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Carbohydrates play a vital role in all living organisms; serving as a cornerstone in primary metabolism through the release of energy from their hydrolysis and subsequent re-utilization (Apriyanto et al., 2022). Starch is the principal carbohydrate reserve in plants, providing essential energy for plant growth. Furthermore, starch serves as a significant carbohydrate source in the human diet. Beyond its nutritional value, starch has extensive industrial application associated with many aspects of human society, such as feed, pharmacy, textiles, and the production of biodegradable plastics. Understanding the mechanisms underlying starch metabolism in plants carries multifaceted benefits. Not only does it contribute to increasing crop yield and refining grain quality, but also can improve the efficiency of industrial applications.
Starch in plants is categorized into two classes based on their location and function: transitory starch and storage starch. Transitory starch is produced in chloroplasts of autotrophic tissues/organs, such as leaves. It is synthesized during the day and degraded during the night. Storage starch is synthesized in heterotrophic tissues/organs, such as endosperm, roots and tubers, which is utilized for plant reproduction and industrial application in human life. Most studies aiming to comprehend starch metabolism of Arabidopsis thaliana primarily focus on transitory starch.
Starch is stored as granular form in chloroplast and amyloplast. The parameters of starch granules, including size, morphology, and quantity per chloroplast serve as indicators of starch metabolism status. However, the understanding of their regulatory mechanism is still incomplete. In this research, I initially employed a simple and adapted method based on laser confocal scanning microscopy (LCSM) to observe size, morphology and quantity of starch granules within chloroplasts in Arabidopsis thaliana in vivo. This method facilitated a rapid and versatile analysis of starch granule parameters across numerous samples. Utilizing this approach, I compared starch granule number per chloroplast between mesophyll cells and guard cells in both wild type plants (Col-0) and several starch related mutants. The results revealed that the granule number is distinct between mesophyll cells and guard cells, even within the same genetic background, suggesting that guard cells operate a unique regulatory mechanism of starch granule number.
Subsequently, I redirected my attention toward examining starch morphology. Through microscopy analyses, I observed a gradual alteration in starch granule morphology in certain mutants during leaf aging. Specifically, in mutants such as sex1-8 and dpe2phs1ss4, there was a progressive alteration in starch granule morphology over time. Conversely, in Col-0 and ss4 mutant, these morphological alterations were not evident. This discovery suggests a new perspective to understand the development of starch morphology.
Further investigation revealed that mutants lacking either Disproportionating enzyme 2 (DPE2) or MALTOSE-EXCESS 1 (MEX1) exhibited gradual alterations in starch morphology with leaf aging. Notably, the most severe effects on starch morphology occurred in double mutants lacking either DPE2 or MEX1 in conjunction with a lack of starch synthase 4 (SS4). In these mutations, a transformation of the starch granule morphology from the typical discoid morphology to oval and eventually to a spherical shape.
To investigate the changes in the internal structure of starch during this alteration, I analyzed the chain length distribution (CLD) of the amylopectin of young, intermediate and old leaves of the mutants. Throughout starch granule development, I found an increased presence of short glucan chains within the granules, particularly evident in dpe2ss4 and mex1ss4 mutants, as well as their parental single mutants. Notably, the single mutant ss4 also showed an affected granule morphology, albeit not influenced by leaf aging..
The CLD pattern of the amylopectin reflects an integrative regulation involving several participants in starch synthesis, including starch synthases (SSs), starch branching/debranching enzymes (SBEs/DBEs). Therefore, I further detected the expression of related genes on transcription level and the enzymatic activity of their respective proteins. Results indicated altered gene expression of several regulators in these mutants, particularly demonstrating dramatic alterations in dpe2 and dpe2ss4 with leaf aging. These changes corresponded with the observed alterations in starch granule morphology.
Taken together, I have identified and characterized a progressive alteration in starch granule morphology primarily resulting from the deficiencies in DPE2 and MEX1. Furthermore, I have associated the CLD pattern with the granule morphogenesis, as well as the gene expression and enzymatic activity of proteins involved in starch synthesis. Unlike SS4, which is implicated in starch initiation, MEX1 and DPE2 are involved into starch degradation. MEX1 is located in chloroplast envelope and DPE2 is situated in the cytosol. Considering the locations and known functions of DPE2/MEX1 and SS4, I infer that there might be two pathways influencing starch morphology: an initiation-affected pathway via SS4 and a degradation-affected pathway via DPE2/MEX1.
Massive stars (Mini > 8 Msol) are the key feedback agents within galaxies, as they shape their surroundings via their powerful winds, ionizing radiation, and explosive supernovae. Most massive stars are born in binary systems, where interactions with their companions significantly alter their evolution and the feedback they deposit in their host galaxy. Understanding binary evolution, particularly in the low-metallicity environments as proxies for the Early Universe, is crucial for interpreting the rest-frame ultraviolet spectra observed in high-redshift galaxies by telescopes like Hubble and James Webb.
This thesis aims to tackle this challenge by investigating in detail massive binaries within the low-metallicity environment of the Small Magellanic Cloud galaxy. From ultraviolet and multi-epoch optical spectroscopic data, we uncovered post-interaction binaries. To comprehensively characterize these binary systems, their stellar winds, and orbital parameters, we use a multifaceted approach. The Potsdam Wolf-Rayet stellar atmosphere code is employed to obtain the stellar and wind parameters of the stars. Additionally, we perform consistent light and radial velocity fitting with the Physics of Eclipsing Binaries software, allowing for the independent determination of orbital parameters and component masses. Finally, we utilize these results to challenge the standard picture of stellar evolution and improve our understanding of low-metallicity stellar populations by calculating our binary evolution models with the Modules for Experiments in Stellar Astrophysics code.
We discovered the first four O-type post-interaction binaries in the SMC (Chapters 2, 5, and 6). Their primary stars have temperatures similar to other OB stars and reside far from the helium zero-age main sequence, challenging the traditional view of binary evolution. Our stellar evolution models suggest this may be due to enhanced mixing after core-hydrogen burning. Furthermore, we discovered the so-far most massive binary system undergoing mass transfer (Chapter 3), offering a unique opportunity to test mass-transfer efficiency in extreme conditions. Our binary evolution calculations revealed unexpected evolutionary pathways for accreting stars in binaries, potentially providing the missing link to understanding the observed Wolf-Rayet population within the SMC (Chapter 4). The results presented in this thesis unveiled the properties of massive binaries at low-metallicity which challenge the way the spectra of high-redshift galaxies are currently being analyzed as well as our understanding of massive-star feedback within galaxies.
Astrophysical shocks, driven by explosive events such as supernovae, efficiently accelerate charged particles to relativistic energies. The majority of these shocks occur in collisionless plasmas where the energy transfer is dominated by particle-wave interactions.Strong nonrelativistic shocks found in supernova remnants are plausible sites of galactic cosmic ray production, and the observed emission indicates the presence of nonthermal electrons. To participate in the primary mechanism of energy gain - Diffusive Shock Acceleration - electrons must have a highly suprathermal energy, implying a need for very efficient pre-acceleration. This poorly understood aspect of the shock acceleration theory is known as the electron injection problem. Studying electron-scale phenomena requires the use of fully kinetic particle-in-cell (PIC) simulations, which describe collisionless plasma from first principles.
Most published studies consider a homogenous upstream medium, but turbulence is ubiquitous in astrophysical environments and is typically driven at magnetohydrodynamic scales, cascading down to kinetic scales. For the first time, I investigate how preexisting turbulence affects electron acceleration at nonrelativistic shocks using the fully kinetic approach. To accomplish this, I developed a novel simulation framework that allows the study of shocks propagating in turbulent media. It involves simulating slabs of turbulent plasma separately, which are further continuously inserted into a shock simulation. This demands matching of the plasma slabs at the interface. A new procedure of matching electromagnetic fields and currents prevents numerical transients, and the plasma evolves self-consistently. The versatility of this framework has the potential to render simulations more consistent with turbulent systems in various astrophysical environments.
In this Thesis, I present the results of 2D3V PIC simulations of high-Mach-number nonrelativistic shocks with preexisting compressive turbulence in an electron-ion plasma. The chosen amplitudes of the density fluctuations ($\lesssim15\%$) concord with \textit{in situ} measurements in the heliosphere and the local interstellar medium. I explored how these fluctuations impact the dynamics of upstream electrons, the driving of the plasma instabilities, electron heating and acceleration. My results indicate that while the presence of the turbulence enhances variations in the upstream magnetic field, their levels remain too low to influence the behavior of electrons at perpendicular shocks significantly. However, the situation is different at oblique shocks. The external magnetic field inclined at an angle between $50^\circ \lesssim \theta_\text{Bn} \lesssim 75^\circ$ relative to the shock normal allows the escape of fast electrons toward the upstream region. An extended electron foreshock region is formed, where these particles drive various instabilities. Results of an oblique shock with $\theta_\text{Bn}=60^\circ$ propagating in preexisting compressive turbulence show that the foreshock becomes significantly shorter, and the shock-reflected electrons have higher temperatures. Furthermore, the energy spectrum of downstream electrons shows a well-pronounced nonthermal tail that follows a power law with an index up to -2.3.
The methods and results presented in this Thesis could serve as a starting point for more realistic modeling of interactions between shocks and turbulence in plasmas from first principles.
Condensation and crystallization are omnipresent phenomena in nature. The formation of droplets or crystals on a solid surface are familiar processes which, beyond their scientific interest, are required in many technological applications. In recent years, experimental techniques have been developed which allow patterning a substrate with surface domains of molecular thickness, surface area in the mesoscopic scale, and different wettabilities (i.e., different degrees of preference for a substance that is in contact with the substrate). The existence of new patterned surfaces has led to increased theoretical efforts to understand wetting phenomena in such systems.
In this thesis, we deal with some problems related to the equilibrium of phases (e.g., liquid-vapor coexistence) and the kinetics of phase separation in the presence of chemically patterned surfaces. Two different cases are considered: (i) patterned surfaces in contact with liquid and vapor, and (ii) patterned surfaces in contact with a crystalline phase. One of the problems that we have studied is the following: It is widely believed that if air containing water vapor is cooled to its dew point, droplets of water are immediately formed. Although common experience seems to support this view, it is not correct. It is only when air is cooled well below its dew point that the phase transition occurs immediately. A vapor cooled slightly below its dew point is in a metastable state, meaning that the liquid phase is more stable than the vapor, but the formation of droplets requires some time to occur, which can be very long.
It was first pointed out by J. W. Gibbs that the metastability of a vapor depends on the energy necessary to form a nucleus (a droplet of a critical size). Droplets smaller than the critical size will tend to disappear, while droplets larger than the critical size will tend to grow. This is consistent with an energy barrier that has its maximum at the critical size, as is the case for droplets formed directly in the vapor or in contact with a chemically uniform planar wall. Classical nucleation theory describes the time evolution of the condensation in terms of the random process of droplet growth through this energy barrier. This process is activated by thermal fluctuations, which eventually will form a droplet of the critical size.
We consider nucleation of droplets from a vapor on a substrate patterned with easily wettable (lyophilic) circular domains. Under certain conditions of pressure and temperature, the condensation of a droplet on a lyophilic circular domain proceeds through a barrier with two maxima (a double barrier). We have extended classical nucleation theory to account for the kinetics of nucleation through a double barrier, and applied this extension to nucleation on lyophilic circular domains.
Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.
Organizations are investing billions on innovation and agility initiatives to stay competitive in their increasingly uncertain business environments. Design Thinking, an innovation approach based on human-centered exploration, ideation and experimentation, has gained increasing popularity. The market for Design Thinking, including software products and general services, is projected to reach 2.500 million $ (US-Dollar) by 2028. A dispersed set of positive outcomes have been attributed to Design Thinking. However, there is no clear understanding of what exactly comprises the impact of Design Thinking and how it is created. To support a billion-dollar market, it is essential to understand the value Design Thinking is bringing to organizations not only to justify large investments, but to continuously improve the approach and its application.
Following a qualitative research approach combined with results from a systematic literature review, the results presented in this dissertation offer a structured understanding of Design Thinking impact. The results are structured along two main perspectives of impact: the individual and the organizational perspective. First, insights from qualitative data analysis demonstrate that measuring and assessing the impact of Design Thinking is currently one central challenge for Design Thinking practitioners in organizations. Second, the interview data revealed several effects Design Thinking has on individuals, demonstrating how Design Thinking can impact boundary management behaviors and enable employees to craft their jobs more actively.
Contributing to innovation management research, the work presented in this dissertation systematically explains the Design Thinking impact, allowing other researchers to both locate and integrate their work better. The results of this research advance the theoretical rigor of Design Thinking impact research, offering multiple theoretical underpinnings explaining the variety of Design Thinking impact. Furthermore, this dissertation contains three specific propositions on how Design Thinking creates an impact: Design Thinking creates an impact through integration, enablement, and engagement. Integration refers to how Design Thinking enables organizations through effectively combining things, such as for example fostering balance between exploitation and exploration activities. Through Engagement, Design Thinking impacts organizations involving users and other relevant stakeholders in their work. Moreover, Design Thinking creates impact through Enablement, making it possible for individuals to enact a specific behavior or experience certain states.
