Physicochemical parameters aid microbial community? A case study from marine recreational beaches, Southern India.

A total of 176 (water and sediment) samples from 22 stations belonging to four different (urban, semi-urban, rural, and holy places) human habitations of Tamil Nadu beaches were collected and analyzed for physiochemical and microbial parameters during 2008-2009. Bacterial counts were two- to tenfold higher in sediments than in water due to strong bacterial aggregations by dynamic flocculation and rich organic content. The elevated bacterial communities during the monsoon explain rainfalls and several other wastes from inlands.

Molecular cloning of prophenoloxidase and the effects of dietary β-glucan and rutin on immune response in hemocytes of the fleshy shrimp, Fenneropenaeus chinensis.

To investigate the effects of dietary β-glucan (0.5 or 1 g kg⁻¹ diet: 0.5-BG, 1-BG) and rutin (0.

Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression.

Functional gene clustering is a statistical approach for identifying the temporal patterns of gene expression measured at a series of time points. By integrating wavelet transformations, a power dimension-reduction technique, noisy gene expression data is smoothed and clustered allowing for new patterns of functional gene expression profiles to be identified. We implement the idea of wavelet dimension reduction into the mixture model for gene clustering, aimed to de-noise the data by transforming an inherently high-dimensional biological problem to its tractable low-dimensional representation.

A computational approach to the functional clustering of periodic gene-expression profiles.

DNA microarray analysis has emerged as a leading technology to enhance our understanding of gene regulation and function in cellular mechanism controls on a genomic scale. This technology has advanced to unravel the genetic machinery of biological rhythms by collecting massive gene-expression data in a time course. Here, we present a statistical model for clustering periodic patterns of gene expression in terms of different transcriptional profiles.

Identification of quantitative trait nucleotides that regulate cancer growth: a simulation approach.

A general growth model derived from basic cellular properties can be used to describe the dynamic process of cancer growth with mathematical equations. It has been recognized that cancer growth is under genetic control, with a multitude of interacting genes each segregating in a Mendelian fashion and displaying environmental sensitivity. In this article, we integrate the mathematical aspects of the pervasive growth model into a statistical framework for the identification of quantitative trait nucleotides that underlie cancer growth.

Standardized risk and description of results from multivariable modeling of a binary response.

Descriptions of significant associations found from a logistic regression analysis typically are based on adjusted odds ratios. Unfortunately, odds ratios provide no information about the prevalence of response. In this paper, we justify and recommend using standardized risks, i.