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The present study discusses money and conflicts and anxiety over money in late Ming vernacular stories and contextualizes these stories in the contemporary society of economic prosperity and rapid changes. The high monetization and extensive use of silver and copper cash as currency brought both wealth and conflicts in various aspects of society. Eleven vernacular stories from several collections are adopted as source materials for the close examination, including Jingshi tongyan (Stories to Caution the World, 1624) and Xingshi hengyan (Stories to Awaken the World, 1627) by Feng Menglong (1574-1646) and the two Pai’an jingqi (Slapping the Table in Amazement, 1628 and 1632) collections by Ling Mengchu (1580-1644), etc. The analysis then focuses on the relationship between money and four topics, the late Ming context, social relations, gender ideals, and religion. Multiple voices and various viewpoints in these narratives show human beings’ struggles in taming and dominating money, the increasingly familiar and essential object in everyday life. Generally, when people cannot control money properly, there is a fear of its detrimental power to humans and social relations within and beyond families. On the contrary, characters, who are able to control money, are praised.
Glioblastoma (GB) is the most aggressive malignant adult brain tumour with a median survival rate of only 15 months. GB tumours are characterized by necrotic and hypoxic core, which leads to nutrient deficient areas contributing to invasive, diffuseinfiltrative and angiogenic nature of these tumours. Cells exposed to nutrient deficient conditions and are known to reprogram their metabolism to produce or procure macro molecules from their environment. This makes cancer cells uniquely dependent on transcriptional regulators and a window of opportunity to target them. Sterol regulatory element binding protein 1 (SREBP1) is a transcriptional regulator of de-novo fatty acid synthesis in cells. The aim of this thesis was to investigate if SREBP1 was involved in restructuring the transcriptional regulation of genes involved in fatty acid biosynthesis upon low serum condition, in mediating interaction with other cell types in the tumour bulk such as endothelial cells, in regulating cancer stem like cells and finally to study its upstream regulation in GB. Global transcriptional analysis on GB cells exposed to low serum conditions revealed that SREBP1 regulated several fatty acid biosynthesis and phospholipid metabolic processes. PLA2G3 was identified as a novel target of SREBP1 in GB that was uniquely regulated in low serum condition. Analysis of total fatty acid and lipid species revealed that loss of SREBP1 in low serum condition changes the proportion of saturated, MUFAs and PUFAs. These changes were not specific to loss of PLA2G3 but as a result of downregulation of many genes regulated by SREBP1 in the fatty acid biosynthetic pathway. Next, treatment of HUVEC’s (endothelial cells) with condition medium from SREBP1-silenced U87 cells inhibited sprouting and tube formation capacity compared to the control condition, emphasizing the role of SREBP1 in angiogenesis and release of signalling mediators. Further, SREBP1 was shown to be important for proliferation of patient derived stem like cells and becomes indispensable for forming neurospheres in long term cultures, indicating its role in maintaining stemness. Also, inhibition of SREBP function by blocking the esterification of cholesterol using inhibitors targeting SOAT1 showed impairment in the viability of GB cells exposed to serum-depleted condition. Overall, SREBP1 plays an important role in maintaining tumour growth in nutrient deficient conditions and help in interaction with tumour microenvironment contributing to the aggressiveness of this tumour and poses itself as an attractive and unique target for GB treatment
Diabetes mellitus is an incurable, metabolic disease, which is associated with severe long-term complications. The in vitro generation of pancreatic β-cells from human induced pluripotent stem cells (hiPSCs) represent a promising strategy for a curative therapy of diabetes mellitus. However, current differentiation strategies largely fail to produce functional β-cells in vitro and require an additional in vivo transplantation to achieve terminal maturation. Previous studies demonstrated a beneficial effect of the extracellular matrix (ECM) on the survival and sustained function of adult, isolated islets of Langerhans. This raises the question whether organ-specific cell-ECM interactions might represent the missing link driving the final stage of β-cell development. In order to address this issue, this study investigated the impact of the pancreas ECM on in vitro β-cell differentiation and its use for the establishment of a pancreatic endocrine organ model.
To this purpose, a pancreas-specific ECM scaffolds (PanMa) was derived from porcine pancreata using whole organ decellularization with Sodium Deoxycholate. In a first step, the generated PanMa was thoroughly characterized using (immuno-) histological stainings, scanning electron microscopy and DNA quantification as well as perfusion and recellularization experiments with endothelial cells. Based on these data, a scoring system (PancScore) for a standardized PanMa generation was developed. Next, the generated PanMa was tested for the presence of tissue-specific ECM features. Therefore, the biophysical and physico-structural characteristics, such as rigidity, porosity and hygroscopy were analyzed using rheological measurements, particle diffusion analyses as well as a water evaporation assay and compared to the properties of ECM scaffolds derived from porcine small intestine (SISser) and lung (LungMa) to examine organ-specific scaffold cues. Following the thorough scaffold characterization, the impact of the PanMa on pluripotency and early development of hiPSC was studied. To this purpose, gene and protein expression of hiPSCs during maintenance culture and spontaneous differentiation on the PanMa were assessed. In a next step, the impact of the PanMa on the pancreatic endocrine differentiation of hiPSCs was tested. Therefore, the PanMa was used as a liquid media supplement or as a solid scaffold during the directed differentiation of hiPSC towards either pancreatic hormone-expressing cells (Rezania et al. 2012; Rezania et al. 2014) or maturing β-cells (Rezania et al. 2014). The impact of the PanMa on the generated cells was examined by gene expression analysis, immunohistochemical staining of important stage markers, as well as glucose stimulated insulin secretion assays. In a last part of this study, the potential of the PanMa for the prolonged culture of hiPSC derived endocrine cells for the establishment of an in vitro organ model of the endocrine pancreas was examined. Therefore, a PanMa-derived hydrogel was generated and used for the encapsulation and culture of hiPSC-derived hormone-expressing cells (HECs). The influence of the PanMa-hydrogel culture was analyzed on gene, protein and functional level by gene expression analysis, immunohistochemical stainings and glucose stimulated insulin secretion.
Whole organ decellularization resulted in the generation of an acellular PanMa scaffold, with low amounts of residual DNA and a preserved ECM micro- and ultrastructure, including important ECM components, such as collagen I, III and IV. Furthermore, the PanMa maintained an intact vessel system and was verified as cytocompatible as demonstrated by the successful recellularization of the arterial system with human endothelial cells. In comparison to SISser and LungMa, the PanMa was characterized as a relative soft, hygroscopic scaffold with a collagen-fiber based structure. Furthermore, the findings indicate that the ECM-specific properties have a relevant effect on the stem cell character and early multi-lineage decisions of hiPSCs. In this regard, maintenance of hiPSCs on the PanMa resulted in a slightly changed expression of pluripotency genes (OCT4, SOX2 and NANOG) and a weak immunohistochemical signal for NANOG protein, indicating a PanMa-dependent impact on hiPSC pluripotency. Strikingly, this presumption was corroborated by the finding that culture on the PanMa promoted an endodermal development of hiPSCs during spontaneous differentiation. In line with that, pancreatic differentiation of hiPSC on both the PanMa and SISser resulted in a significant decrease of glucagon and somatostatin gene expression as well as an unaltered insulin expression, suggesting an ECM-driven suppression of the development of non β-cell endocrine cells. However, this change did not result in an improved glucose stimulated insulin secretion of the generated HECs. Moreover, use of the PanMa as a hydrogel allowed prolonged culture of these cells in a defined culture system. HECs were viable after 21 days of culture, however already showed an altered islet morphology as well as a slightly decreased glucose stimulated insulin secretion.
Altogether, this study demonstrates a relevant biological effect of tissue specific ECM cues on the in vitro differentiation of hiPSCs. More specifically, the data indicate an involvement of the ECM in the endocrine commitment of hiPSC-derived pancreatic cells during directed differentiation highlighting the ECM as an important regulator of pancreatic development. Collectively, these findings emphasize the relevance of the ECM for the fabrication of functional hiPSC-derived cell types and suggest a much stronger consideration of organ specific ECM cues for tissue engineering approaches as well as clinical translation in regenerative medicine.
The Wuertual Reality XR Meeting 2023 was initiated to bring together researchers from many fields who use VR/AR/XR. There was a focus on applied XR and social VR.
In this conference band, you can find the abstracts of the two keynotes, the 34 posters and poster pitches, the 29 talks and the four workshops.
To reach their target site, systemic pesticides must enter the plant from a spray droplet applied in the field. The uptake of an active ingredient (AI) takes place via the barrier-forming cuticular membrane, which is the outermost layer of the plant, separating it from the surrounding environment. Formulations are usually used which, in addition to the AI, also contain stabilizers and adjuvants. Adjuvants can either have surface-active properties or they act directly as barrier-modifying agents. The latter are grouped in the class of accelerating adjuvants, whereby individual variants may also have surface-active properties. The uptake of a pesticide from a spray droplet depends essentially on its permeability through the cuticular barrier. Permeability defines a combined parameter, which is the product of AI mobility and AI solubility within the cuticle. In recent decades, several tools have been developed that allowed the determination of individual parameters of organic compound penetration across the cuticular membrane. Nevertheless, earlier studies showed that mainly cuticular waxes are the barrier-determining component of the cuticular membrane and additionally, it was shown that mainly the very-long-chain aliphatic compounds (VLCAs) are responsible for establishing an effective barrier. However, the barrier-determining role of the individual VLCAs, being classified according to their respective functional groups, is still unknown.
Therefore, the following objectives were pursued and achieved in this work: (1) A new ATR-FTIR-based approach was developed to measure the temperature-dependent real-time diffusion kinetics of organic models for active ingredients (AIs) in paraffin wax, exclusively consisting of very-long chain alkanes. (2) The developed ATR-FTIR approach was applied to determine the diffusion kinetics of self-accelerating adjuvants in cuticular model waxes of different VLCA composition. At the same time, wax-specific changes were recorded in the respective IR spectra, which provided information about the respective wax modification. (3) The ATR-FTIR method was used to characterize the diffusion kinetics, as well as to determine the wax-specific sorption capacities for an AI-modeling organic compound and water in cuticular model waxes after adjuvant treatment. Regarding the individual chemical compositions and structures, conclusions were drawn about the adjuvant-specific modes of action (MoA).
In the first chapter, the ATR-FTIR based approach to determine organic compound diffusion kinetics in paraffin wax was successfully established. The diffusion kinetics of the AI modelling organic compounds heptyl parabene (HPB) and 4-cyanophenol (CNP) were recorded, comprising different lipophilicities and molecular volumes typical for AIs used in pesticide formulations. Derived diffusion coefficients ranged within 10-15 m2 s-1, thus being thoroughly higher than those obtained from previous experiments using an approach solely investigating desorption kinetics in reconstituted cuticular waxes. An ln-linear dependence between the diffusion coefficients and the applied diffusion temperature was demonstrated for the first time in cuticular model wax, from which activation energies were derived. The determined activation energies were 66.2 ± 7.4 kJ mol-1 and 56.4 ± 9.8 kJ mol-1, being in the expected range of already well-founded activation energies required for organic compound diffusion across cuticular membranes, which again confirmed the significant contribution of waxes to the cuticular barrier. Deviations from the assumed Fickian diffusion were attributed to co-occurring water diffusion and apparatus-specific properties.
In the second and third chapter, mainly the diffusion kinetics of accelerating adjuvants in the cuticular model waxes candelilla wax and carnauba wax were investigated, and simultaneously recorded changes in the wax-specific portion of the IR spectrum were interpreted as indications of plasticization. For this purpose, the oil derivative methyl oleate, as well as the organophosphate ester TEHP and three non-ionic monodisperse alcohol ethoxylates (AEs) C12E2, C12E4 and C12E6 were selected. Strong dependence of diffusion on the respective principal components of the mainly aliphatic waxes was demonstrated. The diffusion kinetics of the investigated adjuvants were faster in the n-alkane dominated candelilla wax than in the alkyl ester dominated carnauba wax. Furthermore, the equilibrium absorptions, indicating equilibrium concentrations, were also higher in candelilla wax than in carnauba wax. It was concluded that alkyl ester dominated waxes feature higher resistance to diffusion of accelerating adjuvants than alkane dominated waxes with shorter average chain lengths due to their structural integrity. This was also found either concerning candelilla/policosanol (n-alcohol) or candelilla/rice bran wax (alkyl-esters) blends: with increasing alcohol concentration, the barrier function was decreased, whereas it was increased with increasing alkyl ester concentration. However, due to the high variability of the individual diffusion curves, only a trend could be assumed here, but significant differences were not shown. The variability itself was described in terms of fluctuating crystalline arrangements and partial phase separation of the respective wax mixtures, which had inevitable effects on the adjuvant diffusion. However, diffusion kinetics also strongly depended on the studied adjuvants. Significantly slower methyl oleate diffusion accompanied by a less pronounced reduction in orthorhombic crystallinity was found in carnauba wax than in candelilla wax, whereas TEHP diffusion was significantly less dependent on the respective wax structure and therefore induced considerable plasticization in both waxes. Of particular interest was the AE diffusion into both waxes. Differences in diffusion kinetics were also found here between candelilla blends and carnauba wax. However, these depended equally on the degree of ethoxylation of the respective AEs. The lipophilic C12E2 showed approximately Fickian diffusion kinetics in both waxes, accompanied by a drastic reduction in orthorhombic crystallinity, especially in candelilla wax, whereas the more hydrophilic C12E6 showed significantly retarded diffusion kinetics associated with a smaller effect on orthorhombic crystallinity. The individual diffusion kinetics of the investigated adjuvants sometimes showed drastic deviations from the Fickian diffusion model, indicating a self-accelerating effect. Hence, adjuvant diffusion kinetics were accompanied by a distinct initial lag phase, indicating a critical concentration in the wax necessary for effective penetration, leading to sigmoidal rather than to exponential diffusion kinetics.
The last chapter dealt with the adjuvant-affected diffusion of the AI modelling CNP in candelilla and carnauba wax. Using ATR-FTIR, diffusion kinetics were recorded after adjuvant treatment, all of which were fully explicable based on the Fickian model, with high diffusion coefficients ranging from 10-14 to 10-13 m2 s-1. It is obvious that the diffusion coefficients presented in this work consistently demonstrated plasticization induced accelerated CNP mobilities. Furthermore, CNP equilibrium concentrations were derived, from which partition- and permeability coefficients could be determined. Significant differences between diffusion coefficients (mobility) and partition coefficients (solubility) were found on the one hand depending on the respective waxes, and on the other hand depending on treatment with respective adjuvants. Mobility was higher in candelilla wax than in carnauba wax only after methyl oleate treatment. Treatment with TEHP and AEs resulted in higher CNP mobility in the more polar alkyl ester dominated carnauba wax. The partition coefficients, on the other hand, were significantly lower after methyl oleate treatment in both candelilla and carnauba wax as followed by TEHP or AE treatment. Models were designed for the CNP penetration mode considering the respective adjuvants in both investigated waxes. Co-penetrating water, which is the main ingredient of spray formulations applied in the field, was likely the reason for the drastic differences in adjuvant efficacy. Especially the investigated AEs favored an enormous water uptake in both waxes with increasing ethoxylation level. Surprisingly, this effect was also found for the lipophilic TEHP in both waxes. This led to the assumption that the AI permeability is not exclusively determined by adjuvant induced plasticization, but also depends on a “secondary plasticization”, induced by adjuvant-attracted co-penetrating water, consequently leading to swelling and drastic destabilization of the crystalline wax structure.
The successful establishment of the presented ATR-FTIR method represents a milestone for the study of adjuvant and AI diffusion kinetics in cuticular waxes. In particular, the simultaneously detectable wax modification and, moreover, the determinable water uptake form a perfect basis to establish the ATR-FTIR system as a universal screening tool for wax-adjuvants-AI-water interaction in crop protection science.
