Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.     

Definition and detection of data-based uniqueness in evaluating bilinear (two-way) chemical measurements

Multivariate curve resolution methods, frequently used in analyzing bilinear data sets, result in ambiguous decomposition in general. Implementing the adequate constraints may lead to reduce the so-called rotational ambiguity drastically, and in the most favorable cases to the unique solution. However, in some special cases, non-negativity constraint as minimal information of the system is a sufficient condition to resolve profiles uniquely. Although, several studies on exploring the uniqueness of the bilinear non-negatively constrained multivariate curve resolution methods have been made in the literature, it has still remained a mysterious question. In 1995, Manne published his profile-based theorems giving the necessary and sufficient conditions of the unique resolution. In this study, a new term, i.e., data-based uniqueness is defined and investigated in details, and a general procedure is suggested for detection of uniquely recovered profile(s) on the basis of data set structure in the abstract space. Close inspection of Borgen plots of these data sets leads to realize the comprehensive information of local rank, and these argumentations furnish a basis for data-based uniqueness theorem. The reported phenomenon and its exploration is a new stage (it can be said fundament) in understanding and describing the bilinear (matrix-type) chemical data in general.     

Estimation of multivariate binary density using orthogonal functions

In this paper, the authors studied certain properties of the estimate of Liang and Krishnaiah (1985, J. Multivariate Anal. 16, 162–172) for multivariate binary density. An alternative shrinkage estimate is also obtained. The above results are generalized to general orthonormal systems.     

“网格式平行线法”在螺纹参数测量中的应用

在计算机视觉理论基础上发展起来的视觉检测(vision inspection)技术具有非接触、速度快、精度适中、可实现在线等优点，已广泛地应用于工业产品的在线检测。在计算机视觉检测技术中CCD摄像机是一个最关键的器件，其参数是否准确决定了检测的精度。所以，摄像机标定是视觉检测技术中最基本的也是最重要的一步。在比较其他标定方法的基础上，为了解决传统标定方法对螺纹图像测量系统所带来的一系列问题，采用了一种新的图像测量系统的标定方法——网格式平行线标定方法，该方法运用了CCD亚像素细分技术及调焦技术，可直接得到纵横2个方向的像素，经过理论分析及实验结果均表明，采用该方法具有标定简单、精度高、重复性好等优点，是一种较好的螺纹图像测量系统标定方法。     

Some remarks to multivariate regression model

Some remarks to problems of point and interval estimation, testing and problems of outliers are presented in the case of multivariate regression model. This work was supported by the Council of Czech Government J14/98:153100011.     

The Nonexistence of Procedures with Bounded Performance Characteristics in Certain Parametric Inference Problems

This paper gives a condition which implies the nonexistence of parametric statistical procedures with bounded risk or error performance characteristics. Many examples for which such a condition is satisfied are considered.     

Calorimeter energy calibration using the energy conservation law

A new calorimeter energy calibration method was developed for the proposed ILC detectors. The method uses the center-of-mass energy of the accelerator as the reference. It has been shown that using the energy conservation law it is possible to make ECAL and HCAL cross calibration to reach a good energy resolution for the simple calorimeter energy sum.      

江汉平原上空QTEC变化特征的统计分析

提出了一种观测台网用多接收机性能一致性的标定方法，并利用江汉平原GPS接收台网的观测数据，分析了该地区上空QTEC的日变化、季节变化、年变化和逐年变化的统计特征．标定结果表明，不同接收机之间的仪器偏差是从双频GPS卫星信标推算QTEC的主要误差来源之一，必须校正．统计分析证实，江汉平原上空QTEC的日变化特征同时受电离层和等离子层控制，其年变化以半年周期为主，峰值和谷值分别出现在两分点和两至点附近，QTEC值的逐年变化从太阳活动峰年到低年逐年下降，与以太阳黑子数为代表的太阳活动密切相关．     

Mathematical topographical correction of XPS images using multivariate statistical methods

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from multispectral XPS images. Principal component analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography, which is common to all of the images, will be resolved in the first most significant component. The score of this component contains spatial information about the topography of the surface, whereas the loading is a quantitative representation of the topography contribution to each elemental/chemical image. The simple‐to‐use self‐modelling mixture analysis (Simplisma) method is a pure variable method that searches for the source of most differences in the data and therefore has the potential to distinguish between chemical and topographical phases in images. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for samples with differing degrees of topography. Copyright © 2004 John Wiley & Sons, Ltd.     

Confidence regions in a multivariate regression model with constraints II

The multivariate model, where not only parameters of the mean value of the observation matrix, but also some other parameters occur in constraints, is considered in the paper. Some basic inference is presented under the condition that the covariance matrix is either unknown, or partially unknown, or known. Supported by the grant of the Council of Czech Republic MSM 6 198 959 214.