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1.
mSystems ; 6(5) (no pagination), 2021.
Article in English | EMBASE | ID: covidwho-2318454

ABSTRACT

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).Copyright © 2021 Rando et al.

2.
International Journal of Transportation Science and Technology ; 12(1):301-314, 2023.
Article in English | Scopus | ID: covidwho-2288785

ABSTRACT

During the pandemic, to prevent the spread of the virus, countries all adopted various safety measures, including masking, social distancing, and vaccination. However, there is a lack of methods that can quantitively evaluate the effectiveness of these countermeasures. This research first develops a model to quantitively evaluate the infection risk of riding public transit. By utilizing the developed model, the effectiveness of different countermeasures could be evaluated and compared. For demonstration purposes, the developed model is applied to a particular bus route in the City of Houston, Texas. The modeling results show that masking, social distancing, and vaccination can all reduce the infection risk for passengers. And among all these countermeasures, face masking is the most effective one. In addition, model results approve that the COVID-19 infection risk is highly related to the exposure time and the risk can be controlled by reducing the exposure time. Thus, a new strategy named the "split route strategy” is proposed and compared with the "capacity reduction strategy” using the model developed. In addition, a cost-benefit analysis is performed to assess the feasibility of the proposed "split route strategy”. Furthermore, two interviews were conducted with practitioners at Houston Metro. Both interviewees believe that face masking could significantly prevent the spread of the virus, which validated the model results. © 2022

3.
Acta Veterinaria et Zootechnica Sinica ; 54(1):281-292, 2023.
Article in Chinese | EMBASE | ID: covidwho-2234619

ABSTRACT

The aim of this paper was to prepare specific monoclonal antibody (mAb) against African swine fever virus (ASFV) p54 protein. The p54 protein was expressed in Escherichia coli expression system and used as the antigen in mAb production. The spleen cells from the immunized BALB/c mice were fused with myeloma cells SP2/0. To screen the positive hybridoma cells, the purified p54 protein was used as envelope antigen for indirect ELISA. After four times' subcloning, the supernatant of hybridoma cells were used to identify mAb subtype, ascites were prepared via in vivo induction method in mice and then the mAb was purified. The titer of the mAb was detected by indirect ELISA, and the specificity of the mAb was identified by cross reactivity assay, IFA and Western blot. According to the predicted secondary structure of p54 protein, using the stepwise truncation method identified the epitope region of mAbs, and labeled the region in tertiary structure of p54 protein. Results were as follows: six hybridoma cells secreting p54 monoclonal antibody were successfully screened and named 28G12-1, 31G7-1, 31G7-2, 35F10-1, 35F10-2, 38D3-1, respectively. The heavy chains of 28G12-1, 31G7-1, and 31G7-2 were IgG2a type, the heavy chains of 35F10-1, 35F10-2, 38D3-1 were IgG1 type, light chains were all kappa chains. The lowest titer of mAb was 1:25 600, and having no cross reaction with PRRSV, PRV, PEDV, PPV, SADS-CoV, PCV2, the specificity was strong. All six monoclonal antibodies could recognize the 127-146 aa on carboxyl end. In this study, ASFV p54 protein and p54 monoclonal antibody were successfully obtained, and the epitopes of six mAbs were identified, these experimental data laid a foundation for the functional research of p54 protein and the study of ASFV epitope vaccine. Copyright © 2023 Editorial Board, Institute of Animal Science of the Chinese Academy of Agricultural Sciences. All rights reserved.

4.
International Journal of Biomathematics ; 2022.
Article in English | Web of Science | ID: covidwho-2194047

ABSTRACT

Recent evidences show that individuals who recovered from COVID-19 can be reinfected. However, this phenomenon has rarely been studied using mathematical models. In this paper, we propose an SEIRE epidemic model to describe the spread of the epidemic with reinfection. We obtain the important thresholds R-0 (the basic reproduction number) and R-c (a threshold less than one). Our investigations show that when R-0 > 1, the system has an endemic equilibrium, which is globally asymptotically stable. When R-c < R-0 < 1, the epidemic system exhibits bistable dynamics. That is, the system has backward bifurcation and the disease cannot be eradicated. In order to eradicate the disease, we must ensure that the basic reproduction number R0 is less than Rc. The basic reinfection number is obtained to measure the reinfection force, which turns out to be a new tipping point for disease dynamics. We also give definition of robustness, a new concept to measure the difficulty of completely eliminating the disease for a bistable epidemic system. Numerical simulations are carried out to verify the conclusions.

