Understanding the Spatial Variation of the flood source area within watersheds as they affect of the flood characteristics at the outlet is an important issue in flood control studies. Determining the flood severity index in a watershed requires study of hydrogeomorphic properties, recorded rainfall-runoff events and use of mathematical models in the context of the methodology to delineate various watersheds areas with respect to the flood downstream. In this paper, Roodzard watershed was selected as the case study since it has suitable rainfall-runoff record. The watershed consists of five tributary subwatershed and three intermediate subwatersheds. ModClark distributed hydrologic model was calibrated in subwatersheds with hydrometric stations. Using HECRAS routing model the whole of the Roodzard watershed model was calibrated at Mashin Hydrometric Station at the outlet. Following the “Unit Flood Response” approach, 2*2 km2 grid squares within the watershed were removed one by one in the simulation process and their effect on the flood peak at the outlet was determined. Such effect was quantified by a flood index and used for preparing the map of “flood severity”. Furthermore, the profile of flood index along the main stream was plotted in grid-scale as well as for each sub-basin.

For evaluating the Variation of geometry and hydraulic cross section coefficient and its simulation both in irrigation events (seasonal), and in furrow length (Spatial), some experiments were developed on 24 furrows with a 60m length and 0.75m width in a sandy-loam soil in Isfahan, in summer 2001. Furrow cross sections were measured before and after of every irrigation event using profilemeter at 5, 30 and 55 m from upstream of the furrow. Geometry and hydraulic cross section coefficient were calculated were by using of CSCM, a computer based model. Based on the results, it was seen Variation both seasonal and Spatial, in the furrow cross section area (A) and its dependent coefficient. Evaluation of the geometric and hydraulic coefficient Variation showed no systematic trend. In addition the effect of the σ1,σ2 coefficients were evaluated on the y-An equation. Similarly the effect of the ρ1, ρ2 evaluated on the y-Z2 equation. There was significant seasonal Variation of furrow cross section area. But there wasn't any significant Variation on squared shape factor (Z2) as a hydrawlically index. A linear equation was developed for the seasonal Variation of both A and Z2. Based on the results, no systematic trend was observed between Spatial Variations of the parameters in a furrow because of complexity.

To evaluate the trend of the maximum mean and minimum temperature of Maroon basin during the last half century, a linear regression model has been fitted to the interpolated data. A 95 percent confidence interval for the slope of the regression model was used to detect areas with negative and positive trends. This analysis showed that the temperature of basin confirm an increasing trend during the last half century. This trend is stronger for warm months and for warmer parts of the basin. Meanwhile, the increasing trend of minimum temperature is stronger than the maximum temperature.

