Introduction: Leishmania major is the causative agent of zoonotic cutaneous leishmaniasis (ZCL), affecting millions of people worldwide. About ten percent of the world's population is at risk and two million new cases are reported annually. This disease is endemic in some parts of Iran and is also a major health problem in this country. LACK protein "among the vaccine candidate proteins of Leishmania" has considerable importance because of its role in regulating the immune responses against this disease.Materials and methods: In this study and for the first time, the LACK gene of the Iranian strain of Leishmania major was cloned and sequenced. A pair of primers was designed based on the LACK gene sequence in the gene bank, then the LACK gene of Leishmania major was PCR amplified using the Taq polymerase enzyme. The amplified LACK gene was ligated into the pTZ57R plasmid and transformed into the E. coli Xllblue. The transformed clone was cultured under the appropriate conditions, and the obtained colonies were screened using the different restriction enzymes. After verification by restriction digestion, one of the recombinant plasmids was sequenced using the automated DNA sequencing method.Results: Comparison between the LACK gene sequence from the Iranian strain of Leishmania major and the LACK gene of Leishmania major in the gene bank (Accession number U27568) showed a %99 homology. The amplified LACK sequence contained 939 bp, hence encoding a protein encompassing 313 amino acids.Discussion: Complete homology among the LACK gene sequence of the Iranian strain of Leishmania major and the LACK gene sequence of other Leishmania major strains and Leishmania genera is in agreement with other studies which have indicated that the LACK gene sequence is conserved among different Leishmania genera. Therefore it can be concluded that the results from the LACK gene of different Leishmania genera can be extended to each other. It is feasible to cut out the LACK sequence from pTZ57R-LACK clone and sub clone it into an appropriate plasmid for the expression and production of the LACK protein. Additionally it would be possible to produce mutations or knocking out a part of this gene to modify the pathogenesis of Leishmania.

Canine visceral leishmaniasis is a zoonosis protozoan disease that is potentially fatal to humans and dogs. In Iran, this disease is common and it is caused by Leishmania infantum Iranian standard strain (MCAN/IR/07/Moheb-gh). Several proteins are used for vaccination, one of them is LACK (Leishmania homologue for receptors for activated C kinase), a 36 KD protein. Because of the wide spread of canine visceral leishmaniasis in Iran, in this study molecular cloning of the LACK protective antigen of Leishmania infantum Iran strain was considered to produce an effective recombinant protein. Therefore, Genomic DNA of Leishmania infantum was extracted and used as a template for PCR test, Then, PCR product of the LACK was cloned into pTZ57R/T vector. Recombinant plasmid was extracted and analyzed by sequencing, restriction enzyme digestion and PCR test. The results showed that LACK gene was correctly cloned into pTZ57R/T vector and as expected it was 939bp. This study was the first step for designing a DNA vaccine based on LACK Leishmania infantum Iran stain for expression recombinant protein for subsequent studies.

The neoclassical growth model is tested by use of panel data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Generally in flood studies, two types of information will be offered which are namely as peak instantaneous discharge and flood hydrograph with different return periods. The main purpose of the present research is determination of regional coefficient Diken’s equation for calculation of peak flood with any return period. In this study, because of lacking measurement data in Sheyvand basin, the peak instantaneous discharge data of existing hydrometric stations in outline basins has been used. The outline rivers peak flow data was collected, controlled, validated and accomplished.The long set of peak flow data was analyzed by different statistical distributions. According to the results of statistical tests, the best distributions for each hydrometric station was selected and the floot peak values with different return periods was estimated by the selected distributions. By creating correlation terms between area parameters and flood data with any return period, one area discharge equation was achieved according to Diken’s equation. By correlating between C & N parameters that are equation coefficients and return period, two equations for calculating both of the considered parameters were obtained. Replacing these equations instead of C & N in Diken’s equation, final equation in order to estimating flood amount in study region was achieved. Finally, flood values were estimated flood amounts with different return period for Sheyvand basin by this method.

The precise estimation of reference crop evapotranspiration (ETo) has a significant role in improving water management in field and irrigation management. Different methods are available for ETo estimating. The FAO-PM equation is the most reliable method for ETo estimating, but this method requires large input data that is not fully measured in most meteorological stations. Also, the measured data lacks enough precision, especially in developing countries. Therefore, the researchers have used the empirical equations with minimum input data. In this study, the daily values of ETowere calculated at the synoptic station of Mashhad using Priestley-Taylor, Hargreaves, the FAO-PM proposed method by the lack of data and two forms of the Valiantzas equation. Then, the calculated values were compared with FAO Penman Monteith (PM) method. In this paper, new models for estimating ETo were presented by lack of wind speed and relative humidity data by modifying two forms of the Valiantzas equations. The results showed that by the lack of wind speed and relative humidity data, the modified Valiantzas model had the highest accuracy compared with the other methods. It was showed that the R2, RMSE and MAE indices were 0. 85, 1 and 1. 26 mm per day, respectively. Also, the results showed that the modified Hargreaves method had the highest accuracy when only temperature data is available.

Extended Abstract Background: Honeybees have an important role in the pollination of plants. Apis florea Fabricius, 1787, has been described over the last two centuries.   A. florea is distributed in Vietnam, south-eastern China, India, Nepal, southern Thailand, Sri Lanka, Sudan, southern Iran, Pakistan, Saudi Arabia, and Oman. The climatic zones of A. florea change from tropical rainforest in the east to savannah in the west. Furthermore, westwards, the climatic zones change from tropical to subtropical steppe and semi-desert. Morphometric data were used in addition to analyses of molecular data to study the honeybees. Morphometric analyses are flexible tools employed by users in different areas of the world. The geometric morphometric method uses landmarks that can be located precisely on the vein junctions of wings. The geometric morphometric method has been used to compare the populations of A. florea. In addition, traditional or standard morphometric methods have been used to study the populations of A. florea in various areas of the world. The landmark configurations eliminate the effects of position, orientation, and size of shapes. Data on shapes are converted into Procrustes coordinates using the geometric morphometric method. Regression analysis between centroid size and Procrustes coordinates creates new data on the residuals, which can be applied in other analyses. No published research has utilized residual coordinates (residual data) to compare the populations of A. florea. Therefore, the current research aimed to compare the performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world. Methods: Samples of the A. florea honeybee species were prepared from their distributed areas. The right forewings of honeybees were used to study the relationships among the various populations. Eighty samples were selected from each area. A tps file was built by importing the right forewing pictures into TpsUtil V. 1. 64 software. Then, the tps file was loaded into tpsDig V. 2. 18. Twenty landmarks were digitized in the vein junctions of the forewings. This was followed by the raw data obtained from landmarks, loaded into the MorphoJ software V. 1. 06d, and converted into Procrustes coordinates for use in future analyses, followed by analyzing the Procrustes coordinates. Mahalanobis distances and canonical variates were obtained using permutation tests. The regression between Procrustes data and centroid sizes was calculated, and an allometry test was performed afterward. The residual coordinates (residual data) were obtained after the removal of the size effect (size correction) from shape variables. The residual and Procrustes data were imported into PAST software v. 3. 19, and the populations of A. florea were compared using Canonical Variate Analysis (CVA). Moreover, clusters were drawn with the residual and Procrustes data using SAS v. 8 software. Results: Multivariate (MANOVA) and pair-wise analyses of residual and Procrustes data were tested for the populations of A. florea. The Procrustes and residual data of the populations showed statistically significant differences using MANOVA (p < 0. 001). Moreover, the tested pairwise comparisons indicated that all populations were significantly different in Procrustes and residual data (p < 0. 001). The landmark configurations of forewings were superimposed, and variations were obtained between the populations. The highest variation was found in the vein junction of R and Rs, landmark 19 (S2 = 0. 0000622). The lowest variation was observed in the vein junction of Cu and 1m-cu, landmark 8 (S2 = 0. 0000109). The populations of various areas were compared using Canonical Variate Analysis (CVA). In the CVA of Procrustes data (Procrustes coordinates), the first and second components included 75. 94% of all variation (CV1 = 28. 74% and CV2 = 47. 20%). In addition, in the CVA of residual data, the first and second components included 83. 06 % of all variation (CV1 = 31. 46 % and CV2 = 51. 60 %). The CVA results of Procrustes data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples overlapped with the Iranian samples of Bushehr, Shiraz, and Sistan and Balochestan. Additionally, Oman samples showed partial overlapping with South India and Kerman (Iran). The CVA results of residual data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples were differentiated from Iranian samples. In addition, the Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated the populations of Thailand and Vietnam from the other populations. Cluster analysis was used to compare the populations of A. florea in various areas. The cluster derived from Procrustes data indicated that Sri Lanka was closer to the populations of Iran, except for the Kerman samples. Furthermore, the Sudan and Pakistan populations were categorized under one group. In addition, the cluster derived from the residual data indicated that the Pakistan population was closer to the populations of Iran, except for the Kerman samples. The Sri Lankan population indicated a closer relationship with India, and the Sudanese population was differentiated from the other populations.    Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of A. florea. The results of the derived cluster from residual data indicated closer relationships of A. florea populations from Pakistan and Sri Lanka with Iran and South India, respectively.

Todays gamma knife radiosurgery is used widely for treatment of very small brain tumors. In order to investigate accuracy of dosimetry and treatment planning calculations, using Monte Carlo simulation with dedicated code named as beamnrc including non-CT data and CT data options is necessary. The aim of this study is choosing the best options in order to have an accurate tools based on their advantages and disadvantages. In this study, gamma knife unit 4C along with standard water equivalent phantom and EBT3 films were used to obtain dose distributions. Monte Carlo simulation was done with non-CT data and CT data options of the code and their resulting dose were compared. Comparison the calculated and measured dose distributions at X, Y and Z axis showed gamma value below 1 which verified Monte Carlo simulations. Also comparing the dose distributions from both non-CT data and CT data with each other implies that there is no significant difference between two methods. Based on the obtained results using non-CT data and CT data results in the same dose distribution. So for simplicity, using non CT data for regular phantom shapes is preferred.

Introduction The large temporal and spatial changes of precipitation, especially in mountainous areas, have turned it into a controversial variable in climate models. Measuring precipitation (rain and snow) along with its distribution and changes is very important to improve our understanding of global water cycle and energy, water resources monitoring, hydrological modeling. Lack of reliable data is one of the most important challenges in rainfall analysis. Due to the significant temporal and spatial variability of precipitation in mountainous areas, accurate spatially distributed data is crucial for effective water resource assessment and management. However, many mountainous regions have limited rain gauge stations. Today, satellite products are commonly used to measure precipitation in these areas, but the variability among these products raises concerns about their accuracy in mountainous regions. Additionally, the quality of satellite products differs between various products and across different climatic regions, making it essential to thoroughly evaluate them before use. The purpose of this research was to evaluate the precipitation data of two satellite products (GPM, PERSIAN) and reanalysis data (ECMWF) in the estimation of precipitation in mountainous areas without stations in Lorestan province.   Method This study utilized rainfall data from 24 synoptic and rain gauge stations across Lorestan province. Emphasis was placed on stations situated in or near mountainous regions. The selected stations were chosen based on their suitable spatial distribution and record length. The rainfall data spanned the period from 2015 to 2021 and included daily, monthly, and annual measurements. To evaluate satellite rainfall algorithms and estimate rainfall in regions with limited data, data from the GPM and PERSIAN satellites were employed, along with ECMWF reanalysis data. The PERSIAN rainfall algorithm is a remote sensing-based method that utilizes artificial neural networks. It calibrates infrared data with passive microwave estimates and converts longwave infrared images into rainfall estimates using a three-step process. The spatial resolution of this product is 0.25° x 0.25°, and it offers hourly, daily, and monthly temporal resolution. The PERSIAN rainfall algorithm data can be accessed from https://chrsdata.eng.uci.edu. The GPM mission aims to provide continuous observations of Earth's precipitation. It employs the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) to observe both snow and rain. The final product, called IMERG, is generated through multiple runs of the algorithm for each observation time. Initial estimates are quickly provided, and subsequent estimates improve as more information becomes available. The spatial resolution of the GPM product is 1° x 1°, and it offers hourly, daily, and monthly temporal resolution. IMERG data can be obtained from https://gpm.nasa.gov/data. CMWF reanalysis data is derived from the combination of short-term simulations of numerical weather prediction models with ground-based observational data. These simulations are controlled with observational data, and the resulting reanalysis database provides global coverage from 1979 with a spatial resolution ranging from 0.125° x 0.125° to 3°. The temporal resolution of ECMWF reanalysis data is hourly, daily, and monthly. More information about ECMWF data can be found at https://www.ecmwf.int/ (Azizi, 2019).  To evaluate the accuracy of the products, R-squared correlation (R2), root mean square error (RMSE), standard deviation (MAD), correlation coefficient (R), error deviation (MBE) and Nash-Sutcliffe coefficient (NS) were used. Also, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) indices were used to validate the data.   Results The results showed that none of the three products are suitable for estimating daily precipitation in mountainous areas. However, on a monthly scale, these products provide reasonable estimates. Among the three, the GPM satellite product demonstrated better accuracy on a monthly scale, based on error levels and the spatial distribution of estimated precipitation. On an annual scale, GPM also performed best, as indicated by both statistical errors and the spatial patterns of average annual precipitation. According to the MBE index, on daily and monthly scales, the ECMWF product tended to overestimate precipitation, while the PERSIANN and GPM products underestimated it. On an annual scale, GPM and ECMWF products overestimated precipitation, whereas PERSIANN underestimated it.