TY - JOUR A1 - Heinke, Lars N. A1 - Knicker, Axel J. A1 - Albracht, Kirsten T1 - Test-retest reliability of the internal shoulder rotator muscles' stretch reflex in healthy men JF - Journal of Electromyography and Kinesiology N2 - Until now the reproducibility of the short latency stretch reflex of the internal rotator muscles of the glenohumeral joint has not been identified. Twenty-three healthy male participants performed three sets of external shoulder rotation stretches with various pre-activation levels on two different dates of measurement to assess test-retest reliability. All stretches were applied with a dynamometer acceleration of 104°/s2 and a velocity of 150°/s. Electromyographical response was measured via surface EMG. Reflex latencies showed a pre-activation effect (ƞ2 = 0,355). ICC ranged from 0,735 to 0,909 indicating an overall “good” relative reliability. SRD 95% lay between ±7,0 to ±12,3 ms.. The reflex gain showed overall poor test-retest reproducibility. The chosen methodological approach presented a suitable test protocol for shoulder muscles stretch reflex latency evaluation. A proof-of-concept study to validate the presented methodical approach in shoulder involvement including subjects with clinically relevant conditions is recommended. KW - stretch reflex KW - shoulder KW - test-retest reliability KW - intraclass correlation coefficient KW - standard error of measurement Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.jelekin.2021.102611 SN - 1050-6411 VL - 62 IS - Article 102611 PB - Elsevier CY - Amsterdam ER - TY - RPRT A1 - Stölzle-Feix, Sonja A1 - Thomas, Ulrich A1 - Engelstädter, Max A1 - Goßmann, Matthias A1 - Linder, Peter A1 - Staat, Manfred A1 - Raman, Aravind Hariharan A1 - Jung, Alexander A1 - Fertig, Niels T1 - Plattformtechnologie für kardiale Sicherheitspharmakologie basierend auf teilsynthetischem Herzmuskelgewebe (FLEXcyte) : gemeinsamer FuE-Abschlussbericht aller Partner des Verbundprojektes : Projektlaufzeit: 01.10.2018 bis 30.09.2020 Y1 - 2021 U6 - http://dx.doi.org/10.2314/KXP:1813208581 N1 - Förderkennzeichen BMBF 02P18K020-021 Verbundnummer 01185221 PB - Nanion Technologies GmbH CY - München ER - TY - JOUR A1 - Alexyuk, Madina A1 - Bogoyavlenskiy, Andrey A1 - Alexyuk, Pavel A1 - Moldakhanov, Yergali A1 - Berezin, Vladimir A1 - Digel, Ilya T1 - Epipelagic microbiome of the Small Aral Sea: Metagenomic structure and ecological diversity JF - MicrobiologyOpen N2 - Microbial diversity studies regarding the aquatic communities that experienced or are experiencing environmental problems are essential for the comprehension of the remediation dynamics. In this pilot study, we present data on the phylogenetic and ecological structure of microorganisms from epipelagic water samples collected in the Small Aral Sea (SAS). The raw data were generated by massive parallel sequencing using the shotgun approach. As expected, most of the identified DNA sequences belonged to Terrabacteria and Actinobacteria (40% and 37% of the total reads, respectively). The occurrence of Deinococcus-Thermus, Armatimonadetes, Chloroflexi in the epipelagic SAS waters was less anticipated. Surprising was also the detection of sequences, which are characteristic for strict anaerobes—Ignavibacteria, hydrogen-oxidizing bacteria, and archaeal methanogenic species. We suppose that the observed very broad range of phylogenetic and ecological features displayed by the SAS reads demonstrates a more intensive mixing of water masses originating from diverse ecological niches of the Aral-Syr Darya River basin than presumed before. KW - ecological structure KW - metagenomics KW - microbial diversity KW - shotgun sequencing KW - Small Aral Sea Y1 - 2021 U6 - http://dx.doi.org/10.1002/mbo3.1142 SN - 2045-8827 VL - 10 IS - 1 SP - 1 EP - 10 PB - Wiley CY - Weinheim ER - TY - CHAP A1 - Mandekar, Swati A1 - Jentsch, Lina A1 - Lutz, Kai A1 - Behbahani, Mehdi A1 - Melnykowycz, Mark T1 - Earable design analysis for sleep EEG measurements T2 - UbiComp '21 N2 - Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms). KW - EEG KW - sensors KW - Impedance Spectroscopy KW - Sleep EEG KW - biopotential electrodes Y1 - 2021 U6 - http://dx.doi.org/10.1145/3460418.3479328 N1 - UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, September 21–26, 2021, Virtual, USA SP - 171 EP - 175 ER - TY - CHAP A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Multi-attribute relation extraction (MARE): simplifying the application of relation extraction T2 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA N2 - Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations. Y1 - 2021 SN - 978-989-758-526-5 U6 - http://dx.doi.org/10.5220/0010559201480156 N1 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021 SP - 148 EP - 156 ER - TY - JOUR A1 - Jung, Alexander A1 - Staat, Manfred T1 - Erratum to "Modeling and simulation of human induced pluripotent stem cell-derived cardiac tissue" [GAMM-Mitteilungen, (2019), 42, 4, 10.1002/gamm.201900002] JF - GAMM-Mitteilungen Y1 - 2020 U6 - http://dx.doi.org/10.1002/gamm.202000011 SN - 1522-2608 N1 - Refers to: Modeling and simulation of human induced pluripotent stem cell-derived cardiac tissue. Alexander Jung, Manfred Staat. Volume 42, Issue 4. GAMM-Mitteilungen, 2019. https://doi.org/10.1002/gamm.201900002 VL - 43 IS - 4 PB - Wiley-VCH GmbH CY - Weinheim ER - TY - GEN A1 - Jung, Alexander A1 - Müller, Wolfram A1 - Staat, Manfred T1 - Corrigendum to “Wind and fairness in ski jumping: A computer modelling analysis” [J. Biomech. 75 (2018) 147–153] T2 - Journal of Biomechanics Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.jbiomech.2021.110690 SN - 0021-9290 N1 - Refers to: Alexander Jung, Wolfram Müller, Manfred Staat: Wind and fairness in ski jumping: A computer modelling analysis. Journal of Biomechanics, Volume 75. 25 June 2018. Pages 147-153. https://doi.org/10.1016/j.jbiomech.2018.05.001 VL - 128 IS - Article number: 110690 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Heel, Mareike van A1 - Dikta, Gerhard A1 - Braekers, Roel T1 - Bootstrap based goodness‑of‑fit tests for binary multivariate regression models JF - Journal of the Korean Statistical Society N2 - We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set. Y1 - 2021 U6 - http://dx.doi.org/10.1007/s42952-021-00142-4 SN - 2005-2863 (Online) SN - 1226-3192 (Print) N1 - Corresponding author: Mareike van Heel VL - 51 PB - Springer Nature CY - Singapur ER - TY - BOOK A1 - Dikta, Gerhard A1 - Scheer, Marsel T1 - Bootstrap Methods: With Applications in R N2 - This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics. Y1 - 2021 SN - 978-3-030-73480-0 U6 - http://dx.doi.org/10.1007/978-3-030-73480-0 PB - Springer CY - Cham ER - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Winkens, Marvin A1 - Zündorf, Albert T1 - Catalog integration of heterogeneous and volatile product data T2 - DATA 2020: Data Management Technologies and Applications N2 - The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations. Y1 - 2021 SN - 978-3-030-83013-7 U6 - http://dx.doi.org/10.1007/978-3-030-83014-4_7 N1 - International Conference on Data Management Technologies and Applications, DATA 2020, 7-9 July SP - 134 EP - 153 PB - Springer CY - Cham ER -