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Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated 2,431 variants over the course of its global transmission over the past 3 years. To better evaluate the genomic variation of SARS-CoV-2 before and after the optimization of coronavirus disease 2019 (COVID-19) prevention and control strategies, we analyzed the genetic evolution branch composition and genomic variation of SARS-CoV-2 in both domestic and imported cases in China (the data from Hong Kong and Macau Special Administrative Regions and Taiwan, China were not included) from September 26, 2022 to January 29, 2023. Methods: Analysis of the number of genome sequences, sampling time, dynamic changes of evolutionary branches, origin, and clinical typing of SARS-CoV-2 variants submitted by 31 provincial-level administrative divisions (PLADs) and Xinjiang Production and Construction Corps (XPCC) was conducted to assess the accuracy and timeliness of SARS-CoV-2 variant surveillance. Results: From September 26, 2022 to January 29, 2023, 20,013 valid genome sequences of domestic cases were reported in China, with 72 evolutionary branches. Additionally, 1,978 valid genome sequences of imported cases were reported, with 169 evolutionary branches. The prevalence of the Omicron variants of SARS-CoV-2 in both domestic and imported cases was consistent with that of international epidemic variants. Conclusions: This study provides an overview of the prevalence of Omicron variants of SARS-CoV-2 in China. After optimizing COVID-19 prevention and control strategies, no novel Omicron variants of SARS-CoV-2 with altered biological characteristics or public health significance have been identified since December 1, 2022.
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Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is the dominant circulating strain worldwide. To assess the importation of SARS-CoV-2 variants in the mainland of China during the Omicron epidemic, the genomic surveillance data of SARS-CoV-2 from imported coronavirus disease 2019 (COVID-19) cases in the mainland of China during the first half of 2022 were analyzed. Methods: Sequences submitted from January to July 2022, with a collection date before June 30, 2022, were incorporated. The proportions of SARS-CoV-2 variants as well as the relationships between the origin and destination of each Omicron imported case were analyzed. Results: 4,946 sequences of imported cases were submitted from 27 provincial-level administrative divisions (PLADs), and the median submission interval was within 1 month after collection. In 3,851 Omicron sequences with good quality, 1 recombinant (XU) and 4 subvariants under monitoring (BA.4, BA.5, BA.2.12.1, and BA.2.13) were recorded, and 3 of them (BA.4, BA.5, and BA.2.12.1) caused local transmissions in the mainland of China later than that recorded in the surveillance. Omicron subvariants dominated in the first half of 2022 and shifted from BA.1 to BA.2 then to BA.4 and BA.5. The percentage of BA.2 in the imported SARS-CoV-2 surveillance data was far higher than that in the Global Initiative on Sharing All Influenza Data (GISAID). The imported cases from Hong Kong Special Administrative Region, China, accounted for 32.30% of Omicron cases sampled, and 98.71% of them were BA.2. Conclusions: The Omicron variant showed the intra-Omicron evolution in the first half of 2022, and all of the Omicron subvariants were introduced into the mainland of China multiple times from multiple different locations.
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Background: Based on the control-value theory (CVT), learning strategies and academic emotions are closely related to learning achievement, and have been considered as important factors influencing student's learning satisfaction and learning performance in the online learning context. However, only a few studies have focused on the influence of learning strategies on academic emotions and the interaction of learning strategies with behavioral engagement and social interaction on learning satisfaction. Methods: The participants were 363 pre-service teachers in China, and we used structural equation modeling (SEM) to analyze the mediating and moderating effects of the data. Results: The main findings of the current study showed that learning strategies influence students' online learning satisfaction through academic emotions. The interaction between learning strategies and behavioral engagement was also an important factor influencing online learning satisfaction. Conclusions: We explored the internal mechanism and boundary conditions of how learning strategies influenced learning satisfaction to provide intellectual guarantee and theoretical support for the online teaching design and online learning platform. This study provides theoretical contributions to the CVT and practical value for massive open online courses (MOOCs), flipped classrooms and blended learning in the future.
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The pandemic due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), has caused immense global disruption. With the rapid accumulation of SARS-CoV-2 genome sequences, however, thousands of genomic variants of SARS-CoV-2 are now publicly available. To improve the tracing of the viral genomes' evolution during the development of the pandemic, we analyzed single nucleotide variants (SNVs) in 121,618 high-quality SARS-CoV-2 genomes. We divided these viral genomes into two major lineages (L and S) based on variants at sites 8782 and 28144, and further divided the L lineage into two major sublineages (L1 and L2) using SNVs at sites 3037, 14408, and 23403. Subsequently, we categorized them into 130 sublineages (37 in S, 35 in L1, and 58 in L2) based on marker SNVs at 201 additional genomic sites. This lineage/sublineage designation system has a hierarchical structure and reflects the relatedness among the subclades of the major lineages. We also provide a companion website (www.covid19evolution.net) that allows users to visualize sublineage information and upload their own SARS-CoV-2 genomes for sublineage classification. Finally, we discussed the possible roles of compensatory mutations and natural selection during SARS-CoV-2's evolution. These efforts will improve our understanding of the temporal and spatial dynamics of SARS-CoV-2's genome evolution.
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The SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since impacted a large portion of China and raised major global concern. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses. Although we found only 4% variability in genomic nucleotides between SARS-CoV-2 and a bat SARS-related coronavirus (SARSr-CoV; RaTG13), the difference at neutral sites was 17%, suggesting the divergence between the two viruses is much larger than previously estimated. Our results suggest that the development of new variations in functional sites in the receptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin SARSr-CoVs are likely caused by natural selection besides recombination. Population genetic analyses of 103 SARS-CoV-2 genomes indicated that these viruses had two major lineages (designated L and S), that are well defined by two different SNPs that show nearly complete linkage across the viral strains sequenced to date. We found that L lineage was more prevalent than the S lineage within the limited patient samples we examined. The implication of these evolutionary changes on disease etiology remains unclear. These findings strongly underscores the urgent need for further comprehensive studies that combine viral genomic data, with epidemiological studies of coronavirus disease 2019 (COVID-19).