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The geometric arena here is a smooth manifold of dimension n equipped with a Riemannian or pseudo-Riemannian metric and an affine connection. Field theories following from a variational principle are considered on this basis. In this context, all invariants which are quadratic in the curvature are determined. The work derives several manifestly covariant formulas for the Euler-Lagrange derivatives or the field equations. Some of these field theories can be interpreted as gravitational theories alternatively to Einstein´s general relativity theory. The work also touches the difficult problem to define and to calculate energy and momentum of a gravitational field.
Tuberculosis (TB) has tremendous public health relevance. It most frequently affects the lung and is characterized by the development of unique tissue lesions, termed granulomas. These lesions encompass various immune populations, with macrophages being most extensively investigated. Myeloid derived suppressor cells (MDSCs) have been recently identified in TB patients, both in the circulation and at the site of infection, however their interactions with Mycobacterium tuberculosis (Mtb) and their impact on granulomas remain undefined. We generated human monocytic MDSCs and observed that their suppressive capacities are retained upon Mtb infection. We employed an in vitro granuloma model, which mimics human TB lesions to some extent, with the aim of analyzing the roles of MDSCs within granulomas. MDSCs altered the structure of and affected bacterial containment within granuloma-like structures. These effects were partly controlled through highly abundant secreted IL-10. Compared to macrophages, MDSCs activated primarily the NF-κB and MAPK pathways and the latter largely contributed to the release of IL-10 and replication of bacteria within in vitro generated granulomas. Moreover, MDSCs upregulated PD-L1 and suppressed proliferation of lymphocytes, albeit with negligible effects on Mtb replication. Further comprehensive characterization of MDSCs in TB will contribute to a better understanding of disease pathogenesis and facilitate the design of novel immune-based interventions for this deadly infection.
Die Arbeit befasst sich mit der Parameterbestimmung in gewöhnlichen Differentialgleichungssystemen aus gegebenen Messdaten. Als Zielfunktion wird die quadratische Abweichungen betrachtet, ebenso wie die Betragssummen- und Tschebyschev-Norm der Differenz von der Lösung der gewöhnlichen Differentialgleichung und des Messwert-Vektors. Zur Anwendung kommen dabei sowohl iterative Optimierungsverfahren als auch direkte Methoden der optimalen Steuerung.
Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
(2020)
A New Kind of Permutation Entropy Used to Classify Sleep Stages from Invisible EEG Microstructure
(2017)
Self-similar sets with the open set condition, the linear objects of fractal geometry, have been considered mainly for crystallographic data. Here we introduce new symmetry classes in the plane, based on rotation by irrational angles. Examples without characteristic directions, with strong connectedness and small complexity, were found in a computer-assisted search. They are surprising since the rotations are given by rational matrices, and the proof of the open set condition usually requires integer data. We develop a classification of self-similar sets by symmetry class and algebraic numbers. Examples are given for various quadratic number fields.
Anaplasma phagocytophilum and Anaplasma ovis–Emerging Pathogens in the German Sheep Population
(2021)
Knowledge on the occurrence of pathogenic tick-borne bacteria Anaplasma phagocytophilum and Anaplasma ovis is scarce in sheep from Germany. In 2020, owners from five flocks reported ill thrift lambs and ewes with tick infestation. Out of 67 affected sheep, 55 animals were clinically examined and hematological values, blood chemistry and fecal examinations were performed to investigate the underlying disease causes. Serological tests (cELISA, IFAT) and qPCR were applied to all affected sheep to rule out A. phagocytophilum and A. ovis as a differential diagnosis. Ticks were collected from selected pastures and tested by qPCR. Most animals (n = 43) suffered from selenium deficiency and endoparasites were detected in each flock. Anaplasma spp. antibodies were determined in 59% of examined sheep. Seventeen animals tested positive for A. phagocytophilum by qPCR from all flocks and A. phagocytophilum was also detected in eight pools of Ixodes ricinus. Anaplasma phagocytophilum isolates from sheep and ticks were genotyped using three genes (16S rRNA, msp4 and groEL). Anaplasma ovis DNA was identified in six animals from one flock. Clinical, hematological and biochemical changes were not significantly associated with Anaplasma spp. infection. The 16S rRNA analysis revealed known variants of A. phagocytophilum, whereas the msp4 and groEL showed new genotypes. Further investigations are necessary to evaluate the dissemination and health impact of both pathogens in the German sheep population particularly in case of comorbidities.
Discovering Latent Structure in High-Dimensional Healthcare Data: Toward Improved Interpretability
(2022)
This cumulative thesis describes contributions to the field of interpretable machine learning in the healthcare domain. Three research articles are presented that lie at the intersection of biomedical and machine learning research. They illustrate how incorporating latent structure can provide a valuable compression of the information hidden in complex healthcare data.
Methodologically, this thesis gives an overview of interpretable machine learning and the discovery of latent structure, including clusters, latent factors, graph structure, and hierarchical structure. Different workflows are developed and applied to two main types of complex healthcare data (cohort study data and time-resolved molecular data). The core result builds on Bayesian networks, a type of probabilistic graphical model. On the application side, we provide accurate predictive or discriminative models focusing on relevant medical conditions, related biomarkers, and their interactions.
Background
The alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments.
Results
We present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-likelihood, and thus, our approach is different from existing HMM training algorithms like Baum–Welch. Our method does not involve progressive, regressive, or divide-and-conquer heuristics. We use uniform batch sampling to adapt to large datasets in linear time without the requirement of a tree. When tested on ultra-large protein families with up to 3.5 million sequences, learnMSA is both more accurate and faster than state-of-the-art tools. On the established benchmarks HomFam and BaliFam with smaller sequence sets, it matches state-of-the-art performance. All experiments were done on a standard workstation with a GPU.
Conclusions
Our results show that learnMSA does not share the counterintuitive drawback of many popular heuristic aligners, which can substantially lose accuracy when many additional homologs are input. LearnMSA is a future-proof framework for large alignments with many opportunities for further improvements.
Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.
Diese Arbeit beschäftigt sich mit der Analyse und Modellierung des Microarrayexperiments. Hierfür wird das gesamte Experiment in fünf Teilprozesse zerlegt, die Reverse Transkription, die Hybridisierung, das Waschen, die Fluoreszenz und die Detektion. Jeder Teilprozess wurde separat modelliert und analysiert. Anschließend wurde die Teilprozesse im Gesamtmodell vereint und dieses für verschiedene Parametersituationen simuliert. Diese Arbeit ermöglicht eine mathematische Handhabung des Microarrayexperiments und deckt seine Abhängigkeit von den einzelnen Schritten des Experiments auf. Dies kann benutzt werden, um Normalisierung und Analyse zu verbessern.
Abstract
The expected signature is an analogue of the Laplace transform for probability measures on rough paths. A key question in the area has been to identify a general condition to ensure that the expected signature uniquely determines the measures. A sufficient condition has recently been given by Chevyrev and Lyons and requires a strong upper bound on the expected signature. While the upper bound was verified for many well‐known processes up to a deterministic time, it was not known whether the required bound holds for random time. In fact, even the simplest case of Brownian motion up to the exit time of a planar disc was open. For this particular case we answer this question using a suitable hyperbolic projection of the expected signature. The projection satisfies a three‐dimensional system of linear PDEs, which (surprisingly) can be solved explicitly, and which allows us to show that the upper bound on the expected signature is not satisfied.
Neutrophils in Tuberculosis: Cell Biology, Cellular Networking and Multitasking in Host Defense
(2021)
Neutrophils readily infiltrate infection foci, phagocytose and usually destroy microbes. In
tuberculosis (TB), a chronic pulmonary infection caused by Mycobacterium tuberculosis (Mtb),
neutrophils harbor bacilli, are abundant in tissue lesions, and their abundances in blood correlate
with poor disease outcomes in patients. The biology of these innate immune cells in TB is complex.
Neutrophils have been assigned host-beneficial as well as deleterious roles. The short lifespan of
neutrophils purified from blood poses challenges to cell biology studies, leaving intracellular
biological processes and the precise consequences of Mtb–neutrophil interactions ill-defined. The
phenotypic heterogeneity of neutrophils, and their propensity to engage in cellular cross-talk and
to exert various functions during homeostasis and disease, have recently been reported, and such
observations are newly emerging in TB. Here, we review the interactions of neutrophils with Mtb,
including subcellular events and cell fate upon infection, and summarize the cross-talks between
neutrophils and lung-residing and -recruited cells. We highlight the roles of neutrophils in TB
pathophysiology, discussing recent findings from distinct models of pulmonary TB, and emphasize
technical advances that could facilitate the discovery of novel neutrophil-related disease
mechanisms and enrich our knowledge of TB pathogenesis
Interactive Visualization for the Exploration of Aligned Biological Networks and Their Evolution
(2011)
Network Visualization is a widely used tool in biology. The biological networks, as protein-interaction-networks are important for many aspects in life. Today biologists use the comparison of networks of different species (network alignment) to understand the networks in more detail and to understand the underlying evolution. The goal of this work is to develop a visualization software that is able to visualize network alignments and also their evolution. The presented software is the first software for such visualization tasks. It uses 3D graphics and also animations for the dynamic visualization of evolution. This work consists of a review of the Related Work, a chapter about our Graph-based Approach for Interactive Visualization of Evolving Network Alignments, an explanation of the Graph Layout Algorithm and some hints for the Software System.
Trade of cattle between farms forms a complex trade network. We investigate partitions of this network for cattle trade in Germany. These partitions are groups of farms with similar properties and they are inferred directly from the trade pattern between farms. We make use of a rather new method known as stochastic block modeling (SBM) in order to divide the network into smaller units. SBM turns out to outperform the more established community detection method in the context of disease control in terms of trade restriction. Moreover, SBM is also superior to geographical based trade restrictions and could be a promising approach for disease control.
Background
The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes.
Results
Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments.
Conclusions
Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.
Maligne Erkrankungen zeigen oft charakteristische genetische Veränderungen. Das Auffinden derartiger Veränderungen wurde in den letzten Jahren durch verfeinerte molekulare Techniken erleichtert. Viele genetische Ereignisse in den maligne transformierten Zellen sind jedoch noch ungeklärt. Die präzise Bestimmung der Bruchpunktregionen chromosomaler Veränderungen bei T-Zell akuten lymphatischen Leukämien ist Inhalt dieser Arbeit. Hierzu wurde die „Fine Tiling-Comparative Genomhybridisierung“ (FT-CGH) mit der „Ligation mediated-PCR“ (LM-PCR) kombiniert. Diese Methoden wurden zunächst an Zelllinien etabliert und anschließend in verschiedenen Leukämieproben eingesetzt. Chromosomale Aberrationen gehen häufig mit Verlust oder Gewinn von genetischem Material einher. Diese unbalancierten Anomalien lassen sich durch die Comparative Genomhybridisierung (CGH) ermitteln. Dieses Verfahren ermöglicht Differenzen der DNA-Menge einer zu untersuchenden Probe bezogen auf eine interne Kontrollprobe zu detektieren. Bei der Fine Tiling-CGH werden gezielt chromosomale Abschnitte hochauflösend auf eventuelle Abweichungen des DNA-Gehaltes analysiert. Anschließend werden die detektierten Bruchpunktregionen der DNA Schwankungen mittels der LM-PCR untersucht. Ein Abgleich mit einer internen Kontrollzelllinie HEK 293-T lässt atypische PCR-Fragmente bei der untersuchten Probe aufspüren. Der anschließende Sequenzabgleich unter der Verwendung des BLASTn Suchprogramms (National Center for Biotechnology Information) führte in den untersuchten Zelllinien, wie auch in den T-Zell akuten lymphatischen Leukämieproben zur Identifizierung verschiedener genomischer Veränderungen. Neben einfachen Deletionen wurden auch bisher ungeklärte komplexere chromosomale Translokationen nachgewiesen. So konnte unter anderem bei einer lymphoblastischen T-Zell-Leukämie die Translokation t(12;14)(q23;q11.2) auf genomischer Ebene geklärt werden. Hierbei fand im Abschnitt 14q11 innerhalb des TRA/D Locus eine Deletion von 89 Kilobasen statt. Die Bruchenden wurden mit der Sequenz des open reading frames C12orf42, welches im 12q23 Chromosomenabschnitt lokalisiert ist, zusammengelagert. Bei dieser chromosomalen Aberration wurde die C12orf42 Sequenz zerstört und 1,3 Kilobasen deletiert. Des Weiteren konnte bei einer akuten lymphoblastischen T-Zell-Leukämie die Inversion inv(14)(q11q32) mit involvierten TRA/D und IGH Locus auf Sequenzebene geklärt werden. Der Bruch des 14q11 Bereiches fand zwischen dem Genabschnitt der konstanten Region (TRAC) des TRA/D Locus und dem DAD1 (defender against cell death 1) Gens statt, wobei im beteiligten genetischen Abschnitt keine Rekombinasesignalsequenz (RSS) zu finden ist. Dieses belegt, dass fehlerhafte Umlagerungen innerhalb des Genoms nicht ausschließlich auf die Rekombinase zurückzuführen sind. Die vorliegende Arbeit zeigt, dass die Kombination aus FT-CGH und LM-PCR eine präzise Bruchpunktanalyse unbekannter chromosomaler Aberrationen, welche mit Imbalancen einhergehen, ermöglicht. Diese genaue Analyse dient der Identifizierung von Genen, welche direkt und indirekt durch diese genomischen Umlagerungen betroffen sind. Das Wissen über diese Veränderungen kann für das Verständnis der Pathogenese, für diagnostische Zwecke und zum Nachweis der minimalen Resterkrankung eingesetzt werden. Eine Klärung beteiligter Gene und Signalwege wird es erlauben, zielgerichtete und individualisierte Therapiestrategien zu entwickeln.