Resumo Objetivo Analisar os fatores associados ao óbito em indivíduos internados por COVID-19 em hospitais do Espírito Santo, Brasil. Métodos Estudo transversal, com dados secundários. Modelos de regressão logística foram empregados para estimar razões de chance (odds ratio: OR) brutas e ajustadas. Resultados Até 14 de maio de 2020, 200 indivíduos receberam alta e 220 foram a óbito. Do total de pessoas estudadas, 57,1% eram do sexo masculino, 46,4% maiores de 60 anos de idade, 57,9% foram notificados por instituição privada e 61,7% apresentaram mais de 1 comorbidade. Na análise ajustada, a mortalidade hospitalar foi maior entre aqueles nas faixas etárias de 51 a 60 (OR=4,33 – IC95% 1,50;12,46) e mais de 60 anos (OR=11,84 – IC95% 4,31;32,54), notificados por instituição pública (OR=8,23 – IC95% 4,84;13,99) e com maior número de comorbidades (duas [OR=2,74 – IC95% 1,40;5,34] e três [OR=2,90 – IC95% 1,07;7,81]). Conclusão Observa-se maior mortalidade em idosos, com comorbidades e usuários de hospitais públicos.

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Resumo Objetivo descrever a completude dos dados e avaliar a qualidade do Banco de dados do Painel COVID-19 no Espírito Santo em 2020, quanto à completude de suas variáveis, bem como analisar a confirmação da doença e sua evolução por crianças, adolescentes e jovens. Métodos estudo descritivo exploratório. A completude no preenchimento da ficha no Painel COVID-19 foi classificada como excelente (menos de 5% de preenchimento incompleto), bom (5% a 10%), regular (10% a 20%), ruim (20% a 50%) ou muito ruim (50% ou mais). Resultados observou-se qualidade regular para o critério de confirmação (16%), ruim para a classificação da doença (44%) e status de notificação (30%) e muito ruim para a evolução (79%). Quanto às variáveis epidemiológicas, destaca-se a variável raça/cor da pele com completude regular (17%). Conclusão e implicações para a prática é necessário educação permanente dos profissionais para o preenchimento dos dados de forma correta. Tratando-se de uma pandemia por um vírus novo, esses dados devem estar disponíveis imediatamente, e com qualidade para que medidas de controle possam ser adotadas.

Cotton is the most widely utilized natural fiber in the world. Brazil is currently one of the world's largest cotton producers. Cotton crops are cultivated in all regions of the country, especially in the Cerrado biome. Studies of genotype x environment (GxE) interactions evaluate the adaptability and stability of cotton genotypes. Adaptability and stability evaluations help understand genotype responses to environmental stimuli and the predictability of genotypes in their response to environmental oscillations. We examined the effect of the genotype x environment interaction on cotton yield and fiber characteristics and compared artificial neural networks (ANNs) with conventional methods for assessing adaptability and stability of colored-fiber cotton genotypes. The experiment was conducted at the experimental farm of Universidade D.B.O. Cardoso et al. 2 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (1): gmr18104 Federal de Uberlândia, during four crop years. Twelve genotypes of colored-fiber cotton were evaluated. The experimental design was randomized complete blocks with three replicates. Seed cotton yield was evaluated. The GxE interaction was analyzed by the F-test at α = 0.05. Adaptability, stability, and the factors of the decomposed GxE interaction were analyzed by the Eberhart and Russell, Centroid and ANN methods. The GxE interaction was significant for the variable seed cotton yield, demonstrating differences in genotype behavior among environments. The interactions were predominantly complex. There was concordance between Eberhart and Russsell and ANN analyses. Genotypes UFUJP-02 and UFUJP-17 were responsive to environmental stimuli; they had high predictability, in addition to high fiber yield. The ANN method reliably evaluated adaptability compared with Eberhartand Russel and Centroid methods.

ABSTRACT. Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index 2 E.G. Silva Júnior et al. Genetics and Molecular Research 16 (3): gmr16039798was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.