Overground vs. Treadmill Running: Do Runners Use the Same Strategy to Adjust Stride Length and Frequency While Running at Different Velocities?

Running speed is determined by stride frequency and stride length. As running speed is adjusted, runners make greater adjustments in stride length at slower speeds with a shift to stride frequency adjustments at the faster speeds. The relationship between stride frequency and stride length is largely based on overground research which leads to the purpose of this study to analyze whether the connection of stride frequency and stride length will adjust similar due to changes in running velocity during overground and treadmill running. The protocol was recently approved by The Institutional Review Board and data collection is currently in progress; - thus the following present abstract does not contain data. In order to compare runner’s gait pattern responses to velocity changes, two wearable technologies (Garmin Fenix2, Garmin, Kansas, USA; runScribe, Scribe Lab, San Francisco, USA) designed to measure stride length and stride frequency will be utilized. Subjects will run at a variety of velocities overground and then on the treadmill with speeds ranging from slow, preferred, and fast. The main dependent variables will be stride frequency and stride length. The null hypothesis is: The relationship between stride length and stride frequency is similar while running overground and on a treadmill at different velocities. The results of this study will be helpful to runners as well as development of wearable technology used to quantify run metrics

Isolation and Characterization of Adenoviruses Persistently Shed from the Gastrointestinal Tract of Non-Human Primates

Adenoviruses are important human pathogens that have been developed as vectors for gene therapies and genetic vaccines. Previous studies indicated that human infections with adenoviruses are self-limiting in immunocompetent hosts with evidence of some persistence in adenoid tissue. We sought to better understand the natural history of adenovirus infections in various non-human primates and discovered that healthy populations of great apes (chimpanzees, bonobos, gorillas, and orangutans) and macaques shed substantial quantities of infectious adenoviruses in stool. Shedding in stools from asymptomatic humans was found to be much less frequent, comparable to frequencies reported before. We purified and fully sequenced 30 novel adenoviruses from apes and 3 novel adenoviruses from macaques. Analyses of the new ape adenovirus sequences (as well as the 4 chimpanzee adenovirus sequences we have previously reported) together with 22 complete adenovirus genomes available from GenBank revealed that (a) the ape adenoviruses could clearly be classified into species corresponding to human adenovirus species B, C, and E, (b) there was evidence for intraspecies recombination between adenoviruses, and (c) the high degree of phylogenetic relatedness of adenoviruses across their various primate hosts provided evidence for cross species transmission events to have occurred in the natural history of B and E viruses. The high degree of asymptomatic shedding of live adenovirus in non-human primates and evidence for zoonotic transmissions warrants caution for primate handling and housing. Furthermore, the presence of persistent and/or latent adenovirus infections in the gut should be considered in the design and interpretation of human and non-human primate studies with adenovirus vectors

Functional Characterization of Transcription Factor Motifs Using Cross-species Comparison across Large Evolutionary Distances

We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score for motif scanning, the use of different scores for predicting targets of different motifs, and new ways to deal with redundancies among significant motif–function associations. This framework is applied to the recently sequenced genome of the jewel wasp, Nasonia vitripennis, making use of the existing knowledge of motifs and gene annotations in another insect genome, that of the fruitfly. The framework uses cross-species comparison to improve the specificity of its predictions, and does so without relying upon non-coding sequence alignment. It is therefore well suited for comparative genomics across large evolutionary divergences, where existing alignment-based methods are not applicable. We also apply the framework to find motifs associated with socially regulated gene sets in the honeybee, Apis mellifera, using comparisons with Nasonia, a solitary species, to identify honeybee-specific associations

Evaluating tools for transcription factor binding site prediction

Background: Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA. Results: We evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes — those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively. Conclusions: Selection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease