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P- and S-wave separation plays an important role in elastic reverse-time migration. It can reduce the artifacts caused by crosstalk between different modes and improve image quality. In addition, P- and Swave separation can also be used to better understand and distinguish wave types in complex media. At present, the methods for separating wave modes in anisotropic media mainly include spatial nonstationary filtering, low-rank approximation, and vector Poisson equation. Most of these methods require multiple Fourier transforms or the calculation of large matrices, which require high computational costs for problems with large scale. In this paper, an efficient method is proposed to separate the wave mode for anisotropic media by using a scalar anisotropic Poisson operator in the spatial domain. For 2D problems, the computational complexity required by this method is 1/2 of the methods based on solving a vector Poisson equation. Therefore, compared with existing methods based on pseudo-Helmholtz decomposition operators, this method can significantly reduce the computational cost. Numerical examples also show that the P and S waves decomposed by this method not only have the correct amplitude and phase relative to the input wavefield but also can reduce the computational complexity significantly. [ABSTRACT FROM AUTHOR]

BACKGROUND: Evidence for a potential link between air pollution and rheumatoid arthritis (RA) is inconsistent, and the modified effect of genetic susceptibility on the relationship between air pollution and RA has not been well studied. OBJECTIVE: Using a general population cohort from the UK Biobank, this study aimed to investigate the associations between various air pollutants and the risk of incident RA and to further estimate the impact of combined exposure to ambient air pollutants on the risk of developing RA under the modification effect of genetic predisposition. METHODS: A total of 342,973 participants with completed genotyping data and who were free of RA at baseline were included in the study. An air pollution score was constructed by summing the concentrations of each pollutant weighted by the regression coefficients with RA from single-pollutant models to assess the combined effect of air pollutants, including particulate matter (PM) with diameters ≤ 2.5 µm (PM2.5), between 2.5 and 10 µm (PM2.5–10), and ≤ 10 µm (PM10), as well as nitrogen dioxide (NO2) and nitrogen oxides (NOx). In addition, the polygenic risk score (PRS) of RA was calculated to characterize individual genetic risk. The Cox proportional hazard model was used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) of associations of single air pollutant, air pollution score, or PRS with incident RA. RESULTS: During a median follow-up time of 8.1 y, 2,034 incident events of RA were recorded. The HRs (95% CIs) of incident RA per interquartile range increment in PM2.5, PM2.5-10, PM10, NO2, and NOx were 1.07 (1.01, 1.13), 1.00 (0.96, 1.04), 1.01 (0.96, 1.07), 1.03 (0.98, 1.09), and 1.07 (1.02, 1.12), respectively. We also found a positive exposure-response relationship between air pollution score and RA risk (푝Trend = 0.000053). The HR(95% CI) of incident RA was 1.14 (1.00, 1.29) in the highest quartile group compared with the lowest quartile group of the air pollution score. Furthermore, the results of the combined effect of air pollution score and PRS on the RA risk showed that the risk of RA incidence in the highest genetic risk and air pollution score group was almost twice that of the lowest genetic risk and air pollution score group [incidence rate (IR) per 100,000 person-years: 98.46 vs. 51.19, and HR= 1.73 (95% CI: 1.39, 2.17) vs. 1 (reference)], although no statistically significant interaction between the air pollution and genetic risk for incident RA was found (푝Interaction >0.05). DISCUSSION: The results revealed that long-term combined exposure to ambient air pollutants might increase the risk of RA, particularly in those with high genetic risk. [ABSTRACT FROM AUTHOR]

Phytochemical investigation of the shells of Metaplexis japonica (Thunb.) Makino, belonging to the family of Apocynaceae, afforded three new pregnane steroids, metajapogenins A-C, along with three known compounds. The structures of the new compounds were elucidated as 12β,14β,17β-trihydroxypregna-3,5-dien-7,20-dione, 12β,14β,17β,20β-tetrahydroxypregna-3,5-dien-7-one; 3β,12β,14β,17β-tetrahydroxypregn-5-ene-7,20-dione on the basis of extensive spectroscopic evidence derived from 1D; 2D-NMR experiments and mass spectrometry. The known compounds included pergularin; 12-О-acetylpergularin; and pergularin-3-О-β-D-oleandropyranose; which were identified for the first time in the shells of M. japonica. [ABSTRACT FROM AUTHOR]