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

SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

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.

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints - Kul Khand; Gabriel B. Senay; MacKenzie Friedrichs; Koong Yi; Joshua B. Fisher; Lixin Wang; Kosana Suvočarev; Arman Ahmadi; Housen Chu; Stephen Good; Kanishka Mallick; Justine Missik; et al. (Journal of Hydrology, 2025)

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.

SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Benjamin R. Goldstein; Christopher Pate; Paula Castiblanco; Maggie E. Abell; Jessie Adley; Briana Agenbroad; Adam A. Ahlers; Peter D. Alexander; et al. (Global Ecology and Biogeography, 2025)
Motivation SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

Towards sustainable water disinfection with peracetic acid in aquaculture: A review - Dibo Liu; David L. Straus; Lars‐Flemming Pedersen; Christopher Good; Carlo C. Lazado; Thomas Meinelt (Reviews in Aquaculture, 2024)
Peracetic acid (PAA) has a long history as an efficacious and eco‐friendly disinfectant. It was first synthesised in 1902, and since then a wide range of applications has been developed in various industries. Aquaculture is a more recent industry wherein the potential of PAA is significant. As the global demand for sustainable development increases, there has likewise been growing interest in using PAA in aquaculture as an alternative to less environmentally friendly practices. PAA has no carcinogenic risk to humans (unlike formalin), has negligible harmful by‐products (unlike chlorine‐based disinfectants) and with appropriate precautions, the risks of causing severe human health damage is easier to control than ozone. Fish show strong physiological recovery and adaptation to PAA, whereas susceptible life stages of pathogens are highly vulnerable, enabling a safe and efficacious disinfection of the entire culture water and not the flow‐restricted disinfection by such processes as ultraviolet radiation or ozone. The effective concentration of PAA against many fish pathogens is usually below 2 mg L −1 , which is tolerable for most fish, and it has very low environmental risk due to rapid degradation. However, such degradation and the hydrodynamics in production‐scale aquaculture systems complicate the practical use of PAA. In this review, we summarise key results of safe concentrations of PAA and its effectiveness specifically for fish farmers. We also outline major difficulties and possible solutions for practical uses of PAA. We intend to bring global attention to this compound and inspire future possibilities for its sustainable use as a water disinfectant in aquaculture.

An IQ Consortium Perspective on Connecting Dissolution Methods to In Vivo Performance: Analysis of an Industrial Database and Case Studies to Propose a Workflow - Aktham Aburub; Yuan Chen; John Chung; Ping Gao; David Good; Simone Hansmann; Michael Hawley; Tycho Heimbach; Martin Hingle; Filippos Kesisoglou; Rong Li; John Rose; et al. (The AAPS Journal, 2022)

Genesis of the low sulfide-high-grade PGE mineralization in the W Horizon, Coldwell Complex, Canada: quantitative modeling for PGE reef-style mineralization in syn-magmatic sills - Yonghua Cao; David Good; Robert Linnen; Iain Samson; Ryan Ruthart (Mineralium Deposita, 2021)

Cu isotope systematics of conduit-type Cu–PGE mineralization in the Eastern Gabbro, Coldwell Complex, Canada - Matthew J. Brzozowski; David J. Good; Changzhi Wu; Weiqiang Li (Mineralium Deposita, 2021)

Effects of fluid-induced oxidation on the composition of Fe–Ti oxides in the Eastern Gabbro, Coldwell Complex, Canada: implications for the application of Fe–Ti oxides to petrogenesis and mineral exploration - Matthew J. Brzozowski; Iain M. Samson; Joel E. Gagnon; Robert L. Linnen; David J. Good (Mineralium Deposita, 2021)

Left hemisphere damage produces deficits in predictive control of bilateral coordination - Jacob E. Schaffer; Candice Maenza; David C. Good; Andrzej Przybyla; Robert L. Sainburg (Experimental Brain Research, 2020)

Utilization of In Vitro, In Vivo and In Silico Tools to Evaluate the pH-Dependent Absorption of a BCS Class II Compound and Identify a pH-Effect Mitigating Strategy - Christoph Gesenberg; Neil R. Mathias; Yan Xu; John Crison; Ishani Savant; Amy Saari; David J. Good; Jeffrey N. Hemenway; Ajit S. Narang; Richard R. Schartman; Naiyu Zheng; Adela Buzescu; et al. (Pharmaceutical Research, 2019)

Igneous architecture and implications for diverse Cu-PGE mineralization styles in a conduit system: an example from the Area 41 Cu-PGE occurrence, Coldwell Complex, Canada - Yonghua Cao; Robert Linnen; David Good; Iain Samson; John McBride (Mineralium Deposita, 2019)

Elephants never forget, should art museums remember too? Historic ivory collections as ambassadors for conservation education - Caroline Good; Peter Tyrrell; Zhaomin Zhou; David W. Macdonald (Biodiversity and Conservation, 2019)

PBPK Absorption Modeling: Establishing the In Vitro–In Vivo Link—Industry Perspective - Cordula Stillhart; Xavier Pepin; Christophe Tistaert; David Good; An Van Den Bergh; Neil Parrott; Filippos Kesisoglou (The AAPS Journal, 2019)

Tundra Trait Team: A database of plant traits spanning the tundra biome - Anne D. Bjorkman; Isla H. Myers‐Smith; Sarah C. Elmendorf; Signe Normand; Haydn J. D. Thomas; Juha M. Alatalo; Heather Alexander; Alba Anadon‐Rosell; Sandra Angers‐Blondin; Yang Bai; Gaurav Baruah; Mariska te Beest; et al. (Global Ecology and Biogeography, 2018)
Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( www.try-db.org ) to be included in the next version release.

Inferring the source of evaporated waters using stable H and O isotopes - Gabriel J. Bowen; Annie Putman; J. Renée Brooks; David R. Bowling; Erik J. Oerter; Stephen P. Good (Oecologia, 2018)

Tree cover shows strong sensitivity to precipitation variability across the global tropics - Xiangtao Xu; David Medvigy; Anna T. Trugman; Kaiyu Guan; Stephen P. Good; Ignacio Rodriguez‐Iturbe (Global Ecology and Biogeography, 2018)
Aim Vegetation is sensitive to mean annual precipitation (MAP), but the sensitivity of vegetation to precipitation variability (PV) is less clear. Tropical ecosystems are likely to experience increased PV in the future. Here we assessed the importance, magnitude and mechanism of PV effects on tree cover in the context of covarying environmental drivers such as fire, temperature and soil properties. Location Tropical land. Time period 2000–2010. Major taxa studied Trees. Methods We compiled climate, soil and remotely‐sensed tree cover data over tropical land. We then comprehensively assessed the contribution of PV at different time‐scales to tropical tree cover variations and estimated the sensitivity of tree cover to PV changes by conducting rolling‐window regression and variance decomposition analyses. We further adopted a mechanistic modelling approach to test whether water competition between trees and grasses can explain the observed effect of PV. Results We find that PV contributes 33–56% to the total explained spatial variation (65–79%) in tree cover. The contribution of PV depends on MAP and is highest under intermediate MAP (500–1,500 mm). Tree cover generally increases with rainy day frequency and wet season length but shows mixed responses to inter‐annual PV. Based on the estimated sensitivity, tropical tree cover can decrease by 3–5% overall and by up to 20% in Amazonia under a 20% decrease in rainy days. Mechanistic modelling analysis reproduced the continental differences in tree cover along an MAP gradient. Main conclusions Under intermediate rainfall regimes (500–1,500 mm), PV can be a more important determinant of tropical tree cover than conventionally proposed drivers such as MAP and fire. The effect of PV likely results from the sensitivity of tree–grass competition to the temporal distribution of water resources. These results show that climate variability can strongly shape the biosphere.

Steric sea level variability (1993–2010) in an ensemble of ocean reanalyses and objective analyses - Andrea Storto; Simona Masina; Magdalena Balmaseda; Stéphanie Guinehut; Yan Xue; Tanguy Szekely; Ichiro Fukumori; Gael Forget; You-Soon Chang; Simon A. Good; Armin Köhl; Guillaume Vernieres; et al. (Climate Dynamics, 2017)

Chemical composition in wild ethiopian Arabica coffee accessions - Maria Brígida dos Santos Scholz; Cíntia Sorane Good Kitzberger; Natalia Ferrarezi Pagiatto; Luiz Filipe Protasio Pereira; Fabrice Davrieux; David Pot; Pierre Charmetant; Thierry Leroy (Euphytica, 2016)

Genome-wide association and linkage analyses localize a progressive retinal atrophy locus in Persian cats - Hasan Alhaddad; Barbara Gandolfi; Robert A. Grahn; Hyung-Chul Rah; Carlyn B. Peterson; David J. Maggs; Kathryn L. Good; Niels C. Pedersen; Leslie A. Lyons (Mammalian Genome, 2014)

Medical Mitigation Model: Quantifying the Benefits of the Public Health Response to a Chemical Terrorism Attack - Kevin Good; David Winkel; Michael VonNiederhausern; Brian Hawkins; Jessica Cox; Rachel Gooding; Mark Whitmire (Journal of Medical Toxicology, 2013)

Dynamic dominance varies with handedness: reduced interlimb asymmetries in left-handers - Andrzej Przybyla; David C. Good; Robert L. Sainburg (Experimental Brain Research, 2012)

OBIS-SEAMAP: The World Data Center for Marine Mammal, Sea Bird, and Sea Turtle Distributions - Patrick Halpin; Andrew Read; Ei Fujioka; Ben Best; Ben Donnelly; Lucie Hazen; Connie Kot; Kim Urian; Erin LaBrecque; Andrew Dimatteo; Jesse Cleary; Caroline Good; et al. (Oceanography, 2009)

Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate - Peter Good; Jason A. Lowe; David P. Rowell (Climate Dynamics, 2009)

Effects of fine sediment inputs from a logging road on stream insect communities:a large-scale experimental approach in a Canadian headwater stream - David P. Kreutzweiser; Scott S. Capell; Kevin P. Good (Aquatic Ecology, 2005)