ABSTRACT
The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need for weight calculation, point evaluation of the proposal distributions is also needed. The Gaussian process has been proven to be a highly expressive non-parametric model for conditional density estimation whose training process is also straightforward. In this paper, we introduce a class of adaptive importance sampling methods where the proposal distribution is constructed in a way that Gaussian processes are combined autoregressively. By numerical experiments of sampling from a high dimensional target distribution, we demonstrate that the method is accurate and efficient compared to existing methods.
ABSTRACT
BACKGROUND: Human interfollicular epidermis is sustained by the proliferation of stem cells and their progeny, transient amplifying cells. Molecular characterization of these two cell populations is essential for better understanding of self renewal, differentiation and mechanisms of skin pathogenesis. The purpose of this study was to obtain gene expression profiles of alpha 6+/MHCI+, transient amplifying cells and alpha 6+/MHCI-, putative stem cells, and to compare them with existing data bases of gene expression profiles of hair follicle stem cells. The expression of Major Histocompatibility Complex (MHC) class I, previously shown to be absent in stem cells in several tissues, and alpha 6 integrin were used to isolate MHCI positive basal cells, and MHCI low/negative basal cells. RESULTS: Transcriptional profiles of the two cell populations were determined and comparisons made with published data for hair follicle stem cell gene expression profiles. We demonstrate that presumptive interfollicular stem cells, alpha 6+/MHCI- cells, are enriched in messenger RNAs encoding surface receptors, cell adhesion molecules, extracellular matrix proteins, transcripts encoding members of IFN-alpha family proteins and components of IFN signaling, but contain lower levels of transcripts encoding proteins which take part in energy metabolism, cell cycle, ribosome biosynthesis, splicing, protein translation, degradation, DNA replication, repair, and chromosome remodeling. Furthermore, our data indicate that the cell signaling pathways Notch1 and NF-kappaB are downregulated/inhibited in MHC negative basal cells. CONCLUSION: This study demonstrates that alpha 6+/MHCI- cells have additional characteristics attributed to stem cells. Moreover, the transcription profile of alpha 6+/MHCI- cells shows similarities to transcription profiles of mouse hair follicle bulge cells known to be enriched for stem cells. Collectively, our data suggests that alpha 6+/MHCI- cells may be enriched for stem cells. This study is the first comprehensive gene expression profile of putative human epithelial stem cells and their progeny that were isolated directly from neonatal foreskin tissue. Our study is important for understanding self renewal and differentiation of epidermal stem cells, and for elucidating signaling pathways involved in those processes. The generated data base may serve those working with other human epithelial tissue progenitors.
Subject(s)
Gene Expression Profiling , Hair Follicle/metabolism , Keratinocytes/metabolism , Stem Cells/metabolism , Animals , Cell Proliferation , Cells, Cultured , Databases, Nucleic Acid , Flow Cytometry , Gene Expression , Genes, MHC Class I , Hair Follicle/cytology , Humans , Infant, Newborn , Integrin alpha6/genetics , Keratinocytes/cytology , Ki-67 Antigen/genetics , Mice , NF-kappa B/genetics , Oligonucleotide Array Sequence Analysis , RNA/genetics , Reverse Transcriptase Polymerase Chain Reaction , Stem Cells/cytology , Transcription, GeneticABSTRACT
We present a general method for the computation of molecular population distributions in a system of first-order chemical reactions. The method is based on the observation that the molecules in first-order reactions do not interact with each other. Therefore, the population distributions are a convolution of densities for one molecule. With this method one can study reactions involving any number of molecules. Such analysis is demonstrated on several examples, including an enzyme catalyst reaction and a first-order reaction chain.
