[Main nutritional components of food herbs in mountainous areas of Guizhou Province].

OBJECTIVE: To investigate the nutritional content of edible medicinal materials in mountainous areas of Guizhou Province and compare the comprehensive nutritional value of different varieties. METHODS: A total of 15 kinds of edible herbs were collected from Guizhou Province. According to the national standard, direct drying method, Kjeldahl nitrogen determination method, Soxhlet extraction method, high performance liquid chromatography, inductively coupled plasma mass spectrometry and other detection method were used to determine the content of general nutrients, fat soluble vitamins, minerals and ash. According to the weight ranking of nutritional indexes in principal component analysis and membership function analysis, the quality of 9 kinds of food and drug substances and 6 kinds of Chinese medicinal materials were evaluated and ranked. RESULTS: The eigenvalues of the first 4 principal components were greater than 1, and the cumulative contribution rate was 82.32%. Compared with membership function analysis, the top 5 in comprehensive evaluation were Eucommia, Epimedium and honeysuckle, all of which were food and drug substances. The contents of calcium(851.69 mg/100 g), phosphorus(270.22 mg/100 g) and potassium(1446.48 mg/100 g) were the highest. The contents of carotene(21 963.87 µg/100 g) and vitamin E(57.82 mg/100 g) were the highest in the fat-soluble vitamins of Herbimedium. The contents of various indexes of honeysuckle were relatively high. CONCLUSION: Food and pharmaceutical substances have both medicinal value and nutritional value, and the overall nutritional benefit is higher than that of Chinese medicinal materials.

Deep Ensemble Learning-Based Sensor for Flotation Froth Image Recognition.

Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners' predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold-antimony froth flotation application.

Optimization of Fuzzy Control Parameters for Wind Farms and Battery Energy Storage Systems Based on an Enhanced Artificial Bee Colony Algorithm under Multi-Source Sensor Data.

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs.

Sequence homology score-based deep fuzzy network for identifying therapeutic peptides.

The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).

Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring multiple communication delays.

This paper investigates the issue of parallel event-triggered (PET) dynamic output feedback control for networked control systems (NCSs) built by the discrete-time T-S fuzzy model. Initially, a novel PET dynamic output feedback controller is designed. Based on saving network resources and enhancing transmission efficiency, the PET strategy makes full use of relative and absolute triggering condition information. And the dynamic output feedback control can not only address unmeasurable states but also provide a better response to the internal information of the system. The random multiple communication delays and the ℓth-order Rice fading model with different channel coefficients, meanwhile, are both applied in the system. It is closer to the actual situation. Subsequently, new sufficient conditions of membership function dependence are proposed via the staircase function approximation method combined with Lyapunov stability. It guarantees that the system is exponentially mean square stable (EMSS) with H∞ performance. Ultimately, the presented results are validated using two examples. In the future, we will explore the correlative research of T-S fuzzy Markov jump NCSs.

Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.

The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.

Root system architecture change in response to waterlogging stress in a 448 global collection of rapeseeds (Brassica napus L.).

MAIN CONCLUSIONS: A novel image-based screening method for precisely identifying genotypic variations in rapeseed RSA under waterlogging stress was developed. Five key root traits were confirmed as good indicators of waterlogging and might be employed in breeding, particularly when using the MFVW approach. Waterlogging is a vital environmental factor that has detrimental effects on the growth and development of rapeseed (Brassica napus L.). Plant roots suffer from hypoxia under waterlogging, which ultimately confers yield penalty. Therefore, it is crucially important to understand the genetic variation of root system architecture (RSA) in response to waterlogging stress to guide the selection of new tolerant cultivars with favorable roots. This research was conducted to investigate RSA traits using image-based screening techniques to better understand how RSA changes over time during waterlogging at the seedling stage. First, we performed a t-test by comparing the relative root trait value between four tolerant and four sensitive accessions. The most important root characteristics associated with waterlogging tolerance at 12 h are total root length (TRL), total root surface area (TRSA), total root volume (TRV), total number of tips (TNT), and total number of forks (TNF). The root structures of 448 rapeseed accessions with or without waterlogging showed notable genetic diversity, and all traits were generally restrained under waterlogging conditions, except for the total root average diameter. Additionally, according to the evaluation and integration analysis of 448 accessions, we identified that five traits, TRL, TRSA, TRV, TNT, and TNF, were the most reliable traits for screening waterlogging-tolerant accessions. Using analysis of the membership function value (MFVW) and D-value of the five selected traits, 25 extremely waterlogging-tolerant materials were screened out. Waterlogging significantly reduced RSA, inhibiting root growth compared to the control. Additionally, waterlogging increased lipid peroxidation, accompanied by a decrease in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). This study effectively improves our understanding of the response of RSA to waterlogging. The image-based screening method developed in this study provides a new scientific guidance for quickly examining the basic RSA changes and precisely predicting waterlogging-tolerant rapeseed germplasms, thus expanding the genetic diversity of waterlogging-tolerant rapeseed germplasm available for breeding.

Optimization of adaptive neuro-fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption.

Prediction of adsorption via Adaptive Neuro-Fuzzy Inference System (ANFIS) can save the cost and time in practical applications. Chromium (VI) adsorption data obtained at different temperature, activated carbon dosage and pH values were evaluated by using MATLAB ANFIS. In order to achieve prediction of adsorption via ANFIS with acceptable error values, optimum membership function (MF) and optimum number of MF were determined by using Box-Behnken experimental design (BBD) method. In order to determine the optimum number of MF for each input, all combinations given in BBD matrix were examined via ANFIS, then, regression models for each MFs were developed between the root mean square error (RMSE) and MF numbers of each input. The most used five membership functions (triangular, trapezoidal, generalized bell shaped, Gaussian, Gaussian 2) were investigated. According to the analysis of variance (ANOVA), regression models developed for the test data with triangular and trapezoidal membership functions were significant in the 95 % confidence level. Predictions were employed via ANFIS by using optimum MF numbers of each inputs (6, 6, 3 for triangular MF and 8, 8, 2 for trapezoidal MF). Consequently, the best Cr(VI) adsorption percentage prediction (RMSE = 1.9084 and R2 = 0.992) was obtained by using triangular membership function with optimum MF numbers. Response surface plots, which gives the relationship between MF numbers and RMSE values for triangular MF were also evaluated. In this study, it was demonstrated that MF type and numbers, which are crucial for good prediction via ANFIS grid partition method, can be determined optimally by applying experimental design methodology.

Screening of salt tolerance of maize (Zea mays L.) lines using membership function value and GGE biplot analysis.

Soil salinization is a widely recognized global environmental concern that has a significant impact on the sustainable development of agriculture at a global scale. Maize, a major crop that contributes to the global agricultural economy, is particularly vulnerable to the adverse effects of salt stress, which can hinder its growth and development from germination to the seedling stage. This study aimed to screen highly salt-tolerant maize varieties by using four NaCl concentrations of 0, 60, 120, and 180 mMol/L. Various agronomic traits and physiological and biochemical indices associated with salt tolerance were measured, and salt tolerance was evaluated using principal component analysis, membership function method, and GGE biplot analysis. A total of 41 local maize varieties were assessed based on their D values. The results show that stem thickness, germ length, radicle length, leaf area, germination rate, germination index, salt tolerance index, and seed vigor all decreased as salt concentration increased, while electrical conductivity and salt injury index increased with the concentration of saline solution. Under the stress of 120 mMol/L and 180 mMol/L NaCl, changes in antioxidant enzymes occurred, reflecting the physiological response mechanisms of maize under salt stress. Principal component analysis identified six major components including germination vigor, peroxidase (POD), plant height, embryo length, SPAD chlorophyll and proline (PRO) factors. After calculating the comprehensive index (D value) of each variety's performance in different environments using principal component analysis and the membership function method, a GGE biplot analysis was conducted to identify maize varieties with good salt tolerance stability: Qun Ce 888, You Qi 909, Ping An 1523, Xin Nong 008, Xinyu 66, and Hong Xin 990, as well as varieties with poor salt tolerance: Feng Tian 14, Xi Meng 668, Ji Xing 218, Gan Xin 2818, Hu Xin 712, and Heng Yu 369. Furthermore, it was determined that a 120 mMol/L NaCl concentration was suitable for screening maize varieties during germination and seedling stages. This study further confirmed the reliability of GGE biplot analysis in germplasm selection, expanded the genetic resources of salt-tolerant maize, and provided theoretical references and germplasm utilization for the introduction of maize in saline-alkali areas. These research findings contribute to a better understanding of maize salt tolerance and promote its cultivation in challenging environments.

A comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis to systematically evaluate spice quality: Cinnamomum cassia as an example.

Spices have long been popular worldwide. Besides serving as aromatic and flavorful food and cooking ingredients, many spices exhibit notable bioactivity. Quality evaluation methods are essential for ensuring the quality and flavor of spices. However, existing methods typically focus on the content of particular components or certain aspects of bioactivity. For a systematic evaluation of spice quality, we herein propose a comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis. Cinnamomum cassia was used as a representative example to illustrate this approach. Near-infrared spectroscopy and chemometric methods were combined to predict the geographical origin, cinnamaldehyde content, ash content, antioxidant activity, and integrated membership function value. All the optimal prediction models displayed good predictive ability (correlation coefficient of prediction > 0.9, residual predictive deviation > 2.1). The proposed approach can provide a valuable reference for the rapid and comprehensive quality evaluation of spices.

An optimal genomic DNA extraction method for shoots of four Dendrocalamus species based on membership function analysis.

High-quality genomic DNA extraction is fundamental for the study of gene cloning and expression in plants. Therefore, this study evaluated several methods for extracting genomic DNA from shoots of four Dendrocalamus species to determine the optimal technique. Genomic DNA was extracted using three different methods: a commercial DNA extraction kit method, a modified cetyltrimethylammonium bromide method and a sodium dodecyl sulfate method. A membership function analysis was employed to compare these methods. The results demonstrated that the commercial DNA extraction kit method was the most effective and comprehensive approach for extracting genomic DNA from shoots of four Dendrocalamus species. Furthermore, this study provided valuable insights into optimizing techniques for extracting genomic DNA in other bamboo species.

Metal-non-tolerant ecotypes of ectomycorrhizal fungi can protect plants from cadmium pollution.

The application of mycorrhizal fungi as a bioaugmentation technology for phytoremediation of heavy metal (HM) contaminated soil has attracted widespread attention. In order to explore whether the adaptation of Pinus massoniana (P. massoniana) to metal polluted soil depends on the metal adaptation potential of their associated ectomycorrhizal fungi (ECMF), we evaluated the cadmium (Cd) tolerance of 10 ecotypes of Cenococcum geophilum (C. geophilum) through a membership function method, and P. massoniana seedlings were not (NM) or inoculated by Cd non-tolerant type (JaCg144), low-tolerant (JaCg32, JaCg151) and high-tolerant (JaCg205) isolates of C. geophilum were exposed to 0 and 100 mg·kg-1 for 3 months. The result showed that, each ecotype of C. geophilum significantly promoted the growth, photosynthesis and chlorophyll content, proline (Pro) content and the activity of peroxidase (POD) of P. massoniana seedlings, and decreased malonaldehyde (MDA) content and catalase (CAT) and superoxide dismutase (SOD) activity. The comprehensive evaluation D value of the tolerance to Cd stress showed that the order of the displaced Cd resistance of the four ecotypic mycorrhizal P. massoniana was: JaCg144 > JaCg151 > JaCg32 > JaCg205. Pearson correlation analysis showed that the Sig. value of the comprehensive evaluation (D) values of the strains and mycorrhizal seedlings was 0.077 > 0.05, indicating that the Cd tolerance of the the C. geophilum isolates did not affect its regulatory effect on the Cd tolerance of the host plant. JaCg144 and JaCg151 which are non-tolerant and low-tolerant ecotype significantly increased the Cd content in the shoots and roots by about 136.64-181.75% and 153.75-162.35%, indicating that JaCg144 and JaCg151 were able to effectively increase the enrichment of Cd from the soil to the root. Transcriptome results confirmed that C. geophilum increased the P. massoniana tolerance to Cd stress through promoting antioxidant enzyme activity, photosynthesis, and lipid and carbohydrate synthesis metabolism. The present study suggests that mental-non-tolerant ecotypes of ECMF can protect plants from Cd pollution, providing more feasible strategies for ectomycorrhizal-assisted phytoremediation.

