Bench Press

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.

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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.

If you’ve never used Google Earth, I’d recommend trying it out, at least once. For those of you not in the know, its an amazing piece of software which lets you access Google’s wealth of geographical and geological information (although with its layers feature, it also gives you access to the oceans, the moon, the sky, historical sites of interest, and Mars).

The magic of new, cheap and powerful technologies and information sources like Google Earth is that they can also be used to push additional experiments and discoveries. Widely available mathematical analysis tools like Mathematica let a professor figure out what instruments made up the mysterious “twang” at the beginning of the Beatles classic “It’s Been a Hard Day’s Night.” Advances in gaming and graphics technology make it possible for gaming consoles and personal computers to do sophisticated number-crunching. And, Google Earth helped make it possible for a team of students to take pictures from near-space for only $150.

More recently, Google Earth helped a team of researchers led by the University of the Witwatersrand’s Professor Lee Berger to find and unearth new fossil remains, including those of the newly discovered Australopithecus sediba (pictured right) in the Cradle of Humankind World Heritage Site in South Africa.

Berger and his associates used Google Earth to learn how to identify cave sites from satellite imagery and to help supplement on-foot exploration to map out ~500 previously unknown caves and, subsequently, 25 new fossil sites!

This discovery has been especially exciting as some have argued it could potentially be “the point from which the genus Homo [the genus we humans are a part of] arises” and “a good candidate for being the transitional species between the southern African ape-man Australopithecus africanus (like the Taung Child and Mrs. Ples) and either Homo habilis or even a director ancestor of Homo erectus (like Turkana Boy, Java man, or Peking man).”

Check out the University’s web coverage of the discovery as well as the Google blog’s coverage of the event as well as the video celebrating Professor Berger’s find (below):

(Image credit – University of the Witwatersrand website)

Science papers by Berger’s group: (1) and (2)

We’ve posted before on the Federation of American Scientist’s Immune Attack computer game as a great example of the use of games in science education. But, science “edutainment” isn’t limited just to computer games. Fans of Wizard’s Magic the Gathering and Konami’s Yu-Gi-Oh trading card games will immediately recognize The Healing Blade, a trading card game designed in the spirit of Magic and Yu-Gi-Oh but designed around the battle between antibiotics and bacteria (HT: AMEDNEWS)

The game was designed by two self-admitted “mega-geek” physicians, Dr. Arun Mathews and Dr. Francis Kong, who met in medical school and created the company Nerdcore Learning to promote The Healing Blade and other medicine-related “edutainment” paraphernalia. As to why they created the concept, Dr. Mathews notes:

I was struck upon the complexity and yet innate nature of gaming within the choice I would make for putting some of my sick patients on particular antibiotics … Essentially, in a similar way, when you are playing a complex multi-tiered video game, we are making similar choices by obtaining data from our cultures [and] making risk-management decisions.

Truer words were never spoken.

Amazingly, while Mathews and Kong had only intended to bring 30 copies of the game to launch at the American Medical Students Association annual meeting, a printing error turned that into over 100 copies, 90% of which sold! Mathews describes the sight:

We had this gaggle of students just sitting down, spreading out on a bunch of tables, all playing the game. That is one memory that will take a while to fade, because it was such a neat thing to see students getting super excited about infectious disease and therapies.

As an unabashed former-Magic-and-Yu-gi-oh player, I can definitely see the appeal. There is something very compelling about the mix of chance and strategy in trading card gameplay. Sadly, at the time of the writing of this blog post, The Healing Blade’s online purchase form shows that the game is sold out. So, in the meantime, I will have to leave you with some pictures of some very nice-looking game card art:

(Images and video from Healing Blade website)

Despite all the cool and meaningful innovations we’ve discussed on this blog, few come as close in terms of impact on a scientific field as the Hubble Space Telescope. And this weekend, you can help celebrate it’s birthday!

Officially launched on April 24, 1990 (can you believe that was 20 years ago!?), it has provided one of humanity’s best looks into deep space and has, among other things:

• Helped refine the field’s understanding of Hubble’s Law and the Hubble Constant
• Showed that the expansion of the universe was not decelerating, but accelerating, suggesting the existence of dark energy
• Helped to establish the existence of massive black holes at the center of galaxies and their relationships
• Provided sharp images of the impact of comet Shoemaker-Levy 9 into Jupiter
• Collect data on extrasolar planets and protoplanetary discs
• Furthered the study of Wolf-Rayet Stars, suspected to be the precursors of Gamma-ray bursts, the most powerful energy bursts known in the universe
• The mindblowing look 13 billion years into the past known as the Hubble Deep Field

And, potentially, most important of all: the gorgeous pictures of deep space (from Space Telescope Science Institute’s HubbleSite website).

