A Survey on Big Data-Enabled Innovative Online Education Systems During the COVID-19 Pandemic

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

The Roles of APOBEC-mediated RNA Editing in SARS-CoV-2 Mutations, Replication and Fitness (preprint)

During COVID-19 pandemic, mutations of SARS-CoV-2 produce new strains that can be more infectious or evade vaccines. Viral RNA mutations can arise from misincorporation by RNA-polymerases and modification by host factors. Analysis of SARS-CoV-2 sequence from patients showed a strong bias toward C-to-U mutation, suggesting a potential mutational role by host APOBEC cytosine deaminases that possess broad anti-viral activity. We report the first experimental evidence demonstrating that APOBEC3A, APOBEC1, and APOBEC3G can edit on specific sites of SARS-CoV-2 RNA to produce C-to-U mutations. However, SARS-CoV-2 replication and viral progeny production in Caco-2 cells are not inhibited by the expression of these APOBECs. Instead, expression of wild-type APOBEC3 greatly promotes viral replication/propagation, suggesting that SARS-CoV-2 utilizes the APOBEC-mediated mutations for fitness and evolution. Unlike the random mutations, this study suggests the predictability of all possible viral genome mutations by these APOBECs based on the UC/AC motifs and the viral genomic RNA structure.

APOBEC-mediated Editing of SARS-CoV-2 Genomic RNA Impacts Viral Replication and Fitness (preprint)

During COVID-19 pandemic, mutations of SARS-CoV-2 produce new strains that can be more virulent and evade vaccines. Viral RNA mutations can arise from misincorporation by RNA-polymerases and modification by host factors. Recent SARS-CoV-2 sequence analyses showed a strong bias toward C-to-U mutation, suggesting that host APOBEC cytosine deaminases with immune functions may cause the mutation. We report the experimental evidence demonstrating that APOBEC3A and APOBEC1 can efficiently edit SARS-CoV-2 RNA to produce C-to-U mutation at specific sites. However, APOBEC-editing does not inhibit the viral RNA accumulation in cells. Instead, APOBEC3A-editing of SARS-CoV-2 promotes viral replication/propagation, suggesting that SARS-CoV-2 utilizes the APOBEC-mediated mutations for fitness and evolution. Unlike the unpredictability of random mutations, this study has significant implications in predicting the potential mutations based on the UC/AC motifs and surrounding RNA structures, thus offering a basis for guiding future antiviral therapies and vaccines against the escape mutants.

Two-Stage Robust Telemedicine Assignment Problem with Uncertain Service Duration and No-Show Behaviours (preprint)

The current pandemic of COVID-19 has caused significant strain on medical center resources, which are now the main places to provide the rapid response to COVID-19 through the adoption of telemedicine, especially for the health care delivery, in order to meet patients' needs and reduce the transmission of the virus and protect medical practitioners from infection while minimizing their exposure to the public and in-person visits. Thus it is crucial for the healthcare managers to make an effective assignment plan for the patients and telemedical doctors when providing telemedicine services in a highly uncertain environment. In this regard, this paper presents the first comprehensive study of a two-stage robust optimization telemedicine assignment problem when different sources of uncertainty are incorporated in practice, including uncertain service duration, no-show behaviours of both patients and telemedical doctors. To overcome the difficulty of resolution for such a model with a max-min based mixed-integer linear programming recourse problem, from the algorithmic viewpoint we propose an efficient nested column-and-constraint generation (CCG) solution scheme that decomposes the model into the outer level problem and inner level problem, which both can be solved by CCG algorithm. Our computational results show that our method can solve the problems of realistic sizes within a reasonable time (e.g., up to 100 patients, 10 telemedical doctors, and 200 empirical scenarios within one and a half hours). On the empirical side, this paper has also demonstrated how to choose the optimal hyper-parameters to make an optimal trade-off between cost management and the coverage level of the served patients in the presence of different sources of uncertainty, and what's the added value of our robust policy when compared with its two-stage stochastic programming counterpart model.

Pyroptosis of syncytia formed by fusion of SARS-CoV-2 Spike and ACE2 expressing cells (preprint)

SARS-Cov-2 infected cells fused with the ACE2-positive neighboring cells forming syncytia. However, the effect of syncytia in disease development is largely unknown. We established an in vitro cell-cell fusion system and used it to mimic the fusion of SARS-CoV-2 infected cells with ACE2-expressing cells to form syncytia. We found that Caspase-9 was activated after syncytia formation, and Caspase-3/7 was activated downstream of Caspase-9, but it triggered GSDME-dependent pyroptosis rather than apoptosis. What is more, single cell RNA-sequencing data showed that both ACE2 and GSDME were expression in alveolar type 2 cells in human lung. We propose that pyroptosis is the fate of syncytia formed by SARS-CoV-2 infected host cells and ACE2-positive cells, which indicated that lytic death of syncytia may contribute to the excessive inflammatory responses in severe COVID-19 patients.

Cause Analysis of PM2.5 pollution during the COVID-19 lockdown in Nanning, China (preprint)

To analyze the cause of atmospheric PM2.5 pollution occurred during the COVID-19 lockdown in Nanning of Guangxi, China, Single Particulate Aerosol Mass Spectrometer, Aethalometer, Particulate Lidar, coupled with the monitoring of near-surface gaseous pollutants, meteorological conditions, remote fire spots sensing by satellite and Backward Trajectory Models were conducted during 18–24, Feb 2020. Three haze stages of pre-pollution period (PPP), pollution accumulation period (PAP) and pollution dissipation period (PDP) were identified. The dominant source of PM2.5 in PPP was biomass burning (BB) (40.4%), followed by secondary inorganics (28.1%) and motor vehicle exhaust (11.7%). The PAP was characterized by a large abundance of secondary inorganics, which contributed for 56.1% of the total PM2.5 concentration, followed by BB (17.4%). The absorption Ångström exponent (2.2) in PPP was higher than those of the other two periods. The analysis of fire spots monitored by remote satellite sensing indicated that open BB in regions around Nanning city could be one of the main facotrs matters. The planetary boundary layer-relative humidity-secondary particles matter-particulate matter positive feedback mechanism was employed to elucidate the atompheric process in this study. This study highlights the importance of understanding the role of BB and meteorology in air pollution formation to call for policy for emission control strategies.

Clinical Characteristics and Predictors of Mortality in Young Adults with Severe COVID-19. A Retrospective Observational Study (preprint)

Background: and objective: Little is yet known whether pathogenesis of COVID-19 is different between young and elder patients. Our study aimed to investigate the clinical characteristics and provide predictors of mortality for young adults with severe COVID-19. Methods: : A total of 77 young adults with confirmed severe COVID-19 were recruited retrospectively at Tongji Hospital. Clinical characteristics, laboratory findings, treatment and outcomes were obtained from electronic medical records. The prognostic effects of variables were analyzed using logistic regression model. Results: : In this retrospective cohort, non-survivors showed higher incidence of dyspnea and co-existing laboratory abnormalities, compared with young survivals in severe COVID-19. Multivariate logistic regression analysis showed that lymphopenia, elevated level of d-dimer, hypersensitive cardiac troponin I (hs-CTnI) and high sensitivity C-reactive protein (hs-CRP) were independent predictors of mortality in young adults with severe COVID-19. Further analysis showed that severely young adults with two or more factors abnormalities above would be more prone to death. The similar predictive effect of above four factors had been observed in all-age patients with severe COVID-19. Conclusion: Lymphopenia, elevated level of d-dimer, hs-CTnI and hs-CRP predicted clinical outcomes of young adults with severe COVID-19.

Physical and Psychological Characteristics of Nitrous Oxide Abusers During the COVID-19 Pandemic: An Observational Study. (preprint)

Background The coronavirus disease 2019 (COVID-19) was classified as a global pandemic by the World Health Organization. In addition to health effects, it has also brought about psychological problems due to its economic and social impact. Psychological effects have led to drug and alcohol abuse. To inform physician decisions, there is a need to characterize the physical and psychological characteristics of nitrous oxide abusers during the COVID-19 Pandemic. Methods: We enrolled six nitrous oxide abusers who sought treatment at Taizhou Hospital of Zhejiang province between May 2020 and June 2020. Clinical data including socio-demographic, physical examination, laboratory examination, electromyography, neuroimaging, and psychological assessment were collected. Results: The enrolled patients had a mean age of 22±4.3. Clinical presentations included varying degrees of limb numbness and an ataxic gait. Under laboratory examination, it was revealed that all the patients did not have pernicious anemia, 4 patients had decreased vitamin B12 while 3 patients exhibited elevated homocysteine levels. MR of the spinal cord revealed that 4 patients had abnormal signals in the cervical spinal cord. These signals were of high symmetry with splayed or inverted V sign after T2WI. Peripheral nerve damage was observed in 5 patients who were subjected to an electromyogram (EMG) test. The Symptom Checklist 90 (SCL-90) psychological evaluation indicated that all patients had severe anxiety, depression and psychosis. The psychological status of nitrous oxide abusers was significantly different from that of health controls. After treatment by vitamin B12, the patients’ physical symptoms improved, and they were discharged from the hospital. For the evaluation of mental and psychological outcomes post-discharge, patients were asked to go to the psychological department for check-up and follow-up.Conclusion: The enrolled 6 patients caused by abuse of nitrous oxide presented with symptoms of subacute combined with spinal degeneration. They had more serious psychological problems related to the COVID-19 pandemic. These problems require immediate attention and interventional approaches. The national government and other departments should take active measures to control abuse of nitrous oxide. Trial registration: Not applicable.

