HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes

Microtearding mode study in NSTX using machine learning enhanced reduced model

This article presents a survey of NSTX cases to study the microtearing mode (MTM) stabilities using the newly developed global reduced model for Slab-Like Microtearing modes (SLiM). A trained neutral network version of SLiM enables rapid assessment (0.05s/mode) of MTM with $98\%$ accuracy providing an opportunity for systemic equilibrium reconstructions based on the matching of experimentally observed frequency bands and SLiM prediction across a wide range of parameters. Such a method finds some success in the NSTX discharges, the frequency observed in the experiment matches with what SLiM predicted. Based on the experience with SLiM analysis, a workflow to estimate the potential MTM frequency for a quick assessment based on experimental observation has been established

DispersiveShallowWater.jl

Structure-Preserving Numerical Methods for Dispersive Shallow Water Model

DispersiveShallowWater.jl

Structure-Preserving Numerical Methods for Dispersive Shallow Water Model

PLANT AND ANIMAL INTERACTIONS IN THE “BOTANICAL LOST WORLD” OF THE BIBB COUNTY GLADES.

As arthropod populations continue to decline, it is imperative that sensitive ecosystems are continually monitored and associations between plants and arthropods are documented. The ketona dolomite glades of Bibb County, AL are prime examples of a sensitive ecosystems as they only occur over a very narrow range, contain eight endemic species and over 60 species of conservation concern. From 2016 to 2018, We observed and tracked the inter-specific interactions between populations of plants and arthropods, specifically class Insecta, in Kathy Stiles Freeland Bibb County Glades Preserve. In May of each year, undergraduate students from the University of North Georgia used ecological field techniques to observe evidence of plant-arthropod associations such as herbivory and parasitical behavior, identified plants to genus or species, and collected/identified insects. Four habitat types were chosen to observe and compare associations: large glade, small glade, mixed hardwood dominated forest, and a pine dominated forest. Multi-Dimensional Scaling Analyses(MDS) were performed between the habitats for each year to determine if habitat type was a good indicator of association types and if associations changed over time. The associations varied by habitat type with variation seen between the glades themselves as well, but most variation was driven temporally by year and due to a burn event that took place during the study time. The size and the habitat type surrounding any glade greatly influences the associations and assemblages found in said glade, and annual variation along with habitat type dictates which associations will be present within a plant community

Prostaglandin E EP2 Receptor Deletion Attenuates Intracerebral Hemorrhage-Induced Brain Injury and Improves Functional Recovery

Intracerebral hemorrhage (ICH) is a devastating type of stroke characterized by bleeding into the brain parenchyma and secondary brain injury resulting from strong neuroinflammatory responses to blood components. Production of prostaglandin E 2 (PGE 2 ) is significantly upregulated following ICH and contributes to this inflammatory response in part through its E prostanoid receptor subtype 2 (EP2). Signaling through the EP2 receptor has been shown to affect outcomes of many acute and chronic neurological disorders; although, not yet explored in the context of ICH. Wildtype (WT) and EP2 receptor knockout (EP2 −/− ) mice were subjected to ICH, and various anatomical and functional outcomes were assessed by histology and neurobehavioral testing, respectively. When compared with age-matched WT controls, EP2 −/− mice had 41.9 ± 4.7% smaller ICH-induced brain lesions and displayed significantly less ipsilateral hemispheric enlargement and incidence of intraventricular hemorrhage. Anatomical outcomes correlated with improved functional recovery as identified by neurological deficit scoring. Histological staining was performed to begin investigating the mechanisms involved in EP2-mediated neurotoxicity after ICH. EP2 −/− mice exhibited 45.5 ± 5.8% and 41.4 ± 8.1% less blood and ferric iron accumulation, respectively. Furthermore, significantly less striatal and cortical microgliosis, striatal and cortical astrogliosis, blood–brain barrier breakdown, and peripheral neutrophil infiltration were seen in EP2 −/− mice. This study is the first to suggest a deleterious role for the PGE 2 -EP2 signaling axis in modulating brain injury, inflammation, and functional recovery following ICH. Targeting the EP2 G protein-coupled receptor may represent a new therapeutic avenue for the treatment of hemorrhagic stroke

Identification of exceptionally potent adenosine deaminases RNA editors from high body temperature organisms.

The most abundant form of RNA editing in metazoa is the deamination of adenosines into inosines (A-to-I), catalyzed by ADAR enzymes. Inosines are read as guanosines by the translation machinery, and thus A-to-I may lead to protein recoding. The ability of ADARs to recode at the mRNA level makes them attractive therapeutic tools. Several approaches for Site-Directed RNA Editing (SDRE) are currently under development. A major challenge in this field is achieving high on-target editing efficiency, and thus it is of much interest to identify highly potent ADARs. To address this, we used the baker yeast Saccharomyces cerevisiae as an editing-naïve system. We exogenously expressed a range of heterologous ADARs and identified the hummingbird and primarily mallard-duck ADARs, which evolved at 40-42°C, as two exceptionally potent editors. ADARs bind to double-stranded RNA structures (dsRNAs), which in turn are temperature sensitive. Our results indicate that species evolved to live with higher core body temperatures have developed ADAR enzymes that target weaker dsRNA structures and would therefore be more effective than other ADARs. Further studies may use this approach to isolate additional ADARs with an editing profile of choice to meet specific requirements, thus broadening the applicability of SDRE