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

SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

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.

Persistent urinary metabolic signatures in children with type 1 diabetes - Ernesto S. Nakayasu; Javier E. Flores; Lisa M. Bramer; Jackson L. Chin; Young-Mo Kim; Farooq Syed; Erika M. Zink; Brigitte I. Frohnert; Carmella Evans-Molina; Paul B. Kaplowitz; Fran E. Cogen; Rembert Pieper; et al. (Next Research, 2025)

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.

Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project - Eleanor G. Seaby; N. Simon Thomas; Amy Webb; Helen Brittain; Ana Lisa Taylor Tavares; J. C. Ambrose; P Arumugam; R Bevers; M Bleda; F Boardman-Pretty; C. R. Boustred; H Brittain; et al. (Human Genetics, 2023)
Background Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient’s phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods. Methods The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes with a LOEUF score < 0.2. We compared our findings with the corresponding patient’s diagnostic reports. Results 324/332 (98%) of the variants identified using DeNovoLOEUF were diagnostic or partially diagnostic (whereby the variant was responsible for some of the phenotype). We identified 39 diagnoses that were “missed” by 100KGP standard analyses, which are now being returned to patients. Conclusion We have demonstrated a highly specific and rapid method with a 98% positive predictive value that has good concordance with standard analysis, low false-positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

Joint exposure to smoking, excessive weight, and physical inactivity and survival of ovarian cancer patients, evidence from the Ovarian Cancer Association Consortium - Albina N. Minlikeeva; Rikki Cannioto; Allan Jensen; Susanne K. Kjaer; Susan J. Jordan; Brenda Diergaarde; J. Brian Szender; Kunle Odunsi; Hani Almohanna; Paul Mayor; Kirsten Starbuck; Emese Zsiros; et al. (Cancer Causes & Control, 2019)

Co-designing adaptation decision support: meeting common and differentiated needs - Robert Webb; David Rissik; Lisa Petheram; Jie-Lian Beh; Mark Stafford Smith (Climatic Change, 2019)

History of hypertension, heart disease, and diabetes and ovarian cancer patient survival: evidence from the ovarian cancer association consortium - Albina N. Minlikeeva; Jo L. Freudenheim; Rikki A. Cannioto; J. Brian Szender; Kevin H. Eng; Francesmary Modugno; Roberta B. Ness; Michael J. LaMonte; Grace Friel; Brahm H. Segal; Kunle Odunsi; Paul Mayor; et al. (Cancer Causes & Control, 2017)

Prioritization of knowledge‐needs to achieve best practices for bottom trawling in relation to seabed habitats - Michel J Kaiser; Ray Hilborn; Simon Jennings; Ricky Amaroso; Michael Andersen; Kris Balliet; Eric Barratt; Odd A Bergstad; Stephen Bishop; Jodi L Bostrom; Catherine Boyd; Eduardo A Bruce; et al. (Fish and Fisheries, 2016)
Management and technical approaches that achieve a sustainable level of fish production while at the same time minimizing or limiting the wider ecological effects caused through fishing gear contact with the seabed might be considered to be ‘best practice’. To identify future knowledge‐needs that would help to support a transition towards the adoption of best practices for trawling, a prioritization exercise was undertaken with a group of 39 practitioners from the seafood industry and management, and 13 research scientists who have an active research interest in bottom‐trawl and dredge fisheries. A list of 108 knowledge‐needs related to trawl and dredge fisheries was developed in conjunction with an ‘expert task force’. The long list was further refined through a three stage process of voting and scoring, including discussions of each knowledge‐need. The top 25 knowledge‐needs are presented, as scored separately by practitioners and scientists. There was considerable consistency in the priorities identified by these two groups. The top priority knowledge‐need to improve current understanding on the distribution and extent of different habitat types also reinforced the concomitant need for the provision and access to data on the spatial and temporal distribution of all forms of towed bottom‐fishing activities. Many of the other top 25 knowledge‐needs concerned the evaluation of different management approaches or implementation of different fishing practices, particularly those that explore trade‐offs between effects of bottom trawling on biodiversity and ecosystem services and the benefits of fish production as food.

Persistence of black‐tailed prairie‐dog populations affected by plague in northern Colorado, USA - Dylan B. George; Colleen T. Webb; Kim M. Pepin; Lisa T. Savage; Michael F. Antolin (Ecology, 2013)
The spatial distribution of prairie dog ( Cynomys ludovicianus ) colonies in North America has changed from large, contiguous populations to small, isolated colonies in metapopulations. One factor responsible for this drastic change in prairie‐dog population structure is plague (caused by the bacterium Yersinia pestis ). We fit stochastic patch occupancy models to 20 years of prairie‐dog colony occupancy data from two discrete metapopulations (west and east) in the Pawnee National Grassland in Colorado, USA, that differ in connectivity among suitable habitat patches. We conducted model selection between two hypothesized modes of plague movement: independent of prairie‐dog dispersal (colony‐area) vs. plague movement consistent with prairie‐dog dispersal (connectivity to extinct colonies). The best model, which fit the data well (area under the curve [AUC]: 0.94 west area; 0.79 east area), revealed that over time the proportion of extant colonies was better explained by colony size than by connectivity to extinct (plagued) colonies. The idea that prairie dogs are not likely to be the main vector that spreads Y. pestis across the landscape is supported by the observation that colony extinctions are primarily caused by plague, prairie‐dog dispersal is short range, and connectivity to extinct colonies was not selected as a factor in the models. We also conducted simulations with the best model to examine long‐term patterns of colony occupancy and persistence of prairie‐dog metapopulations. In the case where the metapopulations persist, our model predicted that the western metapopulation would have a colony occupancy rate approximately 2.5 times higher than that of the eastern metapopulation (∼50% occupied colonies vs. 20%) in 50 years, but that the western metapopulation has ∼80% chance of extinction in 100 years while the eastern metapopulation has a less than 25% chance. Extinction probability of individual colonies depended on the frequency with which colonies of the same size class occurred in the metapopulation. Thus, the long‐term persistence of prairie‐dog metapopulations depended on specific details of the metapopulation.

Phospholipase D modulation by ceramide in senescence - Lisa M. Webb; Alan T. Arnholt; Mark E. Venable (Molecular and Cellular Biochemistry, 2010)

Mid-texas, USA coastal marsh vegetation pattern and dynamics as influenced by environmental stress and snow goose herbivory - Deborah L. Miller; Fred E. Smeins; James W. Webb; Lisa Yager (Wetlands, 2005)

Evidence of acoustic startle hyperreflexia in recently detoxified early onset male alcoholics: modulation by yohimbine and m -Chlorophenylpiperazine (mCPP) - John H. Krystal; Elizabeth Webb; Christian Grillon; N. Cooney; Lisa Casal; C. A. Morgan III.; Steven M. Southwick; Michael Davis; Dennis S. Charney (Psychopharmacology, 1997)

Abstracts of presentations on plant protection issues at the fifth international Mango Symposium Abstracts of presentations on plant protection issues at the Xth international congress of Virology - J. E. Peña; M. Wysoki; Gajendra Singh; Nancy Boscán de M.; Freddy J. Godoy; A. Obligado; C. J. Rossetto; I. J. A. Ribeiro; P. B. Gallo; N. B. Soares; J. C. Sabino; A. L. M. Martins; et al. (Phytoparasitica, 1997)