Nonlinear Relationship Research Articles

Association of dust exposure with anxiety and depression in the occupational population: The important role of sleep duration

BackgroundMental health issues, particularly anxiety and depression, are increasingly prevalent among the occupational population. Environmental factors, such as dust exposure, may contribute to the worsening of these symptoms. While previous studies have examined the association between dust exposure and mental health, the moderating effect of sleep duration on this link in occupational settings remains under-explored.MethodsThis study was conducted from July to October 2023 at The Fifth People's Hospital of Ningxia and recruited dust-expose d occupational workers from different coal enterprises. After a series of screening 1274 valid subjects were finally included. Binary logistic regression was used to explore the association of dust exposure with anxiety and depression. Generalized additive models (GAM) were constructed to explore the nonlinear relationships between dust exposure duration, sleep duration, and mental health outcomes. Mediating variable contributions were isolated using the Karlson-Holm-Breen (KHB) method and mediated effects models were fitted.ResultsThe prevalence of anxiety and depression was found to be 6.44% and 4.24%, respectively. Dust exposure duration was positively associated with both anxiety and depression, while stratification by monthly income level had no significant effect. The contribution of sleep duration to the indirect effect accounted for 21.76% and 43.54% of the total effect of dust exposure duration on anxiety and depression scores, respectively. A nonlinear relationship was observed between dust exposure duration, sleep duration, and the scores of anxiety and depression. In the mediation analysis, shorter sleep duration explained 12.87% of the association between dust exposure duration and anxiety scores.ConclusionsDust exposure duration was associated with anxiety and depression, with a nonlinear relationship between them. Changes in sleep duration may effectively influence mental health problems in occupationally dust-exposed populations.

Scanning Thermal Microscopy Method for Self-Heating in Nonlinear Devices and Application to Filamentary Resistive Random-Access Memory.

Devices with a highly nonlinear resistance-voltage relationship are candidates for neuromorphic computing, which can be achieved by highly temperature dependent processes like ion migration. To explore the thermal properties of such devices, Scanning Thermal Microscopy (SThM) can be employed. However, due to the nonlinearity, the high resolution and quantitative method of AC-modulated SThM cannot readily be used. To this end, an extended nonequilibrium scheme for temperature measurement using SThM is proposed, with which the self-heating of nonlinear devices is studied without the need for calibrating the tip-sample contact for a specific material combination, geometry or roughness. Both a DC and an AC voltage are applied to the device, triggering a periodic temperature rise, which enables the simultaneous calculation of the tip-sample thermal resistance and the device temperature rise. The method is applied to HfO2-based RRAM devices, in which the kinetic processes of filamentary switching are governed by temperature. We image temperature and propagation of thermal waves and extract properties like the number of current filaments, thermal confinement and thermal cross-talk.

Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.

High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients. Nine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app. The random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750-0.780) and a Brier score of 0.183 (95% CI: 0.175-0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/. An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.

Reassessing the Role of Low PSA in Prognosis Across Grades of Prostate Cancer: A Cohort Study.

Prior studies have concentrated exclusively on how different prostate-specific antigen (PSA) levels affect the prognosis of high-grade prostate cancer (PCa), often overlooking the prognosis of low-grade PCa. The present cohort study included individuals diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2021. The all-cause mortality (ACM) and prostate cancer-specific mortality (PCSM) for each treatment group was calculated stratified by the four PSA levels (≤ 4.0, 4.1-10.0, 10.1-20.0, and > 20.0 ng/mL). Fine and Gray competing-risks analyses were conducted to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). Cox proportional hazards regression analyses using PSA as a continuous variable with restricted cubic splines (RCS) were conducted to allow for potential nonlinear relationships. This study encompassed 416,825 male patients diagnosed with PCa. Compared to individuals with PSA value between 4.1 and 10.0 ng/mL, a significant association between low levels of PSA (≤ 4.0 ng/mL) and an increased risk of ACM (AHR = 1.15, 95% CI: 1.12-1.19; p < 0.001) and PCSM (AHR = 1.49, 95% CI: 1.38-1.61; p < 0.001) was observed. Additionally, the increased risk of ACM (AHR = 1.35, 95% CI: 1.29-1.40; p < 0.001) and PCSM (AHR = 1.84, 95% CI: 1.67-2.02; p < 0.001) are more pronounced within the first 5 years post-diagnosis. In most subgroups, similar results were observed. The RCS curves further corroborated the correlation between PSA value and the risk of mortality. Low PSA levels are notably linked to a heightened risk for both ACM and PCSM, irrespective of the grade of PCa being high or low. There is a need to initiate new studies that tackle novel diagnostics and therapeutics.

Association between lactate dehydrogenase levels and all-cause mortality in ICU patients with heart failure: a retrospective analysis of the MIMIC-IV database

BackgroundHeart failure (HF) patients admitted to the intensive care unit (ICU) often face high short-term mortality rates. This study aims to investigate the relationship between lactate dehydrogenase (LDH) levels and all-cause mortality in critically ill patients with HF.MethodsData from the MIMIC-IV database were extracted for subjects eligible for HF diagnosis. We utilized the restricted cubic spline (RCS) method, Kaplan-Meier (K-M) survival curves, and Cox regression analysis to assess the association between lactate dehydrogenase (LDH) levels and all-cause mortality in HF patients. Overlap weighting (OW) and subgroup analysis were employed to enhance the robustness and reliability of the study.ResultsA total of 3,065 subjects were enrolled in this study. RCS analysis revealed a nonlinear relationship between LDH levels and the risk of all-cause mortality in critically ill patients with HF, with a hazard ratio (HR) > 1 when LDH exceeded 315 U/L. The K-M survival curve indicated lower survival rates and shorter survival times in subjects with LDH ≥ 315 U/L. Elevated LDH levels were independently associated with increased in-hospital and 1-year mortality rates, with adjusted HR of 1.39 (95% CI: 1.16, 1.67) and 1.29 (95% CI: 1.14, 1.45), respectively. The results remained consistently robust in the OW analyses.ConclusionsElevated LDH levels were significantly associated with an increased risk of all-cause mortality in ICU-admitted HF patients. Further randomized trials are needed to confirm this association.

Sequential and simultaneous distance-based dimension reduction

This paper introduces a method called Sequential and Simultaneous Distance-based Dimension Reduction (S2D2R) that performs simultaneous dimension reduction for a pair of random vectors based on distance covariance (dCov). Compared with sufficient dimension reduction (SDR) and canonical correlation analysis (CCA)-based approaches, S2D2R is a model-free approach that does not impose dimensional or distributional restrictions on variables and is more sensitive to nonlinear relationships. Theoretically, we establish a non-asymptotic error bound to guarantee the performance of S2D2R. Numerically, S2D2R performs comparable to or better than other state-of-the-art algorithms and is computationally faster. All codes of our S2D2R method can be found on GitHub https://github.com/Yijin911/S2D2R.git, including an R package named S2D2R.

Correlation Analysis Between the Geriatric Nutritional Risk Index Hip Fracture in Male: Based on the National Health and Nutrition Examination Survey Database.

Hip fractures (HFs) are common in elderly patients and are associated with high mortality rates and functional impairment. Malnutrition has been shown to negatively impact postoperative survival rates in HF patients. However, the relationship between the Geriatric Nutrition Risk Index (GNRI) and the risk of HF remains unclear. This study aims to evaluate the association between GNRI and HF risk, with a particular focus on the elderly male population.We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) for the periods 2007-2010, 2013-2014, and 2017-2018. Through multivariate regression analysis, we assessed the association between GNRI and HF and performed stratified and subgroup analyses to further explore this relationship. Additionally, we utilized restricted cubic splines (RCSs) to investigate the potential nonlinear relationship between GNRI and HF risk.The study found that gender significantly influenced the relationship between GNRI and HF (p for interaction =0.002). In males, GNRI was significantly negatively associated with the risk of HF (OR <1, p<0.05). RCS analysis showed that the relationship between GNRI and HF risk in elderly males might be linearly negative. The critical threshold for GNRI was identified as 104.14, beyond which the risk of HF significantly decreased.This study demonstrates a linear negative correlation between GNRI and the risk of HF in elderly males, with a GNRI of 104.14 identified as the critical threshold for predicting the risk of hip fractures.

The link between the atherogenic index of plasma and the risk of hypertension: Analysis from NHANES 2017-2020.

