@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{OenalHartmann2011,
  author    = {{\"O}nal-Hartmann, Cigdem},
  title     = {Emotional Modulation of Motor Memory Formation},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-64838},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {Hintergr{\"u}nde: Wie eine Vielzahl von Studien belegt, kann das explizite Ged{\"a}chtnis, das die bewusste Erinnerung an enkodierte Informationen beinhaltet, durch Emotionen beeinflusst werden, und zwar {\"u}ber den Einfluss auf verschiedene Verarbeitungsebenen (Enkodierung, Konsolidierung, Abruf usw.). Bisher wenig untersucht ist, ob und wie Emotionen Vorg{\"a}nge der motorischen Ged{\"a}chtnisbildung, die nicht auf bewusster Erinnerung beruhen und sich stattdessen durch Ver{\"a}nderungen im Verhalten darstellen, modulieren. Experiment 1: Das Ziel des ersten Experimentes war es, den Einfluss von Emotionen auf motorisches Lernen zu untersuchen. Vier Gruppen von Probanden mussten in einer motorischen Lernaufgabe schnelle, seitliche Bewegungen mit dem Daumen ausf{\"u}hren. W{\"a}hrend dieser Aufgabe h{\"o}rten die Probanden emotionale Kl{\"a}nge, die in Valenz und Arousal variierten: 1. Valenz negativ/ Arousal niedrig (V-/A-), 2. Valenz negativ/ Arousal hoch (V-/A+), 3. Valenz positiv/ Arousal niedrig (V+/A-), 4. Valenz positiv/ Arousal hoch (V+/A+). Die deskriptive Analyse aller Daten sprach f{\"u}r beste Ergebnisse f{\"u}r das motorische Lernen in der Bedingung V-/A-, aber die Unterschiede zwischen den Bedingungen waren nicht signifikant. Die Interaktion zwischen Valenz und Arousal emotionaler T{\"o}ne scheint demnach motorische Enkodierungsprozesse zu modulieren, jedoch m{\"u}ssen zuk{\"u}nftige Studien mit unterschiedlichen emotionalen Stimuli die Annahme weiter untersuchen, dass negative Stimuli mit niedrigem Arousal w{\"a}hrend der Enkodierung einen f{\"o}rdernden Effekt auf das motorische Kurzzeitged{\"a}chtnis haben. Experiment 2: Die Absicht des zweiten Experimentes war es, die Auswirkungen emotionaler Interferenzen auf die Konsolidierung beim Sequenzlernen zu untersuchen. Sechs Gruppen von Probanden trainierten zuerst in getrennten Sitzungen eine SRTT-Aufgabe (serial reaction time task). Um die Konsolidierung der neu erlernten Fertigkeit zu modulieren, wurden die Probanden nach dem Training einer von drei unterschiedlichen Klassen emotionaler Stimuli (positiv, negativ oder neutral) ausgesetzt. Diese bestanden aus einem Set emotionaler Bilder, die mit emotional kongruenten Musikst{\"u}cken oder neutralen Kl{\"a}ngen kombiniert waren. Bei den Probandengruppen wurde die emotionale Interferenz nach zwei unterschiedlichen Zeitintervallen realisiert, entweder direkt nach der Trainingssitzung oder sechs Stunden sp{\"a}ter. 72 Stunden nach der Trainingssitzung wurde jede Gruppe erneut mit der SRTT-Aufgabe getestet. Die Leistung in diesem Nachtest wurde mittels Reaktionszeit und Genauigkeit bei der Ausf{\"u}hrung der Zielsequenz analysiert. Die emotionale Interferenz beeinflusste weder die Nachtestergebnisse f{\"u}r die Reaktionszeit noch die f{\"u}r die Genauigkeit. Allerdings konnte eine Steigerung der expliziten Sequenzerkennung durch erregende negative Stimuli festgestellt werden, wenn diese direkt nach der ersten Trainingseinheit (0h) dargeboten wurden. Diese Ergebnisse lassen vermuten, dass die Konsolidierung der expliziten Aspekte prozeduralen Lernens in einer st{\"a}rkeren Wechselwirkung mit emotionalen Interferenzen stehen k{\"o}nnte als die der impliziten Aspekte. Die Konsolidierung unterschiedlicher Ebenen des Fertigkeitserwerbs k{\"o}nnte demnach von unterschiedlichen Mechanismen gesteuert werden. Da Performanz und explizites Sequenzerkennen nicht korrelierten, vermuten wir, dass implizite und explizite Modalit{\"a}ten bei der Durchf{\"u}hrung der SRTT-Aufgabe nicht komplement{\"a}r sind. Experiment 3: Es sollte untersucht werden, ob es eine Pr{\"a}ferenz der linken Gehirnhemisph{\"a}re bei der Kontrolle von Flexionsreaktionen auf positive Stimuli gibt und der rechten Hemisph{\"a}re bei der Kontrolle von Extensionsreaktionen auf negative Stimuli. Zu diesem Zweck sollten rechtsh{\"a}ndige Probanden einen Joystick zu sich ziehen oder von sich weg dr{\"u}cken, nachdem sie einen positiven oder negativen Stimulus in ihrem linken oder rechten Gesichtsfeld gesehen hatten. Die Flexionsreaktionen waren bei positiven Stimuli schneller, Extensionsreaktion hingegen bei negativen Stimuli. Insgesamt