Examining Oral Health Practices and Policies of Early Care Environments - Selina Agandaa; Susanna Henry; Jenna Webb; Ryan Andrews; Yobanna Taylor; Noereem Mena (Journal of Nutrition Education and Behavior, 2026)

Examining Early Care Environments and Health Practices: Outdoor Play, Physical Activity, Screen Time, and Food Education - Susanna Henry; Jenna Webb; Selina Agandaa; Ryan Andrews; Yobanna Taylor; Noereem Mena (Journal of Nutrition Education and Behavior, 2026)

Deep-learning full-waveform inversion of snowpack GPR: joint permittivity–resistivity imaging for snow–soil hydrological mapping - Yuanjun Jiang; Zohaib Akbar; Ryan Webb; Zhao Binbin; Aftab Anwar; M.M Rehman; M.Z. Mirza (Journal of Hydrology, 2026)

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.

Machine learning-based dual-parameter inversion for estimating snowpack liquid water content and density using common offset GPR data - Zohaib Akbar; Yuanjun Jiang; Ryan Webb; Anja Klotzsche; Yuanjia Zhu; Aftab Anwar; Muhammad Mudassar Rehman (Science China Earth Sciences, 2026)

Stochastic modeling of bovine tuberculosis dynamics in white-tailed deer - Brandon J. Simony; Ryan S. Miller; Lindsay M. Beck-Johnson; Samuel M. Smith; Colleen T. Webb (Research in Veterinary Science, 2025)

Plasma biomarkers of alzheimer’s disease and related dementias are associated with cognitive change in community-dwelling older individuals in Australia and the US - Zimu Wu; Michelle M. Mielke; Anne M. Murray; Katherine Webb; Robyn L. Woods; Alice Owen; Suzanne G. Orchard; Trevor T.-J. Chong; Kerry M. Sheets; Joanne Ryan (GeroScience, 2025)

TropiRoot 1.0: Database of tropical root characteristics across environments - Amanda L. Cordeiro; Daniela F. Cusack; Nathaly Guerrero‐Ramírez; Richard J. Norby; Laura Toro; Michelle Y. Wong; S. Joseph Wright; Kristine Grace M. Cabugao; Kelly M. Andersen; Lucia Fuchslueger; Colleen M. Iversen; Fiona Soper; et al. (Ecology, 2025)
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine‐Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text‐based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

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.

Remotely Sensed Fire Heterogeneity and Biomass Recovery Predicts Empirical Biodiversity Responses - Rebecca K. Gibson; Don A. Driscoll; Kristina J. Macdonald; Grant J. Williamson; Rachael H. Nolan; Tim S. Doherty; Dale G. Nimmo; Euan G. Ritchie; Mark Tozer; Liz Tasker; Aaron Greenville; Adam Roff; et al. (Global Ecology and Biogeography, 2025)
Aim To compare field‐based evidence of plant and animal responses to fire with remotely sensed signals of fire heterogeneity and post‐fire biomass recovery. Location South‐eastern Australia; New South Wales. Time Period 2019–2022. Major Taxa Studied A total of 982 species of plants and animals, in eight taxonomic groups: amphibians, birds, fish, insects, mammals, molluscs, plants and reptiles. Methods We collated 545,223 plant and animal response records from 47 field surveys of 4613 sites that focussed on areas burnt in 2019–2020. For each site, we calculated remotely sensed signals of fire heterogeneity and post‐fire biomass recovery, including the delayed recovery index. Meta‐regression analyses were conducted separately for species that declined after fire (negative effect sizes) and species that increased after fire (positive effect sizes) for each buffer size (250 m, 500 m, 1 km, 1.5 km, 2 km and 2.5 km radius). Results We found that species exposed to homogenous high‐severity fire (i.e., low fire heterogeneity) were more likely to exhibit decreased abundance/occurrence or inhibited recovery. Areas with delayed recovery of biomass also had significant negative on‐ground responses, with lower abundance or occurrence in areas where biomass recovery was slower. Main Conclusions The fire heterogeneity index and the delayed recovery index are suitable for inclusion in monitoring and reporting systems for tracking relative measures over time, particularly when field survey data is not available at the landscape scales required to support reporting and management decisions. Locations with remotely sensed signals of delayed recovery should be prioritised for protection against further disturbances that may interfere with the recovery process. Research attention must next focus on how cumulative fire heterogeneity patterns of successive fires affect the post‐fire recovery dynamics to further inform the application of remote sensing indicators as management tools for biodiversity conservation.

