@article{RichterBraunsteinStaeudleetal.2021, author = {Richter, Charlotte and Braunstein, Bjoern and St{\"a}udle, Benjamin and Attias, Julia and Suess, Alexander and Weber, Tobias and Mileva, Katja N. and Rittweger, Joern and Green, David A. and Albracht, Kirsten}, title = {Gastrocnemius medialis contractile behavior is preserved during 30\% body weight supported gait training}, series = {Frontiers in Sports and Active Living}, volume = {2021}, journal = {Frontiers in Sports and Active Living}, number = {2}, publisher = {Frontiers}, address = {Lausanne}, issn = {2624-9367}, doi = {10.3389/fspor.2020.614559}, pages = {Artikel 614559}, year = {2021}, abstract = {Rehabilitative body weight supported gait training aims at restoring walking function as a key element in activities of daily living. Studies demonstrated reductions in muscle and joint forces, while kinematic gait patterns appear to be preserved with up to 30\% weight support. However, the influence of body weight support on muscle architecture, with respect to fascicle and series elastic element behavior is unknown, despite this having potential clinical implications for gait retraining. Eight males (31.9 ± 4.7 years) walked at 75\% of the speed at which they typically transition to running, with 0\% and 30\% body weight support on a lower-body positive pressure treadmill. Gastrocnemius medialis fascicle lengths and pennation angles were measured via ultrasonography. Additionally, joint kinematics were analyzed to determine gastrocnemius medialis muscle-tendon unit lengths, consisting of the muscle's contractile and series elastic elements. Series elastic element length was assessed using a muscle-tendon unit model. Depending on whether data were normally distributed, a paired t-test or Wilcoxon signed rank test was performed to determine if body weight supported walking had any effects on joint kinematics and fascicle-series elastic element behavior. Walking with 30\% body weight support had no statistically significant effect on joint kinematics and peak series elastic element length. Furthermore, at the time when peak series elastic element length was achieved, and on average across the entire stance phase, muscle-tendon unit length, fascicle length, pennation angle, and fascicle velocity were unchanged with respect to body weight support. In accordance with unchanged gait kinematics, preservation of fascicle-series elastic element behavior was observed during walking with 30\% body weight support, which suggests transferability of gait patterns to subsequent unsupported walking.}, language = {en} } @article{GriegerSchwabedalWendeletal.2021, author = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan}, title = {Automated scoring of pre-REM sleep in mice with deep learning}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {Art. 12245}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-021-91286-0}, year = {2021}, abstract = {Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.}, language = {en} } @article{Kleefeld2021, author = {Kleefeld, Andreas}, title = {The hot spots conjecture can be false: some numerical examples}, series = {Advances in Computational Mathematics}, volume = {47}, journal = {Advances in Computational Mathematics}, publisher = {Springer}, address = {Dordrecht}, issn = {1019-7168}, doi = {10.1007/s10444-021-09911-5}, year = {2021}, abstract = {The hot spots conjecture is only known to be true for special geometries. This paper shows numerically that the hot spots conjecture can fail to be true for easy to construct bounded domains with one hole. The underlying eigenvalue problem for the Laplace equation with Neumann boundary condition is solved with boundary integral equations yielding a non-linear eigenvalue problem. Its discretization via the boundary element collocation method in combination with the algorithm by Beyn yields highly accurate results both for the first non-zero eigenvalue and its corresponding eigenfunction which is due to superconvergence. Additionally, it can be shown numerically that the ratio between the maximal/minimal value inside the domain and its maximal/minimal value on the boundary can be larger than 1 + 10- 3. Finally, numerical examples for easy to construct domains with up to five holes are provided which fail the hot spots conjecture as well.}, language = {en} }