war die Performanz am schnellsten, wenn die emotionalen Stimuli im linken Gesichtsfeld pr{\"a}sentiert wurden. Dieser Vorrang der rechten Gehirnhemisph{\"a}re war besonders deutlich f{\"u}r negative Stimuli, wohingegen die Reaktionszeiten auf positive Bilder keine hemisph{\"a}rische Differenzierung zeigten. Wir konnten keine Interaktion zwischen Gesichtsfeld und Reaktionstyp belegen, auch fand sich keine Dreifachinteraktion zwischen Valenz, Gesichtsfeld und Reaktionstyp. In unserem experimentellen Kontext scheint die Interaktion zwischen Valenz und Gesichtsfeld st{\"a}rker zu sein als die Interaktion zwischen Valenz und motorischem Verhalten. Auf Grund dieser Ergebnisse vermuten wir, dass unter gewissen Bedingungen eine Hierarchisierung der asymmetrischen Muster Vorrang hat, die m{\"o}glicherweise andere vorhandene Asymmetrien maskieren k{\"o}nnte.},
  subject      = {Motorisches Lernen},
  language  = {en}
}
@phdthesis{Zuern2015,
  author    = {Z{\"u}rn, Michael},
  title     = {The Dual Nature of Utility - Categorical and Comparative Evaluations in Economic Decisions},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-120141},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2015},
  abstract  = {Utility is perhaps the most central concept in modern economic theorizing. However, the behaviorist reduction to Revealed Preference not only removed the psychological content of utility but experimental investigations also exposed numerous anomalies in this theory. This program of research focused on the psychological processes by which utility judgments are generated. For this purpose, the standard assumption of a homogeneous concept is substituted by the Utilitarian Duality Hypothesis. In particular, judgments concerning categorical utility (uCat) infer an object's category based on its attributes which may subsequently allow the transfer of evaluative information like feelings or attitudes. In contrast, comparative utility (uCom) depends on the distance to a reference value on a specific dimension of comparison. Importantly, dimensions of comparison are manifold and context dependent. In a series of experiments, we show that the resulting Dual Utility Model is able to explain several known anomalies in a parsimonious fashion. Moreover, we identify central factors determining the relative weight assigned to both utility components. Finally, we discuss the implications of the Utilitarian Duality for both, the experimental practice in economics as well as the consequences for economic theorizing. In sum, we propose that the Dual Utility Model can serve as an integrative framework for both the rational model and its anomalies.},
  subject      = {Nutzen},
  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{Zwettler2021,
  author    = {Zwettler, Fabian Ulrich},
  title     = {Expansionsmikroskopie kombiniert mit hochaufl{\"o}sender Fluoreszenzmikroskopie},
  doi       = {10.25972/OPUS-21236},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-212362},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {Fluorescence microscopy is a form of light microscopy that has developed during the 20th century and is nowadays a standard tool in Molecular and Cell biology for studying the structure and function of biological molecules. High-resolution fluorescence microscopy techniques, such as dSTORM (direct Stochastic Optical Reconstruction Microscopy) allow the visualization of cellular structures at the nanometre scale (10-9 m). This has already made it possible to decipher the composition and function of various biopolymers, such as proteins, lipids and nucleic acids, up to the three-dimensional (3D) structure of entire organelles. In practice, however, it has been shown that these imaging methods and their further developments still face great challenges in order to achieve an effective resolution below ∼ 10 nm. This is mainly due to the nature of labelling biomolecules. For the detection of molecular structures, immunostaining is often performed as a standard method. Antibodies to which fluorescent molecules are coupled, recognize and bind specifcally and with high affnity to the molecular section of the target structure, also called epitope or antigen. The fluorescent molecules serve as reporter molecules which are imaged with the use of a fluorescence microscope. However, the size of these labels with a length of about 10-15 nm in the case of immunoglobulin G (IgG) antibodies, cause