Analysis on Target Genes with Mutation in microRNA of Blue-shelled Chicken and White Leghorn

The study was conducted to explore the potential different characters between Blue-shelled chicken and White leghorn.Global genome microRNA was combined the identified microRNA with complementary lab-predicted microRNA.Then the two breed chicken's SNP data got by GGRS were mapped to the microRNA and focused on SNP that deliberately located in mature-microRNA.Bioinformatics method was adopted for target prediction on microRNA which had SNPs.By further gene enrichment analysis,the study found these genes enriched in 22 GO terms,10 KEGG pathways,and 3 IPA important networks.And they enriched in traits which associated with growth,such as mTOR signaling pathways,Wnt signaling pathways,growth hormone receptor networks and insulin-like growth factor Ⅰ receptor networks.And they also enriched in some laying traits,such as oocyte meiotic signaling pathways and progesterone mature oocytes signaling pathways.The methods and the results might provide references for further studies.     

Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis

Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R² of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture.     

基于HHT变换的PSO-LSSVM耦合模型估算气象资料短缺地区ET_0

为准确估算气象资料短缺地区参考作物腾发量,构建了一种基于HHT变换的PSO-LSSVM耦合模型,并利用新疆和田气象站2000—2009年单日数据做训练、双日数据做验证。结果表明,该模型估算ET0方法明显优于常规的PSO-LSSVM和GRNN,预测精度较二者分别提高了15.7%~85.6%和15.8%~93.7%;该方法预测ET0的气象要素重要性为RsTmaxTminRHWn,利用该方法对气象要素组合为Tmax/Tmin/RH/Wn、Tmax/RH/Wn、Tmin/Wn、Wn条件下的ET0预测,MSE分别为0.407、0.185、0.149、0.135,说明该方法可以很好地估算资料缺失地区ET0。     

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.     

怀孕期高原鼢鼠生物学特征与环境因子的关系分析

选择甘肃天祝县高原鼢鼠(Mospalax baileyi)2个地理位置不同的栖息地为研究区,以高原鼢鼠体重、胎数、妊娠率和种群密度作为其生物学特征指标,以2011~2013年归一化植被指数(NDVI)、年均降水量和年均温度为环境因子,分析二者的相关关系。结果表明:2个研究区域NDVI存在显著性差异(P0.05);除了2011年和2013年雌性体重存在差异外,2个区域高原鼢鼠体重、妊娠率、平均胎数和种群密度均无显著性差异(P0.05);雌雄个体重和平均胎数与NDVI呈负相关关系,妊娠率和种群密度与NDVI呈正相关关系;雄性个体重、胎数和种群密度与年均温呈负相关关系,雌性个体重和妊娠率与年均温呈正相关关系;雄性个体重、妊娠率、胎数和种群密度与年均降水量呈正相关关系,雌性个体重与年均降水量呈负相关关系,但高原鼢鼠各生物学指标与环境因子相关性均不显著(P0.05)。说明,高原鼢鼠体重、妊娠率、胎数和种群密度的变化与栖息地环境因子没有直接关系,生物学特征受环境因子的影响较小。     

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.     

我国松毛虫发生率的空间分布格局及其驱动因子

了解我国松毛虫发生的空间分布格局及其驱动因子, 对松毛虫暴发的预防?控制和治理具有重要意义?本研究基于我国各行政区2004年-2012年间每年松毛虫发生面积及其寄主植物面积, 使用一般线性模型分析气候因子是否以及如何驱动我国松毛虫发生的空间分布格局?结果表明:在全国尺度上, 最暖季降水量是松毛虫发生的主要气候驱动因子, 松毛虫平均发生率与最暖季降水量显著正相关?在最暖季降水量的作用下, 我国松毛虫的发生呈现明显的空间分布格局, 最暖季降水量较高的南方和北方部分地区松毛虫发生率较高, 而降水量较低的西北和青藏地区松毛虫发生率较低?我们的研究结果可为松毛虫暴发的预防和管理工作提供指导?     

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。     

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.