WEBVTT
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Hi, I'm Blaine Mooers.
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I'm going to be talking about
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the use of molecular graphics in Org
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for the purpose of doing
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reproducible research in structural biology.
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I'm an associate professor of biochemistry
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and microbiology at the University of Oklahoma
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Health Sciences Center in Oklahoma City.
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My laboratory uses X-ray crystallography
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to determine the atomic structures
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of proteins like this one
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in the lower left, and of nucleic acids
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important in human health.
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This is a crystal of an RNA,
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which we have placed in this
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X-ray diffraction instrument.
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And after rotating the crystal
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in the X-ray beam for two degrees,
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we obtain this following diffraction pattern,
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which has thousands of spots on it.
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We rotate the crystal for over 180 degrees,
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collecting 90 images to obtain all the data.
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We then process those images
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and do an inverse Fourier transform
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to obtain the electron density.
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This electron density map has been
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contoured at the one-sigma level.
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That level's being shown by
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this blue chicken wire mesh.
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Atomic models have been fitted
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to this chicken wire.
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These lines represent bonds between atoms,
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atoms are being represented by points.
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And atoms are colored by atom type,
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red for oxygen, blue for nitrogen,
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and then in this case,
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carbon is colored cyan.
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We have fitted a drug molecule
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to the central blob of electron density
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which corresponds to that active site
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of this protein, which is RET Kinase.
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It's important in lung cancer.
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When we're finished with model building,
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we will then examine
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the result of the final structure
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to prepare images for publication
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using molecular graphics program.
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In this case,
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we've overlaid a number of structures,
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and we're examining the distance between
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the side chain of an alanine
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and one or two drug molecules.
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This alanine sidechain actually blocks
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the binding of one of these drugs.
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The most popular program
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for doing this kind of analysis
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and for preparing images
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for publication is PyMOL.
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PyMOL was used to prepare these images
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on the covers of these featured journals.
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PyMOL is favored because
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it has 500 commands
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and 600 parameter settings
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that provide exquisite control
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over the appearance of the output.
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PyMOL has over 100,000 users,
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reflecting its popularity.
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This is the GUI for PyMOL.
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It shows in white the viewport area
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where one interacts
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with the loaded molecular object.
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We have rendered the same RET kinase
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with a set of preset parameters
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that have been named "publication".
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The other way of applying
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parameter settings and commands
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is to enter them at the PyMOL prompt.
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Then the third way is to load and run scripts.
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PyMOL is actually written in C for speed,
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but it is wrapped in Python for extensibility.
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In fact, there are over 100 articles
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about various plugins and scripts
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that people have developed
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to extend PyMOL for years.
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Here's some examples
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from the snippet library that I developed.
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On the left is a default
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cartoon representation of a RNA hairpin.
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I find this reduced representation
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of the RNA hairpin to be too stark.
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I prefer these alternate ones
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that I developed.
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So, these three to the right of this one
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are not available through
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pull downs in PyMOL.
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So why developed a PyMOL
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snippet library for Org?
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Well, Org provides great support
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for literate programming,
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where you have code blocks
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that contain code that's executable,
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and the output is shown
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below that code block.
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And then you can fill
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the surrounding area in the document
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with the explanatory prose.
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Org has great support
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for editing that explanatory prose.
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Org can run PyMOL through PyMOL's Python API.
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One of the uses of such an Org document
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is to assemble a gallery of draft images.
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We often have to look at
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dozens of candidate images
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with the molecule in different orientations,
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different zoom settings,
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different representations,
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different colors, and so on.
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And to have those images along with…,
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adjacent to the code
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that was used to generate them,
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can be very effective for
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further editing the code
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and improving the images.
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Once the final images have been selected,
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one can submit the code
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as part of the supplemental material.
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Finally, one can use the journal package
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to use the Org files as
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an electronic laboratory notebook,
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which is illustrated with molecular images.
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This can be very useful
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when assembling manuscripts
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months or years later.
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This shows the YASnippet pull down
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after my library has been installed.
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I have an Org file open,
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so I'm in Org mode.
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We have the Org mode submenu,
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and under it, all my snippets
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are located in these sub-sub-menus
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that are prepended with pymolpy.
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Under the molecular representations menu,
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there is a listing of snippets.
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The top one is for the ambient occlusion effect,
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which we're going to apply
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in this Org file.
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So these lines of code were inserted after,
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as well as these flanking lines
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that define the source block,
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were inserted by clicking on that line.
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Then I've added some additional code.
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So, the first line defines
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the language that we're using.
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We're going to use
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the jupyter-python language.
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Then you can define the session,
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and the name of this is arbitrary.
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Then the kernel is our means
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by which we gain access
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to the Python API of PyMOL.
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The remaining settings apply to the output.
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To execute this code
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and to get the resulting image,
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you put the cursor inside this code block,
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or on the top line,
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and enter Control c Control c (C-c C-c).
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This shows the resulting image
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has been loaded up.
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It takes about 10 seconds for this to appear.
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So the downside of this is
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if you have a large number of these,
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the Org file can lag quite a bit
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when you try to scroll through it,
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so you need to close up these result drawers,
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and only open up the ones
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that you're currently examining.
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These are features I think
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are important in practical work.
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So, the plus is, a feature that's present,
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minus is absent.
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I think tab stops and tab triggers
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are really important.
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Triggers are important for
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the fast assertion code,
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tab stops are important for
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complete, accurate editing of code.
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I already addressed the rendering speed
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and scrolling issue.
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I think the way around this
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is just to export the Org document to a PDF file
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and do your evaluation of different images
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by examining them in the PDF
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rather than the Org file.
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The path to PDF is lightning fast in Emacs
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compared to Jupyter,
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where it's cumbersome in comparison.
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This is a snapshot of my initialization file.
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These parts are relevant to doing this work.
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A full description of them
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can be found in the README file
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of this repository on GitHub.
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I'd like to thank the
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Nathan Shock Data Science Workshop
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for feedback during presentations
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I've made about this work.
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And I would also like to thank
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the following funding sources for support.
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I will now take questions. Thank you.
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[captions by Blaine Mooers and Bhavin Gandhi]
