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| Registros recuperados : 25 | |
| 21. |  | RIBEIRO, R. S.; RODRIGUES, J. P. P.; MAURÍCIO, R. M.; BORGES, A. L. C. C.; REIS E SILVA, R.; BERCHIELLI, T. T.; VALADARES FILHO, S. C.; MACHADO, F. S.; CAMPOS, M. M.; FERREIRA, A. L.; GUIMARAES JUNIOR, R.; AZEVÊDO, J. A. G.; SANTOS, R. D. dos; TOMICH, T. R.; PEREIRA, L. G. R. Predicting enteric methane production from cattle in the tropics. Animal, 2020. 15 p.| Biblioteca(s): Embrapa Cerrados; Embrapa Gado de Leite; Embrapa Semiárido. |
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| 22. | | TEIXEIRA, V. A.; LANA, A. M. Q.; BRESOLIN, T.; TOMICH, T. R.; SOUZA, G. M.; FURLONG, J.; RODRIGUES, J. P. P.; COELHO, S. G.; GONÇALVES, L. C.; SILVEIRA, J. A. G.; FERREIRA, L. D.; FACURY FILHO, E. J.; CAMPOS, M. M.; DOREA, J. R. R.; PEREIRA, L. G. R. Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves. Journal of Dairy Science, v. 105, n. 5, p. 1-13, 2022.| Biblioteca(s): Embrapa Gado de Leite. |
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| 23. | | CONGIO, G. F. S.; BANNINK, A.; MAYORGA, O. L.; RODRIGUES, J. P. P.; BOUGOUIN, A.; KEBREAD, E.; SILVA, R. R.; MAURÍCIO, R. M.; SILVA, S. C. DA; OLIVEIRA, P. P. A.; MUÑOZ, C.; PEREIRA, L. G. R.; GÓMEZ, C.; ARIZA-NIETO, C.; RIBEIRO-FILHO, H. M. N.; CASTELÁN-ORTEGA, O. A.; ROSERO-NOGUERA, J. R.; TIERI, M. P.; RODRIGUES, P. H. M.; MARCONDES, M. I.; ASTIGARRAGA, L.; ABARCA, S.; HRISTOV, A. N. Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. Science of the Total Environment, v. 825, n. 153982, p. 1-11, 2022.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 24. | | CONGIO, G. F. S.; BANNINK, A.; MAYORGA, O. L.; RODRIGUES, J. P. P.; BOUGOUIN, A.; KEBREAB, E.; CARVALHO, P. C. F.; BERCHIELLI, T. T.; MERCADANTE, M. E. Z.; VALADARES-FILHO, S. C.; BORGES, A. L. C. C.; BERNDT, A.; RODRIGUES, P. H. M.; KU-VERA, J. C.; MOLINA-BOTERO, I. C.; ARANGO, J.; REIS, R. A.; POSADA-OCHOA , S. L.; TOMICH, T. R.; CASTELÁN-ORTEGA, O. A.; MARCONDES, M. I.; GÓMEZ, C.; RIBEIRO-FILHO, H. M. N.; GERE, J. I.; ARIZA-NIETO, C.; GIRALDO, L. A.; GONDA, H.; CERÓN-CUCCHI, M. E.; HERNÁNDEZ, O.; RICCI, P.; HRISTOV, A. N. Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries. Science of the Total Environment, v. 856, 159128, 2023.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 25. | | CONGIO, G. F. DE. S.; BANNINK, A.; MAYORGA MOGOLLÓN, O. L.; JAURENA, G.; GONDA, H.; GERE, J. I.; CERÓN-CUCCHI, M. E.; ORTIZ-CHURA, A.; TIERI, M. P.; HERNÁNDEZ, O.; RICCI, P.; JULIARENA, M. P.; LOMBARDI, B.; ABDALLA, A. L.; ABDALLA-FILHO, A. L.; BERNDT, A.; OLIVEIRA, P. P. A.; HENRIQUE, F. L.; MONTEIRO, A. L. G.; BORGES, L. I.; RIBEIRO-FILHO, H. M. N.; PEREIRA, L. G. R.; TOMICH, T. R.; CAMPOS, M. M.; MACHADO, F. S.; MARCONDES, M. I.; MERCADANTE, M. E. Z.; SAKAMOTO, L. S.; ALBUQUERQUE, L. G.; CARVALHO, P. C. DE. F.; ROSSETTO, J.; SAVIAN, J. V.; RODRIGUES, P. H. M.; PERNA JÚNIOR, F.; MOREIRA, T. S.; MAURÍCIO, R. M.; RODRIGUES, J. P. P.; BORGES, A. L. DA C. C.; SILVA, R. R. E.; LAGE, H. F.; REIS, R. A.; RUGGIERI, A. C.; CARDOSO, A. DA. S.; SILVA, S. C. DA; CHIAVEGATO, M. B.; VALADARES-FILHO, S. DE. C.; SILVA, F. A. DE. S.; ZANETTI, D.; BERCHIELLI, T. T.; MESSANA, J. D.; MUÑOZ, C.; ARIZA-NIETO, C. J.; SIERRA-ALARCÓN, A. M.; GUALDRÓN-DUARTE, L. B.; MESTRA-VARGAS, L. I.; MOLINA-BOTERO, I. C.; BARAHONA-ROSALES, R.; ARANGO, J.; GAVIRIA-URIBE, X.; VALDERRAMA, L. A. G.; ROSERO-NOGUERA, J. R.; POSADA-OCHOA, S. L.; ABARCA-MONGE, S.; SOTO-BLANCO, R.; KU-VERA, J. C.; JIMÉNEZ-OCAMPO, R.; FLORES-SANTIAGO, E. DEL. J.; CASTELÁN-ORTEGA, O. A.; VÁZQUEZ-CARRILLO, M. F.; BENAOUDA, M.; GÓMEZ-BRAVO, C. A.; BOLOVICH, V. I. A.; CÉSPEDES, M. A. D.; ASTIGARRAGA, L.; HRISTOV, A. N. Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: a meta-analysis. Journal of Cleaner Production, v.312, 127693, 2021 13 p.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| Registros recuperados : 25 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
15/03/2022 |
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Data da última atualização: |
15/03/2022 |
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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: |
TEIXEIRA, V. A.; LANA, A. M. Q.; BRESOLIN, T.; TOMICH, T. R.; SOUZA, G. M.; FURLONG, J.; RODRIGUES, J. P. P.; COELHO, S. G.; GONÇALVES, L. C.; SILVEIRA, J. A. G.; FERREIRA, L. D.; FACURY FILHO, E. J.; CAMPOS, M. M.; DOREA, J. R. R.; PEREIRA, L. G. R. |
