Use of Napier grass and rice straw hay as exogenous additive improves microbial community and fermentation quality of paper mulberry silage

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

There is a need to standardize the NDF procedure. Procedures have varied because of the use of different amylases in attempts to remove starch interference. The original Bacillus subtilis enzyme Type IIIA (XIA) no longer is available and has been replaced by a less effective enzyme. For fiber work, a new enzyme has received AOAC approval and is rapidly displacing other amylases in analytical work. This enzyme is available from Sigma (Number A3306; Sigma Chemical Co., St. Louis, MO). The original publications for NDF and ADF (43, 53) and the Agricultural Handbook 379 (14) are obsolete and of historical interest only. Up to date procedures should be followed. Triethylene glycol has replaced 2-ethoxyethanol because of reported toxicity. Considerable development in regard to fiber methods has occurred over the past 5 yr because of a redefinition of dietary fiber for man and monogastric animals that includes lignin and all polysaccharides resistant to mammalian digestive enzymes. In addition to NDF, new improved methods for total dietary fiber and nonstarch polysaccharides including pectin and beta-glucans now are available. The latter are also of interest in rumen fermentation. Unlike starch, their fermentations are like that of cellulose but faster and yield no lactic acid. Physical and biological properties of carbohydrate fractions are more important than their intrinsic composition.