H1启动子siRNA载体的构建及应用

利用双链RNA(dsRNA)调控基因表达已经成为研究基因功能的有力工具。用人H1启动子构建了pBS／H1PS小干扰RNA(siRNA)表达载体,用于在哺乳动物细胞中产生特异性dsRNA转录产物。通过对293细胞中的PSMA7分子进行表达抑制,证明该siRNA载体能够有效产生针对靶基因的RNA干扰(RNAi)效应。     

RNA干扰与siRNA（小干扰RNA）研究进展

RNA干扰作为后基因组时代的一种下调基因表达的工具已被广泛用于基因功能的研究以及疾病的治疗。在RNA干扰技术下调基因表达的背后存在着一个复杂的蛋白质网络参与了这一功能的实现。本文对近年来RNA干扰在疾病治疗方面的进展及其机制研究方面的进展作了一些概述。     

基于BP神经网络的SARS传播预测

政府的控制措施作为影响SARS传播的因素,利用BP网络,对SARS的传播规律进行预测.以北京市的SARS数据来进行验证,结果显示,该方法准确率非常高.     

siRNA抑制乙肝病毒的研究进展

乙型肝炎作为一种发病率高、死亡率高的传染性疾病,已严重威胁人类健康,乙肝病毒（hepatitis B virus,HBV）是诱发乙型肝炎的重要病因。目前,最主要的治疗方法是运用抗病毒药物控制病情,但这些药物都不能完全治愈乙型肝炎且复发率高。近年来,RNA干扰技术（RNA interference, RNAi）逐渐成为有效、快速治疗乙型肝炎的新疗法。利用RNA干扰技术体外合成针对HBV基因的siRNA,选择适当的载体将其运送至靶细胞,使HBV基因沉默,从而抑制病毒复制,可有效达到治疗乙肝的效果。本文围绕siRNA沉默HBV基因的设计原理、递送载体、靶向策略、以及治疗效果与应用前景等方面进行了系统综述,为今后siRNA治疗乙肝的临床应用提供参考。     

RNA干扰技术的原理与应用

RNA干扰(RNA interference,RNAi)是由双链RNA(double-stranded RNA,dsRNA)所引起的序列特异性基因沉默,是真核生物中一种非常保守的机制,它与协同抑制(cosuppression)、转座子沉默(transposon silencing)以及发育等许多重要的生物学过程密切相关。RNA干扰依赖于小干扰RNA(Small interference RNA,siRNA)与靶序列之间严格的碱基配对,具有很强的特异性,涉及众多基因和蛋白复合物,构成了一个以小RNA为核心的真核基因表达调控系统,它可以在染色质水平、转录水平、转录后水平和翻译水平参与基因表达的调节。RNA干扰技术为人们迅速、准确的剖析基因的功能,分析基因之间错综复杂的联系和相互作用提供了极为有用的工具,同时也为人们预防和治疗癌症和病毒疾病提供了新的思路。     

siRNA脱靶效应研究进展

RNA干扰过程中,siRNA和mRNA特异结合能够使得靶基因沉默。但研究证实,siRNA可能与非靶基因结合而导致非靶基因沉默,这种现象称为siRNA脱靶效应。多种真核生物中的RNA干扰实验证实了脱靶效应的存在。对脱靶机制的研究发现脱靶可能与模体匹配、结构和长dsRNA等有关,很多新方法被提出来预测脱靶概率和检测脱靶基因。通过利用siRNApool、化学修饰和生物信息学方法能够尽可能地降低脱靶效应,提高RNAi实验的质量。对脱靶效应方面的研究进行了总结论述。     

siRNA在治疗学中的应用

小干扰RNA（small interfering RNA,siRNA）是外源性双链RNA（double strand RNA,dsRNA）的加工产物,在细胞内能介导RNA干扰（RNA interference,RNAi）效应,识别特异性mRNA,沉默同源基因表达。其特异性和高效性显示出很高的实用价值,siRNA已成为许多疾病潜在的治疗手段。对于siRNA的应用,尽管还需要在减少非特异反应,发掘高效递药载体,应对新的基因变异等方面进行深入研究,但其可望在抗病毒、神经系统疾病和肿瘤治疗等许多领域发挥治疗作用。     

