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 | Acesso ao texto completo restrito à biblioteca da Embrapa Suínos e Aves. Para informações adicionais entre em contato com cnpsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Suínos e Aves. |
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Data corrente: |
29/10/2014 |
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Data da última atualização: |
29/10/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HENN, J. D.; BOCKOR, L.; MARX, F. R.; COLDEBELLA, A.; RIBEIRO, A. M. L.; KESSLER, A. de M. |
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Afiliação: |
JOAO DIONISIO HENN, CNPSA; LUCINE BOCKOR, UFGRS; FÁBIO RITTER MARX, UFGRS; ARLEI COLDEBELLA, CNPSA; ANDREA MACHADO LEAL RIBEIRO, UFGRS; ALEXANDRE DE MELLO KESSLER, UFGRS. |
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Título: |
Emissão de dióxido de carbono pela cama de primeiro lote de frangos de corte. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Revista Brasileira de Agropecuária Sustentável, Viçosa, MG, v. 4, n. 1, p. 45-52, 2014. |
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Idioma: |
Português |
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Conteúdo: |
Objetivou-se avaliar as concentrações de gases de efeito estufa (CO2, CH4 e N2O) no ambiente interno das instalações e determinar a emissão de CO2 com base no balanço de C da cama de primeiro lote de frangos de corte de linhagens de médio (C44) e de alto desempenho (Cobb 500). Amostras de gases foram coletadas através de câmaras colocadas sobre a cama dentro dos boxes. Aos 0, 10, 20 e 30 minutos após o fechamento das câmaras, foram retiradas amostras de ar com seringas de polipropileno de 20mL e analisadas por cromatografia gasosa. Com base no balanço de C da cama foi estimado o total de CO2 emitido. A concentração de CO2 foi 3,5 vezes maior no ar do interior do aviário em relação ao ar externo e não houve diferença para N2O e CH4. As emissões de CO2 estimadas pelo balanço de C da cama (em g/frango) foram maiores nos machos Cobb em relação às fêmeas Cobb e ambos maiores que a linhagem C44, independente do sexo, no período de 1 a 49 dias de idade. Quando expressas em g kg PV-1, não houve diferenças entre linhagens e sexos. |
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Palavras-Chave: |
Sustentabilidade. |
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Thesagro: |
Aviário; Cama de galinheiro; Dejeto; Dióxido de carbono; Efeito estufa; Frango de corte; Gaseificação; Impacto ambiental; Meio ambiente. |
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Thesaurus Nal: |
Animal wastes; Aviaries; Broiler chickens; Carbon dioxide; Greenhouse gas emissions. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02102naa a2200361 a 4500
001 1998801
005 2014-10-29
008 2014 bl uuuu u00u1 u #d
100 1 $aHENN, J. D.
245 $aEmissão de dióxido de carbono pela cama de primeiro lote de frangos de corte.$h[electronic resource]
260 $c2014
520 $aObjetivou-se avaliar as concentrações de gases de efeito estufa (CO2, CH4 e N2O) no ambiente interno das instalações e determinar a emissão de CO2 com base no balanço de C da cama de primeiro lote de frangos de corte de linhagens de médio (C44) e de alto desempenho (Cobb 500). Amostras de gases foram coletadas através de câmaras colocadas sobre a cama dentro dos boxes. Aos 0, 10, 20 e 30 minutos após o fechamento das câmaras, foram retiradas amostras de ar com seringas de polipropileno de 20mL e analisadas por cromatografia gasosa. Com base no balanço de C da cama foi estimado o total de CO2 emitido. A concentração de CO2 foi 3,5 vezes maior no ar do interior do aviário em relação ao ar externo e não houve diferença para N2O e CH4. As emissões de CO2 estimadas pelo balanço de C da cama (em g/frango) foram maiores nos machos Cobb em relação às fêmeas Cobb e ambos maiores que a linhagem C44, independente do sexo, no período de 1 a 49 dias de idade. Quando expressas em g kg PV-1, não houve diferenças entre linhagens e sexos.
650 $aAnimal wastes
650 $aAviaries
650 $aBroiler chickens
650 $aCarbon dioxide
650 $aGreenhouse gas emissions
650 $aAviário
650 $aCama de galinheiro
650 $aDejeto
650 $aDióxido de carbono
650 $aEfeito estufa
650 $aFrango de corte
650 $aGaseificação
650 $aImpacto ambiental
650 $aMeio ambiente
653 $aSustentabilidade
700 1 $aBOCKOR, L.
700 1 $aMARX, F. R.
700 1 $aCOLDEBELLA, A.
700 1 $aRIBEIRO, A. M. L.
700 1 $aKESSLER, A. de M.
773 $tRevista Brasileira de Agropecuária Sustentável, Viçosa, MG$gv. 4, n. 1, p. 45-52, 2014.
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Embrapa Suínos e Aves (CNPSA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
22/12/2015 |
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Data da última atualização: |
07/01/2020 |
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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: |
NESHICH, I. A. P.; NISHIMURA, L.; MORAES, F. R. de; SALIM, J. A.; VILLALTA-ROMERO, F.; BORRO, L.; YANO, I. H.; MAZONI, I.; TASIC, L.; JARDINE, J. G.; NESHICH, G. |
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Afiliação: |
IZABELLA AGOSTINHO PENA NESHICH, Unicamp; LETICIA NISHIMURA, IQSC-USP; FABIO ROGÉRIO DE MORAES, Unesp, Sao José do Rio Preto; JOSE AUGUSTO SALIM, Unicamp; FABIAN VILLALTA-ROMERO; LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; LJUBICA TASIC, IQ/Unicamp; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Computational Biology tools for identifying specific ligand binding residues for novel agrochemical and drug design. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Current Protein and Peptide Science, v. 16, n. 8, p. 701-717, 2015. |
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ISBN: |
10.2174/1389203716666150505234923 |
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Idioma: |
Inglês |
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Conteúdo: |
The term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand. MenosThe term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of u... Mostrar Tudo |
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Palavras-Chave: |
Agroquímicos; Bioinformática; Biology; Interaction nanoenvironment; Ligand docking; Nanoambiente; Protein-ligand interactions; Sting structure-function descriptors. |
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Thesagro: |
Biologia; Proteína. |
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Thesaurus NAL: |
Agrochemicals; Bioinformatics; Proteins. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03817naa a2200397 a 4500
001 2032213
005 2020-01-07
008 2015 bl uuuu u00u1 u #d
100 1 $aNESHICH, I. A. P.
245 $aComputational Biology tools for identifying specific ligand binding residues for novel agrochemical and drug design.$h[electronic resource]
260 $c2015
520 $aThe term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand.
650 $aAgrochemicals
650 $aBioinformatics
650 $aProteins
650 $aBiologia
650 $aProteína
653 $aAgroquímicos
653 $aBioinformática
653 $aBiology
653 $aInteraction nanoenvironment
653 $aLigand docking
653 $aNanoambiente
653 $aProtein-ligand interactions
653 $aSting structure-function descriptors
700 1 $aNISHIMURA, L.
700 1 $aMORAES, F. R. de
700 1 $aSALIM, J. A.
700 1 $aVILLALTA-ROMERO, F.
700 1 $aBORRO, L.
700 1 $aYANO, I. H.
700 1 $aMAZONI, I.
700 1 $aTASIC, L.
700 1 $aJARDINE, J. G.
700 1 $aNESHICH, G.
773 $tCurrent Protein and Peptide Science$gv. 16, n. 8, p. 701-717, 2015.
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