Scientists have devised a way to engineer yeast to produce sustainable, eco-friendly commodity chemicals using computing power as a guide.
Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection.
A team including a Lawrence Livermore National Laboratory (LLNL) mathematician and collaborators at the University of Massachusetts, Dartmouth and the University of Mississippi, has developed a machine learning-based technique capable of automatically deriving the motion of binary black holes from raw gravitational wave data.
Researchers at Washington University in St. Louis have recently developed a successful predictive model for hospitalized cancer patients that integrates heterogeneous data available in electronic health records.
A study is the first to reveal detailed behavior of massive goliath groupers. Until now, no studies have documented their fine-scale behavior. What is known about them has been learned from divers, underwater video footage, and observing them in captivity. Using a multi-sensor tag with a three axis accelerometer, gyroscope and magnetometer as well as a temperature, pressure and light sensor, a video camera and a hydrophone, researchers show how this species navigates through complex artificial reef environments, maintain themselves in high current areas, and how much time they spend in different cracks and crevices – none of which would be possible without the tag.
Certain biological events, such as proteins changing their shapes to perform some functions, occur so quickly that current methods of molecular imaging cannot capture them. Now, a research team has created a machine-learning technique that can “fill in” missing data needed to document proteins in action in time scales of a few quadrillionths of a second.
Deep brain stimulation (DBS) has been demonstrated to be an effective treatment for many patients suffering with treatment-resistant depression, but exactly how it works is not known.
Argonne scientists have received two high-profile grants from the U.S. Department of Energy that will help scientists at the U.S. National Laboratories take advantage of the latest developments in machine learning technology.
Experts at the U.S. Department of Energy’s Ames Laboratory and their collaborators have identified the way to tune the strength and ductility of a class of materials called high-entropy alloys. The discovery may help power-generation and aviation industry develop more efficient engines.
The Arkansas Economic Development Commission, using flow-through funding from the National Science Foundation, has awarded a postdoctoral research fellow at UA Little Rock a grant worth more than $40,000 to create a machine learning model to predict refugee counts in the United States.
New AI model called EVE, developed by scientists at Harvard Medical School and Oxford University, outperforms other AI methods in determining whether a gene variant is benign or disease-causing.
When applied to more than 36 million variants across 3,219 disease-associated proteins and genes, EVE indicated more than 256,000 human gene variants of unknown significance that should be reclassified as benign or pathogenic.
John F. McDonald and his research team have created a ‘multi-algorithm’ machine learning approach to boost accuracy in predicting drug responses for ovarian cancer patients.
Researchers reporting in ACS Nano have developed a dynamic respirator that modulates its pore size in response to changing conditions, such as exercise or air pollution levels, allowing the wearer to breathe easier when the highest levels of filtration are not required.
Chuck Stewart, an expert in the ecological applications of computer vision, is part of the newly created Imageomics Institute, founded with a $15 million grant from the National Science Foundation to use images of living organisms to understand biological processes.
A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows.
The NSF grant will enable scientists to elucidate trait evolution across species using statistical and supervised machine learning approaches to vigorously and accurately predict general and specific evolutionary mechanisms that also will be applicable to various genomic and transcriptomic data for evolutionary discovery.
Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.
Argonne and Parallel Works, Inc., won the Federal Laboratory Consortium’s Midwest Regional Award for Excellence in Technology Transfer for bringing Argonne’s Machine Learning-Genetic Algorithm (ML-GA) design optimization software to commercialization.
Argonne recently teamed up with a Colorado-based biofuel company to perform a critical lifecycle analysis of its Next Gen technology to produce renewable jet fuel from corn grain in what could be a game-changer in biofuel industry.
Addenbrooke’s Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients’ oxygen needs on a global scale.
The U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets.
One of the frequently used methods to monitor endangered whales is called passive acoustics technology, which doesn’t always perform well.
Researchers are developing a robot that utilizes deep learning to automate certain aspects of the peach cultivation process, which could be a boon for many Georgia peach farms grappling with a shortage of workers. The self-navigating robot uses an embedded 3D camera to determine which trees need to be pruned or thinned, and removes the branches or peaches using a claw-like device attached to its arm.
The U.S. Department of Energy (DOE) announced $16 million for five collaborative research projects to develop artificial intelligence (AI) and machine learning (ML) algorithms for enabling scientific insights and discoveries from data generated by computational simulations, experiments, and observations.
An experiment to study gravity at the quantum scale, insights into an antibiotic-building enzyme, and the backstory of an incredible new protein prediction algorithm are featured in this month’s roundup of science highlights.
Argonne, industry and academia collaborate to bring innovative AI and simulation tools to the COVID-19 battlefront.
Storing the rechargeable batteries at sub-freezing temperatures can crack the battery cathode and separate it from other parts of the battery, a new study shows.
University of Washington and Microsoft researchers have introduced a new class of reporter proteins that can be directly read by a commercially available nanopore sensing device.
Raquel Assis, Ph.D., associate professor, College of Engineering and Computer Science, and a fellow of FAU’s Institute for Human Health and Disease Intervention, has received a five-year, $1.8 million “Maximizing Investigators’ Research Award” from the NIH. The goal of this early career award is to enhance the ability of investigators to take on ambitious scientific projects and approach problems more creatively.
Research from the lab of Shantanu Chakrabartty reveals constraints can lead to learning in AI systems.
Today, Georgia Tech received two National Science Foundation (NSF) Artificial Intelligence Research Institutes awards, totaling $40 million. A third award for $20 million was granted to the Georgia Research Alliance (GRA), with Georgia Tech serving as one of the leading academic institutions.
The University of Washington will lead a new artificial intelligence research institute that will focus on fundamental AI and machine learning theory, algorithms and applications for real-time learning and control of complex dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict.
New award-winning research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help nonexperts effectively, efficiently and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of AI.
In the U.S., the place where one is born, one’s social and economic background, the neighborhoods in which one spends one’s formative years, and where one grows old are factors that account for a quarter to 60% of deaths in any given year
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.
Machine learning can accurately predict cardiovascular disease and guide treatment — but models that incorporate social determinants of health better capture risk and outcomes for diverse groups, finds a new study by researchers at the New York University Tandon School of Engineering and the NYU School of Global Public Health.
Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have demonstrated that they can use machine learning to optimize the performance of particle accelerators by teaching the algorithms the basic physics principles behind accelerator operations – no prior data needed.
Scientists at Brookhaven and Lawrence Berkeley National Laboratories have been developing an automated experimental setup of data collection, analysis, and decision making.
Johns Hopkins University scientists have developed a new tool for predicting which patients suffering from a complex inflammatory heart disease are at risk of sudden cardiac arrest. Published in Science Advances, their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning.
A team of Argonne scientists has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.
The U.S. Department of Energy has awarded $4.15 million to Argonne National Laboratory to support collaborations with industry aimed at commercializing promising energy technologies.
New study of artificial intelligence tools that analyze tumor images shows how they can make inaccurate predictions based on the institution that submitted the image
Researchers are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. They collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed with 693 dimensional features created to represent each clinical trial. These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.
Prosthetics currently lack the sensation of “touch.” To enable a more natural feeling prosthetic hand interface, researchers are the first to incorporate stretchable tactile sensors using liquid metal and machine learning. This hierarchical multi-finger tactile sensation integration could provide a higher level of intelligence for artificial hands by improving control, providing haptic feedback and reconnecting amputees to a previously severed sense of touch.
Researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval.
Highlights of the two-hour visit included behind-the-scenes looks at one of the most powerful X-ray sources on the planet and at the construction of the world’s largest digital camera for astronomy. She also joined presentations of the lab’s research in machine learning, quantum technology and climate science and engaged in discussions about diversity, equity and inclusion at SLAC.
An interdisciplinary team of Cornell and Harvard University researchers developed a machine learning tool to parse quantum matter and make crucial distinctions in the data, an approach that will help scientists unravel the most confounding phenomena in the subatomic realm.
Conversations between seriously ill people, their families and palliative care specialists lead to better quality-of-life. Understanding what happens during these conversations – and how they vary by cultural, clinical, and situational contexts – is essential to guide healthcare communication improvement efforts. To gain true understanding, new methods to study conversations in large, inclusive, and multi-site epidemiological studies are required. A new computer model offers an automated and valid tool for such large-scale scientific analyses.
The University of South Australia will lead a world-first study, using artificial intelligence, to map the risks of the most fatal reproductive cancer in women worldwide so it can be detected and treated earlier.
Researchers have developed a software tool that identify the regulators of genes. The system leverages a machine learning algorithm to predict which transcription factors are most likely to be active in individual cells.