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| Registros recuperados : 15 | |
| 1. |  | KLAFKE, G. B.; SCHÜLLER, M. da R.; PETERS, J. A.; CASTRO, C. M. Caracterização molecular de Eragrostis plana Nees a partir de marcadores AFLP. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA E PÓS GRADUAÇÃO DA EMBRAPA CLIMA TEMPERADO, 3., 2010, Pelotas. resumos e palestras... Pelotas: Embrapa Clima Temperado, 2010. Anais: Carreira, ética e inovação: o que você está fazendo? Pelotas: Embrapa Clima Temperado, 2010. Editado por Ivan Rodrigues de Almeida, Leonardo Ferreira Dutra e Jamir Luis Silva da Silva. 1 CD-ROM.| Biblioteca(s): Embrapa Clima Temperado. |
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| 2. | | MENDES, M. C.; CALVO, F. D.; MARTINS, J, R.; DOMINGUES, L. N.; KLAFKE, G. M.; BARROS, A. T. M. de. Caracterización poblacional de la resistencia de la garrapata Rhipicephalus (Boophilus) microplus a la cipermetrina, deltametrina y flumetrina en los estados de Rio Grande do Sul, Minas Gerais y Mato Grosso do Sul, Brasil. In: CONGRESO LATINOAMERICANO DE ACAROLOGÍA, 1, 2012, Puebla. Anais... Puebla: Sociedad Mexicana de Entomologia, 2012. p.264-273.| Biblioteca(s): Embrapa Pantanal. |
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| 3. | | MENDES, M. C.; DUARTE, F. C.; MARTINS, J. R.; KLAFKE, G. M.; FIORINI, L. C.; BARROS, A. T. M. de. Characterization of the pyrethroid resistance profile of Rhipicephalus (Boophilus) microplus populations from the states of Rio Grande do Sul and Mato Grosso do Sul, Brazil. Revista Brasileira de Parasitologia Veterinária, Jaboticabal,SP v. 22, n. 3, p. 379-384, jul.-set. 2013.| Biblioteca(s): Embrapa Gado de Corte. |
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| 4. | | MATOS, R. da S.; KAPRITCHKOFF, R. T. I.; OLIVEIRA, E. L. de; KLAFKE, G. M.; CHAGAS, A. C. de S. Sensibilidade de Amblyomma sculptum à deltametrina e ao amitraz. In: JORNADA CIENTÍFICA DA EMBRAPA SÃO CARLOS, 15., 2023, São Carlos, SP. Anais [...]. São Carlos, SP: Embrapa Pecuária Sudeste; Embrapa Instrumentação, 2023. p. 58. (Embrapa Pecuária Sudeste. Eventos Técnicos & Científicos, 1)| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 6. | | RODRIGUES, D. S.; ARAUJO, R. N.; BASTIANETTO, E.; LOPES, L. B.; KLAFKE, G. M.; FREITAS, C. M. V. de; MARTINS, J. R.; LEITE, R. C. Acaricide susceptibility of rhipicephalus (boophilus) microplus (canestrini, 1888) parasitic stages. In: CONGRESSO BRASILEIRO DE PARASITOLOGIA VETERINÁRIA, 19.; NOVEL APPROACHES TO THE CONTROL OF HELMINTH PARASITES OF LIVESTOCK, 8., 2016, Belém, PA. Anais... Jaboticabal: CBPV, 2016. Não paginado.| Biblioteca(s): Embrapa Agrossilvipastoril. |
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| 7. | | SABATINI, G. A.; BARROS, A. T. M.; RIBOLLA, P. E. M.; GONZALEZ, R.; KLAFKE, G. M.; SOUZA, E. R.; SCHUMAKER, T. T. S. Detecção molecular de resistência aos piretróides (KDR) em mosca-dos-chifres (Haematobia irritans irritans) de diferentes regiões do Brasil. Revista Brasileira de Parasitologia Veterinária, v.13, supl. 1, p.337, 2004. In: CONGRESSO BRASILEIRO DE PARASITOLOGIA VETERINÁRIA, 13.; SIMPÓSIO LATINO-AMERICANO DE RICKETISIOSES, 1., Ouro Preto, 2004.| Biblioteca(s): Embrapa Pantanal. |
