Perceived costs as drivers of wildlife management preferences in rural Tanzanian communities - Christian Kiffner; Justin Raycraft; Reilly Becchina; Danielle Bettermann; Stephen Koester; Elana Kriegel; Kiana Lindsay; Edwin Maingo Ole; Emily Ramirez; Bryan Spizuco; Neil H. Carter (Conservation Biology, 2026)
Effectively managing human–wildlife interactions is crucial for fostering coexistence on shared landscapes. Management options are most effective when aligned with the preferences of people directly affected by wildlife, yet little is known about how socioecological factors influence these preferences. Integrating responses from 680 rural residents of northern Tanzania and remotely sensed data, we parameterized a Bayesian hierarchical model to test predictions of the hazard‐acceptance model. We estimated how perceived costs and benefits, distance to protected areas, and the human footprint index mediate preferences for managing (preventing damage, compensating damage, reducing populations, and doing nothing) interactions with herbivore (elephant, giraffe, buffalo, zebra, wildebeest, and impala) and carnivore (lion, hyena, leopard, cheetah, honey badger, and jackal) species. Most respondents preferred management options that supported coexistence: prevention (41.9%), no management (38.0%), and compensation (11.1%). In contrast, population reduction (9.0%) was least preferred but more frequently selected for carnivores (13.4%) than herbivores (5.3%). Perceived costs strongly influenced management preferences. Respondents perceiving tangible costs were more likely to prefer prevention (posterior mean: 0.57 [95% credible interval 0.00 to 0.99]) over compensation (0.07 [0.00 to 0.66]) or population reduction (0.16 [0.00 to 0.87]), whereas those not perceiving costs leaned toward no management (0.40 [−0.74 to 1.78]). Though perceived benefits were less influential than costs, respondents associating species with intangible (0.10 [0.00 to 0.74]) or tourism benefits (0.06 [0.00 to 0.63]) were less likely to support population reduction than those perceiving no benefits (0.12 [0.00 to 0.82]). Distance to protected areas and the human footprint index had weaker, inconsistent effects, but random intercepts indicated substantial village–village variation in preferred management options. Our results suggest that conservation strategies should primarily address wildlife‐related costs and foster coexistence by more equitably distributing benefits. A possible strategy could include investing tourism revenues into comanaged, locally tailored damage prevention measures.

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.

Accounting for human–nature linkages in area‐based conservation monitoring through social–ecological indicator bundles - Natalie C. Ban; Mark H. Carr; Emily M. Rubidge; Anne Salomon; Joachim Claudet; Arielle Levine; Lindsay Aylesworth; Luisa Ramirez; Jenn M. Burt; Mark Andrachuk; Natascia Tamburello; Rebecca Martone; et al. (Conservation Biology, 2025)
As the coverage of area‐based conservation increases across the globe, it is critical to improve understanding of the social and ecological outcomes of such measures and the pathways to their outcomes. A social–ecological systems approach to monitoring and evaluation is increasingly advocated; yet, applications remain scarce. We sought to facilitate operationalization of this approach through prioritization of indicators when resources are scarce and to improve capture of social–ecological interactions. We convened a working group of practitioners and academics to explore linked social and ecological interactions through a case study of marine protected areas (MPAs). We used causal models (implemented through causal loop diagrams) in participatory and future‐oriented approaches to identify interactions among key nodes of the system that can be a focus of monitoring. These nodes and their interactions provided insight into linked indicators of key system components, for example, biomass, compliance, perceived legitimacy, catches, and perceived fairness. We called these indicator bundles . Indicator bundles can be applied to analyze causal modeling diagrams, identify essential elements to monitor, and inform analytical and reporting protocols. The bundles can also help identify key leverage points for adaptive management to improve outcomes of existing interventions. This approach can inform monitoring and evaluation and, ultimately, the design and adaptive management of conservation areas that maximize social and ecological benefits and minimize negative trade‐offs.

Variable social-ecological indicators across a Tanzanian protected area network - Juliette Vallin; Justin Raycraft; Danielle Bettermann; John Kioko; Bernard M. Kissui; Stephen Koester; Kiana Lindsay; Edwin Maingo Ole; Emily Ramirez; Bryan Spizuco; Jacqueline Loos; Christian Kiffner (Biological Conservation, 2025)

Toxicity of six representative polycyclic aromatic compounds in five marine test species - Benjamin P. de Jourdan; David L. Daniel; Piero R. Gardinali; Cesar Ramirez; Chris J. Langdon; Emily Stefansson; Thomas F. Parkerton; William A. Stubblefield (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.

Embryotoxicity of statins and other prescribed drugs with reported off-target effects on cholesterol biosynthesis - Taryn Hartley; Hagir Abdelmagid; Zeenat Abdulsalam; Aliyah Mansion; Emily Howe; Daniel Ramirez; Kaylei White; Emmanuel Tadjuidje (Reproductive Toxicology, 2025)

Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America - Morgan E. Christman; Lori R. Spears; Emily K. Burchfield; William D. Pearse; James P. Strange; Ricardo A. Ramirez (Global Change Biology, 2024)
Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by modeling species‐specific habitat requirements, and assemblage‐level responses across geographic regions. We integrated species richness, abundance, and distribution data for 18 bumble bee species with site‐specific bioclimatic, landscape composition, and landscape configuration data to evaluate the effects of multiple environmental stressors on bumble bee assemblages throughout 433 agricultural fields in Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east versus west groupings emerged when evaluating species‐specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact on the mountain region. Further, forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the United States.

