Search

It is more and more important to consider the dependence structure among multiple testings, especially for the genome-wide association studies (GWAS). The existing procedures, such as local index of significance (LIS) and pooled local index of significance (PLIS), were proposed to test hidden Markov model (HMM)-dependent hypotheses under the framework of compound decision theory, which was successfully applied to GWAS. However, the etiology of complex diseases is not only with respect to the genetic effects, but also the environmental factors. Failure to account for the covariates in multiple testing can produce misleading bias of the association of interest, or suffer from loss of testing efficiency. In this paper, we develop a covariate-adjusted multiple testing procedure, called covariate-adjusted local index of significance (CALIS), to account for the effects of environmental factors via a factorial hidden Markov model. The theoretical results show that our procedure can control the false discovery rate (FDR) at the nominal level and has the smallest false non-discovery rate (FNR) among all valid FDR procedures. We further demonstrate the advantage of our novel procedure over the existing procedures by simulation studies and a real data analysis.

Thermosensitive liposome-based drug delivery systems (DDS) are powerful tools for site-specific delivery of chemotherapeutics, especially when combined with regional hyperthermia. The objective of this work was to develop a novel thermosensitive liposomal DDS loaded with lomustine, a chemotherapeutic compound, and iohexol, a contrast medium for visualization by CT. Thermosensitive compound liposomes (TSCLs) composed of DPPC were prepared by reverse-phase evaporation and investigated for encapsulation efficiency, temperature-sensitivity, release kinetics, and In Vivo pharmacokinetics. The size and zeta-potential of TSCLs ranged from 250 to 300 nm and −15 to −30 mV, respectively. At 41 °C, TSCLs were shown to release over 90% of iohexol and lomustine within 4 h. The in vitro release profiles of iohexol and lomustine at 41 °C conformed to first-order kinetics and Weibullmodel, respectively. Phase-transition did not occur after incorporation of cholesterol and soybean phospholipids. In Vivo evaluation performed with C6 glioma model rats proved the prolonged half-lives and improved bioavailability by liposomal encapsulation for both compounds under mild local hyperthermia. The TSCLs used in this study may offer a clinically promising mean of increasing efficacy and controlling toxicity.

The Blended-Wing-Body (BWB) is an unconventional configuration of aircraft and considered as a potential configuration for future commercial aircraft. One of the difficulties in conceptual design of a BWB aircraft is structural mass prediction due to its unique structural feature. This paper presents a structural mass prediction method for conceptual design of BWB aircraft using a structure analysis and optimization method combined with empirical calibrations. The total BWB structural mass is divided into the ideal load-carrying structural mass, non-ideal mass, and secondary structural mass. Structural finite element analysis and optimization are used to predict the ideal primary structural mass, while the non-ideal mass and secondary structural mass are estimated by empirical methods. A BWB commercial aircraft is used to demonstrate the procedure of the BWB structural mass prediction method. The predicted mass of structural components of the BWB aircraft is presented, and the ratios of the structural component mass to the Maximum TakeOff Mass (MTOM) are discussed. It is found that the ratio of the fuselage mass to the MTOM for the BWB aircraft is much higher than that for a conventional commercial aircraft, and the ratio of the wing mass to the MTOM for the BWB aircraft is slightly lower than that for a conventional aircraft. Keywords: Aircraft conceptual design, Blended-Wing-Body, Finite element method, Mass prediction, Structural optimization

The effect of SRB and applied potential on the stress corrosion sensitivity of X80 pipeline steel was analyzed in high-pH soil simulated solution under different conditions using a slow strain rate tensile test, electrochemical test, and electronic microanalysis. The experimental results showed that X80 pipeline steel has a certain degree of SCC sensitivity in high-pH simulated solution, and the crack growth mode was trans-granular stress corrosion cracking. In a sterile environment, the SCC mechanism of X80 steel was a mixture mechanism of anode dissolution and hydrogen embrittlement at −850 mV potential, while X80 steel had the lowest SCC sensitivity due to the weak effect of AD and HE, after Sulfate Reducing Bacteria (SRB) were inoculated, the SCC mechanism of X80 steel was an AD–membrane rupture mechanism at −850 mV potential. The synergistic effect of Cl− and SRB formed an oxygen concentration cell and an acidification microenvironment in the pitting corrosion pit, and this promoted the formation of pitting corrosion which induced crack nucleation, thus significantly improving the SCC sensitivity of X80 steel. The strong cathodic polarization promoted the local corrosion caused by SRB metabolism in the presence of bacteria, whereby the SCC sensitivity in the presence of bacteria was higher than that in sterile conditions under strong cathodic potential.

