21,318 research outputs found

    Infectious Disease Ontology

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    Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

    Identification of molecular markers of delayed graft function based on the regulation of biological ageing

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    Introduction: Delayed graft function is a prevalent clinical problem in renal transplantation for which there is no objective system to predict occurrence in advance. It can result in a significant increase in the necessity for hospitalisation post-transplant and is a significant risk factor for other post-transplant complications. Methodology: The importance of microRNAs (miRNAs), a specific subclass of small RNA, have been clearly demonstrated to influence many pathways in health and disease. To investigate the influence of miRNAs on renal allograft performance post-transplant, the expression of a panel of miRNAs in pre-transplant renal biopsies was measured using qPCR. Expression was then related to clinical parameters and outcomes in two independent renal transplant cohorts. Results: Here we demonstrate, in two independent cohorts of pre-implantation human renal allograft biopsies, that a novel pre-transplant renal performance scoring system (GRPSS), can determine the occurrence of DGF with a high sensitivity (>90%) and specificity (>60%) for donor allografts pre-transplant, using just three senescence associated microRNAs combined with donor age and type of organ donation. Conclusion: These results demonstrate a relationship between pre-transplant microRNA expression levels, cellular biological ageing pathways and clinical outcomes for renal transplantation. They provide for a simple, rapid quantitative molecular pre-transplant assay to determine post-transplant allograft function and scope for future intervention. Furthermore, these results demonstrate the involvement of senescence pathways in ischaemic injury during the organ transplantation process and an indication of accelerated bio-ageing as a consequence of both warm and cold ischaemia

    Cracking the Code on Stem: A People Strategy for Nevada\u27s Economy

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    Nevada has in place a plausible economic diversification strategy—and it’s beginning to work. Now, the state and its regions need to craft a people strategy. Specifically, the state needs to boost the number of Nevadans who possess at least some postsecondary training in the fields of science, technology, engineering, or math—the so-called “STEM” disciplines (to which some leaders add arts and design to make it “STEAM”). The moment is urgent—and only heightened by the projected worker needs of Tesla Motors’ planned “gigafactory” for lithium-ion batteries in Storey County. Even before the recent Tesla commitment, a number of the more high-tech industry sectors targeted by the state’s new economic diversification strategy had begun to deliver significant growth. Most notable in fast-growing sectors like Business IT Ecosystems (as defined by the Governor’s Office for Economic Development) and large sectors like Health and Medical Services, this growth has begun to increase the demand in Nevada for workers with at least a modicum of postsecondary training in one or more STE M discipline. However, there is a problem. Even though many available opportunities require no more than the right community college certificate, insufficient numbers of Nevadans have pursued even a little STEM training. As a result, too few Nevadans are ready to participate in the state’s emerging STEM economy. The upshot: Without concerted action to prepare more Nevadans for jobs in STEM-intensive fields, skills shortages could limit growth in the state’s most promising target industries and Nevadans could miss out on employment that offers superior paths to opportunity and advancement. Which is the challenge this report addresses: Aimed at focusing the state at a critical moment, this analysis speaks to Nevada’s STEM challenge by providing a new assessment of Nevada’s STEM economy and labor market as well as a review of actions that leaders throughout the state—whether in the public, private, civic, or philanthropic sectors—can take to develop a workforce capable of supporting continued growth through economic diversification

    Engineering simulations for cancer systems biology

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    Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

    The development of non-coding RNA ontology

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    Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data

    The European Institute for Innovation through Health Data

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    The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale

    Drug-therapy networks and the predictions of novel drug targets

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    Recently, a number of drug-therapy, disease, drug, and drug-target networks have been introduced. Here we suggest novel methods for network-based prediction of novel drug targets and for improvement of drug efficiency by analysing the effects of drugs on the robustness of cellular networks.Comment: This is an extended version of the Journal of Biology paper containing 2 Figures, 1 Table and 44 reference

    2011 Strategic roadmap for Australian research infrastructure

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    The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

    A multi-faceted approach to optimising a complex unplanned healthcare system

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    Unscheduled and urgent health care represents the largest area of activity and cost for the UK’s National Health Service (NHS). Like typical complex systems unplanned care has the features of interdependence and having structures at different scales which requires modelling at different levels. The aim of this paper is to discuss the development of a multifaceted approach to study and optimise this complex system. We aim to integrate four different methodologies to gain better understanding of the nature of the system and to develop ways to enhance its performance. These methodologies are: (a) Lean/ Flow theory to look at the process and patients and other flows; (b) Simulation/ System Dynamics to undertake analytical analysis and multi-level modelling; (c) stakeholder consultation and use of system thinking to analyse the system and identify options, barriers and good practice; and (d) visual analytic modelling to facilitate effective decision making in this complex environment. Of particular concern are the boundary issues i.e. how changes in unplanned care will impact on the adjacent facilities and ultimately on the whole Healthcare system
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