Characterization of the Genetic Variability of a Sugarcane Commercial Cross Through Yield Components and Quality Parameters

The phenotypic characterization as well as the knowledge of the correlation among traits, is the first step to quantify the potential of a cross for further QTL (quantitative trait loci) detection. The present work aimed to evaluate the yield components and quality parameters variability of a mapping population derived from a bi-parental cross between IACSP95-3018 and IACSP93-3046 at plant cane and ratoon cane as well as to estimate the heritabilities and pair-wise correlation among the traits evaluated. The progeny clones differed significantly for the traits measures indicating the existence of significant amount of variability among them as also as the presence of transgressive clones. Broad-sense heritabilities values were generally high for stalk diameter, stalk weight, stalk height, Brix and Pol%Cane in plant cane and ratoon cane. Tones of sugarcane per hectare (TCH) were significantly correlated with stalk weight and stalk number in both years. Regarding to all the yield components, stalk number together with stalk weight were the most important components in the determination of TCH. While fiber and Pol%Cane were negative correlated showing that they are inversely correlated traits. © 2012 Society for Sugar Research & Promotion.

Genetic and phenotypic parameters of carcass and organ traits of broiler chickens

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic selection to increase bone strength affects prevalence of keel bone damage and egg parameters in commercially housed laying hens.

The prevalence of keel bone damage as well as external egg parameters of 2 pure lines divergently selected for high (H) and low (L) bone strength were investigated in 2 aviary systems under commercial conditions. A standard LSL hybrid was used as a reference group. Birds were kept mixed per genetic line (77 hens of the H and L line and 201 or 206 hens of the LSL line, respectively, per pen) in 8 pens of 2 aviary systems differing in design. Keel bone status and body mass of 20 focal hens per line and pen were assessed at 17, 18, 23, 30, 36, 43, 52, and 63 wk of age. External egg parameters (i.e., egg mass, eggshell breaking strength, thickness, and mass) were measured using 10 eggs per line at both 38 and 57 wk of age. Body parameters (i.e. tarsus and third primary wing feather length to calculate index of wing loading) were recorded at 38 wk of age and mortality per genetic line throughout the laying cycle. Bone mineral density (BMD) of 15 keel bones per genetic line was measured after slaughter to confirm assignment of the experimental lines. We found a greater BMD in the H compared with the L and LSL lines. Fewer keel bone fractures and deviations, a poorer external egg quality, as well as a lower index of wing loading were found in the H compared with the L line. Mortality was lower and production parameters (e.g., laying performance) were higher in the LSL line compared with the 2 experimental lines. Aviary design affected prevalence of keel bone damage, body mass, and mortality. We conclude that selection of specific bone traits associated with bone strength as well as the related differences in body morphology (i.e., lower index of wing loading) have potential to reduce keel bone damage in commercial settings. Also, the housing environment (i.e., aviary design) may have additive effects.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

Rare earth element and yttrium geochemistry applied to the genetic study of cryolite ore at the Pitinga Mine (Amazon, Brazil)

This work aims at the geochemical study of Pitinga cryolite mineralization through REE and Y analyses in disseminated and massive cryolite ore deposits, as well as in fluorite occurrences. REE signatures in fluorite and cryolite are similar to those in the Madeira albite granite. The highest ΣREE values are found in magmatic cryolite (677 to 1345 ppm); ΣREE is lower in massive cryolite. Average values for the different cryolite types are 10.3 ppm, 6.66 ppm and 8.38 ppm (for nucleated, caramel and white types, respectively). Disseminated fluorite displays higher ΣREE values (1708 and 1526ppm) than fluorite in late veins(34.81ppm). Yttrium concentration is higher in disseminated fluorite and in magmatic cryolite. The evolution of several parameters (REEtotal, LREE/HREE, Y) was followed throughout successive stages of evolution in albite granites and associated mineralization. At the end of the process, late cryolite was formed with low REEtotal content. REE data indicate that the MCD was formed by, and the disseminated ore enriched by (additional formation of hydrothermal disseminated cryolite), hydrothermal fluids, residual from albite granite. The presence of tetrads is poorly defined, although nucleated, caramel and white cryolite types show evidence for tetrad effect.

