Aconitum kusnezoffii is a perennial herbaceous medicinal plant of the family Ranunculaceae, with unique medicinal value. Damping off is one of the most important seedling diseases affecting A. kusnezoffii, occurring widely and often causing large-scale seedling death in the field. To clarify the species of the pathogen causing damping off in A. kusnezoffii and to formulate an effective control strategy, this study conducted pathogen identification, research on biological characteristics, and evaluation of fungicide inhibitory activity. Through morphological characteristics, cultural traits, and phylogenetic tree analysis, the pathogen causing damping off in A. kusnezoffii was identified as Rhizoctonia solani, belonging to the AG5 anastomosis group. The optimal temperature for mycelial growth of the pathogen was 25-30 ℃, with OA medium as the most suitable medium, pH 8 as the optimal pH, and sucrose and yeast as the best carbon and nitrogen sources, respectively. The effect of light on mycelial growth was not significant. In evaluating the inhibitory activity of 45 chemical fungicides, including 30% hymexazol, and 4 biogenic fungicides, including 0.3% eugenol, it was found that 30% thifluzamide and 50% fludioxonil had significantly better inhibitory effects on R. solani than other tested agents, with EC_(50) values of 0.129 6,0.220 6 μg·mL~(-1), respectively. Among the biogenic fungicides, 0.3% eugenol also showed an ideal inhibitory effect on the pathogen, with an EC_(50) of 1.668 9 μg·mL~(-1). To prevent the development of resistance in the pathogen and to reduce the use of chemical fungicides, it is recommended that the three fungicides above be used in rotation during production. These findings provide a theoretical basis for the accurate diagnosis and effective control strategy for R. solani causing damping off in A. kusnezoffii.
Fungicides, Industrial/pharmacology* ; Plant Diseases/microbiology* ; Rhizoctonia/growth & development* ; Aconitum/microbiology* ; Phylogeny ; Mycelium/growth & development*

Analyzing the spatial distribution pattern and formation factors of medicinal plant resources can provide a scientific basis for the protection and development of traditional Chinese medicine(TCM) resources. This study is based on the survey data of medicinal plant resources in 104 county-level administrative regions of Anhui province in the Fourth National Survey of TCM Resources. The global spatial autocorrelation analysis, trend surface analysis, local spatial autocorrelation analysis, hotspot analysis, and a geodetector were employed to analyze the spatial distribution pattern of medicinal plant richness, and its relationship with natural factors was explored. The results can provide a basis for the formulation of development strategies such as the protection and utilization of TCM resources, as well as offer a scientific foundation for the establishment of regional planning schemes for TCM resources in Anhui province. The results indicated that the richness of medicinal plant resources in Anhui province had significant spatial heterogeneity, exhibiting highly clustered distribution characteristics. Cold spots and hot spots presented clustered distribution patterns, with cold spots mostly located north of the Huaihe River and hot spots south of the Yangtze River. Overall, the distribution of medicinal plant resources in Anhui province showed an overall trend of high in the south and low in the north, which was consistent with the overall geomorphic trend of this province. In addition, natural factors such as altitude, precipitation, and vegetation type played an important role in the diversity and spatial distribution pattern formation of medicinal plant resources. The extraction and analysis of the spatial distribution characteristics of natural factors in cold and hot spot regions discovered that the heterogeneity of eco-environments constituted a fundamental condition for the formation of species diversity.
Plants, Medicinal/classification* ; China ; Spatial Analysis ; Conservation of Natural Resources ; Biodiversity

To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.
Hyperspectral Imaging ; Wolfiporia ; China ; Least-Squares Analysis ; Support Vector Machine

