Uso de redes neurais artificiais na avaliação da dissimilaridade de algodoeiro de fibra colorida

Pesquisa sem auxílio de agências de fomentoTrabalho de Conclusão de Curso (Graduação)O algodoeiro é uma planta que produz a fibra natural mais importante no mundo. A fibra colorida e está no nicho de mercado com alto valor agregado. Contudo, esta fibra possui desvantagens quando comparada à branca, por ter menor produção e qualidade inferior produzindo fibras curtas e fracas, desvalorizando o produto na industria têxtil. Com o propósito de resolver esses problemas, o melhoramento de plantas é o método que pode apresentar maior eficácia. Um dos fatores necessários para que ocorra o melhoramento, é que exista variabilidade genética entre os genitores, de forma que possibilite a recombinação de híbridos superiores. Para verificar a existência de varialibilidade genética, uma das técnicas que podem ser utilizadas são as Redes Neurais Artificiais (RNA’s). Essa metodologia utiliza a inteligência computacional e o processamento dos dados de forma a simular o cérebro humano, analisando dados não paramétricas e desbalanceados com erros experimentais e falhas de pressuposições. Conforme apresentado, o objetivou-se com este trabalho avaliar o uso Redes Neurais Artificiais (RNA’s) por meio do Mapa Auto Organizável de Kohonen para distinguir a dissimilaridade dos genótipos de algodão de fibra colorida. O trabalho foi realizado em condições de campo em Uberlândia-MG na safra 18/19. O delineamento experimental utilizado foi o de blocos completos casualizados (DBC) com três repetições e 12 genótipos (UFUJP-01, UFUJP-02, UFUJP-05, UFUJP-08, UFUJP-09, UFUJP-10, UFUJP-11, UFUJP-13, UFUJP-16, UFUJP-17, BRS Rubi (RC) e BRS Topázio (TC)). Foram avaliadas sete características: Produtividade de algodão em caroço, Rendimento de fibras, Comprimento de fibra, Uniformidade do comprimento, Índice de fibra curta, Alongamento e Resistência. A dissimilaridade genética foi realizada pela Distância generalizada de Mahalanobis e o agrupamento dos genótipos pelo método hierárquico da Ligação Média entre grupo (UPGMA). Para a inteligência computacional realizou a análise discriminante e o Mapa Auto Organizável de Kohonen utilizando as Redes Neurais Artificiais (RNA’s). As análises de variência foram realizadas e analisadas pelo Teste F e médias comparadas por Scott e Knott a 5% de probabilidade, por meio do Programa estatístico (GENES), integrado ao software R e Matlab. O Mapa AutoOrganizável de Kohonen utilizando Redes Neurais Artificiais (RNA’s) é eficaz em distinguir a dissimilaridade dos genótipos de algodão de fibra colorida sendo o mais indicado na classificação de grupos

Genetic diversity among colored cotton genotypes to obtain potential parent plants

The objective of this study was to analyze the genetic diversity among colored cotton fiber genotypes using technological characteristics of the fiber, seed cotton yield and percentage of fiber, to identify potential parents with high performance. The experiment was conducted in the Brazilian city of Uberlândia, Minas Gerais, during the 2016/2017 harvest. Twelve colored fiber genotypes were used in a randomized block design. The characteristics that were evaluated included micronaire, maturation, fiber length, length uniformity, short fiber index, fiber resistance, elongation, seed cotton yield and percentage of fiber. Genetic divergence was estimated using the Mahalanobis generalized matrix with Unweighted Pair Group with Arithmetic Mean (UPGMA) and the Tocher method. The Singh method was used to evaluate the relative contributions of the characteristics in the divergence. In detecting divergence, fiber length and maturation were observed to have contributed the most. In order to obtain segregant populations with greater genetic variability and greater productive potential, hybridizations between UFUJP-17 and UFUJP-16 with commercial cultivars could be promising. Crossing UFUJP-16 with commercial controls would have a higher chance of success of producing superior fiber quality

Use of computational intelligence in the genetic divergence of colored cotton plants

The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes

The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics