Comparative study of seasonal changes in some blood parameters and genetic polymorphisms of serum antibodies and antigens on red blood cells surface in immature Caspian salmon

Seasonal sampling from 40 immature Caspian salmon were performed in summer, autumn, winter and spring. The maximum ranges of RBC counts, Hct, Hb, WBC count and clotting times were observed in spring, summer, spring, spring and winter, respectively. The minimum amounts of these factors were counted in summer, winter, winter, winter and winter, respectively. Blood Samples were taken from healthy smolt, immature and adult Caspian salmon in spawning time. Hematological determinations and biochemical serum analysis were performed in 101 fish in the three samples. The ranges of hematological values for sample mean were counted. Red blood cell counts were 866600 mm3 and 1259400 mm3 in smolt and adult respectively. Hematocrit was 48.39% in smolt and 44.29% in adult. Hemoglobin was 8.85 gr/dl in smolt and 10.91 gr/dl in adult. White blood cell count was 8781.58 mm3 in smolt and 5217.55 mm3 in adult and mean were differential of WBC, Lymphocyte 90.57%in smolt and73.22% in adult. Neutrophil was 5.12% in smolt and 16.92% in adult, Monocyte were 1.27% in smolt and 4.24% in adult, Clotting time was 282.34 Seconds in smolt and 291.47 seconds in adult MCV, MCH and MCHC also meagered in smolt and adult. Biochemical parameter in immature and mature Caspian salmon meagered .Glucose concentration was 2.97 mmol.l- in immature and 1.99 mmol.l- in mature .Cholesterol concentration was 4.26 mmol.l- in immature and 7.06 mmol.l- in mature. Triglyceride amount was 2.35 mmol.l- in immature and 2.47 mmol.l- in mature and Calcium was 2.47 in immature and 2.61 mmol.l- in mature. An in situ study was made on erythrocytic isoantigens and hetero-antigen and their corresponding iso-and hetero-antibodies of sera by means of hemoagglutination tests on the blood sample, of 450 immature and 50 mature Caspian salmon. The absence of erythrocyte iso-antigens and hetero-antigen and their corresponding iso-and hetero-antibodies were shown by the experimental. It could be indicated an intra-specific variation and differences in species for kelardasht hatchery.

Geometry parameter optimization of large cross-sectional S-shaped bent rib waveguides for silicon based optical switches

地址: Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China

Genetic transformation in Kappaphycus alvarezii using micro-particle bombardment: a potential strategy for germplasm improvement

This study investigated the delivery of a SV40 promoter driving lacZ gene into cells of Kappaphycus alvarezii using particle bombardment. Thallus pieces 0.5-0.8 mm in diameter and 1 cm in length were prepared as gene recipients. Bombardment parameters of 450 psi (rupture pressures) x 6 cm (particle travel distances), 650 psi x 6 cm, 1,100 psi x 6 cm and 1,100 psi x 9 cm were used. A significant increase in transformation efficiency from about 33% under the rupture pressure of 450 psi to 87% at 650 psi was observed in transformed thalli. Most of the positive cells appeared in epidermal cells bombarded at 450 psi, whereas positive signals were seen in both epidermal and medullary cells at 650 psi. No positive transient expression was detected at a bombardment of 1,100 psi, or in negative or blank controls. For the conditions tested, the best parameter was obtained at 650 psi at a distance of 6 cm. Thus, the strategy of taking vegetative thalli as recipients, using particle bombardment, and combining this with micro-propagation, together with developing an in vivo selectable marker, is a viable way to produce stable transformants, to eliminate chimeric expression, and to achieve transgenic breeding in K. alvarezii.

Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

A new search-space-updating technique for genetic algorithms is proposed for continuous optimisation problems. Other than gradually reducing the search space during the evolution process with a fixed reduction rate set ‘a priori’, the upper and the lower boundaries for each variable in the objective function are dynamically adjusted based on its distribution statistics. To test the effectiveness, the technique is applied to a number of benchmark optimisation problems in comparison with three other techniques, namely the genetic algorithms with parameter space size adjustment (GAPSSA) technique [A.B. Djurišic, Elite genetic algorithms with adaptive mutations for solving continuous optimization problems – application to modeling of the optical constants of solids, Optics Communications 151 (1998) 147–159], successive zooming genetic algorithm (SZGA) [Y. Kwon, S. Kwon, S. Jin, J. Kim, Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems, Computers and Structures 81 (2003) 1715–1725] and a simple GA. The tests show that for well-posed problems, existing search space updating techniques perform well in terms of convergence speed and solution precision however, for some ill-posed problems these techniques are statistically inferior to a simple GA. All the tests show that the proposed new search space update technique is statistically superior to its counterparts.

