用于识别心电图的BP网络系统

基于ＢＰ神经网络算法和心电图（ＥＣＧ）识别的原理,作者系统地探讨了ＢＰ网络用于心电图识别的方法并设计了一个用ＢＰ网络识别三种类型（ＥＣＧ）一正常、下壁心肌心梗塞和前间壁心肌梗搴系统。实验结果较好。     

表面活性物质相关蛋白表达的调控研究进展

特异性的肺表面活性物质相关蛋白（ＳＰ）包括亲水性的ＳＰ－Ａ、ＳＰ－Ｄ和疏水性的ＳＰ－Ｂ、ＳＰ－Ｃ．它们的表达与合成受众多生理、病理因素影响。本文综述了该领域的研究进展。１．ＳＰ表达的组织细胞特异性调控：只有肺内某些细胞（肺泡Ⅱ型细胞、Ｃｌａｒａ细胞等）能合成分泌ＳＰ,这可能是由ＳＰ基因中特定序列决定的,如ＳＰ－Ｂ基因的细胞特异性表达的调控成分。２．ＳＰ表达的发育期调控：ＳＰ基因属发育控制基因家族。在人类妊娠前３ｍｏｎ胎肺中,ＳＰ基本不表达：妊娠１５－１８ｗｋ时,气管、支气管上皮细胞即可见ＳＰ－Ｂ、ＳＰ－ＣｍＲＮＡｓ和表达蛋白,它们可能比ＳＰ－Ａ出现早：妊娠１９－２０ｗｋ时可见ＳＰ－ＡｍＲＮＡ和表达蛋白,胎肺组织在体外无激素条件下培养可很快诱导ＳＰ表达,在妊娠后３ｍｏｎ内,各ＳＰｍＲＮＡｓ及其表达蛋白水平与磷脂水平平行升高,也与ＳＰ降低表面张力的特性逐渐增强相关,羊水中可检出这些蛋白,板层体的出现与ＳＰ－Ｂ的表达密切相关,而比ＳＰ－Ａ的表达早,胎肺发育过程中ＳＰｍＲＮＡｓ增加至少部分是由于其基因转录率升高,可能同时也与翻译增加有关。３．糖皮质激素对ＳＰ表达的调控：糖皮质激素对ＳＰ－Ａ表达的调控极复杂,且与剂量     

正常孕产妇血浆胃动素浓度变化

本文测定了１８０例正常孕产妇血浆胃动素浓度,并与２０例正常育龄妇女进行比较。结果表明：早期妊娠妇女血浆胃动素浓度（３８４．４０±１１０．３０ｎｇ／Ｌ）,比正常育龄妇女稍高（３６６．１２±９６．２３ｎｇ／Ｌ）Ｐ＞０．０５;中期妊娠者（３２３．９６±１２５．１０ｎｇ／Ｌ）比早孕者低Ｐ＜０．０５;晚期孕妇胃动素浓度（１２１．０４±２７．００ｎｇ／Ｌ）明显低于中孕者Ｐ＜０．０１;产妇血浆胃动素（４４３．０５±１４０．７９ｎｇ／Ｌ）迅速上升并高于正常育龄妇女Ｐ＜０．０５。为晚期妊娠者的３．１４倍Ｐ＜０．０１。结果提示妊娠影响了母体胃动素分泌和释放,妊娠时间越长,胃动素水平越低,妊娠终止后,胃动素水平明显升高。本文对妊娠及哺乳期妇女血浆胃动素浓度变化的机理及意义进行了讨论。     

