利用AFLP技术研究海湾扇贝不同养殖群体的遗传结构及其分化

采用AFIP分子标记技术对我国海湾扇贝的4个不同地理群体共80个个体利用7对引物组合进行了遗传多样性分析。4群体的多态位点比例分别为：加拿大养殖群体48．8235％,墨西哥湾养殖群体49．2914％,秦皇岛养殖群体38．2353％,浙江养殖群体41．1765％。平均杂和度分别为0．1878,0．1886,0．1265和0．1618。遗传距离介于0．1188～0．0941之间。4个群体的Fst为0．1883。根据遗传距离绘制了UPGMA聚类图。4个群体间的遗传关系如下：加拿大群体和浙江群体聚为一支,墨西哥湾群体和秦皇岛群体聚成一支,最后这两支聚在一起。试验结果表明,经过长时间累代养殖的浙江、加拿大、墨西哥湾群体仍保持较高的杂合度,与引种时间最短的(2年)、最为接近美国自然群体的秦皇岛群体比较,以上3个群体没有出现多态位点比例和杂合度显著下降的现象。     

中国对虾人工选育快速生长群体不同世代间的AFLP分析

利用7对AFLP引物组合检测了中国对虾连续五代选育群体基因组DNA的遗传结构,根据AFLP分析结果计算了选育世代群体间的遗传相似系数和遗传距离.结果表明,AFLP标记表现出较高的多态检测效率,7对引物共产生500余条带,平均每对引物组合检测到33.7个多态性标记,平均杂合度为0.0854～0.1025,非常适合于遗传多样性分析.结果显示,随着选育世代的增加,选育群体的遗传多样性呈现下降趋势,但随着选育时间的延长,群体之间的分化逐渐降低,群体的遗传结构开始趋于稳定,成为一个品系.     

栉孔扇贝不同地理群体的遗传多样性分析

应用AFLP技术对栉孔扇贝的中国长岛群体和韩国东部、西部群体进行了遗传多样性分析。实验表明，中国长岛群体的群体内遗传相似度最高，为0．6754，韩国东部群体次之，为0．6714，韩国西部群体最低，为0．6309；中国长岛群体与韩国东部、西部群体间的遗传距离分别为0．1703和0．1786，韩国两群体间的遗传距离只有0．057。这一结果说明，长岛群体与韩国群体遗传差异较大，韩国两个群体遗传相似度较高，应视为同一个群体。本研究共获得6个韩国两群体的共享特征标记，4个长岛群体的特异性标记，1个韩国西部群体所特有的标记，这些标记可以用于鉴定和区分这三个群体。为提高我国栉孔扇贝群体遗传多样性，以引进韩国西部群体为好。     

龙须菜(Gracilaria lemaneiformis)选育品系遗传背景的RAPD分析

在优化的RAPD反应条件下,筛选出30条随机引物,用于龙须菜野生型青岛一个群体和选育品系福建莆田、江苏连云港、山东荣成3个栽培群体的DNA多态性分析,共扩增出263条DNA片段。龙须菜野生型青岛群体和选育品系的山东荣成栽培种群的平均杂和度为0．160,0．120,多态位点比例为40．68％,32．32％;两群体问的遗传距离为0．1136,遗传相似性为0．8926。福建莆田、江苏连云港、山东荣成3个栽培群体的平均杂和度为0．141,0．123,0．120,多态位点比例为36．50％,33．08％,32．32％;山东荣成／福建莆田,山东荣成／江苏连云港,福建莆田／江苏连云港栽培群体间的遗传距离分别为0．1358,0．1425,0．0722,遗传相似率分别为0．8730,0．8672,0．9303。研究结果表明,龙须菜选育品系来源于野生型青岛群体,其快速生长的品系特征和遗传物质基础在不同海区环境保持相对稳定;龙须菜野生型青岛群体的遗传变异水平高于选育品系群体,选育品系的变异水平随着栽培海区由北方向南方呈现增加的趋势。     

