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Planetary research is often user-based and requires considerable skill, time, and effort. Unfortunately, self-defined boundary conditions, definitions, and rules are often not documented or not easy to comprehend due to the complexity of research. This makes a comparison to other studies, or an extension of the already existing research, complicated. Comparisons are often distorted, because results rely on different, not well defined, or even unknown boundary conditions. The purpose of this research is to develop a standardized analysis method for planetary surfaces, which is adaptable to several research topics. The method provides a consistent quality of results. This also includes achieving reliable and comparable results and reducing the time and effort of conducting such studies. A standardized analysis method is provided by automated analysis tools that focus on statistical parameters. Specific key parameters and boundary conditions are defined for the tool application. The analysis relies on a database in which all key parameters are stored. These databases can be easily updated and adapted to various research questions. This increases the flexibility, reproducibility, and comparability of the research. However, the quality of the database and reliability of definitions directly influence the results. To ensure a high quality of results, the rules and definitions need to be well defined and based on previously conducted case studies. The tools then produce parameters, which are obtained by defined geostatistical techniques (measurements, calculations, classifications). The idea of an automated statistical analysis is tested to proof benefits but also potential problems of this method. In this study, I adapt automated tools for floor-fractured craters (FFCs) on Mars. These impact craters show a variety of surface features, occurring in different Martian environments, and having different fracturing origins. They provide a complex morphological and geological field of application. 433 FFCs are classified by the analysis tools due to their fracturing process. Spatial data, environmental context, and crater interior data are analyzed to distinguish between the processes involved in floor fracturing. Related geologic processes, such as glacial and fluvial activity, are too similar to be separately classified by the automated tools. Glacial and fluvial fracturing processes are merged together for the classification. The automated tools provide probability values for each origin model. To guarantee the quality and reliability of the results, classification tools need to achieve an origin probability above 50 %. This analysis method shows that 15 % of the FFCs are fractured by intrusive volcanism, 20 % by tectonic activity, and 43 % by water & ice related processes. In total, 75 % of the FFCs are classified to an origin type. This can be explained by a combination of origin models, superposition or erosion of key parameters, or an unknown fracturing model. Those features have to be manually analyzed in detail. Another possibility would be the improvement of key parameters and rules for the classification. This research shows that it is possible to conduct an automated statistical analysis of morphologic and geologic features based on analysis tools. Analysis tools provide additional information to the user and are therefore considered assistance systems.
Virtualized cloud data centers provide on-demand resources, enable agile resource provisioning, and host heterogeneous applications with different resource requirements. These data centers consume enormous amounts of energy, increasing operational expenses, inducing high thermal inside data centers, and raising carbon dioxide emissions. The increase in energy consumption can result from ineffective resource management that causes inefficient resource utilization. This dissertation presents detailed models and novel techniques and algorithms for virtual resource management in cloud data centers. The proposed techniques take into account Service Level Agreements (SLAs) and workload heterogeneity in terms of memory access demand and communication patterns of web applications and High Performance Computing (HPC) applications. To evaluate our proposed techniques, we use simulation and real workload traces of web applications and HPC applications and compare our techniques against the other recently proposed techniques using several performance metrics. The major contributions of this dissertation are the following: proactive resource provisioning technique based on robust optimization to increase the hosts' availability for hosting new VMs while minimizing the idle energy consumption. Additionally, this technique mitigates undesirable changes in the power state of the hosts by which the hosts' reliability can be enhanced in avoiding failure during a power state change. The proposed technique exploits the range-based prediction algorithm for implementing robust optimization, taking into consideration the uncertainty of demand. An adaptive range-based prediction for predicting workload with high fluctuations in the short-term. The range prediction is implemented in two ways: standard deviation and median absolute deviation. The range is changed based on an adaptive confidence window to cope with the workload fluctuations. A robust VM consolidation for efficient energy and performance management to achieve equilibrium between energy and performance trade-offs. Our technique reduces the number of VM migrations compared to recently proposed techniques. This also contributes to a reduction in energy consumption by the network infrastructure. Additionally, our technique reduces SLA violations and the number of power state changes. A generic model for the network of a data center to simulate the communication delay and its impact on VM performance, as well as network energy consumption. In addition, a generic model for a memory-bus of a server, including latency and energy consumption models for different memory frequencies. This allows simulating the memory delay and its influence on VM performance, as well as memory energy consumption. Communication-aware and energy-efficient consolidation for parallel applications to enable the dynamic discovery of communication patterns and reschedule VMs using migration based on the determined communication patterns. A novel dynamic pattern discovery technique is implemented, based on signal processing of network utilization of VMs instead of using the information from the hosts' virtual switches or initiation from VMs. The result shows that our proposed approach reduces the network's average utilization, achieves energy savings due to reducing the number of active switches, and provides better VM performance compared to CPU-based placement. Memory-aware VM consolidation for independent VMs, which exploits the diversity of VMs' memory access to balance memory-bus utilization of hosts. The proposed technique, Memory-bus Load Balancing (MLB), reactively redistributes VMs according to their utilization of a memory-bus using VM migration to improve the performance of the overall system. Furthermore, Dynamic Voltage and Frequency Scaling (DVFS) of the memory and the proposed MLB technique are combined to achieve better energy savings.
The International Project for the Evaluation of Educational Achievement (IEA) was formed in the 1950s (Postlethwaite, 1967). Since that time, the IEA has conducted many studies in the area of mathematics, such as the First International Mathematics Study (FIMS) in 1964, the Second International Mathematics Study (SIMS) in 1980-1982, and a series of studies beginning with the Third International Mathematics and Science Study (TIMSS) which has been conducted every 4 years since 1995. According to Stigler et al. (1999), in the FIMS and the SIMS, U.S. students achieved low scores in comparison with students in other countries (p. 1). The TIMSS 1995 “Videotape Classroom Study” was therefore a complement to the earlier studies conducted to learn “more about the instructional and cultural processes that are associated with achievement” (Stigler et al., 1999, p. 1). The TIMSS Videotape Classroom Study is known today as the TIMSS Video Study. From the findings of the TIMSS 1995 Video Study, Stigler and Hiebert (1999) likened teaching to “mountain ranges poking above the surface of the water,” whereby they implied that we might see the mountaintops, but we do not see the hidden parts underneath these mountain ranges (pp. 73-78). By watching the videotaped lessons from Germany, Japan, and the United States again and again, they discovered that “the systems of teaching within each country look similar from lesson to lesson. At least, there are certain recurring features [or patterns] that typify many of the lessons within a country and distinguish the lessons among countries” (pp. 77-78). They also discovered that “teaching is a cultural activity,” so the systems of teaching “must be understood in relation to the cultural beliefs and assumptions that surround them” (pp. 85, 88). From this viewpoint, one of the purposes of this dissertation was to study some cultural aspects of mathematics teaching and relate the results to mathematics teaching and learning in Vietnam. Another research purpose was to carry out a video study in Vietnam to find out the characteristics of Vietnamese mathematics teaching and compare these characteristics with those of other countries. In particular, this dissertation carried out the following research tasks: - Studying the characteristics of teaching and learning in different cultures and relating the results to mathematics teaching and learning in Vietnam - Introducing the TIMSS, the TIMSS Video Study and the advantages of using video study in investigating mathematics teaching and learning - Carrying out the video study in Vietnam to identify the image, scripts and patterns, and the lesson signature of eighth-grade mathematics teaching in Vietnam - Comparing some aspects of mathematics teaching in Vietnam and other countries and identifying the similarities and differences across countries - Studying the demands and challenges of innovating mathematics teaching methods in Vietnam – lessons from the video studies Hopefully, this dissertation will be a useful reference material for pre-service teachers at education universities to understand the nature of teaching and develop their teaching career.
Transcription factors (TFs) are ubiquitous gene expression regulators and play essential roles in almost all biological processes. This Ph.D. project is primarily focused on the functional characterisation of MYB112 - a member of the R2R3-MYB TF family from the model plant Arabidopsis thaliana. This gene was selected due to its increased expression during senescence based on previous qRT-PCR expression profiling experiments of 1880 TFs in Arabidopsis leaves at three developmental stages (15 mm leaf, 30 mm leaf and 20% yellowing leaf). MYB112 promoter GUS fusion lines were generated to further investigate the expression pattern of MYB112. Employing transgenic approaches in combination with metabolomics and transcriptomics we demonstrate that MYB112 exerts a major role in regulation of plant flavonoid metabolism. We report enhanced and impaired anthocyanin accumulation in MYB112 overexpressors and MYB112-deficient mutants, respectively. Expression profiling reveals that MYB112 acts as a positive regulator of the transcription factor PAP1 leading to increased anthocyanin biosynthesis, and as a negative regulator of MYB12 and MYB111, which both control flavonol biosynthesis. We also identify MYB112 early responsive genes using a combination of several approaches. These include gene expression profiling (Affymetrix ATH1 micro-arrays and qRT-PCR) and transactivation assays in leaf mesophyll cell protoplasts. We show that MYB112 binds to an 8-bp DNA fragment containing the core sequence (A/T/G)(A/C)CC(A/T)(A/G/T)(A/C)(T/C). By electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation coupled to qPCR (ChIP-qPCR) we demonstrate that MYB112 binds in vitro and in vivo to MYB7 and MYB32 promoters revealing them as direct downstream target genes. MYB TFs were previously reported to play an important role in controlling flavonoid biosynthesis in plants. Many factors acting upstream of the anthocyanin biosynthesis pathway show enhanced expression levels during nitrogen limitation, or elevated sucrose content. In addition to the mentioned conditions, other environmental parameters including salinity or high light stress may trigger anthocyanin accumulation. In contrast to several other MYB TFs affecting anthocyanin biosynthesis pathway genes, MYB112 expression is not controlled by nitrogen limitation, or carbon excess, but rather is stimulated by salinity and high light stress. Thus, MYB112 constitutes a previously uncharacterised regulatory factor that modifies anthocyanin accumulation under conditions of abiotic stress.
Poly(A) Polymerase 1 (PAPS1) influences organ size and pathogen response in Arabidopsis thaliana
(2014)
Polyadenylation of pre-mRNAs is critical for efficient nuclear export, stability, and translation of the mature mRNAs, and thus for gene expression. The bulk of pre-mRNAs are processed by canonical nuclear poly(A) polymerase (PAPS). Both vertebrate and higher-plant genomes encode more than one isoform of this enzyme, and these are coexpressed in different tissues. However, in neither case is it known whether the isoforms fulfill different functions or polyadenylate distinct subsets of pre-mRNAs. This thesis shows that the three canonical nuclear PAPS isoforms in Arabidopsis are functionally specialized owing to their evolutionarily divergent C-terminal domains. A moderate loss-of-function mutant in PAPS1 leads to increase in floral organ size, whereas leaf size is reduced. A strong loss-of-function mutation causes a male gametophytic defect, whereas a weak allele leads to reduced leaf growth. By contrast, plants lacking both PAPS2 and PAPS4 function are viable with wild-type leaf growth. Polyadenylation of SMALL AUXIN UP RNA (SAUR) mRNAs depends specifically on PAPS1 function. The resulting reduction in SAUR activity in paps1 mutants contributes to their reduced leaf growth, providing a causal link between polyadenylation of specific pre-mRNAs by a particular PAPS isoform and plant growth. Additionally, opposite effects of PAPS1 on leaf and flower growth reflect the different identities of these organs. The overgrowth of paps1 mutant petals is due to increased recruitment of founder cells into early organ primordia whereas the reduced leaf size is due to an ectopic pathogen response. This constitutive immune response leads to increased resistance to the biotrophic oomycete Hyaloperonospora arabidopsidis and reflects activation of the salicylic acid-independent signalling pathway downstream of ENHANCED DISEASE SUSCEPTIBILITY1 (EDS1)/PHYTOALEXIN DEFICIENT4 (PAD4). Immune responses are accompanied by intracellular redox changes. Consistent with this, the redox-status of the chloroplast is altered in paps1-1 mutants. The molecular effects of the paps1-1 mutation were analysed using an RNA sequencing approach that distinguishes between long- and short tailed mRNA. The results shown here suggest the existence of an additional layer of regulation in plants and possibly vertebrate gene expression, whereby the relative activities of canonical nuclear PAPS isoforms control de novo synthesized poly(A) tail length and hence expression of specific subsets of mRNAs.
