Hidden features within the spleen detected on abdominal imaging scans can signal a person’s risk of developing coronary artery disease, according to a new study published in Science Translational Medicine. Led by investigators from Mass General Brigham, the research leverages artificial intelligence and genomic analyses to uncover how blood-forming and immune functions in the spleen … Read more
ESMDynamic is a deep learning model that predicts residue-residue contact dynamics directly from protein sequence, according to recent structural biology data. By mapping conformational variability across experimental structure ensembles and molecular dynamics simulations, the model forecasts dynamic contact probabilities, contact occupancy fractions, and coarse-grained kinetics across multiple temperatures. How ESMDynamic Predicts Protein Conformational Dynamics Understanding … Read more
The technique aims to untangle overlapping electrochemical signals to monitor multiple brain chemicals simultaneously during neurochemical monitoring. Measuring Neurotransmitters with Voltammetry and Machine Learning Monitoring brain chemistry in real time remains a core challenge in neurochemistry. According to Andrews, traditional methods like microdialysis involve implanting a semi-permeable membrane into the brain’s extracellular space to sample … Read more
Researchers have developed a machine learning and quantitative microbial risk assessment framework—known as ML-QMRA—to predict pathogen concentrations in drinking water source monitoring, according to a study published in Biocontaminant. By analyzing routinely measured water quality indicators, the data-driven approach aims to help water managers estimate health risks faster than conventional microbial testing allows. How the … Read more
Researchers have developed an artificial intelligence model that decodes neural, cardiac, and respiratory signals from routine sleep studies to identify high-risk patients that conventional apnea measurements miss, according to a study published in Nature Communications. Sleep disorders affect nearly one billion adults globally, making accurate long-risk stratification an urgent priority for healthcare systems. Limitations of … Read more
According to research published in Nature Mental Health, autistic children show earlier and less age-related refinement in the brain signals involved in processing faces compared to neurotypical peers. Led by James McPartland, PhD, scientists analyzed whole-scalp electroencephalography (EEG) data from approximately 400 autistic children using measurements from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT), … Read more
Tuberculosis claimed 1.23 million lives in 2024, remaining one of the world’s deadliest infectious diseases due to the bacterium’s highly selective outer shell, the mycomembrane. Researchers at the University of Massachusetts Amherst have now developed a paired screening and machine-learning approach to identify which drug compounds can successfully penetrate this barrier, according to findings published … Read more
Researchers at the University of Massachusetts Amherst have developed an artificial intelligence model called MycoPermeNet to accelerate the discovery of tuberculosis drugs. By combining this AI with a high-throughput screening technique known as PAC-MAN, the team can predict which chemical compounds can penetrate the bacterium’s unique mycomembrane, potentially speeding up the search for effective treatments … Read more
Google has begun incorporating user-generated media, including audio and video, into its artificial intelligence training processes as a default setting. According to the Electronic Frontier Foundation (EFF), this move leverages Google’s immense user base to accelerate AI innovation, effectively shifting the burden of data privacy onto individuals who must now actively opt out of these … Read more
Researchers have identified hundreds of previously undetected earthquakes beneath East Antarctica’s David Glacier by reanalyzing decades of seismic data using artificial intelligence. According to a study published in the journal Science, the events occurred between 100 and 150 kilometers below the surface, challenging the long-held assumption that the region is seismically inactive. The findings suggest … Read more