@phdthesis{Kneer2022,
  author    = {Kneer, Katharina Johanna},
  title     = {The association of three anxiety dimensions in children and adolescents: their influence on the brain and malleability by a prevention program},
  doi       = {10.25972/OPUS-25746},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-257468},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Anxiety disorders are the most prevalent group of neuropsychiatric disorders and go along with high personal suffering. They often arise during childhood and show a progression across the life span, thus making this age a specific vulnerable period during development. Still most research about these disorders is done in adults. In light of this, it seems of utmost importance to identify predictive factors of anxiety disorders in children and adolescents. Temperament or personality traits have been proclaimed as risk markers for the development of subsequent anxiety disorders, but their exact interplay is not clear. In this dissertation an effort is made to contribute to the understanding of how risk markers of early temperamental traits, in this case Trait Anxiety, Anxiety Sensitivity and Separation Anxiety are interplaying. While Trait Anxiety is regarded as a more general tendency to react anxiously to threatening situations or stimuli (Unnewehr, Joormann, Schneider, \& Margraf, 1992), Anxiety Sensitivity is the tendency to react with fear to one's own anxious sensations (Allan et al., 2014; S. Reiss, Peterson, Gursky, \& McNally, 1986), and Separation Anxiety is referring to the extent to which the child is avoiding certain situations because of the fear of being separated from primary care givers (In-Albon \& Schneider, 2011). In addition, it will be addressed how these measurements are associated with negative life events, as well as brain functioning and if they are malleable by a prevention program in children and adolescents. In study 1 the aim was to extend the knowledge about the interrelations of this anxiety dimensions and negative life events. Results indicated positive correlations of all three anxiety traits as well as with negative life events. Thus, a close connection of all three anxiety measures as well as with negative life events could be indicated. The closest association was found between Anxiety Sensitivity and Trait Anxiety and between Separation Anxiety and Anxiety Sensitivity. Furthermore, negative life events functioned as mediator between Anxiety Sensitivity and Trait Anxiety, indicating that a part of the association was explained by negative life events. In study 2 we extended the findings from study 1 with neurobiological parameters and examined the influence of anxiety traits on emotional brain activation by administering the "emotional face matching task". This task activated bilateral prefrontal regions as well as both hippocampi and the right amygdala. Further analyses indicated dimension-specific brain activations: Trait Anxiety was associated with a hyperactivation of the left inferior frontal gyrus (IFG) and Separation Anxiety with a lower activation bilaterally in the IFG and the right middle frontal gyrus (MFG). Furthermore, the association between Separation Anxiety and Anxiety Sensitivity was moderated by bi-hemispheric Separation-Anxiety-related IFG activation. Thus, we could identify distinct brain activation patterns for the anxiety dimensions (Trait Anxiety and Separation Anxiety) and their associations (Separation Anxiety and Anxiety Sensitivity). The aim of study 3 was to probe the selective malleability of the anxiety dimensions via a prevention program in an at-risk population. We could identify a reduction of all three anxiety traits from pre- to post-prevention-assessment and that this effect was significant in Anxiety Sensitivity and Trait Anxiety scores. Furthermore, we found that pre-intervention Separation Anxiety and Anxiety Sensitivity post-intervention were associated. In addition, pre-interventive scores were correlated with the intervention-induced change within the measure (i.e., the higher the score before the intervention the higher the prevention-induced change) and pre-intervention Anxiety Sensitivity correlated with the change in Separation Anxiety scores. All relations, seemed to be direct, as mediation/moderation analyses with negative life events did not reveal any significant effect. These results are very promising, because research about anxiety prevention in children and adolescents is still rare and our results are indicating that cognitive-behavioural-therapy based prevention is gilding significant results in an indicated sample even when samples sizes are small like in our study. In sum the present findings hint towards distinct mechanisms underlying the three different anxiety dimensions on a phenomenological and neurobiological level, though they are highly overlapping (Higa-McMillan, Francis, Rith-Najarian, \& Chorpita, 2016; Taylor, 1998). Furthermore, the closest associations were found between Anxiety Sensitivity and Trait Anxiety, as well as between Separation Anxiety and Anxiety Sensitivity. Specifically, we were able to find a neuronal manifestation of the association between Separation Anxiety and Anxiety Sensitivity (Separation Anxiety-specific IFG activation) and a predictive potential on prevention influence. The results of these studies lead to a better understanding of the etiology of anxiety disorders and the interplay between different anxiety-related temperamental traits and could lead to further valuable knowledge about the intervention as well as further prevention strategies.},
  subject      = {Pr{\"a}vention},
  language  = {en}
}
@article{GernertTonyFroehlichetal.2022,
  author    = {Gernert, Michael and Tony, Hans-Peter and Fr{\"o}hlich, Matthias and Schwaneck, Eva Christina and Schmalzing, Marc},
  title     = {Immunosuppressive therapy after autologous hematopoietic stem cell transplantation in systemic sclerosis patients — high efficacy of Rituximab},
  series = {Frontiers in Immunology},
  volume    = {12},
  journal   = {Frontiers in Immunology},
