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The monsoon development in the early rainy season in East Asia is analysed by the evolution of daily weather types (WTs). The WTs are classified by a k‐means clustering analysis based on 850 hPa winds from April 1 to July 31 in the 40‐year period of 1979–2018. Five WTs are identified, typifying the progression of weather regimes in the monsoonal evolution. WTs 1 and 2, with easterly winds in the South China Sea (SCS), occurs mostly in April and early May, corresponding to typical WTs before the SCS monsoon onset. In WT3, winds in the SCS turns westerly, and a rain band emerges in South China, extending east‐northeastward to the Pacific Ocean south of Japan, which signifies the May–June Mei‐yu in South China. Then, with the rain band moved to Yangtze River Valley (YRV) in WT4, this WT corresponds to the June–July Mei‐Yu in central and eastern China along the YRV. Finally, WT5 occurs more frequently towards the end of July, and corresponds to the post‐Mei‐Yu WT, indicating the ending of the early summer rainfall season in East Asia. The evolution of the WTs is gradual but intermittent, representing the weather‐within‐climate information. Each WT likely persists to itself. But WT1 and WT2 also progress to each other. The long persistence of WT3 and WT4 up to several weeks indicates the quasi‐stationary Mei‐Yu in South China and the YRV, respectively. The year‐to‐year variability of the WTs is also evident in the WTs. The El Niño–Southern Oscillation (ENSO) impact on the precipitation in the East Asian region is analysed for three types of ENSO: Eastern Pacific, Central Pacific, and mixed El Niño and La Niña. The seasonal mean precipitation anomalies can be interpreted by the frequencies of the daily WTs under different ENSO conditions. [ABSTRACT FROM AUTHOR]

Purpose: The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. Methods: We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. Results: Global Moran's I statistics of COVID-19 incidences was 0.31 (P<0.05). The areas with a high risk of COVID-19 were mainly located in the provinces around Hubei and the provinces with a high level of economic development. The relative risk of two socioeconomic factors, the per capita consumption expenditure of households and the proportion of the migrating population from Hubei, were 1.887 [95% confidence interval (CI): 1.469~2.399] and 1.099 (95% CI: 1.053~1.148), respectively. The two factors explained up to 78.2% out of 99.7% of structured spatial variations. Conclusion: Our results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19. [ABSTRACT FROM AUTHOR]

The development, protection, and restoration of bays require works in scientific research and applications, and the success of which depends on a well deployment of monitoring stations for marine water quality. However, for bays without historical data, it is difficult to carry out related research on deployment of the monitoring stations, resulting in very few research works. This paper has introduced the affinity propagation (AP) clustering algorithm and achieved good results by correcting the preferences. The results show that under the given preference, that is, when the value of M is −6800, the number of monitoring stations in the Xincun lagoon area is 24. Simultaneous the sensitivity analysis of preferences shows that the number of exemplars decreases with lower preferences, that is, when M decreased from −4000 to −12000, the number also decreased from 70 to 36. However, some exemplars remain unchanged or being changed to adjacent positioning. This shows the stability of computation results and the rationality of AP. The research results can be well applied to other bays, even open waters. • AP clustering can be used to layout seawater monitoring stations. • Multi-dimensional feature parameters is used to modify the bias parameters of the algorithm. • Sensitivity analysis shows that AP clustering can be applied in bays lacking historical data. [ABSTRACT FROM AUTHOR]

Pollution status and ecological risks associated with sediment heavy metals (Cu, Pb, Zn, Cd, and Cr) were investigated around Xincun Bay, assessing their spatial variations and relationship with sediment physiochemical factors. Higher concentrations and associated risks were observed in the central region, where mariculture activities were concentrated, compared to non-maricultured areas. Despite with overall low concentrations, Cd had a higher ecological risk. Correlation and principal component analyses revealed similar sources for all metals in Xincun Bay. Heavy metal concentrations varied with expansion of mariculture operations in terms of quantity and scale, confirming the influence of mariculture activities. Sediments around mariculture had higher contents of clay, silt, and total organic carbon (TOC), and finer particle sizes. Quantitative analyses through correlation and linear regression indicated that TOC significantly regulated heavy metal concentration and distribution (p < 0.05). Considering its significant association with TOC, the influence of mean grain size should not be overlooked., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Hand, foot, and mouth disease (HFMD) is a major public health concern in the Asia-Pacific region. Previous studies have implied that ambient air pollution may affect the incidence of HFMD, but the results among different regions are inconsistent. We aimed to deepen the understanding of the associations between air pollutants and HFMD by conducting a multicity study. Daily data on childhood HFMD counts and meteorological and ambient air pollution (PM 2.5 , PM 10 , NO 2 , CO, O 3 , and SO 2 ) concentrations in 21 cities in Sichuan Province from 2015 to 2017 were collected. A spatiotemporal Bayesian hierarchical model framework was established, and then a distributed lag nonlinear models (DLNMs) was constructed to reveal exposure-lag-response relationships between air pollutants and HFMD while controlling for spatiotemporal effects. Furthermore, given the differences in the levels and seasonal trends of air pollutants between the basin region and plateau region, we explored whether these associations varied between different areas (basin and plateau). The associations between air pollutants and HFMD were nonlinear, with different lag responses. Low NO 2 concentrations and both low and high PM 2.5 and PM 10 concentrations were associated with a decreased risk of HFMD. No significant associations between CO, O 3 , and SO 2 and HFMD were found. The associations between air pollutant concentrations and HFMD were different between the basin and plateau regions. Our study revealed associations between PM 2.5 , PM 10 , and NO 2 concentrations and HFMD, deepening the understanding of the relationships between air pollutants and HFMD. These findings provide evidence to support the formulation of relevant prevention measures and the establishment of an early warning system., (© 2023. The Author(s).)

Background: Epidemiological studies have investigated the short-term effects of meteorological factors and air pollution on the incidence of hand, foot, and mouth disease (HFMD). Several meteorological indicators, such as relative humidity and the diurnal temperature range (DTR), significantly modify the relationship between short-term exposure to temperature and HFMD incidence. However, it remains unclear whether (and how) long-term air pollution levels modify the short-term relationships of HFMD incidence with meteorological factors and air pollution., Methods: We obtained daily data on meteorological factors, air pollutants, and HFMD counts in children from 21 prefecture-level cities in Sichuan Province in Southwest China from 2015 to 2017. First, we constructed a distributed lag nonlinear model (DLNM) at each prefecture-level site to evaluate the short-term impacts of meteorological variables and air pollutants on HFMD incidence. Then, we assessed the pooled effects of the exposures and incorporated long-term city-specific air pollutant indicators as meta-predictors to examine their potential modification effects by performing multivariate meta-regression models., Results: We found that long-term SO 2 and CO concentrations significantly modified the short-term relationships between climatic variables and HFMD incidence. Specifically, high concentrations of CO (P = 0.027) and SO 2 (P = 0.039) reduced the risk of HFMD at low temperatures. The relationship between relative humidity and HFMD incidence was weakened at high SO 2 concentrations (P = 0.024), especially when the relative humidity was below the median level. When the minimum relative humidity (32%) was compared to the median relative humidity (77%), the risk ratio (RR) was 0.77 (95% CI: 0.51-1.17) in the 90 th percentile of SO 2 (19.6 μg/m 3 ) and 0.41 (95% CI: 0.27-0.64) in the 10 th percentile of SO 2 (10.6 μg/m 3 )., Conclusion: Our results indicated that long-term SO 2 and CO levels modified the short-term associations between HFMD incidence in children and meteorological variables. These findings may inform health authorities to optimize targeted public health policies including reducing ambient air pollution and reinforcing self-protective actions to weaken the adverse health impacts of environmental factors on HFMD incidence., (© 2022. The Author(s).)

Background and Objective: Valsartan is widely used for the treatment of moderate hypertension. However, previous studies have found that efficacy of the valsartan depends on the dose and intake. Cytochrome P450 (CYP) 2C9 metabolizes ∼15% of the clinical drugs. Genetic polymorphisms of CYP2C9 markedly affect the safety and effectiveness of many drugs, which might lead to adverse reactions and therapeutic failure. Twenty-four novel CYP2C9 variants (*36-*60) had been previously discovered via gene sequencing in the Han population. Our study aims to evaluate the impact of 38 CYP2C9 variants from the Chinese population on valsartan metabolism compared with CYP2C9*1 in vitro., Methods: Wild-type CYP2C9*1 and other CYP2C9 variants were expressed in Spodoptera frugiperda 21 insect cells. Incubations were performed at 37 °C with 20-2000 μM substrate for 30 min. The metabolite 4-OH valsartan was determined via UPLC-MS/MS., Results: Among the 38 CYP2C9 variants, the enzymatic activities of most variants were significantly altered compared with the wild-type. Three variants (CYP2C9*27, *40 and *49) exhibited increased intrinsic clearance values (134-153% relative clearance). However, 12 variants (CYP *8, *13, *16, *19, *33, *36, *42, *43, *45, *52, *54, *58) caused >90% decreases in the relative clearance of valsartan compared to CYP2C9*1., Conclusions: Our research provides systematic data for evaluating the effects of CYP2C9 variants on valsartan metabolism in the Chinese population. These results will expand our understanding of the impact of CYP2C9 genetic polymorphisms on valsartan metabolism and will contribute to precision medicine., (Copyright © 2022 Elsevier B.V. All rights reserved.)