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Registro Completo |
Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia; Embrapa Soja. |
Data corrente: |
20/09/2022 |
Data da última atualização: |
19/01/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
BASSO, M. F.; LOURENCO, I. T.; MOREIRA-PINTO, C. E.; MENDES, R. A. G.; PAES-DE-MELO, B.; NEVES, M. R. das; MACEDO, A. F.; FIGUEIREDO, V.; GRANDIS, A.; MACEDO, L. L. P. de; ARRAES, F. B. M.; COSTA, M. M. do C.; TOGAWA, R. C.; ENRICH-PRAST, A.; MARCELINO-GUIMARÃES, F. C.; GOMES, A. C. M. M.; SILVA, M. C. M. da; FLOH, E. I. S.; BUCKERIDGE, M. S.; ENGLER, J. de A.; SA, M. F. G. de. |
Afiliação: |
MARCOS FERNANDO BASSO; ISABELA TRISTAN LOURENCO TESSUTTI, Cenargen; CLIDIA EDUARDA MOREIRA-PINTO, Federal University of Brasília; RENEIDA APARECIDA GODINHO MENDES, Federal University of Brasília; BRUNO PAES-DE-MELO; MAYSA ROSA DAS NEVES; AMANDA FERREIRA MACEDO, University of São Paulo; VIVIANE FIGUEIREDO, Federal University of Rio de Janeiro; ADRIANA GRANDIS, University of São Paulo; LEONARDO LIMA PEPINO DE MACEDO, Cenargen; FABRÍCIO BARBOSA MONTEIRO ARRAES; MARCOS MOTA DO CARMO COSTA, Cenargen; ROBERTO COITI TOGAWA, Cenargen; ALEX ENRICH-PRAST, Federal University of Rio de Janeiro; FRANCISMAR CORREA MARCELINO GUIMARA, CNPSO; ANA CRISTINA MENESES M GOMES, Cenargen; MARIA CRISTINA MATTAR DA SILVA, Cenargen; ENY IOCHEVET SEGAL FLOH, University of São Paulo; MARCOS SILVEIRA BUCKERIDGE, University of São Paulo; JANICE DE ALMEIDA ENGLER, Université Côte d’Azur, France; MARIA FATIMA GROSSI DE SA, Cenargen. |
Título: |
Overexpression of a soybean Globin (GmGlb1-1) gene reduces plant susceptibility to Meloidogyne incognita. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Planta, v. 256, 83, 2022. |
Páginas: |
16 p. |
DOI: |
10.1007/s00425-022-03992-2 |
Idioma: |
Inglês |
Notas: |
Na publicação: Isabela Tristan Lourenço-Tessutti; Leonardo Lima Pepino Macedo; Francismar Corrêa Marcelino-Guimaraes; Maria Cristina Mattar Silva; Maria Fatima Grossi-de-Sa. |
Palavras-Chave: |
BRS133; Glyma 11G121800; New biotechnology tools; Phytoglobins; PI595099; Plant-nematode interaction; Rootknot nematodes. |
Thesagro: |
Glycine Max; Nematóide; Soja. |
Thesaurus Nal: |
Nematoda; Soybeans. |
Categoria do assunto: |
-- X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1151081/1/Basso-et-al.-2022-GmGlb1.pdf
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Marc: |
LEADER 01644naa a2200529 a 4500 001 2151081 005 2023-01-19 008 2022 bl uuuu u00u1 u #d 024 7 $a10.1007/s00425-022-03992-2$2DOI 100 1 $aBASSO, M. F. 245 $aOverexpression of a soybean Globin (GmGlb1-1) gene reduces plant susceptibility to Meloidogyne incognita.$h[electronic resource] 260 $c2022 300 $a16 p. 500 $aNa publicação: Isabela Tristan Lourenço-Tessutti; Leonardo Lima Pepino Macedo; Francismar Corrêa Marcelino-Guimaraes; Maria Cristina Mattar Silva; Maria Fatima Grossi-de-Sa. 650 $aNematoda 650 $aSoybeans 650 $aGlycine Max 650 $aNematóide 650 $aSoja 653 $aBRS133 653 $aGlyma 11G121800 653 $aNew biotechnology tools 653 $aPhytoglobins 653 $aPI595099 653 $aPlant-nematode interaction 653 $aRootknot nematodes 700 1 $aLOURENCO, I. T. 700 1 $aMOREIRA-PINTO, C. E. 700 1 $aMENDES, R. A. G. 700 1 $aPAES-DE-MELO, B. 700 1 $aNEVES, M. R. das 700 1 $aMACEDO, A. F. 700 1 $aFIGUEIREDO, V. 700 1 $aGRANDIS, A. 700 1 $aMACEDO, L. L. P. de 700 1 $aARRAES, F. B. M. 700 1 $aCOSTA, M. M. do C. 700 1 $aTOGAWA, R. C. 700 1 $aENRICH-PRAST, A. 700 1 $aMARCELINO-GUIMARÃES, F. C. 700 1 $aGOMES, A. C. M. M. 700 1 $aSILVA, M. C. M. da 700 1 $aFLOH, E. I. S. 700 1 $aBUCKERIDGE, M. S. 700 1 $aENGLER, J. de A. 700 1 $aSA, M. F. G. de 773 $tPlanta$gv. 256, 83, 2022.
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Registro original: |
Embrapa Soja (CNPSO) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
13/01/2020 |
Data da última atualização: |
06/02/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
SILVA, D. A.; COSTA, C. N.; SILVA, A. A.; SILVA, H. T.; LOPES, P. S.; SILVA, F. F.; VERONEZE, R.; THOMPSON, G.; AGUILAR, I.; CARVALHEIRA, J. |
Afiliação: |
CLAUDIO NAPOLIS COSTA, CNPGL. |
Título: |
Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 3, p. 305-315, 2020. |
DOI: |
https://doi.org/10.1111/jbg.12459 |
Idioma: |
Inglês |
Conteúdo: |
Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder. |
Palavras-Chave: |
Autoregression; Legendre polynomials; Random regression. |
Thesaurus NAL: |
Dairy cattle. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02289naa a2200289 a 4500 001 2118639 005 2024-02-06 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1111/jbg.12459$2DOI 100 1 $aSILVA, D. A. 245 $aAutoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle.$h[electronic resource] 260 $c2020 520 $aAutoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder. 650 $aDairy cattle 653 $aAutoregression 653 $aLegendre polynomials 653 $aRandom regression 700 1 $aCOSTA, C. N. 700 1 $aSILVA, A. A. 700 1 $aSILVA, H. T. 700 1 $aLOPES, P. S. 700 1 $aSILVA, F. F. 700 1 $aVERONEZE, R. 700 1 $aTHOMPSON, G. 700 1 $aAGUILAR, I. 700 1 $aCARVALHEIRA, J. 773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 3, p. 305-315, 2020.
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