A research team has developed FreezeNet, a lightweight deep learning model that uses smartphone-captured images to accurately assess freeze injury in wheat seedlings.

A research team has combined magnetic resonance imaging (MRI) and positron emission tomography (PET) to non-invasively track how the sugar beet disease known as syndrome “basses richesses” (SBR) damages taproot structure and disrupts sugar distribution.

A research team has demonstrated that spectroscopy combined with partial least squares regression (PLSR) can accurately estimate plant leaf traits, but models built at one site often fail elsewhere.

To boost solar water splitting efficiency, researchers used quantum molecular dynamics to track how charge carriers (polarons) stabilize in the NaTaO₃ photocatalyst, a process previously hidden from experiments. They discovered that positive hole polarons stabilize strongly and rapidly (~70 meV in 50 fs) driven by the elongation of oxygen-tantalum (O-Ta) bonds, while electron stabilization is insignificant. This time-resolved, atomistic understanding provides crucial guidelines for rationally engineering O-Ta bond dynamics to create high-performance solar fuel catalysts.

Adiposity—or the accumulation of excess fat in the body—is a known driver of cardiometabolic diseases such as heart disease, stroke, type 2 diabetes, and kidney disease. But getting the full picture of a person’s risk is harder than it may seem. Traditional measures such as body mass index (BMI) are imperfect, conflating fat and muscle mass and not capturing where in the body fat is located. A new study from researchers at Mass General Brigham and their colleagues found that an AI tool designed to measure body composition could accurately capture details in just three minutes from a body scan. Their results, published in Annals of Internal Medicine, show that not all fat is equally harmful and highlight the potential of using AI to repurpose data from routine scans.