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.

Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults - Jujiao Kang; Liu Yang; Tianye Jia; Wei Zhang; Lin-Bo Wang; Yu-Jie Zhao; Jia You; Yue-Ting Deng; Yi-Jun Ge; Wei-Shi Liu; Yi Zhang; Yi-Lin Chen; et al. (Science Bulletin, 2025)

Fungal composition associated with host tree identity mediates nutrient addition effects on wood microbial respiration - Zhenhong Hu; Marcos Fernández‐Martínez; Qinsi He; Zhiyuan Xu; Lin Jiang; Guiyao Zhou; Ji Chen; Ming Nie; Qiang Yu; Hao Feng; Zhiqun Huang; Sean T. Michaletz (Ecology, 2024)
Fungi are key decomposers of deadwood, but the impact of anthropogenic changes in nutrients and temperature on fungal community and its consequences for wood microbial respiration are not well understood. Here, we examined how nitrogen and phosphorus additions (field experiment) and warming (laboratory experiment) together influence fungal composition and microbial respiration from decomposing wood of angiosperms and gymnosperms in a subtropical forest. Nutrient additions significantly increased wood microbial respiration via fungal composition, but effects varied with nutrient types and taxonomic groups. Specifically, phosphorus addition significantly increased wood microbial respiration (65%) through decreased acid phosphatase activity and increased abundance of fast‐decaying fungi (e.g., white rot), while nitrogen addition marginally increased it (30%). Phosphorus addition caused a greater increase in microbial respiration in gymnosperms than in angiosperms (83.3% vs. 46.9%), which was associated with an increase in Basidiomycota:Ascomycota operational taxonomic unit abundance in gymnosperms but a decrease in angiosperms. The temperature dependencies of microbial respiration were remarkably constant across nutrient levels, consistent with metabolic scaling theory hypotheses. This is because there was no significant interaction between temperature and wood phosphorus availability or fungal composition, or the interaction among the three factors. Our results highlight the key role of tree identity in regulating nutrient response of wood microbial respiration through controlling fungal composition. Given that the range of angiosperm species may expand under climate warming and forest management, our data suggest that expansion will decrease nutrient effects on forest carbon cycling in forests previously dominated by gymnosperm species.

Advancing co-production for transformative change by synthesizing guidance from case studies on the sustainable management and governance of natural resources - Fernanda A. van Maurik Matuk; Bas Verschuuren; Piero Morseletto; Torsten Krause; David Ludwig; Steven J. Cooke; Moacir Haverroth; Marieke Meesters; Thomas J.M. Mattijssen; Sabine Keßler; Tomaz R. Lanza; Esther Milberg; et al. (Environmental Science & Policy, 2023)

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales - Jens H. Kuhn; Scott Adkins; Bernard R. Agwanda; Rim Al Kubrusli; Sergey V. Alkhovsky; Gaya K. Amarasinghe; Tatjana Avšič-Županc; María A. Ayllón; Justin Bahl; Anne Balkema-Buschmann; Matthew J. Ballinger; Christopher F. Basler; et al. (Archives of Virology, 2021)

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales - Jens H. Kuhn; Scott Adkins; Bernard R. Agwanda; Rim Al Kubrusli; Sergey V. Alkhovsky; Gaya K. Amarasinghe; Tatjana Avšič-Županc; María A. Ayllón; Justin Bahl; Anne Balkema-Buschmann; Matthew J. Ballinger; Christopher F. Basler; et al. (Archives of Virology, 2021)

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.

Complex inter‐island colonization and peripatric founder speciation promote diversification of flightless Pachyrhynchus weevils in the Taiwan–Luzon volcanic belt - Hui‐Yun Tseng; Wen‐San Huang; Ming‐Luen Jeng; Reagan Joseph T. Villanueva; Olga M. Nuñeza; Chung‐Ping Lin (Journal of Biogeography, 2018)
Aim We investigated the spatial and temporal patterns of diversification among colourful and flightless weevils, the Pachyrhynchus orbifer complex, to test the stepping‐stone hypothesis of colonization across the Taiwan–Luzon volcanic belt. Location Southeast Asia. Methods The phylogeny of the P. orbifer complex was reconstructed from a multi‐locus data set of mitochondrial and nuclear genes using maximum likelihood in RAxML and Bayesian inference in MRBAYES. Likelihood‐based tests in CONSEL were used to evaluate alternative tree topologies. Divergence times were estimated in beast based on a range of mutation rates. Ancestral range and biogeographical history were reconstructed using Bayesian binary MCMC ( BBM ) methods in RASP and in BioGeoBEARS. Demographic histories were inferred using the extended Bayesian skyline plot ( EBSP ). Species boundaries were tested using BPP. Results The phylogeny of the P. orbifer complex indicated strong support for seven reciprocally monophyletic lineages grouped by current island boundaries (Camiguin, Fuga, Dalupiri, Calayan, Babuyan, Orchid and Yaeyama Islands), except for a sister Green + Itbayat lineage. Complex and stochastic colonization of P. orbifer was inferred to have involved both northward and southward directions with short‐ and long‐distance dispersal events, which are strongly inconsistent with the strict stepping‐stone hypothesis. Divergence time estimates for all extant island lineages ( c . 3 Myr) of the islands. The statistically delimited seven cryptic species imply that the diversity of Pachyrhynchus from small peripheral islands continues to be largely under‐estimated. Main conclusions The non‐linear, more complex spatial and temporal settings of the archipelago and stochastic dispersal were probable key factors shaping the colonization history of the P. orbifer complex. Speciation of the P. orbifer complex may have occurred only between islands, indicating that peripatric speciation through the founders of stochastic dispersals was the major evolutionary driver.

Taxonomy of the order Mononegavirales: update 2017 - Gaya K. Amarasinghe; Yīmíng Bào; Christopher F. Basler; Sina Bavari; Martin Beer; Nicolás Bejerman; Kim R. Blasdell; Alisa Bochnowski; Thomas Briese; Alexander Bukreyev; Charles H. Calisher; Kartik Chandran; et al. (Archives of Virology, 2017)

A multivalent vaccine for type 1 diabetes skews T cell subsets to Th2 phenotype in NOD mice - Ming S. Lin; Hubert M. Tse; Meghan M. Delmastro; Suzanne Bertera; Caterina T. Wong; Robert Lakomy; Jing He; Martha M. Sklavos; Gina M. Coudriet; Massimo Pietropaolo; Massimo M. Trucco; Jon D. Piganelli (Immunologic Research, 2011)

An engineering intervention resulting in improvement in lung function and change in urinary 8-hydroxydeoxyguanosine among foundry workers in Taiwan - Ming-Hsiu Lin; Saou-Hsing Liou; Ching-Wen Chang; I-Hsiao Huang; Paul T. Strickland; Ching-Huang Lai (International Archives of Occupational and Environmental Health, 2011)

Cytotoxic kurubasch aldehyde from Trichilia emetica - Maminata Traore; Lin Zhai; Ming Chen; Carl Erik Olsen; Nacoulma Odile; Guissou I. Pierre; Ouédrago J. Bosco; Guigemdé T. Robert; S. Brøgger Christensen (Natural Product Research, 2007)

Streptococcal pyrogenic exotoxin B causes mitochondria damage to polymorphonuclear cells preventing phagocytosis of Group A streptococcus - Chuan Chiang-Ni; Chih-Hung Wang; Pei-Jane Tsai; Woei-Jer Chuang; Yee-Shin Lin; Ming-T. Lin; Ching-Chuan Liu; Jiunn-Jong Wu (Medical Microbiology and Immunology, 2006)

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)