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Effectiveness of Bacterial and Fungal Isolates to Control <i>Phoma lingam</i>on Oilseed Rape <i>Brassica napus</i>

American Journal of Plant Sciences 2012年第6期3卷 773-779页

作者： Omar Hammoudi Mazen Salman Ruba Abuamsha Ralf-Udo Ehlers Agriculture Scientific Research Center Lattakia Syria Department of Biotechnology & Biological Control Institute for Phytopathology Christian-Albrechts-University Kiel Kiel Germany National Agriculture Research Center (NARC) Jenin Palestine Palestine Technical University-Kadoorie (PTUK) Tulkarm Palestine

Blackleg disease caused by Phoma lingam is an important disease of oil seed rape (Brassica napus) causing losses up to 95%. The efficacy of microbial antagonists against P. lingam in greenhouse was tested. Serratia plymuthica HRO-C48 and Gliocladium catenulatum J1446 were able to reduce the disease intensity of OSR cotelydones infested with P. lingam at rates 44% and 52% respectively. The reduction of the infestation of the root collar in BBCH14/15 was evaluated as well. Plants treated with a suspension of the antagonists (2 × 105 cfu/plant) and inoculated with either pycnidiospore suspension (2 × 107 cfu/ml) or agar disks grown with P. lingam mycelium, showed a reduced infestation rate of 53% - 93% in the presence of S. plymuthica and 46% - 77% in the presence of G. catenulatum. The efficacy of the antagonist depends highly on their concentration inside OSR seeds. Below 105 cfu/seed no significant difference was recorded between control untreated and infested plants.

关键词： Biological Control Oilseed Rape Phoma lingam Bacteria

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一种利用负熵局部选择外生变量的Directlingam算法

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小型微型计算机系统 2017年第8期38卷 1834-1839页

作者：郝志峰谢峰蔡瑞初陈薇广东工业大学应用数学学院广州510520 广东工业大学计算机学院广州510006 佛山科技技术学院数学与大数据学院广东佛山528000

非高斯性数据间的因果网络已经在经济学、生物学和环境学等学科得到了广泛应用.Directlingam(Direct Method for Learning a Linear Non-Gaussian Structural Equation Model)算法是其中一个经典解法,但其存在维度达到25维度以上时外生... 详细信息

非高斯性数据间的因果网络已经在经济学、生物学和环境学等学科得到了广泛应用.Directlingam(Direct Method for Learning a Linear Non-Gaussian Structural Equation Model)算法是其中一个经典解法,但其存在维度达到25维度以上时外生变量(exogenous variable)识别率低的问题,进而产生级联效应,使得整个网络的估计误差随着层数增大越来越大.为此提出了一种基于负熵局部选择外生变量的Directlingam算法(LS-Directlingam),把变量的非高斯性作为外生变量选择的标准,用负熵来度量变量的非高斯,选择负熵最大的k个变量存入局部目标变量集合Lv中,在集合Lv中进一步去寻找外生变量,从而提高了外生变量的识别率.与基本的Directlingam算法进行实验比较,结果表明LS-Directlingam算法优于Directlingam算法.

关键词：因果网络 Direct lingam 负熵局部选择非高斯性

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An Integrated Causal Path Identification Method

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Wuhan University Journal of Natural Sciences 2019年第4期24卷 305-313页

作者： FEI Nina YANG Youlong School of Mathematics and Statistics Xidian University

Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(Directlingam) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.

关键词： observed variable path analysis causal order Direct lingam causal path graph causal effect

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