.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/07_advanced/plot_bids_analysis.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_07_advanced_plot_bids_analysis.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_07_advanced_plot_bids_analysis.py:


BIDS dataset first and second level analysis
============================================

Full step-by-step example of fitting a :term:`GLM`
to perform a first and second level
analysis in a :term:`BIDS` dataset and visualizing the results.
Details about the :term:`BIDS` standard can be consulted at
`https://bids.neuroimaging.io/ <https://bids.neuroimaging.io/>`_.

More specifically:

1. Download an :term:`fMRI` :term:`BIDS` dataset
   with two language conditions to contrast.
2. Extract first level model objects automatically
   from the :term:`BIDS` dataset.
3. Fit a second level model on the fitted first level models.
   Notice that in this case the preprocessed :term:`bold<BOLD>`
   images were already normalized to the same :term:`MNI` space.

.. GENERATED FROM PYTHON SOURCE LINES 21-24

.. code-block:: Python


    from nilearn import plotting








.. GENERATED FROM PYTHON SOURCE LINES 25-33

Fetch example :term:`BIDS` dataset
----------------------------------
We download a simplified :term:`BIDS` dataset made available for illustrative
purposes. It contains only the necessary
information to run a statistical analysis using Nilearn. The raw data
subject folders only contain bold.json and events.tsv files, while the
derivatives folder includes the preprocessed files preproc.nii and the
confounds.tsv files.

.. GENERATED FROM PYTHON SOURCE LINES 33-37

.. code-block:: Python

    from nilearn.datasets import fetch_language_localizer_demo_dataset

    data = fetch_language_localizer_demo_dataset(legacy_output=False)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [fetch_language_localizer_demo_dataset] Dataset created in 
    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset
    [fetch_language_localizer_demo_dataset] Downloading data from 
    https://osf.io/3dj2a/download ...
    [fetch_language_localizer_demo_dataset] Downloaded 6684672 of 749503182 bytes 
    (0.9%%,  1.9min remaining)
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    [fetch_language_localizer_demo_dataset]  ...done. (35 seconds, 0 min)

    [fetch_language_localizer_demo_dataset] Extracting data from 
    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset/fMRI-language-loc
    alizer-demo-dataset.zip...
    [fetch_language_localizer_demo_dataset] .. done.





.. GENERATED FROM PYTHON SOURCE LINES 38-39

Here is the location of the dataset on disk.

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: Python

    print(data.data_dir)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset




.. GENERATED FROM PYTHON SOURCE LINES 42-53

Obtain automatically FirstLevelModel objects and fit arguments
--------------------------------------------------------------
From the dataset directory we automatically obtain
the FirstLevelModel objects
with their subject_id filled from the :term:`BIDS` dataset.
Moreover, we obtain for each model a dictionary with run_imgs,
events and confounder regressors
since in this case a confounds.tsv file is available
in the :term:`BIDS` dataset.
To get the first level models we only have to specify the dataset directory
and the task_label as specified in the file names.

.. GENERATED FROM PYTHON SOURCE LINES 53-70

.. code-block:: Python

    from nilearn.glm.first_level import first_level_from_bids

    task_label = "languagelocalizer"
    (
        models,
        models_run_imgs,
        models_events,
        models_confounds,
    ) = first_level_from_bids(
        data.data_dir,
        task_label,
        img_filters=[("desc", "preproc")],
        n_jobs=2,
        space_label="",
        smoothing_fwhm=8,
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:61: UserWarning: 'StartTime' not found in file /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset/derivatives/sub-01/func/sub-01_task-languagelocalizer_desc-preproc_bold.json.
      ) = first_level_from_bids(
    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:61: UserWarning: 'slice_time_ref' not provided and cannot be inferred from metadata.
    It will be assumed that the slice timing reference is 0.0 percent of the repetition time.
    If it is not the case it will need to be set manually in the generated list of models.
      ) = first_level_from_bids(




.. GENERATED FROM PYTHON SOURCE LINES 71-75

Quick sanity check on fit arguments
-----------------------------------
Additional checks or information extraction from pre-processed data can
be made here.

.. GENERATED FROM PYTHON SOURCE LINES 77-78

We just expect one run_img per subject.

.. GENERATED FROM PYTHON SOURCE LINES 78-82

.. code-block:: Python

    from pathlib import Path

    print([Path(run).name for run in models_run_imgs[0]])





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ['sub-01_task-languagelocalizer_desc-preproc_bold.nii.gz']




.. GENERATED FROM PYTHON SOURCE LINES 83-86

The only confounds stored are regressors obtained from motion correction. As
we can verify from the column headers of the confounds table corresponding
to the only run_img present.

.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: Python

    print(models_confounds[0][0].columns)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Index(['RotX', 'RotY', 'RotZ', 'X', 'Y', 'Z'], dtype='object')




.. GENERATED FROM PYTHON SOURCE LINES 89-92

During this acquisition the subject read blocks of sentences and
consonant strings. So these are our only two conditions in events.
We verify there are 12 blocks for each condition.

.. GENERATED FROM PYTHON SOURCE LINES 92-94

.. code-block:: Python

    print(models_events[0][0]["trial_type"].value_counts())





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    trial_type
    language    12
    string      12
    Name: count, dtype: int64




.. GENERATED FROM PYTHON SOURCE LINES 95-102

First level model estimation
----------------------------
Now we simply fit each first level model and plot for each subject the
:term:`contrast` that reveals the language network (language - string).
Notice that we can define a :term:`contrast`
using the names of the conditions specified in the events dataframe.
Sum, subtraction and scalar multiplication are allowed.

.. GENERATED FROM PYTHON SOURCE LINES 104-105

Set the threshold as the z-variate with an uncorrected p-value of 0.001.

.. GENERATED FROM PYTHON SOURCE LINES 105-109

.. code-block:: Python

    from scipy.stats import norm

    p001_unc = norm.isf(0.001)








.. GENERATED FROM PYTHON SOURCE LINES 110-111

Prepare figure for concurrent plot of individual maps.

.. GENERATED FROM PYTHON SOURCE LINES 111-141

.. code-block:: Python

    from math import ceil

    import matplotlib.pyplot as plt
    import numpy as np

    ncols = 2
    nrows = ceil(len(models) / ncols)

    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 12))
    axes = np.atleast_2d(axes)
    model_and_args = zip(models, models_run_imgs, models_events, models_confounds)
    for midx, (model, imgs, events, confounds) in enumerate(model_and_args):
        # fit the GLM
        model.fit(imgs, events, confounds)
        # compute the contrast of interest
        zmap = model.compute_contrast("language-string")
        plotting.plot_glass_brain(
            zmap,
            threshold=p001_unc,
            title=f"sub-{model.subject_label}",
            axes=axes[int(midx / ncols), int(midx % ncols)],
            plot_abs=False,
            colorbar=True,
            display_mode="x",
            vmin=-12,
            vmax=12,
        )
    fig.suptitle("subjects z_map language network (unc p<0.001)")
    plotting.show()




.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_001.png
   :alt: subjects z_map language network (unc p<0.001)
   :srcset: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 142-148

Second level model estimation
-----------------------------
We just have to provide the list of fitted FirstLevelModel objects
to the SecondLevelModel object for estimation. We can do this because
all subjects share a similar design matrix (same variables reflected in
column names).

.. GENERATED FROM PYTHON SOURCE LINES 148-152

.. code-block:: Python

    from nilearn.glm.second_level import SecondLevelModel

    second_level_input = models








.. GENERATED FROM PYTHON SOURCE LINES 153-154

Note that we apply a smoothing of 8mm.

.. GENERATED FROM PYTHON SOURCE LINES 154-157

.. code-block:: Python

    second_level_model = SecondLevelModel(smoothing_fwhm=8.0, n_jobs=2)
    second_level_model = second_level_model.fit(second_level_input)








.. GENERATED FROM PYTHON SOURCE LINES 158-162

Computing contrasts at the second level is as simple as at the first level.
Since we are not providing confounders we are performing a one-sample test
at the second level with the images determined by the specified first level
contrast.

.. GENERATED FROM PYTHON SOURCE LINES 162-166

.. code-block:: Python

    zmap = second_level_model.compute_contrast(
        first_level_contrast="language-string"
    )








.. GENERATED FROM PYTHON SOURCE LINES 167-169

The group level :term:`contrast` reveals a left lateralized fronto-temporal
language network.

.. GENERATED FROM PYTHON SOURCE LINES 169-179

.. code-block:: Python

    plotting.plot_glass_brain(
        zmap,
        threshold=p001_unc,
        title="Group language network (unc p<0.001)",
        plot_abs=False,
        display_mode="x",
        figure=plt.figure(figsize=(5, 4)),
    )
    plotting.show()




.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_002.png
   :alt: plot bids analysis
   :srcset: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 180-181

Generate and save the GLM report at the group level.

.. GENERATED FROM PYTHON SOURCE LINES 181-188

.. code-block:: Python

    report_slm = second_level_model.generate_report(
        contrasts="intercept",
        first_level_contrast="language-string",
        threshold=p001_unc,
        display_mode="x",
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/reporting/utils.py:31: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.
      fig.savefig(




.. GENERATED FROM PYTHON SOURCE LINES 189-190

View the GLM report at the group level.

.. GENERATED FROM PYTHON SOURCE LINES 190-192

.. code-block:: Python

    report_slm






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    <section class="nilearn-report pt-0 border border-1 rounded-2 scroll-y">
      <h1 class="withtooltip m-0 pl-3 pt-3 rounded-top-2 fs-5">
        Statistical Report - Second Level Model
        <span class="tooltiptext m-1 p-1 fw-200 fs-3 border border-1 rounded-2"
          >Implement the :term:`General Linear Model&lt;GLM&gt;` for multiple     subject :term:`fMRI` data.

        </span
        >
      </h1>
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              <a href="#model-details-f382b05435f74da28d858d24b8993502" class="pure-menu-link"
                >Model details</a
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                >Design Matrix</a
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    <div class="report">
      <section>
        <h2>Description</h2>
        <p>
      Data were analyzed using <a href="https://nilearn.github.io">Nilearn</a>
      (version= 0.12.0; RRID:SCR_001362).
    </p>
      <p>
        At the group level, a mass univariate analysis was performed with a linear
        regression at each voxel of the brain.
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        The following contrasts were computed
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          <li>intercept</li>
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          <th>Value</th>
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          <td>8.0</td>
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          <section id="design-matrix-f382b05435f74da28d858d24b8993502-0">
            <h3>Design Matrix</h3>
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"
                title="Plot of design matrix for 0." alt="Plot of design matrix for 0."
                style="min-width: 600px" />
            </div>

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          </section>

          <section id="contrasts-f382b05435f74da28d858d24b8993502-0">
            <h3>Contrasts</h3>

            <div class="d-flex flex-column">
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              <div class="scroll-y">
                <img class="pure-img" src="data:image/png;base64,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"
                  title="Plot of the contrast intercept (run 0)."
                  alt="Plot of the contrast intercept (run 0)." style="min-width: 600px" />
              </div>
              <!--  -->
            </div>

          </section>

        </div>


      </div>

      <section id="mask-f382b05435f74da28d858d24b8993502">
        <h2>Mask</h2>
        <div class="scroll-y">
          <img
            class="pure-img"
            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            alt="Mask image"
            title="Mask image"
            style="min-width: 600px"
          />
        </div>
        <div class="pure-u-1 pure-u-md-3-3">
          <p>
            The mask includes 23640 voxels (23.1 %) of the image.
          </p>
        </div>
      </section>

      <section id="statistical-maps-f382b05435f74da28d858d24b8993502">
        <h2>Statistical Maps</h2>
        <div class="pure-g">
          <!--  -->
          <!--  -->
          <!--  -->

          <div class="pure-u-1">
            <h3>intercept</h3>

            <div class="d-flex flex-column">
                <div class="scroll-y">
                  <img
                    class="pure-img"
                    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bd9+eWX0bFjx8RLYgDo3bs3atWqtd2joAghhBBCiEL0olgIIUSpsGnTpthf6AXfqaeeirFjx+KKK67Aiy++iKOPPhpnnnlm2hcUALBgwQIcffTRqFq1Kp566ikMHToUv/nNb/DDDz/E6i5cuBCXXnophg4diqeeegrr1q3DSSedhI0bNwIo9DYBgEsuuSQRyrdLly5p933yySfjzjvvxODBg/HSSy9h1KhRKQ+UX3jhBfTv3x8dOnTAc889h6uuugr3339/zLMOKHyg2rZtW0yZMgXHHXccrr76arzyyisACkMO+zDD119/fWLbH3/8EZdeeikuu+wyvPLKK+jUqVOx9m1hHlwyZcoUDBgwAPXr10/su0GDBgAKH5gPGDAAAwcOxOTJk9G6dWucdNJJsQdPH3zwAUaNGoWbbroJ06ZNQ5MmTTL2wTJx4kSce+65OPXUU/Hcc89hy5YtOP300/HMM8/gV7/6VeKlDx/42ZckX3zxBdq1axfL3di+fXt88cUXafc5Z84cbNy4EXvuuWdsO7bL/3vssQdq1qxZrPZ3FC+88AJeeOEFjB49GnfffTfeeustXHLJJbF6559/Plq1apUIiblhwwb07t0bL774Im655Ra88MIL6NChA/r27Zu4tqtWrULfvn1RrVo1PP7443juuedw/vnnp7z4BApDxI4aNQp/+tOfMHHiRHzzzTcpY3Dz5s045phjMHXqVNx22214+umnUaVKFfTu3Tv2oNy3e/LJJ6NNmzaYPHkyBg4ciFNPPTWrh9Wvv/46unTpgooVK6aUX3LJJbjqqqvwq1/9ClOnTsXo0aNRtWpVrF69GgDw4Ycfonfv3qhatSqefvpp3HbbbZg6dSqOOeaY2EvWYcOGoaCgAE8//TROOeUUXHHFFbj22mtx9913489//jNGjx6NN954I+F1HroekydPxplnnolhw4YlPNuzZcqUKahfvz769++fkNu+ffsCAO68804MGTIEffr0wdSpU3H11Vfjr3/9K4YNG5bYfuHChejTpw/y8/OL1KsHH3wwlixZgo8++ihRxlyxIUObzZs3Y926dfj3v/+Nm266Cccff3zsRV9RrFixAv/5z38SMgkUeveGIink5+enhKjelrz11lvIy8tDr169tkv7RcnZmDFj0LlzZxxyyCGJ6zxkyBAAhQYRxx9/PPbZZx9MmTIFf/7znzF58mSce+65sf14HRDi73//O84991wce+yxmDZtGh5++GEceeSRMZknXbp0SQmLP2vWLJxwwgkoKChIzAHF6SPp27cv/vCHPwAAXnnlFcyaNQtTpkxJqfPrX/8aRx11FJ577jl07twZgwYNio2B0LyUTX9GjhyJ+++/PxFm/N5770XDhg1juifTnA4Ub31gGTx4MKZMmYI//elPmDRpEj777LMivfmBwtzc3333HQ455JBE2eGHH46GDRviiSeeSKn7xhtvYNGiRYlQsEXpvmrVqmHChAmYPHlyIlLDyy+/jLFjx2LMmDGJaAElWeNdfvnlePHFF3Hrrbdi+vTpGDVqVEzO27dvj5o1a+K1115LlG3LNUFRnH766Xj66acT69rHH38chx12GBo1apTV9kWtTdMxe/Zs3H333bj99ttx//334+2338Z5552H/v3741e/+hUmT56MPfbYA/369UvMY8XlN7/5Dfbee29MmTIFBx10EM466yxcffXVmD59Ou6//37cdNNNePjhh4sMF13SOfvss8/G//3f/+Gvf/0rXnnlFdx4442J+XbIkCE499xzUVBQEEu/8cMPP6Bdu3YYM2YMpk2bhvPPPx833XQT/vjHP8b2MWzYMOTm5uKpp57ChRdeiDvuuAPjxo1L/P7111+jW7du+Prrr/HXv/4VL7zwAs455xx8//33iTq/+93vcMMNN2DgwIGJMMxXX311kXP3jBkzUK5cuaAX7dZc3+effx7Tp0/H2LFjMXLkSDz22GO45JJLcNZZZ2HIkCF4+umn8csvv+CUU06J3Y8dfPDBKbIEFL64tvM0PfnvvfdevP/++1i9ejVef/11PPLII0Xm4P7iiy9i6+ucnBzsueeeO8XaWQghhBCiTBAJIYQQO5Dhw4dHANL+Va5cOVH3jTfeiABE06dPT2ljwIAB0QEHHJD4PnDgwGivvfZKfB86dGhUvXr1aNWqVYmyt99+OwIQDRw4MGW7nJyc6D//+U+i7M0334wARG+//XaiDEA0atSoIo/t1VdfjQBEjz32WNo6nTt3jrp165ZSNnbs2AhA9H//938pfRg6dGiizpYtW6LmzZtH5557btrjtuUAovfee6/E+37//fcTdQYPHhzl5eWlbPe73/0uatasWUoZr+1DDz2UKNu0aVPUokWL6LTTTkuUde/ePSpfvnz0/fffx09QFrRt2zbq2bNn4vunn34aAYjy8/Oj559/PlH+3XffRXl5edGNN96YKOvVq1fUp0+fWJu//e1vozZt2qTd5zvvvBMBiGbNmpVSvnjx4ghA9Le//S2KoigaMmRI1K5du9j2o0aNisqXLx9sO911LAmZ2mrWrFnUuHHjaN26dYmy4cOHR+XLl482b94cRVHy+l944YUp244fPz4qV65c9Omnn6aUH3jggdEpp5wSRVEUvf/++yljKUT37t2jypUrRz/99FOibMKECRGAaN68eVEURdHzzz8fAYheeeWVRJ0NGzZETZs2jfr165fSVt++fRPfBwwYELVo0SLatGlTouyhhx6KAETDhw9P26coKhxTv/3tb1PKvvzyyygnJye69dZb02530kknRU2bNo02bNiQKJs+fXoEIHrhhReiKIqib7/9NgIQnXrqqYk6mzZtiurVqxdVrlw5WrJkSaL8iiuuiGrUqJH4zutx1llnpez3rLPOiho1apS4bqHrvnz58ghANGHChERZs2bNYse5atWqqEqVKtG1116bUn7fffdFBQUFif5dffXVUdWqVaMVK1Yk6syYMSOmV6MoijZu3Bjl5eVF9957b6Js0qRJUV5eXjRz5szI06hRo8QccPTRR0erV6+O1SmK8847L6pcuXJiHEVR4fmsVatWtHbt2kTZ3Llzo5ycnKht27bBdrZWHo8++uiofv36WdX1+xo+fHjKHEiqV6+eGMPZypmVjSgqnEOaNWsWnX766SnlL7/8cso8mE4HhBg1alRUq1atjHUyzZ9PPfVUlJOTEz366KPF6mMI6pHFixcHy0ePHp0oW716dVSpUqXoj3/8Y6IsNC9l25++ffum6CZPtnN6SeboTz/9NMrJyQnOu0Xd6j/22GPBc3bZZZdFjRs3jrZs2ZIoO+ecc1LGaja6L4qi6NJLL42qV68ezZ49O2rQoEHKuSzpGm+vvfaKLr/88ozHFkWF17R///6J79tyTZAO6tiVK1dGFStWjF5//fVoy5YtUZMmTaKxY8dGs2fPjgBEb775ZmwbUtK1KedXez2vuOKKCEB03333Jco++eSTCED03HPPpW0riqLoL3/5S8oYYh+uuuqqRNmKFSuivLy8qEmTJilj4eSTT446deqU8VxlO2d7fVa5cuXor3/9a9p20+lRy5YtW6KNGzdGt9xyS9SgQYNEOedrrmtsH4488sjE9zPOOCOqW7dutHLlymD733zzTZSTkxONHTs2pfzqq6+O6tevn5i7Q5x//vnBOWhrri/Xf+vXr0+UnXzyyRGA6OWXX06UTZ06NQIQffTRRyn7njBhQpSTk5NyX9WzZ8+oVatWKfU2bdoUDRkyJOW+zq8tQrRu3Tq64IILYuV9+/aNevfuXeT2QgghhBDbg9Dz69DzPvLAAw9Ehx56aFSjRo2oRo0a0ZFHHhn985//3IE93jrkUSyEEGKHU1BQgPfffz/2d95556XUe/XVV1GrVi307NkzxfO4d+/emD17dsxjj7z//vs44ogjULVq1UTZoYceilq1asXqNmzYEHvttVfiO3OhzZ8/v9jHNWPGDFSqVCkYWhAozIP60UcfxcLJDRgwAEBhuGwL8+UBhZb17du3z7pftWvXxoEHHljifVseeuihYoWQZd5PoNDD4MQTT8Q///nPlDodO3YslhcxWbVqFb766isce+yxibIOHTqgbt26yMvLSwkf2axZM2zatAk33HBDsfezu9K9e/cUz6sOHTpg48aN+Omnn1Lq0dOUvPrqq9hnn33Qtm3bmCwyVGSrVq1QrVo1/OY3v8FTTz2VEtLa0qlTJ9StWzelD0BS5t5++21Uq1YNffr0SdQpX748+vXrl3Gc/vOf/8Rxxx2HvLy8RJkf7+lYsGBBSp+AQg+6KIoyejK+/fbbOOGEE1LymB511FGoUaNGrK92bObl5aFly5bo1KlTItQyALRt2xYrVqyIeQJZmeJx/fDDDyXSU553330Xq1evximnnJJybXv16oVffvkF//nPfwAUnt8jjjgiJaxkz549g3q1XLlyqFGjBhYsWJAoO/vss7Fp0yZ07949Vn/atGl499138eCDD+Lzzz/Hcccdl1a/h5gwYQIefPBBjB49Go0bN06Un3feeVi1ahUuuOAC/Pjjj/jmm28waNAg5ObmxjwItyXbs+1s5czz1VdfYe7cuTj11FNTrnP37t2Rm5sbCzXsdUCILl26YNmyZRg0aBBee+01rF27Nuvj+PjjjzFo0CBcdtlliVz3xe1jcbDzaeXKldGsWbOY/Ph5Kdv+dOnSBdOmTcOIESPw/vvvp01/kGlOL+kc/f777yOKouC8WxQLFixAbm5uig4CCr1h58+fn9jnhg0bMGXKFJx++umJOtnqvj/96U9o0KABDjroIOTm5mL06NGJ30q6xuvSpQsmTpyI22+/PaGfQtSpUydFB+3INUG1atXQt29fPP744/jHP/6BhQsXZj0fASVfm3bq1Al16tRJfG/bti0ApEQ4YNm8efOy7o/FzmXVq1fHHnvsgcMPPzxlLLRt27bI9ks6Z3fp0gW333477rvvPnzzzTdZ93vdunUYPnw4Wrdujfz8fJQvXx7Dhg3DggULYnOulVWg8Pzbcz9jxoxEaP4Qr7/+OoDCKEN+Xl24cGHGcxNaj5Ctub7du3dHhQoVUurl5uaiZ8+eRW5bp04dRFGERYsWJcpmzJgRO//XXHMNXnrpJYwbNw5vvfUWbrvtNtx9990YNWpU2uMVQgghhNiZ2WuvvbBgwYLEX6ZnUjNnzsTpp5+ON998E7NmzUKTJk1w1FFHBaOw7YzoRbEQQogdTm5uLvbff//YH0MRkiVLlmDZsmUoX758yt+QIUOwadOmlAeAlnQPWfbYY49YWY0aNVK+8yFKcfP2AsDSpUvRoEGDtC8JVqxYgSiKUvJlAoUP2vLz81PymqbrW7b98vso7r5LSvny5WNhl+vVqxe7Vr4f2cKHeQx1Tfbee2/07t0bBQUFGbevWbNmLG8kUJg7MvTCy24HIJhzEkBi25K2v6PIdrz767NkyRLMnj07Jos333xz4oEiw3xWrVoVZ511FurXr48ePXrEwo4X1Yfly5cHZbVevXoZx+mCBQti21WrVi0WTjpEKETx0qVLUa5cuWBfyPLly4NjOdTX0HFnez18H7jPdDqwOCxZsgRA4cN3e23btGkDIPnAOHR+Q30j+fn5+OWXX7LqQ8eOHdGtWzcMGTIEzz//PN58881Y6OB0vPzyyzj//PNx/fXXY+DAgSm/tWvXDg899BCmTp2KRo0aoU2bNqhZsyaOPfbYmA7ZVjRq1AiLFy8u0RySDdnKmYfX+aSTTkq5zpUqVcLmzZtjLway0dE9e/bEI488gk8//RR9+vRBnTp1cPbZZxc5nyxevBgnnHACDj744JQ8vcXtY3HIZj4N6b1s+jNs2DBcffXVmDRpErp27Yr69evjxhtvjOXlzNSHks7RCxYsSDvvFsW6detQvnz52JrlgAMOQKtWrRL5Vl9++WWsWLEi5UVxtrqvoKAAJ554ItavX48zzjgjpZ8lXePdc889OOuss3DHHXdgn332QdOmTXHffffF6mWjg7bnnH366adj8uTJmDhxIvr06VOs9kq6Nk23nS3fmnVuun2UZL1a0jn7ySefxJFHHolhw4ahTZs22HPPPTF58uQi+3311Vdj1KhROO+88zBt2jS8//77iZD1vq9FHc/SpUtj9yyWJUuWIIoi1KlTJ2Vs8yV7Jl2WLmVCun758nTXN7RtQUFBysvjdNuyP5nk6T//+Q9uv/12jB07Fueeey4OP/xwXHXVVbjuuutw/fXX4+eff0677c6+dhZCCCFE2aVcuXKoX79+4s8a7Xn+9re/4aKLLkKnTp2w5557Yty4cdiyZQtmzJixA3tccsqVdgeEEEKIdNSqVQt169bFtGnTgr+ne0HRoEGDoJeV95rc1tSuXRsLFixAFEXBl8U1atRATk5OrB8rV67E+vXrt+nDEL//HbXvjRs3Yvny5SkPgxctWhR7KVNSj7u6desiNzcXq1atSin//vvvU/IspmPPPffE66+/HrtGX3zxBfbZZ5+027Vq1Qrly5fHF198keLpytxpzK225557YtGiRbFzEMq/tjPjr0+tWrXQsWNHPPTQQxm369q1K15++WX88ssvePPNN3HllVfixBNPxJw5c7Led61atYKyumjRoozjtEGDBrHtVq1aldXD8Fq1asXyqtauXRubNm3CTz/9lFbXlLSvxcXvg149lKuKFStiw4YNKXVoxFAU7OfkyZODXv4tWrRI7Ct0rOn06ooVK2KeitnQsWNHlC9fPitPsffeew/9+/fHwIEDcdNNNwXrnH322TjttNPw1VdfoWbNmmjUqBH22msvHH/88cXuWzb06NEDDz30EGbMmJGVV66lYsWKsRykGzdujHm7lUTOeJ3vvffelGgTxL/0yFZHn3nmmTjzzDOxZMkSPP/887jssstQvnz5tLpi48aN6N+/P3Jzc/Hkk0+meBMWt4/bmpDey6Y/+fn5GDFiBEaMGIFvvvkG48ePx4gRI9CyZUucddZZWe27pHN0gwYN0s67RVGrVi2sX78e69ati72cO/300zF27Fj89a9/xRNPPIEDDzwQLVu2TNk2G933f//3f7jzzjvRuXNn3HPPPTjnnHMSecRLusarXr067rrrLtx111345JNPcPfdd+Oiiy7C3nvvjcMOOyxRLxsdVNI1QTb07dsXmzZtwoQJExJ5mndW8vPzSzyHlJSSztkNGjTA+PHjMW7cOHz44Ye4+eabMWDAAHz55ZcpY9Tz9NNP44ILLsDVV1+dKHvppZdK1PfatWvjxx9/TPt7rVq1kJOTg3feeSflRSxp165dxm2/++67EvVre8H1USZ5+uyzzwAUej1bOnfujPXr12P+/PkJ2ffsueeeMWOnKIrw5ZdfpniwCyGEEELsaL7++ms0bNgQFStWRLdu3TBy5Eg0bdo0q23Xrl2LjRs3lujZ0Lp162Lr82yoUKFCVs4SIeRRLIQQYqelV69eWLx4MSpUqBD0QA49fAEKvWHeeOONFOv1t99+u8Res+XLl8/qZVOvXr2wdu1aPPXUU8Hfq1Spgk6dOuGZZ55JKWf9Qw89tFj9Ko6H8bbedyasJ+DmzZvx3HPPBR+yl4Ty5cujffv2iXDHAPDhhx9izpw5+PTTT4vc/phjjsHy5ctTLPq++uorzJ49OyWctSc/Px9HHHFE7Pw9+eSTaN++PZo3bw6gMFxhbm4unn322USd5cuX49VXX83Y/s5Or1698N///hcNGzYMyqKnoKAAxx57LH7zm9/g22+/LZbn0qGHHopVq1bh1VdfTZRt2rQJU6ZMyThOu3btiqlTp6aEK/XXKx3t2rXDt99+m1LWs2dP5OTkYMKECRn7+txzz6WEZn/ttdewYsWK7SZTQOFxNWzYMBFmuXHjxpg/f37KC0V7/khIZ3Tr1g2VKlXC/Pnzg9eWD4a7du2KN998M8Xr54033gjq1cWLF2Pt2rUZH4an45///Cc2btyY8YE/UPhQum/fvujZsyfuv//+jHUrVKiAvffeG40aNcIbb7yBr776CoMGDSp237Khf//+aNKkCa677rqgB9Unn3yS1puscePG2LBhQ8oL3zfeeCNtCN50cha6znvuuScaN26M//73v1lF9CguderUwbnnnovevXvj888/T1vvkksuwYcffojnnnsudsO8NX3cWi/JECXpT+vWrXHrrbeiVq1aGc+Dp6Rz9AEHHAAgPO8WBeXT6z6g8EXx4sWL8cILL+CFF15I8SZmf4rSfRs2bMBZZ52Frl274r333sPee++dCEEPlHyNZ9lnn33wl7/8BQBi5/u7774rUgeVdE2QDRUrVsR1112HE044ASeccMJWtbW9ady4cez8vfbaa9t1n1szZwOF0YkOOOAA3Hzzzdi0aVPCuKhChQpYv359rP4vv/ySMqY2b96MJ554okR979WrF5555pm0XrJHHnkkgELP49DYtqlxPKH1SGnz3XffoXr16qhfv37aOs2aNQMA/Pvf/04p//DDD5GTk5P4PcQxxxyDjz/+GF9//XWibMaMGVi6dOkuvXYWQgghxK7NgQceiIkTJ+KVV17Bfffdh2+//RaHHXZYxkgplquvvhoNGzZMSROSDevWrUPtgiqoXr16sf9atGhR4ntieRQLIYTYaenduzeOO+44HH300bjqqqvQsWNHrFmzBp9++im++eYbjBs3LrjdZZddhjFjxqBv374YOnQoVqxYgRtvvBF16tRBbm7xbaTat2+P559/HocddhgqV66Mdu3aBR/y9OrVC8ceeywGDx6MOXPm4MADD8SyZcvwzDPP4MknnwQAjBgxAieeeGLCC+vLL7/Eddddh5NPPrnY3ivt27fH+PHj8fjjj6NNmzaoU6dO4oVliJLu+9xzz8WkSZOyylNcoUIF3HzzzVi3bh1atGiBMWPGYN68eVk9tM6W3//+97jkkktw/PHH47DDDsM111yDAw44AB988AFmz56Nzp07AwDmzp2LVq1a4YYbbkjkJOzWrRv69OmDwYMH44477kDFihUxbNgwdOzYEf369Uvs46abbsJNN92EOXPmJB5uXX/99ejRowcuuuginHrqqXjzzTfx2GOPJa4tUPiwdciQIRg6dCjy8vLQqFEj3HrrrahevTouuOCCRL21a9cmvKjmzp2LVatWJR6Qdu/ePW1+utLi7LPPxtixY9GjRw9ceeWViVy6s2fPxoYNGzBy5Ei89NJLeOihh3DSSSehadOmWLhwIe655x4ccsghxbJo7Nu3L7p27YozzzwTf/rTn1CvXj3cc889WLBgAa677rq023EcnHjiibjooovw3//+F7fffntW+z7kkENiBh5t27bFhRdeiD/84Q9YtmwZjjzySKxduxYvvfQSRowYgUaNGmHYsGE4+OCD8atf/QqXXHIJFi1ahGuuuQZdu3bdpg8333jjDQwdOhS9e/fGa6+9hkceeQSjR49O6LN+/frhhhtuwODBg3Heeefh008/DerH9u3b44033sBrr72GmjVrokWLFqhduzZuuukmXHXVVZg/fz569OiBvLw8/Pe//8Xzzz+PZ599FpUqVcLvf/97jB49GscccwyuueYaLF++HMOHDw96GDF3q3259fDDD2Pw4MGYMWNGIk9xv379sP/++6Njx44oKCjAxx9/jFGjRqFjx44pOVa9Dvrpp5/Qp08fFBQU4LLLLkvJXVutWrVEPs81a9ZgxIgROPzww1GxYkW89957GDlyJEaMGJHyAmlbymPFihXx1FNP4eijj8b++++Pyy67DB06dMCqVaswffp0PPjgg/jnP/8Z9N4+5phjULlyZZx33nm4+uqrMX/+fNx9990pYzgbOWvfvj0mTZqEqVOnokGDBmjYsCEaNmyIO++8E2eccQbWrFmDvn37onLlypg7dy5eeukl3HrrrYn8lNkyfPhwLF26FD169MAee+yBTz75BK+88gouv/zyYP3HH38cY8eOxRVXXIG1a9fivffeS/zWoUMHVKtWrcR9pKfa6NGjceKJJ6JSpUpb7RGak5OTVX9OPPFE7LfffujcuTMqV66MqVOnYvny5Sm5P7OhJHN0hw4dcNJJJ+H3v/891q1bh+bNm2PMmDFZWZ937doV5cqVw4cffhjz9OvQoQM6duyISy65BOvWrUvkSibZ6L4bbrgBc+bMwccff4wKFSrg4YcfRpcuXXDzzTdjxIgRJV7jHXLIITjppJOw9957Iy8vDw8//DAqVKiQ4k28Zs0afPHFFxg+fHiibGvWBCXlmmuu2eo2dgT9+/fHXXfdhQMOOADt2rXDo48+ut1zqZVkzl65ciX69OmDs846C+3atcOGDRtwzz33oEaNGujSpQuAQl2wadMm3H333Tj44INRrVo1tGvXDr1798aDDz6IDh06oE6dOhgzZkzwhXI2DB8+HC+++CIOPfRQXHXVVWjQoAE+++wzrF27FldddRXatm2L3/72tzjrrLMwdOhQHHjggdi4cSO++uorvPnmmxnXxIcccghuuukmzJ8/P2EMVtp88MEHOPjgg1PuoY488kjMnTs38YKeL8EvuOACLFq0CK1bt8Y///lPjBw5EoMHD0alSpUAhOWwf//+uPXWW3HyySfj1ltvxdq1a3HllVcm1oNCCCGEEKXBMccck/jcsWNHHHjggWjWrBmeeuopnHvuuRm3/dOf/oQnnngCM2fOLLaH74YNG7AWm/FrNEKFYvj5bsAW/G3hD9iwYUPJvIojIYQQYgcyfPjwqHLlyln/tn79+ujGG2+M2rRpE1WoUCGqW7dudMQRR0QPP/xwos7AgQOjvfbaK2W7v//971GnTp2iChUqRO3bt49efPHFqHnz5tHvf//7jNstX748AhBNmDAhUfb2229HXbp0iQoKCiIA0Ztvvpn2+H755ZfommuuiZo2bRqVL18+aty4cTR48OCUOs8880zUsWPHqEKFClH9+vWj3//+99Evv/yS+P3NN9+MAETvv/9+ynYnnHBC1L1798T3lStXRqeddlpUu3btCEA0cODAtMe1NfseOHBg5JcMv/vd76JmzZqllPH6vffee9F+++0XVahQIWrXrl30/PPPp9Tr3r171Ldv3/AJzIItW7ZEN954Y9SwYcOofPny0YEHHhgtWrQouu6666KCgoLEtfv2228jANHw4cNTtl+xYkU0ePDgqEaNGlGVKlWifv36RT/88EPsWABE3377bUr5888/H+2zzz5RhQoVotatW0cPPfRQrH/r1q2LrrjiimiPPfaICgoKol69ekWff/55Sh32LfSXaXwVRaZr36xZs+i3v/1tStmUKVNSjjPd2IuiwvF22WWXJcZ2gwYNomOPPTZ68cUXoyiKoi+++CI6+eSToyZNmkT5+flRw4YNo0GDBkULFixItBG69rNnz44d95IlS6JBgwZFtWrVivLz86Nu3bpFM2fOTNku1NbkyZOjtm3bRvn5+dF+++0Xvffee1H16tVjY8Dz4YcfRgCir776KqV88+bN0Z///OeoTZs2Ufny5aP69etHAwYMiFauXJmoM3PmzKhbt25Rfn5+VKtWrWjQoEHR0qVLE7/zWj/99NNF9n/ChAkRgGjx4sVRFCWvx4svvhgdf/zxUaVKlaJ69epFf/zjH2PH8PDDD0etW7eOCgoKot69e0cfffRRTJf95z//iQ477LCoatWqsd8ef/zx6IADDogKCgqiatWqRZ07d46uv/76aOPGjYk6Ib267777JnQPueSSS6LDDjsseGz2Oo8cOTLq1KlTVLVq1ahy5crRXnvtFV1//fUp5zeK4jqI5yX0Z3Xk2rVroz59+kS1a9eO8vPzo3333TflmMn2kMf//ve/0XnnnRc1adIkKl++fFSzZs2oT58+0eTJk1OOy8vrK6+8Eu21115RxYoVo4MOOiiaPXt2yhjORs7mz58fHXvssVGNGjViOvDVV1+NunfvHlWuXDlxzq+44opoxYoVKec2pAM8U6dOjY488siobt26UX5+ftSqVato+PDhKWMGQDRq1KgoipJ6tajzXFQf0zFixIiocePGUW5ubmJ+8jJF/LjNNC8V1Z8///nP0f777x9Vr149qly5ctSlS5foscceS2yf7ZweRSWbo5cvXx79+te/jipXrhzVrl07uvzyy6NRo0bF5u0Qxx13XHTGGWcEfxs5cmQEIDryyCODv2fSff/4xz+i3Nzc6P7770/Z5i9/+UtUrly5RP9LssYbOnRotM8++0RVqlSJqlWrFh1yyCHR9OnTU/bz7LPPRpUrV45WrVqVKNuaNUG2hOZZS2i+89tkuza1shVF2c0p6bZdvXp1dM4550S1atWK6tSpEw0bNiy64447gnrXj+PQMWda61uymbPtca1bty4aMmRI1K5du6igoCCqVatWdNRRR0X/+te/EvU3btwYXXTRRVG9evWinJychIwtXLgwOvHEE6OqVatG9erVi66++urowQcfTDk/6ebr0Jr3008/jY4//vioWrVqUaVKlaJOnTpFTzzxROL3LVu2RPfcc0+09957RxUqVIhq1aoVdevWLbrzzjsznpP169dHtWvXjh544IGU8q25vtleo9Dxb9iwIapVq1Zsvdu9e/fYOVmwYEE0ZMiQqFmzZlFBQUHUtm3baPjw4dHatWtj+/ByOH/+/Khfv35RlSpVoho1akSDBw+OrQWEEEIIIUqb/fffP7rmmmsy1hk1alRUvXr1rO6pQ6xcuTICEA1Bk+iinGZZ/w1BkwhAiddQOVEURcV/vSyEEELsWnz99dfYc889MX78eAwcOLC0u7NbMmLECNx+++2xfJpC7Arst99+OOGEExIeLjsDM2fOxBFHHIH3338/GOJ7Z2TTpk1o2rQp/vSnP+Hss88u7e4IITIwdepUnHHGGVi0aFHC42934JRTTkHVqlUxfvz40u6KEMXmiiuuwOzZs/HGG2+Udlfw0ksv4YwzzsAPP/yAKlWqlHZ3hBBCCCFKjdWrV6Np06YYMWIELr300mCdP//5z7jlllswffp0HHTQQSXaz6pVqwqjEuY0RYWcYngUR1swNvoeK1euRLVq1Yq9X+UoFkIIsVty7bXX4vHHH8dbb72FCRMm4Nhjj0WDBg1w8sknl3bXhBA7ITfccAPuv//+EoeiFIU89thjqFKlCs4444zS7ooQogh+9atfoW3btmnDPO+KfPvtt3jppZcwbNiw0u6KECXiyiuvxD//+U98/PHHpd0V3HHHHbjiiiv0klgIIYQQZY4rr7wSb731Fr777ju8++67OOmkk5CXl4fTTz8dQGGKtmuvvTZR/7bbbsP111+P8ePHo3nz5li4cCEWLlxYYmea3Bwgrxh/uTlbd7zKUSyEEGK3ZMOGDbj66quxaNEiFBQUoEePHhg1apQedAghgpxwwgn4+uuvMW/ePLRu3bq0u7PLkpubi/Hjx6NcOd1mCLGzk5OTg/vvv3+neCG1rfjhhx/wwAMPoFWrVqXdFSFKRIMGDTBx4kQsXry4VPuxevVqdO/eHZdddlmp9kMIIYQQojSYP38+Tj/9dCxduhR169bFoYceivfeew9169YFAHz//ffIzU364d53333YsGED+vfvn9LO8OHDMWLEiGLvPy8nB3k52b/9zcPWvSlW6GkhhBBCCCGEEEIIIYQQQgghhCglGHr6d+WaIb8YoafXR1tw96a5Cj0thBBCCCGEEEIIIYQQQgghhBAiOxQTTgghhBBCCCGEEEIIIYQQQgghSpkdHXpaL4qFEEIIIYQQQgghhBBCCCGEEKKUycsp/Mu6/lbuTy+KhRBCCCGEEEIIIYQQQgghhBCilJFHsRBCCCGEEEIIIYQQQgghhBBClDFyAOQWs/7WoBfFQgghhBBCCCGEEEIIIYQQQghRysijWAghhBBCCCGEEEIIIYQQQgghyhg7OkdxcbyXhRBCCCGEEEIIIYQQQgghhBBC7AbIo1gIIYQQQgghhBBCCCGEEEIIIUqZQo/i4oSe3jr0olgIIYQQQgghhBBCCCGEEEIIIUqZHR16Wi+KhRBCCCGEEEIIIYQQQgghhBCilMnLySmmR3Ex3ioH0ItiIYQQQgghhBBCCCGEEEIIIYQoZXKL6VGcu5X704tiIYQQQgghhBBCCCGEEEIIIYQoZeRRLIQQQgghhBBCCCGEEEIIIYQQZYwdnaN4az2ShRBCCCGEEEIIIYQQQgghhBBC7GLIo1gIIYQQQgghhBBCCCGEEEIIIUqZHe1RrBfFQgghhBBCCCGEEEIIIYQQQghRyihHsRBCCCGEEEIIIYQQQgghhBBClDHyUEyP4mjr9qcXxUIIIYQQQgghhBBCCCGEEEIIUcrkFtOjOLcYdUPoRbEQQgghhBBCCCGEEEIIIYQQQpQyxc5RvHXvifWiWAghhBBCCCGEEEIIIYQQQgghSpti5yjeSo/i3K3aWgghhBBCCCGEEEIIIYQQQgghxC6HPIqFEEIIIYQQQgghhBBCCCGEEKKUUehpIYQQQgghhBBCCCGEEEIIIYQoY+zo0NN6USyEEEIIIYQQQgghhBBCCCGEEKVMbk4Ocovx8rc4dUPoRbEQQgghhBBCCCGEEEIIIYQQQpQyOXk5yMnN/uVvjl4UCyGEEEIIIYQQQgghhBBCCCHErk1uXg5yi/GiWB7FQgghhBBCCCGEEEIIIYQQQgixq5OXi5zc3Ozr50RbtTu9KBZCCCGEEEIIIYQQQgghhBBCiFImJzcHOXnFCD2NrfMoLsYraSGEEEIIIYQQQgghhBBCCCGEELsD8igWQgghhBBCCCGEEEIIIYQQQohSJjcvB7nF8CjO3UqPYr0oFkIIIYQQQgghhBBCCCGEEEKIUiYnt3g5inMi5SgWQgghhBBCCCGEEEIIIYQQQohdGnkUCyGEEEIIIYQQQgghhBBCCCFEGSMnLwc5xXhRnKMXxUIIIYQQQgghhBBCCCGEEEIIsWtT+KK4GKGnsWWr9qcXxUIIIYQQQgghhBBCCCGEEEIIUcrs6NDT2b+SFkIIIYQQQgghhBBCCCGEEEIIsVsgj2IhhBBCCCGEEEIIIYQQQgghhChlcnJykJNbjBzFW5SjWAghhBBCCCGEEEIIIYQQQgghdmly83KRW4wcxbnR1gWP1otiIYQQQgghhBBCCCGEEEIIIYQoZXLycpBTjBzFOZE8ioUQQgghhBBCCCGEEEIIIYQQYpdGL4qFEEIIIYQQQgghhBBCCCGEEKKModDTQgghhBBCCCGEEEIIIYQQQghR1iimRzG20qN4614zCyGEEEIIIYQQQgghhBBCCCHEbsB9992Hjh07olq1aqhWrRq6deuGl19+OW39yZMnY//990eNGjVQuXJldOrUCY888sgO7PHWIY9iIYQQQgghhBBCCCGEEEIIIUSZp3HjxvjTn/6ENm3aIIoiTJo0CSeccAJmz56NvfbaK1a/Vq1aGDZsGPbcc09UqFABL774Is455xzsscce6NOnT7H3n5uTg9zc7L2Ec3O2zqM4J4qiaKtaEEIIIYQQQgghhBBCCCGEEEKI3ZBatWph1KhROPfcc7Oq36VLF/Tt2xd//OMfs97HqlWrUL16dczs0wNVymfv57t64yb0mD4TK1euRLVq1bLejij0tBBCCCGEEEIIIYQQQgghhBBCGDZv3ownnngCa9asQbdu3YqsH0URZsyYgS+//BKHH354ifaZm5dT7L+tQaGnhRBCCCGEEEIIIYQQQgghhBACwCeffIJu3bph3bp1qFKlCqZMmYIOHTqkrb9y5Uo0atQI69evR15eHsaMGYPevXuXaN85eTnIKcbL35wtelEshBBCCCGEEEIIIYQQQgghhBBbTbt27fDRRx9h5cqVeOaZZzBw4EC89dZbaV8WV61aFR999BFWr16NGTNm4PLLL0fLli3Ro0ePYu87Jy8XOXnZB4TO2bJ1waOVo1gIIYQQQgghhBBCCCGEEEIIIQL06tULrVq1wtixY7OqP2TIEMybNw/Tp0/Peh/MUVzcXMMl3Y4oR7EQQgghhBBCCCGEEEIIIYQQQgTYsmUL1q9fv93qlyYKPS2EEEIIIYQQQgghhBBCCCGEKPNce+21OOaYY9C0aVP8/PPPeOyxxzBz5syEd/DZZ5+NRo0aYeTIkQCAkSNHYv/990erVq2wfv16TJs2DY888gjuu+++0jyMrNGLYiGEEEIIIYQQQgghhBBCCCFEmeenn37C2WefjQULFqB69ero2LEjpk+fjt69ewMAvv/+e+TmJgM2r1mzBhdddBHmz5+PgoIC7Lnnnnj00UcxYMCA0jqEYqEcxUIIIYQQQgghhBBCCCGEEEIIUUooR7EQQgghhBBCCCGEEEIIIYQQQogdgl4UCyGEEEIIIYQQQgghhBBCCCFEGUMvioUQQgghhBBCCCGEEEIIIYQQooyhF8VCCCGEEEIIIYQQQgghhBBCCFHG0ItiIYQQQgghhBBCCCGEEEIIIYQoY+hFsRBCCCGEEEIIIYQQQgghhBBClDH0olgIIYQQQgghhBBCCCGEEEIIIcoYelEshBBCCCGEEEIIIYQQQgghhBBlDL0oFkIIIYQQQgghhBBCCCGEEEKIMoZeFAshhBBCCCGEEEIIIYQQQgghRBlDL4qFEEIIIYQQQgghhBBCCCGEEKKMoRfFQgghhBBCCCGEEEIIIYQQQghRxtCLYiGEEEIIIYQQQgghhBBCCCGEKGPoRbEQQgghhBBCCCGEEEIIIYQQQpQx9KJYCCGEEEIIIYQQQgghhBBCCCHKGHpRLIQQQgghhBBCCCGEEEIIIYQQZQy9KBZCCCGEEEIIIYQQQgghhBBCiDKGXhQLIYQQQgghhBBCCCGEEEIIIUQZQy+KhRBCCCGEEEIIIYQQQgghhBCijKEXxUIIIYQQQgghhBBCCCGEEEIIUcbQi2IhhBBCCCGEEEIIIYQQQgghhChj6EWxEEIIIYQQQgghhBBCCCGEEEKUMfSiWAghhBBCCCGEEEIIIYQQQgghyhjlSrsDQgghhBBi2/Hoo4/Gys4888y09ceOHQsAaNq0KQAgLy8PAHDUUUdth94JIYQQQgghhBBCCCF2FvSiWAghhBBC7FR0unZarKxi5fKxsvf+0HtHdEcIAeDgkTNiZe9ee2Qp9EQIURIOve2NWNmWLVGsTHIthBBCCCFE2UKhp4UQQgghhBBCCCGEEEIIIYQQoowhj2IhhBBCiF2ERx55JPF506ZNAIAtW7YAACpXrgwAqFWrVqJOuXKFS70nnngCALBo0aJYm+3btwcANG7cGACQm1toRzh58mQAwHfffZeou8ceewAA6tWrBwBYv349ACCKkh5JGzZsAACcfPLJaY9jwoQJAIBKlSoBADZv3gwAOOOMM9JuI4TYNowbNw5AUndQ/iz5+fkAgAoVKgCgLqmbti0gqQdycnISZdRTF1544TbouRBiR3DvvfcmPnfs2BEAULduqvx/9NFHAIAFCxYkyqpVqwYAaNKkCQCgatWqAJL6BEiuG7hdprWCEEIIIYQQYscgj2IhhBBCCCGEEEIIIYQQQgghhChjyKNYCCFE1tAr0XLaaaeVQk+EKFu8+OKLAIAWLVokyn755RcAwI8//ggAWLduHYCkly6Q9Aakh5/3PgaAGjVqpJTRu5DbVK9ePVG3U6dOAJKeg+wD9wMkvZjfeustAMCcOXMAABUrVkzUadmyJYCkl1FeXh4A4F//+lf6kyCEKDb3339/4nNBQQGApKxTxsuXT+b/pryzDuV58+bNwLdrYu1XrFgxEYWAuoT7AYCNGzcCAJ599lkAwNKlSwEA559//tYclhDCMGnSJABAzZo1E2WUa87Xq1atArBHVu01bNgw8ZlRSjiHc41QpUoVAKny3rVrVwDJNUEoygDXKOzryy+/DACYN29eog7bbtSoUcqxrF27FgCwbNmyRN1TTz01q2MSQgghhBBCpEcvioUQQgghxE7Fip9WxcoKqhbEyg697Y1Y2TtX99wufRKirHPnEZWxcuXKxPfFixeXYm+EENuL279MGojhy8KUFX87rWUp9SY7Dh45I1a24L/LYmVVasbXElWqV4yVzRrWa9t0TAghhBBCiF0AvSgWQohdBHrz0sqe1vr2oe0pp5ySss0DDzwAAGjevHmirHbt2gCS1vn0BKLXH//bup07d075T69EAPjwww8BAD/99BMA4JhjjinB0QkhLOPHjweQ9OZr27YtgFRvYXrj0ruHnsWFXkNI+Y1effTusR5A1CX8v3r1agDAzz//DABo3bp1oi71ht/G5jil5xD1Bz1/rNci98/tqYdCuVKFEMWHOUZtRADmGKUe4X8rm5RfyiThvM/8ohZ6GtKj2LZHuC+2e9999yV+Yx5jX5c6zuZA5/4ZQeHiiy+O7UuIsgJzhPPegDmBLVxH1KhRA5i7rsT7opyvWVMYWYARRZo1a5aow7mdcs5oIfa+gfJMua9fvz4AYPny5Yk6jRs3Tjku6iXqBKvXZs6cWeJjEkIIIYQQQhSiHMVCCCGEEEIIIYQQQgghhBBCCFHGkEexEELshDzzzDMAUj0DmJuU1vneaw8AHnvsMQBJ7156IdpcY/TgYzv04GvatCkA4IcffkjUbdeuHYBUL0Yg1dOIeQz5n33v379/lkcrRNmEeYf32COZM5BySXm0OfuAVNmjRw1z9tGbz0YZoFcwPXfYvtUt9DZi2/TY27BhA4CkF6Kt4z39rCcwy1iX+7Jeg+wH9QY9EOn5vDW8+uqrOOqoo7a6HSF2dug1DCTljv8pU/TIA5Lyls6jP4T/zco6P/tIA9QpIdgHm9ecfeZv1ImsY/UC9R3/M/qChbqGuuySSy5J2x8hdlYmTpwIILn+B4Bq1aoBSI5xfqd3r5VXzrNcwxfKa8k9itk21wZca9ioI97zNxQtxEYIAJL6gsdi+0wvZh4LdYHVCawDxHOoCyGEEEIIIbJDHsVCCCGEEEIIIYQQQgghhBBCCFHGkEexEEIIIYTYqSioWlB0JQDr1sQ9kG/4Vy5u+NdrKWXv/aH3NumXEEIIsavyl55VE16/ALBgwQIAwNy5cwEkI5N06NAhUcdGJdiZ2bRxc6xsy6YNsbL1v8RzqIc8rTsPezlWNvuWY0rUNyF2N7rd8nqsbNPGLbGy90cowo8QQgixq6AXxUIIsRPw9NNPA0iGg2TINRuGjaHZWIcPehhuzsKQb/Xq1Uv5DiQf+HD7NWsKQ7UxFJwNScuw1qzLbdetSz5Q4fYsY3g4hqAGgFWrVgEABg8enOYMCFF2YMjpBg0aAEgN08oQrvy/fPlyAMCKFSsApMoydQFlljJoQzxTPlnGEI1WztkOdQDbYb/YF/uZ4SQZGpahKP3+bZ+3bEk+QOL2PmRtphC4W8MzzzyjcPhil2XcuHEAknMx51n7Aodya+dnIByKlmFb2Z6VceoB+zIJSMovQz4D8dDT3Jfd1oeip16xeo9t8yWV1U/2d3sMhDqNYauBpM7hcU6aNAlAMhQ/AFx88cUQYmfkkUceAQA0a9YMQKo8cGxz3Ldq1QoA8MknnwBIHeNejjjXW3limPdFixYBSMqRlU/Oz5Rlriso5zY9Dcso7z5dDpDUG/yN7SZDSCf3yeP1Osbi1xxCCCGEEEKI4qPQ00IIIYQQQgghhBBCCCGEEEIIUcaQR3EZ46GHHgKQtM711rpA0sq3X79+O7h3QpQNKIcA0LJlSwBJrwF65a1fvx5AquU8PQHoZUwvXevJR08fehl5S3wgacFPWed/eixyP3b/3oPH9ot9rVmzJgCgSZMmKe0CwNKlSwEADzzwAICkxxPbqVWrVqJu3bp1U/rMYzr22GMhxK7Mo48+CgBo3rw5gOQYD0UOoCcxZeOnn34CkPTyB5JeNNzeRwcAkl48bI/eQiHvO7ZH+aQeCbXn+xvyDuR/Hqfd1nsS8zfr6bQtadq0Kf79738DSHoZzp8/HwBw8sknb5d9CrG1PPjggwCSnnY+6oiVu5UrVwIAlixZAiApt3aNQFjG+dXO+5RNe28Q2sbuw0cECHkUs8zqE+KjI/C4qJPseoJrDv7nubDe1b7v/G7L6aVNfScPY7GzwHFKubDzNWWE8sj5jGuFZcuWJepSb3iPXRt1gGVcR4Q8dymPnJ991BG/Lghh20unE6xnso86wro8XhtNSR7FQgghhBBCbD3yKBZCCCGEEEIIIYQQQgghhBBCiDKGPIp3AN1ueT1WNmtYr+2+X3ohAMk8iPvssw+ApGU+LYGtFT4teD/77DMAScvkQw89dDv3WIjdm4cffhhA0psQAOrUqQMgngOYnjLWu44eBpRRWvTbfGQsY84xegJbryNuT28E/qdHsc1pxv1z36F8hj6/qc9PBiS9IJkzed68eQCA1q1bA0jqKNsOYc7Cjz/+OFHG8/Pjjz8CAE488UQIsTPC3JhAPL+fz6MJJD2H6KHH75Qr683n8w1TFkPeNdyOda3nDvtBDx3qBO47k0exz1Vs61NOqS9s/kHukzrK5z+t3SA1tyIAbN4U9zbOyY17Mq1buzFWdvmbq2Nl9xxVGNHhnXfeAaB1jihdGHHDyhhlmvLFuZyyZOWOckb555xOObS/UW4ZmYRRP4DkfM25l/2hx7Jdc7A/Poep9QAmLKO+s3rFHycJeShznUN95XWJ3c57Jdq1ENdbjGLy7LPPAkiei/PPPz92DEJsTxhxiLIXihzgPWz5Gz2KGVEASM7lXg7svQXliR7Foage3sOfsuujDdnt+T+Uo9hHEOF3e//BvvI/+0l9ZnUMf7upa2F71A2VK1fGNRuTUVjI6hXrYmW//ByPvJBbLu5T0eX6V2Jl//7j0bEyIXZ3QuvvG/ZbH5vHp0+fnpj3+Vzx7LPP3v4dFEIIIUSxkUexEEIIIYQQQgghhBBCCCGEEEKUMeRRvBty7733AkjN+dm0aVMA6XMLWss/Wh3TUpeW9l9++WWizldffQUAOO6447Zp34XYnQl5zFAm0+UNt56BLKMFvrfOtdh8Zh7vscvv9ORr2LBhkdtaLwLui1b+3rPY9pkeC9RP9evXj7VHTyluT11k8yzyPDG381tvvQUAWLRoUaLOqaeemvY4hNhePP300wCA6tWrA0jOoUDSA37BggUAwjnA6eXG7SlXofyD9L6hzPG79bThPiin9FDyOTyBpJxS1rzOsfjcgjbKQCgHqa9DfejzDlrvx+0Nzzv78t577yV+O+igg3ZYP0TZhrlyOQ7pTWihTFK2vTchkBzP3J6RC+zcSU87zsXUC8yBDiQ9En1kAtaxuUEZFYX7DnkU8zNl2+ZHJV4f8fhCUUy87qF+Cukdv16y+6aurVmzJoCkrqQetdEg6E196aWXxvYhREl4/vnnAaTeE3B+poxRZmyEHsosZYbbUM4ZUQhIjn/qiUxRhigPy5cvB5BdJBESiiAQWjd4vI6xUUd8lAKeE+7LRjbg2or3Mexf4fOPovMnCyGyJzk3No791qJFi5h+sLRsWRjJ58MPP0yUfffddwCAk08+eZv1UQghhBAlQx7FQgghhBBCCCGEEEIIIYQQQghRxpBH8S4GvYWtJbDPBcT/IS9hWhLTGpkW+qG8Xj7HoPUgaNeuHQBg6tSpAIA5c+bE+krPiAsuuKBYxyjE7sZjjz0GICmb9EwBkp4AtKqntwtl3HrZUF5tPlMgVdZpje/lOGTZzzr8jd9t7lKfbzjkscN9ss8hD2D2me0wJ3Fon9ajwPbBeixQH/F8cXvrvcm8o6zTq9f2zw0vyi70JKbnbygqAHN/0vOd/21ePo5tegXyP9ux7XFs+1ziNqIAZZYehF5egbiHHuWVcmXl3usAbhtaRxD2y3oF8rNfa4Q8k7YXXKf4SCoAMHPmTADA/PnzAQBnnnnmDuuXKBswJzHljPohFBGEnnJcP3B+tfMlZZGegZR55ioGkhEJuB1loF69eok61CuLFy9O2Tc9lK18e0/ikOcu8yD7+wnrpehzCvvIJFa/+Lyo1IlWfv09Eftu9afPB09C6wly//33A0ief3kYi2yhvDduXOiFR3m3449lP/zwAwBg4cKFAJLRc4CkbHE7yjllLyTLfozb75Qb6gJ6FHtvXwvndMqnrZsu/7CVdx9lwHssA0k592sf6gabV53exf7+obD91DIhRNEw0gnna7v2b9SoUdrtKlSokDF3OdvhOgUA9tlnHwDAm2++CQA44ogjtqbrQgghhNgK9KJ4B5BXLu643e2W12Nl5SrEF1VvD9VCSQghhBBli1sOSq6J+HKMD735oMmG2OQLej5oty+lAeDkiZ9uv84K4Th45IxY2ZbNqQZbG9Y3jdXJy8sFkBqa+Y6eetEhhBAl4Yb9CvUp03BVr14dBw99MVZv7dIfYmVV6jePleUXxPW2nuuIHUXXm16NlRWuG1KZNWz7G0c/cFy9lO/eIEUIIYQQux4KPS2EEEIIIYQQQgghhBBCCCGEEGUMeRTv5DDEGT1j6tSpAyAZHgpIhn9iaJglS5YAAFavXp2o40PUMcRTJss/eux4rxwg6dXTqlUrAMDcuXNT2geSlrvTpk0DkAx3p/CNYnfmkUceSXymvNLDjV5xlFEgGUqNoWcpzz6EtP3MOtyGYZztdpR/hk5k6Ei7vQ/pGAp3HQoZB6SGsma/fCj7ULhrHi9D5flt7fZ+3/ZcsG2GreNvNmQn26S+/PDDDwEkdVHv3r1j/ROiOIwdOzbxuU2bNgCS4dQ47hgSESj0ZAGSYRcZEpahWYGkbLCODUsNpMoi5cDLMNuwv/lQzzY0nA/z6rGyzLo+BLVtz8ssv9tw175tH+aSsg0kdSfLvI4I9YffbQhcD/Ui27NrK/anSZMmAIDXXy/0GLJhfPv375+2bSG2FRUqVEiMZ44/6hU751G+OJ7pZW/nf64NOA/WrFkz1g7lgDJA/USdZOdi3kekS1sDJOWX21MX2bUG8WkqfEhr2w5/8yFpQ+3xuK2eCu3fEgqxzVQAPEdPPfVUog7XdhdddFHGdkXZhOtxjh3+tyFYKTcNGzYEAPz4448AUsMsczvKtQ9db+dZ3v9zG/4Wmju5b+oCO+/7tQHb4X+rY+z6w7YfWl/4+wQrw36d73WBTW/D0NzUZ+yPTZuV7n5GiLLGM888AyD5nMLqIM51++67L4Ck7uGzPiApkz51FZ9BhJ4j+PnZzulsr379+in90xpbCCGE2PHIo1gIIYQQQgghhBBCCCGEEEIIIcoY8ijeyaGFHy3saDUc8r6jhT0tea3l3+LFiwEAjRo1ApC0tKUngG3PW/7yf6gOLQnZT1pAA8ncgbT4pTX/E088ASBpWW3bXrhwIQDg1FNPDZ8QIXZSHnjgAQCp45rePJRRegZSHoGkBwqtaSmjlE1rgcs6lDdazluLfFr3L1iwAEDS+8ha+LMdWvz6/3afXh94Lx/7mf9Zx3rjEO+RRNnP5BnoPZ9D/eF36kj7mVbL1Hv0unj//fcTdQ844IBYX4UoCms1z7mO/zkmraW+lw2O6eXLlyfq0MKf45XbU39Yr1fvYRuSPfaR7VAerKwQtsP5O9SeXxuEvHO8p573fLbbee9AYj2MWZeeTiEPZfaRZbwO1gPYQ29jbmv1JM+39VoCkvoDAN577z0AwEEHHZR2H0JsC/ycSdm3cuI97b2nIZBcZ3PtQbkLrcm5hqGM0gvZyh1lxq9ZbMQi9p3teY9Duw8eD/UU/1sPZcqkj0ZgvQe5T5axHbtv1vFejd4L2faP55L6wW7L8z1u3DgAwJAhQ2LHKcoulBs/11lZ8R76jRs3BpCUWyB1vQAkxz/nR0YuAZLj1mPnbR9NiNuHvAJ99BKOeTvP2jWP3xfxaxYv/0BS5r33Iv9bWaZcss8+ukJon0LszkyfPh1A6jMH6ghGyvGRAYD42p73JNYDmM8YqIsof5RV256PMhbSSdQj1I1s7957703U4TNGtsP+hJ4R8P5p4MCBsX0JIYQQIjN6UbwD2LIlEAptffjGTZScrje9Giv71w1HlUJPhBBCCLEzcW+fWokHS+nCZwtREg4eOSNWtmljfJ0fxbMxxFj/y8ZY2W+m2hdDlVL/f5p8ORptWQvPmONqFL1TIYQoQ7To0iFWtmZVq1jZykXLYmWrl/8SKwsazBXEU3cdetsbsbLQc6J3rz0yVibKHp2unRYrK6gSTxuXkxsff9f/k2V7JMp+WU3Dx+TL43Llky9YyaQBjYrX0d2Qw0a9GSt7e+gRpdATIXYNQvdCmsuE2DXRi+LtzIMPPgigZYm3p/W9tQ4Gwt499ACglbv1IKA1IdsjtLizucm8pW3Ig5D7pOUvrfisRwLL+J95S2gtbfvC9mgd/NhjjwFIzclEK2G2d+6550KI0oaeIyFo9coxTzmzMkbPGMqil0nrzbZmzRoA8fx+VtZp3UvLW3oNWetetk3PRJ+fM+ShlOnFSlH5TYG4J4CPShDKS+a9hkN6z3s3WG/LdDnZvOcnAHzwwQcAgP333z/tMQjhsRbsP/30E4CklyrnPOtdw/yW/I1j0+Yo5rzn9QW3sfsM5RIEUmXRywgJRQWgbqlXrx6ApDdeyCsntC/i86nTgyCUX9nLe8hbmGsg6lJuY+vw/BDqSyvnPB6/PY/JekxwLcVteEy2PbYjz2Kxs5Kbm5uIPgIkPX7oEUS9ZeWaawrqA+ot761ny9JFHwGSckuvoVBuYOoIHyWJhNYwlEWuWew9g/dIZh8y5SWmPFPmrUexX2OEvEN5fDzfkyZNApDM0QwAl156adr9i92PCRMmJD5z7HAsUhatJzzHNtcNlKOmTZsm6nC9wPU95yrKDL8D8XmfWPny87WXdyA+P3OsMwqSPQa/79A9An/jueB8be8/vJexv1+wUVH8M4JQxJN050IIkUpBQUFC3kLPCim38+bNAxCPUGTnRT+nUyatfvHzMvUJc48DyWeMPi+yjywCJNcuU6dOBQAcd9xxRRyxEEIIIYhi7wghhBBCCCGEEEIIIYQQQgghRBlDppXbiUcffRTA/7xglpS8HVrv+fweIe9eWirzN+tBwNxB9DakVS0teK03kvc+opWfrUOrPW7vPROBpDUf6/qcR7YurQJZlzmZmA8NSHo48bhmzCgMbzF//vz/1WgAIXYUzz//PICklT9l6+eff07U4Zinp1vIC46W//Qs4JjnNtbKlnV8vj+b34xe+V6maBEMxPP6+Fxj1mMuXa7RUI4hyrzPVWzL0lkWh/ZBQl4A3qua+7IexdyO54DnlP+tBwQ9h954ozAsXM+ePWP7FOK5554DkMzhu+eeeyZ+o4cevXwoTyErd8ou64TyDvtcXz5Kh8V7z4TWCCRTdAC2zX5RVrJpL1TmPf+t3uB5ob7x0Uys7uF547mgjrB1fI4znj+rz+hxyN+4xuL1sB7F3JePxEKPcSBV3wPAK6+8AgA4+uijIcTOwJYtW1LmUHoqUjY5hq2eohxwDUOZop4Kyaaft62s+/k/U8h3rwcyeSHT8/GHH34AkLwnscfAPnuvSyC5LuIxcBvrAUzoZZ0uzyyQPIdck9EDykaVeOCBBwAA559/fmx7seszfvx4AMm8nnb+4LjiHBKadzhf+XFr5x3Oyyzj+jV0j8Gx52UvtI5gu5S5UAQQ71FM2bHr6UxrFQ/b4bFYPUQos/6+IfQcgcfuIwmEfhNChClfvnxCzkKRhShL/G3RokUA4nmELdQ9/l4ciN8PMGpCu3btUvoUgu2G1hzMxfzMM88AAPr375/miIUQQghB5FEshBBCCCGEEEIIIYQQQgghhBBlDHkUlxJbQp485fTefmvYsil+Trvd8nqsbNawXjuiO0JkzXX/AIA6KWUbPoh7tEwYUH/HdEgIIURG+t7zf7GyipUKvZf+8O70RNkHN/XZYX0SOw9R3KEmRl5efN2/JTe+4ZpV62NllapUiJWVz4/f1v3+9dWuJBf5BeUB7JVSuvlns9//OcrfuH8WByGEELsYjwxonoimwIgEQNLrm57Od37dOLbtvC8WxMrWrop7T+fmxiMmhdi8Oa5n975yaqzsP7crz+ruTvvfP5/yPbSOCM3z5fPj4y/0XOznpX49EKbfQ1/Eyv7SqyBQc/dlxeI1sbJ9rnoxVvbJn3+1I7ojxE7PlsBcJoTYNdGL4m3MlClTAADNmzcH8L/QJ3M2ZNgiO3w4SRvyjJ8ZkoX/bbglhothCGqGomKoJhs+kWUMyRgKPc1QMwz7zJBsNmQcQ8ownIwPo21DzrCvDDXD77xRA5IhdX3ImkRYmjnxF2tCbEveeuutxOdmzZoBSI55ysCcOXMSdShXDN/ow6Ymt89uYcWw1JQhykQo5LsPTWdDT1MmfYhoyqgN/ebDQIbCufrwctQZNmwdP1O2fRg7W5f9Yj9CYex8aHx+D4WpZts8N/xuQ/n5MNxPP/00gNRQVqeddlqsbVE2eOqppwAALVq0AJAMC2nlg/M+Ze+nn34CkDqGGPKQ/znHMUQpEJfPTOEbQ6GmPelkzfaddVjG76FQsdw+lAqjqH6FQln6dijTNjwk++X/25DdlG/qKG7P70BSD/rQ+dTRoVD43Bf1hdUbQuzOcD3DeZtrBK53gLh+otzxfgNIyh1D5LK9TKknqAd9egkL72UaNChMPWNf+LBflG323eoDhqJnP9h33uPYFD7pQvja9rhdurQedruJEycCAAYNGhQ7LrHrwZDivCe1Y4dwvDFMK8eODavOMciyUBoayoZPS+HDt4bIlHKiOHDflEt7T59u3g+tOfxx2ucRrMPz5NcpNhSt74e/f7B1OIeHUuhwHZd4hvJ1di/WhChL+LQ0TL/Fcs63QFLOKOvUjVYfUO4XL16csr29p/drAa8HLZyLqU+5Zrn33nsTdZi6gikA2N7xxx+f8diFEEKI3R25sAohhBBCCCGEEEIIIYQQQgghRBlDHsXbgMcffzzxmd5GqdZtJfcoptUcrXG91bCtE/K6IyyjhR2tmr1Fry3jMXDfdp/0CmB7rVq1AhD2YrYeP3ZftpzWhvxP7wNrhU9rft+vkEWwENuS118vDGFuPQS8RyHHIS1UgaSFrffoi3sAx8NKhqDFLfdBOaFnsd0XPQqsV56vwz7TKp/trFmTDLdEeaOsc58hD0O24z2V7WceO/cd8hIiPAbvjWzL2J73NLTH6b0uQp6GVr8BQJMmTWL9mTFjBgDgyCOPTNtnsXsxbtw4AEkvIVrLU1ZsdADKRr169QAk5y/Ot0By7mQ7HJN2rqtatWqwLyGvnEyexH471g15+/poJen+2+2917E9F6G++u/eK4jbU86tt1A6GbbrCLZHvcE+Wx3IdYT3ZPSRCez+ec3oIWmPMxvefffdlO2PPfbYYm0vRGnRuHFh2FXqNq4D5s6dm6hDuaPshDzvvccu27O6znv7cR1COczk9Uh9avUBy+gtFIpCQLlndCRGbeGxhNYchHrA6heuKTLpZa7f6On86KOPAgDOPPPMtNuInZfbbrsNANCtWzcAybUBx629R+U9BOcozgnWG5djj3VC86G/3+fY5DahdXU2a4VM24