Prioritizing Shark, Ray, and Chimaera Conservation in Rapidly Changing Polar Waters - Vanessa B. Bettcher; Amanda Batlle‐Morera; Peter M. Kyne; Ryan Charles; Emiliano García‐Rodríguez; Christoph A. Rohner; Asia O. Armstrong; Margaret O’Leary Amsler; Natalia V. Chernova; Brynn M. Devine; Andrey V. Dolgov; Meghan M. Donovan; et al. (Global Change Biology, 2026)
Climate‐driven alterations in sea‐ice dynamics and shifting fishing activity are reshaping ecosystems across the Arctic and Antarctic, yet conservation‐relevant information for chondrichthyans (sharks, rays, and chimaeras) remains limited. We present the first assessment of Important Shark and Ray Areas (ISRAs) across Polar Waters, integrating species distributions, extinction risk, critical habitat evidence, spatial protection coverage, apparent fishing effort (2013–2018 vs. 2019–2024), and sea‐ice concentration trends. Of 86 chondrichthyan species reported from Polar Waters, only 15 (17.4%) had sufficient evidence to meet the ISRA Criteria, and 28 (32.6%) are threatened with extinction. We identified 35 ISRAs (27 Arctic; 8 Antarctic) covering 0.75% of the Polar Waters region. ISRAs were delineated predominantly based on data from scientific fishing surveys and harvest fisheries. Most Antarctic ISRAs were delineated under Criterion B—Range Restricted, reflecting endemism and scarce life‐history evidence. Major knowledge gaps persist, particularly for highly adapted Polar Species (10 species) and deepwater taxa (72.1% of regional chondrichthyan fauna). Between 2013 and 2024, the spatial footprint of fishing expanded in both poles for trawlers and longliners, despite stable or declining fishing hours in some fleets. Concurrent areas of increasing fishing effort and decreasing sea‐ice concentration remained minimal. However, apparent fishing effort overlapped with most ISRAs, indicating ongoing fishing pressure in critical habitats. Marine Protected Area (MPA) coverage remains uneven: 8.2% of Arctic ISRA spatial extent overlaps MPAs (0.8% no‐take), compared with 43.8% in the Antarctic (14.8% no‐take), with insufficient effective coverage in areas beyond national jurisdiction. These results reveal substantial mismatches between chondrichthyan critical habitats, fishing exposure, and existing protection measures. Integrating ISRAs into existing management frameworks, such as Target 3 of the Global Biodiversity Framework, High Seas Treaty mechanisms, and fisheries governance bodies, will be essential to safeguard vulnerable polar chondrichthyans as sea‐ice concentration declines and exposure risk increases.

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Remanufacture of Hot Forging Dies By LMD-p Using a Cobalt Based Hard-Facing Alloy - Ryan Devine; Crawford Cullen; Jim Foster; Mykola Kulakov; Conor MacFadden; Stephen Fitzpatrick (BHM Berg- und Hüttenmännische Monatshefte, 2021)

Advancing Research for the Management of Long-Lived Species: A Case Study on the Greenland Shark - Jena E. Edwards; Elizabeth Hiltz; Franziska Broell; Peter G. Bushnell; Steven E. Campana; Jørgen S. Christiansen; Brynn M. Devine; Jeffrey J. Gallant; Kevin J. Hedges; M. Aaron MacNeil; Bailey C. McMeans; Julius Nielsen; et al. (Frontiers in Marine Science, 2019)

A global database of nitrogen and phosphorus excretion rates of aquatic animals - Michael J. Vanni; Peter B. McIntyre; Dennis Allen; Diane L. Arnott; Jonathan P. Benstead; David J. Berg; Åge Brabrand; Sébastien Brosse; Paul A. Bukaveckas; Adriano Caliman; Krista A. Capps; Luciana S. Carneiro; et al. (Ecology, 2017)
Animals can be important in modulating ecosystem‐level nutrient cycling, although their importance varies greatly among species and ecosystems. Nutrient cycling rates of individual animals represent valuable data for testing the predictions of important frameworks such as the Metabolic Theory of Ecology ( MTE ) and ecological stoichiometry ( ES ). They also represent an important set of functional traits that may reflect both environmental and phylogenetic influences. Over the past two decades, studies of animal‐mediated nutrient cycling have increased dramatically, especially in aquatic ecosystems. Here we present a global compilation of aquatic animal nutrient excretion rates. The dataset includes 10,534 observations from freshwater and marine animals of N and/or P excretion rates. These observations represent 491 species, including most aquatic phyla. Coverage varies greatly among phyla and other taxonomic levels. The dataset includes information on animal body size, ambient temperature, taxonomic affiliations, and animal body N:P. This data set was used to test predictions of MTE and ES , as described in Vanni and McIntyre (2016; Ecology DOI : 10.1002/ecy.1582 ).