Bench Press

We’ve blogged before about applying gaming technology to science, but much of that has been about using games or gaming system chips. A recent Wired magazine article reveals another interesting use case: taking the capabilities of something like Microsoft’s Xbox360 Kinect system and applying it directly to science research!

Apparently, a number of groups have decided to try out the Kinect as a “poor man’s” LIDAR (a tool that can be used to see and measure where things are in three dimensions)/complicated 3D camera setups which are expensive and require sophisticated calibration/post-processing analysis.

Of course, the Kinect is not a panacea: it has much more limited range, requires researchers to build their own analytical software, and the Kinect can’t do high-speed video (yet). But, because of its much lower price, its size, and the availability of drivers because of the active Kinect hacking/DIY community (and the support that even Microsoft is providing for people using Kinect beyond gaming), a number of researchers have decided to embrace the Kinect as a scientific tool.

The article profiles two potential use cases which only begin to scratch the surface of what this technology could be capable of: mapping meltwater lakes that form on top of glaciers (see images below) and studying small body impacts in space.

But, potentially the most valuable use of Kinect? As the Wired article puts it:

The Kinect’s best asset may be that it inspires students, Tedesco said. Rather than a daunting black box with convoluted cables and arcane software, the Kinect is something that many students are already familiar with.

“This creates a different mindset in students,” he said. “They’re not so scared about using the Kinect, and they can really get involved in learning and basic research.”

“I’m actually on my way to buy two of them right now,” he added.

(Image credit – Kinect) (Image credit – Kinect glacier map)

A few years ago, I blogged about an ingenious crowdsourced game called Fold.It. The concept was pretty simple:

• Use human intuition to help solve complicated three-dimensional protein folding challenges which is oftentimes as effective but significantly faster & cheaper than computational algorithms
• Pool together lots of human volunteers
• Turn the whole experience into a game to get more volunteers to spend more time

The result was a nifty little game which contributed findings which have made it, to date, into a number of peer-reviewed publications (see PNAS paper here and Nature Structure & Molecular Biology paper here)!

Well some researchers at McGill University in Canada want to take a page out of this playbook with a game they built called Phylo (HT: MedGadget) to help deal with another challenging issue in bioinformatics: multiple sequence alignment. In a nutshell, to better understand DNA and how it impacts life, we need to see how stretches of DNA line up with one another. Now, computers are extremely good at taking care of this problem for short stretches of DNA and for “roughly” aligning longer stretches of DNA – but its fairly difficult and costly to do it accurately for long stretches using computer algorithms.

People, however, are curiously intuitive about patterns and shapes. So, the researchers turned the multiple sequence alignment problem into a puzzle game they’ve called Phylo (see image below) where the goal is to line up multiple colored blocks. Players tackle the individual puzzles (in a browser or even on their mobile phone) and the researchers aggregate all of this into improved sequence alignments which help them better understand the underlying genetics of disease.

And how has it been doing? According to the McGill University press release:

So far, it has been working very well. Since the game was launched in November 2010, the researchers have received more than 350,000 solutions to alignment sequence problems. “Phylo has contributed to improving our understanding of the regulation of 521 genes involved in a variety of diseases. It also confirms that difficult computational problems can be embedded in a casual game that can easily be played by people without any scientific training,” Waldispuhl said. “What we’re doing here is different from classical citizen science approaches. We aren’t substituting humans for computers or asking them to compete with the machines. They are working together. It’s a synergy of humans and machines that helps to solve one of the most fundamental biological problems.”

With the new games and platforms, the researchers are hoping to encourage even more gamers to join the fun and contribute to a better understanding of genetically-based diseases at the same time.

Try it out – I have to admit I’m not especially good with puzzle games, so I haven’t been doing particularly well, but the researchers have done a pretty good job with the design of the game (esp. relative to many other academic-inspired gaming programs that I’ve seen) – and who knows, you might be a key contributor to the next big drug treatment!

Singapore is a fascinating country – despite the lack of what most in the West would recognize as democratic freedom, it consistently ranks well in terms of lack of corruption and high and growing standard of living for its people.

It is also one of the boldest when it comes to instituting policies and reforms: they were the first to implement a congestion tax to help manage traffic. Unlike most countries, Singapore is open to competition and investment from foreigners in strategic areas like telecommunications, power generation, and financial services. Singapore has also been extremely active in attempting to build up its capabilities as a center for life sciences excellence.

