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Among the Microbacteriaceae the species of Subtercola and Agreia form closely associated clusters. Phylogenetic analysis demonstrated three major phylogenetic branches of these species. One of these branches contains the two psychrophilic species Subtercola frigoramans and Subtercola vilae, together with a larger number of isolates from various cold environments. Genomic evidence supports the separation of Agreia and Subtercola species. In order to gain insight into the ability of S. vilae to adapt to life in this extreme environment, we analyzed the genome with a particular focus on properties related to possible adaptation to a cold environment. General properties of the genome are presented, including carbon and energy metabolism, as well as secondary metabolite production. The repertoire of genes in the genome of S. vilae DB165\(^T\) linked to adaptations to the harsh conditions found in Llullaillaco Volcano Lake includes several mechanisms to transcribe proteins under low temperatures, such as a high number of tRNAs and cold shock proteins. In addition, S. vilae DB165\(^T\) is capable of producing a number of proteins to cope with oxidative stress, which is of particular relevance at low temperature environments, in which reactive oxygen species are more abundant. Most important, it obtains capacities to produce cryo-protectants, and to combat against ice crystal formation, it produces ice-binding proteins. Two new ice-binding proteins were identified which are unique to S. vilae DB165\(^T\). These results indicate that S. vilae has the capacity to employ different mechanisms to live under the extreme and cold conditions prevalent in Llullaillaco Volcano Lake.
Magnetic Particle Imaging (MPI) is a promising new tomographic modality for fast as well as three-dimensional visualization of magnetic material. For anatomical or structural information an additional imaging modality such as computed tomography (CT) is required. In this paper, the first hybrid MPI-CT scanner for multimodal imaging providing simultaneous data acquisition is presented.
Synapse-associated protein 1 (Syap1) is the mammalian homologue of synapse-associated protein of 47 kDa (Sap47) in Drosophila. Genetic deletion of Sap47 leads to deficiencies in short-term plasticity and associative memory processing in flies. In mice, Syap1 is prominently expressed in the nervous system, but its function is still unclear. We have generated Syap1 knockout mice and tested motor behaviour and memory. These mice are viable and fertile but display distinct deficiencies in motor behaviour. Locomotor activity specifically appears to be reduced in early phases when voluntary movement is initiated. On the rotarod, a more demanding motor test involving control by sensory feedback, Syap1-deficient mice dramatically fail to adapt to accelerated speed or to a change in rotation direction. Syap1 is highly expressed in cerebellar Purkinje cells and cerebellar nuclei. Thus, this distinct motor phenotype could be due to a so-far unknown function of Syap1 in cerebellar sensorimotor control. The observed motor defects are highly specific since other tests in the modified SHIRPA exam, as well as cognitive tasks like novel object recognition, Pavlovian fear conditioning, anxiety-like behaviour in open field dark-light transition and elevated plus maze do not appear to be affected in Syap1 knockout mice.
Energy efficiency of computing systems has become an increasingly important issue over the last decades. In 2015, data centers were responsible for 2% of the world's greenhouse gas emissions, which is roughly the same as the amount produced by air travel.
In addition to these environmental concerns, power consumption of servers in data centers results in significant operating costs, which increase by at least 10% each year.
To address this challenge, the U.S. EPA and other government agencies are considering the use of novel measurement methods in order to label the energy efficiency of servers.
The energy efficiency and power consumption of a server is subject to a great number of factors, including, but not limited to, hardware, software stack, workload, and load level.
This huge number of influencing factors makes measuring and rating of energy efficiency challenging. It also makes it difficult to find an energy-efficient server for a specific use-case. Among others, server provisioners, operators, and regulators would profit from information on the servers in question and on the factors that affect those servers' power consumption and efficiency. However, we see a lack of measurement methods and metrics for energy efficiency of the systems under consideration.
Even assuming that a measurement methodology existed, making decisions based on its results would be challenging. Power prediction methods that make use of these results would aid in decision making. They would enable potential server customers to make better purchasing decisions and help operators predict the effects of potential reconfigurations.
Existing energy efficiency benchmarks cannot fully address these challenges, as they only measure single applications at limited sets of load levels. In addition, existing efficiency metrics are not helpful in this context, as they are usually a variation of the simple performance per power ratio, which is only applicable to single workloads at a single load level. Existing data center efficiency metrics, on the other hand, express the efficiency of the data center space and power infrastructure, not focusing on the efficiency of the servers themselves. Power prediction methods for not-yet-available systems that could make use of the results provided by a comprehensive power rating methodology are also lacking. Existing power prediction models for hardware designers have a very fine level of granularity and detail that would not be useful for data center operators.
This thesis presents a measurement and rating methodology for energy efficiency of servers and an energy efficiency metric to be applied to the results of this methodology. We also design workloads, load intensity and distribution models, and mechanisms that can be used for energy efficiency testing. Based on this, we present power prediction mechanisms and models that utilize our measurement methodology and its results for power prediction.
Specifically, the six major contributions of this thesis are:
We present a measurement methodology and metrics for energy efficiency rating of servers that use multiple, specifically chosen workloads at different load levels for a full system characterization.
We evaluate the methodology and metric with regard to their reproducibility, fairness, and relevance. We investigate the power and performance variations of test results and show fairness of the metric through a mathematical proof and a correlation analysis on a set of 385 servers. We evaluate the metric's relevance by showing the relationships that can be established between metric results and third-party applications.
We create models and extraction mechanisms for load profiles that vary over time, as well as load distribution mechanisms and policies. The models are designed to be used to define arbitrary dynamic load intensity profiles that can be leveraged for benchmarking purposes. The load distribution mechanisms place workloads on computing resources in a hierarchical manner.
Our load intensity models can be extracted in less than 0.2 seconds and our resulting models feature a median modeling error of 12.7% on average. In addition, our new load distribution strategy can save up to 10.7% of power consumption on a single server node.
We introduce an approach to create small-scale workloads that emulate the power consumption-relevant behavior of large-scale workloads by approximating their CPU performance counter profile, and we introduce TeaStore, a distributed, micro-service-based reference application. TeaStore can be used to evaluate power and performance model accuracy, elasticity of cloud auto-scalers, and the effectiveness of power saving mechanisms for distributed systems.
We show that we are capable of emulating the power consumption behavior of realistic workloads with a mean deviation less than 10% and down to 0.2 watts (1%). We demonstrate the use of TeaStore in the context of performance model extraction and cloud auto-scaling also showing that it may generate workloads with different effects on the power consumption of the system under consideration.
We present a method for automated selection of interpolation strategies for performance and power characterization. We also introduce a configuration approach for polynomial interpolation functions of varying degrees that improves prediction accuracy for system power consumption for a given system utilization.
We show that, in comparison to regression, our automated interpolation method selection and configuration approach improves modeling accuracy by 43.6% if additional reference data is available and by 31.4% if it is not.
We present an approach for explicit modeling of the impact a virtualized environment has on power consumption and a method to predict the power consumption of a software application. Both methods use results produced by our measurement methodology to predict the respective power consumption for servers that are otherwise not available to the person making the prediction.
Our methods are able to predict power consumption reliably for multiple hypervisor configurations and for the target application workloads. Application workload power prediction features a mean average absolute percentage error of 9.5%.
Finally, we propose an end-to-end modeling approach for predicting the power consumption of component placements at run-time. The model can also be used to predict the power consumption at load levels that have not yet been observed on the running system.
We show that we can predict the power consumption of two different distributed web applications with a mean absolute percentage error of 2.2%. In addition, we can predict the power consumption of a system at a previously unobserved load level and component distribution with an error of 1.2%.
The contributions of this thesis already show a significant impact in science and industry. The presented efficiency rating methodology, including its metric, have been adopted by the U.S. EPA in the latest version of the ENERGY STAR Computer Server program. They are also being considered by additional regulatory agencies, including the EU Commission and the China National Institute of Standardization. In addition, the methodology's implementation and the underlying methodology itself have already found use in several research publications.
Regarding future work, we see a need for new workloads targeting specialized server hardware. At the moment, we are witnessing a shift in execution hardware to specialized machine learning chips, general purpose GPU computing, FPGAs being embedded into compute servers, etc. To ensure that our measurement methodology remains relevant, workloads covering these areas are required. Similarly, power prediction models must be extended to cover these new scenarios.
Solitary bees build their nests by modifying the interior of natural cavities, and they provision them with food by importing collected pollen. As a result, the microbiota of the solitary bee nests may be highly dependent on introduced materials. In order to investigate how the collected pollen is associated with the nest microbiota, we used metabarcoding of the ITS2 rDNA and the 16S rDNA to simultaneously characterize the pollen composition and the bacterial communities of 100 solitary bee nest chambers belonging to seven megachilid species. We found a weak correlation between bacterial and pollen alpha diversity and significant associations between the composition of pollen and that of the nest microbiota, contributing to the understanding of the link between foraging and bacteria acquisition for solitary bees. Since solitary bees cannot establish bacterial transmission routes through eusociality, this link could be essential for obtaining bacterial symbionts for this group of valuable pollinators.
Cristae architecture is important for the function of mitochondria, the organelles that play the central role in many cellular processes. The mitochondrial contact site and cristae organizing system (MICOS) together with the sorting and assembly machinery (SAM) forms the mitochondrial intermembrane space bridging complex (MIB), a large protein complex present in mammalian mitochondria that partakes in the formation and maintenance of cristae. We report here a new subunit of the mammalian MICOS/MIB complex, an armadillo repeat-containing protein 1 (ArmC1). ArmC1 localizes both to cytosol and mitochondria, where it associates with the outer mitochondrial membrane through its carboxy-terminus. ArmC1 interacts with other constituents of the MICOS/MIB complex and its amounts are reduced upon MICOS/MIB complex depletion. Mitochondria lacking ArmC1 do not show defects in cristae structure, respiration or protein content, but appear fragmented and with reduced motility. ArmC1 represents therefore a peripheral MICOS/MIB component that appears to play a role in mitochondrial distribution in the cell.
Background
Epidural catheters are state of the art for postoperative analgesic in abdominal surgery. Due to neurolysis it can lead to postoperative urinary tract retention (POUR), which leads to prolonged bladder catheterization, which has an increased risk for urinary tract infections (UTI). Our aim was to identify the current perioperative management of urinary catheters and, second, to identify the optimal time of suprapubic bladder catheter removal in regard to the removal of the epidural catheter.
Methods
We sent a questionnaire to 102 German hospitals and analyzed the 83 received answers to evaluate the current handling of bladder drainage and epidural catheters. Then, we conducted a retrospective study including 501 patients, who received an epidural and suprapubic catheter after abdominal surgery at the University Hospital Würzburg. We divided the patients into three groups according to the point in time of suprapubic bladder drainage removal in regard to the removal of the epidural catheter and analyzed the onset of a UTI.
Results
Our survey showed that in almost all hospitals (98.8%), patients received an epidural catheter and a bladder drainage after abdominal surgery. The point in time of urinary catheter removal was equally distributed between before, simultaneously and after the removal of the epidural catheter (respectively: ~28–29%). The retrospective study showed a catheter-associated UTI in 6.7%. Women were affected significantly more often than men (10,7% versus 2,5%, p<0.001). There was a non-significant trend to more UTIs when the suprapubic catheter was removed after the epidural catheter (before: 5.7%, after: 8.4%).
Conclusion
The point in time of suprapubic bladder drainage removal in relation to the removal of the epidural catheter does not seem to correlate with the rate of UTIs. The current handling in Germany is inhomogeneous, so further studies to standardize treatment are recommended.
Chronic Kidney Disease as an Important Co-morbid Condition in Coronary Heart Disease Patients
(2019)
In patients with coronary heart disease (CHD) the control of the modifiable “traditional” cardiovascular risk factors such as hypertension, dyslipidemia, diabetes, achieving/maintaining normal body weight and smoking cessation is of major importance to improve prognosis. Guideline recommendations for secondary CHD prevention include specific treatment targets for blood pressure, lipid levels, and markers of glucose metabolism for both younger and older patients. Chronic kidney disease (CKD) has been identified as a “non-traditional” risk factor for worse outcome in CHD patients, as it is associated with a markedly increased risk for subsequent CV events and mortality.
The specific objectives of the current thesis-project are to investigate (a) the quality of care in a recent sample of German CHD patients and to investigate variation of risk factor control between younger and elder patients (≤70 versus >70 years), (b) to analyze the prevalence of CKD across Europe in stable CHD patients in the outpatient setting and during a hospital stay for CHD, (c) to investigate the level of awareness of CKD in German CHD patients and their treating physicians.
Data from the European-wide EUROASPIRE IV study were used that include data on 7998 CHD patients in the ambulatory setting (study visit) and during a hospital stay for CHD (index). The German EUROASPIRE IV study center in Würzburg recruited 536 patients in 2012-2013. Risk factor control was compared against the current recommendations of the European Society of Cardiology. CKD was described by stages of glomerular filtration rate (eGFR) and albuminuria. German patients were asked in an additional kidney specific module whether they have ever been told by a physician about renal impairment. The fact that CKD or acute kidney injury (AKI) was mentioned in prominent parts of the hospital discharge letter as well as correct ICD-coding of CKD or AKI served as a proxy for physician’s awareness of CKD.
The majority of German CHD patients was treated with the recommended drug therapies including e.g. β-blockers, anti-platelets and statins. However, treatment targets for blood pressure and LDL-cholesterol levels were not achieved in many patients (45% and 53%, respectively) and glycemic control in diabetic CHD patients with HbA1-levels <7% was insufficient (61%). A minority of patients reported on current smoking (10%), but unhealthy life-styles e.g. overweight/obesity (85%/37%) were frequent. Patterns of care differed between younger and older CHD patients while older patients were less likely to receive the recommended medical CHD-therapy, were more likely to have uncontrolled blood pressure and also to be diabetic. However, a greater proportion of diabetic patients >70 years was achieving the HbA1c target, and less elder patients were current smokers or were obese. About 17% of patients on average had CKD (eGFR< 60 ml/min/1.73m²) in the entire European sample at the study visit, and an additional 10% had albuminuria despite preserved eGFR, with considerable variation among countries. Impaired kidney function was observed in every fifth patient admitted for CHD in the entire European dataset of the EUROASPIRE IV study. Of the German CHD patients with CKD at the study visit, only a third were aware of their renal impairment. A minority of these patients was being seen by nephrologists, however, with a higher likelihood of CKD awareness and specialist care in more advanced stages of CKD. About a third of patients admitted for CHD showed either CKD or AKI during the hospital stay, but the discharge letter mentioned chronic or acute kidney disease only in every fifth of these patients. In contrast, correct ICD coding of CKD or AKI was more complete, but still suboptimal.
In summary, quality of secondary prevention in German CHD patients indicates considerably room for improvement, with life-style modifications may become an even greater factor in prevention campaigns than medical treatment into certain target ranges. Preventive therapies should also consider different needs in older individuals acknowledging physical and mental potential, other comorbidities and drug-interactions with co-medication. CKD is common in CHD patients, not only in the elderly. Since CHD and CKD affect each other and impact on worse prognosis of each other, raising the awareness of CKD among patients and physicians and considering CKD in medical therapy may improve prognosis and slow disease progression of CHD as well as CKD.
Supramolecular Block Copolymers by Seeded Living Supramolecular Polymerization of Perylene Bisimides
(2019)
The research on supramolecular polymerization has undergone a rapid development in the last two decades, particularly since supramolecular polymers exhibit a broad variety of functionalities and applications in organic electronics, biological science or as functional materials (Chapter 2.1). Although former studies have focused on investigation of the thermodynamics of supramolecular polymerization (Chapter 2.2), the academic interest in the recent years shifted towards gaining insight into kinetically controlled self-assembly and pathway complexity to generate novel out-of-equilibrium architectures with interesting nanostructures and features (Chapter 2.3). Along this path, the concepts of seeded and living supramolecular polymerization were recently developed to enable the formation of supramolecular polymers with controlled length and low polydispersity under precise kinetic control (Chapter 2.4). Besides that, novel strategies were developed to achieve supramolecular copolymerization resulting in complex multicomponent nanostructures with different structural motives. The classification of these supramolecular copolymers on the basis of literature examples and an overview of previously reported principles to create such supramolecular architectures are provided in Chapter 2.5.
The aim of the thesis was the non-covalent synthesis of highly desirable supramolecular block copolymers by the approach of living seeded supramolecular polymerization and to study the impact of the molecular shape of the monomeric building blocks on the supramolecular copolymerization. Based on the structure of the previously investigated PBI organogelator H-PBI a series of novel PBIs, bearing identical hydrogen-bonding amide side-groups in imide-position and various kind or number of substituents in bay-position, was synthesized and analyzed within this thesis. The new PBIs were successfully obtained in three steps starting from the respective bromo-substituted perylene-3,4:9,10-tetracarboxylic acid tetrabutylesters or from the N,N’-dicyclohexyl-1,7-dibromoperylene-3,4:9,10-tetracarboxylic acid bisimide. All target compounds were obtained in the final step by imidization reactions of the respective perylene tetracarboxylic acid bisanhydride precursors with N-(2-aminoethyl)-3,4,5-tris(dodecyloxy)-benzamide and were fully characterized by 1H and 13C NMR spectroscopy as well as high resolution mass spectrometry.
The variation of bay-substituents strongly changes the optical properties of the monomeric PBIs which were investigated by UV/vis and fluorescence spectroscopy. The increase of the number of the methoxy-substituents provokes, for example, a red-shift of the absorption maxima concomitant with a decrease of extinction coefficients and leads to a drastic increase of the fluorescence quantum yields. Furthermore, the molecular geometry of the PBIs is also affected by variations of the bay-substituents. Thus, increasing the steric demand of the bay-substituents leads to an enlargement of the twist angles of the PBI cores as revealed by DFT calculations.
Especially the 1,7-dimethoxy bay-substituted MeO-PBI proved to be very well-suited for the studies envisioned within this thesis. The self-assembly of this PBI derivative was analyzed in detail by UV/vis, fluorescence and FT-IR spectroscopy as well as atomic force microscopy (Chapter 3). These studies revealed that MeO-PBI forms in a solvent mixture of methylcyclohexane and toluene (2:1, v/v) kinetically trapped off-pathway H-aggregated nanoparticles upon fast cooling of a monomeric solution from 90 to 20 °C. However, upon slow cooling of the monomer solution fluorescent J-type nanofibers are formed by π π interactions and intermolecular hydrogen-bonding.
The kinetically metastable off-pathway H-aggregates can be transformed into the thermodynamically more favored J-type aggregates by addition of seeds, which are produced by ultrasonication of the polymeric nanofibers. Interestingly, the living character of this seed-induced supramolecular polymerization process was proven by a newly designed multicycle polymerization experimental protocol. This living polymerization experiment clearly proves, that the polymerization can only occur at the “active” ends of the polymeric seed and that almost no recombination or chain termination processes are present. Hence, the approach of living supramolecular polymerization enables the formation of supramolecular polymers with controlled length and narrow polydispersity.
In Chapter 4 the copolymerization of MeO-PBI with the structurally similar 1,7-dichloro (Cl-PBI) and 1,7-dimethylthio (MeS-PBI) bay-substituted PBIs is studied in detail. Both PBIs form analogous to MeO-PBI kinetically trapped off-pathway aggregates, which can be converted into the thermodynamically stable supramolecular polymers by seed-induced living supramolecular polymerization under precise kinetic control. However, the stability of the kinetically trapped aggregates of Cl-PBI and MeS-PBI is distinctly reduced compared to that of MeO-PBI, because the π-π-interactions of the kinetically metastable aggregates are hampered through the increased twisting of the PBI-cores of the former PBIs. UV/vis studies revealed that the two-component seeded copolymerization of the kinetically trapped state of MeO-PBI with seeds of Cl-PBI leads to the formation of unprecedented supramolecular block copolymers with A-B-A pattern by a living supramolecular polymerization process at the termini of the seeds. Remarkably, the resulting A-B-A block pattern of the obtained copolymers was clearly confirmed by atomic force microscopy studies as the respective blocks formed by the individual monomeric units could be distinguished by the pitches of the helical nanofibers.
Moreover, detailed UV/vis and AFM studies have shown that by inverted two-component seed-induced polymerization, e.g., upon addition of seeds of MeO-PBI to the kinetically trapped aggregates of Cl-PBI, triblock supramolecular copolymers with B-A-B pattern can be generated. The switching of the block pattern could only be achieved because of the perfectly matching conditions for the copolymerization process and the tailored molecular geometry of the individual building blocks of both PBIs. These studies have demonstrated for the first time, that the block pattern of a supramolecular copolymer can be modulated by the experimental protocol through the approach of living supramolecular polymerization. Furthermore, by UV/vis analysis of the living copolymerization of MeO-PBI and MeS-PBI similar results were obtained showing also the formation of both A-B-A and B-A-B type supramolecular block copolymers. Although for these two PBIs the individual blocks could not be identified by AFM because the helical nanofibers of both PBIs exhibit identical helical pitches, these studies revealed for the first time that the approach of seeded living polymerization is not limited to a special pair of monomeric building blocks.
