Serological Epidemiological Survey Of Bovine Diarrhea In Large-Scale Cattle Farms In Inner Mongolia, China from 2019 to 2022 (preprint)

Bovine diarrhea is a multi-factorial disease, and remains one of the most serious health problems in cattle husbandry. However, the endemic trends of the main pathogens responsible for bovine diarrhea have not been analyzed before, therefore, the purpose of this study was to estimate the prevalence of bovine diarrhea pathogens in the large-scale cattle farms of Inner Mongolia in China, we assessed the differences in the prevalence of infection based on age and area, as well as determined local prevalence rates and the rates of mixed infections. Using stratified random sampling strategy, cattle were divided into four age stages (0-2 months, 3-6 months, 7-18 months and ＞18 months), 2,216 serum samples were collected from 38 cattle herds in 12 leagues and cities in Inner Mongolia of China. Positive rates of Bovine Viral Diarrhoea Virus (BVDV), Bovine Rotavirus (BRV), Bovine Coronavirus (BCoV), Escherichia coli K99 (E.coli K99), and M. paratuberculosis (Mptb) antibodies were detected in the samples by ELISA to determine the epidemic trends and differences of the five pathogens in Inner Mongolia. The positive rate of BVDV vaccine higher than 90% in each age category of cattle, and the prevalence rates of BRV and Mptb in other ages significantly higher than ＞18 months (P <0.05). Except BVDV, the prevalence of BCoV was the highest in 0-2 months old (74.88%), the highest positive rate in ＞18 months old is BRV (63.31%), the infection of viral pathogens were serious in Inner Mongolia. With the highest mixed infection (33.74%) being BRV and BCoV in the category of 0-2 months, and there was a mixed infection of BRV and BCoV at four age stages, Only double and triple infections were detected in cattle aged 2-6 months and 6-18 months, quadruple infections were detected in cattle aged 0-2 months and ＞18 months. Furthermore, obvious regional epidemiological differences among the five pathogens were observed, 5 pathogens were detected in 11 regions, Hulun Buir city was seriously infected with bacterial pathogens, only antibodies to viral pathogens were detected in Ulanqab city. All the above detected pathogens can trigger high mortality and poor prognosis in cattle. This study is the first to report the prevalence and characteristics of the main pathogens of bovine diarrhea in large-scale cattle farms in Inner Mongolia, China, which has practical significance for the future prevention and control of bovine diarrhea and reduce economic losses in cattle farming.

Research on the Design of Virtual Reality Online Education Information Presentation Based on Multi-Sensory Cognition

The popularity of the online teaching model increased during the COVID-19, and virtual reality online education is now firmly established as a future trend in educational growth. Human–computer interaction and collaboration between virtual models and physical entities, as well as virtual multi-sensory cognition, have become the focus of research in the field of online education. In this paper, we analyze the mapping form of teaching information and cue information on users' cognition through an experimental system and investigate the effects of the presentation form of online virtual teaching information, the length of the material, users' memory of the information, and the presentation form of information cues on users' cognitive performance. The experimental results show that different instructional information and cue presentation designs have significant effects on users' learning performance, with relatively longer instructional content being more effective and users being more likely to mechanically remember the learning materials. By studying the impact of multi-sensory information presentation on users' cognition, the output design of instructional information can be optimized, cognitive resources can be reasonably allocated, and learning effectiveness can be ensured, which is of great significance for virtual education research in digital twins.

System Threat during a Pandemic: How Conspiracy Theories Help to Justify the System

During the COVID-19 pandemic, many people have endorsed conspiracy theories about foreign governments yet shown increased trust and support for their own government. Whether there is a potential correlation between these social phenomena and the psychological mechanisms behind them is still unclear. Integrating insights from the existential threat model of conspiracy theories and system justification theory, two experimental studies were conducted to investigate whether belief in out-group conspiracy theories can play a mediating role in the effects of system threat on people's system justification beliefs against the background of the pandemic. The results show that system threat positively predicts individuals' system-justifying belief, and belief in out-group conspiracy theories mediated this relationship.

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning

Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters. For complex machine learning models such as deep neural networks, it is difficult to determine their hyperparameters. In addition, existing hyperparameter optimization algorithms easily converge to a local optimal solution. This paper proposes a method for hyperparameter optimization that combines the Sparrow Search Algorithm and Particle Swarm Optimization, called the Hybrid Sparrow Search Algorithm. This method takes advantages of avoiding the local optimal solution in the Sparrow Search Algorithm and the search efficiency of Particle Swarm Optimization to achieve global optimization. Experiments verified the proposed algorithm in simple and complex networks. The results show that the Hybrid Sparrow Search Algorithm has the strong global search capability to avoid local optimal solutions and satisfactory search efficiency in both low and high-dimensional spaces. The proposed method provides a new solution for hyperparameter optimization problems in deep learning models.

SARS-CoV-2-triggered mast cell rapid degranulation induces alveolar epithelial inflammation and lung injury.

SARS-CoV-2 infection-induced hyper-inflammation links to the acute lung injury and COVID-19 severity. Identifying the primary mediators that initiate the uncontrolled hypercytokinemia is essential for treatments. Mast cells (MCs) are strategically located at the mucosa and beneficially or detrimentally regulate immune inflammations. In this study, we showed that SARS-CoV-2-triggered MC degranulation initiated alveolar epithelial inflammation and lung injury. SARS-CoV-2 challenge induced MC degranulation in ACE-2 humanized mice and rhesus macaques, and a rapid MC degranulation could be recapitulated with Spike-RBD binding to ACE2 in cells; MC degranulation altered various signaling pathways in alveolar epithelial cells, particularly, the induction of pro-inflammatory factors and consequential disruption of tight junctions. Importantly, the administration of clinical MC stabilizers for blocking degranulation dampened SARS-CoV-2-induced production of pro-inflammatory factors and prevented lung injury. These findings uncover a novel mechanism for SARS-CoV-2 initiating lung inflammation, and suggest an off-label use of MC stabilizer as immunomodulators for COVID-19 treatments.

Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model

Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics.

Comparison of epidemiological and clinical characteristics of COVID-19 patients with and without Wuhan exposure / 浙江省有无武汉暴露史新型冠状肺炎患者的流行 病学和临床特征的比较研究

Background: A novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first identified in Wuhan, China, has been rapidly spreading around the world. This study investigates the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) patients in Zhejiang Province who did or did not have a history of Wuhan exposure. Methods: We collected data from medical records of confirmed COVID-19 patients in Zhejiang Province from Jan. 17 to Feb. 7, 2020 and analyzed epidemiological, clinical, and treatment data of those with and without recorded recent exposure in Wuhan. Results: Patients in the control group were older than those in the exposure group ((48.19±16.13) years vs. (43.47±13.12) years, P<0.001), and more were over 65 years old (15.95% control vs. 5.60% exposure, P<0.001). The rate of clustered onset was also significantly higher in the control group than in the exposure group (31.39% vs. 18.66%, P<0.001). The symptom of a sore throat in patients in the exposure group was significantly higher than that in the control group (17.30% vs. 10.89%, P=0.01); however, headache in the exposure group was significantly lower than that in the control group (6.87% vs. 12.15%, P=0.015). More patients in the exposure group had a significantly lower level of lactate dehydrogenase (LDH) and aspartate aminotransferase (AST) than those in the control group. There was no significant difference in any degree of COVID-19 including mild, severe, and critical between the two groups. Conclusions: From the perspective of epidemiological and clinical characteristics, there was no significant difference between COVID-19 patients with and without Wuhan exposure history.

The multidisciplinary nature of COVID-19 research (preprint)

Objective: We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue. Design/Methodology/Approach: We examined bibliometric measures for COVID-19 and the rest of studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages. Results/Discussion: The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation. Conclusions: COVID-19 literature showed the broadest subject coverage, which is clearly a consecuence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics. Originality/Value: The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective.

N439K variant in spike protein may alter the infection efficiency and antigenicity of SARS-CoV-2 based on molecular dynamics simulation (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), has been undergoing various mutations. The analysis of the structural and energetic effects of mutations on protein-protein interactions between the receptor binding domain (RBD) of SARS-CoV-2 and angiotensin converting enzyme 2 (ACE2) or neutralizing monoclonal antibodies will be beneficial for epidemic surveillance, diagnosis, and optimization of neutralizing agents. According to the molecular dynamics simulation, a key mutation N439K in the SARS-CoV-2 RBD region created a new salt bridge which resulted in greater electrostatic complementarity. Furthermore, the N439K-mutated RBD bound hACE2 with a higher affinity than wild-type, which may lead to more infectious. In addition, the N439K-mutated RBD was markedly resistant to the SARS-CoV-2 neutralizing antibody REGN10987, which may lead to the failure of neutralization. These findings would offer guidance on the development of neutralizing antibodies and the prevention of COVID-19.

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.

Interferon-α2b spray inhalation did not shorten virus shedding time of SARS-CoV-2 in hospitalized patients: a preliminary matched case-control study.

BACKGROUND: Currently, there are no drugs that have been proven to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because of its broad antiviral activity, interferon (IFN) should be evaluated as a potential therapeutic agent for treatment of coronavirus disease 2019 (COVID-19), especially while COVID-19-specific therapies are still under development. METHODS: Confirmed COVID-19 patients hospitalized in the First Affiliated Hospital, School of Medicine, Zhejiang University in Hangzhou, China, from January 19 to February 19, 2020 were enrolled in a retrospective study. The patients were separated into an IFN group and a control group according to whether they received initial IFN-α2b inhalation treatment after admission. Propensity-score matching was used to balance the confounding factors. RESULTS: A total of 104 confirmed COVID-19 patients, 68 in the IFN group and 36 in the control group, were enrolled. Less hypertension (27.9% vs. 55.6%, P=0.006), dyspnea (8.8% vs. 25.0%, P=0.025), or diarrhea (4.4% vs. 19.4%, P=0.030) was observed in the IFN group. Lower levels of albumin and C-reactive protein and higher level of sodium were observed in the IFN group. Glucocorticoid dosage was lower in the IFN group (median, 40 vs. 80 mg/d, P=0.025). Compared to the control group, fewer patients in the IFN group were ventilated (13.2% vs. 33.3%, P=0.015) and admitted to intensive care unit (ICU) (16.2% vs. 44.4%, P=0.002). There were also fewer critical patients in the IFN group (7.4% vs. 25.0%, P=0.017) upon admission. Although complications during admission process were comparable between groups, the discharge rate (85.3% vs. 66.7%, P=0.027) was higher and the hospitalization time (16 vs. 21 d, P=0.015) was shorter in the IFN group. When other confounding factors were not considered, virus shedding time (10 vs. 13 d, P=0.014) was also shorter in the IFN group. However, when the influence of other factors was eliminated using propensity score matching, virus shedding time was not significantly shorter than that of the control group (12 vs. 15 d, P=0.206). CONCLUSIONS: IFN-α2b spray inhalation did not shorten virus shedding time of SARS-CoV-2 in hospitalized patients.

