Bench Press

In order to deal with the global outbreak of swine flu effectively, tracking the number of swine flu cases is imperative. Having as much accurate data as possible regarding the epidemic is essential for evaluating what moves the global community ought to start taking to make it through this outbreak. Thus, using quick and accurate tools to evaluate the countless samples  being collected around the world is an absolute necessity. Luckily scientists at the University of Colorado and InDevR, a small biotech in Colorado, may have exactly what the world needs in a microarray chip dubbed the FluChip.

In 2005 Dr. Kathy Rowlen, CEO of InDevR, led a team at the University of Colorado working with the Centers for Disease Control and Prevention (CDC) in developing the FluChip in order to allow labs across the world to quickly distinguish samples between 72 different influenza strains. Her group’s work produced a viable testing platform that produced results in less than 12 hours with impressive accuracy.

Now Dr. Kathy Rowlen and InDevR have licensed the FluChip technology from the University of Colorado. InDevR has arranged to begin testing samples of the swine flu on a M-gene variant of the FluChip while also working on improving the initial design by incorporating new technologies, hopefully making a new assay basic enough that any lab with PCR capabilities will be able to utilize it. Here’s to hoping the FluChip will help us get a better picture of the current state of the swine flu epidemic.

InDevR Press Release:

InDevR, a small biotech company in Boulder, CO, announced today that they have licensed the FluChip technology from the University of Colorado.  The FluChip was invented by a joint team of scientists at the University of Colorado and the Centers for Disease Control and Prevention in an NIH sponsored effort led by Professor Kathy Rowlen.  Rowlen, now the CEO of InDevR, said that InDevR has arranged to test genetic material from the recent swine H1N1 virus on the MChip as well as other versions of the FluChip which are under development.  According to Rowlen “Based on work we conducted a couple of years ago, it appears that the M-gene version of the FluChip will be able to distinguish human H1N1 viruses from the new swine H1N1 virus.  If that proves to be the case, the FluChip will be a much needed and powerful new tool for surveillance since all of the current influenza diagnostics on the market are unable to subtype this virus.” The most popular diagnostic tests for influenza include rapid immunoassays, which are only able to identify the type (A or B) of influenza virus, and reverse-transcriptase polymerase chain reaction assays, which were designed for human-adapted influenza viruses and are not able to identify the swine H1N1 subtype.  State Public Health Laboratories must now send any influenza A viruses that cannot be subtyped using existing diagnostics to the CDC for analysis by genome sequencing or viral isolation.  The CDC must select viruses to analyze since it is not possible to run every sample collected from a large number of Public Health Labs.

The M-gene based FluChip has been demonstrated to delineate human-adapted viruses from non-human viruses, such as the H1N1 virus that caused the 1918 “Spanish Flu”.  “Since the FluChip assay can be conducted within a single day it could be employed in State Public Health Laboratories to greatly enhance influenza surveillance and our ability to track the virus,” Rowlen said.  InDevR will combine the FluChip technology with an innovative detection technology (NESATM), which InDevR also licensed from the University of Colorado and further developed with NIH sponsorship, to make the FluChip assay inexpensive and easy to use in any lab that has basic PCR capabilities.  “Kathy and her team have been engaged with this and similar diagnostic technology for many years,” said Mary Tapolsky, Senior Licensing Manager at the University of Colorado Technology Transfer Office. “CU TTO is excited about this experienced and motivated group developing and commercializing this promising technology.

Pedro Torres-Carrasquillo working on identifying different dialects using an automated computer program.

We live in such a diverse world today. With over 6 billion people of different ethnic backgrounds, spread across six continents, it’s sometimes hard to fathom the immensity and beauty of it all. However, one of the drawbacks of this diversity is the barriers it can create, in particular one of language. However, even within a single language, there may exist several different dialects that even native speakers can’t decipher. That’s where Pedro Torres-Carrasquillo and his colleagues at Lincoln Laboratory have come in.

While there exists language-detecting software currently on the market, no such identification system has been made for the different dialects that exist within a certain language. Torres-Carrasquillo, an electrical engineer specializing in speech processing, believes that by analyzing the frequency spectra of several short bursts of sound (using tools such as the Fast Fourier Transform), he will be able to pinpoint the key differences between different dialects.

Previously, Torres-Carrasquillo says, the approach was to “get a lot of examples, and then build a model that looks like your examples.” But he is tackling the problem in a different way. “Our group’s idea is that we don’t need a model that looks like our data – we need a model that can classify our data,” he explains. “We take very small pieces – snippets of speech – turn them into frequencies, add up all these contributions, and make a model that can tell them apart. We’re looking for patterns from just milliseconds of speech.”

