A Case Study of Regional Sport Organization Development in Triathlon

The current qualitative case study examined how a Regional Sport Organization (RSO) in New Zealand dealt with macro and micro environmental pressures in the development and evolution of increasing participation in the sport of triathlon over a 15-year period. Organizers managed a total participation increase from 300 adult participants in 1999 to close to 1,200 in 2012–13. An increase in child participants from 300 in 2004 to 3,400 in 2012–13 also took place. Archives of the results of the respective adult and children’s series of triathlon events from 1998–2013 were analyzed along with the environmental pressures. The findings indicated that informal and formal organizational responses to internal and external pressures directly or indirectly assisted in increasing sport participation of adults and children. These responses involved new events targeting different groups and periodic adaptive organizational infrastructure review/change. Implications of the research included the highlighted importance of ongoing sport product changes and enhancements and the use of formal internal and external review processes, such as the Organization Development Tool, for community or regional sports to support increased physical activity and participation

A new mathematical model for the interpretation of translational research evaluating six CTLA-4 polymorphisms in high-risk melanoma patients receiving adjuvant interferon

Adjuvant therapy of stage IIB/III melanoma with interferon reduces relapse and mortality by up to 33% but is accompanied by toxicity-related complications. Polymorphisms of the CTLA-4 gene associated with autoimmune diseases could help in identifying interferon treatment benefits. We previously genotyped 286 melanoma patients and 288 healthy (unrelated) individuals for six CTLA-4 polymorphisms (SNP). Previous analyses found no significant differences between the distributions of CTLA-4 polymorphisms in the melanoma population vs. controls, no significant difference in relapse free and overall survivals among patients and no correlation between autoimmunity and specific alleles. We report new analysis of these CTLA-4 genetic profiles, using Network Phenotyping Strategy (NPS). It is graph-theory based method, analyzing the SNP patterns. Application of NPS on CTLA-4 polymorphism captures allele relationship pattern for every patient into 6-partite mathematical graph P. Graphs P are combined into weighted 6-partite graph S, which subsequently decomposed into reference relationship profiles (RRP). Finally, every individual CTLA-4 genotype pattern is characterized by the graph distances of P from eight identified RRP's. RRP's are subgraphs of S, collecting equally frequent binary allele co-occurrences in all studied loci. If S topology represents the genetic "dominant model", the RRP's and their characteristic frequencies are identical to expectation-maximization derived haplotypes and maximal likelihood estimates of their frequencies. The graphrepresentation allows showing that patient CTLA-4 haplotypes are uniquely different from the controls by absence of specific SNP combinations. New function-related insight is derived when the 6-partite graph reflects allelic state of CTLA-4. We found that we can use differences between individual P and specific RRPs to identify patient subpopulations with clearly different polymorphic patterns relatively to controls as well as to identify patients with significantly different survival. © 2014 Pancoska et al

First Insights on Organic Cosolvent Effects on FhuA Wildtype and FhuA Δ1-159

Circular dichroism (CD) and deconvolution were used to study the structural integrity of a “plugged” and an “open” FhuA transmembrane channel protein in the presence of varied concentrations of tetrahydrofuran (THF), ethanol (EtOH) and chloroform/methanol (C/M). FhuA is an Escherichia coli outer membrane protein (78.9 kDa) consisting of 22 β-sheets and an internal globular cork domain which acts as an iron transporter. FhuA and the deletion variant FhuA Δ1-159 showed comparable and remarkable resistance in the presence of THF (≤40 vol%) and EtOH (≤10 vol%). In C/M, significant differences in structural resistance were observed (FhuA stable ≤10 vol%; FhuA Δ1-159 ≤1 vol%). Deconvolution of CD-spectra for FhuA and FhuA Δ1-159 yielded β-sheet contents of 61 % (FhuA) and 58% (FhuA Δ1-159). Interestingly, FhuA and FhuA Δ1-159 had comparable β-sheet contents in the presence and absence of all three organic cosolvents. Additionally, precipitated FhuA and FhuA Δ1-159 (in 40 vol% C/M or 65 vol% THF) redissolved by supplementing the detergent n-octyl-oligo-oxyethylene (oPOE)

Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency

Motivation: Short interfering RNA (siRNA)-induced RNA interference is an endogenous pathway in sequence-specific gene silencing. The potency of different siRNAs to inhibit a common target varies greatly and features affecting inhibition are of high current interest. The limited success in predicting siRNA potency being reported so far could originate in the small number and the heterogeneity of available datasets in addition to the knowledge-driven, empirical basis on which features thought to be affecting siRNA potency are often chosen. We attempt to overcome these problems by first constructing a meta-dataset of 6483 publicly available siRNAs (targeting mammalian mRNA), the largest to date, and then applying a Bayesian analysis which accommodates feature set uncertainty. A stochastic logistic regression-based algorithm is designed to explore a vast model space of 497 compositional, structural and thermodynamic features, identifying associations with siRNA potency

Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities

We developed a simple algorithm, i-Score (inhibitory-Score), to predict active siRNAs by applying a linear regression model to 2431 siRNAs. Our algorithm is exclusively comprised of nucleotide (nt) preferences at each position, and no other parameters are taken into account. Using a validation dataset comprised of 419 siRNAs, we found that the prediction accuracy of i-Score is as good as those of s-Biopredsi, ThermoComposition21 and DSIR, which employ a neural network model or more parameters in a linear regression model. Reynolds and Katoh also predict active siRNAs efficiently, but the numbers of siRNAs predicted to be active are less than one-eighth of that of i-Score. We additionally found that exclusion of thermostable siRNAs, whose whole stacking energy (ΔG) is less than −34.6 kcal/mol, improves the prediction accuracy in i-Score, s-Biopredsi, ThermoComposition21 and DSIR. We also developed a universal target vector, pSELL, with which we can assay an siRNA activity of any sequence in either the sense or antisense direction. We assayed 86 siRNAs in HEK293 cells using pSELL, and validated applicability of i-Score and the whole ΔG value in designing siRNAs

Validation of food store environment secondary data source and the role of neighborhood deprivation in Appalachia, Kentucky

Background Based on the need for better measurement of the retail food environment in rural settings and to examine how deprivation may be unique in rural settings, the aims of this study were: 1) to validate one commercially available data source with direct field observations of food retailers; and 2) to examine the association between modified neighborhood deprivation and the modified retail food environment score (mRFEI). Methods Secondary data were obtained from a commercial database, InfoUSA in 2011, on all retail food outlets for each census tract. In 2011, direct observation identifying all listed food retailers was conducted in 14 counties in Kentucky. Sensitivity and positive predictive values (PPV) were compared. Neighborhood deprivation index was derived from American Community Survey data. Multinomial regression was used to examine associations between neighborhood deprivation and the mRFEI score (indicator of retailers selling healthy foods such as low-fat foods and fruits and vegetables relative to retailers selling more energy dense foods). Results The sensitivity of the commercial database was high for traditional food retailers (grocery stores, supermarkets, convenience stores), with a range of 0.96-1.00, but lower for non-traditional food retailers; dollar stores (0.20) and Farmer’s Markets (0.50). For traditional food outlets, the PPV for smaller non-chain grocery stores was 38%, and large chain supermarkets was 87%. Compared to those with no stores in their neighborhoods, those with a supercenter [OR 0.50 (95% CI 0.27. 0.97)] or convenience store [OR 0.67 (95% CI 0.51, 0.89)] in their neighborhood have lower odds of living in a low deprivation neighborhood relative to a high deprivation neighborhood. Conclusion The secondary commercial database used in this study was insufficient to characterize the rural retail food environment. Our findings suggest that neighborhoods with high neighborhood deprivation are associated with having certain store types that may promote less healthy food options