Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.     

Human and Nonhuman Recognition Using Pyroelectric Infrared Detector

The auto regressive (AR) model of time series is utilized in this paper to recognize a human and nonhuman from pyroelectric infrared (PIR) signals. Through the wavelet transform, the signals are reconstructed by removing the noise from the original signals. The coefficients of the AR model are selected as the features for human and nonhuman recognition and calculated by the Burg algorithm. The classification experiments of a human and nonhuman are performed with a support vector machine. The recognition results for different PIR signals using the proposed AR-based features show high performance with an optimal recognition rate, which is up to 94.6 % and higher than that of the traditional time domain feature and transform domain method, such as the wavelet entropy and wavelet entropy of the double-density dual-tree complex wavelet transform.     

基于小波包分解和PCA-Attention-LSTM的舰船辐射噪声识别技术

为了改进舰船辐射噪声分类系统的性能,进一步提高识别准确率,文章提出了一种基于多特征的小波包分解在长短期记忆(LongShort-TermMemory,LSTM)网络中分类的方法。该方法首先通过小波包分解技术,分频段提取舰船辐射噪声的多种特征,将提取的特征利用主成分分析法(Principal Component Analysis, PCA)进行数据降维,通过添加注意力机制(Attention Mechanism)算法的LSTM网络,对辐射噪声结果分类,提高了学习效率和识别准确率。为了更精细地提取特征,分频段提取了舰船辐射噪声的时频域特征、小波变换特征和梅尔倒谱系数等特征,并将分频段与不分频段的特征、多特征与单一特征、不同信噪比间的算法性能进行对比。实验结果表明,基于小波包分解和PCA-Attention-LSTM的模型可以有效地提高舰船辐射噪声分类的性能,是一种可行的分类方法。     

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.     

Characteristic spectral features of the polarized fluorescence of human breast cancer in the wavelet domain

Wavelet transform of polarized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate normal and malignant tissue types. The intensity differences of parallel and perpendicularly polarized fluorescence spectra are subjected to investigation, since they are relatively free of diffusive background. A number of parameters, capturing spectral variations and subtle changes in the diseased tissues in the visible wavelength regime, are clearly identifiable in the wavelet domain. These manifest both in the average low-pass and high frequency high-pass wavelet coefficients.     

Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry

The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10,000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.     

一种基于提升小波变换的故障特征提取方法及其应用

为了实现旋转机械的早期故障诊断和预示，提出一种采用滑动窗提取瞬态冲击故障特征的方法。该方法以提升小波变换为基础，采用提升模式构造具有冲击特征的小波，用来获取振动信号中的冲击故障特征。然后，采用一种基于回转周期的滑动窗处理小波分解的细节信号，提取每个滑动窗的模最大值作为故障的时域特征。该方法在转子早期碰摩故障和齿轮箱滑动轴承的轴瓦损坏故障的应用中取得了较满意的效果。     

双自适应小波局部极大模方法及其在信号特征提取中的应用

将小波分析中的局部极大模方法采用双自适应提升算法进行改进,用于机械故障冲击信号特征的提取,获取了信号时域和频域冲击特征。将该方法应用于滚动轴承微弱冲击特征的提取,并将原始信号直接进行包络分析、原始信号极大模包络分析、经典小波分析方法、第二代小波的细节信号方法进行了对比。结果表明,双自适应局部极大模方法可以更有效的提取信号中的冲击特征,对小波分解层数极不敏感,表现出了很好的鲁棒性。新方法为进一步实施冲击型故障的诊断工作提供新的思路。     

Application of linear and nonlinear discrete wavelet transforms to MALDI-MS measurements of bacteria for classification

The linear and nonlinear discrete wavelet transforms (DWTs) were used to compress matrix-assisted laser desorption/ionization mass spectra to address two key challenges: the relatively high noise level and the underdetermined format of the data set. By applying the DWT to MALDI-MS spectra, the spectra were simultaneously smoothed and compressed. Multivariate projected difference resolution was used to evaluate the effects of the linear and nonlinear DWT on classification. The cross-validation study using bootstrapped Latin partition and partial least-squares (PLS-2) has proved that the classification accuracy increased after data compression. The best result was obtained when using Fisher's criterion to choose wavelet coefficients for compression. With the aid of principal component analysis (PCA), different wavelet filters may provide different mathematical perspectives to visualize the clustering of bacteria. The effect of growth time was directly observed with wavelet transform, which could not be observed using the original spectra.     

Two-dimensional correlation analysis of nuclear magnetic resonance metabonomics data

Two-dimensional (2D) correlation analysis has been used in this study to identify changes in complex nuclear magnetic resonance (NMR) metabonomics spectra of rat urine samples obtained during a study in which vasculitis (vascular injury), an important safety element in preclinical trials, was induced. Two types of correlation analysis were performed, along the variables and along the samples, and both 2D covariance and correlation coefficient maps were calculated. The binned and 'raw' NMR spectra were analyzed (0.04 and 0.001 ppm resolution, respectively). Good correlation was found among the major peaks of the binned spectra, and two groups of samples were identified using sample-sample 2D correlation maps. Much more complex correlation features were obtained from the 'raw' spectra, in which the specific, butterfly-like patterns were obtained in the covariance map but with only a few significant correlation coefficients in the corresponding 2D correlation maps. In terms of classification, the same group of the last nine spectra that indicated the end of the process and clustered in the 2D sample-sample covariance map of the binned data was also found in the 2D sample-sample covariance map of the raw NMR spectra but, again, not in the 2D correlation coefficient map. A discussion is given on the details of the application of the correlation analysis with regard to spectral data resolution, alignment, the effect of actual intensities of the NMR signal, and reference to various results from 2D correlation analysis of vibrational spectra.     

A complex nonlinear exponential autoregressive model approach toshape recognition using neural networks

A complex nonlinear exponential autoregressive (CNEAR) process which models the boundary coordinate sequence for invariant feature extraction to recognize arbitrary shapes on a plane is presented. A neural network structure is constructed to calculate all the CNEAR coefficients synchronically. The network is simple in structure and easy to implement. The nonlinear parameter is easy to determine using the network. The coefficients are adopted to constitute the feature set. They are proven to be invariant to the transformation of a boundary such as translation, rotation, scale, and choice of the starting point in tracing the boundary. Afterwards, the feature set is used as the input to a complex multilayer perceptron (C-MLP) network for learning and classification. Experimental results show that complicated shapes can be recognized with high accuracy, even in the low-order models. It is also seen that the CNEAR model performs better than the complex autoregressive (CAR) model when shapes have random noise on the boundaries or have differentiating features at detailed levels. Finally, an extended training scheme is developed in which the network is gradually retrained sequentially with shapes containing small increments of noise to improve the robustness of the C-MLP classifier     

An automated methodology for the classification of focal and nonfocal EEG signals using a hybrid classification approach

The uncertainty in human brain leads to the formation of epilepsy disease in human. The automatic detection and severity analysis of epilepsy disease is proposed in this article using a hybrid classification algorithm. The proposed method consists of decomposition stage, feature extraction, and classification stages. The electroencephalogram (EEG) signals are decomposed using dual-tree complex wavelet transform and then features are extracted from these coefficients. These features are then classified using the neural network classification approach in order to classify the EEG signals into either focal or nonfocal EEG signals. Furthermore, severity of the focal EEG signal is analyzed using an adaptive neuro-fuzzy inference system classification approach. The proposed hybrid classification method for the classification of focal signals and nonfocal signals achieved 98.6% of sensitivity, 99.1% of specificity, and 99.4% of accuracy. The average detection rate for both focal and nonfocal dataset is about 98.5%.     

Multiscale cluster analysis

This article describes how the concept of multiresolution is used with cluster analysis of spectral data. Multiresolution analysis progressively increases the resolution of a spectrum profile by adding levels of details contained in scales obtained from a discrete wavelet transform. At each resolution level a cluster analysis is performed on all the spectra profiles in the data set. This allows the relating of changes in the cluster pattern to various broad and narrow features in the spectral data profiles. The analysis also provides an approximate location of the important features in the original wavenumber domain.     

基于蚁群算法和LBP-HF的场景文本定位

针对复杂背景下文本误检率较高的问题,提出了一种基于蚁群聚类和LBP-HF特征验证的复杂场景文本定位算法。该算法首先利用小波高频系数统计特征表达文本模式,采用蚁群聚类算法对文本像素和背景像素进行分类,得到所有可能的文本区域;然后提取更具区分力的LBP-HF纹理特征对侯选的文本区进行验证,获得文本的准确位置。实验结果表明,所提出的基于LBP-HF特征的验证方法能够有效区分文本和非文本区域,使复杂背景下的文本误检率明显下降。     

Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples

Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR spectroscopy with statistical total correlation spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.     

Metabolite projection analysis for fast identification of metabolites in metabonomics. Application in an amiodarone study

The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.     

基于改进零树小波的红外图像盲水印算法

提出了一种小波域自适应盲水印算法,采用（7,4）汉明码技术进行纠错编码。基于整数小波变换,在中高频区采用不重复零树小波编码,自适应的量化小波系数,将水印嵌入到重要系数上。水印提取过程不需要原始图像的参与。实验结果表明,算法自适应性强,实现速度快,具有较好的不可见性,对常见的JPEG压缩、滤波、加噪、剪切等攻击具有较强的鲁棒性。     

循环小波变换及其应用

介绍了循环小波的概念及其循环小波变换的快速算法,详细描述了由原正交小波获得其相应的循环小波的过程,从其中的缠绕叠加过程中,给出了信号的循环小波分解的一般公式,对任意长度数据的信号使用任意偶数的Daubechies小波的变换矩阵的构成给出了统一的描述。接着对使用循环小波变换识别结构系统脉冲响应函数的思想进行了仿真研究。在仿真中以两自由度和悬臂梁结构系统为例考虑了不同的小波对识别精度的影响,还讨论了循环小波变换方法的总体平均性能。     

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.     

基于脊波变换的旋转不变性纹理特征提取方法

提出了一种新的基于脊波变换的旋转不变性纹理特征提取方法。该方法是先在脊波变换过程中的一维小波变换后所形成的每个频率子波段中提取特征,然后采用构建直方图的方法来提取同一尺度下高、低频子波段之间的关系特征,最后将这些特征进行一维傅里叶变换后取幅值并进行特征级融合,从而得到旋转不变性纹理特征。实验结果表明所提出的方法与两种已有的方法相比能够取得更好的分类效果。