A new algorithm could drive breakthroughs in understanding cancer, Alzheimer’s disease, and other potentially fatal conditions.

Researchers from the University of Waterloo developed the machine learning algorithm, called  RNovA, to detect changes in the proteins in human cells.

Brian Adams, MBA, MOT, OTR/L, newly appointed Chief Operating Officer of UH Portage Medical Center, recently celebrated his 20th anniversary with University Hospitals, marking two decades of service rooted in patient care and operational leadership.

Electronic devices capable of perceiving and responding to environmental changes are essential for applications in human–machine interaction, monitoring systems, and robotics. However, most existing devices suffer from the separation of sensing and actuation, leading to complex integration and limited responsiveness. In this study, the researchers introduce an intelligent thermoelectric device inspired by the interplay between sensory and muscle cells in sea anemones. The device seamlessly combines multimodal sensing with autonomous thermal actuation, enabling a closed-loop sensory–motor reflex. It demonstrates excellent temperature sensitivity (0.2 ℃) and pressure resolution (0.03 mm), owing to its three-dimensional architecture and hierarchical conductive network. Molecular dynamics simulations show that its dynamic hydrogen-bonding network enhances stress dissipation and interfacial adhesion, resulting in exceptional mechanical stability over 140,000 cycles. Furthermore, the device exhibits thermal self-adaptation, actively initiating a protection mechanism through thermoresponsive deformation to avoid high-temperature stimuli, with an adjustable actuation threshold. This work represents a significant advancement toward intelligent electronics capable of real-time decision-making and active environmental interaction.

A new graph-based deep learning framework may improve the reconstruction of gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data by integrating global network structure learning with biologically informed statistical modeling, according to a study published in Computational Biomedicine.