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From its first use in the field of biochemistry, instrumental analysis offered a variety of invaluable tools for the comprehensive description of biological systems. Multi-selective methods that aim to cover as many endogenous compounds as possible in biological samples use different analytical platforms and include methods like gene expression profile and metabolite profile analysis. The enormous amount of data generated in application of profiling methods needs to be evaluated in a manner appropriate to the question under investigation. The new field of system biology rises to the challenge to develop strategies for collecting, processing, interpreting, and archiving this vast amount of data; to make those data available in form of databases, tools, models, and networks to the scientific community. On the background of this development a multi-selective method for the determination of phytohormones was developed and optimised, complementing the profile analyses which are already in use (Chapter I). The general feasibility of a simultaneous analysis of plant metabolites and phytohormones in one sample set-up was tested by studies on the analytical robustness of the metabolite profiling protocol. The recovery of plant metabolites proved to be satisfactory robust against variations in the extraction protocol by using common extraction procedures for phytohormones; a joint extraction of metabolites and hormones from plant tissue seems practicable (Chapter II). Quantification of compounds within the context of profiling methods requires particular scrutiny (Chapter II). In Chapter III, the potential of stable-isotope in vivo labelling as normalisation strategy for profiling data acquired with mass spectrometry is discussed. First promising results were obtained for a reproducible quantification by stable-isotope in vivo labelling, which was applied in metabolomic studies. In-parallel application of metabolite and phytohormone analysis to seedlings of the model plant Arabidopsis thaliana exposed to sulfate limitation was used to investigate the relationship between the endogenous concentration of signal elements and the ‘metabolic phenotype’ of a plant. An automated evaluation strategy was developed to process data of compounds with diverse physiological nature, such as signal elements, genes and metabolites – all which act in vivo in a conditional, time-resolved manner (Chapter IV). Final data analysis focussed on conditionality of signal-metabolome interactions.
Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users’ Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users’ private traits from what they share and act on different social networking platforms.
In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user’s personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users’ long-term personality development and further unlock the potential of cooperation between psychologists and data scientists.
This work analyzes the saving and consumption behavior of agents faced with the possibility of unemployment in a dynamic and stochastic life cycle model. The intertemporal optimization is based on Dynamic Programming with a backward recursion algorithm. The implemented uncertainty is not based on income shocks as it is done in traditional life cycle models but uses Markov probabilities where the probability for the next employment status of the agent depends on the current status. The utility function used is a CRRA function (constant relative risk aversion), combined with a CES function (constant elasticity of substitution) and has several consumption goods, a subsistence level, money and a bequest function.
In the living cell, the organization of the complex internal structure relies to a large extent on molecular motors. Molecular motors are proteins that are able to convert chemical energy from the hydrolysis of adenosine triphosphate (ATP) into mechanical work. Being about 10 to 100 nanometers in size, the molecules act on a length scale, for which thermal collisions have a considerable impact onto their motion. In this way, they constitute paradigmatic examples of thermodynamic machines out of equilibrium. This study develops a theoretical description for the energy conversion by the molecular motor myosin V, using many different aspects of theoretical physics. Myosin V has been studied extensively in both bulk and single molecule experiments. Its stepping velocity has been characterized as a function of external control parameters such as nucleotide concentration and applied forces. In addition, numerous kinetic rates involved in the enzymatic reaction of the molecule have been determined. For forces that exceed the stall force of the motor, myosin V exhibits a 'ratcheting' behaviour: For loads in the direction of forward stepping, the velocity depends on the concentration of ATP, while for backward loads there is no such influence. Based on the chemical states of the motor, we construct a general network theory that incorporates experimental observations about the stepping behaviour of myosin V. The motor's motion is captured through the network description supplemented by a Markov process to describe the motor dynamics. This approach has the advantage of directly addressing the chemical kinetics of the molecule, and treating the mechanical and chemical processes on equal grounds. We utilize constraints arising from nonequilibrium thermodynamics to determine motor parameters and demonstrate that the motor behaviour is governed by several chemomechanical motor cycles. In addition, we investigate the functional dependence of stepping rates on force by deducing the motor's response to external loads via an appropriate Fokker-Planck equation. For substall forces, the dominant pathway of the motor network is profoundly different from the one for superstall forces, which leads to a stepping behaviour that is in agreement with the experimental observations. The extension of our analysis to Markov processes with absorbing boundaries allows for the calculation of the motor's dwell time distributions. These reveal aspects of the coordination of the motor's heads and contain direct information about the backsteps of the motor. Our theory provides a unified description for the myosin V motor as studied in single motor experiments.
