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Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data. [ABSTRACT FROM AUTHOR]

We studied the alpha-globin gene genotypes, hematologic values, and transfusion-dependence of patients with Hb H disease. Molecular characterization of alpha-thalassemia was performed. We identified 120 patients with Hb H disease. Of these patients, 35 (29.16%) had deletional form of Hb H disease, and 85 (70.83%) had different form of non-deletional Hb H disease. The most frequently observed Hb H genotypes were --Med/–α3.7 in 33 patients (27.5%), αCD19(-G) α/αCD19(-G) α in 25 cases (20.83%), αpolyA2α/αpolyA2α in 15 (12.5%), and αpolyA1α/αpolyA1α in 13 (10.83%) respectively. The probability of receiving at least one transfusion blood in deletional form was observed in 3 of 35 (8.57%) patients which just seen in 3 of 33 (9%) patients with --Med/–α3.7 genotype. This form was also observed in 8 of 85 (9.4%) patients in non-deletional Hb H diseases which five of them had Med deletion in compound with alpha globin point mutations. Nondeletional Hb H disease was more severe than deletional Hb H disease requiring more blood transfusions. We can recommend that Med deletion in compound with alpha-globin point mutations, polyA1 and constant spring in homozygous form needs to be taken into consideration when offering counseling to high-risk couples. [ABSTRACT FROM AUTHOR]

The Angstrom–Prescott (A–P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A–P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A–P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A–P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A–P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m−2 day−1 for the A–P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively. [ABSTRACT FROM AUTHOR]

This study evaluated the relationship between arsenic uptake via drinking water ingestion and arsenic concentration in fingernails as a biomarker for human exposure. For this purpose, we collected fingernail samples from 40 healthy participants of arsenic-affected rural regions of Kaboudrahang County, the west of Iran. A total of 49 fingernail samples were also collected from individuals who lived in areas where contamination of drinking water sources with arsenic had not been reported. It was found that the fingernails arsenic contents in 50 and 4.08% of the samples collected from arsenic-contaminated and reference villages were higher than the normal arsenic values of nails (0.43–1.08 µg/g), respectively. Based on the results of adjusted multiple linear regression, a significant association was found between groundwater and fingernails arsenic concentration (p < 0.001). Moreover, a statistically significant association was shown between arsenic in the fingernail samples and gender (p = 0.037). Fingernails arsenic contents were not significantly affected by other variables including age, smoking habits, and BMI (p > 0.05). In light of the results of this study, the use of biological indicators such as fingernail tissues due to easier sampling and less risk of external contamination is suitable for assessing exposure to heavy metals in contaminated areas. [ABSTRACT FROM AUTHOR]

Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: (1) traditional range test (TRT), Kruskal–Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT + KW + DFA); (2) traditional range test using mean values (RTM) and two additional tests (RTM + KW + DFA); (3) TRT + KW + PCA (principle component analysis), and; 4) RTM + KW + PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT + KW + DFA and RTM + KW + DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10–60% (overall mean contribution 36%), 0.3–16% (overall mean contribution 6%) and 38–77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT + KW + PCA generated respective corresponding contributions of 1–42% (overall mean 20%), 0.5–30% (overall mean 12%) and 55–84% (overall mean 68%) compared with 17–69% (overall mean 41%), 0.2–12% (overall mean 5%) and 29–76% (overall mean 54%) using the final tracers selected by RTM + KW + PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT + KW + PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT + KW + DFA and RTM + KW + DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM + KW + PCA (mean MAE = 14% and mean RMSE = 16%) and GLUE with TRT + KW + PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources. [ABSTRACT FROM AUTHOR]

The present study investigates different scenarios to project the chance of achieving SDG 3.4 in Iran. In this study, the Iranian Death Registry System data was employed to estimate the Unconditional Probability of Dying (UPoD) for the four major categories of NCDs; then, the Bayesian model averaging was used to project the UPoD at the national and sub-national levels. Also, the prevalence of the risk factors was projected by 2030 based on STEPs as well as some other study data. Plus, UPoD and the possibility of achieving the target were estimated once again based on the assumption that the global reduction in risk factors proposed by WHO would be adopted in Iran. The UPoDs for the four NCDs in Iran were 17.5% (95% UI: 16.3–19.2) and 14.7% (13.3–16.2) in 2010 and 2015 respectively and if the current trend continues, 2030 will mark the UPoD of 10.8% (7.9–14.3). However, If the risk factors are reduced to the WHO target level by 2030, the UPoDs will be reduced to 5.44% (3.51–7.39) and 6.55% (5.00–8.13) of the 2010 and 2015 baseline scenarios, respectively, to enable some provinces to meet SDG 3.4. If the current trend continues, Iran will and will not achieve the SDG 3.4 in 2010 and 2015 baseline scenarios, respectively. However, if the global target set for reducing risk factors is achieved, Iran will meet all expectations in SDG 3.4 except in Asthma and COPD. Therefore, effective interventions are recommended to be designed and followed to reduce Asthma and COPD. [ABSTRACT FROM AUTHOR]

Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work's results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development. [ABSTRACT FROM AUTHOR]

Short QT-interval is a condition that bear the suspicion of short QT syndrome (SQTS). SQTS is known to increase risk of life-threatening arrythmias and sudden cardiac death (SCD). Due to the insufficient population-based studies and use of various QT cut-off values, it accounts for as an undiagnosed condition. In this study, we sought for prevalence of short QT interval in Kherameh cohort study, one of the southern branches of the Prospective Epidemiological Research Studies in Iran (PERSIAN). Data of 4363 adult subjects were analyzed from phase 1 of the cohort during 2014–2017. The corrected QT (QTc) intervals were calculated and electrocardiograms (ECGs) with QTc of less than 370 ms (msec) were reanalyzed for bradycardia, early repolarization, atrial fibrillation (AF), arrhythmias, and other electrical conduction abnormalities. Seventy-two subjects (1.65%) had a QTc of less than 370 ms (mean QTc of 360.72 ± 11.72). A male predominance and a lower mean heart rate observed in SQTS susceptible group (M/F of 1/0.26 vs. 1/1.145, p-value < 0.0001; 58.389 ± 9.787 vs. 70.899 ± 11.775; p-value < 0.0001) compare to the subjects with normal QTc. At least, 2 subjects with high-probability SQTS and 3 with intermediate-probability SQTS identified. The frequency of AF, syncope, bradycardia, early repolarization, low voltage ECG, and infantile SCD in first- and second-degree relatives were 16.67, 4.17, 33.33, 11.11, 11.11, 11.11%, respectively. The prevalence of short QT interval in our cohort was in line with previous studies. The incidence of cardiac symptoms/events, familial SCDs and ECG derived specific findings were high amongst SQTS-susceptible index persons. However, these variables could not predict the symptomatic subjects, which emphasizes gene studies and family screening. [ABSTRACT FROM AUTHOR]

Evaluation of forage yield and antioxidant activity in Poa pratensis with high quality and good spring green-up forage might help variety improvement for use under water deficit condition. Germplasm and phenotypic diversity evaluations lay a foundation for genotype selection and improvement of varieties for drought tolerance in P. pratensis. The present study was conducted to assess the genetic potential of a collection of P. pratensis accessions for drought stress and to identify the association between polyphenol compounds and forage yield traits. Vegetative clone samples of 100 accessions collected from a diverse geographical area of Iran were clonally propagated in a greenhouse and evaluated in the field under two moisture regimes (non-stress and drought stress) in 2018 and 2019. Drought stress had negative effects on fresh and dry forage yields and reduced phenotypic variances. On average, drought stress reduced fresh and dry forage yields by 45% and 28%, respectively. The results of Mantel test showed no significant correlation between forage yield traits and geographical distances. Genetic coefficients of variation for forage yield and most of the phytochemicals were lower under drought stress, suggesting that deficit irrigation may reduce genetic variation for the tested traits. The estimates of heritability were higher under non-stress conditions than under drought stress treatment for forage yield traits and few polyphenols. However, the majority of polyphenol compounds had higher heritability than forage yield traits under drought stress, which suggests the potential for indirect selection. The 'Ciakhor', 'Damavand', 'Karvandan', 'Abrumand', and 'Abr2' accessions had high quantities for polyphenols and yield traits under both moisture regimes. These accessions are promising candidates for use in variety crossing programs and for developing high-yielding varieties under water-deficit conditions. [ABSTRACT FROM AUTHOR]

