苦豆碱及顺式氯氰菊酯对菜田蚜虫群落的毒力研究

研究了植物源杀虫剂苦豆碱及顺式氯氰菊酯对菜田蚜虫群落结构的影响,并测定了其对第一优势种菜缢管蚜的杀伤毒力.苦豆碱在1%浓度下,以药膜法和喷雾法,测定其击倒作用,KT50分别为29.74和25.38min;顺式氯氰菊酯在0.1%浓度下,KT50分别为1.73和0.78min.以点滴法测定此两种杀虫剂对菜缢管蚜的触杀作用,LD50分别为7.7×10-4及9.201μg/头,田间以0.1%喷雾,苦豆碱10d校正防效均可达到80%,而顺式氯氰菊酯仅能达到30%.结果显示,苦豆碱对菜缢管蚜有极强的毒杀作用,同时喷药前后菜田蚜虫群落结构也发生着明显的变化.     

苦豆碱及顺式氯氰菊酯对莱田蚜虫群落的毒力研究

研究了植物源杀虫剂苦豆碱及顺式氯氰菊酯对菜田蚜虫群落结构的影响 ,并测定了其对第一优势种菜缢管蚜的杀伤毒力。苦豆碱在 1 %浓度下 ,以药膜法和喷雾法 ,测定其击倒作用 ,KT50 分别为2 9 74和 2 5 38min ;顺式氯氰菊酯在 0 1 %浓度下 ,KT50 分别为 1 73和 0 78min。以点滴法测定此两种杀虫剂对菜缢管蚜的触杀作用 ,LD50 分别为 7 7× 1 0 -4 及 9 2 0 1 μg 头 ,田间以 0 1 %喷雾 ,苦豆碱 1 0d校正防效均可达到 80 % ,而顺式氯氰菊酯仅能达到 30 %。结果显示 ,苦豆碱对菜缢管蚜有极强的毒杀作用 ,同时喷药前后菜田蚜虫群落结构也发生着明显的变化。     

一种用高效液相色谱-电喷雾/串联质谱(HPLC-ESI/MSn)定量检测植物组织中微量1-氨基环丙烷-1-羧酸(ACC)含量的方法

介绍一种用高效液相色谱碳18柱(HPLC-C18)分离.电喷雾串联质谱(ESI-MSn)鉴定和定量检测植物组织中微量1-氨基环丙烷-1-羧酸(ACC)含量的方法,其最低检测限达0.7 pmol,标准曲线线性符合系数为0.9992.建立了从20～100 mg微量植物样品中提取ACC和固相萃取(SPE)预纯化的方法,加样回收率为95.1%±4.2%.此种提取方法结合HPLC-ESI/MSn分析与定性定量检测苹果'2001富士'中ACC含量为306.6 ng·g-1(FW),表明此法适合于定性定量检测植物样品中的微量ACC.     

国外利用干扰交配技术防治害虫的研究动态

德国的Buttenandt花了近二十年的时间,在1957年从20万头雌性家蚕Bombyx mori腺体,获得了12毫克活性物质并鉴定了它的结构,简称蚕蛾醇(Bombykol)。之后,随着近代微量化学分析仪器的迅速发展,大大促进了昆虫信息素的研究,迄今已鉴定出100多种昆虫的信息素化学结构。其中,有30—40种信息素已商品化,主要用于预测预报,指导适时防治。 许多研究人员曾提出用信息素弥散于大气     

微量量热法在生命科学中的应用

介绍了微量量热系统的基本结构和工作原理,以及微量量热法在酶学、细胞生物学、微生物学、植物生理学研究中的应用。     

银杏嫩枝大棚扦插育苗

利用大棚进行银杏嫩枝扦插全光喷雾育苗,可节约大量的育苗土地,扩大繁育系数,保持优良品种的遗传特性,降低育苗成本,经济效益显著。一、大棚设施大棚骨架为南北向的钢筋混凝土拱型结构,长４３ｍ、宽ｓｉｎ,中顶高３．ｓｉｎ,边高１．Ｚｍ。棚顶覆聚氯乙烯无滴塑料膜,厚０．ｌｍｍ。南北砖墙各留作业门一个,高１．ｓｉｎ,宽１．Ｚｍ,四周用条砖砌埂,床埂高０．４５ｍ,宽０．１５ｍ。棚内设微喷头１２４个,用长回．Ｚｍ,口径４ｍｍ的毛支管与径粗２５ｍｍ的支管道相连,每隔０．７ｍ设一个,分布于支管道两侧。微喷头用支杆固…     

