Kernel Methods Research Articles

Using three statistical methods to analyze the associations between a mixture of multi-nutrients and risk of mild cognitive impairment in an elderly population in Northern China

Few studies have considered nutrients as a mixture and their impact on Mild Cognitive Impairment (MCI). The generalized linear regression (GLM), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models are fitted to estimate the association between intake of a mixture of nutrients and MCI. Comparing the results from these three models, vitamin E and vitamin B6 were identified as the most important factors associated with the risk of MCI. Considering the characteristics of BKMR, it may be more advantageous to use BKMR to estimate the combined the joint effects of nutrients mixture. In the future, studies need to move from a “one nutrient at a time” approach to simultaneous analyses of multiple nutrients intakes in order to understand and quantify the joint effect of nutrients mixture on health.

Association of joint exposure to organophosphorus flame retardants and phthalate acid esters with gestational diabetes mellitus: a nested case-control study

BackgroundOrganic phosphate flame retardants (OPFRs) and phthalate acid esters (PAEs) are common endocrine-disrupting chemicals that cause metabolic disorders. This study aimed to assess the association between joint exposure to OPFRs and PAEs during early pregnancy in women with gestational diabetes mellitus (GDM).MethodsSeven OPFRs and five PAEs were detected in the urine of 65 GDM patients and 100 controls using gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS). The association of OPFRs and PAEs with GDM was assessed using logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models.ResultsLevels of dibutyl phthalate (DBP), di-2-ethylhexyl phthalate (DEHP), diethyl phthalate (DEP), dimethyl phthalate (DMP), tris (2-butoxyethyl) phosphate (TBEP), tributyl phosphate (TBP), tris (2-chloroethyl) phosphate (TCEP), tris (1,3-dichloro-2-propyl) phosphate (TDCPP), tri-ortho-cresyl phosphate (TOCP), and triphenyl phosphate (TPHP) increased in the GDM group, and the OPFRs and PAEs, except for BBP and TMCP, were associated with GDM in the logistic regression analysis. In the WQS model, the mixture of OPFRs and PAEs was significantly positively associated with GDM (OR = 3.29, 95%CI = 1.27–8.51, P = 0.014), with TDCPP having the highest WQS index weight. BKMR analysis reinforced these results, showing that the overall association of joint exposure to the OPFRs and PAEs with GDM increased at exposure levels of the 55th to 75th percentiles. Independent exposure to TDCPP (OR = 1.42, 95%CI = 1.09–1.86, P = 0.011) and TBEP (OR = 1.29, 95%CI = 1.04–1.60, P = 0.023) were associated with an increased risk of GDM.ConclusionsEnvironmental exposure to OPFRs and PAEs is significantly associated with GDM. These findings provide evidence for the adverse effects of exposure to OPFRs and PAEs on the health of pregnant women.

Phase-space negativity as a computational resource for quantum kernel methods

Phase-space negativity as a computational resource for quantum kernel methods

Effects of fine particulate matter bound heavy metals on intentional self-harm deaths in Guangzhou, China, insight from core chemical constituents.

Fine particulate matter (PM2.5) is one of the major threats to human health, and may partly responsible for intentional self-harm deaths, while the limited results seemed contradictory. Further analysis on PM2.5 constituents may provide more reliable evidence. Heavy metals are crucial toxic components of PM2.5 that may induce suicide behavior. What role do PM2.5-bound heavy metals play in a threat to intentional self-harm death is still unclear. Two-year data of daily PM2.5-bound heavy metals (including metalloids) and daily intentional self-harm deaths were collected in Guangzhou. Bayesian kernel machine regression, weighted quantile sum, and quantile-based g-computation models were employed to depict the relationships between heavy metals and intentional self-harm deaths. The number of intentional self-harm deaths was 217 and 283 for 2015 and 2016, respectively. A positive correlation was found between the combined effect of the 13 heavy metals and intentional self-harm deaths. Nickel, cadmium, and iron were the primary contributors to this positive correlation. Heavy metal components play significant roles in PM2.5-related intentional self-harm deaths, and targeted source control measures are warranted to protect residents from suicide.

