A new AI tool can identify aging patterns in blood stem cells by analyzing DAPI-stained nuclear images and chromatin architecture. It may accelerate the discovery and testing of cellular rejuvenation strategies.

A new graph-theoretic framework that provides a unified description of atom-light interactions across regimes ranging from weak to deep-strong coupling has been proposed.

Coronary artery disease remains the top global cause of death, yet standard angiography cannot visualize dangerous artery plaque, creating gaps for guiding percutaneous coronary intervention. This narrative review synthesizes 10 years of human studies, clinical trials and consensus documents covering established and emerging intravascular imaging plus computational tools. Hybrid catheter systems have completed first‑in‑human testing, while artificial‑intelligence plaque analysis delivers diagnostic accuracy comparable to expert readers. These technologies enable combined anatomical, compositional and functional vessel assessment, though broad clinical roll‑out still requires standardized validation and streamlined workflow integration.

Chemists routinely optimize reactions to maximize the yield of their desired products, but understanding why those reactions work can require laborious experiments that track reactions through time. Researchers at the University of Tokyo have developed a method to extract hidden information on reaction speeds from yield data obtained during reaction optimization using machine learning together with rate equations developed by chemists. Concentration-dependent yield analysis (CYAN) uses machine learning to expand the yield data from reaction optimization experiments, then uses this information to calculate how quickly the different steps of a reaction occur. This gives chemists a tool to understand, improve and design complex high-yield chemical reactions. 

Cameras have long been regarded as tools for capturing what the human eye can see. But that assumption may soon be challenged.

“If we change the way we capture data, the potential of Artificial Intelligence (AI) expands dramatically,” says Associate Professor Hiroyuki Kubo of the Graduate School of Informatics. Dr. Kubo is developing methods to visualize what is normally invisible—such as blood vessels beneath the skin or vehicles hidden in fog—using not specialized equipment, but systems built with ordinary projectors and cameras. His approach could significantly accelerate advances in AI. Beyond imaging, Dr. Kubo is also exploring new possibilities in AI-assisted coloring. By designing systems in which humans and AI collaborate to efficiently color large volumes of animation cels, his research aims to enhance productivity in anime production.

We spoke with Dr. Kubo about his work and his vision for the future as he continues to push the boundaries of what cameras—and AI—can do.