So it shouldn’t surprise me that they are among the first countries to actively utilize social media applications like Facebook and Twitter to help deal with a public health risk like Dengue Fever (from The Jakarta Globe):

The city-state’s National Environment Agency (NEA) plans to roll out … providing information on the latest dengue clusters or areas that have been earmarked as high-risk – on these new media platforms within the next three months … Through Facebook and Twitter, the public will also be able to post feedback or provide tip-offs. For example, if Singaporeans notice an increase in the number of mosquitoes in your neighbourhood or find potential breeding sites, they can alert NEA officers by posting on the agency’s Facebook page or tweeting the NEA account. “We need to put more information out in the public space, so more people can be informed and take action,” said Derek Ho, director of the environmental health department at NEA. “Leveraging on new media channels such as Facebook and Twitter is a good way to do that.”

A refreshing understanding of the uses of social media by a government agency – more interesting than that, though, is the work Singapore’s NEA is doing to build image recognition capabilities into smartphone apps like the NEA’s iPhone app to help field workers (and potentially the public) track and identify mosquitos and mosquito larvae!

The NEA is also in the process of developing a mosquito-recognition program that can identify the species of mosquito from a photograph of its pupae or larvae. With such software, and with the help of a mini microscope that attaches to the camera on a personal digital assistant or cellphone, NEA officers will be able to take photographs of larvae or pupae found in mosquito-breeding sites and instantly find out if they belong to the Aedes species, which spreads dengue … When it is ready, the agency hopes to be able to integrate it with the NEA iPhone application, so that the public or grassroots members conducting checks around the neighbourhood can use the technology as well.
Early identification will allow the NEA to act more swiftly to curb the spread of dengue in potential high-risk zones.

Very cool demonstration of the power of smartphones and of a government that is motivated to try out new technologies to tackle serious problems.

I’ve posted before on plush particles, plush organs, knitted dissections, and even plush microbes. It seems in the math/science world, there’s always some creative soul who wants to cute-ify subjects of study.

Well, that set is joined by the latest plush craze… plush… statistical distributions? (HT: Flowing Data). Less stuffed-animal-like per se than say a pillow, but the Etsy store Nausicaa Distribution is selling normal distribution shaped pillows:

If the normal distribution isn’t your thing, how about some of its friends (like t, chi-square, log-normal, uniform, weibull, cauchy, poisson, gumbel, or erlang)?

And not to mention a wide range of statistical-relevant material:

If you’re interested, check out Nausicaa Distribution’s Etsy website!

(Image credit: all from Nausicaa Distribution’s Etsy site)

We’ve commented before about the ability of telemedicine solutions to bring cutting edge technology and quality of care to emerging economies. One example is some of the recent work that the Camera Culture Group from MIT’s Media Lab has done by building a clever optometry solution into an accessory and an application for Android-powered smartphones (HT: Engadget).

The concept is very cool. Many optometrists today use autorefractors, machines which scans the images formed on the back of a patient’s retina to get a rough, but automated, measure on the quality of your vision. Optometrists will then use phoropters to get to a precise enough measure of your eyes as to be able to prescribe lenses for contacts/glasses.

The problem with this approach is that autorefractors and phoropters are too expensive and too time consuming for widespread use in many places around the world. The NETRA solution that the Camera Culture Group came up with was to build an accessory and an application which force a user to make a pair of lines overlap using controls on the phone. Doing this repeatedly lets the application do a calculation similar to what is done by an autorefractor to calculate the quality of the user’s vision in a process which is much faster (several minutes) and cheaper than a standard eye exam (more details in the video and images below)

The project website shows a very compelling table which compares the relative prices and accuracies of optometry solutions in existence today.

It’ll be interesting to see where this technology can go once displays similar to Apple’s Retina Display become cheaper and more prevalent. This example definitely shows the power of telemedicine approaches and is hopefully a harbinger for more equally compelling and innovative solutions for the needs of scientists and doctors around the world.

(Images and video from NETRA website)

The always thoughtful Deepak Singh brings up a great point in a recent post on his personal blog:

Not all data should be published via a peer-reviewed publication. Not every protocol needs to be. But making the data available via wikis, open data resources is pretty much a no-brainer and not just for the future. You enrich currently available data, and have the ability to leverage an additional layer of resources.

