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Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.
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Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.
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Objective To understand the knowledge of COVID-19 and plague prevention and control in Qinghai Province, so as to carry out targeted health education and improve people's ability to prevent and control COVID–19, plague and other publichealth emergencies. Methods Six counties were randomly selected from three cities (states) by two-stage sampling. A self- designed questionnaire was randomly distributed to the public to investigate the awareness and behavior of COVID-19 and plague prevention and control. The Chinese version of Epidate was used for database construction and data entry. After checking and verifying, the data was exported as an Excel file and analyzed by SPSS 21.0 software. Results Accordign to the recovered questionnaires, the passing rate of knowledge of COVID-19 prevention and control was 78.46%, and the average score was (75. 82±16.43). The passing rate of plague prevention and control knowledge was 91.89%, and the average score was (86.46±15.94). The survey area, occupation category, gender and education level affected the knowledge of COVID-19 prevention and control. The average score was statistically significant (P<0.05). The survey area, occupation category, age and education level affected the knowledge of plague prevention and control, and the average score was statistically significant (P<0. 05). Conclusion People in Qinghai have poor knowledge of COVID - 19 prevention and control, but have good knowledge of plague prevention and control. Health education and health promotion activities on COVID - 19 and plague prevention and control should be increased in the future. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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The transmission rate is an important indicator for characterizing a virus and estimating the risk of its outbreak in a certain area, but it is hard to measure. COVID-19, for instance, has greatly affected the world for more than 3 years since early 2020, but scholars have not yet found an effective method to obtain its timely transmission rate due to the fact that the value of COVID-19 transmission rate is not constant but dynamic, always changing over time and places. Therefore, in order to estimate the timely dynamic transmission rate of COVID-19, we performed the following: first, we utilized a rolling time series to construct a time-varying transmission rate model and, based on the model, managed to obtain the dynamic value of COVID-19 transmission rate in mainland China;second, to verify the result, we used the obtained COVID-19 transmission rate as the explanatory variable to conduct empirical research on the impact of the COVID-19 pandemic on China's stock markets. Eventually, the result revealed that the COVID-19 transmission rate had a significant negative impact on China's stock markets, which, to some extent, confirms the validity of the used measurement method in this paper. Notably, the model constructed in this paper, combined with local conditions, can not only be used to estimate the COVID-19 transmission rate in mainland China but also in other affected countries or regions and would be applicable to calculate the transmission rate of other pathogens, not limited to COVID-19, which coincidently fills the gaps in the research. Furthermore, the research based on this model might play a part in regulating anti-pandemic governmental policies and could also help investors and stakeholders to make decisions in a pandemic setting.
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Background: In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2 (AF2), a breakthrough in the field of structural biology, provides a solution to this protein structure prediction problem by learning a deep learning model. However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions limit its applications in de novo antibody design. Method(s): To learn informative representation of antibodies, we trained a deep antibody language model (ALM) on curated sequences from observed antibody space database via a well-designed transformer model. We also developed a novel model named xTrimoABFold++ to predict antibody structure from antibody sequence only based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame aligned point loss. Result(s): xTrimoABFold++ outperforms AF2 and OmegaFold, HelixFold-Single with 30+% improvement on RMSD. Also, it is 151 times faster than AF2 and predicts antibody structure in atomic accuracy within 20 seconds. In recently released antibodies, for example, cemiplimab of PD1 (PDB: 7WVM) and cross-neutralizing antibody 6D6 of SARS-CoV-2 (PDB: 7EAN), the RMSD of xTrimoABFold++ are 0.344 and 0.389 respectively. Conclusion(s): To the best of our knowledge, xTrimoABFold++ achieved the state-of-the-art in antibody structure prediction. Its improvement on both accuracy and efficiency makes it a valuable tool for de novo antibody design, and could make further improvement in immuno-theory.
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Discovered in December 2019, Coronavirus (Covid-19) is an infectious disease that has spread rapidly around the world. The World Health Organization (WHO) declared Covid-19 a pandemic in March 2020. The pandemic has increased the severity and amount of mental health problems, including depression, stress, and anxiety. This research uses real-life Covid-19 Tweets collected from March 2020 until October 2021. The objective is to analyze tweets from the US, UK, and India to discover Covid-19's impact on mental health in the three countries and identify influential users in each country when discussing this topic. The result shows that the major themes in the US were related to government and politics. Some dominant users in the US are news accounts and people who have occupations such as journalists, hosts, and presenters. The UK's theme focuses on relationships between friends and families, with doctors and medical workers as dominant users. India focuses on mental health and education, with dominant users including news-related accounts and some politicians. © 2022 Authors. All rights reserved.
