news, journals and articles from all over the world.
Engineers have developed a thread-based sensor capable of monitoring the direction, angle of rotation and degree of displacement of the head. The design is a proof of principle that could be extended to measuring movements of other limbs by sensors attached like tatoos to the skin.
A new study led by a University at Buffalo researcher has identified how specific communication among different brain regions, known as brain connectivity, can serve as a biomarker for attention deficit hyperactivity disorder (ADHD).
Mount Sinai researchers have published one of the first studies using a machine learning technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress.
Vaccines take time to work. After getting a COVID-19 vaccine, it takes a while for the immune system to fully respond and provide protection from the virus. For the Moderna and Pfizer COVID-19 vaccines, it takes up to two weeks after the second shot to become appropriately protected.
Mount Sinai researchers have published one of the first studies using federated learning to examine electronic health records to better predict how COVID-19 patients will progress.
A team of scientists from Argonne is using artificial intelligence to decode X-ray images faster, which could aid innovations in medicine, materials and energy.
Artificial intelligence is a game-changer in nuclear physics, able to enhance and accelerate fundamental research and analysis by orders of magnitude. DOE’s Jefferson Lab is exploring the expanding synergy between nuclear physics and computer science as it co-hosts together with The Catholic University of America and the University of Maryland a virtual weeklong series of lectures and hands-on exercises Jan. 11-15 for graduate students, postdoctoral researchers and even “absolute beginners.”
A cross-platform Argonne collaboration is optimizing a tool developed after Hurricane Maria to find essential connections between critical infrastructure that will help owners and operators plan for and mitigate a variety of potential hazards.
A University of Washington-led team has come up with a system that could help speed up AI performance and find ways to reduce its energy consumption: an optical computing core prototype that uses phase-change material.
Fermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.
Argonne scientists and research facilities have made a difference in the fight against COVID-19 in the year since the first gene sequence for the virus was published.
Irvine, Calif., Jan. 5, 2021 — Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving.
A research team at Sandia National Laboratories has successfully used machine learning — computer algorithms that improve themselves by learning patterns in data — to complete cumbersome materials science calculations more than 40,000 times faster than normal.
Operators of Jefferson Lab’s primary particle accelerator are getting a new tool to help them quickly address issues that can prevent it from running smoothly. The machine learning system has passed its first two-week test, correctly identifying glitchy accelerator components and the type of glitches they’re experiencing in near-real-time. An analysis of the results of the first field test of the custom-built machine learning system was recently published in the journal Physical Review Accelerators and Beams.
Machine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in the Endocrine Society’s Journal of Clinical Endocrinology & Metabolism.
SUMMARYResearchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network accelerator capable of processing large amounts of information, on the…
Irvine, Calif., Dec. 17, 2020 —University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. “The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset,” said Daniel S.
A model based solely on the past 40 years of weather events uses 7,000 times less computer power than today’s weather forecasting tools. An A.I.-powered model could someday provide more accurate forecasts for rain, snow and other weather events.
Artificial intelligence (AI) has the potential to revolutionize healthcare, but integrating AI-based techniques into routine medical practice has proven to be a significant challenge. A plenary session at the virtual 2020 AACC Annual Scientific Meeting & Clinical Lab Expo will explore how one clinical lab overcame this challenge to implement a machine learning-based test, while a second session will take a big picture look at what machine learning is and how it could transform medicine.
Researchers at the University of Pittsburgh School of Medicine have combined synthetic biology with a machine learning algorithm to create human liver organoids with blood and bile handling systems. When implanted into mice with failing livers, the lab-grown replacement livers extended life.
Berkeley Lab-developed machine learning tool can also calculate the optical properties of a known structure; CUORE experiment in Italy is designed to find theorized process called neutrinoless double-beta decay
Researchers from multiple institutions in North America have developed a fully automated, deep-learning (DL), artificial-intelligence clinical tool that can measure the volume of cerebral ventricles on magnetic resonance images (MRIs) in children within about 25 minutes.
Yager, a group leader at the Center for Functional Nanomaterials, is exploring challenges and opportunities for the U.S. Department of Energy.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
Using a combination of AI and supercomputing resources, Argonne researchers are examining the dynamics of the SARS-CoV-2 spike protein to determine how it fuses with the human host cell, advancing the search for drug treatments.
Creating nanomaterials with flame spray pyrolysis is complex, but scientists at Argonne have discovered how applying artificial intelligence can lead to an easier process and better performance.
The new projects will use DOE’s leadership-class supercomputers to pursue transformational advances in science and engineering.
The Argonne Leadership Computing Facility’s internship program went virtual this year, providing students with an opportunity to work on real-world research projects that address issues at the forefront of scientific computing.
A machine learning model developed by a team of Lawrence Livermore National Laboratory (LLNL) scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.
The Department of Energy’s Oak Ridge National Laboratory and three partnering institutions have received $4.2 million over three years to apply artificial intelligence to the advancement of complex systems in which human decision making could be enhanced via technology.
Data from a new study presented this week at The Liver Meeting Digital Experience® – held by the American Association for the Study of Liver Diseases – found that using neural networks, a type of machine learning algorithm, is a more accurate model for predicting waitlist mortality in liver transplantation, outperforming the older model for end-stage liver disease (MELD) scoring. This advancement could lead to the development of more equitable organ allocation systems and even reduce liver transplant waitlist death rates for patients.
Mount Sinai researchers have developed machine learning models that predict the likelihood of critical events and mortality in COVID-19 patients within clinically relevant time windows.
Argonne computational resources supported the largest comprehensive analysis of COVID-19 genome sequences in the U.S. and helped corroborate growing evidence of a protein mutation.
New York University researchers have created a “developmental atlas” of gene expression in neurons, using gene sequencing and machine learning to categorize more than 250,000 neurons in the brains of fruit flies. Their study, published in Nature, finds that neurons exhibit the most molecular diversity during development and reveals a previously unknown type of neurons only present before flies hatch.
The event summary for the second annual National Health Symposium, organized by the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, is now available.
Researchers used informatics to examine 5,000+ patient records and five years of data related to nursing skin assessments and hospital-acquired pressure injuries. The results underscore the importance of treating and monitoring irritated skin early and eliminating the cause as an important step to prevent pressure injuries.
A new study confirms the success of a natural-gas leak-detection tool pioneered by Los Alamos National Laboratory scientists that uses sensors and machine learning to locate leak points at oil and gas fields, promising new automatic, affordable sampling across vast natural gas infrastructure.
UW researchers have developed SoundWatch, a smartwatch app for deaf, Deaf and hard-of-hearing people who want to be aware of nearby sounds.
What about the kidneys make them a hotspot for COVID-19’s cytokine storm? A research team says it’s the presence of a protein found on specialized renal transport cells.
UC San Diego researchers use experimental artificial intelligence system called DrugCell to predict the best approach to treating cancer.
Lawrence Livermore National Laboratory (LLNL) has installed a state-of-the-art artificial intelligence (AI) accelerator from SambaNova Systems, the National Nuclear Security Administration (NNSA) announced today, allowing researchers to more effectively combine AI and machine learning (ML) with complex scientific workloads.
The development of a new method to make non-volatile computer memory may have unlocked a problem that has been holding back machine learning and has the potential to revolutionize technologies like voice recognition, image processing and autonomous driving.
An electronic nudge to clinicians—triggered by an algorithm that used machine learning methods to flag patients with cancer who would most benefit from a conversation around end-of-life goals—tripled the rate of those discussions.
Researchers nationwide are building the software and applications that will run on some the world’s fastest supercomputers. Among them are members of DOE’s Exascale Computing Project who recently published a paper highlighting their progress so far.
In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications.
The range of AI technologies available for dealing with brain disease is growing fast, and exciting new methods are being applied to brain problems as computer scientists gain a deeper understanding of the capabilities of advanced algorithms. In APL Bioengineering, Italian researchers conducted a systematic literature review to understand the state of the art in the use of AI for brain disease. Their qualitative review sheds light on the most interesting corners of AI development.
Researchers from the Morgridge Institute for Research, the University of Wisconsin-Madison, and Albany Medical College have identified more than 200 molecular features that strongly correlate with COVID-19 severity, offering insight into potential treatment options for those with advanced disease.
The annual Argonne Training Program on Extreme-Scale Computing went virtual this year, providing two weeks of instruction to ready attendees for science in the exascale era.
Precision medicine is a rapidly growing approach to health care that focuses on finding treatments and interventions that work for people based on their genetic makeup, rather than their symptoms.
Zeeshan Ahmed, director of the new Ahmed Lab at Rutgers Institute for Health, Health Care Policy and Aging Research, discusses the future of precision medicine, what needs to be done to successfully analyze the data necessary to develop individualized treatments and the role genetics play during the COVID-19 pandemic.
Australia’s position as one of the world leaders in artificial intelligence (AI) and machine learning will be further boosted thanks to $20 million towards a new national centre, to be based at the University of Adelaide.
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