Magnetic hybrid materials interact with biological matrices. - In: Physical sciences reviews, ISSN 2365-659X, Bd. 7 (2022), 12, S. 1443-1500
Magnetic hybrid materials are a promising group of substances. Their interaction with matrices is challenging with regard to the underlying physical and chemical mechanisms. But thinking matrices as biological membranes or even structured cell layers they become interesting with regard to potential biomedical applications. Therefore, we established in vitro blood-organ barrier models to study the interaction and processing of superparamagnetic iron oxide nanoparticles (SPIONs) with these cellular structures in the presence of a magnetic field gradient. A one-cell-type-based blood-brain barrier model was used to investigate the attachment and uptake mechanisms of differentially charged magnetic hybrid materials. Inhibition of clathrin-dependent endocytosis and F-actin depolymerization led to a dramatic reduction of cellular uptake. Furthermore, the subsequent transportation of SPIONs through the barrier and the ability to detect these particles was of interest. Negatively charged SPIONs could be detected behind the barrier as well as in a reporter cell line. These observations could be confirmed with a two-cell-type-based blood-placenta barrier model. While positively charged SPIONs heavily interact with the apical cell layer, neutrally charged SPIONs showed a retarded interaction behavior. Behind the blood-placenta barrier, negatively charged SPIONs could be clearly detected. Finally, the transfer of the in vitro blood-placenta model in a microfluidic biochip allows the integration of shear stress into the system. Even without particle accumulation in a magnetic field gradient, the negatively charged SPIONs were detectable behind the barrier. In conclusion, in vitro blood-organ barrier models allow the broad investigation of magnetic hybrid materials with regard to biocompatibility, cell interaction, and transfer through cell layers on their way to biomedical application.
https://doi.org/10.1515/psr-2019-0114
Synaptic scaling - an artificial neural network regularization inspired by nature. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-2388, Bd. 33 (2022), 7, S. 3094-3108
Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the objective of finding new strategies to enhance the learning capabilities of neural networks, we focus on a phenomenon that is closely related to learning tasks and neural stability in biological neural networks, called homeostatic plasticity. Among the theories that have been developed to describe homeostatic plasticity, synaptic scaling has been found to be the most mature and applicable. We systematically discuss previous studies on the synaptic scaling theory and how they could be applied to artificial neural networks. Therefore, we utilize information theory to analytically evaluate how mutual information is affected by synaptic scaling. Based on these analytic findings, we propose two flavors in which synaptic scaling can be applied in the training process of simple and complex, feedforward, and recurrent neural networks. We compare our approach with state-of-the-art regularization techniques on standard benchmarks. We found that the proposed method yields the lowest error in both regression and classification tasks compared to previous regularization approaches in our experiments across a wide range of network feedforward and recurrent topologies and data sets.
https://doi.org/10.1109/TNNLS.2021.3050422
On the uncertainty of defocus methods for 3D particle tracking velocimetry. - In: International Symposium on Particle Image Velocimetry, ISSN 2769-7576, Bd. 1 (2021), 1, insges. 2 S.
Defocus methods have become more and more popular for the estimation of the 3D position of particles in flows (Cierpka and Kähler, 2011; Rossi and Kähler, 2014). Typically the depth positions of particles are determined by the defocused particle images using image processing algorithms. As these methods allow the determination of all components of the velocity vector in a volume using only a single optical access and a single camera, they are often used in, but not limited to microfluidics. Since almost no additional equipment is necessary they are low-cost methods that are meanwhile widely applied in different fields. To overcome the ambiguity of perfect optical systems, often a cylindrical lens is introduced in the optical system which enhances the differences of the obtained particle images for different depth positions. However, various methods are emerging and it is difficult for non-experienced users to judge what method might be best suited for a given experimental setup. Therefore, the aim of the presentation is a thorough evaluation of the performance of general advanced methods, including also recently presented neural networks (Franchini and Krevor, 2020; König et al., 2020) based on typical images.
https://doi.org/10.18409/ispiv.v1i1.80
SmartPIV - an app for flow visualization by cross-correlation and optical flow using smartphones. - In: International Symposium on Particle Image Velocimetry, ISSN 2769-7576, Bd. 1 (2021), 1, insges. 2 S.
In recent years smartphones considerably changed our communication and are used on a daily (or even every minute) basis especially by students without any difficulties. Fluid flows also belong to our daily experiences. However, the education of the basic principles of fluid mechanics is sometimes cumbersome due to its non-linear nature. This problem may be tackled in practical sessions applying flow visualization techniques in wind or water tunnels and directly learn from own observations. Nowadays, often optical methods like particle imaging velocimetry (PIV) or particle tracking velocimetry (PTV) are used for these purposes. A typical PIV/PTV setup consists of a (double)pulse laser, a scientific camera and a synchronization device. The costs for this equipment can easily add up to more than 100,000 euros and the installations and set up of the systems requires experiences and is complex. For these reasons Universities often only offer practical courses for a small amount of students and the students may not be allowed to use and set up the systems by their own as the equipment is also needed for scientific research. Due to the COVID-19 pandemic it is also often not allowed to share equipment or even to work in larger groups during practical sessions.
