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Polyneuropathien sind eine ätiologisch heterogene Erkrankung des peripheren Nervensystems. In bis zu 30% der Fälle ist eine Zuordnung zu einem bestimmten PNP Subtyp auch nach aufwändiger und zum Teil invasiver Diagnostik nicht möglich. Bislang fehlt ein diagnostischer Biomarker bei PNP, der z.B. bei der Unterscheidung zwischen einzelnen diagnostischen Subgruppen oder entzündlichen und nicht-entzündlichen Erkrankungsformen helfen könnte. In einer prospektiven Studie mit insgesamt 97 Patienten mit Neuropathien verschiedenster Ätiologie und 17 gesunden Kontrollpersonen erstellten wir Genexpressionsprofile von inflammatorischen Markern und Markern der Regeneration peripherer Nerven in Haut- und N. suralis-Biopsaten. Es wurden Inflammationsmarker (TAC1, CRMP2, AIF1, IL-6) und Marker, die in die Regeneration peripherer Nerven involviert sind (SCD, Netrin-1, DCC, UNC5H2, NEO1, Netrin-G1, Netrin-G2), mittels qRT-PCR untersucht. Alle Patienten erhielten eine N. suralis-Biopsie und/oder eine Hautbiopsie von Ober- beziehungsweise Unterschenkel. Weder in den Haut- noch in den N. suralis-Biopsaten konnten Unterschiede in der Genexpression dieser Marker zwischen einzelnen diagnostischen Subgruppen gefunden werden. Der Inflammationsmarker AIF1 war jedoch in Patienten-Hautproben sowohl proximal als auch distal höher exprimiert als bei gesunden Kontrollpersonen (p < 0,05 bzw. p < 0,01). Zudem fand sich in den Hautproben von PNP-Patienten eine deutlich reduzierte Genexpression von Regenerationsmarkern aus der Netrin-Familie verglichen mit den Hautproben gesunder Probanden (Netrin-1, DCC, UNC5H2, NEO1 sowie Netrin-G1 und G2; p < 0,05 bis p < 0,001). Ferner wies Netrin-1 in distalen Hautproben bei Patienten mit einer entzündlichen PNP eine niedrigere Genexpression auf, als bei Patienten mit einer nicht-entzündlichen Erkrankungsform (p < 0,05). Die Genexpression von NEO1 in distalen Hautproben war bei schmerzloser PNP und gesunden Kontrollpersonen höher als bei schmerzhafter PNP (p < 0,05). Sowohl eine Erhöhung bestimmter Inflammationsmarker als auch eine Verminderung von Regenerationsmarkern peripherer Nerven können bei der Pathophysiologie von Polyneuropathien involviert sein. Insbesondere Mitglieder der Netrin-Familie scheinen eine komplexe Rolle für das Axonwachstum, jedoch auch für entzündliche Prozesse zu spielen.
The limited intrinsic self-healing capability of articular cartilage requires treatment of
cartilage defects. Material assisted and cell based therapies are in clinical practice but
tend to result in formation of mechanical inferior fibro-cartilage in long term follow up. If
a lesion has not been properly restored degenerative diseases are diagnosed as late sequela
causing pain and loss in morbidity. Complex three dimensional tissue models mimicking
physiological situation allow investigation of cartilage metabolism and mechanisms involved
in repair. A standardized and reproducible model cultured under controllable conditions
ex vivo to maintain tissue properties is of relevance for comparable studies.
Topic of this thesis was the establishment of an cartilage defect model that allows for
testing novel biomaterials and investigate the effect of defined defect depths on formation
of repair tissue.
In part I an ex vivo osteochondral defect model was established based on isolation of
porcine osteochondral explants (OCE) from medial condyles, 8 mm in diameter and 5 mm
in height. Full thickness cartilage defects with 1 mm to 4 mm in diameter were created
to define ex vivo cartilage critical size after 28 days culture with custom developed static
culture device. In part II of this thesis hydrogel materials, namely collagen I isolated from
rat tail, commercially available fibrin glue, matrix-metalloproteinase clevable poly(ethylene
glycol) polymerized with heparin (starPEGh), methacrylated poly(N-(2-hydroxypropyl)
methacrylamide mono-dilactate-poly(ethylene glycol) triblock copolymer/methacrylated
hyaluronic acid (MP/HA), thiol functionalized HA/allyl functionalized poly(glycidol)
(P(AGE/G)-HA-SH), were tested cell free and chondrocyte loaded (20 mio/ml) as implant
in 4 mm cartilage defects to investigate cartilage regeneration. Reproducible chondral
defects, 8 mm in diameter and 1 mm in height, were generated with an artificial tissue
cutter (ARTcut®) to investigate effect of defect depth on defect regeneration in part III.
In all approaches OCE were analyzed by Safranin-O staining to visualize proteoglycans
in cartilage and/or hydrogels. Immuno-histological and -fluorescent stainings (aggrecan,
collagen II, VI and X, proCollagen I, SOX9, RUNX2), gene expression analysis (aggrecan,
collagen II and X, SOX9, RUNX2) of chondrocyte loaded hydrogels (part II) and proteoglycan
and DNA content (Part I & II) were performed for detailed analysis of cartilage
regeneration.
Part I: The development of custom made static culture device, consisting of inserts in which OCE is fixed and deep well plate, allowed tissue specific media supply without
supplementation of TGF . Critical size diameter was defined to be 4 mm.
Part II: Biomaterials revealed differences in cartilage regeneration. Collagen I and fibrin
glue showed presence of cells migrated from OCE into cell free hydrogels with indication
of fibrous tissue formation by presence of proCollagen I. In chondrocyte loaded study
cartilage matrix proteins aggrecan, collagen II and VI and transcription factor SOX9 were
detected after ex vivo culture throughout the two natural hydrogels collagen I and fibrin
glue whereas markers were localized in pericellular matrix in starPEGh. Weak stainings resulted
for MP/HA and P(AGE/G)-HA-SH in some cell clusters. Gene expression data and
proteoglycan quantification supported histological findings with tendency of hypertrophy
indicated by upregulation of collagen X and RunX2 in MP/HA and P(AGE/G)-HA-SH.
Part III: In life-dead stainings recruitment of cells from OCE into empty or cell free
collagen I treated chondral defects was seen.
Separated and tissue specific media supply is critical to maintain ECM composition in
cartilage. Presence of OCE stimulates cartilage matrix synthesis in chondrocyte loaded
collagen I hydrogel and reduces hypertrophy compared to free swelling conditions and
pellet cultures. Differences in cartilage repair tissue formation resulted in preference of
natural derived polymers compared to synthetic based materials. The ex vivo cartilage
defect model represents a platform for testing novel hydrogels as cartilage materials, but
also to investigate the effect of cell seeding densities, cell gradients, cell co-cultures on
defect regeneration dependent on defect depth. The separated media compartments allow
for systematic analysis of pharmaceutics, media components or inflammatory cytokines on
bone and cartilage metabolism and matrix stability.
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.
RNA-binding proteins (RBPs) have been extensively studied in eukaryotes, where they post-transcriptionally regulate many cellular events including RNA transport, translation, and stability. Experimental techniques, such as cross-linking and co-purification followed by either mass spectrometry or RNA sequencing has enabled the identification and characterization of RBPs, their conserved RNA-binding domains (RBDs), and the regulatory roles of these proteins on a genome-wide scale. These developments in quantitative, high-resolution, and high-throughput screening techniques have greatly expanded our understanding of RBPs in human and yeast cells. In contrast, our knowledge of number and potential diversity of RBPs in bacteria is comparatively poor, in part due to the technical challenges associated with existing global screening approaches developed in eukaryotes.
Genome- and proteome-wide screening approaches performed in silico may circumvent these technical issues to obtain a broad picture of the RNA interactome of bacteria and identify strong RBP candidates for more detailed experimental study. Here, I report APRICOT (“Analyzing Protein RNA Interaction by Combined Output Technique”), a computational pipeline for the sequence-based identification and characterization of candidate RNA-binding proteins encoded in the genomes of all domains of life using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences of an input proteome using position-specific scoring matrices and hidden Markov models of all conserved domains available in the databases and then statistically score them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them according to functionally relevant structural properties. APRICOT performed better than other existing tools for the sequence-based prediction on the known RBP data sets. The applications and adaptability of the software was demonstrated on several large bacterial RBP data sets including the complete proteome of Salmonella Typhimurium strain SL1344. APRICOT reported 1068 Salmonella proteins as RBP candidates, which were subsequently categorized using the RBDs that have been reported in both eukaryotic and bacterial proteins. A set of 131 strong RBP candidates was selected for experimental confirmation and characterization of RNA-binding activity using RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-Seq) experiments. Based on the relative abundance of transcripts across the RIP-Seq libraries, a catalogue of enriched genes was established for each candidate, which shows the RNA-binding potential of 90% of these proteins. Furthermore, the direct targets of few of these putative RBPs were validated by means of cross-linking and co-immunoprecipitation (CLIP) experiments.
This thesis presents the computational pipeline APRICOT for the global screening of protein primary sequences for potential RBPs in bacteria using RBD information from all kingdoms of life. Furthermore, it provides the first bio-computational resource of putative RBPs in Salmonella, which could now be further studied for their biological and regulatory roles. The command line tool and its documentation are available at https://malvikasharan.github.io/APRICOT/.
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.
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.
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.
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.