a detection of the fluorescent molecules shifted to the real position of the studied antigen. In dense regions where epitopes are located close to each other, steric hindrance between antibodies can also occur and leads to an insuffcient label density. Together with the shifted detection of fluorescent molecules, these factors can limit the achievable resolution of a microscopy technique. Expansion microscopy (ExM) is a recently developed technique that achieves a resolution improvement by physical expansion of an investigated object. Therefore, biological samples such as cultured cells, tissue sections, whole organs or isolated organelles are chemically anchored into a swellable polymer. By absorbing water, this so-called superabsorber increases its own volume and pulls the covalently bound biomolecules isotropically apart. Routinely, this method achieves a magnifcation of the sample by about four times its volume. But protocol variants have already been developed that result in higher expansion factors of up to 50-fold. Since the ExM technique includes in the frst instance only the sample treatment for anchoring and magnifcation of the sample, it can be combined with various standard methods of fluorescence microscopy. In theory, the resolution of the used imaging technique improves linearly with the expansion factor of the ExM treated sample. However, an insuffcient label density and the size of the antibodies can here again impair the effective achievable resolution. The combination of ExM with high-resolution fluorescence microscopy methods represents a promising strategy to increase the resolution of light microscopy. In this thesis, I will present several ExM variants I developed which show the combination of ExM with confocal microscopy, SIM (Structured Illumination Microscopy), STED (STimulated Emission Depletion) and dSTORM. I optimized existing ExM protocols and developed different expansion strategies, which allow the combination with the respective imaging technique. Thereby, I gained new structural insights of isolated centrioles from the green algae Chlamydomonas reinhardtii by combining ExM with STED and confocal microscopy. In another project, I combined 3D-SIM imaging with ExM and investigated the molecular structure of the so-called synaptonemal complex. This structure is formed during meiosis in eukaryotic cells and contributes to the exchange of genetic material between homologous chromosomes. Especially in combination with dSTORM, the ExM method showed its high potential to overcome the limitations of modern fluorescence microscopy techniques. In this project, I expanded microtubules in mammalian cells, a polymer of the cytoskeleton as well as isolated centrioles from C. reinhardtii. By labelling after expansion of the samples, I was able to signifcantly reduce the linkage error of the label and achieve an improved label density. In future, these advantages together with the single molecule sensitivity and high resolution obtained by the dSTORM method could pave the way for achieving molecular resolution in fluorescence microscopy},
  subject      = {Fluoreszenzmikroskopie},
  language  = {en}
}
@phdthesis{Zusan2014,
  author    = {Zusan, Andreas},
  title     = {The Effect of Morphology on the Photocurrent Generation in Organic Solar Cells},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-117852},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Organic solar cells have great potential to become a low-cost and clean alternative to conventional photovoltaic technologies based on the inorganic bulk material silicon. As a highly promising concept in the field of organic photovoltaics, bulk heterojunction (BHJ) solar cells consist of a mixture of an electron donating and an electron withdrawing component. Their degree of intermixing crucially affects the generation of photocurrent. In this work, the effect of an altered blend morphology on polaron pair dissociation, charge carrier transport, and nongeminate recombination is analyzed by the charge extraction techniques time delayed collection field (TDCF) and open circuit corrected transient charge extraction (OTRACE). Different comparative studies cover a broad range of material systems, including polymer and small-molecule donors in combination with different fullerene acceptors. The field dependence of polaron pair dissociation is analyzed in blends based on the polymer pBTTT-C16, allowing a