This paper presents the application of metaheuristic methods to the minimum CROSSING NUMBER problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The CROSSING NUMBER problem is NP-hard and has important applications in engineering. The proposed algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.

The CROSSING NUMBER of a graph G is the minimum NUMBER of edge CROSSINGs over all possibl e drawings of G in a plane. The CROSSING NUMBER is an important measure of the non-planarity of a graph, with ap plications in discrete and computational geometry and VLSI circuit design. In this paper we introduce vertex cente red CROSSING NUMBER and study the same for maximal planar graph.

In 325 B. C. Alexander the Great decided to return to the west at the mouth of Indus. The great Macedonian army provided by a NUMBER of nations started its campaign through Gedrosia (Baluchestan) commanded by Alexander. His fleet supported the ground army under the command of Nearchus on Oman Sea. Greek geographers divided Baluchestan into three regions: Oritiae, the Coast of Fish Eaters and Gedrosia. Alexander faced a NUMBER of challenges while CROSSING the three regions, partly due to his army’s unfamiliarity with the area. In fact, the severity of these problems was far greater than were anticipated from the beginning of the war on the border of Granicus up to the end of it on the mouth of Indus. Alexander, in spite of knowing that Gedrosia had a severe nature and lack of the foodstuffs and grass plots, decided to cross it because of his self-pride and his supernatural ability. Did he succeed to reach all his ambitious goals? Since Gedrosia did not have a central advanced urbanization system, how it this perceived by historians and geographers?This article aims not only to answer the two mentioned questions but also explain the whole operations by Alexander and his army in Gedrosia, the circumstances of their coming in contact with environment and geographical phenomenon’s of the region according to Greek historians and geographers on the basis of descriptive-analysis method.

Purpose: To report results of A/V CROSSING adventitial sheathotomy for the treatment of branch retinal vein occlusion (BRVO) associated with macular edema and hemorrhage. Methods: We performed surgical decompression of BRVO via pars plana deep vitrectomy (lens sparing), PVD induction, and A/V CROSSING adventitial sheathotomy in 3 patients with severely decreased visual acuity (20/200<). Because of macular hemorrhage, edema, and ischemia, these cases were not good candidates for grid macular photocoagulation. Reperfusion of the retina was observed by surgically separating the overlying retinal arteriole from the venule following vitrectomy and adventitial sheathotomy techniques.Results: Intraoperative decompression of the A/V CROSSING was achieved in all three patients. All patients showed improvement as determined by fundus examination, photography, and fluorescein angiography. Post operative visual acuities improved in all cases.Conclusion: Surgical decompression of BRVO via A/V CROSSING sheathotomy is a technically feasible procedure that can result in rapid reperfusion of the retina. Resolution of macular hemorrhage, edema, and ischemia may improve visual prognosis in patients with this common retinal vascular disorder.

Teaching English in an Iranian and Islamic culture poses complex questions for both teachers and learners. In this paper, the authors intend to shed light on what it means to teach English as a foreign language (TEFL) in an Islamic-Iranian context. Having reviewed the colonial and postmodern views of English language teaching, the authors took a look beyond the current state of TEFL in Iran, which is marked by its continuing global tendency, and into the future with an emphasis on the importance of including the local specificities of the Iranian culture and religion. The status of the TEFL in Iran and the direction it should take in the future are accompanied by offering some solutions to inherent problems. Iranian TEFL is introduced as the successful assertion of Iranian local culture against the cultural and ideological domination of the West, which can be an antidote to the harshness of all marginalizations Iranians have suffered for centuries.

The CROSSING NUMBER cr(G) of a graph G is the minimum NUMBER of edge CROSSINGs over all drawings of G in the plane. In the paper, we extend known results concerning CROSSING NUMBERs of join products of two small graphs with cycles. The CROSSING NUMBER of the join product G∗ + Cn for the disconnected graph G∗ consisting of the complete graph K4 and one isolated vertex is given, where Cn is the cycle on n vertices. The proof of the main result is done with the help of lemma whose proof is based on a special redrawing technique. Up to now, the CROSSING NUMBERs of G + Cn are done only for a few disconnected graphs G. Finally, by adding new edge to the graph G∗, we are able to obtain the CROSSING NUMBER of G1 + Cn for one other graph G1 of order five.

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.

Introduction: One of the causes of error and perturbation in isodose curves in conventional radiotherapy treatment planning is the existence of tissues having either very high density (bone, prosthesis) or very low density (lung, air cavities). Nowadays, the use of CT images to solve this problem is growing, and so is the need for calibration curves that convert the CT-NUMBER of the tissue to electron density for dose calculation. The conventional method to obtain this curve is purely measurement-based, in which a phantom containing various materials of known electron densities is imaged. Alternatively, a more fundamental method of stoichiometry has been used in this work.Material and Methods: For the stoichiometric method, initially a cylindrical polyethylene phantom was built. The phantom consists of inserts of high-purity aluminum, PVC, polyethylene, water and cork to model hard bone, skeleton, fat, muscle and lung tissues, respectively. CT imaging was then performed at 120 kVp using a spiral CT scanner (GE model NXI). A system of simultaneous equations was solved to get the appropriate CT-NUMBER to electron-density conversion for each tissue type using the CT-NUMBERs from the phantom images, the physical and radiological data of the materials. A conversion curve showing the variation of CT-NUMBER with relative electron density was also plotted. The result of the stoichiometric conversion was then compared to that from other methods.Results: The system of simultaneous equations yielded the factors, the experimental and the computed CT-NUMBERs were compared. The highest uncertainty was estimated to be approximately 5.6% for a relatively high-density material such as aluminum and 4% for polyethylene. The curves representing the electron density based on CT-NUMBER start diverging at CT-NUMBER equal to zero and above.Discussion and Conclusion: Comparing the results obtained from the experimental and computational methods suggest an acceptable level of accuracy for the computational (stoichiometric) conversion. The uncertainty in the electron density obtained is greater for materials of higher electron density.