The aim of this study was to compare the neuromuscular function of the plantar flexors following caffeine or placebo administration. Thirteen subjects (25 ± 3 years) ingested caffeine or placebo in a randomized, controlled, counterbalanced, double-blind crossover design. Neuromuscular tests were performed before and 1 h after caffeine or placebo intake. During neuromuscular testing, rate of torque development, isometric maximum voluntary torque, and neural drive to the muscles were measured. Triceps surae muscle activation was assessed by normalized root mean square of the EMG signal during the initial phase of contraction (0-100 ms, 100-200 ms) and maximal voluntary contraction (MVC). Furthermore, evoked spinal reflex responses of the soleus muscle (H-reflex evoked at rest and during MVC, V-wave) and peak twitch torques were evaluated. The isometric maximum voluntary torque and evoked potentials were not different. However, we found a significant difference between groups for rate of torque development in the time intervals 0-100 ms [41.1 N · m/s (95% CI: 8.3-73.9 N · m/s, P = 0.016)] and 100-200 ms [32.8 N · m/s (95% CI: 2.8-62.8 N · m/s, P = 0.034)]. These changes were accompanied by enhanced neural drive to the plantar flexors. Data suggest that caffeine solely increased explosive voluntary strength of the triceps surae because of enhanced neural activation at the onset of contraction whereas MVC strength was not affected.
This study analyzed the relationships between isometric as well as concentric maximum voluntary contraction (MVC) strength of the leg muscles and the times as well as speeds over different distances in 17 young short track speed skaters. Isometric as well as concentric single-joint MVC strength and multi-joint MVC strength in a stable (without skates) and unstable (with skates) condition were tested. Furthermore, time during maximum skating performances on ice was measured. Results indicate that maximum torques during eversion and dorsal flexion have a significant influence on skating speed. Concentric MVC strength of the knee extensors was higher correlated with times as well as speeds over the different distances than isometric MVC strength. Multi-joint MVC testing revealed that the force loss between measurements without and with skates amounts to 25%, while biceps femoris and soleus showed decreased muscle activity and peroneus longus, tibialis anterior, as well as rectus femoris exhibited increased muscle activity. The results of this study depict evidence that the skating times and speeds are primarily influenced by concentric MVC strength of the leg extensors. To be able to transfer the strength onto ice in an optimal way, it is necessary to stabilize the knee and ankle joints.
BackgroundThe data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects.ResultsIn this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges.ConclusionsThis paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation.
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