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| 8. | | BARROS, A. T. M. de; SCHUMAKER, T. T. S.; KOLLER, W. W.; KLAFKE, G. M.; ALBUQUERQUE, T. A. de; GONZALEZ, R. Mechanisms of pyrethroid resistance in Haematobia irritans (Muscidae) from Mato Grosso do Sul state, Brazil. Revista Brasileira de Parasitologia Veterinária v. 22, n. 1, p. 136-142, 2013.| Biblioteca(s): Embrapa Gado de Corte. |
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| 9. | | BARROS, A. T. M. de; SCHUMAKER, T. T. S.; KOLLER, W. W.; KLAFKE, G. M.; ALBUQUERQUE, T. A. de; GONZALEZ, R. Mechanisms of pyrethroid resistance in Haematobia irritans (Muscidae) from Mato Grosso do Sul state, Brazil. Revista Brasileira de Parasitologia Veterinária, v. 22, n. 1, p. 136-142, 2013.| Biblioteca(s): Embrapa Pantanal. |
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| 10. | | CABANA, A. L.; XAVIER, M. O.; POESTER, V.; KLAFKE, G. B.; B. FILHO, P. L.; MARTINS, A.; S. FILHO, R. P.; MEIRELES, M. C. A. Serological monitoring of antibodies for an early diagnosis of aspergillosis in captive penguins. Pesquisa Veterinária Brasileira, Brasília, DF, v. 35, n. 6, p. 573-578, jun. 2015| Biblioteca(s): Embrapa Unidades Centrais. |
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| 11. | | MARCHESINI, P.; NOVATO, T. P.; CARDOSO, S. J.; PRATA, M. C. de A.; NASCIMENTO, R. M. do; KLAFKE, G.; COSTA-JÚNIOR, L. M.; MATURANO, R.; LOPES, W. D. Z.; BITTENCOURT, V. R. E. P.; MONTEIRO, C. Acaricidal activity of (E)-cinnamaldehyde and alpha-bisabolol on populations of Rhipicephalus microplus (Acari: Ixodidae) with different resistance profiles. Veterinary Parasitology, v. 286, 109226, 2020.| Biblioteca(s): Embrapa Gado de Leite. |
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| 12. | | PÉREZ DE LEÓN, A. A.; RODRÍGUEZ VIVAS, I. R.; ROMERO SALAS, D.; ANDREOTTI, R.; ROSARIO CRUZ, R.; CHAPARRO, J.; KLAFKE, G. M.; VILLAR, D.; COSTA-JUNIOR, L.; SOLTERO, F.; TEMEYER, K.; URDAZ, J.; LI, A. Acaricide resistance and strategies to mitigate economic impact of the southern cattle fever tick (Rhipicephalus microplus) on livestock production systems in the Americas. In: INTERNATIONAL CONGRESS OF PARASITOLOGY, 13., 2014, Mexico. Conference abstracts... Mexico City, Mexico: Academic Committee ICOPA Congress, 2014.| Biblioteca(s): Embrapa Gado de Corte. |
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| 13. | | MARCHESINI, P.; TEIXEIRA, A. L. C.; CARDOSO, S. J.; PRATA, M. C. de A.; NASCIMENTO, R. M.; KLAFKE, G.; COSTA-JUNIOR, L. M.; MATURANO, R.; LOPES, W. D. Z.; BITTENCOURT, V. R. E. P.; MONTEIRO, C. M. Atividade carrapaticida do alfa-bisabolol sobre populações de Rhipicephalus microplus (acari: ixodidae) com diferentes perfis de resistência. In: SIMPÓSIO BRASILEIRO DE ACAROLOGIA, 7., 2021. Acarologia em tempos de conectividade: [resumos]. México: Sociedad Latinoamericana de Acarologia, 2022. Evento online.| Biblioteca(s): Embrapa Gado de Leite. |
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| 14. | | SANTOS, I. S.; TAVARES, C. P.; KLAFKE, G. M.; RECK, J.; MONTEIRO, C. M. O.; PRATA, M. C. de A.; GOLO. P. S.; SILVA, A. C.; COSTA-JUNIOR, L. M. Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching. Medical and Veterinary Entomology, v. 37, p. 665-674, 2023.| Biblioteca(s): Embrapa Gado de Leite. |
