The Effects of Sport Specific, Governed, and Non-Controllable Focal Point on Female Vertical Jump Performance

Graduate Applie

Effects of Lab Technician Administered vs. Subject Administered Resistance on Wingate Performance

Graduate Applie

Assessing the Relationship between Body Composition and 50-km Running Performance

Graduate Applie

Structure of the TPR Domain of AIP: Lack of Client Protein Interaction with the C-Terminal alpha-7 Helix of the TPR Domain of AIP Is Sufficient for Pituitary Adenoma Predisposition

PMCID: PMC3534021This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Setting an Optimal α That Minimizes Errors in Null Hypothesis Significance Tests

Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which we have the highest possible confidence, then the only logical decision-making threshold is the value that minimizes the probability (or occasionally, cost) of making errors. Setting α to minimize the combination of Type I and Type II error at a critical effect size can easily be accomplished for traditional statistical tests by calculating the α associated with the minimum average of α and β at the critical effect size. This technique also has the flexibility to incorporate prior probabilities of null and alternate hypotheses and/or relative costs of Type I and Type II errors, if known. Using an optimal α results in stronger scientific inferences because it estimates and minimizes both Type I errors and relevant Type II errors for a test. It also results in greater transparency concerning assumptions about relevant effect size(s) and the relative costs of Type I and II errors. By contrast, the use of α = 0.05 results in arbitrary decisions about what effect sizes will likely be considered significant, if real, and results in arbitrary amounts of Type II error for meaningful potential effect sizes. We cannot identify a rationale for continuing to arbitrarily use α = 0.05 for null hypothesis significance tests in any field, when it is possible to determine an optimal α

Rates of Mutation and Host Transmission for an Escherichia coli Clone over 3 Years

Although over 50 complete Escherichia coli/Shigella genome sequences are available, it is only for closely related strains, for example the O55:H7 and O157:H7 clones of E. coli, that we can assign differences to individual evolutionary events along specific lineages. Here we sequence the genomes of 14 isolates of a uropathogenic E. coli clone that persisted for 3 years within a household, including a dog, causing a urinary tract infection (UTI) in the dog after 2 years. The 20 mutations observed fit a single tree that allows us to estimate the mutation rate to be about 1.1 per genome per year, with minimal evidence for adaptive change, including in relation to the UTI episode. The host data also imply at least 6 host transfer events over the 3 years, with 2 lineages present over much of that period. To our knowledge, these are the first direct measurements for a clone in a well-defined host community that includes rates of mutation and host transmission. There is a concentration of non-synonymous mutations associated with 2 transfers to the dog, suggesting some selection pressure from the change of host. However, there are no changes to which we can attribute the UTI event in the dog, which suggests that this occurrence after 2 years of the clone being in the household may have been due to chance, or some unknown change in the host or environment. The ability of a UTI strain to persist for 2 years and also to transfer readily within a household has implications for epidemiology, diagnosis, and clinical intervention

Viral non-coding RNA inhibits HNF4α expression in HCV associated hepatocellular carcinoma

BACKGROUND: Hepatitis C virus (HCV) infection is an established cause of chronic hepatitis, cirrhosis and hepatocellular carcinoma (HCC); however, it is unclear if the virus plays a direct role in the development of HCC. Hepatocyte nuclear factor 4α (HNF4α) is critical determinant of epithelial architecture and hepatic development; depletion of HNF4α is correlated with oncogenic transformation. We explored the viral role in the inhibition of HNF4α expression, and consequent induction of tumor-promoting genes in HCV infection-associated HCC. METHODS: Western blot analysis was used to monitor the changes in expression levels of oncogenic proteins in liver tissues from HCV-infected humanized mice. The mechanism of HNF4α depletion was studied in HCV-infected human hepatocyte cultures in vitro. Targeting of HNF4α expression by viral non-coding RNA was examined by inhibition of Luciferase HNF4α 3’-UTR reporter. Modulation of invasive properties of HCV-infected cells was examined by Matrigel cell migration assay. RESULTS: Results show inhibition of HNF4α expression by targeting of HNF4α 3’-UTR by HCV-derived small non-coding RNA, vmr11. Vmr11 enhances the invasive properties of HCV-infected cells. Loss of HNF4α in HCV-infected liver tumors of humanized mice correlates with the induction of epithelial to mesenchymal transition (EMT) genes. CONCLUSIONS: We show depletion of HNF4α in liver tumors of HCV-infected humanized mice by HCV derived small non-coding RNA (vmr11) and resultant induction of EMT genes, which are critical determinants of tumor progression. These results suggest a direct viral role in the development of hepatocellular carcinoma