Nonlinear Relationship Research Articles

Association of the triglyceride glucose index with all cause and CVD mortality in the adults with diabetes aged < 65 years without cardiovascular disease

Although the triglyceride-glucose (TyG) index has been established as a valuable predictor for cardiovascular disease (CVD) and cardiovascular mortality, there is limited research exploring its association with all-cause or CVD mortality specifically in adults with diabetes aged < 65 years without cardiovascular disease. This study aimed to investigate the relationship between the TyG index and both all-cause and CVD mortality in this population within the United States. Our study recruited 1778 adults with diabetes aged < 65 years without cardiovascular disease from the National Health and Nutrition Examination Survey (NHANES) 2003–2018. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Cox regression modeling was employed to examine the association between the TyG index and mortality in this population. The nonlinear relationship between the TyG index and mortality was assessed using restricted cubic splines (RCS). Additionally, subgroup analyses and interaction tests were conducted to explore potential effect modifiers. A total of 1788 participants were included in the final cohort, with an average age of 49.61 ± 0.32 years. During a median follow-up of 7.92 years, the occurrence of 150 all-cause deaths and 33 CVD-related deaths were recorded. To investigate the independent association between the TyG index and the risks of all-cause and CVD mortality, three Cox regression models were developed. In Model 1, a significant positive association was observed between the TyG index and the risk of all-cause mortality (HR 1.38, 95% CI 1.09–1.74). This association persisted in the minimally adjusted model (HR 1.44, 95% CI 1.13–1.83), which was adjusted for age, gender and race. Even after full adjustment, this positive association remained significant (HR 1.91, 95% CI 1.36–2.70). We also found that the relationship between the TyG index and all-cause mortality was linear. Subgroup analyses revealed no significant interactions between the TyG index and the stratification variables. However, we did not observe a significant association between the TyG index and CVD mortality in this population. Our results suggested that a significantly positive association between the TyG index and all-cause mortality. The positive association between the TyG index and all-cause mortality was linear. We did not observe a significant association between the TyG index and CVD mortality.

Association of anxiety status and anxiety duration with constipation in adult Americans: a cross-sectional study using data from the NHANES 2007–2010

BackgroundConstipated patients may exhibit anxious behaviors, but the relationship between the two remains unclear. This population-based study aims to investigate the relationships of anxiety status and anxiety duration with constipation among U.S. adults.MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2007 to 2010, a cross-sectional study included 8945 participants was performed to assess the relationships of anxiety status and anxiety duration with constipation. Participants’ characteristics were presented using mean or proportion. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable logistic regression models. The analysis accounted for potential confounding factors, including gender, age, race/ethnicity, education level, marital status, PIR, BMI, smoking status, drinking status, chronic diseases, and dietary intake. Subgroup analyses were conducted to ensure the stability of the results. Smoothed curve fitting was applied to investigate both linear and nonlinear associations between anxiety duration and constipation risk. Threshold effects were assessed using two-piecewise linear regression models.ResultsOf the 8945 participants, 922 reported constipation, with a prevalence of 10.3%. After adjusting for confounding factors, individuals with anxiety status exhibited an increased risk of constipation (OR: 1.49, 95% CI: 1.28–1.73). Stratified analyses indicated that the association between anxiety status and constipation remained relatively stable across different stratification levels. Smoothed curve fitting and threshold effect analyses revealed a linear association between anxiety duration and constipation among adults aged 20–60, while a nonlinear inverted U-shaped relationship was observed in those aged 60 and older, with an inflection point at 10 days. The two variables correlated positively on the left side of the inflection point but not on the right.ConclusionThis study showed that a significant association between anxiety status and constipation among U.S. adults. Additionally, an age-dependent association between anxiety duration and constipation was observed, particularly among individuals aged 60 and older.

Research on Coal and Gas Outburst Prediction and Sensitivity Analysis Based on an Interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine Model

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations.

Development of per Capita GDP Forecasting Model Using Deep Learning: Including Consumer Goods Index and Unemployment Rate

In the 21st century, the increasing complexity and uncertainty of the global economy have heightened the need for accurate economic forecasting. Per capita GDP, a critical indicator of living standards, economic growth, and productivity, plays a key role in government policy-making, corporate strategy, and investor decisions. However, predicting per capita GDP poses significant challenges due to its sensitivity to various economic and social factors. Traditional methods such as statistical analysis, regression, and time-series models have shown limitations in capturing nonlinear interactions and volatility of economic data. To address these limitations, this study develops a per capita GDP forecasting model based on deep learning, incorporating key macroeconomic variables—the Consumer Price Index (CPI) and unemployment rate (UR)—to enhance predictive accuracy. This study employs five deep-learning regression models (RNN, LSTM, GRU, TCN, and Transformer) applied to real and placebo datasets, each incorporating combinations of CPI and UR. The results demonstrate that deep learning models can effectively capture complex, nonlinear relationships in economic data, significantly improving predictive accuracy compared to traditional models. Among the models, the Transformer consistently achieves the highest R-squared and lowest error values across various metrics (MSE, RMSE, and MSLE), indicating its superior ability to model intricate economic patterns. In addition, including CPI and UR as additional predictors enhances model robustness, with the TCN and Transformer models showing particularly strong performance in capturing short-term economic fluctuations. The findings suggest that the deep learning models, especially the Transformer, offer valuable tools for policymakers and business leaders, providing reliable GDP forecasts that support economic decision-making, resource allocation, and strategic planning. Academically, this study advances the understanding of deep learning applications in economic forecasting, particularly in integrating significant macroeconomic variables for enhanced predictive performance. The developed model is a foundation for informed economic policy and strategic decisions, offering a robust and actionable framework for managing economic uncertainties. This research contributes to theoretical and applied economics, providing insights that bridge academic innovation with practical utility in economic forecasting.

Association between stress hyperglycemia ratio and all-cause mortality in critically ill patients with sepsis: results from the MIMIC-IV database

BackgroundThis study aimed to explore the association between the stress hyperglycemia ratio (SHR) and short- and long-term outcomes in critically ill patients with sepsis.MethodsThis retrospective observational cohort study was conducted using the Medical Information Mart for Intensive Care-IV (MIMIC-IV v2.2) database. Patients were categorized into 4 SHR quartiles. The main focus was on in-hospital mortality and 1-year all-cause mortality as primary endpoints, while intensive care unit and hospital stays were considered as secondary outcomes. Regression and subgroup analyses were used to assess the correlation between SHR and the primary and secondary outcomes. Restricted cubic spline analysis was utilized to explore the nonlinear relationships between SHR and in-hospital and 1-year all-cause mortality.ResultsThis study included two groups of patients, comprising 7456 and 6564 individuals. The in-hospital and 1-year mortality was 11.96% and 17.96% in Cohort 1 and 2, respectively. SHR was associated with an elevated risk of in-hospital mortality (OR: 2.08, 95%CI 1.66–2.61) and 1-year mortality (HR: 1.70, 95% CI 1.42–2.04). Patients in SHR quartile 4 had a higher risk of in-hospital (OR: 1.86, 95% CI 1.51–2.30) and 1-year (HR: 1.44, 95% CI 1.23–1.69) mortality than those in quartile 2. Restricted cubic spline analysis showed a “J-shaped” relationship between SHR and all-cause mortality in both cohorts. The relationship between high SHR and mortality remained consistent across almost all predefined subgroups.ConclusionsOur study suggests that high SHR is associated with increased in-hospital and 1-year mortality in critically ill sepsis patients. Further investigations are needed to validate these results.

Documentation of lead exposure-associated comorbidity among 4538 workers in China

BackgroundSystematic documentation of morbidity with exposure assessment in a large group of lead (Pb)-exposed workers in China.MethodsUsing the cluster sampling method, Pb-exposed workers were recruited from a factory in the Henan Province, China. The morbidity information and blood lead concentration (BLC) from each worker were collected. Relationships between the two sets of data were analyzed using the logistic regression and restricted cubic spline (RCS) models.ResultsAmong the 4538 recruited workers, there were 2780 males and 1758 females, with a median age of 34 years old, and BLC ranging from 7.70 to 722.70 μg/l. Among them, 1247 (27.4%) had multiple morbidities (comorbidity). Those with the highest BLC showed a 1.79-fold (significant) increase in comorbidities compared to those with the lowest BLC (95% confidence interval = 1.43, 2.25; p < 0.001). In addition, the comorbidities exhibited a BLC dose–response relationship (p < 0.001), after adjusting for potential confounding factors. The RCS curve analysis revealed a nonlinear relationship between BLC and risk of comorbidity (p-overall < 0.001, and p-nonlinear < 0.001). Sensitivity analysis further confirmed the dose–response relationship between BLC and the risk of developing comorbidities. Additionally, the data indicate that female workers were more susceptible to comorbidities.ConclusionOur study demonstrates that comorbidity was prevalent and was dose-dependent among lead exposed workers.

Zero–zero-birefringence polymer using N-substituted maleimide and styrene

The orientational and photoelastic birefringence of alternating copolymers has not been studied in detail. In this study, N-substituted maleimide (RMI) and styrene (St) as alternating copolymers were investigated, and their birefringence, refractive index, and glass transition temperature (T g ) of the alternating copolymers were analyzed. It was found that the photoelastic coefficient and T g exhibited a nonlinear relationship with the composition ratio of RMI and St. A zero–zero-birefringence polymer exhibiting no birefringence was obtained by using N-ethylmaleimide and St and adjusting the composition ratio. This polymer exhibited high T g and low haze.

The curvilinear relationship between hepatic steatosis index and depression: Findings from the NHANES.

Previous studies have established a link between depression and fatty liver disease, yet the relationship with the Hepatic Steatosis Index (HSI) has not been thoroughly investigated. This study aims to explore the potential association between HSI and depression. We investigated the association between HSI and depression (PHQ-9 depression score≥10) using a weighted multiple logistic regression model and stratified analysis. Non-linear associations were explored using fitted smooth curves. A recursive method was employed to identify inflection points and construct a two-part linear regression model. Subgroup analysis was conducted to examine the differences in this association within various subgroups. Our study included 43,888 participants. The mean HSI was 37.06, with a range of 0 to 135.92. Multivariate logistic regression analysis, showed that the OR for the association between HSI and depression was 1.02 (95% CI: 1.01, 1.02). This means that there is 2% increase likelihood of depression for each unit increase in HSI. A nonlinear relationship was observed, with an inflection point identified at an HSI value of 29.72, indicating a negative correlation below the threshold and a positive correlation above it. Stratified analysis indicated that, aside from participants with BMI≥30 who displayed a linear positive association, HSI showed a curvilinear association with depression in other groups are similar to that observed in the overall sample. In subgroup analyses, statistically significant interactions were observed between HSI and depression within groups defined by education level and smoking status. We found a curvilinear relationship between HSI and Depression.

Non-linear thermoelastic analysis of circular sandwich porous plates reinforced with functionally graded graphene platelets

The investigation of graphene-reinforced porous composites has garnered significant interest due to their exceptional mechanical, thermal, and electrical properties, positioning them as promising materials for advanced structural applications. To fully harness the potential of these materials, numerical modeling becomes essential for understanding their behavior and optimizing their performance for specific engineering applications. In the present study, the non-linear bending response of functionally graded (FG) circular sandwich porous plates reinforced with graphene platelets is systematically analyzed under varying boundary conditions. The plates are subjected to a uniform transverse load and a thermal gradient, with distributions of graphene platelets and porosity graded along the thickness. The effective material properties, including Poisson’s ratio, are computed using the Gaussian random field scheme in conjunction with the Halpin-Tsai micromechanical model. The governing equations of motion are derived from Von Kármán’s non-linear relations, utilizing both first-order shear deformation theory (FSDT) and modified higher-order shear deformation theory (MHSDT). These equations are subsequently solved using dynamic relaxation (DR) coupled with finite difference methods. To validate the results, comparisons with existing literature and finite element method (FEM) are conducted, ensuring the accuracy and reliability of the obtained solutions. A comprehensive parametric study is performed to investigate the influence of various factors, including boundary conditions, graphene distribution patterns, porosity variations, and the thickness-to-radius ratio, on the non-linear bending behavior of the composite plates.

Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines

The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineers are increasingly focusing on snow-drifting phenomena and the associated disasters. This study, based on a GIS platform, conducts a case study of a transport infrastructure project in Xinjiang to analyze the geographical and environmental conditions along the route. Through on-site monitoring and investigation, factors related to the occurrence of snow-drifting disasters were implied. The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. This led to the establishment of a susceptibility evaluation index system for snow-drifting disasters, improving both the computational efficiency and the accuracy of the evaluation model. The results indicate that the evaluation accuracies of the WOE, WOE-BP, and WOE-BP-GA models were 72.32%, 75.32%, and 85.18%, respectively. The use of the GA-BP algorithm effectively captured the complex nonlinear relationships among various factors, producing evaluation results highly consistent with real-world conditions. This method may efficiently identify high-risk areas of snow-drifting along transportation infrastructure lines, providing valuable insights for disaster prevention using ArcGIS.

The nonlinear association between body roundness index and infertility in married women

Infertility is increasingly linked to obesity, especially visceral fat accumulation. The Body Roundness Index (BRI) provides a precise quantification of body and visceral fat but its implications for infertility risks and diagnostic value are yet to be fully determined. This study analyzed data from 1,305 married women aged 20–45 years, drawn from the National Health and Nutrition Examination Survey (NHANES). Logistic regression was utilized to examine the relationship between BRI and infertility, incorporating univariate and multivariate regression analyses, smooth curve fittings, and subgroup analyses. Additionally, Receiver Operating Characteristic (ROC) analysis assessed the predictive capability of BRI for infertility. An increase in one unit of BRI correlated with a 13% higher prevalence of infertility after adjusting for covariates (OR = 1.13; 95% CI: 1.05, 1.21). Women with higher BRI exhibited greater infertility odds compared to the reference group (OR: 2.20, 95% CI: 1.42, 3.42). Smooth curve fitting analysis revealed a positive, nonlinear relationship, with an inflection point observed at a BRI value of 7.95. Subgroup analyses upheld the robustness of this association across varied demographic segments. ROC curves indicated significant diagnostic potential of BRI in younger women, whereas its predictive relevance diminished in those over 35, akin to other obesity markers. This study is the first to rigorously affirm a strong link between increased BRI and the higher prevalence of infertility among married women aged 20–45. To optimize fertility, maintaining a healthy weight and waist circumference is recommended. The integration of BRI into clinical practice could assist in early intervention for women at risk of infertility. However, further research is required to identify effective predictive markers for infertility in women aged 36–45.

Association between estrogen and kidney function: population based evidence and mutual bidirectional Mendelian randomization study.

Previous studies have suggested a potential role of estrogen in the pathophysiology of chronic kidney disease (CKD); however, the association and causality between estrogen and kidney function remain unclear. The cross-sectional correlation between serum estradiol concentration and estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR) was analyzed using data from the National Health and Nutrition Examination Survey 2013-2016. Causality was tested using mutual bidirectional Mendelian randomization (MR) approaches based on six large-scale GWAS studies. Weighted generalized multivariate linear regression was employed to estimate the association between estradiol and eGFR and ACR, and a restricted cubic spline analysis was utilized to investigate potential nonlinear relationships. A total of 8932 participants were included. Serum estradiol concentration was positively associated with eGFR after adjusting for potential covariates (β, 0.76; 95% CI 0.24 to 1.27) and with ACR (β, 5.99; 95% CI 1.62 to 10.36). A nonlinear positive association was found between estradiol and eGFR, while an inverse "V"-shaped relationship was seen with ACR. Sensitivity analyses confirmed the stability of the relationship between estradiol and eGFR but indicated a less robust association with ACR. Stratified analysis showed that the association between estradiol and eGFR was particularly significant in populations with CKD and hypertension. All forward MR analyses demonstrated a positive causal relationship between estradiol and eGFR, but no causality was found between estradiol and ACR. No reverse causal association was observed. Serum estradiol concentration was causally associated with eGFR. Further longitudinal research is needed to validate these findings.

Association between neutrophil-albumin ratio and ultrasound-defined metabolic dysfunction-associated fatty liver disease in U.S. adults: evidence from NHANES 2017–2018

BackgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly prevalent, and systemic inflammation markers may play a role in its pathogenesis. This study aimed to investigate the relationship between neutrophil-albumin ratio (NAR) and MAFLD.MethodsThis population-based study was performed using data from NHANES 2017–2018 and included 4526 individuals with a median age of 44 years old, and the males account for 46.13% (n = 2088). Ultrasound-defined MAFLD was diagnosed using a controlled attenuation parameter (CAP) threshold of ≥ 285 dB/m. Differences in baseline characteristics between patients with CAP ≥ 285 dB/m and < 285 dB/m were analyzed. A generalized additive model (GAM) and restricted cubic splines (RCS) were applied to explore the nonlinear relationship between NAR and CAP, followed by generalized linear models (GLMs). Threshold effect analysis was performed to identify the inflection point in the nonlinear relationship. CAP-related variables were ranked using XG Boost and random forest algorithms, and predictive models were developed and evaluated.ResultsThe study population included 1,503 patients with CAP ≥ 285 dB/m. NAR was significantly elevated in subjects with CAP ≥ 285 dB/m (P < 0.001), and nonlinear relationships between NAR and CAP were observed. NAR was positively associated with CAP in three GLMs, and this relationship remained after adjusting for confounding factors or dividing NAR into tertiles. Additionally, when NAR < 1.436, a one-unit rise in NAR was linked to a 3.304-fold increase in the risk of NAFLD (OR = 3.304, 95% CI: 2.649–4.122). The NAR-based random forest model showed the best predictive performance with AUC values of 0.978 (training) and 0.813 (validation).ConclusionNAR is positively associated with CAP, and the NAR-based random forest model is optimal for predicting MAFLD risk, highlighting the importance of NAR in predicting MAFLD.

Association between body roundness index and stroke results from the 1999-2018 NHANES.

Obesity, especially visceral obesity, is a controllable risk factor associated with the incidence of stroke. The body roundness index (BRI) bridges the gap between traditional anthropometric methods of assessing fat distribution to predict the percentage of body fat and visceral adipose tissue, which can be used to decipher its population-based characteristics and potential association with stroke. The negative impact of accumulated visceral obesity on cerebrovascular health has been widely documented. However, the association between BRI and stroke has never been reported before. The purpose of this study was to elucidate the connection between BRI and the general incidence of stroke in the US population. This cohort study extracted cross-sectional data from 39,454 U.S. adults (aged ≥20 years) from documents related to the National Health and Nutrition Examination Survey (NHANES) from 1999 through 2018. We evaluated the relationship between BRI and stroke using weighted logistic regression analysis, and we looked at any possible nonlinear relationships using restricted cubic spline (RCS) regression. Additionally, subgroup analysis and interaction tests were carried out. The study involved 39,454 participants, of whom 1,427 (3.6 %) had a stroke. Fully adjusted logistic regression models showed that BRI was positively associated with stroke, with a 5.7 % increase in stroke incidence per unit increase in BRI (OR = 1.057, 95 % CI = 1.009,1.108, P = 0.020). RCS analysis revealed a nonlinear association, suggesting an elevated risk of stroke before the inflection point of 8.489. This positive correlation was consistent across settings, according to subgroup analyses and interaction tests (P > 0.05 for all interactions). There is a nonlinear positive correlation between BRI and stroke, according to this national cohort study. These results support the use of the BRI as a screening tool for assessing stroke risk; yet, because cross-sectional studies have inherent limitations, more thorough research is required until the BRI has been consistently validated in additional independent cohorts.

