Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

卡尔曼滤波滴定分析法研究(Ⅰ)——热力学平衡滴定模型及其应用

卡尔曼滤波(简称KF)为一种最佳滤波方法,是化学计量学分辨和校正的有效工具。滴定时,考察滴定过程中滴定体积与量测信号的变化,可建立相应的状态与量测方程,用KF递推算法估计状态并确定终点。本文拟分析热力学平衡滴定模型并考察某些参数的影响。     

基于小波分析的条纹图滤波方法

本文提出了一种新颖的干涉条纹图非线性滤波方法,可以选择性地在频域图像的不同区域选用不同的滤波方式,在滤掉图像中大部分散斑噪声的同时,能够减少信息的损失,使图像的内部边界仍然保持清晰。文中首先简介了正交小波变换的原理,然后介绍了去除散斑噪声的具体算法,最后给出了计算机模拟去噪声的结果     

联合卡尔曼滤波及其在综合惯性导航系统初始标定中的应用

本文针对由ＩＮＳ及ＥＳＧＭ 组成的综合惯性导航系统的特点,设计了该系统标校阶段的 联合卡尔曼滤波器。文中给出了该联合卡尔曼滤波器的结构及其算法,该算法具有计算量少、 数据传输量小的优点。理论分析及仿真结果表明该联合卡尔曼滤波算法具有全局最优性,能够 满足系统的标定精度要求,且应用该联合滤波器可大大提高系统的容错性。     

小波多分辨分析用于化学发光光谱的噪声滤除

本文利用小波变换对化学发光光谱进行了多分辨信号分解 ,有效地滤除噪声 ,提高了光谱信噪比。讨论了不同小波基和分解级次对分析结果的影响。分析表明小波分析对离散信号处理具有一定优势。     

Scale invariant amplitude modulated phase-only filtering

A new synthetic discriminant-function-based amplitude modulated phase-only filtering technique is proposed for scale invariant pattern recognition. This technique has been found to yield improved correlation output when compared with the alternate techniques. The proposed technique is inherently suitable for optical implementation using the currently available spatial light modulators. Simulation results confirm the effectiveness of the proposed technique.     

结合多尺度空间滤波和层级网络的基于结构保持的高光谱特征选择

为了充分利用高光谱图像蕴含的丰富的光谱信息和空间信息,提出了结合多尺度空间滤波和层级网络的基于结构保持的高光谱特征选择算法.算法利用基于l2,1范数的数学模型,选出同时保存全局相似性结构和局部流形结构的特征子集;在多个尺度的窗口中使用双边滤波,自适应计算滤波核,自动在光谱数据中融入空间信息,增强了类内相似性和类间相异性,避免了参量选择;引入层级结构实现空间信息和光谱信息的深入融合,提高了分类准确度;讨论了层级数目和窗口尺度个数对分类准确度的影响.在Indian Pines和PaviaU两个数据集的实验表明,该算法在大部分地物种类上的分类准确度都有较大幅度的提升,总体分类准确度分别达到90.98%和94.20%,相比其他方法明显提高了地物分类准确度.     

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove “salt-and-pepper” noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.     

基于PSO 实现的级联H 桥七电平APF 在外超导电源中的研究

针对强磁场实验装置(SHMFF)的链式高压有源电力滤波方案规划,基于有源滤波系统的非线性特性, 采用分数阶值。PI 控制器替代普通PI 控制器,在此基础上引入粒子群算法优化指令电流PI 控制器参数,并通过 实验对比分析了人工整定的PI 控制器与PSO 整定的PI 控制器的运行效果。经样机实验表明,采用的PSO 算法 整定的电流内环控制器是正确的,且具有一定的参考价值。     

基于小波变换的便携式X射线荧光光谱仪检测模型的建立与改进

应用便携式X射线荧光光谱仪对土壤中Cr,Cu,Zn,As,Pb等重金属含量进行检测, 每个样品扫描检测3次,利用小波阈值滤噪的方法对所测谱线进行光滑去噪处理后, 根据土壤重金属的标准值和相应的计数率(取三次处理后检测谱线的平均值)建立各重金属的标准曲线。运用小波阈值滤噪方法时, 为确定最佳的小波基和小波分解层数, 以信噪比(SNR),均方根误差(MSE)和信息熵(H)作为评估指标评价降噪效果。为验证仪器的稳定性, 根据土壤样品中重金属浓度的不同挑选部分样品并同时选用H₃BO₃(空白对照)进行重测。结果表明: 运用小波变换方法时,选取coif3小波基对谱线进行三层分解, 取得了最佳的去噪效果; 建立好模型后,仪器的决定系数R2范围是0.990~0.996, 表明在0~1 500 mg·kg-1范围内, 土壤样品中各重金属元素含量与X射线荧光光谱特征峰强度之间的线性关系良好; 经过重复检测和计算得知仪器的检出限均低于国家一级土壤标准。将小波变换的方法实际运用到X射线荧光光谱仪检测模型的建立与改进中,有效的提高了模型的准确性,同时经验证,仪器具有良好的精密度,可运用于实际土壤重金属污染的现场快速筛查。