Die Biopolyester Cutin und Suberin stellen hydrophobe Grenzbarrieren dar, die sich im Laufe der Evolution der Landpflanzen entwickelt haben. Cutin bildet den Hauptbestandteil der Cuticula, die den Pflanzen Schutz vor unkontrollierter Transpiration bietet. Die Einlagerung von Suberin in die Zellwände definierter Zellen des Wurzelgewebes ermöglicht eine kontrollierte Aufnahme von Wasser und Nährstoffen. Zu den wichtigsten monomeren Bestandteilen dieser biologischen Polyester gehören langkettige α,ω-Dicarbonsäuren und ω-Hydroxycarbonsäuren. Bisher wurde der mikrobielle Abbau der Makromoleküle unzureichend erforscht. Zur Entschlüsselung der Zersetzung ist es notwendig, den Kreislauf der monomeren Bestandteile im Boden zu betrachten. Hierzu eignen sich vor allem Experimente mit positionsspezifisch ¹³C -markierten α,ω-Dicarbonsäuren und ω-Hydroxycarbonsäuren, die in der vorliegenden Arbeit erstmals synthetisch zugänglich gemacht wurden. Die Synthesen umfassten Dicarbonsäuren der geradzahligen Kettenlängen C12 bis C30, deren Carboxygruppen ¹³C -markiert sind. Ebenfalls wurde die Synthese von ω-Hydroxycarbonsäuren der Kettenlängen C14, C18, C22 und C30 mit ¹³C-Markierung an der Carboxygruppe realisiert. Weitere Zielverbindungen waren ω-Hydroxycarbonsäuren der Kettenlängen C14, C15, C18, C22 und C30, deren terminales hydroxyliertes Kohlenstoffatom mit ¹³C markiert ist. Im Rahmen der durchgeführten Arbeit gelang es, alle 19 Zielcarbonsäuren erfolgreich in hohen Ausbeuten und Reinheiten darzustellen. Die Synthese der isotopenmarkierten Verbindungen erforderte die Entwicklung spezieller auf die jeweiligen Zielsubstanzen individuell angepasster Syntheserouten, die den Einbau des Kohlenstoffisotops ¹³C ermöglichten. Für alle Zielverbindungen erfolgte die Einführung des ¹³C durch die Verwendung von ¹³C -markiertem Kaliumcyanid (99 at%). Wegen der hohen Kosten des ¹³C -markierten Ausgangsstoffes wurden alle Reaktionen zunächst unter der Verwendung analoger unmarkierter Edukte optimiert. Der letzte Teil der Arbeit bestand in der Ausführung eines Inkubationsexperimentes mit den ¹³C -markierten α,ω-Dicarbonsäuren der Kettenlängen C12, C18, C22 und C30. Mittels Phospholipidfettsäure-Analyse konnte gezeigt werden, dass die ¹³C -Dicarbonsäuren zu unterschiedlichen Anteilen von verschiedenen Mikroorganismengruppen zum Aufbau von Phospholipidfettsäuren verwendet wurden. Außerdem konnte durch die Anreicherung des CO2 mit dem Isotop ¹³C nachgewiesen werden, dass die ¹³C -markierten Fettsäuren von den Mikroorganismen zur Energiegewinnung abgebaut wurden. Für zukünftige Arbeiten wäre es interessant, Ausschnitte der Cutin- und Suberinstruktur nachzubilden. Durch die Veresterung der ¹³C -markierten α,ω-Dicarbonsäuren und der ¹³C -markierten ω-Hydroxycarbonsäuren untereinander oder mit Alkoholen könnten Dimere und Oligomere hergestellt werden.

In dieser Arbeit wird die Konzeption, Implementierung und Evaluierung einer Augmented Reality-App beschrieben. Diese wurde mit dem Ziel entwickelt, Objekte im realen Raum mit virtuellen Hilfsmitteln auszumessen, sodass diese Anwendung einen Holzgliedermaßstab ersetzen kann. Hinzu kommt die praktische Speicherung der Messwerte. Angefertigt wurde die App mit der Unity Engine und programmiert in C#. Schwerpunkte dieser Arbeit sind die Benutzerfreundlichkeit der App, sowie die Eignung von AR Foundation für das Ausmessungstool. Die Anwendung wird auf die genannten Kriterien im Rahmen eines Nutzertests in einer abschließenden Evaluation bewertet. Als Ergebnis ließ sich festhalten, dass sich die AR-App noch im Prototyp-Stadium befindet, aber im Allgemeinen schon als benutzerfreundlich gilt. Kleinere Änderungen sollen und müssen noch vorgenommen werden, um auch den Umgang mit dem AR-Tool zu vereinfachen.

Bio-medical data comes in various shapes and with different representations. Domain experts use such data for analysis or diagnosis, during research or clinical applications. As the opportunities to obtain or to simulate bio-medical data become more complex and productive, the experts face the problem of data overflow. Providing a reduced, uncluttered representation of data, that maintains the data’s features of interest falls into the area of Data Abstraction. Via abstraction, undesired features are filtered out to give space - concerning the cognitive and visual load of the viewer - to more interesting features, which are therefore accentuated. To address this challenge, the dissertation at hand will investigate methods that deal with Data Abstraction in the fields of liver vasculature, molecular and cardiac visualization. Advanced visualization techniques will be applied for this purpose. This usually requires some pre-processing of the data, which will also be covered by this work. Data Abstraction itself can be implemented in various ways. The morphology of a surface may be maintained, while abstracting its visual cues. Alternatively, the morphology may be changed to a more comprehensive and tangible representation. Further, spatial or temporal dimensions of a complex data set may be projected to a lower space in order to facilitate processing of the data. This thesis will tackle these challenges and therefore provide an overview of Data Abstraction in the bio-medical field, and associated challenges, opportunities and solutions.

The analysis of three-dimensional and complex motion sequences of human gait and therefore the most important question of kenesiology ”why are we falling?” is the essence of this paper. The gerontology and its science of movement is currently limited to simple and one-dimensional methods and models. An extensive literature research shows the latest state of research of the three common methods to determine the stability of human gait. To assess the margin of stability, local stability and orbital stability it is shown, how those methods are applicable to evaluate the subject’s ability to recover from any disturbences of the subject’s gait. Based on this assessment and the method’s advantages and disadvantages a new method will be derived that allows spatial analysis of dynamic instability of linear and non-linear human gait. A motion capture system and the timed up and go test serve as a basis for this new method and will be explained. A numerical approximation to optimise the number of markers within a marker-set of a motion capture analysis and its maximum correlation with the full-body-marker-set will be given. This simplification is very helpful for further clinical or scientific research. To validate the new method a trial with subjects will be shown and discussed. New appreciable variables and snapshots of specific situations during the gait offer new and different interpretations of the human gait. The new method is the most applicable and appropriate assessment of human gait and the individual development of the human gait while aging, as well as to detect and prevent falling and associated injuries. Especially directional change of a non-linear gait become assessable with the new method.

The increase in plastic particles (< 5 mm) in the environment is a global problem, which is in direct correlation to the increasing production quantity and variety. Through direct input (primary) or through the degradation of macroplastics (secondary), particles enter the environmental compartments water and/or soil via conventional material transportation paths. The research and development work on the sustainable removal of microplastic particles (inert organic chemical stressors, IOCS) from wastewater is based on the construction of polymer inclusion compounds. IOCS describe a group of organic chemical molecules, which demonstrate a high level of persistence upon entry in the ecosystem and whose degradation is limited. Following the principle of Cloud Point Technology, a novel separation technique has been developed which induces particle growth in microplastics and allows easier separation from the water by volume increase according to the state of the art. The concept for the sustainable removal of microplastics from Herbort and Schuhen is based on a three-step synthesis. This concept was further optimized as part of the research and adapted to the criteria of resource efficiency and profitability. The fundamental research is premised on the hypothesis that van der Waals forces with short ranges and localized hydrophobic interactions between precursors and/or material and the IOCS to be connected can induce a fixation through the formation of an inclusion compound with particle growth. Through the addition of silicon-based ecotoxicologically irrelevant coagulation and inclusion units, it is possible to initiate molecular self-organization with the hydrophobic stressors in an aggregation process induced through water. It results in adhesive particle growth around the polymer particles and between particles. Subsequently, the polymer extract can be separated from aquatic media through simple and cost-effective filtration processes (e.g. sand trap, grease trap), due to the 10,000 times larger volume microplastic agglomerates.

In der Computergrafik stellte die Berechnung von Reflexionen lange ein Problem dar. Doch mit der ständigen Weiterentwicklung der Hardware und Vorstellung neuer Verfahren ist eine realitätsnahe, echtzeitfähige(durchschnittlich 60 FPS) Berechnung von Reflexionen möglich. In der folgenden Ausarbeitung werden verschiedene Reflexionsverfahren vorgestellt. Alle mathematischen und physikalischen Grundlagen werden gegeben, um die Algorithmen nachvollziehen zu können. Da eine Reflexion immer das Abtasten eines reflektierten Vektors bedeutet, werden zwei verschiedene Abtastungsverfahren für blickabhängige Reflexionen vorgestellt und anschließend implementiert. Zuletzt werden die Verfahren auf Basis von Qualität und Performance gegenübergestellt.

The European landscape is dominated by intensive agriculture which leads to widespread impact on the environment. The frequent use of agricultural pesticides is one of the major causes of an ongoing decline in flower-visiting insects (FVIs). The conservation of this ecologically diverse assemblage of mobile, flying insect species is required by international and European policy. To counteract the decrease in species numbers and their abundances, FVIs need to be protected from anthropogenic stressors. European pesticide risk assessment was devised to prevent unacceptable adverse consequences of pesticide use on FVIs. However, there is an ongoing discussion by scientists and policy-makers if the current risk assessment actually provides adequate protection for FVI species. The first main objective of this thesis was to investigate pesticide impact on FVI species. The scientific literature was reviewed to identify groups of FVIs, summarize their ecology, and determine their habitat. This was followed by a synthesis of studies about the exposure of FVIs in their habitat and subsequent effects. In addition, the acute sensitivity of one FVI group, bee species, to pesticides was studied in laboratory experiments. The second main objective was to evaluate the European risk assessment for possible deficits and propose improvements to the current framework. Regulatory documents were screened to assess the adequacy of the guidance in place in light of the scientific evidence. The suitability of the honey bee Apis mellifera as the currently only regulatory surrogate species for FVIs was discussed in detail. The available scientific data show that there are far more groups of FVIs than the usually mentioned bees and butterflies. FVIs include many groups of ecologically different species that live in the entire agricultural landscape. Their habitats in crops and adjacent semi-natural areas can be contaminated by pesticides through multiple pathways. Environmentally realistic exposure of these habitats can lead to severe effects on FVI population parameters. The laboratory studies of acute sensitivity in bee species showed that pesticide effects on FVIs can vary greatly between species and pesticides. The follow-up critical evaluation of the European FVI risk assessment revealed major shortcomings in exposure and effect assessment. The honey bee proved to be a sufficient surrogate for bee species in lower tier risk assessment. Additional test species may be chosen for higher tier risk assessment to account for ecological differences. This thesis shows that the ecology of FVIs should generally be considered to a greater extent to improve the regulatory process. Data-driven computational approaches could be used as alternative methods to incorporate ecological trait data in spatio-temporal scenarios. Many open questions need to be answered by further research to better understand FVI species and promote necessary changes to risk assessment. In general, other FVI groups than bees need to be investigated. Furthermore, comprehensive data on FVI groups and their ecology need to be collected. Contamination of FVI habitat needs to be linked to exposure of FVI individuals and ecologically complex effects on FVI populations should receive increased attention. In the long term, European FVI risk assessment would benefit from shifting its general principles towards more scientifically informed regulatory decisions. This would require a paradigm shift from arbitrary assumptions and unnecessarily complicated schemes to a substantiated holistic framework.

Since the invention of U-net architecture in 2015, convolutional networks based on its encoder-decoder approach significantly improved results in image analysis challenges. It has been proven that such architectures can also be successfully applied in different domains by winning numerous championships in recent years. Also, the transfer learning technique created an opportunity to push state-of-the-art benchmarks to a higher level. Using this approach is beneficial for the medical domain, as collecting datasets is generally a difficult and expensive process. In this thesis, we address the task of semantic segmentation with Deep Learning and make three main contributions and release experimental results that have practical value for medical imaging. First, we evaluate the performance of four neural network architectures on the dataset of the cervical spine MRI scans. Second, we use transfer learning from models trained on the Imagenet dataset and compare it to randomly initialized networks. Third, we evaluate models trained on the bias field corrected and raw MRI data. All code to reproduce results is publicly available online.

The distributed setting of RDF stores in the cloud poses many challenges. One such challenge is how the data placement on the compute nodes can be optimized to improve the query performance. To address this challenge, several evaluations in the literature have investigated the effects of existing data placement strategies on the query performance. A common drawback in theses evaluations is that it is unclear whether the observed behaviors were caused by the data placement strategies (if different RDF stores were evaluated as a whole) or reflect the behavior in distributed RDF stores (if cloud processing frameworks like Hadoop MapReduce are used for the evaluation). To overcome these limitations, this thesis develops a novel benchmarking methodology for data placement strategies that uses a data-placement-strategy-independent distributed RDF store to analyze the effect of the data placement strategies on query performance. With this evaluation methodology the frequently used data placement strategies have been evaluated. This evaluation challenged the commonly held belief that data placement strategies that emphasize local computation, such as minimal edge-cut cover, lead to faster query executions. The results indicate that queries with a high workload may be executed faster on hash-based data placement strategies than on, e.g., minimal edge-cut covers. The analysis of the additional measurements indicates that vertical parallelization (i.e., a well-distributed workload) may be more important than horizontal containment (i.e., minimal data transport) for efficient query processing. Moreover, to find a data placement strategy with a high vertical parallelization, the thesis tests the hypothesis that collocating small connected triple sets on the same compute node while balancing the amount of triples stored on the different compute nodes leads to a high vertical parallelization. Specifically, the thesis proposes two such data placement strategies. The first strategy called overpartitioned minimal edge-cut cover was found in the literature and the second strategy is the newly developed molecule hash cover. The evaluation revealed a balanced query workload and a high horizontal containment, which lead to a high vertical parallelization. As a result these strategies showed a better query performance than the frequently used data placement strategies.

Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.