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Institute
- Institut für Physik und Astronomie (356)
- Institut für Geowissenschaften (304)
- Institut für Biochemie und Biologie (270)
- Institut für Chemie (194)
- Extern (132)
- Hasso-Plattner-Institut für Digital Engineering GmbH (90)
- Institut für Umweltwissenschaften und Geographie (86)
- Department Linguistik (66)
- Institut für Informatik und Computational Science (65)
- Institut für Mathematik (58)
Terrestrial landscape dynamics are dominated by the production, mobilisation, transfer and deposition of sediment. Numerous chemical elements are carried by sediments, making them a key component for ecological processes, as soil constitution and thus plants and animal ecosystems depends on them, and by extension the human species. They are also essential for climate evolution and regulation as marine sedimentation acts as a carbon sink. However, the processes at the origin of their production, mobilisation, transfer can occur suddenly with high-energy content – such as volcanic eruption, mass wasting or flooding events and wildfires – shaking ecosystems and shaping landforms. Besides, in the last era, the human species has shown its ability to disturb landscape dynamics and change sediments cycles. Thus, there is a need for predictive understanding of the processes involved. This relies on un-derstanding of the mechanisms of key processes and their controls, and knowledge of the state and evolution of the Earth’s surface. Classic approaches to these challenges include empirical observations and numerical modeling of geochemical fluxes and surface processes, as well as the study of terrestrial sedimentary archives to better understand the parameters at stake in landscape dynamics and climate changes and the different actions and retroactions between the production, mobilisation, transfer and deposition of sediments which ultimately shape landscapes. Environmental seismology complements these approaches.
Environmental seismology is the science topic investigating the source functions and propagation properties of seismic vibrations triggered by processes happening at or near the Earth’s surface, below and above it - cryosphere, hydrosphere, atmosphere, human habitat, biosphere, etc., to obtain insight into all these physical processes. Indeed, from mass wasting event to rivers, from wild species to hu-man, all these processes are generating seismic waves. Environmental seismology is a rather recent field, with new branches rapidly expanding and at various stages of scientific progress. This thesis is motivated by the goal of learning more on two major natural process hazards (river bedload transport and mass wasting) as well as on human-generated acoustic hazard, while covering the axis of funda-mental research progression, from data exploration and method and theory development to proof of concept, with the twin aims of developing a better understanding of the operation of these specific processes, and of advancing the methods we have at our disposal to study them.
First, I provide a benchmark for assessment of the reliability of existing seismic bedload model inver-sion to retrieve bedload flux from seismic data. Bedload flux measurements are essential to better understand river dynamics, and this can be achieved with environmental seismology. However, due to a lack of well-constrained validation data, the accuracy of the resulting inversions is unknown. I address this gap in Chapter 2.2, reporting a seismic field experiment, and comparing the results to high-quality independent bedload measurements to constrain a seismic bedload model. The study shows that the quality of bedload flux estimates from seismic data strongly depends on the quality of the input data for the model. Direct measurements of relevant parameters, chiefly seismic ground proper-ties needed for the Green's function and the grain size distribution of the moving bedload, considerably improve the model quality over generic approaches using empirical or theoretical functions. I also pro-vide a numerical tool to facilitate the use of water turbulence and bedload seismic inversion models: seismic models for bedload flux and water turbulence require painstaking work to constrain parame-ters describing the ground properties by active seismic study or analysis of passive seismic data, and the grain size distribution via independent measurements. Reasonable predictions can be achieved by using a Monte Carlo approach to optimize the free parameters with respect to the target parameters. The validation of the tool, in Chapter 2.3, with independent measurements of water depth and bedload flux at a study site on the Eshtemoa river in Israël makes it available for reliable use at other sites. The work reported in this chapter has been published in Lagarde et al. 2021 and Dietze et al. 2019b.
In a second study, reported in Chapter 3, I investigate the formation of a failure plane prior to a rock-slide. A better understanding of the dynamics of the preparation phase is essential to determine the timing, volume and mobilization mechanism of a rock slope failure, and this can be achieved with envi-ronmental seismology. I take advantage of a network of seismic stations close to an instable slope recording cracking signals prior to the slope failure, and use a machine learning technique based on hidden Markov models to isolate these signals from the seismic data, retrieving the cumulative number of cracking events in a period of 20 days prior to a large rockslide and 10 days after. The trajectory of the cumulative number of cracks shifts from a rather linear shape in the two weeks prior to the rock-slide to an S-shaped development in the last 27 h before failure. I interpret this change as a switch from initial distributed cracking to localised damage accumulation in the hours prior to the failure. I develop a simple physical model to explain the temporal evolution of crack activity during the S-shape phase, revealing the importance of an internal parameter, the total crack boundary length as the dominant control parameter of failure plane evolution. This study has been published as Lagarde et al. 2023.
Third, I develop a model converting acoustic signals to seismic signals. A part of the acoustic vibrations generated on the Earth’s surface is converted to seismic signals at the ground interface. Consequently, noise pollution may be translated to slope fatigue and rock micro (or macro) fracturing resulting in a degrading effect on landforms. Moreover, this pollution can have negative impacts, such as physical, physiological as well as psychological effects on animal species. At present, the impact of seismic pol-lution generated by acoustic sources is difficult to evaluate. In Chapter 4, I improve and implement a model converting the acoustic pressure generated by a source in the atmosphere to the corresponding seismic signal for a receiver within the ground. The ground is considered to be a porous elastic medium in which wave behaviour can be approximated by the Biot-Stoll model. The model is extended for use of a temporal pressure pulse as an input, and to produce output on a plan 2D map, where the wind effect on the acoustic to seismic coupling can be reproduced. I invest extensive effort in making the model user-friendly, as the project aims at reaching a large audience, comprising, for example, geo-morphologists, biologists and sociologists. Finally, the model is subjected to synthetic testing as well as a qualitative comparison of the predicted ground particle velocity and the seismic signal of a real heli-copter flight as a source of acoustic input.
These studies advance understanding of the operation of specific natural processes in channels and on hillslopes, and bring us closer to designing functioning early warning systems for mass-wasting and flood events. This thesis also raises questions that have not been considered before, such as the con-tribution of human acoustic pollution to the seismic hum and its impact on the natural environment, or the importance of cracks in the self-development of the failure plane prior to slope. Together, these studies question general assumptions usually made regarding the triggering of mass wasting or the hillslope-channel connectivity. Beyond this, the thesis covers the axis of fundamental research progres-sion, from data exploration and method and theory development to proof of concept, and shows how in the rapidly developing field of environmental seismology, an active awareness of progress can help strengthen and accelerate general advances.
The European Alps are amongst the regions with highest glacier mass loss rates over the last decades. Under the threat of ongoing climate change, the ability to predict glacier mass balance changes for water and risk management purposes has become imperative. This raises an urgent need for reliable glacier models. The European Alps do not only host glaciers, but also numerous caves containing carbonate formations, called speleothems. Previous studies have shown that those speleothems also grew during times when the cave was covered by a warm-based glacier. In this thesis, I utilise speleothems from the European Alps as archives of local, environmental conditions related to mountain glacier evolution.
Previous studies have shown that speleothem isotope data from the Alps can be strongly affected by in-cave processes. Therefore, part of this thesis focusses on developing an isotope evolution model, which successfully reproduces differences between contemporaneous growing speleothems. The model is used to propose correction approaches for prior calcite precipitation effects on speleothem oxygen isotopes (δ18O). Applications on speleothem records from caves outside of the Alps demonstrate that corrected δ18O agrees better with other records and climate model simulations.