红鳍东方鲀与假睛东方鲀的微卫星DNA多态性分析

为了对中国近海分布的红鳍东方鲀（Takifugu rubripes）资源进行深入调查,以确定红鳍东方鲀与假睛东方鲀（Takifugu pseudommus）的种间关系,利用根据红鳍东方鲀基因组序列设计的9对微卫星引物,对野生红鳍（WR）、野生假睛东方鲀（WP）两个地理种群和一个养殖红鳍东方鲀（CR）群体进行了遗传多样性分析。结果表明：9对引物在三个群体中均表现为多态;共获得106个等位基因,每个位点的等位基因数目在5～25之间,9个位点在三群体观测杂合度的范围从0．8167到1．0000,三群体平均杂合度观测值E,排列顺序为wR（0．8661）〉WP（0．8272）〉CR（0．7832）;3个群体之间的遗传距离较小,其中WR和WP群体间的遗传距离最小（0．0586）,WR和CR群体的遗传距离最大（0．0923）;3群体的遗传分化系数Gsr很小,其中,WR和WP两群体间遗传分化系数最小（0．0177）,WR和CR群体间的遗传分化系数最大（0．0290）,与遗传距离的分析结果相一致。群体遗传结构的分析结果表明：红鳍东方鲀养殖群体遗传多样性显著下降,应及早加强对其种质的保护;从分子遗传学的角度来看,红鳍东方鲀与假睛东方鲀应为同一个种。     

基于遗传算法的传感器网络拓扑控制研究

无线传感器网络的首要设计目标是延长网络生命期,网络的拓扑控制是实现这一目标的支撑 基础。针对传统拓扑控制方案所获拓扑的连通冗余度高或结构健壮性低等弊端,将问题转化 为多判据最小生成树模型,提出了一种基于遗传算法的拓扑控制方案。仿真实验结果 表明,该方案可获得具有网络整体功耗低、结构健壮性高和节点间通信干扰小等特点的拓扑 结构,因而能够有效地延长传感器网络生命期。     

无线传感器网络几类拓扑控制及其抗毁性应用简述

无线传感器网络的拓扑结构随着网络中节点的增加、减少和移动实时变化,为保证网络的连通性和覆盖性不被影响,拓扑控制技术所要解决的问题正是传感器节点如何更好地自组织构建全局网络拓扑．本文首先概述了四类拓扑控制算法的理论基础及算法步骤．然后,对提高网络抗毁性的两类拓扑演化算法进行了详细叙述,即无标度网络生长与构建$k$连通网络,分别构建了基于节点位置偏好的移动网络拓扑模型和基于$k$连通的节点调度优化模型．最后,分别从移动节点的引入、折中控制算法的探索、复杂网络理论的应用和传统算法与智能算法的结合这四方面对拓扑控制算法的前景进行了阐述．     

汽轮机故障诊断的遗传神经网络法

阐明了遗传算法与神经网络结合的必要必邮多层感知器神经网络作为遗传搜索的问题表示方式的思想。设计了遗传神经网络故障诊断模型,用这种新方法解决了汽轮机多故障诊断问题。新方法可以简化神经网络的结构并逃逸局部极小。诊断结果与实际相符,从而验证了该方法的有效性和实用性。     

Evolution on neutral networks accelerates the ticking rate of the molecular clock

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive ‘phenotypic entrapment’ entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives.     

Evolving Nonlinear Time-Series Models of the Hot Metal Silicon Content in the Blast Furnace

Neural networks are versatile tools for nonlinear modeling, but in time-series modeling of complex industrial processes the choice of relevant inputs and time lags can be a major problem. A novel method for the simultaneous detection of relevant inputs and an appropriate structure of the lower part of the networks has been developed by evolving neural networks by a genetic algorithm, where the approximation error and the number of weights are minimized simultaneously by multiobjective optimization. The networks on the Pareto front are considered possible candidate models that are evaluated on an independent test set. In order to consider the problem of drift in the variables, which may cause parsimonious models to perform poorly on the test set, the weights in the upper layer of the networks are recursively estimated by a Kalman filter. The method is illustrated on a data set from ironmaking industry, where time-series models of the hot metal silicon content in a blast furnace are evolved. The technique is demonstrated to synthesize models with a choice of inputs in agreement with findings presented in the literature and process know-how.     

