The Relationship between athlete motivation, strategies used to cope with stress and affective outcomes in Canadian University athletes

Motivation to perform and coping with stress during performance are key factors in determining numerous outcomes of sporting performance. However, less evidence is in place assessing their relationship. The aim of this investigation was to assess the relationship between athlete motivation and the coping strategies used to deal with stress during their sporting performance, as well as the relationship between motivation and affect and coping and affect. One hundred and forty five university athletes completed questionnaires. Regressions revealed that two of the three self determined levels of motivation, identified and integrated regulation, predicted increased task-oriented coping strategies. Two of the three non-self determined levels of motivation, amotivation and external regulation, significantly predicted disengagement-oriented coping. Additionally, intrinsic motivation and task-oriented coping predicted increase positive affect. Increased disengagement-oriented coping predicted decreased positive affect. Disengagement-oriented coping significantly predicted increased negative affect. These findings increase understanding of motivations role in predicting athletes coping.

Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.