碘酰脲类除草剂特点及其环境归趋

磺酰脲类除草剂是一类具有优良毒理和环境特性的超高效除草剂，它作用于乙酰乳酸合成酶（ＡＬＳ），抑制支链氨基酸－缬按酸、亮氨酸、异亮氨酸的生物合成，从而抑制细胞分裂和植物生长。磺酰脲类除草剂可被植物的根、茎和叶强烈吸收，并在植株内迅速传导转移和代谢，快速代谢失活是作物对该类除草剂的选择性基础。环境中的磺酰脲化合物主要通过化学水解和微生物降解及少量的光化学分解而消失。     

作物保护剂对磺酰脲类除草剂解毒效应述评

结合国内外学者以及作者自己对作物保护剂和磺酰脲类除草剂的研究,概述了保护剂对磺酰脲类除草剂的解毒作用。不同环境条件对保护剂的解毒作用有一定的影响,并从理论上证明保护剂通过抑制核苷二磷酸（RDP）,并间接抑制乙酰乳酸合成酶（ALS）的活性,从而对磺酰脲类除草剂进行解毒效应．     

定量结构—活性关系研究中量挑选的进化算法

本文将进化算法引入定量结构－活性关系研究中，以解决变量挑选的难题。对杀幼虫剂和磺酰脲除草剂两个体系的应用表明，该进化算法是一种非常有效的变量选方法，并且可以获得多个 高质量扔定量结构－活性关系模型。另外，算法中用于评价模型质量的评价函数的选取是非常关键的。     

香豆素磺酰脲类化合物降血糖活性的定量构效关系研究

目的：研究新型香豆素磺酰脲化合物降血糖作用与化学结构之间的关系，为进一步设计合成该类化合物提供理论依据。方法：选择10个降血糖活性较强的化合物，利用MM!分子力学和CNDO／2量子化学方法计算有关结构参数并进行定量构效关系研究。结果：香豆素-6-磺酰脲类化合物的降血糖活性与磺酰脲基与母核的二面角呈负相关；与母核1位碳原子LUMO轨道本征矢量负相关；与化合物分子的偶极矩正相关。结论：推测分子中7-位甲基的引入不利于磺酰脲基与香豆素母环共平面，降低化合物的降血糖活性；香豆素母环上引入供电子基团不利于增加降血糖活性，可尝试引入吸电子基团；化合物分子偶极矩的增加对降血糖活性有利。     

吡嘧磺隆对土壤中与氮素转化有关微生物活性的影响

在室内培养条件下,研究了水田除草剂吡嘧磺隆在田间用量下对水稻土中与氮素转化有关的生物活性的影响.结果表明,吡嘧磺隆在培养7～30天内能显著抑制土壤脲酶的活性;在培养的前20天内表现为对土壤硝化作用和土壤微生物量氮的抑制作用;施用吡嘧磺隆处理的土壤脲酶活性、土壤硝化作用及土壤微生物量氮间存在显著相关关系.     

磺酰脲类除草剂定量结构-生物降解性关系(QSBR)研究

选取了分子量、熔点、蒸气压、溶解度、LogKOW pKa,MCI,ELUMO和几个代表性原子所带的电荷等具有代表性的参数,结合化合物好氧生物降解作用的半衰期(HL),通过多元线性回归方法对磺酰脲类除草剂的生物降解性进行了定量结构一生物降解性(QSBR)研究,建立了相应的QSBR模型,R^2=0．941。并利用该模型对氯磺隆进行预测,结果表明,该模型对氯磺隆的好氧生物降解性具有良好的预测能力。     

苯磺隆和2,4-D胁迫对土壤微生物影响的研究

以盆栽试验研究了除草剂苯磺隆、2,4-D对石灰性褐土中土壤微生物群落的影响。结果表明,在供试浓度下,土壤微生物对两种除草剂的感应存在一定的差异性。苯磺隆高浓度处理在最初3d极显著地抑制细菌生长,之后对土壤细菌具有刺激作用,特别是培养结束时激活率还高达917.6%,低浓度处理呈极显著的刺激作用;苯磺隆对土壤放线菌具有显著的刺激作用,对土壤真菌有强烈的抑制作用,最高抑制率达100%,到培养结束时(60d)真菌数量还未恢复到对照水平。2,4-D处理对土壤放线菌具有明显的抑制、激活的波动性,对土壤细菌、真菌有强烈的抑制作用。     

基于结构的组蛋白去乙酰化酶抑制剂虚拟筛选

组蛋白去乙酰化酶是抗肿瘤作用的新靶点,基于该酶复合物的三维结构,首先对具有分子多样性的数据库进行了虚拟筛选;然后根据已知HDAC抑制剂的结构特征和筛选的结果,以及与生物大分子互补性,选择合理的构建单元,组建靶向的虚拟组合库;最后进行数据库虚拟筛选,对分子对接的结果进行评分,选择出理论上与HDAC有较好结合能力的化合物,设计了酰胺类、脲类和酰肼类全新结构类型的HDAC抑制剂,初步生物活性评价结果表明,预期有生物活性的化合物显示出一定的HDAC酶抑制活性。     

苯磺隆除草剂对农田土壤动物影响的研究

通过苯磺隆除草剂的模拟实验,研究了除草剂对土壤动物的影响。本实验共获得土壤动物1031个,隶属3门、5纲、9目。其中弹尾目和甲螨亚目为优势类群,其余为常见类群。本实验结果表明,随着苯磺隆除草剂处理浓度的提高,土壤动物种类和数量呈递减趋势。弹尾目和甲螨亚目可作为农药污染的重要指示生物。     

氯嘧磺隆对土壤微生物类群及土壤呼吸强度的影响

通过实验室培养试验研究了氯嘧磺隆对土壤微生物种群动态变化和土壤呼吸强度的影响.测定了0、5、10、20、100μkg-1的氯嘧磺隆对土壤中三大类微生物种群动态变化的影响,结果表明,氯嘧磺隆促进土壤细菌和真菌的生长,抑制放线菌的生长,真菌是氯嘧磺隆胁迫下的优势菌群.密闭法测定了0.01、0.1、1、10、100μg g-1的氯嘧磺隆对土壤呼吸的影响,结果表明,0.01、0.1、1、10μg g-1的氯嘧磺隆轻微抑制土壤呼吸,但受抑制的土壤逐渐恢复,除10μg g-1外,其它处理可恢复到对照水平,高浓度100μg g-1的氯嘧磺隆促进土壤呼吸,不能恢复到对照水平,但根据危害系数法的分级方法计算,氯嘧磺隆属于无实际毒害的农药.     

Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data

Although herbicide drifts are known worldwide and recognized as one of the major risks for crop security in the agriculture sector, the traditional assessment of damage in cotton crops caused by herbicide drifts has several limitations. The aim of this study was to assess proximal sensor and modelling techniques in the detection of phenoxy herbicide dosage in cotton crops. In situ hyperspectral data (400–900 nm) were collected at four different times after ground-based spraying of cotton crops in a factorial randomized complete block experimental design with dose and timing of exposure as factors. Three chemical doses: nil, 5% and 50% of the recommended label rate of the herbicide 2,4-D were applied to cotton plants at specific growth stages (i.e. 4–5 nodes, 7–8 nodes and 11–12 nodes). Results have shown that yield had a significant correlation (p-values <0.05) to the green peak (~550 nm) and NIR range, as the pigment and cell internal structure of the plants are key for the assessment of damage. Prediction models integrating raw spectral data for the prediction of dose have performed well with classification accuracy higher than 80% in most cases. Visible and NIR range were significant in the classification. However, the inclusion of the green band (around 550 nm) increased the classification accuracy by more than 25%. This study shows that hyperspectral sensing has the potential to improve the traditional methods of assessing herbicide drift damage.     

Plant spectral responses to gas leaks and other stresses

The study aimed to assess the ability of remote sensing to differentiate between plant stress caused by natural gas leakage and other stresses. In order to use satellite remote sensing to detect gas leaks it is necessary to determine whether the cause of the stress can be identified in the spectral response and distinguished from other stress factors. Field plots of oilseed rape (Brassica napus) were stressed using elevated levels of natural gas in the soil, dilute herbicide solution and extreme shade. Visible stress response, spectral stress response and chlorophyll content of plants from these three treatments were compared to control plants receiving no treatment. The reflectance from isolated leaves was measured in the laboratory. Spectral responses to stress included increased reflectance in the visible wavelengths and decreased reflectance in the near‐infrared. A shift of the red edge position towards shorter wavelengths was observed as a result of all three stresses, although the shift was greatest when stressed via extreme shade. Red edge position was strongly correlated with chlorophyll content across all the treatments. The ratio of reflectances centred on the wavelengths 670 and 560 nm was used to detect increases in red pigmentation in gassed and herbicide‐stressed leaves. Stress due to extreme shade could be distinguished from stress caused by natural gas or herbicide by changes in the reflectance spectra, however, stress caused by herbicide or natural gas could not be distinguished from one another in the spectra although symptoms of stress caused by elevated gas levels were identified earlier than symptoms caused by herbicide‐induced stress.     

Unbinding pathway energy of glyphosate from the EPSPs enzyme binding site characterized by Steered Molecular Dynamics and Potential of Mean Force

The quantification of herbicides in the environment, like glyphosate, is extremely important to prevent contamination. Nanobiosensors stands out in the quantization process, because of the high selectivity, sensitivity and short response time of the method. In order to emulate the detection of glyphosate using a specific nanobiossensor through an Atomic Force Microscope (AFM), this work carried out Steered Molecular Dynamics simulations (SMD) in which the herbicide was unbinded from the active site of the enzyme 5- enolpyruvylshikimate 3 phosphate synthase (EPSPS) along three different directions.After the simulations, Potential of Mean Force calculations were carried, from a cumulant expansion of Jarzynski’s equation to obtain the profile of free energy of interaction between the herbicide and the active site of the enzyme in the presence of shikimate-3 substrate phosphate (S3P). The set of values for external work, had a Gaussian distribution. The PMF values ranged according to the directions of the unbindong pahway of each simulation, displaying energy values of 10.7, 14.7 and 19.5 KJ mol⁻¹. The results provide a theoretical support in order to assist the construction of a specific nanobiossensor to quantify the glyphosate herbicide.     

A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water

Modelling stream water pollution by herbicides in agricultural areas is a critical issue since numerous and incompletely known processes are involved. A decision-oriented model, SACADEAU-Transf, which represents water and pesticide transfer in medium-sized catchments (10–50 km²) is presented. This model aims at evaluating the effect of land use, agricultural practice and landscape on the contamination of stream water in rural catchments. The processes are represented in an easily understandable way with a moderate amount of information, producing semi-quantitative and spatialized outputs. Modelling focuses on the first few months after herbicide application when high levels of contamination are generally observed, by considering transfer through the catchment area via surface and subsurface flow. The surface flow, based on a tree plot network representation of the catchment, is controlled by soil-surface properties and saturated conditions. The subsurface flow based on Topmodel concepts is controlled by the topography. Herbicide transfer is coupled to water transfer by taking into account the main characteristics of the chemicals. The model simulates the daily water and herbicide outflow at the outlets of the farmers' fields as well as from the catchment. Preliminary results on maize herbicide transfer are presented for an agricultural catchment with an area of 17 km² located in north-western France. The relevance of SACADEAU-Transf model is discussed in view of the qualities required for the decision-oriented models developed for improving agro-environmental management.     

In silico target prediction for elucidating the mode of action of herbicides including prospective validation

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model (“HerbiMod”) is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of ≥ 80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.     

A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.     

Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands

Digital images of soybean canopies [Glycine max (L.) Merrill] were obtained within selected narrow wavebands (6–10 nm bandwidths) to determine their capability for early detection of plant stress. Images and physiological measurements of stress were acquired 2 days, 4 days, and 7 days following application of control, drought, and herbicide [(3,4-dichlorophenyl)-1, 1-dimethylurea, or DCMU] treatments. As a result of frequent rainfall, drought stress never occurred. However, exposure to herbicide rapidly induced plant stress. By day 4, the ratio of variable to maximum leaf fluorescence (F_v/F_m) decreased and leaf water potentials (ψ_w) increased in the herbicide treated soybean, indicating damage to the photosynthetic apparatus and stomatal closure. Also, Munsell leaf color had increased from approximately 5GY 4.6/5.7 to a lighter green-yellow value. Canopy reflectances at 670 nm, 694 nm, and in the 410–740 nm band (R_vis), as well as reflectance at 694 nm divided by reflectance at 760 nm (R₆₉₄/R₇₆₀), detected stress simultaneously with the physiological measurements and increased consistently with stress through day 7. Reflectances at 420 nm and 600 nm, together with R₆₀₀/R₇₆₀ and R_vis/R₇₆₀, did not increase until leaves were yellow or brown and wilted and canopies had begun to collapse on day 7. None of the reflectance or reflectance ratio images detected stress prior to visible color changes. This was attributed primarily to the rapid inducement of chlorosis by the herbicide. Reflectance in narrow wavebands within the 690–700 nm region and its ratio with near-infrared reflectance should provide earlier detection of stress-induced chlorosis compared with broad band systems or narrow bands located at lesser wavelengths.     

利用机器视觉防治草莓苗期杂草的研究

在草莓苗期杂草防治中，由于除草剂残留的危害使得喷洒方法成为研究的热点。本文提出了一种基于机器视觉的除草剂喷洒方法。在图像分割去除背景后，通过开操作删除杂草的像素点，得到只有草莓像素点的处理结果。接着将草莓图像分为6行8列的子区域，根据每个子区域是否有草莓的像素点决定是否喷洒除草剂。在实验结果中，该方法基本上实现了草莓苗期杂草的防治，并节省了50％左右的除草剂。     

Classification of soil and vegetation by kernel Fisher and kernel PCA

Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e. fertilization, water) or for the agrochemicals use for pest control (i.e. herbicide). In particular, weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, the first stage of recognition method of vegetal species, the classification of soil and vegetation, is described and is based upon the kernel Fisher discriminant method (KFDM) and on Kernel Principal Analysis (KPCA).     

Classification of soil and vegetation by kernel fisher and kernel PCA

Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e., fertilization, water) or for the agrochemicals used for pest control (i.e. herbicide). In particular, weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, the first stage of recognition method of vegetal species, the classification of soil and vegetation, is described and is based upon the kernel Fisher discriminant method (KFDM) and on Kernel Principal Analysis (KPCA).