[Application of Intelligent Optimization Algorithms to Wavelength Selection of Near-Infrared Spectroscopy].

Near infrared spectroscopy (NIRS) is a kind of indirect analysis technology, whose application depends on the setting up of relevant calibration model. In order to improve interpretability, accuracy and modeling efficiency of the prediction model, wavelength selection becomes very important and it can minimize redundant information of near infrared spectrum. Intelligent optimization algorithm is a sort of commonly wavelength selection method which establishes algorithm model by mathematical abstraction from the background of biological behavior or movement form of material, then iterative calculation to solve combinatorial optimization problems. Its core strategy is screening effective wavelength points in multivariate calibration modeling by using some objective functions as a standard with successive approximation method. In this work, five intelligent optimization algorithms, including ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO), random frog (RF) and simulated annealing (SA) algorithm, were used to select characteristic wavelength from NIR data of tobacco leaf for determination of total nitrogen and nicotine content and together with partial least squares (PLS) to construct multiple correction models. The comparative analysis results of these models showed that, the total nitrogen optimums models of dataset A and B were PSO-PLS and GA-PLS models. GA-PLS and SA-PLS models were the optimums for nicotine, respectively. Although not all predicting performance of these optimization models was superior to that of full spectrum PLS models, they were simplified greatly and their forecasting accuracy, precision, interpretability and stability were improved. Therefore, this research will have great significance and plays an important role for the practical application. Meanwhile, it could be concluded that the informative wavelength combination for total nitrogen were 4 587~4 878 and 6 700~7 200 cm(-1), and that for tobacco nicotine were 4 500~4 700 and 5 800~6 000 cm(-1). These selected wavelengths have actually physical significance.

Calibration transfer via an extreme learning machine auto-encoder.

In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.

The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis.

Variable selection and outlier detection are important processes in chemical modeling. Usually, they affect each other. Their performing orders also strongly affect the modeling results. Currently, many studies perform these processes separately and in different orders. In this study, we examined the interaction between outliers and variables and compared the modeling procedures performed with different orders of variable selection and outlier detection. Because the order of outlier detection and variable selection can affect the interpretation of the model, it is difficult to decide which order is preferable when the predictabilities (prediction error) of the different orders are relatively close. To address this problem, a simultaneous variable selection and outlier detection approach called Model Adaptive Space Shrinkage (MASS) was developed. This proposed approach is based on model population analysis (MPA). Through weighted binary matrix sampling (WBMS) from model space, a large number of partial least square (PLS) regression models were built, and the elite parts of the models were selected to statistically reassign the weight of each variable and sample. Then, the whole process was repeated until the weights of the variables and samples converged. Finally, MASS adaptively found a high performance model which consisted of the optimized variable subset and sample subset. The combination of these two subsets could be considered as the cleaned dataset used for chemical modeling. In the proposed approach, the problem of the order of variable selection and outlier detection is avoided. One near infrared spectroscopy (NIR) dataset and one quantitative structure-activity relationship (QSAR) dataset were used to test this approach. The result demonstrated that MASS is a useful method for data cleaning before building a predictive model.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat.

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals.

In this study, a new algorithm for wavelength interval selection, known as interval variable iterative space shrinkage approach (iVISSA), is proposed based on the VISSA algorithm. It combines global and local searches to iteratively and intelligently optimize the locations, widths and combinations of the spectral intervals. In the global search procedure, it inherits the merit of soft shrinkage from VISSA to search the locations and combinations of informative wavelengths, whereas in the local search procedure, it utilizes the information of continuity in spectroscopic data to determine the widths of wavelength intervals. The global and local search procedures are carried out alternatively to realize wavelength interval selection. This method was tested using three near infrared (NIR) datasets. Some high-performing wavelength selection methods, such as synergy interval partial least squares (siPLS), moving window partial least squares (MW-PLS), competitive adaptive reweighted sampling (CARS), genetic algorithm PLS (GA-PLS) and interval random frog (iRF), were used for comparison. The results show that the proposed method is very promising with good results both on prediction capability and stability. The MATLAB codes for implementing iVISSA are freely available on the website: .

Multiscale peak detection in wavelet space.

Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation.

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

Quantitative and qualitative determination of LiuweiDihuang preparations by ultra high performance liquid chromatography in dual-wavelength fingerprinting mode and random forest.

The classical traditional Chinese formulation LiuweiDihuang, shown to have clinical efficacy for "nourishing kidney-yin" in traditional Chinese medicine, has been used for thousands of years in China. Little attention, however, has been paid to quality control methods for this formulation. Hence, a rapid and sensitive analytical technique is urgently needed for the evaluation of LiuweiDihuang preparations to assess its quality and pharmacological functionality. In this study, an ultra high performance liquid chromatography dual-wavelength method was developed to simultaneously determine 11 constituents in LiuweiDihuang preparations. This robust approach provided a fast and comprehensive quantitative determination of the major bioactive markers within LiuweiDihuang preparations. To distinguish four dosage forms of LiuweiDihuang preparations, a random forest technique was applied on the spectrometric fingerprint data obtained. This combination approach of chromatographic techniques and data analyses might serve as a rapid and efficient tool to ensure the quality of LiuweiDihuang preparations and other Chinese medicinal formulations and can support quality control and scientific research into the pharmacological potential for these formulations.