A SARS-CoV-2 neutralizing antibody with exceptional spike binding coverage and optimized therapeutic potentials (preprint)

The Coronavirus Disease of 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) threatens global public health and economy. Therapeutic options such as monoclonal antibodies (mAbs) against SARS-CoV-2 are in urgent need. We have identified potent monoclonal antibodies binding to SARS-CoV-2 Spike protein from COVID-19 convalescent patients and one of these antibodies, P4A1, interacts directly and covers the majority of the Receptor Binding Motif (RBM) of Spike receptor-binding domain (RBD), shown by high-resolution complex structure analysis. We further demonstrated P4A1 binding and neutralizing activities against wild type and mutant spike proteins. P4A1 was subsequently engineered to reduce the potential risk for antibody-dependent enhancement (ADE) of infection and to extend its half-life. The engineered mAb exhibits optimized pharmacokinetic and safety profile, and results in complete viral clearance in a rhesus monkey model of COVID-19 following a single injection.

Single-cell transcriptome analysis of the immunosuppressive effect of differential expression of tumor PD-L1 on responding TCR-T cells (preprint)

PD-L1 expression levels in tumors do not consistently predict cancer patients’ response to PD-(L)1 inhibitors. We therefore evaluated how tumor PD-L1 levels affect the anti-PD-(L)1 efficacy and T cell function. We used MART-1-specific TCR-T cells (TCR-T MART-1 ) stimulated with MART-1 27-35 peptide-loaded MEL-526 tumor cells with different proportions of them expressing PD-L1 to perform cellular assays and high-throughput single-cell RNA sequencing. Compared to control T cells, TCR-T MART-1 were more sensitive to exhaustion and secreted lower pro-inflammatory but higher anti-inflammatory cytokines with increasing proportions of PD-L1 + tumor cells. The colocalization of T cells and tumor cells in gene clusters correlated negatively with the proportion of PD-L1 + tumor cells and positively with immune cell cytotoxicity. Moreover, elevated proportion of PD-L1 + tumor cells increased PD-L1 expression and decreased PD-1 expression on T cells and enhanced T cell death. The expression of PD-1 and PD-L1 in T cells and macrophages also correlated positively with COVID-19 severity.

Profiling B cell immune responses to identify neutralizing antibodies from convalescent COVID-19 patients (preprint)

The pandemic Coronavirus Disease 2019 (COVID-19) causes noticeable morbidity and mortality worldwide. In addition to vaccine and antiviral drug therapy, the use of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) neutralizing antibodies for treatment purposes is a viable alternative. In this study, we aimed to profile the humoral responses and identify neutralizing antibodies against SARS-CoV-2 using high-throughput single-cell sequencing that tailored to B cell receptor sequencing. From two convalescent patients with high serum titer against SARS-COV-2, we identified seven antibodies specifically binding to SARS-CoV-2. Among these, the most potent antibody, P4A1 was demonstrated to block the binding of spike protein to its receptor angiotensin-converting enzyme 2 (ACE2), and prevent the viral infection in neutralization assays with pseudovirus as well as live virus at nM to sub-nM range. Moreover, antibody P4A1 can also bind strongly to spike protein with N354D/D364Y, R408I, W436R, V367F or D614G mutations respectively, suggesting that the antibody alone or in combination with other antibodies that recognize different variations of SARS-CoV-2, may provide a broad spectrum therapeutic option for COVID-19 patients. Authors Lisu Huang, Bingqing Shen, Yu Guo, and Shu Shen contributed equally to this work.

