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The loquat (Eriobotrya japonica L.) is a special evergreen tree, and its fruit is of high medical and health value as well as having stable market demand around the world. In recent years, research on the accumulation of nutrients in loquat fruit, such as carotenoids, flavonoids, and terpenoids, has become a hotspot. The SBP-box gene family encodes transcription factors involved in plant growth and development. However, there has been no report on the SBP-box gene family in the loquat genome and their functions in carotenoid biosynthesis and fruit ripening. In this study, we identified 28 EjSBP genes in the loquat genome, which were unevenly distributed on 12 chromosomes. We also systematically investigated the phylogenetic relationship, collinearity, gene structure, conserved motifs, and cis-elements of EjSBP proteins. Most EjSBP genes showed high expression in the root, stem, leaf, and inflorescence, while only five EjSBP genes were highly expressed in the fruit. Gene expression analysis revealed eight differentially expressed EjSBP genes between yellow- and white-fleshed fruits, suggesting that the EjSBP genes play important roles in loquat fruit development at the breaker stage. Notably, EjSBP01 and EjSBP19 exhibited completely opposite expression patterns between white- and yellow-fleshed fruits during fruit development, and showed a close relationship with SlCnr involved in carotenoid biosynthesis and fruit ripening, indicating that these two genes may participate in the synthesis and accumulation of carotenoids in loquat fruit. In summary, this study provides comprehensive information about the SBP-box gene family in the loquat, and identified two EjSBP genes as candidates involved in carotenoid synthesis and accumulation during loquat fruit development. [ABSTRACT FROM AUTHOR]

The aim of this paper is to construct two new classes of multivariate risk statistics, and to study their properties. We, first, introduce the multivariate shortfall risk statistics and multivariate divergence risk statistics. Then, their basic properties are studied, and their representation results are provided. Furthermore, their coherency is also characterized by means of the corresponding loss function. Finally, entropic risk statistics are given to illustrate the proposed new classes of multivariate risk statistics. The relationship between multivariate shortfall and divergence risk statistics is also discussed. [ABSTRACT FROM AUTHOR]

Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding. China is one of the main producers of buckwheat, and the cultivation of Hongshan buckwheat plays an important role in agricultural production. The quality of seeds affects the final yield, and improving buckwheat breeding technology is particularly important. In order to quickly and accurately identify broken Hongshan buckwheat seeds, an identification algorithm based on an improved YOLOv5s model is proposed. Firstly, this study added the Ghost module to the YOLOv5s model, which improved the model's inference speed. Secondly, we introduced the bidirectional feature pyramid network (BiFPN) to the neck of the YOLOv5s model, which facilitates multi-scale fusion of Hongshan buckwheat seeds. Finally, we fused the Ghost module and BiFPN to form the YOLOV5s+Ghost+BiFPN model for identifying broken Hongshan buckwheat seeds. The results show that the precision of the YOLOV5s+Ghost+BiFPN model is 99.7%, which is 11.7% higher than the YOLOv5s model, 1.3% higher than the YOLOv5+Ghost model, and 0.7% higher than the YOLOv5+BiFPN model. Then, we compared the FLOPs value, model size, and confidence. Compared to the YOLOv5s model, the FLOPs value decreased by 6.8 G, and the model size decreased by 5.2 MB. Compared to the YOLOv5+BiFPN model, the FLOPs value decreased by 8.1 G, and the model size decreased by 7.3MB. Compared to the YOLOv5+Ghost model, the FLOPs value increased by only 0.9 G, and the model size increased by 1.4 MB, with minimal numerical fluctuations. The YOLOv5s+Ghost+BiFPN model has more concentrated confidence. The YOLOv5s+Ghost+BiFPN model is capable of fast and accurate recognition of broken Hongshan buckwheat seeds, meeting the requirements of lightweight applications. Finally, based on the improved YOLOv5s model, a system for recognizing broken Hongshan buckwheat seeds was designed. The results demonstrate that the system can effectively recognize seed features and provide technical support for the intelligent selection of Hongshan buckwheat seeds. [ABSTRACT FROM AUTHOR]

Previous studies indicated that extensive genetic variations could be generated due to polyploidy, which is considered to be closely associated with the manifestation of polyploid heterosis. Our previous studies confirmed that triploid loquats demonstrated significant heterosis, other than the ploidy effect, but the underlying mechanisms are largely unknown. This study aimed to overcome the narrow genetic distance of loquats, increase the genetic variation level of triploid loquats, and systematically illuminate the heterosis mechanisms of triploid loquats derived from two cross combinations. Here, inter-simple sequence repeats (ISSRs) and simple sequence repeats (SSRs) were adopted for evaluating the genetic diversity, and transcriptome sequencing (RNA-Seq) was performed to investigate gene expression as well as pathway changes in the triploids. We found that extensive genetic variations were produced during the formation of triploid loquats. The polymorphism ratios of ISSRs and SSRs were 43.75% and 19.32%, respectively, and almost all their markers had a PIC value higher than 0.5, suggesting that both ISSRs and SSRs could work well in loquat assisted breeding. Furthermore, our results revealed that by broadening the genetic distance between the parents, genetic variations in triploids could be promoted. Additionally, RNA-Seq results suggested that numerous genes differentially expressed between the triploids and parents were screened out. Moreover, KEGG analyses revealed that "photosynthetic efficiency" and "glyco-metabolism" were significantly changed in triploid loquats compared with the parents, which was consistent with the results of physiological indicator analyses, leaf micro-structure observations, and qRT-PCR validation. Collectively, our results suggested that extensive genetic variations occurred in the triploids and that the changes in the "photosynthetic efficiency" as well as "glyco-metabolism" of triploids might have further resulted in heterosis manifestation in the triploid loquats. [ABSTRACT FROM AUTHOR]

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil. [ABSTRACT FROM AUTHOR]