Investigating the relationship between energy expenditure, walking speed and angle of turning in humans

Recent studies have suggested that changing direction is associated with significant additional energy expenditure. A failure to account for this additional energy expenditure of turning has significant implications in the design and interpretation of health interventions. The purpose of this study was therefore to investigate the influence of walking speed and angle, and their interaction, on energy expenditure in 20 healthy adults (7 female; 28±7 yrs). On two separate days, participants completed a turning protocol at one of 16 speed- (2.5, 3.5, 4.5, 5.5 km∙h-1) and angle (0, 45, 90, 180°) combinations, involving three minute bouts of walking, interspersed by three minutes seated rest. Each condition involved 5 m of straight walking before turning through the pre-determined angle with the speed dictated by a digital, auditory metronome. Tri-axial accelerometry and magnetometry were measured at 60 Hz, in addition to gas exchange on a breath-by-breath basis. Mixed models revealed a significant main effect for speed (F = 121.609, P < 0.001) and angle (F = 19.186, P < 0.001) on oxygen uptake () and a significant interaction between these parameters (F = 4.433, P < 0.001). Specifically, as speed increased, increased but significant increases in relative to straight line walking were only observed for 90° and 180° turns at the two highest speeds (4.5 and 5.5 km∙hr-1). These findings therefore highlight the importance of accounting for the quantity and magnitude of turns completed when estimating energy expenditure and have significant implications within both sport and health contexts

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Hierarchical event selection for video storyboards with a case study on snooker video visualization

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Soil fungal : Bacterial ratios are linked to altered carbon cycling

Acknowledgments We thank Steffen Ruehlow, Agnes Fastnacht, Karl Kuebler, Iris Kuhlmann, Heike Geilmann, and Petra Linke for technical support in establishing the experiment and with stable isotope analyses. We also thank Markus Lange, Daniel Read, and Hyun Gweon for helpful discussions. Funding AM has received funding from Max Planck Society and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 655240. AM has also received a career orientation grant from the Jena School for Microbial Communication (JSMC) that funded the laboratory visits. DFG SFB Aquadiva funded part of this work.Peer reviewedPublisher PD

A novel microarray approach reveals new tissue-specific signatures of known and predicted mammalian microRNAs

Microarrays to examine the global expression levels of microRNAs (miRNAs) in a systematic in-parallel manner have become important tools to help unravel the functions of miRNAs and to understand their roles in RNA-based regulation and their implications in human diseases. We have established a novel miRNA-specific microarray platform that enables the simultaneous expression analysis of both known and predicted miRNAs obtained from human or mouse origin. Chemically modified 2′-O-(2-methoxyethyl)-(MOE) oligoribonucleotide probes were arrayed onto Evanescent Resonance (ER) microchips by robotic spotting. Supplementing the complementary probes against miRNAs with carefully designed mismatch controls allowed for accurate sequence-specific determination of miRNA expression profiles obtained from a panel of mouse tissues. This revealed new expression signatures of known miRNAs as well as of novel miRNAs previously predicted using bioinformatic methods. Systematic confirmation of the array data with northern blotting and, in particular, real-time PCR suggests that the described microarray platform is a powerful tool to analyze miRNA expression patterns with rapid throughput and high fidelity

Targeted LC–MS derivatization for aldehydes and carboxylic acids with a new derivatization agent 4-APEBA

Based on the template of a recently introduced derivatization reagent for aldehydes, 4-(2-(trimethylammonio)ethoxy)benzeneaminium dibromide (4-APC), a new derivatization agent was designed with additional features for the analysis and screening of biomarkers of lipid peroxidation. The new derivatization reagent, 4-(2-((4-bromophenethyl)dimethylammonio)ethoxy)benzenaminium dibromide (4-APEBA) contains a bromophenethyl group to incorporate an isotopic signature to the derivatives and to add additional fragmentation identifiers, collectively enhancing the abilities for detection and screening of unknown aldehydes. Derivatization can be achieved under mild conditions (pH 5.7, 10 °C). By changing the secondary reagent (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide instead of sodium cyanoborohydride), 4-APEBA is also applicable to the selective derivatization of carboxylic acids. Synthesis of the new label, exploration of the derivatization conditions, characterization of the fragmentation of the aldehyde and carboxylic acid derivatives in MS/MS, and preliminary applications of the labeling strategy for the analysis of aldehydes in urine and plasma are described

Soil bacteria and carbon flux : the correlation with diversity and perturbation

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