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Institute
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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.
Long-term bacteria-fungi-plant associations in permafrost soils inferred from palaeometagenomics
(2024)
The arctic is warming 2 – 4 times faster than the global average, resulting in a strong feedback on northern ecosystems such as boreal forests, which cover a vast area of the high northern latitudes. With ongoing global warming, the treeline subsequently migrates northwards into tundra areas. The consequences of turning ecosystems are complex: on the one hand, boreal forests are storing large amounts of global terrestrial carbon and act as a carbon sink, dragging carbon dioxide out of the global carbon cycle, suggesting an enhanced carbon uptake with increased tree cover. On the other hand, with the establishment of trees, the albedo effect of tundra decreases, leading to enhanced soil warming. Meanwhile, permafrost thaws, releasing large amounts of previously stored carbon into the atmosphere. So far, mainly vegetation dynamics have been assessed when studying the impact of warming onto ecosystems. Most land plants are living in close symbiosis with bacterial and fungal communities, sustaining their growth in nutrient poor habitats. However, the impact of climate change on these subsoil communities alongside changing vegetation cover remains poorly understood. Therefore, a better understanding of soil community dynamics on multi millennial timescales is inevitable when addressing the development of entire ecosystems. Unravelling long-term cross-kingdom dependencies between plant, fungi, and bacteria is not only a milestone for the assessment of warming on boreal ecosystems. On top, it also is the basis for agriculture strategies to sustain society with sufficient food in a future warming world.
The first objective of this thesis was to assess ancient DNA as a proxy for reconstructing the soil microbiome (Manuscripts I, II, III, IV). Research findings across these projects enable a comprehensive new insight into the relationships of soil microorganisms to the surrounding vegetation. First, this was achieved by establishing (Manuscript I) and applying (Manuscript II) a primer pair for the selective amplification of ancient fungal DNA from lake sediment samples with the metabarcoding approach. To assess fungal and plant co-variation, the selected primer combination (ITS67, 5.8S) amplifying the ITS1 region was applied on samples from five boreal and arctic lakes. The obtained data showed that the establishment of fungal communities is impacted by warming as the functional ecological groups are shifting. Yeast and saprotroph dominance during the Late Glacial declined with warming, while the abundance of mycorrhizae and parasites increased with warming. The overall species richness was also alternating. The results were compared to shotgun sequencing data reconstructing fungi and bacteria (Manuscripts III, IV), yielding overall comparable results to the metabarcoding approach. Nonetheless, the comparison also pointed out a bias in the metabarcoding, potentially due to varying ITS lengths or copy numbers per genome.
The second objective was to trace fungus-plant interaction changes over time (Manuscripts II, III). To address this, metabarcoding targeting the ITS1 region for fungi and the chloroplast P6 loop for plants for the selective DNA amplification was applied (Manuscript II). Further, shotgun sequencing data was compared to the metabarcoding results (Manuscript III). Overall, the results between the metabarcoding and the shotgun approaches were comparable, though a bias in the metabarcoding was assumed. We demonstrated that fungal shifts were coinciding with changes in the vegetation. Yeast and lichen were mainly dominant during the Late Glacial with tundra vegetation, while warming in the Holocene lead to the expansion of boreal forests with increasing mycorrhizae and parasite abundance. Aside, we highlighted that Pinaceae establishment is dependent on mycorrhizal fungi such as Suillineae, Inocybaceae, or Hyaloscypha species also on long-term scales.
The third objective of the thesis was to assess soil community development on a temporal gradient (Manuscripts III, IV). Shotgun sequencing was applied on sediment samples from the northern Siberian lake Lama and the soil microbial community dynamics compared to ecosystem turnover. Alongside, podzolization processes from basaltic bedrock were recovered (Manuscript III). Additionally, the recovered soil microbiome was compared to shotgun data from granite and sandstone catchments (Manuscript IV, Appendix). We assessed if the establishment of the soil microbiome is dependent on the plant taxon and as such comparable between multiple geographic locations or if the community establishment is driven by abiotic soil properties and as such the bedrock area. We showed that the development of soil communities is to a great extent driven by the vegetation changes and temperature variation, while time only plays a minor role. The analyses showed general ecological similarities especially between the granite and basalt locations, while the microbiome on species-level was rather site-specific. A greater number of correlated soil taxa was detected for deep-rooting boreal taxa in comparison to grasses with shallower roots. Additionally, differences between herbaceous taxa of the late Glacial compared to taxa of the Holocene were revealed.
