Structure and genetic relationships between Brazilian naturalized and exotic purebred goat domestic goat (Capra hircus) breeds based on microsatellites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Association of the Leptin Gene with Carcass Characteristics in Nellore Cattle

Advances in DNA technology have created biotechnological tools that can be used in animal selection and new strategies for increasing herd productivity and quality. The objective of the present work was to associate the genotypes of leptin gene exon 2 polymorphisms with productive traits in Nellore cattle. Blood was collected from Nellore males and PCR-RFLP reactions were performed with the restriction enzymes ClaI and Kpn2I. The gene frequencies resulting from digestion by ClaI were 0.60 and 0.40 for allele A and T, respectively; the genotypic frequencies were AA = 0.20 and AT = 0.80. The gene frequencies from digestion by Kpn2I were 0.81 for allele C and 0.194 for allele T; the genotypic frequencies were CC = 0.62 and CT = 0.38. The populations in both cases were not in Hardy-Weinberg equilibrium (p > 0.05), and the TT genotype was not found. Significant associations were noted between leptin gene exon 2 polymorphisms and five productive traits in Nellore cattle: carcass fat distribution, the intensity of red muscle coloration, pH, marbling, and post-slaughter fat thickness. © 2013 Copyright Taylor and Francis Group, LLC.

Genetic parameters and relationships between heifers rebreeding and hip height in Nellore cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Transmission system expansion planning by an extended genetic algorithm

The paper presents an extended genetic algorithm for solving the optimal transmission network expansion planning problem. Two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique, the proposed method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Excellent performance is reported in the test results section of the paper for a difficult large-scale real-life problem: a substantial reduction in investment costs has been obtained with regard to previous solutions obtained via conventional optimisation methods and simulated annealing algorithms; statistical comparison procedures have been employed in benchmarking different versions of the genetic algorithm and simulated annealing methods.

Use of survival analysis as a tool for the genetic improvement of age at first conception in Nellore cattle

Restricted breeding seasons in beef cattle lead to censoring of reproductive data. In this paper, age at first conception (AFC) of Nellore females exposed to the sires for the first time between 11 and 16 months of age, was studied aiming to verify the possibility of genetically advance sexual precocity using a survival model. The final data set contained 6699 records of AFC in days. Records of females that did not calve in the next year following exposure to the sire were considered censored (77.5% of total). The model used was a Weibull mixed survival model including effects of contemporary groups, period (fixed) and animal (random). The effect of the contemporary groups on AFC was important (p < 0.01). Heritabilities were 0.51 and 0.76 in logarithmic and original scales respectively. Results indicate that it is possible to genetically advance sexual precocity, using the outcome of survival analysis of AFC as selection criterion. They also suggest that improvements of the environment could advance sexual precocity too, thus an adequate pregnancy rate for farmers could quickly be achieved.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Biotechnology of polyketides: new breath of life for the novel antibiotic genetic pathways discovery through metagenomics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Improvements in the sensibility of MSA-GA tool using COFFEE objective function

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic relationship among reproductive traits in Nellore cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)