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The root of Isatis tinctoria L. is highly appreciated as a Traditional Chinese herbal medicine for the prevention and adjuvant treatment of respiratory diseases caused by coronaviruses viruses such as SARS and COVID-19. I. tinctoria hairy root cultures (ITHRCs) provide a better alternative to field cultivation for the production of antiviral flavonoids. For the first time, ITHRCs were exposed to different colors of LED lights i.e., red, green, blue, red/green/blue (1/1/1, RGB), and white, in an attempt to promote the root growth and enhance the production of bioactive flavonoids. Results revealed that the biomass productivity (7.15 ± 0.63 g/L) in ITHRCs with an initial inoculum size of 0.2% cultured for 50 days under blue light increased by 1.86-fold relative to that under dark (control), and yields of rutin (320.49 ± 27.56 μg/g DW), quercetin (388.75 ± 9.17 μg/g DW), kaempferol (787.90 ± 83.43 μg/g DW), and isorhamnetin (269.11 ± 20.08 μg/g DW) increased by 4.15-fold, 9.31-fold, 9.09-fold, and 2.88-fold as compared with control, respectively. Interestingly, the emergence of adventitious buds was noticed in ITHRCs under all light treatments. Additionally, the enhanced densities of chloroplasts and root hairs were found in blue-light grown ITHRCs as against control, which might account for the elevated biomass productivity. Moreover, blue light induced oxidative stress in ITHRCs in terms of the overproduction of oxidation products and the enhancement of antioxidant enzyme activity. Furthermore, blue light significantly activated photoreceptor (CRY1) and key regulator of light signaling (HY5), thus leading to the up-regulated expression of MYB4 and structural genes (such as CHS and FLS) responsible for flavonoid biosynthesis. And, the transcriptional activation of CUC1 was likely related to the formation of adventitious buds in ITHRCs. Overall, the simple supplementation of blue LED light makes ITHRCs more attractive as plant factories for obtaining higher productivity of biomass and medicinally important flavonoids. © 2023 Elsevier B.V.
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The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.
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The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people's travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin's Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users' behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor's degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area;therefore, it is vital to extend e-scooter service areas to cover underserved communities.
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Objective: To understand the characteristics and associated factors of viral nucleic acid conversion in children infected with Omicron variant strain of SARS-CoV-2 in Shanghai. Methods: The clinical symptoms, laboratory results and other data of 177 children infected with SARS-CoV-2 who were hospitalized in Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University (designated hospital for SARS-CoV-2 infection in Shanghai) from April 25 to June 8, 2022 were retrospectively analyzed. According to the chest imaging findings, the children were divided into mild and common type groups. According to their age, the unvaccinated children were divided into<3 years old group and 3-<18 years old group. According to the vaccination status, the children aged 3-<18 year were divided into non-vaccination group, 1-dose vaccination group and 2-dose vaccination group. Comparison between groups was performed by independent sample t-test and analysis of variance, and multivariate linear regression analysis was used for multivariate analysis. Results: Among the 177 children infected with Omicron variant of SARS-CoV-2, 96 were males and 81 were females, aged 3 (1, 6) years. The time of viral nucleic acid negative conversion was (10.3±3.1) days. The 177 children were 138 cases of mild type and 39 cases of common type. Among the children aged 3-<18 years old, 55 cases were not vaccinated, 5 cases received 1-dose and 36 cases received 2-dose vaccination. Among the 36 children who received 2 doses of vaccination, the time of viral nucleic acid negative conversion was shorter in those vaccinated within 6 months than those over 6 months ((7.1±1.9) vs. (10.8±3.0) d, t=-3.23, P=0.004). Univariate analysis showed that the time of nucleic acid negative conversion of SARS-CoV-2 was associated with age, underlying diseases, gastrointestinal symptoms, white blood cell count, proportion of neutrophils, proportion of lymphocytes, and the number of doses of SARS-CoV-2 vaccine (t=3.87, 2.55, 2.04, 4.24, 3.51, 2.92, F=16.27, all P<0.05). Multiple linear regression analysis showed that older age (ß=-0.33, 95% CI -0.485--0.182, P<0.001) and more doses of vaccination (ß=-0.79, 95% CI -1.463--0.120, P=0.021) were associated with shortened nucleic acid negative conversion time in children, while lower lymphocyte proportion (ß=-0.02, 95% CI -0.044--0.002, P=0.031) and underlying diseases (ß=1.52, 95% CI 0.363-2.672, P=0.010) were associated with prolonged nucleic acid negative conversion time in children. Conclusion: The children infected with Omicron variant of SARS-CoV-2 with reduced lymphocyte proportion and underlying diseases may have longer time of viral nucleic acid negative conversion,while children with older age and more doses of vaccination may have shorter time of viral nucleic acid negative conversion.
Subject(s)
COVID-19 , Nucleic Acids , Child , Female , Male , Humans , Child, Preschool , Adolescent , SARS-CoV-2 , COVID-19 Vaccines , Retrospective Studies , China/epidemiology , Translocation, Genetic , Hospitals, PediatricABSTRACT
This article analyzes the development status, development trend and prospects of China's Internet of Medical Things (IoMT) industry from a macro perspective. Our survey mainly includes: analyzing the necessity and urgency of China's medical system reform from the various dilemmas faced by China's medical system, and analyzing the development of the IoMT industry based on the current basic conditions of development of the Internet of Things (IoT), information technology and background of COVID-19 epidemic. Opportunities and the evolution of China's IoMT policy were also analyzed. Moreover, from the five aspects of medical industry informatization, Internet hospitals, smart wearable devices, medical AI industry and medical industry digitization, the development status and trends of China's IoMT industry are analyzed. Finally, it looks forward to the development prospects and directions of IoMT industry for health care in China. © 2022 The authors and IOS Press.
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Purpose This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects. Design/methodology/approach This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla's and Amazon's new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location. Findings The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla's relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon's relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation. Originality/value Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.
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The application of IoT in cities is a critical component in constructing a smart city. Seoul Metropolitan Government began installing IoT sensors known collectively as S-DoT in 2019. S-DoT collects real-time climate and floating population data. This study aims to introduce a smart city planning application in Seoul, to validate the S-DoT application, and to suggest a research framework for using S-DoT data. We analyzed S-DoT collected floating population data to examine travel behavior, volume, and patterns during the COVID-19 pandemic. The result showed that micro-level spatiotemporal analysis was possible using S-DoT data, and we identified different floating population patterns. The panel regression result that explained the effects of urban factors on the floating population revealed that the degree of COVID-19 seems to impact people’s travel behavior significantly. As more S-DoT technologies are planning to be deployed in Seoul, the city will begin to collect more sophisticated real-time data. However, planners and policymakers should be attentive to the issues and limitations of newly installed S-DoT systems and find better strategies to use S-DoT data. © International Journal of Sustainable Building Technology and Urban Development.
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In this research, a quantitative model is built to predict people's susceptibility to COVID-19 based on their genomes. Identifying people vulnerable to COVID-19 infections is crucial in stopping the spread of the virus. In previous studies, researchers have found that individuals with comorbid diseases have higher chances of being infected and developing more severe COVID-19 conditions. However, these patterns are only observed through correlational analyses between patient phenotypes and the severity of their COVID-19 infection. In this study, genetic variants underlying the observed comorbidity patterns are analyzed through machine learning of COVID-19 data from GWAS studies, which may reveal biological pathways underlying COVID-19 contraction that are essential to the development of effective and targeted therapeutics. Furthermore, through combining genetic variants with the individual's phenotypes, this study built a Neural Network model and Random Forest classifier to predict an individual's likelihood of COVID-19 infection. The Random Forest Classifier in this study shows that on-going symptoms are generally better predictors of COVID-19 condition (higher impurity-based feature importance) than diseases or medical histories. In addition, when trained with genomic data, the comorbid disease impact ranking deduced by the resulting RF model is highly consistent with phenotypic comorbidity patterns observed in past studies.
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Purpose: This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects. Design/methodology/approach: This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic. Findings: The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature. Originality/value: Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation. © 2021, Emerald Publishing Limited.
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Childhood obesity continues to be a serious problem. The outbreak of the COVID-19 pandemic has led to great life-changing challenges amongst schoolchildren, which might subsequently aggravate existing childhood obesity issues. We suggest that educators and schools need to provide solutions to the problem. © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
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With the vast applications of massive online learning platforms during the coVID-19 outbreak, the personalized exercise recommendation methods play an import role on computer aided instruction(CAI). Most existing methods generates the exercises according to the contents and knowledge system structure, lacking semantic relationships between exercises and its knowledge. Knowledge graph is widely used to represent the semi-structured and schemaless information (nodes) and their relation (edges), and indicate the sentence embedding grammatical structure and semantic relations, thus it can be applied on computer aided instruction to automatically generate the personalized exercises. Aiming to improve the efficiency of exercise recommendation, this paper studies the feature information of computer network course, and proposes a content and knowledge graph based personalized exercise recommendation method. More specifically, knowledge graph is firstly constructed from entities and relations of computer network course, and the information vectors of exercises are generated by combining the knowledge with the exercises content. And then the learner's historical log data is analyzed, and the semantic similarity between exercises and their knowledge are generated for the wrong answers. According the semantic similarity of knowledge, the final exercises are recommended for the learners. Experimental results show that the proposed method can improve the efficiency of exercises recommendation. © 2020 IEEE.
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Online education has long been suffering from high dropout rate and low achievement. However, both asynchronous and synchronous online instructions have to become effective to serve as a quick response to maintain undisrupted learning during the COVID-19 outbreak. The purpose of the present study was to examine student engagement, learning outcome, and students' perceptions of an online course featured with frequent tasks, quizzes, and tests as formative assessment. Data were collected from the first five weeks of a course that was temporarily converted from blended learning to be fully online in time of school closure. Analysis of students' learning records and scores indicated that students engaged themselves actively in all of the online learning activities and had gained high scores in all tasks, quizzes, and tests. In addition, students held positive perceptions towards the formative assessment. © This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.