Impact of recent influenza A virus infection on clinical characteristics and outcomes in severe coronavirus disease 2019 adult inpatients (preprint)

Background: Coronavirus disease 2019 (COVID-19) is a current global pandemic. However, impact of recent influenza A virus infection on the clinical course and outcomes of severe COVID-19 adult inpatients needs to be further explored.Methods: In this retrospective cohort study, severe, laboratory confirmed COVID-19 adult patients from Wuhan Tongji Hospital were included. Data were obtained from electronic medical records and compared between patients with and without recent influenza A virus infection.Results: 200 patients were included, 51.5% with recent influenza A virus infection. Recent influenza A virus infection group presented with longer persistence of cough and sputum from illness onset (35.0 vs. 27.0 days, P = 0.018) and (33.0 vs. 26.0 days, P = 0.015), respectively. Median time of progression to critical illness from illness onset was shorter (day 11.5 vs. day 16.0, P = 0.034). Time to clinical improvement and length of hospital stay were longer in recent infection group (23.0 vs. 19.0 days, P = 0.044) and (22.0 vs. 18.0 days, P = 0.030), respectively.Conclusions: Patients with recent influenza A virus infection showed a delay in time to clinical improvement and increased length of hospital stay. There is a high clinical need to improve the detection of common respiratory pathogens to identify co-infection during the epidemic of COVID-19.

Outdoor Air Pollutant Concentration and COVID-19 Infection in Wuhan, China (preprint)

COVID-19 infection, first reported in Wuhan, China in December 2019, has become a global pandemic, causing significantly high infections and mortalities in Italy, the UK, the US, and other parts of the world. Based on the statistics reported by John Hopkins University, 4.7M people worldwide and 84,054 people in China have been confirmed positive and infected with COVID-19, as of 18 May 2020. Motivated by the previous studies which show that the exposures to air pollutants may increase the risk of influenza infection, our study examines if such exposures will also affect Covid-19 infection. To the best of our understanding, we are the first group in the world to rigorously explore the effects of outdoor air pollutant concentrations, meteorological conditions and their interactions, and lockdown interventions, on Covid-19 infection in China. Since the number of confirmed cases is likely to be under-reported due to the lack of testing capacity, the change in confirmed case definition, and the undiscovered and unreported asymptotic cases, we use the rate of change in the daily number of confirmed infection cases instead as our dependent variable. Even if the number of reported infections is under-reported, the rate of change will still accurately reflect the relative change in infection, provided that the trend of under-reporting remains the same. In addition, the rate of change in daily infection cases can be distorted by the government imposed public health interventions, including the lockdown policy, inter-city and intra-city mobility, and the change in testing capacity and case definition. Hence, the effects of the lockdown policy and the inter-city and intra-city mobility, and the change in testing capacity and case definition are all taken into account in our statistical modelling. Furthermore, we adopt the generalized linear regression models covering both the Negative Binomial Regression and the Poisson Regression. These two regression models, when combined with different time-lags (to reflect the COVID-19 incubation period and delay due to official confirmation) in air pollutant exposure (PM2.5), are used to fit the COVID-19 infection model. Our statistical study has shown that higher PM2.5 concentration is significantly correlated with a higher rate of change in the daily number of confirmed infection cases in Wuhan, China (p < 0.05). We also determine that a higher dew point interacting with a higher PM2.5 concentration is correlated with a higher rate of change in the daily number of confirmed infection cases, while a higher UV index and a higher PM2.5 concentration are correlated with a lower rate of change. Furthermore, we find that PM2.5 concentration eight days ago has the strongest predictive power for COVID-19 Infection. Our study bears significance to the understanding of the effect of air pollutant (PM2.5) on COVID-19 infection, the interaction effects of both the air pollutant concentration (PM2.5) and the meteorological conditions on the rate of change in infection, as well as the insights into whether lockdown should have an effect on COVID-19 infection.

Systemic Inflammation and Clinical Outcomes in COVID-19: a retrospective study (preprint)

Background：COVID-19 causes epidemics and pandemics worldwide, but the role of pathophysiological parameters particularly systemic inflammation in COVID-19 has not been understood. We aimed to investigate clinical outcomes in view of systemic inflammation in COVID-19.Methods：In this retrospective study, the demographic and clinical data of 225 confirmed COVID-19 cases on admission at Tongji Hospital from January 28 to February 15, 2020, were extracted and analyzed. These patients were categorized by inflammation state on the basis of the expression of inflammatory factors or classified as severe and non-severe according to 2019 American Thoracic Society / Infectious Disease Society of America guidelines.Results: Among 225 patients with confirmed COVID-19, 155 patients (68.9%) categorized into hyperinflammation group and 70 (31.1%) were non- hyperinflammation group. Compared to non-hyperinflammation group, hyperinflammation group more frequently had chest tightness/dyspnea and lymphopenia, aberrant multiple indexes of organ function including the heart, liver, kidney, and coagulation, with higher level of C-reactive protein (hsCRP) as well as interleukin (IL)-6, IL-8, tumour necrosis factor α (TNF-α), etc. Hyperinflammation group were more likely to admit to intensive care unit (ICU) (52.3% vs 5.7%), receive ventilation (84.5% vs 10.0%) and be with higher mortality (44.5% vs 5.7%) than non-hyperinflammation group. The mortality of severe patients with hyperinflammation (60/99, 60.6%) was significantly higher than without hyperinflammation (2/20, 10.0%). Non-severe patients with hyperinflammation even tended to have higher mortality (9/56, 16.1%) than those in severe cases without hyperinflammation (2/20, 10%).Conclusion: Excessive systemic inflammation was correlated highly with poor clinical outcomes in COVID-19, particularly in severe cases. Non-severe patients with hyperinflammation even tended to have higher mortality than those in severe cases without hyperinflammation.Trial registration: This is a retrospective observational study without a trial registration number.

The Effects of Outdoor Air Pollution Concentrations and Lockdowns on Covid-19 Infections in Wuhan and Other Provincial Capitals in China (preprint)

Background: Covid-19 was first reported in Wuhan, China in Dec 2019. Since then, it has been transmitted rapidly in China and the rest of the world. While Covid-19 transmission rate has been declining in China, it is increasing exponentially in Europe and America. Although there are numerous studies examining Covid-19 infection, including an archived paper looking into the meteorological effect, the role of outdoor air pollution has yet to be explored rigorously. It has been shown that air pollution will weaken the immune system, and increase the rate of respiratory virus infection. We postulate that outdoor air pollution concentrations will have a negative effect on Covid-19 infections in China, whilst lockdowns, characterized by strong social distancing and home isolation measures, will help to moderate such negative effect. Methods: We will collect the number of daily confirmed Covid-19 cases in 31 provincial capital cities in China during the period of 1 Dec 2019 to 20 Mar 2020 (from a popular Chinese online platform which aggregates all cases reported by the Chinese national/provincial health authorities). We will also collect daily air pollution and meteorology data at the city-level (from the Chinese National Environmental Monitoring Center and the US National Climatic Data Center), daily inter-city migration flows and intra-city movements (from Baidu). City-level demographics including age distribution and gender, education, and median household income can be obtained from the statistical yearbooks. City-level co-morbidity indicators including rates of chronic disease and co-infection can be obtained from related research articles. A regression model is developed to model the relationship between the infection rate of Covid-19 (number of confirmed cases/population at the city level) and outdoor air pollution at the city level, after taking into account confounding factors such as meteorology, inter- and intra-city movements, demographics, and co-morbidity and co-infection rates. In particular, we shall study how air pollution affects infection rates across different cities, including Wuhan. Our model will also study air pollution would affect infection rates in Wuhan before and after the lockdown. Expected findings: We expect there be a correlation between Covid-19 infection rate and outdoor air pollution. We also expect that reduced intra-city movement after the lockdowns in Wuhan and the rest of China will play an important role in reducing the infection rate. Interpretation: Infection rate is growing exponentially in major cities worldwide. We expect Covid-19 infection rate is related to the air pollution concentration, and is strongly dependent on inter- and intra-city movements. To reduce the infection rate, the international community may deploy effective air pollution reduction plans and social distancing policies.

Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 (preprint)

Background. The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including (1) the time when the number of daily confirmed cases decreases, (2) the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), (3) the time when the number of daily confirmed cases becomes zero, and (4) the time when the number of patients treated in hospital is zero, which indicates the end of the epidemic. Intuitively, the former two can be regarded as two important turning points which indicate the alleviation of epidemic to some extent, while the latter two as two ``zero" points, respectively. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at a early stage of the outbreak. Method. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Specially, we first establish the iconic indicators to characterize the extent of epidemic spread, yielding four periods of the whole process corresponding to the four meaningful milepost moments: two turning points and two ``zero" points. Then we develop the tracking and forecasting procedure with mild and reasonable assumption. Finally we apply it to analyze and evaluate the COVID-19 using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results. Results show that our model can clearly outline the development of the epidemic at a very early stage. The first prediction results on Jan 29th reveal that the first and second milepost moments for mainland China beyond Hubei Province would appear on Jan 31st and Feb 14th respectively, which are only one day and three days behind the real world situations. Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-late March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. The framework proposed in this paper can help people get a general understanding of the epidemic trends in counties where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.