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Digital inclusion
(2021)
In this thesis, we tackle two social disruptions: recent refugee waves in Germany and the COVID-19 pandemic. We focus on the use of information and communication technology (ICT) as a key means of alleviating these disruptions and promoting social inclusion. As social disruptions typically lead to frustration and fragmentation, it is essential to ensure the social inclusion of individuals and societies during such times.
In the context of the social inclusion of refugees, we focus on the Syrian refugees who arrived in Germany as of 2015, as they form a large and coherent refugee community. In particular, we address the role of ICTs in refugees’ social inclusion and investigate how different ICTs (especially smartphones and social networks) can foster refugees’ integration and social inclusion. In the context of the COVID-19 pandemic, we focus on the widespread unconventional working model of work from home (WFH). Our research here centers on the main constructs of WFH and the key differences in WFH experiences based on personal characteristics such as gender and parental status.
We reveal novel insights through four well-established research methods: literature review, mixed methods, qualitative method, and quantitative method. The results of our research have been published in the form of eight articles in major information systems venues and journals. Key results from the refugee research stream include the following: Smartphones represent a central component of refugee ICT use; refugees view ICT as a source of information and power; the social connectedness of refugees is strongly correlated with their Internet use; refugees are not relying solely on traditional methods to learn the German language or pursue further education; the ability to use smartphones anytime and anywhere gives refugees an empowering feeling of global connectedness; and ICTs empower refugees on three levels (community participation, sense of control, and self-efficacy).
Key insights from the COVID-19 WFH stream include: Gender and the presence of children under the age of 18 affect workers’ control over their time, technology usefulness, and WFH conflicts, while not affecting their WFH attitudes; and both personal and technology-related factors affect an individual’s attitude toward WFH and their productivity. Further insights are being gathered at the time of submitting this thesis.
This thesis contributes to the discussion within the information systems community regarding how to use different ICT solutions to promote the social inclusion of refugees in their new communities and foster an inclusive society. It also adds to the growing body of research on COVID-19, in particular on the sudden workplace transformation to WFH. The insights gathered in this thesis reveal theoretical implications and future opportunities for research in the field of information systems, practical implications for relevant stakeholders, and social implications related to the refugee crisis and the COVID-19 pandemic that must be addressed.
Diabetes is a major public health problem with increasing global prevalence. Type 2 diabetes (T2D), which accounts for 90% of all diagnosed cases, is a complex polygenic disease also modulated by epigenetics and lifestyle factors. For the identification of T2D-associated genes, linkage analyses combined with mouse breeding strategies and bioinformatic tools were useful in the past. In a previous study in which a backcross population of the lean and diabetes-prone dilute brown non-agouti (DBA) mouse and the obese and diabetes-susceptible New Zealand obese (NZO) mouse was characterized, a major diabetes quantitative trait locus (QTL) was identified on chromosome 4. The locus was designated non-insulin dependent diabetes from DBA (Nidd/DBA). The aim of this thesis was (i) to perform a detailed phenotypic characterization of the Nidd/DBA mice, (ii) to further narrow the critical region and (iii) to identify the responsible genetic variant(s) of the Nidd/DBA locus. The phenotypic characterization of recombinant congenic mice carrying a 13.6 Mbp Nidd/DBA fragment with 284 genes presented a gradually worsening metabolic phenotype. Nidd/DBA allele carriers exhibited severe hyperglycemia (~19.9 mM) and impaired glucose clearance at 12 weeks of age. Ex vivo perifusion experiments with islets of 13-week-old congenic mice revealed a tendency towards reduced insulin secretion in homozygous DBA mice. In addition, 16-week-old mice showed a severe loss of β-cells and reduced pancreatic insulin content. Pathway analysis of transcriptome data from islets of congenic mice pointed towards a downregulation of cell survival genes. Morphological analysis of pancreatic sections displayed a reduced number of bi-hormonal cells co-expressing glucagon and insulin in homozygous DBA mice, which could indicate a reduced plasticity of endocrine cells in response to hyperglycemic stress. Further generation and phenotyping of recombinant congenic mice enabled the isolation of a 3.3 Mbp fragment that was still able to induce hyperglycemia and contained 61 genes. Bioinformatic analyses including haplotype mapping, sequence and transcriptome analysis were integrated in order to further reduce the number of candidate genes and to identify the presumable causative gene variant. Four putative candidate genes (Ttc39a, Kti12, Osbpl9, Calr4) were defined, which were either differentially expressed or carried a sequence variant. In addition, in silico ChIP-Seq analyses of the 3.3 Mbp region indicated a high number of SNPs located in active regions of binding sites of β-cell transcription factors. This points towards potentially altered cis-regulatory elements that could be responsible for the phenotype conferred by the Nidd/DBA locus. In summary, the Nidd/DBA locus mediates impaired glucose homeostasis and reduced insulin secretion capacity which finally leads to β-cell death. The downregulation of cell survival genes and reduced plasticity of endocrine cells could further contribute to the β-cell loss. The critical region was narrowed down to a 3.3 Mbp fragment containing 61 genes, of which four might be involved in the development of the diabetogenic Nidd/DBA phenotype.
Investigation of Sirtuin 3 overexpression as a genetic model of fasting in hypothalamic neurons
(2021)
The controlled dosage of substances from a device to its environment, such as a tissue or an organ in medical applications or a reactor, room, machinery or ecosystem in technical, should ideally match the requirements of the applications, e.g. in terms of the time point at which the cargo is released. On-demand dosage systems may enable such a desired release pattern, if the device contain suitable features that can translate external signals into a release function. This study is motivated by the opportunities arising from microsystems capable of an on-demand release and the contributions that geometrical design may have in realizing such features. The goals of this work included the design, fabrication, characterization and experimental proof-of-concept of geometry-assisted triggerable dosing effect (a) with a sequential dosing release and (b) in a self-sufficient dosage system. Structure-function relationships were addressed on the molecular, morphological and, with a particular attention, the device design level, which is on the micrometer scale. Models and/or computational tools were used to screen the parameter space and provide guidance for experiments.
With the downscaling of CMOS technologies, the radiation-induced Single Event Transient (SET) effects in combinational logic have become a critical reliability issue for modern integrated circuits (ICs) intended for operation under harsh radiation conditions. The SET pulses generated in combinational logic may propagate through the circuit and eventually result in soft errors. It has thus become an imperative to address the SET effects in the early phases of the radiation-hard IC design. In general, the soft error mitigation solutions should accommodate both static and dynamic measures to ensure the optimal utilization of available resources. An efficient soft-error-aware design should address synergistically three main aspects: (i) characterization and modeling of soft errors, (ii) multi-level soft error mitigation, and (iii) online soft error monitoring. Although significant results have been achieved, the effectiveness of SET characterization methods, accuracy of predictive SET models, and efficiency of SET mitigation measures are still critical issues. Therefore, this work addresses the following topics: (i) Characterization and modeling of SET effects in standard combinational cells, (ii) Static mitigation of SET effects in standard combinational cells, and (iii) Online particle detection, as a support for dynamic soft error mitigation.
Since the standard digital libraries are widely used in the design of radiation-hard ICs, the characterization of SET effects in standard cells and the availability of accurate SET models for the Soft Error Rate (SER) evaluation are the main prerequisites for efficient radiation-hard design. This work introduces an approach for the SPICE-based standard cell characterization with the reduced number of simulations, improved SET models and optimized SET sensitivity database. It has been shown that the inherent similarities in the SET response of logic cells for different input levels can be utilized to reduce the number of required simulations. Based on characterization results, the fitting models for the SET sensitivity metrics (critical charge, generated SET pulse width and propagated SET pulse width) have been developed. The proposed models are based on the principle of superposition, and they express explicitly the dependence of the SET sensitivity of individual combinational cells on design, operating and irradiation parameters. In contrast to the state-of-the-art characterization methodologies which employ extensive look-up tables (LUTs) for storing the simulation results, this work proposes the use of LUTs for storing the fitting coefficients of the SET sensitivity models derived from the characterization results. In that way the amount of characterization data in the SET sensitivity database is reduced significantly.
