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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.
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.
Amphibian populations are declining worldwide for multiple reasons such as habitat destruction and climate change. An example for an endangered European amphibian is the yellow-bellied toad Bombina variegata. Populations have been declining for decades, particularly at the northern and western range margin. One of the extant northern range centres is the Westerwald region in Rhineland-Palatinate, Germany. To implement informed conservation activities on this threatened species, knowledge of its life-history strategy is crucial. This study therefore focused on different developmental stages to test predictions of life-history theory. It addressed (1) developmental, (2) demographic and (3) genetic issues of Bombina variegata as a model organism: (1) Carry-over effects from larval environment to terrestrial stages and associated vulnerability to predators were investigated using mesocosm approaches, fitness tests and predation trials. (2) The dynamics and demography of B. variegata populations were studied applying a capture-mark-recapture analysis and skeletochronology. The study was complemented through (3) an analysis of genetic diversity and structuring of B. variegata populations using 10 microsatellite loci. In order to reveal general patterns and characteristics among B. variegata populations, the study focused on three geographical scales: local (i.e. a former military training area), regional (i.e. the Westerwald region) and continental scale (i.e. the geographical range of B. variegata). The study revealed carry-over effects of larval environment on metamorph phenotype and behaviour causing variation in fitness in the early terrestrial stage of B. variegata. Metamorph size and condition are crucial factors for survival, as small-sized individuals were particularly prone to predator attacks. Yellow-bellied toads show a remarkable fast-slow continuum of the life-history trait longevity. A populations’ position within this continuum may be determined by local environmental stochasticity, i.e. an extrinsic source of variation, and the efficiency of chemical antipredator protection, i.e. an intrinsic source of variation. Extreme longevity seems to be an exception in B. variegata. Senescence was absent in this study. Weather variability affected reproductive success and thus population dynamics. The dispersal potential was low and short-term fragmentation of populations caused significant genetic differentiation at the local scale. Long-term isolation resulted in increased genetic distance at the regional scale. At the continental scale, populations inhabiting the marginal regions were deeply structured with reduced allelic richness. As consequence of environmental changes, short-lived and isolated B. variegata populations at the range margin may face an increased risk of extinction. Conservation measures should thus improve the connectivity among local populations and reinforce annual reproductive success. Further research on the intraspecific variation in B. variegata skin toxins is required to reveal potential effects on palatability and thus longevity.
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.
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.
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.
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.
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.
Despite widespread plans of big companies like Amazon and Google to develop unmanned delivery drones, scholarly research in this field is scarce, especially in the information systems field. From technical and legal perspectives, drone delivery in last-mile scenarios is in a quite mature state. However, estimates of user acceptance are varying between high skepticism and exaggerated optimism. This research follows a mixed method approach consisting both qualitative and quantitative research, to identify and test determinants of consumer delivery drone service adoption. The qualitative part rests on ten interviews among average consumers, who use delivery services on a regular basis. Insights gained from the qualitative part were used to develop an online survey and to assess the influence of associated risks on adoption intentions. The quantitative results show that especially financial and physical risks impede drone delivery service adoption. Delivery companies who are currently thinking about providing a delivery drone service may find these results useful when evaluating usage behaviors in the future market for delivery drones.
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.