ABSTRACT
Objective: The gold immunochromatographic assay for detection of SARS-CoV-2 antigen was evaluated by international multi-center clinical trial. Methods: A total of 1 855 clinical parallel samples with valid test results (for nucleic acid and antigen tests, respectively) were collected from nine countries, including Germany, the United Kingdom, Ukraine, France, India, Thailand, Malaysia, the United States of America and Brazil, with sampling period from January 3, 2021 to September 22, 2021. These samples were detected by SARS-CoV-2 antigen test kit (colloidal gold immunochromatography assay) and nucleic acid detection kit (real-time fluorescent quantitative reverse transcription polymerase chain reaction). Positive coincidence rates [(number of antigen-positive cases/nucleic acid-positive cases)×100%], negative coincidence rates [(number of antigen-negative cases/nucleic acid-negative cases)×100%], total coincidence rates [(number of cases with consistent results for both antigen and nucleic acid detection/number of total cases) ×100%], as well as Kappa values were calculated. The differences of the above indictors among different countries were evaluated by the coefficient of variation. The detection rates of the antigen test for samples with different cycle threshold values (Ct values) for the nucleic acid detection, different characteristics and different mutant strains were analyzed. Results: For all samples, the positive, negative, and total coincidence rate between the antigen test and nucleic acid assay was 90.8% (569/627), 99.7% (1 224/1 228) and 96.7% (1 793/1 855), respectively, and the consistency coefficient Kappa value was 0.924. Among these countries, the coefficient of variation for positive coincidence rates (except for Malaysia with a lot of samples with Ct value>30), negative coincidence rates (except for France without negative samples) and total coincidence rates (except for France) was 6%,<1%, and 6%, respectively. When Ct values were less than 25, the detection rates of antigen test were 83.3%-100% for each countries (the coefficient of variation was 6%); The total detection rate and the coefficient of variation was 93.4% (428/458) and 5%, respectively, for asymptomatic infected persons and cases within 7 days post onset of symptoms; the total detection rate for various SARS-CoV-2 mutant strains was 97.5% (119/122); and it showed negative results for samples from cases infected with other viruses, including influenza A virus subtype H1N1, influenza B virus, respiratory syncytial virus subgroups A and B, coxsackievirus 16, human metapneumovirus, parainfluenza virus types 1 and 4, Epstein-Barr virus and adenovirus. Conclusion: The SARS-CoV-2 antigen test kit showed excellent authenticity, and there were few differences for its indictors among nine countries, therefore it can meet the needs of large-scale early screening of SARS-CoV-2 infection.
ABSTRACT
Given the coronavirus disease (COVID-19) pandemic, people need to wear masks to protect themselves and reduce the spread of COVID, which brings new challenge to the traditional face recognition task. Since features like the nose and mouth, which are well distinguishable, are hidden under the mask, traditional methods are no longer simply applicable, even though they once achieved a high degree of accuracy. In response to this problem, the Masked Face Recognition Challenge & Workshop (MFR) was held in conjunction with the International Conference on Computer Vision (ICCV) 2021. This article details a method that combining the classic ArcFace and pairwise loss to target the new masked face recognition task. So far, our method has achieved the second place in the competition.
ABSTRACT
In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.