An ultra high performance liquid chromatography with tandem mass spectrometry method for plasma and cerebrospinal fluid pharmacokinetics of rhein in patients with traumatic brain injury after administration of rhubarb decoction.

Damage of blood-brain barrier is a common result of traumatic brain injury. This damage can open the blood-brain barrier and allow drug passage. An ultraperformance liquid chromatography with tandem mass spectrometry method was established to determine the concentration of rhein in the biofluids (plasma and cerebrospinal fluid) of patients with a compromised blood-brain barrier following traumatic brain injury after rhubarb administration. Furthermore, the pharmacokinetic profiles were analyzed. A triple-quadruple tandem mass spectrometer with electrospray ionization was used for rhein detection. The mass transition followed was m/z 283.06→239.0. The calibration curve was linear in the concentration range of 10-8000 ng/mL for the biofluids. The intra- and interday precisions were less than 10%. The relative standard deviation of recovery was less than 15% in biological matrices. The pharmacokinetic data showed that rhein was rapidly transported into biofluids, and exhibited a peak concentration 1 h after rhubarb administration. The elimination rate of rhein was slow. The AUCcerebrospinal fluid /AUCplasma (AUC is area under curve) of rhein was approximately 17%, indicating that portions of rhein could pass the impaired blood-brain barrier. The method was successfully applied to quantify rhein in the biofluids of all patients. The data presented can help to guide clinical applications of rhubarb for treating traumatic brain injury.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

propy: a tool to generate various modes of Chou's PseAAC.

SUMMARY: Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. AVAILABILITY: The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChemoPy: freely available python package for computational biology and chemoinformatics.

MOTIVATION: Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. AVAILABILITY: The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

pH-dependent coordination of Pb2+ to metallothionein2: structures and insight into lead detoxification.

Lead is a toxic heavy metal whose detoxification in organisms is mainly carried out by its coordination with some metalloproteins such as metallothioneins (MTs). Two Pb-MT complexes, named as Pb7-MT2(I) and Pb7-MT2(II), form under neutral and weakly acidic conditions, respectively. However, the structures of the two complexes, which are crucial for a better understanding of the detoxification mechanism of Pb-MTs, have not been clearly elucidated. In this Work, coordination of Pb(2+) with rabbit liver apo-MT2, as well as with the two individual domains (apo-αMT2 and apo-ßMT2) at different pH, were studied by combined spectroscopic (UV-visible, circular dichroism, and NMR) and computational methods. The results showed that in Pb7-MT2(I) the Pb(2+) coordination is in the trigonal pyramidal Pb-S3 mode, whereas the Pb7-MT2(II) complex contains mixed trigonal pyramidal Pb-S3, distorted trigonal pyramidal Pb-S2O1, and distorted quadrilateral pyramidal Pb-S3O1 modes. The O-donor ligand in Pb7-MT2(II) was identified as the carboxyl groups of the aspartic acid residues at positions 2 and 56. Our studies also revealed that Pb7-MT2(II) has a greater acid tolerance and coordination stability than Pb7-MT2(I), thereby retaining the Pb(2+) coordination at acidic pH. The higher flexibility of Pb7-MT2(II) renders it more accessible to lysosomal proteolysis than Pb7-MT2(I). Similar spectral features were observed in the coordination of Pb(2+) by human apo-MT2, suggesting a commonality among mammalian MT2s in the Pb(2+) coordination chemistry.

Selective iteratively reweighted quantile regression for baseline correction.

Extraction of qualitative and quantitative information from large numbers of analytical signals is difficult with drifted baselines, particularly in multivariate analysis. Baseline drift obscures and "fuzzies" signals, and even deteriorates analytical results. In order to obtain accurate and clear results, some effective methods should be proposed and implemented to perform baseline correction before conducting further data analysis. However, most of the classic methods require user intervention or are prone to variability, especially with low signal-to-noise signals. In this study, a novel baseline correction algorithm based on quantile regression and iteratively reweighting strategy is proposed. This does not require user intervention and prior information, such as peak detection. The iteratively reweighting strategy iteratively changes weights of residuals between fitted baseline and original signals. After a series of tests and comparisons with several other popular methods, using various kinds of analytical signals, the proposed method is found to be fast, flexible, robust, and easy to use both in simulated and real datasets.

Separation of nine compounds from Salvia plebeia R.Br. using two-step high-speed counter-current chromatography with different elution modes.

Nine compounds were successfully separated from Salvia plebeia R.Br. using two-step high-speed counter-current chromatography with three elution modes. Elution-extrusion counter-current chromatography was applied in the first step, while classical counter-current chromatography and recycling counter-current chromatography were used in the second step. Three solvent systems, n-hexane/ethyl acetate/ethanol/water (4:6.5:3:7, v/v), methyl tert-butyl ether/ethyl acetate/n-butanol/methanol/water (6:4:1:2:8, v/v) and n-hexane/ethyl acetate/methanol/water (5:5.5:5:5, v/v) were screened and optimized for the two-step separation. The separation yielded nine compounds, including caffeic acid (1), 6-hydroxyluteuolin-7-glucoside (2), 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside (3), nepitrin (4), rosmarinic acid (5), homoplantaginin (6), nepetin (7), hispidulin (8), and 5,6,7,4'-tertrahydroxyflavone (9). To the best of our knowledge, 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside and 5,6,7,4'-tertrahydroxyflavone have been separated from Salvia plebeia R.Br. for the first time. The purities and structures of these compounds were identified by high-performance liquid chromatography, electrospray ionization mass spectrometry, (1)H and (13)C NMR spectroscopy. This study demonstrates that high-speed counter-current chromatography is a useful and flexible tool for the separation of components from a complex sample.

Joint MS-based platforms for comprehensive comparison of rat plasma and serum metabolic profiling.

Metabolomics is a rapidly growing field in the comprehensive understanding of cellular and organism-specific responses associated with perturbations induced by medicines, chemicals and environment. Blood matrices are frequently used in clinical and biological studies. In this study, we compared metabolic profiling between rat plasma and serum using complementary platforms of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadruple time-of-flight-mass spectrometry (LC-QTOF-MS). The sample types that were tested included plasma prepared with K2 EDTA and serum collected using venous blood collection protocols. The results of peak area variation for each detected metabolite/feature in the quality control samples showed a good reproducibility in LC-QTOF-MS and better reproducibility in GC-MS. In GC-MS analysis: (a) 25.8% of the defined metabolites differed serum from plasma profiling (t-test, p < 0.05); and (b) serum possessed higher sensitivity than plasma for its generally higher peak intensity in the metabolic profiling. In LC-QTOF-MS analysis, 13 (in positive ion mode) and seven (in negative ion mode) important metabolites were identified as mainly contributing to the separation between serum and plasma.

Ferrocenoyl phenylalanine: a new strategy toward supramolecular hydrogels with multistimuli responsive properties.

In this paper we present a new paradigm for designing hydrogelators that exhibit sharp phase transitions in response to a series of disparate stimuli, including oxidation-reduction reactions (redox), guest-host interactions, and pH changes. We have serendipitously discovered that ferrocenoyl phenylalanine (Fc-F) monomers aggregate in water via a rapid self-assembly mechanism to form stable, multistimuli hydrogels. In comparison to other known mono- and multiresponsive gelators, Fc-F is unique because of its small size, economy of gel-forming components, and exceptionally simple molecular structure. Density functional theory (DFT) ab initio calculations suggest gel formation initially involves an antiparallel, noncovalent dimerization step wherein the ferrocenoyl moiety of one axe-like monomer conjoins with the phenyl group of the second monomer via a π-π stacking interaction to form brick-like dimers. This stacking creates a cavity in which the carboxylic acid groups of each monomer mutually interact via hydrogen bond formation, which affords additional stability to the dimer. On the basis of structural analysis via optical and electrical measurements and additional DFT calculations, we propose a possible stepwise hierachical assembly mechanism for fibril formation. Insights into the self-assembly pathway of Fc-F should prove useful for understanding gelation processes of more complex systems. We expect that Fc-F will serve as a helpful archetypical template for others to use when designing new, stimuli specific hydrogelation agents.

Designing of molecularly imprinted polymer-based potentiometric sensor for the determination of heparin.

A novel potentiometric sensor with high selectivity and sensitivity has been developed for the determination of heparin, based on the modification of heparin-imprinted polymer film onto a glassy carbon. The performance of the developed heparin sensor was evaluated, and the results indicated that a sensitive potentiometric sensor could be fabricated. The obtained heparin sensor shows high-selectivity monitoring of heparin, shorter response time (<4 min), wider linear range (0.003-0.7 µM), lower detection limit (0.001 µM), and satisfactory long-term stability (>2 months). The potentiometric sensor was successfully applied to the determination of heparin in heparin sodium injection with recoveries between 97.1% and 110.0%.

A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems.

Classical calibration and inverse calibration are two kinds of multivariate calibration in chemical modeling. They use strategies of modeling in component spectral space and in measured variable space, respectively. However, the intrinsic difference between these two calibration models is not fully investigated. Besides, in the case of complex analytical systems, the net analyte signal (NAS) cannot be well defined in inverse calibration due to the existence of uninformative and/or interfering variables. Therefore, application of the NAS cannot improve the predictive performance for this kind of calibration, since it is essentially a technique based on the full-spectrum. From our perspective, variable selection can significantly improve the predictive performance through removing uninformative and/or interfering variables. Although the need for variable selection in the inverse calibration model has already been experimentally demonstrated, it has not aroused so much attention. In this study, we first clarify the intrinsic difference between these two calibration models and then use a new perspective to intrinsically prove the importance of variable selection in the inverse calibration model for complex analytical systems. In addition, we have experimentally validated our viewpoint through the use of one UV dataset and two generated near infrared (NIR) datasets.