Reliability of and relationship between flight time to contraction time ratio and reactive strength index modified

Countermovement jump (CMJ) force-time testing is commonly used to monitor seasonal alterations in athletes’ CMJ strategy (to infer alterations in neuromuscular function). The flight time to contraction time (FT:CT) ratio and reactive strength index modified (RSImod) are considered to be two primary CMJ variables of interest. Due to similar calculations, it is likely that the FT:CT ratio and RSImod share similar reliability and an almost perfect relationship. Consequently, there may be no requirement to include both variables in CMJ monitoring reports. This study aimed to investigate this by recruiting twenty-five males to perform three CMJs on a force platform across two sessions that were separated by one week. The FT:CT ratio and two calculations of RSImod (based on the jump height from either flight time or take-off velocity) were then calculated using robust methods. The between-day reliability was good-excellent for all of the variables (95% confidence interval range of the coefficient of variation = 2.02–9.22%) with no significant between-day differences noted (p ≥ 0.231). There was an almost perfect positive relationship between the FT:CT ratio and RSImod regardless of the calculation method (r = 0.944–0.947, p < 0.001). As the FT:CT ratio and RSImod yield similar absolute reliability and share 90% of common variance, there is little reason to include both variables in CMJ monitoring reports

Understanding the key phases of the countermovement jump force-time curve

The countermovement jump (CMJ) test is commonly conducted to assess neuromuscular function and is being increasingly performed using force platforms. Comprehensive insight into athletes’ neuromuscular function can be gained through detailed analyses of force-time curves throughout specific phases of the CMJ, beyond jump height alone. Confusingly, however, many different terms and methods have been used to describe the different phases of the CMJ. This article describes how six key phases of the CMJ (weighing, unweighting, braking, propulsion, flight, and landing) can be derived from force-time records to facilitate researchers’ and practitioners’ understanding and application to their own practice

The validity of the Push Band 2.0 during vertical jump performance

The Push Band has the potential to provide a cheap and practical method of measuring velocity and power during countermovement vertical jumping (CMJ). However, very little is known about whether it conforms to laboratory-based gold standards. The aim of this study was to assess the agreement between peak and mean velocity and power obtained from the belt-worn Push Band, and derived from three-dimensional motion capture, and vertical force from an in-ground force platform. Twenty-two volunteers performed 3 CMJ on a force platform, while a belt-worn Push Band and a motion capture system (a marker affixed to the Push Band) simultaneously recorded data that enabled peak and mean velocity and power to be calculated and then compared using ordinary least products regression. While the Push Band is reliable, it tends to overestimate peak (9⁻17%) and mean (24⁻27%) velocity, and when compared to force plate-derived peak and mean power, it tends to underestimate (40⁻45%) and demonstrates fixed and proportional bias. This suggests that while the Push Band may provide a useful method for measuring peak and mean velocity during the CMJ, researchers and practitioners should be mindful of its tendency to systematically overestimate and that its measures of peak and mean power should not be used

A proposed method for evaluating drop jump performance with one force platform

The drop jump (DJ) is commonly utilised to assess athletes. The criterion two force platform (2FP) method of assessing DJ performance involves two adjacent force platforms, one for the box and one for the athlete to rebound from. Most researchers and practitioners only have access to one force platform (1FP) and they rarely account for the often considerable discrepancy between box height and drop height (DH). Therefore, this study aimed to determine the criterion validity of evaluating DJ performance with 1FP. Twenty-six young male sports students performed three DJs, from a 0.30 m and 0.40 m high box, on two adjacent force platforms. The DH, touchdown velocity and several performance variables were calculated using the 2FP and 1FP methods. Ordinary least-products regression identified no fixed or proportional bias between methods for any DJ variable. The mean DH was 10% lower than the 0.30 m box and 14% lower than the 0.40 m high box. This discrepancy highlights the importance of accounting for DH when conducting DJ assessments. In conclusion, the 1FP method of evaluating DJ performance is a valid alternative to the criterion 2FP method and could be embedded into automated force analysis software for researchers and practitioners to utilise

Countermovement jump standards in rugby league : what is a “good” performance?

The countermovement jump (CMJ) is considered an important test in rugby league and the force platform is the recommended tool for assessing CMJ performance in this cohort. Due to inconsistent methods applied across previous studies, there is currently a lack of understanding of what constitutes a ‘good’ CMJ performance, with respect to the typical CMJ metrics that are reported for rugby league players. The purpose of this study was, therefore, to produce a scale of reference values for the jump height (JH), reactive strength index modified (RSImod) and mean (PPmean) and peak (PPpeak) propulsion power (relative to body mass) for top-level senior rugby league players competing in the global ‘forward’ and ‘back’ positional groups. One hundred and four players (55 forwards and 49 backs) from the top two tiers of English rugby league performed three CMJs on a force platform at the beginning of pre-season training. The JH, RSImod, PPmean and PPpeak were calculated using criterion methods and a scale of norm-referenced values (percentiles) was produced for each positional group. The backs outperformed the forwards for each CMJ metric reported, thus supporting the production of position-specific norm-referenced values. When each positional group was separated into quartile subgroups, the respective JH, RSImod, PPmean and PPpeak values were mostly largely and significantly different both within and between positions. The presented scale of reference values can, therefore, be used to determine the performance standards of rugby league forwards and backs with respect to the most commonly reported CMJ-derived variables for this cohort