“In Other News”: China’s International Media Strategy on Xinjiang — CGTN and New China TV on YouTube
(2023)
In the Western world China stands accused of severe human rights violations regarding its treatment of the Uyghurs and other predominantly Muslim minorities in its northwestern Xinjiang Uyghur Autonomous Region. This is the first article to systematically analyze the response of China’s international state media to these allegations. By studying the YouTube channels of two leading Chinese state media, China Global Television Network (CGTN) and New China TV (operated by Xinhua News Agency), it presents an indepth understanding of how China’s foreign-facing propaganda works in a crucial case. The quantitative content analysis highlights how China reacted to increasing international (mostly United States) pressure regarding its Xinjiang policies by producing higher volumes of videos and putting out new counternarratives. The qualitative analysis that follows provides in-depth treatment of the most important discourses that Chinese media engage in to salvage the nation’s international image, namely those on development, culture, nature, and terrorism. It finds several ways of countering criticism, ranging from presenting a positive image of China, in line with traditional propaganda guidelines and President Xi Jinping’s assignment to state media to “tell the China story well,” to more innovative approaches. Thus the development narrative becomes more personalized, the discourse on culture supports the “heritagization process” to incorporate minority cultures into a harmonized “Chinese civilization,” representations of nature firmly tie Xinjiang into the discourse of “beautiful China,” the “terror narrative” strategically employs shocking footage in an attempt to gain international “discourse power,” etc. The article provides an up-to-date picture of China’s state media strategy on a highly contentious international issue.
Magnetism is a phenomenon ubiquitously found in everyday life. Yet, together with superconductivity and superfluidity, it is among the few macroscopically realized quantum states. Although well-understood on a quasi-classical level, its microscopic description is still far from being solved. The interplay of strong interactions present in magnetic condensed-matter systems and the non-trivial commutator structure governing the underlying spin algebra prevents most conventional approaches in solid-state theory to be applied.
On the other hand, the quantum limit of magnetic systems is fertile land for the development of exotic phases of matter called spin-liquids. In these states, quantum fluctuations inhibit the formation of magnetic long-range order down to the lowest temperatures. From a theoretical point of view, spin-liquids open up the possibility to study their exotic properties, such as fractionalized excitations and emergent gauge fields. However, despite huge theoretical and experimental efforts, no material realizing spin-liquid properties has been unambiguously identified with a three-dimensional crystal structure. The search for such a realization is hindered by the inherent difficulty even for model calculations. As most numerical techniques are not applicable due to the interaction structure and dimensionality of these systems, a methodological gap has to be filled.
In this thesis, to fill this void, we employ the pseudo-fermion functional renormalization group (PFFRG), which provides a scheme to investigate ground state properties of quantum magnetic systems even in three spatial dimensions.
We report the status quo of this established method and extend it by alleviating some of its inherent approximations. To this end, we develop a multi-loop formulation of PFFRG, including hitherto neglected terms in the underlying flow equations consistently, rendering the outcome equivalent to a parquet approximation. As a necessary prerequisite, we also significantly improve the numerical accuracy of our implementation of the method by switching to a formulation respecting the asymptotic behavior of the vertex functions as well as employing state-of-the-art numerical algorithms tailored towards PFFRG. The resulting codebase was made publicly accessible in the open-source code PFFRGSolver.jl.
We subsequently apply the technique to both model systems and real materials. Augmented by a classical analysis of the respective models, we scan the phase diagram of the three-dimensional body-centered cubic lattice up to third-nearest neighbor coupling and the Pyrochlore lattice up to second-nearest neighbor. In both systems, we uncover in addition to the classically ordered phases, an extended parameter regime, where a quantum paramagnetic phase appears, giving rise to the possibility of a quantum spin liquid.
Additionally, we also use the nearest-neighbor antiferromagnet on the Pyrochlore lattice as well as the simple cubic lattice with first- and third-nearest neighbor couplings as a testbed for multi-loop PFFRG, demonstrating, that the inclusion of higher loop orders has quantitative effects in paramagnetic regimes and that the onset of order can be signaled by a lack of loop convergence.
Turning towards material realizations, we investigate the diamond lattice compound MnSc\(_2\)S\(_4\), explaining on grounds of ab initio couplings the emergence of a spiral spin liquid at low temperatures, but above the ordering transition.
In the Pyrochlore compound Lu\(_2\)Mo\(_2\)O\(_5\)N\(_2\), which is known to not magnetically order down to lowest temperatures, we predict a spin liquid state displaying a characteristic gearwheel pattern in the spin structure factor.
We introduce fluorescence-detected pump–probe microscopy by combining a wavelength-tunable ultrafast laser with a confocal scanning fluorescence microscope, enabling access to the femtosecond time scale on the micrometer spatial scale. In addition, we obtain spectral information from Fourier transformation over excitation pulse-pair time delays. We demonstrate this new approach on a model system of a terrylene bisimide (TBI) dye embedded in a PMMA matrix and acquire the linear excitation spectrum as well as time-dependent pump–probe spectra simultaneously. We then push the technique towards single TBI molecules and analyze the statistical distribution of their excitation spectra. Furthermore, we demonstrate the ultrafast transient evolution of several individual molecules, highlighting their different behavior in contrast to the ensemble due to their individual local environment. By correlating the linear and nonlinear spectra, we assess the effect of the molecular environment on the excited-state energy.
In this thesis, we are interested in numerically preserving stationary solutions of balance laws. We start by developing finite volume well-balanced schemes for the system of Euler equations and the system of MHD equations with gravitational source term. Since fluid models and kinetic models are related, this leads us to investigate AP schemes for kinetic equations and their ability to preserve stationary solutions. Kinetic models typically have a stiff term, thus AP schemes are needed to capture good solutions of the model. For such kinetic models, equilibrium solutions are reached after large time. Thus we need a new technique to numerically preserve stationary solutions for AP schemes. We find a criterion for SP schemes for kinetic equations which states, that AP schemes under a particular discretization are also SP. In an attempt to mimic our result for kinetic equations in the context of fluid models, for the isentropic Euler equations we developed an AP scheme in the limit of the Mach number going to zero. Our AP scheme is proven to have a SP property under the condition that the pressure is a function of the density and the latter is obtained as a solution of an elliptic equation. The properties of the schemes we developed and its criteria are validated numerically by various test cases from the literature.
Current therapeutic strategies efficiently improve survival in patients after myocardial infarction (MI). Nevertheless, long-term consequences such as heart failure development, are still one of the leading causes of death worldwide. Inflammation is critically involved in the cardiac healing process after MI and has a dual role, contributing to both tissue healing and tissue damage. In the last decade, a lot of attention was given to targeting inflammation as a potential therapeutic approach in MI, but the poor understanding of inflammatory cell heterogeneity and function is a limit to the development of immune modulatory strategies. The recent development of tools to profile immune cells with high resolution has provided a unique opportunity to better understand immune cell heterogeneity and dynamics in the ischemic heart.
In this thesis, we employed single-cell RNA-sequencing combined with detection of epitopes by sequencing (CITE-seq) to refine our understanding of neutrophils and monocytes/macrophages heterogeneity and dynamic after experimental myocardial infarction.
Neutrophils rapidly invade the infarcted heart shortly after ischemic damage and have previously been proposed to display time-dependent functional heterogeneity. At the single-cell level, we observed dynamic transcriptional heterogeneity in neutrophil populations during the acute post-MI phase and defined previously unknown cardiac neutrophil states. In particular, we identified a locally acquired SiglecFhi neutrophil state that displayed higher ROS production and phagocytic ability compared to newly recruited neutrophils, suggesting the acquisition of specific function in the infarcted heart. These findings highlight the importance of the tissue microenvironment in shaping neutrophil response.
From the macrophage perspective, we characterized MI-associated monocyte-derived macrophage subsets, two with a pro-inflammatory gene signature (MHCIIhiIl1βhi) and three Trem2hi macrophage populations with a lipid associated macrophage (LAM) signature, also expressing pro-fibrotic and tissue repair genes. Combined analysis of blood monocytes and cardiac monocyte/macrophages indicated that the Trem2hi LAM signature is acquired in the infarcted heart.
We furthermore characterized the role of TREM2, a surface protein expressed mainly in macrophages and involved in macrophage survival and function, in the post-MI macrophage response and cardiac repair. Using TREM2 deficient mice, we demonstrate that acquisition of the LAM signature in cardiac macrophages after MI is partially dependent on TREM2. While their cardiac function was not affected, TREM2 deficient mice showed reduced collagen deposition in the heart after MI. Thus, our data in Trem2-deficient mice highlight the role of TREM2 in promoting a macrophage pro-fibrotic phenotype, in line with the pro-fibrotic/tissue repair gene signature of the Trem2hi LAM-signature genes.
Overall, our data provide a high-resolution characterization of neutrophils and macrophage heterogeneity and dynamics in the ischemic heart and can be used as a valuable resource to investigate how these cells modulate the healing processes after MI. Furthermore, our work identified TREM2 as a regulator of macrophage phenotype in the infarcted heart
The universal two-child policy was introduced by the central government of China in 2016 to respond to the country’s deteriorating population problems, but it was soon replaced by a three-child policy in 2021 given that it failed to continuously boost fertility in Chinese society. This dissertation empirically investigates the implementation of universal two-child policy in three Chinese major cities. Based on the data collected through semi-structured interviews with leaders of local family planning agencies, it finds that local officials are primarily devoted to coping with the discontent of the bereaved single-child parents (shidu families), which is an unexpected consequence of the historical one-child policy, rather than working on the tasks regarding birth encouragement. The dissertation suggests understanding the implementation of China’s population policy within the framework of both historical and rational choice institutionalism. The target responsibility system as an effective tool of the central authority drives local agents to fix their attention at tasks that have larger impact on their career. The shifted focus in the implementation of the universal two-child policy is a result of local officials’ emphasis on the task of maintaining social stability. Shidu families are deemed as a salient threat to social order because their discontent with the state support has incurred continuous petitions at both the national and local level, which would severely undermine local officials’ career advancement. However, in the meantime, stability maintenance is found to have become alienated as reflected by the rising costs and that it replaced birth support to be the focus of local family planning agents in the universal two-child policy era. Since the conflict between the shidu group and the state is unlikely to be resolved, the future population policy design and enforcement will continue to be constrained by the shidu problem.
The nervous system relies on an orchestrated assembly of complex cellular entities called neurons, which are specifically committed to information management and transmission. Inter-neuronal communication takes place via synapses, membrane-membrane junctions which ensure efficient signal transfer. Synaptic neurotransmission involves release of presynaptic neurotransmitters and their reception by cognate receptors at postsynaptic terminals. Inhibitory neurotransmission is primarily mediated by the release of neurotransmitters GABA (γ-Aminobutyric acid) and glycine, which are precisely sensed by GABA type-A receptors (GABAARs) and glycine receptors (GlyRs), respectively. GABAAR assembly and maintenance is coordinated by various postsynaptic neuronal factors including the scaffolding protein gephyrin, the neuronal adaptor collybistin (CB) and cell adhesion proteins of the neuroligin (NL) family, specifically NL2 and NL4.
At inhibitory postsynaptic specializations, gephyrin has been hypothesized to form extended structures underneath the plasma membrane, where its interaction with the receptors leads to their stabilization and impedes their lateral movement. Gephyrin mutations have been associated with various brain disorders, including autism, schizophrenia, Alzheimer’s disease, and epilepsy. Furthermore, gephyrin loss is lethal and causes mice to die within the first post-natal day. Gephyrin recruitment from intracellular deposits to postsynaptic membranes primarily relies on the adaptor protein CB.
As a moonlighting protein, CB, a guanine nucleotide exchange factor (GEF), also catalyzes a nucleotide exchange reaction, thereby regenerating the GTP-bound state of the small GTPase Cdc42 from its GDP-bound form. The CB gene undergoes alternative splicing with the majority of CB splice variants featuring an N-terminal SH3 domain followed by tandem Dbl-homology (DH) and pleckstrin-homology (PH) domains. Previous studies demonstrated that the most widely expressed, SH3-domain containing splice variant (CB2SH3+) preferentially adopts a closed conformation, in which the N-terminally located SH3 domain forms intra-molecular interaction with the DH-PH domain tandem. Previous cell-based studies indicated that SH3 domain-encoding CB variants remain untargeted and colocalize with intracellular gephyrin deposits and hence require additional factors which interact with the SH3 domain, thus inducing an open or active conformation. The SH3 domain-deficient CB isoform (CB2SH3-), on the contrary, adopts an open conformation, which possess enhanced postsynaptic gephyrin-clustering and also effectively replenishes the GTP-bound small GTPase-Cdc42 from its GDP-bound state.
Despite the fundamental role of CB as a neuronal adaptor protein maintaining the proper function of inhibitory GABAergic synapses, its interactions with the neuronal scaffolding protein gephyrin and other post synaptic neuronal factors remain poorly understood. Moreover, CB interaction studies with the small GTPase Cdc42 and TC10, a closely related member of Cdc42 subfamily, remains poorly characterized. Most importantly, the roles of the neuronal factors and small GTPases in CB conformational activation have not been elucidated.
This PhD dissertation primarily focuses on delineating the molecular basis of the interactions between CB and postsynaptic neuronal factors. During the course of my PhD dissertation, I engineered a series of CB FRET (Förster Resonance Energy Transfer) sensors to characterize the CB interaction with its binding partners along with outlining their role in CB conformational activation. Through the aid of these CB FRET sensors, I analyzed the gephyrin-CB interaction, which, due to technical limitations remained unaddressed for more than two decades (refer Chapter 2 for more details). Subsequently, I also unraveled the molecular basis of the interactions between CB and the neuronal cell adhesion factor neuroligin 2 (refer chapter 2) and the small GTPases Cdc42 and TC10 (refer chapter 3) and describe how these binding partners induce a conformational activation of CB.
In summary, this PhD dissertation provides strong evidence of a closely knit CB communication network with gephyrin, neuroligin and the small GTPase TC10, wherein CB activation from closed/inactive to open/active states is effectively triggered by these ligands.
As part of the parasympathetic nervous system, muscarinic receptors are involved in the regulation of numerous functions in the human body. However, targeting a specific subtype of muscarinic receptors is challenging due to the high degree of similarity within the binding site of the endogenous neurotransmitter acetylcholine. Therefore, this study focused on the investigation of dualsteric ligands. Such hybrid ligands target the orthosteric acetylcholine binding site and, simultaneously, a distinct allosteric binding site. Since allosteric binding regions show significant structural differences throughout muscarinic receptor subtypes, it was aimed to produce selective ligands by means of combination of two pharmacophores in one molecule. Herein, the thienopyridine derivatives LY2033298 and LY2119620 were chosen as allosteric moieties. Based on literature studies, the investigated allosteric modulators were analyzed in terms of adequate attachment points for the combination with an orthosteric agonist. As orthosteric units, muscarinic superagonist iperoxo, xanomeline, and TMA were applied in this work. Since the distance between orthosteric and allosteric moieties plays a crucial role for dualsteric ligand binding, the linker chain length was also varied. Pharmacological investigations of the synthesized hybrid ligands were perfomed via FRET- and BRET-assay measurements.
Environmental issues have emerged especially since humans burned fossil fuels, which led to air pollution and climate change that harm the environment. These issues’ substantial consequences evoked strong efforts towards assessing the state of our environment.
Various environmental machine learning (ML) tasks aid these efforts. These tasks concern environmental data but are common ML tasks otherwise, i.e., datasets are split (training, validatition, test), hyperparameters are optimized on validation data, and test set metrics measure a model’s generalizability. This work focuses on the following environmental ML tasks: Regarding air pollution, land use regression (LUR) estimates air pollutant concentrations at locations where no measurements are available based on measured locations and each location’s land use (e.g., industry, streets). For LUR, this work uses data from London (modeled) and Zurich (measured). Concerning climate change, a common ML task is model output statistics (MOS), where a climate model’s output for a study area is altered to better fit Earth observations and provide more accurate climate data. This work uses the regional climate model (RCM) REMO and Earth observations from the E-OBS dataset for MOS. Another task regarding climate is grain size distribution interpolation where soil properties at locations without measurements are estimated based on the few measured locations. This can provide climate models with soil information, that is important for hydrology. For this task, data from Lower Franconia is used.
