RDFScape: Semantic Web meets Systems Biology

<p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.</p> <p>Results</p> <p>RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.</p> <p>Conclusions</p> <p>The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.</p

Examining the psychological wellbeing of refugee children and the role of friendship and bullying

BACKGROUND: Refugee children might have experienced violent and traumatic events before settling into a new country. In the United Kingdom, the number of refugee children is increasing; however, little is known about their psycho-social and physical well-being. AIM: This study aims to investigate the psychological well-being and behaviour of refugee children compared to British-born children on a number of psychological, social, behavioural, and health-related issues and to investigate the role of friendship as a protective factor. SAMPLES: This study utilized a sample of 149 refugee children recruited from two charities, 79 of which are children aged 6-10 years and 70 older refugee children aged 11-16 years. The study also included 120 non-refugee children recruited from primary schools aged 6-10 years. METHODS: This is a cross-sectional study that investigates the psycho-social well-being of refugee children compared to non-refugee British-born children. The study explored symptoms of posttraumatic stress disorder, emotional and behavioural problems (Strengths and Difficulties Questionnaire), self-esteem, friendships and popularity, bullying and victimization, physical health, and psychosomatic problems. RESULTS: Young refugee children reported more peer problems, functional impairment, physical health, and psychosomatic problems compared to the control children and older refugee children groups. On the other hand, older refugee children had lower self-esteem (academic and social self-peers) compared to the younger refugee children group. The differences between the groups were explained by friendship quality, number of friends, peer bullying/victimization, or sibling bullying/victimization except for physical health and psychosomatic problems. CONCLUSIONS: While refugee children were found to be at risk on various levels, the findings also point to the fact that social relationships including friendship quality and number of friends played an essential protective role. Conversely, bullying was a risk factor that explained many of the refugees' problems. These findings pave the way for future research to further probe into the well-being of refugee children in the United Kingdom while also targeting relevant intervention schemes specifically tailored to address their needs

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results