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Previous studies have shown that the composition and function of gut microbiota possibly contribute to the oxidative stress and host metabolism of sows. However, a functional link between gut bacteria with oxidative stress and stillbirth rate of sows remain unclear. To address this issue, the reproductive performance, oxidative stress and gut microbiota of sows with high (H) and low (L) stillbirth rate were analyzed. Results showed that, compared with the H group, the L group had a shorter farrowing duration as well as higher concentration of serum total antioxidant capacity and hydroxyl radical scavenging capacity. For the gut microbiota composition of the tested sows, 6 genera differed between the two groups, 7 genera were correlative with stillbirth rate, and 2 genera were correlated with farrowing duration. The relative abundance of Lachnospiraceae_UCG-001, Marvinbryantia and Ruminococcaceae_UCG-004 were negatively correlated with antioxidant capacity, but positively correlated with the stillbirth rate of sows. Furthermore, the microbiota functions in the polyketide sugar unit biosynthesis and nitrotoluene degradation were found to be different between the two groups through the phylotypic investigation of communities by reconstruction of unobserved states. Collectively, gut microbiota and their functions vary between sows with high or low stillbirth rate, while stillbirth rate and farrowing duration are significantly correlated with the gut microbiota composition and oxidative stress status of sows.

With the advances in innovative instrumentation and various valuable applications, near-infrared (NIR) spectroscopy has become a mature analytical technique in various fields. Variable (wavelength) selection is a critical step in multivariate calibration of NIR spectra, which can improve the prediction performance, make the calibration reliable and provide simpler interpretation. During the last several decades, there have been a large number of variable selection methods proposed in NIR spectroscopy. In this paper, we generalize variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences. We also introduce some hybrid and modified methods, highlighting their improvements. Finally, we summarize the limitations of existing variable selection methods, providing our remarks and suggestions on the development of variable selection methods, to promote the development of NIR spectroscopy.

Gallic acid (GA) is a natural polyphenolic compound with many health benefits. To assess the potential risk of long-term consumption of GA to gut health, healthy dogs were fed a basal diet supplemented with GA (0%, 0.02%, 0.04%, and 0.08%) for 45 d, and fecal microbiota and metabolomics were evaluated. This study demonstrated that GA supplementation regulated serum lipid metabolism by reducing serum triglyceride, fat digestibility, and Bacteroidetes/Firmicutes ratio. In addition, the relative abundance of

Model evaluation plays a central role in chemical modeling. Model population analysis (MPA), a general framework for designing new types of chemometrics algorithms, has shown its advantage in the field of model evaluation. The core idea of MPA is to statistically analyze the outputs of randomly generated sub-models to extract interesting information from the data. One of the most obvious characteristics of MPA-based methods is that they use multiple models instead of a single model for model evaluation. In this review, we described the concept of MPA, and then discussed the application of MPA in model evaluation, including the relationship between MPA and cross-validation, model comparison, randomization tests, model stability, variable importance and sum of rank differences. Finally, we prospected the potential application of MPA in model evaluation.

Electron energy–loss spectroscopy (EELS) equipped on a modern scanning transmission electron microscope is a powerful high resolution elemental analysis technique. However, the conventional method of extracting elemental information quantitatively from EELS spectra is non-trivial. In this article we tried to interpret EELS data from the viewpoint of chemometrics. An EELS spectrum image collected from a vicinal SiC/SiO2 interface was employed as an example. By analyzing the EELS data with sophisticated chemometrics methods, chemical components and elemental ratios of C/Si and O/Si across the interface were derived. Compared with conventional EELS analysis, the chemometrics methods gave more reliable and accurate results with higher calculation efficiency. The chemometrics analysis confirmed that the transition from SiC to SiO2 was chemically sharp, and the transition layer in the vicinal interface was a linear combination of SiC and SiO2. Chemometrics analysis can be a fast and robust complementary method for EELS analysis.

Variable selection methods have been widely used for dimension reduction and improved interpretability when analyzing high-dimension data, such as spectral data and gene expression microarray data. An interesting property of the spectral data is that consecutive variables carry similar information. In other words, a spectral variable is naturally akin to another spectral variable with a close wavelength, which indicates that the regression coefficients of consecutive wavelengths have close values. Based on the above fact, a new block variable selection method named Fisher optimal subspace shrinkage (FOSS), is proposed by using the Fisher optimal partitions algorithm. Unlike most of the existing interval selection methods, FOSS uses information from the regression coefficients of partial least squares (PLS) models to adaptively split variables into some intervals that can have unequal-width. Then, these intervals are selected by weighted block bootstrap sampling (WBBS). The weights of sub-intervals are determined by the mean of the absolute values of regression coefficients of the corresponding interval. The FOSS method is useful particularly when the correlations among the variables are high. We illustrate the performance of the proposed method on three near-infrared (NIR) spectroscopy datasets. Five high-performance variable selection methods are used for comparison. Empirical studies on three real-world datasets under different performance metrics show that FOSS compares favorably to its competitors.

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Identifying damage initiation and development in engineering structures non-parametrically in the form of a nonlinear restoring force (NRF) after strong dynamic loading is attractive. Due to the individuality of various engineering structures, it is quite challenging to assume, in advance, a general parametric model describing the nonlinear behavior. Although a traditional extended Kalman filter (EKF) is efficient in state vector estimation and structural parameter identification with partially available output measurements, a known structural mass is usually required. In this study, a simultaneous NRF and mass identification approach is developed for multi-degree-of-freedom (MDOF) structures using the EKF with weighted global iteration (EKF-WGI) based on limited available absolute acceleration response. The NRF is modeled in a nonparametric way with a power series polynomial model (PSPM) as a function of unknown structural displacement and velocity responses. Then, the performance of the new approach is numerically evaluated using multi-story structures equipped with magneto-rheological (MR) dampers having known applied excitations and partially available noise-contaminated acceleration measurements, but unknown mass. No parametric model for the NRF of the MR dampers is employed. The effect of different noise levels and different initial estimation errors of structural mass on both NRF and mass identification results and the convergence of the approach are investigated. Finally, a dynamic test on a four-story frame structure equipped with an MR damper is carried out and the algorithm is experimentally validated. Comparisons show that the identified NRF provided by the MR damper matches the measurement and that the identified mass is also accurate.

Partial least squares (PLS) and principal component regression (PCR) are two widely used techniques for dimension reduction in chemometrics. However, the relationship between PLS and PCR is not entirely understood. In this paper, we introduce the idea of sufficient dimension reduction (SDR) to chemometrics, and show that PLS and PCR are methods of SDR. Furthermore, this paper shows that these two methods are equivalent within the framework of SDR which means that there is no theoretical advantage of PLS over PCR in terms of prediction performance. The above conclusion is supported by the results of a simulated dataset and three real datasets.

Model population analysis (MPA) is a general framework for designing new types of chemometrics algorithms that has attracted increasing interest in the chemometrics community in recent years. The goal of MPA is to extract statistical information from the model, towards better understanding of the chemical data. Two key elements of MPA are random sampling and statistical analysis. The core idea of MPA is quite universal with potential applications in the fields, such as chemoinformatics, biostatistics and bioinformatics. In this article, we review the development of MPA in chemometrics. We first present the key elements of MPA. Then, the application of MPA in chemometrics is discussed, such as variable selection, model evaluation, outlier detection, applicability domain definition and so on. Finally, the potential application areas of MPA in future research are prospected.

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the kernel fusion technique, together with the state-of-the-art support vector machine (SVM) algorithm, was developed to classify the toxicity of chemicals from Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, different kernels were firstly constructed by applying different molecular fingerprint systems, including FP2, FP4 and MACCS, and then these kernels were integrated to form a new fused kernel strictly under the algorithmic framework of kernel methods. The fused kernel can accurately measure the similarities of molecules for the toxicity classification, taking advantage of the complementarity in multiple kernels and therefore improving the prediction performance. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The obtained results indicate that the kernel fusion-based SVM gave the best prediction ability compared to single fingerprint kernels, and therefore could be regarded as a very promising and alternative modeling approach for potential toxicity prediction of chemicals.

Partial least squares (PLS) is one of the most widely used methods for chemical modeling. However, like many other parameter tunable methods, it has strong tendency of over-fitting. Thus, a crucial step in PLS model building is to select the optimal number of latent variables (nLVs). Cross-validation (CV) is the most popular method for PLS model selection because it selects a model from the perspective of prediction ability. However, a clear minimum of prediction errors may not be obtained in CV which makes the model selection difficult. To solve the problem, we proposed a new strategy for PLS model selection which combines the cross-validated coefficient of determination (Qcv(2)) and model stability (S). S is defined as the stability of PLS regression vectors which is obtained using model population analysis (MPA). The results show that, when a clear maximum of Qcv(2) is not obtained, S can provide additional information of over-fitting and it helps in finding the optimal nLVs. Compared with other regression vector based indictors such as the Euclidean 2-norm (B2), the Durbin Watson statistic (DW) and the jaggedness (J), S is more sensitive to over-fitting. The model selected by our method has both good prediction ability and stability.

Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of 'survival of the fittest' from Darwin's natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm-partial least squares (GA-PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750.

Host defense peptides (HDPs) are crucial components of the body's first line of defense that protect organisms from infections and mediate immune responses. Defensins and cathelicidins are the two most important families of HDPs in mammals. In this review, we summarize the nutrients that are involved in inducible expression of endogenous defensins and cathelicidins. In addition, the mitogen-activated protein kinases (MAPK), nuclear factor kappa B (NF-κB) and histone deacetylase (HDAC) signaling pathways that play vital roles in the induction of defensin and cathelicidin expression are highlighted. Endogenous defensins and cathelicidins induced by nutrients may be potential alternatives to antibiotic treatments against infection and diseases. This review mainly focuses on the inducible expression and regulatory mechanisms of defensins and cathelicidins in multiple species by different nutrients and the potential applications of defensin- and cathelicidin-inducing nutrients.

article i nfo Available online xxxx This review focuses on the recent and potential advances of currently available chemometric methods in relation to data processing in plant metabolomics, especially for the data generated from the mass spectrometry (MS) tech- niques. Recently, plant metabolomics has been gradually regarded as a valuable and promising biotechnology rather than an ambitious advancement. We here outline some significant developments of plant metabolomics, especially, in the combination of modern chemical analysis techniques, dedicated statistical, and chemometric data analysis strategies. The advanced skills in the preprocessing of raw data, identification of metabolites, variable selection and modeling are illustrated. We believe that the insights into these developments are helpful to narrow down the knowledge gap between the molecular organization and metabolism control of plants. We here also discuss the limitations and perspectives in extracting information from high-throughput datasets.