Assessment of Dog-Leash Pulling Force and the Impact of Dog-Walking on Gait Kinematics - Alex Peebles; Michael Bennett; Samantha Morrison; Julio Ramirez-Reyes; Ji Chen; Lara Thompson (Annals of Biomedical Engineering, 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

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.

Conservation planning for environmental water to climate refugia in the manageable Murray–Darling Basin - Joanne M. Bennett; Simon Linke; Shane Brooks; Alex Bush; James Hitchcock; Carmel Pollino; Ross M. Thompson (Journal of Environmental Management, 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.

Pediment formation and subsequent erosion in Gale crater: Clues to the climate history of Mars - A.B. Bryk; W.E. Dietrich; K.A. Bennett; V.K. Fox; C.M. Fedo; M.P. Lamb; E.S. Kite; L.M. Thompson; S.G. Banham; J. Schieber; J.A. Grant; A.R. Vasavada; et al. (Icarus, 2025)

EuPPollNet : A European Database of Plant‐Pollinator Networks - Jose B. Lanuza; Tiffany M. Knight; Nerea Montes‐Perez; Will Glenny; Paola Acuña; Matthias Albrecht; Maddi Artamendi; Isabelle Badenhausser; Joanne M. Bennett; Paolo Biella; Ricardo Bommarco; Andree Cappellari; et al. (Global Ecology and Biogeography, 2025)
Motivation Pollinators play a crucial role in maintaining Earth's terrestrial biodiversity. However, rapid human‐induced environmental changes are compromising the long‐term persistence of plant‐pollinator interactions. Unfortunately, we lack robust, generalisable data capturing how plant‐pollinator communities are structured across space and time. Here, we present the EuPPollNet (European Plant‐Pollinator Networks) database, a fully open European‐level database containing harmonised taxonomic data on plant‐pollinator interactions referenced in both space and time, along with other ecological variables of interest. In addition, we evaluate the taxonomic and sampling coverage of EuPPollNet, and summarise key structural properties in plant‐pollinator networks. We believe EuPPollNet will stimulate research to address data gaps in plant‐pollinator interactions and guide future efforts in conservation planning. Main Types of Variables Included EuPPollNet contains 1,162,109 interactions between plants and pollinators from 1864 distinct networks, which belong to 52 different studies distributed across 23 European countries. Information about sampling methodology, habitat type, biogeographic region and additional taxonomic rank information (i.e. order, family, genus and species) is also provided. Spatial Location and Grain The database contains 1214 different sampling locations from 13 different natural and anthropogenic habitats that fall in 7 different biogeographic regions. All records are geo‐referenced and presented in the World Geodetic System 1984 (WGS84). Time Period and Grain Species interaction data was collected between 2004 and 2021. Major Taxa and Level of Measurement The database contains interaction data at the species level for 94% of the records, including a total of 1411 plant and 2223 pollinator species. The database includes data on 6% of the European species of flowering plants, 34% of bees, 26% of butterflies and 33% of syrphid species at the European level. Software Format The database was built with R and is stored in ‘.rds’ and ‘.csv’ formats. Its construction is fully reproducible and can be accessed at: https://doi.org/10.5281/zenodo.14747448 .

On-orbit calibration and performance of the EMIT imaging spectrometer - David R. Thompson; Robert O. Green; Christine Bradley; Philip G. Brodrick; Natalie Mahowald; Eyal Ben Dor; Matthew Bennett; Michael Bernas; Nimrod Carmon; K. Dana Chadwick; Roger N. Clark; Red Willow Coleman; et al. (Remote Sensing of Environment, 2024)

Harnessing water fleas for water reclamation: A nature-based tertiary wastewater treatment technology - Muhammad Abdullahi; Iestyn Stead; Sophie Bennett; Rafael Orozco; Mohamed Abou-Elwafa Abdallah; Sara Jabbari; Lynne E. Macaskie; Alexandra Tzella; Stefan Krause; Bushra Al-Duri; Robert G. Lee; Ben Herbert; et al. (Science of The Total Environment, 2023)

Overview of the search for signs of space weathering on the low-albedo asteroid (101955) Bennu - B.E. Clark; A. Sen; X.-D. Zou; D.N. DellaGiustina; S. Sugita; N. Sakatani; M. Thompson; D. Trang; E. Tatsumi; M.A. Barucci; M. Barker; H. Campins; et al. (Icarus, 2023)

Complex crop rotations improve organic nitrogen cycling - Lauren C. Breza; Maria Mooshammer; Timothy M. Bowles; Virginia L. Jin; Marty R. Schmer; Bennett Thompson; A. Stuart Grandy (Soil Biology and Biochemistry, 2023)

