ABSTRACT
People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people's stance, which we consider as their online behavior in each topic-related event. Our results reveal a strong connection between the stances toward the three topical events and demonstrate the power of past behaviors in predicting one's future behavior. © 2023 ACM.
ABSTRACT
Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.
ABSTRACT
Anodic aluminium oxide-copper (AAO-Cu) coatings were prepared on the aluminium (Al) alloy substrates to attain excellent antibacterial performance and mechanical stability. The nanoporous AAO interlayer was ob-tained by anodic oxidation with an outer Cu layer deposited by electroplating. The intermediate zone (similar to 2 mu m thick) of the AAO-Cu coating plays a significant role in the coating properties. The interlocking effect in the AAO-Cu intermediate zone significantly enhances the coating adhesion and curbs the coating defoliation. The anti-bacterial tests show that the AAO-Cu zone provides excellent antibacterial ability even when the outer Cu coating was removed. The sustained antibacterial rate of the AAO-Cu intermediate zone against E. coli exceeded 95%. The Cu ions released from the embedded Cu in the nanoporous AAO structure ensure a long-term antibacterial capability. This coating system can be promoted in a large wide range of antibacterial products in public.
ABSTRACT
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.
ABSTRACT
This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they in-crease dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers.
ABSTRACT
Background: Shengmai decoction, which has been included in the diagnosis and treatment of coronavirus disease 2019 (COVID-19), is effective in the early treatment of patients with severe COVID-19. Yiqi Fumai lyophilized injection (YQFM) is a modern Chinese medicine preparation of the Shengmai decoction. The mechanism of its intervention at the molecular level in the severe stage of COVID-19 remains unclear. Therefore, it is necessary to investigate the mechanism of YQFM in the treatment of patients with severe COVID-19. Methods: The corresponding target genes of the main active ingredients in YQFM and COVID-19 were obtained by using multiple databases and literature retrieval. A protein-protein interaction network was constructed, and enrichment analysis of the target was performed using Cytoscape 3.8.1. Lastly, the docking of all the identified compounds with angiotensin-converting enzyme II was confirmed by applying molecular docking technology. Results: YQFM has anti-inflammatory effects on RAW267.4 macrophages. The main active compounds of YQFM are all effective anti-inflammatory agents, and these active compounds also show beneficial physiological functions, such as anti-oxidation, anti-bacterial, and anticancer activities. Gene Ontology analysis showed enrichment in the following pathways: lipopolysaccharides, interleukins, NF-kappa B, interleukin-2 and others, revealing that YQFM may play a role in the treatment of patients with severe COVID-19 through these pathways. Conclusion: YQFM has multicomponent and multitarget characteristics, and it could reduce lung injury by inhibiting inflammatory reactions, promoting antiviral activities, and regulating immunity, among other functions, to treat patients with severe COVID-19.
ABSTRACT
Due to the continuous growth of disease types and past cases, it is more and more difficult to diagnose diseases only by manpower. Machine learning is a model mechanism that is sensitive to data and relies on a large amount of data to complete training. It is very suitable for medical diagnosis. Many scholars have tried to use ML to develop medical diagnosis systems, but they are basically not used in the real world at this stage. This article reviews the work related to medical detection of three major diseases (heart disease, cancer, and COVID-19), aiming to summarize previous experiences to help future scholars conduct research. Specifically, this paper summarizes the research status of the prediction of these three types of diseases based on machine learning methods, evaluate the accuracy and universality of the corresponding prediction models based on time as a clue, and use a comparative method to find out the progress researchers have made in this area and limitations still exist at this stage. And at the end of the article, the results and some potential work fields of the future in these studies are summarized. © 2022 IEEE.
ABSTRACT
Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81∶1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers′ markets, medical workers and other key areas and groups, and ensure early detection and timely response. © 2022 China Tropical Medicine. All rights reserved.
ABSTRACT
On May 13, 2022, World Health Organization(WHO) Position Paper on Influenza Vaccine (2022 edition) was published. This position paper updates information on influenza epidemiology, high risk population, the impact of immunization on disease, influenza vaccines and effectiveness and safety, and propose WHO's position and recommendation that all countries should consider implementing seasonal influenza vaccine immunization programmes to prepare for an influenza pandemic. In addition, it proposes that the influenza surveillance platform can be integrated with the surveillance of other respiratory viruses, such as SARS-CoV-2 and Respiratory Syncytial Virus. This position paper has some implications for the prevention and control of influenza and other respiratory infectious diseases in China: (1) Optimize influenza vaccine policies to facilitate the implementation of immunization services; (2) Influenza prevention and control should from the perspective of Population Medicine focus on the individual and community to integrate with "Promotion, Prevention, Diagnosis, Control, Treatment, Rehabilitation"; (3) Incorporate prevention and control of other respiratory infectious diseases such as influenza, COVID-19, respiratory syncytial virus and adenovirus, and intelligently monitor by integrating multi-channel data to achieve the goal of co-prevention and control of multiple diseases.
Subject(s)
COVID-19 , Influenza Vaccines , Influenza, Human , Humans , Influenza, Human/epidemiology , Influenza, Human/prevention & control , SARS-CoV-2 , World Health OrganizationABSTRACT
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary- Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. IEEE
ABSTRACT
Context. Children and young people with life-limiting or life-threatening conditions and their families are potentially vulnerable during COVID-19 lockdowns due to pre-existing high clinical support needs and social participation limitations. Objectives. To explore the impact of the COVID-19 pandemic and lockdowns on this population. Methods. Sub-analysis of an emergent COVID-19 related theme from a larger semi-structured interview study investigating priority pediatric palliative care outcomes. One hundred and six United Kingdom-wide purposively-sampled Children and young people with life-limiting or life-threatening conditions, parent/carers, siblings, health professionals, and commissioners. Results. COVID-19 was raised by participants in 12/44 interviews conducted after the United Kingdom's first confirmed COVID-19 case. Key themes included loss of vital social support, disruption to services important to families, and additional psychological distress. Conclusion. Continued delivery of child- and family-centered palliative care requires innovative assessment and delivery of psycho-social support. Disruptions within treatment and care providers may compound support needs, requiring cordination for families facing multiagency delays. (C) 2022 The Authors. Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine.
ABSTRACT
Windows are the communication medium between indoor and outdoor, but their influence and the corresponding landscape outside the window are often ignored due to the outdoor frequent activities of people. The coronavirus disease 2019 (COVID-19) has been a better choice to show the window performance, especially for the anxiety level alleviation of people isolated at home. A national survey was conducted on the anxiety of self-separation people and the window influence. The results showed that the average anxiety level was 1.54, between a little anxious and anxious, due to the COVID-19. The best satisfaction with the landscape outside the window was waterscape (2.98), followed by green plants (2.33) and buildings (0.83). During the COVID-19, the average number of overlook times increased by 1.49 times/day, which is higher 0.42 ties/day than the normal condition. The landscape types had the certain influence on the overlook frequency, the window opening times and even the anxiety level. The average anxiety levels are 1.36 and 1.68 with natural landscapes and human landscapes, respectively. Optimizing the landscapes outside the window plays an important role in alleviating the anxiety of residents and improving their mental health.
ABSTRACT
Background. Molnupiravir (MOV) is an orally administered ribonucleoside prodrug of β-D-N4-hydroxycytidine (NHC) against SARS-CoV-2. Here we present viral dynamics analysis of Phase 2 clinical virology data to inform MOV Phase 3 study design and development strategy. Methods. An Immune-Viral Dynamics Model (IVDM) was developed with mechanisms of SARS-CoV-2 infection, replication, and induced immunity, which together describe the dynamics of viral load (VL) during disease progression. Longitudinal virology data from ferret studies (Cox, et al. Nat. Microbiol 2021:6-11) were used to inform IVDM, which was further translated to human by adjusting parameter values to capture clinical data from MOVe-IN/MOVe-OUT studies. Different placements of drug effects (on viral infectivity vs. productivity) and representations of immune response were explored to identify the best ones to describe data. A simplified 95% drug effect was implemented to represent a highly effective dose of MOV. Results. IVDM showed data were best described when MOV acts on viral infectivity, consistent with the error catastrophe mechanism of action. A cascade of innate and adaptive immune response and a basal level activation enabled durable immunity and continued viral decay after treatment end. IVDM reasonably describes VL and viral titer data from animals and humans. Influence of MOV start time was explored using simulations. Consistent with the ferret studies, simulations showed when treatment is started within the first week post infection, MOV reduces viral growth, resulting in a lower and shortened duration of detectable VL. When started later (e.g. >7 days since symptom onset), the magnitude of drug effect is substantially diminished in a typical patient with an effective immune response which reduces VL prior to treatment start. Further work is needed to model response in patients with longer term infection, where MOV drug effects may have more persistent utility. Conclusion. A COVID-19 IVDM developed using multiscale MOV virology data supports drug action on viral infectivity and importance of interplay of treatment and immune response and can describe infection time course and drug effect. IVDM provided mechanistic interpretations for VL drug effect in clinical studies.
ABSTRACT
Background. Molnupiravir (MOV, MK-4482, EIDD-2801) is an orally administered prodrug of N-hydroxycytidine (NHC, EIDD-1931), a nucleoside with broad antiviral activity against a range of RNA viruses. MOV acts by driving viral error catastrophe following its incorporation by the viral RdRp into the viral genome. Given its mechanism of action, MOV activity should not be affected by substitutions in the spike protein present in SARS-CoV-2 variants of concern which impact efficacy of therapeutic neutralizing antibodies and vaccine induced immunity. We characterized MOV activity against variants by assessing antiviral activity in vitro and virologic response from the Phase 2/3 clinical trials (MOVe-In, MOVe-Out) for treatment of COVID-19. Methods. MOV activity against several SARS-CoV-2 variants, was evaluated in an in vitro infection assay. Antiviral potency of NHC (IC50) was determined in Vero E6 cells infected with virus at MOI ~0.1 by monitoring CPE. Longitudinal SARSCoV-2 RNA viral load measures in participants enrolled in MOVe-In and MOVe-Out were analyzed based on SARS-CoV-2 genotype. Sequences of SARS-CoV-2 from study participants were amplified from nasal swabs by PCR and NGS was performed on samples with viral genome RNA of >22,000 copies/ml amplified by primers covering full length genome with Ion Torrent sequencing to identify clades represented in trial participants. SARS-CoV-2 clades were assigned using clade.nextstrain.org. Results. In vitro, NHC was equally effective against SARS-CoV-2 variants B.1.1.7 (20I), B.1351 (20H), and P1 (20J), compared with the original WA1 (19B) isolate. In clinical trials, no discernable difference was observed in magnitude of viral response measured by change from baseline in RNA titer over time across all clades represented including 20A through 20E and 20G to 20I. No participants at the time of the study presented with 20F, 20J, or 21A. Conclusion. Distribution of clades in participants in MOVe-In and MOVe-Out was representative of those circulating globally at the time of collection (Oct 2020 -Jan 2021). Both in vitro and clinical data suggest that spike protein substitutions do not impact antiviral activity of MOV and suggest its potential use for the treatment of SARS-CoV-2 variants.
ABSTRACT
In this paper, we analyzed and found the solution for a suitable nonlinear fractional dynamical system that describes coronavirus (2019-nCoV) using a novel computational method. A compartmental model with four compartments, namely, susceptible, infected, reported and unreported, was adopted and modified to a new model incorporating fractional operators. In particular, by using a modified predictor–corrector method, we captured the nature of the obtained solution for different arbitrary orders. We investigated the influence of the fractional operator to present and discuss some interesting properties of the novel coronavirus infection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
Objective To solve the data difference between COVID-19 confirmed cases and actual number of COVID-19 infections, a new model is proposed to predict the spread of the disease. The data difference has been mainly caused by insufficient understanding in the early stage of transmission, limited detection capabilities and the long incubation period. Methods The historical data of the number of confirmed cases are analyzed based on Window-Time. A Long Short-Term Memory (LSTM) network model is combined with the Window-Time strategy to analyze and predict the actual number of infections according to data published of various regions in the world. Results The LSTM network model with Window-Time strategy has higher accuracy than other models. Tuning the width of the Window-Time to the width of 5, the prediction result shows that it is closest to the real actual number of infections, which is consistent with the incubation period of COVID-19 generally known as 3-7 days. Conclusion This method provides a reference for the analysis of the transmission rate of COVID-19 and the incubation period of the epidemic. © 2021, Publication Centre of Anhui Medical University. All rights reserved.
ABSTRACT
Building density parameter is one of the main indicators of urban monitoring. The accurate and rapid monitoring of building density changes can provide an important basis for urbanization management. It is of great significance to extract building density using dual-polarimetric Sentinel-1 SAR data which provides a short revisit period and a wide coverage. Therefore, this paper proposes a monitoring method of urban building density change based on dual-polarimetric Sentinel-1 data, integrating dual polarization information and texture features. Firstly, a dual polarimetric Sentinel-1 building index is constructed by analyzing the descriptive effects of SAR images on buildings. Then, the building index is used to monitor the change of building density. The experimental results show the effectiveness and practicality of this method by using the dual-polarimetric Sentinel-1 SAR data of Wuhan from 2018 to 2020. The experiments on the Huoshenshan and Leishenshan hospital before and after the construction during the COVID-19 pandemic can also prove the short-term monitoring ability of Sentinel-1 satellite in the rapid construction and expansion of urban areas. © 2021 IEEE.