Discrimination of aqueous and vinegary extracts of Shixiao San using metabolomics coupled with multivariate data analysis and evaluation of antihyperlipidemic effect

A novel study using LCeMS(Liquid chromatography tandem mass spectrometry)coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of Shixiao San.Batches of these two kinds of samples were subjected to analysis,and the datasets of sample codes,tR-m/z pairs and ion intensities were processed with principal component analysis(PCA).The result of score plot showed a clear classification of the aqueous and vinegar groups.And the chemical markers having great contributions to the differentiation were screened out on the loading plot.The identities of the chemical markers were performed by comparing the mass fragments and retention times with those of reference compounds and/or the known compounds published in the literatures.Based on the proposed strategy,quercetin-3-Oneohesperidoside,isorhamnetin-3-O-neohespeeridoside,kaempferol-3-O-neohesperidoside,isorhamnetin-3-O-rutinoside and isorhamnetin-3-O-(2G-a-l-rhamnosyl)-rutinoside were explored as representative markers in distinguishing the vinegar extract from the aqueous extract.The anti-hyperlipidemic activities of two processed extracts of Shixiao San were examined on serum levels of lipids,lipoprotein and blood antioxidant enzymes in a rat hyperlipidemia model,and the vinegary extract,exerting strong lipid-lowering and antioxidative effects,was superior to the aqueous extract.Therefore,boiling with vinegary was predicted as the greatest processing procedure for anti-hyperlipidemic effect of Shixiao San.Furthermore,combining the changes in the metabolic profiling and bioactivity evaluation,the five representative markers may be related to the observed antihyperlipidemic effect.

3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features

Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.

Recent advancement of chemical imaging in pharmaceutical quality control:From final product testing to industrial utilization

Chemical imaging(CI)possesses a strong ability of pharmaceutical analysis.Its great strength relies on the integration of traditional spectroscopy(one dimension)and imaging technique(two dimensions)to generate three-dimensional data hypercubes.Data pre-processing or processing methods are proposed to analyze vast data matrixes and thereby realizing different research objectives.In this review paper,various pharmaceutical applications of quality control over the past few years are summed up in two groups of final product test and industrial utilization.The scope of"quality control"here includes traditional analytical use,process understanding and manufactural control.Finally,two major challenges about undesirable sample geometry and lengthy acquisition time are discussed for prospective commercial or industrial application.

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

Education and sustainable development in Romania and the European Union countries

The sustainable development supposes a development strategy that would ensure the interdependence and complementarily of objectives from the social, economic and environmental fields. The degree of priority established for the three dimensions of sustainable development differs from one country to another, a fact that confers a national and local meaning to this issue. For the Central and Eastern European countries, balanced economic development represents one of the fundamental objectives of the reforms started in 1990. Education represents a priority of any country＇s economic development and an extremely important element of economic growth. This paper presents the characteristics of the Romanian educational system while achieving a comparative analysis regarding different countries of the European Union, both from a quantitative viewpoint （using the main indicators in the education field） and a qualitative viewpoint （using student performances in international evaluations）. In the end, we present some proposals for the improvement of the present state of the Romanian educational system.

Identification of Lignin and Polysaccharide Modifications in Populus Wood by Chemometric Analysis of 2D NMR Spectra from Dissolved Cell Walls

2D ^13C-^1H HSQC NMR spectroscopy of acetylated cell walls in solution gives a detailed fingerprint that can be used to assess the chemical composition of the complete wall without extensive degradation. We demonstrate how multivariate analysis of such spectra can be used to visualize cell wall changes between sample types as high-resolution 2D NMR loading spectra. Changes in composition and structure for both lignin and polysaccharides can subsequently be interpreted on a molecular level. The multivariate approach alleviates problems associated with peak picking of overlapping peaks, and it allows the deduction of the relative importance of each peak for sample discrimination. As a first proof of concept, we compare Populus tension wood to normal wood. All well established differences in cellulose, hemicellulose, and lignin compositions between these wood types were readily detected, confirming the reliability of the multivariate approach, In a second example, wood from transgenic Populus modified in their degree of pectin methylesterification was compared to that of wild-type trees. We show that differences in both lignin and polysaccharide composition that are difficult to detect with traditional spectral analysis and that could not be a priori predicted were revealed by the multivariate approach. 2D NMR of dissolved cell wall samples combined with multivariate analysis constitutes a novel approach in cell wall analysis and provides a new tool that will benefit cell wall research.

Nuclear Magnetic Resonance technique in tumor metabolism

Cancer is one of the most serious diseases that cause an enormous number of deaths all over the world.Tumor metabolism has great discrimination from that of normal tissues.Exploring the tumor metabolism may be one of the best ways to find biomarkers for cancer detection,diagnosis and to provide novel insights into internal physiological state where subtle changes may happen in metabolite concentrations.Nuclear Magnetic Resonance(NMR)technique nowadays is a popular tool to analyze cell extracts,tissues and biological fluids,etc,since it is a relatively fast and an accurate technique to supply abundant biochemical information at molecular levels for tumor research.In this review,approaches in tumor metabolism are discussed,including sample collection,data profiling and multivariate data analysis methods etc.Some typical applications of NMR are also summarized in tumor metabolism.

LC–MS-based metabolomics in profiling of drug metabolism and bioactivation

Since metabolism significantly affects drug safety and efficacy,determining the metabolic profile of a drug is a critical part of drug development.The application of an LC–MS-based metabolomic approach has gained more widespread use in identifying drug metabolites,developing metabolic maps and lending clues to mechanisms of bioactivation.Thus,the LC–MS-based metabolomic approach is a powerful tool for profiling of drug metabolism and bioactivation.

A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices

We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.