Deepak isn’t the only guy to think this, Derek Lowe from In the Pipeline raised a similar point:

Perhaps there should be a way to dump chemical data directly into some archives, the way X-ray data goes into the Protein Data Bank. That wouldn’t count for much, but it would capture things for future use. Having it not count much would decrease the incentive for anyone to fill it full of fakery, too, since there would be even less point than usual. And before anyone objects to having a big pile of non-peer-reviewed chemical data like this, keep in mind that we already have one: it’s called the patent literature, and it can be quite worthwhile.

(all emphases mine)

I think they both have a very good point. Some form of centralized data repository, even if non-peer reviewed, could help tackle the problem that everyone hears about but nobody ever tries to solve of not having a central place to share negative results and protocols (akin to what this blog proposed previously for bio/pharma companies).

It could also help us re-prioritize publication and peer review efforts away from sheer data collation which, while extremely important, is distinct from experimental/study design, data analyses, and drawing conclusions where peer-review is more valuable (there’s only so much peer-review can do to when looking at a data collection effort in isolation).

With modern internet technologies being as fast and as scalable as they are now, there’s simply no reason to use the traditional journal to chronicle every single discovery or achievement. Better to collect most of it in API-accessible/index-able repositories so that others can share in it and curate it and instead focus publications on building analytical insights.

(Image credit)

A recent NBER paper (gated) by Benjamin Jones from Northwestern conducts a systematic review of trends in scientific research and made a couple of conclusions that won’t come as a surprise to anyone in science (HT: Inside Higher Ed):

As science advances and knowledge accumulates, ensuing generations of innovators spend longer in training and become more narrowly expert, shifting key innovations (i) later in the life cycle and (ii) from solo researchers toward teams

As evidence to this, the average age at which a scientist made a discovery which later qualified for a Nobel prize has increased by 6 years over the course of the 20th century. When looking at publications, the researchers found that the average author list on a publication grew, on average, by 15-20% per decade!

We’ve discussed before the “decline of the Lone Ranger model of science”, but Jones’ paper focuses on looking at the policy implications for such a change. He concludes that the government (and, probably, the academic and private institutions which support researchers) need to adapt policy to reflect this new reality by:

• Tailoring funding and messaging to help keep young researchers interested despite the longer and more difficult training period
• Finding new ways to evaluate the worthiness of proposals as scientist’s expertise becomes more and more specialized
• Altering incentive structures as the team of collaborators replaces the Lone Ranger scientist model of discovery

These policy suggestions are definitely good ones, and are certainly necessary to adapt to a new scientific environment, but one dimension of this which Jones doesn’t discuss as much are the technological (the focus of this blog!) innovations which can help further research in this brave new world.

• Improving science communication with the public. We’ve made multiple mentions of this in the past, but they are no less true here. Active public communications management not only helps secure funding and raise public awareness of the good scientists can do, but it also helps attract the interest of future generations of researchers and policymakers.
• Embracing new collaboration tools. To really kick-start collaboration between scientists across geographies and specialties, we need tools that go beyond just email and fax machines. Tools like Google Wave, wikis, distributed version control, and social media forums like Friendfeed are an early taste of the sort of live collaboration that new web technology can bring about.
• Leveraging cloud computing and heterogeneous compute. One of the reasons discoveries are taking longer and are more expensive is that there is so much more data to collect and to analyze than before. One technological innovation which we’ve talked about at lengths here is the ability of graphics cards/GPUs to make supercomputer-level processing power more readily accessible to research labs. Another is the use of new cloud computing services like Amazon’s to rapidly increase the computational resources that a lab/company has access to. Neither are panaceas for all the data analysis issues which scientists face, but they are definitely ways to make things easier for research groups who have stringent IT budgets.
• Using crowdsourcing to speed innovation. Who says research has to take longer and be more expensive? Perhaps its time to pull on new technological levers which let scientists borrow on the resources and brains of a wider group of people. While new platforms like ChemBioConnect, distributed computing systems like Folding@Home, and volunteer crowdsourcing initiatives like Fold.IT are far from perfect, they hint at a future where researchers can call on resources beyond what their personal computers and brains are capable of.
• Building new research attribution models. When I say new attribution models, I’m referring to two things. The first is embodied by new standards like ORCID which make it easier to understand which person is the author/researcher in question (something which will become more and more important as more people with the same initials/names enter the sciences). The second, and more substantive, is finding new ways to understand who contributed what to a particular study. In today’s digital age, I find it laughable that we still rely on simple author list order to determine the relative roles and positions of the researchers listed on a publication. Employing metadata and other graphical cues can help scientists achieve the recognition they deserve, as well as provide appropriate incentives for teams of researchers to contribute.
• Contributing negative and after-publication results to open repository. While I can understand the hesitation for most research groups to pursue a pure open access strategy, those concerns should not hold with negative or post-publication experimental data. While opening up access to data from failed/negative experiments does little to hurt a lab’s ability to publish first, it can be a dramatic boon for other research groups (especially new labs or labs with interdisciplinary focuses) who can not only use the data for their own analyses and experimental designs, but avoid committing resources to experiments which have already been conducted. If it can work for biotechs and pharma companies, then there’s no reason it should be any different for non-commercial groups.

