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Amoeboid cell motility takes place in a variety of biomedical processes such as cancer metastasis, embryonic morphogenesis, and wound healing. In contrast to other forms of cell motility, it is mainly driven by substantial cell shape changes. Based on the interplay of explorative membrane protrusions at the front and a slower-acting membrane retraction at the rear, the cell moves in a crawling kind of way. Underlying these protrusions and retractions are multiple physiological processes resulting in changes of the cytoskeleton, a meshwork of different multi-functional proteins. The complexity and versatility of amoeboid cell motility raise the need for novel computational models based on a profound theoretical framework to analyze and simulate the dynamics of the cell shape.
The objective of this thesis is the development of (i) a mathematical framework to describe contour dynamics in time and space, (ii) a computational model to infer expansion and retraction characteristics of individual cell tracks and to produce realistic contour dynamics, (iii) and a complementing Open Science approach to make the above methods fully accessible and easy to use.
In this work, we mainly used single-cell recordings of the model organism Dictyostelium discoideum. Based on stacks of segmented microscopy images, we apply a Bayesian approach to obtain smooth representations of the cell membrane, so-called cell contours. We introduce a one-parameter family of regularized contour flows to track reference points on the contour (virtual markers) in time and space. This way, we define a coordinate system to visualize local geometric and dynamic quantities of individual contour dynamics in so-called kymograph plots. In particular, we introduce the local marker dispersion as a measure to identify membrane protrusions and retractions in a fully automated way.
This mathematical framework is the basis of a novel contour dynamics model, which consists of three biophysiologically motivated components: one stochastic term, accounting for membrane protrusions, and two deterministic terms to control the shape and area of the contour, which account for membrane retractions. Our model provides a fully automated approach to infer protrusion and retraction characteristics from experimental cell tracks while being also capable of simulating realistic and qualitatively different contour dynamics. Furthermore, the model is used to classify two different locomotion types: the amoeboid and a so-called fan-shaped type.
With the complementing Open Science approach, we ensure a high standard regarding the usability of our methods and the reproducibility of our research. In this context, we introduce our software publication named AmoePy, an open-source Python package to segment, analyze, and simulate amoeboid cell motility. Furthermore, we describe measures to improve its usability and extensibility, e.g., by detailed run instructions and an automatically generated source code documentation, and to ensure its functionality and stability, e.g., by automatic software tests, data validation, and a hierarchical package structure.
The mathematical approaches of this work provide substantial improvements regarding the modeling and analysis of amoeboid cell motility. We deem the above methods, due to their generalized nature, to be of greater value for other scientific applications, e.g., varying organisms and experimental setups or the transition from unicellular to multicellular movement. Furthermore, we enable other researchers from different fields, i.e., mathematics, biophysics, and medicine, to apply our mathematical methods. By following Open Science standards, this work is of greater value for the cell migration community and a potential role model for other Open Science contributions.
The field of exercise psychology has established robust evidence on the health benefits of physical activity. However, interventions to promote sustained exercise behavior have often proven ineffective. This dissertation addresses challenges in the field, particularly the neglect of situated and affective processes in understanding and changing exercise behavior. Dual process models, considering both rational and affective processes, have gained recognition. The Affective Reflective Theory of Physical Inactivity and Exercise (ART) is a notable model in this context, positing that situated processes in-the-moment of choice influence exercise decisions and subsequent exercise behavior.
The dissertation identifies current challenges within exercise psychology and proposes methodological and theoretical advancements. It emphasizes the importance of momentary affective states and situated processes, offering alternatives to self-reported measures and advocating for a more comprehensive modeling of individual variability. The focus is on the affective processes during exercise, theorized to reappear in momentary decision-making, shaping overall exercise behavior.
The first publication introduces a new method by using automated facial action analysis to measure variable affective responses during exercise. It explores how these behavioral indicators covary with self-reported measures of affective valence and perceived exertion. The second publication delves into situated processes at the moment of choice between exercise and non-exercise options, revealing that intraindividual factors play a crucial role in explaining exercise-related choices. The third publication presents an open-source research tool, the Decisional Preferences in Exercising Test (DPEX), designed to capture repeated situated decisions and predict exercise behavior based on past experiences.
The findings challenge previous assumptions and provide insights into the complex interplay of affective responses, situated processes, and exercise choices. The dissertation underscores the need for individualized interventions that manipulate affective responses during exercise and calls for systematic testing to establish causal links to automatic affective processes and subsequent exercise behavior. This dissertation highlights the necessity for methodological and conceptual refinements in understanding and promoting exercise behavior, ultimately contributing to the broader goal of combating increasing inactivity trends.
This thesis is concerned with the phenomenon of quantifier scope ambiguities. This phenomenon has been researched extensively, both from a theoretical and from an empirical point of view. Nevertheless, there are still a number of under-researched topics in the field of quantifier scope, which will be the main focus of this thesis. I will take a closer look at three languages, English, German, and the Asante Twi dialect of Akan (Kwa, Niger-Kongo). The goal is a better understanding of the phenomenon of quantifier scope both within each language, as well as from a cross-linguistic perspective. First, this thesis will provide a series of experiments that allow a direct cross-linguistic comparison between English and German – two languages about which specific claims have been made in the literature. I will also provide exploratory research in the case of Asante Twi, where so far, no work has been dedicated specifically to the study of quantifier scope. The work on Asante Twi will go beyond quantifier scope and also target the quantifier and determiner system in general. The question is not only if particular scope readings are possible or not, but also which factors contribute to an increase or decrease of scope availability, and if there are factors that block certain scope readings altogether. While some of the results confirm and thereby strengthen previous claims, other results contradict general assumptions in the literature. This is particularly the case for inverse readings in German and inverse readings across clause-boundaries.
Volcanic hazard assessment relies on physics-based models of hazards, such as lava flows and pyroclastic density currents, whose outcomes are very sensitive to the location where future eruptions will occur. On the contrary, forecast of vent opening locations in volcanic areas typically relies on purely data-driven approaches, where the spatial density of past eruptive vents informs the probability maps of future vent opening. Such techniques may be suboptimal in volcanic systems with missing or scarce data, and where the controls on magma pathways may change over time. An alternative approach was recently proposed, relying on a model of stress-driven pathways of magmatic dikes. In that approach, the crustal stress was optimized so that dike trajectories linked consistently the location of the magma chamber to that of past vents. The retrieved information on the stress state was then used to forecast future dike trajectories. The validation of such an approach requires extensive application to nature. Before doing so, however, several important limitations need to be removed, most importantly the two-dimensional (2D) character of the models and theoretical concepts. In this thesis, I develop methods and tools so that a physics-based strategy of stress inversion and eruptive vent forecast in volcanoes can be applied to three dimensional (3D) problems. In the first part, I test the stress inversion and vent forecast strategy on analog models, still within a 2D framework, but improving on the efficiency of the stress optimization. In the second part, I discuss how to correctly account for gravitational loading/unloading due to complex 3D topography with a Boundary-Element numerical model. Then, I develop a new, simplified but fast model of dike pathways in 3D, designed for running large numbers of simulations at minimal computational cost, and able to backtrack dike trajectories from vents on the surface. Finally, I combine the stress and dike models to simulate dike pathways in synthetic calderas. In the third part, I describe a framework of stress inversion and vent forecast strategy in 3D for calderas. The stress inversion relies on, first, describing the magma storage below a caldera in terms of a probability density function. Next, dike trajectories are backtracked from the known locations of past vents down through the crust, and the optimization algorithm seeks for the stress models which lead trajectories through the regions of highest probability. I apply the new strategy to the synthetic scenarios presented in the second part, and I exploit the results from the stress inversions to produce probability maps of future vent locations for some of those scenarios. In the fourth part, I present the inversion of different deformation source models applied to the ongoing ground deformation observed across the Rhenish Massif in Central Europe. The region includes the Eifel Volcanic Fields in Germany, a potential application case for the vent forecast strategy. The results show how the observed deformation may be due to melt accumulation in sub-horizontal structures in the lower crust or upper mantle. The thesis concludes with a discussion of the stress inversion and vent forecast strategy, its limitations and applicability to real volcanoes. Potential developments of the modeling tools and concepts presented here are also discussed, as well as possible applications to other geophysical problems.
Reactive eutectic media based on ammonium formate for the valorization of bio-sourced materials
(2023)
In the last several decades eutectic mixtures of different compositions were successfully used as solvents for vast amount of chemical processes, and only relatively recently they were discovered to be widely spread in nature. As such they are discussed as a third liquid media of the living cell, that is composed of common cell metabolites. Such media may also incorporate water as a eutectic component in order to regulate properties such as enzyme activity or viscosity. Taking inspiration form such sophisticated use of eutectic mixtures, this thesis will explore the use of reactive eutectic media (REM) for organic synthesis. Such unconventional media are characterized by the reactivity of their components, which means that mixture may assume the role of the solvent as well as the reactant itself.
The thesis focuses on novel REM based on ammonium formate and investigates their potential for the valorization of bio-sourced materials. The use of REM allows the performance of a number of solvent-free reactions, which entails the benefits of a superior atom and energy economy, higher yields and faster rates compared to reactions in solution. This is evident for the Maillard reaction between ammonium formate and various monosaccharides for the synthesis of substituted pyrazines as well as for a Leuckart type reaction between ammonium formate and levulinic acid for the synthesis of 5-methyl-2-pyrrolidone. Furthermore, reaction of ammonium formate with citric acid for the synthesis of yet undiscovered fluorophores, shows that synthesis in REM can open up unexpected reaction pathways.
Another focus of the thesis is the study of water as a third component in the REM. As a result, the concept of two different dilution regimes (tertiary REM and in REM in solvent) appears useful for understanding the influence of water. It is shown that small amounts of water can be of great benefit for the reaction, by reducing viscosity and at the same time increasing reaction yields.
REM based on ammonium formate and organic acids are employed for lignocellulosic biomass treatment. The thesis thereby introduces an alternative approach towards lignocellulosic biomass fractionation that promises a considerable process intensification by the simultaneous generation of cellulose and lignin as well as the production of value-added chemicals from REM components. The thesis investigates the generated cellulose and the pathway to nanocellulose generation and also includes the structural analysis of extracted lignin.
Finally, the thesis investigates the potential of microwave heating to run chemical reactions in REM and describes the synergy between these two approaches. Microwave heating for chemical reactions and the use of eutectic mixtures as alternative reaction media are two research fields that are often described in the scope of green chemistry. The thesis will therefore also contain a closer inspection of this terminology and its greater goal of sustainability.
The Lyman-𝛼 (Ly𝛼) line commonly assists in the detection of high-redshift galaxies, the so-called Lyman-alpha emitters (LAEs). LAEs are useful tools to study the baryonic matter distribution of the high-redshift universe. Exploring their spatial distribution not only reveals the large-scale structure of the universe at early epochs, but it also provides an insight into the early formation and evolution of the galaxies we observe today. Because dark matter halos (DMHs) serve as sites of galaxy formation, the LAE distribution also traces that of the underlying dark matter. However, the details of this relation and their co-evolution over time remain unclear. Moreover, theoretical studies predict that the spatial distribution of LAEs also impacts their own circumgalactic medium (CGM) by influencing their extended Ly𝛼 gaseous halos (LAHs), whose origin is still under investigation. In this thesis, I make several contributions to improve the knowledge on these fields using samples of LAEs observed with the Multi Unit Spectroscopic Explorer (MUSE) at redshifts of 3 < 𝑧 < 6.
Soft-template strategy enables the fabrication of composite nanomaterials with desired functionalities and structures. In this thesis, soft templates, including poly(ionic liquid) nanovesicles (PIL NVs), self-assembled polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) particles, and glycopeptide (GP) biomolecules have been applied for the synthesis of versatile composite particles of PILs/Cu, molybdenum disulfide/carbon (MoS2/C), and GP-carbon nanotubes-metal (GP-CNTs-metal) composites, respectively. Subsequently, their possible applications as efficient catalysts in two representative reactions, i.e. CO2 electroreduction (CO2ER) and reduction of 4-nitrophenol (4-NP), have been studied, respectively.
In the first work, PIL NVs with a tunable particle size of 50 to 120 nm and a shell thickness of 15 to 60 nm have been prepared via one-step free radical polymerization. By increasing monomer concentration for polymerization, their nanoscopic morphology can evolve from hollow NVs to dense spheres, and finally to directional worms, in which a multi-lamellar packing of PIL chains occurred in all samples. The obtained PIL NVs with varied shell thickness have been in situ functionalized with ultra-small Cu nanoparticles (Cu NPs, 1-3 nm) and subsequently employed as the electrocatalysts for CO2ER. The hollow PILs/Cu composite catalysts exhibit a 2.5-fold enhancement in selectivity towards C1 products compared to the pristine Cu NPs. This enhancement is primarily attributed to the strong electronic interactions between the Cu NPs and the surface functionalities of PIL NVs. This study casts new aspects on using nanostructured PILs as novel electrocatalyst supports in efficient CO2 conversion.
In the second work, a novel approach towards fast degradation of 4-NP has been developed using porous MoS2/C particles as catalysts, which integrate the intrinsically catalytic property of MoS2 with its photothermal conversion capability. Various MoS2/C composite particles have been prepared using assembled PS-b-P2VP block copolymer particles as sacrificed soft templates. Intriguingly, the MoS2/C particles exhibit tailored morphologies including pomegranate-like, hollow, and open porous structures. Subsequently, the photothermal conversion performance of these featured particles has been compared under near infrared (NIR) light irradiation. When employing the open porous MoS2/C particles as the catalyst for the reduction of 4-NP, the reaction rate constant has increased by 1.5-fold under light illumination. This catalytic enhancement mainly results from the open porous architecture and photothermal conversion performance of the MoS2 particles. This proposed strategy offers new opportunities for efficient photothermal-assisted catalysis.
In the third work, a facile and green approach towards the fabrication of GP-CNTs-metal composites has been proposed, which utilizes a versatile GP biomolecule both as a stabilizer for CNTs in water and as a reducing agent for noble metal ions. The abundant hydrogen bonds in GP molecules bestow the formed GP-CNTs with excellent plasticity, enabling the availability of polymorphic CNTs species ranging from dispersion to viscous paste, gel, and even dough by increasing their concentration. The GP molecules can reduce metal precursors at room temperature without additional reducing agents, enabling the in situ immobilization of metal NPs (e.g. Au, Ag, and Pd) on the CNTs surface. The combination of excellent catalytic property of Pd NPs with photothermal conversion capability of CNTs makes the GP-CNTs-Pd composite a promising catalyst for the efficient degradation of 4-NP. The obtained composite displays a 1.6-fold increase in conversion under NIR light illumination in the reduction of 4-NP, mainly owing to the strong light-to-heat conversion effect of CNTs. Overall, the proposed method opens a new avenue for the synthesis of CNTs composite as a sustainable and versatile catalyst platform.
The results presented in the current thesis demonstrate the significance of using soft templates for the synthesis of versatile composites with tailored nanostructure and functionalities. The investigation of these composite nanomaterials in the catalytic reactions reveals their potential in the development of desired catalysts for emerging catalytic processes, e.g. photothermal-assisted catalysis and electrocatalysis.
The Security Operations Center (SOC) represents a specialized unit responsible for managing security within enterprises. To aid in its responsibilities, the SOC relies heavily on a Security Information and Event Management (SIEM) system that functions as a centralized repository for all security-related data, providing a comprehensive view of the organization's security posture. Due to the ability to offer such insights, SIEMS are considered indispensable tools facilitating SOC functions, such as monitoring, threat detection, and incident response.
Despite advancements in big data architectures and analytics, most SIEMs fall short of keeping pace. Architecturally, they function merely as log search engines, lacking the support for distributed large-scale analytics. Analytically, they rely on rule-based correlation, neglecting the adoption of more advanced data science and machine learning techniques.
This thesis first proposes a blueprint for next-generation SIEM systems that emphasize distributed processing and multi-layered storage to enable data mining at a big data scale. Next, with the architectural support, it introduces two data mining approaches for advanced threat detection as part of SOC operations.
First, a novel graph mining technique that formulates threat detection within the SIEM system as a large-scale graph mining and inference problem, built on the principles of guilt-by-association and exempt-by-reputation. The approach entails the construction of a Heterogeneous Information Network (HIN) that models shared characteristics and associations among entities extracted from SIEM-related events/logs. Thereon, a novel graph-based inference algorithm is used to infer a node's maliciousness score based on its associations with other entities in the HIN. Second, an innovative outlier detection technique that imitates a SOC analyst's reasoning process to find anomalies/outliers. The approach emphasizes explainability and simplicity, achieved by combining the output of simple context-aware univariate submodels that calculate an outlier score for each entry.
Both approaches were tested in academic and real-world settings, demonstrating high performance when compared to other algorithms as well as practicality alongside a large enterprise's SIEM system.
This thesis establishes the foundation for next-generation SIEM systems that can enhance today's SOCs and facilitate the transition from human-centric to data-driven security operations.
Inflammatory bowel diseases (IBD), characterised by a chronic inflammation of the gut wall, develop as consequence of an overreacting immune response to commensal bacteria, caused by a combination of genetic and environmental conditions. Large inter-individual differences in the outcome of currently available therapies complicate the decision for the best option for an individual patient. Predicting the prospects of therapeutic success for an individual patient is currently only possible to a limited extent; for this, a better understanding of possible differences between responders and non-responders is needed.
In this thesis, we have developed a mathematical model describing the most important processes of the gut mucosal immune system on the cellular level. The model is based on literature data, which were on the one hand used (qualitatively) to choose which cell types and processes to incorporate and to derive the model structure, and on the other hand (quantitatively) to derive the parameter values. Using ordinary differential equations, it describes the concentration-time course of neutrophils, macrophages, dendritic cells, T cells and bacteria, each subdivided into different cell types and activation states, in the lamina propria and mesenteric lymph nodes. We evaluate the model by means of simulations of the healthy immune response to salmonella infection and mucosal injury.
A virtual population includes IBD patients, which we define through their initially asymptomatic, but after a trigger chronically inflamed gut wall. We demonstrate the model's usefulness in different analyses: (i) The comparison of virtual IBD patients with virtual healthy individuals shows that the disease is elicited by many small or fewer large changes, and allows to make hypotheses about dispositions relevant for development of the disease. (ii) We simulate the effects of different therapeutic targets and make predictions about the therapeutic outcome based on the pre-treatment state. (iii) From the analysis of differences between virtual responders and non-responders, we derive hypotheses about reasons for the inter-individual variability in treatment outcome. (iv) For the example of anti-TNF-alpha therapy, we analyse, which alternative therapies are most promising in case of therapeutic failure, and which therapies are most suited for combination therapies: For drugs also directly targeting the cytokine levels or inhibiting the recruitment of innate immune cells, we predict a low probability of success when used as alternative treatment, but a large gain when used in a combination treatment. For drugs with direct effects on T cells, via modulation of the sphingosine-1-phosphate receptor or inhibition of T cell proliferation, we predict a considerably larger probability of success when used as alternative treatment, but only a small additional gain when used in a combination therapy.
The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance.
In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics.
The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model.
The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions.
This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.
This thesis discusses heat and charge transport phenomena in single-crystalline Silicon penetrated by nanometer-sized pores, known as mesoporous Silicon (pSi). Despite the extensive attention given to it as a thermoelectric material of interest, studies on microscopic thermal and electronic transport beyond its macroscopic characterizations are rarely reported. In contrast, this work reports the interplay of both.
PSi samples synthesized by electrochemical anodization display a temperature dependence of specific heat 𝐶𝑝 that deviates from the characteristic 𝑇^3 behaviour (at 𝑇<50𝐾). A thorough analysis reveals that both 3D and 2D Einstein and Debye modes contribute to this specific heat. Additional 2D Einstein modes (~3 𝑚𝑒𝑉) agree reasonably well with the boson peak of SiO2 in pSi pore walls. 2D Debye modes are proposed to account for surface acoustic modes causing a significant deviation from the well-known 𝑇^3 dependence of 𝐶𝑝 at 𝑇<50𝐾.
A novel theoretical model gives insights into the thermal conductivity of pSi in terms of porosity and phonon scattering on the nanoscale. The thermal conductivity analysis utilizes the peculiarities of the pSi phonon dispersion probed by the inelastic neutron scattering experiments. A phonon mean-free path of around 10 𝑛𝑚 extracted from the presented model is proposed to cause the reduced thermal conductivity of pSi by two orders of magnitude compared to p-doped bulk Silicon. Detailed analysis indicates that compound averaging may cause a further 10-50% reduction. The percolation threshold of 65% for thermal conductivity of pSi samples is subsequently determined by employing theoretical effective medium models.
Temperature-dependent electrical conductivity measurements reveal a thermally activated transport process. A detailed analysis of the activation energy 𝐸𝐴𝜎 in the thermally activated transport exhibits a Meyer Neldel compensation rule between different samples that originates in multi-phonon absorption upon carrier transport. Activation energies 𝐸𝐴𝑆 obtained from temperature-dependent thermopower measurements provide further evidence for multi-phonon assisted hopping between localized states as a dominant charge transport mechanism in pSi, as they systematically differ from the determined 𝐸𝐴𝜎 values.
Biomolecules such as proteins and lipids have vital roles in numerous cellular functions, including biomolecule transport, protein functions, cellular homeostasis and biomembrane integrity. Traditional biochemistry methods do not provide precise information about cellular biomolecule distribution and behavior under native environmental conditions since they are not transferable to live cell samples. Consequently, this can lead to inaccuracies in quantifying biomolecule interactions due to potential complexities arising from the heterogeneity of native biomembranes. To overcome these limitations, minimal invasive microscopic techniques, such as fluorescence fluctuation spectroscopy (FFS) in combination with fluorescence proteins (FPs) and fluorescence lipid analogs, have been developed. FFS techniques and membrane property sensors enable the quantification of various parameters, including concentration, dynamics, oligomerization, and interaction of biomolecules in live cell samples.
In this work, several FFS approaches and membrane property sensors were implemented and employed to examine biological processes of diverse context. Multi-color scanning fluorescence fluctuation spectroscopy (sFCS) was used the examine protein oligomerization, protein-protein interactions (PPIs) and protein dynamics at the cellular plasma membrane (PM). Additionally, two-color number and brightness (N&B) analysis was extended with the cross-correlation analysis in order to quantify hetero-interactions of proteins in the PM with very slow motion, which would not accessible with sFCS due strong initial photobleaching. Furthermore, two semi-automatic analysis pipelines were designed: spectral Förster resonance energy transfer (FRET) analysis to study changes in membrane charge at the inner leaflet of the PM, and spectral generalized polarization (GP) imaging and spectral phasor analysis to monitor changes in membrane fluidity and order.
An important parameter for studying PPIs is molecular brightness, which directly determines oligomerization and can be extracted from FFS data. However, FPs often display complex photophysical transitions, including dark states. Therefore, it is crucial to characterize FPs for their dark-states to ensure reliable oligomerization measurements. In this study, N&B and sFCS analysis were applied to determine photophysical properties of novel green FPs under different conditions (i.e., excitation power and pH) in living cells. The results showed that the new FPs, mGreenLantern (mGL) and Gamillus, exhibited the highest molecular brightness at the cost of lower photostability. The well-established monomeric enhanced green fluorescent protein (mEGFP) remained the best option to investigate PPIs at lower pH, while mGL was best suited for neutral pH, and Gamillus for high pH. These findings provide guidance for selecting an appropriate FP to quantify PPIs via FFS under different environmental conditions.
Next, several biophysical fluorescence microscopy approaches (i.e., sFCS, GP imaging, membrane charge FRET) were employed to monitor changes in lipid-lipid-packing in biomembranes in different biological context. Lipid metabolism in cancer cells is known to support rapid proliferation and metastasis. Therefore, targeting lipid synthesis or membrane integrity holds immense promise as an anticancer strategy. However, the mechanism of action of the novel agent erufosine (EPC3) on membrane stability is not fully under
stood. The present work revealed that EPC3 reduces lipid packing and composition as well as increased membrane fluidity and dynamic, hence, modifies lipid-lipid-interaction. These effects on membrane integrity were likely triggered by modulations in lipid metabolism and membrane organization. In the case of influenza A virus (IAV) infection, regulation of lipid metabolism is crucial for multiple steps in IAV replication and is related to the pathogenicity of IAV. Here, it is shown for the first time that IAV infection triggers a local enrichment of negatively charged lipids at the inner leaflet of the PM, which decreases membrane fluidity and dynamic, as well as increases lipid packing at the assembly site in living cells. This suggests that IAV alters lipid-lipid interactions and organization at the PM. Overall, this work highlights the potential of biophysical techniques as a screening platform for studying membrane properties in living cells at the single-cell level.
Finally, this study addressed remaining questions about the early stage of IAV assembly. The recruitment of matrix protein 1 (M1) and its interaction with other viral surface proteins, hemagglutinin (HA), neuraminidase (NA), and matrix protein 2 (M2), has been a subject of debate due to conflicting results. In this study, different FFS approaches were performed in transfected cells to investigate interactions between IAV proteins themselves and host factors at the PM. FFS measurements revealed that M2 interacts strongly with M1, leading to the translocation of M1 to the PM. This interaction likely took place along the non-canonical pathway, as evidenced by the detection of an interaction between M2 and the host factor LC3-II, leading to the recruitment of LC3-II to the PM. Moreover, weaker interaction was observed between HA and membrane-bound M1, and no interaction between NA and M1. Interestingly, higher oligomeric states of M1 were only detectable in infected cells. These results indicate that M2 initiates virion assembly by recruiting M1 to the PM, which may serve as a platform for further interactions with viral proteins and host factors.
Knowledge graphs are structured repositories of knowledge that store facts
about the general world or a particular domain in terms of entities and
their relationships. Owing to the heterogeneity of use cases that are served
by them, there arises a need for the automated construction of domain-
specific knowledge graphs from texts. While there have been many research
efforts towards open information extraction for automated knowledge graph
construction, these techniques do not perform well in domain-specific settings.
Furthermore, regardless of whether they are constructed automatically from
specific texts or based on real-world facts that are constantly evolving, all
knowledge graphs inherently suffer from incompleteness as well as errors in
the information they hold.
This thesis investigates the challenges encountered during knowledge graph
construction and proposes techniques for their curation (a.k.a. refinement)
including the correction of semantic ambiguities and the completion of missing
facts. Firstly, we leverage existing approaches for the automatic construction
of a knowledge graph in the art domain with open information extraction
techniques and analyse their limitations. In particular, we focus on the
challenging task of named entity recognition for artwork titles and show
empirical evidence of performance improvement with our proposed solution
for the generation of annotated training data.
Towards the curation of existing knowledge graphs, we identify the issue of
polysemous relations that represent different semantics based on the context.
Having concrete semantics for relations is important for downstream appli-
cations (e.g. question answering) that are supported by knowledge graphs.
Therefore, we define the novel task of finding fine-grained relation semantics
in knowledge graphs and propose FineGReS, a data-driven technique that
discovers potential sub-relations with fine-grained meaning from existing pol-
ysemous relations. We leverage knowledge representation learning methods
that generate low-dimensional vectors (or embeddings) for knowledge graphs
to capture their semantics and structure. The efficacy and utility of the
proposed technique are demonstrated by comparing it with several baselines
on the entity classification use case.
Further, we explore the semantic representations in knowledge graph embed-
ding models. In the past decade, these models have shown state-of-the-art
results for the task of link prediction in the context of knowledge graph comple-
tion. In view of the popularity and widespread application of the embedding
techniques not only for link prediction but also for different semantic tasks,
this thesis presents a critical analysis of the embeddings by quantitatively
measuring their semantic capabilities. We investigate and discuss the reasons
for the shortcomings of embeddings in terms of the characteristics of the
underlying knowledge graph datasets and the training techniques used by
popular models.
Following up on this, we propose ReasonKGE, a novel method for generating
semantically enriched knowledge graph embeddings by taking into account the
semantics of the facts that are encapsulated by an ontology accompanying the
knowledge graph. With a targeted, reasoning-based method for generating
negative samples during the training of the models, ReasonKGE is able to
not only enhance the link prediction performance, but also reduce the number
of semantically inconsistent predictions made by the resultant embeddings,
thus improving the quality of knowledge graphs.
Life on Earth is diverse and ranges from unicellular organisms to multicellular creatures like humans. Although there are theories about how these organisms might have evolved, we understand little about how ‘life’ started from molecules. Bottom-up synthetic biology aims to create minimal cells by combining different modules, such as compartmentalization, growth, division, and cellular communication.
All living cells have a membrane that separates them from the surrounding aqueous medium and helps to protect them. In addition, all eukaryotic cells have organelles that are enclosed by intracellular membranes. Each cellular membrane is primarily made of a lipid bilayer with membrane proteins. Lipids are amphiphilic molecules that assemble into molecular bilayers consisting of two leaflets. The hydrophobic chains of the lipids in the two leaflets face each other, and their hydrophilic headgroups face the aqueous surroundings. Giant unilamellar vesicles (GUVs) are model membrane systems that form large compartments with a size of many micrometers and enclosed by a single lipid bilayer. The size of GUVs is comparable to the size of cells, making them good membrane models which can be studied using an optical microscope. However, after the initial preparation, GUV membranes lack membrane proteins which have to be reconstituted into these membranes by subsequent preparation steps. Depending on the protein, it can be either attached via anchor lipids to one of the membrane leaflets or inserted into the lipid bilayer via its transmembrane domains.
The first step is to prepare the GUVs and then expose them to an exterior solution with proteins. Various protocols have been developed for the initial preparation of GUVs. For the second step, the GUVs can be exposed to a bulk solution of protein or can be trapped in a microfluidic device and then supplied with the protein solution. To minimize the amount of solution and for more precise measurements, I have designed a microfluidic device that has a main channel, and several dead-end side channels that are perpendicular to the main channel. The GUVs are trapped in the dead-end channels. This design exchanges the solution around the GUVs via diffusion from the main channel, thus shielding the GUVs from the flow within the main channel. This device has a small volume of just 2.5 μL, can be used without a pump and can be combined with a confocal microscope, enabling uninterrupted imaging of the GUVs during the experiments. I used this device for most of the experiments on GUVs that are discussed in this thesis.
