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The Antarctic Ice Sheet stores ~91% of the global ice volume which is equivalent to a sea-level rise of 58.3 meters. Recent disintegration events of ice shelves and retreating glaciers along the Antarctic Peninsula and West Antarctica indicate the current vulnerable state of the Antarctic Ice Sheet. Glacier tongues and ice shelves create a safety band around Antarctica with buttressing effects on ice discharge. Current decreases in glacier and ice shelf extent reduce the effective buttressing forces and increase ice discharge of grounded ice. The consequence is a higher contribution to sea-level rise from the Antarctic Ice Sheet. So far, it is unresolved which proportion of Antarctic glacier retreat can be attributed to climate change and which part to the natural cycle of growth and decay in the lifetime of a glacier. The quantitative assessment of the magnitude, spatial extent, distribution, and dynamics of circum-Antarctic glacier and ice shelf retreat is of utmost importance to monitor Antarctica’s weakening safety band. In remote areas like Antarctica, earth observation provides optimal properties for large-scale mapping and monitoring of glaciers and ice shelves. Nowadays, the variety of available satellite sensors, technical advancements regarding spatial resolution and revisit times, as well as open satellite data archives create an ideal basis for monitoring calving front change. A systematic review conducted within this thesis revealed major gaps in the availability of glacier and ice shelf front position measurements despite the improved satellite data availability. The previously limited availability of satellite imagery and the time-consuming manual delineation of calving fronts did neither allow a circum-Antarctic assessment of glacier retreat nor the assessment of intra-annual changes in glacier front position. To advance the understanding of Antarctic glacier front change, this thesis presents a novel automated approach for calving front extraction and explores drivers of glacier retreat.
A comprehensive review of existing methods for glacier front extraction ascertained the lack of a fully automatic approach for large-scale monitoring of Antarctic calving fronts using radar imagery. Similar backscatter characteristics of different ice types, seasonally changing backscatter values, multi-year sea ice, and mélange made it challenging to implement an automated approach with traditional image processing techniques. Therefore, the present abundance of satellite data is best exploited by integrating recent developments in big data and artificial intelligence (AI) research to derive circum-Antarctic calving front dynamics. In the context of this thesis, the novel AI-based framework “AntarcticLINES” (Antarctic Glacier and Ice Shelf Front Time Series) was created which provides a fully automated processing chain for calving front extraction from Sentinel-1 imagery. Open access Sentinel-1 radar imagery is an ideal data source for monitoring current and future changes in the Antarctic coastline with revisit times of less than six days and all-weather imaging capabilities. The developed processing chain includes the pre-processing of dual-polarized Sentinel-1 imagery for machine learning applications. 38 Sentinel-1 scenes were used to train the deep learning architecture U-Net for image segmentation. The trained weights of the neural network can be used to segment Sentinel-1 scenes into land ice and ocean. Additional post-processing ensures even more accurate results by including morphological filtering before extracting the final coastline. A comprehensive accuracy assessment has proven the correct extraction of the coastline. On average, the automatically extracted coastline deviates by 2-3 pixels (93 m) from a manual delineation. This accuracy is in range with deviations between manually delineated coastlines from different experts.
For the first time, the fully automated framework AntarcticLINES enabled the extraction of intra-annual glacier front fluctuations to assess seasonal variations in calving front change. Thereby, for example, an increased calving frequency of Pine Island Glacier and a beginning disintegration of Glenzer Glacier were revealed. Besides, the extraction of the entire Antarctic coastline for 2018 highlighted the large-scale applicability of the developed approach. Accurate results for entire Antarctica were derived except for the Western Antarctic Peninsula where training imagery was not sufficient and should be included in future studies.
Furthermore, this dissertation presents an unprecedented record of circum-Antarctic calving front change over the last two decades. The newly extracted coastline for 2018 was compared to previous coastline products from 2009 and 1997. This revealed that the Antarctic Ice Sheet shrank 29,618±1193 km2 in extent between 1997-2008 and gained an area of 7,108±1029 km2 between 2009-2018. Glacier retreat concentrated along the Antarctic Peninsula and West Antarctica. The only East Antarctic coastal sector primarily experiencing calving front retreat was Wilkes Land in 2009-2018. Finally, potential drivers of circum-Antarctic glacier retreat were identified by combining data on glacier front change with changes in climate variables. It was found that strengthening westerlies, snowmelt, rising sea surface temperatures, and decreasing sea ice cover forced glacier retreat over the last two decades. Relative changes in mean air temperature could not be identified as a driver for glacier retreat and further investigations on extreme events in air temperature are necessary to assess the effect of atmospheric forcing on frontal retreat. The strengthening of all identified drivers was closely connected to positive phases of the Southern Annular Mode (SAM). With increasing greenhouse gases and ozone depletion, positive phases of SAM will occur more often and force glacier retreat even further in the future.
Within this thesis, a comprehensive review on existing Antarctic glacier and ice shelf front studies was conducted revealing major gaps in Antarctic calving front records. Therefore, a fully automated processing chain for glacier and ice shelf front extraction was implemented to track circum-Antarctic calving front fluctuations on an intra-annual basis. The large-scale applicability was certified by presenting two decades of circum-Antarctic calving front change. In combination with climate variables, drivers of recent glacier retreat were identified. In the future, the presented framework AntarcticLINES will greatly contribute to the constant monitoring of the Antarctic coastline under the pressure of a changing climate.
Inflammatory mechanisms in the pathophysiology of diabetic peripheral neuropathy (DN) — new aspects
(2021)
The pathogenesis of diabetic neuropathy is complex, and various pathogenic pathways have been proposed. A better understanding of the pathophysiology is warranted for developing novel therapeutic strategies. Here, we summarize recent evidence from experiments using animal models of type 1 and type 2 diabetes showing that low-grade intraneural inflammation is a facet of diabetic neuropathy. Our experimental data suggest that these mild inflammatory processes are a likely common terminal pathway in diabetic neuropathy associated with the degeneration of intraepidermal nerve fibers. In contrast to earlier reports claiming toxic effects of high-iron content, we found the opposite, i.e., nutritional iron deficiency caused low-grade inflammation and fiber degeneration while in normal or high non-heme iron nutrition no or only extremely mild inflammatory signs were identified in nerve tissue. Obesity and dyslipidemia also appear to trigger mild inflammation of peripheral nerves, associated with neuropathy even in the absence of overt diabetes mellitus. Our finding may be the experimental analog of recent observations identifying systemic proinflammatory activity in human sensorimotor diabetic neuropathy. In a rat model of type 1 diabetes, a mild neuropathy with inflammatory components could be induced by insulin treatment causing an abrupt reduction in HbA1c. This is in line with observations in patients with severe diabetes developing a small fiber neuropathy upon treatment-induced rapid HbA1c reduction. If the inflammatory pathogenesis could be further substantiated by data from human tissues and intervention studies, anti-inflammatory compounds with different modes of action may become candidates for the treatment or prevention of diabetic neuropathy.
Treatment-induced neuropathy in diabetes (TIND) — Developing a disease model in type 1 diabetic rats
(2021)
Treatment-induced neuropathy in diabetes (TIND) is defined by the occurrence of an acute neuropathy within 8 weeks of an abrupt decrease in glycated hemoglobin-A1c (HbA1c). The underlying pathogenic mechanisms are still incompletely understood with only one mouse model being explored to date. The aim of this study was to further explore the hypothesis that an abrupt insulin-induced fall in HbA1c may be the prime causal factor of developing TIND. BB/OKL (bio breeding/OKL, Ottawa Karlsburg Leipzig) diabetic rats were randomized in three groups, receiving insulin treatment by implanted subcutaneous osmotic insulin pumps for 3 months, as follows: Group one received 2 units per day; group two 1 unit per day: and group three 1 unit per day in the first month, followed by 2 units per day in the last two months. We serially examined blood glucose and HbA1c levels, motor- and sensory/mixed afferent conduction velocities (mNCV and csNCV) and peripheral nerve morphology, including intraepidermal nerve fiber density and numbers of Iba-1 (ionized calcium binding adaptor molecule 1) positive macrophages in the sciatic nerve. Only in BB/OKL rats of group three, with a rapid decrease in HbA1c of more than 2%, did we find a significant decrease in mNCV in sciatic nerves (81% of initial values) after three months of treatment as compared to those group three rats with a less marked decrease in HbA1c <2% (mNCV 106% of initial values, p ≤ 0.01). A similar trend was observed for sensory/mixed afferent nerve conduction velocities: csNCV were reduced in BB/OKL rats with a rapid decrease in HbA1c >2% (csNCV 90% of initial values), compared to those rats with a mild decrease <2% (csNCV 112% of initial values, p ≤ 0.01). Moreover, BB/OKL rats of group three with a decrease in HbA1c >2% showed significantly greater infiltration of macrophages by about 50% (p ≤ 0.01) and a decreased amount of calcitonin gene related peptide (CGRP) positive nerve fibers as compared to the animals with a milder decrease in HbA1c. We conclude that a mild acute neuropathy with inflammatory components was induced in BB/OKL rats as a consequence of an abrupt decrease in HbA1c caused by high-dose insulin treatment. This experimentally induced neuropathy shares some features with TIND in humans and may be further explored in studies into the pathogenesis and treatment of TIND.
Prerequisite to any biological laboratory assay employing living animals is consideration about its necessity, feasibility, ethics and the potential harm caused during an experiment. The imperative of these thoughts has led to the formulation of the 3R-principle, which today is a pivotal scientific standard of animal experimentation worldwide. The rising amount of laboratory investigations utilizing living animals throughout the last decades, either for regulatory concerns or for basic science, demands the development of alternative methods in accordance with 3R to help reduce experiments in mammals. This demand has resulted in investigation of additional vertebrate species displaying favourable biological properties. One prominent species among these is the zebrafish (Danio rerio), as these small laboratory ray-finned fish are well established in science today and feature outstanding biological characteristics. In this review, we highlight the advantages and general prerequisites of zebrafish embryos and larvae before free-feeding stages for toxicological testing, with a particular focus on cardio-, neuro, hepato- and nephrotoxicity. Furthermore, we discuss toxicokinetics, current advances in utilizing zebrafish for organ toxicity testing and highlight how advanced laboratory methods (such as automation, advanced imaging and genetic techniques) can refine future toxicological studies in this species.
These days, we are living in a digitalized world. Both our professional and private lives are pervaded by various IT services, which are typically operated using distributed computing systems (e.g., cloud environments). Due to the high level of digitalization, the operators of such systems are confronted with fast-paced and changing requirements. In particular, cloud environments have to cope with load fluctuations and respective rapid and unexpected changes in the computing resource demands. To face this challenge, so-called auto-scalers, such as the threshold-based mechanism in Amazon Web Services EC2, can be employed to enable elastic scaling of the computing resources. However, despite this opportunity, business-critical applications are still run with highly overprovisioned resources to guarantee a stable and reliable service operation. This strategy is pursued due to the lack of trust in auto-scalers and the concern that inaccurate or delayed adaptations may result in financial losses.
To adapt the resource capacity in time, the future resource demands must be "foreseen", as reacting to changes once they are observed introduces an inherent delay. In other words, accurate forecasting methods are required to adapt systems proactively. A powerful approach in this context is time series forecasting, which is also applied in many other domains. The core idea is to examine past values and predict how these values will evolve as time progresses. According to the "No-Free-Lunch Theorem", there is no algorithm that performs best for all scenarios. Therefore, selecting a suitable forecasting method for a given use case is a crucial task. Simply put, each method has its benefits and drawbacks, depending on the specific use case. The choice of the forecasting method is usually based on expert knowledge, which cannot be fully automated, or on trial-and-error. In both cases, this is expensive and prone to error.
Although auto-scaling and time series forecasting are established research fields, existing approaches cannot fully address the mentioned challenges: (i) In our survey on time series forecasting, we found that publications on time series forecasting typically consider only a small set of (mostly related) methods and evaluate their performance on a small number of time series with only a few error measures while providing no information on the execution time of the studied methods. Therefore, such articles cannot be used to guide the choice of an appropriate method for a particular use case; (ii) Existing open-source hybrid forecasting methods that take advantage of at least two methods to tackle the "No-Free-Lunch Theorem" are computationally intensive, poorly automated, designed for a particular data set, or they lack a predictable time-to-result. Methods exhibiting a high variance in the time-to-result cannot be applied for time-critical scenarios (e.g., auto-scaling), while methods tailored to a specific data set introduce restrictions on the possible use cases (e.g., forecasting only annual time series); (iii) Auto-scalers typically scale an application either proactively or reactively. Even though some hybrid auto-scalers exist, they lack sophisticated solutions to combine reactive and proactive scaling. For instance, resources are only released proactively while resource allocation is entirely done in a reactive manner (inherently delayed); (iv) The majority of existing mechanisms do not take the provider's pricing scheme into account while scaling an application in a public cloud environment, which often results in excessive charged costs. Even though some cost-aware auto-scalers have been proposed, they only consider the current resource demands, neglecting their development over time. For example, resources are often shut down prematurely, even though they might be required again soon.
To address the mentioned challenges and the shortcomings of existing work, this thesis presents three contributions: (i) The first contribution-a forecasting benchmark-addresses the problem of limited comparability between existing forecasting methods; (ii) The second contribution-Telescope-provides an automated hybrid time series forecasting method addressing the challenge posed by the "No-Free-Lunch Theorem"; (iii) The third contribution-Chamulteon-provides a novel hybrid auto-scaler for coordinated scaling of applications comprising multiple services, leveraging Telescope to forecast the workload intensity as a basis for proactive resource provisioning. In the following, the three contributions of the thesis are summarized:
Contribution I - Forecasting Benchmark
To establish a level playing field for evaluating the performance of forecasting methods in a broad setting, we propose a novel benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Besides proposing a new data set, we introduce two new measures that describe different aspects of a forecast. We applied the developed benchmark to evaluate Telescope.
Contribution II - Telescope
To provide a generic forecasting method, we introduce a novel machine learning-based forecasting approach that automatically retrieves relevant information from a given time series. More precisely, Telescope automatically extracts intrinsic time series features and then decomposes the time series into components, building a forecasting model for each of them. Each component is forecast by applying a different method and then the final forecast is assembled from the forecast components by employing a regression-based machine learning algorithm. In more than 1300 hours of experiments benchmarking 15 competing methods (including approaches from Uber and Facebook) on 400 time series, Telescope outperformed all methods, exhibiting the best forecast accuracy coupled with a low and reliable time-to-result. Compared to the competing methods that exhibited, on average, a forecast error (more precisely, the symmetric mean absolute forecast error) of 29%, Telescope exhibited an error of 20% while being 2556 times faster. In particular, the methods from Uber and Facebook exhibited an error of 48% and 36%, and were 7334 and 19 times slower than Telescope, respectively.
