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.

Environmental identities of college students reveal potential conflicts and common ground for wildlife conservation - Richard Von Furstenberg; Lincoln R. Larson; M. Nils Peterson; Kangjae Jerry Lee; Victoria R. Vayer; kathryn stevenson; Stacy A.C. Nelson; Jeremy Bruskotter; Adam A. Ahlers; Christine Anhalt-Depies; Taniya Bethke; Chris Chizinski; et al. (Biological Conservation, 2025)

Network of networks: Time series clustering of AmeriFlux sites - David E Reed; Housen Chu; Brad G. Peter; Jiquan Chen; Michael Abraha; Brian Amiro; Ray G. Anderson; M. Altaf Arain; Paulo Arruda; Greg A. Barron-Gafford; Carl Bernacchi; Daniel P. Beverly; et al. (Agricultural and Forest Meteorology, 2025)

Seasonal variation of trifluoroacetic acid (TFA) in Antarctic ice cores analysed by direct injection non-suppressed ion chromatography-mass spectrometry (nsIC-MS) - Estrella Sanz Rodriguez; Mingxia Lai; Harrison Stevens; Meredith K. Nation; Christopher T. Plummer; Lenneke M. Jong; Jason L. Roberts; Andrew D. Moy; Andrew R. Bowie; Mark A. Curran; Brett Paull (Science of The Total Environment, 2025)

Biochar-enhanced bioreactors for agricultural nitrogen mitigation - Jason P. Andras; Rachel L. Rubin; William G. Rodriguez-Reillo; Casey D. Chatelain; Oleander Morrill; Kate A. Ballantine (Journal of Environmental Management, 2025)

Evaluating 3D-Printed Pre-separator Inserts in a Fast Screening Impactor for Physicochemical Characterization of Fine Particle Dose Release from Dry Powder Inhalers - Nathan A. Reed; Roee Dar; Nicholas Holtgrewe; Leo N. Y. Cao; Xiaofei Liu; Denise S. Conti; Bryan Newman; Elizabeth Bielski; Jason D. Rodriguez; Changning Guo (AAPS PharmSciTech, 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.

Deployment-invariant probability of detection characterization for aerial LiDAR methane detection - Michael J. Thorpe; Aaron Kreitinger; Dominic T. Altamura; Cameron D. Dudiak; Bradley M. Conrad; David R. Tyner; Matthew R. Johnson; Jason K. Brasseur; Peter A. Roos; William M. Kunkel; Asa Carre-Burritt; Jerry Abate; et al. (Remote Sensing of Environment, 2024)

Smoking, blood DNA methylation sites and lung cancer risk - Arce Domingo-Relloso; Roby Joehanes; Zulema Rodriguez-Hernandez; Lies Lahousse; Karin Haack; M. Daniele Fallin; Miguel Herreros-Martinez; Jason G. Umans; Lyle G. Best; Tianxiao Huan; Chunyu Liu; Jiantao Ma; et al. (Environmental Pollution, 2023)

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)

Development of In Vitro Dissolution Testing Methods to Simulate Fed Conditions for Immediate Release Solid Oral Dosage Forms - Timothy R. Lex; Jason D. Rodriguez; Lei Zhang; Wenlei Jiang; Zongming Gao (The AAPS Journal, 2022)

