The most detailed study ever of the complex motions of octopus arms as related to their behaviors will be published in Nature Scientific Reports this week by researchers from the Marine Biological Laboratory (MBL) and Florida Atlantic University (FAU). This study represents the first full ethogram, or detailed catalog, of octopus arm behaviors in the wild and builds on prior studies from the Roger Hanlon lab at MBL.

BUFFALO, NY — September 11, 2025 — A new research paper was published  in Volume 17, Issue 8 of Aging-US on August 8, 2025, titled “AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging.”

Diagnosing dementia early is challenging due to variability in symptoms and limited access to advanced imaging tools. In a pilot study, researchers tested broadband near-infrared spectroscopy (bNIRS), a portable and noninvasive neuromonitoring technology that measures both blood oxygenation and concentration of oxidized cytochrome c oxidase (oxCCO), a metabolic mitochondrial enzyme linked to Alzheimer’s disease. In this study, participants with mild cognitive impairment, early Alzheimer’s dementia, and healthy controls observed a visual stimulus while bNIRS recorded brain responses. Statistical analysis showed a strong correlation between derivatives of bNIRS signals and cognitive test scores, which weakened significantly when oxCCO metrics were excluded. These results suggest that bNIRS, particularly with its ability to measure oxCCO, could provide an accessible portable technique for diagnosing and monitoring dementia.

A research team from the Hong Kong University of Science and Technology (HKUST), led by Professors Gan Jianping (Department of Ocean Science) and Professor Yang Can (Department of Mathematics), has developed a novel AI-powered tool named STIMP for diagnosing coastal ocean productivity and ecosystem health. STIMP introduces a novel paradigm that imputes missing data and then predicts Chlorophyll-a (Chl-a) concentrations across large spatiotemporal scales. In tests across four representative global coastal regions, STIMP significantly outperformed existing geoscience tools, reducing the mean absolute error (MAE) for imputation by up to 81.39% and for prediction by 58.99%. Accurate Chl-a prediction aids in early detection of harmful algal blooms, ecosystem protection, and provides data-driven insights for evidence-based policy-making.