Analysis of the factors that significantly influence the stability of fluoroquinolone-metal complexes

The aim of the present study was to evaluate factors contributing to the differences between the overall stability constants (log β_pqr) of the fluoroquinolone-metal ion complexes.The experiments were performed using potentiometric titration method in wide pH range. The overall stability constants (log β_pqr) were determined using the Hyperquad program. Complexation equilibria of eight different fluoroquinolones with six divalent and trivalent metal ions were investigated in this study.The authors employed a multifactorial ANOVA analysis, fixed effect model to describe the influence of particular variables affecting the stability of the analyzed complex species. Four different variables were set at different levels labeled. The ligand number (LF) was the first factor. LF determined the number of fluorochinolone molecules in the complex structure, and could take the values 1, 2 or 3. The second factor (Me) was connected with the type of the metal ion bonded in the complex. Since six different metal cations were studied, the Me factor was described with six levels. The number of hydrogen or hydroxide groups substituted into the complex molecule was the third variable (HR) with many levels labeled: q, a, s, d, f and g. The last factor FQ described the type of the fluorochinolone used for complex formations. All variables analyzed here were statistically significant (p value lower than 0.01), which indicates that all of them strongly affect the log β_pqr value. Binary interactions (LF-Me, LF-FQ, Me-HR and Me-FQ) between variables were also stated, which suggests that the effects of these variables were higher than we could calculate based on the effect of each variable alone.The ANOVA analysis has shown that the following factors Me, LF and HR were the most important for the stability of the fluoroquinolone-metal ion complexes. It was also found that according to the FQ factor (type of ligand molecule) all analyzed fluoroquinolones formed stable complexes with metals. It was proved that the application of ANOVA for the entire complexation profile of analyzed fluoroquinolones with polyvalent metal ions was a valid technique for detecting the statistically significant differences in the complexation profiles. Such information may be very useful for better understanding and interpretation of differences in bioavailability of fluoroquinolones and their interactions with antacids and other multimineral drugs.     

Exploring Omics data from designed experiments using analysis of variance multiblock Orthogonal Partial Least Squares

Many experimental factors may have an impact on chemical or biological systems. A thorough investigation of the potential effects and interactions between the factors is made possible by rationally planning the trials using systematic procedures, i.e. design of experiments. However, assessing factors' influences remains often a challenging task when dealing with hundreds to thousands of correlated variables, whereas only a limited number of samples is available. In that context, most of the existing strategies involve the ANOVA-based partitioning of sources of variation and the separate analysis of ANOVA submatrices using multivariate methods, to account for both the intrinsic characteristics of the data and the study design. However, these approaches lack the ability to summarise the data using a single model and remain somewhat limited for detecting and interpreting subtle perturbations hidden in complex Omics datasets.     

Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes

The goal of present work is to analyse the effect of having non-informative variables (NIV) in a data set when applying cluster analysis and to propose a method computationally capable of detecting and removing these variables. The method proposed is based on the use of a genetic algorithm to select those variables important to make the presence of groups in data clear. The procedure has been implemented to be used with k-means and using the cluster silhouettes as fitness function for the genetic algorithm.The main problem that can appear when applying the method to real data is the fact that, in general, we do not know a priori what the real cluster structure is (number and composition of the groups).The work explores the evolution of the silhouette values computed from the clusters built by using k-means when non-informative variables are added to the original data set in both a literature data set as well as some simulated data in higher dimension. The procedure has also been applied to real data sets.     

Statistical analysis of data from accelerated ageing tests of PES UF membranes

In this research, membrane life-time was evaluated by means of accelerated ageing experiments. A pressure pulse unit was used to perform the ageing experiments in an accelerated way. An experimental design has been set up and four ageing factors were varied at two levels. The four ageing factors studied were: fouling status of the membrane, cleaning agent concentration, magnitude of the back pulse and number of applied back pulses. The integrity of the membrane modules was evaluated by means of permeability testing, pressure decay tests and bubble tests. Also tensile tests were performed to investigate the mechanical properties of the membrane modules. The collected data was used for an analysis of variance to determine which ageing factors and which combination of ageing factors influence membrane life time. The analysis showed that the fouling status in combination with the number of applied pressure pulses were significant ageing factors. Additional tensile tests confirmed these results.     

Identification of discriminatory variables in proteomics data analysis by clustering of variables

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods.     

Different approaches to proximate analysis by thermogravimetry analysis

The experimental optimization by the simplex method of the proximate analysis of coal and biomass by thermogravimetry analysis (TGA) is reported. Heating rate, final temperature, holding time, Ar flow rate and sample size were the control variables. The response function used was chosen to minimize the difference in percentage of volatile matter with the ASTM characterization. The relative accuracy of the method was demonstrated by determination of the volatile matter contents of a number of coals in parallel with the ASTM certified method. The method is successfully used with biomass samples.     