By synthesizing multiple theoretical streams into these three overarching themes, the results of this research can help bridge disciplinary boundaries, for example between business, psychology and design, and enhance future collaborative research. Practitioners benefit from the results as multiple desirable outcomes are detailed in this thesis, such as successful individual job crafting behaviors, which can be expected from practicing Design Thinking. This allows practitioners to enact more evidence-based decision-making concerning Design Thinking implementation. Overall, considering multiple levels of impact as well as a broad range of theoretical underpinnings are paramount to understanding and fostering Design Thinking impact.
Plate tectonic boundaries constitute the suture zones between tectonic plates. They are shaped by a variety of distinct and interrelated processes and play a key role in geohazards and georesource formation. Many of these processes have been previously studied, while many others remain unaddressed or undiscovered. In this work, the geodynamic numerical modeling software ASPECT is applied to shed light on further process interactions at continental plate boundaries. In contrast to natural data, geodynamic modeling has the advantage that processes can be directly quantified and that all parameters can be analyzed over the entire evolution of a structure. Furthermore, processes and interactions can be singled out from complex settings because the modeler has full control over all of the parameters involved. To account for the simplifying character of models in general, I have chosen to study generic geological settings with a focus on the processes and interactions rather than precisely reconstructing a specific region of the Earth.
In Chapter 2, 2D models of continental rifts with different crustal thicknesses between 20 and 50 km and extension velocities in the range of 0.5-10 mm/yr are used to obtain a speed limit for the thermal steady-state assumption, commonly employed to address the temperature fields of continental rifts worldwide. Because the tectonic deformation from ongoing rifting outpaces heat conduction, the temperature field is not in equilibrium, but is characterized by a transient, tectonically-induced heat flow signal. As a result, I find that isotherm depths of the geodynamic evolution models are shallower than a temperature distribution in equilibrium would suggest. This is particularly important for deep isotherms and narrow rifts. In narrow rifts, the magnitude of the transient temperature signal limits a well-founded applicability of the thermal steady-state assumption to extension velocities of 0.5-2 mm/yr. Estimation of the crustal temperature field affects conclusions on all temperature-dependent processes ranging from mineral assemblages to the feasible exploitation of a geothermal reservoir.
In Chapter 3, I model the interactions of different rheologies with the kinematics of folding and faulting using the example of fault-propagation folds in the Andean fold-and-thrust belt. The evolution of the velocity fields from geodynamic models are compared with those from trishear models of the same structure. While the latter use only geometric and kinematic constraints of the main fault, the geodynamic models capture viscous, plastic, and elastic deformation in the entire model domain. I find that both models work equally well for early, and thus relatively simple stages of folding and faulting, while results differ for more complex situations where off-fault deformation and secondary faulting are present. As fault-propagation folds can play an important role in the formation of reservoirs, knowledge of fluid pathways, for example via fractures and faults, is crucial for their characterization.
Chapter 4 deals with a bending transform fault and the interconnections between tectonics and surface processes. In particular, the tectonic evolution of the Dead Sea Fault is addressed where a releasing bend forms the Dead Sea pull-apart basin, while a restraining bend further to the North resulted in the formation of the Lebanese mountains. I ran 3D coupled geodynamic and surface evolution models that included both types of bends in a single setup. I tested various randomized initial strain distributions, showing that basin asymmetry is a consequence of strain localization. Furthermore, by varying the surface process efficiency, I find that the deposition of sediment in the pull-apart basin not only controls basin depth, but also results in a crustal flow component that increases uplift at the restraining bend.
Finally, in Chapter 5, I present the computational basis for adding further complexity to plate boundary models in ASPECT with the implementation of earthquake-like behavior using the rate-and-state friction framework. Despite earthquakes happening on a relatively small time scale, there are many interactions between the seismic cycle and the long time spans of other geodynamic processes. Amongst others, the crustal state of stress as well as the presence of fluids or changes in temperature may alter the frictional behavior of a fault segment. My work provides the basis for a realistic setup of involved structures and processes, which is therefore important to obtain a meaningful estimate for earthquake hazards.
While these findings improve our understanding of continental plate boundaries, further development of geodynamic software may help to reveal even more processes and interactions in the future.
Mantodea, commonly known as mantids, have captivated researchers owing to their enigmatic behavior and ecological significance. This order comprises a diverse array of predatory insects, boasting over 2,400 species globally and inhabiting a wide spectrum of ecosystems. In Iran, the mantid fauna displays remarkable diversity, yet numerous facets of this fauna remain poorly understood, with a significant dearth of systematic and ecological research. This substantial knowledge gap underscores the pressing need for a comprehensive study to advance our understanding of Mantodea in Iran and its neighboring regions.
The principal objective of this investigation was to delve into the ecology and phylogeny of Mantodea within these areas. To accomplish this, our research efforts concentrated on three distinct genera within Iranian Mantodea. These genera were selected due to their limited existing knowledge base and feasibility for in-depth study. Our comprehensive methodology encompassed a multifaceted approach, integrating morphological analysis, molecular techniques, and ecological observations.
Our research encompassed a comprehensive revision of the genus Holaptilon, resulting in the description of four previously unknown species. This extensive effort substantially advanced our understanding of the ecological roles played by Holaptilon and refined its systematic classification. Furthermore, our investigation into Nilomantis floweri expanded its known distribution range to include Iran. By conducting thorough biological assessments, genetic analyses, and ecological niche modeling, we obtained invaluable insights into distribution patterns and genetic diversity within this species. Additionally, our research provided a thorough comprehension of the life cycle, behaviors, and ecological niche modeling of Blepharopsis mendica, shedding new light on the distinctive characteristics of this mantid species. Moreover, we contributed essential knowledge about parasitoids that infect mantid ootheca, laying the foundation for future studies aimed at uncovering the intricate mechanisms governing ecological and evolutionary interactions between parasitoids and Mantodea.
Virtual Reality (VR) leads to the highest level of immersion if presented using a 1:1 mapping of virtual space to physical space—also known as real walking. The advent of inexpensive consumer virtual reality (VR) headsets, all capable of running inside-out position tracking, has brought VR to the home. However, many VR applications do not feature full real walking, but instead, feature a less immersive space-saving technique known as instant teleportation. Given that only 0.3% of home users run their VR experiences in spaces more than 4m2, the most likely explanation is the lack of the physical space required for meaningful use of real walking. In this thesis, we investigate how to overcome this hurdle. We demonstrate how to run 1:1-mapped VR experiences in small physical spaces and we explore the trade-off between space and immersion. (1) We start with a space limit of 15cm. We present DualPanto, a device that allows (blind) VR users to experience the virtual world from a 1:1 mapped bird’s eye perspective—by leveraging haptics. (2) We then relax our space constraints to 50cm, which is what seated users (e.g., on an airplane or train ride) have at their disposal. We leverage the space to represent a standing user in 1:1 mapping, while only compressing the user’s arm movement. We demonstrate our 4 prototype VirtualArms at the example of VR experiences limited to arm movement, such as boxing. (3) Finally, we relax our space constraints further to 3m2 of walkable space, which is what 75% of home users have access to. As well- established in the literature, we implement real walking with the help of portals, also known as “impossible spaces”. While impossible spaces on such dramatic space constraints tend to degenerate into incomprehensible mazes (as demonstrated, for example, by “TraVRsal”), we propose plausibleSpaces: presenting meaningful virtual worlds by adapting various visual elements to impossible spaces. Our techniques push the boundary of spatially meaningful VR interaction in various small spaces. We see further future challenges for new design approaches to immersive VR experiences for the smallest physical spaces in our daily life.
Phobic cosmopolitanism
(2024)
The inclusion of exotic germplasm serves as a crucial means to enhance allelic and
consequently phenotypic diversity in inbred crop species. Such species have experienced a reduction in diversity due to artificial selection focused on a limited set of traits. The natural biodiversity within ecosystems presents an opportunity to explore various traits influencing plant survival, reproductive fitness and yield potential. In agricultural research, the study of wild species closely related to cultivated plants serves as a means to comprehend the genetic foundations of past domestication events and the polymorphisms essential for future breeding efforts to develop superior varieties. In order to examine the metabolic composition, pinpoint quantitative trait loci (QTL) and facilitate their resolution an extensive large-scale analysis of metabolic QTL (mQTL) was conducted on tomato backcross inbred lines (BILs) derived from a cross between the wild species S. pennellii (5240) incorporated into the background of S. lycopersicum cv. LEA determinate inbred which can be grown in open fields and cv. TOP indeterminate which can be grown in greenhouse conditions. A large number of mQTL associated with primary secondary and lipid metabolism in fruit were identified across the two BIL populations. Epistasis, the interactions between genes at different loci, has been an interest in molecular and quantitative genetics for many decades. The study of epistasis requires the analysis of very large populations with multiple independent genotypes that carry specific genomic regions. In order to understand the genetic basis of tomato fruit metabolism, I extended the work to investigate epistatic interactions of the genomic regions. In addition, two candidate genes were identified through quantitative trait loci underlying fruit-specific sucrose and jasmonic acid derivatives. Finally, in this study, I assessed the genetic framework of fruit metabolic traits with a high level of detail, utilizing the newly created Solanum pennellii (5240) backcrossed introgression lines (n=3000). This investigation resulted in the discovery of promising candidate loci associated with significant fruit quality traits, including those to the abundance of glutamic acid and aspartic acid crucial elements contributing to the development of acidity and flavors.
Floods continue to be the leading cause of economic damages and fatalities among natural disasters worldwide. As future climate and exposure changes are projected to intensify these damages, the need for more accurate and scalable flood risk models is rising. Over the past decade, macro-scale flood risk models have evolved from initial proof-of-concepts to indispensable tools for decision-making at global-, nationaland, increasingly, the local-level. This progress has been propelled by the advent of high-performance computing and the availability of global, space-based datasets. However, despite such advancements, these models are rarely validated and consistently fall short of the accuracy achieved by high-resolution local models. While capabilities have improved, significant gaps persist in understanding the behaviours of such macro-scale models, particularly their tendency to overestimate risk. This dissertation aims to address such gaps by examining the scale transfers inherent in the construction and application of coarse macroscale models. To achieve this, four studies are presented that, collectively, address exposure, hazard, and vulnerability components of risk affected by upscaling or downscaling.
The first study focuses on a type of downscaling where coarse flood hazard inundation grids are enhanced to a finer resolution. While such inundation downscaling has been employed in numerous global model chains, ours is the first study to focus specifically on this component, providing an evaluation of the state of the art and a novel algorithm. Findings demonstrate that our novel algorithm is eight times faster than existing methods, offers a slight improvement in accuracy, and generates more physically coherent flood maps in hydraulically challenging regions. When applied to a case study, the algorithm generated a 4m resolution inundation map from 30m hydrodynamic model outputs in 33 s, a 60-fold improvement in runtime with a 25% increase in RMSE compared with direct hydrodynamic modelling. All evaluated downscaling algorithms yielded better accuracy than the coarse hydrodynamic model when compared to observations, demonstrating similar limits of coarse hydrodynamic models reported by others. The substitution of downscaling into flood risk model chains, in place of high-resolution modelling, can drastically improve the lead time of impactbased forecasts and the efficiency of hazard map production. With downscaling, local regions could obtain high resolution local inundation maps by post-processing a global model without the need for expensive modelling or expertise.
The second study focuses on hazard aggregation and its implications for exposure, investigating implicit aggregations commonly used to intersect hazard grids with coarse exposure models. This research introduces a novel spatial classification framework to understand the effects of rescaling flood hazard grids to a coarser resolution. The study derives closed-form analytical solutions for the location and direction of bias from flood grid aggregation, showing that bias will always be present in regions near the edge of inundation. For example, inundation area will be positively biased when water depth grids are aggregated, while volume will be negatively biased when water elevation grids are aggregated. Extending the analysis to effects of hazard aggregation on building exposure, this study shows that exposure in regions at the edge of inundation are an order of magnitude more sensitive to aggregation errors than hazard alone. Among the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. The study provides the first mathematical proof and generalizeable treatment of flood hazard grid aggregation, demonstrating important mechanisms to help flood risk modellers understand and control model behaviour.
The final two studies focus on the aggregation of vulnerability models or flood damage functions, investigating the practice of applying per-asset functions to aggregate exposure models. Both studies extend Jensen’s inequality, a well-known 1906 mathematical proof, to demonstrate how the aggregation of flood damage functions leads to bias. Applying Jensen’s proof in this new context, results show that typically concave flood damage functions will introduce a positive bias (overestimation) when aggregated. This behaviour was further investigated with a simulation experiment including 2 million buildings in Germany, four global flood hazard simulations and three aggregation scenarios. The results show that positive aggregation bias is not distributed evenly in space, meaning some regions identified as “hot spots of risk” in assessments may in fact just be hot spots of aggregation bias. This study provides the first application of Jensen’s inequality to explain the overestimates reported elsewhere and advice for modellers to minimize such artifacts.