systematic tuning of the blend morphology by varying the acceptor type and fraction. The effect of both excess photon energy and intercalated phases are minor compared to the influence of excess fullerene, which reduces the field dependence of photogeneration. The study demonstrates that the presence of neat fullerene domains is the major driving force for efficient polaron pair dissociation that is linked to the delocalization of charge carriers. Furthermore, the influence of the processing additive diiodooctane (DIO) is analyzed using the photovoltaic blends PBDTTT-C:PC71BM and PTB7:PC71BM. The study reveals amulti-tiered alteration of the blend morphology of PBDTTT-C based blends upon a systematic increase of the amount of DIO. Domains on the hundred nanometers length scale in the DIO-free blend are identified as neat fullerene agglomerates embedded in an intermixed matrix. With the addition of the additive, 0.6\% and 1\% DIO already substantially reduces the size of these domains until reaching the optimum 3\% DIO mixture, where a 7.1\% power conversion efficiency is obtained. It is brought into connection with the formation of interpenetrating polymer and fullerene phases. Similar to PBDTTT-C, the morphology of DIO-free PTB7:PC71BM blends is characterized by large fullerene domains being decreased in size upon the addition of 3\% DIO. OTRACE measurements reveal a reduced Langevin-type, super-second order recombination in both blends. It is demonstrated that the deviation from bimolecular recombination kinetics cannot be fully attributed to the carrier density dependence of the mobility but is rather related to trapping in segregated PC71BM domains. Finally, with regard to small-molecule donors, a higher yield of photogeneration and balanced transport properties are identified as the dominant factors enhancing the efficiency of vacuum deposited MD376:C60 relative to its solution processed counterpart MD376:PC61BM. The finding is explained by a higher degree of dimerization of the merocyanine dye MD376 and a stronger donor-acceptor interaction at the interface in the case of the vacuum deposited blend.},
  subject      = {Organische Solarzelle},
  language  = {en}
}
@phdthesis{Zude2014,
  author    = {Zude, Ingmar},
  title     = {Characterization of virulence-associated traits of Escherichia coli bovine mastitis isolates},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-100934},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Bacterial mastitis is caused by invasion of the udder, bacterial multiplication and induction of inflammatory responses in the bovine mammary gland. Disease severity and the cause of disease are influenced by environmental factors, the cow's immune response as well as bacterial traits. Escherichia coli (E. coli) is one of the main causes of acute bovine mastitis, but although pathogenic E. coli strains can be classified into different pathotypes, E. coli causing mastitis cannot unambiguously be distinguished from commensal E. coli nor has a common set of virulence factors been described for mastitis isolates. This project focussed on the characterization of virulence- associated traits of E. coli mastitis isolates in comprehensive analyses under conditions either mimicking initial pathogenesis or conditions that E. coli mastitis isolates should encounter while entering the udder. Virulence-associated traits as well as fitness traits of selected bovine mastitis or faecal E. coli strains were identified and analyzed in comparative phenotypic assays. Raw milk whey was introduced to test bacterial fitness in native mammary secretion known to confer antimicrobial effects. Accordingly, E. coli isolates from bovine faeces represented a heterogeneous group of which some isolates showed reduced ability to survive in milk whey whereas others phenotypically resembled mastitis isolates that represented a homogeneous group in that they showed similar survival and growth characteristics in milk whey. In contrast, mastitis isolates did not exhibit such a uniform phenotype when challenged with iron shortage, lactose as sole carbon source and lingual antimicrobial peptide (LAP) as a main defensin of milk. Reduced bacterial fitness could be related to LAP suggesting that bacterial adaptation to an intramammary lifestyle requires resistance to host defensins present in mammary secretions, at least LAP. E. coli strain 1303 and ECC-1470 lack particular virulence genes