Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbertben Huang transform

Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.     

基于递归量化分析与支持向量机的癫痫脑电自动检测方法

癫痫脑电信号的自动检测对癫痫的临床诊断与治疗具有重要意义.基于递归图(recurrence plot)的递归量化分析(recurrence quantification analysis,RQA)重现了非线性时间序列的动力学行为,分析了其递归特性,本文提出了基于RQA的癫痫脑电信号特征提取方法.实验结果表明:直接基于RQA特征的癫痫脑电的检测准确率较高,其中直接基于确定率DET的分类准确率可达到90.25%.本文还把提取的RQA特征值和变化系数、波动指数相结合组成特征向量,输入到SVM分类器,实现癫痫脑电信号的自动检测;实验结果表明:该方法的分类准确率可达到99%.     

Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series

The aim of the article is to answer the question if the Taiwan unemployment rate dynamics is generated by a non-linear deterministic dynamic process. This paper applies a recurrence plot and recurrence quantification approach based on the analysis of non-stationary hidden transition patterns of the unemployment rate of Taiwan. The case study uses the time series data of the Taiwan’s unemployment rate during the period from 1978/01 to 2010/06. The results show that recurrence techniques are able to identify various phases in the evolution of unemployment transition in Taiwan.     

Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins

In this paper, AdaBoost algorithm, a popular and effective prediction method, is applied to predict the subcellular locations of Prokaryotic and Eukaryotic Proteins-a dataset derived from SWISSPROT 33.0. Its prediction ability was evaluated by re-substitution test, Leave-One-Out Cross validation (LOOCV) and jackknife test. By comparing its results with some most popular predictors such as Discriminant Function, neural networks, and SVM, we demonstrated that the AdaBoost predictor outperformed these predictors. As a result, we arrive at the conclusion that AdaBoost algorithm could be employed as a robust method to predict subcellular location. An online web server for predicting subcellular location of prokaryotic and eukaryotic proteins is available at http://chemdata.shu.edu.cn/subcell/ .     

Border effect corrections for diagonal line based recurrence quantification analysis measures

Recurrence Quantification Analysis (RQA) defines a number of quantifiers, which base upon diagonal line structures in the recurrence plot (RP). Due to the finite size of an RP, these lines can be cut by the borders of the RP and, thus, bias the length distribution of diagonal lines and, consequently, the line based RQA measures. In this letter we investigate the impact of the mentioned border effects and of the thickening of diagonal lines in an RP (caused by tangential motion) on the estimation of the diagonal line length distribution, quantified by its entropy. Although a relation to the Lyapunov spectrum is theoretically expected, the mentioned entropy yields contradictory results in many studies. Here we summarize correction schemes for both, the border effects and the tangential motion and systematically compare them to methods from the literature. We show that these corrections lead to the expected behavior of the diagonal line length entropy, in particular meaning zero values in case of a regular motion and positive values for chaotic motion. Moreover, we test these methods under noisy conditions, in order to supply practical tools for applied statistical research.     

Application of wavelet transform to hologram analysis: three-dimensional location of particles

The wavelet analysis provides an efficient tool in numerous signal-processing problems and has been implemented in optical processing techniques, such as in-line holography. When the diffraction pattern recorded on a hologram is analyzed by means of a wavelet transform, the 3-D location of small particles can be determined very accurately. The diffraction process can, in fact, be interpreted as a convolution with a family of wavelet functions, or, merely, as a wavelet transform. The scale parameter of the wavelet family is related to the axial distance z that the wave propagates. The original field is then reconstructed by searching for the optimum value of the scale parameter which produces a maximum of the wavelet transform modulus. The technique proposed is implemented and experimental results are presented.     

基于Hilbert-Huang变换的水声信号特征提取及分类技术

水下目标噪声信号是一种典型的非线性非平稳随机信号，Hilbert-Huang变换较传统方法在处理此类信号时具有很大优势，据此本文提出了一种基于Hilbert-Huang变换的水下目标噪声特征提取方法。本文将环境噪声当作一类目标，利用上述方法对涉及两种航行船舶、海洋生物和海洋环境噪声的四类目标信号的特征进行了详细分析，最后对各类目标噪声信号进行了分类识别实验。实验结果表明基于Hilbert-Huang变换提取的特征对各类目标具有较好的可分性，达到了较高的识别率。     

Recurrence quantification analysis of global stock markets

This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of RQA measures when compared to their developed counterparts. The behavior of stock markets during critical financial events, such as the burst of the technology bubble, the Asian currency crisis, and the recent subprime mortgage crisis, is analyzed by performing RQA in sliding windows. It is shown that during these events stock markets exhibit a distinctive behavior that is characterized by temporary decreases in the fraction of recurrence points contained in diagonal and vertical structures.     

两相流流型动力学特征多尺度递归定量分析

基于垂直上升管中测取的气液两相流电导波动信号,采用递归定量分析方法,从多尺度角度研究了气液两相流泡状流、段塞流及混状流三种典型流型的动力学运动特征.研究结果表明,低频泡状流及混状流在递归图表现为沿对角线方向比较发育的混沌递归线条纹理特征,表明了低频运动的泡状流及混状流具有较好的确定性运动行为,而随着泡状流及混状流运动频率增加,混沌递归特征变差,其运动特征逐渐向随机方向发展.对于段塞流,在混沌递归图上逐渐呈现间歇的矩形块纹理结构,且段塞流中液塞与气塞的间歇运动特征出现在高频段,而段塞流中的泡状流运动则出现在低频段上,且随着泡状流运动频率增加,泡状流逐渐失去确定性运动行为.表明了基于电导波动信号的多尺度非线性分析方法是理解与表征气液两相流动力学特性的有效途径. 关键词： 两相流 流动特性 多尺度分析 递归分析     

基于HHT与LSSVM的远区核爆电磁脉冲识别

为实现远区核爆电磁脉冲（NEMP）和闪电电磁脉冲（LEMP）的有效识别,提出一种基于希尔伯特黄变换（HHT）和最小二乘支持向量机（LSSVM）的识别算法。采用希尔伯特黄变换对远区NEMP和LEMP进行分析,利用两种信号的Hilbert谱在不同频带上分布的差异性,选择谱图中两个区域的能量占比作为信号的特征,选择LSSVM作为分类器进行分类识别。实验结果表明,采用能量占比特征可有效识别NEMP和LEMP,且综合识别率可达到98.59%。     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

基于QMU的高空电磁脉冲下电气电子设备易损性评估方法

高空电磁脉冲（HEMP）可能造成广域基础设施的故障或损毁,考虑到经济原因,需要科学合理地评估其中关键电气电子设备在HEMP辐照下的易损性。将不确定性量化与设备效应评估相结合,总结出基于裕量与不确定性量化（QMU）的电气电子设备易损性评估方法及其工作流程,包括：筛选设备关键参数,通常为耦合通道电流、电压的范数;通过HEMP环境及其与设备耦合的数值仿真及不确定性量化,得到HEMP下设备关键参数的概率分布,作为设备的威胁水平;对工作状态下设备进行HEMP效应试验,通过统计推断得到设备效应阈值概率分布,作为设备在威胁下的强度;计算威胁水平与设备强度间的距离,量化设备关键参数的裕量及其不确定性,评估HEMP下的设备易损性。基于QMU的电气电子设备易损性评估方法还可为后续防护设计提供基础数据和评估方法。     

激光雷达精确修正对流层目标定位误差

提出了一种利用纯转动拉曼激光雷达修正对流层目标定位误差的方法,其基本思想是通过接收氮气和氧气的纯转动拉曼回波信号反演大气折射率垂直廓线,根据目标定位误差理论修正不同高度处目标物的总折射角和高度定位修正值.结果表明：通过纯转动拉曼激光雷达反演大气折射率廓线,可较好修正目标定位误差.计算定位误差时得出相同高度处目标物的总折射角和高度定位修正值随视仰角的增加而减小.当视仰角为10°时,位于8 km高度处的目标物总偏折角可达3.15′,高度定位修正值为14.55 m.当视仰角为30°时,相同高度处目标物总偏折角仅 关键词： 激光雷达 定位误差 大气折射指数 大气温度     

Linear quantification calibration of crystallinity at subpercent and its evaluation based on spectral and spatial information inherited in Raman chemical images

In this paper, the author reported two methods to extract spectral or spatial information inherited in the Raman chemical images for linear quantification calibration of crystallinity. The two approaches reported quantification results according to the spectral mean score of overall pixels or the spatial percentage of the pixels with a score greater than and equal to the threshold of the chemical images, respectively. From this study, it can be concluded that, first, sampling method for data collection in mapping has to be optimized to achieve linear quantification calibration through simple univariate analysis approaches. Second, the ordinary way of evaluating/validating a linear quantification technique by best linear correlation coefficient (R²) and root‐mean‐square error of calibration is disputable and has to be reconsidered. Lastly, with further consideration of root‐mean‐square relative error of calibration and predicted crystallinity at subpercent, it was found that the spectral mean score method cannot generate reliable quantification results at subpercent crystallinity. Copyright © 2014 John Wiley & Sons, Ltd.     

Chaos game representation of functional protein sequences, and simulation and multifractal analysis of induced measures

Investigating the biological function of proteins is a key aspect of protein studies. Bioinformatic methods become important for studying the biological function of proteins. In this paper, we first give the chaos game representation (CGR) of randomly-linked functional protein sequences, then propose the use of the recurrent iterated function systems (RIFS) in fractal theory to simulate the measure based on their chaos game representations. This method helps to extract some features of functional protein sequences, and furthermore the biological functions of these proteins. Then multifractal analysis of the measures based on the CGRs of randomly-linked functional protein sequences are performed. We find that the CGRs have clear fractal patterns. The numerical results show that the RIFS can simulate the measure based on the CGR very well. The relative standard error and the estimated probability matrix in the RIFS do not depend on the order to link the functional protein sequences. The estimated probability matrices in the RIFS with different biological functions are evidently different. Hence the estimated probability matrices in the RIFS can be used to characterise the difference among linked functional protein sequences with different biological functions. From the values of the D_q curves, one sees that these functional protein sequences are not completely random. The D_q of all linked functional proteins studied are multifractal-like and sufficiently smooth for the C_q (analogous to specific heat) curves to be meaningful. Furthermore, the D_q curves of the measure \mu based on their CGRs for different orders to link the functional protein sequences are almost identical if q\geq 0. Finally, the C_q curves of all linked functional proteins resemble a classical phase transition at a critical point.     

Building and analyzing the US airport network based on en-route location information

From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators.     

基于形态学的人工标志中心亚像素定位方法研究

在使用圆形人工标志的计算机视觉检测中,标志的定位精度直接影响到检测的整体精度。以往的中心定位算法对人工标志的中心定位精度差、速度慢,已不能满足精密、快速的检测要求。在经典数学形态学的基础上提出采用改进的扩展形态学的方法提取圆形人工标志的边缘,通过最小二乘拟合方法计算人工标志中心。该方法具有精度高和速度快的优点。实验表明其定位精度在0.02~0.03像素之间,能够满足高精度计算机视觉检测的要求。