Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces.We propose a solution named manifold Bayesian regression.First a n...Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces.We propose a solution named manifold Bayesian regression.First a novel distance metric,the geodesic manifold distance,is introduced to replace the Euclidean distance.The problem of facial animation can be formulated as a sparse warping kernels regression problem,in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models.The geodesic manifold distance can be adopted in traditional regression methods,e.g.radial basis functions without much tuning.We put facial animation into the framework of Bayesian regression.Bayesian approaches provide an elegant way of dealing with noise and uncertainty.After the covariance matrix is properly modulated,Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results.The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.展开更多
Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs...Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs)play a crucial role in the structural design of fault-crossing engineering and in the hazard analysis of fault-intensive areas.In this study,a new global coseismic surface rupture database was constructed by compiling 171 earthquake events(Mw:5.5-7.9)that caused surface rupture.In contrast to the fault classification in traditional empirical relationships,this study categorizes earthquake events as strike-slip,dip-slip,and oblique-slip.CSRPMs utilizing Bayesian ridge regression(BRR)were developed to estimate parameters such as surface rupture length,average displacement,and maximum displacement.Based on Bayesian theory,BRR combines the benefits of both ridge regression and Bayesian linear regression.This approach effectively addresses the issue of overfitting while ensuring the strong model robustness.The reliability of the CSRPMs was validated by residual analysis and comparison with post-earthquake observations from the 2023 Türkiye earthquake doublet.The BRR-CSRPMs with new fault classification criteria are more suitable for the probabilistic hazard analysis of complex fault systems and dislocation design of fault-crossing engineering.展开更多
Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumpt...Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and an...Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and anemia among pregnant women,as well as involved biological mechanisms.So,we conducted this study including 1717 late pregnant women fromZunyi Birth Cohort and collected urine samples for PAHs metabolites detection.Logistic regression and restricted cubic spline regression were used to examine exposuredisease risks and dose-response relationships.We conducted Bayesian kernel machine regression,weighted quantile sum regression,and quantile g-computation regression to fit the joint impacts of multiple PAHs in the real-world scenario on hypocalcemia and anemia.Results showed single exposure to 2-OHNap,2-OHFlu,9-OHFlu,1-OHPhe,2-OHPhe,3-OHPhe,and 1-OHPyr(all P-trend<0.05)increased the risks of hypocalcemia and anemia.Moreover,PAHs mixture was significantly related to higher risks of hypocalcemia and anemia,with 3-OHPhe and 1-OHPyr identified as their major drivers,respectively.Importantly,hypocalcemia served as a significant biological mechanism responsible for PAHs and anemia.Our findings suggest that individual and joint exposure to PAHs during late pregnancy elevate the anemia risk,and calcium supplementation might be a low-cost intervention target for reducing the PAHs-related impairment on anemia for pregnant women.展开更多
Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1...Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1059 participants were included,and eleven metals in whole blood samples and serum IgA,IgG,IgE and IgM levels were measured.Linear regression,quantile-based g-computation(QGC),and Bayesian kernel machine regression(BKMR)models were used to evaluate the associations.Compared with the first tertile of metal levels,arsenic(As)was negatively associated with IgE(β=-0.25,95%confidence interval(CI)=-0.48 to-0.02).Moreover,significant associations of manganese(Mn)with IgA,IgG and IgM were demonstrated(β=0.10,95%CI=0.04 to 0.18;β=0.07,95%CI=0.03 to 0.12;β=0.10,95%CI=0.03 to 0.18,respectively).Cadmium(Cd)were associated with higher levels of IgM.QGC models showed the positive association of the metalmixtures with IgA and IgG,with Mn playing amajor role.Mn and Cd had positive contributions to IgM,while As had negative contributions to IgE.In the BKMR models,the latent continuous outcomes of IgA and IgG showed a significant increase when all the metals were at their 60th percentile or above compared to those at their 50th percentile.Therefore,exposure to metals was associated with maternal Igs,and mainly showed that Mn was associated with increased levels of IgA,IgG and IgM,and As was associated with low IgE levels.展开更多
Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance...Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance of structures have epistemic or aleatory uncertainties.Furthermore,sliding failure is one of the most important failure modes of caisson breakwaters.In most previous studies,for assessment purposes,uncertainties,such as wave and wave period variation,were ignored.Therefore,in this study,Bayesian reliability analysis is implemented to assess the failure probability of the sliding of Tombak port breakwater in the Persian Gulf.The mean and standard deviations were taken as random variables to consider dismissed uncertainties.For this purpose,the frst-order reliability method(FORM)and the frst principal curvature cor-rection in FORM are used to calculate the reliability index.The performances of these methods are verifed by importance sampling through Monte Carlo simulation(MCS).In addition,the reliability index sensitivities of each random variable are calculated to evaluate the importance of diferent random variables while calculating the caisson sliding.The results show that the reliability index is most sensitive to the coefcients of friction,wave height,and caisson weight(or concrete density).The sensitivity of the failure probability of each of the random variables and their uncertainties are calculated by the derivative method.Finally,the Bayesian regression is implemented to predict the statistical properties of breakwater sliding with non-informative priors,which are compared to Goda’s formulation,used in breakwater design standards.The analysis shows that the model posterior for the sliding of a caisson breakwater has a mean and standard deviation of 0.039 and 0.022,respectively.A normal quantile analysis and residual analysis are also performed to evaluate the correctness of the model responses.展开更多
Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear re...Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.展开更多
Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain do...Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain dominant variables.However,the BART model fails to fully utilize the knowledge.To tackle this problem,the paper introduces a modification to BART known as the Partially Fixed BART model.By fixing a portion of the trees’structure,this model enables more efficient utilization of prior knowledge,resulting in enhanced estimation accuracy.Moreover,the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent.Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.展开更多
In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it ...In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.展开更多
This study computes the durability of Return on Assets （ROA） in small and medium enterprises from different sample datasets. Utilizing information from the Financial Statements Statistics of Corporations by Industry...This study computes the durability of Return on Assets （ROA） in small and medium enterprises from different sample datasets. Utilizing information from the Financial Statements Statistics of Corporations by Industry, it verifies the precision of correlation coefficients using the Non-iterative Bayesian-based Imputation （NIBAS） and multiple imputation method for all combinations of common variables with auxiliary files. The following are the three important findings of this paper. First, statistical matching estimates of higher precision can be obtained using key variable sets with higher canonical correlation coefficients. Second, even if the key variable sets have high canonical correlation coefficients, key variables that are correlated extremely strongly with target variables and have high kurtosis should not be used. Finally, using auxiliary flies can improve the precision of statistical matching estimates. Accordingly, the durability of ROA in small and medium enterprises is computed. The author finds that the series of ROA correlation fluctuates for smaller enterprises compared to larger ones, and thus, the vulnerability of ROA in small and medium enterprises can be clarified via statistical matching.展开更多
Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations betwe...Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations between urinary metals(cobalt(Co)and molybdenum(Mo))and diabetes-related indicators(fasting plasma glucose(FPG),hemoglobin A1c(HbA1c),homeostasis model assessment for insulin resistance(HOMA-IR),and insulin)in a cross-sectional study based on the United States National Health and Nutrition Examination Survey.The urinary metal concentrations of 1423 eligible individuals were stratified on the basis of the quartile distribution.Our results showed that the urinary Co level in males at the fourth quartile(Q4)was strongly correlated with increased FPG(β=0.61,95%CI:0.17–1.04),HbA1c(β=0.31,95%CI:0.09–0.54),insulin(β=8.18,95%CI:2.84–13.52),and HOMA–IR(β=3.42,95%CI:1.40–5.44)when compared with first quartile(Q1).High urinary Mo levels(Q4 vs.Q1)were associated with elevated FPG(β=0.46,95%CI:0.17–0.75)and HbA1c(β=0.27,95%CI:0.11–0.42)in the overall population.Positive linear dose-response associations were observed between urinary Co and insulin(Pnonlinear=0.513)and HOMA–IR(Pnonlinear=0.736)in males,as well as a positive linear dose-response relationship between urinary Mo and FPG(Pnonlinear=0.826)and HbA1c(Pnonlinear=0.376)in the overall population.Significant sex-specific and dose-response relationships were observed between urinary metals(Co and Mo)and diabetes-related indicators,and the potential mechanisms should be further investigated.展开更多
The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In a...The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In addition,any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high.In this paper,the authors propose a novel algorithm for dealing with MVs depending on the feature selec-tion(FS)of similarity classifier with fuzzy entropy measure.The proposed algo-rithm imputes MVs in cumulative order.The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure.The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression(BRR)technique.Furthermore,any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature.The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs gener-ated from the three missingness mechanisms.The evaluation metrics of mean abso-lute error(MAE),root mean square error(RMSE)and coefficient of determination(R2 score)were used to measure the performance.The results exhibited that perfor-mance vary depending on the size of the dataset,amount of MVs and the missing-ness mechanism type.Moreover,compared to other methods,the results showed that the proposed method gives better accuracy and less error in most cases.展开更多
This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECL...This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECLSK).Bayesian Additive Regression Trees(BARTs)are the primary methodology used,and average,conditional,and individual-level treatment impacts on children’s mathematics achievement are estimated.BART estimates a negative Average Treatment Effect(ATE)of−1.5421 with increasingly larger adverse effects for children with higher Socioeconomic Status(SES),suggesting diminishing marginal returns.This finding demonstrates the strength of BART to detect nonlinear moderation patterns that are evasive to conventional models.It also implies that Head Start and other preschool interventions will yield greater policy returns when targeted at low-SES children,in order to enable more efficient and fair distribution of public funds.For comparison,Causal Forest estimates a larger ATE(−2.4340)and determines SES to be the overarching moderator,while Propensity Score Matching offers a conservative estimate(−1.2606)without considering effect heterogeneity.These findings underscore the utility of BART in estimating subtle,SES-varying effects of Head Start,and suggest the potential value of more targeted intervention strategies guided by adaptive causal inference.展开更多
The association between the exposure of organochlorine pesticides(OCPs)and serum uric acid(UA)levels remained uncertain.In this study,to investigate the combined effects of OCP mixtures on hyperuricemia,we analyzed se...The association between the exposure of organochlorine pesticides(OCPs)and serum uric acid(UA)levels remained uncertain.In this study,to investigate the combined effects of OCP mixtures on hyperuricemia,we analyzed serum OCPs and UA levels in adults from the National Health and Nutrition Examination Survey(2005–2016).Four statistical models including weighted logistic regression,weighted quantile sum(WQS),quantile g-computation(QGC),and bayesian kernel machine regression(BKMR)were used to assess the relationship between mixed chemical exposures and hyperuricemia.Subgroup analyses were conducted to explore potential modifiers.Among 6,529 participants,the prevalence of hyperuricemia was 21.15%.Logistic regression revealed a significant association between both hexachlorobenzene(HCB)and trans-nonachlor and hyperuricemia in the fifth quintile(OR:1.54,95%CI:1.08–2.19;OR:1.58,95%CI:1.05–2.39,respectively),utilizing the first quintile as a reference.WQS and QGC analyses showed significant overall effects of OCPs on hyperuricemia,with an OR of 1.25(95%CI:1.09–1.44)and 1.20(95%CI:1.06–1.37),respectively.BKMR indicated a positive trend between mixed OCPs and hyperuricemia,with HCB having the largest weight in all three mixture analyses.Subgroup analyses revealed that females,individuals aged 50 years and above,and those with a low income were more vulnerable to mixed OCP exposure.These results highlight the urgent need to protect vulnerable populations from OCPs and to properly evaluate the health effects of multiple exposures on hyperuricemia using mutual validation approaches.展开更多
Studies have shown that exposure to phthalates can affect neonatal birth weight.However,epidemiological evidence on the mediating role of maternal thyroid hormones is limited.Therefore,this study,based on the Complian...Studies have shown that exposure to phthalates can affect neonatal birth weight.However,epidemiological evidence on the mediating role of maternal thyroid hormones is limited.Therefore,this study,based on the Compliance Birth Cohort,aimed to reveal the potential mediating function of maternal thyroid hormones during pregnancy between phthalic acid ester(PAE)exposure and neonatal birth weight.The study included 1274 mother−infant pairs.Linear regression analysis revealed a negative association between MIBP and neonatal birth weight(β=−62.236;95%CI:−118.842,−5.631).Bayesian kernel-machine regression(BKMR)indicated a nonlinear negative association between PAE metabolites(PAEs)and birth weight.Linear regression analysis revealed a positive association between neonatal birth weight and FT3(β=41.605;95%CI:2.631,80.380).The BKMR model also found a positive association between thyroid hormones and birth weight but in a nonlinear manner.Additionally,linear regression analyses showed that TSH,TT3,TT4,FT3,and FT4 were associated with PAEs.The BKMR model revealed an inverted U-shaped association of PAEs with TT3 and FT3 and a nonlinear association with TSH,TT4,and FT4.Structural equation modeling revealed that MMP,MIBP,MBP,MEHP,MOP,MBZP,and MEOHP contributed to a net reduction in neonatal birth weight of 32 g through the TT3,FT3,TT4,and FT4 pathways.The findings suggest that exposure to PAEs during pregnancy leads to a reduction in neonatal birth weight,possibly due to the involvement of maternal thyroid hormones as mediators.Controlling maternal thyroid hormone levels during pregnancy may be a viable method to reduce the harmful effects of phthalate exposure on the developing fetus.展开更多
Exposure to metals may potentially impact cognitive health in the elderly;however,the evidence remains ambiguous.The specific role of serum folate in this relationship is also unclear.We aimed to evaluate the individu...Exposure to metals may potentially impact cognitive health in the elderly;however,the evidence remains ambiguous.The specific role of serum folate in this relationship is also unclear.We aimed to evaluate the individual and joint impact of metals on cognition in the elderly from the United States and explore the potential mediating effect of serum folate.Data from the NHANES 2011-2014 were used,with inductively coupled plasma mass spectrometry(ICP-MS)employed to measure blood metal concentrations.Cognitive function was assessed using tests for immediate,delayed,and working memory:Immediate Recall test(IRT),the Delayed Recall test(DRT),the Animal Fluency test(AFT),and the Digit Symbol Substitution test(DSST).Generalized linear regression models(GLMs),Bayesian kernel machine regression model(BKMR),and quantile g-computation(QG-C)models were used to assess associations between metals(lead,cadmium,mercury,selenium,manganese)and cognition,with mediation analyses examining serum folate’s involvement in metal effects.This study included 2002 participants aged≥60.GLMs revealed the negative association between cadmium and the z-scores of IRT(β:-0.17,95%CI:-0.30,-0.04)and DSST(β:-0.15,95%CI:-0.27,-0.04),with negative effects also observed in the BKMR and QG-C models.Selenium displayed significantly positive association with cognition across various statistical models,including GLMs,QG-C,and BKMR.Serum folate played a mediating role in the effects of cadmium and selenium exposure on DSST z-scores,with a proportion of mediation of 17%and 10%,respectively.Our study assessed the impact of metal mixtures on cognition in the elderly population,finding that high selenium level was strongly associated with better cognitive performance,while cadmium was associated with lower cognitive function scores.Serum folate might partially mediate the association between cadmium,selenium,and DSST z-scores.展开更多
基金Project supported by the National Natural Science Foundation of China(No.60272031)National Basic Research Program(973)of China(No.2002CB312101)the Technology Plan Program of Zhejiang Province(No.2003C21010),China
文摘Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces.We propose a solution named manifold Bayesian regression.First a novel distance metric,the geodesic manifold distance,is introduced to replace the Euclidean distance.The problem of facial animation can be formulated as a sparse warping kernels regression problem,in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models.The geodesic manifold distance can be adopted in traditional regression methods,e.g.radial basis functions without much tuning.We put facial animation into the framework of Bayesian regression.Bayesian approaches provide an elegant way of dealing with noise and uncertainty.After the covariance matrix is properly modulated,Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results.The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.
基金Foundation of China under Grant Nos. U2139207 and 52378517the Natural Science Foundation of Hubei Province under Grant No. 2023AFB934
文摘Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs)play a crucial role in the structural design of fault-crossing engineering and in the hazard analysis of fault-intensive areas.In this study,a new global coseismic surface rupture database was constructed by compiling 171 earthquake events(Mw:5.5-7.9)that caused surface rupture.In contrast to the fault classification in traditional empirical relationships,this study categorizes earthquake events as strike-slip,dip-slip,and oblique-slip.CSRPMs utilizing Bayesian ridge regression(BRR)were developed to estimate parameters such as surface rupture length,average displacement,and maximum displacement.Based on Bayesian theory,BRR combines the benefits of both ridge regression and Bayesian linear regression.This approach effectively addresses the issue of overfitting while ensuring the strong model robustness.The reliability of the CSRPMs was validated by residual analysis and comparison with post-earthquake observations from the 2023 Türkiye earthquake doublet.The BRR-CSRPMs with new fault classification criteria are more suitable for the probabilistic hazard analysis of complex fault systems and dislocation design of fault-crossing engineering.
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
基金The maize and wheat data set used in this study comes from the Drought Tolerance Maize for Africa Project and from CIMMYT's Global Wheat Program.We are thankful to everyone who generated the data used in this article.
文摘Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金supported by the Science&Technology Program of Guizhou Province(Nos.QKHPTRC-GCC[2022]039-1,QKHPTRC-CXTD[2022]014,and QKHHBZ[2020]3002)the Scientific Research Programof Guizhou Provincial Department of Education(No.QJJ[2023]019).
文摘Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and anemia among pregnant women,as well as involved biological mechanisms.So,we conducted this study including 1717 late pregnant women fromZunyi Birth Cohort and collected urine samples for PAHs metabolites detection.Logistic regression and restricted cubic spline regression were used to examine exposuredisease risks and dose-response relationships.We conducted Bayesian kernel machine regression,weighted quantile sum regression,and quantile g-computation regression to fit the joint impacts of multiple PAHs in the real-world scenario on hypocalcemia and anemia.Results showed single exposure to 2-OHNap,2-OHFlu,9-OHFlu,1-OHPhe,2-OHPhe,3-OHPhe,and 1-OHPyr(all P-trend<0.05)increased the risks of hypocalcemia and anemia.Moreover,PAHs mixture was significantly related to higher risks of hypocalcemia and anemia,with 3-OHPhe and 1-OHPyr identified as their major drivers,respectively.Importantly,hypocalcemia served as a significant biological mechanism responsible for PAHs and anemia.Our findings suggest that individual and joint exposure to PAHs during late pregnancy elevate the anemia risk,and calcium supplementation might be a low-cost intervention target for reducing the PAHs-related impairment on anemia for pregnant women.
基金supported by the National Natural Science Foundation of China(No.U22A20358)Zhejiang Provincial Program for the Cultivation of High-Level Innovative Health Talents(No.2020-18).
文摘Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1059 participants were included,and eleven metals in whole blood samples and serum IgA,IgG,IgE and IgM levels were measured.Linear regression,quantile-based g-computation(QGC),and Bayesian kernel machine regression(BKMR)models were used to evaluate the associations.Compared with the first tertile of metal levels,arsenic(As)was negatively associated with IgE(β=-0.25,95%confidence interval(CI)=-0.48 to-0.02).Moreover,significant associations of manganese(Mn)with IgA,IgG and IgM were demonstrated(β=0.10,95%CI=0.04 to 0.18;β=0.07,95%CI=0.03 to 0.12;β=0.10,95%CI=0.03 to 0.18,respectively).Cadmium(Cd)were associated with higher levels of IgM.QGC models showed the positive association of the metalmixtures with IgA and IgG,with Mn playing amajor role.Mn and Cd had positive contributions to IgM,while As had negative contributions to IgE.In the BKMR models,the latent continuous outcomes of IgA and IgG showed a significant increase when all the metals were at their 60th percentile or above compared to those at their 50th percentile.Therefore,exposure to metals was associated with maternal Igs,and mainly showed that Mn was associated with increased levels of IgA,IgG and IgM,and As was associated with low IgE levels.
文摘Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance of structures have epistemic or aleatory uncertainties.Furthermore,sliding failure is one of the most important failure modes of caisson breakwaters.In most previous studies,for assessment purposes,uncertainties,such as wave and wave period variation,were ignored.Therefore,in this study,Bayesian reliability analysis is implemented to assess the failure probability of the sliding of Tombak port breakwater in the Persian Gulf.The mean and standard deviations were taken as random variables to consider dismissed uncertainties.For this purpose,the frst-order reliability method(FORM)and the frst principal curvature cor-rection in FORM are used to calculate the reliability index.The performances of these methods are verifed by importance sampling through Monte Carlo simulation(MCS).In addition,the reliability index sensitivities of each random variable are calculated to evaluate the importance of diferent random variables while calculating the caisson sliding.The results show that the reliability index is most sensitive to the coefcients of friction,wave height,and caisson weight(or concrete density).The sensitivity of the failure probability of each of the random variables and their uncertainties are calculated by the derivative method.Finally,the Bayesian regression is implemented to predict the statistical properties of breakwater sliding with non-informative priors,which are compared to Goda’s formulation,used in breakwater design standards.The analysis shows that the model posterior for the sliding of a caisson breakwater has a mean and standard deviation of 0.039 and 0.022,respectively.A normal quantile analysis and residual analysis are also performed to evaluate the correctness of the model responses.
基金supported by the National Natural Science Foundation of China[rant Nos.81960583,81760577,81560523 and 82260629]Major Science and Technology Projects in Guangxi[GKAA22399 and AA22096026]+3 种基金the Guangxi Science and Technology Development Project[Grant Nos.AD 17129003 and 18050005]the Guangxi Natural Science Foundation for Innovation Research Team[2019GXNSFGA245002]the Innovation Platform and Talent Plan in Guilin[20220120-2]the Guangxi Scholarship Fund of Guangxi Education Department of China。
文摘Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.
文摘Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain dominant variables.However,the BART model fails to fully utilize the knowledge.To tackle this problem,the paper introduces a modification to BART known as the Partially Fixed BART model.By fixing a portion of the trees’structure,this model enables more efficient utilization of prior knowledge,resulting in enhanced estimation accuracy.Moreover,the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent.Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.
基金the National Natural Science Foundation of China(Grant No.12072261)。
文摘In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.
文摘This study computes the durability of Return on Assets （ROA） in small and medium enterprises from different sample datasets. Utilizing information from the Financial Statements Statistics of Corporations by Industry, it verifies the precision of correlation coefficients using the Non-iterative Bayesian-based Imputation （NIBAS） and multiple imputation method for all combinations of common variables with auxiliary files. The following are the three important findings of this paper. First, statistical matching estimates of higher precision can be obtained using key variable sets with higher canonical correlation coefficients. Second, even if the key variable sets have high canonical correlation coefficients, key variables that are correlated extremely strongly with target variables and have high kurtosis should not be used. Finally, using auxiliary flies can improve the precision of statistical matching estimates. Accordingly, the durability of ROA in small and medium enterprises is computed. The author finds that the series of ROA correlation fluctuates for smaller enterprises compared to larger ones, and thus, the vulnerability of ROA in small and medium enterprises can be clarified via statistical matching.
基金supported by the National Institutes of Health (U.S.)-(NIH Grant Number: 1R01ES029082)
文摘Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations between urinary metals(cobalt(Co)and molybdenum(Mo))and diabetes-related indicators(fasting plasma glucose(FPG),hemoglobin A1c(HbA1c),homeostasis model assessment for insulin resistance(HOMA-IR),and insulin)in a cross-sectional study based on the United States National Health and Nutrition Examination Survey.The urinary metal concentrations of 1423 eligible individuals were stratified on the basis of the quartile distribution.Our results showed that the urinary Co level in males at the fourth quartile(Q4)was strongly correlated with increased FPG(β=0.61,95%CI:0.17–1.04),HbA1c(β=0.31,95%CI:0.09–0.54),insulin(β=8.18,95%CI:2.84–13.52),and HOMA–IR(β=3.42,95%CI:1.40–5.44)when compared with first quartile(Q1).High urinary Mo levels(Q4 vs.Q1)were associated with elevated FPG(β=0.46,95%CI:0.17–0.75)and HbA1c(β=0.27,95%CI:0.11–0.42)in the overall population.Positive linear dose-response associations were observed between urinary Co and insulin(Pnonlinear=0.513)and HOMA–IR(Pnonlinear=0.736)in males,as well as a positive linear dose-response relationship between urinary Mo and FPG(Pnonlinear=0.826)and HbA1c(Pnonlinear=0.376)in the overall population.Significant sex-specific and dose-response relationships were observed between urinary metals(Co and Mo)and diabetes-related indicators,and the potential mechanisms should be further investigated.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU)Jeddah,Saudi Arabia,under grant No.(PH:13-130-1442).
文摘The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In addition,any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high.In this paper,the authors propose a novel algorithm for dealing with MVs depending on the feature selec-tion(FS)of similarity classifier with fuzzy entropy measure.The proposed algo-rithm imputes MVs in cumulative order.The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure.The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression(BRR)technique.Furthermore,any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature.The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs gener-ated from the three missingness mechanisms.The evaluation metrics of mean abso-lute error(MAE),root mean square error(RMSE)and coefficient of determination(R2 score)were used to measure the performance.The results exhibited that perfor-mance vary depending on the size of the dataset,amount of MVs and the missing-ness mechanism type.Moreover,compared to other methods,the results showed that the proposed method gives better accuracy and less error in most cases.
文摘This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECLSK).Bayesian Additive Regression Trees(BARTs)are the primary methodology used,and average,conditional,and individual-level treatment impacts on children’s mathematics achievement are estimated.BART estimates a negative Average Treatment Effect(ATE)of−1.5421 with increasingly larger adverse effects for children with higher Socioeconomic Status(SES),suggesting diminishing marginal returns.This finding demonstrates the strength of BART to detect nonlinear moderation patterns that are evasive to conventional models.It also implies that Head Start and other preschool interventions will yield greater policy returns when targeted at low-SES children,in order to enable more efficient and fair distribution of public funds.For comparison,Causal Forest estimates a larger ATE(−2.4340)and determines SES to be the overarching moderator,while Propensity Score Matching offers a conservative estimate(−1.2606)without considering effect heterogeneity.These findings underscore the utility of BART in estimating subtle,SES-varying effects of Head Start,and suggest the potential value of more targeted intervention strategies guided by adaptive causal inference.
基金funded by the National Nature Science Foundation of China(82173613,82373681)the Scientific Project of Shanghai Municipal Health Commission(202140018).
文摘The association between the exposure of organochlorine pesticides(OCPs)and serum uric acid(UA)levels remained uncertain.In this study,to investigate the combined effects of OCP mixtures on hyperuricemia,we analyzed serum OCPs and UA levels in adults from the National Health and Nutrition Examination Survey(2005–2016).Four statistical models including weighted logistic regression,weighted quantile sum(WQS),quantile g-computation(QGC),and bayesian kernel machine regression(BKMR)were used to assess the relationship between mixed chemical exposures and hyperuricemia.Subgroup analyses were conducted to explore potential modifiers.Among 6,529 participants,the prevalence of hyperuricemia was 21.15%.Logistic regression revealed a significant association between both hexachlorobenzene(HCB)and trans-nonachlor and hyperuricemia in the fifth quintile(OR:1.54,95%CI:1.08–2.19;OR:1.58,95%CI:1.05–2.39,respectively),utilizing the first quintile as a reference.WQS and QGC analyses showed significant overall effects of OCPs on hyperuricemia,with an OR of 1.25(95%CI:1.09–1.44)and 1.20(95%CI:1.06–1.37),respectively.BKMR indicated a positive trend between mixed OCPs and hyperuricemia,with HCB having the largest weight in all three mixture analyses.Subgroup analyses revealed that females,individuals aged 50 years and above,and those with a low income were more vulnerable to mixed OCP exposure.These results highlight the urgent need to protect vulnerable populations from OCPs and to properly evaluate the health effects of multiple exposures on hyperuricemia using mutual validation approaches.
