The influence of organizational factors on the acceptance of online teaching among college faculty during the COVID-19 pandemic: a nationwide study in mainland China.

The outbreak of the coronavirus disease 2019 (COVID-19) promoted online teaching on an unprecedented scale, raising researchers' attention to the importance of faculty's acceptance of this urgent teaching shift. This study aimed to explore the influence of organizational factors on faculty's acceptance of online teaching in terms of behavioral intention and perceived usefulness. A multilevel structural equation model was employed to analyze data on 209,058 faculty in 858 higher education institutions based on a nationwide survey conducted in mainland China. The results showed that three key organizational factors, namely strategic planning, leadership, and teaching quality monitoring, impacted faculty's acceptance of online teaching, although in different ways. Strategic planning had a direct impact on perceived usefulness, while leadership had a direct impact on behavioral intentions, and teaching quality monitoring had a direct impact on both perceived usefulness and behavioral intentions. In addition, an indirect effect was found between strategic planning and faculty's behavioral intentions through the mediation of the perceived usefulness of online teaching. The findings of this study have practical implications for college administrators and policymakers, which should effectively implement and promote online teaching and learning, and consider key organizational factors to increase faculty acceptance.

Proteomic profiling of single extracellular vesicles reveals colocalization of SARS-CoV-2 with a CD81/integrin-rich EV subpopulation in sputum from COVID-19 severe patients.

Background: The global outbreak of COVID-19, and the limited availability of clinical treatments, forced researchers around the world to search for the pathogenesis and potential treatments. Understanding the pathogenesis of SARS-CoV-2 is crucial to respond better to the current coronavirus disease 2019 (COVID-19) pandemic. Methods: We collected sputum samples from 20 COVID-19 patients and healthy controls. Transmission electron microscopy was used to observe the morphology of SARS-CoV-2. Extracellular vesicles (EVs) were isolated from sputum and the supernatant of VeroE6 cells, and were characterized by transmission electron microscopy, nanoparticle tracking analysis and Western-Blotting. Furthermore, a proximity barcoding assay was used to investigate immune-related proteins in single EV, and the relationship between EVs and SARS-CoV-2. Result: Transmission electron microscopy images of SARS-COV-2 virus reveal EV-like vesicles around the virion, and western blot analysis of EVs extracted from the supernatant of SARS-COV-2-infected VeroE6 cells showed that they expressed SARS-COV-2 protein. These EVs have the infectivity of SARS-COV-2, and the addition can cause the infection and damage of normal VeroE6 cells. In addition, EVs derived from the sputum of patients infected with SARS-COV-2 expressed high levels of IL6 and TGF-ß, which correlated strongly with expression of the SARS-CoV-2 N protein. Among 40 EV subpopulations identified, 18 differed significantly between patients and controls. The EV subpopulation regulated by CD81 was the most likely to correlate with changes in the pulmonary microenvironment after SARS-CoV-2 infection. Single extracellular vesicles in the sputum of COVID-19 patients harbor infection-mediated alterations in host and virus-derived proteins. Conclusions: These results demonstrate that EVs derived from the sputum of patients participate in virus infection and immune responses. This study provides evidence of an association between EVs and SARS-CoV-2, providing insight into the possible pathogenesis of SARS-CoV-2 infection and the possibility of developing nanoparticle-based antiviral drugs.

Primary students' online homework completion and learning achievement

Online homework has become an important teaching and learning activity due to the impact of COVID-19 pandemic-related restrictions. This study explored the relationship between primary students' online homework completion and learning achievement. It also investigated the moderating effects of key factors including the role of the students and the involvement of their parents on this relationship. Based on a total of 3,210 Chinese online homework assigned in the Spring of 2020 to fourth grade primary school students in Wuhan, China, hierarchical linear modeling was employed to examine the relationship between primary students' online homework completion and their learning achievement. Simultaneously, the effects of potential moderators including students' information literacy, students' prior academic achievement, parental digital self-efficacy, and parent - teacher partnership were investigated. The results showed a significant positive effect of students' online homework completion on learning achievement. Moreover, students' information literacy, students' prior achievement, and parent - teacher partnership positively moderated this relationship. Based on the findings, practical implications for school administrators, teachers, and parents are discussed herein to promote online homework completion and enhance students' learning achievement. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Impact of information literacy, self-directed learning skills, and academic emotions on high school students' online learning engagement: A structural equation modeling analysis.

