Bench Press

Watch out. That person who just fragged you in Counter-Strike might’ve been using eye-tracking software to do so. COGAIN, an EU-funded network of excellence on Communication by Gaze Interaction. While most eye-tracking software is used simply for typing on an on-screen keyboard, COGAIN enhances eye-tracking capability by providing a real-world gaming experience. Aimed at helping those disabled with motor neuron disease and cerebral palsy, COGAIN’s eye-tracking software allows users to play computer games with just the movement of their eyes. Their innovative technology uses commercially available eye-trackers which use cameras to monitor the gamer’s eyes, and COGAIN’s intuitive interface allows for a user-friendly experience.

Glancing to the left or right will turn the virtual character in that direction, for example, while staring at the centre of the screen will make the avatar run forwards. Because the software is independent of the game itself, it can be used to play virtually any game that requires mouse and keyboard inputs.

Currently, COGAIN has pre-programmed 12 different gesture sequences which will activate different keyboard and mouse events. More commands can be programmed if the user wishes. COGAIN hopes their free software will be fast enough to play real-time 3D games.

COGAIN’s eye-tracking software is particularly refreshing because it demonstrates how technology can impact and aid the disabled. As technology evolves and progresses, it’s important that we not only think up what sort of applications we can adapt technology to we should be mindful of people with disabilities and allow them to have as rich a computer experience as others.

As Ben mentioned on Monday, we at Bench Press were disappointed that we were unable to attend the 2009 Science Online London Conference, but we were glad to see the amazing coverage within the blogosphere. One of the panels I was interested in was the first breakout session titled “What is a scientific paper?”. A discussion on the essence of scientific papers could be incredibly enlightening about steps needed to modernize scientific communication and publishing.

Having read through various notes and posts about the panel I have to agree with Cameron Neylon that while the panel’s discussion on methods to modernize papers themselves had some interesting ideas, a key issue with papers was glossed over; their continued publication in antiquated vessels known as journals. As Cameron Neylon writes:

The journal used to play an important role in publication. The publisher still has an important role but we need to step outside the notion of the journal and present different types of content and objects in the best way for that set of objects. The journal as brand may still have a role to play although I think that is increasingly going to be important only at the very top of the market. The idea of the journal is both constraining our thinking about how best to publish different types of research object and distorting the way we do and communicate science. Data publication should be optimized for access to and discoverability of data, software publication should make the software available and useable. Neither are particularly helped by putting “papers” in “journals”. They are helped by creating stable, appropriate publication mechanisms, with appropriate review mechanisms, making them citeable and making them valued. The point at which our response to needing to publish things stops being “well we’d better create a journal for that” then we might just have made it into the 21st century.

Cameron argues effectively that the journal, as used today, does little if anything to optimize access and discovery of data thereby constraining scientific communication and handicapping scientific progress.

While the panel may not have addressed this key issue thoroughly enough, I was happy to see that PLoS has taken a first step to address the limitations of the traditional journal with their new project PLoS Currents: Influenza. As described in their FAQ PLoS Currents: Influenza is

a website for immediate, open communication and discussion of new scientific data, analyses, and ideas in a critical research area. Submissions are screened by a group of leading researchers in the field, and those deemed appropriate are posted immediately and publicly archived at the National Center for Biotechnology Information (NCBI). All content is open access, available under the terms of the Creative Commons Attribution License.

The explicit goal of this project is to provide easy and efficient access to data on Influenza, in hopes that scientific discussion and breakthroughs can be made in a more timely manner. While PLoS still differentiates Currents from Journals, their step out of the typical scientific publishing space with this project is commendable and looks like a great first step at expanding scientific publication. Hopefully this experiment works out and other publishers will begin to experiment as well.

For more coverage on PLoS Currents: Influenza, Bora Zivkovic has a great introductory post.

Here at Bench Press, we’re very interested in how to leverage new internet technologies to help scientists work better, collaborate more effectively, and reach out to the general public in a meaningful way. So, when we found out we wouldn’t be able to make it to the 2009 Science Online London conference (or solo09 as the Twitter-verse seems to be calling it), we were disappointed to say the least (although for some reason it completely escaped our mind that we could’ve attended for a nominal fee via Second Life).

Thankfully, the people who attend a conference dedicated to talking about better ways for scientists to use the internet are also the most likely to live-blog the event. So, thanks to the always-wonderful Allyson Lister over at the Mind Wobbles, the public has access to an avid conference blogger’s account of the conference. And, as a bonus, on each of her posts, she’s also linked to the FriendFeed discussions of each of the panels!

