October 20, 2016

OpenWorm Blog: Announcing the OpenWorm Open House 2016

The content is being cross-posted from the OpenWorm blog, and will be updated periodically.

Hello Everybody!

We want to announce our first Open House for 2016 that will happen on October 25th from 10:30am to 4pm EST (UTC-4) (check here for your timezone), so mark the date on your calendars! The event will be live streamed at this link.

If you were waiting for an opportunity to look at the recent progress we’ve made across all the projects, this is your chance. During the meetings many contributors will present a number of flash talks and various demos, so if you are interested to hear the latest about PyOpenWorm, c302, Sibernetic, Geppetto, Analysis toolbox or any other thing happening under our roof don’t miss this opportunity!

Click below for the schedule of events.

Streamed Online:

10:30 AM - 11AM: Welcome (Stephen Larson)

Flash talks
          11:00 - 11:05: Recent progress in OpenWorm (Stephen Larson)
          11:10 - 11:15: C. elegans nervous system simulation (Padraig Gleeson)
          11:20 - 11:25: C. elegans body simulation (Andrey Palyanov)
          11:30 - 11:35: OpenWorm Badge System (Chee-Wai Lee)
          11:40 - 11:45: DevoWorm Overview (Bradly Alicea)
          11:50 - 11:55: Neuroinformatics (Rick Gerkin)
          12:00 - 12:05: Geppetto (Matteo Cantarelli)
          12:10 - 12:15: Movement Validation (Michael Currie)
          12:20 - 12:25: WormSim (Giovanni Idili)

On social media channels
          12:30 - 1:30: Social media interactions & break out signup

Streamed online (links to be added)
          1:30 PM - 3:00: Multiple track breakout sessions
                    Morphozoic Tutorial - Tom Portegys

On social media channels
3:00 - 3:30: Wrap up & Social Media Networking

Oh and bring along your nerdy friends, the more the merrier!

Hope to see you there!

The OpenWorm team

September 23, 2016

Learning by Doing, Where Doing is Earning Badges

As a member of the OpenWorm Foundation community committee (see previous post), we have been trying to find a means of engaging potential contributors within the context of the various projects. One type of activity is the Badge, a bite-sized [1] learning opportunity that we plan to use as both certifications of competency and concrete goals for the various projects. The OpenWorm Badge System is being spearheaded by Chee-Wai Lee, and is an emerging method in Educational Technology [2]. More details about this will be shared to the community by Chee-Wai in the form of a tutorial at the upcoming OpenWorm Open House.

An example of how semantic data on phenotypes can be extracted from the scientific literature. PICTURE: Tagxedo.com, BLOGPOST: Phenoscape blog

Each badge is designed to impart a specific skill. The OpenWorm badge system currently covers scientific topics (Muscle Model Builder, Hodgkin-Huxley) and research skills (Literature Mining). My contribution is the Literature Mining (LM) series. Literature mining is a technique used to organize the scientific literature, extract useful metadata (e.g. semantic data) from these sources, and identify secondary datasets for re-analysis [3]. Learning skills in Literature Mining will be useful to a wide range of badge earners, particularly those interested in Bioinformatics and Open Science research. These are skills used extensively in the DevoWorm project, and we will be planning more badges on related topical areas in the future.

The first LM badge is focused on working with the scientific literature, while the second (LMII) badge introduces learners to open-access secondary datasets. The only prerequisite is that you must earn Badge I in order to earn Badge II. Both of these badges recently went live, and you may start working on them immediately.

Example of the badge curriculum for LMI. The badgelist system requires learners to complete each step one at a time, and then request feedback (if applicable) from the Admin (e.g. instructor).

[1] why not "byte-sized", you say? Well, the Literature Mining badges are almost byte-sized (seven requirements apiece), so you could say that we are headed in that direction!

[2] Ferdig, R. and Pytash, K. (2014).  There's a badge for that. Tech and Learning, February 26.

[3] For examples of how Literature Mining can be useful, please see the Nature site for news on literature mining research.

September 6, 2016

Now Announcing the OpenWorm Open House

OpenWorm Browser. Courtesy Christian Grove, WormBase and Caltech.

About two years ago, I announced the start of the DevoWorm project to the OpenWorm community. Now both OpenWorm and DevoWorm have grown up a bit, with the former (OpenWorm) now being a Foundation and the latter (DevoWorm) resulting in multiple publications. Now we will be celebrating all of the projects that make up the OpenWorm Foundation in an Open House format, taking place in cyberspace and tentatively scheduled for October.

Image courtesy Matteo Farinella: http://matteofarinella.com/Open-Worm. These posters are the outcome of an OpenWorm Kickstarter campaign several years ago.

The details of the schedule are still being worked out, but the format is to include both short, 5-minute talks (Ignite-style) and longer tutorials (45-60 minutes, plus questions). The short talks will highlight the various ongoing projects within OpenWorm, while the tutorials will focus on specific methods or procedures employed by the projects. If you happen to be a project leader or major contributor, I have probably already asked you for content. Interested in either contributing content or attending? Please let me know

Dr. Stephen Larson (pre-PhD), discussing the connection between Lt. Data and C. elegans at Ignite San Diego.

I have also been involved in committee work for the OpenWorm foundation. One of the initiatives we are in the process of establishing is the OpenWorm badge system, which is being spearheaded by Dr. Chee-Wai Lee. Currently trendy in the online learning world, this is an experiment in open learning that provides micro-credentials to a global community. Badges are a great way to learn new skills, as well as a means to motivate people's contributions to different projects within OpenWorm. Currently, OpenWorm is offering tutorials on the Hodgkin-Huxley model, the Muscle Model builder, and the Muscle Model explorer. If there are any tutorials you would like to see us offer, or if you think there is a need for a particular skill to be highlighted, please let me know.

August 19, 2016

From Toy Models to Quantifying Mosaic Development

Time travel in the Terminator metaverse. COURTESY: Michael Talley.

Almost two years ago, Richard Gordon and I published a paper in the journal Biosystems called "Toy Models for Macroevolutionary Patterns and Trends" [1]. Now, almost exactly two years later [2], we have published a second paper (not quite a follow-up) called "Quantifying Mosaic Development: towards an evo-devo postmodern synthesis of the evolution of development via differentiation trees of embryos". While the title is quite long, the approach can be best described as computational/ statistical evolution of development (evo-devo).

Sketch of a generic differentiation tree, which figures prominently in our theoretical synthesis and analysis. COURTESY: Dr. Richard Gordon.

This paper is part of a special issue in the journal Biology called "Beyond the Modern Evolutionary Synthesis- what have we missed?" and a product of the DevoWorm project. The paper itself is a hybrid theoretical synthesis/research report, and introduces a variety of comparative statistical and computational techniques [3] that are used to analyze quantitative spatial and temporal datasets representing early embryogenesis. Part of this approach was previewed in our most recent public lecture to the OpenWorm Foundation.

The comparative data analysis involves investigations within and between two species from different parts of the tree of life: Caenorhabditis elegans (Nematode, invertebrate) and Ciona intestinalis (Tunicate, chordate). The main comparison involves different instances of early mosaic development, or a developmental process that is deterministic with respect to cellular fate. We also reference data from the regulative developing Axolotl (Amphibian, vertebrate) in one of the analyses. All of the analyses involve the reuse and analysis of secondary data, which is becoming an important part of the scientific process for many research groups.

One of the techniques featured in the paper is an information-theoretic technique called information isometry [4]. This method was developed within the DevoWorm group, and uses a mathematical representation called an isometric graph to visualize cell lineages organized in different ways (e.g. a lineage tree vs. a differentiation tree). This method is summarized and validated in our paper "Information Isometry Technique Reveals Organizational Features in Developmental Cell Lineages" [4]. Briefly, each level of the cell lineage is represented as an isoline, which contains points of a specific Hamming distance. The Hamming distance is the distance between that particular cell in two alternative cell lineage orderings (the forementioned lineage and differentiation trees).

An example of an isometric graph from Caenorhabditis elegans, taken from Figure 12 in [5]. The position of a point representing a cell is based on the depth of its node in the cell lineage. The positions of all points are rotated 45 degrees clockwise from a bottom-to-top differentiation tree (in this case) ordering, where the one-cell stage is at the bottom of the graph.

A final word on the new Biology paper as it related to the use of references. Recently, I ran across a paper called "The Memory of Science: Inflation, Myopia, and the Knowledge Network" [6], which introduced me to the statistical definition of citation age. This inspired me to calculate the citation age of all journal references from three papers: Toy Models, Quantifying Mosaic Development, and a Nature Reviews Neuroscience paper from Bohil, Alicea (me), and Biocca, published in 2011. This was used as an analytical control -- as it is a review, it should contain papers which are older than the contemporary literature. Here are the age distributions for all three papers.

Distribution of Citation Ages from "Toy Models for Macroevolutionary Patterns and Trends" (circa 2014).

Distribution of Citation Ages from "Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development Via Differentiation Trees of Embryos" (circa 2016).

Distribution of Citation Ages from "Virtual Reality in Neuroscience Research and Therapy" (circa 2011).

What is interesting here is that both "Toy Models" and "Quantifying Mosaic Development" show a long tail with respect to age, while the review article shows very little in terms of a distributional tail. While there are differences in topical literatures (the VR and associated perceptual literature is not that old, after all) that influence the result, it seems that the recurrent academic Terminators utilize the literature in a way somewhat differently than most contemporary research papers. While the respect for history is somewhat author and topically dependent, it does seem to add a extra dimension to the research.

[1] the Toy Models paper was part of a Biosystems special issue called "Patterns in Evolution".

[2] This is a Terminator metaverse reference, in which the Terminator comes back every ten years to cause, effect, and/or stop Judgement Day.

[3] Gittleman, J.L. and Luh, H. (1992). On Comparing Comparative Methods. Annual Review of Ecology and Systematics, 23, 383-404.

[4] Alicea, B., Portegys, T.E., and Gordon, R. (2016). Information Isometry Technique Reveals Organizational Features in Developmental Cell Lineages. bioRxiv, doi:10.1101/062539

[5] Alicea, B. and Gordon, R. (2016). Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development Via Differentiation Trees of Embryos. Biology, 5(3), 33.

[6] Pan, R.K., Petersen, A.M., Pammolli, F., and Fortunato, S. (2016). The Memory of Science: Inflation, Myopia, and the Knowledge Network. arXiv, 1607.05606.

August 3, 2016

Slate and the Solitary Ethnographic Diagram

While his style and message does not resonate with me at all, I've always thought that Donald Trump's speeches were highly-structured rhetoric. He seems to be using a form of intersubjective signaling [1] understood by a number of constituencies as communicating their values in an authentic manner. Specifically, the speeches have a sentence structure and cadence that can be differentiated from the literalism of contemporary mainstream society or more traditional forms of doublespeak ubiquitous in American politics.

This is why the most recent challenge from Slate Magazine was too good to pass up. The challenge (which has the feel of a Will Shortz challenge): diagram a passage from a Donald Trump speech given on July 21 in Sun City, South Carolina. The passage is as follows:
"Look, having nuclear—my uncle was a great professor and scientist and engineer, Dr. John Trump at MIT; good genes, very good genes, OK, very smart, the Wharton School of Finance, very good, very smart—you know, if you’re a conservative Republican, if I were a liberal, if, like, OK, if I ran as a liberal Democrat, they would say I’m one of the smartest people anywhere in the world—it’s true!—but when you’re a conservative Republican they try—oh, do they do a number—that’s why I always start off: Went to Wharton, was a good student, went there, went there, did this, built a fortune—you know I have to give my like credentials all the time, because we’re a little disadvantaged—but you look at the nuclear deal, the thing that really bothers me—it would have been so easy, and it’s not as important as these lives are (nuclear is powerful; my uncle explained that to me many, many years ago, the power and that was 35 years ago; he would explain the power of what’s going to happen and he was right—who would have thought?), but when you look at what’s going on with the four prisoners—now it used to be three, now it’s four—but when it was three and even now, I would have said it’s all in the messenger; fellas, and it is fellas because, you know, they don’t, they haven’t figured that the women are smarter right now than the men, so, you know, it’s gonna take them about another 150 years—but the Persians are great negotiators, the Iranians are great negotiators, so, and they, they just killed, they just killed us"
Okay, here you go -- an ethnographic-style diagram [2] based on one man, but perhaps instructive of an entire American subculture (click to enlarge). The diagram focuses on the relationship between John and Donald Trump (context-specific braintrust) and a specific worldview of power wielded through nuclear weapons, financial ability, and persuasion.

[1] In this case, intersubjective signaling could be used as a mechanism to reinforce group cohesion, particularly when the group's belief structure is defined by epistemic closure.

[2] Perceived lack of agency shown as red arcs terminated with a dot.