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Abstract The immune escape of Omicron variants significantly subsides by the third dose of an mRNA vaccine. However, it is unclear how Omicron variant-neutralizing antibodies develop under repeated vaccination. We analyze blood samples from 41 BNT162b2 vaccinees following the course of three injections and analyze their B-cell receptor (BCR) repertoires at six time points in total. The concomitant reactivity to both ancestral and Omicron receptor-binding domain (RBD) is achieved by a limited number of BCR clonotypes depending on the accumulation of somatic hypermutation (SHM) after the third dose. Our findings suggest that SHM accumulation in the BCR space to broaden its specificity for unseen antigens is a counterprotective mechanism against virus variant immune escape.

The mask parameters of a coded aperture are critical design features when optimizing the performance of a gamma-ray camera. In this paper, experiments and Monte Carlo simulations were performed to derive the minimum detectable activity (MDA) when one seeks a real-time imaging capability. First, the impact of the thickness of the modified uniformly redundant array (MURA) mask on the image quality is quantified, and the imaging of point, line, and surface radiation sources is demonstrated using both cross-correlation (CC) and maximum likelihood expectation maximization (MLEM) methods. Second, the minimum detectable activity is also derived for real-time imaging by altering the factors used in the image quality assessment, consisting of the peak-to-noise ratio (PSNR), the normalized mean square error (NMSE), the spatial resolution (full width at half maximum; FWHM), and the structural similarity (SSIM), all evaluated as a function of energy and mask thickness. Sufficiently sharp images were reconstructed when the mask thickness was approximately 2 cm for a source energy between 30 keV and 1.5 MeV and the minimum detectable activity for real-time imaging was 23.7 MBq at 1 m distance for a 1 s collection time.

Many air pollutants are inhaled by human breathing, increasing the prevalence of respiratory disease and even mortality. With the recent COVID-19 issue, the number of air pollutants affecting humans is demands further investigation. However, there are not many adequate air measuring devices that can cover a variety of air pollutants. In this study, the developed air measurement system is able to measure sixteen air pollutants (PM10, PM2.5, PM4.0, PM1.0, CO2, CH4, temperature, humidity, VOCs, O2, H2S, NH3, SO2, CO, O3, NO2) in real time. The developed ‘multi-item air quality monitoring system’ can measure sixteen air pollutants in real time and transmit them to the server and the smartphone application at the same time. It was developed to reduce air pollutant damage to humans by emergency alerts using the smartphone application. The development system is composed of hardware development (measurement device) and software development (smartphone application, server). To verify the reliability of the developed equipment, a comparative test, temperature–humidity accuracy test, and operating temperature test were conducted. In the comparative test, difference ratios of ±5% for PM10, ±6% for PM2.5, ±4% for O3, ±5% for NO2, ±7% for CO, and ±7% for SO2 were found compared to the professional measuring devices. The temperature and humidity accuracy test result showed high reliability at ±1% and humidity ± 2%. The result of the operating temperature test showed that there was no problem in normal operation, However, further tests including the long-term comparative test and the closed chamber test will be carried out for all sensors. Additional work including a long-term test for more clear reliability of the device and closed chamber accuracy test for all 16-item sensors, data acquisition rate, and data transmit rate are in progress for commercializing the device.