AI and the Clinical Immunology/Immunoinformatics for COVID-19

Artificial Intelligence (AI) is contributing to the campaign against the Coronavirus Disease 2019 (COVID-19). Since 2019, more and more AI frameworks and applications in COVID-19 have been proposed, and the recent research has shown that AI is a promising technology because AI can achieve a higher degree of scalability, a more comprehensive and identification of patterns in the vast amount of unstructured and noisy data, accelerated processing power, and strategies to outperform traditional methods in many specific tasks. In this chapter, we focus on the specific AI applications in the clinical immunology/immunoinformatics for COVID-19. More precisely, on one hand, we discuss the application of deep learning in designing SARS-CoV-2 vaccines, and, on the other hand, we discuss the development of a machine learning framework for investigating the SARS-CoV-2 mutations that can help us better respond to the future mutant viruses, including designing more robust vaccines based on such AI approaches. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Paper-Based Devices for Virus Detection in Water

Viral infections are global health concerns that can cause high infection and mortality rates, as of the example from SARS-CoV-2 pandemic. Although conventional methods, e.g., polymerize chain reaction (PCR), can provide reliable and robust detection results, they are often time- and cost-consuming, limiting their application in resource-poor settings. Recently, paper-based devices, as a new biosensing technique, have emerged as a promising tool to conventional methods for pathogen detection including bacteria and virus. In this chapter, we provide a comprehensive introduction and insights on the development of paper-based devices for the pathogen detection in water. Firstly, the substrate materials and fabrication methods for paper-based devices are introduced. Engineering assay onto paper-based devices for virus detection is subsequently discussed for the rapid and on-site monitoring. We also compare the strengths and drawbacks between paper-based devices and the conventional analytical methods for virus detection, including culture method, biochemical test, immune assay, and molecular method. This chapter also discusses the feasibility of paper-based devices for point-of-use detection in water matrix, and the challenges and prospects of paper-based devices in water and environmental monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. IEEE

U.S. Public Transportation Ridership Analysis and Prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.

Coronavirus murine hepatitis virus strain 3 induces intestinal injury via caspase 3 dependent apoptosis

Background: Diarrhea was typical symptoms of the coronavirus disease 2019 (COVID-19). However, the underlying mechanism had not been fully understood. Aim(s): The study aimed to explore the mechanism of intestinal injury during COVID-19 in a coronavirus murine hepatitis virus strain 3 (MHV-3) induced acute mouse model. Method(s): MHV-3 induced acute infection Balb/cJ mice model was established. Intestine samples were collected at indicated time points as 0 h, 24 h, 48 h and 60 h post infection. The mRNA and protein expression of IL1b, TNFalpha, IL6, caspase 3 and cleaved caspase 3 were examined by real-time quantitative PCR (qPCR) and western blot respectively. The intestine injury and apoptosis were measured by HE staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). Moreover, Z-DEVD-FMK (caspase 3 inhibitor) pre-treated MHV-3 infection mice model were established, in which the apoptosis of intestine was evaluated as well. Meanwhile, the murine intestinal cell MODE-K was infected by MHV-3 in vitro for evaluation of virus induced apoptosis. Result(s): Post MHV-3 infection, the histopathology of intestine tissue showed extraordinary injury with time dependence, as well as high level of TUNEL positivity. The mRNA levels of inflammatory cytokine IL1b, TNFalpha and IL6 were significantly increased. The protein expressions of caspase 3 and cleaved caspase 3 in the intestine was found significantly elevated from 24 to 48 h post MHV-3 infection. Z-DEVD-FMK pretreatment inhibited caspase 3 and cleaved caspase 3 expression and decreased TUNEL positivity. Meanwhile, alleviated gut injury and inhibited TNFalpha expression were observed. In vitro treated by MHV-3, intestinal cell line MODE-K showed nine-fold increase of apoptosis by comparison with saline treated ones. The expressions of apoptosis crucial protein caspase3 and cleaved caspase3 significantly elevated, as well as TNFalpha. Conclusion(s): Coronavirus murine hepatitis virus strain 3 induces intestinal injury via caspase 3 dependent apoptosis, which might shed light on the treatment of intestinal complications in COVID-19.

