{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T11:46:30Z","timestamp":1792842390645,"version":"4.5.0"},"reference-count":20,"publisher":"American Society for Microbiology","issue":"1","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/100016804","name":"Natural Science Foundation of Shenzhen City","doi-asserted-by":"crossref","award":["JCYJ20170307150444573"],"award-info":[{"award-number":["JCYJ20170307150444573"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100016804","name":"Natural Science Foundation of Shenzhen City","doi-asserted-by":"crossref","award":["JCYJ20180306172131515"],"award-info":[{"award-number":["JCYJ20180306172131515"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSystems"],"published-print":{"date-parts":[[2020,2,11]]},"abstract":"<jats:p>\n            <jats:named-content content-type=\"genus-species\">Toxoplasma gondii<\/jats:named-content>\n            , one of the world\u2019s most common parasites, can infect all types of warm-blooded animals, including one-third of the world\u2019s human population. Artificial intelligence (AI) could provide accurate and rapid diagnosis in fighting\n            <jats:italic>Toxoplasma<\/jats:italic>\n            . So far, none of the previously reported deep learning methods have attempted to explore the advantages of transfer learning for\n            <jats:italic>Toxoplasma<\/jats:italic>\n            detection. The knowledge from parasitologists is that the\n            <jats:italic>Toxoplasma<\/jats:italic>\n            parasite is generally banana or crescent shaped. Based on this, we built connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves high accuracy and effectiveness in \u00d7400 and \u00d71,000\n            <jats:italic>Toxoplasma<\/jats:italic>\n            microscopic images.\n          <\/jats:p>","DOI":"10.1128\/msystems.00445-19","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T09:40:22Z","timestamp":1580204422000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":22,"title":["Transfer Learning for Toxoplasma gondii Recognition"],"prefix":"10.1128","volume":"5","author":[{"given":"Sen","family":"Li","sequence":"first","affiliation":[{"name":"College of Science, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aijia","family":"Li","sequence":"additional","affiliation":[{"name":"College of Science, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego Alejandro","family":"Molina Lara","sequence":"additional","affiliation":[{"name":"GEPAMOL Group, Faculty of Health Sciences, University of Quind\u00edo, Armenia, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge Enrique","family":"G\u00f3mez Mar\u00edn","sequence":"additional","affiliation":[{"name":"GEPAMOL Group, Faculty of Health Sciences, University of Quind\u00edo, Armenia, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Juhas","sequence":"additional","affiliation":[{"name":"Institute of Medical Microbiology, University of Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-5161","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpmc.26"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-018-2632-z"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.03.040"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18020513"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.022"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2766168"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.4568"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.22"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/01969727308546047"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_12_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Simonyan K","year":"2015","unstructured":"Simonyan K, Zisserman A. 2015. 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