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This study presents a methodology based on multiple local support vector regression (SVR) to calibrate the spectra taken in the field in relative to lab-derived spectra. Laboratory based foliar spectral measurement is a common method to provide lab-derived spectra as a service where a grower sends sample leaves collected manually. The drawback of this method is being time-consuming when the samples are collected and analyzed. In contrast, in-field spectral measurements can be an alternative method capable of providing immediate readings. While both methods work based on the same priniciple, the insturmental differences as well as the conditional difference under which the instruments operate may cause differences in the spectral patterns of the same target. In this work, after developing the calibration method, we validated it by estimating NPK measurements in potato plants using in-field, lab, and field calibrated spectral measurements over two testing modes: dried and fresh. The results showed that the calibration using SVR models could minimize the percentage relative error (PRE) between lab and field spectra within the visible range by considering the influence of the neighboring wavebands up to 32 nm width which improved the alignment of the local maxima of the specral curves. Also, a substantial PRE reduction from 120 % to 20 % for some wavebands in the short-wave infrared (SWIR) region of the fresh mode was observed due to the influence of scaling within the SVR method. The calibration improved the alignment of NPK estimated values between lab and field calibrated spectra of both modes with an emphasis on its necessity to estimate nutrients in the fresh mode as the root mean square error was < 0.1 for the three elements.

Summary: Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture. The bigger picture: Life is a complex and varied phenomenon with a wide range of phenotypic and genotypic variations. The search for the putative genetic mechanisms associated with—and eventually playing causal roles in—the phenotypic differences between species remains a key question in biology. We introduce CALANGO, a comparative genomics tool to search for genome-wide genotype-phenotype associations across species, taking advantage of the large amounts of phenotypic data available for species with complete genomes. Our tool uses phylogeny-aware linear models to account for the non-independence of species data and can be used to detect both homologous regions and molecular functional convergences associated with phenotypes. Through two case studies, we show how CALANGO can be used to investigate the genomic and functional evolution of distinct complex phenotypes and to select targets for experimental characterization.

Francisco Pereira Lobo, Giovanni Marques de Castro, and Felipe Campelo are part of an international team of collaborators that developed CALANGO, a comparative genomics tool to investigate quantitative genotype-phenotype relationships. Their Patterns article highlights how the tool integrates species-centric data to perform genome-wide search and detect genes potentially involved in the emergence of complex quantitative traits across species. Here, they talk about their view of data science, their experience with interdisciplinary research, and the potential applications of their tool.

During epidemics, data from different sources can provide information on varying aspects of the epidemic process. Serology-based epidemiologic surveys could be used to compose a consistent epidemic scenario. We assessed the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG in serum samples collected from 7,837 blood donors in 7 cities of Brazil during March–December 2020. Based on our results, we propose a modification in a compartmental model that uses reported number of SARS-CoV-2 cases and serology results from blood donors as inputs and delivers estimates of hidden variables, such as daily values of SARS-CoV-2 transmission rates and cumulative incidence rate of reported and unreported SARS-CoV-2 cases. We concluded that the information about cumulative incidence of a disease in a city’s population can be obtained by testing serum samples collected from blood donors. Our proposed method also can be extended to surveillance of other infectious diseases.

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package.

Este estudo tem como propósito avaliar os antecedentes e as conseqüências da compra por impulso em lojas virtuais da Internet. Para tanto, foi desenvolvido um modelo teórico que procura apresentar as relações existentes entre o ambiente de loja (site), a impulsividade individual, a circulação na loja (navegação no site) e a realização de compras impulsivas. A partir de uma pesquisa survey realizada com compradores de produtos pela Internet, por meio de modelagem de equações estruturais, foi evidenciado que o ambiente do site afeta o nível de impulsividade, a navegação no site e a ocorrência de compras por impulso, bem como se encontrou uma relação entre compra por impulso e emoções positivas. Elementos ambientais que influenciam a compra por impulso foram identificados e rotas para futuras pesquisas apresentadas, juntamente com implicações acadêmicas e gerenciais dos resultados.