Clinical characteristics of 654 patients with COVID-19 in Dongxihu Shelter Hospital in Wuhan

Objective To analyze the clinical features of patients with coronavirus disease 2019COVID-19in Wuhan, and we provide reference for further prevention and control of the disease. Methods We collected the clinical data of patients with COVID-19 in Dongxihu Shelter Hospital of Wuhan from February 7 to March 6, 2020. The main symptoms, blood test results, lung CT results, and nucleic acid negative conversion were analyzed. Results A total of 654 patients were included, 17526.76%were mild, and 47973.24%were general. There were 344 males (52.60%), and 310 females (47.40%). The patients were with a mean age of49.36+/-10.30years, and 97 patients (14.83%) with a history of hypertension, 51 patients (7.80%) had a history of diabetes. The main clinical symptoms were fever with 547(83.64%) patients, 186 cases (28.44%) had chills, 15 cases (2.29%) had shiver, 342(52.29%) had fatigue symptoms, 413(63.15%) had cough, 137(20.95%) had chest tightness, and 109(16.67%) had diarrhea during the course of the disease. Blood routine tests of 395 patients, the white blood cell count (WBC) was (4.12+/-1.46)x109/L. The total white blood cell count was normal in 378 cases(95.70%), increased in 7(1.77%), and decreased in 10(2.53%). The lymphocyte percentage was (23.10+/-10.02)%, lymphocyte1.06+/-0.37x109/L. The percentage and count of lymphocyte were low. All the 654 cases were examined by CT, 175 cases (26.76%) showed normal lung CT, 422 cases (64.52%) showed patchy or segmental ground-glass opacity, and 57 cases (8.72%) showed multilobar consolidation, ground-glass shadow coexisted with consolidation or streak shadow. The interval between positive nucleic acid test before admission and negative test after admission was as short as 5 days and as long as 24 days, the average was (12.35+/-3.73) days. Conclusion Fever, coughing, and fatigue are the main symptoms in patients with COVID-19. The typical lung CT findings can be used as the basis for clinical diagnosis and disease evaluation. Patients with mild and common type had better prognosis.Copyright © 2021 Editorial Office of Chinese Journal of Schistosomiasis Control. All rights reserved.

Price leadership in China's oil futures market: take two

This study is the first to conduct a comprehensive analysis of the price discovery and market liquidity aspects of China's crude oil futures market compared to WTI and Brent. With intraday-day data consolidated into 1-second intervals and three measures of price discovery, we find that China's crude oil futures market reports encouraging signs in terms of price discovery and efficiency, also showing great resilience during the COVID-19 pandemic. The market has obtained a dominant role in price discovery relative to WTI and Brent during its day trading hours, and has almost caught up with Brent in terms of market liquidity. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Model of Transnational Online Serial Sociocultural Lectures for Overseas Chinese Children and Its Evaluation

Previous lectures targeted at overseas Chinese children have mostly been offline in the form of youth camps sponsored by the Chinese governments before the COVID-19 pandemic and are now usually administered online in a single-topic format trans-nationally post-pandemic. This form of "one-to-many” singular online lectures comes along with discontinuity, mass audiences, chaos, and being less tailored to the individual needs and context, failing to meet the evaluation metrics in different evaluation levels. This paper proposes a model of transnational online livestreaming serial socio-cultural lectures for overseas Chinese children, which puts forward the synergy of lecturing staff from higher educational institutions (HEIs) in China and students at Chinese language schools (CLSs) worldwide as participants. Lecturing staff from needs and context HEIs can guarantee sufficient keynote speakers for "one-on-one” mode and cope with the above-mentioned problems. After the implementation of five sessions of these serial lectures, evaluation of this model shows that the pre-lecture, during-lecture, and post-lecture stages have are applicable. Furthermore, for sociocultural lectures, the audience may be more interested in literature classics than folk arts from a specific region. Correlation analysis reveals that younger children have a better evaluation of this model and an improved inclination to attend lectures, which sheds light on the improvement of sociocultural lectures as online lectures are preferred among overseas Chinese children. © ZHILING YANG, 2023.

Key Contributory Factors Influencing Cycling Safety: Comprehensive Review Based on Accident Data and Literature Survey

In the last years, it is evident that cycling is becoming an alternative transportation mode to driving and has gained more popularity among all age groups, particularly in metropolitan cities, due to COVID-19. Although cycling is beneficial to individuals and urban cities (i.e., reduction of traffic congestion and promotion of a healthy lifestyle), it could also expose cyclists to risky situations, resulting in serious consequences. Therefore, this research aims at conducting a comprehensive analysis of the key contributory factors by using data derived from cycling accident and literature reports. More specifically, the accident data are first used to prioritize contributory factors contributing to a high level of cycling risk, and then the results guide the development of the literature review. The literature review analysis emphasized the characteristics, relationships, and control measures against different selected contributory factors identified from cycling accident reports. The in-depth analysis aids to figure out and better understand what the characteristics and relationships of these factors are, how they affect the safety of cyclists individually and jointly, and what to do to control their negative effects. The findings will not only provide practical insights for transport authorities to control contributory factors influencing cycling safety, but also engage more research for the improvement of cycling popularity, prevention of cycling risks, and enhancement of cycling safety in future. © 2023 American Society of Civil Engineers.

