Synergies and end-effector kinematics in upper limb movements

When humans perform movements repeatedly, they are never completely the same. This is possible because many degrees of freedom (DOF) of the human motor system are involved when performing a motor action. In most cases, the number of DOF involved exceeds the minimum necessary to complete the motor task at hand. This results in many possible solutions for a given task, which is the so-called redundancy problem. To coordinate these redundant degrees of freedom (DOF), synergies are often proposed. A synergy is defined as the temporary linking of DOF into task-specific units. Kay (1988) described the emergence of a synergy as the first step of a two-step constraining process due to the interactions amongst environment, organism, and task constraints. In the second step, the constraints act on the synergy, resulting in the specific behavior. This two-step process was examined by looking at the influence of task constraints on synergies, on end-effector kinematics, and on both levels concurrently. The first step of the two-step process was assessed using the uncontrolled manifold analysis of joint angle variability and the second step was assessed using end-effector kinematics. The results revealed that task constraints influenced synergies and end-effector kinematics independently. More importantly, the results of both synergy and end-effector level demonstrated that some constraints are mainly involved in the first step of the process, whereas other constraints mainly influence the second step of the process. This suggests that a two-step process is at play to coordinate the redundant DOF

Epidemiology of rhizomania disease of sugar beet = Epidemiologie van rhizomanie bij suikerbiet

Rhizomania disease of sugar beet is caused by beet necrotic yellow vein virus (BNYVV). The virus is transmitted by the soil-borne fungus Polymyxa betae. The disease can cause severe losses in sugar yield, depending on the level of infestation in the soil, the environmental conditions during the growing season and the susceptibility of the beet cultivar. Several aspects of the epidemiology of the disease were studied. A quantitative bioassay was developed to assess inoculum potentials of virus and vector in soil. The bioassay allowed to estimate most probable numbers (MPN) of infective units of P.betae with or without virus from the incidence of infected bait plants in a dilution series of infested soil. The recovery of P.betae by bioassay, the effect of duration of the bioassay on detection level of BNYVV and the effect of soil treatments on infectivity of viruliferous resting spores of the vector were assessed. The MPN method enabled the establishment of a nonlinear relationship between inoculum potential of BNYVV in soil before sowing and disease incidence and yield parameters at harvest in an artificially infested field. In the same field, the dynamics of pathogen and vector populations during two successive beet crops in the absence or presence of drip irrigation was studied. A rapid increase of inoculum of BNYVV was found and, at the highest initial inoculum level (inoculum applied in 50 g infested soil m -2), sugar yield was reduced by 10% in the first and by 66% in the second year. Horizontal dispersal of viruliferous inoculum and spread of disease by movement of zoospores of the vector and by root growth of the host was limited to small distances. Displacement of infested soil by tillage practices resulted in spread over larger distances. Newly formed resting spores in roots of BNYVV-resistant plants were less viruliferous than those formed in roots of susceptible plants. A high level of BNYVV-resistance will be needed to reduce the build-up of virus inoculum in the field, which will contribute to the durability of disease resistance

Functional or not functional; that's the question Can we predict the diagnosis functional movement disorder based on associated features?

Background and purpose Functional movement disorders (FMDs) pose a diagnostic challenge for clinicians. Over the years several associated features have been shown to be suggestive for FMDs. Which features mentioned in the literature are discriminative between FMDs and non-FMDs were examined in a large cohort. In addition, a preliminary prediction model distinguishing these disorders was developed based on differentiating features. Method Medical records of all consecutive patients who visited our hyperkinetic outpatient clinic from 2012 to 2019 were retrospectively reviewed and 12 associated features in FMDs versus non-FMDs were compared. An independentttest for age of onset and Pearson chi-squared analyses for all categorical variables were performed. Multivariate logistic regression analysis was performed to develop a preliminary predictive model for FMDs. Results A total of 874 patients were eligible for inclusion, of whom 320 had an FMD and 554 a non-FMD. Differentiating features between these groups were age of onset, sex, psychiatric history, family history, more than one motor phenotype, pain, fatigue, abrupt onset, waxing and waning over long term, and fluctuations during the day. Based on these a preliminary predictive model was computed with a discriminative value of 91%. Discussion Ten associated features are shown to be not only suggestive but also discriminative between hyperkinetic FMDs and non-FMDs. Clinicians can use these features to identify patients suspected for FMDs and can subsequently alert them to test for positive symptoms at examination. Although a first preliminary model has good predictive accuracy, further validation should be performed prospectively in a multi-center study

Next move in movement disorders (NEMO):Developing a computer-aided classification tool for hyperkinetic movement disorders

Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process. Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision. Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases

Synergies et cinématiques de l'effecteur final dans les mouvements des membres supérieurs

Lorsque des êtres humains exécutent des mouvements de manière répétée, ceux-ci ne sont jamais complètement les mêmes. Cela s'explique par le fait que de nombreux degrés de liberté (DDL) du système moteur humain sont impliqués dans l'exécution d'un acte moteur. Dans la plupart des cas, le nombre de DDL mis en jeu excède le minimum nécessaire pour exécuter la tâche motrice à accomplir. Pour coordonner ces DDL redondants, des synergies sont souvent proposées. Une synergie est définie comme la liaison temporaire de DDL au sein d'unités spécifiques à une tâche. Kay a décrit l'émergence d'une synergie comme étant la première étape d'un processus contraignant en deux étapes dû aux interactions entre l'environnement, l'organisme et les contraintes de la tâche. Au cours de la seconde étape, les contraintes agissent sur la synergie, entraînant le comportement spécifique. Ce processus en deux étapes a été étudié en considérant l'influence des contraintes de la tâche sur les deux niveaux. La première étape du processus en deux étapes a été évaluée au moyen de l'analyse Uncontrolled Manifold de la variabilité des angles articulaires et la seconde étape à l'aide de la cinématique de l'effecteur final. Les résultats du niveau simultané des synergies et de l'effecteur final ont démontré que certaines contraintes sont principalement impliquées dans la première étape du processus, alors que d'autres influencent principalement la seconde étape du processus. En d'autres termes, des contraintes de tâche différentes sont impliquées dans chaque étape du processus contraignant en deux étapes, ce qui semble suggérer qu'un processus en deux étapes est à l'œuvre pour coordonner les DDL redondants.When humans perform movements repeatedly, they are never completely the same. This is possible because many degrees of freedom (DOF) of the human motor system are involved when performing a motor action. In most cases, the number of DOF involved exceeds the minimum necessary to complete the motor task at hand. To coordinate these DOF, synergies are often proposed. A synergy is defined as the temporary linking of DOF into task-specific units. Kay (1988) described the emergence of a synergy as the first step of a two-step constraining process due to the interactions amongst environment, organism, and task constraints. In the second step, the constraints act on the synergy, resulting in the specific behavior. This two-step process was examined by looking at the influence of task constraints on synergies, on end-effector kinematics, and on both levels concurrently. To analyze the first step of the two-step process, the emergence of a synergy, was assessed using the uncontrolled manifold analysis of joint angle variability and the second step, the emergence of the specific behavior, was assessed using end-effector kinematics. The results revealed that task constraints influenced synergies and end-effector kinematics independently. More importantly, the results of both synergy and end-effector level demonstrated that some constraints are mainly involved in the first step of the process, whereas other constraints mainly influence the second step of the process. That is, different task constraints are involved in each step of the two-step constraining process, suggesting that a two-step process is at play to coordinate the redundant DOF