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Background Both the triglyceride-glucose (TyG) index, a predictor of insulin resistance (IR), and inflammation are risk factors for stroke in hypertensive patients. However, only a handful of studies have coupled the TyG index and inflammation indices to predict stroke risk in hypertensive patients. The C-reactive protein-triglyceride-glucose index (CTI) is a novel marker that comprehensively assesses the severity of IR and inflammation. The present study explored the association between CTI and the risk of stroke in patients with hypertension. Methods A total of 3,834 hypertensive patients without a history of stroke at baseline were recruited from the China Health and Retirement Longitudinal Study (CHARLS). Multivariate Cox regression and restricted cubic spline (RCS) analyses were employed to assess the relationship between CTI and stroke risk in hypertensive patients. Furthermore, the Boruta algorithm was applied to evaluate the importance of CTI and construct prediction models to forecast the incidence of stroke in the study cohort. Results After 7 years of follow-up, the incidence of stroke in hypertensive patients was 9.6% (368 cases). Multivariate Cox regression analysis revealed a 21% increase in stroke risk with an increase in each CTI unit (hazard ratio (HR) = 1.21, 95% confidence interval (CI) = 1.08–1.37). The top quartile group was 66% more likely to have a stroke than the bottom quartile group (HR = 1.66, 95% CI = 1.23–2.25). RCS analysis confirmed a linear relationship between CTI and stroke risk. The Boruta algorithm validated CTI as a crucial indicator of stroke risk. The Support Vector Machine (SVM) survival model exhibited the best predictive performance for stroke risk in hypertensive patients, with an area under the curve (AUC) of 0.956. Conclusions An increase in CTI levels is associated with a higher risk of stroke in hypertensive patients. This study suggests that CTI may emerge as a unique predictive marker for stroke risk.

Background Medical service efficiency is an important indicator for measuring the equity of medical services. Therefore, this study primarily focuses on investigating the spatiotemporal domain to explore both spatial and temporal characteristics, as well as influencing factors that affect medical service efficiency across diverse provinces in China. Methods The super Epsilon-based Measure (EBM) unexpected model has previously been utilized to quantify energy eco-efficiency, carbon emission efficiency, and green development efficiency. However, limited studies have applied this method to assess the efficiency of healthcare services. Therefore, this study investigates the application of the super-EBM-unexpected model in evaluating medical service efficiency, and further integrates spatial econometric models to explore the influencing factors of medical service efficiency and aims to identify potential avenues for improvement. Results The average efficiency of medical services in the 31 provinces of China ranges from 0.6 to 0.7, indicating predominantly low efficiency values. However, economically developed coastal areas exhibit relatively high efficiency levels above 1. Conversely, regions with relatively lower levels of economic development demonstrate lower efficiency rates at approximately 0.3. Evidently, substantial regional disparities exist. For the influencing factors, the enhancement of residents' living standards can effectively foster the medical service efficiency, while residential living standards of nearby areas can also exert an impact in this region. The influence of educational attainment on medical service efficiency exhibits a significant inhibitory effect. Conclusions The majority of China's 31 provinces exhibit suboptimal medical service efficiency, with notable regional disparities. Future policy initiatives should be tailored to address the unique challenges faced by regions with lower levels of economic development, prioritizing enhancements in both the efficacy and quality of their healthcare systems.

Rock mass engineering serves as a critical foundation for infrastructure construction and resource development [...]

A transversely isotropic rock, slate, was utilized to investigate the size effect and anisotropy on its deformation, tensile strength, and failure mechanism. A series of Brazilian tests were conducted on slate samples of six different sizes from 25 to 100 mm in diameter at seven different loading-foliation angles from 0° to 90°. The results indicate that the Young’s modulus in the plane of transverse isotropy increases, while the Young’s modulus and shear modulus perpendicular to the plane of transverse isotropy decrease with specimen size. The tensile strength of the slate increases with increasing loading-foliation angle, the variation of which is well captured by the Nova–Zaninetti criterion. Furthermore, the tensile strength of the slate increases with specimen size at loading-foliation angles from 0° to 45°, while it increases first and then decreases with specimen size at loading-foliation angles from 60° to 90°. A unified size-effect relation including two equations is proposed and verified against the experimental data on slate. The size-effect relation reveals the relationship among the tensile strength, specimen size, and loading-foliation angle for the transversely isotropic rock. Finally, the slate samples exhibit an increased brittle failure with specimen size, which is consistent with the observations in various isotropic rocks. It is also found that the specimen size, loading-foliation angle, and loading configuration together control the failure mechanism of transversely isotropic rocks in the Brazilian test.

Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By constructing the approximation function, we extend the hard-assignment quantization to soft-assignment quantization. Thanks to the differentiable property of the soft-assignment quantization, the product quantization operation can be integrated as a layer in a convolutional neural network, constructing the proposed product quantization network (PQN). Meanwhile, by extending the triplet loss to the asymmetric triplet loss, we directly optimize the retrieval accuracy of the learned representation based on asymmetric similarity measurement. Utilizing PQN, we can learn a discriminative and compact image representation in an end-to-end manner, which further enables a fast and accurate image retrieval. By revisiting residual quantization, we further extend the proposed PQN to residual product quantization network (RPQN). Benefited from the residual learning triggered by residual quantization, RPQN achieves a higher accuracy than PQN using the same computation cost. Moreover, we extend PQN to temporal product quantization network (TPQN) by exploiting temporal consistency in videos to speed up the video retrieval. It integrates frame-wise feature learning, frame-wise features aggregation and video-level feature quantization in a single neural network. Comprehensive experiments conducted on multiple public benchmark datasets demonstrate the state-of-the-art performance of the proposed PQN, RPQN and TPQN in fast image and video retrieval.

