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Sonstige beteiligte Institutionen
- IZKF Nachwuchsgruppe Geweberegeneration für muskuloskelettale Erkrankungen (5)
- Bernhard-Heine-Centrum für Bewegungsforschung (4)
- Zentraleinheit Klinische Massenspektrometrie (3)
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Spin- and \(k\)-resolved hard X-ray photoelectron spectroscopy (HAXPES) is a powerful tool to probe bulk electronic properties of complex metal oxides. Due to the low efficiency of common spin detectors of about \(10^{-4}\), such experiments have been rarely performed within the hard X-ray regime since the notoriously low photoionization cross sections further lower the performance tremendously. This thesis is about a new type of spin detector, which employs an imaging spin-filter with multichannel electron recording. This increases the efficiency by a factor of \(10^4\) and makes spin- and \(k\)-resolved photoemission at high excitation energies possible. Two different technical approaches were pursued in this thesis: One using a hemispherical deflection analyzer (HDA) and a separate external spin detector chamber, the other one resorting to a momentum- or \(k\)-space microscope with time-of-flight (TOF) energy recording and an integrated spin-filter crystal. The latter exhibits significantly higher count rates and - since it was designed for this purpose from scratch - the integrated spin-filter option found out to be more viable than the subsequent upgrade of an existing setup with an HDA. This instrumental development is followed by the investigation of the complex metal oxides (CMOs) KTaO\(_3\) by angle-resolved HAXPES (HARPES) and Fe\(_3\)O\(_4\) by spin-resolved HAXPES (spin-HAXPES), respectively.
KTaO\(_3\) (KTO) is a band insulator with a valence-electron configuration of Ta 5\(d^0\). By angle- and spin-integrated HAXPES it is shown that at the buried interface of LaAlO\(_3\)/KTO - by the generation of oxygen vacancies and hence effective electron doping - a conducting electron system forms in KTO. Further investigations using the momentum-resolution of the \(k\)-space TOF microscope show that these states are confined to the surface in KTO and intensity is only obtained from the center or the Gamma-point of each Brillouin zone (BZ). These BZs are furthermore square-like arranged reflecting the three-dimensional cubic crystal structure of KTO. However, from a comparison to calculations it is found that the band structure deviates from that of electron-doped bulk KTaO\(_3\) due to the confinement to the interface.
There is broad consensus that Fe\(_3\)O\(_4\) is a promising material for spintronics applications due to its high degree of spin polarization at the Fermi level. However, previous attempts to measure the spin polarization by spin-resolved photoemission spectroscopy have been hampered by the use of low photon energies resulting in high surface sensitivity. The surfaces of magnetite, though, tend to reconstruct due to their polar nature, and thus their magnetic and electronic properties may strongly deviate from each other and from the bulk, dependent on their orientation and specific preparation. In this work, the intrinsic bulk spin polarization of magnetite at the Fermi level (\(E_F\)) by spin-resolved photoelectron spectroscopy, is determined by spin-HAXPES on (111)-oriented thin films, epitaxially grown on ZnO(0001) to be \(P(E_F) = -80^{+10}_{-20}\) %.
Bacterial small RNAs (sRNAs) are widespread post-transcriptional regulators that control bacterial stress responses and virulence. Nevertheless, little is known about how they arise and evolve. Homologs can be difficult to identify beyond the strain level using sequence-based approaches, and similar functionalities can arise by convergent evolution. Here, we found that the virulence-associated CJnc190 sRNA of the foodborne pathogen Campylobacter jejuni resembles the RepG sRNA from the gastric pathogen Helicobacter pylori. However, while both sRNAs bind G-rich sites in their target mRNAs using a C/U-rich loop, they largely differ in their biogenesis. RepG is transcribed from a stand-alone gene and does not require processing, whereas CJnc190 is transcribed from two promoters as precursors that are processed by RNase III and also has a cis-encoded antagonist, CJnc180. By comparing CJnc190 homologs in diverse Campylobacter species, we show that RNase III-dependent processing of CJnc190 appears to be a conserved feature even outside of C. jejuni. We also demonstrate the CJnc180 antisense partner is expressed in C. coli, yet here might be derived from the 3’UTR (untranslated region) of an upstream flagella-related gene. Our analysis of G-tract targeting sRNAs in Epsilonproteobacteria demonstrates that similar sRNAs can have markedly different biogenesis pathways.
Comparative genomics provides structural and functional insights into Bacteroides RNA biology
(2022)
Bacteria employ noncoding RNA molecules for a wide range of biological processes, including scaffolding large molecular complexes, catalyzing chemical reactions, defending against phages, and controlling gene expression. Secondary structures, binding partners, and molecular mechanisms have been determined for numerous small noncoding RNAs (sRNAs) in model aerobic bacteria. However, technical hurdles have largely prevented analogous analyses in the anaerobic gut microbiota. While experimental techniques are being developed to investigate the sRNAs of gut commensals, computational tools and comparative genomics can provide immediate functional insight. Here, using Bacteroides thetaiotaomicron as a representative microbiota member, we illustrate how comparative genomics improves our understanding of RNA biology in an understudied gut bacterium. We investigate putative RNA-binding proteins and predict a Bacteroides cold-shock protein homolog to have an RNA-related function. We apply an in silico protocol incorporating both sequence and structural analysis to determine the consensus structures and conservation of nine Bacteroides noncoding RNA families. Using structure probing, we validate and refine these predictions and deposit them in the Rfam database. Through synteny analyses, we illustrate how genomic coconservation can serve as a predictor of sRNA function. Altogether, this work showcases the power of RNA informatics for investigating the RNA biology of anaerobic microbiota members.
In this thesis, I study entanglement in quantum field theory, using methods from operator algebra theory. More precisely, the thesis covers original research on the entanglement properties of the free fermionic field. After giving a pedagogical introduction to algebraic methods in quantum field theory, as well as the modular theory of Tomita-Takesaki and its relation to entanglement, I present a coherent framework that allows to solve Tomita-Takesaki theory for free fermionic fields in any number of dimensions. Subsequently, I use the derived machinery on the free massless fermion in two dimensions, where the formulae can be evaluated analytically. In particular, this entails the derivation of the resolvent of restrictions of the propagator, by means of solving singular integral equations. In this way, I derive the modular flow, modular Hamiltonian, modular correlation function, R\'enyi entanglement entropy, von-Neumann entanglement entropy, relative entanglement entropy, and mutual information for multi-component regions. All of this is done for the vacuum and thermal states, both on the infinite line and the circle with (anti-)periodic boundary conditions. Some of these results confirm previous results from the literature, such as the modular Hamiltonian and entanglement entropy in the vacuum state. The non-universal solutions for modular flow, modular correlation function, and R\'enyi entropy, however are new, in particular at finite temperature on the circle. Additionally, I show how boundaries of spacetime affect entanglement, as well as how one can define relative (entanglement) entropy and mutual information in theories with superselection rules. The findings regarding modular flow in multi-component regions can be summarised as follows: In the non-degenerate vacuum state, modular flow is multi-local, in the sense that it mixes the field operators along multiple trajectories, with one trajectory per component. This was already known from previous literature but is presented here in a more explicit form. In particular, I present the exact solution for the dynamics of the mixing process. What was not previously known at all, is that the modular flow of the thermal state on the circle is infinitely multi-local even for a connected region, in the sense that it mixes the field along an infinite, discretely distributed set, of trajectories. In the limit of high temperatures, all trajectories but the local one are pushed towards the boundary of the region, where their amplitude is damped exponentially, leaving only the local result. At low temperatures, on the other hand, these trajectories distribute densely in the region to either---for anti-periodic boundary conditions---cancel, or---for periodic boundary conditions---recover the non-local contribution due to the degenerate vacuum state. Proceeding to spacetimes with boundaries, I show explicitly how the presence of a boundary implies entanglement between the two components of the Dirac spinor. By computing the mutual information between the components inside a connected region, I show quantitatively that this entanglement decreases as an inverse square law at large distances from the boundary. In addition, full conformal symmetry (which is explicitly broken due to the presence of a boundary) is recovered from the exact solution for modular flow, far away from the boundary. As far as I know, all of these results are new, although related results were published by another group during the final stage of this thesis. Finally, regarding relative entanglement entropy in theories with superselection sectors, I introduce charge and flux resolved relative entropies, which are novel measures for the distinguishability of states, incorporating a charge operator, central to the algebra of observables. While charge resolved relative entropy has the interpretation of being a ``distinguishability per charge sector'', I argue that it is physically meaningless without placing a cutoff, due to infinite short-distance entanglement. Flux resolved relative entropy, on the other hand, overcomes this problem by inserting an Aharonov-Bohm flux and thus passing to a variant of the grand canonical ensemble. It takes a well defined value, even without putting a cutoff, and I compute its value between various states of the free massless fermion on the line, the charge operator being the total fermion number.
One consequence of the recent coronavirus pandemic is increased demand and use of online services around the globe. At the same time, performance requirements for modern technologies are becoming more stringent as users become accustomed to higher standards. These increased performance and availability requirements, coupled with the unpredictable usage growth, are driving an increasing proportion of applications to run on public cloud platforms as they promise better scalability and reliability.
With data centers already responsible for about one percent of the world's power consumption, optimizing resource usage is of paramount importance. Simultaneously, meeting the increasing and changing resource and performance requirements is only possible by optimizing resource management without introducing additional overhead. This requires the research and development of new modeling approaches to understand the behavior of running applications with minimal information.
However, the emergence of modern software paradigms makes it increasingly difficult to derive such models and renders previous performance modeling techniques infeasible. Modern cloud applications are often deployed as a collection of fine-grained and interconnected components called microservices. Microservice architectures offer massive benefits but also have broad implications for the performance characteristics of the respective systems. In addition, the microservices paradigm is typically paired with a DevOps culture, resulting in frequent application and deployment changes. Such applications are often referred to as cloud-native applications. In summary, the increasing use of ever-changing cloud-hosted microservice applications introduces a number of unique challenges for modeling the performance of modern applications. These include the amount, type, and structure of monitoring data, frequent behavioral changes, or infrastructure variabilities. This violates common assumptions of the state of the art and opens a research gap for our work.
In this thesis, we present five techniques for automated learning of performance models for cloud-native software systems. We achieve this by combining machine learning with traditional performance modeling techniques. Unlike previous work, our focus is on cloud-hosted and continuously evolving microservice architectures, so-called cloud-native applications. Therefore, our contributions aim to solve the above challenges to deliver automated performance models with minimal computational overhead and no manual intervention. Depending on the cloud computing model, privacy agreements, or monitoring capabilities of each platform, we identify different scenarios where performance modeling, prediction, and optimization techniques can provide great benefits. Specifically, the contributions of this thesis are as follows:
Monitorless: Application-agnostic prediction of performance degradations.
To manage application performance with only platform-level monitoring, we propose Monitorless, the first truly application-independent approach to detecting performance degradation. We use machine learning to bridge the gap between platform-level monitoring and application-specific measurements, eliminating the need for application-level monitoring. Monitorless creates a single and holistic resource saturation model that can be used for heterogeneous and untrained applications. Results show that Monitorless infers resource-based performance degradation with 97% accuracy. Moreover, it can achieve similar performance to typical autoscaling solutions, despite using less monitoring information.
SuanMing: Predicting performance degradation using tracing.
We introduce SuanMing to mitigate performance issues before they impact the user experience. This contribution is applied in scenarios where tracing tools enable application-level monitoring. SuanMing predicts explainable causes of expected performance degradations and prevents performance degradations before they occur. Evaluation results show that SuanMing can predict and pinpoint future performance degradations with an accuracy of over 90%.
SARDE: Continuous and autonomous estimation of resource demands.
We present SARDE to learn application models for highly variable application deployments. This contribution focuses on the continuous estimation of application resource demands, a key parameter of performance models. SARDE represents an autonomous ensemble estimation technique. It dynamically and continuously optimizes, selects, and executes an ensemble of approaches to estimate resource demands in response to changes in the application or its environment. Through continuous online adaptation, SARDE efficiently achieves an average resource demand estimation error of 15.96% in our evaluation.
DepIC: Learning parametric dependencies from monitoring data.
DepIC utilizes feature selection techniques in combination with an ensemble regression approach to automatically identify and characterize parametric dependencies. Although parametric dependencies can massively improve the accuracy of performance models, DepIC is the first approach to automatically learn such parametric dependencies from passive monitoring data streams. Our evaluation shows that DepIC achieves 91.7% precision in identifying dependencies and reduces the characterization prediction error by 30% compared to the best individual approach.
Baloo: Modeling the configuration space of databases.
To study the impact of different configurations within distributed DBMSs, we introduce Baloo. Our last contribution models the configuration space of databases considering measurement variabilities in the cloud. More specifically, Baloo dynamically estimates the required benchmarking measurements and automatically builds a configuration space model of a given DBMS. Our evaluation of Baloo on a dataset consisting of 900 configuration points shows that the framework achieves a prediction error of less than 11% while saving up to 80% of the measurement effort.
Although the contributions themselves are orthogonally aligned, taken together they provide a holistic approach to performance management of modern cloud-native microservice applications.
Our contributions are a significant step forward as they specifically target novel and cloud-native software development and operation paradigms, surpassing the capabilities and limitations of previous approaches.
In addition, the research presented in this paper also has a significant impact on the industry, as the contributions were developed in collaboration with research teams from Nokia Bell Labs, Huawei, and Google.
Overall, our solutions open up new possibilities for managing and optimizing cloud applications and improve cost and energy efficiency.
Efficacy of transcranial direct current stimulation in people with multiple sclerosis: a review
(2022)
Background and purpose
Multiple sclerosis (MS) is a chronic inflammatory disease causing a wide range of symptoms including motor and cognitive impairment, fatigue and pain. Over the last two decades, non-invasive brain stimulation, especially transcranial direct current stimulation (tDCS), has increasingly been used to modulate brain function in various physiological and pathological conditions. However, its experimental applications for people with MS were noted only as recently as 2010 and have been growing since then. The efficacy for use in people with MS remains questionable with the results of existing studies being largely conflicting. Hence, the aim of this review is to paint a picture of the current state of tDCS in MS research grounded on studies applying tDCS that have been done to date.
Methods
A keyword search was performed to retrieve articles from the earliest article identified until 14 February 2021 using a combination of the groups (1) ‘multiple sclerosis’, ‘MS’ and ‘encephalomyelitis’ and (2) ‘tDCS’ and ‘transcranial direct current stimulation’.
Results
The analysis of the 30 articles included in this review underlined inconsistent effects of tDCS on the motor symptoms of MS based on small sample sizes. However, tDCS showed promising benefits in ameliorating fatigue, pain and cognitive symptoms.
Conclusion
Transcranial direct current stimulation is attractive as a non-drug approach in ameliorating MS symptoms, where other treatment options remain limited. The development of protocols tailored to the individual's own neuroanatomy using high definition tDCS and the introduction of network mapping in the experimental designs might help to overcome the variability between studies.
The genetic modification of T cells for the expression a chimeric antigen receptor (CAR) endows them with a new specificity for an antigen. Adoptive immunotherapy with CD19-CAR T cells has achieved high rates of sustained complete remissions in B cell malignancies. However, the downregulation or loss of the targeted antigen after mono-specific CAR T cell therapy, e.g. against CD19 or CD22, has been reported. Targeting multiple antigens on tumour cells, sequentially or simultaneously, could overcome this limitation. Additionally, targeting multiple antigens with CAR T cells could drive the translation from hematologic malignancies to prevalent solid cancers, which often express tumour-associated antigens heterogeneously. We hypothesised that expression of a universal CAR, which can be programmed with hapten-like molecules, could endow T cells with specificities for multiple antigens.
