Cloning and Bioinformatic Analysis of L1 Gene of BPV from Guizhou Province

In order to study and analyze L1 gene of bovine papillomavirus（BPV）in Guizhou province,the L1 gene of BPV-GZ01 strain was amplified,cloned and sequenced using bioinformatic softwares and methods,and the secondary structure,tertiary structure,B-cell preponderant epitope,conserved domains analysis, transmembrane domain and signal peptide of L1 gene were predicted.The results showed that the length of L1 gene was 1 494 bp,encoding 497 amino acids.The L1 gene of BPV-GZ01 strain shared an amino acid identities of 98.6%,99.4%,98.4%,94.4% and 91.3%,and a nucleotide identities of 99.1%,99.8%,99.4%,87.6% and 82.8% with those of BPV2,BPV2-SW01,BPV2-AKS01,BPV13 and BPV1 strains,respectively.The results of phylogenetic tree analysis indicated that there was a close relationship between BPV-GZ01 and BPV2-SW01 strains.The prediction of secondary structure of L1 protein indicated that the random coil,extended strand and alphahelix took a higher percentage.The L1 protein was supposed contain 6 potential antigen epitopes.And no transmembrane domains and no signal peptide were found.The tertiary structure of L1 protein was curved spiral structure.These results provided a theoretical basis for immunologic diagnosis and further research of nucleic acid vaccine of BPV.     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

生物多样性与地形及土壤因素对坡面产流产沙的耦合影响

生物多样性关系到生态系统的功能,而坡度和土壤因素是影响坡面产流产沙的重要因子。本研究基于黄土丘陵区生物多样性,并引入坡度、降雨前土壤含水量和土壤容重,探究其与坡面产流产沙总量的耦合关系。通过野外人工降雨试验,记录草本、灌木和乔木群落下的产流时间和产流产沙总量,并开展土壤植被调查。结果表明,与草本和乔木群落相比,灌木群落在延长产流开始时间和降低产沙总量上表现更优;植被群落功能离散度指数(FDiv)对产沙总量影响最大,Simpson指数(D)对产流总量影响最大,坡度仍然是不可忽视的第二重要影响因素;产流总量与坡度、土壤容重、D和功能丰富度指数(FRic)的回归关系优于产流总量分别与其的回归关系;产沙总量与坡度和FDiv指数的回归关系优于产沙总量分别与其的回归关系。植被保护与坡度改善将是该区水土保持规划工作的重点。     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

黑龙江省水稻单产增长潜力预测方法研究

水稻是黑龙江省的主要粮食作物。加入WTO后,水稻生产、贸易、进出口等方面都面临新的形势,同时水稻产量的稳定和增长对于国家粮食安全、人民生活水平提高以及农民奔小康都有着重要意义。因此,探讨了应用Peal曲线函数模型对黑龙江省水稻单产进行预测的方法。     

激活标签法及其在番茄功能基因组的应用

激活标签法是近年来新发展起来的一种研究基因功能有价值的补充方法。该文综述了激活标签法的研究进展及其在番茄功能基因组研究中的应用。     

Prediction of the Economic Development for the Circumjacent Reservoir Region of the Longtan Hydropower Project Using Grey Quantitative Analog

In order to overcome some technical difficulties,we adopt ingenously the grey models combined with the quantitative analog method,and select the Dan Jiangkou Project which has accumulated practical operation data for a long time as the analog project to predict the economic influence for the circumjacent reservoir region of the Longtan hydropower project.On the basis of analog we have built various industrial economic grey models,and compare them with the corresponding predictions for the unconstructed reservoir case.The predictions of the former are greatly larger than the later ones.and hence provides a certain scientific basis for the central leaders to make a decision for constructing the huge hydropower project ultimateiy.     

A Kind of Directing Operation Expert Systems Based on Self-adaptive Predictor

In the general directing-operation expert systems.the information of directingoperation is often shown after the subprocess has finished,The aim of the thesis is to enhance thefunction of directing-operation,and suggest that we add the self-tuning predictor in the expertsystems and use it to direct the present operation.We have shown the case oftemperature-predicting experiment made in the small wine distillatorv so that we can illustrate thatit is feasible to use the predictor to direct the operation.     

煤与瓦斯突出综合预测指标临界值的优化

为了提高煤与瓦斯突出预测的准确性、保障采掘工作面的安全、高效推进和降低防突工程成本,根据“三率”(预测突出率、突出预测准确率、不突出预测准确率)各自具有的合理区间属性和权重,以张集煤矿采掘工作面为例,采用区间数关联决策方法,得出综合预测指标 F ′的最优化区间为［370,380］;根据加权灰靶决策方法,得出预测指标临界值排序为400、390、380等;取最优化区间和排序靠前的临界值的交集,得到煤与瓦斯突出综合预测指标 F ′的临界值为380,并开展现场应用。结果表明,考虑评价指标的区间属性,并取预测指标区间与排序靠前的临界值的交集作为煤与瓦斯突出预测综合指标 F ′的最优临界值方法是合理的。