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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental; Embrapa Unidades Centrais. |
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Data corrente: |
08/04/2009 |
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Data da última atualização: |
04/05/2018 |
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Tipo da produção científica: |
Autoria/Organização/Edição de Livros |
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Autoria: |
LEÃO, N. V. M.; FREITAS, A. D. D. de; NASCIMENTO, M. R. |
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Afiliação: |
NOEMI VIANNA MARTINS LEAO, CPATU; Alessandra Doce Dias de Freitas; Maria Ruth Nascimento. |
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Título: |
Apostila Curso de Colheita de sementes e produção de mudas de espécies florestais. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Belém, PA: Embrapa Amazônia Oriental: Ideflor: Ufra: Governo do Estado do Pará, 2008. |
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Páginas: |
[55 p.] |
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Idioma: |
Português |
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Conteúdo: |
Ecologia e sucessão florestal; Fatores que afetam a produção de sementes; Fenologia reprodutiva; Métodos de colheita; Extração, secagem e beneficiamento de sementes; Germinação e dormência de sementes; Viveiro e produção de mudas; Morfologia de frutos, sementes e plântulas; Conservação e armazenamento de sementes de espécies arbóreas. |
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Palavras-Chave: |
Brasil. |
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Thesagro: |
Comercialização; Embriogénese; Essência Florestal; Floresta; Fruto; Madeira; Maturação; Produção de Sementes. |
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Thesaurus Nal: |
Amazonia. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01110nam a2200265 a 4500
001 1409938
005 2018-05-04
008 2008 bl uuuu 00u1 u #d
100 1 $aLEÃO, N. V. M.
245 $aApostila Curso de Colheita de sementes e produção de mudas de espécies florestais.
260 $aBelém, PA: Embrapa Amazônia Oriental: Ideflor: Ufra: Governo do Estado do Pará$c2008
300 $a[55 p.]
520 $aEcologia e sucessão florestal; Fatores que afetam a produção de sementes; Fenologia reprodutiva; Métodos de colheita; Extração, secagem e beneficiamento de sementes; Germinação e dormência de sementes; Viveiro e produção de mudas; Morfologia de frutos, sementes e plântulas; Conservação e armazenamento de sementes de espécies arbóreas.
650 $aAmazonia
650 $aComercialização
650 $aEmbriogénese
650 $aEssência Florestal
650 $aFloresta
650 $aFruto
650 $aMadeira
650 $aMaturação
650 $aProdução de Sementes
653 $aBrasil
700 1 $aFREITAS, A. D. D. de
700 1 $aNASCIMENTO, M. R.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
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Data corrente: |
05/05/2021 |
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Data da última atualização: |
14/05/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
LOPES, F. B.; MAGNABOSCO, C. de U.; PASSAFARO, T. L.; BRUNES, L. C.; COSTA, M. F. O. e; EIFERT, E. da C.; NARCISO, M. G.; ROSA, G. J. M.; LOBO, R. B.; BALDI, F. |
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Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
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Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
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Páginas: |
p. 438-448 |
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Idioma: |
Inglês |
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Conteúdo: |
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle MenosThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relative... Mostrar Tudo |
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Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
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Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
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Thesaurus NAL: |
Animal breeding; Zebu. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223045/1/Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf
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Marc: |
LEADER 02672naa a2200373 a 4500
001 2131678
005 2021-05-14
008 2020 bl uuuu u00u1 u #d
100 1 $aLOPES, F. B.
245 $aImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.$h[electronic resource]
260 $c2020
300 $ap. 438-448
520 $aThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle
650 $aAnimal breeding
650 $aZebu
650 $aCarne
650 $aGado de Corte
650 $aGenética Animal
650 $aSeleção Genética
653 $aBayesian regression models
653 $aCarne macia
653 $aDeep learning
653 $aGenomic selection
653 $aMaciez da carne
700 1 $aMAGNABOSCO, C. de U.
700 1 $aPASSAFARO, T. L.
700 1 $aBRUNES, L. C.
700 1 $aCOSTA, M. F. O. e
700 1 $aEIFERT, E. da C.
700 1 $aNARCISO, M. G.
700 1 $aROSA, G. J. M.
700 1 $aLOBO, R. B.
700 1 $aBALDI, F.
773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 5, 2020.
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Embrapa Cerrados (CPAC) |
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