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- Kaiserslautern - Fachbereich Mathematik (280)
- Kaiserslautern - Fachbereich Informatik (219)
- Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik (144)
- Kaiserslautern - Fachbereich Chemie (79)
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- Kaiserslautern - Fachbereich Biologie (55)
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- Landau - Fachbereich Natur- und Umweltwissenschaften (26)
- Kaiserslautern - Fachbereich Wirtschaftswissenschaften (19)
- Kaiserslautern - Fachbereich Physik (9)
Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for reaction and
process monitoring. Process monitoring is often carried out online on flowing samples. Benchtop NMR spectrometers are especially well-suited for these applications because they can be installed close to the studied process. However, it is a challenge to analyze a fast-flowing liquid with NMR spectroscopy because short residence times in the magnetic field of the spectrometer result in inefficient polarization build-up and thus poor signal intensity. This is particularly problematic for benchtop NMR spectrometers because of
their compact design. Therefore, different methods to counteract this prepolarization problem in benchtop NMR spectroscopy were studied experimentally in the present work. Established approaches that were studied gave only poor results at high flow velocities. To overcome this, signal enhancement by Overhauser DNP (ODNP) was used, which is based on polarization transfer from unpaired electron spins to nuclear spins and happens on very short time scales, resulting in high signal enhancements, also in fast-flowing liquids. A corresponding set-up was developed and used for the studies: the line leading to the 1 Tesla benchtop NMR spectrometer first passes a fixed bed of a radical matrix which is placed in a Halbach magnet equipped with a microwave cavity to facilitate the polarization transfer. With this ODNP set-up, excellent results were obtained also for the highest studied flow velocities. This shows that ODNP is an enabler for fast-flow benchtop NMR spectroscopy.
ODNP requires the presence of unpaired electrons in the sample which is usually accomplished by addition of stable radicals. However, radicals affect the nuclear relaxation times and can hamper the NMR detection. This was circumvented by immobilizing radicals in a fixed bed, allowing for the measurement of radical-free samples when using ex situ DNP techniques (DNP build-up and NMR detection happen at different places) with flow-induced separation of the hyperpolarized liquid from the radicals. Therefore, the synthesis of robust and chemically inert immobilized radical matrices is mandatory. This was accomplished by immobilizing the radical glycidyloxy-tetramethylpiperidinyloxyl (GT) with a polyethyleneimine (PEI) linker on the surface of controlled porous glasses (CPG). Both the porosity of the CPGs and also the size of the PEI-linker were varied resulting in a set of distinct radical matrices for continuous-flow ODNP. The study shows that CPGs with PEI linkers provide robust, inert, and efficient ODNP matrices.
Another method to address the prepolarization problem in continuous-flow NMR applications is paramagnetic relaxation enhancement (PRE) by using a T1 relaxation agent. In the present work, a PRE agent was developed that was again based on PEI-grafted CPGs with PEI-linker and GT. Here, the interaction of the studied liquid with this PRE agent significantly accelerates the buildup of nuclear polarization prior to NMR detection, which enables quantitative measurements in continuous-flow benchtop NMR applications. The results show that the flow regime for quantitative measurements can be greatly extended by the use of the synthesized PRE agent.
