Perceptions of preparedness: How hospital-based orientation can enhance the transition from academic to clinical learning.

Background: Clinical placements are essential for applied learning experiences in health professions education. Unfortunately, there is little consensus on how best to prepare learners for the transition between academic and clinical learning. We explored learners’ perceptions of hospital-based orientation and resulting preparedness for clinical placement. Methods: Sixty-three learners participated in a total of 18 semi-structured focus groups, during their clinical placements. Data were analyzed thematically. Results: We organized learners’ perceptions of hospital-based orientation that support their preparedness for placement into three themes: (1) adequate site orientation for learner acquisition of organization acumen and (2) clinical preceptor training to support unit/service and (3) individual components. Conclusion: Thoughtful attention to hospital-based orientation can support learners in transitioning from academic to clinical learning. Hospital organizations should attend to all three components during orientation to better support learners’ preparedness for clinical learning

Alphaflexivirus genomes in stony coral tissue loss disease-affected, disease-exposed, and disease-unexposed coral colonies in the U.S. Virgin Islands

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Veglia, A., Beavers, K., Van Buren, E., Meiling, S., Muller, E., Smith, T., Holstein, D., Apprill, A., Brandt, M., Mydlarz, L., & Correa, A. Alphaflexivirus genomes in stony coral tissue loss disease-affected, disease-exposed, and disease-unexposed coral colonies in the U.S. Virgin Islands. Microbiology Resource Announcements, 11(2), (2022): e01199–e01121, https://doi.org/10.1128/mra.01199-21.Stony coral tissue loss disease (SCTLD) is decimating Caribbean corals. Here, through the metatranscriptomic assembly and annotation of two alphaflexivirus-like strains, we provide genomic evidence of filamentous viruses in SCTLD-affected, -exposed, and -unexposed coral colonies. These data will assist in clarifying the roles of viruses in SCTLD.This work was supported by the National Science Foundation (Biological Oceanography) award numbers 1928753 to M.E.B. and T.B.S., 1928609 to A.M.S.C., 1928817 to E.M.M., 19228771 to L.D.M., 1927277 to D.M.H., and 1928761 to A.A., as well as by VI EPSCoR (NSF numbers 0814417 and 1946412)

Using Machine Learning Algorithms to Classify Three Disease States in a Caribbean Coral

Stony coral tissue loss disease (SCTLD) is one of the most pervasive and virulent coral disease outbreaks on record and affects over half of the reef-building coral species in the Caribbean. Restoration efforts and effective treatment of SCTLD requires an in-depth understanding of both the holobiont response to SCTLD as well as the mechanisms of disease resistance. Here, we identify and compare the gene expression profiles of both naturally infected and disease resistant Montastraea cavernosa, a dominant reef-building coral, as well as its endosymbionts (family Symbiodiniaceae), and microbiota. First, we perform classic differential expression analysis to identify and enumerate the differentially expressed genes (DEGs) between three tissue types: healthy tissue on a resistant colony (HR), healthy tissue on a diseased colony (HD), and lesion tissue on a diseased colony (LD). Then, we use support vector machine learning (SVM) algorithms to extract the features (genes) in the coral animal, the endosymbionts, and the microbiome that best classify the three tissue types. To get a holistic understanding of SCTLD resistance and disease progression, we analyze all significant holobiont features within each tissue type and report on the application of these methods for disease monitoring and intervention.Texas Advanced Computing Center (TACC

Viral consortia in Stony Coral Tissue Loss Disease- affected, disease-exposed, and disease-unexposed coral colonies from a transmission experiment conducted on samples collected from Rupert’s Rock in St. Thomas, U.S. Virgin Islands in 2019

Dataset: Viral Consortia in Stony CoralsTo understand the extent to which (if any) viruses are associated with stony coral tissue loss disease (SCTLD) in stony corals of the U.S. Virgin Islands, we leveraged viral metatranscriptomes generated from SCTLD-affected, SCTLD-exposed, and control (unexposed) coral holobionts sampled during a SCTLD transmission experiment. Sequence data is available in NCBI Genbank under BioProject accession PRJNA788911. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/875283NSF Division of Ocean Sciences (NSF OCE) OCE-192860

Applications of Machine Learning Algorithms for Coral Disease Fate in Caribbean Corals

The Caribbean is known as a coral disease “hot spot” due to the high prevalence of acute and chronic diseases that have plagued corals in the area. Two diseases, Stony Coral Tissue Loss Disease (SCTLD) and White Plague (WP), are common and infect many coral species. These two diseases have been studied in a genotype- matching study that looked at transcriptomics of baseline, and post-exposure to disease in four species of corals. While transcriptomic studies have improved our knowledge of host response, a knowledge gap regarding the disease risk corals have prior to disease exposure still exists. Understanding disease risk before an outbreak is an important step in modeling disease dynamics of corals as it will help conservation efforts and disease response protocols. One way to identify disease risk is the application of machine learning to identify patterns of expression based on disease outcome. By applying novel but proven layers of machine learning programs from medical research and using healthy corals whose disease fate are known, we can identify which biological processes are relevant to disease susceptibility. We examined six different types of machine learning algorithms for detection of presence/absence of genes and expression patterns correlated o whether the coral got disease when exposed or not. We will report what types of data these algorithms provide and how it can be applied for disease motoring and modeling.Texas Advanced Computing Center (TACC

Effect of DNA-Induced Corrosion on Passivated Porous Silicon Biosensors

This work examines the influence of charge density and surface passivation on the DNA-induced corrosion of porous silicon (PSi) waveguides in order to improve PSi biosensor sensitivity, reliability, and reproducibility when exposed to negatively charged DNA molecules. Increasing the concentration of either DNA probes or targets enhances the corrosion process and masks binding events. While passivation of the PSi surface by oxidation and silanization is shown to diminish the corrosion rate and lead to a saturation in the changes by corrosion after about 2 h, complete mitigation can be achieved by replacing the DNA probe molecules with charge-neutral PNA probe molecules. A model to explain the DNA-induced corrosion behavior, consistent with experimental characterization of the PSi through Fourier transform infrared spectroscopy and prism coupling optical measurements, is also introduced