@phdthesis{Uereyen2022,
  author    = {{\"U}reyen, Soner},
  title     = {Multivariate Time Series for the Analysis of Land Surface Dynamics - Evaluating Trends and Drivers of Land Surface Variables for the Indo-Gangetic River Basins},
  doi       = {10.25972/OPUS-29194},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-291941},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument. In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed. To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere. In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area. These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions.},
  subject      = {Multivariate Analyse},
  language  = {en}
}
@phdthesis{Uenzelmann2022,
  author    = {{\"U}nzelmann, Maximilian},
  title     = {Interplay of Inversion Symmetry Breaking and Spin-Orbit Coupling - From the Rashba Effect to Weyl Semimetals},
  doi       = {10.25972/OPUS-28310},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-283104},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Breaking inversion symmetry in crystalline solids enables the formation of spin-polarized electronic states by spin-orbit coupling without the need for magnetism. A variety of interesting physical phenomena related to this effect have been intensively investigated in recent years, including the Rashba effect, topological insulators and Weyl semimetals. In this work, the interplay of inversion symmetry breaking and spin-orbit coupling and, in particular their general influence on the character of electronic states, i.e., on the spin and orbital degrees of freedom, is investigated experimentally. Two different types of suitable model systems are studied: two-dimensional surface states for which the Rashba effect arises from the inherently broken inversion symmetry at the surface, and a Weyl semimetal, for which inversion symmetry is broken in the three-dimensional crystal structure. Angle-resolved photoelectron spectroscopy provides momentum-resolved access to the spin polarization and the orbital composition of electronic states by means of photoelectron spin detection and dichroism with polarized light. The experimental results shown in this work are also complemented and supported by ab-initio density functional theory calculations and simple model considerations. Altogether, it is shown that the breaking of inversion symmetry has a decisive influence on the Bloch wave function, namely, the formation of an orbital angular momentum. This mechanism is, in turn, of fundamental importance both for the physics of the surface Rashba effect and the topology of the Weyl semimetal TaAs.},
  subject      = {Rashba-Effekt},
  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{Zoran2022,
  author    = {Zoran, Tamara},
  title     = {Multilevel analysis of the human immune response to \(Aspergillus\) \(fumigatus\) infection: Characteristic molecular signatures and individual risk factors},
  doi       = {10.25972/OPUS-29851},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-298512},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Although the field of fungal infections advanced tremendously, diagnosis of invasive pulmonary aspergillosis (IPA) in immunocompromised patients continues to be a challenge. Since IPA is a multifactorial disease, investigation from different aspects may provide new insights, helpful for improving IPA diagnosis. This work aimed to characterize the human immune response to Aspergillus fumigatus in a multilevel manner to identify characteristic molecular candidates and risk factors indicating IPA, which may in the future support already established diagnostic assays. We combined in vitro studies using myeloid cells infected with A. fumigatus and longitudinal case-control studies investigating patients post allogeneic stem cell transplantation (alloSCT) suffering from IPA and their match controls. Characteristic miRNA and mRNA signatures indicating A. fumigatus-infected monocyte-derived dendritic cells (moDCs) demonstrated the potential to differentiate between A. fumigatus and Escherichia coli infection. Transcriptome and protein profiling of alloSCT patients suffering from IPA and their matched controls revealed a distinctive IPA signature consisting of MMP1 induction and LGAL2 repression in combination with elevated IL-8 and caspase-3 levels. Both, in vitro and case-control studies, suggested cytokines, matrix-metallopeptidases and galectins are important in the immune response to A. fumigatus. Identified IPA characteristic molecular candidates are involved in numerous processes, thus a combination of these in a distinctive signature may increase the specificity. Finally, low monocyte counts, severe GvHD of the gut (grade ≥ 2) and etanercept administration were significantly associated with IPA diagnosis post alloSCT. Etanercept in monocyte-derived macrophages (MDM) infected with A. fumigatus downregulates genes involved in the NF-κB and TNF-α pathway and affects the secretion of CXCL10. Taken together, identified characteristic molecular signatures and risk factors indicating IPA may in the future in combination with established fungal biomarkers overcome current diagnostic challenges and help to establish tailored antifungal therapy. Therefore, further multicentre studies are encouraged to evaluate reported findings.},
  subject      = {Aspergillus fumigatus},
  language  = {en}
}
@phdthesis{Youssef2022,
  author    = {Youssef, Almoatazbellah},
  title     = {Fabrication of Micro-Engineered Scaffolds for Biomedical Application},
  doi       = {10.25972/OPUS-23545},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235457},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Thermoplastic polymers have a history of decades of safe and effective use in the clinic as implantable medical devices. In recent years additive manufacturing (AM) saw increased clinical interest for the fabrication of customizable and implantable medical devices and training models using the patients' own radiological data. However, approval from the various regulatory bodies remains a significant hurdle. A possible solution is to fabricate the AM scaffolds using materials and techniques with a clinical safety record, e.g. melt processing of polymers. Melt Electrowriting (MEW) is a novel, high resolution AM technique which uses thermoplastic polymers. MEW produces scaffolds with microscale fibers and precise fiber placement, allowing the control of the scaffold microarchitecture. Additionally, MEW can process medical-grade thermoplastic polymers, without the use of solvents paving the way for the production of medical devices for clinical applications. This pathway is investigated in this thesis, where the layout is designed to resemble the journey of a medical device produced via MEW from conception to early in vivo experiments. To do so, first, a brief history of the development of medical implants and the regenerative capability of the human body is given in Chapter 1. In Chapter 2, a review of the use of thermoplastic polymers in medicine, with a focus on poly(ε-caprolactone) (PCL), is illustrated, as this is the polymer used in the rest of the thesis. This review is followed by a comparison of the state of the art, regarding in vivo and clinical experiments, of three polymer melt AM technologies: melt-extrusion, selective laser sintering and MEW. The first two techniques already saw successful translation to the bedside, producing patient-specific, regulatory-approved AM implants. To follow in the footsteps of these two technologies, the MEW device parameters need to be optimized. The MEW process parameters and their interplay are further discussed in Chapter 3 focusing on the importance of a steady mass flow rate of the polymer during printing. MEW reaches a balance between polymer flow, the stabilizing electric field and moving collector to produce reproducible, high-resolution scaffolds. An imbalance creates phenomena like fiber pulsing or arcing which result in defective scaffolds and potential printer damage. Chapter 4 shows the use of X-ray microtomography (µCT) as a non-destructive method to characterize the pore-related features: total porosity and the pore size distribution. MEW scaffolds are three-dimensional (3D) constructs but have long been treated in the literature as two-dimensional (2D) ones and characterized mainly by microscopy, including stereo- and scanning electron microscopy, where pore size was simply reported as the distance between the fibers in a single layer. These methods, together with the trend of producing scaffolds with symmetrical pores in the 0/90° and 0/60/120° laydown patterns, disregarded the lateral connections between pores and the potential of MEW to be used for more complex 3D structures, mimicking the extracellular matrix. Here we characterized scaffolds in the aforementioned symmetrical laydown patterns, along with the more complex 0/45/90/135° and 0/30/60/90/120/150° ones. A 2D pore size estimation was done first using stereomicroscopy, followed by and compared to µCT scanning. The scaffolds with symmetrical laydown patterns resulted in the predominance of one pore size, while those with more complex patterns had a broader distribution, which could be better shown by µCT scans. Moreover, in the symmetrical scaffolds, the size of 3D pores was not able to reach the value of the fiber spacing due to a flattening effect of the scaffold, where the thickness of the scaffold was less than the fiber spacing, further restricting the pore size distribution in such scaffolds. This method could be used for quality assurance of fabricated scaffolds prior to use in in vitro or in vivo experiments and would be important for a clinical translation. Chapter 5 illustrates a proof of principle subcutaneous implantation in vivo experiment. MEW scaffolds were already featured in small animal in vivo experiments, but to date, no analysis of the foreign body reaction (FBR) to such implants was performed. FBR is an immune reaction to implanted foreign materials, including medical devices, aimed at protecting the host from potential adverse effects and can interfere with the function of some medical implants. Medical-grade PCL was used to melt electrowrite scaffolds with 50 and 60 µm fiber spacing for the 0/90° and 0/60/120° laydown patterns, respectively. These implants were implanted subcutaneously in immunocompetent, outbred mice, with appropriate controls, and explanted after 2, 4, 7 and 14 days. A thorough characterization of the scaffolds before implantation was done, followed by a full histopathological analysis of the FBR to the implants after excision. The scaffolds, irrespective of their pore geometry, induced an extensive FBR in the form of accumulation of foreign body giant cells around the fiber walls, in a manner that almost occluded available pore spaces with little to no neovascularization. This reaction was not induced by the material itself, as the same reaction failed to develop in the PCL solid film controls. A discussion of the results was given with special regard to the literature available on flat surgical meshes, as well as other hydrogel-based porous scaffolds with similar pore sizes. Finally, a general summary of the thesis in Chapter 6 recapitulates the most important points with a focus on future directions for MEW.},
