超分子与纯分子连接性指数的研究与应用

根据有关原理与方法,计算了色谱分离过程中溶质分子的分子连接性指数及其与固定相形成超分子的超分子连接性指数.并用这些指数对硫醇类化合物在聚乙二醇固定相上分离的定量结构-保留关系(QSRR)进行了研究,所得回归方程相关系数高达0.99.该方法不仅保留了分子连接性指数的优点,还能够反映溶质分子与固定相相互作用模型对色谱保留值的影响,因此超分子连接性指数比分子连接性指数能更全面地反映影响色谱保留值的因素,使用范围更广泛,具有良好应用前景.     

药物设计中定量结构和性质关系的方法概述

定量结构和性质关系(QSPR)在结构优化中起着重要的作用,它通过优化选择最可能的样本从而减少了化合物合成的数量。该文的目的是介绍QSPR的发展概况,为开发新药提供信息。综述了构建QSPR的方法,分别从分子结构的相关描述子,性质的信息数据,和两者间有意义的连接三个方面进行了讨论。     

饱和脂肪酮拓扑指数对其色谱保留指数的预测

采用SPSS统计软件,用多元线性回归分析方法将所构建的饱和脂肪酮一级分子连接性指数(1T),官能团距离拓扑指数(Dco)和脂肪酮分子中甲基数(XCH3)与其色谱保留指数(RI)相关联,获得了好的相关关系。     

烃及衍生物的性质与结构之间定量关系的研究

本文根据有机分子结构的特点，建立了烃及其衍生物物理化学性质与其所含原子数Ｎ和Ｈｏｓｏｙａ拓扑指数ＺＧ之间的定量关系。对若干同类化合物物理化学性质计算结果表明。计算值与实验值相当吻合，标准偏差较小．相关性优良。     

一种预测烷烃衍生物的色谱保留指数的新方法

针对烷烃衍生物的气相色谱保留指数进行预测，利用主原子数（N）、主链碳原子数（N＇）、相对电负性（Xr）、取代参量（P）和团位置参量（q）等分子参数建立了定量预测醇、硫醇、醚、硫醚、醛、酮、酸和酯等烷烃衍生物的气相色谱保留指数（RI）的数学模型，相关系数均大于0.999。240多个标的物的计算值与其实验值都非常吻合，平均相对误差小于1%。研究结果表明，分子参数法的物理意义明确、操作简单、使用方便，是进行定量构效关系（QSPR）研究的一种简便可行的方法。     

分子连接性指数mXz与脂环烃和脂肪烃沸点的定量关系

提出了计算分子中成键原子点价δzi的新方法,并以δzi为基础建构了新的分子连接性指数mXz,研究了mXz(m=0,1)与脂环烃和脂肪烃沸点的定量关系。结果表明,mXz指数与脂环烃和脂肪烃的沸点具有优良的性质相关性,并且mXz指数的性质相关性明显优于Kier指数。以0Xz,1Xz和碳原子数N为自变量的三元回归方程可以预测脂环烃和脂肪烃的沸点。对于碳原子数N为4～30的化合物,预测值与实验值符合较好。     

超分子与纯分子连接性指数的研究与应用

根据有关原理与方法,计算了色谱分离过程中溶质分子的分子连接性指数及其与固定相形成超分子的超分子连接性指数。并用这些指数对硫醇类化合物在聚乙二醇固定相上分离的定量结构-保留关系(QSRR)进行了研究,所得回归方程相关系数高达0．99。该方法不仅保留了分子连接性指数的优点,还能够反映溶质分子与固定相相互作用模型对色谱保留值的影响,因此超分子连接性指数比分子连接性指数能更全面地反映影响色谱保留值的因素,使用范围更广泛,具有良好应用前景。     

醇类化合物对欧洲林蛙蝌蚪毒性的QSAR研究

采用分子描述符计算软件PCLIENT获得123个醇类有机小分子化合物的1 666个理化性质参数,通过相关性分析与逐步线性回归筛选,最终获得14个分子描述符。基于保留的14个关键理化性质,分别以多元线性回归(MLR)、偏最小二乘回归(PLS)与支持向量回归(SVR)构建醇类化合物对欧洲林蛙蝌蚪毒性的QSAR模型。结果表明:3种模型的独立预测决定系数Q2从初始的-163.350、-0.019、0.686分别提升到0.860、0.903与0.936,剔除无关描述符能显著提升模型的预测精度;基于SVR的训练拟合精度和独立预测精度均较好,表明其泛化能力强,鲁棒性好;SVR模型独立测试集预测值和真实值比较结果证明最终筛选出的14个描述符具有较好的显著性,模型具有较好的稳健性。本方法在有毒化合物等QSAR研究领域有较广泛应用前景。     

定量构性关系研究——利用新分子拓扑指数预测烷烃热力函数

基于分子的键距矩阵和键联矩阵提出一种新的分子拓扑指数Y.利用 Y计算了烷烃 ( 2～ 2 0个碳原子共 97个分子 )的 5种热力学性质 .结果表明 ,它比现有的拓扑指数具有更好的结构选择性和性质相关性     

基于纳米金自组装应用的多甲硫基芳炔化合物的设计与合成

基于含硫化合物易于在贵金属表面形成分子膜的事实以及多头含硫有机分子有望成为新一代纳米自组装理想材料的预测,设计并合成了一系列几何形状、功能基数迥异,具有π共轭特征的刚性芳炔硫醚化合物1～7.通过MS,IR,1H和13C NMR对它们进行了结构表征.此外,对两个X型化合物参与纳米金自组装的行为进行了初步的考察.实验结果表明:多MeS-基有机分子具有良好的自组装和可拆卸效果.     

Ant alarm pheromone activity: correlation with molecular shape by scanning computer

The ant Iridomyrmex pruinosus utilizes 2-heptanone as an alarm pheromone. The activities of 49 ketones and 35 nonketones as alarm pheromones for this species were determined. The molecular shapes of these compounds were assessed by submitting silhouette photographs of their molecular models to a pattern recognition machine. A highly significant correlation exists between molecular shape and alarm activity.     

东阳万亩园区土壤养分综合评价研究

通过引入GIS建立园区空间数据库和相应的属性数据库.在园区数据库的基础上,以水田为评价对象,选取有机质、速效氮、速效磷、速效钾和pH五项指标,采用模糊线性隶属度函数(生长指数型和抛物线型)计算隶属度值,分别用相关分析法和主成分分析法确定权重,确定养分评价综合指数值.此外,通过比较相关分析法和主成分分析法的评价结果,表明这两种评价方法效果几乎相同.最后,将离散的综合评价数据通过点状克里格空间内插法转化成面状连续数据,运用ARCVIEV软件制作了养分评价等级图.     

Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning

Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the values simulated by the coupled model were 0.92, 1.12 g m^?2, and 409.70 kg ha^?1, respectively, which were obtained when the soil-adjusted vegetation index was used as a partitioning zone and assimilating parameter. These results demonstrated that the coupled model of the crop model and RS data, based on the simulation sub-zones had a good prediction accuracy. The results provide important technical support for increasing model efficiency, when crop models need to be applied at the spatial scale.     

Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.

     

基于分层多元复合模型的广东省农田土壤有机碳高精度制图

农田有机碳库是唯一可在较短时间尺度上通过合理利用而进行适度调节的碳库,农田土壤有机碳高精度制图对进一步明析地理环境背景,提升区域土壤固碳潜力,促进碳交易、碳中和等具有重要的意义。本研究以广东省为研究区,在中大空间尺度区域综合特征分区的基础上,基于地理探测器确定农田土壤有机碳空间分异的变量结构,分区构建分层多元复合模型,根据208 503个土壤采样点数据编制研究区高精度农田土壤有机碳密度空间分布图。结果表明：耦合自然地理特征和社会经济特征,引入多距离空间聚类进行中大空间尺度综合特征分区,能够显著收敛样本离散程度,土壤有机碳样本标准偏差均值、方差均值较未分区前分别下降0.55、3.53,Moran''s I指数上升0.08。受自然环境与人为扰动双重影响,农田土壤有机碳空间变异的变量众多,且不同综合特征分区内变量结构差异较大,年均降水量、海拔高度、地形坡度等变量在不同特征分区的影响力存在显著差异,土地利用方式及土壤理化性质等变量对不同特征分区均存在较大的影响力。基于地理探测器构建的分层多元复合模型,较好地解决了中大尺度和复杂情景下土壤有机碳空间分异规律与空间突变的同步表达矛盾,抑制了多变量插值噪声增加,其综合精度较地理加权回归模型（GWRK）、径向基函数神经网络（RBFNN）和普通克里格（OK）分别提升6.45%、10.45%和7.50%。在大密度样本集支持下,综合区域综合特征分区、地理探测器、分层多元复合模型等技术手段编制的广东省高精度农田土壤有机碳空间分布图,预测结果准确,空间细节表达清晰,为编制大空间尺度的土壤有机碳分布图探索了有效路径。     

Nutrient Management Zones for Citrus Based on Variation in Soil Properties and Tree Performance

Site-specific soil management can improve profitability and environmental protection of citrus groves having large spatial variation in soil and tree characteristics. The objectives of this study were to identify soil factors causing tree performance decline in a variable citrus grove, and to develop soil-specific management zones based on easily measured soil/tree parameters for variable rate applications of appropriate soil amendments. Selected soil properties at six profile depths (0–1.5 m), water table depth, ground conductivity, leaf chlorophyll index, leaf nutrients and normalized difference vegetation index were compared at 50 control points in a highly variable 45-ha citrus grove. Regression analysis indicated that 90% of spatial variation in tree growth, assessed by NDVI, was explained by average soil profile properties of organic matter, color, near-infrared reflectance, soil solution electrical conductivity, ground conductivity and water table depth. Regression results also showed that soil samples at the surface only (0–150 mm) explained 78% of NDVI variability with NIR and DTPA-extractable Fe. Excessive available copper in low soil organic matter areas of the grove apparently induced Fe deficiency, causing chlorotic foliage disorders and stunted tree growth. The semivariograms of selected variables showed a strong spatial dependence with large ranges (varied from 230 m to 255 m). This grove can be divided into different management zones on the basis of easily measured NDVI and/or soil organic matter for variable rate application of dolomite and chelated iron to improve tree performance.     

冬枣绿盲蝽种群数量时间格局的地统计学分析

运用地统计学方法分析了冬枣绿盲蝽种群数量的时间格局,建立了半变异函数曲线模型。结果表明：冬枣绿盲蝽种群数量的半变异函数均为指数模型,时间格局均为聚集型;2007、2008年该虫的后效时间依次为36．94、35．16d,两者高度一致,这是由该虫本身的生物学特性所决定的;时间序列绿盲蝽种群数量的结构性占总变异的值介于70．44％～99．66％,表明绿盲蝽在时间序列上均有较强的后效时间性,结构性因子是引起变异的主要因子。据此,可以用前36d种群数量预测后面某一时间绿盲蝽的种群数量。     

伊犁绢蒿荒漠草地土壤养分和植被指数的空间异质性研究

[目的]土壤养分与植被分布格局之间的关系.[方法]应用地统计学方法,研究伊犁绢蒿(Seriphidium transiliense)荒漠草地土壤养分与植被指数的空间异质性.[结果](1)绢蒿荒漠土壤碱解氮、速效钾含量空间自相关性较差,而有机质、全磷、速效磷、全钾、全氮、植被指数(NDVI)自相关性较好.(2)通过半方差分析各项目都能用很好的模型来拟合,有机质符合线形模型,全钾、全磷、全氮、速效磷、碱解氮、速效钾和植被指数均符合高斯模型.(3)土壤养分元素和植被指数的变程在23.58～110 m,相差比较大.[结论]通过普通克立格插值,绘制了植被指数与土壤有机质和土壤养分的等值线分布图.对绢蒿蒿类荒漠草地土壤养分与植被指数空间异质性分析有助于分析植被指数的空间异质化程度与土壤元素伊犁(OM,N、P、K)空间异质化的相关性.     

不同有机肥对烤烟生长发育、产质量及青枯病的影响

 通过田间试验,比较研究了不同有机肥（精制有机肥和生物有机肥）与化肥配施对烤烟农艺性状、叶绿素含量、产质量及烤烟青枯病的影响。结果表明：与当地常规施肥（100%化肥）相比,精制有机肥、生物有机肥配施80%化肥,可促进烤烟生长,显著提高烤烟产质量,减轻青枯病的危害。与当地常规施肥（100%化肥）相比,在化肥减量20%的基础上,配施生物有机肥烤烟产量提高了4.5 %,产值提高了9.8%,上中等烟比例提高了8.4%,烤烟青枯病病情指数降低了47.3%;配施精制有机肥烤烟产量提高了5.3%,烤烟青枯病病情指数降低了15.8%。与配施精制有机肥处理相比,配施生物有机肥烤烟产值提高了9.5%,上中等烟比例提高了6.2%,烤烟青枯病病情指数降低了37.5%。     

福建省表层土壤有机碳密度空间分布及影响因素分析

为准确估算福建省表层土壤有机碳密度，确定其空间分布和影响因素，基于2016—2019年采集的4 350个表层土壤样本，利用地统计学方法与ArcGIS技术分析土壤有机碳密度空间分布，并通过随机森林模型分析影响土壤有机碳密度的重要因素。结果表明：福建省表层土壤有机碳密度范围为0.03~14.68 kg·m^-2，平均值为4.06 kg·m^-2。全省表层土壤有机碳密度在空间上聚集分布，具有中等程度空间自相关性，空间变异主要由结构性因素主导，呈现自南向北、从沿海向内陆山地逐渐升高的空间分布格局。降雨量、海拔、土壤质地和土壤pH是影响有机碳储量的主要因素，地貌类型、土壤类型和作物类型影响相对较弱，且随着化肥（磷肥和钾肥）施用量的增加，表层土壤有机碳密度显著降低。研究表明，福建省表层土壤有机碳密度不高，空间分布聚集，自然气候变化、地形因子和人类活动都会影响土壤有机碳密度，其中降雨量、海拔、土壤质地和土壤pH为主导因素。