Collaboration patterns in theoretical population genetics

The paper points out that the characteristic properties of general social networks are reflected in co-authorship patterns of theoretical population genetics as studied from 1900 to 1980. The results are consistent with the analyses of bibliographies where the co-authorship networks in invisible colleges probably have shown the same behavioural patterns as the non-scientific populations. The patterns of behaviour are portrayed in two-dimensional as well as three-dimensional representations of co-authorship data in theoretical population genetics.     

USE OF COUNTERPROPAGATION NEURAL NETWORKS TO ENHANCE THE CONCURRENT SUBSPACE OPTIMIZATION STRATEGY

The paper explores the use of artificial neural networks in a concurrent optimization strategy that derives from a decomposition based approach to design of large-scale engineering systems. These problems are characterized by complex couplings that render parametric design methods inappropriate as solution tools. Decomposition methods reduce the large dimensionality problem into a sequence of smaller, more tractable optimization problems, each with a smaller set of design variables and constraints. The decomposed subproblems are rarely decoupled completely, and design changes in one subproblem have a profound influence on changes in another subproblem. Essential components of decomposition based design methods are strategies to identify a topology for problem decomposition, and to develop coordination strategies which account for couplings among the decomposed problems. The paper examines the effectiveness of artificial neural networks as a tool to both account for the coupling, and to develop methods to coordinate the solution in the different subproblems to a converged optimal design     

牙鲆选择性养殖群体遗传结构的微卫星分析

应用10对微卫星引物对三个牙鲆群体各30个个体进行了群体遗传多样性分析.普通群体的平均等位基因数为7.6,显著高于感病群体的5.80,略高于抗病群体的7.2.普通群体的10个位点的平均基因型数为11.4,而感病群体和抗病群体分别只有8.3和9.0.普通群体的平均有效等位基因数为3.97,略高于感病群体和抗病群体的3.78和3.72.普通群体10个位点最高频率等位基因的平均频率值为0.388,而感病群体和抗病群体分别为0.407和0.425.普通群体低频等位基因(0-0.05)总共有27个,而感病群体和抗病群体分别只有15个和19个.普通群体共得到14个私有等位基因,感病群体和抗病群体仅各得到3个等位基因.研究结果表明了感病群体和抗病群体两个选择性养殖群体遗传多样性的降低.     

Multi-period response management to contaminated water distribution networks: dynamic programming versus genetic algorithms

Previous studies on consequence management assume that the selected response action including valve closure and/or hydrant opening remains unchanged during the entire management period. This study presents a new embedded simulation-optimization methodology for deriving time-varying operational response actions in which the network topology may change from one stage to another. Dynamic programming (DP) and genetic algorithm (GA) are used in order to minimize selected objective functions. Two networks of small and large sizes are used in order to illustrate the performance of the proposed modelling schemes if a time-dependent consequence management strategy is to be implemented. The results show that for a small number of decision variables even in large-scale networks, DP is superior in terms of accuracy and computer runtime. However, as the number of potential actions grows, DP loses its merit over the GA approach. This study clearly proves the priority of the proposed dynamic operation strategy over the commonly used static strategy.     

利用AFLP技术分析中国明对虾的韩国南海种群和养殖群体的遗传差异

利用AFLP技术对新发现的中国明对虾的一个地理种群——韩国南海种群（SP）和中国明对虾的养殖群体（CP）进行了遗传分析。每个群体随机取样30个,5对AFLP引物获得326个位点。其中SP多态位点比例（P0.99）46.93％,Shannon多样性指数（Ⅰ）0．1884,Nei（1978）基因多样性指数（h）0．1197,群体差异性位点9个,占检测位点总数的2．7％。CP多态位点比例（R9q）51．84％,Shannon多样性指数（Ⅰ）0．1954,Nei（1978）基因多样性指数（h）0．1229,群体差异性位点19个,占检测位点总数的5．8％。SP种群各项遗传参数都低干CP种群。两个群体的非偏差遗传相似度和遗传距离分别为0．9899和0．0102。利用中国明对虾的韩国南海种群和养殖群体杂交可以获得新的种质资源,这将为获得最大变异数量性状表型和基因多样性的产生提供可能。     

Creating targeted initial populations for genetic product searches in heterogeneous markets

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct ‘targeted’ initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.     

Identification of genetically and oceanographically distinct blooms of jellyfish

Reports of nuisance jellyfish blooms have increased worldwide during the last half-century, but the possible causes remain unclear. A persistent difficulty lies in identifying whether blooms occur owing to local or regional processes. This issue can be resolved, in part, by establishing the geographical scales of connectivity among locations, which may be addressed using genetic analyses and oceanographic modelling. We used landscape genetics and Lagrangian modelling of oceanographic dispersal to explore patterns of connectivity in the scyphozoan jellyfish Rhizostoma octopus, which occurs en masse at locations in the Irish Sea and northeastern Atlantic. We found significant genetic structure distinguishing three populations, with both consistencies and inconsistencies with prevailing physical oceanographic patterns. Our analyses identify locations where blooms occur in apparently geographically isolated populations, locations where blooms may be the source or result of migrants, and a location where blooms do not occur consistently and jellyfish are mostly immigrant. Our interdisciplinary approach thus provides a means to ascertain the geographical origins of jellyfish in outbreaks, which may have wide utility as increased international efforts investigate jellyfish blooms.     

Stress‐enhanced clonal selection algorithm for structural topology optimization

Recently, numerous modified versions of immune algorithms (IAs) have been adopted in both theoretical and practical applications. However, few have been proposed for solving structural topology optimization problems. In addition, the design connectivity handling and one‐node connected hinge prevention, which are vital in the application of population‐based methods with binary representation for structural topology optimization, have not been applied to IAs in the literature. A stress‐enhanced clonal selection algorithm (SECSA) incorporating an IA with a dominance‐based constraint‐handling technique and a new stress‐enhanced hypermutation operator is proposed to rectify those deficiencies. To demonstrate the high viability of the presented method, comparisons between the presented SECSA and genetic algorithm‐based methods were made on minimum compliance and minimum weight benchmark structural topology design problems in two‐dimensional, three‐dimensional, and multiloading cases. In each case, SECSA was shown to be competitive in terms of convergence speed and solution quality. The main goal of this study is not only to further explore the capabilities of IAs, but also to show that an IA with appropriate enhancements can lead to the development of attractive computational tools for global search in structural topology optimization. Copyright © 2011 John Wiley & Sons, Ltd.     

基于遗传算法和拓扑优化的结构多孔洞损伤识别

鉴于拓扑优化和遗传算法在结构损伤识别中各自的优点,本文将遗传算法、有限元和拓扑优化三种方法相结合,提出了一种用于二维结构多损伤识别的新方法。这种方法将拓扑优化的设计变量和遗传算法的参数统一化,将拓扑优化中的目标函数和约束方程与遗传算法的适应度函数联系起来,并以拓扑优化的约束方程作为控制条件参与整个遗传运算的控制。采用二进制编码遗传算法代替连续变量拓扑优化的方式对发生孔洞损伤形式的二维结构进行损伤识别,避免了利用连续变量拓扑优化进行损伤识别时参数阈值的确定可能给识别结果带来的不良影响。通过对两个二维结构模型的多损伤识别仿真计算,结果显示本方法能够很好地识别二维结构中多个位置的损伤,对于仅用拓扑优化法很难识别的轻微孔洞损伤情况,该方法也能得出与实际情况吻合良好的结果。