Contribution III - Chamulteon
To enable reliable auto-scaling, we present a hybrid auto-scaler that combines proactive and reactive techniques to scale distributed cloud applications comprising multiple services in a coordinated and cost-effective manner. More precisely, proactive adaptations are planned based on forecasts of Telescope, while reactive adaptations are triggered based on actual observations of the monitored load intensity. To solve occurring conflicts between reactive and proactive adaptations, a complex conflict resolution algorithm is implemented. Moreover, when deployed in public cloud environments, Chamulteon reviews adaptations with respect to the cloud provider's pricing scheme in order to minimize the charged costs. In more than 400 hours of experiments evaluating five competing auto-scaling mechanisms in scenarios covering five different workloads, four different applications, and three different cloud environments, Chamulteon exhibited the best auto-scaling performance and reliability while at the same time reducing the charged costs. The competing methods provided insufficient resources for (on average) 31% of the experimental time; in contrast, Chamulteon cut this time to 8% and the SLO (service level objective) violations from 18% to 6% while using up to 15% less resources and reducing the charged costs by up to 45%.
The contributions of this thesis can be seen as major milestones in the domain of time series forecasting and cloud resource management. (i) This thesis is the first to present a forecasting benchmark that covers a variety of different domains with a high diversity between the analyzed time series. Based on the provided data set and the automatic evaluation procedure, the proposed benchmark contributes to enhance the comparability of forecasting methods. The benchmarking results for different forecasting methods enable the selection of the most appropriate forecasting method for a given use case. (ii) Telescope provides the first generic and fully automated time series forecasting approach that delivers both accurate and reliable forecasts while making no assumptions about the analyzed time series. Hence, it eliminates the need for expensive, time-consuming, and error-prone procedures, such as trial-and-error searches or consulting an expert. This opens up new possibilities especially in time-critical scenarios, where Telescope can provide accurate forecasts with a short and reliable time-to-result.
Although Telescope was applied for this thesis in the field of cloud computing, there is absolutely no limitation regarding the applicability of Telescope in other domains, as demonstrated in the evaluation. Moreover, Telescope, which was made available on GitHub, is already used in a number of interdisciplinary data science projects, for instance, predictive maintenance in an Industry 4.0 context, heart failure prediction in medicine, or as a component of predictive models of beehive development. (iii) In the context of cloud resource management, Chamulteon is a major milestone for increasing the trust in cloud auto-scalers. The complex resolution algorithm enables reliable and accurate scaling behavior that reduces losses caused by excessive resource allocation or SLO violations. In other words, Chamulteon provides reliable online adaptations minimizing charged costs while at the same time maximizing user experience.
Several new N-substituted 1,2-benzisothiazol-3(2H)-ones (BITs) were synthesised through a facile synthetic route for testing their anti-dengue protease inhibition. Contrary to the conventional multistep synthesis, we achieved structurally diverse BITs with excellent yields using a two-step, one-pot reaction strategy. All the synthesised compounds were prescreened for drug-like properties using the online Swiss Absorption, Distribution, Metabolism and Elimination (SwissADME) model, indicating their favourable pharmaceutical properties. Thus, the synthesised BITs were tested for inhibitory activity against the recombinant dengue virus serotype-2 (DENV-2) NS2BNS3 protease. Dose–response experiments and computational docking analyses revealed that several BITs bind to the protease in the vicinity of the catalytic triad with IC\(_{50}\) values in the micromolar range. The DENV2 infection assay showed that two BITs, 2-(2-chlorophenyl)benzo[d]isothiazol-3(2H)-one and 2-(2,6-dichlorophenyl)benzo[d]isothiazol-3(2H)-one, could suppress DENV replication and virus infectivity. These results indicate the potential of BITs for developing new anti-dengue therapeutics.
A relevant issue on the treatment of adrenocortical carcinoma (ACC) concerns the optimal duration of adjuvant mitotane treatment. We tried to address this question, assessing whether a correlation exists between the duration of adjuvant mitotane treatment and recurrence-free survival (RFS) of patients with ACC. We conducted a multicenter retrospective analysis on 154 ACC patients treated for ≥12 months with adjuvant mitotane after radical surgery and who were free of disease at the mitotane stop. During a median follow-up of 38 months, 19 patients (12.3%) experienced recurrence. We calculated the RFS after mitotane (RFSAM), from the landmark time-point of mitotane discontinuation, to overcome immortal time bias. We found a wide variability in the duration of adjuvant mitotane treatment among different centers and also among patients cared for at the same center, reflecting heterogeneous practice. We did not find any survival advantage in patients treated for longer than 24 months. Moreover, the relationship between treatment duration and the frequency of ACC recurrence was not linear after stratifying our patients in tertiles of length of adjuvant treatment. In conclusion, the present findings do not support the concept that extending adjuvant mitotane treatment over two years is beneficial for ACC patients with low to moderate risk of recurrence.
