BACKGROUND AND OBJECTIVE: Nowadays, besides the ordinary staining, a variety of cell markers and immunohistochemistry methods are incorporated for distinction between neoplasms. But in the current situation, due to the high cost of most of these methods, cheaper and simpler way like silver nitrate staining should be sought for analyzing the nucleolar organizer dots. Therefore, this study was conducted to evaluate the distinction capability of silver nitrate within the benign and malignant neoplasms of the salivary glands. METHODS: Silver nitrate staining was carried out for nucleolar organizer regions in 22 cases of the most common benign neoplasms of the salivary glands, namely pleomorphic adenoma, and 22 cases of common malignant neoplasms of salivary glands, consisting of 11 adenoid cystic carcinoma and 11 mucoepidermoid carcinoma cases. Twenty-two cases of normal salivary glands of the peripheral regions of the same samples were analyzed. FINDINGS: The mean dots occurrences for normal salivary glands, pleomorphic adenoma, adenoid cystic carcinoma and mucoepidermoid carcinoma were 1.67±0.2, 2.32±0.39, 3.92±1.11 and 3.25±0.78 (for malignancies was 3.58±1), respectively.  There were significant differences among benign and malignant, malignant and normal, and benign and normal groups (p<0.05). CONCLUSION: This study purposed that the use of silver nitrate staining method is suitable for distinction of the benign and malignant neoplasms of salivary glands.

BACKGROUND AND OBJECTIVES: Cattle farming is an important pillar of dry climate agriculture. Cattle farming has even become the main engine of the community's economy. Currently, there are significant obstacles in the implementation of ecological, economic, technological, and infrastructural dimensions, alongside legal and institutional frameworks, that are essential for promoting sustainable livestock farming practices. Specific studies on different dimensional attributes affecting the sustainability of beef cattle are required. The objective of this study is to investigate different factors within four dimensions that affect the sustainability of cattle farming practices in dry climatic conditions.METHODS: This study was conducted using an interview approach to respondents using a questionnaire or list of questions. The questionnaire contains questions on 37 dimensional attributes, including ten environmental, ten economic, ten technological and infrastructure attributes, and seven legal and institutional attributes. A range of responses was collected from individuals engaged in livestock management in the dry climatic conditions of Timor Tengah Utara Regency. The data that has been collected is analyzed employing Multidimensional Scaling and expedited appraisal software. To evaluate sustainability and leverage attributes, Monte Carlo analysis and the alternating least squares algorithm are applied.FINDINGS: The study's outcomes revealed that the sustainability index pertaining to beef cattle farming in dry climates recorded an average score of 53.09 percent. This finding is based on the economic dimension of 45.58 percent, the environmental dimension of 54.18 percent, the technology and infrastructure dimension of 35.15 percent, and the legal and institutional dimension of 77.46 percent. The sustainability index, reflecting the technology and infrastructure aspects, ranks the lowest, primarily because the technological advancements necessary for achieving sustainable livestock farming are still underdeveloped. The availability of livestock waste processing technology is still relatively low and has not been processed optimally. This situation has an impact on the environment.CONCLUSION: The sustainability index of dry climate beef cattle farming is greatly influenced by the attributes of technology and infrastructure dimensions. The enhancement of sustainability in the future necessitates careful consideration of specific factors, including the accessibility of waste and product processing technologies, advancements in information technology, and the development of road infrastructure. Attributes in the legal and institutional dimensions are the main factors supporting beef cattle's sustainability. Optimizing extension services by increasing the number of livestock extension workers is crucial in accelerating the dissemination of eco-friendly livestock technoloBeef Cattle; Dry climate; Environmentally friendly; Multidimensional scaling (MDS); Sustainability assessmenty and innovation.

Dry and desert regions cover a significant amount of the earth surface, nearly one third of the glob. Dry deserts cover almost %4 of the earth"s surface. Though, in studying desert areas the effects of hydrology and geomorphology are of great importance, climatological factors play a more crucial role. Studing the effect of these factors indicate that climatic component determine the limits of arid and desert areas. Due to non-living condition of the desert and lack of suitable climatology stations, remote sensing (RS) technology is of great help in desert regions with various capability in different weather and climate. Furthermore, satellite data such as;RADARST,NIMBUS,IRS, SPOT,LANDSAT, GEOS, METEOSAT, Tirros- N/NOAA, etc with special capability in studying climate and meteorology, can be applied to different aspects of climate in studying desert. In this paper, the significance and role of remote sensing as related to the climate of arid and desert regions would be considered.

In this project, domestication of one of the wild flowering plants, Achillea millefolium L. (yarrow plant) was done to find out its resistance to drought stress condition. Plant samples were collected from the Isfahan region of Iran and were further multiplied by divisions of plants. The study was conducted on the improvement of seeds germination using GA3, morphological and phenological study and to estimate drought tolerance of yarrow plants. The seeds of selected plants were treated with GA3 hormone (0, 250 and 500 ppm) to break the dormancy and improve the germination percentage. GA3 treatment improved the seed quality parameters and the best results were obtained with GA3 @ 500 ppm. The plants propagated through division were cultivated in the field for the domestication of plants and to estimate their potential for landscape purposes. Also, the Phenological cycle of plants was monitored. Attractive flowers, Long duration of flowering and applying green cover during the year was positive points of yarrow for using in the landscape. Irrigation was applied at 25%, 50%, 75% and 100% levels of available water from April to September. Morphological and physiological parameters showed that A. millefolium could significantly tolerate drought treatments until 50% of available water and even at 75%, plants could survive and produce new stems.