烟田中静电喷雾和电动喷雾下噻虫嗪的防效及对昆虫群落的影响

【目的】探明静电喷雾与电动喷雾对噻虫嗪防治害虫效果及烟田昆虫群落的影响,为静电喷雾在烟草上的推广应用提供理论依据。【方法】利用静电喷雾与电动喷雾法喷施0.66 g·L~(-1)25%噻虫嗪水分散粒剂,施药1、3、7 d后,测定2种方法对烟蚜与斜纹夜蛾的防治效果、在烟株与土壤中的沉积以及对昆虫群落的影响。【结果】喷施25%噻虫嗪水分散粒剂1、3、7 d后,静电喷雾对烟蚜和斜纹夜蛾的防治效果均显著高于电动喷雾的防治效果,且防治效果均随时间的推移而升高。噻虫嗪在静电喷雾处理区烟叶上的沉积量高于电动喷雾处理区烟叶上的沉积量,而在静电喷雾处理区土壤中的沉积量低于电动喷雾处理区土壤中的沉积量,且都随时间的推移而降低。静电喷雾与电动喷雾均引起烟田昆虫群落的变化,静电喷雾引起的害虫和中性昆虫的数量变化大于电动喷雾,但天敌昆虫的数量变化小于电动喷雾,且静电喷雾区烟田的物种丰富度明显高于电动喷雾区,可见静电喷雾施药对烟田昆虫群落的影响小于电动喷雾施药。静电喷雾的S_t/S_i与S_n/S_p比值明显高于电动喷雾,说明静电喷雾烟田中昆虫群落稳定性高于电动喷雾烟田中昆虫群落的稳定性。【结论】相比电动喷雾,静电喷雾能够提高噻虫嗪在烟叶上的沉积量,从而提高噻虫嗪对害虫的防治效果,同时减少噻虫嗪在土壤中的沉积量,提高噻虫嗪的生态安全性,因此,静电喷雾在烟草病虫害防治上具有较好的应用前景。     

高岭土颗粒涂布对中国梨喀木虱寄主选择、产卵的影响及应用效果

为探索可替代化学药剂防治中国梨喀木虱的措施,开展了高岭土颗粒涂布对其寄主选择、产卵影响及应用效果的研究。分别采用3.5%高岭土水溶液、清水对梨休眠枝和当年生枝叶进行喷雾涂布处理,于24、48、72、96 h监测选择条件下成虫分布情况,于96 h记录选择和非选择条件下各枝条上的卵量。采用高岭土水溶液对梨苗均匀喷雾涂布,通过被害叶率、单叶若虫量、虫龄指数、为害指数对应用效果进行评价。研究结果显示,在选择条件下,经高岭土涂布的枝条上成虫数量明显少于对照,高岭土涂布的枝条上冬型成虫数量在96 h时最低(0.5头/枝),96 h时着卵量(1.83头/枝)显著低于对照(30.83头/枝);高岭土涂布的枝叶上夏型成虫在72 h时最低(1.17头/枝),96 h时着卵量(12.33头/枝)显著低于对照(242.5头/枝);在非选择条件下,冬型和夏型成虫在高岭土涂布的枝条上着卵量(2.83和15.17头/枝)分别显著低于对照(25.17和170.42头/枝);供试植株在接虫45 d后为害最严重,高岭土涂布的植株被害叶率(54.17%)、单叶若虫量(2.29头/叶)、虫龄指数(3.79)、为害指数(0.41)分别显著低于为对照植株(98.61%、11.72头/叶、4.13、0.95)。上述结果表明,高岭土涂布对中国梨喀木虱成虫的寄主选择具有驱避效果,对雌成虫的产卵具有抑制作用;高岭土涂布可限制中国梨喀木虱种群增长,降低危害水平。研究结果为中国梨喀木虱的综合治理提供了现实依据。     

旱柳广头叶蝉的生物学特性及防治

报道了危害白榆的新害虫——旱柳广头叶蝉Macropsis matsudanisCaietWei。首次记述其幼期形态特征、生物学特性、天敌种类,进行了防治试验。该虫在山东商河1年发生1代,以卵在白榆枝条内越冬;天敌主要有螯蜂、瓢虫等;用阿维菌素和吡虫啉5000倍液喷雾防治若虫具有很好的防治效果。     

世纪之交的蛋白质序列测定技术

对蛋白质序列测定技术的发展过程作了简要回顾，介绍了近年在该方法学领域的主要进展，其中包括Ｎ－端顺序测定的微量化、毛细管ＨＰＬＣ与毛细管电泳的应用，新的Ｃ－端化学降解测序方法，以及快原子轰击（ＦＡＢ）、电喷雾（ＥＳＩ）和基质辅助激光解吸电离－飞行时间（ＭＡＬＤＩ－ＴＯＦ）质谱在蛋白质序列测定中的应用，还对世纪之交该领域的发展趋势作了扼要的展望。     