  issn      = {1664-3224},
  doi       = {10.3389/fimmu.2021.817893},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-254345},
  year      = {2022},
  abstract  = {Background Systemic sclerosis (SSc) patients often need immunosuppressive medication (IS) for disease control. If SSc is progressive despite IS, autologous hematopoietic stem cell transplantation (aHSCT) is a treatment option for selected SSc patients. aHSCT is effective with good available evidence, but not all patients achieve a treatment-free remission after aHSCT. Thus far, data about the need of IS after aHSCT in SSc is not published. The aim of this study was to investigate the use of IS after aHSCT, its efficacy, and the occurrence of severe adverse events (SAEs). Methods Twenty-seven patients with SSc who had undergone aHSCT were included in this single-center retrospective cohort study. Clinical data, including IS, SAEs, and lung function data, were collected. Results Sixteen of 27 (59.3\%) patients received IS after aHSCT. Methotrexate, rituximab, mycophenolate, cyclophosphamide, and hydroxychloroquine were most commonly used. The main reason for starting IS was SSc progress. Nine patients received rituximab after aHSCT and showed an improvement in modified Rodnan skin score and a stabilization of lung function 2 years after rituximab. SAEs in patients with IS after aHSCT (50.0\%) were not more common than in patients without IS (54.6\%). SAEs were mostly due to SSc progress, secondary autoimmune diseases, or infections. Two deaths after aHSCT were transplantation related and three during long-term follow-up due to pulmonary arterial hypertension. Conclusion Disease progression and secondary autoimmune diseases may necessitate IS after aHSCT in SSc. Rituximab seems to be an efficacious treatment option in this setting. Long-term data on the safety of aHSCT is reassuring.},
  language  = {en}
}
@phdthesis{Ji2022,
  author    = {Ji, Changhe},
  title     = {The role of 7SK noncoding RNA in development and function of motoneurons},
  doi       = {10.25972/OPUS-22463},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-224638},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {In mammals, a major fraction of the genome is transcribed as non-coding RNAs. An increasing amount of evidence has accumulated showing that non-coding RNAs play important roles both for normal cell function and in disease processes such as cancer or neurodegeneration. Interpreting the functions of non-coding RNAs and the molecular mechanisms through which they act is one of the most important challenges facing RNA biology today. In my Ph.D. thesis, I have been investigating the role of 7SK, one of the most abundant non-coding RNAs, in the development and function of motoneurons. 7SK is a highly structured 331 nt RNA transcribed by RNA polymerase III. It forms four stem-loop (SL) structures that serve as binding sites for different proteins. Larp7 binds to SL4 and protects the 3' end from exonucleolytic degradation. SL1 serves as a binding site for HEXIM1, which recruits the pTEFb complex composed of CDK9 and cyclin T1. pTEFb has a stimulatory role for transcription and is regulated through sequestration by 7SK. More recently, a number of heterogeneous nuclear ribonucleoproteins (hnRNPs) have been identified as 7SK interactors. One of these is hnRNP R, which has been shown to have a role in motoneuron development by regulating axon growth. Taken together, 7SK's function involves interactions with RNA binding proteins, and different RNA binding proteins interact with different regions of 7SK, such that 7SK can be considered as a hub for recruitment and release of different proteins. The questions I have addressed during my Ph.D. are as follows: 1) which region of 7SK interacts with hnRNP R, a main interactor of 7SK? 2) What effects occur in motoneurons after the protein binding sites of 7SK are abolished? 3) Are there additional 7SK binding proteins that regulate the functions of the 7SK RNP? Using in vitro and in vivo experiments, I found that hnRNP R binds both the SL1 and SL3 region of 7SK, and also that pTEFb cannot be recruited after deleting the SL1 region but is able to bind to a 7SK mutant with deletion of SL3. In order to answer the question of how the 7SK mutations affect axon outgrowth and elongation in mouse primary motoneurons, we proceeded to conduct rescue experiments in motoneurons by using lentiviral vectors. The constructs were designed to express 7SK deletion mutants under the mouse U6 promoter and at the same time to drive expression of a 7SK shRNA from an H1 promoter for the depletion of endogenous 7SK. Using this system we found that 7SK mutants harboring deletions of either SL1 or SL3 could not rescue the axon growth defect of 7SK-depleted motoneurons suggesting that 7SK/hnRNP R complexes are integral for this process. In order to identify novel 7SK binding proteins and investigate their functions, I proceeded to conduct pull-down experiments by using a biotinylated RNA antisense oligonucleotide that targets the U17-C33 region of 7SK thereby purifying endogenous 7SK complexes. Following mass spectrometry of purified 7SK complexes, we identified a number of novel 7SK interactors. Among these is the Smn complex. Deficiency of the Smn complex causes the motoneuron disease spinal muscular atrophy (SMA) characterized by loss of lower motoneurons in the spinal cord. Smn has previously been shown to interact with hnRNP R. Accordingly, we found Smn as part of 7SK/hnRNP R complexes. These proteomics data suggest that 7SK potentially plays important roles in different signaling pathways in addition to transcription.},
  subject      = {Spliceosome},
  language  = {en}
}
@phdthesis{Goettler2022,
  author    = {G{\"o}ttler, David Johannes},
  title     = {Smoking cessation patterns in patients with established coronary heart disease},