associated to mastitis isolates. To find out whether differences in gene expression may contribute to the ability of E. coli variants to cause mastitis, the transcriptome of E. coli model mastitis isolates 1303 and ECC-1470 were analyzed to identify candidate genes involved in bacterium-host interaction, fitness or even pathogenicity during bovine mastitis. DNA microarray analysis was employed to assess the transcriptional response of E. coli 1303 and ECC-1470 upon cocultivation with MAC-T immortalized bovine mammary gland epithelial cells to identify candidate genes involved in bacterium-host interaction. Additionally, the cell adhesion and invasion ability of E. coli strain 1303 and ECC-1470 was investigated. The transcriptonal response to the presence of host cells rather suggested competition for nutrients and oxygen between E. coli and MAC-T cells than marked signs of adhesion and invasion. Accordingly, mostly fitness traits that may also contribute to efficient colonization of the E. coli primary habitat, the gut, have been utilized by the mastitis isolates under these conditions. In this study, RNA-Seq was employed to assess the bacterial transcriptional response to milk whey. According to our transcriptome data, the lack of positively deregulated and also of true virulence-associated determinants in both of the mastitis isolates indicated that E. coli might have adapted by other means to the udder (or at least mammary secretion) as an inflammatory site. We identified traits that promote bacterial growth and survival in milk whey. The ability to utilize citrate promotes fitness and survival of E. coli that are thriving in mammary secretions. According to our results, lactoferrin has only weak impact on E. coli in mammary secretions. At the same time bacterial determinants involved in iron assimilation were negatively regulated, suggesting that, at least during the first hours, iron assimilation is not a challenge to E. coli colonizing the mammary gland. It has been hypothesized that cellular iron stores cause temporary independency to extracellular accessible iron. According to our transcriptome data, this hypothesis was supported and places iron uptake systems beyond the speculative importance that has been suggested before, at least during early phases of infection. It has also been shown that the ability to resist extracytoplasmic stress, by oxidative conditions as well as host defensins, is of substantial importance for bacterial survival in mammary secretions. In summary, the presented thesis addresses important aspects of host-pathogen interaction and bacterial conversion to hostile conditions during colonization of the mastitis inflammatory site, the mammary gland.},
  subject      = {Escherichia coli},
  language  = {en}
}
@phdthesis{Zube2008,
  author    = {Zube, Christina},
  title     = {Neuronal representation and processing of chemosensory communication signals in the ant brain},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-30383},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2008},
  abstract  = {Ants heavily rely on olfaction for communication and orientation and ant societies are characterized by caste- and sex-specific division of labor. Olfaction plays a key role in mediating caste-specific behaviours. I investigated whether caste- and sex-specific differences in odor driven behavior are reflected in specific differences and/or adaptations in the ant olfactory system. In particular, I asked the question whether in the carpenter ant, Camponotus floridanus, the olfactory pathway exhibits structural and/or functional adaptations to processing of pheromonal and general odors. To analyze neuroanatomical specializations, the central olfactory pathway in the brain of large (major) workers, small (minor) workers, virgin queens, and males of the carpenter ant C. floridanus was investigated using fluorescent tracing, immunocytochemistry, confocal microscopy and 3D-analyzes. For physiological analyzes of processing of pheromonal and non-pheromonal odors in the first odor processing neuropil , the antennal lobe (AL), calcium imaging of olfactory projection neurons (PNs) was applied. Although different in total glomerular volumes, the numbers of olfactory glomeruli in the ALs were similar across the female worker caste and in virgin queens. Here the AL contains up to ~460 olfactory glomeruli organized in 7 distinct clusters innervated via 7 antennal sensory