基于决策树的红色籽用西瓜数据挖掘

利用Clementine软件提供的决策树C5．0算法,探索红色籽用西瓜自交系多个数量性状之间的相互制约关系。初步建立了单瓜种子重决策树模型,预测精度达到68％。可据此模型对红籽瓜自交系的各经济性状进行定量分析,为育种生产者进行优良自交系和亲本选配提供决策。     

红籽瓜数量性状分析软件的设计与实现

主要介绍了如何使用面向对象技术对红籽瓜数量性状分析软件的设计,阐述了软件的设计过程,代码的编写。实现了对红籽瓜数量性状的分析。     

棉花亲本选配模式的研究

根据棉花的数量性状,利用聚类分析方法将１７个亲本分为４类．根据棉花的综合性状和性状距离,给出了两套亲本选配方案．试验结果表明,这些方案能提高育种工作中亲本选配的效率．     

利用SSR标记对西瓜果肉硬度性状的连锁分析

果肉硬度作为西瓜的重要品质属性,越来越受到生产者和消费者的重视。本研究以野生种西瓜PI271769为供体亲本,栽培种西瓜203Z为轮回亲本进行高代回交,在BC7F1世代发现了果肉硬度变硬的材料。用平均分布于西瓜11条染色体的185对SSR标记对PI271769、203Z和构建的软肉池、硬肉池进行筛选,发现位于第6染色体上的标记BVWS00954具有多态性。通过单向方差分析在BC7F2分离群体的验证,表明BVWS00954与控制西瓜果肉硬度的基因连锁,且BVWS00954标记分析基因型与不同的西瓜资源果肉硬度表现型完全吻合。研究结果为控制西瓜果肉硬度基因的精细定位、图位克隆及分子标记辅助选择育种奠定了基础。     

奇特的西瓜

奇特的西瓜迷你西瓜一种经人工改造过的“迷你西瓜”在日本市场推出并受到欢迎。这种用一只手就可托起的小型酉瓜名为“红小玉”,重约１～Ｚｋｇ,肉为红色,皮薄,汁多而甜。什锦西瓜美国阿肯色州的布拉、农拉兄弟栽培的一种特大西瓜,最重达９０ｋｇ。它成熟后肉色有３...     

籽瓜种子蛋白组分和生化特性的研究(简报)

籽瓜是西瓜的变种,其种子板大、味香,是人们喜爱的食品。籽瓜种子蛋白质含量约为38%,其中78%是球蛋白。Blagrove(1980)报道。南瓜、西瓜、甜瓜及黄瓜等瓜类种子球蛋     

平榛种质资源坚果农艺性状鉴定与评价研究

平榛是我国重要的榛属植物资源,为鉴定、评价和保护平榛种质资源,提高其利用效率,采用主成分分析和聚类分析方法对99份平榛种质资源坚果的主要农艺性状进行了鉴定和综合评价。结果表明:平榛种质资源坚果的24个性状具有丰富的遗传多样性;10个数量性状的主成分分析确定了4类影响因子,反映了育种选择方向及潜力;基于各种质间形态性状的遗传差异,将99份平榛种质划分为5大类群,第Ⅰ类群可作为选育果面光滑品种的育种和候选亲本材料,第Ⅱ类群可作为选育大粒型、适宜机械化加工的育种和候选亲本材料,第Ⅲ类群具有作为市场产品的潜力,第Ⅳ类群可作为选育圆形、果面无条纹的育种和候选亲本材料,第Ⅴ类群可作为选育高丰产性品种的育种和候选亲本材料。上述研究为平榛种质资源的评价、保护和选优工作提供了基础数据。     

长豇豆荚色、籽粒色及生长习性的遗传研究

以性状多样的长豇豆为材料,对其荚色、籽粒色和生长习性进行遗传分析。结果表明：籽粒色的相对性状间均表现1对等位基因差异的遗传,黑籽对红籽为显性,黑籽对白籽、红籽对白籽均为显性,花斑籽对红籽为显性。生长习性由2对等位基因控制,其中蔓生对矮生为显性上位性。荚角因材料不同有的表现为质量性状,且紫荚对浅绿荚为显性、浅绿荚对绿荚为显性、白荚对浅绿荚为显性;有的表现为数量性状,即由多基因控制。     

作物数量性状的遗传距离及其测定

在杂交育种过程中,亲本的选配是杂种后代出现理想的性状组合的关键环节。遗传学研究指出,双亲的基因型差异越大,其后代的分离幅度也就越大。因此许多育种工作者总是把基因型差异的大小作为杂交育种中选配亲本的重要依据之一。但是,由于育种过程中所遇到的主要性状多数是数量性状,在表现型上呈连续分布。另外,不同性状值之间彼此常有不同程度的相关。因而给按照遗传差异来选配亲本增加了困难。     

江淮下游地区大豆地方品种的初步研究——Ⅱ.数量性状的遗传变异

本文研究江淮下游地区大豆地方品种有关生育期、产量、机械化栽培共17种数量性状的遗传变异。根据这些性状的表型和遗传型方差、遗传型变异系数及遗传力,估计预期遗传进度,从而揭示从大豆地方品种自然群体进行选择的潜力和预期效果。研究证实了本区大豆地方品种数量性状的遗传资源非常丰富;系统育种仍是大豆育种主要方法之一,应加以重视;并提出用数量遗传方法整理和研究大豆地方品种是育种理论和实践上都有意义的方法。     

基于神经网络的异位妊娠发病率发展趋势研究

本文阐述了ＢＰ神经网络的基本算法,并根据近十年来搜集整理的异位妊娠发病率的统计资料;采用 ＢＰ神经网络对异位妊娠的发病率的发展趋势进行预测;预测结果和实际结果比较吻合．进一步指出了神经网络可作为一种新的预测方法．     

Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm

Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.     

New learning automata based algorithms for adaptation of backpropagation algorithm parameters

One popular learning algorithm for feedforward neural networks is the backpropagation (BP) algorithm which includes parameters, learning rate (eta), momentum factor (alpha) and steepness parameter (lambda). The appropriate selections of these parameters have large effects on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase speed of convergence. In this paper, we shall present several classes of learning automata based solutions to the problem of adaptation of BP algorithm parameters. By interconnection of learning automata to the feedforward neural networks, we use learning automata scheme for adjusting the parameters eta, alpha, and lambda based on the observation of random response of the neural networks. One of the important aspects of the proposed schemes is its ability to escape from local minima with high possibility during the training period. The feasibility of proposed methods is shown through simulations on several problems.     

基于BP神经网络与ETM+遥感数据的盐城滨海自然湿地覆被分类

高效而精确的湿地遥感分类是大范围湿地资源动态监测与管理的必要保障。本研究使用ETM 遥感数据,借助Matlab神经网络工具箱,构建了基于BP神经网络的滨海湿地覆被分类模型,并将其应用于江苏盐城沿海湿地珍禽国家级自然保护区的核心区的自然湿地覆被分类研究中。本研究选择3、4、7、8波段作为输入层变量,单隐藏层设为10个节点,输出层变量对应待划分的8种覆被类型,构建三层式BP神经网络滨海湿地覆被分类模型。结果显示,BP分类总精度为85.91%,Kappa系数为0.8328,与最小距离法和极大似然法的分类总精度相比,分别提高了7.99%和6.08%,Kappa系数也相比提高。研究结果表明,BP神经网络分类法是一种较为有效的湿地遥感影像分类技术,能够提高分类精度。     

A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns

The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.     

Reduced bio-basis function neural networks for protease cleavage site prediction

This paper presents a new neural learning algorithm for protease cleavage site prediction. The basic idea is to replace the radial basis function used in radial basis function neural networks by a so-called bio-basis function using amino acid similarity matrices. Mutual information is used to select bio-bases and a corresponding selection algorithm is developed. The algorithm has been applied to the prediction of HIV and Hepatitis C virus protease cleavage sites in proteins with success.     

Discriminating signal from noise: recognition of a movement-based animal display by artificial neural networks

In this study, we investigated the feasibility of applying neural networks to understanding movement-based visual signals. Networks based on three different models were constructed, varying in their input format and network architecture: a Static Input model, a Dynamic Input model and a Feedback model. The task for all networks was to distinguish a lizard (Amphibolurus muricatus) tail-flick from background plant movement. Networks based on all models were able to distinguish the two types of visual motion, and generalised successfully to unseen exemplars. We used curves defined by the receiver-operating characteristic (ROC) to select a single network from each model to be used in regression analyses of network response and several motion variables. Collectively, the models predicted that tail-flick efficacy would be enhanced by faster speeds, greater acceleration and longer durations.     

基于MODIS时间序列反射率数据的雷竹林LAI反演

本文以雷竹林为研究对象,基于MODIS地表反射率数据构建了归一化植被指数(NDVI)、比值植被指数(SR)、Gitelson绿色植被指数(GI)、增强型植被指数(EVI)和土壤调整植被指数(SAVI)5种植被指数,并将其与MODIS 7个波段原始反射率数据作为遥感变量,采用逐步回归和相关分析两种方法进行变量筛选,结合LAI实测数据构建了逐步回归和BP神经网络两种模型,对雷竹林生态系统观测站点2014年1月-2017年3月LAI时间系列数据进行反演,并将反演结果与同时期MOD15A2 LAI产品进行对比分析.结果表明: SR为唯一入选逐步回归模型的变量;b₁、b₂、b₃和b₇以及5种植被指数与LAI之间的相关性均达到显著水平,可作为BP神经网络模型的输入变量.使用BP神经网络反演得到的LAI与实测LAI之间的相关性显著,R²为0.71,RMSE为0.34,RMSE_r为13.6%,其R²比逐步回归模型提高了10.9%,RMSE降低了5.6%,RMSE_r降低了12.3%,与MODIS LAI相比,其R²提高了54.5%,RMSE降低了79.3%,RMSE_r降低了79.1%.结合MODIS时间序列反射率和BP神经网络模型能够精确地反演雷竹林LAI,为实现基于遥感技术快速监测区域雷竹林LAI提供可行的方法.