In the last part of the thesis (Chapter 5) a systematic study on the two-component living copolymerization of PBIs with various sterical demanding bay-substituents is provided. Thus, a series of PBIs containing identical hydrogen-bonding amide groups in imide position but variable number (1-MeO-PBI, MeO-PBI, 1,6,7-MeO-PBI, 1,6,7,12-MeO-PBI) or size (EtO-PBI, iPrO-PBI) of alkoxy bay-substituents was investigated. The molecular geometry of the monomeric building blocks has a strong impact on the thermodynamically and even more pronounced on the kinetically controlled aggregation in solvent mixtures of MCH and Tol. While the mono- and dialkoxy-substituted PBIs form kinetically metastable species, the self-assembly of the tri- and tetramethoxy-substituted PBIs (1,6,7-MeO-PBI and 1,6,7,12-MeO-PBI) is completely thermodynamically controlled. The two 1,7-alkoxy substituted PBIs (EtO-PBI, iPrO-PBI) form very similar to MeO-PBI kinetically off-pathway H-aggregates and thermodynamically more favored J-type aggregates. However, the stability of the kinetically metastable state is drastically lower and the conversion into the thermodynamically favored state much faster than for MeO-PBI. In contrast, the monomethoxy-substituted PBI derivative (1-MeO-PBI) forms a kinetically trapped species by intramolecular hydrogen-bonding of the monomers, which can be transformed into the thermodynamically favored nanofibers by seeded polymerization.
Importantly, the two-component seeded copolymerization of the kinetically trapped MeO PBI with seeds of other PBIs of the present series was studied by UV/vis and AFM revealing that the formation of supramolecular block copolymers is only possible for appropriate combinations of PBI building blocks. Thus, the seeded polymerization of the trapped state of the moderately core-twisted MeO-PBI with the, according to DFT-calculations, structurally similar PBIs (EtO-PBI and iPrO-PBI) leads to the formation of A-B-A block copolymers, like in the seeded copolymerization of MeO-PBItrapped with seeds of Cl-PBI and MeS-PBI already described in Chapter 4. However, by addition of seeds of the almost planar PBIs (H-PBI and 1-MeO-PBI) or seeds of the strongly core-twisted PBIs (1,6,7-MeO-PBI and 1,6,7,12-MeO-PBI) to the kinetically trapped state of MeO-PBI no block copolymers can be obtained. The mismatching geometry of these molecular building blocks strongly hampers both the intermolecular hydrogen-bonding and the π-π-interactions between the two different PBIs and consequently prevents the copolymerization process.
Furthermore, the studies of the two-component seeded copolymerization of the kinetically trapped species of 1-MeO-PBI with seeds of the other PBIs also corroborated that a precise shape complementarity is crucial to generate supramolecular block copolymers. Thus, by addition of seeds of H-PBI to the kinetically trapped monomers of 1-MeO-PBI supramolecular block copolymers were generated. Both PBIs exhibit an almost planar PBI core according to DFT-calculations leading to strong non-covalent interactions between these PBIs. This perfectly matching geometry of both PBIs also enables the inverted seeded copolymerization of the kinetically trapped monomers of H-PBI with 1-MeO-PBIseed concomitant with a switching of the block pattern of the supramolecular copolymer from A-B-A to B-A-B type. In contrast, the seeding with the moderately twisted (MeO-PBI, EtO-PBI and iPrO-PBI) and the strongly twisted PBIs (1,6,7-MeO-PBI and 1,6,7,12 MeO-PBI) has no effect on the kinetically trapped state of 1-MeO-PBI, because the copolymerization of these PBIs is prevented by the mismatching geometry of the molecular building blocks.
In conclusion, the supramolecular polymerization and two-component seeded copolymerization of a series of PBI monomers was investigated within this thesis. The studies revealed that the thermodynamically and kinetically controlled self-assembly can be strongly modified by subtle changes of the monomeric building blocks. Moreover, the results have shown that living supramolecular polymerization is an exceedingly powerful method to generate unprecedented supramolecular polymeric nanostructures with controlled block pattern and length distribution. The formation of supramolecular block copolymers can only be achieved under precise kinetic control of the polymerization process and is strongly governed by the shape complementarity already imparted in the individual components. Thus, these insightful studies might enable a more rational design of monomeric building blocks for the non-covalent synthesis of highly complex supramolecular architectures with interesting properties for possible future applications, e.g., as novel functional materials.
Two chiral chemical molecules being mirror images of each other, also referred to as enantiomers, may have different pharmacokinetic, pharmacodynamic, and toxicological effects. Thus, pharmaceutical manufacturers and authorities are increasingly interested in the approval of enantiopure drugs. However, the isomeric purity and the limits for isomeric impurities have to be specified applying enantioselective analytical methods, such as capillary electrophoresis.
The separation of enantiomers in capillary electrophoresis may be improved by the addition of ionic liquids to the background electrolyte. The aim of this work was to investigate the influence of different separation conditions on the enantioseparation of phenethylamines in background electrolytes containing ionic liquids based on tetrabutylammonium cations.
Best chiral separations were achieved at acidic pH values using phosphate buffers containing 125 mmol/L tetrabutylammonium based salts. Different reasons explaining enhanced enantioseparations in buffers containing ionic liquids were found. First, due to an improvement of the cyclodextrin solubility, the addition of ionic liquids to the background electrolyte enables the use of higher concentrations of these chiral selector. Furthermore, the adsorption of tetrabutylammonium cations to the negatively charged capillary surface results in a reduction of the electroosmotic flow. Hence, the resulting prolongation of migration times leads to a longer period of time for the separation of temporarily formed diastereomeric analyte cyclodextrin complexes, which yields improved enantioseparation. Additionally, due to a decrease of the adsorption of positively charged phenethylamine analyte molecules to capillary surface silanol groups, the adsorption of ionic liquid cations inhibits peak broadening. A further reason explaining an enhanced enantioseparation by the addition of ionic liquids to the background electrolyte is a competition between tetrabutylammonium cations and analyte enantiomers for the inclusion into cyclodextrin cavities.
Furthermore, the influence of different chiral counterions, combined with tetrabutylammonium cations, on the enantioseparation of phenethylamines was investigated. Solely anions based on the basic proteinogenic amino acids L lysine and L arginine yielded chiral separation results superior to those achieved using achiral tetrabutylammonium chloride as background electrolyte additive. Especially the application of tetrabutylammonium L argininate gave very good enantioseparations of all investigated ephedrine derivatives, which might be explained by the ability of L arginine to affect the formation of complexes between analytes and cyclodextrins.
Besides the investigation of the influence of ionic liquids on the enantioseparation, complexes between phenethylamine enantiomers and β cyclodextrin derivatives were characterized by affinity capillary electrophoresis. The binding constants between analyte enantiomers and cyclodextrins and the electrophoretic mobilities of the temporarily formed complexes were determined and compared to the observed chiral resolution values. While neither the calculated binding constants nor their differences correlated with the quality of the enantioseparation, a strong correlation between the differences of the electrophoretic mobilities of the complexes and the chiral resolution values was found.
Brain serotonin (5-hydroxytryptamine, 5-HT) system dysfunction is implicated in exaggerated fear responses triggering various anxiety-, stress-, and trauma-related disorders. However, the underlying mechanisms are not well understood. Here, we investigated the impact of constitutively inactivated 5-HT synthesis on context-dependent fear learning and extinction using tryptophan hydroxylase 2 (Tph2) knockout mice. Fear conditioning and context-dependent fear memory extinction paradigms were combined with c-Fos imaging and electrophysiological recordings in the dorsal hippocampus (dHip). Tph2 mutant mice, completely devoid of 5-HT synthesis in brain, displayed accelerated fear memory formation and increased locomotor responses to foot shock. Furthermore, recall of context-dependent fear memory was increased. The behavioral responses were associated with increased c-Fos expression in the dHip and resistance to foot shock-induced impairment of hippocampal long-term potentiation (LTP). In conclusion, increased context-dependent fear memory resulting from brain 5-HT deficiency involves dysfunction of the hippocampal circuitry controlling contextual representation of fear-related behavioral responses.
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its death receptors TRAILR1/death receptor 4 (DR4) and TRAILR2/DR5 trigger cell death in many cancer cells but rarely exert cytotoxic activity on non-transformed cells. Against this background, a variety of recombinant TRAIL variants and anti-TRAIL death receptor antibodies have been developed and tested in preclinical and clinical studies. Despite promising results from mice tumor models, TRAIL death receptor targeting has failed so far in clinical studies to show satisfying anti-tumor efficacy. These disappointing results can largely be explained by two issues: First, tumor cells can acquire TRAIL resistance by several mechanisms defining a need for combination therapies with appropriate sensitizing drugs. Second, there is now growing preclinical evidence that soluble TRAIL variants but also bivalent anti-TRAIL death receptor antibodies typically require oligomerization or plasma membrane anchoring to achieve maximum activity. This review discusses the need for oligomerization and plasma membrane attachment for the activity of TRAIL death receptor agonists in view of what is known about the molecular mechanisms of how TRAIL death receptors trigger intracellular cell death signaling. In particular, it will be highlighted which consequences this has for the development of next generation TRAIL death receptor agonists and their potential clinical application.
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its death receptors TRAILR1/death receptor 4 (DR4) and TRAILR2/DR5 trigger cell death in many cancer cells but rarely exert cytotoxic activity on non-transformed cells. Against this background, a variety of recombinant TRAIL variants and anti-TRAIL death receptor antibodies have been developed and tested in preclinical and clinical studies. Despite promising results from mice tumor models, TRAIL death receptor targeting has failed so far in clinical studies to show satisfying anti-tumor efficacy. These disappointing results can largely be explained by two issues: First, tumor cells can acquire TRAIL resistance by several mechanisms defining a need for combination therapies with appropriate sensitizing drugs. Second, there is now growing preclinical evidence that soluble TRAIL variants but also bivalent anti-TRAIL death receptor antibodies typically require oligomerization or plasma membrane anchoring to achieve maximum activity. This review discusses the need for oligomerization and plasma membrane attachment for the activity of TRAIL death receptor agonists in view of what is known about the molecular mechanisms of how TRAIL death receptors trigger intracellular cell death signaling. In particular, it will be highlighted which consequences this has for the development of next generation TRAIL death receptor agonists and their potential clinical application.
