MODELLING OF MUSCLE CONTRACTION OF HUMAN TRICEPS BRACHll

A mathematical model for description of the time course of muscle contractions has been developed by investigations on isolated muscles (animal experiments). This model can be used to describe several types of contractions. Concentric muscle work was subdivided in three partial processes overlapping each other: - the activation process (characterized by the velocity of force development) - the shortening process - the process of force decrease, which is due to the increasing overlap of filaments at the end of contraction. Each process could be described by a separate function. The complex model equation is a multiplicative connection of these function terms. The equation entail seven free parameters representing physiological qualities of the investigated muscles. The parameter values can be estimated by fitting the function to the F(v(t))-curve of the muscle contraction (see right figure: contraction of m.triceps brachii during arm extension). The calculation of muscle forces and contraction velocities during human elbow extension is based on a geometrical model of the arm extensor system (left figure). Needed individual anatomical data were estimated by a special measurement procedure. The parameter values call be used to monitor changes ~n strength fitness achieved by special training protocols and to yield information about the physiological processes involved

SIMULATION OF THE FLIGHT DISTANCES OF JAVELINS BASED ON A NEURAL NETWORK APPROACH

INTRODUCTION: The flight distances of javelins are determined by the release parameters as well as by the forces acting on the javelin during flight. The flight phase of the javelin has been under investigation by many researchers using engineering approaches to model the flight phase. The objective is to allow an optimization of the release parameters for maximizing the flight distance. The measurement of release parameters as well as wind influence is not very precise. This means that the models are based on already distorted data. Artificial neural networks (NNs, Haykin 1994) are powerful information processing tools that allow to construct a input-output model of a problem by learning from examples. They are able to generalize , i.e. to produce reasonable outputs for inputs that have not been encountered during learning. NNs handle imprecise data well and could be suitable for modeling the flight distance of javelins as a result of the release parameters. METHODS: Release parameters have been measured using three dimensional film and video analysis. Relevant parameters were determined: the angle of release, the angle of attack (seen from the side), the angle of side attack (seen from behind) as well as the velocity of release. The overall flight was measured as the distance between the throwing line and the athlete’s hand at the point of release plus the distance between the line and the point of touch down of the javelin. Other parameters such as javelin brand, wind speed, etc., were not considered in the model. Multi-Layer-Perceptron Neural Networks (MLPs) were used to construct a model with the release parameters as inputs and the overall distance as output. RESULTS: Several setups were used for the training of the MLPs and 40 sets of release parameters were processed. We used 37 sets for the training of the MLPs and 3 sets were kept for examining the MLPs’ generalization performance (crossvalidation). This was repeated with randomly selected sets for training and crossvalidation. Predictions of the total flight distance using the release parameters were exact up to 5 percent of the overall distance for the cross validation sets. CONCLUSIONS: The MLP simulation of the flight distance is a suitable instrument even though it uses only a small number of parameters. This can be helpful for coaching and provides an alternative to other models. Using more data sets may improve the quality of prediction, and further work will include recording more data sets as well as studies on optimal javelin release parameters. REFERENCES: Haykin, S. (1994). Neural Networks. Englewood Cliffs: Macmillan Publishing Company

Genetic Fingerprint of Immunosuppression Following Half-marathon Running in Microarray Study

ABSTRACT Introduction: An acute bout of exhaustive exercise such as marathon or half-marathon running can interfere with immunity, reflected by transient immunosuppression and inflammation like reaction following the event. To gain more insights into these mechanisms, the capacity of whole blood cultures in profiling gene expression in response to endotoxin (LPS) was studied in athletes before, 30min after, 3h after and 24h after a half-marathon run. Methods: Four well trained men and 4 well trained women participated and gene expression patterns were assessed in LPS-stimulated (1h) and unstimulated whole blood using Affymetrix GeneChip microarrays. Results: exercise significantly altered several genes in LPS-stimualted and unstimulated blood cultures of male and female athletes. A row of genes with prominent anti-inflammatory function were strongly up-regulated in unstimulated cultures in both sexes (ARG-1, SOCS3, DUSP-1, BMX, GOS2, CD177, and GJB6). In the same cultures a row of highly inflammatory and apoptotic genes were down-regulated (Granzymes A-M-B-K-H, PRF1, SPON2, Granulysin, KLRF1, PLEKHF1). Some of these genes which were significantly up-or down-regulated in unstimulated cultures were also strongly regulated in LPS-stimulated cultures (GJB6, ARG-1, ORM2, KLRF1, TRA@///TRD@, Granzymes, SPON2). In addition, there were some strongly regulated genes which could only be detected in LPS-stimulated cultures but not in unstimulated cultures. Among these, TNIP3, PLAU, HIVEP1, and SLED were up-regulated and IFN-β, IFN-γ, L-12B, CXCL4. CXCL10 and TRAF1 were significantly down-regulated. Conclusion: there is a row of genes which are strongly regulated through exercise but can only be detected in (endotoxin) stimulated cultures. This is direct evidence showing that the response to pathogens is strongly down-regulated following prolonged exhaustive exercise through different ways