Clade A protein phosphatase 2Cs (PP2Cs) are abscisic acid (ABA) co-receptors that block ABA signalling by inhibiting the downstream protein kinases. ABA signalling is activated after PP2Cs are inhibited by ABA-bound PYR/PYL/RCAR ABA receptors (PYLs) in Arabidopsis . However, whether these PP2Cs are regulated by other factors remains unknown. Here, we report that ABI1 (ABA-INSENSITIVE 1) can interact with the U-box E3 ligases PUB12 and PUB13, but is ubiquitinated only when it interacts with ABA receptors in an in vitro assay. A mutant form of ABI1-1 that is unable to interact with PYLs is more stable than the wild-type protein. Both ABI1 degradation and all tested ABA responses are reduced in pub12 pub13 mutants compared with the wild type. Introducing the abi1-3 loss-of-function mutation into pub12 pub13 mutant recovers the ABA-insensitive phenotypes of the pub12 pub13 mutant. We thus uncover an important regulatory mechanism for regulating ABI1 levels by PUB12 and PUB13. Signaling by the plant hormone abscisic acid (ABA) is regulated by the ABI1 protein phosphatase. Here Kong et al. propose that ABA signaling is fine-tuned by ubiquitination of ABI1 which promotes ABI degradation in response to ABA.

Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi’an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies.

This article discussed air quality changes in the Beijing-Tianjin-Tangshan (BTT) region. The air quality index (AQI) values, and the concentrations of PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 in the BTT region during the COVID-19 outbreak in 2020 were, respectively, 79.4, 47.2 µg m −3 , 73.4 µg m −3 , 10.3 µg m −3 , 0.87 mg m −3 , 33.6 µg m −3 , and 90.7 µg m −3 . However, they were, respectively, 102.7, 61.4 µg m −3 , 121.0 µg m −3 , 9.0 µg m −3 , 0.88 mg m −3 , 40.1 µg m −3 , and 84.0 µg m −3 during the same period in 2021, which is an increase of 29.2%, 30.1%, 64.8%, –12.9%, 1.94 %, 19.5%, and –7.4% compared with the values in 2020. The combined proportions of grade I and grade II during the COVID-19 outbreak in 2020 were 16.7% higher than those in the same period in 2021, so the air quality has deteriorated rapidly from 2020 to the post-COVID era in 2021. The possible reasons for poorer air quality are that the frequency of dusty weather and air pollutant discharge has increased, and meteorological conditions have been relatively unfavorable. The average AQI values, and concentrations of PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 during the post-COVID period in 2021 respectively decreased by 14.8%, 29.0%, 14.6%, 22.5%, 37.4%, 14.8%, and 8.7%, compared with those in 2020. It is also worth noting that all the changes in air pollution during the post-COVID era have been consistent. The combined proportions of grade I and grade II during post-COVID period in 2021 were 18.4% higher than those during the same period of 2020, which indicates that the air quality during post-COVID 2021 has obviously improved compared with those in the same period of 2020. The possible reasons are a series of clean air policies and clean air actions, as well as favorable atmospheric diffusion conditions. These results indicate that clean air policies play a very important role in improving air quality.

Sulfate aerosols exert profound impacts on human and ecosystem health, weather, and climate, but their formation mechanism remains uncertain. Atmospheric models consistently underpredict sulfate levels under diverse environmental conditions. From atmospheric measurements in two Chinese megacities and complementary laboratory experiments, we show that the aqueous oxidation of SO₂ by NO₂ is key to efficient sulfate formation but is only feasible under two atmospheric conditions: on fine aerosols with high relative humidity and NH₃ neutralization or under cloud conditions. Under polluted environments, this SO₂ oxidation process leads to large sulfate production rates and promotes formation of nitrate and organic matter on aqueous particles, exacerbating severe haze development. Effective haze mitigation is achievable by intervening in the sulfate formation process with enforced NH₃ and NO₂ control measures. In addition to explaining the polluted episodes currently occurring in China and during the 1952 London Fog, this sulfate production mechanism is widespread, and our results suggest a way to tackle this growing problem in China and much of the developing world.

Extracellular vesicles (EVs) are effective mediators of intercellular communications between enterocytes and immune cells. The current study showed that EVs isolated from mouse and human intestinal organoids modulated inflammatory responses of various immune cells including mouse bone-marrow derived-macrophages, dendritic cells, microglia cells, and human monocytes. EVs suppressed LPS-elicited cytokine production in these cells while morphine abolished EVs' immune modulatory effects. Microarray analysis showed that various microRNAs, especially Let-7, contributed to EV-mediated immune modulation. Using murine models, we showed that injection of EVs derived from intestinal organoids reduced endotoxin-induced systemic inflammation and alleviated the symptoms of DSS-induced colitis. EVs derived from morphine-treated organoids failed to suppress the immune response in both these models. Our study suggests that EVs derived from intestinal crypt cells play crucial roles in maintaining host homeostasis and opioid use is a risk factor for exacerbating inflammation in patients with inflammatory diseases such as sepsis and colitis.