UAH researcher wins NASA FINESST award to study solar weather to better protect against threats to humans, satellites and near-Earth technologies

A doctoral student at The University of Alabama in Huntsville has been awarded a NASA Future Investigators in NASA Earth and Space Science and Technology (FINESST) fellowship to study space weather to improve predictive methods for coronal mass ejections (CME) from the Sun.

Shaking Up Earthquake Studies by Increasing Access to Data, Tools and Research Results

Earthquake rupture forecast studies provide information about the probabilities of when earthquakes will occur, where they’ll take place and how strong they’ll be, but the computational tools and data aren’t available to a wide scientific community. That’s about to change.

Not too big: Machine learning tames huge data sets

A machine-learning algorithm demonstrated the capability to process data that exceeds a computer’s available memory by identifying a massive data set’s key features and dividing them into manageable batches that don’t choke computer hardware. Developed at Los Alamos National Laboratory, the algorithm set a world record for factorizing huge data sets during a test run on Oak Ridge National Laboratory’s Summit, the world’s fifth-fastest supercomputer.
Equally efficient on laptops and supercomputers, the highly scalable algorithm solves hardware bottlenecks that prevent processing information from data-rich applications in cancer research, satellite imagery, social media networks, national security science and earthquake research, to name just a few.

Internationally recognized computational researcher Spyridon Bakas, PhD, to serve as inaugural director of Division of Computational Pathology

Indiana University School of Medicine Department of Pathology is launching a new Division of Computational Pathology and a Research Center for Federated Learning in Precision Medicine.

Department of Energy Announces $29 Million for Research on Machine Learning, Artificial Intelligence, and Data Resources for Fusion Energy Sciences

WASHINGTON, D.C. – Today, the U.S. Department of Energy (DOE) announced $29 million in funding for seven team awards for research in machine learning, artificial intelligence, and data resources for fusion energy sciences.

Autonomous discovery defines the next era of science

Argonne National Laboratory is reimagining the lab spaces and scientific careers of the future by harnessing the power of robotics, artificial intelligence and machine learning in the quest for new knowledge.

AI model isolates olive oil ingredients that may fight Alzheimer’s

A growing body of evidence suggests extra virgin olive oil can help prevent cognitive decline due to Alzheimer’s disease. In a new study, Yale School of Medicine researchers led by Natalie Neumann, MD, trained a machine learning algorithm on current…

MD Anderson Research Highlights for August 2, 2023

The University of Texas MD Anderson Cancer Center’s Research Highlights showcases the latest breakthroughs in cancer care, research and prevention. These advances are made possible through seamless collaboration between MD Anderson’s world-leading clinicians and scientists, bringing discoveries from the lab to the clinic and back.

Recent developments include a novel biomarker that may predict the aggressiveness of pancreatic cancer precursors, insights into the structure and function of a breast and ovarian cancer susceptibility gene, a new approach to overcoming treatment resistance in ovarian cancer, distinguishing features of young-onset rectal cancer, a biomarker and potential target for metastatic lung cancer, machine learning models to better predict outcomes of patients with mantle cell lymphoma (MCL), and a promising therapy for patients with relapsed/refractory MCL.

Machine learning, blockchain technology could help counter spread of fake news

A proposed machine learning framework and expanded use of blockchain technology could help counter the spread of fake news by allowing content creators to focus on areas where the misinformation is likely to do the most public harm, according to new research from Binghamton University, State University of New York.

Study Identifies Pitfalls, Solutions for Using AI to Predict Opioid Use Disorder

Researchers examined peer-reviewed journal papers and conducted the first systematic review analyzing not only the technical aspects of machine learning applied to predicting opioid use, but also the published results.

Researchers use Argonne X-rays to find the best antibodies

Antibody therapies are only effective if the antibodies do what we want them to do. This research can help scientists determine if an antibody is likely to stick to something other than the intended target, which should lessen the amount of time wasted with overly sticky antibodies.

nference and Vanderbilt University Medical Center sign agreement to advance real-world evidence generation in complex disease populations

nference, a science-first software company transforming health care by making biomedical data computable, and Vanderbilt University Medical Center, a leading academic medical center, have announced a strategic agreement aimed at advancing research through the deployment of nference’s state-of-the-art federated clinical analytics platform.

Researchers Design Multiclass Cancer Diagnostic Tool Using AI, MicroRNA

MicroRNAs, or miRNAs, regulate genes and biological processes in the human body, including cancer formation and development. To explore the feasibility of miRNAs as cancer biomarkers, researchers created a multiclass cancer diagnostic model using miRNA expression profiles. The study examined the relationship between the composition of miRNAs and various types of cancers. Findings suggest that miRNAs may be highly unique to specific cancerous tissues and can be strong biomarkers for detection and classification in both research and the clinical field

AI and CRISPR Precisely Control Gene Expression

The study by researchers at New York University, Columbia Engineering, and the New York Genome Center, combines a deep learning model with CRISPR screens to control the expression of human genes in different ways—such as flicking a light switch to shut them off completely or by using a dimmer knob to partially turn down their activity. These precise gene controls could be used to develop new CRISPR-based therapies.

Create an independent body to regulate AI and prevent it from discriminating against disadvantaged groups

Qihang Lin, associate professor of business analytics at the University of Iowa’s Tippie College of Business, studies artificial intelligence and discrimination with a National Science Foundation grant. Based on his research, he believes an independent third-party organization must be created…

We are in the midst of an AI-driven revolution in materials research where the confluence of automated experiments and machine learning are redefining the pace of materials discovery.

Keith A. Brown BS Physics, Massachusetts Institue of Technology PhD Applied Physics, Harvard University Postdoc in Chemistry, Northwestern University Contact: [email protected] Keith currently runs the KABlab, a research group at Boston University that studies approaches to accelerate the development of advanced…

New Articles on Using Machine Learning to Predict Mammalian Acute Oral Toxicity and the Effects of Vinyl Chloride on Metabolism

The May 2023 issue of Toxicological Sciences includes articles on profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning and how vinyl chloride enhances high-fat diet-induced proteome alterations in the mouse pancreas related to metabolic dysfunction.

Artificial Intelligence Catalyzes Gene Activation Research and Uncovers Rare DNA Sequences

Biologists have used machine learning, a type of AI, to identify “synthetic extreme” DNA sequences with specifically designed functions in gene activation. They tested 50 million DNA sequences and found synthetic DNA sequences with activities that could be useful in biotechnology and medicine.

Researchers Show That a Machine Learning Model Can Improve Mortality Risk Prediction for Cardiac Surgery Patients

A machine learning-based model that enables medical institutions to predict the mortality risk for individual cardiac surgery patients has been developed by a Mount Sinai research team, providing a significant performance advantage over current population-derived models.

Rensselaer Researcher Uses Artificial Intelligence To Discover New Materials for Advanced Computing

A team of researchers led by Rensselaer Polytechnic Institute’s Trevor David Rhone, assistant professor in the Department of Physics, Applied Physics, and Astronomy, has identified novel van der Waals (vdW) magnets using cutting-edge tools in artificial intelligence (AI). In particular, the team identified transition metal halide vdW materials with large magnetic moments that are predicted to be chemically stable using semi-supervised learning.

Fire Hydrant Hydrophones Find Water Leaks #ASA184

Acoustic monitoring is the go-to solution for locating a leak in a large urban pipe network, as the sounds from leaks are unique and travel far in water, but even this method struggles in complex systems. To tackle the problem, Pranav Agrawal and Sriram Narasimhan from UCLA developed algorithms that operate on acoustic signals collected via hydrophones mounted on fire hydrants. In doing so, the team can avoid costly excavation and reposition the devices as needed. Combined with novel probabilistic and machine-learning techniques to analyze the signals and pinpoint leaks, this technology could support water conservation efforts.