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Using the OpenSim software and verified anatomical data, a computer model for the calculation of biomechanical parameters is developed and used to determine the effect of a reattachment of the Supraspinatus muscle with a medial displacement of the muscle attachment point, which may be necessary for a rupture of the supraspinatus tendon. The results include the influence of the operation on basic biomechanical parameters such as the lever arm, as well as the calculated the muscle activations for the supraspinatus and deltoid. In addition, the influence on joint stability is examined by an analysis of the joint reaction force. The study provides a detailed description of the used model, as well as medical findings to a reattachment of the supraspinatus.
Mit der Software OpenSim und überprüften anatomischen Daten wird ein Computermodell zur Berechnung von biomechanischen Parametern entwickelt und genutzt, um den Effekt einer Refixierung des Supraspinatusmuskels mit einer medialen Verschiebung des Muskelansatzpunktes zu ermitteln, wie sie unter anderem nach einem Riss der Supraspinatussehne notwendig sein kann. Die Ergebnisse umfassen hierbei den Einfluss der Operation auf grundlegende biomechanische Parameter wie den Hebelarm sowie die berechneten Muskelaktivierungen für den Supraspinatus und Deltoideus. Zusätzlich wird der Einfluss auf die Gelenkstabilität betrachtet und durch eine Analyse der Gelenkreaktionskraft untersucht. Die Studie bietet eine detaillierte Beschreibung des genutzten Modells, sowie medizinische Erkenntnisse zu einer Refixierung des Supraspinatus.
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.