牡丹5个管家基因的克隆及其在系统进化分析中的应用

利用RT-PCR技术从牡丹组7个野生种和1个栽培种中分别克隆得到泛素延伸蛋白基因(ubiquitin,UBI)、素环蛋白基因(cyclophilin,CYP)、肌动蛋白基因(Actin)、葡萄糖六磷酸脱氢酶基因(glucose-6-phosphate 1-dehydrogenase,G6PD)和β–微管蛋白基因(beta-tubulin,TUB)等5个管家基因的编码序列(coding sequence,CDS)。序列比对分析发现,5个管家基因序列均相对保守,其在牡丹7个野生种和1个栽培种中的保守性在99.04%~99.69%。利用5个管家基因分别构建了其在牡丹组8份材料中的系统进化树。5个系统进化树均将8份材料分成两组,即革质花盘亚组和肉质花盘亚组;由于5个管家基因核苷酸序列各自的保守性不同,在区分亚组成员间略有差异。综合分析5个管家基因构建的系统进化树可知,杨山牡丹(Paeonia ostii)和栽培牡丹(P.suffruticosa‘Luoyanghong’)常聚为一支,二者亲缘关系较近,因而推测杨山牡丹可能参与了栽培牡丹的形成;紫斑牡丹(P.rockii)和四川牡丹(P.decomposita)一直聚为一支,推测二者亲缘关系较近;黄牡丹(P.lutea)和紫牡丹(P.delavayi)一直聚为一支,推测这二者亲缘关系较近。狭叶牡丹(P.potaninii)与黄牡丹和紫牡丹分化较早,结合前人研究结果建议将狭叶牡丹作为一个独立的种。     

Infections Investigation of ALV and REV in Guangxi Local Poultry Breeds and Sequence Analysis of gp85 Gene of ALV Isolates

In order to investigate the infection status of avian leukosis virus (ALV) and avian reticuloendotheliosis virus (REV) in the major local breeds of Qinzhou,Guangxi,totally 953 samples of egg white,cloaca swab and serum of Ma duck,Shitou goose,Tiejiao-Ma chicken,turkey and pigeon were collected from the representing flocks and detected by the commercial ELISA kits.ALV was isolated for the ALV p27 positive samples by culturing on DF-1 cells,and gp85 gene was sequenced.The results showed that the detections of ALV were negative in the samples except those of Tiejiao-Ma chicken,while REV antibody was found positive in Ma duck,Tiejiao-Ma chicken and turkey.The nucleotide sequences of gp85 gene of two isolates shared 94.5% identity with each other,and shared 86.9% to 94.9% with reference strains.The amino acid sequences of gp85 gene of two isolates shared 91.5% identity with each other,and shared 84.0% to 91.6% with reference strains.There were many variable sites in the hyper variable region hr1 and hr2,and the vr2 and vr3 variable regions were relatively conservative.Phylogenetic tree analysis showed that the two isolates shared the highest homology with SCAU11-XG strain.     

K步稳定的鞋印花纹图像自动聚类

目的 鞋印是刑事侦查的重要物证之一,如何对积累的大量鞋印花纹图像进行自动归类管理是刑事技术迫切需要解决的问题之一。与其他类图像不同,鞋印花纹图像具有种类多但数目未知、同类花纹分布不均匀且同类花纹数目少的特点。基于鞋印花纹图像的这些特点,用目前典型的聚类算法对鞋印花纹图像集进行聚类,并不能取得很好的效果。在对鞋印花纹图像进行分析的基础上,提出一种K步稳定的鞋印花纹图像自动聚类算法。方法 对已标记的鞋印花纹图像进行统计发现,各类鞋印花纹之间在特征空间上存在互不相交的区域(本文称为隔离带)。算法的核心思想是寻找各类鞋印花纹之间的隔离带,来将各类分开。过程为:以单调递增或递减的方式调整特征空间中判定两点为一类的阈值,得到数据集的多次划分;若在连续K次划分的过程中,某一类的成员不发生变化,则说明这K次调整是在隔离带中进行的,即聚出一类,并从数据集中删除已标记的数据;选择下一个阈值对剩余的数据集进行划分,输出K步不变的类;依此类推,直到剩余数据集为空,聚类完成。结果 在两类公开测试数据集和实际鞋印花纹数据集上进行实验,本文算法的主要性能指标都超过典型算法,其中在包含5792枚实际鞋印花纹数据集上的聚类准确率和F-Measure值分别达到了99.68%和95.99%。结论 针对鞋印花纹图像特点,提出了一种通过寻找各类之间的隔离带进行自动聚类的算法,并在实际应用中取得了很好的效果。且算法性能受参数的变化以及类的形状影响较小。本文算法同样适用于具有类似特点的其他数据集的自动聚类。     

2008-2015年橡胶蜂蜜产量与气候的相关性分析

为了进一步发掘橡胶林区潜在经济价值,预测下一年橡胶泌蜜规律,试验采用连续8年记录转地到西双版纳傣族自治州采集橡胶蜜的蜂场收获蜂蜜情况,并以气象观测仪记录当时的气象变化的方法。表明进入橡胶场地的蜂群势变化不大[(13.450±0.113)脾,=0.854],但产量变化显著[(72.613±0.349)kg,<0.01]。气象观测表明气温8年内波动范围不大,而降雨量波动大,表现为2011年洪涝出现早,2014年干旱持续时间长。产量与日平均气温(相关系数r=0.337)有极显著的正相关性,与日降雨量(相关系数r=-0.269)及日最低气温(相关系数r=-0.483)呈极显著的负相关,与群势表现出轻微的负相关(相关系数r=-0.031)。说明持续的干旱和温暖湿热的气温及合理的群势能够增加橡胶蜜的产量,适宜的气候及大面积橡胶林区蕴含着巨大的养蜂潜力。     

几种修剪树型对'珍珠'番石榴开花坐果及产量的影响

选用4年生'珍珠'番石榴为试脸材料,采用开心形、圆头形、平头形树型结构,以果树生产中采用的自然圆形树型结构为对照,研究4种树型修剪方法对'珍珠'番石榴新梢数、花蕾数、开花结果数及果实产量的影响.试验结果表明,'珍珠'番石榴树型圆头形、开心形促进开花坐果较好,圆头形有利于提高产量.     

保定地区几个早熟苹果品种(系)生长观察

为筛选适宜保定地区栽植的早熟苹果品种,以嫁接在SH40中间砧上的不同早熟苹果品种(系)为试材,测定了不同品种(系)早熟苹果树体生长发育指标和果实品质。结果表明:除嘎啦早熟变异外,其他供试品种(系)树体生长较好,3年生时Xu2-7、Xu3-2树高330.0~352.5 cm,4年生时为394.0~417.5 cm,Xu3-2、藤牧1号、信浓红4年生为336.5~381.0 cm。4年生时,干径以Xu2-5最大;短枝、中枝、叶丛枝均以Xu2-5最多,长枝以Xu2-7最多,总枝量以Xu2-5最多;藤牧1号短枝所占比例最高(57.4%)。应用模糊综合评判法得出Xu2-5、Xu2-7、信浓红、藤牧1号果实品质较高。     

温室一茄多果快速育成技术

随着嫁接技术在蔬菜育苗中的广泛应用,根据近年来兴起的一茄多果嫁接技术与园艺景观造型相结合的趋势,介绍了一种改进的一茄多果嫁接技术,技术的流程更简洁,也更易于操作,可使经济效益与人文景观效果得到共同提升。     

施肥对海南省西华农场2龄橡胶树径围生长的影响

用7种配方肥料，在海南省西华农场上进行了为期1年的2年生橡胶树施肥试验。结果表明：不同施肥处理对胶树径围增长效应之间有极显著的差异。其中，处理6(施氧化镁0.4 kg/株)、处理8(施尿素0.6 kg/株、施过磷酸钙1.2 kg/株、施氧化镁0.4 kg/株、施干牛粪6 kg/株)与对照相比，差异极显著，分别比对照增长25.5%、26.4%；其余处理与对照相比，差异不显著，其中，单施尿素(0.6 kg/株)效果最差，单施氯化钾(0.4 kg/株)效果较差。     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.