A newly launched project seeks volunteers willing to lend their idle computing power to help researchers with computationally difficult problems. The Compute Against Alzheimer’s Disease project is being developed by George Mason University and Parabon Computation. Participants will download software that works only when the computer is not otherwise in use. The spare computational cycles will be tasked with molecular modeling at the cellular level. This form of distributed computing approach has been used by groups including Search for Extraterrestrial Intelligence. The researchers are trying to find what causes Alzheimer’s, which is the sixth leading cause of death in the US. (EurekAlert)(George Mason University)(Compute Against Alzheimer’s Disease)
Friday, June 6, 2014
Sunday, March 31, 2013
Scientists Detect Magnetic Fingerprints of Defects in Solar Cells
Scientists Detect Magnetic Fingerprints of Defects in Solar CellsA new highly sensitive method of measurement allowed physicists form Helmholtz-Zentrum Berlin for Materials and Energy to directly detect defects in solar cells with atomic resolution. This findings can be used to optimize solar cells’ efficiency and decrease production costs.HZB physicists have managed to localize defects in amorphous/crystalline silicon heterojunction solar cells. Now, for the first time ever, using computer simulations at Paderborn University, the scientists were able to determine the defects’ exact locations and assign them to certain structures within the interface between the amorphous and crystalline phases.In theory, silicon-based solar cells are capable of converting up to 30 percent of sunlight to electricity—although, in reality, the different kinds of loss mechanisms ensure that even under ideal lab conditions it does not exceed 25 %. Advanced heterojunction cells shall affront this problem: On top of the wafer’s surface, at temperatures below 200 °C, a layer of 10 nanometer disordered (amorphous) silicon is deposited. This thin film is managing to saturate to a large extent the interface defects and to conduct charge carriers out of the cell. Heterojunction solar cells have already high efficiency factors up to 24,7%—even in industrial scale. However, scientists had until now only a rough understanding of the processes at the remaining interface defects.Now, physicists at HZB’s Institute for Silicon Photovoltaics have figured out a rather clever way for detecting the remaining defects and characterizing their electronic structure. “If electrons get deposited on these defects, we are able to use their spin, that is, their small magnetic moment, as a probe to study them,” Dr. Alexander Schnegg explains. With the help of EDMR, electrically detected magnetic resonance, an ultrasensitive method of measurement, they were able to determine the local defects’ structure by detecting their magnetic fingerprint in the photo current of the solar cell under a magnetic field and microwave radiation.Theoretical physicists of Paderborn University could compare these results with quantum chemical computer simulations, thus obtaining information about the defects’ positions within the layers and the processes they are involved to decrease the cells’ efficiency. “We basically found two distinct families of defects”, says Dr. Uwe Gerstmann from Paderborn University, who collaborates with the HZB Team in a program sponsored by Deutsche Forschungsgemeinschaft (DFG priority program 1601). “Whereas in the first one, the defects are rather weakly localized within the amorphous layer, a second family of defects is found directly at the interface, but in the crystalline silicon.”For the first time ever the scientists have succeeded at directly detecting and characterizing processes with atomic resolution that compromise these solar cells’ high efficiency. The cells were manufactured and measured at the HZB; the numerical methods were developed at Paderborn University. “We can now apply these findings to other types of solar cells in order to optimize them further and to decrease production costs”, says Schnegg.George, B., Behrends, J., Schnegg, A., Schulze, T., Fehr, M., Korte, L., Rech, B., Lips, K., Rohrmüller, M., Rauls, E., Schmidt, W., & Gerstmann, U. (2013). Atomic Structure of Interface States in Silicon Heterojunction Solar Cells Physical Review Letters, 110 (13) DOI: 10.1103/PhysRevLett.110.136803If you liked this story, please consider sharing it. You can also follow us on Twitter, Facebook or Google+ to stay up to date on the breaking news and events of the energy industry.
Monday, February 4, 2013
Computer scientists develop new way to study molecular networks
His group authored an article on their new approach to address these shortcomings, titled "Reverse Engineering Molecular Hypergraphs," that received the Best Paper Award at the recent 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine.
Intricate networks of connections among molecules control the processes that occur within cells. The "analysis of these interaction networks has relied almost entirely on graphs for modeling the information. Since a link in a graph connects at most two molecules (e.g., genes or proteins), such edges cannot accurately represent interactions among multiple molecules. These interactions occur very often within cells," the computer scientists wrote in their paper.