用BP神经网络基于氨基酸特性预测非同源蛋白质二级结构含量

依据蛋白质氨基酸特性,以氨基酸组成和有偏自协方差函数为特征矢量,用BP神经网络提出了一种预测非同源蛋白质中α螺旋和β折叠二级结构含量的计算方法。采用相互独立的非同源蛋白质数据库对该方法进行了检验。用Ponnuswamy值时,对二级结构α螺旋和β折叠含量的预测结果是;自检验平均绝对误差分别为0.069和0.065,相应标准偏差分别为0.044和0.047;他检验平均绝对误差分别为0.077和0.070,相应标准偏差分别为0.051和0.049。与仅以氨基酸组成为特征矢量的BP神经网络方法比较,相应的他检验平均绝对误差分别减小了0.024和0.016,标准偏差分别减小了0.031和0.018;与改进的多元线性回归方法比较,相应的他检验平均绝对误差分别减小了0.018和0.011,准偏差分别减小了0.020和0.012。表明：基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。     

基于BP神经网络的京津冀城市群可持续发展综合评价

在综合分析了京津冀城市群各城市功能定位的基础上,构建了包含经济发展、社会发展、科技创新和生态环境4个子系统的城市可持续发展评价指标体系,运用2006—2015年的数据,采用熵值法和BP神经网络对京津冀城市群可持续发展能力进行非线性测度与分类,结果较为理想。结果表明:(1)北京和天津处于高可持续发展水平,可持续发展能力在空间上呈现出以京、津为中心随距离递减的趋势,最南端的邯郸和邢台处于低可持续发展水平;(2)北京可持续发展能力呈现下滑趋势,其他城市可持续发展能力逐年稳步上升,大城市可持续发展压力较大;(3)城市在不同子系统中存在各自的优劣势。各个子系统在可持续发展中均起到重要作用,城市宜结合各自子系统的优、劣势制定具有针对性的发展对策。     

基于BP神经网络的甲状腺癌无创诊断模型的研究

目的:运用BP神经网络技术建立甲状腺癌的无创诊断模型,评估该模型的预测诊断价值。方法:回顾性分析经术后病理证实为甲状腺癌39例与良性病变11例,提取出以上50例病例中手术前经过B超检查与甲状腺癌相关的8项图形特征,并进行评分量化,利用BP神经网络对50例病例进行学习和检验,建立甲状腺癌无创诊断模型。用该无创诊断模型对疑为甲状腺癌20例患者进行术前预测并与术后病理进行比较。结果:本文所建立的基于BP神经网络技术的无创诊断模型在甲状腺癌及甲状腺良性病变的预测诊断中达到了100%的准确率。结论:基于BP神经网络技术的无创诊断模型,在甲状腺癌及良性病变的预测诊断中具有较高的应用价值,这无疑对辅助B超诊断甲状腺良恶性病变提供了新的技术支撑和研究思路。     

Predicting siRNA activity based on back-propagation neural network

RNA interference (RNAi) is a phenomenon of gene silence induced by a double-stranded RNA (dsRNA) homologous to a target gene.RNAi can be used to identify the function of genes or to knock down the targeted genes.In RNAi technology,19 bp double-stranded short interfering RNAs (siRNA) with characteristic 3' overhangs are usually used.The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA.Moreover,there are many factors influencing siRNA activity and these factors are usually nonlinear.To find the motif features and the effect on siRNA activity,we carried out a feature extraction on some published experimental data and used these features to train a backpropagation neural network (BP NN).Then,we used the trained BP NN to predict siRNA activity.     

Predicting siRNA activity based on back-propagation neural network

RNA interference (RNAi) is a phenomenon of gene silence induced by a double-stranded RNA (dsRNA) homologous to a target gene. RNAi can be used to identify the function of genes or to knock down the targeted genes. In RNAi technology, 19 bp double-stranded short interfering RNAs (siRNA) with characteristic 39 overhangs are usually used. The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA. Moreover, there are many factors influencing siRNA activity and these factors are usually nonlinear. To find the motif features and the effect on siRNA activity, we carried out a feature extraction on some published experimental data and used these features to train a back-propagation neural network (BP NN). Then, we used the trained BP NN to predict siRNA activity. __________ Translated from Acta Biophysica Sinica, 2006, 22(6): 429–434 [译自: 生物物理学报]     

基于人工神经网络的昆虫鸣声识别

以常见的7种飞虱雄虫求偶鸣声信号的频率峰值作为输入向量,用人工神经网络来识别它们的鸣声,平均识别率达90．6％。人工神经网络可以用于昆虫鸣声识别。     

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.     

Specific gene silencing in the pre-implantation stage mouse embryo by an siRNA expression vector system

Recently, small interfering RNAs (siRNAs) have become a powerful and widely used tool for the analysis of gene function in mammalian cells. Here we report that the microinjection of an siRNA expression vector into the nucleus is an efficient and powerful method of specific gene silencing in pre-implantation mouse embryos. We used this method to examine the expression of two genes EGFP and Oct4. Vectors encoding siRNAs targeted against EGFP or Oct4 were injected into the pronucleus or nucleus of zygotes, which were then cultured until the blastocyst stage. When the effects of RNAi were examined in blastocyst stage eggs, there was robust inhibition of the gene product in a concentration-dependent manner at both the mRNA and the protein level. The expression of other endogenous genes was not affected, showing the specificity of the vector-mediated RNAi. In addition, this method was effective for inhibiting maternally expressed mRNA. To demonstrate that RNAi of Oct4 induced a similar phenotype to that of Oct4-null embryos, the blastocysts were further cultured in ES medium. After the fourth day of culture, the embryos either had outgrown only a layer of trophoblast cells or showed developmental arrest at the blastocyst stage (>90%). Moreover, concomitant with Oct4 suppression at the blastocyst stage, we observed inhibition of Fgf4, a gene that is known to be induced downstream of Oct4 expression. Taken together, these results demonstrate that the use of siRNA expression vector is a powerful way to achieve gene silencing in the pre-implantation stage embryo.     

Convergence in Continuous Hopfield Neural Network with Delays

IIntroductionWeconsiderthenetworkbasedonHopfleldcircuitequationwiththeadditionofdelayswhereu;（t）IstheInPutvoltageofthel一thneuron,C;IscaPacitance,KIsthetotalresls－tanceattheInputofneuron。,人Isslgmoldaltransferfunction．Inthispaper,weconsiderthenormalizedsystemof（l）。fthetypeThereexistsanextensivekeratureonvariousaspectsofsystemsoftheform（2）withandwithouttimedelays,werefertoEI,2,3jandthereferencescitedtherein．ThepurposeofthispaperIstoderivesufficientconditionsfortheglobal…     

Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of less than 5%. The experimental result indicates the effectiveness of this approach, and is useful for providing an objective basis for evaluating the malignancy of breast tumors.     

Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network

Background and objectiveAn important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.MethodsIn order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.ResultsThe experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.ConclusionsFurther analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.     

基于熵准则的鲁棒的RBF谷胱甘肽发酵建模

在谷胱甘肽的发酵过程建模中, 当试验数据含有噪音时, 往往会导致模型预测精度和泛化能力的下降。针对该问题, 提出了一种新的基于熵准则的RBF神经网络建模方法。与传统的基于MSE准则函数的建模方法相比, 新方法能从训练样本的整体分布结构来进行模型参数学习, 有效地避免了传统的基于MSE准则的RBF网络的过学习和泛化能力差的缺陷。将该模型应用到实际的谷胱甘肽发酵过程建模中, 实验结果表明: 该方法具有较高的预测精度、泛化能力和良好的鲁棒性, 从而对谷胱甘肽的发酵建模有潜在的应用价值。