A new multivariate grey prediction model for forecasting China's regional energy consumption.

Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.

Affinity binding of COVID-19 drug candidates (chloroquine/hydroxychloroquine) and serum albumin: Based on photochemistry and molecular docking.

Chloroquine (CQ) and hydroxychloroquine (HCQ) show good efficacy in the treatment of SARS-CoV-2 in the early stage, while they are no longer recommended due to their side effects. As an important drug delivery carrier, serum albumin (SA) is closely related to the efficacy of drugs. Here, the affinity behaviour of chloroquine and hydroxychloroquine with two SA were investigated through the multispectral method of biochemistry and computer simulation. The results showed that the intrinsic emission of both SA was quenched by CQ and HCQ in a spontaneous exothermic entropy reduction static process, which relied mainly on hydrogen bonding and van der Waals forces. The lower binding constants suggested weak binding between the two drugs and SA, which might lead to differences in efficacy and possibly even to varying side effects. Binding site recognition demonstrated that CQ preferred to bind to the two sites of both SA, while HCQ tended to bind to site I of SA. The results of conformational studies demonstrated that CQ and HCQ could affect the structure of both SA by slightly increasing the α-helix content of SA. Finally, we combine the results from experimental start with molecular simulations to suggest drug modifications to guide the design of drugs. This work has important implications for guiding drug design improvements to select CQ derivatives with fewer side effects for the treatment of COVID-19.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen.

BACKGROUND: Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. RESULTS: In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. CONCLUSIONS: Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Analysis of the Geographic Transmission Differences of COVID-19 in China Caused by Population Movement and Population Density.

The coronavirus disease (COVID-19) has led to a global pandemic and caused huge healthy and economic losses. Non-pharmaceutical interventions, especially contact tracing and social distance restrictions, play a vital role in the control of COVID-19. Understanding the spatial impact is essential for designing such a control policy. Based on epidemic data of the confirmed cases after the Wuhan lockdown, we calculate the invasive reproduction numbers of COVID-19 in the different regions of China. Statistical analysis indicates a significant positive correlation between the reproduction numbers and the population input sizes from Wuhan, which indicates that the large-scale population movement contributed a lot to the geographic spread of COVID-19 in China. Moreover, there is a significant positive correlation between reproduction numbers and local population densities, which shows that the higher population density intensifies the spread of disease. Considering that in the early stage, there were sequential imported cases that affected the estimation of reproduction numbers, we classify the imported cases and local cases through the information of epidemiological data and calculate the net invasive reproduction number to quantify the local spread of the epidemic. The results are applied to the design of border control policy on the basis of vaccination coverage.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen

Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Modeling of a coronavirus disease outbreak at the beginning of 2021 in Shijiazhuang, Hebei Province, China

Objective: To model an outbreak of coronavirus disease 2019 (COVID-19) in Shijiazhuang and forecast its spread trend. Method: We collected confirmed COVID-19 cases from the Health Commission of Hebei Province during the period of January 2 to January 27, 2021. We built a new model (SEIaIcRK), including the contribution of asymptomatic cases, based on the traditional SEIR model to explore and analyze the transmission of COVID-19.

Induction of Broadly Cross-Reactive Antibody Responses to SARS-CoV-2 Variants by S1 Nanoparticle Vaccines.

Despite the rapid deployment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, the emergence of SARS-CoV-2 variants and reports of their immune evasion characteristics have led to an urgent need for novel vaccines that confer potent cross-protective immunity. In this study, we constructed three different SARS-CoV-2 spike S1-conjugated nanoparticle vaccine candidates that exhibited high structural homogeneity and stability. Notably, these vaccines elicited up to 50-times-higher neutralizing antibody titers than the S1 monomer in mice. Crucially, it was found that the S1-conjugated nanoparticle vaccine could elicit comparable levels of neutralizing antibodies against wild-type or emerging variant SARS-CoV-2, with cross-reactivity to SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), the effect of which could be further enhanced using our designed nanoparticles. Our results indicate that the S1-conjugated nanoparticles are promising vaccine candidates with the potential to elicit potent and cross-reactive immunity against not only wild-type SARS-CoV-2, but also its variants of concern, variants of interest, and even other pathogenic betacoronaviruses. IMPORTANCE The emergence of SARS-CoV-2 variants led to an urgent demand for a broadly effective vaccine against the threat of variant infection. The spike protein S1-based nanoparticle designed in our study could elicit a comprehensive humoral response toward different SARS-CoV-2 variants of concern and variants of interest and will be helpful to combat COVID-19 globally.

Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study.

BACKGROUND: Human infections with zoonotic coronaviruses (CoVs), including severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV, have raised great public health concern globally. Here, we report a novel bat-origin CoV causing severe and fatal pneumonia in humans. METHODS: We collected clinical data and bronchoalveolar lavage (BAL) specimens from five patients with severe pneumonia from Wuhan Jinyintan Hospital, Hubei province, China. Nucleic acids of the BAL were extracted and subjected to next-generation sequencing. Virus isolation was carried out, and maximum-likelihood phylogenetic trees were constructed. RESULTS: Five patients hospitalized from December 18 to December 29, 2019 presented with fever, cough, and dyspnea accompanied by complications of acute respiratory distress syndrome. Chest radiography revealed diffuse opacities and consolidation. One of these patients died. Sequence results revealed the presence of a previously unknown ß-CoV strain in all five patients, with 99.8% to 99.9% nucleotide identities among the isolates. These isolates showed 79.0% nucleotide identity with the sequence of SARS-CoV (GenBank NC_004718) and 51.8% identity with the sequence of MERS-CoV (GenBank NC_019843). The virus is phylogenetically closest to a bat SARS-like CoV (SL-ZC45, GenBank MG772933) with 87.6% to 87.7% nucleotide identity, but is in a separate clade. Moreover, these viruses have a single intact open reading frame gene 8, as a further indicator of bat-origin CoVs. However, the amino acid sequence of the tentative receptor-binding domain resembles that of SARS-CoV, indicating that these viruses might use the same receptor. CONCLUSION: A novel bat-borne CoV was identified that is associated with severe and fatal respiratory disease in humans.

Total infectome characterization of respiratory infections in pre-COVID-19 Wuhan, China.

At the end of 2019 Wuhan witnessed an outbreak of "atypical pneumonia" that later developed into a global pandemic. Metagenomic sequencing rapidly revealed the causative agent of this outbreak to be a novel coronavirus denoted SARS-CoV-2. To provide a snapshot of the pathogens in pneumonia-associated respiratory samples from Wuhan prior to the emergence of SARS-CoV-2, we collected bronchoalveolar lavage fluid samples from 408 patients presenting with pneumonia and acute respiratory infections at the Central Hospital of Wuhan between 2016 and 2017. Unbiased total RNA sequencing was performed to reveal their "total infectome", including viruses, bacteria and fungi. We identified 35 pathogen species, comprising 13 RNA viruses, 3 DNA viruses, 16 bacteria and 3 fungi, often at high abundance and including multiple co-infections (13.5%). SARS-CoV-2 was not present. These data depict a stable core infectome comprising common respiratory pathogens such as rhinoviruses and influenza viruses, an atypical respiratory virus (EV-D68), and a single case of a sporadic zoonotic pathogen-Chlamydia psittaci. Samples from patients experiencing respiratory disease on average had higher pathogen abundance than healthy controls. Phylogenetic analyses of individual pathogens revealed multiple origins and global transmission histories, highlighting the connectedness of the Wuhan population. This study provides a comprehensive overview of the pathogens associated with acute respiratory infections and pneumonia, which were more diverse and complex than obtained using targeted PCR or qPCR approaches. These data also suggest that SARS-CoV-2 or closely related viruses were absent from Wuhan in 2016-2017.

The Development and Issues of Energy-ICT: A Review of Literature with Economic and Managerial Viewpoints

This paper reviews the economic and managerial literature on the relationship between energy-ICT and the development of the green energy economy. It is summarized that there are four lines of existing literature on energy-ICT: cost and benefit analysis, fair competition issues, cybersecurity issues, and promotion policy issues. Even though ICT is energy-consuming, most of the existing empirical studies support the idea that energy-ICT has net positive effects on energy savings, energy efficiency improvement, emission reduction, and economic growth at both enterprise and economy-wide levels. Energy-ICT equips the platform operator with higher bargaining power, such that a governance mechanism to assure the fair access right of each entitled participant is required. A smarter energy-ICT network also becomes riskier, and hence the cybersecurity protection is more important than before. Future research and development opportunities remain on these issues of the fair competition, cybersecurity, and promotion policy of energy-ICT.

