MIT researchers have discovered that short bursts of pink noise during sleep can increase the size of cerebrospinal fluid waves, potentially improving cognitive function and slowing neurodegenerative diseases. The study, published in Science Translational Medicine, aims to explore the effects of this technology on clinical populations.
MIT researchers have developed an electrochemical approach to purify ammonia for hydrogen, reducing the temperature and energy required for hydrogen recovery from ammonia and other hydrogen carriers. The method uses electricity to drive thermodynamically uphill and kinetically difficult dehydrogenation reactions, with the potential to be translated to other important dehydrogenation reactions.
A study evaluates how transfer learning from European cohorts improves genomic risk prediction in small underrepresented populations but degrades accuracy when target cohort sample sizes grow. The research suggests a potential solution for optimizing predictive performance in non-European populations by combining large-scale European GWAS results with targeted population-specific GWAS.
BepiColombo, ESA's mission to Mercury, separates from the rest of the spacecraft on 3 September 2026, marking the start of its arrival sequence. The mission's livestream, broadcast from ESA's European Space Operations Centre, includes live views, expert analysis, and the first signal from the spacecraft after separation.
A new deep-learning framework called Methane Analysis and Plume Localization with EMIT (MPL) automates the detection, quantification, and source estimation of methane emissions globally. This technology, powered by satellite data, helps track localized point sources in waste, agriculture, and energy sectors, offering a critical
TimesFM-3 is a state-of-the-art time series foundation model that enables highly accurate multivariate time series forecasting in a single forward pass. It significantly outperforms other forecasting models across major benchmarks. Since its debut in 2024, TimesFM has been adopted for real-world time-series forecasting tasks across multiple domains. TimesFM-3 is pre-trained on a real-world and synthetic time-series corpus comprising more than 1 trillion time points and supports joint prediction of multiple coevolving time series without requiring task-specific fine-tuning.
The briefing also provided an update on the mission’s status and explored the scientific opportunities that await as BepiColombo prepares to begin operations around Mercury.
Google Earth AI introduces the planetary prediction engine (PPE), an experimental research capability that automates geospatial modeling workflows, improving public health, food security, environmental risk, and socioeconomics. PPE autonomously discovers, cleans, trains, and evaluates predictive models from natural-language queries, reducing model building time from weeks to minutes.
A lightweight, self-supervised continuous glucose monitoring (CGM) foundation model called GlucoFM has been developed by Google Research. GlucoFM models slower glucose trends and short-term deviations separately, improving performance across diverse metabolic prediction tasks. Unlike existing models, GlucoFM separates CGM data into distinct streams, enabling better interpretation and analysis of glucose regulation.
AgentHands is an LLM-powered XR prototype that augments conversational agents with synchronized, expressive hand gestures to provide spatially grounded guidance, bridging the mental mapping gap and enhancing user engagement in physical tasks. It leverages the spatial understanding capabilities of XR to replicate natural co-speech gestures, making conversations about surroundings more intuitive and engaging.
U.S. National Science Foundation-supported researchers are behind an educational effort to introduce super intelligence (SI) techniques to elementary school children.
National Science Foundation··1 min read
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