In this study we introduce a novel chemically programmable CAR (cpCAR) based on monoclonal antibody h38C2. Our data show, that cpCARs form a reversible chemical bond to molecules containing a diketone-group and therefore can be programmed to acquire multiple specificities. We programmed cpCAR T cells with hapten-like compounds against integrins αvβ3 and α4β1 as well as the folate receptor. We observed tumour cell lysis, IFN ɣ and IL-2 production and proliferation of programmed cpCAR T cells against tumour cells expressing the respective target antigen in vitro.
As a reference to cpCARs programmed against αvβ3, we further introduced novel conventional αvβ3-CARs. These CARs, based on humanised variants of monoclonal antibody LM609 (hLM609), directly bind to integrin αvβ3 via their scFv. The four αvβ3-CAR constructs comprised either an scFv with higher affinity (hLM609v7) or lower affinity (hLM609v11) against αvβ3 integrin and either a long (IgG4 hinge, CH2, CH3) or short (IgG4 hinge) extracellular spacer. We selected the hLM609v7-CAR with short spacer, which showed potent anti-tumour reactivity both in vitro and in a murine xenograft model, for comparison with the cpCAR programmed against αvβ3. Our data show specific lysis of αvβ3-positive tumour cells, cytokine production and proliferation of both hLM609-CAR T cells and cpCAR T cells in vitro. However, conventional hLM609-CAR T cells mediated stronger anti-tumour effects compared to cpCAR T cells in the same amount of time. In line with the in vitro data, complete destruction of tumour lesions in a murine melanoma xenograft model was only observed for mice treated with conventional αvβ3-CAR T cells.
Collectively, we introduce a cpCAR, which can be programmed against multiple tumour antigens, and hLM609-CARs specific for the integrin αvβ3. The cpCAR technology bears the potential to counteract current limitations, e.g. antigen loss, of current monospecific CAR T cell therapy. Targeting αvβ3 integrin with CAR T cells could have clinical applications in the treatment of solid malignancies, because αvβ3 is not only expressed on a variety of solid malignancies, but also on tumour-associated vasculature and fibroblast.
DNA damage occurs frequently during normal cellular progresses or by environmental factors. To preserve the genome integrity, DNA damage response (DDR) has evolved to repair DNA and the non-properly repaired DNA induces human diseases like immune deficiency and cancer. Since a large number of proteins involved in DDR are enzymes of ubiquitin system, it is critical to investigate how the ubiquitin system regulates cellular response to DNA damage. Hereby, we reveal a novel mechanism for DDR regulation via activation of SCF ubiquitin ligase upon DNA damage.
As an essential step for DNA damage-induced inhibition of DNA replication, Cdc25A degradation by the E3 ligase β-TrCP upon DNA damage requires the deubiquitinase Usp28. Usp28 deubiquitinates β-TrCP in response to DNA damage, thereby promotes its dimerization, which is required for its activity in substrate ubiquitination and degradation. Particularly, ubiquitination at a specific lysine on β-TrCP suppresses dimerization.
The key mediator protein of DDR, 53BP1, forms oligomers and associates with β-TrCP to inhibit its activity in unstressed cells. Upon DNA damage, 53BP1 is degraded in the nucleoplasm, which requires oligomerization and is promoted by Usp28 in a β-TrCP-dependent manner. Consequently, 53BP1 destruction releases and activates β-TrCP during DNA damage response.
Moreover, 53BP1 deletion and DNA damage promote β-TrCP dimerization and recruitment to chromatin sites that locate in the vicinity of putative replication origins. Subsequently, the chromatin-associated Cdc25A is degraded by β-TrCP at the origins. The stimulation of β-TrCP binding to the origins upon DNA damage is accompanied by unloading of Cdc45, a crucial component of pre-initiation complexes for replication. Loading of Cdc45 to origins is a key Cdk2-dependent step for DNA replication initiation, indicating that localized Cdc25A degradation by β-TrCP at origins inactivates Cdk2, thereby inhibits the initiation of DNA replication.
