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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.
The overall objective of this study is to develop a new external fixator, which closely maps the native kinematics of the elbow to decrease the joint force resulting in reduced rehabilitation time and pain. An experimental setup was designed to determine the native kinematics of the elbow during flexion of cadaveric arms. As a preliminary study, data from literature was used to modify a published biomechanical model for the calculation of the joint and muscle forces. They were compared to the original model and the effect of the kinematic refinement was evaluated. Furthermore, the obtained muscle forces were determined in order to apply them in the experimental setup. The joint forces in the modified model differed slightly from the forces in the original model. The muscle force curves changed particularly for small flexion angles but their magnitude for larger angles was consistent.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Malaria infection remains a significant risk for much of the population of tropical and subtropical areas, particularly in developing countries. Therefore, it is of high importance to develop sensitive, accurate and inexpensive malaria diagnosis tests. Here, we present a novel aptamer-based electrochemical biosensor (aptasensor) for malaria detection by impedance spectroscopy, through the specific recognition between a highly discriminatory DNA aptamer and its target Plasmodium falciparum lactate dehydrogenase (PfLDH). Interestingly, due to the isoelectric point (pI) of PfLDH, the aptasensor response showed an adjustable detection range based on the different protein net-charge at variable pH environments. The specific aptamer recognition allows sensitive protein detection with an expanded detection range and a low detection limit, as well as a high specificity for PfLDH compared to analogous proteins. The specific feasibility of the aptasensor is further demonstrated by detection of the target PfLDH in human serum. Furthermore, the aptasensor can be easily regenerated and thus applied for multiple usages. The robustness, sensitivity, and reusability of the presented aptasensor make it a promising candidate for point-of-care diagnostic systems.
Multi-analyte biosensors may offer the opportunity to perform cost-effective and rapid analysis with reduced sample volume, as compared to electrochemical biosensing of each analyte individually. This work describes the development of an enzyme-based biosensor system for multi-parametric determination of four different organic acids. The biosensor array comprises five working electrodes for simultaneous sensing of ethanol, formate, d-lactate, and l-lactate, and an integrated counter electrode. Storage stability of the biosensor was evaluated under different conditions (stored at +4 °C in buffer solution and dry at −21 °C, +4 °C, and room temperature) over a period of 140 days. After repeated and regular application, the individual sensing electrodes exhibited the best stability when stored at −21 °C. Furthermore, measurements in silage samples (maize and sugarcane silage) were conducted with the portable biosensor system. Comparison with a conventional photometric technique demonstrated successful employment for rapid monitoring of complex media.
A nonparametric goodness-of-fit test for random variables with values in a separable Hilbert space is investigated. To verify the null hypothesis that the data come from a specific distribution, an integral type test based on a Cramér-von-Mises statistic is suggested. The convergence in distribution of the test statistic under the null hypothesis is proved and the test's consistency is concluded. Moreover, properties under local alternatives are discussed. Applications are given for data of huge but finite dimension and for functional data in infinite dimensional spaces. A general approach enables the treatment of incomplete data. In simulation studies the test competes with alternative proposals.
The light-addressable potentiometric sensor (LAPS) and scanning photo-induced impedance microscopy (SPIM) are two closely related methods to visualise the distributions of chemical species and impedance, respectively, at the interface between the sensing surface and the sample solution. They both have the same field-effect structure based on a semiconductor, which allows spatially resolved and label-free measurement of chemical species and impedance in the form of a photocurrent signal generated by a scanning light beam. In this article, the principles and various operation modes of LAPS and SPIM, functionalisation of the sensing surface for measuring various species, LAPS-based chemical imaging and high-resolution sensors based on silicon-on-sapphire substrates are described and discussed, focusing on their technical details and prospective applications.