Genetic Assessment Of Staphylococcus Aureus In An Underreported Locality: Ambulatory Care Clinic

Background: Staphylococcus aureus has strong association with anthropogenic environments. This association has not been well supported by use of genetic tools. The aim of this study was to phylogenetically relate numerous isolates from three environments — NCBI samples from hospitals, a community, and a previously unexplored healthcare environment: an ambulatory care clinic (ACC). Methods: This study incorporated hospital samples from NCBI, a community database from the University of Central Florida (UCF), and newly added samples taken from employees of an ambulatory care clinic located at UCF. Samples were collected from nasal swabs of employees, and positive samples were cultured, extracted, and sequenced at seven MLST loci and one virulence locus (spa). MLST sequences were used in eBURST and TCS population structure analyses and all sequences were incorporated into a phylogenetic reconstruction of relationships. Results: A total of 185 samples were incorporated in this study (15 NCBI sequences from hospital infections, 29 from the ACC, and 141 from the community). In both phylogenetic and population genetics analyses, samples proved to be panmixic, with samples not segregating monophyletically based on sample origin. Conclusion: Samples isolated from ambulatory care clinics are not significantly differentiated from either community or hospital samples at the representative loci chosen. These results strengthen previous conclusions that S. aureus may exhibit high genetic similarity across anthropogenic environments

Molecular Evolution Of Fibropapilloma-Associated Herpesviruses Infecting Juvenile Green And Loggerhead Sea Turtles

Chelonid Alphaherpesvirus 5 (ChHV5) has long been associated with fibropapillomatosis (FP) tumor disease in marine turtles. Presenting primarily in juvenile animals, FP results in fibromas of the skin, connective tissue, and internal organs, which may indirectly affect fitness by obstructing normal turtle processes. ChHV5 is near-universally present in tumorous tissues taken from affected animals, often at very high concentrations. However, there is also considerable asymptomatic carriage amongst healthy marine turtles, suggesting that asymptomatic hosts play an important role in disease ecology. Currently, there is a paucity of studies investigating variation in viral genetics between diseased and asymptomatic hosts, which could potentially explain why only some ChHV5 infections lead to tumor formation. Here, we generated a database containing DNA from over 400 tissue samples taken from green and loggerhead marine turtles, including multiple tissue types, a twenty year time span, and both diseased and asymptomatic animals. We used two molecular detection techniques, quantitative (q)PCR and nested PCR, to characterize the presence and genetic lineage of ChHV5 in each sample. We found that nested PCR across multiple loci out-performed qPCR and is a more powerful technique for determining infection status. Phylogenetic reconstruction of three viral loci from all ChHV5-positive samples indicated widespread panmixia of viral lineages, with samples taken across decades, species, disease states, and tissues all falling within the same evolutionary lineages. Haplotype networks produced similar results in that viral haplotypes were shared across species, tissue types and disease states with no evidence that viral lineages associated significantly with disease dynamics. Additionally, tests of selection on viral gene trees indicated signals of selection dividing major clades, though this selection did not divide sample categories. Based on these data, neither the presence of ChHV5 infection nor neutral genetic divergence between viral lineages infecting a juvenile marine turtle is sufficient to explain the development of FP within an individual

Spectroscopic observation of gold-dicarbide: photodetachment and velocity map imaging of the AuC2 anion

Photoelectron spectra following photodetachment of the gold dicarbide anion, AuC2(-), have been recorded using the velocity map imaging technique at several excitation wavelengths. The binding energy spectra show well-defined vibrational structure which, with the aid of computational calculations and Franck-Condon simulations, was assigned to a progression in the Au-C stretching mode, ν3. The experimental data indicate that the features in the spectrum correspond to a (2)A' ← (3)A' transition, involving states which we calculate to have bond angles ~147° but with a low barrier to linearity.Bradley R. Visser, Matthew A. Addicoat, Jason R. Gascooke, Warren D. Lawrance, and Gregory F. Meth

Photodetachment velocity map imaging of the 1A′←2A′ transition in the AuOH anion

Abstract not availableBradley R. Visser, Matthew A. Addicoat, Jason R. Gascooke, Xinxing Zhang, Kit Bowen, Warren D. Lawrance, Gregory F. Meth

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers