共享交通的时空轨迹检索与群体发现

为解决共享交通下的共乘用户群体发现效率低、准确率不高问题，依据R-树原理建立GeoOD-Tree索引，并在此基础上提出以最大化共乘率为目标的群体发现策略。首先，对原始时空轨迹数据进行特征提取与标定处理，挖掘有效出行起讫点（OD）轨迹；其次，针对用户起讫点轨迹的特征，建立GeoOD-Tree索引进行有效的存储管理；最后，给出以最大化共乘行程为目标的群体发现模型，并运用K最近邻（KNN）查询对搜索空间剪枝压缩，提高群体发现效率。采用西安市近12000辆出租车营运轨迹数据，选取动态时间规整（DTW）等典型算法与所提算法在查询效率与准确率上进行性能对比分析。与DTW算法相比，所提算法的准确率提高了10.12%，查询效率提高了约15倍。实验结果表明提出的群体发现策略能有效提高共乘用户群体发现的准确率和效率，可有效提升共乘出行方式的出行率。     

移动式数据挖掘平台模型

本文提出了一种移动式网络海量数据挖掘平台，给出了数据挖掘任务的形式化描述，详细讨论了平台的体系结构、工作原理与流程。该平台克服了传统网络计算模式的缺陷，支持对异质、分散数据源和知识源的挖掘和检索，具有开放性、可伸缩性、灵活性、可扩展性、健壮性。     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

Bivariate Scientific Function Finding in a Sampled,Real-Data Testbed

This article reports the results of a study of domain-independent function finding based on a collection of several hundred real scientific data sets. Prior studies have introduced the controversial idea of discovering functional relationships of interest to scientists directly from the data they collect. The evidence presented here supports the view that this is sometimes possible, but it also suggests how often purely data-driven discovery is not possible and how much more difficult it may be than has often been assumed. Experience with sampled examples of real scientific data suggests as well that emphasis on search in prior studies may have been misplaced. For the function-finding problems studied here, scientists typically propose only a handful of different functional relationships. The difficulty is not in searching through a large space of relationships but in evaluating a few common ones to determine if they are likely to be of scientific interest.     

Freud's narrow escape and the discovery of transference.

Contends that Freud discovered transference in connection with material derived from his treatment of Emma Eckstein. The last chapter of Studies on Hysteria by J. Breuer and S. Freud (1895) in which Freud's 1st published use of the term transference occurs, can be read as a working through of the crisis that occurred when Eckstein nearly died. This concept, it is argued, explained Freud's patient's disturbed feelings toward him as a "false connection" and thereby helped to free him of feelings of personal involvement in her libidinal demands. The story of the troubled circumstances under which Freud discovered transference provides insight into the defensive nature of the concept. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Biflavonoid-Induced Disruption of Hydrogen Bonds Leads to Amyloid-β Disaggregation

Deposition of amyloid β (Aβ) fibrils in the brain is a key pathologic hallmark of Alzheimer’s disease. A class of polyphenolic biflavonoids is known to have anti-amyloidogenic effects by inhibiting aggregation of Aβ and promoting disaggregation of Aβ fibrils. In the present study, we further sought to investigate the structural basis of the Aβ disaggregating activity of biflavonoids and their interactions at the atomic level. A thioflavin T (ThT) fluorescence assay revealed that amentoflavone-type biflavonoids promote disaggregation of Aβ fibrils with varying potency due to specific structural differences. The computational analysis herein provides the first atomistic details for the mechanism of Aβ disaggregation by biflavonoids. Molecular docking analysis showed that biflavonoids preferentially bind to the aromatic-rich, partially ordered N-termini of Aβ fibril via the π–π interactions. Moreover, docking scores correlate well with the ThT EC₅₀ values. Molecular dynamic simulations revealed that biflavonoids decrease the content of β-sheet in Aβ fibril in a structure-dependent manner. Hydrogen bond analysis further supported that the substitution of hydroxyl groups capable of hydrogen bond formation at two positions on the biflavonoid scaffold leads to significantly disaggregation of Aβ fibrils. Taken together, our data indicate that biflavonoids promote disaggregation of Aβ fibrils due to their ability to disrupt the fibril structure, suggesting biflavonoids as a lead class of compounds to develop a therapeutic agent for Alzheimer’s disease.     

Screening of Inhibitors Targeting Heat Shock Protein 47 Involved in the Development of Idiopathic Pulmonary Fibrosis

Heat shock protein 47 (HSP47), a collagen-specific molecular chaperone, is causally related to fibrotic diseases, including idiopathic pulmonary fibrosis. The identification of Compounds that interfere with the HSP47-collagen interaction is essential for the development of relevant therapeutics. Herein, we prepared human HSP47 as a soluble fusion protein expressed in E. coli and established an assay system for HSP47 inhibitor screening. We screened a natural and synthetic Compound library established at Nagasaki University. Among 1023 Compounds, 13 exhibited inhibitory activity against human HSP47, of which three inhibited its function in a dose-dependent manner. Epigallocatechin-3-O-gallate, one of these three Compounds, is a typical polyphenol Compound derived from tea leaves. Structurally related Compounds were synthesized and examined for their activity, revealing a hydroxyl group at A-ring position 5 as important for its activity. The present findings provide valuable insight for the development of natural product-derived therapeutics for fibrotic diseases, including idiopathic pulmonary fibrosis.     

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.