ABSTRACT
Objective To prepare an egg yolk antibody ( IgY) against the recombinant human A rotavirus VP8 and evaluate its biological characteristics .Methods The complete VP4 gene was used as the template to clone VP8 DNA fragment by PCR .The VP8 gene was then cloned into the vector pET 28a for ex-pression of VP8 protein in the prokaryotic system.The expression plasmid of pET28a-VP8,identified by DNA sequencing,was transformed into E.coli BL21 to induce the expression of protein by 0.5 mmol/L IPTG at 30℃.The purified and refolded recombinant VP 8 protein was used to immunize laying hens in combination with complete Freund′s adjuvant ( CFA ) .Anti-VP8 IgY antibodies were extracted from egg yolks by using water dilution-ultrafiltration assay and were further analyzed by SDS-PAGE,ELISA,Western blot and Dot blot assay,respectively.Results The recombinant VP8 protein was expressed in E.coli BL21 as an inclusion body and its molecular weight was about 35 ×10 3 .The VP8 protein was well refolded in a buffer containing Tris-HCl (pH8.0).The isolated anti-VP8 IgY antibodies showed a high titer of 1∶100 000 and a high stabil-ity under the condition of pH3-11 and temperature 25-65℃.Moreover,the anti-VP8 IgY antibodies specific-ally recognized the wild type human A rotavirus .Conclusion The prepared anti-VP8 IgY antibodies showed the advantages of high titer ,good specificity and stability .It might be used as the tool in further investigation for the prevention and treatment of diarrhea caused by human A rotavirus .
ABSTRACT
Action surface electromyography (SEMG) signals can be acquired from human skin surface. Its pattern recognition plays a very important role in practical applications such as human prosthesis and human-computer interface systems. For the purpose of increasing the recognition accuracy, we proposed a new recognition method combining fuzzy entropy (FuzzyEn) with multi-scale analysis. Considering the nonlinear and non-stationary characteristics of the SEMG, a multi-scale fuzzy entropy (MSFuzzyEn) feature was introduced and applied to the pattern recognition of six type action SEMG signals of the forearm. Firstly, multi-scale decomposition was applied to original signal using wavelet decomposition. Then MSFuzzyEn of the decomposed signals were calculated and inputted to support vector machine (SVM) for classification as feature vectors. The mean recognition accuracy reached 97%, which was 3% greater than that when FuzzyEn of original signal is applied to the classification of SEMG signals. The results have proved that the MSFuzzyEn is effective and precise in the classification of action SEMG signals.