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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

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dc.contributor.author García-Fortea, Edgar es_ES
dc.contributor.author García-Pérez, Ana es_ES
dc.contributor.author Gimeno -Páez, Esther es_ES
dc.contributor.author Sánchez-Gimeno, Alfredo es_ES
dc.contributor.author Vilanova Navarro, Santiago es_ES
dc.contributor.author Prohens Tomás, Jaime es_ES
dc.contributor.author Pastor-Calle, David es_ES
dc.date.accessioned 2021-06-08T03:31:22Z
dc.date.available 2021-06-08T03:31:22Z
dc.date.issued 2020-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/167458
dc.description.abstract [EN] The development of double haploids (DHs) is a straightforward path for obtaining pure lines but has multiple bottlenecks. Among them is the determination of the optimal stage of pollen induction for androgenesis. In this work, we developed Microscan, a deep learning-based system for the detection and recognition of the stages of pollen development. In a first experiment, the algorithm was developed adapting the RetinaNet predictive model using microspores of different eggplant accessions as samples. A mean average precision of 86.30% was obtained. In a second experiment, the anther range to be cultivated in vitro was determined in three eggplant genotypes by applying the Microscan system. Subsequently, they were cultivated following two different androgenesis protocols (Cb and E6). The response was only observed in the anther size range predicted by Microscan, obtaining the best results with the E6 protocol. The plants obtained were characterized by flow cytometry and with the Single Primer Enrichment Technology high-throughput genotyping platform, obtaining a high rate of confirmed haploid and double haploid plants. Microscan has been revealed as a tool for the high-throughput efficient analysis of microspore samples, as it has been exemplified in eggplant by providing an increase in the yield of DHs production. es_ES
dc.description.sponsorship This research was funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion and Fondo Europeo de Desarrollo Regional (grant RTI-2018-094592-B-I00 from MCIU/AEI/FEDER, UE). This work was also undertaken as part of the initiative "Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives", which is supported by the Government of Norway. The project is managed by the Global Crop Diversity Trust with the Millennium Seed Bank of the Royal Botanic Gardens, Kew, and implemented in partnership with national and international gene banks and plant breeding institutes around the world. For further information, see the project website: http://www.cwrdiversity.org/.The Spanish Ministerio de Educacion, Cultura y Deporte funded a predoctoral fellowship granted to Edgar Garcia-Fortea (FPU17/02389). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Biology es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Androgenesis es_ES
dc.subject Anther culture es_ES
dc.subject Microspores es_ES
dc.subject RetinaNet es_ES
dc.subject Solanum melongena es_ES
dc.subject.classification GENETICA es_ES
dc.title A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/biology9090272 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTI2018-094592-B-100/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU17%2F02389/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana - Institut Universitari de Conservació i Millora de l'Agrodiversitat Valenciana es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Biotecnología - Departament de Biotecnologia es_ES
dc.description.bibliographicCitation García-Fortea, E.; García-Pérez, A.; Gimeno -Páez, E.; Sánchez-Gimeno, A.; Vilanova Navarro, S.; Prohens Tomás, J.; Pastor-Calle, D. (2020). A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant. Biology. 9(9):1-19. https://doi.org/10.3390/biology9090272 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/biology9090272 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 9 es_ES
dc.identifier.eissn 2079-7737 es_ES
dc.identifier.pmid 32899465 es_ES
dc.identifier.pmcid PMC7564724 es_ES
dc.relation.pasarela S\431659 es_ES
dc.contributor.funder Government of Norway es_ES
dc.contributor.funder Crop Trust es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
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dc.subject.ods 02.- Poner fin al hambre, conseguir la seguridad alimentaria y una mejor nutrición, y promover la agricultura sostenible es_ES


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