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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/03/2007 |
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Data da última atualização: |
17/05/2017 |
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Autoria: |
BORRO, L. C.; OLIVEIRA, S. R. M.; YAMAGISHI, M. E. B.; MANCINI, A. L.; JARDINE, J. G.; MAZONI, I.; SANTOS, E. H. dos; HIGA, R. H.; KUSER, P. R.; NESHICH, G. |
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Afiliação: |
LUIZ C. BORRO, CNPTIA; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; ADAUTO LUIZ MANCINI, CNPTIA; JOSE GILBERTO JARDINE, CNPTIA; IVAN MAZONI, CNPTIA; EDGARD HENRIQUE DOS SANTOS, CNPTIA; ROBERTO HIROSHI HIGA, CNPTIA; PAULA REGINA KUSER FALCAO, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Predicting enzyme class from protein structure using Bayesian classification. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 5, n. 1, p. 193-202, 2006. |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT. Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods. |
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Palavras-Chave: |
Bayesian classification; Bayesian classifier; Bioinformática; Classe de enzima; Data classification; Enzyme classification number; Estrutura de proteína; Naive Bayes; Protein function prediction. |
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Thesaurus Nal: |
Bioinformatics; Protein structure. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/159942/1/AP-Predicting-Borro-GMR-2006.pdf
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Marc: |
LEADER 01540naa a2200361 a 4500
001 1009196
005 2017-05-17
008 2006 bl uuuu u00u1 u #d
100 1 $aBORRO, L. C.
245 $aPredicting enzyme class from protein structure using Bayesian classification.$h[electronic resource]
260 $c2006
520 $aABSTRACT. Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.
650 $aBioinformatics
650 $aProtein structure
653 $aBayesian classification
653 $aBayesian classifier
653 $aBioinformática
653 $aClasse de enzima
653 $aData classification
653 $aEnzyme classification number
653 $aEstrutura de proteína
653 $aNaive Bayes
653 $aProtein function prediction
700 1 $aOLIVEIRA, S. R. M.
700 1 $aYAMAGISHI, M. E. B.
700 1 $aMANCINI, A. L.
700 1 $aJARDINE, J. G.
700 1 $aMAZONI, I.
700 1 $aSANTOS, E. H. dos
700 1 $aHIGA, R. H.
700 1 $aKUSER, P. R.
700 1 $aNESHICH, G.
773 $tGenetics and Molecular Research$gv. 5, n. 1, p. 193-202, 2006.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agrobiologia. Para informações adicionais entre em contato com cnpab.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
09/11/2021 |
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Data da última atualização: |
09/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
DEPABLOS, L.; HOMEM, B. G. C.; FERREIRA, I. M.; BERNARDES, T. F.; BODDEY, R. M.; LARA, M. A. S.; CASAGRANDE, D. R. |
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Afiliação: |
LUIS DEPABLOS, UNIVERSIDAD CENTRAL DE VENEZUELA; BRUNO G. C. HOMEM, UFLA; IGOR M. FERREIRA, UFLA; THIAGO F. BERNARDES, UFLA; ROBERT MICHAEL BODDEY, CNPAB; MÁRCIO A. S. LARA, UFLA; DANIEL R. CASAGRANDE, UFLA. |
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Título: |
Nitrogen cycling in tropical grass-legume pastures managed under canopy light interception. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Nutrient Cycling in Agroecosystems, V. 121,p. 51-67, 2021. |
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ISSN: |
1385-1314 |
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DOI: |
https://doi.org/10.1007/s10705-021-10160-7 |
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Idioma: |
Inglês |
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Conteúdo: |
In grass-legume pastures, grazing management strategies are an essential factor affecting nitrogen (N) cycling. This study assessed the impact of grazing management on N cycling in rotationally-stocked mixed pastures of ?Marandu? palisadegrass (Brachiaria brizantha) and ?Comum? calopo (Calopogonium mucunoides). Treatments included three grazing management strategies, defined by interruption of the rest period when the canopy reached 90 (90LI), 95 (95LI) and 100% (100LI) of the interception of photosynthetically active radiation. A 2-yr experimental period was adopted. Plant litter responses, forage intake and livestock excretion were evaluated. No differences between grazing management were obtained for existing (294 g OM m−2) and deposited litter (6.7 g OM m−2 d−1, P > 0.10). Compared to the dry season, the litter decomposition rate increased 24.0%, and the half-life decreased 37.8% in the rainy season (P < 0.10). The N cycling via litter (553 g ha−1 d−1) was similar in all grazing management (P > 0.10). Less frequent defoliation (100LI) resulted in reduced proportion of legume intake (P < 0.10, 94.4 vs. 168.5 g kg−1; an average of 90LI and 95LI, respectively), lower N intake (123.1 vs. 194.1 g animal unit−1 d−1) and a lower input of N from biological fixation (73.2 vs. 97.8 kg ha−1 yr−1). Less frequent defoliation should be avoided because it reduces the N intake and N retained by animals, which caused a reduction in N utilisation efficiency by heifers. Thus, 95% light interception is the maximum limit to interrupt the regrowth in palisadegrass-calopo pastures. MenosIn grass-legume pastures, grazing management strategies are an essential factor affecting nitrogen (N) cycling. This study assessed the impact of grazing management on N cycling in rotationally-stocked mixed pastures of ?Marandu? palisadegrass (Brachiaria brizantha) and ?Comum? calopo (Calopogonium mucunoides). Treatments included three grazing management strategies, defined by interruption of the rest period when the canopy reached 90 (90LI), 95 (95LI) and 100% (100LI) of the interception of photosynthetically active radiation. A 2-yr experimental period was adopted. Plant litter responses, forage intake and livestock excretion were evaluated. No differences between grazing management were obtained for existing (294 g OM m−2) and deposited litter (6.7 g OM m−2 d−1, P > 0.10). Compared to the dry season, the litter decomposition rate increased 24.0%, and the half-life decreased 37.8% in the rainy season (P < 0.10). The N cycling via litter (553 g ha−1 d−1) was similar in all grazing management (P > 0.10). Less frequent defoliation (100LI) resulted in reduced proportion of legume intake (P < 0.10, 94.4 vs. 168.5 g kg−1; an average of 90LI and 95LI, respectively), lower N intake (123.1 vs. 194.1 g animal unit−1 d−1) and a lower input of N from biological fixation (73.2 vs. 97.8 kg ha−1 yr−1). Less frequent defoliation should be avoided because it reduces the N intake and... Mostrar Tudo |
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Palavras-Chave: |
BNF; Efoliation frequency; Marandu palisadegrass. |
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Thesagro: |
Capim Urochloa. |
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Thesaurus NAL: |
Excreta. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02532naa a2200277 a 4500
001 2135955
005 2021-11-09
008 2021 bl uuuu u00u1 u #d
022 $a1385-1314
024 7 $ahttps://doi.org/10.1007/s10705-021-10160-7$2DOI
100 1 $aDEPABLOS, L.
245 $aNitrogen cycling in tropical grass-legume pastures managed under canopy light interception.$h[electronic resource]
260 $c2021
520 $aIn grass-legume pastures, grazing management strategies are an essential factor affecting nitrogen (N) cycling. This study assessed the impact of grazing management on N cycling in rotationally-stocked mixed pastures of ?Marandu? palisadegrass (Brachiaria brizantha) and ?Comum? calopo (Calopogonium mucunoides). Treatments included three grazing management strategies, defined by interruption of the rest period when the canopy reached 90 (90LI), 95 (95LI) and 100% (100LI) of the interception of photosynthetically active radiation. A 2-yr experimental period was adopted. Plant litter responses, forage intake and livestock excretion were evaluated. No differences between grazing management were obtained for existing (294 g OM m−2) and deposited litter (6.7 g OM m−2 d−1, P > 0.10). Compared to the dry season, the litter decomposition rate increased 24.0%, and the half-life decreased 37.8% in the rainy season (P < 0.10). The N cycling via litter (553 g ha−1 d−1) was similar in all grazing management (P > 0.10). Less frequent defoliation (100LI) resulted in reduced proportion of legume intake (P < 0.10, 94.4 vs. 168.5 g kg−1; an average of 90LI and 95LI, respectively), lower N intake (123.1 vs. 194.1 g animal unit−1 d−1) and a lower input of N from biological fixation (73.2 vs. 97.8 kg ha−1 yr−1). Less frequent defoliation should be avoided because it reduces the N intake and N retained by animals, which caused a reduction in N utilisation efficiency by heifers. Thus, 95% light interception is the maximum limit to interrupt the regrowth in palisadegrass-calopo pastures.
650 $aExcreta
650 $aCapim Urochloa
653 $aBNF
653 $aEfoliation frequency
653 $aMarandu palisadegrass
700 1 $aHOMEM, B. G. C.
700 1 $aFERREIRA, I. M.
700 1 $aBERNARDES, T. F.
700 1 $aBODDEY, R. M.
700 1 $aLARA, M. A. S.
700 1 $aCASAGRANDE, D. R.
773 $tNutrient Cycling in Agroecosystems, V. 121,p. 51-67, 2021.
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