According to Torres-Carrasquillo and his team, their technique will allow them to quantify the linguistic differences between dialects, such as the different pronunciations of vowel sounds between Cuban and Puerto Rican Spanish. So far, they have already been able to discriminate against American English and Indian-accented English with an error rate of only about 7 percent.

To me, this breakthrough illustrates how technology can not only simplify our lives, but can also break down barriers between people of different ethnicities and backgrounds. While it may be a stretch now, maybe one day this will pave the way for speech translators or digital language tutors.  Vacation in Spain anyone?

(Image Credit)

Most of the time scientists develop Youtube videos to illustrate a concept, they usually result in creations which only a mother could love. That’s why when there is a good video, we should celebrate. Enter Tom McFadden, a Human Biology course associate at Stanford University who, in addition to helping students grasp biology, dabbles in a little hip-hop on the side.

Here’s McFadden “explaining” the role of Hox in regulating body pattern development to the tune of what sounds like Jay-Z’s “Money Ain’t a Thang”:

There are more videos at McFadden’s youtube page – I’m a big fan of “I Just Want a Function” – which takes some basic population ecology and spits it out to the tune of Jay-Z’s “I Just Want to Love Ya”:

I’m a population (population) – of big cane toads (gotta love the toads).
It aint where I live, but where I’m about to grow (talkin’ about australia).
I just want a function, to show who I am (exponential baby),
To see if I’ll crash (mo’ toads, mo’ problems), or if I’ll expand.

Now that’s applying technology (YouTube, social media) to science!

As genomes have been sequenced over the past few decades scientists have looked for new ways to analyze and interpret the wealth of information. They’ve developed numerous algorithms with goals ranging from organizing evolutionary family trees (inspired by plagiarism detecting software) to aligning genetic sequences. All of this to answer the numerous questions that can now be asked thanks to sequence databases. One of the many things scientists have attempted to study is positive selection in protein-coding genes.

Positive selection of advantageous gene mutation is particularly interesting to scientists as it can provide insight into the function of new genes. However, positive selection is difficult to detect and analyze as neutral and deleterious mutations predominate advantageous mutations in frequency. Initially scientists looked for positive selection by simply comparing the ratio (/omega) of nonsynonymous nucleotide substitutions (d_N) to the number of synonymous nucleotide substitutions (d_S) between homologous protein-coding gene sequences while utilizing Fisher exact tests to accept or reject a null hypothesis of neutral selection¹.

Over the years scientists developed additional statistical analyses to infer positive selection. Two of the most popular methods are the branch-site method (BSM) and site-specific method. The BSM utilizes a likelihood ratio test to detect positive selection within a given phylogenic branch. The site-specific method on the other hand utilizes /omega to look for specific amino acid substitutions that are positively selected. Both of these methods have been utilized in hundreds of papers and seemingly provided a great deal of insight into potential points of positive selection within various genomes. What would you say then when told that both of these methods contain significant flaws which provide an inordinate number of false positives?

Bovine Rhodopsin protein with predicted sites in red and experimentally determined in blue. (Adapted from Yokoyama et al. 2008 PNAS)

That’s exactly what Masatoshi Nei and his group believe to have shown in a recent paper evaluating the reliability of the branch-site and site-specific methods. Nei’s group utilized several controlled computer simulations as well as data collected by Shozo Yokoyama, at Emory University, on dim-light vision opsins in vertebrates² in their studies determining that both the branch-site and site-specific methods yielded far too many false positives. Nei and his group contend:

This low rate of predictability occurs because most of the current statistical methods are designed to identify codon sites with high /omega values, which may not have anything to do with functional changes. The codon sites showing functional changes generally do not show a high /omega value. To understand adaptive evolution, some form of experimental confirmation is necessary.

From this paper it looks like scientists looking for high /omega values may have been chasing ghosts by assuming that amino acid changes result in functional changes indicating proof of positive selection. The potential impact this will have on hundreds of papers is stunning. In the end the take home message is that statistical analyses, no matter how elegant, have their limits and ought to be utilized in conjunction with experimental data as much as possible.

(Sources: 1 – Reliabilities of identifying positive selection by the branch-site and the site-prediction methods , 2 – Elucidation of phenotypic adaptations: Molecular analyses of dim-light vision proteins in vertebrates )

updated: Had to change all the &omega to /omega because WordPress kept changing it into ? for some reason…bah

You better believe it. In an effort to push computing power forward and circumvent the traditional problems that go along with a two dimensional chip structure, Eby Friedman and other researchers at the University of Rochester have developed  three dimensional processor capable of running up to 1.4 GHz. This design is supposedly the first of its kind and may be the prototype of the next generation processor and, possibly, a means to rolling out the extra computational power needed for next-generation supercomputers for scientific computing.

