Reduced multiple empirical kernel learning machine.

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient.

Analysis of epitopes in the capsid protein of avian hepatitis E virus by using monoclonal antibodies.

Avian hepatitis E virus (HEV) is related genetically and antigenically to human and swine HEVs and capsid protein of avian HEV shares approximately 48-49% amino acid sequence identities with those of human and swine HEVs. Six monoclonal antibodies (MAbs) were produced and used to locate different epitopes in the ORF2 region of aa 339-570 of avian HEV Chinese isolate. The results showed that five epitopes were located in the aa 339-414 region and one in the aa 510-515 region. Two epitopes located in aa 339-355 and aa 384-414 regions are the immunodominant epitopes on the surface of the avian HEV particles as demonstrated by immune capture of viral particles and immunohistochemical detection of the ORF2 antigens with two MAbs.