Collectively, this study suggests a novel mechanism for the regulation of DNA replication upon DNA damage, which involves 53BP1- and Usp28-dependent activation of the SCF(β-TrCP) ligase in Cdc25A degradation.
Articular cartilage is a highly specialized tissue which provides a lubricated gliding surface in joints and thereby enables low-friction movement. If damaged once it has a very low intrinsic healing capacity and there is still no treatment in the clinic which can restore healthy cartilage tissue. 3D biofabrication presents a promising perspective in the field by combining healthy cells and bioactive ink materials. Thereby, the composition of the applied bioink is crucial for defect restoration, as it needs to have the physical properties for the fabrication process and also suitable chemical cues to provide a supportive environment for embedded cells. In the last years, ink compositions with high polymer contents and crosslink densities were frequently used to provide 3D printability and construct stability. But these dense polymeric networks were often associated with restricted bioactivity and impaired cell processes like differentiation and the distribution of newly produced extracellular matrix (ECM), which is especially important in the field of cartilage engineering. Therefore, the aim of this thesis was the development of hyaluronic acid (HA)-based bioinks with a reduced polymer content which are 3D printable and additionally facilitate chondrogenic differentiation of mesenchymal stromal cells (MSCs) and the homogeneous distribution of newly produced ECM. Starting from not-printable hydrogels with high polymer contents and restricted bioactivity, distinct stepwise improvements were achieved regarding stand-alone 3D printability as well as MSC differentiation and homogeneous ECM distribution. All newly developed inks in this thesis made a valuable contribution in the field of cartilage regeneration and represent promising approaches for potential clinical applications. The underlying mechanisms and established ink design criteria can further be applied to other biofabricated tissues, emphasizing their importance also in a more general research setting.
Lack of acid sphingomyelinase (ASM) activity, either through genetic deficiency or through pharmacological inhibition, is linked with increased activity and frequency of Foxp3+ regulatory T cells (Treg) among cluster of differentiation (CD) 4+ T cells in mice in vivo and in vitro1. Thus, pharmacological blockade of ASM activity, which catalyzes the cleavage of sphingomyelin to ceramide and phosphocholine, might be used as a new therapeutic mechanism to correct numeric and/ or functional Treg de-ficiencies in diseases like multiple sclerosis or major depression.
In the present study, the effect of pharmacological inhibition of ASM in humans, in vitro and in vivo, was analyzed. In the in vitro experiments, peripheral blood mono-nuclear cells (PBMC) of healthy human blood donors were treated with two widely prescribed antidepressants with high (sertraline, Ser) or low (citalopram, Cit) capaci-ty to inhibit ASM activity. Similar to the findings in mice an increase in the frequency of Treg among human CD4+ T cells upon inhibition of ASM activity was observed. For the analysis in vivo, a prospective study of the composition of the CD4+ T cell com-partment of patients treated for major depression was done. The data show that pharmacological inhibition of ASM activity was superior to antidepressants with little or no ASM-inhibitory activity in increasing CD45RA- CD25high effector Treg (efTreg) frequencies among CD4+ T cells to normal levels. Independently of ASM inhibition, correlating the data with the clinical response, i.e. improvement of the Hamilton rat-ing scale for depression (HAMD) by at least 50 per cent (%) after four weeks of treatment, it was found that an increase in efTreg frequencies among CD4+ cells dur-ing the first week of treatment identified patients with a clinical response.
Regarding the underlying mechanism, it could be found that the positive effect of ASM inhibition on Treg required CD28 co-stimulation suggesting that enhanced CD28 co-stimulation was the driver of the observed increase in the frequency of Treg among human CD4+ T cells. Inhibition of ASM activity was further associated with changes in the expression and shuttling of CTLA-4, a key inhibitory molecule ex-pressed by Treg, between cellular compartments but the suppressive activity of CTLA-4 through its transendocytosis activity was unaffected by the inhibition of ASM activity.
In summary, the frequency of (effector) Treg among CD4+ T cells in mice and in hu-mans is increased after inhibition of ASM activity suggesting that ASM blockade might beneficially modulate autoimmune diseases and depression-promoting in-flammation.