The adoption of online learning for adolescent students accelerated with the outbreak of the COVID-19 pandemic. However, few studies have investigated the mechanisms influencing adolescent students' online learning engagement systematically and comprehensively. This study applied the Presage-Process-Product (3P) model of learning to investigate the direct effects of presage factors (i.e., information literacy and self-directed learning skills) and process factors (i.e., academic emotions) on high school students' online learning engagement; and the mediating role of process factors. Data from 1993 high school students in China (49.3% males and 50.7% females) were analyzed using structural equation modeling. The result showed that students' information literacy, self-directed learning skills, and positive academic emotions positively predicted their online learning engagement. Moreover, the positive impact of self-directed learning skills on students' online learning engagement was significantly and largely enhanced through the mediation effects of positive academic emotions (ß = 0.606, 95% CI = [0.544, 0.674]). Based on these results, to enhance adolescent students' online learning engagement, it is important for school administrators, teachers, and parents to improve students' information literacy, self-directed learning skills, and positive academic emotions.

Cost Accounting in Multiple-Source and Multiple-Sink Logistics Management From Network Flow Perspectives

Transportation and logistics costs are becoming a large portion of the operating expenses for many businesses. Recently, supply chain disruptions caused by the COVID-19 pandemic and inflation crisis have brought challenges, especially, to many small- and medium-sized companies. Not only are companies struggling with logistics costs, but logistics bottlenecks are often preventing businesses from growing, expanding, and obtaining additional market shares. According to both academics and practitioners, there must be more literature and studies to address these logistics management challenges from cost accounting perspectives. This study focuses on multiple-source and multiple-sink scenarios, in which products are delivered from various production units to various stores. Optimized solutions to these cases may suggest optimal logistics strategies in terms of the minimized costs, as well as provide insights for later profitability analysis through common cost allocations and segment income statement reports. This paper can contribute to the practical examples in logistics management for businesses and is an addition to the current literature on cost accounting issues.

Impact of information literacy, self-directed learning skills, and academic emotions on high school students' online learning engagement: A structural equation modeling analysis

The adoption of online learning for adolescent students accelerated with the outbreak of the COVID-19 pandemic. However, few studies have investigated the mechanisms influencing adolescent students' online learning engagement systematically and comprehensively. This study applied the Presage-Process-Product (3P) model of learning to investigate the direct effects of presage factors (i.e., information literacy and self-directed learning skills) and process factors (i.e., academic emotions) on high school students' online learning engagement;and the mediating role of process factors. Data from 1993 high school students in China (49.3% males and 50.7% females) were analyzed using structural equation modeling. The result showed that students' information literacy, self-directed learning skills, and positive academic emotions positively predicted their online learning engagement. Moreover, the positive impact of self-directed learning skills on students' online learning engagement was significantly and largely enhanced through the mediation effects of positive academic emotions (AN - PMC10061396

A conserved oligomerization domain in the disordered linker of coronavirus nucleocapsid proteins.

The nucleocapsid (N-)protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a key role in viral assembly and scaffolding of the viral RNA. It promotes liquid-liquid phase separation (LLPS), forming dense droplets that support the assembly of ribonucleoprotein particles with as-of-yet unknown macromolecular architecture. Combining biophysical experiments, molecular dynamics simulations, and analysis of the mutational landscape, we describe a heretofore unknown oligomerization site that contributes to LLPS, is required for the assembly of higher-order protein-nucleic acid complexes, and is coupled to large-scale conformational changes of N-protein upon nucleic acid binding. The self-association interface is located in a leucine-rich sequence of the intrinsically disordered linker between N-protein folded domains and formed by transient helices assembling into trimeric coiled-coils. Critical residues stabilizing hydrophobic and electrostatic interactions between adjacent helices are highly protected against mutations in viable SARS-CoV-2 genomes, and the oligomerization motif is conserved across related coronaviruses, thus presenting a target for antiviral therapeutics.

Leflunomide: Traditional immunosuppressant with concurrent antiviral effects.

Leflunomide is a classic disease-modifying anti-rheumatic drug that is widely used to treat autoimmune diseases. Studies also show its antiviral effects in in vitro and/or in vivo experiments. Considering glucocorticoids, immunosuppressants and newly emerged antibodies commonly used in autoimmune diseases and autoinflammatory disorders bring risk of infection such as viral infection, leflunomide with combination of anti-viral and immunosuppressive features to maintain the balance between infection and anti-inflammation are attractive. Here we summarize the actions and mechanisms of leflunomide in immunoregulatory and antiviral effects.

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein.

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by three to four different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced coassembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.

Incidence and prevalence of influenza like illness in the college campus in Guangzhou city, 2008-2020

Objective: To analyze the epidemiology of influenza-like illness (ILI) in Guangzhou Higher Education Mega Center (Guangzhou HEMC) from 2008 to 2020. Method: Based on the monitoring and early warning system of public health emergencies in Guangzhou, the surveillance data of influenza-like illness from 2008 to 2020 in universities in the Guangzhou HEMC were derived and analyzed statistically.

Epidemiological investigation and analysis of an outbreak of human rhinovirus infection in a college in Guangzhou

Objective: To analyze the epidemiological characteristics of human rhinovirus outbreak in a college in Guangzhou, and provide a better evidence for the prevention and control of human rhinovirus outbreaks. Method: Epidemiological investigation was carded out for the influenza-like illness outbreak epidemic in the a college in Guangzhou city on 3rd November, 2020, and the progress of the cases were followed up, and samples were tested for SAILS-COV-2, influenza virus, and respiratory poly-pathogens.

Molecular basis for antiviral activity of two pediatric neutralizing antibodies targeting SARS-CoV-2 Spike RBD.

Neutralizing antibodies (NAbs) hold great promise for clinical interventions against SARS-CoV-2 variants of concern (VOCs). Understanding NAb epitope-dependent antiviral mechanisms is crucial for developing vaccines and therapeutics against VOCs. Here we characterized two potent NAbs, EH3 and EH8, isolated from an unvaccinated pediatric patient with exceptional plasma neutralization activity. EH3 and EH8 cross-neutralize the early VOCs and mediate strong Fc-dependent effector activity in vitro. Structural analyses of EH3 and EH8 in complex with the receptor-binding domain (RBD) revealed the molecular determinants of the epitope-driven protection and VOC evasion. While EH3 represents the prevalent IGHV3-53 NAb whose epitope substantially overlaps with the ACE2 binding site, EH8 recognizes a narrow epitope exposed in both RBD-up and RBD-down conformations. When tested in vivo, a single-dose prophylactic administration of EH3 fully protected stringent K18-hACE2 mice from lethal challenge with Delta VOC. Our study demonstrates that protective NAbs responses converge in pediatric and adult SARS-CoV-2 patients.

Construction and application of a cold chain logistics supervision platform for agricultural products in port areas during the COVID-19 pandemic

Diversification of agricultural imports is an important strategy for ensuring food security in China. During the COVID-19 pandemic, agricultural cold chain logistics are facing new challenges but also a new round of rapid development opportunities. It's particularly critical to determine how to build the first line of defense in port areas for the supervision of agricultural products to ensure the safety and reliability of incoming cargo. In recent years, China has continued to strengthen the construction of its regulatory traceability system for imported agricultural products, but there are still weak links and a lack of connectivity in the overall construction of the system. In this paper, we propose a supervision platform that addresses the problems of weak correlation and traceability of the cold chain logistics information on agricultural products in port areas during the COVID-19 pandemic. Relying on low-power-based radio frequency identification technology, the platform is combined with wireless sensor networks to collect key real-time data on the microenvironment, including temperature and humidity, influencing the cold chain logistics links of imported agricultural products. The platform unites multiple entities, including production, distribution, operation, and supervision entities, to build a pool of basic information data, such as inspection and quarantine certificates, nucleic acid test reports, and disinfection certificates for imported agricultural products. It integrates the core technologies of the new generation, the Internet of Things, the Internet, big data, cloud computing, and block chain, and builds a standard data flow and database to identify the key links and elements affecting the quality and safety of agricultural products from a large amount of data with the help of advanced algorithms. We establish risk controls and early warnings for imported agricultural products, build a traceability model, and then explore the creation of a comprehensive supervision platform in the port area, undertaking key data cleaning, integration, analysis, and sharing. The platform provides powerful support for government supervision, analysis, and decision-making, including emergency responses and public inquiries, combines "physical defense" and "human defense" in the cold chain logistics network for imported agricultural products, and promotes the improvement of the supervisory system for cold chain logistics of agricultural products in port areas.

Development of antiviral peptides as potential therapeutic drugs for COVID-19

With the rapid spread of severe acute respiratory syndrome coronavirus-2 (SARS -CoV -2) . the respiratory syndrome that it caused has become a pandemic. The World Health Organization officially named COVID-19 on 11 February 2020. COVID-19 has wrought havoc on everyday life and healthcare systems. and development of new antiviral drugs to treat it is an important issue. Many anti-viral peptides (AVPs) have been reported to have good activity. Here, we summarize the process of SARS-CoV-2 infection and the potential application of AVPs to provide new ideas for the development of new antiviral drugs.

Was the Coronavirus Disease 2019 Pandemic Associated with an Increased Rate of Cracked Teeth?

INTRODUCTION: There is lack of data on whether the coronavirus disease 2019 (COVID-19) pandemic was associated with changes in the etiology of pathosis in endodontic patients. The aim of this study was to determine the rate of cracks and other etiologic factors during the period of March 16th to May 31st in 2020 (COVID-19 initial outbreak) and 2021 (COVID-19 ongoing pandemic) compared with figures from the same period in 2019 (pre-COVID era) in 2 endodontists' practices. METHODS: The etiologies of patients' chief complaints were determined from records of 2440 teeth (740 in 2019, 651 in 2020, and 1049 in 2021). Changes in the proportion of etiologic factors among all 3 periods were analyzed. The association between the rate of cracked teeth and patients' age and sex was determined using a logistic regression model. RESULTS: The rates of all etiologies collectively during the studied periods showed a significant change (P < .0001). The rate of cracks significantly increased in 2020 (11.8%) (P = .0001) and 2021 (8%) (P = .0018) compared with 2019 (4.3%). The rate of persistent infections decreased in 2020 (22.3%) (P = .0013) and then increased in 2021 (27.5%) (P = .0153) compared with 2019 (30%). Cracked teeth were associated with the age group of 40-60 years (odds ratio [OR] = 1.882; 95% confidence interval [CI], 1.063-3.330) in 2020 and with age ranges of 40-60 years (OR = 2.051; 95% CI, 1.120-3.759) and >60 years (OR = 2.038; 95% CI, 1.050-3.956) and male sex (OR = 1.599; 95% CI, 1.019-2.510) in 2021. CONCLUSIONS: The rate of cracked teeth increased during the initial outbreak of the COVID-19 pandemic and 1 year later. This study provided evidence on the association between the COVID-19 pandemic and changes in the rate and presentation of endodontic etiologic factors.

Plasma metabolome and cytokine profile reveal glycylproline modulating antibody fading in convalescent COVID-19 patients.

