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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.
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.
Schizophrenia is a chronic mental health disorder, which changes rapidly the life of the persons and their families, who suffer from it. It causes high biological and psychological vulnerability as well as cognitive, emotional and behavioral disorders. Nowadays, evidence-based pharmacotherapy and psychotherapy are available aiming the rehabilitation and recovery of individuals with schizophrenia. A democratic society is obliged to give these people the opportunity to have an access to those treatments.
The following three published studies present this dissertation thesis and have a common focus on the implementation of evidence-based psychotherapy in individuals with schizophrenia.
The first study evaluates the efficacy of the Integrated Psychological Therapy (IPT) in Greece, one of the most evaluated rehabilitation programs. IPT was compared to
Treatment as Usual (TAU) in a randomized controlled trial (RCT) with 48 individuals with schizophrenia. Significant effects favouring IPT were found in working memory,
in social perception, in negative symptoms, in general psychopathology and in insight. This study supports evidence for the efficacy of IPT in Greece.
The second study evaluates a second hypothesis, when IPT is more and less effective regarding treatment resistant schizophrenia (TRS) and non treatment resistant
schizophrenia (NTRS). It is a part of the first paper. Significant effects favouring NTRS were found for verbal memory, for symptoms, for functioning and quality of
life. Effect sizes showed superiority of NTRS in comparison to TRS. IPTTRS showed on the other side some significant improvements. This study presents the initial findings of a larger study to be conducted internationally for the first time.
The third study is a systematic review, which aims to evaluate the efficacy of Cognitive Behavioral Therapy (CBT), of Meta Cognitive Therapy (MCT), Metacognitive Training (MCTR), Metacognitive Reflection and Insight Therapy
(MERIT), of various Rehabilitation Programs and Recovery Programs in individuals with schizophrenia. 41 RCTs and 12 Case Studies were included. The above interventions are efficacious in the improvement of cognitions, symptoms, functional outcome, insight, self-esteem, comorbid disorders and metacognitive capacity.
The three studies provide insight regarding the importance of evidence-based psychotherapy in persons with schizophrenia leading to recovery and reintegration into
society. Future RCTs with larger samples and long-term follow up, combining evidence-based psychotherapies for individuals with schizophrenia need to be done.
Blockchain in Healthcare
(2020)
The underlying characteristics of blockchain can facilitate data provenance, data integrity, data security, and data management. It has the potential to transform the healthcare sector. Since the introduction of Bitcoin in the fintech industry, the blcockhain technology has been gaining a lot of traction and its purpose is not just limited to finance. This thesis highlights the inner workings of blockchain technology and its application areas with possible existing solutions. Blockchain could lay the path for a new revolution in conventional healthcare systems. We presented how individual sectors within the healthcare industry could use blockchain and what solution persists. Also, we have presented our own concept to improve the existing paper-based prescription management system which is based on Hyperledger framework. The results of this work suggest that healthcare can benefit from blockchain technology bringing in the new ways patients can be treated.
Interest in crowdfunding has been increasing in recent years, both from the economy and the scientific community. Besides artists and entrepreneurs, researchers are now also funding their projects through many small contributions from the crowd. However, the perceived use in Germany does not reflect the benefits of a crowdfunding campaign, especially in international comparison. This study investigates this issue by identifying the motives and barriers for crowdfunding in order to formulate recommendations for research institutions to encourage the use of crowdfunding.
By means of a literature review, first insights are gained which are then used to conduct qualitative interviews with eleven researchers who successfully completed a crowdfunding campaign. The results indicate that researchers in Germany use crowdfunding primarily to raise awareness for the subject and the scientific community in general. The initial assumption of the speed of crowdfunding as a motive was contradicted by the experts. The major barriers are the immense effort involved in a campaign and the lack of reputation for the concept of crowdfunding by German scientists. In addition, only subjects and projects with a high public relevance and funding volume of up to five digits are recommended for crowdfunding. Furthermore, the public exposure of the experts during the campaign was identified as an additional barrier.
These findings lead to three recommendations for research institutions to increase the use of crowdfunding: Firstly, universities should raise awareness for the subject of crowdfunding as an additional form of research funding and highlight the benefits of a crowdfunding campaign. Secondly, universities should cooperate with crowdfunding partners and utilize the networking capacities of a university. Lastly, universities should provide support to distribute the workload among interdisciplinary teams in order to enhance the effortreturn ratio of a crowdfunding campaign.
The chosen methodology and the scope of the thesis enable further research that might examine the perspective of the universities and the conditions in other countries. In addition, a largescale quantitative survey is required to validate the identified concepts statistically.
Data-minimization and fairness are fundamental data protection requirements to avoid privacy threats and discrimination. Violations of data protection requirements often result from: First, conflicts between security, data-minimization and fairness requirements. Second, data protection requirements for the organizational and technical aspects of a system that are currently dealt with separately, giving rise to misconceptions and errors. Third, hidden data correlations that might lead to influence biases against protected characteristics of individuals such as ethnicity in decision-making software. For the effective assurance of data protection needs,
it is important to avoid sources of violations right from the design modeling phase. However, a model-based approach that addresses the issues above is missing.
To handle the issues above, this thesis introduces a model-based methodology called MoPrivFair (Model-based Privacy & Fairness). MoPrivFair comprises three sub-frameworks: First, a framework that extends the SecBPMN2 approach to allow detecting conflicts between security, data-minimization and fairness requirements. Second, a framework for enforcing an integrated data-protection management throughout the development process based on a business processes model (i.e., SecBPMN2 model) and a software architecture model (i.e., UMLsec model) annotated with data protection requirements while establishing traceability. Third, the UML extension UMLfair to support individual fairness analysis and reporting discriminatory behaviors. Each of the proposed frameworks is supported by automated tool support.
We validated the applicability and usability of our conflict detection technique based on a health care management case study, and an experimental user study, respectively. Based on an air traffic management case study, we reported on the applicability of our technique for enforcing an integrated data-protection management. We validated the applicability of our individual fairness analysis technique using three case studies featuring a school management system, a delivery management system and a loan management system. The results show a promising outlook on the applicability of our proposed frameworks in real-world settings.
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.
Because silver nanoparticles (Ag NPs) are broadly applied in consumer products, their leaching will result in the continuous release of Ag NPs into the natural aquatic environment. Therefore, bacterial biofilms, as the prominent life form of microorganisms in the aquatic environment, are most likely confronted with Ag NPs as a pollutant stressor. Notwithstanding the significant ecological relevance of bacterial biofilms in aquatic systems, and though Ag NPs are expected to accumulate within these biofilms in the environment, the knowledge on the environmental and ecological impact of Ag NPs, is still lagging behind the industrial growth of nanotechnology. Consequently, aim of this thesis was to perform effect assessment of Ag NP exposure on bacterial biofilms with ambient Ag NPs concentrations and under environmentally relevant conditions. Therefore, a comprehensive set of methods was applied in this work to study if and how Ag NPs of two different sizes (30 and 70 nm) affect bacterial biofilms i.e. both monospecies biofilms and freshwater biofilms in environmentally relevant concentrations (600 - 2400 µg l-1). Within the first part of this work, a newly developed assay to test the mechanical stability of
monospecies biofilms of the freshwater model bacterium Aquabacterium citratiphilum was validated. In the first study, to investigate the impact of Ag NPs on the mechanical stability of bacterial biofilms, sublethal effects on the mechanical stability of the biofilms were observed with negative implications for biostabilization. Furthermore, as it is still challenging to monitor the ecotoxicity of Ag NPs in natural freshwater environments, a mesocosm study was performed in this work to provide the possibility for the detailed investigation of effects of Ag NPs on freshwater biofilms under realistic environmental conditions. By applying several approaches to analyze biofilms as a whole in response to Ag NP treatment, insights into the resilience of bacterial freshwater biofilms were obtained. However, as revealed by t-RFLP fingerprinting combined with phylogenetic studies based on the 16S gene, a shift in the bacterial community composition, where Ag NP-sensitive bacteria were replaced by more Ag NP-tolerant species with enhanced adaptability towards Ag NP stress was determined. This shift within the bacterial community may be associated with potential detrimental effects on the functioning of these biofilms with respect to nutrient loads, transformation and/or degradation of pollutants, and biostabilization. Overall, bringing together the key findings of this thesis, 4 general effect mechanisms of Ag NP treatment have been identified, which can be extrapolated to natural freshwater biofilms i.e. (i) the identification of Comamonadaceae as Ag NP-tolerant, (ii) a particular resilient behaviour of the biofilms, (iii) the two applied size fractions of Ag NPs exhibited similar effects independent of their sizes and their synthesis method, and (iv) bacterial biofilms show a high uptake capacity for Ag NPs, which indicates cumulative enrichment.
In this thesis we examined the question whether personality traits of early child care workers influence process quality in preschool.
Research has shown that in educational settings such as preschool, pedagogical quality affects children’s developmental outcome (e.g. NICHD, 2002; Peisner-Feinberg et al., 1999). A substantial part of pedagogical quality known to be vital in this respect is the interaction between teacher and children (e.g., Tietze, 2008). Results of prior classroom research indicate that the teachers’ personality might be an important factor for good teacher-child-interaction (Mayr, 2011). Thus, personality traits might play a vital role for the interaction in preschool. Therefore, the aims of this thesis were to a) identify pivotal personality traits of child care workers, b) assess ideal levels of the identified personality traits and c) examine the relationship between pivotal personality traits and process quality. On that account, we conducted two requirement analyses and a video study. The results of these studies showed that subject matter experts (parents, child care workers, lecturers) partly agreed as to which personality traits are pivotal for child care workers. Furthermore, the experts showed high consensus with regard to the minimum, ideal and maximum personality trait profiles. Furthermore, child care workers whose profiles lay closer to the experts’ ideal also showed higher process quality. In addition, regression analyses showed that the child care workers’ levels of the Big Two (Communion and Agency) related significantly to their process quality.
Environmental processes transforming inorganic nanoparticles: implications on aquatic invertebrates
(2020)
Engineered inorganic nanoparticles (EINPs) are produced and utilized on a large scale and will end up in surface waters. Once in surface waters, EINPs are subjected to transformations induced by environmental processes altering the particles’ fate and inherent toxicity. UV irradiation of photoactive EINPs is defined as one effect-inducing pathway, leading to the formation of reactive oxygen species (ROS), increasing EINP toxicity by exerting oxidative stress in aquatic life. Simultaneously, UV irradiation of photoactive EINP alters the toxicity of co-occurring micropollutants (e.g. pesticides) by affecting their degradation. The presence of natural organic matter (NOM) reduces the agglomeration and sedimentation of EINPs, extending the exposure of pelagic species, while delaying the exposure of benthic species living in and on the sediment, which is suggested as final sink for EINPs. However, the joint impact of NOM and UV irradiation on EINP-induced toxicity, but also EINP-induced degradation of micropollutants, and the resulting risk for aquatic biota, is poorly understood. Although potential effects of EINPs on benthic species are increasingly investigated, the importance of exposure pathways (waterborne or dietary) is unclear, along with the reciprocal pathway of EINPs, i.e. the transport back from aquatic to terrestrial ecosystems. Therefore, this thesis investigates: (i) how the presence of NOM affects the UV-induced toxicity of the model EINP titanium dioxide (nTiO2) on the pelagic organism Daphnia magna, (ii) to which extent UV irradiation of nTiO2 in the presence and absence of NOM modifies the toxicity of six selected pesticides in D. magna, (iii) potential exposure pathway dependent effects of nTiO2 and silver (nAg) EINPs on the benthic organism Gammarus fossarum, and (iv) the transport of nTiO2 and gold EINPs (nAu) via the merolimnic aquatic insect Chaetopteryx villosa back to terrestrial ecosystems. nTiO2 toxicity in D. magna increased up to 280-fold in the presence of UV light, and was mitigated by NOM up to 12-fold. Depending on the pesticide, UV irradiation of nTiO2 reduced but also enhanced pesticide toxicity, by (i) more efficient pesticide degradation, and presumably (ii) formation of toxic by-products, respectively. Likewise, NOM reduced and increased pesticide toxicity, induced by (i) protection of D. magna against locally acting ROS, and (ii) mitigation of pesticide degradation, respectively. Gammarus’ energy assimilation was significantly affected by both EINPs, however, with distinct variation in direction and pathway dependence between nTiO2 and nAg. EINP presence delayed C. villosa emergence by up to 30 days, and revealed up to 40% reduced lipid reserves, while the organisms carried substantial amounts of nAu (~1.5 ng/mg), and nTiO2 (up to 2.7 ng/mg). This thesis shows, that moving test conditions of EINPs towards a more field-relevant approach, meaningfully modifies the risk of EINPs for aquatic organisms. Thereby, more efforts need to be made to understand the relative importance of EINP exposure pathways, especially since a transferability between different types of EINPs may not be given. When considering typically applied risk assessment factors, adverse effects on aquatic systems might already be expected at currently predicted environmental EINP concentrations in the low ng-µg/L range.
On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.
In Western personnel psychology, competence- and control beliefs (CCB) are of widespread use to predict typical work-related outcomes such as well-being, achievement motivation and job performance. The predictive value and comprehension of CCB in East Africa is examined, comparing a Kenyan target with a German source sample (N=143). Responses to personality tests included qualitative interviews on items capturing control orientations (self concept of ability, internality, powerful others, and chance). Linear regression analyses,
explorative factor analyses, and a procrustean target rotation showed comparable, but not fully congruent predictability for the connection of CCB with outcome variables. Factor structures of control responses did not resemble each other sufficiently. Content analyses including scale intercorrelations, quantitative and qualitative item information served for an explanation of this predictability gap, specifying differences between the German and Kenyan samples that are associated with the social-relational domain of personality. Results
fit in the picture depicted by the African Ubuntu philosophy and the South African Personality Inventory project (SAPI), both emphasizing social-relational aspects. In particular, the powerful others control orientation diverges the most between the cultures. Being perceived as a negative and external factor in the German sample with its individualistic culture, powerful others is of mixed emotional quality and just as well internal, when asked for in the Kenyan sample with its Ubuntu-worldview. An uncritical transfer of CCB measures from one culture to another is assumed to be inappropriate. More emic-etic based research is demanded concerning intra- and intercultural variability of CCB to depict a
transcultural applicable model.
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.
Gel effect induced by mucilage in the pore space and consequences on soil physical properties
(2020)
Water uptake, respiration and exudation are some of the biological functions fulfilled by plant roots. They drive plant growth and alter the biogeochemical parameters of soil in the vicinity of roots, the rhizosphere. As a result, soil processes such as water fluxes, carbon and nitrogen exchanges or microbial activity are enhanced in the rhizosphere in comparison to the bulk soil. In particularly, the exudation of mucilage as a gel-like substance by plant roots seems to be a strategy for plants to overcome drought stress by increasing soil water content and soil unsaturated hydraulic conductivity at negative water potentials. Although the variations of soil properties due to mucilage are increasingly understood, a comprehensive understanding of the mechanisms in the pore space leading to such variations is lacking.
The aim of this work was to elucidate the gel properties of mucilage in the pore space, i.e. interparticulate mucilage, in order to link changes of the physico-chemical properties in the rhizosphere to mucilage. The fulfilment of this goal was confronted to the three following challenges: The lack of methods for in situ detection of mucilage in soil; The lack of knowledge concerning the properties of interparticulate mucilage; The unknown relationship between the composition and the properties of model substances and root mucilage produced by various species. These challenges are addressed in several chapters.
In a first instance, a literature review picked information from various scientific fields about methods enabling the characterization of gels and gel phases in soil. The variation of soil properties resulting from biohydrogel swelling in soil was named the gel effect. The combined study of water entrapment of gels and gel phases in soil and soil structural properties in terms of mechanical stability or visual structures proved promising to disentangle the gel effect in soil.
The acquired methodical knowledge was used in the next experiments to detect and characterize the properties of interparticulate gel. 1H NMR relaxometry allows the non-invasive measure of water mobility in porous media. A conceptual model based on the equations describing the relaxation of water protons in porous media was developed to integrate the several gel effects into the NMR parameters and quantify the influence of mucilage on proton relaxation. Rheometry was additionally used to assess mucilage viscosity and soil microstructural stability and ESEM images to visualize the network of interparticulate gel. Combination of the results enabled to identify three main interparticulate gel properties: The spider-web effect restricts the elongation of the polymer chains due to the grip of the polymer network to the surface of soil particles. The polymer network effect illustrates the organization of the polymer network in the pore space according to the environment. The microviscosity effect describes the increased viscosity of interparticulate gel in contrast to free gel. The impact of these properties on soil water mobility and microstructural stability were investigated. Consequences on soil hydraulic and soil mechanical properties found in the literature are further discussed.
The influence of the chemical properties of polymers on gel formation mechanism and gel properties was also investigated. For this, model substances with various uronic acid content, degree of esterification and amount of calcium were tested and their amount of high molecular weight substances was measured. The substances investigated included pectic polysaccharides and chia seed mucilage as model polymers and wheat and maize root mucilage. Polygalacturonic acid and low-methoxy pectin proved as non-suitable model polymers for seed and root mucilage as ionic interactions with calcium control their properties. Mucilage properties rather seem to be governed by weak electrostatic interactions between the entangled polymer chains. The amount of high molecular weight material varies considerably depending on mucilage´s origin and seems to be a straight factor for mucilage’s gel effect in soil. Additionally to the chemical characterization of the high molecular weight compounds, determination of their molecular weight and of their conformation in several mucilages types is needed to draw composition-property profiles. The variations measured between the various mucilages also highlight the necessity to study how the specific properties of the various mucilages fulfill the needs of the plant from which they are exuded.
