Notes

Testicular torsion-detorsion brings about dysfunction involving mitochondrial oxidative phosphorylation.
Staphylococcus chromogenes is a common skin commensal in cattle and has been identified as a frequent cause of bovine mastitis and intramammary infections. We have developed a seven locus Multilocus Sequence Typing (MLST) scheme for typing S. chromogenes. Sequence-based typing systems, such as MLST, have application in studies of genetic diversity, population structure, and epidemiology, including studies of strain variation as a factor in pathogenicity or host adaptation. The S. chromogenes scheme was tested on 120 isolates collected from three geographic locations, Vermont and Washington State in the United States and Belgium. A total of 46 sequence types (STs) were identified with most of the STs being location specific. The utility of the typing scheme is indicated by a discrimination power of 95.6% for all isolates and greater than 90% for isolates from each of the three locations. Phylogenetic analysis placed 39 of the 46 STs into single core group consistent with a common genetic lineage; the STs in this group differ by less than 0.5% at the nucleotide sequence level. Most of the diversification in this lineage group can be attributed to mutation; recombination plays a limited role. This lineage group includes two clusters of single nucleotide variants in starburst configurations indicative of recent clonal expansion; nearly 50% of the isolates sampled in this study are in these two clusters. The remaining seven STs were set apart from the core group by having alleles with highly variable sequences at one or more loci. Recombination had a higher impact than mutation in the diversification of these outlier STs. Alleles with hypervariable sequences were detected at five of the seven loci used in the MLST scheme; the average sequence distances between the hypervariable alleles and the common core alleles ranged from 12 to 34 nucleotides. The extent of these sequence differences suggests the hypervariable alleles may be remnants of an ancestral genotype.The conserved fungal velvet family regulatory proteins link development and secondary metabolite production. The velvet domain for DNA binding and dimerization is similar to the structure of the Rel homology domain of the mammalian NF-κB transcription factor. A comprehensive study addressed the functions of all four homologs of velvet domain encoding genes in the fungal life cycle of the soil-borne plant pathogenic fungus Verticillium dahliae. Genetic, cell biological, proteomic and metabolomic analyses of Vel1, Vel2, Vel3 and Vos1 were combined with plant pathogenicity experiments. Different phases of fungal growth, development and pathogenicity require V. dahliae velvet proteins, including Vel1-Vel2, Vel2-Vos1 and Vel3-Vos1 heterodimers, which are already present during vegetative hyphal growth. The major novel finding of this study is that Vel1 is necessary for initial plant root colonization and together with Vel3 for propagation in planta by conidiation. Vel1 is needed for disease symptom induction in tomato. Vel1, Vel2, and Vel3 control the formation of microsclerotia in senescent plants. Vel1 is the most important among all four V. dahliae velvet proteins with a wide variety of functions during all phases of the fungal life cycle in as well as ex planta.We present a combined molecular and morphological phylogenetic analysis of the Loricariinae, with emphasis on the Harttiini (Cteniloricaria, Harttia, and Harttiella) and Farlowellini (Aposturisoma, Farlowella, Lamontichthys, Pterosturisoma, Sturisoma, and Sturisomatichthys). read more Character sampling comprised seven molecular markers (the mitochondrial Cytb, nd2, 12S and 16S, and the nuclear MyH6, RAG1 and RAG2) and 196 morphological characters. A total of 1,059 specimens, and 159 tissue samples were analized, representing 100 species. A Bayesian Inference analysis was performed using the concatenated data matrix, which is comprised of 6,819 characters. The Loricariinae were found to comprise the tribes (Hartiini (Loricariini, Farlowellini)), the latter two elevated from subtribes. A Maximum Parsimony analysis was also performed using the same data matrix in order to reveal phenotypical synapomorphies to diagnose each clade. Two MP trees were found with a length of 14,704 steps, consistency index of 0.29 and retention index of 0.61, which were summarized in a strict consensus tree. Harttiini includes (Harttiella (Cteniloricaria, Harttia), and Farlowellini includes (Lamontichthys (Pterosturisoma (Sturisoma (Sturisomatichthys, Farlowella)))). Aposturisoma was recovered nested within Farlowella and is synonymyzed to the latter. Sturisoma was corroborated as strictly cis-Andean, while Sturisomatichthys encompasses, besides the valid species already included in the genus, the trans-Andean species once belonging to Sturisoma sensu lato. Identification keys and phylogenetic diagnoses of family-group taxa and genera of both the Harttiini and the Farlowellini are provided.Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.
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