In the first project of the thesis, a lipid mixture doped with an anchor lipid was used that can bind to a histidine chain (referred to as His-tag(ged) or 6H) via the metal cation Ni2+. This method is widely used for the biofunctionalization of GUVs by attaching proteins without a transmembrane domain. Fluorescently labeled His-tags which are bound to a membrane can be observed in a confocal microscope. Using the same lipid mixture, I prepared the GUVs with different protocols and investigated the membrane composition of the resulting GUVs by evaluating the amount of fluorescently labeled His-tagged molecules bound to their membranes. I used the microfluidic device described above to expose the outer leaflet of the vesicle to a constant concentration of the His-tagged molecules. Two fluorescent molecules with a His-tag were studied and compared: green fluorescent protein (6H-GFP) and fluorescein isothiocyanate (6H-FITC). Although the quantum yield in solution is similar for both molecules, the brightness of the membrane-bound 6H-GFP is higher than the brightness of the membrane-bound 6H-FITC. The observed difference in the brightness reveals that the fluorescence of the 6H-FITC is quenched by the anchor lipid via the Ni2+ ion. Furthermore, my measurements also showed that the fluorescence intensity of the membranebound His-tagged molecules depends on microenvironmental factors such as pH. For both 6H-GFP and 6H-FITC, the interaction with the membrane is quantified by evaluating the equilibrium dissociation constant. The membrane fluorescence is measured as a function of the fluorophores’ molar concentration. Theoretical analysis of these data leads to the equilibrium dissociation constants of (37.5 ± 7.5) nM for 6H-GFP and (18.5 ± 3.7) nM for 6H-FITC.
The anchor lipid mentioned previously used the metal cation Ni2+ to mediate the bond between the anchor lipid and the His-tag. The Ni2+ ion can be replaced by other transition metal ions. Studies have shown that Co3+ forms the strongest bonds with the His-tags attached to proteins. In these studies, strong oxidizing agents were used to oxidize the Co2+ mediated complex with the His-tagged protein to a Co3+ mediated complex. This procedure puts the proteins at risk of being oxidized as well. In this thesis, the vesicles were first prepared with anchor lipids without any metal cation. The Co3+ was added to these anchor lipids and finally the His-tagged protein was added to the GUVs to form the Co3+ mediated bond. This system was also established using the microfluidic device.
The different preparation procedures of GUVs usually lead to vesicles with a spherical morphology. On the other hand, many cell organelles have a more complex architecture with a non spherical topology. One fascinating example is provided by the endoplasmic reticulum (ER) which is made of a continuous membrane and extends throughout the cell in the form of tubes and sheets. The tubes are connected by three-way junctions and form a tubular network of irregular polygons. The formation and maintenance of these reticular networks requires membrane proteins that hydrolyize guanosine triphosphate (GTP). One of these membrane proteins is atlastin. In this thesis, I reconstituted the atlastin protein in GUV membranes using detergent-assisted reconstitution protocols to insert the proteins directly into lipid bilayers.
This thesis focuses on protein reconstitution by binding His-tagged proteins to anchor lipids and by detergent-assisted insertion of proteins with transmembrane domains. It also provides the design of a microfluidic device that can be used in various experiments, one example is the evaluation of the equilibrium dissociation constant for membrane-protein interactions. The results of this thesis will help other researchers to understand the protocols for preparing GUVs, to reconstitute proteins in GUVs, and to perform experiments using the microfluidic device. This knowledge should be beneficial for the long-term goal of combining the different modules of synthetic biology to make a minimal cell.
Housing in metabolic cages can induce a pronounced stress response. Metabolic cage systems imply housing mice on metal wire mesh for the collection of urine and feces in addition to monitoring food and water intake. Moreover, mice are single-housed, and no nesting, bedding, or enrichment material is provided, which is often argued to have a not negligible impact on animal welfare due to cold stress. We therefore attempted to reduce stress during metabolic cage housing for mice by comparing an innovative metabolic cage (IMC) with a commercially available metabolic cage from Tecniplast GmbH (TMC) and a control cage. Substantial refinement measures were incorporated into the IMC cage design. In the frame of a multifactorial approach for severity assessment, parameters such as body weight, body composition, food intake, cage and body surface temperature (thermal imaging), mRNA expression of uncoupling protein 1 (Ucp1) in brown adipose tissue (BAT), fur score, and fecal corticosterone metabolites (CMs) were included. Female and male C57BL/6J mice were single-housed for 24 h in either conventional Macrolon cages (control), IMC, or TMC for two sessions. Body weight decreased less in the IMC (females—1st restraint: 6.94%; 2nd restraint: 6.89%; males—1st restraint: 8.08%; 2nd restraint: 5.82%) compared to the TMC (females—1st restraint: 13.2%; 2nd restraint: 15.0%; males—1st restraint: 13.1%; 2nd restraint: 14.9%) and the IMC possessed a higher cage temperature (females—1st restraint: 23.7°C; 2nd restraint: 23.5 °C; males—1st restraint: 23.3 °C; 2nd restraint: 23.5 °C) compared with the TMC (females—1st restraint: 22.4 °C; 2nd restraint: 22.5 °C; males—1st restraint: 22.6 °C; 2nd restraint: 22.4 °C). The concentration of fecal corticosterone metabolites in the TMC (females—1st restraint: 1376 ng/g dry weight (DW); 2nd restraint: 2098 ng/g DW; males—1st restraint: 1030 ng/g DW; 2nd restraint: 1163 ng/g DW) was higher compared to control cage housing (females—1st restraint:
640 ng/g DW; 2nd restraint: 941 ng/g DW; males—1st restraint: 504 ng/g DW; 2nd restraint: 537 ng/g DW). Our results show the stress potential induced by metabolic cage restraint that is markedly influenced by the lower housing temperature. The IMC represents a first attempt to target cold stress reduction during metabolic cage application thereby producing more animal welfare friendly data.
Sulfur is essential for the functionality of some important biomolecules in humans. Biomolecules like the Iron-sulfur clusters, tRNAs, Molybdenum cofactor, and some vitamins. The trafficking of sulfur involves proteins collectively called sulfurtransferase. Among these are TUM1, MOCS3, and NFS1.
This research investigated the role of TUM1 for molybdenum cofactor biosynthesis and cytosolic tRNA thiolation in humans. The rhodanese-like protein MOCS3 and the L-cysteine desulfurase (NFS1) have been previously demonstrated to interact with TUM1. These interactions suggested a dual function of TUM1 in sulfur transfer for Moco biosynthesis and cytosolic tRNA thiolation. TUM1 deficiency has been implicated to be responsible for a rare inheritable disorder known as mercaptolactate cysteine disulfiduria (MCDU), which is associated with a mental disorder. This mental disorder is similar to the symptoms of sulfite oxidase deficiency which is characterised by neurological disorders. Therefore, the role of TUM1 as a sulfurtransferase in humans was investigated, in CRISPR/Cas9 generated TUM1 knockout HEK 293T cell lines.
For the first time, TUM1 was implicated in Moco biosynthesis in humans by quantifying the intermediate product cPMP and Moco using HPLC. Comparing the TUM1 knockout cell lines to the wild-type, accumulation and reduction of cPMP and Moco were observed respectively. The effect of TUM1 knockout on the activity of a Moco-dependent enzyme, Sulfite oxidase, was also investigated. Sulfite oxidase is essential for the detoxification of sulfite to sulfate. Sulfite oxidase activity and protein abundance were reduced due to less availability of Moco. This shows that TUM1 is essential for efficient sulfur transfer for Moco biosynthesis. Reduction in cystathionin -lyase in TUM1 knockout cells was quantified, a possible coping mechanism of the cell against sulfite production through cysteine catabolism.
Secondly, the involvement of TUM1 in tRNA thio-modification at the wobble Uridine-34 was reported by quantifying the amount of mcm5s2U and mcm5U via HPLC. The reduction and accumulation of mcm5s2U and mcm5U in TUM1 knockout cells were observed in the nucleoside analysis. Herein, exogenous treatment with NaHS, a hydrogen sulfide donor, rescued the Moco biosynthesis, cytosolic tRNA thiolation, and cell proliferation deficits in TUM1 knockout cells.
Further, TUM1 was shown to impact mitochondria bioenergetics through the measurement of the oxygen consumption rate and extracellular acidification rate (ECAR) via the seahorse cell Mito stress analyzer. Reduction in total ATP production was also measured. This reveals how important TUM1 is for H2S biosynthesis in the mitochondria of HEK 293T.
Finally, the inhibition of NFS1 in HEK 293T and purified NFS1 protein by 2-methylene 3-quinuclidinone was demonstrated via spectrophotometric and radioactivity quantification. Inhibition of NFS1 by MQ further affected the iron-sulfur cluster-dependent enzyme aconitase activity.
Due to anthropogenic greenhouse gas emissions, Earth’s average surface temperature is steadily increasing. As a consequence, many weather extremes are likely to become more frequent and intense. This poses a threat to natural and human systems, with local impacts capable of destroying exposed assets and infrastructure, and disrupting economic and societal activity. Yet, these effects are not locally confined to the directly affected regions, as they can trigger indirect economic repercussions through loss propagation along supply chains. As a result, local extremes yield a potentially global economic response. To build economic resilience and design effective adaptation measures that mitigate adverse socio-economic impacts of ongoing climate change, it is crucial to gain a comprehensive understanding of indirect impacts and the underlying economic mechanisms.
Presenting six articles in this thesis, I contribute towards this understanding. To this end, I expand on local impacts under current and future climate, the resulting global economic response, as well as the methods and tools to analyze this response.
Starting with a traditional assessment of weather extremes under climate change, the first article investigates extreme snowfall in the Northern Hemisphere until the end of the century. Analyzing an ensemble of global climate model projections reveals an increase of the most extreme snowfall, while mean snowfall decreases.
Assessing repercussions beyond local impacts, I employ numerical simulations to compute indirect economic effects from weather extremes with the numerical agent-based shock propagation model Acclimate. This model is used in conjunction with the recently emerged storyline framework, which involves analyzing the impacts of a particular reference extreme event and comparing them to impacts in plausible counterfactual scenarios under various climate or socio-economic conditions. Using this approach, I introduce three primary storylines that shed light on the complex mechanisms underlying economic loss propagation.
In the second and third articles of this thesis, I analyze storylines for the historical Hurricanes Sandy (2012) and Harvey (2017) in the USA. For this, I first estimate local economic output losses and then simulate the resulting global economic response with Acclimate. The storyline for Hurricane Sandy thereby focuses on global consumption price anomalies and the resulting changes in consumption. I find that the local economic disruption leads to a global wave-like economic price ripple, with upstream effects propagating in the supplier direction and downstream effects in the buyer direction. Initially, an upstream demand reduction causes consumption price decreases, followed by a downstream supply shortage and increasing prices, before the anomalies decay in a normalization phase. A dominant upstream or downstream effect leads to net consumption gains or losses of a region, respectively. Moreover, I demonstrate that a longer direct economic shock intensifies the downstream effect for many regions, leading to an overall consumption loss.
The third article of my thesis builds upon the developed loss estimation method by incorporating projections to future global warming levels. I use these projections to explore how the global production response to Hurricane Harvey would change under further increased global warming. The results show that, while the USA is able to nationally offset direct losses in the reference configuration, other countries have to compensate for increasing shares of counterfactual future losses. This compensation is mainly achieved by large exporting countries, but gradually shifts towards smaller regions. These findings not only highlight the economy’s ability to flexibly mitigate disaster losses to a certain extent, but also reveal the vulnerability and economic disadvantage of regions that are exposed to extreme weather events.
The storyline in the fourth article of my thesis investigates the interaction between global economic stress and the propagation of losses from weather extremes. I examine indirect impacts of weather extremes — tropical cyclones, heat stress, and river floods — worldwide under two different economic conditions: an unstressed economy and a globally stressed economy, as seen during the Covid-19 pandemic. I demonstrate that the adverse effects of weather extremes on global consumption are strongly amplified when the economy is under stress. Specifically, consumption losses in the USA and China double and triple, respectively, due to the global economy’s decreased capacity for disaster loss compensation. An aggravated scarcity intensifies the price response, causing consumption losses to increase.
Advancing on the methods and tools used here, the final two articles in my thesis extend the agent-based model Acclimate and formalize the storyline approach. With the model extension described in the fifth article, regional consumers make rational choices on the goods bought such that their utility is maximized under a constrained budget. In an out-of-equilibrium economy, these rational consumers are shown to temporarily increase consumption of certain goods in spite of rising prices.
The sixth article of my thesis proposes a formalization of the storyline framework, drawing on multiple studies including storylines presented in this thesis. The proposed guideline defines eight central elements that can be used to construct a storyline.
Overall, this thesis contributes towards a better understanding of economic repercussions of weather extremes. It achieves this by providing assessments of local direct impacts, highlighting mechanisms and impacts of loss propagation, and advancing on methods and tools used.
Magmatic-hydrothermal systems form a variety of ore deposits at different proximities to upper-crustal hydrous magma chambers, ranging from greisenization in the roof zone of the intrusion, porphyry mineralization at intermediate depths to epithermal vein deposits near the surface. The physical transport processes and chemical precipitation mechanisms vary between deposit types and are often still debated.
The majority of magmatic-hydrothermal ore deposits are located along the Pacific Ring of Fire, whose eastern part is characterized by the Mesozoic to Cenozoic orogenic belts of the western North and South Americas, namely the American Cordillera. Major magmatic-hydrothermal ore deposits along the American Cordillera include (i) porphyry Cu(-Mo-Au) deposits (along the western cordilleras of Mexico, the western U.S., Canada, Chile, Peru, and Argentina); (ii) Climax- (and sub−) type Mo deposits (Colorado Mineral Belt and northern New Mexico); and (iii) porphyry and IS-type epithermal Sn(-W-Ag) deposits of the Central Andean Tin Belt (Bolivia, Peru and northern Argentina).
The individual studies presented in this thesis primarily focus on the formation of different styles of mineralization located at different proximities to the intrusion in magmatic-hydrothermal systems along the American Cordillera. This includes (i) two individual geochemical studies on the Sweet Home Mine in the Colorado Mineral Belt (potential endmember of peripheral Climax-type mineralization); (ii) one numerical modeling study setup in a generic porphyry Cu-environment; and (iii) a numerical modeling study on the Central Andean Tin Belt-type Pirquitas Mine in NW Argentina.
Microthermometric data of fluid inclusions trapped in greisen quartz and fluorite from the Sweet Home Mine (Detroit City Portal) suggest that the early-stage mineralization precipitated from low- to medium-salinity (1.5-11.5 wt.% equiv. NaCl), CO2-bearing fluids at temperatures between 360 and 415°C and at depths of at least 3.5 km. Stable isotope and noble gas isotope data indicate that greisen formation and base metal mineralization at the Sweet Home Mine was related to fluids of different origins. Early magmatic fluids were the principal source for mantle-derived volatiles (CO2, H2S/SO2, noble gases), which subsequently mixed with significant amounts of heated meteoric water. Mixing of magmatic fluids with meteoric water is constrained by δ2Hw-δ18Ow relationships of fluid inclusions. The deep hydrothermal mineralization at the Sweet Home Mine shows features similar to deep hydrothermal vein mineralization at Climax-type Mo deposits or on their periphery. This suggests that fluid migration and the deposition of ore and gangue minerals in the Sweet Home Mine was triggered by a deep-seated magmatic intrusion.
The second study on the Sweet Home Mine presents Re-Os molybdenite ages of 65.86±0.30 Ma from a Mo-mineralized major normal fault, namely the Contact Structure, and multimineral Rb-Sr isochron ages of 26.26±0.38 Ma and 25.3±3.0 Ma from gangue minerals in greisen assemblages. The age data imply that mineralization at the Sweet Home Mine formed in two separate events: Late Cretaceous (Laramide-related) and Oligocene (Rio Grande Rift-related). Thus, the age of Mo mineralization at the Sweet Home Mine clearly predates that of the Oligocene Climax-type deposits elsewhere in the Colorado Mineral Belt. The Re-Os and Rb-Sr ages also constrain the age of the latest deformation along the Contact Structure to between 62.77±0.50 Ma and 26.26±0.38 Ma, which was employed and/or crosscut by Late Cretaceous and Oligocene fluids. Along the Contact Structure Late Cretaceous molybdenite is spatially associated with Oligocene minerals in the same vein system, a feature that precludes molybdenite recrystallization or reprecipitation by Oligocene ore fluids.
Ore precipitation in porphyry copper systems is generally characterized by metal zoning (Cu-Mo to Zn-Pb-Ag), which is suggested to be variably related to solubility decreases during fluid cooling, fluid-rock interactions, partitioning during fluid phase separation and mixing with external fluids. The numerical modeling study setup in a generic porphyry Cu-environment presents new advances of a numerical process model by considering published constraints on the temperature- and salinity-dependent solubility of Cu, Pb and Zn in the ore fluid. This study investigates the roles of vapor-brine separation, halite saturation, initial metal contents, fluid mixing, and remobilization as first-order controls of the physical hydrology on ore formation. The results show that the magmatic vapor and brine phases ascend with different residence times but as miscible fluid mixtures, with salinity increases generating metal-undersaturated bulk fluids. The release rates of magmatic fluids affect the location of the thermohaline fronts, leading to contrasting mechanisms for ore precipitation: higher rates result in halite saturation without significant metal zoning, lower rates produce zoned ore shells due to mixing with meteoric water. Varying metal contents can affect the order of the final metal precipitation sequence. Redissolution of precipitated metals results in zoned ore shell patterns in more peripheral locations and also decouples halite saturation from ore precipitation.
The epithermal Pirquitas Sn-Ag-Pb-Zn mine in NW Argentina is hosted in a domain of metamorphosed sediments without geological evidence for volcanic activity within a distance of about 10 km from the deposit. However, recent geochemical studies of ore-stage fluid inclusions indicate a significant contribution of magmatic volatiles. This study tested different formation models by applying an existing numerical process model for porphyry-epithermal systems with a magmatic intrusion located either at a distance of about 10 km underneath the nearest active volcano or hidden underneath the deposit. The results show that the migration of the ore fluid over a 10-km distance results in metal precipitation by cooling before the deposit site is reached. In contrast, simulations with a hidden magmatic intrusion beneath the Pirquitas deposit are in line with field observations, which include mineralized hydrothermal breccias in the deposit area.
Hybrid nanomaterials offer the combination of individual properties of different types of nanoparticles. Some strategies for the development of new nanostructures in larger scale rely on the self-assembly of nanoparticles as a bottom-up approach. The use of templates provides ordered assemblies in defined patterns. In a typical soft-template, nanoparticles and other surface-active agents are incorporated into non-miscible liquids. The resulting self-organized dispersions will mediate nanoparticle interactions to control the subsequent self-assembly. Especially interactions between nanoparticles of very different dispersibility and functionality can be directed at a liquid-liquid interface.
In this project, water-in-oil microemulsions were formulated from quasi-ternary mixtures with Aerosol-OT as surfactant. Oleyl-capped superparamagnetic iron oxide and/or silver nanoparticles were incorporated in the continuous organic phase, while polyethyleneimine-stabilized gold nanoparticles were confined in the dispersed water droplets. Each type of nanoparticle can modulate the surfactant film and the inter-droplet interactions in diverse ways, and their combination causes synergistic effects. Interfacial assemblies of nanoparticles resulted after phase-separation. On one hand, from a biphasic Winsor type II system at low surfactant concentration, drop-casting of the upper phase afforded thin films of ordered nanoparticles in filament-like networks. Detailed characterization proved that this templated assembly over a surface is based on the controlled clustering of nanoparticles and the elongation of the microemulsion droplets. This process offers versatility to use different nanoparticle compositions by keeping the surface functionalization, in different solvents and over different surfaces. On the other hand, a magnetic heterocoagulate was formed at higher surfactant concentration, whose phase-transfer from oleic acid to water was possible with another auxiliary surfactant in ethanol-water mixture. When the original components were initially mixed under heating, defined oil-in-water, magnetic-responsive nanostructures were obtained, consisting on water-dispersible nanoparticle domains embedded by a matrix-shell of oil-dispersible nanoparticles.
Herein, two different approaches were demonstrated to form diverse hybrid nanostructures from reverse microemulsions as self-organized dispersions of the same components. This shows that microemulsions are versatile soft-templates not only for the synthesis of nanoparticles, but also for their self-assembly, which suggest new approaches towards the production of new sophisticated nanomaterials in larger scale.
Arthur Schopenhauer (1788–1860) was perhaps the last polymath among the great Germanic philosophers. Switching with ease and elegance between epistemic positions and fields as diverse as idealism and empiricism, fideism and rationalism, realism and nominalism, art and religion, jurisprudence and politics, psychology and occultism, Schopenhauer erected an imposing edifice bearing testimony to his universal learning. This study is an investigation into the very conclusion of Schopenhauer’s philosophy and endeavours to answer the following question: did Schopenhauer’s doctrine of salvation issue forth organically from his intellectual output or was it annexed to his philosophy as a result of his critical engagement with religion? The labyrinthine paths through which Schopenhauer arrives at the soteriological culmination of his philosophy are subjected to critical assessment; the picture that emerges is of a philosopher who seemed convinced that he had solved some of the most pressing cosmic riddles to have tormented mankind through the ages.
Volcanoes are one of the Earth’s most dynamic zones and responsible for many changes in our planet. Volcano seismology aims to provide an understanding of the physical processes in volcanic systems and anticipate the style and timing of eruptions by analyzing the seismic records. Volcanic tremor signals are usually observed in the seismic records before or during volcanic eruptions. Their analysis contributes to evaluate the evolving volcanic activity and potentially predict eruptions. Years of continuous seismic monitoring now provide useful information for operational eruption forecasting. The continuously growing amount of seismic recordings, however, poses a challenge for analysis, information extraction, and interpretation, to support timely decision making during volcanic crises. Furthermore, the complexity of eruption processes and precursory activities makes the analysis challenging.
A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving our understanding of the underlying physical processes. Some similar issues (data reduction, source separation, extraction, and classification) are addressed in the context of music information retrieval (MIR). The signal characteristics of acoustic and seismic recordings comprise a number of similarities. This thesis is going beyond classical signal analysis techniques usually employed in seismology by exploiting similarities of seismic and acoustic signals and building the information retrieval strategy on the expertise developed in the field of MIR.
First, inspired by the idea of harmonic–percussive separation (HPS) in musical signal processing, I have developed a method to extract harmonic volcanic tremor signals and to detect transient events from seismic recordings. This provides a clean tremor signal suitable for tremor investigation along with a characteristic function suitable for earthquake detection. Second, using HPS algorithms, I have developed a noise reduction technique for seismic signals. This method is especially useful for denoising ocean bottom seismometers, which are highly contaminated by noise. The advantage of this method compared to other denoising techniques is that it doesn’t introduce distortion to the broadband earthquake waveforms, which makes it reliable for different applications in passive seismological analysis. Third, to address the challenge of extracting information from high-dimensional data and investigating the complex eruptive phases, I have developed an advanced machine learning model that results in a comprehensive signal processing scheme for volcanic tremors. Using this method seismic signatures of major eruptive phases can be automatically detected. This helps to provide a chronology of the volcanic system. Also, this model is capable to detect weak precursory volcanic tremors prior to the eruption, which could be used as an indicator of imminent eruptive activity. The extracted patterns of seismicity and their temporal variations finally provide an explanation for the transition mechanism between eruptive phases.
Predator-forager interactions are a major factor in evolutionary adaptation of many species, as predators need to gain energy by consuming prey species, and foragers needs to avoid the worst fate of mortality while still consuming resources for energetic gains. In this evolutionary arms race, the foragers have constantly evolved anti-predator behaviours (e.g. foraging activity changes). To describe all these complex changes, researchers developed the framework of the landscape of fear, that is, the spatio-temporal variation of perceived predation risk. This concept simplifies all the involved ecological processes into one framework, by integrating animal biology and distribution with habitat characteristics. Researchers can then evaluate the perception of predation risk in prey species, what are the behavioural responses of the prey and, therefore, understand the cascading effects of landscapes of fear at the resource levels (tri-trophic effects). Although tri-trophic effects are well studied at the predator-prey interaction level, little is known on how the forager-resource interactions are part of the overall cascading effects of landscapes of fear, despite the changes of forager feeding behaviour - that occur with perceived predation risk - affecting directly the level of the resources.
This thesis aimed to evaluate the cascading effects of the landscape of fear on biodiversity of resources, and how the feeding behaviour and movement of foragers shaped the final resource species composition (potential coexistence mechanisms). We studied the changes caused by landscapes of fear on wild and captive rodent communities and evaluated: the cascading effects of different landscapes of fear on a tri-trophic system (I), the effects of fear on a forager’s movement patterns and dietary preferences (II) and cascading effects of different types of predation risk (terrestrial versus avian, III).
In Chapter I, we applied a novel measure to evaluate the cascading effects of fear at the level of resources, by quantifying the diversity of resources left after the foragers gave-up on foraging (diversity at the giving-up density). We tested the measure at different spatial levels (local and regional) and observed that with decreased perceived predation risk, the density and biodiversity of resources also decreased. Foragers left a very dissimilar community of resources based on perceived risk and resources functional traits, and therefore acted as an equalising mechanism.
In Chapter II, we wanted to understand further the decision-making processes of rodents in different landscapes of fear, namely, in which resource species rodents decided to forage on (based on three functional traits: size, nutrients and shape) and how they moved depending on perceived predation risk. In safe landscapes, individuals increased their feeding activity and movements and despite the increased costs, they visited more often patches that were further away from their central-place. Despite a preference for the bigger resources regardless of risk, when perceived predation risk was low, individuals changed their preference to fat-rich resources.
In Chapter III, we evaluated the cascading effects of two different types of predation risk in rodents: terrestrial (raccoon) versus avian predation risk. Raccoon presence or absence did not alter the rodents feeding behaviour in different landscapes of fear. Rodent’s showed risk avoidance behaviours towards avian predators (spatial risk avoidance), but not towards raccoons (lack of temporal risk avoidance).
By analysing the effects of fear in tri-trophic systems, we were able to deepen the knowledge of how non-consumptive effects of predators affect the behaviour of foragers, and quantitatively measure the cascading effects at the level of resources with a novel measure. Foragers are at the core of the ecological processes and responses to the landscape of fear, acting as variable coexistence agents for resource species depending on perceived predation risk. This newly found measures and knowledge can be applied to more trophic chains, and inform researchers on biodiversity patterns originating from landscapes of fear.
Continental rifts are key geodynamic regions where the complex interplay of magmatism and faulting activity can be studied to understand the driving forces of extension and the formation of new divergent plate boundaries. Well-preserved rift morphology can provide a wealth of information on the growth, interaction, and linkage of normal-fault systems through time. If rift basins are preserved over longer geologic time periods, sedimentary archives generated during extensional processes may mirror tectonic and climatic influences on erosional and sedimentary processes that have varied over time. Rift basins are furthermore strategic areas for hydrocarbon and geothermal energy exploration, and they play a central role in species dispersal and evolution as well as providing or inhibiting hydrologic connectivity along basins at emerging plate boundaries.
The Cenozoic East African rift system (EARS) is one of the most important continental extension zones, reflecting a range of evolutionary stages from an early rift stage with isolated basins in Malawi to an advanced stage of continental extension in southern Afar. Consequently, the EARS is an ideal natural laboratory that lends itself to the study of different stages in the breakup of a continent. The volcanically and seismically active eastern branch of the EARS is characterized by multiple, laterally offset tectonic and magmatic segments where adjacent extensional basins facilitate crustal extension either across a broad deformation zone or via major transfer faulting. The Broadly Rifted Zone (BRZ) in southern Ethiopia is an integral part of the eastern branch of the EARS; in this region, rift segments of the southern Ethiopian Rift (sMER) and northern Kenyan Rift (nKR) propagate in opposite directions in a region with one of the earliest manifestations of volcanism and extensional tectonism in East Africa. The basin margins of the Chew-Bahir Basin and the Gofa Province, characterized by a semi-arid climate and largely uniform lithology, provide ideal conditions for studying the tectonic and geomorphologic features of this complex kinematic transfer zone, but more importantly, this area is suitable for characterizing and quantifying the overlap between the propagating structures of the sMER and nKR and the resulting deformation patterns of the BRZ transfer zones.
In this study, I have combined data from thermochronology, thermal modeling, morphometry, paleomagnetic analysis, geochronology, and geomorphological field observations with information from published studies to reconstruct the spatiotemporal relationship between volcanism and fault activity in the BRZ and quantify the deformation patterns of the overlapping rift segments. I present the following results: (1) new thermochronological data from the en-échelon basin margins and footwall blocks of the rift flanks and morphometric results verified in the field to link different phases of magmatism and faulting during extension and infer geomorphological landscape features related to the current tectonic interaction between the nKR and the sMER; (2) temporally constrained paleomagnetic data from the BRZ overlap zone between the Ethiopian and Kenyan rifts to quantitatively determine block rotation between the two segments. Combining the collected data, time-temperature histories of thermal modeling results from representative samples show well-defined deformation phases between 25–20 Ma, 15–9Ma, and ~5 Ma to the present. Each deformation phase is characterized by the onset of rapid cooling (>2°C/Ma) of the crust associated with uplift or exhumation of the rift shoulder. After an initial, spatially very diffuse phase of extension, the rift has gradually evolved into a system of connected structures formed in an increasingly focused rift zone during the last 5 Ma. Regarding the morphometric analysis of the rift structures, it can be shown that normalized slope indices of the river courses, spatial arrangement of knickpoints in the river longitudinal profiles of the footwall blocks, local relief values, and the average maximum values of the slope of the river profiles indicate a gradual increase in the extension rate from north (Sawula basin: mature) to south (Chew Bahir: young). The complexity of the structural evolution of the BRZ overlap zone between nKR and sMER is further emphasized by the documentation of crustal blocks around a vertical axis. A comparison of the mean directions obtained for the Eo-Oligocene (Ds=352.6°, Is=-17.0°, N=18, α95=5.5°) and Miocene (Ds=2.9°, Is=0.9°, N=9, α95=12.4°) volcanics relative to the pole for stable South Africa and with respect to the corresponding ages of the analyzed units record a significant counterclockwise rotation of ~11.1°± 6.4° and insignificant CCW rotation of ~3.2° ± 11.5°, respectively.
My thesis chiefly aims to shed light on the favourable properties of LHP semiconductors from the point of view of their electronic structure.
Currently, various hypotheses are circulating to explain the exceptionally favourable transport properties of LHPs. Seeking an explanation for the low non-radiative recombination rates and long carrier lifetimes is particularly interesting to the halide perovskites research community.
The first part of this work investigates the two main hypotheses that are believed to play a significant role: the existence of a giant Rashba effect and large polarons. The experimental method of ARPES is mainly applied to verify their credibility.
The first hypothesis presumes that a giant Rashba effect restricts the recombination losses of the charge carriers by making the band gap slightly indirect. The Rashba effect is based on a strong SOC that could appear in LHPs thanks to incorporating the heavy element Pb in their structure. Earlier experimental work had pointed out this effect at the VBM of a hybrid LHP as a viable explanation for the long lifetimes of the charge carriers.
My systematic ARPES studies on hybrid MAPbBr3 and spin-resolved ARPES studies on the inorganic CsPbBr3 disprove the presence of any Rashba effect in the VBM of the reported order of magnitude. Therefore, neither the spin texture nor an indirect band gap character at the VBM in the bulk or at the surface can explain the high efficiency of LHP. In case of existence, this effect is in terms of the Rashba parameter at least a factor of a hundred smaller than previously assumed.
The second hypothesis proposes large polaron formation in the electronic structure of LHPs and attributes it to their high defect tolerance and low non-radiative recombination rate. Because the perovskite structure consists of negative and positive ions, polarons of this kind can be expected due to the Coulomb interaction between carriers and the polar lattice at intermediate electron-phonon coupling strength. Their existence is proposed to screen the carriers and defects to avoid recombination and trapping, thus leading to long carrier lifetimes. ARPES results by one group supported this assumption, reporting a 50% effective mass enhancement over the theoretical effective mass for CsPbBr3 in the orthorhombic structure.
The current thesis examines this hypothesis experimentally by photon-energy-dependent ARPES spectra and theoretically by GW band calculations of CsPbBr3 perovskites. The investigation is based on the fact that a polaron contribution in charge transport can become evident by an increase of the effective mass as measured by ARPES over the calculated one without polaron effects. However, my experiments on crystalline CsPbBr3 did not imply a larger effective mass for which one could postulate large polarons. In fact, the effective masses determined from ARPES agree with that of theoretical predictions.
The second part of my thesis thoroughly investigates the possibility of spontaneously magnetizing LHPs by introducing Mn2+ ions. Mn doping was reported to cause ferromagnetism in one of the most common LHPs, MAPbI3, mediated by super-exchange. The current work investigates the magnetic properties of a wide concentration range of Mn-doped MAPbI3 and triple-cation films by XAS, XMCD, and SQUID measurements. Based on the XAS line shape and a sum-rule analysis of the XMCD spectra, a pure Mn2+ configuration has been confirmed. Negative Curie temperatures are extracted from fitting the magnetization with a Curie-Weiss law. However, a remanent magnetization, which would be an indication of the absence of ferromagnetism down to 2K. As far as the double exchange is concerned, the element-specific XAS excludes a sufficient amount of Mn3+ as a prerequisite for this mechanism. All the findings show no evidence of significant double exchange or ferromagnetism in Mn-doped LHPs. The magnetic behavior is paramagnetic rather than ferromagnetic.
In the dissertation's last chapter, orthorhombic features of CsPbBr3 are revealed by ARPES, including an extra VBM at the Γ-point. The VBM of CsPbBr3 shows a temperature-dependent splitting, which decreases by 190 meV between 38K and 300K and tracks a shift of a saddle point at the cubic M-point. It is possible to reproduce the energy shift using an atomic model with a larger unit cell for room temperature, allowing local inversion symmetry breaking. This indicates the importance of electric dipoles for the inorganic LHPs, which may contribute to their high efficiency by breaking inversion symmetry and a Berry-phase effect.
Modern datasets often exhibit diverse, feature-rich, unstructured data, and they are massive in size. This is the case of social networks, human genome, and e-commerce databases. As Artificial Intelligence (AI) systems are increasingly used to detect pattern in data and predict future outcome, there are growing concerns on their ability to process large amounts of data. Motivated by these concerns, we study the problem of designing AI systems that are scalable to very large and heterogeneous data-sets.
Many AI systems require to solve combinatorial optimization problems in their course of action. These optimization problems are typically NP-hard, and they may exhibit additional side constraints. However, the underlying objective functions often exhibit additional properties. These properties can be exploited to design suitable optimization algorithms. One of these properties is the well-studied notion of submodularity, which captures diminishing returns. Submodularity is often found in real-world applications. Furthermore, many relevant applications exhibit generalizations of this property.
In this thesis, we propose new scalable optimization algorithms for combinatorial problems with diminishing returns. Specifically, we focus on three problems, the Maximum Entropy Sampling problem, Video Summarization, and Feature Selection. For each problem, we propose new algorithms that work at scale. These algorithms are based on a variety of techniques, such as forward step-wise selection and adaptive sampling. Our proposed algorithms yield strong approximation guarantees, and the perform well experimentally.
We first study the Maximum Entropy Sampling problem. This problem consists of selecting a subset of random variables from a larger set, that maximize the entropy. By using diminishing return properties, we develop a simple forward step-wise selection optimization algorithm for this problem. Then, we study the problem of selecting a subset of frames, that represent a given video. Again, this problem corresponds to a submodular maximization problem. We provide a new adaptive sampling algorithm for this problem, suitable to handle the complex side constraints imposed by the application. We conclude by studying Feature Selection. In this case, the underlying objective functions generalize the notion of submodularity. We provide a new adaptive sequencing algorithm for this problem, based on the Orthogonal Matching Pursuit paradigm.
Overall, we study practically relevant combinatorial problems, and we propose new algorithms to solve them. We demonstrate that these algorithms are suitable to handle massive datasets. However, our analysis is not problem-specific, and our results can be applied to other domains, if diminishing return properties hold. We hope that the flexibility of our framework inspires further research into scalability in AI.
Depressive disorders are associated with reduced life satisfaction and ability to work. The waiting time for psychotherapy in Germany is currently between three and six months. Accordingly, there is a need for alternative, evidence-based treatment options that are made accessible to patients at a low threshold. A large number of empirical studies prove the effectiveness of exercise in mild and moderate depression. For further conceptualization and quality assurance of exercise as a treatment option, it is necessary to understand the concrete mechanisms of action. In addition to physiological factors, psychological factors also play a role in the effect. As a meta-theory of human experience and behavior, Self-Determination Theory (SDT) provides a useful frame for understanding psychological mechanisms of action with concrete implications for treatment practice. The conceptual extension of SDT to include the frustration of basic psychological needs in addition to need satisfaction is proving useful in the study of mental illness. The first part of this dissertation consists of two publications that validate relevant measurement instruments in this context. The first questionnaire measures the extent of generally experienced satisfaction and frustration of the basic psychological needs for autonomy, competence, and relatedness. The second questionnaire measures the experienced satisfaction of needs by the instructor (here: exercise therapist). The second part of the dissertation includes two publications that examine and classify the satisfaction and frustration of basic psychological needs in depressive symptoms. Differences in the extent of need satisfaction and need frustration between a sample with depression and a sample without depressive symptoms are examined. Further, the relationship between need frustration and depressive symptoms is placed in the context of established pathological processes (emotional dysregulation, rumination). The main findings of this work show that by adding the dimension of need frustration to Basic Psychological Needs Theory, SDT now covers a broader spectrum on the health-disease continuum. In doing so, SDT focuses on the psychological impact of social environments. In addition to the nonfulfillment of basic psychological needs, it is primarily experienced need frustration that is a general vulnerability factor for the occurrence of psychological illness. Moreover, the unbalanced satisfaction of basic psychological needs possibly indicates a conflicting experience between the needs. For the treatment practice it can be deduced that an autonomy-supporting atmosphere, which enables the balanced satisfaction of all three needs, is central for the treatment success.
Solar photocatalysis is the one of leading concepts of research in the current paradigm of sustainable chemical industry. For actual practical implementation of sunlight-driven catalytic processes in organic synthesis, a cheap, efficient, versatile and robust heterogeneous catalyst is necessary. Carbon nitrides are a class of organic semiconductors who are known to fulfill these requirements.
First, current state of solar photocatalysis in economy, industry and lab research is overviewed, outlining EU project funding, prospective synthetic and reforming bulk processes, small scale solar organic chemistry, and existing reactor designs and prototypes, concluding feasibility of the approach.
Then, the photocatalytic aerobic cleavage of oximes to corresponding aldehydes and ketones by anionic poly(heptazine imide) carbon nitride is discussed. The reaction provides a feasible method of deprotection and formation of carbonyl compounds from nitrosation products and serves as a convenient model to study chromoselectivity and photophysics of energy transfer in heterogeneous photocatalysis.
Afterwards, the ability of mesoporous graphitic carbon nitride to conduct proton-coupled electron transfer was utilized for the direct oxygenation of 1,3-oxazolidin-2-ones to corresponding 1,3-oxazlidine-2,4-diones. This reaction provides an easier access to a key scaffold of diverse types of drugs and agrochemicals.
Finally, a series of novel carbon nitrides based on poly(triazine imide) and poly(heptazine imide) structure was synthesized from cyanamide and potassium rhodizonate. These catalysts demonstrated a good performance in a set of photocatalytic benchmark reactions, including aerobic oxidation, dual nickel photoredox catalysis, hydrogen peroxide evolution and chromoselective transformation of organosulfur precursors.
Concluding, the scope of carbon nitride utilization for net-oxidative and net-neutral photocatalytic processes was expanded, and a new tunable platform for catalyst synthesis was discovered.
Essays in public economics
(2023)
This cumulative dissertation uses economic theory and micro-econometric tools and evaluation methods to analyse public policies and their impact on welfare and individual behaviour. In particular, it focuses on policies in two distinct areas that represent fundamental societal challenges in the 21st century: the ageing of society and life in densely-populated urban agglomerations. Together, these areas shape important financial decisions in a person's life, impact welfare, and are driving forces behind many of the challenges in today's societies. The five self-contained research chapters of this thesis analyse the forward looking effects of pension reforms, affordable housing policies as well as a public transport subsidy and its effect on air pollution.
The Andes reflect Cenozoic deformation and uplift along the South American margin in the context of regional shortening associated with the interaction between the subducting Nazca plate and the overriding continental South American plate. Simultaneously, multiple levels of uplifted marine terraces constitute laterally continuous geomorphic features related to the accumulation of permanent forearc deformation in the coastal realm. However, the mechanisms responsible for permanent coastal uplift and the persistency of current/decadal deformation patterns over millennial timescales are still not fully understood. This dissertation presents a continental-scale database of last interglacial terrace elevations and uplift rates along the South American coast that provides the basis for an analysis of a variety of mechanisms that are possibly responsible for the accumulation of permanent coastal uplift. Regional-scale mapping and analysis of multiple, late Pleistocene terrace levels in central Chile furthermore provide valuable insights regarding the persistency of current seismic asperities, the role of upper-plate faulting, and the impact of bathymetric ridges on permanent forearc deformation.
The database of last interglacial terrace elevations reveals an almost continuous signal of background-uplift rates along the South American coast at ~0.22 mm/yr that is modified by various short- to long-wavelength changes. Spatial correlations with crustal faults and subducted bathymetric ridges suggest long-term deformation to be affected by these features, while the latitudinal variability of climate forcing factors has a profound impact on the generation and preservation of marine terraces. Systematic wavelength analyses and comparisons of the terrace-uplift rate signal with different tectonic parameters reveal short-wavelength deformation to result from crustal faulting, while intermediate- to long-wavelength deformation might indicate various extents of long-term seismotectonic segments on the megathrust, which are at least partially controlled by the subduction of bathymetric anomalies. The observed signal of background-uplift rate is likely accumulated by moderate earthquakes near the Moho, suggesting multiple, spatiotemporally distinct phases of uplift that manifest as a continuous uplift signal over millennial timescales.
Various levels of late Pleistocene marine terraces in the 2015 M8.3 Illapel-earthquake area reveal a range of uplift rates between 0.1 and 0.6 mm/yr and indicate decreasing uplift rates since ~400 ka. These glacial-cycle uplift rates do not correlate with current or decadal estimates of coastal deformation suggesting seismic asperities not to be persistent features on the megathrust that control the accumulation of permanent forearc deformation over long timescales of 105 years. Trench-parallel, crustal normal faults modulate the characteristics of permanent forearc-deformation; upper-plate extension likely represents a second-order phenomenon resulting from subduction erosion and subsequent underplating that lead to regional tectonic uplift and local gravitational collapse of the forearc. In addition, variable activity with respect to the subduction of the Juan Fernández Ridge can be detected in the upper plate over the course of multiple interglacial periods, emphasizing the role of bathymetric anomalies in causing local increases in terrace-uplift rate. This thesis therefore provides new insights into the current understanding of subduction-zone processes and the dynamics of coastal forearc deformation, whose different interacting forcing factors impact the topographic and geomorphic evolution of the western South American coast.
Extreme flooding displaces an average of 12 million people every year. Marginalized populations in low-income countries are in particular at high risk, but also industrialized countries are susceptible to displacement and its inherent societal impacts. The risk of being displaced results from a complex interaction of flood hazard, population exposed in the floodplains, and socio-economic vulnerability. Ongoing global warming changes the intensity, frequency, and duration of flood hazards, undermining existing protection measures. Meanwhile, settlements in attractive yet hazardous flood-prone areas have led to a higher degree of population exposure. Finally, the vulnerability to displacement is altered by demographic and social change, shifting economic power, urbanization, and technological development. These risk components have been investigated intensively in the context of loss of life and economic damage, however, only little is known about the risk of displacement under global change.
This thesis aims to improve our understanding of flood-induced displacement risk under global climate change and socio-economic change. This objective is tackled by addressing the following three research questions. First, by focusing on the choice of input data, how well can a global flood modeling chain reproduce flood hazards of historic events that lead to displacement? Second, what are the socio-economic characteristics that shape the vulnerability to displacement? Finally, to what degree has climate change potentially contributed to recent flood-induced displacement events?
To answer the first question, a global flood modeling chain is evaluated by comparing simulated flood extent with satellite-derived inundation information for eight major flood events. A focus is set on the sensitivity to different combinations of the underlying climate reanalysis datasets and global hydrological models which serve as an input for the global hydraulic model. An evaluation scheme of performance scores shows that simulated flood extent is mostly overestimated without the consideration of flood protection and only for a few events dependent on the choice of global hydrological models. Results are more sensitive to the underlying climate forcing, with two datasets differing substantially from a third one. In contrast, the incorporation of flood protection standards results in an underestimation of flood extent, pointing to potential deficiencies in the protection level estimates or the flood frequency distribution within the modeling chain.
Following the analysis of a physical flood hazard model, the socio-economic drivers of vulnerability to displacement are investigated in the next step. For this purpose, a satellite- based, global collection of flood footprints is linked with two disaster inventories to match societal impacts with the corresponding flood hazard. For each event the number of affected population, assets, and critical infrastructure, as well as socio-economic indicators are computed. The resulting datasets are made publicly available and contain 335 displacement events and 695 mortality/damage events. Based on this new data product, event-specific displacement vulnerabilities are determined and multiple (national) dependencies with the socio-economic predictors are derived. The results suggest that economic prosperity only partially shapes vulnerability to displacement; urbanization, infant mortality rate, the share of elderly, population density and critical infrastructure exhibit a stronger functional relationship, suggesting that higher levels of development are generally associated with lower vulnerability.
Besides examining the contextual drivers of vulnerability, the role of climate change in the context of human displacement is also being explored. An impact attribution approach is applied on the example of Cyclone Idai and associated extreme coastal flooding in Mozambique. A combination of coastal flood modeling and satellite imagery is used to construct factual and counterfactual flood events. This storyline-type attribution method allows investigating the isolated or combined effects of sea level rise and the intensification of cyclone wind speeds on coastal flooding. The results suggest that displacement risk has increased by 3.1 to 3.5% due to the total effects of climate change on coastal flooding, with the effects of increasing wind speed being the dominant factor.
In conclusion, this thesis highlights the potentials and challenges of modeling flood- induced displacement risk. While this work explores the sensitivity of global flood modeling to the choice of input data, new questions arise on how to effectively improve the reproduction of flood return periods and the representation of protection levels. It is also demonstrated that disentangling displacement vulnerabilities is feasible, with the results providing useful information for risk assessments, effective humanitarian aid, and disaster relief. The impact attribution study is a first step in assessing the effects of global warming on displacement risk, leading to new research challenges, e.g., coupling fluvial and coastal flood models or the attribution of other hazard types and displacement events. This thesis is one of the first to address flood-induced displacement risk from a global perspective. The findings motivate for further development of the global flood modeling chain to improve our understanding of displacement vulnerability and the effects of global warming.
The present thesis focuses on the synthesis of nanostructured iron-based compounds by using β-FeOOH nanospindles and poly(ionic liquid)s (PILs) vesicles as hard and soft templates, respectively, to suppress the shuttle effect of lithium polysulfides (LiPSs) in Li-S batteries. Three types of composites with different nanostructures (mesoporous nanospindle, yolk-shell nanospindle, and nanocapsule) have been synthesized and applied as sulfur host material for Li-S batteries. Their interactions with LiPSs and effects on the electrochemical performance of Li-S batteries have been systematically studied.
In the first part of the thesis, carbon-coated mesoporous Fe3O4 (C@M-Fe3O4) nanospindles have been synthesized to suppress the shuttle effect of LiPSs. First, β-FeOOH nanospindles have been synthesized via the hydrolysis of iron (III) chloride in aqueous solution and after silica coating and subsequent calcination, mesoporous Fe2O3 (M-Fe2O3) have been obtained inside the confined silica layer through pyrolysis of β-FeOOH. After the removal of the silica layer, electron tomography (ET) has been applied to rebuild the 3D structure of the M-Fe2O3 nanospindles. After coating a thin layer of polydopamine (PDA) as carbon source, the PDA-coated M-Fe2O3 particles have been calcinated to synthesize C@M-Fe3O4 nanospindles. With the chemisorption of Fe3O4 and confinement of mesoporous structure to anchor LiPSs, the composite C@M-Fe3O4/S electrode delivers a remaining capacity of 507.7 mAh g-1 at 1 C after 600 cycles.
In the second part of the thesis, a series of iron-based compounds (Fe3O4, FeS2, and FeS) with the same yolk-shell nanospindle morphology have been synthesized, which allows for the direct comparison of the effects of compositions on the electrochemical performance of Li-S batteries. The Fe3O4-carbon yolk-shell nanospindles have been synthesized by using the β-FeOOH nanospindles as hard template. Afterwards, Fe3O4-carbon yolk-shell nanospindles have been used as precursors to obtain iron sulfides (FeS and FeS2)-carbon yolk-shell nanospindles through sulfidation at different temperatures. Using the three types of yolk-shell nanospindles as sulfur host, the effects of compositions on interactions with LiPSs and electrochemical performance in Li-S batteries have been systematically investigated and compared. Benefiting from the chemisorption and catalytic effect of FeS2 particles and the physical confinement of the carbon shell, the FeS2-C/S electrode exhibits the best electrochemical performance with an initial specific discharge capacity of 877.6 mAh g-1 at 0.5 C and a retention ratio of 86.7% after 350 cycles.
In the third part, PILs vesicles have been used as soft template to synthesize carbon nanocapsules embedded with iron nitride particles to immobilize and catalyze LiPSs in Li-S batteries. First, 3-n-decyl-1-vinylimidazolium bromide has been used as monomer to synthesize PILs nanovesicles by free radical polymerization. Assisted by PDA coating route and ion exchange, PIL nanovesicles have been successfully applied as soft template in morphology-maintaining carbonization to prepare carbon nanocapsules embedded with iron nitride nanoparticles (FexN@C). The well-dispersed iron nitride nanoparticles effectively catalyze the conversion of LiPSs to Li2S, owing to their high electrical conductivity and strong chemical binding to LiPSs. The constructed FexN@C/S cathode demonstrates a high initial discharge capacity of 1085.0 mAh g-1 at 0.5 C with a remaining value of 930.0 mAh g-1 after 200 cycles.
The results in the present thesis demonstrate the facile synthetic routes of nanostructured iron-based compounds with controllable morphologies and compositions using soft and hard colloidal templates, which can be applied as sulfur host to suppress the shuttle behavior of LiPSs. The synthesis approaches developed in this thesis are also applicable to fabricating other transition metal-based compounds with porous nanostructures for other applications.
Background: The concept self-compassion (SC), a special way of being compassionate with oneself while dealing with stressful life circumstances, has attracted increasing attention in research over the past two decades. Research has already shown that SC has beneficial effects on affective well-being and other mental health outcomes. However, little is known in which ways SC might facilitate our affective well-being in stressful situations. Hence, a central concern of this dissertation was to focus on the question which underlying processes might influence the link between SC and affective well-being. Two established components in stress processing, which might also play an important role in this context, could be the amount of experienced stress and the way of coping with a stressor. Thus, using a multi-method approach, this dissertation aimed at finding to which extent SC might help to alleviate the experienced stress and promotes the use of more salutary coping, while dealing with stressful circumstances. These processes might ultimately help improve one’s affective well-being. Derived from that, it was hypothesized that more SC is linked to less perceived stress and intensified use of salutary coping responses. Additionally, it was suggested that perceived stress and coping mediate the relation between SC and affective well-being.
Method: The research questions were targeted in three single studies and one meta-study. To test my assumptions about the relations of SC and coping in particular, a systematic literature search was conducted resulting in k = 136 samples with an overall sample size of N = 38,913. To integrate the z-transformed Pearson correlation coefficients, random-effects models were calculated. All hypotheses were tested with a three-wave cross-lagged design in two short-term longitudinal online studies assessing SC, perceived stress and coping responses in all waves. The first study explored the assumptions in a student sample (N = 684) with a mean age of 27.91 years over a six-week period, whereas the measurements were implemented in the GESIS Panel (N = 2934) with a mean age of 52.76 years analyzing the hypotheses in a populationbased sample across eight weeks. Finally, an ambulatory assessment study was designed to expand the findings of the longitudinal studies to the intraindividual level. Thus, a sample of 213 participants completed questionnaires of momentary SC, perceived stress, engagement and disengagement coping, and affective well-being on their smartphones three times per day over seven consecutive days. The data was processed using 1-1-1 multilevel mediation analyses.
Results: Results of the meta-analysis indicated that higher SC is significantly associated with more use of engagement coping and less use of disengagement coping. Considering the relations between SC and stress processing variables in all three single studies, cross-lagged paths from the longitudinal data, as well as multilevel modeling paths from the ambulatory assessment data indicated a notable relation between all relevant stress variables. As expected, results showed a significant negative relation between SC and perceived stress and disengagement coping, as well as a positive connection with engagement coping responses at the dispositional and intra-individual level. However, considering the mediational hypothesis, the most promising pathway in the link between SC and affective well-being turned out to be perceived stress in all three studies, while effects of the mediational pathways through coping responses were less robust.
Conclusion: Thus, a more self-compassionate attitude and higher momentary SC, when needed in specific situations, can help to engage in effective stress processing. Considering the underlying mechanisms in the link between SC and affective well-being, stress perception in particular seemed to be the most promising candidate for enhancing affective well-being at the dispositional and at the intraindividual level. Future research should explore the pathways between SC and affective well-being in specific contexts and samples, and also take into account additional influential factors.
The purpose of this thesis was to investigate the developmental dynamics between interest, motivation, and learning strategy use during physics learning. The target population was lower secondary school students from a developing country, given that there is hardly in research that studies the above domain-specific concepts in the context of developing countries. The aim was addressed in four parts.
The first part of the study was guided by three objectives: (a) to adapt and validate the Science Motivation Questionnaire (SMQ-II) for the Ugandan context; (b) to examine whether there are significant differences in motivation for learning Physics with respect to students’ gender; and (c) to establish the extent to which students’ interest predicts their motivation to learn Physics. Being a pilot study, the sample comprised 374 randomly selected students from five schools in central Uganda who responded to anonymous questionnaires that included scales from the SMQ-II and the Individual Interest Questionnaire. Data were analysed using confirmatory factor analyses, t-tests and structural equation modelling in SPSS-25 and Mplus-8. The five-factor model solution of the SMQ-II fitted adequately with the study data, with deletion of one item. The modified SMQ-II exhibited invariant factor loadings and intercepts (i.e., strong measurement invariance) when administered to boys and girls. Furthermore, on assessing whether motivation for learning Physics varied with gender, no significant differences were noted. On assessing the predictive effects of individual interest on students’ motivation, individual interest significantly predicted all motivational constructs, with stronger predictive strength on students’ self-efficacy and self-determination in learning Physics.
In the second part whilst using comprised 934 Grade 9 students from eight secondary schools in Uganda, Latent profile analysis (LPA) - a person-centred approach was used to investigate motivation patterns that exist in lower secondary school students during physics learning. A three-step approach to LPA was used to answer three research questions: RQ1, which profiles of secondary school students exist with regards to their motivation for Physics learning; RQ2, are there differences in students’ cognitive learning strategies in the identified profiles; and RQ3, does students’ gender, attitudes, and individual interest predict membership in these profiles? Six motivational profiles were identified: (i) low-quantity motivation profile (101 students; 10.8%); (ii) moderate-quantity motivation profile (246 students; 26.3%); (iii) high-quantity motivation profile (365 students; 39.1%); (iv) primarily intrinsically motivated profile (60 students,6.4%); (v) mostly extrinsically motivated profile (88 students, 9.4%); and (vi) grade-introjected profile (74 students, 7.9%). Low-quantity and grade introjected motivated students mostly used surface learning strategies whilst the high-quantity and primarily intrinsically motivated students used deep learning strategies. On examining the predictive effect of gender, individual interest, and students’ attitudes on the profile membership, unlike gender, individual interest and students’ attitudes towards Physics learning strongly predicted profile membership.
In the third part of the study, the occurrence of different secondary school learner profiles depending on their various combinations of cognitive and metacognitive learning strategy use, as well as their differences in perceived autonomy support, intrinsic motivation, and gender was examined. Data were collected from 576 9th grade student. Four learner profiles were identified: competent strategy user, struggling user, surface-level learner, and deep-level learner profiles. Gender differences were noted in students’ use of elaboration and organization strategies to learn Physics, in favour of girls. In terms of profile memberships, significant differences in gender, intrinsic motivation and perceived autonomy support were also noted. Girls were 2.4 - 2.7 times more likely than boys to be members of the competent strategy user and surface-level learner profiles. Additionally, higher levels of intrinsic motivation predicted an increased likelihood membership into the deep-level learner profile, whilst higher levels of perceived teacher autonomy predicted an increased likelihood membership into the competent strategy user profile as compared to other profiles.
Lastly, in the fourth part, changes in secondary school students’ physics motivation and cognitive learning strategies use during physics learning across time were examined. Two waves of data were collected from initially 954 9th students through to their 10th grade. A three-step approach to Latent transition analysis was used. Generally, students’ motivation decreased from 9th to 10th grade. Qualitative students’ motivation profiles indicated strong with-in person stability whilst the quantitative profiles were relatively less stable. Mostly, students moved from the high quantity motivation profile to the extrinsically motivated profiles. On the other hand, the cognitive learning strategies use profiles were moderately stable; with higher with-in person stability in the deep-level learner profile. None of the struggling users and surface-level learners transitioned into the deep-level learners’ profile. Additionally, students who perceived increased support for autonomy from their teachers had higher membership likelihood into the competent users’ profiles whilst those with an increase in individual interest score had higher membership likelihood into the deep-level learner profile.
