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The study of spatial distribution characteristics of the adsorbed compounds for absorbent materials has significant importance in understanding the behaviors of aerosols while they migrating in the absorbent materials. Herein, for the first time, desorption corona beam ionization–mass spectrometry (DCBI–MS) has proposed for direct in-situ analysis of adsorbed aerosol for absorbent materials. DCBI is a novel atmospheric pressure chemical ionization (APCI)-related technique developed by our group in recent years. It can facilitate accurately localizing sampling by forming a visible thin corona beam and avoid the risk of sample contamination and matrix interference compared with other similar techniques. The advantages of DCBI–MS allow rapid screening of the spatial distribution characteristics of the adsorbed compounds for absorbent materials. The distribution characteristic of phenol in cigarette filter tip filled with cellulose acetate fiber was studied as a model case for demonstrating the feasibility of the developed method. As a comparison, conventional HPLC was also used for the study of the distribution characteristic of phenol. The results revealed DCBI–MS had highly improved assay simplicity in spatial distribution characteristic analysis of phenol for the acetate fiber tip, therefore, exhibiting a great potential for convenient, rapid and cost-efficient analysis of the spatial distribution characteristic investigation of adsorbed compounds for adsorbent materials. [ABSTRACT FROM AUTHOR]

Abstract: One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene mining approach is proposed based on the similarity between probability density functions on each gene for the class of interest with respect to the others. This method allows the ascertainment of significant genes that are informative for discriminating each individual class rather than maximizing the separability of all classes. Then one can select genes containing important information about the particular subtypes of diseases. Based on the mined significant genes for individual classes, a support vector machine with local kernel transform is constructed for the classification of different diseases. The combination of the gene mining approach with support vector machine is demonstrated for cancer classification using two public data sets. The results reveal that significant genes are identified for each cancer, and the classification model shows satisfactory performance in training and prediction for both data sets. [Copyright &y& Elsevier]

Metabonomics has been widely used in disease diagnosis and clinically practical methods often require the detection of multi-class bio-samples. In this work, multi-class classification methods were investigated to simultaneously discriminate among 6 inherited metabolic diseases (IMDs) and the normal instances using gas chromatography-mass spectrometry (GC-MS) of urine samples. Two common multi-class classification strategies, one-against-all (OAA) and one-against-one (OAO) were compared and enhanced using a novel ensemble classification strategy (ECS), which developed a set of sequential sub-classifiers by fusion of OAA and OAO and made the final classification decisions using softmax function. GC-MS data of 240 instances of 6 IMDs and healthy controls were classified by different strategies based on orthogonal partial least squares discriminant analysis (OPLS-DA) and particle swarm optimization (PSO) algorithm was performed for feature selection. By OAA and OAO, the classification accuracies were 70.00% and 82.86%, respectively. Using the two methods based on ECS, the total classification accuracies were 0.9143 and 0.9429. The newly proposed ECS will provide a useful multi-class classification tool for simultaneous detection of clinically similar IMDs and promote practical and reliable diagnosis of IMDs using metabonomics data. [ABSTRACT FROM AUTHOR]

Abstract: Rapid, sensitive and quantitative assays for peptide hydrolysis enzymes are of paramount importance for drug development and in the diagnosis of disease. Here, we proposed a novel biosensor for sensitive and selective active screening of peptidases. This strategy relies on the proteolysis-mediated protection of gold nanoparticles (AuNPs) that were decorated with biotin-labeled substrate peptides and can be aggregated by streptavidin. Enzyme-mediated protection of AuNPs offers this strategy high specificity, and the use of AuNPs additionally allows a visual and homogeneous assay format, thus permitting improved simplicity and throughput of the assays. As a model case, desirable selectivity and sensitivity in peptidase assay were achieved in the active assays of pancreatic elastase with a wide linear response range from 0.005 to 0.10U/mL and a detection limit of 0.003U/mL. The results indicated that this strategy can offer a simple, robust and convenient platform for visualized peptidase activity analysis and related biochemical studies with high sensitivity and selectivity. [Copyright &y& Elsevier]

