diff --git a/README.rst b/README.rst
index 61f2479c..3eed7617 100644
--- a/README.rst
+++ b/README.rst
@@ -93,11 +93,7 @@ Required at runtime:
     (for ``Data`` and ``NetCDFDictionary``)
 
 Optional *(used only in certain classes and methods)*:
-  - `PyNGL <http://www.pyngl.ucar.edu/Download/>`_
-    (for ``NetCDFDictionary``)
   - `Matplotlib <http://matplotlib.org/>`_
-  - `Matplotlib Basemap Toolkit <http://matplotlib.org/basemap/>`_
-    (for drawing maps)
   - `Cartopy <https://scitools.org.uk/cartopy/docs/latest/index.html>`_
     (for some plotting features)
   - `mpi4py <https://github.com/mpi4py/mpi4py>`_
diff --git a/docs/source/api/climate/map_plots.rst b/docs/source/api/climate/map_plots.rst
index 415c618d..5ba17481 100644
--- a/docs/source/api/climate/map_plots.rst
+++ b/docs/source/api/climate/map_plots.rst
@@ -1,8 +1,8 @@
 
-climate.map_plots
+climate.map_plot
 =================
 
-.. automodule:: pyunicorn.climate.map_plots
+.. automodule:: pyunicorn.climate.map_plot
     :synopsis: spatially embedded complex networks, multivariate data,
                time series surrogates
     :members:
diff --git a/notebooks/tutorial_ClimateNetworks.ipynb b/notebooks/tutorial_ClimateNetworks.ipynb
index 2174ef44..d0650e42 100644
--- a/notebooks/tutorial_ClimateNetworks.ipynb
+++ b/notebooks/tutorial_ClimateNetworks.ipynb
@@ -13,7 +13,7 @@
    "id": "677ae7d7",
    "metadata": {},
    "source": [
-    "The objective of this tutorial is to introduce climate networks and explain and illustrate their application with the __pyunicorn__ package. First some theoretical background for understanding general climate networks will be given and then some methods provided by `pyunicorn.climate.ClimateNetwork` will be illustrated. An introduction and application of coupled climate networks will follow. For a detailed discussion and further references, please consult __[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based. "
+    "The objective of this tutorial is to introduce climate networks, and to explain and illustrate their application with the `pyunicorn` package. First some theoretical background for understanding general climate networks will be given, and then some methods provided by `pyunicorn.climate.ClimateNetwork` will be illustrated. An introduction and application of coupled climate networks will follow. For a detailed discussion and further references, please consult __[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based. "
    ]
   },
   {
@@ -29,9 +29,9 @@
    "id": "a56c11e0",
    "metadata": {},
    "source": [
-    "_Climate networks (CN)_ are a way to apply complex network theory to the climate system, by assuming that each node represents a varying dynamical system. Of interest is then the collective behaviour of these interacting dynamical system and the structure of the resulting network. This approach was first introduced by __[Tsonis and Roebber, 2004](https://www.sciencedirect.com/science/article/abs/pii/S0378437103009646)__.\n",
+    "_Climate networks (CN)_ are a way to apply complex network theory to the climate system, by assuming that each node represents a varying dynamical system. Of interest is then the collective behaviour of these interacting dynamical systems and the structure of the resulting network. This approach was first introduced by __[Tsonis and Roebber, 2004](https://www.sciencedirect.com/science/article/abs/pii/S0378437103009646)__.\n",
     "\n",
-    "Climate network analysis is a versatile approach for investigating climatological data and can be used as a complementary method to classical techniques from multivariate statistics. The approach allows for the analysis of single fields of climatological time series, e.g. surface air temperature observed on a grid, or even two or more fields. It has been succesfully applied in many cases, for example to dynamics and predictability of the El Niño Phenomenon \\[__[Radebach et al., 2013](https://arxiv.org/abs/1310.5494)__\\]."
+    "CN analysis is a versatile approach for investigating climatological data, and it can be used as a complementary method to classical techniques from multivariate statistics. The approach allows for the analysis of single fields of climatological time series, e.g., surface air temperature observed on a grid, or even two or more fields. It has been successfully applied in many cases, for example to dynamics and predictability of the El Niño Phenomenon (__[Radebach et al., 2013](https://arxiv.org/abs/1310.5494)__)."
    ]
   },
   {
@@ -39,7 +39,7 @@
    "id": "05e76cc7",
    "metadata": {},
    "source": [
-    "## Theory of Climate Networks (CN)"
+    "## Theory of Climate Networks (CNs)"
    ]
   },
   {
@@ -47,7 +47,7 @@
    "id": "fcc79d2d",
    "metadata": {},
    "source": [
-    "Climate networks (class `climate.ClimateNetwork`) are a typical application of _functional networks_, which allow to study the dynamical relationships between subsystems of a high-dimensional complex system by constructing networks from it. The package provides classes for the construction and analysis of such networks, representing the statistical interdependency structure within and between fields of time series using various similarity measures."
+    "CNs are a typical application of _functional networks_, which allow to study the dynamical relationships between subsystems of a high-dimensional complex system by constructing networks from it. `pyunicorn` provides classes for the construction and analysis of such networks, representing the statistical interdependency structure within and between fields of time series using various similarity measures."
    ]
   },
   {
@@ -63,13 +63,9 @@
    "id": "30cd9555",
    "metadata": {},
    "source": [
-    "Climate Networks represent strong statistical interrelationships between time series of climatological fields. These statistical interrelationships can be estimated with methods from the `timeseries.CouplingAnalysis` class in terms of matrices of _statistical similarities_ $\\textbf{S}$, such as the _(lagged) classical linear Pearson product-moment correlation coefficient_ (CC). \n",
-    "\n",
-    "The CC of two zero-mean time series Variable $X$,$Y$, implemented in `CouplingAnalysis.cross_correlation`, is given by \n",
-    "\n",
-    "$$\\rho_{XY}(\\tau)=\\frac{\\langle X_{t-\\tau}, Y_t \\rangle}{\\sigma_X \\sigma_Y}$$\n",
-    "\n",
-    "which depents on the covariance $\\langle X_{t-\\tau}, Y_t \\rangle$ and standard deviations $\\sigma_X, \\sigma_Y$. Lags $\\tau > 0$ correspond to the linear association of past values of $X$ with $Y$, and vice versa for $\\tau < 0$. "
+    "CNs represent strong statistical interrelationships between time series of climatological fields. These statistical interrelationships can be estimated with methods from the `funcnet.CouplingAnalysis` class in terms of matrices of _statistical similarities_ $\\textbf{S}$, such as the _(lagged) classical linear Pearson product-moment correlation coefficient_ (CC). The CC of two zero-mean time series variables $X,Y$, as implemented in `funcnet.CouplingAnalysis.cross_correlation()`, is given by \n",
+    "$$\\rho_{XY}(\\tau)=\\frac{\\langle X_{t-\\tau}, Y_t \\rangle}{\\sigma_X \\sigma_Y}\\,,$$\n",
+    "which depends on the covariance $\\langle X_{t-\\tau}, Y_t \\rangle$ and the standard deviations $\\sigma_X, \\sigma_Y$. Lags $\\tau > 0$ correspond to the linear association of past values of $X$ with $Y$, and vice versa for $\\tau < 0$. "
    ]
   },
   {
@@ -77,7 +73,7 @@
    "id": "70377c40",
    "metadata": {},
    "source": [
-    "#### Similarity Measures for Climate Networks"
+    "### Similarity Measures for CNs"
    ]
   },
   {
@@ -85,13 +81,9 @@
    "id": "fadb2909",
    "metadata": {},
    "source": [
-    "By thresholding the matrix of a statistical similarity measure $\\textbf{S}$, e.g. based on the CC from above, the interellationships between time series of climate networks can be reconstructed:\n",
-    "\n",
-    "$$A_{pq} = \\Theta(S_{pq}-\\beta), \\text{ if } p \\neq q$$\n",
-    "\n",
-    "and 0 otherwise. $\\Theta$ is the Heaviside function, $\\beta$ denotes a threshold parameter and $A_{pp} = 0$ is set for all nodes $p$ to exclude self-loops. \n",
-    "\n",
-    "A climate network that is reconstructed using the pearson correlation  from above is call _pearson correlation climate network_."
+    "By thresholding the matrix of a statistical similarity measure $\\textbf{S}$, the interrelationships between time series of climate networks can be reconstructed:\n",
+    "$$A_{pq} = \\Theta(S_{pq}-\\beta)\\quad \\text{ if } p \\neq q; \\qquad 0\\quad\\text{otherwise}\\,,$$\n",
+    "where $\\Theta$ is the Heaviside function, $\\beta$ denotes a threshold parameter, and $A_{pp} = 0$ for all nodes $p$ to exclude self-loops. A CN that is reconstructed using the Pearson CC from above is called a _Pearson correlation CN_."
    ]
   },
   {
@@ -99,79 +91,43 @@
    "id": "9c64c013",
    "metadata": {},
    "source": [
-    "## Constructing CN with pyunicorn"
+    "## Constructing CNs"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "3027c7f8",
+   "id": "ff7f5d81-129e-4966-a7fc-a0d25aea87f3",
    "metadata": {},
    "source": [
-    "After establishing some basic theoretic background, we can use pyunicorn to try out some tools for climate networks. First, download the data set following this __[link](https://psl.noaa.gov/repository/entry/show?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j)__ and copy it to the directory \"notebooks\" of this script ot change the path below."
+    "Having established some basic theoretic background, we will now use `pyunicorn` to construct a CN. We start with some imports and some specifications regarding an example __[NOAA dataset](https://psl.noaa.gov/repository/entry/show?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j)__, which is already contained in this notebook's directory."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "35c8e273",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATA_FILENAME = \"./air.mon.mean.nc\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "543a9d17",
-   "metadata": {},
-   "source": [
-    "Now we will start with some imports and some specifications regarding the data set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
    "id": "e793f1a2",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from pyunicorn import climate\n",
-    "from matplotlib import pyplot as plt"
+    "from matplotlib import pyplot as plt\n",
+    "from pyunicorn import climate"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "a1e3f614",
+   "execution_count": 2,
+   "id": "6f1a55f9-8560-484d-ab6a-17ba8b8cd67c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "FILE_TYPE = \"NetCDF\"\n",
+    "DATA_FILENAME = \"./air.mon.mean.nc\"\n",
+    "#  Indicate data source (optional)\n",
+    "DATA_SOURCE = \"ncep_ncar_reanalysis\"\n",
     "#  Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n",
     "#  lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n",
-    "#  For example, the \"NetCDF\" FILE_TYPE is compatible with data from the IPCC\n",
-    "#  AR4 model ensemble or the reanalysis data provided by NCEP/NCAR."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "766f7c90",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  Indicate data source (optional)\n",
-    "DATA_SOURCE = \"ncep_ncar_reanalysis\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "30518b4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "FILE_TYPE = \"NetCDF\"\n",
     "#  Name of observable in NetCDF file (\"air\" indicates surface air temperature\n",
     "#  in NCEP/NCAR reanalysis data)\n",
     "OBSERVABLE_NAME = \"air\""
@@ -179,68 +135,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "24efb40c",
+   "execution_count": 3,
+   "id": "12e44ccb-ba25-4bf9-8170-ed12310b739b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#  Select a subset in time and space from the data (e.g., a particular region\n",
-    "#  or a particular time window, or both)\n",
+    "#  Select a region in time and space from the data (here the whole dataset)\n",
     "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 0, \"lon_min\": 0,\n",
-    "          \"lat_max\": 30, \"lon_max\": 0}  # selects the whole data set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6373f01d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "          \"lat_max\": 30, \"lon_max\": 0}\n",
     "#  Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n",
-    "#  data). This is used for calculating climatological anomaly values\n",
-    "#  correctly.\n",
+    "#  data). This is used for calculating climatological anomaly values.\n",
     "TIME_CYCLE = 12"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "ef63ac55",
-   "metadata": {},
-   "source": [
-    "Now we set some values related to the climate network construction, the first being the threshold $\\beta$ from above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b6cf01fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  For setting fixed threshold\n",
-    "THRESHOLD = 0.5\n",
-    "\n",
-    "#  For setting fixed link density\n",
-    "LINK_DENSITY = 0.005\n",
-    "\n",
-    "#  Indicates whether to use only data from winter months (DJF) for calculating\n",
-    "#  correlations\n",
-    "WINTER_ONLY = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc905586",
+   "id": "9f54ffe5-02a5-47e4-a459-870e1e8afef6",
    "metadata": {},
    "source": [
-    "Now we create a ClimateData object containing our data and then print the information."
+    "Now we set some parameters for the CN construction, the first being the threshold $\\beta$ from above, and create a `ClimateData` object containing our data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "4e49c74a",
+   "execution_count": 4,
+   "id": "baf245bd-2f3e-401d-bc1d-9943fee4bbf2",
    "metadata": {
     "scrolled": true
    },
@@ -250,7 +170,6 @@
      "output_type": "stream",
      "text": [
       "Reading NetCDF File and converting data to NumPy array...\n",
-      "File format: NETCDF4_CLASSIC\n",
       "Global attributes:\n",
       "description: Data from NCEP initialized reanalysis (4x/day).  These are the 0.9950 sigma level values\n",
       "platform: Model\n",
@@ -280,26 +199,33 @@
     }
    ],
    "source": [
+    "#  For setting fixed threshold\n",
+    "THRESHOLD = 0.5\n",
+    "#  For setting fixed link density\n",
+    "LINK_DENSITY = 0.005\n",
+    "#  Indicates whether to use only data from winter months (DJF) for calculating\n",
+    "#  correlations\n",
+    "WINTER_ONLY = False\n",
+    "\n",
     "data = climate.ClimateData.Load(\n",
     "    file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME,\n",
     "    data_source=DATA_SOURCE, file_type=FILE_TYPE,\n",
     "    window=WINDOW, time_cycle=TIME_CYCLE)\n",
-    "\n",
-    "#  Print some information on the data set\n",
     "print(data)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "2980a2c0",
+   "id": "2fade6f6-8457-436f-a52a-77464e92fd54",
    "metadata": {},
    "source": [
-    "Now we create a climate network based on Pearson correlation without lag and with fixed threshold."
+    "Next, we construct a CN based on the Pearson CC, without lag and with fixed threshold. Alternatively, several other similarity measures and construction mechanisms may be used as well."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "c5326b90",
    "metadata": {},
    "outputs": [
@@ -322,30 +248,38 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "5e7b5963",
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2cdee7ef-7d2f-46d9-83ed-c9253e0100c0",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Alternatively, several similarity measures and construction mechanisms may be chosen here."
+    "#  Create a climate network based on Pearson correlation without lag and with\n",
+    "#  fixed link density\n",
+    "# net = climate.TsonisClimateNetwork(\n",
+    "#     data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "b8c963fd",
+   "execution_count": 7,
+   "id": "e6bf3b44-193b-48c0-b7be-f056bd35d72c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#  Create a climate network based on Pearson correlation without lag and with\n",
-    "#  fixed link density\n",
-    "# net = climate.TsonisClimateNetwork(\n",
-    "#     data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY)\n",
-    "\n",
     "#  Create a climate network based on Spearman's rank order correlation without\n",
     "#  lag and with fixed threshold\n",
     "# net = climate.SpearmanClimateNetwork(\n",
-    "#     data, threshold=THRESHOLD, winter_only=WINTER_ONLY)\n",
-    "\n",
+    "#     data, threshold=THRESHOLD, winter_only=WINTER_ONLY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5cdcfc8a-6448-4df3-b49f-f3e5d220f0f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "#  Create a climate network based on mutual information without lag and with\n",
     "#  fixed threshold\n",
     "# net = climate.MutualInfoClimateNetwork(\n",
@@ -357,12 +291,12 @@
    "id": "b443476e",
    "metadata": {},
    "source": [
-    "We finish by doing some calculations and saving them to text files."
+    "We finish by calculating some basic network measures for the resulting CN, optionally saving them to text files."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "4f568b0f",
    "metadata": {},
    "outputs": [
@@ -396,15 +330,10 @@
     "#  Get maximum link distance\n",
     "mld = net.max_link_distance()\n",
     "\n",
-    "#\n",
-    "#  Save results to text file\n",
-    "#\n",
-    "\n",
     "#  Save the grid (mainly vertex coordinates) to text files\n",
-    "data.grid.save_txt(filename=\"grid.txt\")\n",
-    "\n",
+    "#data.grid.save_txt(filename=\"grid.txt\")\n",
     "#  Save the degree sequence. Other measures may be saved similarly.\n",
-    "np.savetxt(\"degree.txt\", degree)"
+    "#np.savetxt(\"degree.txt\", degree)"
    ]
   },
   {
@@ -412,63 +341,28 @@
    "id": "15af9941",
    "metadata": {},
    "source": [
-    "### Plotting"
+    "## Plotting CNs"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "4ee5b44c",
+   "id": "b26e5953-53c6-418a-b08b-509ad415081f",
    "metadata": {},
    "source": [
-    "`pyunicorn` provides a basic plotting feature based on the cartopy package and matplotlib that can be used to have a first look at the generated data. Also the plotting with the `pyNGL` package is still supported but not recommended, as it is deprecated and its development currently at halt in favor for the cartopy project. For plotting in pyunicorn with `pyNGL` an old tutorial can be found in `examples\\tutorials\\climate_networks.py`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ba76b85",
-   "metadata": {},
-   "source": [
-    "#### Cartopy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80c32759",
-   "metadata": {},
-   "source": [
-    "For more info on and how to install cartopy please check out their webpage: https://scitools.org.uk/cartopy/docs/latest/ !\n",
-    "\n",
-    "*Copyright: Cartopy. Met Office. git@github.com:SciTools/cartopy.git.* "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51440d40",
-   "metadata": {},
-   "source": [
-    "We start by creating a plot class, which later on we can modify by acessing its axes. "
+    "`pyunicorn` provides a basic plotting feature based on the __[`cartopy`](https://scitools.org.uk/cartopy/docs/latest/)__ and `matplotlib` packages, which can be used to have a first look at the generated data. We start by initializing a `MapPlot` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "b823297c",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created plot class.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# create a Cartopy plot instance called cn_plot (cn for climate network)\n",
+    "# create a Cartopy plot instance called map_plot\n",
     "# from the data with title DATA_SOURCE\n",
-    "cn_plot = climate.CartopyPlots(data.grid, DATA_SOURCE)"
+    "map_plot = climate.MapPlot(data.grid, DATA_SOURCE)"
    ]
   },
   {
@@ -476,45 +370,20 @@
    "id": "422af668",
    "metadata": {},
    "source": [
-    "Now we add the network measures that we want to plot out via the `.add_dataset()` method, which takes a title and a network measure. The title will also be the name of the plot that will be saved."
+    "With `MapPlot.plot()`, we can now plot some of our previously calculated measures on the given grid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "056f3a92",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add network measures to the plotting queue\n",
-    "cn_plot.add_dataset(\"Degree\", degree)\n",
-    "cn_plot.add_dataset(\"Closeness\", closeness)\n",
-    "cn_plot.add_dataset(\"Betweenness (log10)\", np.log10(betweenness + 1))\n",
-    "cn_plot.add_dataset(\"Clustering\", clustering)\n",
-    "cn_plot.add_dataset(\"Average link distance\", ald)\n",
-    "cn_plot.add_dataset(\"Maximum link distance\", mld)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a76114b5",
-   "metadata": {},
-   "source": [
-    "Before plotting, we can change the plots by accessing `ax`, since cartopy is based on `maplotlib`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "9a001877",
-   "metadata": {
-    "scrolled": true
-   },
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -522,45 +391,56 @@
     }
    ],
    "source": [
-    "ax = plt.set_cmap('plasma') "
+    "# plot degree\n",
+    "map_plot.plot(degree, \"Degree\")\n",
+    "\n",
+    "# add matplotlib.pyplot or cartopy commands to customize figure\n",
+    "plt.set_cmap('plasma')\n",
+    "# optionally save figure\n",
+    "#plt.savefig('degree.png')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "33c0f03c",
+   "id": "b8feb1e0",
    "metadata": {},
    "source": [
-    "Now we can generate the plots in the current directory."
+    "Try plotting more measures if you like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "73aa161c",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 12,
+   "id": "8b67424d",
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created and saved plots @ current directory.\n"
-     ]
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAESCAYAAAAVAhemAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydZ3RcxdmAn+270qr33i1ZxUXuVe4F24DBgGnGhg9ICIRAKKGFGhJIgBB6KCZ0gik2Bhvce++WZFu9d2klbW/3+7HWWmt1W67Z5xyfY929ZWbu3Hln3nmLSBAEATdu3Lhx48bNZYn4QhfAjRs3bty4cXPucAt6N27cuHHj5jLGLejduHHjxo2byxi3oHfjxo0bN24uY9yC3o0bN27cuLmMcQt6N27cuHHj5jLGLejduHHjxo2byxi3oHfjxo0bN24uY9yC3o0bN27cuLmMcQt6N27cXDQUFxcjEon4+OOPz8n9Y2NjWbx48Tm5txs3FytuQe/GjRs3btxcxkgvdAHcuHHj5nxx/PhxxGL3+sbN/xbuHu/GzSWA3W7HaDSe8fV6vb4fS3PpolAokMlkF7oYbtycV9yC3s055ZlnnkEkEpGfn8/ixYvx9fXFx8eHJUuWdBA+n332GSNHjsTDwwM/Pz8mTpzIr7/+6nLOqlWrmDBhAp6ennh5eTFnzhyys7Ndzlm8eDFqtZrCwkJmzpyJp6cn4eHhPPfcc/Q1WeOkSZNIT08nJyeHyZMn4+HhQUREBC+//HKHc41GI8888wwDBgxAqVQSFhbGNddcQ0FBgfOcf/zjH4wdO5aAgABUKhXDhg1j2bJlHe4lEom49957+fzzz0lLS0OhULB69eo+lXnfvn1MnDgRDw8PHn/8cQBMJhNPP/00iYmJKBQKoqKieOSRRzCZTC73WLp0KVOmTCE4OBiFQkFqairvvPNOh2fFxsYyd+5ctm7dysiRI1EqlcTHx/PJJ5+4nNfY2MhDDz1ERkYGarUab29vZs+ezaFDh7qty9KlSxGJRBw4cKDDby+++CISiYSKigoA8vLyuPbaawkNDUWpVBIZGcnChQtpbm52KW/7PXqLxcKzzz5LUlISSqWSgIAAxo8fz5o1a7pvZDduLiHcgt7NeeH666+ntbWVv/71r1x//fV8/PHHPPvss87fn332WW699VZkMhnPPfcczz77LFFRUaxfv955zqeffsqcOXNQq9W89NJLPPXUU+Tk5DB+/HiKi4tdnmez2Zg1axYhISG8/PLLDBs2jKeffpqnn366z2Vvampi1qxZDB48mFdeeYWUlBQeffRRVq1a5fK8uXPn8uyzzzJs2DBeeeUV7r//fpqbmzl69KjzvNdff52hQ4fy3HPP8eKLLyKVSrnuuuv46aefOjx3/fr1PPDAA9xwww28/vrrxMbG9rrMDQ0NzJ49myFDhvDPf/6TyZMnY7fbufLKK/nHP/7BvHnzeOONN7j66qt57bXXuOGGG1yuf+edd4iJieHxxx/nlVdeISoqinvuuYe33nqrw7Py8/NZsGAB06dP55VXXsHPz4/Fixe7TMAKCwv54YcfmDt3Lq+++ioPP/wwR44cISsri8rKyi7rsWDBAlQqFZ9//nmH3z7//HMmTZpEREQEZrOZmTNnsnPnTu677z7eeust7rrrLgoLC9FoNF3e/5lnnuHZZ59l8uTJvPnmmzzxxBNER0ezf//+XrSyGzeXCIIbN+eQp59+WgCE22+/3eX4/PnzhYCAAEEQBCEvL08Qi8XC/PnzBZvN5nKe3W4XBEEQWltbBV9fX+HOO+90+b26ulrw8fFxOX7bbbcJgHDfffe53GfOnDmCXC4X6urqel3+rKwsARA++eQT5zGTySSEhoYK1157rfPYRx99JADCq6++2uEebXUQBEHQ6/Uuv5nNZiE9PV2YMmWKy3FAEIvFQnZ2dq/LenqZ3333XZfjn376qSAWi4UtW7a4HH/33XcFQNi2bVuX5RQEQZg5c6YQHx/vciwmJkYAhM2bNzuP1dbWCgqFQvjjH//oPGY0Gju826KiIkGhUAjPPfecyzFAWLp0qfPYjTfeKISHh7tcv3//fpfzDhw4IADCN99801WzOMt72223Of8ePHiwMGfOnG6vcePmUse9ondzXvjNb37j8veECRNoaGigpaWFH374Abvdzp///OcOhlIikQiANWvWoNFouPHGG6mvr3f+k0gkjBo1ig0bNnR45r333utyn3vvvRez2czatWv7VHa1Ws0tt9zi/FsulzNy5EgKCwudx7799lsCAwO57777OlzfVgcAlUrl/H9TUxPNzc1MmDCh0xVkVlYWqampfSprGwqFgiVLlrgc++abbxg4cCApKSkubThlyhQAlzZsX87m5mbq6+vJysqisLDQRRUOkJqayoQJE5x/BwUFkZyc7NI+CoXC+W5tNhsNDQ2o1WqSk5N7XD0vWrSIyspKl/J9/vnnqFQqrr32WgB8fHwA+OWXX/pkj+Dr60t2djZ5eXm9vsaNm0sNt6B3c16Ijo52+dvPzw9wCLuCggLEYnG3Qq1tIJ4yZQpBQUEu/3799Vdqa2tdzheLxcTHx7scGzBgAEAHNX9PREZGugjrtvI3NTU5/y4oKCA5ORmptHtHlpUrVzJ69GiUSiX+/v4EBQXxzjvvdBCeAHFxcX0qZ3siIiKQy+Uux/Ly8sjOzu7Qfm3t0r4Nt23bxrRp0/D09MTX15egoCDnPv/pZT393ULH9rHb7bz22mskJSWhUCgIDAwkKCiIw4cPd1r39kyfPp2wsDCn+t5ut/Pll19y1VVX4eXlBTja6sEHH+SDDz4gMDCQmTNn8tZbb/V47+eeew6NRsOAAQPIyMjg4Ycf5vDhw91e48bNpYbbvc7NeUEikXR6XOilcZzdbgcc+/ShoaEdfu9JwJ4NZ1v2NrZs2cKVV17JxIkTefvttwkLC0Mmk7F06VK++OKLDue3X1X3lc6utdvtZGRk8Oqrr3Z6TVRUFOCYtEydOpWUlBReffVVoqKikMvl/Pzzz7z22mvOd9FGb9rnxRdf5KmnnuL222/n+eefx9/fH7FYzB/+8IcO9zsdiUTCTTfdxPvvv8/bb7/Ntm3bqKysdNGyALzyyissXryY5cuX8+uvv/L73/+ev/71r+zcuZPIyMhO7z1x4kQKCgqc13zwwQe89tprvPvuu/zf//1ft+Vy4+ZSwS3o3VxwEhISsNvt5OTkMGTIkC7PAQgODmbatGk93tNut1NYWOhcrQKcOHECoE9Gbb0lISGBXbt2YbFYunTf+vbbb1Eqlfzyyy8oFArn8aVLl/Z7eboq46FDh5g6dWoHDUV7fvzxR0wmEytWrHBZrXe2PdJbli1bxuTJk/nwww9djms0GgIDA3u8ftGiRbzyyiv8+OOPrFq1iqCgIGbOnNnhvIyMDDIyMnjyySfZvn0748aN49133+WFF17o8t7+/v4sWbKEJUuWoNVqmThxIs8884xb0Lu5bHCr7t1ccK6++mrEYjHPPfdch9Vd26pw5syZeHt78+KLL2KxWDrco66ursOxN9980+U+b775JjKZjKlTp/ZzDeDaa6+lvr7e5Zntnw2OlalIJMJmszl/Ky4u5ocffuj38nTG9ddfT0VFBe+//36H3wwGAzqdzllOcF2RNzc3n9WERCKRdNCAfPPNN07XuJ4YNGgQgwYN4oMPPuDbb79l4cKFLlqclpYWrFaryzUZGRmIxeIOroPtaWhocPlbrVaTmJjY7TVu3FxquFf0bi44iYmJPPHEEzz//PNMmDCBa665BoVCwZ49ewgPD+evf/0r3t7evPPOO9x6661kZmaycOFCgoKCKC0t5aeffmLcuHEuQlapVLJ69Wpuu+02Ro0axapVq/jpp594/PHHCQoK6vc6LFq0iE8++YQHH3yQ3bt3M2HCBHQ6HWvXruWee+7hqquuYs6cObz66qvMmjWLm266idraWt566y0SExPPy77wrbfeyn//+19+85vfsGHDBsaNG4fNZuPYsWP897//5ZdffmH48OHMmDEDuVzOvHnzuPvuu9Fqtbz//vsEBwdTVVV1Rs+eO3cuzz33HEuWLGHs2LEcOXKEzz//vIMdRXcsWrSIhx56CKCD2n79+vXce++9XHfddQwYMACr1cqnn36KRCJxGux1RmpqKpMmTWLYsGH4+/uzd+9eli1b5mLI6cbNJc+FM/h3879Am3vd6S5tS5cuFQChqKjIeeyjjz4Shg4dKigUCsHPz0/IysoS1qxZ43Ldhg0bhJkzZwo+Pj6CUqkUEhIShMWLFwt79+51nnPbbbcJnp6eQkFBgTBjxgzBw8NDCAkJEZ5++ukOLl49kZWVJaSlpXU4fttttwkxMTEux/R6vfDEE08IcXFxgkwmE0JDQ4UFCxYIBQUFznM+/PBDISkpSVAoFEJKSoqwdOlSZxu1BxB+97vf9amsPZVZEBzufC+99JKQlpbmbOdhw4YJzz77rNDc3Ow8b8WKFcKgQYMEpVIpxMbGCi+99JLThbD9O4uJienUPS0rK0vIyspy/m00GoU//vGPQlhYmKBSqYRx48YJO3bs6HBeZ+51bVRVVQkSiUQYMGBAh98KCwuF22+/XUhISBCUSqXg7+8vTJ48WVi7dq3Leae7173wwgvCyJEjBV9fX0GlUgkpKSnCX/7yF8FsNnfafm7cXIqIBKGPFkVu3FzkLF68mGXLlqHVai90Udz0I/X19YSFhfHnP/+Zp5566kIXx42bSwb3Hr0bN24uCT7++GNsNhu33nrrhS6KGzeXFO49ejf/kzQ2NmI2m7v8XSKRnJO9/LPhUixzf7B+/XpycnL4y1/+wtVXX31OvCbcuLmccQt6N/+TXHPNNWzatKnL32NiYvocWOdccymWuT947rnnnK5yb7zxxoUujhs3lxzuPXo3/5Ps27fPJXLb6ahUKsaNG3ceS9Qzl2KZ3bhxc+FxC3o3bty4cePmMsZtjOfGjRs3btxcxrgFvRs3bty4cXMZ4xb055n58+fj5+fHggULANDr9cyePZuUlBTS0tJcjI3q6+uZPHkySUlJXHPNNRiNRgDKy8vJysripptuwmazsXz5chYuXOi87t5772XWrFnOv5988kleeuml81TDM6OoqIjJkyeTmppKRkYGOp2O3bt3k5aWRmJiIs8995zz3HXr1jFs2DD+/ve/A/DAAw/w7rvvOn9PT0/nb3/7m/Pv8ePHs2vXrvNXmXPIypUrSU5OJikpiQ8++ACAzz77jMzMTD777LMLXLr+52z6BTiSHQ0ZMsT575NPPrkQ1egz52KcAJg0aRIpKSnO9vj9739//ivn5vxzIaP1/C+yYcMGYcWKFcK1114rCIIg6HQ6YePGjYIgCEJra6uQnJws5OXlCYIgCH/84x+FN954o8P/H3nkESE3N1d46623hFWrVgk1NTUuUdrGjx8vjB49WrDb7YIgCMKUKVOEzZs3n68qnhETJ050lrGhoUGwWCzC8OHDhUOHDglWq1UYNWqUcPjwYUEQBOH6668XDAaDcOONNwqtra3C119/7Yx21tzcLGRmZgpXXXWVIAiOSHB+fn6XRaQzi8UiJCUlCeXl5UJra6swYMAAob6+Xrj66qsFs9nsrPPlxNn0C0EQhICAgAtW9rPhXIwTguCIWHjkyJHzXR03Fxj3iv48M2nSJGcObQAPDw+ysrIAR0KN5ORkZzzxFStWOIOD3HLLLfz444+AIzObWCxGKpVit9sJDg5GIpFQVVWFwWBAJpORmprKsWPHsNvtHDp0iOHDh5/nmvae7OxsZDIZEyZMABzZxGpra7FarQwaNAiJRMLChQtZuXIl4Ki/SCRyJkoZM2YMO3bsAGDXrl1cccUVztzqBw8eJDU1tcuMcpcSbSvZiIgI1Go1s2fP5tdff0UQBEQiUbcZ6S5FzrZfXMqci3HCzf8ubkF/EVFWVsbhw4fJzMwEHBnDfHx8AIiIiHBm+rrnnnu4/fbb2bBhgzNVZ5uw27t3L8OGDWP06NHs2LGD7Oxs4uPjzyq3+bkmLy8PtVrNvHnzyMzM5MUXX6SyspKIiAjnOe3rv2TJEsaMGUN6ejpeXl5ERUWh1+tpaGhg586djB49mri4OAoLC9m5cydjx469UFXrV7pqk3nz5jFixAjmz59/AUvX/5xtvwBHGtz2qvuzSbV7sXA24wTAggULnO3RfpvDzeWLO2DORYLJZOKGG27g73//O56ent2eGxcXx9atW12OtQn64OBgRo8eTVJSEm+88QY2m40xY8acy6KfNVarlS1btnDw4EGCg4OZNWtWtyvwK664giuuuMLl2OjRo9m5cye7du3innvuoaCggB07drBz507nPuflyh133MEdd9xxoYvR7/RHv/D19eXgwYPnuKTnj7MdJwCWLVtGenr6uSqim4sQ94r+IkAQBBYtWsQVV1zhIpR8fHxobm4GoKKigvDw8C7vMWbMGKegGz16NGlpaRw9evSSWNFGREQwfPhwoqKiUCgUXHHFFej1epdc5b2p/44dO2hoaCAgIIBRo0Y52+Nir39vCQ8P71ObXOr0R7+4nOiPccLN/yZuQX8R8Nhjj+Hh4cGTTz7pcnzu3Ll8+umngMOyet68eV3eY9CgQeTm5lJaWkpERAQSiQQPDw/WrVt30a/oR4wYQW1tLU1NTdjtdjZv3sywYcOQSCQcPnwYm83GV1991W39x4wZwxdffEFiYiIAQ4cOZc2aNQiCQEhIyPmqyjll5MiRHD16lIqKCrRaLatWrXJRyV5u9Ee/uJzoj3HCzf8oF9IS8H+RqVOnCoGBgYJKpRIiIiKEzZs3C4CQmpoqDB48WBg8eLCwevVqQRAEoba2Vpg4caKQkJAgXHXVVYJer+/23llZWcKCBQucfz/66KNCRETEOa1Pf/Hzzz8L6enpQlpamvDAAw8IgiAIO3bsEFJTU4X4+Hjh6aef7vZ6o9EoyOVy4c0333QeGzVqlHDzzTefy2Kfd5YvXy4kJSUJCQkJwnvvvXehi3POOdt+IZFInN/V4MGDhVdfffU8lPrsOVfjRFZWlpCcnOy8x0033XS+quTmAuIOgevGjRs3btxcxrhV927cuHHjxs1ljFvQu3Hjxo0bN5cxbkHvxo0bN27cXMa4Bb0bN27cuHFzGeMW9G7cuHHjxs1ljFvQu3Hjxo0bN5cxbkHvxo0bN27cXMZcErHujUYjZrP5QhfDjRs3btxcAsjlcpRK5YUuxkXDRS/ojUYjMTExzrSjbty4cePGTXeEhoZSVFTkFvYnuegFvdlspra2lrKyMry9vTlwzTMXukgXjNIgCdF1tgtdjIsCd1s4cLeDA3c7OHC3A+isJuZsehuz2ewW9Ce56AV9G97e3nh7e6OWKi50US4YHnIJaun/9kfchrstHLjbwYG7HRy428FNZ1wygt6NGzdu3Jx/olKLL8hzy3JiL8hzLxSxsbGUlJR0OH7PPffw1ltvndW9LzlBfyE63cXS4cIb3DP1Ntxt4cDdDg7c7eCgv9vhQgn5s3l2q8kC6/q3LOeDPXv2YLOden9Hjx5l+vTpXHfddWd970tO0P8vo1OKkGvdyQbB3RZtXOh2OH0wLsuJ7dUA3d+T5wvdDhcL/dkOF1LIXwr0xhtMEAREIpHLMYVCgULRcQs6KCjI5e+//e1vJCQkkJWVddZldQv6Swi59UKX4OywCXbsgoBUJEYkEqG1GEEkwma3E5xUTJCHssNH0RXeYhk+dkufy2C127HaBRoMRhr0JjQmMwEqJXZBYH+2B3KxlDXVuQQp1JhsVmzYUfg0IxWLaDVZ0FttNBlMVGnsKMRSgpVqFGIZcomEqSEpjAqIRSmR9blcbUSlFvdJCF5sfaK3wqGr8850AnCxtcOFor/awS3ku8doNBIXraa6B8NHtVqNVqt1Ofb000/zzDPPdHud2Wzms88+48EHH+z1mNgdbkHfC9o6/cWiwr+YKNU1UmHQoLeaCVCo0VvN/H7/f13OGRrqz4HqRuffM+LD+bWwEoCBgT7k1jdzY20cdXoja4uqnOdNiQ0DYH1xFYsHJRLl7cnMhAiS/L0BsNjsNJvMGKw2WkwWNEYT8X5e1OmMJPp7s66oikaDCW+ljAi1B8tPlPLpkQJMNnuHeqQG+nLn0AEcrGnkp8p8vrh6IjE+/sglYuSSKCx2O95yGSM/WonGdGoWf7y1xvn/nyuzifL2wGi1ozVbMNnsfDvuLqI8/TrM7E/vU+0H1gvR39o/v7NVeWdlORfCoLt79qY93N/q2eMW8j1jNpuprrNRuDkWb3XncedatHbiJxY7Pcba6Gw1fzo//PADGo2GxYsX90t53YL+EqLOR4ynsXd7cP3xserMFkpbdKjlMtYVVfLo+n0uv4covakxtgBwz7AUGqUyXtp9xOWcFyZlMi8pih0VtaQH+SGXiInxUQNgFwTE7YSf1W5nxYkyCppaOFjTyM6KOhL9vBkS4s/Hh/NRSSX8mFdGkIcSTWAEe/bsAWBsZBCZoYEEeihYtGILdgEmx4TyU345ADelxxPro6ZIoyXYU0WjwYTOYuXIXVcR5KFEZ7GikkqQiMXclB7PS1OHd9kmx++5BoBPjxTw0No9HX4va9G7/P3M0Z+YFZbKS7m/8n/xY8n0j2ZPYwm1BWUA2HSH8ZYpUVe3cu+IFCK8PHt+Me3oS5/ob3qrpu8volKLMVptlLfoONbQzBt7cjlY08jbs0aTnDKEQH09yW+vQCSCnxdOJ97Pq8d79teEQBAc6vLTV19GmwVBEFBJ5c46nA09lfdC9of/RbzV4i4FvfOckx5jfeHDDz9k9uzZhIeHn03xnIiEth56kdLS0oKPjw/Nzc14e3tT++CNnZ7X1YrjcprZF4dIiK059RGfyaDhP7Tna349rGHOP445/54UE0qryYJSKmFbec+Bi25IjcVTJmNzaTX5Ta3O498umMz4qJAO51e06tBbbBxvaOaOldtcfksO8KbFZKFKa2BaXBgTokL4zbAUDkl9qMvL5dMjBcxJjOT61DgAanUG/JQKZBLXj699P7ALAha7DYWk+3numbSv3mKloKmVaZ//AkC8rxe3RE7AU6JAY9Gjs5o5oqkg1SeMnyqP0mjS4a/wxEMi552rhpAS6NOhvN1xep84G6x2G3ZBoNLQTJBSjadUgTw6FwEwFA9AKpb06j59aTeb3c7D6/ZSrTWwv6KZJoue+cnR7K9uoKRZx7jIYL64eiJlLXomf7Yai91VGyPG0UfU8cns2bOHzFB/9rfTHuX+Zj7+qt675La1++rKbJ488iMLo4fx0MDpPV73S1UOTxxegQhYEJXJN2X7mRCUgFgkRoyI3Q3FzEgM5d0rxva6LL0ta3v6qz9cyqv6VpOFxLe/dcqMc0GbXKrfH9/tij4ws7DP5SgpKSE+Pp7vvvuOq666ql/Ke0kL+stJiPeG9h9x24fYG8HdE0kPHqCkwUTJ65mYLHaS/njQ+dvaxwaSGavGUyHmp5UeHKlt4unNB7u8V3vifNX8feoIAj0UyCVibHaBCG9P7vhxKxtLqnl83CD+su2w8/zHxw1ickwo3+QW8+8DJ5zHdy2ZQ5S3JxLxqQ/qqNyXdLOm13U8075yJgOeXRBoNJgI9HAE67jus71sqy/EJtjxk3vQZNZza+woZoYNJFEd1GsB2hl9GdhXVhyh2tjC1roCKgwaro/KJMM3gk+LdxGs9OLHCldtzKiAWEp0jVQbWxgbGI8IETsbilBLFayZ/HsXbcxN2z/iRKtjErhj+sPIelknq93OK8fWUqitY19TWYffx0QEsaOiDoBhYf6UN+up0Rs7nHfHnBl41ZUzOSYUmyCgkkmJ9PIk2LP3AVP2VNZT0qwlKyYUD6mEZpMFf5UCpbR3danS6hny/grn32FqFWabHbEI6vQmAIaHBWC1C/xpbAaTY8Mw22zIxOIz3oc9vV/358Svv40qz9fk4VIX9M888wzvvfceZWVlSKX9o3S/ZAX9/4qQb/9xnC7cThfyFqudmhYLxyoN+KgkDI7xRC491Qlli3aeutZTyvQMHyL95QyO9sRbJaZJZ8NLJaGuxcJvlxZ1KMump9IYFOVBy5FYHl67hxUnyjDaXAeVAJWCBoOJQcF+3JqRwKyESDL+/UOHe4mAJ8YNwkMuxVchRyWTsrGkGp3ZQqvZil0QMNts/HXKMBL8On4knQn6rlTJ56OvdDWI7aqo4/0DJ/gxr4zn0ucyfbjgVCn3R7n6MrBfs+U9SvVNzr8z/aIo1TdRb9LyzvCFPHTwO3TWU/YHqd6h5LRUk+YTho9MhUQkItrDnxhPf8r0Gr4p28/iuNH8X8K4s65He6JSi7ELAjU6A54yKd4KeZfnmqw2DFYb5d7BfZr4dcYrO4/y8o6jvHfFWK5Oju7z9YIgsL+6keMNGv62/Qg1OiNzEiPxVynwVymo1xtpMVn4Ma+MGXFhFGq0To3XtSkxjI0M5sa0OJcJbU+cC0F/Ka/m4dIW9Ha7nbi4OG688Ub+9re/9Vt53YL+Iqb9B+c/tJi9zdEM9yl1Oaew1sgDnxYTGSDn3+tPqdXVCjFPzo/kj1c49niadFaW7W7gnnYCPNxPRkOrFZNVYFicJ/uKdC73XjQhkD0FOnIrDQCIRNBdb5kZH8GWshr+fcVYpsc7nisIAp8dLeRwTSOfHCkAIC3Il3U3zyT0n18DMCjYjwCVgutSY5kQFYqHTIIggJeia+v10wX9xdIfXCZmtU1M/fwX5iRGcrCmgTdmjibYU+U0JmzjbMrel4H9nr1fsbuhmEC5GoPNjM7m6hq0cuI9PH54Of5yTzbWOjQqMZ7+lOga+dPAGUwOSabepGV9zXEGeAVTZWwhzSeMoX5RZ1z+nuit0Omrhud8oTNb0JjMNOhNlLbo0Jqt3P/rLsChrShu1hKhVlGrN2GwWEkN8mNTaTW3DUrk5W5sRdrTvv+cjaDvqq37Q2sI0Hggtl/u0xOXsqD/9ddfmTlzJsePH2fAgAH9Vt5LTtDvm/VYv927Jwvf3lgenytOV803tFr4ZncD6442U9ZodgrlCQPUGCwCYX4yftyvITpATmmDmdEJnnxwZwIxgUqUcjGrDjVx5SvHGRLjwfan01mX3czdHxZyzUh/ogMUJIUqmX6HlefeaGTZKi2Fpaf8dIo2x6CQQ/hoR9Smf98Rx10fOiYMk2NC2VBS7Tx3dEQQO0+qWtvz8ZXj8ZBKSQvydaq0Pz9awINrOhq0tVH9hxu6VGleLIK9M6JSizFZbXx7rIS/bDtEfTu17e+GD+SKxMgO15zvvrW1rIZrl20AwEMiRy1VEKby4Y74sYwNiueNExv5T9FOl+veG3ETw/z7vtLtb/qy4qzTGzlU04jGaGaAvzeDQvz7vTzZdRpe3nGE/1w5ocNv64oqeXbzQV6eOpxREUEIwIbiKmRiMasLK8hvbMVXKWP5iVPbFtemxCAViXl+0lDsCNjsAl5yGYoethDOtg+draBvNdiw2gX8PLtWN58PYX8pC/pzhVvQ9xPnYqAOTykk7b0f0BjNCMBVkzJYvzOHVqONxGAF+bUm57mR/nJ+OzWEYdIUwtUe/N9P2zDZbByqaeL766bQbDKTMbKZic9nozPZO13BD4nxYEySF++sdbiMyaVg7sEvNy3Il4EBPuyvbsBDJuVonQYAb7mU0ZHB/FpYSYiHku/G3us0fmvf7oIg8PTmg3x+tADtyYc9OCqNilYdyQE+NBrM6C2O435KOaFqFckBPviGR5IsMrmU5WIS/kfkW1ny41YA1HIpPgo5Sf7efH3NpC6vOZPy1/qKCdZ0dBfsjNP7e5PRxJbSGlYVVJBd28R/ht3dwV7Aarcj7YMq+XwSlVpMk9FEQVMr+8wSCvMLUMul6K026nRGylp0NBhMBHsqqdYaqGjVdzt5PFOsdjstJ/fzwbGKf2HbYcSIqNLq+Sm/nKEh/sT6qvn+uKtGruS+61BKJRytbeJ4YzOFTa38Y2c2QR5KsqJDEItEiMUi8htb2FvVQLCnks2LZuOn7NzAsFTqSbRV1+e+1L5vtL3zLaU1ZNdryByuZ1SiF78e0RDmK2dguAoPhRhPxam+IggC8tt28ad54Tx/nWMyeLRcT4vBxtikU94PbkF/YXAL+vNMXz7AiIFFvLDl0Mn96yoCvb1Yfby4w3mRXh6Ut+oZHhbA3qoGAJRiEUa7gFouRWu2EuPjSYyPmkaDySmM2xPiqXT6owMMDvKjqFnLjamxaMwWRIgIUat4fXcOAKPCA7EYVNSbtOhtFhLVQSgkUmQiCVbBRk5zFbUmLUEKNT9O/G2XBmft30GxRnvSra4Wf6WCsVHB+CnlqKRSpGIRGqOZCq2e4w3N7KxuZkNBKYfuvIpQteqM2/hcEZ5SyLfHSlhTWMmKvDLGRwZjttu5fUgS85NjOr2mL+XOzY0HICGjsMcgKWfSzy+GNuwMm2Dnm9L95LRUcdxQSlqgL/F+XlSbrLTo9JxoaEYiFhPsoSTCy4MYH09ifb0YGOjDAH/vPu1/t/Gbn7fjo5C7uF0arTa+PVaMxWanUKOlWKPFLggIQKPBxP7qBrzkMp4cP4hH1+9jUnQot8z0ICVcxfhns2n9YCRKubhPgu+2FVtYXVDBlQOieH9O53YRRpEYpXBq4tfb99jWR/ZU1jP367X835ABfHDwBF/Nz2JPZT0ba4sZEa/m3XWORYCvQo7GZOZfM0by+193MykmlFC1irRAX3yVcmbGR2Cy2ak3GEkN9O11HfsDt6DvyCUn6DtzrzufFtX9RXcBSKx2O3/ddgSRCHRmKxqTmXC1Cv+QUCIEMyabjYMnxKhlClosRj4+TcUqF0u4OmIwxboGFBIZgQo1IUovpoQM4KfKbDJ8w5kU7Nj/yW2u5vO6NQTbItnfVIavTInBZiGnpZpkrxAqzHW8ODmT13fncrROw+yECFYVVCBGRFZwEvnaOmI8/ZGJJGyoPUG0hz9D/SLJbanmRGstT6bN5urIwb1qk768j2qxgv0nCnh60wFKW3RU3n+9cxC/GISUR9xxUt/93vl3pJcHcb5epAT68MKkzE6vaV/uNkHeE6EjivDrIeRpX9r1mc0HWDI4yRnr4GJoyzasdjsv5qxmRcVh/jIpk5Hhgfxpwz6KNVrCwsL5+6hEUgN9e20l31ty6zVUtuow2wSaTWZ0FiuPb9gPwDXJMTwwKpU4X68OLp3t6U793ZWwN9tsbCyp5r85xRysaaRBbyTB34vl103FU965/UqNREmIraNXQk/vsa2PVLXq2F/dSJhaxceH82kwmDFZbbSaLUR5e7K/uoHKVj1Pjh/snIh/dqSQvMYWtpfXYrXbMdvsHLzzqj55PPSmjO3L2R1uQd+RyyJgzoWytD4buuuwYpGIN/fmsigjgd+PTMVHIaOsRc8KvYSS/HJGhgeiDtTwzr7jTI4N5f05Y/FqTMGOQIBCjb/cw6me3FFfSEFrHf88sYH9TWWk+YQxKXgAdkGgzHs3t/6y4eRTK5zP/3Hib5m3+R0Oasq5J2kiB6tr8bD5ABomq0dw/aipfFWyB72sEalOzNa6Akb5xzLCPwaZWEy1oRkJYpLUQWyvL2R7fSExHv7Mi8gg2rPrPdLOosR1Rb1UxRWJkZS36Hhq0wEmf/YL626e2e1ge674uHAHOS3VrK85DjgmWncMTXT+rpCIeWf2GEZGBHW49mz7abOnGD9t/wVIuTEtnlDPUxqSM50M9/f3t6Y6l8cOLSdU4cXC6GFsKavmiY37+WDOOOYmRZKt8OuVMV6RppVPDhcQ56tm0SDHO2o1WdhQXMWbe3O5bXAiN6cnYD+5/nl60wGqdQYO1zRR0apnRHgg7RX/g0P8GBDg0+Nze7tyz65rYumhfHaU17rEoACHR8uRWg1Xf7OeWB81RpsNpUSCXCJmbVEVv9w8nUZ/b0JwCPo39uSyLLcYL7mMMLWKYE8VUm0IWRliJsWEurhHtmG02bln1U62L7mCf80cDcD3x0r4zaodHKxpRCWVMCjYj5sz4p3bB09NcJ3Il+XEYiqBNquD7sId9yaSYW/6YPuJlNTgjod8OpfFir4nuupEp4f9bH/8bPa42t+zrwOlIAg8u+UgP54oY+eSuS6Ca6vdg5UbN7MqvwK7IBDiqWJwqD+fnbRmnx4XzpoiR2jZRD8vYnzUrCuu4vbBieTUazje0MIT4wZz66AEFizbwJayU+FbC393LfFvfQvAFQkRVOuMPDwmnRu/38QViZEcqW3k0TEZWOui+aFhCy9NGYaPUs6kT1ZTrXNY5b8x7HpGB8RhEWyMXfMPvKVKwlU+lOqbMNutWAU79yZlsSA6E7W0d0FMumq/9lbWHx/K59H1e4n1UfP27DEENw3rU5ufLcN/cXWDUUklGKwO4bv8uikMDPTFR3nKRay7vtXblbzzWZNKem1lfaHdps5E+AuCwKKd/yG3pZqF0cMp0Naxp7GEV6eP4IbUOKf9QG+s7r/MLuTxDfuw2gUCVAqS/L15acpwJn2yCrPdzpPjByERi1iZV87xhmYygvzYUVHHGzNHMTk2DH+VArFIhF0QmPTpauYlRfLwmIwzaInOuXPlNlbklRHv68XUuDBSA32J81WT2q7/hLz2FVcmRfH6zFF4yKQYrFYaDSYeXbeP3HoNQ4YNw1RS4BwHdi2Zi79Kzv7qBiw2gY0l1Xxw8ASTYkK7tRdpw2yz8fyWQ3ydU0SzyUJ6kC92QSDWR82h2iaCPJQ8MX4QHjIpHjIpMd6eNOYn9Vub9FXIA7QYrATcvde9om/HJSPo33jjDTIzM4n/5l+dzkTPFoPVSlWrAVlV3z7c/h4891U1cMVXa7gmJYaFqXFMjA4hu07DodpGdqHm2KGDHKpt4sO548ip0+Bzcj/MQyYl2FPJ73/ZyYjwIOYnR3O8oYUrvlpDnK+anUvmsvx4KeuKK3l9xijqDSa+O1bC3qp6DBYbiwYl8M6+Y7SardTrjVhsdoxWGymBPiydN4FAD8cgl/T2t3w5P4vM0ADEIhHj//MzeY2OMLj591yLl0LmVJ2159mJQ0j09+bmHzY7j/1pbAaRXh5MjQt3iV7Wm9l9+4FdZ7ES/+YyrkiI4OcCh2ZiXngGT2fM6Yc30jNHNZW8enwdCeogSnQN7G8X+KXqDzc4+2tf9+AHDizsVPAPHFjoPKcvgr4nzvVE4EwE/Y3bPqRI18D9yVNYGD2MBw98y5WDfJ2r8Ta6E/RNRhPriqr4Kb+cj+aOQyRyGMn99ucdjv19rYF1xVUu1yxMjSPYU8l3x0oobz0V1ritj18oqrUG/JTyLi3we5rwPLlxP++fDEZV88BCpzFsoErB5NgwqrR6Spp1KCRidlXW4ymT8J/DBQwN9eeTKyfgKZNittt5ZN1eVrTzFPjT2Aw2FFexq7KeaXHh/GVSJuKKtD4bPp5pH2wT9na7wJZjLUz7W65b0LfjkhH0bXy3YDLjOgmj2leqtQYGv7+c4WEBJAf4cLS2iUO1jmAiyf7epAX7cbBcx43Rw8nwDccmCCglMiJUPkjFkn4fFI/UNhHro3YOIjVaA//ck0OJRkuSvzfhXh5kDh9OUGMlo5b+5LzORyHj+axMLHY7sb5q0oJ8XSxyC5paCPJQOoOOTPv8F+YlRZEZGsDYyCBONLZgtNpYmVdOs8nMzenxzPpyDZmhASxbMJlfCyt4fsshVt84wznw/XbVjg7lT/TzYttiV8EqCAK7KuoYHh6IVCzGYrOzoaSKu37aTrSPmhqtwZkg5sqkKNKCfZkaG05GsF+3WpiynFiKQyRMCHBoMspyYvm58ihvnthErclV3blnxqP9bmXdRrlew9Vb3nU5Nj4qmLlJURyubeKLo4X8etMMBof490nIfVy4kzfzNvLDhLuJ9PDr8ry+Cvq+aqrqTVr+nvMrzVYDOquFQm09IpHgDJKUEezHNckxDA31Z3REULftfKaq/N31Rdyz72vn339InsI/j68H4Mv5WaQH+eKvUnBM6d+pgNtWVsM1yzbw5PjB3DEkCQ+ZY7cyv7GFG77biEQsYnZCJBnBfvx122EXoZ5999UEeiidYwXA7tvnOu0XLkZ6o9k4WtvE81sPAY7AVSPDA0kN8uNgTQOBKiUJfl5sK6/hjT3HmJsYSVqQLzelJ3Qweq3WGhABISeP5x4Oo0C1mztWbmNkeCC7K+u5NSOBZqMj8ZRRqzr5RAGJSEyqTxgykYR5EYPIGFLD2eI/tJjF7+Xz+bZ6ALegb8clI+ibmprw9fU9I9V9G/V6I79bvZONJdXMS4rix7wy3p49mrGRwYR6qtBbrIjFIur1JrzlMraV1/LE2iNMC0nBL7gVndlKoaYVyckBTSQCmViMyWbHSy5lbGQwif7eKKUSmo1mJp/MvgaOCF6HahpJCfThm9xi9lTWO11tJCIRNkEg0suD12aMZEd5Ha/uymbbbVewtrgKmVhEaqAv/jHxJNu0FDS1nFQ3dnx1SomE4vsWdBh0bXY7ErEYs83GS9uP8OZeRyz7UeGB3DN8IDPiw/nHjqOEeKp4ZP1elgxO5Jb0BKK8PRnwzncAfDU/i/FRwfxSWMmPeWWszCvDahdI8PNCJhLhp1KgkErYeNKvPsRTSY3OiLdcxtBQf2YmRHD74CRn2ZqMJuZ8tZaCplZnRD2AWB81O5fMoTw3rst3WREoJqL+lHWx1mpi0rrXkIkk2LFjO9mtp4Wk8LchV/e+k/SBr0r28vrxDVgEh+BLC/LFSy5ziSOw+/a5iCvS+3RfrcWIUiLv0a0tNzce7wnFLu3QGV1tUbWn2WxgX1Mpjxz8HqlIgkQkwmS3IhNJsAg2bo4Zwecle5gTlo7WZqLK0OwMeQtw++AktpXV8J+rJhDn27fIf6drLSITc1BKZC5hdDVmA8W6BjRmPYc05Xj6N3PdwFimnswpsHh6FkmWVrzkMvxVCgYEeBPt7UlBUwvj/rOKb66dxMToUJfn3LZiC5NiQtlYUu3iAy8IAha7HbnE8Xyb3c6ET1ZR0NTKttuuINH//A3ap28p9sQJmTcDLC292jZsNpqp0xt7XZ/T32d3GRjB8X0brTa8FXI8Za7mYCarjT9vOkCt3sj9IwZittmpN5hOhsn2QCWVoJJKeXtfLr8UVHDn0AH8XFCBn1KBj0KGj1KOWiZFIZFgttvJCPIj0d+LIA8lJquNzA9/dAv6dlwygv6DDz7g1ltvRfOn2/p0/U95ZdTqjTQZzWwsruKpCUMYER7YL2Wz2BwCRSmVUK83srOijk0l1YSqVXx3rIR4Py9SA33ZVVHnjNd9On8am4FSKuGW9AQEBF7fncNP+eXIxGICVAruykxGKZVQ0NTCppIaBBz7iwqJmNUFlXgpZASoFMT7evHPGSMxn0zd2mq2UKzRsrWsht2V9RRrtASoFIyKCGJ/dYPTxW5ggA8pgT7OScftgxNZW1RFacspH/vrBsZyoLqB/KZWBgX7oZbLmBEfzuJBiYiAmDeXcW1KDCPCHFbQAHMSI53Z41RSiSOf+0k/+YGBPnw0d7xLdjG7IBD2z1MrtxcmZXLnUIdnQG8EhsVu4969X7OvqZR4z0CSvYNZVZXT66QkZ0OTWY85JIcPD+axqaSaCC8Pns8ayojwQCqP9W3P/Xyyu6GY9wu2caCTGPOeEhm3x48lK3gAoUovbAi0WIwcb61hUvAALDYbL+f+itZqIlTpTajKh78fWwPAhKAEbIKAQixlsF8EE4OSOjXCrNBr8JYpEQAvqQKRSMSm2jyePrISrdXE8xnzmBmWyrGWan6sOMLyikOsnXw/jWY9CrGEA03lHLIexlch470DJ3hr1mgUEgkNBiOHa5vYUlrD+OgQ1hRW4i/xIV4dyAPJU/BXeBKVWsxvft5OoIeSjw/lO5PlRHh58Nr0kWTFhHYo7/nifKUE7i3nKp59RauOr7KLeHnHUW5Miztp6CiiWmdAb7GiMzuySn57rIRBwX78WlTJP6aN4MqkKCq0Ds2L2WpHKhZxqLaRgsZW8ptaMVqtbCqtcQv6dlwygt7Pz4+mpiYyQwNYPCiRKXFhBHl0777xxdFCHlizm9ERQdySHs+cpCin6u5csaG4yrmSN1kdGdkKNK2sLazkzVmj+exoIRqjmftGDOzzvdtm67U6I8WaVoqbtXx+tJCdFXVcNSCKJqNDDS4RibDZBeqaIbfllEpMDCglckYGxBAoV1NlbGZ6yED03sVsKa11mYy0zyHfts+8Mq+MP286wJZFs4l/61v+MimTH/PKsNkFXpo6jLQgP/686QDv7T/O99dNYWR4ILf8sJl/zRzFnsp6nt96CI3RjM5sxWy38+zEIfxmWApwygjxi6OFNJ/05R8bGczcpEgWZSRSfdxVYJ6+om/jrRObWFp0amuhLUDK2ViBt6m8/1u6j5dz1zA2MJ56k5ZgpWOi4iVVEuXhx6iAWAb5RpyzrYLO6KoduuNESw2Ld32CSBBhEk5ZKC+IGsLEoAHka+uYFpJMuUFDkbYevc3CEL9IFGIpA71DEZ00SPukaCdzIzIIVJxSZVvtNmfMBKPNwsGmctZU51JtbKHJrOeRgTMYfLKNHty/jM11+UwISmBXQzH/HnEzoUpv3srfhBgRdSYtNsHOroZi5/0zfMIZ6BOKyWYlxTuUUl0jX5bu5dGZC5lvj+6gBak2NLO/qYw11cco0TXyxdglKCWO7bEibT3XbfuARRkJWOx2bhuUyF0/bae0RUfRvQv6bazoizdJ+/Pb6IvwbBsj+oNz6bnUVqeSZi3zv1nP7iVzkEq6dovcVFJNo9HEtNjwHm0kanQGBv17uVvQt+OSEfRtjZX9m/lsLq3mnlU7GRYWgAgI8lDywKg0BncS3tJgtbK3soGvc4rwlEkx2mwMCw3oYMzTX7QX9J1xukrNf2hxp643vf3IVlYcoUTfiNZiwirY8ZEp8ZV7IAI+KdpFg9mxMt8+/SHkYimlukZ0VjP7mkqpMbbgJVXiIZVRIS7ih+OlZMWEsvakxe6+O+YR6d15fvTntxxiS1kNEhG8d8VYluUWc9+IVOyCwLTPf+Ge4SncmBbvjLJ16/LNeMlllDRrGRrqj8Hi8BF+tZPVk85i5fktB8lrbGFrmUM9PMArmJtihjM7PB2JqGuV9ukW8Htun0u0j/qsViWCIPDwD0U0mHXcGjsKmUhCuIcPcvGl6Z2a31rLtroC3sjbBMAwv2ieSptNiMq706xzX5fs49qoIR2CHrVajPxUeZSFMcPJbq7irt2f8/bwhaT7hnd4RzbBzhFNBV+X7iNR7XiXuS3VvJC9mlJ9I4N8I7gyYhD/KdqJl1TB7wdMZohfpMszBUHAfnJ/90zQWU1U6puJ8vRjd0MxX5bsZXKyF5tLa3h83CBGRQTxS0EFi1ZsAboPwXw5ciFCfJ8L3H70HblkBH1oaCgbNmzA/9/PAvBzfrkzxGisj5ppceF8cPAEcxMj+XDeeARBIL+plYfX7aGsWUdFq57TK7p03vhO446fDe0F/dn6hrbRdp/Tw532dA+zzUbCm99jtlvZO/NP3Z7b/l43fb+JdcVV1Dyw0HlMZ7awoaSaMLUHqUE+qKRSZxQth3mNg7RAX65JieG2QYkuM2+zzUar2YqPQuYQ8LuymREfzp1Dk537d8WaVkYv/QkBh4FQcoAPBU0txPt68dlRh6X5Y6kzuTZqaJehX9sL+rdmjWbBwFiXNuwJQRC4cvM7mO02/jL4Sp46/COxngFMCh7ADTG9c9v7sGA77+RvZrh/NE+mzaZM30TzyT1muyaB9a2bCPEWc7ylhrVT7u/VPbuiLyFwwVG/Px36gXU1xxnsG8Hk4GQG+UUwyDei0/O1VhP5LbVIxGLSfcJdBF+5volKQzMjA2LRmPXctfsLCnUOQ6hMvyiiPPzwlXuQoA5EKpJQrGvgu/KDRKp8abYYEIlEFGrrWTnxtyyvOIwgCIwLSiCji7KcbTv8WHGEZ4+eMmSN9Qwg0y+KR6dHEOvr1c2Vlw5tIXD7woWIOeIW9OeXS0bQA4wYMQLvunLUchl/mZyJCFDJpMS/ucx5/t2ZyTyXNZQBb3/rVAGvvXkmUd6erCooRxBAIRUzIiwQyvvu/tETO+oLGRPY877smXT0M83QJQhCp/Xsi0rxeEMzEz9Z1eF4jLcn9QYjNruA0Wbnm2uymBjjqtGo1Rl4atMB/FUKdpbXYbXbOdHY4jKRAFzc8v4+dTgPr9vr8vtNMSN4MGUq0HWWrrfyNrG00KG6T1QH8eXY23v1jnObq7l158eEKL2oMTos98NVPtyVMJ7JIQPw7KXf/7/zt/Lvgq0ux1K8Q4gVMsgzFTDKYzhJigQkIgl/rv4L0Lf87afTl2xlBquZpYU7+KhoB+MC45kRlsrUkGSnKrs9giAwYd2rmGwWxgUmUKxrQCoWs2z8XS7nfVS4ncVxY5wuhPUmLY8fWk5WUCIDvEPJbq5ELBITqPDk/YJtDPAKZm54BuOCEijU1lNv0jI6sGujy/5sh7b0ty0mCyarDYlY5EywdLlwVO6Lz0Ff4My3Cs4X/SXstWYL64urmBobhqdcRmWrnqEfrHAL+nZcMrrHxsZGPD09aXp0Ebsr67l39U6kYjHrTqqZD/zflYSpVc5B/dhvr+GNPbm8uO0wnx4poLJWxprqXGfaTT+5B2sm980auj85m324vtKVoOtNVKoGkw5/uQfJaSXUPLCQ3HoNRquNfx84wXfHSojy8eSXm2eQ39jK2qJKXtx+hK9zinlqwhCnO85dP2/HSy7DWy4jp14DgL9Szi8FFcxMOLV681LIqHlgIWsKK7llucPf/soBUfiaw7k9fgxqWcdBuc3fvI3fJWUxNSSZW3Z8TL62jhG/vtSjNqNCr+HWnR8D8OXYO/Du5DltzwJcnnc6S+LHuAj6J9Nmk9w63+Uck91ErukEw1RD2Gc4iE2wI6N/w7aeTpNZz207/0OloZnxQQn8JmEC5QZNp0IeYFdDMUabhT0zHqXa2MLjh5ZTYdB0mDSKEdFiMeIrd7zrQIWa66OH8eaJjWT4RrCqKhsxIkYFxFJpaGZOeDoTgh3bZoleQSR6dYwWeC4Ri0T4KrvOb3850VeL/UuNZqPZ6RHUhrdCxvCwgAtUoouXPgv6oqIibr/9dmpqapBIJOzcuZPs7GyWLFmCyWRi0aJF/PnPfwZg3bp1PPLIIyxcuJCHH34YcAidJ554ghdeeAGAhx56iPT0dBYvXtztcyUSCXK5HJFIxKiIIL6+ZhKtJgs/5ZcR6eXJHSu3sb+6gWtTYpgaF8b85BjuH5nKlR4zqTNp0XgYWBw3mh31hdSbtBzWVPa16ueUvhrs9CedCfy2Y8Nf+8px4NdT5wd5KFmUkcDN6fF8frSQL48W4SGTsKnU4Va37FgJy46V8NthyTw0Oh25WMyRmkYGZySw8dZZ+CsVVGr1PLxuL4tWbHH6mn+dXcTvf93FgpQYti++gmmf/8qKE2XsmnFjt/uypwvgZK9TcRZ+k9gxdWh7BEFAY3FE9nsgeUqXQr79/btDJpawd+af+FvOLywrO8AL2atI8znIzNBUinT1lOga8ZTKGRUQx2/j0hjsO7PftEqn+8kLgsDDB75jT2OJM/e8h0TOzNBUGs16l3ZqY1NtHhtrTlCqb+SRlOks2vkfgpVePJV+BfHqjt4qk0OS+bU6h+uiMp31mBqSTKjSm231BcR6BuAhkfGvYdef1f56f3C6fUxX39yZRLS8WGiqCsCHjpqNiy0keH+0r49S7gz6IxKJWFtUyc0/bOaR0emsL67u8fqzlWXnkiVLlvTqvKVLl/bqvD4L+sWLF/PCCy8wYcIEGhsbUSgU/O53v+PLL78kLS2NcePGMX/+fDIyMvj3v//Ntm3buP3229FqtajVatRqNZ9//jmPPvooXl593xc7vcOOEyWBFq4N9mKmv4m/567hWJWZodYJyMQSpGL4T9FOlpUdIEDuyfXRw5gUMoC7exAAF4rzudI/nc6eVXjvAuZ+so2clipUEhk/TrzHuXpr8TTy3O/K+OfuHNRyT/yFYHbWF3JNcjS/Gz4QEGg0mFFKJQwJDcBPpcBis/PYhn0MCw1gxfVTeX7LQWZ88SsDvUOZHjqQVO8w50Shjb4KB5FIxM7pj/DP4+t4N38L10cP61KAX7P135Tpm7guKpObYkb06Tnd8afUmfwpdSYAlQYNZXqNwzJ/YGyvjfh0VhN1Ri2x6u5XKB38l816/rD/G7KbHdHeUr1DWRQ3Gm+ZkiSvYAq0dQz3d82gV65v4uot7yERiXhj2A1U6pt5IWcVz6TPYW5E19EiYzz9kYnF/Fqdy8ywVMDR/um+4aT7hrt8ZxL6d5usfb2b5b5Q49vjNZ0JvN4cu9gFv7O8Zx9L7JzTVVtuKK7i86OOBDlfzs8i3MujV/er0xv549o9mG12ZsZHMOurtb267mxk2bmioqKCRx99lM8//xyxWIxarSYzMxO5XM769euZN28eACaTiVWrVp0bQZ+dnY1MJmPCBMfH6+/vT2VlJVarlUGDBgGwcOFCVq5cSUZGBna7HZFIhEQioc0UQKFQcPPNN/P222/z6KOP9uXx3dI2yFwbNRQ4pdKdsPYVJCIxH49aRLpv+Bnd22K3cUhTTqxngIsr0fmg/arD2245r88222xO+4fPxiwmxdvVOt5bpqQpP4nb/JMw262sFf3MddGZPDwpFKlYTPI737EoIwGj1cbQ0AAMFhsPrNmN1mwlPciXuDeXkaAO4ttxdyIWiTigKSdBHYhaKmd3YwljA+PRWjtm4gLwMHVvWiIVi3kgZSpN0mqmrP8nayf/Hl95x4EjQR2I1W7j0dQZZ9hKPROu8iVc5dvn6+7d+zXHWqrZMeORLs85vR1qja1csektwOGK9tDA6aT5nLKZEASBakNH96u793zJ/MjBPJE2G4BiRQNJ6qBuhTw4XPUqDBqygvsvvvmZcK6/jf4S/OfCJ739PXv6Li4kUanFrC6o4I11VbSYzLSarcT5qnlm4hC+O1bCfb/sQiWVsGBgLGZb1/YWdkFgTWElm0trONagIVztwZTYMKK8PanWGrgxLY7FP27t8nroH1nW3zQ1NTFu3DgmT56MWCxmx44daDQaEhISEIvFDBo0iO++c2xV1NXVERLS+1ldnwR9Xl4earWaefPmUVFRwYIFC5gxYwYREaf2WSMiIti0yeG2s2TJEsaMGcMNN9zgsnq///77GT16NPfff+YWx23qWp1dT6utlVBZx0rn5sZzdeRgvizZy+JdnwDw8ahFiEUiUn26doFro8Go5c49n1Oqb3I5LkaEHYGXBs8nxtOPRK/gM65HbynLiXWsh1L7ZlF7Njy/5RC3ZSTw4pRhVB3rPoCIXCzlL4OvBMBQ7Piof71pBl5yGTO/+BW7AMsWTObedvED7spMZsh7K3kx5xcqDRqWxI/lzsRxBCrUyEQS1tUc51hL5yq4Ngvr7tTpsWmlfJI2gb9tP8y0Df9iULAf2XXNTA9NYUFUJg0mLRqzgX8MvbaPLXN++GfmdWyty+9wXG8183HhDh6dFQaFAyjTxDp/K9UWO/9/pLmSKkOzi6AXiUTUGFuxC4JLzghfmYqJQUkIgoBNsOMv96DC0EyTSY+f4tQEqc2Art7YSp1JR1ZIEpNDkvu34r2kvYDrja6gP7Nc9hSiuf3zzvYZfZlU9MUD43ziEXecR9c5Um/fOzwFb4Wc9/Yf59Vd2eTWa4g+GVY4zteLf0wbQavJgs5ipbxFR43OQKCHkigvh0H1G3tyCVWreD4rkyR/L2d66jZaTT1P+s5WlrW09E+sgva89NJLREVFsXTpUj755BNiYmIYOXIkAIWFruNcXycbfRL0VquVLVu2cPDgQYKDg5k1axYyWdfBC6644gquuOKKDseDgoKYO3cuH330UZ8K2xn11gaerH6etyJewVtyajJhsBu4q9wxkRjgFcyJ1lqiPfxYvOsTfpM4oVtBLwgCf8lezQ8VjnjQ9yRNpEjbwPqa45jsVkc6WLknTxxejlWwk+ETjq/cg2aLAR+Zkn8eX4/WYuKHib9BJpZgtdtpMusIUvZuq6KrAaIiUAznSaXYarKwqqCCPbfP7XIP+fTQpe2FbnF2NKtKarkqIpV5YbV8XrKnw/VquYwNUx9ga10Bf9j/DQBPHV7JU+mzifUM4LCmgi9K9hDrGdBhZdmXQDF/GjuIeUlRNBrMnGho5vGN+1ldlQNAmk8Yyd4Xp77TR65iTicr6l+Ma/mo6DAvKm6gdnCjM+QpQEw7Nf9w/2imhaY4/z7eUkOw0osoD78OiaGeHzSPz4p38UHBNrJbqrgtdjRKsYTpG/+Fr0xFmk84Bdo6qo0tTA1JpljXQIG2njfyNrJ8wm+I8PA9J23QW7rrD919I+2/tc726vtK+2v6a1+8K5uBzu5/JgGUzpa2PfJms4Ed9YUEKDyJ8DilxdpSm88Xh7fw6NgMJkSf+tYeGp1GtI8nOyvqEASBJ8YN4uucIuZ/s47t5XVIxSLuHDqAMLUHB2uK2F1Rx4Oj01h5wzRnNr8ztafoL1nWG06fFCgUChSKjl48K1asYObMmVx33XUIgsCkSZN48MEHufPOO2lqakKn0zm3wKurq/H37zrl9+n0SdBHREQwfPhwoqKiAEfl9Xo9FRWncplXVFQQHt6zivyhhx5i2rRpzJ49u1fP3rt3L2q1mrIQh3Wyd2AxLVti8U8J4FP5v9FHGGn2dl3tvhbxEpWFFQybosC/xsgRbxMShYzPi3cjCCfwl3tSU1XFofJCQsJD+b1PJtpoH/Y2llARLuelUXezrGw/XxUcZV54Bkszp0CrEUukj9PdqsrYTFK0hRRbCycMdnYdsvNt2QH8w/x4zXIEmVhCobYeq2DnOk0Eqckp+OtApxTR7Ok6E5XZBCLq7eysjWd0cCG1vmL0CtcBuc1fuO2D3lkbj0Uiwi+swXlOkM2INjcUWWod0VYdR+W+Hdoz6qSvrVEkwcduJl/m6v5RZzXSYhP4td5Ahp8n3ukVHK6LdjmnGR0+uZ7oI4wo6uQcqIvEFGQhNKQeiwDeymgaPJUcP1jDDeOmU5YT61Inv7AGGKJhhEVNwFFPfpRU8EjKNGQxQSxvrSAvGP6YfiM/1ebToDnCjYEZaNSO+lskIopDXC3Vg5odA5xZCgNiSjgqa1fvCF/8gJtjWpg7fBBNrVqya5uIT0ikuFaCX1gDTVUB+IU1kG7WUCr1xJIT1O17ansHnb0nD5Pg8p4qAsVYJK7n+OjseBoFZ53a16ftfba9J40NDhaV8J9qE0uGZLB85Eiy5dKT71BMk1qE3AoeSm9GjHDYGjyQPIVisYQ6k5YHlr1NZFwM4QYJT42+huJ2dWqxGGhWilmx7Qgjh2SyN+Q2qrxh8tjxfFq0C63NhEKiYJItBnmtDnOkL2WHmvj7vP/D21ONQaai+KQdRXd16uw9eRoFKgM6nhPeYEOndNQJoM6no51GbI2NWl8xvlo7Hkahw7NO/55KpZ60iF0Hc2+7hWirDsPgRrA4IsuZ22xChmgAsOf79VinQUGOENIlxZH9Vqf2fa/55DcsF+wMsLSwszaeCDrve6ePEX3te22c/p6O+1jYVJuHwWZBhEOLkpOdQ4C/PyKzjQOacmYOHk2JvhiLwQYGGOgdyicHfiQxMZEg/TAOl8oQJzo0pPurGjgs9mbmmDgyBD2/P1hOo8HEzRPH8Nj1MYDgjE44227BsN+HpmAlLYV2ctvqFALFDQn4hTUQZDPiYzdTJu15b7+/ZJlojz8iVediVGSwAoXOZ7Tx9NNP88wzz3Q4v7CwkHfeeYcHH3yQtLQ0AgICuO+++5BKpWzdupXw8HAeeughbr31Vv72t785v/Pe0Cc/eqvVyogRI1i/fj0+Pj5cddVV3H333TzzzDN89NFHTgOG999/n4yMzvf2AgMDqa93BNW4/fbbWbNmDc8//3yXVved5aM/k3SfbVQaNNyy/WMG+UYwMyyVPx/5EdFJVfyuGY+wr7GUhw98h4dETp1Zy/0DJnFr3OgO9+1uFlmWE4sgCGypy+fhA99haxeqx0+m4ouxt/d6dd+e/pqtn26d3Vld7ILAqI9WMjoiCE+5lL9NGd7rPOrt27vFYuTxQ8uJ8fTnvgGTUEpknT7v0XV72JjfRLHeEXY3Xh3IjNCB3BE/lonrXsVgs/DPzOsYH5QA9NwW/aHpuNCBRN7bd5w/bz7ApOgQNpY6QhlHenmw8dbZzmBE7ZOYtLGlNp8HDixjuH808epA8lvruCI8nXClN/86sZHPxrpa9N6w7UMKtHW8O/xGjjSVMzdyEEFKLx4+8B0bak+gEssQAS8PvYZ5Y+00GU1UthoYdoZuTH3Z7+7tO+jrt9Gfe+z9oQnoDb3ZCjgXK3qbYOez4t0c0VRwT1JWp94X7Wk06SjQ1iEWiQlVeuMv90AldazAy/VN7GssZa/xMHG+Xjw1YTAFRyOZvuFfXBkxiEpDM69dOZCoTiJy9jYHQG8C5pytLGuTSw3vDce7C0HfYrAScPdeysrKXMrR1YpeLpczfPhwtm/fzg8//MCCBQuQSCRYrVaCgoJYvXo1CxcuJC8vj6ioKH788ccu5ezp9GlFL5VKefHFF5k4cSKCIDBjxgzmzp1LYGAgN954I0ajkVtvvbXXD3/00Uf5z3/+05ciAF2n26zQa6gyNrtYFJ++hxuu8mX91D84//aVq7h/3zfMjxzCdVs/YFRALDqb2emONDogvsMzexok2n6fmJPEtukPs2Tnf8htdQzUTRYDL2Sv4oVBV+LVjStXZ5w+Kz9TejMQiUUi9tzhsPDMeO8HLDY7csk+wq2JpHqHkuQV7KLS72yvfF3VMVJ9w3hz+A1sqs3j/YJtvHx1589+aeoItifX0mwyE9oyxGn0GJVazJHEK3li4z7+sP8bvhp7B4leQd22xcVuId0Vp5f7vf3HAdhYWsO6m2cy9fNfGBcVTOp733PinmtQSaXO1Wf7/jkhOJGt0/7IEU0FYpGYcr2Gd/I2Y7BZGOEfw4qKw1wZMch53ZXFGayoOMy7+Vs4qClnXuRgjDYLjWYdGT7hDPGLYnt9AVNHmNle3kiLyQwCZyzo+/J+eptat6/fRn+5s15sfa2/xog28j128NbeY0yIDuHvsdf0yhXUX+FJTaFDBrSc/Aegs+t4uPo17kocz+8jryZI6UVFLsjFAmMD41lbfYxfJt8H5dAxzZKD/mrv/pZl3eHt7d2rgDlhYWGkpjqMyq+++mrWr1/P888/z86dO9mzZw9RUVEcO3aMhoYGAgL69u312b1u9uzZHdTto0ePJjs7u1fXt63mAZKTk7F1Y13ZHZ0NAN4yJaursglX+XRr5Vyu13CwqYyVlUfY2+hQuYmAV4ZeQ2RSFRu+VpDo78W/ZowmxlcPFDufeSZlfGP4QqZteN0ZKnZbfSFzN73NXQnjmROejq+id24k54qeBr1bMhIYGOjD4BB/DlY3sqWmkNf3mbgtbjRD/CK7dBf7x/G11Jm0fD32DkYFxLK8/BAQ22U5xka2GTXWn/znwEshY2CgLwAvFX7PqoXTKW48N7kK2jjfq/nO2v6vUzJ5bP1eKrRGHtuwj3dmj+G3q3bw8Oh0VNKObd5+xaeUyBgREMtVm9+hwtDM/8WP5YPC7dwWN5rbd39KtIc/AgLfrG7k0+LdBCnULI4fQ7SHH4FKNV8U72aIXxTD/KI5oqng6oghHGtoZnxUMHKJhHf3HUNntuAp7z7BSH/Q9h2drXFbZ/ftCxdbrItzSdCAAl7dlU1LtZmP5o3DWyGnLMch5E+3zektnxjf4JWh15LpH33ynTq0miKg1WqiwazDaLN0qfXrb85WlvU348aN4/jx486/J06cSHp6OjqdzkX931chD5dQCNz2qvuu6O4jMNosHGoq5959X7vEvL8yKYo/jEolLcgPrdnCroo6psSG9XtoXKvdzpaSahb+sLnDb6sn3dsrt73mIRrSzZqLIvlEk9HEBwdOcKRWQ269hsWDEzHWBxOs9GJsYDxysZQbt39EXmstfx88n2/K9vPY1IR2wrzvrM6vYPGPWxCA5266htn1ST1G/euJcyFAekNP5dtVUcfC7zchF4vRmBzapd+PGMiy3GIS/b15YtxghoT6dxsWuSwnlicPr2CQTzgvH1uLQiTBJDgm1ileIZToG7klZiSjAmJJ8w13huG12m1MWPMK42OC+Oqayc52Uccf5+e8MmwCJPl5YbYL1BuMWGx2Fqb1PPj3Z/rV0+/VUwhcq93O0WZHkCxvmZJ4deAZua/1dM2FVNtD30Iid4bBamZ5xWHWaw4wJiKQP45Od07m2p7dV0E/cGAhtcZW3svfwr+vT3X5rSwnlnyPHXyVU8SUmDCuT41FJBLx+IZ93DU0mVjfvrszn89Y971R3fe2HHv27GHs2LE8++yzXH/99WzdupW7776bCRMmkJmZyeDBg7nhhhuQdjLJ74lLJgRub+gsMlh+Sx3ZrZW8kL3aefzp9Cso0zVx3FLA/w0dQFqQH+CwAp8ad2a+9j0hFYuZHBfO74al8Pa+Y4QovKg2OWKq+8pUPV4flVpMM77npGzd0ZVVq59SwcNjHGqtFSdKUUmliAJMfLarEp3VzJzwdO5LmsRDB7+lQFfP0uuGnnVc8VmJEWy8dTZZn65ic2k1f976HSP9Y3h7RNeTv95wMUUNW1dUyd6qBl7d5VhV3DgkkQ8POlzs/rUnl5Hhgbw+YxR3/rSNB0elEZLs2+W9olKLMebW8/Ixh4eBVXDs3YYqvCnRN/JU6mwKdHWc0Nbyn+KdFGobqDBoGBTkhwU7AwN9+fPG/fxhlBF/lZIde0NobDSyOMvDJWHRr4UVFGu0XQ7K5zu/utVuo1TfRI2xBZsgIEaE2W5lsF8kMpGYcoOGo5pKfExmvBWnrLe94k/QYDCS4OfdZbm74lwL+PPFt+UH+OfxDQSoFOyvbiDez5tIbw+Ghzn25VcfkpFT3UhQJ5q54dEdXc7atvTkYglmu+sExGyz8WXJHvbqjxHgoeSGtFM5D4xWG79bvYO9VQ1sXjSb5ACffqzlxcmIESP4/vvveeyxx3j2WUfyNplMhk6n47XXXiMpKYkXX3yRX375hcjIviVju6xW9G2UNGv527bDfHfcoZZ/ZEw6c5OiUFUPojX4EGlBvmdUlq5Wf335GDeXVnPdtxsBGOwTwaFmh5VnqNKbHyf+tltNQpNahJ/21Os630ZAveGzjRaOaiq5LX40rRYj8za/w+4ZjyIWifpl0KrTG0l/7wciIiKcFrJjA+N5IHkKcacZCfXmeRfa4O50Qk6GHP5gzji8FTJe25WNxmQmLdCXAQHevLjtCDUPLCS7TsOUz1bz853XMUx9ymLabLPx3OaDFGm01BuMFGq0tJgs+CrkZIYFYLbZuTUjnqc3HaRa5wguMjE6lLlJkcjEYnZU1BGuVhGm9sBks2G22ViZV46sMRaD3cKogDhnZMT29fjheClquZRp52ii3Bnt31250sLSvWuYHJKMVCQm2tMPX5kHHtKOce2tdjt7GotpMuvZ2VDE2IB45BIpSrGMAm0ds8JSuzSW7erd9Xc/OlMDxSa1CEmTgXJ9Ey0WIxEqX6I8/fr0bKvdzug1Lzv//v2AyWypy+Om6BE8fOh7AO7nq06vPV3Yt7fd+f2+//LdLSMQiUQIgsDjK8oQBIGFMcPxkamQtvOHb6u/xWZHIhZ1cAftjkt1Rd+eJUuWUFpayvLly6mrq2PQoEG0trZy11130dzczNdff92n+12SK/q2jm4NP0K42oManYEob0/WFFWyp7Kef+3JZWiIP0sGJ/L4uEHOWTsBJXCGq+K2Z57tBz0hKgQfhYxmk4UXZibz+AYdR+o0VBtbmLv5bT4bswS/TiK4gcPFpafynQv6cm8fWTkfF+1gadEO3hm+kOujM9nbWMLIgNhe73F25xurlEi4bmAsT00egbkggWJtA+/mb+GpIz/y2Zglvb7PxYq/Uk6j0UyoWkVI0zCGeUCIvxevHV/HsmMljAtMQBAE0oJ8+U1mMtd+tpK5sSG8OcvhGfLy9iN8e6yEm9PjKWgS89cpwwnxVCJC5BJS9OrkGJYeymPJYNeIdu23VhRSxwRiQIAPNrM/sZ6ue4Pt2ze/sYWxUec+cFT7Z7enuakJuVhKklcQIcruB1WpWOzMMDkjdCBikdgpSMYGxbOnoRi9zUKMZ0c/5c768Nl+e70R6r3px2a7lS+O7mKCbyxRHn4M8AqhVN/I5to89DYLM0MH9mpLUioWs3XaHynVNeItUxKq8iFeHcgf9n/DQLJopR4rZnZL9c5rxlp9Adhb6t3pyh4g2sOPo3UaMoL9CP3n12QFJ3FjzHACFB0t7NuQSTq6ILalKF9380zSg/s2iblU+Pnnn/n2229Rq9XU1tY6j//hD39g3Lhxfb7fJSPoBUFAIpEQpfDBJgh4ShUc1zos2e8YnMSfJw4h2lvN9Lhwnhg/uF+f3Z9CVCQScfy313DT95v5Ma+ctbfM4sWth3h9Ty41xlY21pxgftSQTq9t83m9mBniF8mEoESaLQZMdiurKrPxkakYGRDrPKe7wauzCVX787wUMt6cNZp9lfGoLHparUb2NpZgE+xY7Takp6V7vZSEvdlmo9F4cj/+l13I7Uco1jXyt8FX02jWM8ArmBTvEEqadcSeDB16TFCQXVaKIAjcvnIbP+eXU3rfAjaU1DAwyJfM0K4NdyK9PGlpp77uinUHbKT4tFCpbwYcgmBwWgsKqQS74MGuijoivFVojGbWFVVSozNyU/qZGWy1p6CpBalYjNFqI8HPy2XF1x6NWc9WazUPpEzpdR6BNk7vLwAjAmLZWV+Ep1Tepe3MmfQri93GhpoTqCQyVBIZaT5hTreznujN8yQiMRER4QySnYruFq8OJF4dSL1Jy97GEka0+w5Pp31yKKVExoB2gaRSvEOQiEQM9wllg6YOET3nnzjdE2dWWBo/5e/laF0TdwxJ4rchXUek7K6+VyRGsuam6dToDKTjEPSlzVru/3UXw8MCqdEZeizbxU5ra2un6nmJRIK4i++gOy4ZQS8SiVi+fDkHH3uPaE9/0nzDO3SElMALs4/T149eJBLx5TVZzr8fGpPON7nFVGoN/CVnNRqznuEBMWT4Rrhcd7qQv5j2ltvzauYC7tnzJX/Y74iTf03kkE7PO9M9UIPVzH/yHGFzm81GZ/a5zgbt9tf25yrsTOmun8glEh4enY7GZGZ+cgyZof6IRCL0FivvlvhwrKGWE6216FVxHKltYnp8OBsP5DAqPJA5X61hX7UjBsE761qpNFi4b8AkynI6f26VVk9hUwsHqhuYrMgiVNX1KnhEgByT3eq0ltZbrBRpbDQZzeQ1tqCQiLkxLcF5/tqiSpYfL+Wq5Ogu79kdFa069lc1Eu+nxma34imXsqG4miqtngQ/L1QyKcdqHREq1VIFwQovhpi9+a7xIAtjhp/RM09ndGAcqyqzMdkd0W1iPP3RWU0kqoMIVTnGmc76kM5q4nhLDeEqHwIVrpOTDTUnmBSShFwsxWizkN1cRavFyCQG9CryXU99dnNtHjMD4jr9LVCh5rCmwmnV3lfey9/K60Ov543s/cQykiL2M9Y6ssN5bav504V8laGZtVW5LK/O457hKTw7cSgySe9cJztjUIjrBDbK25NvF0xhbVElP57oyjnv0iEiIoLi4mJiY2Odx8xmszMJT1+5ZAQ9wNy5cwl7c5vz7zOZVXd3zdm4z5zNylEiElGpPTULfSt/M+RDjIc/n49d4vwwz9ai9nzy9ogbsQl2WizGLrciuqLAcwcvbyhCIZFSb9Ki2WggWOmFSiJDLpZiF+xcnTWDmRV6Hj7gSPIQqvRmX2MJYd24Vl7oiVFv+sdDY9I7HJv15RqONzQzMjyQ6wbGcrCqgZQAHzJDA/jjtXNZv/4gbw1fyN7wUvK0tWT4hFN1Wu74trpb7Dbezd/C5OAB3DlGRWlODKsqszHYLNgEO9NCU1xWsSablSpji0tKWw+Z1GnA2hnT4sIp0rSyuqDC4TplthDiqWJYWIAz0llnbCmtocFgorJVzz3DU1x+S/DzpkqrpzovGqvdzoRgx8REYzZQomtAmRTOgvozm1h0xcywVMQiEUabBYvdhpdMya6GYmqMrQzyjUBvMzsjZGY3V1Fv0uIhkRGvDqLO1Mr+pjLUJ39XSxXobWZaLEaGDnW4jiYBh2q0HNNtZ+umEGe8fovdhlqmYHgnEU7zWmupMbZgFezIRBKCFV4keAWxv7HUkccgQkVsja1T+52xgfFsrStwCYvcnu7yRmQ3V/JQyjQ8xLkMYCxb+JREXAV9Vyp7gB/KD/FZ6R5+vXE6g9tpmbqLk9AXOyiRSIQImBEfwZiIYP6b2/35FztTp07l+++/Z9KkSQAYDAb8/PyIjo5m9erV3V/cCZecMd6+WY91et65MEzrbbCO7srRF77KLuL+X3d1OP7GsOsBMMb6oSxu6vD7uaDW2MoArxAG+nSfzKY/Oaqp5P92f4ZVsDMxKBEBgfzWeiyClXqTIxTsrhmPIBGJ2edn5O6v/smTabP5b+k+TrSe2sfaNPUB5wB8MXGmfcNmt6MzWyjQaClv0fH05oPUak3YBDt3zpzPbfZ4p8paEAQONJWT21xBiNKHAKWaoX6uITj3N5ZislkZFRBLtd8+BgR4gwD7qhuo1Rmw2gVCPFWIRLC3sp6Hx2Q490rPdEKrM1v4y7bDPDVhcKcxAACW5RZzbUoMVrvQ6d5sd9/iuZgEd+XSVqxtoFTfyP7GMiJUvgQqPYn08COpm+RWWosRqVjS69V0uV5DkbYesUiEgAA4BFmEyteZtji3uQqN2YANO0P9ovCUKpzt0NV4eKylmkpDM3ZBIMU7hEiPnve4IwcWMevLNbyatohHd2xGQGCv4QB3ByxhvOeYDuefPmHY31jKJ4U7WXbriE5tBPo7o9/lYIyn1WppaWkhPDyc1tZWvvjiCxISEpg0adL/tnvd6QPQmQjpzu55ptecyWC4MC2O6wbG8ObeY7y07Sg2HKp6D4mcwX6RFHtKiA08f8YnRzWVbKnNxyLYkIkkCAh4SOUIgoDJbiXFO7Rf0vbO3PAGDWaHIB/uH821UUN568QmwlU+VBmb+WjUrQz0DkVrMSLGYbH7QYFDs3NVxCCmhaZgsJo50VrL/fu/Ycr619nVTWrXC8HZTAAlYjFJ7zisnedHDsFL5MWDQ2aTFZxEcYgEeTsBJxKJyPSPItM/CqvdzqbaEx3uF6L0Znt9Ia01xxhkGkC+KI+tZdUsHpREtc5ASbMOq92ORCTCVynnUE0jYV4q/JQKyr128flPIkJU3vjJPcj0iyI6raTHOiilEkI8VV0KebsgYLM7NBCydtHdLjavCIBQlTd52lquihxEg1lHi8WISiJ30aCcjrqPUTAjPXyJ7CZRkNVuo0TXyNTQFOpMrRhtVpfJbVftluIdSop3KIIgsKLiMGEqn5OTwzJ0NjMJnkGMHdHsPF8QBF7bncPU2DD0VhN7DPt5KGUaDwfcwbbjrpOWzjQCzWYD9SZdl0K+r1xKNjdng1qtRq12jK1eXl7cfffdAOh0Ov7yl7/w9NNP9+l+l42gP50LraY9U4EvEYv5/YiBfHqkAE/UHGupQWPW93zhOSDd95SrlF0QEAEGmwWxSIRCLCW7uYqc5iq8ZSoG+Ub0yQWmDUEQyPSLYk3NMW6LHcUQvyhGBcTSZNbzcu4aAPxkHsjEEqZvfIM3h11PtKfDAtzmF8WDB5axpa7A5Z42wd4hDeuFor8GpS+unsiqgyY+LXZofMYEdr4X24bVbue78gMoxVKWlx/iUKmIodEOQ7oGk46JwUlEn7QqzzGYCFd7cqCmkTC1ilkJDtuQ7LomLHaBaB9P6vRG8htbSQvyxW+UiaIT3vjHVNAqbwD8+Dm/nCEh/i6W/YIgcKKxha+yi3h73zE+nje+y/KKRSJGRQSyqqCccLUHg4L9WLldSo0xhxClNzGe/n3eAjoTOntfbYuGI5oKojz80FgMhKt8iVMHEkcgFruNAm0d62uqmdqFWry/kYjEqGVK9jWW4iNTYbK3ktdaS2hI7yJGikQipoamsLWuAIlIxDC/aJoDD+Mh0wGnFhOv7MxGLIKHx6RTqS1HskVMvUlLvGcgCcNEQOfqfoPVzIqKI6T7hjMjbCAiUfHZV/ok5zsuw4Wgvr6e999/n+LiYsxms/O4wWDgv//9L8XFxQAsXbq0V/e7rAT9hRbunXEmM1CRSIRcIuZYUw2Pp84iK2TAuSlcH2gTmu39ktsmAhqzge/LDzIqILZXqkDHNXpmbnwT28kgLn9Mnsr1McMo12uoNWm5PnoYCeogfrf3K+ZvfQ+AOaFp3LvvvwB8fevjJCiGAPB+3haKdI0siB7KY4eWc03UkItCyPcnU+PCmRoH92ZnceP2j/iyZC+3dZJsqY1Xj6/l6ojB2BEw2CzkeB9FZw0iwy8CuVjKltoCYtSNeIVXsa6gklszEsgMC0QQBAqbWqjVmWgwGJk3wKH2D/Y8FdQp0ENJQVMBFrucjw7mYbHb+TqnGIA3Z45mRHgAo5b+xISoEKJ8PPniqEMY9BQbP9bXi2gfNfO+XkudzkiWfzqRHn6srnLYEDybMfcsW7FnukpXm99ai1wspcLQjMlmYUi77RCZWEKKdyhWu51Kg6bb8Nv9hUgkYnRAHDsbiijTN+EhldFk1hNC73di1VIFWcEO90pBEDihMzA36VS9NpZUUaXV849pjtV4SbMOm2Dn46KdZPhGOK9tj95q5uvSfYwKiMXsXUGaz7Aey9Ef2tfLjVtuuYXjx48zaNAgJJJTRsYmk8mREri5uZurO3JZCfqLlTMR9k9NGMxDvxzkivA057G2tJEXG75yFddEDmF9zXGqDM3duvAA3Lv3a3Y2FDEpeAAvDrqKrfX5RHv4IxGJ8ZIq+LU6lyaznsOaCmc0N4Cfqk/FoPZuNAEO1eGdSaesUH+ZfF+/1u1iQyISs3TkrWStf43h/jHEKjsPUPPbxIl8Ubyb8cFJtFqMPJo2C7FIhN5q5radjkRSg3zCmSzx5YFRaVRrDfyUV87BmnrmJUWzr6qeWzISOr03gMUucNiWx4aKWl66MZrJo+J48IsS7v1lJ5/d41hVNgotbDnqcIHdvGi2y2ShMzaWVLHwu03ckBrHM4nXIRZL+Lkymy11BS4+7YIgUGlopkTn8DIY5h9NUHP/TezaCx2r3Uaxrpb9TWVcH9290Ko3a7tVt/fE6aFluzKOa7EYOaKpoMmsZ3roQBSSU8O4rrlvJld6qxm9zUyN9yHGhp+yMfj+eAnfHSvh3dljEIlE6MwWfi0oZ35yNNf4TSbstMnMVyV7+anyKCX6OiZEh/LnMWHMZBBteULOtcq9/TvTWk3n7Dnni+3bt7N3714GDHBd5NXV1RESEsJ3333Xp/u5Bf15oq8dXWe2cv+AyS7GOwX5Uchau35l3VnNnmvaVIH7GksxWM2d+gdrrSZ2NxTTbDFwW+wohgfEsLuxiCG+kfgrPHk3bzPLKw5TZ9J2uHakfwwRHr6MDYxnckgyOqkIrBe1HWmv8R9a3OVvjQdiOxxTSh19wmLv2vhMIZHiKVMQofIht6WaSoOGSA8/AhILCdqh5OkJg7nvl13k7qtCQODKAdGo5TaMVjvG8gTmeAzhvzlrkEnELB7cceV2ZVIkYa9v545JQdww2hGRcHyKN/NfPc5XOxpIi1AhNisBA8PDAqjXG7sNY9psNHPDd5vY/3/zsJel8W7eZj4o3A7Au8NvxL+d2v6nyqOk+YQxJjAOq2BnT0MJZqOEsaqILv3oz2TVWKStZ0XFEa6NGsIQ355Djo7yjyW7uQqz3YaAgNluJc0nnOBuUlJ3Fze+vV/7zvoizHYrEpEYESLGBsb3ac+7My2FTbCzouIwMzLFxErU+KsUjj35XdnU6U18NHe80yhShIg6vYlqrYH/avdzd8J4irV6vi7ZS3ZLFTkt1dw/YDKPXZHVZbn+V/bX+wOdTtdp8pru7EC6wy3oL1LWFlUywyvW5ZhNYUfW6nrehRTunZHsHcKx1hqnpXd+ax21RkehDTYzk0OSmRKS7HJNvUnL8F/+BsDc8AweHTidvx9bw+6GEuLVgdw3YJKLRfMD+7/hgTFX4snFZ1nfns789nsa6BoPxFKrM/DhwTwKNa1EeWkQAH9jHCabFU9pKxa7jVtjRzHEL5ImKbTFFdvXWEpuSzWxngHMHmUjvyiPR3IOcWdmMu+eWMnVydFcrYpmUkwom0pr2LNkLiOWrmRfVQM6s4UXpwzHYrOxJ7+YKyMHM0s5k6UF21kmL8ZPKUckEmGy2ZCKxBypa2T+cD/evf3Uqj85TMUvfxqI0WInKVTlnKSUtej4+FAenx4pYEFKLHH6kcjF0g6BkMZGBnHXT9v5aaEnt1lHU2VsIcbTn+EBp9JOA/jLPZ3hjmUiCWOD4qn3EFifn4O/whOr3U5gdDXeCjkjwk+FRW5vRd+bULZx6kAG+0X0ejtKJZW7lNUuCOysL6LJrCe5XfCZ9rT/ftsL/bbjubnxCIJAramVMQFxXYbmbcPcrj90R1v9k6U6pGJvUgJ9MFpt/GPnUeRiMS9OznQRKBVaPUarjccSrqXOqOWd/C38VHkUgBF+0U5vmM724tu3qVtF3zs2bNiAj0/HibG/vz8bNmzo8/3cgv480pcZbU6dhj9GuQbMkbW4vq6LTciDw1I/zScMgAaTjv1NpT2qPB8/tJw3h93A6HYGZk+lXUG+tq5Tl6XXMq/DfGmEEwA6DnRd9YE2wTjnq7WMjw7hd+FX02IxYBMEym1NSORiWqxGms0GAhVqttTmY24ED5sYT5mcjwq2839jwtlRfpBfftWzZHAS46KC+e3POxCL4IkN+wnzVPHmrNF8nV3E8rwyqh9YyP+t3MqHh/L58FA+9w5LQaluZKW+ErU2AT+FJzPiA12i5zUYTDy2YR/fXDsJaHCpQ1SAgsYDsTRWtTtYnsbigDT2i8p4fctWghSNTA8biH6dGaVEilQk4apxAo+PG8zb+44hCAIqqZzHU2dx//7/ckRTwctDrnFm13O2pa6JUn0jowPj8DaLmRmW6vAvF0uIihNT3qLjq+zCDpn1QpILWFNYg1gEOosVmUSMTCxGEECsqqS+NBSbYCfWM4DjLTVMCj4zGxmxSMTYoHi21OZTb9IiwhGzXSoWgwCBSrVLWOH237PZbsVst1FpqaKkOpeR/rE9CnnoPkx2e/ZVNfD50QK+O1aCwWpjVkIEOrOVm9LjmZ8c7SLk91c1MPurNcT7elHp14yvzIMms54guZpM/yieyZiLRHTK/RIuP+O4883EiRM7HDt69CgbN25k06ZNnf7eHW5Bf5GSGuRLnVHrkpBCPbYMw8aYbq66sJjtVnJaqjDaLchEEnzlHlwXldnl+YIgsL2ukP1NZezf9zV7Z/7J+ZtIJOrWL7ky4OIOHnS6qyf0fjVjt8gorobV1TbEIiVDVINoWw9OOG1yVxwiwXC8nOWVh5F46LhyQDRXDjgVOOZYfTMr8sq4a+gA/jA/jWc2H2RWQgRLBify8o6jXLtsPUqplPLfX8/eynoeW7+XiTGhmJuCEMlN2Ox2SnOjiEurwFMmZVNJNeXNOsSAv1LhnJyUNmtZeiif4w3NiEWl1OmN/HZYCjMTTtkQZPpH8ZbfQn6z5wsk1SOYmlaPHYFWi5GPDm5hYnQwqYG+PLXpAHcFxaKQSHl3xE1krX2F+ZvfY1RgLNdGDXX49zeW0Gw2EOHhyxfFexg2Yjip9RJkIomzvSO9PfGQSanRGghRO+wDDtU08N+cYm5Miyc1yJfCplYS/V19nMsMsdQYW/i1KpdRPdib9IYJwacs4W2CHYvdhlgkotLQzNa6fATBcdxXrqLVYkIsEiEXS5CLpSQn6on0SO3m7q60/y5OF7b7qxvwVcg5VNtIbp2GY3UafrxhGhnBfjQZTfgpO9eQ/fuAI0e61SxjT0MJm2rzeHv4Qvy7iVHfhnsFf2YIgsCRI0ecgn3z5s00NTWRmppKVlZWzzc4DbegP8/0dlXvIZOiDcgB46kEBtU1gVzMyRrlYim3xI5EJpJ0u4+0q66I3+0/lX0pSuWLTHJ5dcXO3nNPK/k2/pAyhYLWeoaYBrFeu7nbfblvy/ZTXX2ACbEhvDyo4yx/xYlSorw9qGzVMzjEn+8WTOajQ3nM/2Y9CqmEN2aO4q/bjjD367XcPzKVV2aMYmVeGZ4SBR5SOQarha22TTy7ogq9xYpcImZkeBAlLToGvOMwCJoWF4ZMLOb+kakMCRmMSCTiREMzX2UX8dGuShTig0hEYmyCnYZWGVHSZMqklezaU8WwOBubak4Qrfan0t9IerAveyvr+bxkDwuihqKUyBgeEEtuczXLKw4zxDcSlUSGUipzurKFKX3YZdBQU1NPqk8Y7cMDzU2KYvmJUsxWO3KJGH11GC2NXvy410xubDEZwQ4jv+LsaESI2FlfhEXIw0/uwS2xI/vF97s9EpEYycl971jPAOeK3ibYabWYnJkBe6KrkLkai4FXK35gelw4UTjsCoxWG2uLKon29qRQ08rI8CDe3nuM/147ySncuxLyAHdnJvP98VJK9Y38JnECv03qfjXZ3/FM/hcJCgpyCvZJkybx3nvvkZWV1em+fW+4vEbXy4ganRHJJegi1l1SEbPdypsnNnFEU4FUJOaZ9DlMDkl2sRq+FDiXPvplObFUG/YS6eGLzqAjWBrUpbD5rHg3vikKXpufxZfZRfx12xH8PRS88BtP5+TBJti5fmAcm0sd1u8ikYg7hgzg6uRoXt5+lEXLt3BjWjxby2pY8uNWbh+cxPfHSlgSG84tk+QM//AXKo454jgk+XuR19jK9vI6Zxn+PnU4k6XTHH80QrnDEB4VsCRwMEsCHe/dLgicOB6PWClGInKo4KUiCSVleqZFi6k1aZkR741dEIhszWTa3n8hAm6OHUm0hx+bavN4Mm02M8JSO/QXH7mKAWo1sSEBrKrMZqhdhUQsdgqY4cRhtFv4e/Ya/OVVNJn0eEoUNFQGIbYO4NMjxRhtudgQGBUQ2y9BoPqKRCQ+KyFvFwRW6Fdx3B6AFPjP4XyUUglbS6uRiEX8cVQ6CqmE9/Yf5409uTw0Or1b4d6e7eW1LMpIQKYLYXnFYa6O7DppmFtl3z8kJydz4MABlEolCoUCmUzm4mbXVy6tEfZ/iDdnjeKuZUd4tWvN9yVFuV7D1Vvedf79fMZcZrVzHTyX9GcyG4vdxgHpFq5NiaUkO4Zms4FWq5ERmU3IJZKzvn94SiH/+GUtX4+7gx9LdzNefcpX/nSbjJ8rj/L8jCt4dddevswuJNnflxvS4mg8cGq7J9hTxbObDzIrIdJl/zRApeSlqcOp1RkZ8eGPeMgk7Fwyh0CVktUF5XxQsI1rm6aiPJmq9g/DBxKiVvHYxv34KeUc++01va5r2+RPJjotkpoymR81q8g+3ozNsxz7rwkUaOsw247zUMo0Z4KaGaGpfFq8m2GpJhIjyzt9RrPcl6gADbckKVhdUEGwp4pQHOXTmA28l7+FG6KHUWHQEOfpj8Vuw0MiY3VVDgN9wkjpwljuYqA74SkIAk9uPMCJxmbGRgZz+5AkZq5ZfzIAURCR3p78ZdthJkSHEu2t5unNBzly11U9ujq2UZYTi0R0jGFhAdjqwshprur5IjdnxDPPPMOzzz7rcqy6uhq5XM5f//pXrrvuOlJTU5k4cSL//Oc/+3Tvy1bQ93Vf9HzSW6tfH1k+w3/5G6neoXw0ahFtmSHbW+T21SDvTK7pjL4kBirLiXUR8lckRHLXNDXtfWzPF2ejSmyrUzyxlOXEktNcyV17Puf6qEyiB4RQ2qIjc4C1yzCvvcF+0pbqq5J9DI6MAI1jNX/6OzvWUo1FoscmgEwsZu8dV3Z6v5HCeF4enMCodlnNPj1SQGmzlshEPe+srcZos/GPW6MZNtlhWPeLdQYZ/17OuP/87Lzmn3tzeWfmKACajGZs9q5jOnRmPd4VATJ/5vjMxDf2ICFKL8oNGh47+AN/HDjNeU6Mpz/PTBhCTp2GKG9Pcus1AIyLDMZT7jp58JBJmZMURa3OwF79VloqwhjqF0VWcBLBSi+SvILZXl+It0xJhm8EWqvJ6bo26LRskZcCVrvArso6xkQEcceQJA6YzPx2WDL3DEsh2FNFtdbAYxv2s7awkjFRwTw7cQj3rNrB19dMQtJDutMqrZ7fZ3+IUafk0dQZPJm3gndG3NTtNW4XurMjLS2NtWvXOv+WSqUEBARw5MgRVq5cyeuvv86//vUvt6A/nd6kf+zqvJ6uORt680H8KXUmP1YeIaelGolIhLrAdRZ+JkK+P+iu3F2FEFVvkKI1O1J+vjV7VK/vB523fXjDmRvi9de+YbpvONunPwxAoGc+f1izm8fHDe4xAlx3yCRiHkqZxs6GIvY1lhLl8R1L4sfAyRj/NsHODxWH+axoFx9fM44kuZmpI1MRBIEGg4lGg4kILw9y6pspb9WhMipoMuu5acdH3JUwnrEBcSzPK+FonYbbAgIoqjOx8qEUZg7yBRz2AkEeAsP9ojnYXIa/UkGt3kiISsHTmw8S76umUKPlqU0HWBwQiUoqR2M2oJLIUEik5ObGYxNsiBChs+ucE8u2vldvbUBn11NtqUEhVlBprqJR3kjayUAzsZ4BXBudyeMnPuPalBiuGxgLwN5sGVtKa8gI9mNqbJhTwBmtNlRSKSNiXIVWkIcSlVRCc0AJqytaMdktmOwWNGYDGb4RHG+pQVc+mKPGHDwCW6g0aC5JQS+TiPnhuik8uXE/d/20nUqjFU+RwDv7lrNzyRyCPJRclRTFjoo6PjmST/F91+OnVPD6nlyuTYnBZheI9+tozZ/x3g+Mjw6hrEXH39Pn0WoxMTU0pcfthbZvy71Pf2ZIpVJCQ0M5dOgQGzduZOPGjWzZsgWFQkFWVhbPP/+8M6Ndn+7b/0U9t/R3TvG2Ttid8D9XnbSn+yokUl4Nf5GXa/8JQMTgEvy0FzZIzJnO1n+6YTpZn64CwEPW+3zYXbWRTilCfoZtcSbvs6d+p5JKefeKsWjNljMqU3uG+8eglirwkal44MAy6kxaCrR1zvDDUR5+fD/hbmQeubx0oIi84hLMNhshniqCPJTkNjQTIkQgCAIt1n0keAZyQ/Qw/nToB97IvJ5/TBuJVCziw5Jd3DA6wCnk2xCJRLw78ibeydvMh4XbmZsUib9SwaHqRqQSMUEeFj48mMfP6ne4Lnw0y8r2E+sZwFC/KCJ8c/m+/CA6q5mclioeS51JQ50XBOYDIsKU3hxtrmSCdzgJ3p7UGuM40LgDOLWN88BMPyy2iby5N5ebf9iMSCSivEXHmMggRkUEASARw8RoR2bFKq2eVRUaBsjspAU56rKzoo4BAd5Mjg2DdNix14cghRcSkQipWML+xlIOa7cSI4vG3pBKfHQNexpKGBFwbrxaeqNJ60og9rQoUMtlvDApk+3ltXxZ2kRBSSmLByUSqnYkEfr33HE8vG4Ph2sdmS+vT43lqv+u48FRp9p8f3UDg4P9kIjFbC6tplZvJM5HzXNZQzEWB/P33DUsGutHVFjX5Ti9Hp15npxer/8FWlpc0/cqFAoUis7tI/Ly8ggPD6eqqgqRSMS1117Lpk2bSEs7u23OS07Qt+f0D6O3Qqi3510MHTFIGsgtiYN4MecX7s+cdUHLcjYquS+z+75d0F37y61nVo6zeac9XRvkoSTIo/MsZX1RaSZ6BZHo5RBobw67gXv3fc3iuNGkeIfSYjESrw7kkYPfYz1g44+z03h6yCnhVJYTC6dixLCroZhMvyhEiFhZcYQoT39ifR2rX7lUwr/vcFwrjK5HtDMQ/6HFTkO+3yZNZEHUUN6uWM4nRwq4c8gA5BIxZpudaXHh/F/IlRxqKue9ETcRqvTmrRMbWd5wiD2NJXw99g7+cWwtV0cOdvpYt1FtbCbB21G/YKUXab4RfFq0i8fnnNonl0nEPDCqd4NbmNqDLLWEL/YcwWKzc6imkTGRQUR4nXL/GjO8mbKcdq6qUgWPjm6fgEbN3oYSvi7Zi6dEwdzIjF49uztO16B1Jez7Y/XrIZPyxdFCBKUHG26d1UEtvyAlhnVFVRitNgqaWqjVG52/7aqo44W1BXhJFZQZmmgw6fhl0n0MGVpHTr2GO3e/RX5TK3dP7P3405u+fjkI/6ZDMVgVnS9cWk0WYC9RUa5pop9++mmeeeaZDuePGjWKjz/+mOTkZP7617/y008/sWzZMlavXs3YsWPJysoiKyuLkSNHIuvDYgkucUEPnc8e+8LF3rEGDixkIMN55MB3/KdwJ7N9EonzDDgjt5/2KtTzSZVWz7v7Hb64T4w9+wG0M87FvmB7jc65nCB09/v35Qf5YcJvyG6uRGs1UaZvYndDMfcnT2bM8GaaxTKwd61FaDYbEAE1xhasgp3c5irG4FC//v0m19Vrm7BvT5DSiwWBUxGJ1vPBwRMk+Xuz/Pqp+KsUlOWomNguKcrHJzPr3Z0wHrVMgdFuwS4ItMs6S7leg6/s9Cx0AmPSXPf820842tqou3e8r6qRuUnRiESwaFACIpGoSxVyW254wJnK11Mqx2K34y/3JEzlw8GmMpfENX2lq++sv2xkTud4QzNH65p455rhTo+D9nX/0/p9VLTqiXnjGwJUcqbGhnPoQBD/KdrJ2upjTA1JYWtdHi1WEy8MupKZG9/Ac6sC3cm48Y+OSXdqSy52IpNLYd2FLsUpysrKXPLRd7Wanz17tvP/X375JRqNhujoaO688048PDxYtWoVzz33HFKpFK22Y5jw7uiToNdoNEybNg2r1YrVauX+++/nzjvvZPfu3SxZsgSTycSiRYv485//DMC6det45JFHWLhwIQ8/7NjLFIlEPPHEE7zwwgsAPPTQQ6Snp7N48eI+FbyNMxHyF7tw74yFMSP4RlbGq8fWUmlo5vXM612C6fSW8xlNrywnlndrvuODgyf4YM445iZFntEEpbM43XU+YlLiCzq/oA/3Ov2+/T1hONNIYXsaivmu/BAZvhEuucmjUovZUFzFiOgmQEyZ1BMfs8blWW3Um7QEKjyRiiVsqs2j0tB8MtVrxy2P0wV8e2aNMTGLcc5n6IpAd/r1IhG7ZjyCIEBceikvbN3AAHUIJrsVmVhCpV7DidZagpVeLumPAZrMeiJUroPf6UL+9Pqd3p4nRAqm+/ac873Gby/5jS0M8RsJOKLXlek1LI53zQT4a1WO8/+9TTbTlXDfW3pqkB8e3dKjsD8TTeXAQF+WXz+Vl0pbCa5MQXzaZ/bQ6HT+m1PEP2eMQmux8rtVO3ig/lOO1WsAEV+U7gEgSK7mnfzNPJwynd/P8EUickTzO5Pv9mKOktcfsqy3eHt7uwj63uLr60tycjJyuZznn38eAKPRyPbt2/t8rz4Jei8vLzZv3oyHhwc6nY709HSuueYafve73/Hll1+SlpbGuHHjmD9/PhkZGfz73/9m27Zt3H777Wi1WtRqNWq1ms8//5xHH30UL6+eQzp2R18H5ktRwLeR6R+FxD8EjcrArkp48thX/GP6SApO+BOs9CJe3fVA3V/0ZfBZtgXey/+URpPOEfHOSp9zUkelFtNoMMFpMe2jUotplvuCudPL+p3O1KndqR2725v8z6F8ZBIxN6V3rVn5pGgXVYZm/pA8mRDlqQHCafXv58WB6kaXOO6d9e1AhZpdDcVY7XZujBmOj0zFP46tZVmDDLEIhu0OYG5iVIfIcCarjc+Kd7G3sRSVRMYDflG9MjCUiMQgAq3ZwsaiSq5LjcVss7KhoZg6o5brYzoPhZzhG8HGXB01hUpSvEOITivptl7tj7e1icIQSFlO99+AIwVvK1kxoYSp6ynLUSMWiQhUeKIxG5yGZoIgEHAyxHBgw4wO9zkbrdjeUu8zEva9wV6WRpikmBWlHX3dPWRS4vy88FMpeGLjfnZV1gOQGRrA/uoGVFIJj48bxMLUOEpbdKQHn1pEHG9oxl+l6HJb6nS6K/f5GIPLj0f3eM7ZyrLzgVarJT8/n4kTJ5KTk0NiYiJKpZIpU6b0+V7d+1echkQiwcPDoXYzmUwIgoBOp8NqtTrz5i5cuJCVK1cCYLfbEYlESCQSBMGxilAoFNx88828/fbbfS7s2XAxCfnc3Hjnv56ISi12/vORqbhlUAKvzxzF0NAAdpbXEihX8+zRn/i6ZC9b6wpoNLmutXKaq9hRX0h+a10XT+g7PQn53MNh/G7vV0wISuSHib854+eYrDZWF1Sc8fV95Wy2f3qbuOPK5CimxIZ1+dwqQzP7m0p5ZOB0FyEPUKzRsuJEKQVNrfw/e+cd3mZ1tvGftrxkeW/HK3a8Yjtx9p6ssKGssvcoFEoZHUBbSssHhUKhUCirLSVljxASIJBF9nQSOx7x3ntqS+/3hyxZkiVb8kgI7X1dXMSvznves+9znvMMnck7q4OVUdPY2FyMSbCgkEh5Iu983jh3Aa+tWUB+VCh3bthFr95gr0NdcRIPf1qNWubP25fP4Mk1U/nznmNc/uFmHttykO1tFTRouu35C4LAuoYjbG4to89ovff9uLQGP7mMBYlRVPa3o5L6YRYsfNtSyq72KkxuTPOWRqYTKFWwv7PWpw2lrcxS8cjORE509bKuvJ6cyBBiAp2vDmaGJLKp5bjTs9KeZmaGJk6IBKwwsdftv2F0CxZfxqQx5gjftZ/g8WNfUO6/0+m3ZUkxPLa4AIC7Z2Xxs9mZAIhEUHTzeRy//SJumZGBSil3InmAjLBgr0l+JJyMNTghq9oquh8FE8FlE43777+fLVu2UF1dzY4dO5g+fTrd3d08++yz5OTkEBoayu9///sxfd/nO/ru7m6WLFlCeXk5Tz31FK2trcTFDZmlxMXFsWXLFgCuv/565s2bx2WXXeZ0er/nnnuYO3cu99xzj88FdsTparbh7eLhOslDYjrAYBU3/nJhHpd9uJm8xaFoS/rZ2LmfH0Un0aTz53hvCyIRCAKoZErmhadQ2d/OttYKFkSkDvPq5ovdc0JWNYIgUNXdj0QsIkguI9RB7FpXnMRvj37EisiMQbMw93XxBgqpZMST76mEr+POForWFlXdXfhZgK+aj/OjhJnDRKU6s5EN5XXcVTjNJzGqQiIlJSCc/Z015ATHEu2nQi6xnkiWTolBpZDzk427MZh3khkWzF2zMjGWmJmdY0ApDcJSl80TU7OxCALPlX5Do38PW1rLadD2EKsMpl7bxb7OoYV1YUIkFotAnl8GORGh7DmiYYp/KFckzbLXY0f7CULk/uS6mLMlBIRwoKuWLFMMZovFrlDmjaVNhDKQtcX77E52bGjp17KnsR2lVMKypGgCB+3uHfM51tvEmTFD/uRFIhGpQZHs6ahmdliSk98Kb+Eala4wsXfSr81SQoL45cIYLti7lxNdvcM2lLYxOB8QEkX8aY9VYSwq0FVnwjfp3Wg4WQTvK8bDZa6a9BOB+vp6rrjiCjo6OvDz80On0/GnP/2J2bNnc/bZZ/PVV19xww03IBaLefjhh33K22eiV6vVHD58mJaWFi666CIKCws9pj377LM5++yzhz2PiIhgzZo1vP76675+/rSFY2xpbzDawA31U3B1bioHmjsJ8ZPz/Oo5PLr1EP86X0e9mwUpJTCc7prpvFtfzuUzAugz6jhwPIIISTjueKPPqKO4t5nd7VWsis6kZqCDtKAICp9d67Y81+SmotIlcG3yFFZEZdiVeLypyw8RjtdK7uLN2565Er6/VE6Trsf+d5dBQ3lfK/6x9dyWleFE8nXFSXRFSahrUY9YlsSAEN6vrWZ2WPKwvpgRHcZb5y1CEAT2NLbzk427SQwNIC8qzSldp2GAVdGZTvfrDZpu1HI/AqTWjd76xqOcGZNNUXc9zdo+ao9NIVxRT62mi/RBz3NKiYzFkVNp0HTzdfNxFkem2T3nVfa382VTCb879gXib0R8eMky5sU7BzbyZPJqspiHSUASsqr58HgrF00bbjJny2frnkACpQp7HWyYE55ETMYJntxwgHB5EIEyOYumOccccEf87ua3pznvi/WPN2l1JjO/33UYgI+P13JzgTUctLvxtyBDxfkzQzgjMmPYb2P9vg2nizb9RHDZRGLt2qG1NSsri4ceeohrrrmGyspKBEFgzpw5PPfcc9xyyy2TT/Q2REVFkZeXR2lpKQ0NQ+LVhoYGYmNjR3jTivvvv5+VK1c6aRqOhH379hEYGEi/XA2AXLCQbuxFm9dJurGXWmkAvWJnkwN9dSiR3RYawsXEtVv/b5Q4s1p6ZAPBFgOtEj8STQMcHcy/q2noTjKixypqNEitYSAbw4aLCWM7zFbb7kGzr7Zg51sRv6gaklrMtKrFqPstDChF9AQMpQmJ6bDXqUymAiNu61QqKDhaWkpu7nQuzUri9+UdTMkK4d6SNmKS0nihSc+PcpvpL4mmO1BMZLeF6igJzS3hkG0m0KjiPYMOf20dyZla9jXVM8OQR3+qFoCqKDEDJgNfN1fwwbFNrEmfyfreCvZ11qBWRjNrlvVk9uiifPxkEnIM3Tx4qBZzdyeK8GBeE1WwKHca79TuY1leN0osYIQymcpjP6ksRhJNA/Y0ZTIVBheTrAizzqmfAHtf2ZAw+FwnkhBsMVAhG64Ak2bspUcsRylYRd910uERuHIM3dRKA4g0a+kRy2mTKOmKGupzmVmwj6e4dgutajEahfO4mhLVDCZrvecCR/tj6NVL0BosBPtbp12MoodffHuAqxfMIgLYFTzACT1cEJfHXpOWLsMAKq2BtKg4JLJkak+4jL1BazSDFI9jr7klHKSQHFhLe4iEKJHYXidHyAULc+JEPJqU6tRPtnqbBRWdbRoQsNebxFDaJCJsl0KfdbZg0CgRDFo2meuRtoiJyU4jTaoY9IE42Jc9FtTKEPLDg/muvhIhIRQBgVbJAJbUCP503myWKc3cve0oz0RGoFbKh/dTfrdTP4WURlAap2STooug0D4sgsC+LjN5USEex57GaKIq0MTywGS3a0RZZwoJuWHkEwJ9OjYKDUR1C4RMSwKsc9qxTgBdUpF9jWjQdtNn1NOh72dmaCIp3WIGlCKmJFnd+B6VqnGFu7FHfjc9qO39ZJsjw+okhz8uD+PWri7a25rtaY72x6AzO8+5GEUPzy85i1aJHzisezZ0RUmc1j1DWRKNYRKrVNEB3s4nYXqbvU5lrS7ht72YT/56we1abitPD2r7GlEnHS6hGAnj5bLJQGVlJQsXLhz2PC0tjebmZp/z84noW1pa8Pf3JygoiJ6eHrZu3crtt9+ORCKhqKiI7Oxs1q5dy6uvvjpqXgkJCSxYsIAPPviA/Pz8UdMXFhYiFotp+0ebk9vLdKNVhGJb/J0Q2w2xWKNZRYJbY5nBq07b+zmDGsyEdQ+lcdFDSnWXz2Aa2841QOf+DjWy2zp55P0C0xMdtMYdFMs81alWGkACOszhamsaqZTArhYO17USG+RPc4OW9nIRkVnJXDRNg7zUeuJIajGTRAslJSkEE0DGtBOIZGmIRCKiIwf4+thxckusostHdvyRkr4Wrk+ex6aFdw4FqUlYSNjUCgRBoLZ3gEyhz17mJ/MTgUTqipNY8c2fuXbpT1i37RvCe2p5ckXhiHVyhC2N7f/uYHvftjlwhO3k0VscRxu4jfTXNrhgGrCePHoPWf92PYWIiiLsBBYMBDO8P+ParX1p61MbErKqYZB05+YUAZAT2MS68k4+3d/FG1vbuP/sGDJi/fj3oUrOT47l36XlNGl7eSBzFaHtYr5urmF19DRrdBgTMOgcyDU0b6tajNyEk/Mg29iz9rcVXeESIrsttHUn2uvkijpCSR9sh0TTAHXFSU71bm3vh/BIe71t/7fhAn0Mv9z7PgAZQVGsnH8WCVHVwz/kMJ8KkiSAVYJhsliQdmk5evAAP15eyLVJYby64yAPz88lR+7ehNA2BgICA5hV10+3uojzAhIGxf4SQAUuY+/IoUgkIjHHOqpZEZUBIpgbWQmRgMPJU2c2UdfWTI2ij1x1HGeK46lXdLF3335i/IKJ9w8hQhFIm76PE3oNGrOBBP8QzBIpx+tqyVJFkxgQTr1GyqaKPZTIlFwQn0fwoDmkzVrCU52UZh2GI9FuT8ie5tPXPSYqKirYf+O5xA+myQl09k//5ecBzPvPJpp+ehmJgsu6Z0NYN3Ut1rawUfeisBNuFWCV5iGbfK/q5CG8tKf55C5NXLvF2i5uypPgIE30hInkssmAWq2mp6dn2POtW7eSkTG6FMYVPhF9TU0Nt9xyC4IgIAgCP/nJT8jNzeWFF17giiuuQKfTcfXVV5Ob652t9IMPPshbb73l9fcDAgLolvikP3jSMZli6kizFpFIRFa42v4sLUTFs7uL+fPq2XxV2ciaqQm8cbicAaOJVXJPosWh3XKoIgA/MQQm7qdR10NJbQvvzb+RxXMGgHonkZu/zDpcMh2+74oeo45DXXXcWpDOa4fL+ePymbRpdJgsArFBvu20R0KkWWv/t7eKcK4Y63tjRXufiXMK1Fy1MJyP9nYxMymAt85dyNvHTnBp9ApSAsOQiaVsaSmnfsDqxcyddrbjNZC63/2i6CpWtuB58XSEbbM0lvY4IyaLTxqsG5sE/xAOy7aR4H577RZSsZj75uZwwbubeO1QGTfkTaVDq+fyj7bw09lZrEiORZpRwTNfNLEkM4hlWWr71Ye638LMOQPsbwrkgItFgiPWbjEjohmZWMzyKOtViKc5q5RYQ+G26frY3FKGTCzBIgisisnEL7GMbl0XG+qaQYDzchIJUSp4Z1s9erOZ8+On28X8OrORbqOWldHTOCjdRiKja4WPBQE5lTz1RDnv3T2VeMnwk7WtrXr1DfznwiVeR2CcyDXt+yLCn2gum2jMmDGD7777joICqwKl0Wjk5ptv5u233+af//ynz/n5RPSzZ8/m0KFDw57PnTuXY8eOeZVHe3u7/d8ZGRmYzd77LBeJRMjHEarvdEePWO60ewa4JDOJaWHB3P7FTvxlUm7/YifvX7yU90qq+bL9Q54quMie1tNd4Tl5Ausa65gebBVTJQdF4Gop7a2nq2fMs7j967U0//Qyitt7uP6z7XwxqDlfevtFqJVyH2rsGY5tMZpt/PcF1y0eum9eMi2YjZ8H8vKBw/wq7VIkIjFfNBUTrggk1j+Ydn0/m5qPMzNVS0mJZw9x7lwBu5L8tGkn2NA0+inHhvG02f3TVvJOzV5+Nm0FDxf9k4LoUCcPdd7g3YuXsvCt9dT0DHB9XhoXZUzh+b0lvHyglLKOXpoHtLy9OZDt105HJhm07Q8Ucbwohma/OkL8PIdfHTAZmBmaSJhiqEyj3UFHKINYEW31omeyWNjZXom+XYYIGd2aUFp0vXzRC4FSCJQqyQsJRyQSUVKSwhFtMWHSRG7NVqOUyOiXD19yvXGoNNr8GzAYibpxD/evXshiSbzTb456IFHPruWXC6Zz9+wsRoO7b26uaWLplJjhiU8zTASXTSZ+8YtfUFVVBVgt1WbMmIFWq2Xjxo0sWrTI5/xOe894/01okyiJciF6gJzIELZdezb/OVbF64fLeXrXMZ5eUcjCf3xh1xweCWKRiDWxuRzrbiRWGTyuHfxVuanU9Q6w6B9fIJeIef+SZZR39vL7bYe48qMtfHbZilGjZnkDT23hCm+iBE4kvGk728L79K7NvHXeInorVTx+9AtWRFtFcu36fpZETSVMHsD+rlp04Y2EtK1A7KK3UFKSQk/mAMEl7onUtrETBOtp+WQgJTAck8WCNvII+d2hnPnvrzh8y/nDTo8jkZdcIuGRRfncuO47Pi2r5dDN5/PrRVa78PUV9Vz/2XaWTInmove/IUEVgMpQQ2RWCpraBhL9I2kJreVYWzcSkYiHv93Pc6tnc3m2dfMzOyyJXR1VTlr2jhhNmiEVi1kUmWbfTKUm7OVYbxNN2l6uSpqFVCSm9HgqPSIxOosevaAnVhaDUqKleqADi9J5U+btOBzN+czmGuu97YKsdLbXVVEQHUaAbPjy/vqaBZwzdewe/yaa5N05w/IWP+RIeQsXLrTf0cfFxY3JSY4j/kf0JwkT7SXKVZO282ASRa2dHG/vRqWUc6y9m303nkvha2u5N2M5VyXNHjE/sUiEv0zOkkGXpjaMpbwBMgntA1qumZ6G2WJh4VvrWZMWz/7mDi7/aAtvX7B4XJKZ0IJqOJY/5vfh5IUx9mRGV9TSSZifgmClnG86a8hRxzIvfPhVS5dBg0wkYdvADtIUKcTJvFMOcpTeiEQiYpTBbGutYLo6juBRIpCNF7dNXcSTOz/mzXMX0tivZVNVE6tShpd7JBOuc9LiSQwOoLZngA07FDRpuynuaaLN0M/CsBRmRofx5PKZFB2KpFHbQ6VETKtJR722m6DWSOKndOMnlfLP8xfxyJaDLJsSg0opIyO3kV3fCnTqBwh1OdWPBE+mdYcrQmkzmZnht4r1RbWYMVKp38B5wWfTYGwkUZ5AZmYlCVnV7DhWxWUpyW7z8Rbu1pE/fFfEn/cU86eVszjQ1MmbX+7h4QW5zI+P5L3iakKUcubFR5IWqhoXyY8X3jidOl1NpicDTU1NvPjiixw4cIDAwECmT5/OT37yE4KD3WnYjIz/Ef1JgKe7YMdB7QuhejLXelyYQUpIEL/49gC3rt/JhstXsvv6c7j0PztJCghjQYRbNUI7KvvbSVdFjmtTIggCl2Unc8fMaUgGyXxVciyHW7tYf/kq7tqwkzcPV3BzQfqY3Gra6u4f3wmV4z+lnizCd0RRSyer/v0lx2+/kC01zYQrpqIzu1c2C5AomB+RwtIo+PKwhX2ag+T75SIVjTx1XYlJQgohgpl3anaRr0xgbu7QFZrWZMBPOjFXKgBRShVao5m3j1byyKJ8fvHtfvoMRruZm6cIba54b86d3Lrn3/y1Ygvt+n6m+IdyddIcJNFVfFpWx0eHupgTnswlCQUEBks4Lz166GUzdkXbJ5dLmP7qJyglEt44byEzsuWoOtxvdnwdD1PlKbSb2pGL5JTqK0iK6WehfyiN5vWoxBJ7LAAAsyDwtwOl3DpjSJnKXayO0b5tSysIAo9tPcTLB0r54JJlpKiDeK5hgAUJkcyJjUAukRARoOTyrOQxzbWJhrck7ksfuEujTi33rWDfQ5w4cYIFCxYQGhpKdnY2n3zyCX19ffzlL39h06ZN5OTk+JTffz3R+7LAT7RP/ZGUwcZCsmEzavj5DDk/yryYRf9YT5/RREFMONtuXMENn21nano3kV3u3ZACdBu0RCmHHBv5Ihor7+zlD98V8XmF1XQoPyqUTq2e1cmxnJESyz/OX0S7Rk9MoD8fHK+hV2dkpmQOaUERY5ZyuAY+GQ8mgvC9qUddcRIfth/k3xcsJlAmo7UmClHEERakh8MoTgBX5xk4UpzLIe0RIqURJMrjR37BARbBwjFdCaGSEDrMnfxzv1Wjt8/UR7gsnMi4MrKCY4hUOruldtowRHjv8OWG/Kl8WdnAFdkpvHz2PKb+9UPOTotHKfVekiMRiXl19lVsaStHPrix6VAd5+rUVLL0C9HEGXi5Yhv/qt7Dwqh5Hn3SpwzM5fjtIZz3n03c8Nl2ciJCWHd5GHqTGblETH3J6KdsTwqRJkzEx7URHXqMDzZ/QqQuiJ9mLANErBgk+YSsakILqrmrQMyROjOb91vvuY1mC2UdPTQqGzlcbqbPYCRQJkPcLWO6izMhG87c/ALKHRbmxUdS09OPWRAoue1CQv0UHGruZMBoor1fS7zKKq24Ivv75XDKV3v8/9bT/YMPPsjixYtZu3YtNTU1bNiwgS+++IJf//rXPPDAA6xfv96n/E47ondHPmPx4lTfO8BdG3axQl1IoFTBsqj0YeE03X37ZMFdPeWCxe1p3hVBChmHbj7f6dmdhdN4dvcx/nVBmMd6HOqqZ3dHFV8vH/JY6K4cHVo9R1o7ae7XcmHGFHQmMwvfWk+gTMrM6DAOtXRwtK2LGdFhbKpp4q0jFTy58wj7bjyPDy9dzuuHytnb2Ead+GvMXSKemzbbq3t7x7orJeOP+36y4Njex3ubOV4ncEPYPPY07mFpYTBHWhUkBgdCsPuFTRCGHLXkZtUiLSmgydjMXs1+0nRTh6V3RbWhhnZTJznKLJRiq6JaCkmYBTPHdCXk+mWRGaXkWE8TxT1NTFNFE+2nGkace09IAPdk36Lrs28SIxJb8ENCVIAfd27YySvnLOCCjETaNDoSVAE+bapEIhFLI9Pt7VAb1MbhgxGEKqzOhe6YupjLv3uNpTPnuvVgZ7Na6K/MYM3UMv667zivnDOfln4t01/9hPvmZHOl2ndxemZmJc3aHtZsfQnqhp7rzEbWNx6zewJ0nTuhzekUt1WQGxnKurI6DGYLs+LCmBqispsNP32iGb3ZyIf1h5gVmsRZMVkc7KrnieINtOv7CTBLOHdqAvPjIwlyCJGaFxXCieLj3JM3+pg4XeBO6vnfQP7ffvstGzduRCwWO7m8veaaa/jzn//sc36nHdHDxHR0TKAfb52/CH+phPjn30MqEpOvjueFwstG9Zt9suA6wNONvfYTrLs7ek+wCAKGxmQONO+g3+CZIO9MX8zGrcVOxOJYjnpNF787+gVJUSKyI9T8dtthGvs03Dsnmx3XnQ0CPLnzKOsuX4lYJKKopZOvqxrZ39zB11VNLP/XBpRSCa+vWchrh8qYER3GdXlpXivndR5Mstfb1TZ4InAyFpCXDx2nzNLEayd2kJSowWC2EDaClrhKpqTHqLMHXLEhRhZNlDSSvUcPME2ZTrBkuHOgPnMfJfoykuSJFPoP9w4nEUnQC3rMg85OsoNjIDiGkp5mjvc2IzEFEy4dMnqPkkUyL3z0+/2EJGt/LkuKIekv79GtM5Cfa+aotJi8Amsc+s6DI2/Y3UEkElFXFc78cKuZZrdBw6+KPuPBrNXEdwyZDzqevm3/Tsiq5gEhh169kTcOl9Olsxpga4wmn8oAQ5uHXxZ9xk/Sl/Jd2wluSV1IVnAMh7vqebLkSy5YYEEqdq5f58Ek/KRWacevPqshPFLDXbOm8VFpHQMGE/PjIzELAu837aRHI7A0aipPFG/gieIN3D0rkxtmJLFkSjT50aHDymSbF7sKlGj0Wip295OgChy1LhMFd2aZE6WP5JrPaOPGm6A233fo9XpCQ4f3s0ajISDANysWOE2JfiSM5nrUERFYJ1/LvZfz+XdS3m7dxK8q/sXLZ82n+8T3Z1dsq5PN0xWMTOyu7wJMCTDTodWPKDqN9VNzX8YK3qraPSxsJ8CejhouTihgdUwmYVMrePVgGelhwVbf4CFWonnlnPn29NOjQpkeFco3xv18/QKcOyeQt7d2MeeNdTy9YibvldRQ3zNASXsPs2PDyQgbXcnEVm/HtpgITBbJ2/K1nTKvCI3gN+3HODs2mzn53aOWIdpPRYuu14noMzMrKSlJQSwSk5mTyYniEwiChQxlOgFiKwk2GVtoNbUy29/zVQ1AtjKTo7pichhaPDKDo8kMjuaL5gGqNbUkyhNoNDYRH98EpHnOjOGL8LlTEzBZLNyxMoqr/lpOY5eBn6+Jc5qbjmN5pKhu3QYNOrPRrsF/9c430ZgMzA2zerabGzny1YJIJOLxpQVsrW0hIkBJVrgaeXfSiO84wtaHR7THeOfI1yjEEq5Nnsu1yUNzZV5ECuLjIuKee5fvrj2LtNBgex2NZgsNfQNsrW0h1i+ES0OW01UBhcIUNNKjfFvTzP6mdrIi1Dy5YiYWC1R9Wsvh1k6uzEkhWT082qfrGndsIBZlfyUn/EpJYOS+d4fxaLKfqpO2L/oNpwumTJlCeXk5KSlD0qn6+noefPBBVq8eHlFxNHy/vc+ME77c356zwEReVCjfVjezu3HiIr2NBm8j2dUVJ+F3ePgOzzVN7bEplBTF2P+2QSaW8NjifO7euJt6h+hjrrhiSiHb2irY0lpuFxkdLk7g3QM6Xi3dR3DHYgCe+8p6H7jGRYs3tKCa6pBjvLivxP7sjFw179+TzuXzwnh65Sy2XXMWATIZuxvb2VrXQpBcxgXvbiLq2bW0DmjxBuMlecdobRO9OLjm69i3KkkQrfo+YtM9X8g7pg+VB9Bp8OxNUFUVQIHfdAr88tjUt5m9Awc4rD2CxqIhz290Zx/9lgEipRFux99ZeSaunhlMzJRy4uIaWRQ5Msm7Q2yQP13R5ShkYt67O536TgObi4d7/LLBkeT1ZhMnetvY0lLG9rYK6jRdLI+yKrId7KojSqli04qfIhKJRiV5G0QiEUumRJMVrmZzTTNfdHtvtpSZWYk66SD/1/Yc0cpg/jzzUrfp3pl/AwEyKQve+sLp+bqKOtq1epZJV5IcGM6ONmuZA6UKPi+vIybQj4PNnby2ZgER/n5c+9k2rshOYVqY2i6BGA3T/Br50+eN5CZOnHMqTxAEgZZ+7Q+GXL9POPPMM/nPf/5j/1uj0ZCYmIjRaOTZZ5/1Ob8f3IneFY7iXndw/O1WXQaXZSWTpA6krnhyy+VuYR0tilyrWuzkotMT9nXWEFicPuz5Gr8zyQ5r5/Fj61mTE8q8uEi3YsAb2udxz4H3yFZmIkOGGRNHdMVcpb6MOTlttOn6+HPpN/wqy32cghlJAcy4GToPWv8ueKCYhn4NZyTH8dqabH6ycTfvlVQDUNzWjcFssW6yapp5dOsh+vRGvqpqpCA6FDFWKcGW2hauyhmyGqiVBozoTtcTJnNR8jbvR3LO5kBTBwkqz+Zdjt7vXKNSOo4TTZwO/wYlIpGIs1SrKNGVMt3Pe43cXnMfMbJoe76u4y4hqxqKk2jXD/h80gstqObWWDlXv1TOby5KYPV0NQ+eG8dtr1eyeJoKsVjktBlPyKqmoW+Ab/fLkYkkyMWSQeVAEQsjrJuM/cei2aM5QI1sPz/PXGV/z5fxYDu1Xp6dzI761lGVvmz1/ma3P0+XfM3Hi24l3t85lKtjfykkMp4/Yw43rvuOLp2eEKUCk8XCicpACqdYT9kzBkPjlvQ006TrJl8+k0/3NNPWLaK6p59kdRAJqgDWHmxh3pQIZkS7+OHGvcTyjvcMFNUMcOnzZXxzaaZX7eHYLr7AaLFwuLUTT18Zi4KxJ0nPSPghbjT+9Kc/2Q9aMTExrF+/ntTUVNLSfN9sww+U6F3FT6ORvQ1qpXzCPLfZ4EtoS9f3XBdd10AP7uCovORu45AcGM5HP57D9rpWXjtURrBSzg15U0kJGRILLohIZd8ZD7H9iBqVJAiZSIbeoueLvq/Z0VZJraaDaEUQMX6qEU3xbG3+8tnz+eN3RagUMiyCwDOrZnHbjAxkEjFbapoJkEs5b2oicokYkQgONXeyMjmGqAAl22pbiAzwszrd2X6Ih+dPRywWDwuMMxJO1kJQ2tuCIAhMC47GLFiQiMRu+99ssThFDBypfCUlKYgctN1d8zOqzHZtfZnI+zaxQWPR4C+yXgu4JfnB/0dUtwC+O0sJC5Ry2/IofvdxPfnG6SjFYs4t6OKON6o4M0+NodY638QiEQmqAMo7e1kS6RzMo2agE43JwJdHJLzf81fOSorn4dgznPwBeDseHNt6yZRo3i2ustdxpH54++gJPm+s581L56CvCfGYzoY1UxN4bHE+B5s7WZ4Uwz0bd1Mgm47ObEQpkdFr1NFj1JESKGFGyBRadL0UhiZQr+3iwne28dGiW3kk+Sr643UEypTgFBbIM8yyIHb+JpfCn5dhEQSv3dy6swAajZjlEgnz4iIJUkyMdrw3a7Qrfogkb4NNT8rPz48zzjhjXHn9IIkehg9Wb8l+IjFWknd839cY1t6EzpRLJCxPimF5Ugx7G9t5bm8xLf1a1kxN4MJBW+dvW0r5eeNH/HPuddA4H7lIzowpAjnqWJp1PaSroshXx/PzQx9yYVw+55ks+Emdh5NtRz4jGt69eJn9uQSrNz/A7b387LgIZsdZlbbOSrNeDZw7NYE/7y7mw+M1XJI1upb0qVgAqvo72NpWxpywZA511fNIztn2+3RHfNF0lLtSprDhRANxQf7kZjkrM7nGMtdbTK6f8gizlz7tAaZmVFDTUk1WzHAx77AgP6Pk5elud93BLq6cH05th4FDzR3kRYWxvayPzSU95CmTuSwr1v7+8eZmMgOznS4ULYLA7o4q3qraxaywKVwbk8mq6KEzpK9SBkdCD5BJMZgtw5RPHWG0mHl651G21bXw0aXLEYtEjkr2djgq/dnwwr4SZkaH8cCmvdT1atijakMS0cj1uVOhOIlLEgrsadVyPza3lnF3+jKMFgs/2fcffpd3HuEKzwp1rmva1uO9aAyhdA2YkYhFmCwWrxxTeZoro93Xmy0WtCYzQQqZR414X/rH1zX6h0zyy5cvd9K2d8W3337rU34/WKK3wXGw+nJnP1kKHu5MgEaCYzrHsJjubIa9IXnXCRlNEvfHFxKRfoJ3i6u444uddHbvI0TmT5QyiLv2r+XJPD0N2m7CpAHIxRKOdDewta2Cnx58n16jlpfKt3D3gRYA+l+bjUImnjD7dltZI0ki1K+CvU3tIxL9ePvKUQTrLWxtenOWP9eZc9mwU06jpgezICB1IRCNRYMIsd1D2ZHWLr6pbiIltp9ttQMIgc009mkQdwzWI6KSLq3G47cFQaDV1IbWoqPB2EiaYmSnSI74pqWU5VHDr3gc0W8w8m11M0nq0TW4XYlBozcTq5Yjk4pZlRvMkt9tYkVyLP86fyU3tm4nKkDp1F/TVENXCAARKUV83ngUg8XM3+f8eNj3xqo05vheoFzGkzuPcJX6vGHpqgc6uHX/P/jZ3GyezrwGsajBp+9mhAazsbKRe8+K4bELszjz/0oIknuWPARJlTRqe7g4IZ8j3Y38/tgG/lRw8Yincsd59p/D35AYksJNzxzivjnZyCUSt8Q7YeuZIFDX209kwFDI4/Fq2v83i+sd4RrR1Wg0UlRURFFREddcc43P+f3gif5UYiSy8OSAY6R01Ug8pvOG5MHzBGkrS2WZNJVlg1dA1gk7nbKiWH5Z9Cm56ljOj8/DZDGToYrmkZxzOH481W5PXOl3gLs37uYPr+u8CpgxmhmOu3JeFX4Wl2x/lT8uL6SrKYy6FvWo3xkN45W6uOL94zV8UN3JRfF5dv/yjpuwW+t/yu+nD5FKbmQIxe3dfFvdyLKkWGKD/JCJPTtxEQSBZlML9cZG+sx9hBoiiBBCCBIHMtt/5qge82zQRW9hhn/CUBhiD/i2upnlyTFu/aY7wh2BbP1KRbAinNeOdqOSh3F2ajwtAzrWV9QTpwpg6xFITRneB8d0JXzR+xX+7f6ckxrFVVOGu2+eKNOtPyyfyeUfbub+KyppOu5cjrLeVu5KXcFq+XTAd430Dy9dbv+3v7KanRUDPHZxDxic26tTP4DGbCQ62eqvvrMyigCpnKlBkRzurqcgxDu3teeE5fB4dQuVDa00doiJ7i0gxOUmciIJskdv5KvKRmbGuI8UOFn4oZM8wDPPPMMf//hHHn74Ye655x7+8pe/APC73/2O/v5+n/P7ryB6X9wunip4c4L01wsk+SjK94TRdva23/yk8Ie8C/hr+Rbu2f8eFgRmqON5s2oXc2Na+KyhlQCpgmtnJnOwuYMVyb7f43o7caOVKnIigzGYzSRGNkFL5OgvuWCiid113FyRncJC0fJh6Wz9+8/Y67h615vs1MazJi2BufERZIWrqejsHfXUbLKY2dS/hRR5ErP8Z2ASTOzvKSJWGm0XPVfqq+m19CFGTI4y0ykQjkWwIEKEERNlPS3kqz171qsrTiJmWiUGi3lUkvfUFgazmab+AQqiwogIUDIvPpJvq5v4Z1EFKlMkDdpqrk+Zh8FioNxwglpDPfu1h4iWRnF3+G3IxXLoAWms+w2r4/dUlrE5UArzU3Du1AS+q2tlSZbYnjdAr0nHV00lnBs33eN33aFPb6Skvdt+/WRDRoySF79q5k9XKdj4pYJVKQYqunrRGE3EBvpT2W2gqU/LvCwFFKsJkir5W8U2Xp51pVd1iQ3yp7e7jg6tgQ5tLau+fZ4AqZz3Ftw8zOOht/BUX73JzL+OnuCOwmljyne0b/63Y+/evfztb39j+vTpTs+vvPJKZs+ezZNPPulTfv8VRO8NTofBFdnt/f2rJzhOWm89TSkkUu6dtgKwijNfr9zB+sZjvMgW1i++g5kzOwF483AFf1g2ZLs7klKPbaPRoutFJVVSq+ki3l+Nv0Tu8b40KLWMu5VZ7Khvw2RpZVWWxut+84XgfdWLcMRo5QmSWcWcvXojnUH1fF6u46y0eKYEB3KouZP86FBMluH9XNHXypHuRmb5r7Q7x5GKpAQ2+LPPcpAs5TQsggWNoCHfLxedRcduzT5kIhkSkQSDYMBP5IcFCxbBzFTLWfyj6lMunzILhcR5GbD1zabdfoTHKp1+89YhisFspq5PwxXZKdQVJxEwmPacqQkMNMXx17KtlPQ18/tdxziqfY8Z/nnESmNYqX8EqV5G0aACfWFiLyUlKZQGfUSrro8bUubbJSWOZUkcx0Z96ZRoPimrZcmUaKfnaYHh7BhDDIDfbjvE20crafzpZfZnnQeTuHlZI69+08qr37by+o5WNlY2MiM6jNtnZiASiUgLHXJ6VBvfQGBLCnWaLq9dK5stZgbqtgMwLSia433NDJgMHO1uZHFk2pgdgbkj+16DkalhwagU41dePh3W3pOJ/v5+rrrqKl599VUef/xxp9927NiBXO57m/+P6L/ncJxgNicxY5kYY5FYuN5XW/9O4be5Yfw291zW1uzjnzV7mDEjFZFIhKPqyGhltAgCH9Qd5A/FGzkvbjqfNhSRFhhBSkAYT+Rf4PadVw6UcduMDIKVcspkKjD2jiqZmOgT/HgRKrcqvm2tbeGqlSnUN1lo7NPSodFS0tFDfd8AZR09rFLG2J3kdOgHKO9r48KEfEhop6RkiBASsxKxlJv4vGcjMpGMC4LPAUApVjIvYOSIhWq5P/s7a5kf4eHaSBXNlqo+6gaSRq2X2WLh9cMVZISpWJwYzdqtJhZGLKGueDi5pAVGkKeOo6SvmUxFOmcFrcRP7Me+WpV9QeqllYqAtSi0s8j1y6akXk6DtAFwr6A0HgdKuZEh/P67Ihr6BogLGjJ53NxazjUOznC8nUNfVTaSFa4e9nzTLhOlzTrKmrW8cmMKuuporvx4K3qTiXvnWk0ij7f3cKy9i4KoML7qOkyAclg2HvH4sS8oyM8jsrSSqr6hoEUPHP6Im0OvY3Hg/DFvYl037RH+Ss5K9T7Wgqf8/hvQ2+s8LhUKBQqFZ2+Yd955JxaLhRdffJGjR49SX19PdXU1TU1N7Nu3j0ceecTnMvyP6B3gCxmOd6B6upceqQy2hcybGOve1mWkenhaFGzWAJdPKeT50m+5bf0OLsiYQnqoyn4ad1VmtBHy3o5qbt+3lvSgSOaGWe+hD3VZA+FkqKIQAQ2abvpMOo5WhNCh2suBrlpWRGUgJ45ffLuf22ZOI3cUqf2pIHhvxoS/VM7vFufx662HufX1So7degHh/tbV/MxBCwNTahwv7vuWi+RnozebKO1tYdmg0tzx3ma6wiuxIHCkSom4SIJcJOMs1UqOa8vJzKx0koh4aodWUzt9LcmEJQ53DmWrh95ion6gi1ZtH5F+Q6JfW/87jlcBq1tprdHM/gNhSETNyMTudUpO9FdR0y4hUhyBn9iPdlMnJwyVVAXW0GbqQG/REyuLJliiotJQTa5fNhcEn0Nmpme7cL/DoZDlG9HX9vTjL5MS7q/kd0sKeHrXMZ5dNbQ5+qallJtSF/i8ST4jNY5Lpg13OZybaWZzvYi6dj1rCkLoEsWy89qzmPfWFyQEB5IaEsS7JdXcVpDBsfZuPjxew9/XzHfzhSHYrxmMOjY2FvNG4tUEZE/jwu1/Q4IYsGAGjom3spiR8/q+obS3hfSgyO9F5D1PqC9NJFDqnrT7TXoAEhKcdSweffRRHnvsMbfvrF27lgMHDrBgwQIkEgkymQyFQkF4eDhZWVk88cQTrFixwudy/o/oJwljOUGP9s5op5aJjq7nDZZHZXDd7t0kqAJZlBDJe8VVXDqoFW+2WLAIQ6JgQRC4fd9aHoq8j2mKqRzqPwLsJlNSwO8TrWLOzdI3+Vf1Hjq6AmkwNlHZXo1RMLK/s9b+zRfPmjdiW4yH5L058YxXn+NM5ZksXb6CO46+wQv7Snho/nT2NbUTHeBHWqgKqVhMcnAAH1cfoqK/jfSgKGoHOhGLxFT1d3BWbDYAkYoWioMNRBRlMjunje6mJoxCoD3Sm2N9XNuk1lCHWhLM/s59zAlLQuYi1jVZLHzReIxLEmawrb2C2Kqz2KPZj1KsJN/Pmt+0aSdYu8VMpDKIyv52lkbOo6i7nqP9ZZwfn+c2uAxAcNsKVqsqkSDmy75vkIvkZCszuCk3lXTVfP5zQMN0vxz+3PZXOkydnD8opRgJ2rxO8PGaPjF4SCciPSyYjSca2BkSTLw/HO1uxGgxY4w6BozskdIV6WEq8lwcUYUWVLNSEcL/fd7CmXlq+rTW+AIpocHcOTODn3+9l1mx4WypbeH1Q+XcnD+V9y9e5hS0xhW2+SsIAhdsfRkjFrRxKtRtZpIDwqga6LCnbdH2kFpQhi9LvkUQEIHbzfvJQIYq6qR+b7JQV1eHSjUkhfN0mq+rq+Oee+7hq6++st/NL126lPz8/DEFsnHE/4h+kjAen9GeYBglup6v8DUalKP2uG3hzg6O4bHF+fyj6ASLE6P4y74Snt9bwgPzcnly5xHKO3uZE5bEzzOD2GveycUJBVySJadBcxhVZy3p+kiM/vX2byw1XUdmlvU7BsG6cudl1VHd38Ejpe9S1tGLzmjCIPfcFq6mhr6KK0e6DhipT701XRKJRPjJ5Lyefws3HXqFX20+wCOL8thR30ZkgBKVQo7aT8H0OVIkjVbvb8U9TahlCtr0Vo3bkpIUolIGkCsGMEXtoV0fg1wi8ahF72S90d/BbIuJ473V3Bm7xOnutk3XR81AJ+36fs6Jy0EuliIXS6k21JKuSMMoGDmkPYJFMLPngIk1OdH0m/TMD09BIZGSq46jUdNDZdlwsz3HPklTpHBQW8T5qrOJklnFM+YGQFWJQTAybdoJFJpe3px+EXUVI/dhQlY1R0XqUdt9NCSpA9nQVMyZMdlct/sfhMkDWPXvL9l9/TkkufEz7wkXZEyhsU87TLlSa7CQFK7g5U2tJIQpuDDYauLyyOICbshP59IPNgNw96xMfrkwz6tvmSwWfvTdq/SadHy38n4O9sfRVR1AnqSWKtaRr45DZzJxvL+FZm0viQHeb1oOdNUSLPNj+RzP5p2nAt4oQ7Lp5JTFG6hUKiei94T9+/fT2trKjBkz7M/MZjNbt27lhRdeQK/XI/HCL4I7nHZEP5mhCieanCcjkpMN7hxLuLNB9SadL7a1riQqEolY43cma+ZA4cY/2p//Z38HDT06CtTx7O6o5pLtrzI7NpxPf7QCkaiaBKBqSwB/mH4BYcoA6sqHvmHLPy9ryDVJUmAYF0XN5/H2L5j/1npuXLMaVUeGR7GerZzuHJmMBZNxmhGJRNyUsIqf7H+Xp1YUkh0ezAt7S1gzNYEurQGJaIAFg32TFRxDSUkKCq2cQ5o4FGIIkfuDaACZSEJZb6vdI+JoONrTyJq4XOL81Wxrq2BZVAaCINBhGOCwZBdXzEtBKQ2grti6PCQFhLFZtYMF8dNRy/1hMABOSUkKYYpKwhRD99q7OqpIDYogM2G4XwdX6UKB33TqDPUc1BYhAqKkkZQcMLF4mpnNrWVMU0UTKFOOSvJjhc5kpryzl9xB503/PH8xN637jqKqUr6+6gzOePtLXph5Gb/cvI23L1jsdb7+UglvHCzn+vw0wgavZToPJlF62IjZIOUXc6ezeXc7FzrEJgmUyzALFopuPo+q7tHNp2zz9e8nvqNB082WFfc6KVVOkScQJFFwqLuBVdHTmBGWyJ7OGp+I/sKFAjDxJD/aWnOqLaBOFVasWMGRI0cAyMvLszvMEQQBi8WCTDYk3bG4UdgdCacd0dvgre21rxhJU3w8+U8k6XvyHhVaUO2Vwwl36cZD9jbsXf0gZX2t/O7oer5qPk5aYARPFlzA5d+9zjmxuVy3QI1IJLJ/P21qF+H+Goy1GU7eyTwt7BfE55EaGI7JYuF4r4W3qnZxXcq8Ectpq5s7eGpHdyFUvYWvG9G5YckEShVc/u8DXD4znDsKM3n76AmMZgvnpQ+3ny5pk2EggsLEXo4fT0U6sxORCMxuvGi5lsVksXCsp5HZYda/A6UKtCYDXzQeQyISkxgQwgL/JSiljU75TAkI5dqUuXzWUMSKqGn4D2qiu46DjGknsAgCykHCcfzdNXSs7XmCPJ4E4hEEgWO6EkJiq4lWpvGH4g08N+NHI7bdeOeRQiIm1cH1c5ifglfPmU+3zkBaqIooPxXnL7Cw4xslx9t7mBY+enRFAD+ZlG6dnt0NbZztEPSpIDqU6AAlj28/TJfOwGOL8wlRWsW4aqWcPTecC0BU4MgBaRzHVqdhgFdmXUmAVGFt08wBus3dPNf+kj3Nw5ln8kTxBgQPioy2dvyysoGGPg3XT0A8+9qefjq0egpcfPTXFSdhslgwCWaUkuHXEu769GhrF8khQcjFYrbUNjMvPtJrk8/TCUFBQeTkWJUyP/roIwB+8YtfkJKSwrXXXsuRI0d44403/qeMN5FenyZaYjCRu9TJcuc7FrJ3XMBFIhEZqijuC/4lXQHdhEiDaTnhx8uzruSpkq/IiUx2Kves2HD2N3VQ29tEc3AphaFTEBDoNsiHxV+3obK/ndlhSfQoBe4vfweN2cgNKfPQmU1IRaJBv+DD29uX9hotrTebKV/6u3TaeayvaODVQ2Wkh6m4feY0/n6wDKMbF6ZTw7VYhhSq0Zj17G8px2Ax8dMD73HFlEJuTV1IoGzI65zObOTL5hLMFguBMgV5IVZt6W6DljBlEHPCnOvjSbJ1dmwO6xuPsiY2160kpfR4KjGkcrjxOAHTeojxC3a72fryg+F+BhpNTchEMlZGT+Pd2v1cEJc3zOxvoiESifB3IYxwf6VdOfK8zGh21rdhMJnRmLx3QwywOi2OotYup2fTo8JYd/kq5rz+GaFKOT/+eCs35afb3U6PBte5ebCrjg/rD2Hy62B6yJVkZlayXQinRFtChDicJ2Ie4S8DT/JJwyG+bjnO47nnjpj/iqQYD1sB3xEV4OcUtMkRtZpOWnV9zA0fcgo10nyJCvRDKgKxCHbUt7Io4Ydxdz8SzjvP6lTrmWeeISUlhYsvvpiLL76YrKws1q5dy4033uhTfiJhJIe63wP09vYSHBxMT08PKpWK1vuuGDH999FsY6JIvkWiJMqsG/Z8LBGfPGE87edJCW5qRjmLvn6G+p9e6rGstcemcKynCalYTLOul6WR6cM0900WMxsai5nSew6GcBOydgk31N1JSkA4Ziyo/C0caevm4awzuGf1kPTAWwhz2xHtCkeY2+72d9Eu7z2AOdbN22uWut4BHj+0nSvnR7B6ejDvfCjlomlTKGrp5B87uvGXyGloDeRH6gupMtTwVf+3SON1zBGFkxkcg1IipVXXz90H3uWb5T9FNbjp2dxSxqLINCQuOh4bm4pZFZ3pdeATrclAn1HPga5azhxUCPTU59XBn7IkMp2M6Y3D2uDw20ud3ms2ttBt7uHCAilak4F7D77Pn2dcytRcz6F8XeFpbowH1d39vLCvhDNT41iZHDumPB76Zh8ysZhccT5JAWHE+AWzs/UEr1ftZE54MuV9rcwOS+JiB7/33qC4p4nflH7I+stXkfHSh6SHqrhtyioa9fFsrtzKzeHX4i/2pyv8a+4+8C6XJc7kZ9NWWoMHnWLRuCfpZku/lqhA9xv8vx0o5aqcFAJHcCFsQ5/eSNpfP7BzxmTAxkubV9w7otb90k3PTmg5Kisryc3NZWDAt8idP6gT/fcREzmpgi3uY1JPpF/5CfWFPQipWEK6arg9nOMVQmJ2DaLiJMp6W8hSxQxrt4SsaiqPJtBt1FDU8wXnys+mxdTK3eG3cc1MNQlZ1XxaVkteZCh/29JJ9J83UnLbhV7pSzsSuyeSd/zNG8IfbYPh7vokb0kb/14wlQfX1vLB3g4UA2reL6mmtKOH+j4N01RRHO9roUZcRH5IAueJ1xBpjkBiEJM5xXpyTguKJEap4tnSTXzWcIQgqRJ/qYylbnzaRylVdBoGCFcE2om3SHsMo2BkWnIX+WprNLV6jfVkKkKEQizFIgh813aCcEUgltgmxI0LnPLtNvewInoaXzcfJ2RAwrSFLfb2E+0KJyGrmiPFiRzWHkUmkqIQKZimTAcqea7sW65Pme8TyYPnuTEaBowmj2LgJHUgT6+cNaZ8bYgN9OdIaxeB4g5ePHqIW8OuJ1iylH7NAW5KXYAgCNx38H3OiMlyIgzDYCAjR+VKm6XD74s38HH9YV45ez4CAg/Ny+GPO49SGtKCccCfeyJvt78zPyKFfWc8BMDXzce5frkPhvmTBHdroiAIGEe4d751RsYkluj0gEaj4fnnnycuLs7nd08K0a9bt46f/exnWCwWHnzwQW666Sb+9a9/8cwzz3Dffffx4x8PD1gxFngiKE9+4Ju0PXzTUspVSSM7FpnIMo3Fvt32TqvEb0wx2L39jieMxVxtX62KwkSr+Zv1brgJncnslMadnoBKZ+BwaxVg9VL2xHdF5EaEUHrCn3h/M5cmzmRdt4WOsC56+nrIkKcB7VZxc2gHIhHcPnUxhzSl3Ll1Exvnj+53f6RTvDs4pvXllO8Id5sz25XMMz9O4vCWCOsdb1o8/jKph34aoFWtHeYx8Z0FN/L7Y18wK3QKVyQW8nTp1xRu/KN9wbchPySezxuPckZ0FvrorVgECypdH2fFZtOhl7CvswY/icweC94RXQYNOrORw2Xh1Bh24CdWIBPJMQgG+s0DzBPHsygijZqeo2x4JobpmQN0f6ygT29iy3fQ013DBQnZ9tC6mZmVvF97gACpgjlhSdQV+7ZJHm1ueLqOaOrXkKgK8CrKmyu8kaTdWTiNOz8oo1Xaxy8K5vFs6YvcP20VWZYAtredYGFEKv4SBeZBkrOFlr33u80oRHJuDrvOKb+3qnbhL5Gz9sLF1PdpeWnfcZ7bW8K/zl/ENN0C9vfG2sMW265NKvvb6dQP8FzFBq5ffoHP9TwZEIlExHsQ9U8WThYvjQWhoaFO0esEQaC3t5fAwEDefvttn/ObdKI3mUzcd999fPvttwQHBzNz5kwuvPBCPvjgA3bv3s2ll146aQ06EkHZNX+FfEpKnEWX3kaY86SU5piHK8ZyWra9IwLqiBgx7Vgw0c5l9tUOianSp1Xw+okdXDRtCkqpZFTpg0kQqOsdWrB/nJOCxmhmunE6YpFo0OzuCFHN4dS1prA/ZB2zLLEYBQtd1Qn0ikSIRO08s2o2vz+0A7NFQCIeXTRtI2xfCN8xvTvCH4ukxfZOggqnO05PkhZ3bpHryjP5Q571dFjV387ZMdl821ruNj75zJBE3qnZw5q46ehMBrtSX5gigDCF5yiBIXJ/SkpSmCKHVmMbM/3yMWNGJpJxTHecboMGtdyfoO5COjuSKd/ahxgResGArttAMhnIRNaNYGZmJR/XH6asr5WHs4bibvtiBeOJ5EeLs+4nlYyJ5L2F0SLQQiOrEmI4bjnIPy6dyX1fbeSGWWn8YvMXnDVnNZGNera0lrM8Kp2/ndjOfNOPuCn0Wg5pizAKRmQiGX9rf4NeSx8zYgK5b9oKKtjJyuQYyjp6eG5vCRna+SAC/wbrid1x/TGYTfSb9By65QJMFgulHT1kR4RgEQSa+jVO3gBPBxjNZl49WMYdhZ4dKI2GU8lL3sDVbl4sFhMZGcns2bNRq9U+5zfpRL9nzx6ys7Pt4oazzjqLL7/80q5lPVFej1wXQW/JSyQSUaY/wUvtf+fZuD/49K43Gwl38DVUrQ09mQMEl3ielO7y9fVbjiQ9Gmwn9pHef6ViO3s7a/j4qrnD0jqiW2egrneAss4eKjr77OMjLsifX24+wB1RVgcSmZmVHDwg0J7RTa5Fz36tksd3HWV6ooAgCATJlCglUhaHB5MaqeSMJ4v56qEs+zgbTZHR19P9qUZ1lISkFvOw5zY/BGnpem5NW8Tujmp2d1TRMNBFrH8ILbpewhWBmLE6NQqR+7O9p5EF4d6HubUhUBJAvbGRRLlVyS9LkcEXR4+S72cdc/vrVEAwO6TdxJs6iWdIytJh6uShQx+TGBDKQ1lnDFsPPFnBuD4/KleTY+ge9rs7OG4gJoLkRtrQKSRiZsaE0aUzsL+pHZPFwl/PmkvWyx+jlIg49z9fc830VF7evYPz4qdzpuUmxBKrLoWhYxa/7fgrK0Kns097kJfjniV7ajUWQaBlQEuaZh4pwJSAI5T2tSA0zMdvaY3TeBAEgXptN/PCkhGERrJe/ogguYz9N52HWCQaV/0nw1eIN5CKxfZ4B2PFyeKlsWIsoWhHwqQTfWNjo9OdQlxcHA0NDZx77rnMmjWLe++9d0z5TqR/82R5IjeFTWzDjoTJcs/qLt/RvuULsfv67ub+bbzXa72LxGHiuOu7EuV3WARYkhiNv0zKpupG+g1mpGIRv16UT2f50PuXFfizKbCcUq2FmX55yDtWktHnvOmoK67kvvT5LD/4ORqDBX3xEIGNpijneDp3JX3XjcB4TvK+OOUZiySo1djGFIuFor4GtGYj4YktRIlELJki5pWvZZixsDQyHV2YzaWcbwtcZmYl24+oGbBomKYY0gGoMtQQIR0yqypM7HUaKwIChYm9lOtPsMG4lgeyVpMU4GyG5YqRzF7dwVUCMhmE5E0/i0QifrUwj99sPcR39W2E+SsxmC3U3X0pL+4t4XffFfH4tsPozBbWVu8jTzQ0X7/h7wQSxkHNYfKVuezS7qW5pROpSExh6HwY1EuLUgTx451v8lRMKgfqDrB/335y4vxQ6WMo62tlR3slO1bdz56GNnr0Rnr0Qy4E3Y2r0drKnZTEHSZrEyASifiuroVbxnFvP1m8NJFoamrixRdf5MCBAwQGBpKXl8ddd91FcLB3Zp6OOGXKeDfeeKPPJgIwOVr1MpGMbOXYxUCTCccFUhFlRj/4t7vT9HjyHg0lbKOPdmZyLhKHYbND2g3AfJPaKb0JA9/0b2XDsrtIzKzlixP1mCwCvY2RhHcmIHe4mwVrNLfMcDXhg8Ey2jU6LIJAZIBVC7fTIe+k7FrSO1JZXWimpEQ5jEgADEICD21+kTa9gbuf7+Tu2dFuzX1cF2tX4ndH5J7u5V3zGutYnYgxvluzD73FQPyJ+XwnfZOK/jaS++eQnGN1JXzZQhnrdllJ3Z09syNG2niYo/ZS2DEkbrcIFjrNXRQq3GuRi7B+s0JfySbz+zxVcBEBDkporiabnr7rDU61drkjrg07D0tqBMFhXfxy8wFeOmsed83OYv2JevY3dzI9OJZOw4DTdWCWKow0eQr5frmIReLBdnFWau0xaOnql/JAxE95p+t9JP1yIhVBVHd3cbyjgUCxH2/OuYbUnHpqq4ZMBNeV1fKPIyc4M2Qu88JTUIplTlEBT2XbeeMP5NeL8ifl22PlpYnGiRMnWLBgAaGhoWRnZ/PJJ5/Q19fH888/z6ZNm+z29t5i0ok+NjaWhoYhDdqGhgZmz/Zd+W3fvn0EBgZSF2W9T5OZBeLaLTSEi4lrt9CqFqNRiGhuCYdM632drFeCf4OSvhQNQZX+9KVosCgEajuHNE9NjUosnXIkcVpMFYEoZnWRGOpspuNfb12IzAoLsl4p/anaYeULPOGHUWVCordOFk28flia4JIANHE6FG1yjCoT+ghn59xivchezqBKfzRxOqZmDt09aqP0qKRGt3VyhKJNhqxXij7CgH+Dkp7M4feX/vUK8rI77XUqDhluZWk4qkIcakDQSmihCHmqjmo+R00UsVh30/P3hlCe3oKozow41IA01tp2VWxnlnw2bZFy5KUpTMtT43c4lJeEJppSj5MkS0QqllIdJcFfLxBv8adHHQkYKJOpMAzeQ7UOliUit5lgi4FWiR+YQBuvpRo//KJqaG4JZ2rmACf2Wk+FIj8zz/X+lbS8LFYFhCGWSPl5cSd6SytLlSbOyEknQm4V/9VJXcj/WD6Lsw9xQhNOrKKHTqM/TXrnHbRSYiQnsImj/THkBDZx6Eg2vWIZOESP1FeHEsnQ+GwIF2OUOJ+WgwcsBOgEugPFRHZbqI4aflcc0WO9gzdIIUAn0BjmnKZDJSa2w8yAUoR8cB33W1rDQiGM1pYwlNHVHCkx8mr4VXSGSokRiekRy2kMCKY30UB1sDW/Aa0Gv07ZsPkEUN2RSkhMByqLkUTTALtaU4gbrJspIJza0CbMmGnqVRCp7UdqkaKJ0zmNvamZAyg6lXQhJrL7OOv6NnFNwh3IlG1UD9apuSWc6KU1VCMZVqe2YGdRbUhMBzmGbmqlAUSatfSJZByVq53SyAUL6cZee1yEWmmAtZ8cYKuTLU2ZTDXM1XSEWWcfe4mmgWHfAUgY1BHQiSQEWwxUyIY2nl1NYRAN83Xp/Lt3F0tSk3jhaC3LZ+bxf9f+iNcOldHca6BPYXWZa1sjpsancSToOFNUSfa54rrufdCtZdbs2ST4JyLpklOYng3qE3y1r5XUaZk8H7GC3hh/jso7+crYyaxZs6x26AqBPW29dCnL+ORQEXG56UQqAvGXKtjaWs4vhelkSQxu69TlME5jO8wca4oftu5FR7VT3WGVoi0KO2Hvpx6xnDaJs+a/az8dOpJNr1xGl85AkFyGVCxCotOQKTbY01ii4vmoqx+tycz0Qc+GEWYd/SXRNCpGD3owUbw0WXjwwQdZvHgxa9eupaamhg0bNvDFF1/w61//mgceeID169f7lN+k29GbTCYyMzPZvHmzXelhx44dhIWNLKqzwdWOfv+ZD3tM64tIfKTT7ESclicDo93RTya21Jpp5gQKAgghhgDUI6Z/n9/wYOz1hEjV9hOayWLh3wd7KPDLY/vALub4z2RGdpP9t+rA3SxLivGqPI53snXFSZSUpNj79Ftep4gvkSAhVOFPm74PESJ2rrqf9U3HeKb0S4IUMg7dfP6wfG0nel+cEnk6fZwMnw6e7ugdUbjxj0wJCOWDhbeQkFVNdXc/SepA/r3ZxKLINPYcjaDCUEWB3/Rh7450um7S9rCj7QR1TWrmBBRyuMHqZS4yogKtoCXHL9suubHhhK6aN7v+yT0RdxAuDRtz2FTXE6frHf1Y4drnE2G66jgOVmz+E9fnpXG4pYuXzp5HiFKBxmjivq/2YOoL5rPGI7yR8FekIinf9e/i2/6tLA9cwvzAOcPaakdbpd1ngtZs4M+l35BVkMffv/6AQLmUlgEd1ybN5eOGQ4TI/Xnx3Bmcs/ZrPvnRCvQmM7/dVMaxnkZ+mX0WZ8Zkcf3+V7hr1jTu+XIvORFqguQyZsaEcVNBOjEO3vps9bGtt6/X7iaWDMJJ5DveoZDzWZBo3aG5lnmskgKTxTLsXt5oNrO1toUVDj4O6oqTvLJfnyhemiw7+rCwMDZu3EhhYSGVlZXk5eXR19dHeXk5M2bMoK+vz6f8Jv1EL5VK+dOf/sSyZcuwWCw88MADXjemL3hrfxeFfhbEExz45fsEm2ThVGBJogRwtMW2boY8bZjESLi78QGeiX0CoTiZKkMNbaZOZvjnIRVJWRAwhz2a/fS0dCFChMFiIkefSZ1GDYy+ICS40bK2ifCXcQNLuZ6k2Gre7/6YNnbzVsLLSMVVnBc3nZuWBZD4l/eIenYtZ6TEsXRKNMnZvehNAhcaQtEesy5g4/VAOBk+CVxhO/GPhA8X3sJF21/BHHuUT0o7MVos7G5oQyCekpIU1jeUkcIC9uEscShM7B1RlL6trYKelnhgALVETQA2k8oMTIKJo7oSZCIpWYppiEQiWoytPNP+F84PPodFuT1Az5jq7G5suBsP3sAbfweu8JX8HV0Rf7rwLn6y7z9kxCp45UAZD87PxV8m5ZLMJK76eCsAjzY+QaA0gOtCrmJWvoFXT3zD9MgBtKZYeow6ov2sc25zaxmLItJQyZS8W7uf+eEpiIUG0sNULEqI4uKMKewoUnCzciHvVO/F0jCVV2dFceBYNw3aLhZGpPL8zB+x/Js/8/ixLwBo6tNyQ14aR1u7efns+dT29PPY1kO09GvJCAvm9pnTcJQp7axVUMRXFItqSBBfjJbpvGX+NZ/WBrGYa4CRr2K8hTvlO5lE4kTytrYuKRr9sHCyeGms0Ov1hIYO9wKi0WgICPD9sHdS7ujPO+88u0u/ycLcqXo6a3tRS9RepXc8tTuS1YyEbmDkzcJEDNyxYEApIkA3dgHMZCgB2trRlfAzWYpG3EiYJJTiwA9YGpVOuCKUkhLrCU8mkrEgYC6ZUZXUa7rZ3FJGjNJ3JRNPECGipjGZmdzDTQldiAZN8zIzK2ktSyUtJIh3LlxCoFzGd3WtNJVal68z11Vz9yw/u2TBtqhPhsthbzARm4XEgFD+Pe967vj4a5ICwnjxEmswoLriJEo6ICNCh6lN5FHfwhPZS9unc36OlneOWK+yHOeUVCQl3y8XjUXDLs1e5gXM5qv+b7kn4nbSFWnAqZlDEwFvY0q4wkb4L8Xnc+v6HQTJ5fyjqIJrpqexIimGhnt+xJxXvmROWATrG0rRCjoyVFEESOW8VrmDst5Wek3Wq7FHc87m04Yiuw+QOk0XUnU7/97TwpUpUayvqOeuwkxWxVg353s6qjl320v4iWVoLUOi7aWR6eSr4znUbY0g+eTOo9xckM6nl61AJBIRHejH7LgI+gwGPiip4dndx6hpOcbCiDTOTNUyj2xktf/HDkk3AH5CLCniGzhm+R1r+QVTtQ8gFKeSlVU1xtaePJwMXhorpkyZQnl5OSkpQ2t2fX09Dz74IKtXrx7hTfc4bT3jCYJAu76fCGUQ3QYNUcpgtCL37hNHg+MC1WPuxyJYCJGq3aY9VSQPg/e043jfXdknivwd27DR2MS3Xd9SIJ1Og7GRDFUU4YrAYe8IgsChrjoaNT3oLUaf4l7rRBKCvQhALkbM4bqwYdcxa2ffSUKw9TvnOgSPOT8jkZ99tYeann6umZ5mtzv3dLqfSK+ErrC1w0jtUVSbPOKYsJF0uiqK29OW8LeKbexqaKW9Oo5eUzmW0DJiWwupZTjBu+bjiihZF1+1bOTqvGxCFZ5NO9X9Al+WbKPe0MiPQy5zKpevKO5pQqUzEKyUOz33djx8H5AWqmLTj89EEATu/WoPRouFG/KmIhWLSYtU8uC0M3gw+wzAAsh4Mv9CTBYzZkFAIZFyvLeZv1Vs57y4XC6Ozychq5qXMzK5/KMtzJkSy7ryaqp7+pnx90/ZvPx+SkpS+FX2WezprCFGoaKsOoJLCuTsaK+koyaL6/yn8rT+j2Sqovmq5TiBchllnb2kh6rsc1IqFpMRFsx1eVMxWSy8sqmfnx38gLNijzMj4UKoUzvUMIRgLmef6AN+1/Z/vDHnasB3b25jgXVTPFw/6nTDmWeeyX/+8x/OOMOq6KrRaEhMTGTZsmU8++yzPud32hI9QKO2hwhlEGKRGLlIjEQ0PscXWouOQHHAuPOBydH4jRCJUQoji2p9Pf2N5PRnrFCIFICIq0Mupy38S3KCp9pPpo7fq1WvY25ACvkhw6O0jQaby1PX+rrTwHeEI8G40y4OkEl54cy5vLy/lCs+2oLZIvDKOfMJ9VOc8tO9O6RPqaHtWOKIaWztnZ8JS6PT+cuWWq5MiiPWT024IgB1VC8lUud280ZPZUZmC4qeKEIdwtS6I+/UwAiq9Ts5pi9xilI4FlgEAZ3JjKvsZ6wucE81ssLVmC0CV3y0hSuyU9AY3QfPkYol6Ex6xBYRq+bqWDW3cPCXbnuatgEdzfomLs9O5o87jtBvNNGstfZjS2UuZ2Za+ykv1DomQkkFCXSZulkWlcGKqAzOi5tOAApe3LeNiq4+nls9h6mhKpeyiLljlYprDXOY/9pXnKWMoAZ/dPTSQR3tkhI6zJ0gwJ/yL8YsWPjZwQ/4z5UzgFNrn3664E9/+pPdM15MTAzr168nNTWVtLThXiq9wWlL9CKRyB6Fyxa8QyoaX3VEiDAKJo9EP1lxsb1FhUw1osLRWES8kyHODxAHcFRXTIOxidUOwWkcxdDxacUM9AV6jFI3GipkKoIPqd3+5nqd4Eha3pwim46ncH5ACnddVM0Hx6vZcKKBK3OG2sl2uh/tND9ekbs3Zk4VMtUw0nOEq8OkFVEZPFXyFRnBUUwLiqZV14dZsJCQ2EEhs4a95wrnsLNyNGbvCLZId4w8dRxZWVXjGnM56liiAod/c7S5MZGYKCmOSCRiZXIsKSFBiEUiXj9URkPvAFft/StRkkgujM9nUWQaJT3NmAUL4YoAFCm1QNCwvOQSCffMzuLNDjN3FSaTEarC0JzIR0f6WDo8uRNK9eVsb2/m7eq9pAaG886CGzhn/lxe3n+cqz/Zyq7r12Bxc2MYIJfx+gVzeG7zfmoC2jBZzNRou1AIUuaEJXHftBWkBkbQb9SxpbWch74V+NmcbLvJ7ETj+xjQbKw4fvw4vb29zJ49Gz8/P1atWsXhw4dpbm4mOjra5/xOW6KfDCjFY1N2O9X2ut+3Aa4UK/hT7O95V/8KmQOrcT2vW12elnJmzOh+6G1wrWNXlIRgrNrmnojDm1PpSGRaV5zE4iQdj2456ET0MPpiP5HhkkdVTPThHj9cEcg5sTksjpjKsjkDg99IYXNLGUsHyX1Lazml9RrSAiPIUTsrO7luAAKlCho03cT5q0csxw3pBRztbnSbh6/w1XmOJzy54wgPkgtMnpRmtH5JGazDTQXpHGvrZldjOw/MS6VANJt/VO3m3dr9GAUzCf6h7OusoW5rFwVRoayZmsDs2HBmx0XYvzGHJFpimjl+JJhceQqEwcwwgErMgoU+owGpSMzuzmqWZlrnzasdb5KlnMZvo3/JAw2PMDcs0V7uJdJoHu0+xCtfDzAnLIlurT91vbGEp1fQ3K8l0l/JzJgwEqJr0Mol/OeiFXTrDBjNlsEodAOD/8G/VYt5+Jv9vHm4gqdWFHJWWjwR/hMXYMfWBkbLyBYopwvuvfdeFi9ebDf5O+OMM/jmm2+QSCT8+9//5pJLLvEpvx8E0bszvThZOF1JfrK889kQLg3j3uQV/LnsG+YVzhgmrhXh3lGLL17hxlMHb/utuWwK/d1lXufrS39UD3QQKFW41V9wzXM848zWTrZri8sTC/mo/hDL5kwFrG0xTd3LlrJyBATi/UJYGJHKtraKEfMVBIGkgDCOSHcyNyvNnpe7NgiVB7C1rQKzYBkWLvdUITN8SBYy0boW3o4Dx03Ls6tn88jiPP65Rcs/unbTY9QiEomIUQYzXR1LckAYfy7dRF1PP3sb20hSB/Lx8RrWHjxAgr+a2aHJkOBHu76fAx01vFO7n0hlIEFSBe/XHyI5IIw5YUlcnljIi+VbSFeXsLV2Bz/JzyI5sJLPMq9zKtvPDn6IGPj1kc+4I20xeSHxPHN8E99tq6BG08WsmHDiVf7cWZjJI1sOUtLeQ3aEGosg0DqgIzJgiMhXJMey7rIQFv3jC17cW8LPN+0D4PPLVzA1JHiYzsVY2/p4TzORypHn0+mAgwcP8vjjjwOwc+dO9uzZQ3V1NR999BG//e1v/zuJvtMwgFruh96iRzHGU/lI8HQCcbf4Nvdr+UdRBdflTXUa6JOB79tJ3hUpgeFkB8ewobKBs1LjncqbHRzL8d5mpqmsYihviMyR2HsYGFFkDb71myfsaD9BQah3OgS+9sdobl/d5T0WwnfVw9CYDWxtLQem2p/Nig1nVqxzHeQiKfGZVdSXDA9uYxYsfFpfxJJ8C/miMD44Xs30yFCmhqrckn2MXzBnxmRPKMmPdwN0XvrIug0nE7a69FdmcGECXJiQb/+tQdNNSW8zOrOJ3y6dwdO7jrKhspENlY1IRWAaFKuvbzxGsiSL3+7dC0CARE6rrpduo9Uqomqgg3vSlxGuDOTW1EU0aXv4++wfkxzo3tPjmrgcMuOkrEyO4dWDx+lu0VLU00CNpounVhRyzfQ09ja28+vNB/jz6tncun4nD8zLoUdnZFN1Iz/KSuZQSyf3zLZK7iIClPx8XjaPbz/MfXOy2Fbbwh3rdzFgNHHstgvH3G6OmBoUQa2me0x5nWq89NJLvPTSS1RXV9PX18fNN9/M73//e/bs2cMZZ5xBQkICF1xwAQ899NDombngB0H0kcogLIKACTMTTfO+kkXGgmZWh5q59cOv2PyrHLoOeY4ANlZ83wneEZcmzOCRnevI0S90ej4lIJSNTcVU9rfTrjzBvcZs/AfjgutNZrbXtaA1mVkz1TPJuird2UT1jn02FiKwEdXx3mZeKt/KrpvPGPWdk9UnE3G6n5LSz9SgSLe/O+ad3txFY7/SbUAZs2AhJ0NLXpTVDDEnMoRjrV388bsi0kJVXJLl3CaZqmgePvwx3QatR72Msfj4t/+e3z1iupOJkfzHjxZsxx3i/NXE+avtf998u9VkzmyxUN3Tj6YmjR3tlTxyZB2pIhHrl9xBpFLFR3WHeL7sWwKlCv6v4CJmhiQ6ST4TAkKYP6sHqHZbhjtWqQCr6HhxYjRrt5lQy/24J2MZBdIE6ophVhYsS4rhyZ1HmRuUzZUfb0UiEpEdoWZ6VAiB8iGpnUgk4qaCDG4qyGDAaKKso4e9TR0oJGI6tXpC/Xxbvd21l1IqJ9Z/4sx0Tybi4+P54x//yNSpUznzzDNJSkrivPPOIyUlhZ///OcA9PX14e/vP0pOw/GDIHoAsUhEYXbzqS4GErGI5dlqQgKkVLToGK8LBseTXJpxYjz2TZTY3nZSHOnONUimJNE/hP2dtcwMHTpBycQS1sTlYrKYKfXrobKrF6PFgs5koaw8mKuWRPNNdZPHMgee8JtUD4aVAbv4895K3px7LUpp24hpT/bGy5HsbWPC13v6IJmS0o4eMsI8L4oF0daTekGUBa3JRHZEiP0bMpEEs4uGVnZkCEVtXUT4K9GbzMPKdOfcVC7Z8RKld1w0rKyeNi/eECSA31E1pHePmOZkYay+D1p1fRR1N7AyetqI6ZykLoBcDmfH5lAYOoXP20s4om+kXV/Gs6XfcFF8PjelLnCyjLChx6DFX6vjSGsXM6LDUCmGxOflfa1OujUBchk3rpAx92/bWBo15DirrjiJiwKTOGis41hPIyuTY/m6qpFlU6IJUSqYGaNwa2kRIJPy9zUL6dDqufSDzWS+/BHNP73Mnu6Wz3fw+NKCERX3ToZDqpOJc8891/7v+++/n/vuuw+z2Uxzc7NdVL9t2zZmzJjhc94/GKKfLDRqu4n1Uzs9G+lEZdPIntLlW9ABd3CNEtUVKCKkf1I9FnsNG7l7o1h1VdJsflX0KX+bdSUikchpgibn1BFjiqZ1QMvmmmZyzXNZHhVI03Fob7VQ0hdNXcXwzcTBtngk7UMLk6dwvGMReZssFn704WZKbruQgaoAwDPRe7PQjNVm3Bv0iOUozVYnKp5I0d3GLksVQ0OfxonobRsIx/cVXVIaev3Y2FRMiMJCntqPNn0f01TRqEOG36uqlXKUEhEfHq8iSKFApmhAazKjlEpYERdj37wlZFUz79WveXXWVeTktzjlYTMxs0l4vMGAUnTKg7E4wlNY3ZEQqQwaRvIjKYq6e/+CaXMI6RcwWcwsjEhzkgQ4oqKvjct3vAbfWv8OlMr52bSVLC04xK+/KKNR28O7QTeTkFVtV1Qs3hZFjaaTd2r28cvsM53yKwhJoCAkgYSsKKfnSX95j+wINR9duhy5xNmaqaKrj6s/2cp3155Nh1bvtBl4csVM3i+p4cb8qXZfFu7grn369EbY5PGVk47eXucDiUKhQKEYWXpxyy23UFtby1NPPcUHH3xgj0F/zTXXcO211/pchv8R/SiwkbwvC8h4w5R6gty9ia1PmIjTvK+kFakMYllUOv+p3c/Pz7LeBzoSilIqITE4kNggf5R9Q6K+BRGp/OtAN1MSNhKsjXDacKUnNBAQMXzTM15CLWnr4r6v9/L86jmE+ikIHaHffek/d45kxgPbt1U5DSMndEFmZiUtul629x3iusgFHvO1IS8kHr3ZxIzQBGaFJdGm68NPIuNwVz1SaQ+HWzq5qcB6wjOYzbT0a+nU6jl3aqLTCRGsynttA0MBoy7NjeE3FWs5U4jjxoKhU+KlH3xLZIAfr69Z4LXNvdx06hVjR8JYTp8j1cfTps62RkjFEo8kDxDnpyZGGUybrg9/qZz/K7iQZm0vL+8/zvb2arfff+NwOT/OTWVLZS39Rh2BMvc6SI4brqq7rCdRd/04NVTFvy9YgkQsHnZyD1EquHZ66ogk74jSjh7UCvmgtv/JQ1lpEn5i99/UWqy6EQkJztePjz76KI899pjbd44cOcK8efPQarUEBATw6aefsnLlSgC6urpQqVRIJL77eTktif67thMsiLBGRrIIAnfvf5crp8xifsT4SOxkLxQ/NNHTSLgssZCb9vyLO43z7Cc11/a2CAKZ060nvrriJMQiEbP8Z0IHdMi3caynCZXMj8LQRGD8To3c4Zb1OwlWyPlRVpLTc3enXW/gbmPl+uxke1t8vnQzT6yaSfgo5k3lfa2c6G8nUKogO9h6Fx+htBplJwaEsqVVgn94nT19UWsXS6dE0zKgo0OrH0b0ZkFAbx5y+HTf3Gwgm/UV9fZnP/tqD2aLQFSAkpKOHrLC1eOs7cRjLHHuJ2ueuyN8dxIkndlIaW8LGaoolBIZX7eUMCM0gd/krnFKd3tWEMUNRiLcaK73Gows9ptHeHwTR3oamRee4j7uwOA8SciqHnWjlhLi2chfLBJR2dU3YhobRrqCOtWoq6tzCmoz0mk+IyODQ4cOccstt6DT6bj22mvZsmULTz/9NG+99RYqlYqPPvqIpUuX+lSG04ro161bRwzYSR6sg2FJ5FRer9zBlrYyHsw8w2dRz+mCtmAxAbqx24lO1N28r6JoW5uHl1k40dVH7mBYSVcMOHgFc/WkJ21aRDwwYNHwYW01CQVT7JH8vCmLt2Ldbr2Bdy9eOmyBmsxNmbMTGt9QJw0g2AdHMXqzCfxHJ9CErGq6WjrJDfInwl8CtA67BlnCVGAqVUcttIceoK63n4NNHUwLV1PXO4C/VOp0wpKKxRzv6MFotiCTDCmFnZ1mdXzVrtHxr6OVlN1+EVtrWzjc0uk10bcFiwkYp+jeGwJ3HQPeXA2NddyM5drpiC6SuS7j6B9Vu3i+bDNTAyMIkftzS9oifnN0PVtX3Dfs/YMHw9GY9XzT0gAUAEMSyh5dE3Pn9BPRKXDbZxu4b0E6Vwupbsnckey9hWN9LYLA7vpWvjjRyOPLvLuTtn2vvvT7Y00BoFKpvI5eJ5fLSUtLo6ysjPfff59f/epXPPLII3z++eds3ryZzz//nAcffJDdu3f7VIbTiujXrFnD/he+G/b80sQZXJo4w0np42QQus5kRiERj8ulp7fKRqcrHPvhudVzuP2LHdwzO4vFiUPenWx1F0lqgaGgIe7c5ooR0WXqJoEp9ve93Xh4s/AsmxKNzAufDO7IebwbqbHc5Xc1hUFYt1dpMzMr2dFWy9y4iBHT2drI3WbHFb1GLV80FnOGKZtZcj8sCoEdVZUsjEjlm6oTLIwYanOLIJCiDkLA+cpFEAR2N7Rx/nvfcGlmEsFKOVXdfZR19nJFtm9tOpZ7endzz9f5OJnz1xvFRdtciY5qBweVh+2tJ3i+bDP3pC/j6uQ53HvgfW7a8y+AYf0A0KLrI1wF986fg0UQnA5NflIJOpOZufGRfHPdCn729V5mxoSTHaF2Cu/sWs6xtGVE+gku+mAzC+IjeGbXUe6b61nnyVWX6Yfg676jo4PY2FgsFgtVVVWsWrWKRYsWkZCQwAsvvOBzfqcV0Y+GySB5i8UCItEwKUGf3shjWw/yi4V5NPYNoDGamTPKAjoSTgbhT6Rf+7GQUmSAktfXLOTPe4p543AFt8/IYFbskA1vr97q2tR1sXAkez+xH2bMNBga6TX4kyCf2GAZQXI5QkoVoTF+XulaeGpPT8qB3ubn2LbftJSyPCrD4zuO1woJWdUjSh82Nhfzi1WJw95393dnfyDBGc3D0tjG6va2CuKS24mTaDjS3kCOOpYQuT8DJj0lPc2EyYc0vQVB4L6v9nD7zAwnpay1xyr56/5StEYTb1+wmJWDYUd//10RT60oxBGO83okE7aR8EPYUHtD/Dqzkc8bj/KH4o2EywO4OnkOAE/lX4hIJPLozyA7OIb7ks7nJ9vfZkdDG48tyefTsjquzk2loU9DfJDVtCvUT8EZKbEc7+gmO0Jtf3+skf1c0VaWyhXZybxzrAo/D3fS7vqyqr+d2/a8M+7vnwo8/PDDnHXWWSQmJhIWFsYDDzzA5s2byczMtN/T6/V6ZLLhjsZGw2lD9Fu3bmXNmjWjppsIknd0h/nyphbOnB6MzmghLkRBkJ910IUCvxUV4C+T8n5JNbfOcL8Q+zrwJ/ve/lSTfbBSzqOL82ns0/DKwVJeOVjGnXERKCUyghXuvWO5tsnSwEX0yAcoMx23E70vp3rHfF1R3dNHerT1asG177ztG8dyjLW9HQl/JJK3wdsx02/Us+WQjIopDWRHqBEBzdoeKiqsEpK52Z12W+ukgDA27G1hUWQMAZIhaVnNsSkckm4jP1tFZrhVMmM0W1i7tZ6pQZEsjkjjrapd3DZ1MSaLmX8freS9kiqWTYnhmulDQTl0JjNrj1Xx1ZWrUUiHFvMBg5FAmZQzU4c2ca595Ur6ITEdMIrb/R8CybuDqxQjIauam9Z9x2fldSjEUl6fe7X9N6l4dN2W1KAIjtx6AZtrmrjiwy20aXSkhwSRrA5ykvIsmRLNVR9v5cKMKU7RHd25Ex5L2//fikKCFTIQRrc0atB0s6ejmudLv6HPy/gL3ze0trZyzTXX0NTUhFQq5cMPPyQzM5P6+np+9KMfAbBnzx5ycny36DptiL62tpa9e/c6RYq3RfcZj+h8NNy2wmou8ugHdXT0mXjhuiEHOAlzGug8mORE8qv/vZENV6xGLBIRWlDN3sp+UtPKoWLqsLw9wdNmpUeuJsGDmNaXiTTW06Y7jNV0LDbIn8cWF7C3sZ1Hvl7H0oipBMpCqTMnAd5t2GJlsRzXlTFNmT5qWndwXSBrevoxmi1ejSdPBO6uLdy1t7d9MNb7e8dNiWNZpSIxs0OnkJmsoKKzD7EINGYjOkGHIMA7B/tITezAYDHToe+n0zDAP6p2IRWJMVsEctSxDJgNXLkohgAHZygyiZgzZls4fERHnL+a26YuxmAxcf/BD7kwL5R/X7gEP6nzctPUr2FaeLATyQN0aPVcPG2K18FPErKq6UHtU/ucrhjtyigkpgO9xkxZZy+fXLocSUsG0X6+K6rVFSexNAvmx0fy4t4S3mko4hdznK00YgL9iQv0Z9e+YBICQoGJixlgnZcSfpSVzHWfbuc3S2eOeC2jMRt4+vjX6C0m7khbzF8rtk5IOU4mXnvtNae/X3nlFYqLi7nyyivtgWyWLVvmsyIenEZEP2PGDDIzMyl1eNag7UZjMpCuGrLd/GibiAsXTbyt+W8udu+hzfXU99TyQicxf2KonA1HujlzAqw+RorONRbR/2SEqPUV0d2F3J4Ww2cNR1gQkeqVL3SLYCGwWIlUAp2mLvvz8dqrf1PdxE0F6XQedH8d4K5tffmet5sAd/BUt6QWz8qZrmQPcHXyHP6v5Cv+mZ/HtEFf734t4aRMt5m9BQPB7GirZH5EClFKFfWaLmL8gukyaGjUdrMoMo3OCghwOVUf6qonxs8q2m3W9nDVzjf5Te45XDHd/TITHxRAZVffsOeba5qZl+Dec58nnKzIdacSo10TAQQfUvPQ8f1cG7eUuN5MGMe6YyPWe+fmAO5PkQp9GP+u2csfz0sBZE4ne0e4k4b1GnUMmPTEjLARyY4I4fGlM7jq4y28fYHn9S01MIKfT1vJkZ4mzovLPS2J3hW33HLLsGfx8fFjyuv7EV3CC0RERFjvyx0Q7x/iRPJg9al9KkV0edHOvvCi1HKunD/2u3tH1EqHe7dyhe2O1vbfaMjMrBy/7fkYNwu2fspQRdFpGCA7OIad7ZVOvzn+u0RXymHtEY7qSiiJLGddz0YyXU7z3pTFU/vsamgjLmj0NrbBm3bzti+8yaukJGVY/VrVzlO4rjhpxOuJHHUssX7BvLdVGHWexPgFIxaJSAwIRSaWEKkMQmc2cbx36N7e8XsBUjlmwcL2thNcvP1V/jH3WhZGeI6fLZOIkUmsCl6O2FjZyNmpIy9o7RodO+pb7X+PNDdc2+R0xEjjWhAEjnY38mblTp7stEpfVsdkjvqeN3DsY3f//TL7TFICI7j4/W9591gVAwaj11eVvUYtPYN++G040d82LALdgoRIQpUKBgxGj3mJRSIWRKRyYXweSunYA+T8UHHanOh1Oh1BQUP2lCf620gNHE6gNicR3mjfak2mYeJEwOOu1BdoTSa2He9l0TTVhEXGijRrR080RoxXe9yTbbinfnBUIJOLpZgEy7DTvOPirDMbMQom8vysYUXNPRY08gEsgsUaCo8hCcVY7NQ3nGggVKlwUiwaL3zVFxnL6V7db3GbZqTxf13yXH5R9ClzwnwnwLnhyZT0NFPU3cB0tbPko1HbwyULxfTXtXC7NH1Ehy1gDQAlwuowyYamfg0hSvkwcb4NX1Y2kKIOoryrly8rG5kfbz35u5sbP2RyL+5poryvlc2tZWhMRjJUUSyKSOUcdRQRoX5O70+mZ0apWMKliTM4KyaLM79+AUEQuCwnxas1NN5/uJntr0reo6q7j6+jV3PDZ99xYcYUHpifyz2zs+g3mEbUk4lQBhGhDKLfdPpr3U80ThuiT0hIcLo7/cXhT/lj3vkeIy/B6GR/orOPHA823SPBG+JWSiRkDOTQeXDihCaO7k4nE76K9H2983fsF9v/hf0QpVRRMnhabNf3o5b5IxWLMVrMfHVERqJ8SHpjVJkI6g3EjGWY8puvEASBvx0o5T8XLfH5XUeMRuy+KPN5S/YDShFyN26RRypLoExJnjqejc0lnBmTNex3s2DBIHh2w5gZHM03LaUc6KxlxmD8gk79AFKRGKVUwvKkGJ7fW8JD891HvrPhgU37eHhBrtOzryobWZ0S6/GdopYumvu1XDM9jbMcTv2uc+N0J3lwPw4EQWCHZg8nqncTKvfn99PPRyGR2jfIXYEicDMeJpPswTqm3lt4M38u/5hz0xPwl8uGrZOj+RpIyKpmY9oqLnzvG5b+cyP3z81id0Mb6yvq7b4WHPOpOTaFLoOGMDd+/P8HZ5w2RO+K/yy40at0IzmdGInkx3sKF4lESCdYSbBNoiTqJBA9uCdMb5XPXDHaotutMxAotXqLkoukHOluoNOgQSoSU97XhqgrA41Zi0oyJNHRRxiZH6ihon8rXzebCFcEkh/i/f1Vx6DoVyIW88bhclYmxwzzxe2KkYja29O7t45VvN089QSICen33YnS1cmzuWH3P7lmiYLWslSnbx3QHCJAnEpJh2dFwCxVDNvbKzhWpSY1sZ12XR/nJeRRV2yt466GNvY1dTAry/McMFosToFyErKq2VjZwN/XDHfNa8P989zfFTvOjR8CyXtCpaGalzte4405V5OrHrI4saEnc8DuSMoVE+2G2RUxfsGUdfaQ/OIH7Lr+HJLVo3u0s8E2LwLkMjZeuZp3i6v5+dd7eefCJcz3oK8xJbsGjk1x+9v/4IzTluh9xVi8TJ3umOgFbzTy8XbxcO2D9RX1JAdadRtmhCawruEIlyRavWHNCpuCPEUKhLD2QDMmwUSszOqONdov2K5RvK21YpiDD3foMmh4Y+shXj5QilkQuGTaFK7KSeWCDO+8abkLVjJRY8qdQqVrm3pq+9E2IcN8oouliBBRVzIFhcv+RiqSkaEcshJx1fQ+3ttMl0HDJQkzsIY4U7GttcIpj2unpyERj9wXkf5Ktu4JJHnQ22rx4Wj8ZVK312kjoa44ia4oCXUtap/eOx3Ra+njCvUl5KrjxnX/Plkn/A/n/oT1uo38344jvHT2/DHlIRKJuCw7mcuyk4cFu3GFt77wT2cYjUY6Ojrw8/MjOHhsrn7/a4jehu9ThCsbjGZrXOmpod65SfQGk22LPx7Y+sBmsdDTFEGCv5VtjnQ3OF3HyMVDQ/TyGQGU99XQpC2i1ziNEoeodtF+Kt6q2sW1yXPdTn5BEHi39gA7OypRy/z4etk9BA0G5UjIqB5zXRzHkuud5FilQr5aUDhegziOb0dvZe7IPi0wgl6jlgiJ68nLvdVKSUkKnaYuGoxacv3mQ/jQOPCTyNCYDPhL5RQfjmZv9QAXqFKo6xySmrnOO7UhnhZdn72/v24+zhkpIztA+iGf1t3BVXx/SHuEM4NWAJpx5z0ZZC8SiVgpX8ku0zv8btshrp2eRmKwdSfnydHPSGtymI8x6n8oKCkp4bXXXmPjxo0UFxfbTcmDg4NZuHAhl112GZdeeumoUfBsOG207seL0bxqnUrIJGISVBN3z/R9q58rhgWzQcAkWLAIAt1GrVPceldMDYpkceRUEqN0tIRuoGag0/483l+NwTL8brlB082sL5+kXtvFMwWX8FjuGjvJw+S1l7cKnaEF1R5Nkmz/t/17tPDAvmxiF0Sksq3thP1vW54ai4YyvfW51qLlsPYIJsGE1qKj3HCCHOXwe/0QhT8n+tr4xeFP+N2xL7hj6mISApyvxlw1ti+Iz+OVE9t5p2Yvjx5Zx7etZZwzNX7Ed/6bYRSMBIj8OO63AZgY8ftkmNcqJFLePG8RWqOZWa+vY8Bo8uhm+L+9T11RW1vLZZddxqxZs6itreXmm29m48aN7N27l23btvHKK6+QlZXFE088QUpKyjDbe0/4rzrRjyXQwsmC0oOWsSPkgnsNa0ecThPHduKt7u9gZfQ0RDif4EdCmFiBWe6HwsHTl9liNe1yjem9o70SmVjCz6at9JjfRI8LX07zI6V19XhmcwdsQ1R0G7hYHXmqi+upPt5fzeeNR4el8xf702e22rcf0BYxy38GxbpSBCzM8psxTJRa0dfGgc4aPqg7xAUJeVwxZZbb77kiXBHIS4VXsK2tgsWpU4nzV9Ne5jH5iJCZJ953xkRhvAFvbKd6vcXAZ30boA/umLrYbVqxXuT0HoxM5hN1one9/68rTuKRRSa+rmokQCYlYJSxMFExCk53fPbZZ8yYMYO///3vTlZmjrj00kt58skn2bVrF4899hg33ji6vprPJ/ru7m4KCwvJz88nJyeHV199FbC65svOziYtLY3f/va39vSbNm1i5syZPPXUU/Znv/3tb8nOziY3N5fCwkKqqqp8LYbPOB3u6EcbuOnGXq/fb9P18XXz8Qko1cTBkz15g7abRP8QmnW9w2xoPSG6zcTejhr8JHJ6jTq2t50gziFevSPi/dSsjs4cNc/xLhydB5Ps/00WXP0kuBsTtjYerSzpQVGU9bbaxYK2xTohoZn0pE5awjZwyfRA8rLqmO6XTZ5fLmI3zoy6DAMEy/3JilWSM1XvtQ8HsJ7+VkZPG9UUbzTEtY++CT5Z8NWXhTfIzKxkVk4LFycUOI1zmx8MG2EHVfq79Y3hmG6ixfWeNhJt5WmIzUqa+ifeLHissQ5smAgemwzceeedPPjggx5J3hFz585lw4YNXuXr84k+KCiIrVu34u/vz8DAADk5OVx00UXceeedvPPOO2RnZ7NgwQIuvPBCcnNzeeWVV/juu++44YYb6O/vp6ioiG+//ZZDhw4hk8mor68nIODkmEc47hq/byd728AdiWwawsXEtVu8KneoIoDZYZ7zmmx4e1ViMJsxK3vwlyrY0V5ld/QxGprCpfzIOIM6TRdas8FqZ28xsTRyuDtck2Chqr/Dq3zHu4Cc7LzLZKpRN4CeIBaJmBU2hQ/qD5EeFMn0wabf0cawseN6V+xIFrPCkug16viuZj+LE4dMIE/mics2N043+KqP8VDmau7YtxaTxTzMb31mZiV79NHD3vHkkXGiXGC7e2b7ZrDMj017Jfx42bg/ZcdEjKvx8pivYWJPNXwmeolEgr+/1c2lXq9HEAQGBgYwmUxMnz4dgMsvv5x169aRm5uLxWL1HS6RSBAEgebmZsLDw+0ReMbq0m+sOB1O9p7KlgAwgmdQV3JVOdxDn0qM1NZfVjayID6SvZ01zA93trkeabMQ32EBuT9quT872ytJDgxz60CpUz/A2zV7+EX2GRNe9rHm5er1z9M3vBmrvpK8axmuT5nHE8c20KDppsegxczoZCkIAltbyzFYzCyMSEUpkbGu4QgKfQRdFVPpGjWHicf3ieQnc40RiUSsjs7kvboD9isSR8xWNHud12Ta1dvI/syYLHQWEzCytzpv22o08b+3GC+PtbS0jJT9uHH99dd7nfaNN94YNc2Y7ui7u7tZsmQJ5eXlPPXUU7S2thIXN6QtGxcXx5YtWwBrgefNm8dll11GUFAQq1at4tFHHyUrK4tVq1Zx9dVXU1hY6OlTk4JTRfKeTnSjRVSzoVYaQKJpwKtvuTMDm0iMxbe+K+qKk+ho0aMM1qDTGAmUKYeZcnmK/teqFhPZbWF3exXh8sBhJK8zGynuaeKdmn3cP20laUG++U6fTLhuYEZrw5HGx0hjYqRNhG3cSURiHs46g7v3v8vd6UuH3b+7C6KyT3uA7LZFKKJ30WXQcKyniTZ9H7/I8m4zNdpJciwEZBsPE4XJdjDjCm+dKQGcFz+dew+875boJ7odxosDXXU8mLkaaPGpju4w0WvYeHhs2bIJFFG4QU9Pz4TmNyaiV6vVHD58mJaWFi666KIRifrss8/m7LPPtv8dFBTEwYMH+fbbb9m0aROrVq3i3XffZdWqVSN+c9++fQQGBlIXZRVXycwCce0Wu8iuVS1Go3BepPz1ApHdQ2kawsUYJSJ65Gp7mgizjmCLgVaJH4mmAY46/GZDwuBCqhNJCLYYqJANN4NLM/ZavXMJ1jvmOhff211NYSRhplUtRt1vYUApoidATHVHKgxKO2VmgQSGxLG10gB6xUMRwhol1h1oomnAnqZMpsLgcm/qrk5dUc5ivoge62JgkEKATqAxbLgyYGyH2ep5bVCRvS1YbA0HCvZoYTlZ1exvTLHXSZzW5fS7XLDYy2mrkymxD019KF1REkrMFhb6z6e6vZKGcGs9+lI0VEdJ6JGr8e9JRCNXYwkUEaAT6A60LmR1ERKaxDqaFAo6xNCt0JClC7DX6VBdLe9STXBaNNK0GKpHqJMrklqG+qlFoqRN4iwZcVcnx34CUFmMXvWTQYq9TtVRw/vAtZ9cx2ejxJ9Is9bt2OsabEOwBn2plQbY07ZJlA5jQsK8gkK2t50gJSvdaT75RdUAUI2EMHU1kd0WLCp/Koo3kpqcQZdcxj6xlkumr6BGJCZ4wOLUT651am4Jxz/I2vhmhQVZr5T+VOc73F1EEXjCD6PKhERvbTNNvNWtaXRUu9t+alcNn//jWiMYYNfgxFS0yZD1StFHGPBvUNKTObSxspXH3Xzqcemr0dYI8rsJPqQetkYMrxNEpyejMxvpDVM41alDZU3vrk6OGK2fPNXJFeYTFrf9BFAdJUE9bQrBcj/72OsKdF+nkda9rqYwaz/hXZ3aA7xTPRsvj00mPvzwwwnNb1xa91FRUeTl5VFaWkpDQ4P9eUNDA7Gxnt1YSqVSVq1axapVqwgPD+eTTz4ZlegLCwtRqVRIHvnI6blNZDfSLtaWxi7ea1EPO+nYTkUjRcEKHlRv9pTG0QVnsEOauuIkgjE7lVPeL7j3aBY5JI4ddlKTDz2zpRlJdCsqiqCOiMF4ZO6V3GxLjacoaI7uVQN0ZrdhcmfGWrVsUxOrPMYEt5UzP/cYAJ2VYgjr5qMtjfSFypgaFElcu4VeBsWPLVi/VSkGuqnrVwND7RfWa+G7r/T8qCAKs2ChpKGZnYZaqyhZEPj5nvcA2Lz8pwS61M21Tu5g+47hSDSuLipsY8djP7mpt6d+ikq35kXs4NWMC+pakqzlHPw71WVcdUVJUIZZ3I69uha1U3/Zyqk064gy65wczEQxhXtr3ufpkHiQKUecT9m9CrLj86hr6uL3xR9aJSatAjiMMdv7ruNK6+C1TTYYuM6TJzdJ+5C4N7hEan/f8aRt+46/XnD6VklJClrA1uq2/3s6pbuuEVo3ZfJvUA4rbxLOIlyn+eTQvrYx42mNsKEO9ehrBDB7IIj32g9wtWTOsN9s7w9b97A6jPqquYSjPY1oTUbMgoWzY3PI0cYSpQxy65xmxDUitdpJQmPrp8zMSizSo9ByHBLzSTQNUFecRAju61TXnoQfUEcoInCac45rl7s6uSJc55tEY6w8djKg0Wg4ccJq6pqammq/bvAVPhN9S0sL/v7+BAUF0dPTw9atW7n99tuRSCQUFRWRnZ3N2rVr7VqMrigtLUUqlZKammqNunT0KFlZw+1yJxMnU3Q/keKmrqYw6+I9SvlPhdmJVwqCDrbiNpF8pDIIAdhVLkMUMCQuHS2/5pZwvun/hILjPyErq4qC0ARMFjNft5SiM1s3ZNtW3IffaR7JypsrGF/Mljyl9ZPKiVaqKOtrtfuvH+m9w131vFyxjd/kriFKObqjp4m01x5JrO5NjAAY/XrAW4+EEy3i91a8vXqWmUe/aB81XbdBw4amYra2lqOQSAmQKlgamc7PMlbiL5WjNxv5rPEIL1ds5UR/O2/OvcbuN39tzT4uSZiBVOybcVZmZiWhaeVc8dEuHl86g4Soap/ePxkYL4+Vl5dPavn0ej0PPfQQL730EkajdT2TSqXcdtttPPXUU8jlvq1rPhN9TU0Nt9xyC4IgIAgCP/nJT8jNzeWFF17giiuuQKfTcfXVV5Obm+v2/f7+fu666y56e6177JkzZ/KTn/zE12L4jFNxLz/RhBsS0+H2RD1Z35sIFLd3ExPoR4jSvQen46ZSlgedR3dMDZlx3i2YJSUp6DI6Oaw7iuDgxU0qlqAzGVnXeISvl91z2pP8aEjIqrZe/fj4jqdxUtrbQkiy84lBEAQsg3O916jjYFcduzuq0ZmNPJV/IYHfE4XP5pZwt6dwT/A1bsNIGwtvNw/eYjSyT8iq5vHtFaTGiZx8g5gsFmoIpLRcIFCqQG8x8cfijVyaOIPnZ17mlrBlYon9rv/Ppd/w7PFN3J+5im2tFTx9/GtWRGUQofReTJ2ZWUlCVjW3rt/LNdPTmB4VCnz/1qaJ4LGJxh/+8Ac+/PBDjh8/jtlsRhAE5owgFwAAQKpJREFUnnjiCS699FIAduzYwf33348gCDz//PM+5e0z0c+ePZtDhw4Nez537lyOHTs26vszZ85k586dvn52XDhVyneOE3YiTuEqi+d4zN9XBCtkTs6AhLnWU4hoVzhmi4UevYEopQqFyx33aBAPymLf6/kYZX8cfhIZRouFdY1FPD79fNRyvwkpvy/Kko6YyIXNVfHRsQxToppJCHO+OhitvJ6IZGV0Jpd+93d+kXUmDdpuWop66Ze2I9IFYbDsQWc2sTgyjfPippMZPGTGNdFE5wts35bFjT/Y01jqMVF1dnRXDKMru756oIwnV8xEEATiM6v4uLSONw+Xk5s5jQBLBxUnJEjFEu6btoJpquEmd+7w04zlnL/1JaoHOgmWWefP3s4azo51H0jIFTaSL27vZlttC38bo6/70dChH+DVE9t5MHP1iH7wR8J4eaygoGBM3x0JW7Zs4c4772TWrFksXryY1NRUnnvuOW699VYCAgK47LLLCAkJ4eqrr558ov8ffIMvUc1sGtie3vFW434y4av/gbggz6esr/QHWJEUizq1nKAO731aZ2ZWMk0QuDV1Ia9XfoW02roQCQj8Mvssov0mLmYAOBOjK9lOBKG7ni5dyWOkb7gbE970jzuyvzF1PmfFZvFJfRFLIqcS6xdMuCJwxHy8FclPlN22p+/a7s8nKu+J3rSMNG/cXWmNhrI7LuLPe4r58SdbMVsEFiRE8dq5Cwd9w8fZv+krfp65mv8r+ZJgqZXoHzmyDplYwqoRHE65ttXTO4/y5rkL7X9P9Gk+UKrg/bqDvF93kDfnXEOO+tTeo48Gm/TaBoVC4dZHvaPzG61WyzPPPMOiRYvYv38/ixdbPSEmJiYOy88b/I/ov0cYbYEeyTnK90005gmiXdYAJoIg8NdPu3n34gKqu/sJlvlm/tYYIeFm0UJ2dVRzUUI+WcExk1HcCbn/HgmjORwZDeNxmOMOsX5qbvfgXnUkeFPesd6re4O+FA1BlWNTVLLBsXwn07yu82CSz2SvkEp4cP5wsbLjeBjL2FwYkUph6E0s/PpPAMwISeDRI+tYGpmOTCwZVerR0DeAUiphdtxwnxYTBYVEyhPTz+cXRZ9w055/8fvp57HCxe31ycKB+iAUuB93+kF6TUhwVrN99NFHeeyxx0bMt7CwkGeeeQaA0FDr9YfJZOLJJ58ckzn6/4j+NILf4VDqCJ3Q0+TJgmvQlide0zM7LoK2slS2NxVzRoxvRG3Tur1ySiEbm4o9Ev14bXfd5efu3+7+Hs93vSWaiST5sWAiyHAsebhuDsZD8p48x000RrrGcyV7G9yN35GkA57GgzfzwDHPltzL7f+++4Nyvj4iJ0lujf1uE9G7w6dlHcwdB8l7u7atjsnkn9W7Kelt5sHDH7Mn6kHMggWzF/FATjbq6upQqYakjN5EnHvmmWeYP38+crmcRx55BLC659VqtWzcuNHnMvyP6E8j2GxITzXBj8Xzl+2EYlvMPi6t5emsHwPgLxmuNDdaHRvCxRhr2vhP7X6eyr/IbRrX8K3jwVj0PLz5rieR9khE45inNq/zlJP9qYBr+7i6wPUkJZhIAh/rqd/T/BntFO/NGByrhGck74wXxPvz2P53me8/m0XpYsxCDOsr6rn+s+1s/vEZxAYF8MR3RTT0aTBaLPzl9ihCY727gvD0fW/mzmtzfsz8r57m4oQCrtv1FlKxBLNg4cXCy0d872RDpVI5Eb03eO2114iOjub666+3m/zde++93HjjjajVap/L8F9B9KeD21tvEBjfCe3qU10MO8YTLyAvKpRuoxa5WEqfaXRFqqPdDTxZ8hWFoVNo1vUSmpmEobqJX2WfRbAHxTtb+U7lxsgb8el4yKelOQK/ltBJ24icLnB1njLZYveJuG6YjHgbrk6ZHL/lCxzTm+rncm/EdOpUG9ja2s1fy7cgUmgAWPov6+ly/eWrmBkTBkBobLX1/w4SitGsCHyFRRD4R9Vurk+ex53pS2jS9vD7YxuYEerOG8Xphbvuuot169axdetWkpOTR3/BC/xXEL0NPwTCd1f2yVAQm2xIxSI69APUDnSyJHKqx3S2Oh2tqKdqbzu/zV1DpDKItjh/kuSjR7r7PrXFZBLrWEljvCFUvclnLPl93zFRGwlv+208a5a37e4unW1DU5jdTCH5JGRVozMlcPmHm2m593LaNDoGDEbeLKqgS6tnZUqs/RpCmNuOaFe4nfBd8x+tTiPNF4PFxDctpcwJS8JkMfN82Wby1HFcljiT72/A4pFhM/P76KOP2Lx5MyKRiIcffpjS0lIA0tPTufXWW8dE/v9VRG/D6UD4Y124J+s+eiKh0Zup6u7n9oxENjQVEyAdfmeVkFVNUUsnT39Zzi0F6exubOPBrNUkB1qV+dompWSnN75PERlPVVnGqxx5KjCZ5RwPyYP7DY1cIuaqnFTWrP2KOwozEYtE7KhrZVd9KytThmvAu4rwxzsuHj78MeW9rQTL/TjR3843LWXozAZuSJ2PRCSm36QfPZPvIfLz8ykrK2PDhg3s2rWLm2++mbS0NAoLC5FKpXzxxRc8++yzvPfee5x33nk+5f1fSfQ2fJ8WxrHCNRLaRGCy2sS2s2/sMrC1tpnC6j9yd/pSj9+vCzyBf/gAH7UeJtI/mqtmK+k9+aba1rKcBptD8F1EOlFjxt01ieP8OhmkO5Z73lOJ0ZQ3v49jzdany5KieW5vMTU9/VR09rI8OYZ2jd6eBiDU5T0Yf532dFQDIvJDEvi44TBgPd3/Ie8Cu0e/0xVFRUUALF261P7s2LFj3H///Vx33XUAPPTQQ9x///3/I3pf8H2YSN6EXbSVM8I8fqcg3wfsrOjHZLEK2PqN7nffoQXVJJcquOEVq5/nlnuXAkM+y4MHTo127feN8H1ph4kwBxwtf2+eTQbSIxvAPNzxzOmI8YytyVgjPEVbLO/8mN3lRh7KOp8zNj/HjME7els613+Pd85UD3Rwx761bFlxLyW9zbQb+nky7wJkYiniMTrO+T7Bz8+PY8eOkZycjL+/P4cPH2bqVOdrzRtvvJHnnnvO57xPO6Kv13Sxra3CbYhGX3G6nOht5Qy2eIgYM0nfmwzoDBZ2V1ijmWSpooe510zIqrYv1lVt1k3AS9cPv5MK0J3am7hT7S3PBl/b4ft8wh0PHOfG6Uzy48VkrBGeJA27Y89hzhuf80WT1ZPc/y2fZf/dcUMw1rXE9r7GZOCF8s28W3uAW0Ov594d33BAe5itK+5DIfHNo+b3GYmJiWzfvp3k5GTmzZvHwYMHhxH9wYMHmT/fd4+Dpx3Rb2gq5uWKbRNC9HB6kf3JjDXtiRDG01adB5PQGk201Dejksto0/ejlEhp0vYQ4xfsRPIAH+/rZO8Na0gMDhxWJlt4TV/LOBlEdyrJc6R2mCxM5hXAWGELyexI8qe6TKPB3dXGeNciWztMFhzLl6QO+v/2zjs+yirf/+/pJZM2qSQkIUAIJEDoEBRQEQVW1q7o2tDf1b2uuhd1WXf1Cm7x7l5cdS33qrvu3rtrb1fFtbMoFjqEQGihhPReJ5Ppz++PIcMkmV7SeN6vly+ZmSfnOec85zmfU77f7+HAnZdzvK2LOWOSUMpkHq8Lh/8u38rXjeWszJjKy2OfY0PDf1BlrWFVxjS0o+wsi1tuuYX77ruPyspKrr/+eh588EEOHDjAvHnzAKcf/f/8z//w29/+Nui0R5zQ/78J57FmfPFQZyNgItnZDHaH7olwB0bdVhvfVDXQabEixUZFdyt5samM0Zw9mPLDPa08/0U9OrWMrDjPIXQ91UWooV9HMsNB5H19H2xdh+r/LilNgYKhDxEdLKGsDPnCXeQHo52nxmhIjYnMuRK99OZ7zme/A+Dbix9ALVNwtHMPjpZudl/0UETvN1x46KGHMJvNvPTSS1RXVwPw+OOPD7jutttu45Zbbgkq7REn9MCIN7oIlYo0mddz40cKf9hRRkuPc0n+jfPu4EB7Dfmxaa7fj32XznPfHOLvFy9HITv7nPt3Wu51EWwHOZrEfri3iWDrur+g1/V0kKDQAL5nbxVpMtw9qAfz+UZqkBMJDioTmHrmnPuR5G7rTlZBBQdLnH3C8vQplLbXECNX8ZuDH/Pr6auGOHfRQyqV8thjj/HYY49hsVgwm80IQmS2KEek0Ecaf7NUTy9KMOIylHu40SDUWYjFbucvJc5znC9MncR4XTKHO+tdJ1BVHRpHxuSTpJHEPZ9t5+ZpEzg/K23AfSI5CxqpnaE/orG8Hkpdm212XmvfxM8XTgvp/i3m7jNC7x9f6UcrUp6vOhkNA8rB2tbsH98/XqnhkyU/YcXXz1NpbGNGYhbPzL4uqCNzRzJKpTLoM+d9IQp9iIz0FzhSBLOUr5TJmDsmiV11LWxpPMbO5gqmxff1u609Mp4Hs8Zzy54X+OPOQy6hdyeroIIOZQJZSe1h5X0k2GYEiz/h8UQ027K3w1cCJVInk/U/sKb/v0MRfE/16W5j0rov8MiM0WyLI3XAkaKO5eV5N5Efl4Z6FBndRQKj0YhMJgsobj6IQg8EFqHJH8Gccjba8ORD7am8giCwq64FgDxdCtU9bUxPzOxzjdlu498PbGJp7hjWzi8MKS++8uDt2nOZQFY1ItF+PQlOpN6LQAZ+C176gm6bhYcLl6NvXtbnt2AF3z3fng6j6f0+GLGPFuHce7D6Lm+eEkWJY6N+7+FIbm4uH3/8MVOmeD4e+Gc/+xlGo5G//vWvAaU3KoR+qF+kSORhqPMfLoHkX+Lm61puaOKC1Pw+I/XXKnbxVuUers6ayS/PHziTj0aeRhMjYTDZ+5705tWbSAZCsG50p7qdg8yf7n2b3ZdOCDlefSAi7/67P7EPdiLhPjALdDAbzmpOOO/RSGiTw5HKykrMZu8R/mbNmhWUP/2oEPqR0KFHYiaT0jH0VvfhEqeU02mxAbCz5RTnpUwgVqEG4Mmjm5mjz+aqrBlAnc90sqLoQjSSiGSb8Dazj3RnHY3OP5D20LD27KlmVYeif/CNL4Kpg/7Pw1c0TLlaQpUpIeR8DTbe+u7fH/qc+cnjuCB10uBmaBixfv1611n0/WloaODw4cMBpzUqhD5cIu3iEgr9R+kjYfDiC2+zjTljkvnn6XoAHjmwif+as5p5SePosjojem2ccZXH2Pdw5tCHT7fz9pHT6PV6Wltb+fC6pcwP4/zrwSaah79Eapl1MOLF+0s32Jl+fEENeoXR5zXuKwDhznB7/77/8cu+GMrZ7VBEdAx16yyroIJ7UtPIipcSowgujayCCrrMVtgceD6HKwaDAZlbbAJ31Go1q1YF7oEgCn0/QrWwD7ZD9NYp937naXZlkYNnr3LP+Iul7evaQP/OF57K+POF09hV10zXmVn93bvf4IeZ09nf5vQb/ff9m3h6zrUe05NIJBxu6SBRrUSj0bC6IJcZaZ5HvMONUL00/NV9sG0iWCId0MU93d7oacEsRXujx64AP/ZansTYYLITo5IikUjCiqgXzWh8wQz8/bWHwRL8cEQeYHLy2bgaJ9o6qe40siQnPej7jFQEQeDJJ5+kqKgoIumJQh8Aw2V2Hc1wp9462XDL3j/dGelJTE6KdxnlAXxY4zzM4byk8XzbcgJl9mHAs0vV5puWA2CSSFELnoPmDNXzisqStJ/yDEYo4GjVZ//6cv/cf6bsT0glR5JgRkfQeTj/sYM8d1suBcbprjwEO6sfTgTaHqL9jniqn1DvmROvIy3CQXmGO5IIx+4XhX4ICbbh1yZFNzhK/5czWp3BR6uX8Z/fH+APO8r6fH+sp4Y/XjKP+7/cyeSkBIrHppAZqyVBrWSMTtvn2uOKOFdgkP6EKva9Zf/r/nIe+uceAPb/y+Wk65ydzHAZ8LnTv034mykFu03Vf8bdv430nyVHemYbaHrHFXGog7h3b77/ed3l4HvFf0QR7T4iGHy1tSOd9aSqYtGrYgb8Tf9r5VIpOqX/IGm99xupx9S6Y7dH9hmKQi/Sh1DFzNdqgKeXd93CaaxbOI31X+/jhb1HkUqgyWjmp5/vBODrijpeLjlGslbF9QW5/Kw4cF/sQP2WvS1jrinK44bC8Tz45S42lVfxLzOHziAoUoMLT+kEmnbv8wtkYNA/8MlgUHVoHMxoD+pvQsnjUM7gh6PdTjgxCEx2G3YPK3KhMtzqZrghCr1IRPC1lOmrg3xsyUw2LJ5Bm8nMW4dPU9bYhsFq5ZlLFhCrCj9IhrcO0pPlsns+1XIZzy1f4PF6b4TjPhXMfYYCb5b4gyHqgdRJW10ShBlAKdB7DRWBtKMOZQI0JAxKxMdegT98eLxfse+fjxkR9I8fzs8sHCoqKnjhhRfYtm0b9fVOA+b09HQWLFjAv/7rvzJu3LiA0wpJ6E+dOsXtt99OQ0MDMpmM7du3U1ZWxpo1azCbzdxyyy08+uijAGzevJl169axevVqfvaznwHwq1/9ijfffBOpVIpKpeLtt98mN3fgUaQiI4v+Yh+owEkkEvQaNT+elR/WvcMl1DT8dXSjsSMabnvUiWNaYHBOcY4Y0apDX+9gNNriULgpRqIc4epYpNm6dSsbN25kz5491NXVoVarmTRpEkuXLiUtzRlXpKGhgU8++YTnn3+ejz76iAsuuCCgtEMS+ttuu43f/OY3LFq0iNbWVlQqFT/5yU94/fXXKSws5LzzzuPKK69k2rRpvPTSS3z33XfcfvvtGAwGSktL2bJlCyUlJSgUCqqrq4mJiabdsMhoZagE1FOglb1lGaglqgFGNEPpqx1tIiVU/izB/S1bZxVU0EFC2PnwRzSEebDd3gK11RjOg9NI5S0cHduxY0dE8uBOd3c3RUVF3H777Vx11VWsWLGC9957z+O169at44EHHmDPnj0BpR200JeVlaFQKFi0aBEAer2e2tpabDYb06c7LVdXr17NRx99xLRp03A4HEgkEmQyGYIgUF9fT3JyMgqFc1l27NjglnACfSGGa0MNZ0kto2V4GNn0Z7A6Kfc60wTYcgdrBrOpppSL0yajGeQzsoNtE5E2tgxGqPzdLxw3zomFlRFLz9+9fM2SQ62HUF1f+6ehkUOVLcHv34cb9nso+tdI3jNcHWtoaIhYXnpZsWIFK1ascH1eunSp12vvuOMOnnnmmYDTDlroy8vL0el0rFq1ipqaGq655houueQSMjPPxizPzMzk66+/BmDNmjUUFxdz/fXXExsby7Jly1i/fj0FBQUsW7aMm2++mTlz5gSbDb8MxpJVoAT7onnLa7dagtIQujtVJPftBkPcfeUz3LqINKsypw/Kffo/w0DrIZTOPlj3zKHk2Okcpmefcn0OJT++QuK6D+58pR2twUWgeGoP0Vg1GI7GgcEQro5deOGFAd+rs7Ozz2eVShXQYTSlpaVef/vyyy/Jzs4OOA9BC73NZuObb76hpKSE1NRUli9f7pqde2LlypWsXLnS9Tk2NpZ9+/axZcsWNm/ezLJly3jrrbdYtmyZ1zQAdu/ejU6nw6BMAEApOJhk7eSYIo5J1k4q5TF0SvvmI85hJdvWzfbG8WTioCZZilXWd2k1vttBjEmgXScltd1BRdrASES9YUYtcqefam3SwGsyWuzOl8wZB4am+LPuIBUtE0gc08JUSzuV8hhS7T10SJU0ydR90ugtU09RK5r9eoTpTX3KZJbIEOwmsm3drnIfU8RhkUidxkg+ypQ4puXs8uaMdlfIUJNERrzDwnFF3IAyTbR20iFVohbs1B0f26dMHWeeQ6BlCuQ5uZepoT4F3MLd9y9TY4KUDrcdH6PdQkyziXilGkEp9/qchMoW/q18E08WXkG6Jq5PmXoZ12CnMUFKgsFBt1pCR0zfaxR2gcxmZ3vKbHbmxajq2660ZoHU9rPXRKrtHTxT721nrjUrIMbkve31kgVBP6fT/fITrTJ5e5/K6sYyKasGgAOmVNdv6WnNQN/nZFJKztZNXRKkQWNlCrEntcQtqujznOobkl1pKTplaGvUdI03Egt0jTfiUPUVSlWTgoMnxmFOsaCtUdMxpduVh2DL5O85+Wp79Q3JSM0S5qnqXc9gT2cG1rizqzp2qx11p9JVpt5rrTKJqx8CSLGbiHdYaJRpyLZ1u+rOHX99RFuaLOwywcD3aU/teM/vE4G1veYY/654kdAxgJ2yDmQSz8YhdqEH7JCVldXn+/Xr17Nhwwa/efzLX/5CV1cXl1xySZ89+s8++4x33nmHP//5z37T6EUiBHmy/bZt29iwYQOfffYZABs3bsRoNPL++++zb98+AJ5++mmMRiO//OUv/ab3xBNPUFFRwXPPPefx987OTuLj4+no6CAuLo49y3/h8brB8P+ONv5G3B1SBfEO6+BkJgpE8rm0Kx0cq6skNyaJFHUsvz/0OQ2mTn40bh6z9b5HugarCbVMiVzqv0Pwx1Dvb3arJUEFzYnm1lek30FPM2xvNg/B1oOve3hjKOwtgj14xxprQ9HVd/4Wbr6H0+poIBhsZi7Y/JRLMzwRro716tIs2VPIJJ6D+diFHvba11JVVdUnH4HM6CUSCRs2bGDHjh1s27bNtSoQFxdHcXExa9eu9Ts5difoGf3cuXNpbGykra2N+Ph4tm7dyl133cWmTZsoLS2lsLCQN954gz/96U8e//7o0aPI5XImTJiAIAgcPHiQgoKCYLMxgOHU+Pq/nIG+aP6MfarkMcR7CRIz3An0+Xjr2LodRuSCjI1Nz2BVtJI1bTJf7/6OF+feSKxCzbopy5BIJGfqryKsfIW7xDkYrk29NMVLiTEFvk8fLeMvb1HuwvFkcMffOxRsPfhL1/3+gyXwoZ6m545xrJn4w3279XDLMpz61kgRro6Vl5cHfK+4uDivAw5fFBUVsX79egDXSXaBnj/fn6CFXi6X8/jjj7N48WIEQeCSSy7hsssuIzk5mRtuuAGTycTNN9/MtGmeA5wYDAbuuece1whl9uzZ3HvvvSFlfrgyZcrJPi9XMDOTwbbCjRbBdA7+OrgpU07y5xPfcVFaPnqTjSxtDkVjptAQe5y7dr2GDAl2BD65YRlZJPlMK9Q8hvJchvMqk69BZbB5jVQ67gwHb4XBzkMg9xvuKxAjhUjo2GASqsD3EvTS/WAT6NL9cCfUWb47FWl9w1v2N4gZzJlkuAQi7oBzOf77v2JzONDIFXRZTZgcNubOncuuXbsA+MPS2SzMSiM3ITagGNGBRs7zd22wg7FoPJf+bSIYIrXtMNTbFxBePQw1kTxQKhL1MBReNJEkkKX7cAlm6T7QfBgMBp544gmsViuPP/44Tz75JBdeeCF6vd6j4V1ZWRm/+c1veP311/2mLUbGGyTco0i5/z+Qv/FG/xdluAh8JJYgu21mGkxdHO1soN3aA4DBbiZNHceDE85DmTGGx29M5LvqRq6cPI4Y5UBDmnDqI5hIeIF2jJ4slYdiibiXSJ9tMFza31BbhJvtNlQyudfBvSAI/F/1fibFppKd18CqN/fw4XVLPQ5SB2MQNVSrh0P9nIYbu3fv5rHHHnN9vv/++wGnIeAf//hHenp6qK2tZefOnWzatIkTJ07w8MMPB5T2iBP64ejfGQzDcTktWGEOZF8zkDQ8iZxdcNBu6eHnJR9xtLOBNqsRvVLLWG0ipe01NJg6KUrIRBKfxvQkI9P7HVMb7ecfif179zwOdXsYyoiCvrA5HGxuOMKOlgraLN1nvpXgEARydUlclJbP9ITMAX/X/5jnaOSz22YmRj5wKdUuOGgydfGHI5u5e+JisvPMNJu7Od3dyvPHvuIPxqvZ11bFWE0ir5zagUauJKtBislm59YPv2FcQiz5SXHkxOuYpI8nResMwPTO4Qr+72glEkBAIDN2NzdNHc/U1ESkEonXcgmCQLfdQo/NQllHHZPj0kjXOI9/rehu4cv6I9gcdsxHbbRbepiU5SBWpaDHamNcQiw6pZw8fRx6tYp4dXTjQ5R11DE+JinkOBTuz3uknkd/wQUX0LvAvmvXLl566SU+//xzPv30UyZNmoREIkGpVDJv3jzWrFnDTTfd5LL+98eIW7pvvP8Gn9cPd6EfjkRK6MNlb2sld+56Da1MidFuYVZiFnvbqgDI06VwxdgZ5MelMiMxy09K0SFSM59IHRw0mtp6XU8HB9prkUulrCv5P/KUEzhuOclTGf9BkvzsYM4m2FhTdTcAV2fNpCghk8lx6dgcdhbM6kSrkNNlsaKUyVDLB7q4QeD11r++G7t7+Mfxal7ae5R2k4WxcTGkxmiQACaDc/k2TR2HBAlmh41um5lUVSwtlm4UUhlamZJjXQ2M1SSSHaPnssypzJnV6iyXw0FFu4EjLR1Ud3ZT0tBKu8mCVCIhRavmqUvmIZVIEASBq9/dwndVjUxLTaQ4MwVzu566ng4+rz8MwA05czjd3YpUIkEjUyBBwhx9Dl81HkOv1JKo1HK0s4G7Ji5CJZOjksqJU2g40FGDFAlxCg2GhGMYrTZOtRtoNpowWGwIOKVCLpXS1mNm/eKZzB4TuE2Mr2fxj9qDHGivYW3+Utos3a4BSSjvXJfZysT/enfELd17o7W1laamJrRaLenp6T7dAL0x4mb0/hjM5aBQXJXCEYtKeQzZtu6Il2+oZ5W9vF3ldGsx2i3M0+ews/U0t02fyIYlMxAEaCmf6Lq2McHppz0SCSZ6mq9rswoqXG0iEIbLwMBst1HWUcu+tmqazF3U9nTQaOqiwdSJUurskm5MvBaVRNVH5AFkyLhTfxuTVBOZkFPFnrZKNtWUostM460v99NtsaFTyrHYHVjsfduHTCpBKZXy0g8Eqg/7PlujpK2ad3eWcby1izaTGZCglcu4cnIOL65ciEImZWxsjOvgpdDr1in0cqmUifo4Jur9C8LExDhmpiWxJCeNDJ2WqvIx7GypoN3aww9nnc9ko4acGD0SieTsIN4AkzUCHY5O0jKPctfEeNSyvoJxQar7KY1jyZpRMeDeNocDhyCQ9czbrP18BxmxWvQaFY+cX0ScSoHZ7vxdK5e5ttNqu4xkxGoHpAVn2/ePC3RAPl3mU7yx6xCPzC7yWw/nCnq9Hr1e7/9CH4y6GT2E16FFK5Z1qLjnxySRonY72tGfodhw6di9YXXY+Z+T26jpaWeMJp47J5yPxWHnvC+f4L/mrObu3W+4rp2jz+bRqSv5y4lt3J23GF1MjCtIx2AxHD0h+reJYIlkG3mrcg9vVu7BZLfxavFtJCj7du6f1JbxSV0ZCqmMSbGpZBgWkSCLRy9LRCpxxjRwCA5sgo3S6mRPt2BOdt8oY72D1JTCSp/1IAgCJrudcc++w+EfX4le49mK+XSHgXcOV1De2sntRXnkJcWRqD57bbTeqVDbVv/8HDwxDpkluPgQvgb63vJldziQnYlD8cLeo5xo7aTLYkUlk2FzOPiuupGpKYkAHG/rpDA5gYvHZ5ATr0OnkJOsVTNGpwnIeDZYRtuMPhKMuBl9oDOd4UCkO4UOqRK13eT67E3Mfe1T+iIUF7Jgythjs/C7w5+jksr5RcGlFH+xEQnw4twbebtqL4+Ufshn9YdZmDyeKXHp7L70IQCOdzXy8/3v88OtL/DyvJvQq2Joi3II3Gi3oVDdKN07WIDyHjsxPQbGJzr36oxWG1pF39faYLEil0pdS9l761vQq5XkxOtoTNzNyje+5PMbL2F6aiIdZisJZ/ZjbQ7HgKBCh5vbydBpXXu2giBgsNhoNZk5cuoId8zJwSEIPHTkVfQaFRJjAjqFimRlDEe7GrhLs84p6lbATWt3V57tCL+Xt4O8fUDZF9oS2F0ZN0DsYeC74Y7JZue9I6f57Xf7WZWXhV6jwiEIGCxW/n7gBPWGHhI1Kr6prGd8QixLczO4f36hS4QGY8Ds7R6BeH+4b71Zky3ImoPb5/blERTIZOIH6nGQ0fc3e7YDmURK4sRydEoFjd09bK9pYm9dC+aEJpprbBxv6OHSaQncd+kY1Mrgg1cNxhHJo4URJ/S9RNLtKRpEI558k0xNmpfOLBKEUm/BrBxsbjhCaXsNVcY29rfX8P6iu3hk/4fcues1AJam5jM5Np0rxhYRqzgbonVibCpr85fyb3vf5rdln/KTSYvJSZtCoiH0A12GCk+eEr35EgSBL07VMjcjGUGAVw+e5IOjlYzRafj7FYv57bf7eWbXYZ5aNpcbp04AYI/BTs2Rk2yrbkKnlLOnroWLx2dgsdnpsdlxCAJyqRQBgRiFgjE6DS/sPcq8jGQS1SpMNjuz0vX86psSHALsqWvm4fOLkEslvHbwJM1GM0ty0jjd0c09cyZz76c7OC87lfEJsZS3dmKxO4hXK0lUK1mcnc41U3LQyOX8eFY+EomEJqOJo2WpfFJ7kN8VXUH50b4durvAwxmR78dCt0NaPIl8VkEFB2UJXt+NnbVNrP1iJwDXTHHW/y0fbKW8tYt1C6eyLDeDTouV24vyXIOcQImEh4knesXWl5eGJ8wpVg7u9bwa4o6nenS/byD4eu9lZ1Zo2o7noSuoIDVGww8nOV3E9DOdM2CHQ+Dlrxu5+o9H+fxAB0sL44nXyjBbHcSqZSTEyJk5Loabz0tGIfc/EOjNj8FmDrgM5wojVuh9EY1oZ5EgmmdPBzuwsNodlLd10m6yYHcIOASB5h4T2XE6WnrMXJiTjsqLMZO3PPjLx/72WgRBIFau5oShiSu+eRGABIWG2fpsHpt2GT/d+zavVOykOCm3jwXuwmRnB3eyu5lJujR8vcrRetahHFMayHM52tzB4r9/4vp80bgxaOUyqruM/CBvLL/7/gA3f7CVTrMFlUzKyyXl/GlfOZOS4ojNnUScQ+CGqbmkaNX8+6IiUmM0xCkVA55fh8lCjcHIo4uK+qwKuFPRbmBHTRMxSjnvX7uUmi4jcqkEmVTClop67pw1idQYDTPT9eQmxA5YPeildzacolWTMreTnEOLgYHeFr2C0yv4C/udvNb/ulDsSeZnpHDtlHFo5DLy9LF8U1mPwWLjkxsuRq9RD7g+3CiOkaA37f5uueHiTeDd79WfcI1A+7837jPxqxNyyZvWyg9nl3P74hSsdgGNUorB5KDFYOXLgx1c92y5M4pqtZEtDxeSlaQaMJsf7tuUQ82oFHpPBBMkJdIMxiAjkHvYHA5kEgm1BiNvlJ3iP7cd9HptqlbNtNREnlo2jzSdho+PV/PV6XrunTuF0oZWUrRq5mWm+LyXTbD3Mfh5uHA5r1Xs4h+1BznadXb21W7tYXPDURyCQFF8Jgc7ajncWc8st5j1nWd86QEazJ0k4Nk4ZbCW3H3hLw+vVuzk0jEFSJCwv72a10p2UJAcD0jYcvNy7A4HR1o6yU3QoVXIWTu/cEAanWYLHWYrHcmZTLX4n8EBxKuVft2kxiXoGJegc32erIp3/Tt3RmCuPJ5wX/npL/bgW4B6/6Y3nWB5dtdhpBIJn56sYXtNE5eMz+S/VxbTfWoynswYoyngwRJMXnZXxqFKc65y+avP/vQXeX9bo/7eg/4DFV9bVdPT9ExnPl0HnJ9NOFe35DY7VyXI+fej/0ez0Tm0byrNICahbzsURd4/54zQB0IkVwKC/TuL3c61737FybYuLsodw12z8olTKihtbCNRrUQjl9OTFsPpTgM58c6O2Gyzo5LLBuzbemNTeRV/P3CCH00d71HkizNTcO16CwK1BiPXvfcV2fExlLd2cqrdwIm2TqalJvLfe45ycW4GCqkEuVTKvIxkcuJ1CB0qbA47d+x8BXDOxOcljeOUoRm5RMq71SUAqKRylqVPpslsoMdudfr82szU9HRwQ87cPiIP9DHs+n87X+WdyQMDRQxl4A9vGFJLSFSrsFZOcX13sKOOD2pK6bZZ0Cu13L9oMnMzkkmP0bjaYBzQ0gQtfu7dZpVR1ZAQbhE8Ek7MCn+zwMHy9DjU3M7G7QeZlZ7EFzdeSlqMGokP3/OhFvn+M/hgZvRzsjvp0JuID2HWHiz+xD7U+wiCwKNf78MuCLxc4ownX5CcQLPRzJ9+UMy4BO+Dzd466nH0eL3mXEUUej9EwhbAaLVR02UkXqVEr1Hy6Ff72FrVwIPzp3LF5GzMNjuPfLUXhyCQrFXxRtkp3ig7xfyMFHbUNiGTgF2AmUXTOVB6gH+bX8AT28sAuH9+If+sqKOkoZXKe6/lzo+/RyGVcseMPIrHpvbJx5X5OVyZnwPAKwdO8n11I1qFnDE6Davysnho4TQkZ3x13f/fi0MQkJ75vGHxTKxnXGm6rTZKG1s53d5NI7s5Xafi5Xk3cfF8MwaLlcPNHRhOmGg2mnhgQSGnquUUxmVQmDCGJJWOeIXGla4nbA47Jw1njwXduvR+2ux9DfEGcyYf6L2KXvqA+u4eHlxQyM+KNRwtzeC107v44oy/s1am5B9L7gYb2CqhOoR8KexCWOLkq0MON7LgYA68lF4s7guSE2hYu7rPd8NR5Ps/B3+f3XHPt9R89j0KRWyDPcsh3GX8XuwOB+8fq+R0ezcv7TtGqlbNq1cspiA5gYxYLd9WNbis+EWCZ8S51w2XWPcnDE04BIFFcwwcbemgzWQhWaviVLuBkoZWTrV30WO1o5A5g0vMSNfT1mOh1WSm2Wii22IjTqUgQa1CJZPSbbVhcwjk6eOQSSVY7Q56bDa6LU4fsmStmiSNion6OMYnxNJhtiAgYHMISIAb39/KxbkZfHmqFoCxsVoW56Q7o1qpFFgdDqo6u+kwW3EIwpn/oKypjTqDcwT88eplHgNgRMvwMZBOYmtjOffvexcAhUTGb4t+yIWpk/oMQIaD/YU7docDg8WGXRAoa2qj3tCDye7gwS938dTMa8jVJWGy25igSw7Lvci9g+9v1AYDDds87X+Hs/cdTQbbzdVfXXoj2CVyT0Sq7vuvArgzGO9IKIGIWnvMrHrzS061G7i2YBwHGtq4tmAcV+bnkK7z7Lbm7Z7uM/o7q38qute5Ic7oA+RIZz1/PbmNKmMb1cY2jHbnufCX1mcyOTkevUbFnrpmPjxWxWc3XEJeUhxauQzrmX3xQJbW/XFMEcckq+eOpXfWYrU7aDdbAJxBZnpMdJmtyM8E+EhQK5BKJMgkEpqMZvbUN6NTKFDJpExJjg9phN4fT52K7Yz4CQi0Hp/gCozSS5Opi12tp6kytlHf00m7tQfHmTHowuTxPDbtMhLdlu9rkqVkNkcvYI4/9zej1UZJQyvlLZ3UGowcb+3EdCZAi1wqQaeQI5dK0SrljIvXYXcI/LJgOYtSJ3pML1h6O7Wu8UaOfpXe5zdPluvu37sLfq+7mi+RGAqCdT/s/24E247d7QYiId69aQZLOK6tU6acpCZZCs2er482/gyDPZVNq5Bz8/SJbPh6H1Ud3fzz5uUh37+3vveWjQk5jdHKOS/0DkHglYqdJCg1NJkMCAhoZUryYlNIVcdxtLOBR0o/xIHnhY+/Xb7I7VM+f/pB39+VsvDCcLqjAaq8GKG54/5CpcYMtCruxXx6MlOBXhP2lo6gs+QRT4el3LXzNfa0VTI1PgO5ZBdyqRSNy1BPgk6u4rzk8SxKmUiGJp44P8v54Yp8//r3tAzZWwabw8GeuhbKmtqpMxg51NyBXCphdnoSk5PjmT0mifGJ3i3QXel6Dg4WMJ6WlvuLfCB8L2/3KPa99xguYg+BvyeBvhvRItg6i8QM21MaWQBnduyG6sjrYO6nlsv48ax8TncYeO/wadpNFhLUSrotVp7bfYTF2WnEqZRkxmoHuD56axuT8itC2wcbxZzzQl/e1chLJ75lccpE0tRxSCUSjDYLRruFLusJqo3tqGVy0nQqKjoMJGvV3Dd3Cp+cqGFZbobPtCNtDRpo2NdoWaH6m/X9/tDnXJs9i4Mdtfzqs48BUMtk5MakUBA/Bqtg53R3C2aHHZPdynnJE/jj7Gv7pGFzOKg3daCRKVHJvDdP97rwtX9utNp498hpSupbaDSaEASQ9MQzLqaaJal55MelDUijF4vdzkt7j/HlqVoWZ6cxPU3PnDFJ/HzhtAGBZDwxGCfVFRQ3cmhbX1sMby5qvvAWiGakEImQyO7PJBKnS8Lgi6x7SOThtqXli18vmcmFOenk//d7PLZ4BrVdRl7cd4wndzhtke6cOYlfXzCrz9+MhOifw4VzRuj3t1XzXnUJBpuZbK2e2fpsZiSOZVJsKn+a+yOOdjXwm7KzvsxPzbwGqUTCtdl6xmoHGoGsKIzsOdKBYFRFPlwkBG+E5G3W93bVXt6u2gtAVpyW6k4jJrudw531dNvMPLG8iA5zDg1VyWRpE5kSl44i+zDvH6vkUFMbHx+vocvi3BL5y/ybPZ5O1ou3unBfTfjnDi237/xfNi6dw7riaaSesbh+YttBNm7/lpdOfMsXF97XZ0vAnXvfPcZXjce4YXo201ITyYjVYrTa+K6qkZz4mAEWwMEYeUVK9K1x9hEt0JEi0u9GpAZi0TZK7N/mTqfJkIR5Hn0k8Vf2Yy0d3Pj+Vlp7zFyWNxaA9VtLXL//8rzp/HRegd/0RcH3zTljjPfm6T18ULOfZJWOFnM3Fd0tmB02LsuYyoZplwFQY2zj1u1/Y4wmnhVjCrAKDkraqrE4bFR2t9Jjt3JhWj4PF4a+jxQOFWkyxvl4ib3NuKNhTeypI3QIAtXGNva3V/NeVQltFiPVPe19rtl1+2Vkx5/1077n0+18VF5FslbF+VlpvF52yvXbDTlzeGDyxa7PB9treb96P3bBwcpFFzG3faARTKfVRKOpk7qeTn5d9jFLJ6Ty3PIFfa6x2O10mq3857aDtPaYWT/+Rx7L+C87XmFfu3MN8PzkCUglUrY1n8Qq2JEiIUubCBKQIUUqkbi2GmLkSjbOuJq6EwP9333NnEMRF19tIthjg0cy/t6Nc4XhVg/ehN4hCPxv6XEe+ucenrlkPpOS4siKiyFJowrLQLXq0DgMNjMXbH5KNMZz45yZ0V+fM5vrc2YjCAIdVhPNZgNWh50kVYzrGp1czY/GzWNHcwXzk3JBAvOTxvFqxS52tFQAsKe1cohKEDjRdBPyJQhSiYTsGD3ZMXpWZU6noruFRw6/Rb2hB6kEchJ0/HLLXn6QNxYJzhO7suJi+OGkLOZnpDApKZ6fzJmCyWrj4tc+5/XTu3n99O4B94mTq8lsn8Rc8vp8/1ndIR4u/ZAYuYoUlY5LxxSQl2zl/i920tpjdnooSJxGinKphBNtXTx76QJodZ4l3mw2cMrQgtVhJ0Wt444J57G/vZpqYztGuwW7w8H0hExaLN1IgOkJmejkKhwINLfE4RCcHazapqby+CQUbv1VryX3YM6+A1mKHukCP5qIxMy/Q5lAVlL7gO+HasbrbUXj8rc2s7PWaTVYmJLA1NTIuM5lFVRwuHRkG+M9//zzbNy4kfr6eoqKinj22WeZN29eWGmOaKH31IC8NejjXY2s/v4vfb7bcck6tjQcI0MTz9tVe9lUc8D12wvHvyE7Ro9SKmOCLpm1+RdRZWxjYmxq/6TPCUIRhEVzu/h67oo+351s6+JQczsAZrudFRMySddp2FJRz/fVjdR2GanpMjIzXc+++tY+f3vnzElMSIwlI1ZLen4KWdaKPr8vHysgk1yBXCIFBHrsNhK0jRSkJBCjkJOq1QACOfE6JBIJDkHg3W/gyZoPMdrNJKt0TNClIJfKONBRi16pZX7SOG7ISUErU3rdlz98eDz+7MAiOYsPBVHQhzfR3k8fyv1sT2K/6XrnSl3aU2/w4OZdXJmfw8W5Y5iQGP7Md2x+JWwOO5kh4c033+T+++/nhRdeYP78+Tz99NNceumlHD16lNTU0LVnxC3d9x5TW3VoHI7Mg7SbLCSqlVgdDtJ1WlrLPbsvCYLAoY56VDI5sXI1DhyM0cTzX+VbebViJ18vvR+5VEpFdwvXfPsnAF4tXuMy1hoOBGJwFOnZvL/419HysX+j7CQ//Xynx9+23fYD5CnpjLUaONTUzt76Fg40tlFr6KF3Eq2USUnWqtHIZWgVchQyKc1GEy+XlHNeVirxKiXNbXJmJ2bzg4yppGtC62BCqe9Iim4kjNBGAyO5HnrfE/3MirDTOmFMZoJ2oH9db2z4UMU+0HYeaLx8d7KfeQvzGddUjVzGRePGMCNNT2FKAvMzU9Aq5D49cPozko+pnT9/PnPnzuW5554DwOFwkJWVxb333stDDz0Ucn5HrNADvLj3KI9+vc/j3yUqtSQrY7AJDuyCwMzELD6o2Q9AjEzJmvELkUsl2AWBrxvLKW2vYVHKRBKVWvJj0xinS2J2YhZyqfeDXQI5zCXQMKKDbdgXLqH4zIbD99WNXP3OFhyCwE/nFdBg6KGhu4fClERmj0li+hljOX8dQrPRhNlux3Q6f4AvfyTw1SGKs+rhS7QGbIG+B5EQeV94O9I1mL4lErEWPNVHY3cPZrsDu8PBGJ0WBwL76lvZVt3oCtV9e1EecSoFcSoF+UnxxKuUpMWo0SkV6DWqPukNptAXyf4DGZ5dmO2Y2G//BVVVVX3yoVKpUKlUA663WCxotVreeecdrrjiCtf3t956K+3t7XzwwQch53fECP3atWvR6XQIWz5Ep5SjlsuwOwRUMilKmQylXIpSKqXHZqex28TRlg6kUglyiYSX9h0DYEJiLGuK8jBanVHpVDIZcSoFOfE6xifoiFEq/ORmaPEVMCdUojGIiNYypNlmp6qzG4vDgTExnTmy0I7sHY4Dp1CJduCgkUKg9RCpFa8pU04G3c6jLeYABw1jmKqrC+jacGf5oRDKGSBtPRYsDgcdJgtNRhMVHQY6zBYaDCa6LFbaTRaX7Q04V/P+cbw6qkJvMpnIzc2lvr7e53U6nQ6DwdDnu/Xr17Nhw4YB19bW1pKZmcn3339PcXGx6/t169bx9ddfs2PHjpDzO2L26FevXo1cLqfm6Pd0mq1Y7HYkEmeoWKvDQZfRitFqo9tqw2CxYbbbaTE4I8GkatU0Gk2caOvika/2er1HVlwMu+9YNVhFCppIizwEfsRsMLjvyUUyaIdKLmOivvfFDVzkR5Ow90cUeSee6iGaZ8VnFVRERbiFBcGFtZNs73tyYaAiD86Bh7eZfrQI1t1QKZORdiYUblZcjJ+rndR0dfOP49GNmKNWqzl16hQWi8Xndf3PCwE8zuajzYgR+smTJxMXF8dYH0ejhoIgCPTY7BgsVnTn4Iw+WvQX10gfExxIXYxmge9FnNE78VQPkdgq8TVY8CaS4QwA+gt3sIQyox9s/L2X4U4K4pS+j2KOFGq1GrXae+TRYElOTkYmk9HQ0NDn+4aGBtLTg4+A6c6IEfpeotl5m/F/LGg08dfALZLw4+V7YyhEMZwXOpp1MZKwyqITRGmkEa16iLZdRaTFtlWZQKsl8BnjcBwMh5sng80cmYwMMkqlktmzZ7N582bXHr3D4WDz5s3cc889YaU94oR+NOOvgbelRe/s8cFmsKKFiZGzRKKBLyNUf+LdO+Nv3Tcu4u1yNPUR5yL3338/t956K3PmzGHevHk8/fTTdHd3s2bNmrDSFYVeZEgIt4MLtEMTBV5kuDFUS+Yiw5/rr7+epqYmHn30Uerr65kxYwaffvopaWnhuXmPGKHv7HTux47UZZlIYLTIMNiGT3jLoUSsCydiPTgZLvUQfFS2yPZnw6UehpLuEa4R99xzT9hL9f0Z9kKvVCpJT08nKytrqLMiIiIiIjICSE9PRzlIRnkjgWHvRw9On0V/bgwiIiIiIiLgnCBG0iJ+pDMihF5EREREREQkNEQfJRERERERkVGMKPQiIiIiIiKjGFHoRURERERERjGi0IuIiIiIiIxiRKEXEREREREZxYhCP8hceeWVJCYmcs011wBgNBpZsWIFkydPprCwkGeffdZ1bXNzMxdeeCF5eXlcddVVmEzOE9uqq6tZsmQJN954I3a7nQ8++IDVq1e7/u6ee+5h+fLlrs+PPPIIv//97wephKFx6tQpLrzwQgoKCpg2bRrd3d3s3LmTwsJCJk6cyK9+9SvXtZs3b2b27Nls3LgRgLVr1/LCCy+4fp86dSq/+93vXJ/PP//8sI54HE589NFH5Ofnk5eXx5///GcAXnnlFWbNmsUrr7wyxLmLPOG0CwC5XM6MGTNc//3tb38bimIETTT6CYALLriAyZMnu+rjvvvuG/zCiQw+gsigsmXLFuHDDz8Urr76akEQBKG7u1v46quvBEEQhK6uLiE/P18oLy8XBEEQHnjgAeHZZ58d8O9169YJhw8fFp5//nnhk08+ERoaGoScnBzXPc4//3xhwYIFgsPhEARBEC666CJh69atg1XEkFi8eLErjy0tLYLVahXmzJkj7N+/X7DZbML8+fOF0tJSQRAE4brrrhN6enqEG264Qejq6hLefPNN4dZbbxUEQRA6OjqEWbNmCZdffrkgCIJgsViExMREwWKxDEWxIorVahXy8vKE6upqoaurS5g0aZLQ3NwsXHHFFYLFYnGVeTQRTrsQBEFISkoasryHQzT6CUEQhCVLlggHDhwY7OKIDDHijH6QueCCC4iNjXV91mq1LFmyBACdTkd+fj51dc5jJj/88ENuvvlmAG666SY2bdoEOE80kkqlyOVyHA4HqampyGQy6urq6OnpQaFQUFBQwJEjR3A4HOzfv585c+YMckkDp6ysDIVCwaJFiwDQ6/U0NjZis9mYPn06MpmM1atX89FHHwHO8kskEmQyGYIgUFxczLZt2wDYsWMHK1eupLGxEYCSkhIKCgpQKIb3EcSB0DuTzczMRKfTsWLFCj7//HPXmdf9z70e6YTbLkYy0egnRM5dRKEfRlRVVVFaWsqsWbMA6OjoID4+HoDMzExqamoAuPvuu7n99tvZsmULl156KYBL7Hbv3s3s2bNZsGAB27Zto6ysjPHjx6PRaIamUAFQXl6OTqdj1apVzJo1i8cff5za2loyMzNd17iXf82aNRQXFzN16lRiY2PJysrCaDTS0tLC9u3bWbBgAbm5uZw8eZLt27ezcOHCoSpaRPFWJ6tWrWLu3LlceeWVQ5i7yBNuuwBob2/vs3S/ZcuWISlLJAmnnwC45pprXPXhvs0hMnoZ9rHuzxXMZjPXX389GzduJCYmxue1ubm5fPvtt32+6xX61NRUFixYQF5eHs8++yx2u53i4uJoZj1sbDYb33zzDSUlJaSmprJ8+XKfM/CVK1eycuXKPt8tWLCA7du3s2PHDu6++25OnDjBtm3b2L59u2ufc7Ryxx13cMcddwx1NiJOJNpFQkICJSUlUc7p4BFuPwHwzjvvMHXq1GhlUWQYIs7ohwGCIHDLLbewcuXKPqIUHx9PR0cHADU1NWRkZHhNo7i42CV0CxYsoLCwkIMHD46IGW1mZiZz5swhKysLlUrFypUrMRqNrpkJBFb+bdu20dLSQlJSEvPnz3fVx3Avf6BkZGQEVScjnUi0i9FEJPoJkXMTUeiHAb/4xS/QarU88sgjfb6/7LLL+Pvf/w44LatXrVrlNY3p06dz+PBhKisryczMRCaTodVq2bx587Cf0c+dO5fGxkba2tpwOBxs3bqV2bNnI5PJKC0txW6388Ybb/gsf3FxMa+99hoTJ04EYObMmXzxxRcIghD2Wc7DhXnz5nHw4EFqamowGAx88sknfZZkRxuRaBejiUj0EyLnKENpCXgusnTpUiE5OVnQaDRCZmamsHXrVgEQCgoKhKKiIqGoqEj49NNPBUEQhMbGRmHx4sXChAkThMsvv1wwGo0+016yZIlwzTXXuD7//Oc/FzIzM6Nankjx8ccfC1OnThUKCwuFtWvXCoIgCNu2bRMKCgqE8ePHC+vXr/f59yaTSVAqlcJzzz3n+m7+/PnCj370o2hme9D54IMPhLy8PGHChAnCiy++ONTZiTrhtguZTOZ6r4qKioQnn3xyEHIdPtHqJ5YsWSLk5+e70rjxxhsHq0giQ4h4ep2IiIiIiMgoRly6FxERERERGcWIQi8iIiIiIjKKEYVeRERERERkFCMKvYiIiIiIyChGFHoREREREZFRjCj0IiIiIiIioxhR6EVEREREREYxotCLiIiIiIiMYkShFxERERERGcWIQi8iIiIiIjKK+f8005EHSDIwvgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Plot with cartopy and matplotlib\n",
-    "cn_plot.generate_plots(file_name=\"climate_network_measures\",\n",
-    "                                 title_on=False, labels_on=True)"
+    "# plot betwenness\n",
+    "map_plot.plot(np.log10(betweenness + 1), \"Betweenness (log10)\")\n",
+    "\n",
+    "# add matplotlib.pyplot or cartopy commands to customize figure\n",
+    "plt.set_cmap('plasma')\n",
+    "# optionally save figure\n",
+    "#plt.savefig('degree.png')"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pyunicorn",
    "language": "python",
-   "name": "python3"
+   "name": "pyunicorn"
   },
   "language_info": {
    "codemirror_mode": {
@@ -572,7 +452,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/src/pyunicorn/__init__.py b/src/pyunicorn/__init__.py
index 2babe2f2..93a9215f 100644
--- a/src/pyunicorn/__init__.py
+++ b/src/pyunicorn/__init__.py
@@ -16,7 +16,6 @@
 pyunicorn
 =========
 
-
 Subpackages
 -----------
 core
@@ -27,12 +26,6 @@
     Functional networks
 timeseries
     Time series surrogates
-
-To Do
------
-   - A lot - See current product backlog.
-   - Clean up MapPlots class -> Alex!?
-
 """
 
 from .version import __version__
diff --git a/src/pyunicorn/climate/__init__.py b/src/pyunicorn/climate/__init__.py
index f620023d..3ff47560 100644
--- a/src/pyunicorn/climate/__init__.py
+++ b/src/pyunicorn/climate/__init__.py
@@ -22,15 +22,6 @@
 ~~~~~~~~~~~~~~~~~~~~
 [Donges2009c]_, [Donges2009a]_, [Donges2009b]_, [Donges2011a]_, [Zou2011]_,
 [Tominski2011]_, [Heitzig2012]_
-
-To do
-~~~~~
-  - A lot - See current product backlog.
-
-Known Bugs
-~~~~~~~~~~
-  - ...
-
 """
 
 from ..core import GeoNetwork, GeoGrid, Network
@@ -41,8 +32,7 @@
 from .coupled_tsonis import CoupledTsonisClimateNetwork
 from .havlin import HavlinClimateNetwork
 from .hilbert import HilbertClimateNetwork
-from .map_plots import MapPlots
-from .cartopy_plots import CartopyPlots
+from .map_plot import MapPlot
 from .mutual_info import MutualInfoClimateNetwork
 from .partial_correlation import PartialCorrelationClimateNetwork
 from .rainfall import RainfallClimateNetwork
diff --git a/src/pyunicorn/climate/cartopy_plots.py b/src/pyunicorn/climate/cartopy_plots.py
deleted file mode 100644
index 4ffdd92a..00000000
--- a/src/pyunicorn/climate/cartopy_plots.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# This file is part of pyunicorn.
-# Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors
-# URL: <http://www.pik-potsdam.de/members/donges/software>
-# License: BSD (3-clause)
-#
-# Please acknowledge and cite the use of this software and its authors
-# when results are used in publications or published elsewhere.
-#
-# You can use the following reference:
-# J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,
-# L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra,
-# and J. Kurths, "Unified functional network and nonlinear time series analysis
-# for complex systems science: The pyunicorn package"
-
-"""
-Provides classes for analyzing spatially embedded complex networks, handling
-multivariate data and generating time series surrogates.
-"""
-
-import matplotlib.pyplot as plt
-
-try:
-    import cartopy.crs as ccrs
-    import cartopy.feature as cf
-except ImportError:
-    print("climate: Package cartopy could not be loaded. Some functionality "
-          "in class MapPlots might not be available!")
-
-#
-#  Define class CartopyPlots
-#
-
-
-class CartopyPlots:
-
-    """
-    Encapsulates map plotting functions via cartopy and matplotlib.
-
-    """
-
-    def __init__(self, grid, title):
-        """
-        Initialize an instance of MapPlots.
-
-        Plotting of maps is powered by cartopy and matplotlib.
-
-        :type grid: :class:`.Grid`
-        :arg grid: The Grid object describing the map data to be plotted.
-        :arg str title: The title describing the map data.
-        """
-        self.grid = grid
-        """(Grid) - The Grid object describing the map data to be plotted."""
-        self.title = title
-        """(string) - The title describing the map data."""
-
-        #  Initialize list to store data sets and titles
-        self.map_data = []
-        """(list) - The list storing map data and titles."""
-        #  Also for multiple maps
-        self.map_mult_data = []
-        """(list) - The list storing map data and titles for multiple maps."""
-
-        #
-        #  Adjust cartopy settings, fine tuning can be done externally
-        #
-
-        # Specify Coordinate Refference System for Map Projection
-        # pylint: disable-next=abstract-class-instantiated
-        self.projection = ccrs.PlateCarree()
-
-        # Specify CRS (where data should be plotted)
-        # pylint: disable-next=abstract-class-instantiated
-        self.crs = ccrs.PlateCarree()
-
-        # get spatial dims
-        self.lon = self.grid.convert_lon_coordinates(self.grid.lon_sequence())
-        self.lat = self.grid.lat_sequence()
-        self.gridsize_lon = len(self.lon)
-        self.gridsize_lat = len(self.lat)
-        self.lon_min = self.grid.boundaries()["lon_min"]
-        self.lon_max = self.grid.boundaries()["lon_max"]
-        self.lat_min = self.grid.boundaries()["lat_min"]
-        self.lat_max = self.grid.boundaries()["lat_max"]
-
-        # extent of data will also give extent of world map
-        self.data_extent = [self.lon_min, self.lon_max,
-                            self.lat_min, self.lat_max]
-
-        print("Created plot class.")
-
-    def add_dataset(self, title, data):
-        """
-        Add a map data set for plotting.
-
-        Data sets are stored as dictionaries in the :attr:`map_data` list.
-
-        :arg str title: The string describing the data set.
-        :type data: 1D array [index]
-        :arg data: The numpy array containing the map to be drawn
-        """
-        self.map_data.append({"title": title, "data": data})
-
-    def generate_plots(self, file_name, title_on=True, labels_on=True):
-        """
-        Generate and map plots.
-
-        Store the plots in the file indicated by ``file_name`` in the current
-        directory.
-
-        Map plots are stored in a PDF file, with each map occupying its own
-        page.
-
-        :arg str file_name: The name for the PDF file containing map plots.
-        :arg bool title_on: Determines, whether main title is plotted.
-        :arg bool labels_on: Determines whether individual map titles are
-            plotted.
-        """
-
-        for dataset in self.map_data:
-
-            #  Generate figue
-            fig = plt.figure()
-
-            # create map plot
-            ax = plt.axes(projection=self.projection)
-
-            # make it a class feature, as to work with it from outside
-            # self.ax = ax
-
-            # create some standards of plotting that can be adjusted
-            # before calling generate_cartopy_plot
-            # adjust size and plot coastlines and borders
-            ax.set_extent(self.data_extent, crs=self.crs)
-            # ax.set_global()
-            ax.add_feature(cf.COASTLINE.with_scale("50m"), lw=0.5)
-            ax.add_feature(cf.BORDERS.with_scale("50m"), lw=0.2)
-
-            # Draw gridlines in degrees over map
-            gl = ax.gridlines(crs=self.crs, draw_labels=True,
-                              linewidth=.6, color='gray',
-                              alpha=0.5, linestyle='-.')
-            gl.xlabel_style = {"size": 7}
-            gl.ylabel_style = {"size": 7}
-            # plot data upon map
-            ax = plt.tricontourf(self.lon, self.lat, dataset["data"],
-                                 extent=self.data_extent, transform=self.crs)
-            ax = plt.colorbar(shrink=0.5)
-
-            # plot main title
-            if title_on:
-                plt.suptitle(self.title)
-
-            # plot subtitles
-            if labels_on:
-                plt.title(dataset["title"])
-
-            # save figures at current dir
-            file_extension = dataset["title"] + ".png"
-            file_extension = file_extension.replace(" ", "")
-
-            fig.savefig(file_name + "_" + file_extension)
-
-            plt.close()
-
-        print("Created and saved plots @ current directory.")
diff --git a/src/pyunicorn/climate/map_plot.py b/src/pyunicorn/climate/map_plot.py
new file mode 100644
index 00000000..1611f1aa
--- /dev/null
+++ b/src/pyunicorn/climate/map_plot.py
@@ -0,0 +1,119 @@
+# This file is part of pyunicorn.
+# Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors
+# URL: <http://www.pik-potsdam.de/members/donges/software>
+# License: BSD (3-clause)
+#
+# Please acknowledge and cite the use of this software and its authors
+# when results are used in publications or published elsewhere.
+#
+# You can use the following reference:
+# J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,
+# L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra,
+# and J. Kurths, "Unified functional network and nonlinear time series analysis
+# for complex systems science: The pyunicorn package"
+
+"""
+Provides classes for analyzing spatially embedded complex networks, handling
+multivariate data and generating time series surrogates.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+try:
+    import cartopy.crs as ccrs
+    import cartopy.feature as cf
+except ImportError:
+    print("climate: Package cartopy could not be loaded. Some functionality "
+          "in class MapPlot might not be available!")
+
+from ..core import Grid
+
+#
+#  Define class MapPlot
+#
+
+
+# pylint: disable=too-few-public-methods
+class MapPlot:
+    """
+    Encapsulates map plotting functions via Cartopy and Matplotlib.
+    """
+
+    def __init__(self, grid: Grid, title: str):
+        """
+        :arg grid: The `Grid` object describing the map data to be plotted.
+        :arg str title: The title describing the map data.
+        """
+        self.grid: Grid = grid
+        self.title: str = title
+
+        #
+        #  Adjust Cartopy settings, fine tuning can be done externally
+        #
+
+        # Specify Coordinate Refference System for Map Projection
+        # pylint: disable-next=abstract-class-instantiated
+        self.projection = ccrs.PlateCarree()
+
+        # Specify CRS (where data should be plotted)
+        # pylint: disable-next=abstract-class-instantiated
+        self.crs = ccrs.PlateCarree()
+
+        # get spatial dims
+        self.lon = self.grid.convert_lon_coordinates(self.grid.lon_sequence())
+        self.lat = self.grid.lat_sequence()
+        self.gridsize_lon = len(self.lon)
+        self.gridsize_lat = len(self.lat)
+        self.lon_min = self.grid.boundaries()["lon_min"]
+        self.lon_max = self.grid.boundaries()["lon_max"]
+        self.lat_min = self.grid.boundaries()["lat_min"]
+        self.lat_max = self.grid.boundaries()["lat_max"]
+
+        # extent of data will also give extent of world map
+        self.data_extent = [self.lon_min, self.lon_max,
+                            self.lat_min, self.lat_max]
+
+    def plot(self, data: np.ndarray, label: str):
+        """
+        Plot dataset onto ``self.grid``. A simple setup to get a quick view of
+        your data.
+
+        The plot can be customized by calling additional Matplotlib or Cartopy
+        methods afterwards. It can then be saved via ``plt.savefig()``.
+
+        :arg ndarray data: The dataset to be plotted on the Grid.
+        :arg str label: A name for the dataset to print as label.
+        """
+
+        #  Generate figure
+        plt.figure()
+
+        # create GeoAxes object
+        gax = plt.axes(projection=self.projection)
+
+        # create some standards of plotting that can be adjusted
+        # before calling ``generate_cartopy_plot()``
+        # adjust size and plot coastlines and borders
+        gax.set_extent(self.data_extent, crs=self.crs)
+        # ax.set_global()
+        gax.add_feature(cf.COASTLINE.with_scale("50m"), lw=0.5)
+        gax.add_feature(cf.BORDERS.with_scale("50m"), lw=0.2)
+
+        # Draw gridlines in degrees over map
+        gl = gax.gridlines(
+            crs=self.crs, draw_labels=True,
+            linewidth=.6, color='gray',
+            alpha=0.5, linestyle='-.'
+        )
+        gl.xlabel_style = {"size": 7}
+        gl.ylabel_style = {"size": 7}
+
+        # plot data upon map
+        plt.tricontourf(self.lon, self.lat, data,
+                        extent=self.data_extent, transform=self.crs)
+        cbar = plt.colorbar(shrink=0.5)
+        cbar.set_label(label, rotation=270)
+
+        # add title
+        plt.title(self.title)
diff --git a/src/pyunicorn/climate/map_plots.py b/src/pyunicorn/climate/map_plots.py
deleted file mode 100644
index 56ea6336..00000000
--- a/src/pyunicorn/climate/map_plots.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# This file is part of pyunicorn.
-# Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors
-# URL: <http://www.pik-potsdam.de/members/donges/software>
-# License: BSD (3-clause)
-#
-# Please acknowledge and cite the use of this software and its authors
-# when results are used in publications or published elsewhere.
-#
-# You can use the following reference:
-# J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,
-# L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra,
-# and J. Kurths, "Unified functional network and nonlinear time series analysis
-# for complex systems science: The pyunicorn package"
-
-"""
-Provides classes for analyzing spatially embedded complex networks, handling
-multivariate data and generating time series surrogates.
-"""
-
-import os
-import glob
-
-import numpy as np
-
-#  Import Ngl support functions for plotting, map projections etc.
-try:
-    import Ngl
-except ImportError:
-    print("climate: Package Ngl could not be loaded. Some functionality "
-          "in class MapPlots might not be available!")
-
-#
-#  Define class MapPlots
-#
-
-
-class MapPlots:
-
-    """
-    Encapsulates map plotting functions.
-
-    Provides functionality to easily bundle multiple geo-datasets
-    into a single file.
-    """
-
-    def __init__(self, grid, title):
-        """
-        Initialize an instance of MapPlots.
-
-        Plotting of maps is powered by PyNGL.
-
-        :type grid: :class:`.Grid`
-        :arg grid: The Grid object describing the map data to be plotted.
-        :arg str title: The title describing the map data.
-        """
-        self.grid = grid
-        """(Grid) - The Grid object describing the map data to be plotted."""
-        self.title = title
-        """(string) - The title describing the map data."""
-
-        #  Initialize list to store data sets and titles
-        self.map_data = []
-        """(list) - The list storing map data and titles."""
-        #  Also for multiple maps
-        self.map_mult_data = []
-        """(list) - The list storing map data and titles for multiple maps."""
-
-        #
-        #  Adjust PyNGL settings, fine tuning can be done externally
-        #
-
-        #  Set PyNGL resources
-        resources = Ngl.Resources()
-
-        #  Define grid
-        resources.sfXArray = self.grid.lon_sequence()
-        resources.sfYArray = self.grid.lat_sequence()
-
-        #  Change the map projection
-        resources.mpProjection = "Robinson"
-
-        #  Rotate the projection
-        #  Center in the middle of lonMin and lonMax
-        # resources.mpRelativeCenterLon = "true"
-
-        #  Set plot limits
-        resources.mpLimitMode = "LatLon"
-        resources.mpMinLonF = self.grid.boundaries()["lon_min"]
-        resources.mpMaxLonF = self.grid.boundaries()["lon_max"]
-        resources.mpMinLatF = self.grid.boundaries()["lat_min"]
-        resources.mpMaxLatF = self.grid.boundaries()["lat_max"]
-
-        #  Change the color map
-        resources.wkColorMap = "wh-bl-gr-ye-re"
-
-        #  Change thickness of geophysical lines
-        resources.mpGeophysicalLineThicknessF = 2.0
-
-        #  Configure the legend
-        resources.lbAutoManage = False
-        resources.lbOrientation = "Horizontal"
-        resources.lbLabelFont = "Helvetica"
-        resources.lbLabelFontHeightF = 0.0075
-        resources.lbTitleFontHeightF = 0.01
-
-        #  Larger font for regional networks
-        # resources.lbLabelFontHeightF = 0.014
-        # resources.lbTitleFontHeightF = 0.02
-
-        #  Configure the contour plots
-        resources.cnFillOn = True
-        resources.cnLinesOn = False
-        resources.cnLineLabelsOn = False
-        resources.cnInfoLabelOn = False
-        resources.cnMaxLevelCount = 22
-
-        resources.cnFillMode = "RasterFill"
-
-        #  Make resources object accessible from outside
-        self.resources = resources
-        """The PyNGL resources allow fine tuning of plotting options."""
-
-    def add_dataset(self, title, data):
-        """
-        Add a map data set for plotting.
-
-        Data sets are stored as dictionaries in the :attr:`map_data` list.
-
-        :arg str title: The string describing the data set.
-        :type data: 1D array [index]
-        :arg data: The numpy array containing the map to be drawn
-        """
-        self.map_data.append({"title": title, "data": data})
-
-    def generate_map_plots(self, file_name, title_on=True, labels_on=True):
-        """
-        Generate and save map plots.
-
-        Store the plots in the file indicated by ``file_name`` in the current
-        directory.
-
-        Map plots are stored in a PDF file, with each map occupying its own
-        page.
-
-        :arg str file_name: The name for the PDF file containing map plots.
-        :arg bool title_on: Determines, whether main title is plotted.
-        :arg bool labels_on: Determines whether individual map titles are
-            plotted.
-        """
-        #  Set resources
-        resources = self.resources
-
-        #  Set plot title
-        if title_on:
-            resources.tiMainString = self.title
-
-        #  Open a workstation, display in X11 window
-        #  Alternatively wks_type = "ps", wks_type = "pdf" or wks_type = "x11"
-        wks_type = "pdf"
-        wks = Ngl.open_wks(wks_type, file_name, resources)
-
-        #
-        #  Generate map plots
-        #
-        for dataset in self.map_data:
-            #  Set title
-            if labels_on:
-                resources.lbTitleString = dataset["title"]
-
-            #  Generate map plot
-            cmap = Ngl.contour_map(wks, dataset["data"], resources)
-
-        #  Clean up
-        del cmap
-        del resources
-
-        Ngl.end()
-
-    #  FIXME: Clean this up (Jakob)
-    def add_multiple_datasets(self, map_number, title, data):
-        """
-        Add a map-dataset consisting of a title and the dataset itself
-        to the :attr:`map_data` list of dictionaries (pure dictionaries have no
-        order) and reshapes data array for plotting.
-
-        INPUT: title a string describing the dataset
-               data a numpy array containing the map to be drawn
-        """
-        if map_number > len(self.map_mult_data) - 1:
-            self.map_mult_data.append([])
-        self.map_mult_data[map_number].append({"title": title, "data": data})
-
-    #  FIXME: Clean this up (Jakob)
-    def generate_multiple_map_plots(self, map_names, map_scales, title_on=True,
-                                    labels_on=True):
-        """
-        Generate map plots from the datasets stored in the :attr:`map_data`
-        list of dictionaries. Stores the plots in the file indicated by
-        filename in the current directory.
-        """
-        for k, map_data in enumerate(self.map_mult_data):
-            #  Set resources
-            resources = self.resources
-
-            #  Set plot title
-            if title_on:
-                resources.tiMainString = self.title
-
-            #  Open a workstation for every map, only wks_type = "ps" allows
-            #  multiple workstations
-
-            # Define own levels
-            resources.cnLevelSelectionMode = "ExplicitLevels"
-            resources.cnLevels = map_scales[k]
-
-            wks_type = "pdf"
-            wks = Ngl.open_wks(wks_type, map_names[k], resources)
-
-            #
-            #  Generate map plots
-            #
-
-            for dataset in map_data:
-                #  Set title
-                if labels_on:
-                    resources.lbTitleString = dataset["title"]
-
-                #  Reshape for visualization on the sphere
-                dataset["data"].shape = (self.grid.grid_size()["lat"],
-                                         self.grid.grid_size()["lon"])
-
-                #  Generate map plot
-                cmap = Ngl.contour_map(wks, dataset["data"], resources)
-
-            # Clear map
-            del cmap
-            Ngl.destroy(wks)
-
-        #  Clean up
-        del resources
-
-        Ngl.end()
-
-    #  FIXME: Clean this up (Jakob)
-    def add_multiple_datasets_npy(self, map_number, title, data):
-        """
-        Method for very large data sets (RAM issues) and useful for PARALLEL
-        code. Data is copied to npy files (titles still in the list) that
-        can be loaded afterwards.
-
-        INPUT: title a string describing the data set
-               data a Numpy array containing the map to be drawn
-        """
-        if map_number > len(self.map_mult_data) - 1:
-            self.map_mult_data.append([])
-        self.map_mult_data[map_number].append(title)
-
-        np.save(str(map_number) + "_" + title, data)
-
-    #  FIXME: Clean this up (Jakob)
-    def generate_multiple_map_plots_npy(self, map_names, map_scales,
-                                        title_on=True, labels_on=True):
-        """
-        Method for very large datasets (RAM issues) and useful for PARALLEL
-        code. Generates map plots from the datasets stored in the npy files
-        and the list of titles. The data is sorted as parallel computation
-        mixes it up. Stores the plots in the file indicated by filename in the
-        current directory.
-        """
-        #  Set resources
-        resources = self.resources
-
-        #  Set plot title
-        if title_on:
-            resources.tiMainString = self.title
-
-        for k, map_data in enumerate(self.map_mult_data):
-            #  Open a workstation for every map, only wks_type = "ps" allows
-            #  multiple workstation
-
-            #  Sort dataset, as parallel code will mix it
-            map_data.sort()
-
-            # Define own levels
-            resources.cnLevelSelectionMode = "ExplicitLevels"
-            resources.cnLevels = map_scales[k]
-
-            wks_type = "pdf"
-            wks = Ngl.open_wks(wks_type, map_names[k], resources)
-
-            #
-            #  Generate map plots
-            #
-            for ititle in map_data:
-                #  Set title
-                if labels_on:
-                    resources.lbTitleString = ititle
-
-                data = np.load(str(k) + "_" + ititle + ".npy")
-                #  Reshape for visualization on the sphere
-                data.shape = (self.grid.grid_size()["lat"],
-                              self.grid.grid_size()["lon"])
-
-                #  Generate map plot
-                cmap = Ngl.contour_map(wks, data, resources)
-
-            # Clear map
-            del cmap
-            Ngl.destroy(wks)
-
-        #  Clean up
-        for file_name in glob.glob('*.npy'):
-            os.remove(file_name)
-        del resources
-
-        Ngl.end()
-
-    #  FIXME: Possibly bogus method?
-    def save_ps_map(self, title, data, labels_on=True):
-        """
-        Directly create a PS file of data with filename=title.
-        Assumes normalized data between 0 and 1.
-
-        INPUT: title a string describing the dataset data a numpy array
-               containing the map to be drawn
-        """
-        #  Change the levels of contouring
-        resources = self.resources
-        resources.cnLevelSelectionMode = "ExplicitLevels"  # Define own levels.
-        resources.cnLevels = np.arange(0., 1., 0.05)
-
-        wks_type = "ps"
-        wks = Ngl.open_wks(wks_type, title, resources)
-
-        if labels_on:
-            resources.lbTitleString = title
-
-        #  Reshape for visualization on the sphere
-        data.shape = (self.grid.grid_size()["lat"],
-                      self.grid.grid_size()["lon"])
-
-        #  Generate map plot
-        cmap = Ngl.contour_map(wks, data, resources)
-
-        # Clear map
-        del cmap
-        Ngl.destroy(wks)
-
-        #  Clean up
-        del resources
-
-        Ngl.end()
diff --git a/src/pyunicorn/core/__init__.py b/src/pyunicorn/core/__init__.py
index c95cf801..e9c16302 100644
--- a/src/pyunicorn/core/__init__.py
+++ b/src/pyunicorn/core/__init__.py
@@ -22,16 +22,6 @@
 Related Publications
 ~~~~~~~~~~~~~~~~~~~~
 [Donges2011a]_, [Heitzig2012]_, [Donges2012]_
-
-To do
-~~~~~
-  - A lot - See current product backlog.
-  - Clean up MapPlots class -> Alex!?
-
-Known Bugs
-~~~~~~~~~~
-  - ...
-
 """
 
 #
diff --git a/src/pyunicorn/eventseries/__init__.py b/src/pyunicorn/eventseries/__init__.py
index e1e857b3..852fd1bf 100644
--- a/src/pyunicorn/eventseries/__init__.py
+++ b/src/pyunicorn/eventseries/__init__.py
@@ -27,11 +27,6 @@
 To do
 ~~~~~
   - Combine precursor and trigger coincidence rate to obtain one ECA measure
-
-Known Bugs
-~~~~~~~~~~
-  - ...
-
 """
 
 from .event_series import EventSeries
diff --git a/src/pyunicorn/funcnet/__init__.py b/src/pyunicorn/funcnet/__init__.py
index 857b20e2..e7e69dd8 100644
--- a/src/pyunicorn/funcnet/__init__.py
+++ b/src/pyunicorn/funcnet/__init__.py
@@ -16,18 +16,9 @@
 funcnet
 =======
 
-
 Related Publications
 ~~~~~~~~~~~~~~~~~~~~
 
-To do
-~~~~~
-  - ...
-
-Known Bugs
-~~~~~~~~~~
-  - ...
-
 """
 
 from .coupling_analysis import CouplingAnalysis
diff --git a/src/pyunicorn/utils/__init__.py b/src/pyunicorn/utils/__init__.py
index c19a007a..9f22ed4d 100644
--- a/src/pyunicorn/utils/__init__.py
+++ b/src/pyunicorn/utils/__init__.py
@@ -16,15 +16,6 @@
 utils
 =====
 
-
-To do
-~~~~~
-  - ...
-
-Known Bugs
-~~~~~~~~~~
-  - ...
-
 """
 
 __all__ = ['mpi', 'navigator']
diff --git a/tests/test_climate/test_map_plot.py b/tests/test_climate/test_map_plot.py
new file mode 100644
index 00000000..cd6f5577
--- /dev/null
+++ b/tests/test_climate/test_map_plot.py
@@ -0,0 +1,54 @@
+# This file is part of pyunicorn.
+# Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors
+# URL: <http://www.pik-potsdam.de/members/donges/software>
+# License: BSD (3-clause)
+#
+# Please acknowledge and cite the use of this software and its authors
+# when results are used in publications or published elsewhere.
+#
+# You can use the following reference:
+# J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,
+# L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra,
+# and J. Kurths, "Unified functional network and nonlinear time series analysis
+# for complex systems science: The pyunicorn package"
+
+import matplotlib.pyplot as plt
+
+from pyunicorn.climate.climate_data import ClimateData
+from pyunicorn.climate.tsonis import TsonisClimateNetwork
+from pyunicorn.climate.map_plot import MapPlot
+
+
+# pylint: disable=too-few-public-methods
+class TestMapPlot:
+    """
+    Simple tests for the `MapPlot` class.
+    """
+
+    @staticmethod
+    def test_plot():
+        """
+        Check error-free execution.
+        """
+        # prepare ClimateNetwork fixture
+        # (select subset of data to speed up loading and calculation)
+        title = "ncep_ncar_reanalysis"
+        file = 'notebooks/air.mon.mean.nc'
+        window = {
+            "time_min": 0., "time_max": 0.,
+            "lat_min": 30, "lon_min": 0,
+            "lat_max": 50, "lon_max": 30}
+        data = ClimateData.Load(
+            file_name=file, observable_name="air",
+            file_type="NetCDF", window=window, time_cycle=12)
+        net = TsonisClimateNetwork(data, threshold=.05, winter_only=False)
+
+        # create MapPlot
+        map_plot = MapPlot(data.grid, title)
+        assert map_plot.title == title
+
+        # plot with suppressed display
+        plt.ioff()
+        map_plot.plot(net.degree(), "Degree")
+        assert plt.gca().get_title() == title
+        plt.close()