  doi       = {10.25972/OPUS-22395},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-223955},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Background Tobacco smoking is accountable for more than one in ten deaths in patients with cardiovascular disease. Thus, smoking cessation has a high priority in secondary prevention of coronary heart disease (CHD). The present study meant to assess smoking cessation patterns, identify parameters associated with smoking cessation and investigate personal reasons to change or maintain smoking habits in patients with established CHD. Methods Quality of CHD care was surveyed in 24 European countries in 2012/13 by the fourth European Survey of Cardiovascular Disease Prevention and Diabetes. Patients 18 to 79 years of age at the date of the CHD index event hospitalized due to first or recurrent diagnosis of coronary artery bypass graft, percutaneous coronary intervention, acute myocardial infarction or acute myocardial ischemia without infarction (troponin negative) were included. Smoking status and clinical parameters were iteratively obtained a) at the cardiovascular disease index event by medical record abstraction, b) during a face-to-face interview 6 to 36 months after the index event (i.e. baseline visit) and c) by telephone-based follow-up interview two years after the baseline visit. Parameters associated with smoking status at the time of follow-up interview were identified by logistic regression analysis. Personal reasons to change or maintain smoking habits were assessed in a qualitative interview and analyzed by qualitative content analysis. Results One hundred and four of 469 (22.2\%) participants had been classified current smokers at the index event and were available for follow-up interview. After a median observation period of 3.5 years (quartiles 3.0, 4.1), 65 of 104 participants (62.5\%) were classified quitters at the time of follow-up interview. There was a tendency of diabetes being more prevalent in quitters vs non-quitters (37.5\% vs 20.5\%, p=0.07). Higher education level (15.4\% vs 33.3\%, p=0.03) and depressed mood (17.2\% vs 35.9\%, p=0.03) were less frequent in quitters vs non-quitters. Quitters more frequently participated in cardiac rehabilitation programs (83.1\% vs 48.7\%, p<0.001). Cardiac rehabilitation appeared as factor associated with smoking cessation in multivariable logistic regression analysis (OR 5.19, 95\%CI 1.87 to 14.46, p=0.002). Persistent smokers at telephone-based follow-up interview reported on addiction as wells as relaxation and pleasure as reasons to continue their habit. Those current and former smokers who relapsed at least once after a quitting attempt, stated future health hazards as their main reason to undertake quitting attempts. Prevalent factors leading to relapse were influence by their social network and stress. Successful quitters at follow-up interview referred to smoking-related harm done to their health having had been their major reason to quit. Interpretation Participating in a cardiac rehabilitation program was strongly associated with smoking cessation after a cardiovascular disease index event. Smoking cessation counseling and relapse prophylaxis may include alternatives for the pleasant aspects of smoking and incorporate effective strategies to resist relapse.},
  subject      = {Tabakkonsum},
  language  = {en}
}
@phdthesis{Klinke2022,
  author    = {Klinke, Christopher Matthias},
  title     = {Experimental investigation of the effect of distal stress induction on threat conditioning in humans},
  doi       = {10.25972/OPUS-22556},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225562},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Stress constitutes a major risk factor for the development of psychiatric disorders, such as PTSD and anxiety disorders, by shifting the brain into a state of sensitization and makes it more vulnerable when being exposed to further aversive events. This was experimentally in-vestigated in rodents by examining the effect of a distal stress induction on threat conditioning, where stress impaired extinction learning and caused spontaneous recovery. However, this effect has never been experimentally investigated in humans, so far. Thus, the aim of this dissertation was to investigate the effect of distal stress on threat conditioning in humans. Therefore, two subsequent studies were conducted. For both studies, the threat conditioning paradigm comprised threat acquisition, extinction learning, and re-extinction. In the threat acquisition phase, two geometrical shapes were used as conditioned stimulus (CS), from which one (CS+) was paired with a painful electric stimulus (unconditioned stimulus, US), but not the other one (CS-). During extinction learning 24 h later and re-extinction seventeen days later, CSs were again presented but without any US delivery. In Study 1, 69 participants underwent either a stress (socially evaluated cold pressor test; SECPT) or sham protocol 10 days prior to threat conditioning. Furthermore, context effects were examined by placing the stress protocol in the same context (context-A stress, and sham group) or a different context (context-B stress group) than conditioning. Results revealed that the context-A, but not context-B, stress group displayed impaired safety learning (i.e. potenti-ation towards CS-) for startle response during threat acquisition. Moreover, the same stress group showed impaired threat extinction, evident in sustained CS discrimination in valence and arousal ratings during extinction learning, and memory recall. In sum, distal stress on the one hand impaired safety learning during threat conditioning on a level of startle response. On the other hand, stress impaired threat extinction on a level of ratings. Noteworthy, the effect of distal stress was only found when the stressor was placed in the same context as later threat learning. Hence, suggesting that the combination of stressor and stressor-associated context exerted the effect on threat extinction. In Study 2, it was examined if distal stress induction could also have an impact on threat and extinction processes without the necessity of context association. Therefore, the same stress (n = 45) or sham protocol (n = 44) as in Study 1 was conducted in a different context than and 24 h prior to a threat