Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic...Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic males with female genotypes, neo-males are harmful in C. semilaevis aquaculture because they reduce overall production. The present study evaluated the difference in the growth-related traits： total length （TL）, body weight （BW） and square root of body weight （SQ_BW） at the age of 570 days between normal and neo-male offspring （neo-males used as male parents）. The difference in the proportion of females between normal and neo-male offspring was also assessed. Based on the linear mixed model, restricted maximum likelihood （REML） and best linear unbiased prediction （BLUP） were used to estimate various （co）variance components and estimated breeding values （EBVs） of growth-related traits. As a result, all the mean values of the three studied traits were significantly larger in normal offspring than in neo-male offspring. Additionally, the female proportion was significantly larger in normal offspring than in neo-male offspring. Heritability was 0.128＋0.066 2 for TL, 0.128-4-0.065 5 for BW and 0.132~0.062 9 for SQBW, all of which were low level heritabilities. The correlation coefficients of EBVs and phenotypic values of the target traits were 0.516 for TL, 0.524 for BW and 0.506 for SQ_BW, all of which were highly significant （P〈0.01）. Genetic correlations among TL, BW and SQ_BW were positive high （0.921-0.969） and higher than those of phenotype （0.711-0.748）, both of which had low standard errors （0.063-0.123 for genotype, and 0.010-0.018 for phenotype）. Compared with normal offspring, neo-male offspring have lower breeding values for each studied trait through EBVs comparison. Therefore, neo-male offspring should not be used as broodstock in a C. semilaevis breeding programs.展开更多
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accur...Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA10A403-2)the Taishan Scholar Project of Shandong Province of China
文摘Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic males with female genotypes, neo-males are harmful in C. semilaevis aquaculture because they reduce overall production. The present study evaluated the difference in the growth-related traits： total length （TL）, body weight （BW） and square root of body weight （SQ_BW） at the age of 570 days between normal and neo-male offspring （neo-males used as male parents）. The difference in the proportion of females between normal and neo-male offspring was also assessed. Based on the linear mixed model, restricted maximum likelihood （REML） and best linear unbiased prediction （BLUP） were used to estimate various （co）variance components and estimated breeding values （EBVs） of growth-related traits. As a result, all the mean values of the three studied traits were significantly larger in normal offspring than in neo-male offspring. Additionally, the female proportion was significantly larger in normal offspring than in neo-male offspring. Heritability was 0.128＋0.066 2 for TL, 0.128-4-0.065 5 for BW and 0.132~0.062 9 for SQBW, all of which were low level heritabilities. The correlation coefficients of EBVs and phenotypic values of the target traits were 0.516 for TL, 0.524 for BW and 0.506 for SQ_BW, all of which were highly significant （P〈0.01）. Genetic correlations among TL, BW and SQ_BW were positive high （0.921-0.969） and higher than those of phenotype （0.711-0.748）, both of which had low standard errors （0.063-0.123 for genotype, and 0.010-0.018 for phenotype）. Compared with normal offspring, neo-male offspring have lower breeding values for each studied trait through EBVs comparison. Therefore, neo-male offspring should not be used as broodstock in a C. semilaevis breeding programs.
文摘Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
