北票市畜牧技术推广体系改革与建设项目实施效果及建议

深化基层畜牧技术推广体系改革与建设，旨在提升基层畜牧技术推广体系公共服务能力。北票市畜牧技术推广站通过一系列措施，建立健全新型基层畜牧技术推广服务体系。基层畜牧技术推广体系改革与建设推进了体制机制创新，完善了工作机制，夯实了人才基础，提高了服务效能。通过两年的实践证明，基层畜牧技术推广体系改革与建设项目实施，有效解决了畜牧技术推广服务体系“最后一公里”问题。     

新疆南疆棉花品种试验分析

本研究收集新疆南疆大面积种植的56个棉花品种,对非抗虫棉、常规转基因抗虫棉、杂交抗虫棉F1及F2共4类品种的产量及纤维品质进行了比较分析。结果表明,转基因抗虫棉比非抗虫棉增产29.8%,差异达显著水平;杂交棉比常规棉平均增产4.6%,铃重增加0.38g,衣分提高0.91百分点,均未达到显著水平;杂交抗虫棉F1明显优于F2,主要表现在单株结铃性的显著提高导致产量显著提高,而且断裂比强度和纤维整齐度指数等主要品质指标显著提高。由于在河南安阳试验的密度与新疆有明显差异,其准确性需要在新疆高密度条件下继续验证。     

酒石酸泰万菌素对特定畜禽疫病防治功效研究进展

酒石酸泰万菌素(简称泰万菌素)系大环内酯类动物专用抗生素。本文综述了泰万菌素防治敏感细菌感染、支原体病、猪痢疾以及猪繁殖与呼吸综合征等畜禽疫病的功效研究成果,证实泰万菌素对于防治上述畜禽疫病功效显著。近年来研究发现,泰万菌素活性不同于其他类抗生素,可作为抗病毒药物使用,特别适用于对猪繁殖与呼吸综合征的治疗。泰万菌素可抑制病毒复制,缩短病毒血症周期,降低死亡率,减轻临床症状;同时还可通过降低母猪流产率,提高产健康仔猪数以及改善母猪、仔猪病毒血症等,达到控制病毒垂直传播的目的。在当前日益关注兽药残留、动物源细菌耐药性的社会背景下,泰万菌素制剂能很好满足畜禽养殖场用药需求。     

梨属野生种质资源SRAP-PCR反应体系优化研究

采用正交直观分析法和新复极差法对影响梨属野生种质资源SRAP-PCR反应的5种因素(Mg2+浓度、d NTPs浓度、引物浓度、Taq DNA聚合酶、模板DNA)4个水平进行优化筛选。结果表明,优化后的梨属SRAP-PCR反应体系为25μL,包括10×PCR buffer 2.5μL,2.0 mmol/L Mg2+,200μmol/L d NTPs,0.4μmol/L引物,1.5 U Taq酶,60 ng模板DNA。     

棉花单核苷酸多态性标记研究进展

单核苷酸多态性标记已在农作物研究中得到广泛应用并取得重大进展。为了便利棉花SNP(Single nucleotide polymorphism)标记的研究和应用,介绍了利用基因芯片、简化基因组测序、重测序等在棉花中开发SNP标记的方法 ,综述了SNP标记在棉花遗传图谱构建、数量位点的定位和分子标记辅助育种、基因组测序以及系统进化等研究中的应用。并对异源四倍体棉花中SNP标记开发时,同源序列位点和部分同源序列位点上的SNP标记辨别问题进行了系统探讨,对其快捷的开发、检测方式和在数量基因定位中的应用前景进行了展望。     

农垦精神:高校践行社会主义核心价值观的重要维度

在中国革命和社会主义建设中萌芽、凝练的农垦精神,成为各项事业发展,尤其是农业发展的核心价值观。农垦精神与社会主义核心价值观有着密切的内在关联性,二者具有相同的爱国属性、文化基因和理想依托。以农垦精神为核心,开展思想教育、实践教育、文化教育,是高校践行社会主义核心价值观的重要维度。     

浅析新疆矿产资源上市公司会计信息监管

文章对新疆矿产资源上市公司的会计信息监管做了研究,首先介绍了会计信息监管的模式以及新疆矿产资源上市公司监管现状,然后指出了会计信息监管中存在的问题,紧接着对所提出的问题给出了相关的建议对策。     

“中国春”小麦不同缺体的RAPD分析

利用ＰＣＲ－９０Ａ型ＤＮＡ扩增仪，对７个中国春小麦缺体核ＤＮＡ进行了扩增。结果显示，中国春小麦缺体核ＤＮＡ的ＲＡＰＤ产物可分为二种类型：（１）所有供试材料均能扩增出的片段，代表了核ＤＮＡ在进化上的保守性。有一个引物检测到这类片段。（２）全部供试材料间存在的差异片段，这类片段是用来基因定位的主要依据。另外，建立中国春小麦缺体系统的ＲＡＰＤ指纹图谱对研究小麦的系统进化也将有重要意义     

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.