Simultaneous Detection of Seven Human Coronaviruses by Multiplex PCR and MALDI-TOF MS

Human coronaviruses (HCoVs) are associated with a range of respiratory symptoms. The discovery of severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome, and SARS-CoV-2 pose a significant threat to human health. In this study, we developed a method (HCoV-MS) that combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), to detect and differentiate seven HCoVs simultaneously. The HCoV-MS method had high specificity and sensitivity, with a 1–5 copies/reaction detection limit. To validate the HCoV-MS method, we tested 163 clinical samples, and the results showed good concordance with real-time PCR. Additionally, the detection sensitivity of HCoV-MS and real-time PCR was comparable. The HCoV-MS method is a sensitive assay, requiring only 1 μL of a sample. Moreover, it is a high-throughput method, allowing 384 samples to be processed simultaneously in 30 min. We propose that this method be used to complement real-time PCR for large-scale screening studies.

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

This study aims to identify the key factors affecting individuals’behavioral vaccination intention against COVID-19 in Vietnam through an online questionnaire survey. Differing from previous studies, a novel three-staged approach combining Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Partial Least Squares-Structural Equation Model (PLS-SEM), and Artificial Neural Network (ANN) is proposed. Five factors associated with individuals’behavioral vaccination intention (INT) based on 15 experts’opinions are considered in SF-AHP analysis, including Perceived Severity of COVID-19 (PSC), Perceived COVID-19 vaccines (PVC), Trust in government intervention strategies (TRS), Social Influence (SOI), and Social media (SOM). First, the results of SF-AHP indicated that all proposed factors correlate with INT. Second, the data of 474 valid respondents were collected and analyzed using PLS-SEM. The PLS-SEM results reported that INT was directly influenced by PVC and TRS. In contrast, SOI had no direct effect on INT. Further, PSC and SOM moderated the relationship between PVC, TRS and INT, respectively. The ANN was deployed to validate the previous stages and found that the best predictors of COVID-19 vaccination intention were PVC, TRS, and SOM. These results were consistent with the SF-AHP and PLS-SEM models. This research provides an innovative new approach employing quantitative and qualitative techniques to understand individuals’vaccination intention during the global pandemic. Furthermore, the proposed method can be used and expanded to assess the perceived efficacy of COVID-19 measures in other nations currently battling the COVID-19 outbreak.

Multisystem inflammatory syndrome in children and adolescents after infection with SARS-CoV-2. / Multiorgan inflammatorisk syndrom hos barn og ungdom etter SARS-CoV-2-infeksjon.

Multisystem inflammatory syndrome is a rare immune-mediated complication of infection with SARS-CoV-2 in children and adolescents. The patients can rapidly become seriously ill with high fever, gastrointestinal symptoms and cardiogenic shock. The goal of treatment is to ensure adequate circulation and prevent late complications by providing anti-inflammatory therapy.

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.

The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R 2. Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study.

BACKGROUND: The consequences of COVID-19 in those who recover from acute infection requiring hospitalisation have yet to be clearly defined. We aimed to describe the temporal trends in respiratory outcomes over 12 months in patients hospitalised for severe COVID-19 and to investigate the associated risk factors. METHODS: In this prospective, longitudinal, cohort study, patients admitted to hospital for severe COVID-19 who did not require mechanical ventilation were prospectively followed up at 3 months, 6 months, 9 months, and 12 months after discharge from Renmin Hospital of Wuhan University, Wuhan, China. Patients with a history of hypertension; diabetes; cardiovascular disease; cancer; and chronic lung disease, including asthma or chronic obstructive pulmonary disease; or a history of smoking documented at time of hospital admission were excluded at time of electronic case-note review. Patients who required intubation and mechanical ventilation were excluded given the potential for the consequences of mechanical ventilation itself to influence the factors under investigation. During the follow-up visits, patients were interviewed and underwent physical examination, routine blood test, pulmonary function tests (ie, diffusing capacity of the lungs for carbon monoxide [DLCO]; forced expiratory flow between 25% and 75% of forced vital capacity [FVC]; functional residual capacity; FVC; FEV1; residual volume; total lung capacity; and vital capacity), chest high-resolution CT (HRCT), and 6-min walk distance test, as well as assessment using a modified Medical Research Council dyspnoea scale (mMRC). FINDINGS: Between Feb 1, and March 31, 2020, of 135 eligible patients, 83 (61%) patients participated in this study. The median age of participants was 60 years (IQR 52-66). Temporal improvement in pulmonary physiology and exercise capacity was observed in most patients; however, persistent physiological and radiographic abnormalities remained in some patients with COVID-19 at 12 months after discharge. We found a significant reduction in DLCO over the study period, with a median of 77% of predicted (IQR 67-87) at 3 months, 76% of predicted (68-90) at 6 months, and 88% of predicted (78-101) at 12 months after discharge. At 12 months after discharge, radiological changes persisted in 20 (24%) patients. Multivariate logistic regression showed increasing odds of impaired DLCO associated with female sex (odds ratio 8·61 [95% CI 2·83-26·2; p=0·0002) and radiological abnormalities were associated with peak HRCT pneumonia scores during hospitalisation (1·36 [1·13-1·62]; p=0·0009). INTERPRETATION: In most patients who recovered from severe COVID-19, dyspnoea scores and exercise capacity improved over time; however, in a subgroup of patients at 12 months we found evidence of persistent physiological and radiographic change. A unified pathway for the respiratory follow-up of patients with COVID-19 is required. FUNDING: National Natural Science Foundation of China, UK Medical Research Council, and National Institute for Health Research Southampton Biomedical Research Centre. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Selecting Bloggers for Hotels via an Innovative Mixed MCDM Model

The global coronavirus disease 2019 (COVID-19) outbreak had a great impact on the tourism industry. Numerous hotels have ceased operations. Because of the increasing influence of blogs, various industries have adopted blogs as a publicity and marketing strategy. Companies utilize consumers’ trust and loyalty toward bloggers to effectively contact them. Hence, bloggers play a crucial role in the hotel industry. No past study has researched blogger selection by hotel managers. In this study, an innovative mixed multiple-criteria decision-making (MCDM) model including importance-performance analysis (IPA), analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) is established to assist hotel managers in selecting bloggers. We firstly collect the selection criteria via interviews with hotel managers and a review of literature on blogger selection. Messages with stick are understood, remembered, and have an enduring influence on opinions and behavior. Hence, we also introduce the concept of stick to the selection criteria. Based on IPA and the literature review, a hierarchical structure for blogger selection is constructed. Then, AHP and TOPSIS are integrated to assist the case company managers to select suitable bloggers.

Could COVID-19 pandemic be stopped with joint efforts of travel restrictions and public health countermeasures? A modelling study.

OBJECTIVE: We aim to explore and compare the effect of global travel restrictions and public health countermeasures in response to COVID-19 outbreak. DESIGN: A data-driven spatio-temporal modelling to simulate the spread of COVID-19 worldwide for 150 days since 1 January 2020 under different scenarios. SETTING: Worldwide. INTERVENTIONS: Travel restrictions and public health countermeasures. MAIN OUTCOME: The cumulative number of COVID-19 cases. RESULTS: The cumulative number of COVID-19 cases could reach more than 420 million around the world without any countermeasures taken. Under timely and intensive global interventions, 99.97% of infections could be avoided comparing with non-interventions. The scenario of carrying out domestic travel restriction and public health countermeasures in China only could contribute to a significant decrease of the cumulative number of infected cases worldwide. Without global travel restriction in the study setting, 98.62% of COVID-19 cases could be avoided by public health countermeasures in China only compared with non-interventions at all. CONCLUSIONS: Public health countermeasures were generally more effective than travel restrictions in many countries, suggesting multinational collaborations in the public health communities in response to this novel global health challenge.