Riparian areas are an important transition zone in freshwater systems, connecting and regulating both aquatic and terrestrial systems. They are characterized by a high diversity and are important for conservation. However, riparian areas are frequently under stress from human activities. Two of these stressors are agricultural activity and introduction of invasive plant species. One example of the impact of agriculture is pollution with heavy metals such as copper. Copper is commonly used as a fungicide in agriculture and can be introduced into riparian areas via flooding events via streams in areas previously unaffected by pollution. There, copper is toxic to animals, plants and microorganisms at high concentrations, damaging DNA, enzymes, cell membranes and chloroplasts. This leads to a reduction in growth and reproduction of the affected organisms, potentially disrupting the ecosystems. Invasive alien plants are another major cause of biodiversity loss in animals, plants and microorganisms. This can negatively affect entire ecosystems above as well as belowground, leading to alterations of resources and ecosystem functions. Especially soil fungi are important for ecosystem functions. For example by forming symbiotic interactions with plants, which can be disrupted by plant invasion and copper pollution. Two common invasive plant species in riparian areas are Fallopia japonica and Impatiens glandulifera. They frequently invade stands of the native Urtica dioica. The aim of this project was to investigate the impact of these two plant invaders, especially of F. japonica, on native soil communities further modified by copper pollution. This was done in two parts: a field study, investigating the impact of the invasive plants on soil properties, invertebrates, fungi and activity and a mesocosm experiment under the influence of copper pollution, comparing the impact of copper on plants, soil invertebrates, microorganisms and activity depending on the presence of a native or invasive plant species. Under field conditions, plant invasion mainly reduced the diversity of fungi directly associated with the plants but not the biomass of fungi. Direct impacts on soil invertebrates were also observed. In the mesocosms, microbial biomass was reduced under the invasive plant and no impact on invertebrates was observed. Similarly, the soil activity was not affected in the field but was strongly reduced by the presence of F. japonica in the mesocosms. These results align with the enemy release hypothesis, indicating that these invasive species, especially F. japonica, may be less associated with fungal parasites in the invasive range, allowing them to perform better than native species. These findings also indicate that these invaders have various and contrasting impacts on belowground systems, making their effects highly context-dependent and site-specific. Copper pollution inhibited growth in both F. japonica and U. dioica. Urtica dioica seemed to be more sensitive to copper pollution compared to the invasive plant. In the soil, copper pollution further amplified the reduction of soil activity by the invasive plant and had variable effects on invertebrates and microbial biomass. This indicates that F. japonica may gain an advantage against the commonly occurring U. dioica, especially in polluted areas. The negative impact of copper pollution on soil functions could therefore be amplified by facilitating invasion by F. japonica, which also negatively impacts soil functions. Therefore, disturbances by agricultural activity, one major source of copper pollution, could have an even stronger impact across much wider distances and in previously undisturbed areas.
Product manufacturing is performed in a massively automated and increasingly customized manner.
However, overall production speed is limited by automation of inspection since each product has to ensure the required quality.
A widespread and often-used quality assurance method is visual surface inspection.
Automated surface inspection relies on an inspection plan and defect recognition algorithms.
Both inspection planning and defect recognition algorithms development heavily rely on the availability of representative image data containing various product surface textures and imperfections showing a wide variety of possible surface responses to different viewing and lighting conditions.
Due to the advancements in manufacturing, defects in products occur rarely, with different frequencies of appearance, followed by a subjective and laborious annotation process.
Further, since the surface texture is often not relevant to product performance and thus not controlled, products with different surface textures are not treated as different product samples and thus not provided.
Motivated by aforementioned problems, this work introduces the following contributions: (1) image synthesis requirements for industrial quality inspection and a novel realistic image synthesis pipeline satisfying those requirements (Chapter 4), (2) texture synthesis requirements for industrial quality inspection and a procedural approach to parameterized surface texture modeling incorporating domain knowledge (Chapter 5) and (3) defect synthesis requirements for industrial quality inspection as well as a procedural approach to parameterized defect modeling (Chapter 6).
The contributions presented in this thesis, make it possible to obtain, in a controllable and automated manner, the required amount of image data, containing realistic and varying surface textures resembling machining surfaces as well as diversified geometrical defects with automated, pixel-precise annotations (Chapters 7,8).
The presented contributions enable the inspection planning and development of machine vision algorithms for defect recognition to be performed completely virtually, by inspection planning experts, without computer graphics knowledge.
Machine learning and artificial intelligence are pivotal pillars in the area of
computer vision, especially object detection and classification. They support
or replace conventional methods such as morphological operators or manual
surveillance. These models, tailored and trained for various use cases, typically possess a vast number of trainable parameters to cover a wide range of scenarios.
However, their sizes have reached a point where classical computers struggle
to train them efficiently, both in terms of time and computational resources.
Moreover, the data itself is becoming increasingly detailed and thus larger. In
our case, we are dealing with 2D or 3D image data, specifically gray value
images.
One promising avenue to mitigate computational demands is quantum computing.
With properties like superposition, entanglement, and other quantum
mechanical properties, there exists a theoretical advantage over classical methods.
In this doctoral thesis, we aim to investigate the practical utility of quantum
hardware in several application scenarios.
The first part of our study focuses on encoding classical image data into
quantum states. To design quantum algorithms, we must first transform image
information, represented as gray values, into quantum states. This step is
crucial and a main part for the development of quantum algorithms. Image
information is converted into quantum states through methods like basis encoding,
amplitude encoding, or phase encoding. We contribute to this field by
enhancing a phase encoding method called Flexible Representation of Quantum
Images (FRQI). This contribution is included in our two papers [1, 2] and in
Chapter 4 in this thesis. Our approach reduces the number of so called CXoperations
and consequently the errors observed on current quantum hardware.
We also evaluate the scalability in terms of feasibility and usability on existing
hardware.
We adapted our research for the following parts of the thesis based on the
results of the first part. We can not encode and retrieve large images on current
quantum devices. Either we simulate the quantum hardware as in the second
part of this thesis, reduce the image size, or use hybrid approaches as in the
other parts of this thesis.
In the second part, we concentrate on amplitude encoding, with Quantum
Probability Image Encoding (QPIE), and apply the Quantum Fourier Transform
(QFT) to the quantum states. We can detect the orientation of objects in images
with this approach by using additional post-processing methods. We compare
the results of the QFT with those of the Fast Fourier Transform (FFT) and
demonstrate that, at least on the simulator, we get the same results as with the
classical method (see Chapter 5).
The third part of the study is about edge detection of objects in gray value
images. We use the idea of a quantum artificial neuron as the core building block
of our algorithm (see [3] and Chapter 6). In this part, our primary focus is on
the algorithm’s robustness in the face of current hardware limitations. To tailor
it further to the current hardware, we developed six variations of the algorithm
with the aim of reducing the number of quantum circuits. We compare the
results of the six variations. Our adaption of the algorithm allows to examine
image sizes that were previously unattainable by quantum algorithms on existing
quantum hardware.
In the fourth part, we focus on hybrid algorithms in the form of quantum
transfer learning. Drawing from the experiences of the first part regarding the
practical usability of current hardware, quantum transfer learning offers a way
to circumvent these limitations by keeping some parts of the algorithm classical
while executing other parts on quantum hardware. Our algorithm demonstrates
its utility in detecting small cracks with a thickness of approximately one or
two pixels in concrete samples (see [4] and Chapter 7). We highlight differences
between simulators and current quantum computers and demonstrate the capability
to detect the cracks in the images with the current quantum hardware.
Tropical dry forests are crucial for climate adaptation, economic development, and poverty alleviation, offering vital ecosystem services. However, this understudied, and inadequately protected biome faces severe threats like deforestation and land-use changes and is often overlooked in national policies. This neglect poses risks to services like clean water provision, diverse habitats, and climate change mitigation. Changes in land use within these forests impact environmental conditions, causing reduced biodiversity and vegetation restructuring. The regeneration process relies on abiotic factors and natural soil recovery. In this dissertation, I investigated the role of two keystone organism groups—Biological soil crusts ('biocrusts') and leaf-cutting ants (LCA)—in dry forest regeneration. These ecosystem engineers can enhance topsoil quality, introduce essential nutrients and water, and influence plant germination and growth, thereby potentially affecting dry forest regeneration. My primary objectives were to determine the relevance of biocrusts in the Caatinga dry forest, their interaction with LCA, as well as both of their provision of essential ecosystem services, and their response to chronic anthropogenic disturbance. I employed various techniques to document biocrust diversity, and distribution, along with the abiotic environment alterations caused by biocrusts and LCA.
