Publications by authors named "J R Pauly"
Background: Alcohol use in adolescence may increase susceptibility to substance use disorders (SUDs) in adulthood. This study determined if voluntary ethanol (EtOH) consumption during adolescence, combined with social isolation, alters the trajectory of EtOH and nicotine intake during adulthood, as well as activating brain neuroinflammation.
Methods: Adolescent male isolate- and group-housed rats were given 0.
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans.
View Article and Find Full Text PDFObjective: This study investigates the feasibility of non-contact retrospective respiratory gating and cardiac sensing using continuous wave Doppler radar deployed in an MRI system. The proposed technique can complement existing sensors which are difficult to apply for certain patient populations.
Methods: We leverage a software-defined radio for continuous wave radar at 2.
An escalating trend of antipsychotic drug use in children with ADHD, disruptive behavior disorder, or mood disorders has raised concerns about the impact of these drugs on brain development. Since antipsychotics chiefly target dopamine receptors, it is important to assay the function of these receptors after early-life antipsychotic administration. Using rats as a model, we examined the effects of early-life risperidone, the most prescribed antipsychotic drug in children, on locomotor responses to the dopamine D/D receptor agonist, apomorphine, and the D/D receptor agonist, quinpirole.
View Article and Find Full Text PDFArticle Synopsis
- Cryogenic electron tomography (cryoET) is an advanced imaging technique that captures detailed 3D images of biological specimens but struggles with data collection limitations like the missing wedge problem.
- Recent advancements using supervised deep learning methods, particularly convolutional neural networks (CNNs), have helped improve cryoET quality but require substantial pretraining, which can lead to inaccuracies when training data is limited.
- To address these issues, a new unsupervised learning approach using coordinate networks (CNs) has been proposed, significantly speeding up reconstruction times and enhancing image quality without needing pretraining, as demonstrated by improved shape completion and fewer artifacts in experimental results.