Visibility graph-based segmentation of multivariate time series data and its application.

In this paper, we propose an efficient segmentation approach in order to divide a multivariate time series through integrating principal component analysis (PCA), visibility graph theory, and community detection algorithm. Based on structural characteristics, we can automatically divide the high-dimensional time series into several stages. First, we adopt the PCA to reduce the dimensions; thus, a low dimensional time series can be obtained. Hence, we can overcome the curse of dimensionality conduct, which is incurred by multidimensional time sequences. Later, the visibility graph theory is applied to handle these multivariate time series, and corresponding networks can be derived accordingly. Then, we propose a community detection algorithm (the obtained communities correspond to the desired segmentation), while modularity Q is adopted as an objective function to find the optimal. As indicated, the segmentation determined by our method is of high accuracy. Compared with the state-of-art models, we find that our proposed model is of a lower time complexity (O(n3)), while the performance of segmentation is much better. At last, we not only applied this model to generated data with known multiple phases but also applied it to a real dataset of oil futures. In both cases, we obtained excellent segmentation results.

Study on modification of collagen with chondroitin sulfate on the microcosmic level.

To observe the changes during the process of modificating collagen with chondroitin sulfate (CS), a series of experiments was designed. Collagen scaffold was constructed by lyophilization. CS was attached into the scaffold through crosslinking. Solid-liquid phase separation method was used to shape 3D porous structure. Both kinds of scaffolds were investigated by Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), and Electron Spectroscopy for Chemical Analysis (ESCA), Amino Acid Analyses (AAA), and so on for structures and characteristics. The results showed that CS could improve the properties of collagen both in biocompatibility and mechanical property. It increased the polar groups on the surface of scaffold and protected amino acid residues of collagen fiber. At the same time, the structure of scaffold became more regular because of the increase of polar groups. It was indicated that those were the real reasons for the improvement of modification.

Evaluation of modifying collagen matrix with RGD peptide through periodate oxidation.

The aim of the study is to evaluate the effect of modifying collagen matrices with Arg-Gly-Asp (RGD) peptide through periodate oxidation. The collagen matrices were modified with RGD peptide, by periodate activation. The modified collagen matrices and unmodified matrices were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and electron spectroscopy for chemical analysis (ESCA). Mesenchymal stem cells (MSCs) were used to evaluate the cell compatibility of collagen matrices. In terms of cell growth, the MSCs attached much better on the modified matrix than on the unmodified one. But there was no significant difference between two groups regarding the MSC proliferation. Compared to the unmodified matrices, the mechanical strength of the modified matrix decreased sharply, and its 3D structure was destroyed. Introducing specific RGD receptor-mediated adhesion sites on matrices obviously enhanced the MSC adhesion on collagen matrices, but the coupled method of periodate oxidation would likely result in the declination of the mechanical strength of the matrix, as well as the destruction of the matrix structure. This would affect the cell growth on the matrix, and decrease the histocompatibility of the matrices.