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The c-erbB-2 proto-oncogene encodes a 185kDa protein p185, which belongs to epidermal growth factor receptor family. Amplification of this gene has been shown to correlate with poor clinical prognosis for certain cancer patients. The monoclonal antibody A21 which directed against p185 specifically inhibits proliferation of tumor cells overexpressing p185, hence allows it to be a candidate for targeted therapy. In order to overcome several drawbacks of murine MAb, we cloned its VH and VL genes and constructed the single-chain Fv (scFv) through a peptide linker. The recombinant scFvA21 was expressed in Escherichia coli and purified by the affinity column. Subsequently it was characterized by ELISA, Western blot, cell immunohistochemistry and FACS. All these assays showed the binding activity to extracellular domain (ECD) of p185. Based on those properties of scFvA21, we further constructed the scFv-Fc fusion molecule with a homodimer form and the recombinant product was expressed in mammalian cells. In a series of subsequent analysis this fusion protein showed identical antigen binding site and activity with the parent antibody. These anti-p185 engineered antibodies have promised to be further modified as a tumor targeting drugs, with a view of application in the diagnosis and treatment of human breast cancer.

Purpose -Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach -In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings -The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value -In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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