Such environmental ML tasks commonly have a number of properties: (i) geospatiality, i.e., their data refers to locations relative to the Earth’s surface. (ii) The environmental variables to estimate or predict are usually continuous. (iii) Data can be imbalanced due to relatively rare extreme events (e.g., extreme precipitation). (iv) Multiple related potential target variables can be available per location, since measurement devices often contain different sensors. (v) Labels are spatially often only sparsely available since conducting measurements at all locations of interest is usually infeasible. These properties present challenges but also opportunities when designing ML methods for such tasks.
In the past, environmental ML tasks have been tackled with conventional ML methods, such as linear regression or random forests (RFs). However, the field of ML has made tremendous leaps beyond these classic models through deep learning (DL). In DL, models use multiple layers of neurons, producing increasingly higher-level feature representations with growing layer depth. DL has made previously infeasible ML tasks feasible, improved the performance for many tasks in comparison to existing ML models significantly, and eliminated the need for manual feature engineering in some domains due to its ability to learn features from raw data. To harness these advantages for environmental domains it is promising to develop novel DL methods for environmental ML tasks.
This thesis presents methods for dealing with special challenges and exploiting opportunities inherent to environmental ML tasks in conjunction with DL. To this end, the proposed methods explore the following techniques: (i) Convolutions as in convolutional neural networks (CNNs) to exploit reoccurring spatial patterns in geospatial data. (ii) Posing the problems as regression tasks to estimate the continuous variables. (iii) Density-based weighting to improve estimation performance for rare and extreme events. (iv) Multi-task learning to make use of multiple related target variables. (v) Semi–supervised learning to cope with label sparsity. Using these techniques, this thesis considers four research questions: (i) Can air pollution be estimated without manual feature engineering? This is answered positively by the introduction of the CNN-based LUR model MapLUR as well as the off-the-shelf LUR solution OpenLUR. (ii) Can colocated pollution data improve spatial air pollution models? Multi-task learning for LUR is developed for this, showing potential for improvements with colocated data. (iii) Can DL models improve the quality of climate model outputs? The proposed DL climate MOS architecture ConvMOS demonstrates this. Additionally, semi-supervised training of multilayer perceptrons (MLPs) for grain size distribution interpolation is presented, which can provide improved input data. (iv) Can DL models be taught to better estimate climate extremes? To this end, density-based weighting for imbalanced regression (DenseLoss) is proposed and applied to the DL architecture ConvMOS, improving climate extremes estimation. These methods show how especially DL techniques can be developed for environmental ML tasks with their special characteristics in mind. This allows for better models than previously possible with conventional ML, leading to more accurate assessment and better understanding of the state of our environment.
The use of digital media by children and young people offers opportunities for communication, collaboration, and participation. However, to prepare them for the risks and challenges of media usage, promoting digital competencies of students and teachers is an indispensable goal for educational institutions. To meet this requirement, teacher education must be opened to innovative pedagogical concepts for initial teacher education that considers new technologies in a reflective, action-oriented way to promote competencies. Therefore, this work aims to promote the technological pedagogical content knowledge (TPACK) of prospective teachers that enables the purposeful integration of social virtual reality (social VR) into the classroom. Consequently, a pedagogical concept is developed and evaluated in an iterative research and development process following the design- based research approach (DBR) through four consecutive studies. The first study involved an analysis of the requirements of teachers and students for the effective use of social VR in the classroom. The second study examined how prospective teachers perceive teaching and learning activities within two theory-driven scenarios in social VR. The third study investigated the development of Technological Pedagogical Content Knowledge (TPACK) among students in social VR compared to video-based communication. Finally, the fourth study measured the development of TPACK in social VR using epistemic network analysis, finding that social VR can be an effective tool for teacher education, emphasizing the importance of authentic contexts and practical experiences for effective teaching in social VR. In the concluding chapter, appropriate implications for teacher education research and practice are derived from findings. For example, that a deeper understanding of TPACK as metacognitive awareness could enhance teacher education for media integration. It also highlights the need for digital literacy in seminars that address new technologies, emphasizing the importance of considering moral values and sustainability when using VR.
The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established.
As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 Å. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery.
Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
Enterococcus faecalis and Enterococcus faecium are major nosocomial pathogens. Despite their relevance to public health and their role in the development of bacterial antibiotic resistance, relatively little is known about gene regulation in these species. RNA–protein complexes serve crucial functions in all cellular processes associated with gene expression, including post-transcriptional control mediated by small regulatory RNAs (sRNAs). Here, we present a new resource for the study of enterococcal RNA biology, employing the Grad-seq technique to comprehensively predict complexes formed by RNA and proteins in E. faecalis V583 and E. faecium AUS0004. Analysis of the generated global RNA and protein sedimentation profiles led to the identification of RNA–protein complexes and putative novel sRNAs. Validating our data sets, we observe well-established cellular RNA–protein complexes such as the 6S RNA–RNA polymerase complex, suggesting that 6S RNA-mediated global control of transcription is conserved in enterococci. Focusing on the largely uncharacterized RNA-binding protein KhpB, we use the RIP-seq technique to predict that KhpB interacts with sRNAs, tRNAs, and untranslated regions of mRNAs, and might be involved in the processing of specific tRNAs. Collectively, these datasets provide departure points for in-depth studies of the cellular interactome of enterococci that should facilitate functional discovery in these and related Gram-positive species. Our data are available to the community through a user-friendly Grad-seq browser that allows interactive searches of the sedimentation profiles (https://resources.helmholtz-hiri.de/gradseqef/).
The present cumulative dissertation summarizes three clinical studies, which examine
subgroups of patients within the fibromyalgia syndrome (FMS). FMS entails chronic pain and
associated symptoms, and its pathophysiology is incompletely understood (1). Previous studies
show that there is a subgroup of patients with FMS with objective histological pathology of the
small nerve fibers of the peripheral nervous system (PNS). Another subgroup of FMS patients
does not show any signs of pathological changes of the small nerve fibers. The aim of this
dissertation was to compare FMS patients with healthy controls, and these two FMS subgroups
for differences in the central nervous system (CNS) in order to explore possible interactions
between PNS and the CNS. Regarding the CNS, differences of FMS patients with healthy
controls have already been found in studies with small sample sizes, but no subgroups have yet
been identified. Another aim of this thesis was to test whether the subgroups show a different
response to different classes of pain medication. The methods used in this thesis are structural
and functional magnetic resonance imaging (MRI), magnetic resonance diffusion imaging and
magnetic resonance spectroscopy. For the evaluation of clinical symptoms, we used
standardized questionnaires. The subgroups with and without pathologies of the PNS were
determined by skin biopsies of the right thigh and lower leg based on the intraepidermal nerve
fiber density (IENFD) of the small nerve fibers.
1) In the first MRI study, 43 female patients with the diagnosis of FMS and 40 healthy
control subjects, matched in age and body mass index, were examined with different MRI
sequences. Cortical thickness was investigated by structural T1 imaging, white matter integrity
by diffusion tensor imaging and functional connectivity within neuronal networks by functional
resting state MRI. Compared to the controls, FMS patients had a lower cortical volume in
bilateral frontotemporoparietal regions and the left insula, but a higher cortical volume in the
left pericalcarine cortex. Compared to the subgroup without PNS pathology, the subgroup with
PNS pathology had lower cortical volume in both pericalcarine cortices. Diffusion tensor
imaging revealed an increased fractional anisotropy (FA) of FMS patients in corticospinal
pathways such as the corona radiata, but also in regions of the limbic systems such as the fornix
and cingulum. Subgroup comparison again revealed lower mean FA values of the posterior
thalamic radiation and the posterior limb of the left internal capsule in the subgroup with PNS
pathology. In the functional connectivity analysis FMS patients, compared to controls, showed
a hypoconnectivity between the right median frontal gyrus and the posterior cerebellum and
the right crus cerebellum, respectively. In the subgroup comparisons, the subgroup with PNS
pathology showed a hyperconnectivity between both inferior frontal gyri, the right posterior
parietal cortex and the right angular gyrus. In summary, these results show that differences in
brain morphology and functional connectivity exist between FMS patients with and without
PNS pathology. These differences were not associated with symptom duration or severity and,
in some cases, have not yet been described in the context of FMS. The differences in brain
morphology and connectivity between subgroups could also lead to a differential response to
treatment with centrally acting drugs. Further imaging studies with FMS patients should take
into account this heterogeneity of FMS patient cohorts.
2) Following the results from the first MRI study, drug therapies of FMS patients and
their treatment response were compared between PNS subgroups. As there is no licensed drug
for FMS in Europe, the German S3 guideline recommends amitriptyline, duloxetine and
pregabalin for temporary use. In order to examine the current drug use in FMS patients in
Germany on a cross-sectional basis, 156 patients with FMS were systematically interviewed.
The drugs most frequently used to treat pain in FMS were non-steroidal anti-inflammatory
drugs (NSAIDs) (28.9%), metamizole (15.4%) and amitriptyline (8.8%). Pain relief assessed by
patients on a numerical rating scale from 0-10 averaged 2.2 points for NSAIDs, 2.0 for
metamizole and 1.5 for amitriptyline. Drugs that were discontinued for lack of efficacy and not
for side effects were acetaminophen (100%), flupirtine (91.7%), selective serotonin reuptake
inhibitors (81.8%), NSAIDs (83.7%) and weak opioids (74.1%). Patients were divided into
subgroups with and without PNS pathology as determined by skin biopsies. We found no
differences in drug use and effect between the subgroups. Taken together, these results show
that many FMS patients take medication that is not in accordance with the guidelines. The
reduction of symptoms was best achieved with metamizole and NSAIDs. Further longitudinal
studies on medication in FMS are necessary to obtain clearer treatment recommendations.
3) Derived from previous pharmacological and imaging studies (with smaller case
numbers), there is a hypothesis in the FMS literature that hyperreactivity of the insular cortex
may have an impact on FMS. The hyperreactivity seems to be due to an increased concentration
of the excitatory neurotransmitter glutamate in the insular cortex of FMS patients. The
hypothesis is supported by magnetic resonance spectroscopy studies with small number of
cases, as well as results from pharmacological studies with glutamate-inhibiting medication.
Studies from animal models have also shown that an artificially induced increase in glutamate
in the insular cortex can lead to reduced skin innervation. Therefore, the aim of this study was
to compare glutamate and GABA concentrations in the insular cortex of FMS patients with
those of healthy controls using magnetic resonance imaging. There was no significant
difference of both neurotransmitters between the groups. In addition, there was no correlation
between the neurotransmitter concentrations and the severity of clinical symptoms. There
were also no differences in neurotransmitter concentrations between the subgroups with and
without PNS pathology. In conclusion, our study could not show any evidence of a correlation
of glutamate and GABA concentrations with the symptoms of FMS or the pathogenesis of
subgroups with PNS pathologies.
The monarch butterfly (Danaus plexippus) performs one of the most astonishing behaviors in the animal kingdom: every fall millions of these butterflies leave their breeding grounds in North Amerika and migrate more than 4.000 km southwards until they reach their overwintering habitat in Central Mexico. To maintain their migratory direction over this enormous distance, the butterflies use a time-compensated sun compass. Beside this, skylight polarization, the Earth’s magnetic field and specific mountain ranges seem to guide the butterflies as well the south. In contrast to this fascinating orientation ability, the behavior of the butterflies in their non-migratory state received less attention. Although they do not travel long distances, they still need to orient themselves to find food, mating partners or get away from competitors. The aim of the present doctoral thesis was to investigate use of visual cues for orientation in migrating as well as non-migrating monarch butterflies. For this, field experiments investigating the migration of the butterflies in Texas (USA) were combined with experiments testing the orientation performance of non-migratory butterflies in Germany.
In the first project, I recorded the heading directions of tethered butterflies during their annual fall migration. In an outdoor flight simulator, the butterflies maintained a southwards direction as long as they had a view of the sun’s position. Relocating the position of the sun by 180° using a mirror, revealed that the sun is the animals’ main orientation reference. Furthermore, I demonstrated that when the sun is blocked and a green light stimulus (simulated sun) is introduced, the animals interpreted this stimulus as the ‘real’ sun. However, this cue was not sufficient to set the migratory direction when simulated as the only visual cue in indoor experiments. When I presented the butterflies a linear polarization pattern additionally to the simulated sun, the animals headed in the correct southerly direction showing that multiple skylight cues are required to guide the butterflies during their migration.
In the second project, I, furthermore, demonstrated that non-migrating butterflies are able to maintain a constant direction with respect to a simulated sun. Interestingly, they ignored the spectral component of the stimulus and relied on the intensity instead. When a panoramic skyline was presented as the only orientation reference, the butterflies maintained their direction only for short time windows probably trying to stabilize their flight based on optic-flow information. Next, I investigated whether the butterflies combine celestial with local cues by simulating a sun stimulus together with a panoramic skyline. Under this conditions, the animals’ directedness was increased demonstrating that they combine multiple visual cues for spatial orientation.
Following up on the observation that a sun stimulus resulted in a different behavior than the panoramic skyline, I investigated in my third project which orientation strategies the butterflies use by presenting different simulated cues to them. While a bright stripe on a dark background elicited a strong attraction of the butterflies steering in the direction of the stimulus, the inverted version of the stimulus was used for flight stabilization. In contrast to this, the butterflies maintained arbitrary directions with a high directedness with respect to a simulated sun. In an ambiguous scenery with two identical stimuli (two bright stripes, two dark stripes, or two sun stimuli) set 180° apart, a constant flight course was only achieved when two sun stimuli were displayed suggesting an involvement of the animals’ internal compass. In contrast, the butterflies used two dark stripes for flight stabilization and were alternatingly attracted by two bright stripes. This shows that monarch butterflies use stimulus-dependent orientation strategies and gives the first evidence for different neuronal pathways controlling the output behavior.
Acknowledgements
(2023)
Beyond the four canonical nucleosides as primary building blocks of RNA, posttranscriptional modifications give rise to the epitranscriptome as a second layer of genetic information. In eukaryotic mRNA, the most abundant posttranscriptional modification is N6-methyladenosine (m6A), which is involved in the regulation of cellular processes. Throughout this thesis, the concept of atomic mutagenesis was employed to gain novel mechanistic insights into the substrate recognition by human m6A reader proteins as well as in the oxidative m6A demethylation by human demethylase enzymes. Non-natural m6A atomic mutants featuring distinct steric and electronic properties were synthesized and incorporated into RNA oligonucleotides. Fluorescence anisotropy measurements using these modified oligonucleotides revealed the impact of the atomic mutagenesis on the molecular recognition by the human m6A readers YTHDF2, YTHDC1 and YTHDC2 and allowed to draw conclusions about structural prerequisites for substrate recognition. Furthermore, substrate recognition and demethylation mechanism of the human m6A demethylase enzymes FTO and ALKBH5 were analyzed by HPLC-MS and PAGE-based assays using the modified oligonucleotides synthesized in this work.
Modified nucleosides not only expand the genetic alphabet, but are also extensively researched as drug candidates. In this thesis, the antiviral mechanism of the anti-SARS-CoV-2 drug remdesivir was investigated, which causes delayed stalling of the viral RNA-dependent RNA polymerase (RdRp). Novel remdesivir phosphoramidite building blocks were synthesized and used to construct defined RNA-RdRp complexes for subsequent studies by cryogenic electron microscopy (cryo-EM). It was found that the 1'-cyano substituent causes Rem to act as a steric barrier of RdRp translocation. Since this translocation barrier can eventually be overcome by the polymerase, novel derivatives of Rem with potentially improved antiviral properties were designed.
A New International
(2023)
Contributors
(2023)
Can cultural studies attend to the problems of our globalized world? Or is this project of “engaged scholarship” too deeply rooted in the parochial terrain of the national?
This collection of essays – the first volume in the new JMU Cultural Studies publication series – attends to this vital yet difficult question. Based on joint seminars bringing together emerging scholars from Germany and India, the contributions confront “classic texts” from US-American, British, and Indian cultural studies with the specific concerns and contemporary perspectives of the authors.
The collection thus tests the potentials of the tradition to speak to the transnational as well as the national environments of the very present. Emphasis is placed on Marxist and feminist legacies, which are then projected into the domains of contemporary disability, food, and film studies.
Pulmonary artery embolism (PE) is a common condition and an even more common clinical suspect. The computed tomography pulmonary angiogram (CTPA) is the main medical imaging tool used to diagnose a suspected case of PE. To gain a better impression of the effects of a PE on the perfusion and hence the gas exchange, a functional imaging method is beneficial. One approach for functional imaging using radiation exposure is the generation of color-coded iodine perfusion maps acquired by Dual-Energy Computed Tomography (DECT), which enable the detection of perfusion defects in the pulmonary parenchyma. In contrast to the existing approach of DECT with iodine color-coded maps, the SElf-gated Non-Contrast-Enhanced FUnctional Lung (SENCEFUL) MRI technique offers the possibility to interpret perfusion maps without any radiation exposure or application of contrast agents. The measurement in SENCEFUL MRI can be performed during conditions of free breathing and without electrocardiogram triggering.
The purpose of this study was to determine whether PE can be diagnosed on the basis of visible perfusion defects in the perfusion maps of SENCEFUL MRI and in the iodine-coded maps of DECT and to compare the diagnostic performance of these methods. Both SENCEFUL-MRI and iodine distribution maps from DECT have been compared with the CTPA of ten patients with PE. Additionally, the functional images were compared with each other on a per-patient basis.
The iodine perfusion maps of DECT had a sensitivity of 84.2 % and specificity of 65.2 % for the diagnosis of PE. The SENCEFUL technique in MRI showed a sensitivity of 78.9 % and a specificity of 26.1 %. When comparing the whole lung depicted in both series of functional images, the main perfusion defect location matched in four of ten patients (40 %).
In conclusion, this work found that DECT iodine maps have higher sensitivity and specificity in the diagnosis of pulmonary embolism compared with SENCEFUL MRI.
Coxiella burnetii, a Gram negative obligate intracellular bacterium, is the causative
agent of Q fever. It has a world wide distribution and has been documented to
be capable of causing infections in several domestic animals, livestock species,
and human beings. Outbreaks of Q fever are still being observed in livestock
across animal farms in Europe, and primary transmission to humans still oc-
curs especially in animal handlers. Public health authorities in some countries
like Germany are required by law to report human acute cases denoting the
significance of the challenge posed by C. burnetii to public health.
In this thesis, I have developed a platform alongside methods to address the
challenges of genomic analyses of C. burnetii for typing purposes. Identification
of C. burnetii isolates is an important task in the laboratory as well as in the
clinics and genotyping is a reliable method to identify and characterize known
and novel isolates. Therefore, I designed and implemented several methods
to facilitate the genotyping analyses of C. burnetii genomes in silico via a web
platform. As genotyping is a data intensive process, I also included additional
features such as visualization methods and databases for interpretation and
storage of obtained results. I also developed a method to profile the resistome
of C. burnetii isolates using a machine learning approach. Data about antibiotic
resistance in C. burnetii are scarce majorly due to its lifestyle and the difficulty
of cultivation in laboratory media. Alternative methods that rely on homology
identification of resistance genes are also inefficient in C. burnetii, hence, I
opted for a novel approach that has been shown to be promising in other
bacteria species. The applied method relied on an artificial neural network as
well as amino acid composition of position specific scoring matrix profile for
feature extraction. The resulting model achieved an accuracy of ≈ 0.96 on test
data and the overall performance was significantly higher in comparison to
existing models. Finally, I analyzed two new C. burnetii isolates obtained from
an outbreak in Germany, I compared the genome to the RSA 493 reference
isolate and found extensive deletions across the genome landscape.
This work has provided a new digital infrastructure to analyze and character-
ize C. burnetii genomes that was not in existence before and it has also made a
significant contribution to the existing information about antibiotic resistance
genes in C. burnetii.
The discovery, heterologous expression, and characterization of channelrhodopsin-2 (ChR2) – a light-sensitive cation channel found in the green alga Chlamydomonas reinhardtii – led to the success of optogenetics as a powerful technology, first in neuroscience. ChR2 was employed to induce action potentials by blue light in genetically modified nerve cells. In optogenetics, exogenous photoreceptors are expressed in cells to manipulate cellular activity. These photoreceptors were in the beginning mainly microbial opsins. During nearly two decades, many microbial opsins and their mutants were explored for their application in neuroscience. Until now, however, the application of optogenetics to plant studies is limited to very few reports. Several optogenetic strategies for plant research were demonstrated, in which most attempts are based on non-opsin optogenetic tools. Opsins need retinal (vitamin A) as a cofactor to generate the functional protein, the rhodopsin. As most animals have eyes that contain animal rhodopsins, they also have the enzyme - a 15, 15'-Dioxygenase - for retinal production from food-supplied provitamin A (beta-carotene). However, higher plants lack a similar enzyme, making it difficult to express functional rhodopsins successfully in plants. But plant chloroplasts contain plenty of beta-carotene. I introduced a gene, coding for a 15, 15'-Dioxygenase with a chloroplast target peptide, to tobacco plants. This enzyme converts a molecule of β-carotene into two of all-trans-retinal. After expressing this enzyme in plants, the concentration of all-trans-retinal was increased greatly. The increased retinal concentration led to increased expression of several microbial opsins, tested in model higher plants. Unfortunately, most opsins were observed intracellularly and not in the plasma membrane. To improve their localization in the plasma membrane, some reported signal peptides were fused to the N- or C-terminal end of opsins. Finally, I helped to identify three microbial opsins -- GtACR1 (a light-gated anion channel), ChR2 (a light-gated cation channel), PPR (a light-gated proton pump) which express and work well in the plasma membrane of plants. The transgene plants were grown under red light to prevent activation of the expressed opsins. Upon illumination with blue or green light, the activation of these opsins then induced the expected change of the membrane potential, dramatically changing the phenotype of plants with activated rhodopsins.
This study is the first which shows the potential of microbial opsins for optogenetic research in higher plants, using the ubq10 promoter for ubiquitous expression. I expect this to be just the beginning, as many different opsins and tissue-specific promoters for selective expression now can be tested for their usefulness. It is further to be expected that the here established method will help investigators to exploit more optogenetic tools and explore the secrets, kept in the plant kingdom.
This thesis examines the electronic properties of two materials that promise the realization and observation of novel exotic quantum phenomena. For this purpose, angle-resolved photoemission forms the experimental basis for the investigation of the electronic properties. Furthermore, the magnetic order is investigated utilizing X-ray dichroism measurements.
First, the bulk and surface electronic structure of epitaxially grown HgTe in its three-dimensional topological insulator phase is investigated. In this study, synchrotron radiation is used to address the three-dimensional band structure and orbital composition of the bulk states by employing photon-energy-dependent and polarization-dependent measurements, respectively. In addition, the topological surface state is examined on in situ grown samples using a laboratory photon source. The resulting data provide a means to experimentally localize the bulk band inversion in momentum space and to evidence the momentum-dependent change in the orbital character of the inverted bulk states.
Furthermore, a rather new series of van der Waals compounds, (MnBi\(_2\)Te\(_4\))(Bi\(_2\)Te\(_3\))\(_n\), is investigated. First, the magnetic properties of the first two members of the series, MnBi\(_2\)Te\(_4\) and MnBi\(_4\)Te\(_7\), are studied via X-ray absorption-based techniques. The topological surface state on the two terminations of MnBi\(_4\)Te\(_7\) is analyzed using circular dichroic, photon-energy-dependent, and spin-resolved photoemission. The topological state on the (MnBi\(_2\)Te\(_4\))-layer termination shows a free-standing Dirac cone with its Dirac point located in the bulk band gap. In contrast, on the (Bi\(_2\)Te\(_3\))-layer termination the surface state hybridizes with the bulk valences states, forming a spectral weight gap, and exhibits a Dirac point that is buried within the bulk continuum. Lastly, the lack of unambiguous evidence in the literature showing a temperature-dependent mass gap opening in these magnetic topological insulators is discussed through MnBi\(_2\)Te\(_4\).