  language  = {en}
}
@phdthesis{Ye2022,
  author    = {Ye, Mingyu},
  title     = {Immunotherapy with Vaccinia virus co-expressing tumor-associated antigens and mouse IL-2 cytokine in mice with mammary cancer},
  doi       = {10.25972/OPUS-25309},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-253095},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Interleukin 2 (IL-2) was the first cytokine applied for cancer treatment in human history. It has been approved as monotherapy for renal cell carcinoma and melanoma by the FDA and does mediate the regression of the tumors in patients. One of the possible mechanisms is that the administration of IL-2 led to T lymphocytes expansion, including CD4+ and CD8+ T cells. In addition, a recent study demonstrated that antigen-specific T cells could also be expanded through the induction of IL-2, which plays a crucial role in mediating tumor regression. However, despite the long-term and extensive use of IL-2 in the clinic, the ratio of patients who get a complete response was still low, and only about one-fifth of patients showed objective tumor regression. Therefore, the function of IL-2 in cancer treatment should continue to be optimized and investigated. A study by Franz O. Smith et al. has shown that the combination treatment of IL-2 and tumor-associated antigen vaccine has a strong trend to increased objective responses compared to patients with melanoma receiving IL-2 alone. Peptide vaccines are anti-cancer vaccines able to induce a powerful tumor antigenspecific immune response capable of eradicating the tumors. According to the type of antigens, peptide vaccines can be classified into two distinct categories: Tumor-associated antigens (TAA) vaccine and tumor-specific neoantigens (TSA) vaccine. Currently, Peptide vaccines are mainly investigated in phase I and phase II clinical trials of human cancer patients with various advanced cancers such as lung cancer, gastrointestinal tumors, and breast cancers. Vaccinia virus (VACV) is one of the safest viral vectors, which has been wildly used in cancer treatment and pathogen prevention. As an oncolytic vector, VACV can carry multiple large foreign genes, which enable the virus to introduce diagnostic and therapeutic agents without dramatically reducing the viral replication. Meanwhile, the recombinant vaccinia virus (rVACV) can be easily generated by homologous recombination. Here, we used the vaccinia virus as the therapeutic cancer vector, expressing mouse Interleukin 2 (IL-2) and tumor-associated antigens simultaneously to investigate the combined effect of anti-tumor immune response in the 4T1 mouse tumor model. As expected, the VACV driven mIL-2 expression remarkably increased both CD4+ and CD8+ populations in vivo, and the virus-expressed tumor-associated peptides successfully elicited theantigen-specific T cell response to inhibit the growth of tumors. Furthermore, the experiments with tumor-bearing animals showed that the mIL-2 plus tumor antigens expressing VACV vector gave a better anti-cancer response than the mIL-2 alone expressing vector. The combinations did significantly more inhibit tumor growth than mIL-2 treatment alone. Moreover, the results confirmed our previous unpublished data that the mIL-2 expression driven by synthetic early/late promoter in the Lister strain VACV could enhance the tumor regression in the 4T1 mouse model.},
  language  = {en}
}
@phdthesis{Yang2022,
  author    = {Yang, Shang},
  title     = {Characterization and engineering of photoreceptors with improved properties for optogenetic application},
  doi       = {10.25972/OPUS-20527},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205273},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Optogenetics became successful in neuroscience with Channelrhodopsin-2 (ChR2), a light-gated cation channel from the green alga Chlamydomonas reinhardtii, as an easy applicable tool. The success of ChR2 inspired the development of various photosensory proteins as powerful actuators for optogenetic manipulation of biological activity. However, the current optogenetic toolbox is still not perfect and further improvements are desirable. In my thesis, I engineered and characterized several different optogenetic tools with new features. (i) Although ChR2 is the most often used optogenetic actuator, its single-channel conductance and its Ca2+ permeability are relatively low. ChR2 variants with increased Ca2+ conductance were described recently but a further increase seemed possible. In addition, the H+ conductance of ChR2 may lead to cellular acidification and unintended pH-related side effects upon prolonged illumination. Through rational design, I developed several improved ChR2 variants with larger photocurrent, higher cation selectivity, and lower H+ conductance. (ii) The light-activated inward chloride pump NpHR is a widely used optogenetic tool for neural silencing. However, pronounced inactivation upon long time illumination constrains its application for long-lasting neural inhibition. I found that the deprotonation of the Schiff base underlies the inactivation of NpHR. Through systematically exploring optimized illumination schemes, I found illumination with blue light alone could profoundly increase the temporal stability of the NpHR-mediated photocurrent. A combination of green and violet light eliminates the inactivation effect, similar to blue light, but leading to a higher photocurrent and therefore better light-induced inhibition. (iii) Photoactivated adenylyl cyclases (PACs) were shown to be useful for light-manipulation of cellular cAMP levels. I developed a convenient in-vitro assay for soluble PACs that allows their reliable characterization. Comparison of different PACs revealed that bPAC from Beggiatoa is the best optogenetic tool for cAMP manipulation, due to its high efficiency and small size. However, a residual activity of bPAC in the dark is unwanted and the cytosolic localization prevents subcellular precise cAMP manipulation. I therefore introduced point mutations into bPAC to reduce its dark activity. Interestingly, I found that membrane targeting of bPAC with different linkers can remarkably alter its activity, in addition to its localization. Taken together, a set of PACs with different activity and subcellular localization were engineered for selection based on the intended usage. The membrane-bound PM-bPAC 2.0 with reduced dark activity is well-tolerated by hippocampal neurons and reliably evokes a transient photocurrent, when co-expression with a CNG channel. (iv) Bidirectional manipulation of cell activity with light of different wavelengths is of great importance in dissecting neural networks in the brain. Selection of optimal tool pairs is the first and most important step for dual-color optogenetics. Through N- and C-terminal modifications, an improved ChR variant (i.e. vf-Chrimson 2.0) was engineered and selected as the red light-controlled actuator for excitation. Detailed comparison of three two-component potassium channels, composed of bPAC and the cAMP-activated potassium channel SthK, revealed the superior properties of SthK-bP. Combining vf-Chrimson 2.0 and improved SthK-bP "SthK(TV418)-bP" could reliably induce depolarization by red light and hyperpolarization by blue light. A residual tiny crosstalk between vf-Chrimson 2.0 and SthK(TV418)-bP, when applying blue light, can be minimized to a negligible level by applying light pulses or simply lowering the blue light intensity.},
  language  = {en}
}
@phdthesis{Xu2022,
  author    = {Xu, Wenshan},
  title     = {Regulation of the DNA Damage Response by the Ubiquitin System},
  doi       = {10.25972/OPUS-16006},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-160064},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {DNA damage occurs frequently during normal cellular progresses or by environmental factors. To preserve the genome integrity, DNA damage response (DDR) has evolved to repair DNA and the non-properly repaired DNA induces human diseases like immune deficiency and cancer. Since a large number of proteins involved in DDR are enzymes of ubiquitin system, it is critical to investigate how the ubiquitin system regulates cellular response to DNA damage. Hereby, we reveal a novel mechanism for DDR regulation via activation of SCF ubiquitin ligase upon DNA damage. As an essential step for DNA damage-induced inhibition of DNA replication, Cdc25A degradation by the E3 ligase β-TrCP upon DNA damage requires the deubiquitinase Usp28. Usp28 deubiquitinates β-TrCP in response to DNA damage, thereby promotes its dimerization, which is required for its activity in substrate ubiquitination and degradation. Particularly, ubiquitination at a specific lysine on β-TrCP suppresses dimerization. The key mediator protein of DDR, 53BP1, forms oligomers and associates with β-TrCP to inhibit its activity in unstressed cells. Upon DNA damage, 53BP1 is degraded in the nucleoplasm, which requires oligomerization and is promoted by Usp28 in a β-TrCP-dependent manner. Consequently, 53BP1 destruction releases and activates β-TrCP during DNA damage response. Moreover, 53BP1 deletion and DNA damage promote β-TrCP dimerization and recruitment to chromatin sites that locate in the vicinity of putative replication origins. Subsequently, the chromatin-associated Cdc25A is degraded by β-TrCP at the origins. The stimulation of β-TrCP binding to the origins upon DNA damage is accompanied by unloading of Cdc45, a crucial component of pre-initiation complexes for replication. Loading of Cdc45 to origins is a key Cdk2-dependent step for DNA replication initiation, indicating that localized Cdc25A degradation by β-TrCP at origins inactivates Cdk2, thereby inhibits the initiation of DNA replication. Collectively, this study suggests a novel mechanism for the regulation of DNA replication upon DNA damage, which involves 53BP1- and Usp28-dependent activation of the SCF(β-TrCP) ligase in Cdc25A degradation.},
  subject      = {DNS-Sch{\"a}digung},
  language  = {en}
}
@phdthesis{Wu2022,
  author    = {Wu, Zhu},
  title     = {Room Temperature Phosphorescence (RTP): Experimental And Theoretical Studies on Boron-Containing Materials},
  doi       = {10.25972/OPUS-26084},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260844},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Persistent room temperature phosphorescent (RTP) luminophores have gained remarkable interest recently for a number of applications in security printing, OLEDs, optical storage, time-gated biological imaging and oxygen sensors. We report the first persistent RTP with lifetimes up to 0.5 s from simple triarylboranes which have no lone pairs. We also have prepared 3 isomeric (o, m, p-bromophenyl)-bis(2,6-dimethylphenyl)boranes. Among the 3 isomers (o-, m- and p-BrTAB) synthesized, the ortho-one is the only one which shows dual phosphorescence, with a short lifetime of 0.8 ms and a long lifetime of 234 ms in the crystalline state at room temperature. At last, we checked the RTP properties from the boric acid. We found that the pure boric acid does not show RTP in the solid state.},
  language  = {en}
}
@phdthesis{Wohlfart2022,
  author    = {Wohlfart, Jonas},
  title     = {Analysis of Drug Impurities by Means of Chromatographic Methods: Targeted and Untargeted Approaches},
  doi       = {10.25972/OPUS-27387},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-273878},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {The presented works aimed on the analysis of new impurities in APIs and medicinal products. Different subtypes of LC were coupled to suitable detection methods, i.e. UV and various MS techniques, depending on the chemical natures of the analytes and the analytical task. Unexpected impurities in medicinal products and APIs caused several scandals in the past, concomitant with fatalities or severe side effects in human and veterinary patients. The detection of nitrosamines in sartans led to the discovery of nitrosamines in various other drugs, of which the antibiotic rifampicin was analyzed in this work. An examination of the synthesis of rifampicin revealed a high potential for the formation of 4-methyl-1-nitrosopiperazine (MeNP). An LC-MS/HRMS method suitable for the quantification of MeNP was applied in the analysis of drugs collected from Brazil, Comoros, India, Nepal, and Tanzania, where a single dose of rifampicin is used in the post-exposure prophylaxis of leprosy. All batches were contaminated with MeNP, ranging from 0.7-5.1 ppm. However, application of rifampicin containing up to 5 ppm MeNP was recommended by the regulatory authorities for the post-exposure prophylaxis of leprosy. In the 1990s the aminoglycoside antibiotic gentamicin attracted attention after causing fatalities in the USA, but the causative agent was never identified unequivocally. The related substance sisomicin was recognized as a lead impurity by the Holzgrabe lab at the University of W{\"u}rzburg: sisomicin was accompanied by a variety of other impurities and batches containing sisomicin had caused the fatalities. In 2016, anaphylactic reactions were reported after application of gentamicin. A contamination of the medicinal products with histamine, an impurity of the raw material fish peptone used upon the production, could be identified as the cause of the adverse effects. Batches of gentamicin sulfate, which had been stored at the University of W{\"u}rzburg since the earlier investigations, were analyzed regarding their contamination with histamine to determine whether the biogenic amine was responsible for the 1990s fatalities as well. Furthermore, a correlation with the lead impurity sisomicin was checked. Histamine could be detected in all analyzed batches, but at a lower level than in the batches responsible for the anaphylactic reactions. Moreover, there is no correlation of histamine with the lead impurity sisomicin. Hence, the causative agent for the 1990s fatalities was not histamine and remains unknown. Another source of impurities is the reaction of APIs with excipients, e.g. the esterification of naproxen with PEG 600 in soft gel capsules. The influence of the formulation's composition on this reaction was investigated by means of LC-UV. Therefore, the impurity naproxen-PEG-ester (NPEG) was synthesized and used for the development of a method suitable for the analysis of soft gel capsule formulations. Different formulations were stressed for 7 d at 60 °C and the relative amount of NPEG was determined. The formation of NPEG was influenced by the concentrations of water and lactic acid, the pH, and the drug load of the formulation, which can easily be explained by the chemistry behind esterification reactions. Keeping in mind the huge variety of sources of impurities, it might be impossible to predict all potential impurities of a drug substance/product. Targeted and untargeted approaches were combined in the impurity profiling of bisoprolol fumarate. Eight versions of an LC-HRMS method were developed to enable the detection of a maximum number of impurities: an acidic and a basic buffered LC was coupled to MS detection applying ESI and APCI, both in positive in negative mode. MS and MS/MS data were acquired simultaneously by information dependent acquisition. In the targeted approach, potential impurities were derived from a reaction matrix based on the synthesis route of the API, while the untargeted part was based on general unknown comparative screening to identify additional signals. 18 and 17 impurities were detected in the targeted and the untargeted approach, respectively. The molecular formulae were assessed based on the exact mass and the isotope pattern. Theoretical fragment spectra generated by in silico fragmentation were matched with experimental data to estimate the plausibility of proposed/elucidated structures. Moreover, the detected impurities were quantified with respect to an internal standard.},
  subject      = {LC-MS},
  language  = {en}
}