“Right diet for the right person”: a focus group study of nutritionist-dietitians’ perspectives on nutritional genomics and gene-based nutrition advice - Jacus S. Nacis; Marilou R. Galang; Jason Paolo H. Labrador; Milflor S. Gonzales; Aurora Maria Francesca D. Dablo; Diana Glades A. Domalanta-Ronquillo; Victor Franco J. Alfonso; Idelia G. Glorioso; Marietta P. Rodriguez (Journal of Community Genetics, 2022)
Advances in nutritional genomics are intended to revolutionize nutrition practice. A basic understanding of nutritional genomics among nutritionist-dietitians is critical for such advancements to occur. As a precedent to the development and integration of gene-based nutrition advice, this study aimed to assess hospital-based nutritionist-dietitians’ perceptions of nutritional genomics. A total of ten focus group discussions (FGDs) with sixty-one registered nutritionist-dietitians (RNDs) from hospitals in the National Capital Region (NCR), Philippines, were conducted from October to November 2019. Data were collected using a pretested semistructured discussion guide, and thematic analysis was subsequently performed. Diverging perceptions about nutritional genomics were noted among the FGD participants. Five themes emerged relating to the enablers and barriers of gene-based nutrition advice: training and capacity building, the extent of information to be disclosed, cost, ethical considerations, and government support. Themes related to the desired features of the gene-based nutrition advice included being consent-driven, cost-effective, technology-oriented, and guided by standards. The results of this study suggest that training and continued learning will equip RNDs to provide nutrition advice based on genetic information. However, other factors, such as cost and ethical considerations, are critical dimensions that need to be acknowledged and addressed before integrating gene-based advice into nutrition practice.

A Cautionary Tale: Quantitative LC-HRMS Analytical Procedures for the Analysis of N-Nitrosodimethylamine in Metformin - Jingyue Yang; Tim Andres Marzan; Wei Ye; Cynthia D. Sommers; Jason D. Rodriguez; David A. Keire (The AAPS Journal, 2020)
A private testing laboratory reported in a Citizen Petition (CP) to FDA that 16 of 38 metformin drug products they tested had N -nitrosodimethyl amine (NDMA) amounts above the allowable intake (AI) of 96 ng/day. Because the FDA had been monitoring drugs for nitrosamines, orthogonal analytical procedures had been developed, validated and applied to detect the following nitrosamines in metformin drug products (if present): (i) NDMA (with a dedicated method) or (ii) NDMA (with a second confirmatory method), N -nitroso-diethylamine (NDEA), N -ethyl- N -nitroso-2-propanamine (NEIPA), N -nitroso-diisopropylamine (NDIPA), N -nitroso-di-n-propylamine (NDPA), N -nitroso-methylphenylamine (NMPA), N -nitroso-di-n-butylamine (NDBA) and N -nitroso- N -methyl-4-aminobutyric acid (NMBA). In contrast to the private laboratory results, FDA testing on the same set of 38 samples with orthogonal procedures observed amounts over the AI in only 8 of the 38 products and generally observed lower values than reported by the private testing laboratory. As described here, the investigation into the cause of the discrepancy revealed that N,N -dimethylformamide (DMF) can interfere with NDMA measurements. The data showed that the use of sufficient mass accuracy in the data acquisition and appropriate mass tolerance setting in the data processing to assure the selectivity of mass spectrometry measurements of NDMA in the presence of co-eluting DMF was necessary to prevent overestimation of the level of NDMA in metformin drug products. Overall, care should be taken to assure the necessary specificity in analytical procedures for adequate assessment of the nitrosamine level in drug products that also contain DMF or other potential interfering substances.

An NMR Protocol for In Vitro Paclitaxel Release from an Albumin-Bound Nanoparticle Formulation - Min Sung Suh; Sharadrao M. Patil; Darby Kozak; Eric Pang; Stephanie Choi; Xiaohui Jiang; Jason D. Rodriguez; David A. Keire; Kang Chen (AAPS PharmSciTech, 2020)

An Expandable Mechanopharmaceutical Device (1): Measuring the Cargo Capacity of Macrophages in a Living Organism - Phillip Rzeczycki; Tehetina Woldemichael; Andrew Willmer; Mikhail D. Murashov; Jason Baik; Rahul Keswani; Gi Sang Yoon; Kathleen A. Stringer; Nair Rodriguez-Hornedo; Gus R. Rosania (Pharmaceutical Research, 2019)

Transcriptomic changes in Echinochloa colona in response to treatment with the herbicide imazamox - Alice A. Wright; Rajkumar Sasidharan; Liisa Koski; Marianela Rodriguez-Carres; Daniel G. Peterson; Vijay K. Nandula; Jeffery D. Ray; Jason A. Bond; David R. Shaw (Planta, 2018)