Bench Press

The “2.0” moniker is often abused to hype up connections with the new dynamic web applications of today. This is why I’ve co-opted it to describe a new application which is to WebMD’s 1.0 as Gmail/Twitter are to old-school webmail/message boards.

As many of you know, WebMD is a leading health information internet portal, providing a wide range of medical information for both casual patients who browse the site and medical professionals. But, while the information WebMD provides is rich and valuable, it is still a static website, valuable, but not dynamic or intuitive or mobile.

Wolfram Research (maker of popular computational algebra system/scientific computing software Mathematica)’s new WolframAlpha search engine attacks the first two challenges. One of the fundamental problems with traditional portals like WebMD is that to find specific information, the user needs to have some idea of where the information is or how it’s stored/used in conjunction with other information – in other words, it requires contextual knowledge. For example, to figure out how to determine if someone is overweight, the user needs to know:

1. That BMI is the relevant metric
2. Where to find how to calculate BMI and where/how to get the information that goes into the BMI formula
3. Compare BMI with relevant comparisons
4. Understand the limitations and implications of the BMI metric

WolframAlpha tries to simplify this by reducing the dependence of the quality of the search results on contextual knowledge. In the BMI example above, WolframAlpha hasn’t quite solved how to easily and quickly answer steps 1 and 4, but it has made it much easier to do steps 2 and 3. For instance, the BMI of a person who is 5’ 10” and weighs 165 lbs and the relevant comparisons (to the US population as a whole, and to clinical definitions of “overweight”, etc.) What’s incredible is that WolframAlpha won’t only provide you with the data, if it’s feasible, it will even provide you with charts and graphs to illustrate them. While this is a far cry from Jeopardy-playing supercomputers, it is a much welcomed change to the current heavily context-dependent search paradigm.

And, WolframAlpha does a lot more than that. On their sample page of examples of health/medical requests, WolframAlpha breaks out several other capabilities:

• find growth charts/body statistics
• compute calories burned
• figure out Blood Alcohol Content levels
• estimate risks/mortality rates for diseases
• look up diagnoses by ICD-9 code
• understand medical test results
• look up information about drugs, hospitals, and public health information

Very cool, and, despite the more technical bent to information, much more usable than a standard search engine query for finding relevant health information. There is great promise in this technology, especially if it’s natural language processing algorithms improve to the point where it can provide useful information for healthcare professionals or curious/nervous patients (perhaps by combing through/organizing the information in WebMD’s healthcare-professional-oriented).

(Image Credits)

A few weeks ago I wrote a post about the development of the Standoff Patient Triage Tool, an impressive use of lasers in order to make health critical readings of patients from a distance. Well one of the best things about science is that many people can utilize the same tools to come up with unique methods and solutions for any given problem. In this case, researchers have used lasers to develop a technology that could someday revolutionize imaging procedures in medicine.

Photoacoustic imaging of melanoma in vivo.

Photoacoustic tomography is the basis behind a new imaging technology being developed in hopes of providing more flexible and cost effective devices for physicians. The technique takes advantage of ultrasonic emissions produced when a non-ionizing laser pulse is directed towards a tissue. The emissions, resulting from transient thermoelastic expansion of the target tissue due to absorption of the laser energy, are detected and analyzed with various algorithms to construct an image (2D or 3D) of the targeted area. This differs from the reliance on the doppler shift produced by the reflected laser beam in the SPTT.

Images of vasculature like the one seen on the right can be produced by using photoacoustic tomography without the injection of contrast as differences between the molecular composition of the target can be used instead. In the example to the right, the difference between oxygenated and deoxygenated blood is an effective natural contrast. Photoacoustic tomography also presents other benefits over traditional imaging techniques as explained by The Economist:

CT scans also involve potentially harmful ionising radiation. And MRI and CT scans are very expensive, using machines that cost millions of dollars and require dedicated staff to operate them. Photoacoustic tomography, by contrast, could eventually be performed using portable hand-held devices, similar to those used for ultrasound scanning. This would allow doctors to diagnose and monitor patients in clinics, and reduce the need to refer them to consultants.

The adaptability of this nascent technology is also impressive as researchers are already looking at using it to detect specific ailments such as brain lesions and cancer. In the case of cancer, the ability to accurately image vasculature could allow doctors to monitor patients for the development of new blood vessels (angiogenesis) a hallmark of cancer development.

While there are some issues to work out with this new technique, such as the lack of imaging depth (ultrasound signal emitted is reduced the deeper the tissue lies) and ultrasound distortion from varying tissue types within the human body (e.g. bone vs muscle), photoacoustic imaging is a very promising new technology.

(Source)

A lot of the “hot” research occurs at the edges of disciplines. In my humble opinion, this is a natural effect stemming from the fact that the research that occurs at those edges typically involves the use of new equipment/techniques (e.g. applying physical techniques to biology) or because of the need for experts in different fields to come together and exchange thoughts and perspectives (e.g. astrophysics requires an appreciation of the very large [traditional astronomy], the very small [quantum mechanics, statistical mechanics], and the very weird [relativity, ok so it’s not that weird, but from the perspective of someone who’s never moved at relativistic velocities, I think it’s weird]).

But, of course, interdisciplinary research, has its limits (HT: Abstruse Goose):

Anyone else have any equally bad ideas for “interdisciplinary research”?

(Image Credit – Abstruse Goose)

Computers can do some pretty incredible stuff. We’ve seen computers analyze evolutionary trees, master the game of Go, and even take on the challenge of Jeopardy!. However, as amazing as computers are, the one glaring deficit of computers is its inability to extrapolate and make connections, something that comes easy to humans. The culprit is how computers are designed. Computers are built to follow algorithms: a strict set of guidelines which, when executed faithfully, will yield the correct answer. However, ask a computer to build upon these algorithms is a whole different issue. Humans, on the other hand, quickly learn to develop schemas and a general model of understanding. From these basic concepts, we can use deduction to answer a question we’ve never been asked before by combining different skills together to achieve this goal. For example, humans will quickly identify that a certain object on a street is a car, even if we’ve never seen that certain model of car before.

Enter Cognitive-Level Annotation Using Latent Statistical Structure (CLASS), a project that is trying to push computers to recognize specific classes of objects the way humans can. Luc Van Gool of Belgium’s Leuven University (KUL), a member of the CLASS team, sees great promise in CLASS and has already found a marketable use for it in cell phones. Through a mobile service, CLASS will be able to let its users take a picture of an object, say a famous monument, identify what it is, and provide further information to the user via the internet.

“It’s like the object itself becomes the link to further information,” observes Van Gool. He expects the application of this technology to expand rapidly. For instance, cities and museums may offer interactive guided tours or guide books through kooaba.

While CLASS is still a long way off from being complete, it is clearly a giant step towards extending the capabilities of computers and furthering the field of artificial intelligence.

Like my fellow Bench Press blogger Ben, I’m a fairly avid Star Trek fan. Having spent many hours during my formative years watching syndicated episodes I always wondered if we’d ever have some of the amazing devices in the show. One of the devices that captured my imagination was the Tricorder (pictured right). It amazed me that such a tiny device could provide so much utility throughout the show. The Tricorder’s versatility allowed it to do pretty much anything in the show, but what I always remembered was it’s medical utility. It allowed characters like “the Doctor” to analyze any number of ailments quickly and accurately.

While we don’t have the technology to make a Tricorder as effective as the ones used in Star Trek, the U.S. Department of Homeland Security’s Science and Technology Directorate (S&T) is developing a tool called the Standoff Patient Triage Tool (SPTT). The purpose of the SPTT is to aid first responders at a disaster triage patients quickly and accurately. Triaging patients with traditional methods can take 3-5 minutes per person. This can become an extremely difficult and time intensive task during a disaster, exactly the opposite of what we’d like. Therefore, the goal of the SPTT is to reduce triage time to 30 seconds per patient by providing accurate readings of pulse, body temperature, and respiration from up to twenty paces away all in a portable package about the size of a legal notebook.

A drawing of the proposed 15 inch by 8.5 inch x 6 inch Standoff Patient Triage Tool (SPTT) with the following features. 1) 4 x 6 display window; 2) Control button; 3) Infrared camera window; 4) Visible camera window; 5) Ranging subassembly window; 6) Shock bumpers. (Source: DHS S&T)

The SPTT takes advantage of Laser Doppler Vibrometry (LDV) in conjunction with a visual and infrared camera to make it’s readings. LDV takes advantage of the doppler shift produced when a laser bounces off a moving target. The shift in frequency upon return of the laser beam is measured and then analyzed in order to determine the velocity over time of the target. In this case, the SPTT’s vibrometer detects the movement of blood vessels and utilizes algorithms to extrapolate relevant data. So far researchers have found that the SPTT can produce strong readings from the head, chest, abdomen and foot. Currently, taking readings from the cartoid artery region of the neck appears to be the best option. Further testing needs to be done on patients in awkward positions, as well as with differing layers of clothing.

While the SPTT can’t do everything a tricorder can do, it appears to be taking a great first step in providing a portable device capable of providing first responders with accurate job critical data that will help them save lives. Maybe it’s only a matter of time before doctors start waving a small device around the patients while asking them what brings them into the office that day.

