A Pictorial Food and Physical Activity Behavior Checklist Demonstrates Reliability for Both Spanish and English Speakers - Mical Shilts; Jasmyn Aulakh; Christine Davidson; Christiana Drake; Daisy Beltran; Jennifer Joseph; Daniel Perales; Karina Diaz Rios (Journal of Nutrition Education and Behavior, 2026)

Road salt induced mobilization and accumulation of heavy metals in roadside bioretention in the field: the roles of season, plant uptake, media, and catchment properties - Bhabishya Khaniya; Christopher Szota; Tim D. Fletcher; Jennifer Drake (Chemosphere, 2026)

Lessons learned from the modeling of nature-based solutions for urban flood mitigation in Ottawa, Canada - Ali Zoghi; Ryan Cooke; Muhammad Naveed Khaliq; Jennifer Drake (Journal of Hydrology, 2026)

Comparison of short versus long treatment duration of ESBL pneumonia in critically ill patients: a retrospective cohort study - Daniela Wall; Ty Drake; Gabriel Patarroyo Aponte; Shaojie Zhang; Jennifer Cortes (Infectious Diseases, 2026)

A practical, low-cost floating flux chamber for measuring greenhouse gas emissions from small urban waterbodies - Bryn E. Reynolds; Cole J. C. Van De Ven; Evan Heyes; Jennifer Drake (Environmental Monitoring and Assessment, 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

Pleistocene geochemical signatures of the transgressive, overflow, and regressive phases of Lake Bonneville - B. Lee Drake; Jennifer DeGraffenried; Andrea Brunelle; Isaac Hart; Kaylee Barket Jones; Bruce Kaiser; Rachel Quist (Quaternary Science Reviews, 2026)

Compact bioretention cell for urban stormwater management: Assessment of hydrologic, hydraulic, and water quality performance via laboratory and SWMM modelling - Shaahin Nazarpour Tameh; Jennifer Drake; Anna Palla; Ilaria Gnecco (Environmental Modelling & Software, 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.

Road salt reduces plant cover in bioretention systems within road rights-of-way - Bhabishya Khaniya; Christopher Szota; Tim D. Fletcher; Jennifer Drake (Science of The Total Environment, 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.

Pavement wear generates microplastics in stormwater runoff - Kelsey Smyth; Shuyao Tan; Tim Van Seters; Vimy Henderson; Elodie Passeport; Jennifer Drake (Journal of Hazardous Materials, 2025)

Biochar and vegetation effects on discharge water quality from organic-substrate green roofs - Wenxi Liao; Virinder Sidhu; Melanie A. Sifton; Liat Margolis; Jennifer A.P. Drake; Sean C. Thomas (Science of The Total Environment, 2024)

The development of an eco-friendly cold mix asphalt using wastewater sludge ash - Hassan Al Nageim; Anmar Dulaimi; Shakir Al-Busaltan; Mustafa Amoori Kadhim; Aaraf Al-Khuzaie; Linda Seton; Jennifer Croft; Jonathan Drake (Journal of Environmental Management, 2023)

Year-round monitoring of chloride releases from three zero-exfiltration permeable pavements and an asphalt parking lot - Kirti Sehgal; Virinder Sidhu; Claire Oswald; Jennifer Drake (Journal of Environmental Management, 2023)

Greenhouse gases and green roofs: carbon dioxide and methane fluxes in relation to substrate characteristics - Md Abdul Halim; Juliana Vantellingen; Adam S. Gorgolewski; William K. Rose; Jennifer A. P. Drake; Liat Margolis; Sean C. Thomas (Urban Ecosystems, 2022)

A Full Evaporation Static Headspace Gas Chromatography Method with Nitrogen Phosphorous Detection for Ultrasensitive Analysis of Semi-volatile Nitrosamines in Pharmaceutical Products - Jinjian Zheng; Christine L. Kirkpatrick; Daniel Lee; Xinxin Han; Ana I. Martinez; Kimberly Gallagher; Rebecca K. Evans; Sanjay V. Mudur; Xihui Liang; Jennifer Drake; Leah A. Buhler; Mark D. Mowery (The AAPS Journal, 2022)
The recent detection of potent carcinogenic nitrosamine impurities in several human medicines has triggered product recalls and interrupted the supply of critical medications for hundreds of millions of patients, illuminating the need for increased testing of nitrosamines in pharmaceutical products. However, the development of analytical methods for nitrosamine detection is challenging due to high sensitivity requirements, complex matrices, and the large number and variety of samples requiring testing. Herein, we report an analytical method for the analysis of a common nitrosamine, N-nitrosodimethylamine (NDMA), in pharmaceutical products using full evaporation static headspace gas chromatography with nitrogen phosphorous detection (FE-SHSGC-NPD). This method is sensitive, specific, accurate, and precise and has the potential to serve as a universal method for testing all semi-volatile nitrosamines across different drug products. Through elimination of the detrimental headspace-liquid partition, a quantitation limit of 0.25 ppb is achieved for NDMA, a significant improvement upon traditional LC-MS methods. The extraction of nitrosamines directly from solid sample not only simplifies the sample preparation procedure but also enables the method to be used for different products as is or with minor modifications, as demonstrated by the analysis of NDMA in 10+ pharmaceutical products. The in situ nitrosation that is commonly observed in GC methods for nitrosamine analysis was completely inhibited by the addition of a small volume solvent containing pyrogallol, phosphoric acid, and isopropanol. Employing simple procedures and low-cost instrumentation, this method can be implemented in any analytical laboratory for routine nitrosamine analysis, ensuring patient safety and uninterrupted supply of critical medications.

COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data - Ben Bond‐Lamberty; Danielle S. Christianson; Avni Malhotra; Stephanie C. Pennington; Debjani Sihi; Amir AghaKouchak; Hassan Anjileli; M. Altaf Arain; Juan J. Armesto; Samaneh Ashraf; Mioko Ataka; Dennis Baldocchi; et al. (Global Change Biology, 2020)
Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO 2 flux, commonly though imprecisely termed soil respiration ( R S ), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency R S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R S , the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Determining the Sources of Variance in the Preparation of Analytical Standards for Chromatographic Analysis of a Lyophilized Peptide Drug Substance by Nested ANOVA Statistical Analysis - Paul L. Walsh; John Lena; Jennifer Drake; David Lavrich (AAPS PharmSciTech, 2020)

Automated analysis of temperature variance to determine inundation state of wetlands - Thomas L. Anderson; Jennifer L. Heemeyer; William E. Peterman; Michael J. Everson; Brittany H. Ousterhout; Dana L. Drake; Raymond D. Semlitsch (Wetlands Ecology and Management, 2015)

Early life sun exposure, vitamin D-related gene variants, and risk of non-Hodgkin lymphoma - Jennifer L. Kelly; Matthew T. Drake; Zachary S. Fredericksen; Yan W. Asmann; Mark Liebow; Tait D. Shanafelt; Andrew L. Feldman; Stephen M. Ansell; William R. Macon; Megan M. Herr; Alice H. Wang; Grzegorz S. Nowakowski; et al. (Cancer Causes & Control, 2012)