TY - JOUR A1 - Grieger, Niklas A1 - Schwabedal, Justus T. C. A1 - Wendel, Stefanie A1 - Ritze, Yvonne A1 - Bialonski, Stephan T1 - Automated scoring of pre-REM sleep in mice with deep learning JF - Scientific Reports N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-91286-0 SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 11 IS - Art. 12245 PB - Springer Nature CY - London ER - TY - CHAP A1 - Hoffschmidt, Bernhard A1 - Alexopoulos, Spiros A1 - Rau, Christoph A1 - Sattler, Johannes Christoph A1 - Anthrakidis, Anette A1 - Teixeira Boura, Cristiano José A1 - O’Connor, B. A1 - Chico Caminos, Ricardo Alexander A1 - Rendón, C. A1 - Hilger, P. T1 - Concentrating Solar Power T2 - Earth systems and environmental sciences N2 - The focus of this chapter is the production of power and the use of the heat produced from concentrated solar thermal power (CSP) systems. The chapter starts with the general theoretical principles of concentrating systems including the description of the concentration ratio, the energy and mass balance. The power conversion systems is the main part where solar-only operation and the increase in operational hours. Solar-only operation include the use of steam turbines, gas turbines, organic Rankine cycles and solar dishes. The operational hours can be increased with hybridization and with storage. Another important topic is the cogeneration where solar cooling, desalination and of heat usage is described. Many examples of commercial CSP power plants as well as research facilities from the past as well as current installed and in operation are described in detail. The chapter closes with economic and environmental aspects and with the future potential of the development of CSP around the world. KW - Central receiver power plant KW - Concentrated systems KW - Concentrating solar power KW - Fresnel power plant KW - Gas turbine Y1 - 2021 SN - 978-0-12-409548-9 U6 - https://doi.org/10.1016/B978-0-12-819727-1.00089-3 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Schulte, Maximilian A1 - Eggert, Mathias T1 - Predicting hourly bitcoin prices based on long short-term memory neural networks T2 - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021 N2 - Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %. Y1 - 2021 N1 - 16th International Conference on Wirtschaftsinformatik, March 2021, Essen, Germany ER - TY - JOUR A1 - Ball, Christopher Stephen A1 - Vögele, Stefan A1 - Grajewski, Matthias A1 - Kuckshinrichs, Wilhelm T1 - E-mobility from a multi-actor point of view: Uncertainties and their impacts JF - Technological Forecasting and Social Change Y1 - 2021 SN - 0040-1625 U6 - https://doi.org/10.1016/j.techfore.2021.120925 VL - 170 IS - Art. 120925 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Jablonski, Melanie A1 - Poghossian, Arshak A1 - Severin, Robin A1 - Keusgen, Michael A1 - Wege, Christian A1 - Schöning, Michael Josef T1 - Capacitive Field-Effect Biosensor Studying Adsorption of Tobacco Mosaic Virus Particles JF - Micromachines N2 - Plant virus-like particles, and in particular, tobacco mosaic virus (TMV) particles, are increasingly being used in nano- and biotechnology as well as for biochemical sensing purposes as nanoscaffolds for the high-density immobilization of receptor molecules. The sensitive parameters of TMV-assisted biosensors depend, among others, on the density of adsorbed TMV particles on the sensor surface, which is affected by both the adsorption conditions and surface properties of the sensor. In this work, Ta₂O₅-gate field-effect capacitive sensors have been applied for the label-free electrical detection of TMV adsorption. The impact of the TMV concentration on both the sensor signal and the density of TMV particles adsorbed onto the Ta₂O₅-gate surface has been studied systematically by means of field-effect and scanning electron microscopy methods. In addition, the surface density of TMV particles loaded under different incubation times has been investigated. Finally, the field-effect sensor also demonstrates the label-free detection of penicillinase immobilization as model bioreceptor on TMV particles. KW - capacitive field-effect sensor KW - plant virus detection KW - tobacco mosaic virus (TMV) KW - TMV adsorption KW - Ta₂O₅ gate Y1 - 2021 U6 - https://doi.org/10.3390/mi12010057 VL - 12 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harzheim, Thomas A1 - Mühmel, Marc A1 - Heuermann, Holger T1 - A SFCW harmonic radar system for maritime search and rescue using passive and active tags JF - International Journal of Microwave and Wireless Technologies N2 - This paper introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested while attached to life jackets and a small boat. In this demonstration test carried out on the Baltic Sea, the system was able to detect and range the active tags up to a distance of 5800 m using an illumination signal transmit-power of 100 W. Special attention is given to the development, performance, and conceptual differences between passive and active tags used in the system. Guidelines for achieving a high HR dynamic range, including a system components description, are given and a comparison with other HR systems is performed. System integration with a commercial maritime X-band navigation radar is shown to demonstrate a solution for rapid search and rescue response and quick localization. KW - Radar KW - microwave measurements KW - harmonic radar KW - harmonic radar tags KW - nonlinear VNA measurements Y1 - 2021 U6 - https://doi.org/10.1017/S1759078721000520 VL - 13 IS - Special Issue 7 SP - 691 EP - 707 PB - Cambridge University Press CY - Cambridge ER - TY - THES A1 - Jung, Alexander T1 - Electromechanical modelling and simulation of hiPSC-derived cardiac cell cultures Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?https://nbn-resolving.org/urn:nbn:de:hbz:464-20210624-134942-7 SN - 978-3-9821811-1-0 N1 - Dissertation, Universität Duisburg-Essen, 2021 PB - Universität Duisburg-Essen ER - TY - JOUR A1 - Monakhova, Yulia A1 - Diehl, Bernd W.K. T1 - Novel approach of qNMR workflow by standardization using 2H integral: Application to any intrinsic calibration standard JF - Talanta N2 - Quantitative nuclear magnetic resonance (qNMR) is routinely performed by the internal or external standardization. The manuscript describes a simple alternative to these common workflows by using NMR signal of another active nuclei of calibration compound. For example, for any arbitrary compound quantification by NMR can be based on the use of an indirect concentration referencing that relies on a solvent having both 1H and 2H signals. To perform high-quality quantification, the deuteration level of the utilized deuterated solvent has to be estimated. In this contribution the new method was applied to the determination of deuteration levels in different deuterated solvents (MeOD, ACN, CDCl3, acetone, benzene, DMSO-d6). Isopropanol-d6, which contains a defined number of deuterons and protons, was used for standardization. Validation characteristics (precision, accuracy, robustness) were calculated and the results showed that the method can be used in routine practice. Uncertainty budget was also evaluated. In general, this novel approach, using standardization by 2H integral, benefits from reduced sample preparation steps and uncertainties, and can be applied in different application areas (purity determination, forensics, pharmaceutical analysis, etc.). KW - qNMR KW - Deuterium NMR KW - Deuterated solvents KW - Standardization Y1 - 2021 SN - 0039-9140 U6 - https://doi.org/10.1016/j.talanta.2020.121504 VL - 222 IS - Article number: 121504 PB - Elsevier ER - TY - JOUR A1 - Monakhova, Yulia A1 - Diehl, Bernd W. K. T1 - A step towards optimization of the qNMR workflow: proficiency testing exercise at an GxP-accredited laboratory JF - Applied Magnetic Resonance N2 - Quantitative nuclear magnetic resonance (qNMR) is considered as a powerful tool for multicomponent mixture analysis as well as for the purity determination of single compounds. Special attention is currently paid to the training of operators and study directors involved in qNMR testing. To assure that only qualified personnel are used for sample preparation at our GxP-accredited laboratory, weighing test was proposed. Sixteen participants performed six-fold weighing of the binary mixture of dibutylated hydroxytoluene (BHT) and 1,2,4,5-tetrachloro-3-nitrobenzene (TCNB). To evaluate the quality of data analysis, all spectra were evaluated manually by a qNMR expert and using in-house developed automated routine. The results revealed that mean values are comparable and both evaluation approaches are free of systematic error. However, automated evaluation resulted in an approximately 20% increase in precision. The same findings were revealed for qNMR analysis of 32 compounds used in pharmaceutical industry. Weighing test by six-fold determination in binary mixtures and automated qNMR methodology can be recommended as efficient tools for evaluating staff proficiency. The automated qNMR method significantly increases throughput and precision of qNMR for routine measurements and extends application scope of qNMR. Y1 - 2021 U6 - https://doi.org/10.1007/s00723-021-01324-3 SN - 1613-7507 N1 - Corresponding author: Yulia Monakhova VL - 52 SP - 581 EP - 593 PB - Springer Nature CY - Wien ER - TY - JOUR A1 - Engelmann, Ulrich M. A1 - Shalaby, Ahmed A1 - Shasha, Carolyn A1 - Krishnan, Kannan M. A1 - Krause, Hans-Joachim T1 - Comparative modeling of frequency mixing measurements of magnetic nanoparticles using micromagnetic simulations and Langevin theory JF - Nanomaterials N2 - Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory. KW - Magnetic nanoparticles KW - Frequency mixing magnetic detection KW - Langevin theory KW - Micromagnetic simulation KW - Nonequilibrium dynamics Y1 - 2021 SN - 2079-4991 U6 - https://doi.org/10.3390/nano11051257 N1 - This article belongs to the Special Issue Applications and Properties of Magnetic Nanoparticles VL - 11 IS - 5 SP - 1 EP - 16 PB - MDPI CY - Basel ER -