The two sessions I found most interesting (and wished I had been there to see) were:

• The live-demo of the not-yet-public Google Wave by Cameron Neylon, Chris Thorpe, and Ian Mulvany and the discussion of its potential as a means for collaboration (Google, if you’re listening, I’m still waiting for my chance to try it!)
• The discussion on how science communication will be done in 50 years with science fiction author John Gilbey. I’ll be honest, I couldn’t quite tell from Allyson’s notes what exactly happened, but when the notes talk about creating a “University of Rural England where things are not always as they seem” with “machine-enhanced clairvoyance for science quality auditors” and “a temporal portal to allow historic research leaders to be employed on projects”, you know it must’ve been fascinating.

A picture may be worth a thousand words, but a video is worth, depending on the frame rate, a thousand pictures.

We previously posted a breathtaking set of pictures showing just how large the universe really is. But, although the images conveyed a sense of what is now referred to as the Ultra Deep Field but you haven’t seen anything, until you’ve watched a video construction of what the ultra-deep field looks like in 3D (courtesy of Tony Darnell at DeepAstronomy.com):

The key quote from the video:

“We pointed the most powerful telescope ever built by human beings at absolutely nothing for no other reason than because we were curious, and discovered that we occupy a very tiny place in the heavens.”

My thoughts:

1. Did you not get it just by watching the video? We occupy a very tiny place in the heavens.
2. Science video is a great way of reaching out to the public and communicating in a way that pure pictures and text cannot.
3. There’s something to be said about the spirit and essence of the scientific community: willing to explore “nothing” for the sake of exploring it, and still deriving great value from it.

If you like the video, check out Tony’s very informative (and amateur astronomer-friendly) site.

We previously discussed using the powerful Wolfram|Alpha tool to look up medical/biological information, but did you know it also works for astronomical information also?

The Wolfram|Alpha blog lists a couple of great tricks, including:

• the ability to identify the stars in the sky based on your location
• looking up information about a specific star’s distance, brightness, spectral class (astronomy nerds out there know the “Oh Be A Fine Girl Kiss Me!” classification system), mass, and even surface temperature!
• looking up information about the next lunar and solar eclipses, sunrises, and sunsets
• looking up general information about the planets in the solar system

What I find most impressive, however, is Wolfram|Alpha’s ability to generate graphical depictions of the information you’re looking for, whether it be understanding what you’ll see when you look up in the sky:

Visualizing the path of the sun for a particular day:

Or getting a sense of the 3-body-configuration of the sun, moon, and earth:

Chalk this up as yet another cool thing you can do with Wolfram|Alpha!

(Image credit: Wolfram|Alpha and Wolfram|Alpha blog)

If you’ve surfed around the internet, you’ve surely seen the various websites which have a panel of judges evaluate whether certain people meet some standard of attractiveness and consequently label them as “hot or not.” Most of these websites, while highly amusing, hardly set a gold standard for evaluating beauty and attractiveness. However, research done by Amit Kagian, an M.Sc. graduate from the Tel Aviv University School of Computer Science, have shown that computers can be programmed to detect attractiveness in female faces and may bring a whole new meaning to what’s hot and what’s not.

While computers have been programmed to detect basic facial characteristics, for example whether a given face is male or female, Kagian’s program is also asked to make a judgment call on aesthetics. While hard-wired to look specifically for face symmetry, skin smoothness, and hair color, Kagian’s computer program also received the results of a survey from a group of people who were asked to rate the attractiveness of a list of faces which the program uses to analyze and learn what people generally consider to be “attractive.”

Says Kagian, “The computer produced impressive results – its rankings were very similar to the rankings people gave.” This is considered a remarkable achievement, believes Kagian, because it’s as though the computer “learned” implicitly how to interpret beauty through processing previous data it had received.

Kagian’s breakthrough highlights another breakthrough for artificial intelligence. We’ve seen how difficult it is for computers to interpret data, whether it’s categorizing different items or answering questions in Jeopardy. However, Kagian’s program shows how computers can even learn human habits and pinpoint the salient features humans look for when they view an object for the first time. While humans are pushing the boundary of what computers are capable of, we, too, are discovering more about ourselves.

One challenge with getting scientists to collaborate over the internet is the difficulty of representing scientific data in a way that can be readily manipulated and analyzed. Take Chemistry for an example. How does one share information about pathways and chemical structures in a way which allows for an entire group to collaborate on particular problems (e.g. synthesis pathways)?

Imaginitik, a startup specializing in software to help companies and institutions use crowdsourcing, and partially funded by Pfizer, has one such idea (HT: VentureBeat). Most scientists who are working or have worked in chemistry or biology are familiar with software company CambridgeSoft’s scientific software products like ChemDraw or BioDraw. What Imaginitik did was combine CambridgeSoft’s software with the collaborative features of Imaginitik’s Idea Central software to create ChemBioConnect, a crowdsourcing platform for a company or institution to deploy.

The idea is pretty simple. Imagitinik’s Idea Central platform creates web portal where scientists and management can list topics that can benefit from a multi-person collaborative approach and organize responses/suggestions/workflow and to rate individual ideas and contributions. But what differentiates ChemBioConnect from other life sciences collaboration solutions or more generic crowdsourcing platforms is integration with ChemBioDraw’s interface which provides more features than a standard collaboration platform (which will only let you share pictures/text) and a more familiar and robust user-interface than other life sciences-targeted solutions. Interestingly, Imagitinik’s platform also allows the creation of personality profiles (e.g. “creative” or “inquisitive”) to better help scientists network and target the right set of people to solve these problems. Not surprisingly, Imagitinik’s funder Pfizer has been rolling out this solution since Spring 2009!

A poorly scripted demo video is below (I personally think the speaker focuses too much time on basic ChemDraw functionality and less time on how this ties together with the collaborative features for my taste):

I, unfortunately, haven’t had the chance to actually try out the software (although reasonable pricing for enterprise software, I don’t have $50,000 – $500,000 to shell out to evaluate the software), but I think this is a great look into what a prototype for scientific collaborative software:

• Web-based: The need for ease of access across many machines and locations and the need for a central repository with which to organize a group’s information generally means that collaborative platforms should be web-based or, if not, sufficiently web-like as to not be an issue.
• Social networking features: It doesn’t have to be a full-fledged version of Facebook or MySpace, but a collaborative tool should encourage its users to network with one another and allow people to show off what projects they’ve contributed to. Not doing this fails to create the sense of community and personal attachment that crowdsourcing/community collaboration need
• Integration with existing tools: It’s a sad fact of life that inertia is a big factor when people are deciding whether or not to use something. But it’s a fact nevertheless. The best way to encourage quality adoption is to make sure that tools that are commonly used by the target user base tie in nicely for two reasons. First, new users won’t have to learn a new set of techniques, interfaces, and processes to adopt. And secondly, the tools that currently exist oftentimes support features that are harder to develop and more useful than developers of new platforms would like to admit. Sure, lots of people (including this humble commentator) have bashed ChemDraw as clunky and awkward, but someone developing a chemistry crowdsourcing platform is likely to skimp on things like NMR-simulation or smooth rotation of a structure.
• Managed workflow: Collaboration, even face-to-face, can be very difficult because information and suggestions and ideas are not organized effectively. It’s not enough to let people share their information and insights. You have to organize them and create tools with which to evaluate and encourage action on them.

As I haven’t actually put my hands on the software, I’m not sure if ChemBioConnect already supports these, but there are two additional features that I’d strongly suggest a collaboration platform to have:

• Easy way to export work: Too often, developers of a platform or tool forget that there is a world beyond their innovations. This is especially true when people are testing out a piece of software for the first time – it’s important that they can quickly move a piece of work off the tool to integrate with the rest of their work schedule, whether it be in printed form, in the form of a presentation, on a PDF, in web page/HTML form, or even just as a industry file format to share with another. Going the extra mile to make this easy makes it easier for someone to try out your software as well as provides a valuable service that just may win an adopter over.
• Semantics: This is harder to describe, but many web-based tools are very rigid, requiring a user to identify exactly what they want to do and figure out what part of the website is best suited for that particular type of work. Better, instead, to apply semantics/language processing to figure this out for the user. One example of a product that has done this is Google Calendar. Instead of requiring a user to try to figure out which fields correspond to what data when trying to create a calendar entry, a user can simply enter “Lunch with Jenny at Chez Carla on Sept 9, 2009 from 9 PM to 11 PM”. Google will decode the string and fill in the appropriate data. This feature is especially powerful for a collaborative tool where a user doesn’t want to have to figure out if something is a “task” or an “event” or an “idea” and doesn’t want to have to memorize what each of the tool’s special quirks and vocabulary are.

Does anyone else have any thoughts on ChemBioConnect or on other principles of good collaborative tool design?

Previously, Ben wrote a post about innovative use of the virtual world Second Life for simulating N-body problems. One of the groups behind the impressive OpenSim mod, the Meta Institute of Computational Astrophysics (MICA), is incredibly unique in that the organization itself is an exploration into the utility of emerging virtual world (VW) technologies (e.g. SecondLife) for scientific and academic work.

A group of scientists from the California Institute of Technology, Princeton, Drexel University, and the Massachusetts Institute of Technology founded MICA in the spring of 2008 in order to explore and take advantage of what they saw as a new frontier in collaboration and information dissemination. MICA’s goals are¹:

• Exploration, development and promotion of VWs and VR technologies for professional research in astronomy and related fields.
• To provide and develop novel social networking venues and mechanisms for scientific collaboration and communications, including professional meetings, effective telepresence, etc.
• Use of VWs and VR technologies for education and public outreach.
• To act as a forum for exchange of ideas and joint efforts with other scientific disciplines in promoting these goals for science and scholarship in general.

In addition to the collaborative research we’ve written about before MICA also “conducts weekly professional seminars, bi-weekly popular lectures, and many other regularly scheduled and occasional professional discussions and public outreach events, all of them in [SecondLife].” A screenshot of one of their astrophysics seminars can been seen below. MICA has also begun experimenting with various teaching formats for undergraduate and graduate level courses.

MICA members attending a weekly astrophysics seminar by Dr. M. Trenti, given in the StellaNova sim in SecondLife.

What really impresses me about MICA however is their belief in the platform.

[W]e wish to lead by example, and demonstrate the utility of VWs and immersive VR environments generally for scientific research in fields other than humanities and social sciences (where we believe the case is already strong). In that process, we hope to define the “best practices” and optimal use of VR tools in research and education, including scholarly communications. This is the kind of activity that we expect will engage a much broader segment of the academic community in exploration and use of VR technologies. Second, we hope to develop new research tools and techniques, and help lay the foundations of the informational environments for the next generation of VR-enabled Web.

Hopefully MICA’s innovative use of SecondLife will prompt other scientists to follow. I definitely want to check out one of the lectures one of these days.

(Exploring Use of Virtual Worlds as a Scientific Research Platform: The Meta-Institute for Computational Astrophysics)

In case you weren’t aware, biology is really complicated.

It’s complicated not only because understanding specific proteins and pathways is difficult, but because understanding how a biological system of many proteins and pathways functions is even more difficult. In recent years, however, computer technology has made it possible to convert vast databases of biological information into an understanding not only of how individual genes and pathways work, but of how those individual genes and pathways work together.

The Diseasome project is one such project (and one of many -ome/omics words that you’ll encounter in the field of biology) which has converted human gene-disease relationship data from NCBI’s Online Mendelian Inheritance in Man (OMIM) into an annotated network exploring the genetic relationships between all the diseases covered in OMIM. The picture above is a small piece of the full poster (warning the file size is 20 MB) available at the site. An interactive version of the map is also available at the web page (and includes many links to Wikipedia entries explaining each of the genes and diseases) and is a fascinating browse-through for anyone who’s even remotely interested in how new network analysis techniques may be used in understanding human disease.

The Barbabasi Lab at Northeastern published an interesting paper in PNAS about how the data was compiled and analyzed for insights into how diseases and disease genes are connected and how that differs from how “normal” genes are connected. Although the work is subject to the standard Garbage-in-Garbage-Out criticism (if the data being entered is facetious or not necessarily relevant to the study then the results aren’t necessarily relevant or good) and the conclusions thus far are still relatively generic, two conclusions immediately stood out to me.

The first conclusion that jumped out at me was a very interesting analysis done where the researchers shuffled the precise disease-to-gene relationships while keeping the total number of relationships per gene and per disease the same (and repeated this 10,000 times). The finding from that analysis was that the “network clusters” from a random shuffle tended to be much larger than the actual network cluster size or, in other words, diseases which are genetically related to one another tend to be 8 times more related to one another than one would expect at random. This suggests that there are probably types of disease gene profiles with which most diseases tend to cluster around, something which ties to the groups finding that genes which are “shared” by multiple diseases tend to encode proteins which interact with one another!

The second striking conclusion was that disease-associated genes tend to interact and associate with fewer genes than non-disease associated genes. This is in sharp contrast to diseases like cancer which arise from somatic mutations (mutations which happen after birth and are not passed down from past generations) which almost always affect genes which interact with many other genes. The reasoning the paper gives, while speculative, rings true to me:

“This unexpected peripherality of most disease genes can be best explained by using an evolutionary argument. Mutations in topologically central, widely expressed genes are more likely to result in
severe impairment of normal developmental and/or physiological function, leading to lethality in utero or early extrauterine life and to eventual deletion from the population. Only mutations compatible with survival into  the reproductive years are likely to be maintained in a population.”

I’m sure I’m not the only one who eagerly awaits what other insights can arise from mining large biological databases for network information: the holy grail, of course, being enhancements to the quality of medical diagnoses and treatments.

(Image credit – Diseaseome poster)