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

Inhibiting Amikacin Resistance in Multidrug-Resistant Bacteria with Cadmium and Pyrithione - Angel J. Magaña; David Ngo; Kenneth Burgos; Carolina Dominguez Maldonado; Omniya Abdelmaksoud; Jan Sklenicka; Tung Tran; Fernando Pasteran; Verónica Jimenez; María S. Ramirez; Marcelo E. Tolmasky (Current Microbiology, 2025)
The ongoing antibiotic resistance crisis is one of the most pressing public health challenges. Multidrug-resistant bacterial pathogens are reaching the point where some are becoming untreatable. Consequently, besides discovering novel antibiotics, alternative strategies must be explored to manage the problem. One approach is developing inhibitors that overcome resistance to antibiotics currently in use. Resistance to aminoglycosides such as amikacin is mainly due to aminoglycoside-modifying enzymes. Despite being refractory to most resistance enzymes, the semisynthetic amikacin is inactivated by aminoglycoside 6′- N -acetyltransferases type I [AAC(6′)-I], of which AAC(6′)-Ib is the most common in Gram-negative pathogens. The discovery that certain divalent and monovalent cations interfere with enzymatic acetylation catalyzed by AAC(6′)-Ib opens possibilities for developing formulations combining antibiotics with these cations to enhance efficacy. Addition of CdCl₂ to in vitro enzymatic assays inhibited transfer of an acetyl group to the 6′- N position of amikacin, kanamycin, and tobramycin. Hence, Cd 2 ⁺ is a potential adjuvant to aminoglycosides for treating AAC(6′)-Ib-mediated resistant infections. It was initially disappointing that, as with other divalent cations, CdCl₂ addition to cultures of bacteria harboring AAC(6′)-Ib did not reverse resistance. However, the inhibitory action of Cd 2 ⁺ became evident when combined with the ionophore pyrithione. The complex efficiently inhibited resistance in Acinetobacter baumann ii and Klebsiella pneumoniae harboring AAC(6′)-Ib. Furthermore, the combination inhibited amikacin resistance in carbapenem-resistant K. pneumoniae clinical isolates. These results add another cation to the arsenal of potential aminoglycoside adjuvants, which could be developed alone or in coordination complexes with ionophores to treat multidrug-resistant infections.

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.

Nanomaterials: A critical review of impact on food quality control and packaging - Yasmin Abdul Wahab; Lina A. Al-Ani; Ibrahim Khalil; Svenja Schmidt; Nam Nghiep Tran; Marc Escribà-Gelonch; Meng Wai Woo; Kenneth Davey; Sally Gras; Volker Hessel; Nurhidayatullaili Muhd Julkapli (Food Control, 2024)

Stratification of polyisocyanate in two-component waterborne polyurethane films - Yang Liu; Kenneth Tran; Kevin Ho; Tianjia Yang; Xiaochong Li; Sze Hang Wong; Margaret Zhang; Gilbert Walker; Frédéric Lucas; Mitchell A. Winnik (Chemical Engineering Journal, 2024)

The global abundance of tree palms - Robert Muscarella; Thaise Emilio; Oliver L. Phillips; Simon L. Lewis; Ferry Slik; William J. Baker; Thomas L. P. Couvreur; Wolf L. Eiserhardt; Jens‐Christian Svenning; Kofi Affum‐Baffoe; Shin‐Ichiro Aiba; Everton C. de Almeida; et al. (Global Ecology and Biogeography, 2020)
Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests.

Loss of vacuolar acidity results in iron-sulfur cluster defects and divergent homeostatic responses during aging in Saccharomyces cerevisiae - Kenneth L. Chen; Toby N. Ven; Matthew M. Crane; Matthew L. C. Brunner; Adrian K. Pun; Kathleen L. Helget; Katherine Brower; Dexter E. Chen; Ha Doan; Justin D. Dillard-Telm; Ellen Huynh; Yen-Chi Feng; et al. (GeroScience, 2020)

Cell Wall Composition and Bioenergy Potential of Rice Straw Tissues Are Influenced by Environment, Tissue Type, and Genotype - Paul Tanger; Miguel E. Vega-Sánchez; Margaret Fleming; Kim Tran; Seema Singh; James B. Abrahamson; Courtney E. Jahn; Nicholas Santoro; Elizabeth B. Naredo; Marietta Baraoidan; John M. C. Danku; David E. Salt; et al. (BioEnergy Research, 2015)

Poly(β-hydroxybutyrate) production in oilseed leukoplasts of Brassica napus - Kathryn L. Houmiel; Steven Slater; Debra Broyles; Laura Casagrande; Susan Colburn; Kathleen Gonzalez; Timothy A. Mitsky; Steven E. Reiser; Devang Shah; Nancy B. Taylor; Mintien Tran; Henry E. Valentin; et al. (Planta, 1999)