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News Article | May 18, 2017
Site: www.eurekalert.org

Scientists at Princeton University have developed a new algorithm to track neurons in the brain of the worm Caenorhabditis elegans while it crawls. The algorithm, presented in PLOS Computational Biology by Jeffrey Nguyen and colleagues, could save hundreds of hours of manual labor in studies of animal behavior. To investigate the brain's role in behavior, scientists use advanced imaging techniques that record the activity of individual neurons as an animal moves. However, tracking neurons in a moving brain is difficult, especially in the soft-bodied worm C. elegans, whose small size and transparency make it otherwise well suited to such studies. "When the worm crawls, its brain bends as it moves, which poses challenges for imaging," says study co-author Andrew Leifer. His team has spent many hours manually tracking neurons in recordings of the bendy brains of crawling worms, prompting them to develop a new algorithm to streamline this laborious process. The new approach, dubbed Neuron Registration Vector Encoding, draws on computer vision and machine learning techniques. It uses 3D fluorescent recordings of the C. elegans brain to assign a unique identity to each neuron it can detect. Based in part on the relative locations of the neurons, the algorithm keeps track of each neuron over time. Tracking is also enhanced by accounting for ways in which certain worm movements are known to deform the brain. The researchers found that the new algorithm consistently identified more neurons more quickly than did manual tracking approaches or other approaches that are partly automated. They were able to use the new approach to correlate neural activity with specific worm movements, particularly reversal maneuvers. "This research demonstrates the value of employing automated computer vision- and machine learning-based approaches to help tackle laborious tasks in neuroscience research that previously could only be done by humans," Leifer says. His team plans to use their new approach to study neural activity during complex C. elegans behaviors, such as foraging, sleeping, and mating. The algorithm could help reveal how different patterns of brain activity generate these behaviors. In the future, the same approach could also be tested in other species, such as zebrafish. In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Citation: Nguyen JP, Linder AN, Plummer GS, Shaevitz JW, Leifer AM (2017) Automatically tracking neurons in a moving and deforming brain. PLoS Comput Biol 13(5): e1005517. https:/ Funding: This work was supported by Simons Foundation Grant SCGB 324285 to AML and Princeton University's Inaugural Dean for Research Innovation Fund for New Ideas in the Natural Sciences to JWS and AML. JPN is supported by grants from the Swartz Foundation and the Glenn Foundation for Medical Research. ANL is supported by a National Institutes of Health institutional training grant NIH T32 MH065214 through the Princeton Neuroscience Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.


News Article | May 19, 2017
Site: www.scientificcomputing.com

Scientists have identified thousands of previously ignored genetic mutations that, although rare, likely contribute to cancer growth. The findings, which could help pave the way to new treatments, are published in PLOS Computational Biology. Cancer arises when genetic mutations in a cell cause abnormal growth that leads to a tumor. Some cancer drugs exploit this to attack tumor cells by targeting proteins that are mutated from their usual form because of mutations in the genes that encode them. However, only a fraction of all the mutations that contribute significantly to cancer have been identified. Thomas Peterson, at the University of Maryland, and colleagues developed a new statistical analysis approach that uses genetic data from cancer patients to find cancer-causing mutations. Unlike previous studies that focused on mutations in individual genes, the new approach addresses similar mutations shared by families of related proteins. Specifically, the new method focuses on mutations in sub-components of proteins known as protein domains. Even though different genes encode them, different proteins can share common protein domains. The new strategy draws on existing knowledge of protein domain structure and function to pinpoint locations within protein domains where mutations are more likely to be found in tumors. Using this new approach, the researchers identified thousands of rare tumor mutations that occur in the same domain location as mutations found in other proteins in other tumors-- suggesting that they are likely to be involved in cancer. "Maybe only two patients have a mutation in a particular protein, but when you realize it is in exactly the same position within the domain as mutations in other proteins in cancer patients," says senior author of the study Maricel Kann, "you realize it's important to investigate those two mutations." The researchers have coined the term "oncodomain" to refer to protein domains that are more likely to contain cancer-causing mutations. Further study of oncodomains could help inform drug development: "Because the domains are the same across so many proteins," Kann says, "it is possible that a single treatment could tackle cancers caused by a broad spectrum of mutated proteins."


Receive press releases from iHealthcareAnalyst, Inc.: By Email Global Biotechnology Market Research Reports, Competitive Analytics, Trends and Forecast 2017-2021 by iHealthcareAnalyst, Inc. Biotechnology Market reports by iHealthcareAnalyst, Inc. include Aptamers, Array Instruments, Bioinformatics, Biopreservation, Biosensors, Cancer Genome Sequencing, Cell Culture, Cell Culture Media, Sera, Reagents, Cell Culture Protein Surface Coatings, Cell Separation Technologies, Cell Surface Markers, Computational Biology, E. coli Diagnostics Testing, Genome Engineering or Genome Editing, Human Insulin, Immunoassay Analyzers, etc. Maryland Heights, MO, May 19, 2017 --( The global biotechnology market report provides market size (Revenue USD Million 2014 to 2021), market share, market trends and forecasts growth trends (CAGR%, 2017 to 2021). The global market research reports are divided by geography into North America (U.S., Canada), Latin America (Brazil, Mexico, Rest of LA), Europe (U.K., Germany, France, Italy, Spain, Rest of EU), Asia Pacific (Japan, China, India, Rest of APAC), and Rest of the World. The global reports also provide detailed market landscape (market drivers, restraints, opportunities), market attractiveness and profitability analysis as well as profiles of major competitors in the global market which includes company overview, financial snapshot, key products, technologies and services offered, and recent developments. Browse Global Biotechnology Reports, Competitive Analytics, Growth Trends and Forecast 2017-2021 by iHealthcareAnalyst, Inc. at https://www.ihealthcareanalyst.com/reports/biotechnology/ Table of Contents 1. Introduction 2. Executive Summary 2.1. Market Size Estimation (Revenue USD Million, 2014-2021) 2.2. Forecast Estimation (Revenue USD Million and CAGR%, 2017-2021) 3. Research Methodology 4. Market Landscape 4.1. Market Dynamics 4.1.1. Drivers 4.1.2. Barriers 4.1.3. Opportunities 4.2. Market Share Analysis 4.2.1. Companies 4.2.2. Products 4.3. Market Trends Analysis 4.3.1. Key success factors 4.3.2. Market Growth Rate 4.4. Market Attractiveness Analysis 4.5. Market Profitability Analysis 4.5.1. Buyer power 4.5.2. Supplier power 4.5.3. Barriers to entry 4.5.4. Threat of substitute products 4.5.5. Rivalry among firms in the industry 4.6. Distribution Channels 5. Market Segmentation 5.1. Product Type (cells, media, serum, vaccine, protein, instrument, etc.) 5.2. Source Type 5.3. Reagents and Consumables Type 5.4. Diagnostic Test 5.5. Indication Type 5.6. Technology 5.7. Diagnostics, Therapeutic or Surgical Application 5.8. End User Groups 6. Geography (Region, Country) 6.1. North America (U.S., Canada) 6.2. Latin America (Brazil, Mexico, Rest of LA) 6.3. Europe (U.K., Germany, France, Italy, Spain, Rest of EU) 6.4. Asia Pacific (Japan, China, India, Rest of APAC) 6.5. Rest of the World 7. Company Profiles 7.1. Company Overview 7.2. Financial Snapshot (FY 2014-2016) 7.3. Product Portfolio 7.4. Business Strategies 7.5. Recent Developments 8. Recommendations 9. References To request Table of Contents and Sample Pages of these reports visit: https://www.ihealthcareanalyst.com/reports/biotechnology/ About Us iHealthcareAnalyst, Inc. is a global healthcare market research and consulting company providing market analysis, and competitive intelligence services to global clients. The company publishes syndicate, custom and consulting grade healthcare reports covering animal healthcare, biotechnology, clinical diagnostics, healthcare informatics, healthcare services, medical devices, medical equipment, and pharmaceuticals. In addition to multi-client studies, we offer creative consulting services and conduct proprietary single-client assignments targeted at client’s specific business objectives, information needs, time frame and budget. Please contact us to receive a proposal for a proprietary single-client study. Contact Us iHealthcareAnalyst, Inc. 2109, Mckelvey Hill Drive, Maryland Heights, MO 63043 United States Email: sales@ihealthcareanalyst.com Website: https://www.ihealthcareanalyst.com Maryland Heights, MO, May 19, 2017 --( PR.com )-- The published titles of Biotechnology Market reports by iHealthcareAnalyst, Inc. include Aptamers, Array Instruments, Bioinformatics, Biopreservation, Biosensors, Cancer Genome Sequencing, Cell Culture, Cell Culture Media, Sera, Reagents, Cell Culture Protein Surface