Biocrusts, diverse components in the Caatinga dry forest, were present in various successional stages, including agricultural fields, regenerating areas, and old-growth forests. Dominated by cyanobacteria, their coverage depended on factors like leaf-litter burial, disturbance levels, soil stability, seasonality, and the presence/ activity of LCA nests. A balance between vascular plant cover and disturbance pressure was also crucial for biocrust distribution. Both biocrusts and LCA impacted key abiotic factors for dry forest resilience but with significantly differing ecological consequences and reactions to anthropogenic disturbances. Biocrusts, by reducing water infiltration, promoted runoff, fostering small-scale source-sink patterns, benefiting vascular vegetation. They enhanced soil fertility and provided erosion protection, with older biocrusts exhibiting more significant positive effects. Anthropogenic disturbance disrupted biocrust succession, limiting their services and leading to negative feedback loops. LCA nests increased compaction, and reduced water infiltration, potentially hindering forest regeneration. These physico-hydrological barriers persisted, especially in disturbed areas, impacting forest dynamics and resilience for years, even after colony death. Adverse effects of LCA on water availability and soil resistance escalated with anthropogenic disturbance, though LCA refuse had the potential to mitigate some negative soil property changes.
Both biocrusts and LCA act as edaphic ecosystem engineers in the Caatinga dry forest, impacting vascular plants through their abiotic influence. A greenhouse experiment demonstrated the positive effects of both organisms on plant germination, development, and survival across various functional groups. This dissertation also showed for the first time that LCA can accelerate germination time. These facilitative effects are attributed to improved soil conditions, including enhanced water availability and nutrient richness. Given species-specific responses and the prevalence of LCA nests and biocrust coverage in regenerating areas, their activities likely play a pivotal role in shaping successional trajectories and regeneration dynamics in dry forests. This underscores the significant potential of both ecosystem engineers in influencing the regeneration and resilience of tropical dry forests.
In summary, in the human-modified landscapes of the Caatinga, biocrusts and LCA act as ecosystem engineers, influencing vital soil properties. Biocrusts protect degraded soils and facilitate plant establishment, while the impact of LCA depends on the nest structure. These engineers play a crucial role in dry forest regeneration and sustainability. However, climate change and land degradation pose significant threats to both ecosystems and engineers, impacting their effects diametrically. This research enhances understanding of the biome's functioning, regeneration, and resilience, providing insights for sustainable management, restoration, and conservation to support biodiversity and human well-being.
Reaktive Strömungen sind ein wichtiger Bestandteil vieler umwelttechnischer und industrieller Prozesse und ein Forschungsgegenstand in vielen Bereichen. Ein Beispiel eines solchen Prozesses ist die Reinigung von Abgasen eines Verbrennungsmotors in der Automobilindustrie. Hierzu werden Katalysatoren und poröse Filter benutzt. In allen Forschungsbereichen wird mathematische Modellierung und Simulation eingesetzt, die es ermöglicht, die Effizienz des zu entwickelnden Prozesses oder Produkts zu steigern und Daten zu erhalten, die für theoretische oder experimentelle Forschungsmethoden unzugänglich sind. Numerische Algorithmen zur Simulation reaktiver Strömungen werden seit Jahrzehnten entwickelt und haben in vielen Bereichen ihre Leistungsfähigkeit bei der Lösung angewandter Industrie- und Umweltprobleme bewiesen. Die Klasse der reaktiven Strömungen und insbesondere die der reaktiven Strömungen auf der Porenskala ist aber sehr reichhaltig, und es gibt keinen allgemeinen Algorithmus, der für alle Strömungen dieser Art effizient ist. Eine Anpassung der Algorithmen für bestimmte Klassen von Problemen ist erforderlich. In dieser Arbeit liegt der Schwerpunkt auf der Entwicklung effizienter Algorithmen zur porenskaligen Simulation von Prozessen in katalytischen Filtern. Ein besonderes Merkmal dieser Filter ist, dass das Filtermaterial ein inertes, undurchlässiges Grundgerüst und nanoporöse aktive (Washcoat) Partikel enthält, in denen die Reaktionen stattfinden. Der Stofftransport findet innerhalb der Poren statt und in den Washcoat-Partikeln kann der Transport durch Konvektion (oft vernachlässigt) und Diffusion beschrieben werden. Die mathematischen Modelle basieren auf einer Konvektions-Diffusions-Reaktions-Gleichung oder Systemen solcher Gleichungen. Zu den größten Herausforderungen bei der Lösung solcher Probleme gehören die Nichtlinearitäten der reaktiven Terme und die Heterogenität des Strömungsfeldes, die durch die Heterogenität der porösen Medien verursacht wird. Letzteres bedeutet, dass in ein und derselben Materialprobe schnelle und langsame Zonen koexistieren, was bedeutet, dass sich die Art der maßgeblichen