What is OPUS?
Siegen University Library provides a free of charge repository named OPUS Siegen (OPUS = Online PUblication Server) with the purpose to publish, archive and retrieve electronical documents produced at the University of Siegen.
What will you find here?
You will find Open-Access-Publications from all faculties of Siegen University and from the "universi" publishing house. The University Library applies acknowledged quality standards and offers support for publishing your documents.
How to participate?
For uploading documents, sign on to OPUS via Shibboleth using your ZIMT-Account.
Recently published
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Publication Open Access Beziehung auf Bewährung?!(2023)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.Source Type: - Some of the metrics are blocked by yourconsent settings
Publication Open Access Investigating the Crosstalk between Small Extracellular vesicles and RNA granules in Huntington’s Disease(2025)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.Source Type: - Some of the metrics are blocked by yourconsent settings
Publication Open Access Transcriptomic analysis of intracellular RNA granules and small extracellular vesicles: Unmasking their overlap in a cell model of Huntington's disease(2025)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.Source Type: - Some of the metrics are blocked by yourconsent settings
Publication Open Access Flexibilität im Mittelstand – eine multiperspektivische Analyse der Schaffung des nachhaltigen Wettbewerbsvorteils in eigentümergeführten Familienunternehmen(2025)German family businesses are the backbone of the German economy. With exceptional innovative capacity and a strong sense of responsibility, the families behind these traditional enterprises have guided Germany through multiple crises and transitions. Despite their substantial significance, uncertainties still exist as to why family businesses exhibit such resilience. Their intergenerational drive and unique corporate culture distinguish them from larger, far more visible companies. Given that most family businesses achieve market leadership unnoticed within niches, the mystery surrounding the success of this critical sector of the German economy endures. The aim of this dissertation is to discover how the sustainable competitive advantage of family businesses is driven by the person at the heart of the company: the owner-manager. This intersection of ownership and management is the unique characteristic of Germany's typical so-called Mittelstand. Using a multi-method approach, this study examines how the flexibility of the owner-manager, through the development of innovation, influences competitive advantage. The owner-manager is where the corporate and family systems overlap. The multiperspective approach of this dissertation addresses a holistic aim, allowing a comprehensive exploration of the uniqueness of German family businesses. The result of the study is a multi-stage model demonstrating that the long-term success of family businesses depends in many ways on the personal flexibility of the owner-manager. However, the secret of success cannot be captured in numbers and data alone. A key factor is the owner-manager's intuition – the often-highlighted gut feeling. In an integrative manner, qualitative and quantitative results are combined to create an in-depth approach that explains how the German Mittelstand has crafted success stories over decades and centuries that the rest of the world admires.Source Type: - Some of the metrics are blocked by yourconsent settings
Publication Open Access Towards Human-Centered Actionable Explainable AI-enabled Systems(2025)Recently, the applications of complex artificial Intelligence (AI) models have increased exponentially in almost every sector due to the enormous advancement of computing power and the availability of high-quality annotated data for training complex machine learning (ML) models. Generally, AI models are very complex in structure, and they often need to learn thousands, even millions, of parameters in the training phase. Though the predictions are accurate, due to the complex decision-making process, the predictions from such AI models are not understandable to users. Hence, AI systems lack explainability, transparency, and trustworthiness in making the decision explainable to the users. AI models with such complexity are often referred to as black-box models. To interpret the black-box AI models, recently, there has been a high interest in the AI research community concentrating on extracting facts and rationale to explain the reasons behind the prediction and overall models' decision-making priorities. The field that practices interpreting complex AI models and explaining the predictions to uncover the reasons behind particular predictions is known as eXplainable Artificial Intelligence (XAI). Improvements in interpreting ML models have been evident in this decade. However, the current explainability techniques are helpful for AI practitioners in the way that they can employ explanations to debug and eventually improve the models' performance. However, the primary objective of XAI is to help laypeople understand the predictions by providing human-centric explanations, which will eventually increase transparency and trust and lead to faster AI adoption in real-world applications. A significant gap exists in achieving human-centered explainability for AI systems due to associated challenges, including user experience variability, context sensitivity, bias and data deficiency, and actionability. This dissertation aims to advocate the human-understandable explainability of AI-enabled systems and introduces explainable models in different real-world application scenarios. We strive to answer research questions, including i) How can we achieve high-performance ML models addressing technical challenges, including data imbalance, data inadequacy, and model bias? ii) How do explanations vary across different application contexts? iii) What underlying facts and rationale should be considered when explaining prediction for a given context? and iv) How could we achieve actionable explanations? We adopted an exploratory, experimental approach to answering these questions by conducting a wide range of experiments, introducing explainability techniques, and demonstrating explanations in three application areas: smart home, business, and natural language processing (NLP). After carefully selecting application scenarios considering the mentioned questions, this thesis proposed multiple high-performance ML models for energy demand forecasting, occupants' thermal comfort preference modeling, product backorder prediction, multi-class patent classification, and fake review identification. Then, it introduced explainability to provide comprehensible, actionable explanations so that users and stakeholders could understand the predictions and take necessary action accordingly. The results from a wide range of experiments demonstrated high performance compared to state-of-the-art methods. They provided explanations that capture relevant facts and rationale to make users understand the proposed ML models' predictions and overall priorities. The technical and empirical evaluation of the generated explanations for explainable AI-enabled systems highlighted what information needs to be considered and how they should be represented in explanations. The broad contribution of this thesis is three-fold: i) We achieved high-performance ML models in different application areas addressing the challenges, including data inadequacy and extreme imbalance; ii) With a wide range of experiments, this thesis gives a holistic conclusion on what facts and rationale should be employed in generating explanations for a given application context; iii) Lastly, this dissertation highlighted how we can achieve actionable explanations so that users can take necessary actions to earn more system efficiency in the given application context.Source Type: