无线光刺激与神经传感系统

光遗传神经调控正在向闭环技术发展,针对实验动物可自由活动的需求,研发具有大范围光刺激、神经信号同步记录、无线程控等功能的无线光刺激与神经传感系统。该系统包括光刺激—电记录神经接口、刺激传感模块、无线收发模块以及上位机软件。可无线实时调节光刺激频率、幅度和占空比等参数,并同步采集神经信号。给予小鼠刺激运动皮层30Hz光刺激,小鼠运动明显增强,同时记录到刺激诱发的脑电信号。系统为光遗传技术提供多功能神经调控平台,有助于神经环路和神经疾病的长时程研究。

神经元钙传感蛋白的研究进展及其在脑缺血中的作用

神经元钙传感蛋白NCS-1(neuronal calcium sensor-1,NCS-1)是一种具有较强的钙离子结合能力的蛋白质。NCS-1可以结合、传导细微的钙离子浓度变化;而当钙离子浓度超过微摩尔/升数量级时(即生理浓度),其结合能力即达到饱和,这也是它与钙调素(可感受较高浓度的钙离子变化)等其它钙结合蛋白的重要区别之一。它在脑缺血损伤中的作用,还未引起足够的重视,但是根据现有报道,NCS-1极有可能是脑缺血损伤的一个重要的保护因子。另外有研究显示,NCS-1还与神经细胞金属沉积性疾病的发生有关,可以明显增加神经元突触的传导功能。

神经元钙传感蛋白研究前沿与热点分析

背景:神经元钙传感蛋白的研究前沿和热点始终是这一领域的研究者共同关注的焦点。目的:从定量的层面探测神经元钙传感蛋白的前沿领域与研究热点。方法:以ISI的Web of Science数据库中1982至2014年363篇神经元钙传感蛋白相关文献为分析对象,采用文献共被引分析方法和词频分析方法,运用CiteSpaceⅢ可视化软件绘制神经元钙传感蛋白文献共被引网络图谱和关键词共现图谱,结合突现节点文献二次检索的方法,梳理并揭示神经元钙传感蛋白的研究前沿与热点。结果与结论:神经元钙传感蛋白的研究前沿与热点是蛋白质的生理功能,研究热点转变的时间点是1994至1996年,2000年,2008年和2012年。在不同时间阶段,其研究热点也表现出一定的差异性,1992至2000年研究热点是蛋白质的结构和性质,2004至2012年主要集中于研究蛋白质的功能和作用机制。而2008至2014年研究热点是在蛋白质生理功能的研究基础上,更侧重于这一蛋白质的高级功能(如记忆)和各种疾病(如精神分裂症、肿瘤、抑郁症、老年痴呆症、神经元损伤等)的研究。神经元钙传感蛋白前沿领域与研究热点的确定,为今后的研究提供一定的参考。

神经元钙传感蛋白R102Q突变体的动力学构象特征

背景:神经元钙传感蛋白参与多种生理功能,在大脑皮质不同脑区都有很高的分布。在自闭症患者基因测序中识别出神经元钙传感蛋白第102个氨基酸精氨酸ARG102突变成谷氨酰胺Glu102(R102Q)。实验研究显示,R102Q突变对神经元钙传感蛋白局部区域影响很大,发生本质性的构象改变。目的:确定神经元钙传感蛋白单一氨基酸R102Q突变引起结构构象动力学变化的具体原因。方法:采用计算机分子动力学模拟的方法,进行6个独立的、模拟时间是450 ns的全原子动力学模拟。结果与结论:1神经元钙传感蛋白R102Q突变对蛋白整体结构影响不大,在整个模拟过程中都没有进行大的构象重组,但导致螺旋改变,结构更加稳定。2R102Q突变导致盐桥网络发生改变,一方面降低了L2的柔性,使其更加稳定;另一方面改变L3在疏水口袋中的位置,使其在疏水口袋中更加舒展。结果表明,螺旋在蛋白结构稳定中起到一定的作用,盐桥改变也是蛋白动力学变化的重要原因。这项研究可能从分子的层面和结构的视角,为与R102Q突变有关的蛋白质功能缺失提供理论参考。

分子动力学模拟:神经元钙传感蛋白生理机制研究的新方向

背景:神经元钙传感蛋白的生理功能及其发挥、结构折叠与解折叠的研究多采用实验方法进行研究,并提出蛋白可能的作用机制模型及维持结构稳定的可能因素,但实验手段受到时间和空间分辨的局限以及蛋白结构的复杂性,研究受到一定的限制,导致实验中提出的很多理论模型无法得以检验。分子动力学能够从原子水平上观察并解释实验现象,对理论假设和(或)模型进行验证,为实验提供参考和启示;也可以预测新的结构和现象,为建立理论模型和作用机制提供依据。目的:分别对采用实验方法和分子动力学模拟的方法对神经元钙传感蛋白生理功能及其机制研究的进展进行梳理,并对今后的研究做一展望。方法:以"Neuronal Calcium Sensor-1 or Neuronal Calcium Sensor1 or Neuronal Calcium Sensor 1 or NCS-1"为主题词,检索Pub Med数据库中有关神经元钙传感蛋白研究的相关文献,下载全文进行阅读,排除与蛋白生理机制无关的文章,最终对72篇文献进行归纳总结。结果与结论:(1)实验中主要对神经元钙传感蛋白在不同条件、不同位置中调控分泌、调控多巴胺D2受体、在肝细胞内调控腺苷A2A受体以及调控不同刺激下心肌细胞质和细胞核Ca^(2+)等方面提出相关理论模型;(2)分子动力学模拟从结构的视角,对维持蛋白结构稳定的关键因素进行了分析和总结;(3)建议将两种方法结合起来,不断加深对蛋白生理机制的理解,共同推动研究的深入发展。

NaCl浓度改变对人类神经元钙传感蛋白结构的影响:来自分子动力学模拟证据

背景:人类神经元钙传感蛋白是一个含有高电荷的蛋白,对溶液温度极其敏感,但溶液中离子浓度的变化是否能够改变蛋白结构,进而影响其正常功能的发挥,尚未发现相关研究。目的:探讨溶液中NaCl浓度的微小改变对人类神经元钙传感蛋白结构动力学影响的分子机制。方法:采用全原子计算机动力学模拟的方法,将蛋白质数据库中ID为2LCP的人类神经元钙传感蛋白前两个能量最低的结构作为初始结构,能量最小化后,将蛋白置于NaCl浓度分别为0.1,0.3和1 mol/L的水溶液中,以高速摄像速度(ps,10-12s)记录蛋白在模拟体系中的运行和变化情况。结果与结论:溶液中NaCl浓度升高,(1)人类神经元钙传感蛋白柔性增加,整体结构和局部的疏水口袋体积增大;(2)蛋白C端尾部L3蜷缩,首尾氨基酸D176和V190之间的次联系路径数量明显减少,导致两者之间的联系减弱;(3)蛋白N段和C段氨基酸之间的联系削弱,但两段段内氨基酸联系明显增强;(4)蛋白正负电荷间形成的盐桥数量和百分比均降低,尤其在0.3 mol/L NaCl水溶液中的减少更为明显。(5)结果说明,人类神经元钙传感蛋白对溶液NaCl浓度的改变非常敏感,NaCl浓度的微小变化能够引起蛋白关键结构的改变,从原子-分子的视角,为人类神经元钙传感蛋白在不同溶液环境中的结构变化提供理论参考。

多模态神经调控与检测技术研究进展

脑科学被誉为人类自然科学“最后的前沿”,如何精准理解与控制包括大脑在内的生物神经系统,已成为多学科交叉的前沿和热点问题。本文基于“多模态神经调控与检测技术专题研讨会”的研讨成果,从物理化学信号与神经信号相互作用机制、神经接口材料器件系统、神经成像方法、神经信号编码解码技术、临床医学应用等方面,总结回顾了在神经调控与检测技术相关研究领域的科学研究与技术进展,以及未来多学科协同发展面临的机遇与挑战,并探讨了重点领域和发展方向。

针型化学传感器研制及其在体检测的应用及前景展望

针型化学传感器是以针状物(针刺针、不锈钢针头、钨丝、针型塑料和铂丝等)为基体的一类检测仪器,能够实现生命体内某些特定化学物质的微创、在体和实时监测。本文结合团队国家中医药管理局科研三级实验室——医用化学传感器实验室多年的工作,参考国内针型化学传感器研究,对针型化学传感器检测钙离子、氢离子、氧分压、多巴胺、5-羟色胺、乙酰胆碱、去甲肾上腺素和腺苷等化学量的基本原理和研究、应用进展特别是在中医研究中的应用进行介绍,以期推动针型化学传感器的研究,为中医针灸研究提供技术支撑。

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Computational analysis of genetic loci required for amphid structure and functions and their possibly corresponding microRNAs in C. elegans

Objective To examine the important roles of microRNAs （miRNAs） in regulating amphid structure and function, we performed a computational analysis for the genetic loci required for the sensory perception and their possibly corresponding miRNAs in C. elegans. Methods Total 55 genetic loci required for the amphid structure and function were selected. Sequence alignment was combined with E value evaluation to investigate and identify the possible corresponding miRNAs. Results Total 30 genes among the 55 genetic loci selected have their possible corresponding regulatory miRNA（s）, and identified genes participate in the regulation of almost all aspects of amphid structure and function. In addition, our data suggest that both the amphid structure and the amphid functions might be regulated by a series of network signaling pathways. Moreover, the distribution of miRNAs along the 3＇ untranslated region （UTR） of these 30 genes exhibits different patterns. Conclusion We present the possible miRNA-mediated signaling pathways involved in the regulation of chemosensation and thermosensation by controlling the corresponding sensory neuron and interneuron functions. Our work will be useful for better understanding of the miRNA-mediated control of the chemotaxis and thermotaxis in C. elegans.

视锥蛋白样蛋白:一种新兴的脑损伤生物标志物

视锥蛋白样蛋白(VILIPs)是神经钙传感蛋白的一个亚家族,研究表明该蛋白家族具有较高的神经系统特异性,对缺血性卒中、阿尔茨海默病等中枢神经系统疾病的诊断有较高的特异度和敏感度,有望成为重要的脑损伤标志物。本文结合国内外最新研究,就VILIPs在不同中枢神经系统疾病所致脑损伤中的作用机制及临床意义予以阐述。

Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective...

A novel fully-integrated miniature six-axis force/torque sensor

This paper presents a new designed miniature six DOF （degree of freedom） force/torque sensor. This sensor is fully integrated with a micro DSP （digital signal processor）, so all the signal conditioning, A/D, decoupling, digital-signals serial output are performed in the sensor. Some experimental results are presented to demonstrate the capability of the proposed design. Finally, a neural network was used for decoupling the interacting signals, compared with the conventional method using the inverse matrix, this new method is more accurate.

Optical Fiber Type Slide Tactile Sensor Used for Underwater Robot

Because of the special underwater environment, many sensors used well in robots working in space or on the land can not be used in the underwater. So an optical fiber type slide tactile sensor is designed by the inner modulation mechanism of the intensity type optical fiber. The principle and structure of the sensor are introduced in detail. The static and dynamic characteristics are analyzed theoretically and experimentally. The dynamic characteristic model is built and the simulation is made by using genetic algorithm based on neural network. In order to use the sensor perfectly, the recognition model of the sensor is built on the basis of the principle of “inverse solution” using neural networks. The control precision and sensitivity of the manipulator are improved.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

4-CBA Soft Sensor Based on Fuzzy CMAC Neural Networks

Soft sensor is attractive in dealing with online product quality measurement by virtue of other easily measured variables. In AMOCO PTA (purified terephthalic acid) production process, the unavailability of real-time measurement of 4-CBA makes it impossible for timely adjustment and thereby influences the product quality and the plant economy benefit. In this paper, a kind of FCMAC (fuzzy cerebellar model articulation controller) method is presented to solve the online measurement problem. Different from the conventional CMAC (cerebellar model articulation controller) networks, which has inferior smoothing ability because of its table look-up based technology. Integrating fuzzy model into CMAC networks, it becomes more accurate in functional mapping without weakening its generalization ability. Numerical example and industrial application results show the method proposed here is satisfactory and feasible.

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.

Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.

A novel speed sensor-less direct torque control system for mining locomotive haulage

A novel speed sensor-less direct torque control induction motor drive system for the mining locomotive haulage is presented in the paper. Rotor speed identification is based on the model reference adaptive control theory with neural network using back propagation algorithm. The system is implemented using a real-time TMS320F240 digital signal processor. The simulation study and experiment results indicate that the suggested system has good performance.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.