54 research outputs found

    Long-Chain Polyunsaturated Fatty Acid Supplementation in Infancy Reduces Heart Rate and Positively Affects Distribution of Attention

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    A double-blind, randomized, controlled, parallel-group prospective trial was conducted to determine whether a dose-response existed for four different levels of docosahexaenoic acid (DHA) supplementation on the cognitive performance of infants. A total of 122 term infants were fed one of four different formulas varying in their DHA composition (0.00%, 0.32%, 0.64% and 0.96% of total fatty acids as DHA) from birth to 12 months. The three DHA-supplemented formulas also contained 0.64% of total fatty acids as arachidonic acid (ARA, 20:4n-6). Infants were tested at 4, 6, and 9 months of age on a visual habituation protocol that yielded both behavioral and psychophysiological indices of attention. Infants in all DHA+ARA-supplemented conditions had lower heart rates than those in the unsupplemented condition; there was no dose-response for this effect. The distribution of time that infants spent in different phases of attention (a cognitive index derived from the convergence of behavioral and cardiac responses) varied as a function of dosage. Infants supplemented at the two lower DHA doses spent proportionately more time engaged in active stimulus processing than infants fed the unsupplemented formula, while infants fed the highest dose were intermediate and did not differ from any other group

    Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

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    Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy

    Long Chain Polyunsaturated Fatty Acid Supplementation in Infancy Reduces Heart Rate and Positively Affects Distribution of Attention

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    A double-blind, randomized, controlled, parallel-group prospective trial was conducted to determine whether a dose-response existed for four different levels of docosahexaenoic acid (DHA) supplementation on the cognitive performance of infants. A total of 122 term infants were fed one of four different formulas varying in their DHA composition (0.00%, 0.32%, 0.64% and 0.96% of total fatty acids as DHA) from birth to 12 months. The three DHA-supplemented formulas also contained 0.64% of total fatty acids as arachidonic acid (ARA, 20:4n-6). Infants were tested at 4, 6, and 9 months of age on a visual habituation protocol that yielded both behavioral and psychophysiological indices of attention. Infants in all DHA+ARA-supplemented conditions had lower heart rates than those in the unsupplemented condition; there was no dose-response for this effect. The distribution of time that infants spent in different phases of attention (a cognitive index derived from the convergence of behavioral and cardiac responses) varied as a function of dosage. Infants supplemented at the two lower DHA doses spent proportionately more time engaged in active stimulus processing than infants fed the unsupplemented formula, while infants fed the highest dose were intermediate and did not differ from any other group

    Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

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    A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

    Measurements of top-quark pair differential cross-sections in the eÎŒe\mu channel in pppp collisions at s=13\sqrt{s} = 13 TeV using the ATLAS detector