Correlation of wastewater surveillance data with traditional influenza surveillance measures in Cook County, Illinois, October 2022–April 2023 - Emily A.G. Faherty; Deniz Yuce; Colin Korban; Kelley Bemis; Rishi Kowalski; Stephanie Gretsch; Enrique Ramirez; Rachel Poretsky; Aaron Packman; Katelyn Plaisier Leisman; Melissa Pierce; Alyse Kittner; et al. (Science of The Total Environment, 2024)

Homestyles-2 Nutrition Education Curriculum Improves Aspects of Mental Health of Parents/Caregivers of Children Ages 6 to 11 - Emily Najduch; Isabella Ramirez; Kerri-Ann Chambers; Logan Brunson; Xinlin Lu; Latoya O'Neal; Melissa Olfert; Carol Byrd-Bredbenner; Karla Shelnutt (Journal of Nutrition Education and Behavior, 2023)

Hot Vents Beneath an Icy Ocean: The Aurora Vent Field, Gakkel Ridge, Revealed - Eva Ramirez-Llodra; Claudio Argentino; Maria Baker; Antje Boetius; Carolina Costa; Håkon Dahle; Emily Denny; Pierre-Antoine Dessandier; Mari Eilertsen; Benedicte Ferre; Christopher German; Kevin Hand; et al. (Oceanography, 2023)
Evidence of hydrothermal venting on the ultra-slow spreading Gakkel Ridge in the Central Arctic Ocean has been available since 2001, with first visual evidence of black smokers on the Aurora Vent Field obtained in 2014. But it was not until 2021 that the first ever remotely operated vehicle (ROV) dives to hydrothermal vents under permanent ice cover in the Arctic were conducted, enabling the collection of vent fluids, rocks, microbes, and fauna. In this paper, we present the methods employed for deep-sea ROV operations under drifting ice. We also provide the first description of the Aurora Vent Field, which includes three actively venting black smokers and diffuse flow on the Aurora mound at ~3,888 m depth on the southern part of the Gakkel Ridge (82.5°N). The biological communities are dominated by a new species of cocculinid limpet, two small gastropods, and a melitid amphipod. The ongoing analyses of Aurora Vent Field samples will contribute to positioning the Gakkel Ridge hydrothermal vents in the global biogeographic puzzle of hydrothermal vents.

Seroprevalence of porcine reproductive and respiratory syndrome virus on swine farms in a tropical country of the Middle Americas: the case of Costa Rica - Ronald Meléndez; Mónica Guzmán; Carlos Jiménez; Marta Piche; Emily Jiménez; Bernal León; Juan M. Cordero; Lisbeth Ramirez-Carvajal; Alberto Uribe; Arie Van Nes; Arjan Stegeman; Hans Vernooij; et al. (Tropical Animal Health and Production, 2021)
Porcine reproductive and respiratory syndrome virus (PRRSV) causes significant economic losses to the swine industry worldwide. Little is known regarding the epidemiology of this infection in tropical countries. To address this problem in Costa Rica, a seroepidemiological study was carried out in two phases. In the first phase, a pilot study was conducted in nine farms with the clinical diagnosis of PRRSV. In total, 265 pig serum samples were collected from animals ranging in age from 1 to 15 weeks of age. This study aimed to establish the duration of maternal immunity in piglets, to identify the period of viremia, and to determine when seroconversion occurs. In the second phase, a cross-sectional serology study was performed on a representative sample of the Costa Rican national herds in the second phase. The twenty-five selected farms represent all provinces and were classified according to herd size (100 to 2000 sows). In each farm, pigs aged 8, 10, and 12 weeks were sampled, as well as gilts based on the pilot study. In total 1281 pigs were sampled across all 25 farms. The aim of the cross-sectional study was to quantify the seroprevalence of PRRSV in Costa Rican pig farms and to describe its geographical distribution in this tropical country. The prevalence of positive farms was 44% (11/25), and these farms were located in six of the seven provinces of Costa Rica. Overall, 58% (344/596) of the pigs were seropositive to PRRSV. The age of the pigs and the ecozone where farms were located were significantly related with PRRSV seroprevalence in animals and herds, respectively.

Individual differences in blink rate modulate the effect of instrumental control on subsequent Pavlovian responding - Catherine A. Hartley; Cesar A. O. Coelho; Emily Boeke; Franchesca Ramirez; Elizabeth A. Phelps (Psychopharmacology, 2019)

Selective Contrast Enhancement of Individual Alzheimer’s Disease Amyloid Plaques Using a Polyamine and Gd-DOTA Conjugated Antibody Fragment Against Fibrillar Aβ42 for Magnetic Resonance Molecular Imaging - Muthu Ramakrishnan; Thomas M. Wengenack; Karunya K. Kandimalla; Geoffry L. Curran; Emily J. Gilles; Marina Ramirez-Alvarado; Joseph Lin; Michael Garwood; Clifford R. Jack Jr.; Joseph F. Poduslo (Pharmaceutical Research, 2008)