Correlation analysis is one of the most important tasks in the field of visualization research and data mining. This paper proposes a novel dissimilarity-preserving cluster algorithm that characterizes not only the time-varying patterns but also the spatial positions to summary the correlation connection in multi-variable and time-varying data sets. A temporal multi-variable structure is defined to express temporal information of a voxel in multi-dimensional space. Furthermore, a method based on structural similarity index measurement is proposed to compute the difference of time-varying pattern. In order to further explore some abnormal phenomena, spatial similarity is embedded as spatial distance metric by building the kernel density estimate for the neighborhood of each voxel. To verify the effectiveness of the method, the voxels are classified based on the time-varying similarity and spatial distance. Moreover, the combinations of two metrics are rebalanced to be suitable for the different datasets. The approach proposed in this paper is used on both synthetic and real-world data sets to demonstrate its usefulness and effectiveness.

Identification of epitopes which invokes strong humoral responses is an essential issue in the field of immunology. Various computational methods that have been developed based on the antigen structures and the mimotopes these years narrow the search for experimental validation. These methods can be divided into two categories: antigen structure-based methods and mimotope-based methods. Though new methods of the two kinds have been proposed in these years, they cannot maintain a high degree of satisfaction in various circumstances. In this paper, we proposed a new conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis. The method classifies the antigen surface residues into “epitopes” and “nonepitopes” by six epitope propensity scales, removing the “nonepitopes” and using the preprocessed antigen for epitope prediction based on mimotope sequences. The proposed method gives out the meanFscore of 0.42 on the testing dataset. When compared with other publicly available servers by using the testing dataset, the new method yields better performance. The results demonstrate the proposed method is competent for the conformational B-cell epitope prediction.

Generating large scale terrains that conform to the morphology of real scenes is a great challenge for terrain modeling, as simulating complex geometric details is time-consuming and the realistic geographical features are hard to be controlled. In this paper, we propose an efficient modeling method for large scale terrain visualization based on hydrology. To simulate real geographic features, we introduce the hydrology-based Tokunaga river network to guide the terrain generation, and propose a production rule set of river network using procedural modeling. The distribution and structure of river network can be adjusted by user interactions. Ridges are extracted based on river network to provide more skeleton features, and the enrichment method of skeleton features is presented to maintain the morphology of valleys and ridges. Based on the enriched features, diffusion equation is exploited to compute the full elevation field, which can achieve the nature transitions of the regions between skeleton features. Large scale terrain with real morphological features can be generated online through the parallel implementation of diffusion equation. According to user requirements, the augmented virtual terrain can be obtained by blending the selected real terrain with the synthesis terrain seamlessly. The experiments are conducted on digital elevation model, and the results show that the proposed methods can generate large scale terrains that conform to the morphology of real terrain and can well simulate various natural scenes.