Coatings, Cell Separation Technologies, Cell Surface Markers, Computational Biology, E. coli Diagnostics Testing, Genome Engineering or Genome Editing, Human Insulin, Immunoassay Analyzers, Immunoprotein Diagnostic Testing, In Vitro Colorectal Cancer Testing, In Vitro Diagnostics, Life Science Reagents, Medical Cameras, Medical Microscopes, Meningococcal Vaccines, Metabolomics, Microbiology Cultures, Molecular Cytogenetics, Molecular Diagnostics, Monoclonal Antibody Therapeutics, Multiplexed Diagnostics, Next-Generation Sequencing, Non-alcoholic Steatohepatitis Biomarkers, Orthobiologics, Platelet Rich Plasma, Polymerase Chain Reaction, Prenatal and Newborn Genetic Testing, Protein Engineering, Regenerative Medicines, Separation Systems for Commercial Biotechnology, Single Cell Analysis, Single Nucleotide Polymorphism Genotyping, Sperm Bank, Stem Cells, Tissue Engineered Skin Substitutes, Transcriptomics Technologies, and Transfection Technologies.The global biotechnology market report provides market size (Revenue USD Million 2014 to 2021), market share, market trends and forecasts growth trends (CAGR%, 2017 to 2021). The global market research reports are divided by geography into North America (U.S., Canada), Latin America (Brazil, Mexico, Rest of LA), Europe (U.K., Germany, France, Italy, Spain, Rest of EU), Asia Pacific (Japan, China, India, Rest of APAC), and Rest of the World. The global reports also provide detailed market landscape (market drivers, restraints, opportunities), market attractiveness and profitability analysis as well as profiles of major competitors in the global market which includes company overview, financial snapshot, key products, technologies and services offered, and recent developments.Browse Global Biotechnology Reports, Competitive Analytics, Growth Trends and Forecast 2017-2021 by iHealthcareAnalyst, Inc. at https://www.ihealthcareanalyst.com/reports/biotechnology/Table of Contents1. Introduction2. Executive Summary2.1. Market Size Estimation (Revenue USD Million, 2014-2021)2.2. Forecast Estimation (Revenue USD Million and CAGR%, 2017-2021)3. Research Methodology4. Market Landscape4.1. Market Dynamics4.1.1. Drivers4.1.2. Barriers4.1.3. Opportunities4.2. Market Share Analysis4.2.1. Companies4.2.2. Products4.3. Market Trends Analysis4.3.1. Key success factors4.3.2. Market Growth Rate4.4. Market Attractiveness Analysis4.5. Market Profitability Analysis4.5.1. Buyer power4.5.2. Supplier power4.5.3. Barriers to entry4.5.4. Threat of substitute products4.5.5. Rivalry among firms in the industry4.6. Distribution Channels5. Market Segmentation5.1. Product Type (cells, media, serum, vaccine, protein, instrument, etc.)5.2. Source Type5.3. Reagents and Consumables Type5.4. Diagnostic Test5.5. Indication Type5.6. Technology5.7. Diagnostics, Therapeutic or Surgical Application5.8. End User Groups6. Geography (Region, Country)6.1. North America (U.S., Canada)6.2. Latin America (Brazil, Mexico, Rest of LA)6.3. Europe (U.K., Germany, France, Italy, Spain, Rest of EU)6.4. Asia Pacific (Japan, China, India, Rest of APAC)6.5. Rest of the World7. Company Profiles7.1. Company Overview7.2. Financial Snapshot (FY 2014-2016)7.3. Product Portfolio7.4. Business Strategies7.5. Recent Developments8. Recommendations9. ReferencesTo request Table of Contents and Sample Pages of these reports visit:https://www.ihealthcareanalyst.com/reports/biotechnology/About UsiHealthcareAnalyst, Inc. is a global healthcare market research and consulting company providing market analysis, and competitive intelligence services to global clients. The company publishes syndicate, custom and consulting grade healthcare reports covering animal healthcare, biotechnology, clinical diagnostics, healthcare informatics, healthcare services, medical devices, medical equipment, and pharmaceuticals.In addition to multi-client studies, we offer creative consulting services and conduct proprietary single-client assignments targeted at client’s specific business objectives, information needs, time frame and budget. Please contact us to receive a proposal for a proprietary single-client study.Contact UsiHealthcareAnalyst, Inc.2109, Mckelvey Hill Drive,Maryland Heights, MO 63043United StatesEmail: sales@ihealthcareanalyst.comWebsite: https://www.ihealthcareanalyst.com Click here to view the list of recent Press Releases from iHealthcareAnalyst, Inc.


News Article | May 19, 2017
Site: www.scientificcomputing.com

Scientists have identified thousands of previously ignored genetic mutations that, although rare, likely contribute to cancer growth. The findings, which could help pave the way to new treatments, are published in PLOS Computational Biology. Cancer arises when genetic mutations in a cell cause abnormal growth that leads to a tumor. Some cancer drugs exploit this to attack tumor cells by targeting proteins that are mutated from their usual form because of mutations in the genes that encode them. However, only a fraction of all the mutations that contribute significantly to cancer have been identified. Thomas Peterson, at the University of Maryland, and colleagues developed a new statistical analysis approach that uses genetic data from cancer patients to find cancer-causing mutations. Unlike previous studies that focused on mutations in individual genes, the new approach addresses similar mutations shared by families of related proteins. Specifically, the new method focuses on mutations in sub-components of proteins known as protein domains. Even though different genes encode them, different proteins can share common protein domains. The new strategy draws on existing knowledge of protein domain structure and function to pinpoint locations within protein domains where mutations are more likely to be found in tumors. Using this new approach, the researchers identified thousands of rare tumor mutations that occur in the same domain location as mutations found in other proteins in other tumors-- suggesting that they are likely to be involved in cancer. "Maybe only two patients have a mutation in a particular protein, but when you realize it is in exactly the same position within the domain as mutations in other proteins in cancer patients," says senior author of the study Maricel Kann, "you realize it's important to investigate those two mutations." The researchers have coined the term "oncodomain" to refer to protein domains that are more likely to contain cancer-causing mutations. Further study of oncodomains could help inform drug development: "Because the domains are the same across so many proteins," Kann says, "it is possible that a single treatment could tackle cancers caused by a broad spectrum of mutated proteins."


News Article | May 22, 2017
Site: www.eurekalert.org

New Rochelle, NY, May 22, 2017 - Mary Ann Liebert, Inc., publishers announces the launch of The CRISPR Journal, a broad-based international peer-reviewed publication dedicated to the dissemination of critical research on the myriad applications and underlying technology of CRISPR. Debuting in 2018, The CRISPR Journal will be published online and in print with open access options and provide a high-profile forum for groundbreaking international research, editorials, analysis, debate, and commentary. The CRISPR Journal adds an exciting and dynamic component to the Mary Ann Liebert, Inc. portfolio of publications which includes GEN (Genetic Engineering & Biotechnology News) and over 80 leading peer-reviewed journals. Leading the Liebert team to launch The CRISPR Journal is recently appointed Executive Vice President of Strategic Development Kevin Davies, PhD, the founding editor of Nature Genetics and author of several books on genome research including Cracking the Genome. "The extraordinary excitement and profound implications of CRISPR research exceed anything I've seen in the past 30 years of research and scientific publishing," said Davies. "I firmly believe that The CRISPR Journal, devoted to capturing critical advances in CRISPR research and genome editing applications, will serve a huge unmet need across the scientific community." The recent development of the new precision genome editing technology known as CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) has captivated and transformed the world of biomedical research. The power of CRISPR technology has the potential to transform many aspects of human life, developing new materials and biofuels, producing new strains of long-lived, disease-resistant plants and crops, and much more. The biotech and pharma industries are abuzz about the potential of CRISPR to treat cancer and a growing list of genetic diseases. But with this technology comes concerns about accidental or malicious misuse, including fears of eugenics (germline gene editing) and bioterrorism. The CRISPR Journal will address all of these issues. "Like GEN (Genetic Engineering & Biotechnology News), the first and the leading global publication in its field, The CRISPR Journal will be the predominant source for all professionals in its field - from academic research through industrial applications," said Mary Ann Liebert, president and CEO of the company that bears her name, "We have assembled a highly experienced team for this major initiative which is expandable well beyond a traditional journal." Davies adds: "Mary Ann Liebert, Inc. has earned a strong reputation for identifying important medical and scientific trends and serving those communities, with authoritative journals such as Human Gene Therapy, The Journal of Neurotrauma, and Soft Robotics, and its flagship publication GEN, to name a few. The CRISPR Journal will add an exciting new dimension to the Liebert portfolio." A multidisciplinary, global editorial team of CRISPR experts will oversee the peer review selection process for The CRISPR Journal. The Journal will appeal to researchers in wide range of disciplines, including cell biology, genetics and genomics, immunology, infectious diseases, microbiology, molecular biology, neuroscience, plant biology, and much more. In addition to a broad selection of peer review research, The CRISPR Journal will also feature an array of provocative front matter content including editorials, analysis, and commentary. For further information or submission inquiries about The CRISPR Journal, please contact Dr. Kevin Davies, Executive Vice President of Strategic Development, Mary Ann Liebert, Inc. Email: kdavies@liebertpub.com; Cell: 914.336.0888 Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative medical and biomedical peer-reviewed journals, including Journal of Computational Biology, OMICS, Human Gene Therapy, and Soft Robotics. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News), remains the industry's most respected and widely read publication worldwide. A complete list of the firm's more than 80 journals, newsmagazines, and books is available at the Mary Ann Liebert, Inc., publishers website.


News Article | April 17, 2017
Site: www.scientificamerican.com

The brain processes sights, sounds and other sensory information—and even makes decisions—based on a calculation of probabilities. At least, that’s what a number of leading theories of mental processing tell us: The body’s master controller builds an internal model from past experiences, and then predicts how best to behave. Although studies have shown humans and other animals make varied behavioral choices even when performing the same task in an identical environment, these hypotheses often attribute such fluctuations to “noise”—to an error in the system. But not everyone agrees this provides the complete picture. After all, sometimes it really does pay off for randomness to enter the equation. A prey animal has a higher chance of escaping predators if its behavior cannot be anticipated easily, something made possible by introducing greater variability into its decision-making. Or in less stable conditions, when prior experience can no longer provide an accurate gauge for how to act, this kind of complex behavior allows the animal to explore more diverse options, improving its odds of finding the optimal solution. One 2014 study found rats resorted to random behavior when they realized nonrandom behavior was insufficient for outsmarting a computer algorithm. Perhaps, then, this variance cannot simply be chalked up to mere noise. Instead, it plays an essential role in how the brain functions. Now, in a study published April 12 in PLoS Computational Biology, a group of researchers in the Algorithmic Nature Group at LABORES Scientific Research Lab for the Natural and Digital Sciences in Paris hope to illuminate how this complexity unfolds in humans. “When the rats tried to behave randomly [in 2014],” says Hector Zenil, a computer scientist who is one of the study’s authors, “researchers saw that they were computing how to behave randomly. This computation is what we wanted to capture in our study.” Zenil’s team found that, on average, people’s ability to behave randomly peaks at age 25, then slowly declines until age 60, when it starts to decrease much more rapidly. To test this, the researchers had more than 3,400 participants, aged four to 91, complete a series of tasks—“a sort of reversed Turing test,” Zenil says, determining how well a human can outcompete a computer when it comes to producing and recognizing random patterns. The subjects had to create sequences of coin tosses and die rolls they believed would look random to another person, guess which card would be drawn from a randomly shuffled deck, point to circles on a screen and color in a grid to form a seemingly random design. The team then analyzed these responses to quantify their level of randomness by determining the probability that a computer algorithm could generate the same decisions, measuring algorithmic complexity as the length of the shortest possible computer program that could model the participants’ choices. In other words, the more random a person’s behavior, the more difficult it would be to describe his or her responses mathematically, and the longer the algorithm would be. If a sequence were truly random, it would not be possible for such a program to compress the data at all—it would be the same length as the original sequence. After controlling for factors such as language, sex and education, the researchers concluded age was the only characteristic that affected how randomly someone behaved. “At age 25, people can outsmart computers at generating this kind of randomness,” Zenil says. This developmental  trajectory, he adds, reflects what scientists would expect measures of higher cognitive abilities to look like. In fact, a sense of complexity and randomness is based on cognitive functions including attention, inhibition and working memory (which were involved in the study’s five tasks)—although the exact mechanisms behind this relationship remain unknown. “It is around 25, then, that minds are the sharpest.” This makes biological sense, according to Zenil: Natural selection would favor a greater capacity for generating randomness during key reproductive years. The study’s results may even have implications for understanding human creativity. After all, a large part of being creative is the ability to develop new approaches and test different outcomes. “That means accessing a larger repository of diversity,” Zenil says, “which is essentially randomness. So at 25, people have more resources to behave creatively.” Zenil’s findings support previous research, which also showed a decline in random behavior with age. But this is the first study to employ an algorithmic approach to measuring complexity as well as the first to do so over a continuous age range. “Earlier studies considered groups of young and older adults, capturing specific statistical aspects such as repetition rate in very long response sequences,” says Gordana Dodig-Crnkovic, a computer scientist at Mälardalen University in Sweden, who was not involved in the research. “The present article goes a step further.” Using algorithmic measures of randomness, rather than statistical ones, allowed Zenil’s team to examine true random behavior instead of statistical, or pseudorandom, behavior—which, although satisfying statistical tests for randomness, would not necessarily be “incompressible” the way truly random data is. The fact that algorithmic capability differed with age implies the brain is algorithmic in nature—that it does not assume the world is statistically random but takes a more generalized approach without the biases described in more traditional statistical models of the brain. These results may open up a wider perspective on how the brain works: as an algorithmic probability estimator. The theory would update and eliminate some of the biases in statistical models of decision-making that lie at the heart of prevalent theories—prominent among them is the Bayesian brain hypothesis, which holds that the mind assigns a probability to a conjecture and revises it when new information is received from the senses.  “The brain is highly algorithmic,” Zenil says. “It doesn’t behave stochastically, or as a sort of coin-tossing mechanism.” Neglecting an algorithmic approach in favor of only statistical ones gives us an incomplete understanding of the brain, he adds. For instance, a statistical approach does not explain why we can remember sequences of digits such as a phone number—take “246-810-1214,” whose digits are simply even counting numbers: This is not a statistical property, but an algorithmic one. We can recognize the pattern and use it to memorize the number. Algorithmic probability, moreover, allows us to more easily find (and compress) patterns in information that appears random. “This is a paradigm shift,” Zenil says, “because even though most researchers agree that there is this algorithmic component in the way the mind works, we had been unable to measure it because we did not have the right tools, which we have now developed and introduced in our study.” Zenil and his team plan to continue exploring human algorithmic complexity, and hope to shed light on the cognitive mechanisms underlying the relationship between behavioral randomness and age. First, however, they plan to conduct their experiments with people who have been diagnosed with neurodegenerative diseases and mental disorders, including Alzheimer’s and schizophrenia. Zenil predicts, for example, that participants diagnosed with the latter will not generate or perceive randomness as well as their counterparts in the control group, because they often make more associations and observe more patterns than the average person does. The researchers’ colleagues are standing by. Their work on complexity, says Dodig-Crnkovic, “presents a very promising approach.”


News Article | April 27, 2017
Site: www.eurekalert.org

Researchers have developed a personalized algorithm that predicts the impact of particular foods on an individual's blood sugar levels, according to a new study published in PLOS Computational Biology. The algorithm has been integrated into an app, Glucoracle, which will allow individuals with type 2 diabetes to keep a tighter rein on their glucose levels -- the key to preventing or controlling the major complications of a disease that affects 8 percent of Americans. Medications are often prescribed to help patients with type 2 diabetes manage their blood sugar levels, but exercise and diet also play an important role. "While we know the general effect of different types of food on blood glucose, the detailed effects can vary widely from one person to another and for the same person over time," said lead author David Albers, PhD, associate research scientist in Biomedical Informatics at Columbia University Medical Center (CUMC). "Even with expert guidance, it's difficult for people to understand the true impact of their dietary choices, particularly on a meal-to-meal basis. Our algorithm, integrated into an easy-to-use app, predicts the consequences of eating a specific meal before the food is eaten, allowing individuals to make better nutritional choices during mealtime." The algorithm uses a technique called data assimilation, in which a mathematical model of a person's response to glucose is regularly updated with observational data--blood sugar measurements and nutritional information--to improve the model's predictions, explained co-study leader George Hripcsak, MD, MS, the Vivian Beaumont Allen Professor and chair of Biomedical Informatics at CUMC. Data assimilation is used in a variety of applications, notably weather forecasting. "The data assimilator is continually updated with the user's food intake and blood glucose measurements, personalizing the model for that individual," said co-study leader Lena Mamykina, PhD, assistant professor of biomedical informatics at CUMC, whose team has designed and developed the Glucoracle app. Glucoracle allows the user to upload fingerstick blood measurements and a photo of a particular meal to the app, along with a rough estimate of the nutritional content of the meal. This estimate provides the user with an immediate prediction of post-meal blood sugar levels. The estimate and forecast are then adjusted for accuracy. The app begins generating predictions after it has been used for a week, allowing the data assimilator has learned how the user responds to different foods. The researchers initially tested the data assimilator on five individuals using the app, including three with type 2 diabetes and two without the disease. The app's predictions were compared with actual post-meal blood glucose measurements and with the predictions of certified diabetes educators. For the two non-diabetic individuals, the app's predictions were comparable to the actual glucose measurements. For the three subjects with diabetes, the apps forecasts were slightly less accurate, possibly due to fluctuations in the physiology of patients with diabetes or parameter error, but were still comparable to the predictions of the diabetes educators. "There's certainly room for improvement," said Dr. Albers. "This evaluation was designed to prove that it's possible, using routine self-monitoring data, to generate real-time glucose forecasts that people could use to make better nutritional choices. We have been able to make an aspect of diabetes self-management that has been nearly impossible for people with type 2 diabetes more manageable. Now our task is to make the data assimilation tool powering the app even better." Encouraged by these early results, the research team is preparing for a larger clinical trial. The researchers estimate that the app could be ready for widespread use within two years. This release is based on text provided by the authors. In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Funding: GH, ML, and DJA are supported by a grant from the National Library of Medicine LM006910. LM, DJA and ML are supported by a grant from the Robert Wood Johnson Foundation RWJF 73070. LM is supported by a grant from the National Institute of Diabetes and Digestive Kidney diseases R01DK090372. Competing Interests: The authors have declared that no competing interests exist.


Scientists have identified two small molecules that could be pursued as potential treatments for chronic inflammatory diseases. According to a paper published in PLOS Computational Biology, the researchers singled out the molecules using a new drug screening approach they developed. Both molecules, known as T23 and T8, inhibit the function of a protein called tumor necrosis factor (TNF), which is involved in inflammation in diseases such as rheumatoid arthritis, Crohn's disease, psoriasis, multiple sclerosis, and more. Drugs that inhibit TNF's function are considered the most effective way to combat such diseases. However, not all patients respond to them, and their effectiveness can wear off over time. To aid discovery of better TNF inhibitor drugs, Georgia Melagraki and colleagues from Greece and Cyprus developed a new computer-based drug screening platform. The platform incorporates proprietary molecular properties shared between TNF and another protein called RANKL, which is also involved in chronic inflammatory diseases. The researchers developed the platform based on a combination of advanced computational tools. The platform was then used to virtually screen nearly 15,000 small molecules with unknown activity and to predict their interactions with the TNF and RANKL proteins; specifically, how well the small molecules might disrupt the protein-protein interactions (PPIs) leading to the trimerization and activation of these crucial proteins. "This virtual experiment identified nine promising molecules out of thousands of candidates," says study co-corresponding author Antreas Afantitis of NovaMechanics Ltd, Cyprus. To further evaluate their potential, the scientists studied how the nine small molecules interacted with TNF and RANKL in real-world laboratory experiments. Of the nine molecules, T23 and T8 surfaced as particularly strong TNF inhibitors. Both molecules bind to TNF and RANKL, preventing them from interacting properly with other proteins. Both also show low potential for causing toxic side effects in humans. With further research, T23 and T8 could be "further optimized to develop improved treatments for a range of inflammatory, autoimmune, and bone loss diseases," says study co-corresponding author George Kollias of the Biomedical Sciences Research Center 'Alexander Fleming', Greece. Meanwhile, the new virtual drug screening approach could enable discovery of other promising TNF inhibitors, and could be modified to search for potential treatments for additional diseases. In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Citation: Melagraki G, Ntougkos E, Rinotas V, Papaneophytou C, Leonis G, Mavromoustakos T, et al. (2017) Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLoS Comput Biol 13(4): e1005372. https:/ Funding: This work was funded by Greek "Cooperation" Action project TheRAlead (09SYN-21-784) co-financed by the European Regional Development Fund and NSRF 2007-2013, the Innovative Medicines Initiative (IMI) funded project BTCure (No 115142) and Advanced European Research Council (ERC) grant MCs-inTEST (No 340217) to GKol. AA would like to acknowledge funding from Cyprus Research Promotion Foundation, DESMI 2008, ΕΠΙΧΕΙΡΗΣΕΙΣ/ΕΦΑΡΜ /0308/20 http://www. . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Georgia Melagraki, Georgios Leonis and Antreas Afantitis are employed by Novamechanics Ltd, a drug design company. Other authors declare that there are no conflicts of interest.


News Article | April 20, 2017
Site: www.eurekalert.org

Findings could help guide development of treatments that target many mutated proteins at once Scientists have identified thousands of previously ignored genetic mutations that, although rare, likely contribute to cancer growth. The findings, which could help pave the way to new treatments, are published in PLOS Computational Biology. Cancer arises when genetic mutations in a cell cause abnormal growth that leads to a tumor. Some cancer drugs exploit this to attack tumor cells by targeting proteins that are mutated from their usual form because of mutations in the genes that encode them. However, only a fraction of all the mutations that contribute significantly to cancer have been identified. Thomas Peterson, at the University of Maryland, and colleagues developed a new statistical analysis approach that uses genetic data from cancer patients to find cancer-causing mutations. Unlike previous studies that focused on mutations in individual genes, the new approach addresses similar mutations shared by families of related proteins. Specifically, the new method focuses on mutations in sub-components of proteins known as protein domains. Even though different genes encode them, different proteins can share common protein domains. The new strategy draws on existing knowledge of protein domain structure and function to pinpoint locations within protein domains where mutations are more likely to be found in tumors. Using this new approach, the researchers identified thousands of rare tumor mutations that occur in the same domain location as mutations found in other proteins in other tumors-- suggesting that they are likely to be involved in cancer. "Maybe only two patients have a mutation in a particular protein, but when you realize it is in exactly the same position within the domain as mutations in other proteins in cancer patients," says senior author of the study Maricel Kann, "you realize it's important to investigate those two mutations." The researchers have coined the term "oncodomain" to refer to protein domains that are more likely to contain cancer-causing mutations. Further study of oncodomains could help inform drug development: "Because the domains are the same across so many proteins," Kann says, "it is possible that a single treatment could tackle cancers caused by a broad spectrum of mutated proteins." In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Funding: This work was funded by NSF (award #1446406, PI: MGK), NIH (award #1K22CA143148, PI: MGK and Award #R01LM009722 CoPI: MGK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.


News Article | April 17, 2017
Site: www.eurekalert.org

VIDEO:  This is an attempt to explain what we think are some of the most salient results of our research packed in a 4-minute video. People's ability to make random choices or mimic a random process, such as coming up with hypothetical results for a series of coin flips, peaks around age 25, according to a study published in PLOS Computational Biology. Scientists believe that the ability to behave in a way that appears random arises from some of the most highly developed cognitive processes in humans, and may be connected to abilities such as human creativity. Previous studies have shown that aging diminishes a person's ability to behave randomly. However, it had been unclear how this ability evolves over a person's lifetime, nor had it been possible to assess the ways in which humans may behave randomly beyond simple statistical tests. To better understand how age impacts random behavior, Nicolas Gauvrit and colleagues at the Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, assessed more than 3,400 people aged 4 to 91 years old. Each participant performed a series of online tasks that assessed their ability to behave randomly. The five tasks included listing the hypothetical results of a series of 12 coin flips so that they would "look random to somebody else," guessing which card would appear when selected from a randomly shuffled deck, and listing the hypothetical results of 10 rolls of a die--"the kind of sequence you'd get if you really rolled a die." The scientists analyzed the participants' choices according to their algorithmic randomness, which is based on the idea that patterns that are more random are harder to summarize mathematically. After controlling for characteristics such as gender, language, and education, they found that age was the only factor that affected the ability to behave randomly. This ability peaked at age 25, on average, and declined from then on. "This experiment is a kind of reverse Turing test for random behavior, a test of strength between algorithms and humans," says study co-author Hector Zenil. "25 is, on average, the golden age when humans best outsmart computers," adds Dr. Gauvrit. The study also demonstrated that a relatively short list of choices, say 10 hypothetical coin flips, can be used to reliably gauge randomness of human behavior. The authors are now using a similar approach to study potential connections between the ability to behave randomly and such things as cognitive decline and neurodegenerative diseases. The authors have produced a video to summarize the key results of their research, which can be found, with a caption and further details, here: https:/ In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Funding: HZ received partial funding the Swedish Research Council (VR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.

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