5.
4th International Conference on Computer Science and Technologies in Education, CSTE 2022 ; : 184-188, 2022.
Article in English | Scopus | ID: covidwho-2191704

ABSTRACT

The global COVID-19 is spreading, and online teaching is developing rapidly. The continuous deepening of the integration of new technologies such as artificial intelligence and big data with education and teaching has prompted new changes in education and teaching, especially online and offline integrated teaching has become a new form of teaching. Combining the characteristics of open education, this study proposed a design model of online-merge-offline (OMO) intelligent learning space under the framework of PSST on the basis of sorting out the connotation of OMO intelligent learning space, in order to provide reference for future research on intelligent learning space. © 2022 IEEE.

6.
Journal of Operations Management ; 2022.
Article in English | Scopus | ID: covidwho-2148400

ABSTRACT

The outbreak of the COVID-19 pandemic has disrupted supply chains and increased the uncertainties faced by firms. While firms are struggling to survive and recover from the pandemic, Chinese e-commerce platforms have demonstrated resilient supply chains. We develop a framework that investigates the impacts of integration between an e-commerce platform and suppliers on supply chain resilience and the moderating effect of the suppliers' product flexibility. An analysis of data from a Chinese e-commerce platform using operational indicators finds that integration between the e-commerce platform and suppliers in terms of information sharing, joint planning and logistics cooperation has positive impacts on supply chain resilience, while procurement automation has the opposite effect. Furthermore, product flexibility positively moderates the impacts of information sharing, joint planning and logistics cooperation. The results enhance current understandings of the factors that contribute to the development of supply chain resilience and reveal that the relationship between integration and resilience should be examined within a contingency framework. The findings also provide guidelines for managers taking measures to mitigate the negative influences of supply chain disruptions. © 2022 Association for Supply Chain Management, Inc.

7.
Shanghai Ligong Daxue Xuebao/Journal of University of Shanghai for Science and Technology ; 44(3):288-298, 2022.
Article in Chinese | Scopus | ID: covidwho-2056452

ABSTRACT

During the outbreak of COVID-2019, the problem of excessive fatigue in doctors and the decline in treatment rate were caused by unreasonable medical resources scheduling. The influence factors of emergency doctors' fatigue degree were studied quantitatively. The entropy weight fuzzy analytic hierarchy process (EW-FAHP) was used to calculate the influence weight of each factor. Through the methods of reasonable allocation, policy relief and scientific rotation, the fatigue degree of doctors in emergency diagnosis and treatment was minimized. At the same time, the variable service rate was set according to the length of doctors' working time, and the birth death process model was used to analyze the queuing sequence of patients. Finally, the model was visualized by using ProModel simulation software, and the experimental group and the control group were set for comparative study. The simulation results show that the output rate of patients, the utilization rate of doctors' seats and other service indicators of considering fatigue queuing model are better than those of the traditional model. At the same time, a dynamic balance state for doctors' fatigue and patients' satisfaction is reached. © 2022 Shanghai Institute of Mechanical Engineering. All rights reserved.

8.
Journal of Geo-Information Science ; 24(9):1701-1716, 2022.
Article in Chinese | Scopus | ID: covidwho-2056379

ABSTRACT

With the proposal of "carbon peak" and "carbon neutralization", Liquefied Natural Gas (LNG) has gradually garnered the attention of energy market as a clean and low-carbon energy. In this context, it is of great significance to analyze the evolution mode of the LNG maritime transport network, so as to master the dynamic of global energy pattern and the status of China's import trade. In this paper, the evolution trend of the global LNG maritime transport network from 2018 to 2020 is explored based on the ship trajectory data and complex network theory. Meanwhile, according to China's trade status, LNG import sources, distribution of main import ports, and the inflow status of the top three import ports in China are analyzed. The results show that: (1) From 2018 to 2020, the global LNG maritime transport network expanded with a "scale-free" characteristic. The "breadth" and "depth" of node connections in the backbone network are increasing, and there is a risk that global LNG trade will become monopolistic;(2) The countries along the "Belt and Road Initiative" actively participated in trade. The numbers of import ports and import voyages in Central and North America, South and Southeast Asia have significantly increased, and in particular, Sabetta and Bonny ranked the top eight globally according to their export volume;(3) The average shortest path length of the network is increasing year by year from 2018 to 2020, and the new mode of "transshipment port" business is gradually emerging. By 2020, 21 transshipment ports have participated in LNG trade, and the United States occupies the dominant position in global transshipment;(4) In recent three years, China's LNG import scale has developed rapidly, and the flow direction of the maritime transport network tends to be diversified. However, Australia is still the main LNG source for China. In terms of import volume, the ports of Tianjin, Shenzhen, and Yung'an rank the top three in China, and the pressure to reduce carbon emissions has prompted the economically developed regions to build terminals and increase imports. © 2022, Science Press. All right reserved.

