转型期中国特色社会主义政治文化建设面临的挑战和困境探析

从主导政治文化社会整合难度加大、传统政治文化的影响将会长期存在、全球化对中国政治文化的冲击、网络化对中国政治文化的影响、政治文化及相关学科发展滞后、西方政治文化的冲击和渗透、政治社会化渠道不完善等七个方面分析了处在转型期的中国政治文化面临的挑战，此研究对于构建中国特色社会主义的民主的、法治的政治文化具有重要的现实意义。     

水杨酸对盐胁迫下豇豆幼苗生长发育的影响

在300 mmol/L Na Cl胁迫条件下,研究了水杨酸对豇豆[Vigna unguiculata(L.)Walp]幼苗生长发育的影响。结果表明,经水杨酸处理的豇豆幼苗株高、茎粗明显提高,促进了幼苗的形态构建。并且幼苗的超氧化物歧化酶和过氧化物酶活性均显著高于对照,丙二醛含量和电解质渗透率显著或明显降低;水杨酸在适宜的施用浓度范围内能够缓解豇豆幼苗的盐害症状,提高抗盐能力,对豇豆幼苗的生长有一定的促进作用,当水杨酸的处理浓度为100~150 mg/L时,作用效果最明显。     

钼蓝比色法测定笃斯越橘成熟果实中还原型抗坏血酸含量及体系优化

以笃斯越橘(Vaccinium uliginosum)成熟果实为试材,以钼蓝比色法测定笃斯越橘果实还原型抗坏血酸(Reduced ascorbic acid,As A)含量进行条件优化。结果表明,3%偏磷酸-乙酸用量为100μL、5%硫酸和5%钼酸铵用量均为200μL、以去离子水补充至1 500μL,30℃干热恒温浴显色40 min,取出室温下放置1 h后,在700 nm下测定,所得数据稳定,准确性适合用于笃斯越橘果实As A含量的测定;测得含量为(40.20±6.23)mg/100 g FW。     

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。     

不同生理时期梅花鹿血液GSH-Px含量测定及其纯化

研究不同生理时期梅花鹿全血中GSH-Px含量变化及其纯化技术,为开发鹿血抗衰老资源奠定理论基础.用DINB法测定梅花鹿的溶血液、血细胞内容物、血浆和血细胞细胞膜中GSH-Px含量并计算全血GSH-Px含量；利用10％～100％饱和度的(NH4)2SO4盐析和柱层析技术纯化GSH-Px;用SDS-聚丙烯酰胺凝胶电泳测定其亚基相对分子质量.试验结果表明:全血GSH-Px含量以生茸期最高为(1 973.07士25.43)U·mL-1,与配种期的(1 727.74士12.46)U·mL-1差异极显著(P＜0.01),与生茸前期的(1 961.83士16.54)U·mL-1差异不显著(P＞0.05)；GSH-Px的初始比活力为24.9 U·mg-1,经纯化后得到GSH-Px 0.37 mg· mL-1,最终比活力1 347.7U·mg-1,纯化倍数为54.1倍,回收率25.00％；鹿血GSH-Px亚基的相对分子质量为17.2 ku.结果提示生茸期鹿血的GSH-Px含量最高,开发利用价值最大,GSH-Px含量与生理特性相关；鹿血GSH-Px盐析条件为40％～80％饱和度的(NH4)2SO4,此时所得GSH-Px纯化倍数较高.     

陆生野生动物通道设计方法

野生动物通道是缓解由铁路、公路等引起的野生动物生境破碎化的有效措施之一。本文介绍了野生动物通 道的定义和适用目标,提出了野生动物通道的设计原则、依据,以及野生动物通道的位置、数量、形式、宽度、高度、 地面基质和开口处环境的设置要求;以云南思小高速公路野生动物通道为例,分析了2005 年9 月至2008 年5 月间 亚洲象对通道的利用情况。结果表明:亚洲象对通道的利用率仅为40%;“通道位置是否与活动路线重合冶是亚洲 象对通道选择的决定性因素。建议在通道建设完成后进行持续的野生动物利用情况监测,以评估通道的有效性, 进而进行有针对性的改造设计,直到野生动物通道利用效果良好。      

Visual Basic程序设计实践教学探讨与研究

讨论了独立院校针对非计算机专业学生Visual Basic程序设计课程的重要性,并探讨了实践教学方法.     

高校图书馆流通窗口一站式服务模式的探讨

高校图书馆要想发展壮大,必须开拓创新,实行新的服务模式。本文通过对当前图书馆流通现状的具体分析,结合一站式服务模式的特点,提出了几点提升服务水平的建议。     

浅谈地下滴灌的堵塞问题及处理方法

通过对地下滴灌堵塞问题的研究，寻求合理有效的解决方法，利用科学的管理手段和切实可行的处理方法，真正达到节水灌溉的目的，从而有效地提高生产效益。     

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.