The Applications of Pressure Test in the B/S System

Liú, Dan; Li, Li; Yang, Xiaohui; Zhai, Hongyu

doi:10.1109/cise.2009.5363411

Cited by 3 publications

(18 citation statements)

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“…During the growth of bamboo, nutrients and metabolites enter and exit the vessel through the pit, and these substances deposit gradually on the vessel walls; this forms bumps and increases the surface roughness of the vessel (average R q = 58.7 nm). 40 It is difficult for an over-rough surface to trap MOF precursors for nucleation because the surface contains large cavities which are similar to smooth surfaces for the molecules. 37 Therefore, fewer nucleation sites and sufficient space are conducive to the deposition of large MOF crystals.…”

Section: Resultsmentioning

confidence: 99%

In situ deposition of MOF199 onto hierarchical structures of bamboo and wood and their antibacterial properties

Zhang

et al. 2019

RSC Adv.

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Section: Resultsmentioning

confidence: 99%

In situ deposition of MOF199 onto hierarchical structures of bamboo and wood and their antibacterial properties

Zhang

et al. 2019

RSC Adv.

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show abstract

“…Then, the molecular marker electropherogram has three states: mm, Mm and MM, which could be quantified and coding with -1, 0 and 1 respectively. If we randomly sample the codes from -1, 0 and 1, we could get a series of polymorphism data of individuals, and these data are just the marker materials in our research [5,6].…”

Section: Materials Preparation and Research Methodsmentioning

confidence: 99%

“…Where I i is the genotype state in the ith locus, when the genotype is q i q i , the genotype state is -1; when the genotype is Q i q i , the genotype state is 0; when the genotype is Q i Q i , the genotype state is 1 [5,6] Artificial Neural Networks Model. The back-propagation artificial neural networks with 3 layers, one input layer, one hidden layer and one output layer, is used to build the non-linear functions expressing the interdependency between the collected marker data and the phenotype value, which is polygenic genotype value in this paper.…”

Section: Materials Preparation and Research Methodsmentioning

confidence: 99%

“…The number of input neurons was the number of gene locus N, the number of output neurons was the number of quantitative traits, while the number of hidden neurons was 1-1.5 times of input neurons for discussing the influencing of the number of hidden neurons on estimating the genotype value. The neural network structure was described as in [5,6] Simulation Method. The input materials of the gene locus polymorphism including 1000 animals', each with 10 gene loci, were randomized sampled for training the artificial neural networks structure.…”

Section: Materials Preparation and Research Methodsmentioning

confidence: 99%

“…Back-propagation (BP) artificial neural network, a parallel adaptive system simulating the way in which the human brain processes information has been very successfully used in biological sequence analysis for purposes as diverse as protein secondary structure prediction, recognition of signal peptide cleavage sites, gene recognition, design of a genome-wide siRNA library, classification and diagnostic prediction of cancers using gene expression profiling, prediction of specific class I MHC binding peptide sequences [1][2][3][4]. For predicting animal phenotype value, recently, we had primarily used back-propagation artificial neural network to simulate the genomic breeding values, which performed very well in predicting animal phenotype value [5,6]. Now, Largescale, high-density single-nucleotide polymorphism (SNP) marker information on haplotype structure and frequencies become available [7].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Gene Effect on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

Guo

et al. 2011

KEM

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Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.

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Influence of Number of Gene Loci on Prediction of Animal Phenotype Value Using Back-Propagation Artificial Neural Network

Zhang

2011

KEM

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Vast amount of bioinformation immerged in the past, HapMap Project had genotyped more than 3.1 million Single Nucleotide Polymorphisms (SNPs) information by 2007, a prediction equation based on SNPs was derived to calculate genomic breeding values. However, the simple mathematical function could not reflect the complex relation between genome and phenotypes. Unlike the methods of regression, artificial neural networks could perform well for optimization in complex non-linear systems; artificial neural networks have not been used to calculate genomic breeding values. In this paper, back-propagation neural network is used to simulate and predict the genomic breeding values or polygenic genotype value, and the different numbers of gene loci and hidden neurons were used to discuss the influence of the learning rate on estimating the polygenic genotype value. The result showed normalization was very important for training prediction model. After phenotype value normalized, optimum neural network for estimating the animal phenotype could be established without considering the gene number, but the optimum neural network should be selected from amount of neuronal networks with different hidden neuron number. No matter what the gene number is, as well as the number of hidden neurons is right, BP networks could be used to predict the animal phenotypes.

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The Applications of Pressure Test in the B/S System

Cited by 3 publications

References 1 publication

In situ deposition of MOF199 onto hierarchical structures of bamboo and wood and their antibacterial properties

In situ deposition of MOF199 onto hierarchical structures of bamboo and wood and their antibacterial properties

Influence of Gene Effect on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

Influence of Number of Gene Loci on Prediction of Animal Phenotype Value Using Back-Propagation Artificial Neural Network

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