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages. [ABSTRACT FROM AUTHOR]

Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province. [ABSTRACT FROM AUTHOR]

To identify apple canker casual agents and evaluate their pathogenicity and virulence in apple production hubs including West Azarbaijan, Isfahan and Tehran provinces; samples were collected from symptomatic apple trees. Pathogenic isolates on the detached branches were identified as Cytospora cincta, Diplodia bulgarica, Neoscytalidium dimidiatum and Eutypa cf. lata. E. cf. lata was reported as a potential apple canker causal agent in Iran for the first time based on the pathogenicity test on the detached branches, whereas it caused no canker symptoms in apple trees until 6 months after inoculation. Currently, E. cf. lata seems to be adapted to a single city. C. cincta, D. bulgarica and N. dimidiatum caused canker symptoms in apple trees. "C. cincta" and also "C. cincta and N. dimidiatum" were the most widespread and aggressive apple canker species, respectively, associated with apple canker in Iran. Therefore, they are considered to be the main threat to apple production in Iran and should be carefully monitored. Disease progress curve, area under the disease progress curve and optimum temperatures were determined for mentioned species. It is concluded that the establishment of each species occurs in appropriate areas and times in terms of the optimum temperature for their growth. [ABSTRACT FROM AUTHOR]

Water use by the agricultural sector along with inefficient irrigation methods and climate change has led to the depletion and insecurity of water resources and consequent instability of the agricultural system. Defining benchmarks and comparing them is essential for sustainable system management performance. The sustainability performance of an agricultural system depends on various factors related to water, energy, and food. This study selects and ranks sustainability performance indicators (SPIs) of agricultural systems with the analytical hierarchy process (AHP). Expert opinions on agricultural sustainability were obtained from Iran's Regional Water Organization. The factors and variables affecting the management of water resources in agricultural systems in a basin area are evaluated with 17 SPIs (10 indicators of water resources sustainability, 3 energy sustainability indicators, and 4 food sustainability indicators) that measure the sustainability of agricultural systems. The AHP reduced the number of indicators to a small number of effective indicators. Results of pairwise comparison and the subsequent determination of the weight of each indicator show that the indicators of water consumption, groundwater level stability, vulnerability of water resources, and water stress have the largest weights (i.e., importance) for agricultural system sustainability at the basin scale. These selected indicators can be applied to agricultural water systems (AWSs). [ABSTRACT FROM AUTHOR]

Insufficient physical activity (IPA) caused approximately 5% of mortalities in 2017 in Iran, almost double its global average. Despite the relatively considerable burden, a knowledge gap exists regarding the trend of IPA in recent years. We described the trend of IPA prevalence utilizing the data from six rounds of STEPwise approach to risk factor Surveillance (STEPS) in Iran. We estimated the physical activity status of Iranian adults from 2006 to 2016 after adjusting for years of schooling, urbanization percentage, and wealth index. We used the spatiotemporal model to interpolate and extrapolate the IPA prevalence for the years in-between the series and from 2001 to 2006, respectively. We used the data of 177,910 participants from six STEPS surveys and found that the national prevalence of IPA had steadily increased over the course of 16 years and had almost doubled in this time period (23.1% in 2001 to 55.4% in 2016). The increase was persistent across all age and gender strata and in every province. Moreover, IPA was more prevalent among women than their male peers regardless of their age category or province of residence. The prevalence of IPA in Khuzestan (highest prevalence) was almost double compared to that in Lorestan (lowest prevalence) in 2016. The IPA prevalence increased considerably and almost doubled in 16 years among Iranian adults, particularly women. Policies need to target IPA as a high priority contributing to the burden of Non-communicable diseases. [ABSTRACT FROM AUTHOR]

The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19–45%) and a decrease in WFs (2–3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran's major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification. [ABSTRACT FROM AUTHOR]

Lavandula genus is a considerable medicinal plant in pharmaceutical and cosmetics industries. Considering increasing threat of drought in the world, it is important to identify genotypes which can tolerate drought. It is also important to characterize quantity and quality of essential oils, and tolerance indicators of these genotypes against drought stress. Therefore, an experiment was conducted in Gorgan University of Agricultural Sciences and Natural Resources, Iran, during 2017 and 2018, to investigate these factors. It was a factorial experiment based on randomized complete block design with two treatments, three genotypes (Lavandula angustifolia cv. Hidcote, Lavandula angustifolia cv. Munstead, and Lavandula stricta), and four levels of drought stress (irrigation regimes) (I1: 100–90% (control), I2: 80–70%, I3: 60–50% and I4: 30–40% of field capacity) which was done with three repetitions. Drought increased amount of proline in leaves, antioxidant activity, activity of catalase, peroxidase, ascorbate peroxidase, and superoxide enzymes, malondialdehyde content, total flavonoids, total phenol, total sugar and essential oil percentage. The PCA analysis of different irrigation regimes showed that in the first component, the best traits are antioxidant enzymes CAT, SOD, APX, while in the second component, only the trait Catalase is the best trait. The results of PCA analysis in lavender genotypes showed that L. stricta exhibits the most affected physiological changes while trying to adjust to changes in the water status of the environment, under the imposed conditions and shows the highest resistance. But it reduced dry weight of aerial parts, relative water content of leaves, and efficacy of essential oil. Lavandula stricta genotype had the highest amount of essential oil, but the highest dry weight of the aerial parts and essential oil yield were related to L. angustifolia cv. Hidcote and L. angustifolia cv. Munstead genotypes. In all evaluated genotypes, with increasing drought stress, monoterpene compounds were decreased and sesquiterpene compounds were increased. Totally it was shown that drought effect on evaluated traits depends on genotype and nature of traits; this indicates that by choosing drought-tolerant genotypes in breeding programs, high quantity and quality of essential oil, as well as tolerance to drought stress can be achieved. [ABSTRACT FROM AUTHOR]

Automated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. In this study, for the first time, it's been attempted to compare the accuracy of a series of state-of-the-art machine learning methods, ranging from the holistic to neural network algorithms. The study is performed on six snake species in Lar National Park, Tehran Province, Iran. In this research, the holistic methods [k-nearest neighbors (kNN), support vector machine (SVM) and logistic regression (LR)] are used in combination with a dimension reduction approach [principle component analysis (PCA) and linear discriminant analysis (LDA)] as the feature extractor. In holistic methods (kNN, SVM, LR), the classifier in combination with PCA does not yield an accuracy of more than 50%, But the use of LDA to extract the important features significantly improves the performance of the classifier. A combination of LDA and SVM (kernel = 'rbf') is achieved to a test accuracy of 84%. Compared to holistic methods, convolutional neural networks show similar to better performance, and accuracy reaches 93.16% using MobileNetV2. Visualizing intermediate activation layers in VGG model reveals that just in deep activation layers, the color pattern and the shape of the snake contribute to the discrimination of snake species. This study presents MobileNetV2 as a powerful deep convolutional neural network algorithm for snake image classification that could be used even on mobile devices. This finding pave the road for generating mobile applications for snake image identification. [ABSTRACT FROM AUTHOR]

Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73–0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p < 0.05; McNemar's test). In this study, the association of psychiatric risk factors and the importance of modifiable nutrition and lifestyle factors were emphasized. However, as a cross-sectional study, no causality can be inferred. [ABSTRACT FROM AUTHOR]

Ending hunger and ensuring food security are among targets of 2030's SDGs. While food trade and the embedded (virtual) water (VW) may improve food availability and accessibility for more people all year round, the sustainability and efficiency of food and VW trade needs to be revisited. In this research, we assess the sustainability and efficiency of food and VW trades under two food security scenarios for Iran, a country suffering from an escalating water crisis. These scenarios are (1) Individual Crop Food Security (ICFS), which restricts calorie fulfillment from individual crops and (2) Crop Category Food Security (CCFS), which promotes "eating local" by suggesting food substitution within the crop category. To this end, we simulate the water footprint and VW trades of 27 major crops, within 8 crop categories, in 30 provinces of Iran (2005–2015). We investigate the impacts of these two scenarios on (a) provincial food security (FSp) and exports; (b) sustainable and efficient blue water consumption, and (c) blue VW export. We then test the correlation between agro-economic and socio-environmental indicators and provincial food security. Our results show that most provinces were threatened by unsustainable and inefficient blue water consumption for crop production, particularly in the summertime. This water mismanagement results in 14.41 and 8.45 billion m3 y−1 unsustainable and inefficient blue VW exports under ICFS. "Eating local" improves the FSp value by up to 210% which lessens the unsustainable and inefficient blue VW export from hotspots. As illustrated in the graphical abstract, the FSp value strongly correlates with different agro-economic and socio-environmental indicators, but in different ways. Our findings promote "eating local" besides improving agro-economic and socio-environmental conditions to take transformative steps toward eradicating food insecurity not only in Iran but also in other countries facing water limitations. [ABSTRACT FROM AUTHOR]

Epilepsy has garnered increased public health focus because patients who suffer from epilepsy experience pronounced and persistent health and socioeconomic disparities despite treatment and care advances. The epidemiology of epilepsy is diverse in different countries and regions. This nationwide population-based cross-sectional study was conducted to determine the life time prevalence and health related factors of epilepsy for the first time in Iran through a two-phase door-to-door survey method. In phase I, a screening for epilepsy was performed on 68,035 people. Then in phase II, after the neurological evaluation of participants and reviewing medical records, 1130 subjects with epilepsy was confirmed. The life time prevalence of epilepsy was achieved to be 16.6 per 1000 people (95% CI 15.4–17.8) with the average age onset 19.1 ± 21.1 (active prevalence 9.5 per 1000 people). Focal seizure (59.3%), generalized epilepsy (38%) and unknown types of epilepsy (2.7%) were detected among participants. The overall life time prevalence of febrile convulsion was 4.1 per 1000 people. The frequency of attacks per year and per month were 3.0 ± 1.6 and 0.5 ± 0.1, respectively. Age-specific life time prevalence was highest among the age group of 15–19 years old [32.7 per 1000 persons (95% CI 29.1–36.8)] and it was higher in male (53.8%) than female (46.2%) participants. Our results showed that the life time prevalence of epilepsy in Iran is higher than worldwide average. [ABSTRACT FROM AUTHOR]

