Machine learning sharpens earthquake risk assessment maps for Tokyo

Tokyo, one of the world's most densely populated megacities, sits on a highly active seismic zone where the threat of major ...
RMD Open

Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage

Objective Organ damage is a key determinant of poor prognosis and increased mortality in systemic lupus erythematosus (SLE).

Meta’s new CWM model learns how code works, not just what it looks like

Moving beyond static code prediction, the model learns an internal world model of computational environments for more ...
Eos

Unveiling What’s Under the Hood in AI Weather Models

Artificial intelligence models have improved weather forecasting, but their inner workings are largely opaque. A new approach ...

The Machines Finding Life That Humans Can’t See

Across a Swiss meadow and into its forested edges, the drone dragged a jumbo-size cotton swab from a 13-foot tether. Along ...

Deep learning tool developed to unravel molecular mechanisms of convergent evolution

Convergent evolution—where distinct species independently evolve similar traits or functions, such as the wings of birds and ...
eLife

Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models

Non-reversible nucleotide substitution models best describe strand-specific nucleotide substitution biases of viral genomes.
The American Journal of Managed Care

Predicting Patient Response in the Move Toward Precision Medicine in Myasthenia Gravis

Ratna Kiran Bhavaraju-Sanka, MD, and Beth Stein, MD, explore how understanding MG subtypes and patient characteristics enhances targeted therapy and personalized treatment.