With this thesis, I demonstrate the necessity to investigate subsoil community dynamics on millennial time scales as it enables further understanding of long-term ecosystem as well as soil development processes and such plant establishment. Further, I trace long-term processes leading to podzolization which supports the development of applied carbon capture strategies under future global warming.
The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt.
The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm.
Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas.
For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season.
Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.
This work analyzed functional and regulatory aspects of the so far little characterized EPSIN N-terminal Homology (ENTH) domain-containing protein EPSINOID2 in Arabidopsis thaliana. ENTH domain proteins play accessory roles in the formation of clathrin-coated vesicles (CCVs) (Zouhar and Sauer 2014). Their ENTH domain interacts with membranes and their typically long, unstructured C-terminus contains binding motifs for adaptor protein complexes and clathrin itself. There are seven ENTH domain proteins in Arabidopsis. Four of them possess the canonical long C-terminus and participate in various, presumably CCV-related intracellular transport processes (Song et al. 2006; Lee et al. 2007; Sauer et al. 2013; Collins et al. 2020; Heinze et al. 2020; Mason et al. 2023). The remaining three ENTH domain proteins, however, have severely truncated C-termini and were termed EPSINOIDs (Zouhar and Sauer 2014; Freimuth 2015). Their functions are currently unclear. Preceding studies focusing on EPSINOID2 indicated a role in root hair formation: epsinoid2 T DNA mutants exhibited an increased root hair density and EPSINOID2-GFP was specifically located in non-hair cell files in the Arabidopsis root epidermis (Freimuth 2015, 2019).
In this work, it was clearly shown that loss of EPSINOID2 leads to an increase in root hair density through analyses of three independent mutant alleles, including a newly generated CRISPR/Cas9 full deletion mutant. The ectopic root hairs emerging from non-hair positions in all epsinoid2 mutant alleles are most likely not a consequence of altered cell fate, because extensive genetic analyses placed EPSINOID2 downstream of the established epidermal patterning network. Thus, EPSINOID2 seems to act as a cell autonomous inhibitor of root hair formation. Attempts to confirm this hypothesis by ectopically overexpressing EPSINOID2 led to the discovery of post-transcriptional and -translational regulation through different mechanisms. One involves the little characterized miRNA844-3p. Interference with this pathway resulted in ectopic EPSINOID2 overexpression and decreased root hair density, confirming it as negative factor in root hair formation. A second mechanism likely involves proteasomal degradation. Treatment with proteasomal inhibitor MG132 led to EPSINOID2-GFP accumulation, and a KEN box degron motif was identified in the EPSINOID2 sequence associated with degradation through a ubiquitin/proteasome-dependent pathway. In line with a tight dose regulation, genetic analyses of all three mutant alleles indicate that EPSINOID2 is haploinsufficient. Lastly, it was revealed that, although EPSINOID2 promoter activity was found in all epidermal cells, protein accumulation was observed in N-cells only, hinting at yet another layer of regulation.
Mindful Eating
(2024)
Maladaptive eating behaviors such as emotional eating, external eating, and loss-of-control eating are widespread in the general population. Moreover, they are associated to adverse health outcomes and well-known for their role in the development and maintenance of eating disorders and obesity (i.e., eating and weight disorders). Eating and weight disorders are associated with crucial burden for individuals as well as high costs for society in general. At the same time, corresponding treatments yield poor outcomes. Thus, innovative concepts are needed to improve prevention and treatment of these conditions.
The Buddhist concept of mindfulness (i.e., paying attention to the present moment without judgement) and its delivery via mindfulness-based intervention programs (MBPs) has gained wide popularity in the area of maladaptive eating behaviors and associated eating and weight disorders over the last two decades. Though previous findings on their effects seem promising, the current assessment of mindfulness and its mere application via multi-component MBPs hampers to draw conclusions on the extent to which mindfulness-immanent qualities actually account for the effects (e.g., the modification of maladaptive eating behaviors). However, this knowledge is pivotal for interpreting previous effects correctly and for avoiding to cause harm in particularly vulnerable groups such as those with eating and weight disorders.
To address these shortcomings, recent research has focused on the context-specific approach of mindful eating (ME) to investigate underlying mechanisms of action. ME can be considered a subdomain of generic mindfulness describing it specifically in relation to the process of eating and associated feelings, thoughts, and motives, thus including a variety of different attitudes and behaviors. However, there is no universal operationalization and the current assessment of ME suffers from different limitations. Specifically, current measurement instruments are not suited for a comprehensive assessment of the multiple facets of the construct that are currently discussed as important in the literature. This in turn hampers comparisons of different ME facets which would allow to evaluate their particular effect on maladaptive eating behaviors. This knowledge is needed to tailor prevention and treatment of associated eating and weight disorders properly and to explore potential underlying mechanisms of action which have so far been proposed mainly on theoretical grounds.
The dissertation at hand aims to provide evidence-based fundamental research that contributes to our understanding of how mindfulness, more specifically its context-specific form of ME, impacts maladaptive eating behaviors and, consequently, how it could be used appropriately to enrich the current prevention and treatment approaches for eating and weight disorders in the future.
Specifically, in this thesis, three scientific manuscripts applying several qualitative and quantitative techniques in four sequential studies are presented. These manuscripts were published in or submitted to three scientific peer-reviewed journals to shed light on the following questions:
I. How can ME be measured comprehensively and in a reliable and valid way to advance the understanding of how mindfulness works in the context of eating?
II. Does the context-specific construct of ME have an advantage over the generic concept in advancing the understanding of how mindfulness is related to maladaptive eating behaviors?
III. Which ME facets are particularly useful in explaining maladaptive eating behaviors?
IV. Does training a particular ME facet result in changes in maladaptive eating behaviors?
To answer the first research question (Paper 1), a multi-method approach using three subsequent studies was applied to develop and validate a comprehensive self-report instrument to assess the multidimensional construct of ME - the Mindful Eating Inventory (MEI). Study 1 aimed to create an initial version of the MEI by following a three-step approach: First, a comprehensive item pool was compiled by including selected and adapted items of the existing ME questionnaires and supplementing them with items derived from an extensive literature review. Second, the preliminary item pool was complemented and checked for content validity by experts in the field of eating behavior and/or mindfulness (N = 15). Third, the item pool was further refined through qualitative methods: Three focus groups comprising laypersons (N = 16) were used as a check for applicability. Subsequently, think-aloud protocols (N = 10) served as a last check of comprehensibility and elimination of ambiguities.
The resulting initial MEI version was tested in Study 2 in an online convenience sample (N = 828) to explore its factor structure using exploratory factor analysis (EFA). Results were used to shorten the questionnaire in accordance with qualitative and quantitative criteria yielding the final MEI version which encompasses 30 items. These items were assigned to seven ME facets: (1) ‘Accepting and Non-attached Attitude towards one’s own eating experience’ (ANA), (2) ‘Awareness of Senses while Eating’ (ASE), (3) ‘Eating in Response to awareness of Fullness‘ (ERF), (4) ‘Awareness of eating Triggers and Motives’ (ATM), (5) ‘Interconnectedness’ (CON), (6) ‘Non-Reactive Stance’ (NRS) and (7) Focused Attention on Eating’ (FAE).
Study 3 sought to confirm the found facets and the corresponding factor structure in an independent online convenience sample (N = 612) using confirmatory factor analysis (CFA). The study served as further indication of the assumed multidimensionality of ME (the correlational seven-factor model was shown to be superior to a single-factor model). Psychometric properties of the MEI, regarding factorial validity, internal consistency, retest-reliability, and observed criterion validity using a wide range of eating-specific and general health-related outcomes, showed the inventory to be suitable for a comprehensive, reliable and valid assessment of ME. These findings were complemented by demonstrating measurement invariance of the MEI regarding gender. In accordance with the factor structure of the MEI, Paper 1 offers an empirically-derived definition of ME, succeeding in overcoming ambiguities and problems of previous attempts at defining the construct.
To answer the second and third research questions (Paper 2) a subsample of Study 2 from the MEI validation studies (N = 292) was analyzed. Incremental validity of ME beyond generic mindfulness was shown using hierarchical regression models concerning the outcome variables of maladaptive eating behaviors (emotional eating and uncontrolled eating) and nutrition behaviors (consumption of energy-dense food). Multiple regression analyses were applied to investigate the impact of the seven different ME facets (identified in Paper 1) on the same outcome variables. The following ME facets significantly contributed to explaining variance in maladaptive eating and nutrition behaviors: Accepting and Non-attached Attitude towards one`s own eating experience (ANA), Eating in Response to awareness of Fullness (ERF), the Awareness of eating Triggers and Motives (ATM), and a Non-Reactive Stance (NRS, i.e., an observing, non-impulsive attitude towards eating triggers). Results suggest that these ME facets are promising variables to consider when a) investigating potential underlying mechanisms of mindfulness and MBPs in the context of eating and b) addressing maladaptive eating behaviors in general as well as in the prevention and treatment of eating and weight disorders.
To answer the fourth research question (Paper 3), a training on an isolated exercise (‘9 Hunger’) based on the previously identified ME facet ATM was designed to explore its particular association with changes in maladaptive eating behaviors and thus to preliminary explore one possible mechanism of action. The online study was realized using a randomized controlled trial (RCT) design. Latent Change Scores (LCS) across three measurement points (before the training, directly after the training and three months later) were compared between the intervention group (n = 211) and a waitlist control group (n = 188). Short- and longer-term effects of the training could be shown on maladaptive eating behaviors (emotional eating, external eating, loss-of-control eating) and associated outcomes (intuitive eating, ME, self-compassion, well-being). Findings serve as preliminary empirical evidence that MBPs might influence maladaptive eating behaviors through an enhanced non-judgmental awareness of and distinguishment between eating motives and triggers (i.e., ATM). This mechanism of action had previously only been hypothesized from a theoretical perspective. Since maladaptive eating behaviors are associated with eating and weight disorders, the findings can enhance our understanding of the general effects of MBPs on these conditions.
The integration of the different findings leads to several suggestions of how ME might enrich different kinds of future interventions on maladaptive eating behaviors to improve health in general or the prevention and treatment of eating and weight disorders in particular. Strengths of the thesis (e.g., deliberate specific methodology, variety of designs and methods, high number of participants) are emphasized. The main limitations particularly regarding sample characteristics (e.g., higher level of formal education, fewer males, self-selected) are discussed to arrive at an outline for future studies (e.g., including multi-modal-multi-method approaches, clinical eating disorder samples and youth samples) to improve upcoming research on ME and underlying mechanisms of action of MBPs for maladaptive eating behaviors and associated eating and weight disorders.
This thesis enriches current research on mindfulness in the context of eating by providing fundamental research on the core of the ME construct. Thereby it delivers a reliable and valid instrument to comprehensively assess ME in future studies as well as an operational definition of the construct. Findings on ME facet level might inform upcoming research and practice on how to address maladaptive eating behaviors appropriately in interventions. The ME skill ‘Awareness of eating Triggers and Motives (ATM)’ as one particular mechanism of action should be further investigated in representative community and specific clinical samples to examine the validity of the results in these groups and to justify an application of the concept to the general population as well as to subgroups with eating and weight disorders in particular.
In conclusion, findings of the current thesis can be used to set future research on mindfulness, more specifically ME, and its underlying mechanism in the context of eating on a more evidence-based footing. This knowledge can inform upcoming prevention and treatment to tailor MBPs on maladaptive eating behaviors and associated eating and weight disorders appropriately.
Heat stress (HS) is a major abiotic stress that negatively affects plant growth and productivity. However, plants have developed various adaptive mechanisms to cope with HS, including the acquisition and maintenance of thermotolerance, which allows them to respond more effectively to subsequent stress episodes. HS memory includes type II transcriptional memory which is characterized by enhanced re-induction of a subset of HS memory genes upon recurrent HS. In this study, new regulators of HS memory in A. thaliana were identified through the characterization of rein mutants.
The rein1 mutant carries a premature stop in CYCLIN-DEPENDENT-KINASE 8 (CDK8) which is part of the cyclin kinase module of the Mediator complex. Rein1 seedlings show impaired type II transcriptional memory in multiple heat-responsive genes upon re-exposure to HS. Additionally, the mutants exhibit a significant deficiency in HS memory at the physiological level. Interaction studies conducted in this work indicate that CDK8 associates with the memory HEAT SHOCK FACTORs HSAF2 and HSFA3. The results suggest that CDK8 plays a crucial role in HS memory in plants together with other memory HSFs, which may be potential targets of the CDK8 kinase function. Understanding the role and interaction network of the Mediator complex during HS-induced transcriptional memory will be an exciting aspect of future HS memory research.
The second characterized mutant, rein2, was selected based on its strongly impaired pAPX2::LUC re-induction phenotype. In gene expression analysis, the mutant revealed additional defects in the initial induction of HS memory genes. Along with this observation, basal thermotolerance was impaired similarly as HS memory at the physiological level in rein2. Sequencing of backcrossed bulk segregants with subsequent fine mapping narrowed the location of REIN2 to a 1 Mb region on chromosome 1. This interval contains the At1g65440 gene, which encodes the histone chaperone SPT6L. SPT6L interacts with chromatin remodelers and bridges them to the transcription machinery to regulate nucleosome and Pol II occupancy around the transcriptional start site. The EMS-induced missense mutation in SPT6L may cause altered HS-induced gene expression in rein2, possibly triggered by changes in the chromatin environment resulting from altered histone chaperone function.
Expanding research on screen-derived factors that modify type II transcriptional memory has the potential to enhance our understanding of HS memory in plants. Discovering connections between previously identified memory factors will help to elucidate the underlying network of HS memory. This knowledge can initiate new approaches to improve heat resilience in crops.
Column-oriented database systems can efficiently process transactional and analytical queries on a single node. However, increasing or peak analytical loads can quickly saturate single-node database systems. Then, a common scale-out option is using a database cluster with a single primary node for transaction processing and read-only replicas. Using (the naive) full replication, queries are distributed among nodes independently of the accessed data. This approach is relatively expensive because all nodes must store all data and apply all data modifications caused by inserts, deletes, or updates.
In contrast to full replication, partial replication is a more cost-efficient implementation: Instead of duplicating all data to all replica nodes, partial replicas store only a subset of the data while being able to process a large workload share. Besides lower storage costs, partial replicas enable (i) better scaling because replicas must potentially synchronize only subsets of the data modifications and thus have more capacity for read-only queries and (ii) better elasticity because replicas have to load less data and can be set up faster. However, splitting the overall workload evenly among the replica nodes while optimizing the data allocation is a challenging assignment problem.
The calculation of optimized data allocations in a partially replicated database cluster can be modeled using integer linear programming (ILP). ILP is a common approach for solving assignment problems, also in the context of database systems. Because ILP is not scalable, existing approaches (also for calculating partial allocations) often fall back to simple (e.g., greedy) heuristics for larger problem instances. Simple heuristics may work well but can lose optimization potential.
In this thesis, we present optimal and ILP-based heuristic programming models for calculating data fragment allocations for partially replicated database clusters. Using ILP, we are flexible to extend our models to (i) consider data modifications and reallocations and (ii) increase the robustness of allocations to compensate for node failures and workload uncertainty. We evaluate our approaches for TPC-H, TPC-DS, and a real-world accounting workload and compare the results to state-of-the-art allocation approaches. Our evaluations show significant improvements for varied allocation’s properties: Compared to existing approaches, we can, for example, (i) almost halve the amount of allocated data, (ii) improve the throughput in case of node failures and workload uncertainty while using even less memory, (iii) halve the costs of data modifications, and (iv) reallocate less than 90% of data when adding a node to the cluster. Importantly, we can calculate the corresponding ILP-based heuristic solutions within a few seconds. Finally, we demonstrate that the ideas of our ILP-based heuristics are also applicable to the index selection problem.