Influence of DNA structures on the conversion of sanguinarine alkanolamine form to iminium form

Sanguinarine exhibits pH dependent structural equilibrium between iminium form (structure I) and alkanolamine form (structure II) with a pKa of 7.4 as revealed from spectrophotometric titration. The titration data show that the compound exists almost exclusively as structure I and structure II in the pH range 1 to 6 and 8.5 to 11, respectively. The interaction of structure I and structure II to several B-form natural and synthetic double and single stranded DNAs has been studied by spectrophotometric, spectrofluorimetric and circular dichroic measurements in buffers of pH 5.2 and pH 10.4 where the physicochemical properties of DNA remain in B-form structure. The results show that structure I bind strongly to all B-form DNA structures showing typical hypochromism and bathochromism of the alkaloid's absorption maximum, quenching of steady-state fluorescence intensity and perturbations in circular dichroic spectrum. The structure II does not bind to DNA, but in presence of large amount of DNA significant population of structure I is generated, which binds to DNA and forms a structure I-DNA intercalated complex. The nature and magnitude of the spectral pattern are very much dependent on the structure as well as base composition of each DNA. The generation of the structure I from structure II is significantly affected by increasing ionic strength of the medium. The conversion of structure II to structure I in presence of high concentration of DNA in solution is explained through formation of a binding equilibrium process between structure II and structure I-DNA intercalated complex.     

蛋白质结构研究现状与展望

本文综述了近年来蛋白质结构的确定、预测、比较、分类和功能五个问题的研究概况,并分析了解决每个问题的方法,最后指出蛋白质结构研究的发展前景。     

Measurements of protein sequence-structure correlations

Correlations between protein structures and amino acid sequences are widely used for protein structure prediction. For example, secondary structure predictors generally use correlations between a secondary structure sequence and corresponding primary structure sequence, whereas threading algorithms and similar tertiary structure predictors typically incorporate interresidue contact potentials. To investigate the relative importance of these sequence-structure interactions, we measured the mutual information among the primary structure, secondary structure and side-chain surface exposure, both for adjacent residues along the amino acid sequence and for tertiary structure contacts between residues distantly separated along the backbone. We found that local interactions along the amino acid chain are far more important than non-local contacts and that correlations between proximate amino acids are essentially uninformative. This suggests that knowledge-based contact potentials may be less important for structure predication than is generally believed.     

RNA-SSPT: RNA Secondary Structure Prediction Tools

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.     

The functional significance of leaf structure: a search for generalizations

The coupling between leaf structure and function is illustrated with reference to two examples, the C₄ photosynthetic pathway and leaf pubescence. A distinction is made between function and functional significance. The latter is defined as the role, significance or consequence of a structure, whereas the former is more simply the action that a structure is capable of performing. Using the two examples, four generalizations are made concerning the relationships between structure, function and functional significance: the functional significance of leaf structure is environment-dependent; the relationship between functional significance and structure is sometimes non-intuitive; functional equivalency means that there is often more than one 'solution' to the same 'constraint'; and the consequences of leaf structure can exert profound effects at levels of organization beyond those of the individual organism and may play a critical role in determining community structure and function, through interactions with other species and trophic levels. The importance of understanding the consequences in variation in leaf structure at the global scale is illustrated with reference to the issue of global climate change.     

Prediction of RNA secondary structure by free energy minimization

RNA secondary structure is often predicted from sequence by free energy minimization. Over the past two years, advances have been made in the estimation of folding free energy change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.     

Gaussian process functional regression modeling for batch data

A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.     

Structure prediction and fold recognition for the ferrochelatase family of proteins

An α/β barrel is predicted for the three-dimensional (3D) structure of Bacillus subtilis ferrochelatase. To arrive at this structure, the THREADER program was used to find possible homologous 3D structures and to predict the secondary structure for the ferrochelatase sequence. The secondary structure was fit by hand to the selected homologous 3D structure then the MODELLER program was used to predict the fold of ferrochelatase. Molecular biological information about the conserved residues of ferrochelatase was used as the criteria to help select the homologous 3D structure used to predict the fold of ferrochelatase. Based on the predicted structure possible, ligands binding to the iron and protoporphyrin IX are discussed. The structure has been deposited in the Brookhaven database as ID 1FJI. © 1997 Wiley-Liss Inc.     

Fully automated ab initio protein structure prediction using I-SITES,HMMSTR and ROSETTA

MOTIVATION: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.