西藏扎布耶盐湖晚第四纪孢粉植物群的初步研究

扎布耶盐湖位于西藏青藏高原腹地。通过该湖ＺＫ２钻孔的孢粉分析,揭示了约３６０００ａＢ．Ｐ．以来扎布耶地区的气候和湖盆演变。约在３６０００－３３０００ａＢ．Ｐ．,扎布耶地区为森林草原,气候冷、湿;约３３０００－３００００ａＢ．Ｐ．,为灌丛草原,气候温、干;约３００００－２７５００ａＢ,Ｐ．为干旱草原,气候偏暖、湿;约１８０００－１２０００ａＢ．Ｐ．,为干旱草原,环境寒冷,干燥;约１２０００－４０００ａＢ．Ｐ．,扎布耶地区各类草本植物繁茂,气候偏温、干。另外,根据水生藻类化石群的分析,自３６０００ａＢ．Ｐ以来,扎布耶盐湖有三次湖进、淡水湖期,同时也有三次湖退事件。     

BV检测在胎膜早破发生中的临床应用

目的 探讨ＢＶ在胎膜早破发生中的临床意义。方法 对９８例孕２８－３６周门诊健康孕妇定期采用试纸法检测ＢＶ,按最终是否发生胎膜早破分成ＰＲ组（１４例）,正常妊娠组（８４例）。结果 ＰＲ组检测ＢＶ（＋）为１３例,正常组为１０例,ＰＲ组ＢＶ（＋）发生率明显高于正常妊娠组（Ｐ〈０．０５）。结论 在妊娠晚期,孕妇的ＢＶ与胎膜早破有相关性,提示孕晚期出现ＢＶ（＋）,则应及时治疗,否则易导致ＰＲ。     

大鼠睾丸金属结合蛋白含量对睾酮合成速率的影响

通过观察具有不同睾丸金属结合蛋白（ｔｅｓｔｉｓｍｅｔａｌｂｉｎｄｉｎｇｐｒｏｔｅｉｎ,ＴＭＢＰ）含量的对照和缺锌后补锌大鼠安静时和力竭性游泳后不同时间睾丸和血清睾酮和锌含量变化,以探讨ＴＭＢＰ含量对睾酮合成速率的影响．实验结果证实了如下推测：ＴＭＢＰ含量不同,睾酮合成的速率也不同,高起始含量ＴＭＢＰ的状况下,睾丸睾酮的合成加快．这一结果提示：ＴＭＢＰ可能参与睾酮的合成．     

根据孢粉资料推论长江三角洲地区1200年以来的环境变迁

根据长江三角洲地区全新世９个具代表性钻孔的孢粉分析结果讨论了本区约自１２０００年以来的环境变迁。孢粉序列表明：本区约在距今１２０００年前就已发育了亚热带性质的落叶常绿阔叶林,约在９０００—５０００ａＢ．Ｐ．常绿阔叶林获得大发展。孢粉序列良好地记录了古季风盛衰的历史,表现在：１２０００—１０８００ａＢ．Ｐ．较温湿,东南季风强度加强;１０８００（或１１０００－１００００（或１０３００）ａＢ．Ｐ．偏闵干,东南季风强度减弱,对应于新仙女木期;约自１００００ａ．Ｐ．起,气候好转,气温明显上升,东南季风强度再次增强,约在９０００—５０００ａＢ．Ｐ．温暖湿润,为东南季风强盛期。此外．还讨论了约在距今１１０００年前的海侵,全新世下限和哥德堡事件的年代问题,并确定哥德堡事件的年限为１１０００—１０００ａＢ。Ｐ。     

25例异位妊娠临床分析

２５例异位妊娠临床分析邱玉莲广西昭平县人民医院妇产科５４６８００异位妊娠是妇科常见的急腹症之一,近年来国内外报道的异位妊娠发病率有明显增加。本病多数起病急,病情重,若延误诊断及治疗可引起严重后果,甚至死亡。为了提高诊疗水平,现将我院１９９２年１月～１...     

膜去极化与cAMP在胰岛细胞株HIT中诱导CBP片段的转录活性

为了研究 Ｃ Ｂ Ｐ在胰岛 Ｈ Ｉ Ｔ 细胞中调节基因转录的机制,将不同的 Ｃ Ｂ Ｐ片段瞬时转染到细胞中,观察其转录活性．实验表明,在胰岛 Ｈ Ｉ Ｔ 细胞中,膜去极化及 ｃ Ａ Ｍ Ｐ 均可诱导 Ｃ Ｂ Ｐ３０（ Ｃ Ｒ Ｅ Ｂ结合功能区）转录活性增强,并有协同效应． Ｐ Ｋ Ｃ对 Ｃ Ｂ Ｐ３０ 的转录活性无影响;与 Ｃ Ｒ Ｅ Ｂ有更强结合力的 Ｃ Ｂ Ｐ Ｋ Ｉ Ｘ Ｓ／ Ｂ（氨基酸序列短于 Ｃ Ｂ Ｐ３０ 的 Ｃ Ｒ Ｅ Ｂ结合功能区）其基本转录活性及膜去极化、ｃ Ａ Ｍ Ｐ诱导下的转录活性均比 Ｃ Ｂ Ｐ３０ 更强．反义 Ｃ Ｒ Ｅ Ｂ 的过度表达可降低 ｃ Ａ Ｍ Ｐ诱导的 Ｃ Ｂ Ｐ的转录活性．提示在胰岛 Ｈ Ｉ Ｔ 细胞中,膜去极化及 ｃ Ａ Ｍ Ｐ对共转录因子 Ｃ Ｂ Ｐ转录活性的调节作用通过 Ｃ Ｒ Ｅ Ｂ介导．     

晚更新世晚期以来昆仑山垭口区的植被与环境

昆仑山垭口区小南川剖面和雪水河热水剖面的孢粉分析揭示了该区４４０００ａＢ．Ｐ．以来的植被演替和气候变化。４００００ａＢ．Ｐ．前,推测昆仑山垭口区为典型的灌木荒漠植被,气候极端干燥。４００００－３００００ａＢ．Ｐ．,气候稍有好转。３００００－２４０００ａＢ．Ｐ．为草原荒漠,降水增加。２４０００－１８０００ａＢ．Ｐ．为末次冰期极盛期,气候干冷。１８０００－１３０００ａＢ．Ｐ．气候凉偏干。１３０００－８０００ａＢ．Ｐ．气候凉干。８０００－６０００ａＢ．Ｐ．为全新世温暖期,但由于植被的滞后现象,本区仍为草原荒漠景观。６０００－５０００ａＢ．Ｐ．,本区可能也存在着中全新世的降温事件。５０００－３０００ａＢ．Ｐ．在全新世大暖期的影响下,小南川附近的植被演变成温性草原植被,并有人类的放牧活动。３０００ａＢ．Ｐ．以后,气候变凉,草原迅速地演变为灌木和小乔木荒漠植被,其中３０００－１０００ａＢ．Ｐ．为凉干时期,１０００ａＢ．Ｐ．以后,可能有过一段凉湿时期。     

基于神经网络数据挖掘的红色籽用西瓜亲本选择方法研究

根据多年来红色籽用西瓜自交系育种过程中,积累的多代性状复杂、数量庞大的种质资源及数据,利用数据挖掘中的反向传播（Back propagation,BP）神经网络方法,建立红色籽用西瓜BP神经网络模型。以红籽瓜数量性状作为预测依据,选择出具有丰产性状的红籽瓜亲本材料,为育种工作者进行亲本选择时提供一种新的途径和方法。     

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神经网络和RBF神经网络预测老年痴呆症疾病进展的对比研究

目的:比较反向传播算法(BP)神经网络和径向基函数(RBF)神经网络预测老年痴呆症疾病进展的效果。方法:以老年痴呆症随访数据为研究对象,以性别、年龄、受教育程度、有无高血压、有无高胆固醇、有无心脏病、有无中风史、有无家族史8个指标作为输入变量,以五年随访的MMSE差值为输出变量,构建基于BP神经网络和RBF神经网络的老年痴呆症疾病进展预测模型。结果:与BP神经网络模型相比,RBF神经网络预测的结果更好,能够有效地预测老年痴呆症疾病进展。结论:神经网络模型将老年痴呆症疾病进展预测问题转化为随访数据中相关测量指标与MMSE差值的非线性问题,为复杂的老年痴呆症疾病进展预测提供了新思路。     

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.     

基于蛋白质二级结构内容的域结构类预测

运用加入竞争层的BP网络,研究了基于蛋白质二级结构内容的域结构类预测问题.在BP网络中嵌入一竞争,层显著提高了网络预测性能.仅使用了一个小的训练集和简单的网络结构,获得了很高的预测精度自支持精度97.62%,jack-knife测试精度97.62%,及平均外推精度90.74%.在建立更完备的域结构类特征向量和更有代表性的训练集的基础上,所述方法将为蛋白质域结构分类领域提供新的分类基准.     

Fast time delay neural networks

This paper presents a new approach to speed up the operation of time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.     

A cascading structure and training method for multilayer neural networks

A new structure and training method for multilayer neural networks is presented. The proposed method is based on cascade training of subnetworks and optimizing weights layer by layer. The training procedure is completed in two steps. First, a subnetwork, m inputs and n outputs as the style of training samples, is trained using the training samples. Secondly the outputs of the subnetwork is taken as the inputs and the outputs of the training sample as the desired outputs, another subnetwork with n inputs and n outputs is trained. Finally the two trained subnetworks are connected and a trained multilayer neural networks is created. The numerical simulation results based on both linear least squares back-propagation (LSB) and traditional back-propagation (BP) algorithm have demonstrated the efficiency of the proposed method.     

人工神经网络模型及其在农业和生态学研究中的应用

对于一些复杂的农业生态系统,人们对其生态过程了解较少,且这些系统的不确定性和模糊性较大,用传统的方法难以模拟这些系统的行为,神经网络模型因为能较精确地模拟这些系统的行为,而引起生态学者们的广泛兴趣。该文着重介绍了误差逆传神经网络模型的结构、算法及其在农业和生态学中的应用研究。误差逆传神经网络模型一般采用三层神经网络模型结构,三层的神经网络模型能模拟任意复杂程度的连续函数,而且因为它的结构小而不容易产生与训练数据的过度吻合。误差逆传神经网络模型算法的主要特征是:利用当前的输入误差对权值进行调整。在生态学和农业研究中,误差逆传神经网络模型通常作为非线性函数模拟器用于预测作物产量、生物生产量、生物与环境之间的关系等。已有的研究表明:误差逆传神经网络模型的模拟精度要远远高于多元线性方程,类似于非线性方程,而在样本量足够的情况下,有一定的外推能力。但是误差逆传神经网络模型需要大量的样本量来保证所求取参数的可靠性,但这在实际研究中很难做到,因而限制了误差逆传神经网络模型的应用。近年来人们提出了强制训练停止、复合模型等多种技术来提高误差逆传神经网络模型的外推能力,也提出了Garson算法、敏感性分析以及随机化检验等技术对误差逆传神经网络模型的机理进行解释。误差逆传神经网络模型的真正优势在于模拟人们了解较少或不确定性和模糊性较大系统的行为,这些是传统模型所无法实现的,因而是对传统机理模型的重要补充。     

基于神经网络和遗传算法的木糖醇发酵培养基优化研究

发酵过程机理复杂、影响因素众多。菌种的生理生化特性及发酵的工艺确定之后 ,适宜的培养基配方成了发酵水平、原料成本高低的决定因素。为了优化培养基配方 ,采用遗传算法是一种行之有效的方法。遗传算法 (ＧＡ)是基于达尔文进化论和孟德尔遗传学说来实现随机、自适应、并行性全局搜索的一种无须数学模型的优化算法。与其它搜索方法相比 ,ＧＡ的优越性主要有 :(1)在搜索过程中ＧＡ不易陷入局部最优 ,即使所定义的目标函数非连续、不规则或伴有噪声 ,它也能以很大的概率找到全局最优解 ;(2 )由于ＧＡ固有的并行性 ,使得它非常适合于大规模并…     

Selecting neural networks for a committee decision

To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.