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Afiliação: |
Universidade Federal de Minas Gerais; Universidade Federal de Minas Gerais; University of Wisconsin; THIERRY RIBEIRO TOMICH, CNPGL; Universidade Federal de Lavras; Universidade do Sul e Sudeste do Pará; Universidade Federal de Minas Gerais; Universidade Federal de Minas Gerais; Universidade Federal de Minas Gerais; Universidade Federal de Mina Gerais; Universidade Federal de Minas Gerais; MARIANA MAGALHAES CAMPOS, CNPGL; University of Wisconsin; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL. |
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Título: |
Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Journal of Dairy Science, v. 105, n. 5, p. 1-13, 2022. |
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DOI: |
https://doi.org/10.3168/jds.2021-20952 |
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Idioma: |
Inglês |
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Conteúdo: |
Bovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly, and difficult to systematically implement in large-scale operations. The objectives of this study were to evaluate (1) rumination and activity data collected by Hr-Tag sensors (SCR Engineers Ltd.) in heifer calves exposed to anaplasmosis; and (2) the predictive ability of recurrent neural networks in early identification of anaplasmosis. Additionally, we aimed to investigate the effect of time series length before disease diagnosis (5, 7, 10, or 12 consecutive days) on the predictive performance of recurrent neural networks, and how early anaplasmosis disease can be detected in dairy calves (5, 3, and 1 d in advance). Twenty-three heifer calves aged 119 ± 15 (mean ± SD) d and weighing 148 ± 20 kg of body weight were challenged with 2 × 107 erythrocytes infected with UFMG1 strain (GenBank no. EU676176) isolated from Anaplasma marginale. After inoculation, animals were monitored daily by assessing PCV. The lowest PCV value (14 ± 1.8%) and the finding of rickettsia on blood smears were used as a criterion to classify an animal as sick (d 0). Rumination and activity data were collected continuously and automatically at 2-h intervals, using SCR Heatime Hr-Tag collars. Two time series were built including last sequence of ?5, ?7, ?10, or ?12 d preceding d 0 or a sequence of 5, 7, 10, or 12 d randomly selected in a window from ?50 to ?15 d before d 0 to ensure a sequence of days in which PCV was considered normal (32 ± 2.4%). Long shortterm memory was used as a predictive approach, and a leave-one-animal-out cross-validation (LOAOCV) was used to assess prediction quality. Anaplasmosis disease reduced 34 and 11% of rumination and activity, respectively. The accuracy, sensitivity, and specificity of long short-term memory in detecting anaplasmosis ranged from 87 to 98%, 83 to 100%, and 83 to 100%, respectively, using rumination data. For activity data, the accuracy, sensitivity, and specificity varied from 70 to 98%, 61 to 100%, and 74 to 100%, respectively. Predictive performance did not improve when combining rumination and activity. The use of longer time-series did not improve the performance of models to predict anaplasmosis. The accuracy and sensitivity in predicting anaplasmosis up to 3 d before clinical diagnosis (d 0) were greater than 80%, confirming the possibility for early identification of Anaplasmosis disease. These findings indicate the great potential of wearable sensors in early identification of anaplasmosis diseases. This could positively affect the profitability of dairy farmers and animal welfare. MenosBovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly, and difficult to systematically implement in large-scale operations. The objectives of this study were to evaluate (1) rumination and activity data collected by Hr-Tag sensors (SCR Engineers Ltd.) in heifer calves exposed to anaplasmosis; and (2) the predictive ability of recurrent neural networks in early identification of anaplasmosis. Additionally, we aimed to investigate the effect of time series length before disease diagnosis (5, 7, 10, or 12 consecutive days) on the predictive performance of recurrent neural networks, and how early anaplasmosis disease can be detected in dairy calves (5, 3, and 1 d in advance). Twenty-three heifer calves aged 119 ± 15 (mean ± SD) d and weighing 148 ± 20 kg of body weight were challenged with 2 × 107 erythrocytes infected with UFMG1 strain (GenBank no. EU676176) isolated from Anaplasma marginale. After inoculation, animals were monitored daily by assessing PCV. The lowest PCV value (14 ± 1.8%) and the finding of rickettsia on blood smears were used as a criterion to classify an animal as sick (d 0). Rumination and activity data were collected continuously and automatically at 2-h intervals, using SCR Heatime Hr-Tag collars. Two time series... Mostrar Tudo |
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Palavras-Chave: |
Inteligência artificial. |
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Thesagro: |
Anaplasma Marginale; Animal Invertebrado; Doença Animal; Gado Leiteiro; Ruminação; Ruminante. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/232520/1/Using-rumination.pdf
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Marc: |
LEADER 04002naa a2200385 a 4500
001 2140888
005 2022-03-15
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3168/jds.2021-20952$2DOI
100 1 $aTEIXEIRA, V. A.
245 $aUsing rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves.$h[electronic resource]
260 $c2022
520 $aBovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly, and difficult to systematically implement in large-scale operations. The objectives of this study were to evaluate (1) rumination and activity data collected by Hr-Tag sensors (SCR Engineers Ltd.) in heifer calves exposed to anaplasmosis; and (2) the predictive ability of recurrent neural networks in early identification of anaplasmosis. Additionally, we aimed to investigate the effect of time series length before disease diagnosis (5, 7, 10, or 12 consecutive days) on the predictive performance of recurrent neural networks, and how early anaplasmosis disease can be detected in dairy calves (5, 3, and 1 d in advance). Twenty-three heifer calves aged 119 ± 15 (mean ± SD) d and weighing 148 ± 20 kg of body weight were challenged with 2 × 107 erythrocytes infected with UFMG1 strain (GenBank no. EU676176) isolated from Anaplasma marginale. After inoculation, animals were monitored daily by assessing PCV. The lowest PCV value (14 ± 1.8%) and the finding of rickettsia on blood smears were used as a criterion to classify an animal as sick (d 0). Rumination and activity data were collected continuously and automatically at 2-h intervals, using SCR Heatime Hr-Tag collars. Two time series were built including last sequence of ?5, ?7, ?10, or ?12 d preceding d 0 or a sequence of 5, 7, 10, or 12 d randomly selected in a window from ?50 to ?15 d before d 0 to ensure a sequence of days in which PCV was considered normal (32 ± 2.4%). Long shortterm memory was used as a predictive approach, and a leave-one-animal-out cross-validation (LOAOCV) was used to assess prediction quality. Anaplasmosis disease reduced 34 and 11% of rumination and activity, respectively. The accuracy, sensitivity, and specificity of long short-term memory in detecting anaplasmosis ranged from 87 to 98%, 83 to 100%, and 83 to 100%, respectively, using rumination data. For activity data, the accuracy, sensitivity, and specificity varied from 70 to 98%, 61 to 100%, and 74 to 100%, respectively. Predictive performance did not improve when combining rumination and activity. The use of longer time-series did not improve the performance of models to predict anaplasmosis. The accuracy and sensitivity in predicting anaplasmosis up to 3 d before clinical diagnosis (d 0) were greater than 80%, confirming the possibility for early identification of Anaplasmosis disease. These findings indicate the great potential of wearable sensors in early identification of anaplasmosis diseases. This could positively affect the profitability of dairy farmers and animal welfare.
650 $aAnaplasma Marginale
650 $aAnimal Invertebrado
650 $aDoença Animal
650 $aGado Leiteiro
650 $aRuminação
650 $aRuminante
653 $aInteligência artificial
700 1 $aLANA, A. M. Q.
700 1 $aBRESOLIN, T.
700 1 $aTOMICH, T. R.
700 1 $aSOUZA, G. M.
700 1 $aFURLONG, J.
700 1 $aRODRIGUES, J. P. P.
700 1 $aCOELHO, S. G.
700 1 $aGONÇALVES, L. C.
700 1 $aSILVEIRA, J. A. G.
700 1 $aFERREIRA, L. D.
700 1 $aFACURY FILHO, E. J.
700 1 $aCAMPOS, M. M.
700 1 $aDOREA, J. R. R.
700 1 $aPEREIRA, L. G. R.
773 $tJournal of Dairy Science$gv. 105, n. 5, p. 1-13, 2022.
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Embrapa Gado de Leite (CNPGL) |
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