Existing speleothem growth histories and carbon isotope (δ13C) records from Alpine caves located at different elevations are used to infer soil vs. glacier cover and the thermal regime of the glacier over the last glacial cycle. The compatibility with glacier evolution models is statistically assessed. A general agreement between speleothem δ13C-derived information on soil vs. glacier presence and modelled glacier coverage is found. However, glacier retreat during Marine Isotope Stage (MIS) 3 seems to be underestimated by the model. Furthermore, speleothem data provides evidence of surface temperature above the freezing point which is, however, not fully reproduced by the simulations.
History of glacier cover and their thermal regime is explored for the high-elevation cave system Melchsee-Frutt in the Swiss Alps. Based on new (MIS 9b – MIS 7b, MIS 2) and available speleothem δ13C (MIS 7a – 5d) data, warm-based glacier cover is inferred for MIS 8, 7d, 6, and 2. Also a short period of cold-based ice coverage is found for early MIS 6. In a detailed multi-proxy analysis (δ18O, δ13C, Mg/Ca and Sr/Ca), millennial-scale changes in the glacier-related source of the water infiltrating in the karst during MIS 8 and 7d are found and linked to Northern Hemisphere climate variability.
While speleothem records from high-elevation cave sites in the Alps exhibit huge potential for glacier reconstruction, several limitations remain, which are discussed throughout this thesis. Ultimately, recommendations are given to further leverage subglacial speleothems as an archive of glacier dynamics.
Galaxy morphology is a fossil record of how galaxies formed and evolved and can be regarded as a function of the dynamical state of a galaxy. It encodes the physical processes that dominate its evolutionary history, and is strongly aligned with physical properties like stellar mass, star formation rate and local environment. At a distance of ∼50 and 60 kpc, the Magellanic Clouds represent the nearest interacting pair of dwarf irregular galaxies to the Milky Way, rendering them an important test bed for galaxy morphology in the context of galaxy interactions and the effect of the local environment in which they reside. The Large Magellanic Cloud is classified as the prototype for Magellanic Spiral galaxies, with one prominent spiral arm, an offset bar and an inclined rotating disc while the Small Magellanic Cloud is classified as a dwarf Irregular galaxy and is known for its unstructured shape and large depth across the line–of–sight. Resolved stellar populations are powerful probes of a wide range of astrophysical phenomena, the proximity of the Magellanic Clouds allows us to resolve their stellar populations to individual stars that share coherent chemical and age distributions. The coherent properties of resolved stellar populations enable us to analyse them as a function of position within the Magellanic Clouds, offering a picture of the growth of the galaxies’ substructures over time and yielding a comprehensive view of their morphology. Furthermore, investigating the kinematics of the Magellanic Clouds offers valuable insights into their dynamics and evolutionary history. By studying the motions and velocities of stars within these galaxies, we can trace their past interactions, with the Milky Way or with each other and unravel the complex interplay of forces that have influenced the Magellanic Clouds’ formation and evolution.
In Chapter 2, the VISTA survey of the Magellanic Clouds was employed to generate unprecedented high-resolution morphological maps of the Magellanic Clouds in the near-infrared. Utilising colour-magnitude diagrams and theoretical evolutionary models to segregate stellar populations, this approach enabled a comprehensive age tomography of the galaxies. It revealed previously uncharacterised features in their central regions at spatial resolutions of 0.13 kpc (Large Magellanic Cloud) and 0.16 kpc (Small Magellanic Cloud), the findings showcased the impact of tidal interactions on their inner regions. Notably, the study highlighted the enhanced coherent structures in the Large Magellanic Cloud, shedding light on the significant role of the recent Magellanic Clouds’ interaction 200 Myr ago in shaping many of the fine structures. The Small Magellanic Cloud revealed asymmetry in younger populations and irregularities in intermediate-age ones, pointing towards the influence of past tidal interactions.
In Chapter 3, an examination of the outskirts of the Magellanic Clouds led to the identification of new substructures through the use of near-infrared photometry from the VISTA Hemisphere Survey and multi-dimensional phase-space information from Gaia. The distances and proper motions of these substructures were investigated. This analysis revealed the impact of past Magellanic Clouds’ interactions and the influence of the Milky Way’s tidal field on the morphology and kinematics of the Magellanic Clouds. A bi-modal distance distribution was identified within the luminosity function of the red clump stars in the Small Magellanic Cloud, notably in its eastern regions, with the foreground substructure being attributed to the Magellanic Clouds’ interaction around 200 Myr ago. Furthermore, associations with the Counter Bridge and Old Bridge were uncovered through the detection of background and foreground structures in various regions of the SMC.
In chapter 4, a detailed kinematic analysis of the Small Magellanic Cloud was conducted using spectra from the European Southern Observatory Science Archive Facility. The study reveals distinct kinematics in the Wing and bar regions, attributed to interactions with the Large Magellanic Cloud and variations in star formation history. Notably, velocity disparities are observed in the bar’s young main sequence stars, aligning with specific star-forming episodes, and suggesting potential galactic stretching or tidal stripping, as corroborated by proper motion studies.
As followers are becoming more educated and better connected, empowering leadership has gained traction in recent times as an alternative to traditional top-down models of leadership. Several scholars have investigated the relationship between empowering leadership and other variables in different contexts. As most previous studies have focused on the positive aspects of empowering leadership, research on its potential dark side is scarce. Furthermore, no previous study has examined whether and how the transfer of workload from followers to leaders can occur over time, which I proposed can lead to emotional exhaustion and work-family conflict among leaders. Therefore, I proposed that despite the positive outcomes of empowering leadership for both followers and leaders, it may also trigger negative outcomes capable of affecting the well-being of leaders. Drawing on the Conservation of Resources (COR) theory, Job Demand-Resources (JD-R) theory, and Too-Much-of-a-Good-Thing (TMGT) effect model, I investigated this idea. Using follower workload as a moderator, I proposed that the relationship between empowering leadership and leader workload is positive when follower workload is high and negative when follower workload is low. In addition, I examined how empowering leadership interacts with follower workload to affect leader emotional exhaustion and work-family conflict, mediated by leader workload. I proposed that this interaction results in a negative relationship between empowering leadership and both outcomes when follower workload is low, and a positive relationship when it is high.
I tested these hypotheses using data from a three-wave time-lagged design field study with 65 leader-follower dyads consisting of civil servants from different administrative entities of India and Pakistan. The time lag between each study variable was four weeks. At Time 1 (T1), followers answered questions about demographic characteristics, virtual interaction with their leaders, their workload, and the extent to which their leaders practice empowering leadership. At the same time, leaders answered questions about demographic characteristics and their job satisfaction. At Time 2 (T2), leaders provided data on their own workload. Finally, at Time 3 (T3), leaders rated their emotional exhaustion and work-family conflict. A moderated mediation model was tested using PROCESS Model 7 in R. The findings of the study reveal that a significant increase in follower workload through empowering leadership will also increase the leader's workload. Consequently, this increased leader workload leads to a crossover of this interactive effect onto the level of emotional exhaustion and work-family conflict experienced by leaders.
This research offers various contributions to the leadership literature. While empowering leadership has been commonly associated with positive outcomes, my study reveals that it can also lead to negative outcomes. In addition, it shifts the focus of existing research from the effect of empowering leadership on followers to the consequences that it might have for leaders themselves. Overall, my research underscores the need for leaders to consider the potential counterproductive effects of empowering leadership and tailor their approach accordingly.
Human-induced climate change is impacting the global water cycle by, e.g., causing changes in precipitation patterns, evapotranspiration dynamics, cryosphere shrinkage, and complex streamflow trends. These changes, coupled with the increased frequency and severity of extreme hydrometeorological events like floods, droughts, and heatwaves, contribute to hydroclimatic disasters, posing significant implications for local and global infrastructure, human health, and overall productivity.
In the tropical Andes, climate change is evident through warming trends, glacier retreats, and shifts in precipitation patterns, leading to altered risks of floods and droughts, e.g., in the upper Amazon River basin. Projections for the region indicate rising temperatures, potential glacier disappearance or substantial shrinkage, and altered streamflow patterns, highlighting challenges in water availability due to these expected changes and growing human water demand. The evolving trends in hydroclimatic conditions in the tropical Andes present significant challenges to socioeconomic and environmental systems, emphasizing the need for a comprehensive understanding to guide effective adaptation policies and strategies in response to the impacts of climate change in the region.
The main objective of this thesis is to investigate current hydrological dynamics in the tropical Andes of Peru and Ecuador and their responses to climate change. Given the scarcity of hydrometeorological data in the region, this objective was accomplished through a comprehensive data preparation and analysis in combination with hydrological modeling using the Soil and Water Assessment Tool (SWAT) eco-hydrological model. In this context, the initial steps involved assessing, identifying, and/or generating more reliable climate input data to address data limitations.
The thesis introduces RAIN4PE, a high-resolution precipitation dataset for Peru and Ecuador, developed by merging satellite, reanalysis, and ground-based data with surface elevation through the random forest method. Further adjustments of precipitation estimates were made for catchments influenced by fog/cloud water input on the eastern side of the Andes using streamflow data and applying the method of reverse hydrology. RAIN4PE surpasses other global and local precipitation datasets, showcasing superior reliability and accuracy in representing precipitation patterns and simulating hydrological processes across the tropical Andes. This establishes it as the optimal precipitation product for hydrometeorological applications in the region.
Due to the significant biases and limitations of global climate models (GCMs) in representing key atmospheric variables over the tropical Andes, this study developed regionally adapted GCM simulations specifically tailored for Peru and Ecuador. These simulations are known as the BASD-CMIP6-PE dataset, and they were derived using reliable, high-resolution datasets like RAIN4PE as reference data. The BASD-CMIP6-PE dataset shows notable improvements over raw GCM simulations, reflecting enhanced representations of observed climate properties and accurate simulation of streamflow, including high and low flow indices. This renders it suitable for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.
In addition to generating more accurate climatic input data, a reliable hydrological model is essential for simulating watershed hydrological processes. To tackle this challenge, the thesis presents an innovative multiobjective calibration framework integrating remote sensing vegetation data, baseflow index, discharge goodness-of-fit metrics, and flow duration curve signatures. In contrast to traditional calibration strategies relying solely on discharge goodness-of-fit metrics, this approach enhances the simulation of vegetation, streamflow, and the partitioning of flow into surface runoff and baseflow in a typical Andean catchment. The refined hydrological model calibration strategy was applied to conduct reliable simulations and understand current and future hydrological trajectories in the tropical Andes.
By establishing a region-suitable and thoroughly tested hydrological model with high-resolution and reliable precipitation input data from RAIN4PE, this study provides new insights into the spatiotemporal distribution of water balance components in Peru and transboundary catchments. Key findings underscore the estimation of Peru's total renewable freshwater resource (total river runoff of 62,399 m3/s), with the Peruvian Amazon basin contributing 97.7%. Within this basin, the Amazon-Andes transition region emerges as a pivotal hotspot for water yield (precipitation minus evapotranspiration), characterized by abundant rainfall and lower atmospheric water demand/evapotranspiration. This finding underlines its paramount role in influencing the hydrological variability of the entire Amazon basin.
Subsurface hydrological pathways, particularly baseflow from aquifers, strongly influence water yield in lowland and Andean catchments, sustaining streamflow, especially during the extended dry season. Water yield demonstrates an elevation- and latitude-dependent increase in the Pacific Basin (catchments draining into the Pacific Ocean), while it follows an unimodal curve in the Peruvian Amazon Basin, peaking in the Amazon-Andes transition region. This observation indicates an intricate relationship between water yield and elevation.
