Competitive advantage, relationship, and benefit: Primary and secondary influencing factors of supply chain collaboration in China's advanced manufacturing industry.

The advanced manufacturing industry is located at the top of the manufacturing value chain. Its development is restricted by supply chain collaboration (SCC), the level of which is affected by many factors. Few studies comprehensively summarize what influences SCC and distinguish the impact level of each factor. Practitioners have difficulty isolating the primary factors that affect SCC and managing them effectively. Therefore, based on synergetics and the theory of comparative advantage, this study analyzes what influences SCC in the advanced manufacturing industry and how these influencing factors work, using data from 94 manufacturing enterprises and the Haken model to identify the influencing factors. The results show that China's advanced manufacturing supply chain underwent a phase change and entered a new stage during 2017-2018. In the new stage, the competitive advantages of enterprises are one order parameter (slow variable) and are primary factors affecting SCC. The interest demands of enterprises are a fast variable and are secondary factors affecting SCC. The competitive advantages of enterprises dominate the interests of enterprises in affecting the collaboration level of China's advanced manufacturing supply chain. In addition, in the process of influencing SCC, there is a positive correlation between the competitive advantages of enterprises and the interest demands of enterprises, and the two factors have a positive feedback mechanism. Finally, when the enterprises in the supply chain cooperate based on their differential advantages, the collaboration capability of the supply chain is at the highest level, and the overall operation of the supply chain is orderly. In terms of theoretical contribution, this study is the first to propose a collaborative motivation framework that conforms to the characteristics of sequential parameters, which provides a theoretical reference for subsequent studies on SCC. In addition, the theory of comparative advantage and synergetics are linked for the first time in this study, and both of them are enriched and developed. Equally importantly, this study compares the bidirectional influence between firms' competitive advantages and firms' interest demands and the ability of both to influence SCC, enriching previous validation studies of unidirectional influence. In terms of practical implications, this study guides top managers to focus on the management practice of collaborative innovation in the supply chain and advises purchasing managers and sales managers on selecting supply chain partnerships.

The post-COVID-19 future of digital learning in higher education: Views from educators, students, and other professionals in six countries

Predictions about the post-pandemic future of digital learning vary among higher education scholars. Some foresee dramatic, revolutionary change while others speculate that growth in educational technology will be buffeted both by modest expansion and unevenness. To this debate we contribute evidence from four groups across six countries on four continents: college and university educators (n = 281), students (n = 4243), senior administrators (n = 15), and instructional design specialists (n = 43). Our focus is on the future of digital learning after the pandemic-induced pivot to emergency remote instruction. Using data from interviews and self-administered questionnaires, our findings reveal a high degree of congruency between respondent groups, with most envisioning more blended/hybrid instruction post-pandemic and some modest increases in fully online courses. Student opinion is more sceptical about future change than within the other groups. Among respondents in all groups there is little expectation for a full-blown, revolutionary change in online or digital learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

SARS-CoV-2 NSP12 utilizes various host splicing factors for replication and splicing regulation (preprint)

Background The RNA-dependent RNA polymerase (RdRp) is a crucial element in the replication and transcription of RNA viruses. Although the RdRps of lethal human coronaviruses SARS-CoV-2, SARS-CoV, and MARS-CoV have been extensively studied, the molecular mechanism of the catalytic subunit NSP12, which is involved in pathogenesis, remains unclear. Results In this study, the biochemical and cell biological results demonstrate the interactions between SARS-CoV-2 NSP12 and seven host proteins, including three splicing factors (SLU7, PPIL3, and AKAP8), suggesting that the polymerase activity and stability of SARS-CoV-2 RdRp were affected by them to varying degrees. Furthermore, the entry efficacy of SARS-CoV-2 pseudovirus considerably decreased when SLU7 or PPIL3 was knocked out, indicating that abnormal splicing of the host genome was responsible for this occurrence. In addition, NSP12 and its homologues from SARS-CoV and MARS-CoV suppressed thealternative splicing (AS) of cellular genes, which were influenced by the three splicing factors. Conclusions Overall, our research illustrates that SARS-CoV-2 NSP12 can engage with various splicing factors, thereby impacting virus entry, replication, and gene splicing. This not only improves our understanding of how viruses cause diseases but also lays the foundation for the development of antiviral therapies.

Smartphone-based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Application to SARS-CoV-2 Lateral Flow Immunoassays.

BACKGROUND: Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed altering a correct epidemiological surveillance. OBJECTIVE: To evaluate an artificial intelligence-based smartphone application, connected to a cloud web platform, to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management. METHODS: Overall, 252 human sera were used to inoculate a total of 1,165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. RESULTS: Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8-96.1%) for reading IgG band of COVID-19 antibodies RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100% and specificity was 95.8%, CI 94.3-97.3%. All COVID-19 antigen RDTs were correctly read by the app. CONCLUSIONS: The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDTs brands. The web platform serves as a real time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.

