204 research outputs found

    The potential of text mining in data integration and network biology for plant research : a case study on Arabidopsis

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    Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies

    ‘Being different’: realities of life experiences as constructed by persons with albinism in Nigeria

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    In Nigeria, persons with albinism (PWA) continue to face a higher burden of health and social challenges in the society compared with the general population. PWA experience multi-faceted social injustices such as stigma, discrimination and exclusion from education, employment and social participation. These injustices are driven by the Nigerian society because of sociocultural perceptions and stereotypes associated with albinism which can be attributable to the lack of adequate understanding of the condition. This research aimed to understand how the realities of being a PWA in Nigeria could be conceptualised based on their life experiences to develop a substantive theory of their social wellbeing status. By adopting constructivist grounded theory methodology, forty-two in-depth interviews were conducted amongst eleven PWA. Analysis identified three categories each of which embodies the multiple realities of disadvantages and exclusion experienced within the home, schooling, working and social environments at different stages of life. The concept of ‘Being different’ emerged from these categories to offer a theoretical explanation of what it means to be a PWA in Nigeria. The realities of ‘being different’ constitute processual social injustices for PWA because of how the Nigerian society is socio-culturally and institutionally configured to magnify the limitations of albinism above the rights and social liberties of the individual. This research identified albinism as a disability and concluded that PWA are owed a moral and ethical obligation by the Nigerian society for them to be able to access the liberties and support necessary to secure their health and social wellbeing. The sustainable fulfilment of this moral and ethical obligation necessitates an inter-institutional collaboration and vigilance that should address the layers of injustices meted to PWA. This study adds an original contribution to knowledge by offering a theoretical concept to qualify the social status of PWA in Nigeria, and thus, could be useful to inform appropriate health and social care interventions

    Planning Framework for Human Resources for Health for Maternal and Newborn Care

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    With approximately 1.3 billion births estimated to be taking place globally over a decade up to 2020, the demand for maternal and newborn health (MNH) workforce continues to be a key aspect of public health service delivery. Human resources for health (HRH) projection models can contribute the quantitative evidence required for policy design for education commissioning and distribution of skilled personnel. To date, HRH supply and requirement projection models have not been developed specifically for system-based subnational planning within maternal and newborn care. In addition, current methodologies are often limited to national level and have a professional silo approach to considering the workforce, with informing policy and planning as a secondary consideration. The aim of this thesis was to fill the gap through improved understanding of the role of HRH projections for policy and development of a new model for projecting the future MNH clinical teams with spatial equity and system perspective at the centre of the planning framework. The specific objectives were to ‱ review the literature for strengths and limitations for current HRH planning and outline the main components of an evidence-informed MNH-HRH planning framework with relevance to subnational contexts and MNH systems ‱ translate the main components into a working prototype as a spreadsheet-based model to estimate and MNH-HRH requirements and supply for each occupation ‱ apply the MNH-HRH planning model in three countries from low to high income contexts and critique the implications for future research and development in this field. Following the construction of a new planning framework, a working prototype called the ‘MNH.HRH Planning App’ was developed. The spreadsheet-based model was applied using secondary data sources to England, Bangladesh, and Ethiopia which have varied health systems, levels of spatial disaggregation and HRH structures for MNH care. The thesis concludes by highlighting the implications of the new planning framework for the future development of a web-based MNH.HRH Planning App, potential for engaging policy-makers for evidence-informed planning and contributes to the wider discourse on the use of quantitative projection models for planning the future human resources for healthcare

    Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

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    Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

    The Effectiveness of Maintenance and Its Impact on Capital Expenditures

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    2019 November 15 – Board of Trustees Agenda and Minutes

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    Improving Roadway Diagnostics Using Network-Level Data

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    Large amounts of empirical data on transportation infrastructure assets continue to be collected at the network-level due to advancements in technology and in response to data-driven processes. These vast amounts of new data, combined with existing data, leave practitioners searching for ways to transform disparate datasets into effective information. This study expands the use of these data into new areas of application, namely roadway and roadside diagnostics. Providing diagnostics informs practitioners not only about the needs of an infrastructure project, but the causes of those needs. Using network-level data to diagnose fundamental causes improves the engineering aspect of early project development decision making. Mobile light detection and ranging (LiDAR) technology was used to create a new dataset by evaluating road and roadside surface geometry and drainage conditions. Temporal patterns in pavement condition data were mined to inform engineers about the health of the pavement. The geometric and drainage information was combined with information gleaned from mining the pavement condition data and publicly available soils data to provide improved diagnostic analysis of roadway projects. The study capitalizes on graph theory to convert network-level data into diagnostic information. The primary contribution of this study lies in developing new analytical methods that use network-level data to provide comprehensive diagnoses of roadway infrastructure projects and systems. Using these diagnostics early in project development has the potential to reduce late project problems that cost both time and money
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