Endemic plants of the Khorassan-Kopet Dagh (KK) floristic province in northeastern Iran, southern Turkmenistan, and northwestern Afghanistan are often rare and range-restricted. Because of these ranges, plants in the KK are vulnerable to the effects of climate change. Species distribution modelling (SDM) can be used to assess the vulnerability of species under climate change. Here, we evaluated range size changes for three (critically) endangered endemic species that grow at various elevations (Nepeta binaloudensis, Phlomoides binaludensis, and Euphorbia ferdowsiana) using species distribution modelling. Using the HadGEM2-ES general circulation model and two Representative Concentration Pathways Scenarios (RCP 2.6 and RCP 8.5), we predicted potential current and future (2050 and 2070) suitable habitats for each species. The ensemble model of nine algorithms was used to perform this prediction. Our results indicate that while two of species investigated would benefit from range expansion in the future, P. binaludensis will experience range contraction. The range of E. ferdowsiana will remain limited to the Binalood mountains, but the other species will have suitable habitats in mountain ranges across the KK. Using management efforts (such as fencing) with a focus on providing elevational migration routes at local scales in the KK is necessary to conserve these species. Additionally, assisted migration among different mountains in the KK would be beneficial to conserve these plants. For E. ferdowsiana, genetic diversity storage employing seed banks and botanical garden preservation should be considered. [ABSTRACT FROM AUTHOR]

Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002–2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran's groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran's land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran's planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country's water, food, socio-economic security. [ABSTRACT FROM AUTHOR]

Aspergillus species are emerging causative agents of non-dermatophyte mold onychomycosis. In this study, 48 Aspergillus isolates were obtained from patients with onychomycosis in Mashhad, Iran, during 2015–2018. The aim is to identify the Aspergillus isolates to the species level by using partial calmodulin and beta-tubulin gene sequencing and MALDI-TOF MS, and to evaluate their in vitro susceptibility to ten antifungal drugs: terbinafine, itraconazole, voriconazole, posaconazole, ravuconazole, isavuconazole, caspofungin, micafungin, anidulafungin and amphotericin B according to CLSI M38-A3. Our results indicate that A.flavus (n = 38, 79%) is the most common Aspergillus species causing onychomycosis in Mashhad, Iran. Other detected species were A. terreus (n = 3), A. tubingensis (n = 2), A. niger (n = 1), A. welwitschiae (n = 1), A. minisclerotigenes (n = 1), A. citrinoterreus (n = 1) and A. ochraceus (n = 1). Aspergillus flavus, A. terreus and A. niger isolates were correctly identified at the species level by MALDI-TOF MS, while all cryptic species were misidentified. In conclusion, A. flavus is the predominant Aspergillus species causing onychomycosis due to Aspergillus spp. in Mashhad, Iran. MALDI-TOF MS holds promise as a fast and accurate identification tool, particularly for common Aspergillus species. It is important that the current database of reference spectra, representing different Aspergillus species is expanded to increase the precision of the species-level identification. Terbinafine, posaconazole and echinocandins were in vitro most active against the studies Aspergillus isolates and terbinafine could be the first choice for treatment of onychomycosis due to Aspergillus. [ABSTRACT FROM AUTHOR]

To determine levels of change in risk factors for cardiovascular disease among people with and without a previous diagnosis of diabetes from 2007 to 2016 in Iran. Data were obtained from five rounds of the World Health Organization STEPwise approach to Surveillance (STEPS) cross-sectional surveys. Participants were 7665 and 93,733 adults with and without known diabetes, respectively, aged 25–65 years. We used logistic and linear regressions to assess the trends of risk factors. Individuals with known diabetes compared to those without the condition, experienced greater reductions in mean levels of systolic blood pressure (3.0 vs. 0.5 mmHg among women and 3.9 vs. 1.6 mmHg among men), diastolic blood pressure (6.4 vs. 5.11 mmHg in women and 3.3 vs. 1.8 mmHg in men), and non-HDL cholesterol (42.4 vs. 27.2 mg/dL among women and 30.3 vs. 21.0 mg/dL among men) throughout these years. Men with diabetes also showed a greater reduction in the prevalence of daily cigarette smoking compared to their non-diabetic counterparts (7.3% vs. 2.3%). Fasting plasma glucose decreased among subjects with diabetes but increased among those without diabetes. Significant increases were observed in proportions who met goals for blood pressure, triglycerides, non-HDL cholesterol and LDL cholesterol in both groups; however, almost half of diabetic subjects did not achieve risk factor goals in 2016. Secondary prevention in diabetic patients was more effective than primary prevention in the general population; however, the rate of diabetic patients who met the designated goals for each risk factor was still suboptimal. [ABSTRACT FROM AUTHOR]

