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In der Arbeit werden hydrodynamische Modelle und numerische Verfahren zur theoretischen Beschreibung von anisothermen Plasmen untersucht und zur Analyse von Argonentladungen eingesetzt. Es wird ein neues Vier-Momenten-Modell sowie ein neues Drift-Diffusionsmodell zur Beschreibung der Elektronen hergeleitet. Die Beschreibung der Schwerteilchen erfolgt auf Basis eines Zwei-Momenten-Modells bzw. eines Drift-Diffusionsmodells. Zur selbstkonsistenten Bestimmung des elektrischen Feldes wird die Poisson-Gleichung gelöst. Es wird gezeigt, dass die neu entwickelten Fluid-Modelle eingesetzt werden können, um nichtlokale Transporteffekte der Elektronen zu studieren. Zur Diskretisierung der Mehr-Momenten-Modelle werden neue FCT-Verfahren auf Basis der Finiter-Differenzen- und der Finite-Elemente-Methode hergeleitet. Die Diskretisierung der Drift-Diffusionsmodelle erfolgt mittels einer modifizierten Scharfetter-Gummel-Methode. Zur Unterstützung experimenteller Untersuchungen werden neben einer Niederdruckglimmentladung, einer RF-Entladung bei Niederdruck und einer gepulsten Atmosphärendruckentladung auch eine dielektrisch behinderte Entladung bei Atmosphärendruck analysiert. Es wird gezeigt, dass die experimentell beobachteten Schichtstrukturen auf die lange Lebensdauer metastabiler Argonatome zurückzuführen sind.
In classical models of tumorigenesis, the accumulation of tumor promoting chromosomal aberrations is described as a gradual process. Next-generation sequencing-based methods have recently revealed complex patterns of chromosomal aberrations, which are beyond explanation by these classical models of karyotypic evolution of tumor genomes. Thus, the term chromothripsis has been introduced to describe a phenomenon, where temporarily and spatially confined genomic instability results in dramatic chromosomal rearrangements limited to segments of one or a few chromosomes. Simultaneously arising and misrepaired DNA double-strand breaks are also the cause of another phenomenon called chromoplexy, which is characterized by the presence of chained translocations and interlinking deletion bridges involving several chromosomes. In this study, we demonstrate the genome-wide identification of chromosomal translocations based on the analysis of translocation-associated changes in spatial proximities of chromosome territories on the example of the cutaneous T-cell lymphoma cell line Se-Ax. We have used alterations of intra- and interchromosomal interaction probabilities as detected by genome-wide chromosome conformation capture (Hi-C) to infer the presence of translocations and to fine-map their breakpoints. The outcome of this analysis was subsequently compared to datasets on DNA copy number alterations and gene expression. The presence of chained translocations within the Se-Ax genome, partly connected by intervening deletion bridges, indicates a role of chromoplexy in the etiology of this cutaneous T-cell lymphoma. Notably, translocation breakpoints were significantly overrepresented in genes, which highlight gene-associated biological processes like transcription or other gene characteristics as a possible cause of the observed complex rearrangements. Given the relevance of chromosomal aberrations for basic and translational research, genome-wide high-resolution analysis of structural chromosomal aberrations will gain increasing importance.
Discrimination of Streptococcus pneumoniae from other Streptococcus mitis group (SMG) species is still challenging but very important due to their different pathogenic potential. In this study, we aimed to develop a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based optochin susceptibility test with an objective read-out. Optimal test performance was established and evaluated by testing consecutively collected respiratory isolates. Optochin in different concentrations as a potential breakpoint concentration was added to a standardized inoculum. Droplets of 6 µL with optochin and, as growth control, without optochin were spotted onto a MALDI target. Targets were incubated in a humidity chamber, followed by medium removal and on-target protein extraction with formic acid before adding matrix with an internal standard. Spectra were acquired, and results were interpreted as S. pneumoniae in the case of optochin susceptibility (no growth), or as non-S. pneumoniae in the case of optochin non-susceptibility (growth). Highest test accuracy was achieved after 20 h incubation time (95.7%). Rapid testing after 12 h incubation time (optochin breakpoint 2 µg/mL; correct classification 100%, validity 62.5%) requires improvement by optimization of assay conditions. The feasibility of the MALDI-TOF MS-based optochin susceptibility test was demonstrated in this proof-of-principle study; however, confirmation and further improvements are warranted.
Matrix-assisted laser desorption/ionization time-of-flight-mass spectrometry (MALDI-TOF MS)-based direct-on-target microdroplet growth assay (DOT-MGA) was recently described as a novel method of phenotypic antimicrobial susceptibility testing (AST). Here, we developed the application of MALDI-TOF MS-based DOT-MGA for Gram-positive bacteria including AST from agar cultures and directly from positive blood cultures (BCs) using the detection of methicillin resistance as example. Consecutively collected, a total of 14 methicillin-resistant Staphylococcus aureus (MRSA) and 14 methicillin-susceptible S. aureus (MSSA) clinical isolates were included. Furthermore, a collection of MRSA challenge strains comprising different SCCmec types, mec genes, and spa types was tested. Blood samples were spiked with MRSA and MSSA and positive BC broth processed by three different methods: serial dilution of BC broth, lysis/centrifugation, and differential centrifugation. Processed BC broth was directly used for rapid AST using DOT-MGA. Droplets of 6 μl with and without cefoxitin at the EUCAST breakpoint concentration were spotted in triplicates onto the surface of a MALDI target. Targets were incubated in a humidity chamber, followed by medium removal and on-target protein extraction with formic acid before adding matrix with an internal standard as a quality control (QC). Spectra were acquired and evaluated using MALDI Biotyper software. First, tests were considered as valid, if the growth control achieved an identification score of ≥1.7. For valid tests, same score criterion was used for resistant isolates when incubated with cefoxitin. An identification score <1.7 after incubation with cefoxitin defined susceptible isolates. On-target protein extraction using formic acid considerably improved detection of methicillin resistance in S. aureus and DOT-MGA showed feasible results for AST from agar cultures after 4 h incubation time. Comparing the different processing methods of positive BC broth, lysis/centrifugation method with a final dilution step 10–1 of the 0.5 McFarland suspension resulted in best test performance after 4 h incubation time. Overall, 96.4% test validity, 100% sensitivity, and 100% specificity were achieved for detection of methicillin resistance in clinical isolates. All strains of the MRSA challenge collection were successfully tested as methicillin-resistant. This first study on Gram-positive organisms showed feasibility and accuracy of MALDI-TOF MS-based DOT-MGA for rapid AST of S. aureus from agar cultures and directly from positive BCs.
Background
Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.
Method
We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.
Results
Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.
Conclusion
We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.