Reversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversible combinational logic. Some automated reversible logic synthesis methods use optimization algorithms Optimization algorithms are used in some automated reversible logic synthesis techniques. In these methods, the process of finding a circuit for a given function is a very time-consuming task, so it’, s better to design a processor which speeds up the process of synthesis. Application specific instruction set processors (ASIP) can benefit the advantages of both custom ASIC chips and general DSP chips. In this paper, a new architecture for automatic reversible logic synthesis based on an Application Specific Instruction set Processors is presented. The essential purpose of the design was to provide the programmability with the specific necessary instructions for automated synthesis reversible. Our proposed processor that we referred to as ARASP is a 16-bit processor with a total of 47 instructions, which some specific instruction has been set for automated synthesis reversible circuits. ARASP is specialized for automated synthesis of reversible circuits using Genetic optimization algorithms. All major components of the design are comprehensively discussed within the processor core. The set of instructions is provided in the Register Transform Language completely. Afterward, the VHDL code is used to test the proposed architecture.

In this paper, we have proposed a novel hardware caching technique to reduce the communication latency in ODYSSEY methodology. The main idea is to implement small fast split caches in a parallel way to connect the medium to capture and store ownership information as the data flows from the memory module to the requesting processor. One of the main factors for estimating the rate of modem processors is their memory access time. Using cache memory enhances the access time; therefore it remarkably increases the rate. However, this rate is not sure enough for the systems like ODYSSEY which require parallel memory access time. In this paper, we present a new method in which cache memory is split into parts, so that simultaneous accesses to these parts are possible; therefore, the memory access time is highly improved.

Introduction: Sheep are the main source of wool and its fiber characteristics, such as diameter, length, and color, which are determined by genetics and environmental factors, are key features in the economic value of sheep wool. In sheep, white wool has the highest economic value due to its dyeability, thus, the identification of mechanisms responsible for coating color determination is very important from an economic point of view. In general, the coat color is determined based on the amounts and types of melanin produced and released by the melanocytes in the skin tissue (Ito et al., 2000). The genetic basis and genes involved in coat color are well understood in rodents, although many of these genes are incorporated in coat color regulation in other species; including Sheep also have a common role. In Iran, the Lori Bakhtiari sheep is one of the most important breeds of sheep in terms of the use of its wool in the textile and carpet industries. In his breed, the dominant coat of the wool is white, although sometimes a percentage of dark brown and pale brown is also observed (Saadat Nouri, M. & Siah Mansour, 1368). However, in this breed, some animals have black spots on their coat, which leads to a decrease in wool quality. Since MC1R, ASIP, KLF4 and MITF genes play an important role in controlling and determining coat color in mammals, the purpose of this study was to investigate the expression of these genes in two phenotypes of white and black spots in the skin tissue of Lori Bakhtiari sheep.Materials and Methods: Skin samples were obtained from both white and dark parts of 14 white-coated sheep with black spots and total RNA was extracted. The quality and quantity of extracted RNAs were evaluated by agarose gel electrophoresis and spectrophotometer. Extracted RNA samples were exposed to DNase1 enzyme digestion to remove the possible contamination of genomic DNA. Also, the quality of synthesized cDNA was evaluated using 1% agarose gel. In this research, in order to amplify a fragment of the studied genes, using the mRNA sequence of these genes in the GenBank database, appropriate primers were designed by Primer3plus software. To evaluate the relative expression of the target genes, β-actin and GAPDH genes were used as reference genes to normalize the data. Finally, BestKeeper and REST 2009 V2.0.13 software were used for the analysis of gene expression data.Results and Discussion: Based on the descriptive results of Ct values, MC1R and MITF genes revealed minimum and maximum expression stability among the target genes in skin samples with standard deviations of 1.34 and 3.62, respectively. In addition, the reference genes (β-actin and GAPDH) showed the highest stability among all the studied genes. No significant differences were observed in mRNA levels of MC1R, ASIP, KLF4, and MITF genes in the spotted skin tissue compared to the white part of the skin (p>0.05). However, the expression of the ASIP gene was more than 2 times in the spotted part compared to the white skin, but this difference was not significant (p=0.21). In addition, the MC1R gene showed minimum expression differences in black spots and white parts of the skin tissue. In addition, the MITF and MC1R genes showed the highest and lowest levels of expression in skin samples of Lori Bakhtiari breed sheep with average Ct of 25.86 and 30.42, respectively. However, among all the studied genes, the lowest mRNA level was observed for the GAPDH gene with an average Ct of 35.96.Conclusion: Mammalian coat color results from various factors such as the degree and distribution of melanin pigment and the interaction between genotype and environment (B. Li et al., 2018). In addition, melanogenesis is a complex process that includes melanocyte growth, melanosome formation, melanin synthesis, melanin transport, and melanosome release (Ito & Wakamatsu, 2011). According to the conducted studies, a large number of genes are involved in the mechanism of coat color determination, but two genes, MC1R and ASIP, play an essential role in the regulation and control of coat color (Searle, 1968). In our study, no significant difference was observed in the expression of MC1R, ASIP, KFL4, and MITF genes in the spotted compared to the white part of the skin tissue in Lori Bakhtiari sheep. These results showed that the development of skin spots is not under the control of the studied genes in Lori Bakhtiari sheep, and genes or other factors can play roles in the creation of dark spots in this breed.

Basic color in horses, such as black and white, brown and chestnut ASIP and MC1R gene is affected. The relationship between color horse and purity the study of gene polymorphism Aguti put special attention. The purpose of this study was to evaluate the polymorphism in exon 2 gene Aguti population of Arabic horse in Khuzestan. In this study, PCR-RFLP method for the detection of different alleles of the Aguti gene in horses used. Bleed extension of the 45 head of horses in the city of Ahvaz, Dezful and Shush were collected. Polymerase chain reaction 101 bp fragment of exon 2 gene and gene-specific primers were used Aguti. 11 bp deletion in exon 2 gene ASIP (Aguti signaling protein) occurred because of the In / del (delete and Insertion) of the gene Aguti. Finally three band pattern AA, Aa, aa abundance 0.414, 0.457, 0.127, respectively. The allele frequencies of A and a 0.644 and 0.356 respectively.The results showed that this fragment of gene polymorphism and genotype AA, Aa, aa population was established. The results showed that there is Hardy-Weinberg dis equilibrium in the studied population.

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.