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 global population of SARS-CoV-2 is composed of six major subtypes.

The World Health Organization characterized COVID-19 as a pandemic in March 2020, the second pandemic of the twenty-first century. Expanding virus populations, such as that of SARS-CoV-2, accumulate a number of narrowly shared polymorphisms, imposing a confounding effect on traditional clustering methods. In this context, approaches that reduce the complexity of the sequence space occupied by the SARS-CoV-2 population are necessary for robust clustering. Here, we propose subdividing the global SARS-CoV-2 population into six well-defined subtypes and 10 poorly represented genotypes named tentative subtypes by focusing on the widely shared polymorphisms in nonstructural (nsp3, nsp4, nsp6, nsp12, nsp13 and nsp14) cistrons and structural (spike and nucleocapsid) and accessory (ORF8) genes. The six subtypes and the additional genotypes showed amino acid replacements that might have phenotypic implications. Notably, three mutations (one of them in the Spike protein) were responsible for the geographical segregation of subtypes. We hypothesize that the virus subtypes detected in this study are records of the early stages of SARS-CoV-2 diversification that were randomly sampled to compose the virus populations around the world. The genetic structure determined for the SARS-CoV-2 population provides substantial guidelines for maximizing the effectiveness of trials for testing candidate vaccines or drugs.