The associations between exposure to mixed environmental endocrine disruptors and sex steroid hormones in men: a comparison of different statistical models

In recent years, worldwide fertility rates have continued to decrease. Humans are frequently exposed to a combination of environmental endocrine disruptors, which can cause male reproductive disorders. The study employed three distinct analytical models to examine the correlation between exposure to a combination of 25 chemicals and sex steroid hormone levels in adult males. This involved evaluating 12 chemicals and their metabolites from personal care and consumer products, as well as 13 metabolites linked to phthalates and plasticisers. The study analysed 25 chemicals and 3 measured sex steroid hormone outcomes, as well as two calculated hormonal outcomes (free androgen index, TT/E2 ratio) in 1262 adult men who participated in the National Health and Nutrition Examination Survey (NHANES) 2013–2016 in the United States. The study employed several statistical methods to estimate the relationships between single chemicals or chemical blends and sex hormones. These methods included linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine (BKMR) regression. The results of the linear regression analysis indicate that chemical exposure has a negative correlation with E2, TT, and FAI, and a positive correlation with SHBG and TT/E2. The mixture effect analyses using the WQS and BKMR models further confirmed that BP3, MECPP, and MECOP were the most highly weighted chemical mixtures. The analyses also suggested that there were differences in the effects of different concentrations of EDCs on sex steroid hormones. Exposure to environmental endocrine-disrupting chemicals (EDCs) has been found to have a negative correlation with estradiol and total testosterone, as well as FAI. Conversely, this exposure has been found to have a positive correlation with sex hormone binding globulin and the TT/E2 ratio. The study also revealed differences in the effects of different concentrations of EDCs.

Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model.

Quantum support vector machines: theory and applications

Abstract. Quantum Support Vector Machines (QSVMs) combine the fundamental principles of quantum computing and classical Support Vector Machines (SVMs) to improve machine learning performance. In this paper, the author will further explore QSVM. Firstly, introduce the basics of classical SVM, including hyperplane, margin, support vector, and kernel methods. Then, introduce the basic theories of quantum computing, including quantum bits, entanglement, quantum states, superposition, and some related quantum algorithms. Focuses on the concept of QSVM, quantum kernel methods, and how SVM runs on a quantum computer. Key topics include quantum state preparation, measurement, and output interpretation. Theoretical advantages of QSVMs, such as faster computation speed, stronger performance to process high-dimensional data, and kernal computation. In addition, the author discussed the implementation of QSVM, quantum algorithms, quantum gradient descent, and optimization techniques for SVM training. The article also discusses practical issues such as error mitigation and quantum hardware requirements. The purpose of this paper is to show the advantages of QSVM by comparing SVM and QSVM.

Triglyceride-glucose Index and Its combination with obesity indicators mediating the association between 2-hydroxyfluorene and the prevalence of cardiovascular disease: Evidence from the NHANES (2005–2018)

BackgroundPolycyclic aromatic hydrocarbons (PAHs) were increasingly recognized as a group of environmental pollutants associated with various health issues. The rise in their prevalence in the environment was concerning. Yet, the effects of PAH exposure on cardiovascular diseases (CVDs) were still not well understood. PurposeThis study investigated the impact of exposure to 2-hydroxyfluorene on CVDs prevalence, with a special focus on the mediating role of triglyceride-glucose (TyG) index and its combination with obesity indicators. MethodUsing National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018, this study assessed how 2-hydroxyfluorene affects CVDs prevalence through various statistical techniques. The investigation began with restricted cubic spline (RCS) analysis to explore the relationship between 2-hydroxyfluorene levels and CVDs prevalence. Logistic regression was then used to examine associations within PAH mixtures, alongside the Bayesian Kernel Machine Regression (BKMR) model. Furthermore, the Quantile G-Computation (QG-comp) model was used to evaluate the influence of weights and directions. A mediation analysis was also performed to assess the mediating role of TyG-related indicators on the relationship between 2-hydroxyfluorene and CVDs prevalence. Besides, the association between 2-hydroxyfluorene and the prevalence of each specific CVDs, congestive heart failure, myocardium infarction, angina pectoris and coronary heart disease, was also assessed and so was the mediated effect. Finally, the subgroup analysis was conducted to assess the association in each specific subgroup. ResultThe study, involving 3645 participants, found a significant positive association between 2-hydroxyfluorene exposure and CVD (OR (95 %CI) = 115.8013 (5.0521 ∼ 2654.3248), P=0.0029), with 2-hydroxyfluorene showing the positive contribution to CVDs prevalence within the phthalate mixture. The positive association also existed between 2-hydroxyfluorene and the prevalence of each specific CVDs. TyG and TyGWC (Triglyceride Glucose-Waist Circumference) were identified as mediators in the link between 2-hydroxyfluorene exposure and the prevalence of total CVD and each specific CVD, while TyGBMI (Triglyceride Glucose-Body Mass Index) can only mediate the association between 2-hydroxyfluorene and the prevalence of total CVD, congestive heart failure and angina pectoris ConclusionThe findings highlighted a significant association between 2-hydroxyfluorene exposure and CVDs prevalence, with TyG-related indicators acting as mediators.

Exposure to alkylphenols during early pregnancy and the risk of gestational diabetes mellitus: Fetal sex-specific effects

Alkylphenols (APs) may cause gestational diabetes mellitus (GDM) in pregnant women by impairing glucose metabolism through endocrine disruption. However, the current literature has limited epidemiological evidence on the association between APs exposure and the risk of GDM, especially the lack of evidence on joint exposure. Thus, we evaluated the effect of exposure to APs during early pregnancy on the risk of GDM. The study involved 2035 pregnant women from Guangxi Zhuang Birth Cohort (GZBC) in China. Poisson regression model, restricted cubic spline (RCS), Bayesian kernel machine regression (BKMR), and quantile g-computation (Qgcomp) were conducted to evaluate the effects of serum APs levels on the risk of GDM in pregnant women. For each Ln-unit increase in the serum nonylphenol (NP) of pregnant women, the adjusted relative risk (RR) for GDM risk was 1.12 (95 % CI: 1.00, 1.24). After sex stratification, the effect was more pronounced among pregnant women carrying female fetuses（RR=1.22; 95 % CI: 1.09, 1.38). The serum 4-t-octylphenol (4-T-OP) of the medium-exposure (adjusted RR = 3.25: 95 % CI: 1.30, 8.12) and high-exposure groups (adjusted RR = 2.90: 95 % CI: 1.15, 7.31) were related to a significantly increased risk of GDM in pregnant women carrying female fetuses only when compared to the low-exposure group. A reverse U-shaped nonlinear association was found between 4-n-octylphenol (4-N-OP) and 4-n-nonylphenol (4-N-NP) concentrations and GDM risk, and it was more susceptible in pregnant women carrying female fetuses. The Qgcomp and BKMR models showed that exposure to APs mixtures was correlated with an elevated risk of GDM in pregnant women carrying female fetuses (adjusted OR = 1.90: 95 % CI: 1.07, 3.38). Exposure to APs during early pregnancy may have potential fetal sex-specific effects on the risk of GDM, with pregnant women carrying female fetuses being more susceptible.

