The effects of accumulated muscle fatigue on the mechanomyographic waveform: implications for injury prediction.

PURPOSE: Muscle fatigue has been identified as a risk factor for spontaneous muscle injuries in sport. However, few studies have investigated the accumulated effects of muscle fatigue on human muscle contractile properties. This study aimed to determine whether repeated bouts of exercise inducing acute fatigue leads to longer-term fatigue-related changes in muscle contractile properties. METHODS: Maximum voluntary contraction (MVC), electromyographic (EMG) and mechanomyographic (MMG) measures were recorded in the biceps brachii of 11 participants for 13 days, before and after a maximally fatiguing exercise protocol. The exercise protocol involved participants repetitively lifting a weight (concentric contractions only) equal to 40 % MVC, until failure. RESULTS: A significant (p < 0.05) acute pre- to post-exercise decline of biceps brachii MVC and median power frequency (MPF) was observed each day, whilst no difference existed between pre-exercise MVC or MPF values on subsequent days (days 2-13). However, decreases in number of lift repetitions and in pre-exercise MMG values of muscle belly displacement, contraction velocity and half-relaxation velocity were observed through to day 13. CONCLUSIONS: Whilst MVC and MPF measures resolved by the following day's test session, MMG measures indicated an ongoing decrement in muscle performance through days 2-13 consistent with the decline in lift repetitions observed. These results suggest that MMG may be more sensitive in detecting accumulated muscle fatigue than the 'gold standard' measures of MVC/MPF. Considering that muscle fatigue leads to injury, the on-going monitoring of MMG derived contractile properties of muscles in athletes may aid in the prediction of fatigued-induced muscle injury.

Efficient inference of bacterial strain trees from genome-scale multilocus data.

MOTIVATION: In bacterial evolution, inferring a strain tree, which is the evolutionary history of different strains of the same bacterium, plays a major role in analyzing and understanding the evolution of strongly isolated populations, population divergence and various evolutionary events, such as horizontal gene transfer and homologous recombination. Inferring a strain tree from multilocus data of these strains is exceptionally hard since, at this scale of evolution, processes such as homologous recombination result in a very high degree of gene tree incongruence. RESULTS: In this article we present a novel computational method for inferring the strain tree despite massive gene tree incongruence caused by homologous recombination. Our method operates in three phases, where in phase I a set of candidate strain-tree topologies is computed using the maximal cliques concept, in phase II divergence times for each of the topologies are estimated using mixed integer linear programming (MILP) and in phase III the optimal tree (or trees) is selected based on an optimality criterion. We have analyzed 1898 genes from nine strains of the Staphylococcus aureus bacteria, and identified a fully resolved (binary) strain tree with estimated divergence times, despite the high degrees of sequence identity at the nucleotide level and gene tree incongruence. Our method's efficiency makes it particularly suitable for analysis of genome-scale datasets, including those of strongly isolated populations which are usually very challenging to analyze. AVAILABILITY: We have implemented the algorithms in the PhyloNet software package, which is available publicly at http://bioinfo.cs.rice.edu/phylonet/.