Sprint starts and the minimum auditory reaction time

The simple auditory reaction time is one of the fastest reaction times and is thought to be rarely less than 100 ms. The current false start criterion in a sprint used by the International Association of Athletics Federations is based on this assumed auditory reaction time of 100 ms. However, there is evidence, both anecdotal and from reflex research, that simple auditory reaction times of less than 100 ms can be achieved. Reaction time in nine athletes performing sprint starts in four conditions was measured using starting blocks instrumented with piezoelectric force transducers in each footplate that were synchronized with the starting signal. Only three conditions were used to calculate reaction times. The pre-motor and pseudo-motor time for two athletes were also measured across 13 muscles using surface electromyography (EMG) synchronized with the rest of the system. Five of the athletes had mean reaction times of less than 100 ms in at least one condition and 20% of all starts in the first two conditions had a reaction time of less than 100 ms. The results demonstrate that the neuromuscular-physiological component of simple auditory reaction times can be under 85 ms and that EMG latencies can be under 60 ms

Visualization methods for statistical analysis of microarray clusters

BACKGROUND: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. RESULTS: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at . CONCLUSION: Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters

The effect of a 12 week core training regimen on electromyographic activation in national-level junior swimmers

Knowledge of muscle activation during core training exercises over the duration of a training program would enhance our understanding of the physiological responses to training. The purpose of this study was to quantify the effect of a 12-week core training regimen on neuromuscular activation in swimmers. Ten national-level junior swimmers performed a core exercise regimen three times a week over a 12-week training period. Surface electromyographic (EMG) measurements from 6 core muscles were taken pre- (0 weeks), mid- (6 weeks) and post training (12 weeks). Analysis was carried out on the EMG activity during maximal voluntary isometric contractions (MVCs) and on the normalized and non-normalized EMG values during the core exercises. MVC EMG activity increased with the intervention in all muscles. The magnitudes of changes in MVC EMG activity were greater during the initial phase (effect sizes - standardized mean differences 0.32 to 1.01) compared to the second phase (effect sizes -0.20 to 1.04). Substantial reductions were observed in the normalized EMG data, with these effects being greater during the initial phase (effect sizes -1.54 to -0.28) compared to the second phase (effects sizes -1.12 to -0.22). There were also substantial reductions in non-normalized absolute EMG activity in both the initial (effect sizes -2.73 to -0.27) and second (effects sizes -1.27 to -0.20) phases. Over the 12 week training program substantial neuromuscular adaptations occurred in the core muscles; activation during the core exercises reduced, whilst activation during the MVCs increased. These adaptations are indicative of improvements in neuromuscular strength and efficiency. Changes in EMG data provide objective measures of neuromuscular adaptation which can inform future iterations of training regimens for athletic populations

GOLEM: an interactive graph-based gene-ontology navigation and analysis tool

BACKGROUND: The Gene Ontology has become an extremely useful tool for the analysis of genomic data and structuring of biological knowledge. Several excellent software tools for navigating the gene ontology have been developed. However, no existing system provides an interactively expandable graph-based view of the gene ontology hierarchy. Furthermore, most existing tools are web-based or require an Internet connection, will not load local annotations files, and provide either analysis or visualization functionality, but not both. RESULTS: To address the above limitations, we have developed GOLEM (Gene Ontology Local Exploration Map), a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet at

Finding function: evaluation methods for functional genomic data

BACKGROUND: Accurate evaluation of the quality of genomic or proteomic data and computational methods is vital to our ability to use them for formulating novel biological hypotheses and directing further experiments. There is currently no standard approach to evaluation in functional genomics. Our analysis of existing approaches shows that they are inconsistent and contain substantial functional biases that render the resulting evaluations misleading both quantitatively and qualitatively. These problems make it essentially impossible to compare computational methods or large-scale experimental datasets and also result in conclusions that generalize poorly in most biological applications. RESULTS: We reveal issues with current evaluation methods here and suggest new approaches to evaluation that facilitate accurate and representative characterization of genomic methods and data. Specifically, we describe a functional genomics gold standard based on curation by expert biologists and demonstrate its use as an effective means of evaluation of genomic approaches. Our evaluation framework and gold standard are freely available to the community through our website. CONCLUSION: Proper methods for evaluating genomic data and computational approaches will determine how much we, as a community, are able to learn from the wealth of available data. We propose one possible solution to this problem here but emphasize that this topic warrants broader community discussion

Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes

BACKGROUND: The continuous and non-synchronous nature of postnatal male germ-cell development has impeded stage-specific resolution of molecular events of mammalian meiotic prophase in the testis. Here the juvenile onset of spermatogenesis in mice is analyzed by combining cytological and transcriptomic data in a novel computational analysis that allows decomposition of the transcriptional programs of spermatogonia and meiotic prophase substages. RESULTS: Germ cells from testes of individual mice were obtained at two-day intervals from 8 to 18 days post-partum (dpp), prepared as surface-spread chromatin and immunolabeled for meiotic stage-specific protein markers (STRA8, SYCP3, phosphorylated H2AFX, and HISTH1T). Eight stages were discriminated cytologically by combinatorial antibody labeling, and RNA-seq was performed on the same samples. Independent principal component analyses of cytological and transcriptomic data yielded similar patterns for both data types, providing strong evidence for substage-specific gene expression signatures. A novel permutation-based maximum covariance analysis (PMCA) was developed to map co-expressed transcripts to one or more of the eight meiotic prophase substages, thereby linking distinct molecular programs to cytologically defined cell states. Expression of meiosis-specific genes is not substage-limited, suggesting regulation of substage transitions at other levels. CONCLUSIONS: This integrated analysis provides a general method for resolving complex cell populations. Here it revealed not only features of meiotic substage-specific gene expression, but also a network of substage-specific transcription factors and relationships to potential target genes. BMC Genomics 2016 Aug 12; 17(1):628

Assessing the accuracy of perceptions of intelligence based on heritable facial features

Perceptions of intelligence based on facial features can have a profound impact on many social situations, but findings have been mixed as to whether these judgements are accurate. Even if such perceptions were accurate, the underlying mechanism is unclear. Several possibilities have been proposed, including evolutionary explanations where certain morphological facial features are associated with fitness-related traits (including cognitive development), or that intelligence judgements are over-generalisation of cues of transitory states that can influence cognition (e.g., tiredness). Here, we attempt to identify the morphological signals that individuals use to make intelligence judgements from facial photographs. In a genetically informative sample of 1660 twins and their siblings, we measured IQ and also perceptions of intelligence based on facial photographs. We found that intelligence judgements were associated with both stable morphological facial traits (face height, interpupillary distance, and nose size) and more transitory facial cues (eyelid openness, and mouth curvature). There was a significant association between perceived intelligence and measured IQ, but of the specific facial attributes only interpupillary distance (i.e., wide-set eyes) significantly mediated this relationship. We also found evidence that perceived intelligence and measured IQ share a familial component, though we could not distinguish between genetic and shared environmental sources