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.

Pathogen diversity of the non-native narrow-clawed crayfish (Pontastacus leptodactylus) in a UK water body - Matthew Harwood; Josie South; Alison M. Dunn; Paul D. Stebbing; Amy Burgess; Jamie Bojko (Journal of Invertebrate Pathology, 2026)

Correction to: Antiretroviral drugs induce oxidative stress and neuronal damage in the central nervous system - Cagla Akay; Michael Cooper; Akinleye Odeleye; Brigid K. Jensen; Michael G. White; Fair Vassoler; Patrick J. Gannon; Joseph Mankowski; Jamie L. Dorsey; Alison M. Buch; Stephanie A. Cross; Denise R. Cook; et al. (Journal of NeuroVirology, 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.

Deep learning to extract the meteorological by‐catch of wildlife cameras - Jamie Alison; Stephanie Payne; Jake M. Alexander; Anne D. Bjorkman; Vincent Ralph Clark; Onalenna Gwate; Maria Huntsaar; Evelin Iseli; Jonathan Lenoir; Hjalte Mads Rosenstand Mann; Sandy‐Lynn Steenhuisen; Toke Thomas Høye (Global Change Biology, 2024)
Microclimate—proximal climatic variation at scales of metres and minutes—can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro‐scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti‐Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two‐class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open‐top chambers shortens early snow events in autumn, and (2) image‐derived sunshine marginally outperforms sensor‐derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

Equitable implementation of lung cancer screening: avoiding its potential to mirror existing inequities among people who use tobacco - Emily Bilenduke; Shacoria Anderson; Alison Brenner; Jessica Currier; Jan M. Eberth; Jaron King; Stephanie R. Land; Betsy C. Risendal; Jackilen Shannon; Leeann N. Siegel; Mary Wangen; Austin R. Waters; et al. (Cancer Causes & Control, 2023)
Purpose Lung cancer is the leading cause of cancer death, but the advent of lung cancer screening using low-dose computed tomography offers a tremendous opportunity to improve lung cancer outcomes. Unfortunately, implementation of lung cancer screening has been hampered by substantial barriers and remains suboptimal. Specifically, the commentary emphasizes the intersectionality of smoking history and several important sociodemographic characteristics and identities that should inform lung cancer screening outreach and engagement efforts, including socioeconomic considerations (e.g., health insurance status), racial and ethnic identity, LGBTQ + identity, mental health history, military experience/veteran status, and geographic residence in addressing specific community risk factors and future interventions in efforts to make strides toward equitable lung cancer screening. Methods Members of the Equitable Implementation of Lung Cancer Screening Interest Group with the Cancer Prevention and Control Network (CPCRN) provide a critical commentary based on existing literature regarding smoking trends in the US and lung cancer screening uptake to propose opportunities to enhance implementation and support equitable distribution of the benefits of lung cancer screening. Conclusion The present commentary utilizes information about historical trends in tobacco use to highlight opportunities for targeted outreach efforts to engage communities at high risk with information about the lung cancer screening opportunity. Future efforts toward equitable implementation of lung cancer screening should focus on multi-level implementation strategies that engage and work in concert with community partners to co-create approaches that leverage strengths and reduce barriers within specific communities to achieve the potential of lung cancer screening.

Correction to: Determining puma habitat suitability in the Eastern USA - Veronica Yovovich; Nathaniel Robinson; Hugh Robinson; Michael J. Manfredo; Shelby Perry; Jeremy T. Bruskotter; John A. Vucetich; Luis Aníbal Solórzano; Lydia A. Roe; Alison Lesure; Jamie Robertson; Tom Butler; et al. (Biodiversity and Conservation, 2023)

Determining puma habitat suitability in the Eastern USA - Veronica Yovovich; Nathaniel Robinson; Hugh Robinson; Michael J. Manfredo; Shelby Perry; Jeremy T. Bruskotter; John A. Vucetich; Luis Aníbal Solórzano; Lydia A. Roe; Alison Lesure; Jamie Robertson; Tom Butler; et al. (Biodiversity and Conservation, 2023)
Pumas ( Puma concolor ) were eliminated from most of the eastern USA a century ago. In the past couple of decades, their recovery in the West has increased puma dispersal into the Midwest, with some individuals even traveling to the East Coast. We combined published expert opinion data and a habitat suitability index in an analysis that identified 17 areas in the Upper Midwest, Ozarks, Appalachia, and New England that could potentially host puma populations in the future. Thirteen of these were larger than 10,000 km 2 and so likely to ensure a puma population’s long-term genetic health. Further, we quantified patch size, human density, livestock density, percent public land, and a sociocultural index reflecting wildlife values for comparing patches, as well as present a summary of current legislation relevant to puma management in the East. Our work may be useful in identifying suitable areas to restore pumas based not only on the quality of their biophysical habitat, but also on social values conducive to puma-human coexistence.

Towards the fully automated monitoring of ecological communities - Marc Besson; Jamie Alison; Kim Bjerge; Thomas E. Gorochowski; Toke T. Høye; Tommaso Jucker; Hjalte M. R. Mann; Christopher F. Clements (Ecology Letters, 2022)
High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high‐resolution, multidimensional and standardised data across complex ecologies.

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.

Structural characterization and decontamination of dental calculus for ancient starch research - María Soto; Jamie Inwood; Siobhán Clarke; Alison Crowther; Danielle Covelli; Julien Favreau; Makarius Itambu; Steve Larter; Patrick Lee; Marina Lozano; Jason Maley; Aloyce Mwambwiga; et al. (Archaeological and Anthropological Sciences, 2019)

Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales - Christel Prudhomme; Jamie Hannaford; Shaun Harrigan; David Boorman; Jeff Knight; Victoria Bell; Christopher Jackson; Cecilia Svensson; Simon Parry; Nuria Bachiller-Jareno; Helen Davies; Richard Davis; et al. (Hydrological Sciences Journal, 2017)

Electricity generation by Propionibacterium freudenreichii in a mediatorless microbial fuel cell - Alison Reiche; Jamie-lynn Sivell; Kathlyn M. Kirkwood (Biotechnology Letters, 2016)

Operationalizing resilience for adaptive coral reef management under global environmental change - Kenneth R.N. Anthony; Paul A. Marshall; Ameer Abdulla; Roger Beeden; Chris Bergh; Ryan Black; C. Mark Eakin; Edward T. Game; Margaret Gooch; Nicholas A.J. Graham; Alison Green; Scott F. Heron; et al. (Global Change Biology, 2015)
Cumulative pressures from global climate and ocean change combined with multiple regional and local‐scale stressors pose fundamental challenges to coral reef managers worldwide. Understanding how cumulative stressors affect coral reef vulnerability is critical for successful reef conservation now and in the future. In this review, we present the case that strategically managing for increased ecological resilience (capacity for stress resistance and recovery) can reduce coral reef vulnerability (risk of net decline) up to a point. Specifically, we propose an operational framework for identifying effective management levers to enhance resilience and support management decisions that reduce reef vulnerability. Building on a system understanding of biological and ecological processes that drive resilience of coral reefs in different environmental and socio‐economic settings, we present an Adaptive Resilience‐Based management ( ARBM ) framework and suggest a set of guidelines for how and where resilience can be enhanced via management interventions. We argue that press‐type stressors (pollution, sedimentation, overfishing, ocean warming and acidification) are key threats to coral reef resilience by affecting processes underpinning resistance and recovery, while pulse‐type (acute) stressors (e.g. storms, bleaching events, crown‐of‐thorns starfish outbreaks) increase the demand for resilience. We apply the framework to a set of example problems for Caribbean and Indo‐Pacific reefs. A combined strategy of active risk reduction and resilience support is needed, informed by key management objectives, knowledge of reef ecosystem processes and consideration of environmental and social drivers. As climate change and ocean acidification erode the resilience and increase the vulnerability of coral reefs globally, successful adaptive management of coral reefs will become increasingly difficult. Given limited resources, on‐the‐ground solutions are likely to focus increasingly on actions that support resilience at finer spatial scales, and that are tightly linked to ecosystem goods and services.

Antiretroviral drugs induce oxidative stress and neuronal damage in the central nervous system - Cagla Akay; Michael Cooper; Akinleye Odeleye; Brigid K. Jensen; Michael G. White; Fair Vassoler; Patrick J. Gannon; Joseph Mankowski; Jamie L. Dorsey; Alison M. Buch; Stephanie A. Cross; Denise R. Cook; et al. (Journal of NeuroVirology, 2014)
HIV-associated neurocognitive disorder (HAND), characterized by a wide spectrum of behavioral, cognitive, and motor dysfunctions, continues to affect approximately 50 % of HIV(+) patients despite the success of combination antiretroviral drug therapy (cART) in the periphery. Of note, potential toxicity of antiretroviral drugs in the central nervous system (CNS) remains remarkably underexplored and may contribute to the persistence of HAND in the cART era. Previous studies have shown antiretrovirals (ARVs) to be neurotoxic in the peripheral nervous system in vivo and in peripheral neurons in vitro. Alterations in lipid and protein metabolism, mitochondrial damage, and oxidative stress all play a role in peripheral ARV neurotoxicity. We hypothesized that ARVs also induce cellular stresses in the CNS, ultimately leading to neuronal damage and contributing to the changing clinical and pathological picture seen in HIV-positive patients in the cART era. In this report, we show that ARVs are neurotoxic in the CNS in both pigtail macaques and rats in vivo. Furthermore, in vitro, ARVs lead to accumulation of reactive oxygen species (ROS), and ultimately induction of neuronal damage and death. Whereas ARVs alone caused some activation of the endogenous antioxidant response in vitro, augmentation of this response by a fumaric acid ester, monomethyl fumarate (MMF), blocked ARV-induced ROS generation, and neuronal damage/death. These findings implicate oxidative stress as a contributor to the underlying mechanisms of ARV-induced neurotoxicity and will provide an access point for adjunctive therapies to complement ARV therapy and reduce neurotoxicity in this patient population.