This paper is devoted to the numerical analysis of non-smooth ensemble optimal control problems governed by the Liouville (continuity) equation that have been originally proposed by R.W. Brockett with the purpose of determining an efficient and robust control strategy for dynamical systems. A numerical methodology for solving these problems is presented that is based on a non-smooth Lagrange optimization framework where the optimal controls are characterized as solutions to the related optimality systems. For this purpose, approximation and solution schemes are developed and analysed. Specifically, for the approximation of the Liouville model and its optimization adjoint, a combination of a Kurganov–Tadmor method, a Runge–Kutta scheme, and a Strang splitting method are discussed. The resulting optimality system is solved by a projected semi-smooth Krylov–Newton method. Results of numerical experiments are presented that successfully validate the proposed framework.
Theoretical and numerical investigation of optimal control problems governed by kinetic models
(2021)
This thesis is devoted to the numerical and theoretical analysis of ensemble optimal control problems governed by kinetic models. The formulation and study of these problems have been put forward in recent years by R.W. Brockett with the motivation that ensemble control may provide a more general and robust control framework for dynamical systems. Following this formulation, a Liouville (or continuity) equation with an unbounded drift function is considered together with a class of cost functionals that include tracking of ensembles of trajectories of dynamical systems and different control costs. Specifically, $L^2$, $H^1$ and $L^1$ control costs are taken into account which leads to non--smooth optimization problems. For the theoretical investigation of the resulting optimal control problems, a well--posedness theory in weighted Sobolev spaces is presented for Liouville and related transport equations. Specifically, existence and uniqueness results for these equations and energy estimates in suitable norms are provided; in particular norms in weighted Sobolev spaces. Then, non--smooth optimal control problems governed by the Liouville equation are formulated with a control mechanism in the drift function. Further, box--constraints on the control are imposed. The control--to--state map is introduced, that associates to any control the unique solution of the corresponding Liouville equation. Important properties of this map are investigated, specifically, that it is well--defined, continuous and Frechet differentiable. Using the first two properties, the existence of solutions to the optimal control problems is shown. While proving the differentiability, a loss of regularity is encountered, that is natural to hyperbolic equations. This leads to the need of the investigation of the control--to--state map in the topology of weighted Sobolev spaces. Exploiting the Frechet differentiability, it is possible to characterize solutions to the optimal control problem as solutions to an optimality system. This system consists of the Liouville equation, its optimization adjoint in the form of a transport equation, and a gradient inequality. Numerical methodologies for solving Liouville and transport equations are presented that are based on a non--smooth Lagrange optimization framework. For this purpose, approximation and solution schemes for such equations are developed and analyzed. For the approximation of the Liouville model and its optimization adjoint, a combination of a Kurganov--Tadmor method, a Runge--Kutta scheme, and a Strang splitting method are discussed. Stability and second--order accuracy of these resulting schemes are proven in the discrete $L^1$ norm. In addition, conservation of mass and positivity preservation are confirmed for the solution method of the Liouville model. As numerical optimization strategy, an adapted Krylow--Newton method is applied. Since the control is considered to be an element of $H^1$ and to obey certain box--constraints, a method for calculating a $H^1$ projection is presented. Since the optimal control problem is non-smooth, a semi-smooth adaption of Newton's method is taken into account. Results of numerical experiments are presented that successfully validate the proposed deterministic framework. After the discussion of deterministic schemes, the linear space--homogeneous Keilson--Storer master equation is investigated. This equation was originally developed for the modelling of Brownian motion of particles immersed in a fluid and is a representative model of the class of linear Boltzmann equations. The well--posedness of the Keilson--Storer master equation is investigated and energy estimates in different topologies are derived. To solve this equation numerically, Monte Carlo methods are considered. Such methods take advantage of the kinetic formulation of the Liouville equation and directly implement the behaviour of the system of particles under consideration. This includes the probabilistic behaviour of the collisions between particles. Optimal control problems are formulated with an objective that is constituted of certain expected values in velocity space and the $L^2$ and $H^1$ costs of the control. The problems are governed by the Keilson--Storer master equation and the control mechanism is considered to be within the collision kernel. The objective of the optimal control of this model is to drive an ensemble of particles to acquire a desired mean velocity and to achieve a desired final velocity configuration. Existence of solutions of the optimal control problem is proven and a Keilson--Storer optimality system characterizing the solution of the proposed optimal control problem is obtained. The optimality system is used to construct a gradient--based optimization strategy in the framework of Monte--Carlo methods. This task requires to accommodate the resulting adjoint Keilson--Storer model in a form that is consistent with the kinetic formulation. For this reason, we derive an adjoint Keilson--Storer collision kernel and an additional source term. A similar approach is presented in the case of a linear space--inhomogeneous kinetic model with external forces and with Keilson--Storer collision term. In this framework, a control mechanism in the form of an external space--dependent force is investigated. The purpose of this control is to steer the multi--particle system to follow a desired mean velocity and position and to reach a desired final configuration in phase space. An optimal control problem using the formulation of ensemble controls is stated with an objective that is constituted of expected values in phase space and $H^1$ costs of the control. For solving the optimal control problems, a gradient--based computational strategy in the framework of Monte Carlo methods is developed. Part of this is the denoising of the distribution functions calculated by Monte Carlo algorithms using methods of the realm of partial differential equations. A standalone C++ code is presented that implements the developed non--linear conjugated gradient strategy. Results of numerical experiments confirm the ability of the designed probabilistic control framework to operate as desired. An outlook section about optimal control problems governed by non--linear space--inhomogeneous kinetic models completes this thesis.
Background: The majority of breast cancer patients are severely psychologically affected by breast cancer diagnosis and subsequent therapeutic procedures. The COVID-19 pandemic and associated restrictions on public life have additionally caused significant psychological distress for much of the population. It is therefore plausible that breast cancer patients might be particularly susceptible to the additional psychological stress caused by the pandemic, increasing suffering. In this study we therefore aimed to assess the level of psychological distress currently experienced by a defined group of breast cancer patients in our breast cancer centre, compared to distress levels preCOVID-19 pandemic.
Methods: Female breast cancer patients of all ages receiving either adjuvant, neoadjuvant, or palliative therapies were recruited for the study. All patients were screened for current or previous COVID-19 infection. The participants completed a self-designed COVID-19 pandemic questionnaire, the Stress and Coping Inventory (SCI), the National Comprehensive Cancer Network (R) (NCCN (R)) Distress Thermometer (DT), the European Organization for Research and Treatment of Cancer (EORTC) QLQ C30, and the BR23.
Results: Eighty-two breast cancer patients were included. Therapy status and social demographic factors did not have a significant effect on the distress caused by the COVID-19 pandemic. The results of the DT pre and during COVID-19 pandemic did not differ significantly. Using the self-designed COVID-19 pandemic questionnaire, we detected three distinct subgroups demonstrating different levels of concerns in relation to SARS-CoV-2. The subgroup with the highest levels of concern reported significantly decreased life quality, related parameters and symptoms.
Conclusions: This monocentric study demonstrated that the COVID-19 pandemic significantly affected psychological health in a subpopulation of breast cancer patients. The application of a self-created "COVID-19 pandemic questionnaire"could potentially be used to help identify breast cancer patients who are susceptible to increased psychological distress due to the COVID-19 pandemic, and therefore may need additional intensive psychological support.
Realistic and lifelike 3D-reconstruction of virtual humans has various exciting and important use cases. Our and others’ appearances have notable effects on ourselves and our interaction partners in virtual environments, e.g., on acceptance, preference, trust, believability, behavior (the Proteus effect), and more. Today, multiple approaches for the 3D-reconstruction of virtual humans exist. They significantly vary in terms of the degree of achievable realism, the technical complexities, and finally, the overall reconstruction costs involved. This article compares two 3D-reconstruction approaches with very different hardware requirements. The high-cost solution uses a typical complex and elaborated camera rig consisting of 94 digital single-lens reflex (DSLR) cameras. The recently developed low-cost solution uses a smartphone camera to create videos that capture multiple views of a person. Both methods use photogrammetric reconstruction and template fitting with the same template model and differ in their adaptation to the method-specific input material. Each method generates high-quality virtual humans ready to be processed, animated, and rendered by standard XR simulation and game engines such as Unreal or Unity. We compare the results of the two 3D-reconstruction methods in an immersive virtual environment against each other in a user study. Our results indicate that the virtual humans from the low-cost approach are perceived similarly to those from the high-cost approach regarding the perceived similarity to the original, human-likeness, beauty, and uncanniness, despite significant differences in the objectively measured quality. The perceived feeling of change of the own body was higher for the low-cost virtual humans. Quality differences were perceived more strongly for one’s own body than for other virtual humans.
Chronotype is defined as the behavioral manifestation of circadian rhythms related to the external light–dark cycle. Evening chronotype has been associated with an increased risk of developing cardiometabolic diseases in obesity. Menopause is a lifestage associated with an increased risk of developing cardiometabolic diseases and a change in circadian rhythmicity compared to pre-menopause. However, the prevalence of chronotype categories in menopause and their role in determining menopause-related cardiometabolic risk, mostly in obesity, have not been investigated. Thus, we aimed to investigate the prevalence of chronotype categories in post-menopausal women with obesity and their role in menopause-related cardiometabolic risk. In this cross-sectional study we enrolled 49 pre-menopausal and 74 post-menopausal women with obesity. Anthropometric parameters, lifestyle habits, adherence to the Mediterranean Diet (MD), sleep quality, chronotype and the presence of type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD) were studied. No significance differences were detected in terms of lifestyle and adherence to the MD between pre- and post-menopausal women. Chronotype was classified as morning in 66 (53.6%), evening in 20 (16.3%) and intermediate in 37 (30.1%) women. In addition, pre-menopausal women with obesity showed a significantly higher chance to have an intermediate chronotype (OR = 2.21, 95% CI 1.28–3.83; p = 0.004), whereas post-menopausal women with obesity showed a trend to have a higher morning chronotype (OR = 1.42, 95% CI 0.98–2.06; p = 0.051), although this did not reach statistical significance. No significant differences were detected in terms of prevalence of evening chronotype between the two groups. However, the evening chronotype had a significantly higher risk to have T2DM compared to the morning (OR = 17.29, 95% CI 2.40–124.27; p = 0.005) and intermediate chronotypes (OR = 30.86, 95% CI 2.05–464.32; p = 0.013) in both pre- and post-menopausal women with obesity. In conclusion, the intermediate chronotype was significantly more prevalent in pre-menopausal women with obesity compared to post-menopausal women. Evening chronotype was associated to T2DM in both pre- and post-menopause. These results support the importance of including the assessment of chronotype in the management of women with obesity in post-menopause.
