Researchers from the University of Osaka and collaborating institutions developed an autonomous solid-state nanopore that combines sensing, memory, and signal generation in a single device. Unlike conventional nanopores, the sensor continually reshapes itself through chemical reactions, producing unique electrical signatures as molecules pass through, providing a basis for memory. The device successfully identified a variety of nucleotides and amino acids, providing a new platform for molecular sensing and future iontronic computing.

Researchers identify critical disconnects between end users and developers, offering a roadmap to align cost, usability, and design priorities for aging populations

How does the brain learn? Does it acquire new knowledge by creating new neural pathways, or by strengthening the connections that already exist? A new study from Bar-Ilan University offers evidence in favor of the latter, suggesting that learning is driven primarily by changes in the strength of existing neural connections rather than by expanding the brain's underlying architecture.

Insilico Medicine (“Insilico”, HKEX:3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today announced the launch of the Drug Discovery and Development (DDD) Benchmarks as a Service (BaaS). It is a pioneering standardized evaluation framework anchored in carefully decontaminated real-world data and proprietary validated programs. for measuring how frontier AI and foundation models built on other architectures perform on real-world tasks across medicinal chemistry, chemical synthesis, disease biology, clinical development, and longevity research.

A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.

Artificial intelligence is increasingly being used in finance, from investment management to financial analysis. However, recent research suggests that highly accurate market predictions do not necessarily result in better investment outcomes. In two complementary studies from Pusan National University, researchers investigated how financial AI can be improved to make better decisions and how the performance of these systems can be evaluated using a proposed framework. The findings support the development of reliable financial AI.