人神经元性AADC基因的克隆、测序及其对帕金森病的治疗作用(英文)

背景:帕金森病患者脑内黑质多巴胺能神经元变性导致芳香族氨基酸脱羧酶(aromaticL-aminoaciddecarboxylase,AADC)活性水平的降低及波动是产生帕金森患者临床症状和在L-dopa替代治疗时“症状波动”等不良反应出现的重要原因之一。目的:探讨AADC基因脑内移植对帕金森病的治疗作用及其在左旋多巴治疗过程中的地位,并探讨阳离子脂质体介导的基因治疗方法的有效性。设计:2×2析因设计。地点和材料:中山大学附属第二医院林百欣医学研究中心,材料主要包括人嗜铬细胞瘤组织,TRIzol,真核表达载体质粒pCDNA3,阳离子脂质体,6-OHDA,阿朴吗啡,AADC单克隆抗体,SD大鼠。干预:使用基因克隆、亚克隆技术构建pCDNA3-AADC真核表达载体;将帕金森病模型的SD大鼠随机分为实验组与对照组,分别以pCD-NA3-AADC重组体和pCDNA3空载体进行阳离子脂质体包裹后,注射于帕金森病SD大鼠纹状体内,并在该基础上对两组大鼠分别采用一定浓度的L-dopa进行治疗。采用免疫组化方法确定AADC的表达。主要观察指标:①AADC基因脑内移植后两组大鼠的旋转行为。②采用一定浓度的L-dopa进行治疗对大鼠旋转行为的影响。结果:①阳离子脂质体包裹的pCDNA3-AADC重组体脑内注射后,在3,7,14,21,28d时大鼠的旋转行为获得了一定的改善(P<0.05),分别改善了56.0%。

EIF2B5表达下调的人星形胶质细胞与人神经元在内质网应激后细胞存活及miRNA表达的差异

目的探讨白质消融性白质脑病中胶质细胞选择性受累而神经元受累轻微的原因。方法将EIF2B5-RNAi表达载体转染至人星形胶质细胞和人神经元,检测基础状态下及内质网应激(endoplasmic reticulum stress,ERS)后细胞凋亡和活力,检测参与ERS调控的已知和未知miRNA,筛选EIF2B5-RNAi人星形胶质细胞在ERS后miRNA变化。结果与EIF2B5-RNAi人神经元相比,星形胶质细胞自发凋亡及细胞活力下降。较之神经元,更多miRNA参与星形胶质细胞ERS刺激后的调控,EIF2B5-RNAi组参与调控的miRNA数目显著减少。聚类分析发现,5条已知miRNA是通路连接的关键组分。结论人星形胶质细胞在ERS后可能更加依赖众多促细胞增殖分化的miRNA修复,而EIF2B5-RNAi人星形胶质细胞存在自发凋亡,ERS后严重减少的miRNA可能导致细胞无法存活。

信息生物学技术在人神经元蛋白17.3(NP17.3)结构和功能研究中的应用

本文利用信息生物学技术,对新近克隆的人神经元蛋白基因(np17.3)所编码的人神经元蛋白NP17.3的一、二级结构特点及其理化特性进行分析,同时就其空间三维构象进行了模拟,为研究其在活体内的功能提供了理论指导。

人神经元特异性烯醇化酶在肿瘤细胞系中的表达检测及意义

目的探讨从蛋白及细胞水平检测人神经元特异性烯醇化酶(NSE)在非神经内分泌性肿瘤细胞系中的表达及其意义。方法用NSE特异引物和逆转录聚合酶链反应(RTPCR)、蛋白印迹、免疫细胞化学(ICC)染色技术检测NSE在肿瘤细胞中的表达。结果从转录蛋白及细胞水平都能从黑色素瘤细胞株WM451和WM983A、前列腺癌高转移细胞株PC3M和低转移细胞株PC3、乳腺癌细胞株MCF7、结直肠癌细胞株HT29和SL180、胃癌细胞株MGC803中检测到NSE的表达,阳性率为100%。结论NSE除在神经内分泌细胞表达外,在肿瘤细胞亦有更为广泛的表达谱,其作为神经内分泌来源肿瘤标志物的作用值得深入研究。

人神经元钾离子通道α亚基基因的分子克隆

为克隆新的钾离子通道基因并研究其与疾病的关系，将大鼠的神经元钾离子通道α亚基编码区序列在人的ＥＳＴ数据库进行ＢＬＡＳＴ分析，得到一个与其有８７％的一致性的ＥＳＴ，通过重叠群的构建和５’－ＲＡＣＥ反应，克隆了人神经元钾离子通道α亚基（ｈｏｍｏ ｓａｐｉｅｎｓ ｎｅｕｒｏｎａｌ ｐｏｔａｓｓｉｕｍ ｃｈａｎｎｅｌ ａｌｐｈａｓｕｂｕｎｉｔ，ＨＮＫＡ）基因，该基因仅在成人脑和胎脑中有表达．经与大规模测序数据库比较后将该基因定位在８ｑ２３．１～ｑ２３．２位点．选择定位于此区域的痉挛性截瘫为ＨＮＫＡ基因的候选疾病，利用ＰＣＲ产物直接测序的方法，对１４个痉挛性截瘫家系进行了突变检测分析，但未检测到突变．

两种帕金森体外模型的构建及相关研究

为检测a-突触核蛋白(a-Synuclein)在人神经元母细胞瘤细胞(SH-SY5Y)中的表达,构建人野生型(WT)和A53T突变型a-Synuclein质粒(p EGFP-SNCA-WT和p EGFP-SNCA-A53T),经酶切鉴定无误后转染SH-SY5Y细胞,并对转染完成的细胞进行嘌呤霉素筛选,然后通过PCR法扩增转染后SH-SY5Y细胞的a-Synuclein片段,并对其进行测序,检测目的基因,运用Western blot法检测转染后SH-SY5Y细胞中a-Synuclein的表达.酶切鉴定结果表明p EGFP-SNCA-WT和p EGFP-SNCA-A53T质粒构建成功,嘌呤霉素筛选和转基因细胞a-Synuclein片段测序结果显示慢病毒表达载体能成功的整合到SH-SY5Y细胞基因组中,Western blot结果表明,转染后的SH-SY5Y细胞能成功的过表达a-Synuclein.a-Synuclein慢病毒表达载体构建成功,并且SH-SY5Y细胞作为宿主细胞能够表达a-Synuclein.为今后帕金森病(PD)体外模型的建立及帕金森病发病机制的研究奠定了基础.

FPGA implementation of bit-stream neuron and perceptron based on sigma delta modulation

To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（ΣΔ） modulation is presented.The bit-stream adder,multiplier,threshold function unit and fully digital ΣΔ modulator are implemented in a field programmable gate array（FPGA）,and these bit-stream arithmetical units are employed to build the bit-stream artificial neuron.The function of the bit-stream artificial neuron is verified through the realization of the logic function and a linear classifier.The bit-stream perceptron based on the bit-stream artificial neuron with the pre-processed structure is proved to have the ability of nonlinear classification.The FPGA resource utilization of the bit-stream artificial neuron shows that the bit-stream ANN hardware implementation method can significantly reduce the demand of the ANN hardware resources.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

The applying of BP network in forecasting the demand and its growth rate for coal

Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.

A forming load analysis for extrusion process of AZ31 magnesium

The effect of extrusion parameters on the extrusion load for AZ31 magnesium alloy was investigated with the support of numerical methods.With this regard,the process temperature,extrusion ratio,friction factor and punch velocity were selected as main parameters for the experiments.Besides,the experimental results were analyzed by using the finite element method(FEM)and artificial neural network(ANN)method to build a numerical model for predicting the forming load.All the experimental and numerical results were compared to each other and it was concluded from the results that the effect of friction factor on the extrusion load is more dominant at lower extrusion temperature for all given extrusion ratios and punch velocities.Besides this,higher extrusion ratios require higher process temperatures to obtain the lower extrusion load.Also,it was observed that the increase in the extrusion speed causes a significant increase in the forming load for all extrusion ratios and extrusion temperatures.

Design and Simulation of Single Neuron Adaptive PID Controller

The purpose of this paper is to design a neuron adaptive PID controller based on the theory of intelligent control of the extens- ive research on the characteristics of neuronss, neurons and PID controller. Artificial neurons have the adaptive, parallel processing, selflearning learning, and mare fault-tolerant characteristics. When the artificial neurons are used to control the process, the syste^n will enabled to en-sure that the accused has strong anti-interference capability and ro. bustness.

Correlation of Vapour Liquid Equilibria of Binary Mixtures Using Artificial Neural Networks

In this paper, a back propagation artificial neural network （BP-ANN） model is presented for the simultaneous estimation of vapour liquid equilibria （VLE） of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature （222.04-343.23K） and pressure （0.105 to 7.46MPa）. BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave （RKS） equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation （the root mean square error in the range of 0.115-0.1546）.

Predicting Model for Complex Production Process Based on Dynamic Neural Network

Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.

Adult Medulloblastoma Associated with Syringomyelia:A Case Report

The association between cerebellar medulloblastoma and syringomyelia is uncommon and only found in pediatric patients.To date,adult medulloblastoma associated with syringomyelia has not been reported in the literature.Paroxysmal bradycardia is an uncommon clinical manifestation in posterior fossa tumors and likely to be vagally mediated via brainstem preganglionic cardiac motor neurons.This report introduces the diagnosis and treatment of a case of adult medulloblastoma associated with syringomyelia, which presented with paroxysmal bradycardia.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Apaf-l-deficient fog mouse cell apoptosis involves hypopolarization of the mitochondrial inner membrane, ATP depletion and citrate accumulation

To explore how the intrinsic apoptosis pathway is controlled in the spontaneous fog （forebrain overgrowth） mutant mice with an Apafl splicing deficiency, we examined spleen and bone marrow cells from Apafl＋/＋ （＋/＋） and Apafl^fog/fog （fog/fog） mice for initiator caspase-9 activation by cellular stresses. When the mitochondrial inner membrane potential （△ψm） was disrupted by staurosporine, ＋/＋ cells but not fog/fog cells activated caspase-9 to cause apoptosis, indicating the lack of apoptosome （apoptosis protease activating factor 1 （Apaf-l）/cytochrome c/（d）ATP/procaspase-9） function in fog/fog cells. However, when a marginal （-20%） decrease in △ψm was caused by hydrogen peroxide （0.1 mM）, peroxynitrite donor 3-morpholinosydnonimine （0.1 mM） and UV-C irradiation （20 J/m^2）, both ＋/＋ and fog/fog cells triggered procaspase-9 auto-processing and its downstream cascade activation. Supporting our previous results, procaspase-9 pre-existing in the mitochondria induced its auto-processing before the cytosolic caspase activation regardless of the genotypes. Cellular ATP concentration significantly decreased under the hypoactive △ψm condition. Furthermore, we detected accumulation of citrate, a kosmotrope known to facilitate procaspase-9 dimerization, probably due to a feedback control of the Krebs cycle by the electron transfer system. Thus, mitochondrial in situ caspase-9 activation may be caused by the major metabolic reactions in response to physiological stresses, which may represent a mode of Apaf-l-independent apoptosis hypothesized from recent genetic studies.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

Implementing Semantic Deduction of Propositional Knowledge in an Extension Multi-layer Perceptron

The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of prop ositional knowledge base can be implement by the extension multi-layer perceptr on, and by learning, an unknown formula set can be found.