Transplantation of human fetal blood stem cells in the osteogenesis imperfecta mouse leads to improvement in multiscale tissue properties.

Osteogenesis imperfecta (OI or brittle bone disease) is a disorder of connective tissues caused by mutations in the collagen genes. We previously showed that intrauterine transplantation of human blood fetal stem/stromal cells in OI mice (oim) resulted in a significant reduction of bone fracture. This work examines the cellular mechanisms and mechanical bone modifications underlying these therapeutic effects, particularly examining the direct effects of donor collagen expression on bone material properties. In this study, we found an 84% reduction in femoral fractures in transplanted oim mice. Fetal blood stem/stromal cells engrafted in bones, differentiated into mature osteoblasts, expressed osteocalcin, and produced COL1a2 protein, which is absent in oim mice. The presence of normal collagen decreased hydroxyproline content in bones, altered the apatite crystal structure, increased the bone matrix stiffness, and reduced bone brittleness. In conclusion, expression of normal collagen from mature osteoblast of donor origin significantly decreased bone brittleness by improving the mechanical integrity of the bone at the molecular, tissue, and whole bone levels.

Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.

Multiscale object features from clustered complex wavelet coefficients

This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complex wavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an Expectation-Maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a look at potential applications of these clusters, such as rotation- and scale-invariant object recognition. © 2005 IEEE.