[Construction of Recombinant Full-length Hepatitis E Virus Fused with EGFP and Assessment of Infectivity].

The purpose of this study was to construct recombinant full-length hepatitis E virus(HEV)fused with enhanced green fluorescent protein(EGFP),and assess its infectivity in A549 cells. Two fragments from the full-length HEV genome and the EGFP gene were amplified by PCR. The EGFP gene was inserted downstream of the HEV ORF2 and then cloned into the pGEM® -7Zf(+)vector containing the T7 and SP6RNA polymerase promoters, producing pGEM-HEV-EGFP. The construction of the pGEM-HEV-EGFP recombinant plasmid was confirmed by restriction enzyme digest and sequencing. The pGEM-HEV-EGFP recombinant plasmid was transfected into A549 cells to assess infectivity using Lipofectamine. EGFP expression was observed at 24hpost-transfection,and expression of the HEV ORF2 was detected by immunofluorescence, confirming the presence of the HEV ORF2 and EGFP fusion protein. Cytopathic effects were observed at day seven post-transfection. The infectivity of pGEM-HEV-EGFP was confirmed by the presence of fluorescence after three continuous passages. The recombinant pGEM-HEV-EGFP vector was successfully constructed and effectively infected A549 cells, which will facilitate future studies on the mechanisms of HEV infection and pathogenesis.

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.