The atherogenic index of plasma (AIP) is a newly identified metabolic marker for atherosclerosis. However, there are inconsistent conclusions regarding the relationship between AIP and hypertension. The study subjects were sourced from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020. Logistic regression analyses were employed to explore the correlation between AIP and hypertension. The value of AIP in predicting hypertension was assessed using ROC curves, and their nonlinear relationship was described using restricted cubic splines (RCS). Subgroup analyses, interactions, and sensitivity analyses were also conducted. The study included 7,067 participants who were sourced from the NHANES database. There were 2723 participants diagnosed hypertension. We observed a notable correlation between AIP and hypertension (OR:1.89, 95%CI: 1.11-3.22, P = 0.019). ROC curve showed AIP had a good predictive value for the onset of hypertension, with the AUC of 0.652 (95% CI:0.639-0.664, p<0.001). RCS found that there existed a nonlinear association between AIP and the incidence of hypertension (p<0.001). Even after excluding individuals under the age of 40 years old, the results still indicate a strong association between AIP and hypertension. AIP may serve as an early biological marker for identifying hypertension, facilitating early screening of susceptible populations.

Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application

The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. Firstly, an atmospheric dispersion model is utilized to predict the distribution concentration of VOCs emitted by enterprises in the park at the target monitoring stations based on the ozone generation mechanism. Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. The model integrates atmospheric pollutants and related meteorological data to explore the nonlinear influence relationship of ozone formation in depth. The performance of the model is validated by the comprehensive evaluation indexes of R2, MAE and RMSE, and the results show that the present model outperforms the IPSO-CNN and SHAP-PSO-CNN models with the performance indexes of R2 of 0.9492, MAE of 0.0061 mg/m3 and RMSE of 0.0084 mg/m3. This study not only advances the understanding of ozone pollution formation mechanisms, but also provides an assessment of the impact of VOCs emissions from enterprises in the park, which provides empirical support for environmental management.

Study of Seepage Laws of Deep Rock Mass Under Different Water Temperatures Based on Porous Media

Deep coal mining is faced with high temperature, high seepage pressure, and high ground stress, and there is a complex nonlinear coupling relationship between the temperature of water in deep rock mass and its seepage. Based on the background of deep mining in Zhaolou Coal Mine in Shandong Province, China, the hydraulic conductivity of artificial rock samples with similar materials was tested indoors under different water temperatures of 30~80 °C. On the basis of deep rock samples collected in the field, the hydraulic conductivity has a nonlinear positive correlation with the rise of water temperature. The difference in hydraulic conductivity at the highest and lowest temperatures is two to three times. By means of multi-physics coupling finite element software (COMSOL Multiphysics, COMSOL Inc., Stockholm, Sweden), combined with the actual geological background, the regularity was found to be consistent with the laboratory experiment and further proves that the inlet pressure has no effect on the hydraulic conductivity. Subsequent analyses revealed that the influence of temperature on the seepage field is mainly reflected in the change of fluid kinematic viscosity with temperature, which causes the change in the hydraulic conductivity. According to the formula, the hydraulic conductivity of the rock at 80 °C is 2.31 times higher than its hydraulic conductivity at 30 °C, which is matched by the indoor test results. The engineering performance is that as the temperature of the deep rock body increases, the ability of water to penetrate rocks increases, and the water inflow of the working face increases. The results can be applied to the prevention of water hazard threats in deep coal mining.

A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R2 compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R2 values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability.

The lean body mass to visceral fat mass ratio is negatively associated with cardiometabolic disorders: a cross-sectional study

