ELEVATE-LNP: Empty lipid nanoparticle enabled validation and AI-guided therapeutic evaluation for efficient formulation optimization for mRNA delivery - Mingzhi Yu; Luhan Wang; Timothy Elliott; Xianqing Wang; Allen Mathew; Bei Qiu; Liang Yao; Wenxin Wang; Nan Zhang (Chemical Engineering Journal, 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

Lengthening vegetation carbon turnover time across China’s forests - Rong Ge; Xiaoli Ren; Honglin He; Guirui Yu; Li Zhang; Qian Xu; Mengyu Zhang; Shilong Piao; Kailiang Yu; Mathew Williams; Xiaobo Zhu; Shiyong Yu; et al. (Science Bulletin, 2026)

Impact of temperature-stress synergistic effects on coal structural alignment - Song Yu; Geng Guorui; Feng Guangjun; Gao Jie; Veerle Vandeginste; Jonathan P. Mathews (Fuel, 2026)

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.

The Toxicology Investigators Consortium 2024 Annual Report - Kim Aldy; Shao Li; Mari Costantini; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; Michael Abesamis; Faiz Ahmed; Salman Ahsan; Peter Akpunonu; Adam Algren; et al. (Journal of Medical Toxicology, 2026)

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.

Molecular simulation of the potential effects of oxygen functionalities on the adsorption and diffusion of methane in kerogen - Guanwen Lu; Caineng Zou; Jonathan P. Mathews; Guosheng Zhang; Songqi Pan; Hanlin Liu; Yu Song; Yu Liu; Ganlin Hua; Zhengfu Zhao; Ming Yuan; Zhenhua Jing (Fuel, 2025)

Toward an integrated pantropical ocean observing system - Gregory R. Foltz; Yassir A. Eddebbar; Janet Sprintall; Antonietta Capotondi; Sophie Cravatte; Peter Brandt; Adrienne J. Sutton; Tamaryn Morris; Juliet Hermes; Clive R. McMahon; Michael J. McPhaden; Lev B. Looney; et al. (Frontiers in Marine Science, 2025)
Global climate is regulated by the ocean, which stores, releases, and transports large amounts of mass, heat, carbon, and oxygen. Understanding, monitoring, and predicting the exchanges of these quantities across the ocean’s surface, their interactions with the atmosphere, and their horizontal and vertical pathways through the global oceans, are key for advancing fundamental knowledge and improving forecasts and longer-term projections of climate, weather, and ocean ecosystems. The existing global observing system provides immense value for science and society in this regard by supplying the data essential for these advancements. The tropical ocean observing system in particular has been developed over decades, motivated in large part by the far-reaching and complex global impacts of tropical climate variability and change. However, changes in observing needs and priorities, new challenges associated with climate change, and advances in observing technologies demand periodic evaluations to ensure that stakeholders’ needs are met. Previous reviews and assessments of the tropical observing system have focused separately on individual basins and their associated observing needs. Here we provide a broader perspective covering the tropical observing system as a whole. Common gaps, needs, and recommendations are identified, and interbasin differences driven by socioeconomic disparities are discussed, building on the concept of an integrated pantropical observing system. Finally, recommendations for improved observations of tropical basin interactions, through oceanic and atmospheric pathways, are presented, emphasizing the benefits that can be achieved through closer interbasin coordination and international partnerships.

New Paleomagnetic results from Late Triassic limestone of the Eastern Qiangtang Terrane: Implications for the closure of the Paleo-Jinshajiang Ocean - Liang Yu; Maodu Yan; Mathew Domeier; Bingshuai Li; Miaomiao Shen; Chong Guan; Qiang Fu; Wanlong Xu; Zunbo Xu; Zhichao Niu; Dawen Zhang; Liye Yang (Palaeogeography, Palaeoclimatology, Palaeoecology, 2025)

Novel community data in ecology-properties and prospects - Florian Hartig; Nerea Abrego; Alex Bush; Jonathan M. Chase; Gurutzeta Guillera-Arroita; Mathew A. Leibold; Otso Ovaskainen; Loïc Pellissier; Maximilian Pichler; Giovanni Poggiato; Laura Pollock; Sara Si-Moussi; et al. (Trends in Ecology & Evolution, 2024)

Corrigendum to “Structural transformations for a subbituminous coal, impact of temperature on gold-tube pyrolysis chars evaluated using HRTEM” [Fuel 311 (2022) 122581] - Wu Li; Yu Song; Wen-bin Yang; Jonathan P. Mathews (Fuel, 2024)

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change - Enqing Hou; Shuang Ma; Yuanyuan Huang; Yu Zhou; Hyung‐Sub Kim; Efrén López‐Blanco; Lifen Jiang; Jianyang Xia; Feng Tao; Christopher Williams; Mathew Williams; Daniel Ricciuto; et al. (Global Change Biology, 2023)
Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Number of COVID-19 cases required in a population to detect SARS-CoV-2 RNA in wastewater in the province of Alberta, Canada: Sensitivity assessment - Qiaozhi Li; Bonita E. Lee; Tiejun Gao; Yuanyuan Qiu; Erik Ellehoj; Jiaao Yu; Mathew Diggle; Graham Tipples; Rasha Maal-Bared; Deena Hinshaw; Christopher Sikora; Nicholas J. Ashbolt; et al. (Journal of Environmental Sciences, 2023)

A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer - Chang Gong; Ziliang Cheng; Yaping Yang; Jun Shen; Yingying Zhu; Li Ling; Wanyi Lin; Zhigang Yu; Zhihua Li; Weige Tan; Chushan Zheng; Wenbo Zheng; et al. (Science China Life Sciences, 2022)

Registered clinical trials investigating treatment of long COVID: a scoping review and recommendations for research - Felicia Ceban; Alexia Leber; Muhammad Youshay Jawad; Mathew Yu; Leanna M. W. Lui; Mehala Subramaniapillai; Joshua D. Di Vincenzo; Hartej Gill; Nelson B. Rodrigues; Bing Cao; Yena Lee; Kangguang Lin; et al. (Infectious Diseases, 2022)

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)

Point of Care Blood Gas and Electrolyte Analysis in Anesthetized Olive Baboons (Papio anubis) in a Field Setting - Maureen W. Kamau; James M. Hassell; Ellie L. Milnes; Lee-Ann C. Hayek; Mathew M. Mutinda; Roi Harel; Akiko Matsumoto-Oda; Jennifer H. Yu; Dawn Zimmerman; Margaret Crofoot; Suzan Murray (International Journal of Primatology, 2021)

Quantifying particulate matter accumulated on leaves by 17 species of urban trees in Beijing, China - Yansen Xu; Wen Xu; Li Mo; Mathew R. Heal; Xiaowu Xu; Xinxiao Yu (Environmental Science and Pollution Research, 2018)

Contributory presentations/posters - N. Manoj; V. R. Srinivas; A. Surolia; M. Vijayan; K. Suguna; R. Ravishankar; K. Suguna; A. Surolia; M. Vijayan; R. Schwarzenbacher; K. Zeth; Diederichs; et al. (Journal of Biosciences, 1999)

Abstracts of presentations on plant protection issues at the xth international congress of virology - E. Ramirez; A. Hernádez; E. Ham; B. L. Barrón; S. W. Ding; W. X. Li; B. J. Shi; R. H. Symons; Q. Li; K. H. Ryu; P. Palukaitis; I. B. Kaplan; et al. (Phytoparasitica, 1997)