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Year of publication
- 2024 (91) (remove)
Keywords
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.
Die Klimakrise stellt eine Bedrohung für das menschliche Wohlergehen und die planetare Gesundheit dar, welcher u.a. durch Lebens- und Verhaltensstiländerungen begegnet werden kann. Eine dieser individuellen und gesamtgesellschaftlichen Veränderungen könnte eine geschlechtergerechte Aufteilung der Care-Arbeit sein, weshalb es notwendig ist, an vorderster Stelle die dahinterliegenden Mechanismen und Zusammenhänge zu verstehen. Aus diesem Grund beschäftigt sich die vorliegende Bachelorarbeit mit der Frage „Wie kann geschlechtergerechte Care-Arbeit ausgestaltet werden, um einen Beitrag zum Klimaschutz zu leisten?“. Um die Forschungsfrage zu beantworten, wird eine systematische Literaturrecherche durchgeführt, welche durch den theoretischen Rahmen analysiert wird. Dieser setzt sich aus der Externalisierungsgesellschaft von Lessenich, dem Gerechtigkeitsansatz von Fraser und dem soziologischen Geschlecht von Pimminger zusammen. Die Analyse ergibt, dass sowohl die Ursachen, Auswirkungen und Lösungsansätze zur Klimakrise abhängig vom Geschlecht sind und ein Eco Gender Gap existiert.Des Weiteren ist die Aufteilung der Care-Arbeit durch das soziologische Geschlecht geprägt und weist sowohl im lokalen und globalen Kontext Parallelen zur Klimakrise auf. Lösungsansätze für beide Herausforderungen finden sich im Ökofeminismus und einer Verkürzung der Arbeitszeit wieder. In zukünftigen Wirtschaftsmodellen sollte die Care-Arbeit daher mehr Beachtung finden, da sie die unsichtbare Grundlage der derzeitigen Wirtschaftsweise ist, die zur Klimakrise geführt hat.
A firm link between endoplasmic reticulum (ER) stress and tumors has been wildly reported. Endoplasmic reticulum oxidoreductase 1 alpha (ERO1α), an ER-resident thiol oxidoreductase, is confirmed to be highly upregulated in various cancer types and associated with a significantly worse prognosis. Of importance, under ER stress, the functional interplay of ERO1α/PDI axis plays a pivotal role to orchestrate proper protein folding and other key processes. Multiple lines of evidence propose ERO1α as an attractive potential target for cancer treatment. However, the unavailability of specific inhibitor for ERO1α, its molecular inter-relatedness with closely related paralog ERO1β and the tightly regulated processes with other members of flavoenzyme family of enzymes, raises several concerns about its clinical translation. Herein, we have provided a detailed description of ERO1α in human cancers and its vulnerability towards the aforementioned concerns. Besides, we have discussed a few key considerations that may improve our understanding about ERO1α in tumors.
Blickpunkt
(2024)
In recent years, eXtended Reality (XR) technology like Augmented Reality and Virtual Reality became both technically feasible as well as affordable which lead to a drastic demand of professionally designed and developed applications. However, this demand combined with a rapid pace of innovation revealed a lack of design tool support for professional interaction designers as well as a knowledge gap regarding their approaches and needs. To address this gap, this thesis engages with the work of professional XR interaction designers in a qualitative research into XR interaction design approach. Therefore, this thesis applies two complementary lenses stemming from scientific design and social practice theory discourses to observe, describe, analyze, and understand professional XR interaction designers' challenges and approaches with a focus on application prototyping.
Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.
The human gut microbiota harbors untapped potential for biotechnological applications. Within the phylum of Bacteroidota, Phocaeicola vulgatus stands out as a promising candidate for sustainable production of key platform chemicals like succinate. However, genetic engineering of Phocaeicola sp. remains challenging due to its intricate molecular landscape. This study lays the groundwork for manipulating the central carbon pathways in Phocaeicola vulgatus, offering insights into overcoming genetic hurdles for increased succinate yields.
Angesichts der raschen Entwicklungen und der Besonderheiten von Softwaresystemen, welche Künstliche Intelligenz (KI) nutzen, ist ein angepasstes Requirements Engineering (RE) erforderlich. Die spezifischen Anforderungen von KI-Projekten müssen dabei erkannt und angegangen werden. Hierfür wird eine systematische Überprufung bestehender Herausforderungen des RE in KI-Projekten durchgeführt. Darauf aufbauend werden neue RE-Ansätze und Empfehlungen präsentiert, die auf die Datensicht von KI-Projekten abzielen. Mithilfe der Analyse bestehender Lösungsansatze, Methoden, Frameworks und Tools soll aufgezeigt werden, inwiefern die Herausforderungen im RE bewältigt werden können. Noch bestehende Lücken im Forschungsstand werden identifiziert und aufgezeigt.
This study addresses the common occurrence of cell-to-cell variations arising from manufacturing tolerances and their implications during battery production. The focus is on assessing the impact of these inherent differences in cells and exploring diverse cell and module connection methods on battery pack performance and their subsequent influence on the driving range of electric vehicles (EVs). The analysis spans three battery pack sizes, encompassing various constant discharge rates and nine distinct drive cycles representative of driving behaviours across different regions of India. Two interconnection topologies, categorised as “string” and “cross”, are examined. The findings reveal that cross-connected packs exhibit reduced energy output compared to string-connected configurations, which is reflected in the driving range outcomes observed during drive cycle simulations. Additionally, the study investigates the effects of standard deviation in cell parameters, concluding that an increased standard deviation (SD) leads to decreased energy output from the packs. Notably, string-connected packs demonstrate superior performance in terms of extractable energy under such conditions.
Design and characterization of geopolymer foams reinforced with Miscanthus x giganteus fibers
(2024)
This paper presents the effects of different amounts of fibers and foaming agent, as well as different fiber sizes, on the mechanical and thermal properties of fly ash-based geopolymer foams reinforced with Miscanthus x giganteus fibers. The mechanical properties of the geopolymer foams were measured through compressive strength, and their thermal properties were characterized by thermal conductivity and X-ray micro-computed tomography. Furthermore, design of experiment (DoE) were used to optimize the thermal conductivity and compressive strength of Miscanthus x giganteus reinforced geopolymer foams. In addition, the microstructure was studied using X-ray diffraction (XRD), Field emission scanning electron microscopy (SEM) and Fourier-Transform Infrared Spectroscopy (FTIR). Mixtures with a low thermal conductivity of 0.056 W (m K)−1 and a porosity of 79 vol% achieved a compressive strength of only 0.02 MPa. In comparison, mixtures with a thermal conductivity of 0.087 W (m K)−1 and a porosity of 58 vol% achieved a compressive strength of 0.45 MPa.
Die Wirtschaft
(2024)
Introduction: A multitude of findings from cell cultures and animal studies are available to support the anti-cancer properties of cannabidiol (CBD). Since CBD acts on multiple molecular targets, its clinical adaptation, especially in combination with cancer immunotherapy regimen remains a serious concern.
Methods: Considering this, we extensively studied the effect of CBD on the cytokine-induced killer (CIK) cell immunotherapy approach using multiple non-small cell lung cancer (NSCLC) cells harboring diverse genotypes.
Results: Our analysis showed that, a) The Transient Receptor Potential Cation Channel Subfamily V Member 2 (TRPV2) channel was intracellularly expressed both in NSCLC cells and CIK cells. b) A synergistic effect of CIK combined with CBD, resulted in a significant increase in tumor lysis and Interferon gamma (IFN-g) production. c) CBD had a preference to elevate the CD25+CD69+ population and the CD62L_CD45RA+terminal effector memory (EMRA) population in NKT-CIK cells, suggesting early-stage activation and effector memory differentiation in CD3+CD56+ CIK cells. Of interest, we observed that CBD enhanced the calcium influx, which was mediated by the TRPV2 channel and elevated phosphor-Extracellular signal-Regulated Kinase (p-ERK) expression directly in CIK cells, whereas ERK selective inhibitor FR180204 inhibited the increasing cytotoxic CIK ability induced by CBD. Further examinations revealed that CBD induced DNA double-strand breaks via upregulation of histone H2AX phosphorylation in NSCLC cells and the migration and invasion ability of NSCLC cells suppressed by CBD were rescued using the TRPV2 antagonist (Tranilast) in the absence of CIK cells. We further investigated the epigenetic effects of this synergy and found that adding CBD to CIK cells decreased the Long Interspersed Nuclear Element-1 (LINE-1) mRNA expression and the global DNA methylation level in NSCLC cells carrying KRAS mutation. We further investigated the epigenetic effects of this synergy and found that adding CBD to CIK cells decreased the Long Interspersed Nuclear Element-1 (LINE-1) mRNA expression and the global DNA methylation level in NSCLC cells carrying KRAS mutation.