Nonlinear association between red cell distribution width/albumin ratio and peripheral arterial disease in the NHANES: a cross-section study with propensity score matching analysis

BackgroundPeripheral arterial disease (PAD) is a prevalent cardiovascular condition that affects up to 200 million people globally, and has significant implications for morbidity and mortality. Recent studies have identified the red cell distribution width-to-albumin ratio (RDW/ALB) as a potential systemic inflammatory marker that is correlated with several cardiovascular and inflammatory diseases including PAD. However, the comprehensive role of RDW/ALB in PAD remains underexplored. The objective of this study was to elucidate the relationship between RDW/ALB and PAD and to provide novel insights into the prevention and treatment of PAD.MethodsThis retrospective cross-sectional study leveraged data from the NHANES data (1999–2004) identifying PAD using ankle-brachial index measurements (&amp;lt;0.90). The association with PAD was assessed using multivariable logistic regression, complemented by a restricted cubic spline for nonlinearity, and propensity score methods for robustness checks, including propensity score matching and subgroup analyses.ResultsThis study included 6,421 participants, 452 of whom had PAD. The mean age was 70.1 years; the patients were predominantly male. After adjustment, the RDW/ALB ratio showed a significant association with PAD (OR 1.71, 95% CI 1.29–2.26, p &amp;lt; 0.001). After quartiles of RDW/ALB, the risk of PAD was determined to be 2.03 (95% CI 1.31–3.14) in the Q4 group compared with the Q1 group in the adjusted complete model. The restricted sample cubic spline of RDW/ALB and risk of developing PAD demonstrated a nonlinear relationship. The risk of PAD increased considerably with higher RDW/ALB ratios less than 4.08. Subgroup and PSM analyses underscored the consistency of these findings.ConclusionsThe study demonstrated a significant association between RDW/ALB and PAD, with a nonlinear relationship and a threshold effect. Further prospective clinical research is required to validate the relationship between the RDW/ALB ratio and PAD.

Association of TyG index with mortality at 28 days in sepsis patients in intensive care from MIMIC IV database

The relationship between the triglyceride‒glucose (TyG) index and the clinical prognosis of septic patients in intensive care units (ICUs) remains unclear. This study aimed to explore the correlation between the TyG index and 28-day all-cause mortality in septic patients. A retrospective observational cohort study was conducted, including 8955 septic patients from the MIMIC IV 2.2 database. The primary outcome was 28-day all-cause mortality. Multivariate logistic regression analysis and restricted cubic spline regression analysis were used to assess the relationship between the TyG index and 28-day all-cause mortality in septic patients. Subgroup analyses and sensitivity analyses were performed to further validate the robustness of the results. A total of 8955 septic patients were included, 5219 (58.3%) of whom were male, with a mean age of 66.3 (15.8) years and an average TyG index of 9.08 (0.70) and the number of deaths within 28 days was 1639 (18.3%). The RCS curve demonstrated a U-shaped relationship between the TyG index and 28-day all-cause mortality (nonlinear P value = 0.0003). The risk of 28-day all-cause mortality was negatively associated with the TyG index until it decreased to 9.03 (adjusted odds ratio [OR] 0.727, 95% confidence interval [CI] 0.577–0.915). However, when the TyG index exceeded 9.03, the odds ratio for 28-day all-cause mortality significantly increased (adjusted OR 1.185, 95% CI 1.001–1.404). These findings were consistent across subgroups and various sensitivity analyses. Our study revealed a nonlinear U-shaped relationship between the TyG index and 28-day all-cause mortality, with a critical point at a TyG index of 9.03. Our results suggest that the TyG index may be a novel and important factor for the short-term clinical prognosis of critically ill septic patients.

3D polycatenated architected materials.

Architected materials derive their properties from the geometric arrangement of their internal structural elements. Their designs rely on continuous networks of members to control the global mechanical behavior of the bulk. In this study, we introduce a class of materials that consist of discrete concatenated rings or cage particles interlocked in three-dimensional networks, forming polycatenated architected materials (PAMs). We propose a general design framework that translates arbitrary crystalline networks into particle concatenations and geometries. In response to small external loads, PAMs behave like non-Newtonian fluids, showing both shear-thinning and shear-thickening responses, which can be controlled by their catenation topologies. At larger strains, PAMs behave like lattices and foams, with a nonlinear stress-strain relation. At microscale, we demonstrate that PAMs can change their shapes in response to applied electrostatic charges. The distinctive properties of PAMs pave the path for developing stimuli-responsive materials, energy-absorbing systems, and morphing architectures.

More than Just Chilling and Forcing: Deconstructing the Climate Windows and Drivers of Leaf Emergence and Fall in Woody Plant Species

Climate warming is impacting vegetation productivity and plant leaf phenology, but the precise climate drivers and windows of key leaf phenological phases, such as emergence and fall, are still not well understood. Recent intensive computational approaches based on pinpointing the optimal climate window of leaf phenophases by maximizing the signal could help to advance in this question. In this study, we assess the climate variables, the climate windows, and the type of relationship (linear or nonlinear) that drive leaf emergence and fall in 21 deciduous and 13 evergreen woody plant species inhabiting two sites in Mediterranean Spain. We used precipitation, solar radiation, and different temperature measures, including forcing and chilling, as climate variables. We found that forcing variables were the best predictors of leaf phenology, but other temperature variables, as well as precipitation and radiation, were also important. However, chilling was not a good predictor. Most selected models showed nonlinear relationships. The best thresholds for calculating forcing were different from those commonly used. In addition, the best climate window for leaf phenology was species-specific and contingent on climatic and phenological conditions. This optimum climate window often covered longer periods than those usually considered in phenology studies. Our approach could be used to assess and better forecast future plant phenological responses to climate warming.

Maternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: The Upstate KIDS study (2008-2010).

There is growing concern that exposure to per/polyfluoroalkyl substances (PFAS), persistent chemicals used widely to make consumer products water- or grease-proof, may alter immune function, leading to reduced vaccine response or greater susceptibility to infections. We investigated associations between two legacy PFAS (PFOA and PFOS) and infant cytokine levels measured in newborn dried bloodspots (NDBS) from a large population-based birth cohort in Upstate New York, to determine whether exposure to legacy PFAS is associated with variability in cytokine profiles in newborns. We performed adjusted mixed effects regressions for each cytokine against PFOS and PFOA followed by exploratory factor analysis (EFA) on specific cytokine subsets selected via the prior regressions. Among 3448 neonates (2280 singletons and 1168 twins), significant cytokines were dominated by cytokines negatively associated with the given PFAS. Adjusted single-pollutant models with continuous log-transformed PFOA showed significant negative associations with IL-16 (-0.07, 95% CI: -0.3, -0.1), IL-5 (-0.05, 95%CI: -0.09, -0.02), IL-6 (-0.06, 95%CI: -0.1, -0.02), 6-Ckine (0.06, 95% CI: -0.10, -0.02) and significant positive associations with IL-1α (0.066, 95%CI: 0.03, 0.11), MCP-1 (0.06, 95%CI: 0.03, 0.10). Estimates for PFOS were slightly larger than estimates for PFOA but only significant for 6-Ckine (-0.21, 95%CI: -0.09, -0.33) after correction for multiplicity. Our data consistently suggest that legacy PFAS exposures are associated with disrupted, typically reduced, cytokine levels in neonates, with PFOA exposure resulting in more significant differences in individual cytokines and cytokine groupings than PFOS. Regression by PFAS quartile shows evidence of nonlinear dose-response relationships for most cytokines and cytokine groupings.

Assessment of Peak Particle Velocity of Blast Vibration using Hybrid Soft Computing Approaches

Abstract Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) and the CatBoost algorithm, with hyperparameters globally optimized using the improved hippocampus optimization algorithm (IHO). Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.