Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

Study on the mechanism of active components of Liupao tea on 3CLpro based on HPLC‐DAD fingerprint and molecular docking technique

Liupao tea, a drink homologous to medicine and food. It can treat dysentery, relieve heat, remove dampness, and regulate the intestines and stomach. The objective of this study is to explore the material basis and mechanism of Liupao tea intervention in COVID‐19 and to provide a new prevention and treatment programme for COVID‐19. We used high performance liquid chromatography to analyze the extract of Liupao tea and establish its fingerprint. The main index components of the fingerprint were determined using SARS‐COV‐2 3‐chymotrypsin‐like protease (3CLᵖʳᵒ), and an in vitro drug screening model based on fluorescence resonance energy transfer was used to evaluate its inhibitory activity in vitro. The fingerprint results showed that the alcohol extract of Liupao tea contained gallic acid, epigallocatechin gallate (EGCG), caffeine, epicatechin gallate, rutin, and ellagic acid. The molecular docking binding energies of the six index components of SARS‐CoV‐2 3Clᵖʳᵒ were all less than −5.0 kJ/mol and showed strong binding affinity. The results of in vitro activity showed that the IC₅₀ of EGCG was 8.84 μmol/L, which could inhibit SARS‐CoV‐2 3Clᵖʳᵒ to a certain extent. This study unleashed that EGCG has a certain inhibitory effect on SARS‐CoV‐2 3CLᵖʳᵒ, and Liupao tea has a certain significance as a tea drink for the prevention of COVID‐19. PRACTICAL APPLICATIONS: The objective of this study was to explore the material basis and mechanism of Liupao tea intervention in COVID‐19 and to provide a new prevention and treatment programme for COVID‐19. The molecular docking binding energies of the six index components of Liupao tea with SARS‐CoV‐2 3CLᵖʳᵒ were all less than −5.0 kJ/mol, among them, the enzyme activity experiment shows that EGCG has a certain inhibitory effect on SARS‐CoV‐2 3CLᵖʳᵒ, it can be used as a potential SARS‐CoV‐2 3CLᵖʳᵒ inhibitor. We predicted that the understandings gained in the current research may evidence that Liupao tea has a certain significance as a tea drink for the prevention of COVID‐19.

The Mechanisms of Luteolin in Treating COVID-19 Based on Bioinformatics Analysis and Molecular Docking

Luteolin is a natural flavonoid that has a variety of pharmacological activities, such as anti-inflammatory, anti-allergic, anti-bacterial, anti-viral, apoptosis inhibition, cell autophagy regulation, and anti-tumor activity. It is one of the main ingredients of an expert-recommended herbal formula for the prevention and treatment of coronavirus disease 2019 (COVID-19). This suggests that luteolin has strong pharmacological effects on the prevention and treatment of COVID-19. The aims of this study were to identify the molecular targets of luteolin and to infer the possible mechanisms by which it exerts its pharmacological effects. The GSE159787 data set was obtained from the Gene Expression Omnibus online database, and differentially expressed genes were analyzed. There were 22 upregulated differentially expressed genes enriched in the COVID-19 signaling pathway, suggesting that the upregulation of these genes may be closely related to the occurrence of COVID-19. Molecular docking results showed that luteolin had strong binding efficiency to 20 of these 22 key genes. Six of these genes (CFB, EIF2AK2, OAS1, MAPK11, OAS3, and STAT1) showed strong binding activity. Luteolin can regulate the COVID-19 signaling pathway by combining with these targets, which may have a therapeutic effect on COVID-19.

Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution.

Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology-convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index–variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Genetic predispositions to psychiatric disorders and the risk of COVID-19.

BACKGROUND: Whether a genetic predisposition to psychiatric disorders is associated with coronavirus disease 2019 (COVID-19) is unknown. METHODS: Our analytic sample consisted of 287,123 white British participants in UK Biobank who were alive on 31 January 2020. We performed a genome-wide association study (GWAS) analysis for each psychiatric disorder (substance misuse, depression, anxiety, psychotic disorder, and stress-related disorders) in a randomly selected half of the study population ("base dataset"). For the other half ("target dataset"), the polygenic risk score (PRS) was calculated as a proxy of individuals' genetic predisposition to a given psychiatric phenotype using discovered genetic variants from the base dataset. Ascertainment of COVID-19 was based on the Public Health England dataset, inpatient hospital data, or death registers in UK Biobank. COVID-19 cases from hospitalization records or death records were considered "severe cases." The association between the PRS for psychiatric disorders and COVID-19 risk was examined using logistic regression. We also repeated PRS analyses based on publicly available GWAS summary statistics. RESULTS: A total of 143,562 participants (including 10,868 COVID-19 cases) were used for PRS analyses. A higher genetic predisposition to psychiatric disorders was associated with an increased risk of any COVID-19 and severe COVID-19. The adjusted odds ratio (OR) for any COVID-19 was 1.07 (95% confidence interval [CI] 1.02-1.13) and 1.06 (95% CI 1.01-1.11) among individuals with a high genetic risk (above the upper tertile of the PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Slightly higher ORs were noted for severe COVID-19, and similar result patterns were obtained in analyses based on publicly available GWAS summary statistics. CONCLUSIONS: Our findings suggest a potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19. Our data underscore the need for increased medical surveillance for this vulnerable population during the COVID-19 pandemic.

Public awareness, emotional reactions and human mobility in response to the COVID-19 outbreak in China - a population-based ecological study.

BACKGROUND: The outbreak of COVID-19 generated severe emotional reactions, and restricted mobility was a crucial measure to reduce the spread of the virus. This study describes the changes in public emotional reactions and mobility patterns in the Chinese population during the COVID-19 outbreak. METHODS: We collected data on public emotional reactions in response to the outbreak through Weibo, the Chinese Twitter, between 1st January and 31st March 2020. Using anonymized location-tracking information, we analyzed the daily mobility patterns of approximately 90% of Sichuan residents. RESULTS: There were three distinct phases of the emotional and behavioral reactions to the COVID-19 outbreak. The alarm phase (19th-26th January) was a restriction-free period, characterized by few new daily cases, but a large amount public negative emotions [the number of negative comments per Weibo post increased by 246.9 per day, 95% confidence interval (CI) 122.5-371.3], and a substantial increase in self-limiting mobility (from 45.6% to 54.5%, changing by 1.5% per day, 95% CI 0.7%-2.3%). The epidemic phase (27th January-15th February) exhibited rapidly increasing numbers of new daily cases, decreasing expression of negative emotions (a decrease of 27.3 negative comments per post per day, 95% CI -40.4 to -14.2), and a stabilized level of self-limiting mobility. The relief phase (16th February-31st March) had a steady decline in new daily cases and decreasing levels of negative emotion and self-limiting mobility. CONCLUSIONS: During the COVID-19 outbreak in China, the public's emotional reaction was strongest before the actual peak of the outbreak and declined thereafter. The change in human mobility patterns occurred before the implementation of restriction orders, suggesting a possible link between emotion and behavior.

The inhibition of Mpro, the primary protease of COVID-19, by Poria cocos and its active compounds: a network pharmacology and molecular docking study.

Poria cocos is a traditional Chinese medicine (TCM) that can clear dampness, promote diuresis, and strengthen the spleen and stomach. Poria cocos has been detected in many TCM compounds that are used for COVID-19 intervention. However, the active ingredients and mechanisms associated with the effect of Poria cocos on COVID-19 remain unclear. In this paper, the active ingredients of Poria cocos, along with their potential targets related to COVID-19, were screened using TCMSP, GeneCards, and other databases, by means of network pharmacology. We then investigated the active components, potential targets, and interactions, that are associated with COVID-19 intervention. The primary protease of COVID-19, Mpro, is currently a key target in the design of potential inhibitors. Molecular docking techniques and molecular dynamics simulations demonstrated that the active components of Poria cocos could bind stably to the active site of Mpro with high levels of binding activity. Pachymic acid is based on a triterpene structure and was identified as the main component of Poria cocos; its triterpene active component has low binding energy with Mpro. The pachymic acid of Mpro activity was further characterized and the IC50 was determined to be 18.607 µmol L-1. Our results indicate that pachymic acid exhibits a certain inhibitory effect on the Mpro protease.

