Corrigendum to “Revisiting the modifiable areal unit problem in the era of exposome-wide association studies: Assessing the performance of the CDC/ATSDR social vulnerability index at privacy-protecting spatial scales” [Environ. Res. (2026) 124912] - Jonathan V. Lewis; Nathaniel MacNell; Ian D. Buller; Gary Larson; David Fargo; Charles P. Schmitt; Lawrence S. Kirschner; David M. Reif; Farida S. Akhtari; Cristina M. Justice; John S. House; Kyle P. Messier; et al. (Environmental Research, 2026)

Corrigendum to “Revisiting the modifiable areal unit problem in the era of exposome-wide association studies: Assessing the performance of the CDC/ATSDR social vulnerability index at privacy-protecting spatial scales” [Environ. Res. (2026) 124912] - Jonathan V. Lewis; Nathaniel MacNell; Ian D. Buller; Gary Larson; David Fargo; Charles P. Schmitt; Lawrence S. Kirschner; David M. Reif; Farida S. Akhtari; Cristina M. Justice; John S. House; Kyle P. Messier; et al. (Environmental Research, 2026)

Revisiting the modifiable areal unit problem in the era of exposome-wide association studies: Assessing the performance of the CDC/ATSDR social vulnerability index at privacy-protecting spatial scales - Jonathan V. Lewis; Nathaniel MacNell; Ian D. Buller; Gary Larson; David C. Fargo; Charles P. Schmitt; Lawrence S. Kirschner; David M. Reif; Farida S. Akhtari; Cristina M. Justice; John S. House; Kyle P. Messier; et al. (Environmental Research, 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.

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.

Development and in-vitro assessment of novel oxygen-releasing feed additives to reduce enteric ruminant methane emissions - Alison Graham; Camilla Thorn; Michael McDonagh; Caroline O'Donnell; Stephen Nolan; Stuart F. Kirwan; Sandra O'Connor; Corine O. Nzeteu; Alejandra C.V. Montoya; Andrew Bartle; Alison Hall; Cathy Abberton; et al. (Science of The Total Environment, 2025)

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom - Nahid Mohajeri; Alina Walch; Alison Smith; Agust Gudmundsson; Dan Assouline; Tom Russell; Jim Hall (Sustainable Cities and Society, 2023)

Performance on the balloon analogue risk task and anticipatory response inhibition task is associated with severity of impulse control behaviours in people with Parkinson’s disease - Alison Hall; Matthew Weightman; Ned Jenkinson; Hayley J. MacDonald (Experimental Brain Research, 2023)
Dopamine agonist medication is one of the largest risk factors for development of problematic impulse control behaviours (ICBs) in people with Parkinson’s disease. The present study investigated the potential of dopamine gene profiling and individual performance on impulse control tasks to explain ICB severity. Clinical, genetic and task performance data were entered into a mixed-effects linear regression model for people with Parkinson’s disease taking (n = 50) or not taking (n = 25) dopamine agonist medication. Severity of ICBs was captured via the Questionnaire for Impulsive-compulsive disorders in Parkinson’s disease Rating Scale. A cumulative dopamine genetic risk score (DGRS) was calculated for each participant from variance in five dopamine-regulating genes. Objective measures of impulsive action and impulsive choice were measured on the Anticipatory Response Inhibition Task and Balloon Analogue Risk Task, respectively. For participants on dopamine agonist medication, task performance reflecting greater impulsive choice (p = 0.014), and to a trend level greater impulsive action (p = 0.056), as well as a longer history of DA medication (p < 0.001) all predicted increased ICB severity. DGRS however, did not predict ICB severity (p = 0.708). No variables could explain ICB severity in the non-agonist group. Our task-derived measures of impulse control have the potential to predict ICB severity in people with Parkinson’s and warrant further investigation to determine whether they can be used to monitor ICB changes over time. The DGRS appears better suited to predicting the incidence, rather than severity, of ICBs on agonist medication.

