Single-Round DNA Aptamer Selection by Combined Use of Capillary Electrophoresis and Next Generation Sequencing: An Aptaomics Approach for Identifying Unique Functional Protein-Binding DNA Aptamers

In DNA aptamer selection, existing methods do not discriminate aptamer sequences based on their binding affinity and function and the reproducibility of the selection is often poor, even for the selection of well-known aptamers like those that bind the commonly used model protein thrombin. In the present study, a novel single-round selection method (SR-CE selection) was developed by combining capillary electrophoresis (CE) with next generation sequencing. Using SR-CE selection, a successful semi-quantitative and semi-comprehensive aptamer selection for thrombin was demonstrated with high reproducibility for the first time. Selection rules based on dissociation equilibria and kinetics were devised to obtain families of analogous sequences. Selected sequences of the same family were shown to bind thrombin with high affinity. Furthermore, data acquired from SR-CE selection was mined by creating sub-libraries that were categorized by the functionality of the aptamers (e. g., pre-organized aptamers versus structure-induced aptamers). Using this approach, a novel fluorescent molecular recognition sensor for thrombin with nanomolar detection limits was discovered. Thus, in this proof-of-concept report, we have demonstrated the potential of a “DNA Aptaomics” approach to systematically design functional aptamers as well as to obtain high affinity aptamers.     

决策树算法的教育应用探讨

简单介绍了数据挖掘的概念和决策树算法技术,尤其是它所牵涉的信息论原理,并结合相关事例简要概述了决策树算法在教育层面上的应用．     

证据理论合成规则的改进

D-S证据理论是一种有用的不确定性推理方法,由于证据合成公式存在不足,影响了证据理论的应用．该文提出了一种证据理论合成的改进方法,该方法不仅能处理相互冲突的证据,把支持证据冲突的概率按各个命题的平均支持程度加权进行分配,还能根据证据携带的信息量决定聚焦的权重,使合成结果符合实际情况．     

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.     

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.     

试论网络信息技术在图书馆管理中的应用

阐述了网络信息技术管理的概念及实施网络信息技术管理的必要性,探讨了网络信息技术管理的基本内容,提出了网络信息技术管理实现的机制。     

知识服务——新世纪图书馆工作的核心

分析了知识服务产生的原因和意义,介绍了知识服务的概念和特征,探讨了新世纪图书馆与知识服务的关系。     

麦肯锡公司知识管理研究综述

使用文献统计法,对有关麦肯锡公司（Mikinsey）知识管理研究文献的数量、年代、主题分布情况进行了统计分析,并通过对文献主题内容的分析,对现有的研究成果进行了综述。     

我国高校图书馆知识管理创新的探讨

阐述了图书馆知识管理的内涵和高校图书馆知识管理目标,从借鉴企业管理方法、高校图书馆管理制度、高校图书馆人力资源管理等方面,探讨了我国高校图书馆如何进行知识管理创新。     

高校图书馆知识地图的构建

分析了高校图书馆知识资源的分类,阐述了知识地图的相关概念,探讨了高校图书馆知识地图的构建方式。