Design and Implementation of Epidemic Big Data Visualization Platform Based on Vue + ECharts

Data visualization technology makes massive data more intuitive and easy to analyze. Based on the epidemic data from the National Bureau of Statistics of China, with the help of ECharts chart, elementUI component library and Vue technology, the data are visualized by using visualization technology and map integration. Through node. JS, Express The framework and MySQL technology realize the annual data management, regional data management and user management of the epidemic situation, display the epidemic situation of each region from multiple perspectives, and provide users with a reliable and convenient understanding channel and data management platform. It provides convenience for people to understand the data of the new coronavirus epidemic, analyze the development trend of the epidemic and manage the big data of the epidemic. © 2023 IEEE.

Mental health status survey in the first⁃line healthcare workers of designated hospitals for COVID⁃19 under the delta strain outbreak

Objective: To investigate mental health status of first-line healthcare workers in designated hospitals for coronavirus disease 2019 (COVID-19) under the Delta strain outbreak, so as to provide a basis for the implementation of effective countermeasures. Methods: We conducted an online survey on mental health status among 227 first-line medical staff treating COVID-19 patients in Wuhan Jinyintan Hospital during the period from September 4 to 6, 2021 using Self-Rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS). Results：The surveyed first-line medical staff had significantly higher levels of anxiety and depression than the national norms (P<0. 01), and among them, 54 (23. 70%) individuals had anxiety, 33(14. 5%) had depressive emotions, and 26 (11. 5%) had both. Nurses or female staff scored significantly higher than clinicians or male staff for anxiety and depression (P<0. 01). Married or middle-aged and young (30 to 45 years old) first-line medical staff had the relatively high scores of depression and anxiety (P<0. 01). Anxiety and depression of medical staff were associated with the time of working in the first-line, and those who continued to work 10-20 days had the relatively high scores of depression and anxiety (P<0. 05). Conclusion: In the face of SARS-CoV -2 Delta variant outbreak, the first-line medical staff still had negative mentality, such as anxiety and depression, psychological intervention support system should be established in order to help improve the level of the first-line medical staff's mental health and improve the working condition. © 2022 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

Multiplex and Ultrasensitive Detection of Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza A/B Nucleocapsid Proteins Based on Nanometer-Sized Core-Shell Superparamagnetic Antifouling Probes

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A (Flu A), and influenza B (Flu B) show similar clinical symptoms, such as cough, fever, and dyspnea, but patients infected by these viruses should be treated differently. The rapid and accurate diagnosis of infections caused by SARS-CoV-2, Flu A or Flu B is critical during the influenza season. Herein, we synthesized core-shell magnetic particles (MNPs) with excellent antifouling properties and applied them in the MNP-based immunochromatographic test (MICT) for simultaneous detection of SARS-CoV-2, Flu A, and Flu B nucleocapsid(N) proteins in 20 min. Two kinds of carboxyl -modified MNPs, MNP@pMBAA and MNP@Si-SA, were prepared and evaluated as probes in the MICT. Among them, the MNP@ pMBAA showed lower nonspecific adsorption of proteins and low background noise in the application in MICTs. Particularly, the MNP@pMBAA50 bead-based MICT strip exhibited the highest signal-to-noise ratio for SARS-CoV-2 N protein detection with a limit of detection (LOD) of 0.072 ng/mL. Moreover, the proposed MICT strip demonstrated a minimal cross-reactivity and a broad linear dynamic detection range under a magnetic assay reader in the simultaneous detection of SARS-CoV-2, Flu A, and Flu B N proteins with relative LOD values of 0.0086, 0.012, and 0.018 ng/mL, respectively. The results demonstrated that the synthesized MNPs showed great potential for use as MICT probes for sensitive and multiplex detection of biomarkers in the development of point-of-care testing systems.

A risk-averse two-stage stochastic programming model for a joint multi-item capacitated line balancing and lot-sizing problem

In this paper, a comprehensive production planning problem under uncertain demand is investigated. The problem intertwines two NP-hard optimization problems: an assembly line balancing problem and a capacitated lot-sizing problem. The problem is modelled as a two-stage stochastic program assuming a risk-averse decision maker. Efficient solution procedures are proposed for tackling the problem. A case study related to mask production is presented. Several insights are provided stemming from the COVID-19 pandemic. Finally, the results of a series of computational tests are reported. (c) 2021 Elsevier B.V. All rights reserved.

