<正>动物检疫证明网络机打出证系统是在我市畜牧业快速发展,出栏动物大幅增加,动物卫生检疫监督管理相对置后的情况下,为了提高动物检疫监督管理水平,更好地履行动物卫生监管职责,通过多方研究探索实践开发出来的。实施动物检疫证明网络机打出证,解决了很多以前无法解决的问题,弥补了手工出证的不足,实现了规范出证、便捷高效、网络查询、安全追溯、有效防伪、准确统计。为动物卫生及动物产品安全监管提供强有力的支撑平台和工作手段,加

<正>据统计,农村散养鸡的免疫效果明显低于规模养殖场,究其原因,虽然农民自行免疫预防的积极性高,但并非按专业部门规定的科学免疫程序进行,加之操作不规范,免疫后管理不科学等因素影响,笔者针对以上原因进行了阐述,并结合农村

A sensitive and specific SYBR GreenⅠreal-time PCR assay for the detection of red-sea bream iridovirus(RSIV)was established.The real-time PCR primers were designed according to the conserved region of major capsid protein(MCP)gene by using Primer Express 3.0 software.The RSIV MCP gene was inserted into pMD18-T vector to construct the recombinant plasmid.The resulted plasmid was serially diluted and used as the standards.The relationship between plasmid copy number(X)and Ct value was described as a standard curve:Ct=-3.184 lgX+40.270(with an R2 value of 0.996 9).The detection limit of the assay was 2.20×102 virus copies per reaction.The assay showed specificity and could not be amplified with RSIV and epizootic haematopoietic necrosis virus(EHNV),lymphocystis disease virus(LCDV),frog virus 3(FV 3),or soft-shelled turtle iridovirus(STIV).The Tm of the specific product was obtained as 82.5 ℃ through the melting curve analysis.This assay was applied in detecting whether the sea fish samples(stone flounder,turbot,and weever)of 84 batches were infected by RSIV.It was found that the samples of 5 batches presented positive signals,and the virus was quantified by using the obtained standard curve.The results indicate that the real-time PCR assay is specific,sensitive,fast,and accurate and it has great potential in detecting RSIV and studying the pathogenic mechanism of RSIV.

Two specific primers were designed based on conservative gene of white spot syndrome virus(WSSV) using Primer Explorer V3 software.A detection method for WSSV using the denaturing high-performance liquid chromatography(DHPLC) was established.The WSSV LAMP assay did not react with infetious hypodermal and haematopoietic necrosis virus(IHHNV),monodon baculovirus(MBV),hepatopancreatic parvovirus(HPV),baculovirus penaei(BP) or genome DNA of shrimp,indicating the good specificity.The detection limit of DHPLC assay was approximately 10-5 ng/μL WSSV DNA,which was higher than that of conventional PCR and the real-time PCR.100 clinical shrimp samples were detected with the DHPLC method and the results were consistent with the real-time PCR method.The DHPLC assay was accurate,specific and sensitive,having great potential in diagnosis of WSSV infection.

A real-time quantitative PCR assay was developed for detection and quantification of White Spot Syndrome Virus(WSSV).Primers and probe were designed based on the conserved region of WSSV with Primer Express 2.0 software.The real-time PCR assay had a detection limit of 15 DNA copies,with a dynamic range of detection between 1.5 10~7～1.5 10~1 copies.The linear relationship between virus concentration(X)and Ct was Ct=-3.77lgX+43.73 with the correlation coefficient R~2=0.9967.The primers and probe were specific for WSSV and did not react with either Infectious Hypodermal and Hematopoietic Necrosis Virus(IHHNV), Monodon Baculovirus(MBV),Hepatopancreatic Parvovirus(HPV),or shrimp genomic DNA.The real-time PCR assay detected 14 positive WSSV samples from 100 shrimps,while the nested PCR detected 4 positive samples. The real-time PCR assay is highly sensitive,specific,time-saving and easy to handle.It is considered to be a powerful tool for the rapid detection and quantification of WSSV in shrimps.

Aimed at the puzzle of uncertainty resulted from qualitative selection for fracturing candidate of well and layer in oilfield, the paper proposed a sort of prediction model for fracturing candidate effect based on FNNS. In the paper, it was analyzed to the influence factors resulted in predictive precision being poor, discussed the FNNS and its fuzzy decision method, and also explored the modeling and forecasting of optimization layer in candidate wells. The application example shows that the well or layer of fracturing candidate can been optimized quantitatively by means of prediction model based on FNNS.