Gleichungen lokal ändert. Letzteres impliziert, dass nicht einfach Algorithmen für parabolische oder hyperbolische Probleme ausgewählt werden können. Die Algorithmen sollten für jede Art von Strömung robust sein oder sich an die Änderung der Art der Gleichungen anpassen können. Darüber hinaus kann die Eigenschaft ausgenutzt werden, dass der Washcoat (in dem die Reaktionen stattfinden und den Algorithmen eine wesentliche Änderung auferlegen) nur einen begrenzten Teil des Rechengebiets einnimmt. All dies motiviert dazu, die Klasse der Splitting-Verfahren erneut zu untersuchen, an die betrachtete Klasse von Problemen anzupassen und ihre Stabilität und Leistung numerisch zu untersuchen. Dies ist das Hauptthema der Dissertation. Die Steigerung der Rechenleistung und sich ändernde Rechnerarchitekturen erfordern eine Überarbeitung der bisherigen Softwareimplementierung und der bisher verwendeten Datenstrukturen. Ein weiteres Ziel dieser Arbeit ist daher die Entwicklung von Softwarelösungen, die die Simulation reaktiver Strömungen für hochdimensionale Probleme ermöglichen. Zur reaktiven Strömungssimulation auf der Porenskala wurden verschiedene Methoden verwendet. Die Arbeit konzentrierte sich auf zwei allgemeine Methodenklassen: Splitting-Algorithmen und implizit-explizite Schemata. Zum Vergleich werden vollständig implizite Algorithmen verwendet. Eine Reihe von Benchmark-Geometrien und chemischen Reaktionen, deren Komplexität von einfachen 1D- und linearen Fällen bis hin zu CT-Scan-basierten echten Filterdomänen und echten nichtlinearen komplexen chemischen Reaktionen reichte, wurden berücksichtigt und untersucht. Die vorliegende Arbeit zeigt, dass für alle betrachteten Fälle durch die Nutzung von Splitting-Verfahren für den (schwachen) Transport- und den Reaktionsterm, Verbesserungen in Bezug auf Speichernutzung und Konvergenzgeschwindigkeit erzielt werden können.
European crayfish species are considered keystone in freshwater ecosystems. As such, their conservation is of paramount importance to prevent biodiversity decline and loss of ecosystem function. Unfortunately, today, European crayfish species are among the most threatened crayfish species worldwide. An especially relevant threat is represented by the invasive pathogen Aphanomyces astaci. This oomycete, native of North America, has been one of the main causes of crayfish population declines across Europe since its first introduction 150 years ago, to the point of causing the local extinction of many populations. Over the years, several introductions of A. astaci strains into Europe took place through translocation of infected North American crayfish, and were followed by mass mortalities across European crayfish populations. However, in the past 20 years, more and more reports emerged of European crayfish populations surviving A. astaci infections or being latently infected with the pathogen. The survival of infected crayfish can be ascribed to both increased resistance of some crayfish populations and decreased virulence of some A. astaci strains. As the relationship between host and pathogen in Europe is changing, it is imperative to gain insights on what shapes these changes to understand the implications for the long-term coexistence of crayfish and A. astaci in Europe. With this thesis, I focused on the virulence of A. astaci, looking for mechanisms, patterns and determinants underlying the pathogen’s virulence variability. In particular, by characterising the virulence of several A. astaci strains, I identified two possible different mechanisms of loss of virulence. I revealed that A. astaci’s virulence variability is not linked to variation of in vitro growth and sporulation, traits classically associated with a pathogen’s virulence. Based on these results, I suggest that the pathogen’s virulence determinants are likely its “virulence effectors”, of which A. astaci genome is enriched. Additionally, with the present work I provided transcriptomic evidence of coevolution between A. astaci and European crayfish. I showed that the haplogroups based on the canonical mitochondrial markers, often used to assess A. astaci’s virulence to inform management actions, do not differ for some of their characterising phenotypical traits, including virulence. Finally, after experimental characterisation of virulence and assessment of its likely phenotypical determinants, i.e., sporulation and growth, the next and more comprehensive step to study the pathogen’s virulence is through genomic approaches. To this aim, I provided key data for future comparative genomic studies, i.e., highly complete genome assemblies based on Nanopore (3) and Illumina reads (11). These data can be exploited in several ways, from building a pangenome of the species to a genome-wide association study (GWAS), that can offer a much deeper understanding of A. astaci’s virulence and adaptability. In particular, the identification of the loci associated with virulence through a GWAS has the potential to be revolutionary for the management of A. astaci, as it can become the basis to create a genomic tool to quickly and accurately assess the virulence of newly introduced strains, directing management actions towards the more dangerous strains.
