(Tokyo Metropolitan University) Tokyo, Japan - Researchers from Tokyo Metropolitan University have studied the properties of mixtures of silicone-coated "magic sand", a popular kid's toy, and normal sand. Silicone-coated sand particles were found to interact with each other only, and not with other sand particles. The team discovered that adding silicone-coated sand beyond a certain threshold leads to an abrupt change in clustering and rigidity, a simple, useful way to potentially tune the flow […]

(Geisinger Health System) A team of scientists from Geisinger and Tempus have found that artificial intelligence can predict risk of new atrial fibrillation (AF) and AF-related stroke.

(University of the Basque Country ) Researchers from the IXA group at the UPV/EHU are collaborating with Osakidetza (the Basque Regional Health Service) to create a system for automatically extracting adverse drug reactions from electronic health records written in Spanish. The researchers have conducted different tests using both machine learning and deep learning, with the aim of building a robust model for extracting relations between drug-disease pairs based on clinical text mining.

(University of California - San Diego) New research published in two papers by UC San Diego scientists describes novel achievements designed to make the implementation of gene drives safer and more controllable. The new split drive and home-and-rescue systems address concerns about the release of gene drives in wild populations.

(Kanazawa University) A researcher from Kanazawa University devised a way to speed up a fundamental task in computer vision and graphics known as non-rigid point set registration. Unlike previous registration techniques, the proposed method is computationally efficient even for large data sets. Moreover, the computing times for this method are shorter than those for a state-of-the-art approach. The results of this study could have implications for various fields, such as autonomous driving, […]