These suggestions only scratch the surface of what new technologies and policies can do to help scientists in a world where scientific training takes longer and where scientific discoveries need to be more collaborative. If anyone else has any other suggestions, feel free to leave them in the comments!

I’ve mentioned before that I’m a sucker for the indirect observational techniques that one engages in when doing astronomy/astrophysics. One phenomena that astronomers have yet to be able to observe in any depth is also one of the most fascinating consequences of Einstein’s theory of General Relativity: gravitational waves.

A rigorous understanding of gravitational waves is far beyond the scope of this post (and also far far far far far far beyond my limited comprehension), but the basic concept comes from Einstein’s idea that gravity as we know it is actually a “bending” in spacetime that happens wherever there is mass. This curvature is what causes most of the effects of gravity that we can observe (i.e. you feel a pull towards the center of the Earth because the Earth’s mass bends the neighboring spacetime that you are in). As the Earth moves around the Sun (and as the Sun moves around the Milky Way galaxy and as the Milky Way moves…), the curvature in spacetime also moves with it. In certain types of motion (i.e. when an object is part of a binary orbiting system), this movement in spacetime curvature actually results in “waves” of spacetime curvature emanating outwards, kind of like ripples in a pond. These “waves” are called gravitational waves and, because they carry energy, are also called gravitational radiation. Because of the nature of these waves, they have three unique properties which make them interesting tools in the study of astronomy:

• they move at the speed of light
• unlike light, these waves don’t get significantly scattered/blocked
• they don’t require the existence of matter (so they can be used to study black holes)

The problem? They are extremely difficult to detect, because their effects are remarkably small. In fact, the first indirect observation of gravitational waves, found in the change in orbits of the Hulse-Taylor binary system (pictured above-right) won the researchers the 1993 Nobel Prize in Physics.

So, what to do? While there have been many attempts to do this, they are plagued by the difficulty of detecting such weak waves in the presence of as much noise on Earth. Potential solution? The use of a multinational space-borne laser interferometry setup called LISA (Laser Interferometer Space Antenna). With the use of laser light and interferometry (which allows you to measure small changes in distance by observing interference between a beam of light and a reflection), three identical solar-powered spacecraft will be set up in an equilateral triangle orbiting the sun at an angle relative to where the plane of the orbit of the other planets in the solar system (see below).

What’s especially remarkable is the precautions NASA is taking with the LISA spacecraft to correct for error and insure greater accuracy and precision in their measurements:

• Use of microthrusters to maintain drag-free flight by constantly monitoring the position of the test-weights the LISA spacecraft is flying around (and maintaining position of the instruments relative to the test-weights of ~10nm)
• Use of a transponder (which calculates the phase of an incoming beam of laser light and electronically setting the phase of the outgoing beams) instead of a mirror for interferometry to avoid diffraction (light scattering) from a traditional reflection approach
• Use of time-delay interferometry and continuous frequency monitoring/stabilization to correct for the effects of frequency noise

Given the technology and the theory involved, LISA’s potential to change astronomy could potentially rival the Hubble telescope’s, opening up new ways to study distant astronomical phenomena and potentially some of the more exotic topics in physics like string theory and strong-field gravity. There’s a lot more information on the potential topics of scientific inquiry which LISA could be used to study on NASA’s LISA science page.