Olive mill wastewater (OMW) is a by-product of olive oil extraction and its disposal on soil has been associated with significant environmental challenges, including toxic effects on soil organisms and quality of groundwater due to its high phenolic content. Recent studies focusing on the dynamics of OMW degradation in soil are handling the environmental conditions as main factors influencing the fate and transport of polyphenols in the soil-water system. The understanding of seasonal-dependent phenol leaching from OMW-treated soil remained elusive, as field studies are hindered by spatial variability and complex environmental dynamics. Therefore, controlled lysimeter experiments were conducted to investigate the leaching and transport mechanisms of OMW-derived phenolic compounds in soil.
This thesis presents the results of an 18-week lysimeter experiment conducted in a laboratory setting, aimed at monitoring and comprehending the distribution and leaching of OMW-derived phenolic compounds in soil after OMW application. The experiment spanned four seasonal simulation phases, including two winter, one spring, and one summer, under semi-arid climate Tunisian conditions. The effects of OMW on soil leachates properties, soil water repellency, and soil water retention capacity were assessed.
The soil leachates exhibited varying degrees of recovery across the different simulation phases. However, persistent salinity in the leachates and high soil water repellency at the top treated OMW-soils were recorded. The findings revealed also that OMW application changed the pore size distribution in treated OMW-soils. Most of the OMW-derived phenols were immobilized in the upper 5 cm of the soil. Notably, soluble phenolic compounds exhibited the formation of coarser pores for the sake of fine pores, suggesting that OMW- organic carbon played a crucial role in controlling the depth-dependent transport mechanisms of OMW within the soil matrix.
In conclusion, this study provides valuable insights into the fate and impact of OMW-derived phenolic compounds in soil. It emphasizes the significance of conducting OMW applications with careful irrigation practices and thorough phenol leaching surveys to minimize the risk of potential groundwater contamination. Additionally, more experiments are warranted to investigate the sorption capacity of the soil during and after OMW application and its influence on the stability of soluble phenolic compounds
in soils.
Many amphibians and insects have a biphasic life cycle, linking aquatic and terrestrial ecosystems. In temperate wetlands, insect communities are largely dominated by midges, such as non-biting chironomids and mosquitoes. Particularly chironomids and their aquatic larvae play a key role for both aquatic and terrestrial predators, e.g., dragonflies and damselflies (Odonata), birds, riparian spiders and amphibians. Therefore, adverse effects on chironomid larvae induced by pesticides or biocides can have implications on food webs across ecosystem boundaries.
In floodplains of the Upper Rhine Valley in southwest Germany, the biocide Bacillus thuringiensis var. israelensis (Bti) has been applied for over 40 years to reduce nuisance by mass emergence of mosquitoes. Due to its specific mode of action, Bti is presumed to be a more environmentally friendly alternative to non-selective, highly toxic pesticides used in the past. However, research on indirect effects of Bti on non-target organisms inhabiting these wetlands is still relatively scarce. The aim of this thesis was the investigation of direct and indirect effects of Bti on non-target organisms and, consequently, bottom-up effects on aquatic food webs and propagation to the terrestrial ecosystem. Effects were examined in outdoor floodplain pond mesocosms (FPMs) with natural flora and fauna communities.
Benthic macroinvertebrate communities were significantly altered in Bti-treated FPMs, largely due to the reduction of chironomid density by over 40% compared to untreated FPMs. Sampling of exuviae indicated that the emergence of Libellulidae (Odonata) was reduced by Bti, while larger Aeshnidae were not affected. This finding suggested increased intraguild predation (predation of competing predators) in Bti-treated FPMs as a result of decreased prey availability, i.e. chironomid larvae. This conclusion was partly confirmed in food web analyses using stable isotopes of C and N and fatty acids, with Aeshnidae experiencing a slight diet shift towards larger prey (i.e., newts, Aeshnidae) in Bti-treated FPMs. In contrast, the diet proportions of newt larvae were not affected by Bti treatment, but showed a marginal trend in lower omega-6 fatty acid content. Analyses of oxidative stress biomarkers did not reveal any direct effects of Bti on common frog tadpoles under natural climatic conditions.
This thesis emphasizes that adverse effects of Bti on the base of aquatic-terrestrial food webs, i.e., reduction of larval chironomids, can have implications for higher trophic levels and cascade to terrestrial ecosystems. Affected organisms also include species of concern, such as protected Odonata species. In view of the global insect and amphibian decline, the large-scale use of Bti in (partially protected) wetlands should be carefully considered.
Living systems incessantly engage in the regulation of their cellular processes to fulfill their biological functions. Beyond development-related adjustments or cell cycle oscillations, environmental fluctuations compel the system to reorganize metabolic pathways, structural components, or molecular repair and reconstitution mechanisms. These responses manifest across diverse temporal scales, necessitating an intricate regulatory orchestration. Time series experiments have become increasingly popular for charting the chronological order and elucidating the underlying mechanisms. In the era of high-throughput technologies, the majority of cellular molecules can be analyzed in one fell swoop, generating a comprehensive snapshot of the status quo of most present molecules. Methodological advancements also permit the monitoring not only of molecular abundances but also the functional status of transcripts and proteins. However, due to the still high efforts associated with such experiments, the number of measured time points and the replication of measurements remains limited. Resulting datasets contain signals from thousands of molecules, yet they are sparse in temporal resolution and are often imprecise due to biological variability and technical measurement inaccuracies.
This thesis explores the complexities arising from the examination of short time series data and introduces pioneering tools that offer fresh insights into the realm of biological time series analysis. The broad spectrum of analytic possibilities ranges from a molecule-centric investigation of individual time courses to a holistic aggregation of the system’s response to its main characteristics. By creating a modeling framework that applies domain-specific constraints, time-course signals can be transformed from a series of discrete data points into a continuous curve. These curves align with current biological conjectures about molecule kinetics being smooth and devoid of superfluous oscillations. Noise present at individual time points is judiciously accounted for during curve fitting, mitigating the impact of time points with high variance on the curve. Subsequent classification is based on the features of these curves (extreme points and inflection points) and ensures a reduction in data amount and complexity. Succinct labels assigned to each molecule's kinetics encapsulate the signal's most notable features. Besides this modeling approach, an innovative enrichment strategy is introduced, that is independent of prior data partitioning and capable of segregating the temporal response into its thermodynamically relevant components. This approach allows for a continuous assessment of each molecule's contribution to these components, obviating the need for exclusive allocation. The application of various analytical approaches to heat acclimation experiments in Chlamydomonas highlights the relevance and potential of time series experiments and specifically tailored analysis techniques. The integration of different system levels has led to the identification of regulatory peculiarities, such as an increased correlation between transcripts and corresponding proteins during acclimation responses. These and other insights may herald new avenues of research that could ultimately enhance plant robustness in the face of increasing environmental perturbations.
The growing popularity of time series experiments necessitates dedicated analytical approaches that empower researchers and analysts to decipher patterns, discern trends, and unravel the underlying structures within the data, facilitating predictions and the derivation of meaningful conclusions that could potentially build bridges between the interweaved systems levels.