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  • April 4, 2012
  • 02:32 PM
  • 803 views

Lubricating with silver nanoparticles for a better performance

by Cath in Basal Science (BS) Clarified

Oil changes are part of the routine maintenance for most drivers to keep their cars in good running condition. Engine oil reduces the friction between moving parts of the engine and minimize its wear and tear. Like cars, a variety of machinery—whether a printing press or an excavator—also relies on lubricants to prevent breakdowns and [...]... Read more »

  • April 4, 2012
  • 01:15 AM
  • 1,000 views

Finding Nanoscale Defects in Memory Devices

by Jason Carr in Wired Cosmos

The future of space travel, artificial intelligence, and AI are dependent upon our ability to store massive amounts of data in really small areas. It’s a complex undertaking to say the least. Fortunately, new research indicates that we may get there a bit faster by enabling engineers to discover defects that lead to memory defects [...]... Read more »

Lee, I., Obukhov, Y., Xiang, G., Hauser, A., Yang, F., Banerjee, P., Pelekhov, D., & Hammel, P. (2010) Nanoscale scanning probe ferromagnetic resonance imaging using localized modes. Nature, 466(7308), 845-848. DOI: 10.1038/nature09279  

  • April 3, 2012
  • 12:16 AM
  • 985 views

Next Generation Artificial Intelligence

by Jason Carr in Wired Cosmos

As computer scientists this year celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing, who set out the basis for digital computing in the 1930s to anticipate the electronic age, they still quest after a machine as adaptable and intelligent as the human brain. Now, computer scientist Hava Siegelmann of the [...]... Read more »

  • April 2, 2012
  • 08:04 AM
  • 1,133 views

Open Data Manchester: Twenty Four Hour Data People

by Duncan Hull in O'Really?

According to Francis Maude, Open Data is the “next industrial revolution”. Now you should obviously take everything politicians say with a large pinch of salt (especially Maude) but despite the political hyperbole, when it comes to data he is onto something.... Read more »

  • March 31, 2012
  • 01:10 PM
  • 758 views

Automated Science, Deep Data, and the Paradox of Information

by Bradley Voytek in Oscillatory Thoughts

Note: this is was originally published by me over on the O'Reilly Radar.A lot of great pieces have been written about the (relatively) recent surge in interest in “big data” and "data science", but in this piece I want to address the importance of deep data analysis: what we can learn from the statistical outliers by drilling down and asking, “What’s different here? What’s special about these outliers and what do they tell us about our models and assumptions?”The reason that big data proponents are so excited about the burgeoning data revolution isn’t just because of the math. Don’t get me wrong, the math is fun, but we’re excited because we can begin to distill patterns that were previously invisible to us due to a lack of information.That’s big data.Of course, data are just a collection of facts; bits of information that are only given context--assigned meaning and importance--by human minds. It’s not until we do something with the data that any of it matters. You can have the best machine learning algorithms, the tightest statistics, and the smartest people working on them, but none of that means anything until someone makes a story out of the results.And therein lies the rub.Do all these data tell us a story about ourselves and the universe in which we live, or are we simply hallucinating patterns that we want to see?(Semi)Automated ScienceIn 2010, Cornell researchers Michael Schmidt and Hod Lipson published a groundbreaking paper in Science titled, "Distilling Free-Form Natural Laws from Experimental Data". The premise was simple, and it essentially boiled down to the question, "can we algorithmically extract models to fit our data?"So they hooked up a double pendulum--a seemingly chaotic system whose movements are governed by classical mechanics--and trained a machine learning algorithm on the motion data.Their results were astounding.In a matter of minutes the algorithm converged on Newton's second law of motion: f = ma. What took humanity tens of thousands of years to accomplish was completed on 32-cores in essentially no time at all.In 2011 some neuroscience colleagues of mine, lead by Tal Yarkoni, published a paper in Nature Methods titled "Large-scale automated synthesis of human functional neuroimaging data". In this paper the authors sought to extract patterns from the overwhelming flood of brain imaging research.To do this they algorithmically extracted the 3D coordinates of significant brain activations from thousands of neuroimaging studies, along with words that frequently appeared in each study. Using these two pieces of data along with some simple (but clever) mathematical tools, they were able to create probabilistic maps of brain activation for any given term.In other words, you type in a word such as "learning" on their website search and visualization tool, NeuroSynth, and they give you back a pattern of brain activity that you should expect to see during a learning task.But that's not all. Given a pattern of brain activation, the system can perform a reverse inference, asking, "given the data that I'm observing, what is the most probable behavioral state that this brain is in?"Similarly, in late 2010, my wife (Jessica Voytek) and I undertook a project to algorithmically discover associations between concepts in the peer-reviewed neuroscience literature. As a neuroscientist, the goal of my research is to understand relationships between the human brain, behavior, physiology, and disease. Unfortunately, the facts that tie all that information together are locked away in more than 21 million static peer-reviewed scientific publications.How many undergrads would I need to hire to read through that many papers? Any volunteers?Even more mind-boggling, each year more than 30,000 neuroscientists attend the annual Society for Neuroscience conference. If we assume that only two-thirds of those people actually do research, and if we assume that they only work a meager (for the sciences) 40 hours a week, that's around 40 million person-hours dedicated to but one branch of the sciences.Annually.This means that in the 10 years I've been attending that conference, more than 400 million person-hours have gone toward the pursuit of understanding the brain. Humanity built the pyramids in 30 years. The Apollo Project got us to the moon in about 8.So my wife and I said to ourselves, "there has to be a better way".Which lead us to create brainSCANr, a simple (simplistic?) tool (currently itself under peer review) that makes the assumption that the more often that two concepts appear together in the titles or abstracts of published papers, the more likely they are to be associated with one another.For example, if 10,000 papers mention "Alzheimer's disease" that also mention "dementia", then Alzheimer's disease is probably related to dementia. In fact, there are 17087 papers that mention Alzheimer's and dementia, whereas there are only 14 papers that mention Alzheimer's and, for example, creativity.From this, we built what we're calling the "cognome", a mapping between brain structure, function, and disease.Big data, data mining, and machine learning are becoming critical tools in the modern scientific arsenal. Examples abound: text mining recipes to find cultural food taste preferences, analyzing cultural trends via word use in books ("culturomics"), identifying seasonality of mood from tweets, and so on.But so what?Deep DataWhat those three studies show us is that it's possible to automate, or at least semi-automate, critical aspects of the scientific method itself. Schmidt and Lipson show that it is possible to extract equations that perfectly model even seemingly chaotic systems. Yarkoni and colleagues show that it is possible to infer a complex behavioral state given input brian data.My wife and I wanted to show that brainSCANr could be put to work for something more useful than just quantifying relationships between terms. So we created a simple algorithm to perform what we're calling "semi-automated hypothesis generation", which is predicated on a basic "the friend of a friend should be a friend" concept.In the example below, the neurotransmitter "serotonin" has thousands of shared publications with "migraine", as well as with the brain region "striatum". However migraine and striatum only share 16 publications.That's very odd. Because in medicine there is a serotonin hypothesis for the root cause of migraines. And we (neuroscientists) know that serotonin is released in the striatum to modulate brain activity in that region. Given that those two things are true, why is there so little research regarding the role of the striatum in migraines?Perhaps there's a missing connection?Such missing links and other outliers in our models are the essence of deep data analytics. Sure, any data scientist worth their salt can take a mountain of data and reduce it down to a few simple plots. And such plots are important because they tell a story. But those aren't the only stories that our data can tell us.For example, in my geoanalytics work as the Data Evangelist for Uber, I put some of my (definitely rudimentary) neuroscience network analytic skills to work to figure out how people move from neighborhood to neighborhood in San Francisco.At one point, I checked to see if men and women moved around the city differently. A very simple regression model showed that the number of men who go to any given neighborhood significantly predicts the number of woman who go to that same neighborhood.No big deal.But what's cool was seeing where the outliers were. ... Read more »

Yarkoni, T., Poldrack, R., Nichols, T., Van Essen, D., & Wager, T. (2011) Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665-670. DOI: 10.1038/nmeth.1635  

Ahn, Y., Ahnert, S., Bagrow, J., & Barabási, A. (2011) Flavor network and the principles of food pairing. Scientific Reports. DOI: 10.1038/srep00196  

Michel, J., Shen, Y., Aiden, A., Veres, A., Gray, M., , ., Pickett, J., Hoiberg, D., Clancy, D., Norvig, P.... (2010) Quantitative Analysis of Culture Using Millions of Digitized Books. Science, 331(6014), 176-182. DOI: 10.1126/science.1199644  

  • March 28, 2012
  • 05:01 PM
  • 1,003 views

Rare earth materials—the key to clean energy technology

by Cath in Basal Science (BS) Clarified

What are rare earth materials? Rare earth elements (REE) is a term generally used to describe the elements in the lanthanide series—the second last row of the periodic table. However yttrium and scandium, both Group IIIB transition metals, are often included as REE since they are naturally found together with other lanthanides and have similar [...]... Read more »

Wu, C., Yu, D., Law, C., & Wang, L. (2004) Properties of lead-free solder alloys with rare earth element additions. Materials Science and Engineering: R: Reports, 44(1), 1-44. DOI: 10.1016/j.mser.2004.01.001  

Alonso, E., Sherman, A., Wallington, T., Everson, M., Field, F., Roth, R., & Kirchain, R. (2012) Evaluating Rare Earth Element Availability: A Case with Revolutionary Demand from Clean Technologies. Environmental Science , 2147483647. DOI: 10.1021/es203518d  

Zawisza, B., Pytlakowska, K., Feist, B., Polowniak, M., Kita, A., & Sitko, R. (2011) Determination of rare earth elements by spectroscopic techniques: a review. Journal of Analytical Atomic Spectrometry, 26(12), 2373. DOI: 10.1039/c1ja10140d  

  • March 23, 2012
  • 12:05 PM
  • 653 views

Video: Bioinspired Robojelly Fuelled by Hydrogen

by United Academics in United Academics

It’s a small robot powered by hydrogen that resembles the movements of a jellyfish. ... Read more »

Tadesse, Y., Villanueva, A., Haines, C., Novitski, D., Baughman, R., & Priya, S. (2012) Hydrogen-fuel-powered bell segments of biomimetic jellyfish. Smart Materials and Structures, 21(4), 45013. DOI: 10.1088/0964-1726/21/4/045013  

  • March 21, 2012
  • 09:00 PM
  • 994 views

DNA nanoRobot — the next-generation of targeted drug delivery systems?

by Char in Basal Science (BS) Clarified

Targeted drug delivery is a highly sought after technology. Not only does it increase the efficiency of the drugs, but it may also reduce the side effects by localizing drug only where it’s needed. Shawn Douglas and co-researchers at the Harvard Hansjorg Wyss Institute for Biologically Inspired Engineering have created a nano-robot made of DNA strands [...]... Read more »

  • March 14, 2012
  • 09:30 PM
  • 1,152 views

Sonification: Listen To The Sun / Listen To CERN's LHC

by DJ Busby in Astronasty

Solar storm data has recently been translated through a process called sonification into audio. CERN uses this technology too. Here are videos and explanations for both.... Read more »

Alberto de Campo, Natascha Hormann, Harald Markum, Willibald Plessas, & Bianka Sengi. (2005) Sonification of lattice data: The spectrum of the Dirac operator across the deconfinement transition. Proceedings of Science. info:other/PoS: LAT2005-152

Katharina Vogt, Robert H¨oldrich, David Pirr`o, Martin Rumori, Stefan Rossegger, Werner Riegler, & Matevˇz Tadel. (2010) A SONIC TIME PROJECTION CHAMBER. SONIFIED PARTICLE DETECTION AT CERN. International Conference on Auditory Display. info:other/ISBN: 0-9670904-3-1

  • March 9, 2012
  • 01:46 PM
  • 760 views

Have connectionist models killed off beliefs?

by Ben in Critical Science

Connectionist models are widely held to have had a revolutionary impact upon cognitive science (Marcus, 2001). However, they are also employed in a highly controversial doctrine known as ‘eliminative materialism’, which claims the central posits of our common understanding of human psychology, including our conception of ‘beliefs’, are entirely false (Ramsey, Stitch & Garon, 1990). If such arguments are accepted, a radical reorientation is necessary in how we perceive and predict human behaviour, one that does not allow for human desire, intention or beliefs of any kind.... Read more »

  • March 8, 2012
  • 01:44 PM
  • 618 views

Getting to the Root of Microfluidics

by Hector Munoz in Microfluidic Future

It’s not hard to see that a lot here at Microfluidic Future focuses on the medical applications of microfluidics, but that doesn’t mean that I’m not interested in other ways the technology can be used. I love to see novel applications of microfluidics because progress for anyone is progress for everyone. That brings me to today’s post on the RootChip. If the name isn’t a total give away, I recently came across an article that uses a microfluidic chip to study the roots of plants. In the article, “The RootChip: An Integrated Microfluidic Chip for Plant Science” by Stephen Quake and other researchers from Stanford University, a device is developed to study the roots of Arabidopsis thaliana.... Read more »

Grossmann, G., Guo, W., Ehrhardt, D., Frommer, W., Sit, R., Quake, S., & Meier, M. (2011) The RootChip: An Integrated Microfluidic Chip for Plant Science. THE PLANT CELL ONLINE, 23(12), 4234-4240. DOI: 10.1105/tpc.111.092577  

  • March 7, 2012
  • 05:37 AM
  • 494 views

How do children learn and represent music?

by Henkjan Honing in Music Matters


Last month the peer-reviewed online journal Visions of Research in Music Education published a tribute to Jeanne Bamberger. See here for more information. ... Read more »

Desain, P., & Honing, H. (1988) LOCO: A Composition Microworld in Logo. Computer Music Journal, 12(3), 30. DOI: 10.2307/3680334  

