Altitudinal Patterns in Adaptive Evolution of Genome Size and Inter-Genome Hybridization Between Three Elymus Species From the Qinghai–Tibetan Plateau

Genome size variation and hybridization occur frequently within or between plant species under diverse environmental conditions, which enrich species diversification and drive the evolutionary process. Elymus L. is the largest genus in Triticeae with five recognized basic genomes (St, H, P, W, and Y). However, the data on population cytogenetics of Elymus species are sparse, especially whether genome hybridization and chromosomal structure can be affected by altitude are still unknown. In order to explore the relationship between genome sizes, we studied interspecific hybridization and altitude of Elymus species at population genetic and cytological levels. Twenty-seven populations at nine different altitudes (2,800–4,300 m) of three Elymus species, namely, hexaploid E. nutans (StHY, 2n = 6x = 42), tetraploid E. burchan-buddae (StY, 2n = 4x = 28), and E. sibiricus (StH, 2n = 4x = 28), were sampled from the Qinghai–Tibetan Plateau (QTP) to estimate whether intraspecific variation could affect the genomic relationships by genomic in situ hybridization (GISH), and quantify the genome size of Elymus among different altitude ecological groups by flow cytometry. The genome size of E. nutans, E. burchan-buddae, and E. sibiricus varied from 12.38 to 22.33, 8.81 to 18.93, and 11.46 to 20.96 pg/2C with the averages of 19.59, 12.39, and 16.85 pg/2C, respectively. The curve regression analysis revealed a strong correlation between altitude and nuclear DNA content in three Elymus species. In addition, the chromosomes of the St and Y genomes demonstrated higher polymorphism than that of the H genome. Larger genome size variations occurred in the mid-altitude populations (3,900–4,300 m) compared with other-altitude populations, suggesting a notable altitudinal pattern in genome size variation, which shaped genome evolution by altitude. This result supports our former hypothesis that genetic richness center at medium altitude is useful and valuable for species adaptation to highland environmental conditions, germplasm utilization, and conservation

A dual-target molecular mechanism of pyrethrum repellency against mosquitoes

Pyrethrum extracts from flower heads of Chrysanthemum spp. have been used worldwide in insecticides and repellents. While the molecular mechanisms of its insecticidal action are known, the molecular basis of pyrethrum repellency remains a mystery. In this study, we find that the principal components of pyrethrum, pyrethrins, and a minor component, (E)-β-farnesene (EBF), each activate a specific type of olfactory receptor neurons in Aedes aegypti mosquitoes. We identify Ae. aegypti odorant receptor 31 (AaOr31) as a cognate Or for EBF and find that Or31-mediated repellency is significantly synergized by pyrethrin-induced activation of voltage-gated sodium channels. Thus, pyrethrum exerts spatial repellency through a novel, dual-target mechanism. Elucidation of this two-target mechanism may have potential implications in the design and development of a new generation of synthetic repellents against major mosquito vectors of infectious diseases

Identification Method of Wheat Grain Phenotype Based on Deep Learning of ImCascade R-CNN

