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dc.contributor.authorLopes, Williampt_BR
dc.contributor.authorCruz, Giuliano Netto Florespt_BR
dc.contributor.authorRodrigues, Márcio L.pt_BR
dc.contributor.authorVainstein, Mendeli Henningpt_BR
dc.contributor.authorSilva, Lívia Kmetzsch Rosa ept_BR
dc.contributor.authorStaats, Charley Christianpt_BR
dc.contributor.authorVainstein, Marilene Henningpt_BR
dc.contributor.authorSchrank, Augustopt_BR
dc.date.accessioned2021-12-17T04:31:04Zpt_BR
dc.date.issued2020pt_BR
dc.identifier.issn2045-2322pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/233103pt_BR
dc.description.abstractPhenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to defne four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifer whose overall accuracy reached 85% on the test dataset, with per-class specifcity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classifcation. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identifed and characterized using computational models so that future work may unveil morphological associations with yeast virulence.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofScientific reports. London. Vol. 10 (Feb. 2020), 2362, 11 p.pt_BR
dc.rightsOpen Accessen
dc.subjectPatógenopt_BR
dc.subjectMicroorganismopt_BR
dc.subjectMicroscopia eletrônica de varredurapt_BR
dc.subjectCryptococcuspt_BR
dc.titleScanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.pt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001128385pt_BR
dc.type.originEstrangeiropt_BR


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