利用WGCNA进行玉米花期基因共表达模块鉴定

权重基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)是系统生物学的一种研究方法,在挖掘生物学数据与特定性状之间的生物学关系方面具有十分重要的作用。本研究利用玉米(Zea mays L.)自交系B73的14份不同发育阶段的转录组数据,筛选掉低表达丰度的基因,最终得到了22,426个高表达的基因用于创建基因表达矩阵;利用不同组织作为性状,创建表型矩阵。然后利用R软件中的WGCNA包建立了共表达网络,共得到20个模块。本研究将与组织相关性高于0.65的模块定义为组织特异性模块,最终鉴定到14个组织特异性模块。利用在线网站Agrigo对组织特异性模块中的基因进行GO (gene ontology)富集分析,发现14个模块中均可以得到富集种类。开花作为玉米生育周期中的一个重要生理过程,不仅代表着植物从营养生长到生殖生长的转变,也关系到产量、株高和抗逆性等农艺性状。本研究发现8个组织特异性模块中的基因可以富集到与开花调控的代谢通路。此外,有17个已经报道过的开花时间调控基因存在于共表达模块中,并且主要分布在Blue模块和Darkmagenta模块,因此本研究重点关注了这2个模块内部的基因调控网络。本研究通过计算不同组织中的基因表达丰度,并联合权重基因共表达网络分析的方法,鉴定到了具有生物学意义的共表达基因模块,挖掘到了数个开花相关的模块,有助于揭示玉米开花调控的遗传机制。     

Application of modified Deep Belief Network in Forecasting Cotton Diseases and Insect Pests

[Objective] The occurrence and development of cotton diseases and insect pests are mainly related to environmental information. Because this environmental information is various, complex and unstable, the study on the prediction methods of cotton diseases and insect pests is a certain challenge. This study aims to establish a forecasting model for the timely and accurate prediction of cotton diseases and insect pests. [Method] A forecasting model of cotton diseases and insect pests is proposed based on environmental information and a modified Deep Belief Network (DBN) that is constructed by a three-layer restricted Boltzmann machine (RBM) and a supervised back-propagation (BP) network. In the method, the RBM is used to transform the original environmental information vectors into a new feature space related to the diseases and pests; the BP network is trained to classify and forecast the features generated by the last RBM layer and two rules of dynamic learning and comparison and dispersion are adopted to accelerate the training process of RBM. The proposed model was validated on a dataset of cotton bollworm, aphids, spider, cotton Verticillium wilt, and Fusarium wilt in a recent six-year period. [Result] Compared with the traditional prediction models of cotton diseases and insect pests, the proposed model can deeply explore the extensive correlation between the occurrence of cotton diseases and pests and environmental information. The results show that the proposed model has a higher accuracy compared with the classical predictive models, and the average forecasting accuracy is above 83%. [Conclusion] The proposed method is an effective crop disease and pest forecasting method that can provide a technical support for preventing cotton disease and insect pests.     

A correlation network approach to metabolic data analysis for tomato fruits

Network analysis of correlations between abundances of metabolites across tomato genotypes can help in unraveling the biological basis of organoleptic variation in tomato. We illustrate how to construct and interpret simple correlations networks using metabolic data collected on a diverse set of tomato genotypes. Various types of correlations are calculated and displayed in the form of networks. Interpretations on the basis of network analyses are compared to interpretations following principal components analysis.     

Distribution of trust model in P2P network based on equilibrium theory

There are a large number of malicious attack nodes and collusion groups in P2P(peer to peer) network, and the existing trust models improve the P2P network environment to some extent, but the emphasis of the models are different, which are unable to fully solve large-scale malicious attacks and deception. Therefore, a P2P trust model based on equilibrium theory is presented. The model consists of the construction of the trust structure, the malicious node detection and trust speculation. It constructs trust network according to the equilibrium theory, uses the equilibrium theory to define nodes balance factor, detects balance malicious nodes by calculating the impact of malicious behavior on the network, and adopts trust inference algorithm to estimate trust nodes to prevent distrust network nodes being added. Experimental results show that the model is reliable, and the algorithm is efficient and robust.     

农民工社会网络研究的3种范式：一个反思性的文献综述

本文通过对已有的农民工社会网络研究成果的综述,总结出农民工社会网络研究的三种传统,即结构制约研究传统、个体利用和再建构研究传统和面向实践的研究传统。前两种传统虽然对立却都是源于结构与个体的二元划分范式,关注静态的逻辑和理论的逻辑。第三种传统与他们完全不同,面向运行实践的研究传统通过对农民工社会网络实际运作过程的描述和分析,把农民工社会网络视为在具体实践过程中不断生产的动态的流动的社会事实,联系实践的微观本土环境,在描述过程的基础上解释农民工社会网络的运行逻辑。本文认为,农民工社会网络以及整个农民工社会现象是一个在中国转型过程中产生的极具实践感的动态的社会事实。与前两种研究传统比较,第三种研究传统显然适合对这一对象的研究也更有希望提出更有解释力的理论。     

拟南芥APETALA1基因在花发育中的网络调控及其生物学功能

为了阐明拟南芥花分生组织特征基因--APETALA1（AP1）基因在开花调控网络的中心位置,笔者重点综述了AP1在花发育中的网络调控及其生物学功能,探讨了花发育机制的研究重点。AP1既是花分生组织决定基因,也是花器官发育特征基因,处于成花调控网络的关键位置。AP1基因需要通过与其他调控基因的相互作用,决定花分生组织的形成、花芽的起始及花器官的发育。     

基于网络教学平台的“生物统计学”教学改革及实践效果分析

在构建“生物统计学”课程网络教学平台的基础上,对3个教学环节共77名本科生利用该平台进行的教学活动及教学改革实践进行了研究,旨在进一步提高该课程的教学水平和教学质量。结果表明：利用网络教学平台,有利于开展网上互动式教学活动;有利于强化教学实践环节;有利于同学们学以致用并扩大学科视野。SAS统计分析结果表明,代表学生网上学习能力的网上“阅读讲义次数”与学生的学习成绩密切相关,达极显著水平（R=0.6004**）。     

ANN Disaster Identifying of Wujiawan Landslide in Wanzhou Urban

The paper aims at ANN disaster-possibility identifying of Wujiawan Landslide. ANN construction and parameter setup are analyzed for landslide disaster identifying by ANN, based on a typical landslide-Wujiawan landslide in Wanzhou urban, by confirming evaluation factor and establishing sample data. The ANN model is trained by the similar landslide sample in Wanzhou urban, then the disaster is identified in several different conditions of Wujiawan landslide. Finally, the same conclusion are found by analyzing combined ANN Disaster-Identifying and limit-equilibrium-method calculation. The results show that AAN is accurate and satisfied to be used landslide disaster-possibility identifying.     

构建主义学习理论与基于网络的包装材料学教学应用

构建主义学习理论强调学习的主动性,认为学习是学习者主动构建意义的过程。这一全新的学习理论对如何利用网络技术进行包装材料学的教学,对提高包装工程专业学生包装材料学课程的学习兴趣与其综合能力的提高有着重要的启示。     

网络农业信息标准化问题思考

针对指数级增长、庞杂无序的网络农业信息,部分学者提出了自动采集、智能搜索等技术性解决方案,而对问题关键之所在--网络农业信息标准化的系统研究和报道并不多见。文章概述了网络农业信息资源的建设现状与存在的问题,对国内外标准化研究现状进行综述,并深入探讨了网络农业信息资源标准化中最为基础也较为关键的分类、编码、描述元数据、内容标准化及质量控制五方面问题,以期为相关研究提供建设性观点和参考。