RESUMEN
Carrot (Daucus carota) is an Apiaceae plant with multi-colored fleshy roots that provides a model system for carotenoid research. In this study, we assembled a 430.40 Mb high-quality gapless genome to the telomere-to-telomere (T2T) level of "Kurodagosun" carrot. In total, 36 268 genes were identified and 34 961 of them were functionally annotated. The proportion of repeat sequences in the genome was 55.3%, mainly long terminal repeats. Depending on the coverage of the repeats, 14 telomeres and 9 centromeric regions on the chromosomes were predicted. A phylogenetic analysis showed that carrots evolved early in the family Apiaceae. Based on the T2T genome, we reconstructed the carotenoid metabolic pathway and identified the structural genes that regulate carotenoid biosynthesis. Among the 65 genes that were screened, 9 were newly identified. Additionally, some gene sequences overlapped with transposons, suggesting replication and functional differentiation of carotenoid-related genes during carrot evolution. Given that some gene copies were barely expressed during development, they might be functionally redundant. Comparison of 24 cytochrome P450 genes associated with carotenoid biosynthesis revealed the tandem or proximal duplication resulting in expansion of CYP gene family. These results provided molecular information for carrot carotenoid accumulation and contributed to a new genetic resource.
RESUMEN
The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.
RESUMEN
BACKGROUND: Water shortage caused by global warming seriously affects the yield and quality of vegetable crops. ß-carotene, the lipid-soluble natural product with important pharmacological value, is abundant in celery. Transcription factor MYB family extensively disperses in plants and plays regulatory roles in carotenoid metabolism and water scarcity response. RESULTS: Here, the AgMYB5 gene encoding 196 amino acids was amplified from celery cv. 'Jinnanshiqin'. In celery, the expression of AgMYB5 exhibited transactivation activity, tissue specificity, and drought-condition responsiveness. Further analysis proved that ectopic expression of AgMYB5 increased ß-carotene content and promoted drought tolerance in transgenic Arabidopsis thaliana. Moreover, AgMYB5 expression promoted ß-carotene biosynthesis by triggering the expression of AtCRTISO and AtLCYB, which in turn increased antioxidant enzyme activities, and led to the decreased contents of H2O2 and MDA, and the inhibition of O2- generation. Meanwhile, ß-carotene accumulation promoted endogenous ABA biosynthesis of transgenic Arabidopsis, which resulted in ABA-induced stomatal closing and delayed water loss. In addition, ectopic expression of AgMYB5 increased expression levels of AtERD1, AtP5CS1, AtRD22, and AtRD29. CONCLUSIONS: The findings indicated that AgMYB5 up-regulated ß-carotene biosynthesis and drought tolerance of Arabidopsis.
