Dr. Thomas Hartung, Director of the Center for Alternatives to Animal Testing (CAAT) at the Johns Hopkins Bloomberg School of Public Health, has endorsed the public launch of ToxIndex, an agentic AI platform developed by Insilica Inc. that produces comprehensive, source-traceable toxicological risk assessments in just a few hours.

A new computational tool infers changes occurring at the ends of the chromosomes housing our DNA. It does so by detecting structural alterations in cells and tissues captured in images taken of routine medical biopsies, according to findings published March 16, 2026, in Cell Reports Methods.

In testing the new tool called TLPath, the scientists were able to more accurately predict telomere length from the imaged biopsies than if they based their prediction solely on the age of patients when they donated their samples. The scientists further evaluated the model’s prediction capabilities by demonstrating that it could identify telomere length differences between individuals of the exact same chronological age.

If more histopathology slides from routine clinical diagnostic tests can be scanned, stored and made accessible to scientists, tools such as TLPath can enable large-scale studies with the potential to transform the study of telomere biology and human aging.

Anglia Ruskin University (ARU) and Cambridge-based global semiconductor and software design leader Arm have officially opened the ARU Arm AI Lab in Cambridge, England.

Made possible through a donation from Arm, the new facility includes powerful computers built using Arm technology, enabling researchers and engineers from both organisations to collaborate on cutting-edge AI innovation.

Traditional trial-and-error methods for developing BaTiO₃ (BT)-based high-entropy energy storage ceramics are highly inefficient, and such materials struggle to balance high energy storage density and efficiency. The research team adopted a machine learning acceleration strategy, building a random forest model to screen 660,000 candidate compositions and identify the optimal one. This ceramic achieves an ultrahigh energy storage density of 10.8 J·cm^-3 and an efficiency of 86%, along with excellent temperature and frequency stability and charge-discharge performance. It provides an efficient new approach for designing high-performance energy storage ceramics, boasting significant application potential in the electronic device field.

While satellite navigation has become an essential part of modern life, it still struggles to work reliably indoors and in dense urban environments where high-rise buildings deteriorate signal propagation. In his doctoral dissertation at the University of Vaasa, Finland, Akpojoto Siemuri investigates how adaptive machine learning and advanced sensor fusion methods can improve positioning accuracy, robustness, and efficiency.