Characterization of Adaptive Immune Responses to SARS-CoV-2 Updated Bivalent Booster

Bivalent COVID-19 vaccines that contain two mRNAs encoding Wuhan-1 and Omicron BA.4/5 spike proteins are successful in preventing infection from the original strain and Omicron variants, but the quality of adaptive immune responses is still not well documented. This study aims at characterizing adaptive immune responses to the bivalent booster vaccination in 46 healthy participants. Plasma and PBMC were collected prior and three weeks after bivalent booster. We measured anti-N, anti-S, and RBD IgM, IgA, IgG plasma titers against original, Omicron BA.1, and BA.5 variants (pending) as well as total anti-S IgG titers and surrogate Virus Neutralization capacity against the Alpha, Delta, and BA.1 variant. With spectral flow-cytometry we identified peripheral blood B-cells specific for the RBD of the S-protein of the original and BA.1 variants. T-cell-specific responses were assessed by cytokine release assay after stimulation with SARS-CoV-2 peptides from the original, BA.1, BA.4, and BA.5 variants (pending). Finally, we performed TRB and IGH repertoire studies on sorted CD4+, CD8+, CD19+ lymphocytes, to study breadth of SARS-CoV-2 specific clonotypes (pending). 27/46 participants were analyzed;9 had SARS-CoV-2 infection (COVID+), while 18 are infection naive (COVID-). In both groups, median time since last dose of SARS-CoV-2 vaccine (3rd or 4th) was 11 months. All subjects were positive for anti-S IgG prior to bivalent booster. The COVID + group displayed anti-S IgG pre-booster levels and neutralization against BA.1 higher than the COVID- group. Significant increase post-boost of total anti-S IgG and BA.1 neutralizing activity was detected in the COVID- but not in the COVID+ group;however, no difference in neutralization activity post-boost was detected between the two groups. Furthermore, the COVIDgroup showed significant increase in the frequency of CD19+ and CD27+ switched memory B-cells specific for BA.1 RBD in post-boost compared to pre-boost samples. However, post-boost frequencies of the same B-cells were higher in the COVID+ compared to the COVID- group. These preliminary findings confirm that among individual immunized with the original COVID-19 mRNAvaccine, prior COVID infection provides increased protection against SARS-CoV-2 variants. They also demonstrate that booster immunization with the bivalent vaccine induces robust adaptive immune responses against Omicron variant.[Formula presented][Formula presented]Copyright © 2023 Elsevier Inc.

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

An Efficient Medicine Identification and Delivery System Based on Mobile Manipulation Robot

In recent years, with the emergence of COVID-19, the shortage of medical resources has become increasingly obvious. However, current environments such as hospital wards still require a large number of medical staff to deliver medicines. In this paper, we propose a mobile robot that can complete medicine grabbing and delivery in a hospital ward scenario. First, a lightweight neural network is built to improve the detection efficiency of Faster R-CNN algorithm for boxed medicine. Then, the pose of the robotic arm grasping the pill box is determined by point cloud matching to control the mechanical grasping of the pill box. Finally, a discomfort function representing the collision risk between the robot and the pedestrian is incorporated into the Risk-RRT algorithm to improve the navigation performance of the algorithm. By building a real experimental platform, the experiments verify the performance of our proposed medicine delivery robot system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Satellite-based estimates of daily NO2 exposure in urban agglomerations of China and application to spatio-temporal characteristics of hotspots

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd

Design and Autonomous Navigation of a New Indoor Disinfection Robot Based on Disinfection Modeling

The COVID-19 pandemic shows growing demand of robots to replace humans for conducting multiple tasks including logistics, patient care, and disinfection in contaminated areas. In this paper, a new autonomous disinfection robot is proposed based on aerosolized hydrogen peroxide disinfection method. Its unique feature lies in that the autonomous navigation is planned by developing an atomization disinfection model and a target detection algorithm, which enables cost-effective, point-of-care, and full-coverage disinfection of the air and surface in indoor environment. A prototype robot has been fabricated for experimental study. The effectiveness of the proposed concept design for automated indoor environmental disinfection has been verified with air and surface quality monitoring provided by a qualified third-party testing agency. Note to Practitioners - Robots are desirable to reduce the risk of human infection of highly contagious virus. For such purpose, a novel autonomous disinfection robot is designed herein for automated disinfection of air and surface in indoor environment. The robot structure consists of a mobile carrier platform and an atomizer disinfection module. The disinfection modeling is conducted by using the measurement data provided by a custom-built PM sensor array. To achieve cost-effective and qualified disinfection, a full-coverage path planning scheme is proposed based on the established disinfection model. Moreover, for specifically disinfecting the frequently contacted objects (e.g., tables and chairs in offices and hospitals), a target perception algorithm is proposed to mark the localization of these objects in the map, which are disinfected by the robot more carefully in these marked areas. Experimental results indicate that the developed disinfection robot offers great effectiveness to fight against the COVID-19 pandemic. © 2004-2012 IEEE.

Uncertainty-aware transfer learning to evolve digital twins for industrial elevators

Digital twins are increasingly developed to support the development, operation, and maintenance of cyber-physical systems such as industrial elevators. However, industrial elevators continuously evolve due to changes in physical installations, introducing new software features, updating existing ones, and making changes due to regulations (e.g., enforcing restricted elevator capacity due to COVID-19), etc. Thus, digital twin functionalities (often built on neural network-based models) need to evolve themselves constantly to be synchronized with the industrial elevators. Such an evolution is preferred to be automated, as manual evolution is time-consuming and error-prone. Moreover, collecting sufficient data to re-train neural network models of digital twins could be expensive or even infeasible. To this end, we propose unceRtaInty-aware tranSfer lEarning enriched Digital Twins LATTICE, a transfer learning based approach capable of transferring knowledge about the waiting time prediction capability of a digital twin of an industrial elevator across different scenarios. LATTICE also leverages uncertainty quantification to further improve its effectiveness. To evaluate LATTICE, we conducted experiments with 10 versions of an elevator dispatching software from Orona, Spain, which are deployed in a Software in the Loop (SiL) environment. Experiment results show that LATTICE, on average, improves the Mean Squared Error by 13.131% and the utilization of uncertainty quantification further improves it by 2.71%. © 2022 ACM.

Attribute Analysis and Diagnosis of LUNG CT Images of COVID-19

Computed Tomography (CT) is an authoritative verification standard for patients with Corona Virus Disease 2019 (COVID-19). Automatic detection of lung infection through CT is of great significance for epidemic prevention and control and prevention of cross-infection. The accuracy of existing lung CT image segmentation methods is not high, and due to the privacy protection measures of hospitals, the number of COVID-19 lung CT data sets is too small, which is prone to over-fitting during training. In this paper, we propose a qualitative mapping model for the diagnosis and localization of COVID-19 lesions. The binary image processed by U-net network is used as input, and lung CT is segmented as four attributes, and attribute diagnosis is carried out with the help of correlation matrix and transformation degree function. Experiments show that this method not only avoids the over-fitting risk of data sets, but also increases the robustness of data. Experiments also prove that this design has higher accuracy than the simple neural network learning. © 2022 IEEE.

Implementation and characterisation of a sterilisation module consisting of novel 265 nm UVC LED packages

To efficiently fight against the COVID-19 pandemic, a sterilisation module using 265 nm UVC LED packages was developed. In this paper, the performance of the sterilisation module in terms of irradiance uniformity, junction temperature increase and sterilisation efficiency were characterised. The irradiance uniformity fluctuation across the four corners and the centre point in a 130 mm × 130 mm area was below 10%, exhibiting good uniformity. Uniform irradiance was important to achieve consistent sterilisation, which was the primary difference between the UVC LED package developed and commercial UVC LED packages. Key to achieving uniform irradiance was the structure, consisting of a stacked silicon reflector and a secondary optical lens designed by ray tracing simulation. The junction temperature increase of the 265 nm UVC LED package driving at 200 mA was only 28°C, sufficiently low to exhibit better reliability and performance. A 99.99% sterilisation efficiency on E. coli bacteria was achieved within one minute with UV dosage of 2.7 mJ/cm2 at 200 mA driving current. From the results, the novel 265 nm UVC LED package was a time-efficient solution for disinfection purposes. © 2021 The Hong Kong Institution of Engineers.

POLLINATOR-FLOWER INTERACTIONS IN GARDENS DURING THE COVID-19 PANDEMIC LOCKDOWN OF 2020

During the main COVID-19 global pandemic lockdown period of 2020 an impromptu set of pollination ecologists came together via social media and personal contacts to carry out standardised surveys of the flower visits and plants in gardens. The surveys involved 67 rural, suburban and urban gardens, of various sizes, ranging from 61.18° North in Norway to 37.96° South in Australia, resulting in a data set of 25,174 rows, with each row being a unique interaction record for that date/site/plant species, and comprising almost 47,000 visits to flowers, as well as records of flowers that were not visited by pollinators, for over 1,000 species and varieties belonging to more than 460 genera and 96 plant families. The more than 650 species of flower visitors belong to 12 orders of invertebrates and four of vertebrates. In this first publication from the project, we present a brief description of the data and make it freely available for any researchers to use in the future, the only restriction being that they cite this paper in the first instance. The data generated from these global surveys will provide scientific evidence to help us understand the role that private gardens (in urban, rural and suburban areas) can play in conserving insect pollinators and identify management actions to enhance their potential. © 2022 The authors.

Pandemic Bonds Issued by the Chinese Government Supported Post-Disaster Recovery from COVID-19 Pandemic

During the SARS-CoV-2 (COIVD-19) outbreak, China repeatedly stressed that the response to the pandemic required action at all levels of government, including the issuance of Pandemic Bonds to help the country return to work and production. However, studies on the effectiveness of Pandemic Bonds during that period are rare. Starting with China's national financial bond market data after COVID-19 in 2020, this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return, based on the Copula model. The empirical analysis is also carried out for multiple dimensional groupings such as enterprises, industries, provinces, and bond maturities. The results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market returns. In addition, the market correlation is higher for Pandemic Bonds issued in Hubei Province, which is at the center of the 2020 pandemic, and the shorter the maturity of the Pandemic Bond issued, the stronger the relationship with market returns. Finally, this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic, fiscal, and social pressures. © 2020 Tsinghua University Press.

Sequential immunization with SARS-CoV-2 vaccines

Sequential immunization is one of the special means to solve the shortage of vaccines, respond to SARS-CoV-2 variants and improve the efficacy of vaccines in the current pandemic period. This article mainly reviewed five sequential immunization strategies using the vaccines authorized by World Health Organization: priming with inactivated vaccine and boosting with recombinant protein vaccine, vector vaccine or mRNA vaccine;priming with vector vaccine and boosting with mRNA vaccine;prime-boost immunization with mRNA vaccines produced by different manufactures. Results of the related studies showed that heterologous sequential immunization strategies were safe and effective, and higher immunogenicity and efficacy could be achieved by sequential immunization. In addition, sequential immunization could provide certain protective effects against SARS-CoV-2 variants.

Emergency response report of a positive COVID-19 nucleic test case

Background: Eight pm on April 13, 2022,a10:1 mixed test tube was found to be positive in the COVID-19 nucleic acid test site set up outside the hospital. In order to identify the infected case and control the spread of COVID-19 rapidly, we conducted this emergency investigation. Methods: According to the National COVID-19 Control and Prevention Protocol (8th edition), Guideline on Emergency Response to COVID-19 Case Found in Hospital in Shandong Province, and the Emergency Response Plan for COVID-19 in our hospital, information reporting, hospitalblockading, potential COVID- 19 cases tracing, close contact screening, environmental sampling and disinfecting, COVID-19 nucleic acid testing and risk assessment were carried out by our team. Results: A female COVID-19 case aged 50 years was identified. She is aodd-jobber who works in the labour market near the hospital. The virus strain was sequenced as Omicron BA.2. A total of 65 close contacts was controlled in a hotel. The COVID-19 nucleic acid test results for all the staff of hospital, environmental samples were negative. The risk of COVID-19 spread was controlled and the hospital restarted of clinical activities as normal at 8 am on April 14 after blockaded for 12 hours. Inthe following 7 days, the staff of the hospital were tested for COVID-19 nucleic acid twice a day, and the results were negative. Then the testing frequency changed to once a day. Conclusion: Formulating detailed and feasible COVID-19 emergency response plans based on the requirements of the public documents and the actual conditions of the hospital, is useful to improve the efficiency of emergency response to COVID-19 cases and save time for control of COVID-19 spread and restart the clinical activities of hospital. © 2022 Shandong Yinbao Technology Co. Ltd. All Rights Reserved.

Diabetes Analysis with a Dataset Using Machine Learning

Diabetes is a disease that actually impacts the capacity of the body to obtain blood glucose, which is usually referred to as blood sugar. At the end of 2019, a new public health problem (COVID-19) emerged. This disease has greatly harmed people with diabetes. Therefore, we intend to make use of data mining algorithms to prevent death and improve the quality of life through the prediction of diabetes. In this paper, four different algorithms have been used to analyze Diabetes from DAT260x Lab01: Logistic, Decision Tree Classifier, Xgboost and SVC. The models are evaluated for which algorithm is much effective. The paper then provides a quick overview of both the set of data and the fieldwork carried out on the subject. In the adjoining step, the dataset and its features are discussed. In addition, the paper explains the four algorithms and virtual environments that have been used to clarify the variables, which have the largest impact on raw data. The findings are obtained by evaluating the confusion matrix applied to the whole selected algorithm. The paper outlines the full observations and conclusions taken based on the results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fight Against COVID-19 Misinformation via Clustering-Based Subset Selection Fusion Methods

The worldwide COVID-19 pandemic has brought about a lot of changes in people's life. It also emerges as a new challenge to information search services. This is because up to now our understanding about the virus is still limited, and there is a lot of misinformation online. In such a situation, how to provide useful and correct information to the public is not straightforward. Responsibility of search engines is crucial because many people make decisions based on the information available to them. In this piece of work, we try to improve retrieval quality via the data fusion technique. Especially, a clustering-based approach is proposed for selecting a subset of systems from all available ones for finding relevant, credible, and correct documents. Experimented with a group of runs submitted to the 2020 TREC Health Misinformation Track, we demonstrate that data fusion is a very beneficial approach for this task, whether measured by some traditional metrics such as MAP or some task specific metrics such as CAM. When choosing 17 runs, which is one third of all component retrieval systems available, the linear combination method is better than the best component retrieval system by 31.42% in MAP and 21.72% in CAM. The proposed methods are also better than the state-of-the-art subset selection method by a clear margin. © 2022 Copyright @Anonymous for this paper by its authors.

Forecasting New COVID-19 Cases and Deaths Based on an Intelligent Point and Interval System Coupled With Environmental Variables

The outbreak of Coronavirus disease 2019 (COVID-19) has become a global public health event. Effective forecasting of COVID-19 outbreak trends is still a complex and challenging issue due to the significant fluctuations and non-stationarity inherent in new COVID-19 cases and deaths. Most previous studies mainly focused on univariate prediction and ignored the uncertainty prediction of COVID-19 pandemic trends, which may lead to insufficient results. Therefore, this study utilized a novel intelligent point and interval multivariate forecasting system that consists of a distribution function analysis module, an intelligent point prediction module, and an interval forecasting module. Aimed at the characteristics of the COVID-19 series, eight hybrid models composed of various distribution functions (DFs) and optimization algorithms were effectively designed in the analysis module to determine the exact distribution of the COVID-19 series. Then, the point prediction module presents a hybrid multivariate model with environmental variables. Finally, interval forecasting was calculated based on DFs and point prediction results to obtain uncertainty information for decision-making. The new cases and new deaths of COVID-19 were collected from three highly-affected countries to conduct an empirical study. Empirical results demonstrated that the proposed system achieved better prediction results than other comparable models and enables the informative and practical quantification of future COVID-19 pandemic trends, which offers more constructive suggestions for governmental administrators and the general public. Copyright © 2022 Qu, Sha, Xu and Li.

Design and Autonomous Navigation of a New Indoor Disinfection Robot Based on Disinfection Modeling

The COVID-19 pandemic shows growing demand of robots to replace humans for conducting multiple tasks including logistics, patient care, and disinfection in contaminated areas. In this paper, a new autonomous disinfection robot is proposed based on aerosolized hydrogen peroxide disinfection method. Its unique feature lies in that the autonomous navigation is planned by developing an atomization disinfection model and a target detection algorithm, which enables cost-effective, point-of-care, and full-coverage disinfection of the air and surface in indoor environment. A prototype robot has been fabricated for experimental study. The effectiveness of the proposed concept design for automated indoor environmental disinfection has been verified with air and surface quality monitoring provided by a qualified third-party testing agency.

An Approach and Implementation for Knowledge Graph Construction and QA System

In this paper, we investigate and propose a knowledge graph-based method and implementation of the question-and-answer (QA) system for COVID-19 cases imported from abroad. It mainly analyzes and organizes the knowledge graph construction methods based on knowledge acquisition and visualization. In addition, this paper implements the knowledge graph-based QA system by training term frequency-inverse document frequency (TF-IDF) model and Bidirectional Long Short-Term Memory + Conditional Random Field (Bi-LSTM+CRF) model as well as Cypher query statements using the graph database Neo4j. Finally, the visual intelligent interface of the QA system is designed to meet user requirements and realize the function of accurate QA. © 2021 IEEE.

Correlation analysis between Baidu migration network and COVID-19 epidemic in China

Population mobility affected the spread and risk diffusion of COVID-19. Based on Baidu migration big data and COVID-19 cases data released by the national health commission of people's republic of China combined with mathematical statistics analysis and geographic information technology, OLS test and geographically weighted regression were used to analyze the correlation between the spread of COVID-19 and Baidu migration network from January 10 to March 14, 2020.The results showed that the diffusion process of COVID-19 epidemic in China was characterized by stages, including outbreak, potential diffusion, rapid diffusion, diffusion inhibition and diffusion reduction. In the study period, there is a certain spatial correlation between the COVID-19 epidemic data and the difference coefficient of inflow and outflow and the external connection degree of provinces. Through the OLS test of population migration index, it was found that the correlation between the difference coefficient of inflow and outflow and the spread of epidemic was more significant, and there was no collinear effect. The correlation analysis showed that there was a correlation between the epidemic data and the difference coefficient of inflow and outflow in spatial location, and most of them were negative correlation in the early stage, and gradually became positive correlation in the later stage. The negative correlation between Hubei and Hubei was significant, and the positive correlation between Xinjiang, Tibet and Qinghai was significant. It revealed that provinces with large population mobility and high number of confirmed cases were mainly distributed in Hubei Province and the central cities of China's key urban agglomerations, and the epidemic prevention pressure was mainly due to the risk of transmission and diffusion caused by large population mobility and high number of confirmed cases. © 2021 ACM.