(Image Source)(DHS S&T Press Release)

On Bench Press, we usually discuss technologies to empower scientists to do research or to better communicate with each other and with the public. But, technologies can also help the “casual” fan appreciate science as well. Case in point: Google recently announced a new application available for Android phones called Google Sky Map. The concept is quite ingenious. If ancient navigators used starcharts and compasses to determine their location, why can’t a phone that knows its location (via built-in GPS) and its direction (via magnetometer and accelerometer) be used to figure out what stars are up above?

Or in other words, Google took this physical setup (very cute setup from Google for how Google Sky Map works) of what is basically a sextant, compass, and calendar:

And replaced it with a smartphone:

I haven’t had a chance to play with it (as I don’t own a G1), but the application looks very nicely packaged. You can use it to figure out what stars are right above you. But, the coolest feature is the ability to use the application to point you in the direction of something you’re interested in seeing (e.g. the planet Saturn).

More details are in the video below. You can get Sky Map for Android through the Android Market. For anyone who’s tried it, let us know what you think in the comments!

(Image credit – Google)

The N-body problem is a classic question in physics and mathematics. It is interesting, because while it is very simple to state, it results in a wide array of interesting behavior and very deep insights into how the universe works.

The setup is as follows: if we have N objects in space, with N masses, initial positions, and speeds, where and how fast will each of those N objects be moving if they are subject only to each other’s gravity (and the laws of classical physics – e.g. Newton’s Laws of Motion, etc)?

For a situation with 2 objects (N=2) and some of the cases with 3 objects (N=3), the problem has been well-discussed (the former leads to elliptical orbits, the latter results in the dynamics of the moon orbiting around the earth while subject to the sun’s gravity). But for problems more complex, the solution is anything but pretty. Behavior can emerge which may seem regular and predictable but degenerate into pure chaos. In fact, Poincare’s study of the N=3 problem eventually served as the foundation for the study of chaos theory, or the rise of chaotic, seemingly random behavior (like turbulence) out of “orderly” equations and behavior.

But, the challenge of studying these problems and formulating/testing hypotheses becomes increasingly more challenging as N increases. After all, how do you test an idea for how the N body problem plays out when the problem is that its so difficult to figure out how the N body problem plays out?

Enter the age of the computer simulation. Mathematicians/physicists/astronomers now have the tools to test their ideas (or just pass the time watching simulations) using computers to simulate the behavior of complex systems of N bodies.

But, the fun doesn’t stop with just a handful, or even hundreds, of particles. For an N-body simulation to be sufficient for as astronomer trying to study the structure of the universe, it would have to be scaled up to model billions of particles over distances in the billions of light years.

These super simulations are staggering to comprehend. The Millennium Simulation, run in 2005 to test our understanding of quasars and dark matter, simulated ~10 billion particles (with each “particle” representing a mass about a billion times larger than our sun) across a mind-boggling 8 trillion quadrillion cubic light years expanse of space containing over 20 million galaxies.

But even the Millennium simulation is mere child’s play compared to Project Horizon, which aimed to model “half the observable universe with enough resolution to describe a Milky Way-like galaxy with more than 100 dark matter particles”.

And, probably, this is merely the tip of the iceberg for what such super simulations may be capable of. How the future of this will shape out, I believe, is dependent on the following N=3 (pun intended) questions:

1. It is relatively simple (although computationally challenging) to create a toy simulation to validate or disprove one’s theory. It is much more challenging to build a simulation which can reveal testable/actionable (e.g. not simply if one’s theory is right or wrong) conclusions to further our understanding of the system of interest. For example, will future models of the human metabolome reveal genes or regulatory pathways of interest, or are they simply to validate existing models (something which may bias the model design away from revealing more interesting behavior)? Answering this question in the affirmative is essential, or else these simulations becomes vanity toys – something to use up research funds on without driving real value for the underlying science.
2. Will Moore’s Law/alternative processors/computer science keep up with demands for greater precision and computational power? Otherwise, these super simulations will hit constraints like energy consumption or the introduction of calculation error.
3. Will scientists become more adept at communicating the value of these models? This is potentially the most important question as the investment necessary to run these simulations are significant, not only in terms of cost, but in terms of manpower and energy consumption. With the current financial crisis and a potential future energy crisis, demonstrating value to the public and to more “traditional” scientists/doctors/engineers who rely on non-computational techniques will become more and more important for these future simulations to survive.

For now, I’ll leave you with a video summarizing the gorgeous results of the Millennium simulation: