Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural NetworkSCIEI北大核心CSCD

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.     

Uncertain multiobjective traveling salesman problem

Traveling salesman problem is a fundamental combinatorial optimization model studied in the operations research community for nearly half a century, yet there is surprisingly little literature that addresses uncertainty and multiple objectives in it. A novel TSP variation, called uncertain multiobjective TSP (UMTSP) with uncertain variables on the arc, is proposed in this paper on the basis of uncertainty theory, and a new solution approach named uncertain approach is applied to obtain Pareto efficient route in UMTSP. Considering the uncertain and combinatorial nature of UMTSP, a new ABC algorithm inserted with reverse operator, crossover operator and mutation operator is designed to this problem, which outperforms other algorithms through the performance comparison on three benchmark TSPs. Finally, a new benchmark UMTSP case study is presented to illustrate the construction and solution of UMTSP, which shows that the optimal route in deterministic TSP can be a poor route in UMTSP.     

Numerical and empirical modeling of peak deceleration and stress analysis of polyurethane elastomer under impact loading test

In this work, we present the modeling of the peak deceleration (PD) using data of the experimental drop test. Specimens with different thicknesses and areas tested in the drop test device which has adaptable height and weight. In the empirical modeling of the PD, the thickness, area, drop mass and drop height considered as separable functions. An analytical model and Neural Network (NN) was used as the empirical models. Further, the stress on the material was calculated using differential equations and the Finite Element Method (FEM). The Obtained PD from the experimental test, analytical and NN models was converted to the stress on the material using a derived differential equation. Finally, the best model for analyzing the PD and Stress on the material was presented.     

The physicochemical properties role of a functionalized alkyl-peptide in nanofibre formation and neural progenitor cells viability and survival

Amphiphilic alkyl-peptides as novel biomaterials form 3D scaffolds that are applicable in tissue engineering. Here, the nanofibre formation capability of a distinct alkyl-peptide was investigated using coarse-grained molecular dynamics simulation (CGMD) and experimental methods. The alkyl-peptide (Ace-FAQRVPPEEEGGGAAAAK-Nhe(C16)) was functionalized with a peptide epitope (FAQRVPPP) which can help to maintenance and differentiation of neural stem cells. Two alkyl-peptide systems were investigated: the all-functionalized system (with only bioactive alkyl-peptides) and the distributed system (a combination of bioactive and non-bioactive alkyl-peptides with ratio 1:2). The CGMD and TEM results confirm elongated nanofibres for all-functionalized system and cylindrical nanofibres for the distributed one. Furthermore, PC12 cells show a reliable growth on both 2D alkyl-peptides coated surfaces. Because of the nanofibres negative surface charges, the cell morphologies show clustered form in the distributed system and rounded shape in the all-functionalized one. Since the stem cell state preserves in cluster form, the physicochemical property of these nanofibres allows a potential advantage in stem cell long-time maintains.     

基于上转换纳米粒子的低损伤神经界面技术

Optogenetics is a neuromodulation technology that combines light control technology with genetic technology, thus allowing the selective activation and inhibition of the electrical activity in specific types of neurons with millisecond time resolution. Over the past several years, optogenetics has become a powerful tool for understanding the organization and functions of neural circuits, and it holds great promise to treat neurological disorders. To date, the excitation wavelengths of commonly employed opsins in optogenetics are located in the visible spectrum. This poses a serious limitation for neural activity regulation because the intense absorption and scattering of visible light by tissues lead to the loss of excitation light energy and also cause tissue heating. To regulate the activity of neurons in deep brain regions, it is necessary to implant optical fibers or optoelectronic devices into target brain areas, which however can induce severe tissue damage. Non- or minimally-invasive remote control technologies that can manipulate neural activity have been highly desirable in neuroscience research. Upconversion nanoparticles (UCNPs) can emit light with a short wavelength and high frequency upon excitation by light with a long wavelength and low frequency. Therefore, UCNPs can convert low-frequency near-infrared (NIR) light into high-frequency visible light for the activation of light-sensitive proteins, thus indirectly realizing the NIR optogenetic system. Because NIR light has a large tissue penetration depth, UCNP-mediated optogenetics has attracted significant interest for deep-tissue neuromodulation. However, in UCNP-mediated in vivo optogenetic experiments, as the up-conversion efficiency of UCNPs is low, it is generally necessary to apply high-power NIR light to obtain up-converted fluorescence with energy high enough to activate a photosensitive protein. High-power NIR light can cause thermal damage to tissues, which seriously restricts the applications of UCNPs in optogenetic technology. Therefore, the exploration of strategies to increase the up-conversion efficiency, fluorescence intensity, and biocompatibility of UCNPs is of great significance to their wide applications in optogenetic systems. This review summarizes recent developments and challenges in UCNP-mediated optogenetics for deep-brain neuromodulation. We firstly discuss the correspondence between the parameters of UCNPs and employed opsins in optogenetic experiments, which mainly include excitation wavelengths, emission wavelengths, and luminescent lifetimes. Thereafter, we introduce the methods to enhance the conversion efficiency of UCNPs, including optimizing the structure of UCNPs and modifying the organic dyes in UCNPs. In addition, we also discuss the future opportunities in combining UCNP-mediated optogenetics with flexible microelectrode technology for the long-term detection and regulation of neural activity in the case of minimal injury.     

面向脑机接口的犹他神经电极技术

A human brain is composed of a large number of interconnected neurons forming a neural network. To study the functional mechanism of the neural network, it is necessary to record the activity of individual neurons over a large area simultaneously. Brain-computer interface (BCI) refers to the connection established between the human/animal brain and computers/other electronic devices, which enables direct interaction between the brain and external devices. It plays an important role in understanding, protecting, and simulating the brain, especially in helping patients with neurological disorders to restore their impaired motor and sensory functions. Neural electrodes are electrophysiological devices that form the core of BCI, which convert neuronal electrical signals (carried by ions) into general electrical signals (carried by electrons). They can record or interfere with the state of neural activity. The Utah Electrode Array (UEA) designed by the University of Utah is a mainstream neural electrode fabricated by bulk micromachining. Its unique three-dimensional needle-like structure enables each electrode to obtain high spatiotemporal resolution and good insulation between each other. After implantation, the tip of each electrode affects only a small group of neurons around it even allowing to record the action potential of a single neuron. The availability of a large number of electrodes, high quality of signals, and long service life has made UEA the first choice for collecting neuronal signals. Moreover, UEA is the only implantable neural electrode that can record signals in the human cerebral cortex. This article mainly serves as an introduction to the construction, manufacturing process, and functioning of UEA, with a focus on the research progress in fabricating high-density electrode arrays, wireless neural interfaces, and optrode arrays using silicon, glass, and metal as that material of construction. We also discuss the surface modification techniques that can be used to reduce the electrode impedance, minimize the rejection by brain tissue, and improve the corrosion resistance of the electrode. In addition, we summarize the clinical applications where patients can control external devices and get sensory feedback by implanting UEA. Furthermore, we discuss the challenges faced by existing electrodes such as the difficulty in increasing electrode density, poor response of integrated wireless neural interface, and the problems of biocompatibility. To achieve stability and durability of the electrode, advancements in both material science and manufacturing technology are required. We hope that this review can broaden the scope of ideas for the development of UEA. The realization of a fully implantable neural microsystem can contribute to an improved understanding of the functional mechanisms of the neural network and treatment of neurological diseases.     

Evolution of Artificial Base Pairs with Hydrogen Bond Complementarity

Artificial base pairs,from the perspective of synthetic biology,are designed to contain the features of modularity,orthogonality,and manipulability.And the development of artificial base pairs has beat endowed with responsibility to understand the biological process,improve the recognition capacity and stability of aptamers,and develop the nucleoside drugs,diagnosis,and drag delivery.In this review,we first gave a concise introduction of artificial base pairs based on their interaction modes including alternative hydrogen bonding,hydrophobic interaction,and metal coordination.Then we displayed the detailed information of artificial base pairs with hydrogen bonding interaction,and analyzed how the changes of their structures affect their functions.Subsequently,we highlighted the applications of functional artificial base pairs in aptamer discovery,diagnosis,and drug delivery.Finally,an insight into the remaining challenges and future perspective of the artificial bases was provided.     

白木通人工繁育技术研究

为探讨白木通人工繁育技术,以出苗率和成活率为指标,研究了播种季节、播种方式对有性繁殖的影响,以及扦插种条前处理、扦插用种条的采集时间、扦插用种条的质量、扦插时间4因素对扦插效果的影响。结果表明,播种季节以农历冬至前后播种效果最好;播种方式为种子直播到塑料大棚的营养钵中,并加盖1~2 cm厚的营养土;扦插繁殖中,激素处理对扦插效果影响较大,其中ABT(2号)处理成活率最高;冬季采集种条置湿沙贮藏成活率明显高于其它采集时间所采集的种条;扦插种条的质量直接影响扦插成活率,其中以直接5~8 mm的种条成活率最高;扦插时间对扦插种条的成活起关键作用,其中以3月份扦插效果最好。通过本研究,初步拟定了白木通有性繁殖与无性繁殖技术。     

一种改进的Jacobi正交多项式的BP神经网络算法

 根据多项式理论,构造一种以Jacobi正交多项式作为隐层神经元激励函数的BP(back-propagation)神经网络模型.针对该网络,提出一种改进算法即隐层神经元数可快速确定的权值直接确定算法.首先介绍正交基函数和Jacobi多项式的定义,以及BP神经网络的基本原理.然后进行网络隐层数设计及其隐神经元数的确定,且设置各层连接权值、给出改进算法的步骤.最后,将其与传统矩阵迭代法和Levenberg-Marquardt训练算法进行比较.计算机实验结果表明,该算法具有比传统的BP迭代法更快的计算速度,并且能够达到更高的工作精度.     

补偿模糊神经网络在故障树分析中的应用研究

将补偿模糊神经网络用于故障树分析中的研究,建立了故障树顶事件失效模糊概率计算的补偿模糊神经网络模型,给出了应用实例.结果表明,将补偿模糊神经网络用于故障树分析是可行的,其计算更为方便,结果更为精确.