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

This study explores whether the current external quality assessment (EQA) level and acceptable bias for basic semen analysis in China are clinically useful. We collected data of semen EQA from Andrology laboratories in the Hunan Province (China) in 2022 and searched for data in the published literature from January 2000 to December 2023 in China. On the basis of these data, we analyzed the coefficients of variation and acceptable biases of different quality control materials for basic semen analysis through robust statistics. We compared these findings with quality specifications based on biological variation from optimal, desirable, and minimum levels of bias to seek a unified and more suitable semen EQA bias evaluation standard for China's national conditions. Different sources of semen quality control material exhibited considerable variation in acceptable biases among laboratories, ranging from 8.2% to 56.9%. A total of 50.0% of the laboratories met the minimum quality specifications for progressive motility (PR), whereas 100.0% and 75.0% of laboratories met only the minimum quality specifications for sperm concentration and total motility (nonprogressive [NP] + PR), respectively. The Z value for sperm concentration and PR+NP was equivalent to the desirable performance specification, whereas the Z value for PR was equivalent only to the minimum performance specification. This study highlights the feasibility of operating external quality assessment schemes for basic semen analysis using quality specifications based on biological variation. These specifications should be unified among external quality control (EQC) centers based on biological variation.
Semen Analysis/standards* ; Humans ; China ; Male ; Quality Control ; Sperm Motility ; Sperm Count/standards*

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

Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

The research on endophytes of medicinal plants mainly relies on the traditional culture and isolation methods. Because of their functions such as promoting host growth, improving stress resistance, promoting the accumulation of medicinal active ingredients or directly producing medicinal active ingredients, the endophytes of medicinal plants have gradually attracted wide attention. However, it was found that the strains isolated by traditional methods were not the true dominant endophytes of medicinal plants by comparing the results of traditional culture isolation with high-throughput sequencing. The blind and random nature of traditional methods leads to the lack of standards in terms of medium selection, culture time and interaction between species. On the contrary, high-throughput sequencing technology is an emerging molecular biology technology developed in recent decades. Due to its high resolution level and indepen-dent culture, it can be used for thorough analysis of the community structure and diversity of environmental microorganisms. Therefore, we proposed the strategy of using high-throughput sequencing technology to guide the traditional culture and isolation of endophytes from medicinal plants. Firstly, the endophytic structure and diversity of medicinal plants were completely clear by high-throughput sequencing technology, and the dominant endophytes of the host were unequivocal. Then according to the characteristics of each dominant endophytes design or query suitable medium for its growth to culture and isolation. Finally, the function of the isolates was studied. This method can prevent researchers from missing out on the important functional strains of the host, expand the research scope of endophytes of medicinal plants, and facilitate the in-depth excavation and utilization of endophytes of medicinal plants.
Endophytes/genetics* ; High-Throughput Nucleotide Sequencing ; Plants, Medicinal ; Research Design

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

Using the 260 geographical distribution records of Polygonatum cyrtonema in China, combined with 53 environmental factors, the maximum entropy modeling(MaxEnt) was used to study the ecological factors affecting the suitability distribution of P. cyrtonema. The ArcGIS software was used to predict the potential distribution of the population of P. cyrtonema. The dominant factors were chosen by using the Jackknife test and the Receiver Operating Characteristic(ROC) curve was used to evaluate the simulation. The results showed that high value of area under curve(AUC) denoted good results, which significantly differed from random predictions. Based on the evaluation criterion, the accuracies of the predictions of P. cyrtonema potential distribution in the current periods were excellent. The main environmental factors affecting the suitable growth of P. cyrtonema were the monthly precipitation, the wettest monthly precipitation, the annual average temperature range and the precipitation of November, March, February, April, May and October. There are 9 environmental factors in soil type. The potential fitness of P. cyrtonema in China is high, mainly concentra-ted in Hunan, western Hubei, Guangdong, northeastern Guangxi, southeastern Guizhou, Jiangxi, southwestern Anhui, Fujian, Zhejiang, Shaanxi, southwestern Henan and Chongqing. The growth distribution of the potential distribution area of P. cyrtonema was divided, and the zoning map of the growth suitability of P. cyrtonema was formed. Through the comparative analysis of the potential distribution range based on MaxEnt and the distribution range of literature records, the understanding of the distribution range of P. cyrtonema was expanded.
China ; Ecology ; Entropy ; Polygonatum ; Research Design ; Soil

Based on rDNA ITS sequences of Dendrobium officinale and the other 69 species of Dendrobium, a pair of dismatched allele-specific diagnostic primers, TPSH-AS1F and TPSH-AS1R were designed to authenticate D. officinale from the other species. Thebidirectional PCR amplification were performed using the diagnostic primers with the total DNAs of the original plants or processing products as a template. When the annealing temperature was raised to 60 ℃, only the template DNA of D. officinale could be amplified whereas the diagnostic PCRs of the other Dendrobium species were all negative. Compared with the other authentification methods, the bidirectional PCR amplifications is not only simpler and time-saving but practical and effective.

Nerolidol is an important constituent of terpenoid essential oil and has excellent anti-tumor, anti-bacterial, and anti-oxidative properties. For realizing heterogenous production of nerolidol, our research firstly integrated the codon-optimized Actinidia sinensis nerolidol synthase gene (NES) into the terpenoid chassis strain FPP-001, and obtained NES-001 that could produce 2.71 mg•L⁻¹ nerolidol. Then, the N-terminal of the NES was fused with FPS by linker peptide GGGS. With this strategy, nerolidol production improved by 59.80-fold, reaching 162.07 mg•L⁻¹. Finally, by introduction of auxotrophic marker TRP1 in NES-002, the resulting strain NES-003 could produce 1 711.53 mg•L⁻¹ by high cell density fermentation method. This study provides the basis for the fermentative production of nerolidol and other sesquiterpenoids.