tracts. The AL is divided into two hemispheres regarding innervations of glomeruli by PNs with axons leaving via a dual output pathway. This pathway consists of the medial (m) and lateral (l) antenno-cerebral tract (ACT) and connects the AL with the higher integration areas in the mushroom bodies (MB) and the lateral horn (LH). M- and l-ACT PNs differ in their target areas in the MB calyx and the LH. Three additional ACTs (mediolateral - ml) project to the lateral protocerebrum only. Males had ~45\% fewer glomeruli compared to females and one of the seven sensory tracts was absent. Despite a substantially smaller number of glomeruli, males possess a dual PN output pathway to the MBs. In contrast to females, however, only a small number of glomeruli were innervated by projection neurons of the m-ACT. Whereas all glomeruli in males were densely innervated by serotonergic processes, glomeruli innervated by sensory tract six lacked serotonergic innervations in the female castes. It appears that differences in general glomerular organization are subtle among the female castes, but sex-specific differences in the number, connectivity and neuromodulatory innervations of glomeruli are substantial and likely to promote differences in olfactory behavior. Calcium imaging experiments to monitor pheromonal and non-pheromonal processing in the ant AL revealed that odor responses were reproducible and comparable across individuals. Calcium responses to both odor groups were very sensitive (10-11 dilution), and patterns from both groups were partly overlapping indicating that processing of both odor classes is not spatially segregated within the AL. Intensity response patterns to the pheromone components tested (trail pheromone: nerolic acid; alarm pheromone: n-undecane), in most cases, remained invariant over a wide range of intensities (7-8 log units), whereas patterns in response to general odors (heptanal, octanol) varied across intensities. Durations of calcium responses to stimulation with the trail pheromone component nerolic acid increased with increasing odor concentration indicating that odor quality is maintained by a stable pattern (concentration invariance) and intensity is mainly encoded in the response durations of calcium activities. For n-undecane and both general odors increasing response dynamics were only monitored in very few cases. In summary, this is the first detailed structure-function analyses within the ant's central olfactory system. The results contribute to a better understanding of important aspects of odor processing and olfactory adaptations in an insect's central olfactory system. Furthermore, this study serves as an excellent basis for future anatomical and/or physiological experiments.},
  subject      = {Gehirn},
  language  = {en}
}
@phdthesis{Zott2016,
  author    = {Zott, Maximilian},
  title     = {Extreme Value Theory in Higher Dimensions - Max-Stable Processes and Multivariate Records},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136614},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Die Extremwerttheorie behandelt die stochastische Modellierung seltener und extremer Ereignisse. W{\"a}hrend fundamentale Theorien in der klassischen Stochastik, wie etwa die Gesetze der großen Zahlen oder der zentrale Grenzwertsatz das asymptotische Verhalten der Summe von Zufallsvariablen untersucht, liegt in der Extremwerttheorie der Fokus auf dem Maximum oder dem Minimum einer Menge von Beobachtungen. Die Grenzverteilung des normierten Stichprobenmaximums unter einer Folge von unabh{\"a}ngigen und identisch verteilten Zufallsvariablen kann durch sogenannte max-stabile Verteilungen charakterisiert werden. In dieser Dissertation werden verschiedene Aspekte der Theorie der max-stabilen Zufallsvektoren und stochastischen Prozesse behandelt. Insbesondere wird der Begriff der 'Differenzierbarkeit in Verteilung' eines max-stabilen Prozesses eingef{\"u}hrt und untersucht. Ferner werden 'verallgemeinerte max-lineare Modelle' eingef{\"u}hrt, um einen bekannten max-stabilen Zufallsvektor durch einen max-stabilen Prozess zu interpolieren. Dar{\"u}ber hinaus wird der Zusammenhang von extremwerttheoretischen Methoden mit der Theorie der multivariaten Rekorde hergestellt. Insbesondere werden sogenannte 'vollst{\"a}ndige' und 'einfache' Rekorde eingef{\"u}hrt, und deren asymptotisches Verhalten untersucht.},
  subject      = {Stochastischer Prozess},
  language  = {en}
}