https://doi.org/10.18409/ispiv.v1i1.78
Separating regular languages over infinite words with respect to the Wagner hierarchy. - In: 41st IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, (2021), S. 46:1-46:13
We investigate the separation problem for regular ω-languages with respect to the Wagner hierarchy where the input languages are given as deterministic Muller automata (DMA). We show that a minimal separating DMA can be computed in exponential time and that some languages require separators of exponential size. Further, we show that in this setting it can be decided in polynomial time whether a separator exists on a certain level of the Wagner hierarchy and that emptiness of the intersection of two languages given by DMAs can be decided in polynomial time. Finally, we show that separation can also be decided in polynomial time if the input languages are given as deterministic parity automata.
https://doi.org/10.4230/LIPIcs.FSTTCS.2021.46
Trash or treasure? : machine-learning based PCB layout anomaly detection with AnoPCB. - In: SMACD / PRIME 2021, (2021), S. 48-51
https://ieeexplore.ieee.org/document/9547913
Security by Reconfiguration (SecRec) - Physikalische Sicherheit durch dynamische Hardware-Rekonfiguration, Teilvorhaben: Schutz vor Reverse-Engineering und Fehlerinjektionsangriffen durch dynamische Hardware-Rekonfiguartion : Abschlussbericht : Projektstart: 1. Januar 2017, Laufzeit: 48 Monate : Berichtszeitraum: 1. Januar 2017-30. September 2020. - Ilmenau : [Technische Universität Ilmenau]. - 1 Online-Ressource (17 Seiten, 390,27 KB)Förderkennzeichen BMBF 16KIS0609
https://doi.org/10.2314/KXP:1806258749
Routing of cardiovascular and respiratory oscillations to the retinal vessels. - In: Journal of vascular research, ISSN 1423-0135, Bd. 58 (2021), S. 16
https://doi.org/10.1159/000518026
Coupled CP decomposition of EEG and MEG magnetometer and gradiometer measurements via the coupled SECSI framework. - In: Conference record of the Fifty-Fifth Asilomar Conference on Signals, Systems & Computers, (2021), S. 1661-1667
Recent research has shown that the joint processing of simultaneously recorded EEG-MEG signals can be beneficial compared to the separate analysis of data if the corresponding tensors have one of their factor matrices in common. In this study, we perform a joint CP decomposition of simultaneously recorded EEG and MEG magnetometer as well as gradiometer measurements via a new extension of the coupled SEmi-Algebraic framework for the approximate CP decomposition via SImultaneous matrix diagonalization (SECSI) to jointly factorize several tensors with at least one factor matrix in common. In case of the biomedical data, the four measured tensors have the factor matrix in the frequency domain in common. The developed coupled SECSI algorithm allows extracting the signal sources even in ill-conditioned scenarios. In the final processing step, coupled SECSI chooses the best out of a variety of possible factor matrix estimates, i.e., a combination that leads to the smallest value of the reconstruction error. However, for data tensors that do not exhibit significant coupling, the uncoupled solution can also be selected. The results are evaluated on measured as well as on synthetic data and compared with other state-of-the-art approaches.
https://doi.org/10.1109/IEEECONF53345.2021.9723118
Die digitale Zukunft des Lernens und Lehrens mit Remote-Laboren. - In: Digitalisierung in Studium und Lehre gemeinsam gestalten, (2021), S. 553-575
Der Einsatz von Remote-Laboren in ingenieurwissenschaftlichen Studiengängen ermöglicht Studierenden an einigen Hochschulen die ortsunabhängige Nutzung von Laboren, Maschinen und Robotern. Remote-Labore eignen sich in besonderer Weise dafür, den digitalisierungsbedingten Anforderungen und dem Qualifikationsbedarf aus Wirtschaft und Industrie zu begegnen. Die Onlinebedienung von Laboren bietet viele Ansatzpunkte für den Erwerb digitaler Kompetenzen, wie beispielsweise das Sammeln und Analysieren von Big Data, das Entwickeln geeigneter Schnittstellen für den Onlinezugriff oder den korrekten Einsatz zur Verfügung stehender softwarebasierter Messtechnik. Auch während der Coronapandemie im Sommersemester 2020, als der reguläre Zugang zu Laboren aufgrund der Kontaktbeschränkungen nicht erlaubt war, ermöglichten Remote-Labore den Studierenden praktische Erfahrungen. Jedoch stellen nicht nur die didaktischen, sondern auch die technischen und organisatorischen Aspekte ingenieurwissenschaftliche Studiengänge bei der Umsetzung von Remote-Laboren vor anspruchsvolle Aufgaben. Der nachfolgende Beitrag greift diese Aspekte auf und beschreibt anhand ausgewählter Beispiele, wie die Umsetzung und Integration von Remote-Laboren in Studium und Lehre gelingen kann, aber auch welche Herausforderungen nach wie vor bestehen.
https://doi.org/10.1007/978-3-658-32849-8_31