@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}
}
@inproceedings{BornheimGriegerBialonski2021,
  author    = {Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning},
  series = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  booktitle = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  publisher = {Heinrich Heine University},
  address   = {D{\"u}sseldorf},
  doi       = {10.48415/2021/fhw5-x128},
  pages     = {105 -- 111},
  year      = {2021},
  language  = {en}
}
@unpublished{RingersBialonskiSolovevetal.2021,
  author    = {Ringers, Christa and Bialonski, Stephan and Solovev, Anton and Hansen, Jan N. and Ege, Mert and Friedrich, Benjamin M. and Jurisch-Yaksi, Nathalie},
  title     = {Preprint: Local synchronization of cilia and tissue-scale cilia alignment are sufficient for global metachronal waves},
  series = {bioRxiv},
  journal   = {bioRxiv},
  doi       = {10.1101/2021.11.23.469646},
  pages     = {19 Seiten},
  year      = {2021},
  abstract  = {Motile cilia are hair-like cell extensions present in multiple organs of the body. How cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine here experiments, novel analysis tools, and theory to address this knowledge gap. We investigate collective dynamics of cilia in the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Despite the fact that synchronization is local only, we observed global patterns of traveling metachronal waves across the multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right nose, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment are sufficient to generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.},
  language  = {en}
}