Introduction: Copy number Variation (CNV) consist of deletion, insertion, and duplications. It is an important source of genetic Variation in organisms and thus influences on the gene expression and phenotypic Variation. Copy number Variation (CNV) is one of the structural variant with an intermediate size class larger than 50bp which involves unbalanced rearrangements that increase or decrease the amount of DNA (Pirooznia et al 2015, Alkan et al 2011). The size of CNVs is larger than 50bp, while smaller segments are known as insertions or deletions (indels). Thereupon these structural Variations comprise more polymorphic than SNPs because of enormity, detection of them and their effect on phenotype has caught the attention of many researchers recently. It has been reported that CNVs changes in gene dosage and regulation as well as in transcript structure, and thus contribute to phenotypic variability (Pirooznia et al 2015, Alkan et al 2011). The pea-comb phenotype is caused by a CNV mapping to intron 1 of the SRY (sex determining region Y)-box 5 (SOX5) gene (Wright et al. 2009). Late feathering in chickens is due to incomplete duplication in PRLR and SPEF2 genes (Elfrink et al. 2008). In swine, dominant white colour has been related with a duplication of a 450-kb fragment of the KIT gene (Giuffra et al. 1999) and a splice mutation causing the skipping of exon 17 (Giuffra et al. 1999). In sheep, doubling in the ASIP gene results in the regulation of pigment in body coat (Norris et al. 2008). Doubling the 4.6 k base pair into the six introns of the STX17 gene results in a gray body color in the horse with age. Deletion of the intergenic region with a length of 11.7 kbp in the goat genome leads to the removal of horns (Clop et al. 2012). Chicken is the most intensively farmed animal on earth and is a major food source with billions of birds used in meat and egg production each year. A big share of chicken CNVs involves protein coding or regulatory sequences. A comprehensive study of chicken CNV can provide valuable information on genetic diversity and assist future analyses of associations between CNV and economically important traits in chickens. Unique chicken genome with macro and micro chromosomes and its biology make it an ideal organism for studies in development and evolution, as well as applications in agriculture and medicine (Burt 2005). In the last several years, There has been an increasing interest in the study of CNVs in the chicken. This study focuses on comparison of CNV between the broilers and layers chicken to find evidence of domestication on the genome using whole genome sequencing.Material and methods: we used n=90 female birds of two commercial broiler (n=40) and layer (n=50) chicken. The broilers (BRs) were represented by 20 DNA samples of each of two lines (BRA and BRB) established independently and previously collected as part of the AVIANDIV project. In the layer group (LRs), data from 25 birds each from purebred white (WL) and brown (BL) egg laying populations, sequenced in the frame of the SYNBREED project (http://www.synbreed.tum.de/index.php?id=2 ,(were included. The paired-end reads with a read length of 101bp were mapped against the current reference genome assembly Galgal6 using the Burrows-Wheeler aligner (bwa, 0.6.2-r126 Version, with default parameters. Duplicate reads were masked during post-processing using the Picard tool set (version 2.9.2, http://picard.sourceforge.net). Finally, Genome Analysis Toolkit-3.3.0 was used to realign reads for correcting errors caused by InDels. Using GATK software package and Depth Of Coverage function (McKenna et al 2010), the depth of readings was calculated for each sample. Then filter out reads with mapping quality below 20. Because comparing the genomes of individuals in different groups was time consuming and computationally difficult for all parts of the genome, the genomes of each individual were divided into 1000 bp non-overlapping windows and the average reading depth per window was calculated. Then the results were normalized against the BL sample that showed highest average depth. In short, we created a correction factor per population and applied it on the depth of coverage value for each window. For all the contrasts, we performed an analysis of variance (ANOVA) as described (Carneiro et al 2014). For the Broilers-Layers contrast we scanned 935247 windows. 70372 windows showed significant by FDR with P < 0.001, with ANOVA using the Benjamini-Hochberg FDR method for multiple corrections (Benjamini and Hochberg 1995).Results and discussion: Mapping sequencing data to galGal6 assembly showed an average 98.61% mapping rate and 11.51 depth. Manhattan plot was plotted for regions of the genome that differed significantly between the two groups (FDR = 0.001). The points above the hypothetical line were identified and examined in a 25 Kbp confidence interval to identify possible genes. 39 regions were identified that half of them dose not contain any genes. Although Long noncoding RNAs are under lower selective pressure than protein-coding genes (Batista and Chang 2013), The other 11 regions contained 16 genes related to long non-coding RNAs. Long noncoding RNAs (lncRNAs) play a critical role in organizing the 3-dimensional genome architecture and regulating gene activity in cis or in trans through multiple mechanisms (Zhang et al 2019, Batista and Chang 2013). 6 othere regions also contained 12 coding genes. Most of the identified genes were somehow linked to the immune system disease or cancer. Genes such as DEDs and TNFAIP8 are involved in programmed cell death (apoptosis) and two genes NPAL3 and RCAN, which are involved in the immune system, had a copy number Variation in the studied samples. In addition RCAN is involved in Down syndrome. The PFDN gene, located on chromosome 25, is also involved in Alzheimer's and Parkinson's disease.

Disease mapping includes a set of statistical techniques that lead to provide clean maps based on estimation of the incidence, prevalence and mortality rates for the users to be able to estimate the distribution of disease reliably. The main aims of disease mapping are to: describe the Spatial Variation in disease incidence for the formulation of etiological hypotheses; identify areas of unusually high risk so that action may be taken; provide a clean map of disease risk in a region to allow better resource allocation and risk assessment; disease atlas construction. So far, several case studies have been conducted in which the applications of disease mapping models have been used.However, except in a few small studies and in limited situations, disease mapping models have not been compared and their general terms have not been studied. According to the importance of disease mapping and its growing use, in the present study the most important models have been introduced and their theoretical foundations, applications, advantages and disadvantages have been mentioned briefly and their various aspects have been compared.

