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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agropecuária Oeste. Para informações adicionais entre em contato com cpao.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agropecuária Oeste. |
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Data corrente: |
24/05/2021 |
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Data da última atualização: |
24/05/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PRADO, C. C. A. do; DURRANT, L. R.; SCORZA JUNIOR, R. P.; BONFÁ, M. R. L. |
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Afiliação: |
CAIO CÉSAR ACHILES DO PRADO, UNIVERSIDADE DE SÃO PAULO, LORENA, SP; LUCIA REGINA DURRANT, BIOSAGE, LAWRENCEVILLE, NEW JERSEY, USA; ROMULO PENNA SCORZA JUNIOR, CPAO; MARICY RAQUEL LINDENBAH BONFÁ, UNIVERSIDADE DE SÃO PAULO, LORENA, SP. |
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Título: |
Fipronil biodegradation and metabolization by Bacillus megaterium strain E1. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Journal of Chemical Technology and Biotechnoly, 2021. |
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DOI: |
/10.1002/jctb.6758 |
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Idioma: |
Inglês |
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Conteúdo: |
Fipronil is a broad-spectrum insecticide that is used extensively due to its effective action in pest control. However, environmental studies have shown it has high toxicity towards non-target organisms as well. In addition, the degradation of fipronil can generate even more toxic and reactive metabolites in the environment. In the present study, bioprospecting for bacteria with the potential to degrade fipronil was performed using fipronil as the sole source of nitrogen and main source of carbon. From samples of corn culture soil with a history of fipronil application, isolation was performed using the microcosm enrichment method. |
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Palavras-Chave: |
Biorremediação; Descontaminação; Environmental biotechnology; Poluentes orgânicos persistentes. |
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Thesagro: |
Degradação Ambiental. |
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Thesaurus Nal: |
Biodegradation; Bioremediation; Decontamination; Degradation; Persistent organic pollutants. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01529naa a2200289 a 4500
001 2131964
005 2021-05-24
008 2021 bl uuuu u00u1 u #d
024 7 $a/10.1002/jctb.6758$2DOI
100 1 $aPRADO, C. C. A. do
245 $aFipronil biodegradation and metabolization by Bacillus megaterium strain E1.$h[electronic resource]
260 $c2021
520 $aFipronil is a broad-spectrum insecticide that is used extensively due to its effective action in pest control. However, environmental studies have shown it has high toxicity towards non-target organisms as well. In addition, the degradation of fipronil can generate even more toxic and reactive metabolites in the environment. In the present study, bioprospecting for bacteria with the potential to degrade fipronil was performed using fipronil as the sole source of nitrogen and main source of carbon. From samples of corn culture soil with a history of fipronil application, isolation was performed using the microcosm enrichment method.
650 $aBiodegradation
650 $aBioremediation
650 $aDecontamination
650 $aDegradation
650 $aPersistent organic pollutants
650 $aDegradação Ambiental
653 $aBiorremediação
653 $aDescontaminação
653 $aEnvironmental biotechnology
653 $aPoluentes orgânicos persistentes
700 1 $aDURRANT, L. R.
700 1 $aSCORZA JUNIOR, R. P.
700 1 $aBONFÁ, M. R. L.
773 $tJournal of Chemical Technology and Biotechnoly, 2021.
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Embrapa Agropecuária Oeste (CPAO) |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
18/10/2001 |
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Data da última atualização: |
18/10/2001 |
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Autoria: |
PRADO, H. A. do; HIRTLE, S. C.; ENGEL, P. M. |
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Título: |
Clustering algorithms for data mining. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
Revista Tecnologia da Informacao, Brasilia, v.2, n.1, p.51-58, 2000. |
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Idioma: |
Inglês |
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Conteúdo: |
Knowledge Discovery from Databases (KDD) can be seen as a set of computer-aided Knowledge discovery techniques. Research in this field has heen carried out according to two main dimension: simplifying and scaling of the whole process to cope with very large databases. The first efforts of KDD research community addressed strongly the task of prediction, e.g., regression and classification. The aim of this task is to fit a model over a data set for which it is Known some interesting feature in order to predict the same feature for a new case. Recently, researchers have focussed the task of description that includes clustering and finding associative rules. In using the descriptive approach one is interested in figures out how a set of objects are organized in the space of their dimensions. This paper presents a study on clustering methods focussing, in particular, those that have been the target of research efforts in the KDD realm. Our objective is to state a well-founded basis for forthcoming work.
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Palavras-Chave: |
Clustering; Data mining; Descoberta de conhecimento; Knowledge discovery; Mineracao de dados; Unsupervised learning. |
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Thesagro: |
Base de Dados. |
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Thesaurus NAL: |
databases. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01669naa a2200241 a 4500
001 1555548
005 2001-10-18
008 2000 bl --- 0-- u #d
100 1 $aPRADO, H. A. do
245 $aClustering algorithms for data mining.
260 $c2000
520 $aKnowledge Discovery from Databases (KDD) can be seen as a set of computer-aided Knowledge discovery techniques. Research in this field has heen carried out according to two main dimension: simplifying and scaling of the whole process to cope with very large databases. The first efforts of KDD research community addressed strongly the task of prediction, e.g., regression and classification. The aim of this task is to fit a model over a data set for which it is Known some interesting feature in order to predict the same feature for a new case. Recently, researchers have focussed the task of description that includes clustering and finding associative rules. In using the descriptive approach one is interested in figures out how a set of objects are organized in the space of their dimensions. This paper presents a study on clustering methods focussing, in particular, those that have been the target of research efforts in the KDD realm. Our objective is to state a well-founded basis for forthcoming work.
650 $adatabases
650 $aBase de Dados
653 $aClustering
653 $aData mining
653 $aDescoberta de conhecimento
653 $aKnowledge discovery
653 $aMineracao de dados
653 $aUnsupervised learning
700 1 $aHIRTLE, S. C.
700 1 $aENGEL, P. M.
773 $tRevista Tecnologia da Informacao, Brasilia$gv.2, n.1, p.51-58, 2000.
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