9.
2nd International Conference on Bioinformatics and Intelligent Computing, BIC 2022 ; : 1-5, 2022.
Article in English | Scopus | ID: covidwho-1902107

ABSTRACT

Since the outbreak and spread of COVID-19 in large areas of the world, the importance of rapid diagnosis of COVID-19 has increased. In the first week after the onset of COVID-19, the density of lesions is uneven, and chest CT is often difficult to show local subpleural ground-glass shadows, resulting in missed diagnosis. The COVID-19 intelligent diagnosis system based on the convolutional neural network algorithm can not only accurately identify the feature points, reduce the workload of doctors and improve the diagnosis efficiency, but also reduce the rate of missed diagnosis and misdiagnosis, which is conducive to epidemic control. © 2022 ACM.

10.
Photonics and Electromagnetics Research Symposium (PIERS) ; : 1961-1966, 2021.
Article in English | English Web of Science | ID: covidwho-1883139

ABSTRACT

Wuhai City is an important coal resource area in Inner Mongolia Autonomous Region. High-intensity underground mining will cause large land subsidence. Differential SAR Interferometry (D-InSAR) is a popular monitoring method of land subsidence in recent years. This paper uses two-pass D-InSAR method to monitor land subsidence in Wuhai City. The experimental data selects 7 scenes of C-band Sentinel-1A images from September 2019 to March 2020. The final deformation results are shown in the Figure 3. The two-pass D-InSAR processing flow includes data focusing, baseline estimation, interferogram generation, adaptive filtering and coherence generation, phase unwrapping, orbit refining and re-flattening, deformation map generation. The result shows: During the monitoring time, the most serious subsidence areas are concentrated near the mine clusters on the east and west sides of Wuhai City. Maximum settlement value up to 242 mm. The subsidence values in heavy industrial and residential areas are slightly smaller compared to the former. Settlement values are generally ranged from 56 to 87 mm. The settlement is lightest in the southern part of Hainan district. It indicates that mining can greatly accelerate surface subsidence. Meanwhile, human activities and groundwater extraction can increase subsidence. From the perspective of time, Settlement in Wuhai City is more pronounced during September to December 2019 but it decreases sharply in January 2020.This should be related to the outbreak of COVID-19. The subsidence value increased slightly from February to March 2020, which showed that the epidemic had been preliminarily controlled and people began to return to work.

11.
Emerging Markets Finance and Trade ; 2022.
Article in English | Scopus | ID: covidwho-1860556

ABSTRACT

This paper investigates the wavelet-based quantile dependence between Economic Policy Uncertainty (EPU) and green bond markets over 2014–2021. We first determine how the connectivity between EPU and green bonds differs across different investment horizons by decomposing EPU and green bond series into various frequency bands. Next, we provide a quantile-based framework to characterize the reliance between EPU and green bond markets across various market circumstances. Our findings show that the Granger causality from EPU to the green bond market is non-linear and varies across time scales. Our results benefit policymakers with a policy design to mitigate systematic volatility caused by external shocks in the green bond markets. © 2022 Taylor & Francis Group, LLC.

12.
Msystems ; 6(6):52, 2021.
Article in English | Web of Science | ID: covidwho-1849163

ABSTRACT

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

14.
2nd International Conference on Computer Vision, Image, and Deep Learning ; 11911, 2021.
Article in English | Scopus | ID: covidwho-1511404

ABSTRACT

With the spread of the epidemic in the world, wearing masks has become the most simple and effective way to block the COVID-19. For the lack of data and model design to fit the epidemic scene, we propose an integrated masked face recognition system with three cascaded convolutional neural networks. Firstly, a SSD model is used to detect masked face to eliminate the interference of irrelevant background. Then, we use an Hourglass network to regress the key points of the occluded face and crop the aligned eye-brow area without mask. Finally, we finetune a pretrained FaceNet to fully adapt to the data of eye-brow regions. Experiments on numbers of laboratory and wild images proved that our method can recognize the subjects with mask effectively. © 2021 SPIE.