Endemic species are believed to converge on narrow ranges of traits, with rarity reflecting adaptation to specific environmental regimes. We hypothesized that endemism is characterized by limited trait variability and environmental tolerances in two Dianthus species (Dianthus pseudocrinitus and Dianthus polylepis) endemic to the montane steppes of northeastern Iran. We measured leaf functional traits and calculated Grime's competitor/stress-tolerator/ruderal (CSR) adaptive strategies for these and co-occurring species in seventy-five 25-m2 quadrats at 15 sites, also measuring a range of edaphic, climatic, and topographic parameters. While plant communities converged on the stress-tolerator strategy, D. pseudocrinitus exhibited functional divergence from S- to R-selected (C:S:R = 12.0:7.2:80.8% to 6.8:82.3:10.9%). Canonical correspondence analysis, in concert with Pearson's correlation coefficients, suggested the strongest associations with elevation, annual temperature, precipitation seasonality, and soil fertility. Indeed, variance (s2) in R- and S-values for D. pseudocrinitus at two sites was exceptionally high, refuting the hypothesis of rarity via specialization. Rarity, in this case, is probably related to recent speciation by polyploidy (neoendemism) and dispersal limitation. Dianthus polylepis, in contrast, converged towards stress-tolerance. 'Endemism' is not synonymous with 'incapable', and polyploid neoendemics promise to be particularly responsive to conservation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to gather ethnopharmacological information on plants used by the pastorals of southeastern Iran. The relationships between ecological value of the plant species and ethnobotanical indices were investigated. The loss of medicinal plants and its effective factors were also determined under nomadism and sedentary pastoralism. Ethnopharmacological information of plants was collected through interviews with 85 local people including nomads (43%) and sedentary pastorals (57%). Ethnobotanical indices including relative frequency of citation (RFC), relative importance (RI), cultural value (CV), and use value (UV) were estimated. Canopy cover and density of plant species were measured at 60 sampling plots in the exclosure, nomadic rangelands and sedentary pastorals rangelands. The Importance Value Index (IVI) and Relative Loss Index (RL) were estimated for both nomadic and sedentary pastoral rangelands. Pearson correlation coefficient was used to investigate the relationship between ethnobotanical indices and IVI of plant species. The Bayesian networks was used to investigate the relationship between ethnobotanical indices and plant species loss. In total, 156 medicinal plant species of 50 families were identified in the region by locals. Positive correlation was observed between ethnobotanical indices (RFC and RI) and ecological index (IVI). The mean decline of the ecological importance of medicinal species in sedentary pastoral rangelands was approximately three times higher than in nomadic rangelands. Bayesian networks showed that cultural value, seed exploitation and aerial parts exploitation had direct relationships with species loss in both nomadic and sedentary pastoral rangelands. Bunium persicum, an ecologically and socially important species, had been extensively harvested (more than 60%) in the both nomadic and sedentary pastoral rangelands, making it a priority species in future conservation programs. Medicinal plants had high ecological value and were severely exploited, threatening sustainability of arid and semiarid ecosystems. Local pastorals not only use medicinal plants as herbal remedies but also consider them as a source of income. Popular plants with multiple medicinal uses were more susceptible to loss. Higher medicinal knowledge of pastorals did not help to mitigate medicinal plant loss, requesting new plans to aware them to the circumstances that often leads to species removal from community. Given the importance, abundance and widespread use of medicinal plants, further studies can provide a basis for their conservation and for identifying new therapeutic effects of plants in the region.

