Want faster internet from space? A new study shows how smarter satellite layouts can boost both data and computing power—cutting costs while improving coverage.

A team led by Prof. Fei Ling from South China University of Technology developed PRTS (Pathology-driven Reconstruction of Transcriptomic States), a deep learning framework that predicts single-cell-resolution spatial transcriptomics directly from H&E-stained histology images.

A new study in Engineering reveals how macrophage-released U2AF1 aids heart repair after heart attack by steering Yap1 splicing toward the pro-angiogenic Yap1-2γ isoform, offering a potential biomarker and therapeutic target for improving cardiac recovery.

POSTECH develops a magnetic-field battery technology that prevents explosions and delivers four times the capacity.

Tokyo, Japan – Researchers from Tokyo Metropolitan University have successfully traced the mechanism behind how an industrially important “superbase” catalyst is synthesized in a faster, microwave-assisted reaction. They took measurements using X-rays while the reaction occurred, uncovering how small precursor molecules were formed first before they clustered to create the final product. Their insights promise finer control over a promising technology for speeding up chemical synthesis in industry.

Researchers at the University of Navarra in Spain, have developed RNACOREX, a new open-source software tool that reveals hidden genetic regulatory networks involved in cancer and helps predict patient survival. Tested across 13 different tumor types using data from The Cancer Genome Atlas (TCGA), RNACOREX identifies key interactions between microRNAs and messenger RNAs—molecular relationships that are often missed by conventional analyses.

Unlike many artificial intelligence models that function as “black boxes,” RNACOREX produces interpretable molecular maps that show how genes interact within tumors. These networks can stratify patients according to survival probability with accuracy comparable to advanced AI approaches, while clearly explaining the biological mechanisms behind the predictions.

Published in PLOS Computational Biology, the study demonstrates how RNACOREX can uncover shared molecular patterns across cancers, highlight individual molecules of biomedical interest, and generate new hypotheses about tumor progression. Freely available via GitHub and PyPI, the tool is designed to be accessible for research laboratories worldwide and represents a step forward in explainable AI for precision oncology.

C. Ola Landgren, M.D., Ph.D., received HealthTree Foundation’s prestigious 2025 Innovation Award for his work in developing CORAL, a new research tool that leverages AI to predict individual outcomes and guide treatment decisions in patients with multiple myeloma.