Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.

A practical, evidence-based checklist developed by scientists at the University of Surrey is helping everyone from keen gardeners to local councils plan their next greening project with confidence.  

A recent paper from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), published in Nature Communications, built an active learning model that was able to explore a virtual search space of one million potential battery electrolytes starting from just 58 data points. From this minimal data, the team from the lab of UChicago PME Asst. Prof. Chibueze Amanchukwu identified four distinct new electrolyte solvents that rival state-of-the-art electrolytes in performance.

Our galaxy’s most abundant type of planet could be rich in liquid water due to formative interactions between magma oceans and primitive atmospheres during their early years. New experimental work demonstrates that large quantities of water are created as a natural consequence of planet formation. It represents a major step forward in how we think about the search for distant worlds capable of hosting life.

A new technology can shape the spectrum of light emitted from a laser frequency comb across the visible and near-infrared wavelengths with record precision.

Researchers from China present a new framework to simulate how black hole images change over time, focusing on rotating regular black holes with nonsingular cores. Using spatio-temporal random fields and efficient light ray tracing, the model captures realistic brightness fluctuations, turbulence, and light-travel effects around the black hole. The simulated results reproduce time-varying features like the shifting bright ring seen in M87*, offering a fast, physically grounded alternative to full GRMHD simulations and paving the way for future dynamic black hole imaging studies.