This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the infl...This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.展开更多
Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma we...Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.展开更多
Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presen...Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presence or absence). It has been shown that such a binary trait also can be analyzed with the simple linear regression, subject to virtually no loss in power compared to the generalized linear model analysis. Binary trait is a special case of a multiple categorical trait (e.g., low, medium or high). We propose a mechanism to decompose a multiple categorical trait into an array of correlated binary variables. The categorical trait turned multiple binary traits are analyzed with a multivariate linear regression method. Turning the problem of categorical trait mapping into that of multivariate mapping allows the exploration of pleiotropic effects of QTL for different categories. Efficiency of the method is verified through a series of simulation experiments.展开更多
Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. ...Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.展开更多
This paper constructs and studies a nonlinear multivariate regression-tensor model for substantiation of necessary/sufficient conditions of optimization of technological calculation of multifactor physical and chemica...This paper constructs and studies a nonlinear multivariate regression-tensor model for substantiation of necessary/sufficient conditions of optimization of technological calculation of multifactor physical and chemical process of hardening of complex composite media for metal coatings. An adaptive a posteriori procedure for parametric formation of the target quality functional of integrative physical and mechanical properties of the designed metal coating has been proposed. The results of the research may serve as elements of a mathematical language when creating automated design of precision nanotechnologies for surface hardening of complex composite metal coatings on the basis of complex tribological and anticorrosive tests.展开更多
An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals.Such models should enhance the understanding of the energy behavior of ...An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals.Such models should enhance the understanding of the energy behavior of a building,the impact of the external variables,and the causes of inefficiencies.Energy Signatures can fill this role,with particular regard to the consumption due to air conditioning.Univariate models,neglecting the impact of solar radiation,have been widely adopted for Energy Signature analysis.This paper presents Multivariable Energy Signatures considering outdoor temperature and solar radiation.The application on a real-world dataset of multivariable non-parametric approaches stands out from previous works in the ES sector.This led to a mean improvement of 0.768 to 0.804 of the coefficients of determination calculated over 103 world real-case studies.Moreover,Neural Networks outperformed several literature algorithms regarding accuracy,robustness,and scalability.The paper also discusses issues regarding the time resolution of input data and introduces appropriate visualization tools to employ Multivariable Energy Signatures as diagnostic tools.展开更多
In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically ind...In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.展开更多
Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for...Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. Methods： RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas （TCGA）, including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated IncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify IncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified IncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. Results： We identified nine HNSCC-relevant IncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CY-I-OR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival （OS） and disease free survival （DFS） of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum （DDP）. HCG22, the most dramatically down-regulated IncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values in- dependent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. Conclusions： Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated IncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these IncRNAs in HNSCC as well as clinical applications.展开更多
One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficu...One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.展开更多
Soybean (Glycine max L. Merr.) adaptation to new environments has been hard to predict based on maturity group. The aim of this study was to evaluate the performance of 14 soybean genotypes, from the Soybean Breeding ...Soybean (Glycine max L. Merr.) adaptation to new environments has been hard to predict based on maturity group. The aim of this study was to evaluate the performance of 14 soybean genotypes, from the Soybean Breeding Program of the Federal University of Uberlandia, in their adaptive capacity and seed yield stability at 3 locations and 2 growing seasons. For the adaptability and stability analysis the Toler and Centroid methods were used;5 genotypic groups were identified in the first whereas 4 groups were identified in the latter. By the Toler method group A was composed by 4 genotypes, UFU-001, UFU-003, UFU-0010, and UFU-001. They showed a convex pattern of adaptability and stability. In contrast, the genotypes UFU-008 and UFU-0013 were classified in Group E with a concave pattern of adaptability and stability. Regarding results from the Centroid method, the Genotype UFU-002, with higher seed yield than average, was the only genotype in Ideotype VI with moderate adaptability to favorable environments. In contrast, 10 genotypes were included in the Ideotype V, of medium general adaptability. The genotypes UFU-001, UFU-002, UFU-006, UFU-0010, and UFU-0011 were recommended for use in the Brazilian Cerrado growing region. These genotypes had high seed yield potential in high quality environments.展开更多
We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displaceme...We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displacement (LLID). The results of applying the cluster analysis technique to the sampling data showed that maintenance operation is required for approximately 10% of the total lots, and these lots were further classified into three groups according to the degree of maintenance need. To analyze the background factors for detecting abnormal LLID lots, a principal component analysis was performed;the results showed that the first principal component represents LLIDs from the viewpoints of the rail structure, equipment, and operating conditions. Binomial and ordinal logit regression models (LRMs) were used to quantitatively investigate the determinants of abnormal LLIDs. Binomial LRM was used to characterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significant impact on LLIDs.展开更多
In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of...In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data （MASAL）, which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.展开更多
基金supported by The Hong Kong Polytechnic University through the project RU3Ythe Research Grant Council through the project PolyU 5128/13E+1 种基金National Natural Science Foundation of China(Grant No.51778313)Cooperative Innovation Center of Engineering Construction and Safety in Shangdong Blue Economic Zone
文摘This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.
基金This work was supported by a grant fromthe Hubei Province Natural Science Foundation of China(No.2003 ABA151)
文摘Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.
