Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 380
Filter
1.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-338526

ABSTRACT

Using supervisory data on small and mid-sized nonfinancial enterprises (SMEs), we find that those SMEs with higher leverage faced tighter constraints in accessing bank credit after the COVID-19 outbreak in spring 2020. Specifically, SMEs with higher pre-COVID leverage obtained a smaller volume of new loans and had to pay a higher spread on them during the pandemic period. Consistent with an inward shift in loan supply, these effects were concentrated in loans originated by banks with below-median capital buffers. Highly levered SMEs that relied on low-capital large banks for funding before the pandemic were not able to substitute to other sources of debt financing and thus experienced more of a reduction in total debt as well as a decline in investment and employment. On the other hand, the unprecedented public support, especially the Paycheck Protection Program (PPP), mitigated the adverse real effect stemming from bank credit constraints.

2.
Frontiers in Environmental Science ; 10:13, 2022.
Article in English | Web of Science | ID: covidwho-1869374

ABSTRACT

This paper aims to empirically examine the impact of institutional pressure on green supply chain management (GSCM) efforts and the moderating role of big data analytics capabilities (BDAC) on organizational performance. This study greatly develops a research model by integrating institutional theory, the natural resource-based view (NRBV), and dynamic capability theory to explore this relationship. This article is based on structured questionnaire data of 347 supply chain personnel. We employed structural equation modeling to verify the research hypotheses. The findings provide empirical support for institutional pressures affecting GSCM efforts and organizational performance. The results also showed that the moderating effect of BDAC positively strengthened the impact of GSCM effort on organizational performance. The findings extend and refine the existing GSCM literature, providing new insights for scholars to explore this view further. Practitioners can turn their attention to incorporating institutional pressures and advanced technologies into organizational decision-making, even in times of crisis such as Covid-19.

3.
Bulletin of the American Meteorological Society ; 103(3):S83-S89, 2022.
Article in English | Web of Science | ID: covidwho-1868832

ABSTRACT

Anthropogenic forcing has approximately halved the probability of 2020 June-July persistent heavy mei-yu rainfall event based on HadGEM3-GA6 simulations without considering the COVID-induced aerosol emission reduction.

4.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-337493

ABSTRACT

Background. Monitoring the emergence and spread of SARS-CoV-2 variants is an important public health objective. Travel restrictions, aimed to prevent viral spread, have major economic consequences and unclear effectiveness despite considerable research. We investigated the introduction and establishment of the Gamma variant in New York City (NYC) in 2021. Methods. We performed phylogeographic analysis on 15,967 Gamma sequences available on GISAID and sampled between March 10th through May 1st, 2021, to identify geographic sources of Gamma lineages introduced into NYC. We identified locally circulating Gamma transmission clusters and inferred the timing of their establishment in NYC. Findings. We identified 16 phylogenetically-distinct Gamma clusters established in NYC (cluster sizes ranged 2-108 genomes). Most of the NYC clusters were introduced from Florida and Illinois;only one was introduced from outside the United States (US). By the time the first Gamma case was reported by genomic surveillance in NYC on March 10th, the majority (57%) of circulating Gamma lineages had already been established in the city for at least two weeks. Interpretation. Despite the expansion of SARS-CoV-2 genomic surveillance in NYC, there was a substantial gap between Gamma variant introduction and establishment in January/February 2021, and its identification by genomic surveillance in March 2021. Although travel from Brazil to the US was restricted from May 2020 through the end of the study period, this restriction did not prevent Gamma from becoming established in NYC as most introductions occurred from domestic locations.

5.
Current Issues in Tourism ; : 17, 2022.
Article in English | Web of Science | ID: covidwho-1852773

ABSTRACT

Travellers' mobility decisions are fraught with uncertainty and instability during public health crises. However, existing studies have not revealed the internal mechanism of travellers' mobility changes in a public health crisis. This paper established and trained a Bayesian network model from multiple data to analyse Chinese travellers' mobility decision-making processes under COVID-19 and simulated the changes in mobility decisions in different scenarios. The results show that travellers reformulate mobility decisions in response to various information and negotiate between social customs and personal needs. Mobility can be modified through risk communication and habits adaptation. Bayesian network models provide a methodological contribution to causal exploration and scenario prediction.

8.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-336965

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through 'epitope grafting,' in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of 'epitope grafting' in rational vaccine design.

9.
Msystems ; 6(6):52, 2021.
Article in English | Web of Science | ID: covidwho-1849163

ABSTRACT

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

11.
Mathematics in Applied Sciences and Engineering ; 3(1):60-85, 2022.
Article in English | Scopus | ID: covidwho-1847558

ABSTRACT

We introduce two mathematical models based on systems of differential equations to investigate the relationship between the latency period and the transmission dynamics of COVID-19. We analyze the equilibrium and stability properties of these models, and perform an asymptotic study in terms of small and large latency periods. We fit the models to the COVID-19 data in the U.S. state of Tennessee. Our numerical results demonstrate the impact of the latency period on the dynamical behaviors of the solutions, on the value of the basic reproduction numbers, and on the accuracy of the model predictions. © Mathematics in Applied Sciences and Engineering 2022.