The association between N-nitrosamines exposure and lipid metabolism in the high incidence area of esophageal cancer: A case-control analysis

Environmental carcinogens such as N-nitrosamines are high-risk factors for the development of esophageal cancer (EC). However, the association between nitrosamines exposure and lipid metabolism disorders in human EC remained largely obscure. Therefore, we conducted a population-based case-control study established with esophageal inflammation (BCH), esophageal heterotrophic hyperplasia (DYS), patients with primary EC and matched controls in high prevalence area of EC in China. Our prospective work investigated the joint and independent effects of N-nitrosamines co-exposure on the risk of EC development. Lipidomics analysis was employed to screen differential lipid biomarkers in serum, and the mediating effects of key lipid metabolites in the association between nitrosamines exposure and EC were evaluated. After adjustment for confounders, N-nitrosodimethylamine (NDMA, 1.79 (95% CI: 1.35, 2.39)), N-nitrosodi-n-propylamine (NDPA, 1.55 (95%CI: 1.15, 2.09)), N-nitrosodiethylamine (NDEA, 1.82 (95%CI: 1.36, 2.45)), N-nitrosodibutylamine (NDBA, 1.60 (95%CI: 1.20, 2.13)), N-nitrosomethylethylamine (NMEA, 1.81 (95%CI: 1.36, 2.41)) and N-nitrosomorpholine (NMOR, 1.84 (95%CI: 1.38, 2.45)) exposure all elevated the risks of EC development. The Bayesian kernel machine regression (BKMR) with hierarchical variable selection showed significant positive joint associations of urinary NDMA, NDPA, NMOR, NDEA and EC development, when all nitrosamines were at the 55th percentiles or above, compared with the median. Lipidomic screening of serum samples from the stages of BCH, DYS and EC suggested the perturbation in the biosynthesis of unsaturated fatty acid metabolism pathway, of which myristic acid (FFA 14:0), palmitoleic acid (FFA 16:1), docosahexaenoic acid (FFA 22:6) exerted remarkable mediation effects in the association between N-nitrosamines exposure and EC development. These findings provided new sights for screening early lipid biomarkers and intervention targets in human EC.

Associations of mixed metals exposure with cognitive impairment risk: a cross-sectional study in Chinese adults.

Associations between exposure to single metals and cognitive impairment or related outcomes have been reported in many previous studies. However, co-exposure to more than one metal is common situation. In recent years, studies on the effects of exposure to multiple metals on cognitive impairment or related outcomes have increased, but remain very limited, with a focus on populations with occupational exposure to metals, children, and adolescents. The potential relationships between exposure to metal mixtures and risk of cognitive impairment in adults remain to be clarified. To determine the associations between blood metal mixtures and cognitive impairment risk. Inductively coupled plasma mass spectrometry (ICP-MS) was utilized to detect the blood levels of lead (Pb), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), and zinc (Zn). Multivariable logistic regression and Bayesian kernel machine regression (BKMR) models were employed to assess the relationships of exposure to these blood metal mixtures with the risk of cognitive impairment. It was found that four metals (Pb, Fe, Cu, and Mg) were positively correlated with cognitive impairment in each single metal model. The association of Pb and Cu remained significant after adjusting for these six metals, with the odds ratios (95% confidence intervals) in the highest quartiles of 9.51 (4.41-20.54, p-trend <0.01) and 4.87 (2.17-10.95, p-trend <0.01), respectively. The BKMR models indicated that co-exposure levels of Ca, Cu, Fe, Mg, Pb, and Zn were related to increased risk of cognitive impairment at ≥25th percentile compared with median, and Pb and Cu mainly contributed to the joint effect. In addition, the interaction effects of Mg and Pb/Pb and Cu on the risk of cognitive impairment were observed. Co-exposure of six metals (Pb, Fe, Cu, Ca, Mg, and Zn) increased the risk of cognitive impairment in Chinese adults, with Pb and Cu likely to have greater impact. Potential interaction effects of Mg and Pb, Pb and Cu on the risk of cognitive impairment may exist.

Scene-aware Foveated Rendering.

We propose a new scene-aware foveated rendering method, which incorporates the scene awareness and characteristics of the human visual system into the mapping-based foveated rendering framework. First, we generate the conservative visual importance map that encodes the visual features of the scene, visual acuity, and gaze motion. Second, we construct the pixel size control map using a convolution kernel method. Third, we utilize the pixel size control map to guide the foveated rendering. At last, a temporal coherent refinement strategy is used to maintain the smooth foveated rendering for the adjacent frames. Compared to the state-of-the-art mapping-based foveated rendering methods using the same compression ratio, our method achieves smaller MSE, higher PSNR, and SSIM in the fovea, periphery, salient regions, and the whole image. We also conducted user studies, and the results proved that the perceptual quality of our method has a high visual similarity with the around truth rendered with the full resolution.

Prenatal metal exposures and kidney function in adolescence in Project Viva.

The developing kidney is vulnerable to prenatal environmental factors such as metal exposure, potentially altering the risk of later-life kidney dysfunction. This study examines the relationship between prenatal metal exposures, individually and as mixtures, and adolescent kidney function in Project Viva, a prospective longitudinal birth cohort in Massachusetts, USA. We used data on metals measured in blood during pregnancy including 15 in the first trimester and four in the second trimester. We calculated estimated glomerular filtration rate (eGFR) in adolescents (mean: 17.7 years) using cystatin C- (eGFRcys) and creatinine-based (eGFRcreat) equations for children. We used linear regression for single metal analyses, and Bayesian kernel machine regression and quantile-based g-computation for mixture analyses, adjusting for relevant covariates. To account for multiple comparisons in the single metal analyses, we applied the Holm-Bonferroni procedure to control the false discovery rate. This study included 371 participants with first trimester metals and adolescent eGFR, and 256 with second trimester metals. Each doubling in first trimester cadmium concentration was associated with lower adolescent eGFRcys (β:-1.51; 95% CI:-2.83, -0.18). Each doubling in first trimester chromium (β:-1.45; 95% CI:-2.71, -0.19), nickel (β:-1.91; 95% CI:-3.65, -0.16), and vanadium (β:-1.69; 95% CI:-3.21, -0.17) was associated with lower adolescent eGFRcreat. After adjusting for multiple comparisons, p-values for associations between adolescent eGFR and chromium, nickel, vanadium and cadmium did not meet the criteria for significance. Metal mixture analyses did not identify statistically significant associations with adolescent eGFR. These findings have important implications for future studies investigating the potential mechanisms through which prenatal metal exposures affect long-term kidney health in children.

Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011–2014

BackgroundVolatile organic compounds (VOCs) are ubiquitous environmental pollutants which have been suggested to have adverse effects on human health. While the influence of environmental pollutant exposures on periodontitis has attracted elevating attention in recent years, the epidemiological evidence on the association between VOCs exposure and periodontitis was scarce. This study aimed to investigate the potential mediating role of systemic inflammation factors in the complex association between VOCs exposure and periodontitis.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, we examined the impacts of VOCs exposure on periodontitis. Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. Mediation models were applied to assess the mediated effects of systemic inflammation on the association between mixed VOCs exposure and periodontitis. Besides, we analyzed the association between mixed VOCs exposure and periodontitis in stratified age, gender, and smoking status subgroups.Results1,551 participants were ultimately included for further analyses, of whom 45.20% suffering from periodontitis. Multivariable logistic regression and RCS identified positive associations between single urinary mVOCs and periodontitis (P < 0.05). Notably, BKMR and QGC models suggested that mixed VOCs exposure was significantly associated with periodontitis, with 2-Aminothiazoline-4-carboxylic acid (ATCA) contributing the most (conditional posterior inclusion probability = 0.997). Moreover, systemic inflammation markers (leukocyte and lymphocyte counts) were found to partly mediate the association between VOCs exposure and periodontitis (P < 0.05). No interaction effect was identified between mixed VOCs exposure and periodontitis in age, gender and smoking status subgroups (P > 0.05).ConclusionThis study demonstrated a positive association between VOCs exposure and periodontitis, which was potentially mediated by systemic inflammation factors. Further longitudinal researches are demanded to clarify the underlying mechanisms.

Regularized maximum likelihood estimation for the random coefficients model

. The random coefficients model Y i = β 0 i + β 1 i X 1 i + β 2 i X 2 i + … + β d i X d i , with 𝐗 i , Y i , 𝜷 i i.i.d, and 𝜷 i independent of 𝐗 i is often used to capture unobserved heterogeneity in a population. We propose a quasi-maximum likelihood method to estimate the joint density distribution of the random coefficient model. This method implicitly involves the inversion of the Radon transformation in order to reconstruct the joint distribution, and hence is an inverse problem. To add stability to the solution, we apply Tikhonov-type regularization methods. Nonparametric estimation for the joint density of β i = ( β 0 i , … , β d i ) based on kernel methods or Fourier inversion have been proposed in recent years. Most of these methods assume a heavy tailed design density f 𝐗 . We analyze the convergence of the quasi maximum likelihood method without assuming heavy tails for f 𝐗 and illustrate performance by applying the method on simulated and real data.

Using Four Machine Learning Methods to Analyze the Association Between Polycyclic Aromatic Hydrocarbons and Visual Impairment in American Adults: Evidence from NHANES

The causes of visual impairment are complex and may be influenced by exposure to environmental pollutants. Using data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES), we examined the association between exposure to ten polycyclic aromatic hydrocarbons (PAHs) and vision problems in 1149 U.S. adults. We employed various supervised learning methods, including variable selection techniques such as Lasso and elastic net, weighted quantile sum regression (WQS), and Bayesian kernel machine regression (BKMR), to assess the association between PAHs and the occurrence of visual impairments. The mediation effects between urinary 2-fluorene and inflammation were evaluated using mediation analysis. Both the lasso and elastic net models consistently identified two specific PAH congeners, 2-fluorene and 1-phenanthrene, as significant predictors. The WQS regression revealed a positive relationship between the PAH mixture and visual impairment, with notable contributions from urinary 2-fluorene (weight = 0.39) and 9-fluorene (weight = 0.21). BKMR analysis indicated that the likelihood of visual impairment increases with higher PAH exposure, showing a general upward trend. This trend also revealed a positive association between visual impairment and exposure to four specific PAH metabolites, including 2-fluorene. A significant mediation effect was observed for alkaline phosphatase (p = 0.03), with a proportion mediated of 10.48%. Our findings suggest a significant association between PAHs and visual impairment, with multiple statistical models consistently emphasizing the crucial role of 2-fluorene exposure. This study highlights the importance of considering environmental pollutants as significant contributors to visual health outcomes, providing insights for preventing visual impairment.

Hedging downside risk in agricultural commodities: A novel nonparametric kernel method

Using a nonparametric kernel method, this paper develops a weighted conditional value-at-risk hedge model to hedge downside risks in agricultural commodities. The model exhibits convexity, ensuring the acquisition of its global optimal solution. Simulations show that the nonparametric kernel method enhances the accuracy of the weighted conditional value-at-risk and hedge ratio determination, outperforming traditional estimation methods. Using major agricultural commodities, empirical evidence shows the superiority of the proposed model in reducing downside risks, compared to the minimum variance, minimum value-at-risk, and minimum conditional value-at-risk hedge models.

Integrating Graph Structures into Kernel Regression Models

Kernel methods are highly flexible and powerful tools for capturing complex, nonlinear relationships in data. In this paper, we propose a substantial extension of existing network-based regression models by integrating kernel methods with graph-theoretic constraints. Our approach builds upon the foundational work of Li et al. [1], who incorporated network cohesion into generalized linear models (GLMs). We extend their framework by introducing a kernelized regression model that allows for the modeling of nonlinear interactions while leveraging network data. This kernelized framework relaxes the linearity constraints of GLMs, making the model more versatile and capable of capturing complex patterns in high-dimensional spaces. We demonstrate the effectiveness of our approach using simulated and real-world datasets, such as the Teenage Friends and Lifestyle Study. Results show that our kernelized network regression model not only outperforms traditional linear and generalized linear models in predictive accuracy but also scales efficiently to larger datasets. Our work represents a significant advancement in the modeling of network-linked data, providing a robust, scalable, and interpretable framework that extends the application of kernel methods beyond their traditional constraints. Future directions include exploring more complex graph structures, such as weighted and directed graphs, and developing optimized algorithms for even larger datasets.

