Eliminating background artifacts in super-resolution microscopy: A risk-aware adaptive deep learning framework
Peer-Reviewed Publication
Updates every hour. Last Updated: 30-Oct-2026 23:16 ET (31-Oct-2026 03:16 GMT/UTC)
The research team led by Liying Qu and Weisong Zhao from Harbin Institute of Technology, in collaboration with Peking University, has developed Adaptive-SN2N, an innovative self-supervised learning framework. By introducing a risk-aware adaptive normalization strategy, this method successfully eliminates background artifacts commonly induced by deep learning, preserving high biological fidelity and enabling highly accurate live-cell quantitative analysis.
Researchers at the Indian Institute of Technology Gandhinagar (IITGN) have developed an adaptive charging strategy for lithium-ion batteries that could help Electric Vehicles (EVs) charge efficiently while reducing a major cause of battery degradation known as lithium plating. Published in the peer-reviewed Journal of Energy Storage, the study introduces a self-adjusting charging framework that dynamically protects batteries from internal degradation while optimising charging efficiency and time across varying temperature and health conditions.
Researchers have developed a fish-like robot that shows how some species of modern fish are able to walk on land, and could help unravel how early vertebrates evolved similar abilities hundreds of millions of years ago.
The National Institute of Environmental Health Sciences has awarded a five-year, $3.3 million grant to a University of Cincinnati College of Medicine researcher to study the potential cardiovascular toxicity of microplastics and nanoplastics.
Researchers at IRB Barcelona develop a computational framework that creates molecules with selective activity in specific cell types, without the need to start from a predefined molecular target.
Published in Communications Chemistry, the new strategy combines predictive and generative AI to design new chemical entities with specific biological effects.
Experimental validation confirmed that several of the AI-generated molecules displayed the activity they were designed for, achieving a success rate superior to that of traditional screening methods.