Background Fatigue is a symptom characterized by an elevated prevalence in patients undergoing hemodialysis, which may cause extreme mental and muscular debilitation, significantly influencing social interaction, life quality and well-being. However, the significance of fatigue to patients undergoing hemodialysis has not been recognized yet, and prevention and management of fatigue in this population have not been thoroughly investigated. Additionally, previous studies mainly focused on muscular fatigue, while mental fatigue has been seldom discussed. This study aims to investigate the interaction between nurses and multidisciplinary of nonpharmacological integrated care interventions (NICIs) and assess the impact of fatigue on patients undergoing hemodialysis. Methods The integrative nonpharmacological care interventions in this study included walking, motivational interviewing (MI) and health education regarding behavioral self-management. A single-center randomized controlled trial was conducted in the dialysis center of the nephrological department in a tertiary affiliated hospital of medical university from January to June 2019. A total of 118 patients were selected and randomly divided into the intervention group (IG) and the control group (CG). Four patients dropped out during the study, and 114 patients were enrolled for the eventual analysis. The 60 patients in the IG received routine nursing combined with integrated care interventions, while the 54 patients in the CG received routine nursing only. This study lasted for six months. Results The experimental group exhibited significant reductions of overall fatigue (2.26 vs. 0.48), mental fatigue (1.41 vs. 0.54), muscular fatigue (2.13 vs. 0.75), and some biochemical indicators (e.g., serum urea) (P Conclusions Nurses and multidisciplinary teams have been demonstrated to play a key role and interplay function in chronic disease management. Hence, the nurse-led multidisciplinary NICIs significantly alleviated total fatigue (muscular fatigue and mental fatigue) and improved other parameters. Trial registration ChiCTR-IOR-16008621 (March 18, 2016)

Structural layout design of blended wing body (BWB) aircraft in the preliminary design phase is a challenging optimization problem due to large numbers of design variables and various constraints. A two-loop optimization strategy is proposed to solve the BWB aircraft structural layout design problem considering constraints of the displacement, stress, strain, and buckling. The two-loop optimization consists of an inner loop and an outer loop. The inner loop is to optimize each stiffened panel of the BWB aircraft structure, and outer loop is to find the best layout design. To improve computational efficiency, an equivalent finite element model is applied to BWB aircraft structure analysis, and an analytical method is used for buckling and static analysis of the stiffened panels. The proposed method can efficiently solve the structural layout optimization problem of a notional BWB aircraft with acceptable computational burden. The result indicates the mass of main load-carrying structure of the BWB aircraft is reduced by 9.28% compared to that of the initial structural layout.

In this paper, we deal with the Cauchy image restoration problem under the maximum a posteriori framework. We propose a novel image prior, weighted hyper-Laplacian prior with overlapping group sparsity on the image gradient. This prior allows us to simultaneously promote the structural and pixel-level sparseness of the natural image gradient. The performance can be further improved by introducing the in-group-weights to balance the different scales of the components within each group. To tackle the corresponding optimization problem, we present a novel quadratic majorizer for majorization-minimization. We adopt the non-convex alternating direction method of multipliers as the main algorithm framework. The proposed regularizer can be reduced to the related variational regularizers including the total variation, the hyper-Laplacian, and the total variation with overlapping group sparsity. The comparative experiments with those existing gradient-based regularizers demonstrate the effectiveness of the proposed method in terms of PSNR and SSIM values.

The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.

Thermosensitive liposome-based drug delivery systems (DDS) are powerful tools for site-specific delivery of chemotherapeutics, especially when combined with regional hyperthermia. The objective of this work was to develop a novel thermosensitive liposomal DDS loaded with lomustine, a chemotherapeutic compound, and iohexol, a contrast medium for visualization by CT. Thermosensitive compound liposomes (TSCLs) composed of DPPC were prepared by reverse-phase evaporation and investigated for encapsulation efficiency, temperature-sensitivity, release kinetics, and

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Investigators routinely use unidimensional summaries for multidimensional data. In microarray data analysis, for example, the gene expression level is indeed a unidimensional summary of probe-level or SNP measurements. In this paper, we propose a mixture factor model for the low-level data, which enables us to examine the adequacy of a unidimensional summary while accommodating known or latent subgroups in the population. We also develop screening procedures based on the proposed model to identify potentially informative genes in biomedical studies. As shown in our empirical studies, the proposed methods are often more effective than existing methods because the new model goes beyond the conventional unidimensional summaries of gene expressions.

With the rapid development of statistical genetics, the deep researches of ordinal traits have been gradually emphasized. The data of these traits bear relatively less information than those of continuous phenotypes, therefore it is more complex to map the quantitative trait loci (QTL) of ordinal traits. In this paper, the multiple-interval mapping method is considered in the genetic mapping of ordinal traits. By combining threshold model and statistical model, we build a cumulative logistic regression model to express the relationship between the ordinal data and the QTL genotypes. In order to make the interval mapping more straightforward, we treat the recombination rates as unknown parameters, and then simultaneously obtain the estimates of QTL positions, QTL effects and threshold parameters via the EM algorithm. We perform simulation experiments to investigate and compare the proposed method. We also present a real example to test the reasonableness of the considered model and estimate both model parameters and QTL parameters. Both results of simulations and example show that the method we proposed is reasonable and effective.

The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Specifically, the original sampling scheme of these imaging genetic studies is primarily the retrospective case-control design, whereas most existing statistical analyses of these studies ignore such sampling scheme by directly correlating imaging phenotypes (called the secondary traits) with genotype. Although it has been well documented in genetic epidemiology that ignoring the case-control sampling scheme can produce highly biased estimates, and subsequently lead to misleading results and suspicious associations, such findings are not well documented in imaging genetics. We use extensive simulations and a large-scale imaging genetic data analysis of the Alzheimer’s Disease Neuroimag-ing Initiative (ADNI) data to evaluate the effects of the case-control sampling scheme on GWAS results based on some standard statistical methods, such as linear regression methods, while comparing it with several advanced statistical methods that appropriately adjust for the case-control sampling scheme.

Background Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability analysis. In this paper, we propose a novel multiple testing procedure based on the Cartesian hidden Markov model (CHMM), called repLIS procedure, for replicability analysis across two studies, which can characterize the local dependence structure among adjacent SNPs via a four-state Markov chain. Results Theoretical results show that the repLIS procedure can control the false discovery rate (FDR) at the nominal level α and is shown to be optimal in the sense that it has the smallest false non-discovery rate (FNR) among all α-level multiple testing procedures. We carry out simulation studies to compare our repLIS procedure with the existing methods, including the Benjamini-Hochberg (BH) procedure and the empirical Bayes approach, called repfdr. Finally, we apply our repLIS procedure and repfdr procedure in the replicability analyses of psychiatric disorders data sets collected by Psychiatric Genomics Consortium (PGC) and Wellcome Trust Case Control Consortium (WTCCC). Both the simulation studies and real data analysis show that the repLIS procedure is valid and achieves a higher efficiency compared with its competitors. Conclusions In replicability analysis, our repLIS procedure controls the FDR at the pre-specified level α and can achieve more efficiency by exploiting the dependency information among adjacent SNPs. Electronic supplementary material The online version of this article (10.1186/s12859-019-2707-7) contains supplementary material, which is available to authorized users.

Proof of Theorem 1 and additional simulations. We give a brief proof of Theorem 1 in Additional file 2. The asymptotic optimality can be derived without essential difficulty by extending the proof of Theorem 6 in [20]. We also carried out additional simulation studies to investigate the numerical performance of repLIS in various model settings. (PDF 249 kb)

Brief description of some core code of our repLIS procedure. repLIS is a program to perform replicability analysis in genome-wide association studies, which is written in R code. Here, repLIS program is designed for one chromosome or a segment of chromosome. For the analysis of multiple chromosomes, firstly, the users can make the parallel computing for them, then complete the global analysis by combining all results from multiple chromosomes. (PDF 151 kb)

With the rapid growth of next-generation sequencing technology, more and more rare variants are available in the human genome. In recent years, the point of study has already changed direction to rare variants in genome-wide association studies (GWAS). Although a variety of approaches have been proposed to test associations between rare variants and phenotypes of interest, it is far from the end of this problem, and it is worth exploring new statistical methods based on special features of rare variants. As we all know, the most direct way is to evaluate the association in a two-way contingency table if the phenotype is a discrete variable. The numbers of observations are very close or equal to 0s for most of cells in the contingency table due to the extremely low mutation rates of rare variants. In this paper, we propose a novel association test for rare variants based on a generalization of Fisher’s exact test, and the p-value of this exact test can be computed under the multivariate hypergeometric distribution in the framework of algebraic statistics. Simulation results show that our proposed method outperforms the existing methods, despite there is heterogeneity among causal variants. We also successfully apply our method into the genetic association study of coronary artery disease and hypertension from the Wellcome Trust Case Control Consortium.

