ABSTRACT
Leveraging the unexpected variation in the frequency of eating restaurant prepared food due to the COVID-19 pandemic, we seek to identify and estimate the causal relationship between the frequency of eating restaurant prepared food and people's BMI. We use first-differencing and instrumental variable approaches to correct for potential endogeneity bias due to both the time-invariant and time-varying unobserved factors. Our results show eating more restaurant prepared food has a positive and statistically significant effect on BMI, and in addition to other channels that have been identified in the literature, mood-boosting is another channel through which eating restaurant prepared food leads to weight gain. Heterogeneous effect analysis further shows that eating restaurant prepared food is more likely to have an impact on those people who lead a more stressful lifestyle before the pandemic.
ABSTRACT
Spike proteins of coronaviruses are highly glycosylated and responsible for host recognition and viral entry. The glycans provide a camouflaging shield to help coronaviruses evade host immunity and, in some cases, modulate functional domain structures and dynamics pertinent to host recognition. However, the glycans are chemically and conformationally heterogeneous, making it challenging to determine the chemical compositions and conformations quantitatively. Combining cryo-electron microscopy, mass spectrometry, and molecular modeling, we systematically characterize a panel of spike protein variants of human and animal coronaviruses, including those of the variants of concern of SARS-CoV-2. We have established a robust workflow to quantify the heterogeneity of individual N-glycans by mass spectrometry. We also demonstrated the ability to visualize long glycan structures directly in regions where the dynamics are restricted. In places where the N-glycans are too dynamic, their structural information is generally lost after extended cryo-EM data processing that aims to achieve high resolution. To address this issue, we developed a computational tool called GlycoSHIELD to generate ensembles of glycan conformers to recapitulate the fuzzy structures that are in quantitative agreement with the experimental cryo-EM data. The ability to generate fully glycosylated spike protein models enables the prediction of hitherto unknown receptor and antibody binding sites. This work was supported by Academia Sinica intramural fund, an Academia Sinica Career Development Award, Academia Sinica to STDH (AS-CDA-109- L08), an Infectious Disease Research Supporting Grant to STDH (AS-IDR- 110-08), and the Ministry of Science and Technology (MOST), Taiwan (MOST 109-3114-Y-001-001, MOST 110-2113-M-001- 050-MY3 and MOST 110-2311-B-001-013-MY3) to STDH.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
ABSTRACT
The goal of proactive contact tracing is to diminish the spread of an epidemic by means of contact tracing mobile apps and big data analysis. Finding superspreaders as has been used in Japan and Australia during the early days of the COVID-19 pandemic has proven effective as backward contact tracing can pick up infections that might otherwise be missed. In this paper, we formulate a proactive contact tracing problem to identify the superspreaders using maximum-likelihood estimation, graph traversal and deep learning algorithms. This problem is challenging due to its sheer combinatorial complexity, problem scale and the fact that the underlying infection network topology is rarely known. We propose a deep learning-based framework using Graph Neural Networks to iteratively refine the supervised learning of proactive contact tracing networks using smaller infection networks and to identify the superspreader. By optimizing the graph traversal and topological features for deep learning, proactive contact tracing strategies can be developed to contain superspreading in an epidemic outbreak.
ABSTRACT
In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.
ABSTRACT
Aim. The study is aimed at investigating the incidence of lung cancer (LC) at the population level with the analysis of key analytical indicators in the context of coronavirus. Materials and methods. We have used data from the IARC, statistical reference books of P.A. Herzen Moscow Research Oncological Institute, data from N.N. Petrov NMRC of Oncology, and the database of the Population Cancer Registry of St. Petersburg (DB PCR St. Petersburg) for the current study. Results. The study has established a steady decline in the incidence of LC among the male population in the worldwide, in Russia and in St. Petersburg. Among the female population, a significant increase in the incidence has been revealed. On the basis of the database PCR St. Petersburg, the patterns of age-sex indicators of the incidence of LC have been analyzed. It has been established that the largest number of cases is registered in the age group of patients over 70 years old, here the largest losses due to coronavirus have also been identified. An increase in the proportion of early stages of the disease has been noted. The accounting reliability index decreased from 2000 to 2019, with the exception of 2017. Comparison of yearly lethality in the DB PCR St. Petersburg showed a significant difference in the data. The detailed localization structure of LC has been studied, where it has been found out that the first place is occupied by cancer of the upper lobe, bronchi or lung, although over 19 years of observation, a decrease in the proportion of this localization in the total volume of C34 has been noted. The proportion of patients with LC without histological verification has decreased. The influence of the smoking factor on the increased risk of LC has been studied.Copyright © 2022 Izdatel'stvo Meditsina. All rights reserved.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibits strong stability on conventional stainless steel (SS) surface, with infectious virus detected even after two days, posing a high risk of virus transmission via surface touching in public areas. In order to mitigate the surface toughing transmission, the present study develops the first SS with excellent anti-pathogen properties against SARS-COV-2. The stabilities of SARS-CoV-2, H1N1 influenza A virus (H1N1), and Escherichia coli (E.coli) on the surfaces of Cu-contained SS, pure Cu, Ag-contained SS, and pure Ag were investigated. It is discovered that pure Ag and Ag-contained SS surfaces do not display apparent inhibitory effects on SARS-CoV-2 and H1N1. In comparison, both pure Cu and Cu-contained SS with a high Cu content exhibit significant antiviral properties. Significantly, the developed anti-pathogen SS with 20 wt% Cu can distinctly reduce 99.75% and 99.99% of viable SARS-CoV-2 on its surface within 3 and 6 h, respectively. In addition, the present anti-pathogen SS also exhibits an excellent inactivation ability for H1N1 influenza A virus (H1N1), and Escherichia coli (E.coli). Interestingly, the Cu ion concentration released from the anti-pathogen SS with 10 wt% and 20 wt% Cu was notably higher than the Ag ion concentration released from Ag and the Ag-contained SS. Lift buttons made of the present anti-pathogen SS are produced using mature powder metallurgy technique, demonstrating its potential applications in public areas and fighting the transmission of SARS-CoV-2 and other pathogens via surface touching.
ABSTRACT
Aim. The study is aimed at investigating the incidence of lung cancer (LC) at the population level with the analysis of key analytical indicators in the context of coronavirus. Materials and methods. We have used data from the IARC, statistical reference books of P.A. Herzen Moscow Research Oncological Institute, data from N.N. Petrov NMRC of Oncol¬ogy, and the database of the Population Cancer Registry of St. Petersburg (DB PCR St. Petersburg) for the current study. Results. The study has established a steady decline in the incidence of LC among the male population in the worldwide, in Russia and in St. Petersburg. Among the female population, a significant increase in the incidence has been revealed. On the basis of the database PCR St. Petersburg, the patterns of age-sex indicators of the incidence of LC have been analyzed. It has been established that the largest number of cases is registered in the age group of patients over 70 years old, here the largest losses due to coronavirus have also been identified. An increase in the proportion of early stages of the disease has been noted. The accounting reliability index decreased from 2000 to 2019, with the exception of 2017. Comparison of yearly lethality in the DB PCR St. Petersburg showed a signifi¬cant difference in the data. The detailed localization structure of LC has been studied, where it has been found out that the first place is occupied by cancer of the upper lobe, bronchi or lung, although over 19 years of observation, a decrease in the proportion of this localization in the total volume of C34 has been noted. The proportion of patients with LC without histological verification has decreased. The influence of the smoking factor on the increased risk of LC has been studied.
