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Cell-based sensors for the detection of gases have long been underrepresented, due to the cellular requirement of being cultured in a liquid environment. In this work we established a cell-based gas biosensor for the detection of toxic substances in air, by adapting a commercial sensor chip (Bionas®), previously used for the measurement of pollutants in liquids. Cells of the respiratory tract (A549, RPMI 2650, V79), which survive at a gas phase in a natural context, are used as biological receptors. The physiological cell parameters acidification, respiration and morphology are continuously monitored in parallel. Ammonia was used as a highly water-soluble model gas to test the feasibility of the sensor system. Infrared measurements confirmed the sufficiency of the medium draining method. This sensor system provides a basis for many sensor applications such as environmental monitoring, building technology and public security.
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.
Rationale: Previous studies [Topolnik et al., Cereb Cortex 2003; 13: 883; Schindler et al., Brain 2007; 130: 65] indicate that the termination of focal onset seizures may be causally related to an increase of global neuronal correlation during the second half of the seizures. This increase was observed to occur earlier in complex partial seizures than in secondarily generalized seizures. We here address the question whether such an increase of neuronal correlation prior to seizure end is indeed a global phenomenon, involving both hemispheres or whether there are side-specific differences. Methods: We analyzed 20 focal onset seizures (10 complex partial, 10 secondarily generalized seizures) recorded in 13 patients who underwent presurgical evaluation of focal epilepsies of different origin. EEG was recorded intracranially from bilaterally implanted subdural strip and intrahippocampal depth electrodes. Utilizing a moving window approach, we investigated the evolution of the maximum cross correlation for all channel combinations during seizures. For each moving window the mean value of the maximum cross correlation (MCC) between all electrode contacts was computed separately for each hemisphere. After normalization of seizure durations, MCC values of the ipsi- and contralateral hemisphere for all seizures were determined. Results: We observed that the MCC of the contralateral hemisphere in complex partial seizures increased during the first half of the seizure, whereas, for the same time interval, the MCC of the ipsilateral hemisphere even declined below the level of the pre-seizure period. In contrast, no significant differences between both hemispheres could be observed for secondarily generalized seizures where both hemispheres showed a simultaneous increase of MCC during the second half of the seizures. The level of MCC for the contralateral hemisphere was higher for complex partial seizures than for secondarily generalized seizures during the first half of the seizure. Conclusions: Our findings indicate that there are indeed lateralized differences in the evolution of global neuronal correlation during complex partial and secondarily generalized seizures. The observed contralateral increase of neuronal correlation during complex partial seizures might indicate an emerging self-organizing mechanism for preventing the spread of seizure activity.