A New Method for Figuring the Number of Hidden Layer Nodes in BP Algorithm

In the field of artificial neural network, BP neural network is a multi-layer feed-forward neural network. Because it is difficult to figure the number of hidden layer nodes in a BP neural network, the theoretical basis and the existing methods for BP network hidden layer nodes are studied. Then based on traditional empirical formulas, we propose a new approach to rapidly figure the quantity of hidden layer nodes in two-layer network. That is, with the assistance of experience formulas, the horizon of unit number in hidden layer can be confirmed and its optimal value will be found in this horizon. Finally, a new formula for figuring the quantity of hidden layer codes is obtained through fitting input dimension, output dimension and the optimal value of hidden layer codes. Under some given input dimension and output dimension, efficiency and precision of BP algorithm may be improved by applying the proposed formula

Genetic Basis and Expression Pattern Indicate the Biocontrol Potential and Soil Adaption of Lysobacter capsici CK09

Lysobacter species have attracted increasing attention in recent years due to their capacities to produce diverse secondary metabolites against phytopathogens. In this research, we analyzed the genomic and transcriptomic patterns of Lysobacter capsici CK09. Our data showed that L. capsici CK09 harbored various contact-independent biocontrol traits, such as fungal cell wall lytic enzymes and HSAF/WAP-8294A2 biosynthesis, as well as several contact-dependent machineries, including type 2/4/6 secretion systems. Additionally, a variety of hydrolytic enzymes, particularly extracellular enzymes, were found in the L. capsici CK09 genome and predicted to improve its adaption in soil. Furthermore, several systems, including type 4 pili, type 3 secretion system and polysaccharide biosynthesis, can provide a selective advantage to L. capsici CK09, enabling the species to live on the surface in soil. The expression of these genes was then confirmed via transcriptomic analysis, indicating the activities of these genes. Collectively, our research provides a comprehensive understanding of the biocontrol potential and soil adaption of L. capsici CK09 and implies the potential of this strain for application in the future

Polymorphism in exon 1 of adiponectin gene and its association with water holding capacity, IMF and abdominal fat in duck

The objective of the present study was to investigate the effect of polymorphism in adiponectin gene on meat quality traits, serum total cholesterol, serum triglyceride and abdominal fat of 170 individuals from Cherry Valley duck (CV), Jinding duck (JD) and Hybrid duck (CV × JD) (HB) populations. PCR-SSCP technique was developed to analyze a 230 bp region of the adiponectin gene exon 1. Three genotypes (CC, CD and DD), which were the products of two alleles (C and D) were observed. Alignment sequences’ results showed that four SNPs (C86T, C104T, C146T and C155T) were found and all of those nucleotide variations were nonsense mutations. Association analysis indicated that all of these traits had significant population effects except meat colour (P < 0.05) and then the birds with homozygote (CC) had significant lower than homozygotes (DD) for IMF, water holding capacity and abdominal fat (P < 0.05). The research suggested that genotype DD may be an advantage genotype for fat deposition in duck. The adiponectin gene exon 1 polymorphism could be used in marker assistant selection (MAS) as a genetic marker for the birds’ fat deposition.Key words: Duck, adiponectin gene, polymorphism, meat quality, fatness

IQMNMR: Open source software using time-domain NMR data for automated identification and quantification of metabolites in batches

<p>Abstract</p> <p>Background</p> <p>One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches.</p> <p>Results</p> <p>We created IQMNMR, an R package that integrates a relaxation algorithm, digital filter, and similarity search algorithm. It differs from existing software in that it is a time-domain method; it uses not only frequency to resolve overlapping resonances but also relaxation time constants; it requires only one NMR spectrum per application; is uninfluenced by phase shifts and baseline distortions; and most important, yields a batch of quantified metabolites.</p> <p>Conclusions</p> <p>IQMNMR provides a solution that can automatically identify and quantify metabolites by one-dimensional proton NMR spectroscopy. Its time-domain nature, stability against phase shifts and baseline distortions, requirement for only one NMR spectrum, and capability to output a batch of quantified metabolites are of considerable significance to metabolic modeling and simulation.</p> <p>IQMNMR is available at <url>http://cran.r-project.org/web/packages/IQMNMR/</url>.</p

In vitro specific interactions revealed the infective characteristics of fungal endophytes to grapevine

In the present study a method for co-culture of fungal endophytic strains and grape cells was developed in order to study their interactions, and filter candidates for further safe inoculation in the vineyard. Analysis of morphological and physiological traits was performed by measuring the plant callus and fungal growth, plant cells viability, degree of cell oxidation and the scale of contact or its absence as reaction of the fungal endophyte to the presence of the plant callus. Accordingly, endophytic fungal strains (EFS) were classified on scale of invasion into categories (strong - medium - weak invasive), as well as the contact between the two partners (grow into - grow onto - contact - no contact) and the grape cell oxidation degree (normal (no oxidation) - light - moderate - serious). More included the dominance and distribution of EFS in the plant host, and correlation plots of physiological traits during plant callus and endophytic fungi co–culture were calculated