TuX0J9sPi50n736xGeN6s3bCQkIHkO/P0EkFxD8b+/f7D752+c2+09FPVD6HiEKMtYvcL5i/MZ5+uFCxfGtuM8z/UE5djOnVxb0KOYnv1Wxr1HsX12CYSitSVDyYfutXif76MsvvPOO7FjEEKk8sgjjyQ+J+fQSrF606ZNS3mueMopp2zvrgkhtgHyKBZCCCGEEEIIIYQQQgghhBBCiDKGPIq3Aua56NSpU6LM58jZuHEjRh9dK8WjKC8vD8PeS1oGk1sOSs0jXNhOQwDp83qyvfOeXwhgcUp7d/ZM5gehBR37RWvaBQsWpLQPJC3rrNUdkOp9R0/JPfbYI+W4K1SogHmn/zp2bK0nP5Noz+dABJK5iVlGyyNaLNrtaIlIS0JaH+eWi1v/5uTKIliUnCeffBIA0LJlSwCp1qs2/w6QtIa1csNx7fOQWfmtWLEiKuSnerMCQMVKcR3B3XureGv1T89YygX3beWXVrys42XeW+kC8RyD1gOXloQ+t5o/TiDudRzyEvKegN6D0W5H7wvvnWDLfGQGeniErJn9ObDHyX0yVzUtpwcMGBA7BrF7wCghlBGO45BFux/THOvWg43zO+c4n2vbks5TJ+QJnO673T6TJ2w6r+PQtt7LOBTVI117Fh5z6NjTbeP7kOk3/rfeWtYrC4ivK3idgaR+4HrH5zdeszKeq3jtz/EIMmdOcjkOn38WD55aQ5EJdiOG1Cv0sK1fv36i7I+zK6SrniCvXNxeN78gfmtWIVBWrnx87ry9e2oEny1btuCy19fG6q3/JR5pJC8v3hfO7fzvI6gAyUgnfm1A7yG7PfUIt2Ee9xBc33CdYqO1eA8g7tPey3jvw5A+oW5mjmOuA/w6CkjKf/ny5XHDB7aVwv2sWbUeQDX7A/Irxq8b782on5ifXp7Fuyb77rsvgOQ9gV9DckwByfFPL3feI9CTHYiP05D3vV9jcJ8hr9/ieMZmWk/4edWvEUKRzni8obWHl0uuhRiRBUh6A7JtHi/r2vnc33fwHFsZ5n2Qj2gUikzC/7d3r5Syr0qVKgHHN0gpW7lyJQY9uSB2jIU6IZVy5eO6t3x+XE+Enl+0OO/pWNm3D8o7a1el4eljY2UVKlV33yvG6qxbG4pUGF9vrF0dH3/L5/4nVlZQs36sLKpfPVYWml8pb9RBLLfPPbmdj4RgZdOvH7hut1F++Nnf94ciH/nnB/xuI8PZ9T6Q1FPUB+Xz4/nJK1Upel0nxO7E+PHjAcSjm9jnoElZis/11apVS6k7bdo0AIquJcTOjjyKhRBCCCGEEEIIIYQQQgghhBCijCGP4q2A3rTW2s3nyaLVjbUoLsR/T1q4hvJn8X8op3C6HJ/W0paW+D4vCPtFjxkgnsOPFnrW8m/RokUAgA4dOqT0IV0OMXuO2J49J7QgtF6PQNIi2LbN8+NzlQixrbjzzjsBAJ07dwaQHHvWIs6PeXq/Wy9VWthyXHOc2zGbms88M/Qo8Dm9rRWtt9yn3NJa1x6H90YIed6wPeo2tmcjH3B71g15EVKv8Px4j4CQVzTb89+BuK5h30M51dL9t3V5fF7n0qvJwuNt3bo1AODFF19M/ParX/0qVl/sWrz88suJz3Xq1AGQHKeJCBbOE9V+9p7rIXnnnMt5MFO0gkzes97jN5NnvW8nlM87Ux5jv89MXsPp8rKHZNjvI7SO4HaZ5nu2Q11DvRvK6ew9lHzUFSDp5UWdxQgCSZ0Qj/qQLTVr1sSzzz4LADj55JNL3I4oXe6//34ASUv74szn2wO/xihJXlKL1y8hz0CuS/jfr+OB5Hnh9rVr1wYQjhrkvYS5drH3DPycLm+w7YfP9RqC+pf3dSHPTK55CttrlLatovA6jDqHc82ECRMSv51zzjkl3o/YftD7GwDat28PIB6Zh2PHygPHp79vsPfXHGeUXW5j1xp+jUD54lgKzf9eVjJFAsmU49jP7f5/prqZ4DZWh1ImitomdAwhnczPXkeFPJO9RyLLbV16e4d0nhC7GzZimr8vp2zxeaPVL9yOdbyXL5B8rmgjE/jv/Jwu2pK9X/cyyShO9rli6D7OtyNEWYJ5h21kD+YLpwyF7tGT82xcdvx9Eb36+ayF0U1Da4jBgwcX9xCEENsIvWETQgghhBBCCCGEEEIIIYQQQogyhl4UCyGEEEIIIYQQQgghhBBCCCFEGUOhp0vAk08+CSAZesGGQ2OoI4aOYjiTatWqJeqUL18eE/olQyhz+8WLF6e0C8TDoPI3HzLq6YF7Jj7zN4aIsPjQTmzPhnbhvjKFafNhKvl93bp1qP/wRADJEFgVKlTAli1bEiFeGA7m559/TrTH/a9atQpAMnSN7YMPOU3Y3sT+jRNhoObPnw8AuPX/KsHz+uuvA0heo/79+weP8aCbX4uVvfeH3sG6Yvdizz0L5Ynh3TgOrUwxVBzrMLSSDaVIueBY4zZ2DOfk5ODeo2smQh0xLBLbtftkKFTW9aGZgaRMs88+LDQArFy5EkBSRrk9v9twUGyPv1GX2dBMoRC7QGpYOOochpJjf7it7R/370PuhcLS+BCboTDcPkwV26Oesf1gXeoinpvLZiRDd5NNG9nnpJ4a8a/psXohPripT1b1xI5j/PjxmLC0BYCKpnQ1Nm3YDGBlSt2HTmoIIBluEoiHL/OhVIHkGGToJcq3lTkfTtGHeLa/+zk4FDLSh2fz24T2mS5cZYhQHR+aKhSC0u8jnR6xv4XCXPp+UL82bFh4jbguAJLrLR+WmmFz7XXgdgx9y/8JnejCfwLAxvXxcJ5rVyyOlV36Wk0AhWPgL7e9AQB45+qesXpi58bLkg13eMcRtVPqlitXDpe/GZ9HPJe2WJLQD0BSx3gZvfof8XCn3D/Xyzk5OShXPn0YWcufD8+PlfmQ05nkj3DtEQq560NZh/rE/z48r1038bMPJW9TbPDzTz/9BCAZ7tq2w+2pX/y+8vLycMnLq7hX09vsQlNu3hw/zuRvqSlEiJ1H7r77bgDA7373u6z2J7Yvf/zjHwEARx55ZKKM18uHg+bcYtMZcP7x8hBKJ8X7Bsq/nRfZpl9jhNbKfv0Qkj2/jqY8ZZqvffod264/vpC+CW3n6/oQ8D60fijdhU9pYevwfoj6MRRilvLI+y3//MVuw88rVqzAbT1yE/dHbP83Ly2Ntb/hl02xsk0b4+uGLVvi56ygalxH739D/J5D9xe7BtXq1o+V+TmjYqV4ipPc3Pj8GxpDUWAM1Wqxb6ysdoNqsbJpF3dOtv0/uQvd96dLEWXDVIfCtgOpIWk5P/PZINcP9jmgnd+BpB5kXZsSi/3hOp59t+n9KNP+uSK3HX9y/cRzm88++wxAUvfw+E499VQIsbPR5fpXYmX5FeOvfWYN6wWg8PkHALRs2RJAqpykW/dbeaZcjD6mSkodylJojcPngZSxJUuWJOpQNidNmgQgOZ8PGjQodgxCiO2DPIqFEEIIIYQQQgghhBBCCCGEEKKMIY/iYkBrm/bt2wMIexTTK5iWM7Sss15H3qqdFjqZLFy5L/63VnjecpfWOyFPJ/5GyxzvXWPb9ha71rqIie1pqd+0aVMASasg2y/fnrfuA5Lee7QWtJ5+Hu/hYD2AaE2YvCZxj2J6DPD8TJ48GQDQr1+/tPsUZYMxY8YAADp3LrSkpSUr5c56pnHc8T/Hd2jsUoZCXv7ee85bxVtvBOoM74Vo5Y6yzv+02LPWfN6bznsTZPLcpRWt9Z5gHR+FIORJ6b0QqB+sVzTL6OXHfVm95/fN8xay9ud21GH+WOz+qYO4rfeMFLsvzZo1A+IOIEFC8w/Hl/fKsXgZCc3p6TyAQ797T9uQ17G3Bg5596QjVNd74ZHQMXj9YXWC94L2583Knj8nPiKBre89iuxaiLqE8s3rx+uyfPnyRF0f6WSPPfZAKhuxLXn44Ydx9tlnb9M2xfaB9wOcZzkeGa0DSM5fdg7Phj322AMLFy5MfOd8z7VFaB4k9MbhPrOJCBDCy72XN3tM6fSJ9TbIJiIA5Y3H5+tY3enXSfyN91dAUtZ5XzZv3jwAqfqAn3k8O2K+9x5YXHNwHWfnDZ7Du+66CwDw+9//frv3T6SnXbt2AFLHCcdZurFjxy3XnNQTmTxtfSQj2z4jDrBOpogiHr8Wt5+9nNp9+nk/05rD36d7eQ0R6nu6SAR+XWHr+O+hc8zt7HMNwuPzUd9CXpW8Nqzr7zGE2FXZvHlzLPqglReOccok5YT/rQxwey8XVjdy7qZ8zZ07F0AyMhAQ90xmf/hMwz6P5Xxq11K2vzxGIP4cx+oSrh8YLYDPO1n3vffeS9RlnwcMGAAhdgX43JNy5qP1WLh+8ZGLgKQ8cJ6kTFGH2HsG//6kbt26AJL32kB8Hqb8vfZaMton79fl1S/E9kErWSGEEEIIIYQQQgghhBBCCCGEKGPIo7gIrrrqqsTnPn0Kc754S1t6DQBJK5sFCxYACOfapQUNrZD5n9Z0NgeH95LzHjNA3FOH+7IehNwHLXTYHi1/bP98Pk9aFdn2aM1Mq+gff/wRQNJ70W7PfXovCOuJRasg9ofn0R4nzwu3o0Whte7luW3SpElhwddJD1BC6yRaG9pccKJs06JFCwDxnGMhyzqOVVqUU/5oGQck8w1Tvpjzylq9es97Espdls7TwG7L8cxxHtJTHPve+zhkpZ8uL5n13PEynk2eVF9uj4FePl7Xhtpg/3hMNvc5dYP3+vL5iOxvXvckrSBXxrYRuwf33nsvAKBt27Yorv2c9Wj1shzKwcf5y+fLtnNwulzfmTz+SciLJp23UaYcxb48lPPQe+5m2mfIkzCdZ3LI+9h7L4TwOY6zyWPM88/rYqNGUKdQH8UtrVek7UtJ2HvvvfHOO+8AAA499NBt2rbYNjz88MMAgPr1U3MLhuZ2lrFutt69FSpUSJlfuc7m2Ax5wRFG1eHcV+gBEI+sEyKTbPn8nFZmfUSAkM6gfHnv6pDOSCfHti7nae85bevwt0aNGgFIrg14XezxMFqAX4d53bQtoF6h9wTvf0Jel/SiKE4UCLH9YEQqO19zDkk319lrx7mE44oyHbq+HIuh+bWocWnrenkM5Sr2sh/SVd6bN5O+8PcSoXPhPZMztZNuDRO6V/FeyPZcZYrG4Nvk8xvqrlAENn7mfVc27QuxK7Bp06bEeE4X5QNIzleUhWz0QihqEL0Vfd7Sb775JlGHER3T3ffYffP5iNefNmoQn9HwN/bHejZy3d+gQQMAmfVy4tmjELsIfDboo51Z/LM9zqmhHMV+vqSMhZ5lsh3qAxu1izLI7fnf6iDO0VOmTAEAnHTSSZkPVghRLLSiFbs169bEQ0OeNvHbWFmFgvLAG6+klOVXjItH15tejZX964ajtqKHQohdlbxy8ZvmDevjN8mbNsQf6u195dRY2X9uP27bdEyUmOv33ZgS6ow3UXxoYR8SirJNlRqhsJVx+S9Xvn6sbO3qeIoRsftzR4/KwfJsXr54bu5a+GDG6qT58+P1rt1nbcrDFT6U4b744vnad+NGMiMP1m3ilpChYGBOr1wtboB24wGF59i+IN8eL5+FEDsfD55QL2bwzxdV1uDnwikrYttuXB/XE5UCOuaDm/psi66KUqBmvfB6wHJdx8IXLnxZU6VKFVz7bvzZ1uoV62JlIeo0qh4rG39yg5Q1Ag1ihBC7Jv/+49Fpf5swYYL5pjW+ECKMtEMRWOswWtv43Db2pp/WLXwQQ+t+mwvTW6XSyoYPpK2Fnc9ZFfLO5fbe0jZk+ec9dbitfYhBCyGfS8haDvF4eH7m/+/plLXUq1evXmz/th3bX7bH46Plv83p6uv4/wDQuHFjAPbY5QUoMkNvQgDYZ599ACTli2OV3633un/YSs8AyjyQtIylFwF1ho0a4PP5cJyHvGjpXUKLPZ9zPNT3kHcd5T3dw+mQlb73ELD6gLoxk0eRby8T6XK0hrb158LqA++ZxLqh4/Z5n6kbkzfMqXmOxO4D5zHKVyjXLmWX4z70sD9djk0rTxxX3js15LFLMo1/72nrvWmybackZGonXS7BUO5kb82fqV1/XHad4j0SQ+2lWyeFrhX1Gv/HvaK2rdFA+fLlE/r//fffBwAccMAB23Qfovg89NBDic9c13KtT33A9ft886aWnsCcQ7gOCHn7+bnKrsmpe7xMETuPcazS+j603vaRDmx0n3R4Pch7E1uWKbd6NvnRfWQT345tn3XZD+pTW8eva6jfGSkGSEZ/+umnnwAkPQP9OqykcH23ZMmSRBnPN+8B7QsA+x+Ir9WefvppAMApp5yyVf0SxeO+++4DwKgj4XHG6+rzcdq6vNbUHz5XH5BcR/hoO9Yjh+M/U85v4r3tQt65fn72ebRtO+nWHHbd5HVMaP4nmbyG/X1QukglQNxDmXVCMpxOx1h4jqkTeI2sXqGe8NGZQvchXs/a5yVAyXLJi90fRhdh5L5C4gYDW4OXkWz0isdH6bH6ykcz5PMSOxdz7cL1BNdaNsoP2/Se+5wn7XMJny+c21idxnPqI3fY9mk47J/9cv62zylZ57HHHgMAnHHGGRBiZ2HcuHEAUp/Pe+P3UJQ/P/+G5jQfdcjfa1vZZNt+ncH7FltG2fae/LZN5g2fOHEiAGDQoEH+0IUQJUA5ioUQQgghhBBCCCGEEEIIIYQQoowhj+IisJ7AxHvUWetvWtLQKobWNjYnFq1uaE3qLddDVrmZrPFD+UCLgu3SUtGGQPIedSTkkUhrIFrcWk/JdP3yFrhA3JMrZDm0cGGhRx/zFrAPNkQO20n2/YdgH4Qg9BAAknLgc3WE8vx6rxBirV+91SzboTcBkBy/mbxwCbfzOcdDlnqZvAe9Bb/36AvV9TooG4/FUA5U307I04DHRQt+/mavi48owOth9Q7PVzrrRXvtrEcHELdiFLsfN998MwCgR48eAJJzubVy9/l4OQa9N42tm40sk5BnTLrtM60DfJn1aPEeNqF8XpnyF6bbJ9vzntShfWXKr5xunRPyvAxZ/Kc7Tta1Vs8hD0sgeV2ZhwxIrjW4rqO+sB4J25Lq1asnPCTppfDGG28AAHr27Lld9inS8+ijjwJIXR9zvvZeg5wvrKX+vHnzACSjjFi9Qvw8GIoIkG4tzXFoxzej6oTW0H6fcc+B9OPaexhl8rQNeSN5r3xf15IuX2ooMoC/TwnlPM7kYUhvId6jLV26FEDSG7yw/fQ5oYuCaxDrPch9cVxxTPF+zB6Tv/a815o0aVKibODAgSXun8iOZs2aAUheI3svwPHk5ZFyZdetXn44PqyO4X0D5x3Wtc8a7JofCOe3Jj76SUjmvDduaA7OFDHA9iFUJ9P6IZtIRJlyE5N0OinTvUooSoOPCMNnFvRass9zuD11O7/bHKc+won3dCwk9f6jOLzwwgsAgOOPP77EbYidF45H6v7CKBgNM2xRfMqXL5+yjuBn6q5QdB4vV9RJXANZOfHe9KG1OffloyX4Od7+Rnnj+stGYqH+ZFkoByvrzJkzB0BSxzIaHJA8/+nasc8OuG7gMUybNg1Acl0BAGeddVasH0JsT0aOHAkAaNeuHYBUmfJrEn9PYvGRAWw73I6/pYtGaPfF7UMR2tLdw4SiiPEYuE675557AKSuk7g+u/jii2P7EkKEkUexEEIIIYQQQgghhBBCCCGEEEKUMeQqJXZrXr+iML8fLZIWL16Msx6fl9W2q1eui5WtWbltcxIKIXYNJg/eK+ElQI+PS19fEau3bk083+PG9XFPrU0b494Xzc75W6xs7oRfF7erQogdwJi+NRNext4j1PL7VwMeUuWUk1DsXNx8YDzfpwCu71LoMUTvh0JZz0t4H1lPCe9N7SPOCCGEJyc3u/VA6F7i5o8K/vf/1UTZv244att0TGxXrt6rMApf/fr1ASTnGOupZyP1kQsb/Zji9VqtWjVc/PKGWL3y+fHHvA+euAeApPdsyNtfCCG2JwP+9mWs7K+9auz4jggh0qIXxWm44447AABNmjRJlP38888AkqEM+ADAhozib3zIwt8YRhBILsp8+NNMIRh8mBX78MGHYMsmRAwJhdTNJpyUD6nFdmzYXR/mleeG29qQ0b5dnjeGYQSSIb+++eYbAMkwPDZ0DUPA8PxwIcz2QsciyiajRo0CAHTv3j1Rli7ccCjEs5UZIDnOrfxw/LJdbm/reHkNhZ4k6UIx2XGdbvtQSEYfHpb/Q6FVQ6F2fb/8/1DoaRIKueuhPvFhoS1er9jQUwwH5/Uy9YPVVzxmf6Nub9IZVsrqJbHrwrCjlEGOEyvnodDodptQCORsZNnXzSY8oi/PRChsVKZ9+dCT/B5qx4enDIV8TBdiNlNIXR+iKhSm2pON7gsdA+Xd/2b1B9cN3Df10OLFi2PHxXUI+xAKc1cUlSpVSuyLOsr2R+xYGEKtbt26ibJ04c0pLza8NMOYL1q0KKW9bMZGKFy6h2PN6iAfGp+E7gcyhZgrqm5I72UKR11U+6F2MqWyIF4vZKPbLDwe3o8wfQ2vFcOGA8lwsrwXCeF1bChUN/fBa0U9w3Mauh4+xKZNXyK2H7fffjsAYP/99weQvGZ2De5DJFJ2OYbsuEuX+sC2x8/p1umh37yMZCMrITKlhEkXIppjOzQXZxPiOZt7gXT3GKF7Kd+H0H2IT3cR2pfXG7wuXDfa9vw8YJ/j+HUJjzs1tdmCwFGL3ZHbbrst8Zmpr3zoeLvu4/NDjhvqEDvei5OCzssb17V8xgkk73H9vVFI3vz6hHJi07jw+CgXoTndr61YN3S/7Z9RsD07L/p9+fDw9ph5bv0zRFtGvc5rY5/9Ep6LPfbYI6Uu53wAePDBBwEA5513Xmx7IbYHNCrhnBOa9ygfXg6BuLxmukf3813o3j/dXG/lmnLHuuyfvX/yes+nb7H7obyOGzcOADBkyJBgH4QQSRR6WgghhBBCCCGEEEIIIYQQQgghyhjyKE4DrfxC1mm0eKG1sLVOS1fHWo7S4sxb/nuLQiBp5ea9VqwnnLdgZV3u25axH94jMRMhy13vxUzPXWsFSWsgHi9/43Faj2LvncnjDFkSeksmey68pTOtAhnWp/B6/pDxeEXZoFWrVgBSLc4ob7TgpbUp5caOU+/pxXGYyQLf/wfSh3gM6YPQ9p5MXoMknbdxyMMo3T6t3vMyGfLg8d5PmTwDvKcB9an1suJvLKOesbrN78N7H9tz7z1FuI3VQfyctDbWFLorQ9niGApZsPsoAyWRvZDVfDZ1PCGvHO9FE9rWW/OHZC6dB1w2nsmZPP6y0UPpvPBse/44Q+c63bkIeT/6UNGZri/r0KLZ6hh6HDLyAOumrrFWxtoMEYr6wP+vv/564rdevXpl1Z4oGY899hiA5HxhPUc4TrxMEXvdGfWGY4MeqbVq1UrU8TJDXcS6QNySnuOEesp6Mfv2OHfacrbjZdvKT1Fex6F1Cetk8m5in7nGsnM6Sbe93Sf1sr+vCOmgUJSlour4aEQAsHz5cgDJdSHPI+sCcd3FdaG9RoxUxePk+OC1ssfpI1eE6owdOxYAcMEFF6Q9PlEyKMOZolHxOvpIAbxWVkeE5qKi2iWZ5ibiveiAuPdsNp7F2UQUyaY803nzujMUoYSffQSQbI4l09rF/5Yp+lFR5bY/vL722RHvH3nfEJJheRSXHaxHOtcYPgKfxctQyOu4OHrFewdzTraRu3wULvYhFAabz0U4x2XSGemitdk6/pmhnTu5LqJ8+Sgtdv3F536Ex8B5HIjLNOd7+1yRcst+cL7nvkLRPXgdQ/d7zZo1i9UXYnvCcR2KFEKZ5Hjmdyur/nkk5zS75qEs+kgDofuCdClZQlEN6CUcelbqnyeyD7zHWrJkSaIuzwHld8KECQCAc845J9gXIYQ8ioUQQgghhBBCCCGEEEIIIYQQoswhdyjH8OHDAQA9e/YEkGoF5j0IM+Xj8h7F1hKGVmm0qKFHCj1srYWO9yDwXjBA3JOR+7IeCT6vAPNn0BIuU+6ATPkKCK2u7ba0PPS5+zLlKuR5o7eB9YqmpRGt8Xw+BbvdTz/9BCBpuW+tEF+5tEtK3zdu3IhTH/kSnp+XxfOi/rJiUays0a/Hxcp++JtyH+zs0ErNyjHHn88fHvJopex4j8NQbsFM3rNFeQBbC2NvDRiyDswmV1G6vKshMuUh9XW8x2JIxn3dTJ6LzOtDXWL1aLqcYNbi2edt9/+tJbDX1X4sAPGcgiuX/BTr8y+rN8TKKlaO55gsnx+ffsvnFz/Hoygejz/+eOIzrz8tzH1kC4u1as+WkDc+50HqiZBlLcdXusgfIc8/334mT56Q3GeT19fXzZQfNJMncVH49Yot8/oyG30XOt50eRFDHh1e99nvtFzmGii0rzeubJDy3V+jkGc398GxYD0b//3vfwMAunRJXcuIbQPX1Fwj2Bx8vFYcJ6zL62/HI6+lt24PRePw62Mbhch7k3Kuou6w+e/S5cwsrjx6zx/vQW0t/znnsj/UbXafvL+hNxXX5nZcp8t/zGOx9wPLli0DACxcuBBA2HOXbWeTt9Vf15AXBMcD79l4PW2/eN14nnhOOD6AuEd3tWrVcO0s61ld+HlEly2xOSBTVCmx7aHs85ozX7n1vvNe37xmoVzk6e4FQhFFMuUQT7d9Jq9+n/szNNd5j/hMkYO8zgrdh2Sjh9JFWbHb+3k1lAvYe0qG5vh064VMa4R0a4/QdiHPSZ/jONSXd284HEByXK1atQon3vdFrE9rV8Y9OvPKxY/pnXfeAQAceuihsd9E6XDdddcBAI488shEGXUEx0sod7Yfs5wD+LyL248+tnpijHFNEMpJ6p9neK88IDm/cB+UAepBC8t4P1WvXr0ijyFT9DKeg9Ca+vvvvwcQ934M6VofjYD3efYZAY+T54t9D3k/LliwIGVf3rsZSM4P3IbybKNUcV+TJ08GAPTr1y/WdyG2BTfffDOAeC50O779MzgvW0A8Oopf89g6PuJB6DnGihUrcE/vmgm5WLmyMOrWsmXJ+4rmzZsDSMpbqD2/dqBOCx0Dn+/4iIUPPPBAos75558f66sQZRl5FAshhBBCCCGEEEIIIYQQQgghRBlDHsUOWoOFrGV8njBarFjLHFqKeStya4VHqzafL5iWMOwDELeI95Y1QDyXEC0A7T5p+ca2vQeVPQafW4P7DFnR+jp16tSJ9T2dda9tj8e+aFGhxy6tjKzFM88pvTMy5UOh94L32rK5STJ5dIndlzvvvBMAcPDBBwMI5/bx3jOhPHocvxxrIS8CbyUcIp1HW8iy2G8T8pDx+sB7EdjtvdV/aF9evrzHm+2Ht64Pecr5PmfylqAOYa4hGyXB504KWUpSj1Bf+jyE9nh5/dgO9YLNPxiaF8Suwd133w0AOPDAAxNl9EBZvHgxgOT8YOcxeotxfHivFYvP0R3KUUgLdlqah7zv2LbP8VOcHIMhuUonp/Y333c/x9u2M/UjJI/ptimJN3MouoqvE/I6TpfvLZvc7pm8tLPxiuK15phie9ZjlTqP18FHlQGSa7xZs2YBALp165a2XyJ7nnjiCQDJ+YJrTTsH8LrwGnKOyTSueQ051qw3qG0bCOcH8/A3P46AuOz4HIghQnmR2XeeC7bD/4xKBACNGzdOqcsxHFr3eL2SKdqClyXbHj1/uG9el/nz5yfqzJ07N6U/vGfI5ElJvOeErcP7Os4J1kOJ9108Fo6h0LlI1fPxNebSpUsT59R7sNsxxPFwzz33AAAuueSSWFsie6ZPn5743LJlSwDJ68poVRx/QPL8++hCmQh5EvvfSGi8ZhNJw5ONJ3GmedB7EPvyUJn/LaSrwjl7w/vM5N3r95VpfeKfS9g52HvoZ8pn7OuE1gHp+h6KmEYdHLpGxYE67/nnnwcAnHDCCVvVnth6eB9rPeJ8fnN/rwokn9NxXuT9iX3Gx/Hic/fymaSNaMH9c1z6NYjFP1e0z9DoBcj+sC7nPKsH2XfWyfR8w3s22r77SB3ZyDgjf/goh0A8+iCvg43oQtnkcfG881otXbo0UdffA4bgtbLRVITYHlAfUIf4KIdA+jnayhZlxj9Ts7rM5xb39z12vvN50bmetesqH1mNMmnnYz8323WxPX4gVY/Y47PvALSGFiIVeRQLIYQQQgghhBBCCCGEEEIIIUQZQy+KhRBCCCGEEEIIIYQQQgghhBBiKxg9ejSaN2+OihUr4sADD8S//vWv0u5SkSh+poPhGUIhlhg2wYcRtGFbGBqBIUoYpsWGNWJoBIYYYmgEhmmxYVF8+GbCEGq2jz6Mgg1rsmzZspQyn1yeoVWAZJgWtsdjsSEcfJglXw4kQ95kCnlHeE4YGob/bWgdH86Gx2CPhWEseJwMK8N+2RAT/Lxx40bcclBuIjwGt73opXjomLy8RrGyzZvjx9fmt1NiZV+PPilWJrYPh416M1b29tAjAKSOYyBVtiinHGP87kOGAskxxbrUHTYsfLqQZaEws36bUF0fEiYUdinTb74OQyAxDIwPg2X3n01oNf89U7hrf0y2Db8v6lir9/z2obB66fSU3zZ0XNQDtr/cfyL8drm5sTYrVi4fK6tSvWKsLK9c3E5r/S/xcHNdb3o15fu/bjgqVkcUTceOHQGkhhSj7HJ8MQR1qI4PnZYp/LKfizkv2c9ep1iZY5g31mXIJOqWTOM2FNLV95Hz/X//+99EGedK9qt27doAgFatWgFITYnh53TKXijUfCiEq6+TLrR2SCf4bS3p2rF6yIecTNeG/ex1jA2p589tKMQ+xwHPux0PQOoakp+Z0oDtWL0WOt9i6+E6lPJm1+3Ehyrl9aEch1Iy+DDENsSzD+mcDdmklSAhueP6mvcnDCPduXPnWL/ShZnNFDLXy4ttJ5uQk9mEb0wX5p+hqAGgRYsWAIDvv/8eAPDNN98ASMoWEA/tTtkO6Q5/LrkesPdalHHeu3BMheYUe77+fGjFRF2GzF69OpmOh9eD++JYApLzAucNUTLGjRsHAOjUqVOizIdRnDdvHoBw+FiOe17rTCGos0nb4NNU2LnJp8Dy+si27+UqtC729w0+hY2tn04nZErb4Nuw23k9FtqnPxbfb/vZz8GhdUSm+yy/vd8mdI+Rbh0QOgauB+zzDV5j/pYuJVZObvo0Rhb2y67bROnCZwQWjg0/39r5NV3KBKtfOJa4tuDzN65LQvMYx0goZL6f37lv3hfYfbJ/fn6061w+V2Q/eC7s8xi/BvL9A5LjmXMdfwvpHt7LcE5mqOwff/wxUYfhwLk9z4GdrymL3BfnAP+swPY50/0PP3OdOW3aNADAscceGzsGIbYGrnV5XxmSEx8G2j8ftL9R7kJpb3zaHJ+6L3TPzd+4jrGyTjnxz/qtLvNpeHxaBzv/+e353T5XVCpKsb148skncfnll+P+++/HgQceiLvuugt9+vTBl19+mZLKaWdDHsVCCCGEEEIIIYQQQgghhBBCCFFC7rzzTpx33nk455xz0KFDB9x///2oVKkSxo8fX9pdy4g8iv/HVVddBQDo1q0bgKTVTMhDxlvF0DofSFrx0VKMFjG0ZLPQEsd7FFirOm8tH7K4pWWxt4wNecKxjBZwbC+TZavfxu7Te+eEvCm85W7I49EfJ/vD8wkkLfxYJ+TRTQshWmLzGOhJYC2HvJcxj6lhw4YQuy+87pRxO2YpO7TCo1Ua64Y8XHxZOk81S8gj0Mt2yHPHW/SHvAdoYee3D1nyL126FEBS3rhvazVMfUQ9F/LkZx1vTRs6t97yPpM3gj8Xtj3vPcDjthaJ6byrQ94coX349ryuFTs/I0eOBAD06dMHQOocwGtN+eZ4oxcXkLz+fj70UTmA+JgMWeYS71Ec8kBlO95bLpPnjvfqA5Jz+scffwwA+Oijj1L2bfEROyjb1jvAW/6HPFxDnoxA2GOX22fyBGK/vH61ntjp5DK0Lilqm0zt2OP065yQRwh1J8cZr6f3LAzhrbKB5LXhOJk5cyYAoEePHlkfiyjk2WefTXz261meXzvevdU8oSePve68zt7z1F5vertwrRuSGz/G/HihR4Ddl8fK0oIFCwAAbdu2BZCMGmAJedQWhdd31sPVywD1ie27976h7uV6zHoH8B4rky6kzDRqVBgJiPrz888/T9RhJAW27XW4bd/3LyS3vI4cMxwXdk6hNzCPj+eL58R6M/Ha83i5rR2Tfk566KGHAADnnnturH8iPU2aNAGQKoO8v+S15/nn2tnW955//B5aK2TCjwf+t7qFY49tc/yGIgGkW3uHxi/l8Ycffkj5DiTlsEGDBin7Dq2z/b1JSI+k60+mdYSfr+16IF17ofk/k5yn8yQmIc9LXjPvrRmC665QpBnqi9CasSSwH08++SQAYMCAAdukXVF8OMbsM0PqFz8/hnSQj4ZmIwH6eZ/bh+5ZKdNe3uy4ph7x0ZFsHT/X2WeEQOpzTz/v+wh+vm0g/FyROsc/gwzhn5ty3/aenp99pEgrv5yPeb7r1KmTcnxWjv2anue6adOmsX2yX5zbn3vuuUSdE088Me1xCVEUQ4cOBQAcdNBBADKvk/kbZdzPf/Y3731s61BPcX6z96we6gzv5Rvaxq+fQnM+++U9i+37AeKjONg6dr0jxLZiw4YN+PDDD3HttdcmynJzc9GrVy/MmjWrFHtWNPIoFkIIIYQQQgghhBBCCCGEEEKIErBkyRJs3rwZ9erVSymvV68eFi5cWEq9yg55FP8Pny+DFifWusRbwtGiLeTRwjpsN5Q7yteldYuNkR/ybAZSLc5JcXKIct/egghIn1PQ9oFWOxzg3N4Kgbdw9FbNtj2fI4UWdrYNnyuS/207rE/rTFo+e+tr+5m/0bMgmzxLYtfjvvvuA5D0LmF+PgutWzmeqQ9ocWYtR30+suJ6Dfh2vKV8yDPNb0NC1uve68VuQ/mlfvM50ULW9d6i2FoCWw9kICmbtg7743MUZsoj7D0LQ97V/nuonUz5W9PtM9M5FbsOLVu2BBAeF34sU96ZqxhIzguc23we70w5xKkT7DqCskdrd8qVXSP4yAahyAG+Lutw7rNWisxF7OUptHbh8fnjDeUNzJSXj2SKCsAyn6copPN8fuBMOaJDngjp+uPXI5nGSaYoD/6YQu14j1Uer/WkoNcCx0XIs4jb8fgy5cIUmbFzOteP9BrkeQ5ZsPM6U4Zs9Bvirw/bsbkKqQ/oSca1byiaUbp53x6D9+rhNla/8DibN28e67PfLpNckHR5Ne1547nk8fpcvgCwbNmylL5mitbij2GfffYBgJRcTz7SAf/vtddeiTrc/sMPPwSQPH/e0whIn7vUwvq+HTunMMch103eY8yOJZ4LjiHeJ4bypPtoECI7brvtNgBAly5dAISjXfmIVvba07OM85iP4GXnWT+GMkXL4b59xC1LNrnN/b547zN//vxEHUYZYP5O6hTbd8oKPeo4znzkE7svkk2+8dC6wuc797mYLSEP4nR1fdSjTHV8O6G1kJ+DbXt+DRWa931EpMLjTXqeFhdeC54T60UpSgdeY85zQHLO9N659p6BeoW/Ub/YeZ/j2EfnIXae5X0+8fc/dnuO2ZCeSSeLIY/CdBG7QrLOOY/b236luwcPrVN4vn2EklB+ee/RaPvF88P7MdYJRYHhPtkPrnPs9fTrSm5v1y6TJ08GAPTr1y94vEJkItP9sSddFLFQLmD/3Nzi31f4iIw2gpkf+yGPYi9LITjPeZ3jZR+I39OE9Jdd7wgh9KK41Kh85x/hA8StW1Oo4Gzgg7wK8QcRBbf9dft1bBeh/M3Xg4+9+L/5pCe26T7G9K2ZeKDHhwTH3/efWL0N6+M37xt+2TZho0TJWLE4bpgxaX3rwg+JiHEFuHLPeEh4IbJlxhUHAkhdNPe4LR5GZOP67MJ3btpYdL2Dbn4tVvbeH3pn1b4QQgghhBBi5+Xe46okXgraF73e2OCiaUtj214+M/Ul85Vtt1cvhRDbmtu/LHwpffvIGYmyd689srS6I4QQooTUqVMHeXl5KSmIgMKURPXr1y+lXmWHXhT/D1rxe6tQa43Cl4VcnNOCxVrJ+Fj/XOTbFwnZWPhkYtmyZSmWu7SA8VZzIS8InwOY29r2vPUisXW8FQ/bs5Y/zAfmrYJCx88+83xlstjPZEHN32iZx355byQgaSVIq0xa9du8Kpk8k8SuT/Xq1VM8PTgO6enBsUoLep8jCIh7yIZkPVPuzaJy9oY8WjJZ2GWTc5Nteus5you16ktnURzyHqCcMX+blTeeQ2/pGPIoZr/S5Qiz25ckAkA2+VpCpNtnplwsonS47rrrAAC9exe+RPeWq0By3HIuCOWzoUU4rWKpI7KR91DuLs43lA1aqYfa8R7PobnTl9Fb4ZtvvkmUec/GTHqIayF6FIe81NJ56mWje0KeRV4/hvKE8Ri8V5XVMV4Xh/rj53SfYzAb+Q/13V97u2bw19iff+vt4T0aQjmxWYfrTJvzTmQH88HZ8cR5nwaCIZ3hryHXrCHdkS7PpB3XlDd69PGa2utdlPe79RDykY64rb1B7dixY8rxhTwCiR/fbM+uk3l+vGFlaK3uvSOtpw0jFH311VcAkh6P1Jl2nqXMMN/6p59+CiB53wEAhx56KICkLvM5C4HkeWZ+b7bDvtgoSemuZ0jPeJ1r8yt7j2nvgWr1DM+p9R7zdbw+KE5eaZGMMsRzar3v/dogNE9Q1qiHvVd5aJ713q/2evo5l9czNB96r7ZMcxTHxxtvvAEg1cs93brcyif1jH+eQKwc+Dkum3uXTGv6dL9lWk+E1gGZIi2lq5PpmY0/b5mijvA/x0XDhg0TdZYsWZLSjj8uq+Mp79mst0Tpc/PNNwMA9txzTwCp8yI/+6iDdv3Iz95bLjT2eb/CMcbvdgz7+ZRjy8oz5z3qNh89wEId4dfotn++r17PAMnniXx+wHNi1xrMI58uik5I1n00jlDECNbxz1Pt8XAOYDvea99uR13LtRDXd0B8vg8/Y0md74UoDoySMmHCBADJexufQxuIP38PeRT7Z3w+4gCQXJOny7dun59Sn1AXUQcxmpPth++P3aefC6nvQushP5+HIpDoWb/YHlSoUAH77bcfZsyYkcg/v2XLFsyYMQMXX3xx6XauCPSiWAghhBBCCCGEEEIIIYQQQgghSsjll1+OgQMHYv/990fXrl1x1113Yc2aNTjnnHNKu2sZ0YtiIYQQQgghhBBCCCGEEEIIIYQoIQMGDMDixYtxww03YOHChejUqRNeeeWVlKhVOyN6Ufw/fFhphiRgKAYgGZLEh05jiBEgGdbBh0q137c2XNCWLVuwatWqxHeGjWB4Q/bZhrtkyAeGn2MYWB6LDZ/NsC8MecZ2bHhEhoJg+Am2Y0NV8Lz40EyZQk8xDB+3taFwuM/q1asjlFnWtutDUGcKXetDujF0BZAM62XPt9h9qFKlSsr1pxxwDHBMhfJEpQs5ZsdaurEeChmVTj7ShViy22QKfxsKkcs+UsYZvoV1GfoRSOoV6shQuBa258Ou2XBa3Af3SdniubAyxvaot7y+sZ8zhf72pDs3/rM9htD19GF37Bwgdg4Y1o8hjkIhzgnrcExamfOyb9cEQOp87kM78z9lB0iOQR/KzYZ45HjimoMyEyKbedWHmA+FSmdfuWilXPl0EHaf/rutQ7lmO6EwtF7GON+z3F4Htsff2F6mfmUKd+nDvvlrZz/7MJpWJ/i+h/QQzz/DajGU3vLlywGkrr+8fuU4sdeQ21G/+jWMKBqeczvGfFj5UBhxHzKZ4QhDYYOzCbfK/VPOOA8yVJxvE4ivt+3vXKdT5/A4rX7xOZF4TKF1Msc+t581axaAZGhme3w8f7xnsHqPa3vee1AWbIjtpk2bAkiGj2bo6ffffx9Aaqhcf69Gnfbdd98l6vzwww8AgHbt2gEADj/8cACp15N95/YdOnQAkNSDH374YaJugwYNgvu2+LRDIV3GdY3XwyFd7sOOej0IJMP28RqlC5EtUhk6dCgAoG/fvgCS1yy0Pud14Fxs5zPqX3t/ACTlIBQq0ad9CukN6ha2a68r2/Rze6b0FOwndYQ9Bh9mPzSnU559+MmQ/vDzIH8LnVu/NrDH4NcI/vlLKCWGDyeb6Z6M7VudkC5sdjb9C6XL8dcklE6Czyo4Phi+k9fMrhF8+hIgddyFCKVNEjsGn4rBpiHwYVU5f9g1HceJDxsbuu/neKSMU19ZOabs+FDRdp9+HUI9Y3Ucnw3MmzcPQHKM+Xt7ILlm5bngPu3zNn8u2AcrfywrznjmeeN/q0d5bqmreb7sPnleOL/y+Fg39HyXa/xQWG+eC66JQusuIbaGUaNGAUjqGq6Fuaaw61E/5imjNp2Ofw7Jeco+46P8UkdQBig3dr1LPcft7fsKQj1A+fMpPWyZby/0fMATWsdvbWpQITJx8cUX7/Shpj16UVxKrPlpTaysXMXA5dgQL6o86qaU7z8DqHrHmG3Us12D6nfdl1ik7sicAhUrxx92b9oYuPHNj/dp7yunxsr+c/tx26ZjIoUq1eM3zps3K5+