Cord-blood black carbon particle burden is associated with a C19MC small extracellular vesicle miRNA signature enriched for neurodevelopmental pathways - Houman Kahroba; Kenneth Vanbrabant; Marcel van Herwijnen; Rick Kamps; Matthew Walker; Dagmar Waiblinger; Marcel Ameloot; Michelle Plusquin; John Wright; Tim Nawrot; Theo M. de Kok; Julian Krauskopf (Environmental Research, 2026)

Relationship between positional obstructive sleep apnea and excessive daytime sleepiness in patients with atrial fibrillation - Xiaoyue Liu; Patrick Stafford; Kenneth Bilchick; Sula Mazimba; Jeongok Logan; Yeilim Cho; Paras Patel; McCall Walker; William Healy; Younghoon Kwon (Sleep and Biological Rhythms, 2026)

Cord-blood black carbon burden is associated with coordinated inflammatory and heme-metabolic transcriptional programs at birth in Bradford, United Kingdom - Houman Kahroba; Kenneth Vanbrabant; Marcel van Herwijnen; Rick Kamps; Dagmar Waiblinger; Matthew Walker; Marcel Ameloot; Maarten Roeffaers; John Wright; Tim Nawrot; Theo M. de Kok; Julian Krauskopf (Science of The Total Environment, 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.

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030 - Virginie Thierry; Hervé Claustre; Orens Pasqueron de Fommervault; Nathalie Zilberman; Kenneth S. Johnson; Brian A. King; Susan E. Wijffels; Udaya T. V. S. Bhaskar; Magdalena Alonso Balmaseda; Mathieu Belbeoch; Marine Bollard; Jacqueline Boutin; et al. (Frontiers in Marine Science, 2025)
The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

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.

The seasonal patterns of hydrographic and biogeochemical variables in the Ross Sea: A BGC-Argo analysis - Ruobing Cao; Walker O. Smith; Yisen Zhong; Stephen Riser; Kenneth S. Johnson; Lynne Talley (Deep Sea Research Part II: Topical Studies in Oceanography, 2025)

Gait Velocity Alterations in Essential Tremor: a Meta-Analysis - Kenneth Harrison; Brandon M. Peoples; Keven G. Santamaria Guzman; Emily J. Hunter; Harrison C. Walker; Jaimie A. Roper (The Cerebellum, 2024)
Essential tremor (ET) is a prevalent movement disorder that impairs gait function, including gait speed - a critical marker of mobility disability and adverse outcomes. This meta-analysis aimed to quantify differences in gait speed between individuals diagnosed with ET compared to people without a movement disorder diagnosis. Electronic databases were searched for studies comparing gait speed in ET patients and controls. Effect sizes were calculated using standardized mean differences (Hedges’ g) and pooled using a random-effects model. Eight studies (390 ET, 227 controls) were included. ET patients exhibited significantly slower gait speeds than controls. The effect size (Hedges’ g = -1.06, 95% CI -1.47 to -0.65, p < .001) indicates a large, clinically significant difference. Substantial study heterogeneity was observed (I 2 = 76.9%). These findings suggest that gait speed deficits are a significant feature of ET, potentially reflecting cerebellar dysfunction. This highlights the need for gait assessment and targeted interventions in ET management to reduce fall risk and improve quality of life. Understanding the moderating factors such as medication type and state, disorder severity, and age could provide significant benefits in the treatment and management of ET.

Stratification of polyisocyanate in two-component waterborne polyurethane films - Yang Liu; Kenneth Tran; Kevin Ho; Tianjia Yang; Xiaochong Li; Sze Hang Wong; Margaret Zhang; Gilbert Walker; Frédéric Lucas; Mitchell A. Winnik (Chemical Engineering Journal, 2024)

Fostering greater recognition of Caribbean traditional plant knowledge - Ina Vandebroek; Jason West; Kenneth Otero-Walker; Steve Maldonado Silvestrini (Trends in Ecology & Evolution, 2024)

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.

Neuropsychological Assessment and Screening in Heart Failure: a Meta-Analysis and Systematic Review - Eric J. Connors; Alexander O. Hauson; Brianna D. Barlet; Sharis Sarkissians; Nicholas P. Stelmach; Alyssa D. Walker; Natasha M. Nemanim; Kristina L. Greenwood; Nicholas J. Chesher; Scott C. Wollman; Kenneth E. Allen; Matthew G. Hall; et al. (Neuropsychology Review, 2021)

Using multiple natural tags provides evidence for extensive larval dispersal across space and through time in summer flounder - Jennifer A. Hoey; F. Joel Fodrie; Quentin A. Walker; Eric J. Hilton; G. Todd Kellison; Timothy E. Targett; J. Christopher Taylor; Kenneth W. Able; Malin L. Pinsky (Molecular Ecology, 2020)
Dispersal sets the fundamental scales of ecological and evolutionary dynamics and has important implications for population persistence. Patterns of marine dispersal remain poorly understood, partly because dispersal may vary through time and often homogenizes allele frequencies. However, combining multiple types of natural tags can provide more precise dispersal estimates, and biological collections can help to reconstruct dispersal patterns through time. We used single nucleotide polymorphism genotypes and otolith core microchemistry from archived collections of larval summer flounder ( Paralichthys dentatus , n = 411) captured between 1989 and 2012 at five locations along the US East coast to reconstruct dispersal patterns through time. Neither genotypes nor otolith microchemistry alone were sufficient to identify the source of larval fish. However, microchemistry identified clusters of larvae ( n = 3–33 larvae per cluster) that originated in the same location, and genetic assignment of clusters could be made with substantially more confidence. We found that most larvae probably originated near a biogeographical break (Cape Hatteras) and that larvae were transported in both directions across this break. Larval sources did not shift north through time, despite the northward shift of adult populations in recent decades. Our novel approach demonstrates that summer flounder dispersal is widespread throughout their range, on both intra‐ and intergenerational timescales, and may be a particularly important process for synchronizing population dynamics and maintaining genetic diversity during an era of rapid environmental change. Broadly, our results reveal the value of archived collections and of combining multiple natural tags to understand the magnitude and directionality of dispersal in species with extensive gene flow.

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Lightning fatality risk map of the contiguous United States - William P. Roeder; Benjamin H. Cummins; Kenneth L. Cummins; Ronald L. Holle; Walker S. Ashley (Natural Hazards, 2015)

Ecological mitigation of hillslope instability: ten key issues facing researchers and practitioners - Alexia Stokes; Grant B. Douglas; Thierry Fourcaud; Filippo Giadrossich; Clayton Gillies; Thomas Hubble; John H. Kim; Kenneth W. Loades; Zhun Mao; Ian R. McIvor; Slobodan B. Mickovski; Stephen Mitchell; et al. (Plant and Soil, 2014)

Cumulative geoecological effects of 62 years of infrastructure and climate change in ice‐rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska - Martha K. Raynolds; Donald A. Walker; Kenneth J. Ambrosius; Jerry Brown; Kaye R. Everett; Mikhail Kanevskiy; Gary P. Kofinas; Vladimir E. Romanovsky; Yuri Shur; Patrick J. Webber (Global Change Biology, 2014)
Many areas of the Arctic are simultaneously affected by rapid climate change and rapid industrial development. These areas are likely to increase in number and size as sea ice melts and abundant Arctic natural resources become more accessible. Documenting the changes that have already occurred is essential to inform management approaches to minimize the impacts of future activities. Here, we determine the cumulative geoecological effects of 62 years (1949–2011) of infrastructure‐ and climate‐related changes in the Prudhoe Bay Oilfield, the oldest and most extensive industrial complex in the Arctic, and an area with extensive ice‐rich permafrost that is extraordinarily sensitive to climate change. We demonstrate that thermokarst has recently affected broad areas of the entire region, and that a sudden increase in the area affected began shortly after 1990 corresponding to a rapid rise in regional summer air temperatures and related permafrost temperatures. We also present a conceptual model that describes how infrastructure‐related factors, including road dust and roadside flooding are contributing to more extensive thermokarst in areas adjacent to roads and gravel pads. We mapped the historical infrastructure changes for the Alaska North Slope oilfields for 10 dates from the initial oil discovery in 1968–2011. By 2010, over 34% of the intensively mapped area was affected by oil development. In addition, between 1990 and 2001, coincident with strong atmospheric warming during the 1990s, 19% of the remaining natural landscapes (excluding areas covered by infrastructure, lakes and river floodplains) exhibited expansion of thermokarst features resulting in more abundant small ponds, greater microrelief, more active lakeshore erosion and increased landscape and habitat heterogeneity. This transition to a new geoecological regime will have impacts to wildlife habitat, local residents and industry.

Recommendations for a national agenda to substantially reduce cervical cancer - Jennifer S. Smith; Noel T. Brewer; Debbie Saslow; Kenneth Alexander; Mildred R. Chernofsky; Richard Crosby; Libby Derting; Leah Devlin; Charles J. Dunton; Jeffrey Engle; Maria Fernandez; Mona Fouad; et al. (Cancer Causes & Control, 2013)

In Vivo Quantification of Active Decitabine-Triphosphate Metabolite: A Novel Pharmacoanalytical Endpoint for Optimization of Hypomethylating Therapy in Acute Myeloid Leukemia - Hongyan Wang; Ping Chen; Jiang Wang; Ramasamy Santhanam; Josephine Aimiuwu; U. V. Vijaya Saradhi; Zhongfa Liu; Sebastian Schwind; Alice Mims; John C. Byrd; Michael R. Grever; Miguel A. Villalona-Calero; et al. (The AAPS Journal, 2013)

Haplotype structure in Ashkenazi Jewish BRCA1 and BRCA2 mutation carriers - Kate M. Im; Tomas Kirchhoff; Xianshu Wang; Todd Green; Clement Y. Chow; Joseph Vijai; Joshua Korn; Mia M. Gaudet; Zachary Fredericksen; V. Shane Pankratz; Candace Guiducci; Andrew Crenshaw; et al. (Human Genetics, 2011)

Ligand-Binding Mass Spectrometry to Study Biotransformation of Fusion Protein Drugs and Guide Immunoassay Development: Strategic Approach and Application to Peptibodies Targeting the Thrombopoietin Receptor - Michael P. Hall; Colin Gegg; Kenneth Walker; Christopher Spahr; Robert Ortiz; Vimal Patel; Steven Yu; Liana Zhang; Hsieng Lu; Binodh DeSilva; Jean W. Lee (The AAPS Journal, 2010)

Primary care providers’ willingness to recommend BRCA1/2 testing to adolescents - Suzanne C. O’Neill; Beth N. Peshkin; George Luta; Anisha Abraham; Leslie R. Walker; Kenneth P. Tercyak (Familial Cancer, 2010)

Consumption, Investment, and Future Well‐Being: Reply to Daly et al. - KENNETH J. ARROW; GRETCHEN DAILY; PARTHA DASGUPTA; PAUL EHRLICH; LAWRENCE GOULDER; GEOFFREY HEAL; SIMON LEVIN; KARL‐GÖRAN MÄLER; STEPHEN SCHNEIDER; DAVID STARRETT; BRIAN WALKER (Conservation Biology, 2007)

Rapid Assessment of Primer Combinations and Recovery of AFLP™ Products using Ethidium Bromide Staining - David L. Scott; Michon D. Walker; Clarence W. Clarck; Channapatna S. Prakash; Kenneth L. Deahl (Plant Molecular Biology Reporter, 1998)

Blocky calcite cement from the Suwannee Terrane: Evidence for mixing of middle to high latitude meteoric and sea water - Kenneth J. Tobin; Kenneth R. Walker; Steven G. Goldberg (Carbonates and Evaporites, 1997)

The self-tuning controller: Comparison with human performance in the control of arterial pressure - Kenneth S. Stern; Howard J. Chizeck; Bruce K. Walker; P. S. Krishnaprasad; Paul J. Dauchot; Peter G. Katona (Annals of Biomedical Engineering, 1985)