Reassessment of the Lineage Fusion Hypothesis for the Origin of Double Membrane Bacteria

In 2009, James Lake introduced a new hypothesis in which reticulate phylogeny reconstruction is used to elucidate the origin of Gram-negative bacteria (Nature 460: 967–971). The presented data supported the Gram-negative bacteria originating from an ancient endosymbiosis between the Actinobacteria and Clostridia. His conclusion was based on a presence-absence analysis of protein families that divided all prokaryotes into five groups: Actinobacteria, Double Membrane bacteria (DM), Clostridia, Archaea and Bacilli. Of these five groups, the DM are by far the largest and most diverse group compared to the other groupings. While the fusion hypothesis for the origin of double membrane bacteria is enticing, we show that the signal supporting an ancient symbiosis is lost when the DM group is broken down into smaller subgroups. We conclude that the signal detected in James Lake's analysis in part results from a systematic artifact due to group size and diversity combined with low levels of horizontal gene transfer.Exobiology Program (U.S.) (Grant NNX08AQ10G)Assembling the Tree of Life (Program) (Grant DEB 0830024

Mechanical power production assessment during weightlifting exercises. A systematic review

The assessment of the mechanical power production is of great importance for researchers and practitioners. The purpose of this review was to compare the differences in ground reaction force (GRF), kinematic, and combined (bar velocity x GRF) methods to assess mechanical power production during weightlifting exercises. A search of electronic databases was conducted to identify all publications up to 31 May 2019. The peak power output (PPO) was selected as the key variable. The exercises included in this review were clean variations, which includes the hang power clean (HPC), power clean (PC) and clean. A total of 26 articles met the inclusion criteria with 53.9% using the GRF, 38.5% combined, and 30.8% the kinematic method. Articles were evaluated and descriptively analysed to enable comparison between methods. The three methods have inherent methodological differences in the data analysis and measurement systems, which suggests that these methods should not be used interchangeably to assess PPO in Watts during weightlifting exercises. In addition, this review provides evidence and rationale for the use of the GRF to assess power production applied to the system mass while the kinematic method may be more appropriate when looking to assess only the power applied to the barbell

BranchClust: a phylogenetic algorithm for selecting gene families

BACKGROUND: Automated methods for assembling families of orthologous genes include those based on sequence similarity scores and those based on phylogenetic approaches. The first are easy to automate but usually they do not distinguish between paralogs and orthologs or have restriction on the number of taxa. Phylogenetic methods often are based on reconciliation of a gene tree with a known rooted species tree; a limitation of this approach, especially in case of prokaryotes, is that the species tree is often unknown, and that from the analyses of single gene families the branching order between related organisms frequently is unresolved. RESULTS: Here we describe an algorithm for the automated selection of orthologous genes that recognizes orthologous genes from different species in a phylogenetic tree for any number of taxa. The algorithm is capable of distinguishing complete (containing all taxa) and incomplete (not containing all taxa) families and recognizes in- and outparalogs. The BranchClust algorithm is implemented in Perl with the use of the BioPerl module for parsing trees and is freely available at . CONCLUSION: BranchClust outperforms the Reciprocal Best Blast hit method in selecting more sets of putatively orthologous genes. In the test cases examined, the correctness of the selected families and of the identified in- and outparalogs was confirmed by inspection of the pertinent phylogenetic trees

Sporulation, bacterial cell envelopes, and the origin of life

Electron cryotomography (ECT) enables the 3D reconstruction of intact cells in a near-native state. Images produced by ECT have led to the proposal that an ancient sporulation-like event gave rise to the second membrane in diderm bacteria. Tomograms of sporulating monoderm and diderm bacterial cells show how sporulation can lead to the generation of diderm cells. Tomograms of Gram-negative and Gram-positive cell walls and purified sacculi suggest that they are more closely related than previously thought and support the hypothesis that they share a common origin. Mapping the distribution of cell envelope architectures onto a recent phylogenetic tree of life indicates that the diderm cell plan, and therefore the sporulation-like event that gave rise to it, must be very ancient. One explanation for this model is that during the cataclysmic transitions of the early Earth, cellular evolution may have gone through a bottleneck in which only spores survived, which implies that the last bacterial common ancestor was a spore

Pathways for scale and discipline reconciliation: current socio-ecological modelling methodologies to explore and reconstitute human prehistoric dynamics

International audienceThis communication elaborates a plea for the necessity of a specific modelling methodology which does not sacrifice two modelling principles: explanation Micro and correlation Macro. Three goals are assigned to modelling strategies: describe, understand and predict. One tendency in historical and spatial modelling is to develop models at a micro level in order to describe and by that way, understand the connection between local ecological contexts, acquired through local ecological data, and local social practices, acquired through archaeology. However, such a method faces difficulties for expanding its validity: It is validated by its adequacy with local data, but the prediction step is unreachable and quite nothing can be said for places out where. On the other hand, building models at a far larger scale, for instance at the continent and even the world level, enhances the connection between ecology and its temporal variability. Such connections are based on well-founded theories but lower the " small causes, big effects " emergence corresponding to agent-based approaches and the related inherent variability of socio-ecological dynamics that one can notice at a lower scale. We then propose a plea for combining both elements for building large-scale modelling tools, which aims are to describe and provide predictions on long-term past evolutions, that include the test of explaining socio-anthropological hypotheses, i.e. the emergence and the spread of local social innovations