Germplasm effect reflects the quantitative relation between production ability of germplasm elements and yield （quality） of a certain crop, which can be shown by mathematic function, namely, germplasm effect functio...Germplasm effect reflects the quantitative relation between production ability of germplasm elements and yield （quality） of a certain crop, which can be shown by mathematic function, namely, germplasm effect function. Germplasm effect of a crop variety is an aggregation of many effective factors, and is restrained by different effective factors; constant increase of any one effect of germplasm elements would lead to law of effect decline, therefore, possible modes of transgenic crops effect function were deduced according to the law of effect decline. The possible modes of single transgenic germplasm effect function and multi-transgenic germplasm effect regression equation were discussed, and the characteristics of germplasm effect regression equation were analyzed in this paper.展开更多
This paper deals with the description and the representation of polynomials defined over n-simplices, The polynomials are computed by using two recurrent schemes: the Neville-Aitken one for the Lagrange interpolating ...This paper deals with the description and the representation of polynomials defined over n-simplices, The polynomials are computed by using two recurrent schemes: the Neville-Aitken one for the Lagrange interpolating operator and the De Casteljau one for the Bernstein-Bezier approximating operator. Both schemes fall intothe framework of transformations of the form where the F iare given numbers (forexample, at the initial step they coincide with the values of the function on a given lattice), and the coefficients (x) are linear polynomials valued in x and x is fixed. A general theory for such sequence of transformations can be found in [2] where it is also proved that these tranformations are completely characterized in term of a linear functional, reference functional. This functional is associated with a linear space., characteristic space.The concepts of reference functionals and characteristic spaces will be used and we shall prove the existence of a characteristic space for the reference functional: associated with these operators.展开更多
AIM To compare the performance of 3 published delayed graftfunction(DGF) calculators that compute the theoretical risk of DGF for each patient.METHODS This single-center,retrospective study included 247 consecutive ki...AIM To compare the performance of 3 published delayed graftfunction(DGF) calculators that compute the theoretical risk of DGF for each patient.METHODS This single-center,retrospective study included 247 consecutive kidney transplants from a deceased donor.These kidney transplantations were performed at our institution between January 2003 and December 2012.We compared the occurrence of observed DGF in our cohort with the predicted DGF according to three different published calculators. The accuracy of the calculators was evaluated by means of the c-index(receiver operating characteristic curve).RESULTS DGF occurred in 15.3% of the transplants under study.The c index of the Irish calculator provided an area under the curve(AUC) of 0.69 indicating an acceptable level of prediction,in contrast to the poor performance of the Jeldres nomogram(AUC = 0.54) and the Chapal nomogram(AUC = 0.51). With the Irish algorithm the predicted DGF risk and the observed DGF probabilities were close. The mean calculated DGF risk was significantly different between DGF-positive and DGF-negative subjects(P < 0.0001). However,at the level of the individual patient the calculated risk of DGF overlapped very widely with ranges from 10% to 51% for recipients with DGF and from 4% to 56% for those without DGF.The sensitivity,specificity and positive predictive value of a calculated DGF risk ≥ 30% with the Irish nomogram were 32%,91% and 38%. CONCLUSION Predictive models for DGF after kidney transplantation are performant in the population in which they were derived,but less so in external validations.展开更多
文摘Germplasm effect reflects the quantitative relation between production ability of germplasm elements and yield （quality） of a certain crop, which can be shown by mathematic function, namely, germplasm effect function. Germplasm effect of a crop variety is an aggregation of many effective factors, and is restrained by different effective factors; constant increase of any one effect of germplasm elements would lead to law of effect decline, therefore, possible modes of transgenic crops effect function were deduced according to the law of effect decline. The possible modes of single transgenic germplasm effect function and multi-transgenic germplasm effect regression equation were discussed, and the characteristics of germplasm effect regression equation were analyzed in this paper.
文摘This paper deals with the description and the representation of polynomials defined over n-simplices, The polynomials are computed by using two recurrent schemes: the Neville-Aitken one for the Lagrange interpolating operator and the De Casteljau one for the Bernstein-Bezier approximating operator. Both schemes fall intothe framework of transformations of the form where the F iare given numbers (forexample, at the initial step they coincide with the values of the function on a given lattice), and the coefficients (x) are linear polynomials valued in x and x is fixed. A general theory for such sequence of transformations can be found in [2] where it is also proved that these tranformations are completely characterized in term of a linear functional, reference functional. This functional is associated with a linear space., characteristic space.The concepts of reference functionals and characteristic spaces will be used and we shall prove the existence of a characteristic space for the reference functional: associated with these operators.
文摘AIM To compare the performance of 3 published delayed graftfunction(DGF) calculators that compute the theoretical risk of DGF for each patient.METHODS This single-center,retrospective study included 247 consecutive kidney transplants from a deceased donor.These kidney transplantations were performed at our institution between January 2003 and December 2012.We compared the occurrence of observed DGF in our cohort with the predicted DGF according to three different published calculators. The accuracy of the calculators was evaluated by means of the c-index(receiver operating characteristic curve).RESULTS DGF occurred in 15.3% of the transplants under study.The c index of the Irish calculator provided an area under the curve(AUC) of 0.69 indicating an acceptable level of prediction,in contrast to the poor performance of the Jeldres nomogram(AUC = 0.54) and the Chapal nomogram(AUC = 0.51). With the Irish algorithm the predicted DGF risk and the observed DGF probabilities were close. The mean calculated DGF risk was significantly different between DGF-positive and DGF-negative subjects(P < 0.0001). However,at the level of the individual patient the calculated risk of DGF overlapped very widely with ranges from 10% to 51% for recipients with DGF and from 4% to 56% for those without DGF.The sensitivity,specificity and positive predictive value of a calculated DGF risk ≥ 30% with the Irish nomogram were 32%,91% and 38%. CONCLUSION Predictive models for DGF after kidney transplantation are performant in the population in which they were derived,but less so in external validations.
