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ResearcherID
- M-1240-2017 (1)
T-pattern analysis supports studies of various aspects of human or animal behavior as well as interaction between human subjects and animal or artificial agents. The following proceedings give an overiew on the application of T-pattern analysis in different research fields like media, gaming, human behaviour, social and organisational interaction as well as sports and health.
The synapse-associated protein of 47 kDa (Sap47) in Drosophila melanogaster is the founding member of a phylogenetically conserved protein family of hitherto unknown molecular function. Sap47 is localized throughout the entire neuropil of adult and larval brains and closely associated with glutamatergic presynaptic vesicles of larval motoneurons. Flies lacking the protein are viable and fertile and do not exhibit gross structural or marked behavioral deficiencies indicating that Sap47 is dispensable for basic synaptic function, or that its function is compensated by other related proteins.
Syap1 - the mammalian homologue of Sap47 - was reported to play an essential role in Akt1 phosphorylation in various non-neuronal cells by promoting the association of mTORC2 with Akt1 which is critical for the downstream signaling cascade for adipogenesis. The function of Syap1 in the vertebrate nervous system, however, is unknown so far.
The present study provides a first description of the subcellular localization of mouse Syap1 in cultured motoneurons as well as in selected structures of the adult mouse nervous system and reports initial functional experiments. Preceding all descriptive experiments, commercially available Syap1 antibodies were tested for their specificity and suitability for this study. One antibody raised against the human protein was found to recognize specifically both the human and murine Syap1 protein, providing an indispensable tool for biochemical, immunocytochemical and immunohistochemical studies.
In the course of this work, a Syap1 knockout mouse was established and investigated. These mice are viable and fertile and do not show obvious changes in morphology or phenotype. As observed for Sap47 in flies, Syap1 is widely distributed in the synaptic neuropil, particularly in regions rich in glutamatergic synapses but it was also detected at perinuclear Golgi-associated sites in certain groups of neuronal somata. In motoneurons the protein is especially observed in similar perinuclear structures, partially overlapping with Golgi markers and in axons, dendrites and axonal growth cones. Biochemical and immunohistochemical analyses showed widespread Syap1 expression in the central nervous system with regionally distinct distribution patterns in cerebellum, hippocampus or olfactory bulb. Besides its expression in neurons, Syap1 is also detected in non-neuronal tissue e.g. liver, kidney and muscle tissue. In contrast, non-neuronal cells in the brain lack the typical perinuclear accumulation.
First functional studies with cultured primary motoneurons on developmental, structural and functional aspects reveal no influence of Syap1 depletion on survival and morphological features such as axon length or dendritic length. Contrary to expectations, in neuronal tissues or cultured motoneurons a reduction of Akt phosphorylation at Ser473 or Thr308 was not detected after Syap1 knockdown or knockout.
Soft x-ray spectroscopic study of methanol and glycine peptides in different physical environments
(2017)
Ion-specific effects occur in a huge variety of aqueous solutions of electrolytes and larger molecules like peptides, altering properties such as viscosity, enzyme activity, protein stability, and salting-in and salting-out behavior of proteins. Typically, these type of effects are rationalized in terms of the Hofmeister series, which originally orders cations and anions according to their ability to enhance or suppress the solubility of proteins in water. This empirical order, however, is still not understood yet. Quite some effort was made to gain a molecular level understanding of this phenomenon, yet no consensus has been found about the underlying mechanisms and the determination and localization of the interaction sites.
Resonant inelastic soft x-ray scattering (RIXS) combines x-ray emission (XES) and absorption spectroscopies (XAS), probing the partial local density of states of both occupied and unoccupied electronic states and is thus a promising candidate to shed more light onto the issue. The studies presented in this work are directed towards an improved understanding of the interaction between salts and peptides. In order to address this topic, the impact of different physical environments on the electronic structure of small molecules (i.e., methanol and glycine derived peptides) is investigated systematically using soft x-ray spectroscopic methods, corroborated with density functional theory (DFT) calculations.
In a first step, molecules without any interactions to the surrounding are investigated, using gas-phase methanol as a model system. Thereby, the local and element specific character of RIXS is demonstrated and used to separately probe the local electronic structure of methanol’s hydroxyl and methyl group, respectively. The attribution of the observed emission features to distinct molecular orbitals is confirmed by DFT calculations, which also quantitatively explain the different relative intensities of the emission features. For resonant excitation of the O K pre-edge absorption resonance, strong isotope effects are found that are explained by dynamical processes at the hydroxyl group. This serves as an excellent example for possible consequences of a local change in the geometric structure or symmetry of a molecule on its electronic structure.
In the following, the sample system is expanded to the amino acid glycine and its smallest derived peptides diglycine and triglycine. As a first step, they are studied in their crystalline form in solid state. Again, a comprehensive picture of the electronic structure is developed by measuring RIXS maps at the oxygen and nitrogen K absorption edge, corroborated by DFT calculations. Similar to the case of methanol, dynamic processes at the protonated amino group of the molecules after exciting the nitrogen atom have a strong influence on the emission spectra. Furthermore, it is shown that RIXS can be used to selectively excite the peptide nitrogen to probe the electronic structure around it. A simple building block approach for XES spectra is applied to separate the contribution of the emission attributed to transitions into core holes at the peptide and the amino nitrogen, respectively.
In the aqueous solution, the surrounding water molecules slightly change the electronic structure, probably via interactions with the charged functional groups. The effects on the x-ray emission spectra, however, are rather small. Much bigger changes are observed when manipulating the protonation state of the functional groups by adjusting the pH value of the solution. A protonation of the carboxyl group at low pH values, as well as a deprotonation of the amino group at high pH values lead to striking changes in the shape of the RIXS maps. In a comprehensive study of glycine’s XES spectra at varying pH values, changes in the local electronic structure are not only observed in the immediate surrounding of the manipulated functional groups but also in more distant moieties of the molecule.
Finally, the study is extended to mixed aqueous solutions of diglycine and a variety of different salts as examples for systems where Hofmeister effects are observed. To investigate the influence of different cations and anions on the electronic structure of diglycine, two series of chlorine and potassium salts are used. Ion-specific effects are identified for both cases. Some of the changes in the x-ray emission spectra of diglycine in the mixed solutions qualitatively follow the Hofmeister series as a function of the used salt. The observed trends thereby indicate an increased interaction between the electron density around the peptide oxygen with the cations, whereas anions seem to interact with the amino group of the peptide.
Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks.
The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack.
Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time.
The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy.
The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include:
• Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies.
• Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads.
• Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model.
• A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles.
• A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability.
• An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20% for uncalibrated models and up to 10% for calibrated models depending on the solving technique.
In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits:
• It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment.
• DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios.
• The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving.
• The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms.
The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.
Recent years have seen rapid advances in the chemistry of small molecules containing electron-precise boron-boron bonds. This review provides an overview of the latest methods for the controlled synthesis of B–B single and multiple bonds as well as the ever-expanding range of reactivity displayed by the latter.
The obligate human pathogen Neisseria gonorrhoeae is responsible for the widespread sexually transmitted disease gonorrhoea, which in rare cases also leads to the development of disseminated gonococcal infection (DGI). DGI is mediated by PorBIA-expressing bacteria that invade host cells under low phosphate condition by interaction with the scavenger receptor-1 (SREC-I) expressed on the surface of endothelial cells. The interaction of PorBIA and SREC-I was analysed using different in vitro approaches, including surface plasmon resonance experiments that revealed a direct phosphate-independent high affinity interaction of SREC-I to PorBIA. However, the same binding affinity was also found for the other allele PorBIB, which indicates unspecific binding and suggests that the applied methods were unsuitable for this interaction analysis.
Since N. gonorrhoeae was recently classified as a “super-bug” due to a rising number of antibiotic-resistant strains, this study aimed to discover inhibitors against the PorBIA-mediated invasion of N. gonorrhoeae. Additionally, inhibitors were searched against the human pathogen Chlamydia trachomatis, which causes sexually transmitted infections as well as infections of the upper inner eyelid. 68 compounds, including plant-derived small molecules, extracts or pure compounds of marine sponges or sponge-associated bacteria and pipecolic acid derivatives, were screened using an automated microscopy based approach. No active substances against N. gonorrhoeae could be identified, while seven highly antichlamydial compounds were detected.
The pipecolic acid derivatives were synthesized as potential inhibitors of the virulence-associated “macrophage infectivity potentiator” (MIP), which exhibits a peptidyl prolyl cis-trans isomerase (PPIase) enzyme activity. This study investigated the role of C. trachomatis and N. gonorrhoeae MIP during infection. The two inhibitors PipN3 and PipN4 decreased the PPIase activity of recombinant chlamydial and neisserial MIP in a dose-dependent manner. Both compounds affected the chlamydial growth and development in epithelial cells. Furthermore, this work demonstrated the contribution of MIP to a prolonged survival of N. gonorrhoeae in the presence of neutrophils, which was significantly reduced in the presence of PipN3 and PipN4.
SF2446A2 was one of the compounds that had a severe effect on the growth and development of C. trachomatis. The analysis of the mode of action of SF2446A2 revealed an inhibitory effect of the compound on the mitochondrial respiration and mitochondrial ATP
production of the host cell. However, the chlamydial development was independent of proper functional mitochondria, which excluded the connection of the antichlamydial properties of SF2446A2 with its inhibition of the respiratory chain. Only the depletion of cellular ATP by blocking glycolysis and mitochondrial respiratory chain inhibited the chlamydial growth. A direct effect of SF2446A2 on C. trachomatis was assumed, since the growth of the bacteria N. gonorrhoeae and Staphylococcus aureus was also affected by the compound.
In summary, this study identified the severe antichlamydial activity of plant-derived naphthoquinones and the compounds derived from marine sponges or sponge-associated bacteria SF2446A2, ageloline A and gelliusterol E. Furthermore, the work points out the importance of the MIP proteins during infection and presents pipecolic acid derivatives as novel antimicrobials against N. gonorrhoeae and C. trachomatis.
The topic of this thesis is the theoretical and numerical analysis of optimal control problems, whose differential constraints are given by Fokker-Planck models related to jump-diffusion processes. We tackle the issue of controlling a stochastic process by formulating a deterministic optimization problem. The
key idea of our approach is to focus on the probability density function of the process,
whose time evolution is modeled by the Fokker-Planck equation. Our control framework is advantageous since it allows to model the action of the control over the entire range of the process, whose statistics are characterized by the shape of its probability density function.
We first investigate jump-diffusion processes, illustrating their main properties. We define stochastic initial-value problems and present results on the existence and uniqueness of their solutions. We then discuss how numerical solutions of stochastic problems are computed, focusing on the Euler-Maruyama method.
We put our attention to jump-diffusion models with time- and space-dependent coefficients and jumps given by a compound Poisson process. We derive the related Fokker-Planck equations, which take the form of partial integro-differential equations. Their differential term is governed by a parabolic operator, while the nonlocal integral operator is due to the presence of the jumps. The derivation is carried out in two cases. On the one hand, we consider a process with unbounded range. On the other hand, we confine the dynamic of the sample paths to a bounded domain, and thus the behavior of the process in proximity of the boundaries has to be specified. Throughout this thesis, we set the barriers of the domain to be reflecting.
The Fokker-Planck equation, endowed with initial and boundary conditions, gives rise to Fokker-Planck problems. Their solvability is discussed in suitable functional spaces. The properties of their solutions are examined, namely their regularity, positivity and probability mass conservation. Since closed-form solutions to Fokker-Planck problems are usually not available, one has to resort to numerical methods.
The first main achievement of this thesis is the definition and analysis of conservative and positive-preserving numerical methods for Fokker-Planck problems. Our SIMEX1 and SIMEX2 (Splitting-Implicit-Explicit) schemes are defined within the framework given by the method of lines. The differential operator is discretized by a finite volume scheme given by the Chang-Cooper method, while the integral operator is approximated by a mid-point rule. This leads to a large system of ordinary differential equations, that we approximate with the Strang-Marchuk splitting method. This technique decomposes the original problem in a
sequence of different subproblems with simpler structure, which are separately solved and linked to each other through initial conditions and final solutions. After performing the splitting step, we carry out the time integration with first- and second-order time-differencing methods. These steps give rise to the SIMEX1 and SIMEX2 methods, respectively.