Integrating behavioural, material and environmental science to inform the design and evaluation of a reuse system for takeaway food - Maryam Hoseini; Sarah C. Greenwood; Saima Eman; Paul Mattinson; Harriet M. Baird; Rorie Beswick-Parsons; J. Patrick A. Fairclough; Thomas L. Webb; Anthony J. Ryan; Rachael H. Rothman (Resources, Conservation and Recycling, 2024)

Forecasting industrial induced tipping points in first nations’ land use in northeast British Columbia - Xiaojing Lu; Naomi Owens-Beek; Ryan McKay; Jim Webb; Xulin Guo; David Natcher (The Extractive Industries and Society, 2024)

Quantifying short-term changes in snow strength due to increasing liquid water content above hydraulic barriers - Mikael Schlumpf; Jordy Hendrikx; John Stormont; Ryan Webb (Cold Regions Science and Technology, 2024)

Propagation of barotropic Kelvin waves around Antarctica - David J. Webb; Ryan M. Holmes; Paul Spence; Matthew H. England (Ocean Dynamics, 2022)
Barotropic (i.e., depth-uniform) coastal oceanic Kelvin waves can provide rapid teleconnections from climate and weather events in one location to remote regions of the globe. Studies suggest that barotropic Kelvin waves observed around Antarctica may provide a mechanism for rapidly propagating circulation anomalies around the continent, with implications for continental shelf temperatures along the West Antarctic Peninsula and thus Antarctic ice mass loss rates. However, how the propagation of Kelvin waves around Antarctica is influenced by features such as coastal geometry and variations in bathymetry remains poorly understood. Here we study the propagation of barotropic Antarctic Kelvin waves using a range of idealized model simulations. Using a single-layer linear shallow water model with 1 ∘ horizontal resolution, we gradually add complexity of continental configuration, realistic bathymetry, variable planetary rotation, and forcing scenarios, to isolate sources and sinks of wave energy and the mechanisms responsible. We find that approximately 75 % of sub-inertial barotropic Kelvin wave energy is scattered away from Antarctica as other waves in one circumnavigation of the continent, due mostly to interactions with bathymetry. Super-inertial barotropic Kelvin waves lose nearly 95 % of their energy in one circumpolar loop, due to interactions with both coastal geometry and bathymetry. These results help to explain why only sustained signals of low-frequency resonant barotropic Kelvin waves have been observed around Antarctica, and contribute to our understanding of the role of rapid, oceanic teleconnections in climate.

Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

The Presence of Hydraulic Barriers in Layered Snowpacks: TOUGH2 Simulations and Estimated Diversion Lengths - Ryan W. Webb; Steven R. Fassnacht; Michael N. Gooseff; Stephen W. Webb (Transport in Porous Media, 2018)

Sustainable urban systems: Co-design and framing for transformation - Robert Webb; Xuemei Bai; Mark Stafford Smith; Robert Costanza; David Griggs; Magnus Moglia; Michael Neuman; Peter Newman; Peter Newton; Barbara Norman; Chris Ryan; Heinz Schandl; et al. (Ambio, 2018)

Headwater regions — Physical, ecological, and social approaches to understand these areas: introduction to the special issue - Steven R. Fassnacht; Ryan W. Webb; William E. Sanford (Frontiers of Earth Science, 2017)

Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado - Ryan W. Webb (Frontiers of Earth Science, 2017)

Inferring infection hazard in wildlife populations by linking data across individual and population scales - Kim M. Pepin; Shannon L. Kay; Ben D. Golas; Susan S. Shriner; Amy T. Gilbert; Ryan S. Miller; Andrea L. Graham; Steven Riley; Paul C. Cross; Michael D. Samuel; Mevin B. Hooten; Jennifer A. Hoeting; et al. (Ecology Letters, 2017)
Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI . Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.

Ecophysiological diversity of a novel member of the genus Alteromonas, and description of Alteromonas mediterranea sp. nov. - Elena P. Ivanova; Mario López-Pérez; Mila Zabalos; Song Ha Nguyen; Hayden K. Webb; Jason Ryan; Kiril Lagutin; Mikhail Vyssotski; Russell J. Crawford; Francisco Rodriguez-Valera (Antonie van Leeuwenhoek, 2015)

Development of the ear, hearing capabilities and laterophysic connection in the spotfin butterflyfish (Chaetodon ocellatus) - Jacqueline F. Webb; Ryan M. Walsh; Brandon M. Casper; David A. Mann; Natasha Kelly; Nicole Cicchino (Environmental Biology of Fishes, 2012)

Carbon outcomes of major land‐cover transitions in SE Asia: great uncertainties and REDD + policy implications - Alan D. Ziegler; Jacob Phelps; Jia QI Yuen; Edward L. Webb; Deborah Lawrence; Jeff M. Fox; Thilde B. Bruun; Stephen J. Leisz; Casey M. Ryan; Wolfram Dressler; Ole Mertz; Unai Pascual; et al. (Global Change Biology, 2012)
Policy makers across the tropics propose that carbon finance could provide incentives for forest frontier communities to transition away from swidden agriculture (slash‐and‐burn or shifting cultivation) to other systems that potentially reduce emissions and/or increase carbon sequestration. However, there is little certainty regarding the carbon outcomes of many key land‐use transitions at the center of current policy debates. Our meta‐analysis of over 250 studies reporting above‐ and below‐ground carbon estimates for different land‐use types indicates great uncertainty in the net total ecosystem carbon changes that can be expected from many transitions, including the replacement of various types of swidden agriculture with oil palm, rubber, or some other types of agroforestry systems. These transitions are underway throughout Southeast Asia, and are at the heart of REDD + debates. Exceptions of unambiguous carbon outcomes are the abandonment of any type of agriculture to allow forest regeneration (a certain positive carbon outcome) and expansion of agriculture into mature forest (a certain negative carbon outcome). With respect to swiddening, our meta‐analysis supports a reassessment of policies that encourage land‐cover conversion away from these [especially long‐fallow] systems to other more cash‐crop‐oriented systems producing ambiguous carbon stock changes – including oil palm and rubber. In some instances, lengthening fallow periods of an existing swidden system may produce substantial carbon benefits, as would conversion from intensely cultivated lands to high‐biomass plantations and some other types of agroforestry. More field studies are needed to provide better data of above‐ and below‐ground carbon stocks before informed recommendations or policy decisions can be made regarding which land‐use regimes optimize or increase carbon sequestration. As some transitions may negatively impact other ecosystem services, food security, and local livelihoods, the entire carbon and noncarbon benefit stream should also be taken into account before prescribing transitions with ambiguous carbon benefits.

Physiological responses to simulated stair climbing in professional firefighters wearing rubber and leather boots - Chun-Jung Huang; Ryan S. Garten; Chip Wade; Heather E. Webb; Edmund O. Acevedo (European Journal of Applied Physiology, 2009)

s of papers presented at the 63rd annual PAA meeting - Jose L. Rueda; Donald Berrios; W. R. Amoros; H. A. Mendoza; Jose Gil Archuleta; Gene D. Easton; C. R. Brown; Doug Bruce; Elsa Camadro; S. J. Peloquin; J. R. Davis; W. B. Jones; et al. (American Journal of Potato Research, 1979)