Dynamic evolution of the genetic search region through fuzzy coding

A technique for automatic exploration of the genetic search region through fuzzy coding (Sharma and Irwin, 2003) has been proposed. Fuzzy coding (FC) provides the value of a variable on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree-of-membership. It is an indirect encoding method and has been shown to perform better than other conventional binary, Gray and floating-point encoding methods. However, the static range of the membership functions is a major problem in fuzzy coding, resulting in longer times to arrive at an optimum solution in large or complicated search spaces. This paper proposes a new algorithm, called fuzzy coding with a dynamic range (FCDR), which dynamically allocates the range of the variables to evolve an effective search region, thereby achieving faster convergence. Results are presented for two benchmark optimisation problems, and also for a case study involving neural identification of a highly non-linear pH neutralisation process from experimental data. It is shown that dynamic exploration of the genetic search region is effective for parameter optimisation in problems where the search space is complicated.

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Evolutionary tuning of SVM parameter values in multiclass problems

Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved.

Genetic Algorithm for the Determination of Linear Viscoelastic Relaxation Spectrum from Experimental Data

Conventional procedures employed in the modeling of viscoelastic properties of polymer rely on the determination of the polymer`s discrete relaxation spectrum from experimentally obtained data. In the past decades, several analytical regression techniques have been proposed to determine an explicit equation which describes the measured spectra. With a diverse approach, the procedure herein introduced constitutes a simulation-based computational optimization technique based on non-deterministic search method arisen from the field of evolutionary computation. Instead of comparing numerical results, this purpose of this paper is to highlight some Subtle differences between both strategies and focus on what properties of the exploited technique emerge as new possibilities for the field, In oder to illustrate this, essayed cases show how the employed technique can outperform conventional approaches in terms of fitting quality. Moreover, in some instances, it produces equivalent results With much fewer fitting parameters, which is convenient for computational simulation applications. I-lie problem formulation and the rationale of the highlighted method are herein discussed and constitute the main intended contribution. (C) 2009 Wiley Periodicals, Inc. J Appl Polym Sci 113: 122-135, 2009

Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms

J.A. Ferreira Neto, E.C. Santos Junior, U. Fra Paleo, D. Miranda Barros, and M.C.O. Moreira. 2011. Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms. Cien. Inv. Agr. 38(2): 169-178. The objective of this manuscript is to develop a new procedure to achieve optimal land subdivision using genetic algorithms (GA). The genetic algorithm was tested in the rural settlement of Veredas, located in Minas Gerais, Brazil. This implementation was based on the land aptitude and its productivity index. The sequence of tests in the study was carried out in two areas with eight different agricultural aptitude classes, including one area of 391.88 ha subdivided into 12 lots and another of 404.1763 ha subdivided into 14 lots. The effectiveness of the method was measured using the shunting line standard value of a parceled area lot`s productivity index. To evaluate each parameter, a sequence of 15 calculations was performed to record the best individual fitness average (MMI) found for each parameter variation. The best parameter combination found in testing and used to generate the new parceling with the GA was the following: 320 as the generation number, a population of 40 individuals, 0.8 mutation tax, and a 0.3 renewal tax. The solution generated rather homogeneous lots in terms of productive capacity.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Genetic characterization of egg weight, egg production and age at first egg in quails

O objetivo neste estudo foi obter estimativas de parâmetros genéticos para as características peso do ovo, produção de ovos em 189 dias de postura e dia do primeiro ovo em codornas de três linhagens de postura e uma de corte. Os dados foram analisados por meio de procedimentos bayesianos usando amostragem de Gibbs. As estimativas de herdabilidade para peso do ovo, produção de ovos em 189 dias de postura e dia do primeiro ovo foram, respectivamente, para a linhagem amarela, 0,31; 0,84 e 0,53; azul, 0,14; 0,82 e 0,60; vermelha, 0,70; 0,96 e 0,75; e de corte, 0,73; 0,96 e 0,72. As correlações genéticas entre peso do ovo e produção de ovos em 189 dias de postura, peso do ovo e dia do primeiro ovo e, produção de ovos em 189 dias de postura e dia do primeiro ovo foram, para amarela, 0,58; -0,77; e -0,90; azul, 0,09; -0,01; e -0,95; vermelha, 0,09; 0,03; e -0,76; e de corte, -0,18; 0,19 e -0,91. A partir das probabilidades de superposição das distribuições posteriories dos parâmetros, as linhagens dividem-se em dois grupos distintos: um com as linhagens amarela e azul e outro com as linhagens vermelha e de corte.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Genetic structure of Aedes aegypti populations determined using pairwise comparisons

The biological characteristics of Aedes aegypti (Diptera, Culicidae), which is a vector of dengue and yellow fever, make this organism a good model for studying population structure and the events that may influence it under the effect of human activity. We assessed the genetic variability of five A. aegypti populations using RAPD-PCR technique and six primers. Four populations were from Brazil and one was from the USA. A total of 165 polymorphic DNA loci were generated. Considering the six primers and the five populations, the mean value of inter-population genetic diversity (Gst) was 0.277, which is considered high according to the Wright classification. However, pairwise comparisons of the populations gave variable Gst values ranging from 0.044 to 0.289. This variation followed the population's geographic distance to some extent but was also influenced by human activity. The lowest Gst values were obtained in the comparison of populations from cities with intensive commercial and medical contacts. These mosquito populations were previously classified as insecticide resistant, susceptible, or with decreased susceptibility; this parameter apparently had an effect on the Gst values obtained in the pairwise comparisons. ©FUNPEC-RP.