Structure Design of GFRP Composite Leaf Spring: An Experimental and Finite Element Analysis.

Due to the high load-bearing capacity and light weight, composite leaf spring with variable width and variable thickness has been increasingly used in the automobile industry to replace the conventional steel leaf spring with a heavy weight. The optimum structural design of composite leaf spring is particularly favorable for the weight reduction. In this study, an effective algorithm is developed for structural optimization of composite leaf spring. The mechanical performance of composite leaf spring with designed dimensions is characterized using a combined experimental and computational approach. Specifically, the composite leaf spring with variable width and variable thickness was prepared using the filament winding process, and the three-dimensional finite element (FE) model of the designed composite leaf spring is developed. The experimental sample and FE model of composite leaf spring are tested under the three-point bending method. From experimental and simulation results, it is shown that the bending stiffness of the designed leaf spring meets the design requirement in the automotive industry, while the results of stress calculation along all directions meet the requirements of material strength requirement. The developed algorithm contributes to the design method for optimizing the stiffness and strength performance of the composite leaf spring.

Viewpoint Assessment and Recommendation for Photographing Architectures.

This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice.

Informative SNP selection methods based on SNP prediction.

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs, i.e., tag SNPs, accurately representing the rest of the SNPs. Tag SNP selection can achieve: 1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or 2) necessary reduction of the huge SNP sets (obtained, e.g., from Affymetrix) for further fine haplotype analysis. In this paper, we show that the tag SNP selection strongly depends on how the chosen tags will be used-advantage of one tag set over another can only be considered with respect to a certain prediction method. We show how to separate tag selection from SNP prediction and propose greedy and local-minimization algorithms for tag SNP selection. We give two novel approaches to SNP prediction based on multiple linear regression (MLR) and support vector machines (SVMs). An extensive experimental study on various datasets including ten regions from hapMap project shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. The MLR-based method also uses on average 30% fewer tags than IdSelect for statistical covering all SNPs. The tag selection based on SVM SNP prediction uses fewer tags to achieve the same prediction accuracy as the methods of Halldorsson et al.

MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression.

UNLABELLED: The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html

Combinatorial search methods for multi-SNP disease association.

Recent improvements in the accessibility of high-throughput genotyping have brought a deal of attention to genome-wide association studies for common complex diseases. Although, such diseases can be caused by multi-loci interactions, locus-by-locus studies are prevailing. Recently, two-loci analysis has been shown promising (Marchini et al, 2005), and multi-loci analysis is expected to find even deeper disease-associated interactions. Unfortunately, an exhaustive search among all possible corresponding multi-markers can be unfeasible even for small number of SNPs let alone the complete genome. In this paper we first propose to extract informative (indexing) SNPs that can be used for reconstructing of all SNPs almost without loss (He and Zelikovsky, 2006). In the reduced set of SNPs, we then propose to apply a novel combinatorial method for finding disease-associated multi-SNP combinations (MSCs). Our experimental study shows that the proposed methods are able to find MSCs whose disease association is statistically significant even after multiple testing adjustment. For (Daly et al, 2001) data we found a few unphased MSCs associated with Crohn's disease with multiple testing adjusted p-value below 0.05 while no single SNP or pair of SNPs show any significant association. For (Ueda et al, 2003) data we found a few new unphased and phased MSCs associated with autoimmune disorder.

Multiple linear regression for index SNP selection on unphased genotypes.

The search for the association between complex diseases and single nucleotide polymorphism (SNPs) or haplotypes has recently received great attention. Recent successes in high throughput genotyping technologies drastically increase the length of available SNP sequences. This elevates the importance for the use of a small subset of informative SNPs, called index SNPs, accurately representing the rest of the SNPs (i.e., the rest of the SNPs can be highly predicted from the index SNPs). Index SNP selection achieves the compaction of huge unphased genotype data (obtained, e.g., from Affimetrix Map Array) in order to make feasible fine genotype analysis. In this paper we propose a novel index SNP selection on unphased genotypes based on multiple linear regression (MLR) SNP prediction. We measure the quality of our index SNP selection algorithm by comparing actual SNPs with the SNPs computationally predicted from chosen index SNPs. We obtain an extremely good prediction rates and compression. For example, for region ENm010 (123 SNPs), we can use 2% of SNPs for representing all SNPs with 93.5% accuracy. An experimental study on 4 ENCODE regions from HapMap shows that our method uses significantly fewer index SNPs (e.g., up to two times less index SNPs to reach 90% prediction accuracy) than the state-of-the-art method of Halperin et al. for genotypes.

A combinatorial method for predicting genetic susceptibility to complex diseases.

Recent improvements in the accessibility of high-throughput genotyping have brought a great deal of attention to disease association and susceptibility studies. This paper explores possibility of applying combinatorial methods to disease susceptibility prediction. The proposed combinatorial methods as well as standard statistical methods are applied to publicly available genotype data on Crohn's disease and autoimmune disorders for predicting susceptibility to these diseases. The quality of susceptibility prediction algorithm is assessed using leave-one-out and leave-many-out tests - the disease status of one or several individuals is predicted and compared to the their actual disease status which is initially made unknown to the algorithm. The best prediction rate achieved by the proposed algorithms is 77.78% for Crohn's disease and 64.99% for autoimmune disorders, respectively.

Family trio phasing and missing data recovery.

Although there exist many phasing methods for unrelated adults or pedigrees, phasing and missing data recovery for data representing family trios is lagging behind. This paper is an attempt to fill this gap by considering the following problem. Given a set of genotypes partitioned into family trios, find for each trio a quartet of parent/offspring haplotypes explaining each trio without recombinations and recovering the SNP values missed in given genotype data. Our contributions include: formulating the pure-parsimony trio phasing without recombinations and the trio missing data recovery problems; proposing new greedy and integer linear programming based solution methods; extensive experimental validation of proposed methods showing advantage over the previously known methods.

Linear reduction method for predictive and informative tag SNP selection.

Constructing a complete human haplotype map is helpful when associating complex diseases with their related SNPs. Unfortunately, the number of SNPs is very large and it is costly to sequence many individuals. Therefore, it is desirable to reduce the number of SNPs that should be sequenced to a small number of informative representatives called tag SNPs. In this paper, we propose a new linear algebra-based method for selecting and using tag SNPs. We measure the quality of our tag SNP selection algorithm by comparing actual SNPs with SNPs predicted from selected linearly independent tag SNPs. Our experiments show that for sufficiently long haplotypes, knowing only 0.4% of all SNPs the proposed linear reduction method predicts an unknown haplotype with the error rate below 2% based on 10% of the population.

Linear reduction methods for tag SNP selection.

It is widely hoped that constructing a complete human haplotype map will help to associate complex diseases with certain SNP's. Unfortunately, the number of SNP's is huge and it is very costly to sequence many individuals. Therefore, it is desirable to reduce the number of SNP's that should be sequenced to considerably small number of informative representatives, so called tag SNP's. In this paper, we propose a new linear algebra based method for selecting and using tag SNP's. Our method is purely combinatorial and can be combined with linkage disequilibrium (LD) and block based methods. We measure the quality of our tag SNP selection algorithm by comparing actual SNP's with SNP's linearly predicted from linearly chosen tag SNP's. We obtain an extremely good compression and prediction rates. For example, for long haplotypes (>25000 SNP's), knowing only 0.4% of all SNP's we predict the entire unknown haplotype with 2% accuracy while the prediction method is based on a 10% sample of the population.