The literature has documented conflicting and inconsistent associations between muscle-to-fat ratios and metabolic diseases. Additionally, different adipose tissues can have contrasting effects, with visceral adipose tissue being identified as particularly harmful. This study aimed to explore the relationship between the ratio of the lean mass index (LMI) to the visceral fat mass index (VFMI) and cardiometabolic disorders, including dyslipidemia, hypertension, and diabetes, as previous research on this topic is lacking. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States and included 10,867 individuals. Logistic regression was employed to explore the association between LMI/VFMI and cardiometabolic disorders. Generalized additive models were utilized to examine the nonlinear relationships between variables. Data analysis revealed a consistent inverse association between ln(LMI/VFMI) and dyslipidemia, hypertension, and diabetes. Each 2.7-fold increase in LMI/VFMI (one unit of ln[LMI/VFMI]) was associated with lower odds ratios (ORs) for these conditions. In men, the ORs were 0.21 (95% CI 0.17–0.25) for dyslipidemia, 0.37 (95% CI 0.30–0.45) for hypertension, and 0.16 (95% CI 0.10–0.23) for diabetes. Similarly, in women, the ORs were 0.22 (95% CI 0.19–0.26), 0.51 (95% CI 0.42–0.61), and 0.19 (95% CI 0.13–0.27). Quartile analysis showed that participants in the highest quartile (Q4) had significantly lower ORs compared to those in the lowest quartile (Q1). In men, Q4 ORs were 0.18 (95% CI 0.14–0.23) for dyslipidemia, 0.30 (95% CI 0.23–0.39) for hypertension, and 0.11 (95% CI 0.06–0.20) for diabetes. In women, Q4 ORs were 0.12 (95% CI 0.10–0.15), 0.39 (95% CI 0.29–0.52), and 0.12 (95% CI 0.06–0.25), respectively. Dyslipidemia and diabetes demonstrated nonlinear patterns, while a linear association was found for hypertension. Subgroup analyses across various characteristics confirmed these findings with no substantial directional changes. Maintaining an appropriate ratio of LMI to VFMI may be associated with favorable metabolic health.

Effect of Blue–Green Infrastructure in Mitigating Microenvironmental Heat Islands: Field- and Simulation-Based Insights

Urban heat island (UHI) effects, intensified by urbanization and environmental changes, are critical challenges to urban thermal comfort and sustainability. This study investigates the combined role of water bodies and vegetation in mitigating microclimatic heat island effects within a campus area, focusing on their cooling impacts and interactions. Using field measurements and numerical simulations, the research evaluates the cooling effects and wind flow modifications induced by greenery and water bodies in the surrounding environment. The findings demonstrate that both vegetation and water bodies provide a significant cooling effect, reducing temperatures by 0–6 K in downstream regions, with the impact being more pronounced closer to the blue–green spaces. Furthermore, the combined application of water bodies and vegetation offers enhanced cooling effects; however, their influence on the thermal environment exhibits a nonlinear relationship. These results underscore the importance of strategic blue–green infrastructure planning in mitigating UHI effects and optimizing urban thermal environments.

Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors. The MNN, by simulating the memory effect of biological neurons, effectively captures the complex nonlinear relationships within vulnerability data, thereby enhancing the data insight capabilities of the network security management system. Subsequently, structural optimization and parameter adjustments are made to the TCNN model, incorporating residual connections and attention mechanisms to improve its classification performance, making it more adaptable to the dynamically changing network security environment. Through data preprocessing, feature extraction, and model training, this study conducts experimental validation on a public vulnerability dataset. The experimental results indicate that: The MNN model demonstrates excellent performance across evaluation metrics such as Accuracy (ACC), Precision (P), Recall (R), and F1 Score, achieving an ACC of 89.5%, P of 90.2%, R of 88.7%, and F1 of 89.4%. The improved TCNN model shows even more outstanding performance on the aforementioned evaluation metrics. After structural optimization and parameter adjustments, the TCNN model's ACC increases to 93.8%, significantly higher than the MNN model. The P value also improves, reaching 91.5%, indicating enhanced capability in reducing false positives and improving vulnerability identification accuracy. The integrated classification system, leveraging the strengths of both the MNN and improved TCNN models, achieves an ACC of 95.2%. This improvement not only demonstrates the system's superior capability in accurately classifying vulnerability data but also proves the synergistic effect of MNN and TCNN models in addressing complex network security environments. The comprehensive classification system proposed in this study significantly enhances the classification performance of computer vulnerabilities, providing robust technical support for network security management. The system exhibits higher accuracy and stability in handling complex vulnerability datasets, making it highly valuable for practical applications and research.

A Novel Reinforcement Learning Algorithm-Based Control Strategy for Grid-Configured Inverters

To address the power oscillation problem due to the introduction of inertia and damping, this paper proposes a new deep reinforcement learning algorithm based on the SD3 (Softmax Deep Double Deterministic policy gradients) algorithm for grid configuration inverter control strategy to compensate for the loss of inertia and damping in the grid. Virtual synchronous generator control, as a typical grid configuration technique, inevitably brings stability problems while providing inertia and damping support to the grid. In this paper, we first analyze the nonlinear relationship between inertia and angular velocity, and find the key parameters to maintain the power stability; then, we migrate the deep reinforcement learning strategy, and design the control strategy applicable to the virtual synchronous generator; finally, through the adaption of the key parameters, we combine the control strategy with the grid-connected inverter, and solve the problem of the excessive grid-connected power oscillation of the inverter. The effectiveness and accuracy of the control method compared with other algorithms are verified by building a simulation model in MATLAB/Simulink, which realizes the purpose of reducing power oscillation.

