动物杂种优势利用及其预测方法

杂种优势因具有提高两个不同亲本的遗传组成杂交所产后代的生产性能、生活力和抗逆性等特点,被广泛地运用在动植物杂交育种中。杂种优势利用率越高,种畜产生的价值越高,其后代产生的额外利润就越大。因此,杂种优势的利用率是广大育种工作者所关注的一项重要内容。杂种优势利用率的评估则依赖于对种畜杂种优势大小的预测。本文主要从杂种优势利用和杂种优势预测方法2个方面对已有的研究进行综述,并探讨有关杂种优势预测准确度提高的方法,以期对相关的研究提供一定的参考和帮助。     

基于公共天气预报的三江平原ET0预报模型比较及敏感性分析

为了提出适合我国三江平原的高精度ET0预报方法,基于该区6个气象站点的天气预报数据和实测气象数据,以FAO56-Penman-Monteith(FAO56-PM)公式计算值为基准,比较Hargreaves-Samani(HS)、Thornthwaite(TH)和Blaney-Criddle(BC)3个ET₀预报模型的效果,对最优模型进行敏感性分析。结果表明:3个模型1~7 d预见期平均绝对误差均值分别为0.66、0.65、0.65 mm/d,均方根误差分别为0.93、0.96、0.95 mm/d,相关系数分别为0.857、0.828、0.840。1~5 d预见期最优预报模型为HS模型,6~7 d为TH模型。总体上预报精度由高到低为HS、TH、BC模型,建议采用HS模型在三江平原开展ET₀预报,HS模型预报对最高温预报的敏感性大于最低温。其预报值在夏季受温度预报误差影响最大,冬季最小,4季整体误差较小。研究可为灌溉预报提供较准确的数据基础。     

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.     

黑龙江地区鲤疱疹病毒3型的流行病学调查及其主要囊膜蛋白的抗原表位预测

2015-2016年对来自黑龙江地区46个养殖场送检的鲤鱼进行锦鲤疱疹病毒(Koi herpes virus,KHV)的分离鉴定,并应用DNAStar等软件对检出的阳性样本的主要囊膜蛋白(Major envelop protein,MEP)基因进行生物信息学分析。结果显示,KHV的检出率约为6.5%,3株阳性样本具有相同的MEP基因序列,称之为KHV-HLJ1516。MEP基因开放阅读框为771 bp,编码256个氨基酸,相对分子质量为28 280.58,等电点为8.40。对KHV-HLJ1516的MEP基因序列同源性分析表明,其与KHV-GZ11分离株仅有1个碱基不同,但并未导致氨基酸发生改变,碱基相似性为99%;而与HZ419分离株相比,KHV-HLJ1516的MEP基因有3个碱基发生突变,相应的氨基酸由Asn变为IIe。聚类分析结果显示,KHV-HLJ1516与KHV-GZ11处于同一分支,而HZ419位于另一分支。B细胞抗原表位预测结果显示,KHV-HLJ1516的MEP的抗原表位主要位于4~11,24~40,42~63,82~110,209~218,238~249氨基酸区段。     

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.     

Predictive modes of action of pesticides in uterine adenocarcinoma development in rats

Endometrial adenocarcinoma in the uterine corpus is a malignant cancer that occurs in menopausal women and aged rodents. Because of the similarities in pathogenesis and morphology of endometrial adenocarcinoma in rodents and humans, prediction of the modes of action (MOA) in uterine carcinogenesis is important for extrapolation of rodent data to humans. Three MOAs have been accepted as major pathways for uterine carcinogenesis in rodents: 1) estrogenic activity, 2) increased serum 17beta-estradiiol (E2) to progesterone (P4) ratio and 3) modulation of estrogen metabolism to produce 4-hydroxyestradiol via P450 induction. Inhibition of estrogen excretion and increased aromatase in situ in the tumor are also a potential pathway. Here, chemicals showing uterine carcinogenicity were chosen from approximately 300 pesticides evaluated in Japan within the past decade, and their mechanisms were predicted using parameters from mechanistic and toxicity studies. Seven pesticides increased uterine tumor formation in rats, and the pathways of 4 pesticides could be predicted based on various mechanistic studies. The MOAs of cyenopyrafen and benthiavalicarb-isopropyl were predicted to be modulation of estrogen metabolism, while those of pyriminobac-methyl and spirodiclofen were predicted to be increased E2 to P4 ratio. The driven pathways of metazosulfuron and isopyrazam could not be predicted using several mechanistic studies. No mechanistic studies have been reported for sedaxane, which has a chemical structure and toxicological profile similar to isopyrazam. Our results indicated that appropriate mechanistic studies are useful for mechanism prediction in risk assessment. From this analysis, a flowchart showing a decision tree for predictive MOAs in uterine carcinogenesis was proposed.     

