Achillea spp.: A comprehensive review on its ethnobotany, phytochemistry, phytopharmacology and industrial applications.

The genus Achillea genus houses more than 100 species, a number of them are popularly used in traditional medicine for spasmodic gastrointestinal, gynecological and hepatobiliary disorders, hemorrhages, pneumonia, rheumatic pain, inflammation, wounds healing etc. Members of the genus contain a wide variety of volatile and non-volatile secondary metabolites, including terpenes, polyphenols, flavonoids and others. Multiple studies have assessed the biological effects and other aspects of Achillea spp. In a number of preclinical studies, Achillea plants and their essential oils have demonstrated promising antibacterial properties against a number of human and plant pathogens. Besides, the plants have displayed strong antioxidative and potent anti-proliferative and anticancer properties in various cellular and animal models. Achillea plants have widely been used as food preservative in food industry. Clinical studies have indicated its potential against multiple sclerosis (MS), irritable bowel syndrome (IBS), ulcerative colitis, episiotomy wound, primary dysmenorrhea, oral mucositis etc. The present work focuses to provide a brief overview on folk knowledge, phytochemistry, biological activity and applications of Achillea plants. There is a close relationship between the traditional ethnobotanical usage and pharmacological and clinical data from different Achillea spp. The application of Achillea plants and their extracts seems to be a promising alternative for antimicrobial and antioxidant purposes in food, pharmaceutical and cosmetic industries.

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.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forcasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran).

Air pollution is one of the major concerns for the population and the environment due to its hazardous effects. PM10 has affected significant scientific and regulatory interest because of its strong correlation with chronic health such as respiratory illnesses, lung cancer, and asthma. Forcasting air quality and assessing the health impacts of the air pollutants like particulate matter is crucial for protecting public health.This study incorporated weather, traffic, green space information, and time parameters, to forcst the AQI and PM10. Traffic data plays a critical role in predicting air pollution, as it significantly influences them. Therefore, including traffic data in the ANN model is necessary and valuable. Green spaces also affect air quality, and their inclusion in neural network models can improve predictive accuracy. The key factors influencing the AQI are the two-day lag time, the proximity of a park to the AQI monitoring station, the average distance between each park and AQI monitoring stations, and the air temperature. In addition, the average distance between each park, the number of parks, seasonal variations, and the total number of vehicles are the primary determinants affecting PM10.The straightforward effective Multilayer Perceptron Artificial Neural Network (MLP-ANN) demonstrated correlation coefficients (R) of 0.82 and 0.93 when forcasting AQI and PM10, respectively. This study also used the forcasted PM10 values from the ANN model to assess the health effects of elevated air pollution. The results indicate that elevated levels of PM10 can increase the likelihood of respiratory symptoms. Among children, there is a higher prevalence of bronchitis, while among adults, the incidence of chronic bronchitis is higher. It was estimated that the attributable proportions for children and adults were 6.87% and 9.72%, respectively. These results underscore the importance of monitoring air quality and taking action to reduce pollution to safeguard public health.

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.

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach.

This work presents a simplified method for the application of the Multi-Layer Perceptron (MLP) model that aims to predict the aesthetic quality of the landscape designed by water and plants in different forms and volume. The MLP was prepared by (Rosenblat) in the field of computer science, followed by the application of a MLP in landscape aesthetic quality prediction proposed by (Jahani). In the method of this research, the structure of MLP was structured for aesthetic quality prediction of plants and water in urban park landscapes. The accuracies of designed MLP structures were tested to achieve the most accurate one in aesthetic quality prediction. This method creates an environmental decision support system tool for landscape designers, and it is a platform to predict the quality of environment. In practice, the designed environmental decision support system tool is applied by landscape managers to predict the aesthetic quality of landscape in designing new urban parks.•Applies Multi-Layer Perceptron method in landscape assessment.•Accurate MATLAB extension for landscape aesthetic evaluation.•Defined criteria for aesthetic value of landscape.

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.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air.

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications.

Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers' diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R2 = 0.959) in comparison with MLR results (R2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies.

Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibers. Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFMMLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving. According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFMMLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.

Evaluation and comparison of the antidepressant-like activity of Artemisia dracunculus and Stachys lavandulifolia ethanolic extracts: an in vivo study.

Several studies have supported the preventive and therapeutic values of phenolic compounds including chlorogenic acid, syringic acid, vanillic acid, ferulic acid, caffeic acid, luteolin, rutin, catechin, kaempferol, and quercetin in mental disorders. Since these secondary metabolites are reported as the phenolic compounds of Artemisia dracunculus (A. dracunculus) and Stachys lavandulifolia (S. lavandulifolia), the main aim of this study was the evaluation and comparison of the phenolic contents, flavonoids, and antidepressant-like activity of Artemisia dracunculus with Stachys lavandulifolia. Antidepressant-like activity of the extracts was evaluated in the forced swimming test (FST) and the tail suspension test (TST). Moreover, the open field test was conducted to evaluate the general locomotor activity of mice following treatment with the extracts. Since phenolic compounds and flavonoids play main roles in pharmacological effects, the phenolic and flavonoid contents of the extracts were measured. Though significant difference between the phenolic contents of the extracts was not observed, but S. lavandulifolia exhibited higher flavonoid contents. Animal treatment with extracts decreased the immobility times in both FST and TST compared to the vehicle group without any significant effect on the locomotor activity of animals. Also, S. lavandulifolia at 400 mg/kg showed higher potency in both tests compared to A. dracunculus. Our results provided promising evidence on the antidepressant-like activity of both extracts which could be related to flavonoids as the main components of the extracts, but more studies need to be conducted to specify the main compounds and the mechanisms involved in the observed effects.

Personality patterns and Smoking behavior among students in Tabriz, Iran.

INTRODUCTION: Psychological factors have always been considered for their role on risk taking behavior such as substance abuse, risky driving and smoking. The aim of this study was to determine the association between smoking behavior and potential personality patterns among high school students in Tabriz, Iran. METHODS: Through a multistage sampling in a cross-sectional study, 1000 students were enrolled to represent the final grade high school student population of Tabriz, Iran in 2013. The personality patterns along with smoking status and some background information were collected through standard questionnaires along with Millon Clinical Multiaxial Inventory-III (MCMI-III). Fourteen personality patterns and ten clinical syndromes. ANOVA and Kruskal Wallis tests were used to compare numeric scales among the study participants, with respect to their smoking status. Stata version 13 statistical software package was used to analyze the data. Multivariate logistic regression was used to predict likelihood of smoking by personality status. RESULTS: Two logistic models were developed in both of whom male sex was identified as a determinant of regular smoking (1st model) and ever-smoking (2nd model). Depressive personality increased the likelihood of being a regular smoker by 2.8 times (OR=2.8, 95% CI: 1.3-6.1). The second personality disorder included in the model was sadistic personality with an odds ratio of 7.9 (96% CI: 1.2-53%). Histrionic personality increased the likelihood of experiencing smoking by 2.2 times (OR=2.2, 95% CI: 1.6-3.1) followed by borderline personality (OR=2.8, 95% CI: 0.97-8.1). CONCLUSION: Histrionic and depressive personalities could be considered as strong associates of smoking, followed by borderline and sadistic personalities. A causal relationship couldn't be assumed unless well controlled longitudinal studies reached the same findings using psychiatric interviews.

The impact of listening to pleasant natural sounds on anxiety and physiologic parameters in patients undergoing coronary angiography: A pragmatic quasi-randomized-controlled trial.

OBJECTIVE: This study aimed to investigate the impact of listening to pleasant natural sounds on anxiety and physiological parameters in patients undergoing coronary angiography. METHODS: The present pragmatic quasi-randomized controlled clinical trial was conducted on 130 patients undergone elective angiography. The participants were randomly divided into two groups, including a pleasant natural sounds group, and a control group (n1/2 65 per group). Spielberger's state/trait anxiety inventory was used to assess levels of anxiety. The patients' anxiety level and physiological parameters were measured at baseline, before, during, immediately after, and 20 min after coronary angiography. RESULTS: The mean level of anxiety was similar in both arms at baseline (t = 1.317, df = 128, p = 0.190). The intervention arm displayed significantly lower anxiety levels than the control arm during the intervention (Wilks' lambda 0.11, Pillai's trace 0.89, P 0.001, F 2.05). The physiological parameters (systolic and diastolic blood pressure, mean arterial pressure, heart rate, and oxygen saturation) of both groups showed statistically significant differences (p < 0.05) over time and in group-by time interactions. CONCLUSION: As an effective nursing intervention presenting no side-effects, listening to pleasant natural sounds can be helpful in the management of anxiety.