Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/9039 https://doi.org/10.1139/cjfr-2013-0142 |
Resumo: | Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of fifteen fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R2 values varying between 0.9965 – 0.9971 and 0.9974 – 0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely 0.25 h-1 for surface contaminant fungi, 0.45 h-1 for blue stain fungi and between 0.30 and 0.40 h-1 for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses. |
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Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networksAntifungal ActivityArtificial Neural NetworksBacillus amyloliquefaciensIntelligent Predictive ModelsPhyto-pathogenic FungiBiopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of fifteen fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R2 values varying between 0.9965 – 0.9971 and 0.9974 – 0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely 0.25 h-1 for surface contaminant fungi, 0.45 h-1 for blue stain fungi and between 0.30 and 0.40 h-1 for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.National Research Council of Canada2013-12-04T12:04:39Z2013-12-042013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/9039http://hdl.handle.net/10174/9039https://doi.org/10.1139/cjfr-2013-0142engVicente, H., Roseiro, J.C., Arteiro, J.M., Neves, J. & Caldeira, A.T., Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks. Canadian Journal of Forest Research, 43:985-992, 2013985-9921208-6037 (electronic)0045-5067 (print)http://www.nrcresearchpress.com/doi/abs/10.1139/cjfr-2013-0142#.Ui-T9H_YH0J43Canadian Journal of Forest Research11Departamento de Químicahvicente@uevora.ptjose.roseiro@lneg.ptjmsa@uevora.ptjneves@di.uminho.ptatc@uevora.pt276Vicente, HenriqueRoseiro, JoséArteiro, JoséNeves, JoséCaldeira, A. Teresainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T18:50:08Zoai:dspace.uevora.pt:10174/9039Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:02:59.181134Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
title |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
spellingShingle |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks Vicente, Henrique Antifungal Activity Artificial Neural Networks Bacillus amyloliquefaciens Intelligent Predictive Models Phyto-pathogenic Fungi |
title_short |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
title_full |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
title_fullStr |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
title_full_unstemmed |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
title_sort |
Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks |
author |
Vicente, Henrique |
author_facet |
Vicente, Henrique Roseiro, José Arteiro, José Neves, José Caldeira, A. Teresa |
author_role |
author |
author2 |
Roseiro, José Arteiro, José Neves, José Caldeira, A. Teresa |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Vicente, Henrique Roseiro, José Arteiro, José Neves, José Caldeira, A. Teresa |
dc.subject.por.fl_str_mv |
Antifungal Activity Artificial Neural Networks Bacillus amyloliquefaciens Intelligent Predictive Models Phyto-pathogenic Fungi |
topic |
Antifungal Activity Artificial Neural Networks Bacillus amyloliquefaciens Intelligent Predictive Models Phyto-pathogenic Fungi |
description |
Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of fifteen fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R2 values varying between 0.9965 – 0.9971 and 0.9974 – 0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely 0.25 h-1 for surface contaminant fungi, 0.45 h-1 for blue stain fungi and between 0.30 and 0.40 h-1 for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-04T12:04:39Z 2013-12-04 2013-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/9039 http://hdl.handle.net/10174/9039 https://doi.org/10.1139/cjfr-2013-0142 |
url |
http://hdl.handle.net/10174/9039 https://doi.org/10.1139/cjfr-2013-0142 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Vicente, H., Roseiro, J.C., Arteiro, J.M., Neves, J. & Caldeira, A.T., Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks. Canadian Journal of Forest Research, 43:985-992, 2013 985-992 1208-6037 (electronic) 0045-5067 (print) http://www.nrcresearchpress.com/doi/abs/10.1139/cjfr-2013-0142#.Ui-T9H_YH0J 43 Canadian Journal of Forest Research 11 Departamento de Química hvicente@uevora.pt jose.roseiro@lneg.pt jmsa@uevora.pt jneves@di.uminho.pt atc@uevora.pt 276 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
National Research Council of Canada |
publisher.none.fl_str_mv |
National Research Council of Canada |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136513457389568 |