基金funded by the National Key Research and Development Program of China(2018YF-C1004300,2018YFC1004302)Science&Technology Program of Guizhou Province(QKHHBZ[2020]3002,QKHPTRC-GCC[2022]039-1,QKHPTRC-CXTD[2022]014)+1 种基金Natural Science Foundation of Guizhou Province(QKH-J[2022]YB614)Science and Technology Plan Project of Zunyi City(ZKH-J(2021)294).
文摘Studies have shown that exposure to phthalates can affect neonatal birth weight.However,epidemiological evidence on the mediating role of maternal thyroid hormones is limited.Therefore,this study,based on the Compliance Birth Cohort,aimed to reveal the potential mediating function of maternal thyroid hormones during pregnancy between phthalic acid ester(PAE)exposure and neonatal birth weight.The study included 1274 mother−infant pairs.Linear regression analysis revealed a negative association between MIBP and neonatal birth weight(β=−62.236;95%CI:−118.842,−5.631).Bayesian kernel-machine regression(BKMR)indicated a nonlinear negative association between PAE metabolites(PAEs)and birth weight.Linear regression analysis revealed a positive association between neonatal birth weight and FT3(β=41.605;95%CI:2.631,80.380).The BKMR model also found a positive association between thyroid hormones and birth weight but in a nonlinear manner.Additionally,linear regression analyses showed that TSH,TT3,TT4,FT3,and FT4 were associated with PAEs.The BKMR model revealed an inverted U-shaped association of PAEs with TT3 and FT3 and a nonlinear association with TSH,TT4,and FT4.Structural equation modeling revealed that MMP,MIBP,MBP,MEHP,MOP,MBZP,and MEOHP contributed to a net reduction in neonatal birth weight of 32 g through the TT3,FT3,TT4,and FT4 pathways.The findings suggest that exposure to PAEs during pregnancy leads to a reduction in neonatal birth weight,possibly due to the involvement of maternal thyroid hormones as mediators.Controlling maternal thyroid hormone levels during pregnancy may be a viable method to reduce the harmful effects of phthalate exposure on the developing fetus.
基金supported by the National Natural Science Foundation of China(No.81973007).
文摘Exposure to metals may potentially impact cognitive health in the elderly;however,the evidence remains ambiguous.The specific role of serum folate in this relationship is also unclear.We aimed to evaluate the individual and joint impact of metals on cognition in the elderly from the United States and explore the potential mediating effect of serum folate.Data from the NHANES 2011-2014 were used,with inductively coupled plasma mass spectrometry(ICP-MS)employed to measure blood metal concentrations.Cognitive function was assessed using tests for immediate,delayed,and working memory:Immediate Recall test(IRT),the Delayed Recall test(DRT),the Animal Fluency test(AFT),and the Digit Symbol Substitution test(DSST).Generalized linear regression models(GLMs),Bayesian kernel machine regression model(BKMR),and quantile g-computation(QG-C)models were used to assess associations between metals(lead,cadmium,mercury,selenium,manganese)and cognition,with mediation analyses examining serum folate’s involvement in metal effects.This study included 2002 participants aged≥60.GLMs revealed the negative association between cadmium and the z-scores of IRT(β:-0.17,95%CI:-0.30,-0.04)and DSST(β:-0.15,95%CI:-0.27,-0.04),with negative effects also observed in the BKMR and QG-C models.Selenium displayed significantly positive association with cognition across various statistical models,including GLMs,QG-C,and BKMR.Serum folate played a mediating role in the effects of cadmium and selenium exposure on DSST z-scores,with a proportion of mediation of 17%and 10%,respectively.Our study assessed the impact of metal mixtures on cognition in the elderly population,finding that high selenium level was strongly associated with better cognitive performance,while cadmium was associated with lower cognitive function scores.Serum folate might partially mediate the association between cadmium,selenium,and DSST z-scores.
