Researchers have fully mapped how noise spreads through quantum computers over time to show that glitches link together across different moments, creating a form of 'memory' that undermines calculations.

 

An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on their development of a new statistical method that improves analysis in single-molecule fluorescence experiments, which are used to study important protein complexes in cells.

Researchers at McMaster University have developed a new database that brings together more than 100 years of historical epidemiological data from across Canada, which will help to predict future patterns of infectious disease.

A research team led by Prof. Tao Jiang and Prof. Jing Li from State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, in collaboration with Prof. Shi-Shun Zhao from College of Mathematics, Jilin University and Prof. Jianwei Wang from NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, developed a viral risk prediction framework named GIVAL based on the pre-trained viral protein language model vBERT. This study proposes a general intelligent prediction method for assessing adaptation risks of unknown viruses.

A team from the Institute of Industrial Science, The University of Tokyo has developed MatAgent, an AI system powered by a large language model. By reasoning in natural language, using memory, and learning from feedback, MatAgent explores new inorganic materials with a human-like process while explaining each step it takes.