16.
Pediatric Medicine ; 4, 2021.
Article in English | Scopus | ID: covidwho-1395513

ABSTRACT

The novel coronavirus has rapidly arisen to be a global pandemic since its discovery in December 19th. SARS-CoV-2, a type of betacoronavirus, mainly infects cells which express angiotensin-converting enzyme 2 (ACE2) receptors, causing alveolar damage and excessive inflammation in the lungs, and it can even cause diffuse alveolar damage and thrombosis in severe cases. The clinical manifestations range from mild pneumonia to severe illness, and even death. The prevalence of infection in children is similar to that of adults, though the symptoms are mild or even asymptomatic, among them, fever and cough are the most common symptoms. However, there are also reports of admission to the intensive care unit (ICU) or even death in children. Among them, acute respiratory distress syndrome (ARDS) is a common complication, with high mortality rates. Currently there are no specific drugs for the novel coronavirus pneumonia, and a large number of clinical trials are underway to search out the most suitable treatment. Respiratory support is still the basic management for ARDS induced by the novel coronavirus. This review summarizes the epidemiology, pathogenesis, clinical manifestations, diagnosis and progress of treatment methods in severe pediatric coronavirus disease 19 with ARDS, hoping that when the novel coronavirus continues to spread, clinicians can better understand, diagnose and treat the pediatric patients. © Pediatric Medicine. All rights reserved.

17.
12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1365241

ABSTRACT

Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins. We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus-Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences. Availability: We make all of our data and code available on GitHub https://github.com/QData/DeepVHPPI. © 2021 ACM.

18.
Advances in Climate Change Research ; 2021.
Article in English | Scopus | ID: covidwho-1279520

ABSTRACT

The systemic risk induced by climate change represents one of the most prominent threats facing humanity and has attracted increasing attention since the outbreak of the COVID-19 pandemic at the end of 2019. The existing literature highlights the importance of systemic risk induced by climate change, but there are still deficiencies in understanding its dynamics and assessing the risk. Aiming to bridge this gap, this study develops a theoretical framework and employs two cases to illustrate the concept, origin, occurrence, propagation, evolution, and assessment framework of systemic risk induced by climate change. The key findings include: 1) systemic risk induced by climate change derives from the rapid growth of greenhouse gas emissions, increasingly complex connections among different socioeconomic systems, and continuous changes in exposure and vulnerability;2) systemic risk induced by climate change is a holistic risk generated by the interconnection, interaction, and dynamic evolution of different types of single risks, and its fundamental, defining feature is cascading effects. The extent of risk propagation and its duration depend on the characteristics of the various discrete risks that are connected to make up the systemic risk;3) impact domains, severity of impact, and probability of occurrences are three core indicators in systemic risk assessment, and the impact domains should include the economy, society, homeland security, human health, and living conditions. We propose to deepen systemic risk research from three aspects: to develop theories to understand the mechanism of systemic risk;to conduct empirical research to assess future risks;and to develop countermeasures to mitigate the risk. © 2021 The Authors

19.
2nd IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021 ; : 93-98, 2021.
Article in English | Scopus | ID: covidwho-1247077

ABSTRACT

Stock prediction has been considered as a significant problem in both economics and computer fields. Since stock historical price is extraordinary crucial that it can affect investors' attitude towards whole market, and people's emotions will influence the stock price in the future, sequence-based predict models have received much attention these years. In this paper, we propose using LSTM to predict accurate single stock price, with whose history index as experience. To take public mood into account, numerous tweets were collected and quantified by deep learning method. To revise the LSTM model, we applied attention mechanism to extract vital factors and realign weights between history index and public sentiment. Experiments demonstrate that this model performs well, for the forecast price curve almost coincides with the real price curve. But in some extreme circumstance, models without sentiment as input performed better, which indicates that population sentiment can be a noise of prediction. © 2021 IEEE.

20.
Adv. Intell. Sys. Comput. ; 1305 AISC:433-442, 2021.
Article in English | Scopus | ID: covidwho-1212842

ABSTRACT

The COVID-19 can be transmitted by air droplets, aerosols, and other carriers, the spread of the virus can be effectively prevented by wearing masks in public. Therefore, it is meaningful to identify whether a mask is worn in particular places. In this paper, a method based on multi-task convolutional neural networks (MTCNN) and MobileNet algorithms is proposed to implement mask recognition on human face. Firstly, MTCNN is used to detect facial contours. Then the output image is used to train MobileNet model. By comparing the extracted facial feature data, the human with mask or not can be marked. The method has been tested in a 1.8 GHz Intel Core machine with 160 × 160 static images. Average accuracy rate of 94.73% and detection speed of 1.9 s are achieved. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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