Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Genetic parameters and genetic and phenotypic trends of performance traits of equines from the Brazilian Army

The objective of this research was to compare the magnitude of genetic parameters (coefficients of heritability and genetic correlation) as estimated by the Restricted Maximum Likelihood (REML) method and Bayesian Inference, and to estimate the genetic and phenotypic trends to the traits height at the withers (HW24) and weight at 24 months of age (W24). The average heritability estimated by Bayesian Inference to HW24 was 0.47, and it was lower than that obtained by REML bi-trait analysis (0.52); however, the value estimated to W24 (0.39) was higher than that obtained by REML bi-trait analysis (0.38). The genetic correlation estimate between W24 and HW24 traits obtained by the REML method (0.66) was lower than that obtained by the Bayesian Inference Method (0.72). From the regression of the average additive genetic merit in the year of birth of the animals, it was found that the averaged genetic values of the animals for HW24 showed a genetic trend near zero (-0.0008cm/year), and the averaged genetic values for W24 showed a negative trend of -0.38 kg/year. The values to the direct heritability estimated for HW24 and W24 suggest that the direct selection for these traits can provide genetic gain in this population. The genetic correlation between the traits, high and positive, suggests that the selection for HW24 should promote increase in W24 at this age. The genetic trends obtained for the traits studied, near zero, indicate that the selection performed produced a slight reduction of the weight of the animals at 24 months of age; however, it did not promote increase in height at the wither at this same age, in this population

Generating steep, shear-free gradients of small molecules for cell culture

We present the fabrication, characterization and cell culture results of a microfluidic device for generating steep gradient interfaces of small molecules (< 1 kDa) across cell culture with no convective shear stresses applied to the cells. We use a novel streamline of two fluids to generate stable and uniform gradient interfaces/boundaries by confronting one fluid with the other. We separate a gradient generation channel and a cell culture channel by a polyester membrane so that viscous shear stress by the bottom channel flow does not convectively disturb the chemical environment of cultured cells seeded on the membrane in the top channel. Using two-component dyes to characterize the steepness of the diffusional interface, we demonstrate 50 mu m wide steps for about 400 Da molecules. Using BCECF, a 689 Da pH-sensitive diffusible dye which is actively taken up by living cells, we demonstrate gradient boundaries narrower than five cell diameters in HeLa culture. We also demonstrate steep gradients of pH across cells in the same device. This work should be of interest to researchers attempting to generate gradients of small, rapidly diffusing molecules for studies in cellular differentiation and signaling.close32