Effect of salt concentration and pH on the hydrophobicity parameters of surfactants studied by TLC and spectral mapping technique

The lipophilicity and specific hydrophobic area of 56 surfactants having different hydrophobic moiety and different length of ethylene oxide chain were determined by reversed-phase thin-layer chromatography and the strength and selectivity of the effect of sodium chloride and pH on the hydrophobicity parameters was elucidated using spectral mapping technique followed by two-dimensional nonlinear mapping. In each instance significant linear correlations were found between the lipophilicity and specific hydrophobic surface area of surfactants suggesting that from a chromatographic point of view they behave as a homologous series of solutes. It was established that the strength of the effect of both salt concentration and pH is relatively low and the selectivity of their influence on the hydrophobicity parameters is markedly different.     

Relationship between the hydrophobic and hydrophilic molecular parameters of some monoamine oxidase inhibitory drugs, determined by means of adsorptive and reversed-phase thin-layer chromatography.

The absorption capacity, the specific hydrophilic surface area, the lipophilicity and the specific hydrophobic surface area of 17 monoamine oxidase inhibitory drugs were determined by means of adsorptive and reversed-phase thin-layer chromatography for future application of these molecular parameters in quantitative structure-activity relationship studies. Principal component analysis suggests that most of the physicochemical parameters have a different information content, and their application in the elucidation of their mode of action is therefore justified.     

Influence of salt and pH on the hydrophobicity parameters of antisense nucleosides studied by reversed-phase thin-layer chromatography and multivariate mathematical-statistical methods.

The lipophilicity and specific hydrophobic surface area of 12 8-substituted 2'-deoxyadenosine and 17 5-substituted-2'-deoxyuridine derivatives were determined by reversed-phase thin-layer chromatography in ion-free eluents and in eluents containing sodium chloride, sodium acetate and acetic acid. The strength and selectivity of the effect of eluent additives were separated by use of spectral mapping technique followed by two-dimensional nonlinear mapping. The relationship between the structural characteristics and hydrophobicity parameters was elucidated by stepwise regression analysis. Eluent additives exert a considerable influence on both hydrophobicity parameters. The effect of sodium chloride and acetic acid was higher than that of sodium acetate. The strength and selectivity of the sensitivity of nucleosides towards eluent additives significantly depended on the character of the ring structure and on the length of the apolar alkyl chain. The influence of the degree of unsaturation and the branching of the alkyl substituent was negligible.     

Relationship between the hydrophobic and hydrophilic molecular parameters of some synthetic nucleosides, determined by means of adsorptive and reversed-phase thin-layer chromatography

The adsorption capacity, the specific adsorptive surface, the lipophilicity and the specific hydrophobic surface of 59 natural and synthetic nucleoside derivatives were determined by means of adsorptive chromatography and reversed-phase thin-layer chromatography for the future application of these molecular parameters in quantitative structure-activity relationship studies. Stepwise regression analysis and principal component analysis proved that each of the physico-chemical parameters has a different information content, and their application in the design of new bioactive derivatives is therefore justified.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

Relationship between hydrophobicity parameters and the strength and selectivity of phytotoxicity of sulfosuccinic acid esters

The strength and selectivity of the phytotoxicity of 11 sulfosuccinic acid ester surfactants were determined on the leaves of Tradescantia bicolor, and the data were evaluated by multivariate mathematical-statistical methods. Spectral mapping technique combined with stepwise regression analysis indicated that both the strength and the selectivity of the effect depend significantly on the specific hydrophobic surface area of the anionic surfactants determined in the presence of ions. The significant relationship between this hydrophobicity parameter and phytotoxic activity suggests the involvement of apolar (hydrophobic) forces in the plant-surfactant interaction. It was assumed that the apolar alkyl chains of the surfactants may insert in the hydrophobic part of the phospholipid bilayers causing membrane disorder and malfunction.     

Statistical and multivariate statistical techniques to trace the sources and affecting factors of groundwater pollution in a rapidly growing city on the Chinese Loess Plateau

Abstract

Groundwater quality is defined by various water quality parameters. The aims of the research are to understand the relationships among different groundwater quality parameters and to trace the sources and affecting factors of groundwater pollution via statistical and multivariate statistical techniques. The 36 shallow groundwater samples collected from shallow pumping wells in Yan’an City were analyzed for various water quality parameters. Correlation analysis, principal component analysis (PCA), hierarchical cluster analysis (HCA), and multivariable linear regressions (MLR) were jointly used in this study to explore the sources and affecting factors of groundwater pollution. The study reveals that the mineral dissolution/precipitation and anthropogenic input are the main sources of the physicochemical indices and trace elements in the groundwater. Groundwater chemistry is predominantly regulated by natural processes such as dissolution of carbonates, silicates, and evaporates and soil leaching, followed by human activities as the second factor. Climatic factors and land use types are also important in affecting groundwater chemistry. Cl^– is the greatest contributor to the overall groundwater quality revealed by the two regression models. The first model which has eight dependent variables is high in model reliability and stability, and is recommended for the overall groundwater quality prediction. The study is helpful for understanding groundwater quality variation in urban areas.     

Relationship between the physicochemical parameters and biodegradation rate of sulfosuccinic acid ester surfactants

The relationship between the rate of biodegradation of 10 sulfosuccinic acid derivatives by a bacterial consortium and the physicochemical parameters was elucidated by principal component analysis followed by modified nonlinear mapping technique. It was established that the hydrophobicity parameters determined in the presence of ions and the bulkiness of the surfactant molecule exert a considerable impact on the biodegradation rate. Nonlinear mapping technique using the absolute values of principal component loadings explains more precisely the relationship than the common nonlinear mapping does.     

Hydrophobic regions on protein surfaces. Derivation of the solvation energy from their area distribution in crystallographic protein structures.

For the first time, a direct approach for the derivation of an atomic solvation parameter from macromolecular structural data alone is presented. The specific free energy of solvation for hydrophobic surface regions of proteins is delineated from the area distribution of hydrophobic surface patches. The resulting value is 18 cal/(mol.A2), with a statistical uncertainty of +/-2 cal/mol.A2) at the 5% significance level. It compares favorably with the parameters for carbon obtained by other authors who use the the crystal geometry of succinic acid or energies of transfer from hydrophobic solvent to water for small organic compounds. Thus, the transferability of atomic solvation parameters for hydrophobic atoms to macromolecules has been directly demonstrated. A careful statistical analysis demonstrates that surface energy parameters derived from thermodynamic data of protein mutation experiments are clearly less confident.     

Comparison of partial least-square method and canonical correlation analysis in a quantitative structure–retention relationship study

The retention of 7 monotetrazolium and 9 ditetrazolium salts was determined on alumina and reversed-phase (RP) alumina layers using n-hexane-1-propanol and water-1-propanol mixtures as eluents. The retention capacity and the specific surface area of solutes in contact with the stationary phases were calculated. The relationship between retention characteristics and physicochemical parameters of solutes was elucidated by canonical correlation analysis and partial least-square regression analysis. Both methods found significant relationships between the chromatographic and physicochemical parameters, however, the results were different according to the method applied. Calculations suggested that the retention on both alumina and RP alumina layers is of mixed character, hydrophobic, electronic and steric parameters are equally involved in the retention.     

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits'' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.     

Application of independent component analysis to microarrays

We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.     

The use of cluster and discriminant analysis in the investigations of the role of trace metals in the pathogenesis of Parkinson’s disease

X-ray fluorescence microscopy was applied for two-dimensional elemental analysis of substantia nigra (SN) tissue. The samples representing Parkinson’s disease (PD) and control cases were examined at HASYLAB beamline L and at ESRF beamline ID22. Two-dimensional mapping of P, S, Cl, K, Ca, Fe, Cu, Zn, Se and Br was done with the spatial resolution of 15 and 5 μm. The masses per unit area of elements in neuromelanin reach nerve cells of SN were determined.The elemental data were processed using two multivariate techniques, namely cluster and discriminant analysis. The statistical methods were used for data reduction, both unsupervised and supervised classification as well as for the creation of a model that would simplify case identification based on the elemental analysis of SN tissue. The results of cluster analysis confirmed the statistical significance of the differences in elemental composition of PD and control SN nerve cells. Based on the results of discriminant analysis, the elements (P, Cl, Fe, Cu and Zn) that played the greatest role in the process of differentiation between neurons from examined groups were determined.     

Modeling chondrocyte patterns by elliptical cluster processes

Superficial zone chondrocytes (CHs) of human joints are spatially organized in distinct horizontal patterns. Among other factors, the type of spatial CH organization within a given articular surface depends on whether the cartilage has been derived from an intact joint or the joint is affected by osteoarthritis (OA). Furthermore, specific variations of the type of spatial organization are associated with particular states of OA. This association may prove relevant for early disease recognition based on a quantitative structural characterization of CH patterns. Therefore, we present a point process model describing the distinct morphology of CH patterns within the articular surface of intact human cartilage. This reference model for intact CH organization can be seen as a first step towards a model-based statistical diagnostic tool. Model parameters are fitted to fluorescence microscopy data by a novel statistical methodology utilizing tools from cluster and principal component analysis. This way, the complex morphology of surface CH patters is represented by a relatively small number of model parameters. We validate the point process model by comparing biologically relevant structural characteristics between the fitted model and data derived from photomicrographs of the human articular surface using techniques from spatial statistics.     

A simple approach to guide factor retention decisions when applying principal component analysis to biomechanical data

The use of principal component analysis (PCA) as a multivariate statistical approach to reduce complex biomechanical data-sets is growing. With its increased application in biomechanics, there has been a concurrent divergence in the use of criteria to determine how much the data is reduced (i.e. how many principal factors are retained). This short communication presents power equations to support the use of a parallel analysis (PA) criterion as a quantitative and transparent method for determining how many factors to retain when conducting a PCA. Monte Carlo simulation was used to carry out PCA on random data-sets of varying dimension. This process mimicked the PA procedure that would be required to determine principal component (PC) retention for any independent study in which the data-set dimensions fell within the range tested here. A surface was plotted for each of the first eight PCs, expressing the expected outcome of a PA as a function of the dimensions of a data-set. A power relationship was used to fit the surface, facilitating the prediction of the expected outcome of a PA as a function of the dimensions of a data-set. Coefficients used to fit the surface and facilitate prediction are reported. These equations enable the PA to be freely adopted as a criterion to inform PC retention. A transparent and quantifiable criterion to determine how many PCs to retain will enhance the ability to compare and contrast between studies.     

基于光合系统参数建立马铃薯耐荫性综合评价体系

为构建便捷的马铃薯(Solanum tuberosum)耐荫性综合评价体系并发掘耐荫种质, 以35个马铃薯品种(系)为实验材料, 测定块茎膨大期遮荫下植株叶片叶绿素含量、光合能力和叶绿素荧光等光合参数及收获后块茎单株产量和淀粉含量等指标。根据耐荫系数, 利用主成分分析法、隶属函数法、聚类分析法和逐步回归分析法进行综合评价。通过主成分分析将马铃薯耐荫性相关的13个单项光合指标转换为6个综合指标, 代表了全部信息的87.51%。以此计算各种质的隶属函数值, 并以主成分的贡献率进行加权, 最终获得所用材料耐荫性的综合评价值(D值)。根据D值聚类分析结果将35个马铃薯分为4类, 其中Eshu10和Lishu6分别为耐荫性最强和最弱的品种。通过逐步回归分析建立了马铃薯耐荫性评价数学模型: D=0.060+0.106G_s+0.214qP+0.143NPQ。同时, 用该评价体系鉴定为耐荫性强的品种(系)在遮荫后其产量和/或淀粉含量等指标减幅均低于耐荫性弱的种质, 表明该评价体系可用于快速评价和预测马铃薯种质的耐荫性。