In Amazon lowlands rivers, particularly in the Ucayali River, floodplains play a significant role in shaping streamflow seasonality by attenuating and delaying peak flows for up to two months during periods of high discharge. This observation underscores the critical importance of incorporating floodplain dynamics into hydrological simulations and river management strategies for accurate modeling and effective water resource management.
Hydrological responses vary across different land use types in high Andean catchments. Pasture areas exhibit the highest water yield, while agricultural areas and mountain forests show lower yields, emphasizing the importance of puna (high-altitude) ecosystems, such as pastures, páramos, and bofedales, in regulating natural storage.
Projected future hydrological trajectories were analyzed by driving the hydrological model with regionalized GCM simulations provided by the BASD-CMIP6-PE dataset. The analysis considered sustainable (low warming, SSP1-2.6) and fossil fuel-based development (high-end warming, SSP5-8.5) scenarios for the mid (2035-2065) and end (2065-2095) of the century. The projected changes in water yield and streamflow across the tropical Andes exhibit distinct regional and seasonal variations, particularly amplified under a high-end warming scenario towards the end of the century. Projections suggest year-round increases in water yield and streamflow in the Andean regions and decreases in the Amazon lowlands, with exceptions such as the northern Amazon expecting increases during wet seasons. Despite these regional differences, the upper Amazon River's streamflow is projected to remain relatively stable throughout the 21st century. Additionally, projections anticipate a decrease in low flows in the Amazon lowlands and an increased risk of high flows (floods) in the Andean and northern Amazon catchments.
This thesis significantly contributes to enhancing climatic data generation, overcoming regional limitations that previously impeded hydrometeorological research, and creating new opportunities. It plays a crucial role in advancing hydrological model calibration, improving the representation of internal hydrological processes, and achieving accurate results for the right reasons. Novel insights into current hydrological dynamics in the tropical Andes are fundamental for improving water resource management. The anticipated intensified changes in water flows and hydrological extreme patterns under a high-end warming scenario highlight the urgency of implementing emissions mitigation and adaptation measures to address the heightened impacts on water resources.
In fact, the new datasets (RAIN4PE and BASD-CMIP6-PE) have already been utilized by researchers and experts in regional and local-scale projects and catchments in Peru and Ecuador. For instance, they have been applied in river catchments such as Mantaro, Piura, and San Pedro to analyze local historical and future developments in climate and water resources.
Prediction is often regarded as a central and domain-general aspect of cognition. This proposal extends to language, where predictive processing might enable the comprehension of rapidly unfolding input by anticipating upcoming words or their semantic features. To make these predictions, the brain needs to form a representation of the predictive patterns in the environment. Predictive processing theories suggest a continuous learning process that is driven by prediction errors, but much is still to be learned about this mechanism in language comprehension. This thesis therefore combined three electroencephalography (EEG) experiments to explore the relationship between prediction and implicit learning at the level of meaning.
Results from Study 1 support the assumption that the brain constantly infers und updates probabilistic representations of the semantic context, potentially across multiple levels of complexity. N400 and P600 brain potentials could be predicted by semantic surprise based on a probabilistic estimate of previous exposure and a more complex probability representation, respectively.
Subsequent work investigated the influence of prediction errors on the update of semantic predictions during sentence comprehension. In line with error-based learning, unexpected sentence continuations in Study 2 ¬– characterized by large N400 amplitudes ¬– were associated with increased implicit memory compared to expected continuations. Further, Study 3 indicates that prediction errors not only strengthen the representation of the unexpected word, but also update specific predictions made from the respective sentence context. The study additionally provides initial evidence that the amount of unpredicted information as reflected in N400 amplitudes drives this update of predictions, irrespective of the strength of the original incorrect prediction.
Together, these results support a central assumption of predictive processing theories: A probabilistic predictive representation at the level of meaning that is updated by prediction errors. They further propose the N400 ERP component as a possible learning signal. The results also emphasize the need for further research regarding the role of the late positive ERP components in error-based learning. The continuous error-based adaptation described in this thesis allows the brain to improve its predictive representation with the aim to make better predictions in the future.
Data preparation stands as a cornerstone in the landscape of data science workflows, commanding a significant portion—approximately 80%—of a data scientist's time. The extensive time consumption in data preparation is primarily attributed to the intricate challenge faced by data scientists in devising tailored solutions for downstream tasks. This complexity is further magnified by the inadequate availability of metadata, the often ad-hoc nature of preparation tasks, and the necessity for data scientists to grapple with a diverse range of sophisticated tools, each presenting its unique intricacies and demands for proficiency.
Previous research in data management has traditionally concentrated on preparing the content within columns and rows of a relational table, addressing tasks, such as string disambiguation, date standardization, or numeric value normalization, commonly referred to as data cleaning. This focus assumes a perfectly structured input table. Consequently, the mentioned data cleaning tasks can be effectively applied only after the table has been successfully loaded into the respective data cleaning environment, typically in the later stages of the data processing pipeline.
While current data cleaning tools are well-suited for relational tables, extensive data repositories frequently contain data stored in plain text files, such as CSV files, due to their adaptable standard. Consequently, these files often exhibit tables with a flexible layout of rows and columns, lacking a relational structure. This flexibility often results in data being distributed across cells in arbitrary positions, typically guided by user-specified formatting guidelines.
Effectively extracting and leveraging these tables in subsequent processing stages necessitates accurate parsing. This thesis emphasizes what we define as the “structure” of a data file—the fundamental characters within a file essential for parsing and comprehending its content. Concentrating on the initial stages of the data preprocessing pipeline, this thesis addresses two crucial aspects: comprehending the structural layout of a table within a raw data file and automatically identifying and rectifying any structural issues that might hinder its parsing. Although these issues may not directly impact the table's content, they pose significant challenges in parsing the table within the file.
Our initial contribution comprises an extensive survey of commercially available data preparation tools. This survey thoroughly examines their distinct features, the lacking features, and the necessity for preliminary data processing despite these tools. The primary goal is to elucidate the current state-of-the-art in data preparation systems while identifying areas for enhancement. Furthermore, the survey explores the encountered challenges in data preprocessing, emphasizing opportunities for future research and improvement.
Next, we propose a novel data preparation pipeline designed for detecting and correcting structural errors. The aim of this pipeline is to assist users at the initial preprocessing stage by ensuring the correct loading of their data into their preferred systems. Our approach begins by introducing SURAGH, an unsupervised system that utilizes a pattern-based method to identify dominant patterns within a file, independent of external information, such as data types, row structures, or schemata. By identifying deviations from the dominant pattern, it detects ill-formed rows. Subsequently, our structure correction system, TASHEEH, gathers the identified ill-formed rows along with dominant patterns and employs a novel pattern transformation algebra to automatically rectify errors. Our pipeline serves as an end-to-end solution, transforming a structurally broken CSV file into a well-formatted one, usually suitable for seamless loading.
Finally, we introduce MORPHER, a user-friendly GUI integrating the functionalities of both SURAGH and TASHEEH. This interface empowers users to access the pipeline's features through visual elements. Our extensive experiments demonstrate the effectiveness of our data preparation systems, requiring no user involvement. Both SURAGH and TASHEEH outperform existing state-of-the-art methods significantly in both precision and recall.
Due to their sessile lifestyle, plants are constantly exposed to pathogens and possess a multi-layered immune system that prevents infection. The first layer of immunity called pattern-triggered immunity (PTI), enables plants to recognise highly conserved molecules that are present in pathogens, resulting in immunity from non-adaptive pathogens. Adapted pathogens interfere with PTI, however the second layer of plant immunity can recognise these virulence factors resulting in a constant evolutionary battle between plant and pathogen. Xanthomonas campestris pv. vesicatoria (Xcv) is the causal agent of bacterial leaf spot disease in tomato and pepper plants. Like many Gram-negative bacteria, Xcv possesses a type-III secretion system, which it uses to translocate type-III effectors (T3E) into plant cells. Xcv has over 30 T3Es that interfere with the immune response of the host and are important for successful infection. One such effector is the Xanthomonas outer protein M (XopM) that shows no similarity to any other known protein. Characterisation of XopM and its role in virulence was the focus of this work.
While screening a tobacco cDNA library for potential host target proteins, the vesicle-associated membrane protein (VAMP)-associated protein 1-2 like (VAP12) was identified. The interaction between XopM and VAP12 was confirmed in the model species Nicotiana benthamiana and Arabidopsis as well as in tomato, a Xcv host. As plants possess multiple VAP proteins, it was determined that the interaction of XopM and VAP is isoform specific.
It could be confirmed that the major sperm protein (MSP) domain of NtVAP12 is sufficient for binding XopM and that binding can be disrupted by substituting one amino acid (T47) within this domain. Most VAP interactors have at least one FFAT (two phenylalanines [FF] in an acidic tract) related motif, screening the amino acid sequence of XopM showed that XopM has two FFAT-related motifs. Substitution of the second residue of each FFAT motif (Y61/F91) disrupts NtVAP12 binding, suggesting that these motifs cooperatively mediate this interaction. Structural modelling using AlphaFold further confirmed that the unstructured N-terminus of XopM binds NtVAP12 at its MSP domain, which was further confirmed by the generation of truncated XopM variants.
Infection of pepper leaves, with a XopM deficient Xcv strain did not result in a reduction of virulence in comparison to the Xcv wildtype, showing that the function of XopM during infection is redundant. Virus-induced gene silencing of NbVAP12 in N. benthamiana plants also did not affect Xcv virulence, which further indicated that interaction with VAP12 is also non-essential for Xcv virulence. Despite such findings, ectopic expression of wildtype XopM and XopMY61A/F91A in transgenic Arabidopsis seedlings enhanced the growth of a non-pathogenic Pseudomonas syringae pv. tomato (Pst) DC3000 strain. XopM was found to interfere with the PTI response allowing Pst growth independent of its binding to VAP. Furthermore, transiently expressed XopM could suppress reactive oxygen species (ROS; one of the earliest PTI responses) production in N. benthamiana leaves. The FFAT double mutant XopMY61A/F91A as well as the C-terminal truncation variant XopM106-519 could still suppress the ROS response while the N-terminal variant XopM1-105 did not. Suppression of ROS production is therefore independent of VAP binding. In addition, tagging the C-terminal variant of XopM with a nuclear localisation signal (NLS; NLS-XopM106-519) resulted in significantly higher ROS production than the membrane localising XopM106-519 variant, indicating that XopM-induced ROS suppression is localisation dependent.
To further characterise XopM, mass spectrometry techniques were used to identify post-translational modifications (PTM) and potential interaction partners. PTM analysis revealed that XopM contains up to 21 phosphorylation sites, which could influence VAP binding. Furthermore, proteins of the Rab family were identified as potential plant protein interaction partners. Rab proteins serve a multitude of functions including vesicle trafficking and have been previously identified as T3E host targets. Taking this into account, a model of virulence of XopM was proposed, with XopM anchoring itself to VAP proteins to potentially access plasma membrane associated proteins. XopM possibly interferes with vesicle trafficking, which in turn suppresses ROS production through an unknown mechanism.
In this work it was shown that XopM targets VAP proteins. The data collected suggests that this T3E uses VAP12 to anchor itself into the right place to carry out its function. While more work is needed to determine how XopM contributes to virulence of Xcv, this study sheds light onto how adapted pathogens overcome the immune response of their hosts. It is hoped that such knowledge will contribute to the development of crops resistant to Xcv in the future.
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.