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Objective To investigate the effects of family history of hypertension, overweight/obesity and the interaction between the two factors on hypertension in Han and Manchu adults in Hebei Province, so to provide a reference for the prevention and control of hypertension in Hebei province. Methods A cross-section survey by questionnaires investigation and physical examination was conducted in Hebei province in July to August 2017, a multistage stratified cluster sampling covering 4 633 Han and Manchu people aged 20-80 years old in Hebei province was performed. A multivariate Logistic regression model was used to analyze the effects of family history of hypertension and overweight/obesity on hypertension. Results After adjusting for ethnicity, sex, age, geographic distribution(urban/rural), economic income,education, smoking, drinking, physical exercise and daily activity, the risk of hypertension was 3.6(95% CI: 2.51-5.18) times higher in those with a family history of hypertension than those without the family history. The risk of hypertension in overweight/obese people was 2.90(95% CI: 1.92-4.37) times higher than non-overweight/obese people. The risk of hypertension was 9.78(95% CI: 6.86-13.95) times higher in population with the factors of overweight plus family history as compared to those with neither factors.The relative excess risk due to interaction (RERI) was 4.28(95% CI: 2.59-5.96), the synergy index (SI) was 1.95(95% CI: 1.54-2.47) and the attributable proportion due to interaction (AP) was 0.44(95% CI: 0.33-0.54). Conclusions Both family history of hypertension and overweight/obesity are associated with an increased risk of hypertension, and there is a positive additive interaction between the two factors.

Objective: This study aimed to investigate the heritability of various obesity indices and their shared genetic factors with cardiometabolic traits in the Chinese nuclear family. Methods: A total of 1270 individuals from 538 nuclear families were included in this cross‐sectional study. Different indices were used to quantify fat mass and distribution, including body index mass (BMI), visceral fat index (VFI), and body fat percent (BFP). Heritability and genetic correlations for all quantitative traits were estimated using variance component models. The susceptibility‐threshold model was utilized to estimate the heritability for binary traits. Results: Heritability estimates for obesity indices were highest for BMI (59%), followed by BFP (49%), and VFI (40%). Heritability estimates for continuous cardiometabolic traits varied from 24% to 50%. All obesity measures exhibited consistently significant positive genetic correlations with blood pressure, fasting blood glucose, and uric acid (rG range: 0.26–0.57). However, diverse genetic correlations between various obesity indices and lipid profiles were observed. Significant genetic correlations were limited to specific pairs: BFP and total cholesterol (rG = 0.24), BFP and low‐density lipoprotein cholesterol (rG = 0.25), and VFI and triglyceride (rG = 0.33). Conclusion: The genetic overlap between various obesity indices and cardiometabolic traits underscores the importance of pleiotropic genes. Further studies are warranted to investigate specific shared genetic and environmental factors between obesity and cardiometabolic diseases. [ABSTRACT FROM AUTHOR]

Sample preparation is a critical step to achieve reliable in situ chemical analysis. Sample mounting technique with a tin-based alloy was developed in recent years, which is particularly useful for high-precision volatile analyses by secondary ion mass spectrometry (SIMS). However, the success of this technique is hindered by challenges, such as complex alloy preparation and potential Pb contamination. Herein, we introduce a new Sn–Bi alloy preparation method that may overcome these hurdles and assess its potential as a standard preparation method for in situ volatile and isotope analyses. This new alloy can be manufactured with commercially available pure tin and bismuth metal (atomic Sn : Bi = 42 : 58), and its production requires only a heating plate and clean containers. This ensures its high accessibility to laboratories worldwide. The Pb content of the alloy is dependent on the tin and bismuth used. The material (Sn and Bi) from three different manufacturers were evaluated in this study, resulting in the virtually Pb-free MAC alloy (Pb <0.2 μg g−1). The SIMS U–Pb dating results of the zircon standards (Qinghu, Plešovice, and SA01) are consistent with the recommended values (within error). Furthermore, the mounted samples exhibit satisfactory relief on this alloy, suggesting that this alloy material is appropriate for the analysis of oxygen isotopes. The routine external precision of oxygen isotope ratios is better than 0.30‰ (2sd), on par with that obtained with epoxy mounts. The water background in the SIMS sample chamber can be recovered rapidly after sample transfer from the storage to the sample chamber. Hence, this tin-based alloy is suitable for sample mounting for SIMS volatile and isotope (incl. U–Pb) analyses. [ABSTRACT FROM AUTHOR]

As a complex nonlinear system, the inverted pendulum (IP) system has the characteristics of asymmetry and instability. In this paper, the IP system is controlled by a learned deep neural network (DNN) that directly maps the system states to control commands in an end-to-end style. On the basis of deep reinforcement learning (DRL), the detail reward function (DRF) is designed to guide the DNN learning control strategy, which greatly enhances the pertinence and flexibility of the control. Moreover, a two-phase learning protocol (offline learning phase and online learning phase) is proposed to solve the "real gap" problem of the IP system. Firstly, the DNN learns the offline control strategy based on a simplified IP dynamic model and DRF. Then, a security controller is designed and used on the IP platform to optimize the DNN online. The experimental results demonstrate that the DNN has good robustness to model errors after secondary learning on the platform. When the length of the pendulum is reduced by 25% or increased by 25%, the steady-state error of the pendulum angle is less than 0.05 rad. The error is within the allowable range. The DNN is robust to changes in the length of the pendulum. The DRF and the two-phase learning protocol improve the adaptability of the controller to the complex and variable characteristics of the real platform and provide reference for other learning-based robot control problems. [ABSTRACT FROM AUTHOR]