A Baylor College of Medicine team has overcome a major obstacle that limited their ability to continuously grow human norovirus virus, which they require to conduct experiments needed to develop strategies to prevent and treat these serious infections and better understand norovirus biology. The researchers identified factors that restrict viral replication and developed a way to overcome them to optimize long-term viral cultivation.

Big Earth Data Journal is calling for papers for a Special Issue on 10th Anniversary Special Issue of Big Earth Data. The journal has published pioneering research that leverages Earth observations, big data analytics, and interdisciplinary collaboration to address global challenges, including climate change, biodiversity loss, sustainable urbanization, disaster monitoring, etc. To celebrate the journal’s 10th anniversary, we invite submissions to a special issue that reflects on the journal’s decade-long impact, showcases cutting-edge advancements in Big Earth Data research, and defines future directions for the field. This special issue will not only honor the progress made but also encourage the community to tackle emerging challenges and seize opportunities in the next era of data-driven Earth science. Welcome to submit!

Researchers have developed a compact, high-precision gas mapping system by integrating on-chip dual microcombs with a nanomaterial-functionalized fiber sensor array. This hybrid architecture achieves the simultaneous, highly selective detection of 12 distinct gas species with a LOD (limit of detection) of 24.3 parts per billion. By leveraging the specific chemical interactions of nanomaterials driven by precise optical comb lines, the system offers a robust solution for analyzing complex gas mixtures in environmental and industrial settings.

A new study explains the brain mechanisms behind moments when we first recognize a blurry object, a primal ability that enabled our ancestors to avoid threats.  Based on this understanding, the team built an AI model with a human-like perceptual mechanism that learn new tasks with little training.