Serpentinization in Jezero crater: Comparing SuperCam Mars data to terrestrial serpentinites - J.M. Comellas; S.K. Sharma; P.J. Gasda; A. Cousin; A. Essunfeld; P. Fryer; L.E. Mayhew; L. Sun; A.J. Brown; T.E. Acosta-Maeda; E. Dehouck; M. Veneranda; et al. (Icarus, 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.

Ultramafic float rocks at Jezero crater (Mars): excavation of lower crustal rocks or mantle peridotites by impact cratering? - O. Beyssac; E. Clavé; O. Forni; A. Udry; A.C. Pascuzzo; E. Dehouck; P. Beck; L. Mandon; C. Quantin-Nataf; N. Mangold; G. Lopez-Reyes; C. Royer; et al. (Earth and Planetary Science Letters, 2026)

Database development and LIBS calibration for the LIBS-Raman Sensor for planetary exploration - E.R. Sawyers; G. Lopez-Reyes; A. Barlow; M. Veneranda; E.A. Lymer; E.A. Cloutis; J. Manrique; B. Barrios; S. Julve; J. Freemantle; M. Aznar; M.G. Daly; et al. (Icarus, 2025)

The importance of tropical tree-ring chronologies for global change research - Peter Groenendijk; Flurin Babst; Valerie Trouet; Ze-Xin Fan; Daniela Granato-Souza; Giuliano Maselli Locosselli; Mulugeta Mokria; Shankar Panthi; Nathsuda Pumijumnong; Abrham Abiyu; Rodolfo Acuña-Soto; Eduardo Adenesky-Filho; et al. (Quaternary Science Reviews, 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.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Tropical forest succession increases tree taxonomic and functional richness but decreases evenness - Masha T. van der Sande; Lourens Poorter; Géraldine Derroire; Mario Marcos do Espirito Santo; Madelon Lohbeck; Sandra C. Müller; Radika Bhaskar; Michiel van Breugel; Juan Manuel Dupuy‐Rada; Sandra M. Durán; Catarina C. Jakovac; Horacio Paz; et al. (Global Ecology and Biogeography, 2024)
Aim Successional changes in functional diversity provide insights into community assembly by indicating how species are filtered into local communities based on their traits. Here, we assess successional changes in taxonomic and functional richness, evenness and redundancy along gradients of climate, soil pH and forest cover. Location Neotropics. Time period Last 0–100 years. Major taxa studied Trees. Methods We used 22 forest chronosequence studies and 676 plots across the Neotropics to analyse successional changes in Hill's taxonomic and functional diversity of trees, and how these successional changes vary with continental‐scale gradients in precipitation, soil pH and surrounding forest cover. Results Taxonomic and functional richness and functional redundancy increased, while taxonomic and functional evenness decreased over time. Functional richness and evenness changed strongly when not accounting for taxonomic richness, but changed more weakly after statistically accounting for taxonomic richness, indicating that changes in functional diversity are largely driven by taxonomic richness. Nevertheless, the successional increases in functional richness when correcting for taxonomic richness may indicate that environmental heterogeneity and limiting similarity increase during succession. The taxonomically‐independent successional decreases in functional evenness may indicate that stronger filtering and competition select for dominant species with similar trait values, while many rare species and traits are added to the community. Such filtering and competition may also lead to increased functional redundancy. The changes in taxonomically‐independent functional diversity varied with resource availability and were stronger in harsh, resource‐poor environments, but weak in benign, productive environments. Hence, in resource‐poor environments, environmental filtering and facilitation are important, whereas in productive environments, weaker abiotic filtering allows for high initial functional diversity and weak successional changes. Main conclusion We found that taxonomic and functional richness and functional redundancy increased and taxonomic and functional evenness decreased during succession, mainly caused by the increasing number of rare species and traits due to the arrival of new species and due to changing (a)biotic filters.

Raman-IR spectroscopic, and XRD analysis of selected samples from Fogo Island, Cabo Verde: Implications for ancient Martian volcanology - M. Aznar; A. Sanz-Arranz; S. Shkolyar; E.R. Sawyers; M. Konstantinidis; G. Lopez-Reyes; M. Veneranda; E.A. Lymer; J.R. Freemantle; M.G. Daly; E.A. Cloutis; F. Rull-Perez; et al. (Advances in Space Research, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge - Santiago Lopez-Restrepo; Elias D. Nino-Ruiz; Luis G. Guzman-Reyes; Andres Yarce; O. L. Quintero; Nicolas Pinel; Arjo Segers; A. W. Heemink (Computational Geosciences, 2021)
In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and that based on our prior knowledge about the dynamical system of interest. We perform the combination of both sources of information via optimal shrinkage factors. The method exploits the rank-deficiency of ensemble covariance matrices to provide an efficient and practical implementation of the analysis step in EnKF based formulations. Localization and inflation aspects are discussed, as well. Experimental tests are performed to assess the accuracy of our proposed filter implementation by employing an Advection Diffusion Model and an Atmospheric General Circulation Model. The experimental results reveal that the use of our proposed filter implementation can mitigate the impact of sampling noise, and even more, it can avoid the impact of spurious correlations during assimilation steps.

Synergistic antidepressant-like effect of capsaicin and citalopram reduces the side effects of citalopram on anxiety and working memory in rats - Irving S. Aguilar-Martinez; Miriam E. Reyes-Mendez; J. Manuel Herrera-Zamora; Fernando Osuna-Lopez; Adolfo Virgen-Ortiz; Nestor Mendoza-Munoz; Jose L. Gongora-Alfaro; Eloy G. Moreno-Galindo; Javier Alamilla (Psychopharmacology, 2020)

Efficacy of yeast antagonists used individually or in combination with hot water dipping for control of postharvest brown rot of peaches - D. Zhang; J. G. Lopez-Reyes; D. Spadaro; A. Garibaldi; M. L. Gullino (Journal of Plant Diseases and Protection, 2010)

Bactericidal effect of bovine lactoferrin, LFcin, LFampin and LFchimera on antibiotic-resistant Staphylococcus aureus and Escherichia coli - Héctor Flores-Villaseñor; Adrian Canizalez-Román; Magda Reyes-Lopez; Kamram Nazmi; Mireya de la Garza; Jorge Zazueta-Beltrán; Nidia León-Sicairos; Jan G. M. Bolscher (BioMetals, 2010)