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Reef ecosystems face multiple threats including coral bleaching, which is primarily driven by global warming and can be intensified by local stressors. Despite evidence of global and local stressors, in situ assessments of their combined effects remain scarce for Brazilian reefs (Southwestern Atlantic), which are considered putative climate refugia, notably the subtropical reefs of São Paulo. However, these reefs are exposed t...
Rationale and objectives In monkeys, the muscarinic cholinergic receptor antagonist scopolamine is known to broadly disrupt learned behaviors, though the precise nature of the cognitive deficits has been questioned. Experimentally observable deficits in memory can be ascribed to poor attentional focusing, human interference as well as age, sex, and dosing regimen. The potential stress associated with isolation can also play a...
Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegenerationNARA Subscribed
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 fi...
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 te...
Structural controls of superposed komatiite-hosted Ni and orogenic Au mineralisation at Beta-Hunt, Western AustraliaNARA Subscribed
The Beta-Hunt Au-Ni deposit hosts coinciding orogenic Au and komatiite-hosted Ni-Cu-(PGE) mineralisation. The close spatial relationship between the two diachronous mineralisation styles suggests that long-lived local structural architecture played a role in the superposition of the two deposit types. Deposit-scale field geological and structural analysis was conducted to define the deformation paragenesis and structural frame...
Congo Basin Carbon Cycle Responses to Global ChangeNARA Subscribed
The Congo Basin and its contiguous forests harbor globally significant carbon stocks, estimated at 65 gigatons of C (GtC) above and belowground. Despite rising temperatures and intensifying droughts, they have remained a carbon sink, albeit weak: 0.26–0.50 GtC yr. −1 carbon uptake since 1980. However, these forests' carbon stocks and fluxes, including gross primary productivity, respiration, net primary productivity, and river...
Understanding variation in home range size (HR) provides important insights into the underlying ecological processes driving space use. However, it remains unclear whether the interspecific allometric scaling of mammals' HR can also be consistently observed within species and for both sexes. To address this knowledge gap, we GPS‐tracked 349 resident individuals across Brazil, encompassing 18 mammal species. We estimated indivi...
Motivation and retrospective appraisal of psychedelic study participation: a qualitative study in healthy volunteersNARA Subscribed
Rationale Little is known about motives of healthy volunteers to participate in psychedelic trials and how they appraise their study experience retrospectively. Objectives This paper explored reasons why healthy people register for psychedelic trials, factors that they considered to contribute to either positive or negative study experiences, and under which circumstances they would seek a psychedelic experience again. Methods...
AviList: a unified global bird checklistNARA Subscribed
Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learningNARA Subscribed
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, th...