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Abstract Objective Small dense low-density lipoprotein cholesterol (sdLDL-C), as an emerging atherogenic factor of cardiovascular diseases, requires additional tests. We aimed to establish a sdLDL-C equation using standard lipid profile and evaluate its capacity of identifying the residual cardiovascular risk beyond LDL-C and apolipoprotein B (ApoB). Methods This cross-sectional study included 25 435 participants from Health Management Cohort and 11 628 participants from China Health and Retirement Longitudinal Study (CHARLS) to construct and evaluate the sdLDL-C equation by least-squares regression model. The equation for sdLDL-C depended on low-density lipoprotein cholesterol (LDL-C) and an interaction term between LDL-C and the natural log of triglycerides (TG). Results The modified equation (sdLDL-C = 0.14*ln(TG)*LDL-C − 0.45*LDL-C + 10.88) was more accurate than the original equation in validation set (slope = 0.783 vs. 0.776, MAD = 5.228 vs. 5.396). Using the 80th percentile (50 mg/dL) as a risk-enhancer rule for sdLDL-C, accuracy of the modified equation was higher than the original equation in validation set (90.47% vs. 89.73%). The estimated sdLDL-C identified an additional proportion of high-risk individuals in BHMC (4.93%) and CHARLS (1.84%). Conclusion The newly developed equation in our study provided an accurate tool for estimating sdLDL-C level among the Chinese population as a potential cardiovascular risk-enhancer.

Background The atherogenic effect of remnant cholesterol is being increasingly acknowledged. This study aimed to explore the association of discordance between remnant cholesterol and low‐density lipoprotein cholesterol with stroke onset using 2 Chinese national cohorts. Methods and Results We studied 11 139 participants from CHARLS (China Health and Retirement Longitudinal Study) and 5993 participants from CHNS (China Health and Nutrition Survey) aged 45 years or older. The discordance between remnant cholesterol and low‐density lipoprotein cholesterol was defined using the difference in percentile units (>15 units). There were 988 (8.9%) and 128 (2.1%) stroke events reported during follow‐up in the 2 cohorts. Elevated remnant cholesterol was significantly associated with a higher risk of total stroke in 2 cohorts. After adjusting for remnant cholesterol level, the discordantly high remnant cholesterol group was significantly associated with an increased stroke risk (CHARLS: subdistribution hazard ratio [sHR], 1.31 [95 CI, 1.10–1.55]; CHNS: sHR, 1.84 [95 CI, 1.15–3.08]) compared with the discordantly low group. Consistent results were shown even among those with optimal low‐density lipoprotein cholesterol level. Conclusions The discordance between remnant cholesterol and low‐density lipoprotein cholesterol, representing the intraindividual discrepancy, is significantly associated with stroke onset among Chinese adults.

Abstract Background Triglyceride and glucose (TyG) index, a surrogate marker of insulin resistance, has been validated as a predictor of cardiovascular disease. However, effects of TyG-related indices combined with obesity markers on cardiovascular diseases remained unknown. We aimed to investigate the associations between TyG index and modified TyG indices with new-onset cardiovascular disease and the time-dependent predictive capacity using a national representative cohort. Methods This study is a retrospective observational cohort study using data from China Health and Retirement Longitudinal Study (CHARLS) of 7 115 participants. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. The modified TyG indices were developed combining TyG with body mass index (BMI), waist circumference (WC) and waist-to‐height ratio (WHtR). We used adjusted Cox proportional hazards regression to analyze the association and predictive capacity based on hazard ratio (HR) and Harrell’s C‐index. Results Over a 7-year follow‐up period, 2136 participants developed cardiovascular disease, including 1633 cases of coronary heart disease and 719 cases of stroke. Compared with the lowest tertile group, the adjusted HR (95% CI) for new-onset cardiovascular disease in the highest tertile for TyG, TyG-BMI, TyG-WC, and TyG-WHtR were 1.215 (1.088–1.356), 1.073 (0.967–1.191), 1.078 (0.970–1.198), and 1.112 (1.002–1.235), respectively. The C‐indices of TyG index for cardiovascular disease onset were higher than other modified TyG indices. Similar results were observed for coronary heart disease and stroke. Conclusion TyG and TyG-WhtR were significantly associated with new-onset cardiovascular diseases, and TyG outperformed the modified TyG indices to identify individuals at risk of incident cardiovascular event.

Abstract Background Both the triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, and systemic inflammation are predictors of cardiovascular diseases; however, little is known about the coexposures and relative contributions of TyG index and inflammation to cardiovascular diseases. Using the nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted longitudinal analyses to evaluate the joint and mutual associations of the TyG index and high-sensitivity C-reactive protein (hsCRP) with cardiovascular events in middle-aged and older Chinese population. Methods This study comprised 8 658 participants aged at least 45 years from the CHARLS 2011 who are free of cardiovascular diseases at baseline. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Cardiovascular events were defined as the presence of physician-diagnosed heart disease and/or stroke followed until 2018.We performed adjusted Cox proportional hazards regression and mediation analyses. Results The mean age of the participants was 58.6 ± 9.0 years, and 3988 (46.1%) were females. During a maximum follow-up of 7.0 years, 2606 (30.1%) people developed cardiovascular diseases, including 2012 (23.2%) cases of heart diseases and 848 (9.8%) cases of stroke. Compared with people with a lower TyG index (

In this article, the arbitrary‐oriented object detection problem with application in robotic grasping is addressed. A novel Jensen–Shannon divergence (JSD)– You Only Look Once (YOLO) model is proposed, which enables real‐time grasp detection with high performance. The one‐stage object detection network YOLOv5 is modified with a decoupled head, which solves the angle classification problem and rectangle parameter regression problem separately, such that the YOLOv5 network is applicable for robotic grasping and the detection accuracy is significantly improved. A circular smooth label angle classification method is proposed to tackle the boundary discontinuity problem in angle regression, and the periodicity of the angle prediction is guaranteed. A novel Jensen–Shannon intersection of union is designed to calculate the intersection over union of oriented rectangles, which aims to better measure the discrepancies between the prediction and the ground truth and to avoid the singularity problem when two rectangles are not overlapped. Extensive evaluation on the Cornell and visual manipulation relationship dataset datasets demonstrates the effectiveness of the JSD–YOLO model in general robotic grasp operations, with 99.7% and 95.7% image‐wise split accuracy, respectively.

PurposeThe debate over the causal and longitudinal association between cystatin C and stroke in older adults persists. Our aim was to assess the link between cystatin C levels, both measured and genetically predicted, and stroke risk.MethodsThis study employed a retrospective cohort design using samples of the China Health and Retirement Longitudinal Study (CHARLS), which is a nationally representative cohort recruiting individuals aged 45 years or above. A multivariate logistic model and the two-sample Mendelian randomization framework were used to investigate the longitudinal and genetically predicted effect of serum cystatin C on stroke.ResultsThe study population had a mean age of 59.6 (SD ±9.5), with 2,996 (46.1%) women. After adjusting for confounding factors, compared to those in the first quartile of cystatin C, those in the last quartile had the greatest risk of stroke incidence [odds ratio (OR), 1.380; 95% confidence interval (CI), 1.046–1.825]. The Mendelian randomization analysis showed that a genetically predicted cystatin C level was positively associated with total stroke (OR by inverse variance-weighted method, 1.114; 95% CI, 1.041–1.192).ConclusionsThis national cohort study suggests that higher serum cystatin C is associated with an increased risk of total stroke, which is further supported by Mendelian randomization.

This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data. To do this, the cycle generative adversarial network with special symmetric generation countermeasure network cycle mechanism and "cycle consistency loss" was selected. We improved the performance of the network learning and training by optimizing the network structure. Next, based on the optimized shallow profile sample set training network, random noise was removed from the shallow profile data and the signal-to-noise ratio of the data was improved. The effectiveness and adaptability of this method for denoising shallow profile data were verified by trial calculations of experimental and actual data and by comparison with the traditional band-pass filtering method.

This paper addresses the image-based visual servoing (IBVS) control problem with an uncalibrated camera, unknown dynamics, and constraints. A novel data-driven uncalibrated IBVS (UIBVS) strategy is proposed, incorporated with the Koopman-based model predictive control (KMPC) algorithm and the adaptive robust Kalman filter (ARKF). First, to alleviate the need for calibration of the camera’s intrinsic and extrinsic parameters, the ARKF with an adaptive factor is utilized to estimate the image Jacobian matrix online, thereby eliminating the laborious camera calibration procedures and improving robustness against camera disturbances. Then, a data-driven MPC strategy is proposed, wherein the unknown nonlinear dynamic model is learned using the Koopman operator theory, resulting in a linear Koopman prediction model. Only input–output data are used to construct the prediction model, and hence, the proposed approach is robust against model uncertainties. Furthermore, with a symmetric quadratic cost function, the proposed approach solves the quadratic programming problem online, and visibility constraints as well as joint torque constraints are taken into account. As a result, the proposed KMPC scheme can be implemented in real time, and the UIBVS performance degradation which arises from the control torque constraints can be avoided. Simulations and comparisons for a 2-DOF robotic manipulator demonstrate the feasibility of the proposed approach. Simulation results further validate that the computation time of the proposed approach is comparable to the one of kinematic-based methods.

The development of supervised deep learning technology in seismology and related fields has been restricted due to the lack of training sets. A large amount of unlabeled data is recorded in seismic exploration, and their application to network training is difficult, e.g., fault identification. To solve this problem, herein, we propose an end-to-end training data set generative adversarial network Fault2SeisGAN. This network can expand limited labeled datasets to improve the performance of other neural networks. In the proposed method, the Seis-Loss is used to constrain horizon and amplitude information, Fault-Loss is used to constrain fault location information, and the Wasserstein distance is added to stabilize the network training to generate seismic amplitude data with fault location labels. A new fault identification network model was trained with a combination of expansion and original data, and the model was tested using actual seismic data. The results show that the use of the expanded dataset generated in this study improves the performance of the deep neural network with respect to seismic data prediction. Our method solves the shortage of training data set problem caused by the application of deep learning technology in seismology to a certain extent, improves the performance of neural networks, and promotes the development of deep learning technology in seismology.

Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.

The organic anion of the title compound, {[Na(C15H9O10S)(H2O)2]·H2O}n {systematic name: poly[[diaqua[μ-2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxy-4-oxo-4H-chromene-8-sulfonato]sodium] monohydrate]}, has a nearly planar structure. The Na atom is six-coordinated by O atoms, two from water molecules and four from the anion. The dihedral angle between the ring systems in the anion is 10.1 (1)°. Intramolecular O—H...S and O—H...O interactions occur. In the crystal structure, an extensive network of classical intermolecular O—H...S and O—H...O hydrogen bonds forms layers along the c axis.