Public emotional responses to crisis: The COVID-19 pandemic in Wuhan and London

Online public responses during crises provide a window into how people emotionally react to them. Capitalizing on the international nature of the COVID-19 pandemic, we performed cross-cultural examination of group and individual differences in public emotional responses. We collected 1,106,395 Weibo posts in Wuhan from July 2019 to June 2020 and 6,564,014 tweets in London from October 2019 to July 2020, and found that the public mood in both cities followed a similar pattern during the onset of the COVID-19 pandemic: a stage of plunging mood followed by a period of recovery. We further examined the relationship between individuals' personality and mood changes. Our results showed that in Wuhan, emotionally stable people experienced more dramatic mood changes, while in London, people high in agreeableness and conscientiousness were more negatively affected during the lockdown period. Based on our findings, we suggest effective crisis management strategies for both policymakers and individuals.

HostSeq: a Canadian whole genome sequencing and clinical data resource.

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Optimized synthesis of anti-COVID-19 drugs aided by retrosynthesis software

Considering the millions of COVID-19 patients worldwide, a global critical challenge of low-cost and efficient anti-COVID-19 drug production has emerged. Favipiravir is one of the potential anti-COVID-19 drugs, but its original synthetic route with 7 harsh steps gives a low product yield (0.8%) and has a high cost ($68 per g). Herein, we demonstrated a low-cost and efficient synthesis route for favipiravir designed using improved retrosynthesis software, which involves only 3 steps under safe and near-ambient air conditions. A yield of 32% and cost of $1.54 per g were achieved by this synthetic route. We also used the same strategy to optimize the synthesis of sabizabulin. We anticipate that these synthetic routes will contribute to the prevention and treatment of COVID-19.

A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

What can we learn from the 2008 financial crisis for global power decarbonization after COVID-19?

Since the outbreak of the COVID-19 pandemic, power generation and the associated CO2 emissions in major countries have experienced a decline and rebound. Knowledge on how an economic crisis affects the emission dynamics of the power sector would help alleviate the emission rebound in the post-COVID-19 era. In this study, we investigate the mechanism by which the 2008 global financial crisis sways the dynamics of power decarbonization. The method couples the logarithmic mean Divisia index (LMDI) and environmentally extended input-output analysis. Results show that, from 2009 to 2011, global power generation increased rapidly at a rate higher than that of GDP, and the related CO2 emissions and the emission intensity of global electricity supply also rebounded;the rapid economic growth in fossil power-dominated countries (e.g., China, the United States, and India) was the main reason for the growth of electricity related CO2 emissions;and the fixed capital formation was identified as the major driver of the rebound in global electricity consumption. Lessons from the 2008 financial crisis can provide insights for achieving a low-carbon recovery after the COVID-19 crisis, and specific measures have been proposed, for example, setting electricity consumption standards for infrastructure construction projects to reduce electricity consumption induced by the fixed capital formation, and attaching energy efficiency labels and carbon footprint labels to metal products (e.g., iron and steel, aluminum, and fabricated metal products), large quantities of which are used for fixed capital formation. © 2023 The Authors

Epidemiological Investigation and Analysis of Bovine Coronavirus in Beef Cattle in Jilin Province.

In order to comprehensively understand the epidemiological situation of bovine coronavirus (BCoV) in beef cattle herds in Jilin Province, blood, nasal swabs, fecal swabs and tissue organs of clinically diseased and dead cattle were collected in different seasons from 12 counties and cities in the east, central and western regions of Jilin Province, using serological and molecular diagnostic testing techniques to conduct an epidemiological investigation of BCoV in the The epidemiological situation of BCoV in some areas of Jilin Province. A total of 1 298 clinical serum samples, 462 clinical samples (including fecal samples, liver, lung, spleen, trachea and other tissue samples) were collected, and PCR detection of clinical samples was performed by applying commercial BCoV antibody detection kits to detect serum antibodies and a novel detection technique of nano-PCR, and sequencing and analysis of positive results detected by nucleic acid. The results showed that the serum positive rate of BCoV antibodies was 1.08%, and the positive rate of clinical samples such as feces and liver was 21.10%. The BCoV prevalent strain in the investigated area was more than 99% homologous to the prevalent strain in Sichuan, China, after sequencing analysis. This study provides a comprehensive survey of BCoV prevalence in central Jilin Province, which enriches the epidemiological survey data of bovine coronavirus and lays the foundation for guiding the prevention and control of bovine coronavirus.Copyright © 2023 Acta Veterinaria et Zootechnica Sinica. All rights reserved.

The Effects of Online Production-Oriented Approach (POA) on Chinese Undergraduates' English Speaking Competence Development During COVID-19 Pandemic Era

—The Covid-19 pandemic has expedited Online Teaching and Learning (OTL) following a sudden closure of academic institutions. Although within the past years, POA was fully developed and expanded in various projects that yielded fruitful college English learning results (Matsuda, 2017), recently, in the learning and practice of Oral English by Chinese undergraduates, there are distinct drawbacks and issues affecting language learning. This paper attempts to apply POA to Chinese undergraduates' oral English classes based on OTL during the covid-19 pandemic, specifically focusing on whether POA can increase the effectiveness of oral English learning for undergraduates. Data analysis of both the pre and post-tests revealed significant improvement in the experimental class and minimal improvement in the controlled class. Students' pronunciation, vocabulary, and fluency improved in the experimental group. It is implied that the POA application was effective in enhancing Chinese undergraduates' speaking skills. Some suggestions are put forth to enhance the application of online POA during the covid-19 pandemic era. © 2023 ACADEMY PUBLICATION.

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.

Scalable Adversarial Attack Algorithms on Influence Maximization

In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee 1/2 - ϵ approximation (for any small ϵ >0) and an efficient running time. © 2023 ACM.

Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications IEEE

Semi-Supervised Multi-View Fusion for Identifying CAP and COVID-19 With Unlabeled CT Images

Recently under the condition of reducing nucleic acid testing for COVID-19 in large population, the computer-aided diagnosis with the chest computed tomography (CT) image has become increasingly important in differential diagnosis of community-acquired pneumonia (CAP) and COVID-19. In prac-tice, there usually exist a mass of unlabeled CT images, especially in regions without adequate medical resources, and the existing diagnosis methods cannot take advantage of the useful information among them. Therefore, it is practical and urgent need to develop a computer-aided diagnosis model that can effectively exploit both labeled and unlabeled samples. To this end, in this paper, we pro -pose a semi-supervised multi-view fusion method for the diagnosis of COVID-19. It explores both the discriminative features from labeled samples and the structure information from unlabeled samples and fuses multi-view features extracted from CT images, including image feature, statistical feature, and lesions specific feature, for improving the diagnostic performance. Specifically, in the proposed model, we utilize semi-supervised learning technique with pairwise constraint regularization to learn the model with both labeled samples and unlabeled samples. Simultaneously, we employ low-rank multi-view constraint to capture latent comple-mentary information among different features from CT images. Experimental results show that the proposed method outperforms the state-of-the-art methods in differential diagnosis of CAP vs. COVID-19.

Covid Sepsis Gene Expression Subphenotypes Manifest Differential Plasma Protein Signatures

INTRODUCTION: Transcriptome-derived sepsis subphenotypes, termed 'adaptive', 'inflammopathic' and 'coagulopathic', have been reliably identified in sepsis cohorts, however plasma proteomics in these groups have not been well characterized. We hypothesized that inflammatory and vascular injury markers would be elevated in the inflammopathic and coagulopathic groups compared to the adaptive group. METHOD(S): We prospectively enrolled and obtained blood from 130 inpatients with COVID19-related sepsis. Severity was classified by NIH ordinal scale. Gene expression analysis was performed by Nanostring nCounter (Inflammatix). Inflammatory proteins interleukin (IL)-6, IL8, IL10, IL1RA, IL1RL1, and IFNg and vascular markers ANGPT2, sICAM, vWF, ADAMTS13, and protein C were measured with OLINK proximity extension assay. Clinical variables were compared by chi-square and protein levels were compared using ANOVA with Bonferroni adjustment. RESULT(S): The transcriptomic classifier identified 32% (41) inflammopathic, 50% (65) adaptive and 18% (24) coagulopathic subjects. The inflammopathic group had more patients requiring mechanical ventilation (39% vs 9% vs 21%;p < 0.001) and higher 90-day mortality (32% vs 8% vs 13%, p = 0.016). Inflammatory cytokines IL8 and IL10 were significantly higher in inflammopathic compared to adaptive (p=0.038 and p=0.017 respectively), but not compared to coagulopathic (p>0.99 and p=0.24, respectively). Both the inflammopathic and coagulopathic groups expressed higher IL1RL1 and interferon-gamma compared to adaptive (IL1RL1;p< 0.001, p=0.002, IFNg;p=0.007, p=0.001). Plasma IL6 and IL1RA did not differ between groups, nor did many vascular proteins. The inflammopathic group expressed higher sICAM (p=0.049 vs adaptive) and lower ADAMTS13 compared to the adaptive group, and the coagulopathic group did not differ in its vascular protein expression. CONCLUSION(S): Transcriptomic subphenotypes are present in COVID-19 sepsis at similar proportions to non-COVID-19 sepsis. Inflammopathic subjects manifested higher severity of illness at admission, higher expression of inflammatory proteins and higher mortality. Markers of vascular injury did not distinguish the coagulopathic group. Integrating RNA and protein expression may offer new insights to host immune dysregulation during COVID sepsis.

COVID-19 Forecasting Based on Local Mean Decomposition and Temporal Convolutional Network

Since the outbreak of coronavirus disease 2019 (COVID-19) has resulted in a dramatic loss of human life and economic disruption worldwide from early 2020, numerous studies focusing on COVID-19 forecasting were presented to yield accurate predicting results. However, most existing methods could not provide satisfying forecasting performance due to tons of assumptions, poor capability to learn appropriate parameters, etc. Therefore, in this paper, we combine a traditional time series decomposition: local mean decomposition (LMD) with temporal convolutional network (TCN) as a general framework to overcome these shortcomings. Based on the particular architecture, it can solve weekly new confirmed cases forecasting problem perfectly. Extensive experiments show that the proposed model significantly outperforms lots of state-of-the-art forecasting methods, and achieves desirable performance in terms of root mean squared log error (RMSLE), mean absolute percentage error (MAPE), Pearson correlation (PCORR), and coefficient of determination (R-2). To be specific, it could reach 0.9739, 0.8908, and 0.7461 on R-2 when horizon is 1, 2, and 3 respectively, which proves the effectiveness and robustness of our LMD-TCN model.

Detection of Antibody versus Antigen, Optimal Option of Different Serological Assays Based Tests for COVID-19 Diagnosis: A Meta-Analysis

Background: In this study, the diagnostic efficacy of antigen test and antibody test were assessed. Additional-ly, the difference of sensitivity, specificity, and diagnostic odds ratio were compared concerning efficacy of antibody test versus antigen test for Corona Virus Disease 2019 (COVID-19) diagnosis. Methods: Online databases were searched for full-text publications and STATA software was used for data pooling and analysis before Sep 1st, 2022. Forrest plot was used to show the pooled sensitivity, specificity and diagnostic odds ratio. Combined receiver operating characteristic (ROC) curve was used to show the area of under curve of complex data. Results: Overall, 25 studies were included. The sensitivity (0.68, 95% CI: 0.53-0.80) and specificity (0.99, 95% CI: 0.98-0.99) in antibody or antigen was calculated. The time point of test lead to heterogeneity. The area under curve (AUC) was 0.98 (95% CI: 0.96-0.99), and the diagnostic odds ratio (DOR) was 299.54 (95% CI: 135.61-661.64). Subgroup analysis indicated antibody test with sensitivity (0.59, 95% CI: 0.44-0.73) and specificity (0.98, 95% CI: 0.95-0.99) and antigen test with sensitivity of 0.77 (95% CI: 0.53-0.91) and specificity of 0.99 (95% CI: 0.98-1.00). Higher AUC and DOR were proved in antigen test. Conclusion: The present study compared the efficacy of antibody test versus antigen test for COVID-19 di-agnosis. Better diagnostic efficacy, lower heterogeneity, and less publication bias of rapid antigen testing was suggested in this study. This study would help us to make better strategy about choosing rapid and reliable testing method in diagnosis of the COVID-19 disease. © 2023 Fu et al. Published by Tehran University of Medical Sciences.

Research on factors of marine ranch leisure fisheries development based on multiple orderly logit model

Based on the survey of 43 Marine ranches and 260 consumers, this paper uses the diversified and orderly Logit model to study the significant factors affecting the development of recreational fishery in Marine ranches.The study found that six factors, including consumer gender, individual economic strength, consumer demand for Marine pasture products, brand construction level of Marine pasture recreational fishery, online channel promotion level of Marine pasture recreational fishery and the impact of COVID-19 epidemic, had a significant impact on the development of Marine pasture recreational fishery.This paper divides tower into product development and brand building, marketing and daily operation three dimensions, suggest operators improve the level of recreational fishery product development and brand building, develop differentiation price, develop online channels, strengthen the whole process communication, innovation under the outbreak of daily operation mode, expand the market share, so as to enhance the competitiveness of Marine pasture recreational fishery. © 2022 SPIE.