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Air-borne pollutants in particulate matter (PM) form, produced either physically during industrial processes or certain biological routes, have posed a great threat to human health. Particularly during the current COVID-19 pandemic, effective filtration of the virus is an urgent matter worldwide. In this review, we first introduce some fundamentals about PM, including its source and classification, filtration mechanisms, and evaluation parameters. Advanced filtration materials and their functions are then summarized, among which polymers and MOFs are discussed in detail together with their antibacterial performance. The discussion on the application is divided into end-of-pipe treatment and source control. Finally, we conclude this review with our prospective view on future research in this area. (c) 2022 Institute of Process Engineering, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Guided by cultivation theory and intergroup contact theory, we examined how U.S. college students' traditional media use and social media use for information about COVID-19, and direct contact with Chinese were associated with their behavioral attitudes toward Chinese people in this survey study. Findings indicated that contact quality was positively associated with attitudes toward Chinese people. Moderation analyses indicated that traditional media use negatively predicted behavioral attitudes toward Chinese people for those with no Chinese friends and was a nonsignificant predictor for those with one or more Chinese friends. Furthermore, results indicated that social media use was positively associated with attitudes toward Chinese people for those who had high contact quality with Chinese but was a nonsignificant predictor for those who had low contact quality. Overall findings ruminate the critical role of intergroup contact quality and friendship in reducing intergroup prejudice in COVID-19.
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Information and communication technologies (ICTs) have been making higher education confront great challenges globally, while COVID-19 has been speeding up the necessity to overcome them. Traditionally, geographical teaching in China involves indoor experiments, fieldtrips and other practical activities in addition to lectures in class. We asked what impacts or changes the COVID-19 epidemic would have on geographical education in universities of China. Based on the case of Beijing Normal University, this chapter aims to examine new learning patterns as a response to the outbreak and the real impacts in China. Questionnaire surveys and typical cases are used to examine the teaching arrangements and effects in three periods, viz., the early stage of the outbreak when it was necessary to prepare contingent teaching plans, the middle and late stage of the spring semester to examine the adaptations and feedback of online learning, and later when traditional teaching resumed in the autumn semester of 2020 to evaluate online learning. This research aims to seek some innovative measurements for reforming geographical education in Chinese universities in post COVID-19 pandemic times. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.
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An epidemiological investigation was conducted on a cluster epidemic of COVID-19 in the vaccinated population in Beijing in 2022, and serum samples were collected from 21 infected cases and 61 close contacts (including 20 cases with positive nucleic acid in the isolation observation period). The results of antibody detection showed that the IgM antibody of two infected persons was positive, and the IgG antibody positive rates of patients who were converted, not converted to positive and infected persons were 36.84% (7/19), 63.41% (26/41) and 71.43% (15/21), respectively. About 98.78% of patients had been vaccinated with the SARS-CoV-2 inactivated vaccine. The positive rate of IgG antibody in patients immunized with three doses of vaccine was 86.00% (43/50), which was higher than that in patients with one or two doses [16.12% (5/31)]. The antibody level of M (Q1, Q3) in patients immunized with three doses was 4.255 (2.303, 7.0375), which was higher than that in patients with one or two doses [0.500 (0.500, 0.500)] (all P values<0.001). The antibody level of patients who were vaccinated less than three months [7.335 (1.909, 7.858)] was higher than that of patients vaccinated more than three months after the last vaccination [2.125 (0.500, 4.418)] (P=0.007). The positive rate and level of IgG antibody in patients who were converted to positive after three doses were 77.78% (7/9) and 4.207 (2.216, 7.099), respectively, which were higher than those in patients who were converted after one or two doses [0 and 0.500 (0.500, 0.500)] (all P values<0.05).
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COVID-19 , Humans , SARS-CoV-2 , Disease Outbreaks , COVID-19 Vaccines , Immunoglobulin G , Antibodies, ViralABSTRACT
We investigated the types of novel coronavirus strains present during the Omicron epidemic from late 2022 to early 2023, COVID-19 co-infections with other pathogens, and clinical characteristics of patients with novel coronavirus infections. Adult patients hospitalized due to SARS CoV-2 infection in six hospitals in Guangzhou city were included in the study from November 2022 to February 2023. Clinical information was collected and analyzed, and bronchoalveolar lavage fluid was obtained for pathogen detection using a variety of techniques, including standard methods and mNGS, tNGS. The results showed that the main strain circulating in Guangzhou was Omicron BA.5.2, and the overall detection rate of potentially pathogenic pathogens combined with Omicron COVID-19 infection was 49.8%. In patients with severe COVID-19 infection, special attention should be paid to aspergillosis and combined Mycobacterium tuberculosis infection. In additon, Omicron strain infection could cause viral sepsis, which led to a worse prognosis for COVID-19 patients. Diabetic patients with SARS-CoV-2 infection did not benefit from glucocorticoid treatment, and caution was necessary when using glucocorticoids. These findings highlighted some new features of severe Omicron coronavirus infection that should be noted.
