Efficient and reliable numerical simulation methods play an important role in the design of industrial components using short fiber reinforced thermoplastics (SFRT). The main goal of this dissertation is to develop a Finite Element (FE) based simulation method capable of capturing the complex mechanical behaviors and predicting the crack initiation of SFRT under a variety of loading conditions, while ensuring that the required computation and parameter calibration efforts are acceptable for industrial applications. To establish a comprehensive material database for modeling purposes, this research extensively characterizes PBT-GF30, a commonly used SFRT material in the automotive industry. The experimental investigation considers a range of loading conditions, including short-term and long-term quasi-static loading, as well as cyclic loading. Furthermore, influential factors such as temperature, strain rate, injection flow direction, thermal aging, and micro-damage are comprehensively examined, providing essential insights into the mechanical properties of SFRT. Mechanical responses of the investigated SFRT are modeled utilizing a hybrid approach combining micro-mechanical and macro-mechanical modeling strategies. This approach employs the Mori-Tanaka mean-field homogenization method to determine the effective linear elastic properties of SFRT, while the macroscopic plastic deformation is described using a macro-mechanical anisotropic viscoplastic model. Within the Continuum Damage Mechanics (CDM) framework, the effects of the matrix micro-damage on the macroscopic behaviors of SFRT are taken into account. A nonlinear damage evolution law, correlated with the material deformation history, accurately captures the different processes of damage accumulation observed in quasi-static and low cycle fatigue (LCF) tests. The proposed material model is implemented in the ANSYS software, employing a hybrid time scheme to ensure compatibility and efficiency for industrial applications. In this approach, the standard implicit time scheme is employed for iterative equilibrium solving at each time step, while an explicit scheme is utilized to evaluate and update damage variables. To enhance software compatibility, standard material models available in the commercial FE software are utilized instead of user-defined subroutines. Moreover, to improve computational efficiency, a clustering technique is employed to group similar fiber orientations and damage variables. Finally, the validity of the simulation method, incorporating calibrated material parameters, is demonstrated through accurate predictions of the mechanical responses observed in both testing specimens and a real industrial component subjected to various loading conditions.

Das Ziel einer Bewährungsunterstellung ist das straffreie Leben der verurteilten Person. Oftmals wird das Soziale Netzwerk zu einer entscheidenden Stellschraube bei der Strafaussetzung. In die sozialpädagogische Arbeit der Bewährungshilfe wird es vielfach (un-)mittelbar miteinbezogen. Ob und welche (nicht-)intendierten Folgen sich aus der Sozialen Hilfe für die sozialen Beziehungen der Adressat*innen ergeben, wird in dem Buch empirisch beleuchtet. Dabei rücken insbesondere Paarbeziehungen und das Verhältnis zwischen Fachkraft und Adressat*in in den Fokus.

Huntington’s disease (HD) is a neurodegenerative disorder marked by progressive neuronal degeneration, with no current cure. Recent research suggests that RNA granules (such as stress granules and p-bodies) and small extracellular vesicles (sEVs) play critical roles in cellular dysfunction in HD. Both compartments share features like liquid-liquid phase separation (LLPS) and RNA-binding proteins, but the relationship between mutant huntingtin (mHTT) and their shared content remains unexplored. In this study, we analyze the transcriptomic and proteomic profiles of sEVs and RNA granules in a model expressing mHTT to understand their molecular interactions in HD. Our results show significant changes in gene expression in both sEVs and RNA granules, with a notable decrease in sEVs. Long non-coding RNAs (lncRNAs) were abundant in both compartments, and their expression shifted in HD, suggesting their involvement in disease progression. Additionally, 139 genes in our marker list are regulated by the Repressor Element 1 Silencing Transcription Factor (REST), which is disrupted in HD. Three marker genes (SNHG7, LHR1 LNC1610-1, and lnc-DUXA-1) were validated in RNA granules using RNA-FISH, showing partial co-localization with YB1-positive stress granules. qRT-PCR confirmed increased expression of all five marker genes (lnc-SLC30A5-6, SNHG7, SNHG12, LHR1-LNC1610-1, and lnc-DUXA-1) in HD RNA granules, with three markers showing increased expression in sEVs, though two exhibited high variability. To validate the relevance of our findings, we compared the expression patterns of HD sEV marker genes with a recently published RNA sequencing dataset of plasma EVs from HD patients. Our results show a stronger correlation between the pre-HD group in the patient dataset and our cell model, suggesting that our model better reflects the early stages of HD progression. We also identified distinct protein profiles in HD sEVs and RNA granules, with 13 shared proteins, highlighting a unique molecular signature for HD. STRING and KEGG pathway analyses revealed enriched pathways related to neurodegenerative diseases, suggesting broader impacts on neurodegenerative processes. Overlapping GO terms between RNA granules and sEVs point to functional interactions, particularly in RNA transport and metabolism. Notably, WDR1, a protein associated with mHTT-RNA complexes, was identified in both HD sEVs and RNA granules, suggesting its role in HD pathogenesis by influencing RNA granule formation and facilitating intercellular communication via sEVs. Our findings demonstrate that mHTT alters the composition of sEVs and RNA granules in HD. The detection of miRNAs, zinc finger proteins (ZNFs), and lncRNAs in sEVs suggests that HD cells may attempt to manage stress and intercellular signaling. The identification of overlapping proteins like WDR1, RANBP6, and ITGAV offers potential biomarkers and therapeutic targets. This study enhances our understanding of HD pathology by revealing the differential sorting of RNA and proteins in HD, with implications for early diagnosis and targeted therapies.

Huntington's disease (HD) arises from the abnormal expansion of a CAG repeat in the HTT gene. The mutant CAG repeat triggers aberrant RNA-protein interactions and translates into toxic aggregate-prone polyglutamine protein. These aberrant RNA-protein ineractions also seed the formation of cytoplasmic liquid-like granules, such as stress granules. Emerging evidence demonstrates that granules formed via liquid-liquid phase separation can mature into gel-like inclusions that persist within the cell and may act as precursor to aggregates that occur in patients' tissue. Thus, deregulation of RNA granules is an important component of neurodegeneration. Interestingly, both the formation of intracellular membrane-less organelles like stress granules and the secretion of small extracellular vesicles (sEVs) increase upon stress and under disease conditions. sEVs are lipid membrane-bound particles that are secreted from all cell types and may participate in the spreading of misfolded proteins and aberrant RNA-protein complexes across the central nervous system in neurodegenerative diseases like HD. In this study, we performed a comparative transcriptomic analysis of sEVs and RNA granules in an HD model. RNA granules and sEVs were isolated from an inducible HD cell model. Both sEVs and RNA granules were isolated from induced (HD) and non-induced (control) cells and analyzed by RNA sequencing. Our comparative analysis between the transcriptomics data of HD RNA granules and sEVs showed that: (I) intracellular RNA granules and extracellular RNA vesicles share content, (II) several non-coding RNAs translocate to RNA granules, and (III) the composition of RNA granules and sEVs is affected in HD cells. Our data showing common transcripts in intracellular RNA granules and extracellular sEVs suggest that formation of RNA granules and sEV loading may be related. Moreover, we found a high abundance of lncRNAs in both control and HD samples, with several transcripts under REST regulation, highlighting their potential role in HD pathogenesis and selective incorporation into sEVs. The transcriptome cargo of RNA granules or sEVs may serve as a source for diagnostic strategies. For example, disease-specific RNA-signatures of sEVs can serve as biomarker of central nervous system diseases. Therefore, we compared our dataset to transcriptomic data from HD patient sEVs in blood. However, our data suggest that the cell-type specific signature of sEV-secreted RNAs as well as their high variability may make it difficult to detect these biomarkers in blood.