Transcriptomic and metabolomic profiling provide insight into the role of sugars and hormones in leaf senescence of Pinellia ternata.

KEY MESSAGE: The interaction network and pathway map uncover the potential crosstalk between sugar and hormone metabolisms as a possible reason for leaf senescence in P. ternata. Pinellia ternata, an environmentally sensitive medicinal plant, undergoes leaf senescence twice a year, affecting its development and yield. Understanding the potential mechanism that delays leaf senescence could theoretically decrease yield losses. In this study, a typical senescent population model was constructed, and an integrated analysis of transcriptomic and metabolomic profiles of P. ternata was conducted using two early leaf senescence populations and two stay-green populations. The result showed that two key gene modules were associated with leaf senescence which were mainly enriched in sugar and hormone signaling pathways, respectively. A network constructed by unigenes and metabolisms related to the obtained two pathways revealed that several compounds such as D-arabitol and 2MeScZR have a higher significance ranking. In addition, a total of 130 hub genes in this network were categorized into 3 classes based on connectivity. Among them, 34 hub genes were further analyzed through a pathway map, the potential crosstalk between sugar and hormone metabolisms might be an underlying reason of leaf senescence in P. ternata. These findings address the knowledge gap regarding leaf senescence in P. ternata, providing candidate germplasms for molecular breeding and laying theoretical basis for the realization of finely regulated cultivation in future.

Whole-genome sequencing in medicinal plants: current progress and prospect.

Advancements in genomics have dramatically accelerated the research on medicinal plants, and the development of herbgenomics has promoted the "Project of 1K Medicinal Plant Genome" to decipher their genetic code. However, it is difficult to obtain their high-quality whole genomes because of the prevalence of polyploidy and/or high genomic heterozygosity. Whole genomes of 123 medicinal plants were published until September 2022. These published genome sequences were investigated in this review, covering their classification, research teams, ploidy, medicinal functions, and sequencing strategies. More than 1,000 institutes or universities around the world and 50 countries are conducting research on medicinal plant genomes. Diploid species account for a majority of sequenced medicinal plants. The whole genomes of plants in the Poaceae family are the most studied. Almost 40% of the published papers studied species with tonifying, replenishing, and heat-cleaning medicinal effects. Medicinal plants are still in the process of domestication as compared with crops, thereby resulting in unclear genetic backgrounds and the lack of pure lines, thus making their genomes more difficult to complete. In addition, there is still no clear routine framework for a medicinal plant to obtain a high-quality whole genome. Herein, a clear and complete strategy has been originally proposed for creating a high-quality whole genome of medicinal plants. Moreover, whole genome-based biological studies of medicinal plants, including breeding and biosynthesis, were reviewed. We also advocate that a research platform of model medicinal plants should be established to promote the genomics research of medicinal plants.

Low-intensity red-light therapy in slowing myopic progression and the rebound effect after its cessation in Chinese children: a randomized controlled trial.

PURPOSE: To investigate the effect of low-intensity red-light (LRL) therapy on myopic control and the response after its cessation. METHODS: A prospective clinical trial. One hundred two children aged 6 to 13 with myopia were included in the LRL group (n = 51) and the single-focus spectacles (SFS) group (n = 51). In LRL group, subjects wore SFS and received LRL therapy provided by a laser device that emitted red-light of 635 nm and power of 0.35 ± 0.02 mW. One year after the control trial, LRL therapy was stopped for 3 months. The outcomes mainly included axial length (AL), spherical equivalent refraction (SER), subfoveal choroidal thickness (SFCT), and accommodative function. RESULTS: After 12 months of therapy, 46 children in the LRL group and 40 children in the SFS group completed the trial. AL elongation and myopic progression were 0.01 mm (95%CI: - 0.05 to 0.07 mm) and 0.05 D (95%CI: - 0 .08 to 0.19 D) in the LRL group, which were less than 0.39 mm (95%CI: 0.33 to 0.45 mm) and - 0.64 D (95%CI: - 0.78 to - 0.51 D) in the SFS group (p < 0.05). The change of SFCT in the LRL group was greater than that in the SFS group (p < 0.05). Accommodative response and positive relative accommodation in the LRL group were more negative than those in the SFS group (p < 0.05). Forty-two subjects completed the observation of LRL cessation, AL and SER increased by 0.16 mm (95%CI: 0.11 to 0.22 mm) and - 0.20 D (95%CI: - 0.26 to - 0.14 D) during the cessation (p < 0.05), and SFCT returned to baseline (p > 0.05). CONCLUSIONS: LRL is an effective measure for preventing and controlling myopia, and it may also have the ability to improve the accommodative function. There may be a slight myopic rebound after its cessation. The effect of long-term LRL therapy needs to be further explored. TRIAL REGISTRATION: Chinese Clinical Trial Registry: Chinese Clinical Trails registry: ChiCTR2100045250. Registered 9 April 2021; retrospectively registered. http://www.chictr.org.cn/showproj.aspx?proj=124250.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance.

Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots. In order to detect root diseases at early stages and limit their further spread, an efficient and non-destructive testing (NDT) method is urgently needed. Hyperspectral remote sensing technology was performed in this study to discern whether ginseng roots were diseased. Hyperspectral reflectance of leaves at 325-1,075 nm were collected from the ginsengs with no symptoms on leaves at visual. These spectra were divided into healthy and diseased groups according to the symptoms on roots after harvest. The hyperspectral data were used to construct machine learning classification models including random forest, extreme random tree (ET), adaptive boosting and gradient boosting decision tree respectively to identify diseased ginsengs, while calculating the vegetation indices and analyzing the region of specific spectral bands. The precision rates of the ET model preprocessed by savitzky golay method for the identification of healthy and diseased ginsengs reached 99% and 98%, respectively. Combined with the preliminary analysis of band importance, vegetation indices and physiological characteristics, 690-726 nm was screened out as a specific band for early detection of ginseng root diseases. Therefore, underground root diseases can be effectively detected at an early stage by leaf hyperspectral reflectance. The NDT method for early detection of ginsengs root diseases is proposed in this study. The method is helpful in the prevention and control of root diseases of ginsengs to prevent the reduction of ginseng yield.

Application of Authentication Evaluation Techniques of Ethnobotanical Medicinal Plant Genus Paris: A Review.

Genus Paris mainly distributed in Asia and served as traditional ethnobotanical medicine in China, India, and Nepal. The main aim of this study was to review ethnic minorities medicinal records for Paris in China, the Chinese patent medicines based on Paris materials and the authentication and quality evaluation of Paris based on two parts of analytical technologies and data analysis. This article reviews approximately 100 articles from 1980 to 2019 published on the research of genus Paris materials and their Chinese patent medicines. Additionally, local books were also searched. Five major kinds of chemical techniques have been applied to authentication and quality assessment of Paris and among them, chromatography was the most widely used one, and DNA barcoding technique was the most worth further study. Additionally, more than 70% of the research studies were analyzed by unsupervised pattern-recognition techniques combined with analytical technologies such as spectroscopy. Meanwhile, the data fusion strategy with higher classification ability combined multiple supervised pattern-recognition techniques have been gradually applied to authentication assessment. The optimal analytical technologies and methods applied to Paris qualities for authentication and quality evaluation are greatly important, which could promote the development and utilization for ethnomedicines of Paris.

Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics.

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.