Kamel Fezzaa, a physicist in Argonne’s Photon Sciences directorate, has received the 2025 Gopal K. Shenoy Excellence in Beamline Science award for contributions to the Advanced Photon Source.
RWJBarnabas Health and Rutgers Cancer Institute, the state’s only National Cancer Institute-designated Comprehensive Cancer Center, have proudly unveiled New Jersey’s first and only freestanding, fully comprehensive cancer hospital. The Jack & Sheryl Morris Cancer Center in New Brunswick is one of only 13 freestanding cancer hospitals in the United States.
NYU researchers identified two brain networks involved in word retrieval—the cognitive process of accessing words we need to speak. A semantic network processes meaning in middle/inferior frontal gyri, while an articulatory network in inferior frontal/precentral gyri plans speech production.
The secret to psychedelic drugs’ links to greater empathy and insight may lie in their ability to coax the right hemisphere of the brain into a position of dominance over the left, according to a proposed new theory.
New England Journal of Medicine perspectives piece by UC San Diego Health physicians emphasizes a call for disclosure when using AI to draft patient messaging.
Scientists are developing artificial intelligence and machine learning tools for improving the operations of particle accelerators. Here, the results of three related research studies with recent peer-reviewed journal articles are discussed. All of this research was funded by a DOE Office of Science Funding Opportunity Announcement (FOA) grant [now known as Notice of Funding Opportunities (NOFO)].
Thirty students on the path to achieving doctorates in fields that emphasize the use of computing and mathematics have been selected for the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program.
A four-year, $2.33 million grant from the National Eye Institute of the National Institutes of Health to Wayne State University is supporting research focused on improving and preserving vision and eye health in those with diabetes.
A study in The FASEB Journal reveals PME-1 protein regulates tumor suppressor PP2A through two distinct mechanisms: methyl group removal and direct binding. Using genetically modified mice, researchers found each function affects different developmental aspects—demethylation impacts brain development while binding affects olfaction. These findings could lead to targeted treatments for Alzheimer's disease and cancer by fine-tuning PP2A activity.
Thirty-six high-achieving seniors will graduate from the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska–Lincoln on May 17. The Raikes School is an interdisciplinary honors program known for developing innovative leaders at the intersection of technology and business.
Arizona State University Associate Professor Heni Ben Amor spent a year embedded in Google's DeepMind developing human-robot interaction systems that will improve (human!) lives.
Under a $50,000 1-year grant from the Additional Ventures Foundation and a Cardiac Imaging Suite Pilot Award, Eamon Doyle, PhD, Data Engineer, Data Science and AI at Children’s Hospital Los Angeles, will use novel magnetic resonance imaging (MRI) technologies to scan the hearts and lungs of children between 12-18 years old born with single ventricle hearts, as well as those of healthy control patients. His goal is to assess methods of determining blood flow, ventilation patterns, and oxygen perfusion in the lungs.
This study introduces a deep-learning system for rapid, automated detection and classification of tiny calcium deposits (microcalcifications) in mammograms to aid early breast cancer diagnosis. Leveraging a multi-center dataset of 4,810 biopsy-confirmed mammograms, our pipeline uses a Faster RCNN model with a feature-pyramid backbone to detect and classify microcalcifications—the pipeline requires no hand-tuned rules and provides both the overall cancer risk and highlighted lesion regions in seconds per image. On unseen test data, it achieved overall classification accuracy of 72% for discriminating between benign and malignant breasts and 78% sensitivity of malignant breast cancer prediction, marking a significant step toward AI-assisted, cost-effective breast-cancer screening that can run on standard radiology workstations.