Enhanced sentiment analysis regarding COVID-19 news from global channels.

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

Expression of angiotensin converting enzyme 2 mRNA in different SARS-CoV-2 infection states and times.

To measure the expression of angiotensin converting enzyme 2 (ACE2) mRNA in SARS-CoV-2 infection with different infection status and at different stages during infection, we used RT-qP CR to measure the expression of ACE2 mRNA. Measurements were analyzed by two-way repeated measures analysis of variance (RMANOVA). Expression of ACE2 mRNA was downregulated in initial stages of SARS-CoV-2 infection both in the asymptomatic infection (ASY) group and the confirmed cases (CON) group (t=-8.0845, P < 0.0001; t=-8.1904, P < 0.0001, respectively). The expression of ACE2 mRNA in the incubation period of CON group was lower compared with the intinal period of ASY group (F = 6.084, p = 0.016, partialη2 = 0.070). Relative expression of ACE2 mRNA was upregulated at the late stage of SARS-CoV-2 infection in the ASY and CON groups (F = 23.489, p = 0.000, partialη2 = 0.225; F = 46.555, p = 0.000, partialη2 = 0.365, respectively). The relative expression of ACE2 mRNA was down-regulated (mean ± SEM:0.69 ± 0.03) after inoculation with SARSCoV- 2 Spike pseudovirus, and there was a statistical difference (one-way t test, mean diffience =-0.31, 95%CI: -0.37˜-0.24, t=-8.1904, P < 0.0001). The expression of ACE2 mRNA is downregulated in the initial stage of SARS-CoV-2 infection, and then upregulated in the late stage of SARS-CoV-2 infection. The lower expression of ACE2 mRNA during the incubation period can lead to clinical symptoms. Downregulation of ACE2 mRNA was related to the interaction between SARS-CoV-2 S protein and ACE2.Communicated by Ramaswamy H. Sarma.

Enhanced sentiment analysis regarding COVID-19 news from global channels

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

How Does Persuasion Happen? A Novel Bounded Confidence Opinion Dynamics Model Based on Social Judgment Theory

Persuasion exists in every aspect of social life. It is important to understand how persuasion works and how strong it is. In this article, we improve the classic Hegselmann-Krause model, one of the most famous bounded confidence models, and propose a novel opinion dynamics model to explain the process by which persuasion occurs from a systematic perspective. In our model, the concepts of latitudes of acceptance, noncommitment, and rejection from social judgment theory and the cognitive error in the process of persuasion, namely assimilation, are introduced. When people are exchanging their opinions with their neighbors, the opinions in the latitude of acceptance will be assimilated, those in the latitude of noncommitment will keep unchanged, and those in the latitude of rejection will not be considered. Theoretical proofs show that our model will converge to a stable state in a finite time. Numerical results of extensive simulation experiments on four datasets show the performance of the model. Furthermore, real social platform data and global COVID-19 vaccination data are analyzed to verify the effectiveness of the model in the decision-making process.

COVID-19 Vaccine Sensing: Sentiment Analysis and Subject Distillation from Twitter Data

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and result in a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to December 31st, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. After calculating the number of topics, Latent Dirichlet Allocation (LDA) model is used to extract topics and keywords. We find that people had different sentiments between Chinese vaccine and those in other countries, and the sentiment value might be affected by the number of daily news cases and deaths, and the nature of key issues in the communication network, as well as revealing the intensity and evolution of 10 topics of major public concern, and provides insights into vaccine trust.

Media Bias and Factors Affecting the Impartiality of News Agencies during COVID-19.

When COVID-19 was raging around the world, people were more fearful and anxious. In this context, the media should uphold impartiality and shoulder the responsibility of eliminating misinformation. Therefore, our research adopted sentiment analysis technologies to analyze the impartiality of news agencies and analyzed the factors that affect the impartiality of COVID-19-related articles about various countries. The SentiWordNet3.0 and bidirectional encoder representations from transformers (BERT) models were employed to analyze the articles and visualize the data. The following conclusions were redrawn in our research. During the pandemic, articles of some news agencies were not objective; the impartiality of news agencies was related to the reliability of news agencies instead of the bias of news agencies; there were obvious differences in the coverage and positivity of international news agencies to report the performance of COVID-19 prevention and control in different countries.

A survey of mental health status of obstetric nurses during the novel coronavirus pneumonia pandemic.

ABSTRACT: To investigate the mental health status of obstetric nurses and its influencing factors during the novel coronavirus epidemic period, so as to provide theoretical reference for hospital decision-makers and managers.From February 25 to March 20, 2020, we conducted a cross-sectional survey through online questionnaire, and selected obstetric nurses from Jilin and Heilongjiang Provinces as the research objects by convenience sampling.Three hundred eighteen valid questionnaires were collected; the results of Symptom Checklist 90 showed that the scores of "obsessive-compulsive", "depression", "anxiety", "hostility", "phobia", and "psychosis" were higher than the Chinese norm (P < .01). There were 107 people whose total score of Symptom Checklist 90 was more than 160, and 83 people whose number of positive items was more than 43. Logistic regression results showed that married, temporary employment, lack of support and communication from family and relatives, onerous task, and unbearable responsibility were independent risk factors for mental disorder.There is a great psychological burden for obstetric nurses during the epidemic period. Decision makers should focus on necessary psychological intervention for those that are married, temporarily employed, and those lacking family supports including communication. At the same time, managers should distribute tasks reasonably to avoid psychological burdens caused by overwork.

Topics, Sentiments, and Emotions Triggered by COVID-19-Related Tweets from IRAN and Turkey Official News Agencies.

There is no doubt that the COVID-19 epidemic posed the most significant challenge to all governments globally since January 2020. People have to readapt after the epidemic to daily life with the absence of an effective vaccine for a long time. The epidemic has led to society division and uncertainty. With such issues, governments have to take efficient procedures to fight the epidemic. In this paper, we analyze and discuss two official news agencies' tweets of Iran and Turkey by using sentiment- and semantic analysis-based unsupervised learning approaches. The main topics, sentiments, and emotions that accompanied the agencies' tweets are identified and compared. The results are analyzed from the perspective of psychology, sociology, and communication.

Analysis of Continuous Mutation and Evolution on Circulating SARS-CoV-2

Monitoring the mutation and evolution of the virus is important for tracing its ongoing transmission and facilitating effective vaccine development. A total of 342 complete genomic sequences of SARS-CoV-2 were analyzed in this study. Compared to the reference genome reported in December 2019, 465 mutations were found, among which, 347 occurred in only 1 sequence, while 26 occurred in more than 5 sequences. For these 26 further identified as SNPs, 14 were closely linked and were grouped into 5 profiles. Phylogenetic analysis revealed the sequences formed 2 major groups. Most of the sequences in late period (March and April) constituted the Cluster II, while the sequences before March in this study and the reported S/L and A/B/C types in previous studies were all in Cluster I. The distributions of some mutations were specific geographically or temporally, the potential effect of which on the transmission and pathogenicity of SARS-CoV-2 deserves further evaluation and monitoring. Two mutations were found in the receptor-binding domain (RBD) but outside the receptor-binding motif (RBM), indicating that mutations may only have marginal biological effects but merit further attention. The observed novel sequence divergence is of great significance to the study of the transmission, pathogenicity, and development of an effective vaccine for SARS-CoV-2.

Dynamic Zero-Covid strategy curtails mutagenesis and emergence of new variants of the SARS-CoV-2 (preprint)

Within the local outbreak period of SARS-CoV-2 Delta variant in Nanjing and Yangzhou, China, we analyzed the mutation process of the Delta variants in 520 cases, as well as the production, spread and elimination of new mutant strains under the non-pharmaceutical interventions (NPI) strategy. The investigation on distribution of COVID-19 cases and phylogenetic analysis of SARS-CoV-2 genome sequences attributed to tracking the transmission chains, transmission chains were terminated by the isolation of the COVID-19 patients and quarantine of close-contracts, suggesting the importance of NPI in prompting some mutations to disappear and stopping the transmission of new variants. Dynamic zero-Covid strategy has been implemented successfully to against the second-largest local epidemic caused by an imported COVID-19 case in China.

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-021-00574-w.

MULTIFRACTAL BEHAVIOR OF CRYPTOCURRENCIES BEFORE AND DURING COVID-19

Based on high-frequency data, we study the difference in cryptocurrency market before and during the COVID-19. We analyze the multifractality of three major cryptocurrencies via the multifractal detrended fluctuation analysis (MFDFA). To investigate the source of multifractality, we construct shuffled, surrogated and truncate data. The results show that market efficiency of cryptocurrency has decreased during COVID-19. The cryptocurrency multifractal characteristics mainly come from non-Gaussian distribution. Additionally, the components of multifractal nature have changed during the pandemic. The results provide evidence for the impact of COVID-19 on cryptocurrency market.

Analysis of Continuous Mutation and Evolution on Circulating SARS-CoV-2.

