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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/06/2013 |
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Data da última atualização: |
18/05/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MARON, L. G.; GUIMARAES, C. T.; KIRST, M.; ALBERT, P. S.; BIRCHLER, J. A.; BRADBURY, P. J.; BUCKLER, E. S.; COLUCCIO, A. E.; DANILOVA, T. V.; KUDMA, D.; MAGALHAES, J. V.; PIÑEROS, M. A.; SCHATZ, M. C.; WING, R. A.; KOCHIAN, L. V. |
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Afiliação: |
CLAUDIA TEIXEIRA GUIMARAES, CNPMS; JURANDIR VIEIRA DE MAGALHAES, CNPMS. |
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Título: |
Aluminum tolerance in maize is associated with higher MATE 1 gene copy number. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Proceedings of the National Academy of Sciences of the United States of America, Washington,v. 110, n. 13, p. 5241-5246, Mar. 2013. |
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DOI: |
10.1073/pnas.1220766110 |
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Idioma: |
Inglês |
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Conteúdo: |
Genome structure variation, including copy number variation and presence/absence variation, comprises a large extent of maize genetic diversity; however, its effect on phenotypes remains largely unexplored. Here, we describe how copy number variation underlies a rare allele that contributes to maize aluminum (Al) tolerance. Al toxicity is the primary limitation for crop production on acid soils, which makeup 50% of the world’s potentially arable lands. In arecombinant inbred line mapping population, copy number variation of the Al tolerance gene multidrug and toxic compound extrusion 1(MATE1) is the basis for the quantitative trait locus of largest effect on phenotypic variation. This expansion in MATE1 copy number is associated with higher MATE1 expression, which in turn results in superior Al tolerance. The three MATE1 copies are identical and are part of a tandem triplication. Only three maize inbred lines carrying the three-copy allele were identified from maize and teosinte diversity panels, indicating that copy number variationforMATE1 is a rare,and quite likely recent, event. These maize lines with higher MATE1 copy number are also Al-tolerant, have high MATE1 expression, and originate from regions of highly acidic soils. Our findings show a role for copy number variation in the adaptation of maize to acidic soils in the tropics and suggest that genome structural changes may be a rapid evolutionary response to new environments. |
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Palavras-Chave: |
Tolerância ao alumínio. |
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Thesagro: |
Genética vegetal; Milho. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02485naa a2200337 a 4500
001 1960467
005 2017-05-18
008 2013 bl uuuu u00u1 u #d
024 7 $a10.1073/pnas.1220766110$2DOI
100 1 $aMARON, L. G.
245 $aAluminum tolerance in maize is associated with higher MATE 1 gene copy number.$h[electronic resource]
260 $c2013
520 $aGenome structure variation, including copy number variation and presence/absence variation, comprises a large extent of maize genetic diversity; however, its effect on phenotypes remains largely unexplored. Here, we describe how copy number variation underlies a rare allele that contributes to maize aluminum (Al) tolerance. Al toxicity is the primary limitation for crop production on acid soils, which makeup 50% of the world’s potentially arable lands. In arecombinant inbred line mapping population, copy number variation of the Al tolerance gene multidrug and toxic compound extrusion 1(MATE1) is the basis for the quantitative trait locus of largest effect on phenotypic variation. This expansion in MATE1 copy number is associated with higher MATE1 expression, which in turn results in superior Al tolerance. The three MATE1 copies are identical and are part of a tandem triplication. Only three maize inbred lines carrying the three-copy allele were identified from maize and teosinte diversity panels, indicating that copy number variationforMATE1 is a rare,and quite likely recent, event. These maize lines with higher MATE1 copy number are also Al-tolerant, have high MATE1 expression, and originate from regions of highly acidic soils. Our findings show a role for copy number variation in the adaptation of maize to acidic soils in the tropics and suggest that genome structural changes may be a rapid evolutionary response to new environments.
650 $aGenética vegetal
650 $aMilho
653 $aTolerância ao alumínio
700 1 $aGUIMARAES, C. T.
700 1 $aKIRST, M.
700 1 $aALBERT, P. S.
700 1 $aBIRCHLER, J. A.
700 1 $aBRADBURY, P. J.
700 1 $aBUCKLER, E. S.
700 1 $aCOLUCCIO, A. E.
700 1 $aDANILOVA, T. V.
700 1 $aKUDMA, D.
700 1 $aMAGALHAES, J. V.
700 1 $aPIÑEROS, M. A.
700 1 $aSCHATZ, M. C.
700 1 $aWING, R. A.
700 1 $aKOCHIAN, L. V.
773 $tProceedings of the National Academy of Sciences of the United States of America, Washington,v. 110$gn. 13, p. 5241-5246, Mar. 2013.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
14/04/2020 |
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Data da última atualização: |
07/04/2022 |
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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: |
OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. |
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Afiliação: |
LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
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Título: |
A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, n. 12, v. 6, a. 906, 2020. |
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Páginas: |
1 - 21 |
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DOI: |
10.3390/rs12060906 |
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Idioma: |
Inglês |
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Palavras-Chave: |
Macronutrient; Micronutrient; Proximal sensor. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212333/1/P-A-Machine-Learning-Framework-to-Predict-Nutrient-....pdf
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Marc: |
LEADER 00939naa a2200337 a 4500
001 2121648
005 2022-04-07
008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/rs12060906$2DOI
100 1 $aOSCO, L. P.
245 $aA machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements.$h[electronic resource]
260 $c2020
300 $a1 - 21
653 $aMacronutrient
653 $aMicronutrient
653 $aProximal sensor
700 1 $aRAMOS, A. P. M.
700 1 $aPINHEIRO, M. M. F.
700 1 $aMORIYA, E. A. S.
700 1 $aIMAI, N. N.
700 1 $aESTRABIS, N.
700 1 $aIANCZYK, F.
700 1 $aARAÚJO, F. F.
700 1 $aLIESENBERG, V.
700 1 $aJORGE, L. A. de C.
700 1 $aLI, J.
700 1 $aMA, L.
700 1 $aGONÇALVES, W. N.
700 1 $aMARCATO JUNIOR, J.
700 1 $aCRESTE, J. E.
773 $tRemote Sensing$gn. 12
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