{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:32:02Z","timestamp":1762432322192,"version":"build-2065373602"},"reference-count":38,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003758","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e ao Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico do Maranh\u00e3o","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003758","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["resjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Vet Entomology"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Reports of\n                    <jats:italic>Rhipicephalus microplus<\/jats:italic>\n                    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\u2009days 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\n                    <jats:italic>R.\u2009microplus<\/jats:italic>\n                    larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non\u2010viable 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\n                    <jats:italic>R.\u2009microplus<\/jats:italic>\n                    was evaluated for the software validation process. Groups of three and six images of eggs with 12\u2009days 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%\u2009\u00b1\u20093.33 (Piracanjuba population), 95.98%\u2009\u00b1\u20093.5 (Desterro population) and 0.0%\u2009\u00b1\u20090.0 (Barbalha population). For groups with six images, the values were 94.41%\u2009\u00b1\u20093.84 (Piracanjuba population), 95.93%\u2009\u00b1\u20092.36 (Desterro population) and 0.0%\u2009\u00b1\u20090.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98%\u2009\u00b1\u20091.73 (Piracanjuba population); 96%\u2009\u00b1\u20092.1 (Desterro population); and 0.14%\u2009\u00b1\u20090.25 (Barbalha population). There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of\n                    <jats:italic>R.\u2009microplus<\/jats:italic>\n                    larvae was validated and proved to be effective, with considerable reduction in time to obtain results.\n                  <\/jats:p>","DOI":"10.1111\/mve.12664","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T05:16:00Z","timestamp":1684127760000},"page":"665-674","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic method based on deep learning to identify and account\n                    <i>Rhipicephalus microplus<\/i>\n                    larval hatching"],"prefix":"10.1111","volume":"37","author":[{"given":"Igor S.","family":"Santos","sequence":"first","affiliation":[{"name":"Applied Computing Core Federal University of Maranh\u00e3o \u2013 UFMA  S\u00e3o Lu\u00eds Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caio P.","family":"Tavares","sequence":"additional","affiliation":[{"name":"Parasite Control Laboratory Federal University of Maranh\u00e3o \u2013 UFMA  S\u00e3o Lu\u00eds Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilherme M.","family":"Klafke","sequence":"additional","affiliation":[{"name":"Instituto de Pesquisas Veterin\u00e1rias Desid\u00e9rio Finamor (IPVDF) \u2013 Centro de Pesquisa em Sa\u00fade Animal Secretaria da Agricultura, Pecu\u00e1ria e Desenvolvimento Rural  Eldorado do Sul Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-792X","authenticated-orcid":false,"given":"Jos\u00e9","family":"Reck","sequence":"additional","affiliation":[{"name":"Instituto de Pesquisas Veterin\u00e1rias Desid\u00e9rio Finamor (IPVDF) \u2013 Centro de Pesquisa em Sa\u00fade Animal Secretaria da Agricultura, Pecu\u00e1ria e Desenvolvimento Rural  Eldorado do Sul Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5253-5451","authenticated-orcid":false,"given":"Caio M. O.","family":"Monteiro","sequence":"additional","affiliation":[{"name":"Departamento de Bioci\u00eancias e Tecnologia do Instituto de Patologia Tropical e Sa\u00fade P\u00fablica Universidade Federal de Goi\u00e1s  Goi\u00e2nia Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcia Cristina A.","family":"Prata","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio de Parasitologia Embrapa Gado de Leite  Juiz de Fora Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patr\u00edcia S.","family":"Golo","sequence":"additional","affiliation":[{"name":"Departamento de Parasitologia Animal Universidade Federal Rural do Rio de Janeiro  Serop\u00e9dica Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arist\u00f3fanes C.","family":"Silva","sequence":"additional","affiliation":[{"name":"Applied Computing Core Federal University of Maranh\u00e3o \u2013 UFMA  S\u00e3o Lu\u00eds Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Livio M.","family":"Costa\u2010Junior","sequence":"additional","affiliation":[{"name":"Parasite Control Laboratory Federal University of Maranh\u00e3o \u2013 UFMA  S\u00e3o Lu\u00eds Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pestbp.2021.104776"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.5815\/ijmecs.2018.05.06"},{"key":"e_1_2_9_4_1","first-page":"49","article-title":"Avalia\u00e7\u00e3o da efic\u00e1cia in vitro de dois isolados do fungo entomopatog\u00eanico Beauveria bassiana (Bals) Vuill. em f\u00eameas ingurgitadas de Boophilus microplus (Canestrini, 1887) (Acari:Ixodidae)","volume":"6","author":"Bittencourt V.R.E.P.","year":"1997","journal-title":"Revista Brasileira de Parasitologia Veterinaria"},{"key":"e_1_2_9_5_1","unstructured":"Brasil. 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