Abstract: As a greedy search algorithm, classification and regression tree (CART) is easily relapsing into overfitting while modeling microarray gene expression data. A straightforward solution is to filter irrelevant genes via identifying significant ones. Considering some significant genes with multi-modal expression patterns exhibiting systematic difference in within-class samples are difficult to be identified by existing methods, a strategy that unimodal transform of variables selected by interval segmentation purity (UTISP) for CART modeling is proposed. First, significant genes exhibiting varied expression patterns can be properly identified by a variable selection method based on interval segmentation purity. Then, unimodal transform is implemented to offer unimodal featured variables for CART modeling via feature extraction. Because significant genes with complex expression patterns can be properly identified and unimodal feature extracted in advance, this developed strategy potentially improves the performance of CART in combating overfitting or underfitting while modeling microarray data. The developed strategy is demonstrated using two microarray data sets. The results reveal that UTISP-based CART provides superior performance to k-nearest neighbors or CARTs coupled with other gene identifying strategies, indicating UTISP-based CART holds great promise for microarray data analysis. [ABSTRACT FROM AUTHOR]

Abstract: Representing a compound by a numerous structural descriptors becomes common in quantitative structure–activity relationship (QSAR) studies. As every descriptor carries molecular structure information more or less, it seems more advisable to investigate all the possible descriptor vectors rather than traditional variable selection when building a QSAR model. Based on particle swarm optimization (PSO) algorithm, a more flexible descriptor selection and model construction method variable-weighted support vector machine (VW-SVM) is proposed. The new strategy adopted in this paper is to weight all structural descriptors with continuous non-negative values rather than removing or reserving any ones arbitrarily. The manner of invoking PSO to seek non-negative weights of variables can be regarded as a process of searching optimized rescaling for every molecular structural descriptor. Moreover, PSO is employed to search the optimal parameters of VW-SVM model besides variable weights, enables the construction of a rational and adaptive parameter-free QSAR model according to the performance of the total model. Results obtained by investigating glycogen synthase kinase-3α inhibitors and carbonic anhydrase II inhibitors indicate VW-SVM can hold more useful structure information of compounds than other methods as optimally weighting all the descriptors, consequently leading to precisely QSAR models coupled with developed performance both in training and in prediction. [Copyright &y& Elsevier]

Abstract: An unlooked-for experimental observation that in near-infrared spectroscopy (NIR) the absorption peak of the second overtone of aniline adsorbed by 13X molecular sieve nearly disappeared led us investigate a fundamental question: the behavior of NIR when the outside space surrounding a molecule is too small to allow the molecule to vibrate freely. Through NIR of various organic compounds adsorbed by different porous inorganic materials like 13X molecular sieve, silica gel and active aluminium oxide, and NIR of supramolecular cyanuric acid-melamine, we can reasonably confirm a theoretical inference that in the micro-environment above, all intensities of NIR absorbance decrease, and the second overtone decreases more than the first overtone does. Furthermore, one distinct feature of NIR, higher sensitivity to the size of micro-environmental space as compared with mid-infrared (MIR), and its potential application to the study of supramolecular structure are outlined by our experiments. [Copyright &y& Elsevier]

Abstract: Through randomly arranging samples of a calibration set, treating their NIR spectra with orthogonal discrete wavelet transform, and selecting suitable variables in terms of correlation coefficient test (r-test), it is possible to extract features of each component in a multi-component system respectively and partial least squares (PLS) models based on these features are capable of predicting the concentration of every component. What is perhaps more important, with the proposed strategy, the predictive ability of the model is at least not impaired while the size of the calibration set can be obviously reduced. Therefore, it provides a more economical, rapid, as well as convenient approach of NIR quantitative analysis for multi-component system. In addition, all important factors and parameters related to the proposed strategy are discussed in detail. [Copyright &y& Elsevier]

Abstract Fluorescent probes have been valuable tools for bioanalytical multiplex assays. However, as a common phenomenon in multiplex fluorescence assays, spectral overlap usually leads to difficulty in spectral analysis for multiple analytes. Although multiway calibrations have provided mathematic approaches for complex spectral analysis, it remains a grand challenge for these methods in practical applications because of the problems such as prior rank estimation. Herein, we report a novel second-order calibration algorithm of alternating residual trilinearization (ART) for the decomposition of complex spectra generated from multiplex fluorescence assays. By alternating iterative convergence to the spectral profiles of each component in convergence process, ART enables automatic rank estimation for second-order calibration, thus able to avoid the risk of chemical meaningless fitting of component spectra. Combined with fluorescence excitation-emission matrix (EEM) spectroscopy, the performance of ART has been demonstrated by a simulated example and an analytical experiment performed using molecular beacons (MBs) for the simultaneous assay of three breast cancer related DNA targets. The results revealed that the proposed algorithm is capable of automatic estimating the number of underlying components during its convergence process to produce acceptable performance in spectral profile resolution and concentration estimation. Compared with other existing iterative trilinear decomposition strategies such as parallel factor analysis (PARAFAC) requiring a prior rank estimation, the proposed ART therefore provides a robust second-order calibration strategy for complex spectral analysis in multiplex fluorescence assays. Graphical abstract In this study, fluorescence excitation-emission matrix (EEM) spectroscopy combined with a novel chemometric algorithm, alternating residual trilinearization (ART), was used to simultaneous assay of three nucleic acid targets based on molecular beacons (MBs) with desirable performance. fx1 Highlights • A novel alternating residual trilinearization (ART) algorithm. • Automatic rank estimation of ART for second-order calibration of three-way data. • Good performance of ART demonstrated using data generated from multiple DNA detection. [ABSTRACT FROM AUTHOR]

GC-MS urinary metabolomic analysis coupled with chemometrics is used to detect inborn errors of metabolism (IEMs), which are genetic disorders causing severe mental and physical debility and even sudden infant death. Orthogonal partial least squares discriminant analysis (OPLS-DA) is an efficient multivariate statistical method that conducts data analysis of metabolite profiling. However, performance degradation is often observed for OPLS-DA due to increasing size and complexity of metabolomic datasets. In this study, hybrid particle swarm optimization (HPSO) is employed to modify OPLS-DA by simultaneously selecting the optimal variable subset, associated weights and the appropriate number of orthogonal components, constructing a new algorithm called HPSO-OPLSDA. Investigating two IEMs, methylmalonic acidemia (MMA) and isovaleric acidemia (IVA), results suggest that HPSO-OPLSDA can significantly outperform OPLS-DA in terms of the discrimination between disease samples and healthy controls. Moreover, main discriminative metabolites are identified by HPSO-OPLSDA to aid the clinical diagnosis of IEMs, including methylmalonic-2, methylcitric-4(1) and 3-OH-propionic-2 for MMA and isovalerylglycine-1 for IVA. [ABSTRACT FROM AUTHOR]

Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. [ABSTRACT FROM AUTHOR]

A new, feasible procedure for high-precision bromine isotope analysis using multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS) is described. With a combination of HR mass resolution mode and accurate optimization of the Zoom Optics parameters (Focus Quad: −1.30; Zoom Quad: 0.00), the challenging problem of the isobaric interferences ( 40 Ar 38 ArH + and 40 Ar 40 ArH + ) in the measurement of bromine isotopes ( 79 Br + , 81 Br + ) has been effectively solved. The external reproducibility of the measured 81 Br/ 79 Br ratios in the selected standard reference materials ranged from ±0.03‰ to ±0.14‰, which is superior to or equivalent to the best results from previous contributions. The effect of counter cations on the Br + signal intensity and the instrumental-induced mass bias was evaluated as the loss of HBr aerosol in nebulizer and potential diffusive isotope fractionations. [ABSTRACT FROM AUTHOR]

In order to eliminate boron loss and potential isotopic fractionation during chemical pretreatment of natural samples with complex matrices, a three-column ion-exchange separation/purification procedure has been modified, which ensures more than 98% recovery of boron from each step for a wide range of sample matrices, and is applicable for boron isotope analysis by both TIMS and MC-ICP-MS. The PTIMS-Cs2BO2 +-static double collection method was developed, ensuring simultaneous collection of 133Cs2 11B16O2 +(m/z 309) and 133Cs2 10B16O2 + (m/z 308) ions in adjacent H3–H4 Faraday cups with typical zoom optics parameters (Focus Quad: 15 V, Dispersion Quad: −85V). The external reproducibilities of the measured 11B/10B ratios of the NIST 951 boron standard solutions of 1000ng, 100ng and 10ng of boron by PTIMS method are ±0.06‰, ±0.16‰ and ±0.25‰, respectively, which indicates excellent precision can be achieved for boron isotope measurement at nanogram level boron in natural samples. An on-peak zero blank correction procedure was employed to correct the residual boron signals effect in MC-ICP-MS, which gives consistent δ11B values with a mean of 39.66±0.35‰ for seawater in the whole range of boron content from 5ppb to 200ppb, ensuring accurate boron isotope analysis in few ppb boron. With the improved protocol, consistent results between TIMS and MC-ICP-MS data were obtained in typical geological materials within a wide span of δ 11B values ranging from −25‰ to +40‰. [ABSTRACT FROM AUTHOR]

Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis. [Display omitted] • A strategy was proposed for identifying multiple inherited metabolic diseases (IMDs). • We used combined GC-MS for acquiring metabolomics data with random forest for data handling. • The encouraging results implied the promise of the strategy for robust and reliable identification of multiple IMDs. [ABSTRACT FROM AUTHOR]

Abstract: Activity screening of DNA base excision repair (BER) enzymes is a crucial step for understanding numerous fundamental biochemical processes. A novel label-free homogeneous technique is developed for visualized uracil-DNA glycosylase (UDG) activity assay using gold nanoparticles (AuNPs). This strategy relies on the enzyme-catalyzed assembly of AuNPs decorated with DNA probes. In the presence of endonuclease IV (an enzyme which can further hydrolyze the products from UDG-catalyzed reaction), the substrate DNA selectively interacts with UDG followed by the efficient release of a single-strand probe. The released single-strand probe then makes the network-like assembly of decorated AuNPs to provide a visible signal for UDG activity. This strategy that can be performed in a label-free homogeneous assay format improved the duration, the simplicity and the throughput of UDG activity screening. The results provided in the present study revealed that this strategy could hold great potential as a robust, convenient and visualized platform for activity screening of uracil-DNA glycosylases with high selectivity and desirable sensitivity. [Copyright &y& Elsevier]

Abstract: Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method. [Copyright &y& Elsevier]

Characterization of the protein-peptide interactions are a critical for understanding the functions and signal pathways of proteins. Herein, a new finding of universal terminal protection that protein bind specifically with peptide and provide a protective coating to prevent peptide hydrolysis in the presence of peptidase. On the basis of this mechanism, we first reported a novel label-free fluorescence biosensor strategy that utilizes the protection of specific terminal protein on peptide-templated gold nanocluster (AuNCs) beacon for the detection of proteins. The fluorescence quenching of peptide-templated AuNCs can be effectively inhibited with increasing concentration of the specific protein, exhibiting a satisfactory sensitivity and selectivity toward protein with the detection limit of MDM2 and gp120 are 0.0019 U/mL and 0.0012 U/mL, respectively. The developed label-free fluorescence biosensor strategy provides new ideas to detect and screen protein for analyzing protein-peptide interaction in biomedical applications. [Display omitted] • A novel label-free fluorescence biosensor was developed for the detection and screening of protein. • Using MDM2 and gd120 as an example, peptide-templated AuNCs realized its specific determination. • Peptide-templated AuNCs can be applied in the biomedical for the screening of protein. [ABSTRACT FROM AUTHOR]

Abstract: This paper has demonstrated the study on the adsorption kinetics of orthoxylene on silica gel with a novel experimental methodology. In the method, there was a differential adsorption bed (DAB) where the solid adsorbent always contacted with the same bulk concentration of the adsorbate vapor, and the DAB was monitored with near-infrared diffuse reflectance spectroscopy (NIRDRS) continuously as well as non-invasively. Local partial least squares (PLS) algorithm was suggested to replace normal global PLS method in multivariate calibration models for processing NIRDRS data, because the concentration of the adsorbate on the adsorbent varied greatly as the adsorption process was going on. In this way, we, conveniently as well as promptly, obtained instantaneous adsorption rates of several orthoxylene/silica gel adsorption processes under different conditions like partial pressure of orthoxylene vapor and velocity of gas, and discovered that the adsorption process was physical adsorption, and mainly controlled by external diffusion. [Copyright &y& Elsevier]