Spatial Variations of the water quality parameters of the Merbok estuary were interpreted by multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Data from January to December 2011 were collected to monitor 13 parameters at six sampling stations along the river stretch (two stations at each river section: upstream, midstream, and downstream). Cluster analysis results revealed two different groups between the sampling stations, reflecting different physicochemical properties and pollution levels in the study area. Factor analysis was used for the parameters of the surface and bottom water quality, yielding four factors that were responsible for 72.93 and 68.90 % of the total variance of data sets. PCA also found conductivity, salinity, dissolved oxygen, chlorophyll a, and NO3- to be the most important parameters contributing to the fluctuations of surface water and bottom water quality in the Merbok estuary. This study presents the usefulness of multivariate statistical techniques for assessing water quality data sets and for understanding Spatial Variations in water quality parameters to effectively manage water quality in estuaries.

Multivariate statistical techniques were applied for evaluation of temporal/ Spatial Variations and interpretation of a large complex water-quality data set of Shiroud River that discharges to southern part of Caspian Sea, Iran. Totally 16 parameters of water quality were monitored during 12 months at 8 sites in mountainous, flat and estuary areas. Factor analysis (FA) results showed that the first factor explained 25.76% of the total variance [comprise of electrical conductivity (EC), total dissolved solids (TDS), total hardness, calcium ion and water temperature levels]. The second factor called water quality indicator factor explained 13.99% [comprise of silicate, dissolved oxygen (DO) and pH levels], and the third factor called phosphate pollutant factor explained 10.72% (comprise of orthophosphate and total phosphorus (TP)). Additional factors were affected by part of nutrient, flow rate and general water quality, each of them recorded variance less than 10%. Discriminate analysis (DA) gave the best results for both Spatial and temporal analysis. It has provided an important data reduction as it uses only four parameters (mean river depth, DO, NH4+, and EC). Thus, DA allowed a reduction in the dimensionality of the large data set, explaining a few indicator parameters responsible for large Variations in water quality. The present study shows the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and identifies probable source components in order to explain the pollution of Shiroud River.

The main objective in the present study was to assess the Spatial Variation of chemical and physical soil properties and then use this information to select an appropriate area to install a pasture rehabilitation experiment in the Zereshkin region, Iran. A regular 250 m grid was used for collecting a total of 150 soil samples (from 985 georeferenced soil pits) at 0 to 30, and 30 to 60 cm layers. Soil samples were analyzed for pH, EC, N, K, P, Na, Ca, Mg and SAR. Conventional statistical methods and geostatistics were performed in order to analyze soil properties Spatial dependence. Mean, standard deviation, skewness, and kurtosis for all measured variables were evaluated. All variograms generally were well structured with a relatively large nugget effect. Soil properties such as pH, P semivariograms were best fitted by spherical models, while SAR, Na were best fitted by spherical models. In the beginning kriging were performed in order to analyze Spatial Variation of chemical and physical soil properties, then for enhancing estimation accuracy and comparing results we used cokriging technique. Comparison of the results using statistical techniques showed that kriging technique has acceptable accuracy in characterizing the Spatial variability. Also results showed that although kriging technique has acceptable accuracy in characterizing the Spatial variability of soil properties but if higher accuracy is needed, cokriging is preferred to kriging particularly when the extra variable has been used.

According to recent developments in computer science and applications of softwares in computation and simulation of complicated problems in various fields of science, computer methods grow as a science production basis. Spatial and Temporal Variation of electromagnetic field intensity could be investigated through some simplifications like 2-dim space, thin wire assumption, spotted-source and distant fields approximation. Finite differences method could be used to solving Maxwell’s equations by given boundary conditions in convergence limits. To comparison our solution by what is prospected, we use 2-dim structured grid in several examples.