The rate of photosynthetic induction across different light intensities can be approximated using the light response curve of photosynthesis - Elias Kaiser; Ningyi Zhang; Sarah Berman; Mehrdad Behzadian; Silvere Vialet-Chabrand; Leo F. M. Marcelis (Photosynthesis Research, 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

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.

What makes a cyanobacterial bloom disappear? A review of the abiotic and biotic cyanobacterial bloom loss factors - Ted D. Harris; Kaitlin L. Reinl; Marzi Azarderakhsh; Stella A. Berger; Manuel Castro Berman; Mina Bizic; Ruchi Bhattacharya; Sarah H. Burnet; Jacob A. Cianci-Gaskill; Lisette N. de Senerpont Domis; Inge Elfferich; K. Ali Ger; et al. (Harmful Algae, 2024)

Clinical and imaging modality factors impacting radiological interpretation of breast screening in young women with neurofibromatosis type 1 - Mathilda Wilding; Jane Fleming; Katrina Moore; Ashley Crook; Ranjani Reddy; Sarah Choi; Timothy E. Schlub; Michael Field; Lavvina Thiyagarajan; Jeff Thompson; Yemima Berman (Familial Cancer, 2023)

Meteorological influences on airborne pollen and spores in Johannesburg (Gauteng), South Africa - Linus B. Ajikah; Sarah J. Roffe; Frank H. Neumann; Marion K. Bamford; Nanike Esterhuizen; Dilys Berman; Jonny Peter (Aerobiologia, 2023)
Airborne fungal spores and pollen (aerospora), synergistic with air pollution, are key triggers of allergic respiratory diseases. Effective diagnosis and treatment requires up-to-date location-specific knowledge on the temporal variability of aerospora types and levels. Johannesburg is the largest city in South Africa and has grown substantially in three decades, with changes in ground cover, population density and air pollution, yet until now, no continuous aerospora sampling has occurred. We present a daily two-year (August 2019–July 2021) aerospora assemblage for Johannesburg and explore temporal characteristics of 13 dominant aerospora in relation to daily meteorological variables (pressure, rainfall, relative humidity, temperature and wind characteristics). February–July, July–September and January-July represent high-risk periods for fungal spores [( Alternaria alternata (Fries. ex Keissler), Ascospores, Aspergillus niger (Van Tieghem) , Penicillium chrysogenum (Thom), Cladosporium graminum (Corda), Epicoccum nigrum (Link), Helminthosporium solani (Durieu and Montagne) Nigrospora sphaerica (Saccardo ex. Mason), Smuts Ustilago nuda ( Jensen ex. Rostrup) and Torula herbarum (Link)], trees ( Cupressus , Morus and Platanus ) and grass (Poaceae), respectively. Using a generalised additive model, results show that daily meteorological characteristics explained 7–32% of daily aerospora variability, with the largest effect on tree pollen. Rainfall, relative humidity and temperature influenced daily fungal spore and Poaceae counts, with moderate/low rainfall (

Comparison of Two Nutrition Curricula in Promoting Nutrition Behavior Change in Teens Participating in UGA EFNEP - Sydnee Berman; Tiffany Williams; Alison Berg; Tracey Brigman; Sami Yli-Piipari; Sarah T Henes (Journal of Nutrition Education and Behavior, 2023)

Feasibility of Adding Weekly Goal Setting Activities to an Adolescent Nutrition Curriculum for UGA EFNEP Youth Programming - Sydnee Berman; La Keshia Levi; Tiffany Williams; Laura Tucker; Alison Berg; Tracey Brigman; Sami Yli-Piipari; Sarah T. Henes (Journal of Nutrition Education and Behavior, 2023)

The psychological impact and experience of breast cancer screening in young women with an increased risk of breast cancer due to neurofibromatosis type 1 - Ashley Crook; Rebekah Kwa; Sarah Ephraums; Mathilda Wilding; Lavvina Thiyagarajan; Jane Fleming; Katrina Moore; Yemima Berman (Familial Cancer, 2022)

Effects of methamphetamine on neural responses to visual stimuli - Kathryne Van Hedger; Sarah K. Keedy; Kathryn E. Schertz; Marc G. Berman; Harriet de Wit (Psychopharmacology, 2019)