Gamma irradiation effects on physical properties of squash seeds

In order to study the effects of gamma radiation on some physical properties of squash (Cucurbit pepo. L) seed, five irradiation doses (25, 50, 75, 100 and 200 GY) have been used.  Some physical properties, including dimensional properties (length, width, thickness, geometric mean diameter, sphericity, volume, surface area, projected area, flakiness ratio and elongation ratio), mass, 1,000 seeds mass, bulk density, true density and porosity of gamma irradiated squash seeds were measured.  Statistical indices including maximum, minimum, average, variance, skewness and kurtosis, for dimensional properties and mass of the seeds were calculated.  Results revealed a significant raise in hollow seeds number by increasing gamma irradiation dose from 5% for 25 GY to nearly 100% for 100 and 200 GY.  On the other hand, length, width, thickness, mass of single seed, 1,000 seeds mass and porosity showed an increase followed by a decrease with the increasing gamma irradiation dose.  With the increasing gamma irradiation dose, true and bulk densities were found to decrease from 338.41   kg m-3 to 214.01 kg m-3 and 420.16 kg m-3 to 256.12 kg m-3, respectively.  In 100 and 200 GY all seeds were hollow and very small, therefore dimensions and mass of these seeds were not measured. Keywords: gravimetric properties, dimensional properties, squash seeds, irradiation, gamma ra

Fine mapping of two major QTLs conferring resistance to powdery mildew in tomato

Tomato (Solanum lycopersicum) is the most cultivated crop in the Solanaceae family and is a host for Oidium neolycopersici, the cause agent of powdery mildew disease. In wild species of tomato, genes (Ol-1–Ol-6) for monogenic resistance have been identified. Moreover, three quantitative resistance loci (QRLs), namely Ol-qtl1, Ol-qtl2 and Ol-qtl3, have been mapped in Solanum neorickii G1.1601. In this work, we developed several advanced backcross populations in order to fine-map these Ol-qtls. Resistant lines harboring individual Ol-qtl were produced and used in recombinant screening. Ten recombinants were identified in chromosomal regions carrying Ol-qtl1s. The recombinant individuals were used to produce recombinant families (RFs). By screening these RFs with molecular markers and testing them with O. neolycopersici, we could localize Ol-qtl1 in a region of about 2.3 Mbp on the long arm of chromosome 6 and Ol-qtl2 in a region of 2.5 Mbp on the short arm of chromosome 12. On the other hand, the presence of Ol-qtl3 locus was not confirmed in this study. The fine-mapping results further demonstrated the co-localization between Ol-qtls and genes for monogenic resistance; the Ol-qtl1 interval contains the Ol-1 gene and the Ol-qtl2 interval harbors the Lv gene that confers monogenic resistance to Leveillula taurica, another species of tomato powdery mildew

Light Intensity: The Role Player in Cucumber Response to Cold Stress

Low temperatures are a substantial limitation in the geographic distribution of warm-season crops such as cucumber (Cucumis sativus L.). Tolerance to low temperatures varies among different plant species and genotypes when changes in environmental cues occur. Therefore, biochemical and biophysical events should be coordinated to form a physiological response and cope with low temperatures. We examined how light intensity influences the effects of low temperature on photosynthesis and some biochemical traits. We used chlorophyll fluorescence imaging and polyphasic fluorescence transient to analyze cold stress damage by 4 °C. Photosynthetic Photon Flux Densities (PPFDs) of 0, 300, and 600 μmol m−2 s−1, in four accessions of cucumber, were investigated. The results show that the negative effects of cold stress are PPFD-dependent. The adverse effect of cold stress on the electron transport chain is more pronounced in plants exposed to 600 μmol m−2 s−1 than the control and dark-exposed plants, indicated by a disturbance in the electron transport chain and higher energy dissipation. Moreover, biochemical traits, including the H2O2 content, ascorbate peroxidase activity, electrolyte leakage, and water-soluble carbohydrate, increased under low temperature by increasing the PPFD. In contrast, chlorophyll and carotenoid contents decreased under low temperature through PPFD elevation. Low temperature induced a H2O2 accumulation via suppressing ascorbate peroxidase activity in a PPFD-dependent manner. In conclusion, high PPFDs exacerbate the adverse effects of low temperature on the cucumber seedlings

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle