Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.     

Metabolomic-based investigation of Yinlan alleviating hyperlipidemia by inhibiting blood stasis and phlegm turbidity through the PXR-CYP3A4-ABCB1-FXR pathway

Yinlan lipid regulatory capsule (YL) is a composite traditional Chinese medicine (TCM) new drug to alleviate hyperlipidemia, while its therapeutic mechanism in vivo was not clarified with nontargeted metabolomics investigation. An animal model was established in rats fed a high-fat diet, and their body weights, body mass index (BMI) and blood cholesterol levels were measured. Serum, liver and kidney tissue samples were also extracted for PXR-CYP3A4-ABCB1-FXR signaling pathway research using PCR and UHPLC–MS. The obtained plasma samples were analyzed by UHPLC-Q-TOF-MS metabolomic investigation, which revealed PXR-CYP3A4-related metabolites and changes induced by YL. Finally, the key metabolites were chosen as index components, and their levels in the serum, liver, small intestine and bile were used for simultaneous UHPLC–MS-MS determination. The results indicated that YL was effective in rebalancing blood TG and TC levels (compared to controls). With respect to the PXR-CYP3A4-ABCB1 pathway, as a result of YL’s effect, gene expression or activity of the two targets decreased significantly in both the liver and kidney. The same trend was observed in the serum samples mentioned above. Metabolomics screening and data revealed that 44 metabolites can be regarded as biomarkers related to hyperlipidemia, fatty acids synthesis, and body energy consumption, as well as synthesis, transportation and exertion of cholesterol. YL’s treatment focused on 26 of them, primarily bile acids, indicating that the antihyperlipidemic effect of this drug lies in its inhibitory activity of cholesterol metabolism. Subsequent analysis of those in vivo components revealed that significant increases (compared to the model group) occurred in the blood, liver, small intestine and bile in groups that received medium and high doses of YL (while the low dose was relatively unchanged). Those target components exhibit a close relationship with PXR and/or CYP3A4. The use of YL repressed PXR expression and subsequently decreased CYP3A4 activity. As a result, synthesis of related bile acids increased, while cholesterol levels decreased, consequently leading to the attenuation of hyperlipidemia. This study comprehensively investigated the antihyperlipidemia mechanism of YL based on its repression of PXR-CYP3A4 activity and related metabolite yield, establishing an accurate method for evaluating the therapeutic effect of YL.     

A class of interaction-round-a-face models and its equivalence with an ice-type model

A new model (called the Temperley-Lieb interactions model) is introduced, in two-dimensional lattice statistics, on a square lattice . The Temperley-Lieb equivalence of this model to the six-vertex, self-dual Potts, critical hard-hexagons and critical nonintersecting string models is established. A graphical equivalence of this model to the six-vertex model generalizes this equivalence to noncritical cases of the above models. The order parameters of a specialization of this model are studied.     

Quality evaluation of fingerprints of herbal medicine with chromatographic data

In this study, local least squares (LLS) and principal component analysis (PCA) were applied to deal with the disturbances in a data set of chromatographic fingerprints after necessary data transformations. It has been demonstrated that PCA with standard normal variate (SNV) transformation of data led to meaningful classification of 33 different Erigeron breviscapus herbal samples. The result was also corroborated by variance squares discriminant method. The quality of herbal objects was further evaluated, and the causes of this fact have been explained from a chemical point of view. At the same time, it implied an idea for qualitative evaluation of the herbal objects with a common class pattern of chromatographic fingerprints.     

Determination of molecular orientation distribution of a stable paramagnetic probe in oriented 4-cyano-4’-<Emphasis Type="Italic">n</Emphasis>-pentylbiphenyl

The molecular orientation distribution function of a stable radical 4-hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl in magnetic-field oriented 4-cyano-4’-n-pentylbiphenyl was determined from the angular dependence of the ESR spectra. The preferred molecular orientation of radical species in the liquid crystal matrix was determined. The temperature evolution of the orientation distribution function was studied.__________Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 1, pp. 190–195, January, 2005.     

Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares

Changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS) are proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by a previously proposed spectral interval selection method, moving window partial least squares (MWPLSR) [Anal. Chem. 74 (2002) 3555]. The utilization of informative regions aims to construct better PLS models than those based on the whole spectral points. The purpose of CSMWPLS and SCMWPLS is to optimize the informative regions and their combination to further improve the prediction ability of the PLS models. The results of their application to an open-path (OP)/FT-IR spectra data set show that the proposed methods, especially SCMWPLS can find out an optimized combination, with which one can improve, often significantly, the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP) with the reasonable latent variable (LVs) number, comparing with the results obtained using whole spectra or direct combination of informative regions for a compound. Regions consisting of the combinations obtained can easily be explained by the existence of IR absorption bands in those spectral regions.     

偏最小二乘光度法同时测定铜和铁的研究及应用

７－（１－苯偶氮）－８－羟基喹啉－５－磺酸钠在ＰＨ＝４．７５ＨＡｃ－ＮａＡｃ缓冲溶液中能与Ｃｕ（Ⅱ）和ＦＥ（Ⅲ）形成稳定的络合物，本文研究了Ｃｕ（Ⅱ）－ＰＡＨＱＳ、Ｆｅ（Ⅲ）－ＰＡＨＱＳ体系的显色条件，以偏最小二乘法处理两者重叠吸收峰，建立了光度法同时测定铜和铁的方法。     

Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data

This article discusses problems of validating classification models especially in datasets where sample sizes are small and the number of variables is large. It describes the use of percentage correctly classified (%CC) as an indicator for success of a classification model. For small datasets, %CC should not be used uncritically and its interpretation depends on sample size. It illustrates the use of a common classification method, discriminant partial least squares (D-PLS) on a randomly generated dataset of 200 samples and 200 variables.

An aim of the classifier is to determine whether the null hypothesis (there is no distinction between two classes) can be rejected. Autoprediction gives an 84.5% CC. It is shown that, if there is variable selection, it must be performed independently on the training set to obtain a CC close to 50% on the test set; otherwise, over-optimistic and false conclusions can be reached about the ability to classify samples into groups.

Finally, two aims of determining the quality of a model are frequently confused, namely optimisation (often used to determine the most appropriate number of components in a model) and independent validation; to overcome this, the data should be split into three groups.

There are often difficulties with model building if validation and optimisation have been done on different groups of samples, especially using iterative methods, each group being modelled using properties, such as a different number of components or different variables.     

Chemometric data analysis of pollutants in wastewater—a case study

In this study, chemometric techniques such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and partial least squares (PLS) were used to analyse the wastewater dataset to identify the factors which affect the composition of sewage of domestic origin, spatial and temporal variations, similarity/dissimilarity among the wastewater characteristics of cis- and trans-drains and discriminating variables. Samples collected from 24 wastewater drains in Lucknow city and from three sites on Gomti river in the month of January/February, May, August and November during the period of 5 years (1994-1999) were characterized for 32 parameters. The multivariate techniques successfully described the similarities/dissimilarities among the sewage drains on the basis of their wastewater characteristics and sources signifying the effect of routine domestic/commercial activities in respective drainage areas. Spatial and seasonal variations in wastewater composition were also determined successfully. CA generated six groups of drains on the basis of similar wastewater characteristic. PCA provided information on seasonal influence and compositional differences in sewage generated by domestic and industrial waste dominated drains and showed that drains influenced by mixed industrial effluents have high organic pollution load. DA rendered six variables (TDS, alkalinity, F, TKN, Cd and Cr) discriminating between cis- and trans-drains. PLS-DA showed dominance of Cd, Cr, NO₃, PO₄ and F in cis-drains wastewater. The results suggest that biological-process based STPs could treat wastewater both from the cis- as well as trans-drains, however, prior removal of toxic metals will be required from the cis-drains sewage. Further, seasonal variations in wastewater composition and pollution load could be the guiding factor for determining the STPs design parameters. The information generated would be useful in selection of process type and in designing of the proposed sewage treatment plants (STPs) for safe disposal of wastewater.     

Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.