ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease that gradually affects a person's ability to move, speak, and breathe. It advances differently in every patient. Now, researchers at Nagoya University have developed an AI tool that uses data from patient follow-up studies to estimate the speed of disease progression and identify patterns of muscle decline. The study was published in npj Digital Medicine.

A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features from an AI model trained on atomic structural data and optical absorption spectra, and then groups materials with similar structural and spectral characteristics. This approach can be extended to reveal how atomic arrangements influence other material properties, paving the way for more efficient materials design.

A team led by the Beijing Institute of Technology presents a unified framework in National Science Review for personalized healthcare termed self-powered intelligence. It describes how on-device AI, flexible electronics, and body-based energy harvesting can be co-designed into wearable patches and implantable sensors, enabling them to analyze physiological signals in real time, predict health risks, for continuous and personalized healthcare.

Tokyo, Japan – Researchers from Tokyo Metropolitan University have created a new paradigm for identifying online phishing campaigns. Their new system, PhishLumos, is triggered when links show signs of concealing information, and looks for clues in the “infrastructure” of the website to uncover the whole campaign of which the site is only a tiny part. Real-world testing showed detection which was 8 days faster than an expert, with 190,000 URLs detected over 6 months.