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| 15. | | VIANNA, L. L.; PRADIEÉ, J.; MARTINS, C. T. D. C.; VASCONSCELOS, M. L. M.; ANGHINONI, L. B.; RHEINGANTZ, M. G. T.; LIMA, V. F. M. H.; VIEIRA, A. D.; KLAFKE, G. B.; CORRÊA, M. N.; PEGORARO, L. M. C. Percoll versus mini optiprep: gradiente effect on developmental rates and sex ratio from IVP bovine embryos. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE TECNOLOGIA DE EMBRIÕES, 24., 2010, Porto de Galinhas. Resumos... Porto Alegre: UFRGS, 2010. Revista Acta Scientiae Veterinariae, Porto Alegre, v. 38(Supl 2), s675-s821, 2010.| Biblioteca(s): Embrapa Clima Temperado. |
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| Registros recuperados : 15 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
30/11/2023 |
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Data da última atualização: |
30/11/2023 |
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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: |
SANTOS, I. S.; TAVARES, C. P.; KLAFKE, G. M.; RECK, J.; MONTEIRO, C. M. O.; PRATA, M. C. de A.; GOLO. P. S.; SILVA, A. C.; COSTA-JUNIOR, L. M. |
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Afiliação: |
IGOR S. SANTOS, UNIVERSIDADE FEDERAL DO MARANHÃO; CAIO P. TAVARES, UNIVERSIDADE FEDERAL DO MARANHÃO; GUILHERME M. KLAFKE, INSTITUTO DE PESQUISAS VETERINÁRIAS DESIDÉRIO FINAMOR; JOSÉ RECK, INSTITUTO DE PESQUISAS VETERINÁRIAS DESIDÉRIO FINAMOR; CAIO M. O. MONTEIRO, UNIVERSIDADE FEDERAL DE GOIÁS; MARCIA CRISTINA DE AZEVEDO PRATA, CNPGL; PATRÍCIA S. GOLO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; ARISTOFANES C. SILVA, UNIVERSIDADE FEDERAL DO MARANHÃO; LIVIO M. COSTA-JUNIOR, UNIVERSIDADE FEDERAL DO MARANHÃO. |
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Título: |
Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Medical and Veterinary Entomology, v. 37, p. 665-674, 2023. |
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DOI: |
http://doi.org/10.1111/mve.12664 |
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Idioma: |
Inglês |
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Conteúdo: |
Reports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results. MenosReports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (... Mostrar Tudo |
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Palavras-Chave: |
Controle; Eclosão larval; Larval hatching. |
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Thesagro: |
Carrapato; Larva; Ovo; Resistência. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 03010naa a2200313 a 4500
001 2158936
005 2023-11-30
008 2023 bl uuuu u00u1 u #d
024 7 $ahttp://doi.org/10.1111/mve.12664$2DOI
100 1 $aSANTOS, I. S.
245 $aAutomatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.$h[electronic resource]
260 $c2023
520 $aReports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results.
650 $aCarrapato
650 $aLarva
650 $aOvo
650 $aResistência
653 $aControle
653 $aEclosão larval
653 $aLarval hatching
700 1 $aTAVARES, C. P.
700 1 $aKLAFKE, G. M.
700 1 $aRECK, J.
700 1 $aMONTEIRO, C. M. O.
700 1 $aPRATA, M. C. de A.
700 1 $aGOLO. P. S.
700 1 $aSILVA, A. C.
700 1 $aCOSTA-JUNIOR, L. M.
773 $tMedical and Veterinary Entomology$gv. 37, p. 665-674, 2023.
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