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this article, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the nonrespondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes. Supplementary materials for this article are available online.

Summary In case-control studies, association analysis was designed to test whether genetic variants were associated with human diseases. To evaluate the association, analysing one genetic marker at a time suffered from weak power, because of the correction for multiple testing and possibly small genetic effects. An alternative strategy was to test simultaneous effects of multiple markers, which was believed to be more powerful. However, when the number of markers under investigation was large, they would be subjected to weak power as well, because of the greater degrees of freedom. To conquer these limitations in case-control studies, we proposed a novel method that could test joint association of several loci (i.e. haplotype), with only a single degree of freedom. In this research, we developed a nonparametric approach, which was based on U-statistics. We also introduced a new kernel for U-statistic, which could combine the haplotype structure information, and was expected to enhance the power. Simulations indicated that our proposed approach offered merits in identifying the associations between diseases and haplotypes. Application of our method to a study of candidate genes for internalising disorder illustrated its virtue in utility and interpretation, and provided an excellent result in detecting the associations.

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.

SummaryMore and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall's τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits, especially for multivariate ordinal traits.

Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.

Failures of a flowmeter floater and a spherical valve installed at a pipe transporting maleic acid aqueous solution in a maleic anhydride plant were investigated by means of macroscopic and microscopic examinations, chemical composition analysis, and weight loss tests. The investigation results indicated that the components were made of AISI type 316L stainless steel. The damages of both the floater and the inner surface of the valve were caused by an extensive erosion–corrosion in maleic acid aqueous solution contaminated with sulphuric acid and chlorides, which originated from the feed blending coking benzene and the recycled process water, while that of the back surface of the floater as well as the outer surface of the valve was induced by general and pitting corrosion as a result of formation of a stagnation zone where the acidic chloride-containing solution collected. It was recommended to use a dechlorination device by resin adsorption technique for reducing the content of chloride ions in the maleic acid aqueous solution.

In studies of complex disorders such as nicotine dependence, it is common that researchers assess multiple variables related to a disorder as well as other disorders that are potentially correlated with the primary disorder of interest. In this work, we refer to those variables and disorders broadly as multiple traits. The multiple traits may or may not have a common causal genetic variant. Intuitively, it may be more powerful to accommodate multiple traits in genetic traits, but the analysis of multiple traits is generally more complicated than the analysis of a single trait. Furthermore, it is not well documented as to how much power we may potentially gain by considering multiple traits. Our aim is to enhance our understanding on this important and practical issue. We considered a variety of correlation structures between traits and the disease locus. To focus on the effect of accommodating multiple traits, we examined genetic models that are relatively simple so that we can pinpoint the factors affecting the power. We conducted simulation studies to explore the performance of testing multiple traits simultaneously and the performance of testing a single trait at a time in family-based association studies. Our simulation results demonstrated that the performance of testing multiple traits simultaneously is better than that of testing each trait individually for almost models considered. We also found that the power of association tests varies among the underlying models. The advantage of conducting a multiple traits test is minimized when some traits are influenced by the gene only through other traits; and it is maximized when there are causal relations between the traits and the gene, and among the traits themselves or when there are extraneous traits.

Pitting corrosion behavior of 316L SS was investigated in the presence of aerobic and anaerobic bacteria isolated from cooling water system in oil refinery using polarization measurement, electrochemical impedance spectroscopy, scanning electron microscopy examinations and energy dispersive spectrum analysis. The results show the corrosion potential (Ecorr), pitting potential (Epit) and polarization resistance (RP) of 316L SS had a distinct decrease in the presence of bacteria, in comparison with those observed in the sterile medium for the same exposure time interval. Micrometer-scale pitting was observed on the 316L SS surface in the presence of bacteria. The combination of SRB and IOB demonstrated higher corrosion rates than SRB or IOB alone. The synergy of 0.01 M NaCl + SRB + IOB yielded the highest corrosion rate. The synergies between the metal surface, abiotic corrosion products, chloride anion, and bacterial cells and their metabolic products increased the corrosion damage degree of the passive film and accelerated pitting propagation.