Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

Os recursos de algas marinhas de importáncia económica na República popular de Moçambique com especial referéncia para Euchema (Florideae) em Cabo Delgado

Composition, abundance and environmental characteristics of the sea-weed resources of Mozambique were investigated along the whole Mozambican sea shore between May, 1979 and November, 1980. The limits of distribution of tropical algae were identified and located close to parallel 21 degree 00'S. Important concentrations of sea-weeds were found in the southern region, but only the Euchema resource in the coast of Cabo Delgado looked promising for commercial exploitation. Biological studies of Euchema were undertaken, the results of which permit the recommendation of a strategy for immediate harvesting which could yield 400-500 tons annually. Further proposals for the culture of sea-weeds are also included.

Molecular cloning, characterization and localization of chicken type II procollagen gene

Chicken type II procollagen (ccol2a1) has become as an important oral tolerance protein for effective treatment of rheumatoid arthritis. However, its molecular identity remains unclear. Here, we reported the full-length cDNA and nearly complete genomic DN