Conclusions: Taken together, CBD holds a great potential for treating NSCLC with CIK cell immunotherapy. In addition, we utilized NSCLC with different driver mutations to investigate the efficacy of CBD. Our findings might provide evidence for CBD-personized treatment with NSCLC patients.
DT-13 attenuates inflammation by inhibiting NLRP3-inflammasome related genes in RAW264.7 macrophages
(2024)
Plant derived saponins or other glycosides are widely used for their anti-inflammatory, antioxidant, and anti-viral properties in therapeutic medicine. In this study, we focus on understanding the function of the less known steroidal saponin from the roots of Liriope muscari L. H. Bailey – saponin C (also known as DT-13) in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages in comparison to the well-known saponin ginsenoside Rk1 and anti-inflammatory drug dexamethasone. We proved that DT-13 reduces LPS-induced inflammation by inhibiting nitric oxide (NO) production, interleukin-6 (IL-6) release, cycloxygenase-2 (COX-2), tumour necrosis factor-alpha (TNF-α) gene expression, and nuclear factor kappa-B (NFκB) translocation into the nucleus. It also inhibits the inflammasome component NOD-like receptor family pyrin domain containing protein 3 (NLRP3) regulating the inflammasome activation. This was supported by the significant inhibition of caspase-1 and interleukin-1 beta (IL-1β) expression and release. This study demonstrates the anti-inflammatory effect of saponins on LPS-stimulated macrophages. For the first time, an in vitro study shows the attenuating effect of DT-13 on NLRP3-inflammasome activation. In comparison to the existing anti-inflammatory drug, dexamethasone, and triterpenoid saponin Rk1, DT-13 more efficiently inhibits inflammation in the applied cell culture model. Therefore, DT-13 may serve as a lead compound for the development of new more effective anti-inflammatory drugs with minimised side effects.
Dynamic Programming
(2024)
Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
Gegenwart aufnehmen
(2024)
Die moderne Arbeitswelt erfordert digitale Kompetenz, doch Hochschulen mangelt es an Angeboten zum digitalen Kompetenzaufbau Studierender. Peer-Angebote können ein sinnvoller Ansatz zur Förderung digitaler Kompetenz sein, allerdings fehlen empirische Belege für deren Wirksamkeit. Die Studie setzt hier an und evaluiert den digitalen Kompetenzerwerb von Teilnehmenden fachübergreifender Peer-Trainings auf Grundlage des DigComp Rahmenmodells. Die Ergebnisse zeigen, dass Trainings-Teilnehmende ihre digitale Kompetenz im Vergleich zur Kontrollgruppe signifikant stärker steigern konnten. Die Ausbildung zur bzw. zum Peer-Trainer:in sowie die Peer-Trainings wurden von allen Beteiligten sehr positiv bewertet.
Heuristic Methods
(2024)
In vision tasks, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, convolution requires multiple stacked layers and a hierarchical structure for large context. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to the non-causal two-dimensional image space. We scale the Hyena convolution kernels beyond the feature map size up to 191$\times$191 to maximize the ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5%, respectively, while outperforming other large-kernel networks. Combining HyenaPixel with attention further increases accuracy to 83.6%. We attribute the success of attention to the lack of spatial bias in later stages and support this finding with bidirectional Hyena.
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
(2024)
The indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected.
Interne Audits können mehr
(2024)
Dieser Beitrag zeigt, wie das Deutsche Zentrum für Luft- und Raumfahrt e. V. (DLR) Zufriedenheitsanalysen aus zwei Sichtweisen durchführt: Aus Sicht der Auditoren und aus Sicht der Managementbeauftragten der auditierten Institute und Einrichtungen. Die Ergebnisse fließen in die jährliche Auditprogrammplanung ein. Damit wird der Nutzen von internen Audits gesteigert.
Is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication
(2024)
The Information and Communication Technology (ICT) sector is a significant global industry, and addressing climate change is of critical importance. This paper aims to assess the resources utilized by the ICT sector, the associated negative environmental impacts, and potential mitigation measures. In order to understand these aspects, this study attempts to categorize the resources used by ICT, analyze the amount consumed and the resulting negative impacts, and determine what measures exist to mitigate them. An economic and empirical evaluation shows a negative trend in ICT’s resource consumption, mainly due to increased energy consumption and rising carbon emissions from devices such as smartphones and data centers. The investigated countermeasures focus on Green IT strategies that encompass energy efficiency, carbon awareness, and hardware efficiency principles as outlined by the Green Software Foundation. Special attention is given to reducing the environmental footprint of data center operations and smartphones. This paper concludes that Green IT strategies, although promising in theory, are often not implemented at an industry level.
Trade of wild-caught animals is illegal for many taxa and in many countries. Common regulatory procedures involve documentation and marking techniques. However, these procedures are subject to fraud and thus should be complemented by routine genetic testing in order to authenticate the captive-bred origin of animals intended for trade. A suitable class of genetic markers are SNPSTRs that combine a short tandem repeat (STR) and single nucleotide polymorphisms (SNPs) within one amplicon. This combined marker type can be used for genetic identification and for parentage analyses and in addition, provides insight into haplotype history. As a proof of principle, this study establishes a set of 20 SNPSTR markers for Athene noctua, one of the most trafficked owls in CITES Appendix II. These markers can be coamplified in a single multiplex reaction. Based on population data, the percentage of observed and expected heterozygosities of the markers ranged from 0.400 to 1.000 and 0.545 to 0.850, respectively. A combined probability of identity of 5.3*10-23 was achieved with the whole set, and combined parentage exclusion probabilities reached over 99.99%, even if the genotype of one parent was missing. A direct comparison of an owl family and an unrelated owl demonstrated the applicability of the SNPSTR set in parentage testing. The established SNPSTR set thus proved to be highly useful for identifying individuals and analysing parentage to determine wild or captive origin. We propose to implement SNPSTR-based routine certification in wildlife trade as a way to reveal animal laundering and misdeclaration of wild-caught animals.
Dieses Einführungspapier ist als Orientierungshilfe zum Thema Künstliche Intelligenz (KI) (engl. Artifical Intelligence, AI) im DaF/DaZ-Kontext gedacht. Ausgehend von häufig gestellten Fragen enthält es grundsätzliche Informationen zu technischen und historischen Hintergründen, didaktisch-methodische Reflexionsanregungen sowie praktische Ideen zum Einsatz von KI im DaF/DaZ-Kontext.
Der Klimawandel ist eine immer größer werdende Herausforderung von der besonders Kinder auf Grund ihrer Vulnerabilität betroffen sind. Ein Ansatz, um die Resilienz von Kindern zu stärken ist die Agency von Kindern im Kontext des Klimawandels zu stärken. Diese Arbeit analysiert, wie Maßnahmen zur Stärkung der Agency von Kindern in Form von Klimawandelprojekten wirksam gestaltet werden können. Dieses Vorhaben wird mittels einer qualitativen Inhaltsanalyse von bereits durchgeführten Klimaprojekten umgesetzt. Die Analyse zeigt, dass vor allem Maßnahmen, bei denen eine Aktionskomponente, neben der Wissensbildung und Stimmenschaffung, im Vordergrund stehen die Agency von Kindern stärken.
Küssen
(2024)
The lattice Boltzmann method (LBM) stands apart from conventional macroscopic approaches due to its low numerical dissipation and reduced computational cost, attributed to a simple streaming and local collision step. While this property makes the method particularly attractive for applications such as direct noise computation, it also renders the method highly susceptible to instabilities. A vast body of literature exists on stability-enhancing techniques, which can be categorized into selective filtering, regularized LBM, and multi-relaxation time (MRT) models. Although each technique bolsters stability by adding numerical dissipation, they act on different modes. Consequently, there is not a universal scheme optimally suited for a wide range of different flows. The reason for this lies in the static nature of these methods; they cannot adapt to local or global flow features. Still, adaptive filtering using a shear sensor constitutes an exception to this. For this reason, we developed a novel collision operator that uses space- and time-variant collision rates associated with the bulk viscosity. These rates are optimized by a physically informed neural net. In this study, the training data consists of a time series of different instances of a 2D barotropic vortex solution, obtained from a high-order Navier–Stokes solver that embodies desirable numerical features. For this specific text case our results demonstrate that the relaxation times adapt to the local flow and show a dependence on the velocity field. Furthermore, the novel collision operator demonstrates a better stability-to-precision ratio and outperforms conventional techniques that use an empirical constant for the bulk viscosity.
During robot-assisted therapy, a robot typically needs to be partially or fully controlled by therapists, for instance using a Wizard-of-Oz protocol; this makes therapeutic sessions tedious to conduct, as therapists cannot fully focus on the interaction with the person under therapy. In this work, we develop a learning-based behaviour model that can be used to increase the autonomy of a robot’s decision-making process. We investigate reinforcement learning as a model training technique and compare different reward functions that consider a user’s engagement and activity performance. We also analyse various strategies that aim to make the learning process more tractable, namely i) behaviour model training with a learned user model, ii) policy transfer between user groups, and iii) policy learning from expert feedback. We demonstrate that policy transfer can significantly speed up the policy learning process, although the reward function has an important effect on the actions that a robot can choose. Although the main focus of this paper is the personalisation pipeline itself, we further evaluate the learned behaviour models in a small-scale real-world feasibility study in which six users participated in a sequence learning game with an assistive robot. The results of this study seem to suggest that learning from guidance may result in the most adequate policies in terms of increasing the engagement and game performance of users, but a large-scale user study is needed to verify the validity of that observation.
Linear Optimization
(2024)
Statins are a group of hypolipidemic drugs that act by competitive inhibition of the HMGR enzyme. They are generally considered effective and safe but claimed to have side effects on skeletal muscles. A molecular side effect of statins is the block of terpene biosynthesis and hence of dolichol involved in N-glycosylation and O-mannosylation of proteins. Defects in O-mannosylation lead to α-dystroglycan (α-DG) hypoglycosylation and a series of hereditary dystroglycanopathies. The current project aims to get insight into molecular pathomechanisms induced by statins in mammalian muscle cells and to unravel a potential link between these effects and statin-induced decreases of α-DG O-mannosylation. The study was based on mass spectrometric proteomics supported by western blot analysis to reveal Rosuvastatin effects on cellular pathways under high (micromolar) or low (nanomolar) conditions. Differential proteomics revealed higher statin effects on muscle cell function in micromolar than nanomolar concentration, which is reached in the patient’s plasma. We demonstrated distinct and partially overlapping patterns of fold-changed proteins under high and low statin conditions. Gene ontology term enrichment (GOTE) analyses of fold-changed proteins revealed cellular pathways related to muscle function and development are affected, even under low statin conditions, typically reached in the patient’s plasma during prophylactic medication.
The digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.
Process-induced changes in thermo-mechanical viscoelastic properties and the corresponding morphology of biodegradable polybutylene adipate terephthalate (PBAT) and polylactic acid (PLA) blown film blends modified with four multifunctional chain-extending cross-linkers (CECL) were investigated. The introduction of CECL modified the properties of the reference PBAT/PLA blend significantly. The thermal analysis showed that the chemical reactions were incomplete after compounding, and that film blowing extended them. SEM investigations of the fracture surfaces of blown extrusion films reveal the significant effect of CECL on the morphology formed during the processing. The anisotropic morphology introduced during film blowing proved to affect the degradation processes as well. Furthermore, the reactions of CECL with PBAT/PLA induced by the processing depend on the deformation directions. The blow-up ratio parameter was altered to investigate further process-induced changes proving synergy with mechanical and morphological features. Using blown film extrusion, the elongational behavior represents a very important characteristic. However, its evaluation may be quite often problematic, but with the SER Universal Testing Platform it was possible to determine changes in the duration of time intervals corresponding to the rupture of elongated samples.
Medien-Literatur(en)
(2024)