Article
Refine
Year of publication
- 2021 (39) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (39) (remove)
Language
- English (39)
Document Type
- Article (39) (remove)
Keywords
- constructive alignment (2)
- examination (2)
- long-term retention (2)
- multimodal (2)
- practical learning (2)
- AlterG (1)
- Bacillus sp (1)
- Biosolubilization (1)
- Bone quality and biomechanics (1)
- Bootstrap (1)
Is part of the Bibliography
- no (39)
Lignite biosolubilization and bioconversion by Bacillus sp.: the collation of analytical data
(2021)
The vast metabolic potential of microbes in brown coal (lignite) processing and utilization can greatly contribute to innovative approaches to sustainable production of high-value products from coal. In this study, the multi-faceted and complex coal biosolubilization process by Bacillus sp. RKB 7 isolate from the Kazakhstan coal-mining soil is reported, and the derived products are characterized. Lignite solubilization tests performed for surface and suspension cultures testify to the formation of numerous soluble lignite-derived substances. Almost 24% of crude lignite (5% w/v) was solubilized within 14 days under slightly alkaline conditions (pH 8.2). FTIR analysis revealed various functional groups in the obtained biosolubilization products. Analyses of the lignite-derived humic products by UV-Vis and fluorescence spectrometry as well as elemental analysis yielded compatible results indicating the emerging products had a lower molecular weight and degree of aromaticity. Furthermore, XRD and SEM analyses were used to evaluate the biosolubilization processes from mineralogical and microscopic points of view. The findings not only contribute to a deeper understanding of microbe–mineral interactions in coal environments, but also contribute to knowledge of coal biosolubilization and bioconversion with regard to sustainable production of humic substances. The detailed and comprehensive analyses demonstrate the huge biotechnological potential of Bacillus sp. for agricultural productivity and environmental health.
Delayed cerebral ischemia (DCI) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) and can lead to infarction and poor clinical outcome. The underlying mechanisms are still incompletely understood, but animal models indicate that vasoactive metabolites and inflammatory cytokines produced within the subarachnoid space may progressively impair and partially invert neurovascular coupling (NVC) in the brain. Because cerebral and retinal microvasculature are governed by comparable regulatory mechanisms and may be connected by perivascular pathways, retinal vascular changes are increasingly recognized as a potential surrogate for altered NVC in the brain. Here, we used non-invasive retinal vessel analysis (RVA) to assess microvascular function in aSAH patients at different times after the ictus.
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.
Thrombogenic complications are a main issue in mechanical circulatory support (MCS). There is no validated in vitro method available to quantitatively assess the thrombogenic performance of pulsatile MCS devices under realistic hemodynamic conditions. The aim of this study is to propose a method to evaluate the thrombogenic potential of new designs without the use of complex in-vivo trials. This study presents a novel in vitro method for reproducible thrombogenicity testing of pulsatile MCS systems using low molecular weight heparinized porcine blood. Blood parameters are continuously measured with full blood thromboelastometry (ROTEM; EXTEM, FIBTEM and a custom-made analysis HEPNATEM). Thrombus formation is optically observed after four hours of testing. The results of three experiments are presented each with two parallel loops. The area of thrombus formation inside the MCS device was reproducible. The implantation of a filter inside the loop catches embolizing thrombi without a measurable increase of platelet activation, allowing conclusions of the place of origin of thrombi inside the device. EXTEM and FIBTEM parameters such as clotting velocity (α) and maximum clot firmness (MCF) show a total decrease by around 6% with a characteristic kink after 180 minutes. HEPNATEM α and MCF rise within the first 180 minutes indicate a continuously increasing activation level of coagulation. After 180 minutes, the consumption of clotting factors prevails, resulting in a decrease of α and MCF. With the designed mock loop and the presented protocol we are able to identify thrombogenic hot spots inside a pulsatile pump and characterize their thrombogenic potential.
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.
In the context of the Solvency II directive, the operation of an internal risk model is a possible way for risk assessment and for the determination of the solvency capital requirement of an insurance company in the European Union. A Monte Carlo procedure is customary to generate a model output. To be compliant with the directive, validation of the internal risk model is conducted on the basis of the model output. For this purpose, we suggest a new test for checking whether there is a significant change in the modeled solvency capital requirement. Asymptotic properties of the test statistic are investigated and a bootstrap approximation is justified. A simulation study investigates the performance of the test in the finite sample case and confirms the theoretical results. The internal risk model and the application of the test is illustrated in a simplified example. The method has more general usage for inference of a broad class of law-invariant and coherent risk measures on the basis of a paired sample.
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.
Modern industry and multi-discipline projects require highly trained individuals with resilient science and engineering back-grounds. Graduates must be able to agilely apply excellent theoretical knowledge in their subject matter as well as essential practical “hands-on” knowledge of diverse working processes to solve complex problems. To meet these demands, university education follows the concept of Constructive Alignment and thus increasingly adopts the teaching of necessary practical skills to the actual industry requirements and assessment routines. However, a systematic approach to coherently align these three central teaching demands is strangely absent from current university curricula. We demonstrate the feasibility of implementing practical assessments in a regular theory-based examination, thus defining the term “blended assessment”. We assessed a course for natural science and engineering students pursuing a career in biomedical engineering, and evaluated the benefit of blended assessment exams for students and lecturers. Our controlled study assessed the physiological background of electrocardiograms (ECGs), the practical measurement of ECG curves, and their interpretation of basic pathologic alterations. To study on long time effects, students have been assessed on the topic twice with a time lag of 6 months. Our findings suggest a significant improvement in student gain with respect to practical skills and theoretical knowledge. The results of the reassessments support these outcomes. From the lecturers ́ point of view, blended assessment complements practical training courses while keeping organizational effort manageable. We consider blended assessment a viable tool for providing an improved student gain, industry-ready education format that should be evaluated and established further to prepare university graduates optimally for their future careers.