Mathematical modeling of biological systems is a powerful tool to systematically investigate the functions of biological processes and their relationship with the environment. To obtain accurate and biologically interpretable predictions, a modeling framework has to be devised whose assumptions best approximate the examined scenario and which copes with the trade-off of complexity of the underlying mathematical description: with attention to detail or high coverage. Correspondingly, the system can be examined in detail on a smaller scale or in a simplified manner on a larger scale. In this thesis, the role of photosynthesis and its related biochemical processes in the context of plant metabolism was dissected by employing modeling approaches ranging from kinetic to stoichiometric models. The Calvin-Benson cycle, as primary pathway of carbon fixation in C3 plants, is the initial step for producing starch and sucrose, necessary for plant growth. Based on an integrative analysis for model ranking applied on the largest compendium of (kinetic) models for the Calvin-Benson cycle, those suitable for development of metabolic engineering strategies were identified. Driven by the question why starch rather than sucrose is the predominant transitory carbon storage in higher plants, the metabolic costs for their synthesis were examined. The incorporation of the maintenance costs for the involved enzymes provided a model-based support for the preference of starch as transitory carbon storage, by only exploiting the stoichiometry of synthesis pathways. Many photosynthetic organisms have to cope with processes which compete with carbon fixation, such as photorespiration whose impact on plant metabolism is still controversial. A systematic model-oriented review provided a detailed assessment for the role of this pathway in inhibiting the rate of carbon fixation, bridging carbon and nitrogen metabolism, shaping the C1 metabolism, and influencing redox signal transduction. The demand of understanding photosynthesis in its metabolic context calls for the examination of the related processes of the primary carbon metabolism. To this end, the Arabidopsis core model was assembled via a bottom-up approach. This large-scale model can be used to simulate photoautotrophic biomass production, as an indicator for plant growth, under so-called optimal, carbon-limiting and nitrogen-limiting growth conditions. Finally, the introduced model was employed to investigate the effects of the environment, in particular, nitrogen, carbon and energy sources, on the metabolic behavior. This resulted in a purely stoichiometry-based explanation for the experimental evidence for preferred simultaneous acquisition of nitrogen in both forms, as nitrate and ammonium, for optimal growth in various plant species. The findings presented in this thesis provide new insights into plant system's behavior, further support existing opinions for which mounting experimental evidences arise, and posit novel hypotheses for further directed large-scale experiments.
Mars is one of the best candidates among planetary bodies for supporting life. The presence of water in the form of ice and atmospheric vapour together with the availability of biogenic elements and energy are indicators of the possibility of hosting life as we know it. The occurrence of permanently frozen ground – permafrost, is a common phenomenon on Mars and it shows multiple morphological analogies with terrestrial permafrost. Despite the extreme inhospitable conditions, highly diverse microbial communities inhabit terrestrial permafrost in large numbers. Among these are methanogenic archaea, which are anaerobic chemotrophic microorganisms that meet many of the metabolic and physiological requirements for survival on the martian subsurface. Moreover, methanogens from Siberian permafrost are extremely resistant against different types of physiological stresses as well as simulated martian thermo-physical and subsurface conditions, making them promising model organisms for potential life on Mars. The main aims of this investigation are to assess the survival of methanogenic archaea under Mars conditions, focusing on methanogens from Siberian permafrost, and to characterize their biosignatures by means of Raman spectroscopy, a powerful technology for microbial identification that will be used in the ExoMars mission. For this purpose, methanogens from Siberian permafrost and non-permafrost habitats were subjected to simulated martian desiccation by exposure to an ultra-low subfreezing temperature (-80ºC) and to Mars regolith (S-MRS and P-MRS) and atmospheric analogues. They were also exposed to different concentrations of perchlorate, a strong oxidant found in martian soils. Moreover, the biosignatures of methanogens were characterized at the single-cell level using confocal Raman microspectroscopy (CRM). The results showed survival and methane production in all methanogenic strains under simulated martian desiccation. After exposure to subfreezing temperatures, Siberian permafrost strains had a faster metabolic recovery, whereas the membranes of non-permafrost methanogens remained intact to a greater extent. The strain Methanosarcina soligelidi SMA-21 from Siberian permafrost showed significantly higher methane production rates than all other strains after the exposure to martian soil and atmospheric analogues, and all strains survived the presence of perchlorate at the concentration on Mars. Furthermore, CRM analyses revealed remarkable differences in the overall chemical composition of permafrost and non-permafrost strains of methanogens, regardless of their phylogenetic relationship. The convergence of the chemical composition in non-sister permafrost strains may be the consequence of adaptations to the environment, and could explain their greater resistance compared to the non-permafrost strains. As part of this study, Raman spectroscopy was evaluated as an analytical technique for remote detection of methanogens embedded in a mineral matrix. This thesis contributes to the understanding of the survival limits of methanogenic archaea under simulated martian conditions to further assess the hypothetical existence of life similar to methanogens on the martian subsurface. In addition, the overall chemical composition of methanogens was characterized for the first time by means of confocal Raman microspectroscopy, with potential implications for astrobiological research.
The term Linked Data refers to connected information sources comprising structured data about a wide range of topics and for a multitude of applications. In recent years, the conceptional and technical foundations of Linked Data have been formalized and refined. To this end, well-known technologies have been established, such as the Resource Description Framework (RDF) as a Linked Data model or the SPARQL Protocol and RDF Query Language (SPARQL) for retrieving this information. Whereas most research has been conducted in the area of generating and publishing Linked Data, this thesis presents novel approaches for improved management. In particular, we illustrate new methods for analyzing and processing SPARQL queries. Here, we present two algorithms suitable for identifying structural relationships between these queries. Both algorithms are applied to a large number of real-world requests to evaluate the performance of the approaches and the quality of their results. Based on this, we introduce different strategies enabling optimized access of Linked Data sources. We demonstrate how the presented approach facilitates effective utilization of SPARQL endpoints by prefetching results relevant for multiple subsequent requests. Furthermore, we contribute a set of metrics for determining technical characteristics of such knowledge bases. To this end, we devise practical heuristics and validate them through thorough analysis of real-world data sources. We discuss the findings and evaluate their impact on utilizing the endpoints. Moreover, we detail the adoption of a scalable infrastructure for improving Linked Data discovery and consumption. As we outline in an exemplary use case, this platform is eligible both for processing and provisioning the corresponding information.
In the presented thesis, the most advanced photon reconstruction technique of ground-based γ-ray astronomy is adapted to the H.E.S.S. 28 m telescope. The method is based on a semi-analytical model of electromagnetic particle showers in the atmosphere. The properties of cosmic γ-rays are reconstructed by comparing the camera image of the telescope with the Cherenkov emission that is expected from the shower model. To suppress the dominant background from charged cosmic rays, events are selected based on several criteria. The performance of the analysis is evaluated with simulated events. The method is then applied to two sources that are known to emit γ-rays. The first of these is the Crab Nebula, the standard candle of ground-based γ-ray astronomy. The results of this source confirm the expected performance of the reconstruction method, where the much lower energy threshold compared to H.E.S.S. I is of particular importance. A second analysis is performed on the region around the Galactic Centre. The analysis results emphasise the capabilities of the new telescope to measure γ-rays in an energy range that is interesting for both theoretical and experimental astrophysics. The presented analysis features the lowest energy threshold that has ever been reached in ground-based γ-ray astronomy, opening a new window to the precise measurement of the physical properties of time-variable sources at energies of several tens of GeV.
Software maintenance encompasses any changes made to a software system after its initial deployment and is thereby one of the key phases in the typical software-engineering lifecycle. In software maintenance, we primarily need to understand structural and behavioral aspects, which are difficult to obtain, e.g., by code reading. Software analysis is therefore a vital tool for maintaining these systems: It provides - the preferably automated - means to extract and evaluate information from their artifacts such as software structure, runtime behavior, and related processes. However, such analysis typically results in massive raw data, so that even experienced engineers face difficulties directly examining, assessing, and understanding these data. Among other things, they require tools with which to explore the data if no clear question can be formulated beforehand. For this, software analysis and visualization provide its users with powerful interactive means. These enable the automation of tasks and, particularly, the acquisition of valuable and actionable insights into the raw data. For instance, one means for exploring runtime behavior is trace visualization. This thesis aims at extending and improving the tool set for visual software analysis by concentrating on several open challenges in the fields of dynamic and static analysis of software systems. This work develops a series of concepts and tools for the exploratory visualization of the respective data to support users in finding and retrieving information on the system artifacts concerned. This is a difficult task, due to the lack of appropriate visualization metaphors; in particular, the visualization of complex runtime behavior poses various questions and challenges of both a technical and conceptual nature. This work focuses on a set of visualization techniques for visually representing control-flow related aspects of software traces from shared-memory software systems: A trace-visualization concept based on icicle plots aids in understanding both single-threaded as well as multi-threaded runtime behavior on the function level. The concept’s extensibility further allows the visualization and analysis of specific aspects of multi-threading such as synchronization, the correlation of such traces with data from static software analysis, and a comparison between traces. Moreover, complementary techniques for simultaneously analyzing system structures and the evolution of related attributes are proposed. These aim at facilitating long-term planning of software architecture and supporting management decisions in software projects by extensions to the circular-bundle-view technique: An extension to 3-dimensional space allows for the use of additional variables simultaneously; interaction techniques allow for the modification of structures in a visual manner. The concepts and techniques presented here are generic and, as such, can be applied beyond software analysis for the visualization of similarly structured data. The techniques' practicability is demonstrated by several qualitative studies using subject data from industry-scale software systems. The studies provide initial evidence that the techniques' application yields useful insights into the subject data and its interrelationships in several scenarios.