Sustainable pathways towards climate and biodiversity goals in the UK: the importance of managing land-use synergies and trade-offs - Alison C. Smith; Paula A. Harrison; Nicholas J. Leach; H. Charles J. Godfray; Jim W. Hall; Sarah M. Jones; Sarah S. Gall; Michael Obersteiner (Sustainability Science, 2023)
Agricultural and environmental policies are being fundamentally reviewed and redesigned in the UK following its exit from the European Union. The UK government and the Devolved Administrations recognise that current land use is not sustainable and that there is now an unprecedented opportunity to define a better land strategy that responds fully to the interconnected challenges of climate change, biodiversity loss and sustainable development. This paper presents evidence from three pathways (current trends, sustainable medium ambition, and sustainable high ambition) to mid-century that were co-created with UK policymakers. The pathways were applied to a national integrated food and land-use model (the FABLE calculator) to explore potential synergies and trade-offs between achieving multiple sustainability targets under limited land availability and constraints to balance food supply and demand at national and global levels. Results show that under the Current Trends pathway all unprotected open natural land would be converted to urban, agriculture and afforested land, with the consequence that from 2030 onwards tree planting targets could not be met. In contrast, the two sustainable pathways illustrate how dietary change, agricultural productivity improvements and waste reduction can free up land for nature recovery and carbon sequestration. This enables a transition to a sustainable food and land-use system that provides a net carbon sink with up to 44% of land able to support biodiversity conservation. We highlight key trade-offs and synergies, which are important to consider for designing and implementing emerging national policies. These include the strong dependence of climate, food and biodiversity targets on dietary shifts, sustainable improvements in agricultural productivity, improved land-use design for protecting and restoring nature, and rapid reductions in food loss and waste.

How can diverse national food and land-use priorities be reconciled with global sustainability targets? Lessons from the FABLE initiative - Aline Mosnier; Guido Schmidt-Traub; Michael Obersteiner; Sarah Jones; Valeria Javalera-Rincon; Fabrice DeClerck; Marcus Thomson; Frank Sperling; Paula Harrison; Katya Pérez-Guzmán; Gordon Carlos McCord; Javier Navarro-Garcia; et al. (Sustainability Science, 2023)
There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here, we present a collaborative approach developed with the FABLE—Food, Agriculture, Biodiversity, Land, and Energy—Consortium to reconcile both global and national elements for developing national food and land-use system pathways. This approach includes three key features: (1) global targets, (2) country-driven multi-objective pathways, and (3) multiple iterations of pathway refinement informed by both national and international impacts. This approach strengthens policy coherence and highlights where greater national and international ambition is needed to achieve global goals (e.g., the SDGs). We discuss how this could be used to support future climate and biodiversity negotiations and what further developments would be needed.

Exploring stop signal reaction time over two sessions of the anticipatory response inhibition task - Alison Hall; Ned Jenkinson; Hayley J. MacDonald (Experimental Brain Research, 2022)
Various behavioural tasks measure response inhibition encompassing the ability to cancel unwanted actions, evaluated via stop signal reaction time (SSRT). It is unclear whether SSRT is an unchangeable inherent measure of inhibitory network integrity or whether it can improve with repetition. The current study explored if and how SSRT changed over two sessions for the Anticipatory Response Inhibition Task (ARIT), and how this compared with the Stop Signal Task (SST). Forty-four participants repeated the ARIT and SST over two sessions. SSRT and its constituent measures (Go trial reaction time, stop signal delay) were calculated. SSRT reflecting non-selective response inhibition was consistent between sessions in the ARIT and SST (both p > 0.293). Reaction time and stop signal delay also remained stable across sessions in the ARIT (all p > 0.063), whereas in the SST, reaction time ( p = 0.013) and stop signal delay ( p = 0.009) increased. SSRT reflecting behaviourally selective stopping on the ARIT improved ( p < 0.001) over two sessions, which was underpinned by changes to reaction time ( p < 0.001) and stop signal delay ( p < 0.001). Overall, the maximal efficiency of non-selective inhibition remained stable across two sessions in the ARIT. Results of the SST confirmed that non-selective inhibition can, however, be affected by more than inhibitory network integrity. Behaviourally selective stopping on the ARIT changed across sessions, suggesting the sequential neural process captured by the SSRT occurred more quickly in session two. These findings have implications for future studies that necessitate behavioural measures over multiple sessions.

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.

Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus - Pik Fang Kho; Sally Mortlock; Frederic Amant; Daniela Annibali; Katie Ashton; John Attia; Paul L. Auer; Matthias W. Beckmann; Amanda Black; Louise Brinton; Daniel D. Buchanan; Stephen J. Chanock; et al. (Human Genetics, 2021)

In vivo Validation of Bimolecular Fluorescence Complementation (BiFC) to Investigate Aggregate Formation in Amyotrophic Lateral Sclerosis (ALS) - Emily K. Don; Alina Maschirow; Rowan A. W. Radford; Natalie M. Scherer; Andrés Vidal-Itriago; Alison Hogan; Cindy Maurel; Isabel Formella; Jack J. Stoddart; Thomas E. Hall; Albert Lee; Bingyang Shi; et al. (Molecular Neurobiology, 2021)
Amyotrophic lateral sclerosis (ALS) is a form of motor neuron disease (MND) that is characterized by the progressive loss of motor neurons within the spinal cord, brainstem, and motor cortex. Although ALS clinically manifests as a heterogeneous disease, with varying disease onset and survival, a unifying feature is the presence of ubiquitinated cytoplasmic protein inclusion aggregates containing TDP-43. However, the precise mechanisms linking protein inclusions and aggregation to neuronal loss are currently poorly understood. Bimolecular fluorescence complementation (BiFC) takes advantage of the association of fluorophore fragments (non-fluorescent on their own) that are attached to an aggregation-prone protein of interest. Interaction of the proteins of interest allows for the fluorescent reporter protein to fold into its native state and emit a fluorescent signal. Here, we combined the power of BiFC with the advantages of the zebrafish system to validate, optimize, and visualize the formation of ALS-linked aggregates in real time in a vertebrate model. We further provide in vivo validation of the selectivity of this technique and demonstrate reduced spontaneous self-assembly of the non-fluorescent fragments in vivo by introducing a fluorophore mutation. Additionally, we report preliminary findings on the dynamic aggregation of the ALS-linked hallmark proteins Fus and TDP-43 in their corresponding nuclear and cytoplasmic compartments using BiFC. Overall, our data demonstrates the suitability of this BiFC approach to study and characterize ALS-linked aggregate formation in vivo. Importantly, the same principle can be applied in the context of other neurodegenerative diseases and has therefore critical implications to advance our understanding of pathologies that underlie aberrant protein aggregation.

Risk stratification, genomic data and the law - Alison Hall; Thomas Finnegan; Susmita Chowdhury; Tom Dent; Mark Kroese; Hilary Burton (Journal of Community Genetics, 2018)

Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European ancestry - Zhiguo Zhao; Wanqing Wen; Kyriaki Michailidou; Manjeet K. Bolla; Qin Wang; Ben Zhang; Jirong Long; Xiao-Ou Shu; Marjanka K. Schmidt; Roger L. Milne; Montserrat García-Closas; Jenny Chang-Claude; et al. (Cancer Causes & Control, 2016)

Genetic variation in the immunosuppression pathway genes and breast cancer susceptibility: a pooled analysis of 42,510 cases and 40,577 controls from the Breast Cancer Association Consortium - Jieping Lei; Anja Rudolph; Kirsten B. Moysich; Sabine Behrens; Ellen L. Goode; Manjeet K. Bolla; Joe Dennis; Alison M. Dunning; Douglas F. Easton; Qin Wang; Javier Benitez; John L. Hopper; et al. (Human Genetics, 2016)

Candidate locus analysis of the TERT–CLPTM1L cancer risk region on chromosome 5p15 identifies multiple independent variants associated with endometrial cancer risk - Luis G. Carvajal-Carmona; Tracy A. O’Mara; Jodie N. Painter; Felicity A. Lose; Joe Dennis; Kyriaki Michailidou; Jonathan P. Tyrer; Shahana Ahmed; Kaltin Ferguson; Catherine S. Healey; Karen Pooley; Jonathan Beesley; et al. (Human Genetics, 2015)

Consistent proportional macronutrient intake selected by adult domestic cats (Felis catus) despite variations in macronutrient and moisture content of foods offered - Adrian K. Hewson-Hughes; Victoria L. Hewson-Hughes; Alison Colyer; Andrew T. Miller; Simon R. Hall; David Raubenheimer; Stephen J. Simpson (Journal of Comparative Physiology B, 2013)

A Transient Decrease in Spleen Size Following Stroke Corresponds to Splenocyte Release into Systemic Circulation - Hilary A. Seifert; Aaron A. Hall; Cortney B. Chapman; Lisa A. Collier; Alison E. Willing; Keith R. Pennypacker (Journal of Neuroimmune Pharmacology, 2012)

The spleen contributes to stroke induced neurodegeneration through interferon gamma signaling - Hilary A. Seifert; Christopher C. Leonardo; Aaron A. Hall; Derrick D. Rowe; Lisa A. Collier; Stanley A. Benkovic; Alison E. Willing; Keith R. Pennypacker (Metabolic Brain Disease, 2012)

Administration of a Sigma Receptor Agonist Delays MCAO-Induced Neurodegeneration and White Matter Injury - Christopher C. Leonardo; Aaron A. Hall; Lisa A. Collier; Suzanne M. Green; Alison E. Willing; Keith R. Pennypacker (Translational Stroke Research, 2010)

Influence Diagrams for Representing Uncertainty in Climate-Related Propositions - Jim Hall; Craig Twyman; Alison Kay (Climatic Change, 2005)