In total, this dissertation investigates the complex ways aggregation and disaggregation influence the behaviour of risk models, focusing on the scale-transfers underpinning macro-scale flood risk assessments. Extending a key finding of the flood hazard literature to the broader context of flood risk, this dissertation concludes that all else equal, coarse models overestimate risk. This dissertation goes beyond previous studies by providing mathematical proofs for how and where such bias emerges in aggregation routines, offering a mechanistic explanation for coarse model overestimates. It shows that this bias is spatially heterogeneous, necessitating a deep understanding of how rescaling may bias models to effectively reduce or communicate uncertainties. Further, the dissertation offers specific recommendations to help modellers minimize scale transfers in problematic regions. In conclusion, I argue that such aggregation errors are epistemic, stemming from choices in model structure, and therefore hold greater potential and impetus for study and mitigation. This deeper understanding of uncertainties is essential for improving macro-scale flood risk models and their effectiveness in equitable, holistic, and sustainable flood management.
The global drylands cover nearly half of the terrestrial surface and are home to more than two billion people. In many drylands, ongoing land-use change transforms near-natural savanna vegetation to agricultural land to increase food production. In Southern Africa, these heterogenous savanna ecosystems are also recognized as habitats of many protected animal species, such as elephant, lion and large herds of diverse herbivores, which are of great value for the tourism industry. Here, subsistence farmers and livestock herder communities often live in close proximity to nature conservation areas. Although these land-use transformations are different regarding the future they aspire to, both processes, nature conservation with large herbivores and agricultural intensification, have in common, that they change the vegetation structure of savanna ecosystems, usually leading to destruction of trees, shrubs and the woody biomass they consist of.
Such changes in woody vegetation cover and biomass are often regarded as forms of land degradation and forest loss. Global forest conservation approaches and international programs aim to stop degradation processes, also to conserve the carbon bound within wood from volatilization into earth’s atmosphere. In search for mitigation options against global climate change savannas are increasingly discussed as potential carbon sinks. Savannas, however, are not forests, in that they are naturally shaped by and adapted to disturbances, such as wildfires and herbivory. Unlike in forests, disturbances are necessary for stable, functioning savanna ecosystems and prevent these ecosystems from forming closed forest stands. Their consequently lower levels of carbon storage in woody vegetation have long been the reason for savannas to be overlooked as a potential carbon sink but recently the question was raised if carbon sequestration programs (such as REDD+) could also be applied to savanna ecosystems. However, heterogenous vegetation structure and chronic disturbances hamper the quantification of carbon stocks in savannas, and current procedures of carbon storage estimation entail high uncertainties due to methodological obstacles. It is therefore challenging to assess how future land-use changes such as agricultural intensification or increasing wildlife densities will impact the carbon storage balance of African drylands.
In this thesis, I address the research gap of accurately quantifying carbon storage in vegetation and soils of disturbance-prone savanna ecosystems. I further analyse relevant drivers for both ecosystem compartments and their implications for future carbon storage under land-use change. Moreover, I show that in savannas different carbon storage pools vary in their persistence to disturbance, causing carbon bound in shrub vegetation to be most likely to experience severe losses under land-use change while soil organic carbon stored in subsoils is least likely to be impacted by land-use change in the future.
I start with summarizing conventional approaches to carbon storage assessment and where and for which reasons they fail to accurately estimated savanna ecosystem carbon storage. Furthermore, I outline which future-making processes drive land-use change in Southern Africa along two pathways of land-use transformation and how these are likely to influence carbon storage. In the following chapters, I propose a new method of carbon storage estimation which is adapted to the specific conditions of disturbance-prone ecosystems and demonstrate the advantages of this approach in relation to existing forestry methods. Specifically, I highlight sources for previous over- and underestimation of savanna carbon stocks which the proposed methodology resolves. In the following chapters, I apply the new method to analyse impacts of land-use change on carbon storage in woody vegetation in conjunction with the soil compartment. With this interdisciplinary approach, I can demonstrate that indeed both, agricultural intensification and nature conservation with large herbivores, reduce woody carbon storage above- and belowground, but partly sequesters this carbon into the soil organic carbon stock. I then quantify whole-ecosystem carbon storage in different ecosystem compartments (above- and belowground woody carbon in shrubs and trees, respectively, as well as topsoil and subsoil organic carbon) of two savanna vegetation types (scrub savanna and savanna woodland). Moreover, in a space-for-time substitution I analyse how land-use changes impact carbon storage in each compartment and in the whole ecosystem. Carbon storage compartments are found to differ in their persistence to land-use change with carbon bound in shrub biomass being least persistent to future changes and subsoil organic carbon being most stable under changing land-use. I then explore which individual land-use change effects act as drivers of carbon storage through Generalized Additive Models (GAMs) and uncover non-linear effects, especially of elephant browsing, with implications for future carbon storage. In the last chapter, I discuss my findings in the larger context of this thesis and discuss relevant implications for land-use change and future-making decisions in rural Africa.
Laser induced switching offers an attractive possibility to manipulate small magnetic domains for prospective memory and logic devices on ultrashort time scales. Moreover, optical control of magnetization without high applied magnetic fields allows manipulation of magnetic domains individually and locally, without expensive heat dissipation. One of the major challenges for developing novel optically controlled magnetic memory and logic devices is reliable formation and annihilation of non-volatile magnetic domains that can serve as memory bits in ambient conditions. Magnetic skyrmions, topologically nontrivial spin textures, have been studied intensively since their discovery due to their stability and scalability in potential spintronic devices. However, skyrmion formation and, especially, annihilation processes are still not completely understood and further investigation on such mechanisms are needed. The aim of this thesis is to contribute to better understanding of the physical processes behind the optical control of magnetism in thin films, with the goal of optimizing material parameters and methods for their potential use in next generation memory and logic devices.
First part of the thesis is dedicated to investigation of all-optical helicity-dependent switching (AO-HDS) as a method for magnetization manipulation. AO-HDS in Co/Pt multilayer and CoFeB alloys with and without the presence of Dzyaloshinskii-Moriya interaction (DMI), which is a type of exchange interaction, have been investigated by magnetic imaging using photo-emission electron microscopy (PEEM) in combination with X-ray magnetic circular dichroism (XMCD). The results show that in a narrow range of the laser fluence, circularly polarized laser light induces a drag on domain walls. This enables a local deterministic transformation of the magnetic domain pattern from stripes to bubbles in out-of-plane magnetized Co/Pt multilayers, only controlled by the helicity of ultrashort laser pulses. The temperature and characteristic fields at which the stripe-bubble transformation occurs has been calculated using theory for isolated magnetic bubbles, using as parameters experimentally determined average size of stripe domains and the magnetic layer thickness.
The second part of the work aims at purely optical formation and annihilation of magnetic skyrmions by a single laser pulse. The presence of a skyrmion phase in the investigated CoFeB alloys was first confirmed using a Kerr microscope. Then the helicity-dependent skyrmion manipulation was studied using AO-HDS at different laser fluences. It was found that formation or annihilation individual skyrmions using AO-HDS is possible, but not always reliable, as fluctuations in the laser fluence or position can easily overwrite the helicity-dependent effect of AO-HDS. However, the experimental results and magnetic simulations showed that the threshold values for the laser fluence for the formation and annihilation of skyrmions are different. A higher fluence is required for skyrmion formation, and existing skyrmions can be annihilated by pulses with a slightly lower fluence. This provides a further option for controlling formation and annihilation of skyrmions using the laser fluence. Micromagnetic simulations provide additional insights into the formation and annihilation mechanism.
The ability to manipulate the magnetic state of individual skyrmions is of fundamental importance for magnetic data storage technologies. Our results show for the first time that the optical formation and annihilation of skyrmions is possible without changing the external field. These results enable further investigations to optimise the magnetic layer to maximise the energy gap between the formation and annihilation barrier. As a result, unwanted switching due to small laser fluctuations can be avoided and fully deterministic optical switching can be achieved.
Wars and the world
(2024)
This book offers a descriptive analysis of the Soviet/Russian wars in Afghanistan, Chechnya, and Georgia, as well as an in-depth exploration of the ways in which these wars are framed in the collective consciousness created by global popular culture. Russian and Western modalities of remembrance have been, and remain, engaged in a world war that takes place (not exclusively, but intensively) on the level of popular culture. The action/reaction dynamic, confrontational narratives and othering between the two "camps" never ceased. The Cold War, in many ways and contrary to the views of many others who hoped for the end of history, never really ended.
The trace elements copper, iron, manganese, selenium and zinc are essential micronutrients involved in various cellular processes, all with different responsibilities. Based on that importance, their concentrations are tightly regulated in mammalian organisms. The maintenance of those levels is termed trace element homeostasis and mediated by a combination of processes regulating absorption, cellular and systemic transport mechanisms, storage and effector proteins as well as excretion. Due to their chemical properties, some functions of trace elements overlap, as seen in antioxidative defence, for example, comprising an expansive spectrum of antioxidative proteins and molecules. Simultaneously, the same is true for regulatory mechanisms, causing trace elements to influence each other’s homeostases. To mimic physiological conditions, trace elements should therefore not be evaluated separately but considered in parallel. While many of these homeostatic mechanisms are well-studied, for some elements new pathways are still discovered. Additionally, the connections between dietary trace element intake, trace element status and health are not fully unraveled, yet. With current demographic developments, also the influence of ageing as well as of certain pathological conditions is of increasing interest. Here, the TraceAge research unit was initiated, aiming to elucidate the homeostases of and interactions between essential trace elements in healthy and diseased elderly. While human cohort studies can offer insights into trace element profiles, also in vivo model organisms are used to identify underlying molecular mechanisms. This is achieved by a set of feeding studies including mice of various age groups receiving diets of reduced trace element content. To account for cognitive deterioration observed with ageing, neurodegenerative diseases, as well as genetic mutations triggering imbalances in cerebral trace element concentrations, one TraceAge work package focuses on trace elements in the murine brain, specifically the cerebellum. In that context, concentrations of the five essential trace elements of interest, copper, iron, manganese, selenium and zinc, were quantified via inductively coupled plasma-tandem mass spectrometry, revealing differences in priority of trace element homeostases between brain and liver. Upon moderate reduction of dietary trace element supply, cerebellar concentrations of copper and manganese deviated from those in adequately supplied animals. By further reduction of dietary trace element contents, also concentrations of cerebellar iron and selenium were affected, but not as strong as observed in liver tissue. In contrast, zinc concentrations remained stable. Investigation of aged mice revealed cerebellar accumulation of copper and iron, possibly contributing to oxidative stress on account of their redox properties. Oxidative stress affects a multitude of cellular components and processes, among them, next to proteins and lipids, also the DNA. Direct insults impairing its integrity are of relevance here, but also indirect effects, mediated by the machinery ensuring genomic stability and its functionality. The system includes the DNA damage response, comprising detection of endogenous and exogenous DNA lesions, decision on subsequent cell fate and enabling DNA repair, which presents another pillar of genomic stability maintenance. Also in proteins of this machinery, trace elements act as cofactors, shaping the hypothesis of impaired genomic stability maintenance under conditions of disturbed trace element homeostasis. To investigate this hypothesis, a variety of approaches was used, applying OECD guidelines Organisation for Economic Co-operation and Development, adapting existing protocols for use in cerebellum tissue and establishing new methods. In order to assess the impact of age and dietary trace element depletion on selected endpoints estimating genomic instability, DNA damage and DNA repair were investigated. DNA damage analysis, in particular of DNA strand breaks and oxidatively modified DNA bases, revealed stable physiological levels which were neither affected by age nor trace element supply. To examine whether this is a result of increased repair rates, two steps characteristic for base excision repair, namely DNA incision and ligation activity, were studied. DNA glycosylases and DNA ligases were not reduced in their activity by age or trace element depletion, either. Also on the level of gene expression, major proteins involved in genomic stability maintenance were analysed, mirroring results obtained from protein studies. To conclude, the present work describes homeostatic regulation of trace elements in the brain, which, in absence of genetic mutations, is able to retain physiological levels even under conditions of reduced trace element supply to a certain extent. This is reflected by functionality of genomic stability maintenance mechanisms, illuminating the prioritization of the brain as vital organ.
Hardy inequalities on graphs
(2024)
The dissertation deals with a central inequality of non-linear potential theory, the Hardy inequality. It states that the non-linear energy functional can be estimated from below by a pth power of a weighted p-norm, p>1. The energy functional consists of a divergence part and an arbitrary potential part. Locally summable infinite graphs were chosen as the underlying space. Previous publications on Hardy inequalities on graphs have mainly considered the special case p=2, or locally finite graphs without a potential part.
Two fundamental questions now arise quite naturally: For which graphs is there a Hardy inequality at all? And, if it exists, is there a way to obtain an optimal weight? Answers to these questions are given in Theorem 10.1 and Theorem 12.1. Theorem 10.1 gives a number of characterizations; among others, there is a Hardy inequality on a graph if and only if there is a Green's function. Theorem 12.1 gives an explicit formula to compute optimal Hardy weights for locally finite graphs under some additional technical assumptions. Examples show that Green's functions are good candidates to be used in the formula.
Emphasis is also placed on illustrating the theory with examples. The focus is on natural numbers, Euclidean lattices, trees and star graphs. Finally, a non-linear version of the Heisenberg uncertainty principle and a Rellich inequality are derived from the Hardy inequality.
