Otimização do controle eletrônico do diagrama de radiação de arranjos de antenas usando algoritmos genéticos com codificação real

Antenna arrays are able to provide high and controlled directivity, which are suitable for radiobase stations, radar systems, and point-to-point or satellite links. The optimization of an array design is usually a hard task because of the non-linear characteristic of multiobjective, requiring the application of numerical techniques, such as genetic algorithms. Therefore, in order to optimize the electronic control of the antenna array radiation pattem through genetic algorithms in real codification, it was developed a numerical tool which is able to positioning the array major lobe, reducing the side lobe levels, canceling interference signals in specific directions of arrival, and improving the antenna radiation performance. This was accomplished by using antenna theory concepts and optimization methods, mainly genetic algorithms ones, allowing to develop a numerical tool with creative genes codification and crossover rules, which is one of the most important contribution of this work. The efficiency of the developed genetic algorithm tool is tested and validated in several antenna and propagation applications. 11 was observed that the numerical results attend the specific requirements, showing the developed tool ability and capacity to handle the considered problems, as well as a great perspective for application in future works.

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Algoritmos genéticos canônico e elitista: uma abordagem comparativa

Universidade Federal do Rio Grande do Norte

Análise de fatores clínico-demográficos, psicológicos, hormonais e genéticos na síndrome da ardência bucal e ardor bucal secundário

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Estimação de parâmetros genéticos para resistência à infecção por IMNV em camarões Litopenaeus vannamei por meio de análise de sobrevivência

The main specie of marine shrimp raised at Brazil and in the world is Litopenaeus vannamei, which had arrived in Brazil in the `80s. However, the entry of infectious myonecrosis virus (IMNV), causing the infectious myonecrosis disease in marine shrimps, brought economic losses to the national shrimp farming, with up to 70% of mortality in the shrimp production. In this way, the objective was to evaluate the survival of shrimps Litopenaeus vannamei infected with IMNV using the non parametric estimator of Kaplan-Meier and a model of frailty for grouped data. It were conducted three tests of viral challenges lasting 20 days each, at different periods of the year, keeping the parameters of pH, temperature, oxygen and ammonia monitored daily. It was evaluated 60 full-sib families of L. vannamei infected by IMNV in each viral challenge. The confirmation of the infection by IMNV was performed using the technique of PCR in real time through Sybr Green dye. Using the Kaplan-Meier estimator it was possible to detect significant differences (p <0.0001) between the survival curves of families and tanks and also in the joint analysis between viral challenges. It were estimated in each challenge, genetic parameters such as genetic value of family, it`s respective rate risk (frailty), and heritability in the logarithmic scale through the frailty model for grouped data. The heritability estimates were respectively 0.59; 0.36; and 0.59 in the viral challenges 1; 2; and 3, and it was also possible to identify families that have lower and higher rates of risk for the disease. These results can be used for selecting families more resistant to the IMNV infection and to include characteristic of disease resistance in L. vannamei into the genetic improvement programs

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Determinantes genéticos e ambientais das doenças hipertensivas da gravidez

The development of complex diseases such as preeclampsia are determined by both environmental and genetic factors, but there is also interaction among these factors. Preeclampsia is a pregnancy-specific disorder characterized by de-novo hypertension and proteinuria after 20th week of gestation. There is a broad spectrum of clinical presentations related to hypertensive disorders of pregnancy (HDP) that can range from mild preeclampsia to eclampsia (seizures) or HELLP syndrome (Hemolysis, Elevation of Liver enzymes, Low Platelets). Those clinical outcomes might be linked to different pathological mechanisms. Our work aims to identify factors (i.e. genes and environmental) associated with the HDP’s clinical spectrum. Using a case-control approach, we selected a total of 1498 pregnant women for epidemiological and genetic studies, encompassing 755 normotensive (control); 518 preeclampsia; 84 eclampsia; and 141 HELLP. Women were genotyped for 18 SNPs across 5 candidate genes (FLT1, ACVR2A, ERAP1, ERAP2 and LNPEP). For the environmental factors, we found maternal age, parity status and pre-gestational body mass index as important risk factors associated with disease. Genes were associated in a phenotype-specific manner: ACVR2A with early preeclampsia (rs1424954, p=0.002); FLT1 with HELLP syndrome (rs9513095, p=0.003); and ERAP1 with eclampsia (rs30187, p=0.03). Our results suggest that different genetic mechanisms along with specific environmental factors might determine the clinical spectrum of HDP. In addition, phenotype refinement seems to be an essential step in the search for complex disease genes