Combined short-term exposure to meteorological, pollution factors and pertussis in different groups from Jining, China.

Previous studies have typically explored daily lagged relationships among pertussis and meteorology, with little assessment of effect and interaction among pollutants mixtures. Our researchers collected pertussis cases data from 2017-2022 as well as meteorological and contaminative factors for the Jining region. First, we reported the application of the Moving Epidemic Method (MEM) to estimate epidemic threshold and intensity level. Then we developed a Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) model to assess single, multiple effects and interaction of meteorological and pollution factors on pertussis cases for different sex, delayed and epidemic threshold groups. There has been a yearly upward trend in the incidence of pertussis in Jining regions. High prevalence threshold years were in 2018-2019, the epidemic peak was mainly concentrated in 32 weeks. Totally, pertussis infections disease was separately 2.1% (95% confidence Interval (CI) = 1.3, 2.8) and 1.1% (95% CI = 0.3, 1.9) higher per decile increase in temperature and sulphur dioxide (SO2). And pertussis infections disease was 1.1% lower per decile increase in humidity. In the different stratified analyses, air pressure was a strong negative effect in males and in the lagged 11-20 days group, with 7.3 and 14.7%, respectively. Sulphur dioxide had a relatively weak positive effect in males, females and the group after 20 days lag, ranging from 0.5 to 0.6%. The main positive effectors affecting the onset of disease at low and high threshold levels were ozone (O3) and SO2, respectively, while the negative effectors were SO2 and carbon monoxide (CO), respectively. This is the first mathematically based study of seasonal threshold of pertussis in China, which allows accurate estimation of epidemic level. Our findings support that short-term exposure to pollutants is the risk factor for pertussis. We should concentrate on pollutants monitoring and effect modeling.

Gestational organophosphate esters (OPEs) and executive function in adolescence: The HOME Study

BackgroundEvidence from toxicological studies indicate organophosphate esters (OPEs) are neurotoxic, but few epidemiological studies investigated associations between gestational OPEs and executive function. ObjectiveTo examine the associations between gestational concentrations of OPE urinary metabolites and executive function at 12 years MethodsWe used data from 223 mother-adolescent dyads from the Health Outcomes of Measures of the Environment (HOME) Study. Women provided spot urine samples at 16 weeks gestation, 26 weeks gestation, and at delivery for quantification of bis(1,3-dichloro-2-propyl) phosphate, bis-2-chloroethyl phosphate (BCEP), diphenyl phosphate (DPHP), and di-n-butyl phosphate (DNBP). Executive function was assessed at age 12 years using the parent- and self-report Behavior Rating Inventory of Executive Function (BRIEF2). Covariate-adjusted associations between specific gravity-corrected OPEs and BRIEF2 scores were estimated using multiple informant models. Bayesian Kernel Machine Regression (BKMR) was used to assess the impact of all OPEs simultaneously. ResultsParent- and self-report BRIEF2 indices and composite scores were weakly to moderately correlated (rs=0.32-0.41). A natural-log unit increase in BCEP at 26 weeks was associated with approximately a 1-point increase on the self-report Cognitive Regulation Index [CRI] (95% CI 0.4, 2.3), the Emotion Regulation Index [ERI] (95% CI 0.3, 2.2), and the Global Executive Composite [GEC] (95% CI 0.4, 2.2), indicating poorer performance. Higher DPHP at 16 weeks was associated with lower parent-report GEC score (β=-1.1, 95% CI -2.3, -0.003). BKMR identified BCEP and DNBP at 26 weeks as important contributors to CRI and ERI, respectively. ConclusionOPE metabolites during gestational development, particularly BCEP, may influence adolescent executive function. However, since the FDR p-values failed to reach statistical significance, additional studies would benefit from using larger cohorts.