猪饲料有效能值预测模型的构建

为探索饲料常规成分及碳水化合物组分与饲料有效能值之间的关系方程,本研究以NRC第11版《猪营养需要量》中发布的122套饲料营养成分表为基础,将饲料中11种基础成分[6项常规成分:干物质、粗蛋白质(CP)、粗纤维(CF)、粗脂肪(EE)、酸性醚提取物、粗灰分(ash);5项碳水化合物组分:淀粉(ST)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、半纤维素、酸性洗涤木质素]作为自变量,将饲料中的消化能(DE)、代谢能(ME)及净能(NE)作为因变量,采用SAS软件中的REG过程,分别建立不同性质饲料、自变量的不同组合与DE、ME及NE之间的回归关系方程,并以相关系数(R2)及变异系数(CV)作为评价回归模型的优劣。研究表明,有效能值与CP、ST及纤维类指标显著或极显著相关(P0.05或P0.01)。将所有饲料作为研究对象时,饲料的DE、ME及NE与上述11种基础成分之间建立的普适性回归模型预测效果较差。当将14种玉米及其加工产品形成子集时,建立饲料基础营养成分与DE、ME及NE的关系方程分别为7、6和7套(P0.05),且3组回归模型R2分别为0.632 8~0.772 3、0.646 9~0.684 9和0.670 5~0.822 1,CV分别为6.61%~8.40%、6.58%~7.34%和6.21%~8.27%;当将13种大豆及其加工产品形成子集时,共建立饲料基础成分与DE、ME关系方程分别有3和4套,回归模型R2分别为0.907 1~0.926 9、0.890 7~0.922 3,CV分别为5.40%~6.09%、5.79%~6.78%,NE与基础营养成分指标之间无法建立具有营养学意义的有效回归方程。对于同类饲料中具有相同自变量组合的DE及ME预测模型而言,两者之间的差异主要是自变量CP的系数上,且CP部分对ME的正效应低于DE,这保证模型预测的ME低于DE。同时选用本研究构建的适宜模型,补充了NRC第11版成分表中第97(去皮大豆粕,低寡糖,浸提)、101(全脂大豆,高蛋白质)及102号(全脂大豆,低寡糖)饲料的DE值分别为15.99、17.35、17.27 MJ/kg,ME值分别为14.53、16.15和16.14 MJ/kg。综上,以NRC(2012)饲料营养成分表为基础,建立的普适性有效能值回归模型预测效果较差。按照玉米类和大豆类进行分类,可建立DE、ME和NE与饲料化学成分之间的多元回归方程,其中最优的预测因子为CP、EE、ST、ash、NDF、ADF。具有相同自变量的同类饲料DE和ME预测模型之间的差异是CP系数,CP影响DE转化为ME的效率。     

动态经济评价模块原理及其在林分经营模型中的应用*

通过动态经济评价模块，将成本构成要素分解成材料消耗，用工消耗和资金换算单位乘子等基本元素，而用以基本元素以自变量的函数描述成本及收入核算项目，借助模块内专门设计的解码过程，通过外部文件，修改成本构成要素的分解层次和描述单位，重新设计成本核算项目的描述函数，实现了经济评价模块同林分生长模型间的柔性结构匹配。压缩数据向量及其相应算法的引入，不仅减少了内存空间的占用量，而且提高了经济评价模块的运行速度，     

森林资源动态变化预测模型

本文给出了不等间隔时序数列的CM(1,1)模型,并对广西融水县森林资源动态变化进行了预测。预测结果比较理想,模型精度97.2％,从而为森林资源部分数据更新提供了一个新方法。     

“十二五”期间我国天然橡胶消费量预测

通过分析我国天然橡胶消费量和国内生产总值(GDP)之间的关系,建立天然橡胶消费量的回归模型,依据我国经济发展趋势以不同的GDP增长率和时间变量对我国"十二五"期间天然橡胶消费量做出预测。