outreach.html

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  <h1 align="left">
    Outreach and teaching materials
  </h1>

  <p align="left">
    The CellProfiler team has developed several educational resources to
    introduce people to the joy of answering important biomedical
    research questions using software. Biological image analysis is
    interdisciplinary - it concretely demonstrates mathematics and
    computer science to students in the context of high-impact biomedical
    research.
  </p>

  <h1 align="left">
    In-person workshops
  </h1>

  <blockquote>
    <a href="mailto:imagingadmin@broadinstitute.org">Sign up for
    notifications</a> of workshops open to the public.
  </blockquote>

  <h1 align="left">
    BLOSSOMS video/activity lesson
  </h1>

  <blockquote>
    <p align="left">
      BLOSSOMS (Blended Learning Science or Math Studies) is a large,
      free repository of video modules for high school math and science
      classes created by "star" teachers from around the world. Each
      video is designed for viewing in brief segments, allowing the
      in-class teacher between segments to engage the class in an active,
      goal-oriented exercise. The goal is not just to learn about an
      exciting area of science but also to develop deeper and richer
      thinking skills.
    </p>

    <p align="left">
      <strong>Video lesson:</strong> <a href=
      "http://blossoms.mit.edu/video/carpenter.html">Discovering
      Medicines, Using Robots and Computers</a> [<a href=
      "http://www.cpalms.org/Public/PreviewResourceUrl/Preview/18770">listing
      in CPALMS</a>]
    </p>
  </blockquote>

  <h1 align="left">
    Robots vs Disease activity lesson
  </h1>

  <blockquote>
    <p align="left">
      This activity teaches students about a new technology used to
      identify genes involved in human disease. The activity uses a
      specific example of how to discover genes that promote metastasis,
      which is the process of cancer cells spreading throughout the body
      from the site of the original tumor. This activity teaches students
      how new software works that "looks" at images and learns from the
      biologist what types of cells to look for. This hands-on activity
      models how the computer learns to recognize cells of interest, and
      stars the students as “the computer."
    </p>

    <p align="left">
      <strong>Activity description:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/MuseumOfSciencePresentation/RobotsVsDiseaseActivity.doc">
      Robots vs Disease: Modeling Biomedical Research in your Classroom
      (.doc)</a>
    </p>

    <p align="left">
      <strong>Materials:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/MuseumOfSciencePresentation/CellPictures.zip">Pictures
      of Cells (.pdf)</a>
    </p>

    <p align="left">
      <strong>Presentation Slides (in 3 different formats):</strong>
      (<a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/MuseumOfSciencePresentation/2008_03_31_MuseumScience.pdf">PDF</a>)
      (<a class="contentLink" href=
      "http://d1zymp9ayga15t.cloudfront.net/content/MuseumOfSciencePresentation/2008_03_31_MuseumScience_PPT.zip">PowerPoint.zip</a>)
    </p>
  </blockquote>

  <h1 align="left">
    <i>C. elegans</i> image analysis written exercise
  </h1>

  <blockquote>
    <p align="left">
      This exercise will allow students to learn about how image analysis
      can be applied to screening chemicals for antibiotic drugs. The
      data is from a published study in which the nematode <i>C.
      elegans</i> was used as an animal model to find small molecules
      that cure infection by the <i>E.faecalis</i> pathogen. In this
      exercise, you will have access to the following materials:<br />
    </p>

    <ul>
      <li>Background information on bacterial resistance, antibiotic
      discovery and C. elegans as a model organism for antibiotic
      research.
      </li>

      <li>Images from an actual screen in which several compounds and
      extracts were found to rescue the worms from infection but had not
      previously been reported to have antimicrobial properties.
      </li>
    </ul>

    <p align="left">
      The exercise is written as a set of modules, such that the
      activities can be done up until any point. Modules can be combined
      to create a lesson plan appropriate for students ranging from
      high-school up to upper-level college biology students.
    </p>

    <p align="left">
      <strong>Activity overview and description:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/C_elegansActivity/Overview_Description.pdf">Searching
      for new antibiotics using digital images of infected worms
      (.pdf)</a>
    </p>

    <p align="left">
      <strong>Materials:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/C_elegansActivity/Images.zip">Images of <i>C.
      elegans</i> worms (.TIF)</a>, <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/C_elegansActivity/Pipelines.zip">Pipelines</a>
    </p>

    <p align="left">
      <strong>Written exercise:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/C_elegansActivity/C_elegans_CellProfiler_EducationalExercise.pdf">
      (PDF)</a>
    </p>
  </blockquote>
  <a id="translocation"></a>
  <h1 align="left">
    Image-based screening for quantifying a translocation assay written
    exercise
  </h1>

  <blockquote>
    <p align="left">
      This exercise will demonstrate how image analysis can be used for
      novel drug discovery. Students will use CellProfiler identify and
      delineate cells and cellular sub-compartments, and collect and
      store measurements from each cell. Afterwards, they will use
      CellProfiler Analyst to visualize their data, and use its machine
      learning tool to train the computer to distinguish between treated
      and untreated cells
    </p>

    <p align="left">
      <strong>Activity overview and written exercise:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/TranslocationActivity/TranslocationData_CP_CPA_Exercise.pdf">
      Image-based screening using subcellular localization of FOXO1A in
      osteosarcoma cells (.pdf)</a>
    </p>

    <p align="left">
      <strong>Materials:</strong> <a href=
      "http://cellprofiler-org.s3.amazonaws.com/TranslocationData.zip">Images
      and text file of experimental parameters (.zip)</a>
    </p>
  </blockquote>
  <a id="hepfibcocx"></a>
  <h1 align="left">
    Using cellular co-culture platforms as a tool for drug discovery
    written exercise
  </h1>

  <blockquote>
    <p align="left">
      This exercise will introduce students to the image-analysis
      challenges in using co-culture systems as part of a novel <i>in
      vitro</i> liver model. Students will use CellProfiler to generate a
      suite of cellular measurements, followed by training the supervised
      learning tool in CellProfiler Analyst to discriminate between
      hepatocytes and fibroblasts. The student will then learn how
      including additional tailored image features can improve
      classification.
    </p>

    <p align="left">
      <strong>Activity overview and written exercise:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/HepatocyteCocxActivity/exercise.pdf">Discovering
      drugs by using mixed cell cultures (.pdf)</a>
    </p>

    <p align="left">
      <strong>Materials:</strong> <a href=
      "http://d1zymp9ayga15t.cloudfront.net/content/HepatocyteCocxActivity/input_data.zip">Images,
      pipeline and text file of experimental parameters (.zip)</a>
    </p>
  </blockquote>