小蓬草精油化感作用的生物测定

采用水蒸气蒸馏法从小蓬草[Conyza canadensis (L.)Cronq.]地上部分分离出精油,利用GC-MS技术对该精油的主要化学成分进行了鉴定,结果显示:精油提取率为0.07%;该精油主要含有2,3-二甲基-4(3H)-喹唑啉酮、α-佛手柑油烯、柠檬烯二醇、反式-β-金合欢烯和α-姜黄烯等成分,其中,2,3-二甲基-4(3H)-喹唑啉酮的相对含量最高,达到54.41%.以青菜(Brassica chinensis L.)、白菜[B. pekinensis (Lour.)Rupr.]、小麦(Triticum aestivum L.)和高粱[Sorghum bicolor (L.)Moench]为供试植物,采用培养皿法对不同质量浓度(0.75、1.5、3.0和6.0 g·L-1)小蓬草精油的化感效应进行了生物测定,结果显示:经不同质量浓度小蓬草精油处理后4种供试植物的种子最终发芽率和发芽指数以及幼苗的根长和株高等4个生物测定指标总体上均低于对照,且随精油质量浓度的提高不断降低,其中,0.75 g·L-1 精油处理组的4个生物测定指标均与对照无显著或极显著差异,而6.0 g·L-1精油处理组的4个指标与对照则有显著或极显著的差异.4种供试植物的4个生物测定指标的化感效应敏感指数几乎均为负值,且随精油质量浓度的提高敏感指数的绝对值逐渐增大,说明小蓬草精油对供试植物的化感作用为抑制作用,且抑制作用的强度与精油质量浓度有关.在4种供试植物中,小蓬草精油对青菜种子的最终发芽率的抑制作用最明显,其半抑制浓度(IC50)约为3.0 g·L-1;在4个生物测定指标中,种子最终发芽率以及幼苗根长对小蓬草精油的化感作用较敏感.研究结果表明,小蓬草精油具有一定的化感活性,可将其作为植物源除草剂原料进行开发利用.     

绿僵菌MaFZ-13对橙带蓝尺蛾幼虫致病力及林间防治效果

【目的】橙带蓝尺蛾Milionia basalis近年来在我国南方地区严重危害罗汉松Podocarpusmacrophyllus和竹柏Podocarpus nagi,扩散蔓延速度快,造成了很大的损失。本研究旨在评估金龟子绿僵菌Metarhizium anisopliae MaFZ-13菌株对橙带蓝尺蛾幼虫的致病力,探寻橙带蓝尺蛾生物防治新资源。【方法】通过绿僵菌MaFZ-13不同浓度(1.0×10⁵~1.0×10⁹孢子/mL)孢子悬浮液接种橙带蓝尺蛾幼虫,运用时间-剂量-死亡率(TDM)模型对生物测定结果进行分析,同时通过林间喷施1.0×10⁸孢子/mL绿僵菌孢子悬浮液进行林间防治效果试验。【结果】橙带蓝尺蛾幼虫累计死亡率随着绿僵菌MaFZ-13孢子悬浮液孢子浓度的增加和接种时间的延长而增大。接种1.0×10⁹孢子/mL孢子悬浮液8 d后以及接种1.0×10⁸孢子/mL孢子悬浮液10 d后,幼虫死亡率均达到100%;接种1.0×10⁷孢子/mL孢子悬浮液15 d后幼虫死亡率为95.6%。应用TDM模型对生测数据进行分析,结果表明所建模型通过Hosmer-Lemeshow拟合异质性检验,并由模型估算出了该菌株对橙带蓝尺蛾幼虫的致死剂量和致死时间。绿僵菌MaFZ-13菌株接种4, 5, 6和7 d后的半致死剂量(LC₅₀)对数估计值分别为7.99, 7.12, 6.46和5.83。以1.0×10⁷, 1.0×10⁸和1.0×10⁹孢子/mL的孢子浓度接种时,绿僵菌MaFZ-13菌株对橙带蓝尺蛾幼虫的LT₅₀值分别为5.19,3.99和2.81 d。林间用1.0×10⁸孢子/mL绿僵菌孢子悬浮液喷洒9 d后橙带蓝尺蛾幼虫死亡率可达到85.8%,防治效果为85.05%。【结论】绿僵菌MaFZ-13对橙带蓝尺蛾幼虫具有较强的致病力,林间防治效果良好,具有较好的应用潜力。     

东莨菪内酯与双脱甲氧基姜黄素对朱砂叶螨致死效应模拟

采用玻片浸渍法测定了植物源杀螨活性物质东莨菪内酯、双脱甲氧基姜黄素及两者联合作用最佳增效配比(东:双=7:6)对朱砂叶螨雌成螨的致死效应,并采用时间-剂量-死亡率(TDM)模型进行模拟.结果表明:所建TDM模型均通过Hosmer-Lemoshow拟合异质性检验,朱砂叶螨雌成螨对3种药剂浓度变化的敏感程度为东莨菪内酯＞两者最佳配比＞双脱甲氧基姜黄素,双脱甲氧基姜黄素、东莨菪内酯和两者最佳配比对朱砂叶螨雌成螨的致死率高峰期分别为处理后32、28和32 h;处理后48 h的LC50和LC90分别为0.3324、0.2035、0.2195mg·mL-1和2.1198、0.9521、1.1617 mg·mL-1;浓度为1.0和2.0 mg·mL-1处理下,LT50分别为7.4、6.0、6.1h和6.4、4.8、5.0h.因此,东莨菪内酯与双脱甲氧基姜黄素最佳配比和东莨菪内酯具有相近的杀螨动态及时间-剂量效应,表现出较好的杀螨活性和增效作用,具有一定的开发利用价值.     

玫烟色拟青霉对小菜蛾致病力的时间-剂量-死亡率模型模拟

小菜蛾Plutella xylostella是我国南方十字花科蔬菜上的重要害虫,已对田间常用的化学杀虫剂产生了严重的抗性。为寻找有效的小菜蛾生物防治措施,本实验研究了一株分离自家白蚁的玫烟色拟青霉Paecilomyces fumosoroseus (SCAU-PFCF01)对小菜蛾2～4龄幼虫的致病力。实验采用浸液法,供试浓度为1×10³、1×10⁴、1×10⁵、1×10⁶和1×10⁷个孢子/mL。结果表明：随玫烟色拟青霉孢子浓度的升高,小菜蛾的感病死亡率增加,在浓度为1×10⁷ /mL时,小菜蛾2、3和4龄幼虫的累计死亡率分别为96%、85%和80%。玫烟色拟青霉对小菜蛾各龄幼虫的致病力与供试龄期有关,其感病的敏感顺序为2龄、3龄和4龄。用时间 剂量 死亡率模型（time-dose-mortality model,TDM）对各龄幼虫的致病力数据进行模拟,所建模型均顺利通过Hosmer-Lemeshow拟合异质性检验,表明模型拟合良好,并由模型估计出了该菌株对小菜蛾各龄幼虫的致死剂量与致死时间。2龄幼虫接种后第7天、3龄幼虫接种后第5天、4龄幼虫接种后第4天的LC₅₀估计值分别为1.17×10⁴、1.44×10⁴和5.21×10⁴ /mL,LC₉₀估计值分别为1.98×10⁶、3.82×10⁷和1.29×10⁸ /mL。玫烟色拟青霉对小菜蛾幼虫的致死时间与浓度相关,供试各龄幼虫的LT₅₀值随着孢子悬浮液浓度的增加而递减,在1×10⁵～1×10⁷ /mL的范围内,2龄幼虫的LT₅₀值从3.16天降低到1.72天,3龄幼虫的LT₅₀从3.21天降低到1.83天,4龄幼虫的LT₅₀从3.69天降低到2.04天。即2龄幼虫致死所需的时间最短,其次为3龄幼虫,4龄幼虫致死所需的时间最长。结果显示了该株玫烟色拟青霉在小菜蛾的生物防治中具较强的应用潜力。     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统(SES)模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现:(1)多数模型对景观过程与社会经济决策的反馈关系分析不足;(2)应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;(3)通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括:集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

多变量平行线测定胰岛素效价的统计分析

引言平行线测定(parallel-line assay)是一种特定的实验设计,通过标准品和供试品在一定条件下比较剂量反应比例(dose response ratio),从而测得供试品效价(potency)。量效关系一般只在某一特定时间上观察反应,即通常的单变量平行线测定。如果把时间因素也考虑在反应内,就发展为多变量生物测定(multivariate bioassay)。Vφlund曾描述了多变量剂量反应关系和平行线模型的统计分析,采用协方差矩阵测验变异来源(na-     

湿地沉积物对磷的吸附-解吸动力学模型概述

介绍了常用的吸附-解吸动力学模型(Freundlich方程、Langmuir方程及Temkin模型),分析比较了不同模型之间的相互关系,对目前国内外关于磷吸附-解吸动力学模型研究进行了简要概述,介绍了不同模型的统一表达形式,并提出应加强对不同类型湿地中沉积物对磷的吸附-解吸过程及其动力学模型、对吸附-解吸过程的长期监测和不同环境因子间的交叉效应研究等建议,以深化对动力学模型的认识,从净化机理层面充分理解湿地生态系统的运行机制.     

Kitasatospora sp. MY5-36产ε-聚赖氨酸分批补料发酵动力学

以北里孢菌(Kitasatospora sp.)MY 5-36为供试菌株,对ε-聚赖氨酸分批补料发酵动力学模型进行研究。建立了该菌株发酵合成ε-聚赖氨酸的菌体生长、产物合成和总糖消耗的动力学模型,并通过Origin 8.1软件对模型参数进行非线性拟合。结果表明:菌体量和聚赖氨酸的产量分别为16.25和13.15 g/L,产物合成与菌体生长的关系为部分耦联型。经验证,预测值与实验值有良好的拟合性,拟合度分别为0.999、0.995和0.992,说明所构建模型能够较好地反映ε-聚赖氨酸分批补料发酵过程。     

渗透剂对高效氯氰菊酯毒力和三种鳞翅目害虫上表皮超微结构的影响

以甜菜夜蛾Spodoptera exigua (Hübner)、小菜蛾Plutella xylostella (L.)和棉铃虫Helicoverpa armigera (Hübner)为试虫,通过室内生物测定技术,研究了氮酮、噻酮和N-甲基-2-吡咯烷酮等3种渗透剂对高效氯氰菊酯毒力的影响;利用扫描电镜观察了甜菜夜蛾和小菜蛾4龄幼虫对照组和渗透剂处理组间上表皮超微结构的变化。结果表明：含3.0% N-甲基-2-吡咯烷酮的高效氯氰菊酯对小菜蛾幼虫LD₅₀为0.0074 μg/头、毒力系数为2.0,对高效氯氰菊酯增效作用明显,而对其他两种试虫没有增效作用;氮酮和噻酮对高效氯氰菊酯对3种鳞翅目幼虫毒力均无增效作用。甜菜夜蛾对照组上表皮蜡质层和护蜡层清晰可见,渗透剂处理组上表皮护蜡层有不同程度的消失;而小菜蛾对照组和N-甲基-2-吡咯烷酮处理组上表皮护蜡层没有明显变化,只有氮酮处理组上表皮的护蜡层消失。以上结果提示,渗透剂对上表皮护蜡层的影响不是其对高效氯氰菊酯增效作用的机制。     

田间小麦叶片气孔对环境因子响应的模拟及叶片水分平衡的计算

经实测与数据分析得出田间供试小麦叶片气孔对光强、叶温、叶片水势分别作一定形式的响应,据此估算了冠层总气孔导度,进而建立了预测小麦群体蒸腾速率、冠层总气孔导度、叶温、叶片水势等的水分平衡模型。实验表明模型具有较好的预测性。供试小麦叶片一天中可溶性糖含量变化对叶片水势的影响不大。     

头孢菌素类抗生素的定量构效关系研究

目的:建立一种预测精度较高的定量构效关系(QSAR)模型,为设计和合成活性更高的头孢菌素类抗生素提供理论依据。方法:发展了一种基于支持向量回归(SVR)和k-最近邻(KNN)的非线性组合预测方法(SVR-KNN),系统研究了48种抗流感嗜血杆菌头孢菌素衍生物的QSAR。结果:留一法预测结果表明,非线性筛选描述符和子模型能明显提高预测精度,汰选子模型后的组合预测精度优于单一子模型,SVR-KNN的MSE、MAPE分别为0.019、1.81%;独立样本预测结果显示,SVR-KNN在所有参比模型中具有最优的预测精度及稳定性,其MSE、MAPE分别为0.010、1.33%。结论:SVR-KNN模型具有较强的预测能力和优异的泛化推广能力,在抗生素及其他药物的QSAR研究中有广泛应用前景。     

基于支持向量回归的酚类化合物QSAR建模

目的：采用定量构效关系（QSAR）方法探索酚类化合物的毒性与分子结构参数的关系。方法：基于支持向量回归（SVR）、依均方误差最小原则选择最优核函数,对酚类化合物及其衍生物进行了QSAR研究。结果：不同数据集选取的最优核函数有异,对小样本、非线性等问题,SVR具有较优的稳定性及预测能力,在酚类化合物及其衍生物的QSAR研究中得到了优于原文献方法的独立预测结果。结论：SVR模型具有较好的预测能力,在QSAR及相关研究中可得到更广泛应用。     

Sensitive bioassays for determining residues of sulfonylurea herbicides in soil and their availability to crop plants

Sulfonylurea herbicides are potent inhibitors of plant growth and are extremely active against a wide spectrum of weeds. They are used at very low rates (10–50 g ai/ha) and cause rapid inhibition of root and shoot growth of young plants. Routine chemical assays for detecting low levels of these compounds are difficult and there is need to develop sensitive bioassay methods for detecting their extremely low residue levels in the soil.This paper describes a simple pot bioassay method with a self watering system using turnip (Brassica rapa) seedlings as test plants for quantitative determination of sulfonylurea herbicides. Results are presented with six of these compounds whose activity was investigated in widely differing substrates. The potential availability to plants was calculated from the dose-response curves in different substrates. The dose-response relationship has been described by a specifically developed computer model. Details are also given of a direct seeded bioassay method with controlled watering system using several test species for detection of sulfonylurea herbicides. The potential uses and practical applications of both techniques are discussed.     

芳香羧酸衍生物驱避剂的非线性定量构效关系

【目的】驱避剂可使害虫不敢接近受用者从而保护受用者免遭其害。建立高精度、可解释性强的非线性定量构效关系(quantitative structure activity relationship, QSAR)模型对设计合成新的高效昆虫驱避剂有重要意义。【方法】基于37个芳香羧酸类化合物对家蝇Musca domestica的驱避活性,以量子化学计算软件PCLIENT获取每一化合物初始描述符,以二元矩阵重排过滤器、多轮末尾淘汰实施特征非线性筛选,以支持向量回归(support vector regression, SVR)建立非线性QSAR模型,以SVR非线性解释体系分析各保留描述符对驱避活性的影响。【结果】1 542个初始描述符的SVR模型F=1.2,特征筛选后6个保留描述符的SVR模型F=184.6,特征筛选对QSAR模型精度有重要影响。6个保留分子描述符的重要性依次为p4BCD>GATS7v>T(O..O)> JGI8>SssO>nArCONR2。【结论】保留描述符与芳香羧酸类化合物对家蝇驱避活性的非线性关系明显,获得了高精度、普适性强的非线性SVR-QSAR模型。     

A unified DNA sequence and non‐DNA sequence mapping model of complex traits

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non‐DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open‐pollinated design composed of randomly sampling maternal plants from a natural population and their half‐sib seeds. This design forms a two‐level hierarchical platform for a joint linkage‐linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence‐based effects and non‐DNA sequence‐based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non‐DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA‐based effects and non‐DNA sequence‐based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.     

Bayesian methods for a three-state model for rodent carcinogenicity studies

The objective of a chronic rodent bioassay is to assess the impact of a chemical compound on the development of tumors. However, most tumor types are not observable prior to necropsy, making direct estimation of the tumor incidence rate problematic. In such cases, estimation can proceed only if the study incorporates multiple interim sacrifices or we make use of simplified parametric or nonparametric models. In addition, it is widely accepted that other factors, such as weight, can be related to both dose level and tumor onset, confounding the association of interest. However, there is not typically enough information in the current study to assess such effects. The addition of historical data can help alleviate this problem. In this article, we propose a novel Bayesian semiparametric model for the analysis of data from rodent carcinogenicity studies. We develop informative prior distributions for covariate effects through the use of historical control data and outline a Gibbs sampling scheme. We implement the model by analyzing data from a National Toxicology Program chronic rodent bioassay.     

Application of support vector regression to genome-assisted prediction of quantitative traits

A byproduct of genome-wide association studies is the possibility of carrying out genome-enabled prediction of disease risk or of quantitative traits. This study is concerned with predicting two quantitative traits, milk yield in dairy cattle and grain yield in wheat, using dense molecular markers as predictors. Two support vector regression (SVR) models, ε-SVR and least-squares SVR, were explored and compared to a widely applied linear regression model, the Bayesian Lasso, the latter assuming additive marker effects. Predictive performance was measured using predictive correlation and mean squared error of prediction. Depending on the kernel function chosen, SVR can model either linear or nonlinear relationships between phenotypes and marker genotypes. For milk yield, where phenotypes were estimated breeding values of bulls (a linear combination of the data), SVR with a Gaussian radial basis function (RBF) kernel had a slightly better performance than with a linear kernel, and was similar to the Bayesian Lasso. For the wheat data, where phenotype was raw grain yield, the RBF kernel provided clear advantages over the linear kernel, e.g., a 17.5% increase in correlation when using the ε-SVR. SVR with a RBF kernel also compared favorably to the Bayesian Lasso in this case. It is concluded that a nonlinear RBF kernel may be an optimal choice for SVR, especially when phenotypes to be predicted have a nonlinear dependency on genotypes, as it might have been the case in the wheat data.     

基于核密度估计的动物生境适宜度制图方法

生境适宜度制图能提供动物适宜生境的空间分布信息,对野生动物种群管理、保护地规划等非常重要。生境适宜度制图的关键是构建生境适宜度模型(habitat suitability model, HSM),只基于动物出现位置数据构建HSM的方法在实践中得到了非常广泛的应用。然而现有的只基于动物出现位置数据构建HSM的方法还不能很好地直接表达动物生境适宜度和环境因子之间具有生态学意义的数量关系,因此也就不能很好地体现环境因子对动物生境利用的生态学作用。 本文提出了一种基于核密度估计构建HSM的方法,在地理信息系统技术支持下,通过运用核密度估计从代表性的动物出现位置数据中估计出动物出现对各个环境因子的概率密度函数来直接表达生境适宜度与各个环境因子之间的数量关系,以体现环境因子对动物生境利用的生态学作用,在此基础上对生境适宜度与各个环境因子之间的数量关系进行综合构建了具有明确生态学意义的HSM用于动物生境适宜度制图。以美国Voyageures国家公园的白尾鹿(Odocoileus virginianus)生境适宜度制图为例,基于365个出现位置点位数据并结合积雪深度、地表覆被类型、森林边界长度和坡度等环境因子数据,开展了该方法的案例研究。通过交叉验证计算连续Boyce指数对制图结果进行评价,结果表明：基于核密度估计方法构建的HSM预测能力强,所得出的生境适宜度图经10次交叉验证,连续Boyce指数平均值为0.75,标准差为0.11,达到了较高精度。此外,由于基于核密度估计的方法能以“生境适宜度和环境因子之间具有生态学意义的数量关系”的形式来直接体现环境因子对动物生境利用的生态学作用,就模型的可解释性而言,该方法要优于现有的其他构建HSM的方法。     

A unified Markov chain Monte Carlo framework for mapping multiple quantitative trait loci

In this article, a unified Markov chain Monte Carlo (MCMC) framework is proposed to identify multiple quantitative trait loci (QTL) for complex traits in experimental designs, based on a composite space representation of the problem that has fixed dimension. The proposed unified approach includes the existing Bayesian QTL mapping methods using reversible jump MCMC algorithm as special cases. We also show that a variety of Bayesian variable selection methods using Gibbs sampling can be applied to the composite model space for mapping multiple QTL. The unified framework not only results in some new algorithms, but also gives useful insight into some of the important factors governing the performance of Gibbs sampling and reversible jump for mapping multiple QTL. Finally, we develop strategies to improve the performance of MCMC algorithms.