The inhibition of Mpro, the primary protease of COVID-19, by Poria cocos and its active compounds: a network pharmacology and molecular docking study† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d0ra07035a

Poria cocos is a traditional Chinese medicine (TCM) that can clear dampness, promote diuresis, and strengthen the spleen and stomach. Poria cocos has been detected in many TCM compounds that are used for COVID-19 intervention. However, the active ingredients and mechanisms associated with the effect of Poria cocos on COVID-19 remain unclear. In this paper, the active ingredients of Poria cocos, along with their potential targets related to COVID-19, were screened using TCMSP, GeneCards, and other databases, by means of network pharmacology. We then investigated the active components, potential targets, and interactions, that are associated with COVID-19 intervention. The primary protease of COVID-19, Mpro, is currently a key target in the design of potential inhibitors. Molecular docking techniques and molecular dynamics simulations demonstrated that the active components of Poria cocos could bind stably to the active site of Mpro with high levels of binding activity. Pachymic acid is based on a triterpene structure and was identified as the main component of Poria cocos;its triterpene active component has low binding energy with Mpro. The pachymic acid of Mpro activity was further characterized and the IC50 was determined to be 18.607 μmol L−1. Our results indicate that pachymic acid exhibits a certain inhibitory effect on the Mpro protease. The inhibition of Mpro, the primary protease of COVID-19, by Poria cocos.

Undergraduates' perceptions on emergency remote learning in ecology in the post-pandemic era.

The COVID-19 pandemic has strongly disrupted academic activities, particularly in disciplines with a strong empirical component among other reasons by limiting our mobility. It is thus essential to assess emergency remote teaching plans by surveying learners' opinions and perceptions during these unusual circumstances. To achieve this aim, we conducted a survey during the spring semester of 2021 in an environmental science program to ascertain learners' perceptions on online and onsite learning activities in ecology-based modules. We were particularly interested not only in comparing the performance of these two types of activities but also in understanding the role played by learners' perceptions about nature in shaping this pattern. Environmental science programs are rather heterogeneous from a conceptual point of view and, thus, learners may also be more diverse than in traditional ecology programs, which may affect their interest for ecology-based modules. We assessed connectedness to nature by computing the reduced version of the Nature Relatedness Scale. Here, we found that online activities systematically obtained significantly lower scores than onsite activities regardless of the wording employed, and that altruistic behaviors were prevalent among learners. Interestingly, scores for both onsite and online activities were strongly influenced by learners' connectedness to nature, as learners with a stronger connection to nature gave higher scores to both types of activities. Our results suggest that an effort to improve the efficacy of remote learning activities should be the focus of research about teaching methodologies in predominantly empirical scientific disciplines.

Exploring the Potential Targets and Mechanisms of Huang Lian Jie Du Decoction in the Treatment of Coronavirus Disease 2019 Based on Network Pharmacology.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2; previously known as 2019-nCoV) emerged in Wuhan, China, and caused many infections and deaths. At present, there are no specific drugs for the etiology and treatment of COVID-19. A combination of traditional Chinese and western medicine is proposed to treat COVID-19, in which Huang Lian Jie Du decoction (HLJDD) is recommended for the treatment of COVID-19 in many provinces in China and has been widely used in the clinic. This study explored the potential targets of HLJDD in the treatment of COVID-19 based on network pharmacology. METHODS: First, the chemical composition and targets of HLJDD and COVID-19-related targets were obtained through the TCMSP, UniProt, GeneCards and OMIM databases. Second, HLJDD target and HLJDD-COVID-19 target networks were constructed via the STRING database and Cytoscape software. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the HLJDD-COVID-19 targets was applied via the DAVID database. RESULTS: Our study identified a total of 67 active ingredients of HLJDD and 204 targets of HLJDD. A total of 502 COVID-19-related targets were obtained, of which 47 were intersecting targets of HLJDD and COVID-19. A total of 179 GO terms and 77 KEGG terms, including the TNF signaling pathway, NF-κB signaling pathway and HIF-1 signaling pathway, were identified. CONCLUSION: The present study explored the potential targets and signaling pathways of HLJDD during the treatment of COVID-19, which may provide a basis for the research and development of drugs for the treatment of COVID-19.

COVID-19 and risk of subsequent life-threatening secondary infections: a matched cohort study in UK Biobank.

BACKGROUND: With the increasing number of people infected with and recovered from coronavirus disease 2019 (COVID-19), the extent of major health consequences of COVID-19 is unclear, including risks of severe secondary infections. METHODS: Based on 445,845 UK Biobank participants registered in England, we conducted a matched cohort study where 5151 individuals with a positive test result or hospitalized with a diagnosis of COVID-19 were included in the exposed group. We then randomly selected up to 10 matched individuals without COVID-19 diagnosis for each exposed individual (n = 51,402). The life-threatening secondary infections were defined as diagnoses of severe secondary infections with high mortality rates (i.e., sepsis, endocarditis, and central nervous system infections) from the UK Biobank inpatient hospital data, or deaths from these infections from mortality data. The follow-up period was limited to 3 months after the initial COVID-19 diagnosis. Using a similar study design, we additionally constructed a matched cohort where exposed individuals were diagnosed with seasonal influenza from either inpatient hospital or primary care data between 2010 and 2019 (6169 exposed and 61,555 unexposed individuals). After controlling for multiple confounders, Cox models were used to estimate hazard ratios (HRs) of life-threatening secondary infections after COVID-19 or seasonal influenza. RESULTS: In the matched cohort for COVID-19, 50.22% of participants were male, and the median age at the index date was 66 years. During a median follow-up of 12.71 weeks, the incidence rate of life-threatening secondary infections was 2.23 (123/55.15) and 0.25 (151/600.55) per 1000 person-weeks for all patients with COVID-19 and their matched individuals, respectively, which corresponded to a fully adjusted HR of 8.19 (95% confidence interval [CI] 6.33-10.59). The corresponding HR of life-threatening secondary infections among all patients with seasonal influenza diagnosis was 4.50, 95% CI 3.34-6.08 (p for difference < 0.01). Also, elevated HRs were observed among hospitalized individuals for life-threatening secondary infections following hospital discharge, both in the COVID-19 (HR = 6.28 [95% CI 4.05-9.75]) and seasonal influenza (6.01 [95% CI 3.53-10.26], p for difference = 0.902) cohorts. CONCLUSION: COVID-19 patients have increased subsequent risks of life-threatening secondary infections, to an equal extent or beyond risk elevations observed for patients with seasonal influenza.

Integrating serum microRNAs and electronic health records improved the diagnosis of tuberculosis.

BACKGROUND: To verify the differential expression of miR-30c and miR-142-3p between tuberculosis patients and healthy controls and to investigate the performance of microRNA (miRNA) and subsequently models for the diagnosis of tuberculosis (TB). METHODS: We followed up 460 subjects suspected of TB, and finally enrolled 132 patients, including 60 TB patients, 24 non-TB disease controls (TB-DCs), and 48 healthy controls (HCs). The differential expression of miR-30c and miR-142-3p in serum samples of the TB patients, TB-DCs, and HCs were identified by reverse transcription-quantitative real-time PCR. Diagnostic models were developed by analyzing the characteristics of miRNA and electronic health records (EHRs). These models evaluated by the area under the curves (AUC) and calibration curves were presented as nomograms. RESULTS: There were differential expression of miR-30c and miR-142-3p between TB patients and HCs (p < 0.05). Individual miRNA has a limited diagnostic value for TB. However, diagnostic performance has been both significantly improved when we integrated miR-142-3p and ordinary EHRs to develop two models for the diagnosis of tuberculosis. The AUC of the model for distinguishing tuberculosis patients from healthy controls has increased from 0.75 (95% CI: 0.66-0.84) to 0.96 (95% CI: 0.92-0.99) and the model for distinguishing tuberculosis patients from non-TB disease controls has increased from 0.67 (95% CI: 0.55-0.79) to 0.94 (95% CI: 0.89-0.99). CONCLUSIONS: Integrating serum miR-142-3p and EHRs is a good strategy for improving TB diagnosis.

Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis.

BACKGROUND: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank. METHODS: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections. FINDINGS: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28-1·62) for All COVID-19 cases, 1·55 (1·34-1·78) for Inpatient COVID-19 cases, and 2·03 (1·59-2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58-1·93). INTERPRETATION: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. FUNDING: National Natural Science Foundation of China.

Study on the mechanism of active components of Liupao tea on 3CLpro based on HPLC-DAD fingerprint and molecular docking technique.

Liupao tea, a drink homologous to medicine and food. It can treat dysentery, relieve heat, remove dampness, and regulate the intestines and stomach. The objective of this study is to explore the material basis and mechanism of Liupao tea intervention in COVID-19 and to provide a new prevention and treatment programme for COVID-19. We used high performance liquid chromatography to analyze the extract of Liupao tea and establish its fingerprint. The main index components of the fingerprint were determined using SARS-COV-2 3-chymotrypsin-like protease (3CLpro ), and an in vitro drug screening model based on fluorescence resonance energy transfer was used to evaluate its inhibitory activity in vitro. The fingerprint results showed that the alcohol extract of Liupao tea contained gallic acid, epigallocatechin gallate (EGCG), caffeine, epicatechin gallate, rutin, and ellagic acid. The molecular docking binding energies of the six index components of SARS-CoV-2 3Clpro were all less than -5.0 kJ/mol and showed strong binding affinity. The results of in vitro activity showed that the IC50 of EGCG was 8.84 µmol/L, which could inhibit SARS-CoV-2 3Clpro to a certain extent. This study unleashed that EGCG has a certain inhibitory effect on SARS-CoV-2 3CLpro , and Liupao tea has a certain significance as a tea drink for the prevention of COVID-19. PRACTICAL APPLICATIONS: The objective of this study was to explore the material basis and mechanism of Liupao tea intervention in COVID-19 and to provide a new prevention and treatment programme for COVID-19. The molecular docking binding energies of the six index components of Liupao tea with SARS-CoV-2 3CLpro were all less than -5.0 kJ/mol, among them, the enzyme activity experiment shows that EGCG has a certain inhibitory effect on SARS-CoV-2 3CLpro , it can be used as a potential SARS-CoV-2 3CLpro inhibitor. We predicted that the understandings gained in the current research may evidence that Liupao tea has a certain significance as a tea drink for the prevention of COVID-19.

Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic.

Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.

Clinical Characteristics of Patients Infected With the Novel 2019 Coronavirus (SARS-Cov-2) in Guangzhou, China.

BACKGROUND: The clinical manifestations and factors associated with the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections outside of Wuhan are not clearly understood. METHODS: All laboratory-confirmed cases with SARS-Cov-2 infection who were hospitalized and monitored in Guangzhou Eighth People's Hospital were recruited from January 20 to February 10. RESULTS: A total of 275 patients were included in this study. The median patient age was 49 years, and 63.6% had exposure to Wuhan. The median virus incubation period was 6 days. Fever (70.5%) and dry cough (56.0%) were the most common symptoms. A decreased albumin level was found in 51.3% of patients, lymphopenia in 33.5%, and pneumonia based on chest computed tomography in 86%. Approximately 16% of patients (n = 45) had severe disease, and there were no deaths. Compared with patients with nonsevere disease, those with severe disease were older, had a higher frequency of coexisting conditions and pneumonia, and had a shorter incubation period (all P < .05). There were no differences between patients who likely contacted the virus in Wuhan and those who had no exposure to Wuhan. Multivariate logistic regression analysis indicated that older age, male sex, and decreased albumin level were independently associated with disease severity. CONCLUSIONS: Most of the patients infected with SARS-CoV-2 in Guangzhou, China are not severe cases and patients with older age, male, and decreased albumin level were more likely to develop into severe ones.