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Institute
- Fachbereich Medizintechnik und Technomathematik (1545) (remove)
Projection
(2009)
Production and Characterization of Porous Fibroin Scaffolds for Regenerative Medical Application
(2019)
Biomedical applications of magnetic nanoparticles (MNP) fundamentally rely on the particles’ magnetic relaxation as a response to an alternating magnetic field. The magnetic relaxation complexly depends on the interplay of MNP magnetic and physical properties with the applied field parameters. It is commonly accepted that particle core size is a major contributor to signal generation in all the above applications, however, most MNP samples comprise broad distribution spanning nm and more. Therefore, precise knowledge of the exact contribution of individual core sizes to signal generation is desired for optimal MNP design generally for each application. Specifically, we present a magnetic relaxation simulation-driven analysis of experimental frequency mixing magnetic detection (FMMD) for biosensing to quantify the contributions of individual core size fractions towards signal generation. Applying our method to two different experimental MNP systems, we found the most dominant contributions from approx. 20 nm sized particles in the two independent MNP systems. Additional comparison between freely suspended and immobilized MNP also reveals insight in the MNP microstructure, allowing to use FMMD for MNP characterization, as well as to further fine-tune its applicability in biosensing.
Pressure distribution to the distal biceps tendon at the radial tuberosity: a biomechanical study
(2020)
Purpose
Mechanical impingement at the narrow radioulnar space of the tuberosity is believed to be an etiological factor in the injury of the distal biceps tendon. The aim of the study was to compare the pressure distribution at the proximal radioulnar space between 2 fixation techniques and the intact state.
Methods
Six right arms and 6 left arms from 5 female and 6 male frozen specimens were used for this study. A pressure transducer was introduced at the height of the radial tuberosity with the intact distal biceps tendon and after 2 fixation methods: the suture-anchor and the cortical button technique. The force (N), maximum pressure (kPa) applied to the radial tuberosity, and the contact area (mm²) of the radial tuberosity with the ulna were measured and differences from the intact tendon were detected from 60° supination to 60° pronation in 15° increments with the elbow in full extension and in 45° and 90° flexion of the elbow.
Results
With the distal biceps tendon intact, the pressures during pronation were similar regardless of extension and flexion and were the highest at 60° pronation with 90° elbow flexion (23.3 ± 53.5 kPa). After repair of the tendon, the mean peak pressure, contact area, and total force showed an increase regardless of the fixation technique. Highest peak pressures were found using the cortical button technique at 45° flexion of the elbow and 60° pronation. These differences were significantly different from the intact tendon. The contact area was significantly larger in full extension and 15°, 30°, and 60° pronation using the cortical button technique.
Conclusions
Pressures on the distal biceps tendon at the radial tuberosity increase during pronation, especially after repair of the tendon.
Clinical relevance
Mechanical impingement could play a role in both the etiology of primary distal biceps tendon ruptures and the complications occurring after fixation of the tendon using certain techniques.
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.
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.
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.
We are developing an X-ray computed tomography (CT) system which will be combined with a high resolution animal PET system. This permits acquisition of both molecular and anatomical images in a single machine. In particular the CT will also be utilized for the quantification of the animal PET data by providing accurate data for attenuation correction. A first prototype has been built using a commercially available plane silicon diode detector. A cone-beam reconstruction provides the images using the Feldkamp algorithm. First measurements with this system have been performed on a mouse. It could be shown that the CT setup fulfils all demands for a high quality image of the skeleton of the mouse. It is also suited for soft tissue measurements. To improve contrast and resolution and to acquire the X-ray energy further development of the system, especially the use of semiconductor detectors and iterative reconstruction algorithms are planned.
Successful bone sawing requires a high level of skill and experience, which could be gained by the use of Virtual Reality-based simulators. A key aspect of these medical simulators is realistic force feedback. The aim of this paper is to model the bone sawing process in order to develop a valid training simulator for the bilateral sagittal split osteotomy, the most often applied corrective surgery in case of a malposition of the mandible. Bone samples from a human cadaveric mandible were tested using a designed experimental system. Image processing and statistical analysis were used for the selection of four models for the bone sawing process. The results revealed a polynomial dependency between the material removal rate and the applied force. Differences between the three segments of the osteotomy line and between the cortical and cancellous bone were highlighted.