Researchers have revealed a previously unknown way plants shape their growth in response to light — a breakthrough that could better equip crops to handle environmental stress.  In a first-of-its-kind finding, the team discovered how a compound that’s involved in plant metabolism can actually "reprogram” an unrelated light-sensing protein. This unexpected interaction, which was reported in the journal Nature Communications, is an exciting step toward more fully understanding plant physiology. 

Lower back pain is the most common musculoskeletal issue in the U.S. and a top cause of global disability. To tackle this, researchers have developed a groundbreaking AI-powered system that automates patient-specific lumbar spine modeling. By merging deep learning with biomechanical simulation, the new method slashes model prep time by nearly 98% – from more than 24 hours to just 30 minutes – while preserving clinical accuracy. This innovation enables faster, more consistent diagnoses and personalized treatment planning.

A new artificial intelligence model found previously undetected signals in routine heart tests that strongly predict which patients will suffer potentially deadly complications after surgery. The model significantly outperformed risk scores currently relied upon by doctors.

The federally-funded work by Johns Hopkins University researchers, which turns standard and inexpensive test results into a potentially life-saving tool, could transform decision-making and risk calculation for both patients and surgeons.