ObjectiveWheat serves as the primary source of dietary carbohydrates for the human population, supplying 20% of the required caloric intake. Currently, the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield, ensuring an overall increase in wheat production. Additionally, the consideration of phenotype parameters, such as grain length and width, holds significant importance in the introduction, screening, and evaluation of germplasm resources. Notably, a noteworthy positive association has been observed between grain size, grain shape, and grain weight. Simultaneously, within the scope of wheat breeding, the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains, consequently leading to a direct reduction in both emergence rate and yield. In essence, the integrity of wheat grains directly influences the wheat breeding process. Nevertheless, distinguishing between intact and damaged grains remains challenging due to the minimal disparities in certain characteristics, thereby impeding the accurate identification of damaged wheat grains through manual means. Consequently, this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain phenotype parameters.MethodsThis study presented an enhanced approach for addressing the challenges of low detection accuracy, unclear segmentation of wheat grain contour, and missing detection. The proposed strategy involves utilizing the Cascade Mask R-CNN model and replacing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count. Furthermore, the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model. Additionally, a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains. The Soft-NMS algorithm was employed to identify the candidate frame and achieve accurate segmentation of the wheat kernel adhesion region. Additionally, the ImCascade R-CNN model was developed. Simultaneously, to address the issue of low accuracy in obtaining grain contour parameters due to disordered grain arrangement, a grain contour-based algorithm for parameter acquisition was devised. Wheat grain could be approximated as an oval shape, and the grain edge contour could be obtained according to the mask, the distance between the farthest points could be iteratively obtained as the grain length, and the grain width could be obtained according to the area. Ultimately, a method for wheat kernel phenotype identification was put forth. The ImCascade R-CNN model was utilized to analyze wheat kernel images, extracting essential features and determining the integrity of the kernels through classification and boundary box regression branches. The mask generation branch was employed to generate a mask map for individual wheat grains, enabling segmentation of the grain contours. Subsequently, the number of grains in the image was determined, and the length and width parameters of the entire wheat grain were computed.Results and DiscussionsIn the experiment on wheat kernel phenotype recognition, a comparison and improvement were conducted on the identification results of the Cascade Mask R-CNN model and the ImCascade R-CNN model across various modules. Additionally, the efficacy of the model modification scheme was verified. The comparison of results between the Cascade Mask R-CNN model and the ImCascade R-CNN model served to validate the proposed model's ability to significantly decrease the missed detection rate. The effectiveness and advantages of the ImCascade R-CNN model were verified by comparing its loss value, P-R value, and mAP_50 value with those of the Cascade Mask R-CNN model. In the context of wheat grain identification and segmentation, the detection results of the ImCascade R-CNN model were compared to those of the Cascade Mask R-CNN and Deeplabv3+ models. The comparison confirmed that the ImCascade R-CNN model exhibited superior performance in identifying and locating wheat grains, accurately segmenting wheat grain contours, and achieving an average accuracy of 90.2% in detecting wheat grain integrity. These findings serve as a foundation for obtaining kernel contour parameters. The grain length and grain width exhibited average error rates of 2.15% and 3.74%, respectively, while the standard error of the aspect ratio was 0.15. The statistical analysis and fitting of the grain length and width, as obtained through the proposed wheat grain shape identification method, yielded determination coefficients of 0.9351 and 0.8217, respectively. These coefficients demonstrated a strong agreement with the manually measured values, indicating that the method is capable of meeting the demands of wheat seed testing and providing precise data support for wheat breeding.ConclusionsThe findings of this study can be utilized for the rapid and precise detection of wheat grain integrity and the acquisition of comprehensive grain contour data. In contrast to current wheat kernel recognition technology, this research capitalizes on enhanced grain contour segmentation to furnish data support for the acquisition of wheat kernel contour parameters. Additionally, the refined contour parameter acquisition algorithm effectively mitigates the impact of disordered wheat kernel arrangement, resulting in more accurate parameter data compared to existing kernel appearance detectors available in the market, providing data support for wheat breeding and accelerating the cultivation of high-quality and high-yield wheat varieties

A robust watermarking hybrid algorithm for color image

In order to improve the anti-attack performance of the watermark to meet the requirements of copyright protection and content forensics. This paper proposes a digital watermarking hybrid algorithm based on color images. The specific process is to adopt the idea of multi-algorithm layered embedding, choose the algorithm based on discrete cosine transform (DCT) algorithm, discrete wavelet transform_singular value decomposition (DWT_SVD) algorithm, and hologram algorithm, these three algorithms with robust complementary functions, then embed the same watermark image into the color image R, G and B layers to complete the watermark embedding. Compared with the single-algorithm embedded watermark, the hybrid algorithm can achieve blind extraction, at the same time, the algorithm has better robustness and can resist more types and higher intensity attacks. In the process of digital image transmission, the integrity of the watermark information of the carried picture can be guaranteed to achieve copyright protection, content forensics and other purposes

The Structural Characteristics of an Acidic Water-Soluble Polysaccharide from Bupleurum chinense DC and Its In Vivo Anti-Tumor Activity on H22 Tumor-Bearing Mice

This study explored the preliminary structural characteristics and in vivo anti-tumor activity of an acidic water-soluble polysaccharide (BCP) separated purified from Bupleurum chinense DC root. The preliminary structural characterization of BCP was established using UV, HPGPC, FT-IR, IC, NMR, SEM, and Congo red. The results showed BCP as an acidic polysaccharide with an average molecular weight of 2.01 × 103 kDa. Furthermore, we showed that BCP consists of rhamnose, arabinose, galactose, glucose, and galacturonic acid (with a molar ratio of 0.063:0.788:0.841:1:0.196) in both α- and β-type configurations. Using the H22 tumor-bearing mouse model, we assessed the anti-tumor activity of BCP in vivo. The results revealed the inhibitory effects of BCP on H22 tumor growth and the protective actions against tissue damage of thymus and spleen in mice. In addition, the JC-1 FITC-AnnexinV/PI staining and cell cycle analysis have collectively shown that BCP is sufficient to induce apoptosis and of H22 hepatocarcinoma cells in a dose-dependent manner. The inhibitory effect of BCP on tumor growth was likely attributable to the S phase arrest. Overall, our study presented significant anti-liver cancer profiles of BCP and its promising therapeutic potential as a safe and effective anti-tumor natural agent