Monarch butterflies are famous for their annual long-distance migration. Decreasing temperatures and reduced daylight induce the migratory state in the autumn generation of monarch butterflies. Not only are they in a reproductive diapause, they also produce fat deposits to be prepared for the upcoming journey: Driven by their instinct to migrate, they depart from their eclosion grounds in the northern regions of the North American continent and start their southern journey to their hibernation spots in Central Mexico. The butterflies cover a distance of up to 4000 km across the United States. In the next spring, the same butterflies invert their preferred heading direction due to seasonal changes and start their northward spring migration. The spring migration is continued by three consecutive butterfly generations, until the animals repopulate the northern regions in North America as non-migratory monarch butterflies. The monarch butterflies’ migratory state is genetically and epigenetically regulated, including the directed flight behavior. Therefore, the insect’s internal compass system does not only have to encode the butterflies preferred, but also its current heading direction. However, the butterfly’s internal heading representation has to be matched to external cues, to avoid departing from its initial flight path and increasing its risk of missing its desired destination. During the migratory flight, visual cues provide the butterflies with reliable orientation information. The butterflies refer to the sun as their main orientation cue. In addition to the sun, the butterflies likely use the polarization pattern of the sky for orientation. The sky compass signals are processed within a region in the brain, termed the central complex (CX). Previous research on the CX neural circuitry of the monarch butterflies demonstrated that tangential central complex neurons (TL) carry the visual input information into the CX and respond to a simulated sun and polarized light. However, whether these cells process additional visual cues like the panoramic skyline is still unknown. Furthermore, little is known about how the migratory state affects visual cue processing. In addition to this, most experiments studying the monarch butterfly CX focused on how neurons process single visual cues. However, how combined visual stimuli are processed in the CX is still unknown.
This thesis is investigating the following questions:
1) How does the migratory state affect visual cue processing in the TL cells within the monarch butterfly brain?
2) How are multiple visual cues integrated in the TL cells?
3) How is compass information modulated in the CX?
To study these questions, TL neurons from both animal groups (migratory and non-migratory) were electrophysiologically characterized using intracellular recordings while presenting different simulated celestial cues and visual sceneries. I showed that the TL neurons of migratory butterflies are more narrowly tuned to the sun, possibly helping them in keeping a directed flight course during migration. Furthermore, I found that TL cells encode a panoramic skyline, suggesting that the CX network combines celestial and terrestrial information. Experiments with combined celestial stimuli revealed that the TL cells combine both cue information linearly. However, if exposing the animals to a simulated visual scenery containing a panoramic skyline and a simulated sun, the single visual cues are weighted differently. These results indicate that the CX’s input region can flexibly adapt to different visual cue conditions. Furthermore, I characterize a previously unknown neuron in the monarch butterfly CX which responds to celestial stimuli and connects the CX with other brain neuropiles. How this cell type affects heading direction encoding has yet to be determined.
Regional climate models (RCMs) are tools used to project future climate change at a regional scale. Despite their high horizontal resolution, RCMs are characterized by systematic biases relative to observations, which can result in unrealistic interpretations of future climate change signals. On the other hand, bias correction (BC) is a popular statistical post-processing technique applied to improve the usability of output from climate models. Like every other statistical technique, BC has its strengths and weaknesses. Hence, within the regional context of Germany, and for temperature and precipitation, this study is dedicated to the assessment of the impact of different BC techniques on the RCM output. The focuses are on the impact of BC on the RCM’s statistical characterization, and physical consistency defined as the spatiotemporal consistency between the bias-corrected variable and the simulated physical mechanisms governing the variable, as well as the correlations between the bias-corrected variable and other (simulated) climate variables. Five BC techniques were applied in adjusting the systematic biases in temperature and precipitation RCM outputs. The BC techniques are linear scaling, empirical quantile mapping, univariate quantile delta mapping, multivariate quantile delta mapping that considers inter-site dependencies, and multivariate quantile delta mapping that considers inter-variable dependencies (MBCn). The results show that each BC technique adds value in reducing the biases in the statistics of the RCM output, though the added value depends on several factors such as the temporal resolution of the data, choice of RCM, climate variable, region, and the metric used in evaluating the BC technique. Further, the raw RCMs reproduced portions of the observed modes of atmospheric circulation in Western Europe, and the observed temperature, and precipitation meteorological patterns in Germany. After the BC, generally, the spatiotemporal configurations of the simulated meteorological patterns as well as the governing large-scale mechanisms were reproduced.
However, at a more localized spatial scale for the individual meteorological patterns, the BC changed the simulated co-variability of some grids, especially for precipitation. Concerning the co-variability among the variables, a physically interpretable positive correlation was found between temperature and precipitation during boreal winter in both models and observations. For most grid boxes in the study domain and on average, the BC techniques that do not adjust inter-variable dependency did not notably change the simulated correlations between the climate variables. However, depending on the grid box, the (univariate) BC techniques tend to degrade the simulated temporal correlations between temperature and precipitation. Further, MBCn which adjusts biases in inter-variable dependency has the skill to improve the correlations between the simulated variables towards observations.
Characterization of binding properties of ephedrine derivatives to human alpha-1-acid glycoprotein
(2023)
Most drugs, especially those with acidic or neutral moieties, are bound to the plasma protein albumin, whereas basic drugs are preferentially bound to human alpha-1-acid glycoprotein (AGP). The protein binding of the long-established drugs ephedrine and pseudoephedrine, which are used in the treatment of hypotension and colds, has so far only been studied with albumin. Since in a previous study a stereoselective binding of ephedrine and pseudoephedrine to serum but not to albumin was observed, the aim of this study was to check whether the enantioselective binding behavior of ephedrine and pseudoephedrine, in addition to the derivatives methylephedrine and norephedrine, is due to AGP and to investigate the influence of their different substituents and steric arrangement. Discontinuous ultrafiltration was used for the determination of protein binding. Characterization of ligand-protein interactions of the drugs was obtained by saturation transfer difference nuclear magnetic resonance spectroscopy. Docking experiments were performed to analyze possible ligand-protein interactions. The more basic the ephedrine derivative is, the higher is the affinity to AGP. There was no significant difference in the binding properties between the individual enantiomers and the diastereomers of ephedrine and pseudoephedrine.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Non-aureus staphylococci (NAS) are ubiquitous bacteria in livestock-associated environments where they may act as reservoirs of antimicrobial resistance (AMR) genes for pathogens such as Staphylococcus aureus. Here, we tested whether housing conditions in pig farms could influence the overall AMR-NAS burden. Two hundred and forty porcine commensal and environmental NAS isolates from three different farm types (conventional, alternative, and organic) were tested for phenotypic antimicrobial susceptibility and subjected to whole genome sequencing. Genomic data were analysed regarding species identity and AMR gene carriage. Seventeen different NAS species were identified across all farm types. In contrast to conventional farms, no AMR genes were detectable towards methicillin, aminoglycosides, and phenicols in organic farms. Additionally, AMR genes to macrolides and tetracycline were rare among NAS in organic farms, while such genes were common in conventional husbandries. No differences in AMR detection existed between farm types regarding fosfomycin, lincosamides, fusidic acid, and heavy metal resistance gene presence. The combined data show that husbandry conditions influence the occurrence of resistant and multidrug-resistant bacteria in livestock, suggesting that changing husbandry practices may be an appropriate means of limiting the spread of AMR bacteria on farms.
Aim
Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small‐bodied species, will increase with increasing temperature. However, the specific responses of single species to rising temperatures, such as spatial range shifts, also alter communities, calling for intensive monitoring of real‐world communities over time.
Location
German Alps and pre‐alpine forests in south‐east Germany.
Methods
We empirically examined the temporal and spatial change in wild bee communities and its drivers along two largely well‐protected elevational gradients (alpine grassland vs. pre‐alpine forest), each sampled twice within the last decade.
Results
We detected clear abundance‐based upward shifts in bee communities, particularly in cold‐adapted bumble bee species, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase in small‐bodied species in low‐ and mid‐elevations along the grassland gradient. Community responses in the pre‐alpine forest gradient were only partly consistent with community responses in alpine grasslands.
Main Conclusion
In well‐protected temperate mountain regions, small‐bodied bees may initially profit from warming temperatures, by getting more abundant and diverse. Less severe warming, and differences in habitat openness along the forested gradient, however, might moderate species responses. Our study further highlights the utility of standardized abundance data for revealing rapid changes in bee communities over only one decade.