Wheat transformation efficiency is closely related to several factors such as receptor genotype, constructed plasmid and selection procedure after bombardment or co-cultivation. In our study, several kinds of antibiot...Wheat transformation efficiency is closely related to several factors such as receptor genotype, constructed plasmid and selection procedure after bombardment or co-cultivation. In our study, several kinds of antibiotics, which were normally used in plant transformation to the selection genes of nptII, bar and hpt, were tested for the optimal concentrations for wheat transformation. The results showed that 25 - 50mg/L of geneticin (G418) was suitable for the selection of nptll, kanamycin or neomycin was not suitable for use. 3 -5mg/L of phosphinothricin (PPT) or biolaphos could be used for the selection of bar, 100 - 150mg/L of hygromycin for the selection of hpt. Yangmai 158 and Yangmai 10 with high tissue culture response and good agronomic characteristics were screened from 25 potential Chinese wheat cultivars. The concentration changing of selectable agent in selection medium was helpful to obtain enough regeneration plantlets with strong root system.展开更多
Fast stepwise procedures of selection of variables by using AIC and BIC criteria are proposed inthis paper. We shall use a short name 'FSP' for these new procedures. FSP are similar to the well-known stepwise ...Fast stepwise procedures of selection of variables by using AIC and BIC criteria are proposed inthis paper. We shall use a short name 'FSP' for these new procedures. FSP are similar to the well-known stepwise regression procedures in computing steps. But FSP have two advantages. One of theseadvantages is that FSP are definitely convergent with a faster rate in finite computing steps. Anotheradvantage is that ESP can be used for large number of candidate variables. In this paper we alsoshow some asymptotic properties of FSP, and some simulation results.展开更多
There are four serious problems in the discriminant analysis. We developed an optimal linear discriminant function （optimal LDF） based on the minimum number of misclassification （minimum NM） using integer programm...There are four serious problems in the discriminant analysis. We developed an optimal linear discriminant function （optimal LDF） based on the minimum number of misclassification （minimum NM） using integer programming （IP）. We call this LDF as Revised IP-OLDF. Only this LDF can discriminate the cases on the discriminant hyperplane （Probleml）. This LDF and a hard-margin SVM （H-SVM） can discriminate the lineary separable data （LSD） exactly. Another LDFs may not discriminate the LSD theoretically （Problem2）. When Revised IP-OLDF discriminate the Swiss banknote data with six variables, we find MNM of two-variables model such as （X4, X6） is zero. Because MNMk decreases monotounusly （MNMk 〉= MNM（k＋1））, sixteen MNMs including （X4, X6） are zero. Until now, because there is no research of the LSD, we surveyed another three linear separable data sets such as： 18 exam scores data sets, the Japanese 44 cars data and six microarray datasets. When we discriminate the exam scores with MNM=0, we find the generalized inverse matrix technique causes the serious Problem3 and confirmed this fact by the cars data. At last, we claim the discriminant analysis is not the inferential statistics because there is no standard errors （SEs） of error rates and discriminant coefficients （Problem4）. Therefore, we poroposed the ＂100-fold cross validation for the small sample＂ method （the method）. By this break-through, we can choose the best model having minimum mean of error rate （M2） in the validation sample and obtaine two 95% confidence intervals （CIs） of error rate and discriminant coefficients. When we discriminate the exam scores by this new method, we obtaine the surprising results seven LDFs except for Fisher＇s LDF are almost the same as the trivial LDFs. In this research, we discriminate the Japanese 44 cars data because we can discuss four problems. There are six independent variables to discriminate 29 regular cars and 15 small cars. This data is linear separable by the emission rate （X1） and the number of seats （X3）. We examine the validity of the new model selection procedure of the discriminant analysis. We proposed the model with minimum mean of error rates （M2） in the validation samples is the best model. We had examined this procedure by the exam scores, and we obtain good results. Moreover, the 95% CI of eight LDFs offers us real perception of the discriminant theory. However, the exam scores are different from the ordinal data. Therefore, we apply our theory and procedure to the Japanese 44 cars data and confirmed the same conclution.展开更多
文摘Wheat transformation efficiency is closely related to several factors such as receptor genotype, constructed plasmid and selection procedure after bombardment or co-cultivation. In our study, several kinds of antibiotics, which were normally used in plant transformation to the selection genes of nptII, bar and hpt, were tested for the optimal concentrations for wheat transformation. The results showed that 25 - 50mg/L of geneticin (G418) was suitable for the selection of nptll, kanamycin or neomycin was not suitable for use. 3 -5mg/L of phosphinothricin (PPT) or biolaphos could be used for the selection of bar, 100 - 150mg/L of hygromycin for the selection of hpt. Yangmai 158 and Yangmai 10 with high tissue culture response and good agronomic characteristics were screened from 25 potential Chinese wheat cultivars. The concentration changing of selectable agent in selection medium was helpful to obtain enough regeneration plantlets with strong root system.
文摘Fast stepwise procedures of selection of variables by using AIC and BIC criteria are proposed inthis paper. We shall use a short name 'FSP' for these new procedures. FSP are similar to the well-known stepwise regression procedures in computing steps. But FSP have two advantages. One of theseadvantages is that FSP are definitely convergent with a faster rate in finite computing steps. Anotheradvantage is that ESP can be used for large number of candidate variables. In this paper we alsoshow some asymptotic properties of FSP, and some simulation results.
文摘There are four serious problems in the discriminant analysis. We developed an optimal linear discriminant function （optimal LDF） based on the minimum number of misclassification （minimum NM） using integer programming （IP）. We call this LDF as Revised IP-OLDF. Only this LDF can discriminate the cases on the discriminant hyperplane （Probleml）. This LDF and a hard-margin SVM （H-SVM） can discriminate the lineary separable data （LSD） exactly. Another LDFs may not discriminate the LSD theoretically （Problem2）. When Revised IP-OLDF discriminate the Swiss banknote data with six variables, we find MNM of two-variables model such as （X4, X6） is zero. Because MNMk decreases monotounusly （MNMk 〉= MNM（k＋1））, sixteen MNMs including （X4, X6） are zero. Until now, because there is no research of the LSD, we surveyed another three linear separable data sets such as： 18 exam scores data sets, the Japanese 44 cars data and six microarray datasets. When we discriminate the exam scores with MNM=0, we find the generalized inverse matrix technique causes the serious Problem3 and confirmed this fact by the cars data. At last, we claim the discriminant analysis is not the inferential statistics because there is no standard errors （SEs） of error rates and discriminant coefficients （Problem4）. Therefore, we poroposed the ＂100-fold cross validation for the small sample＂ method （the method）. By this break-through, we can choose the best model having minimum mean of error rate （M2） in the validation sample and obtaine two 95% confidence intervals （CIs） of error rate and discriminant coefficients. When we discriminate the exam scores by this new method, we obtaine the surprising results seven LDFs except for Fisher＇s LDF are almost the same as the trivial LDFs. In this research, we discriminate the Japanese 44 cars data because we can discuss four problems. There are six independent variables to discriminate 29 regular cars and 15 small cars. This data is linear separable by the emission rate （X1） and the number of seats （X3）. We examine the validity of the new model selection procedure of the discriminant analysis. We proposed the model with minimum mean of error rates （M2） in the validation samples is the best model. We had examined this procedure by the exam scores, and we obtain good results. Moreover, the 95% CI of eight LDFs offers us real perception of the discriminant theory. However, the exam scores are different from the ordinal data. Therefore, we apply our theory and procedure to the Japanese 44 cars data and confirmed the same conclution.