To overcome the limitations in the use of the graphs, Murali and his students used hypergraphs, a generalization of a graph in which an hyperedge can connect multiple molecules.
"We used hypergraphs to capture the uncertainty that is inherent in reverse engineering gene to gene networks from systems biology datasets," explained Ahsanur Rahman, the lead author on the paper. "We believe hypergraphs are powerful representations for capturing the uncertainty in a network's structure."
They developed reliable algorithms that can discover hyperedges supported by sets of networks. In ongoing research, the scientists seek to use hyperedges to suggest new experiments. By capturing uncertainty in network structure, hyperedges can directly suggest groups of genes for which further experiments may be required in order to precisely discover interaction patterns. Incorporating the data from these experiments might help to refine hyperedges and resolve the interactions among molecules, resulting in fruitful interplay and feedback between computation and experiment.
Murali, and his students Ahsanur Rahman and Christopher L. Poirel, both doctoral candidates, and David L. Badger, a software engineer in Murali's group, all of Blacksburg, Va., and all in the computer science department, used funding from the National Institutes of Health and the National Science Foundation to better understand this uncertainty in these various forms of interactions.
Murali is also the co-director of the Institute for Critical Technology and Applied Science's Center for Systems Biology of Engineered Tissues and the associate program director for the computational tissue engineering interdisciplinary graduate education program at Virginia Tech.
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Tuesday, September 25, 2012
Scientists create chemical 'brain': Giant network links all known compounds and reactions
A decade in the making, the software optimizes syntheses of drug molecules and other important compounds, combines long (and expensive) syntheses of compounds into shorter and more economical routes and identifies suspicious chemical recipes that could lead to chemical weapons.
"I realized that if we could link all the known chemical compounds and reactions between them into one giant network, we could create not only a new repository of chemical methods but an entirely new knowledge platform where each chemical reaction ever performed and each compound ever made would give rise to a collective 'chemical brain,'" said Bartosz A. Grzybowski, who led the work. "The brain then could be searched and analyzed with algorithms akin to those used in Google or telecom networks."
Called Chematica, the network comprises some seven million chemicals connected by a similar number of reactions. A family of algorithms that searches and analyzes the network allows the chemist at his or her computer to easily tap into this vast compendium of chemical knowledge. And the system learns from experience, as more data and algorithms are added to its knowledge base.
Details and demonstrations of the system are published in three back-to-back papers in the Aug. 6 issue of the journal Angewandte Chemie.
Grzybowski is the senior author of all three papers. He is the Kenneth Burgess Professor of Physical Chemistry and Chemical Systems Engineering in the Weinberg College of Arts and Sciences and the McCormick School of Engineering and Applied Science.
In the Angewandte paper titled "Parallel Optimization of Synthetic Pathways Within the Network of Organic Chemistry," the researchers have demonstrated algorithms that find optimal syntheses leading to drug molecules and other industrially important chemicals.
"The way we coded our algorithms allows us to search within a fraction of a second billions of chemical syntheses leading to a desired molecule," Grzybowski said. "This is very important since within even a few synthetic steps from a desired target the number of possible syntheses is astronomical and clearly beyond the search capabilities of any human chemist."
Chematica can test and evaluate every possible synthesis that exists, not only the few a particular chemist might have an interest in. In this way, the algorithms find truly optimal ways of making desired chemicals.
The software already has been used in industrial settings, Grzybowski said, to design more economical syntheses of companies' products. Synthesis can be optimized with various constraints, such as avoiding reactions involving environmentally dangerous compounds. Using the Chematica software, such green chemistry optimizations are just one click away.
Another important area of application is the shortening of synthetic pathways into the so-called "one-pot" reactions. One of the holy grails of organic chemistry has been to design methods in which all the starting materials could be combined at the very beginning and then the process would proceed in one pot -- much like cooking a stew -- all the way to the final product.
The Northwestern researchers detail how this can be done in the Angewandte paper titled "Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry."
The chemists have taught their network some 86,000 chemical rules that check -- again, in a fraction of a second -- whether a sequence of individual reactions can be combined into a one-pot procedure. Thirty predictions of one-pot syntheses were tested and fully validated. Each synthesis proceeded as predicted and had excellent yields.
In one striking example, Grzybowski and his team synthesized an anti-asthma drug using the one-pot method. The drug typically would take four consecutive synthesis and purification steps.
"Our algorithms told us this sequence could be combined into just one step, and we were naturally curious to check it out in a flask," Grzybowski said. "We performed the one-pot reaction and obtained the drug in excellent yield and at a fraction of the cost the individual steps otherwise would have accrued."
The third area of application is the use of the Chematica network approach for predicting and monitoring syntheses leading to chemical weapons. This is reported in the Angewandte paper titled "Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats."
"Since we now have this unique ability to scrutinize all possible synthetic strategies, we also can identify the ones that a potential terrorist might use to make a nerve gas, an explosive or another toxic agent," Grzybowski said.
Algorithms known from game theory first are applied to identify the strategies that are hardest to detect by the federal government -- the use of substances, for example, such as kitchen salt, clarifiers, grain alcohol and a fertilizer, all freely available from a local convenience store. Characteristic combinations of seemingly innocuous chemicals, such as this example, are red flags.
This strategy is very different from the government's current approach of monitoring and regulating individual substances, Grzybowski said. Chematica can be used to monitor patterns of chemicals that together become suspicious, instead of monitoring individual compounds. Grzybowski is working with the federal government to implement the software.
Chematica now is being commercialized. "We chose this name," Grzybowski said, "because networks will do to chemistry what Mathematica did to scientific computing. Our approach will accelerate synthetic design and discovery and will optimize synthetic practice at large."
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The above story is reprinted from materials provided by Northwestern University. The original article was written by Megan Fellman.
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Journal References:
Mikolaj Kowalik, Chris M. Gothard, Aaron M. Drews, Nosheen A. Gothard, Alex Weckiewicz, Patrick E. Fuller, Bartosz A. Grzybowski, Kyle J. M. Bishop. Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7928 DOI: 10.1002/anie.201202209Chris M. Gothard, Siowling Soh, Nosheen A. Gothard, Bartlomiej Kowalczyk, Yanhu Wei, Bilge Baytekin, Bartosz A. Grzybowski. Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7922 DOI: 10.1002/anie.201202155Patrick E. Fuller, Chris M. Gothard, Nosheen A. Gothard, Alex Weckiewicz, Bartosz A. Grzybowski. Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats. Angewandte Chemie International Edition, 2012; 51 (32): 7933 DOI: 10.1002/anie.201202210Note: If no author is given, the source is cited instead.
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Monday, September 17, 2012
Computer scientists show what makes movie lines memorable
Cornell researchers who applied computer analysis to a database of movie scripts think they may have found the secret of what makes a line memorable.
The study suggests that memorable lines use familiar sentence structure but incorporate distinctive words or phrases, and they make general statements that could apply elsewhere. The latter may explain why lines such as, "You're gonna need a bigger boat" or "These aren't the droids you're looking for" (accompanied by a hand gesture) have become standing jokes. You can use them in a different context and apply the line to your own situation.
While the analysis was based on movie quotes, it could have applications in marketing, politics, entertainment and social media, the researchers said.
"Using movie scripts allowed us to study just the language, without other factors. We needed a way of asking a question just about the language, and the movies make a very nice dataset," said graduate student Cristian Danescu-Niculescu-Mizil, first author of a paper to be presented at the 50th Annual Meeting of the Association for Computational Linguistics July 8-14 in Jeju, South Korea.
The study grows out of ongoing work on how ideas travel across networks.
"We've been looking at things like who talks to whom," said Jon Kleinberg, a professor of computer science who worked on the study, "but we hadn't explored how the language in which an idea was presented might have an effect."
To address that, they collaborated with Lillian Lee, a professor of computer science who specializes in computer processing of natural human language.
They obtained scripts from about 1,000 movies, and a database of memorable quotes from those movies from the Internet Movie Database. Each quote was paired with another from the movie's script, spoken by the same character in the same scene and about the same length, to eliminate every factor except the language itself. Obi-Wan Kenobi, for example, also said, "You don't need to see his identification," but you don't hear that a lot.
They asked a group of people who had not seen the movies to choose which quote in the pairs was most memorable. Two patterns emerged to identify the memorable choice: distinctiveness and generality.
Then the researchers programmed a computer with linguistic rules reflecting these concepts. A line will be less general if it contains third-person pronouns and definite articles (which refer to people, objects or events in the scene) and uses past tense (usually referring to something that happened previously in the story). Distinctive language can be identified by comparison with a database of news stories. The computer was able to choose the memorable quote an average of 64 percent of the time.
Later analysis also found subtle differences in sound and word choice: Memorable quotes use more sounds made in the front of the mouth, words with more syllables and fewer coordinating conjunctions.
In a further test, the researchers found that the same rules applied to popular advertising slogans.
Although teaching a computer how to write memorable dialogue is probably a long way off, applications might be developed to monitor the work of human writers and evaluate it in progress, Kleinberg suggested.
The researchers have set up a website where you can test your skill at identifying memorable movie quotes, and perhaps contribute some data to the research, at www.cs.cornell.edu/~cristian/memorability.html.
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The above story is reprinted from materials provided by Cornell University. The original article was written by Bill Steele.
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Monday, August 20, 2012
Computer scientists present smile database
481 test subjects participated in the research of Theo Gevers and Albert Ali Salah. The researchers made a video recording of a posed smile and a spontaneous smile for each participant. The subjects also were also asked to look angry, happy, sad, surprised and scared. Gevers and Salah analysed certain characteristics, such as how quickly the corners of the mouth turn upwards. This knowledge can be applied to computer software which guesses ages, recognise emotions and analyse human behaviour.
The researchers also asked the test subjects to look at images of other test subjects. They had to guess the age of those people and state how attractive they found them. They were also asked to judge character traits, such as whether the person is helpful by nature, or if that person is perhaps in love?
The data collected allowed the researchers to develop software that can estimate people's age. The software takes into account whether someone is happy, sad or angry, and adjusts its estimate accordingly. The software appears to be slightly better at estimating ages than humans. On average, humans' estimates are seven years off , while the computer is six years off on average .
The research of Gevers and Salah also shows that you look younger when you smile, but only if you are over forty. If you are under forty, you should look neutral if you want to come across younger.
Smile Database: http://www.uva-nemo.org/
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The above story is reprinted from materials provided by Universiteit van Amsterdam (UVA).
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Tuesday, August 14, 2012
New method to find novel connections from gene to gene, drug to drug and between scientists
Led by Avi Ma'ayan, PhD, Assistant Professor of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine, and Neil Clark, PhD a postdoctoral fellow in the Ma'ayan laboratory, the team of investigators used the new algorithm to create 15 different types of gene-gene networks. They also discovered novel connections between drugs and side effects, and built a collaboration network that connected Mount Sinai investigators based on their past publications.
"The algorithm makes it simple to build networks from data," said Dr. Ma'ayan. "Once high dimensional and complex data is converted to networks, we can understand the data better and discover new and significant relationships, and focus on the important features of the data."
The group analyzed one million medical records of patients to build a network that connects commonly co-prescribed drugs, commonly co-occurring side effects, and the relationships between side effects and combinations of drugs. They found that reported side effects may not be caused by the drugs, but by a separate condition of the patient that may be unrelated to the drugs. They also looked at 53 cancer drugs and connected them to 32 severe side effects. When chemotherapy was combined with cancer drugs that work through cell signaling, there was a strong link to cardiovascular related adverse events. These findings can assist in post-marketing surveillance safety of approved drugs.
The approach is presented in two separate publications in the journals BMC Bioinformatics and BMC Systems Biology. The tools that implement the approach Genes2FANs and Sets2Networks can be found online at http://actin.pharm.mssm.edu/genes2FANs and http://www.maayanlab.net/S2N.
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The above story is reprinted from materials provided by The Mount Sinai Hospital / Mount Sinai School of Medicine, via Newswise.
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Journal Reference:
Ruth Dannenfelser, Neil R Clark, Avi Ma'ayan. Genes2FANs: connecting genes through functional association networks. BMC Bioinformatics, 2012; 13 (1): 156 DOI: 10.1186/1471-2105-13-156Note: If no author is given, the source is cited instead.
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Monday, June 18, 2012
Scientists develop biological computer to encrypt and decipher images
The study was published in a recent online-before-print edition of the journal Angewandte Chemie.
Instead of using traditional computer hardware, a group led by Professor Ehud Keinan of Scripps Research and the Technion created a computing system using bio-molecules. When suitable software was applied to the biological computer, it could decrypt, separately, fluorescent images of The Scripps Research Institute and Technion logos.
A Union Between Biology and Computer Science
In explaining the work's union of the often-disparate fields of biology and computer science, Keinan notes that a computer is, by definition, a machine made of four components -- hardware, software, input, and output. Traditional computers have always been electronic, machines in which both input and output are electronic signals. The hardware is a complex composition of metallic and plastic components, wires, and transistors, and the software is a sequence of instructions given to the machine in the form of electronic signals.
"In contrast to electronic computers, there are computing machines in which all four components are nothing but molecules," Keinan said. "For example, all biological systems and even entire living organisms are such computers. Every one of us is a biomolecular computer, a machine in which all four components are molecules that 'talk' to one another logically."
The hardware and software in these devices, Keinan notes, are complex biological molecules that activate one another to carry out some predetermined chemical work. The input is a molecule that undergoes specific, predetermined changes, following a specific set of rules (software), and the output of this chemical computation process is another well-defined molecule.
"Building" a Biological Computer
When asked what a biological computer looks like, Keinan laughs.
"Well," he said, "it's not exactly photogenic." This computer is "built" by combining chemical components into a solution in a tube. Various small DNA molecules are mixed in solution with selected DNA enzymes and ATP. The latter is used as the energy source of the device.
"It's a clear solution -- you don't really see anything," Keinan said. "The molecules start interacting upon one another, and we step back and watch what happens." And by tinkering with the type of DNA and enzymes in the mix, scientists can fine-tune the process to a desired result.
"Our biological computing device is based on the 75-year-old design by the English mathematician, cryptanalyst, and computer scientist Alan Turing," Keinan said. "He was highly influential in the development of computer science, providing a formalization of the concepts of algorithm and computation, and he played a significant role in the creation of the modern computer. Turing showed convincingly that using this model you can do all the calculations in the world. The input of the Turing machine is a long tape containing a series of symbols and letters, which is reminiscent of a DNA string. A reading head runs from one letter to another, and on each station it does four actions: 1) reading the letter; 2) replacing that letter with another letter; 3) changing its internal state; and 4) moving to next position. A table of instructions, known as the transitional rules, or software, dictates these actions. Our device is based on the model of a finite state automaton, which is a simplified version of the Turing machine. "
Unique Biological Properties
Now that he has shown the viability of a biological computer, does Keinan hope that this model will compete with its electronic counterpart?
"The ever-increasing interest in biomolecular computing devices has not arisen from the hope that such machines could ever compete with electronic computers, which offer greater speed, fidelity, and power in traditional computing tasks," Keinan said. "The main advantages of biomolecular computing devices over electronic computers have to do with other properties."
As shown in this work, he continues, a wealth of information can be stored and encrypted in DNA molecules. Although each computing step is slower than the flow of electrons in an electronic computer, the fact that trillions of such chemical steps are done in parallel makes the entire computing process fast. "Considering the fact that current microarray technology allows for printing millions of pixels on a single chip, the numbers of possible images that can be encrypted on such chips is astronomically large," he said.
"Also, as shown in our previous work and other projects carried out in our lab, these devices can interact directly with biological systems and even with living organisms," Keinan explained. "No interface is required since all components of molecular computers, including hardware, software, input, and output, are molecules that interact in solution along a cascade of programmable chemical events." He adds that because of DNA's ability to store information, major computer companies have been extremely interested in the development of DNA-based computing systems.
The first author of the study, "A Molecular Cryptosystem for Images by DNA Computing," is graduate student Sivan Shoshani of Technion. In addition to Keinan and Shoshani, authors include postdoctoral fellow Ron Piran of Scripps Research and Yoav Arava of the Technion.
This work was supported by the National Science Foundation, the Israel-US Binational Science Foundation, and the Skaggs Institute for Chemical Biology, as well as graduate fellowships from the Irwin and Joan Jacobs Foundation, the Fine Foundation, the Russell Berrie Nanotechnology Institute, and the Israel Ministry of Science and Technology.
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The above story is reprinted from materials provided by Scripps Research Institute.
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Sivan Shoshani, Ron Piran, Yoav Arava, Ehud Keinan. A Molecular Cryptosystem for Images by DNA Computing. Angewandte Chemie International Edition, 2012; DOI: 10.1002/anie.201107156Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.