Isozyme systems as markers of genetic deterioration in stored seeds of Brassicaceae species

The main objective of this study was to determine if isozyme systems can be used as markers of genetic deterioration in Brassicaceae seed accessions under different storage conditions. Seed samples of Brassica oleracea, Cardaria draba, Erysimum cheiri, Iberis sempervirens and Rapistrum rugosum were stored for periods of 9 to 30 years at -10°C and 3-4% seed moisture content (long-term or LT conditions) and at 5°C and uncontrolled relative humidity (RH) (short-term or ST conditions). Starch Gel Electrophoresis (SGE) was used to analyse six enzyme systems oriented to determine the genetic deterioration of the accessions studied. The results obtained show that long-term storage conditions (LT) were extremely effective in maintaining the viability of seeds of the five Brassicaceae species studied. The final germination percentages reached by seeds from LT samples ranged from 75 to 100%, while the germination percentages of ST samples (except for B. oleracea) were very low (from 0 to 10%). Similar conclusions were obtained studying the integrity of electrophoretic bands for several isozymes. Two enzyme systems were of special interest: malate dehydrogenase and alcohol dehydrogenase.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.