The initial step in enhancing the robustness of combinational logic is the application of gate-level mitigation techniques. As a result, significant improvement of the overall SER can be achieved with minimum area, delay and power overheads. For the SET mitigation in standard cells, it is essential to employ the techniques that do not require modifying the cell structure. This work introduces the use of decoupling cells for improving the robustness of standard combinational cells. By insertion of two decoupling cells at the output of a target cell, the critical charge of the cell’s output node is increased and the attenuation of short SETs is enhanced. In comparison to the most common gate-level techniques (gate upsizing and gate duplication), the proposed approach provides better SET filtering. However, as there is no single gate-level mitigation technique with optimal performance, a combination of multiple techniques is required. This work introduces a comprehensive characterization of gate-level mitigation techniques aimed to quantify their impact on the SET robustness improvement, as well as introduced area, delay and power overhead per gate. By characterizing the gate-level mitigation techniques together with the standard cells, the required effort in subsequent SER analysis of a target design can be reduced. The characterization database of the hardened standard cells can be utilized as a guideline for selection of the most appropriate mitigation solution for a given design.
As a support for dynamic soft error mitigation techniques, it is important to enable the online detection of energetic particles causing the soft errors. This allows activating the power-greedy fault-tolerant configurations based on N-modular redundancy only at the high radiation levels. To enable such a functionality, it is necessary to monitor both the particle flux and the variation of particle LET, as these two parameters contribute significantly to the system SER. In this work, a particle detection approach based on custom-sized pulse stretching inverters is proposed. Employing the pulse stretching inverters connected in parallel enables to measure the particle flux in terms of the number of detected SETs, while the particle LET variations can be estimated from the distribution of SET pulse widths. This approach requires a purely digital processing logic, in contrast to the standard detectors which require complex mixed-signal processing. Besides the possibility of LET monitoring, additional advantages of the proposed particle detector are low detection latency and power consumption, and immunity to error accumulation.
The results achieved in this thesis can serve as a basis for establishment of an overall soft-error-aware database for a given digital library, and a comprehensive multi-level radiation-hard design flow that can be implemented with the standard IC design tools. The following step will be to evaluate the achieved results with the irradiation experiments.
Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, fifty-two compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron stars are formed, what neutron stars are composed of, how the Universe expands, and allow testing general relativity in the highly-dynamical strong-field regime. It is the goal of GW astrophysics to extract such information as accurately as possible. Yet, this is only possible if the tools and technology used to detect and analyze GWs are advanced enough. A key aspect of GW searches are waveform models, which encapsulate our best predictions for the gravitational radiation under a certain set of parameters, and that need to be cross-correlated with data to extract GW signals. Waveforms must be very accurate to avoid missing important physics in the data, which might be the key to answer the fundamental questions of GW astrophysics. The continuous improvements of the current LIGO-Virgo detectors, the development of next-generation ground-based detectors such as the Einstein Telescope or the Cosmic Explorer, as well as the development of the Laser Interferometer Space Antenna (LISA), demand accurate waveform models. While available models are enough to capture the low spins, comparable-mass binaries routinely detected in LIGO-Virgo searches, those for sources from both current and next-generation ground-based and spaceborne detectors must be accurate enough to detect binaries with large spins and asymmetry in the masses. Moreover, the thousands of sources that we expect to detect with future detectors demand accurate waveforms to mitigate biases in the estimation of signals’ parameters due to the presence of a foreground of many sources that overlap in the frequency band. This is recognized as one of the biggest challenges for the analysis of future-detectors’ data, since biases might hinder the extraction of important astrophysical and cosmological information from future detectors’ data. In the first part of this thesis, we discuss how to improve waveform models for binaries with high spins and asymmetry in the masses. In the second, we present the first generic metrics that have been proposed to predict biases in the presence of a foreground of many overlapping signals in GW data.
For the first task, we will focus on several classes of analytical techniques. Current models for LIGO and Virgo studies are based on the post-Newtonian (PN, weak-field, small velocities) approximation that is most natural for the bound orbits that are routinely detected in GW searches. However, two other approximations have risen in prominence, the post-Minkowskian (PM, weak- field only) approximation natural for unbound (scattering) orbits and the small-mass-ratio (SMR) approximation typical of binaries in which the mass of one body is much bigger than the other. These are most appropriate to binaries with high asymmetry in the masses that challenge current waveform models. Moreover, they allow one to “cover” regions of the parameter space of coalescing binaries, thereby improving the interpolation (and faithfulness) of waveform models. The analytical approximations to the relativistic two-body problem can synergically be included within the effective-one-body (EOB) formalism, in which the two-body information from each approximation can be recast into an effective problem of a mass orbiting a deformed Schwarzschild (or Kerr) black hole. The hope is that the resultant models can cover both the low-spin comparable-mass binaries that are routinely detected, and the ones that challenge current models. The first part of this thesis is dedicated to a study about how to best incorporate information from the PN, PM, SMR and EOB approaches in a synergistic way. We also discuss how accurate the resulting waveforms are, as compared against numerical-relativity (NR) simulations. We begin by comparing PM models, whether alone or recast in the EOB framework, against PN models and NR simulations. We will show that PM information has the potential to improve currently-employed models for LIGO and Virgo, especially if recast within the EOB formalism. This is very important, as the PM approximation comes with a host of new computational techniques from particle physics to exploit. Then, we show how a combination of PM and SMR approximations can be employed to access previously-unknown PN orders, deriving the third subleading PN dynamics for spin-orbit and (aligned) spin1-spin2 couplings. Such new results can then be included in the EOB models currently used in GW searches and parameter estimation studies, thereby improving them when the binaries have high spins. Finally, we build an EOB model for quasi-circular nonspinning binaries based on the SMR approximation (rather than the PN one as usually done). We show how this is done in detail without incurring in the divergences that had affected previous attempts, and compare the resultant model against NR simulations. We find that the SMR approximation is an excellent approximation for all (quasi-circular nonspinning) binaries, including both the equal-mass binaries that are routinely detected in GW searches and the ones with highly asymmetric masses. In particular, the SMR-based models compare much better than the PN models, suggesting that SMR-informed EOB models might be the key to model binaries in the future. In the second task of this thesis, we work within the linear-signal ap- proximation and describe generic metrics to predict inference biases on the parameters of a GW source of interest in the presence of confusion noise from unfitted foregrounds and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simple (yet realistic) LISA sources, and demonstrate its validity against Monte-Carlo simulations. The metrics we describe pave the way for more realistic studies to quantify the biases with future ground-based and spaceborne detectors.
Polymeric semiconductors are strong contenders for replacing traditional inorganic semiconductors in electronic applications requiring low power, low cost and flexibility, such as biosensors, flexible solar cells and electronic displays. Molecular doping has the potential to enable this revolution by improving the conductivity and charge transport properties of this class of materials. Despite decades of research in this field, gaps in our understanding of the nature of dopant–polymer interactions has resulted in limited commercialization of this technology. This work aims at providing a deeper insight into the underlying mechanisms of molecular p-doping of semiconducting polymers in the solution and solid-state, and thereby bring the scientific community closer to realizing the dream of making organic semiconductors commonplace in the electronics industry. The role of 1) dopant size/shape, 2) polymer chain aggregation and 3) charge delocalization on the doping mechanism and efficiency is addressed using optical (UV-Vis-NIR) and electron paramagnetic resonance (EPR) spectroscopies. By conducting a comprehensive study of the nature and concentration of the doping-induced species in solutions of the polymer poly(3-hexylthiophene) (P3HT) with 3 different dopants, we identify the unique optical signatures of the delocalized polaron, localized polaron and charge-transfer complex, and report their extinction coefficient values. Furthermore, with X-ray diffraction, atomic force microscopy and electrical conductivity measurements, we study the impact of processing technique and doping mechanism on the morphology and thereby, charge transport through the doped films.
This work demonstrates that the doping mechanism and type of doping-induced species formed are strongly influenced by the polymer backbone arrangement rather than dopant shape/size. The ability of the polymer chain to aggregate is found to be crucial for efficient charge transfer (ionization) and polaron delocalization. At the same time, our results suggest that the high ionization efficiency of a dopant–polymer system in solution may subsequently hinder efficient charge transport in the solid-state due to the reduction in the fraction of tie chains, which enable charges to move efficiently between aggregated domains in the films. This study demonstrates the complex multifaceted nature of polymer doping while providing important hints for the future design of dopant-host systems and film fabrication techniques.