Monitoring the mutation and evolution of the virus is important for tracing its ongoing transmission and facilitating effective vaccine development. A total of 342 complete genomic sequences of SARS-CoV-2 were analyzed in this study. Compared to the reference genome reported in December 2019, 465 mutations were found, among which, 347 occurred in only 1 sequence, while 26 occurred in more than 5 sequences. For these 26 further identified as SNPs, 14 were closely linked and were grouped into 5 profiles. Phylogenetic analysis revealed the sequences formed 2 major groups. Most of the sequences in late period (March and April) constituted the Cluster II, while the sequences before March in this study and the reported S/L and A/B/C types in previous studies were all in Cluster I. The distributions of some mutations were specific geographically or temporally, the potential effect of which on the transmission and pathogenicity of SARS-CoV-2 deserves further evaluation and monitoring. Two mutations were found in the receptor-binding domain (RBD) but outside the receptor-binding motif (RBM), indicating that mutations may only have marginal biological effects but merit further attention. The observed novel sequence divergence is of great significance to the study of the transmission, pathogenicity, and development of an effective vaccine for SARS-CoV-2.

Modelling and Predicting of Minimal CFR During Initial COVID-19 Epidemics from 214 Nations (preprint)

Background: The basic integrating responding capacity (IRC) against the COVID-19 epidemics is essential fundament for national responding strategy and can’t be evaluated quantitatively worldwide. To explore the applicable parameter labeling IRC from national daily CFRs and predicting method of minimal CFR during initial COVID-19 epidemic.Methods: Daily case fatality rates of COVID-19 cases since the first COVID-19 death in 214 nations were explored and found that similar falling zones marked with two turning points within the fitting curves occurred for many nations. The turning points could be quantified with parameters for the day duration (T1, T2 and ΔT) and for the three-day moving arithmetic average CFRs (CFR1, CFR2, and ΔCFR) under wave theory for 71 nations after screening. Two prediction models of CFR2 were established with multiple linear regressions (M1) and multi-order curve regressions (M2).Findings: Among the 92 nations, the 3DMA CFRs curves were arising continuously for 21 nations. Three types of falling zones could be classified with strong, moderate and weak IRC in the other 71 nations, the range of CFR2 was 0·0682~32·5804 percent. Only the minimal CFR showed significant correlations with 9 independent national indicators in 65 nations with CFRs under 7 percent. Model M1 showed that Log(POPU), B1K, and HHS made significant positive contributions, and Log(GDP), A65, DGDP, P1K, N1K and BMI (21·8 ~ 29·5) made negative contributions to the minimal CFR against COVID-19 epidemics for most nations. CFR2 was predicted well with model M1 for 57 nations and with model M2 for 59 nations for internal evaluation.Interpretation: The national minimal CFR could be predicted with models successfully for most nations based on some national, which provided the essential information in advance to establish suitable national responding strategies against COVID-19 epidemics worldwide.Funding Statement: This study was supported by National Natural Science Foundation of China (21976169) and Beijing Natural Science Foundation Project (8182055). Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Data collection and analysis to be part of a continuing public health outbreak were thus considered exempt from institutional review board approval.

[Important aspects of prevention of health personnel in hospitals during the epidemic novel coronavirus 2019.] / Aspectos importantes de prevencion del personal sanitario en los hospitales durante la epidemia del nuevo coronavirus 2019

Today, the novel coronavirus has spread widely throughout the world and poses new challenges to ensure the health and safety of health personnel. Because health personnel are at the frontlines in the fight against the novel coronavirus, which is one of the groups most affected and vulnerable during the pandemic, it is necessary to remind that the preventive measures adopted by health personnel are essential. Especially in emergency situations, essential measures must be taken to prevent occupational exposure during the novel coronavirus pandemic. Health professionals are working with great intensity and enormous social responsibility. In addition to the applause, they deserve more attention. Al dia de hoy, el nuevo coronavirus SARS-CoV-2 se ha extendido ampliamente por el mundo y plantea nuevos desafios para garantizar la salud y seguridad del personal sanitario. Debido a que dicho personal esta en primera linea de la lucha contra el nuevo coronavirus, siendo uno de los grupos mas afectados y vulnerables durante la pandemia, es necesario tener en cuenta que las medidas preventivas adoptadas por ellos son fundamentales. Especialmente en situaciones de emergencia, hay que tomar las medidas imprescindibles para la prevencion de la exposicion ocupacional durante esta nueva pandemia. Los profesionales sanitarios estan trabajando con una gran intensidad y una enorme responsabilidad social pero, ademas de los aplausos, merecen mas atencion.