Discovery of GPR183 Agonists Based on an Antagonist Scaffold

The G protein-coupled receptor GPR183/EBI2, which is activated by oxysterols, is a therapeutic target for inflammatory and metabolic diseases where both antagonists and agonists are of potential interest. Using the piperazine diamide core of the known GPR183 antagonist (E)-3-(4-bromophenyl)-1-(4-(4-methoxybenzoyl)piperazin-1-yl)prop-2-en-1-one (NIBR189) as starting point, we identified and sourced 79 structurally related compounds that were commercially available. In vitro screening of this compound collection using a Ca²⁺ mobilization assay resulted in the identification of 10 compounds with agonist properties. To enable establishment of initial structure-activity relationship trends, these were supplemented with five in-house compounds, two of which were also shown to be GPR183 agonists. Taken together, our findings suggest that the agonist activity of this compound series is dictated by the substitution pattern of one of the two distal phenyl rings, which functions as a molecular efficacy-switch.     

Role of Post-translational Modifications in Alzheimer's Disease

The global burden of Alzheimer's disease (AD) is growing. Valiant efforts to develop clinical candidates for treatment have continuously met with failure. Currently available palliative treatments are temporary and there is a constant need to search for reliable disease pathways, biomarkers and drug targets for developing diagnostic and therapeutic tools to address the unmet medical needs of AD. Challenges in drug-discovery efforts raise further questions about the strategies of current conventional diagnosis; drug design; and understanding of disease pathways, biomarkers and targets. In this context, post-translational modifications (PTMs) regulate protein trafficking, function and degradation, and their in-depth study plays a significant role in the identification of novel biomarkers and drug targets. Aberrant PTMs of disease-relevant proteins could trigger pathological pathways, leading to disease progression. Advancements in proteomics enable the generation of patterns or signatures of such modifications, and thus, provide a versatile platform to develop biomarkers based on PTMs. In addition, understanding and targeting the aberrant PTMs of various proteins provide viable avenues for addressing AD drug-discovery challenges. This review highlights numerous PTMs of proteins relevant to AD and provides an overview of their adverse effects on the protein structure, function and aggregation propensity that contribute to the disease pathology. A critical discussion offers suggestions of methods to develop PTM signatures and interfere with aberrant PTMs to develop viable diagnostic and therapeutic interventions in AD.     

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。     

大规模数据库中的知识获取

一、前言数据是知识的潭泉,拥有大量的数据与拥有许多有用的知识完全是两回事.为了有效地利用大量的公共数据,必须更好地理解这些数据,并从其中快速、准确地发现知识.这里所说的知识是指大量数据中存在的规律性(r egularity)或不同属性值之间所存在的[I F THEN〕规则.将所获取的知识附加于仅由事实数据(fact data)构成的传统数据库上,既可强化数据库的查询能力,又可给数据库提供推理能力,     

Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

The Design of Discrimination Experiments

Experimentation plays a fundamental role in scientific discovery. Scientists experiment to gather data, investigate phenomena, measure quantities, and test theories. In this article, we address the problem of designing experiments to discriminate between two completing theories. Given an initial situation for which the two theories make the same prediction, the experiment design problem is to determine how to modify the situation such that the two theories make different predictions for the modified situation. The modified situation is called a discrimination experiment. We present a knowledge-intensive method called DEED for designing discrimination experiments. The method analyzes the differences in the two theories' explanations of the prediction for the initial situation. Based on this analysis, it determines modifications to the initial situation that will result in a discrimination experiment. We illustrate the method with the design of experiments to discriminate between several pairs of qualitative theories in the fluids domain.     

A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.     

中国大油田的分布特征与发现前景

大油田在石油储量增长和产量构成中占有重要位置.截至2004年底,中国大油田的可采储量占总探明储量的77%,2004年大油田产量占全国石油产量的70%.中国大油田的储集层主要是白垩系和第三系的陆相碎屑岩,圈闭以背斜圈闭和混合圈闭为主,20世纪70年代是中国大油田发现的高峰期.中国的大油田主要分布在富油气凹陷中;凹陷的资源丰度越大,发现大油田所需的时间越短;凹陷的资源量越大,发现的大油田规模也越大.中国目前整体勘探程度比较低,未来在中西部地区和海域都可能有大油田发现.图6表1参10     

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。