Happy 20th birthday, Hubble!

(Image credits – Hubble Site via Space Telescope Science Institute)

Science News recently put out a very interesting article about the numerous mistakes that many a doctor and scientist have made in evaluating statistics. Given the importance of statistical analyses in research today (who doesn’t worship at the “altar of p < 0.05”?), I was pretty shocked at how poor a typical scientist’s statistical training is.

The article highlights a few key common misconceptions to watch out for:

1. The opposite of a false-positive is not necessarily a true-positive: How many times have you heard the explanation that a p-value of 0.05 means that “it is at least 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance”? Well, that’s an understandable but unfortunate error. A p-value of 0.05 implies that there is a 5% chance that the result observed is what you would get in that particular experiment if the opposite of what you believe is true: in other words, the probability of a false-positive. However, it does not mean a 95% chance that the hypothesis is correct. Or, to use an example from the Science News article: suppose there is a drug test which detects steroid use in athletes. This test has a 5% false-positive rate (kind of like having a p-value of 0.05). Suppose a specific athlete comes back with a positive drug test. What is the chance that he or she is actually a steroid user? By now, you know the answer is not 95%, but what is it? The actual answer depends on how many athletes actually use steroids. Or, to use real numbers, suppose 5% of a group of 400 tested athletes are actual steroid users. Therefore:
   • There are 20 athletes (5%) who use steroids and 380 (95%) who don’t use
   • Of the 20 athletes that are actual steroid users, the test will correctly identify 19 (95%) as users
   • Of the 380 athletes that are not actual steroid users, the test will incorrectly identify 19 (5%) as steroid users.

   The final result? Of the 38 athletes identified as steroid users, 50% (19) will be false positives! So, even though the test/experiment’s results have a p-value of 0.05, the actual probability that the hypothesis is correct was only 50%, not 95%. Keep that in mind the next time you hear someone use a p-value to assert they have a better than 95% shot at being correct.

2. Statistical significance does not necessarily mean actually significant: The poster-children for this type of error are the numerous articles floating around that random food/environmental factor XYZ causes a “significantly increased risk” of cancer/heart disease/death/something bad. Just because something observed is highly unlikely to be explained away by chance, doesn’t mean that the actual impact itself is significant by any actual sense of the word. An increase in a risk might be real, but if its an increase in risk of cancer from 0.01% to 0.011%, I’d hardly call that significant.
3. Large numbers of experiments means large numbers of false positives. The archetype for this type of error are the large genome-wide studies done to find genetic fingerprints which tend to go with a particular disease. If you are studying 20,000 genes with a survey tool which has a p-value of 0.05, elementary multiplication suggests that you’ll find at least 1,000 genes (5%) showing up as hits which aren’t actually related to the disease at all! This isn’t to say that so-called genome-wide association studies are all bunk (the best studies will use multiple means to verify and assess if a gene is related to a given condition), but it should be the first thing that you think about when evaluating claims on a large data set based on standard statistical tools.
4. “Statistically significant” isn’t always statistically significant. Imagine two clinical trials comparing Drug A and Drug B with placebo. Although data shows that both Drug A and Drug B provide improvements over placebo, only Drug A demonstrates a statistically significant improvement. Does this then mean that Drug A is statistically significantly better than Drug B, which the trial suggested does not provide a statistically significant improvement over placebo? The answer, obviously, is that it depends on the level of improvement. The point of that little mental exercise, however, is that the status of being “statistically significant” doesn’t confer any special significance or power when making a different comparison. An illustration of the real-world consequences of this comes from an example from the Science News article:
   

   A number of studies have suggested that children and adolescents taking antidepressants face an increased risk of suicidal thoughts or behavior… One set of such studies, for instance, found that with the antidepressant Paxil, trials recorded more than twice the rate of suicidal incidents for participants given the drug compared with those given the placebo. For another antidepressant, Prozac, trials found fewer suicidal incidents with the drug than with the placebo. So it appeared that Paxil might be more dangerous than Prozac.

   But actually, the rate of suicidal incidents was higher with Prozac than with Paxil. The apparent safety advantage of Prozac was due not to the behavior of kids on the drug, but to kids on placebo — in the Paxil trials, fewer kids on placebo reported incidents than those on placebo in the Prozac trials.

   As the previous examples makes it clear, our ability to compare two forms of statistical comparison is extremely limited and subject to all sorts of extra considerations. This is one reason why many statisticians are skeptical of meta-analyses (studies which combine the data from multiple studies), and clearly illustrates why scientists and doctors everywhere need to bone up on their statistical training and their reading of the fine print on studies they use.

Read the list carefully, and don’t make these mistakes!

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