ArticleScientific reports2017
Molecular Identification of ten species of stored-product psocids through microarray method based on ITS2 rDNA.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- The relationship between taxonomic classification and applied entomology: stored product pests as a model group.Journal of insect science (Online) · 2025Review
- Review
- Rapid identification of the invasive fall armyworm Spodoptera frugiperda (Lepidoptera, Noctuidae) using species-specific primers in multiplex PCR.Scientific reports · 2020Article
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Authors and funding
14 authors.
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Abstract
Stored-product psocids (Psocoptera: Liposcelididae) are cosmopolitan storage pests that can damage stored products and cause serious economic loss. However, because of the body size (~1 mm) of eggs, nymphs, and adults, morphological identification of most stored-product psocids is difficult and hampers effective identification. In this study, 10 economically important stored-product Liposcelis spp. psocids (Liposcelis brunnea, L. entomophila, L. decolor, L. pearmani, L. rufa, L.mendax, L. bostrychophila, L. corrodens, L. paeta, and L. tricolor) were collected from 25 geographic locations in 3 countries (China, Czech Republic, and the United States). Ten species-specific probes for identifying these 10 psocid species were designed based on ITS2 sequences. The microarray method and reaction system were optimized. Specificity of each of the ten probes was tested, and all probes were found suitable for use in identification of the respective10 Liposcelis spp. psocids at 66 °C. This method was also used to identify an unknown psocid species collected in Taian, China. This work has contributed to the development of a molecular identification method for stored-product psocids, and can provide technical support not only to facilitate identification of intercepted samples in relation to plant quarantine, but also for use in insect pest monitoring.
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