In cultivated rice ( Oryza sativa L.), F-1 pollen sterility is controlled by at least 6 loci of the F, pollen sterility genes. To map S-b, one of loci, rice variety Taichung 65 (T65) carrying S-b(j)/S-b(j) and its nea...In cultivated rice ( Oryza sativa L.), F-1 pollen sterility is controlled by at least 6 loci of the F, pollen sterility genes. To map S-b, one of loci, rice variety Taichung 65 (T65) carrying S-b(j)/S-b(j) and its near-isogenic line TIST2 carrying S-b(i)/S-b(i) were used to develop the mapping population. One hundred and fifty-eight microsatellite markers were selected to survey T65 and TISL2. RM13 on chromosome 5 was found to be polymorphic between them. Cosegregation indicated that RM13 was closely linked with locus S-b. Eleven RFLP markers were selected on the corresponding region from the genetic map of Rice Genome Research Program (RGP) of Japan to convert into sequence-tagged site (STS) markers. Amplicon length polymorphism (ALP) was carried out, but none of them was found to be polymorphic between T65 and TISL2. Then PCR-based RFLP (PBR) was done using six 4-nucleotide recognizing restriction endonucleases. Polymorphism was detected when PCR products of R830STS and R2213SSTS were digested with Taq I. Genetic analysis indicated that the distance between locus S-b and markers, R830STS, RM13 and R2213SSTS were 3.3 cM (centi-Morgan), 5.2 cM and 5.5 cM, respectively. These PCR-based markers could be directly used in marker-assisted selection. The technical system combining genetic mapping and PCR-based marker-assisted selection will facilitate the development of molecular breeding.展开更多
The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with ...The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with finite number of inspections. This paper gives the algorithm for an optimal inspection schedule and specifies the computing procedure for a Weibull distribution. Using this algorithm, optimal inspection times are computed as a numerical result. Compared with the periodic point inspection, the policies in this paper reduce the cost successfully.展开更多
基金Supported by the National Natural ScienceFoundation(103410020)Supported by the Guangdong Provincial NaturalScienceFoundation of China(0501332)Supported by the Guangdong EducationalDepartment Natural Science Foundation of China
文摘In cultivated rice ( Oryza sativa L.), F-1 pollen sterility is controlled by at least 6 loci of the F, pollen sterility genes. To map S-b, one of loci, rice variety Taichung 65 (T65) carrying S-b(j)/S-b(j) and its near-isogenic line TIST2 carrying S-b(i)/S-b(i) were used to develop the mapping population. One hundred and fifty-eight microsatellite markers were selected to survey T65 and TISL2. RM13 on chromosome 5 was found to be polymorphic between them. Cosegregation indicated that RM13 was closely linked with locus S-b. Eleven RFLP markers were selected on the corresponding region from the genetic map of Rice Genome Research Program (RGP) of Japan to convert into sequence-tagged site (STS) markers. Amplicon length polymorphism (ALP) was carried out, but none of them was found to be polymorphic between T65 and TISL2. Then PCR-based RFLP (PBR) was done using six 4-nucleotide recognizing restriction endonucleases. Polymorphism was detected when PCR products of R830STS and R2213SSTS were digested with Taq I. Genetic analysis indicated that the distance between locus S-b and markers, R830STS, RM13 and R2213SSTS were 3.3 cM (centi-Morgan), 5.2 cM and 5.5 cM, respectively. These PCR-based markers could be directly used in marker-assisted selection. The technical system combining genetic mapping and PCR-based marker-assisted selection will facilitate the development of molecular breeding.
文摘The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with finite number of inspections. This paper gives the algorithm for an optimal inspection schedule and specifies the computing procedure for a Weibull distribution. Using this algorithm, optimal inspection times are computed as a numerical result. Compared with the periodic point inspection, the policies in this paper reduce the cost successfully.
