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https://www.selleckchem.com/products/nu7026.html The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentnd sustain healthy behaviors as well as community supports also via social media-based preventive interventions.Children's vocabulary ability at school entry is highly variable and predictive of later language and literacy outcomes. Sleep is potentially useful in understanding and explaining that variability, with sleep patterns being predictive of global trajectories of language acquisition. Here, we looked to replicate and extend these findings. Data from 354 children (without English as an additional language) in the Born in Bradford study were analysed, describing the mean intercepts and linear trends in parent-reported day-time and night-time sleep duration over five time points between 6 and 36 months-of-age. The mean difference between night-time and day-time sleep was predictive of receptive vocabulary at age five, with more night-time sleep relative to day-time sleep predicting better language. An exploratory a

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