RESUMEN
The successful implementation of heterosis in rice has significantly enhanced rice productivity, but the genetic basis of heterosis in rice remains unclear. To understand the genetic basis of heterosis in rice, main-effect and epistatic quantitative trait loci (QTLs) associated with heterosis for grain yield-related traits in the four related rice mapping populations derived from Xiushui09 (XS09) (japonica) and IR2061 (indica), were dissected using single nucleotide polymorphism bin maps and replicated phenotyping experiments under two locations. Most mid-parent heterosis of testcross F1s (TCF1s) of XS09 background introgression lines (XSILs) with Peiai64S were significantly higher than those of TCF1s of recombinant inbred lines (RILs) with PA64S at two locations, suggesting that the effects of heterosis was influenced by the proportion of introgression of IR2061's genome into XS09 background. A total of 81 main-effect QTLs (M-QTLs) and 41 epistatic QTLs were identified for the phenotypic variations of four traits of RILs and XSILs, TCF1s and absolute mid-parent heterosis in two locations. Furthermore, overdominance and underdominance were detected to play predominant effects on most traits in this study, suggesting overdominance and underdominance as well as epistasis are the main genetic bases of heterosis in rice. Some M-QTLs exhibiting positive overdominance effects such as qPN1.2, qPN1.5 and qPN4.3 for increased panicle number per plant, qGYP9 and qGYP12.1 for increased grain yield per plant, and qTGW3.4 and qTGW8.2 for enhanced 1000-grain weight would be highly valuable for breeding to enhance grain yield of hybrid rice by marker-assisted selection.
Asunto(s)
Vigor Híbrido/genética , Oryza/genética , Agricultura/métodos , China , Mapeo Cromosómico/métodos , Cromosomas/genética , Cruzamientos Genéticos , Grano Comestible/genética , Epistasis Genética/genética , Genes Dominantes/genética , Genes de Plantas/genética , Genotipo , Fenotipo , Fitomejoramiento/métodos , Polimorfismo de Nucleótido Simple/genética , Sitios de Carácter Cuantitativo/genéticaRESUMEN
A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.