14 research outputs found

    S.K.Warfield, “Highly Accurate Segmentation of Brain Tissue and Subcortical Gray Matter from Newborn

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    Abstract. The segmentation of newborn brain MRI is important for assessing and directing treatment options for premature infants at risk for developmental disorders, abnormalities, or even death. Segmentation of infant brain MRI is particularly challenging when compared with the segmentation of images acquired from older children and adults. We sought to develop a fully automated segmentation strategy and present here a Bayesian approach utilizing an atlas of priors derived from previous segmentations and a new scheme for automatically selecting and iteratively refining classifier training data using the STAPLE algorithm. Results have been validated by comparison to hand-drawn segmentations

    Sense of Community: Issues and Considerations From a Cross-cultural Perspective

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    Behaviour settings such as work, family, church and community are primary settings in which we participate, they provide us with meaningful roles, relationships, and social identities. In fact, these are settings that provide us with a sense of community (SOC). SOC has been heralded as the guiding value for community research and action. It reflects the integration of people into networks and structures that provide feelings of belonging, identification and meaning. The concept has received much attention since the introduction of McMillan and Chavis' initial formulation. It is argued that research into SOC has been hampered by relying on the Sense of Community Index at the expense of the SOC model. Insights are drawn from cross-cultural psychology and research to highlight conceptual issues and to encourage exploration and the utilisation of alternative modes of investigation. Contextualist approaches including substantive theorising and narrative psychology, which have their roots in pragmatism, are promoted as frameworks for bringing community and SOC into focus as central to social and community development

    Comprehensive variation discovery in single human genomes

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    Complete knowledge of the genetic variation in individual human genomes is a crucial foundation for understanding the etiology of disease. Genetic variation is typically characterized by sequencing individual genomes and comparing reads to a reference. Existing methods do an excellent job of detecting variants in approximately 90% of the human genome, however calling variants in the remaining 10% of the genome (largely low-complexity sequence and segmental duplications) is challenging. To improve variant calling, we developed a new algorithm, DISCOVAR, and examined its performance on improved, low-cost sequence data. Using a newly created reference set of variants from finished sequence of 103 randomly chosen Fosmids, we find that some standard variant call sets miss up to 25% of variants. We show that the combination of new methods and improved data increases sensitivity several-fold, with the greatest impact in challenging regions of the human genome
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