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181.
182.
光遗传学是一门涉及神经科学、光学、半导体光电子学及生物医学的交叉科学.把光作为一种遗传学的研究工具,可为神经科学研究提供更高效、精准的神经调控手段,也为临床精神疾病的研究和治疗提供了新的思路.集成式注入型生物光电极是一种集刺激神经元的光源与采集生物电信号的微电极于一体的多功能生物微探针,在利用活体生物进行的光遗传学研究中有着重要的应用.文章回顾了光遗传学的历史,对集成式注入型生物光电极器件的分类和发展进行了分析,详细比较了不同类型光电极器件在结构和性能上的差异,从电学特性、噪声信号、生物兼容性及可靠性等方面进行评价.最后,对光电极器件的未来发展进行了初步的探讨. 相似文献
183.
Dr. Lan Hu Prof. Dr. Yan Zhao 《Chemistry (Weinheim an der Bergstrasse, Germany)》2019,25(32):7702-7710
Outcomes of chemical reactions are generally dominated by the intrinsic reactivities of reaction partners, but enzymes frequently override such constraints to transform less reactive molecules in the presence of more reactive ones. Despite the attractiveness of such catalysis, it is difficult to build synthetic catalysts with these features. Micellar imprinting is a powerful method to create template-complementary binding sites inside protein-sized water-soluble nanoparticles. When a photocleavable functional monomer was used to bind two phosphonate/phosphate templates as transition-state analogues, active sites with predetermined size and shape were formed inside doubly cross-linked micelles through molecular imprinting. Postmodification replaced the binding group with a catalytic pyridyl group, forming highly selective artificial esterases. The catalysts displayed enzyme-like kinetics and turnover numbers that were in the hundreds. The selectivity of the catalysts, derived from the substrate-complementary imprinted active sites, enabled transformation of less reactive esters in the presence of more reactive ones. 相似文献
184.
LI Lianming NIU Xiaokang CHEN Linhui CHAI Yuan ZHANG Tao SHI Jun WANG Aili LUO Ying HE Long CHENG Depeng LIU Nan CUI Tiejun YOU Xiaohu 《中国通信》2014,(6)
With more than 40 years Moore scaling, the speed of CMOS transistors is around 100 GHz. Such fact makes it possible to realize mm-wave circuits in CMOS. However, with the target of achieving broadband and power-efficient operation, 60 GHz CMOS RF transceiver faces severe challenges. After reviewing the technology issues, regarding the 60 GHz applications, this paper discusses design challenges both from the system and the building block levels, and also presents some simulated or measured circuits results. 相似文献
185.
Bentolhoda Hadavi Moghadam Akbar Khodaparast Haghi Shohreh Kasaei 《Journal of Macromolecular Science: Physics》2015,54(11):1404-1425
Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats. 相似文献
186.
This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers
- Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
- Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
- In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
- Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.
187.
188.
Mengmeng Ma Ying Wang Nan Gao Xinping Liu Yuhuan Sun Prof. Jinsong Ren Prof. Dr. Xiaogang Qu 《Chemistry (Weinheim an der Bergstrasse, Germany)》2019,25(51):11852-11858
Proteolysis of amyloid-β (Aβ) is a promising approach against Alzheimer's disease. However, it is not feasible to employ natural hydrolases directly because of their cumbersome preparation and purification, poor stability, and hazardous immunogenicity. Therefore, artificial enzymes have been developed as potential alternatives to natural hydrolases. Since specific cleavage sites of Aβ are usually embedded inside the β-sheet structures that restrict access by artificial enzymes, this strongly hinders their efficiency for practical applications. Herein, we construct a NIR (near-IR) controllable artificial metalloprotease (MoS2-Co) using a molybdenum disulfide nanosheet (MoS2) and a cobalt complex of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (Codota). Evidenced by detailed experimental and theoretical studies, the NIR-enhanced MoS2-Co can circumvent the restriction by simultaneously inhibition of β-sheet formation and destroying β-sheet structures of the preformed Aβ aggregates in living cell. Furthermore, our designed MoS2-Co is an easy to graft Aβ-target agent that prevents misdirected or undesirable hydrolysis reactions, and has been demonstrated to cross the blood brain barrier. This method can be adapted for hydrolysis of other kinds of amyloids. 相似文献
189.
190.
A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (Tme), mold temperature (Tmo), packing time (tp), cooling time (tc), and fill pressure (pf), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was Tme. The rest of the process parameters, Tmo, tp, tc, and pf, were found to be relatively less influential. Warpage increased with elevating Tmo. In contrast, an increase in Pp and Tme caused the warpage to decrease. 相似文献