大葱种子人工老化与膜脂过氧化的研究

大葱种子经过人工老化处理后,其发芽率、发芽指数、活力指数下降,活力指数较发芽率和发芽指数下降明显,种子老化6天后活力指数比对照下降70％。脱氢酶、SOD、POD活性和GSH含量也呈下降趋势,而浸出液可溶性糖、MDA、自由基、H2O2的含量增加,GSH、H2O2、自由基、可溶性糖含量与种子活力显著相关,并且较其他指标变化更为显著。说明大葱种子人工老化过程中膜脂过氧化加剧,膜结构和功能遭到破坏。因此,大葱种子人工老化与膜脂过氧化直接相关。     

醇化烟叶表面细菌筛选及增香效应研究

为明确烤烟醇化过程中烟叶表面增香微生物对其品质的作用,以6个不同产地的烟叶为试材,通过菌株分离和产酶能力筛选,对筛选出的细菌进行16S rDNA测序分析,将烟丝发酵样进行感官质量评吸、常规化学成分和多酚化合物分析。结果表明,菌株GY99处理样的感官评吸质量提升最为明显,能有效增加烟叶香气和甜感,其处理后的牡丹江、庆阳烟叶总分分别增加了2.26、2.03分,舒适度提升,香气质和香气量分别增加0.44、0.33分,刺激性和杂气降低。本研究筛选出1株可同时产蛋白酶、淀粉酶和纤维素酶的芽孢杆菌属(Bacillus sp.)菌株GY99。同时还筛选到3株可明显改善烟草品质的菌株,分别是芽孢杆菌属(Bacillus sp.)菌株GY96、假单胞菌属(Pseudomonas)菌株GY72和微球菌属(Micrococcus)菌株GY67。     

Computation of Three-Dimensional Temperature Distribution and Thermal Analysis to Integrated Circuit Substrates

The exact three di me nsional temperatu re distribution and thermal analysiS to inte-grated circuit substrates have been presen ted by means of numerical method of a fin ite difference.Thismethed.which will overcome the di fficuItv of mathematical deduction mav be consltieredas an analytical way aided by com puters for thermal design of electron ic compon ents.     

热分析法研究水稻花药发育过程及其动力学

植物花药发育是植物个体发育过程中的一个重要组成部分。在此过程中,花药物质和组成不断变化,呈现出显著的动态过程。关于花药发育过程中物质和组成变化的研究,人们已经做了很多工作。但未见用热分析法研究水稻花药发育过程及其动力学的报道。本文用热分析法研究了不同花粉育期野败型和红莲型两类水稻保持系可育花药的热分解过程,测定了花粉不同育期可育花药的热重/差热(TG/DTA)及其差示扫描量热(DSC)曲线,获得了相应的热力学参数,分析了它们在不同时期的热分解规律以及发育过程中花药物质变化与其不同育期的关系,建立了发育过程中水稻花药干物质增长的动力学模型,并计算了它们的表观动力学参数。     

响应面法优化樱桃魔芋葡苷聚糖热喷雾处理参数

通过响应面分析法对樱桃魔芋葡苷聚糖热喷雾处理参数进行优化。根据前期单因素试验结果,采用Box-Behnken design(BBD)法设计试验,考察魔芋葡苷聚糖热喷雾处理的温度、时间、魔芋葡苷聚糖添加量三个因素对樱桃果实呼吸强度的影响,再用Design-Expert软件对BBD试验数据进行分析处理,得出樱桃魔芋葡苷聚糖热喷雾处理的最优参数为:魔芋葡苷聚糖添加量6 g/L,热喷雾温度45℃,处理时间10 min。理论预测处理后樱桃于(1±0.5)℃条件下贮藏15 d时的呼吸强度为1.95 CO2mg·kg-1·h-1,实际测定呼吸强度为2.084 3 CO2mg·kg-1·h-1,二者接近,误差较小,说明该方程与实际情况拟合很好。采用该法优化的魔芋葡苷聚糖热喷雾处理参数,可使采后樱桃果实的呼吸代谢降到较低水平,有利于保持贮藏品质,延长贮藏期限。     

Study on Application of Interrelated Controlling Method for Thermal Environment and Energy Saving in Buildings--Application in Determining the Control Indexes for Heat Insulation and Energy Saving of Enclosure in Bu ildings

In this paper according to the relationships between indoor-outdoor climate parameters, indoor thermal environment parameter and heat insulation parameters of enclosure, the control condition of indoor thermal environment and the control parameters of heat insulation-energy saving are advanced. Prescribing a quality limit for the parameters, the control indexes for the heat insulation energy saving of the enclosure are given.     

AB-QTL分析法及在水稻优异基因资源发掘和利用中的应用

野生稻是水稻育种的重要种质资源,在其漫长的进化过程中形成了极其丰富的遗传多样性,具有栽培稻所不具有或己消失了的优良基因。利用分子标记技术从野生稻中发掘有利基因是目前稻种资源研究利用中的重点。由于野生稻总的农艺性状表现劣于栽培稻,在实际研究中受到了许多不利因素（不良农艺性状和不利连锁基因的存在、平衡群体中野生稻种较高的不利基因频率、遗传累赘等）的影响,这对野生稻种资源中优异基因的发掘和利用带来了困难。高代回交QTL分析法（advancedbackcrossQTLanalysis,简称AB-QTL）为实现野生种中有利基因的发掘提供了一个新途径,许多研究表明AB-QTL分析法对野生资源中有利基因的发掘是可行的。本文对AB-QTL分析法的理论基础、特性、渗入系构建及其在野生稻遗传分化和产量基因发掘中的应用进行综述,并就今后的研究重点进行了探讨。     

Contributions to the Analysis of Genotype × Environment Interactions: Theoretical Results of the Application and Comparison of Clustering Techniques for the Stratification of Field Test Sites

For an analysis of cross-classified data sets with rows = genotypes and columns = environments (locations and/or years), existing genotype × environment interactions are of major importance. Differential responses of genotypes (environments) across environments (genotypes) are expressed by these effects. To reduce the impact of these genotype × environment interaction effects, one commonly stratifies genotypes or environments by cluster analysis techniques into homogeneous groups so that interactions within groups are minimized. The present paper presents a comprehensive overview of the numerous procedures for stratification of genotypes or environments by cluster analysis which have been proposed in the literature. In these studies, two different concepts of interaction have been used: the crossover concept of interaction [different rank orders of genotypes (environments) within environments (genotypes)] or the usual statistical concept of interaction (deviations from additivity of main effects in the linear model). For a quantitative characterization of cluster techniques and for a comparison of two different clustering procedures, two parameters are introduced and discussed: measure of resemblance for two classifications and cluster size for one classification.     

Contributions to the Analysis of Genotype × Environment Interactions: Experimental Results of the Application and Comparison of Clustering Techniques for the Stratification of Field Test Sites

The results from multienvironment field performance trials of cultivars are usually analysed as two‐way classification data with rows=genotypes/cultivars and columns=environments (locations and/or years). To reduce the impact of genotype × environment interaction effects, one commonly stratifies genotypes/cultivars or environments by cluster analysis techniques into homogeneous groups so that interactions within groups are minimized. By such a stratification, for example of test sites, with regard to similarity of genotype × environment interactions and the selection of only one representative test site from each group, the overall number of necessary test sites for yield trials can be reduced. In the literature, many clustering techniques have been proposed. Systematic comparisons between different cluster methods, however, are rather rare. A single cluster method is characterized by `measure of distance', `stopping criterion', `algorithm' and `level of significance'. In this paper, 11 clustering techniques were applied to extensive yield data sets of several agricultural crops (faba bean, fodder beet, oat, winter oilseed rape and sugar beet) from the official registration trials of the German `Bundessortenamt'. The results were compared with each other using two proposed parameters: measure of resemblance (for two classifications) and cluster size (for one classification). Neither the level of significance nor the algorithm has a substantial impact on the resulting clusters. The final results of clustering are therefore mainly determined by the stopping criterion with its associated measure of distance. If one uses tests for crossover interactions as stopping criteria, the resulting clusters are larger than the resulting clusters for the F‐test of conventional interactions in an analysis of variance. The cluster size decreases with increasing sensitivity of the tests that are used as stopping criteria. Finally, recommendations for the choice and handling of clustering techniques for practical applications are given.