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The emergence of the Covid-19 pandemic has drawn great attention to vulnerable people affected by major diseases. Among them, Alzheimer's disease (AD) is the most prevalent disease. However, a long-standing challenge is to achieve early diagnosis of AD by detecting biomarkers such as amyloid beta (Aβ42), thus avoiding the labor of specialized hospital personnel and the high cost of imaging examinations using positron emission tomography. In this paper, we report a straightforward approach to realize a non-invasive lab-around fiber (LaF) optical sensor for AD biomarker detection, which is based on a tilted fiber Bragg grating (TFBG) combined with a nanoscale metallic thin film. We successfully demonstrated the detection of Aβ42 in complex biological matrices with a detection limit of 5 pg/mL. Therefore, our TFBG-SPR biosensor platform enables large-scale early disease screening and has great potential for clinical applications in early AD diagnosis. © 2022 IEEE.
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Due to the impact of the COVID-19, online teaching has become a common teaching method at present, and in-depth research on online teaching methods is of great practical significance. In the computer network course, we organize the online teaching process according to the BOPPPS model combined with online teaching tools such as DingTalk and Rain Classroom. We use Rain Classroom to seize the pre-class preview and use DingTalk to achieve participatory online interactive teaching and complete homework correction and online Q&A. The results of the questionnaire show that the above-mentioned teaching organization method can enable students to actively participate in interactive teaching, and students' approval of online teaching is relatively high. © 2022 IEEE.
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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.
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Introduction: COVID-19 is a public health emergency of international concern. Clinicians are likely to adopt various antithrombotic strategies to prevent embolic events, but the optimal antithrombotic strategy remains uncertain. We performed a Bayesian network meta-analysis to evaluate various antithrombotic strategies comprehensively. Method(s): We systematically searched PubMed, Cochrane Library, Web of Science, EMBASE and Clinical trials. gov to screen trials comparing different antithrombotic strategies. The primary outcome is 28-day mortality, and the secondary outcomes include major thrombotic event, major bleeding and in-hospital mortality, etc. We assessed the risk of bias using the Cochrane Collaboration's tool and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We successively performed traditional pairwise and Bayesian network meta-analysis using R v4.2.1 software. Result(s): Twenty-six eligible randomized controlled trials were included, giving a total of 35 paired comparisons with 32,041 patients randomized to 7 antithrombotic strategies. In comparison to standard of care (SoC) strategy, therapeutic anticoagulation (TA) (RR 0.36, 95% CrI 0.13-0.86) and prophylactic anticoagulation (PA) (RR 0.35, 95% CrI 0.12-0.85) strategy significantly reduced the mortality of COVID-19 patients (Fig. 1). The antiplatelet (AP) strategy was associated with high risk of major bleeding when compared with SoC strategy (RR 2.5, 95% CrI 1.1-8.9), and the TA (RR 0.43, 95% CrI 0.17-0.98), PA (RR 0.27, 95% CrI 0.10-0.63) and PA with Fibrinolytic agents (FA) strategy (RR 0.12, 95% CrI 0.01-0.81) was associated with low risk of major thrombotic event. Conclusion(s): This network meta-analysis indicates that the TA and PA strategies probably reduce mortality and confer other important benefits in COVID-19 patients. These findings provide guidance on how to choose optimal antithrombotic strategies for COVID-19 patients.
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In the current environment where COVID-19 is serious, the space, place and resources required for teaching nuclear power plants are restricted to a great extent. To solve such problems and improve the utilization of education resources, this study improved an accident simulator for nuclear power plants based on the concept of cloud technology. We build the Browser / Server architecture so that the platform has successfully implemented multiterminal, multiplatform and multiuser simultaneous applications. Through the simulation results of the Small Break Loss of Coolant Accident (SBLOCA) and the test results of platform performance by PCTran-Cloud, the correctness of PCTran-Cloud in the accident simulation function and results were verified. In general, PCTran-Cloud has the characteristics of high scalability, high concurrency and high security. The platform can provide an environment for the training and education of nuclear power professionals. © 2022 IEEE.
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Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.
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The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry's ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry. © 2023 by the authors.
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In this paper, we research a type of newfashioned fractional models for COVID-19 outbreak, further improve it to Hilfer fractional mathematical models be called as SEIR compartmental models of order α(0,1) and type β[0,1] on an unbounded domain [0,+f). The existence for the nonlinear Hilfer fractional differential equations are proved via Schauder's fixed point theorem based on appropriate growth conditions in suitable Banach spaces. We conclude that the Hilfer fractional differential system has at least one solution in specified Banach space. © 2022 IEEE.
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We sought to examine the potential environmental protective factors contributing to academic resilience among college students during emergency distance education. Data were collected from a sample of undergraduate students (n = 195) representing various majors and academic classifications. Our study results revealed that students perceived their motivation and comprehension of course material during emergency distance education as significantly lower than those before emergency distance education. Moreover, over 26% of the participants reported a decreased GPA during this period. However, a positive physical learning environment and student-perceived teacher academic support benefited students' academic performance. Interestingly, the physical learning environment positively predicted teacher academic support, beta=.31, t(193) = 4.32, p < .001. The physical learning environment also explained a significant proportion of variance in teacher academic support scores, R-2 = .08, F(1, 193) = 16.25, p < .001, suggesting student perceptions of teacher support partially depends on their physical learning environment. Finally, students that had a higher classification were more likely to report an increased GPA;seniors were better at coping with the negative effects of emergency distance education than juniors and sophomores.
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Introduction: To meet the multi-user, cross-time-and-space, cross-platform online demand of work, and professional training teaching in nuclear reactor safety analysis under the normalization of Coronavirus Disease 2019. Method: Taking the nuclear accident simulation software PCTRAN as an example, this study adopts cloud computing technology to build the NasCloud, a nuclear accident simulation cloud platform based on Browser/Server architecture, and successfully realizes multi-user, cross-time-and-space, cross-platform applications. Targeting the AP1000, a pressurized water reactor nuclear power plant, the simulation of cold-leg Small Break Loss of Coolant Accident and cold-leg Large Break Loss of Coolant Accident were carried out to verify the correctness of the NasCloud's accident simulation function. Results: The result shows that the simulation functions and results of the NasCloud in multi-terminal are consistent with the single version of PCTRAN. At the same time, the platform has high scalability, concurrency and security characteristics. Discussion: Therefore, the nuclear accident simulation cloud platform built in this study can provide solutions for the work and training of nuclear reactor safety analysis, and provide reference for other engineering design and simulation software cloud to computing transformation. Copyright © 2023 Chen, Chen, Xie, Xiong and Yu.
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Rapid and comprehensive lockdowns to contain the coronavirus 2019 (COVID-19) pandemic reduced anthropogenic emissions and, thereby, decreased the aerosol optical depth (AOD) in Xiangyang, Hubei Province. However, their complicated interactions make quantifying the contribution of decreased aerosols to crop growth challenging. Here, we explored the indirect effects of decreased aerosol concentrations on the gross primary productivity (GPP) and water use efficiency (WUE) of winter wheat by quantifying the contributions of key environmental factors. Our results showed high temporal and spatial associations between aerosols (represented by AOD), GPP, and WUE before, during, and after the COVID-19 pandemic. AOD decreased by 23.8% +/- 10.1%, whereas GPP and WUE increased by 16.5% +/- 5.8% and 17.0% +/- 15.3%, respectively. The GeoDetector model revealed that photosynthetically active radiation (PAR) had a major impact on GPP and WUE, followed by precipitation, surface soil moisture, subsurface soil moisture, and surface temperature. Moreover, causality analysis showed a causal relationship between AOD and the dominant factors (PAR and precipitation) during the lockdown, thereby indicating a positive effect of decreased aerosols on GPP and WUE changes of winter wheat. Our findings assist in understanding the mechanisms causing GPP and WUE changes, given the environmental factors that changed significantly during the pandemic. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
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In the study of the impact of cross-border capital flows, most scholars at home and abroad focus on the method of linear time series mainly based on the vector autoregressive model (VAR), ignoring the volatility of variables in time series. In order to make up for the deficiency, the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model can be used to study the nonlinear time-varying correlation between variables. With the help of Eviews12 software and the DCC-MVGARCH model, this paper studies the impact of securities markets on cross-border capital flows in China from domestic and foreign perspectives in the context of two financial crises and COVID-19. The results indicate that financial crises affect the correlation between the securities markets and cross-border capital flows. China's stock market is positively correlated with short-term capital flows and negatively correlated with long-term capital flows. Its booming bond market promotes short-term capital flows but fails to affect the long-term capital flows, and China's short-term capital flows are increasingly linked to the volatility of foreign stock markets. Therefore, it is necessary to improve the mechanism for better monitoring and analyzing cross-border capital flows, promote further development of financial supervision, and guide market players to face the securities market rationally. © 2023 SPIE.
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The COVID-19 epidemic has threatened the human being. The automatic and accurate segmentation for the infected area of the COVID-19 CT images can help doctors to make correct diagnosis and treatment in time. However, it is very challenging to achieve perfect segmentation due to the diffuse infections of the COVID-19 to the patient lungs and irregular shapes of the infected areas and very similar infected areas to other lung tissues. To tackle these challenges, the XR-MSF-Unet model is proposed in this paper for segmenting the COVID-19 lung CT images of patients. The XR (X ResNet) convolution module is proposed in this model to replace the two-layer convolution operations of U-Net, so as to extract more informative features for achieving good segmentation results by multiple branches of XR. The plug and play attention mechanism module MSF (multi-scale features fusion module) is proposed in XR-MSF-Unet to fuse multi-scale features from different scales of reception fields, global, local and spatial features of CT images, so as to strengthen the detail segmentation effect of the model. Extensive experiments on the public COVID-19 CT images demonstrate that the proposed XR module can strengthen the capability of the XR-MSF-Unet model to extract effective features, and the MSF module plus XR module can effectively improve the segmentation capability of the XR-MSF-Unet model for the infected areas of the COVID-19 lung CT images. The proposed XR-MSF-Unet model obtains good segmentation results. Its segmentation performance is superior to that of the original U-Net model by 3.21, 5.96, 1.22 and 4.83 percentage points in terms of Dice, IOU, F1-Score and Sensitivity, and it defeats other same type of models, realizing automatic segmentation to the COVID-19 lung CT images. © 2022, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.
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Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2.
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The Chest X-ray (CXR) images of COVID-19 patients are different from those of normal people, which has been an effective base for making correct diagnosis. It is an important way to help medicine doctors to make the fast and accurate diagnosis for patients by using com puter aided automatic classification technique based on the patient chest X-ray images. The new COVID-SERA-NeXt model was proposed in this paper for classifying COVID-19 CXR images by introducing the cross-stacked channel attention module and residual attention module, as well as the proposed dimensional reduction module, into the ResNeXt model. The performance of the proposed COVID-SERA-NeXt model was tested on the open accessed COVIDx dataset by extensive experiments. The experimental results show that the proposed COVID-SERA-NeXt model is superior to its base model ResNeXt. It achieves the accuracy and Macro_Recall of 96. 11 % and 95. 46%, respectively. Further experiments demonstrate that the proposed COVID-SERA-NeXt model achieves better performance to classify COVID-19 CXR images when it is pre-trained using ChestX-ray8 dataset. © 2022, Taiyuan University of Technology. All rights reserved.
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The outbreak of COVID-19 has had a significant impact on the world. The prediction of COVID-19 can conduct the distribution of medical supplies and prevent further transmission. However, the spread of COVID-19 is affected by various factors, so the prediction results of previous studies are limited in practical application. A deep learning model with multi-channel combined multiple factors including space, time, and environment (STE-COVIDNet) is proposed to predict COVID-19 infection accurately in this paper. The attention mechanism is applied to score each environment to reflect its impact on the spread of COVID-19 and obtain environmental features. The experiments on the data of 48 states in the United States prove that STE-COVIDNet is superior to other advanced prediction models in performance. In addition, we analyze the attention weights of the environment of the 48 states obtained by STE-COVIDNet. It is found that the same environmental factors have inconsistent effects on COVID-19 transmission in different regions and times, which explains why researchers have varying results when studying the impact of environmental factors on transmission of COVID-19 based on data from different areas. STE-COVIDNet has a certain explainability and can adapt to the environmental changes, which ultimately improves our predictive performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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COVID-19 pandemic has spread globally and affected a large number of people. Previous studies collected online survey and interview data to raise awareness of requirements from people with visual impairments (PVI) under the COVID-19 pandemic, however, little has been observed in PVI's daily activities due to the suspension of face-to-face fieldwork. In this study, we utilized an innovative data source-YouTube videos to fill the vacancy of observation data in this specific topic. Compared to previous studies, we got more voices involved and gained a richer dataset by considering both videos from the visually impaired community where PVI are primary authors and news media videos where PVI are involved as active participants. Eventually, we collected 24 videos created by the visually impaired community and 27 videos from the news media community, where 57 PVI were depicted. This study uncovered the problems causing pandemic-related challenges and suggested the need for explicit guidelines that can make the prevention protocols accessible and inclusive for PVI, as this study indicates that accessibility can be easily missed under unforeseen situations like the COVID-19 pandemic. © 2022 ACM.
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Purpose: This study aims to investigate how the public formed their need for information in the early stage of the COVID-19 outbreak. Exploring the formation of information needs can reveal why the public's information needs differ and provide insights on targeted information service during health crises at an essential level. Design/methodology/approach: The data were collected through semi-structured interviews with 46 participants and analyzed using the grounded theory approach. Concepts, sub-categories and categories were developed, and a model was built to examine how the public formed the need for information about the pandemic. Findings: The authors found that participants were stimulated by information asymmetry, severity of the pandemic and regulations to control the pandemic, which triggered their perceptions of information credibility, threat and social approval. After the participants perceived that there was a threat, it activated their basic needs and they actively formed the need for information based on cognitive activities. Moreover, information delivered by different senders resulted in a passive need for information. Participants' individual traits also influenced their perceptions after being stimulated. Research limitations/implications: Long-term follow-up research is needed to help researchers identify more detailed perspectives and do comparative studies. Besides, this study conducted interviews through WeChat voice calls and telephone calls, and might be limited compared with face-to-face interviews. Practical implications: The findings of this study provide theoretical contributions to the information needs research and practical implications for information services and public health management. Originality/value: There is little systematic research on how the public formed information needs in the early stage of the COVID-19 outbreak. © 2022, Emerald Publishing Limited.
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At the beginning of 2020, Coronavirus Disease 2019 (COVID-19) spread widely all over the world, leading to a public health crisis in the world. Automatic COVID-19 CT segmentation can not only assist radiologists in understanding images, but also help physicians to calibrate diagnoses and provide image-guided clinical diagnosis. However, due to the inhomogeneous intensity distribution of COVID-19 in CT scans, the ambiguous and missing boundaries, and highly variable shapes of lesions, it is quite challenging to develop an automatic solution. Therefore, this paper proposes a novel Multi-Attention Guided U-Net++ (MA-UNet++) for COVID-19 segmentation. In this network, we design a novel long-skip channel-wise attention module and introduce a spatial-wise attention module to re-weight the feature representation and capture rich contextual relationships at different scales. The experiment evaluated on the COVID-19 CT Segmentation dataset, demonstrate the MA-UNet++ achieves higher segmentation accuracy than the state-of-art methods. © 2022 ACA.