YjuxNrHVHo/IO0q46V4kpAN7I715Mh4XJEO5kCriTZBc1BtepnrecNk4K29gxqt+u1i3vowi3IbhVusnXTg2JahP3Vft4N2d1g43BDKmde7O+VekvkNYtkQe9YRvt6ZszPpjU02ya84olZ2CeiWDaz6c6dltVwjOkWWUXvWKxfyBqolkVkBTtDRBYpGhYLWWNN9YN7vwqiB8WwkVawkKymv1uNOY1iakEvnUrZKZtfjuwMl6vs9WzvWrCxMF4HuGVYdipbvxKVDAmV6Ea2/nqikimgEt1mKei27JDfgyyWstYrkVUNwl6JkL9xBnUl3PzT7Xq/KCB7RfItyG4VbrJ9kkNAEslr1X7eDdndYONwQyIiXezvlXqbhP0ifSEfqln9Xbsy2isz5jtRfyMMxv34BYG8Xp2Yk9ctAqLA4CWOCF8LGo4xn0g04OtYrO+NepFFkZxarXdOtQh6RXqjjY8IbwZu/WpbDgGvgOlU67aa/m415jSKqSGfSEASOmbzbszfV4vs9WzvWrCxMF4HlDMsVcNCNSws21n6dzQgtTVbf6WMMkNCVtVZLGhohs1iQSNV1NjRF+rKNor1QJkpIFXUSBU0DMsi1SW9K2XP51Tyqsl8Tu2K7L5IkEOjMZI5lfPJHNMZhfPJHMmcyrUj0brbCVr1T7frZb/EkdHS8djT6SI5RWc6XWwouxmYlo1mWpiW7So74PNxeEuMxbzGWDLPQrH0uVbZzehdZuMwLv6/E+PbDbv6YxzdGmMhp3FuNstMKs+52SwLOY2btsS5cXsvOcUgq+ropk1W1ckpBkNxec0Z7eWM+ZxikC5o5LXSZ7vqb4SDIz0c3hwlXTSZTRUpalbpJMSiyaGR6CW3UVT6VKuoHGOzGQVFM5nNKCRzKtdvjrG1N9hwLK1F9jWDcW7aEmOhoDM+n2MhWyh9FnSObI6teRsFVPdrpd+opsX+TVFUw2Ihr5Ip6Czk1VX1d6sxpzKmTqUKLBYNplIFUkWN3QNhhhOBjs2vlW0tqAZF3aSgGl0Z35Wy85qBopc+uy27Wbt2Yq1j2Z2nbNvYY7xO6AY7g5uMrfEg0YCXs8kCc3mVoFfg8GiC/UPxtsm+HFHW78xcnpTi0O/QNb33DsZKLAUzOYpGkaBXZN+m1bEUtIpbd/RxfCLFA+OZpYz5m0Zj3LrDPSGpVf90u/7F144AcGwiw1RaIeQTObIlzh27B1vWwy27uZwRvVL2Xfs2cSGZ5/6xDItpgx41y9EtjfVuFa0yRnSDfeVVt2zl8fEUT8zmMWzwCnDtYJhX3bKV4XhrjAetYj0z5t/xkv1oX32cY9M58gqEgzY3b4nyjpfsd72nXRnzd+4pMUA8MZllMlMk7JW4Y08/d+7ZhHxxW0s9H0kVtDXLfv0dOxlbyPPYTB49U6KDvG5TmNffsbPpOi4FN6aM/qCfh8cXeXIyi2JZBESR/cMRhiKBS9RYi1Zjzq6+CCenMzw0lmFyTifpZDmypcT0EwzUjwntGn9uDBqN2BnahdUwQ7QLq1m3tGutUx6r5cS9TuKKPxK6HVjPI6G7zWPczHnxVzsyBYWHHnmcIzccJBZqPYCvBuVz3m3LwkZAwEYQxabOmF8rvn9ihh+dTRLwSgiCB9t2UAyTZ+3o4/a9jSmdWvXPldeX9VZ0vYbHuFW9y3XJXrGGBzQR8tfILuvtx8Pc7BQDg8NoOE3p3Src7NSN8b0SX7zvLN8+MYdP9CCKApZVyu6/a+8Ar7xlB7A2HuNGKPeRx0MNj3Gn/bws+/xclkeeOskN+65h20C0oexL+VSrSOaKFDSHkN9TN6m13thYq+yyn9sW2I6N4BEQRDri5yv95tx8jlOzOWRJRBA92JaDalrsGYy0zEJSRrNj5tiFBR4eTxEQRSYmL7B5ZCuKZXF4NLHEMtSp8VfuO0n01PD5dsvPvYJniRnCsLsju4zV2HWtfVHWWzBVsrPjHT0SeuOJ8TqjG+wMbjLCAR/hDsu+HBGWfcQCImG5Oz8GqjJzgxWnFXUhkzhdUDk+laU3XM1CMp9TOTGT47rReEMi/lb9s/L6Sr3jwWVPW43ezWY3l2VX6t0T8oEqs2UwwmJBb0rvVtEMY0Q3MJPJ89BYhv6ozKZ4BUtBusixiQx3ZPJsioWbYjxoFZdLxvzB0QRCNsKB0QSG7Z793omM+b5IEDdqcrexsRbZax3fraLSb+qykLA6VopKNDNmVrIMFRa9DMZrWYY6Mf6aYdC4nJghOoHV2LVdbByVp2p2Cht7jDewgQ5jPTNzFcNBteya12wRv5eCYaEYl1cWc7vqWk+91xM5pfTELrriR19U9lHQLXLKldHf3ZB9pbW3HtbTz9eThWQ9mX6uBr+50uCmd6ewsTDewAY6jPXMzA14PciiQE6rzt7OaQYhr0jAe3llMberrvXUez0RCXiQJZGsqleVZ1WdkE8kErgy+rsbsq+09tbDevr5erKQrCfTz9XgN1ca3PTuFDYWxhvYQIfRDRYSN8RDMgeGoyzkVeZzKqpuMZ9TWcir7N0Uaetr1pVop96t1lWpdzKnolkmyS7pvZ7YFAtzZEuM+azKTLpIUTWZSReZz6oc2hxjU6xzm6fW088rZWuGhe04aOuQMb+a9q5F9nqObze2im6wkKwny9DV4DdXGir11k3r0jesVV7HJWxgAxtYU2buWpMWjm4rsTCcmMkxnVUIeUWetaNvqbyTaCf7Sqt1lfV7fDLDfN4m0uN0Te/1xN0HNwPwwFiG8wsFQn6Ju/YOLJV3Et1g27mU7GROJa/bmLbDQETuSsb8WtrbquyV8aDsz09OZZlQFMJeoWt+XmarmFhUmFdVgpK4ZpaTZuNdmU3o8YksUxmTcK/dNZahK9FvrnSU9ZtdUDoua2NhvIENdAHlc94jsrfpRW67qKRkWeL2vZu4bjS+RKHVrSemq9G7XXX5fKVErIgsEVKTXL+zl239EXy+zidvrCdkv8TRnf0EfD4yqkZM9nNwNIbs73y4b2d/rxb2xX2WdhP7Ldezva3KbkRXeHA0QSLkJ6+bhH0Sm3uCXfFzQfAQD/rQTRvVtJElgXiwtfhUxurjnY3H4wG685odriy/udrQwa3rS9hYGG9gA11EK5m5Zc5G2SsS9IkYlr3E4bgaWp54SCbe8l3tQTuzw5utq2y/eNDHUFQiHvStyX5XCsZTBU7N5uiL+dncG0I1TU7N5gBWTaHVKrrNxgHL/e0TBcI+AZ8oNN3f69HeVmW7xYN0UUe3HKJBH70Rec1xohUstcknEQkIa5Ldarx7cjrNw+MpYrKXoahI0C/x8HgKYImurdO4EvzmakHZP7yih04/M37m/MzYwAauIKyk5REFz9K/s4q5qlOynkmotJ/fKyJ4PPifAfZbSaHlkwSiso9IwMt0WkXVzUtXcgXiau9vt3ggiR6mUmqJV7jLcaKdMarVuirp2hIhP15RJBHykwj6OZsskFd0F0kbuBJR6R8+aYOubQMbeEbimUrL0y48U+23nhRa64mrvb/d9BM9Appt41mx1eBKow5rta71pGvbQPexQde2gQ1s4BlLy9MuPFPtt54UWuuJq72/3fSznNKRwM6KHzxXGnVYq3WtJ13bBrqPbtO1PZO2qFyWSBfUtiVEtcpecGY+QypnkohI7OqPNVVXu47YPL+QJZ23iIdFtvVGm7qnXbK/8vA49zwyw3l7nJ+9aduaZLsdret23Pa9T88xliywpS/ErTsHXOWV6WnKe+xWHhnbSH+3tv7lfzzFk7NZ9g9G+fUX76u6x02PVv3ATe8v/vg8p2ez7B6M8sqbt1XJdrunVZuvLC/b73/+xwOcPKVyzen7eNeLb6qyX6v6tXqU8g9Oz3JhPs/W/jDP3j1Y9V2rsptt00giwKnZHL//ufuZKsJwEH7v5TewZzByyTav9QjwZvRuta5my8v9/bH/Os5DTy9yZOIx3vATB9bU362WHxtfYDqlMpSQOTRavc+1Vdu6+bNp21XHEA8nZHTL4V/uO894WmE0HuB51w1fMk6sFZU2//HT80xnigzFgly/pacpm7vpB/XjXWU9Zbq2h8dTfP/ENKcn0uzOjHFwSw+HRxOXpGtr1/x2cjbNQtagN+rlmsF4R2S4Xf/QhSSTiwojPQGObF0bA0mrbXKL2aupqxlU+pqpGpe+YY3wOM4V/o6pDSgWizz11FPs2bOHSKQ7ySmqanL/+STHp7Kolo0sChwYjnJ0Wx+y3NrvlVazeRezCn/7/dPcfyGDalnIosjRrTHecPtu4mHZNfM5pxlrZkhI51T+8d6z/PhCGs208EsiN2+N85pbdxCP1P9h0C52hmPjM7zpbx9iTlsuG/DDJ99whEOjm1qSHfJKTGaKTKYUdNvBJ3gYSQQYDMmcms9yZi6PajnIooddA2GCfnjPPz/GU/NFDBu8AuzrD/K/XnUD2/rjDWVnFRPDLj05iQYkV73d2vrjsXHe8v+drLn+k6/by3N3bWc8VajRYyQWpGCYTftBQBQ5MZup0btoFfnvn36MdAX1ZFyEv33TDVw3NMiT0+mae/YOxlAsq2mbD0UCTOeUmvJvnHySP/33ZI3e7/rpAd5wy40t+bmb7NFECEmqDf5n51L89j89zMmkimmDJMA1fTIffvVhtvXFW5Ldaps++/3v8/cP1PrT62+G9/zM3S35eau+tpAv8Duff6Su3jsGEi3JbtUe3zh1mt/74oWa+j/4qm284rr9bZHhVl5QNf7k/z7Bsakcml16intoOMK7X3qQTfFwS7Y1TbvumCz7+VRKXZIxnJCZzqV586ceI1cxk0c88Ok3H+LotpG6Nm8XTs0s8BuffZCziyYWpcN6d/RI/MUv3siugZ6WYmc55qyMd242f3Rqmjd++jiVyyQv8Ok3XMvtu7bWbW+rvubWR8lMkY9/+yQPjC3PoTdtifFrd11DTyTQFhlubc0pKu/78mM8Np1Ht218gsB1Q2He9/Lr2dzkQ6ZLyXBrk65bdWP2/qE4kiS0ZZ52Q1n2+bk0o0Kaffv2EQwGL33jKrCxMGZ9FsbfPzHDj84m6Q3LSwNmIa/yrB193L63/iLNDamCtpTNu/KXdr1s3g/++2N860SS3oifqCyRVU0WchrP29vHG++8pm5dPtGDbjlNy3DDx/7zSb55Yo6+iExU9pJVDZI5lefvHeCtL9jfFv3ccPS9X6taFJcx4If731d/weAmW9EMZnMakYC3tH/TNMkpBqZpMZvXSAT9hHxeCrpBqqjxpR89zbmUSdAHsuRBNR2KOhwaDvLFX7+zYbub/QXu1tYXfvQe13u++/bbOTWbq9FjMOIn4Pc27Qez6SJnkvkavT/07yfrZhDHRfjMm27h4fFUzT27+sIMxoNN2zwgCSimXVP+5n98xFXvR97zvJb83E32nsFIXaaHl3/02zw+oxLwgl8EzQLFgIObZD79xme3JLvVNjXS+/yftubnbmPM7frf/tyPOTGv19X7X3/zrpZkt2qPRn7ean+3Wv7Bf3uUBydyRAMSYa9A3rDJKia3bYvz/p8/0pJtz83nGo5Jr+DBI3hwbAfDdnjF/76nalFcRlyEY++v39/twk995Js8Oa/jF8Angm6BZsP+fh+ffcvtLcXO8lhaGe/c/ONlH72nbmwJAcdb9PNW57f3/esjfPvUAr0hHxFZJKdaLBR07trTy2+8cH9bZLi19Y+//AgPT+WJyiJBSaBo2mRVi1u2RPn0G5/TUv+1Ou6PXVioG7MPjybY2hduyzzthvK4CHpMgup8RxfGG3uM1wHpgsrxqSy9YZn+iIzsE+mPyPSGZU7M5EgX1KbrajWb98x8hvsvZOiN+BmKBwnJPobiQXojfh4cz3B8IlNTl1fwMJVWkMS1ZT6fX8jy4wtp+iLyRdlehuJB+iIyD42nOb+QXbN+bvjyQ2NLi+KwD2Sh9Akwp5W+b1a2xwNn5gqEfFJV5r/X4+HRyQwhn5eekB+/V6An5Gc2rXJ20USWoDcaIBSU6Y0GCPrgxHyRe5+ea9h2SRTwS+Ilt0/Ua+uXfnSmYd1/+a3HaxgMgn6Jp+cLeBynqQx4x3Y4Pp0lKvuq9H5qMrs0cQWk5T+AtAWfu/csiaC/6p6o7OOp6RyO7TRlc1kUeHI6i98rVpX//Q8fbaj3//jSvU1n+AvAmfk8Qb/UFNPDD07PcjJZWhT3RGRCQZmeiEzACyeTKt9+YrbpMebWJo/j1G3T//o/7otigJd/9GtN+47bGHO7/rGxRU4v6Mh19D6VVPnB6dmmZbvZw638b//ryYZ6v/9rx9Zsc7fys/NZHpvJE5ElBmNBQkGZwViQaEDi2HSOH59ZaNq2bqwilWMyJHsJ+iRCspdvPTq5tCiuN8a++OPzDe2yFnzv5Eypv0XoifoJh/z0RP3IIpxe0PnGE1O1sdPFbyvHUmW8c/OPrz88sRRbfEJpT6jvYngsAP/ww7M17W0Xs8fJ2TQPjGXoDfnYlAgSCvjZlAjSG/Lx4ESGR8cW1zyHurX16dk8T8zmCftF+qMlX+uPBonKIo/P5HnoQu0bMjeshQmkMmYngn5Oz+WZzqhrnqfdUDkuIrL30jesERsL43WAYjiolk3EX93BEb+XgmGhGJ3L5k3lTFTLIrpiu0ZUlihoFnMFpaYuj+BBsxxEz9qyj9N5C820iK5w7KjspaBbpPO1Rz22K/P59Gxp0b3S4YUV3zcjG8eDatk1r9A9Hg+KUdoeUomibmABK7obWfKg2zCWLDSlQyO4tfXpZGPGx/NJvYbBwCeKKKYNKxJe3DLgDdtBNW0C3up6FnONZU+k1Jqs8oBXomhatewJLjaXxNLTYkmoLp9MNg7EY3OFpjP8bUA1HXxidb+6MT1cmM9j2qUnppXwi2BYMJbONT3G3NqEx1O3TZOXIPh8eqa2rNUx5nb9XFrBskGup7ddskuzst3s4Vb+9Hxjxc/M5Ndsc7fyVFZHt2xCvup6wl4B1bCZytT2t5tt3VhF3MbkeLqx3vXiWrtwfi6HZZeeFFfCJ4Jpw3iqdi5x81u3seTmHxdWoXe7mD0WsgaqZRFZ4egRWaSoWRe5dtc2h7q1dT5bwLAh5K0uD0oCmmUzudg8w2+7mUCKmtkxVhi3cdEpbCyM1wEBrwdZFMhp1ZvIc5pByCsS8HYumzcRkZBFkaxa/ZQrq5qE/CIDoUBNXY5d2mRvOWvLPo6HRfySSHbF5vmsahDyicTDtfyE7cp83j1Y2nu1crlkr/i+Gdl4HGRRwDRX2MlxCHhFtBVnuQd9XkRgRXeXJggBtvSt/vjUS7V1Z1+g4X3b+nw1DAa6ZRGQBKgJ1vUz4L2CB1kSUIzqenoijWVvTsg1WeWKYRKUxFr2BBebm5ZNQBIw7erykb7GoW3LQKjpDH+Biz9irOp+dWN62NofRhJK2wgqoVngFWFLPNL0GHNrE45Tt00jjU3Ozjq7tFodY27XD8QDiAKo9fQWSnZpVrabPdzKd/Y3VnzXpvCabe5Wnoj68IkCBX3FEzbDRvYKDMdq+9vNtm6sIm5jcjTeWO96ca1d2DYQQRRK2ycqoVulveWjidq5xM1v3caSm39sXYXe7WL26I16kcXS9olK5FSLoF9kIOJf8xzq1tb+aAivAAWjurxo2vhFgZGeSwSAJmSslgkk6Jc6xgrjNi46hY2F8TogHpI5MBxlIa8yn1NRdYv5nMpCXmXvpkhL7BTlbE3VsFANC8t2lv4dDUg1r9939cc4ujXGQk5jcrFIrlj6XMhp3Dga48DmWE1dhu0wHA9gWk5TMtywrTfKzVvjJHMqk4tFMhdlJ3MqR0bjddkpmtHPtGw002r4uuZnjmxh4OI2p7wOql36hNIe4585sqVp2Y4DuwZCFHSTrKqjmzZZVcdwHK4fiVHQDRYLGpphs1jQGIzL7OiRUE1IZhUKRZVkVqGow97+YEN2imbh1taff9auhvf9+vMOklOMKj2KmsnO/hCOx1NVVzkD3rCr/cAjeDgwFCWr6lV67xuJUg7Tirn8B6X9j//t1h2kilrVPVlVZ99QBI/gacrmqmWzfyiKZlhV5b982/UN9f7fP39rja0q9csUNDJFnUxBwwZ29YcpatWyc4rBUFyuYXp49u5BrumTUQxYyKkUCioLORXFKCWi3XXtYNNjzM3mjsdTt02/+7M3NNT7X3+zdu9lqzGk8vq8ZqDopc/rtvSwu9eHWta7uKz3nj65LjuFm2w3e7iVv+En6ucnlPH7dx9as83dynf0R7luU5icajKbKVIoqsxmimQVk0NDEW7e1du0bcusIo3GZKXNn3f9CJGLa456Y2wlA0w7ccc1m0r9bcFiTqNQ1FjMaagW7O718cJrh2tjZ4XfpooahYufbmPJzT9edHjzUmzRbTAvfkJpj/Frb9tR0163utz61a2PrhmMc9OWGAsFnZlUkYKiMZMqslDQuXFzjOu39Kx5DnVr687BMNcOhslrFvPZkq/NZ4tkVYuDm8ItsVO0Ou7LTCArY3aqqLF7IMxQTG66rlZROS4KXTikaGNhvE44uq2PZ+3ow7EdprMKju3wrB19HN3WOu1KLOBjIOIHB4q6CQ4MRPzEXChrfuU5u7htR4KiYXBusUDRMLhtR4Jfec4u17pGE6GWZLjh1bds57YdPRR0g3MLBQq6wW07enj1Ldtb1i/i95IqaEykiowvFplIFUkVNOyVT9cu4pNvOLK0OC6jzErRquz9Q3H2DEbAhrSigw17BiPcuWcTh0cTOI7DXF7FcRwOjyb46GuPcGBQxrRgUXEwLTgwKPOBVx5qyX6N4NbWv3hN/cXxx167m9FEqK4e+4fiLfnB0W19dfX+1BsPLk3cZUQ88Mk3HmL/ULzuPUe39bVk86Pb+uqWv+PF9VkQfvfuXldbDUUCKJrBY5Np7j+/yGOTaRTNYE9/tK6M0UT9p/0f/IUb2Nvvw7QgrYFpwd5+Hx/8hRtaHmNu5W72+OWb6vvH629u3XfcxnfE78UnephJqZxN5plJqfhEDx96dYXearXercpu1R5/8LP1GRj+8BWb2ybDrfx9L7+e27bFsW2HuaKBbTvcti3Ou196sGXbuo3JvYOxujb/mzde5zrGOo2P/LfD7O6RsCzI6mBZsLtH4iP/7bCr3nsHYwxG/MxlVE7P5ZjLqAxG/IzE6idSudXzqTceZOXz0QDwqTdd59reds1vv3bXNdy1pxfLsZnKqliOzV17evm1u65pmwy3et7/c4e4ZUsUy3ZIKgaW7XDLlijve3njhwGtyHBrk1vM3j8Ub7muVlEeF92gi9hgpWB9WCnKWA8e43ImajKjktF0Yn4ffTG5Knu0UzzGZdmpgk5ONYjIXhIhX1OZq81mK1+qrv/zwHnueeQ4z7nhQNd4jMttfWIyRTKt0BcPcO1Iom0Zu43aWs4k/tGpSZJ5i76wyLP2jHB4NMGhrb0N9Vgrj3FZ728/NsVYpsiWWJC7LnKslvXuFI9xmfnlu09cYHIRRnrgzmu3VjG/rJSxlPnsl/CJIrplUdTMpYz5ZnmMy3o/PpFiNq0wGA9wcHNiTWOsWXuUdfjgVx5hMg8jYfidl93gyqDRyHcupd9KhoRy5v2j44vMpxX64wGuH+1Z1fhutfxrx8b53ql5jo9PMzsPg/1wYHSIO/b0c/eh0bbafD14jC9l8+8en+bsQp4dvWHuPDDUkdiyEuU2PXJhgbm0wkA8wA1bexv6efkej+OU9kw7Do7Hc8n2utXz9QcnePDcOW7cvp0X3bi5I77mhg0e4+7wGFdiYTHN+XNPb9C1dRrruTDuNkzLZiJVxOMpZY2WoRoWODCSCHSMFL6dstdSl2VZHDt2jEOHDiGKtfua2431tHle0fn6E9OIYimDuIzFgobjOLxg/6ZLkuGvFuupd7qg8vkfjyFJAj0hH2MXLrBl61YWCzqO7fDzN43W/BBVdZMHzy8iiB6i8rJNsmrpqd3hrYmmDvVYT73bpUMjuOlXUA2mMwqb4gHCFZmm3dA7mSvyN/91Fq8k0BfxMzkxwcjmzSRzGo7t8Prbt9MX6cwk2g242TyvGcykVIbiMiG5uzZfjZ+3a2xU1uMV4Pjx4xw4cADDpuN6b2B9kcvlOHXq1AZd2wbah3aeb7+estdTj1axnm29VCaxZl0Z/d0qVsP84pb57JYx74b11LtdOjRCq0wS3dC7oDkX2XZqGW/yhklBu3ziwWrQLkaFbrSpkex2jY0rKf5v4MrDxsL4GYZ2nm+/nrLXU49WsZ5tvVQmsV+8Mvq7VayG+cUt89ktY94N66l3u3RohFaZJLqhd8jvuci2U8t4E/ZKhPyXTzxYDdrFqNCNNjWS3a6xcSXF/w1cedhYGF+BaIaFwQ2tZqK2E+2UvZa6TMvGsJw1k453o61rRWUm8WxGIVW4+FnU2NEX6tg2ClhfvSuZX5I5Fc0ySV6C+cWNEcAtY94NlXoXVIOiblJQja7oXanDxEKBsWSeiYVCyzo0QjNMEpXMCd3Quy8S5NBojGROZS6joJoGcxmFZE7l2pFo17ZRrCU2N0IzjArd9rXVjO92xYTKetIFnbRqki7oXdF7A1c/usOWvIG2oNVzzd1QzhLNKiZF3cQrCG3NHu2W7FbrKttvsaAxWzCZSCv0hOy2neXezra2E3v6o0ymijw2maFoWAS9IteNxNjT3zmO0zLWU+8yw8uTU1kWiw5xm0syv5RZJqbTKmlFRxaFhuwTboj4vaSLOlNpZSlJZTgeqNna0Qn0BnxMLuR5YDyNqlvIPpGbRuPcuq330jc3Cbd+DXklJjNFplIqml16mjmckLui9517SgmVj01kSBYhHIc79vQvlXcS7YrNjXBJm6+Dr61mfLcrJgREkdl0keNTWc5NqiSFeQ4MR9kav3L3km/g8sDGwvgKQkbRl1gYgj4Rw7KZy5XOOW4l+1gQPCRCfiKyt6PZo52W3WpdZfv5hNKBDQKsyn7daGs7MVtQkSSRm3f0IeDBxsGwbGYLKtuDnZ0811NvWZa4fe8mDoxEeFBMcePhEXojjRe4kiSwvT/CUCzQFPuEG3KagW45bIoHED0ClmOjWw45zSAhddbX7jkzx4nZPJvjQYJ+iaJmcmI2zz1n5pbYGdYKt35NFTR0y2EoLi8xJ3RL72DQy92HRrlxe5wHHspz05GtDMa6k0zdrtjcCJey+Xr42mrGd7tiwonZDGeSeXrDXoyoRG/Yy5lknmjAu8S2s4ENrAYbC+MrBCvPNQcQhdJnVjGJyN6Wg4skCuvmAO2U3UxdlfbzCiB4PPi9IoK9evt1qq3tROUZ8ytZCqbTKkOxQFter18K6+lr8aBMX1giHmyeDlH2SayWPLHeWAUR1bA67mvJXJFj4xn6IjKDsRLLa0/Ij1cUeGIyy807i23dVlDZr/X1pit6V6IvHGQo5qUv3L3tE+2OzY1waZt3x9fc2tTJe8rIKzpn5vIkgn5iAS9Zn0hfJIBXMTibLLBrINLRbWIbuLqxsRHnCsFGFu7a8Ey1XzdYCjZQjfX0tfVkZ3imjrGrhennSsJ6su1s4OrHxsL4CsFGFu7a8Ey1XzdYCjZQjfX0tfVkZ3imjrGrhennSsJ6su1s4OrHxsK4ApbdHZaC1aATGf6dyqC+HFFpv1ReI62apPJajf06bZNu2LxSRiVLQTKvki7qJPNqW1kKLnfkVZ2MYpFX9abvuVKZXyrZGcYW8sxd/OwGO8N66l2JZL7IdMYgmS92Rd56spCsp+xKdHsuqWTbmc8q5FST+Wx32HYq8UyaQytxtet99c+KLWA2p+FIWldYClaDdmXzdiOD+nJEbRZzcimLudM26YbN3WQMhmTGpQJPTmVRTIuAJLJ/KMJQJNAWuZcrdN3iyek0p2bznJlUSPrm2DMYZv9QHJ+v/omHVwPzy207+zk3l+OB8TSKbhG4yEpx287+jsteT72LRYPvnprhkfEM49N5jhUucMNojDv3bCLY4STT9WQhWU/Z6zmXlNl2Hp3IcGHBQPEXuH5zd9h2nqlz6DNF742FcQW6yVKwGrQrm7cbGdSXI8pZzH1hH0ZUoi/sW8pi3toX7qhNumFzNxmKZqCYNvs2RZBEAdOyUUyb6ZzCdvnqPQL9yek0D4+niMleEgEPogAPj6cAXLPWrwbml/OLBYJBH8/bO4THA44DhmNxfrHAochqUwqbw3rq/d1TM3zv1Dw9IR99QZAE+N6peYC2sXG4YT1ZSNZT9nrOJWW2nZu2JuhzUmzfmsD2eLrCtvNMnUOfKXpvLIwr4JNE8IpdzeZdDdaSzdvtDOrLBSuzmDM+kd6IjKQYnJ7L4/OKhPzejtikGzZ3k6EbFmfm8wzGAiSCy4Gr26wU3cbK/k6JIomQH0EQXLPWrwbml0q9eyomqsWC1tVs/W7rXcnG0RfxY+S8DMQCCEJn2DgqsZ7MEJeb7G7NJZVsOyGvSDogMRALUDCsjse1Z+oc+kzS++rQoo242rN5N7KY62Qx6zZF3eqYTbphczcZNqCaDj6xeuvA1c5KsZqs9athbDxTs/WfqWwcz1TZ68m2czXEidXgmaT3xsJ4Ba72bN6NLOY6Wcw+Yem1UCXaZZNu2NxNhkDpMBPdsqrKr3ZWitVkrV8NY+OZmq3/TGXjeKbKXk+2nashTqwGzyS9NxbGFdDN7mdQdxuXS+Z4t1GZxZwqaBiWRaqgkSpq7B4IMxQLdMwm3bC5mwwb2NUfpqiZTC8WmFosML1YuOpZKSr7e2y+wFRWZWy+0DBr/UodG5UZ4pV6LxY0NMNm8aKfdyJb3y07vdtZ65VsHNOpAnndYDpV6DobR14zUPTSZ7dZKRYLGvPZUr93SnZlv64nI0Yl2066oKEYNumC1pW49kxlArlc9O4Grs5ZcZVwoGsZ1OuJ9cwcX0/sH4oDcGYuT0px6Hfg8GiC/UNxJKk0qDtlk27Y3E2GGPZzcjrDwxOZJZaCw5tj9G/va5vsyxFbEkH+8/FJ7h/LsJhW6UlNcXRLjC3XjbjecyWNDbcM8b2DMQDOJgvM5VWCXmHJzzstO+L3ktOMdclav2P3IHnV4JHxDBNZm9GAxZEtce7YPdhRuVDBDJFS0WwbvyAwnJC7wgyxzLaTQ7UsZFHkwHCErfH2/Rhw6++QV8Inrg8jxkgsSKaoc3ouz0TWJJDX2T0QZiTW+RMPn6lMIOupdzexsTCuwEDET/wqyqx0w3pmjq8nfD6RQ1t72d4XpEeb5cj+AWKhZcqyTtqkGzZ3k/H9EzOcWyyytSdEwCeh6CbnFos8PL7I7Xs3tbUNlxO++eQ0Z5JFtvYG6BULhOMBziSLfPPJaV55y46691xJY8MtQ3wg4ufQ1l52DUSWJq92Pyl2k50u6uiWsy5Z6wYOh7b1cc2mGI+fKHBw7xYCsheDzu99LDNDDMVlPIIHx3a6xgxRZtsZiPrxe0U0w1pi23FjX2kVbv3tE/V1Y8QoGCYBv5frRmJIOR/7R2IgCBQM05WOsV14pjKBrKfe3cTGwrgConB5ToCdQrczxy8XhGUfsYBIWK7/Or2TNumGzStlpAsqx6ey9IZl+i9SdSVCPrw5gRMzOa4bjRMPdZbCaz0wk8nz0FiG/qjMQFRmWs8x1BtmLqtybCLDHZk8m2Jh1/sv97HRTIZ4OODDXcP2yzZtm6mUWnqV3eWs9co2Rfwim8I+BmIyht1d2cvMEHSFGaIeC0lE9iIInraxkDTT36GlJ4bdZ8TwCiJhn0gk4F3H/r76mUDWU+9u4+rQYgMb2EBdKIaDatk1r7oifi8Fw0Ixrp5M4krkFAfVtIiu+PETlX0UdIuccmXrfTmyEYgeAc228ax4nXu1MySsp+xusJBs9PeG7PWW3W1sLIw3sIGrGAGvB1kUyGnV2fo5zSDkFQl4r55M4kpEAh5kSSS74hjorKoT8olEAle23pcjG4HllPbWOiuosq52hoT1lN0NFpKN/t6Qvd6yu42NhfEGNnAVIx6SOTAcZSGvMp9TUXWL+ZzKQl5l76bIVbmNAmBTLMyRLTHmsyozaQXVMJhJK8xnVQ5tjjXcRnElYD0ZNNxkm5bDcELGuPj/9WqTZljYjoO2zvbohuxusJBs9Le77Mtl7F3tsruNy3kbXddxbiHHdZHqI3JNy+5oEs5MJk9OcYgEPGuerN3a6lb+Lw9c4PRslt2DUV5x09am6lJ1E8N28Aqepihx3K4/Nr7AdEplKCFzaLQ9CSIr4abDL/7tNzl+zuLAg9/kc29+UVP3uOnhVp5X9LqJT3/+teMcn85yYCjKb919oEq2W11ubXKTsfL6o9tK7BO/+JmHlq757OuOLJVD6eSwguYQ8nuqqK3c/LNVe3zgq48u6f3Ol17flM1b1TtdUFEMh4DXQzwkc/fBzQD87leeunjFFP/rZfuWylcju9XyLz80tjTGfubIlqb0dsPKvigzZUymFHKqQUT2MpIILJVv+72vLd17/k/vbkp2s20qy5i/mHAX8IoMRPxLrBT/8sAYZ+dz7OiP8IqbtlSxerQ6ZtzK3dr07D/6FgUg9LV7+MF7ntcUo8hK31mtPf7foxOMLyqM9gR4yfWbm5Ldqp+vtF+ZbeQXPnEfChAAPv+rt7SVhcSNraXc3x/69yc4ncyzuy/MO37y2iq93fRzs7lbzFlpj7KMn/yjbzEJjHztHv69yf5e6/guy/j645NL/f2igyMdYa9x0/vP/t8TnJzLcc1AhLe95No16d0syjLOzRdIKRqJgJ/t/aGOyHa7fiJdaLndrcLjOFfRxpBVolgs8tRTT/EX9+cY7gnzWy/cR1802FFKlHxB52uPT/DQWAbVtJAlkSNbYtx9cDPhUGuDq1XqpKeTC7z5bx9hoeLtW68XPvXGwxzavMmVlmcyU2QypaDbDj7Bw0giwGgitER1VgnTtBlPFWquFz02f/pvxzk2lVuiNTo0HOHdLz3IcE+kpp7VwM0ef//DH/EX3ynWXP8/7grxP+66oyW9hyIBpnNKTflgSObUfJYzc3lUy0EWPewaCHM+O89vfu7pGtl/9Yt7eMGenXVtNRILUjDMmjYFRLGUib5Cxt7BGIpl1Vz/5o98i/tr1ebmEPz921/Ad0/NcGw8s0T1dGg0xk3bevjuidka/3zhgWFSqtG0PR6YHON3vnC+RvafvXoHL7t2b0t+66b3jt4wxyZSHJ/Kolo2sihwYDjKH3zmIc7W8Y89wNf/5CUtyW61/NzCIm/59MPMactyB/zwyTcc4bqRwZZii1usuGvfJp6czvL4VBbFsAl4BQ4OR6t+AK3E2Rb1dmtTeXxfWCyiGTZ+r8DWniALxRz//e8eqav3tUMDdf3cbczs6otwJpmrKd8/FK/LOvDmv/wa35is1fmFI/A3v3537ReAqprcfz5Z4zs3bulFc+ym7XFqZoHf+OyDnF00MSk9cdrRI/EXv3gjezbV/+HvFqfc/HxPf5TZglpjvz//8vf5v3Uc/WW74KNvqK/3arFysfJ/nzjJb/zjmZrr/uI1u/jJ/Xvq6uf3CDw4tlBj8/1DUb791EyNnz9//xBjqWKNPT777fv411O1bfy5ffChX6qvd6tzpVv5fC7P2z/3MKcXdEwbJAF29/r489ccYddgT1tsresWT06na/Q+m5njbf9U2+Efe+1ufvLAnpb0bnU9U45FD4wtU3/edIl1S6uy3a63TIu/u+cMF+az/Mp1Afbt20cw2Blqvo2FMcsL448/lOPJOZ07d/fwOz95cIkSxSsKGJaNalgMRPxtoUT54n1n+faJOfqjMlHZR1bVmc+q3LV3wJVKyg2pgla3rT7Rs0SdVFn+yr+8h7RVW0+vF771e8+rW5eiGczmNCIBb+nYTdMkpxjsGYywvb92QXtuPsep2VzN9f94z2kemykQDUiEvQJ5wyarmNy2Lc5f/fKzVmvOpuzxwo/e43rPI+9pTe+AJKCYdk25aVrM5jUSQT8hn5eCbpAqavzhv590lf3dt99e11aDET8Bv7emTbPpImeS+RoZu/rCDMaDLen98Vddx/dOzdMXkYnKXrKqQTKnIjo2C4pZ45/XDUfZPRxv2h5v/sdHWra5m9+66Y1lM1fQ6Q3LSxPZQl7lr79/oW2yWy1/9cfvWfrhKVA6mhtKi8Rv/E592W6xxS1WjET9+GVvR/V2a5Pb+P7dzz+yFFtW6v3Ft9b3c7cxE5IECqZdU354NFGXhqzyCflKrHxiXsb3T8zwo7PJGhseHI6xa1OsaXu86IPf4OSiid8DPhF0CzQHrumR+PrvvLCubLc45ebng2E/kiS2NMbc9G4XGtnczdfOzGR4fCpTY3NNNZjMajV+vqsvSF882FJMddO71bnSrfx/fPZeTi+ayOJyf6sW7O/38W9vf/6a7Qpw7MICD4+nOqp3q+uZ1axbWpXtdv0XfvQ0PzybYm+/n1+6Vu7owvjq2RTSBvREZHpCXh6ayPDAhYUlWhJR8Cz9O6uYaz5pppJKalM8SFCW2BQP0h+VOTaRYSaTb7qulRQq5bZ6BQ9TaQVJ9FSVf+eJ6aWJK+Rd/gNYMOALPxyrqcvjgTNzBUI+iajswycJRGUfkYCX6bSKqq84llM3mUwpRALequtnUxpPzOYJ+yUGY0FCQZnBWJBoQOLR6RzHxhfWZNdG9nj3l37Q8L43/u23mtZblkSenMkie8Wqcq8o8OhElrDPS0/Ij98r0BPy84OT0w1lf/Q/H6+xVdAv8fR8AY/jVLXJtiyenMoSlX1VMqKyj6emc9iWXXV9o0UxwK994TH6IjKDsQABv1T6lAQenMgQkr1V/hkP+XlwPI1pOlVt9XtFnpzOIYtCVflXHzrXUPb7v/ZYjc0l0cNUSi29Jm5Cb58g8tB4mojspT8iI/tE+iNyw8UhwA1/VNvfbrLdxpLb9f/1xPTSojji8xDyeYj4Sk9G5jT4wo/ONR1b3GJFJODlwYkMXlFYs95u+rm1yW18P34+tRRb6up973jNPW5jJiBJPDKRIeCTqsoTQT9nkwXySnVS5Y0NFmhu36+kMizbMB708/hkFsMwm7LHd56a5uzFRXEi4iMU9JGI+PB74NyiyXeeqh3/bnHKtmyOT9f6ecjn5bHJLF6Pp8p+v9NgUQzwnN9vbJe14EP/9njD7z/8/x6t0c+wbI6NZ0kE/VU29wkCD4xniAZ81TEn6OPB8QyO7amyx581WBwCPPcPavVuda50G98PX1go9bcIiYifUNBPIuJHFuHMgs73Ts6sya5QS8NX1vuHJ+ca3veRf3+iab1bXc+sZt3Sqmy36+eyRR4Yz5AI++iLdj4vZmNhvAJhv0jRsFnIah2jJWknlZQbhYpH8KBZDqKnunwiozSs7+xitqYuHA+qZddsmZAlCdWyMVZmJdsOuu0gS9V7kLOaimFDyFtdT9groFo20ym1YduagZs9zs83tum5OZrWWxA9KIaNtMK2HseDYpp4V1w/n9VohLNJpcZWPlFEMW1YkelrI6BYNgFv9fUBr0TRtFgNO1NUrqZ6EiQB1Sy94qxEWJYo6haGXf26QRIEFNOq2Tc2vti4P8/O5ZqmgHLTWxKgqFv4pdYJ/ZuV7TaW3K6fTJfG2MrgWv7/hZTSdGxxixWyV0QxLERa39bVbKxwa5Pb+J7KlPbrrGxRudaxVK7mHrcx4xM9FHUL7wpueTcasmRdTRt/70ZlGPJ5KRgmptMcDdm52SwWpSeH1TqAefH7lXCLU5YDqlnr535JpGiYCCvskamjVyXG67wZbBeOT9fqVYlTc7V+bpgOqmUSWJGfIonC0vaJSgT8EkXDQlxxSMulHh2drRN6Wp0r3cb3XFrBAvz1+tuG83O5S7Tu0nCj4ZvN1tkTV4F6fdIumrXVrFtale12fU6xSrL93UmL21gYr0Beswh6BXqj/o7RkrSTSsqNQsWxS5v1Lae6fHMsQCPs6InW1IXHQRYFTHPFkyPTRBYFvCt5LAUPPsGDalY/SY76ZbwCFIzqevJGaRE2lFj7L0E3e2zrb2zT7QM0rbdtOQS8AuYK2zoeh4AkYay4vj/a+FXVjr5Aja10yyIgCbAicAjYBEQBxai+XjFMgpLIatiZsmo11ZNt2shS6cdKJfKqSdBXItSvhGnbBCSx5tf/aE/j/twxEGmaAspNb9OGoE9EM1tfATQr220suV0/Ei+NsZXPYcr/35oINB1b3GKFalgEvCLWKk51azZWuLXJbXwPXzyKd2WLyrVuSURq7nEbM7rllE71sqvL3WjILnW4eb3v3agMC7pByCsheZqjIds+GEWk9Dq9WofSXuPtg9Ea2W5xSvSALNX6uWZaBL0S9gp7xOroVYnRDh4Ad2CoVq9K7Bmo9XOv5EEWS6dvVsK0bGRJRF0xjhXNJOgVsVb83LpUmvqOOqGn1bnSbXwPxAOIgFavvwXYNrD2XBk3Gr7BaOOtA/X6pF00a6tZt7Qq2+36SEAsydaq/aZT2FgYV2Axp7JYMDiyOcZNW3s7RktSSSU1uVgkWzSYXCyuikrKjULFsB2G4wFMq5pK57nXDhG/GCwLxvIflPYYv+q2LTV1OQ7sGghR0E2yqo5u2mRVnZxilE65WvHrX/ZJjCQC5BSj6vrBhJ9rB8PkNZPZTJFCUWU2UySrmFw/FGkLO4WbPf7455/d8L5PveF5Teutmhb7N0VLJ/5UlBuWzfWbo+R1o4o66dnXDDWU/ZsvOFhjq6JmsrM/hOPxVLVJEEX2D0fJqnqVjKyqs28ogiAKVdd/4zef01D2x191HcmcymxGQdHM0qdpc+PmGAXVYCZdpKiazKSLpAsaN47GkSRPVVs1w2L/UATVsqvKX3pke0PZv3/3dU1TQLnprdsWR0bj5FSjio7uLbdvbSj7kffU9rebbLex5Hb9T1w7RO/FBz053aGgO+T00uQ64IdXPWt707GlmnZuuS9yisGNm2MYlr1mvd30c2uT2/g+uC2xFFtyukN+pd63jtbcUzlm5nMqedVkPqeimCY3bI6h6KYrDZmqm+RUA1U3efASe2nrfe9GZZguahwcieL1SuQ1A0W3yGtGlT1My0YzLUzL5rn7htjRI6E5sJjVyRd0FrM6mgPbeySeu692/LvFKUEUODBU6+cF3eC6kSiG45AqahQ0k1RR44OvuaGh3ve8v3N7jN/xUwcbfv/bL7m+Rj+vKHBoNEqqqFXZXLdtbhqNkVX06phT1LlxNIZHcKrs8bafvKah7O/8Qa3erc6VbuP78NbeUn9bkMppFIoaqZyGasGuXh93XLNpTXYFdxq+264ZaHjf23/y2qb1bnU9UxmLplIFckWDqVSh4bqlVdlu1w9Eg9w0GiOV15m/xFvvdmBjYVwB24E7d/fwWy/cRyzgYyDiBweKugkODET8baNjefG1I9yyvYecqnNmLktO1bllew8vvnak5brc2jqaCNUt/9s33bA0cZdRZqVwq2v/UJw9gxGwIa3oYMOewQijiVDdNo0mQnWvf//PHeK2bXFs22GuaGDbDrdti/PulzYOsu2wx1t/ov4TzF+/M9Cy3ke39dUtv3PPJg6PJnAch7m8iuM4HB5N8OFfqL9I/IvX7HK11f6heN02Hd3WV1fG0W19da+/0eXB7U0BuHPPJu7Y049jO0xmiji2wx17+vmtF+7jrr0D2LbDZLqIbTvctXeA19y6oyV7/Okr6y/UPvwL21v2Wze9X3FkK8/a0YdjO0xnFRzb4Vk7+thSVzLsaOAjbrJbLf+bNyyPsfKzj14v/PXr3ceYW2y5++Dmun3xa3ddU1fvRmhVb7c2ufnsJ994aEnv8nO2st5u99yxe5BdfWEWchqn5rIs5EqJpC++dqRuf+/pj3JuPseD5xd54PwiD55f5Nx8jjv76+t816C7PY5u66trwzt2D+ITPcykVM4m88ykVHyih5BXIlXQmEgVGV8sMpEqkipo/NlrDrMzJmADeavU5ztjAh997ZGW+8LNz+/YPchgxM9cRuX0XI65jMpgxM9LXBz9pxr/Lm0L3OJao/F9555NdW3+a3ddU9fP33D77rr2eOnO+m16eX1iBqB94/4vfvFG9vf7sB3I66W1w/7+EitFu7B/KF5X7w++alvd6z/631wM0kDvVtczS+sWxeD0XJacYlxy3dKqbLfrf/W5e7htRwJtxdulTmCDlYJlVgpv7xDXbRv+/9n78zi5rvLOH3/Xvbeqbu1VvarVam2W2rLkRZa8jDGYcWwgBEgIIRnmO0DYQiDwSiDsJAzDLwEG8x1IWCYhbJkMZEhgIAlfJwEDxhhjbEuyvGhra+1FvXftVXe/vz9K1apu9W11ddfSXXXfr1e/SrpdfZ/znOc55566dZ/PWfC7eukYlysvC4pBXjcIeSWCsrQm1QtXx3ghi30oV9L/5b8+ybk52NEBf/RrNy9Q1qiXjnHZ9g+eHmV4Ns/WzhAvuXHLAtv10jEu266sXv/Saxf6XS8d43Kef/Xhk5ybzLOjN8SbX7BnQZ7XS8e4rDhQWZD2trtKi+i79mxale2VHi/7/dPjE4yli/THAvzHvZtW5LcTTrFY7PfXHhrigRNTPHr+8hOod2yP8aLrenjTCwdX5Z8TTvH+2alJhmdzbO0Mc9e1vQv8dvob27Lnj3sEz/zfOI2lxeoMqZzCubnCsvF2YnEflttUbku5bVdTLzh8fpbxdJG+WICD2ztXNJ+vNM/LbfLYdqn2wLaxPZ55FYv7/r9TKIAMvP/l1zqqd9SSsnLCQ0MXmUop9MRlXji4eYHteukYl23/xf93igwQBd61Qr9rNe4fOjXB+aks23siNblTvBRO+f8vT59jfFahr1Pm12/c4agQVcla1zPlHMwqKqoGfh9EZP+a8nyl7y/bnpxNElJmXLm2elNeGA8ODhKJrP35oKthmBajyQIeT6nisoyim2BDfyLQUrvIrAcUzeDQ+TkE0UPIKzI0dIrBwWvJ66XbOwe2JVa00F+r7crChYyitbTtZuZ5Kq/wfx4bRpIEOkI+hi9cYOu2bczlNWzL5nduHajbrn/N9Hs0meXj3z+BVxLoi1++aIynCliWxQdfdh1bEvWZ41bjd7V/45TPF1MFfnx8gm2dIXpj8pri7dSmvKIzni6yKR4gXFGwl1N1JpIKfXGZUEUhay3j7dSmuWyRh05N0xUtKWvMH8+r2LbNi/duqsnud0uRK2r8+7PjiGJJMaFZtmMBL2fPnmHnzmtIF/W6224m7TqfV9pGV5gZO+/KtbUataoSdVk5TpX0Tsoaru2108w8d1IciPi95HWTot6afqfz1qXK8YV+R2Uvec0kna/f15Cr8bvav3HKZ4CibhJcJA2xmnjXSr2glvF2apOTWouTekctcVJOaHXbzaRd53Mn2/XCXRg3gVpVibqsHKdKeidlDdf22mlmnjspDmRVnZBXJOBtTb9jIeFS5fhCvzOKTsgnEgvVb8pfjd/V/o1TPgMEvCKFRdIQq4l3rdQLahlvpzY5qbU4qXfUEiflhFa33UzadT53sl0vGiMK57LEXuteprIlfdvFu8E06jGKej0/vR4pV9IPTWbJFBRmcjqRdB6PR2SwN7Lqr59W0oeVthXFwOPxYNs2um2vyfZK2lRpeyZTRDfAK4HfK63I9lpyZK15vhbbZcWBX5ydIZPXmM6qWFM5dNvieTu76vYYBSz02zStBc+o1nt8b0lEuHVbnAdOTGGYNmFZJKeYJPMqL7qup26PUcDq/K42RyrzGZh/xrhUmBTj3FwB3TCZyasIs3mKhll1vJ3aVFYv0MzLCgu6ac2rFyw+Xss8r2yTqhnzzxiX1VpOz+TQzNICXbUsFN3gwEBi/nGCesz1ZeWEIyNJFKOkOa1bFkVtoe16UGlb1QyyisFUukjBMOvudyWNvoY65X95F9p6PUYBa5/Pa2VbWIU0Z9X26m6hzVlub3aATNGgoBl4BaGmqheraVO1+6ZvNLqDfh7PTXNkJM3opMJ5fYYDAzG6d1xNBfVKqu3D3pDMcSPF4bE0xUsatDf1x+it4QLNMdd8Ik+dn+Hx4fS8iP5tW2PcujVRM/+cKOdzNXleK9vXb47xy9NTPDqcZC6l0pGf5batMa7ffDX117UT8XtJFTQuporzhTOb44ErHu2oB6+8eYCZrMKhkTTDSZOgV+SWgRivvHmg7rZX43e1OVJWwhlPKaSKGrIoMNgbITIQ51uPnefQcJLJOY1eNcktW+Pc2B+v2g+nNpW3MF7p8VrmecgrUVRznJ7OoRg2suRhV3eY6zfHGE8VeHIkSUErPU5y85YYu7oidZ/rd3VFODWe5skLSQp6KdduHijZrjeD3VHGkgWeGk1zYVan6M9z05YYg93RuvvdzGuoU/47KUTVktXM57W2PV2DjcCuhrswrjPpoja/73fQV9oSs/yJKxHyE5G9Db9re7U2tSpHRuY4N1dgayJAyBDpTAQ4N1fgyMjcVavWF1NtHw5NZ5jMqVzTHcEvlTakmMypDE1nalY57tSmB54e5cnRDL1RmZBfJK+aPDma4Z+eHJlXKVirf04IgqfqPK+V7UfPzjBTMLhxc5Q5X5GOrigzBYNHz87wsv31XSRmVR3NtNkUDyB6BEzbQjNtsqpOQqrvGEurBjdt72JPXwLdMvEKIn6/SFo16Kir5dX5XW2OSJLAju4IfbHAAnWLn52cIGdY7B+IM+Ur0NMbJ2dYPD2Wqnp8L9emhFTdcSeqzfOxdIHJrEpvLIBPFNFMk8msyliyQN6w2L8ljlcS0A2LvGFyeibLtq5wXef60zNZ8obFTQOJeWWOvGFweibL/lB9FTEm8wqSJHLb9g667CQ7t3dgXjoet3119buZ11Cn/G8Eq5nPa23bY/jJTtbXlrswriOL9/0GEC/tGpYpGkRkL5IoNDQIK21Tq5HKKxy7mKEzLNMR8qFlfGxKBPHlNU5OZLlxIF5V1Xo1fbh43/syc3mBszN5dvVE1vy1o1ObJtJ5Hr+QojPip//SHYVECETBw5GRFC9OZq/4er0eObLSPK+V7ZlsgaMjaboiMl0RPx4lRX9XmJmsyrNjGW6/prBAlq6WLOUDiKUNYeo8xhTNYCxZJBLwzscbSlXr4ymFvligrlXra/G72rlQ9kmUR+zi8W3n/Qx0hZlbxfheSZuqPb6YavO8Mq5lNYIQEpphcfh8kl09IXpjl/N5Lq/y3FQOn1ci5JfqMtc7z2tqzeY1Jyr7I+QVSQYkuqMyed1kdK6IZlil/KiD3+vlGlqZ/42m0euWSkSh/n3beiugdcR6VJ9Yj21qBLVUKai2DxtRQe3UpqJS2t9+sd/LqRSsx+rjam3nVRvFXFqdIacb5NX178NqaNeq9WaqkKyGWqlxCB4PRcNAlhaqcZTnloJq1C0ezVSGWC7PC4aJYlh187tdr6HthLswriPrUX1iPbapEdRSpaDaPmxEBbVTmwJyaX/7xX4vp1KwHquPq7Ud8nuQxaXVGcJeiZB//fuwGtq1ar2ZKiSroVZqHJZtE5AklEUFSeW5JeiX6haPZipDLJfnQUlEloS6+d2u19B2wl0Y15Fa7VHe6m1qBGWVgtmcwngyT0bRGU/mmc0p7NkUWVXV+kr70Gnf+2RBZWdXqCZfNzq1KR6UuW1bnNmsyniqUNqkIFVgOqNwYCC+pEpBM3OkVra7IkH2D8SYySpMJAvkNJ2JZIGZrML1/dGaP0ZhmBaqYWKYVlP7r1y1ni3qZBQNzbDIKBrZok5fXG5I1Xoz/K4c31PpIgVdZypdXNX4XguVebAc1faVU1wN0+KmLVFymnHF3LK7J0xfTK5bPCrntZmsQlbRmckqK57XVtpXS1HZH1lFx7AssopOtqizpSNAT7R+frfrNXS9YFr1l2xznzGuM82s4txIbWoEBwY6GE8VSqoUcwZbvAoHBmIcGKi+JKnaPtzbFwfg7EyeqZxC0CtwYCAxf7wWOLXptXfsJOiXODKSYixVIOQTedF1Pbz64Laqz9XI6uO12n7h7l5yis6TI2lGMxaegMHBrXFeuLu3Zm1dj6oz7Vq1fsvWTrKKzlMjaYbTFp6gzk0DMW7ZWt8iMFidSkGt1Dh6QzJD05kl5xZJEqqyUS17emNkijonJjIUdIugV+C6TVH29Dorv9RK0aHcH6NzBbKqjW0zn+fl89TL73a9hjaTct6UixzribslNI3ZEno9agavxzbVk/Ie8x4bzpw7zTU7dmF7WNEe805U24eL972vB05tGk1mSectYiFhxXq2zcyRtdpO5lWmsiqFosazp05y/bV7CAZ89ET8NascL9uQveIVup6JkL+p/adoRsOr1ss0w+9yLBRN5fjxIfbuHUT2+Wsa76vZdsqD5ai2r5ziutzcUq94lP22bBvTBFEsPfe8nN9r6aulyBdVjhx9mgP7byQUWPj3raZj3M6U80YwFDKTI3XdEtq9Y9wgmlnF6cR6bFO9WFzFnA566Y2VqpjXUq1fbR+GAz7CVVupDqc2bUlE2OIsXVzVuRrBWmxXVo5H/AFmwj564wF0q3aV4+tRdaaSdqpaXxjvIF1hL92RYE3jvRLbq1EpWIsaRyXLzS31iMfSKiQsq0JSD0UH2ScR9AlLzt/1zsN2uoY2k8q8AfGq718r7kccl7agmdX6Lo2nEZXjbnX6+qEVlFQ2Gqvxu137ymVtOOVNvXAXxi5tQTOr9V0aTyMqx93q9PVDKyipbDRW43e79pXL2nDKm3rhLoxdmsJaKpJXQ2UV82xOJacZzObUulXrO/nXaL8rUTSDrKKjaMbV37zBqawcV3UTy7ZRa1w5vt6r05uZa42mEfFeie31mAf1YjV+16OvDNNCN+2m5Hkzx1i7jm9tkTRhXezV3YKLSwXN3GO+LxJgZDbPsYsZzk3rKHKWfZuj9EUCNbOxnEpBVtWb4rdhWIwk84wli2iWjU/w0J8IMJAIzVettyLlCvFkXkUxbGyoeeX4eqxOb+YYayaNiPfVbK+nPGgEq/G7Vn1VzvO5vMpk3mA0VaQjZDUkz5s5xtp9fE8nlbrbchfGLg2lmXvMj2eLFA2LPb0R5IKP7b0RiobFeLbIDrk2aiRO/qUKGpppN8XvkWSeockskYCXuN+LYhgMTWYBVq3GsREQBA+JkJ+gV2AqJNEfD+BftEtXrWxEZO+6qU5v5hhrJo2I99Vsr6c8aASr8btWfVXOc58AsuRBgIbleTPHWLuPb4/hJztZX1vuwtilYTRzj/nFqhQzskhHxL9mVYpKnPwzLIuLSaX0yEYT/Y7KpU/cPqn0Wiu/1zuSKOAV67tQWS/V6c0cY+uFRsR7OdvrIQ8azWr8rpXqjFcoScT5vSLCBlAh2ai21wuiUH//WrsHXdYVzaxIboQqhZN/okdAtSw8i77mahW/XdYPbtW/SzvQriok7vhuDO7C2KVhNLMiuRGqFE7+mbaFXxCwFy1CW8Vvl/WDW/Xv0g60qwqJO74bQzt+6+NILfbgrnYnnJlsgbxqE/J76IrUZxcXJybSObJFm0jAw6ZYvbeduFxZOpVVmckWUHSQvRCWS7tT1fMroLIqxdBkluNjSU5cyJD2zTDQEWawN1KTxwkq/QPmd3UyTJvNCRnNtDl8YYZkRiMR9XFNd3RNfq9kZ7NKv39+coqpvEpPyM9N2xIL/K42b53e73T8/GyGVM4kHhbZ3hldkX+1atODJyd4+NgsSXmCe/f118W2E7Xc6XClfV7Owf/zyGlOTea4tjfMb962Y0Gu1XvHrlReoajbBLwe4qG1bTNSba7969NjPPzkFBeFMV5x89Y12aiW5fK8VjaczvO9w8M8N5lhd2+U3zy4Mr+dcJpbnGwfH08yndbojvnY27dwF6F65Fplnv94aJLDx6cZsS5y52DviubUtc4tZdtF9XI/eQTPmubzlbSp0vb56QypokE8ILEpHqqL3040Yl5rJu6W0FzeEjrcO0BPIraq6s5qK0ULBZ0HhyY4OpJGMU1kUWT/QIy7BzcRDNa3YCSX17j/mVEOD6dRDBNZEjm4NcbLbthCOFTfKupMTuU7hy9w6EKKomESkERu2Rbn1Qe3EQ3Xt3Dg7FSS9/79EU7NKKgG+CW4tkvm//1/DrCzp8ot4Rwo50GmaKBbpU/x0YBEtqjwse89zdPjOTTLwicI3NgX5mOvuoktK1wolqlWZeLI8Di/9+UjzOqXj3V64cu/d4D9WzZVlbfVqm7YpsXf//Icj11IoRomfknk9m1xXnvHTuKRpRdM1Y4lp/dPZrK8638f5uycgQb4gJ0dEp97/S0MbuqsiW0nNM3k+HiK01M5FNNGFj3s6gmzty+Oz1fdzk3V9vljwyO87X+duuI8f/OGPdw7uLOuFe2KYvD4+RmOXcygmBayKLBvc5Tbtnchy9V9+KzW75Fkij/4+iFG85fPsSUEf/2mW7m+v6cqG9X2Ryqr8I1Hzy6Z59GQvyY2nNp6bnaOt331CFPq5ff2+OFv3nKQ/QObVnx+cJ5b+mNB8rpxhW1V0/nLH57k0HAaxbKQBYFbtsZ490uuoysarGuuPT06ye9/9RDjxcvH+gLwpTffwo1bepf8m1rNLX6PwKHhWY6NZ1AMC1kS2NdX2zx3atNUKs9nf3BiyT7viYdqYsOJRsxrV2tTNptlaGiorltCr4/l+TqhXNWaLmpV/225UtTj8RD0SXg8nmXP9eDQBA8NTeOVBPpjQbySwEND0zw4NLFGL67O/c+M8uOTU3glD/3xIF7Jw49PTnH/M6N1t/1vz47xy3NzxEI+dvdEiIV8/PLcHP/27FjdbX/0/z7NiWkFvwgxCfwinJhW+Oj/fbpmNsqVs/2JAAMdQfoTARIhP//9+8c4NJrF5xPZFPHj84kcGs3yiX95tmobZZUJQfQQD/gQRA9Dk1lGkvkl3/+Or19eFJenrlm9dLzavHV6/0gyv+Txr/zsOR44OYVPEuiPB/FJAg+cnOIbj5519K9WbXrn3z7BqTkDjwdkwOOBU3MGf/h3h2pm24nj4ymOjCQRRYGesIwoChwZSXJ8PFXVeZZrk1OfL7UoBnjr356smX9OPH5+hl+cnUGSBPqiASRJ4BdnZ3j8/EzV56rW79//amlR7KE0l3uA0Ty87WtPVG2j2v74xqNnHfO8VjaczvN7X768KC5f0KdUeOtXDld1fnCeW46Pp5a0/an7j/Hgc3N4JYG+iB+vJPDgc3N89gcn6p5rf/D1y4vi8jJqvFg67kSt5pZDw7OcnsnRHZEZ7InSHZE5PZPj5GS6aj+qbdNf/WSIR86lCMpetiUCBGUvj5xL8Vc/GaqZDScaMa/VKj/WgrswrsAnlSo9M0WjKtHsxZWiouCZ//dS55rJFjg6kqYrItMbCxDwS/TGAnRFZJ4dyzCTLdTatXkm0jkOD6fpjspsigcJyhKb4kG6ozJHR9NMpHMNsb05ESQS9LE50Rjbj5+b5tnJHGG/h+54kHCA0qvfw7HJHI+fm66pPUkU8EsikihwdGSWoxezRAMSfbEgkaBMXyxINCDx1HiWoyOzKz7vYpUJnyQQlX1EAl7GU8oVm3f8y5Mj8xePmF8g7BeI+UvDfrwI3378worz1inPJdHDxaRS+tq14ngyq/D4hTQdIZm+eJCQ7KUvHqQrInN4JMX52cwV/lU7lpzef+jsDOfTFj4PJMJegv7Sq98D5+YMfnJifM22ncgVNU5P5UgE/XSE/Pi9Ah0hP4mgn7MzeXJVTPxObfIKHi6mikjiwj7/zi8vf+DwVPyU+cIPT63ZPydSeYVjFzN0hmW6IzKyT6Q7ItMZljk5kSWVX7n+aLV+P3JqhvFiydeYLBCUSq8CMJaH+5+68kN/reJ9fjbDYxdSdEWuzPMnhpMcu5hasw2ntv70+MT8h96Iz0PI5yHiK0V8Si09XrFSnOaWoF/izHQeARbYHpvLc2QsQzzopTceJBTw0xsP0hHycng0zZFzc3XLtfufGmU0X1rAxGWBkFR6rWW8nd5vWzbHxjNEZR/dEZmwLNEdkWs6vp3adGoyxRPDaTpDvkvX0NK1tDPk4/BomlOTqTXbcKIR81qt8mOtuAvjRaymurPaStG8aqOYJlF54SMTUdlLTjfIq/V7uiVbtFEMc16667JtH3nNJFtsTdsjswV0CwKLHjUISAKaVfp9vRhPKqiWRdi70HbYK6CYFuNVCJZXqzLx3GRp8bn4y73y/4eTxRXnbbWqGzndRDFNIou+WozKXvKaSSp35Q5G1Y4lp/dPpIuYlL4VqMQnggGcm7xyUV6rim/VtFFMm9AiDd2Qz0tBt1DNtc8tHsGDatqInoXHz0wXWY7TM7m6VbQXdRvFtIj4F/od8XvJ6yZFvX5+X7z0oXrxQ2gSYAOn6xjvVM5ENZaez/OaSSpvrNmGU1vHUqV4L76Ql///3BJ+O+E0t/hEkaJhsXipkixoqKZF2L9oXvOLFDSLyezK55ZqOb3MvFareDu9X7dsFMMi4F1ovZbj26lNsxn90py6cGKLyCIF3WQ2o7OYjTSvrRd1DXdhvIjVVHdWWyka8nuQRZGMsjCJM4pO2CsR8tevsjQS8CBLIhll4ae7jKIR8olEAq1pe6AziFeAorEwRkXDwieUfl8v+hIyfkEgpy+0ndNLz2D2JVZenFStysTu3tLzy4s3gS7/f2sisOK8rVZ1I+wVkUWRrLLQekbRCflE4uErn0mrdiw5vX9TLIAIqIvW3ppZunju6L3yue5aVXz7RQ+y6CGvLRzfeU0n6BXwi2ufW2yrVPhi2guPX9O9/C6Ou7rCdatoD3g9yKJAVl3od1bVCXlFAt76+b35UvHw4mWBQeku8q46xjseFvFLS8/nIZ9IPCSt2YZTW/vjpXgvXrSW/797Cb+dcJpbNNMkIAlXLBYSQR9+USCnLprXVJOgT6A3svK5pVp2LTOv1SreTu/3Ch5kSaCoL7Rey/Ht1KbOqPfSnLpwYssqJkGvSGf0yvqkjTSvrRd1DXdhXIFmrG7P9mr3f++KBNk/EGMmqzCZLlJUDSbTRWayCtf3R+uqTrEpFubg1hjTGYWJVIGCYjCRKjCdUdi/JVZXdYpm2r5tRzfX94bJqTZTqQLZAkylCuRUm329YW7b0V032/sHOtm/OUKmaDCZLpAvKEymC2SKBjf1Rdg/sHQhWBnDtFANE8O05lUmskWdjKKhGRYZRSNb1EsbiCxSp/j1mwfou7RWSqsWOdUifelC1heA375t24rz1inPy6ob+qX/l48nIjK3bYsxl1cYTxXIKzrjqQIzWYWDA/El1SmqHUtO779lZxfbYwKaDcmsTl4tvao27OiQ+JXr+tZs24lwwMeunjDJgspcXkXVLebyKsmCys6uUFVV3E5t0i2bzfEAhrmwz1/9H3bO/61d8VPmnS++ds3+OREPyezbHGU2V5rXMkWdyXSR2ZzCnk2RqtQpKv3OKzoFzSCv6I5+33ltF32Bkq8pxSJnlF4toD8EL7tpy7I21tIf2zuj3L4tzkz2yjy/dWuCfZvja7bh1Nb/uHcTnZfWQlnNJqfZZLVSxHv8VKVO4TS3FFSDa7pDWLDAdn9HiAP9UVIFnclUgXxRZTJVYC6vc3BLjAM7OlB0k5yqU9RKr7XKtZfdtIUtodIHgLRiUTBKr2uNd+Vc6/R+j+BhX1+UjKLVbXw79dO1vXFu3RpjNq8xkSz1+USywGxe4+CWGNf2xtdsw4lGzGu1yo+14i6MK7Bh1fvbxwI+eiJ+sKGgGWAvf667BzfxwsFubMtmLF3AtmxeONjN3YPVVRGvhpfdsIV79vRgWTZjqQKWZXPPnh5edsOVk0kr2f7oq25gV4ePog4pE4o67Orw8dFX3VB323/6Gzdw5/Y4lmUzVdCxLJs7t8f5099wtm1ZNsm8ymiywMhcgdFkgWRepT8WZLA3AhakihpYMNgbYSCxdEXyF994YP7iWb7P0OktHa82b53eP5AILXn8LXft5kV7erAs61K8LV60p4fX3rFzyfMvZ6PaNv3F6w+yLVK6i6hSet0Wgc+89kDNbDuxty/OgYEEtm0zlVOwbZsDAwn29sWrOs9ybXLq8795w54lz/OVN15XM/+cuGVrJzdsjpHMqzw3lSGZV7lhc4xbti7/4W8pIn4vPtHDeLrIuZk84+kiPtFDfyy4pA9ffOMBehYJ2/T44QtvOOhoo1b98do7djrmea1sOJ3nS2+5eX58lz8EdXrhr9/snOdODCRCS84te/viS9r+wMv2cffuDkzLYjyrYloWd+/u4N0vuW4+fhNJhbMzOSaSCj7Rc8WjNqvlr990K1tCJZ+tS76XVUiccOrDiN+75Fwb8XuXfP9t27vqPr6d8uMd91zLPYOdmLbFxYyCaVvcM9jJO+65tmY2nGjEvFaruWgtuHJtXJZru2bXLuKx2JrO5eoYr1/bRy/McmQkyUxS5dzYeXb0b6cr4efAQIL926q/cK+qDSOzjCcV+hLyVe8UJ/MqU1m1VHR0SRNZ0U16In4SIf+KdIwBzk1nGZrMcuzcHOMFlb6gn307OhjsjbCjOwK0po5x2e/nRjM8e/4M12+/ht1bogv8rpVtJ5qhY1zmiz84yYnJDNf1RnnHS/as6FxrpZyzummhGzZeyYNXFOZzdjXnkkQPokfAtEua4OVzOcX72ZE5jp+5wN5rtnH9QEdD490MHeNyPz18apLzszm2d4Z5wbW9q+rzMrXQMS63q6zxa1s2umWvqV1L8f0nh3n48DFecHDfqnWrrzbXOvndTD3fU5MpZjM6nVHvkneKa2HDiWb63Qi5NndhzOWF8eDgIJHI8hOoy8YkV9T492fHES99hXP27Bl27ryGdFHHtm1evHfTmgd4LTFMi9FkAY+nVK1bRtFNsKE/EVjRJKJoBofOzyGIngVFjxmldDfowLZETTY3WW9U+h3yigwNnWJw8FryutnSfjeTWuXsas7VrvGuZZ9v1HaZpsnRo0fZv38/olidlm6j2+qydlwdYxeXGlHLitpGUKuq3WpVLFqFdvW7mdSy0rzac7VrvNdrdf96bddSbKS2ujQGd2Hs0hbUsqK2EdSqardaFYtWoV39bia1rDSv9lztGu/1Wt2/Xtu1FBuprS6NwV0Yu7QFlRW1ybyKbpokV1lR2whqVbVbrYpFq1Dpd1bRMSyLrKK3vN/NpJaV5tWeq13jvV6r+9dru5ZiI7XVpTG05mzhsmbqVZzTTMqVs6enciSLNt02q66obQTl6txM0aCgGXgFYVVVu2W1ivGUQqqoIYvCsioWrULZv3PTOaYLFr2G1ZJ+V1uUV09qlbOrOVc5rhdm8ySLFn2W3ZLxXky5P+byGgXNwCeuvs/r0a5a5EK92UhtLdOK1+j1grswdlmAZdmkixrpoo5h2UiCh1jASyzgQ9jgX0f6fCL7t3WyoytIhzrJwb09xELLb4jQTATBQyLkJyJ71zQBSpLAju4IfbHAilQsWg3TtsG2W+5ZQaexGvF7yap6U8ZwrXJ2NecSBA/xoI+iZjAZFOmJ+IkHN/68tVJswLYX6lY3k1rmQr3ZSG1t5Wv0eqF9ro4uKyJd1OZla4I+Ed20mMqqADWV2GkmYdlHLCASltfv3YBKJFGoyUCVfRIr32Jh4zOSzDM0mSXkE+kOiQS8IkOTWYCryndtBJzGaqqgoZl2U8dwrXK2mnOV+yPgFYnLpXi32ty1FAvzQFp3c3Ytc6HebIS2tsM1utmsz49ELk3BMC3SRR3ZKyJ7RUTBM//vTNHAWFSc4OKyXlE0g7FkkUjAS0T2IgkCEdlLJOBlPKWgaIs3k91YOI1VSfRwMamUvhVoozFc2R9+r4jg8eBvM7/bKd7tihvvxuAujF3mcWVrXFqFVpfvchqrokdAtSw8i75SbfUx3K5zV7v63a648W4M7sLYZR5XtsalVWh1+S6nsWraFn5BwF608G/1Mdyuc1e7+t2uuPFuDO7CuALTaq+vIQzTQjXM+a9fGilbo2gGWUXf8F9pV0sz/V4c70baaITtSirlu2azClnVLL22iHyX01g1TJvNCRn90v/bRXqqsj/yio5qWOQVveF+NzrPF/td0Iym+N1MFM2goFltcS1xpeUaw8a+OtSYyayKLaktX925XFVrvWVrDMNiJJlnLFlEs2x8gof+RICBRAhJat1B3Uy/G1HFvB4VEvoiAUZm8xy7mOHcjIYayLFvc5S+yPpVIqkGp7Fa7vONJD1VCyJ+L6mCxsicysWsQTSjMtARJOL3Xv2P10gzlQLKfl9MFVFNG7/oYXM80BC/m0l5Th2eK3BqRsUaTrG1I9jy15KNKC230XAXxhUI0BbVnVeraq2nbE1ZKSAS8BL3e1EMo6WUApxopt+NqGJejwoJ49kiRcPiuk0R/EUfOzdFKBoW49kiO+SNn2vLSUwlpI0hPVVLsqqOZtpsjslkIhKbYzKaaZNVdRJSfXOtmUoBZb83xQOIHgHTthrmdzOpVJ2J+D2IHtriWrKRpOU2Ku7CuAKfJMKl6s6I7G3JZFtc1QogCqXXSr/rIVtTqRQQvSSV5pNKr+Mphb5YYMN/xb0UzfR7pfGuhw3DsriYVEqPLtTJthOVfR7yikT8Iomwn7xutlyuOY3VjSA9VSsqc9ArgF8SCPoldKv+udaIMVaNbRBRdLOlr2OLx3dZdUYQhZYb30600/huNK03YtZIq1d3NrOqtdWVApxopt+NiPd6VEho11xrV5o5r7Wr7Wbijm+XeuIujBfR6tWdzaxqbXWlACea6Xcj4r0eFRLaNdfalWbOa+1qu5m449ulnrgL4wqKqtHy1Z2VVa05VaeolV5X6vdaqq4rlQIyioZmWGQUbc1KAdW2yTAtdNNuikJCLf1eCZXxThc10gWddFGraZ6vR4WEyj5PFzSKukW60Jg+Xy8sp4BSb/WEZqozzGZVZgo6s1m1brlW6V8zlQJWOr5bLd6V4zur6BiWRVbR22p8u9QPN3sqmMlr9AcbU8XcTOarmJMKqlW6q7c5IS/rd62qrgcSIaD0bG2qqCGLAoO9kfnj1VBtm8rvn8urTOYNRlNFOkJWQyrHa+l3tYS8EkU1x+mpPIppIYsCu3pChDpqZ3s9KiT0x4KkCxrPTeUYzRgEsiq7e8L0x4J1t91MllNAEQRPXdUTmqnO4PcInJ5Ic3QkzfB4kbPqRfYPxBiIbqqZjeXUV6A5SgHLje96x6OZ8S7PnaNzBbKqjW3TsDnVpbVxF8YVdId9bVHNW65i7ovLeAQPtmVf1e9aVV1LksCO7gh9sQC6ZZe2rl3lp/tq21R+v08AWfI0VIWkln5Xy1i6wGRWZVNMRpIEDMNiMqsSCxZqVr29HhUS8rpBwO/lhs1RpKyP6zZH8Ygied3A5xOvfoINynIKKPGgr67qCc1UZzg0PMszF9MkQhJ2VCQRknjmYpqI7OWuPbVZHF/Nv2bk+XLju5XjXZ5Te8I+mPNzYGucUKB1r9sujcNdGFcg+6S2VKUAlq1irkfVteyTkGvsx3JtWly1Lng8+L0iQgOq1itZq9/VspQiBn4QRE9dqrfXi0LCgnj7RcI+kWjQ1xCVgmaynALKyFxhXjqvHuoJzVRnSOUVjl3M0BmW6Qj5UFMSvbEgXknj5ESWGwfixENrG3krVvRZszcrZ7nxXYq3heyVWi7elcg+iaBPcB+fcKkZrXdlWCOtXs27mirm9Vj5XG2b1qMPjaBdq7fdeC8Rb8NC0cy69Ukz+7yo2yimdcXjYBG/l7xuUtTrp77SzJxaLt5F3ULRrZaMt4tLPdmQC2PbtvmHf/gHXvGKV3DzzTdzzz338PGPf5xcLjf/nrNnz/LWt76VgwcPcvvtt/PhD3+YTCZz1XO3ejXvaqqY12Plc7VtWo8+NIJ2rd52471EvCUB+dLX3ZXUqk+a2ecBrwdZFMiq+oLjWVUn5BUJeOunvtLMnFou3gGvgOwVWjLeLi71ZEN+9/CVr3yFz372s7z5zW/mjjvu4MKFC/zlX/4lzz33HF//+tfJZrO84Q1voKenh/vuu4/Z2Vk+/elPMzExwde+9jXH86byKnLIS0/EvyJ1hlo8SzaRzpEt2kQCHjbFwqs+z0rbVK5insqq/OzUJOPpIn2xALft7HL0u/Jv4NIdAdNC0c0V9dVSPH5umpHZAgOdQW7b0b0iPxTNWPB8brlNqmGC7QFPqQBjqTZV+vD5n5zgybMz3Dz8LL/3K9etKd7VHv/e4WGem8ywuzfKbx7cWnW/raaf+hMBhiaz/M1DxxmeKbC1K8h/vm03g72Rq379WCu/Hz0zxfBMnq1dIe64pmdN/jkdT+UVirpNwOshHpLn4/3e//swp0bg2qd+yp//1gvqEm8nFrdpJSyOX7U2yvH+7PcPMzxnsrVD5C333shgb2T+mVNNN7Eo3RmxuDxm1jKvVY6x0Zk8aVUj5vfRFZNXPU/Ayvo8HpLZtznKL87O8Ohz41yYzLAteYHBvgTP29k13/drietK5sHzsxlSOZN4WGR7Z3RV/l6tTZXHK8f3Nx4Z4sJcgW0dQX7jwI4F8XZq71qo7I9nRpPMZRU6IjK7e6MLzl9tTlWb///j/md55NQEd449y/t//aYFv6vVddrpPKPJLOm8RSwksCWxsnqNWvXHT06Mc24yw47eKL9yXV9dbFfbptXYrnpOLSirbvdK8dj2xvq+w7Isbr/9dl7+8pfz0Y9+dP74v/3bv/Gud72L73znO/ziF7/gr/7qr/jJT35CR0cHAA899BBvfetb+eY3v8ktt9yy4JyFQoETJ07wi2kvOzcluHtwE8Hg0goNtarCzeU17n9mlMPDaRTDRJZEDm6N8bIbthAOVVfJXG2bjl+c5g++/jjDWeYvjlsj8D/feBt7N3df8f5KG5migW6V7ghEA1LVfg/PpPmTbx/l2ckcugVeAa7vDfPx397Plo7okn6EvBJj6cIVVfa9IZmh6cwV1dh7++JLFlf949FjvP9b5684ft9rtvM7+/dV1bdltYWVHj83O8fbvnqEKfXyuXv88DdvOcj+geoKg5za5NRPPz4zxJ//08QV5/mvr9rMm267ua5+J/MFPvQPRzkxXZiP93XdQT71mpvZ3h2viW2/R+DQ8CzHLmbm82Df5iiPnz/GF3565ST6rnvDvOveF9bEtlP+K4rB4+dnrmjTbdu7kOWlLyTLqUlI0pUXDCcbh4ZP8LmfFK54/x+/OMrbnv88jo+nOD2dQzFsZMnDru4we3pjFE1zzfPaTLrAF398iidGLs9rtw7EeMc919JVpRpItfPaT4bO8eavHafyguYBvvqmvfzHXdtrElenedA2Lf7+l+d47EIK1TDxSyK3b4vz2jt2Eo9U92xztTn4g6Hn+OA/XLjiPPe9ZjuvvnFvTeZtJ1YznzvZrjb//+XZU/zhN05fcfxzr93Fy/cO1uQ67RQLj2Xz3SPDPHEhdTnPt8V59cFtRMNLFwBWm89O/VHUFf74G0c4O2dgAiKws0Pic6+/hcFNnTWx7US1MVrOdrVjr1DQeXBogjMTSe7s1rnuuusIBuujMLThHqXI5XL8+q//Oi9/+csXHN+xYwcAIyMj/PznP+fgwYPzi2KAF7zgBYRCIX72s585njsql6qYDw3POr6nXIXr8XgI+iQ8Hg9TWZV0UavKj/ufGeXHJ6fwSh7640G8kocfn5zi/mdGqzrPatr0h//rEOezpQEV8pRez2dLx50oqw70JwIMdATpTwRIhPxVT65/8u2jHBrNIQkeOmQRSfBwaDTHn3z7qKMfx8dTDE1mEUQP8YAPQfQwNJnl0PDsfDX2rp4wm2Iyk1mVsfSViwJgyUXxcsfBuW9Hkvmqjv/ely8visuDbkqFt37lcFX9t1ybnPppqUUxwP/vuxfr7vd7//4IRy8WED0Q93sQPXD0YoEPfOvJmtl+cGiCX5ydQZIE+qIBJEngF2dnllwUA/zFj3JLHl+Nbacx9vj5mSXb9Pj5GUfbZTWJxfEbSearsrHUohjgMz/MzCsY9MYC7O6J0BsLMJlVOTmZrsm89rWHT/PI2SQhn5cdHSFCPi+PnE3ytYevXMBcjWrntXcsWhQD2JeO1yquTvPg3//yHA+cnMInCfTHg/gkgQdOTvGNR8/WzG+nti61KIbSvFareduJj33vaY5czOGXBHqCXvySwJGLOT72vaerjl+1+b/Uorh8vFbXaafzfOPRszxwYgrvpXh7JYEHTkzxncNLx2K5c1XbH3/w9cc5NWcgeCAkgeCBU3MGf/h3ztfvWvVHtTFazna1Y+/BoQkeGppGbMATOhtuYRyNRvnIRz7CwYMHFxz/4Q9/CMDu3bs5c+bM/EK5jCAIbNmyhfPnzzueOxHykQj6OH4xw2w2j2maC35UTWcuX5L78gqAbeEVwCdAMq+iavoVf7PUz1gyw6HhNJ1hPz1RGdkn0BOV6Qz7eXIkzVgys6LzrKZNP3hmlPNpCy8QD0kEgxLxkIQXOJ+2+MEzo8va82AjecCDveI2ln9+eWaSZydzBH0eOiIBAgE/HZEAQZ+HZydz/OT4+BV+2KbJc5M5ApJAyCsiemxCXhGv4OHYxQw+j4eILBGQBCKyRMgnMjpXIF9UF9j+8+8dWTav/vx7R1bctyI2I3MFhEv/Lx8XbIuRuQIi9oLjDx0fZ/bSo49hHwR9pVcoLY7/7xPn1xxv2zQZmsohL+qnb/zi+LJ+f/gfH1uz307Hnx2d49SMguyFjohMMOCnIyIT8MGJ6QKPPDexYttOfatoGk+OpInKXjpCPrySh46Qj39/zPkCBfC6L/9wzX47jbHZbJ5nL2ZIBH0L2rTc3JIvqgzPFQj5xAXxc8pnJxs/OrK833/8jZ8R8olE/RKy5CHqL42d56Zy2Ka5pnltaDLJ4xfSJEI++hIyoYCXvoRMIuTj0Eiaoclk3ea1rz30HMVLPnovfdgvP1ZcBL7+k9NrjqvTPHhmOsVjF1J0hPz0xmQCfpHemExHyM+hkRRnplNr9tuprV958Nll4/2Jf3pyzfO2088T56Z4ejxH2C/SFQ0SDPrpigYJ+0Wensjxs6GJFcev2vy/71+eWtbv+/6/J9d8nXaKRSqr8NiFFImgb0G8O8N+Do+kuDCbXnM+O/XH0EiS4Sz4gHjYSzDgJR724vPA2TmDHx0bW7Ntp59qY1TLOXUyneXJkXRpHq3yG/XVsCGfMV7MkSNH+PKXv8y9997L7t27yWQyhEJXinyHQqEFBXqLmZycIFnUmSvYHBKTdIUXdo9u2kzmjZIGbkVhgWXbKIbNVEjCu4KPM8NJjYtTWbqDMF68/MiGoutMF+DxwwpbEysLfrVteviZGQxKA0vXLxdseAAdePjwMbq0yRXZrpaHz6QpKhDx2iiFy3e1BAPyOhx+9jnCSnSBHznN5LlpjWJUIuAVKo4bnJvWkQs+ZuTLj00YlkVWtWHOT9B3+f2PnFr6runl349z9OjirYuX7lvVsLiYNchEJPwVXx85HT90cvpy+67chIyHnzzGdsH5W4qVtCmvmpye0VBjIlPS5f44PawudZp5Hj01w9GjR1dko1q/j5xJoRqlCVEpXr576zFAMUu5Jmfia7I9k9cZGS/iiQmoqctjaXR5tzl2Rl+z305jbCZncH4kT0fQQ2Hu8hyimobj3FLQLE7NqET8HiSh4vk7h3x2sjHiPL0BMDQJI+dPL7BR1C1GMwZS1ke44hGkaue145NF5tIZOoMwN1ecP25pGrMFeOzwMbK9gaueB6qf1x55dqLiPQtfAR4/c5598dya4urE0LTCTDJNVxBmzMt3zgxNY6YAjx8+Rqp7ZY9TVJuDR84klz3fI6cucnSgPrvR/fxchoJiEfVCMX/Zb0G3yelw+JnnkHORFcWv2vy/2nz+xOlZ/uPmE2u6TjvF4kJSYSaZQ44KTBnZ+ePa/PX7GDOd/hWdq9r+eOb8HOVoatrlQlOPDSqlOTWhLOybWq1bqo3RcrarHXvjaZ2R8RxdQZjW/Fy7tb6bNG34hfGhQ4d429vextatW/n4xz8+f9yzREWsbdtLHi/T27sJK28Rt+CWA/3EgwsnM8O0GE0VEQB/hQawqpvYQH88sKKHxzdl8vxi7gySAJvily8UE6kiAxG47eA1bIqubPeeats04xvnWyefwga83svhtzUDCXjBwX3s37uyB/mrRY/P8PfHD2F5PMjBy37njCIB0ebg9bsZ3NaxwI9MQcMIZ+iN+okFL39YmM2pKHKW7b0ROiKXJ6GsomPbcGBrfEFRwJ3DT3HisXHHtt15bR/7919ZuLFU3xZUg2haoS/qJyRfXozlFZ1oRmVzTCbov2z7nDbKD4dLX/stUFW69I3RC27ex/79A45tW0mbMgUN7WKG3oifeEU/7Tp7hPGzzqvEO67tYv/+/Wvy2+l4Rp7mn547hi2AHLg8lvKGgixeyrWdVxZeLmXbqW+ns0XOaBMkQl56Y5dzastTz3B+mcXxvmu8a/bbaYylCgpnzDFEAboqni+dySqOc4uiGVjDKUQPRCpsOOWzk42B489wepnF8WAvDGzftcBGuqARyKpctzlKtCJ3qp3XIjMZ/vncswiCh47E5ViMJ4t0+W1uP7iPa7pWVpBW7bx2Z+4sPx4ZAkpfLVt26dW8tDi+7ZrtDF47sKa4OhGfy/KvI8eRBA9di/zuDdjcdnAv2ztWXphVTQ4eGHmKM085L47vvHYz+/ffuCLb1WJ1zPL3xw9jCR4CoYr53CwSlGwO3rCbwf7EiuJXbf7fOfYsT/3C+bHDW3d1MnjtdWu6TjvFIjqT56GJ03j9Ej2dlxdo48kim0M2tx28li3x8IrOVW1/TOjj/OjCeQB8voprj6bjsy/NqYs2s6nVuqXaGC1nu9qxtyVX4Gj+ApIA3TEJcH50oxZcvTfWMffffz9vfOMb2bx5M3/7t39LPB4HIBwOL3lnuFAoEIk4T1Dpok6yoLF3c5TOSAhRFBf8+H1eOkJ+NAt0C/AI6BZoVmmXH7/Pe8XfLPXTn4hyy9YYszmVqYyCollMZRRmcyo3D8ToT0RXdJ7VtOklN2xhe0xAB9J5g0LRIJ030IHtMYGX3LBlxbar/fkP1/RyfW+YgmYzky1SKKqlV83m+t4wv7K37wo/PKLI7t4wRcMir5uYtoe8bqJbNvs2R9Fse8HxvGaypSNIKOBfYPtPf/PAsrn0p795YMV9a+JhoCOIden/5eOWR2CgI4iJZ8HxF+7to/PS2M9pUNBKr1AqwPutW7evOd4eUWSwJ4yyqJ9e+7y9y/r9id+5fc1+Ox2/fksH13bJKDrMZRUKRZW5rEJRKxXg3bl704ptO/Wt7PNx80CMjKIzl9fQDZu5vMav3r5tWb//9++9eM1+O42xzkiI6zdHSRa0BW1abm4JBfxs7QiS18wV5bOTjXsPLO/3Z1571xU2iobF7p4wHlFc07w22Jvgtm0xknmNybRCUTWYTCsk8xq3DMQY7E3UbV570wt3U16a6TaYl14BAsAbf2XXmuPq9HNNd5zbt8VLW82nFYqqyWRaYS6vcstAnGu642v226mtb7n7+mXj/eFX3ryqudrGg2GDjcfxPbfu6OHGvjA51WQ2W6RYVJnNFsmpJjduCnPX4KYVx6/a/F+sPrGY97/85jVfp51iEY/I3L4tTrKgLYj3bE7l4ECcbZ2xVeVzZZ879cfgQIKtkdI9lWROp1DQSeZ0NLtUgHfvvv41jyWnn2pjVMs5tTcW4eaBWGkezVf3XPRq2LAL46985Su85z3vYf/+/Xzzm9+ku/vynacdO3YwPDy84P2WZTE6OsquXbscz2lb8LydXdy2vcvxPbGAj56IH2woaAZckgeLBap77uVlN2zhnj09WJbNWKqAZdncs6eHl92wparzrKZNX3jDreyKCdhAwSoVqOyKCXzhDbdWbbta/uzVN3HjpiC6YTNTMNENmxs3BfmzV9/k6MfevjiDvRGwIFXUwILB3gi3be9a8vhAYum77f/9Py29aHA6Ds59O5AIVXX8S2+5eX5xXP4qrNMLf/3m5Rfs1bTJqZ/+9JVLq17811dtrrvf/+//c4D9m4NYNqRUG8uG/ZtLqhS1sn334Caet7ML27IZzxSxLZvn7eziD+5aOv//8Fecv4ar1rbTGLtte9eSbVpubhlIhKrKZycb73jh0lXxf3RPyNHG3r54Tea1t9y1m3v3dGFZFmPpIpZlce+eLt5y1+6qzgPVz2tffeuNLH5QI3DpeK3i6sRr79jJi/b0lPxOFbAsixft6eG1d+ysmd+OY+w/71jyPJ/5f6q3bVk2ybzKaLLAyFyB0WSBZF7FctgI6GOvuon/sDWKadnMFHVMy+Y/bI3ysVc5z+dOfVtt/n/utUtfyz/32l01u047nee1d+zkRdctivd1Pbz6YPXXkojfu2Sf98eCS/bHF95wK9fEBCwbsmbp25FrYgJ/8bqDVduutj+qjdFytqsde3cPbuKFg900Qkdtw8m1AXzrW9/iox/9KC996Uu577778PkWduQXvvAFvvrVr/LjH//4Crm2b33rW9x888KLclmurW/rdjZ3Ly13spiNqmNcyQPHLnJ2MsPO3igv2ue8SKolybzKVFbl5MUUM6kiXfEAezaXLsqJUOmCvlJ93qsdX8y56SxDk1m+e+g0xy9k2bstwqtu2cVgb4Qd3ct/1blWPd+y3z89Ns5Yqkh/PMB/3Ne3wO9qWWk/lf3+xqOnGJtT6O+Qee0d1zbE7zLN0DE+emGWIyNJvvXIKYaTsDUBr7nzWg4MJNi/bflxvpF1jO8/OsJDQ9P84vg4cwp0yPC8vX28cLCbl116ZMfJRq3mtdPTaZJZg0REYld3bNXnWU2bvvbQczzy7BB3Xj/Im164cEFeq7g60Wgd48rjn/znpzg2nmFfX5QP/cbyd1SdKM9Tsle8Qvd4uXnq8IUZxuaK9HcEOLht4Ye/eusY3/cvT/HIqVHuvHbLhtQxvlqfL+6P8vsPn5/lYrrI5liAg9s7V3QtaQUd45HJaaZGh+sq17bhFsbT09Pce++9dHZ2ct999yEt2gpz69bSpgm/9mu/Rm9vL+985ztJpVJ8+tOf5qabbuLLX/7yFecsL4wHBweXfdTCZW0YpsVosoDH40GueN5I0U2woT+xsmedVoOiGRw6P4cgekrVvUOnGBy8lrxuggUHtiVWPMCrZb34HZUvf4DMKFrd/W4muaLGvz87jnhpE4KzZ8+wc+c1pIs6tm3z4r2bCFd5t2QjMJMt8KWfnsUrCQueuZ5Ml+4ov/muHXRF6lu40mxM0+To0aPs378fUbxSz9xlaZo5T62FjRzvavt8o8aolmSzWYaGhuq6MN5wV8SHHnoIRVEYGxvjv/yX/3LF7z/5yU/yqle9ir/7u7/jE5/4BO9973sJhUL86q/+Ku9///ub0GKXMqZtY1g2wUWbb3hFgYJmYNp23RJSt2w0yybu90KF2qksSaSKGrplU50M/8pZP35fphF+NxPVtFFMm57AQr9DPi9TOQXVtFnb9zPrk7xqo5gmnYs2GYjKXsbSBfKqTZf72d9lCZo5T7Ur1fa5G6PGsOH68NWvfjWvfvWrr/q+wcFB/vZv/7b+DXJZMaLHgyR40E0LUbg8sHWztCOTuIxiyFrxCh58ggfFMAhVftI2DGRRwFsjwfulWC9++6TLd0gb4Xcz8YseZNFDXtOJBRZWbwe9Av5GqMQ3gZDfgyyKZBSdQIV6R0bRCXslQv7W9Ntl7TRznmpXqu1zN0aNobXvubcohmmhGiaGWR99ynohXfpaW9FNcqpOUSu9KrpJNCDV9Ssg2SfRnwiQLepkFb2kvajoZIs6fXG5ro8TVPqt6CamZc//u5F+ZxQNzbDIKFpD/G4m4YCPXT1hkgWVZF5FN02SeZVkQWVnV6hhj1E0eqx2RYLsH4gxk1V4bjzNmeksz42nmckqXN8fbfnHKKDU57ppb7j5ca0omkFW0VG0JcTSV8BK5imnfG7mNSmnaKSLJjml/moFtabaa0MzryXtRGteFVuUWu133kwifi+pgsbFpIJqWfgFgc0Jmciir/rrQblydnSuQFa1se2rV9TWinKlbaZoUNAMvIKwqqrg1VD2bzylkCpqyKLQML+byd6+OACnp3IkizbdNhwYSMwfryfNHKsHtyX40bMXeXwsg2JayKLAzf1RDm5L1NVusyn3+VxeZTJvMJoq0hGyNtT8uBoMw2IkmWcsWUSzbHyCp7QFdCKEJFW3UHKap8rKCYvzOeL3klX1puS5ppkcH08xNJnj9FiRGd8Ug71h9vbF8fk2zrPG1V4bmnktaRfchfEGorznuOwVCfpEdNNiKlvaxWC1ygaNJqvqaKZNX1zGI3iwLRvNtMmqOgmpvj5IksCO7gg9YR/M+TmwNU4o0Jh+EwQPiZCfiOytSVVwNZT97osFqq4k3sj4fCL7t3WyoytIhzrJwb09xEIr23ltrTRzrP7r0xeZyOkMbooiSwKKYTGR0/nXpy/yphcO1tV2Myn3uU+gtNMWbLj5cTWMJPMMTWaJBLzE/V4Uw2BosrQj29VUZxbjNE9VKidU5nOqoKGZdlPy/Ph4iiMjSWKyl0TAgyjAkZHSZidXU51ZT1R7bWjmtaRdaP2rY4tgmBbpoo7sFeerUcvPGGWKBhHZu+4Hx1I+QKmitpE+yD6JoE9oyuJQEoWmDTrZJ7Vkod3VCMs+YgGRsNy4xyeaNVZHk1meuJCiOyrTF6/YlStV4MhIihcnsyuWldpIVPa5VwDB48HvFRGsjTM/rgZFMxhLFokEvPOqM+VagvGUQl8ssKp5rnKecspn07S4mCqyKR5oeJ7nihqnp3Ikgn5iAS9JUSQR8iMIAmdn8uzqiWw41Zlqrw3NvJa0Oq03U7Qo5WpU76KJxisK6FZJB3C90wo+uLhcjWbmeTpvoRgmUXnho0lR2UteM0nnW/O523adW8qqM/Ii2VJZklBMC91hY45qcOpbj+BBNW1ET+P7vKw6E/JdqTpT0C1UszXj7dIY3IXxBqGyGrWSjVSN2go+uLhcjWbmeSwkIEslVYpKMopOyCcSC7XmlN+uc0ul6kwltVSdcepb27Lxix5Mu/F9Xqk6U0mrq864NIbWnCVbkFaoRm0FH1xcrkYz83xLIsKt2+JMZxTGUwXyis54qsB0RuHAQLwlH6OAhX2u6iaWbaO2wdzSCNUZp3zWLZvN8QCGaTc8z9eL6oxLa+I+orKBaIVq1HJb5/IaBc3AJ248H9ZCrbbkbLYNl+Vp5lh99cFtADwxnOLcbJ6oLPGi63rmj7cq5b6dySrkNAvDsumJyHXp8/U0xsrqMiNzBaaLCgFv7VVnllOryKp6U/K8maoz7c56yv964C6MNxCtVI1qA7ZduQdda9MI+a5WkPNrFZo5VoOyl7v39pEIBUgrGjHZx/5tcYJy/SUR1wPWpWdbrTo847oex5ggeIgHfWimhaJbyF6BeLC27VkunxNSc/K8maoz7cp6zP964C6MNyAbuRp1oYyVtCEl51ZDI+S7WkHOr9Voxlgty3f1JmS2SeE1yXdtJObl2kSBsE/AJwptMcYut0kiIgt1bZNTPjfzmtRo1Zl2Zj3mfz3YqOsrlw1IK0jOrYZG+N2ufeuykHrJd613GiHXth7H2Hpsk0tr0k651hpeuGwI2lVSqRF+t2vfuiykEfJd65F2HWPrsU0urUk75Zq7MHZpGO0qqdQIv9u1b10W0gj5rvVIu46x9dgml9aknXLNXRi7NIz1ItdmmBa6aWOYjdnsoNLvvKJT0Azyil5Tv9dL39YSw7RQDXPNcWp0vJtJpXzXTE4hVdCYySk1le9ajzRCrm09jrH12CaX1qSdcq01Z0mXdUszZazKFbVzeZXJvMFoqkhHyGpIRW3E7yVV0LiYKqKaJWH8zfEAEX/tlAJaQc4Palf53Mx4N5O+SICR2TzHL2YoGiYBSWRvX4S+SGtX7JfzPJlXUQwbG2qe/+txjK3HNrm0Ju2Sa+7C2KWhNFPGar5qXQBZ8iBAwypqs6qOZtpsigcQPQKmbaGZNllVJyHVxnaryPnVqvK5mfFuJuPZIkXD4rpNESRRwDAtiobFeLbIDrl1VSnK+R/0CkyFJPrjAfy+2krUrccxth7b5NKatEuuuQtjl6bQaHmfRlStr8R2uZoXRBTdrIvtjSznV6vK52bGu5kspUoBkFG0llalqEQSBbxifS/Y63GMrcc2ubQmrZ5rrXdlcHFZgmZW1LZTNe9aqVVftWuft6sqhYuLi0utcBfGLm1BMytq26mad63Uqq/atc/bVZXCxcXFpVa08t3wulKrvcKbuef4RDpHtmgTCXjYFAuv6G826h7p5YraqazKeK7A+TmFyGyWRDhIT8Q/70u94lq2PZtTKWomAZ9IyC8tsF1vHj0zxfBMnq1dIe64pqchNsvkitp80WF4mUKNyr4yLGv+eWzDtOf7aiUxWtDnisJERqMznScky2vq82rzo9HjpaxKMTSZ5cxEhoKqE/R76YkFGOyNNOwxCkUz0C0br+Cpm02nvs0pGumiSU7RGr5F8Ey2QF61Cfk9dEWCDbU9msySzlvEQgJbEo19lryWflc7Zk5MpDgylse/KcX1/Z1rsl0tK53XWs12I8Z3M2k9j+pMrSvmm7HneC6vcf8zoxweTqMYJrIkcnBrjJfdsIVwaOkB1gp7pHssmweeHuWx4RQzczl+NnWW27fGee0dO+seV49l88ipCZ64kJrv81u3xXn1wW119LjE+ekUH/jWk5yYLqBb4BXguu4gn3rNzWzvjtfVtqaZHB9PcXoqh2LayKKHXT1h9vbF8fnEJf9mXsEjqaBaFn5BYHNCJuSVSObVFcfIi4ej52c4PJJmbLLAM/kxDg6U8rxaqs2PZo4Xr2Dzj4+e4emJHJpp4RMFbtwU5mOvuqmudgEMw2IkmWcsWUSzbHyCh/5EgIFECEmqzQcDp74NiCInJ9MMTeY4PVZkxjfFYO/yuVYrCgWdB4cmODqSRjFNZFFk/0CMuwc3EQzWtgBwMZmcyncOX1hybomG61tkWku/qx0zU6k8n/3BCQ4Np8nkFf7x1FFu2Rrj3S+5jp54qFYuLslq5rVWsN2I8b0eaB1PGkS50t3j8RD0SXg8HqayKumi1pTzrIb7nxnlxyen8Eoe+uNBvJKHH5+c4v5nRtdle2vFd48M88jZOaI+iS1RgahP4pGzc3z3yHDd4/rdI8P88twc8aCPa3oixIM+fnlujn97dqxO3l7mA996kqMXC4geiPs9iB44erHAB771ZN1tHx9PcWQkiSgK9IRlRFHgyEiS4+Mpx78pK3j0xWV2dIXoi8tops1YulBVjB56bpLDwynissRATCAuSxweTvHQc5NV+1FtfjRzvPyPfzvB0Yt5wn6JgViAsF/i6MU8/+PfTtTd9kgyz9BkFkH0EA/4EEQPQ5NZRpL5mtlw6tvHz8+Uck2ARMCDKHDVXKsVDw5N8NDQNF5JoD8WxCsJPDQ0zYNDE3W3/Z3DF3jgxFTJdrxk+4ETU3zn8IW6266l39WOmc/+4AQPPjeHKHpIyCCKHh58bo7P/qD+eb6aea0VbDdifK8H3IVxFSyumBcFz/y/M0VjxRsI1Oo8q2EinePwcJruqMymeJCgLLEpHqQ7KnN0NM1EOreu2lsrRpNZnriQojsqs7kzSNDnZXNnye/DIymGxrN1i2tWUXliOEVnxM/mjiCxoI/NHcv3ea149MwUJ6YLBH3QGQ0QCsp0RgMEfXByusCjZ6bqZjtX1Dg9lSMR9NMR8uP3CnSE/CSCfs7O5MktccGr7MOQ7CXokwjJXryCh4upIpLoWVGMZrIFjo6k6YrIbEoECXm9bEoE6YrIPDuWYSZbWLEf1eZ/M8fLM2NzHBnNEA9K9CVCRMMyfYkQ8aDE0bEMz4zN1c32YkUMnyQQlX1EAl7GUwqKZlz9JFfBqW9ty+bYeIao7CMR8uMVRRJXybVaUZlrvbEAAb9Ebyywqlyrlsp5rS8eJCR76bs0nx8ZSTGazNbNdi39rnbMHB9Pcmg4TUfIS2/8ku14gI6QlyOjaY6PJ2vt7jyrmddawXYjxvd6wV0YV0ErVMxnizaKYS6QcgKIyj7ymkm2eKXtVqjwT+etS34v/HovKnvJKCZJRatbXFUNFN0k4l95n9eK4Zk8ulXS8a1EljxoVun39UI1bRTTJrRISzbk81LQLVRz5bnmETyopo3oWVmM8qqNYi4d75xukFdX3ufV5n8zx8tkSkW1LMKLvlIN+0SKpsVkSq2b7UYoYjj1rW7ZKIZFwLvQ9nK5VitqmWvVsty8ltdM0vn6fQhr5hibTmsolkXYvyjP/SIFw2I6Xb8F4mrmtVaw3U6KN+7CuApaoWI+EvAgSyIZZeHEkVE0Qj6RSOBK261Q4R8LCZf81hcczyg6UVkkIfvqFle/j9KdY3XlfV4rtnaF8AqgGAsnLcWw8Qml39cLv+hBFj3ktYV9ntd0gl4Bv7jyXLOtUpGJaa8sRiG/B1lcOt5hr0TIv/I+rzb/mzleeuN+/IJATjMXHM9pJgFRoDdev2dOG6GI4dS3XsGDLAkU9YW2l8u1WlHLXKuW5ea1kE8kFqrfJb6ZY6w75kMWBHLqojxXTYKSQHesfsVoq5nXWsF2OyneuAvjKqjVXuHN3HN8UyzMwa0xpjMKE6kCBcVgIlVgOqOwf0tsSXWKVtgjfUsiwq3b4kxnFMaTRYq6zniyyHRG4eBAnMG+SN3iGpH93Lo1zmxW5eJcgXRB4+Lc8n1eK+64pofruoMUNJjNFMkXFGYzRQoa7OkO1lWdIhzwsasnTLKgMpdXUXWLubxKsqCysyu0ZCW1Ux/qls3meADDtFcUo65IkP0DMWayClPpIoqhM5UuMpNVuL4/WlXlfLX538zxckN/Bwe2REkVDCZTBfIFlclUgVTBYH9/lBv6O+pmu6yIkS3qZBQNzbDIKBrZok5fXK5J9bpT33oED/v6omQUjWReRTdNklfJtVpRmWuT6SJF1WBylblWLZXz2tiluWXs0txyYCBeV3WKWvpd7ZjZ25fglq0x5vI6k6lLtlNF5vI6B7bE2NuXqLW786xmXtuotg3TQjVMDNNqyPheL7SOJw2iVnuFN3PP8XJV/tHRNGOpAiGfyD17epat1m+FPdLLChCHR1JMF2BzyOZF1/Xw6oPb5ieUesX1VQe2ztueyBQJ+kT+w44OXnp9fw09XJpPveZmPvCtJzk5XSCllu4U799cUqWoN3v74gCcnckzlVMIegUODCTmjy+FUx9G/F6yqr7iGN09uAmAp0fTzBQgHIcXDnbPH6+GavO/mePlAy/bB/cf4+hYhsm8RkAUuOuaROl4nRlIlL6BGE8ppIoasigw2BuZP14LnPp2WzxINODl9FSOZNGm2+aquVYryjn17FiGsXSBsFdada5Vy6sObKWgGhwaSTGVLc0td+7smJ9z6kkt/a52zLz7JdcBJzg8miatQMxrc/fujkvH68tq5rWNZNtJIaQ/VvqwU8/xvR7w2PYGeEC0zhQKBU6cOMHg4CCRyMo+Ybs6xhtLx7iSC7NpHj98jNsO7mNbZ2zB7+oV12ReZSqrkleNK3SME6H6SiqV2Qg6xpU4xaLaGE2mszxx+Bi3HtxHb2xtd9DWu45xJc+MzTGZUumN++t6p3gpmqljnM4XOfzkMxy8+Ya20DEuzy2ZvErBsAhKAtGQv6FzSzN1jJ8dm+XRw8e44+A+V8e4RpRzSvaKpee8TQtFN+dzqpk6xtlslqGhIa677jqCwfqMMfeO8Sqp1V7hzdxzfFMszKbY1d9XSSvskb4lHmam08+W+NKPjdQ6rpUV1/Hg5QlM0U0yRYOI7G3IoumOa3q445q6m1mScMBHtQ+MOMWi2hh1hYP0xbx0hdc+iVZru5nj5Yb+Dm6o/xcSSyL7JOQ623Dq27DsIxYQCcuN/zarKxKkq4F7ayyYW7ovG2703FJLv6sdM9dtiqP2h7huU7w2DaiC1cxr6932YoUQAFEovZZzqhHju5lszFt+Li4biFZQ9XBxcVl/uHOLS61xc8pdGLu41J1WUPVwcXFZf7hzi0utcXPKXRi7tCGGaaGbdsM2JmkFVQ+XjUdlRblLa7Je5hY311qH9ZJTzWSjPy7q4rJiypW2c3mVybzBaKpIR8giFvAh1FmDsRVUPVw2Bk4V5Y3Ic5fG08y5xc211qTdr1fuwtilbUgXNaayKj6htPObAExlSzuB1bt6WxA8JEJ+IrJ3w6t6uKxvynkue0WCPhHdtBqW5y6Np5lzi5trrUm7X6/chbFLW1BZaesVQPB48HtFBIuGVm+3gqqHy/plJRXl7XSBaycaPbe4udb6tOv1ys1al7bArbR1aQfcPHdpFG6uubQq7sLYpS1wK21d2gE3z10ahZtrLq2KuzB2aQsqK21V3cSybdQ2q7RtVxqtQtJM3Iry5tJMdYZG23Zzrbm4SiD1ox0fH3FpU8oVtcm8imLY2NBWlbbtRjNVSJpJu1eUN4NmqjM007aba43HVQKpP+7C2KVtKFfaBr0CUyGJ/ngAv8/b7Ga51IlmqpA0k3avKG8GzVRnaKZtN9caj6sEUn/chbFL2yGJAl7RncBbmfWiQtJM2rWivNE0U51hvShDuLnWGNZLvFsdtwddXFxaDrdi3qVRNDPX3DxvL9x4NwZ3Yezi4tJyuBXzLo2imbnm5nl74ca7MbgLYxcXl5bDVSFxaRTNVGdwlSHaCzfejcF9LGiVGKblFhu4bCjaLWddFZLqabccWS2L+6mZ6gyuMkTzaMZ4ceNdf9yFcZW4UikuG412zVlXhWTltGuOVMty/dQsdQZXGaLxNHO8uPGuP25vVklZKsXj8RD0SXg8HqayKumi1uymubgsSbvnrKtCcnXaPUdWytX6SRIF/JLYlFxrpu12Yz2MFzfe9cO9Y1wFrlSKy0bDzVmXq+HmyMpw+8kF3DxoB9zoVYErleKy0XBz1uVquDmyMtx+cgE3D9oBd2FcBa5UistGw81Zl6vh5sjKcPvJBdw8aAfcRykqmMoWiEQijr8vS6VMZVUMy0L0CJi2hWHa9ET8q/r6RNEMdMvGK3iQfWsLh9O5nCpnHz83zchsgYHOILft6F6RDadzVevH+dkMqZxJPCyyvTO6Ihu14sjwLD8/l8HqmOXWHT0r+hunNlXb1lOTKWYzOp1RL9f2xldkYy1U5uz/efgMp2dy7OoK85v/Yfuqc3a5tjodX87vevOj4+M8/MwMM75xXnLDlobaTuUVirpNwOshHpLXdK5q+/yhUxOcn8qyvSfCC6/dtOy5yjkCl+58mRaKbl41R2o1LmrJsxeTPD6SQ+pJctNAV83au5J+quV8Xi2PnplieCbP1q4Qd1yzsnmtVpyeTpPMGiQiEru6Yw21vZr5fC1U5kFBMzBNEMXS7pprmVOdcMrN4+NJptMa3TEfe/sSazpXrdrUCNupgrLqv10pHtt27/sXCgVOnDjBd8+YXNMb59UHtxENL73nuGFYjCTzXEwqqJaFXxDYnJAZSISQpJUHu3yesWQRzbLxCR76E4Gqz7PcufpjQfK6cUXlbLpQ5CPfeYpnJ3PoFngFuL43zMd/ez9bu5ae1JyqcENeibF0YcV+pLIK33j0LI9dSKEaJn5J5PZtcV57x06iIX9dK31HZzN89LtP8fR4joJiEZQFbuwL87FX3cSWRYvzq/kd8XvJqvqK2zqTLvDFH5/iieE0imkiiyK3bo3xjnuupSMSqKvfPzp1lrd8/cQVx7/yxuu499qdVZ2r2v7QdYO/+snQkn53xYJr9m05To7P8M6/fYLzaQuD0l2A7TGBL7zhVvb0dV3tz9eEohg8fn6GYxczKKaFLArs2xzltu1dyHJ1C6Zq+3w6m+M93zzCc7MapgWiALs7fXz2tQfZ2Z1Y9lyZooFule58RQOSYw7WalzUkolkjk/df4wjoxmyBY1I0MeBLVE+8LJ99MRCNWlv2e/F/VTtPFhLzk+n+MC3nuTEdGF+Pr+uO8inXnMz27vjdbU9lynylZ89x+MXLo/v27bFeMtdu+mIBupqezXzea24PL6z837v2xxZ1fh2wmmMqZrOX/7wJIeG0yiWhSwI3LI1xrtfch098VBV56p2XK7mPLWyXSjoPDg0wZmJJHd261x33XUEg/W5hriPUlQgCR4eODHFdw5fcHxPVtXRTJu+uMyOrhB9cRnNtMmqelW2RpJ5hiazCKKHeMCHIHoYmswyksxX3W6ncx0fTy1ZOfuBbz3JodEckuChQxaRBA+HRnP8ybePOtpwqsI9Pp6qyo9vPHqWB05O4ZME+uNBfJLAAyen+MajZ+te6fvR7z7FL4czCIKHqLcke/PL4dLkWq3fI8l8VW394o9P8eOhWbyiwOaojFcU+PHQLF/88am6+/17SyyKlzu+HNX2x2d/cMLR73rzzr99gtNpCw/gAzzA6bTFO//2ibrbfvz8DL84O4MkCfRFA0iSwC/OzvD4+Zmqz1Vtn//h3x3i+LSG6IGwD0QPHJ/WePc3DjueK6vqJEL+0mKuI0h/IkAi5He8cNVqXNSST91/jJ+dSSIIHuL+0vj+2Zkkn7r/WM3aW5bKWtxPY+lCzebzavnAt57k6MUCogfifg+iB45eLPCBbz1Zd9tf+dlz/OjkDF5JoD8WwCsJ/OjkDF/52XN1t72a+bxWnJxMc3omR0/Uz+6eCD1RP6dncpycTNfMhlPOfur+Yzz43BxeSaAv4scrCTz43Byf/YHzfF6ra8xqzlMr2w8OTfDQ0DRiA55UcRfGFXRF/HRHZY6MpBhNZq/4fWU1akj2EvRJhGQvslckUzQwFj1z5ISiGYwli0QCXqKyD58kEJV9RAJexlMKimasuM1O5wr6Jc5M5/HYNrJXRBQ8yF6Rk+NpTkwXCPmgKxokFJTpigYJ+z0cm8zx+LnpZf2uPJcAnJ7OEfRLK/Lj/GyGxy6k6IrI9MWDhGQvffEgXRGZJ4aTHLuYusJGtX3rxOELMzw9niMqi3RHAwRlD93RAFFZ5JmJHIcvXLlgcfLbK3i4mCoiiZ4VtfXUZIonhtN0hnxsSgQJBfxsSgTpDPk4NJrmqeG5uvn9Vw+covyVkFDxA2Bf+v1KqbY/xlN5Dg2nSYS8V/h9eDTNqcnUmnxbjgeOXeR82sIHxEMSQV/p1QdcSFs8cOxi3Wyn8grHLmboDMt0R2Rkn0h3RKYzLHNyIksqv/KvAqvt86cupDg7Z+AXIRHxEwr6SUT8yCI8N6vxg6cvLptrK5GAcmqTJHq4mFRKjxHUIZeX45mxOY6MZogHJXrjAYJ+kd54gHhQ4smxDI+dm1nzOK6ksp9qOZ9Xy6NnpjgxXSDog85ogFBQpjMaIOiDk9MFHj0zVTfbp6fTPH4hTWfEf2k+99EXD9IZ8XN4JM3p6dotEhezmvm8VuSKGqenciSCfroiMhHZS1dEJhH0c3YmT64GHwKdxthIMseR0QyJkERvvDSn9saDdIS8HBlNc3w8ueJzVTsuV3OeWtmeyRY4OpKmKyLT4fBtfi1xF8aLiMpe8ppJOn9lwGpVjapbNpplI0sLv3KRJQnFtNCtlT/d4nQunyhSNCxYVAgwmymiWxCQxAXHA5KAZsHIbOEKG05+W4Bi2PjEhedy8iOVM1ENk6i8cJOFcp+n8kbdKn3H5opolkVw0deaQUlANS3G5opX/I2T3x7Bg2raiJ6VtXU2o6OYJhF5YT9FZJGCajKVVevm98nJzJp+X0m1/ZHOGyiWRcS/MDcjskhBN5nNVPctSzWcncxgUvpauRKvAMal39eLom6jmBYR/8I8j/i95HWTor7ymFbb5+PpHCbgX5hq+EQwLBhO5teca05tEj0CqmXhWXSXuRHV+pMpFdWyCPsWOh72iRQNk5m0suZx7EQt5/NqGZ7Jo1sgSwv7XJY8aFbp9/UimTVQTJPookcHorJEXjdJZuv3gWA183mtUE0bxbQJLdosKOTzUtAtVHPt8XYaY+mcgWpZhBY9vx72ixQMi+n0lYvyWq1bVnOeWtnOq/alXGvMBk3uwngRGUUn5BOJha7smlpVo3oFDz7Bg2IsnDgUw0AWBbxVPHfjdC7NNAlIAixKvM5oAK8ARcNccLxoWPgEGOi88pkdJ78FLk3A5sJzOfkRD4v4JZGMsnBBVO7zeEiqW6Vvf0cAnyBQMBaev2BY+EWB/o4rn4dz8tu2bPyiB9NeWVs7o15kUSSrLOynrGIS9Iv0RPx183tP7/LP2l3t95VU2x+xkIQsCGTVhbmZVUyCXpHOaP0muZ29UURAX/T5VrdKzxrvrMLvagl4PciicMXjVVlVJ+QVCXhXHtNq+7wvFkYE1IWphmaCJMDWRGjNuebUJtMu1VzYixaCjajW74378QsCOW2h4znNJCCJdMXkNY9jJ2o5n1fL1q4QXqF0g2KhbRufUPp9vUhEJGRRJKMs9DujGIS8IolI/YoPVzOf1wq/6EEWPeS1heM7r+kEvQL+GnzX7zTGYmEJvyCQX/QtRE41CUoC3bErt4Wu1bplNeeple2Q33Mp1+p3M6USd2FcwVRaYTqjcGAgzpbEleoU5WpURTdRdBPTsuf/HQ1IK660lH0S/YkA2aJORtHQDIuMopEt6vTF5aqqmZ3OVVANrukOYXs8C9q6py/Gdd1B8hrMZArkCwozmQI51WZfb3hJdQonvy1gV3eYgmqsyI/tnVFu3xZnJqswniqQV3TGUwVmsgq3bk2wb3N8zX3rxMFtXdzYFyajmEyn8uQUm+lUnoxicsOmMAe3XVmM5eS3btlsjgcwTHtFbb22N86tW2PM5jUmkgXyRZWJZIHZvMYtW2LctLWjbn6//UXXUp56rIofKD1z+/YXXbvic1XbH33xELdsjZHM61ycK5AtqFycK/l9cEts1eoUhmmhGuayX8O9aN9mtscENCCVNyhopVcN2BYTeNG+zauyvRLiIZl9m6PM5hSmswqKZjKdVZjNKezZFKlKnaLaPr9pW5ydHRKqCTNplUxOZSatopilAryX3Lh5zbnm1CbDtNmckNGtlY2LWnJDfwcHtkRJFQwmU0UKqslkqkiqYHBzf5Tbd3SteRw7Ucv5vFruuKaH67qDFLTSt4H5gsJspkhBgz3dwbqqU+zqjnHbthizWfXSfK4xniowm1U5OBCruTpF5bhfMJ+nCxQUm+l0Ydn5vFaEAz529YRJFlTm8iqqbjGXV0kWVHZ2hQgHrlycVovTGBtIhDmwJUoybzCRypMtqEyk8szldQ5siS2pTlGrdctqzlMr212RIPsHYsxkFWay9VelcBfGFSiGyZ07O3jVga2O74kFfPRE/GBDQTPAhp6In1iVg2EgEWKwNwIWpIoaWDDYG2EgUf0nfKdz7e2LL9nWT73mZm7ZEsa0bOaUUrLesqWkSlGt33v74lX58do7dvKiPT1YlsVYqoBlWbxoTw+vvWNnzfrWiY+88gau7wlSNGySKhQNm+t7gnzklTdU7fdAIlRVW99xz7XcM9iJaVtczCiYtsU9g528455r6+73l994XVXHl6Pa/vijF+/hzh1xirrOhWSBoq5z5444b/+VwaptW5ZNMq8ymiwwMldgNFkgmVexHL6q/tzv3sL2CJiARul1e6R0vN7ctr2L5+3swrZsxjNFbMvmeTu7uG179Rfsavv8v//nm+gJgGJDxii99gTg4//pxprlWq3GRS35wMv2cdc1CWzbJqWCbdvcdU2CD7xsX93bW8v5vFo+9Zqb2b85iGVDSrWxbNi/uaRKUW/ectdu7t3TVZrP00Usy+LePV285a7dNbPhNO4/8sobOLA5jGZaJFXQTIsDm8N89DdvrJltJ/b2xTkwUMq1qZyCbdscGEiwty9eMxtOOfu+X9vLHdtjKJrJaLqIopncsT3GH714T9XnqtW4X+48tbL9wt29HNwaJ7/467A64Mq1cVmuTQ/3kIjF6In4SYSWf8C7Vpp8ro5x43SMz01nGZrMMjab48Tpc1y3awf9nWEGeyPs6HbWr16uTetdxxggmVeZyqp874nznJ7Msas3zG/eun1Fee7ESvujbHsqpTBbVOgMyPTE5VXZLp9L9opXaMguda7y+x87Nc2RoZMcGNzD7dd2r8nvammGjvH9R0d4aGiatFpAUwR8skXMH+SFg928bP/AsueqV5sayVMjM/zi8DGed3BfTXWMV4KrY1x7HWOnce8TPWimzcnxJEePPcf+fbvZ05do6PjOFTVUs/RITi3uFC+F05w6ksyRzhnEwhIDiXBD1y3N0DEu+53LZSE9UVe5NndhzOWFcVf/dvDKYEN/IuDud95CKJrBofNzCKKHkFdkaOgUg4PXktdNsODAtkTDL2SNwDAtRpMFPJ5SNXAZRTfrnue1tF3tuSrf7xXg2LFj7Nu3r/TMcQuP75lsgS/99CxeSaA3dvk5y8l06a71m+/aQVekvvrRzcY0TY4ePcr+/fsRFxUGu2wsnMZ9XtEZTxfZFA8QkIS2Gd/NnM+bSaXf6AozY+ddHeNG4u533po0s3K8mdSqKrjZtqs9VzP9biZO1dtR2UtON8irrem3S2tSK2WgVqFd5zUnv+uFuzBehLvfeWvSzMrxZlKrquBm2672XM30u5k4VW9nFJ2wVyLkb02/XVqTWikDtQrtOq85+V0v3IVxBZrRmArqRrOSKv5Wp7JyPFXQUAyTVKExlePNpLIqOK/oFDSDvKI3JM9rVZG8mnNVvl/VTSzbRm2QQkIzqazeHp3Nk8yXXmeyCtf3Rxv2GIWiGWQVva6bW6xHmjnXtmKfr0SVJVPQyGkmmYLW8PHd6HjXck7dSFT6rRn1L75rzdXAKrFpXAV1I6jVHuWtQn8sSLqg8dxkjpG0iZxR2d0bpj/W2s9cRvxeUgWNi6nifKHI5njgig0o6kF5LGWKBgXNwCsIqx5j1Z6rfDyZV1EMu+XGtxMv2NXDZLrI4ZEUhWmToE/k4ECcF+yqf0GWYViMJPOMJYtolo1P8JS2T06EkKTWvGhDc+faVu9zp3EfEEVOTqZ5bjLHc9MaRjjD7t4woY76K4E0M961nFM3EmX/ppP1l2tzF8YV9ET8xBtUzdoIynuUy16RoE9ENy2msipAw6p21xN53SDg93JDfxRv1see/igeUSSvG/h8rVukk1V1NNNmUzyA6BEwbQvNtMmqOgmpvnkgCB4SIT8R2bvmauhqz1V+f9ArMBWS6I8H8Psas3NSM5ktavR3htncEcaybATBg8dTOh6t83aqI8k8Q5NZIgEvcb8XxTAYmswCXFX5ZSPTzLm21fvcadyfm84ymVXpjfopRiV6o34msyqxYKHufjcz3rWcUzcSZb89hp/sZH1tuQvjCkShdZJr8R7lAKJQes0UDSKyty0GU5nK/vD6RUJ+kWjQh261dn8slQcgouhmQ/2WRKFmk02155JEAa/YHhcPRTMYSxaJBLxE5ct3kDKKxnhKoS8WqNtjQ0vZ9kml13rbbibNnGvbqc8rx32l36XdJAViQR+Sbtbd7/Vyba3lnLqRaMQ6rfWvFG1Ku1avOtGu/dGufrcrzVRfcZVfGj/G2rXPm+m3O6e2Pu7CuEVp1+pVJ9q1P9rV73almeorrvJL48dYu/Z5M/1259TWx10YtygrqV5tJ7WKyv7IFDTyanOqmFfDWuLUrlXMlRimhW7abZHnleorGUVDMywySmPUV5ppuxJFMyhoVsPUGZo5xhao7eRVcmrptRl93khFjEq/ZzIKyaLBTEZpiN/rZU5tp+t3o2nHR1TaBqfq1YjfSzKvtp1aRcgrUVRzDE3lOD2joV3MMNjTmCrm1VCryud2rWIu999cXmUybzCaKtIRslo+zwcSpXweTymkihqyKDDYG5k/3qq2y+oMw3MFTs2oWMMptnYEG6LO0MwxVlbbOTOTm99+/JquxqjtNFMRozckc9xIcWgkzfC4yjRz7B+I0bvGrddXQjPj7apN1R93YdzCOFWvVu49305qFWPpQqmKOeJHjYn0RhpXxbwaalX53K5VzOX+8wkgSx4EaIs8lySBHd0R+mIBdMvGK3gaduewmbbL6gwhn0jE70H00DB1hmaOsXm1nS0JsD3gsbFtGqK200xFjKHpDJM5lWu6QwQ1L33dISZzKkPTGfZv66yr7WbG21Wbqj/uwrgNqKxeXS8VtY1mcRXzlCQSD/rIN6CKeTXUI07tVMW8QIVEAMHjwe8VEVpchaQS2SdR/3tn68P24vEtCQIR2YsgCg0d340eY0urztAQ1ZlmKmLkihqnp3Ikgn5iAS+5WYmeWIB0UefsTJ5dPRHCDbh7ux7i3Q7X70bj9mCb0a4VtRutertd41Qr3P5rLzba+K4V7aqIoZo2imkTWqRLHvJ5KegWqunG22X1uAvjNqNdK2o3WvV2u8apVrj9115stPFdK9pVEcMvepBFD3lNX3A8r+kEvQJ+0Y23y+pxF8YVmFbrV3eul4raRlNZxZxVdAzLIqvoDa/eXintGqdaUdl/qm5i2TZqm/VfO1Wtb7TxXSvWiyLGalRI1pKf4YCPXT1hkgWVmWyRgmYyky2SLKjs7Ao15DEKaPwYc68LjaE1Z4tVMplVsSW15as721WloFwZPzpXIKuWClQaVTG/Gto1TrWi3E/JvIpi2NjQFv3XrlXrG21814pmzhOrUSGpVX7u6Y2RKeocu5hhNGPgjens2xxlT29szX5djWaOMfe6UH/chXEF7VK13q4qBeWK+Z6wD+b8HNgaJxRYv3Fu1zjVinL/Bb0CUyGJ/ngA/6JnEluRdq1a32jju1Y0c55YjQpJrfKzaJr0xoN0hf10WTPsG+xGlESKpolc56VNM8eYe12oP+7CuAKfJIJXbJvqznZSKahE9kkEfcKG+Xq1XeNUKyRRwCu2x8XDrVrfeOO7VjRznlipCkmt8nOB6oxfJC5LxEM+9AaozqyXMeZeF+pHa8+Qq8Ct7nRxcdmouFXrLuuZWuVnM/PcHWOtj7swXoRb3eni4rJRcavWXdYztcrPZua5O8ZaH3dhXIFmuNWdLi4uGxe3at1lPVOr/Gym6ow7xlof9xGVCjTDZnOiPtWdhmkt+aB8Kq/M728fb8Ae7/XCyb9avb+W5BSNdNEkp2jEQoE1nWs9+u1kQ9GMhm/Tux6oZbzXI4vjXZ6/jo1mmMoX6AkF2bclumGr1qsdM4pmUNAsFM0gFKjvlsiLWc34rtWcMJMtkFdtQn4PXZHgqs+zGqqZW1arquCU55PpIinFRDUsemOBhuR52cZcXqOgGfjExitDNHM+b/VrSet5tAam8yrBkEbE762Z5IqTrIvfI3BoeJZjFzMopoUsCuzbHOW27V3I8sYJS7WyNc2UudE0k+PjKYYmc5weKzLjm2KwN8zevjg+X3UX0PXot5ONkFdiLF1gLFlEs2x8gof+RICBRAhJat27G7WM93rEKd6mYfK1h0/z+IU0Rd0k4BW5bVuMt9y1m47oxvlgUO2YMQyLkWSe4bkCp2ZUrOEUWzuCDcnz1YzvWs0JhYLOg0MTHB1Jo5gmsiiyfyDG3YObCAbrq8JS7vNq5pZqVRWWm9dSBY2L6SKzBZOL6SJ+r1jT6/fVsAHbLr02itX0eSvYbiSt40kN8AoehiazjCTzNTtnWdbF4/EQ9El4PB6msioPDk3wi7MzSJJAXzSAJAn84uwMj5+fqZntRuDkX7qo1eT9teT4eIojI0lEARIBD6IAR0aSHB9PVX2u9ei3k43S4jCLIHqIB3wIYu3zfD1Sy3ivR5zi/cUfn+JHJ2fwSgJbEgG8ksCPTs7wlZ891+wmV0W1Y2YkmWdoMovogYjfg+ihYXm+mvFdqznhwaEJHhqaxisJ9MeCeCWBh4ameXBoYq1uXZVyn69mbpFEAb8kXvUu+dXmNZ8okAgI+ESh4fH2igLxoA+vKDTsOraWPt/IthuJuzCuIOSTiAS8jKcUFM24+h9chcWyLqLgQfaK6LrJ0dE08aCf7oiM7BPpjsh0hmVOTmRJ5ZUaeFN/nPyTL0neLd4NqNr315JcUeP0VI5E0E8i5McriiRCfhJBP2dn8uSqmNDWo99ONjy2zenpHEG/RFT24ZMEorKvpnm+HqllvNcjTvGezhR4YjhNIuynLx4kJPvoiwfpjPg5PJLm9HS62U1fEdWOGUUzGEsWiQS8JbksQSAiexuS56sZ37WaE2ayBY6OpOmKyPTGAgT8Er2xAF0RmWfHMsxkC7V2d57KPq/X3OLUTwLMz2sbId61ohF9vh5tNxp3YbwIWZJQTAvdWvuXI06yLrploxgmoUWbDUT8XvK6SVHfGHIv1crWNFPmRjVtFNO+os9DPi8F3UI1V257PfrtZAOPB8Ww8YkLHx2oZZ6vR2oZ7/WIU7wziolimkT9C49HZYm8bpLMboyLV7VjRrdsNMtGlhY+htaIPF/N+K7VnJBX7VK85YV5HpW95HSDvFo/vxvR5079ZEHT5rVmXseamefNtN1o3IXxIhTDQBYFvDV4RslJ1sUreJAlkbymLzieVXVCXpGAd2PIvVQrW9NMmRu/6EEWPVf0eV7TCXoF/OLKba9Hv51sYNvIkgfNNBccrmWer0dqGe/1iFO8o7KILIpk1IXHM4pByCuSiGyM+oVqx4xX8OATPCjGwoV/I/J8NeO7VnNCyO8pxVtZmOcZRSfslQj56+d3I/rcqZ8EaNq81szrWDPzvJm2G427MK4grxlkizp9cbkmlZZOsi5er8j+LTFSBZXprIKimUxnFWZzCns2RTaMOkW1sjXNlLkJB3zs6gmTLKgk8yq6aZLMqyQLKju7QoSrqCZei9/pvEq6oJHOqzX126lNtsfDru4wBdVgNquQLKjMZpWa5vl6pJbxXo84xbs7GuTWrTGSOZWxZJ5sofQ6m1U5OBBjV3es2U1fEdWOMdkn0Z8IkC3qpPIqRd0ilVcbkuermddqNRd2RYLsH4gxk1W4mCyQKepcTBaYySpc3x+tqzpFZZ9nFA3NsMgoWkOuoRbMz2vNjHde0SloBnlFb8h1rBF9vh5tN5rW8aQG2DYM9kYYSIRqdk4naZqB6CYispeTE1nGM0VCXpHn7ezitu1dNbPdCKqV3lmtVE8t2NsXB+D0VI5k0abbhgMDifnj1VCtHyGvRFHNcXo6h2KU7uLu6g4T6qh/rvmjATLFWY5PpCnqFgGvwN5NUfoiG0ehYDXUMt7rEad4v/1XBoEhDo2mmc2rBLwid+5M8KYX7Gpug6uk2jHWHwuSLmg8N5VjNGMQyGns7gnTH6u/dNlq5rVazYUv3N1LTtF5cjTNeLpI0CdycGucF+7uXb1DK6R8rRxPKaSKGrIoNOwaGogHgXRT4h3xe0uKGKkiqmnjFz1sjgeI+OurAgKN6fP1aLuReGzb3b+wUChw4sQJtu+4hs6OeF1suDrGa3t/LUnnixx+8hkO3nxDw3SMz01nGZrMEvRL+EQRzTQpqAaDvRF2dEfW1IartalsW/aKSB4Bw7ZQdLMuttcjtYz3emRxvJN5lamsylSmQLZgEgmK9ESD9ERKhYgbjZWOsbLfWBbHT55g757r4NIiqlF+N0PHuOy3qpkUDZOAJOL3iQ31uxG6tk553ox4l21LogfRI2DaFoZpt1yfr0fb2WyWoaEhrrvuOoLB+nwIcu8YV+Dz1k/bVBKFJTs7HpKJ181q43Dyr1bvryVh2UcsIBKW136XeiV+LK7mBQghIQoexlMKfbFATSeXyjYtZRsgo2h1sb0eqWW81yOV8a6smB/cFJ9/j6KbZIpGqYJ/g+3MtZIxVum3VxAJ+0QiAS+6RUP9Xs28tpa5sNLvePByfjc63rJPot63dZzyvNHxXqxKUUJsyT5fj7YbwcaaIV1cNiBuJbFLo2hmxXwzcf12/YbG+N2ufd5OuAtjF5c641YSuzSKZlbMNxPXb9dvaIzf7drn7URrf4daJaZVP2Hu9Ugzn/NtJ8rVvEOT2dL/JQnFKCmgDPZGGlJJPDSZRTMsJEHAsCzUS88Yt/pjFFDKc920MUwLUdz4W0EvR7lifiqrol2q3hco6b72RPwtO84r/daxUQ2Lgmpg4mkbv1XDBNsDHhvbbmy8G/3MaaXflgCWbaPqJppVf78rbcOlO8VmqW6jlXOtklZfO7T+VbEKJrMqtqRWvVf9RsNp7/lW97uZNLOaty8SYGQ2z/GLmfninL19kZZXpSjn+VxeZTJvMJoq0hGyWj7PG6GAsh4pKwWMzBW5mDWIphUGOoINUQpoJvPxnsqjmBayKLCrJ9SQeBuGxUgyz1iyiGbZ+AQP/YkAA4kQklTfBVNZrSKZV1EMGxsapnDUTHWlZtIuawd3YVyBAPOfAjdi9fZKKe/zLntFgj4R3bTawu9mIkkCO7oj9MUCDa/mHc8WKRoW1/VFEQUPpmVTNEzGs0V2yK2rSlHOc59Q2gygXcb3WLrAZFalNxaYV0CZzKrEgoWWViHJqjqaadMX9ZOJSPRF/WimTVbVSUitH+9NMRlJEjAMq2HxHknmGZrMEgl4ifu9KIYx/81YvW0LgodEyE/QKzAVkuiPB/D7GvMhqGw7Intb+s7pYtpl7eAujCvwSSJc2u98I1Zvr4SlKmpFofTayn6vFxpdzduuqhQLq9ZB8Hjwe0WEBqsUNJpGK6CsFxarFPglgZDceFWKRrPk+PaDINY/3kvZ9kml10bmmiQKeMXmLEybqa7UaNpp7dAaXtSQVq8sdStq24t2VaVo1zx34+3GG1zFG5fa005jzF0YL6LVK0vditr2ol1VKdo1z914u/EGV/HGpfa00xhzF8YVaEb1e9VvNJq5z7tL42mn/e0rqcxzVTfnq9ZbPc8r4z2ZKTKdVZjMFBseb0UzyCo6imZc/c01oDLe09kiM3md6WyxreLd6PG9XuaWStWZdiKVVxhPFUnllYbYqxxjmYJGqqCRKWgtOcZa86q4ShpZ1dpMmrnPu0vjaZf97RfTzKr1ZtIbkjlupDh8PknRMAhIEjdtidLbgC3nm6lS4PcInJ5I8+RImpHxIme0CW4eiDEQ3VRXu82mmeO7mbbbVXVGUQwePz/DsYuZeRWSfZuj3La9C1mu75KuXRRv3IVxBT0RP/EWqqx0oly9vSkemN/nvR2qt9uVZipiNJNmVq03k6HpDJM5lV09YfySgGpYTOZUhqYz7N/WWVfbzVQpODQ8yzMX03SEvHhiAomQl2cuponIXu7a07qL42aO72bablfVmcfPz/CLszN0hmX6Qn6yqs4vzs4A1D3P20XxpvWvjlUgCq3zVYAT62Wfd5fG0+r72zvRzKr1RpMrapyeypEI+umoWBx48wJnZ/Ls6okQrtMd82aqFKTyCscuZugMy3SEfKgpL72xAF5J5ORElhsH4sQbcMe8mTRzfDfadruqzlTmeXek1OOyr3Qdr3eet5PiTetljsuytFNlqYtLu6GaNoppE1p0Zzzk81LQLVSzNVUKirqNYlpXPA4W8XvJ6yZF3Z3XWol2vY41M8/bSYXEXRi3Ge1UWeri0m74RQ+y6CGv6QuO5zWdoFfAL7amSkHA60EWBbLqQr+zqk7IKxLwuvNaK9Gu17Fm5nk7qZC0xn3vGqHp5orfu1H3Cnf3eV9YxSyK4tX/oEXYqDm7Vtop3uGAj109YY6MJClqBgIeLGwUw+TAQKJuj1HAZZWC8jPFsiShGAbZos5gb6SuX7PGQzL7Nkf5xdkZ8gWNmbyCZzqHYlk8b2dXwx6jqOUYq/ZcimY0rYZgNX6vpb2V17G0qjNT0JnJKPj93oZexxo9p1bm+VxWBSxAwPbYdc/zyvFtWvb8M8YF1aj7+K6ksOhDfz1wF8YVHJ/IssUUl62gboW9wtt9n/d2q2JuhZxdDe0a752dYY6cn+Xn5+coaiYBn8gtA3F2dobrbruZKgU39sd5eniOxy+kmE7qdCspbtsW58b+eN1t13KMVXuuZiqBrMbvWrW3rEJydCTN8HiRs+o4+xukQtLMOXWwN8z3Dl3gyFgG1bTwiwIH+qMM3rG9rnYB+mNB0gWNM9N5ioZFQBK4pjtEfyxYd9uaZnJ8PMX5yRQDdb6/4S6MKxA8XLWCuhX2Cm/3fd7brYq5FXJ2NbRrvI+OJpnKa+zdFMPnFdB0i6m8xtHRZN2r1pupUvD0WIqcYXFgW5wpuUBPb5ycYfH0WKruftdyjFV7rmYqgazG71q1t1KFxI6KdDRQhaSZc+p3nhjm7FyRrYkgQb9IQTU5O1fkO08M884X762r7bxuEPB7uXFL/NK96tI967xu4PPVd7V6fDzFkZEk3XL9b2q4C+MKQj4JD5JjhWWr7RXervu8t1MVc6vl7Epp13gvVbUOMJ1VGqrO0GiVgsWqFHbOz0BnmLm8Vne/aznGqj1XM5VAVuN3rdq7ON5KSqInFkBqgApJM+fU87MZHruQoisi0xe/fJd2PFXg8EiK87MZtndG62J7aUUrGqJoVam2E/HbUOc9TVrvyrBGlquwbNdK2FagXWPn+t1efrerOkMz/a5lrlV7rmYqBazG71q1t1XiXS2pnIlqmETlhX5HZS95zSSVW3mdVLU0028ntZ160dIL45/97Ge86lWv4qabbuLuu+/mS1/6EvZVgrdchWW7VsK2Au0aO9fv9vK7XdUZmul3LXOt2nM1UylgNX7Xqr2tEu9qiYdF/JJIRlnod0bRCflE4uH6Pc7QTL+d1HbqRct+k37kyBH+4A/+gJe+9KW8613v4vDhw3z2s5/Fsize/va3L/k3WUWnYNuOFZZrUXRwql5tplLARqtiXguVsSuaJnm1tN+7RxQbWsXc6D6v9Pv8dIZU0SAekNgUD617v51yxOlci99f9ns6rzKWVknMFYiE/Gvyu1o/ckVtftv1xYoQtZoTKttUWbV++PwE2bxNJORhe1d8RVXr1dquNkarYSU2Kv1+emSGi+MZNhcv0p8IL/C7Hu2tzLWsoqMbNl7Jg1cUFuTaSvq28lymaeERPNiWjW7Z8+eqPE+lUsAPnhxlLK3QH5N53p7eBdexWs23TmNsKqOQVw1CfolowFkZorK9lmkjSQKGYZHXrq5sUDmWKuN9fGyOi+MZhtUJemPBuqgzLM6Pst/PTaVJ5wxiYYmBRLjqeK+EyvNs74xy+7Y4D5ycYi6Tx/KAYIPlEXnRnp66PUYBC3OzoGhYCAhYCGu8hq6knyrVdkzVIlJnnYCWXRh/8YtfZM+ePXz6058G4K677sIwDP7mb/6GN77xjcjylQNnLq+zrXf5CstqFR2cqlcjfi9ZVW9KVetGq2KuFeV93oemcpye0dAuZhjsacw+783sc103+NuHhjg0nEaxLGRB4JatMd79kuuA+haKrMZvpxwJeSXG0oUrztUfC5LXjSve77FsHjk1weMXUkzO5vnl7Dlu2xbn1Qe31d2PcgX16akcimkjix529YTZ2xdHkoSazAlObbJEha//7AJqxXv9pHn+tc4XzWrHZbUxWk2eV2tDlk2+/egFZi/dVDo8M0und5YXX99Z9/ZeVkjIoJgGsiixfyDKQHRT1X0b8XtJFTQuporzC8HN8QAhr0Qyr15xngupWd75jWNU3kv7+0MX+epbrmdbZ7gm862TD7Zp8cDTozx2IYVqmPglkdu3xXntHTsdzzWvbDCTo6jbBLwerukKO153ncaSLJt897ELTJUTfWqaHj/86g1dK/brajiNMa9g89c/OsGR0QyqZeEXBA5sifKBl+3DCvjq2ufP25Pg7x45d9lvoMcPz7+uo2Z+OxEQRSZTBY5fzFA0LQKiwN7NUbbFq1elqHZc7OqKcGo8zXMTGXYM1MIbZ1ryUQpN03jsscd48YtfvOD4S17yEgqFAocOHVry73b1hAj4veR1Y8nfw2VFh/5EgIGOIP2JAImQ3zHhy9WrHo+HoE/C4/EwlVUZSeaXPJ4uaqt3fIWUq4IF0UM84EMQPQxNZhlJ5utu26k/GuH3/D7vET8DMZHeiJ/JrMpYulB3283s87/6yRCPnEsRlL1sSwQIyl4eOZfir34yVHfbq/HbKUeOj6eWPNfx8dSS7//ukWF+eW6OqCwxEBWIyhK/PDfHvz07Vnc/yhXUoijQE5YRRYEjI0mOj6dqNic4tekdXz22YFEMoALv/NozVfe5k+1qY7SaPK/Wxh/9r6PM6qWLWvlnVoc//t9H6t7eskJCV8TH7p4oXREfz1xMc2h4tuq+zao6mmmzKR5gZ1eYTfEAmmkzli4seZ7f/+rCRTGADvz+V56t2XzrdJ6//+U5Hjk7RyzgY0d3mFjAxyNn5/jukWHHc5WVDW7YkuC27Z3csCWx7HXXaSz90f86ypQKIqU7fCIwpcJ7v/FkVb4th9MY+8S/PMuj59ME/SJbYgGCfpFHz6f5yx+erHuf//l3j5PWICJC3F96TWvwyX86UTO/nTg5meb0TI6uqMxgT5SuqMzpmRwnJ9NVn6vafjo9kyVvWFy7qb5KK9Cid4xHRkbQdZ3t27cvOL5tW+lO0fnz53n+858/f9yySs/MeDHwGArTSQWP4UcU1va5wbRKEi6ls4jo5dlLMxjLaXSFfIA0f1wwzJrZdkLTTUans4RFCOIBwyAI2KLJ2HSKqGji89bnOSWn/mi03wHJJuqXiEgmkm3V3e9m9vnwXI7R2SzXdfvpjF6+Ozyb8TCWzHLswjhbO+qjb7sav51yxFB0Lkzl6Qx5F5zL9OhcmCqyuydM5VjKphXOTKbYEffSFfEz6/HT2eFnToZzkynOT0zRGQrUxY+CpnN+MkW37ClVUKPh84NkeTg/mcJjagR9ImuZE5zadOTCFD3x0rTur+ha9VJNznd/OcSL9vWtqM+dbC8Xo/PTeTqD3jXnuZMNUzW4MJmjIyQtsPHsxRlkv8QuP4T9IqZpIooiOdXEBv7t0BluvaZrReeqtr1ZRePsRJL+sEg8VBKwSvgFgoLImYk5wpJJRPZW3bcSJdsSYDjkx0+fHmPzpXhLFfdmjEulNN9+eIi7r+9dke1qY5FMFzg7lWZXh4+e2KVvTCMiM7LN+ekUZy5O0hMJOp7LJ13qWxs0hzY5jaXz4xlkv8RuP0QCIoZhIkki2WIp0X/01Dlu37m2O8dOY+xiLstMrsCeLi9dsfIc4mMuq3AxlePI2YtsTYSoR5+fGp5D0Q2u7ZKIV8jCpQoqBU3jFyeHuaE/sSa/nVgQiwCAQUgCPx6Gp1L0hT0EV1gcV+2cU2k75gWMy+u2euCxr1aNtgF58sknec1rXsPXv/51nve8580fNwyDffv28e53v5u3ve1t88dnZ2c5f/58E1rq4uLi4uLi4uJSDdu3b6ezs7Mu527JO8blTxIehypJYdEntlgsxvbt2/H7/Vf8zsXFxcXFxcXFpflYloWqqsRisbrZaMmFcTRaKjLJ5XILjufzpefGwuGFXx1LklS3Tx4uLi4uLi4uLi61YfEarta05O3RrVu3IooiFy5cWHC8/P9du3Y1o1kuLi4uLi4uLi7rmJZcGPv9fm655RYeeOCBBRt6/OAHPyAajXLjjTc2sXUuLi4uLi4uLi7rkZZcGAO8/e1v56mnnuKP/uiPeOihh/iLv/gLvvrVr/L7v//7S2oYu7i4uLi4uLi4tDctuzC+4447+PznP8+5c+d4xzvewfe//33e//7385a3vOWK965m62iX9Y9t2/zDP/wDr3jFK7j55pu55557+PjHP77g2fOzZ8/y1re+lYMHD3L77bfz4Q9/mEwm08RWu9SCd77znfzKr/zKgmNurFuPo0eP8rrXvY79+/fzvOc9jw984APMzs7O/96NeWvxj//4j7zsZS9j//79vPSlL+Wb3/zmgmu1G++Nz/j4OLfccguPPfbYguMriW0ul+O//tf/yp133sn+/ft54xvfyOnTp6tuQ0sW35V50YtexIte9KJl37OaraNdNgZf+cpX+OxnP8ub3/xm7rjjDi5cuMBf/uVf8txzz/H1r3+dbDbLG97wBnp6erjvvvuYnZ3l05/+NBMTE3zta19rdvNdVsk///M/88ADD9Df3z9/LJPJuLFuMZ599lle//rXc8cdd/CFL3yBqakpPvOZz/COd7yDb33rW27MW4xvf/vbfOQjH+F1r3sd99xzD48//jh/9md/hqIovPnNb3bj3QKMjY3x5je/mWw2u+D4SmP7nve8h6effpr3ve99hMNhvvCFL/C7v/u73H///cTj8ZU3xG5z3vSmN9m/9Vu/teDYfffdZ+/fv98uFotNapXLWjFN077lllvs//bf/tuC4//6r/9qDw4O2k8//bT913/91/ZNN91kz87Ozv/+pz/9qT04OGg/8cQTjW6ySw2YmJiwb731Vvuuu+6y77777vnjbqxbj9e97nX27/zO79iGYcwf+8EPfmDfdddd9vDwsBvzFuM//af/ZL/mNa9ZcOxd73rX/Dh3471xMU3T/s53vmPfdttt9m233WYPDg7av/zlL+d/v5LYHjlyxB4cHLR/+tOfzr9ndnbW3r9/v/3FL36xqva07KMUK2G1W0e7rH9yuRy//uu/zstf/vIFx3fs2AGUdkf8+c9/zsGDB+nouLzH/Ate8AJCoRA/+9nPGtpel9rwp3/6p9x5553ccccdC467sW4tkskkjz/+OP/5P/9nRPHy7nQvfvGLeeihhxgYGHBj3mJomkYksnA74EQiQSqVAtwxvpE5deoU/+2//Tde+cpXct99913x+5XE9uc//znBYJA777xz/j0dHR3ceuutVce/rRfGK9k62mVjEo1G+chHPsLBgwcXHP/hD38IwO7duzlz5sz8QrmMIAhs2bLFjf0G5Nvf/jbHjh3jIx/5yBW/c2PdWpw6dQrbtuns7OQ973kPN998MzfffDPvfe97SafTgBvzVuN3f/d3eeSRR/jnf/5nstksDz/8MN/73vf4jd/4DcCN90amr6+PBx54gA996ENLiiOsJLZnzpxhy5YtSNLCJ4S3bt3KuXPnqmpPSz9jfDXKD24vFosOhULAlRuEuGxsjhw5wpe//GXuvfdedu/eTSaTmY91JaFQyI39BmNsbIxPfvKTfPKTn1xwV6GMG+vWYm5uDoAPf/jD3HXXXfzP//k/OX/+PJ/5zGcYGRnh//yf/+PGvMV46Utfyi9/+Uve//73zx97/vOfz4c//GHAHeMbmas9/7uS2Gaz2SU3/giFQvObu62Utl4YV7t1tMvG5dChQ7ztbW9j69atfPzjH58/vlTsbdt2zAmX9Ydt23z4wx/mhS98IS95yUsc3+fGunXQdR2Affv2zY/nO+64g2g0yh//8R/zyCOPAG7MW4m3v/3tHDlyhPe9733ceOONnDp1ii984Qv80R/9EV/84hcBN96tzNVia1mWY5yrjX9bL4yr3TraZWNy//3388EPfpAdO3bw1a9+df7TaTgcXvJOQqFQYNOmTQ1upctq+eY3v8mpU6f4/ve/j2EYAPMSToZhIAiCG+sWo3z36O67715w/AUveAEAJ06ccGPeQhw5coSf//zn/Pmf/zm//du/DcBtt93GwMAAv//7v89Pf/pTN94tzEpiG4lEFkg1lsnn81c8m3412vqWqLt1dOvzla98hfe85z3s37+fb37zm3R3d8//bseOHQwPDy94v2VZjI6OurHfQPzgBz8gmUzy/Oc/n3379rFv3z7+6Z/+ibGxMfbt28cXv/hFN9YtRrkuRNO0BcfLH4xkWXZj3kJcvHgRgAMHDiw4fuuttwLw3HPPufFuYVYS2x07djA6Ojr/JECZ4eFhrrnmmqrstfXC2N06urX51re+xac//Wl+9Vd/la9+9atXfGq88847eeKJJ+afVwR4+OGHyefzCypbXdY3H/vYx/jOd76z4Ofuu++mu7ub73znO/zO7/yOG+sW45prrqG/v5/7779/wfEf//jHANxyyy1uzFuInTt3AlyhFHXkyBEAtmzZ4sa7hVlJbJ///OeTz+d5+OGH598zNzfHE088wfOf//yq7Hlsu723eHv00Ud54xvfyItf/GJ+67d+iyeffJK//uu/5r3vfe+Su+S5bAymp6e599576ezs5L777luyUhXg137t1+jt7eWd73wnqVSKT3/609x00018+ctfbkazXWrEBz/4QR5//HF+8pOfAKUJ0o11a/Hv//7vvOtd7+JXf/VX+e3f/m3Onj3LZz7zGV7wghfwuc99zo15i/GHf/iHPPzww7z97W/npptu4vTp03z+859n8+bN/MM//APZbNaNdwvw2GOP8frXv56/+7u/4/bbbwdWPn+/7nWv49SpU7zvfe8jHo/z+c9/nlQqxfe//31isdiK29D2C2OABx54gM997nOcO3eO3t5e/st/+S+86U1vanazXNbAd77zHf7kT/7E8fef/OQnedWrXsXQ0BCf+MQnePLJJwmFQtx77728//3vd58v3+AsXhgDbqxbkAcffJAvfvGLnDp1ilgsxite8Qre/e534/P5ADfmrYSmafzVX/0V//zP/8zU1BSbN2/m3nvv5R3veMf8M+duvDc+Sy2MYWWxTafT/Pf//t/50Y9+hGVZHDhwgA996EPz3zisFHdh7OLi4uLi4uLi4kKbP2Ps4uLi4uLi4uLiUsZdGLu4uLi4uLi4uLjgLoxdXFxcXFxcXFxcAHdh7OLi4uLi4uLi4gK4C2MXFxcXFxcXFxcXwF0Yu7i4uLi4uLi4uADuwtjFxcXFxcXFxcUFcBfGLi4uLi4uLi4uLoC7MHZxcXGpGlVVmZiYWHDse9/7Hq985SvZv38/z3/+8/nYxz5GOp1uUgtdXFxcXFaDuzB2cXFxqYLjx4/z0pe+lF/84hfzx770pS/xwQ9+kHg8zvve9z5e8YpX8O1vf5vXv/71KIrSxNa6uLi4uFSD1OwGuLi4uGwkTp48ydjY2Pz/JyYm+PznP89dd93Fl770JQShdL9h3759vOc97+F//+//ze/93u81q7kuLi4uLlXg3jF2cXFxWQPf//730XWdN7zhDfOLYoCXv/zl9Pf3893vfreJrXNxcXFxqQZ3Yezi4uKyQj74wQ/yoQ99CIAPfehDXHvttTz11FMA3HTTTVe8/4YbbuDs2bNks9lV2ctkMnzqU5/innvu4frrr+f5z38+H/rQh7h48eIV7/3lL3/J61//em6++WbuuOMO/uzP/ozHH3+ca6+9turFua7r3HHHHbzkJS9Z8veveMUreN7znoeu6wDMzMzwiU98gpe85CXceOON3Pj/b+/OQqr81jiOf7db0235rzRTKDNNK4LModG6aDTUJrTUBjMMyiTsoi6ym+bsqiIziwYElRTE3JVBJRZaEUUQWWFGiI2S2bbJrBzORbT576ajbk//0zm/D+ybdz3v+6x197yL5107KIioqCiysrJobW21uTc/P5+YmBhCQ0MJCQkhLi5OLw8i8l9DrRQiIp0UHx9Pr169KCwsJD4+nrCwMHJzc/nrr7/o06fPd/He3t4APH36lJEjR3Yp1+vXr0lISODZs2csWrSIgIAA6urqKCgo4OLFixQWFuLr6wtAWVkZaWlpeHp6kpKSgsFgoLCwkMrKym6t08nJiXnz5pGTk8Pt27cJCgqyjt27d4+amhqSk5NxcnLi7du3xMXF8fr1axYvXoyvry8WiwWz2cz+/fsxGo2kpKQAkJOTQ0ZGBtHR0cTFxfH582dOnjxJeno6LS0tLFmypFvzFRHpKSqMRUQ6KSQkhNraWgoLCwkODmb+/PkcPHgQV1fXH8a7uLgA0Nzc3OVc+/bt49GjR+Tl5RESEmK9HhMTQ0xMDDt27ODIkSO0t7ezfft2TCYTRUVFeHp6AhAXF8fcuXO7scovFi5cSE5ODmaz2aYwLikpASA2Nhb4chrH06dPyczMJCIiwhqXkJBAeHg4paWl1sK4qKiIYcOGsWfPHmtcbGws8fHxVFdXd3uuIiI9Ra0UIiJ26ujo+OW40Wjs8vPOnj2Lv78/vr6+vHr1yvrz8PAgODiYK1eu8P79e+7cuUN9fT3z58+3FsUA/fr1IykpqVvrAQgMDGT06NGUlpZaWyZaW1spLS1lzJgxBAQEALB8+XKuXr3KzJkzbe63WCy4ubnZvBR4e3tTW1vLvn37ePjwIQCurq6cPn2abdu2dXuuIiI9RTvGIiJ26N27NxaL5YdjHz58APhhm8WvWCwWmpqaaGpqYtKkST+Nq6+v58mTJwDWtoq/CwwM7FLeb8XGxrJlyxYqKyuZPn06ly9f5uXLl6SlpdnEOTg4cOzYMaqqqnjy5Al1dXW8e/cOAJPJZI3btGkTqampZGdnk52djZeXF5MnTyYiIoKpU6diMBjsmq+IiL1UGIuI2GHw4MHcvXuX5ubm71oq6uvrcXBwwMvLq0vPbGtrAyAsLIy1a9f+NM7b25v79+//dPxrK0d3zZkzh927d2M2m5k+fTolJSWYTCaio6OtMTU1NSQmJvLx40cmTJhAeHg4SUlJhIaGkpiYaPM8f39/zp49y82bN6moqODatWuYzWaKi4uJiIggMzPTrvmKiNhLhbGIiB2CgoI4d+4ct2/fZuLEiTZjVVVVBAYGdnnH2N3dHVdXV968eUN4ePh341euXMHBwQFnZ2f8/f0BrK0Jf1dXV9elvN9yc3Nj1qxZXLhwgYaGBi5evMjs2bNt1rNr1y7evHnDmTNnGDZsmPX658+fsVgseHh4AF/aMGpqanB0dGTcuHGMGzcOgMbGRlJTUzl//jw1NTUMHz7crjmLiNhDPcYiIl3w9azi9vZ2ACIjI3FycuLo0aM2vcZnzpzh2bNnxMTEdDmH0Whk5syZPHjwgNLSUpux6upqVq9ezc6dO3F0dGTEiBEEBARw6tQpHj9+bI379OkTubm53VmijdjYWFpaWti6dSstLS3Wj+6+slgsmEwmfHx8bK7n5ubS0tJiPa6tra2NxMRE1q9fb+1ZBvDw8GDo0KHWdYuI/JO0Yywi0gUDBgwA4NSpU3R0dLBgwQJSUlLIzMwkOTmZyMhIamtryc3NZfTo0SQkJHQrz4YNG7hx4wYbNmzg8uXLjBkzhufPn1NQUIDRaGTz5s0AGAwGdu7cyYoVK1i0aBHLli2jT58+lJSU2L1jDDBx4kQGDx7MhQsX8PHxYfz48TbjM2bMICsri+TkZKKioujo6KCiooJLly7h4uJiPcPZ2dmZVatWsWfPHpYuXUpUVBQmk4lbt25hNpuZNm2azY6ziMg/wdDx7z6nFhERq9bWVjZu3EhZWRkGg4Hi4mL8/Pw4ceIEeXl51NXVMWDAAGbMmEFaWhp9+/btdq7Gxkays7MpLy/nxYsX9O/fn+DgYNasWcOoUaNsYqurq9m7dy/Xr1/H0dGRWbNmMXz4cDIyMsjIyOjWzvVXWVlZ7N+/n3Xr1pGammoz1trayuHDhykpKaG+vp6+ffvi5+dHUlISVVVVHDp0iPz8fMaOHQtAcXExJ06c4NGjRzQ3NzNkyBCio6NZuXIlzs7O3Z6jiEhPUGEsIvI/qri4mPT0dLsLYxGR/xfqMRYRERERQT3GIiK/RUNDQ6dj3dzc7D5q7Vvv37/v9D/wGY1G3N3dezS/iMifQIWxiMhvMGXKlE7H/idaH44fP86BAwc6FTto0CDKy8t7NL+IyJ9APcYiIr/B1atXOx0bEBDAwIEDezT/48ePbY5z+xVnZ2fCwsJ6NL+IyJ9AhbGIiIiICPr4TkREREQEUGEsIiIiIgKoMBYRERERAVQYi4iIiIgAKoxFRERERAAVxiIiIiIigApjEREREREA/gUxhWiLJtwcYQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_train.plot(kind='scatter', x='t0_eq_vas', y='t1_eq_vas', xlim=(0,100), ylim=(0,100), alpha=0.1, figsize=S);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQf_PpBUANDw"
      },
      "source": [
        "We start with applying a linear regression to see how much of eq_vas at t1 is explained by eq_vas at t0 in a simple (linear) baseline model.\n",
        "\n",
        "First, we prepare the outcome data by applying median imputation on the missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGiA_FA4Uz5m",
        "outputId": "08229c23-9026-4b71-f8a3-81b7edf41eda"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([80.])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_imputer = SimpleImputer(missing_values=999, strategy = 'median')\n",
        "Y_imputer.fit(df_train['t1_eq_vas'].to_frame())\n",
        "Y_imputer.statistics_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcnJ_O97-VkE"
      },
      "source": [
        "Missings for `t1_eq_vas` are imputed using this median value, and the resulting distribution is shown below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "GMpCjHdOWIZb",
        "outputId": "80d8f0dd-ef6b-46e6-a78a-139c668c88c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 't1_eq_vas'}>]], dtype=object)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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fv9De1dXV6dChQ+rXr1+LXFB84MABRUdHc8FaC6LPrYM+tw763Droc+tpqV435+8398MBAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLafbrFwAAaI8mvXRaeun1tp5Gs5xY/Ou2nkK7xMoNAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwlA5tPQEAANC0HvO2tfUUrkjBnd3a9PNZuQEAAJZCuAEAAJZCuAEAAJZCuAEAAJbS7HBjGIY2btyo8ePHKyYmRiNGjNCCBQtUW1tr1kyePFl9+vRp9M+BAwfMmtraWj3++ONKTExUdHS0UlJSdPTo0Uaft3btWo0cOVJRUVGaMGGCtm/f3qimuLhYEydO1IABAzR8+HCtXLlShmE099AAAIAFNPtuqdWrV+upp57SAw88oMGDB6u8vFz//d//rX/84x9au3atDMPQkSNH9MADD2j06NEuP9urVy/zv8+ZM0fvv/++MjMz5efnp9zcXE2ZMkXbtm1TYGCg+VlLlixRenq6brjhBhUUFGjmzJlav3694uPjJUn79u1TWlqaxo0bp9mzZ2vv3r166qmn5HQ6NX369KtoDQAAaI+aFW6cTqdWrVqlu+66S3PmzJEkDRkyRIGBgZo9e7Y++OAD+fj46KuvvtLNN9+s6OjoJvezf/9+7dixQ6tWrdJNN90kSYqLi9OIESP0/PPPKy0tTfX19Vq5cqWmTp2q9PR0SdKwYcOUnJysvLw8rVu3TpKUl5envn37Kjs726y5cOGCVq1apZSUFHXu3PlK+gIAANqpZp2Wqq2t1W233aZbb73VZTw8PFyS9Mknn+ijjz6SJPXt2/eS+9m5c6d8fHyUmJhojgUFBSk+Pl7FxcWSpPfee0/V1dUuqz8eHh4aNWqUdu/erfr6ep07d067du1qtEI0ZswY1dXVac+ePc05PAAAYAHNWrkJCAjQY4891mj8jTfekHTxtNOWLVvk7++vhQsX6s0331RdXZ1uvPFGzZ8/Xz179pQkHTt2TN27d1eHDq4fHxoaqq1bt5o1ktSjRw+XmrCwMDkcDlVUVMhms+n8+fNN1kjSiRMnlJSU1OSxOBwOORyO5hz+D2rYn7v3C1f0uXXQ59ZBn1sH/W19LfU39nJc9ROK9+3bp2eeeUYjR45Ur169dOjQIdXU1Oiaa65RXl6eTp06pby8PN1zzz3avHmzgoODVVNTIz8/v0b78vX1ld1ulyTV1NRIUqM6X19fSRdXkTw8PH6w5lKOHDlyhUf8w8rKylps3/gGfW4d9Ll10GdYTVt+p68q3OzZs0cPPfSQQkNDtWDBAklSRkaG0tLSFBsbK+nitTQDBw7UuHHj9OyzzyozM1NOp9MMJt/VMO50Opvc3nAXlKenp5niLrUvT89Ln3Xr3bu3fHx8LuMoL5/D4VBZWZmioqJks9ncum98gz63DvrcOuhz63A4HNJLp9t6Gj8p7v5O19XVXfbCxBWHm23btmnevHkKDw9Xfn6+eYdTv379GtWGhIQoIiLCvB7H399flZWVjersdrv8/f0lXTwF1jDWpUsXs6aurs7cR0MA+u4KTcPqT1OrQw1sNluL/SJpyX3jG/S5ddDn1kGfYTXu/k43Z19X9BC/1atXa86cOYqOjtaGDRvUtWtXSdL58+f1yiuvuDzPpkF9fb2uueYaSRcvQD558mSj1ZmKigpFRESYNZJUXl7uUlNeXi4vLy+FhIQoNDRUNputyRpJioyMvJLDAwAA7Vizw82LL76o7OxsjR07Vvn5+eZKiyR17NhROTk55m3ZDQ4ePKiKigoNGjRIkpSUlCS73a6SkhKzpqqqSqWlpeYFwDExMfLx8VFhYaFZYxiGioqKlJCQIC8vL3Xq1ElxcXEqKipyeWhfYWGhAgIC1L9//+YeHgAAaOeadVrqiy++0KJFi3Tdddfp3nvv1YcffuiyPTQ0VOnp6Xr00Uc1b948jR8/XqdOndKyZcvUp08f3XHHHZKk+Ph4JSQkKDMzU5mZmQoMDFROTo78/f2VnJwsSfL29lZqaqry8vLUsWNHxcTEqKCgQAcPHtT69evNz5w+fbpSUlI0a9YsTZo0Sfv371d+fr4yMjJ4xg0AAD9BzQo3b731lurr63Xq1Cndc889jbYvWrRIv/nNb+Tt7a38/Hylp6fL29tbo0aN0sMPP+xy63dubq4WL16srKwsOZ1ODRw4UEuXLnW5vmbGjBmy2WzatGmT1qxZo8jISC1fvty8WFmSBg8erJycHC1btkzp6ekKDg7W3LlzlZqaeiX9AAAA7ZyH8RN7CVNdXZ0OHTqkfv36tcjdUgcOHFB0dDQXBrYg+tw66HProM+tw+FwKOLR19t6Gj8ZBXd2c/t3ujl/v3krOAAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsJRmhxvDMLRx40aNHz9eMTExGjFihBYsWKDa2lqz5uOPP9a0adMUGxurQYMG6ZFHHlF1dbXLfmpra/X4448rMTFR0dHRSklJ0dGjRxt93tq1azVy5EhFRUVpwoQJ2r59e6Oa4uJiTZw4UQMGDNDw4cO1cuVKGYbR3EMDAAAW0Oxws3r1av3hD3/QzTffrLy8PP3ud7/T1q1bNWPGDBmGoerqak2dOlVVVVXKysrSnDlzVFRUpNmzZ7vsp2F8zpw5ysrKUmVlpaZMmaKzZ8+6fFZ2drbuuOMO5ebmKiwsTDNnzlRpaalZs2/fPqWlpSkiIkI5OTm67bbb9NRTT2nFihVX3BQAANB+dWhOsdPp1KpVq3TXXXdpzpw5kqQhQ4YoMDBQs2fP1gcffKB33nlH1dXV2rx5s4KCgiRJwcHBmjZtmvbs2aO4uDjt379fO3bs0KpVq3TTTTdJkuLi4jRixAg9//zzSktLU319vVauXKmpU6cqPT1dkjRs2DAlJycrLy9P69atkyTl5eWpb9++ys7ONmsuXLigVatWKSUlRZ07d3ZLowAAQPvQrJWb2tpa3Xbbbbr11ltdxsPDwyVJn3zyiXbu3KnY2Fgz2EjS0KFD5evrq+LiYknSzp075ePjo8TERLMmKChI8fHxZs17772n6upqjR492qzx8PDQqFGjtHv3btXX1+vcuXPatWuXS40kjRkzRnV1ddqzZ09zDg8AAFhAs1ZuAgIC9NhjjzUaf+ONNyRJvXr10rFjx3TLLbe4bPf09FT37t114sQJSdKxY8fUvXt3dejg+vGhoaHaunWrWSNJPXr0cKkJCwuTw+FQRUWFbDabzp8/32SNJJ04cUJJSUlNHovD4ZDD4fjhg26Ghv25e79wRZ9bB31uHfS5ddDf1tdSf2MvR7PCTVP27dunZ555RiNHjlSvXr1UXV0tX1/fRnW+vr7mRcc1NTXy8/NrssZut5s1khrVNey7trZWHh4eP1hzKUeOHLms47sSZWVlLbZvfIM+tw763DroM6ymLb/TVxVu9uzZo4ceekihoaFasGCBOd4QOr7NMAxz3Ol0Nlnz7Z91Op1Nbm+4C8rT09NMcZfal6fnpc+69e7dWz4+PpfcfiUcDofKysoUFRUlm83m1n3jG/S5ddDn1kGfW4fD4ZBeOt3W0/hJcfd3uq6u7rIXJq443Gzbtk3z5s1TeHi48vPzFRgYKOniKkpTKyZ1dXXq1q2bJMnf31+VlZWNaux2u/z9/SVdPAXWMNalSxeX/TTsoyEAfffzGlZ/mlodamCz2VrsF0lL7hvfoM+tgz63DvoMq3H3d7o5+7qih/itXr1ac+bMUXR0tDZs2KCuXbua28LDw1VRUeFS73Q6dfLkSUVGRpo1J0+ebLQ6U1FRoYiICLNGksrLy11qysvL5eXlpZCQEIWGhspmszVZI8n8PAAA8NPR7HDz4osvKjs7W2PHjlV+fr650tIgMTFRpaWlqqqqMsdKSkpkt9vNu6OSkpJkt9tVUlJi1lRVVam0tNS8ADgmJkY+Pj4qLCw0awzDUFFRkRISEuTl5aVOnTopLi5ORUVFLg/tKywsVEBAgPr379/cwwMAAO1cs05LffHFF1q0aJGuu+463Xvvvfrwww9dtoeGhuruu+/Wc889p5SUFM2YMUNnz55Vdna2hg0bppiYGElSfHy8EhISlJmZqczMTAUGBionJ0f+/v5KTk6WJHl7eys1NVV5eXnq2LGjYmJiVFBQoIMHD2r9+vXmZ06fPl0pKSmaNWuWJk2apP379ys/P18ZGRk84wYAgJ+gZoWbt956S/X19Tp16pTuueeeRtsXLVqkiRMn6tlnn9XChQuVkZEhX19fjR07VnPnznWpzc3N1eLFi5WVlSWn06mBAwdq6dKlLtfXzJgxQzabTZs2bdKaNWsUGRmp5cuXKzY21qwZPHiwcnJytGzZMqWnpys4OFhz585Vampqc3sBAAAswMP4ib2Eqa6uTocOHVK/fv1a5G6pAwcOKDo6mgsDWxB9bh30uXXQ59bhcDgU8ejrbT2Nn4yCO7u5/TvdnL/fvBUcAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYSrPeCg4AgCT1mLetracAXBIrNwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFKuKtx89tlniouL065du1zGJ0+erD59+jT658CBA2ZNbW2tHn/8cSUmJio6OlopKSk6evRoo89Yu3atRo4cqaioKE2YMEHbt29vVFNcXKyJEydqwIABGj58uFauXCnDMK7m0AAAQDvV4Up/8NSpU3rggQdUU1PjMu50OnXkyBE98MADGj16tMu2Xr16mf99zpw5ev/995WZmSk/Pz/l5uZqypQp2rZtmwIDAyVJq1ev1pIlS5Senq4bbrhBBQUFmjlzptavX6/4+HhJ0r59+5SWlqZx48Zp9uzZ2rt3r5566ik5nU5Nnz79Sg8PAAC0U80ON06nU6+++qqysrKa3H78+HF99dVXuvnmmxUdHd1kzf79+7Vjxw6tWrVKN910kyQpLi5OI0aM0PPPP6+0tDTV19dr5cqVmjp1qtLT0yVJw4YNU3JysvLy8rRu3TpJUl5envr27avs7Gyz5sKFC1q1apVSUlLUuXPn5h4iAABox5p9Wurw4cN64okndPvttzcZcD766CNJUt++fS+5j507d8rHx0eJiYnmWFBQkOLj41VcXCxJeu+991RdXe2y+uPh4aFRo0Zp9+7dqq+v17lz57Rr165GK0RjxoxRXV2d9uzZ09zDAwAA7Vyzw80vfvELFRUVaf78+U2uihw6dEj+/v5auHChBg0apKioKP3rv/6rPv74Y7Pm2LFj6t69uzp0cF04Cg0N1fHjx80aSerRo4dLTVhYmBwOhyoqKvTJJ5/o/PnzTdZI0okTJ5p7eAAAoJ1r9mmphuthLuXQoUOqqanRNddco7y8PJ06dUp5eXm65557tHnzZgUHB6umpkZ+fn6NftbX11d2u12SzGt5vlvn6+sr6eIFyR4eHj9YcykOh0MOh+N7j6W5Gvbn7v3CFX1uHfS5ddBnWFVL/Y29HFd8QfGlZGRkKC0tTbGxsZIuXkszcOBAjRs3Ts8++6wyMzPldDrNYPJdDeNOp7PJ7Q13QXl6epoHeql9eXpeemHqyJEjl3dAV6CsrKzF9o1v0OfWQZ9bB32G1bTld9rt4aZfv36NxkJCQhQREWFej+Pv76/KyspGdXa7Xf7+/pKkgIAAc6xLly5mTV1dnbmPhgD03RWahtWfplaHGvTu3Vs+Pj6XfVyXw+FwqKysTFFRUbLZbG7dN75Bn1sHfW4d7bbPL73e1jPAj5y7v9N1dXWXvTDh1nBz/vx5bd26VT179mx0p1R9fb2uueYaSVJ4eLh27twpp9PpsrpSUVGhiIgIs0aSysvL1b9/f7OmvLxcXl5eCgkJkWEYstlsKi8vd/mshv8dGRl5ybnabLYW+0XSkvvGN+hz66DPrYM+w2rc/Z1uzr7c+oTijh07Kicnx7wtu8HBgwdVUVGhQYMGSZKSkpJkt9tVUlJi1lRVVam0tFRJSUmSpJiYGPn4+KiwsNCsMQxDRUVFSkhIkJeXlzp16qS4uDgVFRW5PLSvsLBQAQEBLqEIAAD8NLj9tFR6eroeffRRzZs3T+PHj9epU6e0bNky9enTR3fccYckKT4+XgkJCcrMzFRmZqYCAwOVk5Mjf39/JScnS5K8vb2VmpqqvLw8dezYUTExMSooKNDBgwe1fv168/OmT5+ulJQUzZo1S5MmTdL+/fuVn5+vjIwMnnEDAMBPkNvDzW9+8xt5e3srPz9f6enp8vb21qhRo/Twww+73Pqdm5urxYsXKysrS06nUwMHDtTSpUtdrq+ZMWOGbDabNm3apDVr1igyMlLLly83L1aWpMGDBysnJ0fLli1Tenq6goODNXfuXKWmprr70AAAQDvgYfzEXsJUV1enQ4cOqV+/fi1yQfGBAwcUHR3NufMWRJ9bB31uHe21zz3mbWvrKeBHrODObm7/Tjfn7zdvBQcAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZyVeHms88+U1xcnHbt2uUy/vHHH2vatGmKjY3VoEGD9Mgjj6i6utqlpra2Vo8//rgSExMVHR2tlJQUHT16tNFnrF27ViNHjlRUVJQmTJig7du3N6opLi7WxIkTNWDAAA0fPlwrV66UYRhXc2gAAKCduuJwc+rUKaWkpKimpsZlvLq6WlOnTlVVVZWysrI0Z84cFRUVafbs2S51DeNz5sxRVlaWKisrNWXKFJ09e9asWb16tbKzs3XHHXcoNzdXYWFhmjlzpkpLS82affv2KS0tTREREcrJydFtt92mp556SitWrLjSQwMAAO1Yh+b+gNPp1KuvvqqsrKwmt7/wwguqrq7W5s2bFRQUJEkKDg7WtGnTtGfPHsXFxWn//v3asWOHVq1apZtuukmSFBcXpxEjRuj5559XWlqa6uvrtXLlSk2dOlXp6emSpGHDhik5OVl5eXlat26dJCkvL099+/ZVdna2WXPhwgWtWrVKKSkp6ty5c7ObAgAA2q9mr9wcPnxYTzzxhG6//fYmA87OnTsVGxtrBhtJGjp0qHx9fVVcXGzW+Pj4KDEx0awJCgpSfHy8WfPee++purpao0ePNms8PDw0atQo7d69W/X19Tp37px27drlUiNJY8aMUV1dnfbs2dPcwwMAAO1cs1dufvGLX6ioqEjdunVrdK2NJB07dky33HKLy5inp6e6d++uEydOmDXdu3dXhw6uHx8aGqqtW7eaNZLUo0cPl5qwsDA5HA5VVFTIZrPp/PnzTdZI0okTJ5SUlNTkcTgcDjkcjss65svVsD937xeu6HProM+tgz7Dqlrqb+zlaHa4CQwM/N7t1dXV8vX1bTTu6+ur2tpaSVJNTY38/PyarLHb7WaNpEZ1Dfuura2Vh4fHD9ZcypEjR773OK5GWVlZi+0b36DPrYM+tw76DKtpy+90s8PN5WgIHd9mGIY57nQ6m6z59s86nc4mtzfcBeXp6WmmuEvty9Pz0mfdevfuLR8fn0tuvxIOh0NlZWWKioqSzWZz677xDfrcOuhz62i3fX7p9baeAX7k3P2drquru+yFCbeHGz8/vyZXTOrq6tStWzdJkr+/vyorKxvV2O12+fv7S5ICAgLMsS5durjsp2EfDQHou5/XsPrT1OpQA5vN1mK/SFpy3/gGfW4d9Ll10GdYjbu/083Zl9sf4hceHq6KigqXMafTqZMnTyoyMtKsOXnyZKPVmYqKCkVERJg1klReXu5SU15eLi8vL4WEhCg0NFQ2m63JGknm5wEAgJ8Ot4ebxMRElZaWqqqqyhwrKSmR3W43745KSkqS3W5XSUmJWVNVVaXS0lLzAuCYmBj5+PiosLDQrDEMQ0VFRUpISJCXl5c6deqkuLg4FRUVuTy0r7CwUAEBAerfv7+7Dw8AAPzIuf201N13363nnntOKSkpmjFjhs6ePavs7GwNGzZMMTExkqT4+HglJCQoMzNTmZmZCgwMVE5Ojvz9/ZWcnCxJ8vb2VmpqqvLy8tSxY0fFxMSooKBABw8e1Pr1683Pmz59ulJSUjRr1ixNmjRJ+/fvV35+vjIyMnjGDYB2YdJLp7mGBXAjt4eboKAgPfvss1q4cKEyMjLk6+ursWPHau7cuS51ubm5Wrx4sbKysuR0OjVw4EAtXbrU5fqaGTNmyGazadOmTVqzZo0iIyO1fPlyxcbGmjWDBw9WTk6Oli1bpvT0dAUHB2vu3LlKTU1196EBAIB2wMP4ib2Eqa6uTocOHVK/fv1a5G6pAwcOKDo6mgsDWxB9bh30uXU4HA5FPMqqDayl4M5ubv/d0Zy/37wVHAAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWEqHtp4AALhTj3nb2noKANoYKzcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSOrTETr/66isNHDhQTqfTZdzLy0tlZWWSpI8//liLFy/W3r171aFDB40YMULz5s1TQECAWV9bW6usrCz97W9/k91uV0xMjB599FFFRka67Hft2rXasGGDPv/8c/Xs2VO///3vNXLkyJY4NAAA8CPXIuHm8OHDcjqdWrJkia677jpz3NPz4kJRdXW1pk6dqp///OfKyspSZWWlsrOzdfr0aa1Zs8asnzNnjt5//31lZmbKz89Pubm5mjJlirZt26bAwEBJ0urVq7VkyRKlp6frhhtuUEFBgWbOnKn169crPj6+JQ4PAAD8iLVIuDl06JA6duyo0aNHq2PHjo22v/DCC6qurtbmzZsVFBQkSQoODta0adO0Z88excXFaf/+/dqxY4dWrVqlm266SZIUFxenESNG6Pnnn1daWprq6+u1cuVKTZ06Venp6ZKkYcOGKTk5WXl5eVq3bl1LHB4AAPgRa5Frbg4dOqTIyMgmg40k7dy5U7GxsWawkaShQ4fK19dXxcXFZo2Pj48SExPNmqCgIMXHx5s17733nqqrqzV69GizxsPDQ6NGjdLu3btVX1/fEocHAAB+xFok3Hz00Ufy9PRUSkqKoqOjlZCQoMcff1y1tbWSpGPHjik8PNx1Ip6e6t69u06cOGHWdO/eXR06uC4uhYaG6vjx42aNJPXo0cOlJiwsTA6HQxUVFS1wdAAA4MfM7aelnE6njhw5Ik9PT2VkZCgtLU1lZWXKzc3V0aNH9dxzz6m6ulq+vr6NftbX19cMQDU1NfLz82uyxm63mzWSGtU17LthX01xOBxyOBxXdpDfs89v/ydaBn1uHfQZwNVoqb+xl8Pt4cYwDK1cuVI/+9nPFBERIUmKj4/Xz372M2VmZqqkpETSxdNHTf1sw7jT6Wyy5ts/+927sb69H+mbC5ibcuTIkcs8ouZruCMMLYs+tw76DOBKtOXvDreHG5vNpkGDBjUav/nmmyVdvJPKz8+vyVWVuro6devWTZLk7++vysrKRjV2u13+/v6SZN42brfb1aVLF5f9NOzjUnr37i0fH5/LPKrL43A4VFZWpqioKNlsNrfuG9+gz62j3fb5pdfbegYAJLf/7qirq7vshQm3h5vPP/9cb731loYNG2YGFUnmxb3XXHONwsPDG10P43Q6dfLkSfPi4PDwcO3cuVNOp9NlBaaiosJcEWq4bqe8vFz9+/c3a8rLy+Xl5aWQkJBLztNms7XYL+yW3De+QZ9bB30GcCXc/bujOfty+wXF586d02OPPaaNGze6jL/22mvy9PRUbGysEhMTVVpaqqqqKnN7SUmJ7Ha7eXdUUlKS7Ha7eRpLkqqqqlRaWqqkpCRJUkxMjHx8fFRYWGjWGIahoqIiJSQkyMvLy92HBwAAfuTcvnITEhKiCRMm6JlnnpGXl5eio6O1d+9erVixQnfffbd69uypu+++W88995xSUlI0Y8YMnT17VtnZ2Ro2bJhiYmIkXbxOJyEhQZmZmcrMzFRgYKBycnLk7++v5ORkSZK3t7dSU1OVl5enjh07KiYmRgUFBTp48KDWr1/v7kMDAADtQIs8xO/JJ59UWFiYNm/erOXLlys4OFgzZ87UAw88IOni82qeffZZLVy4UBkZGfL19dXYsWM1d+5cl/3k5uZq8eLFysrKktPp1MCBA7V06VKX62tmzJghm82mTZs2ac2aNYqMjNTy5csVGxvbEocGAAB+5DyMhluLfiLq6up06NAh9evXr0UuKD5w4ICio6O5RqEF0efW0V773GPetraeAvCTV3BnN7f/7mjO32/eCg4AACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACylRd4KDsAaJr10Wnrp9baeBgA0Cys3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUjq09QSAn4oe87a19RQA4CeBlRsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGApvBUc7dKkl05LL73e1tMAAPwIsXIDAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshVvBoR7ztrX1FAAAcBvLhJvi4mItXbpUx44dU1BQkJKTkzVt2jR5eHi06jx4/goAAG3LEqel9u3bp7S0NEVERCgnJ0e33XabnnrqKa1YsaKtpwYAAFqZJVZu8vLy1LdvX2VnZ0uShg0bpgsXLmjVqlVKSUlR586d23iGAACgtbT7lZtz585p165dGj16tMv4mDFjVFdXpz179rTRzAAAQFto9ys3n3zyic6fP68ePXq4jIeFhUmSTpw4oaSkJHPc6XRKkux2uxwOh1vn4nQ6FR7Y7lsKAMBVq62tlaen+9ZQ6uvrJX3zd/z7tPu/xNXV1ZIkPz8/l3FfX19JF5v7bV9//bUkqaKiokXm8+dRP2uR/QIA0J4cPXq0Rfb79ddfN/qb/13tPtw0JLhL3RX13dTYpUsX9ejRQ506dXJrogQAAC3H6XTq66+/VpcuXX6wtt2Hm4CAAEmNV2jsdrukxis6HTp00LXXXts6kwMAAG7zQys2Ddr90kVoaKhsNpvKy8tdxhv+d2RkZFtMCwAAtJF2H246deqkuLg4FRUVyTAMc7ywsFABAQHq379/G84OAAC0tnYfbiRp+vTpeu+99zRr1iy99dZbWrp0qfLz8/Xggw/yjBsAAH5iLBFuBg8erJycHB0/flzp6enaunWr5s6dq9/97netNofi4mJNnDhRAwYM0PDhw7Vy5UqXlSQ0j2EY2rhxo8aPH6+YmBiNGDFCCxYscLm26uOPP9a0adMUGxurQYMG6ZFHHjHvnsOVmTFjhn71q1+5jNFn9zlw4IDuu+8+RUdHa8iQIfr3f/93VVZWmtvptXts2rRJv/71rxUdHa1x48Zpw4YNLr+P6fPV+eyzzxQXF6ddu3a5jF9OX2tra/X4448rMTFR0dHRSklJaZm7qgxctb179xrXX3+9kZGRYbz11lvGkiVLjD59+hjLly9v66m1W6tWrTL69etn/PnPfzbefvtt4/nnnzcGDRpkTJkyxXA6ncaXX35pDB061Jg0aZKxfft2Y+PGjUZcXJyRkpLS1lNvtzZv3mz07t3bGD58uDlGn92nrKzMiIqKMqZNm2aUlJQYBQUFRmJionHXXXcZhkGv3WXTpk1G7969jSeffNJ45513jKVLlxp9+vQxVq9ebRgGfb5aJ0+eNMaMGWP07t3bePfdd83xy+3rtGnTjBtvvNEoKCgwCgsLjfHjxxtDhgwxzpw549Z5Em7cIDU11Zg0aZLLWFZWlhEdHW189dVXbTSr9svhcBhxcXHGE0884TL+2muvGb179zbef/99Y8WKFcaAAQOMyspKc/uOHTuM3r17G6Wlpa095Xbv9OnTRnx8vDFs2DCXcEOf3ee+++4zJk+ebFy4cMEcKywsNIYNG2ZUVFTQaze56667jOTkZJex2bNnm99r+nxlHA6H8fLLLxsJCQlGQkJCo3BzOX3dt2+f0bt3b2PHjh1mTWVlpREdHW3k5eW5db6WOC3Vlnj9g/vV1tbqtttu06233uoyHh4eLuniU6l37typ2NhYBQUFmduHDh0qX19fFRcXt+p8reA//uM/lJiYqMGDB7uM02f3OHPmjHbv3q3f/va3stls5vjo0aP11ltvKSQkhF67yblz5+Tv7+8yds011+js2bOS+E5fqcOHD+uJJ57Q7bffrqysrEbbL6evO3fulI+PjxITE82aoKAgxcfHu733hJurdDmvf0DzBAQE6LHHHlNsbKzL+BtvvCFJ6tWrl44dO2aGnQaenp7q3r07PW+ml156SQcPHtRjjz3WaBt9do/Dhw/LMAxde+21mjNnjmJiYhQTE6OMjAx9+eWXkui1u0yZMkVvv/22tmzZopqaGpWUlOjVV1/VhAkTJNHnK/WLX/xCRUVFmj9/fpM36lxOX48dO6bu3burQwfXR+yFhobq+PHjbp1vu3+IX1tr7usfcGX27dunZ555RiNHjlSvXr1UXV1t9vjbfH196XkznDp1SosWLdKiRYtc/o2rAX12j6qqKknSI488omHDhmn58uU6ceKElixZok8++UQvvPACvXaTcePG6d1339XcuXPNsaSkJD3yyCOS+E5fqcDAwO/dfjl9rampafIhfL6+vuaDd92FcHOVmvv6BzTfnj179NBDDyk0NFQLFiwwx5vquWEYl/z/Aq4Mw9Ajjzyim266SWPGjLlkHX2+eufPn5ckXX/99eZ3ePDgwQoICNDDDz+st99+WxK9dofp06dr3759yszMVP/+/XX48GHl5uZq1qxZysvLk0SfW8oP9dXpdF6yx+7uPeHmKjX39Q9onm3btmnevHkKDw9Xfn6++W8Pfn5+Tf5bVl1dnbp169bKs2yfNmzYoMOHD2vr1q26cOGCJJm3y164cEGenp702U0a/o12+PDhLuNDhw6VJB06dIheu8G+ffu0c+dO/fGPf9Sdd94pSUpISFBISIgefPBB7dixgz63kMvpq7+/v8ujDxrY7fZG10ldLZYVrhKvf2g5q1ev1pw5cxQdHa0NGzaoa9eu5rbw8PBGb3Z3Op06efIkPb9MhYWFOnPmjJKSknT99dfr+uuv1+bNm3Xq1Cldf/31ysvLo89u0nBN3rlz51zGG0Jl586d6bUbfPrpp5KkgQMHuozHx8dLkv7xj3/Q5xZyOX0NDw/XyZMnzTMeDSoqKhQREeHW+RBurhKvf2gZL774orKzszV27Fjl5+c3SvWJiYkqLS01r2WQpJKSEtntdpcr8XFpf/jDH/Tyyy+7/DN8+HB17dpVL7/8siZPnkyf3SQiIkLXXXedtm3b5jL+t7/9TZIUFxdHr92gZ8+ektToLtV9+/ZJkrp3706fW8jl9DUpKUl2u10lJSVmTVVVlUpLS5WUlOTW+XgYBo/RvVr/+7//q5SUFI0ePVqTJk3S/v37tWLFCmVkZLTqU5Kt4osvvtDIkSN17bXXKisrq8kr6yXplltuUXBwsGbMmKGzZ88qOztbAwYM0DPPPNMW07aEefPmaffu3fr73/8u6eIvHvrsHq+//rpmz56tsWPH6s4779THH3+sJUuWaOjQoVq2bBm9dpOZM2eqpKRE06dP14ABA3T06FHl5OTol7/8pTZu3Kiamhr6fJV27dql+++/X88++6wGDRok6fJ/V9x33306fPiwMjMzFRgYqJycHJ09e1Zbt25Vly5d3DZHwo2bFBUVadmyZTp+/LiCg4N1zz33KDU1ta2n1S69/PLLevTRRy+5fdGiRZo4caKOHDmihQsXav/+/fL19dXIkSM1d+5crnO6Ct8NN5Losxu9+eabysvL0+HDh9WlSxeNHz9e//Zv/yYvLy9J9Nodzp07p6efflpbtmzR//3f/+mXv/ylRo4cqfT0dPPaJ/p8dZoKN9Ll9fXLL7/U4sWLtX37djmdTg0cOFDz5883V93chXADAAAshWtuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApfw/ysvtFsjDwUUAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "t1_eq = Y_imputer.transform(df_train['t1_eq_vas'].to_frame())\n",
        "pd.DataFrame(data = t1_eq, columns = ['t1_eq_vas']).hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BJz1rMNmM9R"
      },
      "source": [
        "The same can be done for the outcome measure in the test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "4aY5Y2P0XaJP",
        "outputId": "4063c574-ffe1-4174-89c7-c57522a20f52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 't1_eq_vas_test'}>]], dtype=object)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prepare t1_eq_test for use later on in model assessment\n",
        "t1_eq_test = Y_imputer.transform(df_test['t1_eq_vas'].to_frame())\n",
        "pd.DataFrame(t1_eq_test, columns = ['t1_eq_vas_test']).hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Ma0pQQJ-kSz"
      },
      "source": [
        "The code below fits a linear regression to predict t1_eq_vas using t0_eq_vas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "LkCw6iOgW2XT",
        "outputId": "37c0d573-df13-4cbc-ab18-652c20bda554"
      },
      "outputs": [
        {
          "data": {
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "t0_eq = pd.DataFrame(data = X_train, columns = X_columns)['t0_eq_vas'].to_frame()\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(t0_eq, t1_eq)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRNoCnRj_CNz"
      },
      "source": [
        "### Question 5\n",
        "\n",
        "The intercept, beta-coefficient and the R^2 (coefficient of determination) can be obtained from lin_reg. Consult the documentation for LinearRegression (https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) and extract the intercept, beta-coefficient and the R^2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW-QnWTvj94K"
      },
      "source": [
        "We normally should add **cross-validation** to our model building process to be able to properly compare different model options in terms of performance, which becomes necessarywhen training models that contain hyperparameters. \n",
        "\n",
        "Next to the choices you have on how to build your models, you also have choices to make regarding the performance metric, being the 'thing' that your model will optimize. Let's apply Mean Squared Error (MSE) as our metric in the next iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aaTRRejj94K",
        "outputId": "fe1a81e2-8750-4e46-fdf0-b1d48f752510"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Scores: [16.66593682 16.82464722 16.87729003 16.88592997 16.93669798]\n",
            "Mean: 16.8381\n",
            "Standard deviation: 0.0931\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "def display_scores(scores):\n",
        "    print(f\"Scores: {scores}\")\n",
        "    print(f\"Mean: {scores.mean():.4f}\")\n",
        "    print(f\"Standard deviation: {scores.std():.4f}\")\n",
        "\n",
        "scores = cross_val_score(lin_reg, t0_eq, t1_eq, scoring='neg_mean_squared_error', cv=5)\n",
        "lin_rmse_scores = np.sqrt(-scores)\n",
        "display_scores(lin_rmse_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wy5vRCbnj94L"
      },
      "source": [
        "The reported root of the MSE of around 16.8 tells us that given a predicted value, the true outcome is likely to be in the interval of 2 * 16.8 below and 2 * 16.8 above the predicted value. This results in a range of around 65 points for an outcome measure ranging from 0 to 100, which is substantial, and confirms that a simple linear model will not do the trick. \n",
        "\n",
        "Now that we have seen the simplest setup for a linear regression with only one predictor, let's use linear regression to try to find out which features are the best predictors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CeInvCjj94L"
      },
      "source": [
        "### SelectKBest\n",
        "\n",
        "There are various techniques to determine which features give the best prediction in a regression, such as stepwise selection. Scikit-learn has various [univariate feature selection](https://scikit-learn.org/stable/modules/feature_selection.html) methods for this purpose. We will use [SelectKBest](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHTdsoEMj94L",
        "outputId": "d18fdcec-6d6a-41c8-db29-0c3ebf8b037d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Scores: [15.99162775 16.14324365 16.17794808 16.22819153 16.19174312]\n",
            "Mean: 16.1466\n",
            "Standard deviation: 0.0821\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_selection import SelectKBest, f_regression\n",
        "\n",
        "k10best = Pipeline(\n",
        "    steps=[\n",
        "        (\"prep\", ColumnTransformer(transformers=transformers.values())),\n",
        "        (\"kbest\", SelectKBest(f_regression, k=10)),\n",
        "    ]\n",
        ")\n",
        "\n",
        "X_10best = k10best.fit(df_train, t1_eq).transform(df_train)\n",
        "lin_10best = LinearRegression()\n",
        "scores_10best = cross_val_score(lin_10best, X_10best, t1_eq, scoring='neg_mean_squared_error', cv=5)\n",
        "lin_10best_rmse_scores = np.sqrt(-scores_10best)\n",
        "display_scores(lin_10best_rmse_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glyJVS_Tj94M"
      },
      "source": [
        "Using the 10 Best features, the model performs only slightly better with an RMSE of 16.1 +/- 0.08 compared to the 16.8 +/- 0.09 found when only using t0_eq_vas.\n",
        "\n",
        "The code below shows the features in descending order of importance, which (unexpectedly) puts eq_t0_vas at the top of the list. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6kPaWcd_j94M",
        "outputId": "1eb7fcb1-33e8-4a1f-96e3-133c189661ca"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "      <th>feature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>12988.61</td>\n",
              "      <td>t0_eq_vas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>8612.77</td>\n",
              "      <td>oks_t0_score</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>8036.19</td>\n",
              "      <td>oks_t0_shopping</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7386.24</td>\n",
              "      <td>t0_self_care</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6930.16</td>\n",
              "      <td>t0_disability</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>6620.98</td>\n",
              "      <td>oks_t0_washing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6559.51</td>\n",
              "      <td>t0_anxiety</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>5421.20</td>\n",
              "      <td>oks_t0_stairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5213.64</td>\n",
              "      <td>oks_t0_work</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4438.80</td>\n",
              "      <td>oks_t0_transport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>3610.42</td>\n",
              "      <td>oks_t0_kneeling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3476.54</td>\n",
              "      <td>t0_activity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>3472.68</td>\n",
              "      <td>oks_t0_confidence</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>3408.84</td>\n",
              "      <td>oks_t0_walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3230.02</td>\n",
