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rate detailed structural data sets from a single scan, aiding the quantitative analysis of structurally heterogenous samples, including cellular surfaces.There are increasing evidences showing the contribution of somatic genetic variants to non-cancer diseases. However, their detection using massive parallel sequencing methods still has important limitations. In addition, the relative importance and dynamics of somatic variation in healthy tissues are not fully understood. We performed high-depth whole-exome sequencing in 16 samples from patients with a previously determined pathogenic somatic variant for a primary immunodeficiency and tested different variant callers detection ability. Subsequently, we explored the load of somatic variants in the whole blood of these individuals and validated it by amplicon-based deep sequencing. Variant callers allowing low frequency read thresholds were able to detect most of the variants, even at very low frequencies in the tissue. The genetic load of somatic coding variants detectable in whole blood is low, ranging from 1 to 2 variants in our dataset, except for one case with 17 variants compatible with clonal haematopoiesis under genetic drift. Because of the ability we demonstrated to detect this type of genetic variation, and its relevant role in disorders such as primary immunodeficiencies, we suggest considering this model of gene mosaicism in future genetic studies and considering revisiting previous massive parallel sequencing data in patients with negative results.Properly conducted serological survey can help determine infection disease true spread. This study aims to estimate the seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia accounting for non-response bias. A sample of adults was recruited with random digit dialling, interviewed and invited for anti-SARS-CoV-2 antibodies. The seroprevalence was corrected with the aid of the bivariate probit model that jointly estimated individual propensity to agree to participate in the survey and seropositivity. 66,250 individuals were contacted, 6,440 adults agreed to be interviewed and blood samples were obtained from 1,038 participants between May 27 and June 26, 2020. Naïve seroprevalence corrected for test characteristics was 9.0% (7.2-10.8) by CMIA and 10.5% (8.6-12.4) by ELISA. Correction for non-response decreased estimates to 7.4% (5.7-9.2) and 9.1% (7.2-10.9) for CMIA and ELISA, respectively. The most pronounced decrease in bias-corrected seroprevalence was attributed to the history of any illnesses in the past 3 months and COVID-19 testing. Seroconversion was negatively associated with smoking status, self-reported history of allergies and changes in hand-washing habits. These results suggest that even low estimates of seroprevalence can be an overestimation. Serosurvey design should attempt to identify characteristics that are associated both with participation and seropositivity.Therapeutic antibodies are decorated with complex-type N-glycans that significantly affect their biodistribution and bioactivity. selleck The N-glycan structures on antibodies are incompletely processed in wild-type CHO cells due to their limited glycosylation capacity. To improve N-glycan processing, glycosyltransferase genes have been traditionally overexpressed in CHO cells to engineer the cellular N-glycosylation pathway by using random integration, which is often associated with large clonal variations in gene expression levels. In order to minimize the clonal variations, we used recombinase-mediated-cassette-exchange (RMCE) technology to overexpress a panel of 42 human glycosyltransferase genes to screen their impact on antibody N-linked glycosylation. The bottlenecks in the N-glycosylation pathway were identified and then released by overexpressing single or multiple critical genes. Overexpressing B4GalT1 gene alone in the CHO cells produced antibodies with more than 80% galactosylated bi-antennary N-glycans. Combinatorial overexpression of B4GalT1 and ST6Gal1 produced antibodies containing more than 70% sialylated bi-antennary N-glycans. In addition, antibodies with various tri-antennary N-glycans were obtained for the first time by overexpressing MGAT5 alone or in combination with B4GalT1 and ST6Gal1. The various N-glycan structures and the method for producing them in this work provide opportunities to study the glycan structure-and-function and develop novel recombinant antibodies for addressing different therapeutic applications.Chronic obstructive pulmonary disease (COPD) is the fourth major cause of mortality and morbidity worldwide and is projected to be the third by 2030. However, there is little evidence available on the associations of COPD hospitalizations with meteorological factors and air pollutants in developing countries/regions of Asia. In particular, no study has been done in western areas of China considering the nonlinear and lagged effects simultaneously. This study aims to evaluate the nonlinear and lagged associations of COPD hospitalizations with meteorological factors and air pollutants using time-series analysis. The modified associations by sex and age were also investigated. The distributed lag nonlinear model was used to establish the association of daily COPD hospitalizations of all 441 public hospitals in Chengdu, China from Jan/2015-Dec/2017 with the ambient meteorological factors and air pollutants. Model parameters were optimized based on quasi Akaike Information Criterion and model diagnostics was condure sensitive to extreme temperatures and relative humidity.Inflammatory biomarkers in exhaled breath condensate (EBC) are measured to estimate the effects of air pollution on humans. The present study was conducted to investigate the relationship between particulate matter and inflammatory biomarkers in blood plasma and exhaled air in young adults. The obtained results were compared in two periods; i.e., winter and summer. GRIMM Dust Monitors were used to measure PM10, PM2.5, and PM1 in indoor and outdoor air. A total of 40 healthy young adults exhaling air condensate were collected. Then, biomarkers of interleukin-6 (IL-6), Nitrosothiols (RS-NOs), and Tumor necrosis factor-soluble receptor-II (sTNFRII) were measured by 96 wells method ELISA and commercial kits (HS600B R&D Kit and ALX-850-037-KI01) in EBC while interleukin-6 (IL-6), sTNFRII and White Blood Cell (WBC) were measured in blood plasma in two periods of February 2013 (winter) and May 2013 (summer). Significant association was found between particulate matter and the white blood cell count (p  less then  0.001), as well as plasma sTNFRII levels (p-value = 0.001). No significant relationship was found between particulate matter with RS-NOs (p = 0.128), EBC RSNOs (p-value = 0.128), and plasma IL-6 (p-value = 0.167). In addition, there was no significant relationship between interleukin-6 of exhaled air with interleukin-6 of plasma (p-value  less then  0.792 in the first period and  less then  0.890 in the second period). sTNFRII was not detected in EBC. Considering the direct effect between increasing some biomarkers in blood and EBC and particulate matter, it is concluded that air pollution causes this increasing.A ternary logic decoder (TLD) is demonstrated with independently controlled double-gate (ICDG) silicon-nanowire (Si-NW) MOSFETs to confirm a feasibility of mixed radix system (MRS). The TLD is essential component for realization of the MRS. The ICDG Si-NW MOSFET resolves the limitations of the conventional multi-threshold voltage (multi-Vth) schemes required for the TLD. The ICDG Si-NW MOSFETs were fabricated and characterized. Afterwards, their electrical characteristics were modeled and fitted semi-empirically with the aid of SILVACO ATLAS TCAD simulator. The circuit performance and power consumption of the TLD were analyzed using ATLAS mixed-mode TCAD simulations. The TLD showed a power-delay product of 35 aJ for a gate length (LG) of 500 nm and that of 0.16 aJ for LG of 14 nm. Thanks to its inherent CMOS-compatibility and scalability, the TLD based on the ICDG Si-NW MOSFETs would be a promising candidate for a MRS using ternary and binary logic.Entangled states are crucial for modern quantum enabled technology which makes their creation key for future developments. In this paper, a robust quantum control methodology is presented to create entangled states of two typical classes, the W and the Greenberger-Horne-Zeilinger (GHZ). It was developed from the analysis of a chain of alkali atoms [Formula see text] interaction with laser pulses, which leads to the two-photon transitions from the ground to the Rydberg states with a predetermined magnetic quantum number. The methodology is based on the mechanism of the two-photon excitation, adiabatic for the GHZ and non-adiabatic for the W state, induced by the overlapping chirped pulses and governed by the Rabi frequency, the one-photon detuning, and the strength of the Rydberg-Rydberg interactions.Peritumoral edema (PE) of breast cancer at T2-weighted MR images is considered a poor prognostic sign and may represent the microenvironment surrounding the tumor; however, its histopathological mechanism remains unclear. The purpose of the study was to identify and describe detailed histopathological characteristics associated with PE at preoperative breast MRI in breast cancer patients. This retrospective study included breast cancer patients who had undergone preoperative MRI and surgery between January 2011 and December 2012. Two radiologists determined the presence of PE in consensus based on the signal intensity surrounding the tumor at T2-weighted images. The following detailed histopathological characteristics were reviewed by two breast pathologists using four-tiered grades; lymphovascular invasion, vessel ectasia, stromal fibrosis, growth pattern, and tumor budding. Tumor necrosis and tumor infiltrating lymphocytes were assessed using a percent scale. Baseline clinicopathological characteristics, inth detailed histopathological characteristics of lymphovascular invasion, vessel ectasia, stromal fibrosis, growth pattern, and tumor necrosis, suggesting a relevance for tumor microenvironment.In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group's model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon's eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts.
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