Watershed Continuum Monitoring Approach: combining multiple water quality patterns along stream and river flowpaths to track sources, pathways, and processing of pollutants - Sujay S. Kaushal; Ashley E. Mon; Stanley B. Grant; Paul M. Mayer; Aaron J. Porter; Andrew J. Sekellick; Jason H. Chase; Shantanu V. Bhide; John D. Jastram; Tamara A. Newcomer-Johnson; Sydney A. Shelton; Alexis M. Yaculak; et al. (Ecological Engineering, 2026)

Long-term soil health and microbiome responses to sustainable management under drought stress in tropical coffee agroecosystems - Marina Scalioni Vilela; Lindsey C. Slaughter; Dalyse Toledo Castanheira; Flávio H. V. Medeiros; Márcio R. Nunes; Fernanda C. L. Medeiros; Bruno Montoani Silva; Alisson A. V. Campos; Mauro M. L. Faria; Leandro C. Paiva; Rayan C. Andrade; Milene A. F. Carvalho; et al. (Plant and Soil, 2026)
Background and aims Implementing sustainable crop management to improve soil health can bolster plant and soil resistance to drought stress. This study evaluated the long-term impact of sustainable soil management on soil health in tropical coffee agroecosystems, focusing on how these practices influenced plant yield and quality, as well as soil microbiome composition and function under drought treatment. Methods Plots were selected from a long-term experiment established in January 2016 in Lavras municipality, Minas Gerais State, Brazil, and treatments had been in place for 7 years by the end of the current study. Soil management treatments included: (i) annual coffee husk residue amendment within rows + permanent signal grass ( Urochloa decumbens ) cover between rows (CHUD), (ii) annual agricultural gypsum amendment within rows + permanent signal grass cover between rows (GUD) , and (iii) a control consisting of coffee monoculture with bare soil. Results Combining coffee husk + signal grass management improved chemical, physical, and biological soil health indicators compared to bare soil management. These practices were also associated with higher coffee yield and superior beverage quality. Soil fungal and bacterial communities responded to both management and water availability, and soil functional indicators assessed by enzyme activity showed greater resistance to the drought treatment under coffee husk + signal grass management . Conclusion Overall, this study indicates that combined residue amendment and soil cover may enhance long-term soil and crop responses in this tropical coffee agroecosystem, with evidence of greater resistance of soil functional indicators to the drought treatment used in the incubation assay.

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.

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series - Maria Dornelas; Laura H. Antão; Amanda E. Bates; Viviana Brambilla; Jonathan M. Chase; Cher F. Y. Chow; Ada Fontrodona‐Eslava; Anne E. Magurran; Inês S. Martins; Faye Moyes; Alban Sagouis; Samuel Adu‐Acheampong; et al. (Global Ecology and Biogeography, 2025)
Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

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 of Inspired Therapeutics Pediatric VAD: Computational Analysis and Characterization of VAD V3 - Landon H. Tompkins; Barry N. Gellman; Steven R. Prina; Gino F. Morello; Thomas Roussel; Jonathan A. Kopechek; Stuart J. Williams; Priscilla C. Petit; Mark S. Slaughter; Steven C. Koenig; Kurt A. Dasse (Cardiovascular Engineering and Technology, 2022)

Development of Inspired Therapeutics Pediatric VAD: Benchtop Evaluation of Impeller Performance and Torques for MagLev Motor Design - Landon H. Tompkins; Steven R. Prina; Barry N. Gellman; Gino F. Morello; Thomas Roussel; Jonathan A. Kopechek; Stuart J. Williams; Priscilla C. Petit; Mark S. Slaughter; Steven C. Koenig; Kurt A. Dasse (Cardiovascular Engineering and Technology, 2022)

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.

Spatial scale dependency issues in the application of the Modified Universal Soil Loss Equation (MUSLE) - D. Gwapedza; D. A. Hughes; A. R. Slaughter (Hydrological Sciences Journal, 2018)

Simulating Microbial Water Quality in Data-Scarce Catchments: an Update of the WQSAM Model to Simulate the Fate of Escherichia coli - A. R. Slaughter (Water Resources Management, 2017)

Continuous-Flow Left Ventricular Assist Device Support Improves Myocardial Supply:Demand in Chronic Heart Failure - Kevin G. Soucy; Carlo R. Bartoli; Dustin Phillips; Guruprasad A. Giridharan; Michael A. Sobieski; William B. Wead; Robert D. Dowling; Zhongjun J. Wu; Sumanth D. Prabhu; Mark S. Slaughter; Steven C. Koenig (Annals of Biomedical Engineering, 2017)

Feasibility of Subcutaneous ECG Leads for Synchronized Timing of a Counterpulsation Device - S. Warren; G. A. Giridharan; R. D. Dowling; P. A. Spence; L. Tompkins; Eric Gratz; L. C. Sherwood; M. A. Sobieski; C. R. Bartoli; M. S. Slaughter; Robert S. Keynton; S. C. Koenig (Cardiovascular Engineering and Technology, 2012)

The grapevine polygalacturonase-inhibiting protein (VvPGIP1) reduces Botrytis cinerea susceptibility in transgenic tobacco and differentially inhibits fungal polygalacturonases - Dirk A. Joubert; Ana R. Slaughter; Gabré Kemp; John V. W. Becker; Geja H. Krooshof; Carl Bergmann; Jacques Benen; Isak S. Pretorius; Melané A. Vivier (Transgenic Research, 2006)

High concordance of drug-induced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening - P. J. O’Brien; W. Irwin; D. Diaz; E. Howard-Cofield; C. M. Krejsa; M. R. Slaughter; B. Gao; N. Kaludercic; A. Angeline; P. Bernardi; P. Brain; C. Hougham (Archives of Toxicology, 2006)