Introduction: The increased use of fossil fuels has resulted in the production of pollutants and the release of greenhouse gases, leading to a global rise in temperature and climate change. On the other hand, climate change significantly influences temperature, precipitation, humidity, and cloud cover in local and regional variations. Therefore, considering the varying trends of climate change across different regions, it is crucially important to investigate the long-term trends of key climate parameters, including temperature and precipitation.Global Climate Models (GCMs) are reliable tools for simulating the global climate response to greenhouse gas concentrations. Performing based on greenhouse gas emission scenarios, the models can project the data of future climate variables such as precipitation and temperature for the entire Earth in three dimensions. On the other hand, the temperature is expected to rise in different parts of the world by varying degrees, although the exact amount is uncertain. In this regard, climate change not only raises temperature but also affects the hydrological cycle by accelerating ocean surface evaporation.It is important to acknowledge the uncertainty of GCM outputs in investigations, as its ignorance may reduce the reliability of the results. It should be noted that the raw data obtained from GCMs may not adequately resolve this problem and can diminish the precision of the results. Bias-correction methods have become increasingly common in climate change impact studies over the past decade, ranging from simple averaging methods to complex ones. Therefore, this study sought to investigate the efficiency of LS, NBC, and SDSM bias-correction methods and the combination of SDSM and LS models in reducing the uncertainty of CanESM2 temperature predictions in dry areas. Material and methods: covering an area of approximately 74,650 square kilometers, Yazd province is located in the center of Iran between 29 degrees 48 minutes to 33 degrees 30 minutes north latitude and 52 degrees 45 minutes to 56 degrees 30 minutes east longitude, being characterized by the most unfavorable natural factors that dominate Iran’s central plateau.This study used three groups of data: 1) daily temperature data collected from Yazd synoptic station from 1966 to 2020, provided by the National Meteorological Organization; 2) Atmospheric statistics obtained from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) for the same period; 3) CanESM2 general circulation model of temperature simulations used for the periods of 1966-2005 and 2006-2045, based on the RCP8.5 emission scenario. The study applied three bias-correction methods, including LS, NBC, and SDSM, and performed LS on the outputs of the SDSM model to correct the temperature outputs of the CanESM2 model under the RCP8.5 emission scenario. To assess the efficiency of the methods used, the outputs of each bias-correction method were compared with observational data during the 2006-2020 period. Results: The results revealed a consistent sinusoidal trend in the daily and monthly temperature data. The highest and lowest monthly average temperature rates were found to have been typically recorded in July and January, respectively. Moreover, the average annual temperature indicated an increasing trend from 1966 to 2005, which was found to have continued from 2006 to 2020, but with fewer variations. On the other hand, the results of the CanESM2 temperature simulations based on the RCP8.5 emission scenario suggested that the variations followed a pattern relatively similar to that of the observations. The average annual temperature simulated by the model for both the past (1966-2005) and future (2006-2045) showed a significant increasing trend. However, observational data for the 1966-2005 period indicated a slower increasing trend than the simulated values during the same period. As predicted, compared to the raw model values, the bias-corrected values obtained via the LS method better matched the actual data found for the evaluation period (2006-2020). As for the NBC application, the results suggested that the method improved the accuracy of future average temperature projections of CanESM2. It was also found that SDSM and LS offered relatively acceptable accuracy in terms of SDSM outputs. The results also revealed that the temperature data corrected by all four methods, including LS, SDSM, NBC, and the combination of LS and SDSM agreed well with observational data collected from the synoptic station, whose coefficients of determination were found to be 0.948, 0.968, 0.969, and 0.969, respectively. Moreover, the non-parametric Kendall test revealed a significant increasing trend in the average annual air temperature for both past and future periods in the study area. Discussion and conclusion: the comparison of the coefficients of determination of the average monthly temperature rates corrected by the above-mentioned methods and the ones found for the observational data during the evaluation period indicated that all four bias-correction methods performed acceptably in the study area. It is worth noting that the LS method showed a slightly poorer performance than the other error correction methods. Based on the results of this study, it can be concluded that since the efficiency of methods used for reducing the uncertainty of temperature outputs in dry areas was relatively acceptable, it seems that investigating suitable methods for reducing the uncertainty of precipitation outputs in dry areas would be of greater importance.

Salsola sp. known as shoor or shooreh in most parts of the country, are annual or perennial plants from Chenopodiaceae family. In desert regions, especially in northern margins of Iranian central desert Salsola sp. is available and extended from 1000 m above sea level altitudes. These plants are resistant to drought conditions and tolerate relative salinity of soils. In Semnan province, 13 species of this genus were collected and determined among which six were annual and seven perennial. The most important genera of Sal sola in this region based on their role in supplying forage and in green cover composition of ranges, were S. arbuscoliformis, S. rigida cf Orientalis and S. arbuscula. Although some annual species like S. kali have shown special abilities in forage production under harsh conditions in many parts of the world, this study was carried out to determine Salsola characteristics in Iran. In ranges of arid regions, especially in winter grazing ranges of Semnan province, Salsola sp. is an important component of green cover. Animal husbanderers, especially nomads believe that Sa/sola plays a role by providing salt for domestic animals in addition to its essential role as a forage in grazing cycle from ranges. Usually in ranges with Salsola, a part of grazing time that ordinarily is at the end of the day will be devoted to these ranges. Domestic animals would graze and take needed salts from these plants so that there is no need for putting salt stones in the area. This part of grazing is effective in health of livestock nutrition and helps the shepherd to lead the herds toward watering places. Protein and dry matter production of some Salsola sp. is considerable and can be compared with Medicago sativa.