ABSTRACT
The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has posed many challenges to the medical community, a number of which will remain in the near future. Analysis of clinical cases helps to identify problems, to plan future research, which can change the understanding of the consequences of the disease. As the first year of the pandemic progresses, there is a better understanding of the pathophysiology of the virus and the varied results of imaging COVID-19 in affected organs, which is crucial for improving the treatment of this complex disease and improving health outcomes. It is described a clinical case of exanthema in an 11-year-old patient after COVID-19 Clinical and immunological methods of investigation are used. The child's condition was of the moderate severity, caused by skin and asthenovegetative syndromes. The child was diagnosed with COVID-19;fatigue syndrome after a viral illness;post COVID-19 exanthema infection crustal ichthyosis of the legs. A particular feature of this case is the occurrence of exanthema on the skin of the child after COVID-19. In these cases, the awareness of pediatricians should be raised to increase the level of knowledge of the correct diagnostic algorithm. © 2022 by the Author(s).
ABSTRACT
STEM education is a multidisciplinary teaching approach that aims at helping students develop knowledge in science, technology, engineering, and mathematics. This chapter aims to study the development of online STEM education of K12 group and its innovation in the era of distance education and popularity in China. This chapter will discuss the current situation of STEM education in China and the factors affecting the implementation of it particularly during the COVID-19 period. This chapter will study the learning outcome of K12 group by investigating the impact of STEM education on teaching mode. This chapter will discuss whether and how STEM approach would enhance teaching quality on K12. By applying bibliometric analysis, the chapter will perform statistical and correlation analysis on the STEM education. STEM and STEAM cases will be used for discussion. Finally, the chapter will provide suggestions for stakeholders to promote STEM education. © 2023 by IGI Global. All rights reserved.
ABSTRACT
Background: Convalescent plasma therapy (CPT) has been issued emergency use authorization for the treatment of SARS-CoV-2 (COVID-19) by the United States Food and Drug Administration. The presence of coagulation factors in CPT in combination with the pro-thrombotic state COVID-19 patients are in may potentiate their risk of thrombotic events. Aim(s): To assess the risk of venous thromboembolisms (VTE) and arterial thromboembolisms (ATE) in COVID-19 patients undergoing CPT. Method(s): MEDLINE, Embase, and Cochrane CENTRAL were searched for randomized controlled trials that investigated the safety and efficacy of CPT and standard of care (SOC) against placebo or SOC alone in adult COVID-19 patients. Study selection and data extraction were done in duplicate. The primary outcome was the development of VTE and ATE. The secondary outcomes were 30-day mortality, clinical improvement, length of hospitalization (LOH), sepsis/fever, and major adverse cardiovascular events (MACE). Meta-analysis was conducted using the Mantel-Haenszel random effects model. Binary endpoints and continuous endpoints were synthesized using odd ratios (OR) and mean differences respectively with 95% confidence intervals (CI). Result(s): 17 randomized controlled trials including 18566 patients were included (Table 1). The risk of VTE and ATE did not differ between the CPT and the control group. There were also no significant differences in 30-day mortality, clinical improvement, LOH, risk of sepsis/fever, and MACE. A summary of outcomes is illustrated in Table 2. Conclusion(s): Treatment of COVID-19 with CPT does not appear to be associated with an increased risk of VTE, ATE, or adverse events but also does not appear to provide mortality or clinical benefit.
ABSTRACT
This chapter focuses on the development of social media advertising in China, especially influencer marketing through social media posts. More specifically, this study examines how the personal traits of social media influencers, the characteristics of the content posted by them, the form of advertising, and the users' personal preferences influence the effectiveness of these influencers' advertising. Through this research study, the authors conclude that social media influencers in China act as great assets to advertising and in recent times have gained the attention from many brands. During COVID-19, social media usage increased dramatically and helped influencers increase their followership. In an ever-accelerated pace of this digital and social era, it is worthy for brands to rethink how to collaborate and partner with these influencers. It is also significant for influencers to think about how to maintain long-term relationships and garner meaningful interactions with their fans and fellow influencers. © 2022, IGI Global.
ABSTRACT
Tourism and hospitality that once occupied billions of economic outputs every year was blown seriously by the COVID-19 outbreak. The slow of domestic and international interactions has drained the tourism and hospitality industries' customer flows greatly. Millions of practitioners are struggling for crisis remission. Though scholars have noted the plight of the hospitality industry, most previous studies have focused on the pandemic effects on the hospitality industry in the short run. Few have investigated the whole industry's long-term sustainability under the pandemic, which is increasingly important as the pandemic is going beyond expected. This chapter aims to explore measures and innovations that help hotels recover from pandemic shock and develop long-term sustainability during the outbreak. Based on the framework of Clauss T, the author studied the business model and innovation of the Chinese hotel industry in the post-pandemic era by integrating the importance of hygiene upgrades, local market expansion, and stakeholder management to the industry. © 2022, IGI Global. All rights reserved.
ABSTRACT
This chapter discusses the Chinese EdTech economy in the COVID-19 era. After studying the characteristics of the Chinese online education market and its market size, this research focuses on the differences in business strategies and market responses among online education applications during the COVID-19 era and the subsequent institutional changes, as well as analyzing the factors that cause such differences. For the analysis, a few cases of successful online education companies in China are selected, and their business models and technology applications are discussed. The authors conclude this chapter by addressing the challenges that China's online education faces and providing corresponding suggestions. © 2022, IGI Global. All rights reserved.
ABSTRACT
Digital economy using internet, cloud computing, big data, as well as fintech in order to drive economic activities using digital information as a key factor for production has permeated all aspects of society as a result of unexpected onset of the COVID-19 pandemic. Recently, a white paper titled "The Development and Employment of China's Digital Economy in 2021" was released by The China Academy of Information and Communications Technology (CAICT), analyzing the development pattern of China's digital economy since the onset of COVID-19. This chapter summarizes the development of digital economy, the employment situation in various regions and industries in China, thereby making an in-depth analysis of the digital transformation of traditional industries along with putting forward policy suggestions for promoting further development of China's digital economy. Since the onset of COVID-19, China's digital and smart economy has ushered in creating new opportunities for growth and development as well as simultaneously combatting challenges in the macro environment. © 2022, IGI Global. All rights reserved.
ABSTRACT
This chapter aims to study operation mode of wine tourism and coping strategies of major wine producing regions in the context of COVID-19. This study uses Citespace to analyze the changes of research hotspots in wine tourism industry before and after COVID-19. Through the comparison between the global wine industry management strategy and the Chinese wine industry management strategy, the similarities and differences between the two are analyzed through specific cases. This chapter investigates the driving force and factors of sustainable development of tourism economy with wine tourism as the core sector. It also studies innovative strategies such as the promotional effects of digital tourism in response to COVID-19 on wine tourism, thereby extending the protection and utilization of intellectual property rights of each wine producer. Case studies of China will be used for discussion. Finally, this chapter provides suggestions for policymakers to build a sustainable wine tourism for enterprises. © 2022, IGI Global.
ABSTRACT
The potential impact of epidemics, e.g., COVID-19, H1N1, and SARS, is severe on public health, the economy, education, and society. Before effective treatments are available and vaccines are fully deployed, combining Non-Pharmaceutical Interventions (NPIs) and vaccination strategies is the main approaches to contain the epidemic or live with the virus. Therefore, research for deciding the best containment operations to contain the epidemic based on various objectives and concerns is much needed. In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. We prove the hardness of COOD and propose an approximation algorithm, named Multi-Type Action Scheduling (MTAS), with the ideas of Infected Ratio, Contact Risk, and Severity Score to select and schedule appropriate actions that implement NPIs and allocate vaccines for different groups of people. We evaluate MTAS on real epidemic data of a population with real contacts and compare it against existing approaches in epidemic and misinformation containment. Experimental results demonstrate that MTAS improves at least 200% over the baselines in the test case of sustaining public health and the economy. Moreover, the applicability of MTAS to various epidemics of different dynamics is demonstrated, i.e., MTAS can effectively slow down the peak and reduce the number of infected individuals at the peak. © 2022 IEEE.