Sucrose synthase (Susy) is a key enzyme of sucrose metabolism, catalysing the reversible conversion of sucrose and UDP to UDP-glucose and fructose. Therefore, its activity, localization and function have been studied in various plant species. It has been shown that Susy can play a role in supplying energy in companion cells for phloem loading (Fu and Park, 1995), provides substrates for starch synthesis (Zrenner et al., 1995), and supplies UDP-glucose for cell wall synthesis (Haigler et al., 2001). Analysis of the Arabidopsis genome identifies six Susy isoforms. The expression of these isoforms was investigated using promoter-reporter gene constructs (GUS) and real time RT-PCR. Although these isoforms are closely related at the protein level they have radically different spatial and temporal patterns of expression in the plant with no two isoforms showing the same distribution. More than one isoform is expressed in all organs examined. Some of them have high but specific expression in particular organs or developmental stages whilst others are constantly expressed throughout the whole plant and across various stages of development. The in planta function of the six Susy isoforms were explored through analysis of T-DNA insertion mutants and RNAi lines. Plants without the expression of individual isoforms show no differences in growth and development, and are not significantly different from wild type plants in soluble sugars, starch and cellulose contents under all growth conditions investigated. Analysis of T-DNA insertion mutant lacking Sus3 isoform that was exclusively expressed in stomata cells only had a minor influence on guard cell osmoregulation and/or bioenergetics. Although none of the sucrose synthases appear to be essential for normal growth under our standard growth conditions, they may be necessary for growth under stress conditions. Different isoforms of sucrose synthase respond differently to various abiotic stresses. It has been shown that oxygen deprivation up regulates Sus1 and Sus4 and increases total Susy activity. However, the analysis of the plants with reduced expression of both Sus1 and Sus4 revealed no obvious effects on plant performance under oxygen deprivation. Low temperature up regulates Sus1 expression but the loss of this isoform has no effect on the freezing tolerance of non acclimated and cold acclimated plants. These data provide a comprehensive overview of the expression of this gene family which supports some of the previously reported roles for Susy and indicates the involvement of specific isoforms in metabolism and/or signalling.
Sulphur, a macronutrient essential for plant growth, is among the most versatile elements in living organisms. Unfortunately, little is known about regulation of sulphate uptake and assimilation by plants. Identification of sulphate signalling processes will allow to control sulphate acquisition and assimilation and may prove useful in the future to improve sulphur-use efficiency in agriculture. Many of genes involved in sulphate metabolism are regulated on transcriptional level by products of other genes called transcription factors (TF). Several published experiments revealed TF genes that respond to sulphate deprivation, but none of these have been so far been characterized functionally. Thus, we aimed at identifying and characterising transcription factors that control sulphate metabolism in the model plant Arabidopsis thaliana. To achieve that goal we postulated that factors regulating Arabidopsis responses to inorganic sulphate deficiency change their transcriptional levels under sulphur-limited conditions. By comparing TF transcript profiles from plants grown on different sulphate regimes, we identified TF genes that may specifically induce or repress changes in expression of genes that allow plants to adapt to changes in sulphate availability. Candidate genes obtained from this screening were tested by reverse genetics approaches. Transgenic plants constitutively overproducing selected TF genes and mutant plants, lacking functional selected TF genes (knock out), were used. By comparing metabolite and transcript profiles from transgenic and wild type plants we aimed at confirming the role of selected AP2 TF candidate genes in plant adaptation to sulphur unavailability. After preliminary characterisation of WRKY24 and MYB93 TF genes, we postulate that these factors are involved in a complex multifactorial regulatory network, in which WRKY24 and MYB93 would act as superior factors regulating other transcription factors directly involved in the regulation of S-metabolism genes. Results obtained for plants overproducing TOE1 and TOE2 TF genes suggests that these factors may be involved in a mechanism, which is promoting synthesis of an essential amino acid, methionine, over synthesis of another amino acid, cysteine. Thus, TOE1 and TOE2 genes might be a part of transcriptional regulation of methionine synthesis. Approaches creating genetically manipulated plants may produce plant phenotypes of immediate biotechnological interest, such as plants with increased sulphate or sulphate-containing amino acid content, or better adapted to the sulphate unavailability.
Carbonates play a key role in the chemistry and dynamics of our planet. They are directly connected to the CO2 budget of our atmosphere and have a great impact on the deep carbon cycle. Moreover, recent studies have shown that carbonates are stable along the geothermal gradient down to Earth's lower mantle conditions, changing their crystal structure and related properties. Subducted carbonates may also react with silicates to form new phases. These reactions will redistribute elements, such as calcium (Ca), magnesium (Mg), iron (Fe) and carbon in the form of carbon dioxide (CO2), but also trace elements, that are carried by the carbonates. The trace elements of most interest are strontium (Sr) and rare earth elements (REE) which have been found to be important constituents in the composition of the primitive lower mantle and in mineral inclusions found in super-deep diamonds. However, the stability of carbonates in presence of mantle silicates at relevant temperatures is far from being well understood. Related to this, very little is known about distribution processes of trace elements between carbonates and mantle silicates. To shed light on these processes, we studied reactions between Sr- and REE-containing CaCO3 and Mg/Fe-bearing silicates of the system (Mg,Fe)2SiO4 - (Mg,Fe)SiO3 at high pressure and high temperature using synchrotron radiation based μ-X-ray diffraction (μ-XRD) and μ-X-ray fluorescence (μ-XRF) with μm-resolution in a laser-heated diamond anvil cell. X-ray diffraction is used to derive the structural changes of the phase reactions whereas X-ray fluorescence gives information on the chemical changes in the sample. In-situ experiments at high pressure and high temperature were performed at beamline P02.2 at PETRA III (Hamburg, Germany) and at beamline ID27 at ESRF (Grenoble, France). In addition to μ-XRD and μ-XRF, ex-situ measurements were made on the recovered sample material using transmission electron microscopy (TEM) and provided further insights into the reaction kinetics of carbonate-silicate reactions.
Our investigations show that CaCO3 is unstable in presence of mantle silicates above 1700 K and a reaction takes place in which magnesite plus CaSiO3-perovskite are formed. In addition, we observed that a high content of iron in the carbonate-silicate system favours dolomite formation during the reaction. The subduction of natural carbonates with significant amounts of Sr leads to a comprehensive investigation of the stability not only of CaCO3 phases in contact with mantle silicates but also of SrCO3 (and of Sr-bearing CaCO3). We found that SrCO3 reacts with (Mg,Fe)SiO3-perovskite to form magnesite and gained evidence for the formation of SrSiO3-perovskite.
To complement our study on the stability of SrCO3 at conditions of the Earth's lower mantle, we performed powder X-ray diffraction and single crystal X-ray diffraction experiments at ambient temperature and up to 49 GPa. We observed a transformation from SrCO3-I into a new high-pressure phase SrCO3-II at around 26 GPa with Pmmn crystal structure and a bulk modulus of 103(10) GPa. This information is essential to fully understand the phase behaviour and stability of carbonates in the Earth's lower mantle and to elucidate the possibility of introducing Sr into mantle silicates by carbonate-silicate reactions.
Simultaneous recording of μ-XRD and μ-XRF in the μm-range over the heated areas provides spatial information not only about phase reactions but also on the elemental redistribution during the reactions. A comparison of the spatial intensity distribution of the XRF signal before and after heating indicates a change in the elemental distribution of Sr and an increase in Sr-concentration was found around the newly formed SrSiO3-perovskite. With the help of additional TEM analyses on the quenched sample material the elemental redistribution was studied at a sub-micrometer scale. Contrary to expectations from combined μ-XRD and μ-XRF measurements, we found that La and Eu were not incorporated into the silicate phases, instead they tend to form either isolated oxide phases (e.g. Eu2O3, La2O3) or hydroxyl-bastnäsite (La(CO3)(OH)). In addition, we observed the transformation from (Mg,Fe)SiO3-perovskite to low-pressure clinoenstatite during pressure release. The monoclinic structure (P21/c) of this phase allows the incorporation of Ca as shown by additional EDX analyses and, to a minor extent, Sr too.
Based on our experiments, we can conclude that a detection of the trace elements in-situ at high pressure and high temperature remains challenging. However, our first findings imply that silicates may incorporate the trace elements provided by the carbonates and indicate that carbonates may have a major effect on the trace element contents of mantle phases.
One of the main problems in machine learning is to train a predictive model from training data and to make predictions on test data. Most predictive models are constructed under the assumption that the training data is governed by the exact same distribution which the model will later be exposed to. In practice, control over the data collection process is often imperfect. A typical scenario is when labels are collected by questionnaires and one does not have access to the test population. For example, parts of the test population are underrepresented in the survey, out of reach, or do not return the questionnaire. In many applications training data from the test distribution are scarce because they are difficult to obtain or very expensive. Data from auxiliary sources drawn from similar distributions are often cheaply available. This thesis centers around learning under differing training and test distributions and covers several problem settings with different assumptions on the relationship between training and test distributions-including multi-task learning and learning under covariate shift and sample selection bias. Several new models are derived that directly characterize the divergence between training and test distributions, without the intermediate step of estimating training and test distributions separately. The integral part of these models are rescaling weights that match the rescaled or resampled training distribution to the test distribution. Integrated models are studied where only one optimization problem needs to be solved for learning under differing distributions. With a two-step approximation to the integrated models almost any supervised learning algorithm can be adopted to biased training data. In case studies on spam filtering, HIV therapy screening, targeted advertising, and other applications the performance of the new models is compared to state-of-the-art reference methods.
In this work approaches for new detection system development for an Analytical Ultracentrifuge (AUC) were explored. Unlike its counterpart in chromatography fractionation techniques, the use of a Multidetection system for AUC has not yet been implemented to full extent despite its potential benefit. In this study we tried to couple existing fundamental spectroscopic and scattering techniques that are used in day to day science as tool for extracting analyte information. Trials were performed for adapting Raman, Light scattering and UV/Vis (with possibility to work with the whole range of wavelengths) to AUC. Conclusions were drawn for Raman and Light scattering to be a possible detection system for AUC, while the development for a fast fiber optics based multiwavelength detector was completed. The multiwavelength detector demonstrated the capability of data generation matching the literature and reference measurement data and faster data collection than that of the commercial instrument. It became obvious that with the generation of data in 3-D space in the UV/Vis detection system, the user can select the wavelength for the evaluation of experimental results as the data set contains the whole range of information from UV/Vis wavelength. The detector showed the data generation with much faster speed unlike the commercial instruments. The advantage of fast data generation was exemplified with the evaluation of data for a mixture of three colloids. These data were in conformity with measurement results from normal radial experiments and without significant diffusion broadening. Thus conclusions were drawn that with our designed Multiwavelength detector, meaningful data in 3-D space can be collected with much faster speed of data generation.
The selective infrared (IR) excitation of molecular vibrations is a powerful tool to control the photoreactivity prior to electronic excitation in the ultraviolet / visible (UV/Vis) light regime ("vibrationally mediated chemistry"). For adsorbates on surfaces it has been theoretically predicted that IR preexcitation will lead to higher UV/Vis photodesorption yields and larger cross sections for other photoreactions. In a recent experiment, IR-mediated desorption of molecular hydrogen from a Si(111) surface on which atomic hydrogen and deuterium were co-adsorbed was achieved, following a vibrational mechanism as indicated by the isotope-selectivity. In the present work, selective vibrational IR excitation of adsorbate molecules, treated as multi-dimensional oscillators on dissipative surfaces, has been simulated within the framework of open-system density matrix theory. Not only potential-mediated, inter-mode coupling poses an obstacle to selective excitation but also the coupling of the adsorbate ("system") modes to the electronic and phononic degrees of freedom of the surface ("bath") does. Vibrational relaxation thereby takes place, depending on the availabilty of energetically fitting electron-hole (e/h) pairs and/or phonons (lattice vibrations) in the surface, on time-scales ranging from milliseconds to several hundreds of femtoseconds. On metal surfaces, where the relaxation process of the adsorbate via the e/h pair mechanism dominates, vibrational lifetimes are usually shorter than on insulator or semiconductor surfaces, in the range of picoseconds, being also the timescale of the IR pulses used here. Further inhibiting factors for selectivity can be the harmonicity of a mode and weak dipole activities ("dark modes") rendering vibrational excitation with moderate field intensities difficult. In addition to simple analytical pulses, optimal control theory (OCT) has been employed here to generate a suitable electric field to populate the target state/mode maximally. The complex OCT fields were analyzed by Husimi transformation, resolving the control field in time and energy. The adsorbate/surface systems investigated were CO/Cu(100), H/Si(100) and 2H/Ru(0001). These systems proved to be suitable models to study the above mentioned effects. Further, effects of temperature, pure dephasing (elastic scattering processes), pulse duration and dimensionality (up to four degrees of freedom) were studied. It was possible to selectively excite single vibrational modes, often even state-selective. Special processes like hot-band excitation, vibrationally mediated desorption and the excitation of "dark modes" were simulated. Finally, a novel OCT algorithm in density matrix representation has been developed which allows for time-dependent target operators and thus enables to control the excitation mechanism instead of only the final state. The algorithm is based on a combination of global (iterative) and local (non-iterative) OCT schemes, such that short, globally controlled time-intervals are coupled locally in time. Its numerical performance and accuracy were tested and verified and it was successfully applied to stabilize a two-state linear-combination and to enforce a successive "ladder climbing" in a rather harmonic system, where monochromatic, analytical pulses simultaneously excited several states, leading to a population loss in the target state.
Nowadays, graph data models are employed, when relationships between entities have to be stored and are in the scope of queries. For each entity, this graph data model locally stores relationships to adjacent entities. Users employ graph queries to query and modify these entities and relationships. These graph queries employ graph patterns to lookup all subgraphs in the graph data that satisfy certain graph structures. These subgraphs are called graph pattern matches. However, this graph pattern matching is NP-complete for subgraph isomorphism. Thus, graph queries can suffer a long response time, when the number of entities and relationships in the graph data or the graph patterns increases.
One possibility to improve the graph query performance is to employ graph views that keep ready graph pattern matches for complex graph queries for later retrieval. However, these graph views must be maintained by means of an incremental graph pattern matching to keep them consistent with the graph data from which they are derived, when the graph data changes. This maintenance adds subgraphs that satisfy a graph pattern to the graph views and removes subgraphs that do not satisfy a graph pattern anymore from the graph views.
Current approaches for incremental graph pattern matching employ Rete networks. Rete networks are discrimination networks that enumerate and maintain all graph pattern matches of certain graph queries by employing a network of condition tests, which implement partial graph patterns that together constitute the overall graph query. Each condition test stores all subgraphs that satisfy the partial graph pattern. Thus, Rete networks suffer high memory consumptions, because they store a large number of partial graph pattern matches. But, especially these partial graph pattern matches enable Rete networks to update the stored graph pattern matches efficiently, because the network maintenance exploits the already stored partial graph pattern matches to find new graph pattern matches. However, other kinds of discrimination networks exist that can perform better in time and space than Rete networks. Currently, these other kinds of networks are not used for incremental graph pattern matching.
This thesis employs generalized discrimination networks for incremental graph pattern matching. These discrimination networks permit a generalized network structure of condition tests to enable users to steer the trade-off between memory consumption and execution time for the incremental graph pattern matching. For that purpose, this thesis contributes a modeling language for the effective definition of generalized discrimination networks. Furthermore, this thesis contributes an efficient and scalable incremental maintenance algorithm, which updates the (partial) graph pattern matches that are stored by each condition test. Moreover, this thesis provides a modeling evaluation, which shows that the proposed modeling language enables the effective modeling of generalized discrimination networks. Furthermore, this thesis provides a performance evaluation, which shows that a) the incremental maintenance algorithm scales, when the graph data becomes large, and b) the generalized discrimination network structures can outperform Rete network structures in time and space at the same time for incremental graph pattern matching.
Microsaccades
(2015)
The first thing we do upon waking is open our eyes. Rotating them in our eye sockets, we scan our surroundings and collect the information into a picture in our head. Eye movements can be split into saccades and fixational eye movements, which occur when we attempt to fixate our gaze. The latter consists of microsaccades, drift and tremor. Before we even lift our eye lids, eye movements – such as saccades and microsaccades that let the eyes jump from one to another position – have partially been prepared in the brain stem. Saccades and microsaccades are often assumed to be generated by the same mechanisms. But how saccades and microsaccades can be classified according to shape has not yet been reported in a statistical manner. Research has put more effort into the investigations of microsaccades’ properties and generation only since the last decade. Consequently, we are only beginning to understand the dynamic processes governing microsaccadic eye movements. Within this thesis, the dynamics governing the generation of microsaccades is assessed and the development of a model for the underlying processes. Eye movement trajectories from different experiments are used, recorded with a video-based eye tracking technique, and a novel method is proposed for the scale-invariant detection of saccades (events of large amplitude) and microsaccades (events of small amplitude). Using a time-frequency approach, the method is examined with different experiments and validated against simulated data. A shape model is suggested that allows for a simple estimation of saccade- and microsaccade related properties. For sequences of microsaccades, in this thesis a time-dynamic Markov model is proposed, with a memory horizon that changes over time and which can best describe sequences of microsaccades.
Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of the observations. Unraveling such transitions yields essential information for the understanding of the observed system’s intrinsic evolution and potential external influences. A precise detection of multiple changes is therefore of great importance for various research disciplines, such as environmental sciences, bioinformatics and economics. The primary purpose of the detection approach introduced in this thesis is the investigation of transitions underlying direct or indirect climate observations. In order to develop a diagnostic approach capable to capture such a variety of natural processes, the generic statistical features in terms of central tendency and dispersion are employed in the light of Bayesian inversion. In contrast to established Bayesian approaches to multiple changes, the generic approach proposed in this thesis is not formulated in the framework of specialized partition models of high dimensionality requiring prior specification, but as a robust kernel-based approach of low dimensionality employing least informative prior distributions.
First of all, a local Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of a single transition. The analysis of synthetic time series comprising changes of different observational evidence, data loss and outliers validates the performance, consistency and sensitivity of the inference algorithm. To systematically investigate time series for multiple changes, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the weighted kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. The detection approach is applied to environmental time series from the Nile river in Aswan and the weather station Tuscaloosa, Alabama comprising documented changes. The method’s performance confirms the approach as a powerful diagnostic tool to decipher multiple changes underlying direct climate observations.
Finally, the kernel-based Bayesian inference approach is used to investigate a set of complex terrigenous dust records interpreted as climate indicators of the African region of the Plio-Pleistocene period. A detailed inference unravels multiple transitions underlying the indirect climate observations, that are interpreted as conjoint changes. The identified conjoint changes coincide with established global climate events. In particular, the two-step transition associated to the establishment of the modern Walker-Circulation contributes to the current discussion about the influence of paleoclimate changes on the environmental conditions in tropical and subtropical Africa at around two million years ago.
In the present work synchronization phenomena in complex dynamical systems exhibiting multiple time scales have been analyzed. Multiple time scales can be active in different manners. Three different systems have been analyzed with different methods from data analysis. The first system studied is a large heterogenous network of bursting neurons, that is a system with two predominant time scales, the fast firing of action potentials (spikes) and the burst of repetitive spikes followed by a quiescent phase. This system has been integrated numerically and analyzed with methods based on recurrence in phase space. An interesting result are the different transitions to synchrony found in the two distinct time scales. Moreover, an anomalous synchronization effect can be observed in the fast time scale, i.e. there is range of the coupling strength where desynchronization occurs. The second system analyzed, numerically as well as experimentally, is a pair of coupled CO₂ lasers in a chaotic bursting regime. This system is interesting due to its similarity with epidemic models. We explain the bursts by different time scales generated from unstable periodic orbits embedded in the chaotic attractor and perform a synchronization analysis of these different orbits utilizing the continuous wavelet transform. We find a diverse route to synchrony of these different observed time scales. The last system studied is a small network motif of limit cycle oscillators. Precisely, we have studied a hub motif, which serves as elementary building block for scale-free networks, a type of network found in many real world applications. These hubs are of special importance for communication and information transfer in complex networks. Here, a detailed study on the mechanism of synchronization in oscillatory networks with a broad frequency distribution has been carried out. In particular, we find a remote synchronization of nodes in the network which are not directly coupled. We also explain the responsible mechanism and its limitations and constraints. Further we derive an analytic expression for it and show that information transmission in pure phase oscillators, such as the Kuramoto type, is limited. In addition to the numerical and analytic analysis an experiment consisting of electrical circuits has been designed. The obtained results confirm the former findings.
In this work, an approach of paleoclimate reconstruction for tropical East Africa is presented. After giving a short summary of modern climate conditions in the tropics and the East African climate peculiarity, the potential of reconstructing climate from paleolake sediments is discussed. As demonstrated, the hydrologic sensitivity of high-elevated closed-basin lakes in the Central Kenya Rift yields valuable guaranties for the establishment of long-term climate records. Temporal fluctuations of the limnological characteristics saved in the lake sediments are used to define variations in the Quaternary climate history. Based on diatom analyses in radiocarbon- and 40Ar/39Ar-dated sediments, a chronology of paleoecologic fluctuations is developed for the Central Kenya Rift -lakes Nakuru, Elmenteita and Naivasha. At least during the penultimate interglacial (around 140 to 60 kyr BP) and during the last interglacial (around 12 to 4 kyr BP), these lakes experienced several transgression-regression cycles on time intervals of about 11,000 years. Additionally, a long-term trend of lake evolution is found suggesting the general succession from deep freshwater lakes towards more saline waters during the last million years. Using ecologic transfer functions and a simple lake-balance model, the observed paleohydrologic fluctuations are linked to potential precipitation-evaporation changes in the lake basins. Though also tectonic influences on the drainage pattern and the effect of varied seepage are investigated, it can be shown that already a small increase in precipitation of about 30±10 % may have affected the hydrologic budget of the intra-rift lakes within the reconstructed range. The findings of this study help to assess the natural climate variability of East Africa. They furthermore reflect the sensitivity of the Central Kenya Rift -lakes to fluctuations of large-scale climate parameters, such as solar radiation and sea-surface temperatures of the Indian Ocean.
For more than two centuries, plant ecologists have aimed to understand how environmental gradients and biotic interactions shape the distribution and co-occurrence of plant species. In recent years, functional trait–based approaches have been increasingly used to predict patterns of species co-occurrence and species distributions along environmental gradients (trait–environment relationships). Functional traits are measurable properties at the individual level that correlate well with important processes. Thus, they allow us to identify general patterns by synthesizing studies across specific taxonomic compositions, thereby fostering our understanding of the underlying processes of species assembly. However, the importance of specific processes have been shown to be highly dependent on the spatial scale under consideration. In particular, it remains uncertain which mechanisms drive species assembly and allow for plant species coexistence at smaller, more local spatial scales. Furthermore, there is still no consensus on how particular environmental gradients affect the trait composition of plant communities. For example, increasing drought because of climate change is predicted to be a main threat to plant diversity, although it remains unclear which traits of species respond to increasing aridity. Similarly, there is conflicting evidence of how soil fertilization affects the traits related to establishment ability (e.g., seed mass). In this cumulative dissertation, I present three empirical trait-based studies that investigate specific research questions in order to improve our understanding of species distributions along environmental gradients.
In the first case study, I analyze how annual species assemble at the local scale and how environmental heterogeneity affects different facets of biodiversity—i.e. taxonomic, functional, and phylogenetic diversity—at different spatial scales. The study was conducted in a semi-arid environment at the transition zone between desert and Mediterranean ecosystems that features a sharp precipitation gradient (Israel). Different null model analyses revealed strong support for environmentally driven species assembly at the local scale, since species with similar traits tended to co-occur and shared high abundances within microsites (trait convergence). A phylogenetic approach, which assumes that closely related species are functionally more similar to each other than distantly related ones, partly supported these results. However, I observed that species abundances within microsites were, surprisingly, more evenly distributed across the phylogenetic tree than expected (phylogenetic overdispersion). Furthermore, I showed that environmental heterogeneity has a positive effect on diversity, which was higher on functional than on taxonomic diversity and increased with spatial scale. The results of this case study indicate that environmental heterogeneity may act as a stabilizing factor to maintain species diversity at local scales, since it influenced species distribution according to their traits and positively influenced diversity. All results were constant along the precipitation gradient.
In the second case study (same study system as case study one), I explore the trait responses of two Mediterranean annuals (Geropogon hybridus and Crupina crupinastrum) along a precipitation gradient that is comparable to the maximum changes in precipitation predicted to occur by the end of this century (i.e., −30%). The heterocarpic G. hybridus showed strong trends in seed traits, suggesting that dispersal ability increased with aridity. By contrast, the homocarpic C. crupinastrum showed only a decrease in plant height as aridity increased, while leaf traits of both species showed no consistent pattern along the precipitation gradient. Furthermore, variance decomposition of traits revealed that most of the trait variation observed in the study system was actually found within populations. I conclude that trait responses towards aridity are highly species-specific and that the amount of precipitation is not the most striking environmental factor at this particular scale.
In the third case study, I assess how soil fertilization mediates—directly by increased nutrient addition and indirectly by increased competition—the effect of seed mass on establishment ability. For this experiment, I used 22 species differing in seed mass from dry grasslands in northeastern Germany and analyzed the interacting effects of seed mass with nutrient availability and competition on four key components of seedling establishment: seedling emergence, time of seedling emergence, seedling survival, and seedling growth. (Time of) seedling emergence was not affected by seed mass. However, I observed that the positive effect of seed mass on seedling survival is lowered under conditions of high nutrient availability, whereas the positive effect of seed mass on seedling growth was only reduced by competition. Based on these findings, I developed a conceptual model of how seed mass should change along a soil fertility gradient in order to reconcile conflicting findings from the literature. In this model, seed mass shows a U-shaped pattern along the soil fertility gradient as a result of changing nutrient availability and competition.
Overall, the three case studies highlight the role of environmental factors on species distribution and co-occurrence. Moreover, the findings of this thesis indicate that spatial heterogeneity at local scales may act as a stabilizing factor that allows species with different traits to coexist. In the concluding discussion, I critically debate intraspecific trait variability in plant community ecology, the use of phylogenetic relationships and easily measured key functional traits as a proxy for species’ niches. Finally, I offer my outlook for the future of functional plant community research.
Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors.
Requirements engineers have to elicit, document, and validate how stakeholders act and interact to achieve their common goals in collaborative scenarios. Only after gathering all information concerning who interacts with whom to do what and why, can a software system be designed and realized which supports the stakeholders to do their work. To capture and structure requirements of different (groups of) stakeholders, scenario-based approaches have been widely used and investigated. Still, the elicitation and validation of requirements covering collaborative scenarios remains complicated, since the required information is highly intertwined, fragmented, and distributed over several stakeholders. Hence, it can only be elicited and validated collaboratively. In times of globally distributed companies, scheduling and conducting workshops with groups of stakeholders is usually not feasible due to budget and time constraints. Talking to individual stakeholders, on the other hand, is feasible but leads to fragmented and incomplete stakeholder scenarios. Going back and forth between different individual stakeholders to resolve this fragmentation and explore uncovered alternatives is an error-prone, time-consuming, and expensive task for the requirements engineers. While formal modeling methods can be employed to automatically check and ensure consistency of stakeholder scenarios, such methods introduce additional overhead since their formal notations have to be explained in each interaction between stakeholders and requirements engineers. Tangible prototypes as they are used in other disciplines such as design, on the other hand, allow designers to feasibly validate and iterate concepts and requirements with stakeholders. This thesis proposes a model-based approach for prototyping formal behavioral specifications of stakeholders who are involved in collaborative scenarios. By simulating and animating such specifications in a remote domain-specific visualization, stakeholders can experience and validate the scenarios captured so far, i.e., how other stakeholders act and react. This interactive scenario simulation is referred to as a model-based virtual prototype. Moreover, through observing how stakeholders interact with a virtual prototype of their collaborative scenarios, formal behavioral specifications can be automatically derived which complete the otherwise fragmented scenarios. This, in turn, enables requirements engineers to elicit and validate collaborative scenarios in individual stakeholder sessions – decoupled, since stakeholders can participate remotely and are not forced to be available for a joint session at the same time. This thesis discusses and evaluates the feasibility, understandability, and modifiability of model-based virtual prototypes. Similarly to how physical prototypes are perceived, the presented approach brings behavioral models closer to being tangible for stakeholders and, moreover, combines the advantages of joint stakeholder sessions and decoupled sessions.
Efficiently managing large state is a key challenge for data management systems. Traditionally, state is split into fast but volatile state in memory for processing and persistent but slow state on secondary storage for durability. Persistent memory (PMem), as a new technology in the storage hierarchy, blurs the lines between these states by offering both byte-addressability and low latency like DRAM as well persistence like secondary storage. These characteristics have the potential to cause a major performance shift in database systems.
Driven by the potential impact that PMem has on data management systems, in this thesis we explore their use of PMem. We first evaluate the performance of real PMem hardware in the form of Intel Optane in a wide range of setups. To this end, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the performance of customizable database-related PMem access. Based on experimental results obtained with PerMA-Bench, we discuss findings and identify general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs. We then propose Viper, a hybrid PMem-DRAM key-value store. Based on PMem-aware access patterns, we show how to leverage PMem and DRAM efficiently to design a key database component. Our evaluation shows that Viper outperforms existing key-value stores by 4–18x for inserts while offering full data persistence and achieving similar or better lookup performance. Next, we show which changes must be made to integrate PMem components into larger systems. By the example of stream processing engines, we highlight limitations of current designs and propose a prototype engine that overcomes these limitations. This allows our prototype to fully leverage PMem's performance for its internal state management. Finally, in light of Optane's discontinuation, we discuss how insights from PMem research can be transferred to future multi-tier memory setups by the example of Compute Express Link (CXL).
Overall, we show that PMem offers high performance for state management, bridging the gap between fast but volatile DRAM and persistent but slow secondary storage. Although Optane was discontinued, new memory technologies are continuously emerging in various forms and we outline how novel designs for them can build on insights from existing PMem research.
We do magnetohydrodynamic (MHD) simulations of local box models of turbulent Interstellar Medium (ISM) and analyse the process of amplification and saturation of mean magnetic fields with methods of mean field dynamo theory. It is shown that the process of saturation of mean fields can be partially described by the prolonged diffusion time scales in presence of the dynamically significant magnetic fields. However, the outward wind also plays an essential role in the saturation in higher SN rate case. Algebraic expressions for the back reaction of the magnetic field onto the turbulent transport coefficients are derived, which allow a complete description of the nonlinear dynamo. We also present the effects of dynamically significant mean fields on the ISM configuration and pressure distribution. We further add the cosmic ray component in the simulations and investigate the kinematic growth of mean fields with a dynamo perspective.