              "      <td>depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3030.51</td>\n",
              "      <td>t0_discomfort</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2798.40</td>\n",
              "      <td>oks_t0_standing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1670.23</td>\n",
              "      <td>oks_t0_night_pain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1513.21</td>\n",
              "      <td>oks_t0_pain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1401.19</td>\n",
              "      <td>t0_mobility</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1394.65</td>\n",
              "      <td>circulation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1235.65</td>\n",
              "      <td>t0_assisted</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>988.34</td>\n",
              "      <td>lung_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>892.12</td>\n",
              "      <td>oks_t0_limping</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>848.78</td>\n",
              "      <td>diabetes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>787.41</td>\n",
              "      <td>heart_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>733.27</td>\n",
              "      <td>arthritis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>401.40</td>\n",
              "      <td>nervous_system</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>350.56</td>\n",
              "      <td>high_bp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>340.12</td>\n",
              "      <td>x0_2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>340.12</td>\n",
              "      <td>x0_1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>307.46</td>\n",
              "      <td>t0_previous_surgery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>307.46</td>\n",
              "      <td>t0_previous_surgery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>243.16</td>\n",
              "      <td>stroke</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>193.54</td>\n",
              "      <td>kidney_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>167.61</td>\n",
              "      <td>t0_living_arrangements</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>83.85</td>\n",
              "      <td>liver_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>80.29</td>\n",
              "      <td>cancer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57.17</td>\n",
              "      <td>age_band</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>28.30</td>\n",
              "      <td>t0_symptom_period</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       score                 feature\n",
              "39  12988.61               t0_eq_vas\n",
              "38   8612.77            oks_t0_score\n",
              "36   8036.19         oks_t0_shopping\n",
              "19   7386.24            t0_self_care\n",
              "17   6930.16           t0_disability\n",
              "28   6620.98          oks_t0_washing\n",
              "22   6559.51              t0_anxiety\n",
              "37   5421.20           oks_t0_stairs\n",
              "34   5213.64             oks_t0_work\n",
              "29   4438.80        oks_t0_transport\n",
              "33   3610.42         oks_t0_kneeling\n",
              "20   3476.54             t0_activity\n",
              "35   3472.68       oks_t0_confidence\n",
              "30   3408.84          oks_t0_walking\n",
              "13   3230.02              depression\n",
              "21   3030.51           t0_discomfort\n",
              "31   2798.40         oks_t0_standing\n",
              "27   1670.23       oks_t0_night_pain\n",
              "26   1513.21             oks_t0_pain\n",
              "18   1401.19             t0_mobility\n",
              "6    1394.65             circulation\n",
              "15   1235.65             t0_assisted\n",
              "7     988.34            lung_disease\n",
              "32    892.12          oks_t0_limping\n",
              "8     848.78                diabetes\n",
              "3     787.41           heart_disease\n",
              "14    733.27               arthritis\n",
              "10    401.40          nervous_system\n",
              "4     350.56                 high_bp\n",
              "2     340.12                  x0_2.0\n",
              "1     340.12                  x0_1.0\n",
              "16    307.46     t0_previous_surgery\n",
              "24    307.46     t0_previous_surgery\n",
              "5     243.16                  stroke\n",
              "9     193.54          kidney_disease\n",
              "25    167.61  t0_living_arrangements\n",
              "11     83.85           liver_disease\n",
              "12     80.29                  cancer\n",
              "0      57.17                age_band\n",
              "23     28.30       t0_symptom_period"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat(\n",
        "        {\"score\": pd.Series(k10best[\"kbest\"].scores_), \"feature\": X_columns}, axis=1\n",
        "    ).sort_values(\"score\", ascending=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FXmpcqpV176"
      },
      "source": [
        "It is important to realize that linear regression is simply to be used as a baseline model; do not put too much effort into tuning; mainly use it to increase your understanding of the data and as a means of face validity, i.e. to see if there is nothing striking in the importance ranking, which could be indicative of errors in your data, in your data preparation or in your modeling approach. \n",
        "\n",
        "\n",
        "So even though you are in the CRISP-DM Modeling phase at this point, approach building a baseline model as helping you to increase your Data Understanding. This is exactly why it is important to not only estimate your baseline model and its performance, but also to describe it from different angles, by showing features in descending order of importance, but also by looking into the coefficients for each feature and their signs (i.e. whether they contribute to higher of lower outcomes).\n",
        "\n",
        "To this purpose, let's estimate the regression with the 10 best features, such that we can inspect their coefficients. Let's start with showing the dataframe for these 10 features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "xivAe2JMezS3",
        "outputId": "3745dd34-3935-4915-8e0a-03f60369e73b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t0_disability</th>\n",
              "      <th>t0_self_care</th>\n",
              "      <th>t0_anxiety</th>\n",
              "      <th>oks_t0_washing</th>\n",
              "      <th>oks_t0_transport</th>\n",
              "      <th>oks_t0_work</th>\n",
              "      <th>oks_t0_shopping</th>\n",
              "      <th>oks_t0_stairs</th>\n",
              "      <th>oks_t0_score</th>\n",
              "      <th>t0_eq_vas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>90.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>85.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>70.0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t0_disability  t0_self_care  t0_anxiety  oks_t0_washing  oks_t0_transport  \\\n",
              "0            2.0           1.0         1.0             2.0               2.0   \n",
              "1            2.0           1.0         1.0             3.0               2.0   \n",
              "2            2.0           1.0         1.0             3.0               3.0   \n",
              "3            1.0           1.0         1.0             4.0               2.0   \n",
              "4            2.0           1.0         1.0             2.0               2.0   \n",
              "5            2.0           1.0         1.0             3.0               2.0   \n",
              "6            1.0           2.0         1.0             3.0               2.0   \n",
              "7            1.0           1.0         1.0             3.0               2.0   \n",
              "8            2.0           1.0         1.0             4.0               3.0   \n",
              "9            2.0           1.0         2.0             4.0               2.0   \n",
              "\n",
              "   oks_t0_work  oks_t0_shopping  oks_t0_stairs  oks_t0_score  t0_eq_vas  \n",
              "0          2.0              2.0            2.0          18.0       90.0  \n",
              "1          2.0              2.0            2.0          18.0       85.0  \n",
              "2          1.0              3.0            2.0          21.0       87.0  \n",
              "3          2.0              2.0            2.0          23.0       90.0  \n",
              "4          2.0              2.0            3.0          23.0       50.0  \n",
              "5          2.0              3.0            1.0          19.0       55.0  \n",
              "6          2.0              2.0            1.0          18.0       95.0  \n",
              "7          1.0              3.0            2.0          19.0       70.0  \n",
              "8          2.0              3.0            2.0          24.0       95.0  \n",
              "9          1.0              2.0            3.0          19.0       40.0  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cols = k10best['kbest'].get_support(indices=True)\n",
        "X_set = pd.DataFrame(X_10best, columns=X_columns[cols])\n",
        "X_set.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "d0Xi05ynzFXN",
        "outputId": "ef052860-9886-4a46-a2fa-3ca71f4e9bf2"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t0_disability</th>\n",
              "      <th>t0_self_care</th>\n",
              "      <th>t0_anxiety</th>\n",
              "      <th>oks_t0_washing</th>\n",
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              "      <th>oks_t0_score</th>\n",
              "      <th>t0_eq_vas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.00</td>\n",
              "      <td>97465.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.47</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.39</td>\n",
              "      <td>2.81</td>\n",
              "      <td>2.07</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.85</td>\n",
              "      <td>1.78</td>\n",
              "      <td>18.96</td>\n",
              "      <td>67.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.89</td>\n",
              "      <td>7.70</td>\n",
              "      <td>19.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>19.00</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>24.00</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>48.00</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t0_disability  t0_self_care  t0_anxiety  oks_t0_washing  \\\n",
              "count       97465.00      97465.00    97465.00        97465.00   \n",
              "mean            1.47          1.30        1.39            2.81   \n",
              "std             0.50          0.47        0.56            1.00   \n",
              "min             1.00          1.00        1.00            0.00   \n",
              "25%             1.00          1.00        1.00            2.00   \n",
              "50%             1.00          1.00        1.00            3.00   \n",
              "75%             2.00          2.00        2.00            4.00   \n",
              "max             2.00          3.00        3.00            4.00   \n",
              "\n",
              "       oks_t0_transport  oks_t0_work  oks_t0_shopping  oks_t0_stairs  \\\n",
              "count          97465.00     97465.00         97465.00       97465.00   \n",
              "mean               2.07         1.41             1.85           1.78   \n",
              "std                0.83         0.87             1.20           0.89   \n",
              "min                0.00         0.00             0.00           0.00   \n",
              "25%                2.00         1.00             1.00           1.00   \n",
              "50%                2.00         1.00             2.00           2.00   \n",
              "75%                2.00         2.00             3.00           2.00   \n",
              "max                4.00         4.00             4.00           4.00   \n",
              "\n",
              "       oks_t0_score  t0_eq_vas  \n",
              "count      97465.00    97465.0  \n",
              "mean          18.96       67.5  \n",
              "std            7.70       19.7  \n",
              "min            0.00        0.0  \n",
              "25%           13.00       55.0  \n",
              "50%           19.00       70.0  \n",
              "75%           24.00       80.0  \n",
              "max           48.00      100.0  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_set.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vn645RETyhEV"
      },
      "source": [
        "With these features, we can estimate a Linear Regression and extract the feature coefficients."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "o8pTWPY1tmqP",
        "outputId": "c569116b-8a4b-47c8-9fe8-ef66b2544753"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Coefficients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>t0_disability</th>\n",
              "      <td>4.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>t0_self_care</th>\n",
              "      <td>-3.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>t0_anxiety</th>\n",
              "      <td>-3.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_washing</th>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_transport</th>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_work</th>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_shopping</th>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_stairs</th>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oks_t0_score</th>\n",
              "      <td>-0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>t0_eq_vas</th>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Coefficients\n",
              "t0_disability             4.10\n",
              "t0_self_care             -3.03\n",
              "t0_anxiety               -3.21\n",
              "oks_t0_washing            0.88\n",
              "oks_t0_transport          0.15\n",
              "oks_t0_work               0.39\n",
              "oks_t0_shopping           1.12\n",
              "oks_t0_stairs             0.51\n",
              "oks_t0_score             -0.06\n",
              "t0_eq_vas                 0.20"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lin_reg_full = LinearRegression()\n",
        "lin_reg_full.fit(X_set, t1_eq)\n",
        "pd.DataFrame(lin_reg_full.coef_.transpose(), X_columns[cols], columns=['Coefficients'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkUqnQ9ERpKa"
      },
      "source": [
        "The larger coefficients with contrasting signs for variables containing information that is 'pointing in the same direction' (disability / self care / anxiety) might indicate multicollinearity, which could possibly introduce some model instability. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc4NLsWHj94M"
      },
      "source": [
        "## LASSO Regression\n",
        "\n",
        "We will now apply LASSO regression, applied to the same numeric outcome (in contrast to the applying LASSO to a binary outcome, as seen before). We apply LASSO, a so-called regularized version of a normal regression, using cross-validation (following the example from [scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py)), which is now crucial as we have a hyperparameter to tune. \n",