Medication regularities of traditional Chinese medicine for treating COVID-19: a data mining based literature analysis

Objective: To explore distribution regularities of the nature, flavor, and channel entry of traditional Chinese Medicine (TCM) for treating Corona Virus Disease 2019 (COVID-19)by data mining in the relevant literature to provide basis for clinical prescriptions.

Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic.

Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of - 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.

Proteomic analysis and identification reveal the anti-inflammatory mechanism of clofazimine on lipopolysaccharide-induced acute lung injury in mice.

BACKGROUND AND OBJECTIVE: Acute lung injury (ALI)/ acute respiratory distress syndrome (ARDS) was increasingly recognized as one of the most severe acute hyperimmune response of coronavirus disease 2019 (COVID-19). Clofazimine (CFZ) has attracted attention due to its anti-inflammatory property in immune diseases as well as infectious diseases. However, the role and potential molecular mechanism of CFZ in anti-inflammatory responses remain unclear. METHODS: We analyze the protein expression profiles of CFZ and LPS from Raw264.7 macrophages using quantitative proteomics. Next, the protective effect of CFZ on LPS-induced inflammatory model is assessed, and its underlying mechanism is validated by molecular biology analysis. RESULTS: LC-MS/MS-based shotgun proteomics analysis identified 4746 (LPS) and 4766 (CFZ) proteins with quantitative information. The key proteins and their critical signal transduction pathways including TLR4/NF-κB/HIF-1α signaling was highlighted, which was involved in multiple inflammatory processes. A further analysis of molecular biology revealed that CFZ could significantly inhibit the proliferation of Raw264.7 macrophages, decrease the levels of TNF-α and IL-1ß, alleviate lung histological changes and pulmonary edema, improve the survival rate, and down-regulate TLR4/NF-κB/HIF-1α signaling in LPS model. CONCLUSION: This study can provide significant insight into the proteomics-guided pharmacological mechanism study of CFZ and suggest potential therapeutic strategies for infectious disease.

Dysregulation of Innate and Adaptive Immune Responses in Asymptomatic SARS-CoV-2 Infection with Delayed Viral Clearance.

Asymptomatic infection with SARS-CoV-2 is a major concern in the control of the COVID-19 pandemic. Many questions concerning asymptomatic infection remain to be answered, for example, what are the differences in infectivity and the immune response between asymptomatic and symptomatic infections? In this study, based on a cohort established by the Wuchang District Health Bureau of Wuhan in the early stage of the COVID-19 pandemic in Wuhan in 2019, we conducted a comprehensive analysis of the clinical, virological, immunological, and epidemiological data of asymptomatic infections. The major findings of this study included: 1) the asymptomatic cohort enrolled this study exhibited low-grade but recurrent activity of viral replication; 2) despite a lack of overt clinical symptoms, asymptomatic infections exhibited ongoing innate and adaptive immune responses; 3) however, the immune response from asymptomatic infections was not activated adequately, which may lead to delayed viral clearance. Given the fragile equilibrium between viral infection and host immunity, and the delayed viral clearance in asymptomatic individuals, close viral monitoring should be scheduled, and therapeutic intervention may be needed.

Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy (preprint)

In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as incidence rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves  the effectiveness of the control strategy in restraining the spread of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes of the number of active cases in the COVID-19 epidemic in the United States over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.

Case Report: Pirfenidone in the Treatment of Post-COVID-19 Pulmonary Fibrosis

Background Pulmonary fibrosis is one of the sequelae of the COVID-19, which seriously affects the quality of life of survivors. Currently, there are no optimal evidence based guidelines targeting this population. Case Presentation We report a 66-year-old female patient without underlying comorbidities admitted to Changsha Public Health Center because of COVID-19. During hospitalization, she developed co-bacterial infection and acute respiratory distress syndrome, and received broad-spectrum antibacterial therapy, invasive mechanical ventilation and extracorporeal membrane oxygenation. After the acute phase, she developed post-COVID-19 pulmonary fibrosis subsequently treated with pirfenidone. Over 96 weeks after pirfenidone treatment, her modified Medical Research Council Dyspnea level improved to 2 from 4 at discharge. Her 6 minutes walk test distance, total lung capacity, and diffusion capacity for carbon monoxide all increased. Chest CT performed on 2 years after illness onset showed regressing fibrosis. The Hospital Anxiety and Depression Scale, Athens Insomnia Scale, and 36-Item Short Form Health Survey questionnaire all improved. Conclusion Post-COVID-19 pulmonary fibrosis is a challenging consequence of COVID-19, and our case suggests that pirfenidone may be an effective treatment option.

A prediction model to forecast passenger flow based on flight arrangement in airport terminals

Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals. In this paper, field investigations were carried out in four typical airport terminals with different scales and operation states to reveal the characteristics of passenger flow. A prediction model is established to forecast passengers’ distribution in the main areas of an airport terminal based on its flight arrangement. The results indicate the dislocation peaks of passenger numbers in these areas, due to the airport's departure process. The peak time interval is about 30 minutes between the check-in hall and the security check area, and 60-80 minutes between the check-in hall and the departure hall. RD value (i.e., the ratio of the actual passenger number in a certain area to the design value) is used to describe this peak shifting feature. When the annual passenger throughput of an airport terminal reaches or even exceeds its design value, the total peak RD value is normally 0.6-0.8. For the airport affected by COVID-19, the peak RD is only 0.2, which reflects the decline in terminal passenger numbers during the pandemic. This research provides useful insight into the characteristics of passenger flow in airport terminals, and is beneficial for their design and operation.