Finally, the integration of molecular interactions in gel and interparticulate gel properties to explain the physical properties of the rhizosphere was discussed. This approach offers numerous perspectives to clarify for example how water content or hydraulic conductivity in the rhizosphere vary according to the properties of the exuded mucilage. The hypothesis that the gel effect is general for all soil-born exudates showing gel properties was considered. As a result, a classification of soil-born gel phases including roots, seeds, bacteria, hyphae and earthworm’s exuded gel-like material according to their common gel physico-chemical properties is recommended for future research. An outcome could be that the physico-chemical properties of such gels are linked with the extent of the gel effect, with their impact on soil properties and with the functions of the gels in soil.
Nowadays, almost any IT system involves personal data processing. In
such systems, many privacy risks arise when privacy concerns are not
properly addressed from the early phases of the system design. The
General Data Protection Regulation (GDPR) prescribes the Privacy by
Design (PbD) principle. As its core, PbD obliges protecting personal
data from the onset of the system development, by effectively
integrating appropriate privacy controls into the design. To
operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define privacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate controls, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls.
This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy concerns and comprises three sub-methodologies: model-based privacy analysis, modelbased privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to specify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy impact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evaluate our concepts, showing a promising outlook on the applicability of our methodology in real-world settings.
Molecular dynamics (MD) as a field of molecular modelling has great potential to revolutionize our knowledge and understanding of complex macromolecular structures. Its field of application is huge, reaching from computational chemistry and biology over material sciences to computer-aided drug design. This thesis on one hand provides insights into the underlying physical concepts of molecular dynamics simulations and how they are applied in the MD algorithm, and also briefly illustrates different approaches, as for instance the molecular mechanics and molecular quantum mechanics approaches.
On the other hand an own all-atom MD algorithm is implemented utilizing and simplifying a version of the molecular mechanics based AMBER force field published by \big[\cite{cornell1995second}\big]. This simulation algorithm is then used to show by the example of oxytocin how individual energy terms of a force field function. As a result it has been observed, that applying the bond stretch forces alone caused the molecule to be compacted first in certain regions and then as a whole, and that with adding more energy terms the molecule got to move with increasing flexibility.
The Material Point Method (MPM) has proven to be a very capable simulation method in computer graphics that is able to model materials that were previously very challenging to animate [1, 2]. Apart from simulating singular materials, the simulation of multiple materials that interact with each other introduces new challenges. This is the focus of this thesis. It will be shown that the self-collision capabilities of the MPM can naturally handle multiple materials interacting in the same scene on a collision basis, even if the materials use distinct constitutive models. This is then extended by porous interaction of materials as in[3], which also integrates easily with MPM.It will furthermore be shown that regular single-grid MPM can be viewed as a subset of this multi-grid approach, meaning that its behavior can also be achieved if multiple grids are used. The porous interaction is generalized to arbitrary materials and freely changeable material interaction terms, yielding a flexible, user-controllable framework that is independent of specific constitutive models. The framework is implemented on the GPU in a straightforward and simple way and takes advantage of the rasterization pipeline to resolve write-conflicts, resulting in a portable implementation with wide hardware support, unlike other approaches such as [4].
Initial goal of the current dissertation was the determination of image-based biomarkers sensitive for neurodegenerative processes in the human brain. One such process is the demyelination of neural cells characteristic for Multiple sclerosis (MS) - the most common neurological disease in young adults for which there is no cure yet. Conventional MRI techniques are very effective in localizing areas of brain tissue damage and are thus a reliable tool for the initial MS diagnosis. However, a mismatch between the clinical fndings and the visualized areas of damage is observed, which renders the use of the standard MRI diffcult for the objective disease monitoring and therapy evaluation. To address this problem, a novel algorithm for the fast mapping of myelin water content using standard multiecho gradient echo acquisitions of the human brain is developed in the current work. The method extents a previously published approach for the simultaneous measurement of brain T1, T∗ 2 and total water content. Employing the multiexponential T∗ 2 decay signal of myelinated tissue, myelin water content is measured based on the quantifcation of two water pools (myelin water and rest) with different relaxation times. Whole brain in vivo myelin water content maps are acquired in 10 healthy controls and one subject with MS. The in vivo results obtained are consistent with previous reports. The acquired quantitative data have a high potential in the context of MS. However, the parameters estimated in a multiparametric acquisition are correlated and constitute therefore an ill-posed, nontrivial data analysis problem. Motivated by this specific problem, a new data clustering approach is developed called Nuclear Potential Clustering, NPC. It is suitable for the explorative analysis of arbitrary dimensional and possibly correlated data without a priori assumptions about its structure. The developed algorithm is based on a concept adapted from nuclear physics. To partition the data, the dynamic behavior of electrically even charged nucleons interacting in a d-dimensional feature space is modeled. An adaptive nuclear potential, comprised of a short-range attractive (Strong interaction) and a long-range repulsive term (Coulomb potential), is assigned to each data point. Thus, nucleons that are densely distributed in space fuse to build nuclei (clusters), whereas single point clusters are repelled (noise). The algorithm is optimized and tested in an extensive study with a series of synthetic datasets as well as the Iris data. The results show that it can robustly identify clusters even when complex configurations and noise are present. Finally, to address the initial goal, quantitative MRI data of 42 patients are analyzed employing NPC. A series of experiments with different sets of image-based features show a consistent grouping tendency: younger patients with low disease grade are recognized as cohesive clusters, while those of higher age and impairment are recognized as outliers. This allows for the definition of a reference region in a feature space associated with phenotypic data. Tracking of the individual's positions therein can disclose patients at risk and be employed for therapy evaluation.
Stream ecosystems are one of the most threatened ecosystems worldwide due to their exposure to diverse anthropogenic stressors. Pesticides appear to be the most relevant stressor for agricultural streams. Due to the current mismatch of modelled and measured pesticide concentrations, monitoring is necessary to inform risk assessment or improve future pesticide approvals. Knowing if biotic stress responses are similar across large scales and long time frames could ultimately help in estimating protective stressor thresholds.
This thesis starts with an overview of entry pathways of pesticides to streams as well as the framework of current pesticide monitoring and gives an outline of the objectives of the thesis. In chapter 2, routine monitoring data based on grab sampling from several countries is analysed to identify the most frequently occurring pesticide mixtures. These mixtures are comprised of relatively low numbers of pesticides, of which herbicides are dominating. The detected pesticide mixtures differ between regions and countries, due to differences in the spectrum of analysed compounds and limits of quantification. Current routine monitoring does not include sampling during pesticide peaks associated with heavy rainfall events which likely influences the detected pesticide mixtures. In chapter 3, sampling rates of 42 organic pesticides for passive sampling are provided together with recommendations for the monitoring of field-relevant peaks. Using this information, in chapter 4 a pesticide gradient is established in an Eastern European region where agricultural intensity adjacent to sampled streams ranges from low to high. In contrast to current routine monitoring, rainfall events were sampled and a magnitude of pesticides were analysed. This led to the simultaneous detection of numerous pesticides of which one to three drive the pesticide toxicity. The toxicity, however, showed no relationship to the agricultural intensity. Using microcosms, the stress responses of fungal communities, the hyphomycetes, and the related ecosystem function of leaf decomposition, is investigated in chapter 5. Effects of a field-relevant fungicide mixture are examined across three biogeographical regions for three consecutive cycles of microbial leaf colonisation and decomposition. Despite different initial communities, stress responses as well as recoveries were similar across biogeographical regions, indicating a general pattern.
Overall, this thesis contributes to an improved understanding of occurrence and concentrations of pesticides mixtures in streams, their monitoring and impact on an ecosystem function. We showed that estimated pesticide toxicities reach levels that affect non-target organisms and thereby potentially whole ecosystems. Routine monitoring, however, likely underestimates the threat by pesticides. Effects leading to a loss in biodiversity or functions in streams ecosystems can be reduced by reassessing approved pesticides with ongoing targeted monitoring and increased knowledge of effects caused by these pesticides.
This thesis examined two specific cases of point and diffuse pollution, pesticides and salinisation, which are two of the most concerning stressors of Germany’s freshwater bodies. The findings of this thesis were organized into three major components, of which the first component presents the contribution of WWTPs to pesticide toxicity (Chapter 2). The second component focuses on the current and future background salt ion concentrations under climate change with the absence of anthropogenic activities (Chapter 3). Finally, the third major component shows the response of invertebrate communities in terms of species turnover to levels of salinity change, considered as a proxy for human-driven salinisation (Chapter 4).