Identification of Cald1 as a novel regulator of Linggui Zhugan decoction for improving insulin resistance in vivo and in vitro.

OBJECTIVE: To identify Cald1 as a novel regulator of Linggui Zhugan decoction for improving insulin resistance in vivo and in vitro. METHODS: Sprague-Dawley rats were randomly assigned to 3 groups that were received a normal rat chow diet, high-fat diet (HFD), and an HFD plus LGZGD, respectively. The homeostatic model assessment (HOMA)-insulin resistance (IR) index was used to determine IR. Gene microarray methodology was used to identify differentially expressed genes (DEGs) in the three groups of rats. The DEGs associated with IR were confirmed by quantitative real-time polymerase chain reaction. Additionally, Mouse 3T3-L1 pre-adipocytes were differentiated into mature 3T3-L1 adipocytes, which were then treated with tumor necrosis factor (TNF)-α to induce cellular IR. Lipid accumulations were identified by Oil Red O staining. Glucose uptake was assessed using the 3 H-2-DG test. RESULTS: In this study, we found Cald1 was further screened to validate its biological function in 3T3-L1 adipocytes induced to develop IR. In vitro experiments showed that insulin-stimulated 3H2-DG uptake by IR 3T3-L1 adipocytes was increased after LGZGD intervention, which was associated with a down-regulation of Cald1 expression. CONCLUSION: LGZGD ameliorates HFD-induced IR in rats and TNF-α induced IR in adipocytes by down-regulating Cald1 expression.

Genetic variation of rs3811463 is associated with gestational diabetes mellitus susceptibility.

Gestational diabetes mellitus (GDM) is a growing health concern, and it increases the risk of adverse pregnancy outcomes with substantial long-term adverse health impacts on mothers and their offspring. Several studies have revealed specific associations between genetic variants and the risk of GDM. Single nucleotide polymorphisms (SNPs) are the major type of genetic variation in humans. Let-7 microRNA targets are enriched for genes containing SNPs associated with glucose metabolism, including Lin28. In the present study, the effect of T/C variants of rs3811463 (a SNP located near to the let-7 binding site in Lin28) on GDM risk was investigated. A GDM rat model was successfully constructed using a high fat diet and streptozotocin injection, and the primary skeletal muscle cells were isolated. The cell transfection results demonstrated that rs3811463-T/C significantly affected the glucose uptake and insulin sensitivity. Reverse transcription-quantitative polymerase chain reaction analysis indicated that the C allele at rs3811463 regulated the expression of glucose metabolism-associated genes insulin-like growth factor two binding protein 2 and glucokinase. Western blot analysis data revealed that replacement of the T allele by the C allele at rs3811463 modulated the protein level of Sirtuin 1. Taken together, it was concluded that the let-7/Lin28 axis regulated glucose uptake and insulin sensitivity by modulating the expression of glucose metabolism-associated proteins. These findings provide novel evidence on the association between genetic variations of rs3811463 and GDM susceptibility.

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.

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.

A bootstrapping soft shrinkage approach for variable selection in chemical modeling.

In this study, a new variable selection method called bootstrapping soft shrinkage (BOSS) method is developed. It is derived from the idea of weighted bootstrap sampling (WBS) and model population analysis (MPA). The weights of variables are determined based on the absolute values of regression coefficients. WBS is applied according to the weights to generate sub-models and MPA is used to analyze the sub-models to update weights for variables. The optimization procedure follows the rule of soft shrinkage, in which less important variables are not eliminated directly but are assigned smaller weights. The algorithm runs iteratively and terminates until the number of variables reaches one. The optimal variable set with the lowest root mean squared error of cross-validation (RMSECV) is selected. The method was tested on three groups of near infrared (NIR) spectroscopic datasets, i.e. corn datasets, diesel fuels datasets and soy datasets. Three high performing variable selection methods, i.e. Monte Carlo uninformative variable elimination (MCUVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm partial least squares (GA-PLS) are used for comparison. The results show that BOSS is promising with improved prediction performance. The Matlab codes for implementing BOSS are freely available on the website: http://www.mathworks.com/matlabcentral/fileexchange/52770-boss.

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 .

[Monitoring report of Oncomelania hupensis snail distribution and diffusion in main drainages of Hexi Reservoir].

OBJECTIVE: To understand the status of Oncomelania hupensis snail distribution and diffusion in main drainages of Hexi Reservoir and evaluate the snail control effect of the schistosomiasis control engineering of Hexi Reservoir. METHODS: The O. hupensis snails were investigated by using the straw curtain method and fishing net method in different areas of the main drainages of Hexi Reservoir, and the results were analyzed. RESULTS: A total of 1 800 straw curtains were used and 37 snails were found in Naxi stream. Totally 5 870 kg floats were salved and no snails were found. CONCLUSION: The schistosomiasis control engineering of Hexi Reservoir is effective in the prevention of the snail diffusion, but there are still snails in the upstream. rherefore, the snail surveillance and control need to be strengthened.

Systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient model for real-life counter-current chromatography separation.

Solvent system selection is the first step toward a successful counter-current chromatography (CCC) separation. This paper introduces a systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model, which is efficient in predicting the solute partition coefficient. Firstly, the application of the NRTL-SAC method was extended to the ethyl acetate/n-butanol/water and chloroform/methanol/water solvent system families. Moreover, the versatility and predictive capability of the NRTL-SAC method were investigated. The results indicate that the solute molecular parameters identified from hexane/ethyl acetate/methanol/water solvent system family are capable of predicting a large number of partition coefficients in several other different solvent system families. The NRTL-SAC strategy was further validated by successfully separating five components from Salvia plebeian R.Br. We therefore propose that NRTL-SAC is a promising high throughput method for rapid solvent system selection and highly adaptable to screen suitable solvent system for real-life CCC separation.

Enhancement of secondary metabolites from Bacillus Licheniformis XY-52 on immune response and expression of some immune-related genes in common carp, Cyprinus carpio.

This study was undertaken to isolate active secondary metabolites from immunostimulatory Bacillus Licheniformis XY-52 and evaluate their activities at 0%, 0.5%, 1.0%, and 2.0% doses supplementation with feed on immune response in common carp at weeks 1, 2, and 3. By applying chromatography techniques and successive recrystallization, two purified metabolites were obtained and identified by spectral data (mass spectrometry and nuclear magnetic resonance) as: Cyclo-(Phe-Tyr) and Cyclo-(Phe-Gly). The results revealed that humoral innate immune parameters (lysozyme activity, phagocytic activity and bactericidal activity) were significantly (P < 0.05) increased after feeding on the two active compounds-supplemented diet. Furthermore, administration of the two active compounds significantly (P < 0.05) up regulated IL-1ß, Type 1 IFN, IFN g2b, IL10 and TNF-α gene expression. Heat shock protein 70 (HSP70) gene expression was significantly (P < 0.05) lower as compared to control group at the end of trial. Common carp fed with the two compounds had higher survival rates (69.3%) compared to the controls (32.0%) after challenged with the pathogen, Aeromonas hydrophila. The present study indicates that the two isolated active compounds could positively influence immune response and enhance disease resistance of common carp against A. hydrophila infection.

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.

Using nonrandom two-liquid model for solvent system selection in counter-current chromatography.

Selection of an appropriate solvent system is of great importance for a successful counter-current chromatography separation. In this work, the nonrandom two-liquid (NRTL) model, a thermodynamic method, was used for predicting the partition coefficient based on a few measured partition coefficients. The NRTL method provides quite satisfactory results for model solutes in first correlating measured partition coefficient in a few representative biphasic liquid systems and then successfully predicting partition coefficient in other two-phase liquid systems. According to the predicted partition coefficient, a suitable solvent system can be screened. Assisted with the NRTL method, the solvent system composed of hexane/ethyl acetate/methanol/water (1:4:1:4, v/v) was rapidly screened for the successful separation of two major compounds with high purity from Malus hupehensis leaves. The results demonstrated that the NRTL model can offer a simple and practical strategy to estimate partition coefficients in support of CCC solvent system selection, which will significantly minimize the experimental efforts and cost involved in solvent system selection.

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models.

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC-MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC-MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC-MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC-MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.

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.

[Effect of schistosomiasis control projects in Hexi Reservoir on Oncomelania snail control].

OBJECTIVE: To evaluate the effect of schistosomiasis control projects in Hexi Reservoir on Oncomelania hupensis snail control. METHODS: The canal hardening + main water system widening + the overflow dam project, the concrete slope protection, the banking and reclamation + concrete slope protection project, the environment reform project, and the comprehensive treatment were implemented in the tail area, the hydro-fluctuation belt, the rainwater harvesting zoon of the upstream area, the dam area, and the downstream area of the reservoir, respectively. The changes of the snail situation were investigated before and after the construction of the reservoir, and the snail control effects of the schistosomiasis control projects in different parts of the reservoir were analyzed. RESULTS: There were no Oncomelania snails found 3 years in the bottom area, dam area, hydro-fluctuation belt, tail region and downstream of the dam after the construction and storage of the reservoir and the implementation of the schistosomiasis control projects. In the rainwater harvesting zoon of the upstream area, the density of living snails decreased from 0.620 4 snails/0.1 m2 in 2009 to 0.113 2 snails/0.1 m2 in 2013, but the snail area still remained. CONCLUSIONS: The schistosomiasis control projects in Hexi Reservoir have effectively prevented the diffusion of Oncomelania snails from the rainwater harvesting zone of the upstream area to the dam area, and they are effective in the snail control.

Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features.

Identification and characterization of B-cell epitopes in target antigens was one of the key steps in epitopes-driven vaccine design, immunodiagnostic tests, and antibody production. Experimental determination of epitopes was labor-intensive and expensive. Therefore, there was an urgent need of computational methods for reliable identification of B-cell epitopes. In current study, we proposed a novel peptide feature description method which combined peptide amino acid properties with chemical molecular features. Based on these combined features, a random forest (RF) classifier was adopted to classify B-cell epitopes and non-epitopes. RF is an ensemble method that uses recursive partitioning to generate many trees for aggregating the results; and it always produces highly competitive models. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for current method were 78.31%, 80.05%, 72.23%, 0.5836, and 0.8800, respectively. These results showed that an appropriate combination of peptide amino acid features and chemical molecular features with a RF model could enhance the prediction performance of linear B-cell epitopes. Finally, a freely online service was available at http://sysbio.yznu.cn/Research/Epitopesprediction.aspx.

A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

Using random forest to classify T-cell epitopes based on amino acid properties and molecular features.

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.

Pharmacokinetic comparison of the vasorelaxant compound ferulic acid following the administration of Guanxin II to healthy volunteers and patients with angina pectoris.

Coronary heart disease (CHD) is the leading cause of mortality worldwide. The Chinese medicinal formula Guanxin II has been shown to have a favorable effect in the attenuation of angina. The aim of this study was to compare the pharmacokinetics of ferulic acid (FA), which is a vasorelaxant compound present in Guanxin II, in healthy volunteers and patients with angina pectoris following the administration of Guanxin II. Ex vivo experiments were performed in order to investigate the vasorelaxant effect of FA on the human internal mammary artery (IMA) to provide evidence that it is a bioactive component of Guanxin II. Following the oral administration of Guanxin II, the FA levels in the serum were quantified by a simple and rapid high-performance liquid chromatography (HPLC) method. Treatment with FA (10-8-10-3 M) caused a concentration-dependent relaxation of endothelial IMA rings following precontraction with KCl. Statistically significant differences were identified between the pharmaco-kinetic parameters Cmax, t1/2α, t1/2ß and t1/2Ka of the healthy volunteers and the patients with angina pectoris following the oral administration of Guanxin II. FA is a bioactive compound absorbed from Guanxin II that attenuates angina pectoris, a condition that may modify the pharmacokinetics of FA. Not only do the pharmacokinetic parameters direct the clinical use of Guanxin II, but they may also be useful for exploring the pathology of angina pectoris.

Selective of informative metabolites using random forests based on model population analysis.

One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex 'omics' data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list.

Investigation of scrambled ions in tandem mass spectra, part 2. On the influence of the ions on peptide identification.

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms.