The progress of viral diseases such as the new coronavirus (COVID-19) can be influenced not only by social isolation policies, but also by climatic factors. Understanding how these factors affect the progress of the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be essential to know the risks each country is facing because of the disease. In this study, we verified the existence of a relationship between the basic reproduction number (R0) of SARS-CoV-2 with different climate variables, while also considering the Global Health Security Index (GHS). We collected data from confirmed cases of COVID-19 along with their respective GHS notes and climate data, from December 31, 2019 to April 13, 2020, for 52 countries. The generalized additive model (GAM) was applied to explore the effect of temperature, relative humidity, solar radiation index, and GHS score on the spread rate of COVID-19. The countries that showed similarity to each other were grouped into clusters using the Kohonen self-organizing map methodology to investigate the importance of each variable in the dissemination of the disease. The temperature variable presented a linear relationship ( p < 0.001) with the R0, with an explained variation of 36.2%, while the relative humidity variable did not present a significant relationship with the R0. The response curve of the solar radiation variable presented a significant nonlinear relationship ( p < 0.001) with an explained variation of 32.3%. The GHS index variable, with a significant nonlinear relationship ( p < 0.001), presented the largest explanatory response in the control of COVID-19, with an explained variation of 38.4%; further, it was observed that the countries with the largest GHS index scores were less influenced by climate variables.

ABSTRACT. Methods to obtain phenotypic information were evaluated to help breeders choosing the best methodology for analysis of genetic diversity in backcross populations. Phenotypes were simulated for 13 characteristics generated in 10 populations with 100 individuals each. Genotypic information was generated from 100 loci of which 20 were taken at random to determine the characteristics expressing two alleles. Dissimilarity measures were calculated, and genetic diversity was analyzed through hierarchical clustering and graphic projection of the distances. A backcross was performed from the two most divergent populations. A set of characteristics with variable heritability was taken into account. The environmental effect was simulated assuming ~ (0, ). For hierarchical clusters, the following methods were used: Gower Method, average linkage within the cluster, average linkage among clusters, the furthest neighbor method, the nearest neighbor method, Ward's method, and the median method. The environmental effect and heritability of the analyzed variables had an influence on the pattern of hierarchical clustering populations according to the backcrossed generations. The nearest neighbor method was the most efficient in reconstructing the system of backcrossing, and it presented the highest cophenetic correlation. The efficiency of the nearest neighbor method was the highest when the analysis involved characteristics of high heritability.Keywords: hierarchical clustering, genetic diversity, backcrossing.Agrupamentos genéticos hierárquicos para análises fenotípicas RESUMO. Visando auxiliar o melhorista na escolha da melhor metodologia para análises de diversidade genética em populações de retrocruzamento avaliaram-se os métodos baseados em informações fenotípicas. Os fenótipos foram simulados para 13 características, geradas em 10 populações com 100 indivíduos cada. Geraram-se informações genotípicas de 100 locos, dos quais 20 foram tomados ao acaso para determinar as características, manifestando dois alelos. Medidas de dissimilaridade foram calculadas, e analisou-se a diversidade genética por meio agrupamento hierárquico e projeção gráfica das distâncias. A partir das duas populações mais divergentes fez-se o retrocruzamento. Considerou-se um conjunto de características com herdabilidade variável. Simulou-se o efeito ambiental admitindo distribuição normal, com média zero e variância . Para as análises de agrupamentos hierárquicos utilizaram-se os métodos: Método de Gower, Ligação média dentro de grupo, Ligação média entre grupo, Vizinho mais distante, Vizinho mais próximo, Método de Ward, Método da mediana. O efeito ambiental e a herdabilidade das variáveis analisadas influenciaram o padrão de agrupamento de populações sob retrocruzamento. Em características de herdabilidade elevada, o método do Vizinho mais próximo foi o mais eficiente em reconstituir o retrocruzamento, além de ter apresentado a maior correlação cofenética, sendo considerada a melhor metodologia a priori e a posteriori.Palavras-chave: métodos ...

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Coffee growing is one of the most important agricultural activities in the world market. Among the commercially relevant species, there is Coffea canephora,which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Because of this, hybrids are obtained through the crosses between these groups. Given the difficulty in differentiating between two varietal groups genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the objective was to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of canephora, as well as to identify the most relevant phenotypic traits in these functions. Data from 165 genotypes from the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural e do Centro Agronómico Tropical de Investigación y Enseñanza were used for which different plant traits were measured. The quadratic DA applied was the one with the best performance for genotype discrimination, with an average apparent error rate of 0.0333. Cercosporiose incidence, rust incidence and vegetative vigor were the most important traits in the varietal groups' discrimination.

The selection of better-evaluated genotypes for a target region depends on the characterization of the climate conditions of the environment. With the advancement of computer technology and daily available information about the weather, integrating such information in selection and interaction genotype x environment studies has become a challenge. This article presents the use of the technique of artificial neural networks associated with reaction norms for the processing of climate and geo-referenced data for the study of genetic behaviors and the genotype-environment interaction of soybean genotypes. The technique of self-organizing maps (SOM) consists of competitive learning between two layers of neurons; one is the input, which transfers the data to the map, and the other is the output, where the topological structure formed by the competition generates weights, which represent the dissimilarity between the neural units. The methodologies used to classify these neurons and form the target populations of environments (TPE) were the discriminant analysis (DA) and the principal component analysis (PCA). To study soybean genetic behavior within these TPEs, the random regression model was adopted to estimate the components of variance, and the reaction norms were adjusted through the Legendre polynomials. The SOM methodology allowed for an explanation of 99% of the variance of the climate data and the formation of well-structured TPEs, with the membership probability of the regions within the TPEs above 80%. The formation of these TPEs allowed us to identify and quantify the response of the genotypes to sensitive changes in the environment.