In the April 18, 2025 online issue of the American Association for the Advancement of Science journal Science, Utah State University scientist Zachariah Gompert and colleagues report adaptive divergence in cryptic color pattern is underlain by two distinct, complex chromosomal rearrangements, where millions of bases of DNA were flipped backwards and moved from one part of a chromosome to another, independently in populations of stick insects on different mountains.

How do we learn new things? Neurobiologists using cutting-edge visualization techniques have revealed how changes across our synapses and neurons unfold. The findings depict how information is processed in our brain’s circuitry, offering insights for neurological disorders and brain-like AI systems.

Three investigations funded by the U.S. National Science Foundation (NSF) and sponsored by the International Space Station (ISSInternational Space Station) National Laboratory are launching on SpaceX’s 32nd Commercial Resupply Services (CRS) mission, contracted by NASANational Aeronautics and Space Administration. These experiments leverage the microgravityThe condition of perceived weightlessness created when an object is in free fall, for example when an object is in orbital motion. Microgravity alters many observable phenomena within the physical and life sciences, allowing scientists to study things in ways not possible on Earth. The International Space Station provides access to a persistent microgravity environment. environment to advance fundamental science that could lead to improved pharmaceutical manufacturing, new materials with valuable industrial applications, and the next generation of soft active materials with lifelike properties.

Under Yellowstone lies a magma-filled formation that drives the national park’s famous geysers and other hydrothermal features. New research conducted by University of Utah geoscientists has located the top of the chamber 3.8 kilometers below Earth’s surface and characterized the upper reservoir’s structure, offering fresh insights into the risk of future eruptions.

A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.