The EU chemicals strategy for sustainability (CSS) asserts that both human health and the environment are presently threatened and that further regulation is necessary. In a recent Guest Editorial, members of the German competent authority for risk assessment, the BfR, raised concerns about the scientific justification for this strategy. The complexity and interdependence of the networks of regulation of chemical substances have ensured that public health and wellbeing in the EU have continuously improved. A continuous process of improvement in consumer protection is clearly desirable but any initiative directed towards this objective must be based on scientific knowledge. It must not confound risk with other factors in determining policy. This conclusion is fully supported in the present Commentary including the request to improve both, data collection and the time-consuming and bureaucratic procedures that delay the publication of regulations.
Background: Neuralgic amyotrophy (NA) has been described as a possible extrahepatic manifestation of hepatitis E virus (HEV) infection. Usually, HEV-associated NA occurs bilaterally. The clinical characteristics determining the course of HEV-associated NA have still not been defined. Methods: In this retrospective multicentric case series, 16 patients with HEV-associated NA were studied and compared to 176 HEV patients without NA in terms of their age, sex, and ALT levels. Results: Neither gender distribution (75% vs. 67% male) nor age (47 vs. 48 years median) differed significantly between the NA patients and controls. Eight NA patients (50%) presented with bilateral involvement — seven of these had right-side dominance and one had left-side dominance. Thirteen cases (81%) were hospitalized. Eight of these patients stayed in hospital for five to seven days, and five patients stayed for up to two weeks. The time from the onset of NA to the HEV diagnosis, as well as the diagnostic and therapeutic proceedings, showed a large variability. In total, 13 (81%) patients received treatment: 1/13 (8%) received intravenous immunoglobulins, 8/13 (62%) received glucocorticoids, 3/13 (23%) received ribavirin, and 6/13 (46%) received pregabalin/gabapentin. Patients with ages above the median (47 years) were more likely to be treated (p = 0.001). Conclusion: HEV-associated NA causes a relevant morbidity. In our case series neither the type of treatment nor the time of initiation of therapy had a significant effect on the duration of hospitalization or the course of the disease. The clinical presentation, the common diagnostic and therapeutic procedures, and the patients' characteristics showed large variability, demonstrating the necessity of standardized protocols for this rare but relevant disease.
The comet assay is a commonly used method to determine DNA damage and repair activity in many types of samples. In recent years, the use of the comet assay in human biomonitoring became highly attractive due to its various modified versions, which may be useful to determine individual susceptibility in blood samples. However, in human biomonitoring studies, working with large sample numbers that are acquired over an extended time period requires some additional considerations. One of the most important issues is the storage of samples and its effect on the outcome of the comet assay. Another important question is the suitability of different blood preparations. In this study, we analysed the effect of cryopreservation on DNA damage and repair activity in human blood samples. In addition, we investigated the suitability of different blood preparations. The alkaline and FPG as well as two different types of repair comet assay and an in vitro hydrogen peroxide challenge were applied. Our results confirmed that cryopreserved blood preparations are suitable for investigating DNA damage in the alkaline and FPG comet assay in whole blood, buffy coat and PBMCs. Ex vivo hydrogen peroxide challenge yielded its optimal effect in isolated PBMCs. The utilised repair comet assay with either UVC or hydrogen peroxide-induced lesions and an aphidicolin block worked well in fresh PBMCs. Cryopreserved PBMCs could not be used immediately after thawing. However, a 16-h recovery with or without mitotic stimulation enabled the application of the repair comet assay, albeit only in a surviving cell fraction.
The comet assay is widely used in basic research, genotoxicity testing, and human biomonitoring. However, interpretation of the comet assay data might benefit from a better understanding of the future fate of a cell with DNA damage. DNA damage is in principle repairable, or if extensive, can lead to cell death. Here, we have correlated the maximally induced DNA damage with three test substances in TK6 cells with the survival of the cells. For this, we selected hydrogen peroxide (H\(_{2}\)O\(_{2}\)) as an oxidizing agent, methyl methanesulfonate (MMS) as an alkylating agent and etoposide as a topoisomerase II inhibitor. We measured cell viability, cell proliferation, apoptosis, and micronucleus frequency on the following day, in the same cell culture, which had been analyzed in the comet assay. After treatment, a concentration dependent increase in DNA damage and in the percentage of non-vital and apoptotic cells was found for each substance. Values greater than 20-30% DNA in tail caused the death of more than 50% of the cells, with etoposide causing slightly more cell death than H\(_{2}\)O\(_{2}\) or MMS. Despite that, cells seemed to repair of at least some DNA damage within few hours after substance removal. Overall, the reduction of DNA damage over time is due to both DNA repair and death of heavily damaged cells. We recommend that in experiments with induction of DNA damage of more than 20% DNA in tail, survival data for the cells are provided.
Novel dyes were prepared by simple “click CuAAC” attachment of a triarylborane–alkyne to the azide side chain of an amino acid yielding triarylborane dye 1 which was conjugated with pyrene (dye 2) forming a triarylborane–pyrene FRET pair. In contrast to previous cationic triarylboranes, the novel neutral dyes interact only with proteins, while their affinity to DNA/RNA is completely abolished. Both the reference triarylborane amino acid and triarylborane–pyrene conjugate bind to BSA and the hDPP III enzyme with high affinities, exhibiting a strong (up to 100-fold) fluorescence increase, whereby the triarylborane–pyrene conjugate additionally retained FRET upon binding to the protein. Furthermore, the triarylborane dyes, upon binding to the hDPP III enzyme, did not impair its enzymatic activity under a wide range of experimental conditions, thus being the first non-covalent fluorimetric markers for hDPP III, also applicable during enzymatic reactions with hDPP III substrates.
Expression of the MYC oncoprotein, which binds the DNA at promoters of most transcribed genes, is controlled by growth factors in non-tumor cells, thus stimulating cell growth and proliferation.
Here in this thesis, it is shown that MYC interacts with SPT5, a subunit of the RNA polymerase II (Pol II) elongation factor DSIF. MYC recruits SPT5 to promoters of genes and is required for its association with Pol II. The transfer of SPT5 is mediated by CDK7 activity on TFIIE, which evicts it from Pol II and allows SPT5 to bind Pol II.
MYC is required for fast and processive transcription elongation, consistent with known functions of SPT5 in yeast. In addition, MYC increases the directionality of promoters by stimulating sense transcription and by suppressing the synthesis of antisense transcripts.
The results presented in this thesis suggest that MYC globally controls the productive assembly of Pol II with general elongation factors to form processive elongation complexes in response to growth-factor stimulation of non-tumour cells. However, MYC is found to be overexpressed in many tumours, and is required for their development and progression.
In this thesis it was found that, unexpectedly, such overexpression of MYC does not further enhance transcription but rather brings about squelching of SPT5. This reduces the processivity of Pol II on selected set of genes that are known to be repressed by MYC, leading to a decrease in growth-suppressive gene transcription and uncontrolled tumour growth
As a non-destructive testing method, X-ray imaging has proved to be suitable for the examination of a variety of objects. The measurement principle is based on the attenuation of X-rays caused by these objects. This attenuation can be recorded as shades of intensity using X-ray detectors and thus contains information about the inner structure of the investigated object. Since X-rays are electromagnetic waves, they also experience a change of phase in addition to their attenuation while penetrating an object. In general, imaging methods based on this effect are referred to as phase contrast imaging techniques. In the laboratory, the two mainly used methods are the propagation based phase contrast or in-line phase contrast and the grating interferometry.
While in-line phase contrast - under certain conditions - shows edge enhancement at interfaces due to interference, phase contrast in the grating interferometry is only indirectly measurable by the use of several gratings. In addition to phase contrast, grating interferometry provides access to the so-called dark-field imaging contrast, which measures the scattering of X-rays caused by an object.
These two imaging techniques, together with a novel concept of laboratory X-ray sources, the liquid-metal-jet, form the main part of this work. Compared to conventional X-ray sources, the liquid-metal-jet source offers higher brightness. The term brightness is defined by the number of X-ray photons per second, emitting area (area of the X-ray spot) and solid angle at which they are emitted.
On the basis of this source, a high resolution in-line phase contrast setup was partially developed in the scope of this work. Several computed tomographies show the feasibility of in-line phase contrast and the improvement of image quality by applying phase retrieval algorithms.
Moreover, the determination of optimized sample positions for in-line phase contrast imaging is treated at which the edge enhancement is maximized. Based on primitive fiber objects, this optimization has proven to be a good approximation.
With its high brightness in combination with a high spatial coherence, the liquid-metal-jet source is also interesting for grating interferometry. The development of such a setup is also part of this work. The overall concept and the characterization of the setup is presented as well as the applicability and its limits for the investigation of various objects.
Due to the very unique concept of this grating interferometer it was possible to realize a modified interferometer system by using a single grating only. Its concept and results are also presented in this work.
Furthermore, a grating interferometer based on a microfocus X-ray tube was tested regarding its performance. Thereby, parameters like the anode material, acquisition geometry and gratings were altered in order to find the advantages and disadvantages of each configuration.