Let’s cross our fingers that it will stay on schedule for launch in the 2018-2020 range and deliver not only concrete observations of gravitational waves but a whole wealth of information on the universe we live in.

(Image credit: NASA) (Image credit: NASA)

I have always been impressed with the work of astronomers. Unlike biologists and chemists who can, for a wide array of topics, actually touch and feel what they are studying, astronomers have to make conclusions with only careful observations conducted with powerful telescopes and computers informed by understanding the laws of physics (quantum and relativity included) and backed up by complex computational models.

One phenomena which astronomers can use to better explore deep space is an effect predicted by Einstein’s theory of general relativity called gravitational lensing. General relativity predicts that the path of light can be bent by gravitational fields; the most dramatic example of this would be a black hole, where gravity is so strong that light “falls” back into it. The same effect, on a less dramatic scale, could result in the path of light being bent on its way to Earth by the gravity of another object. The term “gravitational lens” refers to the fact that this bending is similar to the bending of light by a telescope lens.

Now, for the layperson, the fact that light can bend is probably just a cool effect which has no practical importance. But, to a well-trained astronomer, the knowledge of how gravity works lets them use the phenomena of gravitational lensing to understand both the objects that are emitting light (because the lensing effect allows us to see objects which are so far away that they are blocked by another object) and the “lens” itself (understanding the mass, structure, and position of what is bending the light).

Take a look at the pictures (HT: Wikipedia) below of Einstein rings. These occur when the line of sight to a bright, faraway object is being blocked by another object. However, because of the gravitational lens effect, the light from that faraway object can bend around the closer object, resulting in a ring which gives scientists a chance to study not only the faraway object but also understand the structure of the intervening space.

There are countless other examples of the application of gravitational lensing in the study of astronomy, but one of the most clever that I heard about recently was the study of dark matter. The theory in a nutshell: the universe is believed to be mostly dark matter – matter which does not reflect or emit any light whatsoever. Because it doesn’t seem to emit or reflect electromagnetic radiation, there has been no direct observational way to study it. However, dark matter does have mass. This means it has gravity and can thus bend light as a gravitational lens!

Researchers were able to took astronomical survey data from around the world and, using sophisticated computer algorithms and programs, compile a picture of gravitational lensing due to dark matter. From that, they were then able to digitally put together a picture of the structure of the dark matter in (at least part of) the universe and get a sense for how it’s evolved over time (the further from Earth you look, the further back in time):

And with this they made a striking conclusion – we all have dark matter to thank for the existence of the stars and the galaxies:

Our results are consistent with predictions of gravitationally induced structure formation, in which the initial, smooth distribution of dark matter collapses into filaments then into clusters, forming a gravitational scaffold into which gas can accumulate, and stars can be built.

Awesome.

(Image credit) (Image credit)

One of the best ways for scientists to reach out to the general public is through video. This past Friday, I got a chance to experience this firsthand at the IMAX theater at Boston’s New England Aquarium. A while back, I had caught the trailer for Hubble 3D at an IMAX movie and, given my love for all things Hubble, I had wanted to catch a showing. Seeing that the Hubble special was only ~40 minutes long, I decided to also buy a ticket for Under the Sea 3D as well.

And, as my Tweets that day pointed out, I was blown away:

There are some today who think high-def/3D is usually a gimmick by movie studios and digital display sellers, but that was definitely not true for either of these films. The 3D really enhanced the impact of the visuals. It let the audience, many of whom are unlikely to ever conduct spacewalks or scuba-dive where the Under the Sea 3D crew went to really feel what it was like to see undersea life. And, in the case of some of the deep space Hubble 3D shots, it gave the audience a very cool new look at objects so far away that its almost inconceivable that human beings will ever actually get to visit them.

Couple that with strong performances on interesting material by Leonardo DiCaprio in Hubble 3D and Jim Carrey in Under the Sea 3D and you get a strong combination which, if I’m any judge, not only gives the audience a juicy taste of why science is cool, but why its important to continue to study it.

I have definitely been sold on these, and I not only plan to check out more of these as they come out (I’ve got my eye on Sea Rex 3D), but would recommend this to anyone who has an hour to spend or would like to check out a visually stunning way to learn something new.