Resolving the evolutionary history of two hippotragin antelopes using archival and ancient DNA
(2024)
African antelopes are iconic but surprisingly understudied in terms of their genetics, especially when it comes to their evolutionary history and genetic diversity. The age of genomics provides an opportunity to investigate evolution using whole nuclear genomes. Decreasing sequencing costs enable the recovery of multiple loci per genome, giving more power to single specimen analyses and providing higher resolution insights into species and populations that can help guide conservation efforts. This age of genomics has only recently begun for African antelopes. Many African bovids have a declining population trend and hence, are often endangered. Consequently, contemporary samples from the wild are often hard to collect. In these cases, ex situ samples from contemporary captive populations or in the form of archival or ancient DNA (aDNA) from historical museum or archaeological/paleontological specimens present a great research opportunity with the latter two even offering a window to information about the past. However, the recovery of aDNA is still considered challenging from regions with prevailing climatic conditions that are deemed adverse for DNA preservation like the African continent. This raises the question if DNA recovery from fossils as old as the early Holocene from these regions is possible.
This thesis focuses on investigating the evolutionary history and genetic diversity of two species: the addax (Addax nasomaculatus) and the blue antelope (Hippotragus leucophaeus). The addax is critically endangered and might even already be extinct in the wild, while the blue antelope became extinct ~1800 AD, becoming the first extinct large African mammal species in historical times. Together, the addax and the blue antelope can inform us about current and past extinction events and the knowledge gained can help guide conservation efforts of threatened species. The three studies used ex situ samples and present the first nuclear whole genome data for both species. The addax study used historical museum specimens and a contemporary sample from a captive population. The two studies on the blue antelope used mainly historical museum specimens but also fossils, and resulted in the recovery of the oldest paleogenome from Africa at that time.
The aim of the first study was to assess the genetic diversity and the evolutionary history of the addax. It found that the historical wild addax population showed only limited phylogeographic structuring, indicating that the addax was a highly mobile and panmictic population and suggesting that the current European captive population might be missing the majority of the historical mitochondrial diversity. It also found the nuclear and mitochondrial diversity in the addax to be rather low compared to other wild ungulate species. Suggestions on how to best save the remaining genetic diversity are presented. The European zoo population was shown to exhibit no or only minor levels of inbreeding, indicating good prospects for the restoration of the species in the wild. The trajectory of the addax’s effective population size indicated a major bottleneck in the late Pleistocene and a low effective population size well before recent human impact led to the species being critically endangered today.
The second study set out to investigate the identities of historical blue antelope specimens using aDNA techniques. Results showed that six out of ten investigated specimens were misidentified, demonstrating the blue antelope to be one of the scarcest mammal species in historical natural history collections, with almost no bone reference material. The preliminary analysis of the mitochondrial genomes suggested a low diversity and hence low population size at the time of the European colonization of southern Africa.
Study three presents the results of the analyses of two blue antelope nuclear genomes, one ~200 years old and another dating to the early Holocene, 9,800–9,300 cal years BP. A fossil-calibrated phylogeny dated the divergence time of the three historically extant Hippotragus species to ~2.86 Ma and demonstrated the blue and the sable antelope (H. niger) to be sister species. In addition, ancient gene flow from the roan (H. equinus) into the blue antelope was detected. A comparison with the roan and the sable antelope indicated that the blue antelope had a much lower nuclear diversity, suggesting a low population size since at least the early Holocene. This concurs with findings from the fossil record that show a considerable decline in abundance after the Pleistocene–Holocene transition. Moreover, it suggests that the blue antelope persisted throughout the Holocene regardless of a low population size, indicating that human impact in the colonial era was a major factor in the blue antelope’s extinction.
This thesis uses aDNA analyses to provide deeper insights into the evolutionary history and genetic diversity of the addax and the blue antelope. Human impact likely was the main driver of extinction in the blue antelope, and is likely the main factor threatening the addax today. This thesis demonstrates the value of ex situ samples for science and conservation, and suggests to include genetic data for conservation assessments of species. It further demonstrates the beneficial use of aDNA for the taxonomic identification of historically important specimens in natural history collections. Finally, the successful retrieval of a paleogenome from the early Holocene of Africa using shotgun sequencing shows that DNA retrieval from samples of that age is possible from regions generally deemed unfavorable for DNA preservation, opening up new research opportunities. All three studies enhance our knowledge of African antelopes, contributing to the general understanding of African large mammal evolution and to the conservation of these and similarly threatened species.
Background: Societies worldwide have become more diverse yet continue to be inequitable. Understanding how youth growing up in these societies are socialized and consequently develop racial knowledge has important implications not only for their well-being but also for building more just societies. Importantly, there is a lack of research on these topics in Germany and Europe in general.
Aim and Method: The overarching aim of the dissertation is to investigate 1) where and how ethnic-racial socialization (ERS) happens in inequitable societies and 2) how it relates to youth’s development of racial knowledge, which comprises racial beliefs (e.g., prejudice, attitudes), behaviors (e.g., actions preserving or disrupting inequities), and identities (e.g., inclusive, cultural). Guided by developmental, cultural, and ecological theories of socialization and development, I first explored how family, as a crucial socialization context, contributes to the preservation or disruption of racism and xenophobia in inequitable societies through its influence on children’s racial beliefs and behaviors. I conducted a literature review and developed a conceptual model bridging research on ethnic-racial socialization and intergroup relations (Study 1). After documenting the lack of research on socialization and development of racial knowledge within and beyond family contexts outside of the U.S., I conducted a qualitative study to explore ERS in Germany through the lens of racially marginalized youth (Study 2). Then, I conducted two quantitative studies to explore the separate and interacting relations of multiple (i.e., family, school) socialization contexts for the development of racial beliefs and behaviors (Study 3), and identities (Studies 3, 4) in Germany. Participants of Study 2 were 26 young adults (aged between 19 and 32) of Turkish, Kurdish, East, and Southeast Asian heritage living across different cities in Germany. Study 3 was conducted with 503 eighth graders of immigrant and non-immigrant descent (Mage = 13.67) in Berlin, Study 4 included 311 early to mid-adolescents of immigrant descent (Mage= 13.85) in North Rhine-Westphalia with diverse cultural backgrounds.
Results and Conclusion: The findings revealed that privileged or marginalized positions of families in relation to their ethnic-racial and religious background in society entail differential experiences and thus are an important determining factor for the content/process of socialization and development of youth’s racial knowledge. Until recently, ERS research mostly focused on investigating how racially marginalized families have been the sources of support for their children in resisting racism and how racially privileged families contribute to transmission of information upholding racism (Study 1). ERS for racially marginalized youth in Germany centered heritage culture, discrimination, and resistance strategies to racism, yet resistance strategies transmitted to youth mostly help to survive racism (e.g., working hard) by upholding it instead of liberating themselves from racism by disrupting it (e.g., self-advocacy, Study 2). Furthermore, when families and schools foster heritage and intercultural learning, both contexts may separately promote stronger identification with heritage culture and German identities, and more prosocial intentions towards disadvantaged groups (i.e., refugees) among youth (Studies 3, 4). However, equal treatment in the school context led to mixed results: equal treatment was either unrelated to inclusive identity, or positively related to German and negatively related to heritage culture identities (Studies 3, 4). Additionally, youth receiving messages highlighting strained and preferential intergroup relations at home while attending schools promoting assimilation may develop a stronger heritage culture identity (Study 4). In conclusion, ERS happened across various social contexts (i.e., family, community centers, school, neighborhood, peer). ERS promoting heritage and intercultural learning, at least in one social context (family or school), might foster youth’s racial knowledge manifesting in stronger belonging to multiple cultures and in prosocial intentions toward disadvantaged groups. However, there is a need for ERS targeting increasing awareness of discrimination across social contexts of youth and teaching youth resistance strategies for liberation from racism.
Ada (Fishman) Maimon
(2023)
During the last decades, therapeutical proteins have risen to great significance in the pharmaceutical industry. As non-human proteins that are introduced into the human body cause a distinct immune system reaction that triggers their rapid clearance, most newly approved protein pharmaceuticals are shielded by modification with synthetic polymers to significantly improve their blood circulation time. All such clinically approved protein-polymer conjugates contain polyethylene glycol (PEG) and its conjugation is denoted as PEGylation. However, many patients develop anti-PEG antibodies which cause a rapid clearance of PEGylated molecules upon repeated administration. Therefore, the search for alternative polymers that can replace PEG in therapeutic applications has become important. In addition, although the blood circulation time is significantly prolonged, the therapeutic activity of some conjugates is decreased compared to the unmodified protein. The reason is that these conjugates are formed by the traditional conjugation method that addresses the protein's lysine side chains. As proteins have many solvent exposed lysines, this results in a somewhat uncontrolled attachment of polymer chains, leading to a mixture of regioisomers, with some of them eventually affecting the therapeutic performance.
This thesis investigates a novel method for ligating macromolecules in a site-specific manner, using enzymatic catalysis. Sortase A is used as the enzyme: It is a well-studied transpeptidase which is able to catalyze the intermolecular ligation of two peptides. This process is commonly referred to as sortase-mediated ligation (SML). SML constitutes an equilibrium reaction, which limits product yield. Two previously reported methods to overcome this major limitation were tested with polymers without using an excessive amount of one reactant.
Specific C- or N-terminal peptide sequences (recognition sequence and nucleophile) as part of the protein are required for SML. The complementary peptide was located at the polymer chain end. Grafting-to was used to avoid damaging the protein during polymerization. To be able to investigate all possible combinations (protein-recognition sequence and nucleophile-protein as well as polymer-recognition sequence and nucleophile-polymer) all necessary building blocks were synthesized. Polymerization via reversible deactivation radical polymerization (RDRP) was used to achieve a narrow molecular weight distribution of the polymers, which is required for therapeutic use.
The synthesis of the polymeric building blocks was started by synthesizing the peptide via automated solid-phase peptide synthesis (SPPS) to avoid post-polymerization attachment and to enable easy adaptation of changes in the peptide sequence. To account for the different functionalities (free N- or C-terminus) required for SML, different linker molecules between resin and peptide were used.
To facilitate purification, the chain transfer agent (CTA) for reversible addition-fragmentation chain-transfer (RAFT) polymerization was coupled to the resin-immobilized recognition sequence peptide. The acrylamide and acrylate-based monomers used in this thesis were chosen for their potential to replace PEG.
Following that, surface-initiated (SI) ATRP and RAFT polymerization were attempted, but failed. As a result, the newly developed method of xanthate-supported photo-iniferter (XPI) RAFT polymerization in solution was used successfully to obtain a library of various peptide-polymer conjugates with different chain lengths and narrow molar mass distributions.
After peptide side chain deprotection, these constructs were used first to ligate two polymers via SML, which was successful but revealed a limit in polymer chain length (max. 100 repeat units). When utilizing equimolar amounts of reactants, the use of Ni2+ ions in combination with a histidine after the recognition sequence to remove the cleaved peptide from the equilibrium maximized product formation with conversions of up to 70 %.
Finally, a model protein and a nanobody with promising properties for therapeutical use were biotechnologically modified to contain the peptide sequences required for SML. Using the model protein for C- or N-terminal SML with various polymers did not result in protein-polymer conjugates. The reason is most likely the lack of accessibility of the protein termini to the enzyme. Using the nanobody for C-terminal SML, on the other hand, was successful. However, a similar polymer chain length limit was observed as in polymer-polymer SML. Furthermore, in case of the synthesis of protein-polymer conjugates, it was more effective to shift the SML equilibrium by using an excess of polymer than by employing the Ni2+ ion strategy.
Overall, the experimental data from this work provides a good foundation for future research in this promising field; however, more research is required to fully understand the potential and limitations of using SML for protein-polymer synthesis. In future, the method explored in this dissertation could prove to be a very versatile pathway to obtain therapeutic protein-polymer conjugates that exhibit high activities and long blood circulation times.
This thesis explores word order variability in verb-final languages. Verb-final languages have a reputation for a high amount of word order variability. However, that reputation amounts to an urban myth due to a lack of systematic investigation. This thesis provides such a systematic investigation by presenting original data from several verb-final languages with a focus on four Uralic ones: Estonian, Udmurt, Meadow Mari, and South Sámi. As with every urban myth, there is a kernel of truth in that many unrelated verb-final languages share a particular kind of word order variability, A-scrambling, in which the fronted elements do not receive a special information-structural role, such as topic or contrastive focus. That word order variability goes hand in hand with placing focussed phrases further to the right in the position directly in front of the verb. Variations on this pattern are exemplified by Uyghur, Standard Dargwa, Eastern Armenian, and three of the Uralic languages, Estonian, Udmurt, and Meadow Mari. So far for the kernel of truth, but the fourth Uralic language, South Sámi, is comparably rigid and does not feature this particular kind of word order variability. Further such comparably rigid, non-scrambling verb-final languages are Dutch, Afrikaans, Amharic, and Korean. In contrast to scrambling languages, non-scrambling languages feature obligatory subject movement, causing word order rigidity next to other typical EPP effects.
The EPP is a defining feature of South Sámi clause structure in general. South Sámi exhibits a one-of-a-kind alternation between SOV and SAuxOV order that is captured by the assumption of the EPP and obligatory movement of auxiliaries but not lexical verbs. Other languages that allow for SAuxOV order either lack an alternation because the auxiliary is obligatorily present (Macro-Sudan SAuxOVX languages), or feature an alternation between SVO and SAuxOV (Kru languages; V2 with underlying OV as a fringe case). In the SVO–SAuxOV languages, both auxiliaries and lexical verbs move. Hence, South Sámi shows that the textbook difference between the VO languages English and French, whether verb movement is restricted to auxiliaries, also extends to OV languages. SAuxOV languages are an outlier among OV languages in general but are united by the presence of the EPP.
