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The tropical warm pool waters surrounding Indonesia are one of the equatorial heat and moisture sources that are considered as a driving force of the global climate system. The climate in Indonesia is dominated by the equatorial monsoon system, and has been linked to El Niño-Southern Oscillation (ENSO) events, which often result in severe droughts or floods over Indonesia with profound societal and economic impacts on the populations living in the world's fourth most populated country. The latest IPCC report states that ENSO will remain the dominant mode in the tropical Pacific with global effects in the 21st century and ENSO-related precipitation extremes will intensify. However, no common agreement exists among climate simulation models for projected change in ENSO and the Australian-Indonesian Monsoon. Exploring high-resolution palaeoclimate archives, like tree rings or varved lake sediments, provide insights into the natural climate variability of the past, and thus helps improving and validating simulations of future climate changes. Centennial tree-ring stable isotope records | Within this doctoral thesis the main goal was to explore the potential of tropical tree rings to record climate signals and to use them as palaeoclimate proxies. In detail, stable carbon (δ13C) and oxygen (δ18O) isotopes were extracted from teak trees in order to establish the first well-replicated centennial (AD 1900-2007) stable isotope records for Java, Indonesia. Furthermore, different climatic variables were tested whether they show significant correlation with tree-ring proxies (ring-width, δ13C, δ18O). Moreover, highly resolved intra-annual oxygen isotope data were established to assess the transfer of the seasonal precipitation signal into the tree rings. Finally, the established oxygen isotope record was used to reveal possible correlations with ENSO events. Methodological achievements | A second goal of this thesis was to assess the applicability of novel techniques which facilitate and optimize high-resolution and high-throughput stable isotope analysis of tree rings. Two different UV-laser-based microscopic dissection systems were evaluated as a novel sampling tool for high-resolution stable isotope analysis. Furthermore, an improved procedure of tree-ring dissection from thin cellulose laths for stable isotope analysis was designed. The most important findings of this thesis are: I) The herein presented novel sampling techniques improve stable isotope analyses for tree-ring studies in terms of precision, efficiency and quality. The UV-laser-based microdissection serve as a valuable tool for sampling plant tissue at ultrahigh-resolution and for unprecedented precision. II) A guideline for a modified method of cellulose extraction from wholewood cross-sections and subsequent tree-ring dissection was established. The novel technique optimizes the stable isotope analysis process in two ways: faster and high-throughput cellulose extraction and precise tree-ring separation at annual to high-resolution scale. III) The centennial tree-ring stable isotope records reveal significant correlation with regional precipitation. High-resolution stable oxygen values, furthermore, allow distinguishing between dry and rainy season rainfall. IV) The δ18O record reveals significant correlation with different ENSO flavors and demonstrates the importance of considering ENSO flavors when interpreting palaeoclimatic data in the tropics. The findings of my dissertation show that seasonally resolved δ18O records from Indonesian teak trees are a valuable proxy for multi-centennial reconstructions of regional precipitation variability (monsoon signals) and large-scale ocean-atmosphere phenomena (ENSO) for the Indo-Pacific region. Furthermore, the novel methodological achievements offer many unexplored avenues for multidisciplinary research in high-resolution palaeoclimatology.
Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.
In this thesis we consider diverse aspects of existence and correctness of asymptotic solutions to elliptic differential and pseudodifferential equations. We begin our studies with the case of a general elliptic boundary value problem in partial derivatives. A small parameter enters the coefficients of the main equation as well as into the boundary conditions. Such equations have already been investigated satisfactory, but there still exist certain theoretical deficiencies. Our aim is to present the general theory of elliptic problems with a small parameter. For this purpose we examine in detail the case of a bounded domain with a smooth boundary. First of all, we construct formal solutions as power series in the small parameter. Then we examine their asymptotic properties. It suffices to carry out sharp two-sided \emph{a priori} estimates for the operators of boundary value problems which are uniform in the small parameter. Such estimates failed to hold in functional spaces used in classical elliptic theory. To circumvent this limitation we exploit norms depending on the small parameter for the functions defined on a bounded domain. Similar norms are widely used in literature, but their properties have not been investigated extensively. Our theoretical investigation shows that the usual elliptic technique can be correctly carried out in these norms. The obtained results also allow one to extend the norms to compact manifolds with boundaries. We complete our investigation by formulating algebraic conditions on the operators and showing their equivalence to the existence of a priori estimates. In the second step, we extend the concept of ellipticity with a small parameter to more general classes of operators. Firstly, we want to compare the difference in asymptotic patterns between the obtained series and expansions for similar differential problems. Therefore we investigate the heat equation in a bounded domain with a small parameter near the time derivative. In this case the characteristics touch the boundary at a finite number of points. It is known that the solutions are not regular in a neighbourhood of such points in advance. We suppose moreover that the boundary at such points can be non-smooth but have cuspidal singularities. We find a formal asymptotic expansion and show that when a set of parameters comes through a threshold value, the expansions fail to be asymptotic. The last part of the work is devoted to general concept of ellipticity with a small parameter. Several theoretical extensions to pseudodifferential operators have already been suggested in previous studies. As a new contribution we involve the analysis on manifolds with edge singularities which allows us to consider wider classes of perturbed elliptic operators. We examine that introduced classes possess a priori estimates of elliptic type. As a further application we demonstrate how developed tools can be used to reduce singularly perturbed problems to regular ones.
Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not available. Thus, it is useful to automatically generate meta information, such as ontological dependencies, range definitions, and topical classifications. Association rule mining, which was originally applied for sales analysis on transactional databases, is a promising and novel technique to explore such data. We designed an adaptation of this technique for min-ing Rdf data and introduce the concept of “mining configurations”, which allows us to mine RDF data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. To this end, we present rule-based approaches for auto-completion, data enrichment, ontology improvement, and query relaxation. Auto-completion remedies the problem of inconsistent ontology usage, providing an editing user with a sorted list of commonly used predicates. A combination of different configurations step extends this approach to create completely new facts for a knowledge base. We present two approaches for fact generation, a user-based approach where a user selects the entity to be amended with new facts and a data-driven approach where an algorithm discovers entities that have to be amended with missing facts. As knowledge bases constantly grow and evolve, another approach to improve the usage of RDF data is to improve existing ontologies. Here, we present an association rule based approach to reconcile ontology and data. Interlacing different mining configurations, we infer an algorithm to discover synonymously used predicates. Those predicates can be used to expand query results and to support users during query formulation. We provide a wide range of experiments on real world datasets for each use case. The experiments and evaluations show the added value of association rule mining for the integration and usability of RDF data and confirm the appropriateness of our mining configuration methodology.
The Adana Basin of southern Turkey, situated at the SE margin of the Central Anatolian Plateau is ideally located to record Neogene topographic and tectonic changes in the easternmost Mediterranean realm. Using industry seismic reflection data we correlate 34 seismic profiles with corresponding exposed units in the Adana Basin. The time-depth conversion of the interpreted seismic profiles allows us to reconstruct the subsidence curve of the Adana Basin and to outline the occurrence of a major increase in both subsidence and sedimentation rates at 5.45 – 5.33 Ma, leading to the deposition of almost 1500 km3 of conglomerates and marls. Our provenance analysis of the conglomerates reveals that most of the sediment is derived from and north of the SE margin of the Central Anatolian Plateau. A comparison of these results with the composition of recent conglomerates and the present drainage basins indicates major changes between late Messinian and present-day source areas. We suggest that these changes in source areas result of uplift and ensuing erosion of the SE margin of the plateau. This hypothesis is supported by the comparison of the Adana Basin subsidence curve with the subsidence curve of the Mut Basin, a mainly Neogene basin located on top of the Central Anatolian Plateau southern margin, showing that the Adana Basin subsidence event is coeval with an uplift episode of the plateau southern margin. The collection of several fault measurements in the Adana region show different deformation styles for the NW and SE margins of the Adana Basin. The weakly seismic NW portion of the basin is characterized by extensional and transtensional structures cutting Neogene deposits, likely accomodating the differential uplift occurring between the basin and the SE margin of the plateau. We interpret the tectonic evolution of the southern flank of the Central Anatolian Plateau and the coeval subsidence and sedimentation in the Adana Basin to be related to deep lithospheric processes, particularly lithospheric delamination and slab break-off.
Adopting a minimalist framework, the dissertation provides an analysis for the syntactic structure of comparatives, with special attention paid to the derivation of the subclause. The proposed account explains how the comparative subclause is connected to the matrix clause, how the subclause is formed in the syntax and what additional processes contribute to its final structure. In addition, it casts light upon these problems in cross-linguistic terms and provides a model that allows for synchronic and diachronic differences. This also enables one to give a more adequate explanation for the phenomena found in English comparatives since the properties of English structures can then be linked to general settings of the language and hence need no longer be considered as idiosyncratic features of the grammar of English. First, the dissertation provides a unified analysis of degree expressions, relating the structure of comparatives to that of other degrees. It is shown that gradable adjectives are located within a degree phrase (DegP), which in turn projects a quantifier phrase (QP) and that these two functional layers are always present, irrespectively of whether there is a phonologically visible element in these layers. Second, the dissertation presents a novel analysis of Comparative Deletion by reducing it to an overtness constraint holding on operators: in this way, it is reduced to morphological differences and cross-linguistic variation is not conditioned by way of postulating an arbitrary parameter. Cross-linguistic differences are ultimately dependent on whether a language has overt operators equipped with the relevant – [+compr] and [+rel] – features. Third, the dissertation provides an adequate explanation for the phenomenon of Attributive Comparative Deletion, as attested in English, by way of relating it to the regular mechanism of Comparative Deletion. I assume that Attributive Comparative Deletion is not a universal phenomenon, and its presence in English can be conditioned by independent, more general rules, while the absence of such restrictions leads to its absence in other languages. Fourth, the dissertation accounts for certain phenomena related to diachronic changes, examining how the changes in the status of comparative operators led to changes in whether Comparative Deletion is attested in a given language: I argue that only operators without a lexical XP can be grammaticalised. The underlying mechanisms underlying are essentially general economy principles and hence the processes are not language-specific or exceptional. Fifth, the dissertation accounts for optional ellipsis processes that play a crucial role in the derivation of typical comparative subclauses. These processes are not directly related to the structure of degree expressions and hence the elimination of the quantified expression from the subclause; nevertheless, they are shown to be in interaction with the mechanisms underlying Comparative Deletion or the absence thereof.
Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.
One of the most significant current discussions in Astrophysics relates to the origin of high-energy cosmic rays. According to our current knowledge, the abundance distribution of the elements in cosmic rays at their point of origin indicates, within plausible error limits, that they were initially formed by nuclear processes in the interiors of stars. It is also believed that their energy distribution up to 1018 eV has Galactic origins. But even though the knowledge about potential sources of cosmic rays is quite poor above „ 1015 eV, that is the “knee” of the cosmic-ray spectrum, up to the knee there seems to be a wide consensus that supernova remnants are the most likely candidates. Evidence of this comes from observations of non-thermal X-ray radiation, requiring synchrotron electrons with energies up to 1014 eV, exactly in the remnant of supernovae. To date, however, there is not conclusive evidence that they produce nuclei, the dominant component of cosmic rays, in addition to electrons. In light of this dearth of evidence, γ-ray observations from supernova remnants can offer the most promising direct way to confirm whether or not these astrophysical objects are indeed the main source of cosmic-ray nuclei below the knee. Recent observations with space- and ground-based observatories have established shell-type supernova remnants as GeV-to- TeV γ-ray sources. The interpretation of these observations is however complicated by the different radiation processes, leptonic and hadronic, that can produce similar fluxes in this energy band rendering ambiguous the nature of the emission itself. The aim of this work is to develop a deeper understanding of these radiation processes from a particular shell-type supernova remnant, namely RX J1713.7–3946, using observations of the LAT instrument onboard the Fermi Gamma-Ray Space Telescope. Furthermore, to obtain accurate spectra and morphology maps of the emission associated with this supernova remnant, an improved model of the diffuse Galactic γ-ray emission background is developed. The analyses of RX J1713.7–3946 carried out with this improved background show that the hard Fermi-LAT spectrum cannot be ascribed to the hadronic emission, leading thus to the conclusion that the leptonic scenario is instead the most natural picture for the high-energy γ-ray emission of RX J1713.7–3946. The leptonic scenario however does not rule out the possibility that cosmic-ray nuclei are accelerated in this supernova remnant, but it suggests that the ambient density may not be high enough to produce a significant hadronic γ-ray emission. Further investigations involving other supernova remnants using the improved back- ground developed in this work could allow compelling population studies, and hence prove or disprove the origin of Galactic cosmic-ray nuclei in these astrophysical objects. A break- through regarding the identification of the radiation mechanisms could be lastly achieved with a new generation of instruments such as CTA.
In processing and data storage mainly ferromagnetic (FM) materials are being used. Approaching physical limits, new concepts have to be found for faster, smaller switches, for higher data densities and more energy efficiency. Some of the discussed new concepts involve the material classes of correlated oxides and materials with antiferromagnetic coupling. Their applicability depends critically on their switching behavior, i.e., how fast and how energy efficient material properties can be manipulated. This thesis presents investigations of ultrafast non-equilibrium phase transitions on such new materials. In transition metal oxides (TMOs) the coupling of different degrees of freedom and resulting low energy excitation spectrum often result in spectacular changes of macroscopic properties (colossal magneto resistance, superconductivity, metal-to-insulator transitions) often accompanied by nanoscale order of spins, charges, orbital occupation and by lattice distortions, which make these material attractive. Magnetite served as a prototype for functional TMOs showing a metal-to-insulator-transition (MIT) at T = 123 K. By probing the charge and orbital order as well as the structure after an optical excitation we found that the electronic order and the structural distortion, characteristics of the insulating phase in thermal equilibrium, are destroyed within the experimental resolution of 300 fs. The MIT itself occurs on a 1.5 ps timescale. It shows that MITs in functional materials are several thousand times faster than switching processes in semiconductors. Recently ferrimagnetic and antiferromagnetic (AFM) materials have become interesting. It was shown in ferrimagnetic GdFeCo, that the transfer of angular momentum between two opposed FM subsystems with different time constants leads to a switching of the magnetization after laser pulse excitation. In addition it was theoretically predicted that demagnetization dynamics in AFM should occur faster than in FM materials as no net angular momentum has to be transferred out of the spin system. We investigated two different AFM materials in order to learn more about their ultrafast dynamics. In Ho, a metallic AFM below T ≈ 130 K, we found that the AFM Ho can not only be faster but also ten times more energy efficiently destroyed as order in FM comparable metals. In EuTe, an AFM semiconductor below T ≈ 10 K, we compared the loss of magnetization and laser-induced structural distortion in one and the same experiment. Our experiment shows that they are effectively disentangled. An exception is an ultrafast release of lattice dynamics, which we assign to the release of magnetostriction. The results presented here were obtained with time-resolved resonant soft x-ray diffraction at the Femtoslicing source of the Helmholtz-Zentrum Berlin and at the free-electron laser in Stanford (LCLS). In addition the development and setup of a new UHV-diffractometer for these experiments will be reported.
Wood is used for many applications because of its excellent mechanical properties, relative abundance and as it is a renewable resource. However, its wider utilization as an engineering material is limited because it swells and shrinks upon moisture changes and is susceptible to degradation by microorganisms and/or insects. Chemical modifications of wood have been shown to improve dimensional stability, water repellence and/or durability, thus increasing potential service-life of wood materials. However current treatments are limited because it is difficult to introduce and fix such modifications deep inside the tissue and cell wall. Within the scope of this thesis, novel chemical modification methods of wood cell walls were developed to improve both dimensional stability and water repellence of wood material. These methods were partly inspired by the heartwood formation in living trees, a process, that for some species results in an insertion of hydrophobic chemical substances into the cell walls of already dead wood cells, In the first part of this thesis a chemistry to modify wood cell walls was used, which was inspired by the natural process of heartwood formation. Commercially available hydrophobic flavonoid molecules were effectively inserted in the cell walls of spruce, a softwood species with low natural durability, after a tosylation treatment to obtain “artificial heartwood”. Flavonoid inserted cell walls show a reduced moisture absorption, resulting in better dimensional stability, water repellency and increased hardness. This approach was quite different compared to established modifications which mainly address hydroxyl groups of cell wall polymers with hydrophilic substances. In the second part of the work in-situ styrene polymerization inside the tosylated cell walls was studied. It is known that there is a weak adhesion between hydrophobic polymers and hydrophilic cell wall components. The hydrophobic styrene monomers were inserted into the tosylated wood cell walls for further polymerization to form polystyrene in the cell walls, which increased the dimensional stability of the bulk wood material and reduced water uptake of the cell walls considerably when compared to controls. In the third part of the work, grafting of another hydrophobic and also biodegradable polymer, poly(ɛ-caprolactone) in the wood cell walls by ring opening polymerization of ɛ-caprolactone was studied at mild temperatures. Results indicated that polycaprolactone attached into the cell walls, caused permanent swelling of the cell walls up to 5%. Dimensional stability of the bulk wood material increased 40% and water absorption reduced more than 35%. A fully biodegradable and hydrophobized wood material was obtained with this method which reduces disposal problem of the modified wood materials and has improved properties to extend the material’s service-life. Starting from a bio-inspired approach which showed great promise as an alternative to standard cell wall modifications we showed the possibility of inserting hydrophobic molecules in the cell walls and supported this fact with in-situ styrene and ɛ-caprolactone polymerization into the cell walls. It was shown in this thesis that despite the extensive knowledge and long history of using wood as a material there is still room for novel chemical modifications which could have a high impact on improving wood properties.