Neuromuscular Plantar Flexor Performance of Sprinters versus Physically Active Individuals.

INTRODUCTION: Comparison of the neuromuscular performance of different athlete types may give insight into the in vivo variability of these measures and their underpinning mechanisms. The study aims to compare the neuromuscular function of the plantar flexors of sprinters and physically active individuals to assess any differences in explosive force performance. METHODS: Neuromuscular performance of a group of sprinters (highly trained/national level, n = 12; elite/international level, n = 2) and physically active individuals ( n = 14) were assessed during involuntary, explosive, and maximum voluntary isometric plantar flexions, across different muscle-tendon unit (MTU) lengths (10° plantarflexion, 0° (anatomical zero/neutral), and 10° dorsiflexion). Plantarflexion rate of torque development (RTD) was measured in three 50-ms time windows from their onset. The synchronous activation of the plantar flexor agonist muscles was calculated as the time difference between 1) the first and last muscle onset and 2) the onsets of the two gastrocnemii muscles. Muscle size and MTU stiffness were assessed using sonograms of the medial gastrocnemius and myotendinous junction. RESULTS: Sprinters exhibited greater involuntary RTD across time points (0-50 ms, 50-100 ms) and MTU lengths. In addition, sprinters demonstrated greater early phase voluntary RTD (0-50 ms, 50-100 ms) across MTU lengths. Sprinters also demonstrated greater late-phase RTD (100-150 ms), and relative maximal voluntary torque at the DF angle only. The sprinters demonstrated a more synchronous activation of the gastrocnemii muscles. There were no observable differences in muscle size and MTU stiffness between groups. CONCLUSIONS: These findings suggest sprint-specific training could be a contributing factor toward improved explosive performance of the plantar flexors, particularly in the early phase of muscular contraction, evidenced by the greater explosive torque producing capabilities of sprinters.

Ankle and Plantar Flexor Muscle-Tendon Unit Function in Sprinters: A Narrative Review.

Maximal sprinting in humans requires the contribution of various muscle-tendon units (MTUs) and joints to maximize performance. The plantar flexor MTU and ankle joint are of particular importance due to their role in applying force to the ground. This narrative review examines the contribution of the ankle joint and plantar flexor MTUs across the phases of sprinting (start, acceleration, and maximum velocity), alongside the musculotendinous properties that contribute to improved plantar flexor MTU performance. For the sprint start, the rear leg ankle joint appears to be a particularly important contributor to sprint start performance, alongside the stretch-shortening cycle (SSC) action of the plantar flexor MTU. Comparing elite and sub-elite sprinters revealed that elite sprinters had a higher rate of force development (RFD) and normalized average horizontal block power, which was transferred via the ankle joint to the block. For the acceleration phase, the ankle joint and plantar flexor MTU appear to be the most critical of the major lower limb joints/MTUs. The contribution of the ankle joint to power generation and positive work is minimal during the first stance, but an increased contribution is observed during the second stance, mid-acceleration, and late-acceleration. In terms of muscular contributions, the gastrocnemius and soleus have distinct roles. The soleus acts mainly as a supporter, generating large portions of the upward impulse, whereas the gastrocnemius acts as both an accelerator and a supporter, contributing significantly to propulsive and upward impulses. During maximum velocity sprinting the ankle joint is a net dissipater of energy, potentially due to the greater vertical loading placed on the plantar flexors. However, the ankle joint is critical for energy transfer from proximal joints to ground force application to maintain velocity. In terms of the contribution of musculoskeletal factors to ankle joint and plantar flexor performance, an optimal plantar flexor MTU profile potentially exists, which is possibly a combination of several musculoskeletal factors, alongside factors such as footwear and technique.

Assessing network-based methods in the context of system toxicology.

Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark. Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner. Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug's mechanism of action. Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases.

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

Reliability of mechanical properties of the plantar flexor muscle tendon unit with consideration to joint angle and sex.

The reliability of mechanical measures can be impacted by the protocol used, including factors such as joint angle and the sex of participants. This study aimed to determine the inter-day reliability of plantar flexor mechanical measures across ankle joint angles and contraction types and consider potential sex-specific effects. 14 physically-active individuals participated in two identical measurement sessions involving involuntary and voluntary plantar flexor contractions, at three ankle angles (10° plantarflexion (PF), 0° (anatomical zero (AZ)), and 10° dorsiflexion (DF)), while torque and surface EMG were recorded. The reliability of mechanical parameters of maximal voluntary torque (MVT), rate of torque development (RTD), electromechanical delay, and tendon stiffness were assessed using absolute and relative reliability measures. MVT measures were reliable across ankle angles. RTD measures showed good group level reliability and moderate reliability for an individual during the early phase of contraction across ankle angles. Explosive voluntary torque measures tended to be less reliable from 50 ms onward, with varied reliability across angles for late-phase RTD. Tendon stiffness demonstrated the best reliability at the DF angle. Sex-based differences in the reliability of tendon measures found that females had significantly different initial tendon length between testing sessions. Despite this, tendon excursion, force, and stiffness measures demonstrated similar reliability compared to males. Ankle angle changes influence the reliability of plantar flexor mechanical measurements across contraction types, particularly for voluntary contractions. These results highlight the importance of establishing potential protocol effects on measurement reliability prior to quantifying plantar flexor mechanical measures.

Identifying multiscale translational safety biomarkers using a network-based systems approach.

Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.

Genomic and proteomic biomarker landscape in clinical trials.

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications.

Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.

Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression.

Understanding the molecular basis of major depression is critical for identifying new potential biomarkers and drug targets to alleviate its burden on society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of major depression-associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with major depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis and alteration of transcription factor binding sites. The fine-mapping process uncovered putative causally associated variants whose proximal genes were linked with major depression pathophysiology. Four colocalizing genetic variants altered the expression of five genes, highlighting the role of SLC12A5 in neuronal chlorine homeostasis and MYRF in nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of rs62259947 in modulating P4HTM expression by altering the YY1 binding site, altogether regulating hypoxia response. Overall, our pipeline could prioritize putative causal genetic variants in major depression. More importantly, it can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes.

BACKGROUND: A cancer diagnosis is a source of psychological and emotional stress, which are often maintained for sustained periods of time that may lead to depressive disorders. Depression is one of the most common psychological conditions in patients with cancer. According to the Global Cancer Observatory, breast and colorectal cancers are the most prevalent cancers in both sexes and across all age groups in Spain. OBJECTIVE: This study aimed to compare the prevalence of depression in patients before and after the diagnosis of breast or colorectal cancer, as well as to assess the usefulness of the analysis of free-text clinical notes in 2 languages (Spanish or Catalan) for detecting depression in combination with encoded diagnoses. METHODS: We carried out an analysis of the electronic health records from a general hospital by considering the different sources of clinical information related to depression in patients with breast and colorectal cancer. This analysis included ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis codes and unstructured information extracted by mining free-text clinical notes via natural language processing tools based on Systematized Nomenclature of Medicine Clinical Terms that mentions symptoms and drugs used for the treatment of depression. RESULTS: We observed that the percentage of patients diagnosed with depressive disorders significantly increased after cancer diagnosis in the 2 types of cancer considered-breast and colorectal cancers. We managed to identify a higher number of patients with depression by mining free-text clinical notes than the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.8% (441/1269). In addition, the number of patients with depression who received chemotherapy was higher than those who did not receive this treatment, with significant differences (P<.001). CONCLUSIONS: This study provides new clinical evidence of the depression-cancer comorbidity and supports the use of natural language processing for extracting and analyzing free-text clinical notes from electronic health records, contributing to the identification of additional clinical data that complements those provided by coded data to improve the management of these patients.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Effect of Changing Match Format from Halves to Quarters on the Performance Characteristics of Male University Field Hockey Players.

