Network characteristics and stock liquidity:Evidence from the UK

This article examines the effect of the Engle-Granger (E-G) price spillover network characteristics on firm's stock liquidity with a longitudinal dataset of FTSE 350 from 2006 to 2021. We find that the subprime crisis, European debt sovereign crisis, British Brexit and COVID-19 caused dramatic network structure change. We also find that firms with higher centrality are likely to suffer from more or greater price shocks leading to lower stock liquidity. Finally, the robust of our results has also been identified, and a general framework for network characteristics and stock liquidity has been established to some extent.

Highly Time-Resolved and Nontargeted Characterization of Volatile Organic Compound Emissions from Face Masks

Face covering by masks has become a lifeline for humans to prevent the airborne transmission of highly infectious SARS-CoV-2. One of the side effects, however, is the release of volatile organic compounds (VOCs), which can hardly be fully understood based on traditional offline measurements. Here, for the first time, we performed highly time-resolved and nontargeted measurements of VOCs emitted from face masks using an ultrasensitive proton transfer-reaction quadrupole-interface time-of-flight mass spectrometer. We found diverse VOC species, some of which are toxic. The chemical structures of the major VOC species were identified to be from the chemicals and processes involved in mask production. High concentrations of VOCs emitted from surgical masks (predominant mask type) were all concentrated in the initial 1 h and then dropped rapidly to an acceptable level after a process of naturally airing out. Higher emissions from a surgical mask for children are likely due to their colorful cartoon patterns. Despite the lowest emissions, the N95 respirator with an active carbon layer required 6 h to remove the toxic methanol. We support mask wearing to curtail the COVID-19 pandemic, but our results highlight the importance of naturally airing out masks to reduce zero-distance inhalation of mask-emitted VOCs.

Novel Insights Into the Sulfated Glucuronic Acid-Based Anti-SARS-CoV-2 Mechanism of Exopolysaccharides From Halophilic Archaeon Haloarcula hispanica.

The pandemic caused by SARS-CoV-2 is the most widely spread disease in the 21st century. Due to the continuous emergence of variants across the world, it is necessary to expand our understanding of host-virus interactions and explore new agents against SARS-CoV-2. In this study, it was found exopolysaccharides (EPSs) from halophilic archaeon Haloarcula hispanica ATCC33960 can bind to the spike protein of SARS-CoV-2 with the binding constant KD of 2.23 nM, block the binding of spike protein to Vero E6 and bronchial epithelial BEAS-2B cells, and inhibit pseudovirus infection. However, EPSs from the gene deletion mutant △HAH_1206 almost completely lost the antiviral activity against SARS-CoV-2. A significant reduction of glucuronic acid (GlcA) and the sulfation level in EPSs of △HAH_1206 was clearly observed. Our results indicated that sulfated GlcA in EPSs is possible for a main structural unit in their inhibition of binding of SARS-CoV-2 to host cells, which would provide a novel antiviral mechanism and a guide for designing new agents against SARS-CoV-2.

Impact of the COVID-19 Pandemic on China's Stock Market Volatility, During and After the Outbreak: Evidence From an ARDL Approach.

Purpose: In this study, we empirically investigate the impact of the COVID-19 pandemic on China's stock price volatility during and after its initial outbreak, using time-series daily data covering the period from July to October, 2020 and 2021, respectively. Design/Methodology/Approach: In the estimation, the ARDL bounds test approach was employed to examine the existence of co-integration and the relationship of long-run and short-run between the new infection rates and stock price volatility, as stable and unstable variables are mixed. The inner-day and inter-day volatility, based on the Shanghai (securities) composite index, are estimated in separate empirical models. In addition, the Inter-bank overnight lending rate (IBOLR) is controlled in order to consider the effect of liquidity and investment cost. Findings and Implications: We find that in the initial year (2020) of the epidemic, the new infection rate is negatively correlated to stock prices in the short-term, whereas no significant evidence existed in the long-term, regardless of model specifications. However, after the epidemic's outbreak (2021), the result depicts that new infections increased stock prices in the long-term, and depressed its inner-day volatility in the short-term, which is inconsistent with most investigations. This phenomenon may be due to the fact that investors were more concerned about the withdrawal of monetary easing and fiscal stimulus, which were introduced to fight against the epidemic's impact on economy, than the epidemic itself. This study complements the limitations of most existing studies, which just focus on the period of the epidemic's outbreak, and provides insight into macroeconomic policy making in the era of the post COVID-19 epidemic such as the structural and ordered exit of the stimulating policies, intervention in IBOLR and balance social and economic sustainability.

Teaching Reform to the Biology Major During the COVID-19 Pandemic: A Study of the Method of Teaching Industrial Innovation and Entrepreneurial Talents.

The biology major has developed rapidly in recent years. Biology is a science that penetrates every aspect of human life and is one of the core majors in most agricultural colleges and universities. However, many teachers lack practical experience in the subject. To overcome this problem, in recent years, we have been trying to introduce new reforms into our teaching. This article provides some insight into the way that biology majors have been reformed, which will help educators in agricultural colleges and universities. At present, teachers implement the "Industrial Innovation and Entrepreneurship Talent Cultivation" (IIETC) model, but it is not clear whether this helps biology majors to master the course and improve their practical skills. In this study, the IIETC model is outlined, and the academic achievement and satisfaction of students taught under the IIETC model are assessed. A T-test is used to examine potential differences between IIETC and traditional teaching models. In-depth interviews and questionnaires were given to two groups of students who followed different teaching models as part of an exploratory study. The aim was to explore how effective IIETC is at helping biology majors master the course and improve students' wellbeing. Our results show that compared with traditional teaching methods, the IIETC model has a significant positive impact on the academic performance and happiness of biology students. Students trained under the IIETC model were more active and scored more highly in their final exams. They were more likely to feel that they had achieved success and happiness through the course (P = 0.03). The outcomes of this research reveal a novel teaching reform that improved students' enthusiasm for innovation and entrepreneurship during the ongoing COVID-19 pandemic. The effects are very encouraging and deserve further exploration and expansion in future work.

Efficacy and potential mechanisms of Chinese herbal compounds in coronavirus disease 2019: advances of laboratory and clinical studies.

The Coronavirus disease 2019 (COVID-19) pandemic is still spread and has made a severe public health threat around the world. To improve disease progression, emerging Chinese herbal compounds were used in clinical practice and some agents have proven beneficial in treating COVID-19. Here, the relevant literature from basic researches to clinical application were identified and comprehensively assessed. A variety of Chinese herbal compounds have been reported to be effective in improving symptoms and outcomes in patients with COVID-19, particularly together with routine treatment strategy. The pharmacological activities were mainly attributed to the relief of clinical symptoms, inhibition of cytokine storm, and improvement of organ function. Besides, the development of novel antiviral drugs from medicinal herbs were further discussed. The updated laboratory and clinical studies provided the evidence of Chinese herbal compounds such as Lianhua Qingwen prescription, Shufeng Jiedu prescription, and Qingfei Paidu Tang for the relief of COVID-19. However, both of the randomized controlled trials and real world researches need to be done for supporting the evidence including the efficacy and safety in fighting COVID-19.

A Study on Weak Edge Detection of COVID-19's CT Images Based on Histogram Equalization and Improved Canny Algorithm.

The coronavirus disease 2019 (COVID-19) is a substantial threat to people's lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K-means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.

iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients.

Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.

The Influence of the COVID-19 Pandemic on the Imports and Exports in China, Japan, and South Korea.

In this paper, time-series and cross-country data spanning from January 2020 to December 2020 are adopted to empirically investigate the impact of the COVID-19 pandemic on exports and imports in China, Japan, and South Korea. In the models, industrial production, trade openness, government response (including monetary and fiscal intervention), and the pandemic impact of major trade partners are controlled. In addition, the three countries, China, Japan, and South Korea, are also estimated separately in consideration of the cross-country disparity. The results show that domestic epidemics in China, Japan, and South Korea have a non-significant (statistically significant) effect on imports, but are negatively correlated with exports in Japan; epidemics in major trading partners are negatively correlated with imports in Japan and positively correlated with exports in China and South Korea; and government intervention is positively correlated with imports in China and positively correlated with exports in China, Japan, and South Korea.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

Recent advances of traditional Chinese medicine on the prevention and treatment of COVID-19.

Coronavirus disease-2019 (COVID-19) is a new highly infectious disease caused by a novel coronavirus. Recently, the number of new cases infected pneumonia in the world continues to increase, which has aroused great concern from the international community. At present, there are no small-molecule specific anti-viral drugs for the treatment. The high mortality rate seriously threatens human health. Traditional Chinese medicine (TCM) is a unique health resource in China. The combination of TCM and Western medicine has played a positive and important role in combating COVID-19 in China. In this review, through literature mining and analysis, it was found that TCM has the potential to prevent and treat the COVID-19. Then, the network pharmacological studies demonstrated that TCM played roles of anti-virus, anti-inflammation and immunoregulation in the management of COVID-19 via multiple components acting on multiple targets and multiple pathways. Finally, clinical researches also confirmed the beneficial effects of TCM on the treatment of patients. This review may provide meaningful and useful information on further drug development of COVID-19 and other viral infectious diseases.

Proinflammatory cytokines are associated with prolonged viral RNA shedding in COVID-19 patients.

Since December 2019, Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic. We aimed to investigate the clinical characteristics and analyzed the risk factors for prolonged viral RNA shedding. We retrospectively collected data from 112 hospitalized COVID-19 patients in a single center in Wuhan, China. Factors associated with prolonged viral RNA shedding (≥28 days) were investigated. Forty-nine (43.8%) patients had prolonged viral RNA shedding. Patients with prolonged viral shedding were older and had a higher rate of hypertension. Proinflammatory cytokines, including interleukin-2R (IL-2R) and tumor necrosis factor-α (TNF-α), were significantly elevated in patients with prolonged viral shedding. Multivariate analysis revealed that hypertension, older age, lymphopenia and elevated serum IL-2R were independent risk factors for prolonged viral shedding. This comprehensive investigation revealed the distinct characteristics between patients with or without prolonged viral RNA shedding. Hypertension, older age, lymphopenia and high levels of proinflammatory cytokines may be correlated with prolonged viral shedding.

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .

The Trans-omics Landscape of COVID-19 (preprint)

The outbreak of coronavirus disease 2019 (COVID-19) has been causing a global health emergency. Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19.

The Trans-omics Landscape of COVID-19 (preprint)

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.

Sleep problems among Chinese adolescents and young adults during the coronavirus-2019 pandemic.

OBJECTIVE: To assess the prevalence and sociodemographic correlates of insomnia symptoms among Chinese adolescents and young adults affected by the outbreak of coronavirus disease-2019 (COVID-19). METHODS: This cross-sectional study included Chinese adolescents and young adults 12-29 years of age during part of the COVID-19 epidemic period. An online survey was used to collect demographic data, and to assess recognition of COVID-19, insomnia, depression, and anxiety symptoms using the Pittsburgh Sleep Quality Index (PSQI), the Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder (GAD-7) questionnaires, respectively. The Social Support Rate Scale was used to assess social support. RESULTS: Among 11,835 adolescents and young adults included in the study, the prevalence of insomnia symptoms during part of the COVID-19 epidemic period was 23.2%. Binomial logistic regression analysis revealed that female sex and residing in the city were greater risk factors for insomnia symptoms. Depression or anxiety were risk factors for insomnia symptoms; however, social support, both subjective and objective, was protective factors against insomnia symptoms. Furthermore, anxiety and depression symptoms were mediators of social support and insomnia symptoms. CONCLUSIONS: Results of this study revealed a high prevalence of sleep problems among adolescents and young adults during the COVID-19 epidemic, especially senior high school and college students, which were negatively associated with students' projections of trends in COVID-19. The adverse impact of COVID-19 was a risk factor for insomnia symptoms; as such, the government must devote more attention to sleep disorders in this patient population while combating COVID-19.

Prevalence and socio-demographic correlates of psychological health problems in Chinese adolescents during the outbreak of COVID-19.

Psychological health problems, especially emotional disorders, are common among adolescents. The epidemiology of emotional disorders is greatly influenced by stressful events. This study sought to assess the prevalence rate and socio-demographic correlates of depressive and anxiety symptoms among Chinese adolescents affected by the outbreak of COVID-19. We conducted a cross-sectional study among Chinese students aged 12-18 years during the COVID-19 epidemic period. An online survey was used to conduct rapid assessment. A total of 8079 participants were involved in the study. An online survey was used to collect demographic data, assess students' awareness of COVID-19, and assess depressive and anxiety symptoms with the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) questionnaire, respectively. The prevalence of depressive symptoms, anxiety symptoms, and a combination of depressive and anxiety symptoms was 43.7%, 37.4%, and 31.3%, respectively, among Chinese high school students during the COVID-19 outbreak. Multivariable logistic regression analysis revealed that female gender was the higher risk factor for depressive and anxiety symptoms. In terms of grades, senior high school was a risk factor for depressive and anxiety symptoms; the higher the grade, the greater the prevalence of depressive and anxiety symptoms. Our findings show there is a high prevalence of psychological health problems among adolescents, which are negatively associated with the level of awareness of COVID-19. These findings suggest that the government needs to pay more attention to psychological health among adolescents while combating COVID-19.

Multi-chain Fudan-CCDC model for COVID-19 in Iran (preprint)

Background: COVID-19 has been deeply affecting people's lives all over the world. It is significant for prevention and control to model the evolution effectively and efficiently . Methods: We first propose the multi-chain Fudan-CCDC model which is based on the original single-chain model to describe the revival of COVID-19 in some countries. Multi-chains are considered as the superposition of distinctive single chains. Parameter identification is carried out by minimizing the penalty function. Results: From results of numerical simulations, the multi-chain model performs well on data fitting and reasonably interprets the revival phenomena. The band of 25% fluctuation of simulation results could contain most seemly unsteady increments. Conclusion: The multi-chain model has better performance on data fitting in revival situations compared with the single-chain model. It is predicted by the three-chain model with data by Apr 21 that the epidemic curve of Iran would level off on round May 10, and the final cumulative confirmed cases would be around 88820. The upper bound of the 95% confidence interval would be around 96000.