The k-ZIG: Flexible Modeling for Zero-Inflated Counts

Summary Many applications involve count data from a process that yields an excess number of zeros. Zero-inflated count models, in particular, zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, along with Poisson hurdle models, are commonly used to address this problem. However, these models struggle to explain extreme incidence of zeros (say more than 80%), especially to find important covariates. In fact, the ZIP may struggle even when the proportion is not extreme. To redress this problem we propose the class of k-ZIG models. These models allow more flexible modeling of both the zero-inflation and the nonzero counts, allowing interplay between these two components. We develop the properties of this new class of models, including reparameterization to a natural link function. The models are straightforwardly fitted within a Bayesian framework. The methodology is illustrated with simulated data examples as well as a forest seedling dataset obtained from the USDA Forest Service's Forest Inventory and Analysis program.     

Plasticity of regulatory T cells: subversion of suppressive function and conversion to enhancement of lung allergic responses

Activation of CD4(+)CD25(+)Foxp3(+) naturally occurring regulatory T cells (nTregs) resulting in suppression of lung allergic responses requires interaction of MHC class I on nTregs and CD8. In the absence of CD8 (CD8(-/-) recipients), transferred nTregs restored airway hyperresponsiveness, eosinophilic inflammation, and IL-13 levels following allergen exposure. Enhancement of lung allergic responses was accompanied by reduced expression of Foxp3 and increased expression of IL-13 in the transferred nTregs. In CD8(-/-) recipients pretreated with glucocorticoid-induced TNFR-related protein-ligand Ab, the transferred nTregs maintained high levels of Foxp3 and did not result in altered lung responses. Thus, the regulatory function of nTregs can be subverted by reducing the expression of Foxp3 and following signaling through glucocorticoid-induced TNFR-related protein are converted nTregs into IL-13-producing CD4(+) T cells mediating lung allergic responses.     

Leukotriene B4 receptor 1 expression on dendritic cells is required for the development of Th2 responses and allergen-induced airway hyperresponsiveness

Dendritic cells (DC) are important APCs that control allergen-induced airway responses by interacting directly with T cells. Leukotriene B(4) (LTB(4)), interacting with its high-affinity receptor, LTB(4) receptor 1 (BLT1), is known to attract and activate leukocytes during inflammation. We have previously shown that BLT1 expression on Ag-primed T cells is required for the development of airway hyperresponsiveness (AHR; Miyahara et al. 2005. Am. J. Respir. Crit. Care Med. 172: 161-167). However, the role for the LTB(4)-BLT1 pathway in DC function in allergen-induced airway responses has not been defined. Bone marrow-derived DCs (BMDC) were generated. Naive BALB/c mice received OVA-pulsed BLT1-deficient (BLT1(-/-)) BMDCs or wild-type BMDCs intratracheally and were then challenged with OVA for 3 days. Airway responses were monitored 48 h after the last allergen challenge. BLT1(-/-) BMDCs showed normal maturation judged from surface expression of CD markers. Compared with recipients of wild-type BMDCs, mice that received BLT1(-/-) BMDCs developed significantly lower AHR to inhaled methacholine, lower goblet cell metaplasia, and eosinophilic infiltration in the airways and decreased levels of Th2 type cytokines in the bronchoalveolar lavage fluid. Migration of BLT1(-/-) BMDCs into peribronchial lymph nodes was significantly impaired compared with BLT1(+/+) BMDCs after intratracheal instillation. These data suggest that BLT1 expression on DCs is required for migration of DCs to regional lymph nodes as well as in the development of AHR and airway inflammation.     

Modeling disease incidence data with spatial and spatio temporal dirichlet process mixtures

Disease incidence or mortality data are typically available as rates or counts for specified regions, collected over time. We propose Bayesian nonparametric spatial modeling approaches to analyze such data. We develop a hierarchical specification using spatial random effects modeled with a Dirichlet process prior. The Dirichlet process is centered around a multivariate normal distribution. This latter distribution arises from a log-Gaussian process model that provides a latent incidence rate surface, followed by block averaging to the areal units determined by the regions in the study. With regard to the resulting posterior predictive inference, the modeling approach is shown to be equivalent to an approach based on block averaging of a spatial Dirichlet process to obtain a prior probability model for the finite dimensional distribution of the spatial random effects. We introduce a dynamic formulation for the spatial random effects to extend the model to spatio-temporal settings. Posterior inference is implemented through Gibbs sampling. We illustrate the methodology with simulated data as well as with a data set on lung cancer incidences for all 88 counties in the state of Ohio over an observation period of 21 years.     

SELEX_DB: a database on in vitro selected oligomers adapted for recognizing natural sites and for analyzing both SNPs and site-directed mutagenesis data

SELEX_DB is an online resource containing both the experimental data on in vitro selected DNA/RNA oligomers (aptamers) and the applets for recognition of these oligomers. Since in vitro experimental data are evidently system-dependent, the new release of the SELEX_DB has been supplemented by the database SYSTEM storing the experimental design. In addition, the recognition applet package, SELEX_TOOLS, applying in vitro selected data to annotation of the genome DNA, is accompanied by the cross-validation test database CROSS_TEST discriminating the sites (natural or other) related to in vitro selected sites out of random DNA. By cross-validation testing, we have unexpectedly observed that the recognition accuracy increases with the growth of homology between the training and test sets of protein binding sequences. For natural sites, the recognition accuracy was lower than that for the nearest protein homologs and higher than that for distant homologs and non-homologous proteins binding the common site. The current SELEX_DB release is available at http://wwwmgs.bionet.nsc.ru/mgs/systems/selex/.     

Starts of bacterial genes: estimating the reliability of computer predictions.

Exact mapping of gene starts is an important problem in the computer-assisted functional analysis of newly sequenced prokaryotic genomes. We describe an algorithm for finding ribosomal binding sites without a learning sample. This algorithm is particularly useful for analysis of genomes with little or no experimentally mapped genes. There is a clear correlation between the ribosomal binding site (RBS) properties of a given genome and the potential gene start prediction accuracy. This correlation is of considerable predictive power and may be useful for estimating the expected success of future genome analysis efforts. We also demonstrate that the RBS properties depend on the phylogenetic position of a genome.     

MAP2 competes with MAP1 for binding to microtubules

A question whether MAP1 and MAP2 (the major microtubule associated proteins from mammalian brain) bind to common or distinct sites on the microtubule surface was studied. Microtubules were assembled from tubulin and MAP1 and then centrifuged through a layer of MAP2 solution under conditions where no repolymerization of tubulin with MAP2 could occur. During centrifugation, MAP2 displaced most of MAP1 on the microtubules. This implies that MAP1 is reversibly bound to microtubules and that MAP2 binding interferes with MAP1 binding. The latter means that binding sites for MAP1 and MAP2 are identical or overlap.