Exploring forms of life in player development pathways-the case of British rugby league

Social, cultural, and historical constraints can influence attitudes towards learning, developing, and performing in sport. A recent conceptualization of these environmental constraints in athlete development pathways is a form of life, which describes the values, beliefs, traditions, customs, and behaviors that contribute to an athlete’s development. Although a form of life can have a powerful influence on athlete development, research exploring this relationship is limited. In this article we explore the form of life in British rugby league football player development contexts to clarify how social, cultural, and historical constraints influence the development of rugby league players in the United Kingdom. Twenty-four coaches were interviewed through individual semi-structured interviews to collect the data. Findings show how forms of life in rugby league player development pathways are established and maintained by the complex interactions between the microsystem, mesosystem, exosystem, and macrosystem that shape and guide the development of players. We recommend that player development pathways in sport underpin practice with a theoretical framework of the learning process to protect athletes from social, cultural, and historical constraints that are not conducive to their development

A deterministic finite automaton for faster protein hit detection in BLAST

BLAST is the most popular bioinformatics tool and is used to run millions of queries each day. However, evaluating such queries is slow, taking typically minutes on modern workstations. Therefore, continuing evolution of BLAST-by improving its algorithms and optimizations-is essential to improve search times in the face of exponentially increasing collection sizes. We present an optimization to the first stage of the BLAST algorithm specifically designed for protein search. It produces the same results as NCBI-BLAST but in around 59% of the time on Intel-based platforms; we also present results for other popular architectures. Overall, this is a saving of around 15% of the total typical BLAST search time. Our approach uses a deterministic finite automaton (DFA), inspired by the original scheme used in the 1990 BLAST algorithm. The techniques are optimized for modern hardware, making careful use of cache-conscious approaches to improve speed. Our optimized DFA approach has been integrated into a new version of BLAST that is freely available for download at http://www.fsa-blast.org/

General-Purpose Search Techniques for Genomic Text

Fast and accurate techniques for searching large genomic text collections are becoming increasingly important. While Information Retrieval is well-established for general-purpose text retrieval tasks, less is known about retrieval techniques for genomic text data. In this paper, we investigate and propose general-purpose search techniques for genomic text. In particular, we show that significant improvements can result from manual term expansion, where additional words are added to queries and documents. We also show that collection partitioning, where documents are included in or excluded from the search space, is highly effective for some tasks. We experiment with our techniques on four text collections and show, for example, that the collection partitioning scheme can improve effectiveness by almost 9.5% over a standard retrieval baseline. We conclude by recommending techniques that can be considered for most genomic search tasks