A full convergence and stability analysis of our schemes is included. Moreover, we are able to prove that the positivity and the mass conservation of the solution to Fokker-Planck problems are satisfied at the discrete level by the numerical solutions computed with the SIMEX schemes.
The second main achievement of this thesis is the theoretical analysis and the numerical solution of optimal control problems governed by Fokker-Planck models. The field of optimal control deals with finding control functions in such a way that given cost functionals are minimized. Our framework aims at the minimization of the difference between a known sequence of values and the first moment of a jump-diffusion process; therefore, this formulation can also be considered as a parameter estimation problem for stochastic processes. Two cases are discussed, in which the form of the cost functional is continuous-in-time and discrete-in-time, respectively.
The control variable enters the state equation as a coefficient of the Fokker-Planck partial integro-differential operator. We also include in the cost functional a $L^1$-penalization term, which enhances the sparsity of the solution. Therefore, the resulting optimization problem is nonconvex and nonsmooth. We derive the first-order optimality systems satisfied by the optimal solution. The computation of the optimal solution is carried out by means of proximal iterative schemes in an infinite-dimensional framework.
Endogenous clocks help animals to anticipate the daily environmental changes. These
internal clocks rely on environmental cues, called Zeitgeber, for synchronization. The
molecular clock consists of transcription-translation feedback loops and is located in
about 150 neurons (Helfrich-Förster and Homberg, 1993; Helfrich-Förster, 2005). The
core clock has the proteins Clock (CLK) and Cycle (CYC) that together act as a
transcription activator for period (per) and timeless (tim) which then, via PER and TIM
block their own transcription by inhibiting CLK/CYC activity (Darlington et al., 1998;
Hardin, 2005; Dubruille and Emery, 2008). Light signals trigger the degradation of TIM
through a blue-light sensing protein Cryptochrome (CRY) and thus, allows CLK/CYC to
resume per and tim transcription (Emery et al., 1998; Stanewsky et al., 1998).
Therefore, light acts as an important Zeitgeber for the clock entrainment. The
mammalian clock consists of similarly intertwined feedback loops.
Endogenous clocks facilitate appropriate alterations in a variety of behaviors
according to the time of day. Also, these clocks can provide the phase information to the
memory centers of the brain to form the time of day related associations (TOD). TOD
memories promote appropriate usage of resources and concurrently better the survival
success of an animal. For instance, animals can form time-place associations related to
the availability of a biologically significant stimulus like food or mate. Such memories will
help the animal to obtain resources at different locations at the appropriate time of day.
The significance of these memories is supported by the fact that many organisms
including bees, ants, rats and mice demonstrate time-place learning (Biebach et al.
1991; Mistlberger et al. 1997; Van der Zee et al. 2008; Wenger et al. 1991). Previous
studies have shown that TOD related memories rely on an internal clock, but the identity
of the clock and the underlying mechanism remain less well understood. The present
study demonstrates that flies can also form TOD associated odor memories and further
seeks to identify the appropriate mechanism.
Hungry flies were trained in the morning to associate odor A with the sucrose
reward and subsequently were exposed to odor B without reward. The same flies were
exposed in the afternoon to odor B with and odor A without reward. Two cycles of the
65
reversal training on two subsequent days resulted in the significant retrieval of specific
odor memories in the morning and afternoon tests. Therefore, flies were able to
modulate their odor preference according to the time of day. In contrast, flies trained in
a non-reversal manner were unable to form TOD related memories. The study also
demonstrates that flies are only able to form time-odor memories when the two
reciprocal training cycles occur at a minimum 6 h interval.
This work also highlights the role of the internal state of flies in establishing timeodor
memories. Prolonged starvation motivates flies to appropriate their search for the
food. It increases the cost associated with a wrong choice in the T-maze test as it
precludes the food discovery. Accordingly, an extended starvation promotes the TOD
related changes in the odor preference in flies already with a single cycle of reversal
training. Intriguingly, prolonged starvation is required for the time-odor memory
acquisition but is dispensable during the memory retrieval.
Endogenous oscillators promote time-odor associations in flies. Flies in constant
darkness have functional rhythms and can form time-odor memories. In contrast, flies
kept in constant light become arrhythmic and demonstrated no change in their odor
preference through the day. Also, clock mutant flies per01 and clkAR, show compromised
performance compared to CS flies when trained in the time-odor conditioning assay.
These results suggest that flies need a per and clk dependent oscillator for establishing
TOD related memories. Also, the clock governed rhythms are necessary for the timeodor
memory acquisition but not for the retrieval.
Pigment-Dispersing Factor (PDF) neuropeptide is a clock output factor (Park and
Hall, 1998; Park et al., 2000; Helfrich-Förster, 2009). pdf01 mutant flies are unable to
form significant time-odor memories. PDF is released by 8 neurons per hemisphere in
the fly brain. This cluster includes the small (s-LNvs) and large (l-LNvs) ventral lateral
neurons. Restoring PDF in these 16 neurons in the pdf01 mutant background rescues
the time-odor learning defect. The PDF neuropeptide activates a seven transmembrane
G-protein coupled receptor (PDFR) which is broadly expressed in the fly brain (Hyun et
al., 2005). The present study shows that the expression of PDFR in about 10 dorsal
neurons (DN1p) is sufficient for robust time-odor associations in flies.
66
In conclusion, flies use distinct endogenous oscillators to acquire and retrieve
time-odor memories. The first oscillator is light dependent and likely signals through the
PDF neuropeptide to promote the usage of the time as an associative cue during
appetitive conditioning. In contrast, the second clock is light independent and
specifically signals the time information for the memory retrieval. The identity of this
clock and the underlying mechanism are open to investigation.
3D point clouds are a de facto standard for 3D documentation and modelling. The advances in laser scanning technology broadens the usability and access to 3D measurement systems. 3D point clouds are used in many disciplines such as robotics, 3D modelling, archeology and surveying. Scanners are able to acquire up to a million of points per second to represent the environment with a dense point cloud. This represents the captured environment with a very high degree of detail. The combination of laser scanning technology with photography adds color information to the point clouds. Thus the environment is represented more realistically. Full 3D models of environments, without any occlusion, require multiple scans. Merging point clouds is a challenging process. This thesis presents methods for point cloud registration based on the panorama images generated from the scans. Image representation of point clouds introduces 2D image processing methods to 3D point clouds. Several projection methods for the generation of panorama maps of point clouds are presented in this thesis. Additionally, methods for point cloud reduction and compression based on the panorama maps are proposed. Due to the large amounts of data generated from the 3D measurement systems these methods are necessary to improve the point cloud processing, transmission and archiving. This thesis introduces point cloud processing methods as a novel framework for the digitisation of archeological excavations. The framework replaces the conventional documentation methods for excavation sites. It employs point clouds for the generation of the digital documentation of an excavation with the help of an archeologist on-site. The 3D point cloud is used not only for data representation but also for analysis and knowledge generation. Finally, this thesis presents an autonomous indoor mobile mapping system. The mapping system focuses on the sensor placement planning method. Capturing a complete environment requires several scans. The sensor placement planning method solves for the minimum required scans to digitise large environments. Combining this method with a navigation system on a mobile robot platform enables it to acquire data fully autonomously. This thesis introduces a novel hole detection method for point clouds to detect obscured parts of a captured environment. The sensor placement planning method selects the next scan position with the most coverage of the obscured environment. This reduces the required number of scans. The navigation system on the robot platform consist of path planning, path following and obstacle avoidance. This guarantees the safe navigation of the mobile robot platform between the scan positions. The sensor placement planning method is designed as a stand alone process that could be used with a mobile robot platform for autonomous mapping of an environment or as an assistant tool for the surveyor on scanning projects.
Tourism in Würzburg: Suggestions on how to enhance the travel experience for Chinese tourists
(2017)
This report provides suggestions on how to enhance the travel experience for Chinese tourists in the German city of Würzburg. Based on a user experience survey and a market research, this work includes a quantitative and competitive analysis. It further provides concrete and hands-on measurements for the city council to improve the experience of Chinese visitors coming to Würzburg.