Nowadays, the problem with building super-fast processors using a traditional two dimensional structure is that we are reaching the limit on how many transistors we can cram in a given space. This inherently limits the capabilities of a processor and will provide an upper bound on how effective future processors can be. However, by adding one more dimension, processor designers can bypass the usual restrictions and allow for continuing growth in this industry. Additionally, because of the three dimensional structure, new chips can be folded into a tenth the size of their flatter counterparts, yet run at ten times the speed. Such changes could deliver new types of processing capabilities by implementing more complex circuitry than what is allowed in a 2D chip layout:

• Parallel computing: 3D chips will be better able to run computations in parallel by literally stacking individual processes next to and on top of one other
• Memory bandwidth: One of the most important considerations in chip design today is giving processing cores sufficient access to memory such that the cores aren’t left idle during peak computing cycles. 3D chips enable new paths and architectures for memory access which “flat” chip design do not, making systems much faster.
• Reduced energy consumption: Smaller package size and enhanced parallelism will reduce power consumption, making supercomputers/servers consume less power.

However, like all new technologies in their development stage, increasing the complexity of processors introduces several new problems. Synchronizing operating speeds, power requirements, and signal processing are just some of the new concerns chip designers need to deal with.

Friedman says getting all three levels of the 3-D chip to act in harmony is like trying to devise a traffic control system for the entire United States—and then layering two more United States above the first and somehow getting every bit of traffic from any point on any level to its destination on any other level—while simultaneously coordinating the traffic of millions of other drivers.

While 3D chip design is still in its infancy, Professor Friedman’s research group has demonstrated what may one day be the driver for a whole lot of computing power.

How can technology aid scientific discovery? We’ve covered on Bench Press examples such as providing the computing power needed to simulate particle physics and using cosmic muons to scan Mayan temples. But what about something closer to home – like figuring out what the “infamous” opening chord in the Beatles classic “A Hard Days Night” is?

As you probably guessed from the fact that I’m writing this, the answer is yes.

Professor Jason Brown of Dalhousie University applied a technique called Fourier analysis to the problem at hand. Fourier analysis is useful for decomposing a particular waveform into the fundamental frequencies which make it up. Or, to put it more commonly, it lets you take a waveform (like a particular sound) and figure out what all the underlying frequencies are which make it up.

The concept of Fourier analysis has been around at least as early as the 1800s when Jean Baptiste Joseph Fourier used the concept to explore heat propagation. However, the lack of computers made the technique unwieldy for exploring real world analog data. This problem was addressed with the development of programs and integrated circuits adept at deploying a numerical approximation of Fourier analysis called the Fast Fourier Transform (oftentimes abbreviated FFT) which has made an entire realm of sophisticated analyses possible and relatively simple to do.

What Brown did was very elementary. Using a digital recording of the Beatles classic and the widely available program Mathematica, he broke down the opening “twang” into the 29375 frequencies that made it up. He then applied a filter (to filter out harmonics and background noise) to pick out the 48 most important frequencies and compared them to popular “estimates” of what was played.

What Brown found was that most popular guesses of the opening were wrong as they assumed the Beatles had played the G2 note (where C4 is “middle C”) which didn’t show up at all in his Fourier analysis. But, using information about what instruments the Beatles played (which gives you information like knowing that pianos make 3 distinct frequencies due to the hammer hitting 3 strings simultaneously), Brown deduced what countless enthusiasts have guessed at for over 40 years. Without further ado, the opening sound to “It’s a Hard Day’s Night”:

Awesome.

Thanks to the internet the enterprise of playing practical jokes on the world has become incredibly easy and every year now I look forward to seeing what hilarious items pop up throughout the internet. So here’s a quick list of some of my favorite tech/science April Fools jokes for 2009:

Google masters artificial intelligence. The brilliant people over at Google continue to amaze by creating the world’s first “artificial intelligence tasked-array system” which they’ve dubbed the Cognitive Autoheuristic Distributed-Intelligence Entity (CADIE). Apparently it’s already cranking out changes at Google: “Earlier today, for instance, CADIE deduced from a quick scan of the visual segment of the social web a set of online design principles from which she derived this intriguing homepage.”

Gmail Autopilot. Thanks to CADIE e-mail’s even easier than before. By using the Gmail Autopilot one can set simple sliders to manage all your e-mail without going through the hassle of reading and writing. E-mail will never be the same again. Nigeria may become more wealthy though…

Let Gmail Autopilot handle all your e-mail conversations.

Tiny black hole on Earth created by Large Hadron Collider. CERN admits that the real reason they shut down the LHC was due to the creation of a “tiny black hole” that they have “kept under quarantine” and are monitoring as we speak.

Qualcomm, on the cutting edge of Bioengineering. Qualcomm best known for it’s CDMA technology for wireless networks has delved into cutting edge research to improve wireless network coverage around the world. The video below takes an exclusive look behind the scenes of Qualcomm’s latest work.

Happy April Fools!