Subject(s)
Aspergillosis , COVID-19 , Adult , Humans , SARS-CoV-2 , Bronchoalveolar Lavage Fluid , GlucocorticoidsABSTRACT
With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. © 2023 by the authors.
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We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed outcomes, allowing for the application of flexible statistical learning tools to estimate the nuisance functions. We propose an analytic Wald-type confidence interval and a double bootstrap scheme for statistical inference. We demonstrate the performance of the proposed methods through simulation studies and apply these methods to estimate the efficiency gain of covariate adjustment in Covid-19 therapeutic trials.
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During combatting the COVID-19 pandemic, the most widespread change in Spanish as a foreign language instruction is imperative online teaching. It demands that language teachers move all teaching activities to virtual platforms, facilitating the construction of their digital identities. However, there is scarce attention on Spanish teachers' professional development, given the necessity of understanding the evolvement of their identities across virtual learning platforms. Through the lens of a case study, this research explores the digital identities of Spanish as a foreign language teachers during the school lockdown in 2022. The data includes semi-structured interviews, virtual classroom discourse, lesson plans, and reflective writing. The results show that Spanish teachers formed multiple digital identities, including curriculum innovators, vulnerable actors, involuntary team workers, overseas returnees, and academic researchers. Among them, the first three are core identities, while overseas returnees and academic researchers are peripheral identities. Regardless, they were formed and negotiated under the influence of teachers' past experiences, the exercise of agency, emotional vulnerability, and social context. In addition, a contradictory belief in teaching was also identified during the formation of Chinese Spanish teachers' digital identities.
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It is hard to find an empirical study that examines the online learning or blended learning's effect on school pupils' regular exam performance during the COVID-19 epidemic and afterwards. This study attempts to fill in this research gap. An intelligent tutoring system (ITS) was utilised in mathematics online instruction in many elementary and middle schools in China. It supports individualised teaching and learning and has positive effect on students' learning. Two case studies are introduced to illustrate the system's functions and effects on students' mathematics learning performance. In the first case, a mathematics teacher in a junior high school provided the students with differentiated assignments during the epidemic. In the second case, a teacher in a primary school utilised the ITS to implement blended learning after the epidemic. Quasi-experiments were conducted and the regular examination's data analysis result shows that the treatment group outperformed the control group.
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Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.
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COVID-19 has stimulated additional research interest on economic sustainability and ESG in both academia and industry. This study adopts a DEA approach to examine the efficiency of achieving ESG targets and their relationships with financial performance. Using MSCI ESG data from 2015 to 2019 on 1108 Chinese firms, we examine the ESG proportional and pillar mix efficiencies. The dominant strategies for our sampled firms are to improve overall ESG performance by enhancing the E and S pillars through sacrificing G's performance. The second result shows a positive relationship between proportional efficiency and financial performance while a mixed relationship between pillar mix efficiency and financial performance. However, for the technology sector, there exists some trade-offs between ESG performance and financial performance. Specifically, relative to non-technology firms, improving proportional and pillar mix efficiencies for technology firms could result in some sacrifice in stock valuation. © 2023 Elsevier B.V.
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Since the outbreak of the COVID-19 pandemic, power generation and the associated CO2 emissions in major countries have experienced a decline and rebound. Knowledge on how an economic crisis affects the emission dynamics of the power sector would help alleviate the emission rebound in the post-COVID-19 era. In this study, we investigate the mechanism by which the 2008 global financial crisis sways the dynamics of power decarbonization. The method couples the logarithmic mean Divisia index (LMDI) and environmentally extended input-output analysis. Results show that, from 2009 to 2011, global power generation increased rapidly at a rate higher than that of GDP, and the related CO2 emissions and the emission intensity of global electricity supply also rebounded;the rapid economic growth in fossil power-dominated countries (e.g., China, the United States, and India) was the main reason for the growth of electricity related CO2 emissions;and the fixed capital formation was identified as the major driver of the rebound in global electricity consumption. Lessons from the 2008 financial crisis can provide insights for achieving a low-carbon recovery after the COVID-19 crisis, and specific measures have been proposed, for example, setting electricity consumption standards for infrastructure construction projects to reduce electricity consumption induced by the fixed capital formation, and attaching energy efficiency labels and carbon footprint labels to metal products (e.g., iron and steel, aluminum, and fabricated metal products), large quantities of which are used for fixed capital formation. © 2023 The Authors