The increasing global population has led to a growing demand for cost-effective and eco-friendly methods of water purification. This demand has reached a peak due to the increasing presence of impurities and pollutants in water and a growing awareness of waterborne diseases. Advanced oxidation processes (AOPs) are effective methods to address these challenges, due to the generation of highly reactive radicals, such as sulfate radical (SO4•-), hydroxyl radical (•OH), and/or superoxide radical (•O2-) in oxidation reactions. Relative to conventional hydrogen peroxide (H2O2)-based AOPs for wastewater treatment, the persulfate-related AOPs are receiving increasing attention over the past decades, due to their stronger oxidizing capability and a wider pH working window. Further deployment of the seemingly plausible technology as an alternative for the well-established one in industry, however, necessitates a careful evaluation of compounding factors, such as water matrix effects, toxicological consequences, costs, and engineering challenges, etc. To this end, rational design of efficient and environmentally friendly catalysts constitutes an indispensable pathway to advance persulfate activation efficacy and to elucidate the mechanisms in AOPs, the combined endeavors are expected to provide insightful understanding and guidelines for future studies in wastewater treatment. A dozens of transition metal-based catalysts have been developed for persulfate-related AOPs, while the undesirable metal leaching and poor stability in acidic conditions have been identified as major obstacles. Comparatively, the carbonaceous materials are emerging as alternative candidates, which are characterized by metal-free nature, wide availability, and exceptional resistance to acid and alkali, as well as tunable physicochemical and electronic properties, the combined merits make them an attractive option to overcome the aforementioned limitations in metal-based catalytic systems. This dissertation aims at developing novel carbonaceous materials to boost the activity in peroxymonosulfate (PMS) activation processes. Functionalized carbon materials with metal particles or heteroatoms were constructed and further evaluated in terms of their ability to activate PMS for AOPs. The main contents of this thesis are summarized as follows: (1) Iron oxide-loaded biochar: improving stability and alleviating metal leakage Metal leaching constitutes one of the main drawbacks in using transition metals as PMS activators, which is accompanied by the generation of metal-containing sludge, potentially leading to secondary pollution. Meanwhile, the metal nanoparticles are prone to aggregate, causing rapid decay of catalytic performance. The use of carbons as supports for transition metals could mitigate these deficiencies, because the interaction between metals and carbons could in turn disperse and stabilize metal nanoparticles, thus suppressing the metal leaching. In this work, the environmentally benign lignin with its abundant phenolic groups, which is well known to serve as carbon source with high yields and flexibility, was utilized to load Fe ions. The facile low-temperature pre-treatment pyrolytic strategy was employed to construct a green catalyst with iron oxides embedded in Kraft-lignin-derived biochar (termed as γ-Fe2O3@KC). The γ-Fe2O3@KC was capable of activating PMS to generate stable non-radical species (1O2 and Fe (V)=O) and to enhance electron transfer efficiency. A surface-bound reactive complex (catalyst-PMS*) was identified by electrochemical characterizations and discussed with primary surface-bound radical pairs to explain the contradictions between quenching and EPR detection results. The system also showed encouraging reusability for at least 5 times and high stability at pH 3-9. The low concentration of iron in γ-Fe2O3@KC/PMS system implied that the carbon scaffold of biochar substantially alleviated metal leakage. (2) MOF-derived nanocarbon: new carbon crystals Traditional carbon materials are of rather moderate performance in activation PMS, due to the poor electron transfer capacity within the amorphous structure and limited active sites for PMS adsorption. Herein, we established crystalline nanocarbon materials via a simple NaCl-templated strategy using the monoclinic zeolitic imidazolate framework-8 (ZIF-8) sealed with NaCl crystals as the precursors. Specifically, NaCl captured dual advantages in serving as structure-directing agent during hydrolysis and protective salt reactor to facilitate phase transformation during carbonization. The structure-directing agent NaCl provided a protective and confined space for the evolution of MOF upon carbonization, which led to high doping amounts of nitrogen (N) and oxygen elements (O) in carbon framework (N: 14.16 wt%, O: 9.6 wt%) after calcination at a high temperature of 950 oC. We found that N-O co-doping can activate the chemically inert carbon network and the nearby sp2-hybridized carbon atoms served as active sites for adsorption and activation. Besides, the highly crystallized structure with well-established carbon channels around activated carbon atoms could significantly accelerate electron transfer process after initial adsorption of PMS. As such, this crystalline nanocarbon exhibited excellent catalytic kinetics for various pollutants. (3) MOF-derived 2D carbon layers: enhanced mass/electron transfer The two-dimensional (2D) configuration of carbon-based nanosheets with inherent nanochannels and abundant active sites residing on the layer edges or in between the layers, allowed the accessible interaction and close contact between the substrates and reactants, as well as the dramatically improved electron- and mass-transfer kinetics. In this regard, we developed dual-templating strategy to afford 2D assembly of the crystalline carbons, which found efficiency in reinforcing the interactions between the catalyst surface and foreign pollutants. Specifically, we found that the ice crystals and NaCl promoted the evolution of MOF in a 2D fashion during the freezing casting stage, while the later further allowed the formation of a graphitic surface at high calcination temperature, by virtue of the templating effect of molten salt. Due to the highly retained co-doping amounts, N and O heteroatoms created abundant active sites for PMS activation, the 2D configuration of carbon-based nanosheets enable efficient interaction of PMS and pollutants on the surface, which further boosted the kinetics of degradation.
Development of electrochemical antibody-based and enzymatic assays for mycotoxin analysis in food
(2023)
Electrochemical methods are promising to meet the demand for easy-to-use devices monitoring key parameters in the food industry. Many companies run own lab procedures for mycotoxin analysis, but it is a major goal to simplify the analysis. The enzyme-linked immunosorbent assay using horseradish peroxidase as enzymatic label, together with 3,3',5,5' tetramethylbenzidine (TMB)/H2O2 as substrates allows sensitive mycotoxin detection with optical detection methods. For the miniaturization of the detection step, an electrochemical system for mycotoxin analysis was developed. To this end, the electrochemical detection of TMB was studied by cyclic voltammetry on different screen-printed electrodes (carbon and gold) and at different pH values (pH 1 and pH 4). A stable electrode reaction, which is the basis for the further construction of the electrochemical detection system, could be achieved at pH 1 on gold electrodes. An amperometric detection method for oxidized TMB, using a custom-made flow cell for screen-printed electrodes, was established and applied for a competitive magnetic bead-based immunoassay for the mycotoxin ochratoxin A. A limit of detection of 150 pM (60 ng/L) could be obtained and the results were verified with optical detection. The applicability of the magnetic bead-based immunoassay was tested in spiked beer using a handheld potentiostat connected via Bluetooth to a smartphone for amperometric detection allowing to quantify ochratoxin A down to 1.2 nM (0.5 µg/L).
Based on the developed electrochemical detection system for TMB, the applicability of the approach was demonstrated with a magnetic bead-based immunoassay for the ergot alkaloid, ergometrine. Under optimized assay conditions a limit of detection of 3 nM (1 µg/L) was achieved and in spiked rye flour samples ergometrine levels in a range from 25 to 250 µg/kg could be quantified. All results were verified with optical detection. The developed electrochemical detection method for TMB gives great promise for the detection of TMB in many other HRP-based assays.
A new sensing approach, based on an enzymatic electrochemical detection system for the mycotoxin fumonisin B1 was established using an Aspergillus niger fumonisin amine oxidase (AnFAO). AnFAO was produced recombinantly in E. coli as maltose-binding protein fusion protein and catalyzes the oxidative deamination of fumonisins, producing hydrogen peroxide. It was found that AnFAO has a high storage and temperature stability. The enzyme was coupled covalently to magnetic particles, and the enzymatically produced H2O2 in the reaction with fumonisin B1 was detected amperometrically in a flow injection system using Prussian blue/carbon electrodes and the custom-made wall-jet flow cell. Fumonisin B1 could be quantified down to 1.5 µM (≈ 1 mg/L). The developed system represents a new approach to detect mycotoxins using enzymes and electrochemical methods.
This research focuses on empowering leadership, a leadership style that shares autonomy and responsibilities with the followers. Empowering leadership enhances the meaningfulness of work by fostering participation in decision-making, expressing confidence in high performance, and providing autonomy in target setting (Cheong, 2016). I examine how empowering leadership affects followers’ reflection. I used data from 528 individuals across 172 teams and found a positive relationship between empowering leadership and followers’ reflection. Followers’ reflection, in turn, is negatively associated with followers’ withdrawal, which mediates the beneficial effect of empowering leadership on leaders’ emotional exhaustion. As for the leaders, I propose that empowering leadership is negatively related also to leaders’ emotional exhaustion. This research broadens our understanding of empowering leadership's effects on both followers and leaders. Moreover, it integrates empowering leadership, leader emotional exhaustion, and burnout literature. Overall, empowering leadership strengthens members’ reflective attitudes and behaviors, which result in reduced withdrawal (and increased presence and contribution) in teams. Because the members contribute to team effort more, the leaders experience less emotional exhaustion. Hence, my work not only identifies new ways through which empowering leadership positively affects followers but also shows how these positive effects on followers benefit the leaders’ well-being.
Establishment of final leaf size in plants represents a complex mechanism that relies on the precise regulation of two interconnected cellular processes, cell division and cell expansion. In previous work, the barley protein BROAD LEAF1 (BLF1) was identified as a novel negative regulator of cell proliferation, that mainly limits leaf growth in the width direction. Here I identified a novel RING/U-box protein that interacts with BLF1 through a yeast two hybrid screen. Using BiFC, Co-IP and FRET I confirmed the interaction of the two proteins in planta. Enrichment of the BLF1-mEGFP fusion protein and the increase of the FRET signal upon MG132 treatment of tobacco plants, together with an in vivo ubiquitylation assay in bacteria, confirmed that the RING/U-box E3 interacts with BLF1 to mediate its ubiquitylation and degradation by the 26S proteasome system. Consistent with regulation of endogenous BLF1 in barley by proteasomal degradation, inhibition of the proteasome by bortezomib treatment on BLF1-vYFP transgenic barley plants also resulted in an enrichment of the BLF1 protein. I thus demonstrated that RING/U-box E3 is colocalized with BLF1 in nuclei and negatively regulates BLF1 protein levels. Analysis of ring-e3_1 knock-out mutants suggested the involvement of the RING/U-box E3 gene in leaf growth control, although the effect was mainly on leaf length. Together, my results suggest that proteasomal degradation, possibly mediated by RING/U-box E3, contributes to fine-tuning BLF1 protein-level in barley.
An exploration of activity and therapist preferences and their predictors in German-speaking samples
(2023)
According to current definitions of evidence-based practice, patients’ preferences play an important role for the psychotherapeutic process and outcomes. However, whereas a significant body of research investigated preferences regarding specific treatments, research on preferred activities or therapist characteristics is rare, investigated heterogeneous aspects with inconclusive results, lacked validated assessment tools, and neglected relevant preferences, their predictors as well as the perspective of mental health professionals. Therefore, the three studies of this dissertation aimed to address the most fundamental drawbacks in current preference research by providing a validated questionnaire, focus efforts on activity and therapist preferences and add preferences of psychotherapy trainees. To this end, Paper I reports the translation and validation of the 18-item Cooper-Norcross Inventory of Preference (C-NIP) in a broad, heterogeneous sample of N = 969 laypeople, resulting in good to acceptable reliabilities and first evidence of validity. However, the original factor structure was not replicated. Paper II assesses activity preferences of psychotherapists in training using the C-NIP and compares them with the initial laypeople sample. There were significant differences between both samples, with trainees preferring a more patient-directed, emotionally intense and confrontational approach than laypeople. CBT trainees preferred a more therapist-directed, present-focused, challenging and less emotional intense approach than psychodynamic or -analytic trainees. Paper III explores therapist preferences and tests predictors for specific preference choices. For most characteristics, more than half of the participants did not have specific preferences. Results pointed towards congruency effects (i.e., preference for similar characteristics), especially for members of marginalized groups. The dissertation provides both researchers and practitioners with a validated questionnaire, shows potentially obstructive differences between patients and therapists and underlines the importance of therapist characteristics for marginalized groups, thereby laying the foundation for future applications and implementations in research and practice.
Aging is a complex process characterized by several factors, including loss of genetic and epigenetic information, accumulation of chronic oxidative stress, protein damage and aggregates and it is becoming an emergent drug target. Therefore, it is the utmost importance to study aging and agerelated diseases, to provide treatments to develop a healthy aging process. Skeletal muscle is one of the earliest tissues affected by age-related changes with progressive loss of muscle mass and function from 30 years old, effect known as sarcopenia. Several studies have shown the accumulation of protein aggregates in different animal models, as well as in humans, suggesting impaired proteostasis, a hallmark of aging, especially regarding degradation systems. Thus, different publications have explored the role of the main proteolytic systems in skeletal muscle from rodents and humans, like ubiquitin proteasomal system (UPS) and autophagy lysosomal system (ALS), however with contradictory results. Yet, most of the published studies are performed in muscles that comprise more than one fiber type, that means, muscles composed by slow and fast fibers. These fiber types, exhibit different metabolism and contraction speed; the slow fibers or type I display an oxidative metabolism, while fast fibers function towards a glycolytic metabolism ranging from fast oxidative to fast glycolytic fibers. To this extent, the aim of this thesis sought to understand on how aging impacts both fiber types not only regarding proteostasis but also at a metabolome and transcriptome network levels. Therefore, the first part of this thesis, presents the differences between slow oxidative (from Soleus muscle) and fast glycolytic fibers (Extensor digitorum longus, EDL) in terms of degradation systems and how they cope with oxidative stress during aging, while the second part explores the differences between young and old EDL muscle transcriptome and metabolome, unraveling molecular features. More specifically, the results from the present work show that slow oxidative muscle performs better at maintaining the function of UPS and ALS during aging than EDL muscle, which is clearly affected, accounting for the decline in the catalytic activity rates and accumulation of autophagy-related proteins. Strinkingly, transcriptome and metabolome analyses reveal that fast glycolytic muscle evidences significant downregulation of mitochondrial related processes and damaged mitochondria morphology during aging, despite of having a lower oxidative metabolism compared to oxidative fibers. Moreover, predictive analyses reveal a negative association between aged EDL gene signature and lifespan extending interventions such as caloric restriction (CR). Although, CR intervention does not alter the levels of mitochondrial markers in aged EDL muscle, it can reverse the higher mRNA levels of muscle damage markers. Together, the results from this thesis give new insights about how different metabolic muscle fibers cope with age-related changes and why fast glycolytic fibers are more susceptible to aging than slow oxidative fibers.
Evaluation of nitrogen dynamics in high-order streams and rivers based on high-frequency monitoring
(2023)
Nutrient storage, transform and transport are important processes for achieving environmental and ecological health, as well as conducting water management plans. Nitrogen is one of the most noticeable elements due to its impacts on tremendous consequences of eutrophication in aquatic systems. Among all nitrogen components, researches on nitrate are blooming because of widespread deployments of in-situ high-frequency sensors. Monitoring and studying nitrate can become a paradigm for any other reactive substances that may damage environmental conditions and cause economic losses.
Identifying nitrate storage and its transport within a catchment are inspiring to the management of agricultural activities and municipal planning. Storm events are periods when hydrological dynamics activate the exchange between nitrate storage and flow pathways. In this dissertation, long-term high-frequency monitoring data at three gauging stations in the Selke river were used to quantify event-scale nitrate concentration-discharge (C-Q) hysteretic relationships. The Selke catchment is characterized into three nested subcatchments by heterogeneous physiographic conditions and land use. With quantified hysteresis indices, impacts of seasonality and landscape gradients on C-Q relationships are explored. For example, arable area has deep nitrate legacy and can be activated with high intensity precipitation during wetting/wet periods (i.e., the strong hydrological connectivity). Hence, specific shapes of C-Q relationships in river networks can identify targeted locations and periods for agricultural management actions within the catchment to decrease nitrate output into downstream aquatic systems like the ocean.
The capacity of streams for removing nitrate is of both scientific and social interest, which makes the quantification motivated. Although measurements of nitrate dynamics are advanced compared to other substances, the methodology to directly quantify nitrate uptake pathways is still limited spatiotemporally. The major problem is the complex convolution of hydrological and biogeochemical processes, which limits in-situ measurements (e.g., isotope addition) usually to small streams with steady flow conditions. This makes the extrapolation of nitrate dynamics to large streams highly uncertain. Hence, understanding of in-stream nitrate dynamic in large rivers is still necessary. High-frequency monitoring of nitrate mass balance between upstream and downstream measurement sites can quantitatively disentangle multi-path nitrate uptake dynamics at the reach scale (3-8 km). In this dissertation, we conducted this approach in large stream reaches with varying hydro-morphological and environmental conditions for several periods, confirming its success in disentangling nitrate uptake pathways and their temporal dynamics. Net nitrate uptake, autotrophic assimilation and heterotrophic uptake were disentangled, as well as their various diel and seasonal patterns. Natural streams generally can remove more nitrate under similar environmental conditions and heterotrophic uptake becomes dominant during post-wet seasons. Such two-station monitoring provided novel insights into reach-scale nitrate uptake processes in large streams.
