Astragalus membranaceus var. mongholicus (AMM), a member of the Leguminosae, is one of the most important medicinal plants worldwide. The dried roots of AMM have a wide range of pharmacological effects and are a traditional Chinese medicine. Here, we report the first chromosome-level reference genome of AMM, comprising nine pseudochromosomes with a total size of 1.47 Gb and 27 868 protein-encoding genes. Comparative genomic analysis reveals that AMM has not experienced an independent whole-genome duplication (WGD) event after the WGD event shared by the Papilionoideae species. Analysis of long terminal repeat retrotransposons suggests a recent burst of these elements at approximately 0.13 million years ago, which may explain the large size of the AMM genome. Multiple gene families involved in the biosynthesis of triterpenoids and flavonoids were expanded, and our data indicate that tandem duplication has been the main driver for expansion of these families. Among the expanded families, the phenylalanine ammonia-lyase gene family was primarily expressed in the roots of AMM, suggesting their roles in the biosynthesis of phenylpropanoid compounds. The functional versatility of 2,3-oxidosqualene cyclase genes in cluster III may play a critical role in the diversification of triterpenoids in AMM. Our findings provide novel insights into triterpenoid and flavonoid biosynthesis and can facilitate future research on the genetics and medical applications of AMM.

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

The gut microbiota of insects plays a vital role in digestion, nutrient acquisition, metabolism of dietary toxins, pathogen immunity and maintenance of gut homeostasis. Bacillus thuringinensis (Bt) poisons target insects through its toxins that are activated in the insect gut. The effects of Bt toxins on gut microbiota of insects and their underlying mechanisms are not well understood. In this study, we found that Cry1Ab/2Ab toxins significantly changed the gut bacterial community's structure and reduced the total load of gut bacteria in the Locusta migratoria. In addition, Cry toxins significantly increased the level of reactive oxygen species (ROS) in the gut of locusts. Our results also showed that Cry1Ab/2Ab toxins induced the host gut's immune response by up-regulating of key genes in the Immune deficiency (IMD) and Toll pathway. RNA interference showed that knocking down Relish could narrow the difference in the load, diversity, and composition in gut bacteria caused by Cry toxins. Our findings suggest that Bt potentially influences the gut bacterial community of L. migratoria through host immune response.

Epimedium brevicornu Maxim. is a herbal plant with various therapeutic effects, and its aboveground tissues contain flavonol compounds such as icaritin that can be used to produce new drugs for the treatment of advanced liver cancer. Previous studies have shown that ultraviolet-B (UV-B, 280–315 nm) stress can increase the levels of flavonoid substances in plants. In the current study, we observed the microstructure of E. brevicornu leaves after 0, 5, 10, 15, and 20 d of UV-B radiation (60 μw·cm⁻²) and quality formation mechanism of E. brevicornu leaves after 0, 10, and 20 d of UV-B radiation by LC‒ESI‒MS/MS. The contents of flavonols such as icariside I, wushanicaritin, icaritin, and kumatakenin were significantly upregulated after 10 d of radiation. The results indicated that UV-B radiation for 10 d inhibited the morphological development of E. brevicornu but increased the content of active medicinal components, providing a positive strategy for epimedium quality improvement.

It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis, and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties.