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Immature or semi-mature dendritic cells (DCs) represent tolerogenic maturation stages that can convert naive T cells into Foxp3\(^{+}\) induced regulatory T cells (iTreg). Here we found that murine bone marrow-derived DCs (BM-DCs) treated with cholera toxin (CT) matured by up-regulating MHC-II and costimulatory molecules using either high or low doses of CT (CT\(^{hi}\), CT\(^{lo}\)) or with cAMP, a known mediator CT signals. However, all three conditions also induced mRNA of both isoforms of the tolerogenic molecule cytotoxic T lymphocyte antigen 2 (CTLA-2α and CTLA-2β). Only DCs matured under CT\(^{hi}\) conditions secreted IL-1β, IL-6 and IL-23 leading to the instruction of Th17 cell polarization. In contrast, CT\(^{lo}\)- or cAMP-DCs resembled semi-mature DCs and enhanced TGF-β-dependent Foxp3\(^{+}\) iTreg conversion. iTreg conversion could be reduced using siRNA blocking of CTLA-2 and reversely, addition of recombinant CTLA-2α increased iTreg conversion in vitro. Injection of CT\(^{lo}\)- or cAMP-DCs exerted MOG peptide-specific protective effects in experimental autoimmune encephalomyelitis (EAE) by inducing Foxp3\(^{+}\) Tregs and reducing Th17 responses. Together, we identified CTLA-2 production by DCs as a novel tolerogenic mediator of TGF-β-mediated iTreg induction in vitro and in vivo. The CT-induced and cAMP-mediated up-regulation of CTLA-2 also may point to a novel immune evasion mechanism of Vibrio cholerae.
In the present work, the energetic structure and coherence properties of the silicon vacancy point defect in the technologically important material silicon carbide are extensively studied by the optically detected magnetic resonance (ODMR) technique in order to verify its high potential for various quantum applications. In the spin vacancy, unique attributes are arising from the C3v symmetry and the spin-3/2 state, which are not fully described by the standard Hamiltonian of the uniaxial model. Therefore, an advanced Hamiltonian, describing well the appearing phenomena is established and the relevant parameters are experimentally determined. Utilizing these new accomplishments, several quantum metrology techniques are proposed.
First, a vector magnetometry scheme, utilizing the appearance of four ODMR lines, allows for simultaneous detection of the magnetic field strength and the tilting angle of the magnetic field from the symmetry axis of the crystal.
The second magnetometry protocol utilizes the appearance of energetic level anticrossings (LAC) in the ground state (GS) energy levels. Relying only on the change in photoluminescence in the vicinity of this GSLACs, this all-optical method does not require any radio waves and hence provides a much easier operation with less error sources as for the common magnetometry schemes utilizing quantum points.
A similar all-optical method is applied for temperature sensing, utilizing the thermal shift of the zero field splitting and consequently the anticrossing in the excited state (ES). Since the GSLACs show no dependence on temperature, the all-optical magnetometry and thermometry (utilizing the ESLACs) can be conducted subsequently on the same defect.
In order to quantify the achievable sensitivity of quantum metrology, as well as to prove the potential of the Si-vacancy in SiC for quantum processing, the coherence properties are investigated by the pulsed ODMR technique. The spin-lattice relaxation time T1 and the spin-spin relaxation time T2 are thoroughly analyzed for their dependence on the external magnetic field and temperature.
For actual sensing implementations, it is crucial to obtain the best signal-to-noise ratio without loss in coherence time. Therefore, the irradiation process, by which the defects are created in the crystal, plays a decisive role in the device performance. In the present work, samples irradiated with electrons or neutrons with different fluences and energies, producing different defect densities, are analyzed in regard to their T1 and T2 times at room temperature.
Last but not least, a scheme to substantially prolong the T2 coherence time by locking the spin polarization with the dynamic decoupling Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence is applied.
Exciton-polaritons in semiconductor microcavities form a highly nonlinear platform to study a variety of effects interfacing optical, condensed matter, quantum and statistical physics. We show that the complex polariton patterns generated by picosecond pulses in microcavity wire waveguides can be understood as the Cherenkov radiation emitted by bright polariton solitons, which is enabled by the unique microcavity polariton dispersion, which has momentum intervals with positive and negative group velocities. Unlike in optical fibres and semiconductor waveguides, we observe that the microcavity wire Cherenkov radiation is predominantly emitted with negative group velocity and therefore propagates backwards relative to the propagation direction of the emitting soliton. We have developed a theory of the microcavity wire polariton solitons and of their Cherenkov radiation and conducted a series of experiments, where we have measured polariton-soliton pulse compression, pulse breaking and emission of the backward Cherenkov radiation.
Sponges (phylum Porifera) are evolutionary ancient, sessile filter-feeders that harbor a largely diverse microbial community within their internal mesohyl matrix. Throughout this thesis project, I aimed at exploring the adaptations of these symbionts to life within their sponge host by sequencing and analyzing the genomes of a variety of bacteria from the microbiome of the Mediterranean sponge Aplysina aerophoba. Employed methods were fluorescence-activated cell sorting with subsequent multiple displacement amplification and single-cell / ‘mini-metagenome’ sequencing, and metagenomic sequencing followed by differential coverage binning. These two main approaches both aimed at obtaining genome sequences of bacterial symbionts of A. aerophoba, that were then compared to each other and to references from other environments, to gain information on adaptations to the host sponge environment and on possible interactions with the host and within the microbial community.
Cyanobacteria are frequent members of the sponge microbial community. My ‘mini-metagenome’ sequencing project delivered three draft genomes of “Candidatus Synechococcus spongiarum,” the cyanobacterial symbiont of A. aerophoba and many more sponges inhabiting the photic zone. The most complete of these genomes was compared to other clades of this symbiont and to closely related free-living cyanobacterial references in a collaborative project published in Burgsdorf I*, Slaby BM* et al. (2015; *shared first authorship). Although the four clades of “Ca. Synechococcus spongiarum” from the four sponge species A. aerophoba, Ircinia variabilis, Theonella swinhoei, and Carteriospongia foliascens were approximately 99% identical on the level of 16S rRNA gene sequences, they greatly differed on the genomic level. Not only the genome sizes were different from clade to clade, but also the gene content and a number of features including proteins containing the eukaryotic-type domains leucine-rich repeats or tetratricopeptide repeats. On the other hand, the four clades shared a number of features such as ankyrin repeat domain-containing proteins that seemed to be conserved also among other microbial phyla in different sponge hosts and from different geographic locations. A possible novel mechanism for host phagocytosis evasion and phage resistance by means of an altered O antigen of the lipopolysaccharide was identified.
To test previous hypotheses on adaptations of sponge-associated bacteria on a broader spectrum of the microbiome of A. aerophoba while also taking a step forward in methodology, I developed a bioinformatic pipeline to combine metagenomic Illumina short-read sequencing data with PacBio long-read data. At the beginning of this project, no pipelines to combine short-read and long-read data for metagenomics were published, and at time of writing, there are still no projects published with a comparable aim of un-targeted assembly, binning and analysis of a metagenome. I tried a variety of assembly programs and settings on a simulated test dataset reflecting the properties of the real metagenomic data. The developed assembly pipeline improved not only the overall assembly statistics, but also the quality of the binned genomes, which was evaluated by comparison to the originally published genome assemblies.
The microbiome of A. aerophoba was studied from various angles in the recent years, but only genomes of the candidate phylum Poribacteria and the cyanobacterial sequences from my above-described project have been published to date. By applying my newly developed assembly pipeline to a metagenomic dataset of A. aerophoba consisting of a PacBio long-read dataset and six Illumina short-read datasets optimized for subsequent differential coverage binning, I aimed at sequencing a larger number and greater diversity of symbionts. The results of this project are currently in review by The ISME Journal. The complementation of Illumina short-read with PacBio long-read sequencing data for binning of this highly complex metagenome greatly improved the overall assembly statistics and improved the quality of the binned genomes. Thirty-seven genomes from 13 bacterial phyla and candidate phyla were binned representing the most prominent members of the microbiome of A. aerophoba. A statistical comparison revealed an enrichment of genes involved in restriction modification and toxin-antitoxin systems in most symbiont genomes over selected reference genomes. Both are defense features against incoming foreign DNA, which may be important for sponge symbionts due to the sponge’s filtration and phagocytosis activity that exposes the symbionts to high levels of free DNA. Also host colonization and matrix utilization features were significantly enriched. Due to the diversity of the binned symbiont genomes, a within-symbionts genome comparison was possible, that revealed three guilds of symbionts characterized by i) nutritional specialization on the metabolization of carnitine, ii) specialization on sulfated polysaccharides, and iii) apparent nutritional generalism. Both carnitine and sulfated polysaccharides are abundant in the sponge extracellular matrix and therefore available to the sponge symbionts as substrates. In summary, the genomes of the diverse community of symbionts in A. aerophoba were united in their defense features, but specialized regarding their nutritional preferences.
An den Grenzen der Pragmatik
(2017)
Mechanisms of visual memory formation in bees: About immediate early genes and synaptic plasticity
(2017)
Animals form perceptual associations through processes of learning, and retain that information through mechanisms of memory. Honeybees and bumblebees are classic models for insect perception and learning, and despite their small brains with about one million neurons, they are organized in highly social colonies and possess an astonishing rich behavioral repertoire including navigation, communication and cognition. Honeybees are able to harvest hundreds of morphologically divergent flower types in a quick and efficient manner to gain nutrition and, back in the hive, communicate discovered food sources to nest mates. To accomplish such complex tasks, bees must be equipped with diverse sensory organs receptive to stimuli of different modalities and must be able to associatively learn and memorize the acquired information. Particularly color vision plays a prominent role, e.g. in navigation along landmarks and when bees identify inflorescences by their color signals. Once acquired, bees are known to retain visual information for days or even months. Numerous studies on visual perception and color vision have been conducted in the past decades and largely revealed the information processing pathways in the brain. In contrast, there are no data available on how the brain may change in the course of color learning experience and whether pathways differ for coarse and fine color learning. Although long-term memory (LTM) storage is assumed to generally include reorganization of the neuronal network, to date it is unclear where in the bee brain such changes occur in the course of color learning and whether visual memories are stored in one particular site or decentrally distributed over different brain domains. The present dissertation research aimed to dissect the visual memory trace in bees that is beyond mere stimulus processing and therefore two different approaches were elaborated: first, the application of immediate early genes (IEG) as genetic markers for neuronal activation to localize early processes underlying the formation of a stable LTM. Second, the analysis of late consequences of memory formation, including synaptic reorganization in central brain areas and dependencies of color discrimination complexity.
Immediate early genes (IEG) are a group of rapidly and transiently expressed genes that are induced by various types of cellular stimulation. A great number of different IEGs are routinely used as markers for the localization of neuronal activation in vertebrate brains. The present dissertation research was dedicated to establish this approach for application in bees, with focus on the candidate genes Amjra and Amegr, which are orthologous to the two common vertebrate IEGs c-jun and egr-1. First the general requirement of gene transcription for visual LTM formation was proved. Bumblebees were trained in associative proboscis extension response (PER) conditioning to monochromatic light and subsequently injected with an inhibitor of gene transcription. Memory retention tests at different intervals revealed that gene transcription is not required for the formation of a mid-term memory, but for stable LTM. Next, the appliance of the candidate genes was validated. Honeybees were exposed to stimulation with either alarm pheromone or a light pulse, followed by qPCR analysis of gene expression. Both genes differed in their expression response to sensory exposure: Amjra was upregulated in all analyzed brain parts (antennal lobes, optic lobes and mushroom bodies, MB), independent from stimulus modality, suggesting the gene as a genetic marker for unspecific general arousal. In contrast, Amegr was not significantly affected by mere sensory exposure. Therefore, the relevance of associative learning on Amegr expression was assessed. Honeybees were trained in visual PER conditioning followed by a qPCR-based analysis of the expression of all three Amegr isoforms at different intervals after conditioning. No learning-dependent alteration of gene expression was observed. However, the presence of AmEgr protein in virtually all cerebral cell nuclei was validated by immunofluorescence staining. The most prominent immune-reactivity was detected in MB calyx neurons.
Analysis of task-dependent neuronal correlates underlying visual long-term memory was conducted in free-flying honeybees confronted with either absolute conditioning to one of two perceptually similar colors or differential conditioning with both colors. Subsequent presentation of the two colors in non-rewarded discrimination tests revealed that only bees trained with differential conditioning preferred the previously learned color. In contrast, bees of the absolute conditioning group chose randomly among color stimuli. To investigate whether the observed difference in memory acquisition is also reflected at the level of synaptic microcircuits, so called microglomeruli (MG), within the visual domains of the MB calyces, MG distribution was quantified by whole-mount immunostaining three days following conditioning. Although learning-dependent differences in neuroarchitecture were absent, a significant correlation between learning performance and MG density was observed.
Taken together, this dissertation research provides fundamental work on the potential use of IEGs as markers for neuronal activation and promotes future research approaches combining behaviorally relevant color learning tests in bees with examination of the neuroarchitecture to pave the way for unraveling the visual memory trace.
The current dissertation addresses the analysis of technology-enhanced learning processes by using Process Mining techniques. For this purpose, students’ coded think-aloud data served as the measurement of the learning process, in order to assess the potential of this analysis method for evaluating the impact of instructional support.
The increasing use of digital media in higher education and further educational sectors enables new potentials. However, it also poses new challenges to students, especially regarding the self-regulation of their learning process. To help students with optimally making progress towards their learning goals, instructional support is provided during learning. Besides the use of questionnaires and tests for the assessment of learning, researchers make use increasingly of process data to evaluate the effects of provided support. The analysis of observed behavioral traces while learning (e.g., log files, eye movements, verbal reports) allows detailed insights into the student’s activities as well as the impact of interventions on the learning process. However, new analytical challenges emerge, especially when going beyond the analysis of pure frequencies of observed events. For example, the question how to deal with temporal dynamics and sequences of learning activities arises. Against this background, the current dissertation concentrates on the application of Process Mining techniques for the detailed analysis of learning processes. In particular, the focus is on the additional value of this approach in comparison to a frequency-based analysis, and therefore on the potential of Process Mining for the evaluation of instructional support.
An extensive laboratory study with 70 university students, which was conducted to investigate the impact of a support measure, served as the basis for pursuing the research agenda of this dissertation. Metacognitive prompts supported students in the experimental group (n = 35) during a 40-minute hypermedia learning session; whereas the control group (n = 35) received no support. Approximately three weeks later, all students participated in another learning session; however, this time all students learned without any help. The participants were instructed to verbalize their learning activities concurrently while learning. In the three analyses of this dissertation, the coded think aloud data were examined in detail by using frequency-based methods as well as Process Mining techniques.
The first analysis addressed the comparison of the learning activities between the experimental and control groups during the first learning session. This study concentrated on the research questions whether metacognitive prompting increases the number of metacognitive learning activities, whether a higher number of these learning activities corresponds with learning outcome (mediation), and which differences regarding the sequential structure of learning activities can be revealed. The second analysis investigated the impact of the individual prompts as well as the conditions of their effectiveness on the micro level. In addition to Process Mining, we used a data mining approach to compare the findings of both analysis methods. More specifically, we classified the prompts by their effectiveness, and we examined the learning activities preceding and following the presentation of instructional support. Finally, the third analysis considered the long-term effects of metacognitive prompting on the learning process during another learning session without support. It was the key objective of this study to examine which fostered learning activities and process patterns remained stable during the second learning session.
Overall, all three analyses indicated the additional value of Process Mining in comparison to a frequency-based analysis. Especially when conceptualizing the learning process as a dynamic sequence of multiple activities, Process Mining allows identifying regulatory loops and crucial routing points of the process. These findings might contribute to optimizing intervention strategies. However, before drawing conclusions for the design of instructional support based on the revealed process patterns, additional analyses need to investigate the generalizability of results. Moreover, the application of Process Mining remains challenging because guidelines for analytical decisions and parameter settings in technology-enhanced learning context are currently missing. Therefore, future studies need to examine further the potential of Process Mining as well as related analysis methods to provide researchers with concrete recommendations for use. Nevertheless, the application of Process Mining techniques can already contribute to advance the understanding of the impact of instructional support through the use of fine-grained process data.
This work is concerned with the syntheses and photophysical properties of para-xylylene bridged macrocycles nPBI with ring sizes from two to nine PBI units, as well as the complexation of polycyclic aromatic guest compounds.
With a reduced but substantial fluorescence quantum yield of 21% (in CHCl3) the free host 2PBI(4-tBu)4 can be used as a dual fluorescence probe. Upon encapsulation of rather electron-poor guests the fluorescence quenching interactions between the chromophores are prevented, leading to a significant fluorescence enhancement to > 90% (“turn-on”). On the other hand, the addition of electron-rich guest molecules induces an electron transfer from the guest to the electron-poor PBI chromophores and thus quenches the fluorescence entirely (“turn-off”). The photophysical properties of the host-guest complexes were studied by transient absorption spectroscopy. These measurements revealed that the charge transfer between guest and 2PBI(4-tBu)4 occurs in the “normal region” of the Marcus-parabola with the fastest charge separation rate for perylene. In contrast, the charge recombination back to the PBI ground state lies far in the “inverted region” of the Marcus-parabola.
Beside complexation of planar aromatic hydrocarbons into the cavity of the cyclophanes an encapsulation of fullerene into the cyclic trimer 3PBI(4-tBu)4 was observed. 3PBI(4-tBu)4 provides a tube-like structure in which the PBI subunits represent the walls of those tubes. The cavity has the optimal size for hosting fullerenes, with C70 fitting better than C60 and a binding constant that is higher by a factor of 10. TA spectroscopy in toluene that was performed on the C60@3PBI(4-tBu)4 complex revealed two energy transfer processes. The first one comes from the excited PBI to the fullerene, which subsequently populates the triplet state. From the fullerene triplet state a second energy transfer occurs back to the PBI to generate the PBI triplet state.
In all cycles that were studied by TA spectroscopy, symmetry-breaking charge separation (SB-CS) was observed in dichloromethane. This process is fastest within the PBI cyclophane 2PBI(4-tBu)4 and slows down for larger cycles, suggesting that the charge separation takes place through space and not through bonds. The charges then recombine to the PBI triplet state via a radical pair intersystem crossing (RP-ISC) mechanism, which could be used to generate singlet oxygen in yields of ~20%.
By changing the solvent to toluene an intramolecular folding of the even-numbered larger cycles was observed that quenches the fluorescence and increases the 0-1 transition band in the absorption spectra. Force field calculations of 4PBI(4-tBu)4 suggested a folding into pairs of dimers, which explains the remarkable odd-even effect with respect to the number of connected PBI chromophores and the resulting alternation in the absorption and fluorescence properties. Thus, the even-numbered macrocycles can fold in a way that all chromophores are in a paired arrangement, while the odd-numbered cycles have open conformations (3PBI(4-tBu)4, 5PBI(4-tBu)4, 7PBI(4-tBu)4) or at least additional unpaired PBI unit (9PBI(4-tBu)4).
With these experiments we could for the first time give insights in the interactions between cyclic PBI hosts and aromatic guest molecules. Associated with the encapsulation of guest molecules a variety of possible applications can be envisioned, like fluorescence sensing, chiral recognition and photodynamic therapy by singlet oxygen generation. Particularly, these macrocycles provide photophysical relaxation pathways of PBIs, like charge separation and recombination and triplet state formation that are hardly feasible in monomeric PBI dyes. Furthermore, diverse compound specific features were found, like the odd-even effect in the folding process or the transition of superficial nanostructures of the tetrameric cycle influenced by the AFM tip. The comprehensive properties of these macrocycles provide the basis for further oncoming studies and can serve as an inspiration for the synthesis of new macrocyclic compounds.
The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e., sedentary: <1.5 metabolic equivalents (MET); light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e., >480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-week preparation period was assessed by the wrist-worn multisensory device Microsoft Band II (MSBII). A total of 11 rowers provided valid data (i.e., >480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63 ± 1.25 h per day during the week and 12.49 ± 1.10 h per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27 ± 1.15, 0.76 ± 0.37, 0.51 ± 0.44 h per day, and 0.67 ± 0.43, 0.59 ± 0.37, 0.53 ± 0.32 h per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69). Based on our pilot study of 11 national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 h/day.
The effects of circuit-like functional high-intensity training (Circuit\(_{HIIT}\)) alone or in combination with high-volume low-intensity exercise (Circuit\(_{combined}\)) on selected cardio-respiratory and metabolic parameters, body composition, functional strength and the quality of life of overweight women were compared. In this single-center, two-armed randomized, controlled study, overweight women performed 9-weeks (3 sessions·wk\(^{−1}\)) of either Circuit\(_{HIIT}\) (n = 11), or Circuit\(_{combined}\) (n = 8). Peak oxygen uptake and perception of physical pain were increased to a greater extent (p < 0.05) by Circuit\(_{HIIT}\), whereas Circuit\(_{combined}\) improved perception of general health more (p < 0.05). Both interventions lowered body mass, body-mass-index, waist-to-hip ratio, fat mass, and enhanced fat-free mass; decreased ratings of perceived exertion during submaximal treadmill running; improved the numbers of push-ups, burpees, one-legged squats, and 30-s skipping performed, as well as the height of counter-movement jumps; and improved physical and social functioning, role of physical limitations, vitality, role of emotional limitations, and mental health to a similar extent (all p < 0.05). Either forms of these multi-stimulating, circuit-like, multiple-joint training can be employed to improve body composition, selected variables of functional strength, and certain dimensions of quality of life in overweight women. However, Circuit\(_{HIIT}\) improves peak oxygen uptake to a greater extent, but with more perception of pain, whereas Circuit\(_{Combined}\) results in better perception of general health.
Enterprise applications in virtualized data centers are often subject to time-varying workloads, i.e., the load intensity and request mix change over time, due to seasonal patterns and trends, or unpredictable bursts in user requests. Varying workloads result in frequently changing resource demands to the underlying hardware infrastructure. Virtualization technologies enable sharing and on-demand allocation of hardware resources between multiple applications. In this context, the resource allocations to virtualized applications should be continuously adapted in an elastic fashion, so that "at each point in time the available resources match the current demand as closely as possible" (Herbst el al., 2013). Autonomic approaches to resource management promise significant increases in resource efficiency while avoiding violations of performance and availability requirements during peak workloads.
Traditional approaches for autonomic resource management use threshold-based rules (e.g., Amazon EC2) that execute pre-defined reconfiguration actions when a metric reaches a certain threshold (e.g., high resource utilization or load imbalance). However, many business-critical applications are subject to Service-Level-Objectives defined on an application performance metric (e.g., response time or throughput). To determine thresholds so that the end-to-end application SLO is fulfilled poses a major challenge due to the complex relationship between the resource allocation to an application and the application performance. Furthermore, threshold-based approaches are inherently prone to an oscillating behavior resulting in unnecessary reconfigurations.
In order to overcome the deficiencies of threshold-based
approaches and enable a fully automated approach to dynamically control the resource allocations of virtualized applications, model-based approaches are required that can predict the impact of a reconfiguration on the application performance in advance. However, existing model-based approaches are severely limited in their learning capabilities. They either require complete performance models of the application as input, or use a pre-identified model structure and only learn certain model parameters from empirical data at run-time. The former requires high manual efforts and deep system knowledge to create the performance models. The latter does not provide the flexibility to capture the specifics of complex and heterogeneous system architectures.
This thesis presents a self-aware approach to the resource management in virtualized data centers. In this context, self-aware means that it automatically learns performance models of the application and the virtualized infrastructure and reasons based on these models to autonomically adapt the resource allocations in accordance with given application SLOs. Learning a performance model requires the extraction of the model structure representing the system architecture as well as the estimation of model parameters, such as resource demands. The estimation of resource demands is a key challenge as they cannot be observed directly in most systems.
The major scientific contributions of this thesis are:
- A reference architecture for online model learning in virtualized systems. Our reference architecture is based on a set of model extraction agents. Each agent focuses on specific tasks to automatically create and update model skeletons capturing its local knowledge of the system and collaborates with other agents to extract the structural parts of a global performance model of the system. We define different agent roles in the reference architecture and propose a model-based collaboration mechanism for the agents. The agents may be bundled within virtual appliances and may be tailored to include knowledge about the software stack deployed in a specific virtual appliance.
- An online method for the statistical estimation of resource demands. For a given request processed by an application, the resource time consumed for a specified resource within the system (e.g., CPU or I/O device), referred to as resource demand, is the total average time the resource is busy processing the request. A request could be any unit of work (e.g., web page request, database transaction, batch job) processed by the system. We provide a systematization of existing statistical approaches to resource demand estimation and conduct an extensive experimental comparison to evaluate the accuracy of these approaches. We propose a novel method to automatically select estimation approaches and demonstrate that it increases the robustness and accuracy of the estimated resource demands significantly.
- Model-based controllers for autonomic vertical scaling of virtualized applications. We design two controllers based on online model-based reasoning techniques in order to vertically scale applications at run-time in accordance with application SLOs. The controllers exploit the knowledge from the automatically extracted performance models when determining necessary reconfigurations. The first controller adds and removes virtual CPUs to an application depending on the current demand. It uses a layered performance model to also consider the physical resource contention when determining the required resources. The second controller adapts the resource allocations proactively to ensure the availability of the application during workload peaks and avoid reconfiguration during phases of high workload.
We demonstrate the applicability of our approach in current virtualized environments and show its effectiveness leading to significant increases in resource efficiency and improvements of the application performance and availability under time-varying workloads. The evaluation of our approach is based on two case studies representative of widely used enterprise applications in virtualized data centers. In our case studies, we were able to reduce the amount of required CPU resources by up to 23% and the number of reconfigurations by up to 95% compared to a rule-based approach while ensuring full compliance with application SLO. Furthermore, using workload forecasting techniques we were able to schedule expensive reconfigurations (e.g., changes to the memory size) during phases of load load and thus were able to reduce their impact on application availability by over 80% while significantly improving application performance compared to a reactive controller. The methods and techniques for resource demand estimation and vertical application scaling were developed and evaluated in close collaboration with VMware and Google.
The contact of hot melt with liquid water - called Molten Fuel Coolant Interaction (MFCI) - can result in vivid explosions. Such explosions can occur in different scenarios: in steel or powerplants but also in volcanoes. Because of the possible dramatic consequences of such explosions an investigation of the explosion process is necessary.
Fundamental basics of this process are already discovered and explained, such as the frame conditions for these explosions. It has been shown that energy transfer during an MFCI-process can be very high because of the transfer of thermal energy caused by positive feedback mechanisms.
Up to now the influence of several varying parameters on the energy transfer and the explosions is not yet investigated sufficiently. An important parameter is the melt temperature, because the amount of possibly transferable energy depends on it. The investigation of this influence is the main aim of this work. Therefor metallic tin melt was used, because of its nearly constant thermal material properties in a wide temperature range. With tin melt research in the temperature range from 400 °C up to 1000 °C are
possible.
One important result is the lower temperature limit for vapor film stability in the experiments. For low melt temperatures up to about 600 °C the vapor film is so unstable that it already can collapse before the mechanical trigger. As expected the transferred thermal energy all in all increases with higher temperatures. Although this effect sometimes is superposed by other influences such as the premix of melt and water, the result is confirmed after a consequent filtering of the remaining influences. This trend is not only recognizable in the amount of transferred energy, but also in the fragmentation of melt or the vaporizing water. But also the other influences on MFCI-explosions showed interesting results in the frame of this work. To perform the experiments the installation and preparation of the experimental Setup in the laboratory were necessary.
In order to compare the results to volcanism and to get a better investigation of the brittle fragmentation
of melt additional runs with magmatic melt were made. In the results the thermal power during energy transfer could be estimated. Furthermore the model of “cooling fragments “ could be usefully applied.
In this work, multi-particle quantum optimal control problems are studied in the framework of time-dependent density functional theory (TDDFT).
Quantum control problems are of great importance in both fundamental research and application of atomic and molecular systems. Typical applications are laser induced chemical reactions, nuclear magnetic resonance experiments, and quantum computing.
Theoretically, the problem of how to describe a non-relativistic system of multiple particles is solved by the Schrödinger equation (SE). However, due to the exponential increase in numerical complexity with the number of particles, it is impossible to directly solve the Schrödinger equation for large systems of interest. An efficient and successful approach to overcome this difficulty is the framework of TDDFT and the use of the time-dependent Kohn-Sham (TDKS) equations therein.
This is done by replacing the multi-particle SE with a set of nonlinear single-particle Schrödinger equations that are coupled through an additional potential.
Despite the fact that TDDFT is widely used for physical and quantum chemical calculation and software packages for its use are readily available, its mathematical foundation is still under active development and even fundamental issues remain unproven today.
The main purpose of this thesis is to provide a consistent and rigorous setting for the TDKS equations and of the related optimal control problems.
In the first part of the thesis, the framework of density functional theory (DFT) and TDDFT are introduced. This includes a detailed presentation of the different functional sets forming DFT. Furthermore, the known equivalence of the TDKS system to the original SE problem is further discussed.
To implement the TDDFT framework for multi-particle computations, the TDKS equations provide one of the most successful approaches nowadays. However, only few mathematical results concerning these equations are available and these results do not cover all issues that arise in the formulation of optimal control problems governed by the TDKS model.
It is the purpose of the second part of this thesis to address these issues such as higher regularity of TDKS solutions and the case of weaker requirements on external (control) potentials that are instrumental for the formulation of well-posed TDKS control problems. For this purpose, in this work, existence and uniqueness of TDKS solutions are investigated in the Galerkin framework and using energy estimates for the nonlinear TDKS equations.
In the third part of this thesis, optimal control problems governed by the TDKS model are formulated and investigated. For this purpose, relevant cost functionals that model the purpose of the control are discussed.
Henceforth, TDKS control problems result from the requirement of optimising the given cost functionals subject to the differential constraint given by the TDKS equations. The analysis of these problems is novel and represents one of the main contributions of the present thesis.
In particular, existence of minimizers is proved and their characterization by TDKS optimality systems is discussed in detail.
To this end, Fréchet differentiability of the TDKS model and of the cost functionals is addressed considering \(H^1\) cost of the control.
This part is concluded by deriving the reduced gradient in the \(L^2\) and \(H^1\) inner product.
While the \(L^2\) optimization is widespread in the literature, the choice of the \(H^1\) gradient is motivated in this work by theoretical consideration and by resulting numerical advantages.
The last part of the thesis is devoted to the numerical approximation of the TDKS optimality systems and to their solution by gradient-based optimization techniques.
For the former purpose, Strang time-splitting pseudo-spectral schemes are discussed including a review of some recent theoretical estimates for these schemes and a numerical validation of these estimates.
For the latter purpose, nonlinear (projected) conjugate gradient methods are implemented and are used to validate the theoretical analysis of this thesis with results of numerical experiments with different cost functional settings.
In mammals, megakaryocytes (MKs) in the bone marrow (BM) produce blood platelets, required for hemostasis and thrombosis. MKs originate from hematopoietic stem cells and are thought to migrate from an endosteal niche towards the vascular sinusoids during their maturation. Through imaging of MKs in the intact BM, here we show that MKs can be found within the entire BM, without a bias towards bone-distant regions. By combining in vivo two-photon microscopy and in situ light-sheet fluorescence microscopy with computational simulations, we reveal surprisingly slow MK migration, limited intervascular space, and a vessel-biased MK pool. These data challenge the current thrombopoiesis model of MK migration and support a modified model, where MKs at sinusoids are replenished by sinusoidal precursors rather than cells from a distant periostic niche. As MKs do not need to migrate to reach the vessel, therapies to increase MK numbers might be sufficient to raise platelet counts.
Drug delivery of therapeutic gases – strategies for controlled and local delivery of carbon monoxide
(2017)
The isoenzyme heme oxygenase 1 (HO-1) is a key element for maintaining cellular homeostasis. Upregulated in response to cellular stress, the HO-1 degrades heme into carbon monoxide (CO), biliverdin, and Fe2+. By means of a local cell-protective feedback loop the enzyme triggers numerous effects including anti-oxidative, anti-apoptotic, and anti-inflammatory events associated with complex signalling patterns which are largely orchestrated by CO. Various approaches to mimic this physiological HO-1 / CO system aiming for a treatment of medical conditions have been described [1]. These preclinical studies commonly applied CO systemically via (i) inhalation or (ii) using CO-Releasing Molecules (CORMs) [2]. The clinical use of these approaches, however, is challenged by a lack of practicability and substantial safety issues associated with the toxicity of high systemic doses of CO that are required for triggering therapeutic effects. Therefore, one rational of this thesis is to describe and evaluate strategies for the local delivery of CO aiming for safe and effective CO therapeutics of tomorrow.
Im Rahmen der vorliegenden Untersuchung liegt der Fokus auf der Überprüfung und Weiterentwicklung der Methode der Multiagentensysteme für die Prognosezwecke im Einzelhandel. Die konkrete Zielsetzung der Arbeit ist der Entwurf eines integrativen Systems zur Simulation möglicher Zukunftsszenarien des (räumlichen) Konsumentenverhaltens. Mit Hilfe einer agentenbasierten Modellierung ist es möglich die bisher vorherrschenden Top-Down Ansätze flexibel in ein Bottom-Up Modell zu integrieren. Die wichtigsten strukturprägenden Impulse im Einzelhandelssystem und somit auch auf die Konsumenten gehen aktuell von der Digitalisierung des Verkaufsvorgangs aus. Hierbei wird der „Raum-Zeit-Käfig“ der Kunden ausgeweitet und bestimmte Zwänge der räumlichen und zeitlichen Bindung innerhalb des Kaufprozesses entfallen. Die klassische zeitliche Abfolge des Einkaufsverhaltens wird aufgelöst; Information findet vermehrt digital statt. Vielmehr steht der Produktnutzen im Mittelpunkt, und zugehörige Dienstleistungen wie Information, Service und Logistik werden flexibel kombiniert. Vor diesem Hintergrund stellt die agentenbasierte Simulation einen dynamischen Ansatzpunkt dar, in dem eine Reihe der Defizite tradierter, statischer Methoden Berücksichtigung findet und sich vielfältige Einsatzmöglichkeiten für die Analyse der Wechselwirkungen zwischen Konsumentenverhalten und räumlichen Einzelhandelsstrukturen ergeben. Aufgrund der zunehmenden Digitalisierung des Einkaufsprozesses und den daraus entstehenden Informationen zum Konsumentenverhalten in Kombination mit immer komplexeren Fragestellungen ist in den kommenden Jahren eine verstärkte Dynamik bei der Anwendungshäufigkeit von Multiagentensimulationen in Einzelhandelsunternehmen zu erwarten.
Background
Artificial rearing of honey bee larvae is an established method which enables to fully standardize the rearing environment and to manipulate the supplied diet to the brood. However, there are no studies which compare learning performance or neuroanatomic differences of artificially-reared (in-lab) bees in comparison with their in-hive reared counterparts.
Methods
Here we tested how different quantities of food during larval development affect body size, brain morphology and learning ability of adult honey bees. We used in-lab rearing to be able to manipulate the total quantity of food consumed during larval development. After hatching, a subset of the bees was taken for which we made 3D reconstructions of the brains using confocal laser-scanning microscopy. Learning ability and memory formation of the remaining bees was tested in a differential olfactory conditioning experiment. Finally, we evaluated how bees reared with different quantities of artificial diet compared to in-hive reared bees.
Results
Thorax and head size of in-lab reared honey bees, when fed the standard diet of 160 µl or less, were slightly smaller than hive bees. The brain structure analyses showed that artificially reared bees had smaller mushroom body (MB) lateral calyces than their in-hive counterparts, independently of the quantity of food they received. However, they showed the same total brain size and the same associative learning ability as in-hive reared bees. In terms of mid-term memory, but not early long-term memory, they performed even better than the in-hive control.
Discussion
We have demonstrated that bees that are reared artificially (according to the Aupinel protocol) and kept in lab-conditions perform the same or even better than their in-hive sisters in an olfactory conditioning experiment even though their lateral calyces were consistently smaller at emergence. The applied combination of experimental manipulation during the larval phase plus subsequent behavioral and neuro-anatomic analyses is a powerful tool for basic and applied honey bee research.
Bee pollination increases yield quantity and quality of cash crops in Burkina Faso, West Africa
(2017)
Mutualistic biotic interactions as among flowering plants and their animal pollinators are a key component of biodiversity. Pollination, especially by insects, is a key element in ecosystem functioning, and hence constitutes an ecosystem service of global importance. Not only sexual reproduction of plants is ensured, but also yields are stabilized and genetic variability of crops is maintained, counteracting inbreeding depression and facilitating system resilience. While experiencing rapid environmental change, there is an increased demand for food and income security, especially in sub-Saharan communities, which are highly dependent on small scale agriculture. By combining exclusion experiments, pollinator surveys and field manipulations, this study for the first time quantifies the contribution of bee pollinators to smallholders’ production of the major cash crops, cotton and sesame, in Burkina Faso. Pollination by honeybees and wild bees significantly increased yield quantity and quality on average up to 62%, while exclusion of pollinators caused an average yield gap of 37% in cotton and 59% in sesame. Self-pollination revealed inbreeding depression effects on fruit set and low germination rates in the F1-generation. Our results highlight potential negative consequences of any pollinator decline, provoking risks to agriculture and compromising crop yields in sub-Saharan West Africa.