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Registro Completo |
Biblioteca(s): |
Embrapa Amazônia Ocidental. |
Data corrente: |
14/12/2023 |
Data da última atualização: |
15/12/2023 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
NEVES, K.; SILVA, J. C. da; LIMA, E.; SOMAN, L.; QUEIROZ, C.; KOOLEN, H.; SILVA, G. F. da. |
Afiliação: |
KIANDRO NEVES, UNIVERSIDADE DO ESTADO DO AMAZONAS; JOSÉ CARLOS DA SILVA, UNIVERSIDADE DO ESTADO DO AMAZONAS; EMILLY LIMA, UNIVERSIDADE DO ESTADO DO AMAZONAS; LÍVIA SOMAN, UNIVERSIDADE FEDERAL DE SÃO PAULO; CLAUDIA QUEIROZ, BOLSISTA; HECTOR KOOLEN, UNIVERSIDADE DO ESTADO DO AMAZONAS; GILVAN FERREIRA DA SILVA, CPAA. |
Título: |
Prospecting for natural products from a new species of Streptomyces isolated from sediments of the Madeira River, Amazonas, Brazil. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
In: BRAZILIAN CONFERENCE ON NATURAL PRODUCT (BCNP), 9.; MEETING ON MICROMOLECULAR EVOLUTION, SYSTEMATICS AND ECOLOGY (RESEM), 35., 2023, Salvador. Proceedings [...] Campinas: Galoá, 2023. |
Idioma: |
Inglês |
Conteúdo: |
The objective of this study was to prospect a Streptomyces lineage isolated from sediments of the Madeira River against agricultural phytopathogens. The dDDH below 70% (55.2%) indicated MAD 42 as a new species. |
Palavras-Chave: |
Antifungal. |
Thesaurus Nal: |
Actinobacteria; New species. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01006nam a2200217 a 4500 001 2159744 005 2023-12-15 008 2023 bl uuuu u00u1 u #d 100 1 $aNEVES, K. 245 $aProspecting for natural products from a new species of Streptomyces isolated from sediments of the Madeira River, Amazonas, Brazil.$h[electronic resource] 260 $aIn: BRAZILIAN CONFERENCE ON NATURAL PRODUCT (BCNP), 9.; MEETING ON MICROMOLECULAR EVOLUTION, SYSTEMATICS AND ECOLOGY (RESEM), 35., 2023, Salvador. Proceedings [...] Campinas: Galoá$c2023 520 $aThe objective of this study was to prospect a Streptomyces lineage isolated from sediments of the Madeira River against agricultural phytopathogens. The dDDH below 70% (55.2%) indicated MAD 42 as a new species. 650 $aActinobacteria 650 $aNew species 653 $aAntifungal 700 1 $aSILVA, J. C. da 700 1 $aLIMA, E. 700 1 $aSOMAN, L. 700 1 $aQUEIROZ, C. 700 1 $aKOOLEN, H. 700 1 $aSILVA, G. F. da
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Embrapa Amazônia Ocidental (CPAA) |
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Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
03/01/2018 |
Data da última atualização: |
11/01/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. |
Afiliação: |
M. Nascimento, UFV; F. F. e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; C. D. Cruz, UFV; A. C. C. Nascimento, UFV; J. M. S. Viana, UFV; C. F. Azevedo, UFV; L. M. A. Barroso, UFV. |
Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
Páginas: |
12 p. |
Idioma: |
Inglês |
Conteúdo: |
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. MenosGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved be... Mostrar Tudo |
Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
Thesagro: |
Estatística. |
Thesaurus NAL: |
Marker-assisted selection; Simulation models; Statistics. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
LEADER 02634naa a2200313 a 4500 001 2084109 005 2018-01-11 008 2017 bl uuuu u00u1 u #d 100 1 $aNASCIMENTO, M. 245 $aRegularized quantile regression applied to genome-enabled prediction of quantitative traits.$h[electronic resource] 260 $c2017 300 $a12 p. 520 $aGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. 650 $aMarker-assisted selection 650 $aSimulation models 650 $aStatistics 650 $aEstatística 653 $aGenomic selection 653 $aRegularized regression 653 $aSeleção genômica 653 $aSNP effects 700 1 $aSILVA, F. F. e 700 1 $aRESENDE, M. D. V. de 700 1 $aCRUZ, C. D. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aVIANA, J. M. S. 700 1 $aAZEVEDO, C. F. 700 1 $aBARROSO, L. M. A. 773 $tGenetics and Molecular Research$gv. 16, n. 1, gmr16019538, 2017.
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