代谢组学数据分析方法及在糖尿病研究中的应用

对NMR波谱数据的统计分析是基于NMR代谢组学研究的关键问题之一. 鉴于NMR波谱信号可以近似为样品中各种成分谱信号的线性叠加,本文将非负矩阵分解(NMF)方法引入基于NMR代谢组学的数据处理中,并与代谢组学中常用的统计方法--主成分分析(PCA)进行比较. 通过NMF和PCA两种方法对健康志愿者与2型糖尿患者血液和尿液的NMR谱图的统计分析,对所获取的特征代谢物进行比较和验证,并探讨了PCA方法可能存在的不足之处及其原因;阐明了NMF方法是基于NMR的代谢组学研究中较理想的数据分析方法. 最后,讨论了基于NMR代谢组学在糖尿病研究中的前景.     

两种聚醚改性有机硅表面活性剂的NMR数据分析

运用液体核磁共振(NMR)技术(包括~1H NMR、DEPT135、~(13)C qNMR、~1H-~1H COSY、~1H-~(13)C HSQC、~1H-~(13)C HMBC、~(29)Si qNMR和~1H-~(29)Si HMBC)对两种有机硅表面活性剂进行了化学结构表征,对所得波谱数据进行了较为全面的指认.结果表明一维~1H NMR、~(13)C NMR和~(29)Si NMR,与二维NMR方法结合使用可以准确区分这两种有机硅表面活性剂的结构:样品A和样品B均为聚乙二醇改性的聚二甲基硅氧烷有机硅表面活性剂;A具有线型ABA结构,平均分子量约为1 967;B具有梳型BAB结构,平均分子量约为7 191.     

基于最大散度差准则的阈值图像分割

针对目标和背景的面积相差很大时,最大类间方差阈值法(Otsu阈值法)得到的阈值是"有偏"的,从而造成阈值图像分割失败的问题,提出一种最大散度差准则的阈值图像分割方法。最大散度差准则以广义散度差——类间方差减去C倍的类内方差作为分离性度量,同时考虑类间方差和类内方差在可分性中的作用,可有效克服最大类间方差阈值法(Otsu阈值法)的阈值"偏移"现象。实验结果表明:通过选择适当的参数C,该方法能得到比最大类间方差法更好的分割结果。     

瑞芬太尼酸的波谱数据和结构确证

瑞芬太尼是一类新型、理想、手术中常用的一种麻醉镇痛药,具有起效迅速、半衰期短、易于控制、术后苏醒快等优点,进入人体后以肾脏代谢为主,经脱脂代谢后,形成羧酸代谢物,即瑞芬太尼酸,因此对瑞芬太尼及其代谢物瑞芬太尼酸的结构研究很有必要。目前文献只有对原型药物的结构研究,而其代谢物瑞芬太尼酸的结构研究未见报道。建立了紫外吸收光谱法、红外吸收光谱法、核磁共振波谱法(包含~1H NMR,~(13)C NMR,~1H-~1H COSY,~1H-~(13)C HSQC,~1H-~(13)C HMBC和DEPT)以及质谱方法对瑞芬太尼酸的结构信息进行了鉴别研究。其中紫外吸收光谱显示瑞芬太尼酸的芳香结构和共轭体系信息,在紫外末端附近与254.0 nm附近有最大吸收,分别与该物质E_2和B带相对应,说明分子中存在苯环特征结构。采用溴化钾压片法制备瑞芬太尼酸样品,进行红外光谱测试,红外吸收光谱显示各个官能团的特征吸收峰,均与瑞芬太尼酸结构中的主要官能团相一致。利用NMR波谱法(包含~1H NMR,~(13)C NMR,~1H-~1H COSY,~1H-~(13)C HSQC,~1H-~(13)C HMBC和DEPT)对瑞芬太尼酸所有的~1H NMR和~(13)C NMR信号进行了全面归属。通过正离子检测模式进行质谱分析,检测到质荷比(m/z)为362.98, 331.02, 303.10和259.09等碎片离子峰,此检测结果与瑞芬太尼酸的分子量相一致,且与瑞芬太尼酸的结构特征相对应。结合光谱法等多种谱学技术可有效解析瑞芬太尼酸的波谱学数据,并确证其结构。结果表明:多种谱学数据显示与瑞芬太尼酸的结构相一致,可用于瑞芬太尼酸的结构研究,并为其质量及纯度研究提供参考。     

新型拟肽cyclo[-RGD-ψ（triazole）-GD-]波谱学数据与结构确证

多肽RGD是一类含有精氨酸-甘氨酸-天冬氨酸(Arg-Gly-Asp)序列的短肽,该文对新型环状拟肽cyclo[-RGD-ψ(triazole)-GD-]的紫外吸收光谱(UV)、红外吸收光谱(FT-IR)进行了分析,并利用核磁共振(NMR)波谱(包括~1H NMR、~1H-~1H COSY、~(13C) NMR、DEPT、~1H-~(13)C HSQC和~1H-~(13)C HMBC)对其~1H和~(13)C NMR信号进行了全归属.     

基于最大隶属原则的核磁共振波谱模糊识别

介绍了基于最大隶属原则的核磁共振(NMR)波谱模糊识别原理与方法. 提出了关于核磁共振波谱模糊集合的概念,并给出了相应的隶属函数. 通过建立标准谱数据库和相应的模糊识别算法,实现了核磁共振波谱的快速自动定性分析. 对苯酚、邻苯二酚、间苯二酚及对苯二酚4种化合物及其11种混合物样品的¹H NMR谱进行了定性分析. 结果表明,在粗略找峰和隶属度阈值在0.45~0.85之间较大范围内取值的情况下,方法均给出了准确的识别结果.     

基于模拟退火算法的NMR波谱重叠峰解析

提出了基于模拟退火算法的NMR波谱重叠峰全局优化解析方法.对苯酚样品的核磁共振1H谱中6.9ppm和7.2ppm化学位移处的裂分峰组进行了定量解析处理.在分别对4次、8次和16次扫描测量的各自10套1H谱处理中,拟合曲线与实验曲线之间的相关系数大于0.9986,峰面积比值的最大标准差小于0.05.结果表明模拟退火算法适用于NMR波谱重叠峰的解析.     

杀菌剂环氟菌胺波谱学数据全归属

环氟菌胺是一种具有新型母核结构的优秀杀菌剂,该文采用~1H NMR、~(13)C NMR、~(19)F NMR、~1H-~(13)C HSQC、~1H-~(13)C HMBC和~1H-~(15)N HMBC等多种核磁共振(NMR)技术,并结合质谱(MS)、紫外光谱(UV)和红外光谱(IR)等方法对环氟菌胺进行了解析,同时对一维核磁共振(1D NMR)波谱数据进行全归属,讨论了UV和IR的吸收峰与该化合物各官能团的对应关系、~(19)F对其~1H和~(13)C NMR谱峰的影响,以及MS主要离子碎片的归属和化合物裂解规律.     

A型恒星光谱线指数岭回归有效温度的预测分析

天文光谱线指数数据能够较好地保留着恒星的物理特征信息,为此借助线指数特征数据构建多参数模型,有利于更好地回归分析数据的共变关系及谱线的内在规律。世界上光谱获取率最高的施密特天文望远镜LAMOST发布的观测光谱都已经过标记,利用天文可视化工具分析这些标记的恒星光谱线指数会产生预测因子自相关,多元线性回归时因变量存在共线性,导致方差较大、得到最小二乘回归系数不稳定,虽不影响使用回归的有效性,但较难从回归方程中得到独立预测因子的评估系数。利用LAMOST巡天光谱数据中A型恒星Lick线指数为数据源,选取有效温度T_eff为7 000～8 500 K,取信噪比大于50的光谱特征值实现回归分析恒星参数T_eff值,经箱线图呈现DR5星表中,A型光谱86 097条具备T_eff值大样本光谱数据的整体分布,统计分析26种线指数的特征值后,选取分布相似且带宽为12 Å的kp12,halpha12和hgamma12字段,减少解释线指数变量的数目,优化冗余变量方差膨胀因子（VIF）系数。实验选取两两变量间观测数据集,局部拟合回归散点、同样的数据源使用散点图的总体轮廓生成高密度散点图,利用色差透明性突出显示数据密集区域。结果表明多元线性回归和岭回归算法都能从低分辨率光谱中确定A型恒星的有效温度,但经过共线性数据分析有偏估计实验,使用岭回归分析寻找最佳模型,能更准确地确定恒星有效温度,进而得到预测A型恒星有效温度及谱线回归特性。     

磷氨米酮及其β-端位异构体的NMR研究

磷氨米酮是从链霉菌培养液中分离得到的一种天然磷酰糖肽.作为多种金属蛋白酶的抑制剂,磷氨米酮在生物和医学领域有着广泛的应用.该研究通过对化学合成磷氨米酮及其β-端位异构体的1D NMR波谱(1H,13C和31P NMR)和2D NMR波谱(1H-1H COSY,HSQC和NOESY)的解析,对其NMR信号进行了全归属,并确认了磷氨米酮及其β-端位异构体的端位构型.     

Exact ground state of a frustrated integer-spin modified Shastry-Sutherland model

We propose a method to measure real-valued time series irreversibility which combines two different tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the inand outdegree distributions of the associated graph. The method is computationally efficient and does not require any ad hoc symbolization process. We find that the method correctly distinguishes between reversible and irreversible stationary time series, including analytical and numerical studies of its performance for: (i) reversible stochastic processes (uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic processes (a discrete flashing ratchet in an asymmetric potential), (iii) reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv) dissipative chaotic maps in the presence of noise. Two alternative graph functionals, the degree and the degree-degree distributions, can be used as the Kullback-Leibler divergence argument. The former is simpler and more intuitive and can be used as a benchmark, but in the case of an irreversible process with null net current, the degree-degree distribution has to be considered to identify the irreversible nature of the series.     

A color correction algorithm for noisy multi-view images

A novel color correction algorithm for noisy multi-view images is presented. The key idea is to use the improved Karhunen-Loeve (K-L) transform to obtain correction matrix that can eliminate noise effect to the fullest extent. Noise variance estimation is first performed in the algorithm. In the end, wavelet transform is applied to denoise the corrected image. Experimental results show that, compared with traditional correction method, a well-performed correction result is achieved using the proposed method,and the visual effect of the denoised corrected image is almost consistent with ideal corrected image.     

滑动移除小波分析法在动力学结构突变检验中的应用

标度指数计算的即时性与准确性对相关时间序列的动力学结构突变分析至关重要,然而现有方法在即时性与准确性上一直无法兼顾.将小波分析方法与滑动移除窗口技术相融合,提出一种新的动力学结构突变检测方法——滑动移除小波分析法.通过选取不同的滑动移除窗口,分别对构建的线性、非线性理想时间序列进行动力学结构突变分析,结果表明不论是线性时间序列还是非线性时间序列,滑动移除小波分析能够准确地检测到序列的动力学结构突变点及突变区间,对于滑动移除窗口长度依赖性较小,具有很强的稳定性,而且在计算速度上明显优于滑动移除重标极差和滑动移除方差分析方法,将在大数据处理中具有一定的优势.同时分别对线性、非线性理想时间序列添加高斯白噪声,结果表明滑动移除小波分析具有很强的抗噪能力,能够准确地检测到加噪后序列的突变点.对佛坪站日最高温度实测资料的动力学结构突变的准确检测进一步验证了该方法的有效性.滑动移除小波分析法可为具有相关性的系统动力学结构突变的快速、准确检测提供一种途径.     

Comparison of different methods for combining phase-contrast images obtained with multiple coils

The ability to determine coil sensitivities implies that a method optimized in terms of maximized signal-to-noise ratio (SNR) can be applied to the combination of multiple coil images. An optimization of SNR subsequently results in a minimized variance in quantitative velocity measurements using phase-contrast imaging. When coil sensitivities are unknown, the weighted mean method, utilizing the square of the signal magnitude as weights, is suitable for combination of multiple phase images. In this study, the optimized method using estimated coil sensitivities was compared to the weighted mean method both theoretically and experimentally. It is shown that absence of noise correlation between the different coil images implies no difference between the methods regarding the variance of the phase. In the practical situation, noise correlation does exist, implying an opportunity for further reduction of phase variance using the optimized method. In vitro and in vivo studies showed, however, no significant difference between the two methods studied.     

磁流体数值模拟中一种消去磁场散度的方法

充分利用时空守恒元和解元（CESE）方法的特点（在CESE方法中，守恒变量及它们的空间导数都作为独立的更新量并且求解点在每个控制体边界上）给出一种用最小二乘法求解来消去磁场散度的方法.且我们进一步探究了磁场散度限制方程取不同的权重时对结果的影响.通过比较，我们发现当方程权重取为1时，可以非常有效地消去磁场散度误差.     

用相关矩阵特征判据法实现三重不变光学图像识别

本文首先在K-L变换中用相关矩阵作为特征判据进行特征压缩,然后用综合判别函数法制备空间综合匹配滤波器,可以有效地压缩特征图像数目.用这种滤波器实现了平移、旋转、尺度三重不变光学图像识别,且有较高的信噪比.     

Distribution- versus correlation-induced anomalous transport in quenched random velocity fields

We study mechanisms of anomalous transport in quenched random media. Broad disorder point distributions and strong disorder correlations cause anomalous transport and can lead to the same anomalous scaling laws for the mean and variance of the particle displacements. The respective mechanisms, however, are fundamentally different. This difference is reflected in the spatial particle densities and first passage time distributions, which provide an indicator to identify the origins of anomalous transport.     

A New Class of Weighted CUSUM Statistics

A change point is a location or time at which observations or data obey two different models: before and after. In real problems, we may know some prior information about the location of the change point, say at the right or left tail of the sequence. How does one incorporate the prior information into the current cumulative sum (CUSUM) statistics? We propose a new class of weighted CUSUM statistics with three different types of quadratic weights accounting for different prior positions of the change points. One interpretation of the weights is the mean duration in a random walk. Under the normal model with known variance, the exact distributions of these statistics are explicitly expressed in terms of eigenvalues. Theoretical results about the explicit difference of the distributions are valuable. The expansions of asymptotic distributions are compared with the expansion of the limit distributions of the Cramér-von Mises statistic and the Anderson and Darling statistic. We provide some extensions from independent normal responses to more interesting models, such as graphical models, the mixture of normals, Poisson, and weakly dependent models. Simulations suggest that the proposed test statistics have better power than the graph-based statistics. We illustrate their application to a detection problem with video data.     

Wigner-Matrix-Based Normality Test and Application to Weak Signal Detection in SISO/SIMO Systems

Based on the asymptotic spectral distribution of Wigner matrices,a new normality test method is proposed via reforming the white noise sequence.In this work,the asymptotic cumulative distribution function(CDF)of eigenvalues of the Wigner matrix is deduced.A numerical Kullback-Leibler divergence of the empirical spectral CDF based on test samples from the deduced asymptotic CDF is established,which is treated as the test statistic.For validating the superiority of our proposed normality test,we apply the method to weak 8PSK signal detection in the single-input single-output(SISO) system and the single-input multiple-output(SIMO)system.By comparing with other common normality tests and the existing signal detection methods,simulation results show that the proposed method is superior and robust.     

Broad distribution effects in sums of lognormal random variables

The lognormal distribution describing, e.g., exponentials of Gaussian random variables is one of the most common statistical distributions in physics. It can exhibit features of broad distributions that imply qualitative departure from the usual statistical scaling associated to narrow distributions. Approximate formulae are derived for the typical sums of lognormal random variables. The validity of these formulae is numerically checked and the physical consequences, e.g., for the current flowing through small tunnel junctions, are pointed out. Received 8 November 2002 / Received in final form 17 March 2003 Published online 7 May 2003