Long-term in-stream nitrate dynamics can also be evaluated with the application of water quality model. This is among the first time to use a data-model fusion approach to upscale the two-station methodology in large-streams with complex flow dynamics under long-term high-frequency monitoring, assessing the in-stream nitrate retention and its responses to drought disturbances from seasonal to sub-daily scale. Nitrate retention (both net uptake and net release) exhibited substantial seasonality, which also differed in the investigated normal and drought years. In the normal years, winter and early spring seasons exhibited extensive net releases, then general net uptake occurred after the annual high-flow season at later spring and early summer with autotrophic processes dominating and during later summer-autumn low-flow periods with heterotrophy-characteristics predominating. Net nitrate release occurred since late autumn until the next early spring. In the drought years, the late-autumn net releases were not so consistently persisted as in the normal years and the predominance of autotrophic processes occurred across seasons. Aforementioned comprehensive results of nitrate dynamics on stream scale facilitate the understanding of instream processes, as well as raise the importance of scientific monitoring schemes for hydrology and water quality parameters.
Planets outside our solar system, so-called "exoplanets", can be detected with different methods, and currently more than 5000 exoplanets have been confirmed, according to NASA Exoplanet Archive. One major highlight of the studies on exoplanets in the past twenty years is the characterization of their atmospheres usingtransmission spectroscopy as the exoplanet transits. However, this characterization is a challenging process and sometimes there are reported discrepancies in the literature regarding the atmosphere of the same exoplanet. One potential reason for the observed atmospheric inconsistencies is called impact parameter degeneracy, and it is highly driven by the limb darkening effect of the host star. A brief introductionto those topics in presented in chapter 1, while the motivation and objectives of thiswork are described in chapter 2.The first goal is to clarify the origin of the transmission spectrum, which is anindicator of an exoplanet’s atmosphere; whether it is real or influenced by the impactparameter degeneracy. A second goal is to determine whether photometry from space using the Transiting Exoplanet Survey Satellite (TESS), could improve on the major parameters, which are responsible for the aforementioned degeneracy, of known exoplanetary systems. Three individual projects were conducted in order toaddress those goals. The three manuscripts are presented, in short, in the manuscriptoverview in chapter 3.More specifically, in chapter 4, the first manuscript is presented, which is an ex-tended investigation on the impact parameter degeneracy and its application onsynthetic transmission spectra. Evidently, the limb darkening of the host star isan important driver for this effect. It keeps the degeneracy persisting through different groups of exoplanets, based on the uncertainty of their impact parameter and on the type of their host star. The second goal, was addressed in the second and third manuscripts (chapter 5 and chapter 6 respectively). Using observationsfrom the TESS mission, two samples of exoplanets were studied; 10 transiting inflated hot-Jupiters and 43 transiting grazing systems. Potentially, the refinement or confirmation of their major system parameters’ measurements can assist in solving current or future discrepancies regarding their atmospheric characterization.In chapter 7 the conclusions of this work are discussed, while in chapter 8 itis proposed how TESS’s measurements can be able to discern between erroneousinterpretations of transmission spectra, especially on systems where the impact parameter degeneracy is likely not applicable.
Transposable elements (TEs) are loci that can replicate and multiply within the genome of their host. Within the host, TEs through transposition are responsible for variation on genomic architecture and gene regulation across all vertebrates. Genome assemblies have increased in numbers in recent years. However, to explore in deep the variations within different genomes, such as SNPs (single nucleotide polymorphism), INDELs (Insertion-deletion), satellites and transposable elements, we need high-quality genomes. Studies of molecular markers in the past 10 years have limitations to correlate with biological differences because molecular markers rely on the accuracy of the genomic resources. This has generated that a substantial part of the studies of TE in recent years have been on high quality genomic resources such as Drosophila, zebrafinch and maize. As testudine have a slow mutation rate lower only to crocodilians, with more than 300 species, adapted to different environments all across the globe, the testudine clade can help us to study variation. Here we propose Testudines as a clade to study variation and the abundance of TE on different species that diverged a long time ago. We investigated the genomic diversity of sea turtles, identifying key genomic regions associated to gene family duplication, specific expansion of particular TE families for Dermochelyidae and that are important for phenotypic differentiation, the impact of environmental changes on their populations, and the dynamics of TEs within different lineages. In chapter 1, we identify that despite high levels of genome synteny within sea turtles, we identified that regions of reduced collinearity and microchromosomes showed higher concentrations of multicopy gene families, as well as genetic distances between species, indicating their potential importance as sources of variation underlying phenotypic differentiation. We found that differences in the ecological niches occupied by leatherback and green turtles have led to contrasting evolutionary paths for their olfactory receptor genes. We identified in leatherback turtles a long-term low population size. Nonetheless, we identify no correlation between the regions of reduced collinearity with abundance of TEs or an accumulation of a particular TE group. In chapter 2, we identified that sea turtle genomes contain a significant proportion of TEs, with differences in TE abundance between species, and the discovery of a recent expansion of Penelope-like elements (PLEs) in the highly conserved sea turtle genome provides new insights into the dynamics of TEs within Testudines. In chapter 3, we compared the proportion of TE across the Testudine clade, and we identified that the proportion of transposable elements within the clade is stable, regardless of the quality of the assemblies. However, we identified that the proportion of TEs orders has correlation with genome quality depending of their expanded abundancy. For retrotransposon, a highly abundant element for this clade, we identify no correlation. However, for DNA elements a rarer element on this clade, correlate with the quality of the assemblies.
Here we confirm that high-quality genomes are fundamental for the study of transposable element evolution and the conservation within the clade. The detection and abundance of specific orders of TEs are influenced by the quality of the genomes. We identified that a reduction in the population size on D. coriacea had left signals of long-term low population sizes on their genomes. On the same note we identified an expansion of TE on D. coriacea, not present in any other member of the available genomes of Testudines, strongly suggesting that it is a response of deregulation of TE on their genomes as consequences of the low population sizes.
Here we have identified important genomic regions and gene families for phenotypic differentiation and highlighted the impact of environmental changes on the populations of sea turtles. We stated that accurate classification and analysis of TE families are important and require high-quality genome assemblies. Using TE analysis we manage to identify differences in highly syntenic species. These findings have significant implications for conservation and provide a foundation for further research into genome evolution and gene function in turtles and other vertebrates. Overall, this study contributes to our understanding of evolutionary change and adaptation mechanisms.
Distributed decision-making studies the choices made among a group of interactive and self-interested agents. Specifically, this thesis is concerned with the optimal sequence of choices an agent makes as it tries to maximize its achievement on one or multiple objectives in the dynamic environment. The optimization of distributed decision-making is important in many real-life applications, e.g., resource allocation (of products, energy, bandwidth, computing power, etc.) and robotics (heterogeneous agent cooperation on games or tasks), in various fields such as vehicular network, Internet of Things, smart grid, etc.
This thesis proposes three multi-agent reinforcement learning algorithms combined with game-theoretic tools to study strategic interaction between decision makers, using resource allocation in vehicular network as an example. Specifically, the thesis designs an interaction mechanism based on second-price auction, incentivizes the agents to maximize multiple short-term and long-term, individual and system objectives, and simulates a dynamic environment with realistic mobility data to evaluate algorithm performance and study agent behavior.
Theoretical results show that the mechanism has Nash equilibria, is a maximization of social welfare and Pareto optimal allocation of resources in a stationary environment. Empirical results show that in the dynamic environment, our proposed learning algorithms outperform state-of-the-art algorithms in single and multi-objective optimization, and demonstrate very good generalization property in significantly different environments. Specifically, with the long-term multi-objective learning algorithm, we demonstrate that by considering the long-term impact of decisions, as well as by incentivizing the agents with a system fairness reward, the agents achieve better results in both individual and system objectives, even when their objectives are private, randomized, and changing over time. Moreover, the agents show competitive behavior to maximize individual payoff when resource is scarce, and cooperative behavior in achieving a system objective when resource is abundant; they also learn the rules of the game, without prior knowledge, to overcome disadvantages in initial parameters (e.g., a lower budget).
To address practicality concerns, the thesis also provides several computational performance improvement methods, and tests the algorithm in a single-board computer. Results show the feasibility of online training and inference in milliseconds.
There are many potential future topics following this work. 1) The interaction mechanism can be modified into a double-auction, eliminating the auctioneer, resembling a completely distributed, ad hoc network; 2) the objectives are assumed to be independent in this thesis, there may be a more realistic assumption regarding correlation between objectives, such as a hierarchy of objectives; 3) current work limits information-sharing between agents, the setup befits applications with privacy requirements or sparse signaling; by allowing more information-sharing between the agents, the algorithms can be modified for more cooperative scenarios such as robotics.
Unveiling the Local Universe
(2023)
Extreme weather and climate events are one of the greatest dangers for present-day society. Therefore, it is important to provide reliable statements on what changes in extreme events can be expected along with future global climate change. However, the projected overall response to future climate change is generally a result of a complex interplay between individual physical mechanisms originated within the different climate subsystems. Hence, a profound understanding of these individual contributions is required in order to provide meaningful assessments of future changes in extreme events. One aspect of climate change is the recently observed phenomenon of Arctic Amplification and the related dramatic Arctic sea ice decline, which is expected to continue over the next decades. The question to what extent Arctic sea ice loss is able to affect atmospheric dynamics and extreme events over mid-latitudes has received a lot of attention over recent years and still remains a highly debated topic.
In this respect, the objective of this thesis is to contribute to a better understanding on the impact of future Arctic sea ice retreat on European temperature extremes and large-scale atmospheric dynamics.
The outcomes are based on model data from the atmospheric general circulation model ECHAM6. Two different sea ice sensitivity simulations from the Polar Amplification Intercomparison Project are employed and contrasted to a present day reference experiment: one experiment with prescribed future sea ice loss over the entire Arctic, as well as another one with sea ice reductions only locally prescribed over the Barents-Kara Sea.% prescribed over the entire Arctic, as well as only locally over the Barent/Karasea with a present day reference experiment.
The first part of the thesis focuses on how future Arctic sea ice reductions affect large-scale atmospheric dynamics over the Northern Hemisphere in terms of occurrence frequency changes of five preferred Euro-Atlantic circulation regimes. When compared to circulation regimes computed from ERA5 it shows that ECHAM6 is able to realistically simulate the regime structures. Both ECHAM6 sea ice sensitivity experiments exhibit similar regime frequency changes. Consistent with tendencies found in ERA5, a more frequent occurrence of a Scandinavian blocking pattern in midwinter is for instance detected under future sea ice conditions in the sensitivity experiments. Changes in occurrence frequencies of circulation regimes in summer season are however barely detected.
After identifying suitable regime storylines for the occurrence of European temperature extremes in winter, the previously detected regime frequency changes are used to quantify dynamically and thermodynamically driven contributions to sea ice-induced changes in European winter temperature extremes.
It is for instance shown how the preferred occurrence of a Scandinavian blocking regime under low sea ice conditions dynamically contributes to more frequent midwinter cold extreme occurrences over Central Europe. In addition, a reduced occurrence frequency of a Atlantic trough regime is linked to reduced winter warm extremes over Mid-Europe. Furthermore, it is demonstrated how the overall thermodynamical warming effect due to sea ice loss can result in less (more) frequent winter cold (warm) extremes, and consequently counteracts the dynamically induced changes.
Compared to winter season, circulation regimes in summer are less suitable as storylines for the occurrence of summer heat extremes.
Therefore, an approach based on circulation analogues is employed in order to quantify thermodyamically and dynamically driven contributions to sea ice-induced changes of summer heat extremes over three different European sectors. Reduced occurrences of blockings over Western Russia are detected in the ECHAM6 sea ice sensitivity experiments; however, arguing for dynamically and thermodynamically induced contributions to changes in summer heat extremes remains rather challenging.
During the Cenozoic, global cooling and uplift of the Tian Shan, Pamir, and Tibetan plateau modified atmospheric circulation and reduced moisture supply to Central Asia. These changes led to aridification in the region during the Neogene. Afterwards, Quaternary glaciations led to modification of the landscape and runoff.
In the Issyk-Kul basin of the Kyrgyz Tian Shan, the sedimentary sequences reflect the development of the adjacent ranges and local climatic conditions. In this work, I reconstruct the late Miocene – early Pleistocene depositional environment, climate, and lake development in the Issyk-Kul basin using facies analyses and stable δ18O and δ13C isotopic records from sedimentary sections dated by magnetostratigraphy and 26Al/10Be isochron burial dating. Also, I present 10Be-derived millennial-scale modern and paleo-denudation rates from across the Kyrgyz Tian Shan and long-term exhumation rates calculated from published thermochronology data. This allows me to examine spatial and temporal changes in surface processes in the Kyrgyz Tian Shan.
In the Issyk-Kul basin, the style of fluvial deposition changed at ca. 7 Ma, and aridification in the basin commenced concurrently, as shown by magnetostratigraphy and the δ18O and δ13C data. Lake formation commenced on the southern side of the basin at ca. 5 Ma, followed by a ca. 2 Ma local depositional hiatus. 26Al/10Be isochron burial dating and paleocurrent analysis show that the Kungey range to the north of the basin grew eastward, leading to a change from fluvial-alluvial deposits to proximal alluvial fan conglomerates at 5-4 Ma in the easternmost part of the basin. This transition occurred at 2.6-2.8 Ma on the southern side of the basin, synchronously with the intensification of the Northern Hemisphere glaciation. The paleo-denudation rates from 2.7-2.0 Ma are as low as long-term exhumation rates, and only the millennial-scale denudation rates record an acceleration of denudation.
This work concludes that the growth of the ranges to the north of the basin led to creation of the topographic barrier at ca. 7 Ma and a subsequent aridification in the Issyk-Kul basin. Increased subsidence and local tectonically-induced river system reorganization on the southern side of the basin enabled lake formation at ca. 5 Ma, while growth of the Kungey range blocked westward-draining rivers and led to sediment starvation and lake expansion. Denudational response of the Kyrgyz Tian Shan landscape is delayed due to aridity and only substantial cooling during the late Quaternary glacial cycles led to notable acceleration of denudation. Currently, increased glacier reduction and runoff controls a more rapid denudation of the northern slope of the Terskey range compared to other ranges of the Kyrgyz Tian Shan.
The trace elements, selenium (Se) and copper (Cu) play an important role in maintaining normal brain function. Since they have essential functions as cofactors of enzymes or structural components of proteins, an optimal supply as well as a well-defined homeostatic regulation are crucial. Disturbances in trace element homeostasis affect the health status and contribute to the incidence and severity of various diseases. The brain in particular is vulnerable to oxidative stress due to its extensive oxygen consumption and high energy turnover, among other factors. As components of a number of antioxidant enzymes, both elements are involved in redox homeostasis. However, high concentrations are also associated with the occurrence of oxidative stress, which can induce cellular damage. Especially high Cu concentrations in some brain areas are associated with the development and progression of neurodegenerative diseases such as Alzheimer's disease (AD). In contrast, reduced Se levels were measured in brains of AD patients. The opposing behavior of Cu and Se renders the study of these two trace elements as well as the interactions between them being particularly relevant and addressed in this work.