The COVID-19 pandemic has incurred tremendous costs worldwide and is still threatening public health in the "new normal." The association between neutralizing antibody levels and metabolic alterations in convalescent patients with COVID-19 is still poorly understood. In the present work, we conducted absolutely quantitative profiling to compare the plasma cytokines and metabolome of ordinary convalescent patients with antibodies (CA), convalescents with rapidly faded antibodies (CO), and healthy subjects. As a result, we identified that cytokines such as M-CSF and IL-12p40 and plasma metabolites such as glycylproline (gly-pro) and long-chain acylcarnitines could be associated with antibody fading in COVID-19 convalescent patients. Following feature selection, we built machine-learning-based classification models using 17 features (six cytokines and 11 metabolites). Overall accuracies of more than 90% were attained in at least six machine-learning models. Of note, the dipeptide gly-pro, a product of enzymatic peptide cleavage catalyzed by dipeptidyl peptidase 4 (DPP4), strongly accumulated in CO individuals compared with the CA group. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination experiments in healthy mice demonstrated that supplementation of gly-pro down-regulates SARS-CoV-2-specific receptor-binding domain antibody levels and suppresses immune responses, whereas the DPP4 inhibitor sitagliptin can counteract the inhibitory effects of gly-pro upon SARS-CoV-2 vaccination. Our findings not only reveal the important role of gly-pro in the immune responses to SARS-CoV-2 infection but also indicate a possible mechanism underlying the beneficial outcomes of treatment with DPP4 inhibitors in convalescent COVID-19 patients, shedding light on therapeutic and vaccination strategies against COVID-19.

Engineered ACE2-Fc counters murine lethal SARS-CoV-2 infection through direct neutralization and Fc-effector activities.

Soluble angiotensin-converting enzyme 2 (ACE2) constitutes an attractive antiviral capable of targeting a wide range of coronaviruses using ACE2 as their receptor. Using structure-guided approaches, we developed a series of bivalent ACE2-Fcs harboring functionally and structurally validated mutations that enhance severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor binding domain recognition by up to ~12-fold and remove angiotensin enzymatic activity. The lead variant M81 potently cross-neutralized SARS-CoV-2 variants of concern (VOCs), including Omicron, at subnanomolar half-maximal inhibitory concentration and was capable of robust Fc-effector functions, including antibody-dependent cellular cytotoxicity, phagocytosis, and complement deposition. When tested in a stringent K18-hACE2 mouse model, Fc-enhanced ACE2-Fc delayed death by 3 to 5 days or effectively resolved lethal SARS-CoV-2 infection in both prophylactic and therapeutic settings via the combined effects of neutralization and Fc-effector functions. These data add to the demonstrated utility of soluble ACE2 as a valuable SARS-CoV-2 antiviral and indicate that Fc-effector functions may constitute an important component of ACE2-Fc therapeutic activity.

Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model.

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Epidemiology of rhinovirus under the COVID‐19 pandemic in Guangzhou, China, 2020

Background To analyze the epidemic characteristics of the human rhinovirus (HRV) outbreaks in Guangzhou, China, in 2020. Methods Descriptive epidemiological methods were used to analyze the HRV‐related outbreaks in Guangzhou, 2020. Results Seventeen outbreaks were reported in 2020 during the coronavirus disease 2019 (COVID‐19) pandemic in Guangzhou, a total of 465 patients (290 males and 175 females) were enrolled, with a median age of 10. A total of 223 (47.96%) had been tested for HRV, 89 (39.91%) of which were positive;344/465 (73.98%) had a fever, 138/465 (29.68%) had a runny nose, 139/465 (29.89%) had a sore throat, 86/465 (18.49%) had a cough, 41/465 (8.82%) had a headache, and 37/465 (7.96%) had a sneeze. Patients at age of 13–15 had the highest rate of sore throat and runny nose, patients aged 11–12 had the highest rate of sneezing, and patients at age of 12–14 had the highest rate of positive rate. Patients tested positive had a higher rate of fever (χ2 = 11.271, p = .001), cough (χ2 = 6.987, p = .008), runny nose (χ2 = 7.980, p = .005), and sneeze (χ2 = 4.676, p = .031). Conclusion The HRV was restored during the fighting of the COVID‐19 pandemic. The conventional COVID‐19 control measures were not effective enough in preventing rhinovirus. More appropriate control measures should be used to control HRV.

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models.

BACKGROUND: COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation. METHODS: The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). RESULTS: Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India). CONCLUSIONS: This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries.