1,138 research outputs found

    Advances in understanding gray matter pathology in multiple sclerosis: Are we ready to redefine disease pathogenesis?

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    The purpose of this special issue in BMC Neurology is to summarize advances in our understanding of the pathological, immunological, imaging and clinical concepts of gray matter (GM) pathology in patients with multiple sclerosis (MS). Review articles by Lucchinetti and Popescu, Walker and colleagues, Hulst and colleagues and Horakova and colleagues summarize important recent advances in understanding GM damage and its implications to MS pathogenesis. They also raise a number of important new questions and outline comprehensive approaches to addressing those questions in years to come. In the last decade, the use of immunohistochemistry staining methods and more advanced imaging techniques to detect GM lesions, like double inversion recovery, contributed to a surge of studies related to cortical and subcortical GM pathology in MS. It is becoming more apparent from recent biopsy studies that subpial cortical lesions in early MS are highly inflammatory. The mechanisms responsible for triggering meningeal inflammation in MS patients are not yet elucidated, and they should be further investigated in relation to their role in initiating and perpetuating the disease process. Determining the role of antigens, environmental and genetic factors in the pathogenesis of GM involvement in MS is critical. The early involvement of cortical and subcortical GM damage in MS is very intriguing and needs to be further studied. As established in numerous cross-sectional and longitudinal studies, GM damage is a better predictor of physical disability and cognitive impairment than WM damage. Monitoring the evolution of GM damage is becoming an important marker in predicting future disease course and response to therapy in MS patients

    Emotional Sentence Annotation Helps Predict Fiction Genre

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    Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekmanā€™s model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear
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