基金Item supported by national natural sciencefoundation( No.30471236)
文摘Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presence or absence). It has been shown that such a binary trait also can be analyzed with the simple linear regression, subject to virtually no loss in power compared to the generalized linear model analysis. Binary trait is a special case of a multiple categorical trait (e.g., low, medium or high). We propose a mechanism to decompose a multiple categorical trait into an array of correlated binary variables. The categorical trait turned multiple binary traits are analyzed with a multivariate linear regression method. Turning the problem of categorical trait mapping into that of multivariate mapping allows the exploration of pleiotropic effects of QTL for different categories. Efficiency of the method is verified through a series of simulation experiments.
文摘Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.
文摘This paper constructs and studies a nonlinear multivariate regression-tensor model for substantiation of necessary/sufficient conditions of optimization of technological calculation of multifactor physical and chemical process of hardening of complex composite media for metal coatings. An adaptive a posteriori procedure for parametric formation of the target quality functional of integrative physical and mechanical properties of the designed metal coating has been proposed. The results of the research may serve as elements of a mathematical language when creating automated design of precision nanotechnologies for surface hardening of complex composite metal coatings on the basis of complex tribological and anticorrosive tests.
基金support from TIM S.p.A.through the Ph.D.scholarship.The work of D.S.Schiera was supported by the“Network 4 Energy Sustainable Transition-NEST,”through the Project code PE0000021,Concession Decree No.1561 adopted by the Ministero dell’Universitàe della Ricerca(MUR),under Grant CUP E13C22001890001in part by the National Recovery and Resilience Plan(NRRP),Mission 4 Component 2 Investment 1.3-Call for tender No.341 of MURfunded by the European Union-NextGenerationEU.
文摘An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals.Such models should enhance the understanding of the energy behavior of a building,the impact of the external variables,and the causes of inefficiencies.Energy Signatures can fill this role,with particular regard to the consumption due to air conditioning.Univariate models,neglecting the impact of solar radiation,have been widely adopted for Energy Signature analysis.This paper presents Multivariable Energy Signatures considering outdoor temperature and solar radiation.The application on a real-world dataset of multivariable non-parametric approaches stands out from previous works in the ES sector.This led to a mean improvement of 0.768 to 0.804 of the coefficients of determination calculated over 103 world real-case studies.Moreover,Neural Networks outperformed several literature algorithms regarding accuracy,robustness,and scalability.The paper also discusses issues regarding the time resolution of input data and introduces appropriate visualization tools to employ Multivariable Energy Signatures as diagnostic tools.
基金National Natural Science Foundation of China No.40301038
文摘In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.
基金Project supported by the National Natural Science Foundation of China(Nos.31471226 and 91440108)the Fundamental Research Funds for the Central Universities(Nos.WK2070000044 and WK2070000034),China
文摘Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. Methods： RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas （TCGA）, including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated IncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify IncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified IncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. Results： We identified nine HNSCC-relevant IncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CY-I-OR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival （OS） and disease free survival （DFS） of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum （DDP）. HCG22, the most dramatically down-regulated IncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values in- dependent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. Conclusions： Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated IncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these IncRNAs in HNSCC as well as clinical applications.
文摘One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.
文摘Soybean (Glycine max L. Merr.) adaptation to new environments has been hard to predict based on maturity group. The aim of this study was to evaluate the performance of 14 soybean genotypes, from the Soybean Breeding Program of the Federal University of Uberlandia, in their adaptive capacity and seed yield stability at 3 locations and 2 growing seasons. For the adaptability and stability analysis the Toler and Centroid methods were used;5 genotypic groups were identified in the first whereas 4 groups were identified in the latter. By the Toler method group A was composed by 4 genotypes, UFU-001, UFU-003, UFU-0010, and UFU-001. They showed a convex pattern of adaptability and stability. In contrast, the genotypes UFU-008 and UFU-0013 were classified in Group E with a concave pattern of adaptability and stability. Regarding results from the Centroid method, the Genotype UFU-002, with higher seed yield than average, was the only genotype in Ideotype VI with moderate adaptability to favorable environments. In contrast, 10 genotypes were included in the Ideotype V, of medium general adaptability. The genotypes UFU-001, UFU-002, UFU-006, UFU-0010, and UFU-0011 were recommended for use in the Brazilian Cerrado growing region. These genotypes had high seed yield potential in high quality environments.
文摘We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displacement (LLID). The results of applying the cluster analysis technique to the sampling data showed that maintenance operation is required for approximately 10% of the total lots, and these lots were further classified into three groups according to the degree of maintenance need. To analyze the background factors for detecting abnormal LLID lots, a principal component analysis was performed;the results showed that the first principal component represents LLIDs from the viewpoints of the rail structure, equipment, and operating conditions. Binomial and ordinal logit regression models (LRMs) were used to quantitatively investigate the determinants of abnormal LLIDs. Binomial LRM was used to characterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significant impact on LLIDs.
基金The authors thank two anonymous referees for their constructive comments and suggestions. This work was supported by grant R01 DA016750-09 from the National Institute on Drug Abuse. Zhu＇s work was also supported by the National Natural Science Foundation of China （Grant No. 11001044）, the Yhndamental Research ~nds for the Central Universities （11CXPY007, 10JCXK001）, the Natural Science Foundation of Jilin Province （Grant No. 201215007）, the Scientific Research Foundation for Returned Scholars, MOE of China, and the Program for Changjiang Scholars and Innovative Research Team in University. The Framingham Heart Study project is conducted and supported by the National Heart, Lung, and Blood Institute （NHLBI） in collaboration with Boston University （N01 HC25195）. The Framingham data used for the analyses described in this manuscript were obtained through dbGaP （phs000128.v3.p3）.
文摘In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data （MASAL）, which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.