12.
Natural Product Communications ; 17(4), 2022.
Article in English | Scopus | ID: covidwho-1846642

ABSTRACT

Jiedu Huoxue Decoction (JHD), a recommended traditional prescription for patients with severe COVID-19, has appeared in the treatment protocols in China. Based on bioinformatics and computational chemistry methods, including molecular docking, molecular dynamics (MD) simulation, and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculation, we aimed to reveal the mechanism of JHD in treating severe COVID-19. The compounds in JHD were obtained and screened on TCMSP, SwissADME, and ADMETLab platforms. The compound targets were obtained from TCMSP and STITCH, while COVID-19 targets were obtained from Genecards and NCBI. The protein-protein interaction network was constructed by using STRING. Gene Ontology (GO) and KEGG enrichment were performed with ClueGO and R language. AutoDock vina was employed for molecular docking. 100 ns MD simulation of the optimal docking complex was carried out with AmberTools 20. A total of 84 compounds and 29 potential targets of JHD for COVID-19 were collected. The key phytochemicals included quercetin, luteolin, β-sitosterol, puerarin, stigmasterol, kaempferol, and wogonin, which could regulate the immune system. The hub genes included IL6, IL10, VEGFA, IL1B, CCL2, HMOX1, DPP4, and ACE2. ACE2 and DPP4 were related to SARS-CoV-2 entering cells. GO and KEGG analysis showed that JHD could intervene in cytokine storm and endothelial proliferation and migration related to thrombosis. The molecular docking, 100 ns MD simulation, and MM/GBSA calculation confirmed that targets enriched in the COVID-19 pathway had high affinities with related compounds, and the conformations of the puerarin-ACE2, quercetin-EGFR, luteolin-EGFR, and quercetin-IL1B complexes were stable. In a word, JHD could treat COVID-19 by intervening in cytokine storm, thrombosis, and the entry of SARS-CoV-2, while regulating the immune system. These mechanisms were consistent with JHD's therapeutic concept of “detoxification” and “promoting blood circulation and removing blood stasis” in treating COVID-19. The research provides a theoretical basis for the development and application of JHD. © The Author(s) 2022.

13.
2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era, FoNeS-AIoT 2021 ; : 28-32, 2021.
Article in English | Scopus | ID: covidwho-1846087

ABSTRACT

In 2015, the "Internet +"action plan first appeared. Various industries have been developing rapidly, aided by the rise of the Internet, and the "Internet + Education"is certainly among them. Affected by the COVID-19 pandemic, online courses and online and offline hybrid courses have become key breakthrough fields in college curriculum reform. This paper mainly conducts a comprehensive study on the online and offline hybrid teaching mode of "Internet Marketing"carried out by Chaoxing online teaching platform, aiming at constructing a first-class undergraduate course of "Internet marketing". © 2021 IEEE.

14.
4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 ; : 470-474, 2022.
Article in English | Scopus | ID: covidwho-1840265

ABSTRACT

Recent years have witnessed the rapid development of artificial intelligence (AI) in different fields, including biomedical, in which timely detection of anomalies can play a vital role in patients' health monitoring. COVID-19, a contagious disease caused by the Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2), has become a global epidemic. The key to combating this and other epidemics is detecting and isolating the infected patients in time. Therefore, there is an urgent need for a timely, practical detection approach. This paper proposes an AI-enabled pneumonia detection system, AIRBiS, to detect pneumonia (i.e., COVID-19) efficiently. AIRBiS is based on a high-performance Artificial Neural Network and an interactive user interface for effective operation and monitoring. The evaluation results demonstrate that the proposed system achieved 94.4% detection accuracy of pneumonia (i.e., COVID-19) over the collected test data. © 2022 IEEE.

15.
3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022 ; : 507-511, 2022.
Article in English | Scopus | ID: covidwho-1831841

ABSTRACT

The use of computer vision to realize non-contact face mask wearing testing and identify the standardization of wearing can improve the efficiency of mask wearing inspection, which is of great significance to reduce the spread of COVID-19. Based on the YOLO v3 framework in deep learning, this project conducts an in-depth study on the accuracy and efficiency of face mask detection, introduces its structure and principle, and explores and experiments its application based on TensorFlow. The evaluation and identification rate of the experiment was 78∗, which verified the feasibility and practicability of the method. © 2022 IEEE.

16.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-334805

ABSTRACT

Omicron sub-lineage BA.2 has rapidly surged globally, accounting for over 60% of recent SARS-CoV-2 infections. Newly acquired RBD mutations and high transmission advantage over BA.1 urge the investigation of BA.2's immune evasion capability. Here, we show that BA.2 causes strong neutralization resistance, comparable to BA.1, in vaccinated individuals' plasma. However, BA.2 displays more severe antibody evasion in BA.1 convalescents, and most prominently, in vaccinated SARS convalescents' plasma, suggesting a substantial antigenicity difference between BA.2 and BA.1. To specify, we determined the escaping mutation profiles1,2 of 714 SARS-CoV-2 RBD neutralizing antibodies, including 241 broad sarbecovirus neutralizing antibodies isolated from SARS convalescents, and measured their neutralization efficacy against BA.1, BA.1.1, BA.2. Importantly, BA.2 specifically induces large-scale escape of BA.1/BA.1.1effective broad sarbecovirus neutralizing antibodies via novel mutations T376A, D405N, and R408S. These sites were highly conserved across sarbecoviruses, suggesting that Omicron BA.2 arose from immune pressure selection instead of zoonotic spillover. Moreover, BA.2 reduces the efficacy of S309 (Sotrovimab)3,4 and broad sarbecovirus neutralizing antibodies targeting the similar epitope region, including BD55-5840. Structural comparisons of BD55-5840 in complexes with BA.1 and BA.2 spike suggest that BA.2 could hinder antibody binding through S371F-induced N343-glycan displacement. Intriguingly, the absence of G446S mutation in BA.2 enabled a proportion of 440-449 linear epitope targeting antibodies to retain neutralizing efficacy, including COV2-2130 (Cilgavimab)5. Together, we showed that BA.2 exhibits distinct antigenicity compared to BA.1 and provided a comprehensive profile of SARS-CoV-2 antibody escaping mutations. Our study offers critical insights into the humoral immune evading mechanism of current and future variants.

17.
10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021 ; 878 LNEE:557-565, 2022.
Article in English | Scopus | ID: covidwho-1826329

ABSTRACT

The worldwide spread of COVID-19 has greatly hit global economy by now. The world’s major economies including both developed and developing countries have felt the resulting impact on their financial markets. Accordingly, learning residents’ consumption structure is significant for boosting consumption demand and recovering financial market. In this paper, the Extend Linear Expenditure System (ELES) model is explored to learn both urban and rural residents’ consumption structures of China during COVID-19. In specific, the indices of marginal propensity to consume, income elasticity of demand, and price elasticity can be yielded via the ELES model based on the disposable income and the consumption data. Furthermore, the consumption structures before and during the corona virus epidemic can be quantitatively compared. Extensive experimental results demonstrate that the epidemic has made profound impacts on the consumption structure of residents. Among them, the marginal propensities on food and medical services have greatly increased, while the proportions of other expenditures have been decreased. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

18.
10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021 ; 878 LNEE:548-556, 2022.
Article in English | Scopus | ID: covidwho-1826328

ABSTRACT

Since 2019, the sudden outbreak of COVID-19 has made huge impacts on various aspects of society, especially the financial industries that are closely related to the national economy and people’s livelihood. Finance is a data-intensive field and its traditional research models include supervised and unsupervised models, state-based models, econometric models, and stochastic models. However, the above models are prone to lose their effectiveness in the situation of an extremely complex financial ecosystem with a large number of nonlinear unpredictable effects, such as those caused by COVID-19. To address this issue, we comprehensively explore and fuse Stochastic Block Model (SBM) and Cox Proportional Hazards Model (COX) for a reliable and accurate financial risk prediction. Specifically, SBM, which is popular in social network analysis, is employed to capture the impact factors on the financial industry in public emergencies, and COX is then leveraged to determine the duration of the impact factors. An extensive experimental evaluation validates the effectiveness of our framework in predicting financial risk. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

19.
Asian Journal of Organic Chemistry ; 2022.
Article in English | Scopus | ID: covidwho-1825833

ABSTRACT

A sequential protocol of α-diazophosphonates with isatins to access a series of α-diazo-β-hydroxyphosphonate derivatives via the inorganic base catalysis was reported. The resulting α-diazo-β-hydroxyphosphonates could then be readily transformed to 4-phosphonylated-3-hydroxyquinolin-2(1H)-ones with moderate to excellent yields through a catalyst-free regioselective ring-expansion rearrangement. Control experiment demonstrates that intramolecular cyclization pathway is more reasonable for the ring-expansion process. In addition, a benzo[b]thiophene-derived isatin featured with the inhibition of SARS-CoV Mpro was also suitable for this transformation and generated the corresponding scaffolds with potential anti-virus activities for further development. © 2022 Wiley-VCH GmbH.

20.
Applied Economics ; : 8, 2022.
Article in English | Web of Science | ID: covidwho-1805775

ABSTRACT

We develop a pricing model for pandemic European call and put options, in which the underlying variable is the infected population. Interestingly, the economic predictions of the pandemic options are essentially different from the common stock option. For example, the value of pandemic call option is concave in the underlying variable and decreasing in the volatility. In addition, the maturity has ambiguous impact on the valuation of pandemic call option. We show that the basic reproduction number, the unique characteristics of pandemic, has a significant effect on the valuation of each options.

SELECTION OF CITATIONS
SEARCH DETAIL