Changes in the essential oil content and composition of Salvia limbata C.A. Mey at different growth stages and altitudes.

Salvia limbata is of great importance to the pharmaceutical industry owing to its various biological effects. Therefore, it is important to investigate the main factors that affect its essential oil composition. Although some investigations have been performed with regard to the phytochemistry of S. limbata, this study investigates, for the first time, the effect of growth stage and altitude on the content and chemical composition of essential oil extracted from S. limbata. For this purpose, the essential oil was extracted from 45 air-dried samples by hydrodistillation and analyzed by GC-MS and GC-flame methods. The highest content of essential oil was obtained from aerial parts in the vegetative stage at an altitude of 1500 m (0.86% v/w). Our findings show that the vegetative stage at 1500 m is the optimal harvest time to extract the highest content of oil while the highest content of monoterpenes (including α-pinene and ß-pinene) could be obtained in the same phenological stage at 2000 m. By contrast, the content of sesquiterpenes increased to the highest values in the ripening stage at 1500 and 2500 m. The results of this study help to find the optimal conditions to obtain the highest content of S. limbata essential oil, but additional studies are warranted.

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.

BACKGROUND: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, -2, -4, -6, -8, -10 and -12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. RESULTS: Comparing to the MLR, the MLP model represents the significant value of R2 in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. CONCLUSIONS: Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.

Sedative-hypnotic, anxiolytic and possible side effects of Salvia limbata C. A. Mey. Extracts and the effects of phenological stage and altitude on the rosmarinic acid content.

ETHNOPHARMACOLOGICAL RELEVANCE: Salvia limbata C. A. Mey. (Persian name: Maryam Goli-e-labeh dar) has been used for treating central nervous disorders such as insomnia, anxiety and depression in Persian traditional medicine. S. limbata is known for its pharmacological activities which could be at least in a part, upon the presence of rosmarinic acid (RA). However, the sedative-hypnotic effect, anxiolytic activity, possible side effects, and the mechanism of action of S. limbata extract has not yet been examined. AIM OF THE STUDY: In the current study the sedative-hypnotic effect, anxiolytic activity, possible side effects, and the mechanism of action of S. limbata extracts were evaluated. Besides, the effects of altitude and phenological stage on the RA content of S. limbata were investigated. MATERIALS AND METHODS: Sedative-hypnotic and anxiolytic effects were evaluated through the pentobarbital induced loss of righting reflex test and open field test, respectively. Flumazenil was used to reveal the mechanism of action. Possible side effects were investigated in the passive avoidance and grip strength tests. Besides, the effects of altitude and phenological stage (vegetative, flowering, and seed setting) on the RA content of S. limbata were evaluated using reversed-phase high-performance liquid chromatography (RP-HPLC). RESULTS: Following behavioral tests, sedative-hypnotic and anxiolytic effects were observed. Since the observed effects were reversed by flumazenil and no side effect on the memory and muscle strength was reported, modulation of the α1-containing GABA-A receptors could be proposed as one of the involved mechanisms. According to the RP-HPLC analysis, harvesting S. limbata in the vegetative stage at the altitude of 2500 m led to the highest content of RA (8.67 ± 0.13 mg/g dry matter). Among different extract of the plant samples collected in the vegetative stage at the altitude of 2500 m, the hydroalcoholic extract showed the highest rosmarinic acid content. CONCLUSION: The obtained results help to find the optimum situation to gain the highest content of RA as well as the pharmacological activity that could be economically important for the pharmaceutical industries.

Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques.

In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.

Human Activities Impact Prediction in Vegetation Diversity of Lar National Park in Iran Using Artificial Neural Network Model.

The effects of livestock and tourism on vegetation include loss of biodiversity and in some cases species extinction. To evaluate these stressor-effect relationships and provide a tool for managing them in Iran's Lar National Park, we developed a multilayer perceptron (MLP) artificial neural network model to predict vegetation diversity related to human activities. Recreation and restricted zones were selected as sampling areas with maximum and minimum human impacts. Vegetation diversity was measured as the number of species in 210 sample plots. Twelve landform and soil variables were also recorded and used in model development. Sensitivity analyses identified human intensity class and soil moisture as the most significant inputs influencing the MLP. The MLP was strong with R2 values in training (0.91), validation (0.83), and test data sets (0.88). A graphical user interface was designed to make the MLP model accessible within an environmental decision support system tool for national park managers, thus enabling them to predict effects and develop proactive plans for managing human activities that influence vegetation diversity. Integr Environ Assess Manag 2021;17:42-52. © 2020 SETAC.

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats.

Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model (R 2 = .9) is the most suitable and precise model compared with RBF (R 2 = .81) and SVM (R 2 = .74) in predicting hyperforin in H. perforatum based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.

Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks.

BACKGROUND: Hypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)]. RESULTS: According to the results, the MLP model (R2 = 0.87) had an advantage over RBF (R2 = 0.8) and SVM (R2 = 0.54) models and it was relatively accurate in predicting hypericin content in H. perforatum based on the ecological conditions of site including soil types, its characteristics and plant phenological stages of habitat. The results of sensitivity analysis revealed that phenological stages, hill aspects, total nitrogen, altitude and organic carbon are the most influential factors that have an integral effect on the content of hypericin. CONCLUSIONS: The designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.