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. Coliform counts drastically increased at holy and urban places due to pilgrimage and other ritual activities. Higher values of the pollution index (PI) ratio (>1) reveals, human fecal pollutions affect the water quality. The averaged PI ratio shows a substantial higher microbial contamination in holy places than in urban areas and the order of decreasing PI ratios observed were: holy places > urban areas > semi-urban areas > rural areas. Correlation and factor analysis proves microbial communities were not related to physicochemical parameters. Principal component analysis indicates 55.32 % of the total variance resulted from human/animal fecal matters and sewage contaminants whereas 19.95 % were related to organic contents and waste materials from the rivers. More than 80 % of the samples showed a higher fecal coliform and Streptococci by crossing the World Health Organization's permissible limits.

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

To investigate the effects of dietary ß-glucan (0.5 or 1 g kg⁻¹ diet: 0.5-BG, 1-BG) and rutin (0.5 or 1 g kg⁻¹ diet: 0.5-RT, 1-RT) after 10 days in the absence of pathogen challenge on the immune response of Fenneropenaeus chinensis, we determined total hemocyte count (THC) and the expression of four immune-related genes in hemocytes: those for prophenoloxidase (proPO), peroxinectin (PX), lipopolysaccharide and/or ß-glucan binding protein (LGBP), and c-type lectin (CL). As a prerequisite for subsequent experiments, cDNA encoding proPO of the fleshy shrimp, Fenneropenaeus chinensis (f-proPO) was obtained from hemocytes; it had a full length of 3023 bp, with an open reading frame (ORF) of 2061bp, a 105-bp 5'-untranslated region, and a 906-bp 3'-untranslated region containing the poly A signal. The THCs of shrimp fed ß-glucan of 1 g kg⁻¹ diet, and rutin of 1 g kg⁻¹ diet were significantly higher than that of the control (P < 0.05). The expression of proPO mRNA was slightly downregulated and that of LGBP mRNA was upregulated (except in 1-RT). PX and CL mRNA remained constitutively expressed in all groups. Our results reveal that ß-glucan and rutin dietary supplements have minimal effect on immune response in the absence of pathogen challenge.

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. The model incorporates biologically meaningful Fourier series approximations of gene periodic expression into a mixture-model-based likelihood function, thus producing results that are likely to be closer to biological relevance, as compared to those from existing models. Also because the structures of the time-dependent means and covariance matrix are modeled, the new approach displays increased statistical power and precision of parameter estimation. The approach was used to reanalyze a real example with 800 periodically expressed transcriptional genes in yeast, leading to the identification of 13 distinct patterns of gene-expression cycles. The model proposed can be useful for characterizing the complex biological effects of gene expression and generate testable hypotheses about the workings of developmental systems in a more precise quantitative way.

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. As a first attempt of its kind, we capitalize on the simplest Haar wavelet shrinkage technique to break an original signal down into its spectrum by taking its averages and differences and, subsequently, detect gene expression patterns that differ in the smooth coefficients extracted from noisy time series gene expression data. The method is shown to be effective on simulated data and and on recent time course gene expression data. Supplementary Material is available at www.liebertonline.com .

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. This integrative framework is constructed with a single nucleotide polymorphism-based haplotype blocking analysis. Simulation studies have been performed to demonstrate the usefulness of the model. The proposed model provides a generic platform model for testing and detecting specific DNA sequence variants that regulates the timing of cancer emergence, growth and differentiation.

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.e., standardized probabilities, which do provide information about prevalence, in addition to adjusted odds ratios, for pairwise comparisons of the levels of a significant factor. We illustrate the advantages of generally reporting standardized risk estimates, in the context of assessing the effect of blood lead levels during the preschool years on occurrence of academic problems in kindergarten. Results are more meaningfully interpreted when accompanied by standardized risk estimates.