  • March 6, 2012
  • 05:36 PM
  • 830 views

Rapid microfluidics-based measurements aid bitumen extraction

by Cath in Basal Science (BS) Clarified

Here’s a piece of engineering news to kick off Canada’s National Engineering month: A team of researchers led by Professor David Sinton of the University of Toronto’s Mechanical and Industrial Engineering Department developed a process to analyze the behavior of bitumen (an extra heavy oil) using a microfluidic chip, a tool commonly used in the [...]... Read more »

  • March 2, 2012
  • 11:32 AM
  • 887 views

5 Things to Know About SAMtools Mpileup

by Daniel Koboldt in Massgenomics

Next-generation sequencing instruments might be considered a disruptive technology. The incredible throughput of these machines, even 4-5 years ago, clearly mandated the development of a new generation of algorithms and data formats capable of storing, processing, and analyzing huge amounts of sequence data. One key achievement in next-generation sequencing bioinformatics was the specification of sequence [...]... Read more »

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, & 1000 Genome Project Data Processing Subgroup. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England), 25(16), 2078-9. PMID: 19505943  

  • February 28, 2012
  • 11:06 PM
  • 914 views

“Power Felt”–a thermoelectric fabric that uses body heat to power electronics

by Cath in Basal Science (BS) Clarified

Did you know that while you’re sitting down and reading this post, your body can provide about 4-6 watts of power? That’s enough power to run a clock radio. Humans store energy from the food they consumed and some of this energy is then emitted as body heat. This wasted heat can be recovered at [...]... Read more »

Hewitt, C., Kaiser, A., Roth, S., Craps, M., Czerw, R., & Carroll, D. (2012) Multilayered Carbon Nanotube/Polymer Composite Based Thermoelectric Fabrics. Nano Letters, 2147483647. DOI: 10.1021/nl203806q  

Snyder, G., & Toberer, E. (2008) Complex thermoelectric materials. Nature Materials, 7(2), 105-114. DOI: 10.1038/nmat2090  

  • February 22, 2012
  • 05:18 AM
  • 651 views

Leaping lizards [video]

by GrrlScientist in Maniraptora

SUMMARY: Theropod dinosaurs used their long tails as counterbalances to stabilise rapid or irregular movements ... Read more »

Libby, T., Moore, T., Chang-Siu, E., Li, D., Cohen, D., Jusufi, A., & Full, R. (2012) Tail-assisted pitch control in lizards, robots and dinosaurs. Nature, 481(7380), 181-184. DOI: 10.1038/nature10710  

  • February 22, 2012
  • 04:28 AM
  • 1,113 views

Leaping lizards [video] | @GrrlScientist

by GrrlScientist in GrrlScientist

Theropod dinosaurs used their long tails as a counterbalance to stabilise rapid or irregular movements ... Read more »

Libby, T., Moore, T., Chang-Siu, E., Li, D., Cohen, D., Jusufi, A., & Full, R. (2012) Tail-assisted pitch control in lizards, robots and dinosaurs. Nature, 481(7380), 181-184. DOI: 10.1038/nature10710  

  • February 21, 2012
  • 10:10 PM
  • 906 views

Turning waste into energy

by Cath in Basal Science (BS) Clarified

One of the biggest concerns with converting biomass into usable energy has always been the amount of land and water used to obtain it. Crops grown exclusively for biofuel production are often in competition for land and water use in food and fibre production. To overcome this problem, a second-generation of biofuels made from non-food [...]... Read more »

Dang, T., Le, G., Giang Pham, T., Nguyen, T., Dao, D., Hong Vu, T., Thuy Hoang, T., Hoa Tran, T., & Vu, A. (2011) Synthesis of advanced materials for bio-oil production from rice straw by pyrolysis. Advances in Natural Sciences: Nanoscience and Nanotechnology, 2(4), 45012. DOI: 10.1088/2043-6262/2/4/045012  

  • February 21, 2012
  • 03:22 PM
  • 657 views

What is the relation between language and cognition?

by Henkjan Honing in Music Matters

What is the relation between language and cognition? On the one hand, researchers like Noam Chomsky thought of language as an independent function with its own rules. However, other people thought that language as a system is embedded in cognition and subject to all models of cognition. ... Read more »

Hauser MD, Chomsky N, & Fitch WT. (2002) The faculty of language: what is it, who has it, and how did it evolve?. Science (New York, N.Y.), 298(5598), 1569-79. PMID: 12446899  

  • February 18, 2012
  • 09:00 PM
  • 454 views

Does performance matter?

by Henkjan Honing in Music Matters

A nice example of how a single score can give rise to an enormous variety of intriguing performances. ... Read more »

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