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Background: Autism Spectrum Disorder (ASD) is characterized by impairments in social communication, limited repetitive behaviors, impaired language development, and interest or activity patterns, which include a group complex neurodevelopmental syndrome with diverse phenotypes that reveal considerable etiological and clinical heterogeneity and are also considered one of the most heritable disorders (over 90%). Genetic, epigenetic, and environmental factors play a role in the development of ASD.
Aim: This study was designed to investigate the extent of DNA damage in parents of autistic children by treating peripheral blood mononuclear cells (PBMCs) with bleomycin and hydrogen peroxide (H2O2).
Methods: Peripheral blood mononuclear cells (PBMCs) were isolated by the Ficoll method and treated with a specific concentration of bleomycin and H2O2 for 30 min and 5 min, respectively. Then, the degree of DNA damage was analyzed by the alkaline comet assay or single cell gel electrophoresis (SCGE), an effective way to measure DNA fragmentation in eukaryotic cells.
Results: Our findings revealed that there is a significant difference in the increase of DNA damage in parents with affected children compared to the control group, which can indicate the inability of the DNA molecule repair system. Furthermore, our study showed a significant association between fathers’ occupational difficulties (exposed to the influence of environmental factors), as well as family marriage, and suffering from ASD in offspring.
Conclusion: Our results suggested that the influence of environmental factors on parents of autistic children may affect the development of autistic disorder in their offspring. Subsequently, based on our results, investigating the effect of environmental factors on the amount of DNA damage in parents with affected children requires more studies.
Background: In Germany, hospice and palliative care is well covered through inpatient, outpatient, and home-based care services. It is unknown if, and to what extent, there is a need for additional day care services to meet the specific needs of patients and caregivers.
Methods: Two day hospices and two palliative day care clinics were selected. In the first step, two managers from each facility (n = 8) were interviewed by telephone, using a semi-structured interview guide. In the second step, four focus groups were conducted, each with three to seven representatives of hospice and palliative care from the facilities’ hospice and palliative care networks. Interviews and focus groups were audio recorded, transcribed verbatim and analyzed using qualitative content analysis.
Results: The interviewed experts perceived day care services as providing additional patient and caregiver benefits. Specifically, the services were perceived to meet patient needs for social interaction and bundled treatments, especially for patients who did not fit into inpatient settings (due to, e.g., their young age or a lack of desire for inpatient admission). The services were also perceived to meet caregiver needs for support, providing short-term relief for the home care situation.
Conclusions: The results suggest that inpatient, outpatient, and home-based hospice and palliative care services do not meet the palliative care needs of all patients. Although the population that is most likely to benefit from day care services is assumed to be relatively small, such services may meet the needs of certain patient groups more effectively than other forms of care.
The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and financial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions. In the context of pricing cyber insurance policies, issues of interdependence arise for both systematic and systemic cyber risks; classical actuarial valuation needs to be extended to include more complex methods, such as concepts of risk-neutral valuation and (set-valued) monetary risk measures.
Social comparison theories suggest that ingroups are strengthened whenever important outgroups are weakened (e.g., by losing status or power). It follows that ingroups have little reason to help outgroups facing an existential threat. We challenge this notion by showing that ingroups can also be weakened when relevant comparison outgroups are weakened, which can motivate ingroups to strategically offer help to ensure the outgroups' survival as a highly relevant comparison target. In three preregistered studies, we showed that an existential threat to an outgroup with high (vs. low) identity relevance affected strategic outgroup helping via two opposing mechanisms. The potential demise of a highly relevant outgroup increased participants’ perceptions of ingroup identity threat, which was positively related to helping. At the same time, the outgroup’s misery evoked schadenfreude, which was negatively related to helping. Our research exemplifies a group's secret desire for strong outgroups by underlining their importance for identity formation.
Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge.
Mixed-integer NMPC for real-time supervisory energy management control in residential buildings
(2023)
In recent years, building energy supply and distribution systems have become more complex, with an increasing number of energy generators, stores, flows, and possible combinations of operating modes. This poses challenges for supervisory control, especially when balancing the conflicting goals of maximizing comfort while minimizing costs and emissions to contribute to global climate protection objectives. Mixed-integer nonlinear model predictive control is a promising approach for intelligent real-time control that is able to properly address the specific characteristics and restrictions of building energy systems. We present a strategy that utilizes a decomposition approach, combining partial outer convexification with the Switch-Cost Aware Rounding procedure to handle switching behavior and operating time constraints of building components in real-time. The efficacy is demonstrated through practical applications in a single-family home with a combined heat and power unit and in a multi-family apartment complex with 18 residential units. Simulation studies show high correspondence to globally optimal solutions with significant cost savings potential of around 19%.
Catalogs of competency-based learning objectives (CLO) were introduced and promoted as a prerequisite for high-quality, systematic curriculum development. While this is common in medicine, the consistent use of CLO is not yet well established in epidemiology, biometry, medical informatics, biomedical informatics, and nursing informatics especially in Germany. This paper aims to identify underlying obstacles and give recommendations in order to promote the dissemination of CLO for curricular development in health data and information sciences. To determine these obstacles and recommendations a public online expert workshop was organized. This paper summarizes the findings.
Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.
Purpose: Radiology reports mostly contain free-text, which makes it challenging to obtain structured data. Natural language processing (NLP) techniques transform free-text reports into machine-readable document vectors that are important for creating reliable, scalable methods for data analysis. The aim of this study is to classify unstructured radiograph reports according to fractures of the distal fibula and to find the best text mining method.
Materials & Methods: We established a novel German language report dataset: a designated search engine was used to identify radiographs of the ankle and the reports were manually labeled according to fractures of the distal fibula. This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). The extracted document vectors were used to train neural networks (NN), support vector machines (SVM), and logistic regression (LR) to recognize distal fibula fractures. The results were compared via cross-tabulations of the accuracy (acc) and area under the curve (AUC).
Results: In total, 3268 radiograph reports were included, of which 1076 described a fracture of the distal fibula. Comparison of the text representation methods showed that BOW achieved the best results (AUC = 0.98; acc = 0.97), followed by TF-IDF (AUC = 0.97; acc = 0.96), NMF (AUC = 0.93; acc = 0.92), PCA (AUC = 0.92; acc = 0.9), LDA (AUC = 0.91; acc = 0.89) and doc2vec (AUC = 0.9; acc = 0.88). When comparing the different classifiers, NN (AUC = 0,91) proved to be superior to SVM (AUC = 0,87) and LR (AUC = 0,85).
Conclusion: An automated classification of unstructured reports of radiographs of the ankle can reliably detect findings of fractures of the distal fibula. A particularly suitable feature extraction method is the BOW model.
Key Points:
- The aim was to classify unstructured radiograph reports according to distal fibula fractures.
- Our automated classification system can reliably detect fractures of the distal fibula.
- A particularly suitable feature extraction method is the BOW model.