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Background: Congenital hypothyroidism (CH) is one of the most prevalent preventable cause of mental retardation and intellectual disability in newborns. Genetic and environmental factors have been related to congenital hypothyroidism. Hence, this study was conducted to illustrate the incidence and spatial distribution of the CH using the dataset from the national newborn CH screening information system during 2011-2020in counties affiliated to the Kohgiluyeh and Boyer Ahmad University of Medical Sciences. Methods: This was a cross-sectional study. All newborns that were screened for CH in 3–5 days of age in the health centers of Yasuj University of Medical Sciences were enrolled the study since 2011–2020. Meteorological data including information on temperature, humidity, precipitation in a 10-year period (2011–2020) was taken from the Meteorological Organization. The required information layers include slope, slope direction, height, vegetation and political divisions of the province, were purchased from the organizations that made the layers. The data related to the incidence of congenital hypothyroidism was collected from the 10-year reports of the provincial health center. Addresses of patients were transferred to Arc GIS 10.5 software and entered in urban and rural point's layer to prepare and draw disease distribution maps in the province's newborns. The influence of geographical factors on the occurrence of disease was done by SPSS 23 software using multivariate binary logistic regression. P-value < 0.05 was assumed as significant level. Results: Out of 142,674 screened newborns for CH, 827 had positive screening, including 441 boys (53.3%) and 386 girls (46.7%). Total incidence per 1000 live birth was 5.79. Based on multivariate logistic regression, slope (sig = 0.0001, Exp(B) = 0.91, CI95% Exp(B) = 0.87–0.95), thin forest (sig = 0.001, Exp(B) = 0.2, CI95% Exp(B) = 0.07–0.53), water area (sig = 0.0001, Exp(B) = 8.91, CI95% Exp(B) = 3.35–23.69), were predicting factors of CH. Conclusion: Given that, the incidence of CH in the Kohgiluyeh and Boyar Ahmad province is higher than the average of country (Iran), and the geographical factors of land slope and type of vegetation were related to it that cannot be changed, it is very important to carry out necessary nutritional interventions during pregnancy to prevent newborns from contracting this disease. [ABSTRACT FROM AUTHOR]

Leishmaniasis is the most important parasitic infection in Iran. The aim of this study was to model the changing suitability patterns of Leishmania infantum, the causative agent of visceral leishmaniasis for the 21st century in the country. Temperature, precipitation, and aridity-nature distribution limiting bioclimatic variables were involved in the ecological modelling. The altitudinal trends were considered by using 100 m bars. In Iran, the topographical patterns strongly impact the changing patterns of the suitability of L. infantum due to climate change. In general, climate change will decrease the parasite's suitability in the areas at low altitudes and increase in the middle and higher elevation regions. Increasing values are mainly predicted in the West, the decreasing suitability values in the East part of Iran. The altitudinal shifts and the reduced spatial distribution of L. infantum in the arid regions of East and Central Iran were modelled. [ABSTRACT FROM AUTHOR]

The relationship between plant species and climatic factors has always been a fundamental issue in plant ecology, and the use of multivariate statistical methods can be effective in revealing the relationship between climatic factors and plant species distribution. Therefore, in this study, climatic factors affecting the distribution of Artemisia sieberi and Artemisia aucheri, widely distributed in Iran, were investigated. For this purpose, 117 climatic factors were used, and to reduce the number of factors and determine the most important effective ones, a factor analysis was used by principal component analysis. The results showed that six factors including heating temperature, spring and summer precipitation, wind, autumn–winter precipitation, and dusty and cloudiness days explained 37.32%, 22.54%, 7.18%, 6.6%, 4.22%, and 4.15% of data variation, respectively. Together these seven factors account for 82% of data variation. The autumn–winter precipitation and heating temperature had the greatest impact on the presence of Artemisia sieberi and Artemisia aucheri, respectively, so that the autumn–winter precipitation was negative in areas where Ar.sieberi is observed. The heating temperature factor is negative in areas where Ar.aucheri is present, while it is positive in areas lacking Ar.aucheri. The study of the effect of environmental factors on Artemisia species distribution is very important in the planning and management of natural resources, and Artemisia is one of the most important plants in the country's rangelands; therefore, the results of this research can be used for practical planning, management, and reclamation of these rangelands. [ABSTRACT FROM AUTHOR]

Crop cultivar performance is a result of combined effects of genotype, environment and genotype × environment (G × E) interaction. To effectively generate reliable estimates of crop yield the magnitude and patterns of G × E in regional yield trials should be specified. This research aimed to (1) investigate existing possible mega-environments (ME) and suitability of test locations for winter wheat zoning, and (2) determine the role of climatic factors in clustering patterns of G × E. Winter wheat yield data from a three-year nationwide yield trial consisting of 24 genotypes grown in 24 test environments supplemented with 37 climatic factors were subjected to empirical and analytical analyses. Standard deviation-scaled genotype main effect and G × E interaction (SD-GGE) biplot methodology, factorial regression and partial least square regression were applied to both analyses. The combined ANOVA showed that the environmental effect was the main source of variation (83%), and the magnitude of G × E interaction was sixfold greater than genotype alone. The SD-GGE biplot confirmed non-repeatable patterns for grouping of test locations across years, indicating significant (P < 0.01) rank-change location-by-year interactions and existence of strong "crossover" G × E interactions. This led to the conclusion that the winter wheat growing region in Iran consists of a single but complex ME for grain yield, suggesting that high-yielding-and stable winter wheat genotypes should be developed for the entire region rather than genotypes adapted to specific agro-ecological regions. Precipitation (monthly and total) and temperature (minimum, maximum and average) accounted for 25.4% and 56.8% of total G × E. [ABSTRACT FROM AUTHOR]

Background and Objectives: Fascioliasis is a common disease of human and animals caused by Fasciola hepatica and F. gigantica. The life cycle of these trematodes is influenced by climatic and geographical factors. The aim of this study was to determine the geographical features of animal fascioliasis in Kohgiluyeh and Boyer-Ahmad province during 2016-2018 using GIS method. Materials and Methods: In this cross-sectional study, the prevalence of fasciola in slaughtered animals in different cities was extracted from the information of the province's veterinary department. Climatic layers were constructed based on interpolation models. Data on mean annual rainfall, evaporation and humidity, mean annual wind velocity, mean annual temperature, mean annual minimum and maximum temperatures, average height, average slope and area of each vegetation were extracted from the reference ground layers. The relationship between each variable and the prevalence of the disease in the two groups of cities, with high and low prevalence, was evaluated using independent two-sample t-test. Results: The highest prevalence of fasciolosis in slaughtered animals in Kohgiluyeh and Boyer-Ahmad province was 5.3% and belonged to Gachsaran city. There was a direct relationship between the mean annual humidity and the prevalence of fasciolosis (p=0.023). No significant relationship was found between other studied factors and the rate of infection (p>0.05). Conclusion: It seems that the relationship between humidity and the prevalence of fasciolosis is mainly due to the effect of humidity on the life cycle of snails and the host plants. Using these results, the quality of disease control programs can be improved and significant savings in the costs of animal husbandry and health care can be achieved. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis (VL) is the fatal form of leishmaniasis. A newly emerging focus of zoonotic VL (ZVL) including 13 villages has been reported from Maraveh Tappeh County, Golestan province, north‐eastern Iran. We investigated the effect of climatic and environmental factors on the occurrence of disease in this focus by geographical information systems (GIS) approaches and logistic regression models. The incidence rate of ZVL in human and dog population was estimated 1.2% and 27.3%, respectively, in the studied areas. Rock soil type (OR = 11.7), rainfall (OR = 1.04) and elevation (OR = 0.99) were found as determinants of ZVL. Also, almost all infected villages were in close proximity of rivers. Cavities of rock soil around rivers are assumed as suitable areas for sandfly resting and growth by providing shadow and moisture. Rainfall also provides appropriate moisture in semi‐arid regions for survival of sandflies. The negative effect of higher altitude can be explained by some sandfly life‐limiting conditions like freezing weather. Accordingly, regions with lower altitude and higher rainfall, covered by rock and in close proximity of rivers, are the hazard zones for ZVL in the Maraveh Tappeh. Air humidity, temperature, land cover type and slope were not predictors of disease in the current study. Further investigations on human population movement, probable reservoirs and vectors of disease can provide valuable data for modelling the future distribution of ZVL in the Iranian province. [ABSTRACT FROM AUTHOR]

Background: The reports of numerous outbreaks of whiteflies from different parts of the world have increased its medical importance. The aim of this study was to determine relationship between environmental changes and climatic factors with the outbreak of the whitefly population in Tehran, the capital of Iran. Methods: This study was carried out in urban areas of Tehran, where the increasing population of whiteflies was reported frequently during 2018. In order to entrap the whiteflies, 20 yellow sticky cards smeared with white refined grease were installed on the trunks of the trees at twice per month as trapping time intervals. The captured flies were transferred and conserved in cans containing 70% alcohol and were counted accurately under a stereomicroscope. To determine the relationship between air quality index, precipitation, air temperature and air humidity as environmental and climatic factors with the abundance of whiteflies, change point analysis and Generalized Estimating Equations (GEE) was used. Results: The most density of white flies per trap was 256.6 and 155.6 in early October and late September respectively. The number moved closer to zero from November to April. The population of whiteflies was inversely correlated with the level of air quality index (p= 0.99) and precipitation (p= 0.95), and it had a direct correlation with the high temperature. Also, the population of whiteflies had a direct correlation with the level of air humidity in the first half of the year Conclusion: According to these findings, during spring and summer from early May to early October. [ABSTRACT FROM AUTHOR]

Background and Objective: Climate change in recent years has led to a crisis of drought in many countries around the world, including Iran. In such a situation, the supply of safe drinking water is very important and knowing the climate impacts on the chemical quality of these resources can play a significant role in their proper exploitation. Materials and Methods: Using GIS geomorphology models, the effect of climatic factors including minimum and maximum temperatures, mean precipitation, evaporation, transpiration and vegetation index on the quality of groundwater resources of Varamin Plain include pH, electrical conductivity, total dissolved solid, anions and cations concentrations and total hardness were studied and their maps were produced. Correlation between these factors was investigated using spatial correlation analysis tool at a confidence level of 0.95 (p = 0.05). Results: The results showed that these parameters (except pH) along the plain are highly variable and the spatial distribution of data in this region is not normal around the mean, and the frequency of pixels with values less than the spatial mean of the region is higher. Spatial distribution of the factors showed that their amounts (except pH) are low in the central area of the plain and reach its peak in the western and northwest regions. The spatial distribution of pH was almost uniform in all the plain. Conclusion: Correlation analysis showed that the quality of water resources is influenced by climatic factors and the maximum temperature has the highest impact. [ABSTRACT FROM AUTHOR]

• Soil salinity was the most important soil sub-factor deciding best eucalyptus farming sites. • Annual water availability was most important water sub-factor deciding best locations. • Mean annual wind speed was the most critical climate sub-factor deciding best sites. • Uncultivated sandy hills were the most important lands for eucalyptus farming. • Fuzzy AHP provided highly accuracy predictions of the best eucalyptus farming sites. Iran is a low-forest cover country (<10% of country area is forested) and has problems with deforestation and increasing demands for wood products. This wood scarcity has prompted the country to curtail industrial logging to meet other demands for wood. The costs of importing wood are rising, so farming wood industrially with fast-growing species may help with this problem. Eucalyptus is a fast-growing species that can be used for industrial production and has been proven adaptable to the environments of southern Iran. This study identifies the most suitable locations for eucalyptus wood-farming in Khuzestan Province. Twenty-two factors and fuzzy analytic hierarchy process (FAHP) were used to identify the best locations for eucalyptus wood-farming in this Province in southern Iran. The independent variables used reflect four components of the environment: soil, water, climate, and land cover. Maps of twenty-two factors were prepared from satellite data, ground sampling, and other geospatial data sources. Eucalyptus plantations in Khuzestan Province were mapped by combining GPS-located ground surveys with data from the Khuzestan Natural Resources Administration. The ecological needs of E. camaldulensis were determined from a review of eucalyptus adaptation studies conducted in southern Iran. The factor maps were classified based on their suitability for eucalyptus farming and these classes served as within-layer weights. The weights of effective factors were calculated using FAHP and Chang's fuzzy-extent analysis (outside-of-layer weights). The accuracies of the fuzzy weights of effective factors were assessed by consistency ratio. A eucalyptus wood-farming potential map was created by overlaying the weighted sub-factor maps. This map was validated with real data (the map of current eucalyptus plantations), overall accuracy assessment, and the kappa index. The results showed that among the primary factors, water (weight: 0.34) and land cover (weight: 0.32) were the most influential determinants of the best locations for eucalyptus farming. The accuracy assessment revealed that the FAHP method produced an overall accuracy of 82% and kappa index of 0.71. This is acceptably strong for the identification of the best eucalyptus farm locations in Khuzestan Province. The wood-farming potential map developed from this research, can be used as a foundation to plan eucalyptus farming in southern Iran to meet the demand for wood in Iran. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been greatly influenced by several head-cut gullies due to unusual climatic factors and human induced activity. The present study is therefore intended to address this issue by developing head-cut gully erosion prediction maps using boosting ensemble machine learning algorithms, namely Boosted Tree (BT), Boosted Generalized Linear Models (BGLM), Boosted Regression Tree (BRT), Extreme Gradient Boosting (XGB), and Deep Boost (DB). Initially, we produced a gully erosion inventory map using a variety of resources, including published reports, Google Earth images, and field records of the Global Positioning System (GPS). Subsequently, we distributed this information randomly and choose 70% (102) of the test gullies and the remaining 30% (43) for validation. The methodology was designed using morphometric and thematic determinants, including 14 head-cut gully erosion conditioning features. We have also investigated the following: (a) Multi-collinearity analysis to determine the linearity of the independent variables, (b) Predictive capability of piping models using train and test dataset and (c) Variables importance affecting head-cut gully erosion. The study reveals that altitude, land use, distances from road and soil characteristics influenced the method with the greatest impact on head-cut gully erosion susceptibility. We presented five head-cut gully erosion susceptibility maps and investigated their predictive accuracy through area under curve (AUC). The AUC test reveals that the DB machine learning method demonstrated significantly higher accuracy (AUC = 0.95) than the BT (AUC = 0.93), BGLM (AUC = 0.91), BRT (AUC = 0.94) and XGB (AUC = 0.92) approaches. The predicted head-cut gully erosion susceptibility maps can be used by policy makers and local authorities for soil conservation and to prevent threats to human activities. Image 1 • The gully erosion susceptibility map created at a regional scale using GIS. • BT, BGLM, BRT, XGB, and DB models used for gully erosion modelling. • The proposed models present >90% prediction accuracy. • Predicted maps can be used by policy makers for in-situ soil conservation. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland. These models were also evaluated to model the internal DEF (IDEF) across the northwestern, southeastern, northern, and western regions around the wetland. Furthermore, the main factors controlling DEF and IDEF were identified based on the importance value (IV) of predictors in the best model. The results showed that the ERT model increased the prediction accuracies by an average of 40%, compared to the MARS model. According to the IV obtained from the ERT model, aerosol optical depth (IV = 0.28), wetland discharge (IV = 0.25), near-surface wind speed (IV = 0.08), and erosive winds frequency (IV = 0.07) were identified as the most important factors controlling DEF across the northeastern and southwestern regions of the wetland. However, the erosive wind speed was detected as the major factor affecting the IDEF in the northern (IV = 0.16), western (IV = 0.18), and southeastern (IV = 0.65) regions of study wetland. It was also found that vapor pressure with IV of 0.32 had the greatest effect on IDEF variations across the northwestern region of the wetland. Overall, the results demonstrate the effectiveness of the ERT model in modeling the factors affecting DEF and IDEF, and the results may be used to mitigate dust events hazards around the Gavkhouni Wetland, in central Iran.

The COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1 st -level Response to Major Public Health Emergency in each region, peaking after 1-2 days, reaching their inflection points after 10-19 days, and dropping to zero after 11-18 days since the 1 st -level response was launched. By March 29 th , the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25 th , in Italy as in Guangdong on February 25 th , and in Iran as in Xinjiang on March 8 th . The results showed that by June 15 th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objective: By studying the effect of environmental factors on health, it is clear that geographical, climatic and environmental factors have a significant impact on human health. This study, based on the data of the patients with breast cancer in Iran since 2010 to 2014 and using the statistical methods has determined the effect of geographical features of Iran (solar radiation status, radiation angle) on the frequency and distribution of this disease., Results: The maximum amount of total solar radiation occurs in the vicinity (surrounding) of the tropic of cancer, which covers some parts of the south of Iran and in the atmosphere of the northern latitudes of Iran. The amount of humidity and cloudiness is more than the southern latitudes, which causes more reflection of short waves of the sun during the day. Findings showed that the rate of breast cancer in low latitudes is higher than high latitudes. It was also found that with increasing longitude, the rate of cancer increases significantly due to the high thickness of the atmosphere and receiving more sunlight in the electromagnetic spectrum, as well as dry air and low water vapor in low altitude areas of eastern and southeastern Iran.

Strong competitor (i.e. big-sized) trees are globally crucial for promoting aboveground biomass. Still, we do not fully understand the simultaneous influences of different levels of competitor (i.e. strong, moderate, medium and weak) trees at stand level in shaping forest diversity and biomass along a climatic gradient. We hypothesized that few strong competitor trees shape the positive relationship between tree species richness and aboveground biomass better than moderate, medium and weak competitor trees along a climatic gradient. Using the forest inventory data (i.e. tree diameter, height and crown diameter), we quantified strong (i.e. 99th percentile; top 1%), moderate (i.e. 75th percentile; top 25%), medium (i.e. 50th percentile) and weak (i.e. 25th percentile) competitor trees as well as species richness and aboveground biomass of 248 plots (moist temperate, semi-humid, and semi-arid forests) across 12 sites in Iran. The main results from three piecewise structural equation models (i.e. tree diameter, height and crown based models) showed that, after considering the simultaneous fixed effects of climate and random effects of sites or forest types variation, strong competitor trees possessed strong positive effects on tree species richness and biomass whereas moderate, medium and weak competitor trees possessed negligible positive to negative effects. Also, different levels of competitor trees promoted each other in a top-down way but the effects of strong competitor trees on moderate, medium and weak competitor trees were relatively weak. This study suggests that the simultaneous interactions of different tree sizes at stand level across forest sites should be included in the integrative ecological modeling for better understanding the role of different levels of competitor trees in shaping positive forest diversity - functioning relationship in a changing environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Understanding the impacts of multiple climatic and edaphic factors on forest diversity, structure and biomass is crucial to predicting how forests will react to global environmental change. Here, we addressed how do forest structural attributes (i.e. top 1% big, top 25% big medium and small trees; in terms of tree height, diameter, and crown), species richness, and aboveground biomass respond to temperature-related and water-related climatic factors as well as to edaphic factors. By assuming disturbance as a constant factor in the study forests, we hypothesize that water-related and temperature-related climatic factors play contrasting roles whereas edaphic factors play an additional role in shaping forest diversity, structure and aboveground biomass in species-poor and structurally-complex forests. We used forest inventory and environmental factors data from 248 forest plots (moist temperate, semi-humid, and semi-arid) across 12 sites in Iran. We developed multiple linear mixed-effect models for each response variable by using multiple climatic and edaphic factors as fixed effects whereas sites as a random effect. Top 1% big, top 25% big, medium, and small trees enhanced with mean annual temperature but declined with water-related climatic (i.e. mean annual precipitation, cloud cover, potential evapotranspiration, and wet day frequency) factors, whereas soil texture (i.e. sand content) and pH were of additional importance. Species richness increased with precipitation and cloud cover but decreased with temperature, potential evapotranspiration, soil fertility and sand content. Aboveground biomass increased along temperature gradient but decreased with potential evapotranspiration, clay and sand contents. Temperature seemed to be the main driver underlying the increase in forest structure (i.e. diameter-related attributes) and biomass whereas precipitation did so for species richness. We argue that the impacts of multiple climatic factors on forest structural attributes, diversity and biomass should be properly evaluated in order to better understand the responses of species-poor forests to climate change., (Copyright © 2019 Elsevier B.V. All rights reserved.)