Plos One
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The lunchtime and after-school contexts are critical windows in a school day for children to be physically active. While numerous studies have investigated correlates of children's habitual physical activity, few have explored correlates of physical activity occurring at lunchtime and after-school from a social-ecological perspective. Exploring correlates that influence physical activity occurring in specific contexts can potentially improve the prediction and understanding of physical activity. Using a context-specific approach, this study investigated correlates of children's lunchtime and after-school physical activity. ⋯ Increasing specificity of correlate research has enabled the identification of unique features of, and a more in-depth interpretation of, lunchtime and after-school physical activity behaviour and is a potential strategy for advancing the physical activity correlate research field. The findings of this study could be used to inform and tailor gender-specific public health messages and interventions for promoting lunchtime and after-school physical activity in children.
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Optimal care of adults with severe acute respiratory failure requires specific resources and expertise. We sought to measure geographic access to these centers in the United States. ⋯ Geographic access to high capability severe acute respiratory failure centers varies widely across states and regions in the United States. Adequate referral center access in the case of disasters and pandemics will depend highly on local and regional care coordination across political boundaries.
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Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. ⋯ Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger than that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading.
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The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. ⋯ From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.
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Elevated myocardial energy expenditure (MEE) is related with reduced left ventricular ejection fraction, and has also been documented as an independent predictor of cardiovascular mortality. However, the serum small-molecule metabolite profiles and pathophysiological mechanisms of elevated MEE in heart failure (HF) are still lacking. Herein, we used 1H-NMR-based metabolomics analysis to screen for potential biomarkers of MEE in HF. ⋯ These results suggested that in patients with heart failure, MEE elevation was associated with significant changes in serum metabolomics profiles, especially the concentration of 3-hydroxybutyrate, acetone and succinate. These compounds could be used as potential serum biomarkers to study myocardial energy mechanism in HF patients.