Patients with the underlying valvular heart disease are at the high risk of developing sub-acute or chronic endocarditis secondary to Coxiella burnetii. Q fever endocarditis is the most common manifestation along with persistent the infection. There is some serologic and molecular evidence of C. burnetii infection in humans and livestock in Iran. As it is possible to observe chronic Q fever in Iran, it seems necessary to study the prevalence of Q fever endocarditis in this country. In the present study, Infective Endocarditis (IE) patients (possible or definite based on Duke Criteria) hospitalized in Rajaie Cardiovascular Medical and Research Center were enrolled from August 2016 to September 2018. Culture-negative endocarditis patients were evaluated by Raoult criteria for diagnosis Q fever endocarditis. The serological results for brucellosis were negative for all subjects. All blood and tissue samples including valve samples were tested for C. burnetii infection using serology and Polymerase Chain Reaction (PCR). In this study, 126 patients who were admitted to the hospital were enrolled; of which 52 subjects were culture-negative IE. Among the participants, 16 patients (30.77%) were diagnosed with Q fever IE and underwent medical treatment. The mean age of patients was 46.6 years ranging from 23 to 69 years and 75% of them were male. Considering the high prevalence of Q fever IE, evaluation of the patients with culture-negative IE for C. burnetii infections was highly recommended. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs., (© 2022. The Author(s).)

The Anemone genus is a tuberous geophyte which undergoes a dormancy period during unfavorable environmental conditions for growth. Five species of the Anemone genus naturally grow in several regions of Iran. The diverse uses of Anemone in gardens for landscaping, cut flowers, and potted plants indicate its high ornamental potential. Its dormancy and flowering are influenced by various factors. The present paper was conducted to explore the flowering behavior of Anemone accessions in response to different pre-treatments. For this purpose, tubers of 18 Anemone accessions (A. coronaria and A. biflora) were collected from natural regions of six provinces in Iran. These tubers were subjected to different conditions of non-chilling (20 °C, 90 days), chilling (4 °C, 90 days), GA 3 (150 mgL -1 ; 24 h), and 5-azaCitidine (5-azaC; 40 µM; 24 h) prior to the cultivation. Most of the accessions were able to enter the flowering stage without chilling. The shortest period for the sprouting of tubers (16.89 ± 7.83 days) belonged to 5-azaC pre-treatment. In addition, this treatment accelerated the flowering time (about 30 days earlier) and diameter of the stem, bud, and flower. Morphological characteristics, such as stem height, number of leaves, bud, and petal and the longevity of flowers on the plant were significantly affected by GA 3 pre-treatment. Our results indicated a positive correlation between flower length, stem height, and stem diameter with flower longevity under different pre-treatment conditions. The present study demonstrated that accessions Anm3, Anm12, and Anm18 had ornamental values higher than the population mean across four conditions.

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes' habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes' habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human-snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

Plasma treatment is recognized as a suitable technology to improve germination efficiency of numerous seeds. The objective of this paper is to demonstrate whether cold air plasma can change the quality and quantity of wheat yield. Effects of cold plasma treatment on wheat (Pishgam variety) yield were studied by a randomized complete block design experiment at the Faculty of Agriculture of Tarbiat Modarres University, Iran, during 2015-17. Seeds were pre-treated with 80 W of cold plasma at four levels of time, 60, 120, 180 and 240 seconds. Plasma effects on yield and quality of wheat were determined by measuring plant photosynthesis, grain yield, biological yield, 1000-grain weight, total chlorophyll, carotenoid, anthocyanin, protein and starch content. Results showed that plasma treatments had positive effects on wheat characteristics, and treatment of 180 s had the highest stimulatory effect. In both years, cold plasma increased grain yield at 180 s, but decreased it at 240 s compared with control. The rate of plant photosynthesis, grain yield, 1000-grain weight, carotenoid and anthocyanin were enhanced at 180 s. The starch content and grain protein were enhanced at 120 s cold plasma application compared with control.