Systematic Transformations of Entrepreneurship in the New Era: The Commitment to Benefit Corporation Entrepreneurship

Since the 18th National Congress of the Communist Party of China (CPC) was held in 2012, China has comprehensively started shifting economic and social development from the traditional way of extensive growth, driven by labor, land, and capital, to innovation-driven high-quality development. During the transformation process, intricate economic and social contradictions have arisen one after another. Thus, the effective participation of entrepreneurs as market players is indispensable in promoting high-quality social and economic development and modernizing China's governance system and capacity. In particular, the global COVID-19 pandemic has had a vast and lasting impact on production and social order. It is all the more necessary to make the best of the endogenous capability of entrepreneurship to energize market vitality and address the complicated challenges in social and market governance in the face of a major social crisis. In his report to the 19th CPC National Congress in 2017, Chinese President Xi Jinping said, "We will inspire and protect entrepreneurship, and encourage more entities to make innovations and start businesses. We will build an educated, skilled, and innovative workforce, foster respect for model workers, and promote quality workmanship." On July 21, 2020, when presiding over a roundtable with entrepreneurs, Xi once again stressed the great value of entrepreneurship in improving the socialist market economy with Chinese characteristics and accelerating innovation-driven development in the post-COVID-19 era. He pointed out, "Since the reform and opening up, the socialist market economy system has steadily developed and constantly improved;various market entities are thriving;facing the huge attacks on domestic and global economies caused by COVID-19, it is needed to make every effort to protect market entities, spur vitality of market entities, carry forward entrepreneurship, and enable enterprises to play bigger roles and achieve greater development, to accumulate fundamental strength for economic growth."

Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE

Interactive Strategies of Chinese Cloud Classroom Based on the Perspective of Meta Universe

With the American social software Facebook officially announced its name change to "Meta”, the concept of "Meta Universe” has really entered the public field. The meta universe is an Internet social form based on the integrated application of various emerging information technologies. It has transformative value for the future development of all walks of life, including the education industry. Since the outbreak of covid-19, online Chinese learning of most foreign students has changed from a short-term accidental state to a normal state. In this way, we must pay attention to how to seize the opportunity of the development of meta universe technology, combine the meta universe cloud classroom with AI digital people, and create a new scene of educational application integrating multiple technologies under the new AI ecology. Only in this way can we build a new educational paradigm change, and solve some bottlenecks and difficulties in the current development of Chinese cloud classroom interaction for foreign students, so that classroom teaching can truly promote the development of students' knowledge, ability and emotion, and finally cultivate students' innovative spirit and practical ability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Rashes caused by arbidol in a patient with novel coronavirus pneumonia.

A 50-year-old male patient with agitated depression and hyperlipemia received oral amoxicillin and clavulanate potassium 0.5 g once daily and 2 lopinavir and ritonavir tablets twice daily for novel coronavirus infection, based on previous drugs including quetiapine, clonazepam, and atorvastatin calcium. After 3 days, lopinavir and ritonavir was changed to oral arbidol 200 mg, thrice daily due to suspicious drug interaction. After taking arbidol for 3 days, the patient developed red papules on the whole body. Considering that it might be related to amoxicillin and clavulanate potassium, the drug was stopped and loratadine was given. But the rashes were aggravated. Considering that the drug eruption was caused by arbidol, arbidol was discontinued and the rashes subsided in a large area the next day. Then vitamin C injection, calcium gluconate injection, and ribavirin were added. After 5 days, the rashes subsided completely. After 17 days, the patient recovered from pneumonia.Copyright © 2020 by the Chinese Medical Association.

Impact of COVID-19 and green finance on transportation energy carbon emissions in China: From the perspective of an automobile energy consumption structure

Background: In China, the transportation sector is the main energy consumer and the main source of carbon emissions. Reducing carbon emissions in the transportation sector is an important goal for China, especially during the current period of economic development. Due to the impact of pandemic shocks, the rapid development of green finance is conducive to supporting the transportation sector in achieving a carbon peak. Thus, we examined whether the development of green finance is still effective under the impact of a pandemic and the actual effect of green finance on the reduction of carbon emissions. Methods: In this study, we searched the internet for consumption structure data of vehicles and green finance indices of 30 Chinese provinces and cities from 2016 to 2021. A regression discontinuity model was constructed to test the effect of pandemic shock and green finance development on the reduction of transportation energy carbon emissions. Results: The results show that the impact of the COVID-19 pandemic has helped people change their preference toward more energy-efficient vehicles and reduce carbon emissions in the transportation sector. Green finance can effectively contribute to the reduction of transportation energy carbon emissions;however, the overall mitigation effect is limited. Conclusion: The empirical evidence is not only helpful in assessing the long-term impact of the COVID-19 pandemic but also conducive to the appropriate establishment of policy tools for supporting green finance development, which is further conducive to reducing carbon emissions in the transportation sector. Copyright © 2023 Liu, Cheng, Guan, Liu, Zhang, Li and Yang.