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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/68359
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dc.creatorA. V. Teixeirapt_BR
dc.creatorJulia Angelica Gonçalves da Silveirapt_BR
dc.creatorLayanne Duarte Ferreirapt_BR
dc.creatorElias Jorge Facury Filhopt_BR
dc.creatorMariana Magalhães Campospt_BR
dc.creatorJoão R. R. Doreapt_BR
dc.creatorLuiz Gustavo Ribeiro Pereirapt_BR
dc.creatorÂngela Maria Quintão Lanapt_BR
dc.creatorTiago Bresolinpt_BR
dc.creatorThierry Ribeiro Tomichpt_BR
dc.creatorG. M. Souzapt_BR
dc.creatorJ. Furlongpt_BR
dc.creatorJoão Paulo Pacheco Rodriguespt_BR
dc.creatorSandra Gesteira Coelhopt_BR
dc.creatorLucio Carlos Gonçalvespt_BR
dc.date.accessioned2024-05-15T20:15:19Z-
dc.date.available2024-05-15T20:15:19Z-
dc.date.issued2022-03-10-
dc.citation.volume105pt_BR
dc.citation.issue5pt_BR
dc.citation.spage4421pt_BR
dc.citation.epage4433pt_BR
dc.identifier.doi10.3168/jds.2021-20952pt_BR
dc.identifier.issn1525-3198pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/68359-
dc.description.resumoBovine 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.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.description.sponsorshipOutra Agênciapt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentVET - DEPARTAMENTO DE ZOOTECNIApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofJournal of Dairy Sciencept_BR
dc.rightsAcesso Abertopt_BR
dc.subjectPecuária de Precisãopt_BR
dc.subjectanaplasmosept_BR
dc.subjectmachine learningpt_BR
dc.subjectTecnologiapt_BR
dc.subject.otherGado Leiteiropt_BR
dc.subject.otherRuminaçãopt_BR
dc.subject.otherAnaplasma Marginalept_BR
dc.subject.otherDoença Animalpt_BR
dc.titleUsing rumination and activity data for early detection of anaplasmosis disease in dairy heifer calvespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.journalofdairyscience.org/article/S0022-0302(22)00140-0/fulltextpt_BR
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