Association Between Passive Smoking and Health Among Chinese Nurses: A Cross-Sectional Study.

This study aimed to investigate the association between passive smoking and physical and psychological health in Chinese nurses. Participants of this cross-sectional study comprised 2,484 non-smoking nurses. Passive smoking and demographic information were assessed using a self-administered questionnaire. Physical, psychological, and overall health status of nurses were measured using the Cornell Medical Index (CMI) health questionnaire. Multivariate-adjusted odds ratio (OR) and 95% confidence interval (CI) for nurses' health were estimated by exposure to passive smoking using unconditional logistic regression models. A total of 1,219 nurses (49.07%) were exposed to passive smoking. Of these, 609 (24.52%), 160 (6.44%), and 587 (23.63%) nurses had poorer physical, mental, and overall health, respectively. After adjusting for other confounding factors, compared with the non-passive smoking group, passive smoking was associated with poor physical (OR = 1.51, 95% CI: 1.25-1.83), mental (OR = 1.48, 95% CI: 1.07-2.07), and overall (OR = 1.58, 95% CI: 1.30-1.93) health of nurses, respectively. We also carried out subgroup analyses stratified by age, department, and professional title, which showed that most findings supported the main results. This study demonstrated that exposure to passive smoking was a risk factor for overall decreased physical and mental health status among Chinese nurses.

Physical Exercise Ameliorates Anxiety, Depression and Sleep Quality in College Students: Experimental Evidence from Exercise Intensity and Frequency.

BACKGROUND: The main purpose of this study was to investigate the effects of exercise intensity and exercise frequency on anxiety, depression and sleep quality in college students. METHODS: All participants came from a university in northeastern China. All participants were tested for Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and were diagnosed with anxiety disorders. The research subjects were divided into six groups, namely the low-intensity and low-frequency group (group 1), low-intensity and moderate-frequency group (group 2), low-intensity and high-frequency group (group 3), high-intensity and low-frequency group (group 4), and high-intensity and moderate-frequency group (group 5) and high-intensity and high-frequency group (group 6). The duration of each physical exercise for each group was 1 h. Participants' exercise intensity was monitored using Polar H10 HR sensors and the Borg RPE scale. The experiment was carried out for a total of 6 weeks. The researchers conducted pre- and post-test scores on the subjects' anxiety, depression and sleep quality through questionnaires. RESULTS: Exercise intensity improved anxiety and decreased symptoms of depression better than exercise frequency; sleep quality was more closely related to exercise intensity. CONCLUSION: Exercise intensity and exercise frequency have different effects on anxiety, depression and sleep quality improvement, indicating that exercise intensity and exercise frequency have different effects on anxiety, depression and sleep quality of college students.

The post-COVID-19 future of digital learning in higher education: Views from educators, students, and other professionals in six countries

Predictions about the post-pandemic future of digital learning vary among higher education scholars. Some foresee dramatic, revolutionary change while others speculate that growth in educational technology will be buffeted both by modest expansion and unevenness. To this debate we contribute evidence from four groups across six countries on four continents: college and university educators (n = 281), students (n = 4243), senior administrators (n = 15), and instructional design specialists (n = 43). Our focus is on the future of digital learning after the pandemic-induced pivot to emergency remote instruction. Using data from interviews and self-administered questionnaires, our findings reveal a high degree of congruency between respondent groups, with most envisioning more blended/hybrid instruction post-pandemic and some modest increases in fully online courses. Student opinion is more sceptical about future change than within the other groups. Among respondents in all groups there is little expectation for a full-blown, revolutionary change in online or digital learning. Practitioner notes What is already known about this topic Digital learning has been growing in higher education, although a digital disconnect continues whereby the availability of educational technology exceeds its application to learning. Expectations regarding technology-mediated learning post-COVID-19 are mixed, hampering planning for the future. Hesitancy about teaching or taking courses with some or full online components persists. What this paper adds A strong majority of respondents in higher education foresee the most growth in blended/hybrid forms of digital learning post-COVID-19. A solid percentage, between about two-thirds and three-quarters of faculty and students, envision learners and instructors taking or teaching more fully online courses post-pandemic. A strong congruency exists between faculty, students, senior administrators, and instructional design professionals in their ranking of scenarios for the future of digital learning. Implications for practice and/or policy Educational technology in higher learning will not return to a pre-COVID-19 normality?if a pre-COVID-19 ?normal? could even be defined. As post-pandemic institutional planning unfolds, it is important to reflect experiences and incorporate insights of instructors, students, and instructional designers. Successfully building on these insights, where more blended/hybrid learning is foreseen, requires a thoughtful integration of face-to-face learning and educational technology.

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.