旨在通过RNA干扰技术揭示蛋白酪氨酸磷酸酶(phosphatase and tensin homolog deleted on chromosome 10,PTEN)基因在奶山羊乳腺上皮细胞中对乳脂合成及脂肪酸组成的影响。利用RT-PCR方法扩增到西农萨能奶山羊乳腺组织中PTEN基因(GenBan...旨在通过RNA干扰技术揭示蛋白酪氨酸磷酸酶(phosphatase and tensin homolog deleted on chromosome 10,PTEN)基因在奶山羊乳腺上皮细胞中对乳脂合成及脂肪酸组成的影响。利用RT-PCR方法扩增到西农萨能奶山羊乳腺组织中PTEN基因(GenBank登录号:MK158074.1)的CDS区,进行序列分析和不同泌乳时期差异转录分析。合成靶向该基因的siRNA,由RT-qPCR结果筛选出有效siRNA,将其转染至奶山羊的乳腺上皮细胞,进一步检测干扰该基因后对脂质合成相关基因转录水平及脂肪酸组成的影响。结果表明:本研究首次克隆到奶山羊(Capra hircus)PTEN基因的CDS区,全长为1212 bp;经序列比对发现,山羊的PTEN基因核苷酸序列同牛(Bos taurus)、猪(Sus scrofa)和人(Homo sapiens)的相似度分别为99%、98%和97%,编码氨基酸序列相似性均在99%以上;蛋白质结构预测发现:其蛋白不存在跨膜结构域,亚细胞定位于细胞质中,N端具有保守的磷酸酶功能区;该基因在奶山羊泌乳盛期乳腺组织中的转录量较干奶期下降51.5%;合成的siRNA转染至乳腺上皮细胞后,成功筛选出理想的siRNA,干扰效率达到94%(P<0.01);与对照组相比,干扰PTEN基因后显著上调SREBP1、FASN、ACACA、SCD1基因转录量(P<0.01或P<0.05),显著下调LPL、FABP3、ACOX1、CPT1B、GPAM、DGAT2和HSL基因转录量(P<0.01或P<0.05)。脂肪酸检测分析发现:干扰该基因能够显著上调C16:1的去饱和指数(P<0.05),但对C18:1的去饱和指数没有显著影响。综上所述,PTEN基因能够调控乳腺上皮细胞中脂质合成相关基因的转录及脂肪酸组成,在奶山羊乳脂代谢中发挥重要作用。展开更多
This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in the POPN online fau...This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, an improved online fault diagnosis algorithm that integrates generalized mutual exclusion constraints (GMECs) and integer linear programming (ILP) is proposed. Assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. GMEC is used for elementary diagnosis of the system behavior, then the ILP problem of POPN is solved for further diagnosis. Finally, an example of a real DES to test the new fault diagnoser is analyzed. The proposed algorithm increases the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP is verified.展开更多
基金supported by the National Natural Science Foundation of China(61473144)
文摘This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, an improved online fault diagnosis algorithm that integrates generalized mutual exclusion constraints (GMECs) and integer linear programming (ILP) is proposed. Assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. GMEC is used for elementary diagnosis of the system behavior, then the ILP problem of POPN is solved for further diagnosis. Finally, an example of a real DES to test the new fault diagnoser is analyzed. The proposed algorithm increases the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP is verified.
