ABSTRACT
BNT162b2, an mRNA-based SARS-CoV-2 vaccine (Pfizer- BioNTech), is one of the most effective COVID-19 vaccines and has been approved by more than 130 countries worldwide. However, several studies have reported that the COVID-19 vaccine shows high interpersonal variability in terms of humoral and cellular responses, such as those with respect to SARS-CoV-2 spike protein immunoglobulin (Ig)G, IgA, IgM, neutralizing antibodies, and CD4+ & CD8+ T cells. The objective of this study is to investigate the kinetic changes in anti-SARS-CoV-2 spike IgG (IgG-S) profiles and adverse reactions and their associations with HLA profiles among 100 hospital workers from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan. DQA1*03:03:01 (p = 0.017;OR 2.80, 95% CI 1.05-7.25) was significantly associated with higher IgG-S production after two doses of BNT162b2 while DQB1*06:01:01:01 (p = 0.028, OR 0.27, 95% CI 0.05-0.94) was significantly associated with IgG-S declines after two doses of BNT162b2. No HLA alleles were significantly associated with either local symptoms or fever. However, C*12:02:02 (p = 0.058;OR 0.42, 95% CI 0.15-1.16), B*52:01:01 (p = 0.031;OR 0.38, 95% CI 0.14-1.03), DQA1*03:02:01 (p = 0.028;OR 0.39, 95% CI 0.15-1.00) and DPB1*02:01:02 (p = 0.024;OR 0.45, 95% CI 0.21-0.97) appeared significantly associated with protection against systemic symptoms after two doses of BNT162b2 vaccination. Further studies with larger sample sizes are clearly warranted to determine HLA allele associations with the production and long-term sustainability of IgG-S after COVID-19 vaccination.
ABSTRACT
The COVID-19 Pandemic has increased the demands of governments for technologies to estimate the route of infection. In this paper, we propose a new smart city framework that collects anonymized passage information from deployed Bluetooth sensors and analyzes them to reconstruct the multiple trajectories of infected people. We formulate recovering multiple trajectories on the basis of anonymized passage information, including passage time and passage position, obtained by sensors in a smart city as a problem of multiple-trajectory reconstruction in general networks. We propose a new method for reconstructing multiple trajectories on the basis of anonymized passage information. Our method assumes that each trajectory follows a Markov process and estimates transit time for each edge in networks and the transition probability of the Markov process. On the basis of its estimation, our method can find multiple trajectories with maximum likelihood by solving a minimum cost flow problem. We evaluate the performance of our method in experiments using simulation data and actual human trajectory data. © 2022 IEEE.
ABSTRACT
In this research, we investigated the recent trend of the diffusion of cashless payment in Japan by using the survey data of students in our institute during 2018 and 2021. Cashless payment has achieved remarkable development with the wide usage of smartphones and the prevalence of consequent smartphone-based system. Before the bar code, either two- and single-dimensional, was introduced in the cashless system, the major technology that was used is a smart card or integrated circuit card-based system. We investigated the behavior of technology adoptions to cashless payment, which we called "Smart Payment System"in 2018[5]. Since 2018, we conducted surveys and found that the effect of COVID-19 resulted in the increase adoption to the cashless payment system among students and the increase usage of credit cards. The survey showed that the use of cashless payment system increased after 2020 in comparison to 2018 and 2019. Since the COVID-19 pandemic in spring of 2020, the usage of online shopping increased, and the usage of real stores decreased. Although we did not use big data, such as the log data of mobile phone usages, the results indicated that our observation of the use of cashless payment system among young users is consistent with the common sense. © 2021 ACM.
ABSTRACT
This research envisages findings from observations on the behaviors of online learners. As a result of the COVID-19 pandemic in 2020, universities and colleges were forced to use online learning methods, even in places wherein such learning techniques were not previously utilized. However, in 2021, after one year of experience, institutions and students became accustomed to online teaching and learning. Consequently, the behaviors of online learners have a certain resemblance to their behaviors in person face-to-face class situations. This research illustrates the findings on the behaviors of online learners from a case study on a medium-sized private college in rural Japan. © 2021 IEEE