Seismic performance analysis of steel beam to CFST column connection with ductility and energy dissipation components

Concrete-filled steel tubular (CFST) column to steel beam joint with the ductility and energy dissipation components is a type of connection which is used in prefabricated structures, to improve the capacity of connections and construction efficiency. In this paper, two different type of steel beam to CFST column connections with the penetrated high-strength bolts and end-plate are investigated, i.e., steel beam to CFST column connection with end plate (CJ-1), and T-stub bolted (CJ-2) connections. The finite element model (FEM) of steel beam to CFST column connection with the penetrated high-strength bolts under cyclic loading are conducted based on the whole process of the nonlinear explicit analysis method using ABAQUS. The feasibility of FEM is verified by a set of experimental results performed by our research group, as well as available test results from other researchers. The failure modes, bearing capacity, energy dissipation capacity and ductility and rigidity degeneration were studied. As a result, the load-displacement hysteretic loop curve of CJ-2 connection is plump. However, the hysteresis curve of CJ-1 shows pinching phenomenon. The value of buckling load and ultimate load of CJ-2 increased by 28 % and 30 % respectively, compared with CJ-1. With respect of stress analysis, the plastic strain accumulation position distribution is relatively uniform duo to the T-stub connection, avoiding the penetrated high-strength bolt early yield or fracture. The results show that the steel beam to CFST column connection with penetrated bolts and T-stub connection has good seismic capacity

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes

A gene based approach to test genetic association based on an optimally weighted combination of multiple traits.

There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study

The Mechanism Study of Vortex Tools Drainage Gas Recovery of Gas Well

The liquid loading of gas well is an important issue in deep exploitation of natural gas. The technic of vortex drainage has good prospects because the tool construction and construction work is simple, the technic is environmental and efficient. Currently, the mechanism for the vortex drainage and the theory of fluid motion are still missing. Therefore, in order to further understand the downhole flow field, verify drainage mechanism and select best working conditions, based on computational fluid dynamics and mixture model of multiphase flow through Fluent, the study established a three-dimensional structural model of vortex tools and the numerical simulation has been done. By monitoring the wellhead and the radial distribution of the liquid content and observing the state of the gas-liquid flow and the path line, the study analyzed the influence on gas well flow field by vortex tool. The study revealed the working mechanism of vortex tools to facilitate understanding the nature of the vortex drainage process, guide how to select the preferred process conditions and provide theoretical basis for the application and the dynamics simulation of vortex drainage technology.Key words: The liquid loading of gas well; Vortex drainage; Multiphase flow; Numerical simulatio

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with

Genome-wide association tests by using block information in family data

Abstract By applying an association test to analyze the data sets from Genetic Analysis Workshop 15 Problem 3, we compare power using different haplotype-block information. The results from using both of the two different coding schemes show that the test using tight blocks with limited haplotype diversity within each block is more powerful than that using evenly spaced blocks, and the latter is more powerful than that using single-marker blocks. By using carefully chosen haplotype blocks, the power of association tests may be enhanced.http://deepblue.lib.umich.edu/bitstream/2027.42/117371/1/12919_2007_Article_2513.pd