T2LYcMOzNWNmqBd/HyqrWaxIrK6haIVYWeqHg9cu6NYFJQQghhBBCCFFmGNW98H6XL7CA5AswPji3L+SE2NUY9h7vg39I/H920F6l1Z3tztpVcePzw0bFnze8PfSIHdEdIYQQZRC9KP4ffNlICxhaFtuXkN6qL+SR4C1aWcda1q9Zswbb2t57xYoVMatF65Hgk8AvW7YMQNLKzVr+8Xho9cbvfDELJC18aCGUyVuIbdOyLuStzfPMY+Bv9uaGfWcd78Vo+5WursV77rAdWvICSSssekGI3YsKFSokPFuAuJx4b67QizzvZZbJgi2TtVrIe8m2my2UyXRef3YfbJvfKaONGjVK1KWlHren9aH3ZLDtUGdYT1uvE71VtPUo5naMbkALxZAlP60LKaO2X34f/rpm8vQKeYh5D0Ox80IPDY5Fjs2QJzzHDOcxa0nr5wkv51Y+fSSM0D69h3IoegY/e70T8sYh3vPMykHIK9jDY6c+5HwfWiN4ucmkF0mo7977hvvK5N0bii7g4Tnw+g1I792TSd69jg/t268jQl5M7DvXJVxrhK4L9Rof/lI3A/F5iu1OnDgxUWfQoEGxNkUSjjW7rvUeLZk8bHk9uH2mKCHe29yOH+8hx3nQrqm9/HJ8U3dYbzPWYRnbtR44/jj9tiFmzpwJILkmtvjIGjw3dszyuOh9xG3s+afHLvvK70ceeSQA4B//+EeiLj2a7bEDqfLL8/TZZ58BAH788ceU9oCkFzPhOaAe7NGjR+K3t99+G0Dy3ipTpAL2i3rYnlvK9IIFCwAkzxvXJfYY2A7XQiH9zs/eA3Xs2LGJOhdccEGsr2UdrnPp2eUjiwHx+35ec3v+Q157QNxjD4jPddbTn/hxFVpH+LWt1zGh7emRz35a78B087XVgZSJdF609vi9R3HIQ5n45wiZjjM0vxK/bvJthLYPna90UVqyiVqU6VmDP4ZQFDTqPOoq/zuQHKc+GgXHMZB8FuLXHpMmTUrUGThwYNrjENsOXi/qfXstuc7j2MgUPYvzROg+hTLE+ZRjIzT/cDu2y7ES8ij29yt2jLE/jN5EXca5ynob8jeWsd1QJIWwHo2vr6yHYkie+dlHKgxFCPSexXZtxPp+Tg89F/IRlHh89lrxPFNfWQ9zIbYFjMpD2fQROezanM/6OVa9Jy+QHL/875/12c8c+5Q76g4bQcNHUuN8Z6MGUS4o5/FIGvGIgr5/Vpf4e/xQNBdFBxQiFcW7EEIIIYQQQgghhBBCCCGEEEKIMoY8iv+HtxK2li+Ev9E6kJYn1vLM55NhHWs1X6dOHSSze20b1q1bl7B6o2edjeNPC3h6THtvJloAAkkra1rf0NrI5iSjtQ49fn3OMyDuQchzw31ayx1aWvK80xLJWlunyxNk63hvDJ9/yFoasw69F3gsbdq0ie3TehmL3Yfq1auneLbTwsx7foW8Z711eSjfhbeQDVnTh7wF7LaWdJbsofzDmbwPQ95zQFjvUW6pTyjP1kLWHx9/s94+PmpAOq84IO4xFcqBxs/eUtkeg7cYZP94PWl1afuVKbez35fYefGyG/LYT+e9n41HS8hDJl1uu2xyYYf6wd+8p20mOJfbvMY8F7Qips6j17XdZ4sWLQDEc2Hafqbz6g3l6srkuUO8xy7Pv7VoDulg20+7z0zpKLw3pe9naNt03lH2N24XquPh9aD+sWuhUE5TIPVceGtuenrZ0JMizJQpUwAkr7/1quN1yeT9xuvg9YrPlQUk5wkfYSDkUUzZ5NxnLesJxyj7wPV8KMc1+8Hjs8cZynPr++X1HC3+Q7lR0+Vxt+OR6wc/rm0fuAbnueA6guN7//33T9T94IMPACTvS0J5170sMm/jtGnTEnUOPfRQAMA+++yTUjfkOdqzZ08ASe9q3mvZY2B9nyvNnlvvecFrzfvE0Dnh2oPn1kY5shGrbF2tUzLTrFkzAEm59DnKgbgu8JFFfP3QNqH2vCexrZMuilAol573JM4UBeH77wvTonDc2TUCoez5CB5APNenj6CWSX9kE4UnNPf59YePcGLnRX9fk2nN5/cV6p/XH6FznG5dk2ld5+cDC+9f+ByGzx5Cnk6s6+ctID738D89lsWOg/nufXQCW+bnDRuFgPO8z2Ns78E5H3C+YF3Krx2znIv9+LNzKOWK0QfpmWjvr+nx5/MPE/vcbMmSJSn75n2K7RfPC3WQb8+zZcuW2DOWkGxSr4eeNXj94tdYQPK8eFniMdg1Atc8PE6eL/sMmM9juY+5c+emHH8h4fsdIdJx8803Jz537doVQFzGOR6tHPP+getj/mb1i39ex/EdeiZH/PsUKyf+frtVq1YpfQCAOXPmAEg+o/f6xv7m50Afqcni77UsVu6FEPIoFkIIIYQQQgghhBBCCCGEEEKIMkeZ8yjucPkLsbITK76Hww8/HEDSOoUWMNbyzFul+dyH9jMtXmjNYq1LK1eujPWr4taheeXj1qUNJz4MIGn5Qus+a6kXRRGwZUvCyjBkxUzLIB6Xt9y3ljXew5FWPNaC3Vussz3rtcAy7tvnTrMW1WyHVou0PLJ51bzFHy2HrNW8twbicfE4rZe1t5D0eVZsX7mvqzr8mNI/ALh+VlyMcnLj3pwVK8lCcEcxa1gvAEnvIQC446vqsXqDnv0+VvbsoL3SepBZ77V0Vv/ZeAJn4z3gc3yH2vE5woC41xK3D3kd++MMeRR7vUJZsnqPMs59ZZJf6k3KJvtgvRsog96LMOSZ6ftjdZk/h7RWZMSCn376KVGXFvw+37o9t/yNer1qrQJ4Cqq0jpVt+CW7rPT5BfFc7xXyU/XLmlXxnHaikFGjRiU+d+vWDUA8r1Qo/6O3PA957qTzvrOeHrT859wX8qzjGOJcxbk8FAHAy2coEgCPgeOfawTbX++RzD7Y+Zoy5yMHhHIVeu+qUD6gdHVDnrZeH1GmrTWw18EhbyHvtRjy6E6XfzDkCeyjM2TyWshEujreQ8vCfR173wex39ZnoU9uj94CAHTv3r3IumUJP37snJcu72dINukh4udZ6gAgaeXO+SbTWOH2HMPz589P/EZvXi+bxM6T1Cfee8bKEnWPn+ssPAfMo0s4T3O+tP3xa36r9/wY9/dRQHK9wMhA9LRlf+mZBQBt27ZN2TfrhnKpe51jdTbzHlNXM6JCSHcQytTrr78OIHnvcO6TS2N1q9SIeyiUS9zrJfM9ls9PFwWhdsr/DfN4HZPHtGljas7FWw8r/J8pqkxZ5ZJLLkl8Pv744wHEPcXseQvdI+TkxsfE5EF7x7zb2I4d4xyvXKd6z3Mg7oGTydvVy5XXXfYz5dN7u9m++kgs9Oa327Gv3iMn5CHrPZPtNn5ezSRzPoJbaF3mPXZDaw2/9gk9xyGZPIl9nXTex3a7TFFGPP78Z1qThp75UPf6tZ/1Env55ZcBAMccc0zW/RJxOg97OVY2+5ZjcP311wMA+vTpAyC85vAevyEPYI7NRYsWAUiuPew8xnmZa4100ZLsPjivhuZi78FPubVrDeoKHo/PdWzb8+sbzrdWfn0EMd6v16pVCxvWx9e8a9asSRxL6P6O+GhmobzwnpBHMc8Tdbhfa9l+8Dj53NTqPR8dIJTL/rrOhfvg+WF/zjnnnCL7LsomNjpiuuhjoQg3lBmuv3m/YeF84p8ThO6f/D0R+xJ6DuLzhttnhmybkVAoH1xvA8l1v5/PiV3L+eczvp8AcNZZZ8WOXYiyjDyKhRBCCCGEEEIIIYQQQgghhBCijKEXxUIIIYQQQgghhBBCCCGEEEIIUcYoc6GnQ9SsWTMRtoDhEBiixIaDYggQhnZkKJFQsnSGgfGhFG072cAwCj5clQ09x3ATmULNEB4P+8f2bPgIhjhhiCIbzsLD8G9ffPEFgOR5s/iwmb7c7pNhJBo0aAAgNVS0D6/kQ1kDybAYPE6G9WK5vVb8jccZ6rsPUcOwPrZfwPLYdqJ0ufPOOwEAe+21V7G3XblyZdoQYVZOfJi0UHgyH1qN30MhH1knFALV48Ow2rrUAz6cqx37PoQqt2HYJHucNvSc3daGUmLbXgdZ3ZFOftl3e06on6zeBFJD5VJ+KYsMUxUKn+3D1NeuXTul30BS1nkM1L323LLMhscROw9W3jlu/RxqdboPnUjsOEsXvpTb2tCsP/zwA4BkGKU99tgDQKoc+PQIPnyg3T/Htg/FZsMr2ZDtQDLtBcMrAUk5Yj+aN28OIHUch8KfAeFQVTx2npNMYRsz4UPns11eI3tO/LopFHaf+6QeC/WLx8E6PJeZQvT7PmQKsen1r23b9yGED/VXUkLrGREfl3be8XNTKCypnxd4bSl3odBpft8h2WA73MZuu3RpYUhj6jDKPOWEegFIjhte/9D9xieffAIA2GeffQAk9YztF/vz3nvvAUjOma1bF6ZWsOHpGBKToaGp90L4MPV2nPP4uC/K0HfffQcgNWwqPzMMHXWc1Xvpwszbc8J98pyw716v2nZ4DPvvvz8A4NNPP017vKXBwoULAcTXTyI5toDkteb1pBwV5x6dhGSH2Pa4BuVagXJk9UbLli0BxNO+2HZ8iEX/jMDeozLko0//YNvz+oz6x9bxZenmR//ZYs+Tl8vQnM7j8uH2/TYWny7D1uH58frHni+uDTLpDb9/v07MtCbKJsy1Pxar1/iZ58SH9waS15xjnGOJ8xSQKgti28M5yo/D0HhkGON04xyIp56wdf24YXhqjgk75jimuNbk2Ag9VwzdTxM/9nm83CeflQLxNFLsj22fdfif+rgwRVR8Pbty5crEOid0/0KZYh3O6bw/s7DPlCUbHpqfef55rdiOvb9jXd7D8Vhsv7yupu63Ooj1eX+ZTYobUbax+p/jx8sdx7eVOx9mnvJi5YTzCccun2PYdTLHvA0xDaRPrwfE02HZ1D0c823atAGQvA/46quvEnW4/vdrEm5rj4H3Uf5ZrdbJQqRHHsVCCCGEEEIIIYQQQgghhBBCCFHGKHMexZ/deXzK93HjxgGombA889am1hKGFjS0cqNVp7XYoSWNt0aPefzVrAhPhSpxL0NvwU6LH2ud5r2Mae1mLQjZDi2F2Gd6yNr+0aKH+6D1obXmZ9u0TqXFDz2LAaB+/fopbXtPArtP7ouWPTwWe27TWUOHrHzZZ7bDYwpZ7LE91rVWkDyX3If3dACASQMqpdRdtGgRbpkdt34sV152GTuKVq1aAUiOQQC456jUiAC0qLOW+Tk5OVi+fHlsjNHazXrXcgx47yD73Vvph/Cett5yNGSBz317T2Vbn8eVydPZ951169atmyjzOoft+vMGJGU0dCzeit5b4FudRgtdvy/bhre8DnngUR9ze1o80urXHgMtd6kTvUeq7TP1yMsXd47tm+PKWncePvIfsb6FKJ8f9zS456gaAJLRFm6aXSVWRxRixy3PP68Hx5f1zCDeq9SOWz/PcAxyPbBkyZJEXa4N+BvnR84Ndh8cM/6/7SvnQy/nIev0Ro0aAQBatGgBAJgzZ06izt577w0gGQGElrV16tRJ1KFMhM6B/+49I0Mekuks4G05+87joY7i9bBW/enatbqP553/KafW4t+vI3zEk5D1s/8tk9dxqF/ewwkAet/9XpHHBgB55eLt55Vznlh5Wltkix+r2cydFo4fjk2uJ+jBYy31/Rjz49yWeZ1hIw9wHPO+gvJLfRMaj947385jrD9r1iwAyXnRzqmcb3hc9Hr5+uuvY3UpX9Qz1L2htZCXF3v+qe/oBcB98j7A6gPqLt6XsK7tV0h/ePy9H89/ukhNdht663300UeFx7IpPl7WrIr3YdOG9OtBS7kK8X2H2Lgude1zf0HjWJ2Ls2pp94fRqoDkOOV/rg3sGuGavQrHE8cD15TWA9hHEPP3vHZu570oZdeuDQj3z375uTkTlCEbOYXyw/uid955J9YvrjEow6xrSbf/kJ708u7vc2wZYd2QTvbthNpINxeHPJ29R7FdU/Haeq/tTPvJ1M90a4vQcfp7KI4FG7mG15bbX/uPeFS79evmp3x/4qyWsT5k8vQS2bNxffz8A8nnbd4j1cLfqA8499m61C+ZovR4Tzq2G4oIlO7e2+oMrgkoH1yP2DmV23nvY/63zwx9VCQen5UxrjW8h19eXh5uO6zws406uHHjxsT5Csmd15ts30aM4m/UkaHnin4d59cp9jj9uWVdrtlsmY+gYtcavBZ8hkVvSs/+N0yPlX1wU59gXbF7Y+cTji0fWco/xwOSOoKyzmdfVh8UevUnxyr3ZWWVMk358vfLdm3O36gr/DsJIP48hc847FzI9TrL/HN9u8ZJ95zynHPOgRAijFaJQgghhBBCCCGEEEIIIYQQQghRxihzHsUeWq/4/BQhrxpvjUJLlZC3Ci3MaC1jLcU2btyIuE9TZmhtw32FrPC9JZu1pGF99oOWObQ2sjnF2Hda8fDcWOtT7otWSTxvzZo1S9T58ssvU/bl83qGPIqJzxdq8V4Z9hp5bwpa/GXKLcTfuE9rRcVz66+ntfZknUx5EcWO4cILLwQAnHnmmQBSxwat3HgNaZlqr6Ufzxyj9CII5evJlKcrnQdwaHtvfcdxZS32aJHnx6Htl/c6DFmzs1/es5hj11rc0qKVssRt7T79mA95D3mr3Gw8Fv35tu1R3nhOaC1sLSW95SB/o1eH1UFsj5a/tEZmDhS7PdvzVtdAPAft1uoD7jOZTyyeX6msQ7m/6KKLEmVeLjN5vficwHZcpMu15/NyhfbBMWXHh/cSCnnacA7ylv7chrlAbV8pB126dAEAdO7cOVGH54LegOzDt99+m6jTpEmTlPbSyWmoLJt8xCSUH5TtsV+8HrZd7x0YOm++79zGRibx67hM/UuXf96uv9J55dh++by2yjdWemTjlePXlqFc9t7rlXNVqH2OqVAuLI5RtkcLeRuRguOXeU05x4TGk/eUC+Xj430E5SzkKch5n8c7d+5cAMlzY9dNXOP7KB+Z1u+ZcoL7NTkjRdjzx75z3uf8aI83nUexlVkfLYbXM1OEAS+/ybl5Wayu2Lnw+fMsfr0IJGWF44zPDKynjp8XfI5zO4boHUjPZrZrZYdext6TMLTO9B7EbM/2jzLXrl27lHZsrj+uG9hn5h239/RFRSKy8u7voXgOQnWIj+hm+8N1FmU69IwgnbdxNp6z9vzznHKsZJqv00WCCvUj3XoCKDpKiz02H4UCSM1DGYLnKxTNQmwfOEdyXIfyfntY13rW+ed/oXY4FnxkQcqsff7mI2NxTNh7GeZS988KQuOQz1K4Dz5PtLqW8yyPxd8n237wOEPRB3l+5s8v9JjnfQu9DzPBdm3ER7bH/oV0HHUQzymjSPnzZ9uhvIUiR/AZg42ABaQ+d/HHbteDQoSwz6457vwzvdCzSMo95cLLvv3MdQHXvCEP/mwiaLA9rleoK+w4Z7/8PYN9X8F1DtvjOZg3b17KsQDAxRcrro4QxUUexUIIIYQQQgghhBBCCCGEEEIIUcYokx7Fv/nNbxKfjz766JTfQt6zxFvNei8R+9nnyrMeLfZztnDf9CSynna0TmMum1DOQ1oZeo9JWtHbXIW04vG5Daw1EK102K7P7QYkrffotcTvNs8r8Z4HPBZr0USrIu9VGcrhSssmngOeN3tM6XKShKzJvQenvYbeK6o43lVi20L5DVl6cmzyN1rEWStOn7+SeK8hWycbz9hs8g77fbIvNrcNrflYl7JqPQwpy17WQ573PkcR/1t55PnJZFnsczr776F9+m1DXlHe08n2i9fC5yiy19xbdHvvY2tR73MTUofYa24tGe2+7BiiReO20gMhLxeRCq+DvZ7pPNesJxz1Ba+5H+P+s61D61t66QBxD5aQRbz3GGGdkNei9ySmJTs94m07lAPOr1b22I6XK+YdAoDmzZun9CNdDl97DtJ9Dx17pty/XgdyW3utWJfnIiRf6XLCUrZtmz6fWihaRLrjzMZ7qLTz/4VyXwpgwIABAIBJkyYBSPVs8TkpOdZCdbh+yDTPen3CdbMdu7xO9ADwVvRAPD9dJq9I6gZuz37ZdQTrcF+UCTu/+lxm9H7hubAeTzzOb775BgDQuHHjlPbtMXu5DXnjeD1ADz96CQDAfvvtl3Is3jsHAP773/8CSJ53Xgd7nekxeeCBBwJI9TYqinRrmp2R++67D0Dq/W9ZJOQ5ynHP8Zf0EI9HCeM9pB236dar3rPYts15OrRW9rIbWkd4j2L+D0UH8Hl4O3bsCCA1DzGPh7qG5+L7779P1KGs+HGfTe7kkBdtuvk0FNHFRzLgdx/FwBK6J0jndWz7wLWUXxtkmtN9PzNFOiGhOr7vHG92LcTzbZ/xZItdF2TjhSlKDq+d9+YLrR9D0Qc8Xi+EvEwpD97z3uog6jL2i1Gz7DqZcyZlgfdIVt78WtzP14xKAsTPRWjsUtf6aEv2nHDtw3HMKEl8BmIjPPqIItzG3q8TH4nFwv1bj00g+awldJ/C88bzZXUk104+yp1df/n7ft3/i6II3fPT258yz2eZVnf4yGr8zc4PPu841wn2mTo/c+z6PORWBvxzv1B0RD5n52+hdYZfi3Cbli1bpvRXCFEyyuSL4p2BhpMeiYWVpHIuzoMKsXOSkxtf1K1cGg872O2W12Nls4b12i59EkKUDrkBfbBuTfyGdDXWxcoGPfVDyvcPbuqz7TomhCgVXv99t8TnkDGQDwtpH5Zd8kpqOOHy+QoOJERpM25AzYSRK+V1+L/iD/MXfjM3Vrbki3/GyirWqBcrq9N2/1jZulWLU77/vCxgjBx/jy6E2A3YtDEeytjrmIFPxo1ZRh6ml0/bgoqViu/8IcS2YMsuYKQmhBBi10Qviv8HLWBojUKrFpsTi5+9xZ61zPEeCCFrVZ8PgN8zWY55y1hr8cz+eK9A2y9a5PA3WgqFrJlpme+9ouyDS2/N7D0mbNs8l/yN+7b94/HQ0o7t2316S2Bv3Wf3yX3Qo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"
                    alt="Stat map plot for the contrast: intercept"
                    title="Stat map plot for the contrast: intercept"
                    style="min-width: 600px"
                  />
                </div>