Kinematic parameters and membership probabilities of open clusters in the Bordeaux PM2000 catalogue

Aims. We derive lists of proper-motions and kinematic membership probabilities for 49 open clusters and possible open clusters in the zone of the Bordeaux PM2000 proper motion catalogue (+ 11 degrees <= delta <= + 18 degrees). We test different parametrisations of the proper motion and position distribution functions and select the most successful one. In the light of those results, we analyse some objects individually. Methods. We differenciate between cluster and field member stars, and assign membership probabilities, by applying a new and fully automated method based on both parametrisations of the proper motion and position distribution functions, and genetic algorithm optimization heuristics associated with a derivative-based hill climbing algorithm for the likelihood optimization. Results. We present a catalogue comprising kinematic parameters and associated membership probability lists for 49 open clusters and possible open clusters in the Bordeaux PM2000 catalogue region. We note that this is the first determination of proper motions for five open clusters. We confirm the non-existence of two kinematic populations in the region of 15 previously suspected non-existent objects.

Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]

Genetic potential of a maize population from Southern Brazil for the modified convergent-divergent selection scheme

Maize (Zea mays L.) is a very important cereal to world-wide economy which is also true for Brazil, particularly in the South region. Grain yield and plant height have been chosen as important criteria by breeders and farmers from Santa Catarina State (SC), Brazil. The objective of this work was to estimate genetic-statistic parameters associated with genetic gain for grain yield and plant height, in the first cycle of convergent-divergent half-sib selection in a maize population (MPA1) cultivated by farmers within the municipality of Anchieta (SC). Three experiments were carried out in different small farms at Anchieta using low external agronomic inputs; each experiment represented independent samples of half-sib families, which were evaluated in randomized complete blocks with three replications per location. Significant differences among half-sib families were observed for both variables in all experiments. The expected responses to truncated selection of the 25% better families in each experiment were 5.1, 5.8 and 5.2% for reducing plant height and 3.9, 5.7 and 5.0% for increasing grain yield, respectively. The magnitudes of genetic-statistic parameters estimated evidenced that the composite population MPA1 exhibits enough genetic variability to be used in cyclical process of recurrent selection. There were evidences that the genetic structure of the base population MPA1, as indicated by its genetic variability, may lead to expressive changes in the traits under selection, even under low selection pressure.

Analysis of small signal stability margins using genetic optimization

Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.

Genetic polymorphism in an inflammasome component, cervical mycoplasma detection and female infertility in women undergoing in vitro fertilization

The inflammasome is an inducible cytoplasmic structure that is responsible for production and release of biologically active interleukin-1 (IL-1). A polymorphism in the inflammasome component NALP3 has been associated with decreased IL-1 levels and increased occurrence of vaginal Candida infection. We hypothesized that this polymorphism-induced variation would influence susceptibility to infertility. DNA was obtained from 243 women who were undergoing in vitro fertilization (IVF) and tested for a length polymorphism in intron 2 of the gene coding for NALP3 (gene symbol CIAS1). At the conclusion of testing the findings were analyzed in relation to clinical parameters and IVF outcome. The frequency of the 12 unit repeat allele, associated with maximal inflammasome activity, was 62.3% in cases of female infertility vs. 75.6% in cases where only the male partner had a detectable fertility problem (p = 0.0095). Conversely, the frequency of the 7 unit repeat allele was 28.9% in those with a female fertility problem, 17.0% in women with infertile males and 18.4% in idiopathic infertility (p = 0.0124). Among the women who were cervical culture-positive for mycoplasma the frequency of the 7 unit repeat was 53.7% as opposed to 19.5% in those negative for this infection (p < 0.0001). We conclude that the CIAS1 7 unit repeat polymorphism increases the likelihood of mycoplasma infection-associated female infertility. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to and the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum. (C) 1997 Optical Society of America.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.