Determinantes genéticos da hanseníase em uma população do Rio Grande do Norte

Background: Leprosy can cause severe disability and disfigurement and is still a major health in different parts of the world. Only a subset of those individuals exposed to the pathogen will go on to develop clinical disease and there is a broad clinical spectrum amongst leprosy patients. The outcome of infection is in part due to host genes that influence control of the initial infection and the host´s immune response to that infection. Aim: Evaluate if polymorphisms type SNP in the 17q118q21 chromosomic region contribute to development of leprosy in Rio Grande do Norte population. Material and methods: A sample composed of 215 leprosy patients and 229 controls drawn from the same population were genotyped by using a Snapshot assay for eight genes (NOS2A, CCL18, CRLF3, CCL23, TNFAIP1, STAT5B, CCR7 and CSF3) located in chromosomic region 17q118q21. The genotype and allele frequency were measured and statistical analysis was performed by chi-square in SPSS version 15 and graph prism pad version 4 software. Results: Ours results indicated that the markers NOS2A8277, NOS2A8rs16949, CCR78rs11574663 and CSF38rs2227322 presented strong association with leprosy and their risk genotype were GG, TT, AA and GG respectively. The risk genotypes for all markers associated to leprosy presented recessive inheritance standard. When we compared the interaction among the markers in different combination we find that the marker NOS2A8277 associated with CCR78rs11574663 presented highest risk probability to development of leprosy. When we evaluated the haplotype of the risk markers it was found a haplotype associated with increase of the protection (CSF38rs22273228CC, CCR78 rs115746638GA, NOS2A8rs169498CT and NOS2A82778GA). The association of the clinical forms paucibacilary and multibacilary with markers showed that to the markers NOS2A8 2778GG, CCR78rs115746638AA and CSF38rs22273228GG there were a strong influence to migration to multibacilary pole and to marker NOS2A8rs169498TT the high proportion was found to the paucibacilary form. Conclusions: Changes in the genes NOS2A, CCR7 and CSF3 can influence the immune response against Mycobacterium leprae. The combination among these polymorphisms alters the risk probability to develop leprosy. The markers type SNP associated to development of the leprosy also are linked to clinical forms and its severity being the polymorphism NOS2A8rs169498TT associated with paucibacilar form and the polymorphisms NOS2A82778GG, CCR78rs115746638AA and CSF38rs22273228GG associated to multibacilar form

Identificação de genes MUTM em BACs da cana-de-açucar e caracterização preliminar destes genes e seus promotores

Sugarcane has an importance in Brazil due to sugar and biofuel production. Considering this aspect, there is basic research being done in order to understand its physiology to improve production. The aim of this research is the Base Excision Repair pathway, in special the enzyme MUTM DNA-glycosylase (formamidopyrimidine) which recognizes oxidized guanine in DNA. The sugarcane scMUTM genes were analyzed using four BACs (Bacterial Artificial Chromosome) from a sugarcane genomic library from R570 cultivar. The resulted showed the presence in the region that had homology to scMUTM the presence of transposable elements. Comparing the similarity, it was observed a highest similarity to Sorghum bicolor sequence, both nucleotide and peptide sequences. Furthermore, promoter regions from MUTM genes in some grass showed different cis-regulatory elements, among which, most were related to oxidative stress, suggesting a gene regulation by oxidative stress