              <details>
                <summary class="pure-button button-small contrast-button">
                  Cluster Table
                </summary>
                <!--  -->
                <table class="pure-table">
      <tbody>
        <tr>
          <th>Height control</th>
          <td>fpr</td>
        </tr>
        <tr>
          <th>α</th>
          <td>0.001</td>
        </tr>
        <tr>
          <th>Threshold (computed)</th>
          <td>3.291</td>
        </tr>
        <tr>
          <th>Cluster size threshold (voxels)</th>
          <td>0</td>
        </tr>
        <tr>
          <th>Minimum distance (mm)</th>
          <td>8.0</td>
        </tr>
      </tbody>
    </table>
                <!--  -->
                <!--  -->
                  <table class="pure-table">
      <thead>
        <tr style="text-align: right;">
          <th>Cluster ID</th>
          <th>X</th>
          <th>Y</th>
          <th>Z</th>
          <th>Peak Stat</th>
          <th>Cluster Size (mm3)</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>1</td>
          <td>-57.5</td>
          <td>-48.5</td>
          <td>13.5</td>
          <td>4.43</td>
          <td>5103</td>
        </tr>
        <tr>
          <td>1a</td>
          <td>-71.0</td>
          <td>-53.0</td>
          <td>18.0</td>
          <td>3.64</td>
          <td></td>
        </tr>
        <tr>
          <td>2</td>
          <td>-62.0</td>
          <td>-8.0</td>
          <td>49.5</td>
          <td>4.32</td>
          <td>455</td>
        </tr>
        <tr>
          <td>2a</td>
          <td>-53.0</td>
          <td>-8.0</td>
          <td>45.0</td>
          <td>4.17</td>
          <td></td>
        </tr>
        <tr>
          <td>3</td>
          <td>-48.5</td>
          <td>-30.5</td>
          <td>-22.5</td>
          <td>3.98</td>
          <td>364</td>
        </tr>
        <tr>
          <td>4</td>
          <td>46.0</td>
          <td>5.5</td>
          <td>-27.0</td>
          <td>3.91</td>
          <td>2369</td>
        </tr>
        <tr>
          <td>4a</td>
          <td>50.5</td>
          <td>23.5</td>
          <td>-27.0</td>
          <td>3.80</td>
          <td></td>
        </tr>
        <tr>
          <td>4b</td>
          <td>55.0</td>
          <td>14.5</td>
          <td>-18.0</td>
          <td>3.79</td>
          <td></td>
        </tr>
        <tr>
          <td>5</td>
          <td>-71.0</td>
          <td>-17.0</td>
          <td>-4.5</td>
          <td>3.89</td>
          <td>6287</td>
        </tr>
        <tr>
          <td>5a</td>
          <td>-53.0</td>
          <td>-8.0</td>
          <td>-9.0</td>
          <td>3.82</td>
          <td></td>
        </tr>
        <tr>
          <td>5b</td>
          <td>-66.5</td>
          <td>1.0</td>
          <td>-4.5</td>
          <td>3.82</td>
          <td></td>
        </tr>
        <tr>
          <td>5c</td>
          <td>-48.5</td>
          <td>19.0</td>
          <td>-18.0</td>
          <td>3.69</td>
          <td></td>
        </tr>
        <tr>
          <td>6</td>
          <td>-39.5</td>
          <td>-3.5</td>
          <td>-40.5</td>
          <td>3.60</td>
          <td>91</td>
        </tr>
        <tr>
          <td>7</td>
          <td>50.5</td>
          <td>-12.5</td>
          <td>-9.0</td>
          <td>3.51</td>
          <td>364</td>
        </tr>
        <tr>
          <td>8</td>
          <td>-48.5</td>
          <td>14.5</td>
          <td>18.0</td>
          <td>3.43</td>
          <td>91</td>
        </tr>
        <tr>
          <td>9</td>
          <td>-53.0</td>
          <td>19.0</td>
          <td>22.5</td>
          <td>3.34</td>
          <td>91</td>
        </tr>
        <tr>
          <td>10</td>
          <td>-75.5</td>
          <td>-35.0</td>
          <td>4.5</td>
          <td>3.32</td>
          <td>91</td>
        </tr>
        <tr>
          <td>11</td>
          <td>55.0</td>
          <td>10.0</td>
          <td>-13.5</td>
          <td>3.30</td>
          <td>91</td>
        </tr>
      </tbody>
    </table>
              </details>
            </div>
          </div>
        </div>
      </section>

      <section id="about-f382b05435f74da28d858d24b8993502" style="text-align: left">
        <h2>About</h2>
        <ul>
          <li>Date preprocessed: <time>2025-06-21T09:39:56</time></li>
        </ul>
      </section>
    </div>

        </div>
      </div>
    </section>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 193-195

Or in a separate browser window
report_slm.open_in_browser()

.. GENERATED FROM PYTHON SOURCE LINES 197-198

Save the report to disk

.. GENERATED FROM PYTHON SOURCE LINES 198-201

.. code-block:: Python

    output_dir = Path.cwd() / "results" / "plot_bids_analysis"
    output_dir.mkdir(exist_ok=True, parents=True)
    report_slm.save_as_html(output_dir / "report_slm.html")








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 47.289 seconds)

**Estimated memory usage:**  1306 MB


.. _sphx_glr_download_auto_examples_07_advanced_plot_bids_analysis.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.12.0?urlpath=lab/tree/notebooks/auto_examples/07_advanced/plot_bids_analysis.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_bids_analysis.ipynb <plot_bids_analysis.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_bids_analysis.py <plot_bids_analysis.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_bids_analysis.zip <plot_bids_analysis.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_