Word order variability is not restricted to the preverbal field in verb-final languages, as most of them feature postverbal elements (PVE). PVE challenge the notion of verb-finality in a language. Strictly verb-final languages without any clause-internal PVE are rare. This thesis charts the first structural and descriptive typology of PVE. Verb-final languages vary in the categories they allow as PVE. Allowing for non-oblique PVE is a pivotal threshold: when non-oblique PVE are allowed, PVE can be used for information-structural effects. Many areally and genetically unrelated languages only allow for given PVE but differ in whether the PVE are contrastive. In those languages, verb-finality is not at stake since verb-medial orders are marked. In contrast, the Uralic languages Estonian and Udmurt allow for any PVE, including information focus. Verb-medial orders can be used in the same contexts as verb-final orders without semantic and pragmatic differences. As such, verb placement is subject to actual free variation. The underlying verb-finality of Estonian and Udmurt can only be inferred from a range of diagnostics indicating optional verb movement in both languages. In general, it is not possible to account for PVE with a uniform analysis: rightwards merge, leftward verb movement, and rightwards phrasal movement are required to capture the cross- and intralinguistic variation.
Knowing that a language is verb-final does not allow one to draw conclusions about word order variability in that language. There are patterns of homogeneity, such as the word order variability driven by directly preverbal focus and the givenness of postverbal elements, but those are not brought about by verb-finality alone. Preverbal word order variability is restricted by the more abstract property of obligatory subject movement, whereas the determinant of postverbal word order variability has to be determined in the future.
The automotive industry is a prime example of digital technologies reshaping mobility. Connected, autonomous, shared, and electric (CASE) trends lead to new emerging players that threaten existing industrial-aged companies. To respond, incumbents need to bridge the gap between contrasting product architecture and organizational principles in the physical and digital realms. Over-the-air (OTA) technology, that enables seamless software updates and on-demand feature additions for customers, is an example of CASE-driven digital product innovation. Through an extensive longitudinal case study of an OTA initiative by an industrial- aged automaker, this dissertation explores how incumbents accomplish digital product innovation. Building on modularity, liminality, and the mirroring hypothesis, it presents a process model that explains the triggers, mechanisms, and outcomes of this process. In contrast to the literature, the findings emphasize the primacy of addressing product architecture challenges over organizational ones and highlight the managerial implications for success.
Human activities modify nature worldwide via changes in the environment, biodiversity and the functioning of ecosystems, which in turn disrupt ecosystem services and feed back negatively on humans. A pressing challenge is thus to limit our impact on nature, and this requires detailed understanding of the interconnections between the environment, biodiversity and ecosystem functioning. These three components of ecosystems each include multiple dimensions, which interact with each other in different ways, but we lack a comprehensive picture of their interconnections and underlying mechanisms. Notably, diversity is often viewed as a single facet, namely species diversity, while many more facets exist at different levels of biological organisation (e.g. genetic, phenotypic, functional, multitrophic diversity), and multiple diversity facets together constitute the raw material for adaptation to environmental changes and shape ecosystem functioning. Consequently, investigating the multidimensionality of ecosystems, and in particular the links between multifaceted diversity, environmental changes and ecosystem functions, is crucial for ecological research, management and conservation. This thesis aims to explore several aspects of this question theoretically.
I investigate three broad topics in this thesis. First, I focus on how food webs with varying levels of functional diversity across three trophic levels buffer environmental changes, such as a sudden addition of nutrients or long-term changes (e.g. warming or eutrophication). I observed that functional diversity generally enhanced ecological stability (i.e. the buffering capacity of the food web) by increasing trophic coupling. More precisely, two aspects of ecological stability (resistance and resilience) increased even though a third aspect (the inverse of the time required for the system to reach its post-perturbation state) decreased with increasing functional diversity. Second, I explore how several diversity facets served as a raw material for different sources of adaptation and how these sources affected multiple ecosystem functions across two trophic levels. Considering several sources of adaptation enabled the interplay between ecological and evolutionary processes, which affected trophic coupling and thereby ecosystem functioning. Third, I reflect further on the multifaceted nature of diversity by developing an index K able to quantify the facet of functional diversity, which is itself multifaceted. K can provide a comprehensive picture of functional diversity and is a rather good predictor of ecosystem functioning. Finally I synthesise the interdependent mechanisms (complementarity and selection effects, trophic coupling and adaptation) underlying the relationships between multifaceted diversity, ecosystem functioning and the environment, and discuss the generalisation of my findings across ecosystems and further perspectives towards elaborating an operational biodiversity-ecosystem functioning framework for research and conservation.
Homomorphisms are a fundamental concept in mathematics expressing the similarity of structures. They provide a framework that captures many of the central problems of computer science with close ties to various other fields of science. Thus, many studies over the last four decades have been devoted to the algorithmic complexity of homomorphism problems. Despite their generality, it has been found that non-uniform homomorphism problems, where the target structure is fixed, frequently feature complexity dichotomies. Exploring the limits of these dichotomies represents the common goal of this line of research.
We investigate the problem of counting homomorphisms to a fixed structure over a finite field of prime order and its algorithmic complexity. Our emphasis is on graph homomorphisms and the resulting problem #_{p}Hom[H] for a graph H and a prime p. The main research question is how counting over a finite field of prime order affects the complexity.
In the first part of this thesis, we tackle the research question in its generality and develop a framework for studying the complexity of counting problems based on category theory. In the absence of problem-specific details, results in the language of category theory provide a clear picture of the properties needed and highlight common ground between different branches of science. The proposed problem #Mor^{C}[B] of counting the number of morphisms to a fixed object B of C is abstract in nature and encompasses important problems like constraint satisfaction problems, which serve as a leading example for all our results. We find explanations and generalizations for a plethora of results in counting complexity. Our main technical result is that specific matrices of morphism counts are non-singular. The strength of this result lies in its algebraic nature. First, our proofs rely on carefully constructed systems of linear equations, which we know to be uniquely solvable. Second, by exchanging the field that the matrix is defined by to a finite field of order p, we obtain analogous results for modular counting. For the latter, cancellations are implied by automorphisms of order p, but intriguingly we find that these present the only obstacle to translating our results from exact counting to modular counting. If we restrict our attention to reduced objects without automorphisms of order p, we obtain results analogue to those for exact counting. This is underscored by a confluent reduction that allows this restriction by constructing a reduced object for any given object. We emphasize the strength of the categorial perspective by applying the duality principle, which yields immediate consequences for the dual problem of counting the number of morphisms from a fixed object.
In the second part of this thesis, we focus on graphs and the problem #_{p}Hom[H]. We conjecture that automorphisms of order p capture all possible cancellations and that, for a reduced graph H, the problem #_{p}Hom[H] features the complexity dichotomy analogue to the one given for exact counting by Dyer and Greenhill. This serves as a generalization of the conjecture by Faben and Jerrum for the modulus 2. The criterion for tractability is that H is a collection of complete bipartite and reflexive complete graphs. From the findings of part one, we show that the conjectured dichotomy implies dichotomies for all quantum homomorphism problems, in particular counting vertex surjective homomorphisms and compactions modulo p. Since the tractable cases in the dichotomy are solved by trivial computations, the study of the intractable cases remains. As an initial problem in a series of reductions capable of implying hardness, we employ the problem of counting weighted independent sets in a bipartite graph modulo prime p. A dichotomy for this problem is shown, stating that the trivial cases occurring when a weight is congruent modulo p to 0 are the only tractable cases. We reduce the possible structure of H to the bipartite case by a reduction to the restricted homomorphism problem #_{p}Hom^{bip}[H] of counting modulo p the number of homomorphisms between bipartite graphs that maintain a given order of bipartition. This reduction does not have an impact on the accessibility of the technical results, thanks to the generality of the findings of part one. In order to prove the conjecture, it suffices to show that for a connected bipartite graph that is not complete, #_{p}Hom^{bip}[H] is #_{p}P-hard. Through a rigorous structural study of bipartite graphs, we establish this result for the rich class of bipartite graphs that are (K_{3,3}\{e}, domino)-free. This overcomes in particular the substantial hurdle imposed by squares, which leads us to explore the global structure of H and prove the existence of explicit structures that imply hardness.
Among the different meanings carried by numerical information, cardinality is fundamental for survival and for the development of basic as well as of higher numerical skills. Importantly, the human brain inherits from evolution a predisposition to map cardinality onto space, as revealed by the presence of spatial-numerical associations (SNAs) in humans and animals. Here, the mapping of cardinal information onto physical space is addressed as a hallmark signature characterizing numerical cognition.
According to traditional approaches, cognition is defined as complex forms of internal information processing, taking place in the brain (cognitive processor). On the contrary, embodied cognition approaches define cognition as functionally linked to perception and action, in the continuous interaction between a biological body and its physical and sociocultural environment.
Embracing the principles of the embodied cognition perspective, I conducted four novel studies designed to unveil how SNAs originate, develop, and adapt, depending on characteristics of the organism, the context, and their interaction. I structured my doctoral thesis in three levels. At the grounded level (Study 1), I unfold the biological foundations underlying the tendency to map cardinal information across space; at the embodied level (Study 2), I reveal the impact of atypical motor development on the construction of SNAs; at the situated level (Study 3), I document the joint influence of visuospatial attention and task properties on SNAs. Furthermore, I experimentally investigate the presence of associations between physical and numerical distance, another numerical property fundamental for the development of efficient mathematical minds (Study 4).
In Study 1, I present the Brain’s Asymmetric Frequency Tuning hypothesis that relies on hemispheric asymmetries for processing spatial frequencies, a low-level visual feature that the (in)vertebrate brain extracts from any visual scene to create a coherent percept of the world. Computational analyses of the power spectra of the original stimuli used to document the presence of SNAs in human newborns and animals, support the brain’s asymmetric frequency tuning as a theoretical account and as an evolutionarily inherited mechanism scaffolding the universal and innate tendency to represent cardinality across horizontal space.
In Study 2, I explore SNAs in children with rare genetic neuromuscular diseases: spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD). SMA children never accomplish independent motoric exploration of their environment; in contrast, DMD children do explore but later lose this ability. The different SNAs reported by the two groups support the critical role of early sensorimotor experiences in the spatial representation of cardinality.
In Study 3, I directly compare the effects of overt attentional orientation during explicit and implicit processing of numerical magnitude. First, the different effects of attentional orienting based on the type of assessment support different mechanisms underlying SNAs during explicit and implicit assessment of numerical magnitude. Secondly, the impact of vertical shifts of attention on the processing of numerical distance sheds light on the correspondence between numerical distance and peri-personal distance.
In Study 4, I document the presence of different SNAs, driven by numerical magnitude and numerical distance, by employing different response mappings (left vs. right and near vs. distant).
In the field of numerical cognition, the four studies included in the present thesis contribute to unveiling how the characteristics of the organism and the environment influence the emergence, the development, and the flexibility of our attitude to represent cardinal information across space, thus supporting the predictions of the embodied cognition approach. Furthermore, they inform a taxonomy of body-centred factors (biological properties of the brain and sensorimotor system) modulating the spatial representation of cardinality throughout the course of life, at the grounded, embodied, and situated levels.
If the awareness for different variables influencing SNAs over the course of life is important, it is equally important to consider the organism as a whole in its sensorimotor interaction with the world. Inspired by my doctoral research, here I propose a holistic perspective that considers the role of evolution, embodiment, and environment in the association of cardinal information with directional space. The new perspective advances the current approaches to SNAs, both at the conceptual and at the methodological levels.
Unveiling how the mental representation of cardinality emerges, develops, and adapts is necessary to shape efficient mathematical minds and achieve economic productivity, technological progress, and a higher quality of life.
Water stored in the unsaturated soil as soil moisture is a key component of the hydrological cycle influencing numerous hydrological processes including hydrometeorological extremes. Soil moisture influences flood generation processes and during droughts when precipitation is absent, it provides plant with transpirable water, thereby sustaining plant growth and survival in agriculture and natural ecosystems.
Soil moisture stored in deeper soil layers e.g. below 100 cm is of particular importance for providing plant transpirable water during dry periods. Not being directly connected to the atmosphere and located outside soil layers with the highest root densities, water in these layers is less susceptible to be rapidly evaporated and transpired. Instead, it provides longer-term soil water storage increasing the drought tolerance of plants and ecosystems.
Given the importance of soil moisture in the context of hydro-meteorological extremes in a warming climate, its monitoring is part of official national adaption strategies to a changing climate. Yet, soil moisture is highly variable in time and space which challenges its monitoring on spatio-temporal scales relevant for flood and drought risk modelling and forecasting.
Introduced over a decade ago, Cosmic-Ray Neutron Sensing (CRNS) is a noninvasive geophysical method that allows for the estimation of soil moisture at relevant spatio-temporal scales of several hectares at a high, subdaily temporal resolution. CRNS relies on the detection of secondary neutrons above the soil surface which are produced from high-energy cosmic-ray particles in the atmosphere and the ground. Neutrons in a specific epithermal energy range are sensitive to the amount of hydrogen present in the surroundings of the CRNS neutron detector. Due to same mass as the hydrogen nucleus, neutrons lose kinetic energy upon collision and are subsequently absorbed when reaching low, thermal energies. A higher amount of hydrogen therefore leads to fewer neutrons being detected per unit time. Assuming that the largest amount of hydrogen is stored in most terrestrial ecosystems as soil moisture, changes of soil moisture can be estimated through an inverse relationship with observed neutron intensities.
Although important scientific advancements have been made to improve the methodological framework of CRNS, several open challenges remain, of which some are addressed in the scope of this thesis. These include the influence of atmospheric variables such as air pressure and absolute air humidity, as well as, the impact of variations in incoming primary cosmic-ray intensity on observed epithermal and thermal neutron signals and their correction. Recently introduced advanced neutron-to-soil moisture transfer functions are expected to improve CRNS-derived soil moisture estimates, but potential improvements need to be investigated at study sites with differing environmental conditions. Sites with strongly heterogeneous, patchy soil moisture distributions challenge existing transfer functions and further research is required to assess the impact of, and correction of derived soil moisture estimates under heterogeneous site conditions. Despite its capability of measuring representative averages of soil moisture at the field scale, CRNS lacks an integration depth below the first few decimetres of the soil. Given the importance of soil moisture also in deeper soil layers, increasing the observational window of CRNS through modelling approaches or in situ measurements is of high importance for hydrological monitoring applications.
By addressing these challenges, this thesis aids to closing knowledge gaps and finding answers to some of the open questions in CRNS research. Influences of different environmental variables are quantified, correction approaches are being tested and developed. Neutron-to-soil moisture transfer functions are evaluated and approaches to reduce effects of heterogeneous soil moisture distributions are presented. Lastly, soil moisture estimates from larger soil depths are derived from CRNS through modified, simple modelling approaches and in situ estimates by using CRNS as a downhole technique. Thereby, this thesis does not only illustrate the potential of new, yet undiscovered applications of CRNS in future but also opens a new field of CRNS research. Consequently, this thesis advances the methodological framework of CRNS for above-ground and downhole applications. Although the necessity of further research in order to fully exploit the potential of CRNS needs to be emphasised, this thesis contributes to current hydrological research and not least to advancing hydrological monitoring approaches being of utmost importance in context of intensifying hydro-meteorological extremes in a changing climate.
Overcoming natural biomass limitations in gram-negative bacteria through synthetic carbon fixation
(2024)
The carbon demands of an ever-increasing human population and the concomitant rise in net carbon emissions requires CO2 sequestering approaches for production of carbon-containing molecules. Microbial production of carbon-containing products from plant-based sugars could replace current fossil-based production. However, this form of sugar-based microbial production directly competes with human food supply and natural ecosystems. Instead, one-carbon feedstocks derived from CO2 and renewable energy were proposed as an alternative. The one carbon molecule formate is a stable, readily soluble and safe-to-store energetic mediator that can be electrochemically generated from CO2 and (excess off-peak) renewable electricity. Formate-based microbial production could represent a promising approach for a circular carbon economy. However, easy-to-engineer and efficient formate-utilizing microbes are lacking. Multiple synthetic metabolic pathways were designed for better-than-nature carbon fixation. Among them, the reductive glycine pathway was proposed as the most efficient pathway for aerobic formate assimilation. While some of these pathways have been successfully engineered in microbial hosts, these synthetic strains did so far not exceed the performance of natural strains. In this work, I engineered and optimized two different synthetic formate assimilation pathways in gram-negative bacteria to exceed the limits of a natural carbon fixation pathway, the Calvin cycle.
The first chapter solidified Cupriavidus necator as a promising formatotrophic host to produce value-added chemicals. The formate tolerance of C. necator was assessed and a production pathway for crotonate established in a modularized fashion. Last, bioprocess optimization was leveraged to produce crotonate from formate at a titer of 148 mg/L.
In the second chapter, I chromosomally integrated and optimized the synthetic reductive glycine pathway in C. necator using a transposon-mediated selection approach. The insertion methodology allowed selection for condition-specific tailored pathway expression as improved pathway performance led to better growth. I then showed my engineered strains to exceed the biomass yields of the Calvin cycle utilizing wildtype C. necator on formate. This demonstrated for the first time the superiority of a synthetic formate assimilation pathway and by extension of synthetic carbon fixation efforts as a whole.
In chapter 3, I engineered a segment of a synthetic carbon fixation cycle in Escherichia coli. The GED cycle was proposed as a Calvin cycle alternative that does not perform a wasteful oxygenation reaction and is more energy efficient. The pathways simple architecture and reasonable driving force made it a promising candidate for enhanced carbon fixation. I created a deletion strain that coupled growth to carboxylation via the GED pathway segment. The CO2 dependence of the engineered strain and 13C-tracer analysis confirmed operation of the pathway in vivo.
In the final chapter, I present my efforts of implementing the GED cycle also in C. necator, which might be a better-suited host, as it is accustomed to formatotrophic and hydrogenotrophic growth. To provide the carboxylation substrate in vivo, I engineered C. necator to utilize xylose as carbon source and created a selection strain for carboxylase activity. I verify activity of the key enzyme, the carboxylase, in the decarboxylative direction. Although CO2-dependent growth of the strain was not obtained, I showed that all enzymes required for operation of the GED cycle are active in vivo in C. necator.
I then evaluate my success with engineering a linear and cyclical one-carbon fixation pathway in two different microbial hosts. The linear reductive glycine pathway presents itself as a much simpler metabolic solution for formate dependent growth over the sophisticated establishment of hard-to-balance carbon fixation cycles. Last, I highlight advantages and disadvantages of C. necator as an upcoming microbial benchmark organism for synthetic metabolism efforts and give and outlook on its potential for the future of C1-based manufacturing.
Skepticism
(2022)
This dissertation offers new and original readings of three major texts in the history of Western philosophy: Descartes’s “First Meditation,” Kant’s “Transcendental Deduction,” and his “Refutation of Idealism.” The book argues that each text addresses the problem of skepticism and posits that they have a hitherto underappreciated, organic relationship to one another. The dissertation begins with an analysis of Descartes’ “First Meditation,” which I argue offers two distinct and independent skeptical arguments that differ in both aim and scope. I call these arguments the “veil of ideas” argument and the “author of my origin” argument. My reading counters the standard interpretation of the text, which sees it as offering three stages of doubt, namely the occasional fallibility of the senses, the dream hypothesis, and the evil demon hypothesis. Building on this, the central argument of the dissertation is that Kant’s “Transcendental Deduction” actually transforms and radicalizes Descartes’s Author of My Origin argument, reconceiving its meaning within the framework of Kant’s own transcendental idealist philosophy. Finally, I argue that the Refutation of Idealism offers a similarly radicalized version of Descartes’s Veil of Ideas argument, albeit translated into the framework of transcendental idealism.
The experience of premenstrual syndrome (PMS) affects up to 90% of individuals with an active menstrual cycle and involves a spectrum of aversive physiological and psychological symptoms in the days leading up to menstruation (Tschudin et al., 2010). Despite its high prevalence, the precise origins of PMS remain elusive, with influences ranging from hormonal fluctuations to cognitive, social, and cultural factors (Hunter, 2007; Matsumoto et al., 2013).
Biologically, hormonal fluctuations, particularly in gonadal steroids, are commonly believed to be implicated in PMS, with the central factor being varying susceptibilities to the fluctuations between individuals and cycles (Rapkin & Akopians, 2012). Allopregnanolone (ALLO), a neuroactive steroid and progesterone metabolite, has emerged as a potential link to PMS symptoms (Hantsoo & Epperson, 2020). ALLO is a positive allosteric modulator of the GABAA receptor, influencing inhibitory communication (Rupprecht, 2003; Andréen et al., 2006). Different susceptibility to ALLO fluctuations throughout the cycle may lead to reduced GABAergic signal transmission during the luteal phase of the menstrual cycle.
The GABAergic system's broad influence leads to a number of affected physiological systems, including a consistent reduction in vagally mediated heart rate variability (vmHRV) during the luteal phase (Schmalenberger et al., 2019). This reduction in vmHRV is more pronounced in individuals with high PMS symptoms (Baker et al., 2008; Matsumoto et al., 2007). Fear conditioning studies have shown inconsistent associations with cycle phases, suggesting a complex interplay between physiological parameters and PMS-related symptoms (Carpenter et al., 2022; Epperson et al., 2007; Milad et al., 2006).
The neurovisceral integration model posits that vmHRV reflects the capacity of the central autonomous network (CAN), which is responsible for regulatory processes on behavioral, cognitive, and autonomous levels (Thayer & Lane, 2000, 2009). Fear learning, mediated within the CAN, is suggested to be indicative of vmHRV's capacity for successful
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regulation (Battaglia & Thayer, 2022). Given the GABAergic mediation of central inhibitory functional connectivity in the CAN, which may be affected by ALLO fluctuations, this thesis proposes that fluctuating CAN activity in the luteal phase contributes to diverse aversive symptoms in PMS.
A research program was designed to empirically test these propositions. Study 1 investigated fear discrimination during different menstrual cycle phases and its interaction with vmHRV, revealing nuanced effects on acoustic startle response and skin conductance response. While there was heightened fear discrimination in acoustic startle responses in participants in the luteal phase, there was an interaction between menstrual cycle phase and vmHRV in skin conductance responses. In this measure, heightened fear discrimination during the luteal phase was only visible in individuals with high resting vmHRV; those with low vmHRV showed reduced fear discrimination and higher overall responses.
Despite affecting the vast majority of menstruating people, there are very limited tools available to reliably assess these symptoms in the German speaking area. Study 2 aimed at closing this gap, by translating and validating a German version of the short version of the Premenstrual Assessment Form (Allen et al., 1991), providing a reliable tool for future investigations, which closes the gap in PMS questionnaires in the German-speaking research area.
Study 3 employed a diary study paradigm to explore daily associations between vmHRV and PMS symptoms. The results showed clear simultaneous fluctuations between the two constructs with a peak in PMS and a low point in vmHRV a few days before menstruation onset. The association between vmHRV and PMS was driven by psychological PMS symptoms.
Based on the theoretical considerations regarding the neurovisceral perspective on PMS, another interesting construct to consider is attentional control, as it is closely related to functions of the CAN. Study 4 delved into attentional control and vmHRV differences between menstrual cycle phases, demonstrating an interaction between cycle phase and PMS symptoms. In a pilot, we found reduced vmHRV and attentional control during the luteal phase only in participants who reported strong PMS.
While Studies 1-4 provided evidence for the mechanisms underlying PMS, Studies 5 and 6 investigated short- and long-term intervention protocols to ameliorate PMS symptomatology. Study 5 explored the potential of heart rate variability biofeedback (HRVB) in alleviating PMS symptoms and a number of other outcome measures. In a waitlist-control design, participants underwent a 4-week smartphone-based HRVB intervention. The results revealed positive effects on PMS, with larger effect sizes on psychological symptoms, as well as on depressive symptoms, anxiety/stress and attentional control.
Finally, Study 6 examined the acute effects of HRVB on attentional control. The study found positive impact but only in highly stressed individuals.
The thesis, based on this comprehensive research program, expands our understanding of PMS as an outcome of CAN fluctuations mediated by GABAA receptor reactivity. The results largely support the model. These findings not only deepen our understanding of PMS but also offer potential avenues for therapeutic interventions. The promising results of smartphone-based HRVB training suggest a non-pharmacological approach to managing PMS symptoms, although further research is needed to confirm its efficacy.
In conclusion, this thesis illuminates the complex web of factors contributing to PMS, providing valuable insights into its etiological underpinnings and potential interventions. By elucidating the relationships between hormonal fluctuations, CAN activity, and psychological responses, this research contributes to more effective treatments for individuals grappling with the challenges of PMS. The findings hold promise for improving the quality of life for those affected by this prevalent and often debilitating condition.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.
Arctic climate change is marked by intensified warming compared to global trends and a significant reduction in Arctic sea ice which can intricately influence mid-latitude atmospheric circulation through tropo- and stratospheric pathways. Achieving accurate simulations of current and future climate demands a realistic representation of Arctic climate processes in numerical climate models, which remains challenging.
Model deficiencies in replicating observed Arctic climate processes often arise due to inadequacies in representing turbulent boundary layer interactions that determine the interactions between the atmosphere, sea ice, and ocean. Many current climate models rely on parameterizations developed for mid-latitude conditions to handle Arctic turbulent boundary layer processes.
This thesis focuses on modified representation of the Arctic atmospheric processes and understanding their resulting impact on large-scale mid-latitude atmospheric circulation within climate models. The improved turbulence parameterizations, recently developed based on Arctic measurements, were implemented in the global atmospheric circulation model ECHAM6. This involved modifying the stability functions over sea ice and ocean for stable stratification and changing the roughness length over sea ice for all stratification conditions. Comprehensive analyses are conducted to assess the impacts of these modifications on ECHAM6's simulations of the Arctic boundary layer, overall atmospheric circulation, and the dynamical pathways between the Arctic and mid-latitudes.
Through a step-wise implementation of the mentioned parameterizations into ECHAM6, a series of sensitivity experiments revealed that the combined impacts of the reduced roughness length and the modified stability functions are non-linear. Nevertheless, it is evident that both modifications consistently lead to a general decrease in the heat transfer coefficient, being in close agreement with the observations.
Additionally, compared to the reference observations, the ECHAM6 model falls short in accurately representing unstable and strongly stable conditions.
The less frequent occurrence of strong stability restricts the influence of the modified stability functions by reducing the affected sample size. However, when focusing solely on the specific instances of a strongly stable atmosphere, the sensible heat flux approaches near-zero values, which is in line with the observations. Models employing commonly used surface turbulence parameterizations were shown to have difficulties replicating the near-zero sensible heat flux in strongly stable stratification.
I also found that these limited changes in surface layer turbulence parameterizations have a statistically significant impact on the temperature and wind patterns across multiple pressure levels, including the stratosphere, in both the Arctic and mid-latitudes. These significant signals vary in strength, extent, and direction depending on the specific month or year, indicating a strong reliance on the background state.
Furthermore, this research investigates how the modified surface turbulence parameterizations may influence the response of both stratospheric and tropospheric circulation to Arctic sea ice loss.
The most suitable parameterizations for accurately representing Arctic boundary layer turbulence were identified from the sensitivity experiments. Subsequently, the model's response to sea ice loss is evaluated through extended ECHAM6 simulations with different prescribed sea ice conditions.
The simulation with adjusted surface turbulence parameterizations better reproduced the observed Arctic tropospheric warming in vertical extent, demonstrating improved alignment with the reanalysis data. Additionally, unlike the control experiments, this simulation successfully reproduced specific circulation patterns linked to the stratospheric pathway for Arctic-mid-latitude linkages. Specifically, an increased occurrence of the Scandinavian-Ural blocking regime (negative phase of the North Atlantic Oscillation) in early (late) winter is observed. Overall, it can be inferred that improving turbulence parameterizations at the surface layer can improve the ECHAM6's response to sea ice loss.
The wide distribution of location-acquisition technologies means that large volumes of spatio-temporal data are continuously being accumulated. Positioning systems such as GPS enable the tracking of various moving objects' trajectories, which are usually represented by a chronologically ordered sequence of observed locations. The analysis of movement patterns based on detailed positional information creates opportunities for applications that can improve business decisions and processes in a broad spectrum of industries (e.g., transportation, traffic control, or medicine). Due to the large data volumes generated in these applications, the cost-efficient storage of spatio-temporal data is desirable, especially when in-memory database systems are used to achieve interactive performance requirements.
To efficiently utilize the available DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes structures). By considering horizontal data partitioning, we can independently apply different tuning options on a fine-grained level. However, the selection of cost and performance-balancing configurations is challenging, due to the vast number of possible setups consisting of mutually dependent individual decisions.
In this thesis, we introduce multiple approaches to improve spatio-temporal data management by automatically optimizing diverse tuning options for the application-specific access patterns and data characteristics. Our contributions are as follows:
(1) We introduce a novel approach to determine fine-grained table configurations for spatio-temporal workloads. Our linear programming (LP) approach jointly optimizes the (i) data compression, (ii) ordering, (iii) indexing, and (iv) tiering. We propose different models which address cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload, memory budgets, and data characteristics. To yield maintainable and robust configurations, we further extend our LP-based approach to incorporate reconfiguration costs as well as optimizations for multiple potential workload scenarios.
(2) To optimize the storage layout of timestamps in columnar databases, we present a heuristic approach for the workload-driven combined selection of a data layout and compression scheme. By considering attribute decomposition strategies, we are able to apply application-specific optimizations that reduce the memory footprint and improve performance.
(3) We introduce an approach that leverages past trajectory data to improve the dispatch processes of transportation network companies. Based on location probabilities, we developed risk-averse dispatch strategies that reduce critical delays.
(4) Finally, we used the use case of a transportation network company to evaluate our database optimizations on a real-world dataset. We demonstrate that workload-driven fine-grained optimizations allow us to reduce the memory footprint (up to 71% by equal performance) or increase the performance (up to 90% by equal memory size) compared to established rule-based heuristics.
Individually, our contributions provide novel approaches to the current challenges in spatio-temporal data mining and database research. Combining them allows in-memory databases to store and process spatio-temporal data more cost-efficiently.
This thesis presents an attempt to use source code synthesised from Coq formalisations of device drivers for existing (micro)kernel operating systems, with a particular focus on the Linux Kernel.
In the first part, the technical background and related work are described. The focus is here on the possible approaches to synthesising certified software with Coq, namely the extraction to functional languages using the Coq extraction plugin and the extraction to Clight code using the CertiCoq plugin. It is noted that the implementation of CertiCoq is verified, whereas this is not the case for the Coq extraction plugin. Consequently, there is a correctness guarantee for the generated Clight code which does not hold for the code being generated by the Coq extraction plugin. Furthermore, the differences between user space and kernel space software are discussed in relation to Linux device drivers. It is elaborated that it is not possible to generate working Linux kernel module components using the Coq extraction plugin without significant modifications. In contrast, it is possible to produce working user space drivers both with the Coq extraction plugin and CertiCoq. The subsequent parts describe the main contributions of the thesis.
In the second part, it is demonstrated how to extend the Coq extraction plugin to synthesise foreign function calls between the functional language OCaml and the imperative language C. This approach has the potential to improve the type-safety of user space drivers. Furthermore, it is shown that the code being synthesised by CertiCoq cannot be used in kernel space without modifications to the necessary runtime. Consequently, the necessary modifications to the runtimes of CertiCoq and VeriFFI are introduced, resulting in the runtimes becoming compatible components of a Linux kernel module. Furthermore, justifications for the transformations are provided and possible further extensions to both plugins and solutions to failing garbage collection calls in kernel space are discussed.
The third part presents a proof of concept device driver for the Linux Kernel. To achieve this, the event handler of the original PC Speaker driver is partially formalised in Coq. Furthermore, some relevant formal properties of the formalised functionality are discussed. Subsequently, a kernel module is defined, utilising the modified variants of CertiCoq and VeriFFI to compile a working device driver. It is furthermore shown that it is possible to compile the synthesised code with CompCert, thereby extending the guarantee of correctness to the assembly layer. This is followed by a performance evaluation that compares a naive formalisation of the PC speaker functionality with the original PC Speaker driver pointing out the weaknesses in the formalisation and possible improvements. The part closes with a summary of the results, their implications and open questions being raised.
The last part lists all used sources, separated into scientific literature, documentations or reference manuals and artifacts, i.e. source code.
The plant cell wall plays several crucial roles during plant development with its integrity acting as key signalling component for growth regulation during biotic and abiotic stresses. Cellulose microfibrils, the principal load-bearing components is the major component of the primary cell wall, whose synthesis is mediated by microtubule-associated CELLULOSE SYNTHASE (CESA) COMPLEXES (CSC). Previous studies have shown that CSC interacting proteins COMPANION OF CELLULOSE SYNTHASE (CC) facilitate sustained cellulose synthesis during salt stress by promoting repolymerization of cortical microtubules. However, our understanding of cellulose synthesis during salt stress remains incomplete.
In this study, a pull-down of CC1 protein led to the identification of a novel interactor, termed LEA-like. Phylogenetic analysis revealed that LEA-like belongs to the LATE EMBRYOGENESIS ABUNDANT (LEA) protein family, specifically to the LEA_2 subgroup, showing a close relationship with the CC proteins. Roots of the double mutants lea-like and its closest homolog emb3135 exhibited hypersensitivity when grown on cellulose synthesis inhibitors. Further analysis of higher-order mutants of lea-like, emb3135, and cesa6 demonstrated a genetic interaction between them indicating a significant role in cellulose synthesis.
Live-cell imaging revealed that both LEA-like and EMB3135 migrated with the CSC at the plasma membrane along microtubule tracks in control and oryzalin-treated conditions which destabilize microtubules, suggesting a tight interaction. Investigation of fluorescently labeled lines of different domains of the LEA-like protein revealed that the N-terminal cytosolic domain of LEA-like colocalizes with microtubules, suggesting a physical association between the two.
Considering the established role of LEA proteins in abiotic stress tolerance, we performed phenotypic analysis of the mutant under various stresses. Growth of double mutants of lea-like and emb3135 on NaCl containing media resulted in swelling of root cell indicating a putative role in salt stress tolerance. Supportive of this the quadruple mutant, lacking LEA-like, EMB3135, CC1, and CC2 proteins, exhibited a severe root growth defect on NaCl media compared to control conditions. Live-cell imaging revealed that under salt stress, the LEA-like protein forms aggregates in the plasma membrane.
In conclusion, this study has unveiled two novel interactors of the CSC that act with the CC proteins that regulate plant growth in response to salt stress providing new insights into the intricate regulation of cellulose synthesis, particularly under such conditions.
Leaves exhibit cells with varying degrees of shape complexity along the proximodistal axis. Heterogeneities in growth directions within individual cells bring about such complexity in cell shape. Highly complex and interconnected gene regulatory networks and signaling pathways have been identified to govern these processes. In addition, the organization of cytoskeletal networks and cell wall mechanical properties greatly influences the regulation of cell shape. Research has shown that microtubules are involved in regulating cellulose deposition and direc-tion of cell growth. However, comprehensive analysis of the regulation of the actin cytoskele-ton in cell shape regulation has not been well studied.
This thesis provides evidence that actin regulates aspects of cell growth, division, and direction-al expansion that impacts morphogenesis of developing leaves. The jigsaw puzzle piece mor-phology of epidermal pavement cells further serves as an ideal system to investigate the com-plex process of morphogenetic processes occurring at the cellular level. Here we have em-ployed live cell based imaging studies to track the development of pavement cells in actin com-promised conditions. Genetic perturbation of two predominantly expressed vegetative actin genes ACTIN2 and ACTIN7 results in delayed emergence of the cellular protrusions in pave-ment cells. Perturbation of actin also impacted the organization of microtubule in these cells that is known to promote emergence of cellular protrusions. Further, live-cell imaging of actin or-ganization revealed a correlation with cell shape, suggesting that actin plays a role in influencing pavement cell morphogenesis.
In addition, disruption of actin leads to an increase in cell size along the leaf midrib, with cells being highly anisotropic due to reduced cell division. The reduction of cell division further im-pacted the morphology of the entire leaf, with the mutant leaves being more curved. These re-sults suggests that actin plays a pivotal role in regulating morphogenesis at the cellular and tis-sue scales thereby providing valuable insights into the role of the actin cytoskeleton in plant morphogenesis.
This thesis focuses on the molecular evolution of Macroscelidea, commonly referred to as sengis. Sengis are a mammalian order belonging to the Afrotherians, one of the four major clades of placental mammals. Sengis currently consist of twenty extant species, all of which are endemic to the African continent. They can be separated in two families, the soft-furred sengis (Macroscelididae) and the giant sengis (Rhynchocyonidae). While giant sengis can be exclusively found in forest habitats, the different soft-furred sengi species dwell in a broad range of habitats, from tropical rain-forests to rocky deserts.
Our knowledge on the evolutionary history of sengis is largely incomplete. The high level of superficial morphological resemblance among different sengi species (especially the soft-furred sengis) has for example led to misinterpretations of phylogenetic relationships, based on morphological characters. With the rise of DNA based taxonomic inferences, multiple new genera were defined and new species described. Yet, no full taxon molecular phylogeny exists, hampering the answering of basic taxonomic questions. This lack of knowledge can be to some extent attributed to the limited availability of fresh-tissue samples for DNA extraction. The broad African distribution, partly in political unstable regions and low population densities complicate contemporary sampling approaches. Furthermore, the DNA information available usually covers only short stretches of the mitochondrial genome and thus a single genetic locus with limited informational content.
Developments in DNA extraction and library protocols nowadays offer the opportunity to access DNA from museum specimens, collected over the past centuries and stored in natural history museums throughout the world. Thus, the difficulties in fresh-sample acquisition for molecular biological studies can be overcome by the application of museomics, the research field which emerged from those laboratory developments.
This thesis uses fresh-tissue samples as well as a vast collection museum specimens to investigate multiple aspects about the macroscelidean evolutionary history. Chapter 4 of this thesis focuses on the phylogenetic relationships of all currently known sengi species. By accessing DNA information from museum specimens in combination of fresh tissue samples and publicly available genetic resources it produces the first full taxon molecular phylogeny of sengis. It confirms the monophyly of the genus Elephantulus and discovers multiple deeply divergent lineages within different species, highlighting the need for species specific approaches. The study furthermore focuses on the evolutionary time frame of sengis by evaluating the impact of commonly varied parameters on tree dating. The results of the study show, that the mitochondrial information used in previous studies to temporal calibrate the Macroscelidean phylogeny led to an overestimation of node ages within sengis. Especially soft-furred sengis are thus much younger than previously assumed. The refined knowledge of nodes ages within sengis offer the opportunity to link e.g. speciation events to environmental changes.
Chapter 5 focuses on the genus Petrodromus with its single representative Petrodromus tetradactylus. It again exploits the opportunities of museomics and gathers a comprehensive, multi-locus genetic dataset of P. tetradactylus individuals, distributed across most the known range of this species. It reveals multiple deeply divergent lineages within Petrodromus, whereby some could possibly be associated to previously described sub-species, at least one was formerly unknown. It underscores the necessity for a revision of the genus Petrodromus through the integration of both molecular and morphological evidence. The study, furthermore identifies changing forest distributions through climatic oscillations as main factor shaping the genetic structure of Petrodromus.
Chapter 6 uses fresh tissue samples to extent the genomic resources of sengis by thirteen new nuclear genomes, of which two were de-novo assembled. An extensive dataset of more than 8000 protein coding one-to-one orthologs allows to further refine and confirm the temporal time frame of sengi evolution found in Chapter 4. This study moreover investigates the role of gene-flow and incomplete lineage sorting (ILS) in sengi evolution. In addition it identifies clade specific genes of possible outstanding evolutionary importance and links them to potential phenotypic traits affected. A closer investigation of olfactory receptor proteins reveals clade specific differences. A comparison of the demographic past of sengis to other small African mammals does not reveal a sengi specific pattern.
Ecosystems play a pivotal role in addressing climate change but are also highly susceptible to drastic environmental changes. Investigating their historical dynamics can enhance our understanding of how they might respond to unprecedented future environmental shifts. With Arctic lakes currently under substantial pressure from climate change, lessons from the past can guide our understanding of potential disruptions to these lakes. However, individual lake systems are multifaceted and complex. Traditional isolated lake studies often fail to provide a global perspective because localized nuances—like individual lake parameters, catchment areas, and lake histories—can overshadow broader conclusions. In light of these complexities, a more nuanced approach is essential to analyze lake systems in a global context.
A key to addressing this challenge lies in the data-driven analysis of sedimentological records from various northern lake systems. This dissertation emphasizes lake systems in the northern Eurasian region, particularly in Russia (n=59). For this doctoral thesis, we collected sedimentological data from various sources, which required a standardized framework for further analysis. Therefore, we designed a conceptual model for integrating and standardizing heterogeneous multi-proxy data into a relational database management system (PostgreSQL). Creating a database from the collected data enabled comparative numerical analyses between spatially separated lakes as well as between different proxies.
When analyzing numerous lakes, establishing a common frame of reference was crucial. We achieved this by converting proxy values from depth dependency to age dependency. This required consistent age calculations across all lakes and proxies using one age-depth modeling software. Recognizing the broader implications and potential pitfalls of this, we developed the LANDO approach ("Linked Age and Depth Modelling"). LANDO is an innovative integration of multiple age-depth modeling software into a singular, cohesive platform (Jupyter Notebook). Beyond its ability to aggregate data from five renowned age-depth modeling software, LANDO uniquely empowers users to filter out implausible model outcomes using robust geoscientific data. Our method is not only novel but also significantly enhances the accuracy and reliability of lake analyses.
Considering the preceding steps, this doctoral thesis further examines the relationship between carbon in sediments and temperature over the last 21,000 years. Initially, we hypothesized a positive correlation between carbon accumulation in lakes and modelled paleotemperature. Our homogenized dataset from heterogeneous lakes confirmed this association, even if the highest temperatures throughout our observation period do not correlate with the highest carbon values. We assume that rapid warming events contribute more to high accumulation, while sustained warming leads to carbon outgassing. Considering the current high concentration of carbon in the atmosphere and rising temperatures, ongoing climate change could cause northern lake systems to contribute to a further increase in atmospheric carbon (positive feedback loop). While our findings underscore the reliability of both our standardized data and the LANDO method, expanding our dataset might offer even greater assurance in our conclusions.
Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
How do the rights of same-sex couples have to be ensured by states, and which kind of environmental obligations are induced by the right to life and to personal integrity? Questions as diverse and far-reaching as these are regularly dealt with by the Inter-American Court of Human Rights in its advisory function. This book is the first comprehensive, non-Spanish-written treatise on the advisory function of this Court. It analyzes the scope of the Court's advisory jurisdiction and its procedural practice in comparison with that of other international courts. Moreover, the legal effects of the Court’s advisory opinions and the question when the Court should better reject a request for an advisory opinion are examined.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
The dynamic landscape of digital transformation entails an impact on industrial-age manufacturing companies that goes beyond product offerings, changing operational paradigms, and requiring an organization-wide metamorphosis. An initiative to address the given challenges is the creation of Digital Innovation Units (DIUs) – departments or distinct legal entities that use new structures and practices to develop digital products, services, and business models and support or drive incumbents’ digital transformation. With more than 300 units in German-speaking countries alone and an increasing number of scientific publications, DIUs have become a widespread phenomenon in both research and practice.
This dissertation examines the evolution process of DIUs in the manufacturing
industry during their first three years of operation, through an extensive longitudinal single-case study and several cross-case syntheses of seven DIUs. Building on the lenses of organizational change and development, time, and socio-technical systems, this research provides insights into the fundamentals, temporal dynamics, socio-technical interactions, and relational dynamics of a DIU’s evolution process. Thus, the dissertation promotes a dynamic understanding of DIUs and adds a two-dimensional perspective to the often one-dimensional view of these units and their interactions with the main organization throughout the startup and growth phases of a DIU.
Furthermore, the dissertation constructs a phase model that depicts the early stages of DIU evolution based on these findings and by incorporating literature from information systems research. As a result, it illustrates the progressive intensification of collaboration between the DIU and the main organization. After being implemented, the DIU sparks initial collaboration and instigates change within (parts of) the main organization. Over time, it adapts to the corporate environment to some extent, responding to changing circumstances in order to contribute to long-term transformation. Temporally, the DIU drives the early phases of cooperation and adaptation in particular, while the main organization triggers the first major evolutionary step and realignment of the DIU.
Overall, the thesis identifies DIUs as malleable organizational structures that are crucial for digital transformation. Moreover, it provides guidance for practitioners on the process of building a new DIU from scratch or optimizing an existing one.
A comprehensive study on seismic hazard and earthquake triggering is crucial for effective mitigation of earthquake risks. The destructive nature of earthquakes motivates researchers to work on forecasting despite the apparent randomness of the earthquake occurrences. Understanding their underlying mechanisms and patterns is vital, given their potential for widespread devastation and loss of life. This thesis combines methodologies, including Coulomb stress calculations and aftershock analysis, to shed light on earthquake complexities, ultimately enhancing seismic hazard assessment.
The Coulomb failure stress (CFS) criterion is widely used to predict the spatial distributions of aftershocks following large earthquakes. However, uncertainties associated with CFS calculations arise from non-unique slip inversions and unknown fault networks, particularly due to the choice of the assumed aftershocks (receiver) mechanisms. Recent studies have proposed alternative stress quantities and deep neural network approaches as superior to CFS with predefined receiver mechanisms. To challenge these propositions, I utilized 289 slip inversions from the SRCMOD database to calculate more realistic CFS values for a layered-half space and variable receiver mechanisms. The analysis also investigates the impact of magnitude cutoff, grid size variation, and aftershock duration on the ranking of stress metrics using receiver operating characteristic (ROC) analysis. Results reveal the performance of stress metrics significantly improves after accounting for receiver variability and for larger aftershocks and shorter time periods, without altering the relative ranking of the different stress metrics.
To corroborate Coulomb stress calculations with the findings of earthquake source studies in more detail, I studied the source properties of the 2005 Kashmir earthquake and its aftershocks, aiming to unravel the seismotectonics of the NW Himalayan syntaxis. I simultaneously relocated the mainshock and its largest aftershocks using phase data, followed by a comprehensive analysis of Coulomb stress changes on the aftershock planes. By computing the Coulomb failure stress changes on the aftershock faults, I found that all large aftershocks lie in regions of positive stress change, indicating triggering by either co-seismic or post-seismic slip on the mainshock fault.
Finally, I investigated the relationship between mainshock-induced stress changes and associated seismicity parameters, in particular those of the frequency-magnitude (Gutenberg-Richter) distribution and the temporal aftershock decay (Omori-Utsu law). For that purpose, I used my global data set of 127 mainshock-aftershock sequences with the calculated Coulomb Stress (ΔCFS) and the alternative receiver-independent stress metrics in the vicinity of the mainshocks and analyzed the aftershocks properties depend on the stress values. Surprisingly, the results show a clear positive correlation between the Gutenberg-Richter b-value and induced stress, contrary to expectations from laboratory experiments. This observation highlights the significance of structural heterogeneity and strength variations in seismicity patterns. Furthermore, the study demonstrates that aftershock productivity increases nonlinearly with stress, while the Omori-Utsu parameters c and p systematically decrease with increasing stress changes. These partly unexpected findings have significant implications for future estimations of aftershock hazard.
The findings in this thesis provides valuable insights into earthquake triggering mechanisms by examining the relationship between stress changes and aftershock occurrence. The results contribute to improved understanding of earthquake behavior and can aid in the development of more accurate probabilistic-seismic hazard forecasts and risk reduction strategies.
This thesis presents a comprehensive exploration of the application of DNA origami nanofork antennas (DONAs) in the field of spectroscopy, with a particular focus on the structural analysis of Cytochrome C (CytC) at the single-molecule level. The research encapsulates the design, optimization, and application of DONAs in enhancing the sensitivity and specificity of Raman spectroscopy, thereby offering new insights into protein structures and interactions.
The initial phase of the study involved the meticulous optimization of DNA origami structures. This process was pivotal in developing nanoscale tools that could significantly enhance the capabilities of Raman spectroscopy. The optimized DNA origami nanoforks, in both dimer and aggregate forms, demonstrated an enhanced ability to detect and analyze molecular vibrations, contributing to a more nuanced understanding of protein dynamics.
A key aspect of this research was the comparative analysis between the dimer and aggregate forms of DONAs. This comparison revealed that while both configurations effectively identified oxidation and spin states of CytC, the aggregate form offered a broader range of detectable molecular states due to its prolonged signal emission and increased number of molecules. This extended duration of signal emission in the aggregates was attributed to the collective hotspot area, enhancing overall signal stability and sensitivity.
Furthermore, the study delved into the analysis of the Amide III band using the DONA system. Observations included a transient shift in the Amide III band's frequency, suggesting dynamic alterations in the secondary structure of CytC. These shifts, indicative of transitions between different protein structures, were crucial in understanding the protein’s functional mechanisms and interactions.
The research presented in this thesis not only contributes significantly to the field of spectroscopy but also illustrates the potential of interdisciplinary approaches in biosensing. The use of DNA origami-based systems in spectroscopy has opened new avenues for research, offering a detailed and comprehensive understanding of protein structures and interactions. The insights gained from this research are expected to have lasting implications in scientific fields ranging from drug development to the study of complex biochemical pathways. This thesis thus stands as a testament to the power of integrating nanotechnology, biochemistry, and spectroscopic techniques in addressing complex scientific questions.
Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice.
(1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics.
To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions.
(2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures.
Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes.
(3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines.
The mobile-immobile model (MIM) has been established in geoscience in the context of contaminant transport in groundwater. Here the tracer particles effectively immobilise, e.g., due to diffusion into dead-end pores or sorption. The main idea of the MIM is to split the total particle density into a mobile and an immobile density. Individual tracers switch between the mobile and immobile state following a two-state telegraph process, i.e., the residence times in each state are distributed exponentially. In geoscience the focus lies on the breakthrough curve (BTC), which is the concentration at a fixed location over time. We apply the MIM to biological experiments with a special focus on anomalous scaling regimes of the mean squared displacement (MSD) and non-Gaussian displacement distributions. As an exemplary system, we have analysed the motion of tau proteins, that diffuse freely inside axons of neurons. Their free diffusion thereby corresponds to the mobile state of the MIM. Tau proteins stochastically bind to microtubules, which effectively immobilises the tau proteins until they unbind and continue diffusing. Long immobilisation durations compared to the mobile durations give rise to distinct non-Gaussian Laplace shaped distributions. It is accompanied by a plateau in the MSD for initially mobile tracer particles at relevant intermediate timescales. An equilibrium fraction of initially mobile tracers gives rise to non-Gaussian displacements at intermediate timescales, while the MSD remains linear at all times. In another setting bio molecules diffuse in a biosensor and transiently bind to specific receptors, where advection becomes relevant in the mobile state. The plateau in the MSD observed for the advection-free setting and long immobilisation durations persists also for the case with advection. We find a new clear regime of anomalous diffusion with non-Gaussian distributions and a cubic scaling of the MSD. This regime emerges for initially mobile and for initially immobile tracers. For an equilibrium fraction of initially mobile tracers we observe an intermittent ballistic scaling of the MSD. The long-time effective diffusion coefficient is enhanced by advection, which we physically explain with the variance of mobile durations. Finally, we generalize the MIM to incorporate arbitrary immobilisation time distributions and focus on a Mittag-Leffler immobilisation time distribution with power-law tail ~ t^(-1-mu) with 0<mu<1 and diverging mean immobilisation durations. A fit of our model to the BTC of experimental data from tracer particles in aquifers matches the BTC including the power-law tail. We use the fit parameters for plotting the displacement distributions and the MSD. We find Gaussian normal diffusion at short times and long-time power-law decay of mobile mass accompanied by anomalous diffusion at long times. The long-time diffusion is subdiffusive in the advection-free setting, while it is either subdiffusive for 0<mu<1/2 or superdiffusive for 1/2<mu<1 when advection is present. In the long-time limit we show equivalence of our model to a bi-fractional diffusion equation.