Clinical characteristics of different subtypes and risk factors for the severity of illness in patients with COVID-19 in Zhejiang, China.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) is now becoming an enormous threat to public health. The clinical spectrum of COVID-19 is extensive, of which critical cases are with rapid disease progression and high mortality. The aim of our study is to summarize the characteristics of different subtypes and explore risk factors of illness severity for early identification and prompt treatment. METHODS: In this retrospective study, we collected data of patients confirmed COVID-19 in Zhejiang Province from 17 January to 12 February 2020. According to the definition of clinical classification, we divided confirmed cases into four types, and summarize epidemiological and clinical characteristics, laboratory and radiograph findings, treatments, and outcomes, respectively. Moreover, we used univariate and multivariate ordinal logistic regression models to explore risk factors for the severity of illness in patients with COVID-19. RESULTS: A total of 788 patients were enrolled in our study, of whom 52 cases (6.6%) were mild type, 658 cases (83.5%) were common type, 61 cases (7.2%) were severe type, and 17 cases (2.2%) were critical type. Multivariate ordinal logistic regression demonstrated increasing odds of the severity of illness in patients with COVID-19 associated with male (odds ratio [OR] = 1.7, 95% confidence interval [CI]: 1.2-2.6 P = 0.008), fever (OR = 3.6, 95% CI: 2.1-6.3, P <  0.001), cough (OR = 1.7, 95% CI: 1.0-2.9, P = 0.041), hemoptysis (OR = 3.4, 95% CI: 1.1-10.3, P = 0.032), gastrointestinal symptoms (OR = 1.9, 95% CI: 1.0-3.5, P = 0.047), hypertension (OR = 2.6, 95% CI: 1.2-5.6, P = 0.013). With the increase of age-grading, risk for the severity of illness was gradually higher (≤ 18 years [OR = 1.0], 19-40 years [OR = 12.7, 95% CI: 4.5-36.0, P < 0.001], 41-65 years [OR = 14.8, 95% CI: 5.2-42.1, P <  0.001], ≥ 66 years [OR = 56.5, 95% CI: 17.1-186.5, P < 0.001]). CONCLUSIONS: Clinicians should pay close attention to these features in patients with COVID-19 including older age, male, fever, cough, hemoptysis, gastrointestinal symptoms and hypertension to identify the severity of illness as early as possible.

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.

Liver Enzyme Elevation in Coronavirus Disease 2019: A Multicenter, Retrospective, Cross-Sectional Study.

INTRODUCTION: Elevated liver enzyme levels are observed in patients with coronavirus disease 2019 (COVID-19); however, these features have not been characterized. METHODS: Hospitalized patients with COVID-19 in Zhejiang Province, China, from January 17 to February 12, 2020, were enrolled. Liver enzyme level elevation was defined as alanine aminotransferase level >35 U/L for men and 25 U/L for women at admission. Patients with normal alanine aminotransferase levels were included in the control group. Reverse transcription polymerase chain reaction was used to confirm severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and patients symptomatic with SARS-CoV-2 infection were defined as patients with COVID-19. Epidemiological, demographic, clinical, laboratory, treatment, and outcome data were collected and compared. RESULTS: Of 788 patients with COVID-19, 222 (28.2%) patients had elevated liver enzyme levels (median [interquartile range {IQR}] age, 47.0 [35.0-55.0] years; 40.5% women). Being male, overweight, and smoking increased the risk of liver enzyme level elevation. The liver enzyme level elevation group had lesser pharyngalgia and more diarrhea than the control group. The median time from illness onset to admission was 3 days for liver enzyme level elevation groups (IQR, 2-6), whereas the median hospitalization time for 86 (38.7%) discharged patients was 13 days (IQR, 11-16). No differences in disease severity and clinical outcomes were noted between the groups. DISCUSSION: We found that 28.2% of patients with COVID-19 presented with elevated liver enzyme levels on admission, which could partially be related to SARS-CoV-2 infection. Male patients had a higher risk of liver enzyme level elevation. With early medical intervention, liver enzyme level elevation did not worsen the outcomes of patients with COVID-19.