Association between blood test indicators and atrial fibrillation in elderly patients aged 65 and above in the Central Jiangsu region: a cross-sectional study

ObjectivesThe aim of this study was to explore the correlation between blood test indicators and Atrial Fibrillation (AF) in Individuals Aged 65 and Older in Yangzhou, Jiangsu.MethodsFrom January 1, 2019, to August 31, 2023, an epidemiological cross-sectional survey was conducted among the elderly population undergoing health check-ups at Northern Jiangsu People’s Hospital in Jiangsu Province. Patients diagnosed with AF after a 12-lead electrocardiogram were included in the case group, and non-AF individuals matched by age and gender in a 1:4 frequency ratio were included in the control group. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select important variables from routine blood tests and biochemical indicators and their derived indicators (such as TyG, TyG-BMI, TG/HDL-c, RAR, NLR, MHR). Based on the selected variables, participants were divided into four groups (Q1 ~ Q4), and multifactorial Logistic regression analysis, restricted cubic spline regression, gender subgroup analysis and Receiver Operating Characteristic (ROC) curve were used to explore the relationship between the relevant variables and AF.ResultsA total of 5,879 elderly individuals over the age of 65 were included in the study, with a prevalence of AF of 2.96% (174/5,879). The prevalence of AF in the overall population, as well as in male and female populations, showed a continuous increasing trend with age (P for trend < 0.001). A total of 696 individuals without AF after matching served as the control group, and LASSO regression identified albumin, direct bilirubin, and uric acid as three significant indicators. After adjusting for relevant confounding factors, lower levels of albumin, and higher levels of direct bilirubin and uric acid were significantly associated with the occurrence of AF (P < 0.05). Gender subgroup analysis revealed that in the elderly female population, albumin was not significantly associated with AF (P > 0.05), while direct bilirubin and uric acid were significantly associated with AF (P < 0.05). In the male population, albumin, direct bilirubin, and uric acid were significantly associated with AF (P < 0.05). Restricted cubic spline regression analysis showed a significant nonlinear relationship between direct bilirubin and AF (P for nonlinear < 0.001). The ROC curve analysis indicates that albumin, direct bilirubin, and uric acid all have good association strength with AF in elderly patients, with direct bilirubin showing the strongest association effect (AUC (95% CI) = 0.728 (0.686, 0.769)).ConclusionsLow levels of albumin, high levels of direct bilirubin, and uric acid are all significantly associated with AF in the elderly population of the Central Jiangsu region. The conclusions of this study need further validation with a larger sample size.

Triglyceride-glucose index in predicting the risk of new-onset diabetes in the general population aged 45 years and older: a national prospective cohort study

ObjectiveInsulin resistance (IR) is often present in diabetes, which imposes a heavy burden on the prevention and treatment of diabetes. Triglyceride glucose index (TyG) is simple, reliable and reproducible in detecting IR, and has great advantages in predicting the risk of diabetes. The aim of this study was to analyze the potential association between TyG and the risk of diabetes in Chinese middle-aged and older adults using a prospective cohort study design.MethodsThis study used longitudinal data from five waves of the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, 2018, and 2020, involving 5886 participants. We used Cox proportional risk regression modeling to investigate the association between TyG index and the risk of new-onset diabetes, and decision tree analysis to identify high-risk groups for diabetes. Finally, ROC curves were applied in order to construct a predictive model for diabetes.ResultsA total of 1054 (17.9%) participants developed diabetes throughout the 9-year follow-up. Our study utilized a multivariate Cox proportional risk regression model and found a significant correlation between TyG index and diabetes risk. The analysis also revealed a nonlinear relationship between TyG index and diabetes risk.Receiver Operating Characteristic(ROC) curve analysis showed that the Area under the curve(AUC) area of TyG index in predicting the risk of new-onset diabetes was 0.652 (P < 0.05).ConclusionsTyG index can be used as a risk factor for predicting new-onset diabetes in the middle-aged and elderly population in China. In addition, there was a nonlinear relationship between TyG index and diabetes. Improving insulin resistance by regulating glucose and lipid metabolism plays an important role in the primary prevention of diabetes.

Detection and characterization of scale-invariant behaviour and stochastic downscaling of terrestrial water storage anomalies from GRACE and GPS

ABSTRACT Gravity Recovery and Climate Experiments (GRACE) based terrestrial water storage anomalies (TWSA) are used to characterize large-scale hydrology and to estimate regional groundwater depletion around the globe. Due to their 0.25° resolution, GRACE TWSA do not capture fine-resolution variations in TWSA and can miss the extreme TWSA values within a region. This study aims to develop a stochastic downscaling method that relies only on TWSA data and generates realistic statistical properties of fine resolution (0.0625°) TWSA patterns. It is hypothesized that TWSA are scale invariant, which allows the statistical properties of fine scale variations to be inferred from those of coarse scale variations. Three study regions are considered including the Central Amazon in Brazil, the Sindh province in Pakistan, and a region around California, U.S.A. For each region, the relationships between the TWSA moments and the spatial resolution are examined for resolutions between 0.25° and 4°. Nonlinear relationships between scaling exponents and the moment orders indicate that multifractality occurs for the three regions, but the multifractality differs between the three regions and between wet and dry conditions. Based on these results, a multiplicative random cascade model is developed to downscale the TWSA. When the model is used to downscale to 0.0625°, it can produce wider ranges of TWSA within the region than the coarse resolution input.

The optimal annual case volume for acute type A aortic dissection surgery in relation to long-term outcomes.

Previous analyses of the volume-outcome relationship have focused on short-term outcomes such as early mortality. The current study aims to update a novel statistical methodology, facilitating the evaluation of the relation between procedural volume and time-to-event outcomes such as long-term survival, using surgery for acute type A aortic dissection as an illustrative example. This study employed an existing dataset of type A dissection outcomes, retrieved from literature. Studies were included when reporting on annual case load and long-term survival, which served as the primary outcome of interest. Individual patient data were reconstructed from the included studies, and a hazard ratio was determined per study in relation to overall survival, after which the calculated hazard ratios were incorporated in a restricted cubic spline model, facilitating the application of the elbow-method. Fifty-two studies were included (n = 14878 patients), with a median follow-up of 5 years. One-, 3-, 5-, and 10-year survival of the overall cohort were 82% (95% CI 82-83%), 79% (95% CI 78-80%), 74% (95% CI 74-75%), and 60% (95% CI 59-62%) respectively. A significant non-linear volume-outcome relation for long-term survival was observed in both the unadjusted and adjusted analyses (p = 0.030 and p = 0.002), with an optimal annual case load of 32 cases/year (95% CI 31-33). Based on the available data, these findings imply that the annual case volume to achieve optimal long-term survival is located near a procedural volume of 32 cases/year. After accrual of more annual procedures, long-term survival may no longer significantly improve any further.

Driving factors and spatio-temporal evolution of tourist accident clusters in Chinese outbound tourism

ABSTRACT The clustering of tourist accidents is a critical yet underexplored area of research. This study employs cluster theory and safety system theory to develop a comprehensive measurement tool for analysing tourist accident clusters, examining their spatiotemporal evolution patterns, and identifying the driving mechanisms from a holistic destination perspective. By analysing accident records of Chinese outbound tourists across 48 countries from 2011 to 2019, the findings reveal a reduction in disparities among tourist accident clusters (α-convergence effect) and a fluctuating trend over time. Spatial autocorrelation analysis identifies four distinct spatial distribution patterns: low-low (L-L), high-low (H-L), high-high (H-H), and low-high (L-H). Furthermore, Boosted Regression Tree (BRT) analysis uncovers a nonlinear relationship between accident clusters and four primary factors: human, machine, environment, and management. Within this framework, indicators such as tourist volume pressure, traffic risk, and employee training levels emerge as significant contributors. Additionally, geographic detector analysis reveals interactions among the driving mechanisms, offering deeper insights into their interrelated effects. This study provides practical implications for destinations, tourists, and tourism practitioners, emphasising strategies to prevent and mitigate the impact of accident clusters.