Discovering effect of intuitionistic fuzzy transformation in multi-layer perceptron for heart disease prediction: a study.

Cardiovascular disease (CVD) is the one of the most fatal diseases in the world we have seen in last two decades. For heart disease detection, imprecision in clinical parameters may occur due to error in taking readings or in measuring devices or environmental conditions etc. Hence, introducing fuzzy set theory in feature engineering may give better results as it deals with uncertainty. But in fuzzy set theory, only one uncertainty is considered, which is membership degree or degree of belongingness. Intuitionistic fuzzy set (IFS) considers two uncertainties - membership degree and non-membership degree and so IFS may provide efficient results. To reduce the risk of heart disease, an advanced deep learning algorithm will play a significant role in heart disease prediction that will help physicians to diagnose early. In this paper, we have established a transformation of patient features using i) intuitionistic fuzzy parameters, where Sugeno-type fuzzy complement is used and ii) fuzzy parameters, where gamma membership function is used. These transformed attributes are applied on Deep Learning prediction algorithm as Multi-layer Perceptron (MLP). The novelty of the paper lies from feature transformation to deep learning. It is observed that intuitionistic fuzzy transformation approach, keeping model parameters intact, significantly outperforms non-fuzzy method and gammy fuzzy Transformation, which is reflected in evaluation mechanisms.

Effects of mixed substrates of different agricultural and forestry residues on the cutting seedlings of Thuja sutchuenensis.

To screen environment-friendly seedling cultivation substrates which could replace peat and with less cost, we compared the effects of different agricultural and forestry residue mixed substrates on cutting propagation of Thuja sutchuenensis, in an experiment following randomized block design. There were five types of mixed substrates, including peat + vermiculite + perlite (T1), edible mushroom residue (EMR) + vermiculite + perlite (T2), carbo-nized rice husk (CRH) + vermiculite + perlite (T3), EMR + slag + sawdust (T4) and CRH + EMR + slag (T5). The results showed that the bulk density of T3 was the lowest, followed by T2, which significantly differed from other mixed substrates. The non-capillary porosity of T2 was significantly greater than that of T1, while the capillary porosity and the total porosity of T2 was lower than T1 and T3, respectively. T2 had the highest contents of total nitrogen, total phosphorus, total potassium, alkali-hydrolyzed nitrogen, available phosphorus, substrate moisture and the highest pH, which differed significantly from other mixed substrates in most chemical indicators. The membership function values of rooting rate and growth indicators of cuttings with different mixed substrates were in order of T2 > T3 > T1> T5 > T4. Most indicators with larger grey relation values were physical indicators. The top five indicators were capillary water capacity, total potassium, field water capacity, maximum water capacity, and total porosity, with both capillary water capacity and total potassium content ranking first. In general, the physicochemical properties, rooting rate, and growth characteristics of cuttings under T2 were better than those of other mixed substrates. The capillary water capacity and total potassium were the main factors affecting rooting and growth of cuttings. At the early stage of cutting, the physical properties of mixed substrate had greater effect on rooting rate and growth of cuttings than the chemical properties. Overall, our results suggested that T2 should be preferred in the cutting propagation of T. sutchuenensis.

Fault Diagnosis for Complex Equipment Based on Belief Rule Base with Adaptive Nonlinear Membership Function.

Fault diagnosis of complex equipment has become a hot field in recent years. Due to excellent uncertainty processing capability and small sample problem modeling capability, belief rule base (BRB) has been widely used in the fault diagnosis. However, previous BRB models almost did not consider the diverse distributions of observation data which may reduce diagnostic accuracy. In this paper, a new fault diagnosis model based on BRB is proposed. Considering that the previous triangular membership function cannot address the diverse distribution of observation data, a new nonlinear membership function is proposed to transform the input information. Then, since the model parameters initially determined by experts are inaccurate, a new parameter optimization model with the parameters of the nonlinear membership function is proposed and driven by the gradient descent method to prevent the expert knowledge from being destroyed. A fault diagnosis case of laser gyro is used to verify the validity of the proposed model. In the case study, the diagnosis accuracy of the new BRB-based fault diagnosis model reached 95.56%, which shows better fault diagnosis performance than other methods.

High-throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy.

The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline-alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline-alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the QA-reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs.

Membership-function-dependent dynamic output feedback H∞ controller design of continuous-time T-S fuzzy systems via non-quadratic Lyapunov function.

The dynamic output feedback H∞ (DOFH) control under imperfect premise matching (IPM) is studied in this paper for continuous-time Takagi-Sugeno (T-S) fuzzy systems. Different from the existing results, the DOFH switching controller, which enjoys membership functions (MFs) distinct from the fuzzy systems, is designed. First, the non-quadratic Lyapunov function (NQLF) based on MFs is utilized to design the controller. The time derivatives of MFs are addressed by the switching strategy. Second, a method based on linear matrix inequality (LMI) is given to make the controller gains solvable. Third, an improved method is developed to incorporate the more boundary information of MFs into the stability conditions to reduce conservatism. Finally, three examples are used to certify the advantage of the approach.

Comprehensive evaluation of Pinellia ternata germplasm resources based on phenotypic trait classification / 中国中药杂志

The evaluation of germplasm resources is the prerequisite for the development, utilization, and conservation of Chinese medicinal resources. The selection of excellent germplasm is the key to the breeding and orderly production of Pinellia ternata. In this study, 21 germplasm materials of P. ternata from major production areas in China were collected and analyzed for population diversity after phenotypic preliminary screening. The results have revealed that the P. ternata population has abundant phenotypic variation, and the phenotypic changes could be divided into five phenotypes in terms of organ trait variation. Further analysis of variation in 20 quantitative traits of the population revealed that the coefficient of variation for adenosine content(339.05%) was the largest, while the coefficient of variation for the underground plant height(16.35%) was the smallest. Correlation analysis showed that there was a strong correlation among various traits, with 52 pairs of traits showing highly significant correlation(P<0.01) and 19 pairs of traits showing a significant correlation(P<0.05). The 21 germplasms in the test could be classified into three major clusters by cluster analysis, with Cluster Ⅱ having the highest number and content of nucleosides, making it suitable for the selection and breeding of P. ternata varieties with high content of nucleosides. The yield in Cluster Ⅲ was higher than that in other groups, making it suitable for the selection and breeding of P. ternata varieties with a high yield. All trait indicators could be simplified into five principal component factors through principal component analysis, and the cumulative contribution rate was up to 86.04%. Further, comprehensive analysis using membership function and stepwise regression analysis identified nine traits, such as plant height, main leaf length, and underground plant height as characteristic indicators for the comprehensive evaluation of germplasm resources of P. ternata. BX007, BX008, and BX005 were identified as germplasms with both high yield and high uridine content, with BX007 having the highest uridine content of 479.51 μg·g~(-1). It belonged to the germplasm of P. ternata with double bulbils and could be cultivated as a potential good variety. Based on the phenotypic classification of P. ternata, systematic resource evaluation was carried out in this study, which could lay a foundation for the excavation of genetic resources and the breeding of new varieties of P. ternata.

[Comprehensive evaluation of Pinellia ternata germplasm resources based on phenotypic trait classification].

The evaluation of germplasm resources is the prerequisite for the development, utilization, and conservation of Chinese medicinal resources. The selection of excellent germplasm is the key to the breeding and orderly production of Pinellia ternata. In this study, 21 germplasm materials of P. ternata from major production areas in China were collected and analyzed for population diversity after phenotypic preliminary screening. The results have revealed that the P. ternata population has abundant phenotypic variation, and the phenotypic changes could be divided into five phenotypes in terms of organ trait variation. Further analysis of variation in 20 quantitative traits of the population revealed that the coefficient of variation for adenosine content(339.05%) was the largest, while the coefficient of variation for the underground plant height(16.35%) was the smallest. Correlation analysis showed that there was a strong correlation among various traits, with 52 pairs of traits showing highly significant correlation(P<0.01) and 19 pairs of traits showing a significant correlation(P<0.05). The 21 germplasms in the test could be classified into three major clusters by cluster analysis, with Cluster Ⅱ having the highest number and content of nucleosides, making it suitable for the selection and breeding of P. ternata varieties with high content of nucleosides. The yield in Cluster Ⅲ was higher than that in other groups, making it suitable for the selection and breeding of P. ternata varieties with a high yield. All trait indicators could be simplified into five principal component factors through principal component analysis, and the cumulative contribution rate was up to 86.04%. Further, comprehensive analysis using membership function and stepwise regression analysis identified nine traits, such as plant height, main leaf length, and underground plant height as characteristic indicators for the comprehensive evaluation of germplasm resources of P. ternata. BX007, BX008, and BX005 were identified as germplasms with both high yield and high uridine content, with BX007 having the highest uridine content of 479.51 µg·g~(-1). It belonged to the germplasm of P. ternata with double bulbils and could be cultivated as a potential good variety. Based on the phenotypic classification of P. ternata, systematic resource evaluation was carried out in this study, which could lay a foundation for the excavation of genetic resources and the breeding of new varieties of P. ternata.

Agronomic evaluation of shade tolerance of 16 spring Camelina sativa (L.) Crantz genotypes under different artificial shade levels using a modified membership function.

Camelina [Camelina sativa (L.) Crantz] is currently gaining considerable attention as a potential oilseed feedstock for biofuel, oil and feed source, and bioproducts. Studies have shown the potential of using camelina in an intercropping system. However, there are no camelina genotypes evaluated or bred for shade tolerance. The objective of this study was to evaluate and determine the shade tolerance of sixteen spring camelina genotypes (growth stage: BBCH 103; the plants with 4-5 leaves) for intercropping systems. In this study, we simulated three different shade levels, including low (LST), medium (MST), and high shade treatments (HST; 15, 25, and 50% reduction of natural light intensity, respectively), and evaluated the photosynthetic and physiological parameters, seed production, and seed quality. The mean chlorophyll pigments, including the total chlorophyll and chlorophyll a and b across the 16 genotypes increased as shade level increased, while the chlorophyll fluorescence parameter Fv/Fm, chlorophyll a/b, leaf area, the number of silicles and branches plant-1 decreased as shade level increased. The first day of anthesis and days of flowering duration of camelina treated with shade were significantly delayed and shortened, respectively, as shade increased. The shortened lifecycle and altered flowering phenology decreased camelina seed yield. Additionally, the shade under MST and HST reduced the seed oil content and unsaturated fatty acids, but not saturated fatty acids. The dendrograms constructed using the comprehensive tolerance membership values revealed that CamK9, CamC4, and 'SO-40' were the relatively shade-tolerant genotypes among the 16 camelina genotypes. These camelina genotypes can grow under the shade level up to a 25% reduction in natural light intensity producing a similar seed yield and seed oil quality, indicating the potential to intercrop with maize or other small grain crops. The present study provided the baseline information on the response of camelina genotypes to different shade levels, which would help in selecting or breeding shade-tolerant genotypes.