1,555 research outputs found

    ELVIS: Entertainment-led video summaries

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    © ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Multimedia Computing, Communications, and Applications, 6(3): Article no. 17 (2010) http://doi.acm.org/10.1145/1823746.1823751Video summaries present the user with a condensed and succinct representation of the content of a video stream. Usually this is achieved by attaching degrees of importance to low-level image, audio and text features. However, video content elicits strong and measurable physiological responses in the user, which are potentially rich indicators of what video content is memorable to or emotionally engaging for an individual user. This article proposes a technique that exploits such physiological responses to a given video stream by a given user to produce Entertainment-Led VIdeo Summaries (ELVIS). ELVIS is made up of five analysis phases which correspond to the analyses of five physiological response measures: electro-dermal response (EDR), heart rate (HR), blood volume pulse (BVP), respiration rate (RR), and respiration amplitude (RA). Through these analyses, the temporal locations of the most entertaining video subsegments, as they occur within the video stream as a whole, are automatically identified. The effectiveness of the ELVIS technique is verified through a statistical analysis of data collected during a set of user trials. Our results show that ELVIS is more consistent than RANDOM, EDR, HR, BVP, RR and RA selections in identifying the most entertaining video subsegments for content in the comedy, horror/comedy, and horror genres. Subjective user reports also reveal that ELVIS video summaries are comparatively easy to understand, enjoyable, and informative

    Chronic HCV Infection Affects the NK Cell Phenotype in the Blood More than in the Liver

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    Although epidemiological and functional studies have implicated NK cells in protection and early clearance of HCV, the mechanism by which they may contribute to viral control is poorly understood, particularly at the site of infection, the liver. We hypothesized that a unique immunophenotypic/functional NK cell signature exists in the liver that may provide insights into the contribution of NK cells to viral control. Intrahepatic and blood NK cells were profiled from chronically infected HCV-positive and HCV-negative individuals. Baseline expression of activating and inhibitory receptors was assessed, as well as functional responses following stimulation through classic NK cell pathways. Independent of HCV infection, the liver was enriched for the immunoregulatory CD56bright NK cell population, which produced less IFNγ and CD107a but comparable levels of MIP1β, and was immunophenotypically distinct from their blood counterparts. This profile was mostly unaltered in chronic HCV infection, though different expression levels of NKp46 and NKG2D were associated with different grades of fibrosis. In contrast to the liver, chronic HCV infection associated with an enrichment of CD161lowperforinhigh NK cells in the blood correlated with increased AST and 2B4 expression. However, the association of relatively discrete changes in the NK cell phenotype in the liver with the fibrosis stage nevertheless suggests an important role for the NK response. Overall these data suggest that tissue localization has a more pervasive effect on NK cells than the presence of chronic viral infection, during which these cells might be mostly attuned to limiting immunopathology. It will be important to characterize NK cells during early HCV infection, when they should have a critical role in limiting infection

    Gravitating σ\sigma Model Solitons

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    We study axially symmetric static solitons of O(3) nonlinear σ\sigma model coupled to (2+1)-dimensional anti-de Sitter gravity. The obtained solutions are not self-dual under static metric. The usual regular topological lump solution cannot form a black hole even though the scale of symmetry breaking is increased. There exist nontopological solitons of half integral winding in a given model, and the corresponding spacetimes involve charged Ban~\tilde nados-Teitelboim-Zanelli black holes without non-Abelian scalar hair.Comment: 35 pages, RevTe

    miR-132/212 knockout mice reveal roles for these miRNAs in regulating cortical synaptic transmission and plasticity

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    miR-132 and miR-212 are two closely related miRNAs encoded in the same intron of a small non-coding gene, which have been suggested to play roles in both immune and neuronal function. We describe here the generation and initial characterisation of a miR-132/212 double knockout mouse. These mice were viable and fertile with no overt adverse phenotype. Analysis of innate immune responses, including TLR-induced cytokine production and IFNβ induction in response to viral infection of primary fibroblasts did not reveal any phenotype in the knockouts. In contrast, the loss of miR-132 and miR-212, while not overtly affecting neuronal morphology, did affect synaptic function. In both hippocampal and neocortical slices miR-132/212 knockout reduced basal synaptic transmission, without affecting paired-pulse facilitation. Hippocampal long-term potentiation (LTP) induced by tetanic stimulation was not affected by miR-132/212 deletion, whilst theta burst LTP was enhanced. In contrast, neocortical theta burst-induced LTP was inhibited by loss of miR-132/212. Together these results indicate that miR-132 and/or miR-212 play a significant role in synaptic function, possibly by regulating the number of postsynaptic AMPA receptors under basal conditions and during activity-dependent synaptic plasticity

    Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

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    OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates
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