        "\n",
        "Recall that Lasso contains an additional penalty component:\n",
        "\n",
        "$$ J(\\Theta) = MSE (\\Theta) + \\alpha\\sum\\limits_{i=1}^n \\mid{\\Theta_{i}}\\mid$$\n",
        "\n",
        "By default `LassoCV` tries 100 different values for $\\alpha$. \n",
        "\n",
        "We can then easily plot the MSE for all considered $\\alpha$ values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ao0DnJHhj94N"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
            "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LassoCV\n",
        "lasso = LassoCV(cv=5, random_state=42, n_jobs=-1).fit(X_train, t1_eq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "-HlD_UdJj94N",
        "outputId": "49ee2bf7-7152-455c-dcb6-aee7baf2544d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=S)\n",
        "plt.semilogx(lasso.alphas_, np.sqrt(lasso.mse_path_), \":\")\n",
        "plt.plot(\n",
        "    lasso.alphas_,\n",
        "    np.sqrt(lasso.mse_path_.mean(axis=-1)),\n",
        "    \"k\",\n",
        "    label=\"Average across the folds\",\n",
        "    linewidth=2,\n",
        ")\n",
        "plt.axvline(\n",
        "    lasso.alpha_, linestyle=\"--\", color=\"k\", label=\"alpha: CV estimate\"\n",
        ")\n",
        "plt.legend()\n",
        "plt.xlabel(r\"$\\alpha$\")\n",
        "plt.ylabel(\"Root mean square error\")\n",
        "plt.title(\"Root mean square error on each fold: coordinate descent\")\n",
        "plt.axis(\"tight\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APcCOsflj94O"
      },
      "source": [
        "The Lasso model performs best for low $\\alpha$ values. Below we can see how much that results in beta coefficients being pushed to zero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7QufvuEj94O",
        "outputId": "adbb533c-e813-48e8-9e02-9e16335566f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Lasso model:\n",
            "  RMSE: 15.97\n",
            "  alpha: 0.1211\n"
          ]
        }
      ],
      "source": [
        "print(\"Best Lasso model:\\n\"\n",
        "      f\"  RMSE: {np.sqrt(mean_squared_error(lasso.predict(X_train), t1_eq)):.2f}\\n\"\n",
        "      f\"  alpha: {lasso.alpha_:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ib6K1tN4j94O"
      },
      "source": [
        "By ranking the (absolute) regression coefficients from high to low, we can find the excluded variables (due to the LASSO penalty) at the bottom of the list:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "L9aT3qkwj94O",
        "outputId": "32eca936-c1db-4d50-c5bc-4480a6943cac"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>coef_abs</th>\n",
              "      <th>coef</th>\n",
              "      <th>feature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3.57</td>\n",
              "      <td>3.57</td>\n",
              "      <td>t0_disability</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2.99</td>\n",
              "      <td>-2.99</td>\n",
              "      <td>depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2.34</td>\n",
              "      <td>-2.34</td>\n",
              "      <td>t0_self_care</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2.21</td>\n",
              "      <td>-2.21</td>\n",
              "      <td>t0_anxiety</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.06</td>\n",
              "      <td>-2.06</td>\n",
              "      <td>lung_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.77</td>\n",
              "      <td>-1.77</td>\n",
              "      <td>heart_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.46</td>\n",
              "      <td>-1.46</td>\n",
              "      <td>circulation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.32</td>\n",
              "      <td>-1.32</td>\n",
              "      <td>diabetes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.07</td>\n",
              "      <td>-1.07</td>\n",
              "      <td>oks_t0_limping</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>oks_t0_shopping</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>oks_t0_washing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.52</td>\n",
              "      <td>t0_previous_surgery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>arthritis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.38</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>oks_t0_pain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.36</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>high_bp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.35</td>\n",
              "      <td>age_band</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.28</td>\n",
              "      <td>0.28</td>\n",
              "      <td>oks_t0_kneeling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.22</td>\n",
              "      <td>t0_assisted</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>oks_t0_score</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>t0_eq_vas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.15</td>\n",
              "      <td>oks_t0_walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.14</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>oks_t0_standing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.13</td>\n",
              "      <td>t0_previous_surgery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>oks_t0_night_pain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>x0_2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>oks_t0_stairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>stroke</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>oks_t0_confidence</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>oks_t0_work</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>kidney_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>nervous_system</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>x0_1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>t0_discomfort</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>oks_t0_transport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>liver_disease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>cancer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>t0_living_arrangements</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>t0_symptom_period</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>t0_mobility</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>t0_activity</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    coef_abs  coef                 feature\n",
              "17      3.57  3.57           t0_disability\n",
              "13      2.99 -2.99              depression\n",
              "19      2.34 -2.34            t0_self_care\n",
              "22      2.21 -2.21              t0_anxiety\n",
              "7       2.06 -2.06            lung_disease\n",
              "3       1.77 -1.77           heart_disease\n",
              "6       1.46 -1.46             circulation\n",
              "8       1.32 -1.32                diabetes\n",
              "32      1.07 -1.07          oks_t0_limping\n",
              "36      0.74  0.74         oks_t0_shopping\n",
              "28      0.54  0.54          oks_t0_washing\n",
              "24      0.52  0.52     t0_previous_surgery\n",
              "14      0.50 -0.50               arthritis\n",
              "26      0.38 -0.38             oks_t0_pain\n",
              "4       0.36 -0.36                 high_bp\n",
              "0       0.35  0.35                age_band\n",
              "33      0.28  0.28         oks_t0_kneeling\n",
              "15      0.22  0.22             t0_assisted\n",
              "38      0.20  0.20            oks_t0_score\n",
              "39      0.20  0.20               t0_eq_vas\n",
              "30      0.15  0.15          oks_t0_walking\n",
              "31      0.14 -0.14         oks_t0_standing\n",
              "16      0.13  0.13     t0_previous_surgery\n",
              "27      0.03 -0.03       oks_t0_night_pain\n",
              "2       0.00  0.00                  x0_2.0\n",
              "37      0.00  0.00           oks_t0_stairs\n",
              "5       0.00 -0.00                  stroke\n",
              "35      0.00 -0.00       oks_t0_confidence\n",
              "34      0.00  0.00             oks_t0_work\n",
              "9       0.00 -0.00          kidney_disease\n",
              "10      0.00 -0.00          nervous_system\n",
              "1       0.00 -0.00                  x0_1.0\n",
              "21      0.00  0.00           t0_discomfort\n",
              "29      0.00 -0.00        oks_t0_transport\n",
              "11      0.00 -0.00           liver_disease\n",
              "12      0.00 -0.00                  cancer\n",
              "25      0.00 -0.00  t0_living_arrangements\n",
              "23      0.00 -0.00       t0_symptom_period\n",
              "18      0.00 -0.00             t0_mobility\n",
              "20      0.00 -0.00             t0_activity"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat({'coef_abs': pd.Series(abs(lasso.coef_)), 'coef': pd.Series(lasso.coef_), 'feature': X_columns}, axis=1).sort_values('coef_abs', ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaNr8gLSus7z"
      },
      "source": [
        "### Question 6\n",
        "True assessment of model performance is not done by looking at the performance on the train set, but by evaluating performance on the test set. Use the developed LASSO model to predict outcomes for the test set, and compare the RMSE for the test set with the RMSE obtained above during cross-validation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQ0J7bU_j94Q"
      },
      "source": [
        "## KNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLBhRqNmyshQ"
      },
      "source": [
        "The next algorithm we apply is K nearest neighbor, which makes its prediction for a patient based on the outcomes for the K most comparable patients. This algorithm again has a tuning parameter / hyperparameter (K; indicating the number of most comparable patients to consider) that needs to be tuned using cross-validation.\n",
        "\n",
        "There are other hyperparameters to consider, such as how to determine the comparability/distance between instances, and whether to weigh comparable instances with regards to how comparable they are. \n",
        "\n",
        "In the example below, we only focus on the hyperparameter K."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "FLFMeEyP4YM6"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from collections import defaultdict\n",
        "\n",
        "n_neighbors = [1, 3, 5]\n",
        "knn = defaultdict(dict) # use defaultdict for nested dicts\n",
        "for n in n_neighbors:\n",
        "    knn[n][\"model\"] = KNeighborsRegressor(n_neighbors=n, n_jobs=-1).fit(X_train, t1_eq)\n",
        "    knn[n][\"cross validation scores\"] = cross_val_score(\n",
        "        knn[n][\"model\"], X_train, t1_eq, scoring=\"neg_mean_squared_error\", cv=5\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCfHKGX388_z"
      },
      "source": [
        "The graph below shows that these knn models result in very poor performance, which is expected as we used all available features and knn is not good at handling a large number of features. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "_3uLBMcq86hr",
        "outputId": "6be629ca-05d0-47df-c5d0-66985c96771d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot 1/K vs. error rate (such that the X-axis goes from low to high flexibility)\n",
        "knn_rmse = [np.sqrt(np.mean(-knn[n]['cross validation scores'])) for n in n_neighbors]\n",
        "plt.figure(figsize=S)\n",
        "plt.semilogx([1/n for n in n_neighbors], knn_rmse, 'o-', markeredgecolor='w', markeredgewidth='2');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtyJsWSO-Sg4"
      },
      "source": [
        "We could for example only work with the top-3 features found in the importance ranking earlier in this notebook, to see whether that improves performance, which will also speed up computational time considerably."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VZhiZYS6JmK",
        "outputId": "7932eb28-3123-4c39-b75e-5b2291703909"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "39    t0_eq_vas\n",
            "dtype: object\n",
            "38    oks_t0_score\n",
            "dtype: object\n",
            "19    t0_self_care\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(X_columns[X_columns=='t0_eq_vas'])\n",
        "print(X_columns[X_columns=='oks_t0_score'])\n",
        "print(X_columns[X_columns=='t0_self_care'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "tEf9Xlzy-NSY"
      },
      "outputs": [],
      "source": [
        "subset = X_train[:,[39, 38, 19]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xMBA9-W4fYN"
      },
      "source": [
        "### Question 7 \n",
        "\n",
        "- Retrain the knn model using only the subset of features defined above, and add a larger range for the hyperparameter n_neighbors, containing: [1, 2, 3, 5, 10, 20, 30, 50, 100, 200, 300]. Use the same loop as was applied in the KNN model trained above. \n",
        "- After running the model, plot the RMSE values for each of the hyperparameter values as was done above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7dByHxY2Cm_"
      },
      "source": [
        "This already looks better, but we have overlooked one important aspect of training knn models, which is to standardize features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSnMmxEPBTcl"
      },
      "source": [
        "### Question 8 (bonus)\n",
        "- Standardize the subset of features and retrain the knn model, while setting the `weights` hyperparameter to distance (make sure to check the documentation for the appropriate settings).\n",
        "- Plot the performance\n",
        "- Explain why performance did not improve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXRVdLXMUrCy"
      },
      "source": [
        "# Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85SMKoyCj94g"
      },
      "source": [
        "We considered three basic machine learning algorithms:\n",
        "- Regression\n",
        "- LASSO\n",
        "- KNN\n",
        "\n",
        "and applied them for modeling a numeric outcome. LASSO and KNN both used a hyperparameter and therefore needed to be tuned using cross-validation, which allowed us to compare performance for all possible hyperparameter settings. Regression is often used at the start, simply to quickly generate a baseline model. \n",
        "\n",
        "Quickly working towards a baseline model is good practice, as it forces you to set up your full pipeline, and in such a way that it can be tweaked relatively easily.\n",
        "\n",
        "A strategy for quickly getting started, would be to: \n",
        "- Run a regression to get baseline performance and check your results for face validity\n",
        "- Run LASSO and compare performance\n",
        "- With the selected variables in your LASSO, run KNN and compare performance\n",
        "\n",
        "The next notebook will look at more sophisticated machine learning algorithms that tend to perform better than the models considered in this notebook. In that notebook, a binary outcome instead of a numeric outcome will be modeled. \n"
      ]
    }
  ],
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