In this study, visual-near infrared(VNIR), short-wave infrared(SWIR), and VNIR + SWIR fusion hyperspectral data of Polygonatum cyrtonema from different geographical origins were collected and preprocessed by first derivative(FD), second derivative(SD), Savitzky-Golay smoothing(S-G), standard normalized variate(SNV), multiplicative scatter correction(MSC), FD+S-G, and SD+S-G. Three algorithms, namely random forest(RF), linear support vector classification(LinearSVC), and partial least squares discriminant analysis(PLS-DA), were used to establish the identification models of P. cyrtonema origin from three spatial scales, i.e., province, county, and township, respectively. Successive projection algorithm(SPA) and competitive adaptive reweighted sampling(CARS) were used to screen the characteristic bands, and the P. cyrtonema origin identification models were established according to the selected characteristic bands. The results showed that(1)after FD preprocessing of VNIR+SWIR fusion hyperspectral data, the accuracy of recognition models established using LinearSVC was the highest, reaching 99.97% and 99.82% in the province origin identification model, 100.00% and 99.46% in the county origin identification model, and 99.62% and 98.39% in the township origin identification model. The accuracy of province, county, and township origin identification models reached more than 98.00%.(2)Among the 26 characteristic bands selected by CARS, after FD pretreatment, the accuracy of origin identification models of different spatial scales was the highest using LinearSVC, reaching 98.59% and 97.05% in the province origin identification model, 97.79% and 94.75% in the county origin identification model, and 90.13% and 87.95% in the township origin identification model. The accuracy of identification models of different spatial scales established by 26 characteristic bands reached more than 87.00%. The results show that hyperspectral imaging technology can realize accurate identification of P. cyrtonema origin from different spatial scales.
Spectroscopy, Near-Infrared ; Polygonatum ; Algorithms ; Random Forest ; Least-Squares Analysis

Imported medicinal materials are an important part of Chinese medicinal resources. To be specific, about 10% of the around 600 commonly used Chinese medicinal materials are from abroad, and the introduction of foreign medicinal materials has promoted the development of Chinese medicine. Amid the advancement of reform and opening up and the "Belt and Road" Initiative, major headway has been made in the cross-border trade in China, bringing opportunities for the import of medicinal materials from border ports. However, for a long time, there is a lack of systematic investigation on the types of exotic medicinal materials at border ports. In the fourth national census of traditional Chinese medicine resources, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, together with several organizations, investigated the nearly 40 border ports, Chinese medicinal material markets, and border trade markets in 6 provinces/autonomous regions in China for the first time and recorded the types, sources, circulation, and the transaction characteristics of imported medicinal materials. Moreover, they invited experts to identify the origins of the collected 237 medicinal materials. In addition, the status quo and the problems of the medicinal materials were summarized. This study is expected to lay a basis for clarifying the market and origins of imported medicinal materials as well as the scientific research on and supervision of them.
Medicine, Chinese Traditional ; Materia Medica ; Records ; Censuses ; China ; Drugs, Chinese Herbal

Dabie Mountain in Anhui province is a genuine producing area of Poria cocos, commonly known as Anling. Jinzhai county in Anhui province is a traditional producing area of P. cocos, and it is also a key county for poverty alleviation in Dabie Mountains. Poverty alleviation of traditional Chinese medicine producing area is an important measure to implement the major strategic deployment of the central government. The planting of P. cocos is helpful to promote the development of traditional Chinese medicine industry in Dabie Mountains and help poverty alleviation. P. cocos is a saprophytic fungus with special demands on soil and ecological environment, and its planting appears a scattered and irregular distribution. Traditional investigation methods are time-consuming and laborious, and the results are greatly influenced by subjective factors. In order to obtain the suitable planting area of P. cocos in Jinzhai county, according to the field survey, the research team has explored the regional, biological characteristics and cultivation methods of P. cocos in the county, and obtained the altitude distribution area suitable for the growth of P. cocos. Then, the MaxEnt niche model was used to analyze the relationship between ecological factors and distribution areas, and the potential distribution zoning of P. cocos in Jinzhai county was studied. Combined with the characteristics of P. cocos planting pattern, taking ZY-3 remote sensing image as the data source, the maximum likelihood method was used to extract the area that could be used for P. cocos cultivation in Jinzhai county, and the reason why artificial planting P. cocos was mainly distributed in the west of Jinzhai county was analyzed. The suitable regional classification of P. cocos in Jinzhai county was obtained by superposition of suitable altitude distribution area, MaxEnt analysis and area extracted from remote sensing image, which provided data support for the planting planning of P. cocos in Jinzhai county.
Altitude ; China ; Medicine, Chinese Traditional ; Soil ; Wolfiporia

Polygonatum cyrtonema is a famous bulk medicinal material which is the medicinal and edible homologous. With the implementation of the traditional Chinese medicine industry to promote precise poverty alleviation, the planting area of P. cyrtonema in Jinzhai is becoming larger and larger in recent years. Jinzhai is located in the Dabie Mountainous area, which is the largest mountain area and county in Anhui Province. The cultivation of P. cyrtonema is scattered, and the traditional Chinese medicine resources investigation is not only inefficient and accurate. In this study,the "Resource 3"(ZY-3) remote sensing image was used as the best observation phase,and the method of support vector machine classification was used. The method of parallelepiped, minimum distance, mahalanob is distance, maximum likelihood classification and neural net were used to classify and recognize the P. cyrtonema in the whole region. In order to determine the accuracy and reliability of classification results, the accuracy of six supervised classification results was evaluated by confusion matrix method, and the advantages and disadvantages of six supervised classification methods for extracting P. cyrtonema field planting area were compared and analyzed. The results showed that the method of support vector machine classification was more appropriate than that using other classification methods. It provides a scientific basis for monitoring the planting area of P. cyrtonemain field.
Medicine, Chinese Traditional ; Polygonatum ; Reproducibility of Results ; Research Design ; Support Vector Machine

Based on the investigation of wild medicinal plant resources in Dexing city, Jiangxi province, and the collected plant specimens, which were identified by taxonomy, two new record species of geographical distribution were found, which are Meehania zheminensis A. Takano, Pan Li & G.-H. Xia and Corydalis huangshanensis L.Q.Huang & H.S.Peng. The voucher specimens are kept in Dexing museum of traditional Chinese medicine. In this paper, the new distribution species were reported, which provides valuable information for further enriching and supplementing the species diversity of medicinal plant resources in Jiangxi province.
China ; Corydalis ; Humans ; Lamiaceae ; Medicine, Chinese Traditional ; Museums ; Plants, Medicinal

Thoracic aortic dissection (TAD) without familial clustering or syndromic features is known as sporadic TAD (STAD). So far, the genetic basis of STAD remains unknown. Whole exome sequencing was performed in 223 STAD patients and 414 healthy controls from the Chinese Han population (N = 637). After population structure and genetic relationship and ancestry analyses, we used the optimal sequence kernel association test to identify the candidate genes or variants of STAD. We found that COL3A1 was significantly relevant to STAD (P = 7.35 × 10
Aneurysm, Dissecting/genetics* ; Case-Control Studies ; Cluster Analysis ; Cohort Studies ; Collagen Type III/genetics* ; Computational Biology ; Genetic Predisposition to Disease ; Humans

Qizhu, the dried rhizome of Atractylodes macrocephala in Compositae family, is the representative wild variety of Atractylodis Macrocephalae Rhizoma (Baizhu) with modern excellent quality. Through textual research of materia medica works and modern studies, the medication methods between Qizhu and ancient Baizhu were systematically compared. Focusing on seven key issues, this paper systematically summarized the medicinal history, characters, cultivation and other related contents of Qizhu, in order to provide a basis of Qizhu in the recovery and development of its own Daodi-status, and further serve the industrial development of this herb. The name, harvesting time, processing method and other issues had undergone a relatively complicated evolution process. At present, acknowledged points are as following：①The distribution areas of Qizhu include southern areas of the Yangtze River in Anhui province and its surrounding regions. ②Harvesting time is late October. ③Qizhu can be dried in the shade or micro-hot dried after being wrapped with absorbent paper, later it can be divided into two commercial specifications. ④In addition to cutting, there is still a lack of other processing methods. ⑤The superior characters of Qizhu contain white, less oil, fragrant smell and sweet taste and so on. ⑥The history of Qizhu as a genuine medicinal material can be traced back to the Ming dynasty.