conditioning paradigm. Similar to Study 1, weakened extinction learning was found in fear ratings for the stress (vs. sham) group, which was indicated by persistent CS+/CS- differentiation after the first block of extinction trials. Alterations in safety learning towards the CS- during threat acquisition were only supported by significant correlations between stress measures on the stress day and conditioned startle response of the CS- during acquisition. Taken together, in two subsequent studies this dissertation provided first evidence of impaired threat extinction after distal stress induction in humans. Furthermore, impairments in safety learning, as can be observed in PTSD, were additionally demonstrated. Interestingly, the effects were boosted and more profound when associating the stressor to the later learning context. These results have clinical implications as they can be translated to the notion that prior stress exposure makes an individual more vulnerable for later aversive events.},
  subject      = {Stress},
  language  = {en}
}
@phdthesis{Zuefle2022,
  author    = {Z{\"u}fle, Marwin Otto},
  title     = {Proactive Critical Event Prediction based on Monitoring Data with Focus on Technical Systems},
  doi       = {10.25972/OPUS-25575},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-255757},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {The importance of proactive and timely prediction of critical events is steadily increasing, whether in the manufacturing industry or in private life. In the past, machines in the manufacturing industry were often maintained based on a regular schedule or threshold violations, which is no longer competitive as it causes unnecessary costs and downtime. In contrast, the predictions of critical events in everyday life are often much more concealed and hardly noticeable to the private individual, unless the critical event occurs. For instance, our electricity provider has to ensure that we, as end users, are always supplied with sufficient electricity, or our favorite streaming service has to guarantee that we can watch our favorite series without interruptions. For this purpose, they have to constantly analyze what the current situation is, how it will develop in the near future, and how they have to react in order to cope with future conditions without causing power outages or video stalling. In order to analyze the performance of a system, monitoring mechanisms are often integrated to observe characteristics that describe the workload and the state of the system and its environment. Reactive systems typically employ thresholds, utility functions, or models to determine the current state of the system. However, such reactive systems cannot proactively estimate future events, but only as they occur. In the case of critical events, reactive determination of the current system state is futile, whereas a proactive system could have predicted this event in advance and enabled timely countermeasures. To achieve proactivity, the system requires estimates of future system states. Given the gap between design time and runtime, it is typically not possible to use expert knowledge to a priori model all situations a system might encounter at runtime. Therefore, prediction methods must be integrated into the system. Depending on the available monitoring data and the complexity of the prediction task, either time series forecasting in combination with thresholding or more sophisticated machine and deep learning models have to be trained. Although numerous forecasting methods have been proposed in the literature, these methods have their advantages and disadvantages depending on the characteristics of the time series under consideration. Therefore, expert knowledge is required to decide which forecasting method to choose. However, since the time series observed at runtime cannot be known at design time, such expert knowledge cannot be implemented in the system. In addition to selecting an appropriate forecasting method, several time series preprocessing steps are required to achieve satisfactory forecasting accuracy. In the literature, this preprocessing is often done manually, which is not practical for autonomous computing systems, such as Self-Aware Computing Systems. Several approaches have also been presented in the literature for predicting critical events based on multivariate monitoring data using machine and deep learning. However, these approaches are typically highly domain-specific, such as financial failures, bearing failures, or product failures. Therefore, they require in-depth expert knowledge. For this reason, these approaches cannot be fully automated and are not transferable to other use cases. Thus, the literature lacks generalizable end-to-end workflows for modeling, detecting, and predicting failures that require only little expert knowledge. To overcome these shortcomings, this thesis presents a system model for meta-self-aware prediction of critical events based on the LRA-M loop of Self-Aware Computing Systems. Building upon this system model, this thesis provides six further contributions to critical event prediction. While the first two contributions address critical event prediction based on univariate data via time series forecasting, the three subsequent contributions address critical event prediction for multivariate monitoring data using machine and deep learning algorithms. Finally, the last contribution addresses the update procedure of the system model. Specifically, the seven main contributions of this thesis can be summarized as follows: First, we present a system model for meta self-aware prediction of critical events. To handle both univariate and multivariate monitoring data, it offers univariate time series forecasting for use cases where a single observed variable is representative of the state of the system, and machine learning algorithms combined with various preprocessing techniques for use cases where a large number of variables are observed to characterize the system's state. However, the two different modeling alternatives are not disjoint, as univariate time series forecasts can also be included to estimate future monitoring data as additional input to the machine learning models. Finally, a feedback loop is incorporated to monitor the achieved prediction quality and trigger model updates. We propose a novel hybrid time series forecasting method for univariate, seasonal time series, called Telescope. To this end, Telescope automatically preprocesses the time series, performs a kind of divide-and-conquer technique to split the time series into multiple components, and derives additional categorical information. It then forecasts the components and categorical information separately using a specific state-of-the-art method for each component. Finally, Telescope recombines the individual predictions. As Telescope performs both preprocessing and forecasting automatically, it represents a complete end-to-end approach to univariate seasonal time series forecasting. Experimental results show that Telescope achieves enhanced forecast accuracy, more reliable forecasts, and a substantial speedup. Furthermore, we apply Telescope to the scenario of predicting critical events for virtual machine auto-scaling. Here, results show that Telescope considerably reduces the average response time and significantly reduces the number of service level objective violations. For the automatic selection of a suitable forecasting method, we introduce two frameworks for recommending forecasting methods. The first framework extracts various time series characteristics to learn the relationship between them and forecast accuracy. In contrast, the other framework divides the historical observations into internal training and validation parts to estimate the most appropriate forecasting method. Moreover, this framework also includes time series preprocessing steps. Comparisons between the proposed forecasting method recommendation frameworks and the individual state-of-the-art forecasting methods and the state-of-the-art forecasting method recommendation approach show that the proposed frameworks considerably improve the forecast accuracy. With regard to multivariate monitoring data, we first present an end-to-end workflow to detect critical events in technical systems in the form of anomalous machine states. The end-to-end design includes raw data processing, phase segmentation, data resampling, feature extraction, and machine tool anomaly detection. In addition, the workflow does not rely on profound domain knowledge or specific monitoring variables, but merely assumes standard machine monitoring data. We evaluate the end-to-end workflow using data from a real CNC machine. The results indicate that conventional frequency analysis does not detect the critical machine conditions well, while our workflow detects the critical events very well with an F1-score of almost 91\%. To predict critical events rather than merely detecting them, we compare different modeling alternatives for critical event prediction in the use case of time-to-failure prediction of hard disk drives. Given that failure records are typically significantly less frequent than instances representing the normal state, we employ different oversampling strategies. Next, we compare the prediction quality of binary class modeling with downscaled multi-class modeling. Furthermore, we integrate univariate time series forecasting into the feature generation process to estimate future monitoring data. Finally, we model the time-to-failure using not only classification models but also regression models. The results suggest that multi-class modeling provides the overall best prediction quality with respect to practical requirements. In addition, we prove that forecasting the features of the prediction model significantly improves the critical event prediction quality. We propose an end-to-end workflow for predicting critical events of industrial machines. Again, this approach does not rely on expert knowledge except for the definition of monitoring data, and therefore represents a generalizable workflow for predicting critical events of industrial machines. The workflow includes feature extraction, feature handling, target class mapping, and model learning with integrated hyperparameter tuning via a grid-search technique. Drawing on the result of the previous contribution, the workflow models the time-to-failure prediction in terms of multiple classes, where we compare different labeling strategies for multi-class classification. The evaluation using real-world production data of an industrial press demonstrates that the workflow is capable of predicting six different time-to-failure windows with a macro F1-score of 90\%. When scaling the time-to-failure classes down to a binary prediction of critical events, the F1-score increases to above 98\%. Finally, we present four update triggers to assess when critical event prediction models should be re-trained during on-line application. Such re-training is required, for instance, due to concept drift. The update triggers introduced in this thesis take into account the elapsed time since the last update, the prediction quality achieved on the current test data, and the prediction quality achieved on the preceding test data. We compare the different update strategies with each other and with the static baseline model. The results demonstrate the necessity of model updates during on-line application and suggest that the update triggers that consider both the prediction quality of the current and preceding test data achieve the best trade-off between prediction quality and number of updates required. We are convinced that the contributions of this thesis constitute significant impulses for the academic research community as well as for practitioners. First of all, to the best of our knowledge, we are the first to propose a fully automated, end-to-end, hybrid, component-based forecasting method for seasonal time series that also includes time series preprocessing. Due to the combination of reliably high forecast accuracy and reliably low time-to-result, it offers many new opportunities in applications requiring accurate forecasts within a fixed time period in order to take timely countermeasures. In addition, the promising results of the forecasting method recommendation systems provide new opportunities to enhance forecasting performance for all types of time series, not just seasonal ones. Furthermore, we are the first to expose the deficiencies of the prior state-of-the-art forecasting method recommendation system. Concerning the contributions to critical event prediction based on multivariate monitoring data, we have already collaborated closely with industrial partners, which supports the practical relevance of the contributions of this thesis. The automated end-to-end design of the proposed workflows that do not demand profound domain or expert knowledge represents a milestone in bridging the gap between academic theory and industrial application. Finally, the workflow for predicting critical events in industrial machines is currently being operationalized in a real production system, underscoring the practical impact of this thesis.},
  subject      = {Prognose},
  language  = {en}
}
@phdthesis{Upcin2022,
  author    = {Upcin, Berin},
  title     = {Contribution of vascular adventitia-resident progenitor cells to new vessel formation in \(ex\) \(vivo\) 3D models},
  doi       = {10.25972/OPUS-25507},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-255070},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Ongoing research to fight cancer, one of the dominant diseases of the 21st century has led to big progress especially when it comes to understanding the tumor growth and metastasis. This includes the discovery of the molecular mechanisms of tumor vascularization, which is critically required for establishment of tumor metastasis. Formation of new blood vessels is the first step in tumor vascularization. Therefore, understanding the molecular and cellular basis of tumor vascularization attracted a significant effort studying in biomedical research. The blood vessels for supplying tumor can be formed by sprouting from pre-existing vessels, a process called angiogenesis, or by vasculogenesis, that is de novo formation of blood vessels from not fully differentiated progenitor cell populations. Vasculogenic endothelial progenitor cells (EPCs) can either be activated from populations in the bone marrow reaching the pathological region via the circulation or they can be recruited from local reservoirs. Neovessel formation influences tumor progression, hence therapeutic response model systems of angiogenesis/vasculogenesis are necessary to study the underlying mechanisms. Although, initially the research in this area focused more on angiogenesis, it is now well understood that both angiogenesis and postnatal vasculogenesis contribute to neovessel formation in adult under both most pathological as well as physiological conditions. Studies in the last two decades demonstrate that in addition to the intimal layer of fully differentiated mature endothelial cells (ECs) and various smaller supplying vessels (vasa vasorum) that can serve as a source for new vessels by angiogenesis, especially the adventitia of large and medium size blood vessels harbors various vascular wall-resident stem and progenitor cells (VW-SPCs) populations that serve as a source for new vessels by postnatal vasculogenesis. However, little is known about the potential role of VW-SPCs in tumor vascularization. To this end, the present work started first to establish a modified aortic ring assay (ARA) using mouse aorta in order to study the contribution of vascular adventitia-resident VW-SPCs to neovascularization in general and in presence of tumor cells. ARA is already established an ex vivo model for neovascularization allows to study the morphogenetic events of complex new vessel formation that includes all layers of mature blood vessels, a significant advantage over the assays that employ monolayer endothelial cell cultures. Moreover, in contrast to assays employing endothelial cells monocultures, both angiogenic and vasculogenic events take place during new vessel formation in ARA although the exact contribution of these two processes to new vessel formation cannot be easily distinguished in conventional ARA. Thus, in this study, a modified protocol for the ARA (mdARA) was established by either removing or keeping the aortic adventitia in place. The mdARA allows to distinguish the role of VW-SPCs from those of other aortic layers. The present data show that angiogenic sprouting from mature aortic endothelium was markedly delayed when the adventitial layer was removed. Furthermore, the network between the capillary-like sprouts was significantly reduced in absence of aortic adventitia. Moreover, the stabilization of new sprouts by assembling the NG2+ pericyte-like cells that enwrapped the endothelial sprouts from the outside was improved when the adventitial layer remained in place. Next, mimicking the tumor-vessel adventitia-interaction, multicellular tumor spheroids (MCTS) and aortic rings (ARs) with or without adventitia of C57BL/6-Tg (UBC-GFP) mice were confronted within the collagen gel and cultured ex vivo. This 3D model enabled analysis of the mobilization, migration and capillary-like sprouts formation by VW-SPCs within tumor-vessel wall-interface in comparison to tumor-free side of the ARs. Interestingly, while MCTS preferred the uptake of single vascular adventitia-derived cells, neural spheroids were directly penetrated by capillary-like structures that were sprouted from the aortic adventitia. In summary, the model established in this work allows to study new vessel formation by both postnatal vasculogenesis and angiogenesis under same conditions. It can be applied in various mouse models including reporter mouse models, e.g. Cxcr1 CreER+/mTmG+/- mice, in which GFP-marked macrophages of the vessel wall were directly observed as they mobilized from their niche and migrated into collagen gel. Another benefit of the model is that it can be used for testing different factors such as small molecules, growth factors, cytokines, and drugs with both pro- and anti-angiogenic/vasculogenic effects.},
  language  = {en}
}
@phdthesis{Lisowski2022,
  author    = {Lisowski, Clivia},
  title     = {Maturation of the \(Salmonella\) containing vacuole is compromised in G1 arrested host cells},
  doi       = {10.25972/OPUS-18523},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-185239},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {The interaction of bacterial pathogens and the human host is a complex process that has shaped both organisms on a molecular, cellular and population level. When pathogenic bacteria infect the human body, a battle ensues between the host immune system and the pathogen. In order to escape an immune response and to colonize the host, pathogenic bacteria have developed diverse virulence strategies and some pathogens even replicate within host cells. For survival and propagation within the dynamic environment of a host cell, these bacteria interfere with the regulation of host pathways, such as the cell cycle, for their own benefit. The intracellular pathogen Salmonella Typhimurium invades eukaryotic cells and resides and replicates in a modified vacuolar compartment in which it is protected from the innate immune response. To this end, it employs a set of virulence factors that help to invade cells (SPI-1 effectors) and to hijack and modify the host endolysosomal system, in order to stabilize and mature its vacuolar niche (SPI-2 effectors). Previous studies have shown that Salmonella arrests host cells in G2/M phase and that Salmonella infected cells progress faster from G1 into S phase, suggesting that the G1 phase is disadvantageous for Salmonella infection. In fact, it has already been observed that Salmonella replication is impaired in G1 arrested cells. However, the reason for this impairment remained unclear. The current study addressed this question for the first time and revealed that the highly adapted, intracellular lifestyle of Salmonella is drastically altered upon G1 arrest of the host cell. It is shown that proteasomal degradation in G1 arrested cells is delayed and endolysosomal and autophagosomal trafficking is compromised. Accordingly, processing of lysosomal proteins is insufficient and lysosomal activity is decreased; resulting in uneven distribution and accumulation of endolysosomes and autophagosomes, containing undegraded cargo. The deregulation of these cellular signaling pathways affects maturation of the Salmonella containing vacuole (SCV). For the first time it is shown that acidification of SCVs is impaired upon G1 arrest. Thus, an important environmental factor for the switch from SPI-1 to SPI-2 gene expression is missing and the SPI-2 system is not activated. Consequently, targeting and modification of host cell structures by SPI-2 effectors e.g. recruitment of endolysosomal membrane proteins, like LAMP1, or exchange of endosomal cargo, is compromised. In addition, degradation of Salmonella SPI-1 effectors by the host proteasome is delayed. Their prolonged presence sustained the recruitment of early endosomes and contributed to the SCV remaining in an early, vulnerable maturation stage. Finally, it was shown that SCV membrane integrity is compromised; the early SCV ruptures and bacteria are released into the cytoplasm. Depending on the host cell type, SPI-2 independent, cytoplasmic replication is promoted. This might favor bacterial spreading, dissemination into the tissue and provide an advantage in host colonization. Overall, the present study establishes a link between host cell cycle regulation and the outcome of Salmonella infection. It fills the gap of knowledge as to why the host cell cycle stage is of critical importance for Salmonella infection and sheds light on a key aspect of host-pathogen interaction.},
  subject      = {Salmonella Typhimurium},
  language  = {en}
}
@phdthesis{Staiger2022,
  author    = {Staiger, Simona},
  title     = {Chemical and physical nature of the barrier against active ingredient penetration into leaves: effects of adjuvants on the cuticular diffusion barrier},
  doi       = {10.25972/OPUS-19937},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-199375},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Agrochemicals like systemic active ingredients (AI) need to penetrate the outermost barrier of the plant, known as the plant cuticle, to reach its right target site. Therefore, adjuvants are added to provide precise and efficient biodelivery by i.a. modifying the cuticular barrier and increasing the AI diffusion. This modification process is depicted as plasticization of the cuticular wax which mainly consists of very long-chain aliphatic (VLCA) and cyclic compounds. Plasticization of cuticular waxes is pictured as an increase of amorphous domains and/or a decrease of crystalline fractions, but comprehensive, experimental proof is lacking to date. Hence, the objective of this thesis was to i) elucidate the permeation barrier of the plant cuticle to AIs in terms of the different wax fractions and ii) holistically investigate the modification of this barrier using selected oil and surface active adjuvants, an aliphatic leaf wax and an artificial model wax. Therefore, the oil adjuvant methyl oleate (MeO) and other oil derivatives like methyl linolenate (MeLin), methyl stearate (MeSt) and oleic acid (OA) were selected. Three monodisperse, non-ionic alcohol ethoxylates with increasing ethylene oxide monomer (EO) number (C10E2, C10E5, C10E8) were chosen as representatives of the group of surface active agents (surfactants). Both adjuvant classes are commonly used as formulation aids for agrochemicals which are known for its penetration enhancing effect. The aliphatic leaf wax of Schefflera elegantissima was selected, as well as a model wax comprising the four most abundant cuticular wax compounds of this species. Permeation, transpiration and penetration studies were conducted using enzymatically isolated cuticles of Prunus laurocerasus and Garcinia xanthochymus. Cuticular permeability to the three organic solutes theobromine, caffeine and azoxystrobin differing in lipophilicity was measured using a steady-state two-chamber system separated by the isolated leaf cuticles of the evergreen species P. laurocerasus and G. xanthochymus. Treating the isolated cuticles with methanol selectively removed the cyclic fraction, and membrane permeability to the organic compounds was not altered. In contrast, fully dewaxing the membranes using chloroform resulted in a statistically significant increase in permeance for all compounds and species, except caffeine with cuticles of G. xanthochymus due to a matrix-specific influence on the semi-hydrophilic compound. Crystalline regions may reduce the accessibility to the lipophilic pathway across the waxes and also block hydrophilic domains in the cuticle. Knowing that the aliphatic wax fraction builds the cuticular diffusion barrier, the influence of the adjuvants on the phase behaviour of an aliphatic cuticular wax as well as the influence on the cuticular penetration of AIs were investigated. Differential scanning calorimetry (DSC) and Fourier-transform infrared spectroscopy (FTIR) were selected to investigate the phase behaviour and thus possible plasticization of pure Schefflera elegantissima leaf wax, its artificial model wax comprising the four most abundant compounds (n-nonacosane, n-hentriacontane, 1-triacontanol and 1-dotriacontanol) and wax adjuvant mixtures. DSC thermograms showed a shift of the melting ranges to lower temperatures and decreased absolute values of the total enthalpy of transition (EOT) for all adjuvant leaf wax blends at 50 \% (w/w) adjuvant proportion. The highest decrease was found for C10E2 followed by MeO > OA and C10E8 > MeLin > MeSt. The aliphatic crystallinity determined by FTIR yielded declined values for the leaf and the artificial wax with 50 \% MeO. All other adjuvant leaf wax blends did not show a significant decrease of crystallinity. As it is assumed that the cuticular wax is formed by crystalline domains which consist of aliphatic hydrocarbon chains and an amorphous fraction comprising aliphatic chain ends and functional groups, the plasticizers are depicted as wax disruptors influencing amorphization and/or crystallization. The adjuvants can increase crystalline domains using the aliphatic tail whereas their more hydrophilic head is embedded in the amorphous wax fraction. DSC and FTIR showed similar trends using the leaf wax and the model wax in combination with the adjuvants. In general, cuticular transpiration increased after adding the pure adjuvants to the surface of isolated cuticles or leaf envelopes. As waxes build the cuticular permeation barrier not only to AIs but also to water, the adjuvant wax interaction might affect the cuticular barrier properties leading to increased transpiration. Direct evidence for increased AI penetration with the adjuvants was given using isolated cuticles of P. laurocerasus in combination with the non-steady-state setup simulation of foliar penetration (SOFP) and caffeine at relative humidity levels (RH) of 30, 50 and 80 \%. The increase in caffeine penetration was much more pronounced using C10E5 and C10E8 than MeO but always independent of RH. Only C10E2 exhibited an increased penetration enhancing effect positively related to RH. The role of the molecular structure of adjuvants in terms of humectant and plasticizer properties are discussed. Hence, the current work shows for the first time that the cuticular permeation barrier is associated with the VLCAs rather than the cyclic fraction and that adjuvants structurally influence this barrier resulting in penetration enhancing effects. Additionally, this work demonstrates that an artificial model wax is feasible to mimic the wax adjuvant interaction in conformity with a leaf wax, making it feasible for in-vitro experiments on a larger scale (e.g. screenings). This provides valuable knowledge about the cuticular barrier modification to enhance AI penetration which is a crucial factor concerning the optimization of AI formulations in agrochemistry.},
  subject      = {Adjuvans},
  language  = {en}
}
@phdthesis{Kryven2022,
  author    = {Kryven, Myroslav},
  title     = {Optimizing Crossings in Circular-Arc Drawings and Circular Layouts},
  isbn      = {978-3-95826-174-7},
  doi       = {10.25972/WUP-978-3-95826-175-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245960},
  school      = {Universit{\"a}t W{\"u}rzburg},
  pages     = {viii, 129},
  year      = {2022},
  abstract  = {A graph is an abstract network that represents a set of objects, called vertices, and relations between these objects, called edges. Graphs can model various networks. For example, a social network where the vertices correspond to users of the network and the edges represent relations between the users. To better see the structure of a graph it is helpful to visualize it. The research field of visualizing graphs is called Graph Drawing. A standard visualization is a node-link diagram in the Euclidean plane. In such a representation the vertices are drawn as points in the plane and edges are drawn as Jordan curves between every two vertices connected by an edge. Edge crossings decrease the readability of a drawing, therefore, Crossing Optimization is a fundamental problem in Graph Drawing. Graphs that can be drawn with few crossings are called beyond-planar graphs. The topic that deals with definition and analysis of beyond-planar graphs is called Beyond Planarity and it is an important and relatively new research area in Graph Drawing. In general, beyond planar graphs posses drawings where edge crossings are restricted in some way. For example, the number of crossings may be bounded by a constant independent of the size of the graph. Crossings can also be restricted locally by, for example, restricting the number of crossings per edge, restricting the number of pairwise crossing edges, or bounding the crossing angle of two edges in the drawing from below. This PhD thesis defines and analyses beyond-planar graph classes that arise from such local restrictions on edge crossings.},
  subject      = {Graphenzeichnen},
  language  = {en}
}