The effects of hyperoxia on repeated sprint cycling performance & muscle fatigue.

OBJECTIVES: Hyperoxia (>21% oxygen) can evoke performance improvements in aerobic and anaerobic exercise. The aims of the current study were to determine the effects of breathing hyperoxic gas (fraction of inspired oxygen [FiO2] 1.00) on repeated cycle performance, and to assess the nature and extent of fatigue after intermittent sprinting. DESIGN & METHODS: Testing (n=14 males) comprised two visits to the laboratory. Each session involved 10×15s repeated cycle sprints breathing FiO2 1.00 (hyperoxia) or FiO2 0.21 (normoxia). Muscle fatigue was measured pre and post sprints using Maximal Voluntary Contraction (MVC), voluntary activation (VA) and potentiated doublet twitch (PTF). Blood lactate (BLa) was taken between sprints. Paired samples t-tests were used to examine difference between conditions in power output (peak and mean Watts) and BLa. Two-way ANOVA was used to examine fatigue variables pre and post sprints according to condition. RESULTS: Mean power output was 4% greater in hyperoxia (p<0.01), with no difference in peak power (p>0.05). There was a significant increase in BLa in hyperoxia compared with normoxia (p<0.01) in sprints 4 and 8, as well as meaningful difference in sprints 4-10. There was no significant difference in fatigue factors (MVC, VA and PTF) (p>0.05) in response to the cycling, although a large drop in PTF occurred in both conditions. CONCLUSION: Hyperoxia can elicit improvements in mean cycling power, with no significant change in post exercise muscle fatigue. Hyperoxia as a training aid may provide performance enhancing effects during repeated sprint cycling by reducing concurrent muscle fatigue, primarily via peripheral factors.

GenGIS 2: geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework.

GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index) for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA), which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada. GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis.

SPANNER: taxonomic assignment of sequences using pyramid matching of similarity profiles.

BACKGROUND: Homology-based taxonomic assignment is impeded by differences between the unassigned read and reference database, forcing a rank-specific classification to the closest (and possibly incorrect) reference lineage. This assignment may be correct only to a general rank (e.g. order) and incorrect below that rank (e.g. family and genus). Algorithms like LCA avoid this by varying the predicted taxonomic rank based on matches to a set of taxonomic references. LCA and related approaches can be conservative, especially if best matches are taxonomically widespread because of events such as lateral gene transfer (LGT). RESULTS: Our extension to LCA called SPANNER (similarity profile annotater) uses the set of best homology matches (the LCA Profile) for a given sequence and compares this profile with a set of profiles inferred from taxonomic reference organisms. SPANNER provides an assignment that is less sensitive to LGT and other confounding phenomena. In a series of trials on real and artificial datasets, SPANNER outperformed LCA-style algorithms in terms of taxonomic precision and outperformed best BLAST at certain levels of taxonomic novelty in the dataset. We identify examples where LCA made an overly conservative prediction, but SPANNER produced a more precise and correct prediction. CONCLUSIONS: By using profiles of homology matches to represent patterns of genomic similarity that arise because of vertical and lateral inheritance, SPANNER offers an effective compromise between taxonomic assignment based on best BLAST scores, and the conservative approach of LCA and similar approaches. AVAILABILITY: C++ source code and binaries are freely available at http://kiwi.cs.dal.ca/Software/SPANNER. CONTACT: beiko@cs.dal.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.