Canonical partial least squares—a unified PLS approach to classification and regression problems

We propose a new data compression method for estimating optimal latent variables in multi‐variate classification and regression problems where more than one response variable is available. The latent variables are found according to a common innovative principle combining PLS methodology and canonical correlation analysis (CCA). The suggested method is able to extract predictive information for the latent variables more effectively than ordinary PLS approaches. Only simple modifications of existing PLS and PPLS algorithms are required to adopt the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimization of the preparation of nalmefene-loaded sustained-release microspheres using central composite design

Nalmefene-loaded poly(lactic-co-glycolic acid) microspheres were prepared by O/O emulsification/solvent evaporation method. The central composite design-response surface methodology was used to optimize and predict the preparation microspheres. Effects of three independent variable variables i.e., Span80 concentration in outer phase, poly(lactic-co-glycolic acid) concentration in inner phase and theoretical drug content were evaluated on a number of response variables. Response variables selected in this study were drug content, encapsulation efficiency, mean diameter, diameter span and the cumulative percentage of the drug released in the first day after incubation (marked as F1d, and it was also calculated as the initial burst). Multiple linear regression and second-order polynomial model were fitted to the data, and the resulting equations were used to produce five dimensional response graphs, by which optimal experimental conditions were selected. The results showed that all response variables were greatly dependent on three independent variables, and the optimal conditions were Span80 concentration 1.5%, poly(lactic-co-glycolic acid) concentration 17.5%, and theoretical drug content 6%. According to the optimal conditions, the drug content, encapsulation efficiency, mean diameter, diameter span and F1d of prepared microspheres were 4.37%, 72.8%, 64.1 microm, 1.36 and 8.93%, respectively.     

Variable selection for discriminating herbal medicines with chromatographic fingerprints

When discriminating herbal medicines with pattern recognition based on chromatographic fingerprints, typically, the majority of variables/data points contain no discrimination information. In this paper, chemometric approaches concerning forward selection and key set factor analysis using principal component analysis (PCA), unweighted and weighted methods based on the inner- and outer-variances, Fisher coefficient from the between- and within-class variations were investigated to extract representative variables. The number of variables retained was determined based on the cumulative variance percent of principal components, the ratio of observations to variables and the factor indicative function (IND). In order to assess the methods for variable selection and criteria levels to determine the number of variables retained, the original and reduced datasets were compared with Procrustes analysis and a weighted measure of similarity. Moreover, the tri-variate plots of the first three PCA scores were used to visually examine the reduced datasets in low dimensional space. Herbal samples were finally discriminated by use of Bayes discrimination analysis with the reduced subsets. The case study for 79 herbal samples showed that, the methods of forward selection associating the variables with the loadings closest to 0 and key set factor analysis were preferable to determine the representative variables. Procrustes analysis and the weighted measure were not indicative to extract representative variables. High matching between the original and reduced datasets did not suggest high prediction accuracy. Visually examining the PC1-PC2-PC3 scores projection plots with the reduced subsets, not all the herb samples could be separated due to the complexity of chromatographic fingerprints.     

GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms

The non-linear regression technique known as alternating conditional expectations (ACE) method is only applicable when the number of objects available for calibration is considerably greater than the number of considered predictors. Alternating conditional expectations regression with selection of significant predictors by genetic algorithms (GA-ACE), the non-linear regression technique presented here, is based on the ACE algorithm but introducing several modifications to resolve the applicability limitations of the original ACE method, thus facilitating the practical implementation of a very interesting calibration tool. In order to overcome the lack of reliability displayed by the original ACE algorithm when working on data sets characterized by a too large number of variables and prior to the development of the non-linear regression model, GA-ACE applies genetic algorithms as a variable selection technique to select a reduced subset of significant predictors able to accurately model and predict a considered variable response. Furthermore, GA-ACE actually provides two alternative application approaches, since it allows either the performance of prior data compression computing a number of principal components to be subsequently subjected to GA-selection, or working directly on original variables.In this study, GA-ACE was applied to two real calibration problems, with a very low observation/variable ratio (NIR data), and the results were compared with those obtained by several linear regression techniques usually employed. When using the GA-ACE non-linear method, notably improved regression models were developed for the two response variables modeled, with root mean square errors of the residuals in external prediction (RMSEP) equal to 11.51 and 6.03% for moisture and lipid contents of roasted coffee samples, respectively. The improvement achieved by applying the new non-linear method introduced is even more remarkable taking into account the results obtained with the best performance linear method (IPW-PLS) applied to predict the studied responses (14.61 and 7.74% RMSEP, respectively).     

The statistical basis of standardization designs for diagnostic assays

Accuracy and long-term quality of laboratory diagnostic assays depend critically on the standardization process. In this note we review statistically typical procedures and designs used in standardization. Issues of practical relevance such as the number of systems, runs, and replicates to be involved in the standardization design, and quality control aspects, are addressed.     

Model building in multivariate additive partial least squares splines via the GCV criterion

In the literature, much effort has been put into modeling dependence among variables and their interactions through nonlinear transformations of predictive variables. In this paper, we propose a nonlinear generalization of Partial Least Squares (PLS) using multivariate additive splines. We discuss the advantages and drawbacks of the proposed model, building it via the generalized cross validation criterion (GCV) criterion, and show its performance on a real dataset and on simulated datasets in comparison to other methods based on splines. Copyright © 2009 John Wiley & Sons, Ltd.     

土壤样品采集和化学元素分析全过程质量控制比较研究

利用SAX模型，对于中国南方3个省共计13个茶园的土壤中的可提取态P元素的测定实验进行了全程质量控制。通过计算实例以及与相关文献计算结果的对比分析，给出了SAX过程的评价标准，并对于ASX模型的评价过程中的一些问题进行了深入探讨。指出SAX模型评价标准在使用过程中要注意与实验设计和任何改进措施的费用效益分析相结合。     

Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

In this paper, multivariate calibration of complicated process fluorescence data is presented. Two data sets related to the production of white sugar are investigated. The first data set comprises 106 observations and 571 spectral variables, and the second data set 268 observations and 3997 spectral variables. In both applications, a single response, ash content, is modelled and predicted as a function of the spectral variables. Both data sets contain certain features making multivariate calibration efforts non-trivial. The objective is to show how principal component analysis (PCA) and partial least squares (PLS) regression can be used to overview the data sets and to establish predictively sound regression models. It is shown how a recently developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. In addition, signal compression is tested on the larger data set using wavelet analysis. It is demonstrated that a compression down to 4% of the original matrix size — in the variable direction — is possible without loss of predictive power. It is concluded that the combination of OSC for pre-processing and wavelet analysis for compression of spectral data is promising for future use.     

Errors in variables regression with value‐censored data

Samples with analyte concentrations outside a method's dynamic range are a reality of clinical chemistry and are particularly of interest in method comparison studies. The most obvious remedy—to ignore any such values—introduces bias and loses the information that censored data might add to the analysis. Extending conventional errors‐in‐variables methods to incorporate value‐censored data recovers this information. The formulation presented uses a variance model more flexible than either the constant variance or the constant coefficient of variation models. Copyright © 2016 John Wiley & Sons, Ltd.     

原子吸收光谱法测量质量控制研究

以原子吸收光谱法对锌合金标准物质中的铜元素定值为例,运用田口方差分析方法对定值数据进行分析处理,实现元素定值过程的质量控制。从实验设计方案的角度确定实验过程的不确定度,对溶解、等份稀释、仪器校准三部分的实验步骤分别进行数据处理,计算各部分方差波动对总实验数据波动的贡献率,从而确定最主要的不确定度来源。     

Observation‐based missing data methods for exploratory data analysis to unveil the connection between observations and variables in latent subspace models

This paper introduces a class of methods to infer the relationship between observations and variables in latent subspace models. The approach is a modification of the recently proposed missing data methods for exploratory data analysis (MEDA). MEDA is useful to identify the structure in the data and also to interpret the contribution of each latent variable. In this paper, MEDA is augmented with dummy variables to find the data variables related to a given deviation detected among observations, for instance, the difference between one cluster of observations and the bulk of the data. The MEDA extension, referred to as observation‐based MEDA or oMEDA, can be performed in several ways, one of which is theoretically shown to be equivalent to a comparison of means between groups. The use of the proposed approach is demonstrated with a number of examples with simulated data and a real data set of archeological artifacts. Copyright © 2011 John Wiley & Sons, Ltd.     

Non-parametric multivariate analysis of variance in the proteomic response of potato to drought stress

Spot detection is a mandatory step in all available software packages dedicated to the analysis of 2D gel images. As the majority of spots do not represent individual proteins, spot detection can obscure the results of data analysis significantly. This problem can be overcome by a pixel-level analysis of 2D images.     

Informational Analysis of Variance Applied to Method-Comparison. A Comparative Study

Abstract

The results obtained in the determination of mercury in solid wastes by AAS using two preparation methods¹ are compared through statistical parametric and non-parametric tests, linear regression and informational analysis of variance. The informational analysis of variance (IANOVA) method is a distribution-free procedure valid under minimal assumptions. It is not influenced by the range of the data and has very satisfactory robustness properties. Applying this algorithm to compare the effectiveness of the traditional water-bath digestion method with the microwave digestion method discussed in ref. 1, it was possible to assess the proportional errors introduced by microwave digestion method concerning the analysis of mercury in waste samples.     

多批次肝衰竭患者呼出气体的电喷雾萃取电离质谱检测及代谢组学数据分析

采用高分辨电喷雾萃取电离质谱(EESI-MS)技术对肝衰竭患者和健康志愿者呼出气体样本进行快速检测, 结合多块偏最小二乘分析(MB-PLS)方法, 对多批次获取的呼出气体代谢数据进行统计建模分析, 并与传统的PLS方法进行比较. 结果表明, MB-PLS方法能有效消除批次差异对统计建模的影响. 此外, 利用MB-PLS模型变量VIP值对变量进行筛选, 可降低数据的冗余, 消除无关变量对模型的影响, 从而有效提高了模型的性能.