Infection‐competent monkeypox virus contamination identified in domestic settings following an imported case of monkeypox into the UK - Barry Atkinson; Christopher Burton; Thomas Pottage; Katy‐Anne Thompson; Didier Ngabo; Ant Crook; James Pitman; Sian Summers; Kuiama Lewandowski; Jenna Furneaux; Katherine Davies; Timothy Brooks; et al. (Environmental Microbiology, 2022)
An imported case of monkeypox was diagnosed in December 2019 in a traveller returning from Nigeria to the UK. Subsequently, environmental sampling was performed at two adjoining single‐room residences occupied by the patient and their sibling. Monkeypox virus DNA was identified in multiple locations throughout both properties, and monkeypox virus was isolated from several samples 3 days after the patient was last in these locations. Positive samples were identified following the use of both vacuum and surface sampling techniques; these methodologies allowed for environmental analysis of potentially contaminated porous and non‐porous surfaces via real‐time quantitative reverse transcriptase PCR analysis in addition to viral isolation to confirm the presence of infection‐competent virus. This report confirms the potential for infection‐competent monkeypox virus to be recovered in environmental settings associated with known positive cases and the necessity for rapid environmental assessment to reduce potential exposure to close contacts and the general public. The methods adopted in this investigation may be used for future confirmed cases of monkeypox in order to establish levels of contamination, confirm the presence of infection‐competent material and to identify locations requiring additional cleaning.

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)

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network - Jason Beringer; Caitlin E. Moore; Jamie Cleverly; David I. Campbell; Helen Cleugh; Martin G. De Kauwe; Miko U. F. Kirschbaum; Anne Griebel; Sam Grover; Alfredo Huete; Lindsay B. Hutley; Johannes Laubach; et al. (Global Change Biology, 2022)
In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20 th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO 2 sink to a net CO 2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under‐represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long‐term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Biofilm formation in an experimental water distribution system: the contamination of non-touch sensor taps and the implication for healthcare - Ginny Moore; David Stevenson; Katy-Anne Thompson; Simon Parks; Didier Ngabo; Allan M. Bennett; Jimmy T. Walker (Biofouling, 2015)

CTFS ‐Forest GEO : a worldwide network monitoring forests in an era of global change - Kristina J. Anderson‐Teixeira; Stuart J. Davies; Amy C. Bennett; Erika B. Gonzalez‐Akre; Helene C. Muller‐Landau; S. Joseph Wright; Kamariah Abu Salim; Angélica M. Almeyda Zambrano; Alfonso Alonso; Jennifer L. Baltzer; Yves Basset; Norman A. Bourg; et al. (Global Change Biology, 2015)
Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long‐term forest dynamics research sites ( CTFS ‐Forest GEO ) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS ‐Forest GEO spans 25°S–61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS ‐Forest GEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 °C), changes in precipitation (up to ±30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m −2 yr −1 and 3.1 g S m −2 yr −1 ), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS ‐Forest GEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS ‐Forest GEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Nanosecond pulsed electric fields modulate the expression of Fas/CD95 death receptor pathway regulators in U937 and Jurkat Cells - Larry E. Estlack; Caleb C. Roth; Gary L. Thompson; William A. Lambert; Bennett L. Ibey (Apoptosis, 2014)

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data - Paul M. Thompson; Jason L. Stein; Sarah E. Medland; Derrek P. Hibar; Alejandro Arias Vasquez; Miguel E. Renteria; Roberto Toro; Neda Jahanshad; Gunter Schumann; Barbara Franke; Margaret J. Wright; Nicholas G. Martin; et al. (Brain Imaging and Behavior, 2014)

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder - Jillian P. Casey; Tiago Magalhaes; Judith M. Conroy; Regina Regan; Naisha Shah; Richard Anney; Denis C. Shields; Brett S. Abrahams; Joana Almeida; Elena Bacchelli; Anthony J. Bailey; Gillian Baird; et al. (Human Genetics, 2012)

Case Study Part 2: Probabilistic Modelling of Long-term Effects of Pesticides on Individual Breeding Success in Birds and Mammals - W. ROELOFS; D.R. CROCKER; R.F. SHORE; D.R.J. MOORE; G.C. SMITH; H.R. AKCAKAYA; R.S. BENNETT; P.F. CHAPMAN; M. CLOOK; M. CRANE; I.C. DEWHURST; P.J. EDWARDS; et al. (Ecotoxicology, 2005)

Case Study Part 1: How to Calculate Appropriate Deterministic Long-Term Toxicity to Exposure Ratios (TERs) for Birds and Mammals - R.F. SHORE; D.R. CROCKER; H.R. AKCAKAYA; R.S. BENNETT; P.F. CHAPMAN; M. CLOOK; M. CRANE; I.C. DEWHURST; P.J. EDWARDS; A. FAIRBROTHER; S. FERSON; D. FISCHER; et al. (Ecotoxicology, 2005)

High-throughput sequence identification of gene coding variants within alcohol-related QTLs - Marissa A. Ehringer; Jessica Thompson; Otakuye Conroy; Yan Xu; Fan Yang; Jennifer Canniff; Mary Beeson; Lena Gordon; Beth Bennett; Thomas E. Johnson; James M. Sikela (Mammalian Genome, 2001)