An intricate network of molecular and cellular actors orchestrates the delicate balance between effector immune responses and immune tolerance. The pleiotropic cytokine tumor necrosis factor-alpha (TNF) proves as a pivotal protagonist promoting but also suppressing immune responses. These opposite actions are accomplished through specialist cell types responding to TNF via TNF receptors TNFR1 and TNFR2. Recent findings highlight the importance of TNFR2 as a key regulator of activated natural FoxP3+ regulatory T cells (Tregs) in inflammatory conditions, such as acute graft-vs.-host disease (GvHD) and the tumor microenvironment. Here we review recent advances in our understanding of TNFR2 signaling in T cells and discuss how these can reconcile seemingly conflicting observations when manipulating TNF and TNFRs. As TNFR2 emerges as a new and attractive target we furthermore pinpoint strategies and potential pitfalls for therapeutic targeting of TNFR2 for cancer treatment and immune tolerance after allogeneic hematopoietic cell transplantation.
Macrophages stand in the first line of defense against a variety of pathogens but are also involved in the maintenance of tissue homeostasis. To fulfill their functions macrophages sense a broad range of pathogen- and damage-associated molecular patterns (PAMPs/DAMPs) by plasma membrane and intracellular pattern recognition receptors (PRRs). Intriguingly, the overwhelming majority of PPRs trigger the production of the pleiotropic cytokine tumor necrosis factor-alpha (TNF). TNF affects almost any type of cell including macrophages themselves. TNF promotes the inflammatory activity of macrophages but also controls macrophage survival and death. TNF exerts its activities by stimulation of two different types of receptors, TNF receptor-1 (TNFR1) and TNFR2, which are both expressed by macrophages. The two TNF receptor types trigger distinct and common signaling pathways that can work in an interconnected manner. Based on a brief general description of major TNF receptor-associated signaling pathways, we focus in this review on research of recent years that revealed insights into the molecular mechanisms how the TNFR1-TNFR2 signaling network controls the life and death balance of macrophages. In particular, we discuss how the TNFR1-TNFR2 signaling network is integrated into PRR signaling.
Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.
Promising initial insights show that offices designed to permit physical activity (PA) may reduce workplace sitting time. Biophilic approaches are intended to introduce natural surroundings into the workplace, and preliminary data show positive effects on stress reduction and elevated productivity within the workplace. The primary aim of this pilot study was to analyze changes in workplace sitting time and self-reported habit strength concerning uninterrupted sitting and PA during work, when relocating from a traditional office setting to “active” biophilic-designed surroundings. The secondary aim was to assess possible changes in work-associated factors such as satisfaction with the office environment, work engagement, and work performance, among office staff. In a pre-post designed field study, we collected data through an online survey on health behavior at work. Twelve participants completed the survey before (one-month pre-relocation, T1) and twice after the office relocation (three months (T2) and seven months post-relocation (T3)). Standing time per day during office hours increased from T1 to T3 by about 40 min per day (p < 0.01). Other outcomes remained unaltered. The results suggest that changing office surroundings to an active-permissive biophilic design increased standing time during working hours. Future larger-scale controlled studies are warranted to investigate the influence of office design on sitting time and work-associated factors during working hours in depth.
Automation in Software Performance Engineering Based on a Declarative Specification of Concerns
(2019)
Software performance is of particular relevance to software system design, operation, and evolution because it has a significant impact on key business indicators. During the life-cycle of a software system, its implementation, configuration, and deployment are subject to multiple changes that may affect the end-to-end performance characteristics. Consequently, performance analysts continually need to provide answers to and act based on performance-relevant concerns. To ensure a desired level of performance, software performance engineering provides a plethora of methods, techniques, and tools for measuring, modeling, and evaluating performance properties of software systems. However, the answering of performance concerns is subject to a significant semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted. Performance evaluation approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. For the involved stakeholders, it can be an elaborate process to reasonably select, parameterize and correctly apply performance evaluation approaches, and to filter and interpret the obtained results. An additional challenge is that available performance evaluation artifacts may change over time, which requires to switch between different measurement-based and model-based performance evaluation approaches during the system evolution. At model-based analysis, the effort involved in creating performance models can also outweigh their benefits.
To overcome the deficiencies and enable an automatic and holistic evaluation of performance throughout the software engineering life-cycle requires an approach that: (i) integrates multiple types of performance concerns and evaluation approaches, (ii) automates performance model creation, and (iii) automatically selects an evaluation methodology tailored to a specific scenario. This thesis presents a declarative approach —called Declarative Performance Engineering (DPE)— to automate performance evaluation based on a humanreadable specification of performance-related concerns. To this end, we separate the definition of performance concerns from their solution. The primary scientific contributions presented in this thesis are:
A declarative language to express performance-related concerns and a corresponding processing framework:
We provide a language to specify performance concerns independent of a concrete performance evaluation approach. Besides the specification of functional aspects, the language allows to include non-functional tradeoffs optionally. To answer these concerns, we provide a framework architecture and a corresponding reference implementation to process performance concerns automatically. It allows to integrate arbitrary performance evaluation approaches and is accompanied by reference implementations for model-based and measurement-based performance evaluation.
Automated creation of architectural performance models from execution traces:
The creation of performance models can be subject to significant efforts outweighing the benefits of model-based performance evaluation. We provide a model extraction framework that creates architectural performance models based on execution traces, provided by monitoring tools.The framework separates the derivation of generic information from model creation routines. To derive generic information, the framework combines state-of-the-art extraction and estimation techniques. We isolate object creation routines specified in a generic model builder interface based on concepts present in multiple performance-annotated architectural modeling formalisms. To create model extraction for a novel performance modeling formalism, developers only need to write object creation routines instead of creating model extraction software from scratch when reusing the generic framework.
Automated and extensible decision support for performance evaluation approaches:
We present a methodology and tooling for the automated selection of a performance evaluation approach tailored to the user concerns and application scenario. To this end, we propose to decouple the complexity of selecting a performance evaluation approach for a given scenario by providing solution approach capability models and a generic decision engine. The proposed capability meta-model enables to describe functional and non-functional capabilities of performance evaluation approaches and tools at different granularities. In contrast to existing tree-based decision support mechanisms, the decoupling approach allows to easily update characteristics of solution approaches as well as appending new rating criteria and thereby stay abreast of evolution in performance evaluation tooling and system technologies.
Time-to-result estimation for model-based performance prediction:
The time required to execute a model-based analysis plays an important role in different decision processes. For example, evaluation scenarios might require the prediction results to be available in a limited period of time such that the system can be adapted in time to ensure the desired quality of service. We propose a method to estimate the time-to-result for modelbased performance prediction based on model characteristics and analysis parametrization. We learn a prediction model using performancerelevant features thatwe determined using statistical tests. We implement the approach and demonstrate its practicability by applying it to analyze a simulation-based multi-step performance evaluation approach for a representative architectural performance modeling formalism.
We validate each of the contributions based on representative case studies. The evaluation of automatic performance model extraction for two case study systems shows that the resulting models can accurately predict the performance behavior. Prediction accuracy errors are below 3% for resource utilization and mostly less than 20% for service response time. The separate evaluation of the reusability shows that the presented approach lowers the implementation efforts for automated model extraction tools by up to 91%. Based on two case studies applying measurement-based and model-based performance evaluation techniques, we demonstrate the suitability of the declarative performance engineering framework to answer multiple kinds of performance concerns customized to non-functional goals. Subsequently, we discuss reduced efforts in applying performance analyses using the integrated and automated declarative approach. Also, the evaluation of the declarative framework reviews benefits and savings integrating performance evaluation approaches into the declarative performance engineering framework. We demonstrate the applicability of the decision framework for performance evaluation approaches by applying it to depict existing decision trees. Then, we show how we can quickly adapt to the evolution of performance evaluation methods which is challenging for static tree-based decision support systems. At this, we show how to cope with the evolution of functional and non-functional capabilities of performance evaluation software and explain how to integrate new approaches. Finally, we evaluate the accuracy of the time-to-result estimation for a set of machinelearning algorithms and different training datasets. The predictions exhibit a mean percentage error below 20%, which can be further improved by including performance evaluations of the considered model into the training data. The presented contributions represent a significant step towards an integrated performance engineering process that combines the strengths of model-based and measurement-based performance evaluation. The proposed performance concern language in conjunction with the processing framework significantly reduces the complexity of applying performance evaluations for all stakeholders. Thereby it enables performance awareness throughout the software engineering life-cycle. The proposed performance concern language removes the semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted by the user.
Fungi of the order Mucorales colonize all kinds of wet, organic materials and represent a permanent part of the human environment. They are economically important as fermenting agents of soybean products and producers of enzymes, but also as plant parasites and spoilage organisms. Several taxa cause life-threatening infections, predominantly in patients with impaired immunity. The order Mucorales has now been assigned to the phylum Mucoromycota and is comprised of 261 species in 55 genera. Of these accepted species, 38 have been reported to cause infections in humans, as a clinical entity known as mucormycosis. Due to molecular phylogenetic studies, the taxonomy of the order has changed widely during the last years. Characteristics such as homothallism, the shape of the suspensors, or the formation of sporangiola are shown to be not taxonomically relevant. Several genera including Absidia, Backusella, Circinella, Mucor, and Rhizomucor have been amended and their revisions are summarized in this review. Medically important species that have been affected by recent changes include Lichtheimia corymbifera, Mucor circinelloides, and Rhizopus microsporus. The species concept of Rhizopus arrhizus (syn. R. oryzae) is still a matter of debate. Currently, species identification of the Mucorales is best performed by sequencing of the internal transcribed spacer (ITS) region. Ecologically, the Mucorales represent a diverse group but for the majority of taxa, the ecological role and the geographic distribution remain unknown. Understanding the biology of these opportunistic fungal pathogens is a prerequisite for the prevention of infections, and, consequently, studies on the ecology of the Mucorales are urgently needed.
This work deals with the development and application of novel quantum Monte Carlo methods to simulate fermion-boson models. Our developments are based on the path-integral formalism, where the bosonic degrees of freedom are integrated out exactly to obtain a retarded fermionic interaction. We give an overview of three methods that can be used to simulate retarded interactions. In particular, we develop a novel quantum Monte Carlo method with global directed-loop updates that solves the autocorrelation problem of previous approaches and scales linearly with system size. We demonstrate its efficiency for the Peierls transition in the Holstein model and discuss extensions to other fermion-boson models as well as spin-boson models. Furthermore, we show how with the help of generating functionals bosonic observables can be recovered directly from the Monte Carlo configurations. This includes estimators for the boson propagator, the fidelity susceptibility, and the specific heat of the Holstein model. The algorithmic developments of this work allow us to study the specific heat of the spinless Holstein model covering its entire parameter range. Its key features are explained from the single-particle spectral functions of electrons and phonons. In the adiabatic limit, the spectral properties are calculated exactly as a function of temperature using a classical Monte Carlo method and compared to results for the Su-Schrieffer-Heeger model.