Mental health problems are highly prevalent worldwide. Fortunately, psychotherapy has proven highly effective in the treatment of a number of mental health issues, such as depression and anxiety disorders. In contrast, psychotherapy training as is practised currently cannot be considered evidence-based. Thus, there is much room for improvement. The integration of simulated patients (SPs) into psychotherapy training and research is on the rise. SPs originate from the medical education and have, in a number of studies, been demonstrated to contribute to effective learning environments. Nevertheless, there has been voiced criticism regarding the authenticity of SP portrayals, but few studies have examined this to date.
Based on these considerations, this dissertation explores SPs’ authenticity while portraying a mental disorder, depression. Altogether, the present cumulative dissertation consists of three empirical papers. At the time of printing, Paper I and Paper III have been accepted for publication, and Paper II is under review after a minor revision.
First, Paper I develops and validates an observer-based rating-scale to assess SP authenticity in psychotherapeutic contexts. Based on the preliminary findings, it can be concluded that the Authenticity of Patient Demonstrations scale is a reliable and valid tool that can be used for recruiting, training, and evaluating the authenticity of SPs.
Second, Paper II tests whether student SPs are perceived as more authentic after they receive an in-depth role-script compared to those SPs who only receive basic information on the patient case. To test this assumption, a randomised controlled study design was implemented and the hypothesis could be confirmed. As a consequence, when engaging SPs, an in-depth role-script with details, e.g. on nonverbal behaviour and feelings of the patient, should be provided.
Third, Paper III demonstrates that psychotherapy trainees cannot distinguish between trained SPs and real patients and therefore suggests that, with proper training, SPs are a promising training method for psychotherapy.
Altogether, the dissertation shows that SPs can be trained to portray a depressive patient authentically and thus delivers promising evidence for the further dissemination of SPs.
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
The trace elements zinc and manganese are essential for human health, especially due to their enzymatic and protein stabilizing functions. If these elements are ingested in amounts exceeding the requirements, regulatory processes for maintaining their physiological concentrations (homeostasis) can be disturbed. Those homeostatic dysregulations can cause severe health effects including the emergence of neurodegenerative disorders such as Parkinson’s disease (PD). The concentrations of essential trace elements also change during the aging process. However, the relations of cause and consequence between increased manganese and zinc uptake and its influence on the aging process and the emergence of the aging-associated PD are still rarely understood. This doctoral thesis therefore aimed to investigate the influence of a nutritive zinc and/or manganese oversupply on the metal homeostasis during the aging process. For that, the model organism Caenorhabditis elegans (C. elegans) was applied. This nematode suits well as an aging and PD model due to properties such as its short life cycle and its completely sequenced, genetically amenable genome. Different protocols for the propagation of zinc- and/or manganese-supplemented young, middle-aged and aged C. elegans were established. Therefore, wildtypes, as well as genetically modified worm strains modeling inheritable forms of parkinsonism were applied. To identify homeostatic and neurological alterations, the nematodes were investigated with different methods including the analysis of total metal contents via inductively-coupled plasma tandem mass spectrometry, a specific probe-based method for quantifying labile zinc, survival assays, gene expression analysis as well as fluorescence microscopy for the identification and quantification of dopaminergic neurodegeneration.. During aging, the levels of iron, as well as zinc and manganese increased.. Furthermore, the simultaneous oversupply with zinc and manganese increased the total zinc and manganese contents to a higher extend than the single metal supplementation. In this relation the C. elegans metallothionein 1 (MTL-1) was identified as an important regulator of metal homeostasis. The total zinc content and the concentration of labile zinc were age-dependently, but differently regulated. This elucidates the importance of distinguishing these parameters as two independent biomarkers for the zinc status. Not the metal oversupply, but aging increased the levels of dopaminergic neurodegeneration. Additionally, nearly all these results yielded differences in the aging-dependent regulation of trace element homeostasis between wildtypes and PD models. This confirms that an increased zinc and manganese intake can influence the aging process as well as parkinsonism by altering homeostasis although the underlying mechanisms need to be clarified in further studies.