基于MCLP的知识挖掘模型在项目管理中的应用

针对项目管理中不同知识的分类和表达模式,提出了一种基于多目标线性规划的知识挖掘模型.该模型在对文本信息预处理的基础上,采用多目标线型规划方法,实现了对可量化信息和不可量化信息的同时挖掘,从而挖掘出更准确的项目知识模式,实现了项目管理中的知识的再利用,并就应用过程中遇到的具体问题进行了探讨.     

MassBank: a public repository for sharing mass spectral data for life sciences

MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron‐ionization mass spectrometry(EI‐MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)‐MSⁿ data of 2337 authentic compounds of metabolites, 11 545 EI‐MS and 834 other‐MS data of 10 286 volatile natural and synthetic compounds, and 3045 ESI‐MS² data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI‐MS² data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass‐to‐charge ratio are optimized to the ESI‐MS² data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI‐MS² data on an identical compound under different collision‐induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21–23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. Copyright © 2010 John Wiley & Sons, Ltd.     

Computing motif correlations in proteins

Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.     

气相色谱保留指数通用数据库的研究（二）PEG 20M柱间保留指数的相互关系

针对极性柱间保留指数的可比性差这一特点，选择了以常用的ＰＥＧ２０Ｍ为代表，根据气－液色谱保留的一般模型，并考虑柱间选择性作用的微小差异及气－液相界面面积ＡＧＬ大小不等，建立了柱间保留的相互关系方程。对６１个不同结构类型的化合物在１００℃和７５℃柱温下６根ＰＥＧ２０Ｍ石英毛细管柱上的应用表明，结果准确，关联后计算得到的保留指数与实际测定值间的绝对平均偏差为１ｉ．ｕ．，从而为极性柱上保留指数通用数据库的建立提供了方法。     

气相色谱-负化学源-质谱法检测水中10种全氟羧酸化合物

建立了气相色谱-负化学源-质谱（GC-NCI-MS）检测水中10种全氟羧酸化合物的分析方法。使用硅烷衍生化试剂N-甲基-N-三甲基硅基三氟乙酰胺（MSTFA）对全氟羧酸化合物进行衍生化，水样经弱阴离子交换固相萃取柱净化富集后进样。实验优化了样品前处理、衍生化和仪器条件。结果表明，10种全氟羧酸化合物在0.1~10 mg/L范围内线性关系良好，相关系数为0.9956~0.9993；方法的检测限（LOD）和定量限（LOQ）分别为0.5~1.5 μg/L和1.5~4.5 μg/L。在空白水样中进行了3个添加水平的加标回收试验，10种全氟羧酸化合物的平均回收率为70.2%~112.6%，相对标准偏差（RSD）为2.1%~14.5%（n=6）。该法原理简单，灵敏度高，准确、精密，可实现水体中10种全氟羧酸化合物同时检测的要求。     

Application of chiral bidentate NMR solvents for assignment of the absolute configuration of alcohols: scope and limitation

The ¹³C NMR behaviors of several cyclic and biaryl secondary alcohols as well as acyclic tertiary alcohols have been studied in the chiral bidentate NMR solvent BMBA-p-Me (1). An empirical rule has been advanced to correlate the absolute configuration of each type of alcohols with the ¹³C chemical shift behaviors in (R,R)- and (S,S)-BMBA-p-Me.     

Hydrogen and Lithium Bonds—Lewis Acid Units Possessing Multi-Center Covalent Bonds

MP2/aug-cc-pVTZ calculations were carried out on complexes wherein the proton or the lithium cation is located between π-electron systems, or between π-electron and σ-electron units. The acetylene or its fluorine and lithium derivatives act as the Lewis base π-electron species similarly to molecular hydrogen, which acts as the electron donor via its σ-electrons. These complexes may be classified as linked by π-H∙∙∙π/σ hydrogen bonds and π-Li∙∙∙π/σ lithium bonds. The properties of these interactions are discussed, and particularly the Lewis acid units are analyzed, because multi-center π-H or π-Li covalent bonds may occur in these systems. Various theoretical approaches were applied here to analyze the above-mentioned interactions—the Quantum Theory of Atoms in Molecules (QTAIM), the Symmetry-Adapted Perturbation Theory (SAPT) and the Non-Covalent Interaction (NCI) method.     

A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

Measuring the predictability and complexity of time series using entropy is essential tool designing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet neural network model. The LogNNet reservoir matrix is filled with time series elements according to our algorithm. The accuracy of the classification of images from the MNIST-10 database is considered as the entropy measure and denoted by NNetEn. The novelty of entropy calculation is that the time series is involved in mixing the input information in the reservoir. Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values. We introduce a new time series characteristic called time series learning inertia that determines the learning rate of the neural network. The robustness and efficiency of the method is verified on chaotic, periodic, random, binary, and constant time series. The comparison of NNetEn with other methods of entropy estimation demonstrates that our method is more robust and accurate and can be widely used in practice.     

面向西南印度洋多源科考数据的可扩展管理系统

为了使科考数据的存储和管理适应未来科学考察动态化、多样化发展的需要,在全面分析多源科考数据特点的基础上,结合成熟的数据库管理系统SQL Server以及空间数据库引擎ArcSDE,对多源科考数据的存储组织结构和可视化管理进行了分析和研究,提出了一套多源科考数据管理方案.设计并实现了西南印度洋多金属硫化物科学考察数据管理系统,该系统主要由多源科考数据的统一存储管理和可视化空间操作等功能模块组成,具有良好的适应性和扩展性,可满足实际应用需求.