The study examined whether the performance characteristics of male university field hockey players differed when the match format was 2 × 35 min halves compared to 2 × 2 × 17.5 min quarters. Thirty-five male university field hockey players (age 21.2 ± 3.0 years, height 1.81 ± 0.07 m, body mass 75.1 ± 8.9 kg), competing at national level in the UK, were monitored over 52 matches played across the 2018-2019 (2 × 35 min halves) and 2019-2020 (2 × 2 × 17.5 min quarters) seasons using 15 Hz Global Positioning System units and heart rate monitors. Total distance, high-speed running distance (≥15.5 km·h-1), accelerations (≥2 m·s-1), decelerations (≤-2 m·s-1), average heart rate and percentage of time spent at >85% of maximum heart rate were recorded during both match formats. Two-level random intercept hierarchal models (Match-level 1, Player-level 2) suggested that the change in format from 2 × 35 min halves (2018-2019 season) to 2 × 2 × 17.5 min quarters (2019-2020 season) resulted in a reduction in total distance and high-speed running distance completed during a match (by 221 m and 120 m, respectively, both p < 0.001). As no significant cross-level interactions were observed (between season and half), the change from 35 min halves to 17.5 min quarters did not attenuate the reduced physical performance evident during the second half of matches (total distance: -235 m less in second half; high-speed running distance: -70 m less in second half; both p < 0.001). Overall, the findings suggest that the change in match format did alter the performance characteristics of male university field hockey players, but the quarter format actually reduced the total distance and high-speed running distance completed during matches, and did not attenuate the reduction in performance seen during the second half of matches.

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data.

Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

Sources of Error When Measuring Achilles Tendon Mechanics During the Stance Phase of Running.

In recent years, the use of methods to investigate muscle-tendon unit function that combine motion capture with ultrasound (MoCapUS) has increased. Although several limitations and individual errors of these methods have been reported, the total error from all the potential sources together has not been estimated. The aim of this study was to establish the total error in the Achilles tendon (AT) measurements, specifically its length (ATL), strain (ATS), and moment arm (ATMA) acquired with MoCapUS during running. The total error from digitizing, marker movement, ultrasound calibration, and probe rotation errors caused mean ATL error of 4.2 ± 0.6 mm, mean ATMA error of 0.1 ± 0.1 mm, and could potentially alter measured ATS by a mean 2.9 ± 0.2%. Correcting both the calcaneus insertion position (CIP) and properly synchronizing ultrasound and motion capture data caused changes of up to 5.4 ± 1.7 mm in ATL and 11.6 ± 1.3 mm in ATMA. CIP correction and synchronization caused a similar amount of change in ATL, as well as ATS. However, the ATMA change was almost exclusively due to the CIP correction. Finally, if all sources of error were combined, the total ATL error could reach 13.1 mm, the total ATMA error could reach 14.4 mm, and ATS differences could reach up to ± 6.7%. The magnitude of such errors emphasizes the fact that MoCapUS-based AT measurements must be interpreted within the scope of their corresponding errors.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Comorbidity between Alzheimer's disease and major depression: a behavioural and transcriptomic characterization study in mice.

BACKGROUND: Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer's disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. METHODS: We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before ß-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis. RESULTS: We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart, Grin3b, Nr1d1 and Mc4r, and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD. CONCLUSIONS: The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.

Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods.

Objective. Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the 'gold standard' in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events.Approach. This study aimed to determine: (1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets (2) if the Teager-Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique.Main results. For fast, explosive contractions, the TKEO was the best-performing automatic detection method, with a low bias level (4.7 ± 5.6 ms) and excellent intraclass correlation coefficient (ICC) of 0.993, however with wide limits of agreement (LoA) (-6.2 to +15.7 ms). For slow, ramped contractions, the AGLR with TKEO conditioning was the best-performing automatic detection method with the smallest bias (11.3 ± 32.9 ms) and excellent ICC (0.983) but produced wide LoA (-53.2 to +75.8 ms). For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning.Significance. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.

Measurement of instantaneous Achilles tendon moment arm and force during the stance phase of running.

Accurate estimates of the Achilles tendon (AT) moment arm (ATMA) are necessary for investigating triceps surae muscle-tendon unit loading and function. There are limited reported values of ATMA during running. By combining ultrasound and motion capture, ATMA was estimated during the stance phase of running. Group mean ATMA was estimated at 49.2 ± 3.8 mm and 37.5 ± 5.3 mm, relative to the centre of rotation (malleoli markers midpoint) and the ankle finite helical axis respectively. Differences in the corresponding estimated AT forces reached up to 3100 N approximately. Such discrepancies can lead to misinterpretation of the whole muscle-tendon unit function.