CO2 Flux Estimation by Different Regression Methods from an Alpine Meadow on the Qinghai-Tibetan Plateau

National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381]; Chinese Academy of Sciences [KZCX2-YW-432]

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Evaluation of different culture conditions for high-level soluble expression of human cyclin A(2) with pET vector in BL21 (DE3) and spectroscopic characterization of its inclusion body structure

In this paper, we evaluated various parameters of culture condition affecting high-level soluble expression of human cyclin A, in Escherichia coli BL21(DE3), and demonstrated that the highest protein yield was obtained using TB(no glycerol) + 0.5% glucose medium at 25 degrees C. By single immobilized metal ion affinity chromatography, we got highly purified human cyclin A(2) with a yield ranged from 20 to 30 mg/L. By amyloid-diagnostic dye ThT binding and Fourier transform infrared spectroscopy, we observed a significant decrease in alpha-helix content and an increase in beta-sheet structure in cyclin A(2) inclusion body in comparison to its native protein, and confirmed the resemblance of the internal organization of cyclin A(2) inclusion body and amyloid fibrils.

Rapid synthesis of Mn0.65Zn0.35Fe2O4/SiO2 homogeneous nanocomposites by modified sol-gel auto-combustion method

MnZn-ferrite/SiO2 nanocomposites with different silica content were successfully fabricated by a novel modified sol-gel auto-combustion method using citric acid as a chelating agent and tetraethyl orthosilicate (TEOS) as the source of silica matrix. The auto-combustion nature of the dried gel was studied by X-ray diffraction (XRD), Infrared spectra (IR), thermogravimetry (TG) and differential thermal analysis (DTA). Transmission electron microscope (TEM) observation shows that the MnZn-ferrite particles are homogeneously dispersed in silica matrix after auto-combustion of the dried gels. The magnetic properties vary with the silica content. The transition from the ferromagnetic to paramagnetic state is observed by Mossbauer spectra measurement with the increasing silica content. Vibrating sample magnetometer (VSM) shows that the magnetic properties of Mn0.65Zn0.35Fe2O4/SiO2 nanocomposites strongly depend on the silica content.

Synthesis of nanocrystalline Ba(CoxZn1-x)(2)Fe16O27 by sol-gel auto-combustion method

The synthesis of nanocrystalline W-type hexaferrites Ba(CoxZn1-x)(2)Fe16O27 powders by sol-gel auto-combustion method has been investigated. The thermal decomposition process of dried gel was studied by thermogravimetry (TG), differential thermal analysis (DTA) and infrared spectra (IR). The structural and magnetic properties of resultant particles were investigated by X-ray diffraction (XRD), transmission electron microscope (TEM), and vibrating sample magnetometer (VSM). The results reveal that the dried gel exhibits auto-combustion behavior. After combustion, pure nanocrystalline W-type hexaferrite phase starts to appear at the calcination temperature of 800 degrees C. The crystallinity and the grain size increase at higher temperature. The saturation magnetization and coercivity clearly depend on calcination temperature and Co content X.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

Studies on the method of leaps and bounds regression

In this paper, we introduce the method of leaps and bounds regression which can be used to select variables quickly and obtain the best regression models. These models contain one variable, two variables, three variables and so on. The results obtained by using leaps and bounds regression were compared with those achieved by using stepwise regression to lead to the conclusion that leaps and bounds regression is an effective method.

Prediction of molar absorptivities of color reagents during their color reactions with cerium using multiple regression analysis and neural network

A quantitative structure-property study has been made on the relationship between molar absorptivities (epsilon) of asymmetrical phosphone bisazo derivatives of chromotropic acid and their color reactions with cerium by multiple regression analysis and neural network. The new topological indices A(x1) - A(x3) suggested in our laboratory and molecular connectivity indices of 43 compounds have been calculated. The results obtained from the two methods are compared. The neural network model is superior to the regression analysis technique and gave a prediction which was sufficiently accurate to estimate the molar absorptivities of color reagents during their color reactions with cerium.

Study on Phenol Compounds Using Multiple Regression and Neural Network Methods

In this paper, the molecular connectivity indices and the electronic charge parameters of forty-eight phenol compounds nave been calculated. and applied for studying the relationship between partition coefficients and structure of phenol compounds. The results demonstrate that the properties of compounds can be described better with selective parameters, and the results obtained by neural network are superior to that by multiplle regression.

Quantitative structure-property relationships for colour reagents and their colour reactions with ytterbium using regression analysis and computational neural networks

In this paper, the new topological indices A(x1)-A(x3) suggested in our laboratory and molecular connectivity indices have been applied to multivariate analysis in structure-property studies. The topological indices of twenty asymmetrical phosphono bisazo derivatives of chromotropic acid have been calculated. The structure-property relationships between colour reagents and their colour reactions with ytterbium have been studied by A(x1)-A(x3) indices and molecular connectivity indices with satisfactory results. Multiple regression analysis and neural networks were employed simultaneously in this study.

QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION-ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS

Quantitative structure-toxicity models were developed that directly link the molecular structures of a et of 50 alkYlated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitor